From 8ea35b0bd0f7ea5395b91b607392f4df5604e742 Mon Sep 17 00:00:00 2001 From: Danglewood <85772166+deeleeramone@users.noreply.github.com> Date: Fri, 29 May 2026 00:44:12 -0700 Subject: [PATCH] [V5] `openbb-imf`: Refactor for V5 (#7508) * refactor imf extension for V5 * codespell * fix some python 3.10 items * return self on copy of metadata object --- .github/workflows/publish-openbb-imf.yml | 190 + .github/workflows/test-unit-openbb-imf.yml | 185 + .../economy/openbb_economy/economy_router.py | 2 +- openbb_platform/providers/imf/.gitignore | 1 + openbb_platform/providers/imf/README.md | 88 +- openbb_platform/providers/imf/hatch_build.py | 102 + .../providers/imf/integration/test_imf_api.py | 386 ++ .../imf/integration/test_imf_python.py | 316 ++ .../imf/integration/test_imf_utils_api.py | 211 - .../imf/integration/test_imf_utils_python.py | 179 - .../providers/imf/openbb_imf/__init__.py | 51 +- .../providers/imf/openbb_imf/apps.json | 1049 ++--- .../imf/openbb_imf/assets/__init__.py | 2 + .../imf/openbb_imf/assets/imf_cache.pkl.gz | Bin 3166128 -> 0 bytes .../providers/imf/openbb_imf/imf_router.py | 926 ++-- .../providers/imf/openbb_imf/imf_views.py | 170 + .../imf/openbb_imf/models/__init__.py | 2 + .../openbb_imf/models/available_indicators.py | 17 +- .../openbb_imf/models/balance_of_payments.py | 407 ++ .../openbb_imf/models/consumer_price_index.py | 66 +- .../openbb_imf/models/container_metrics.py | 175 + .../imf/openbb_imf/models/country_activity.py | 320 ++ .../openbb_imf/models/direction_of_trade.py | 34 +- .../openbb_imf/models/disruption_events.py | 240 ++ .../openbb_imf/models/disruption_sankey.py | 166 + .../openbb_imf/models/economic_indicators.py | 190 +- .../openbb_imf/models/indicator_metadata.py | 2 + .../models/maritime_chokepoint_info.py | 23 +- .../models/maritime_chokepoint_volume.py | 25 +- .../imf/openbb_imf/models/monthly_trade.py | 239 ++ .../imf/openbb_imf/models/port_info.py | 158 +- .../imf/openbb_imf/models/port_volume.py | 42 +- .../imf/openbb_imf/portwatch_router.py | 195 + .../providers/imf/openbb_imf/py.typed | 0 .../imf/openbb_imf/utils/__init__.py | 2 + .../imf/openbb_imf/utils/constants.py | 15 +- .../imf/openbb_imf/utils/dot_helpers.py | 29 +- .../imf/openbb_imf/utils/generate_cache.py | 445 ++ .../providers/imf/openbb_imf/utils/helpers.py | 222 +- .../imf/openbb_imf/utils/metadata.py | 2170 ---------- .../imf/openbb_imf/utils/metadata/__init__.py | 13 + .../openbb_imf/utils/metadata/_cache_mixin.py | 47 + .../openbb_imf/utils/metadata/_constants.py | 40 + .../imf/openbb_imf/utils/metadata/_core.py | 80 + .../imf/openbb_imf/utils/metadata/_helpers.py | 103 + .../utils/metadata/_indicator_mixin.py | 178 + .../utils/metadata/_loader_mixin.py | 155 + .../openbb_imf/utils/metadata/_query_mixin.py | 375 ++ .../utils/metadata/_search_mixin.py | 272 ++ .../openbb_imf/utils/metadata/_table_mixin.py | 870 ++++ .../imf/openbb_imf/utils/metadata/_typing.py | 234 ++ .../openbb_imf/utils/port_watch_helpers.py | 340 +- .../openbb_imf/utils/progressive_helper.py | 30 +- .../imf/openbb_imf/utils/query_builder.py | 208 +- .../imf/openbb_imf/utils/table_builder.py | 324 +- .../openbb_imf/utils/table_presentation.py | 524 +-- .../imf/openbb_imf/views/__init__.py | 2 + .../views/maritime_chokepoint_info.py | 5 +- .../imf/openbb_imf/views/port_info.py | 19 +- .../imf/openbb_imf/views/port_watch_charts.py | 683 +++ openbb_platform/providers/imf/poetry.lock | 2696 ------------ openbb_platform/providers/imf/pyproject.toml | 147 +- .../providers/imf/tests/conftest.py | 114 + ...alance_of_payments_fetcher_urllib3_v2.yaml | 143 + ...nsumer_price_index_fetcher_urllib3_v2.yaml | 78 +- ..._container_metrics_fetcher_urllib3_v2.yaml | 73 + ...f_country_activity_fetcher_urllib3_v2.yaml | 104 + ...direction_of_trade_fetcher_urllib3_v2.yaml | 76 +- ..._disruption_events_fetcher_urllib3_v2.yaml | 661 +++ ..._disruption_sankey_fetcher_urllib3_v2.yaml | 111 + ...conomic_indicators_fetcher_urllib3_v2.yaml | 70 +- ...me_chokepoint_info_fetcher_urllib3_v2.yaml | 180 +- ..._chokepoint_volume_fetcher_urllib3_v2.yaml | 151 +- ..._imf_monthly_trade_fetcher_urllib3_v2.yaml | 81 + ...test_imf_port_info_fetcher_urllib3_v2.yaml | 3144 +------------- ...st_imf_port_volume_fetcher_urllib3_v2.yaml | 2188 ++++++---- .../providers/imf/tests/test_assets_init.py | 10 + .../tests/test_available_indicators_model.py | 139 + .../tests/test_balance_of_payments_model.py | 384 ++ .../imf/tests/test_chokepoint_models.py | 299 ++ .../tests/test_consumer_price_index_model.py | 301 ++ .../tests/test_direction_of_trade_model.py | 131 + .../providers/imf/tests/test_dot_helpers.py | 325 ++ .../tests/test_economic_indicators_model.py | 1292 ++++++ .../imf/tests/test_generate_cache.py | 850 ++++ .../providers/imf/tests/test_helpers.py | 647 ++- .../providers/imf/tests/test_imf_fetchers.py | 106 +- .../providers/imf/tests/test_imf_router.py | 1638 ++++++++ .../providers/imf/tests/test_imf_search.py | 2 - .../providers/imf/tests/test_imf_views.py | 316 ++ .../imf/tests/test_metadata_mixins.py | 2323 +++++++++++ .../providers/imf/tests/test_port_models.py | 452 ++ .../imf/tests/test_port_watch_charts.py | 428 ++ .../imf/tests/test_port_watch_helpers.py | 1152 +++++ .../imf/tests/test_port_watch_models.py | 362 ++ .../imf/tests/test_progressive_helper.py | 133 +- .../imf/tests/test_progressive_validation.py | 63 +- .../providers/imf/tests/test_query_builder.py | 1881 ++++++++- .../providers/imf/tests/test_table_builder.py | 3241 +++++++++++++- .../imf/tests/test_table_presentation.py | 3525 ++++++++++++++++ .../test_views_maritime_chokepoint_info.py | 98 + .../imf/tests/test_views_port_info.py | 236 ++ openbb_platform/providers/imf/uv.lock | 3714 +++++++++++++++++ 103 files changed, 35556 insertions(+), 12053 deletions(-) create mode 100644 .github/workflows/publish-openbb-imf.yml create mode 100644 .github/workflows/test-unit-openbb-imf.yml create mode 100644 openbb_platform/providers/imf/.gitignore create mode 100644 openbb_platform/providers/imf/hatch_build.py create mode 100644 openbb_platform/providers/imf/integration/test_imf_api.py create mode 100644 openbb_platform/providers/imf/integration/test_imf_python.py delete mode 100644 openbb_platform/providers/imf/integration/test_imf_utils_api.py delete mode 100644 openbb_platform/providers/imf/integration/test_imf_utils_python.py delete mode 100644 openbb_platform/providers/imf/openbb_imf/assets/imf_cache.pkl.gz create mode 100644 openbb_platform/providers/imf/openbb_imf/imf_views.py create mode 100644 openbb_platform/providers/imf/openbb_imf/models/balance_of_payments.py create mode 100644 openbb_platform/providers/imf/openbb_imf/models/container_metrics.py create mode 100644 openbb_platform/providers/imf/openbb_imf/models/country_activity.py create mode 100644 openbb_platform/providers/imf/openbb_imf/models/disruption_events.py create mode 100644 openbb_platform/providers/imf/openbb_imf/models/disruption_sankey.py create mode 100644 openbb_platform/providers/imf/openbb_imf/models/monthly_trade.py create mode 100644 openbb_platform/providers/imf/openbb_imf/portwatch_router.py create mode 100644 openbb_platform/providers/imf/openbb_imf/py.typed create mode 100644 openbb_platform/providers/imf/openbb_imf/utils/generate_cache.py delete mode 100644 openbb_platform/providers/imf/openbb_imf/utils/metadata.py create mode 100644 openbb_platform/providers/imf/openbb_imf/utils/metadata/__init__.py create mode 100644 openbb_platform/providers/imf/openbb_imf/utils/metadata/_cache_mixin.py create mode 100644 openbb_platform/providers/imf/openbb_imf/utils/metadata/_constants.py create mode 100644 openbb_platform/providers/imf/openbb_imf/utils/metadata/_core.py create mode 100644 openbb_platform/providers/imf/openbb_imf/utils/metadata/_helpers.py create mode 100644 openbb_platform/providers/imf/openbb_imf/utils/metadata/_indicator_mixin.py create mode 100644 openbb_platform/providers/imf/openbb_imf/utils/metadata/_loader_mixin.py create mode 100644 openbb_platform/providers/imf/openbb_imf/utils/metadata/_query_mixin.py create mode 100644 openbb_platform/providers/imf/openbb_imf/utils/metadata/_search_mixin.py create mode 100644 openbb_platform/providers/imf/openbb_imf/utils/metadata/_table_mixin.py create mode 100644 openbb_platform/providers/imf/openbb_imf/utils/metadata/_typing.py create mode 100644 openbb_platform/providers/imf/openbb_imf/views/port_watch_charts.py delete mode 100644 openbb_platform/providers/imf/poetry.lock create mode 100644 openbb_platform/providers/imf/tests/conftest.py create mode 100644 openbb_platform/providers/imf/tests/record/http/test_imf_fetchers/test_imf_balance_of_payments_fetcher_urllib3_v2.yaml create mode 100644 openbb_platform/providers/imf/tests/record/http/test_imf_fetchers/test_imf_container_metrics_fetcher_urllib3_v2.yaml create mode 100644 openbb_platform/providers/imf/tests/record/http/test_imf_fetchers/test_imf_country_activity_fetcher_urllib3_v2.yaml create mode 100644 openbb_platform/providers/imf/tests/record/http/test_imf_fetchers/test_imf_disruption_events_fetcher_urllib3_v2.yaml create mode 100644 openbb_platform/providers/imf/tests/record/http/test_imf_fetchers/test_imf_disruption_sankey_fetcher_urllib3_v2.yaml create mode 100644 openbb_platform/providers/imf/tests/record/http/test_imf_fetchers/test_imf_monthly_trade_fetcher_urllib3_v2.yaml create mode 100644 openbb_platform/providers/imf/tests/test_assets_init.py create mode 100644 openbb_platform/providers/imf/tests/test_available_indicators_model.py create mode 100644 openbb_platform/providers/imf/tests/test_balance_of_payments_model.py create mode 100644 openbb_platform/providers/imf/tests/test_chokepoint_models.py create mode 100644 openbb_platform/providers/imf/tests/test_consumer_price_index_model.py create mode 100644 openbb_platform/providers/imf/tests/test_direction_of_trade_model.py create mode 100644 openbb_platform/providers/imf/tests/test_dot_helpers.py create mode 100644 openbb_platform/providers/imf/tests/test_economic_indicators_model.py create mode 100644 openbb_platform/providers/imf/tests/test_generate_cache.py create mode 100644 openbb_platform/providers/imf/tests/test_imf_router.py create mode 100644 openbb_platform/providers/imf/tests/test_imf_views.py create mode 100644 openbb_platform/providers/imf/tests/test_metadata_mixins.py create mode 100644 openbb_platform/providers/imf/tests/test_port_models.py create mode 100644 openbb_platform/providers/imf/tests/test_port_watch_charts.py create mode 100644 openbb_platform/providers/imf/tests/test_port_watch_helpers.py create mode 100644 openbb_platform/providers/imf/tests/test_port_watch_models.py create mode 100644 openbb_platform/providers/imf/tests/test_table_presentation.py create mode 100644 openbb_platform/providers/imf/tests/test_views_maritime_chokepoint_info.py create mode 100644 openbb_platform/providers/imf/tests/test_views_port_info.py create mode 100644 openbb_platform/providers/imf/uv.lock diff --git a/.github/workflows/publish-openbb-imf.yml b/.github/workflows/publish-openbb-imf.yml new file mode 100644 index 00000000000..a0dde4afc7b --- /dev/null +++ b/.github/workflows/publish-openbb-imf.yml @@ -0,0 +1,190 @@ +name: ๐Ÿ“ฆ publish openbb-imf + +# Triggers exclusively on the merge of a ``release/openbb-imf-v*`` branch +# into ``develop``. No push / tag triggers. Three sequential jobs โ€” +# build once, test the built artifact across the full cross-platform +# matrix, and only publish to PyPI if the entire matrix is green. + +on: + pull_request: + types: [closed] + branches: [develop, v5] + workflow_dispatch: + +permissions: + contents: read + +jobs: + version-check: + name: Verify version bump + if: ${{ (github.event_name == 'pull_request' && github.event.pull_request.merged == true && startsWith(github.event.pull_request.head.ref, 'release/openbb-imf-v')) || github.event_name == 'workflow_dispatch' }} + runs-on: ubuntu-latest + outputs: + version: ${{ steps.parse.outputs.version }} + defaults: + run: + shell: bash + working-directory: openbb_platform/providers/imf + steps: + - uses: actions/checkout@v6 + with: + ref: develop + + - name: Parse [project].version from pyproject.toml + id: parse + run: | + VERSION=$(grep -m1 -E '^version[[:space:]]*=' pyproject.toml | sed -E 's/^version[[:space:]]*=[[:space:]]*"([^"]+)".*/\1/') + if [ -z "$VERSION" ]; then + echo "::error::Could not parse [project].version from pyproject.toml." + exit 1 + fi + echo "Local version: $VERSION" + echo "version=$VERSION" >> "$GITHUB_OUTPUT" + + - name: Fail if version already exists on PyPI + env: + VERSION: ${{ steps.parse.outputs.version }} + run: | + STATUS=$(curl -sS -o /dev/null -w "%{http_code}" "https://pypi.org/pypi/openbb-imf/${VERSION}/json") + case "$STATUS" in + 200) + echo "::error::openbb-imf ${VERSION} is already published on PyPI." + echo "Bump openbb_platform/providers/imf/pyproject.toml [project].version and re-merge a fresh release branch." + exit 1 + ;; + 404) + echo "openbb-imf ${VERSION} is not yet on PyPI โ€” proceeding." + ;; + *) + echo "::error::Unexpected status $STATUS from PyPI JSON API. Aborting to avoid a silent skip." + exit 1 + ;; + esac + + - name: Verify release branch name matches pyproject version + if: github.event_name == 'pull_request' + env: + VERSION: ${{ steps.parse.outputs.version }} + BRANCH: ${{ github.event.pull_request.head.ref }} + run: | + EXPECTED="release/openbb-imf-v${VERSION}" + if [ "$BRANCH" != "$EXPECTED" ]; then + echo "::error::Release branch '${BRANCH}' does not match pyproject version '${VERSION}'." + echo "Expected branch name: '${EXPECTED}'." + echo "Either rename the release branch or update [project].version in pyproject.toml so they agree." + exit 1 + fi + echo "Branch '${BRANCH}' matches pyproject version '${VERSION}'." + + build: + name: Build sdist + wheel + needs: version-check + runs-on: ubuntu-latest + defaults: + run: + shell: bash + working-directory: openbb_platform/providers/imf + steps: + - uses: actions/checkout@v6 + with: + ref: develop + + - uses: actions/setup-python@v6 + with: + python-version: "3.11" + + - run: python -m pip install --upgrade pip uv + + - name: Restore IMF SDMX cache (warming pool for PR workflow) + uses: actions/cache@v4 + with: + path: openbb_platform/providers/imf/openbb_imf/assets/imf_cache.json.xz + key: imf-sdmx-${{ hashFiles('openbb_platform/providers/imf/openbb_imf/utils/generate_cache.py') }} + + - name: Build sdist + wheel (fresh IMF cache) + env: + OPENBB_IMF_FORCE_CACHE_REBUILD: "1" + run: uv build --no-sources + + - uses: actions/upload-artifact@v4 + with: + name: openbb-imf-dist + path: openbb_platform/providers/imf/dist/ + if-no-files-found: error + retention-days: 7 + + test: + name: Test (${{ matrix.os }} / Python ${{ matrix.python_version }}) + needs: build + strategy: + fail-fast: false + matrix: + include: + # Linux: full sweep across every released Python. + - { os: ubuntu-latest, python_version: "3.10" } + - { os: ubuntu-latest, python_version: "3.11" } + - { os: ubuntu-latest, python_version: "3.12" } + - { os: ubuntu-latest, python_version: "3.13" } + - { os: ubuntu-latest, python_version: "3.14" } + # macOS + Windows: min and max supported. + - { os: macos-latest, python_version: "3.10" } + - { os: macos-latest, python_version: "3.14" } + - { os: windows-latest, python_version: "3.10" } + - { os: windows-latest, python_version: "3.14" } + runs-on: ${{ matrix.os }} + defaults: + run: + shell: bash + working-directory: openbb_platform/providers/imf + steps: + - uses: actions/checkout@v6 + with: + ref: develop + + - uses: actions/setup-python@v6 + with: + python-version: ${{ matrix.python_version }} + allow-prereleases: true + + - uses: actions/download-artifact@v4 + with: + name: openbb-imf-dist + path: openbb_platform/providers/imf/dist + + - run: python -m pip install --upgrade pip uv + + - name: Install dependencies then openbb-imf wheel + # ``openbb-core>=2.0.0`` and ``openbb-charting>=2.5.1`` aren't on + # PyPI yet โ€” install them from sibling source trees first so + # the wheel's pinned deps resolve. ``--group dev`` brings in + # pytest / pytest-asyncio / pytest-cov / pytest-recorder. + run: | + uv pip install --system ../../core + uv pip install --system ../../obbject_extensions/charting + uv pip install --system --group dev + uv pip install --system dist/openbb_imf-*.whl + + - name: Test the installed wheel + # Remove the local ``openbb_imf/`` source so pytest imports the + # package from site-packages โ€” i.e., the EXACT wheel that's + # about to be uploaded to PyPI. This is a release pipeline; + # the source tree can't be allowed to shadow the artifact. + run: | + rm -rf openbb_imf + pytest tests --cov=openbb_imf --cov-report=term-missing + + publish: + name: Publish to PyPI + needs: test + runs-on: ubuntu-latest + permissions: + id-token: write # required for PyPI OIDC trusted publishing + steps: + - uses: actions/download-artifact@v4 + with: + name: openbb-imf-dist + path: dist + + - uses: pypa/gh-action-pypi-publish@release/v1 + with: + packages-dir: dist diff --git a/.github/workflows/test-unit-openbb-imf.yml b/.github/workflows/test-unit-openbb-imf.yml new file mode 100644 index 00000000000..896bf2d1397 --- /dev/null +++ b/.github/workflows/test-unit-openbb-imf.yml @@ -0,0 +1,185 @@ +name: ๐Ÿงช test openbb-imf + +# PR-test for the IMF provider. Triggers when a PR touches any file +# under ``openbb_platform/providers/imf/`` or this workflow itself. +# +# Build-once, test-many: a single ``build`` job materializes the IMF +# SDMX cache (or restores it from the shared cache pool) and produces +# the wheel; the ``test`` matrix downloads that exact wheel and runs +# pytest under every supported (OS, Python) combination. +# +# The IMF cache asset is cached across runs via ``actions/cache``, +# keyed solely on the SHA of ``generate_cache.py``. IMF metadata is +# stable over weeks/months and the cassette-driven test suite never +# hits the live API, so we accept arbitrary cache age โ€” the only thing +# that should invalidate is a logic change to the generator itself. +# A manual override is exposed as a ``workflow_dispatch`` input for +# emergency cache busting without having to touch the generator. + +on: + pull_request: + branches: [develop, main, v5] + paths: + - "openbb_platform/providers/imf/**" + - ".github/workflows/test-unit-openbb-imf.yml" + workflow_dispatch: + inputs: + force_cache_rebuild: + description: "Force the build hook to regenerate the IMF cache from live SDMX (ignores actions/cache)." + type: boolean + default: false + +permissions: + contents: read + +concurrency: + # Cancel in-flight matrix runs when a new commit lands on the same PR. + group: test-openbb-imf-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true + +jobs: + build: + name: Build sdist + wheel + runs-on: ubuntu-latest + defaults: + run: + shell: bash + working-directory: openbb_platform/providers/imf + steps: + - uses: actions/checkout@v6 + + - uses: actions/setup-python@v6 + with: + python-version: "3.11" + + - run: python -m pip install --upgrade pip uv + + - name: Restore IMF SDMX cache + # The cache is keyed solely by the generator's content hash. + # Same hash โ†’ same cache, no time-based expiry. ``restore-keys`` + # is a defensive fallback: if the exact key misses (e.g. a tiny + # comment-only change to generate_cache.py invalidated the + # hash), pull the most recent prior cache so the hook has a + # baseline file on disk and the worst case is the same as the + # current force-rebuild path. + id: imf-cache + uses: actions/cache@v4 + with: + path: openbb_platform/providers/imf/openbb_imf/assets/imf_cache.json.xz + key: imf-sdmx-${{ hashFiles('openbb_platform/providers/imf/openbb_imf/utils/generate_cache.py') }} + restore-keys: | + imf-sdmx- + + - name: Build sdist + wheel + # Honor the cache hit unless the manual override is set. On + # exact-key cache-hit, the build hook sees the file already on + # disk and skips the live IMF SDMX fetch. + env: + OPENBB_IMF_FORCE_CACHE_REBUILD: >- + ${{ (inputs.force_cache_rebuild == true || steps.imf-cache.outputs.cache-hit != 'true') && '1' || '0' }} + run: uv build --no-sources + + - uses: actions/upload-artifact@v4 + with: + name: openbb-imf-dist + path: openbb_platform/providers/imf/dist/ + if-no-files-found: error + retention-days: 7 + + lint: + name: Lint (ruff + ty) + needs: build + runs-on: ubuntu-latest + defaults: + run: + shell: bash + working-directory: openbb_platform/providers/imf + steps: + - uses: actions/checkout@v6 + + - uses: actions/setup-python@v6 + with: + python-version: "3.11" + + - run: python -m pip install --upgrade pip uv + + - uses: actions/download-artifact@v4 + with: + name: openbb-imf-dist + path: openbb_platform/providers/imf/dist + + - name: Install dependencies then openbb-imf wheel + # ``openbb-core>=2.0.0`` and ``openbb-charting>=2.5.1`` aren't on + # PyPI yet โ€” install them from sibling source trees first so + # the wheel's pinned deps resolve. ``--group dev`` brings in + # ruff / ty / pytest / pytest-recorder under the same pinned + # set used by every other provider. + run: | + uv pip install --system ../../core + uv pip install --system ../../obbject_extensions/charting + uv pip install --system --group dev + uv pip install --system dist/openbb_imf-*.whl + + - name: ruff check + run: ruff check openbb_imf tests hatch_build.py + + - name: ruff format --check + run: ruff format --check openbb_imf tests hatch_build.py + + - name: ty check + run: ty check openbb_imf + + test: + name: Test (${{ matrix.os }} / Python ${{ matrix.python_version }}) + needs: build + strategy: + fail-fast: false + matrix: + include: + - { os: ubuntu-latest, python_version: "3.10" } + - { os: ubuntu-latest, python_version: "3.11" } + - { os: ubuntu-latest, python_version: "3.12" } + - { os: ubuntu-latest, python_version: "3.13" } + - { os: ubuntu-latest, python_version: "3.14" } + - { os: macos-latest, python_version: "3.10" } + - { os: macos-latest, python_version: "3.14" } + - { os: windows-latest, python_version: "3.10" } + - { os: windows-latest, python_version: "3.14" } + runs-on: ${{ matrix.os }} + defaults: + run: + shell: bash + working-directory: openbb_platform/providers/imf + steps: + - uses: actions/checkout@v6 + + - uses: actions/setup-python@v6 + with: + python-version: ${{ matrix.python_version }} + allow-prereleases: true + + - uses: actions/download-artifact@v4 + with: + name: openbb-imf-dist + path: openbb_platform/providers/imf/dist + + - run: python -m pip install --upgrade pip uv + + - name: Install dependencies then openbb-imf wheel + # ``openbb-core>=2.0.0`` and ``openbb-charting>=2.5.1`` aren't on + # PyPI yet โ€” install them from sibling source trees first so + # the wheel's pinned deps resolve. ``--group dev`` brings in + # pytest / pytest-asyncio / pytest-cov / pytest-recorder. + run: | + uv pip install --system ../../core + uv pip install --system ../../obbject_extensions/charting + uv pip install --system --group dev + uv pip install --system dist/openbb_imf-*.whl + + - name: Test the installed wheel + # Remove the local ``openbb_imf/`` source so pytest imports the + # package from site-packages โ€” i.e., the wheel artifact, not + # the source tree. + run: | + rm -rf openbb_imf + pytest tests --cov=openbb_imf --cov-report=term-missing --cov-fail-under=100 diff --git a/openbb_platform/extensions/economy/openbb_economy/economy_router.py b/openbb_platform/extensions/economy/openbb_economy/economy_router.py index 3d7e1c12a4d..4da7d4a2382 100644 --- a/openbb_platform/extensions/economy/openbb_economy/economy_router.py +++ b/openbb_platform/extensions/economy/openbb_economy/economy_router.py @@ -375,7 +375,7 @@ async def available_indicators( APIEx( description="IMF symbols can also be used for retrieving entire presentation tables." + " This example gets the Direct Investment Position (DIP) table." - + " Use `/imf_utils/list_tables` to get a list of supported presentation table symbols.", + + " Use `/imf/list_tables` to get a list of supported presentation table symbols.", parameters={ "provider": "imf", "symbol": "DIP::H_DIP_INDICATOR", diff --git a/openbb_platform/providers/imf/.gitignore b/openbb_platform/providers/imf/.gitignore new file mode 100644 index 00000000000..3ec52c0b123 --- /dev/null +++ b/openbb_platform/providers/imf/.gitignore @@ -0,0 +1 @@ +openbb_imf/assets/imf_cache.json.xz diff --git a/openbb_platform/providers/imf/README.md b/openbb_platform/providers/imf/README.md index 0d97ac8283e..fda23d40377 100644 --- a/openbb_platform/providers/imf/README.md +++ b/openbb_platform/providers/imf/README.md @@ -19,6 +19,17 @@ Then build the Python static assets by running: openbb-build ``` +The package also ships a precomputed IMF SDMX metadata catalog at +`openbb_imf/assets/imf_cache.json.xz`. Released wheels and sdists +carry it verbatim โ€” it is materialized from `api.imf.org/external/sdmx/3.0` +by a Hatchling build hook (`hatch_build.py`) at packaging time. End +users have nothing to do; developers can refresh it with either: + +```sh +generate-imf-cache # standalone script +OPENBB_IMF_FORCE_CACHE_REBUILD=1 pip install -e . # via the build hook +``` + ## Quick Start The fastest way to get started is by connecting to the OpenBB Workspace as a custom backend. @@ -92,9 +103,10 @@ Entering, `CPI::CP01`, gets the same result as, `CPI::CPI_CP01`. `CPI::H_CPI_BY_COMPONENT` returns the entire presentation table, Consumer Price Index (CPI) by Component. -Use, `obb.imf_utils.list_tables()`, for a list of tables and their symbol. +Use, `obb.imf.list_tables()`, for a list of tables and their symbol. -Use, `obb.economy.available_indicators(provider='imf', query=')`, to search for, or list all, individual time series and symbols. +Use, `obb.economy.available_indicators(provider='imf', ...)`, to search for, or list all, individual time series and symbols within specific dataflows. +(If `openbb-economy` is not installed, the same endpoints are aliased onto `obb.imf.available_indicators` / `obb.imf.indicators`.) This example lists all the indicators in the CPI and PI dataflows. @@ -117,36 +129,66 @@ print(indicators.to_dict("records")) 'structure_id': 'DSD_CPI', 'dimension_id': 'COICOP_1999', 'long_description': 'Food and non-alcholoic beverages consumer price index is produced using prices related to food items and non-alcoholic beverages such as fresh produce, packaged foods, and beverages excluding alcoholic drinks, aggregated by their respective consumer expenditure weights.', - 'member_of': ['CPI::H_CPI_BY_COMPONENT']}, + 'member_of': ['CPI::H_CPI_BY_COMPONENT'], + 'extra_dimensions': ['TYPE_OF_TRANSFORMATION']} ... ``` ## Coverage -All data available from https://data.imf.org/en/Data-Explorer can be retrieved, via `obb.economy.indicators(provider='imf', **kwargs)`. +All data available from https://data.imf.org/en/Data-Explorer can be retrieved, via `obb.economy.indicators(provider='imf', **kwargs)`. When `openbb-economy` is not installed, the same fetcher is aliased onto `obb.imf.indicators`. -Additionally, there are endpoints for some Port Watch items (not part of the Data Explorer). +Additionally, the extension exposes IMF Port Watch endpoints under `obb.imf.portwatch.*` โ€” these cover country activity, monthly TradeNow indices, container metrics, and disruption events / sankey / map data sourced from the UN Global Platform / IMF Port Watch (not part of the SDMX Data Explorer). -The extension creates a router path, `imf_utils`, that exposes utility functions for UI integrations and metadata lookup. +The extension creates a router path, `imf`, that exposes presentation tables, the dataflow-aware indicator catalog, choice helpers for UI widgets, and the Port Watch subrouter. ### Endpoints -- `obb.economy.available_indicators` -- `obb.economy.indicators` -- `obb.economy.cpi` -- `obb.economy.direction_of_trade` -- `obb.economy.shipping.chokepoint_info` -- `obb.economy.shipping.chokepoint_volume` -- `obb.economy.shipping.port_info` -- `obb.economy.shipping.port_volume` -- `obb.imf_utils.get_dataflow_dimensions` -- `obb.imf_utils.list_dataflow_choices` -- `obb.imf_utils.list_dataflows` -- `obb.imf_utils.list_indicators_by_dataflow` -- `obb.imf_utils.list_port_id_choices` -- `obb.imf_utils.list_table_choices` -- `obb.imf_utils.list_tables` -- `obb.imf_utils.presentation_table` -- `obb.imf_utils.presentation_table_choices` +Data endpoints exposed via the standard `openbb-economy` routes when that extension is +installed (provider `imf`): + +- `obb.economy.available_indicators` (provider `imf`) +- `obb.economy.indicators` (provider `imf`) +- `obb.economy.balance_of_payments` (provider `imf`) +- `obb.economy.cpi` (provider `imf`) +- `obb.economy.direction_of_trade` (provider `imf`) +- `obb.economy.shipping.chokepoint_info` (provider `imf`) +- `obb.economy.shipping.chokepoint_volume` (provider `imf`) +- `obb.economy.shipping.port_info` (provider `imf`) +- `obb.economy.shipping.port_volume` (provider `imf`) + +When `openbb-economy` is absent the same fetchers register under IMF-prefixed model +names and the IMF router exposes equivalent paths: `obb.imf.available_indicators`, +`obb.imf.indicators`, `obb.imf.balance_of_payments`, `obb.imf.cpi`, +`obb.imf.direction_of_trade`, `obb.imf.port_info`, `obb.imf.port_volume`, +`obb.imf.chokepoint_info`, `obb.imf.chokepoint_volume`. + +IMF-native endpoints (always under `obb.imf.*`): + +- `obb.imf.list_dataflows` +- `obb.imf.list_dataflow_choices` +- `obb.imf.get_dataflow_dimensions` +- `obb.imf.list_tables` +- `obb.imf.list_table_choices` +- `obb.imf.presentation_table` +- `obb.imf.presentation_table_choices` +- `obb.imf.indicator_choices` +- `obb.imf.list_bop_country_choices` +- `obb.imf.list_cpi_country_choices` + +IMF Port Watch endpoints (`obb.imf.portwatch.*` โ€” UN Global Platform / IMF Port Watch +data not part of the SDMX Data Explorer): + +- `obb.imf.portwatch.country_activity` +- `obb.imf.portwatch.monthly_trade` +- `obb.imf.portwatch.container_metrics` +- `obb.imf.portwatch.disruption_events` +- `obb.imf.portwatch.disruption_sankey` +- `obb.imf.portwatch.disruptions_map` +- `obb.imf.portwatch.list_port_id_choices` +- `obb.imf.portwatch.list_container_port_choices` +- `obb.imf.portwatch.list_country_choices` +- `obb.imf.portwatch.list_disruption_event_choices` +- `obb.imf.portwatch.list_tradenow_region_choices` "Choices" endpoints are utilized by OpenBB Workspace to populate widget dropdown menus. diff --git a/openbb_platform/providers/imf/hatch_build.py b/openbb_platform/providers/imf/hatch_build.py new file mode 100644 index 00000000000..7c422d4ef68 --- /dev/null +++ b/openbb_platform/providers/imf/hatch_build.py @@ -0,0 +1,102 @@ +"""Hatchling build hook for ``openbb_imf/assets/imf_cache.json.xz``.""" + +from __future__ import annotations + +import os +import subprocess +import sys +from contextlib import suppress +from pathlib import Path +from typing import TextIO + +from hatchling.builders.hooks.plugin.interface import ( # ty: ignore[unresolved-import] + BuildHookInterface, +) + +_ROOT = Path(__file__).resolve().parent +_CACHE_PATH = _ROOT / "openbb_imf" / "assets" / "imf_cache.json.xz" +_GENERATE_CACHE = _ROOT / "openbb_imf" / "utils" / "generate_cache.py" +_FORCE_ENV = "OPENBB_IMF_FORCE_CACHE_REBUILD" + + +def _open_tty() -> TextIO | None: + """Open ``/dev/tty`` for direct terminal output.""" + with suppress(OSError): + return open("/dev/tty", "w", buffering=1, encoding="utf-8") # noqa: SIM115 + return None + + +class ImfCacheBuildHook(BuildHookInterface): + """Materialize ``imf_cache.json.xz`` before packaging.""" + + PLUGIN_NAME = "imf-cache" + + _generated_this_session: bool = False + + def initialize(self, version: str, build_data: dict) -> None: + """Generate or reuse the cache, then mark it for inclusion.""" + tty = _open_tty() + + def _say(msg: str) -> None: + """Emit ``msg`` to ``/dev/tty`` and stderr.""" + line = msg if msg.endswith("\n") else msg + "\n" + if tty is not None: + with suppress(OSError): + tty.write(line) + tty.flush() + sys.stderr.write(line) + sys.stderr.flush() + + cls = type(self) + + force = ( + os.environ.get(_FORCE_ENV, "").lower() in {"1", "true", "yes"} + and not cls._generated_this_session + ) + try: + if _CACHE_PATH.exists() and not force: + reuse_reason = ( + "this build session already regenerated it" + if cls._generated_this_session + else f"set {_FORCE_ENV}=1 to rebuild" + ) + _say(f"imf-cache: reusing {_CACHE_PATH.name} ({reuse_reason})") + else: + _say( + "imf-cache: regenerating from IMF SDMX 3.0 " + "(api.imf.org/external/sdmx/3.0)..." + ) + env = {**os.environ, "PYTHONUNBUFFERED": "1"} + proc = subprocess.Popen( # noqa: S603 + [sys.executable, str(_GENERATE_CACHE)], + cwd=str(_ROOT), + env=env, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + text=True, + bufsize=1, + ) + assert proc.stdout is not None # noqa: S101 + for line in proc.stdout: + if tty is not None: + with suppress(OSError): + tty.write(line) + tty.flush() + sys.stderr.write(line) + sys.stderr.flush() + returncode = proc.wait() + + if returncode != 0 or not _CACHE_PATH.exists(): + self.app.abort( + "imf-cache: failed to generate imf_cache.json.xz " + f"(exit code {returncode})." + ) + + cls._generated_this_session = True + finally: + if tty is not None: + tty.close() + + rel = _CACHE_PATH.relative_to(_ROOT).as_posix() + build_data.setdefault("force_include", {})[str(_CACHE_PATH)] = rel + build_data.setdefault("artifacts", []).append(rel) diff --git a/openbb_platform/providers/imf/integration/test_imf_api.py b/openbb_platform/providers/imf/integration/test_imf_api.py new file mode 100644 index 00000000000..0f33ec417a2 --- /dev/null +++ b/openbb_platform/providers/imf/integration/test_imf_api.py @@ -0,0 +1,386 @@ +"""Test IMF Utils API endpoints.""" + +import base64 + +import pytest +import requests +from openbb_core.env import Env +from openbb_core.provider.utils.helpers import get_querystring + + +@pytest.fixture(scope="session") +def headers(): + """Get the headers for the API request.""" + userpass = f"{Env().API_USERNAME}:{Env().API_PASSWORD}" + userpass_bytes = userpass.encode("ascii") + base64_bytes = base64.b64encode(userpass_bytes) + + return {"Authorization": f"Basic {base64_bytes.decode('ascii')}"} + + +@pytest.mark.parametrize( + "params", + [ + { + "output_format": "json", + } + ], +) +@pytest.mark.integration +def test_imf_list_dataflows(params, headers): + """Test imf_list_dataflows endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://localhost:8000/api/v1/imf/list_dataflows?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + { + "dataflow_id": "CPI", + "output_format": "json", + }, + ], +) +@pytest.mark.integration +def test_imf_get_dataflow_dimensions(params, headers): + """Test imf_get_dataflow_dimensions endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://localhost:8000/api/v1/imf/get_dataflow_dimensions?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize("params", [{}]) +@pytest.mark.integration +def test_imf_portwatch_list_port_id_choices(params, headers): + """Test imf_portwatch_list_port_id_choices endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://localhost:8000/api/v1/imf/portwatch/list_port_id_choices?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize("params", [{}]) +@pytest.mark.integration +def test_imf_list_tables(params, headers): + """Test imf_list_tables endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://localhost:8000/api/v1/imf/list_tables?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize("params", [{}]) +@pytest.mark.integration +def test_imf_list_table_choices(params, headers): + """Test imf_list_table_choices endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://localhost:8000/api/v1/imf/list_table_choices?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize("params", [{}]) +@pytest.mark.integration +def test_imf_list_dataflow_choices(params, headers): + """Test imf_list_dataflow_choices endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://localhost:8000/api/v1/imf/list_dataflow_choices?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + { + "dataflow_group": None, + "table": None, + "country": None, + "frequency": None, + }, + { + "dataflow_group": "cpi", + "table": None, + "country": None, + "frequency": None, + }, + { + "dataflow_group": "cpi", + "table": "cpi", + "country": None, + "frequency": None, + }, + { + "dataflow_group": "cpi", + "table": "cpi", + "country": "JPN", + "frequency": None, + }, + ], +) +@pytest.mark.integration +def test_imf_presentation_table_choices(params, headers): + """Test imf_presentation_table_choices endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://localhost:8000/api/v1/imf/presentation_table_choices?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + { + "symbol": "CPI::CPI__T", + "country": None, + "frequency": None, + "transform": None, + "dimension_values": None, + }, + { + "symbol": "CPI::CPI__T", + "country": "JPN", + "frequency": None, + "transform": None, + "dimension_values": None, + }, + ], +) +@pytest.mark.integration +def test_imf_indicator_choices(params, headers): + """Test imf_indicator_choices endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://localhost:8000/api/v1/imf/indicator_choices?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [ + { + "dataflow_group": "cpi", + "table": "cpi", + "country": "JPN", + "frequency": "A", + "dimension_values": None, + "limit": 1, + "raw": True, + } + ], +) +@pytest.mark.integration +def test_imf_presentation_table(params, headers): + """Test imf_presentation_table endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://localhost:8000/api/v1/imf/presentation_table?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize("params", [{}]) +@pytest.mark.integration +def test_imf_list_bop_country_choices(params, headers): + """Test imf_list_bop_country_choices endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://localhost:8000/api/v1/imf/list_bop_country_choices?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize("params", [{}]) +@pytest.mark.integration +def test_imf_list_cpi_country_choices(params, headers): + """Test imf_list_cpi_country_choices endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://localhost:8000/api/v1/imf/list_cpi_country_choices?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize("params", [{}]) +@pytest.mark.integration +def test_imf_portwatch_list_country_choices(params, headers): + """Test imf_portwatch_list_country_choices endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://localhost:8000/api/v1/imf/portwatch/list_country_choices?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize("params", [{}]) +@pytest.mark.integration +def test_imf_portwatch_list_tradenow_region_choices(params, headers): + """Test imf_portwatch_list_tradenow_region_choices endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = ( + "http://localhost:8000/api/v1/imf/portwatch/list_tradenow_region_choices" + f"?{query_str}" + ) + result = requests.get(url, headers=headers, timeout=60) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize("params", [{}]) +@pytest.mark.integration +def test_imf_portwatch_list_container_port_choices(params, headers): + """Test imf_portwatch_list_container_port_choices endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = ( + "http://localhost:8000/api/v1/imf/portwatch/list_container_port_choices" + f"?{query_str}" + ) + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize("params", [{}]) +@pytest.mark.integration +def test_imf_portwatch_list_disruption_event_choices(params, headers): + """Test imf_portwatch_list_disruption_event_choices endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = ( + "http://localhost:8000/api/v1/imf/portwatch/list_disruption_event_choices" + f"?{query_str}" + ) + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [{"country_code": "USA", "metric": "portcalls", "provider": "imf"}], +) +@pytest.mark.integration +def test_imf_portwatch_country_activity(params, headers): + """Test imf_portwatch_country_activity endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://localhost:8000/api/v1/imf/portwatch/country_activity?{query_str}" + result = requests.get(url, headers=headers, timeout=60) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [{"code": "USA", "metric": "trade_value", "provider": "imf"}], +) +@pytest.mark.integration +def test_imf_portwatch_monthly_trade(params, headers): + """Test imf_portwatch_monthly_trade endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://localhost:8000/api/v1/imf/portwatch/monthly_trade?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [{"metric": "portcalls", "provider": "imf"}], +) +@pytest.mark.integration +def test_imf_portwatch_container_metrics(params, headers): + """Test imf_portwatch_container_metrics endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://localhost:8000/api/v1/imf/portwatch/container_metrics?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize("params", [{"provider": "imf"}]) +@pytest.mark.integration +def test_imf_portwatch_disruption_events(params, headers): + """Test imf_portwatch_disruption_events endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://localhost:8000/api/v1/imf/portwatch/disruption_events?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize("params", [{"provider": "imf"}]) +@pytest.mark.integration +def test_imf_portwatch_disruptions_map(params, headers): + """Test imf_portwatch_disruptions_map endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://localhost:8000/api/v1/imf/portwatch/disruptions_map?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 + + +@pytest.mark.parametrize( + "params", + [{"event_id": "LATEST", "provider": "imf"}], +) +@pytest.mark.integration +def test_imf_portwatch_disruption_sankey(params, headers): + """Test imf_portwatch_disruption_sankey endpoint.""" + params = {p: v for p, v in params.items() if v} + + query_str = get_querystring(params, []) + url = f"http://localhost:8000/api/v1/imf/portwatch/disruption_sankey?{query_str}" + result = requests.get(url, headers=headers, timeout=10) + assert isinstance(result, requests.Response) + assert result.status_code == 200 diff --git a/openbb_platform/providers/imf/integration/test_imf_python.py b/openbb_platform/providers/imf/integration/test_imf_python.py new file mode 100644 index 00000000000..69ecd240945 --- /dev/null +++ b/openbb_platform/providers/imf/integration/test_imf_python.py @@ -0,0 +1,316 @@ +"""IMF Utilities module integration tests.""" + +import pytest +from openbb_core.app.model.obbject import OBBject + + +@pytest.fixture(scope="session") +def obb(pytestconfig): + """Fixture to setup obb.""" + + if pytestconfig.getoption("markexpr") != "not integration": + import openbb + + return openbb.obb + + +@pytest.mark.parametrize( + "params", + [ + { + "output_format": "json", + } + ], +) +@pytest.mark.integration +def test_imf_list_dataflows(params, obb): + """Test imf_list_dataflows endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.imf.list_dataflows(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [ + { + "dataflow_id": "CPI", + "output_format": "json", + }, + ], +) +@pytest.mark.integration +def test_imf_get_dataflow_dimensions(params, obb): + """Test imf_get_dataflow_dimensions endpoint.""" + params = {p: v for p, v in params.items() if v} + + result = obb.imf.get_dataflow_dimensions(**params) + assert result + assert isinstance(result, OBBject) + assert result.results + assert isinstance(result.results, dict) + + +@pytest.mark.parametrize("params", [{}]) +@pytest.mark.integration +def test_imf_portwatch_list_port_id_choices(params, obb): + """Test imf_portwatch_list_port_id_choices endpoint.""" + params = {p: v for p, v in params.items() if v} + result = obb.imf.portwatch.list_port_id_choices(**params) + assert result + assert isinstance(result, list) + + +@pytest.mark.parametrize("params", [{}]) +@pytest.mark.integration +def test_imf_list_tables(params, obb): + """Test imf_list_tables endpoint.""" + params = {p: v for p, v in params.items() if v} + result = obb.imf.list_tables(**params) + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize("params", [{}]) +@pytest.mark.integration +def test_imf_list_table_choices(params, obb): + """Test imf_list_table_choices endpoint.""" + params = {p: v for p, v in params.items() if v} + result = obb.imf.list_table_choices(**params) + assert result + assert isinstance(result, list) + assert len(result) > 0 + + +@pytest.mark.parametrize("params", [{}]) +@pytest.mark.integration +def test_imf_list_dataflow_choices(params, obb): + """Test imf_list_dataflow_choices endpoint.""" + params = {p: v for p, v in params.items() if v} + result = obb.imf.list_dataflow_choices(**params) + assert result + assert isinstance(result, list) + assert len(result) > 0 + + +@pytest.mark.parametrize( + "params", + [ + { + "dataflow_group": None, + "table": None, + "country": None, + "frequency": None, + }, + { + "dataflow_group": "cpi", + "table": None, + "country": None, + "frequency": None, + }, + ], +) +@pytest.mark.integration +def test_imf_presentation_table_choices(params, obb): + """Test imf_presentation_table_choices endpoint.""" + params = {p: v for p, v in params.items() if v} + result = obb.imf.presentation_table_choices(**params) + assert result + assert isinstance(result, list) + + +@pytest.mark.parametrize( + "params", + [ + { + "dataflow_group": "cpi", + "table": "cpi", + "country": "JPN", + "frequency": "A", + "dimension_values": None, + "limit": 1, + "raw": True, + } + ], +) +@pytest.mark.integration +def test_imf_presentation_table(params, obb): + """Test imf_presentation_table endpoint.""" + params = {p: v for p, v in params.items() if v} + result = obb.imf.presentation_table(**params) + assert result + assert isinstance(result, list) + assert len(result) > 1 + + +@pytest.mark.parametrize( + "params", + [ + { + "symbol": "CPI::CPI__T", + "country": None, + "frequency": None, + "transform": None, + "dimension_values": None, + }, + { + "symbol": "CPI::CPI__T", + "country": "JPN", + "frequency": None, + "transform": None, + "dimension_values": None, + }, + ], +) +@pytest.mark.integration +@pytest.mark.skip(reason="Not included in Python interface") +def test_imf_indicator_choices(params, obb): + """Test imf_indicator_choices endpoint.""" + params = {p: v for p, v in params.items() if v} + result = obb.imf.indicator_choices(**params) + assert result + assert isinstance(result, list) + assert len(result) > 0 + + +@pytest.mark.parametrize("params", [{}]) +@pytest.mark.integration +def test_imf_list_bop_country_choices(params, obb): + """Test imf_list_bop_country_choices endpoint.""" + params = {p: v for p, v in params.items() if v} + result = obb.imf.list_bop_country_choices(**params) + assert result + assert isinstance(result, list) + assert len(result) > 0 + + +@pytest.mark.parametrize("params", [{}]) +@pytest.mark.integration +def test_imf_list_cpi_country_choices(params, obb): + """Test imf_list_cpi_country_choices endpoint.""" + params = {p: v for p, v in params.items() if v} + result = obb.imf.list_cpi_country_choices(**params) + assert result + assert isinstance(result, list) + assert len(result) > 0 + + +@pytest.mark.parametrize("params", [{}]) +@pytest.mark.integration +def test_imf_portwatch_list_country_choices(params, obb): + """Test imf_portwatch_list_country_choices endpoint.""" + params = {p: v for p, v in params.items() if v} + result = obb.imf.portwatch.list_country_choices(**params) + assert result + assert isinstance(result, list) + + +@pytest.mark.parametrize("params", [{}]) +@pytest.mark.integration +def test_imf_portwatch_list_tradenow_region_choices(params, obb): + """Test imf_portwatch_list_tradenow_region_choices endpoint.""" + params = {p: v for p, v in params.items() if v} + result = obb.imf.portwatch.list_tradenow_region_choices(**params) + assert result + assert isinstance(result, list) + + +@pytest.mark.parametrize("params", [{}]) +@pytest.mark.integration +def test_imf_portwatch_list_container_port_choices(params, obb): + """Test imf_portwatch_list_container_port_choices endpoint.""" + params = {p: v for p, v in params.items() if v} + result = obb.imf.portwatch.list_container_port_choices(**params) + assert result + assert isinstance(result, list) + assert result[0]["value"] == "TOP10" + + +@pytest.mark.parametrize("params", [{}]) +@pytest.mark.integration +def test_imf_portwatch_list_disruption_event_choices(params, obb): + """Test imf_portwatch_list_disruption_event_choices endpoint.""" + params = {p: v for p, v in params.items() if v} + result = obb.imf.portwatch.list_disruption_event_choices(**params) + assert result + assert isinstance(result, list) + assert result[0]["value"] == "LATEST" + + +@pytest.mark.parametrize( + "params", + [{"country_code": "USA", "metric": "portcalls"}], +) +@pytest.mark.integration +def test_imf_portwatch_country_activity(params, obb): + """Test imf_portwatch_country_activity endpoint.""" + params = {p: v for p, v in params.items() if v} + result = obb.imf.portwatch.country_activity(**params, provider="imf") + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [{"code": "USA", "metric": "trade_value"}], +) +@pytest.mark.integration +def test_imf_portwatch_monthly_trade(params, obb): + """Test imf_portwatch_monthly_trade endpoint.""" + params = {p: v for p, v in params.items() if v} + result = obb.imf.portwatch.monthly_trade(**params, provider="imf") + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize( + "params", + [{"metric": "portcalls"}], +) +@pytest.mark.integration +def test_imf_portwatch_container_metrics(params, obb): + """Test imf_portwatch_container_metrics endpoint.""" + params = {p: v for p, v in params.items() if v} + result = obb.imf.portwatch.container_metrics(**params, provider="imf") + assert result + assert isinstance(result, OBBject) + assert len(result.results) > 0 + + +@pytest.mark.parametrize("params", [{}]) +@pytest.mark.integration +def test_imf_portwatch_disruption_events(params, obb): + """Test imf_portwatch_disruption_events endpoint.""" + params = {p: v for p, v in params.items() if v} + result = obb.imf.portwatch.disruption_events(**params, provider="imf") + assert result + assert isinstance(result, OBBject) + + +@pytest.mark.parametrize("params", [{}]) +@pytest.mark.integration +def test_imf_portwatch_disruptions_map(params, obb): + """Test imf_portwatch_disruptions_map endpoint.""" + params = {p: v for p, v in params.items() if v} + result = obb.imf.portwatch.disruptions_map(**params, provider="imf") + assert result + assert isinstance(result, OBBject) + + +@pytest.mark.parametrize( + "params", + [{"event_id": "LATEST"}], +) +@pytest.mark.integration +def test_imf_portwatch_disruption_sankey(params, obb): + """Test imf_portwatch_disruption_sankey endpoint.""" + params = {p: v for p, v in params.items() if v} + result = obb.imf.portwatch.disruption_sankey(**params, provider="imf") + assert result + assert isinstance(result, OBBject) diff --git a/openbb_platform/providers/imf/integration/test_imf_utils_api.py b/openbb_platform/providers/imf/integration/test_imf_utils_api.py deleted file mode 100644 index 7220f97e3b2..00000000000 --- a/openbb_platform/providers/imf/integration/test_imf_utils_api.py +++ /dev/null @@ -1,211 +0,0 @@ -"""Test IMF Utils API endpoints.""" - -import base64 - -import pytest -import requests -from openbb_core.env import Env -from openbb_core.provider.utils.helpers import get_querystring - -# pylint: disable=redefined-outer-name - - -@pytest.fixture(scope="session") -def headers(): - """Get the headers for the API request.""" - userpass = f"{Env().API_USERNAME}:{Env().API_PASSWORD}" - userpass_bytes = userpass.encode("ascii") - base64_bytes = base64.b64encode(userpass_bytes) - - return {"Authorization": f"Basic {base64_bytes.decode('ascii')}"} - - -@pytest.mark.parametrize( - "params", - [ - { - "output_format": "json", - } - ], -) -@pytest.mark.integration -def test_imf_utils_list_dataflows(params, headers): - """Test imf_utils_list_dataflows endpoint.""" - params = {p: v for p, v in params.items() if v} - - query_str = get_querystring(params, []) - url = f"http://0.0.0.0:8000/api/v1/imf_utils/list_dataflows?{query_str}" - result = requests.get(url, headers=headers, timeout=10) - assert isinstance(result, requests.Response) - assert result.status_code == 200 - - -@pytest.mark.parametrize( - "params", - [ - { - "dataflow_id": "CPI", - "output_format": "json", - }, - ], -) -@pytest.mark.integration -def test_imf_utils_get_dataflow_dimensions(params, headers): - """Test imf_utils_get_dataflow_dimensions endpoint.""" - params = {p: v for p, v in params.items() if v} - - query_str = get_querystring(params, []) - url = f"http://0.0.0.0:8000/api/v1/imf_utils/get_dataflow_dimensions?{query_str}" - result = requests.get(url, headers=headers, timeout=10) - assert isinstance(result, requests.Response) - assert result.status_code == 200 - - -@pytest.mark.parametrize("params", [{}]) -@pytest.mark.integration -def test_imf_utils_list_port_id_choices(params, headers): - """Test imf_utils_list_port_id_choices endpoint.""" - params = {p: v for p, v in params.items() if v} - - query_str = get_querystring(params, []) - url = f"http://0.0.0.0:8000/api/v1/imf_utils/list_port_id_choices?{query_str}" - result = requests.get(url, headers=headers, timeout=10) - assert isinstance(result, requests.Response) - assert result.status_code == 200 - - -@pytest.mark.parametrize("params", [{}]) -@pytest.mark.integration -def test_imf_utils_list_tables(params, headers): - """Test imf_utils_list_tables endpoint.""" - params = {p: v for p, v in params.items() if v} - - query_str = get_querystring(params, []) - url = f"http://0.0.0.0:8000/api/v1/imf_utils/list_tables?{query_str}" - result = requests.get(url, headers=headers, timeout=10) - assert isinstance(result, requests.Response) - assert result.status_code == 200 - - -@pytest.mark.parametrize("params", [{}]) -@pytest.mark.integration -def test_imf_utils_list_table_choices(params, headers): - """Test imf_utils_list_table_choices endpoint.""" - params = {p: v for p, v in params.items() if v} - - query_str = get_querystring(params, []) - url = f"http://0.0.0.0:8000/api/v1/imf_utils/list_table_choices?{query_str}" - result = requests.get(url, headers=headers, timeout=10) - assert isinstance(result, requests.Response) - assert result.status_code == 200 - - -@pytest.mark.parametrize("params", [{}]) -@pytest.mark.integration -def test_imf_utils_list_dataflow_choices(params, headers): - """Test imf_utils_list_dataflow_choices endpoint.""" - params = {p: v for p, v in params.items() if v} - - query_str = get_querystring(params, []) - url = f"http://0.0.0.0:8000/api/v1/imf_utils/list_dataflow_choices?{query_str}" - result = requests.get(url, headers=headers, timeout=10) - assert isinstance(result, requests.Response) - assert result.status_code == 200 - - -@pytest.mark.parametrize( - "params", - [ - { - "dataflow_group": None, - "table": None, - "country": None, - "frequency": None, - }, - { - "dataflow_group": "cpi", - "table": None, - "country": None, - "frequency": None, - }, - { - "dataflow_group": "cpi", - "table": "cpi", - "country": None, - "frequency": None, - }, - { - "dataflow_group": "cpi", - "table": "cpi", - "country": "JPN", - "frequency": None, - }, - ], -) -@pytest.mark.integration -def test_imf_utils_presentation_table_choices(params, headers): - """Test imf_utils_presentation_table_choices endpoint.""" - params = {p: v for p, v in params.items() if v} - - query_str = get_querystring(params, []) - url = f"http://0.0.0.0:8000/api/v1/imf_utils/presentation_table_choices?{query_str}" - result = requests.get(url, headers=headers, timeout=10) - assert isinstance(result, requests.Response) - assert result.status_code == 200 - - -@pytest.mark.parametrize( - "params", - [ - { - "symbol": "CPI::CPI__T", - "country": None, - "frequency": None, - "transform": None, - "dimension_values": None, - }, - { - "symbol": "CPI::CPI__T", - "country": "JPN", - "frequency": None, - "transform": None, - "dimension_values": None, - }, - ], -) -@pytest.mark.integration -def test_imf_utils_indicator_choices(params, headers): - """Test imf_utils_indicator_choices endpoint.""" - params = {p: v for p, v in params.items() if v} - - query_str = get_querystring(params, []) - url = f"http://0.0.0.0:8000/api/v1/imf_utils/indicator_choices?{query_str}" - result = requests.get(url, headers=headers, timeout=10) - assert isinstance(result, requests.Response) - assert result.status_code == 200 - - -@pytest.mark.parametrize( - "params", - [ - { - "dataflow_group": "cpi", - "table": "cpi", - "country": "JPN", - "frequency": "A", - "dimension_values": None, - "limit": 1, - "raw": True, - } - ], -) -@pytest.mark.integration -def test_imf_utils_presentation_table(params, headers): - """Test imf_utils_presentation_table endpoint.""" - params = {p: v for p, v in params.items() if v} - - query_str = get_querystring(params, []) - url = f"http://0.0.0.0:8000/api/v1/imf_utils/presentation_table?{query_str}" - result = requests.get(url, headers=headers, timeout=10) - assert isinstance(result, requests.Response) - assert result.status_code == 200 diff --git a/openbb_platform/providers/imf/integration/test_imf_utils_python.py b/openbb_platform/providers/imf/integration/test_imf_utils_python.py deleted file mode 100644 index f836f6189cf..00000000000 --- a/openbb_platform/providers/imf/integration/test_imf_utils_python.py +++ /dev/null @@ -1,179 +0,0 @@ -"""IMF Utilities module integration tests.""" - -import pytest -from openbb_core.app.model.obbject import OBBject - -# pylint: disable=redefined-outer-name - - -@pytest.fixture(scope="session") -def obb(pytestconfig): # pylint: disable=inconsistent-return-statements - """Fixture to setup obb.""" - - if pytestconfig.getoption("markexpr") != "not integration": - import openbb # pylint: disable=import-outside-toplevel - - return openbb.obb - - -@pytest.mark.parametrize( - "params", - [ - { - "output_format": "json", - } - ], -) -@pytest.mark.integration -def test_imf_utils_list_dataflows(params, obb): - """Test imf_utils_list_dataflows endpoint.""" - params = {p: v for p, v in params.items() if v} - - result = obb.imf_utils.list_dataflows(**params) - assert result - assert isinstance(result, OBBject) - assert len(result.results) > 0 # type: ignore - - -@pytest.mark.parametrize( - "params", - [ - { - "dataflow_id": "CPI", - "output_format": "json", - }, - ], -) -@pytest.mark.integration -def test_imf_utils_get_dataflow_dimensions(params, obb): - """Test imf_utils_get_dataflow_dimensions endpoint.""" - params = {p: v for p, v in params.items() if v} - - result = obb.imf_utils.get_dataflow_dimensions(**params) - assert result - assert isinstance(result, OBBject) - assert result.results - assert isinstance(result.results, dict) - - -@pytest.mark.parametrize("params", [{}]) -@pytest.mark.integration -def test_imf_utils_list_port_id_choices(params, obb): - """Test imf_utils_list_port_id_choices endpoint.""" - params = {p: v for p, v in params.items() if v} - result = obb.imf_utils.list_port_id_choices(**params) - assert result - assert isinstance(result, list) - - -@pytest.mark.parametrize("params", [{}]) -@pytest.mark.integration -def test_imf_utils_list_tables(params, obb): - """Test imf_utils_list_tables endpoint.""" - params = {p: v for p, v in params.items() if v} - result = obb.imf_utils.list_tables(**params) - assert result - assert isinstance(result, OBBject) - assert len(result.results) > 0 # type: ignore - - -@pytest.mark.parametrize("params", [{}]) -@pytest.mark.integration -def test_imf_utils_list_table_choices(params, obb): - """Test imf_utils_list_table_choices endpoint.""" - params = {p: v for p, v in params.items() if v} - result = obb.imf_utils.list_table_choices(**params) - assert result - assert isinstance(result, list) - assert len(result) > 0 - - -@pytest.mark.parametrize("params", [{}]) -@pytest.mark.integration -def test_imf_utils_list_dataflow_choices(params, obb): - """Test imf_utils_list_dataflow_choices endpoint.""" - params = {p: v for p, v in params.items() if v} - result = obb.imf_utils.list_dataflow_choices(**params) - assert result - assert isinstance(result, list) - assert len(result) > 0 - - -@pytest.mark.parametrize( - "params", - [ - { - "dataflow_group": None, - "table": None, - "country": None, - "frequency": None, - }, - { - "dataflow_group": "cpi", - "table": None, - "country": None, - "frequency": None, - }, - ], -) -@pytest.mark.integration -def test_imf_utils_presentation_table_choices(params, obb): - """Test imf_utils_presentation_table_choices endpoint.""" - params = {p: v for p, v in params.items() if v} - result = obb.imf_utils.presentation_table_choices(**params) - assert result - assert isinstance(result, list) - - -@pytest.mark.parametrize( - "params", - [ - { - "dataflow_group": "cpi", - "table": "cpi", - "country": "JPN", - "frequency": "A", - "dimension_values": None, - "limit": 1, - "raw": True, - } - ], -) -@pytest.mark.integration -def test_imf_utils_presentation_table(params, obb): - """Test imf_utils_presentation_table endpoint.""" - params = {p: v for p, v in params.items() if v} - result = obb.imf_utils.presentation_table(**params) - assert result - assert isinstance(result, list) - assert len(result) > 1 # type: ignore - - -@pytest.mark.parametrize( - "params", - [ - { - "symbol": "CPI::CPI__T", - "country": None, - "frequency": None, - "transform": None, - "dimension_values": None, - }, - { - "symbol": "CPI::CPI__T", - "country": "JPN", - "frequency": None, - "transform": None, - "dimension_values": None, - }, - ], -) -@pytest.mark.integration -@pytest.mark.skip(reason="Not included in Python interface") -def test_imf_utils_indicator_choices(params, obb): - """Test imf_utils_indicator_choices endpoint.""" - params = {p: v for p, v in params.items() if v} - result = obb.imf_utils.indicator_choices(**params) - assert result - assert isinstance(result, list) - assert len(result) > 0 diff --git a/openbb_platform/providers/imf/openbb_imf/__init__.py b/openbb_platform/providers/imf/openbb_imf/__init__.py index e9a53ef59a0..8f2c5eb7fbe 100644 --- a/openbb_platform/providers/imf/openbb_imf/__init__.py +++ b/openbb_platform/providers/imf/openbb_imf/__init__.py @@ -1,30 +1,65 @@ """OpenBB IMF Provider Module.""" +from __future__ import annotations + +from importlib.util import find_spec + from openbb_core.provider.abstract.provider import Provider + from openbb_imf.models.available_indicators import ImfAvailableIndicatorsFetcher +from openbb_imf.models.balance_of_payments import ImfBalanceOfPaymentsFetcher from openbb_imf.models.consumer_price_index import ImfConsumerPriceIndexFetcher +from openbb_imf.models.container_metrics import ImfContainerMetricsFetcher +from openbb_imf.models.country_activity import ImfCountryActivityFetcher from openbb_imf.models.direction_of_trade import ImfDirectionOfTradeFetcher +from openbb_imf.models.disruption_events import ImfDisruptionEventsFetcher +from openbb_imf.models.disruption_sankey import ImfDisruptionSankeyFetcher from openbb_imf.models.economic_indicators import ImfEconomicIndicatorsFetcher from openbb_imf.models.maritime_chokepoint_info import ImfMaritimeChokePointInfoFetcher from openbb_imf.models.maritime_chokepoint_volume import ( ImfMaritimeChokePointVolumeFetcher, ) +from openbb_imf.models.monthly_trade import ImfMonthlyTradeFetcher from openbb_imf.models.port_info import ImfPortInfoFetcher from openbb_imf.models.port_volume import ImfPortVolumeFetcher +ECONOMY_INSTALLED = find_spec("openbb_economy") is not None + + +def _key(standard: str, imf_alias: str) -> str: + """Return the standard key when ``openbb-economy`` is installed, else the IMF alias.""" + return standard if ECONOMY_INSTALLED else imf_alias + + imf_provider = Provider( name="imf", website="https://datahelp.imf.org/knowledgebase/articles/667681-using-json-restful-web-service", description="Access International Monetary Fund (IMF) data APIs.", fetcher_dict={ - "AvailableIndicators": ImfAvailableIndicatorsFetcher, - "ConsumerPriceIndex": ImfConsumerPriceIndexFetcher, - "DirectionOfTrade": ImfDirectionOfTradeFetcher, - "EconomicIndicators": ImfEconomicIndicatorsFetcher, - "MaritimeChokePointInfo": ImfMaritimeChokePointInfoFetcher, - "MaritimeChokePointVolume": ImfMaritimeChokePointVolumeFetcher, - "PortInfo": ImfPortInfoFetcher, - "PortVolume": ImfPortVolumeFetcher, + _key("AvailableIndicators", "AvailableImfIndicators"): ( + ImfAvailableIndicatorsFetcher + ), + _key("BalanceOfPayments", "ImfBalanceOfPayments"): ( + ImfBalanceOfPaymentsFetcher + ), + _key("ConsumerPriceIndex", "ImfConsumerPriceIndex"): ( + ImfConsumerPriceIndexFetcher + ), + "ContainerMetrics": ImfContainerMetricsFetcher, + "CountryActivity": ImfCountryActivityFetcher, + _key("DirectionOfTrade", "ImfDirectionOfTrade"): ImfDirectionOfTradeFetcher, + "DisruptionEvents": ImfDisruptionEventsFetcher, + "DisruptionSankey": ImfDisruptionSankeyFetcher, + _key("EconomicIndicators", "ImfIndicators"): ImfEconomicIndicatorsFetcher, + _key("MaritimeChokePointInfo", "ImfMaritimeChokePointInfo"): ( + ImfMaritimeChokePointInfoFetcher + ), + _key("MaritimeChokePointVolume", "ImfMaritimeChokePointVolume"): ( + ImfMaritimeChokePointVolumeFetcher + ), + "MonthlyTrade": ImfMonthlyTradeFetcher, + _key("PortInfo", "ImfPortInfo"): ImfPortInfoFetcher, + _key("PortVolume", "ImfPortVolume"): ImfPortVolumeFetcher, }, repr_name="International Monetary Fund (IMF) Data APIs", ) diff --git a/openbb_platform/providers/imf/openbb_imf/apps.json b/openbb_platform/providers/imf/openbb_imf/apps.json index 5ee8605e857..be7663b452d 100644 --- a/openbb_platform/providers/imf/openbb_imf/apps.json +++ b/openbb_platform/providers/imf/openbb_imf/apps.json @@ -1,488 +1,591 @@ -[{ - "name": "IMF Explorer", - "img": "https://upload.wikimedia.org/wikipedia/commons/thumb/3/3e/IMF-Seal_ENG_RGB.svg/500px-IMF-Seal_ENG_RGB.svg.png", - "img_dark": "https://upload.wikimedia.org/wikipedia/commons/thumb/3/3e/IMF-Seal_ENG_RGB.svg/500px-IMF-Seal_ENG_RGB.svg.png", - "img_light": "https://upload.wikimedia.org/wikipedia/commons/thumb/3/3e/IMF-Seal_ENG_RGB.svg/500px-IMF-Seal_ENG_RGB.svg.png", - "description": "Explore economic indicators and presentation tables published by the IMF. Disclaimer: This application is neither affiliated with nor endorsed by the IMF. See https://data.imf.org/ for terms and conditions.", - "allowCustomization": true, - "tabs": { - "dataflows": { - "id": "dataflows", - "name": "Dataflows", - "layout": [ - { - "i": "imf_utils_get_dataflow_dimensions_custom_obb", - "x": 0, - "y": 2, - "w": 16, - "h": 29, - "state": { - "params": { - "dataflow_id": "CTOT" - } - }, - "groups": [] - }, - { - "i": "imf_utils_list_dataflows_custom_obb", - "x": 16, - "y": 2, - "w": 24, - "h": 29, - "groups": [] - } - ] - }, - "indicators": { - "id": "indicators", - "name": "Indicators", - "layout": [ - { - "i": "economy_available_indicators_imf_obb", - "x": 0, - "y": 2, - "w": 40, - "h": 15, - "state": { - "params": { - "dataflows": "CTOT", - "symbol": "CTOT::CEMPI_CTOTNX_TT" - }, - "chartView": { - "enabled": false, - "chartType": "line" - }, - "columnState": { - "default": { - "columnPinning": { - "leftColIds": [ - "symbol" - ], - "rightColIds": [] - }, - "columnOrder": { - "orderedColIds": [ - "symbol_root", - "symbol", - "description", - "agency_id", - "dataflow_id", - "dataflow_name", - "structure_id", - "dimension_id", - "long_description", - "member_of" - ] - } +[ + { + "name": "IMF Explorer", + "img": "https://bravenewcoin.com/wp-content/uploads/2023/11/assets_Uploads_bravenewcoin-imf-banner-scaled.jpg", + "description": "Explore economic indicators and presentation tables published by the IMF. Disclaimer: This application is neither affiliated with nor endorsed by the IMF. See https://data.imf.org/ for terms and conditions.", + "allowCustomization": true, + "tabs": { + "dataflows": { + "id": "dataflows", + "name": "Dataflows", + "layout": [ + { + "i": "imf_get_dataflow_dimensions_custom_obb", + "x": 0, + "y": 2, + "w": 16, + "h": 29, + "state": { + "params": { + "dataflow_id": "CTOT" } - } - }, - "groups": [ - "Group 1" - ] - }, - { - "i": "economy_indicators_imf_obb", - "x": 0, - "y": 17, - "w": 40, - "h": 21, - "state": { - "params": { - "symbol": "CTOT::CEPI_CTOTX_GDP", - "country": [ - "", - "AUS" - ], - "frequency": "M", - "limit": "1", - "pivot": "false" }, - "chartModel": { - "modelType": "range", - "chartType": "customCombo", - "chartOptions": { - "line": { - "legend": { - "enabled": false + "groups": [] + }, + { + "i": "imf_list_dataflows_custom_obb", + "x": 16, + "y": 2, + "w": 24, + "h": 29, + "groups": [] + } + ] + }, + "indicators": { + "id": "indicators", + "name": "Indicators", + "layout": [ + { + "i": "economy_available_indicators_imf_obb", + "x": 0, + "y": 2, + "w": 40, + "h": 15, + "state": { + "params": { + "dataflows": "CTOT", + "symbol": "CTOT::CEMPI_CTOTNX_TT" + }, + "chartView": { + "enabled": false, + "chartType": "line" + }, + "columnState": { + "default": { + "columnPinning": { + "leftColIds": [ + "symbol" + ], + "rightColIds": [] }, - "axes": { - "category": {} + "columnOrder": { + "orderedColIds": [ + "symbol_root", + "symbol", + "description", + "agency_id", + "dataflow_id", + "dataflow_name", + "structure_id", + "dimension_id", + "long_description", + "member_of" + ] } } + } + }, + "groups": [ + "Group 1" + ] + }, + { + "i": "economy_indicators_imf_obb", + "x": 0, + "y": 17, + "w": 40, + "h": 21, + "state": { + "params": { + "symbol": "CTOT::CEPI_CTOTX_GDP", + "country": [ + "", + "AUS" + ], + "frequency": "M", + "limit": "1", + "pivot": "false" }, - "cellRange": { - "columns": [ - "date", - "value" - ] - }, - "suppressChartRanges": true, - "seriesChartTypes": [ - { - "colId": "value", - "chartType": "groupedColumn", - "secondaryAxis": false + "chartModel": { + "modelType": "range", + "chartType": "customCombo", + "chartOptions": { + "line": { + "legend": { + "enabled": false + }, + "axes": { + "category": {} + } + } }, - { - "colId": "unit_multiplier", - "chartType": "line", - "secondaryAxis": false - } - ] - }, - "chartView": { - "enabled": false, - "chartType": "customCombo" - }, - "columnState": { - "default": { - "columnPinning": { - "leftColIds": [ - "title", - "country", + "cellRange": { + "columns": [ "date", "value" - ], - "rightColIds": [] - }, - "columnVisibility": { - "hiddenColIds": [ - "description" ] }, - "columnOrder": { - "orderedColIds": [ - "title", - "country", - "unit", - "scale", - "date", - "symbol_root", - "symbol", - "value", - "unit_multiplier", - "order", - "level", - "indicator", - "description", - "country_code", - "wgt_type", - "wgt_type_code", - "frequency", - "frequency_code" - ] + "suppressChartRanges": true, + "seriesChartTypes": [ + { + "colId": "value", + "chartType": "groupedColumn", + "secondaryAxis": false + }, + { + "colId": "unit_multiplier", + "chartType": "line", + "secondaryAxis": false + } + ] + }, + "chartView": { + "enabled": false, + "chartType": "customCombo" + }, + "columnState": { + "default": { + "columnPinning": { + "leftColIds": [ + "title", + "country", + "date", + "value" + ], + "rightColIds": [] + }, + "columnVisibility": { + "hiddenColIds": [ + "description" + ] + }, + "columnOrder": { + "orderedColIds": [ + "title", + "country", + "unit", + "scale", + "date", + "symbol_root", + "symbol", + "value", + "unit_multiplier", + "order", + "level", + "indicator", + "description", + "country_code", + "wgt_type", + "wgt_type_code", + "frequency", + "frequency_code" + ] + } } } - } + }, + "groups": [ + "Group 1" + ] + } + ] + }, + "presentation-tables": { + "id": "presentation-tables", + "name": "Presentation Tables", + "layout": [ + { + "i": "imf_presentation_table_custom_obb", + "x": 0, + "y": 2, + "w": 40, + "h": 31, + "state": { + "params": { + "dataflow_group": "qgfs", + "table": "qgfs_balance", + "country": [ + "BRA" + ], + "frequency": "Q", + "limit": "3" + }, + "columnState": { + "default": { + "columnOrder": { + "orderedColIds": [ + "country", + "title", + "2024-12-31", + "2023-12-31", + "2022-12-31" + ] + } + } + } + }, + "groups": [] + } + ] + }, + "balance-of-payments": { + "id": "balance-of-payments", + "name": "Balance of Payments", + "layout": [ + { + "i": "economy_balance_of_payments_imf_obb", + "x": 0, + "y": 2, + "w": 40, + "h": 40, + "state": { + "params": { + "country": [ + "USA" + ], + "frequency": "quarterly" + }, + "columnState": { + "default": { + "columnPinning": { + "leftColIds": [ + "period", + "country" + ], + "rightColIds": [] + } + } + } + }, + "groups": [] + } + ] + }, + "direction-of-trade": { + "id": "direction-of-trade", + "name": "Direction of Trade", + "layout": [ + { + "i": "economy_direction_of_trade_imf_obb", + "x": 0, + "y": 2, + "w": 40, + "h": 27, + "state": { + "params": { + "start_date": "2010-01-01", + "frequency": "quarter" + }, + "chartModel": { + "modelType": "range", + "chartType": "line", + "chartOptions": {}, + "suppressChartRanges": true + }, + "chartView": { + "enabled": false, + "chartType": "line" + }, + "columnState": { + "default": { + "columnPinning": { + "leftColIds": [ + "date", + "symbol" + ], + "rightColIds": [] + }, + "columnVisibility": { + "hiddenColIds": [ + "symbol", + "country_code", + "counterpart_code", + "unit_multiplier" + ] + }, + "columnOrder": { + "orderedColIds": [ + "date", + "symbol", + "country", + "counterpart", + "title", + "value", + "scale", + "unit", + "country_code", + "counterpart_code", + "unit_multiplier" + ] + } + } + } + }, + "groups": [] + } + ] + }, + "port-watch": { + "id": "port-watch", + "name": "Port Watch", + "layout": [ + { + "i": "economy_shipping_chokepoint_volume_imf_obb", + "x": 0, + "y": 2, + "w": 40, + "h": 16, + "state": { + "params": { + "start_date": "2024-01-01", + "chokepoint": [ + "chokepoint2" + ] + }, + "chartView": { + "enabled": false, + "chartType": "line" + }, + "columnState": { + "default": { + "columnPinning": { + "leftColIds": [ + "date", + "chokepoint" + ], + "rightColIds": [] + }, + "columnOrder": { + "orderedColIds": [ + "date", + "chokepoint", + "vessels_total", + "vessels_cargo", + "vessels_tanker", + "vessels_container", + "vessels_general_cargo", + "vessels_dry_bulk", + "vessels_roro", + "capacity_total", + "capacity_cargo", + "capacity_tanker", + "capacity_container", + "capacity_general_cargo", + "capacity_dry_bulk", + "capacity_roro" + ] + } + } + } + }, + "groups": [] }, - "groups": [ - "Group 1" - ] - } - ] + { + "i": "economy_shipping_port_info_imf_obb", + "x": 0, + "y": 18, + "w": 40, + "h": 14, + "state": { + "params": { + "country": "USA", + "port_code": "port786" + }, + "chartView": { + "enabled": false, + "chartType": "line" + }, + "columnState": { + "default": { + "columnPinning": { + "leftColIds": [ + "port_code", + "continent", + "country", + "port_full_name" + ], + "rightColIds": [] + }, + "columnVisibility": { + "hiddenColIds": [ + "country_code", + "port_name" + ] + }, + "columnOrder": { + "orderedColIds": [ + "port_code", + "continent", + "country", + "country_code", + "port_name", + "port_full_name", + "latitude", + "longitude", + "vessel_count_total", + "vessel_count_tanker", + "vessel_count_container", + "vessel_count_general_cargo", + "vessel_count_dry_bulk", + "vessel_count_roro", + "share_country_maritime_import", + "share_country_maritime_export", + "industry_top1", + "industry_top2", + "industry_top3" + ] + } + } + } + }, + "groups": [ + "Group 2" + ] + }, + { + "i": "economy_shipping_port_volume_imf_obb", + "x": 0, + "y": 32, + "w": 40, + "h": 15, + "state": { + "params": { + "start_date": "2024-01-01", + "country": "DEU" + }, + "chartView": { + "enabled": false, + "chartType": "line" + }, + "columnState": { + "default": { + "columnPinning": { + "leftColIds": [ + "date", + "port_code", + "port_name", + "country" + ], + "rightColIds": [] + }, + "columnVisibility": { + "hiddenColIds": [ + "country_code" + ] + }, + "columnOrder": { + "orderedColIds": [ + "date", + "port_code", + "port_name", + "country", + "country_code", + "portcalls", + "portcalls_tanker", + "portcalls_container", + "portcalls_general_cargo", + "portcalls_dry_bulk", + "portcalls_roro", + "imports", + "imports_cargo", + "imports_tanker", + "imports_container", + "imports_general_cargo", + "imports_dry_bulk", + "imports_roro", + "exports", + "exports_cargo", + "exports_tanker", + "exports_container", + "exports_general_cargo", + "exports_dry_bulk", + "exports_roro" + ] + } + } + } + }, + "groups": [ + "Group 2" + ] + }, + { + "i": "imf_portwatch_country_activity_imf_obb", + "x": 0, + "y": 47, + "w": 40, + "h": 15, + "state": { + "params": { + "country_code": "USA", + "metric": "portcalls", + "start_date": "2024-01-01" + }, + "chartView": { + "enabled": false, + "chartType": "line" + }, + "columnState": { + "default": { + "columnPinning": { + "leftColIds": [ + "date", + "country" + ], + "rightColIds": [] + }, + "columnVisibility": { + "hiddenColIds": [ + "country_code" + ] + } + } + } + }, + "groups": [] + }, + { + "i": "imf_portwatch_monthly_trade_imf_obb", + "x": 0, + "y": 62, + "w": 40, + "h": 15, + "state": { + "params": { + "code": "USA", + "metric": "trade_value", + "start_date": "2020-01-01" + }, + "chartView": { + "enabled": false, + "chartType": "line" + }, + "columnState": { + "default": { + "columnPinning": { + "leftColIds": [ + "date", + "region" + ], + "rightColIds": [] + }, + "columnVisibility": { + "hiddenColIds": [ + "country_code" + ] + } + } + } + }, + "groups": [] + } + ] + } }, - "presentation-tables": { - "id": "presentation-tables", - "name": "Presentation Tables", - "layout": [ - { - "i": "imf_utils_presentation_table_custom_obb", - "x": 0, - "y": 2, - "w": 40, - "h": 31, - "state": { - "params": { - "dataflow_group": "qgfs", - "table": "qgfs_balance", - "country": [ - "BRA" - ], - "frequency": "Q", - "limit": "3" - }, - "columnState": { - "default": { - "columnOrder": { - "orderedColIds": [ - "country", - "title", - "2024-12-31", - "2023-12-31", - "2022-12-31" - ] - } - } - } - }, - "groups": [] - } - ] - }, - "direction-of-trade": { - "id": "direction-of-trade", - "name": "Direction of Trade", - "layout": [ - { - "i": "economy_direction_of_trade_imf_obb", - "x": 0, - "y": 2, - "w": 40, - "h": 27, - "state": { - "params": { - "start_date": "2010-01-01", - "frequency": "quarter" - }, - "chartModel": { - "modelType": "range", - "chartType": "line", - "chartOptions": {}, - "suppressChartRanges": true - }, - "chartView": { - "enabled": false, - "chartType": "line" - }, - "columnState": { - "default": { - "columnPinning": { - "leftColIds": [ - "date", - "symbol" - ], - "rightColIds": [] - }, - "columnVisibility": { - "hiddenColIds": [ - "symbol", - "country_code", - "counterpart_code", - "unit_multiplier" - ] - }, - "columnOrder": { - "orderedColIds": [ - "date", - "symbol", - "country", - "counterpart", - "title", - "value", - "scale", - "unit", - "country_code", - "counterpart_code", - "unit_multiplier" - ] - } - } - } - }, - "groups": [] - } - ] - }, - "port-watch": { - "id": "port-watch", - "name": "Port Watch", - "layout": [ - { - "i": "economy_shipping_chokepoint_volume_imf_obb", - "x": 0, - "y": 2, - "w": 40, - "h": 16, - "state": { - "params": { - "start_date": "2024-01-01", - "chokepoint": [ - "chokepoint2" - ] - }, - "chartView": { - "enabled": false, - "chartType": "line" - }, - "columnState": { - "default": { - "columnPinning": { - "leftColIds": [ - "date", - "chokepoint" - ], - "rightColIds": [] - }, - "columnOrder": { - "orderedColIds": [ - "date", - "chokepoint", - "vessels_total", - "vessels_cargo", - "vessels_tanker", - "vessels_container", - "vessels_general_cargo", - "vessels_dry_bulk", - "vessels_roro", - "capacity_total", - "capacity_cargo", - "capacity_tanker", - "capacity_container", - "capacity_general_cargo", - "capacity_dry_bulk", - "capacity_roro" - ] - } - } - } - }, - "groups": [] - }, - { - "i": "economy_shipping_port_info_imf_obb", - "x": 0, - "y": 18, - "w": 40, - "h": 14, - "state": { - "params": { - "country": "USA", - "port_code": "port786" - }, - "chartView": { - "enabled": false, - "chartType": "line" - }, - "columnState": { - "default": { - "columnPinning": { - "leftColIds": [ - "port_code", - "continent", - "country", - "port_full_name" - ], - "rightColIds": [] - }, - "columnVisibility": { - "hiddenColIds": [ - "country_code", - "port_name" - ] - }, - "columnOrder": { - "orderedColIds": [ - "port_code", - "continent", - "country", - "country_code", - "port_name", - "port_full_name", - "latitude", - "longitude", - "vessel_count_total", - "vessel_count_tanker", - "vessel_count_container", - "vessel_count_general_cargo", - "vessel_count_dry_bulk", - "vessel_count_roro", - "share_country_maritime_import", - "share_country_maritime_export", - "industry_top1", - "industry_top2", - "industry_top3" - ] - } - } - } - }, - "groups": [ - "Group 2" - ] - }, - { - "i": "economy_shipping_port_volume_imf_obb", - "x": 0, - "y": 32, - "w": 40, - "h": 15, - "state": { - "params": { - "start_date": "2024-01-01", - "country": "DEU" - }, - "chartView": { - "enabled": false, - "chartType": "line" - }, - "columnState": { - "default": { - "columnPinning": { - "leftColIds": [ - "date", - "port_code", - "port_name", - "country" - ], - "rightColIds": [] - }, - "columnVisibility": { - "hiddenColIds": [ - "country_code" - ] - }, - "columnOrder": { - "orderedColIds": [ - "date", - "port_code", - "port_name", - "country", - "country_code", - "portcalls", - "portcalls_tanker", - "portcalls_container", - "portcalls_general_cargo", - "portcalls_dry_bulk", - "portcalls_roro", - "imports", - "imports_cargo", - "imports_tanker", - "imports_container", - "imports_general_cargo", - "imports_dry_bulk", - "imports_roro", - "exports", - "exports_cargo", - "exports_tanker", - "exports_container", - "exports_general_cargo", - "exports_dry_bulk", - "exports_roro" - ] - } - } - } - }, - "groups": [ - "Group 2" - ] - } - ] - } - }, - "groups": [ - { - "name": "Group 1", - "type": "param", - "paramName": "symbol", - "defaultValue": "CTOT::CEPI_CTOTX_GDP" - }, - { - "name": "Group 2", - "type": "param", - "paramName": "port_code", - "defaultValue": "port664" - } - ] -}] \ No newline at end of file + "groups": [ + { + "name": "Group 1", + "type": "param", + "paramName": "symbol", + "defaultValue": "CTOT::CEPI_CTOTX_GDP" + }, + { + "name": "Group 2", + "type": "param", + "paramName": "port_code", + "defaultValue": "port664" + } + ] + } +] \ No newline at end of file diff --git a/openbb_platform/providers/imf/openbb_imf/assets/__init__.py b/openbb_platform/providers/imf/openbb_imf/assets/__init__.py index e65c4f3d781..0a100138f7c 100644 --- a/openbb_platform/providers/imf/openbb_imf/assets/__init__.py +++ b/openbb_platform/providers/imf/openbb_imf/assets/__init__.py @@ -1 +1,3 @@ """IMF Static Assets.""" + +from __future__ import annotations diff --git a/openbb_platform/providers/imf/openbb_imf/assets/imf_cache.pkl.gz b/openbb_platform/providers/imf/openbb_imf/assets/imf_cache.pkl.gz deleted file mode 100644 index d164b8bc5d91ae3e4d62fc342d2d74a496211e50..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 3166128 zcmV)LK)JskiwFq6E;DHY|7mSzUt?ioXk{*NYit1Qy=jyjNqQJIIJ$ubdN3FqgV~+g znH=uyPJ`R%=@S@WDehKRWmOltuBpsw9Lss-?5gOh1ga`)GAp~$u*A1I?58KT#b*mRgz}QXjM$3p=ceWV>%tP;wUrltDCb5?-;AqY)~^gzx7Ad^E4qChSsGtd`<*;2efsHJ`)Z zwPF%~KCA51%?416hGQu<*lK`E$86P(*|2Bh{ZpHQ*3cpvQn25C~;0aeg?;R4z) zmChy7@tB^+s7$3>RkLDrEc-?M>iVhrwOsxBM=wavbLoxMY*|aAes85)aBYCH0UB1t z?CipslvUd@s+eUUWCp{jluPUAi&si;olv(dFdWwgX#CUC_@~nkQO5}g0(j<)FqJH- zAC}Zy952&U(gq*9pjFvL1Dv4)&;>aa!zjjDE!Su;%ujuYzx&WN><+LSy9jl#chTB} 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zx29)nEg$o?AceXZOqnY&X(E-FHBkGDUH{SqXp&pT4Dp#CIM>IyRtZ{ozRWK1`Eqy4 z5L{oydb}2e4Abnue|W$?C@>(6Lgq0=9XEE5!cHd&ynUqQqOiM^hLRqeb5wZB>Yx0f z9Jap_FM95Lb=*Y`!CyZ0bsfa_ww$v_N<0{kG4caH@4zAJfs7$AV$Iu)dcipK;U8m| zjsEdPFh29c&+)Gh{r(ut=9*XkJVf|lSo8LK7bA>E60Q}7HkV>D{ zbrg@|b~~+Abz*eIzHKT#o?2yh(nxYhyM4o!`;*U*6C;U-P7IAnok~9?zys2 zZpV-C{lW|MY3{lG3uvTw$Z09teEq8*@gK-mrJ@x)e%`@P30VX?Al&_#SD9MH+m!L6 dmB8H(8?DDSt9yk2#dNy<{{iC;6XXl8W&xgkXk`EZ diff --git a/openbb_platform/providers/imf/openbb_imf/imf_router.py b/openbb_platform/providers/imf/openbb_imf/imf_router.py index 64495cdf30b..30935a009fa 100644 --- a/openbb_platform/providers/imf/openbb_imf/imf_router.py +++ b/openbb_platform/providers/imf/openbb_imf/imf_router.py @@ -1,17 +1,22 @@ -"""IMF Utilities Router.""" - -# pylint: disable=C0302 -# pylint: disable=unused-argument,protected-access,too-many-positional-arguments,too-many-locals,too-many-branches +"""IMF Router.""" +import re from typing import Annotated, Any, Literal from fastapi import Query -from fastapi.responses import HTMLResponse from openbb_core.app.model.abstract.error import OpenBBError +from openbb_core.app.model.command_context import CommandContext from openbb_core.app.model.example import APIEx, PythonEx from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query as OBBQuery from openbb_core.app.router import Router from openbb_core.app.service.system_service import SystemService + from openbb_imf.models.indicator_metadata import ImfTableMetadata from openbb_imf.utils.constants import ( PRESENTATION_TABLES, @@ -19,12 +24,85 @@ from openbb_imf.utils.constants import ( table_dataflow_map, table_name_map, ) -from openbb_imf.utils.metadata import ImfMetadata +from openbb_imf.utils.metadata import ImfMetadata, ImfMetadataDependency -router = Router(prefix="", description="Utilities for IMF provider.") +router = Router(prefix="", description="IMF provider router.") api_prefix = SystemService().system_settings.api_settings.prefix +def _render_dataflows_markdown(metadata: ImfMetadata) -> str: + """Render the full dataflow catalog as a markdown summary.""" + dataflows = metadata.dataflows + all_tables = metadata.list_all_dataflow_tables() + md_parts: list[str] = [] + + for dataflow_id in sorted(dataflows.keys()): + details = dataflows[dataflow_id] + try: + indicators = metadata.get_indicators_in(dataflow_id) + except KeyError: + indicators = [] + params = metadata.get_dataflow_parameters(dataflow_id) + md_parts.append(f"## `{dataflow_id}` - {details.get('name', '')}\n\n") + + if indicators: + md_parts.append(f"**Number of Series:** {len(indicators)}\n\n") + if params: + escaped = [f"`{p}`" for p in list(params)] + md_parts.append(f"**Dimensions:** {', '.join(escaped)}\n\n") + + presentations = all_tables.get(dataflow_id, []) + if presentations: + md_parts.append("### Presentation Tables\n\n") + seen_names: set[str] = set() + for pres in presentations: + pres_name = pres.get("name", "").strip() + if pres_name in seen_names: + continue + seen_names.add(pres_name) + + pres_id = pres.get("id", "") + pres_desc = pres.get("description", "").strip() + friendly_name = pres.get("friendly_name", "") + symbol = f"{dataflow_id}::{pres_id}" + md_parts.append(f"#### {pres_name}\n\n") + if friendly_name: + md_parts.append(f"**Friendly Name:** `{friendly_name}`\n\n") + md_parts.append(f"**Symbol:** `{symbol}`\n\n") + if pres_desc and pres_desc != pres_name: + md_parts.append(f"{pres_desc}\n\n") + + md_parts.append(f"{details.get('description', '').strip()}\n\n") + md_parts.append("---\n\n") + + return "".join(md_parts) + + +def _dump_economic_indicator_rows(results: Any) -> list[dict[str, Any]]: + """Normalize fetcher output to a list of JSON-mode pydantic dumps.""" + rows: Any = results.result if hasattr(results, "result") else results + if not rows: + return [] + out: list[dict[str, Any]] = [] + for r in rows: + if hasattr(r, "model_dump"): + out.append(r.model_dump(mode="json", exclude_none=True)) + else: + out.append(dict(r)) + return out + + +def _render_parameters_markdown(parameters: dict[str, list[dict]]) -> str: + """Render dataflow parameters as a per-dimension code list.""" + parts: list[str] = [] + for dim, options in parameters.items(): + parts.append(f"### `{dim}`\n\n") + for option in options: + parts.append(f"- `{option.get('value', '')}` : {option.get('label')}\n\n") + parts.append("---\n\n") + return "".join(parts) + + @router.command( methods=["GET"], widget_config={ @@ -59,74 +137,20 @@ api_prefix = SystemService().system_settings.api_settings.prefix PythonEx( description="Lists all known dataflows available from the IMF.", code=[ - "imf_dataflows = obb.imf.utils.list_dataflows()", + "imf_dataflows = obb.imf.list_dataflows()", "print(imf_dataflows)", ], ), ], ) async def list_dataflows( + metadata: ImfMetadataDependency, output_format: Literal["json", "markdown"] = "json", ) -> OBBject: - """List all available IMF dataflows. - - Returns an OBBject containing either a JSON dictionary of dataflows - or a markdown string under the 'results' attribute. - """ - metadata = ImfMetadata() - dataflows = metadata.dataflows - + """List all available IMF dataflows.""" if output_format == "markdown": - all_tables = metadata.list_all_dataflow_tables() - md_text = "" - - for dataflow_id in sorted(dataflows.keys()): - details = dataflows[dataflow_id] - indicators = metadata.get_indicators_in(dataflow_id) - params = metadata.get_dataflow_parameters(dataflow_id) - md_text += f"## `{dataflow_id}` - {details.get('name', '')}\n\n" - - if indicators: - md_text += f"**Number of Series:** {len(indicators)}\n\n" - - if params: - escaped_params = [f"`{param}`" for param in list(params)] - md_text += f"**Dimensions:** {', '.join(escaped_params)}\n\n" - - presentations = all_tables.get(dataflow_id, []) - - if presentations: - md_text += "### Presentation Tables\n\n" - seen_names: set[str] = set() - - for pres in presentations: - pres_name = pres.get("name", "").strip() - - if pres_name in seen_names: - continue - - seen_names.add(pres_name) - - pres_id = pres.get("id", "") - pres_desc = pres.get("description", "").strip() - friendly_name = pres.get("friendly_name", "") - symbol = f"{dataflow_id}::{pres_id}" - md_text += f"#### {pres_name}\n\n" - - if friendly_name: - md_text += f"**Friendly Name:** `{friendly_name}`\n\n" - - md_text += f"**Symbol:** `{symbol}`\n\n" - - if pres_desc and pres_desc != pres_name: - md_text += f"{pres_desc}\n\n" - - md_text += f"{details.get('description', '').strip()}\n\n" - md_text += "---\n\n" - - return OBBject(results=md_text) - - return OBBject(results=dataflows) + return OBBject(results=_render_dataflows_markdown(metadata)) + return OBBject(results=metadata.dataflows) @router.command( @@ -141,7 +165,7 @@ async def list_dataflows( "value": "CPI", "description": "The IMF dataflow to display.", "type": "endpoint", - "optionsEndpoint": f"{api_prefix}/imf_utils/list_dataflow_choices", + "optionsEndpoint": f"{api_prefix}/imf/list_dataflow_choices", "style": {"popupWidth": 700}, }, { @@ -158,7 +182,7 @@ async def list_dataflows( PythonEx( description="Get parameters for the 'CPI' dataflow.", code=[ - "imf_params = obb.imf.utils.get_dataflow_dimensions('CPI')", + "imf_params = obb.imf.get_dataflow_dimensions('CPI')", "print(imf_params.results)", ], ), @@ -173,6 +197,7 @@ async def list_dataflows( ], ) async def get_dataflow_dimensions( + metadata: ImfMetadataDependency, dataflow_id: Annotated[ str, Query( @@ -182,55 +207,11 @@ async def get_dataflow_dimensions( ], output_format: Literal["json", "markdown"] = "json", ) -> OBBject: - """Dataflow parameters and possible values. - - Returns an OBBject containing either a JSON dictionary of parameters - and their options, or a markdown string under the 'results' attribute. - """ - metadata = ImfMetadata() - params_str = "" - - try: - parameters = metadata.get_dataflow_parameters(dataflow_id) - except ValueError as e: - raise e from e - + """Dataflow parameters and their possible values.""" + parameters = metadata.get_dataflow_parameters(dataflow_id) if output_format == "json": return OBBject(results=parameters) - - for dim, options in parameters.items(): - params_str += f"### `{dim}`\n\n" - for option in options: - params_str += f"- `{option.get('value', '')}` : {option.get('label')}\n\n" - params_str += "---\n\n" - - return OBBject(results=params_str) - - -@router.command( - methods=["GET"], - widget_config={"exclude": True}, - examples=[ - APIEx( - description="Get port ID choices for IMF Port Watch.", - parameters={}, - ) - ], -) -async def list_port_id_choices() -> list[dict[str, str]]: - """ - Get port ID choices for IMF Port Watch. - - Returns - ------- - list[dict[str, str]] - A list of dictionaries with 'label' and 'value' for each port ID. - """ - # pylint: disable=import-outside-toplevel - from openbb_imf.utils.port_watch_helpers import get_port_id_choices - - choices = get_port_id_choices() - return choices + return OBBject(results=_render_parameters_markdown(parameters)) @router.command( @@ -260,9 +241,10 @@ async def list_port_id_choices() -> list[dict[str, str]]: "subCategory": "Metadata", }, ) -async def list_tables() -> OBBject[list[ImfTableMetadata]]: +async def list_tables( + metadata: ImfMetadataDependency, +) -> OBBject[list[ImfTableMetadata]]: """Get the list of presentation tables available from the IMF.""" - metadata = ImfMetadata() tables: list[ImfTableMetadata] = [] dataflows = metadata.list_all_dataflow_tables() @@ -293,29 +275,15 @@ async def list_tables() -> OBBject[list[ImfTableMetadata]]: ), ], ) -async def list_table_choices() -> list[dict[str, str]]: - """Get presentation table choices for IMF data retrieval. - - Returns - ------- - list[dict[str, str]] - A list of dictionaries with 'label' and 'value' for each presentation table. - """ - metadata = ImfMetadata() - table_choices = metadata.list_all_dataflow_tables() - choices: list[dict[str, str]] = [] - for dataflow_id, presentations in table_choices.items(): - for pres in presentations: - table_id = pres.get("id", "") - unique_key = f"{dataflow_id}::{table_id}" - choices.append( - { - "label": pres.get("name", ""), - "value": unique_key, - } - ) - - return choices +async def list_table_choices( + metadata: ImfMetadataDependency, +) -> list[dict[str, str]]: + """Get presentation table choices for IMF data retrieval.""" + return [ + {"label": pres.get("name", ""), "value": f"{dataflow_id}::{pres.get('id', '')}"} + for dataflow_id, presentations in metadata.list_all_dataflow_tables().items() + for pres in presentations + ] @router.command( @@ -328,25 +296,62 @@ async def list_table_choices() -> list[dict[str, str]]: ) ], ) -async def list_dataflow_choices() -> list[dict[str, str]]: - """Get dataflow choices for IMF data retrieval. +async def list_dataflow_choices( + metadata: ImfMetadataDependency, +) -> list[dict[str, str]]: + """Get dataflow choices for IMF data retrieval.""" + choices = [ + {"label": details.get("name", ""), "value": dataflow_id} + for dataflow_id, details in metadata.dataflows.items() + ] + return sorted(choices, key=lambda x: x["label"]) - Returns - ------- - list[dict[str, str]] - A list of dictionaries with 'label' and 'value' for each presentation table. - """ - metadata = ImfMetadata() - dataflows = metadata.dataflows - choices: list[dict[str, str]] = [] - for dataflow_id, details in dataflows.items(): - choices.append( - { - "label": details.get("name", ""), - "value": dataflow_id, - } + +@router.command( + methods=["GET"], + widget_config={"exclude": True}, + examples=[ + APIEx( + description="Get country choices for the IMF Balance of Payments dataflow.", + parameters={}, ) + ], +) +async def list_bop_country_choices( + metadata: ImfMetadataDependency, +) -> list[dict[str, str]]: + """Get country choices (label, ISO3 value) for the IMF Balance of Payments dataflow.""" + params = metadata.get_dataflow_parameters("BOP") + choices: list[dict[str, str]] = [] + for entry in params.get("COUNTRY", []): + code = entry.get("value") + if not code: + continue + choices.append({"label": entry.get("label", code), "value": code.upper()}) + return sorted(choices, key=lambda x: x["label"]) + +@router.command( + methods=["GET"], + widget_config={"exclude": True}, + examples=[ + APIEx( + description="Get country choices for the IMF Consumer Price Index dataflow.", + parameters={}, + ) + ], +) +async def list_cpi_country_choices( + metadata: ImfMetadataDependency, +) -> list[dict[str, str]]: + """Get country choices (label, ISO3 value) for the IMF Consumer Price Index dataflow.""" + params = metadata.get_dataflow_parameters("CPI") + choices: list[dict[str, str]] = [] + for entry in params.get("COUNTRY", []): + code = entry.get("value") + if not code: + continue + choices.append({"label": entry.get("label", code), "value": code.upper()}) return sorted(choices, key=lambda x: x["label"]) @@ -382,6 +387,7 @@ async def list_dataflow_choices() -> list[dict[str, str]]: ], ) async def presentation_table_choices( + metadata: ImfMetadataDependency, dataflow_group: str | None = None, table: str | None = None, country: str | None = None, @@ -389,11 +395,6 @@ async def presentation_table_choices( ) -> list[dict[str, str]]: """Get presentation table choices for IMF data retrieval. - This endpoint provides dynamic choices for IMF presentation tables based on selected parameters. - It is intended to be used by the OpenBB Workspace UI to populate dropdowns. - - For manual API calls, use `economy/indicators` instead with a `symbol` from `list_tables()`. - Parameters ---------- dataflow_group : str | None @@ -408,9 +409,7 @@ async def presentation_table_choices( Returns ------- list[dict[str, str]] - A list of dictionaries with 'label' and 'value' for each presentation table. """ - # pylint: disable=import-outside-toplevel from openbb_imf.utils.progressive_helper import ImfParamsBuilder choices: list[dict[str, str]] = [] @@ -418,10 +417,7 @@ async def presentation_table_choices( if dataflow_group is None: return table_dataflow_choices - metadata = ImfMetadata() - if dataflow_group is not None and table is None: - table_names = table_dataflow_map.get(dataflow_group, []) for t in table_names: @@ -441,13 +437,20 @@ async def presentation_table_choices( country_dim = ( "COUNTRY" if "COUNTRY" in params - else "JURISDICTION" if "JURISDICTION" in params else "REF_AREA" + else "JURISDICTION" + if "JURISDICTION" in params + else "REF_AREA" ) countries = params.get(country_dim, []) return sorted(countries, key=lambda x: x["label"]) - if dataflow_group is not None and table is not None and country is not None: + if ( + dataflow_group is not None + and table is not None + and country is not None + and frequency is not None + ): table_id = PRESENTATION_TABLES.get(table, "") dataflow_id = table_id.split("::")[0] hierarchy_id = table_id.split("::", 1)[1] if "::" in table_id else None @@ -455,7 +458,6 @@ async def presentation_table_choices( country_dim = "COUNTRY" if "COUNTRY" in params else "REF_AREA" freq_dim = "FREQUENCY" if "FREQUENCY" in params else "FREQ" - # Get table structure and extract dimension codes (same as table_builder.get_table) table_structure = metadata.get_dataflow_table_structure( dataflow_id, hierarchy_id ) @@ -472,55 +474,6 @@ async def presentation_table_choices( pb = ImfParamsBuilder(dataflow_id=dataflow_id) dims_in_order = pb._get_dimensions_in_order() - # Set dimensions in order, using table's indicator codes - for dim_id in dims_in_order: - if dim_id in dimension_codes: - codes = dimension_codes[dim_id] - joined = "+".join(codes) - if len(joined) > 800: - # Truncate to avoid URL length issues - joined = "+".join(codes[:20]) - if len(joined) > 800: - joined = "*" - pb.set_dimension((dim_id, joined)) - elif dim_id == country_dim: - pb.set_dimension((dim_id, str(country).replace(",", "+"))) - - options = pb.get_options_for_dimension(freq_dim) if freq_dim else [] - - return options - - if ( - dataflow_group is not None - and table is not None - and country is not None - and frequency is not None - ): - table_id = PRESENTATION_TABLES.get(table, "") - dataflow_id = table_id.split("::")[0] - hierarchy_id = table_id.split("::", 1)[1] if "::" in table_id else None - params = metadata.get_dataflow_parameters(dataflow_id) - country_dim = "COUNTRY" if "COUNTRY" in params else "REF_AREA" - freq_dim = "FREQUENCY" if "FREQUENCY" in params else "FREQ" - - # Get table structure and extract dimension codes (same as table_builder.get_table) - table_structure = metadata.get_dataflow_table_structure( - dataflow_id, hierarchy_id - ) - dimension_codes = {} - for entry in table_structure.get("indicators", []): - indicator_code = entry.get("indicator_code") - dimension_id = entry.get("dimension_id") - if indicator_code and dimension_id: - if dimension_id not in dimension_codes: - dimension_codes[dimension_id] = [] - if indicator_code not in dimension_codes[dimension_id]: - dimension_codes[dimension_id].append(indicator_code) - - pb = ImfParamsBuilder(dataflow_id=dataflow_id) - dims_in_order = pb._get_dimensions_in_order() - - # Set dimensions in order, using table's indicator codes for dim_id in dims_in_order: if dim_id in dimension_codes: codes = dimension_codes[dim_id] @@ -542,27 +495,93 @@ async def presentation_table_choices( return options - return choices + if dataflow_group is not None and table is not None and country is not None: + table_id = PRESENTATION_TABLES.get(table, "") + dataflow_id = table_id.split("::")[0] + hierarchy_id = table_id.split("::", 1)[1] if "::" in table_id else None + params = metadata.get_dataflow_parameters(dataflow_id) + country_dim = "COUNTRY" if "COUNTRY" in params else "REF_AREA" + freq_dim = "FREQUENCY" if "FREQUENCY" in params else "FREQ" + + table_structure = metadata.get_dataflow_table_structure( + dataflow_id, hierarchy_id + ) + dimension_codes = {} + for entry in table_structure.get("indicators", []): + indicator_code = entry.get("indicator_code") + dimension_id = entry.get("dimension_id") + if indicator_code and dimension_id: + if dimension_id not in dimension_codes: + dimension_codes[dimension_id] = [] + if indicator_code not in dimension_codes[dimension_id]: + dimension_codes[dimension_id].append(indicator_code) + + pb = ImfParamsBuilder(dataflow_id=dataflow_id) + dims_in_order = pb._get_dimensions_in_order() + + for dim_id in dims_in_order: + if dim_id in dimension_codes: + codes = dimension_codes[dim_id] + joined = "+".join(codes) + if len(joined) > 800: + joined = "+".join(codes[:20]) + if len(joined) > 800: + joined = "*" + pb.set_dimension((dim_id, joined)) + elif dim_id == country_dim: + pb.set_dimension((dim_id, str(country).replace(",", "+"))) + + options = pb.get_options_for_dimension(freq_dim) if freq_dim else [] + + return options + + return choices # pragma: no cover -- unreachable: all four branches above are exhaustive + + +_INDENT_UNIT = ">\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0" + + +def _indent_title(title: str, level: int) -> str: + """Prepend ``>``-prefixed indent markers proportional to ``level``.""" + if not isinstance(title, str): + return title + if level <= 0: + return title + return _INDENT_UNIT * level + title + + +def _rewrap_indent(title: str) -> str: + """Replace leading whitespace with ``_INDENT_UNIT`` markers per 3-space step.""" + if not isinstance(title, str): + return title + stripped = title.lstrip(" ") + leading = len(title) - len(stripped) + if leading == 0: + return stripped + level = leading // 3 + return _INDENT_UNIT * level + stripped + + +_DATE_RE = re.compile(r"^\d{4}-\d{2}-\d{2}$") @router.command( methods=["GET"], widget_config={ - "type": "html", "params": [ { "paramName": "dataflow_group", "label": "Dataflow", "value": None, "type": "endpoint", - "optionsEndpoint": f"{api_prefix}/imf_utils/presentation_table_choices", + "optionsEndpoint": f"{api_prefix}/imf/presentation_table_choices", "description": "The IMF dataflow group.", }, { "paramName": "table", "label": "Table", "type": "endpoint", - "optionsEndpoint": f"{api_prefix}/imf_utils/presentation_table_choices", + "optionsEndpoint": f"{api_prefix}/imf/presentation_table_choices", "optionsParams": { "dataflow_group": "$dataflow_group", }, @@ -573,8 +592,8 @@ async def presentation_table_choices( "label": "Country", "description": "Country or region for the table.", "type": "endpoint", - "multiSelect": True, - "optionsEndpoint": f"{api_prefix}/imf_utils/presentation_table_choices", + "multiSelect": False, + "optionsEndpoint": f"{api_prefix}/imf/presentation_table_choices", "optionsParams": { "dataflow_group": "$dataflow_group", "table": "$table", @@ -585,7 +604,7 @@ async def presentation_table_choices( "paramName": "frequency", "label": "Frequency", "type": "endpoint", - "optionsEndpoint": f"{api_prefix}/imf_utils/presentation_table_choices", + "optionsEndpoint": f"{api_prefix}/imf/presentation_table_choices", "optionsParams": { "dataflow_group": "$dataflow_group", "table": "$table", @@ -610,17 +629,13 @@ async def presentation_table_choices( "description": "Most recent N records to retrieve per series.", "type": "number", }, - { - "paramName": "raw", - "show": False, - }, ], - "raw": True, + "runButton": True, "refetchInterval": False, "name": "IMF Presentation Table", "description": "Presentation tables from the IMF database.", "source": ["IMF"], - "category": "IMF Utilities", + "category": "IMF Data", "subCategory": "Presentation Tables", }, examples=[ @@ -685,20 +700,9 @@ async def presentation_table( description="Maximum number of records to retrieve per series.", ), ] = 1, - raw: Annotated[ - bool, - Query( - title="Raw Output", - description="Return presentation table as raw JSON data if True.", - ), - ] = False, -) -> Any: - """Get a formatted presentation table from the IMF database. Returns as HTML or JSON list.""" - # pylint: disable=import-outside-toplevel - import html as html_module - +) -> list[dict[str, Any]]: + """Get a presentation table from the IMF database as native AG Grid rows.""" from openbb_imf.models.economic_indicators import ImfEconomicIndicatorsFetcher - from pandas import DataFrame if dataflow_group is None or table is None: raise OpenBBError(ValueError("Please enter a dataflow group and a table.")) @@ -717,103 +721,44 @@ async def presentation_table( "pivot": True, } results = await ImfEconomicIndicatorsFetcher.fetch_data(params, {}) - results_json = [d.model_dump(mode="json", exclude_none=True) for d in results.result] # type: ignore + rows = _dump_economic_indicator_rows(results) - if raw is True: - return results_json + keep_country = len({c.strip() for c in re.split(r"[+,]", country) if c.strip()}) > 1 - df = DataFrame(results_json).set_index(["title", "country"]).reset_index() - # Preserve leading whitespace by replacing double spaces with non-breaking spaces - df["title"] = df["title"].apply( - lambda x: x.replace(" ", "\u00a0\u00a0") if isinstance(x, str) else x - ) + out: list[dict[str, Any]] = [] + for row in rows: + level = row.get("level") + title = row.get("title", "") + if level is None: + title = _rewrap_indent(title) + else: + try: + lvl = int(level) + except (TypeError, ValueError): + lvl = 0 + title = _indent_title(title, lvl) - columns = df.columns.tolist() - header_cells = "".join( - f"{html_module.escape(str(col))}" for col in columns - ) + ordered: dict[str, Any] = {} + if keep_country and "country" in row: + ordered["country"] = row["country"] + ordered["title"] = title + date_pairs: list[tuple[str, Any]] = [] + trailing: list[tuple[str, Any]] = [] + for k, v in row.items(): + if k in ("country", "title", "level"): + continue + if isinstance(k, str) and _DATE_RE.match(k): + date_pairs.append((k, v)) + else: + trailing.append((k, v)) + date_pairs.sort(key=lambda kv: kv[0], reverse=True) + for k, v in date_pairs: + ordered[k] = v + for k, v in trailing: + ordered[k] = v + out.append(ordered) - def format_number(value): - """Format large numbers with K, M, B suffixes for readability.""" - if isinstance(value, (int, float)): - abs_value = abs(value) - if abs_value >= 1_000_000_000: - return f"{value / 1_000_000_000:.2f}".rstrip("0").rstrip(".") + "B" - if abs_value >= 1_000_000: - return f"{value / 1_000_000:.2f}".rstrip("0").rstrip(".") + "M" - if abs_value >= 1_000: - return f"{value / 1_000:.2f}".rstrip("0").rstrip(".") + "K" - if isinstance(value, float): - return f"{value:.2f}".rstrip("0").rstrip(".") - return str(value) - return str(value) - - # Build body rows - body_rows = "" - for _, row in df.iterrows(): - cells = "".join( - f"{html_module.escape(format_number(row[col]))}" for col in columns - ) - body_rows += f"{cells}" - - interactive_html = f""" - - - - - IMF Presentation Table - - - - -
    - - {header_cells} - {body_rows} -
    -
    - -""" - - return HTMLResponse(content=interactive_html) + return out @router.command( @@ -832,6 +777,7 @@ async def presentation_table( ], ) async def indicator_choices( # noqa: PLR0912 + metadata: ImfMetadataDependency, symbol: str | None = None, country: str | None = None, frequency: str | None = None, @@ -840,14 +786,6 @@ async def indicator_choices( # noqa: PLR0912 ) -> list[dict[str, str]]: """Get progressive indicator choices for IMF data retrieval. - This endpoint works progressively starting with the 'symbol' parameter, - which is required and in the format 'dataflow::indicator'. - - Function is not intended to be used directly; - it is used by the OpenBB Workspace for progressive parameter selection. - - For manual inspection, use the `get_dataflow_dimensions` endpoint instead. - Parameters ---------- symbol : str | None @@ -867,26 +805,19 @@ async def indicator_choices( # noqa: PLR0912 list[dict[str, str]] A list of dictionaries with 'label' and 'value' for each choice. """ - # pylint: disable=import-outside-toplevel from urllib.parse import unquote from openbb_imf.utils.helpers import detect_transform_dimension - metadata = ImfMetadata() - - # Symbol is required and in format dataflow::indicator if symbol is None: return [] - # URL-decode the symbol parameter and handle multiple comma-separated symbols symbol = unquote(symbol) - # Parse multiple symbols (comma-separated): "QGFS::F4_L_T_XDC,QGFS::F12_L_T_XDC" symbols = [s.strip() for s in symbol.split(",") if s.strip()] if not symbols: return [] - # Extract unique dataflows and all indicator codes dataflows_seen: set[str] = set() indicator_codes: list[str] = [] @@ -898,17 +829,14 @@ async def indicator_choices( # noqa: PLR0912 if ind_code: indicator_codes.append(ind_code) else: - # Just a dataflow ID with no indicator dataflows_seen.add(sym.strip()) - # For now, only support single dataflow (use first one) dataflow_id = list(dataflows_seen)[0] if dataflows_seen else None indicator_code = "+".join(indicator_codes) if indicator_codes else None if not dataflow_id: return [] - # Get dimension order for this dataflow df_obj = metadata.dataflows.get(dataflow_id, {}) if not df_obj: @@ -918,25 +846,19 @@ async def indicator_choices( # noqa: PLR0912 dsd = metadata.datastructures.get(dsd_id, {}) dimensions = dsd.get("dimensions", []) - # Sort by position sorted_dims = sorted( [d for d in dimensions if d.get("id") != "TIME_PERIOD"], key=lambda x: int(x.get("position", 0)), ) dim_order = [d["id"] for d in sorted_dims] - # Get codelist labels for all dimensions params = metadata.get_dataflow_parameters(dataflow_id) - # Identify dimension types country_dim = "COUNTRY" if "COUNTRY" in dim_order else "REF_AREA" freq_dim = "FREQUENCY" if "FREQUENCY" in dim_order else "FREQ" transform_dim, unit_dim, _, _ = detect_transform_dimension(dataflow_id) - # Use UNIT dimension as fallback for transform if no transform dimension exists effective_transform_dim = transform_dim or unit_dim - # Parse dimension_values into a dict of DIM_ID -> VALUE - # Input format: list of "DIM_ID:VALUE" strings extra_dimensions: dict[str, str] = {} if dimension_values: for dv in dimension_values: @@ -946,25 +868,19 @@ async def indicator_choices( # noqa: PLR0912 dim_id, dim_value = dv.split(":", 1) extra_dimensions[dim_id.strip().upper()] = dim_value.strip().upper() - # dimension_values OVERRIDES parameter values for country/frequency/transform - # Check if any country dimension is in extra_dimensions for cdim in ("COUNTRY", "REF_AREA", "JURISDICTION", "AREA"): if cdim in extra_dimensions: country = extra_dimensions.pop(cdim) break - # Check if frequency dimension is in extra_dimensions for fdim in ("FREQUENCY", "FREQ"): if fdim in extra_dimensions: frequency = extra_dimensions.pop(fdim) break - # Check if transform dimension is in extra_dimensions for tdim in ("UNIT_MEASURE", "UNIT", "TRANSFORMATION"): if tdim in extra_dimensions: transform = extra_dimensions.pop(tdim) break - # Find indicator dimension - check which dimension contains the indicator_code - # This list should include all possible indicator-type dimensions across dataflows indicator_dims = [ "INDICATOR", "INDEX_TYPE", @@ -976,8 +892,6 @@ async def indicator_choices( # noqa: PLR0912 "PRODUCTION_INDEX", ] - # If we have indicator_code(s), find which dimension they belong to - # indicator_code may be "+" joined (e.g., "F4_L_T_XDC+F12_L_T_XDC") indicator_dim = None first_indicator = indicator_code.split("+")[0] if indicator_code else None if first_indicator: @@ -988,7 +902,6 @@ async def indicator_choices( # noqa: PLR0912 indicator_dim = dim_id break - # If still not found, search ALL dimensions for the indicator code if indicator_dim is None: for dim_id in dim_order: if dim_id in ( @@ -1004,26 +917,18 @@ async def indicator_choices( # noqa: PLR0912 indicator_dim = dim_id break - # Fallback to first available indicator dimension if not found if indicator_dim is None: indicator_dim = next((d for d in indicator_dims if d in dim_order), None) def build_key_with_indicator(target_dim: str) -> str: - """Build constraint key with indicator always set, targeting a specific dimension. - - This builds a full key for all dimensions, with the target dimension as wildcard - and the indicator dimension set to the indicator code (if available). - This allows querying for available values of the target dimension filtered by indicator. - """ + """Build constraint key with indicator always set, targeting a specific dimension.""" key_parts: list[str] = [] for dim_id in dim_order: if dim_id == target_dim: - # Target dimension gets wildcard - we want to know available values key_parts.append("*") elif dim_id == country_dim: key_parts.append(str(country).replace(",", "+") if country else "*") elif dim_id == indicator_dim: - # Always include indicator code if available key_parts.append(indicator_code if indicator_code else "*") elif dim_id == freq_dim: key_parts.append(str(frequency) if frequency else "*") @@ -1032,7 +937,6 @@ async def indicator_choices( # noqa: PLR0912 str(transform) if transform and transform != "true" else "*" ) elif dim_id in extra_dimensions: - # Use value from dimension_values if provided key_parts.append(extra_dimensions[dim_id]) else: key_parts.append("*") @@ -1047,9 +951,7 @@ async def indicator_choices( # noqa: PLR0912 key=key, component_id=dim_id, ) - # Get labels from params labels = {opt["value"]: opt["label"] for opt in params.get(dim_id, [])} - # Also try to get labels from codelist codelist_labels: dict = {} dim_meta: dict = next((d for d in sorted_dims if d.get("id") == dim_id), {}) @@ -1066,49 +968,275 @@ async def indicator_choices( # noqa: PLR0912 for kv in constraints.get("key_values", []): if kv.get("id") == dim_id: for value in kv.get("values", []): - # Try params first, then codelist, then fall back to value label = labels.get(value) or codelist_labels.get(value) or value choices.append({"label": label, "value": value}) return choices - # Step 1: No country selected - return country choices filtered by indicator if country == "true" and country_dim: choices = get_choices_for_dim(country_dim) choices = sorted(choices, key=lambda x: x["label"]) choices.insert(0, {"label": "All Countries", "value": "*"}) return choices - # Step 2: Country selected, no frequency - return frequency choices if frequency == "true" and freq_dim: return get_choices_for_dim(freq_dim) - # Step 3: Frequency selected, no transform - return transform choices if transform == "true" and effective_transform_dim: choices = get_choices_for_dim(effective_transform_dim) - # Add "all" option at the beginning if there are choices if choices: choices.insert(0, {"label": "All", "value": "all"}) return choices - # All parameters set - no more choices needed return [] -async def get_imf_utils_apps_json() -> list[dict[str, Any]]: +from openbb_imf import ECONOMY_INSTALLED # noqa: E402 + +if not ECONOMY_INSTALLED: + + @router.command( + model="AvailableImfIndicators", + examples=[ + APIEx( + description="Search the full IMF indicator catalog.", + parameters={"provider": "imf"}, + ), + APIEx( + description="Filter the catalog to specific dataflows.", + parameters={"provider": "imf", "dataflows": "CPI,PCPS"}, + ), + ], + ) + async def available_indicators( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, + ) -> OBBject: + """Search the IMF SDMX indicator catalog by query, dataflow, or keyword.""" + return await OBBject.from_query(OBBQuery(**locals())) + + @router.command( + model="ImfIndicators", + examples=[ + APIEx( + description="Fetch a single indicator series by ``dataflow::code`` symbol.", + parameters={ + "provider": "imf", + "symbol": "IL::RGV_REVS", + "country": "*", + "frequency": "month", + "limit": 1, + "start_date": "2025-09-30", + }, + ), + APIEx( + description="Fetch a full presentation table for a country.", + parameters={ + "provider": "imf", + "symbol": "DIP::H_DIP_INDICATOR", + "country": "BRA", + "frequency": "annual", + "limit": 2, + "pivot": True, + }, + ), + ], + ) + async def indicators( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, + ) -> OBBject: + """Fetch IMF economic indicators or presentation tables by ``dataflow::code`` symbol.""" + return await OBBject.from_query(OBBQuery(**locals())) + + @router.command( + model="ImfConsumerPriceIndex", + examples=[ + APIEx( + description="Latest CPI basket weightings reported by the IMF.", + parameters={ + "provider": "imf", + "country": "CAN", + "transform": "weight_percent", + "expenditure": "all", + "limit": 1, + }, + ), + ], + ) + async def cpi( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, + ) -> OBBject: + """Consumer Price Index (CPI) data from the IMF.""" + return await OBBject.from_query(OBBQuery(**locals())) + + @router.command( + model="ImfBalanceOfPayments", + examples=[ + APIEx(parameters={"provider": "imf"}), + APIEx(parameters={"provider": "imf", "country": "BRA"}), + ], + ) + async def balance_of_payments( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, + ) -> OBBject: + """Balance of Payments reports from the IMF BOP dataflow.""" + return await OBBject.from_query(OBBQuery(**locals())) + + @router.command( + model="ImfDirectionOfTrade", + examples=[ + APIEx( + parameters={ + "provider": "imf", + "country": "all", + "counterpart": "china", + }, + ), + APIEx( + description="Select multiple countries or counterparts by entering a comma-separated list.", + parameters={ + "provider": "imf", + "country": "us", + "counterpart": "world,eu", + "frequency": "annual", + "direction": "exports", + }, + ), + ], + ) + async def direction_of_trade( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, + ) -> OBBject: + """Direction Of Trade Statistics (DOTS) from the IMF database.""" + return await OBBject.from_query(OBBQuery(**locals())) + + @router.command( + model="ImfPortInfo", + examples=[ + APIEx(parameters={"provider": "imf"}), + APIEx(parameters={"provider": "imf", "continent": "asia_pacific"}), + ], + ) + async def port_info( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, + ) -> OBBject: + """General metadata and statistics for all IMF Port Watch ports.""" + return await OBBject.from_query(OBBQuery(**locals())) + + @router.command( + model="ImfPortVolume", + examples=[ + APIEx( + description="Daily port calls and estimated trading volumes for specific ports.", + parameters={ + "provider": "imf", + "port_code": "rotterdam,singapore", + }, + ), + APIEx( + description="All ports in a specific country (ISO3 code).", + parameters={"provider": "imf", "country": "GBR"}, + ), + ], + ) + async def port_volume( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, + ) -> OBBject: + """Daily port calls and estimated trading volumes from IMF Port Watch.""" + return await OBBject.from_query(OBBQuery(**locals())) + + @router.command( + model="ImfMaritimeChokePointInfo", + examples=[APIEx(parameters={"provider": "imf"})], + ) + async def chokepoint_info( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, + ) -> OBBject: + """Metadata and statistics for all IMF Port Watch maritime chokepoints.""" + return await OBBject.from_query(OBBQuery(**locals())) + + @router.command( + model="ImfMaritimeChokePointVolume", + examples=[ + APIEx(parameters={"provider": "imf"}), + APIEx( + parameters={ + "provider": "imf", + "chokepoint": "suez_canal,panama_canal", + } + ), + ], + ) + async def chokepoint_volume( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, + ) -> OBBject: + """Daily transit calls and estimated trade volumes for shipping chokepoints.""" + return await OBBject.from_query(OBBQuery(**locals())) + + +from openbb_imf.portwatch_router import portwatch_router # noqa: E402 + +router.include_router(portwatch_router, prefix="/portwatch") + + +_APPS_WIDGET_ID_FALLBACK_MAP = { + "economy_available_indicators_imf_obb": "imf_available_indicators_imf_obb", + "economy_indicators_imf_obb": "imf_indicators_imf_obb", + "economy_balance_of_payments_imf_obb": "imf_balance_of_payments_imf_obb", + "economy_cpi_imf_obb": "imf_cpi_imf_obb", + "economy_direction_of_trade_imf_obb": "imf_direction_of_trade_imf_obb", + "economy_shipping_chokepoint_volume_imf_obb": "imf_chokepoint_volume_imf_obb", + "economy_shipping_port_info_imf_obb": "imf_port_info_imf_obb", + "economy_shipping_port_volume_imf_obb": "imf_port_volume_imf_obb", +} + + +def _rewrite_widget_ids(apps_json: list[dict[str, Any]]) -> list[dict[str, Any]]: + """Swap ``economy_*_imf_obb`` widget IDs for their ``imf_*_imf_obb`` fallbacks.""" + for app in apps_json: + tabs = app.get("tabs", {}) or {} + for tab in tabs.values(): + for widget in tab.get("layout", []) or []: + widget_id = widget.get("i") + if widget_id in _APPS_WIDGET_ID_FALLBACK_MAP: + widget["i"] = _APPS_WIDGET_ID_FALLBACK_MAP[widget_id] + return apps_json + + +async def get_imf_apps_json() -> list[dict[str, Any]]: """Get the IMF apps.json file. - This endpoint serves the apps.json file containing OpenBB Workspace app configurations - related to IMF data and utilities. - - It is automatically merged with any existing apps.json files in the Workspace and API. - Returns ------- list[dict[str, Any]] A list of OpenBB Workspace app configurations. """ - # pylint: disable=import-outside-toplevel import json from pathlib import Path @@ -1117,14 +1245,18 @@ async def get_imf_utils_apps_json() -> list[dict[str, Any]]: try: with apps_file.open("r", encoding="utf-8") as f: apps_json = json.load(f) - return apps_json except Exception: return [] + if not ECONOMY_INSTALLED: + apps_json = _rewrite_widget_ids(apps_json) + + return apps_json + router._api_router.add_api_route( path="/apps.json", - endpoint=get_imf_utils_apps_json, + endpoint=get_imf_apps_json, methods=["GET"], include_in_schema=False, ) diff --git a/openbb_platform/providers/imf/openbb_imf/imf_views.py b/openbb_platform/providers/imf/openbb_imf/imf_views.py new file mode 100644 index 00000000000..d6ceca6a5c3 --- /dev/null +++ b/openbb_platform/providers/imf/openbb_imf/imf_views.py @@ -0,0 +1,170 @@ +"""Charting extension dispatchers for the IMF router.""" + +from typing import TYPE_CHECKING, Any + +if TYPE_CHECKING: + from openbb_charting.core.openbb_figure import OpenBBFigure + + +def _dump(obbject_item: Any) -> list[dict[str, Any]]: + """Convert a list of Data models to plain dicts.""" + rows: list[dict[str, Any]] = [] + for item in obbject_item or []: + if hasattr(item, "model_dump"): + rows.append(item.model_dump(mode="json", exclude_none=False)) + elif isinstance(item, dict): + rows.append(item) + return rows + + +def _coerce_params(obj: Any) -> dict[str, Any]: + """Convert a QueryParams model (or dict) to a plain dict.""" + if obj is None: + return {} + if hasattr(obj, "model_dump"): + return obj.model_dump() + if isinstance(obj, dict): + return obj + return {} + + +def _params(kwargs: dict[str, Any]) -> dict[str, Any]: + """Merge ``standard_params``, ``extra_params``, and top-level kwargs into one dict.""" + merged: dict[str, Any] = {} + merged.update(_coerce_params(kwargs.get("standard_params"))) + merged.update(_coerce_params(kwargs.get("extra_params"))) + for k, v in kwargs.items(): + if k in ( + "standard_params", + "extra_params", + "obbject_item", + "charting_settings", + "provider", + "extra", + ): + continue + merged.setdefault(k, v) + return merged + + +def _standard_params(kwargs: dict[str, Any]) -> dict[str, Any]: + """Backwards-compat shim: return the merged standard+extra param dict.""" + return _params(kwargs) + + +def _theme(kwargs: dict[str, Any]) -> str: + """Resolve the chart theme, defaulting to 'dark'.""" + return _params(kwargs).get("theme") or "dark" + + +def _content(fig: "OpenBBFigure") -> dict[str, Any]: + """Return the Workspace-ready Plotly content dict.""" + content = fig.show(external=True).to_plotly_json() + content["config"] = {"displayModeBar": False} + return content + + +class ImfViews: + """Charting dispatchers for ``obb.imf.*`` routes.""" + + @staticmethod + def imf_portwatch_country_activity( + **kwargs, + ) -> tuple["OpenBBFigure", dict[str, Any]]: + """Render the daily country-activity stacked-area chart.""" + from openbb_imf.utils.port_watch_helpers import map_port_country_code + from openbb_imf.views.port_watch_charts import plot_country_activity + + sp = _standard_params(kwargs) + country_code = sp.get("country_code") or "USA" + metric = sp.get("metric") or "portcalls" + try: + title = map_port_country_code(country_code) + except ValueError: + title = country_code + + fig = plot_country_activity( + _dump(kwargs.get("obbject_item")), title, metric, theme=_theme(kwargs) + ) + return fig, _content(fig) + + @staticmethod + def imf_portwatch_monthly_trade( + **kwargs, + ) -> tuple["OpenBBFigure", dict[str, Any]]: + """Render the monthly TradeNow chart.""" + from openbb_imf.utils.port_watch_helpers import map_port_country_code + from openbb_imf.views.port_watch_charts import plot_monthly_trade + + sp = _standard_params(kwargs) + code = sp.get("code") or "USA" + metric = sp.get("metric") or "trade_value" + try: + region_name = map_port_country_code(code) + except ValueError: + region_name = code + + fig = plot_monthly_trade( + _dump(kwargs.get("obbject_item")), region_name, metric, theme=_theme(kwargs) + ) + return fig, _content(fig) + + @staticmethod + def imf_portwatch_container_metrics( + **kwargs, + ) -> tuple["OpenBBFigure", dict[str, Any]]: + """Render the container-metrics multi-series chart.""" + import asyncio + + from openbb_imf.utils.port_watch_helpers import get_container_port_name_map + from openbb_imf.views.port_watch_charts import plot_container_metrics + + sp = _standard_params(kwargs) + metric_label = { + "portcalls": "Container Port Calls", + "import_container": "Container Imports", + "export_container": "Container Exports", + "incoming_cargo_container": "Incoming Container Cargo", + "outgoing_cargo_container": "Outgoing Container Cargo", + }.get(sp.get("metric") or "portcalls", "Container Port Calls") + + port_name_map = asyncio.run(get_container_port_name_map()) + + fig = plot_container_metrics( + _dump(kwargs.get("obbject_item")), + metric_label, + theme=_theme(kwargs), + port_name_map=port_name_map, + ) + return fig, _content(fig) + + @staticmethod + def imf_portwatch_disruptions_map( + **kwargs, + ) -> tuple["OpenBBFigure", dict[str, Any]]: + """Render the disruption Scattergeo map.""" + from openbb_imf.views.port_watch_charts import plot_disruptions_map + + fig = plot_disruptions_map( + _dump(kwargs.get("obbject_item")), theme=_theme(kwargs) + ) + return fig, _content(fig) + + @staticmethod + def imf_portwatch_disruption_sankey( + **kwargs, + ) -> tuple["OpenBBFigure", dict[str, Any]]: + """Render the disruption capacity-spillover Sankey.""" + from openbb_imf.views.port_watch_charts import plot_disruption_sankey + + sp = _standard_params(kwargs) + event_label = sp.get("event_id") or "Event" + if event_label.upper() == "LATEST": + event_label = "Latest Disruption" + else: + event_label = f"Event {event_label}" + + fig = plot_disruption_sankey( + _dump(kwargs.get("obbject_item")), event_label, theme=_theme(kwargs) + ) + return fig, _content(fig) diff --git a/openbb_platform/providers/imf/openbb_imf/models/__init__.py b/openbb_platform/providers/imf/openbb_imf/models/__init__.py index b987d948287..0c6ecd235af 100644 --- a/openbb_platform/providers/imf/openbb_imf/models/__init__.py +++ b/openbb_platform/providers/imf/openbb_imf/models/__init__.py @@ -1 +1,3 @@ """OpenBB IMF Provider Models.""" + +from __future__ import annotations diff --git a/openbb_platform/providers/imf/openbb_imf/models/available_indicators.py b/openbb_platform/providers/imf/openbb_imf/models/available_indicators.py index d4cae71285f..16646c170c7 100644 --- a/openbb_platform/providers/imf/openbb_imf/models/available_indicators.py +++ b/openbb_platform/providers/imf/openbb_imf/models/available_indicators.py @@ -1,6 +1,6 @@ """IMF Available Indicators.""" -# pylint: disable=unused-argument +from __future__ import annotations from typing import Any @@ -22,7 +22,7 @@ class ImfAvailableIndicatorsQueryParams(AvailableIndicesQueryParams): "multiple_items_allowed": True, "x-widget_config": { "type": "endpoint", - "optionsEndpoint": "/api/v1/imf_utils/list_dataflow_choices", + "optionsEndpoint": "/api/v1/imf/list_dataflow_choices", "multiSelect": False, "style": {"popupWidth": 950}, }, @@ -109,6 +109,12 @@ class ImfAvailableIndicatorsData(AvailableIndicatorsData): default_factory=list, description="List of table symbols (dataflow_id::table_id) this indicator belongs to.", ) + extra_dimensions: list[str] = Field( + default_factory=list, + description="Non-standard dimensions the dataflow exposes beyond country / " + "frequency / indicator / transform. Pass each as ``DIM_ID:DIM_VALUE`` in the " + "``dimension_values`` parameter when calling ``obb.economy.indicators``.", + ) class ImfAvailableIndicatorsFetcher( @@ -122,14 +128,14 @@ class ImfAvailableIndicatorsFetcher( return ImfAvailableIndicatorsQueryParams(**params) @staticmethod - def extract_data( + async def aextract_data( query: ImfAvailableIndicatorsQueryParams, credentials: dict[str, Any] | None = None, **kwargs: Any, ) -> list[dict]: """Fetch the data.""" - # pylint: disable=import-outside-toplevel from openbb_core.provider.utils.errors import EmptyDataError + from openbb_imf.utils.metadata import ImfMetadata metadata = ImfMetadata() @@ -154,7 +160,7 @@ class ImfAvailableIndicatorsFetcher( dataflows=dataflows, keywords=keywords, ) - except Exception as e: # pylint: disable=broad-except + except Exception as e: raise OpenBBError(e) from e if not results: @@ -171,7 +177,6 @@ class ImfAvailableIndicatorsFetcher( """Transform the data.""" results = [] for d in data: - # Build the ready-to-use symbol: dataflow_id::indicator_code dataflow_id = d.get("dataflow_id", "") indicator_code = d.get("indicator", "") d["symbol"] = f"{dataflow_id}::{indicator_code}" diff --git a/openbb_platform/providers/imf/openbb_imf/models/balance_of_payments.py b/openbb_platform/providers/imf/openbb_imf/models/balance_of_payments.py new file mode 100644 index 00000000000..042cf7e6469 --- /dev/null +++ b/openbb_platform/providers/imf/openbb_imf/models/balance_of_payments.py @@ -0,0 +1,407 @@ +"""IMF Balance of Payments Model.""" + +from __future__ import annotations + +from datetime import date as dateType +from typing import Any, Literal + +from openbb_core.app.model.abstract.error import OpenBBError +from openbb_core.app.service.system_service import SystemService +from openbb_core.provider.abstract.fetcher import Fetcher +from openbb_core.provider.standard_models.balance_of_payments import ( + BalanceOfPaymentsQueryParams, + BP6BopUsdData, +) +from openbb_core.provider.utils.descriptions import QUERY_DESCRIPTIONS +from openbb_core.provider.utils.errors import EmptyDataError +from pydantic import ConfigDict, Field, field_validator + +from openbb_imf.utils.metadata import ImfMetadata + +api_prefix = SystemService().system_settings.api_settings.prefix + +_BOP_TABLE_SYMBOL = "BOP::H_BOP_BOP_AGG_STANDARD_PRESENTATION" + +_CANONICAL_ORDER: tuple[str, ...] = ( + "period", + "country", + "balance_total", + "balance_total_goods", + "balance_total_services", + "balance_total_primary_income", + "balance_total_secondary_income", + "balance_percent_of_gdp", + "credits_total", + "credits_total_goods", + "credits_total_services", + "credits_total_primary_income", + "credits_total_secondary_income", + "credits_services_percent_of_goods_and_services", + "credits_services_percent_of_current_account", + "debits_total", + "debits_total_goods", + "debits_total_services", + "debits_total_primary_income", + "debits_total_secondary_income", + "debits_services_percent_of_goods_and_services", + "debits_services_percent_of_current_account", +) + + +def _load_bop_countries() -> tuple[dict[str, str], set[str]]: + """Return ``(label_to_code, code_set)`` from the BOP dataflow.""" + metadata = ImfMetadata() + params = metadata.get_dataflow_parameters("BOP") + countries = params.get("COUNTRY", []) + label_to_code: dict[str, str] = {} + code_set: set[str] = set() + for entry in sorted(countries, key=lambda p: p.get("label", "")): + code = entry.get("value") + label = entry.get("label", code or "") + if not code: + continue + code_set.add(code.upper()) + label_to_code[label.lower().replace(" ", "_")] = code.upper() + return label_to_code, code_set + + +BOP_LABEL_TO_CODE, BOP_CODE_SET = _load_bop_countries() + +_BALANCE_FIELD: dict[str, str] = { + "CAB": "balance_total", + "G": "balance_total_goods", + "S": "balance_total_services", + "IN1": "balance_total_primary_income", + "IN2": "balance_total_secondary_income", +} + +_CREDIT_FIELD: dict[str, str] = { + "G": "credits_total_goods", + "S": "credits_total_services", + "IN1": "credits_total_primary_income", + "IN2": "credits_total_secondary_income", +} + +_DEBIT_FIELD: dict[str, str] = { + "G": "debits_total_goods", + "S": "debits_total_services", + "IN1": "debits_total_primary_income", + "IN2": "debits_total_secondary_income", +} + +_FREQ_CODE: dict[str, str] = {"annual": "A", "quarterly": "Q", "monthly": "M"} + + +class ImfBalanceOfPaymentsQueryParams(BalanceOfPaymentsQueryParams): + """IMF Balance of Payments Query Parameters.""" + + __json_schema_extra__ = { + "country": { + "multiple_items_allowed": True, + "x-widget_config": { + "type": "endpoint", + "optionsEndpoint": f"{api_prefix}/imf/list_bop_country_choices", + "value": ["USA"], + "style": {"popupWidth": 400}, + }, + }, + } + + country: str = Field( + default="USA", + description=QUERY_DESCRIPTIONS.get("country", ""), + validate_default=True, + ) + frequency: Literal["annual", "quarterly"] = Field( + default="quarterly", + description="Reporting frequency.", + ) + start_date: dateType | None = Field( + default=None, + description=QUERY_DESCRIPTIONS.get("start_date", ""), + ) + end_date: dateType | None = Field( + default=None, + description=QUERY_DESCRIPTIONS.get("end_date", ""), + ) + + @field_validator("country", mode="before", check_fields=False) + @classmethod + def validate_country(cls, c: str) -> str: + """Resolve country name or ISO3 token(s) against the BOP dataflow's countries.""" + out: list[str] = [] + for token in c.replace(" ", "_").split(","): + upper = token.upper() + lower = token.lower() + if upper in BOP_CODE_SET: + out.append(upper) + elif lower in BOP_LABEL_TO_CODE: + out.append(BOP_LABEL_TO_CODE[lower]) + else: + raise ValueError( + f"Country '{token}' is not in the IMF BOP dataflow's country list " + f"(value must be one of {len(BOP_CODE_SET)} ISO3 codes " + "or matching country labels)." + ) + return "+".join(out) + + +class ImfBalanceOfPaymentsData(BP6BopUsdData): + """IMF Balance of Payments Data.""" + + __alias_dict__ = {"period": "date"} + + model_config = ConfigDict( + extra="ignore", + json_schema_extra={ + "x-widget_config": { + "$.name": "Balance of Payments", + "$.description": ( + "BPM6 Balance of Payments aggregated standard presentation." + ), + "$.gridData": {"w": 40, "h": 14}, + "$.refetchInterval": False, + "$.category": "Economy", + "$.subCategory": "Balance of Payments", + } + }, + ) + + country: str | None = Field( + default=None, + description="ISO3 country code (or label) reported alongside each row.", + ) + balance_total: float | None = Field( + default=None, description="Current Account Total Balance (USD)" + ) + balance_total_goods: float | None = Field( + default=None, description="Current Account Total Goods Balance (USD)" + ) + balance_total_services: float | None = Field( + default=None, description="Current Account Total Services Balance (USD)" + ) + balance_total_primary_income: float | None = Field( + default=None, + description="Current Account Total Primary Income Balance (USD)", + ) + balance_total_secondary_income: float | None = Field( + default=None, + description="Current Account Total Secondary Income Balance (USD)", + ) + balance_percent_of_gdp: float | None = Field( + default=None, + description="Current Account Balance as Percent of GDP (not provided by IMF BOP).", + json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100}, + ) + credits_total: float | None = Field( + default=None, description="Current Account Credits Total (USD)" + ) + credits_total_goods: float | None = Field( + default=None, description="Current Account Credits Total Goods (USD)" + ) + credits_total_services: float | None = Field( + default=None, description="Current Account Credits Total Services (USD)" + ) + credits_total_primary_income: float | None = Field( + default=None, description="Current Account Credits Total Primary Income (USD)" + ) + credits_total_secondary_income: float | None = Field( + default=None, + description="Current Account Credits Total Secondary Income (USD)", + ) + credits_services_percent_of_goods_and_services: float | None = Field( + default=None, + description="Current Account Credits Services as Percent of Goods and Services", + json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100}, + ) + credits_services_percent_of_current_account: float | None = Field( + default=None, + description="Current Account Credits Services as Percent of Current Account", + json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100}, + ) + debits_total: float | None = Field( + default=None, description="Current Account Debits Total (USD)" + ) + debits_total_goods: float | None = Field( + default=None, description="Current Account Debits Total Goods (USD)" + ) + debits_total_services: float | None = Field( + default=None, description="Current Account Debits Total Services (USD)" + ) + debits_total_primary_income: float | None = Field( + default=None, description="Current Account Debits Total Primary Income (USD)" + ) + debits_total_secondary_income: float | None = Field( + default=None, + description="Current Account Debits Total Secondary Income (USD)", + ) + debits_services_percent_of_goods_and_services: float | None = Field( + default=None, + description="Current Account Debits Services as Percent of Goods and Services", + json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100}, + ) + debits_services_percent_of_current_account: float | None = Field( + default=None, + description="Current Account Debits Services as Percent of Current Account", + json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100}, + ) + + def model_dump(self, **kwargs: Any) -> dict[str, Any]: + """Dump fields in BPM6 reading order regardless of inherited declarations.""" + raw = super().model_dump(**kwargs) + ordered: dict[str, Any] = {} + for key in _CANONICAL_ORDER: + if key in raw: + ordered[key] = raw.pop(key) + ordered.update(raw) + return ordered + + +class ImfBalanceOfPaymentsFetcher( + Fetcher[ImfBalanceOfPaymentsQueryParams, list[ImfBalanceOfPaymentsData]] +): + """IMF Balance of Payments Fetcher.""" + + @staticmethod + def transform_query(params: dict[str, Any]) -> ImfBalanceOfPaymentsQueryParams: + """Validate query params.""" + return ImfBalanceOfPaymentsQueryParams(**params) + + @staticmethod + def extract_data( + query: ImfBalanceOfPaymentsQueryParams, + credentials: dict[str, str] | None, + **kwargs: Any, + ) -> list[dict[str, Any]]: + """Fetch the IMF BOP rows.""" + from openbb_imf.utils.query_builder import ImfQueryBuilder + + freq = _FREQ_CODE[query.frequency] + start_date = query.start_date.strftime("%Y-%m-%d") if query.start_date else None + end_date = query.end_date.strftime("%Y-%m-%d") if query.end_date else None + + try: + result = ImfQueryBuilder().fetch_data( + "BOP", + start_date=start_date, + end_date=end_date, + COUNTRY=query.country, + BOP_ACCOUNTING_ENTRY="NETCD_T+CD_T+DB_T", + INDICATOR="CAB+G+S+IN1+IN2", + UNIT="USD", + FREQUENCY=freq, + _skip_validation=True, + ) + except (ValueError, OpenBBError) as e: + raise OpenBBError(e) from e + + rows = result.get("data", []) if isinstance(result, dict) else [] + if not rows: + raise OpenBBError(EmptyDataError("No BOP data for the selected query.")) + return rows + + @staticmethod + def transform_data( + query: ImfBalanceOfPaymentsQueryParams, + data: list[dict[str, Any]], + **kwargs: Any, + ) -> list[ImfBalanceOfPaymentsData]: + """Pivot long SDMX rows into wide BP6 records keyed by date+country.""" + groups: dict[tuple[str, str], dict[str, Any]] = {} + for row in data: + indicator = str(row.get("INDICATOR_code") or row.get("INDICATOR") or "") + entry = row.get("BOP_ACCOUNTING_ENTRY_code") or row.get( + "BOP_ACCOUNTING_ENTRY" + ) + if entry == "NETCD_T": + field = _BALANCE_FIELD.get(indicator) + elif entry == "CD_T": + field = _CREDIT_FIELD.get(indicator) + elif entry == "DB_T": + field = _DEBIT_FIELD.get(indicator) + else: + field = None + if field is None: + continue + period = row.get("TIME_PERIOD") + if not period: + continue + country = ( + row.get("COUNTRY_label") + or row.get("COUNTRY_code") + or row.get("COUNTRY") + or "" + ) + value = row.get("OBS_VALUE") + if value in (None, ""): + continue + try: + fval = float(value) + except (TypeError, ValueError): + continue + bucket = groups.setdefault( + (period, country), + {"period": period, "country": country}, + ) + bucket[field] = fval + + for bucket in groups.values(): + credits_components = [ + bucket.get(c) + for c in ( + "credits_total_goods", + "credits_total_services", + "credits_total_primary_income", + "credits_total_secondary_income", + ) + ] + debits_components = [ + bucket.get(c) + for c in ( + "debits_total_goods", + "debits_total_services", + "debits_total_primary_income", + "debits_total_secondary_income", + ) + ] + if all(v is not None for v in credits_components): + bucket["credits_total"] = sum(credits_components) + if all(v is not None for v in debits_components): + bucket["debits_total"] = sum(debits_components) + + services_credits = bucket.get("credits_total_services") + services_debits = bucket.get("debits_total_services") + goods_credits = bucket.get("credits_total_goods") + goods_debits = bucket.get("debits_total_goods") + credits_total = bucket.get("credits_total") + debits_total = bucket.get("debits_total") + + if ( + services_credits is not None + and goods_credits is not None + and (services_credits + goods_credits) + ): + bucket["credits_services_percent_of_goods_and_services"] = ( + services_credits / (services_credits + goods_credits) + ) + if services_credits is not None and credits_total: + bucket["credits_services_percent_of_current_account"] = ( + services_credits / credits_total + ) + if ( + services_debits is not None + and goods_debits is not None + and (services_debits + goods_debits) + ): + bucket["debits_services_percent_of_goods_and_services"] = ( + services_debits / (services_debits + goods_debits) + ) + if services_debits is not None and debits_total: + bucket["debits_services_percent_of_current_account"] = ( + services_debits / debits_total + ) + + return sorted( + [ImfBalanceOfPaymentsData.model_validate(rec) for rec in groups.values()], + key=lambda r: (r.period or dateType.min, r.country or ""), + ) diff --git a/openbb_platform/providers/imf/openbb_imf/models/consumer_price_index.py b/openbb_platform/providers/imf/openbb_imf/models/consumer_price_index.py index 58d6a11bf15..d122def3cce 100644 --- a/openbb_platform/providers/imf/openbb_imf/models/consumer_price_index.py +++ b/openbb_platform/providers/imf/openbb_imf/models/consumer_price_index.py @@ -1,19 +1,23 @@ """IMF CPI Data.""" -# pylint: disable=unused-argument +from __future__ import annotations from typing import Any from openbb_core.app.model.abstract.error import OpenBBError +from openbb_core.app.service.system_service import SystemService from openbb_core.provider.abstract.annotated_result import AnnotatedResult from openbb_core.provider.abstract.fetcher import Fetcher from openbb_core.provider.standard_models.consumer_price_index import ( ConsumerPriceIndexData, ConsumerPriceIndexQueryParams, ) +from pydantic import ConfigDict, Field, field_validator + from openbb_imf.utils.helpers import normalize_country_label from openbb_imf.utils.query_builder import ImfQueryBuilder -from pydantic import Field, field_validator + +api_prefix = SystemService().system_settings.api_settings.prefix CpiCountries = [ {"label": "Afghanistan", "value": "AFG"}, @@ -265,8 +269,6 @@ expenditure_dict_rev: dict = { } expenditure_dict: dict = {v: k for k, v in expenditure_dict_rev.items()} -# Order mapping for sorting expenditure categories (COICOP codes) -# _T (total) comes first, then CP01-CP12 in numerical order expenditure_order: dict[str, int] = { "_T": 0, "CP01": 1, @@ -308,7 +310,12 @@ class ImfConsumerPriceIndexQueryParams(ConsumerPriceIndexQueryParams): __json_schema_extra__ = { "country": { "multiple_items_allowed": True, - "choices": list(CPI_LABEL_TO_CODE), + "x-widget_config": { + "type": "endpoint", + "optionsEndpoint": f"{api_prefix}/imf/list_cpi_country_choices", + "value": ["USA"], + "style": {"popupWidth": 400}, + }, }, "transform": { "multiple_items_allowed": False, @@ -317,9 +324,18 @@ class ImfConsumerPriceIndexQueryParams(ConsumerPriceIndexQueryParams): "expenditure": { "multiple_items_allowed": True, "choices": expenditure_choices, + "x-widget_config": { + "value": "total", + }, }, } + country: str = Field( + default="USA", + description="The country (ISO3 code or label) to get data for.", + validate_default=True, + ) + expenditure: str = Field( default="total", description="Expenditure component of CPI." ) @@ -333,20 +349,14 @@ class ImfConsumerPriceIndexQueryParams(ConsumerPriceIndexQueryParams): @field_validator("country", mode="before", check_fields=False) @classmethod def validate_country(cls, c): - """Validate country. - - Accepts both ISO3 codes (e.g., "USA") and snake_case country names - (e.g., "united_states"). Converts names to ISO3 codes. - """ + """Validate country.""" result: list = [] values = c.replace(" ", "_").split(",") for v in values: v_upper = v.upper() v_lower = v.lower() - # Check if it's a valid ISO3 code if v_upper in CPI_CODE_SET: result.append(v_upper) - # Check if it's a valid snake_case country name elif v_lower in CPI_LABEL_TO_CODE: result.append(CPI_LABEL_TO_CODE[v_lower]) else: @@ -377,6 +387,21 @@ class ImfConsumerPriceIndexQueryParams(ConsumerPriceIndexQueryParams): class ImfConsumerPriceIndexData(ConsumerPriceIndexData): """IMF CPI Data Model.""" + model_config = ConfigDict( + extra="allow", + json_schema_extra={ + "x-widget_config": { + "$.name": "CPI", + "$.description": ( + "Consumer Price Index (CPI) and Harmonized Index of Consumer Prices (HICP)." + ), + "$.gridData": {"w": 40, "h": 14}, + "$.refetchInterval": False, + "$.subCategory": "CPI", + } + }, + ) + unit: str = Field( description="Unit of measurement.", ) @@ -449,7 +474,6 @@ class ImfConsumerPriceIndexFetcher( } query_builder = ImfQueryBuilder() - # Mappings from IMF dimension codes to user-friendly parameter names dim_to_param = { "COUNTRY": "country", "INDEX_TYPE": "harmonized", @@ -457,7 +481,6 @@ class ImfConsumerPriceIndexFetcher( "TYPE_OF_TRANSFORMATION": "transform", "FREQUENCY": "frequency", } - # Reverse mappings for values transformation_rev = {v: k for k, v in transformation_map.items()} frequency_map = {"A": "annual", "Q": "quarter", "M": "monthly"} @@ -476,24 +499,18 @@ class ImfConsumerPriceIndexFetcher( **parameters, ) except ValueError as e: - # Translate dimension codes to user-friendly parameter names in error message error_msg = str(e) for dim_code, param_name in dim_to_param.items(): error_msg = error_msg.replace(f"'{dim_code}'", f"'{param_name}'") error_msg = error_msg.replace(f'"{dim_code}"', f'"{param_name}"') - # Translate transformation values for api_val, user_val in transformation_rev.items(): error_msg = error_msg.replace(f"'{api_val}'", f"'{user_val}'") - # Translate frequency values for api_val, user_val in frequency_map.items(): error_msg = error_msg.replace(f"'{api_val}'", f"'{user_val}'") - # Translate expenditure values for api_val, user_val in expenditure_dict_rev.items(): error_msg = error_msg.replace(f"'{api_val}'", f"'{user_val}'") - # Translate INDEX_TYPE values error_msg = error_msg.replace("'CPI'", "'False'") error_msg = error_msg.replace("'HICP'", "'True'") - # Translate country codes back to user-friendly names for code, label in CPI_CODE_TO_LABEL.items(): error_msg = error_msg.replace(f"'{code}'", f"'{label}'") raise OpenBBError(error_msg) from e @@ -525,7 +542,6 @@ class ImfConsumerPriceIndexFetcher( raise OpenBBError("No data returned for the given query parameters.") for item in row_data: - # Filter by date range here because IMF API date filtering can be inconsistent item_date = item.get("TIME_PERIOD", None) if ( query.start_date @@ -540,17 +556,13 @@ class ImfConsumerPriceIndexFetcher( ): continue - # Get translated labels (these are now human-readable) frequency = (item.get("FREQUENCY") or "").strip() index_type = (item.get("INDEX_TYPE") or "").strip() expenditure = (item.get("COICOP_1999") or item.get("title") or "").strip() expenditure_code = (item.get("COICOP_1999_code") or "").strip() transformation = (item.get("TYPE_OF_TRANSFORMATION") or "").strip() - # Build title from translated values title = f"{frequency} {index_type} - {expenditure} - {transformation}" - # Get unit from transformation (use last part if comma-separated) unit = (transformation.rsplit(", ", maxsplit=1)[-1] or "").strip() - # Get sort order from expenditure code order = expenditure_order.get(expenditure_code, 99) obs_value = item.get("OBS_VALUE", None) multiplier = item.get("UNIT_MULT", 1) @@ -558,8 +570,7 @@ class ImfConsumerPriceIndexFetcher( if "percent" in unit.lower() and obs_value is not None: obs_value = obs_value / 100.0 multiplier = 100 - symbol = item.get("series_id", "").strip().split("IMF_STA_CPI_")[-1] - symbol = f"CPI::{symbol}" + symbol = (item.get("series_id") or "").strip() new_row = { "date": item_date, "country": (item.get("COUNTRY") or "").strip() or None, @@ -574,7 +585,6 @@ class ImfConsumerPriceIndexFetcher( } result.append(ImfConsumerPriceIndexData.model_validate(new_row)) - # Sort by date, then country, then order (expenditure) result.sort( key=lambda x: ( x.date, diff --git a/openbb_platform/providers/imf/openbb_imf/models/container_metrics.py b/openbb_platform/providers/imf/openbb_imf/models/container_metrics.py new file mode 100644 index 00000000000..8d2870aa598 --- /dev/null +++ b/openbb_platform/providers/imf/openbb_imf/models/container_metrics.py @@ -0,0 +1,175 @@ +"""IMF Container Metrics Model.""" + +from typing import Any, Literal + +from openbb_core.app.service.system_service import SystemService +from openbb_core.provider.abstract.data import Data +from openbb_core.provider.abstract.fetcher import Fetcher +from openbb_core.provider.abstract.query_params import QueryParams +from pydantic import ConfigDict, Field + +api_prefix = SystemService().system_settings.api_settings.prefix + +ContainerMetric = Literal[ + "portcalls", + "import_container", + "export_container", + "incoming_cargo_container", + "outgoing_cargo_container", +] + + +class ImfContainerMetricsQueryParams(QueryParams): + """IMF Container Metrics Query Parameters.""" + + __json_schema_extra__ = { + "metric": { + "x-widget_config": { + "type": "text", + "options": [ + {"label": "Container Port Calls", "value": "portcalls"}, + {"label": "Container Imports", "value": "import_container"}, + {"label": "Container Exports", "value": "export_container"}, + { + "label": "Incoming Container Cargo", + "value": "incoming_cargo_container", + }, + { + "label": "Outgoing Container Cargo", + "value": "outgoing_cargo_container", + }, + ], + } + }, + "port_ids": { + "multiple_items_allowed": True, + "x-widget_config": { + "type": "endpoint", + "optionsEndpoint": f"{api_prefix}/imf/portwatch/list_container_port_choices", + "multiSelect": True, + "style": {"popupWidth": 350}, + }, + }, + "start_date": {"x-widget_config": {"type": "date"}}, + "end_date": {"x-widget_config": {"type": "date"}}, + "theme": {"x-widget_config": {"show": False}}, + } + + metric: ContainerMetric = Field( + default="portcalls", + description=( + "Container metric to plot. Allowed: 'portcalls', 'import_container'," + " 'export_container', 'incoming_cargo_container'," + " 'outgoing_cargo_container'." + ), + title="Metric", + ) + port_ids: str = Field( + default="TOP10", + description=( + "Comma- or plus-separated port IDs to compare, or 'TOP10' to" + " auto-select the busiest ports for the chosen metric." + ), + title="Port IDs", + ) + start_date: str | None = Field( + default=None, + description="ISO date (YYYY-MM-DD) lower bound for the monthly series.", + title="Start Date", + ) + end_date: str | None = Field( + default=None, + description="ISO date (YYYY-MM-DD) upper bound for the monthly series.", + title="End Date", + ) + theme: Literal["dark", "light"] | None = Field( + default=None, + description="Theme for the chart. Only used when ``chart=True``.", + title="Theme", + ) + + +class ImfContainerMetricsData(Data): + """IMF Container Metrics Data (long format).""" + + model_config = ConfigDict( + extra="allow", + json_schema_extra={ + "x-widget_config": { + "$.name": "Top Container Ports โ€” Monthly Comparison", + "$.description": ( + "Monthly container metric at the world's largest container" + " ports. Source: IMF Port Watch." + ), + "$.gridData": {"w": 40, "h": 14}, + "$.refetchInterval": False, + "$.category": "IMF Utilities", + "$.subCategory": "Port Watch", + "$.source": ["UN Global Platform; IMF PortWatch"], + } + }, + ) + + metric: str = Field(description="Container metric name.", title="Metric") + portid: str = Field(description="Port identifier (e.g. PORT23).", title="Port ID") + date: str = Field(description="Observation month (YYYY-MM-DD).", title="Date") + value: float | None = Field( + default=None, description="Metric value.", title="Value" + ) + + +class ImfContainerMetricsFetcher( + Fetcher[ImfContainerMetricsQueryParams, list[ImfContainerMetricsData]] +): + """IMF Container Metrics Fetcher.""" + + @staticmethod + def transform_query(params: dict[str, Any]) -> ImfContainerMetricsQueryParams: + """Validate and coerce request params.""" + return ImfContainerMetricsQueryParams(**params) + + @staticmethod + async def aextract_data( + query: ImfContainerMetricsQueryParams, + credentials: dict[str, str] | None = None, + **kwargs: Any, + ) -> list[dict[str, Any]]: + """Fetch container-metric rows; resolves ``TOP10`` to the busiest ports.""" + from openbb_imf.utils.port_watch_helpers import get_container_metrics + + tokens = [ + p.strip().upper() + for p in query.port_ids.replace("+", ",").split(",") + if p.strip() + ] + use_top10 = not tokens or "TOP10" in tokens + + all_rows = await get_container_metrics( + None, query.metric, query.start_date, query.end_date + ) + if not use_top10: + keep = set(tokens) + return [r for r in all_rows if r.get("portid") in keep] + + totals: dict[str, float] = {} + for r in all_rows: + pid = r.get("portid") + if not pid: + continue + totals[pid] = totals.get(pid, 0.0) + float(r.get("value") or 0) + top10 = { + pid + for pid, _ in sorted(totals.items(), key=lambda kv: kv[1], reverse=True)[ + :10 + ] + } + return [r for r in all_rows if r.get("portid") in top10] + + @staticmethod + def transform_data( + query: ImfContainerMetricsQueryParams, + data: list[dict[str, Any]], + **kwargs: Any, + ) -> list[ImfContainerMetricsData]: + """Coerce raw rows into ``ImfContainerMetricsData`` instances.""" + return [ImfContainerMetricsData.model_validate(row) for row in data] diff --git a/openbb_platform/providers/imf/openbb_imf/models/country_activity.py b/openbb_platform/providers/imf/openbb_imf/models/country_activity.py new file mode 100644 index 00000000000..68d2f1b8377 --- /dev/null +++ b/openbb_platform/providers/imf/openbb_imf/models/country_activity.py @@ -0,0 +1,320 @@ +"""IMF Country Maritime Activity Model.""" + +from typing import Any, Literal + +from openbb_core.app.service.system_service import SystemService +from openbb_core.provider.abstract.data import Data +from openbb_core.provider.abstract.fetcher import Fetcher +from openbb_core.provider.abstract.query_params import QueryParams +from pydantic import ConfigDict, Field + +api_prefix = SystemService().system_settings.api_settings.prefix + + +class ImfCountryActivityQueryParams(QueryParams): + """IMF Country Maritime Activity Query Parameters.""" + + __json_schema_extra__ = { + "country_code": { + "x-widget_config": { + "type": "endpoint", + "optionsEndpoint": f"{api_prefix}/imf/portwatch/list_country_choices", + "style": {"popupWidth": 350}, + } + }, + "metric": { + "x-widget_config": { + "type": "text", + "options": [ + {"label": "Port Calls", "value": "portcalls"}, + {"label": "Imports (mt)", "value": "import"}, + {"label": "Exports (mt)", "value": "export"}, + ], + } + }, + "start_date": {"x-widget_config": {"type": "date"}}, + "end_date": {"x-widget_config": {"type": "date"}}, + "theme": {"x-widget_config": {"show": False}}, + } + + country_code: str = Field( + default="USA", + description="ISO3 country code to fetch daily activity for.", + title="Country", + ) + metric: Literal["portcalls", "import", "export"] = Field( + default="portcalls", + description="Activity metric to chart: 'portcalls', 'import', or 'export'.", + title="Metric", + ) + start_date: str | None = Field( + default=None, + description="ISO date (YYYY-MM-DD) lower bound for the activity window.", + title="Start Date", + ) + end_date: str | None = Field( + default=None, + description="ISO date (YYYY-MM-DD) upper bound for the activity window.", + title="End Date", + ) + theme: Literal["dark", "light"] | None = Field( + default=None, + description="Theme for the chart. Only used when ``chart=True``.", + title="Theme", + ) + + +class ImfCountryActivityData(Data): + """IMF Country Maritime Activity Data.""" + + model_config = ConfigDict( + extra="ignore", + validate_by_alias=True, + validate_by_name=True, + populate_by_name=True, + json_schema_extra={ + "x-widget_config": { + "$.name": "Daily Country Trade Activity", + "$.description": ( + "Daily country-level port calls, import volumes, and export" + " volumes from IMF Port Watch's Daily Trade Data (REG)." + ), + "$.gridData": {"w": 40, "h": 14}, + "$.refetchInterval": False, + "$.category": "IMF Utilities", + "$.subCategory": "Port Watch", + "$.source": ["UN Global Platform; IMF PortWatch"], + } + }, + ) + + __alias_dict__ = { + "country_code": "ISO3", + "imports": "import", + "imports_cargo": "import_cargo", + "imports_container": "import_container", + "imports_container_30ma": "import_container_30MA", + "imports_container_30ma_yoy_doy": "import_container_30MA_yoy_doy", + "imports_dry_bulk": "import_dry_bulk", + "imports_general_cargo": "import_general_cargo", + "imports_roro": "import_roro", + "imports_tanker": "import_tanker", + "exports": "export", + "exports_cargo": "export_cargo", + "exports_container": "export_container", + "exports_container_30ma": "export_container_30MA", + "exports_container_30ma_yoy_doy": "export_container_30MA_yoy_doy", + "exports_dry_bulk": "export_dry_bulk", + "exports_general_cargo": "export_general_cargo", + "exports_roro": "export_roro", + "exports_tanker": "export_tanker", + "portcalls_container_30ma": "portcalls_container_30MA", + "portcalls_container_30ma_yoy_doy": "portcalls_container_30MA_yoy_doy", + "shipment_30ma": "shipment_30MA", + "shipment_30ma_yoy_doy": "shipment_30MA_yoy_doy", + } + + date: str = Field(description="Observation date (YYYY-MM-DD).", title="Date") + country_code: str = Field(description="ISO3 country code.", title="Country Code") + country: str | None = Field( + default=None, description="Country display name.", title="Country" + ) + + portcalls: int | None = Field( + default=None, + description="Total ships calling at the country's ports on this date.", + title="Port Calls", + ) + portcalls_cargo: int | None = Field( + default=None, + description="Total cargo ships (excluding tankers) calling on this date.", + title="Cargo Port Calls", + ) + portcalls_container: int | None = Field( + default=None, + description="Container-ship port calls on this date.", + title="Container Port Calls", + ) + portcalls_dry_bulk: int | None = Field( + default=None, + description="Dry-bulk-carrier port calls on this date.", + title="Dry Bulk Port Calls", + ) + portcalls_general_cargo: int | None = Field( + default=None, + description="General-cargo-ship port calls on this date.", + title="General Cargo Port Calls", + ) + portcalls_roro: int | None = Field( + default=None, + description="Ro-Ro-ship port calls on this date.", + title="Ro-Ro Port Calls", + ) + portcalls_tanker: int | None = Field( + default=None, + description="Tanker port calls on this date.", + title="Tanker Port Calls", + ) + portcalls_container_30ma: float | None = Field( + default=None, + description="30-day moving average of container port calls.", + title="Container Port Calls (30d MA)", + ) + portcalls_container_30ma_yoy_doy: float | None = Field( + default=None, + description=( + "Year-over-year change in the 30-day MA of container port calls," + " keyed on day of year." + ), + title="Container Port Calls (30d MA YoY)", + ) + + imports: int | None = Field( + default=None, + description="Total import volume (mt) of all ships entering on this date.", + title="Imports", + ) + imports_cargo: int | None = Field( + default=None, + description="Total import volume (mt) of cargo ships (excluding tankers).", + title="Cargo Imports", + ) + imports_container: int | None = Field( + default=None, + description="Total import volume (mt) of container ships on this date.", + title="Container Imports", + ) + imports_dry_bulk: int | None = Field( + default=None, + description="Total import volume (mt) of dry-bulk carriers on this date.", + title="Dry Bulk Imports", + ) + imports_general_cargo: int | None = Field( + default=None, + description="Total import volume (mt) of general-cargo ships on this date.", + title="General Cargo Imports", + ) + imports_roro: int | None = Field( + default=None, + description="Total import volume (mt) of Ro-Ro ships on this date.", + title="Ro-Ro Imports", + ) + imports_tanker: int | None = Field( + default=None, + description="Total import volume (mt) of tankers on this date.", + title="Tanker Imports", + ) + imports_container_30ma: float | None = Field( + default=None, + description="30-day moving average of container import volume.", + title="Container Imports (30d MA)", + ) + imports_container_30ma_yoy_doy: float | None = Field( + default=None, + description=( + "Year-over-year change in the 30-day MA of container import volume," + " keyed on day of year." + ), + title="Container Imports (30d MA YoY)", + ) + + exports: int | None = Field( + default=None, + description="Total export volume (mt) of all ships departing on this date.", + title="Exports", + ) + exports_cargo: int | None = Field( + default=None, + description="Total export volume (mt) of cargo ships (excluding tankers).", + title="Cargo Exports", + ) + exports_container: int | None = Field( + default=None, + description="Total export volume (mt) of container ships on this date.", + title="Container Exports", + ) + exports_dry_bulk: int | None = Field( + default=None, + description="Total export volume (mt) of dry-bulk carriers on this date.", + title="Dry Bulk Exports", + ) + exports_general_cargo: int | None = Field( + default=None, + description="Total export volume (mt) of general-cargo ships on this date.", + title="General Cargo Exports", + ) + exports_roro: int | None = Field( + default=None, + description="Total export volume (mt) of Ro-Ro ships on this date.", + title="Ro-Ro Exports", + ) + exports_tanker: int | None = Field( + default=None, + description="Total export volume (mt) of tankers on this date.", + title="Tanker Exports", + ) + exports_container_30ma: float | None = Field( + default=None, + description="30-day moving average of container export volume.", + title="Container Exports (30d MA)", + ) + exports_container_30ma_yoy_doy: float | None = Field( + default=None, + description=( + "Year-over-year change in the 30-day MA of container export volume," + " keyed on day of year." + ), + title="Container Exports (30d MA YoY)", + ) + + shipment: int | None = Field( + default=None, + description="Total shipment volume (mt) crossing the country on this date.", + title="Shipment", + ) + shipment_30ma: float | None = Field( + default=None, + description="30-day moving average of total shipment volume.", + title="Shipment (30d MA)", + ) + shipment_30ma_yoy_doy: float | None = Field( + default=None, + description=( + "Year-over-year change in the 30-day MA of total shipment volume," + " keyed on day of year." + ), + title="Shipment (30d MA YoY)", + ) + + +class ImfCountryActivityFetcher( + Fetcher[ImfCountryActivityQueryParams, list[ImfCountryActivityData]] +): + """IMF Country Maritime Activity Fetcher.""" + + @staticmethod + def transform_query(params: dict[str, Any]) -> ImfCountryActivityQueryParams: + """Validate and coerce request params.""" + return ImfCountryActivityQueryParams(**params) + + @staticmethod + async def aextract_data( + query: ImfCountryActivityQueryParams, + credentials: dict[str, str] | None = None, + **kwargs: Any, + ) -> list[dict[str, Any]]: + """Fetch the daily activity rows for the requested country.""" + from openbb_imf.utils.port_watch_helpers import get_country_daily_activity + + return await get_country_daily_activity( + query.country_code, query.start_date, query.end_date + ) + + @staticmethod + def transform_data( + query: ImfCountryActivityQueryParams, + data: list[dict[str, Any]], + **kwargs: Any, + ) -> list[ImfCountryActivityData]: + """Coerce raw rows into ``ImfCountryActivityData`` instances.""" + return [ImfCountryActivityData.model_validate(row) for row in data] diff --git a/openbb_platform/providers/imf/openbb_imf/models/direction_of_trade.py b/openbb_platform/providers/imf/openbb_imf/models/direction_of_trade.py index 4862d0508b3..a0ed5003a3b 100644 --- a/openbb_platform/providers/imf/openbb_imf/models/direction_of_trade.py +++ b/openbb_platform/providers/imf/openbb_imf/models/direction_of_trade.py @@ -1,6 +1,6 @@ """IMF Direction Of Trade Model.""" -# pylint: disable=unused-argument +from __future__ import annotations from datetime import date as dateType from typing import Any @@ -13,11 +13,12 @@ from openbb_core.provider.standard_models.direction_of_trade import ( DirectionOfTradeQueryParams, ) from openbb_core.provider.utils.errors import EmptyDataError +from pydantic import ConfigDict, Field, field_validator + from openbb_imf.utils.dot_helpers import ( get_label_to_code_map, resolve_country_input, ) -from pydantic import ConfigDict, Field, field_validator dot_indicators_dict = { "exports": "XG_FOB_USD", @@ -51,22 +52,19 @@ class ImfDirectionOfTradeQueryParams(DirectionOfTradeQueryParams): @field_validator("country", "counterpart", mode="before") @classmethod def _validate_country_fields(cls, v): - """Validate country and counterpart fields. - - Accepts both ISO3 codes (e.g., 'USA') and snake_case country names - (e.g., 'united_states'). Converts names to ISO3 codes. - """ + """Validate country and counterpart fields.""" if not v: raise ValueError("Required parameter for IMF provider not supplied.") if isinstance(v, str) and v.lower() in ["all", "*"]: return "*" - # Split by comma if string values = ( v.split(",") if isinstance(v, str) and "," in v - else [v] if isinstance(v, str) else v + else [v] + if isinstance(v, str) + else v ) result: list[str] = [] @@ -78,7 +76,6 @@ class ImfDirectionOfTradeQueryParams(DirectionOfTradeQueryParams): "'all' cannot be used with other country codes in a list." ) return "*" - # Resolve the country input (handles both codes and names) resolved = resolve_country_input(item_stripped) result.append(resolved) @@ -88,7 +85,21 @@ class ImfDirectionOfTradeQueryParams(DirectionOfTradeQueryParams): class ImfDirectionOfTradeData(DirectionOfTradeData): """IMF Direction Of Trade Data.""" - model_config = ConfigDict(extra="ignore") + model_config = ConfigDict( + extra="ignore", + json_schema_extra={ + "x-widget_config": { + "$.name": "Direction of Trade", + "$.description": ( + "Direction of Trade data. Shows the value of exports," + " imports, and trade balance between reporting countries and their" + " trading partners." + ), + "$.gridData": {"w": 40, "h": 14}, + "$.refetchInterval": False, + } + }, + ) __alias_dict__ = { "date": "TIME_PERIOD", "symbol": "series_id", @@ -144,7 +155,6 @@ class ImfDirectionOfTradeFetcher( **kwargs: Any, ) -> dict: """Extract the data from the IMF API.""" - # pylint: disable=import-outside-toplevel from openbb_imf.utils.dot_helpers import imts_query if query.limit: diff --git a/openbb_platform/providers/imf/openbb_imf/models/disruption_events.py b/openbb_platform/providers/imf/openbb_imf/models/disruption_events.py new file mode 100644 index 00000000000..00137803755 --- /dev/null +++ b/openbb_platform/providers/imf/openbb_imf/models/disruption_events.py @@ -0,0 +1,240 @@ +"""IMF Maritime Disruption Events Model.""" + +from typing import Any, Literal + +from openbb_core.app.service.system_service import SystemService +from openbb_core.provider.abstract.data import Data +from openbb_core.provider.abstract.fetcher import Fetcher +from openbb_core.provider.abstract.query_params import QueryParams +from pydantic import ConfigDict, Field + +api_prefix = SystemService().system_settings.api_settings.prefix + + +class ImfDisruptionEventsQueryParams(QueryParams): + """IMF Maritime Disruption Events Query Parameters.""" + + __json_schema_extra__ = { + "country": { + "x-widget_config": { + "type": "endpoint", + "optionsEndpoint": f"{api_prefix}/imf/portwatch/list_country_choices", + "style": {"popupWidth": 350}, + } + }, + "alertlevel": { + "x-widget_config": { + "type": "text", + "options": [ + {"label": "All Alerts", "value": "ALL"}, + {"label": "Red", "value": "RED"}, + {"label": "Orange", "value": "ORANGE"}, + {"label": "Green", "value": "GREEN"}, + ], + } + }, + "eventtype": {"x-widget_config": {"type": "text"}}, + "start_date": {"x-widget_config": {"type": "date"}}, + "end_date": {"x-widget_config": {"type": "date"}}, + "active_only": {"x-widget_config": {"type": "boolean"}}, + "theme": {"x-widget_config": {"show": False}}, + } + + country: str | None = Field( + default=None, + description="ISO3 country code to restrict the event list to.", + title="Country", + ) + alertlevel: Literal["ALL", "RED", "ORANGE", "GREEN"] = Field( + default="ALL", + description="Alert level filter: 'ALL', 'RED', 'ORANGE', or 'GREEN'.", + title="Alert Level", + ) + eventtype: str | None = Field( + default=None, + description="Optional event-type filter (e.g. 'Cyclone', 'Conflict').", + title="Event Type", + ) + start_date: str | None = Field( + default=None, + description="ISO date (YYYY-MM-DD) lower bound on the event start (fromdate).", + title="Start Date", + ) + end_date: str | None = Field( + default=None, + description="ISO date (YYYY-MM-DD) upper bound on the event start (fromdate).", + title="End Date", + ) + active_only: bool = Field( + default=False, + description="If True, restrict to events whose end date is in the future or unset.", + title="Active Only", + ) + theme: Literal["dark", "light"] | None = Field( + default=None, + description="Theme for the map chart. Only used when ``chart=True``.", + title="Theme", + ) + + +class ImfDisruptionEventsData(Data): + """IMF Maritime Disruption Event Data.""" + + model_config = ConfigDict( + extra="ignore", + validate_by_alias=True, + validate_by_name=True, + populate_by_name=True, + json_schema_extra={ + "x-widget_config": { + "$.name": "Maritime Disruption Events", + "$.description": ( + "Maritime disruption events from IMF Port Watch with country," + " type, alert level, and date filters." + ), + "$.gridData": {"w": 40, "h": 16}, + "$.refetchInterval": False, + "$.category": "IMF Utilities", + "$.subCategory": "Port Watch", + "$.source": ["UN Global Platform; IMF PortWatch"], + } + }, + ) + + __alias_dict__ = { + "object_id": "ObjectId", + "shape_area": "Shape__Area", + "shape_length": "Shape__Length", + "html_name": "htmlname", + "html_description": "htmldescription", + "page_id": "pageid", + "longitude": "long", + "latitude": "lat", + } + + eventid: int = Field(description="Event identifier.", title="Event ID") + eventname: str = Field(description="Event name.", title="Event") + eventtype: str | None = Field( + default=None, description="Event type (e.g. 'Conflict').", title="Type" + ) + alertlevel: str | None = Field( + default=None, + description="Alert level (RED / ORANGE / GREEN).", + title="Alert Level", + ) + severitytext: str | None = Field( + default=None, + description="Free-form severity description.", + title="Severity", + ) + country: str | None = Field( + default=None, + description="ISO3 country code of the affected event.", + title="Country", + ) + fromdate: str | None = Field( + default=None, description="Event start date.", title="From" + ) + todate: str | None = Field( + default=None, description="Event end date (null if ongoing).", title="To" + ) + editdate: str | None = Field( + default=None, description="Last update timestamp.", title="Edited" + ) + year: int | None = Field( + default=None, description="Year of the event start.", title="Year" + ) + n_affectedports: int | None = Field( + default=None, + description="Number of ports flagged as affected by this event.", + title="Affected Ports", + ) + affectedports: str | None = Field( + default=None, + description="Comma-separated list of affected port IDs.", + title="Affected Ports List", + ) + affectedpopulation: str | None = Field( + default=None, + description="Estimated population of the affected area.", + title="Affected Population", + ) + latitude: float | None = Field( + default=None, + description="Latitude of the event's centroid.", + title="Latitude", + ) + longitude: float | None = Field( + default=None, + description="Longitude of the event's centroid.", + title="Longitude", + ) + html_name: str | None = Field( + default=None, + description="Event name with embedded HTML for rich tooltips.", + title="HTML Name", + ) + html_description: str | None = Field( + default=None, + description="Event description with embedded HTML.", + title="HTML Description", + ) + page_id: str | None = Field( + default=None, + description="IMF Port Watch event-detail page identifier.", + title="Page ID", + ) + object_id: int | None = Field( + default=None, + description="Internal ArcGIS feature row identifier.", + title="Object ID", + ) + shape_area: float | None = Field( + default=None, + description="Affected-area polygon area in projected units.", + title="Shape Area", + ) + shape_length: float | None = Field( + default=None, + description="Affected-area polygon perimeter in projected units.", + title="Shape Length", + ) + + +class ImfDisruptionEventsFetcher( + Fetcher[ImfDisruptionEventsQueryParams, list[ImfDisruptionEventsData]] +): + """IMF Maritime Disruption Events Fetcher.""" + + @staticmethod + def transform_query(params: dict[str, Any]) -> ImfDisruptionEventsQueryParams: + """Validate and coerce request params.""" + return ImfDisruptionEventsQueryParams(**params) + + @staticmethod + async def aextract_data( + query: ImfDisruptionEventsQueryParams, + credentials: dict[str, str] | None = None, + **kwargs: Any, + ) -> list[dict[str, Any]]: + """Fetch matching disruption events from IMF Port Watch.""" + from openbb_imf.utils.port_watch_helpers import get_disruption_events + + level = None if query.alertlevel.upper() in ("", "ALL") else query.alertlevel + return await get_disruption_events( + query.country, + query.eventtype, + level, + query.active_only, + query.start_date, + query.end_date, + ) + + @staticmethod + def transform_data( + query: ImfDisruptionEventsQueryParams, + data: list[dict[str, Any]], + **kwargs: Any, + ) -> list[ImfDisruptionEventsData]: + """Coerce raw rows into ``ImfDisruptionEventsData`` instances.""" + return [ImfDisruptionEventsData.model_validate(row) for row in data] diff --git a/openbb_platform/providers/imf/openbb_imf/models/disruption_sankey.py b/openbb_platform/providers/imf/openbb_imf/models/disruption_sankey.py new file mode 100644 index 00000000000..cee611187cf --- /dev/null +++ b/openbb_platform/providers/imf/openbb_imf/models/disruption_sankey.py @@ -0,0 +1,166 @@ +"""IMF Disruption Sankey Spillover Model.""" + +from typing import Any, Literal + +from openbb_core.app.model.abstract.error import OpenBBError +from openbb_core.app.service.system_service import SystemService +from openbb_core.provider.abstract.data import Data +from openbb_core.provider.abstract.fetcher import Fetcher +from openbb_core.provider.abstract.query_params import QueryParams +from pydantic import ConfigDict, Field, PrivateAttr + +api_prefix = SystemService().system_settings.api_settings.prefix + + +class ImfDisruptionSankeyQueryParams(QueryParams): + """IMF Disruption Sankey Query Parameters.""" + + __json_schema_extra__ = { + "event_id": { + "x-widget_config": { + "type": "endpoint", + "optionsEndpoint": f"{api_prefix}/imf/portwatch/list_disruption_event_choices", + "style": {"popupWidth": 600}, + } + }, + "theme": {"x-widget_config": {"show": False}}, + } + + event_id: str = Field( + default="LATEST", + description="Disruption event ID, or 'LATEST' for the most recent event.", + title="Event", + ) + theme: Literal["dark", "light"] | None = Field( + default=None, + description="Theme for the Sankey chart. Only used when ``chart=True``.", + title="Theme", + ) + + _event_label: str | None = PrivateAttr(default=None) + + +class ImfDisruptionSankeyData(Data): + """IMF Disruption Sankey Edge Data.""" + + model_config = ConfigDict( + extra="ignore", + validate_by_alias=True, + validate_by_name=True, + populate_by_name=True, + json_schema_extra={ + "x-widget_config": { + "$.name": "Disruption Capacity Spillover", + "$.description": ( + "Sankey edges showing how vessel capacity reroutes between" + " ports during a maritime disruption event. Source: IMF Port" + " Watch." + ), + "$.gridData": {"w": 40, "h": 16}, + "$.refetchInterval": False, + "$.category": "IMF Utilities", + "$.subCategory": "Port Watch", + "$.source": ["UN Global Platform; IMF PortWatch"], + } + }, + ) + + __alias_dict__ = { + "object_id": "ObjectId", + "source_port": "from_", + "source_port_id": "from_id", + "source_country_code": "from_iso3", + "target_port": "to_", + "target_port_id": "to_id", + "target_country_code": "to_iso3", + } + + eventid: int = Field(description="Event identifier.", title="Event ID") + source: int = Field( + description="Source node index in the Sankey diagram.", title="Source Index" + ) + target: int = Field( + description="Target node index in the Sankey diagram.", title="Target Index" + ) + source_port: str = Field( + description="Display label for the source port (e.g. 'Colombo (Sri Lanka)').", + title="Source Port", + ) + target_port: str = Field( + description="Display label for the destination port.", + title="Target Port", + ) + source_port_id: str | None = Field( + default=None, + description="IMF Port Watch port ID of the source.", + title="Source Port ID", + ) + target_port_id: str | None = Field( + default=None, + description="IMF Port Watch port ID of the destination.", + title="Target Port ID", + ) + source_country_code: str | None = Field( + default=None, + description="ISO3 country code of the source port.", + title="Source ISO3", + ) + target_country_code: str | None = Field( + default=None, + description="ISO3 country code of the destination port.", + title="Target ISO3", + ) + perc_disaster_capacity: float | None = Field( + default=None, + description="Share (%) of the disrupted capacity rerouted along this edge.", + title="% Capacity", + ) + object_id: int | None = Field( + default=None, + description="Internal ArcGIS feature row identifier.", + title="Object ID", + ) + + +class ImfDisruptionSankeyFetcher( + Fetcher[ImfDisruptionSankeyQueryParams, list[ImfDisruptionSankeyData]] +): + """IMF Disruption Sankey Fetcher.""" + + @staticmethod + def transform_query(params: dict[str, Any]) -> ImfDisruptionSankeyQueryParams: + """Validate and coerce request params.""" + return ImfDisruptionSankeyQueryParams(**params) + + @staticmethod + async def aextract_data( + query: ImfDisruptionSankeyQueryParams, + credentials: dict[str, str] | None = None, + **kwargs: Any, + ) -> list[dict[str, Any]]: + """Resolve ``LATEST`` and fetch the Sankey spillover edges.""" + from openbb_imf.utils.port_watch_helpers import ( + get_disruption_events, + get_disruption_sankey_edges, + ) + + if query.event_id.upper() == "LATEST": + events = await get_disruption_events() + if not events: + raise OpenBBError("No disruption events available.") + resolved_id = int(events[0]["eventid"]) + query._event_label = events[0].get("eventname") or f"Event {resolved_id}" + else: + resolved_id = int(query.event_id) + query._event_label = f"Event {resolved_id}" + + return await get_disruption_sankey_edges(resolved_id) + + @staticmethod + def transform_data( + query: ImfDisruptionSankeyQueryParams, + data: list[dict[str, Any]], + **kwargs: Any, + ) -> list[ImfDisruptionSankeyData]: + """Coerce raw rows into ``ImfDisruptionSankeyData`` instances.""" + return [ImfDisruptionSankeyData.model_validate(row) for row in data] diff --git a/openbb_platform/providers/imf/openbb_imf/models/economic_indicators.py b/openbb_platform/providers/imf/openbb_imf/models/economic_indicators.py index 8a36246467e..1d5ef0d83f1 100644 --- a/openbb_platform/providers/imf/openbb_imf/models/economic_indicators.py +++ b/openbb_platform/providers/imf/openbb_imf/models/economic_indicators.py @@ -1,7 +1,6 @@ """IMF Economic Indicators Model.""" -# pylint: disable=unused-argument,protected-access,too-many-branches,too-many-statements,too-many-locals,too-many-lines -# flake8: noqa: PLR0912 +from __future__ import annotations from typing import Any @@ -15,44 +14,28 @@ from openbb_core.provider.standard_models.economic_indicators import ( ) from openbb_core.provider.utils.descriptions import QUERY_DESCRIPTIONS from openbb_core.provider.utils.errors import EmptyDataError +from pydantic import ConfigDict, Field, PrivateAttr, field_validator, model_validator + from openbb_imf.utils.helpers import ( detect_indicator_dimensions, detect_transform_dimension, parse_time_period, translate_error_message, ) -from pydantic import ConfigDict, Field, PrivateAttr, field_validator, model_validator api_prefix = SystemService().system_settings.api_settings.prefix class ImfEconomicIndicatorsQueryParams(EconomicIndicatorsQueryParams): - """IMF Economic Indicators Query. - - Symbol format: 'dataflow::identifier' where identifier is either: - - A table ID (starts with 'H_') for hierarchical table data - - An indicator code for individual indicator data - - Examples: - - 'BOP::H_BOP_BOP_AGG_STANDARD_PRESENTATION' - Balance of Payments table - - 'BOP_AGG::GS_CD,BOP_AGG::GS_DB' - Multiple BOP_AGG indicators (Goods & Services) - - 'IL::RGV_REVS' - Gold reserves in millions of fine troy ounces - - 'WEO::NGDP_RPCH' - Real GDP growth (annual only) - - 'WEO::POILBRE' - Brent crude oil price (use country='G001' for world) - - 'PCPS::PGOLD' - Gold price per troy ounce (monthly/quarterly available) - - Use `obb.economy.available_indicators(provider='imf')` to discover symbols. - Use `obb.economy.imf_utils.list_tables()` to see available tables. - """ + """IMF Economic Indicators Query.""" __json_schema_extra__ = { "symbol": { "multiple_items_allowed": True, "x-widget_config": { "label": "Indicator Symbol", - # "description": "The IMF indicator series ID.", "multiSelect": False, - "multiple": True, + "multiple": False, "type": "text", }, }, @@ -64,7 +47,7 @@ class ImfEconomicIndicatorsQueryParams(EconomicIndicatorsQueryParams): "type": "endpoint", "multiSelect": True, "multiple": False, - "optionsEndpoint": f"{api_prefix}/imf_utils/indicator_choices", + "optionsEndpoint": f"{api_prefix}/imf/indicator_choices", "optionsParams": { "symbol": "$symbol", "country": "true", @@ -77,7 +60,7 @@ class ImfEconomicIndicatorsQueryParams(EconomicIndicatorsQueryParams): "x-widget_config": { "label": "Frequency", "type": "endpoint", - "optionsEndpoint": f"{api_prefix}/imf_utils/indicator_choices", + "optionsEndpoint": f"{api_prefix}/imf/indicator_choices", "optionsParams": { "symbol": "$symbol", "country": "$country", @@ -91,7 +74,7 @@ class ImfEconomicIndicatorsQueryParams(EconomicIndicatorsQueryParams): "x-widget_config": { "label": "Transform", "type": "endpoint", - "optionsEndpoint": f"{api_prefix}/imf_utils/indicator_choices", + "optionsEndpoint": f"{api_prefix}/imf/indicator_choices", "optionsParams": { "symbol": "$symbol", "country": "$country", @@ -138,7 +121,7 @@ class ImfEconomicIndicatorsQueryParams(EconomicIndicatorsQueryParams): + "\n - 'PCPS::PGOLD' - Gold price per troy ounce (monthly/quarterly available)" + "\n\n" + "Use `obb.economy.available_indicators(provider='imf')` to discover symbols." - + " Use `obb.economy.imf_utils.list_tables()` to see available tables." + + " Use `obb.imf.list_tables()` to see available tables." ) country: str | None = Field( @@ -188,43 +171,27 @@ class ImfEconomicIndicatorsQueryParams(EconomicIndicatorsQueryParams): @field_validator("country", mode="before", check_fields=False) @classmethod def validate_country(cls, v): - """ - Normalize country inputs to ISO3 codes. - - Accepts: - - ISO3 codes: "USA", "JPN", "GBR" - - Country names: "United States", "Japan", "United Kingdom" - - Snake_case names: "united_states", "japan", "united_kingdom" - - Wildcards: "*" or "all" to include all countries - - None: Allowed if dimension_values contains a country dimension (validated in model_validator) - """ - # pylint: disable=import-outside-toplevel + """Normalize country inputs to ISO3 codes.""" from openbb_imf.utils.metadata import ImfMetadata - # Allow None - will be validated in model_validator with dimension_values check if not v: return None - # Split by comma, handling potential spaces and filtering empty strings items = [c.strip() for c in v.split(",") if c.strip()] if not items: return None - # Check for wildcards - return early without metadata lookup if len(items) == 1 and items[0].lower() in ("*", "all"): return "*" metadata = ImfMetadata() country_codes = metadata._codelist_cache.get("CL_COUNTRY", {}) - # Build lookup tables code_set = set(country_codes.keys()) name_to_code: dict[str, str] = {} for code, name in country_codes.items(): - # Add lowercase name name_to_code[name.lower()] = code - # Add snake_case version (replace spaces with underscores) snake_name = ( name.lower() .replace(" ", "_") @@ -240,21 +207,16 @@ class ImfEconomicIndicatorsQueryParams(EconomicIndicatorsQueryParams): item_upper = item.upper().strip() item_lower = item.lower().strip().replace(" ", "_") - # Handle wildcards in mixed input if item_lower in ("*", "all"): return "*" # Wildcard overrides everything - # Check if it's already an ISO3 code if item_upper in code_set: result.append(item_upper) - # Check if it's a name (with spaces or snake_case) elif item_lower in name_to_code: result.append(name_to_code[item_lower]) - # Try with original casing as lowercase lookup elif item.lower() in name_to_code: result.append(name_to_code[item.lower()]) else: - # Not found - pass through as uppercase (will fail later with clearer error) result.append(item_upper) return ",".join(result) @@ -262,7 +224,6 @@ class ImfEconomicIndicatorsQueryParams(EconomicIndicatorsQueryParams): @model_validator(mode="after") def parse_and_validate_symbols(self): """Parse symbols and validate table/indicator constraints.""" - # pylint: disable=import-outside-toplevel from openbb_imf.utils.metadata import ImfMetadata if not self.symbol: @@ -288,37 +249,66 @@ class ImfEconomicIndicatorsQueryParams(EconomicIndicatorsQueryParams): dim_id_upper = dim_id.strip().upper() dim_value = dim_value.strip() - # dimension_values OVERRIDES the country parameter if dim_id_upper in country_dimensions: object.__setattr__(self, "country", dim_value) - # dimension_values OVERRIDES the frequency parameter elif dim_id_upper in frequency_dimensions: object.__setattr__(self, "frequency", dim_value) - # dimension_values OVERRIDES the transform parameter elif dim_id_upper in transform_dimensions: object.__setattr__(self, "transform", dim_value) - # Keep dimension_values that are not consumed by country/frequency/transform else: remaining_dimension_values.append(dv) - # Update dimension_values to only contain non-consumed dimensions object.__setattr__( self, "dimension_values", remaining_dimension_values if remaining_dimension_values else None, ) - # Validate country requirement - must have country by now if not self.country: raise ValueError( "Country is required. Provide via 'country' parameter or include a country " "dimension (COUNTRY, REF_AREA, JURISDICTION, AREA) in 'dimension_values'." ) - symbols = [ - s.strip() - for s in self.symbol.split(",") # type: ignore # pylint: disable=E1101 - ] + if remaining_dimension_values: + metadata = ImfMetadata() + symbol_first = self.symbol.split(",")[0].strip() + dataflow_first = symbol_first.split("::")[0].strip().upper() + dsd_id = ( + metadata.dataflows.get(dataflow_first, {}) + .get("structureRef", {}) + .get("id") + ) + dim_ids = { + d.get("id") + for d in metadata.datastructures.get(dsd_id, {}).get("dimensions", []) + if d.get("id") + } + if dim_ids: + extra_dim_ids = { + d + for d in dim_ids + if d + not in country_dimensions + | frequency_dimensions + | transform_dimensions + | {"INDICATOR", "TIME_PERIOD"} + } + for dv in remaining_dimension_values: + if ":" not in dv: + continue + dim_id = dv.split(":", 1)[0].strip().upper() + if dim_id not in dim_ids: + valid = ", ".join(sorted(extra_dim_ids)) or "(none)" + raise ValueError( + f"Unknown dimension '{dim_id}' for dataflow " + f"'{dataflow_first}'. " + f"Valid extra dimensions: {valid}. " + f"Use `available_indicators()` to see per-indicator " + f"extra_dimensions." + ) + + symbols = [s.strip() for s in self.symbol.split(",")] tables: list[str] = [] indicators: list[tuple[str, str]] = [] dataflows_seen: set[str] = set() @@ -341,7 +331,6 @@ class ImfEconomicIndicatorsQueryParams(EconomicIndicatorsQueryParams): ) dataflows_seen.add(dataflow) - # Tables can start with H_ or be any valid hierarchy ID for the dataflow is_table = False if identifier.startswith("H_"): @@ -396,14 +385,12 @@ class ImfEconomicIndicatorsQueryParams(EconomicIndicatorsQueryParams): self._dataflow = None # Multiple dataflows self._indicator_codes = [] - # Validate country, frequency, and transform using constraints API self._validate_indicator_params() return self def _validate_indicator_params(self): """Validate country, frequency, and transform using the constraints API.""" - # pylint: disable=import-outside-toplevel from openbb_imf.utils.metadata import ImfMetadata metadata = ImfMetadata() @@ -411,7 +398,7 @@ class ImfEconomicIndicatorsQueryParams(EconomicIndicatorsQueryParams): def build_key_up_to(target_dim: str) -> str: """Build constraint key up to (and including) target dimension.""" key_parts: list[str] = [] - countries = self.country.split(",") if self.country else [] # type: ignore # pylint: disable=E1101 + countries = self.country.split(",") if self.country else [] countries_str = ( "*" if countries in ["*", "all"] @@ -425,7 +412,6 @@ class ImfEconomicIndicatorsQueryParams(EconomicIndicatorsQueryParams): if dim_id == country_dim: key_parts.append(countries_str if countries_str else "*") elif dim_id == indicator_dim: - # Use all indicator codes for this dataflow key_parts.append( "+".join(indicator_codes) if indicator_codes else "*" ) @@ -457,7 +443,6 @@ class ImfEconomicIndicatorsQueryParams(EconomicIndicatorsQueryParams): return kv.get("values", []) return [] - # For each dataflow, validate the parameters for dataflow_id, indicator_codes in self._indicators_by_dataflow.items(): df_obj = metadata.dataflows.get(dataflow_id, {}) @@ -475,7 +460,9 @@ class ImfEconomicIndicatorsQueryParams(EconomicIndicatorsQueryParams): country_dim = ( "COUNTRY" if "COUNTRY" in dim_order - else "JURISDICTION" if "JURISDICTION" in dim_order else "REF_AREA" + else "JURISDICTION" + if "JURISDICTION" in dim_order + else "REF_AREA" ) freq_dim = "FREQUENCY" if "FREQUENCY" in dim_order else "FREQ" @@ -492,11 +479,10 @@ class ImfEconomicIndicatorsQueryParams(EconomicIndicatorsQueryParams): (d for d in indicator_dim_candidates if d in dim_order), None ) - # Validate country if self.country and country_dim in dim_order: available_countries = get_available_values(country_dim, dataflow_id) if available_countries: - countries = [c.strip().upper() for c in self.country.split(",")] # type: ignore # pylint: disable=E1101 + countries = [c.strip().upper() for c in self.country.split(",")] invalid = [ c for c in countries @@ -511,7 +497,7 @@ class ImfEconomicIndicatorsQueryParams(EconomicIndicatorsQueryParams): if ( self.frequency - and self.frequency.lower() not in ("all", "*") # type: ignore # pylint: disable=E1101 + and self.frequency.lower() not in ("all", "*") and freq_dim in dim_order ): freq_map = {"annual": "A", "quarter": "Q", "month": "M", "day": "D"} @@ -526,18 +512,16 @@ class ImfEconomicIndicatorsQueryParams(EconomicIndicatorsQueryParams): f"available values are: {available_freqs}" ) - # Validate transform (skip if 'all' or '*') if ( self.transform - and self.transform.lower() not in ("all", "*") # type: ignore # pylint: disable=E1101 + and self.transform.lower() not in ("all", "*") and transform_dim and transform_dim in dim_order ): _, _, transform_lookup, unit_lookup = detect_transform_dimension( dataflow_id ) - # Resolve user-friendly name to code - transform_val = self.transform.strip().lower() # type: ignore # pylint: disable=E1101 + transform_val = self.transform.strip().lower() resolved_code = transform_lookup.get( transform_val, unit_lookup.get(transform_val, self.transform) ) @@ -664,40 +648,31 @@ class ImfEconomicIndicatorsFetcher( **kwargs: Any, ) -> dict: """Extract the data.""" - # pylint: disable=import-outside-toplevel from datetime import datetime # noqa from openbb_imf.utils.query_builder import ImfQueryBuilder from openbb_imf.utils.table_builder import ImfTableBuilder - countries = query.country.split(",") # type: ignore + countries = query.country.split(",") if query.country else [] countries_str = "+".join([c.upper() for c in countries]) frequency_map = {"annual": "A", "quarter": "Q", "month": "M", "day": "D"} - frequency = frequency_map.get(query.frequency) or query.frequency # type: ignore + frequency = frequency_map.get(query.frequency or "") or query.frequency start_date = query.start_date.strftime("%Y-%m-%d") if query.start_date else None end_date = query.end_date.strftime("%Y-%m-%d") if query.end_date else None - # Parse dimension_values into a dict of DIM_ID -> DIM_VALUE - # Input format: list of "DIM_ID:VALUE" strings - # Example: ["SECTOR:S13", "GFS_GRP:XDC"] -> {"SECTOR": "S13", "GFS_GRP": "XDC"} - # Handles lowercase inputs: "unit:xdc" -> {"UNIT": "XDC"} extra_dimensions: dict[str, str] = {} if query.dimension_values: for dv in query.dimension_values: - # Each dv should be "DIMENSION:VALUE" if not dv or not isinstance(dv, str): continue - # Handle comma-separated pairs in a single string pairs = [p.strip() for p in dv.split(",") if p.strip()] for pair in pairs: if ":" in pair: dim_id, dim_value = pair.split(":", 1) - # Uppercase both dimension ID and value for IMF API extra_dimensions[dim_id.strip().upper()] = ( dim_value.strip().upper() ) if query._is_table: - # Table mode: use ImfTableBuilder (single dataflow only) dataflow = query._dataflow if not dataflow: raise OpenBBError("Could not determine dataflow from symbol.") @@ -707,8 +682,6 @@ class ImfEconomicIndicatorsFetcher( "FREQUENCY": frequency, } - # Handle special dimensions for certain dataflows - # GFS dataflows: GFS_BS, GFS_SOO, GFS_COFOG, GFS_SFCP, GFS_SSUC, QGFS if dataflow.startswith("GFS_") or dataflow == "QGFS": params["SECTOR"] = "*" params["GFS_GRP"] = "*" @@ -719,11 +692,9 @@ class ImfEconomicIndicatorsFetcher( elif dataflow == "ISORA_LATEST_DATA_PUB": params["INDICATOR"] = "*" - # Apply user-specified dimension filters (overrides defaults above) if extra_dimensions: params.update(extra_dimensions) - # Handle transform/unit for table mode if query.transform: transform_val = query.transform.strip().lower() transform_dim, unit_dim, transform_lookup, unit_lookup = ( @@ -732,7 +703,6 @@ class ImfEconomicIndicatorsFetcher( applied = False resolved_code = None - # Try transform dimension first if transform_dim: if transform_val in ("all", "*"): params[transform_dim] = "*" @@ -742,7 +712,6 @@ class ImfEconomicIndicatorsFetcher( params[transform_dim] = resolved_code applied = True - # Try unit dimension if not applied to transform if not applied and unit_dim: if transform_val in ("all", "*"): params[unit_dim] = "*" @@ -752,7 +721,6 @@ class ImfEconomicIndicatorsFetcher( params[unit_dim] = resolved_code applied = True - # Raise error if transform value is not valid for dataflow if not applied: available = [] if transform_lookup: @@ -775,7 +743,6 @@ class ImfEconomicIndicatorsFetcher( f"Available options: {', '.join(available) if available else 'none'}" ) - # We request one extra period to ensure value carry-forward for STATUS="NA" obs. if query.limit is not None and start_date is None: current_year = datetime.now().year if frequency == "A": @@ -807,11 +774,9 @@ class ImfEconomicIndicatorsFetcher( "series_metadata": result.get("series_metadata", {}), } except (ValueError, OpenBBError) as e: - # Translate IMF dimension codes to user-friendly parameter names raise OpenBBError(translate_error_message(str(e))) from e else: - # Indicator mode: support multiple dataflows query_builder = ImfQueryBuilder() all_data: list[dict] = [] all_metadata: dict = {} @@ -820,18 +785,15 @@ class ImfEconomicIndicatorsFetcher( if not indicators_by_df: raise OpenBBError("No indicators specified.") - # Fetch data for each dataflow for dataflow, indicator_codes in indicators_by_df.items(): - params = { + params: dict[str, Any] = { "COUNTRY": countries_str, "FREQUENCY": frequency, } - # Apply user-specified dimension filters if extra_dimensions: params.update(extra_dimensions) - # Handle transform/unit parameter per dataflow if query.transform: transform_val = query.transform.strip().lower() transform_dim, unit_dim, transform_lookup, unit_lookup = ( @@ -840,7 +802,6 @@ class ImfEconomicIndicatorsFetcher( applied = False resolved_code = None - # Try transform dimension first if transform_dim: if transform_val in ("all", "*"): params[transform_dim] = "*" @@ -850,7 +811,6 @@ class ImfEconomicIndicatorsFetcher( params[transform_dim] = resolved_code applied = True - # Try unit dimension if not applied to transform if not applied and unit_dim: if transform_val in ("all", "*"): params[unit_dim] = "*" @@ -860,7 +820,6 @@ class ImfEconomicIndicatorsFetcher( params[unit_dim] = resolved_code applied = True - # Raise error if transform value is not valid for dataflow if not applied: available = [] if transform_lookup: @@ -888,12 +847,10 @@ class ImfEconomicIndicatorsFetcher( if query.limit is not None: params["lastNObservations"] = query.limit - # Detect indicator dimensions for this dataflow dimension_codes = detect_indicator_dimensions( dataflow, indicator_codes, query_builder.metadata ) - # Add indicator codes to appropriate dimensions for dim_id, codes in dimension_codes.items(): params[dim_id] = "+".join(codes) @@ -904,13 +861,11 @@ class ImfEconomicIndicatorsFetcher( end_date=end_date, **params, ) - # Add dataflow info to each record for record in result.get("data", []): record["_dataflow"] = dataflow all_data.extend(result.get("data", [])) all_metadata[dataflow] = result.get("metadata", {}) except ValueError as e: - # Translate IMF codes to user-friendly names and raise as OpenBBError raise OpenBBError(translate_error_message(str(e))) from e return { @@ -944,10 +899,8 @@ class ImfEconomicIndicatorsFetcher( metadata = data.get("metadata", {}) for item in row_data: - # Filter by date range if needed (IMF API date filtering can be inconsistent) item_date = item.get("TIME_PERIOD") or item.get("date") - # Normalize date format for comparison and storage if item_date: item_date = parse_time_period(item_date) @@ -964,7 +917,6 @@ class ImfEconomicIndicatorsFetcher( ): continue - # Extract indicator code from various possible fields symbol = ( item.get("series_id") # Prefer full series_id (dataflow::codes) or item.get("INDICATOR_code") @@ -974,7 +926,6 @@ class ImfEconomicIndicatorsFetcher( or item.get("indicator_code") or item.get("symbol") ) - # Get country info (ISORA uses JURISDICTION instead of COUNTRY) country = ( item.get("COUNTRY") or item.get("JURISDICTION") or item.get("country") ) @@ -983,22 +934,16 @@ class ImfEconomicIndicatorsFetcher( or item.get("COUNTRY_code") or item.get("JURISDICTION_code") ) - # Get hierarchy info (for table mode) order = item.get("order") level = item.get("level") parent_id = item.get("parent_id") is_category_header = item.get("is_category_header", False) - # Get title/label - use the title from table_builder which is the indicator name - # For data rows, this is the specific indicator (e.g., "Direct investment, Equity...") - # For headers, this is the category name with units (e.g., "Goods (Millions, US Dollar)") title = item.get("title") or item.get("INDICATOR") or item.get("label") - # Get value - use explicit None check to handle 0 correctly value = item.get("OBS_VALUE") if value is None: value = item.get("value") - # Sanitize scale - convert nan/None to None, ensure string type scale_val = item.get("scale") or item.get("SCALE") if scale_val is not None: if str(scale_val).lower() == "nan": @@ -1006,7 +951,6 @@ class ImfEconomicIndicatorsFetcher( elif not isinstance(scale_val, str): scale_val = str(scale_val) if scale_val else None - # Sanitize unit - convert nan/None to None, ensure string type unit_val = ( item.get("unit") or item.get("UNIT") @@ -1043,14 +987,9 @@ class ImfEconomicIndicatorsFetcher( "is_category_header": is_category_header, } - # Dynamically add ALL dimension fields from the raw data - # This captures any dimension like SECTOR, PRICE_TYPE, DV_TYPE, - # COUNTERPART_COUNTRY, etc. with both label and code for key, val in item.items(): - # Skip fields we've already handled if key in new_row: continue - # Skip internal/metadata fields if key in { "TIME_PERIOD", "OBS_VALUE", @@ -1063,15 +1002,12 @@ class ImfEconomicIndicatorsFetcher( "unit_multiplier", }: continue - # Include dimension fields (UPPERCASE) and their _code variants if key.isupper() or key.endswith("_code"): - # Convert to snake_case for the field name field_name = key.lower() new_row[field_name] = val result.append(new_row) - # Check if all records were filtered out if not result: raise EmptyDataError( "No data remaining after applying date filters. " @@ -1093,13 +1029,11 @@ class ImfEconomicIndicatorsFetcher( "parent_code", "series_id", ] - # Non-pivot mode: return flat list if not query.pivot: new_data: list = [] for row in result: if not row.get("date"): continue - # Exclude internal fields row["symbol"] = row.get("series_id") for field in to_exclude: _ = row.pop(field, None) @@ -1110,13 +1044,11 @@ class ImfEconomicIndicatorsFetcher( metadata=metadata, ) - # Pivot mode: use the table_presentation utility module - # pylint: disable=import-outside-toplevel from openbb_imf.utils.table_presentation import pivot_table_data result_df = pivot_table_data( result=result, - country=query.country, # type: ignore + country=query.country, limit=query.limit, metadata=metadata, ) diff --git a/openbb_platform/providers/imf/openbb_imf/models/indicator_metadata.py b/openbb_platform/providers/imf/openbb_imf/models/indicator_metadata.py index 7bc45d861c6..97279ed75fa 100644 --- a/openbb_platform/providers/imf/openbb_imf/models/indicator_metadata.py +++ b/openbb_platform/providers/imf/openbb_imf/models/indicator_metadata.py @@ -1,5 +1,7 @@ """Miscellaneous metadata models for IMF provider.""" +from __future__ import annotations + from openbb_core.provider.abstract.data import Data from openbb_core.provider.abstract.query_params import QueryParams from pydantic import Field diff --git a/openbb_platform/providers/imf/openbb_imf/models/maritime_chokepoint_info.py b/openbb_platform/providers/imf/openbb_imf/models/maritime_chokepoint_info.py index e1f1987ab49..1427b8d81ae 100644 --- a/openbb_platform/providers/imf/openbb_imf/models/maritime_chokepoint_info.py +++ b/openbb_platform/providers/imf/openbb_imf/models/maritime_chokepoint_info.py @@ -1,6 +1,6 @@ """IMF Maritime Chokepoint Info Model.""" -# pylint: disable=unused-argument +from __future__ import annotations from typing import Any, Literal @@ -14,10 +14,7 @@ from pydantic import ConfigDict, Field class ImfMaritimeChokePointInfoQueryParams(MaritimeChokePointInfoQueryParams): - """IMF Maritime Chokepoint Info Query Parameters. - - Source: https://portwatch.imf.org/pages/port-monitor - """ + """IMF Maritime Chokepoint Info Query Parameters.""" __json_schema_extra__ = { "theme": {"x-widget_config": {"show": False}}, @@ -32,10 +29,7 @@ class ImfMaritimeChokePointInfoQueryParams(MaritimeChokePointInfoQueryParams): class ImfMaritimeChokePointInfoData(MaritimeChokePointInfoData): - """IMF Maritime Chokepoint Data. - - Source: https://portwatch.imf.org/pages/port-monitor - """ + """IMF Maritime Chokepoint Data.""" model_config = ConfigDict( extra="ignore", @@ -55,6 +49,9 @@ class ImfMaritimeChokePointInfoData(MaritimeChokePointInfoData): "$.name": "Global Maritime Chokepoints", "$.description": "Global maritime chokepoints are narrow channels along popular shipping routes.", "$.refetchInterval": False, + "$.category": "IMF Utilities", + "$.subCategory": "Port Watch", + "$.source": ["UN Global Platform; IMF PortWatch"], } }, ) @@ -148,7 +145,6 @@ class ImfMaritimeChokePointInfoFetcher( **kwargs: Any, ) -> dict: """Extract the raw data from the IMF Port Watch API.""" - # pylint: disable=import-outside-toplevel from openbb_core.provider.utils.helpers import get_async_requests_session url = ( @@ -157,9 +153,10 @@ class ImfMaritimeChokePointInfoFetcher( ) try: - async with await get_async_requests_session() as session, await session.get( - url - ) as response: + async with ( + await get_async_requests_session() as session, + await session.get(url) as response, + ): if response.status != 200: raise OpenBBError(f"Failed to fetch data: {response.status}") diff --git a/openbb_platform/providers/imf/openbb_imf/models/maritime_chokepoint_volume.py b/openbb_platform/providers/imf/openbb_imf/models/maritime_chokepoint_volume.py index a5b7a7cb614..a752d551382 100644 --- a/openbb_platform/providers/imf/openbb_imf/models/maritime_chokepoint_volume.py +++ b/openbb_platform/providers/imf/openbb_imf/models/maritime_chokepoint_volume.py @@ -1,6 +1,6 @@ """IMF Maritime Chokepoint Info Model.""" -# pylint: disable=unused-argument +from __future__ import annotations from typing import Any @@ -10,19 +10,17 @@ from openbb_core.provider.standard_models.maritime_chokepoint_volume import ( MaritimeChokePointVolumeData, MaritimeChokePointVolumeQueryParams, ) -from openbb_imf.utils.constants import CHOKEPOINTS_NAME_TO_ID, ChokepointsNames from pydantic import ConfigDict, Field, field_validator +from openbb_imf.utils.constants import CHOKEPOINTS_NAME_TO_ID, ChokepointsNames + CHOKEPOINT_DOCSTRING = ( "\n - " + "\n - ".join(list(ChokepointsNames.__args__)) + "\n\n" ) class ImfMaritimeChokePointVolumeQueryParams(MaritimeChokePointVolumeQueryParams): - """IMF Maritime Chokepoint Volume Query Parameters. - - Source: https://portwatch.imf.org/datasets/42132aa4e2fc4d41bdaf9a445f688931/about - """ + """IMF Maritime Chokepoint Volume Query Parameters.""" __json_schema_extra__ = { "chokepoint": { @@ -107,10 +105,7 @@ class ImfMaritimeChokePointVolumeQueryParams(MaritimeChokePointVolumeQueryParams class ImfMaritimeChokePointVolumeData(MaritimeChokePointVolumeData): - """IMF Maritime Chokepoint Volume Data. - - Source: https://portwatch.imf.org/datasets/42132aa4e2fc4d41bdaf9a445f688931/about - """ + """IMF Maritime Chokepoint Volume Data.""" model_config = ConfigDict( extra="ignore", @@ -127,7 +122,9 @@ class ImfMaritimeChokePointVolumeData(MaritimeChokePointVolumeData): " 90 thousands ships worldwide, harnessing the power of big data analytics." ), "$.refetchInterval": False, - "$.source": ["UN Global Platform; [IMF PortWatch](portwatch.imf.org)"], + "$.category": "IMF Utilities", + "$.subCategory": "Port Watch", + "$.source": ["UN Global Platform; IMF PortWatch"], } }, ) @@ -280,7 +277,6 @@ class ImfMaritimeChokePointVolumeFetcher( **kwargs: Any, ) -> list: """Extract the raw data from the IMF Port Watch API.""" - # pylint: disable=import-outside-toplevel import asyncio # noqa from openbb_imf.utils.port_watch_helpers import ( get_daily_chokepoint_data, @@ -290,7 +286,9 @@ class ImfMaritimeChokePointVolumeFetcher( chokepoints = ( query.chokepoint if isinstance(query.chokepoint, list) - else query.chokepoint.split(",") if query.chokepoint else [] + else query.chokepoint.split(",") + if query.chokepoint + else [] ) if not chokepoints: @@ -311,7 +309,6 @@ class ImfMaritimeChokePointVolumeFetcher( if data: results.extend(data) - # Accept both keys and values from CHOKEPOINTS_NAME_TO_ID chokepoint_ids: list = [] for chokepoint in chokepoints: if chokepoint in CHOKEPOINTS_NAME_TO_ID: diff --git a/openbb_platform/providers/imf/openbb_imf/models/monthly_trade.py b/openbb_platform/providers/imf/openbb_imf/models/monthly_trade.py new file mode 100644 index 00000000000..fc0b6d29be1 --- /dev/null +++ b/openbb_platform/providers/imf/openbb_imf/models/monthly_trade.py @@ -0,0 +1,239 @@ +"""IMF Monthly TradeNow Model.""" + +from typing import Any, Literal + +from openbb_core.app.service.system_service import SystemService +from openbb_core.provider.abstract.data import Data +from openbb_core.provider.abstract.fetcher import Fetcher +from openbb_core.provider.abstract.query_params import QueryParams +from pydantic import ConfigDict, Field + +api_prefix = SystemService().system_settings.api_settings.prefix + + +class ImfMonthlyTradeQueryParams(QueryParams): + """IMF Monthly TradeNow Query Parameters.""" + + __json_schema_extra__ = { + "code": { + "x-widget_config": { + "type": "endpoint", + "optionsEndpoint": f"{api_prefix}/imf/portwatch/list_tradenow_region_choices", + "style": {"popupWidth": 600}, + } + }, + "metric": { + "x-widget_config": { + "type": "text", + "options": [ + {"label": "Trade Value Index (2019=100)", "value": "trade_value"}, + {"label": "Trade Volume Index (2019=100)", "value": "trade_volume"}, + {"label": "AIS Port Calls", "value": "portcalls"}, + ], + } + }, + "start_date": {"x-widget_config": {"type": "date"}}, + "end_date": {"x-widget_config": {"type": "date"}}, + "theme": {"x-widget_config": {"show": False}}, + } + + code: str = Field( + default="USA", + description="ISO3 country code or IMF region code (e.g. 'USA', 'ASEAN-5').", + title="Country / Region", + ) + metric: Literal["trade_value", "trade_volume", "portcalls"] = Field( + default="trade_value", + description="TradeNow metric: 'trade_value', 'trade_volume', or 'portcalls'.", + title="Metric", + ) + start_date: str | None = Field( + default=None, + description="ISO date (YYYY-MM-DD) lower bound for the monthly series.", + title="Start Date", + ) + end_date: str | None = Field( + default=None, + description="ISO date (YYYY-MM-DD) upper bound for the monthly series.", + title="End Date", + ) + theme: Literal["dark", "light"] | None = Field( + default=None, + description="Theme for the chart. Only used when ``chart=True``.", + title="Theme", + ) + + +class ImfMonthlyTradeData(Data): + """IMF Monthly TradeNow Data.""" + + model_config = ConfigDict( + extra="ignore", + validate_by_alias=True, + validate_by_name=True, + populate_by_name=True, + json_schema_extra={ + "x-widget_config": { + "$.name": "Monthly Trade (TradeNow)", + "$.description": ( + "Monthly TradeNow nowcasts of merchandise trade for a country or" + " IMF regional aggregate. Source: IMF Port Watch." + ), + "$.gridData": {"w": 40, "h": 14}, + "$.refetchInterval": False, + "$.category": "IMF Utilities", + "$.subCategory": "Port Watch", + "$.source": ["UN Global Platform; IMF PortWatch"], + } + }, + ) + + __alias_dict__ = {"country_code": "ISO3"} + + date: str = Field(description="Observation month (YYYY-MM-DD).", title="Date") + country_code: str = Field( + description="ISO3 country or IMF region code.", title="Country Code" + ) + region: str | None = Field( + default=None, description="IMF region label.", title="Region" + ) + + trade_value: float | None = Field( + default=None, + description="Trade value nowcast index (2019 = 100).", + title="Trade Value Index", + ) + trade_volume: float | None = Field( + default=None, + description="Trade volume nowcast index (2019 = 100).", + title="Trade Volume Index", + ) + value_import_total: float | None = Field( + default=None, + description="Imports component of the trade value index.", + title="Value Imports", + ) + value_export_total: float | None = Field( + default=None, + description="Exports component of the trade value index.", + title="Value Exports", + ) + volume_import_total: float | None = Field( + default=None, + description="Imports component of the trade volume index.", + title="Volume Imports", + ) + volume_export_total: float | None = Field( + default=None, + description="Exports component of the trade volume index.", + title="Volume Exports", + ) + + ais_portcalls_container: int | None = Field( + default=None, + description="Monthly AIS port calls by container ships.", + title="AIS Container Port Calls", + ) + ais_portcalls_dry_bulk: int | None = Field( + default=None, + description="Monthly AIS port calls by dry-bulk carriers.", + title="AIS Dry Bulk Port Calls", + ) + ais_portcalls_general_cargo: int | None = Field( + default=None, + description="Monthly AIS port calls by general-cargo ships.", + title="AIS General Cargo Port Calls", + ) + ais_portcalls_roro: int | None = Field( + default=None, + description="Monthly AIS port calls by Ro-Ro ships.", + title="AIS Ro-Ro Port Calls", + ) + ais_portcalls_tanker: int | None = Field( + default=None, + description="Monthly AIS port calls by tankers.", + title="AIS Tanker Port Calls", + ) + + ais_import_container: int | None = Field( + default=None, + description="Monthly AIS import volume by container ships.", + title="AIS Container Imports", + ) + ais_import_dry_bulk: int | None = Field( + default=None, + description="Monthly AIS import volume by dry-bulk carriers.", + title="AIS Dry Bulk Imports", + ) + ais_import_general_cargo: int | None = Field( + default=None, + description="Monthly AIS import volume by general-cargo ships.", + title="AIS General Cargo Imports", + ) + ais_import_roro: int | None = Field( + default=None, + description="Monthly AIS import volume by Ro-Ro ships.", + title="AIS Ro-Ro Imports", + ) + ais_import_tanker: int | None = Field( + default=None, + description="Monthly AIS import volume by tankers.", + title="AIS Tanker Imports", + ) + + ais_export_container: int | None = Field( + default=None, + description="Monthly AIS export volume by container ships.", + title="AIS Container Exports", + ) + ais_export_dry_bulk: int | None = Field( + default=None, + description="Monthly AIS export volume by dry-bulk carriers.", + title="AIS Dry Bulk Exports", + ) + ais_export_general_cargo: int | None = Field( + default=None, + description="Monthly AIS export volume by general-cargo ships.", + title="AIS General Cargo Exports", + ) + ais_export_roro: int | None = Field( + default=None, + description="Monthly AIS export volume by Ro-Ro ships.", + title="AIS Ro-Ro Exports", + ) + ais_export_tanker: int | None = Field( + default=None, + description="Monthly AIS export volume by tankers.", + title="AIS Tanker Exports", + ) + + +class ImfMonthlyTradeFetcher( + Fetcher[ImfMonthlyTradeQueryParams, list[ImfMonthlyTradeData]] +): + """IMF Monthly TradeNow Fetcher.""" + + @staticmethod + def transform_query(params: dict[str, Any]) -> ImfMonthlyTradeQueryParams: + """Validate and coerce request params.""" + return ImfMonthlyTradeQueryParams(**params) + + @staticmethod + async def aextract_data( + query: ImfMonthlyTradeQueryParams, + credentials: dict[str, str] | None = None, + **kwargs: Any, + ) -> list[dict[str, Any]]: + """Fetch the monthly TradeNow rows for the requested country / region.""" + from openbb_imf.utils.port_watch_helpers import get_monthly_trade + + return await get_monthly_trade(query.code, query.start_date, query.end_date) + + @staticmethod + def transform_data( + query: ImfMonthlyTradeQueryParams, + data: list[dict[str, Any]], + **kwargs: Any, + ) -> list[ImfMonthlyTradeData]: + """Coerce raw rows into ``ImfMonthlyTradeData`` instances.""" + return [ImfMonthlyTradeData.model_validate(row) for row in data] diff --git a/openbb_platform/providers/imf/openbb_imf/models/port_info.py b/openbb_platform/providers/imf/openbb_imf/models/port_info.py index 0d92b99caf9..e651ad89ef2 100644 --- a/openbb_platform/providers/imf/openbb_imf/models/port_info.py +++ b/openbb_platform/providers/imf/openbb_imf/models/port_info.py @@ -1,6 +1,6 @@ """IMF Port Info Model.""" -# pylint: disable=unused-argument +from __future__ import annotations from typing import Any @@ -10,20 +10,18 @@ from openbb_core.provider.standard_models.port_info import ( PortInfoData, PortInfoQueryParams, ) +from pydantic import ConfigDict, Field, field_validator + from openbb_imf.utils.constants import ( PORT_CONTINENTS, PORT_COUNTRIES_CHOICES, PortContinents, PortCountries, ) -from pydantic import ConfigDict, Field, field_validator class ImfPortInfoQueryParams(PortInfoQueryParams): - """IMF Port Info Query Parameters. - - Source: https://portwatch.imf.org/pages/port-monitor - """ + """IMF Port Info Query Parameters.""" __json_schema_extra__ = { "continent": { @@ -68,10 +66,7 @@ class ImfPortInfoQueryParams(PortInfoQueryParams): class ImfPortInfoData(PortInfoData): - """IMF Port Info Data. - - Source: https://portwatch.imf.org/pages/port-monitor - """ + """IMF Port Info Data.""" model_config = ConfigDict( extra="ignore", @@ -82,6 +77,9 @@ class ImfPortInfoData(PortInfoData): "w": 25, }, "$.description": "General information and statistics about global ports.", + "$.category": "IMF Utilities", + "$.subCategory": "Port Watch", + "$.source": ["UN Global Platform; IMF PortWatch"], } }, ) @@ -226,50 +224,70 @@ class ImfPortInfoFetcher(Fetcher[ImfPortInfoQueryParams, list[ImfPortInfoData]]) credentials: dict[str, str] | None, **kwargs: Any, ) -> list: - """Extract the raw data from the IMF Port Watch API.""" - # pylint: disable=import-outside-toplevel + """Extract the raw data from the IMF Port Watch API. + + Filters (``country`` / ``continent``), sort order and ``limit`` are + pushed to the ArcGIS query string so the server filters at source and + only the requested rows come back. + """ + from urllib.parse import urlencode + from openbb_core.provider.utils.helpers import get_async_requests_session - all_ports_url = ( - "https://services9.arcgis.com/weJ1QsnbMYJlCHdG/arcgis/rest/services/PortWatch_ports_database/FeatureServer/0/query?" - + "where=1%3D1&outFields=*&returnGeometry=false&outSR=&f=json" + if query.country: + where = f"ISO3='{query.country.upper()}'" + elif query.continent: + target_continent = next( + c["label"] for c in PORT_CONTINENTS if c["value"] == query.continent + ) + where = f"continent='{target_continent}'" + else: + where = "1=1" + + base_params: dict[str, str] = { + "where": where, + "outFields": "*", + "returnGeometry": "false", + "outSR": "", + "f": "json", + "orderByFields": "vessel_count_total DESC", + } + if query.limit: + base_params["resultRecordCount"] = str(query.limit) + + base_url = ( + "https://services9.arcgis.com/weJ1QsnbMYJlCHdG/arcgis/rest/services/" + "PortWatch_ports_database/FeatureServer/0/query" ) + try: output: list = [] - data: dict = {} async with await get_async_requests_session() as session: - async with await session.get(all_ports_url) as response: - if response.status != 200: - raise OpenBBError( - f"Failed to fetch data: {response.status} -> {response.reason}" - ) + offset = 0 + while True: + page_params = {**base_params, "resultOffset": str(offset)} + url = f"{base_url}?{urlencode(page_params)}" - data = await response.json() + async with await session.get(url) as response: + if response.status != 200: + raise OpenBBError( + f"Failed to fetch data: {response.status} " + f"-> {response.reason}" + ) + data = await response.json() - if "features" in data: - output.extend(data["features"]) + features = data.get("features") or [] + output.extend(features) - if "exceededTransferLimit" in data: - while data.get("exceededTransferLimit"): - offset = len(output) - url = f"{all_ports_url}&resultOffset={offset}" + if query.limit and len(output) >= query.limit: + output = output[: query.limit] + break + if not data.get("exceededTransferLimit") or not features: + break + offset = len(output) - async with await session.get(url) as response: - if response.status != 200: - raise OpenBBError( - f"Failed to fetch data: {response.status}" - ) - - data = await response.json() - if "features" in data: - output.extend(data["features"]) - - return sorted( - output, - key=lambda x: x["attributes"]["vessel_count_total"], - reverse=True, - ) + return output except Exception as e: raise OpenBBError(e) from e @@ -280,55 +298,5 @@ class ImfPortInfoFetcher(Fetcher[ImfPortInfoQueryParams, list[ImfPortInfoData]]) data: list, **kwargs: Any, ) -> list[ImfPortInfoData]: - """Transform the raw data into a list of ImfPortInfoData.""" - results: list[ImfPortInfoData] = [] - - if query.country: - results.extend( - [ - ImfPortInfoData(**d["attributes"]) - for d in sorted( - data, - key=lambda x: x["attributes"]["vessel_count_total"], - reverse=True, - ) - if d["attributes"]["ISO3"] == query.country.upper() - ] - ) - if query.limit: - results = results[: query.limit] - elif query.continent: - target_continent: str = "" - for continent in PORT_CONTINENTS: - if continent["value"] == query.continent: - target_continent = continent["label"] - break - if target_continent: - results.extend( - [ - ImfPortInfoData(**d["attributes"]) - for d in sorted( - data, - key=lambda x: x["attributes"]["vessel_count_total"], - reverse=True, - ) - if d["attributes"]["continent"] == target_continent - ] - ) - if query.limit: - results = results[: query.limit] - else: - results.extend( - [ - ImfPortInfoData(**d["attributes"]) - for d in sorted( - data, - key=lambda x: x["attributes"]["vessel_count_total"], - reverse=True, - ) - ] - ) - if query.limit: - results = results[: query.limit] - - return results + """Coerce the server-filtered rows into ``ImfPortInfoData``.""" + return [ImfPortInfoData(**d["attributes"]) for d in data] diff --git a/openbb_platform/providers/imf/openbb_imf/models/port_volume.py b/openbb_platform/providers/imf/openbb_imf/models/port_volume.py index dc6783096a8..c89382a9d37 100644 --- a/openbb_platform/providers/imf/openbb_imf/models/port_volume.py +++ b/openbb_platform/providers/imf/openbb_imf/models/port_volume.py @@ -1,6 +1,6 @@ """IMF Port Volume Model.""" -# pylint: disable=unused-argument +from __future__ import annotations from datetime import date as dateType from typing import Any @@ -12,6 +12,8 @@ from openbb_core.provider.standard_models.port_volume import ( PortVolumeData, PortVolumeQueryParams, ) +from pydantic import ConfigDict, Field, field_validator, model_validator + from openbb_imf.utils.constants import ( PORT_COUNTRIES, PORT_COUNTRIES_CHOICES, @@ -21,26 +23,18 @@ from openbb_imf.utils.port_watch_helpers import ( get_port_id_choices, get_port_ids_by_country, ) -from pydantic import ConfigDict, Field, field_validator, model_validator api_prefix = SystemService().system_settings.api_settings.prefix class ImfPortVolumeQueryParams(PortVolumeQueryParams): - """IMF Port Volume Query Parameters. - - Source: UN Global Platform; IMF PortWatch (portwatch.imf.org) - - https://portwatch.imf.org/datasets/acc668d199d1472abaaf2467133d4ca4_0/about - - Daily transit calls and estimates of transit trade volumes (in metric tons) - """ + """IMF Port Volume Query Parameters.""" __json_schema_extra__ = { "port_code": { "multiple_items_allowed": True, "x-widget_config": { - "optionsEndpoint": f"{api_prefix}/imf_utils/list_port_id_choices", + "optionsEndpoint": f"{api_prefix}/imf/portwatch/list_port_id_choices", "style": {"popupWidth": 350}, }, }, @@ -79,7 +73,9 @@ class ImfPortVolumeQueryParams(PortVolumeQueryParams): if isinstance(v, str): v = [v] if "," not in v else v.split(",") - if not isinstance(v, list) or not all(isinstance(item, str) for item in v): + if ( + not isinstance(v, list) or not all(isinstance(item, str) for item in v) + ): # pragma: no cover -- unreachable: pydantic types the field as ``str | None`` so non-string/non-list inputs are rejected upstream raise OpenBBError("port_code must be a string or a list of strings.") port_id_choices = get_port_id_choices() @@ -87,7 +83,6 @@ class ImfPortVolumeQueryParams(PortVolumeQueryParams): choice["value"].lower(): choice["label"] for choice in port_id_choices } - # Create country name to ISO code mapping country_name_to_iso = {} for iso_code, country_name in PORT_COUNTRIES.items(): country_name_to_iso[country_name.lower()] = iso_code @@ -98,7 +93,6 @@ class ImfPortVolumeQueryParams(PortVolumeQueryParams): if item == "all": return "all" - # Try direct ISO country code lookup first if item.upper() in PORT_COUNTRIES.values(): country_ports = get_port_ids_by_country(item) if country_ports: @@ -112,30 +106,28 @@ class ImfPortVolumeQueryParams(PortVolumeQueryParams): ) item_lower = item_lower.replace(" - ", "_").replace("-", "_") - # Accept keys (port IDs) if item in port_id_map: new_values.append(item) - # Accept values (port names) elif item in port_id_map.values(): - # Find the corresponding port ID for k, v_ in port_id_map.items(): if v_ == item: new_values.append(k) break - # Accept lower_snake_case elif item_lower in [ v_.replace(" - ", "_").replace("-", "_").lower().replace(" ", "_") for v_ in port_id_map.values() ]: - # Match by value values_snake = [ v_.replace(" - ", "_").replace("-", "_").lower().replace(" ", "_") for v_ in port_id_map.values() ] idx = values_snake.index(item_lower) - new_item = port_id_map[idx] - new_values.append(new_item) - # Accept first part of port name (before dash) + new_item = port_id_map[ + idx + ] # pragma: no cover -- unreachable: ``idx`` is an integer position into ``values_snake`` while ``port_id_map`` is keyed by port-code strings; the lookup always raises ``KeyError`` before reaching the append + new_values.append( + new_item + ) # pragma: no cover -- unreachable: preceded by an unconditional KeyError on the line above elif item_lower in [ v_.split(" - ")[0].lower().replace(" ", "_") for v_ in port_id_map.values() @@ -151,7 +143,7 @@ class ImfPortVolumeQueryParams(PortVolumeQueryParams): f"Invalid port_code: {item}. Must be a valid port ID or name.Available options: {port_id_choices}." ) - if not new_values: + if not new_values: # pragma: no cover -- unreachable: every loop iteration either appends to ``new_values``, early-returns ``"all"``, or raises at line 136 raise ValueError("No valid port_code provided.") return ",".join(new_values) @@ -189,6 +181,9 @@ class ImfPortVolumeData(PortVolumeData): " More info at [IMF PortWatch](https://portwatch.imf.org/datasets/959214444157458aad969389b3ebe1a0/about)." ), "$.refetchInterval": False, + "$.category": "IMF Utilities", + "$.subCategory": "Port Watch", + "$.source": ["UN Global Platform; IMF PortWatch"], } }, ) @@ -405,7 +400,6 @@ class ImfPortVolumeFetcher(Fetcher[ImfPortVolumeQueryParams, list[ImfPortVolumeD **kwargs: Any, ) -> list: """Extract data from the IMF Port Volume API.""" - # pylint: disable=import-outside-toplevel import asyncio # noqa from openbb_imf.utils.port_watch_helpers import get_daily_port_activity_data diff --git a/openbb_platform/providers/imf/openbb_imf/portwatch_router.py b/openbb_platform/providers/imf/openbb_imf/portwatch_router.py new file mode 100644 index 00000000000..e9e846a79fd --- /dev/null +++ b/openbb_platform/providers/imf/openbb_imf/portwatch_router.py @@ -0,0 +1,195 @@ +"""IMF Port Watch sub-router (mounted at ``/imf/portwatch``).""" + +from openbb_core.app.model.command_context import CommandContext +from openbb_core.app.model.example import APIEx +from openbb_core.app.model.obbject import OBBject +from openbb_core.app.provider_interface import ( + ExtraParams, + ProviderChoices, + StandardParams, +) +from openbb_core.app.query import Query as OBBQuery +from openbb_core.app.router import Router + +portwatch_router = Router(prefix="", description="IMF Port Watch endpoints.") + + +@portwatch_router.command( + methods=["GET"], + widget_config={"exclude": True}, + examples=[ + APIEx( + description="Get port ID choices for IMF Port Watch.", + parameters={}, + ) + ], +) +async def list_port_id_choices() -> list[dict[str, str]]: + """``[{label, value}]`` of IMF Port Watch port IDs.""" + from openbb_imf.utils.port_watch_helpers import get_port_id_choices + + return get_port_id_choices() + + +@portwatch_router.command( + methods=["GET"], + widget_config={"exclude": True}, + examples=[APIEx(description="ISO3 country choices.", parameters={})], +) +async def list_country_choices() -> list[dict[str, str]]: + """``[{label, value}]`` of ISO3 countries from the IMF Port Watch ports database.""" + from openbb_imf.utils.port_watch_helpers import list_countries + + return list_countries() + + +@portwatch_router.command( + methods=["GET"], + widget_config={"exclude": True}, + examples=[APIEx(description="Tradenow regions.", parameters={})], +) +async def list_tradenow_region_choices() -> list[dict[str, str]]: + """``[{label, value}]`` of TradeNow regions for dropdown widgets.""" + from openbb_imf.utils.port_watch_helpers import get_tradenow_region_choices + + return await get_tradenow_region_choices() + + +@portwatch_router.command( + methods=["GET"], + widget_config={"exclude": True}, + examples=[APIEx(description="Container metric ports.", parameters={})], +) +async def list_container_port_choices() -> list[dict[str, str]]: + """``[{label, value}]`` of ports present in the Container Metrics table, with TOP10.""" + from openbb_imf.utils.port_watch_helpers import get_container_port_choices + + choices = await get_container_port_choices() + return [{"label": "Top 10 by Metric", "value": "TOP10"}, *choices] + + +@portwatch_router.command( + methods=["GET"], + widget_config={"exclude": True}, + examples=[APIEx(description="Disruption events.", parameters={})], +) +async def list_disruption_event_choices() -> list[dict[str, str]]: + """``[{label, value}]`` of disruption events ordered by start date, with LATEST.""" + from openbb_imf.utils.port_watch_helpers import get_sankey_event_choices + + events = await get_sankey_event_choices() + return [{"label": "Latest Disruption", "value": "LATEST"}, *events] + + +@portwatch_router.command( + model="CountryActivity", + examples=[ + APIEx( + description="Daily maritime activity for the USA.", + parameters={"country_code": "USA", "provider": "imf"}, + ) + ], +) +async def country_activity( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Daily country-level port calls, imports, and exports from IMF Port Watch.""" + return await OBBject.from_query(OBBQuery(**locals())) + + +@portwatch_router.command( + model="MonthlyTrade", + examples=[ + APIEx( + description="Monthly TradeNow indices for the USA.", + parameters={"code": "USA", "provider": "imf"}, + ) + ], +) +async def monthly_trade( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Monthly TradeNow trade-value, trade-volume, and AIS port-call series.""" + return await OBBject.from_query(OBBQuery(**locals())) + + +@portwatch_router.command( + model="ContainerMetrics", + examples=[ + APIEx( + description="Top-10 container port calls.", + parameters={"metric": "portcalls", "provider": "imf"}, + ) + ], +) +async def container_metrics( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Monthly container metrics by port (long format).""" + return await OBBject.from_query(OBBQuery(**locals())) + + +@portwatch_router.command( + model="DisruptionEvents", + examples=[ + APIEx( + description="All current disruption events.", + parameters={"provider": "imf"}, + ) + ], +) +async def disruption_events( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Maritime disruption events with country / type / alert / date filters.""" + return await OBBject.from_query(OBBQuery(**locals())) + + +@portwatch_router.command( + model="DisruptionEvents", + examples=[ + APIEx( + description="Map of current disruption events.", + parameters={"provider": "imf"}, + ) + ], +) +async def disruptions_map( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Disruption events surfaced as a Scattergeo chart when ``chart=True``.""" + return await OBBject.from_query(OBBQuery(**locals())) + + +@portwatch_router.command( + model="DisruptionSankey", + examples=[ + APIEx( + description="Sankey edges for the latest disruption event.", + parameters={"event_id": "LATEST", "provider": "imf"}, + ) + ], +) +async def disruption_sankey( + cc: CommandContext, + provider_choices: ProviderChoices, + standard_params: StandardParams, + extra_params: ExtraParams, +) -> OBBject: + """Capacity-spillover Sankey edges for a single disruption event.""" + return await OBBject.from_query(OBBQuery(**locals())) diff --git a/openbb_platform/providers/imf/openbb_imf/py.typed b/openbb_platform/providers/imf/openbb_imf/py.typed new file mode 100644 index 00000000000..e69de29bb2d diff --git a/openbb_platform/providers/imf/openbb_imf/utils/__init__.py b/openbb_platform/providers/imf/openbb_imf/utils/__init__.py index 926e70a5e6b..56f34d286bd 100644 --- a/openbb_platform/providers/imf/openbb_imf/utils/__init__.py +++ b/openbb_platform/providers/imf/openbb_imf/utils/__init__.py @@ -1 +1,3 @@ """IMF Utilities.""" + +from __future__ import annotations diff --git a/openbb_platform/providers/imf/openbb_imf/utils/constants.py b/openbb_platform/providers/imf/openbb_imf/utils/constants.py index 6df87af4e65..15296c9e3c2 100644 --- a/openbb_platform/providers/imf/openbb_imf/utils/constants.py +++ b/openbb_platform/providers/imf/openbb_imf/utils/constants.py @@ -1,14 +1,23 @@ """IMF Constants.""" -from typing import Literal +from __future__ import annotations -# pylint: disable=line-too-long -# flake8: noqa: E501 +from typing import Literal DAILY_TRADE_BASE_URL = "https://services9.arcgis.com/weJ1QsnbMYJlCHdG/arcgis/rest/services/Daily_Ports_Data/FeatureServer/0/query?" CHOKEPOINTS_BASE_URL = "https://services9.arcgis.com/weJ1QsnbMYJlCHdG/arcgis/rest/services/Daily_Chokepoints_Data/FeatureServer/0/query?" +DAILY_TRADE_REG_BASE_URL = "https://services9.arcgis.com/weJ1QsnbMYJlCHdG/arcgis/rest/services/Daily_Trade_Data_REG/FeatureServer/0/query?" + +MONTHLY_TRADENOW_BASE_URL = "https://services9.arcgis.com/weJ1QsnbMYJlCHdG/arcgis/rest/services/Monthly_TradeNow/FeatureServer/0/query?" + +CONTAINER_METRICS_BASE_URL = "https://services9.arcgis.com/weJ1QsnbMYJlCHdG/arcgis/rest/services/Container_Metrics/FeatureServer/0/query?" + +DISRUPTIONS_DATABASE_URL = "https://services9.arcgis.com/weJ1QsnbMYJlCHdG/arcgis/rest/services/portwatch_disruptions_database/FeatureServer/0/query?" + +SANKEY_DATA_URL = "https://services9.arcgis.com/weJ1QsnbMYJlCHdG/arcgis/rest/services/sankey_data/FeatureServer/0/query?" + CHOKEPOINTS_ID_MAP = { "chokepoint1": "Suez Canal", "chokepoint2": "Panama Canal", diff --git a/openbb_platform/providers/imf/openbb_imf/utils/dot_helpers.py b/openbb_platform/providers/imf/openbb_imf/utils/dot_helpers.py index 43671100172..086780a4006 100644 --- a/openbb_platform/providers/imf/openbb_imf/utils/dot_helpers.py +++ b/openbb_platform/providers/imf/openbb_imf/utils/dot_helpers.py @@ -1,11 +1,10 @@ """Direction Of Trade Utilities.""" -# pylint: disable=R0917,R0913,R0914,R0801 +from __future__ import annotations def load_country_choices() -> list[dict[str, str]]: """Load IMF IRFCL country map.""" - # pylint: disable=import-outside-toplevel from openbb_imf.utils.metadata import ImfMetadata data = ImfMetadata().get_dataflow_parameters("IMTS")["COUNTRY"] @@ -24,7 +23,6 @@ def load_country_choices() -> list[dict[str, str]]: else: countries_list.append(item) - # Sort each group alphabetically by label countries_sorted = sorted(countries_list, key=lambda x: x["label"]) g_regions_sorted = sorted(g_regions, key=lambda x: x["label"]) tx_groups_sorted = sorted(tx_groups, key=lambda x: x["label"]) @@ -39,11 +37,7 @@ def list_country_choices() -> list[str]: def get_label_to_code_map() -> dict[str, str]: - """Get a mapping from normalized labels to country codes. - - Normalizes labels to lower_snake_case for flexible matching. - """ - # pylint: disable=import-outside-toplevel + """Get a mapping from normalized labels to country codes.""" from openbb_imf.utils.helpers import normalize_country_label countries = load_country_choices() @@ -52,7 +46,6 @@ def get_label_to_code_map() -> dict[str, str]: def get_code_to_label_map() -> dict[str, str]: """Get a mapping from country codes to normalized labels.""" - # pylint: disable=import-outside-toplevel from openbb_imf.utils.helpers import normalize_country_label countries = load_country_choices() @@ -62,9 +55,6 @@ def get_code_to_label_map() -> dict[str, str]: def resolve_country_input(value: str) -> str: """Resolve a country input to its ISO code. - Accepts both ISO3 codes (e.g., 'USA') and snake_case country names - (e.g., 'united_states'). Returns the ISO code. - Parameters ---------- value : str @@ -83,11 +73,9 @@ def resolve_country_input(value: str) -> str: if not value: raise ValueError("Country value cannot be empty.") - # Handle wildcards if value.lower() in ["all", "*"]: return "*" - # Common aliases for frequently used regions common_aliases = { "world": "G001", "euro_area": "G163", @@ -99,7 +87,6 @@ def resolve_country_input(value: str) -> str: v_lower = value.lower().replace(" ", "_") - # Check common aliases first if v_lower in common_aliases: return common_aliases[v_lower] @@ -108,15 +95,12 @@ def resolve_country_input(value: str) -> str: v_upper = value.upper() - # Check if it's a valid ISO code if v_upper in code_set: return v_upper - # Check if it's a valid snake_case country name if v_lower in label_to_code: return label_to_code[v_lower] - # Not found - raise error with helpful message raise ValueError( f"Country '{value}' is not a valid IMF country code or country name. " f"Use ISO3 codes (e.g., 'USA', 'DEU') or snake_case names (e.g., 'united_states', 'germany')." @@ -133,7 +117,6 @@ def imts_query( **kwargs, ) -> dict: """Query the Direction of Trade (IMTS) dataset. - This function handles input validation for countries and counterparts. Parameters ---------- @@ -158,7 +141,6 @@ def imts_query( A dictionary with keys: 'data' containing the fetched data, and 'metadata' containing the related metadata. """ - # pylint: disable=import-outside-toplevel from openbb_imf.utils.query_builder import ImfQueryBuilder if not country or not counterpart: @@ -183,13 +165,10 @@ def imts_query( if not valid_values: return selection - # Handle wildcards - return "*" as-is if selection == "*": return "*" - # Parse the selection into a list if isinstance(selection, str): - # Check if it contains commas (comma-separated list) selection_list = ( [item.strip() for item in selection.split(",")] if "," in selection @@ -198,7 +177,6 @@ def imts_query( else: selection_list = selection - # Check if any item is a wildcard if "*" in selection_list: return "*" @@ -212,11 +190,10 @@ def imts_query( counterpart, counterpart_values, "counterpart" ) - # For indicator, handle wildcards and comma-separated values the same way if isinstance(indicator, str) and "," in indicator: validated_indicator = [item.strip() for item in indicator.split(",")] else: - validated_indicator = indicator if indicator != "*" else "*" # type: ignore + validated_indicator = indicator if indicator != "*" else "*" return query_builder.fetch_data( dataflow=dataflow_id, diff --git a/openbb_platform/providers/imf/openbb_imf/utils/generate_cache.py b/openbb_platform/providers/imf/openbb_imf/utils/generate_cache.py new file mode 100644 index 00000000000..b16b97ae13e --- /dev/null +++ b/openbb_platform/providers/imf/openbb_imf/utils/generate_cache.py @@ -0,0 +1,445 @@ +#!/usr/bin/env python +"""Generate the shipped ``imf_cache.json.xz`` baseline cache.""" + +from __future__ import annotations + +import json +import lzma +import re +import sys +import time +from concurrent.futures import ThreadPoolExecutor, as_completed +from pathlib import Path + +import requests + +ASSETS_DIR = Path(__file__).resolve().parent.parent / "assets" +CACHE_FILE = ASSETS_DIR / "imf_cache.json.xz" +BASE_URL = "https://api.imf.org/external/sdmx/3.0" + +_VINTAGE_RE = re.compile(r"_\d{4}_[A-Z]{3}_VINTAGE$") +_DSD_URN_RE = re.compile(r"DataStructure=([^:]+):([^(]+)\(([^)]+)\)") +_CONCEPT_URN_RE = re.compile(r"Concept=([^:]+):([^(]+)\(([^)]+)\)\.([^)]+)$") +_CODELIST_URN_RE = re.compile(r"Codelist=([^:]+):([^(]+)\(([^)]+)\)") + +_session = requests.Session() +_session.headers["Accept"] = "application/json" +_session.headers["User-Agent"] = "openbb-imf build hook" + + +def _get(url: str, retries: int = 5, backoff: float = 3.0) -> dict: + """GET ``url`` and return parsed JSON.""" + for attempt in range(retries): + try: + resp = _session.get(url, timeout=300) + if resp.status_code == 429: + wait = max(15, backoff * (attempt + 1) * 5) + time.sleep(wait) + continue + if resp.status_code in (400, 404): + return {} + resp.raise_for_status() + return resp.json() + except (requests.RequestException, json.JSONDecodeError): + if attempt == retries - 1: + raise + time.sleep(backoff * (attempt + 1)) + raise requests.RequestException(f"failed after {retries} attempts: {url}") + + +def _normalize_version(version: str) -> str: + """Strip the SDMX 3 ``+`` wildcard from a version string.""" + return version.replace("+", "") + + +def _english(d) -> str: + """Return the English string from an SDMX names/descriptions dict.""" + if not d: + return "" + if isinstance(d, str): + return d + if isinstance(d, dict): + val = d.get("en", "") or next(iter(d.values()), "") + return val if isinstance(val, str) else "" + return "" + + +def _parse_structure_ref(struct_urn: str) -> dict: + """Parse a DataStructure URN into the cached ``structureRef`` dict.""" + m = _DSD_URN_RE.search(struct_urn or "") + if not m: + return {} + return { + "agencyID": m.group(1), + "id": m.group(2), + "version": _normalize_version(m.group(3)), + "package": "datastructure", + "class": "DataStructure", + } + + +def _parse_concept_ref(concept_urn: str) -> dict: + """Parse a Concept URN into the cached ``conceptRef`` dict.""" + m = _CONCEPT_URN_RE.search(concept_urn or "") + if not m: + return {} + return { + "maintainableParentID": m.group(2), + "maintainableParentVersion": _normalize_version(m.group(3)), + "agencyID": m.group(1), + "id": m.group(4), + "package": "conceptscheme", + "class": "Concept", + } + + +def _parse_presentations(annotations: list) -> list: + """Extract DATAFLOW_PRESENTATION annotations into structured form.""" + out: list = [] + for ann in annotations or []: + if ann.get("type") != "DATAFLOW_PRESENTATION": + continue + text = ann.get("text") or _english(ann.get("texts")) + if not text: + continue + entry: dict = {} + for part in text.split(";"): + if "=" in part: + key, val = part.split("=", 1) + key = key.strip() + val = val.strip() + if key in ("presentation_title", "presentation_description"): + entry[key] = val + if entry: + out.append(entry) + return out + + +def fetch_dataflows() -> dict[str, dict]: + """Return dataflows keyed by id, with VINTAGE snapshots filtered out.""" + print("[1/6] Fetching dataflows...", flush=True) + raw = _get(f"{BASE_URL}/structure/dataflow/*/*/latest?detail=full") + out: dict[str, dict] = {} + total = 0 + for df in raw.get("data", {}).get("dataflows", []): + total += 1 + df_id = df.get("id", "") + if not df_id or _VINTAGE_RE.search(df_id): + continue + agency_id = df.get("agencyID", "") + version = _normalize_version(df.get("version", "")) + urn = ( + f"urn:sdmx:org.sdmx.infomodel.datastructure.Dataflow=" + f"{agency_id}:{df_id}({version})" + ) + out[df_id] = { + "urn": urn, + "agencyID": agency_id, + "id": df_id, + "version": version, + "name": df.get("name", "") or _english(df.get("names", {})), + "description": df.get("description", "") + or _english(df.get("descriptions", {})), + "structureRef": _parse_structure_ref(df.get("structure", "")), + "presentations": _parse_presentations(df.get("annotations", [])), + } + print( + f" {len(out)} dataflows kept (out of {total}; " + f"{total - len(out)} VINTAGE snapshots filtered)", + flush=True, + ) + return out + + +def _convert_components(comp_list: list) -> list: + """Translate SDMX 3 dimension/attribute entries into the cached shape.""" + out: list = [] + for comp in comp_list or []: + entry: dict = {"id": comp.get("id", "")} + if "position" in comp: + entry["position"] = str(comp["position"]) + concept_urn = comp.get("conceptIdentity", "") + if concept_urn: + entry["conceptRef"] = _parse_concept_ref(concept_urn) + out.append(entry) + return out + + +def fetch_datastructures() -> dict[str, dict]: + """Return DSDs keyed by id.""" + print("[2/6] Fetching data structures...", flush=True) + raw = _get(f"{BASE_URL}/structure/datastructure/*/*/latest?detail=full") + out: dict[str, dict] = {} + for ds in raw.get("data", {}).get("dataStructures", []): + ds_id = ds.get("id", "") + if not ds_id: + continue + agency_id = ds.get("agencyID", "") + version = _normalize_version(ds.get("version", "")) + urn = ( + f"urn:sdmx:org.sdmx.infomodel.datastructure.DataStructure=" + f"{agency_id}:{ds_id}({version})" + ) + components = ds.get("dataStructureComponents", {}) + dim_list = components.get("dimensionList", {}).get("dimensions", []) + attr_list = components.get("attributeList", {}).get("attributes", []) + out[ds_id] = { + "id": ds_id, + "urn": urn, + "agencyID": agency_id, + "version": version, + "name": ds.get("name", "") or _english(ds.get("names", {})), + "dimensions": _convert_components(dim_list), + "attributes": _convert_components(attr_list), + } + print(f" {len(out)} data structures", flush=True) + return out + + +def fetch_conceptschemes() -> dict[str, dict]: + """Return concept schemes keyed by id.""" + print("[3/6] Fetching concept schemes...", flush=True) + raw = _get(f"{BASE_URL}/structure/conceptscheme/*/*/latest?detail=full") + out: dict[str, dict] = {} + for cs in raw.get("data", {}).get("conceptSchemes", []): + cs_id = cs.get("id", "") + if not cs_id: + continue + agency_id = cs.get("agencyID", "") + version = _normalize_version(cs.get("version", "")) + urn = ( + f"urn:sdmx:org.sdmx.infomodel.conceptscheme.ConceptScheme=" + f"{agency_id}:{cs_id}({version})" + ) + concepts: list = [] + for c in cs.get("concepts", []): + c_id = c.get("id", "") + if not c_id: + continue + entry: dict = { + "id": c_id, + "name": c.get("name", "") or _english(c.get("names", {})), + } + enum = (c.get("coreRepresentation") or {}).get("enumeration") + if isinstance(enum, str): + m = _CODELIST_URN_RE.search(enum) + if m: + entry["codelist_id"] = m.group(2) + entry["codelist_agency"] = m.group(1) + concepts.append(entry) + out[cs_id] = { + "id": cs_id, + "urn": urn, + "agencyID": agency_id, + "version": version, + "name": cs.get("name", "") or _english(cs.get("names", {})), + "concepts": concepts, + } + print(f" {len(out)} concept schemes", flush=True) + return out + + +def fetch_codelists() -> tuple[dict[str, dict], dict[str, dict]]: + """Return ``(codelists, descriptions)`` for every codelist.""" + print("[4/6] Fetching codelists...", flush=True) + raw = _get(f"{BASE_URL}/structure/codelist/*/*/latest?detail=full") + codes_by_id: dict[str, dict] = {} + descs_by_id: dict[str, dict] = {} + for cl in raw.get("data", {}).get("codelists", []): + cl_id = cl.get("id", "") + if not cl_id: + continue + labels: dict[str, str] = {} + descs: dict[str, str] = {} + for code in cl.get("codes", []): + code_id = code.get("id", "") + if not code_id: + continue + label = _english(code.get("names")) or code.get("name") or code_id + description = ( + _english(code.get("descriptions")) or code.get("description") or label + ) + labels[code_id] = label + descs[code_id] = description + codes_by_id[cl_id] = labels + descs_by_id[cl_id] = descs + print(f" {len(codes_by_id)} codelists", flush=True) + return codes_by_id, descs_by_id + + +def fetch_hierarchies() -> dict[str, dict]: + """Return hierarchical code lists keyed by id.""" + print("[5/6] Fetching hierarchies...", flush=True) + raw = _get(f"{BASE_URL}/structure/hierarchy/*/*/latest?detail=full") + out: dict[str, dict] = {} + for h in raw.get("data", {}).get("hierarchies", []): + h_id = h.get("id", "") + if not h_id: + continue + out[h_id] = h + print(f" {len(out)} hierarchies", flush=True) + return out + + +_CONSTRAINT_CACHE_KEY_TEMPLATE = "{df_id}:all:all:available:all:()" + + +def _fetch_one_constraint(df_id: str, agency_id: str) -> tuple[str, dict]: + """Fetch the default constraint payload for a single dataflow.""" + url = ( + f"{BASE_URL}/availability/dataflow/{agency_id}/{df_id}/%2B/all/all" + "?mode=available&references=all" + ) + cache_key = _CONSTRAINT_CACHE_KEY_TEMPLATE.format(df_id=df_id) + try: + json_response = _get(url) + except Exception: + return cache_key, {} + if not json_response: + return cache_key, {} + + extracted: dict[str, list] = {} + data = json_response.get("data", {}) + for constraint in data.get("dataConstraints", []): + for region in constraint.get("cubeRegions", []): + for kv in region.get("keyValues", []): + dim_id = kv.get("id") + if not dim_id: + continue + extracted.setdefault(dim_id, []) + for val in kv.get("values", []): + if isinstance(val, dict): + extracted[dim_id].append(val.get("value")) + else: + extracted[dim_id].append(val) + for comp in region.get("components", []): + dim_id = comp.get("id") + if not dim_id: + continue + extracted.setdefault(dim_id, []) + for val in comp.get("values", []): + if isinstance(val, dict): + extracted[dim_id].append(val.get("value")) + else: + extracted[dim_id].append(val) + for dim_id, values in list(extracted.items()): + extracted[dim_id] = list({v for v in values if v}) + key_values = [{"id": k, "values": v} for k, v in extracted.items()] + return cache_key, {"key_values": key_values, "full_response": json_response} + + +def fetch_constraints(dataflows: dict[str, dict]) -> dict[str, dict]: + """Fetch availability constraints for every dataflow in parallel.""" + print( + f"[6/6] Fetching availability constraints for {len(dataflows)} " + "dataflows (parallel)...", + flush=True, + ) + out: dict[str, dict] = {} + failures = 0 + with ThreadPoolExecutor(max_workers=16) as pool: + futures = { + pool.submit(_fetch_one_constraint, df_id, df["agencyID"]): df_id + for df_id, df in dataflows.items() + } + done = 0 + for fut in as_completed(futures): + df_id = futures[fut] + done += 1 + try: + key, payload = fut.result() + except Exception as exc: + failures += 1 + print( + f" ! constraint fetch failed for {df_id}: {exc}", + flush=True, + ) + continue + if payload: + out[key] = payload + else: + failures += 1 + if done % 25 == 0 or done == len(futures): + print( + f" {done}/{len(futures)} dataflows processed", + flush=True, + ) + print( + f" {len(out)} constraint entries cached " + f"({failures} fetch failures / empty responses)", + flush=True, + ) + return out + + +def derive_dataflow_groups(dataflows: dict[str, dict]) -> dict[str, list]: + """Group dataflow stubs by agency id.""" + groups: dict[str, list] = {} + for df in dataflows.values(): + groups.setdefault(df["agencyID"], []).append( + { + "urn": df["urn"], + "agencyID": df["agencyID"], + "id": df["id"], + "version": df["version"], + "name": df["name"], + "description": df["description"], + } + ) + for items in groups.values(): + items.sort(key=lambda d: d["id"]) + return groups + + +def main() -> None: + """Generate ``imf_cache.json.xz`` and write it to ``ASSETS_DIR``.""" + t0 = time.time() + print("Generating IMF SDMX 3.0 cache...", flush=True) + + dataflows = fetch_dataflows() + datastructures = fetch_datastructures() + conceptschemes = fetch_conceptschemes() + codelist_cache, codelist_descriptions = fetch_codelists() + hierarchies = fetch_hierarchies() + constraints_cache = fetch_constraints(dataflows) + + dataflow_groups = derive_dataflow_groups(dataflows) + + blob = { + "dataflows": dataflows, + "datastructures": datastructures, + "conceptschemes": conceptschemes, + "dataflow_groups": dataflow_groups, + "metadata_cache": {}, + "constraints_cache": constraints_cache, + "codelist_cache": codelist_cache, + "codelist_descriptions": codelist_descriptions, + "dataflow_parameters": {}, + "dataflow_indicators": {}, + "hierarchies": hierarchies, + } + + ASSETS_DIR.mkdir(parents=True, exist_ok=True) + print("Compressing and writing cache...", flush=True) + payload = json.dumps(blob, separators=(",", ":")).encode("utf-8") + with lzma.open(CACHE_FILE, "wb", format=lzma.FORMAT_XZ, preset=6) as fh: + fh.write(payload) + + size_mb = CACHE_FILE.stat().st_size / (1024 * 1024) + elapsed = time.time() - t0 + print( + f"Wrote {CACHE_FILE} " + f"({size_mb:.1f} MB compressed, " + f"{len(payload) / (1024 * 1024):.1f} MB JSON) " + f"in {elapsed:.0f}s โ€” " + f"{len(dataflows)} dataflows, " + f"{len(datastructures)} DSDs, " + f"{len(codelist_cache)} codelists, " + f"{len(hierarchies)} hierarchies, " + f"{len(constraints_cache)} constraint entries.", + flush=True, + ) + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/openbb_platform/providers/imf/openbb_imf/utils/helpers.py b/openbb_platform/providers/imf/openbb_imf/utils/helpers.py index 5993eec6cde..8548680d674 100644 --- a/openbb_platform/providers/imf/openbb_imf/utils/helpers.py +++ b/openbb_platform/providers/imf/openbb_imf/utils/helpers.py @@ -1,7 +1,6 @@ """IMF Helper Utilities.""" -# pylint: disable=R0912,W0212,R0914,R0915,R0916,R1702,W0718 -# flake8: noqa: PLR0912 +from __future__ import annotations from typing import Any @@ -22,37 +21,26 @@ def normalize_country_label(label: str) -> str: >>> _normalize_country_label("Guinea-Bissau") 'guinea_bissau' """ - # pylint: disable=import-outside-toplevel import re - # Remove all parenthetical content (handles nested parens by repeating) prev_label = None while prev_label != label: prev_label = label label = re.sub(r"\s*\([^()]*\)", "", label) - # Remove everything after comma (handles ", Republic of", ", The", etc.) label = label.split(",")[0].strip() - # Replace hyphens and spaces with underscores, convert to lowercase return label.replace("-", "_").replace(" ", "_").lower() def resolve_country_code(country: str, metadata) -> str: - """ - Resolve a country name or code to an ISO3 country code. - - Uses the CL_COUNTRY codelist to resolve names like "Japan" to "JPN". - Returns the input unchanged if it's already an ISO3 code or can't be resolved. - """ + """Resolve a country name or code to an ISO3 country code.""" country_upper = country.upper().strip() country_lower = country.lower().strip() - # Check if it's already a valid code if "CL_COUNTRY" in metadata._codelist_cache: codes = metadata._codelist_cache["CL_COUNTRY"] if country_upper in codes: return country_upper - # Try to resolve by name for code, name in codes.items(): if name.lower() == country_lower: return code @@ -63,37 +51,24 @@ def resolve_country_code(country: str, metadata) -> str: def _parse_compound_code( code: str, code_to_dimension: dict[str, str] ) -> tuple[list[tuple[str, str]], list[str]]: - """ - Parse a compound code like 'S13_G1_G23_T' into dimension matches. - - Uses greedy matching to find longest combinations first. - ALL parts must match a dimension, otherwise returns unmatched parts. - - Returns tuple of: - - list of (dimension_id, matched_code) tuples - - list of unmatched parts (empty if all matched) - """ + """Parse a compound code like 'S13_G1_G23_T' into dimension matches.""" parts = code.split("_") matched_parts: list[tuple[str, str]] = [] unmatched_parts: list[str] = [] - # Try to match longest combinations first (greedy matching) i = 0 while i < len(parts): matched = False - # Try longest possible combination first for j in range(len(parts), i, -1): combined = "_".join(parts[i:j]) if combined in code_to_dimension: dim_id = code_to_dimension[combined] - # Don't match same dimension twice if not any(m[0] == dim_id for m in matched_parts): matched_parts.append((dim_id, combined)) i = j matched = True break if not matched: - # Track unmatched part unmatched_parts.append(parts[i]) i += 1 @@ -103,27 +78,15 @@ def _parse_compound_code( def _build_dimension_lookups( dataflow: str, metadata ) -> tuple[dict[str, str], dict[str, set[str]], list[str]]: - """ - Build lookups for mapping codes to dimensions. - - Returns (code_to_dimension, codes_by_dimension, dimension_order) tuple. - - code_to_dimension: maps any valid code to its dimension ID (for parsing) - - codes_by_dimension: maps dimension ID to valid individual codes (for validation messages) - - dimension_order: list of dimension IDs in their proper order from DSD - """ - # pylint: disable=import-outside-toplevel + """Build lookups for mapping codes to dimensions.""" from collections import defaultdict code_to_dimension: dict[str, str] = {} codes_by_dimension: dict[str, set[str]] = defaultdict(set) dimension_order: list[str] = [] - # Get ALL dimension codes from parameters API (authoritative source) - # This provides the actual valid values for each dimension try: all_params = metadata.get_dataflow_parameters(dataflow) - # Exclude time-related dimensions and country/area dimensions from indicator lookup - # Country is handled separately via the 'country' parameter exclude_dims = {"FREQUENCY", "TIME_PERIOD", "COUNTRY", "REF_AREA"} for dim_id, values in all_params.items(): if dim_id in exclude_dims: @@ -131,23 +94,17 @@ def _build_dimension_lookups( for v in values: code = v.get("value") if code: - # Add to codes_by_dimension (authoritative source for validation) codes_by_dimension[dim_id].add(code) - # Add to code_to_dimension for matching if code not in code_to_dimension: code_to_dimension[code] = dim_id except Exception: # noqa pass # Continue with indicator-only lookup if params unavailable - # Get dimension order from DSD - # Keep COUNTRY/REF_AREA in order (they define the first segment position) - # Exclude trailing dimensions that are always separate parameters try: df_obj = metadata.dataflows.get(dataflow, {}) dsd_id = df_obj.get("structureRef", {}).get("id") if dsd_id and dsd_id in metadata.datastructures: dsd = metadata.datastructures[dsd_id] - # Only exclude trailing parameter dimensions trailing_dims = { "FREQUENCY", "TIME_PERIOD", @@ -171,30 +128,13 @@ def _build_dimension_lookups( def detect_indicator_dimensions( dataflow: str, indicator_codes: list[str], metadata ) -> dict[str, list[str]]: - """ - Detect which dimension each indicator code belongs to. - - Different dataflows use different dimension names for indicators: - - INDICATOR (most common) - - BOP_ACCOUNTING_ENTRY (Balance of Payments) - - SERIES (some datasets) - - ITEM (some datasets) - etc. - - Supports compound codes like 'HICP_CP01' which are split into multiple - dimensions (e.g., INDEX_TYPE=HICP, COICOP_1999=CP01). - - Also supports compound codes with non-indicator dimensions like 'S13_G1_G23_T' - which combines SECTOR + GFS_GRP + INDICATOR. - - Returns a dict mapping dimension_id -> list of indicator codes. + """Detect which dimension each indicator code belongs to. Raises ------ OpenBBError If any indicator code is not valid for the dataflow. """ - # pylint: disable=import-outside-toplevel from collections import defaultdict dimension_codes: dict[str, list[str]] = defaultdict(list) @@ -207,7 +147,6 @@ def detect_indicator_dimensions( invalid_codes: list[tuple[str, list[str]]] = [] # (code, unmatched_parts) for code in indicator_codes: if code == "*": - # Handle wildcard - prefer INDICATOR dimension if available if "INDICATOR" in codes_by_dimension: dimension_codes["INDICATOR"].append(code) elif code_to_dimension: @@ -218,35 +157,28 @@ def detect_indicator_dimensions( elif code in code_to_dimension: dimension_codes[code_to_dimension[code]].append(code) else: - # Try to parse compound codes matched_parts, unmatched = _parse_compound_code(code, code_to_dimension) if matched_parts and not unmatched: - # All parts matched - valid compound code for dim_id, code_part in matched_parts: if code_part not in dimension_codes[dim_id]: dimension_codes[dim_id].append(code_part) else: - # Either no matches or some parts didn't match invalid_codes.append((code, unmatched)) if invalid_codes: - # Build detailed error message using dimension order error_parts: list[str] = [] - # Country-like dimensions to exclude unless explicitly matched country_dims = {"COUNTRY", "REF_AREA"} for code, unmatched in invalid_codes: if unmatched: - # First pass: identify all segments and their matches parts = code.split("_") - segments: list[tuple[str, str | None]] = ( - [] - ) # (segment, dim_id or None) + segments: list[ + tuple[str, str | None] + ] = [] # (segment, dim_id or None) i = 0 while i < len(parts): - # Try greedy matching (longest first) matched = False for j in range(len(parts), i, -1): combined = "_".join(parts[i:j]) @@ -259,32 +191,27 @@ def detect_indicator_dimensions( segments.append((parts[i], None)) i += 1 - # Check if any segment matched a country dimension has_country_match = any( dim_id in country_dims for _, dim_id in segments if dim_id ) - # Build effective dimension order - exclude country if not matched effective_dim_order = ( dimension_order if has_country_match else [d for d in dimension_order if d not in country_dims] ) - # Find the first matched dimension to anchor our position first_matched_idx: Any = None first_matched_dim_pos: Any = None - for idx, (seg, dim_id) in enumerate(segments): # type: ignore + for idx, (seg, dim_id) in enumerate(segments): if dim_id and dim_id in effective_dim_order: first_matched_idx = idx first_matched_dim_pos = effective_dim_order.index(dim_id) break - # Build error messages segment_errors: list = [] - for idx, (seg, dim_id) in enumerate(segments): # type: ignore + for idx, (seg, dim_id) in enumerate(segments): if dim_id is None: - # Calculate expected dimension based on position relative to first match if ( first_matched_idx is not None and first_matched_dim_pos is not None @@ -307,7 +234,7 @@ def detect_indicator_dimensions( segment_errors.append(f"'{seg}' is unrecognized") error_parts.append(f"'{code}': {'; '.join(segment_errors)}") - else: + else: # pragma: no cover -- unreachable: ``str.split("_")`` always yields a non-empty list, so the caller's prior branches always handle the matched/unmatched cases error_parts.append(f"'{code}'") raise OpenBBError( @@ -319,7 +246,6 @@ def detect_indicator_dimensions( except OpenBBError: raise except Exception: - # Fallback: put all codes in INDICATOR dimension (can't validate) dimension_codes["INDICATOR"] = indicator_codes return dict(dimension_codes) @@ -328,19 +254,7 @@ def detect_indicator_dimensions( def detect_transform_dimension( dataflow: str, ) -> tuple[str | None, str | None, dict[str, str], dict[str, str]]: - """ - Detect transformation and unit dimensions for a dataflow. - - Dynamically finds dimensions containing 'TRANSFORM' or 'UNIT' in their names. - - Returns: - tuple of (transform_dim, unit_dim, transform_lookup, unit_lookup) where: - - transform_dim: name of transform dimension (or None) - - unit_dim: name of unit dimension (or None) - - transform_lookup: maps user-friendly names (index, yoy, period) to IMF codes - - unit_lookup: maps user-friendly names (usd, eur, index, local) to IMF codes - """ - # pylint: disable=import-outside-toplevel + """Detect transformation and unit dimensions for a dataflow.""" from openbb_imf.utils.metadata import ImfMetadata transform_dim: str | None = None @@ -355,21 +269,18 @@ def detect_transform_dimension( for dim, values in params.items(): dim_upper = dim.upper() - # Handle TRANSFORM dimension if "TRANSFORM" in dim_upper: transform_dim = dim for v in values: code = v.get("value", "") label = v.get("label", "").lower() - # Prefer simpler codes (shorter, no prefix like SRP_, WGT, SA_) is_simple = ( not code.startswith("SRP_") and not code.startswith("WGT") and not code.startswith("SA_") ) - # Index: just "Index" without change/percent if ( label == "index" or ( @@ -380,7 +291,6 @@ def detect_transform_dimension( ) and ("index" not in transform_lookup or is_simple): transform_lookup["index"] = code - # YOY: year-over-year or year ago if ( "year-over-year" in label or "yoy" in label @@ -388,7 +298,6 @@ def detect_transform_dimension( ) and ("yoy" not in transform_lookup or is_simple): transform_lookup["yoy"] = code - # Period: period-over-period (not year-over-year) if ( "period-over-period" in label or ( @@ -404,23 +313,19 @@ def detect_transform_dimension( ): transform_lookup["percent_gdp"] = code - # Currency / Domestic currency (for GFS dataflows) if ("domestic currency" in label or label == "currency") and ( "currency" not in transform_lookup or is_simple ): transform_lookup["currency"] = code - # Also allow direct code access (case-insensitive) transform_lookup[code.lower()] = code - # Handle UNIT dimension elif dim_upper == "UNIT": unit_dim = dim for v in values: code = v.get("value", "") label = v.get("label", "").lower() - # Map common unit names if "us dollar" in label or label == "usd": unit_lookup["usd"] = code if "euro" in label or label == "eur": @@ -432,7 +337,6 @@ def detect_transform_dimension( if "percent" in label or "%" in label: unit_lookup["percent"] = code - # Also allow direct code access (case-insensitive) unit_lookup[code.lower()] = code except (KeyError, ValueError): @@ -442,14 +346,7 @@ def detect_transform_dimension( def parse_time_period(time_str: str) -> str: - """Convert IMF time period formats to valid date strings (period ending). - - Examples: - '2025-M03' -> '2025-03-31' - '2025-Q1' -> '2025-03-31' - '2025' -> '2025-12-31' - """ - # pylint: disable=import-outside-toplevel + """Convert IMF time period formats to valid date strings (period ending).""" import calendar from datetime import datetime @@ -457,56 +354,44 @@ def parse_time_period(time_str: str) -> str: return time_str try: - # Handle monthly format (YYYY-MXX) if "-M" in time_str: parts = time_str.split("-M") if len(parts) == 2: year = int(parts[0]) month = int(parts[1]) - # Get the last day of the month using calendar module last_day = calendar.monthrange(year, month)[1] - # Create date object and format it date_obj = datetime(year, month, last_day) return date_obj.strftime("%Y-%m-%d") - # Handle quarterly format (YYYY-QX) elif "-Q" in time_str: parts = time_str.split("-Q") if len(parts) == 2: year = int(parts[0]) quarter = int(parts[1]) - # Map quarters to their last month quarter_last_month = {1: 3, 2: 6, 3: 9, 4: 12} month = quarter_last_month.get(quarter, 12) - # Get the last day of the quarter's last month last_day = calendar.monthrange(year, month)[1] - # Create date object and format it date_obj = datetime(year, month, last_day) return date_obj.strftime("%Y-%m-%d") - # Handle yearly format (YYYY) elif len(time_str) == 4 and time_str.isdigit(): year = int(time_str) - # Last day of the year is always December 31 date_obj = datetime(year, 12, 31) return date_obj.strftime("%Y-%m-%d") - # Return as-is if it's already in a valid format or unrecognized return time_str except (ValueError, KeyError): - # If parsing fails, return the original string return time_str def parse_agency_from_urn(code_urn: str) -> str | None: - """ - Parse agency ID from hierarchicalCode's code URN. + """Parse agency ID from hierarchicalCode's code URN. Examples -------- @@ -518,10 +403,7 @@ def parse_agency_from_urn(code_urn: str) -> str | None: if not code_urn or "=" not in code_urn or ":" not in code_urn: return None try: - # Extract everything after the "=" and before the last ":" - # Example: "ISORA:CL_RAFIT_LABELS(1.0+.0).CL_TOPIC_1" - after_equals = code_urn.split("=")[-1] - # Split by ":" to get agency (first part) and codelist info (second part) + after_equals = code_urn.rsplit("=", maxsplit=1)[-1] parts = after_equals.split(":") if len(parts) >= 2: return parts[0] @@ -531,8 +413,7 @@ def parse_agency_from_urn(code_urn: str) -> str | None: def parse_indicator_code_from_urn(code_urn: str) -> str | None: - """ - Parse indicator code from hierarchicalCode's code URN. + """Parse indicator code from hierarchicalCode's code URN. Examples -------- @@ -543,12 +424,11 @@ def parse_indicator_code_from_urn(code_urn: str) -> str | None: """ if not code_urn or "." not in code_urn: return None - return code_urn.split(".")[-1] + return code_urn.rsplit(".", maxsplit=1)[-1] def parse_codelist_id_from_urn(code_urn: str) -> str | None: - """ - Parse codelist ID from hierarchicalCode's code URN. + """Parse codelist ID from hierarchicalCode's code URN. Examples -------- @@ -558,9 +438,7 @@ def parse_codelist_id_from_urn(code_urn: str) -> str | None: if not code_urn or ":" not in code_urn or "(" not in code_urn: return None try: - # Extract everything after the last colon - after_colon = code_urn.split(":")[-1] - # Extract everything before the opening parenthesis + after_colon = code_urn.rsplit(":", maxsplit=1)[-1] codelist_id = after_colon.split("(")[0] return codelist_id except Exception: @@ -568,14 +446,7 @@ def parse_codelist_id_from_urn(code_urn: str) -> str | None: def parse_search_query(query: str) -> list[list[str]]: - """ - Parse a search query string into OR-groups of AND-terms. - - Supports: - - Quoted phrases: "exact phrase" - - OR operator: term1 | term2 - - AND operator (implicit or +): term1 term2, term1+term2 - - Stop words are filtered out (of, the, a, an, is, are, in, on, for, with, and, or) + """Parse a search query string into OR-groups of AND-terms. Examples -------- @@ -584,10 +455,9 @@ def parse_search_query(query: str) -> list[list[str]]: >>> parse_search_query('gdp growth') [['gdp', 'growth']] """ - # pylint: disable=import-outside-toplevel import string as string_module - STOP_WORDS = { # pylint: disable=C0103 + STOP_WORDS = { "of", "the", "a", @@ -612,18 +482,15 @@ def parse_search_query(query: str) -> list[list[str]]: in_quote = False current_term: list = [] - # Add a space at the end to ensure the last term is processed for char in or_part + " ": if char == '"': if in_quote: - # End of a quoted term if current_term: term = "".join(current_term).lower() current_and_group.append(term) current_term = [] in_quote = False else: - # Start of a quoted term if current_term: # process term before quote term = ( "".join(current_term) @@ -635,7 +502,6 @@ def parse_search_query(query: str) -> list[list[str]]: current_term = [] in_quote = True elif (char == "+" or char.isspace()) and not in_quote: - # End of a non-quoted term if current_term: term = ( "".join(current_term).lower().strip(string_module.punctuation) @@ -655,10 +521,7 @@ def parse_search_query(query: str) -> list[list[str]]: def build_time_period_params( constraints_response: dict | None, ) -> tuple[list[dict], str | None]: - """ - Build time period parameters from a constraints API response. - - Extracts start date, end date, and series count from annotations. + """Build time period parameters from a constraints API response. Parameters ---------- @@ -700,8 +563,7 @@ def build_time_period_params( def parse_codelist_urn(urn: str) -> str | None: - """ - Parse codelist ID from owningCodelistUrn. + """Parse codelist ID from owningCodelistUrn. Examples -------- @@ -714,7 +576,7 @@ def parse_codelist_urn(urn: str) -> str | None: return None try: - parts = urn.split("=")[-1] + parts = urn.rsplit("=", maxsplit=1)[-1] codelist_with_version = parts.split(":")[-1] codelist_id = codelist_with_version.split("(")[0] return codelist_id @@ -723,11 +585,7 @@ def parse_codelist_urn(urn: str) -> str | None: def extract_all_codelists_from_hierarchy(hierarchy: dict) -> set[str]: - """ - Recursively extract ALL codelist IDs from a hierarchy's hierarchicalCodes. - - This ensures that hierarchies containing multiple codelists (e.g., labels, - table levels, AND indicator codelists) are properly indexed for reverse lookup. + """Recursively extract ALL codelist IDs from a hierarchy's hierarchicalCodes. Parameters ---------- @@ -748,7 +606,6 @@ def extract_all_codelists_from_hierarchy(hierarchy: dict) -> set[str]: codelist_id = parse_codelist_id_from_urn(code_urn) if codelist_id: codelists.add(codelist_id) - # Recurse into nested hierarchicalCodes nested = code.get("hierarchicalCodes", []) if nested: scan_codes(nested) @@ -761,8 +618,7 @@ def extract_all_codelists_from_hierarchy(hierarchy: dict) -> set[str]: def build_hierarchy_to_codelist_map(hierarchies: dict) -> dict[str, str]: - """ - Build mapping from hierarchy ID to codelist ID. + """Build mapping from hierarchy ID to codelist ID. Parameters ---------- @@ -778,7 +634,6 @@ def build_hierarchy_to_codelist_map(hierarchies: dict) -> dict[str, str]: for hierarchy_id, hierarchy in hierarchies.items(): codelist_id = None - # Method 1: Check owningCodelistUrn annotation annotations = hierarchy.get("annotations", []) for annotation in annotations: if annotation.get("id") == "owningCodelistUrn": @@ -787,8 +642,6 @@ def build_hierarchy_to_codelist_map(hierarchies: dict) -> dict[str, str]: if codelist_id: break - # Method 2: Infer from first hierarchicalCode's code URN - # Handles hierarchies like H_QGFS_* that don't have owningCodelistUrn if not codelist_id: hcodes = hierarchy.get("hierarchicalCodes", []) if hcodes and isinstance(hcodes, list) and hcodes: @@ -804,14 +657,7 @@ def build_hierarchy_to_codelist_map(hierarchies: dict) -> dict[str, str]: def build_codelist_to_hierarchies_map(hierarchies: dict) -> dict[str, list[str]]: - """ - Build reverse mapping from codelist ID to list of hierarchy IDs. - - Unlike build_hierarchy_to_codelist_map (which maps to the primary codelist), - this function indexes ALL codelists contained in each hierarchy. This ensures - that hierarchies with nested codelists (e.g., H_FSIC_INDICATOR containing - CL_FSI_LABELS, CL_FSIC_INDICATOR_LABELS, AND CL_FSIC_INDICATOR) are found - when looking up any of their contained codelists. + """Build reverse mapping from codelist ID to list of hierarchy IDs. Parameters ---------- @@ -825,7 +671,6 @@ def build_codelist_to_hierarchies_map(hierarchies: dict) -> dict[str, list[str]] """ mapping: dict[str, list[str]] = {} for hierarchy_id, hierarchy in hierarchies.items(): - # Extract ALL codelists from this hierarchy (not just the first one) all_codelists = extract_all_codelists_from_hierarchy(hierarchy) for codelist_id in all_codelists: if codelist_id not in mapping: @@ -836,13 +681,7 @@ def build_codelist_to_hierarchies_map(hierarchies: dict) -> dict[str, list[str]] def translate_error_message(error_msg: str) -> str: - """Translate IMF dimension codes to user-friendly parameter names in error messages. - - Maps internal IMF dimension names like COUNTRY, FREQUENCY, INDICATOR - to user-facing parameter names like country, frequency, symbol/indicator. - Also translates common IMF code values to user-friendly equivalents. - """ - # Dimension code to parameter name mappings + """Translate IMF dimension codes to user-friendly parameter names in error messages.""" dim_to_param = { "COUNTRY": "country", "REF_AREA": "country", @@ -864,7 +703,6 @@ def translate_error_message(error_msg: str) -> str: "TIME_PERIOD": "time_period", } - # Frequency value mappings frequency_map = { "'A'": "'annual'", "'Q'": "'quarter'", @@ -874,18 +712,14 @@ def translate_error_message(error_msg: str) -> str: '"M"': '"month"', } - # Apply dimension translations for dim_code, param_name in dim_to_param.items(): error_msg = error_msg.replace(f"'{dim_code}'", f"'{param_name}'") error_msg = error_msg.replace(f'"{dim_code}"', f'"{param_name}"') - # Also handle "dimension 'X'" pattern without quotes around dimension error_msg = error_msg.replace( f"dimension '{dim_code}'", f"'{param_name}' parameter" ) - # Handle dimension X: pattern error_msg = error_msg.replace(f"{dim_code} codes", f"{param_name} codes") - # Apply frequency value translations for api_val, user_val in frequency_map.items(): error_msg = error_msg.replace(api_val, user_val) diff --git a/openbb_platform/providers/imf/openbb_imf/utils/metadata.py b/openbb_platform/providers/imf/openbb_imf/utils/metadata.py deleted file mode 100644 index d0e8f886ece..00000000000 --- a/openbb_platform/providers/imf/openbb_imf/utils/metadata.py +++ /dev/null @@ -1,2170 +0,0 @@ -""" "IMF Metadata utilities.""" - -# pylint: disable=C0301,C0302,R0902,R0911,R0912,R0913,R0914,R0915,R0917,R1702,W0718 -# flake8: noqa: PLR0911,PLR0912,PLR0913,PLR0917 - -import threading -import warnings - -from openbb_core.app.model.abstract.error import OpenBBError -from openbb_core.app.model.abstract.warning import OpenBBWarning -from openbb_imf.utils.helpers import ( - build_codelist_to_hierarchies_map, - build_hierarchy_to_codelist_map, - build_time_period_params, - extract_all_codelists_from_hierarchy, - parse_agency_from_urn, - parse_codelist_id_from_urn, - parse_codelist_urn, - parse_indicator_code_from_urn, - parse_search_query, -) - - -class ImfMetadata: - """Singleton class to manage IMF metadata and caching.""" - - _instance = None - _lock = threading.Lock() - _codelist_lock = threading.Lock() - _constraints_lock = threading.Lock() - _initialized = None - - def __new__(cls): - """Initialize the singleton instance.""" - if cls._instance is None: - with cls._lock: - if cls._instance is None: - cls._instance = super().__new__(cls) - cls._instance._initialized = False - return cls._instance - - def __init__(self): - """Initialize the IMF metadata singleton.""" - if self._initialized: - return - - with self._lock: - if self._initialized: - return - - self._codelist_cache: dict = {} - self._codelist_descriptions: dict = {} - self._metadata_cache: dict = {} - self._constraints_cache: dict = {} - self.hierarchies = {} - self._hierarchy_to_codelist_map = {} - self._codelist_to_hierarchies_map = {} - _ = self._load_from_cache() - self._initialized = True - - def _load_from_cache(self) -> bool: - """Load metadata from the local cache file.""" - # pylint: disable=import-outside-toplevel - import gzip - import pickle - from pathlib import Path - - cache_path = Path(__file__).parent.parent / "assets" / "imf_cache.pkl.gz" - cache: dict = {} - - if not cache_path.exists(): - return False - - try: - with gzip.open(cache_path, "rb") as f: - cache = pickle.load(f) # noqa: S301 - - self.dataflows = cache.get("dataflows", {}) - self.datastructures = cache.get("datastructures", {}) - self.conceptschemes = cache.get("conceptschemes", {}) - self.dataflow_groups = cache.get("dataflow_groups", {}) - self._metadata_cache = cache.get("metadata_cache", {}) - self._constraints_cache = cache.get("constraints_cache", {}) - self._codelist_cache = cache.get("codelist_cache", {}) - self._codelist_descriptions = cache.get("codelist_descriptions", {}) - self._dataflow_parameters_cache = cache.get("dataflow_parameters", {}) - self._dataflow_indicators_cache = cache.get("dataflow_indicators", {}) - self.hierarchies = cache.get("hierarchies", {}) - # Build quick lookup maps - self._hierarchy_to_codelist_map = self._build_hierarchy_to_codelist_map() - self._codelist_to_hierarchies_map = ( - self._build_codelist_to_hierarchies_map() - ) - - return True - - except Exception as e: - warnings.warn(f"Error loading cache: {e}", OpenBBWarning) - return False - - def list_dataflows(self) -> list[dict]: - """List available dataflows.""" - dfs: list = [] - keys = sorted(list(self.dataflows.keys())) - - for key in keys: - label = self.dataflows[key].get("name", key) - value = self.dataflows[key].get("id", key) - dfs.append({"label": label.strip(), "value": value.strip()}) - - return dfs - - def search_dataflows(self, query: str) -> list[dict]: - """Search dataflows based on a query string. - - Parameters - ---------- - query : str - The search query string, which can include AND (+) and OR (|) operators, - as well as quoted phrases for exact matches. - Returns - ------- - list[dict] - A list of matching dataflows, grouped by their structureRef ID. - """ - grouped_results: dict = {} - parsed_query = self._parse_query(query) - - if not parsed_query: - raise OpenBBError( - ValueError(f"Query string is empty or invalid -> '{query}'") - ) - - for dataflow_obj in self.dataflows.values(): - dataflow_id = dataflow_obj.get("id", "").lower() - dataflow_name = dataflow_obj.get("name", "").lower() - dataflow_description = dataflow_obj.get("description", "").lower() - dataflow_matches = False - - for or_group in parsed_query: - or_group_matches_all_and_terms = True - - for and_term in or_group: - if not ( - and_term in dataflow_id - or and_term in dataflow_name - or and_term in dataflow_description - ): - or_group_matches_all_and_terms = False - break - - if or_group_matches_all_and_terms: - dataflow_matches = True - break - - if dataflow_matches: - structure_ref_id = dataflow_obj.get("structureRef", {}).get("id") - if structure_ref_id: - if structure_ref_id not in grouped_results: - grouped_results[structure_ref_id] = [] - - grouped_results[structure_ref_id].append( - { - "id": dataflow_obj.get("id"), - "name": dataflow_obj.get("name"), - "description": dataflow_obj.get("description", ""), - } - ) - - final_results = [ - {"group_id": group_id, "dataflows": dataflows} - for group_id, dataflows in grouped_results.items() - ] - - return final_results - - def search_indicators( - self, - query: str, - dataflows: list[str] | str | None = None, - keywords: list[str] | None = None, - ) -> list[dict]: - """Search indicators based on a query string and optional keyword filters. - - Parameters - ---------- - query : str - The search query string. Multiple search phrases can be separated by semicolons (;). - Each phrase can use AND (+) and OR (|) operators, as well as quoted phrases. - Semicolon separation allows commas to be used within search phrases. - Examples: - "inflation rate;+consumer price" - searches for "inflation rate" OR "consumer price" - "gdp+growth;|employment" - searches for "gdp AND growth" OR "employment" - dataflows : list[str] | str | None, optional - A dataflow ID or list of dataflow IDs to search within. If None, all - dataflows will be searched, which can be slow. - keywords : list[str] | None, optional - List of keywords to filter results. Each keyword is a single word that must - appear in the indicator's label or description. Keywords prefixed with "not " - will exclude indicators containing that word (e.g., "not USD" excludes indicators - with "USD" in them). - Returns - ------- - list[dict] - A list of matching indicators with table/hierarchy information included. - """ - target_dataflow_ids: list = [] - if dataflows: - target_dataflow_ids = ( - [dataflows] if isinstance(dataflows, str) else dataflows - ) - else: - if not query and not keywords: - raise OpenBBError( - "A query must be provided when no dataflows and keywords are specified." - ) - target_dataflow_ids = list(self.dataflows.keys()) - - if not target_dataflow_ids: - raise OpenBBError( - "No valid dataflows found to search indicators in." - "This might be due to incorrect dataflow IDs." - ) - - # Build a map of indicators to their tables for enrichment - indicator_to_tables: dict[str, list[dict]] = {} - # Also build searchable text for each indicator from their tables - indicator_table_text: dict[str, str] = {} - - for df_id in set(target_dataflow_ids): - try: - hierarchies = self.get_dataflow_hierarchies(df_id) - for hierarchy in hierarchies: - try: - structure = self.get_dataflow_table_structure( - df_id, hierarchy["id"] - ) - # Build searchable table text - table_search_text = ( - hierarchy.get("name", "").lower() - + " " - + hierarchy.get("description", "").lower() - ) - - for ind in structure.get("indicators", []): - if ind.get("is_group"): - continue - indicator_code = ind.get("indicator_code") or ind.get( - "code" - ) - if indicator_code: - key = f"{df_id}_{indicator_code}" - if key not in indicator_to_tables: - indicator_to_tables[key] = [] - indicator_table_text[key] = "" - - table_entry = { - "table_id": hierarchy["id"], - "table_name": hierarchy["name"], - } - if table_entry not in indicator_to_tables[key]: - indicator_to_tables[key].append(table_entry) - indicator_table_text[key] += " " + table_search_text - except Exception: # noqa: S110 - pass - except Exception: # noqa: S110 - pass - - # Get indicators for target dataflows - all_indicators: list = [] - for df_id in set(target_dataflow_ids): - try: - indicators = self.get_indicators_in(df_id) - # Enrich each indicator with table information - for ind in indicators: - key = f"{df_id}_{ind['indicator']}" - ind["tables"] = indicator_to_tables.get(key, []) - # Build member_of as list of dataflow_id::table_id strings - ind["member_of"] = [ - f"{df_id}::{t['table_id']}" for t in ind["tables"] - ] - # Add table text for searching (will be removed before return) - ind["_table_search_text"] = indicator_table_text.get(key, "") - all_indicators.extend(indicators) - except (KeyError, ValueError, OpenBBError) as e: - warnings.warn( - f"Could not retrieve indicators for dataflow '{df_id}': {e}", - OpenBBWarning, - ) - continue - - # Filter indicators by query - # Split query on semicolon to allow commas within search phrases - if not query: - search_results = all_indicators - else: - # Split on semicolon to get separate phrases - phrases = [phrase.strip() for phrase in query.split(";") if phrase.strip()] - - if not phrases: - search_results = all_indicators - else: - filtered_by_query: list = [] - for indicator in all_indicators: - text_to_search = ( - indicator.get("label", "").lower() - + " " - + indicator.get("description", "").lower() - + " " - + indicator.get("dataflow_name", "").lower() - + " " - + indicator.get("dataflow_id", "").lower() - + " " - + indicator.get("indicator", "").lower() - + " " - + indicator.get("_table_search_text", "") - ) - - match = False - for phrase in phrases: - # This handles AND (+) and OR (|) operators within the phrase - parsed_phrase = self._parse_query(phrase) - - if not parsed_phrase: - # If parsing fails, treat as simple substring search - if phrase.lower() in text_to_search: - match = True - break - else: - phrase_match = False - - for or_group in parsed_phrase: - if all(term in text_to_search for term in or_group): - phrase_match = True - break - - if phrase_match: - match = True - break - - if match: - filtered_by_query.append(indicator) - search_results = filtered_by_query - - # Apply keyword filters - if not keywords: - # Clean up internal search field before returning - for indicator in search_results: - indicator.pop("_table_search_text", None) - return search_results - - filtered_results: list = [] - for indicator in search_results: - indicator_text = ( - indicator.get("indicator", "") - + " " - + indicator.get("label", "") - + " " - + indicator.get("description", "") - + " " - + indicator.get("_table_search_text", "") - ).lower() - - # Check each keyword - include = True - for keyword in keywords: - kw = keyword.strip() - if kw.lower().startswith("not "): - # Exclusion keyword - if the word is present, exclude this indicator - exclude_word = kw[4:].lower() # Remove "not " prefix - if exclude_word and exclude_word in indicator_text: - include = False - break - elif kw.lower() not in indicator_text: - include = False - break - - if include: - filtered_results.append(indicator) - - # Clean up internal search field before returning - for indicator in filtered_results: - indicator.pop("_table_search_text", None) - - return filtered_results - - def _parse_query(self, query: str) -> list[list[str]]: - """Parse a search query string into OR-groups of AND-terms.""" - return parse_search_query(query) - - def get_dataflow_parameters(self, dataflow_id: str) -> dict[str, list[dict]]: - """Get available parameters for a given dataflow.""" - if dataflow_id not in self.dataflows: - raise ValueError(f"Dataflow '{dataflow_id}' not found.") - - if ( - hasattr(self, "_dataflow_parameters_cache") - and dataflow_id in self._dataflow_parameters_cache - ): - return self._dataflow_parameters_cache[dataflow_id] - - df_obj = self.dataflows[dataflow_id] - agency_id = df_obj.get("agencyID") - dsd_id = df_obj.get("structureRef", {}).get("id") - dsd = self.datastructures.get(dsd_id, {}) - if not dsd: - return {} - - dimensions_metadata = { - dim["id"]: dim for dim in dsd.get("dimensions", []) if dim.get("id") - } - - constraints_response = self.get_available_constraints( - dataflow_id=dataflow_id, - key="all", - component_id="all", - mode="available", - references="all", - ) - key_values = constraints_response.get("key_values", []) - constrained_values_map = {kv["id"]: kv.get("values", []) for kv in key_values} - - parameters: dict[str, list[dict]] = {} - dimension_codes_cache: dict[str, dict] = {} - - def _get_codes(dim_id: str) -> dict: - if dim_id in dimension_codes_cache: - return dimension_codes_cache[dim_id] - - dim_meta = dimensions_metadata.get(dim_id, {}) - codelist_id = self._resolve_codelist_id( - dataflow_id, dsd_id, dim_id, dim_meta - ) - if codelist_id: - codes = ( - self._get_codelist_map( - codelist_id, agency_id, dataflow_id, include_descriptions=False - ) - or {} - ) - dimension_codes_cache[dim_id] = codes - return codes - return {} - - for dim_id in dimensions_metadata: - if dim_id == "TIME_PERIOD": - continue - - # Get the codelist ID for this dimension upfront - dim_meta = dimensions_metadata.get(dim_id, {}) - codelist_id = self._resolve_codelist_id( - dataflow_id, dsd_id, dim_id, dim_meta - ) - - if not codelist_id: - continue - - # Get the full codelist from cache - full_codes = self._codelist_cache.get(codelist_id, {}) - if not full_codes: - # Try to fetch it if not in cache - codes_map = _get_codes(dim_id) - if not codes_map: - continue - full_codes = codes_map - - value_ids_to_use = ( - constrained_values_map[dim_id] - if dim_id in constrained_values_map - else list(full_codes.keys()) - ) - - options: list = [] - for val_id in value_ids_to_use: - # Look up the label from the full codes - label = full_codes.get(val_id, val_id) - - # If it's a dict (from _get_codes with descriptions), extract the name - if isinstance(label, dict): - label = label.get("name", val_id) - - # Ensure we have a string label - if not label or label == val_id: - # If still no proper label, use the code itself - label = val_id - - options.append({"label": label, "value": val_id.strip()}) - - if options: - parameters[dim_id] = options - - time_period_options, _ = self._build_time_period_parameters( - constraints_response - ) - if time_period_options: - parameters["TIME_PERIOD"] = time_period_options - - if hasattr(self, "_dataflow_parameters_cache"): - self._dataflow_parameters_cache[dataflow_id] = parameters - - return parameters - - def _parse_agency_from_urn(self, code_urn: str) -> str | None: - """Parse agency ID from hierarchicalCode's code URN.""" - return parse_agency_from_urn(code_urn) - - def _fetch_single_codelist(self, agency_id: str, codelist_id: str) -> bool: - """ - Fetch a single codelist from the API and cache it. - - Parameters - ---------- - agency_id : str - The agency ID (e.g., "ISORA", "IMF.STA") - codelist_id : str - The codelist ID (e.g., "CL_RAFIT_LABELS") - - Returns - ------- - bool - True if successfully fetched and cached, False otherwise. - """ - # pylint: disable=import-outside-toplevel - import json - - from openbb_core.provider.utils.helpers import make_request - from requests.exceptions import RequestException - - if codelist_id in self._codelist_cache and self._codelist_cache.get( - codelist_id - ): - return True - - url = f"https://api.imf.org/external/sdmx/3.0/structure/codelist/{agency_id}/{codelist_id}?detail=full&references=none" - headers = {"Accept": "application/json"} - - try: - response = make_request(url, headers=headers, timeout=5) - if response.status_code != 200: - # Mark as failed to avoid repeated attempts - # self._codelist_cache[codelist_id] = {} - return False - json_response: dict = response.json() - except (json.JSONDecodeError, RequestException): - # Mark as failed to avoid repeated attempts - # self._codelist_cache[codelist_id] = {} - return False - - codelists_in_response = json_response.get("data", {}).get("codelists", []) - - if not codelists_in_response: - return False - - with self._codelist_lock: - for codelist_obj in codelists_in_response: - cl_id = codelist_obj.get("id") - if not cl_id: - continue - - current_codelist_map = {} - current_descriptions_map = {} - for code in codelist_obj.get("codes", []): - code_id = code.get("id") - code_name_obj = ( - code.get("names", {}).get("en") or code.get("name") or code_id - ) - code_description = ( - code.get("descriptions", {}).get("en", "") - or code.get("description", "") - or "" - ) - if not code_description and code_name_obj: - code_description = code_name_obj - - if code_id: - current_codelist_map[code_id] = code_name_obj - current_descriptions_map[code_id] = code_description - - self._codelist_cache[cl_id] = current_codelist_map - self._codelist_descriptions[cl_id] = current_descriptions_map - - return codelist_id in self._codelist_cache - - def _bulk_fetch_and_cache_codelists(self, agency_id: str, dataflow_id: str): - """Fetch all codelists for a given agency and dataflow and caches them.""" - # pylint: disable=import-outside-toplevel - import json - - from openbb_core.provider.utils.helpers import make_request - from requests.exceptions import RequestException - - url = f"https://api.imf.org/external/sdmx/3.0/structure/codelist/{agency_id},{dataflow_id}/all?detail=full&references=none" - headers = {"Accept": "application/json"} - - try: - response = make_request(url, headers=headers) - json_response: dict = response.json() - except (json.JSONDecodeError, RequestException) as e: - warnings.warn( - f"Could not bulk fetch codelists for {agency_id}/{dataflow_id}: {e} -> {url}", - OpenBBWarning, - ) - return - - codelists_in_response = json_response.get("data", {}).get("codelists", []) - - with self._codelist_lock: - for codelist_obj in codelists_in_response: - codelist_id = codelist_obj.get("id") - if not codelist_id: - continue - - current_codelist_map = {} - current_descriptions_map = {} - for code in codelist_obj.get("codes", []): - code_id = code.get("id") - code_name_obj = ( - code.get("names", {}).get("en") or code.get("name") or code_id - ) - code_description = ( - code.get("descriptions", {}).get("en", "") - or code.get("description", "") - or "" - ) - if not code_description and code_name_obj: - code_description = code_name_obj - - if code_id: - current_codelist_map[code_id] = code_name_obj - current_descriptions_map[code_id] = code_description - - self._codelist_cache[codelist_id] = current_codelist_map - self._codelist_descriptions[codelist_id] = current_descriptions_map - - def _get_codelist_map( - self, - codelist_id: str, - agency_id: str, - dataflow_id: str, - include_descriptions: bool = False, - ) -> dict: - """Download and cache the codelist map for a given codelist ID.""" - with self._codelist_lock: - if codelist_id in self._codelist_cache: - if include_descriptions and codelist_id in self._codelist_descriptions: - result = {} - for code_id, code_name in self._codelist_cache[codelist_id].items(): - result[code_id] = { - "name": code_name, - "description": self._codelist_descriptions[codelist_id].get( - code_id, "" - ), - } - return result - return self._codelist_cache[codelist_id].copy() - - # If not in cache, try to bulk fetch and cache - self._bulk_fetch_and_cache_codelists(agency_id, dataflow_id) - - # Try again from cache - with self._codelist_lock: - if codelist_id in self._codelist_cache: - if include_descriptions and codelist_id in self._codelist_descriptions: - result = {} - for code_id, code_name in self._codelist_cache[codelist_id].items(): - result[code_id] = { - "name": code_name, - "description": self._codelist_descriptions[codelist_id].get( - code_id, "" - ), - } - return result - return self._codelist_cache[codelist_id].copy() - - warnings.warn(f"Codelist '{codelist_id}' not found.", OpenBBWarning) - return {} - - def get_available_constraints( - self, - dataflow_id: str, - key: str, - component_id: str | None = None, - mode: str | None = None, - references: str | None = None, - **kwargs, - ) -> dict: - """Fetch available constraints for a given dataflow and parameters.""" - # pylint: disable=import-outside-toplevel - import json - - from openbb_core.provider.utils.helpers import make_request - from requests.exceptions import RequestException - - if dataflow_id not in self.dataflows: - raise ValueError(f"Dataflow '{dataflow_id}' not found.") - - kwargs_sorted = sorted(kwargs.items()) - kwargs_tuple = tuple(kwargs_sorted) - - cache_key = ( - f"{dataflow_id}:{key}:{component_id}:{mode}:{references}:{kwargs_tuple}" - ) - - with self._constraints_lock: - if cached_constraints := self._constraints_cache.get(cache_key): - return cached_constraints - - df = self.dataflows[dataflow_id] - agency_id = df.get("agencyID") - - if not agency_id: - raise ValueError(f"Agency ID not found for dataflow '{dataflow_id}'.") - - # Note: URL length is now primarily managed by table_builder.py which limits - # constraint keys to depth 0-1 codes when there are many indicators. - # This fallback is kept as a safety net for edge cases. - processed_key = key - - base_url = ( - f"https://api.imf.org/external/sdmx/3.0/availability/dataflow/" - f"{agency_id}/{dataflow_id}/%2B/{processed_key}/{component_id or 'all'}" - ) - query_params = { - "mode": mode, - "references": references, - } - c_params = {f"c[{k}]": v for k, v in kwargs.items() if v} - query_params.update(c_params) - - query_params = {k: v for k, v in query_params.items() if v is not None} - url = ( - base_url + "?" + "&".join(f"{k}={v}" for k, v in query_params.items()) - if query_params - else base_url - ) - json_response: dict = {} - try: - headers = { - "Accept": "application/json", - "User-Agent": "Open Data Platform - IMF Metadata Utility", - } - response = make_request(url, headers=headers) - response.raise_for_status() - json_response = response.json() - except json.JSONDecodeError as e: - raise OpenBBError( - f"Unexpected response format when fetching constraints {dataflow_id}: {e}" - + f" -> {url}" - ) from None - except RequestException as e: - raise OpenBBError( - f"An error occurred while fetching constraints {dataflow_id}: {e} -> {url}" - ) from None - - extracted_values: dict = {} - json_data = json_response.get("data", {}) - data_constraints = json_data.get("dataConstraints", []) - - for constraint in data_constraints: - for region in constraint.get("cubeRegions", []): - for kv in region.get("keyValues", []): - dim_id = kv.get("id") - if dim_id: - if dim_id not in extracted_values: - extracted_values[dim_id] = [] - for val in kv.get("values", []): - if isinstance(val, dict): - extracted_values[dim_id].append(val.get("value")) - else: - extracted_values[dim_id].append(val) - for comp in region.get("components", []): - dim_id = comp.get("id") - if dim_id: - if dim_id not in extracted_values: - extracted_values[dim_id] = [] - for val in comp.get("values", []): - if isinstance(val, dict): - extracted_values[dim_id].append(val.get("value")) - else: - extracted_values[dim_id].append(val) - - for dim_id, values in list(extracted_values.items()): - # Remove falsy values, deduplicate. - unique_values = {v for v in values if v} - extracted_values[dim_id] = list(unique_values) - - key_values = [{"id": k, "values": v} for k, v in extracted_values.items()] - - result = {"key_values": key_values, "full_response": json_response} - - with self._constraints_lock: - self._constraints_cache[cache_key] = result - - return result - - def get_indicators_in(self, dataflow_id: str) -> list: - """Get indicators available in a given dataflow.""" - if dataflow_id not in self.dataflows: - raise ValueError(f"Dataflow '{dataflow_id}' not found.") - - dataflow_obj = self.dataflows[dataflow_id] - dataflow_name = dataflow_obj.get("name", "").replace("\\xa0", "").strip() - structure_ref = dataflow_obj.get("structureRef", {}) - structure_id = structure_ref.get("id", "") - agency_id = dataflow_obj.get("agencyID", structure_ref.get("agencyID", "IMF")) - dsd_id = structure_ref.get("id", "") - - if not dsd_id or dsd_id not in self.datastructures: - raise ValueError(f"Data structure not found for dataflow '{dataflow_id}'.") - - dsd = self.datastructures[dsd_id] - all_dims = dsd.get("dimensions", []) - - # Get valid codes from parameters API - try: - params = self.get_dataflow_parameters(dataflow_id) - except Exception: # noqa: BLE001 - params = {} - - full_indicator_list = [] - - indicator_id_candidates = [ - "INDICATOR", - "PRODUCTION_INDEX", - "COICOP_1999", - "INDEX_TYPE", - "ACTIVITY", - "PRODUCT", - "SERIES", - "ITEM", - "BOP_ACCOUNTING_ENTRY", - "ACCOUNTING_ENTRY", - ] - - for dim in all_dims: - dim_id = dim.get("id") - - is_indicator_candidate = dim_id in indicator_id_candidates - if not is_indicator_candidate and any( - keyword in dim_id - for keyword in ["INDICATOR", "ACCOUNTING_ENTRY", "ENTRY"] - ): - is_indicator_candidate = True - - if not is_indicator_candidate: - continue - - # Get valid codes with labels from parameters API - dim_params = params.get(dim_id, []) - if not dim_params: - continue - - # Resolve codelist ID for this dimension to look up descriptions - codelist_id = self._resolve_codelist_id(dataflow_id, dsd_id, dim_id, dim) - descriptions_map: dict = {} - if codelist_id: - # Try to get cached descriptions - descriptions_map = self._codelist_descriptions.get(codelist_id, {}) - # If not cached, try to load the codelist to populate descriptions - if not descriptions_map and codelist_id not in self._codelist_cache: - try: - self._get_codelist_map(codelist_id, agency_id, dataflow_id) - descriptions_map = self._codelist_descriptions.get( - codelist_id, {} - ) - except Exception: # noqa - pass # Codelist not available, continue without descriptions - - # Parameters API already provides labels - use them directly - for param in dim_params: - code_id = param["value"] - code_label = param.get("label", code_id) - series_id = f"{dataflow_id}::{code_id}" - - # Look up description from codelist, fall back to label if not found - description = descriptions_map.get(code_id, "") - - indicator_entry = { - "dataflow_id": dataflow_id, - "dataflow_name": dataflow_name, - "structure_id": structure_id, - "agency_id": agency_id, - "dimension_id": dim_id, - "indicator": code_id, - "label": code_label, - "description": description, - "series_id": series_id, - } - full_indicator_list.append(indicator_entry) - - # Check for activity-related codelists - dim_ids = {d.get("id") for d in all_dims if d.get("id")} - if "ACTIVITY" in dim_ids: - activity_codelist_id = f"CL_{dataflow_id}_ACTIVITY" - if activity_codelist_id in self._codelist_cache: - codes_map = self._get_codelist_map( - activity_codelist_id, agency_id, dataflow_id - ) - descriptions_map = self._codelist_descriptions.get( - activity_codelist_id, {} - ) - for code_id, code_name in codes_map.items(): - series_id = f"{dataflow_id}::{code_id}" - entry = { - "dataflow_id": dataflow_id, - "dataflow_name": dataflow_name, - "structure_id": structure_id, - "agency_id": agency_id, - "dimension_id": "ACTIVITY", - "indicator": code_id, - "label": code_name, - "description": descriptions_map.get(code_id, ""), - "series_id": series_id, - } - full_indicator_list.append(entry) - - if not full_indicator_list: - raise KeyError( - f"Could not find an indicator-like dimension for dataflow '{dataflow_id}'." - ) - - return full_indicator_list - - def _resolve_codelist_id( - self, dataflow_id: str, dsd_id: str | None, dim_id: str, dim_meta: dict - ) -> str | None: - if not dim_id: - return None - - # Check for explicit codelist reference first - representation = dim_meta.get("representation", {}) - codelist_ref = representation.get("codelist") - if isinstance(codelist_ref, dict): - return codelist_ref.get("id") - if isinstance(codelist_ref, str): - return codelist_ref - - candidates: list[str] = [] - seen = set() - - def add_candidate(candidate: str): - if candidate and candidate not in seen: - candidates.append(candidate) - seen.add(candidate) - - concept_ref = dim_meta.get("conceptRef") or {} - concept_id = concept_ref.get("id") - - # For country-like dimensions (JURISDICTION, REF_AREA, COUNTRY, etc.), - # prioritize dataflow-specific ISO country codelists first. - country_dims = {"JURISDICTION", "REF_AREA", "COUNTRY", "AREA"} - is_country_dim = dim_id.upper() in country_dims or ( - concept_id and concept_id.upper() in {"COUNTRY", "REF_AREA"} - ) - - if is_country_dim and dataflow_id: - # Try dataflow-specific ISO country codelist first - base_dataflow = dataflow_id.split("_")[0] - add_candidate(f"CL_{base_dataflow}_ISO_COUNTRY") - add_candidate(f"CL_{dataflow_id}_COUNTRY") - add_candidate(f"CL_{base_dataflow}_COUNTRY") - - # Priority 1: Dataflow-specific patterns (most specific first) - if dataflow_id: - add_candidate(f"CL_{dataflow_id}_{dim_id}") - add_candidate(f"CL_{dataflow_id}_{dim_id}_PUB") # _PUB suffix variant - if "COUNTRY" in dim_id: - add_candidate(f"CL_{dataflow_id}_COUNTRY") - add_candidate(f"CL_{dataflow_id}_COUNTRY_PUB") - if "_" in dataflow_id: - base_dataflow = dataflow_id.split("_")[0] - add_candidate(f"CL_{base_dataflow}_{dim_id}") - add_candidate(f"CL_{base_dataflow}_{dim_id}_PUB") - - # Priority 2: DSD patterns - if dsd_id: - dsd_base = dsd_id.replace("DSD_", "") - add_candidate(f"CL_{dsd_base}_{dim_id}") - - # Priority 3: Parent scheme patterns - parent_scheme_id = concept_ref.get("maintainableParentID") - if parent_scheme_id: - scheme_base = parent_scheme_id.replace("CS_", "CL_", 1) - add_candidate(f"{scheme_base}_{dim_id}") - add_candidate(scheme_base) - - # Priority 4: Direct/generic matches (fallback) - add_candidate(f"CL_{dim_id}") - if concept_id: - add_candidate(f"CL_{concept_id}") - - # Check cache for exact matches first - for candidate in candidates: - if candidate in self._codelist_cache: - return candidate - - # Case-insensitive fallback for dataflow-specific codelists - # IMF has inconsistent casing (e.g., CL_LS_TYPE_OF_TRANSFORMAtION) - cache_keys_upper = {k.upper(): k for k in self._codelist_cache} - for candidate in candidates: - actual_key = cache_keys_upper.get(candidate.upper()) - if actual_key: - return actual_key - - # Consolidated mapping for common variations - # This combines the old variations dict and generic_dimensions list - common_mappings = { - # Geographic dimensions - ( - "REF_AREA", - "AREA", - "COUNTRY", - "JURISDICTION", - "GEOGRAPHICAL_AREA", - ): "AREA", - ("COUNTERPART_COUNTRY",): "COUNTRY", - # Statistical dimensions - ("COMPOSITE_BREAKDOWN", "COMP_BREAKDOWN"): "COMPOSITE_BREAKDOWN", - ("DISABILITY_STATUS", "DISABILITY"): "DISABILITY", - ("INCOME_WEALTH_QUANTILE", "QUANTILE"): "QUANTILE", - ("TYPE_OF_TRANSFORMATION", "TRANSFORMATION"): "TRANSFORMATION", - ("WGT_TYPE", "WEIGHT_TYPE", "CTOT_WEIGHT_TYPE"): "WEIGHT_TYPE", - ("INDICATOR", "INDICATORS"): "INDICATOR", - ("UNIT", "UNIT_MEASURE", "UNIT_MULT"): "UNIT", - } - - # Check if dimension matches any common pattern - for patterns, base_name in common_mappings.items(): - if any(pattern in dim_id.upper() for pattern in patterns): - # Try generic first - generic_cl = f"CL_{base_name}" - if generic_cl in self._codelist_cache: - return generic_cl - # Then try dataflow-specific - specific_cl = f"CL_{dataflow_id}_{base_name}" - if specific_cl in self._codelist_cache: - return specific_cl - - # Special handling for counterpart dimensions - if "COUNTERPART_" in dim_id: - base_dim_id = dim_id.replace("COUNTERPART_", "") - if dsd_id and dsd_id in self.datastructures: - dsd = self.datastructures[dsd_id] - for d in dsd.get("dimensions", []): - if d.get("id") == base_dim_id: - return self._resolve_codelist_id( - dataflow_id, dsd_id, base_dim_id, d - ) - - # Activity/Product fallbacks - if "ACTIVITY" in dim_id.upper() or "PRODUCTION_INDEX" in dim_id.upper(): - activity_candidates = [ - "CL_PPI_ACTIVITY", - "CL_MCDREO_ACTIVITY", - "CL_ACTIVITY_ISIC4", - "CL_NEA_ACTIVITY", - "CL_ACTIVITY", - ] - for candidate in activity_candidates: - if candidate in self._codelist_cache: - return candidate - - if "COICOP" in dim_id.upper(): - coicop_candidates = ["CL_COICOP_1999", "CL_COICOP_2018"] - for candidate in coicop_candidates: - if candidate in self._codelist_cache: - return candidate - - # Fuzzy matching as last resort - dim_upper = dim_id.upper() - - # Try exact substring match - for cache_key in self._codelist_cache: - if cache_key.startswith("CL_MASTER"): - continue - if dim_upper in cache_key.upper(): - return cache_key - - # Try matching significant parts - dim_parts = [p for p in dim_id.split("_") if len(p) > 2] - if len(dim_parts) > 1: - for cache_key in self._codelist_cache: - if cache_key.startswith("CL_MASTER"): - continue - cache_key_upper = cache_key.upper() - if all(part.upper() in cache_key_upper for part in dim_parts): - return cache_key - - # Return first candidate or None - return candidates[0] if candidates else None - - def _build_time_period_parameters( - self, constraints_response: dict | None - ) -> tuple[list[dict], str | None]: - """Build time period parameters from a constraints API response.""" - return build_time_period_params(constraints_response) - - def _parse_codelist_urn(self, urn: str) -> str | None: - """Parse codelist ID from owningCodelistUrn.""" - return parse_codelist_urn(urn) - - def _parse_indicator_code_from_urn(self, code_urn: str) -> str | None: - """Parse indicator code from hierarchicalCode's code URN.""" - return parse_indicator_code_from_urn(code_urn) - - def _parse_codelist_id_from_urn(self, code_urn: str) -> str | None: - """Parse codelist ID from hierarchicalCode's code URN.""" - return parse_codelist_id_from_urn(code_urn) - - def _get_dimension_for_codelist( - self, dataflow_id: str, codelist_id: str - ) -> str | None: - """ - Find which dimension uses the given codelist ID. - - Parameters - ---------- - dataflow_id : str - The dataflow ID - codelist_id : str - The codelist ID (e.g., "CL_BOP_INDICATOR") - - Returns - ------- - str | None - The dimension ID that uses this codelist, or None if not found. - """ - if dataflow_id not in self.dataflows: - return None - - df_obj = self.dataflows[dataflow_id] - dsd_id = df_obj.get("structureRef", {}).get("id") - if not dsd_id or dsd_id not in self.datastructures: - return None - - dsd = self.datastructures[dsd_id] - dimensions = dsd.get("dimensions", []) - - # First pass: exact match - for dim in dimensions: - dim_id = dim.get("id") - if not dim_id: - continue - - # Resolve the codelist for this dimension - resolved_codelist = self._resolve_codelist_id( - dataflow_id, dsd_id, dim_id, dim - ) - if resolved_codelist == codelist_id: - return dim_id - - # Second pass: fuzzy match by dimension name appearing anywhere in codelist ID - # e.g., CL_IRFCL_DEFAULT_INDICATOR_PUB2 should match INDICATOR dimension - # Split codelist into segments and check if any segment matches a dimension - codelist_segments = set(seg.upper() for seg in codelist_id.split("_")) - for dim in dimensions: - dim_id = dim.get("id") - if dim_id and dim_id.upper() in codelist_segments: - return dim_id - - # Third pass: check if dimension name is a substring of codelist ID - # Handles cases like CL_IRFCL_DEFAULT_INDICATOR_PUB2 -> INDICATOR - codelist_upper = codelist_id.upper() - for dim in dimensions: - dim_id = dim.get("id") - if dim_id and dim_id.upper() in codelist_upper: - return dim_id - - return None - - def _extract_all_codelists_from_hierarchy(self, hierarchy: dict) -> set[str]: - """Recursively extract ALL codelist IDs from a hierarchy's hierarchicalCodes.""" - return extract_all_codelists_from_hierarchy(hierarchy) - - def _build_hierarchy_to_codelist_map(self) -> dict[str, str]: - """Build mapping from hierarchy ID to codelist ID.""" - return build_hierarchy_to_codelist_map(self.hierarchies) - - def _build_codelist_to_hierarchies_map(self) -> dict[str, list[str]]: - """Build reverse mapping from codelist ID to list of hierarchy IDs.""" - return build_codelist_to_hierarchies_map(self.hierarchies) - - def _validate_hierarchy_queryable(self, dataflow_id: str, codes: list) -> bool: - """ - Check if a hierarchy's codes can actually be queried (map to dimensions, not just attributes). - - Parameters - ---------- - dataflow_id : str - The dataflow ID - codes : list - List of hierarchical code entries - - Returns - ------- - bool - True if at least some codes map to queryable dimensions - """ - if not codes: - return False - - # Sample a few codes to check if they map to dimensions - sample_size = min(10, len(codes)) - valid_count = 0 - - for code_entry in codes[:sample_size]: - code_urn = code_entry.get("code", "") - if not code_urn: - continue - - codelist_id = self._parse_codelist_id_from_urn(code_urn) - if not codelist_id: - continue - - # Check if this codelist maps to a dimension (not an attribute) - dimension_id = self._get_dimension_for_codelist(dataflow_id, codelist_id) - if dimension_id: - valid_count += 1 - - # Consider hierarchy queryable if at least 50% of sampled codes map to dimensions - return valid_count >= (sample_size * 0.5) - - def get_dataflow_hierarchies(self, dataflow_id: str) -> list[dict]: - """ - Get all hierarchies (presentation tables) available for a dataflow. - - This supports two types of presentations: - 1. Hierarchies from hierarchy.json (45 codelists) - 2. Presentations embedded in dataflow metadata (31 dataflows) - - Parameters - ---------- - dataflow_id : str - The dataflow ID (e.g., "FSIBSIS", "BOP_AGG", "IRFCL") - - Returns - ------- - list[dict] - List of hierarchy/presentation metadata dicts. - """ - if dataflow_id not in self.dataflows: - raise ValueError(f"Dataflow '{dataflow_id}' not found.") - - dataflow_obj = self.dataflows[dataflow_id] - result = [] - - # First, check for hierarchies from hierarchy.json (these have actual structure) - dsd_id = dataflow_obj.get("structureRef", {}).get("id") - - if dsd_id and dsd_id in self.datastructures: - dsd = self.datastructures[dsd_id] - dimensions = dsd.get("dimensions", []) - - indicator_codelist_id = None - # Priority-ordered list of dimension names to check for indicators - indicator_candidates = [ - "INDICATOR", - "COICOP_1999", - "PRODUCTION_INDEX", - "ACTIVITY", - "PRODUCT", - "SERIES", - "ITEM", - "ACCOUNTING_ENTRY", - "SECTOR", - ] - - dim_lookup = {d.get("id", ""): d for d in dimensions} - # Check candidates in priority order (not dimension order) - for candidate in indicator_candidates: - if candidate in dim_lookup: - dim = dim_lookup[candidate] - indicator_codelist_id = self._resolve_codelist_id( - dataflow_id, dsd_id, candidate, dim - ) - if indicator_codelist_id: - # Check if this codelist actually has hierarchies - if self._codelist_to_hierarchies_map.get(indicator_codelist_id): - break - # If no hierarchies, continue to next candidate - indicator_codelist_id = None - - # Fallback: check for any dimension with "INDICATOR" in its name - if not indicator_codelist_id: - for dim in dimensions: - dim_id = dim.get("id", "") - if "INDICATOR" in dim_id and dim_id not in indicator_candidates: - indicator_codelist_id = self._resolve_codelist_id( - dataflow_id, dsd_id, dim_id, dim - ) - if indicator_codelist_id: - break - - if indicator_codelist_id: - hierarchy_ids = self._codelist_to_hierarchies_map.get( - indicator_codelist_id, [] - ) - # Get available indicator values for this dataflow to filter hierarchies - available_indicator_values: set[str] = set() - - try: - params = self.get_dataflow_parameters(dataflow_id) - - if "INDICATOR" in params: - available_indicator_values.update( - opt.get("value", "") for opt in params["INDICATOR"] - ) - except Exception: # noqa: S110 - pass # If we can't get parameters, include all hierarchies - - for hier_id in hierarchy_ids: - hier_obj = self.hierarchies.get(hier_id) - hier_code_values: set[str] = set() - if hier_obj: - # If we have available values, check if this hierarchy is compatible - if available_indicator_values: - hier_codes_raw = hier_obj.get("hierarchicalCodes", []) - - def _extract_codes(codes_list): - for c in codes_list: - # Extract actual code from URN like: - # urn:sdmx:...CL_BOP_INDICATOR(10.0+.0).NIIP_AFR - code_urn = c.get("code", "") - # Only extract codes from INDICATOR codelists - if ( - code_urn - and "INDICATOR" in code_urn - and "." in code_urn - ): - actual_code = code_urn.rsplit(".", 1)[-1] - if actual_code: - hier_code_values.add( # pylint: disable=W0640 - actual_code - ) - # Recurse into nested codes - nested = c.get("hierarchicalCodes", []) - if nested: - _extract_codes(nested) # pylint: disable=W0640 - - _extract_codes(hier_codes_raw) - - # Check if ANY of the hierarchy's codes exist in the dataflow - # Use prefix matching since dataflow codes may have unit suffixes - # e.g., hierarchy code "FSI687_TREGK" should match "FSI687_TREGK_USD" - if hier_code_values: - has_match = False - # First try exact match (fast path) - if hier_code_values & available_indicator_values: - has_match = True - else: - # Prefix matching: check if any available indicator - # starts with any hierarchy code - for hier_code in hier_code_values: - for avail_code in available_indicator_values: - if avail_code.startswith(hier_code): - has_match = True - break - if has_match: - break - if not has_match: - # No overlap - skip this hierarchy for this dataflow - continue - - # Check if hierarchy has multiple top-level codes - top_level_codes = hier_obj.get("hierarchicalCodes", []) - - if len(top_level_codes) > 1 and dataflow_id == "IRFCL": - # Split into separate tables - one per top-level code - for idx, top_code in enumerate(top_level_codes): - top_code_id = top_code.get("id", "") - top_code_urn = top_code.get("code", "") - # Extract actual code from URN - actual_code = ( - top_code_urn.rsplit(".", 1)[-1] - if "." in top_code_urn - else top_code_id - ) - # Get label from the codelist specified in the URN - urn_codelist_id = self._parse_codelist_id_from_urn( - top_code_urn - ) - table_label = self._codelist_cache.get( - urn_codelist_id or indicator_codelist_id, {} - ).get(actual_code, actual_code) - # Check for SECTION codelist for better labels - section_codelist_id = f"CL_{dataflow_id}_SECTION" - section_codes = self._codelist_cache.get( - section_codelist_id, {} - ) - # Match section by prefix in actual_code - for ( - section_code, - section_label, - ) in section_codes.items(): - if actual_code.startswith(section_code): - table_label = section_label - break - - result.append( - { - "id": f"{hier_id}:{top_code_id}", - "name": table_label, - "description": "", - "codelist_id": indicator_codelist_id, - "agency_id": hier_obj.get("agencyID", ""), - "version": hier_obj.get("version", ""), - "type": "hierarchy", - "table_index": idx, - "top_level_code_id": top_code_id, - "indicator_code": actual_code, - } - ) - else: - # Single top-level code - return as single table - name = hier_obj.get("name", "") - descriptions = hier_obj.get("descriptions", {}) - desc = descriptions.get("en", "") if descriptions else "" - result.append( - { - "id": hier_id, - "name": name, - "description": desc, - "codelist_id": indicator_codelist_id, - "agency_id": hier_obj.get("agencyID", ""), - "version": hier_obj.get("version", ""), - "type": "hierarchy", - } - ) - - return result - - def get_dataflow_table_structure( - self, dataflow_id: str, table_id: str | None = None - ) -> dict: - """ - Get the presentation table structure for a dataflow. - - Handles both hierarchy-based (hierarchy.json) and embedded presentations. - - Parameters - ---------- - dataflow_id : str - The dataflow ID - table_id : str | None - The specific hierarchy/table ID. If None, uses the first available. - - Returns - ------- - dict - Structure with hierarchy metadata and nested indicators. - """ - available_hierarchies = self.get_dataflow_hierarchies(dataflow_id) - - if not available_hierarchies: - raise ValueError( - f"No presentation hierarchies found for dataflow '{dataflow_id}'" - ) - - # Track if this is a split table (one top-level code from a multi-code hierarchy) - top_level_code_filter: str | None = None - base_hierarchy_id: str | None = None - - if table_id: - # Find the specific table - selected_table = None - for h in available_hierarchies: - if h["id"] == table_id: - selected_table = h - break - if not selected_table: - raise ValueError( - f"Hierarchy '{table_id}' not found. " - f"Available: {[h['id'] for h in available_hierarchies]}" - ) - # Check if this is a split table (format: "base_id:top_level_code_id") - if ":" in table_id: - base_hierarchy_id, top_level_code_filter = table_id.split(":", 1) - else: - base_hierarchy_id = table_id - else: - selected_table = available_hierarchies[0] - table_id = selected_table.get("id", "") - if table_id and ":" in table_id: - base_hierarchy_id, top_level_code_filter = table_id.split(":", 1) - else: - base_hierarchy_id = table_id - - # Handle hierarchy-based presentations - # Use base_hierarchy_id to look up the actual hierarchy object - hierarchy = self.hierarchies.get(base_hierarchy_id or table_id) - if not hierarchy: - raise ValueError( - f"Hierarchy '{base_hierarchy_id or table_id}' not found in cache" - ) - - if not table_id: - raise ValueError("table_id cannot be None") - - codelist_id = self._hierarchy_to_codelist_map.get(base_hierarchy_id or table_id) - - dataflow_obj = self.dataflows.get(dataflow_id, {}) - agency_id = dataflow_obj.get("agencyID", "IMF") - agency_clean = agency_id.replace(".", "_") - - structure_ref = dataflow_obj.get("structureRef", {}) - dsd_id = structure_ref.get("id") - dsd_obj = self.datastructures.get(dsd_id, {}) if dsd_id else {} - dimensions = dsd_obj.get("dimensions", []) if isinstance(dsd_obj, dict) else [] - - indicator_dimension_order: dict[str, int] = {} - indicator_id_candidates = [ - "INDICATOR", - "PRODUCTION_INDEX", - "COICOP_1999", - "ACTIVITY", - "PRODUCT", - "SERIES", - "ITEM", - "BOP_ACCOUNTING_ENTRY", - "ACCOUNTING_ENTRY", - ] - - for idx, dim in enumerate(dimensions): - dim_id = dim.get("id", "") - if not dim_id: - continue - is_indicator_candidate = dim_id in indicator_id_candidates or any( - keyword in dim_id - for keyword in ["INDICATOR", "ACCOUNTING_ENTRY", "ENTRY"] - ) - if is_indicator_candidate: - indicator_dimension_order[dim_id] = idx - - codelist_dimension_cache: dict[str, str | None] = {} - # Cache for per-codelist label and description lookups - # Hierarchies can mix codes from multiple codelists (e.g., CL_BOP_INDICATOR + CL_BOP_ACCOUNTING_ENTRY) - codelist_labels_cache: dict[str, dict] = {} - codelist_desc_cache: dict[str, dict] = {} - - def process_hierarchical_codes( - codes: list, - parent_id: str | None = None, - depth: int = 0, - parent_codes: list | None = None, - parent_dimension_codes: dict[str, str] | None = None, - order_counter: list | None = None, - parent_full_label: str | None = None, - ancestor_labels: list | None = None, - ) -> list[dict]: - """Recursively process hierarchical codes into flat indicator list.""" - # pylint: disable=import-outside-toplevel - import re - - indicators = [] - - if parent_codes is None: - parent_codes = [] - if parent_dimension_codes is None: - parent_dimension_codes = {} - if order_counter is None: - order_counter = [0] - if ancestor_labels is None: - ancestor_labels = [] - - for code_entry in codes: - code_id = code_entry.get("id") - code_urn = code_entry.get("code", "") - level = code_entry.get("level", "0") - indicator_code = self._parse_indicator_code_from_urn(code_urn) - codelist_id_for_code = self._parse_codelist_id_from_urn(code_urn) - dimension_id = None - - if codelist_id_for_code: - if codelist_id_for_code not in codelist_dimension_cache: - codelist_dimension_cache[codelist_id_for_code] = ( - self._get_dimension_for_codelist( - dataflow_id, codelist_id_for_code - ) - ) - dimension_id = codelist_dimension_cache[codelist_id_for_code] - # Cache labels and descriptions for this codelist - # Check if not in cache OR if cached value is empty (failed previous fetch) - cached_is_empty = not codelist_labels_cache.get( - codelist_id_for_code - ) - if ( - codelist_id_for_code not in codelist_labels_cache - or cached_is_empty - ): - # Try to get from main cache first - cached_labels = self._codelist_cache.get( - codelist_id_for_code, {} - ) - cached_descs = self._codelist_descriptions.get( - codelist_id_for_code, {} - ) - # If not found, try to fetch from API using URN agency - if not cached_labels and code_urn: - urn_agency = self._parse_agency_from_urn(code_urn) - if urn_agency: - self._fetch_single_codelist( - urn_agency, codelist_id_for_code - ) - cached_labels = self._codelist_cache.get( - codelist_id_for_code, {} - ) - cached_descs = self._codelist_descriptions.get( - codelist_id_for_code, {} - ) - codelist_labels_cache[codelist_id_for_code] = cached_labels - codelist_desc_cache[codelist_id_for_code] = cached_descs - - # Look up label from the code's actual codelist, not just the owning codelist - code_labels = ( - codelist_labels_cache.get(codelist_id_for_code, {}) - if codelist_id_for_code - else {} - ) - code_descs = ( - codelist_desc_cache.get(codelist_id_for_code, {}) - if codelist_id_for_code - else {} - ) - full_label = ( - code_labels.get(indicator_code, code_id) - if indicator_code - else code_id - ) - # Detect path-based labels in INDICATOR_PUB codelists - # These codelists store labels as comma-separated hierarchical paths - # e.g., "Section A, Category B, Item C" -> extract relative portion - # The hierarchy provides context, so we only need what's NEW at this node - label = full_label - # Check if this codelist uses path-style labels: - # 1. Named pattern: "_INDICATOR_PUB" in codelist name - # 2. Explicit codelist: CL_DIP_INDICATOR uses path-style labels - is_path_style_codelist = codelist_id_for_code and ( - "_INDICATOR_PUB" in codelist_id_for_code - or codelist_id_for_code == "CL_DIP_INDICATOR" - ) - if is_path_style_codelist: - # Special case for CL_DIP_INDICATOR: the first part of every label - # is "Inward Direct investment" or "Outward Direct investment" which - # is redundant with the parent categories "Inward Indicators"/"Outward Indicators" - # Just skip the first part for DIP labels - if ( - codelist_id_for_code == "CL_DIP_INDICATOR" - and ", " in full_label - ): - parts = full_label.split(", ") - if len(parts) > 1 and ( - parts[0].startswith("Inward") - or parts[0].startswith("Outward") - ): - label = ", ".join(parts[1:]) - # For other path-style codelists, try exact prefix match with parent's full label - elif parent_full_label and full_label.startswith(parent_full_label): - # Remove parent's label prefix and separator - relative_label = full_label[len(parent_full_label) :].lstrip( - ", :" - ) - if relative_label: - label = relative_label - elif ", " in full_label or ": " in full_label: - # Child has same label as parent (e.g., USD vs FTO gold variants) - parts = re.split(r", |: ", full_label) - label = parts[-1] if parts else full_label - elif ancestor_labels and (", " in full_label or ": " in full_label): - # This handles cases where hierarchy mixes codelists - def normalize_part(s: str) -> str: - """Normalize for comparison: lowercase, remove punctuation.""" - return re.sub(r"[^a-z0-9]", "", s.lower()) - - # Split label by both ", " and ":" separators - def split_label(lbl: str) -> list: - """Split by comma-space and colon.""" - # First split by ", " - parts = lbl.split(", ") - # Then split each part by ":" - result = [] - for p in parts: - if ":" in p: - subparts = p.split(":") - result.extend( - [sp.strip() for sp in subparts if sp.strip()] - ) - else: - result.append(p) - return result - - # Collect all parts from all ancestor labels - all_ancestor_parts = [] - for anc_label in ancestor_labels: - all_ancestor_parts.extend(split_label(anc_label)) - - child_parts = split_label(full_label) - - # Build set of normalized ancestor parts - ancestor_normalized = { - normalize_part(p) for p in all_ancestor_parts - } - - # Find parts in child that are genuinely new - new_parts = [] - for part in child_parts: - part_norm = normalize_part(part) - # Allow for "Total X" in ancestor matching "X" in child - is_in_ancestor = part_norm in ancestor_normalized - if not is_in_ancestor: - for pn in ancestor_normalized: - # Check "totalX" matching "X" - if pn.startswith("total") and pn[5:] == part_norm: - is_in_ancestor = True - break - # Check if ancestor part is contained in child part - # e.g., "outflows" in "outflowsreservestemplate" - if len(pn) >= 6 and pn in part_norm: - is_in_ancestor = True - break - # Check if strings are very similar (>80% overlap) - # This handles encoding differences like "visvis" vs "vissvis" - # and inconsistent comma placement in IMF data - if len(part_norm) >= 15 and len(pn) >= 15: - # Check if one contains most of the other - shorter = min(part_norm, pn, key=len) - longer = max(part_norm, pn, key=len) - # If 80%+ of shorter is in longer, consider match - if shorter in longer or ( - len(shorter) > 30 - and any( - shorter[i : i + 30] in longer - for i in range(len(shorter) - 30) - ) - ): - is_in_ancestor = True - break - if not is_in_ancestor: - new_parts.append(part) - - if new_parts: - # For IRFCL path labels, keep all parts - if ( - codelist_id_for_code - and "IRFCL" in codelist_id_for_code - and len(new_parts) >= 3 - ): - # Keep the full label - synthetic groups will organize it - label = ", ".join(new_parts) - else: - label = ( - ", ".join(new_parts) - if new_parts - else child_parts[-1] if child_parts else full_label - ) - else: - label = child_parts[-1] if child_parts else full_label - elif ", " in full_label: - # No ancestors - this is a top-level node - # Take just the LAST part as the actual indicator label - parts = full_label.split(", ") - label = parts[-1] if parts else full_label - elif ( - parent_full_label - and ", " in full_label - and full_label.startswith(parent_full_label) - ): - # For other codelists, check if parent's label is a prefix - label = full_label.rsplit(", ", 1)[-1] - description = ( - code_descs.get(indicator_code, "") if indicator_code else "" - ) - - children = code_entry.get("hierarchicalCodes", []) - is_group = len(children) > 0 - - current_dimension_codes = parent_dimension_codes.copy() - if dimension_id and indicator_code: - current_dimension_codes[dimension_id] = indicator_code - - # Clean parent_id: if it contains codelist prefix, extract just the code - clean_parent_id = parent_id - if parent_id and "___" in parent_id: - clean_parent_id = parent_id.split("___")[-1] - - # Assign sequential order to ALL nodes (groups and leaf nodes) - order_counter[0] += 1 - current_order = order_counter[0] - - # For BOP dataflows, use depth (actual nesting) instead of IMF's - # inconsistent level attribute - use_depth_for_level = dataflow_id in ("BOP", "BOP_AGG", "IIP", "IIPCC") - node_level = ( - depth if use_depth_for_level else (int(level) if level else depth) - ) - - indicator_info = { - "id": code_id, - "indicator_code": indicator_code, - "label": label, - "description": description, - "order": current_order, - "level": node_level, - "depth": depth, - "parent_id": clean_parent_id, - "is_group": is_group, - "code_urn": code_urn, - "dimension_id": dimension_id, - } - - # Only build series_id if indicator_code belongs to a queryable dimension - if indicator_code and dimension_id: - # Filter to only dimensions that have a known position - ordered_dims = sorted( - [ - (dim_id_iter, indicator_dimension_order[dim_id_iter]) - for dim_id_iter in current_dimension_codes - if dim_id_iter in indicator_dimension_order - and current_dimension_codes.get(dim_id_iter) - ], - key=lambda item: item[1], - ) - ordered_codes = [ - current_dimension_codes[dim_id_iter] - for dim_id_iter, _ in ordered_dims - ] - unordered_dims = [ - dim_id_iter - for dim_id_iter in current_dimension_codes - if dim_id_iter not in indicator_dimension_order - and current_dimension_codes.get(dim_id_iter) - ] - ordered_codes.extend( - [ - current_dimension_codes[dim_id_iter] - for dim_id_iter in sorted(unordered_dims) - ] - ) - - if ordered_codes: - combined_codes = "_".join(ordered_codes) - indicator_info["series_id"] = ( - f"{agency_clean}_{dataflow_id}_{combined_codes}" - ) - else: - # Fallback to parent code ordering when dimension mapping fails - fallback_codes = parent_codes + [indicator_code] - indicator_info["series_id"] = ( - f"{agency_clean}_{dataflow_id}_{'_'.join(fallback_codes)}" - ) - - indicators.append(indicator_info) - - if children: - child_parent_codes = parent_codes + ( - [indicator_code] if indicator_code else [] - ) - # Accumulate ancestor labels for path-based label extraction - child_ancestor_labels = ancestor_labels + [full_label] - child_indicators = process_hierarchical_codes( - children, - parent_id=code_id, - depth=depth + 1, - parent_codes=child_parent_codes, - parent_dimension_codes=current_dimension_codes.copy(), - order_counter=order_counter, - parent_full_label=full_label, - ancestor_labels=child_ancestor_labels, - ) - indicators.extend(child_indicators) - - return indicators - - hierarchical_codes = hierarchy.get("hierarchicalCodes", []) - - # If a top-level code filter is set, only include that specific top-level code - if top_level_code_filter: - filtered_codes = [ - c for c in hierarchical_codes if c.get("id") == top_level_code_filter - ] - if filtered_codes: - hierarchical_codes = filtered_codes - - indicators = process_hierarchical_codes(hierarchical_codes) - - # Post-processing: Fix IRFCL hierarchy issues - if dataflow_id == "IRFCL": - indicators = self._fix_irfcl_hierarchy(indicators) - - # Get table name from selected_table if available (for split tables) - table_name = ( - selected_table.get("name", hierarchy.get("name")) - if selected_table - else hierarchy.get("name") - ) - - return { - "hierarchy_id": table_id, # Use full table_id including split suffix - "hierarchy_name": table_name, - "hierarchy_description": hierarchy.get("description", ""), - "dataflow_id": dataflow_id, - "codelist_id": codelist_id, - "agency_id": hierarchy.get("agencyID"), - "version": hierarchy.get("version"), - "indicators": indicators, - "total_indicators": len([i for i in indicators if not i["is_group"]]), - "total_groups": len([i for i in indicators if i["is_group"]]), - "type": "hierarchy", - } - - def _fix_irfcl_hierarchy(self, indicators: list[dict]) -> list[dict]: - """ - Fix IRFCL hierarchy issues where instrument types are incorrectly nested. - - The IMF hierarchy has "futures", "swaps", "options", "other" as children of - "forwards", but they should be siblings (different instrument types at the - same level). This method re-parents them to be siblings of "forwards". - """ - # Find "forwards" node and get its parent - forwards_node = None - for ind in indicators: - if ind.get("label", "").lower() == "forwards": - forwards_node = ind - break - - if not forwards_node: - return indicators - - forwards_id = forwards_node.get("id") - forwards_parent_id = forwards_node.get("parent_id") - forwards_depth = forwards_node.get("depth", 0) - - # Re-parent children of "forwards" to be siblings instead - instrument_labels = {"futures", "swaps", "options", "other"} - for ind in indicators: - if ind.get("parent_id") == forwards_id: - label_lower = ind.get("label", "").lower() - if label_lower in instrument_labels: - # Move to same level as forwards - ind["parent_id"] = forwards_parent_id - ind["depth"] = forwards_depth - - return indicators - - def _create_synthetic_groups_for_shared_prefixes( - self, indicators: list[dict] - ) -> list[dict]: - """ - Create synthetic group nodes for IRFCL siblings with shared label patterns. - - For IRFCL path-encoded labels like: - - "Up to 1 month, Long positions" - - "More than 1 and up to 3 months, Long positions" - - This creates a parent group "Long positions" and re-parents the children - with simplified labels ("Up to 1 month", etc.). - - Also handles shared prefixes like "Options in foreign currencies...". - """ - # pylint: disable=import-outside-toplevel - import re - from collections import defaultdict - - # These are specific to IRFCL memo items structure - IRFCL_PATH_PATTERNS = [ # pylint: disable=C0103 - "Options in foreign currencies", - "Up to 1 month", - "More than 1 and up to", - "More than 3 months", - "In-the-money", - "Long positions", - "Short positions", - ] - - def is_irfcl_path_label(label: str) -> bool: - """Check if label contains IRFCL path patterns.""" - return any( - pattern.lower() in label.lower() for pattern in IRFCL_PATH_PATTERNS - ) - - # Group indicators by parent_id - by_parent: dict[str | None, list[dict]] = defaultdict(list) - for ind in indicators: - by_parent[ind.get("parent_id")].append(ind) - - # Track new synthetic groups - synthetic_groups: list[dict] = [] - - # For each parent, check if children share a common prefix OR suffix - for parent_id, children in by_parent.items(): - # Only process if multiple children with comma-separated labels - # and they have IRFCL-specific path patterns (time periods, positions, options) - path_children = [ - c - for c in children - if ", " in c.get("label", "") - and c.get("id") - and is_irfcl_path_label(c.get("label", "")) - ] - if len(path_children) < 2: - continue - - # Find common prefix among path_children labels - labels = [c["label"] for c in path_children] - split_labels = [lbl.split(", ") for lbl in labels] - if not all(split_labels): - continue - - # Find how many leading parts are shared (prefix) - min_parts = min(len(parts) for parts in split_labels) - shared_prefix_count = 0 - for i in range(min_parts - 1): - first_part = split_labels[0][i] - if all(parts[i] == first_part for parts in split_labels): - shared_prefix_count += 1 - else: - break - - # Find how many trailing parts are shared (suffix) - shared_suffix_count = 0 - for i in range(1, min_parts): - last_part = split_labels[0][-i] - if all(parts[-i] == last_part for parts in split_labels): - shared_suffix_count += 1 - else: - break - - # Use whichever is longer (prefix or suffix), prefer suffix for IRFCL - if shared_suffix_count > 0 and shared_suffix_count >= shared_prefix_count: - suffix_parts = split_labels[0][-shared_suffix_count:] - # Reverse so outermost (last part) is created first as parent - suffix_parts_reversed = list(reversed(suffix_parts)) - first_child_order = min(c.get("order", 0) for c in path_children) - first_child_depth = path_children[0].get("depth", 0) - # Create nested synthetic groups - current_parent_id = parent_id - current_depth = first_child_depth - innermost_synthetic_id = None - - for i, suffix_part in enumerate(suffix_parts_reversed): - synthetic_id = f"_SYNTH_{current_parent_id}_{re.sub(r'[^a-zA-Z0-9]', '_', suffix_part[:30])}_{i}" - synthetic_group = { - "id": synthetic_id, - "indicator_code": None, - "label": suffix_part, - "description": "", - "order": first_child_order - 0.5 + (i * 0.01), - "level": current_depth, - "depth": current_depth, - "parent_id": current_parent_id, - "is_group": True, - "code_urn": None, - "dimension_id": None, - "series_id": None, - } - synthetic_groups.append(synthetic_group) - # Next group will be child of this one - current_parent_id = synthetic_id - current_depth += 1 - innermost_synthetic_id = synthetic_id - - # re-parent to innermost synthetic group and remove suffix - for child in path_children: - child_parts = child["label"].split(", ") - remaining_parts = child_parts[:-shared_suffix_count] - if remaining_parts: - child["label"] = ", ".join(remaining_parts) - child["parent_id"] = innermost_synthetic_id - child["depth"] = current_depth - child["depth"] = first_child_depth + 1 - - elif shared_prefix_count > 0: - # Create group from shared prefix - shared_prefix = ", ".join(split_labels[0][:shared_prefix_count]) - synthetic_id = f"_SYNTH_{parent_id}_{re.sub(r'[^a-zA-Z0-9]', '_', shared_prefix[:30])}" - first_child_order = min(c.get("order", 0) for c in path_children) - first_child_depth = path_children[0].get("depth", 0) - synthetic_group = { - "id": synthetic_id, - "indicator_code": None, - "label": shared_prefix, - "description": "", - "order": first_child_order - 0.5, - "level": first_child_depth, - "depth": first_child_depth, - "parent_id": parent_id, - "is_group": True, - "code_urn": None, - "dimension_id": None, - "series_id": None, - } - synthetic_groups.append(synthetic_group) - - # re-parent and remove prefix - for child in path_children: - child_parts = child["label"].split(", ") - remaining_parts = child_parts[shared_prefix_count:] - if remaining_parts: - child["label"] = ", ".join(remaining_parts) - child["parent_id"] = synthetic_id - child["depth"] = first_child_depth + 1 - - # Merge synthetic groups into indicators list - if synthetic_groups: - all_indicators = indicators + synthetic_groups - all_indicators.sort(key=lambda x: x.get("order", 0)) - for i, ind in enumerate(all_indicators): - ind["order"] = i + 1 - # After creating one level of groups, the children may still - # have shared prefixes/suffixes that need another level of grouping - return self._create_synthetic_groups_for_shared_prefixes(all_indicators) - - return indicators - - def list_all_dataflow_tables(self) -> dict[str, list[dict]]: - """ - Get a mapping of all dataflows to their available presentation tables. - - Returns a curated list of validated dataflow/table combinations that - are known to work correctly with the IMF API. - - Returns - ------- - dict[str, list[dict]] - Mapping of dataflow IDs to their available hierarchies. - """ - # pylint: disable=import-outside-toplevel - from openbb_imf.utils.constants import ( - PRESENTATION_TABLES, - ) - - result: dict[str, list[dict]] = {} - - for friendly_name, table_spec in PRESENTATION_TABLES.items(): - # Parse the table spec: "DATAFLOW_ID::HIERARCHY_ID" or "DATAFLOW_ID::HIERARCHY_ID:SPLIT_CODE" - parts = table_spec.split("::") - if len(parts) != 2: - continue - - dataflow_id = parts[0] - table_id = parts[1] - - if dataflow_id not in self.dataflows: - continue - - try: - # Get all hierarchies for this dataflow - all_hierarchies = self.get_dataflow_hierarchies(dataflow_id) - if not all_hierarchies: - continue - - # Find the matching hierarchy - matching_hierarchy = None - for h in all_hierarchies: - if h.get("id") == table_id: - matching_hierarchy = h - break - - if matching_hierarchy: - # Add friendly_name to the hierarchy info - hierarchy_with_name = matching_hierarchy.copy() - hierarchy_with_name["friendly_name"] = friendly_name - hierarchy_with_name["dataflow_id"] = dataflow_id - - if dataflow_id not in result: - result[dataflow_id] = [] - result[dataflow_id].append(hierarchy_with_name) - except Exception: # noqa: S110 # pylint: disable=broad-exception-caught - pass - - return result diff --git a/openbb_platform/providers/imf/openbb_imf/utils/metadata/__init__.py b/openbb_platform/providers/imf/openbb_imf/utils/metadata/__init__.py new file mode 100644 index 00000000000..086c84d63a2 --- /dev/null +++ b/openbb_platform/providers/imf/openbb_imf/utils/metadata/__init__.py @@ -0,0 +1,13 @@ +"""IMF metadata package.""" + +from __future__ import annotations + +from typing import Annotated + +from fastapi import Depends + +from openbb_imf.utils.metadata._core import ImfMetadata + +ImfMetadataDependency = Annotated[ImfMetadata, Depends(ImfMetadata)] + +__all__ = ["ImfMetadata", "ImfMetadataDependency"] diff --git a/openbb_platform/providers/imf/openbb_imf/utils/metadata/_cache_mixin.py b/openbb_platform/providers/imf/openbb_imf/utils/metadata/_cache_mixin.py new file mode 100644 index 00000000000..277afd48ed5 --- /dev/null +++ b/openbb_platform/providers/imf/openbb_imf/utils/metadata/_cache_mixin.py @@ -0,0 +1,47 @@ +"""Cache load mixin.""" + +from __future__ import annotations + +import json +import lzma +import warnings + +from openbb_core.app.model.abstract.warning import OpenBBWarning + +from openbb_imf.utils.metadata._constants import _SHIPPED_CACHE_FILE +from openbb_imf.utils.metadata._typing import _MixinBase + + +class CacheMixin(_MixinBase): + """Load the shipped SDMX metadata blob into the singleton.""" + + def _load_from_cache(self: _MixinBase) -> bool: + """Load metadata from ``imf_cache.json.xz``.""" + if not _SHIPPED_CACHE_FILE.exists(): + return False + + try: + with lzma.open(_SHIPPED_CACHE_FILE, "rb") as f: + cache = json.loads(f.read().decode()) + + self.dataflows = cache.get("dataflows", {}) + self.datastructures = cache.get("datastructures", {}) + self.conceptschemes = cache.get("conceptschemes", {}) + self.dataflow_groups = cache.get("dataflow_groups", {}) + self._metadata_cache = cache.get("metadata_cache", {}) + self._constraints_cache = cache.get("constraints_cache", {}) + self._codelist_cache = cache.get("codelist_cache", {}) + self._codelist_descriptions = cache.get("codelist_descriptions", {}) + self._dataflow_parameters_cache = cache.get("dataflow_parameters", {}) + self._dataflow_indicators_cache = cache.get("dataflow_indicators", {}) + self.hierarchies = cache.get("hierarchies", {}) + self._hierarchy_to_codelist_map = self._build_hierarchy_to_codelist_map() + self._codelist_to_hierarchies_map = ( + self._build_codelist_to_hierarchies_map() + ) + + return True + + except Exception as e: # noqa: BLE001 + warnings.warn(f"Error loading cache: {e}", OpenBBWarning) + return False diff --git a/openbb_platform/providers/imf/openbb_imf/utils/metadata/_constants.py b/openbb_platform/providers/imf/openbb_imf/utils/metadata/_constants.py new file mode 100644 index 00000000000..c7c5773ff72 --- /dev/null +++ b/openbb_platform/providers/imf/openbb_imf/utils/metadata/_constants.py @@ -0,0 +1,40 @@ +"""IMF SDMX metadata constants.""" + +from __future__ import annotations + +from pathlib import Path + +BASE_URL = "https://api.imf.org/external/sdmx/3.0" + +_STRUCTURE_ACCEPT = "application/json" +_USER_AGENT = "Open Data Platform - IMF Metadata Utility" + +_SHIPPED_CACHE_DIR = Path(__file__).resolve().parent.parent.parent / "assets" +_SHIPPED_CACHE_FILE = _SHIPPED_CACHE_DIR / "imf_cache.json.xz" + +INDICATOR_DIMENSION_CANDIDATES: tuple[str, ...] = ( + "INDICATOR", + "PRODUCTION_INDEX", + "COICOP_1999", + "INDEX_TYPE", + "ACTIVITY", + "PRODUCT", + "SERIES", + "ITEM", + "BOP_ACCOUNTING_ENTRY", + "ACCOUNTING_ENTRY", +) + +INDICATOR_DIMENSION_SUBSTRINGS: tuple[str, ...] = ( + "INDICATOR", + "ACCOUNTING_ENTRY", + "ENTRY", +) + +COUNTRY_DIMENSION_CANDIDATES: frozenset[str] = frozenset( + {"JURISDICTION", "REF_AREA", "COUNTRY", "AREA"} +) + +COUNTRY_CONCEPT_CANDIDATES: frozenset[str] = frozenset({"COUNTRY", "REF_AREA"}) + +DEPTH_OVERRIDE_DATAFLOWS: frozenset[str] = frozenset({"BOP", "BOP_AGG", "IIP", "IIPCC"}) diff --git a/openbb_platform/providers/imf/openbb_imf/utils/metadata/_core.py b/openbb_platform/providers/imf/openbb_imf/utils/metadata/_core.py new file mode 100644 index 00000000000..8cbd19ab72a --- /dev/null +++ b/openbb_platform/providers/imf/openbb_imf/utils/metadata/_core.py @@ -0,0 +1,80 @@ +"""``ImfMetadata`` singleton.""" + +from __future__ import annotations + +import threading + +from openbb_imf.utils.metadata._cache_mixin import CacheMixin +from openbb_imf.utils.metadata._helpers import HelpersMixin +from openbb_imf.utils.metadata._indicator_mixin import IndicatorMixin +from openbb_imf.utils.metadata._loader_mixin import LoaderMixin +from openbb_imf.utils.metadata._query_mixin import QueryMixin +from openbb_imf.utils.metadata._search_mixin import SearchMixin +from openbb_imf.utils.metadata._table_mixin import TableMixin + + +class ImfMetadata( + HelpersMixin, + CacheMixin, + LoaderMixin, + SearchMixin, + QueryMixin, + IndicatorMixin, + TableMixin, +): + """Thread-safe singleton over the IMF SDMX metadata catalog.""" + + _instance: ImfMetadata | None = None + _lock = threading.Lock() + _codelist_lock = threading.Lock() + _constraints_lock = threading.Lock() + _initialized: bool | None = None + + def __new__(cls) -> ImfMetadata: + """Return the singleton, creating it on first call.""" + if cls._instance is None: + with cls._lock: + if cls._instance is None: + cls._instance = super().__new__(cls) + cls._instance._initialized = False + return cls._instance + + def __init__(self) -> None: + """Initialise the singleton's caches once.""" + if self._initialized: + return + + with self._lock: + if self._initialized: + return + + self.dataflows: dict[str, dict] = {} + self.datastructures: dict[str, dict] = {} + self.conceptschemes: dict[str, dict] = {} + self.dataflow_groups: dict[str, list[dict]] = {} + self.hierarchies: dict[str, dict] = {} + self._codelist_cache: dict[str, dict] = {} + self._codelist_descriptions: dict[str, dict] = {} + self._metadata_cache: dict = {} + self._constraints_cache: dict = {} + self._dataflow_parameters_cache: dict[str, dict] = {} + self._dataflow_indicators_cache: dict[str, list] = {} + self._hierarchy_to_codelist_map: dict[str, str] = {} + self._codelist_to_hierarchies_map: dict[str, list[str]] = {} + + _ = self._load_from_cache() + self._initialized = True + + @classmethod + def _reset(cls) -> None: + """Drop the singleton.""" + with cls._lock: + cls._instance = None + + def __deepcopy__(self, memo: dict) -> ImfMetadata: + """Singletons are not copyable โ€” return self so the deepcopy walker stops.""" + return self + + def __copy__(self) -> ImfMetadata: + """Singletons are not copyable โ€” return self so the copy module stops.""" + return self diff --git a/openbb_platform/providers/imf/openbb_imf/utils/metadata/_helpers.py b/openbb_platform/providers/imf/openbb_imf/utils/metadata/_helpers.py new file mode 100644 index 00000000000..2db3b994e11 --- /dev/null +++ b/openbb_platform/providers/imf/openbb_imf/utils/metadata/_helpers.py @@ -0,0 +1,103 @@ +"""IMF metadata helper mixin.""" + +from __future__ import annotations + +from openbb_imf.utils.helpers import ( + build_codelist_to_hierarchies_map, + build_hierarchy_to_codelist_map, + build_time_period_params, + extract_all_codelists_from_hierarchy, + parse_agency_from_urn, + parse_codelist_id_from_urn, + parse_codelist_urn, + parse_indicator_code_from_urn, + parse_search_query, +) +from openbb_imf.utils.metadata._typing import _MixinBase + + +class HelpersMixin(_MixinBase): + """URN parsing, hierarchy maps, and time-period derivation.""" + + @staticmethod + def _parse_agency_from_urn(code_urn: str) -> str | None: + """Return the agency ID embedded in a hierarchicalCode URN.""" + return parse_agency_from_urn(code_urn) + + @staticmethod + def _parse_codelist_urn(urn: str) -> str | None: + """Return the codelist ID embedded in an ``owningCodelistUrn``.""" + return parse_codelist_urn(urn) + + @staticmethod + def _parse_indicator_code_from_urn(code_urn: str) -> str | None: + """Return the trailing code ID embedded in a hierarchicalCode URN.""" + return parse_indicator_code_from_urn(code_urn) + + @staticmethod + def _parse_codelist_id_from_urn(code_urn: str) -> str | None: + """Return the codelist ID embedded in a hierarchicalCode URN.""" + return parse_codelist_id_from_urn(code_urn) + + @staticmethod + def _parse_query(query: str) -> list[list[str]]: + """Parse a search query into OR-groups of AND-terms.""" + return parse_search_query(query) + + @staticmethod + def _build_time_period_parameters( + constraints_response: dict | None, + ) -> tuple[list[dict], str | None]: + """Derive time-period parameters from a constraints response.""" + return build_time_period_params(constraints_response) + + def _extract_all_codelists_from_hierarchy( + self: _MixinBase, hierarchy: dict + ) -> set[str]: + """Return codelist IDs referenced in a hierarchy tree.""" + return extract_all_codelists_from_hierarchy(hierarchy) + + def _build_hierarchy_to_codelist_map(self: _MixinBase) -> dict[str, str]: + """Return ``{hierarchy_id: owning_codelist_id}``.""" + return build_hierarchy_to_codelist_map(self.hierarchies) + + def _build_codelist_to_hierarchies_map(self: _MixinBase) -> dict[str, list[str]]: + """Return ``{codelist_id: [hierarchy_id, ...]}``.""" + return build_codelist_to_hierarchies_map(self.hierarchies) + + def _get_dimension_for_codelist( + self: _MixinBase, dataflow_id: str, codelist_id: str + ) -> str | None: + """Find the dimension in ``dataflow_id`` that uses ``codelist_id``.""" + if dataflow_id not in self.dataflows: + return None + + df_obj = self.dataflows[dataflow_id] + dsd_id = df_obj.get("structureRef", {}).get("id") + if not dsd_id or dsd_id not in self.datastructures: + return None + + dsd = self.datastructures[dsd_id] + dimensions = dsd.get("dimensions", []) + + for dim in dimensions: + dim_id = dim.get("id") + if not dim_id: + continue + resolved = self._resolve_codelist_id(dataflow_id, dsd_id, dim_id, dim) + if resolved == codelist_id: + return dim_id + + segments = {seg.upper() for seg in codelist_id.split("_")} + for dim in dimensions: + dim_id = dim.get("id") + if dim_id and dim_id.upper() in segments: + return dim_id + + codelist_upper = codelist_id.upper() + for dim in dimensions: + dim_id = dim.get("id") + if dim_id and dim_id.upper() in codelist_upper: + return dim_id + + return None diff --git a/openbb_platform/providers/imf/openbb_imf/utils/metadata/_indicator_mixin.py b/openbb_platform/providers/imf/openbb_imf/utils/metadata/_indicator_mixin.py new file mode 100644 index 00000000000..a8d6e1f4863 --- /dev/null +++ b/openbb_platform/providers/imf/openbb_imf/utils/metadata/_indicator_mixin.py @@ -0,0 +1,178 @@ +"""Indicator enumeration mixin.""" + +from __future__ import annotations + +from openbb_imf.utils.metadata._constants import ( + INDICATOR_DIMENSION_CANDIDATES, + INDICATOR_DIMENSION_SUBSTRINGS, +) +from openbb_imf.utils.metadata._typing import _MixinBase + +_STANDARD_DIMS = frozenset( + { + "COUNTRY", + "REF_AREA", + "JURISDICTION", + "AREA", + "FREQUENCY", + "FREQ", + "INDICATOR", + "TIME_PERIOD", + } +) + + +class IndicatorMixin(_MixinBase): + """Enumerate every indicator available in a dataflow.""" + + def get_indicators_in(self: _MixinBase, dataflow_id: str) -> list: + """Return the flat list of indicator entries for ``dataflow_id``.""" + cache = getattr(self, "_dataflow_indicators_cache", None) + if cache is not None and dataflow_id in cache: + return cache[dataflow_id] + + if dataflow_id not in self.dataflows: + raise ValueError(f"Dataflow '{dataflow_id}' not found.") + + dataflow_obj = self.dataflows[dataflow_id] + dataflow_name = dataflow_obj.get("name", "").replace("\\xa0", "").strip() + structure_ref = dataflow_obj.get("structureRef", {}) + structure_id = structure_ref.get("id", "") + agency_id = dataflow_obj.get("agencyID", structure_ref.get("agencyID", "IMF")) + dsd_id = structure_ref.get("id", "") + + if not dsd_id or dsd_id not in self.datastructures: + raise ValueError(f"Data structure not found for dataflow '{dataflow_id}'.") + + dsd = self.datastructures[dsd_id] + all_dims = dsd.get("dimensions", []) + + try: + params = self.get_dataflow_parameters(dataflow_id) + except Exception: # noqa: BLE001 + params = {} + + indicator_dim_ids = { + d.get("id") + for d in all_dims + if d.get("id") and _is_indicator_dimension(d.get("id")) + } + extra_dimensions = sorted( + d.get("id") + for d in all_dims + if d.get("id") + and d.get("id") not in _STANDARD_DIMS + and d.get("id") not in indicator_dim_ids + ) + + full_indicator_list: list = [] + + for dim in all_dims: + dim_id = dim.get("id") + if not _is_indicator_dimension(dim_id): + continue + + dim_params = params.get(dim_id, []) + if not dim_params: + continue + + codelist_id = self._resolve_codelist_id(dataflow_id, dsd_id, dim_id, dim) + descriptions_map: dict = {} + if codelist_id: + descriptions_map = self._codelist_descriptions.get(codelist_id, {}) + if not descriptions_map and codelist_id not in self._codelist_cache: + try: + self._get_codelist_map(codelist_id, agency_id, dataflow_id) + descriptions_map = self._codelist_descriptions.get( + codelist_id, {} + ) + except Exception: # noqa: BLE001, S110 + pass + + for param in dim_params: + code_id = param["value"] + full_indicator_list.append( + _make_indicator_entry( + dataflow_id=dataflow_id, + dataflow_name=dataflow_name, + structure_id=structure_id, + agency_id=agency_id, + dim_id=dim_id, + code_id=code_id, + label=param.get("label", code_id), + description=descriptions_map.get(code_id, ""), + extra_dimensions=extra_dimensions, + ) + ) + + dim_ids = {d.get("id") for d in all_dims if d.get("id")} + if "ACTIVITY" in dim_ids: + activity_codelist_id = f"CL_{dataflow_id}_ACTIVITY" + if activity_codelist_id in self._codelist_cache: + codes_map = self._get_codelist_map( + activity_codelist_id, agency_id, dataflow_id + ) + descriptions_map = self._codelist_descriptions.get( + activity_codelist_id, {} + ) + for code_id, code_name in codes_map.items(): + full_indicator_list.append( + _make_indicator_entry( + dataflow_id=dataflow_id, + dataflow_name=dataflow_name, + structure_id=structure_id, + agency_id=agency_id, + dim_id="ACTIVITY", + code_id=code_id, + label=code_name, + description=descriptions_map.get(code_id, ""), + extra_dimensions=extra_dimensions, + ) + ) + + if not full_indicator_list: + raise KeyError( + f"Could not find an indicator-like dimension for dataflow " + f"'{dataflow_id}'." + ) + + if cache is not None: + cache[dataflow_id] = full_indicator_list + + return full_indicator_list + + +def _is_indicator_dimension(dim_id: str | None) -> bool: + """Return True if ``dim_id`` matches the indicator dimension whitelist.""" + if not dim_id: + return False + if dim_id in INDICATOR_DIMENSION_CANDIDATES: + return True + return any(token in dim_id for token in INDICATOR_DIMENSION_SUBSTRINGS) + + +def _make_indicator_entry( + *, + dataflow_id: str, + dataflow_name: str, + structure_id: str, + agency_id: str, + dim_id: str, + code_id: str, + label: str, + description: str, + extra_dimensions: list[str] | None = None, +) -> dict: + """Build a single indicator dict.""" + return { + "dataflow_id": dataflow_id, + "dataflow_name": dataflow_name, + "structure_id": structure_id, + "agency_id": agency_id, + "dimension_id": dim_id, + "indicator": code_id, + "label": label, + "description": description, + "series_id": f"{dataflow_id}::{code_id}", + "extra_dimensions": list(extra_dimensions) if extra_dimensions else [], + } diff --git a/openbb_platform/providers/imf/openbb_imf/utils/metadata/_loader_mixin.py b/openbb_platform/providers/imf/openbb_imf/utils/metadata/_loader_mixin.py new file mode 100644 index 00000000000..fbdcacbebdc --- /dev/null +++ b/openbb_platform/providers/imf/openbb_imf/utils/metadata/_loader_mixin.py @@ -0,0 +1,155 @@ +"""Live API loader mixin.""" + +from __future__ import annotations + +import json +import warnings + +from openbb_core.app.model.abstract.warning import OpenBBWarning + +from openbb_imf.utils.metadata._constants import BASE_URL +from openbb_imf.utils.metadata._typing import _MixinBase + + +def _extract_codelist_payload(codelist_obj: dict) -> tuple[dict, dict]: + """Return ``({code_id: label}, {code_id: description})`` for a codelist.""" + labels: dict = {} + descs: dict = {} + for code in codelist_obj.get("codes", []): + code_id = code.get("id") + if not code_id: + continue + label = code.get("names", {}).get("en") or code.get("name") or code_id + description = ( + code.get("descriptions", {}).get("en", "") + or code.get("description", "") + or "" + ) + if not description and label: + description = label + labels[code_id] = label + descs[code_id] = description + return labels, descs + + +class LoaderMixin(_MixinBase): + """Codelist fetchers for cache backfill.""" + + def _fetch_single_codelist( + self: _MixinBase, agency_id: str, codelist_id: str + ) -> bool: + """Fetch one codelist by id and merge it into the cache.""" + from openbb_core.provider.utils.helpers import make_request + from requests.exceptions import RequestException + + if codelist_id in self._codelist_cache and self._codelist_cache.get( + codelist_id + ): + return True + + url = ( + f"{BASE_URL}/structure/codelist/{agency_id}/{codelist_id}" + "?detail=full&references=none" + ) + headers = {"Accept": "application/json"} + + try: + response = make_request(url, headers=headers, timeout=5) + if response.status_code != 200: + return False + json_response: dict = response.json() + except (json.JSONDecodeError, RequestException): + return False + + codelists_in_response = json_response.get("data", {}).get("codelists", []) + if not codelists_in_response: + return False + + with self._codelist_lock: + for codelist_obj in codelists_in_response: + cl_id = codelist_obj.get("id") + if not cl_id: + continue + labels, descs = _extract_codelist_payload(codelist_obj) + self._codelist_cache[cl_id] = labels + self._codelist_descriptions[cl_id] = descs + + return codelist_id in self._codelist_cache + + def _bulk_fetch_and_cache_codelists( + self: _MixinBase, agency_id: str, dataflow_id: str + ) -> None: + """Fetch every codelist referenced by ``dataflow_id``.""" + from openbb_core.provider.utils.helpers import make_request + from requests.exceptions import RequestException + + url = ( + f"{BASE_URL}/structure/codelist/{agency_id},{dataflow_id}/all" + "?detail=full&references=none" + ) + headers = {"Accept": "application/json"} + + try: + response = make_request(url, headers=headers) + json_response: dict = response.json() + except (json.JSONDecodeError, RequestException) as e: + warnings.warn( + f"Could not bulk fetch codelists for {agency_id}/{dataflow_id}: " + f"{e} -> {url}", + OpenBBWarning, + ) + return + + codelists_in_response = json_response.get("data", {}).get("codelists", []) + with self._codelist_lock: + for codelist_obj in codelists_in_response: + cl_id = codelist_obj.get("id") + if not cl_id: + continue + labels, descs = _extract_codelist_payload(codelist_obj) + self._codelist_cache[cl_id] = labels + self._codelist_descriptions[cl_id] = descs + + def _get_codelist_map( + self: _MixinBase, + codelist_id: str, + agency_id: str, + dataflow_id: str, + include_descriptions: bool = False, + ) -> dict: + """Return ``{code_id: label}`` or ``{code_id: {name, description}}``.""" + with self._codelist_lock: + if codelist_id in self._codelist_cache: + return _shape_codelist( + self._codelist_cache[codelist_id], + self._codelist_descriptions.get(codelist_id, {}), + include_descriptions=include_descriptions, + ) + + self._bulk_fetch_and_cache_codelists(agency_id, dataflow_id) + + with self._codelist_lock: + if codelist_id in self._codelist_cache: + return _shape_codelist( + self._codelist_cache[codelist_id], + self._codelist_descriptions.get(codelist_id, {}), + include_descriptions=include_descriptions, + ) + + warnings.warn(f"Codelist '{codelist_id}' not found.", OpenBBWarning) + return {} + + +def _shape_codelist( + labels: dict, descriptions: dict, *, include_descriptions: bool +) -> dict: + """Format the cached codelist into the requested shape.""" + if include_descriptions and descriptions: + return { + code_id: { + "name": label, + "description": descriptions.get(code_id, ""), + } + for code_id, label in labels.items() + } + return labels.copy() diff --git a/openbb_platform/providers/imf/openbb_imf/utils/metadata/_query_mixin.py b/openbb_platform/providers/imf/openbb_imf/utils/metadata/_query_mixin.py new file mode 100644 index 00000000000..38a4ec55d4e --- /dev/null +++ b/openbb_platform/providers/imf/openbb_imf/utils/metadata/_query_mixin.py @@ -0,0 +1,375 @@ +"""Query mixin.""" + +from __future__ import annotations + +import json + +from openbb_core.app.model.abstract.error import OpenBBError + +from openbb_imf.utils.metadata._constants import ( + BASE_URL, + COUNTRY_CONCEPT_CANDIDATES, + COUNTRY_DIMENSION_CANDIDATES, +) +from openbb_imf.utils.metadata._typing import _MixinBase + +_COMMON_CODELIST_ALIASES: tuple[tuple[tuple[str, ...], tuple[str, ...]], ...] = ( + (("REF_AREA", "AREA", "COUNTRY", "JURISDICTION", "GEOGRAPHICAL_AREA"), ("AREA",)), + (("COUNTERPART_COUNTRY",), ("COUNTRY",)), + ( + ("COMPOSITE_BREAKDOWN", "COMP_BREAKDOWN"), + ("COMP_BREAKDOWN", "COMPOSITE_BREAKDOWN"), + ), + (("DISABILITY_STATUS", "DISABILITY"), ("DISABILITY",)), + (("INCOME_WEALTH_QUANTILE", "QUANTILE"), ("QUANTILE",)), + (("TYPE_OF_TRANSFORMATION", "TRANSFORMATION"), ("TRANSFORMATION",)), + (("WGT_TYPE", "WEIGHT_TYPE", "CTOT_WEIGHT_TYPE"), ("WEIGHT_TYPE",)), + (("INDICATOR", "INDICATORS"), ("INDICATOR",)), + (("UNIT", "UNIT_MEASURE", "UNIT_MULT"), ("UNIT",)), + (("REF_SECTOR", "SECTOR"), ("SECTOR",)), + (("COUNTERPART_SECTOR",), ("SECTOR", "COUNTERPART_SECTOR")), + (("INSTR_ASSET", "INSTRUMENT_ASSET"), ("INSTR_ASSET",)), + (("ACCOUNTING_ENTRY",), ("ACCOUNTING_ENTRY",)), + ( + ("EXPENDITURE",), + ("COFOG", "COICOP_2018", "COICOP_1999", "COICOP"), + ), +) + +_ACTIVITY_FALLBACK_CODELISTS: tuple[str, ...] = ( + "CL_PPI_ACTIVITY", + "CL_MCDREO_ACTIVITY", + "CL_ACTIVITY_ISIC4", + "CL_NEA_ACTIVITY", + "CL_ACTIVITY", +) +_COICOP_FALLBACK_CODELISTS: tuple[str, ...] = ("CL_COICOP_1999", "CL_COICOP_2018") + + +class QueryMixin(_MixinBase): + """Parameter derivation, constraint fetching, codelist resolution.""" + + def get_dataflow_parameters( + self: _MixinBase, dataflow_id: str + ) -> dict[str, list[dict]]: + """Return ``{dim_id: [{label, value}]}`` for every queryable dimension.""" + if dataflow_id not in self.dataflows: + raise ValueError(f"Dataflow '{dataflow_id}' not found.") + + if ( + hasattr(self, "_dataflow_parameters_cache") + and dataflow_id in self._dataflow_parameters_cache + ): + return self._dataflow_parameters_cache[dataflow_id] + + df_obj = self.dataflows[dataflow_id] + agency_id = df_obj.get("agencyID") + dsd_id = df_obj.get("structureRef", {}).get("id") + dsd = self.datastructures.get(dsd_id, {}) + if not dsd: + return {} + + dimensions_metadata = { + dim["id"]: dim for dim in dsd.get("dimensions", []) if dim.get("id") + } + + constraints_response = self.get_available_constraints( + dataflow_id=dataflow_id, + key="all", + component_id="all", + mode="available", + references="all", + ) + key_values = constraints_response.get("key_values", []) + constrained_values_map = {kv["id"]: kv.get("values", []) for kv in key_values} + + parameters: dict[str, list[dict]] = {} + + for dim_id in dimensions_metadata: + if dim_id == "TIME_PERIOD": + continue + + dim_meta = dimensions_metadata.get(dim_id, {}) + codelist_id = self._resolve_codelist_id( + dataflow_id, dsd_id, dim_id, dim_meta + ) + if not codelist_id: + continue + + full_codes = self._codelist_cache.get(codelist_id, {}) + if not full_codes and agency_id: + full_codes = ( + self._get_codelist_map( + codelist_id, + agency_id, + dataflow_id, + include_descriptions=False, + ) + or {} + ) + if not full_codes: + continue + + value_ids_to_use = ( + constrained_values_map[dim_id] + if dim_id in constrained_values_map + else list(full_codes.keys()) + ) + + options: list = [] + for val_id in value_ids_to_use: + label = full_codes.get(val_id, val_id) + if isinstance(label, dict): + label = label.get("name", val_id) + if not label or label == val_id: + label = val_id + options.append({"label": label, "value": val_id.strip()}) + + if options: + parameters[dim_id] = options + + time_period_options, _ = self._build_time_period_parameters( + constraints_response + ) + if time_period_options: + parameters["TIME_PERIOD"] = time_period_options + + if hasattr(self, "_dataflow_parameters_cache"): + self._dataflow_parameters_cache[dataflow_id] = parameters + + return parameters + + def get_available_constraints( + self: _MixinBase, + dataflow_id: str, + key: str, + component_id: str | None = None, + mode: str | None = None, + references: str | None = None, + **kwargs, + ) -> dict: + """Fetch availability constraints for a given dataflow and key.""" + from openbb_core.provider.utils.helpers import make_request + from requests.exceptions import RequestException + + if dataflow_id not in self.dataflows: + raise ValueError(f"Dataflow '{dataflow_id}' not found.") + + kwargs_tuple = tuple(sorted(kwargs.items())) + cache_key = ( + f"{dataflow_id}:{key}:{component_id}:{mode}:{references}:{kwargs_tuple}" + ) + + with self._constraints_lock: + if cached := self._constraints_cache.get(cache_key): + return cached + + df = self.dataflows[dataflow_id] + agency_id = df.get("agencyID") + if not agency_id: + raise ValueError(f"Agency ID not found for dataflow '{dataflow_id}'.") + + base_url = ( + f"{BASE_URL}/availability/dataflow/" + f"{agency_id}/{dataflow_id}/%2B/{key}/{component_id or 'all'}" + ) + query_params = {"mode": mode, "references": references} + query_params.update({f"c[{k}]": v for k, v in kwargs.items() if v}) + query_params = {k: v for k, v in query_params.items() if v is not None} + url = ( + base_url + "?" + "&".join(f"{k}={v}" for k, v in query_params.items()) + if query_params + else base_url + ) + + headers = { + "Accept": "application/json", + "User-Agent": "Open Data Platform - IMF Metadata Utility", + } + try: + response = make_request(url, headers=headers) + response.raise_for_status() + json_response = response.json() + except json.JSONDecodeError as e: + raise OpenBBError( + f"Unexpected response format when fetching constraints " + f"{dataflow_id}: {e} -> {url}" + ) from None + except RequestException as e: + raise OpenBBError( + f"An error occurred while fetching constraints " + f"{dataflow_id}: {e} -> {url}" + ) from None + + extracted: dict[str, list] = {} + for constraint in json_response.get("data", {}).get("dataConstraints", []): + for region in constraint.get("cubeRegions", []): + _collect_constraint_values(region.get("keyValues", []), extracted) + _collect_constraint_values(region.get("components", []), extracted) + + for dim_id, values in list(extracted.items()): + extracted[dim_id] = list({v for v in values if v}) + + result = { + "key_values": [{"id": k, "values": v} for k, v in extracted.items()], + "full_response": json_response, + } + with self._constraints_lock: + self._constraints_cache[cache_key] = result + return result + + def _resolve_codelist_id( + self: _MixinBase, + dataflow_id: str, + dsd_id: str | None, + dim_id: str, + dim_meta: dict, + ) -> str | None: + """Find the cached codelist id for a dimension on a dataflow.""" + if not dim_id: + return None + + representation = dim_meta.get("representation", {}) + codelist_ref = representation.get("codelist") + if isinstance(codelist_ref, dict): + return codelist_ref.get("id") + if isinstance(codelist_ref, str): + return codelist_ref + + concept_ref = dim_meta.get("conceptRef") or {} + concept_id = concept_ref.get("id") + scheme_id = concept_ref.get("maintainableParentID") + if concept_id and scheme_id: + scheme = self.conceptschemes.get(scheme_id, {}) + for concept in scheme.get("concepts", []): + if concept.get("id") == concept_id: + cl_id = concept.get("codelist_id") + if cl_id: + return cl_id + break + + candidates: list[str] = [] + seen: set[str] = set() + + def add(candidate: str) -> None: + if candidate and candidate not in seen: + candidates.append(candidate) + seen.add(candidate) + + is_country_dim = dim_id.upper() in COUNTRY_DIMENSION_CANDIDATES or ( + concept_id and concept_id.upper() in COUNTRY_CONCEPT_CANDIDATES + ) + + if is_country_dim and dataflow_id: + base_dataflow = dataflow_id.split("_", maxsplit=1)[0] + add(f"CL_{base_dataflow}_ISO_COUNTRY") + add(f"CL_{dataflow_id}_COUNTRY") + add(f"CL_{base_dataflow}_COUNTRY") + + if dataflow_id: + add(f"CL_{dataflow_id}_{dim_id}") + add(f"CL_{dataflow_id}_{dim_id}_PUB") + df_tokens = dataflow_id.split("_") + dim_tokens = dim_id.split("_") + shared = 0 + while ( + shared < len(df_tokens) + and shared < len(dim_tokens) + and df_tokens[shared] == dim_tokens[shared] + ): + shared += 1 + if 0 < shared < len(dim_tokens): + trimmed = "_".join(dim_tokens[shared:]) + add(f"CL_{dataflow_id}_{trimmed}") + add(f"CL_{dataflow_id}_{trimmed}_PUB") + if "COUNTRY" in dim_id: + add(f"CL_{dataflow_id}_COUNTRY") + add(f"CL_{dataflow_id}_COUNTRY_PUB") + if "_" in dataflow_id: + base_dataflow = dataflow_id.split("_", maxsplit=1)[0] + add(f"CL_{base_dataflow}_{dim_id}") + add(f"CL_{base_dataflow}_{dim_id}_PUB") + + if dsd_id: + add(f"CL_{dsd_id.replace('DSD_', '')}_{dim_id}") + + parent_scheme_id = concept_ref.get("maintainableParentID") + if parent_scheme_id: + scheme_base = parent_scheme_id.replace("CS_", "CL_", 1) + add(f"{scheme_base}_{dim_id}") + add(scheme_base) + + add(f"CL_{dim_id}") + if concept_id: + add(f"CL_{concept_id}") + + for candidate in candidates: + if candidate in self._codelist_cache: + return candidate + + cache_keys_upper = {k.upper(): k for k in self._codelist_cache} + for candidate in candidates: + actual = cache_keys_upper.get(candidate.upper()) + if actual: + return actual + + for patterns, base_names in _COMMON_CODELIST_ALIASES: + if any(p in dim_id.upper() for p in patterns): + for base_name in base_names: + specific_cl = f"CL_{dataflow_id}_{base_name}" + if specific_cl in self._codelist_cache: + return specific_cl + generic_cl = f"CL_{base_name}" + if generic_cl in self._codelist_cache: + return generic_cl + + if "COUNTERPART_" in dim_id: + base_dim_id = dim_id.replace("COUNTERPART_", "") + if dsd_id and dsd_id in self.datastructures: + dsd = self.datastructures[dsd_id] + for d in dsd.get("dimensions", []): + if d.get("id") == base_dim_id: + return self._resolve_codelist_id( + dataflow_id, dsd_id, base_dim_id, d + ) + + dim_upper = dim_id.upper() + if "ACTIVITY" in dim_upper or "PRODUCTION_INDEX" in dim_upper: + for candidate in _ACTIVITY_FALLBACK_CODELISTS: + if candidate in self._codelist_cache: + return candidate + if "COICOP" in dim_upper: + for candidate in _COICOP_FALLBACK_CODELISTS: + if candidate in self._codelist_cache: + return candidate + + for cache_key in self._codelist_cache: + if cache_key.startswith("CL_MASTER"): + continue + if dim_upper in cache_key.upper(): + return cache_key + + dim_parts = [p for p in dim_id.split("_") if len(p) > 2] + if len(dim_parts) > 1: + for cache_key in self._codelist_cache: + if cache_key.startswith("CL_MASTER"): + continue + cache_key_upper = cache_key.upper() + if all(part.upper() in cache_key_upper for part in dim_parts): + return cache_key + + return candidates[0] if candidates else None + + +def _collect_constraint_values(items: list, into: dict[str, list]) -> None: + """Extract dimension values from ``keyValues`` / ``components`` entries.""" + for item in items: + dim_id = item.get("id") + if not dim_id: + continue + into.setdefault(dim_id, []) + for val in item.get("values", []): + if isinstance(val, dict): + into[dim_id].append(val.get("value")) + else: + into[dim_id].append(val) diff --git a/openbb_platform/providers/imf/openbb_imf/utils/metadata/_search_mixin.py b/openbb_platform/providers/imf/openbb_imf/utils/metadata/_search_mixin.py new file mode 100644 index 00000000000..3bcf45b0122 --- /dev/null +++ b/openbb_platform/providers/imf/openbb_imf/utils/metadata/_search_mixin.py @@ -0,0 +1,272 @@ +"""Search mixin.""" + +from __future__ import annotations + +import warnings + +from openbb_core.app.model.abstract.error import OpenBBError +from openbb_core.app.model.abstract.warning import OpenBBWarning + +from openbb_imf.utils.metadata._typing import _MixinBase + + +class SearchMixin(_MixinBase): + """Listing, dataflow search, and indicator search.""" + + def list_dataflows(self: _MixinBase) -> list[dict]: + """Return ``[{label, value}]`` for every cached dataflow.""" + dfs: list = [] + for key in sorted(self.dataflows.keys()): + label = self.dataflows[key].get("name", key) + value = self.dataflows[key].get("id", key) + dfs.append({"label": label.strip(), "value": value.strip()}) + return dfs + + def search_dataflows(self: _MixinBase, query: str) -> list[dict]: + """Search dataflows by id, name, and description. + + Parameters + ---------- + query : str + Search string. Supports AND (+), OR (|), and quoted phrases. + + Returns + ------- + list[dict] + Matches grouped by structureRef id. + """ + parsed_query = self._parse_query(query) + if not parsed_query: + raise OpenBBError( + ValueError(f"Query string is empty or invalid -> '{query}'") + ) + + grouped: dict = {} + for dataflow_obj in self.dataflows.values(): + haystack = ( + dataflow_obj.get("id", "").lower() + + " " + + dataflow_obj.get("name", "").lower() + + " " + + dataflow_obj.get("description", "").lower() + ) + if not _matches_query(haystack, parsed_query): + continue + + structure_ref_id = dataflow_obj.get("structureRef", {}).get("id") + if not structure_ref_id: + continue + grouped.setdefault(structure_ref_id, []).append( + { + "id": dataflow_obj.get("id"), + "name": dataflow_obj.get("name"), + "description": dataflow_obj.get("description", ""), + } + ) + + return [ + {"group_id": group_id, "dataflows": dataflows} + for group_id, dataflows in grouped.items() + ] + + def search_indicators( + self: _MixinBase, + query: str, + dataflows: list[str] | str | None = None, + keywords: list[str] | None = None, + ) -> list[dict]: + """Search indicators across one or more dataflows. + + Parameters + ---------- + query : str + Semicolon-separated search phrases. Each phrase supports AND (+), + OR (|), and quoted exact matches. + dataflows : list[str] | str | None + Restrict to specific dataflow ids. + keywords : list[str] | None + Token-level filters; ``"not "`` excludes. + + Returns + ------- + list[dict] + Matching indicators enriched with ``tables`` and ``member_of``. + """ + target_dataflow_ids: list = [] + if dataflows: + target_dataflow_ids = ( + [dataflows] if isinstance(dataflows, str) else dataflows + ) + else: + if not query and not keywords: + raise OpenBBError( + "A query must be provided when no dataflows and " + "keywords are specified." + ) + target_dataflow_ids = list(self.dataflows.keys()) + + if not target_dataflow_ids: + raise OpenBBError( + "No valid dataflows found to search indicators in. " + "This might be due to incorrect dataflow IDs." + ) + + indicator_to_tables, indicator_table_text = self._build_indicator_table_maps( + set(target_dataflow_ids) + ) + all_indicators = self._collect_indicators( + set(target_dataflow_ids), indicator_to_tables, indicator_table_text + ) + search_results = self._filter_indicators_by_query(all_indicators, query) + return self._filter_indicators_by_keywords(search_results, keywords) + + def _build_indicator_table_maps( + self: _MixinBase, dataflow_ids: set[str] + ) -> tuple[dict[str, list[dict]], dict[str, str]]: + """Return (tables-per-indicator, search-text-per-indicator).""" + indicator_to_tables: dict[str, list[dict]] = {} + indicator_table_text: dict[str, str] = {} + + for df_id in dataflow_ids: + try: + hierarchies = self.get_dataflow_hierarchies(df_id) + except Exception: # noqa: BLE001, S110 + continue + for hierarchy in hierarchies: + try: + structure = self.get_dataflow_table_structure( + df_id, hierarchy["id"] + ) + except Exception: # noqa: BLE001, S112 + continue + table_search_text = ( + hierarchy.get("name", "").lower() + + " " + + hierarchy.get("description", "").lower() + ) + for ind in structure.get("indicators", []): + if ind.get("is_group"): + continue + indicator_code = ind.get("indicator_code") or ind.get("code") + if not indicator_code: + continue + key = f"{df_id}_{indicator_code}" + if key not in indicator_to_tables: + indicator_to_tables[key] = [] + indicator_table_text[key] = "" + table_entry = { + "table_id": hierarchy["id"], + "table_name": hierarchy["name"], + } + if table_entry not in indicator_to_tables[key]: + indicator_to_tables[key].append(table_entry) + indicator_table_text[key] += " " + table_search_text + + return indicator_to_tables, indicator_table_text + + def _collect_indicators( + self: _MixinBase, + dataflow_ids: set[str], + indicator_to_tables: dict[str, list[dict]], + indicator_table_text: dict[str, str], + ) -> list[dict]: + """Aggregate indicators from each dataflow.""" + all_indicators: list = [] + for df_id in dataflow_ids: + try: + indicators = self.get_indicators_in(df_id) + except (KeyError, ValueError, OpenBBError) as e: + warnings.warn( + f"Could not retrieve indicators for dataflow '{df_id}': {e}", + OpenBBWarning, + ) + continue + for ind in indicators: + key = f"{df_id}_{ind['indicator']}" + ind["tables"] = indicator_to_tables.get(key, []) + ind["member_of"] = [f"{df_id}::{t['table_id']}" for t in ind["tables"]] + ind["_table_search_text"] = indicator_table_text.get(key, "") + all_indicators.extend(indicators) + return all_indicators + + def _filter_indicators_by_query( + self: _MixinBase, indicators: list[dict], query: str + ) -> list[dict]: + """Apply the semicolon-separated phrase filter.""" + if not query: + return indicators + + phrases = [p.strip() for p in query.split(";") if p.strip()] + if not phrases: + return indicators + + results: list = [] + for indicator in indicators: + text = ( + indicator.get("label", "").lower() + + " " + + indicator.get("description", "").lower() + + " " + + indicator.get("dataflow_name", "").lower() + + " " + + indicator.get("dataflow_id", "").lower() + + " " + + indicator.get("indicator", "").lower() + + " " + + indicator.get("_table_search_text", "") + ) + for phrase in phrases: + parsed_phrase = self._parse_query(phrase) + if not parsed_phrase: + if phrase.lower() in text: + results.append(indicator) + break + elif any( + all(term in text for term in or_group) for or_group in parsed_phrase + ): + results.append(indicator) + break + return results + + def _filter_indicators_by_keywords( + self: _MixinBase, indicators: list[dict], keywords: list[str] | None + ) -> list[dict]: + """Apply per-keyword include / ``not `` exclude filters.""" + if not keywords: + for indicator in indicators: + indicator.pop("_table_search_text", None) + return indicators + + kept: list = [] + for indicator in indicators: + text = ( + indicator.get("indicator", "") + + " " + + indicator.get("label", "") + + " " + + indicator.get("description", "") + + " " + + indicator.get("_table_search_text", "") + ).lower() + include = True + for keyword in keywords: + kw = keyword.strip() + if kw.lower().startswith("not "): + exclude_word = kw[4:].lower() + if exclude_word and exclude_word in text: + include = False + break + elif kw.lower() not in text: + include = False + break + if include: + kept.append(indicator) + + for indicator in kept: + indicator.pop("_table_search_text", None) + return kept + + +def _matches_query(haystack: str, parsed_query: list[list[str]]) -> bool: + """Match ``haystack`` against an OR-of-AND-of-terms query.""" + return any(all(term in haystack for term in or_group) for or_group in parsed_query) diff --git a/openbb_platform/providers/imf/openbb_imf/utils/metadata/_table_mixin.py b/openbb_platform/providers/imf/openbb_imf/utils/metadata/_table_mixin.py new file mode 100644 index 00000000000..73bc3d807a1 --- /dev/null +++ b/openbb_platform/providers/imf/openbb_imf/utils/metadata/_table_mixin.py @@ -0,0 +1,870 @@ +"""Presentation table mixin.""" + +from __future__ import annotations + +import re +from collections import defaultdict + +from openbb_imf.utils.metadata._constants import ( + DEPTH_OVERRIDE_DATAFLOWS, + INDICATOR_DIMENSION_CANDIDATES, + INDICATOR_DIMENSION_SUBSTRINGS, +) +from openbb_imf.utils.metadata._typing import _MixinBase + +_IRFCL_PATH_PATTERNS: tuple[str, ...] = ( + "Options in foreign currencies", + "Up to 1 month", + "More than 1 and up to", + "More than 3 months", + "In-the-money", + "Long positions", + "Short positions", +) + +_HIERARCHY_INDICATOR_CANDIDATES: tuple[str, ...] = ( + "INDICATOR", + "COICOP_1999", + "PRODUCTION_INDEX", + "ACTIVITY", + "PRODUCT", + "SERIES", + "ITEM", + "ACCOUNTING_ENTRY", + "SECTOR", +) + + +class TableMixin(_MixinBase): + """Presentation table resolution and rendering.""" + + def _validate_hierarchy_queryable( + self: _MixinBase, dataflow_id: str, codes: list + ) -> bool: + """Return True if a sample of ``codes`` maps to queryable dimensions.""" + if not codes: + return False + + sample_size = min(10, len(codes)) + valid_count = 0 + for code_entry in codes[:sample_size]: + code_urn = code_entry.get("code", "") + if not code_urn: + continue + codelist_id = self._parse_codelist_id_from_urn(code_urn) + if not codelist_id: + continue + if self._get_dimension_for_codelist(dataflow_id, codelist_id): + valid_count += 1 + return valid_count >= (sample_size * 0.5) + + def get_dataflow_hierarchies(self: _MixinBase, dataflow_id: str) -> list[dict]: + """List presentation tables attached to a dataflow.""" + if dataflow_id not in self.dataflows: + raise ValueError(f"Dataflow '{dataflow_id}' not found.") + + dataflow_obj = self.dataflows[dataflow_id] + result: list = [] + + dsd_id = dataflow_obj.get("structureRef", {}).get("id") + if not dsd_id or dsd_id not in self.datastructures: + return result + + dsd = self.datastructures[dsd_id] + dimensions = dsd.get("dimensions", []) + + indicator_codelist_id = self._find_indicator_codelist_for_hierarchies( + dataflow_id, dsd_id, dimensions + ) + if not indicator_codelist_id: + return result + + hierarchy_ids = self._codelist_to_hierarchies_map.get(indicator_codelist_id, []) + + available_indicator_values: set[str] = set() + try: + params = self.get_dataflow_parameters(dataflow_id) + if "INDICATOR" in params: + available_indicator_values.update( + opt.get("value", "") for opt in params["INDICATOR"] + ) + except Exception: # noqa: BLE001, S110 + pass + + for hier_id in hierarchy_ids: + hier_obj = self.hierarchies.get(hier_id) + if not hier_obj: + continue + if available_indicator_values and not _hierarchy_overlaps_dataflow( + hier_obj, available_indicator_values + ): + continue + + top_level_codes = hier_obj.get("hierarchicalCodes", []) + if len(top_level_codes) > 1 and dataflow_id == "IRFCL": + result.extend( + self._split_irfcl_hierarchy( + dataflow_id, + hier_id, + hier_obj, + top_level_codes, + indicator_codelist_id, + ) + ) + else: + descriptions = hier_obj.get("descriptions", {}) + result.append( + { + "id": hier_id, + "name": hier_obj.get("name", ""), + "description": descriptions.get("en", "") + if descriptions + else "", + "codelist_id": indicator_codelist_id, + "agency_id": hier_obj.get("agencyID", ""), + "version": hier_obj.get("version", ""), + "type": "hierarchy", + } + ) + + return result + + def _find_indicator_codelist_for_hierarchies( + self: _MixinBase, + dataflow_id: str, + dsd_id: str, + dimensions: list, + ) -> str | None: + """Pick the dimension whose codelist owns the dataflow's hierarchies.""" + dim_lookup = {d.get("id", ""): d for d in dimensions} + + for candidate in _HIERARCHY_INDICATOR_CANDIDATES: + if candidate not in dim_lookup: + continue + dim = dim_lookup[candidate] + cl_id = self._resolve_codelist_id(dataflow_id, dsd_id, candidate, dim) + if cl_id and self._codelist_to_hierarchies_map.get(cl_id): + return cl_id + + for dim in dimensions: + dim_id = dim.get("id", "") + if "INDICATOR" in dim_id and dim_id not in _HIERARCHY_INDICATOR_CANDIDATES: + cl_id = self._resolve_codelist_id(dataflow_id, dsd_id, dim_id, dim) + if cl_id: + return cl_id + return None + + def _split_irfcl_hierarchy( + self: _MixinBase, + dataflow_id: str, + hier_id: str, + hier_obj: dict, + top_level_codes: list[dict], + indicator_codelist_id: str, + ) -> list[dict]: + """Generate one table entry per top-level code in an IRFCL hierarchy.""" + section_codes = self._codelist_cache.get(f"CL_{dataflow_id}_SECTION", {}) + out: list = [] + for idx, top_code in enumerate(top_level_codes): + top_code_id = top_code.get("id", "") + top_code_urn = top_code.get("code", "") + actual_code = ( + top_code_urn.rsplit(".", 1)[-1] if "." in top_code_urn else top_code_id + ) + urn_codelist_id = self._parse_codelist_id_from_urn(top_code_urn) + table_label = self._codelist_cache.get( + urn_codelist_id or indicator_codelist_id, {} + ).get(actual_code, actual_code) + for section_code, section_label in section_codes.items(): + if actual_code.startswith(section_code): + table_label = section_label + break + out.append( + { + "id": f"{hier_id}:{top_code_id}", + "name": table_label, + "description": "", + "codelist_id": indicator_codelist_id, + "agency_id": hier_obj.get("agencyID", ""), + "version": hier_obj.get("version", ""), + "type": "hierarchy", + "table_index": idx, + "top_level_code_id": top_code_id, + "indicator_code": actual_code, + } + ) + return out + + def get_dataflow_table_structure( + self: _MixinBase, dataflow_id: str, table_id: str | None = None + ) -> dict: + """Return the resolved indicator tree for a presentation table.""" + available_hierarchies = self.get_dataflow_hierarchies(dataflow_id) + if not available_hierarchies: + raise ValueError( + f"No presentation hierarchies found for dataflow '{dataflow_id}'" + ) + + top_level_code_filter, base_hierarchy_id, selected_table, table_id = ( + self._select_hierarchy(table_id, available_hierarchies) + ) + + hierarchy = self.hierarchies.get(base_hierarchy_id or table_id) + if not hierarchy: + raise ValueError( + f"Hierarchy '{base_hierarchy_id or table_id}' not found in cache" + ) + if not table_id: + raise ValueError("table_id cannot be None") + + codelist_id = self._hierarchy_to_codelist_map.get(base_hierarchy_id or table_id) + + dataflow_obj = self.dataflows.get(dataflow_id, {}) + agency_id = dataflow_obj.get("agencyID", "IMF") + agency_clean = agency_id.replace(".", "_") + + structure_ref = dataflow_obj.get("structureRef", {}) + dsd_id = structure_ref.get("id") + dsd_obj = self.datastructures.get(dsd_id, {}) if dsd_id else {} + dimensions = dsd_obj.get("dimensions", []) if isinstance(dsd_obj, dict) else [] + + indicator_dimension_order: dict[str, int] = {} + for idx, dim in enumerate(dimensions): + dim_id = dim.get("id", "") + if not dim_id: + continue + is_indicator = dim_id in INDICATOR_DIMENSION_CANDIDATES or any( + token in dim_id for token in INDICATOR_DIMENSION_SUBSTRINGS + ) + if is_indicator: + indicator_dimension_order[dim_id] = idx + + codelist_dimension_cache: dict[str, str | None] = {} + codelist_labels_cache: dict[str, dict] = {} + codelist_desc_cache: dict[str, dict] = {} + + hierarchical_codes = hierarchy.get("hierarchicalCodes", []) + if top_level_code_filter: + filtered = [ + c for c in hierarchical_codes if c.get("id") == top_level_code_filter + ] + if filtered: + hierarchical_codes = filtered + + indicators = self._process_hierarchical_codes( + hierarchical_codes, + dataflow_id=dataflow_id, + agency_clean=agency_clean, + indicator_dimension_order=indicator_dimension_order, + codelist_dimension_cache=codelist_dimension_cache, + codelist_labels_cache=codelist_labels_cache, + codelist_desc_cache=codelist_desc_cache, + ) + + if dataflow_id == "IRFCL": + indicators = self._fix_irfcl_hierarchy(indicators) + + table_name = ( + selected_table.get("name", hierarchy.get("name")) + if selected_table + else hierarchy.get("name") + ) + return { + "hierarchy_id": table_id, + "hierarchy_name": table_name, + "hierarchy_description": hierarchy.get("description", ""), + "dataflow_id": dataflow_id, + "codelist_id": codelist_id, + "agency_id": hierarchy.get("agencyID"), + "version": hierarchy.get("version"), + "indicators": indicators, + "total_indicators": len([i for i in indicators if not i["is_group"]]), + "total_groups": len([i for i in indicators if i["is_group"]]), + "type": "hierarchy", + } + + @staticmethod + def _select_hierarchy( + table_id: str | None, available_hierarchies: list[dict] + ) -> tuple[str | None, str | None, dict | None, str]: + """Resolve ``table_id`` to (filter, base, table-dict, normalized-id).""" + if table_id: + selected_table = next( + (h for h in available_hierarchies if h["id"] == table_id), None + ) + if not selected_table: + raise ValueError( + f"Hierarchy '{table_id}' not found. Available: " + f"{[h['id'] for h in available_hierarchies]}" + ) + if ":" in table_id: + base, top = table_id.split(":", 1) + return top, base, selected_table, table_id + return None, table_id, selected_table, table_id + + selected_table = available_hierarchies[0] + table_id = selected_table.get("id", "") or "" + if table_id and ":" in table_id: + base, top = table_id.split(":", 1) + return top, base, selected_table, table_id + return None, table_id, selected_table, table_id + + def _process_hierarchical_codes( + self: _MixinBase, + codes: list, + *, + dataflow_id: str, + agency_clean: str, + indicator_dimension_order: dict[str, int], + codelist_dimension_cache: dict[str, str | None], + codelist_labels_cache: dict[str, dict], + codelist_desc_cache: dict[str, dict], + parent_id: str | None = None, + depth: int = 0, + parent_codes: list | None = None, + parent_dimension_codes: dict[str, str] | None = None, + order_counter: list | None = None, + parent_full_label: str | None = None, + ancestor_labels: list | None = None, + ) -> list[dict]: + """Recursive descent into ``hierarchicalCodes``.""" + indicators: list = [] + + if parent_codes is None: + parent_codes = [] + if parent_dimension_codes is None: + parent_dimension_codes = {} + if order_counter is None: + order_counter = [0] + if ancestor_labels is None: + ancestor_labels = [] + + for code_entry in codes: + code_id = code_entry.get("id") + code_urn = code_entry.get("code", "") + level = code_entry.get("level", "0") + indicator_code = self._parse_indicator_code_from_urn(code_urn) + codelist_id_for_code = self._parse_codelist_id_from_urn(code_urn) + + dimension_id = self._resolve_dimension_for_code( + dataflow_id, + codelist_id_for_code, + code_urn, + codelist_dimension_cache, + codelist_labels_cache, + codelist_desc_cache, + ) + + code_labels = ( + codelist_labels_cache.get(codelist_id_for_code, {}) + if codelist_id_for_code + else {} + ) + code_descs = ( + codelist_desc_cache.get(codelist_id_for_code, {}) + if codelist_id_for_code + else {} + ) + full_label = ( + code_labels.get(indicator_code, code_id) if indicator_code else code_id + ) + label = _derive_node_label( + codelist_id_for_code=codelist_id_for_code, + full_label=full_label, + parent_full_label=parent_full_label, + ancestor_labels=ancestor_labels, + ) + description = code_descs.get(indicator_code, "") if indicator_code else "" + + children = code_entry.get("hierarchicalCodes", []) + is_group = len(children) > 0 + + current_dimension_codes = parent_dimension_codes.copy() + if dimension_id and indicator_code: + current_dimension_codes[dimension_id] = indicator_code + + clean_parent_id = parent_id + if parent_id and "___" in parent_id: + clean_parent_id = parent_id.split("___")[-1] + + order_counter[0] += 1 + current_order = order_counter[0] + + use_depth = dataflow_id in DEPTH_OVERRIDE_DATAFLOWS + node_level = depth if use_depth else (int(level) if level else depth) + + indicator_info: dict = { + "id": code_id, + "indicator_code": indicator_code, + "label": label, + "description": description, + "order": current_order, + "level": node_level, + "depth": depth, + "parent_id": clean_parent_id, + "is_group": is_group, + "code_urn": code_urn, + "dimension_id": dimension_id, + } + if indicator_code and dimension_id: + indicator_info["series_id"] = _build_series_id( + agency_clean=agency_clean, + dataflow_id=dataflow_id, + current_dimension_codes=current_dimension_codes, + indicator_dimension_order=indicator_dimension_order, + parent_codes=parent_codes, + indicator_code=indicator_code, + ) + + indicators.append(indicator_info) + + if children: + child_parent_codes = parent_codes + ( + [indicator_code] if indicator_code else [] + ) + child_indicators = self._process_hierarchical_codes( + children, + dataflow_id=dataflow_id, + agency_clean=agency_clean, + indicator_dimension_order=indicator_dimension_order, + codelist_dimension_cache=codelist_dimension_cache, + codelist_labels_cache=codelist_labels_cache, + codelist_desc_cache=codelist_desc_cache, + parent_id=code_id, + depth=depth + 1, + parent_codes=child_parent_codes, + parent_dimension_codes=current_dimension_codes.copy(), + order_counter=order_counter, + parent_full_label=full_label, + ancestor_labels=ancestor_labels + [full_label], + ) + indicators.extend(child_indicators) + + return indicators + + def _resolve_dimension_for_code( + self: _MixinBase, + dataflow_id: str, + codelist_id_for_code: str | None, + code_urn: str, + codelist_dimension_cache: dict[str, str | None], + codelist_labels_cache: dict[str, dict], + codelist_desc_cache: dict[str, dict], + ) -> str | None: + """Memoize the dimension + labels + descriptions for a codelist.""" + if not codelist_id_for_code: + return None + if codelist_id_for_code not in codelist_dimension_cache: + codelist_dimension_cache[codelist_id_for_code] = ( + self._get_dimension_for_codelist(dataflow_id, codelist_id_for_code) + ) + + cached_is_empty = not codelist_labels_cache.get(codelist_id_for_code) + if codelist_id_for_code not in codelist_labels_cache or cached_is_empty: + cached_labels = self._codelist_cache.get(codelist_id_for_code, {}) + cached_descs = self._codelist_descriptions.get(codelist_id_for_code, {}) + if not cached_labels and code_urn: + urn_agency = self._parse_agency_from_urn(code_urn) + if urn_agency: + self._fetch_single_codelist(urn_agency, codelist_id_for_code) + cached_labels = self._codelist_cache.get(codelist_id_for_code, {}) + cached_descs = self._codelist_descriptions.get( + codelist_id_for_code, {} + ) + codelist_labels_cache[codelist_id_for_code] = cached_labels + codelist_desc_cache[codelist_id_for_code] = cached_descs + + return codelist_dimension_cache[codelist_id_for_code] + + def _fix_irfcl_hierarchy(self: _MixinBase, indicators: list[dict]) -> list[dict]: + """Re-parent IRFCL ``forwards`` children to be siblings.""" + forwards_node = next( + (i for i in indicators if i.get("label", "").lower() == "forwards"), + None, + ) + if not forwards_node: + return indicators + + forwards_id = forwards_node.get("id") + forwards_parent_id = forwards_node.get("parent_id") + forwards_depth = forwards_node.get("depth", 0) + + instrument_labels = {"futures", "swaps", "options", "other"} + for ind in indicators: + if ind.get("parent_id") != forwards_id: + continue + if ind.get("label", "").lower() in instrument_labels: + ind["parent_id"] = forwards_parent_id + ind["depth"] = forwards_depth + return indicators + + def _create_synthetic_groups_for_shared_prefixes( + self: _MixinBase, indicators: list[dict] + ) -> list[dict]: + """Wrap IRFCL siblings with shared path components under synthetic groups.""" + by_parent: dict[str | None, list[dict]] = defaultdict(list) + for ind in indicators: + by_parent[ind.get("parent_id")].append(ind) + + synthetic_groups: list[dict] = [] + + for parent_id, children in by_parent.items(): + path_children = [ + c + for c in children + if ", " in c.get("label", "") + and c.get("id") + and _is_irfcl_path_label(c.get("label", "")) + ] + if len(path_children) < 2: + continue + + labels = [c["label"] for c in path_children] + split_labels = [lbl.split(", ") for lbl in labels] + if not all( + split_labels + ): # pragma: no cover -- unreachable: ``str.split(", ")`` always returns a non-empty list, so every element of ``split_labels`` is truthy + continue + + min_parts = min(len(parts) for parts in split_labels) + shared_prefix_count = 0 + for i in range(min_parts - 1): + first_part = split_labels[0][i] + if all(parts[i] == first_part for parts in split_labels): + shared_prefix_count += 1 + else: + break + + shared_suffix_count = 0 + for i in range(1, min_parts): + last_part = split_labels[0][-i] + if all(parts[-i] == last_part for parts in split_labels): + shared_suffix_count += 1 + else: + break + + if shared_suffix_count > 0 and shared_suffix_count >= shared_prefix_count: + self._wrap_with_suffix_groups( + path_children, + split_labels, + shared_suffix_count, + parent_id, + synthetic_groups, + ) + elif shared_prefix_count > 0: + self._wrap_with_prefix_group( + path_children, + split_labels, + shared_prefix_count, + parent_id, + synthetic_groups, + ) + + if synthetic_groups: + all_indicators = indicators + synthetic_groups + all_indicators.sort(key=lambda x: x.get("order", 0)) + for i, ind in enumerate(all_indicators): + ind["order"] = i + 1 + return self._create_synthetic_groups_for_shared_prefixes(all_indicators) + + return indicators + + @staticmethod + def _wrap_with_suffix_groups( + path_children: list[dict], + split_labels: list[list[str]], + shared_suffix_count: int, + parent_id: str | None, + synthetic_groups: list[dict], + ) -> None: + """Build nested synthetic groups from a shared label suffix.""" + suffix_parts = split_labels[0][-shared_suffix_count:] + suffix_parts_reversed = list(reversed(suffix_parts)) + first_child_order = min(c.get("order", 0) for c in path_children) + first_child_depth = path_children[0].get("depth", 0) + + current_parent_id = parent_id + current_depth = first_child_depth + innermost_synthetic_id: str | None = None + + for i, suffix_part in enumerate(suffix_parts_reversed): + synthetic_id = ( + f"_SYNTH_{current_parent_id}_" + f"{re.sub(r'[^a-zA-Z0-9]', '_', suffix_part[:30])}_{i}" + ) + synthetic_groups.append( + { + "id": synthetic_id, + "indicator_code": None, + "label": suffix_part, + "description": "", + "order": first_child_order - 0.5 + (i * 0.01), + "level": current_depth, + "depth": current_depth, + "parent_id": current_parent_id, + "is_group": True, + "code_urn": None, + "dimension_id": None, + "series_id": None, + } + ) + current_parent_id = synthetic_id + current_depth += 1 + innermost_synthetic_id = synthetic_id + + for child in path_children: + child_parts = child["label"].split(", ") + remaining = child_parts[:-shared_suffix_count] + if remaining: + child["label"] = ", ".join(remaining) + child["parent_id"] = innermost_synthetic_id + child["depth"] = first_child_depth + 1 + + @staticmethod + def _wrap_with_prefix_group( + path_children: list[dict], + split_labels: list[list[str]], + shared_prefix_count: int, + parent_id: str | None, + synthetic_groups: list[dict], + ) -> None: + """Build a single synthetic group from a shared label prefix.""" + shared_prefix = ", ".join(split_labels[0][:shared_prefix_count]) + synthetic_id = ( + f"_SYNTH_{parent_id}_{re.sub(r'[^a-zA-Z0-9]', '_', shared_prefix[:30])}" + ) + first_child_order = min(c.get("order", 0) for c in path_children) + first_child_depth = path_children[0].get("depth", 0) + synthetic_groups.append( + { + "id": synthetic_id, + "indicator_code": None, + "label": shared_prefix, + "description": "", + "order": first_child_order - 0.5, + "level": first_child_depth, + "depth": first_child_depth, + "parent_id": parent_id, + "is_group": True, + "code_urn": None, + "dimension_id": None, + "series_id": None, + } + ) + for child in path_children: + child_parts = child["label"].split(", ") + remaining = child_parts[shared_prefix_count:] + if remaining: + child["label"] = ", ".join(remaining) + child["parent_id"] = synthetic_id + child["depth"] = first_child_depth + 1 + + def list_all_dataflow_tables(self: _MixinBase) -> dict[str, list[dict]]: + """Map curated dataflow ids to their validated presentation tables.""" + from openbb_imf.utils.constants import PRESENTATION_TABLES + + result: dict[str, list[dict]] = {} + for friendly_name, table_spec in PRESENTATION_TABLES.items(): + parts = table_spec.split("::") + if len(parts) != 2: + continue + dataflow_id, table_id = parts + if dataflow_id not in self.dataflows: + continue + try: + all_hierarchies = self.get_dataflow_hierarchies(dataflow_id) + except Exception: # noqa: BLE001 + continue + matching = next( + (h for h in all_hierarchies if h.get("id") == table_id), None + ) + if not matching: + continue + enriched = matching.copy() + enriched["friendly_name"] = friendly_name + enriched["dataflow_id"] = dataflow_id + result.setdefault(dataflow_id, []).append(enriched) + return result + + +def _is_irfcl_path_label(label: str) -> bool: + """Return True if ``label`` contains an IRFCL path-pattern phrase.""" + return any(p.lower() in label.lower() for p in _IRFCL_PATH_PATTERNS) + + +def _hierarchy_overlaps_dataflow( + hier_obj: dict, available_indicator_values: set[str] +) -> bool: + """Return True if the hierarchy references at least one dataflow code.""" + hier_codes_raw = hier_obj.get("hierarchicalCodes", []) + hier_code_values: set[str] = set() + + def _extract(codes_list: list) -> None: + for c in codes_list: + code_urn = c.get("code", "") + if code_urn and "INDICATOR" in code_urn and "." in code_urn: + actual_code = code_urn.rsplit(".", 1)[-1] + if actual_code: + hier_code_values.add(actual_code) + nested = c.get("hierarchicalCodes", []) + if nested: + _extract(nested) + + _extract(hier_codes_raw) + if not hier_code_values: + return True + if hier_code_values & available_indicator_values: + return True + return any( + avail.startswith(hier) + for hier in hier_code_values + for avail in available_indicator_values + ) + + +def _derive_node_label( + *, + codelist_id_for_code: str | None, + full_label: str, + parent_full_label: str | None, + ancestor_labels: list[str], +) -> str: + """Strip ancestor-redundant prefixes from a path-style codelist label.""" + label = full_label or "" + is_path_style = codelist_id_for_code and ( + "_INDICATOR_PUB" in codelist_id_for_code + or codelist_id_for_code == "CL_DIP_INDICATOR" + ) + + if is_path_style: + if codelist_id_for_code == "CL_DIP_INDICATOR" and ", " in full_label: + parts = full_label.split(", ") + if len(parts) > 1 and ( + parts[0].startswith("Inward") or parts[0].startswith("Outward") + ): + return ", ".join(parts[1:]) + + if parent_full_label and full_label.startswith(parent_full_label): + relative = full_label[len(parent_full_label) :].lstrip(", :") + if relative: + return relative + if ", " in full_label or ": " in full_label: + parts = re.split(r", |: ", full_label) + return parts[-1] if parts else full_label + return label + + if ancestor_labels and (", " in full_label or ": " in full_label): + return _strip_known_ancestors( + codelist_id_for_code, full_label, ancestor_labels + ) + + if ", " in full_label: + parts = full_label.split(", ") + return parts[-1] if parts else full_label + + elif ( + parent_full_label + and ", " in full_label + and full_label.startswith(parent_full_label) + ): + return full_label.rsplit(", ", 1)[-1] + + return label + + +def _strip_known_ancestors( + codelist_id_for_code: str | None, + full_label: str, + ancestor_labels: list[str], +) -> str: + """Remove label parts that match normalised ancestor labels.""" + + def normalize(s: str) -> str: + return re.sub(r"[^a-z0-9]", "", s.lower()) + + def split(lbl: str) -> list[str]: + out: list[str] = [] + for p in lbl.split(", "): + if ":" in p: + out.extend(sp.strip() for sp in p.split(":") if sp.strip()) + else: + out.append(p) + return out + + all_ancestor_parts: list[str] = [] + for ancestor in ancestor_labels: + all_ancestor_parts.extend(split(ancestor)) + ancestor_normalised = {normalize(p) for p in all_ancestor_parts} + + child_parts = split(full_label) + new_parts: list[str] = [] + for part in child_parts: + part_norm = normalize(part) + if part_norm in ancestor_normalised: + continue + is_in_ancestor = False + for pn in ancestor_normalised: + if pn.startswith("total") and pn[5:] == part_norm: + is_in_ancestor = True + break + if len(pn) >= 6 and pn in part_norm: + is_in_ancestor = True + break + if len(part_norm) >= 15 and len(pn) >= 15: + shorter, longer = ( + (part_norm, pn) if len(part_norm) <= len(pn) else (pn, part_norm) + ) + if shorter in longer or ( + len(shorter) > 30 + and any( + shorter[i : i + 30] in longer for i in range(len(shorter) - 30) + ) + ): + is_in_ancestor = True + break + if not is_in_ancestor: + new_parts.append(part) + + if not new_parts: + return child_parts[-1] if child_parts else full_label + if codelist_id_for_code and "IRFCL" in codelist_id_for_code and len(new_parts) >= 3: + return ", ".join(new_parts) + return ( + ", ".join(new_parts) + if new_parts + else (child_parts[-1] if child_parts else full_label) + ) + + +def _build_series_id( + *, + agency_clean: str, + dataflow_id: str, + current_dimension_codes: dict[str, str], + indicator_dimension_order: dict[str, int], + parent_codes: list[str], + indicator_code: str, +) -> str: + """Compose ``__``, ordered by dimension position.""" + ordered_dims = sorted( + [ + (dim_id, indicator_dimension_order[dim_id]) + for dim_id in current_dimension_codes + if dim_id in indicator_dimension_order + and current_dimension_codes.get(dim_id) + ], + key=lambda item: item[1], + ) + ordered_codes = [current_dimension_codes[dim_id] for dim_id, _ in ordered_dims] + unordered_dims = sorted( + dim_id + for dim_id in current_dimension_codes + if dim_id not in indicator_dimension_order + and current_dimension_codes.get(dim_id) + ) + ordered_codes.extend(current_dimension_codes[dim_id] for dim_id in unordered_dims) + + if ordered_codes: + return f"{agency_clean}_{dataflow_id}_{'_'.join(ordered_codes)}" + fallback_codes = parent_codes + [indicator_code] + return f"{agency_clean}_{dataflow_id}_{'_'.join(fallback_codes)}" diff --git a/openbb_platform/providers/imf/openbb_imf/utils/metadata/_typing.py b/openbb_platform/providers/imf/openbb_imf/utils/metadata/_typing.py new file mode 100644 index 00000000000..43c827c53e3 --- /dev/null +++ b/openbb_platform/providers/imf/openbb_imf/utils/metadata/_typing.py @@ -0,0 +1,234 @@ +"""Type stubs for the IMF metadata mixin pattern.""" + +from __future__ import annotations + +import threading + + +class _MixinBase: + dataflows: dict[str, dict] + datastructures: dict[str, dict] + conceptschemes: dict[str, dict] + dataflow_groups: dict[str, list[dict]] + hierarchies: dict[str, dict] + _metadata_cache: dict + _constraints_cache: dict + _codelist_cache: dict[str, dict] + _codelist_descriptions: dict[str, dict] + _dataflow_parameters_cache: dict[str, dict] + _dataflow_indicators_cache: dict[str, list] + _hierarchy_to_codelist_map: dict[str, str] + _codelist_to_hierarchies_map: dict[str, list[str]] + _codelist_lock: threading.Lock + _constraints_lock: threading.Lock + + @staticmethod + def _parse_agency_from_urn(code_urn: str) -> str | None: + raise NotImplementedError + + @staticmethod + def _parse_codelist_urn(urn: str) -> str | None: + raise NotImplementedError + + @staticmethod + def _parse_indicator_code_from_urn(code_urn: str) -> str | None: + raise NotImplementedError + + @staticmethod + def _parse_codelist_id_from_urn(code_urn: str) -> str | None: + raise NotImplementedError + + @staticmethod + def _parse_query(query: str) -> list[list[str]]: + raise NotImplementedError + + @staticmethod + def _build_time_period_parameters( + constraints_response: dict | None, + ) -> tuple[list[dict], str | None]: + raise NotImplementedError + + def _extract_all_codelists_from_hierarchy(self, hierarchy: dict) -> set[str]: + raise NotImplementedError + + def _build_hierarchy_to_codelist_map(self) -> dict[str, str]: + raise NotImplementedError + + def _build_codelist_to_hierarchies_map(self) -> dict[str, list[str]]: + raise NotImplementedError + + def _get_dimension_for_codelist( + self, dataflow_id: str, codelist_id: str + ) -> str | None: + raise NotImplementedError + + def _load_from_cache(self) -> bool: + raise NotImplementedError + + def _fetch_single_codelist(self, agency_id: str, codelist_id: str) -> bool: + raise NotImplementedError + + def _bulk_fetch_and_cache_codelists(self, agency_id: str, dataflow_id: str) -> None: + raise NotImplementedError + + def _get_codelist_map( + self, + codelist_id: str, + agency_id: str, + dataflow_id: str, + include_descriptions: bool = False, + ) -> dict: + raise NotImplementedError + + def list_dataflows(self) -> list[dict]: + raise NotImplementedError + + def search_dataflows(self, query: str) -> list[dict]: + raise NotImplementedError + + def search_indicators( + self, + query: str, + dataflows: list[str] | str | None = None, + keywords: list[str] | None = None, + ) -> list[dict]: + raise NotImplementedError + + def get_dataflow_parameters(self, dataflow_id: str) -> dict[str, list[dict]]: + raise NotImplementedError + + def get_available_constraints( + self, + dataflow_id: str, + key: str, + component_id: str | None = None, + mode: str | None = None, + references: str | None = None, + **kwargs, + ) -> dict: + raise NotImplementedError + + def _resolve_codelist_id( + self, dataflow_id: str, dsd_id: str | None, dim_id: str, dim_meta: dict + ) -> str | None: + raise NotImplementedError + + def get_indicators_in(self, dataflow_id: str) -> list: + raise NotImplementedError + + def get_dataflow_hierarchies(self, dataflow_id: str) -> list[dict]: + raise NotImplementedError + + def get_dataflow_table_structure( + self, dataflow_id: str, table_id: str | None = None + ) -> dict: + raise NotImplementedError + + def list_all_dataflow_tables(self) -> dict[str, list[dict]]: + raise NotImplementedError + + def _validate_hierarchy_queryable(self, dataflow_id: str, codes: list) -> bool: + raise NotImplementedError + + def _fix_irfcl_hierarchy(self, indicators: list[dict]) -> list[dict]: + raise NotImplementedError + + def _create_synthetic_groups_for_shared_prefixes( + self, indicators: list[dict] + ) -> list[dict]: + raise NotImplementedError + + def _build_indicator_table_maps( + self, dataflow_ids: set[str] + ) -> tuple[dict[str, list[dict]], dict[str, str]]: + raise NotImplementedError + + def _collect_indicators( + self, + dataflow_ids: set[str], + indicator_to_tables: dict[str, list[dict]], + indicator_table_text: dict[str, str], + ) -> list[dict]: + raise NotImplementedError + + def _filter_indicators_by_query( + self, indicators: list[dict], query: str + ) -> list[dict]: + raise NotImplementedError + + def _filter_indicators_by_keywords( + self, indicators: list[dict], keywords: list[str] | None + ) -> list[dict]: + raise NotImplementedError + + def _find_indicator_codelist_for_hierarchies( + self, dataflow_id: str, dsd_id: str, dimensions: list + ) -> str | None: + raise NotImplementedError + + def _split_irfcl_hierarchy( + self, + dataflow_id: str, + hier_id: str, + hier_obj: dict, + top_level_codes: list[dict], + indicator_codelist_id: str, + ) -> list[dict]: + raise NotImplementedError + + @staticmethod + def _select_hierarchy( + table_id: str | None, available_hierarchies: list[dict] + ) -> tuple[str | None, str | None, dict | None, str]: + raise NotImplementedError + + def _process_hierarchical_codes( + self, + codes: list, + *, + dataflow_id: str, + agency_clean: str, + indicator_dimension_order: dict[str, int], + codelist_dimension_cache: dict[str, str | None], + codelist_labels_cache: dict[str, dict], + codelist_desc_cache: dict[str, dict], + parent_id: str | None = None, + depth: int = 0, + parent_codes: list | None = None, + parent_dimension_codes: dict[str, str] | None = None, + order_counter: list | None = None, + parent_full_label: str | None = None, + ancestor_labels: list | None = None, + ) -> list[dict]: + raise NotImplementedError + + def _resolve_dimension_for_code( + self, + dataflow_id: str, + codelist_id_for_code: str | None, + code_urn: str, + codelist_dimension_cache: dict[str, str | None], + codelist_labels_cache: dict[str, dict], + codelist_desc_cache: dict[str, dict], + ) -> str | None: + raise NotImplementedError + + @staticmethod + def _wrap_with_suffix_groups( + path_children: list[dict], + split_labels: list[list[str]], + shared_suffix_count: int, + parent_id: str | None, + synthetic_groups: list[dict], + ) -> None: + raise NotImplementedError + + @staticmethod + def _wrap_with_prefix_group( + path_children: list[dict], + split_labels: list[list[str]], + shared_prefix_count: int, + parent_id: str | None, + synthetic_groups: list[dict], + ) -> None: + raise NotImplementedError diff --git a/openbb_platform/providers/imf/openbb_imf/utils/port_watch_helpers.py b/openbb_platform/providers/imf/openbb_imf/utils/port_watch_helpers.py index 69050a9c0cd..43cd51219c1 100644 --- a/openbb_platform/providers/imf/openbb_imf/utils/port_watch_helpers.py +++ b/openbb_platform/providers/imf/openbb_imf/utils/port_watch_helpers.py @@ -1,16 +1,98 @@ """IMF Port Watch helpers.""" -# pylint: disable=R0914 +from __future__ import annotations from typing import Any from async_lru import alru_cache + from openbb_imf.utils.constants import ( CHOKEPOINTS_BASE_URL, + CONTAINER_METRICS_BASE_URL, DAILY_TRADE_BASE_URL, + DAILY_TRADE_REG_BASE_URL, + DISRUPTIONS_DATABASE_URL, + MONTHLY_TRADENOW_BASE_URL, + SANKEY_DATA_URL, ) +async def _arcgis_query( + base_url: str, + where: str = "1=1", + *, + order_by: str | None = None, + page_size: int = 1000, + extra_params: str = "", +) -> list[dict[str, Any]]: + """Paginate an ArcGIS FeatureServer query and return raw attribute dicts.""" + from urllib.parse import quote + + from openbb_core.app.model.abstract.error import OpenBBError + from openbb_core.provider.utils.helpers import get_async_requests_session + + encoded_where = quote(where, safe="") + order_clause = f"&orderByFields={quote(order_by, safe='')}" if order_by else "" + base_query = ( + f"where={encoded_where}&outFields=*{order_clause}" + f"&returnZ=true&resultRecordCount={page_size}" + f"&maxRecordCountFactor=5&outSR=&f=json{extra_params}" + ) + + rows: list[dict[str, Any]] = [] + offset = 0 + async with await get_async_requests_session(timeout=120) as session: + while True: + url = f"{base_url}{base_query}&resultOffset={offset}" + async with await session.get(url) as response: + if response.status != 200: + raise OpenBBError( + f"ArcGIS query failed: {response.status} -> {url}" + ) + data = await response.json() + features = data.get("features") or [] + for feat in features: + attrs = feat.get("attributes") or {} + if attrs: + rows.append(attrs) + if not data.get("exceededTransferLimit") or not features: + break + offset += len(features) + return rows + + +def _epoch_to_iso_date(value: Any) -> str | None: + """Convert a millisecond-epoch integer to ``YYYY-MM-DD`` (or pass through strings).""" + from datetime import datetime, timezone + + if value is None: + return None + if isinstance(value, str): + return value[:10] + if isinstance(value, (int, float)) and value > 1e10: + return datetime.fromtimestamp(value / 1000, tz=timezone.utc).strftime( + "%Y-%m-%d" + ) + return str(value) + + +def _normalize_ymd(row: dict[str, Any]) -> dict[str, Any]: + """Replace ``year``/``month``/``day`` triplet with a single ``date`` string.""" + from contextlib import suppress + from datetime import datetime + + if {"year", "month", "day"}.issubset(row): + with suppress(TypeError, ValueError): + row["date"] = datetime( + int(row["year"]), int(row["month"]), int(row["day"]) + ).strftime("%Y-%m-%d") + return { + k: v + for k, v in row.items() + if k not in ("year", "month", "day", "ObjectId", "GlobalID") + } + + def list_countries() -> list[dict[str, str]]: """List available countries for IMF Port Watch. @@ -113,7 +195,6 @@ async def get_daily_chokepoint_data( chokepoint_id : str The ID of the chokepoint (e.g., "chokepoint1"). 1-24 are valid IDs """ - # pylint: disable=import-outside-toplevel from datetime import datetime # noqa from openbb_core.app.model.abstract.error import OpenBBError from openbb_core.provider.utils.helpers import get_async_requests_session @@ -200,7 +281,6 @@ async def get_all_daily_chokepoint_activity_data( start_date: str | None = None, end_date: str | None = None ) -> list: """Get the complete historical volume dataset for all chokepoints.""" - # pylint: disable=import-outside-toplevel import asyncio # noqa from openbb_core.app.model.abstract.error import OpenBBError @@ -240,15 +320,11 @@ async def get_all_daily_chokepoint_activity_data( async def get_all_daily_port_activity_data() -> list: """Get all port activity data as a bulk download CSV. - This function fetches a large file containing daily global port activity. - Expect the file to be around 800 MB in size. - Returns ------- list A list of dictionaries, each representing a row of port activity data. """ - # pylint: disable=import-outside-toplevel from io import StringIO # noqa from openbb_core.app.model.abstract.error import OpenBBError from openbb_core.provider.utils.helpers import get_async_requests_session @@ -260,9 +336,10 @@ async def get_all_daily_port_activity_data() -> list: ) content = "" try: - async with await get_async_requests_session( - timeout=120 - ) as session, await session.get(url) as response: + async with ( + await get_async_requests_session(timeout=120) as session, + await session.get(url) as response, + ): if response.status != 200: raise OpenBBError( f"Failed to fetch port activity data: {response.status} - {response.reason}" @@ -303,7 +380,6 @@ async def get_daily_port_activity_data( list A list of dictionaries, each representing daily activity data for the specified port. """ - # pylint: disable=import-outside-toplevel from datetime import datetime # noqa from openbb_core.app.model.abstract.error import OpenBBError from openbb_core.provider.utils.helpers import get_async_requests_session @@ -325,7 +401,7 @@ async def get_daily_port_activity_data( return ( ( DAILY_TRADE_BASE_URL - + f"where=portid%20%3D%20%27{port_id.upper()}%27&" # type: ignore + + f"where=portid%20%3D%20%27{port_id.upper()}%27&" + f"outFields=*&orderByFields=date&returnZ=true&resultOffset={offset}&resultRecordCount=1000" + "&maxRecordCountFactor=5&outSR=&f=json" ) @@ -399,7 +475,6 @@ async def list_ports() -> list[dict[str, Any]]: list[dict] A list of dictionaries, each representing a port with its details. """ - # pylint: disable=import-outside-toplevel from openbb_core.app.model.abstract.error import OpenBBError from openbb_core.provider.utils.helpers import get_async_requests_session @@ -411,9 +486,10 @@ async def list_ports() -> list[dict[str, Any]]: ports: list[dict] = [] try: - async with await get_async_requests_session() as session, await session.get( - url - ) as response: + async with ( + await get_async_requests_session() as session, + await session.get(url) as response, + ): if response.status != 200: raise OpenBBError( f"Failed to fetch ports data: {response.status} - {response.reason}" @@ -429,24 +505,224 @@ async def list_ports() -> list[dict[str, Any]]: raise OpenBBError(f"Error fetching ports data: {e} -> {e.args}") from e -def get_ports() -> list[dict[str, Any]]: - """Get the list of all ports synchronously.""" - # pylint: disable=import-outside-toplevel +def _run_list_ports_sync() -> list[dict[str, Any]]: + """Run ``list_ports`` synchronously in a worker thread.""" import asyncio - try: - loop = asyncio.get_running_loop() - except RuntimeError: - loop = None + return asyncio.run(list_ports()) - if loop is not None: - # Already in an async context - import concurrent.futures - with concurrent.futures.ThreadPoolExecutor() as executor: - future = executor.submit(asyncio.run, list_ports()) - ports = future.result() - else: - ports = asyncio.run(list_ports()) +def get_ports() -> list[dict[str, Any]]: + """Get the list of all ports synchronously.""" + import concurrent.futures - return ports + with concurrent.futures.ThreadPoolExecutor() as executor: + future = executor.submit(_run_list_ports_sync) + return future.result() + + +def _date_where_clause(field: str, start_date: str | None, end_date: str | None) -> str: + """Compose an ArcGIS ``WHERE`` clause restricting ``field`` to a date range.""" + clauses: list[str] = [] + if start_date: + clauses.append(f"{field} >= TIMESTAMP '{start_date} 00:00:00'") + if end_date: + clauses.append(f"{field} <= TIMESTAMP '{end_date} 23:59:59'") + if not clauses: + return "1=1" + return " AND ".join(clauses) + + +@alru_cache(maxsize=64) +async def get_country_daily_activity( + iso3: str, + start_date: str | None = None, + end_date: str | None = None, +) -> list[dict[str, Any]]: + """Daily country-level maritime activity from ``Daily_Trade_Data_REG``.""" + code = iso3.strip().upper() + where = _date_where_clause("date", start_date, end_date) + where = f"ISO3 = '{code}'" + ("" if where == "1=1" else f" AND ({where})") + rows = await _arcgis_query(DAILY_TRADE_REG_BASE_URL, where=where, order_by="date") + return [_normalize_ymd(r) for r in rows] + + +@alru_cache(maxsize=64) +async def get_monthly_trade( + iso3: str, + start_date: str | None = None, + end_date: str | None = None, +) -> list[dict[str, Any]]: + """Monthly TradeNow indices for an ISO3 country.""" + code = iso3.strip().upper() + where = _date_where_clause("date", start_date, end_date) + where = f"ISO3 = '{code}'" + ("" if where == "1=1" else f" AND ({where})") + rows = await _arcgis_query(MONTHLY_TRADENOW_BASE_URL, where=where, order_by="date") + out: list[dict[str, Any]] = [] + for r in rows: + r["date"] = _epoch_to_iso_date(r.get("date")) + out.append(_normalize_ymd(r)) + return out + + +@alru_cache(maxsize=64) +async def get_container_metrics( + port_ids: tuple[str, ...] | None = None, + metric: str | None = None, + start_date: str | None = None, + end_date: str | None = None, +) -> list[dict[str, Any]]: + """Container-specific port metrics from ``Container_Metrics`` in long format. + + Always returns ``[{metric, portid, date, value}]``. When ``port_ids`` is + ``None``, every port column from the wide source row is emitted; pass a + tuple of port ids (uppercase) to restrict the output. + """ + where = _date_where_clause("date_in", start_date, end_date) + if metric: + where = f"metric = '{metric}'" + ("" if where == "1=1" else f" AND ({where})") + rows = await _arcgis_query( + CONTAINER_METRICS_BASE_URL, where=where, order_by="date_in" + ) + keep = {p.upper() for p in port_ids} if port_ids else None + skip = { + "OBJECTID", + "OBJECTID_1", + "METRIC", + "DATE", + "DATE_IN", + "GLOBALID", + "YEAR", + "MONTH", + "DAY", + } + out: list[dict[str, Any]] = [] + for r in rows: + date_str = _epoch_to_iso_date(r.get("date") or r.get("date_in")) + metric_val = r.get("metric") + for col, val in r.items(): + col_up = col.upper() + if col_up in skip or not col_up.startswith("PORT"): + continue + if val is None: + continue + if keep is not None and col_up not in keep: + continue + out.append( + { + "metric": metric_val, + "portid": col_up, + "date": date_str, + "value": val, + } + ) + return out + + +@alru_cache(maxsize=32) +async def get_disruption_events( + iso3: str | None = None, + eventtype: str | None = None, + alertlevel: str | None = None, + active_only: bool = False, + start_date: str | None = None, + end_date: str | None = None, +) -> list[dict[str, Any]]: + """Disruption events from ``portwatch_disruptions_database``.""" + clauses: list[str] = [] + if iso3: + clauses.append(f"country = '{iso3.strip().upper()}'") + if eventtype: + clauses.append(f"eventtype = '{eventtype}'") + if alertlevel: + clauses.append(f"alertlevel = '{alertlevel.upper()}'") + if active_only: + clauses.append("(todate IS NULL OR todate >= CURRENT_TIMESTAMP)") + date_clause = _date_where_clause("fromdate", start_date, end_date) + if date_clause != "1=1": + clauses.append(date_clause) + where = " AND ".join(clauses) if clauses else "1=1" + rows = await _arcgis_query( + DISRUPTIONS_DATABASE_URL, where=where, order_by="fromdate DESC" + ) + for r in rows: + for field in ("fromdate", "todate", "editdate"): + if field in r: + r[field] = _epoch_to_iso_date(r[field]) + return rows + + +@alru_cache(maxsize=32) +async def get_disruption_sankey_edges(event_id: int) -> list[dict[str, Any]]: + """Capacity spillover edges for a disruption event.""" + where = f"eventid = {int(event_id)}" + return await _arcgis_query(SANKEY_DATA_URL, where=where) + + +async def get_tradenow_region_choices() -> list[dict[str, str]]: + """Distinct ``[{label: region, value: ISO3}]`` from ``Monthly_TradeNow``.""" + rows = await _arcgis_query( + MONTHLY_TRADENOW_BASE_URL, + where="ISO3 IS NOT NULL", + extra_params="&returnDistinctValues=true&outFields=region,ISO3", + ) + seen: set[str] = set() + out: list[dict[str, str]] = [] + for r in rows: + iso3 = r.get("ISO3") + region = r.get("region") or iso3 + if not iso3 or iso3 in seen: + continue + seen.add(iso3) + out.append({"label": str(region), "value": str(iso3)}) + return sorted(out, key=lambda c: c["label"]) + + +async def get_container_port_choices() -> list[dict[str, str]]: + """Distinct port columns from the wide ``Container_Metrics`` table.""" + rows = await _arcgis_query( + CONTAINER_METRICS_BASE_URL, where="1=1", extra_params="&resultRecordCount=1" + ) + if not rows: + return [] + ignored = {"OBJECTID", "OBJECTID_1", "METRIC", "DATE", "DATE_IN", "GLOBALID"} + ports = sorted( + k.upper() + for k in rows[0] + if k.upper().startswith("PORT") and k.upper() not in ignored + ) + name_map = await get_container_port_name_map() + return [{"label": name_map.get(p, p), "value": p} for p in ports] + + +@alru_cache(maxsize=1) +async def get_container_port_name_map() -> dict[str, str]: + """Map every container-metrics port id (e.g. ``PORT1065``) to a friendly name.""" + ports = await list_ports() + out: dict[str, str] = {} + for p in ports: + pid = (p.get("portid") or "").upper() + if not pid: + continue + out[pid] = p.get("fullname") or p.get("portname") or pid + return out + + +async def get_sankey_event_choices() -> list[dict[str, str]]: + """Distinct disruption events that have Sankey spillover edges.""" + rows = await _arcgis_query( + DISRUPTIONS_DATABASE_URL, + where="1=1", + order_by="fromdate DESC", + extra_params="&outFields=eventid,eventname,fromdate", + ) + out: list[dict[str, str]] = [] + for r in rows: + eid = r.get("eventid") + if eid is None: + continue + name = r.get("eventname") or f"event {eid}" + fromdate = _epoch_to_iso_date(r.get("fromdate")) or "" + label = f"{name} ({fromdate})" if fromdate else str(name) + out.append({"label": label, "value": str(eid)}) + return out diff --git a/openbb_platform/providers/imf/openbb_imf/utils/progressive_helper.py b/openbb_platform/providers/imf/openbb_imf/utils/progressive_helper.py index 7f98e5e7d82..f0aa809d2a4 100644 --- a/openbb_platform/providers/imf/openbb_imf/utils/progressive_helper.py +++ b/openbb_platform/providers/imf/openbb_imf/utils/progressive_helper.py @@ -1,14 +1,12 @@ """IMF Progressive Query Helper.""" -# pylint: disable=W0212 +from __future__ import annotations -from openbb_imf.utils.query_builder import ImfQueryBuilder +from openbb_imf.utils.query_builder import ImfQueryBuilder # noqa: F401 class ImfParamsBuilder: - """A helper class to build IMF queries progressively by making sequential dimension selections, - for each dimension of a dataflow, filtering the available options at each step based on previous selections. - """ + """Build IMF queries progressively by making sequential dimension selections.""" def __init__(self, dataflow_id: str): """Initialize the ImfParamsBuilder object. @@ -18,7 +16,9 @@ class ImfParamsBuilder: dataflow_id : str The ID of the dataflow to build a query for. """ - self._builder = ImfQueryBuilder() + import sys + + self._builder = sys.modules[__name__].ImfQueryBuilder() if dataflow_id not in self._builder.metadata.dataflows: raise KeyError( f"Dataflow '{dataflow_id}' not found." @@ -42,12 +42,12 @@ class ImfParamsBuilder: """Get the list of dimension IDs in their specified order.""" dimensions_metadata = self.dsd.get("dimensions", []) - # Sort by position if available, otherwise keep original order if dimensions_metadata and all( d.get("position") is not None for d in dimensions_metadata ): dimensions_metadata = sorted( - dimensions_metadata, key=lambda x: int(x.get("position")) # type: ignore + dimensions_metadata, + key=lambda x: int(x.get("position")), ) return [ @@ -57,8 +57,7 @@ class ImfParamsBuilder: ] def get_next_dimension_to_select(self) -> str | None: - """ - Get the ID of the next dimension that needs a selection. + """Get the ID of the next dimension that needs a selection. Returns ------- @@ -99,8 +98,6 @@ class ImfParamsBuilder: if self._selections[dim] is not None: key_parts.append(self._selections[dim]) else: - # Use wildcard '*' for unselected dimensions instead of empty string - # Empty string creates malformed URLs like '../' key_parts.append("*") key = ".".join(key_parts) @@ -109,7 +106,6 @@ class ImfParamsBuilder: key=key, component_id=dimension_id, ) - # Store the last constraints response for time period validation self._last_constraints_response = constraints options: list[dict[str, str]] = [] @@ -170,8 +166,6 @@ class ImfParamsBuilder: f" Valid dimensions: {list(self._selections.keys())}" ) self._selections[dimension[0]] = dimension[1] - # When a selection is made, we clear selections for downstream dimensions - # as they might now be invalid. found_dim = False for dim in self._dimensions: if found_dim: @@ -196,8 +190,7 @@ class ImfParamsBuilder: def build_url( self, start_date: str | None = None, end_date: str | None = None ) -> str: - """ - Build the final API URL based on the current selections. + """Build the final API URL based on the current selections. Parameters ---------- @@ -219,8 +212,7 @@ class ImfParamsBuilder: ) def fetch(self, start_date: str | None = None, end_date: str | None = None) -> dict: - """ - Build the URL and fetch the data based on the current selections. + """Build the URL and fetch the data based on the current selections. Parameters ---------- diff --git a/openbb_platform/providers/imf/openbb_imf/utils/query_builder.py b/openbb_platform/providers/imf/openbb_imf/utils/query_builder.py index fad18bd329d..f983433740d 100644 --- a/openbb_platform/providers/imf/openbb_imf/utils/query_builder.py +++ b/openbb_platform/providers/imf/openbb_imf/utils/query_builder.py @@ -1,7 +1,6 @@ """IMF Query Builder utilities.""" -# pylint: disable=C0302,R0911,R0912,R0913,R0914,R0915,R0917,R1702,W0212 -# flake8: noqa: PLR0911,PLR0912,PLR0913,PLR0917 +from __future__ import annotations import warnings @@ -37,18 +36,15 @@ class ImfQueryBuilder: dsd = self.metadata.datastructures[dsd_id] all_dimensions = dsd.get("dimensions", []) dimension_ids = {d["id"] for d in all_dimensions if d.get("id")} - # Create a map for case-insensitive matching of dimension IDs dimension_id_map = {d_id.lower(): d_id for d_id in dimension_ids} final_kwargs: dict = {} for key, value in kwargs.items(): - # Try to match the key (case-insensitive) to a known dimension ID matched_dim_id = dimension_id_map.get(key.lower()) if matched_dim_id: final_kwargs[matched_dim_id] = value else: - # If not a dimension, keep the original key final_kwargs[key] = value dimensions = sorted( @@ -60,15 +56,12 @@ class ImfQueryBuilder: key=lambda x: int(x.get("position")), ) key_parts: list = [] - # Use a set to keep track of dimensions that have been added to the key_parts - # to avoid adding them again to query_params dimensions_in_key: set = set() for dim in dimensions: dim_id = dim.get("id") param_value = final_kwargs.get(dim_id) - # Handle wildcards and empty values if ( param_value is None or param_value in ["", "*"] @@ -90,35 +83,29 @@ class ImfQueryBuilder: f"https://api.imf.org/external/sdmx/3.0/data/dataflow/" f"{agency_id}/{dataflow}/+/{key}" ) - # Only include parameters in query_params that are not dimensions query_params = { k: v for k, v in final_kwargs.items() if k not in dimensions_in_key } - # Format dates for TIME_PERIOD filter frequency = (final_kwargs.get("FREQUENCY") or "").upper() def format_date( date_str: str, frequency: str, is_end_date: bool = False ) -> str: """Format date string based on frequency to match IMF TIME_PERIOD format.""" - if not date_str: + if not date_str: # pragma: no cover -- unreachable: every caller wraps the call in ``if start_date:`` / ``if end_date:`` so an empty value never reaches this branch return date_str - # Parse the date - could be YYYY, YYYY-MM, or YYYY-MM-DD parts = date_str.split("-") year = int(parts[0]) month = int(parts[1]) if len(parts) >= 2 else 1 if frequency == "A" or len(parts) == 1: - # Annual frequency or year-only input if is_end_date: - # For end date, use first day of next year return f"{year + 1}-01-01" return f"{year}-01-01" if is_end_date: - # For end date, use first day of next month month += 1 if month > 12: month = 1 @@ -155,10 +142,7 @@ class ImfQueryBuilder: return url def validate_dimension_constraints(self, dataflow: str, **kwargs) -> None: - """ - Validate that the provided dimension parameter combinations are valid according - to IMF API constraints. Uses progressive constraint checking to ensure the - parameters are actually available for the dataflow. + """Validate that the provided dimension parameter combinations are valid according Parameters ---------- @@ -172,21 +156,18 @@ class ImfQueryBuilder: ValueError If the parameter combination is invalid according to API constraints """ - # pylint: disable=import-outside-toplevel from openbb_core.app.model.abstract.warning import OpenBBWarning + from openbb_imf.utils.progressive_helper import ImfParamsBuilder try: builder = ImfParamsBuilder(dataflow) dimensions_in_order = builder._get_dimensions_in_order() - # Build up selections progressively and validate each step for dim_id in dimensions_in_order: if dim_id in kwargs: user_value = kwargs[dim_id] - # Normalize to list for checking - # Handle comma-separated or plus-separated strings if isinstance(user_value, str): if "," in user_value: user_values = [v.strip() for v in user_value.split(",")] @@ -199,29 +180,24 @@ class ImfQueryBuilder: else: user_values = [user_value] if user_value else [] - # Filter out empty strings user_values = [v for v in user_values if v] if not user_values: continue - # Skip wildcards - they're always valid if user_values == ["*"] or len("+".join(user_values)) > 2000: builder.set_dimension((dim_id, "*")) continue - # Get available options for this dimension given prior selections available_options = builder.get_options_for_dimension(dim_id) available_values = {opt["value"] for opt in available_options} - # Check if user's values are valid invalid_values = [] for val in user_values: if val != "*" and val not in available_values: invalid_values.append(val) if invalid_values: - # Build helpful error message prior_selections = { d: kwargs.get(d) for d in dimensions_in_order @@ -230,7 +206,6 @@ class ImfQueryBuilder: < dimensions_in_order.index(dim_id) } - # Show all available values without truncation all_values = sorted(available_values) error_msg = ( f"Invalid value(s) for dimension '{dim_id}': {invalid_values}. " @@ -239,11 +214,8 @@ class ImfQueryBuilder: ) raise ValueError(error_msg) - # Set the valid value to progress the builder builder.set_dimension((dim_id, user_values[0])) - # Check time period constraints from the last dimension validation - # The _last_constraints_response already contains contentConstraints with TIME_PERIOD info start_date = kwargs.get("start_date") end_date = kwargs.get("end_date") @@ -253,12 +225,9 @@ class ImfQueryBuilder: full_response = constraints.get("full_response", {}) data = full_response.get("data", {}) - # Time period annotations can be in contentConstraints or dataConstraints - # Check both places time_start = None time_end = None - # Try contentConstraints first (primary location) content_constraints = data.get("contentConstraints", []) for constraint in content_constraints: for annotation in constraint.get("annotations", []): @@ -269,7 +238,6 @@ class ImfQueryBuilder: elif ann_id == "time_period_end": time_end = ann_title - # Fall back to dataConstraints if not found if not (time_start and time_end): data_constraints = data.get("dataConstraints", []) for constraint in data_constraints: @@ -282,9 +250,6 @@ class ImfQueryBuilder: time_end = ann_title if time_start and time_end: - # Use >= because time_end represents the END of the last period - # e.g., time_end=2025-01-01 means data up to end of 2024 - # So start_date=2025-01-01 would be requesting data AFTER the available range if start_date and start_date >= time_end: raise ValueError( f"Requested start_date '{start_date}' is after the latest available data '{time_end}'. " @@ -297,7 +262,6 @@ class ImfQueryBuilder: ) except KeyError as e: - # Dataflow not found or other metadata issue - let it pass through warnings.warn( f"Could not validate constraints for dataflow '{dataflow}': {e}", OpenBBWarning, @@ -314,8 +278,6 @@ class ImfQueryBuilder: ) -> dict: """Fetch data from the IMF API for a given dataflow and parameters. - Uses XML format for data retrieval as the JSON format has data truncation issues. - Parameters ---------- dataflow : str @@ -329,19 +291,18 @@ class ImfQueryBuilder: **kwargs Dimension parameters """ - # pylint: disable=import-outside-toplevel from openbb_core.app.model.abstract.error import OpenBBError from openbb_core.provider.utils.errors import EmptyDataError from openbb_core.provider.utils.helpers import make_request + from pandas import DataFrame, to_numeric + from requests.exceptions import RequestException + from openbb_imf.utils.helpers import parse_time_period from openbb_imf.utils.table_presentation import ( extract_unit_from_label, parse_unit_and_scale, ) - from pandas import DataFrame, to_numeric - from requests.exceptions import RequestException - # Validate dimension constraints before making the API call if not _skip_validation: self.validate_dimension_constraints( dataflow, start_date=start_date, end_date=end_date, **kwargs @@ -365,74 +326,57 @@ class ImfQueryBuilder: f"An error occurred during the HTTP request: {url} -> {e} -> {res_content}" ) from e - # Parse XML try: import defusedxml.ElementTree as DefusedET root = DefusedET.fromstring(xml_content) - except Exception as e: # pylint: disable=broad-except + except Exception as e: raise OpenBBError(f"Failed to parse XML response: {url} -> {e}") from e - # Define namespaces used in IMF SDMX responses namespaces = { "message": "http://www.sdmx.org/resources/sdmxml/schemas/v3_0/message", "ss": "http://www.sdmx.org/resources/sdmxml/schemas/v3_0/data/structurespecific", "common": "http://www.sdmx.org/resources/sdmxml/schemas/v3_0/common", } - # Find all Series elements dataset = root.find(".//message:DataSet", namespaces) if dataset is None: - # Try without namespace prefix dataset = root.find(".//DataSet") if dataset is None: - # Try with ss namespace dataset = root.find(".//ss:DataSet", namespaces) if dataset is None: raise OpenBBError( EmptyDataError(f"No data found in the response. URL: {url}") ) - # Parse Group elements to extract group-level attributes (UNIT, ACCOUNTING_ENTRY, etc.) - # Group structure: - # USD - # NETLA - # group_attributes: dict[str, dict[str, str]] = {} - # Find all Group elements - they can have namespace prefix for group in dataset.findall("Group") + dataset.findall("ss:Group", namespaces): - # The group key is typically the INDICATOR code or similar dimension group_key = None for attr_name, attr_value in group.attrib.items(): - # Skip namespace type attributes like ns1:type if "type" in attr_name.lower() and "group" in attr_value.lower(): continue - # The first non-type attribute is the key (e.g., INDICATOR) group_key = attr_value break if not group_key: continue - # Extract Comp elements containing group-level attribute values group_attrs: dict[str, str] = {} for comp in group.findall("Comp") + group.findall("ss:Comp", namespaces): comp_id = comp.attrib.get("id") if comp_id: - # Value is in a child element - value_elem = comp.find("Value") or comp.find("ss:Value", namespaces) + value_elem = comp.find("Value") + if value_elem is None: + value_elem = comp.find("ss:Value", namespaces) if value_elem is not None and value_elem.text: group_attrs[comp_id] = value_elem.text if group_attrs: group_attributes[group_key] = group_attrs - # Get dataflow metadata dataflow_obj = self.metadata.dataflows.get(dataflow, {}) - # Build translation maps for dimension values translation_maps = self._get_cached_translations(dataflow) - # Build dimension order mapping for proper series_id construction structure_ref = dataflow_obj.get("structureRef", {}) dsd_id = structure_ref.get("id") indicator_dimension_order: dict[str, int] = {} @@ -465,20 +409,16 @@ class ImfQueryBuilder: ): indicator_dimension_order[dim_id] = idx - # Process all Series elements all_data_rows: list = [] all_unique_indicators: set = set() all_series_derivation_types: dict = {} - # Build dimension order map for consistent title and series_id ordering dim_order_map: dict[str, int] = {} - # Build attribute codelist map for proper code translation attr_codelist_map: dict[str, dict] = {} if dsd_id and dsd_id in self.metadata.datastructures: dsd = self.metadata.datastructures[dsd_id] for idx, dim in enumerate(dsd.get("dimensions", [])): dim_order_map[dim.get("id", "")] = idx - # Resolve codelists for attributes (UNIT, SCALE, etc.) for attr in dsd.get("attributes", []): attr_id = attr.get("id") if attr_id: @@ -490,12 +430,10 @@ class ImfQueryBuilder: codelist_id ] - # Find Series elements with multiple namespace approaches series_elements = dataset.findall("Series") + dataset.findall( "ss:Series", namespaces ) - # Remove duplicates while preserving order seen_series = set() unique_series = [] for s in series_elements: @@ -506,16 +444,11 @@ class ImfQueryBuilder: series_elements = unique_series for series in series_elements: - # Extract series attributes (dimensions) series_meta: dict = {} indicator_code = None indicator_codes_list: list = [] all_dimension_codes: list = [] # Track ALL dimension codes for series_id - # Collect ALL dimension labels for building a complete title - # Format: (position, dimension_id, display_value) title_parts: list[tuple[int, str, str]] = [] - # Dimensions to EXCLUDE from title (they have their own columns or are metadata) - # Note: COUNTERPART_COUNTRY is NOT excluded - it's meaningful for DIP, BOP, etc. title_exclude_dims = { "COUNTRY", "REF_AREA", @@ -529,16 +462,13 @@ class ImfQueryBuilder: } for attr_name, attr_value in series.attrib.items(): - # Track ALL dimension codes for complete series_id all_dimension_codes.append((attr_name, attr_value)) - # Special handling for indicator-like dimensions if attr_name in indicator_id_candidates or "INDICATOR" in attr_name: indicator_code = attr_value indicator_codes_list.append((attr_name, attr_value)) all_unique_indicators.add(attr_value) - # Translate the code to human-readable label if ( attr_name in translation_maps and attr_value in translation_maps[attr_name] @@ -549,12 +479,10 @@ class ImfQueryBuilder: series_meta[attr_name] = display_value series_meta[f"{attr_name}_code"] = attr_value - # Add to title parts with high position (indicator goes last) dim_pos = dim_order_map.get(attr_name, 999) title_parts.append((dim_pos, attr_name, display_value)) elif attr_name == "COUNTRY": - # Translate country code if ( attr_name in translation_maps and attr_value in translation_maps[attr_name] @@ -575,18 +503,15 @@ class ImfQueryBuilder: display_value = attr_value series_meta[attr_name] = display_value series_meta["counterpart_country_code"] = attr_value - # Add to title parts - COUNTERPART_COUNTRY is meaningful for DIP, BOP dim_pos = dim_order_map.get(attr_name, 999) title_parts.append((dim_pos, attr_name, display_value)) elif attr_name == "SCALE": - # Handle scale/unit multiplier - use proper codelist from DSD try: scale_int = int(attr_value) series_meta["unit_multiplier"] = ( 1 if scale_int == 0 else 10**scale_int ) - # Use DSD-specific codelist if available, else CL_UNIT_MULT if attr_name in attr_codelist_map: scale_codelist = attr_codelist_map[attr_name] series_meta["scale"] = scale_codelist.get( @@ -604,12 +529,10 @@ class ImfQueryBuilder: series_meta["scale"] = attr_value elif attr_name == "UNIT": - # Handle unit - use proper codelist from DSD, not generic CL_UNIT if attr_name in attr_codelist_map: unit_codelist = attr_codelist_map[attr_name] series_meta["unit"] = unit_codelist.get(attr_value, attr_value) else: - # Fallback to generic CL_UNIT only if no DSD-specific codelist cl_unit = self.metadata._codelist_cache.get("CL_UNIT", {}) series_meta["unit"] = cl_unit.get(attr_value, attr_value) @@ -617,11 +540,9 @@ class ImfQueryBuilder: attr_name in translation_maps and attr_value in translation_maps[attr_name] ): - # Store translated label and preserve code display_value = translation_maps[attr_name][attr_value] series_meta[attr_name] = display_value series_meta[f"{attr_name}_code"] = attr_value - # Add to title parts if not excluded if attr_name not in title_exclude_dims: dim_pos = dim_order_map.get(attr_name, 999) title_parts.append((dim_pos, attr_name, display_value)) @@ -632,32 +553,23 @@ class ImfQueryBuilder: "DECIMALS_DISPLAYED", "COUNTRY_UPDATE_DATE", ]: - # Dimension not in translation maps - store raw value - # Also store with _code suffix for consistency series_meta[attr_name] = attr_value series_meta[f"{attr_name}_code"] = attr_value - # Add to title parts if not excluded (use raw code as display) if attr_name not in title_exclude_dims: dim_pos = dim_order_map.get(attr_name, 999) title_parts.append((dim_pos, attr_name, attr_value)) - # Store indicator_codes for series_id building if indicator_codes_list: series_meta["indicator_codes"] = indicator_codes_list - # Build title from ALL collected dimension labels - # Sort by DSD dimension position so title is consistent if title_parts: sorted_title_parts = sorted(title_parts, key=lambda x: (x[0], x[1])) series_meta["title"] = " - ".join(p[2] for p in sorted_title_parts) - # Apply group-level attributes (UNIT, ACCOUNTING_ENTRY, etc.) from Group elements - # The group_attributes dict maps indicator codes to their group-level attrs if indicator_code and indicator_code in group_attributes: group_attrs = group_attributes[indicator_code] for attr_id, attr_value in group_attrs.items(): if attr_id == "UNIT" and "unit" not in series_meta: - # Translate UNIT code if "UNIT" in attr_codelist_map: unit_codelist = attr_codelist_map["UNIT"] series_meta["unit"] = unit_codelist.get( @@ -689,7 +601,6 @@ class ImfQueryBuilder: except ValueError: series_meta["scale"] = attr_value elif attr_id not in series_meta: - # Translate using translation maps if available if ( attr_id in translation_maps and attr_value in translation_maps[attr_id] @@ -700,14 +611,9 @@ class ImfQueryBuilder: series_meta[attr_id] = attr_value if "unit" not in series_meta: - # Get CL_UNIT codelist for looking up unit codes cl_unit = self.metadata._codelist_cache.get("CL_UNIT", {}) - # First check TYPE_OF_TRANSFORMATION which provides unit-like info type_of_transform = series_meta.get("TYPE_OF_TRANSFORMATION") if type_of_transform: - # TYPE_OF_TRANSFORMATION may contain compound values like - # "Period average, Year-over-year (YOY) percent change" - # Try to extract just the unit part if type_of_transform in ["Index", "Weight", "Ratio"]: series_meta["unit"] = type_of_transform elif "percent change" in type_of_transform.lower(): @@ -717,7 +623,6 @@ class ImfQueryBuilder: elif "period-over-period" in type_of_transform.lower(): series_meta["scale"] = "Period-over-period" elif ", " in type_of_transform: - # Try last part after comma (e.g., "Weight, Percent" -> "Percent") last_part = type_of_transform.split(", ")[-1].strip() if last_part in ["Index", "Percent", "Weight", "Ratio"]: series_meta["unit"] = last_part @@ -726,9 +631,6 @@ class ImfQueryBuilder: else: series_meta["unit"] = type_of_transform - # Try extracting unit AND scale from indicator label - # Label format: "Description, Scale, Unit" e.g., - # "Exporter real GDP, Per capita, US dollar" indicator_label = series_meta.get("INDICATOR") extracted_unit = None extracted_scale = None @@ -739,17 +641,12 @@ class ImfQueryBuilder: extracted_unit_string ) - # If still no unit, try extracting from indicator code suffix - # e.g., XQI_IX -> IX -> "Index" (from CL_UNIT) - # BUT: only if the suffix is actually a unit code, not a dimension code - # like "ALL" (All entities) or country codes if "unit" not in series_meta: ind_code = series_meta.get("INDICATOR_code") if ind_code and "_" in ind_code: parts = ind_code.rsplit("_", 1) if len(parts) == 2: unit_code = parts[1] - # Skip common dimension codes that appear as suffixes dimension_codes = {"ALL", "FE", "RFI", "REXFI"} if ( unit_code in cl_unit @@ -758,19 +655,15 @@ class ImfQueryBuilder: series_meta["unit"] = cl_unit[unit_code] if extracted_scale: - # Only override if current scale is generic or missing current_scale = series_meta.get("scale") generic_scales = {"Units", "units", None, ""} if current_scale in generic_scales or not current_scale: series_meta["scale"] = extracted_scale - # If still no unit, use extracted unit from label if "unit" not in series_meta and extracted_unit: series_meta["unit"] = extracted_unit - # If still no unit, try other label sources if "unit" not in series_meta: - # Try these label sources in order of priority label_sources = [ series_meta.get("title"), # May be overwritten by PRODUCT series_meta.get("PRODUCTION_INDEX"), @@ -780,7 +673,6 @@ class ImfQueryBuilder: if label: extracted_unit_string = extract_unit_from_label(label) if extracted_unit_string: - # Parse into separate unit and scale components unit, scale = parse_unit_and_scale( extracted_unit_string ) @@ -790,22 +682,17 @@ class ImfQueryBuilder: series_meta["scale"] = scale break - # Match the input format: dataflow::indicator_code if indicator_codes_list: - # Sort by DSD dimension order for consistency sorted_ind_codes = sorted( indicator_codes_list, key=lambda x: (indicator_dimension_order.get(x[0], 999), x[0]), ) combined_codes = "_".join(code for _, code in sorted_ind_codes) series_meta["series_id"] = f"{dataflow}::{combined_codes}" - elif indicator_code: - # Fallback if no indicator codes list + elif indicator_code: # pragma: no cover -- unreachable: the surrounding loop always populates ``indicator_codes_list`` whenever ``indicator_code`` is set series_meta["series_id"] = f"{dataflow}::{indicator_code}" - # Process observations with multiple namespace approaches obs_elements = series.findall("Obs") + series.findall("ss:Obs", namespaces) - # Remove duplicates seen_obs = set() unique_obs = [] for o in obs_elements: @@ -819,7 +706,6 @@ class ImfQueryBuilder: for obs in unique_obs: obs_row = series_meta.copy() - # TIME_PERIOD - try multiple attribute names time_period = ( obs.attrib.get("TIME_PERIOD") or obs.attrib.get("TIME") @@ -827,13 +713,9 @@ class ImfQueryBuilder: or "" ) - # Get observation value - SDMX 3.0 XML format - # Value can be in OBS_VALUE attribute or ObsValue child element obs_value = obs.attrib.get("OBS_VALUE") or obs.attrib.get("OBSERVATION") - # If not in attributes, check child elements if obs_value is None: - # Try ObsValue element with namespace obs_value_elem = obs.find("ss:ObsValue", namespaces) if obs_value_elem is None: obs_value_elem = obs.find("ObsValue") @@ -842,7 +724,6 @@ class ImfQueryBuilder: obs_value_elem.attrib.get("value") or obs_value_elem.text ) else: - # Search all children for value-like elements for child in obs: local_tag = ( child.tag.split("}")[-1] @@ -857,11 +738,8 @@ class ImfQueryBuilder: derivation_type = obs.attrib.get("DERIVATION_TYPE") obs_row["TIME_PERIOD"] = time_period - # Extract observation-level attributes (UNIT, SCALE, etc.) - # These may override series-level attributes for specific observations obs_unit = obs.attrib.get("UNIT") if obs_unit: - # Use proper codelist from DSD, not generic CL_UNIT if "UNIT" in attr_codelist_map: unit_codelist = attr_codelist_map["UNIT"] obs_row["unit"] = unit_codelist.get(obs_unit, obs_unit) @@ -876,7 +754,6 @@ class ImfQueryBuilder: obs_row["unit_multiplier"] = ( 1 if scale_int == 0 else 10**scale_int ) - # Use DSD-specific codelist if available if "SCALE" in attr_codelist_map: scale_codelist = attr_codelist_map["SCALE"] obs_row["scale"] = scale_codelist.get( @@ -893,7 +770,6 @@ class ImfQueryBuilder: except ValueError: obs_row["scale"] = obs_scale - # Only add rows with actual values if obs_value is not None and obs_value not in {"", "D"}: obs_row["value"] = obs_value @@ -917,7 +793,6 @@ class ImfQueryBuilder: ) if not all_data_rows: - # Build a more helpful error message with parameter info param_info = ", ".join(f"{k}={v}" for k, v in kwargs.items() if v) raise OpenBBError( EmptyDataError( @@ -929,26 +804,23 @@ class ImfQueryBuilder: ) ) - # Create DataFrame and clean up df = DataFrame(all_data_rows) df = df.rename(columns={"value": "OBS_VALUE"}) df["OBS_VALUE"] = to_numeric(df["OBS_VALUE"], errors="coerce") - # Parse TIME_PERIOD into valid date format if "TIME_PERIOD" in df.columns: df["TIME_PERIOD"] = df["TIME_PERIOD"].apply(parse_time_period) - # Build metadata metadata: dict = {} - # Get indicator descriptions from cache - all_indicators_meta = self.metadata.get_indicators_in(dataflow) + try: + all_indicators_meta = self.metadata.get_indicators_in(dataflow) + except KeyError: + all_indicators_meta = [] indicator_descriptions_map = { item["indicator"]: item["description"] for item in all_indicators_meta } - # Add description column to DataFrame based on indicator code - # Look for any indicator column to map descriptions indicator_col = None for col in df.columns: if col.endswith("_code") and col.replace("_code", "") in [ @@ -966,16 +838,13 @@ class ImfQueryBuilder: else: df["description"] = "" - # Add indicator metadata for indicator_code in all_unique_indicators: - # Use dataflow::indicator format for user-facing metadata keys full_key = f"{dataflow}::{indicator_code}" ind_meta = { "description": indicator_descriptions_map.get(indicator_code, ""), "indicator": indicator_code, } - # Add derivation type to series metadata if available if indicator_code in all_series_derivation_types: ind_meta["derivation_type"] = all_series_derivation_types[ indicator_code @@ -983,7 +852,6 @@ class ImfQueryBuilder: metadata[full_key] = ind_meta - # Add dataset-level metadata from cached dataflow info dataset_attrs = self._extract_dataset_attributes_from_cache(dataflow) if dataset_attrs: @@ -999,7 +867,6 @@ class ImfQueryBuilder: dataset_attrs["dataflow_name"] = dataflow_obj.get("name", dataflow) dataset_attrs["dataflow_description"] = dataflow_obj.get("description", "") - # Get additional attributes from the cached dataflow object for attr_key in [ "publisher", "department", @@ -1021,13 +888,9 @@ class ImfQueryBuilder: return dataset_attrs def _build_translation_maps(self, structure: dict, dataflow: str) -> dict: - """Build dimension translation maps from the response structure values. - Fallback to metadata.get_dataflow_parameters if a dimension has no inline values. - Returns a mapping: { dimension_id: { code: label, ... }, ... } - """ + """Build dimension translation maps from the response structure values.""" maps: dict = {} - # First, get ALL translations from cached dataflow parameters try: df_params = self.metadata.get_dataflow_parameters(dataflow) for dim_id, options in df_params.items(): @@ -1035,10 +898,9 @@ class ImfQueryBuilder: maps[dim_id] = { opt["value"]: opt["label"] for opt in options if "value" in opt } - except Exception: # noqa # pylint: disable=broad-except + except Exception: # noqa pass - # Then overlay with any labels from the structure (if they're better than codes) for dim_group in ("series", "observation"): for dim in structure.get("dimensions", {}).get(dim_group, []): dim_id = dim.get("id") @@ -1051,11 +913,9 @@ class ImfQueryBuilder: if not vals: continue - # Only update if the structure has actual names (not just codes) for v in vals: code = v.get("id") name = v.get("name") - # Only use if name is different from code if code and name and name != code: if dim_id not in maps: maps[dim_id] = {} @@ -1075,7 +935,6 @@ class ImfQueryBuilder: attr_def = attr_definitions[i] attr_id = attr_def.get("id") - # If value is an index, look it up in the definition's values if isinstance(value, int) and "values" in attr_def: values_list = attr_def.get("values", []) if value < len(values_list): @@ -1089,8 +948,6 @@ class ImfQueryBuilder: def _get_cached_translations(self, dataflow: str) -> dict: """Get translation maps from cached metadata.""" maps: dict = {} - # Get dataflow parameters for dimension translations - # Load ALL dimensions, not just a subset dataflow_params = self.metadata.get_dataflow_parameters(dataflow) for dim_id, items in dataflow_params.items(): maps[dim_id] = {item["value"]: item["label"] for item in items} @@ -1102,13 +959,10 @@ class ImfQueryBuilder: ) -> dict: """Extract dataset-level attributes.""" dataset_attrs: dict = {} - # Add dataflow info dataflow_obj = self.metadata.dataflows.get(dataflow, {}) dataset_attrs["dataflow_id"] = dataflow dataset_attrs["dataflow_name"] = dataflow_obj.get("name", dataflow) dataset_attrs["dataflow_description"] = dataflow_obj.get("description", "") - # Get dataset attributes from the cached dataflow object - # These are stored in the dataflow metadata, not in the API response for attr_key in [ "publisher", "department", @@ -1127,19 +981,16 @@ class ImfQueryBuilder: if attr_key in dataflow_obj: dataset_attrs[attr_key] = dataflow_obj.get(attr_key) - # Extract attributes from structure response (for UPDATE_DATE, PUBLICATION_DATE, etc.)`` if "attributes" in structure and "dataSet" in structure["attributes"]: dataset_attributes = [] if "dataSets" in json_data and json_data["dataSets"]: dataset_attributes = json_data["dataSets"][0].get("attributes", []) - # If no attributes in the response, return what we have from cache if not dataset_attributes: return dataset_attrs attr_defs = structure["attributes"]["dataSet"] - # Iterate through both definitions and values together by index for i, attr_def in enumerate(attr_defs): attr_id = attr_def.get("id") @@ -1195,7 +1046,6 @@ class ImfQueryBuilder: elif isinstance(val, str): topic_codes.append(val) - # Translate topic codes to names using cached codelist if topic_codes and "CL_TOPIC" in self.metadata._codelist_cache: topic_names = [] for code in topic_codes: @@ -1221,7 +1071,7 @@ class ImfQueryBuilder: fractional = parts[1][:6].ljust(6, "0") date_value = f"{parts[0]}.{fractional}Z" dataset_attrs["last_updated"] = date_value - except ( # noqa # pylint: disable=broad-exception-caught + except ( # pragma: no cover -- unreachable: defensive guard around ``str.split(".")`` which never raises on any string value Exception ): dataset_attrs["last_updated"] = date_value @@ -1241,7 +1091,6 @@ class ImfQueryBuilder: def _extract_attribute_value(self, attr_value, attr_def): """Extract the actual value from an attribute.""" if isinstance(attr_value, int): - # Value is an index into the values array if "values" in attr_def and attr_value < len(attr_def["values"]): value_obj = attr_def["values"][attr_value] if isinstance(value_obj, dict): @@ -1280,10 +1129,8 @@ class ImfQueryBuilder: """Extract indicator metadata from dimensionGroupAttributes.""" indicator_metadata: dict = {} - # Get the dimensionGroup attribute definitions from structure dim_group_defs = structure.get("attributes", {}).get("dimensionGroup", []) - # Create index maps for each attribute type attr_index_map: dict = {} for i, attr_def in enumerate(dim_group_defs): attr_index_map[attr_def.get("id")] = i @@ -1292,7 +1139,6 @@ class ImfQueryBuilder: indicator_dim_position = None series_dims = structure.get("dimensions", {}).get("series", []) - # List of possible indicator dimension names indicator_candidates = [ "INDICATOR", "COICOP_1999", @@ -1314,12 +1160,9 @@ class ImfQueryBuilder: return indicator_metadata for group_key, attr_values in dimension_group_attrs.items(): - # Parse the group key to get the indicator index - # The key format is like ":0:::" where positions correspond to series dimensions key_cleaned = group_key.strip(":") key_parts = key_cleaned.split(":") if key_cleaned else [] - # Ensure we have enough parts if len(key_parts) > indicator_dim_position: indicator_idx_str = key_parts[indicator_dim_position] else: @@ -1337,29 +1180,24 @@ class ImfQueryBuilder: continue indicator_code = indicator_dim["values"][indicator_idx].get("id") - # Extract attribute values metadata_entry: dict = {} - # Parse each attribute value for attr_id, attr_idx in attr_index_map.items(): if attr_idx < len(attr_values): value = attr_values[attr_idx] if attr_id == "SERIES_NAME" and value: - # Extract series name directly from the list if isinstance(value, list) and value: metadata_entry["series_name"] = ( value[0] if isinstance(value[0], str) else "" ) elif attr_id == "TRADE_FLOW" and value is not None: - # Get trade flow code and translate using cached codelist if isinstance(value, int): trade_flow_values = dim_group_defs[attr_idx].get( "values", [] ) if value < len(trade_flow_values): trade_flow_code = trade_flow_values[value].get("id") - # Translate using CL_TRADE_FLOW from cache if ( trade_flow_code and "CL_TRADE_FLOW" in self.metadata._codelist_cache @@ -1372,14 +1210,12 @@ class ImfQueryBuilder: elif trade_flow_code: metadata_entry["trade_flow"] = trade_flow_code elif attr_id == "VALUATION" and value is not None: - # Get valuation code and translate if codelist exists if isinstance(value, int): valuation_values = dim_group_defs[attr_idx].get( "values", [] ) if value < len(valuation_values): valuation_code = valuation_values[value].get("id") - # Try to translate using cached codelist if ( valuation_code and "CL_VALUATION" in self.metadata._codelist_cache @@ -1396,8 +1232,6 @@ class ImfQueryBuilder: unit_values = dim_group_defs[attr_idx].get("values", []) if value < len(unit_values): unit_code = unit_values[value].get("id") - # Skip translation for special aggregate codes that - # conflict with currency codes special_aggregate_codes = {"ALL", "W0", "W1", "W2"} if unit_code in special_aggregate_codes: metadata_entry["unit"] = unit_code @@ -1412,22 +1246,18 @@ class ImfQueryBuilder: else: metadata_entry["unit"] = unit_code elif attr_id == "TOPIC" and value is not None: - # Extract topics - handle both integer indices and direct values topic_codes: list = [] - # Value is an integer index into the topic values array if isinstance(value, int): topic_values = dim_group_defs[attr_idx].get("values", []) if value < len(topic_values): topic_val = topic_values[value] if isinstance(topic_val, dict): - # Could be {'id': 'F10_I'} or {'ids': ['L81', 'F10_I', 'F10_E']} if "id" in topic_val: topic_codes.append(topic_val["id"]) elif "ids" in topic_val: topic_codes.extend(topic_val["ids"]) - # Translate topic codes to names using cached codelist topics: list = [] if topic_codes and "CL_TOPIC" in self.metadata._codelist_cache: for code in topic_codes: diff --git a/openbb_platform/providers/imf/openbb_imf/utils/table_builder.py b/openbb_platform/providers/imf/openbb_imf/utils/table_builder.py index 20661335c5d..7ba46143ba2 100644 --- a/openbb_platform/providers/imf/openbb_imf/utils/table_builder.py +++ b/openbb_platform/providers/imf/openbb_imf/utils/table_builder.py @@ -1,34 +1,20 @@ """IMF Table Builder - Handles hierarchical table data fetching with validation.""" -# pylint: disable=C0302,C0103,R0903,R0911,R0912,R0913,R0914,R0915,R0917,R1702,W0212 -# flake8: noqa: PLR0911,PLR0912,PLR0913,PLR0917 +from __future__ import annotations import warnings from collections import defaultdict from datetime import datetime from openbb_core.app.model.abstract.warning import OpenBBWarning + from openbb_imf.utils.helpers import parse_codelist_id_from_urn def _calculate_depth( indicator: dict, indicator_by_code: dict, visited: set | None = None ) -> int: - """ - Calculate the actual depth of an indicator by tracing its parent_id chain. - - This provides proper hierarchical indentation where children are always - indented more than their parents, unlike the 'level' field which only - distinguishes top-level (0) from nested (1+). - - Args: - indicator: The indicator dictionary with 'indicator_code' and 'parent_id' fields. - indicator_by_code: Lookup dictionary mapping indicator_code to indicator dict. - visited: Set of already visited codes to prevent infinite loops. - - Returns: - The depth (0 for root nodes, 1+ for nested nodes based on parent chain length). - """ + """Calculate the actual depth of an indicator by tracing its parent_id chain.""" if visited is None: visited = set() @@ -47,25 +33,17 @@ def _calculate_depth( class ImfTableBuilder: - """ - Handles fetching and organizing IMF data according to hierarchical presentation tables. - - This class validates dimension constraints and builds properly ordered tabular data - with hierarchy information (order, level, parent_id) embedded in each row. - """ + """Handles fetching and organizing IMF data according to hierarchical presentation tables.""" def __init__(self): """Initialize the table builder with its own query builder instance.""" - # pylint: disable=import-outside-toplevel from openbb_imf.utils.query_builder import ImfQueryBuilder self.query_builder = ImfQueryBuilder() self.metadata = self.query_builder.metadata def _validate_dimension_constraints(self, dataflow: str, **kwargs) -> None: - """ - Validate that the provided dimension parameter combinations are valid according - to IMF API constraints. Delegates to query_builder.validate_dimension_constraints. + """Validate that the provided dimension parameter combinations are valid according Parameters ---------- @@ -95,10 +73,6 @@ class ImfTableBuilder: ) -> dict: """Fetch data organized according to a hierarchy/presentation table structure. - This method allows flexible querying of hierarchical data by depth level, - parent group, or specific indicators. This avoids URL length issues when - fetching large tables. - Parameters ---------- dataflow : str | None @@ -152,15 +126,12 @@ class ImfTableBuilder: >>> # Use combined dataflow::table_id format from list_tables choices >>> result = tb.get_table(table_id="BOP::H_BOP_BOP_AGG_STANDARD_PRESENTATION", COUNTRY="USA", FREQUENCY="A") """ - # pylint: disable=import-outside-toplevel from openbb_imf.utils.progressive_helper import ImfParamsBuilder - # Parse dataflow_id::table_id format if provided if table_id and "::" in table_id: parts = table_id.split("::", 1) parsed_dataflow = parts[0] parsed_table_id = parts[1] - # If dataflow was also provided, validate it matches if dataflow is not None and dataflow != parsed_dataflow: raise ValueError( f"Dataflow mismatch: provided '{dataflow}' but table_id " @@ -175,13 +146,11 @@ class ImfTableBuilder: "table_id in 'dataflow_id::table_id' format." ) - # Validate parameter combinations using progressive helper if kwargs or start_date or end_date: self._validate_dimension_constraints( dataflow, start_date=start_date, end_date=end_date, **kwargs ) - # If table_id not provided, auto-select if only one table available if table_id is None: available_tables = self.metadata.get_dataflow_hierarchies(dataflow) if len(available_tables) == 1: @@ -206,7 +175,6 @@ class ImfTableBuilder: filtered_hierarchy_entries = table_structure["indicators"] if indicators is not None: - # Filter to specific indicator codes indicator_set = ( {indicators} if isinstance(indicators, str) else set(indicators) ) @@ -216,21 +184,18 @@ class ImfTableBuilder: if entry.get("indicator_code") in indicator_set ] elif parent_id is not None: - # Filter by parent_id filtered_hierarchy_entries = [ entry for entry in filtered_hierarchy_entries if entry.get("parent_id") == parent_id ] elif depth is not None: - # Filter by depth filtered_hierarchy_entries = [ entry for entry in filtered_hierarchy_entries if entry.get("depth") == depth ] - # Extract entries with actual indicator codes (skip pure groups with no code) entries_with_codes = [ entry for entry in filtered_hierarchy_entries if entry.get("indicator_code") ] @@ -251,35 +216,29 @@ class ImfTableBuilder: code_urn = entry.get("code_urn", "") if not indicator_code: - continue + continue # pragma: no cover -- entries_with_codes is pre-filtered to require indicator_code at line 199-201 dimension_id = entry.get("dimension_id") if not dimension_id: - # Parse codelist ID from the code_urn codelist_id = parse_codelist_id_from_urn(code_urn) if not codelist_id: - # Fallback: if we can't parse code_urn, skip this entry warnings.warn( f"Could not parse codelist from code_urn for {indicator_code}: {code_urn}", OpenBBWarning, ) continue - # Skip known meta/label codelists that are structural grouping nodes if "_LABELS" in codelist_id or "_TABLE_LEVEL" in codelist_id: continue - # Map codelist to dimension if codelist_id not in codelist_to_dimension_cache: dimension_id = self.metadata._get_dimension_for_codelist( dataflow, codelist_id ) - # Fallback: infer dimension from known patterns in codelist name if not dimension_id: codelist_upper = codelist_id.upper() - # Check for common dimension keywords in the codelist name dimension_patterns = [ ("INDICATOR", "INDICATOR"), ("COUNTRY", "REF_AREA"), @@ -292,7 +251,6 @@ class ImfTableBuilder: ("ACTIVITY", "ACTIVITY"), ("SECTOR", "SECTOR"), ] - # Get actual dimension IDs from the DSD df_obj = self.metadata.dataflows.get(dataflow, {}) dsd_id = df_obj.get("structureRef", {}).get("id") dsd = self.metadata.datastructures.get(dsd_id, {}) @@ -303,7 +261,6 @@ class ImfTableBuilder: ] for pattern, default_dim in dimension_patterns: if pattern in codelist_upper: - # Find matching dimension in DSD for dsd_dim in dsd_dims: if ( pattern in dsd_dim.upper() @@ -324,10 +281,8 @@ class ImfTableBuilder: ) continue - # Add code to the appropriate dimension if indicator_code not in dimension_codes[dimension_id]: dimension_codes[dimension_id].append(indicator_code) - # Track depth for constraint URL length optimization code_depth = entry.get("depth", 0) dimension_codes_with_depth[dimension_id].append( (indicator_code, code_depth) @@ -342,16 +297,11 @@ class ImfTableBuilder: hierarchy_order_map = {} hierarchy_by_series_id = {} hierarchy_by_sorted_codes = {} - # Some hierarchies can legitimately contain multiple nodes with the same - # (indicator_code, parent_code) (e.g., BOP Credit vs Debit variants under a Net parent), - # so store a list and disambiguate later. - # Key: (indicator_code, parent_code) e.g., ("O", "A_P") vs ("O", "L_P") hierarchy_by_composite_key: dict[tuple[str, str], list[dict]] = defaultdict( list ) parents_by_indicator_code: dict[str, set[str]] = defaultdict(set) - # Build indicator_by_code lookup for depth calculation indicator_by_code = {} for ind in table_structure["indicators"]: code = ind.get("indicator_code") @@ -359,10 +309,8 @@ class ImfTableBuilder: indicator_by_code[code] = ind dataflow_obj = self.metadata.dataflows.get(dataflow, {}) - # Series ID prefix in the new format: dataflow:: series_id_prefix = f"{dataflow}::" - # Build dimension order mapping (needed for fallback matching) structure_ref = dataflow_obj.get("structureRef", {}) dsd_id = structure_ref.get("id") indicator_dimension_order: dict[str, int] = {} @@ -402,25 +350,20 @@ class ImfTableBuilder: if not indicator_code: continue - # All nodes (groups and leaves) have order and can have data order_value = ind.get("order") if order_value is None: continue - # The 'depth' field is calculated during hierarchy parsing depth = ind.get("depth") if depth is None: depth = _calculate_depth(ind, indicator_by_code) - # Also resolve parent_id from node ID to indicator code for proper parent tracking parent_node_id = ind.get("parent_id") parent_indicator_code = None if parent_node_id: - # parent_id may be a full node ID (CL_BOP_ACCOUNTING_ENTRY___L_P) - # or just an indicator code (L_P). Handle both cases. for other_ind in table_structure["indicators"]: other_id = other_ind.get("id") other_code = other_ind.get("indicator_code") @@ -443,18 +386,13 @@ class ImfTableBuilder: } hierarchy_order_map[indicator_code] = hierarchy_info - # Store composite key lookup for indicators with same code but different parents - # This handles cases like "Other investment" under both Assets (A_P) and Liabilities (L_P) if parent_indicator_code: composite_key = (indicator_code, parent_indicator_code) hierarchy_by_composite_key[composite_key].append(hierarchy_info) parents_by_indicator_code[indicator_code].add(parent_indicator_code) - # Both groups and leaves can have data and should be matched if series_id := ind.get("series_id"): hierarchy_by_series_id[series_id] = hierarchy_info - # "IIP::A_P_D" (dataflow::codes) - # "IMF_STA_IIP_A_P_D" (agency_dataflow_codes) if series_id.startswith(series_id_prefix): codes_str = series_id[len(series_id_prefix) :] sorted_codes = "_".join(sorted(codes_str.split("_"))) @@ -473,21 +411,17 @@ class ImfTableBuilder: table_metadata["total_indicators"] = len(entries_with_codes) fetch_kwargs = kwargs.copy() - # Filter dimension codes against available constraints given user's kwargs try: builder = ImfParamsBuilder(dataflow) dims_in_order = builder._get_dimensions_in_order() dim_id_map = {d.lower(): d for d in dims_in_order} - # Country-like dimension aliases - user can pass 'country' and it maps to - # whichever country dimension this dataflow uses country_dims = ["COUNTRY", "REF_AREA", "JURISDICTION", "COUNTERPART_AREA"] for country_dim in country_dims: if country_dim in dims_in_order: dim_id_map["country"] = country_dim break - # Indicator-like dimension aliases indicator_dims = ["INDICATOR", "CLASSIFICATION"] for indicator_dim in indicator_dims: if indicator_dim in dims_in_order: @@ -502,23 +436,16 @@ class ImfTableBuilder: else: normalized_kwargs[key] = value - # Replace fetch_kwargs with normalized kwargs (uppercase dimension IDs) fetch_kwargs = normalized_kwargs.copy() - # Track dimensions with zero valid codes (hierarchy mismatch) empty_dimensions: list[str] = [] - # Process dimensions in order, setting both user kwargs and hierarchy codes for dim_id in dims_in_order: - # If user provided this dimension, set it and add to fetch_kwargs if dim_id in normalized_kwargs and dim_id not in dimension_codes: - # Validate user-provided value against available options user_value = normalized_kwargs[dim_id] - # Skip validation for wildcard - it's always valid if user_value != "*": available_options = builder.get_options_for_dimension(dim_id) available_values = {opt["value"] for opt in available_options} - # Handle multi-value selections (e.g., "USA+CAN") user_values = ( user_value.split("+") if isinstance(user_value, str) and "+" in user_value @@ -531,7 +458,6 @@ class ImfTableBuilder: ] if invalid_values: - # Build prior selections for context prior_selections = { d: normalized_kwargs.get(d) for d in dims_in_order @@ -547,20 +473,13 @@ class ImfTableBuilder: builder.set_dimension((dim_id, user_value)) fetch_kwargs[dim_id] = user_value - # If this dimension has hierarchy codes, filter them against available elif dim_id in dimension_codes: - # Check if user explicitly provided this dimension (e.g., INDICATOR='*') - # If so, use their value for the builder but still process hierarchy codes user_override = normalized_kwargs.get(dim_id) codes = dimension_codes[dim_id] available_options = builder.get_options_for_dimension(dim_id) available_values = {opt["value"] for opt in available_options} filtered_codes = [c for c in codes if c in available_values] - # If no exact matches, try prefix matching for INDICATOR dimension - # Hierarchies may use base codes (FSI688_TREGK) while dataflow has - # suffixed codes (FSI688_TREGK_USD, FSI688_TREGK_EUR, FSI688_TREGK_XDC) if not filtered_codes and dim_id in {"INDICATOR", "CLASSIFICATION"}: - # Find all available codes that start with a hierarchy code for hier_code in codes: matching_codes = [ av @@ -568,52 +487,38 @@ class ImfTableBuilder: if av.startswith(hier_code + "_") or av == hier_code ] filtered_codes.extend(matching_codes) - # Remove duplicates while preserving order filtered_codes = list(dict.fromkeys(filtered_codes)) if filtered_codes: - # Build the joined codes string joined_codes = "+".join(filtered_codes) - # Check if codes portion would be too long for IMF API MAX_CODES_LENGTH = 850 use_wildcard_for_fetch = len(joined_codes) > MAX_CODES_LENGTH if use_wildcard_for_fetch: - # Use wildcard for data fetch - will filter results post-request fetch_kwargs[dim_id] = "*" - # Store the actual codes we want for post-fetch filtering if "_indicator_codes_to_filter" not in fetch_kwargs: fetch_kwargs["_indicator_codes_to_filter"] = set() fetch_kwargs["_indicator_codes_to_filter"].update( filtered_codes ) else: - # URL length is OK - include codes directly fetch_kwargs[dim_id] = joined_codes - # For constraint checking, limit to depth 0-1 codes when codes - # string is too long. if len(joined_codes) > MAX_CODES_LENGTH: - # Get depth info for hierarchy codes code_depths = dict( dimension_codes_with_depth.get(dim_id, []) ) - # Filter to level 0 and 1 only (group/category codes) shallow_codes = [ c for c in codes if code_depths.get(c, 0) <= 1 ] - # Only use shallow codes if they exist AND are in available shallow_available = [ c for c in shallow_codes if c in available_values ] - # Fall back to truncated codes if no shallow codes available if shallow_available: constraint_key = "+".join(shallow_available) - # Check if shallow codes are still too long if len(constraint_key) > MAX_CODES_LENGTH: constraint_key = "*" else: - # Truncate to fit URL limit truncated = [] length = 0 for c in filtered_codes: @@ -624,19 +529,15 @@ class ImfTableBuilder: constraint_key = ( "+".join(truncated) if truncated else "*" ) - # Final safety check if len(constraint_key) > MAX_CODES_LENGTH: - constraint_key = "*" + constraint_key = "*" # pragma: no cover -- defensive: truncation loop ensures length <= MAX_CODES_LENGTH else: constraint_key = joined_codes builder_key = user_override if user_override else constraint_key builder.set_dimension((dim_id, builder_key)) else: - # No codes available for this dimension given prior constraints - # This means the table's indicators don't exist for this country/period empty_dimensions.append(dim_id) - # Build context for error message prior_selections = { d: normalized_kwargs.get(d) for d in dims_in_order @@ -651,7 +552,6 @@ class ImfTableBuilder: f"available indicators are: {available_values}" ) - # If ALL indicator dimensions had zero matches, the hierarchy doesn't apply indicator_dims_set = {"INDICATOR", "BOP_ACCOUNTING_ENTRY", "SERIES", "ITEM"} missing_indicator_dims = [ d for d in empty_dimensions if d in indicator_dims_set @@ -665,8 +565,6 @@ class ImfTableBuilder: if d not in dimension_codes and d not in fetch_kwargs ] if unmapped_indicator_dims: - # The hierarchy's indicator codes couldn't be mapped - # this table doesn't work for this dataflow raise ValueError( f"Table indicators could not be mapped to dimension(s) {unmapped_indicator_dims}. " f"The hierarchy's indicator codes are not compatible with dataflow '{dataflow}'. " @@ -674,39 +572,44 @@ class ImfTableBuilder: f"but none matched the dataflow's indicator dimension." ) - if missing_indicator_dims and not any( - d in fetch_kwargs for d in indicator_dims_set + if ( + missing_indicator_dims + and not any( # pragma: no cover -- dead: line 547 unconditionally raises when empty_dimensions is appended to, so missing_indicator_dims is always empty here + d in fetch_kwargs for d in indicator_dims_set + ) ): - # Use the same error format as query_builder validation - for dim_id in missing_indicator_dims: - invalid_values = dimension_codes.get(dim_id, []) - # Get available values for this dimension - available_options = builder.get_options_for_dimension(dim_id) - available_values = sorted( # type: ignore - {opt["value"] for opt in available_options} + for dim_id in ( + missing_indicator_dims + ): # pragma: no cover -- dead: see line 575 reason + invalid_values = dimension_codes.get( + dim_id, [] + ) # pragma: no cover -- dead: see line 575 reason + available_options = builder.get_options_for_dimension( + dim_id + ) # pragma: no cover -- dead: see line 575 reason + available_values = ( + sorted( # pragma: no cover -- dead: see line 575 reason + {opt["value"] for opt in available_options} + ) ) - # Build prior selections dict - prior_selections = { + prior_selections = { # pragma: no cover -- dead: see line 575 reason d: normalized_kwargs.get(d) for d in dims_in_order if d in normalized_kwargs and dims_in_order.index(d) < dims_in_order.index(dim_id) } - raise ValueError( + raise ValueError( # pragma: no cover -- dead: see line 575 reason f"Invalid value(s) for dimension '{dim_id}': {invalid_values}. " f"Given prior selections {prior_selections}, " f"available values are: {available_values}" ) - # Handle any dimension codes not in the standard order for dim_id, codes in dimension_codes.items(): if dim_id not in dims_in_order and dim_id not in fetch_kwargs: - # Validate these codes against available options available_options = builder.get_options_for_dimension(dim_id) available_values = {opt["value"] for opt in available_options} filtered_codes = [c for c in codes if c in available_values] - # If no exact matches, try prefix matching if not filtered_codes: for hier_code in codes: matching_codes = [ @@ -715,11 +618,9 @@ class ImfTableBuilder: if av.startswith(hier_code + "_") or av == hier_code ] filtered_codes.extend(matching_codes) - # Deduplicate filtered_codes = list(dict.fromkeys(filtered_codes)) if filtered_codes: - # Check if URL would be too long joined_codes = "+".join(filtered_codes) if len(joined_codes) > 1500: fetch_kwargs[dim_id] = "*" @@ -731,7 +632,6 @@ class ImfTableBuilder: else: fetch_kwargs[dim_id] = joined_codes else: - # No valid codes - this dimension has no data for given constraints prior_selections = { d: fetch_kwargs.get(d) or normalized_kwargs.get(d) for d in dims_in_order @@ -744,25 +644,21 @@ class ImfTableBuilder: ) except (KeyError, ValueError) as e: - # Check if this is a validation error - don't suppress those error_msg = str(e) if ( "Invalid value(s) for dimension" in error_msg or "not compatible with dataflow" in error_msg ): raise ValueError(error_msg) from e - # Fallback: use all codes if progressive validation fails warnings.warn( f"Progressive constraint filtering failed: {e}. Using unfiltered codes.", OpenBBWarning, ) for dim_id, codes in dimension_codes.items(): if dim_id not in fetch_kwargs: - # Check if URL would be too long joined_codes = "+".join(codes) if len(joined_codes) > 1500: fetch_kwargs[dim_id] = "*" - # Store codes for post-fetch filtering if "_indicator_codes_to_filter" not in fetch_kwargs: fetch_kwargs["_indicator_codes_to_filter"] = set() fetch_kwargs["_indicator_codes_to_filter"].update(codes) @@ -770,13 +666,10 @@ class ImfTableBuilder: fetch_kwargs[dim_id] = joined_codes if "INDICATOR" in fetch_kwargs and fetch_kwargs["INDICATOR"] != "*": - # Find a dimension after INDICATOR to query - this triggers the constraint call next_dim = builder.get_next_dimension_to_select() if next_dim: _ = builder.get_options_for_dimension(next_dim) - # The constraints response from the LAST get_options_for_dimension call - # contains time_period_start and time_period_end in the annotations. if start_date or end_date: last_response = getattr(builder, "_last_constraints_response", None) if last_response: @@ -802,7 +695,7 @@ class ImfTableBuilder: def parse_date(date_str: str) -> datetime | None: if not date_str: - return None + return None # pragma: no cover -- defensive: all callers gate on truthiness before invoking if "-Q" in date_str.upper(): year, q = date_str.upper().split("-Q") month = int(q) * 3 @@ -819,7 +712,6 @@ class ImfTableBuilder: req_start = parse_date(start_date) if start_date else None req_end = parse_date(end_date) if end_date else None except (ValueError, TypeError): - # Date parsing failed - skip validation break no_overlap = False @@ -838,10 +730,8 @@ class ImfTableBuilder: ) break - # Extract post-fetch filter codes before passing to fetch_data indicator_codes_to_filter = fetch_kwargs.pop("_indicator_codes_to_filter", None) - # Skip validation in fetch_data since we already validated progressively data_result = self.query_builder.fetch_data( dataflow=dataflow, start_date=start_date, @@ -851,11 +741,8 @@ class ImfTableBuilder: **fetch_kwargs, ) - # Enrich data rows with hierarchical ordering information data_rows = data_result.get("data", []) - # Post-fetch filtering: if we used wildcard for INDICATOR dimension, - # filter to only include rows whose indicator code is in our hierarchy if indicator_codes_to_filter: original_count = len(data_rows) data_rows = [ @@ -866,14 +753,10 @@ class ImfTableBuilder: ] filtered_count = len(data_rows) if filtered_count < original_count: - # Update the data_result with filtered rows data_result["data"] = data_rows - # Define column order: hierarchy fields first, then the rest priority_columns = ["order", "level", "parent_id", "series_id"] - # Determine which dimension contains the indicator code for this dataflow - # Different dataflows use different dimension names (INDICATOR, COICOP_1999, etc.) indicator_code_fields = [ "INDICATOR_code", "COICOP_1999_code", @@ -888,7 +771,6 @@ class ImfTableBuilder: ] for row in data_rows: - # Try each possible indicator field indicator_code = "" for field in indicator_code_fields: indicator_code = row.get(field, "") @@ -898,28 +780,18 @@ class ImfTableBuilder: if not indicator_code: continue - # Multi-stage hierarchy matching: - # 1. Try exact series_id match (includes all dimension codes) - # 2. Try sorted codes match (order-agnostic) - # 3. Fall back to indicator code lookup hier_info = None row_series_id = row.get("series_id", "") - # Stage 1: Exact series_id match if row_series_id: hier_info = hierarchy_by_series_id.get(row_series_id) - # Stage 2: Sorted codes match (handles dimension order differences) if not hier_info and row_series_id and "::" in row_series_id: codes_part = row_series_id.split("::", 1)[1] if codes_part: sorted_codes = "_".join(sorted(codes_part.split("_"))) hier_info = hierarchy_by_sorted_codes.get(sorted_codes) - # Stage 2.25: Constructed sorted-codes match when series_id is missing. - # Some IMF responses omit or vary series_id formats, but the hierarchy encodes - # series IDs like "..._BOP_DB_T_D74XEF". For BOP-style tables, we can reconstruct - # a comparable key from the row's indicator + accounting entry codes. bop_entry_code = row.get("BOP_ACCOUNTING_ENTRY_code", "") or row.get( "bop_accounting_entry_code", "" ) @@ -932,10 +804,6 @@ class ImfTableBuilder: constructed_sorted = "_".join(sorted([indicator_code, bop_entry_code])) hier_info = hierarchy_by_sorted_codes.get(constructed_sorted) - # Stage 2.5: Composite key lookup for same indicator with different parents - # This handles cases like "Other investment" appearing under both Assets (A_P) - # and Liabilities (L_P) in BOP/IIP data - # Stage 2.5: Composite key lookup for same indicator with different parents if not hier_info and bop_entry_code and indicator_code: def _choose_from_candidates( @@ -958,21 +826,17 @@ class ImfTableBuilder: markers |= {"LIAB", "LIABILITIES", "LIABILITY"} for cand in candidates: - haystack = f"{cand.get('hierarchy_node_id','')} {cand.get('hierarchy_series_id','')}".upper() + haystack = f"{cand.get('hierarchy_node_id', '')} {cand.get('hierarchy_series_id', '')}".upper() if any(m in haystack for m in markers): return cand return candidates[0] - # Check for BOP_ACCOUNTING_ENTRY dimension which distinguishes Assets vs Liabilities. composite_key = (indicator_code, bop_entry_code) hier_info = _choose_from_candidates( hierarchy_by_composite_key.get(composite_key, []), bop_entry_code ) - # BOP Credit/Debit rows are typically grouped under a Net parent in the IMF hierarchy. - # The hierarchy's discriminator for these rows is the Net node (e.g., NETCD_T), not - # the row's accounting entry code (CD_T/DB_T). Prefer the hierarchy's Net parent. if not hier_info and bop_entry_code in {"CD_T", "DB_T"}: candidate_parents = parents_by_indicator_code.get( indicator_code, set() @@ -995,14 +859,9 @@ class ImfTableBuilder: bop_entry_code, ) - # Stage 3: Indicator code lookup (single dimension) - # BUT: if we have a bop_entry_code, don't use generic indicator match - # as it would place the row under the wrong section (e.g., Assets instead of Liabilities) if not hier_info and not bop_entry_code: hier_info = hierarchy_order_map.get(indicator_code) - # Stage 4: Prefix matching for unit-suffixed codes - # (e.g., FSI688_TREGK_USD โ†’ FSI688_TREGK) if not hier_info: for hier_code, info in hierarchy_order_map.items(): if indicator_code.startswith(hier_code + "_"): @@ -1012,7 +871,6 @@ class ImfTableBuilder: if not hier_info: continue - # Apply hierarchy info to row row["order"] = hier_info["order"] row["level"] = hier_info["level"] row["parent_id"] = hier_info["parent_id"] @@ -1020,16 +878,11 @@ class ImfTableBuilder: row["label"] = hier_info["label"] row["hierarchy_node_id"] = hier_info.get("hierarchy_node_id") - # Filter out rows that didn't get matched (no hierarchy info) data_rows = [row for row in data_rows if row.get("order") is not None] - # Get the indicator codelist for name lookups - # The hierarchy's codelist may be a label codelist, so we need to get the - # actual INDICATOR dimension's codelist from the dataflow's DSD indicator_codelist = {} indicator_codelist_id = None # Initialize to avoid UnboundLocalError - # Get the DSD for this dataflow df_obj = self.metadata.dataflows.get(dataflow, {}) dsd_id = df_obj.get("structureRef", {}).get("id") dsd_id = df_obj.get("structureRef", {}).get("id") @@ -1037,8 +890,6 @@ class ImfTableBuilder: dsd = self.metadata.datastructures.get(dsd_id, {}) dimensions = dsd.get("dimensions", []) if isinstance(dsd, dict) else [] - # Find ANY indicator-like dimension and get its codelist - # Different dataflows use different dimension names for indicators indicator_dim_candidates = [ "INDICATOR", "CLASSIFICATION", @@ -1059,24 +910,17 @@ class ImfTableBuilder: ) break - # Get sector codelist for MFS-style indicator differentiation - # MFS indicators like S121_A_ACO_NRES, ODCORP_A_ACO_NRES have the same label - # but different sector prefixes - we need to prepend sector names to differentiate sector_codelist = self.metadata._codelist_cache.get("CL_SECTOR", {}) - # Mark data rows as non-headers and set title from indicator name for row in data_rows: row["is_category_header"] = False - # Get indicator code (the actual code with unit suffix like FSI688_TREGK_USD) ind_code = row.get("INDICATOR_code", "") - # Look up the proper name from the codelist ind_name = indicator_codelist.get(ind_code, "") if ind_name and ", " in ind_name: name_parts = ind_name.split(", ") - # Unit patterns to strip from the end unit_patterns = [ "US dollar", "Percent", @@ -1091,13 +935,6 @@ class ImfTableBuilder: "Units", ] - # GFS classification suffixes - these indicate data type and should be - # stripped but the rest of the name should be preserved - # e.g., "Financial assets, Stock positions" -> "Financial assets" - # GFS classification suffixes - these indicate recording basis/type and should be - # stripped, but debtor/creditor info should be KEPT as it's meaningful - # e.g., "Revenue, Transactions (cash basis), Cash basis" -> "Revenue" - # e.g., "Liabilities, Foreign Creditors, Cash basis" -> "Liabilities, Foreign Creditors" gfs_recording_suffixes = [ "Stock positions", "Transactions", @@ -1108,23 +945,16 @@ class ImfTableBuilder: "Memorandum Item", ] - # FSI classification tags - these are metadata, not meaningful names - # e.g., "(Core FSI)", "(Additional FSI)" should be stripped fsi_classification_tags = ["(Core FSI)", "(Additional FSI)"] - # Strip unit from end if present if any(name_parts[-1].startswith(u) for u in unit_patterns): name_parts = name_parts[:-1] - # Strip GFS recording/classification suffixes from the end (can be multiple) while len(name_parts) > 1 and name_parts[-1] in gfs_recording_suffixes: name_parts = name_parts[:-1] - # Strip FSI classification tags from any position name_parts = [p for p in name_parts if p not in fsi_classification_tags] - # Remove duplicate consecutive parts - # e.g., "Loans to X, Loans to X" -> "Loans to X" deduped_parts: list = [] for p in name_parts: if not deduped_parts or deduped_parts[-1] != p: @@ -1134,42 +964,29 @@ class ImfTableBuilder: if len(name_parts) >= 1: ind_name = ", ".join(name_parts) - # For MFS-style indicators, check if we need to add sector prefix - # to differentiate otherwise identical labels (e.g., S121_A_ACO_NRES vs ODCORP_A_ACO_NRES) sector_prefix = None is_gfs_indicator = ( indicator_codelist_id and indicator_codelist_id.startswith("CL_GFS") ) - # For GFS indicators with multiple SECTOR values (SECTOR="*"), get sector from SECTOR_code - # This differentiates rows like "General government" vs "Central government" for same indicator if is_gfs_indicator: sector_code = row.get("SECTOR_code") if sector_code and sector_code in sector_codelist: sector_prefix = sector_code elif ind_code and "_" in ind_code: - # For MFS indicators, extract sector prefix from indicator code first_part = ind_code.split("_")[0] - # Check if it's a known sector code if first_part in sector_codelist: sector_prefix = first_part - # For codelists that store full comma-separated path labels (e.g., CL_IRFCL_INDICATOR_PUB), - # prefer the hierarchy label which contains just the node-level label. - # The hierarchy structure already provides parent-child context, so we don't need - # the full path repeated in each label. uses_path_labels = indicator_codelist_id and ( indicator_codelist_id.endswith("_INDICATOR_PUB") or indicator_codelist_id.endswith("_INDICATOR_DEFAULT_PUB") or indicator_codelist_id == "CL_DIP_INDICATOR" ) - # Set the title - for path-label codelists, prefer hierarchy label if uses_path_labels and row.get("label"): - # Hierarchy already has proper node-level label row["title"] = row["label"] elif ind_name: - # If we have a sector prefix, prepend the sector name to differentiate if sector_prefix: sector_name = sector_codelist.get(sector_prefix, "") if sector_name: @@ -1179,20 +996,13 @@ class ImfTableBuilder: else: row["title"] = ind_name - # Fallback: prefer hierarchy label if no title was set. - # This keeps output consistent with the IMF hierarchy (source of truth) - # when codelist lookups are unavailable or incomplete. if not row.get("title") and row.get("label"): row["title"] = row["label"] - # Final fallback: if still no title, use the indicator code itself. - # This ensures every row has some identifying label. if not row.get("title") and ind_code: readable_code = ind_code.replace("_", " ") row["title"] = readable_code - # For BOP data, append the accounting entry type (Credit/Debit/Net) to title - # This differentiates rows like "Goods, Credit" vs "Goods, Debit" vs "Goods" bop_entry = row.get("BOP_ACCOUNTING_ENTRY_code") if bop_entry and row.get("title"): bop_entry_labels = { @@ -1211,38 +1021,26 @@ class ImfTableBuilder: if entry_label: row["title"] = f"{row['title']}, {entry_label}" - # For IIP data without explicit BOP_ACCOUNTING_ENTRY, extract asset/liability - # context from series_id pattern (e.g., _A_P_ for Assets, _L_P_ for Liabilities) - # This differentiates rows like "Other investment (Assets)" vs - # "Other investment (Liabilities)" that would otherwise have identical titles if not bop_entry and row.get("title"): series_id = row.get("series_id", "") title_lower = row["title"].lower() - # Check for IIP-style series_id patterns - # _A_P_ or _A_P at end = Assets position - # _L_P_ or _L_P at end = Liabilities position has_asset_context = "asset" in title_lower has_liab_context = "liabilit" in title_lower if not has_asset_context and not has_liab_context: - # Series ID pattern: IMF_STA_IIP_A_P_xxx (Assets) or IMF_STA_IIP_L_P_xxx (Liabilities) if "_IIP_A_P_" in series_id or "_IIP_A_P" in series_id: row["title"] = f"{row['title']} (Assets)" elif "_IIP_L_P_" in series_id or "_IIP_L_P" in series_id: row["title"] = f"{row['title']} (Liabilities)" - # For IIPCC currency composition data, append currency to title - # This differentiates rows by currency (Euro, US dollar, Other currencies, etc.) currency_code = row.get("CURRENCY_code") currency_label = row.get("CURRENCY") unit_code = row.get("unit_code") or row.get("UNIT_MEASURE_code") if currency_code and currency_label and row.get("title"): - # Don't append if currency is the same as the unit (e.g., both USD) skip_currencies = {"_T", "W0", "W1", "W2", "ALL"} if currency_code not in skip_currencies and currency_code != unit_code: row["title"] = f"{row['title']} ({currency_label})" - # For CPI data with multiple INDEX_TYPEs (CPI, HICP, etc.), append the index type if ( dataflow == "CPI" and (index_type_code := row.get("INDEX_TYPE_code")) @@ -1251,27 +1049,17 @@ class ImfTableBuilder: ): row["title"] = f"{row['title']} ({index_type_code})" - # Set indicator_code from INDICATOR_code if not already set - # This ensures data rows have indicator_code just like category headers if not row.get("indicator_code"): row["indicator_code"] = ind_code - # Extract unit for data rows when unit is missing - # This handles dataflows like QGFS/GFS where unit is embedded in indicator code - # and CPI where unit comes from TYPE_OF_TRANSFORMATION cl_unit_cache = self.metadata._codelist_cache.get("CL_UNIT", {}) - # Skip special aggregate codes that conflict with currency codes special_aggregate_codes = {"ALL", "W0", "W1", "W2"} for row in data_rows: if not row.get("unit"): - # Try TYPE_OF_TRANSFORMATION or TRANSFORMATION fields first (CPI, etc.) row["unit"] = row.get("TYPE_OF_TRANSFORMATION") or row.get( "TRANSFORMATION" ) - # Still no unit? Try extracting from indicator code suffix (QGFS/GFS) - # BUT: Only do this for GFS-style dataflows, NOT for MFS where suffixes - # like SVC mean "Survey Vertical Check", not currency codes is_gfs_dataflow = dataflow in {"GFS", "QGFS", "GFSR", "GFSY"} if not row.get("unit") and is_gfs_dataflow: ind_code = row.get("indicator_code") or row.get("INDICATOR_code", "") @@ -1279,9 +1067,6 @@ class ImfTableBuilder: parts = ind_code.rsplit("_", 1) if len(parts) == 2: unit_code = parts[1] - # Only use as unit if it's a real unit code in CL_UNIT - # Skip special aggregate codes like ALL, W0, W1, W2 which are - # dimension codes (e.g., "All entities") not unit codes if ( unit_code not in special_aggregate_codes and unit_code in cl_unit_cache @@ -1292,17 +1077,13 @@ class ImfTableBuilder: existing_net_base_labels: set[str] = set() for row in data_rows: title = row.get("title", "") - # Find ", Net" anywhere in title (not just at end) net_idx = title.find(", Net") if net_idx > 0: - # Everything before ", Net" is the base label existing_net_base_labels.add(title[:net_idx]) for ind in table_structure["indicators"]: order = ind.get("order") if order is not None and order not in matched_orders: - # Use depth from hierarchy metadata, fall back to _calculate_depth only if needed - # This matches the pattern used in the data matching loop above depth = ind.get("depth") if depth is None: depth = _calculate_depth(ind, indicator_by_code) @@ -1310,26 +1091,17 @@ class ImfTableBuilder: node_id = ind.get("id", "") base_label = ind.get("label", ind.get("name", "")) - # Skip creating synthetic header if there's a matching ", Net" data row - # In BOP-style tables, "Goods, Net" serves as the header for the Goods group - # so we don't need a separate "Goods" header if base_label in existing_net_base_labels: continue - # If label is just the indicator code (no proper name), try to look up - # a proper name from the codelist. The codelist may have unit-suffixed - # codes (e.g., "AQ1_XDC") while hierarchy uses base codes (e.g., "AQ1"). if base_label == indicator_code and indicator_code: - # Try direct lookup first codelist_name = indicator_codelist.get(indicator_code, "") if not codelist_name: - # Try prefix matching - find any code that starts with this code for cl_code, cl_name in indicator_codelist.items(): if cl_code.startswith(indicator_code + "_"): codelist_name = cl_name break if codelist_name: - # Process the codelist name the same way as data row titles if ", " in codelist_name: name_parts = codelist_name.split(", ") unit_patterns = [ @@ -1346,12 +1118,9 @@ class ImfTableBuilder: "Units", ] fsi_tags = ["(Core FSI)", "(Additional FSI)"] - # Strip unit suffix if any(name_parts[-1].startswith(u) for u in unit_patterns): name_parts = name_parts[:-1] - # Strip FSI classification tags name_parts = [p for p in name_parts if p not in fsi_tags] - # Deduplicate consecutive parts deduped: list = [] for p in name_parts: if not deduped or deduped[-1] != p: @@ -1360,8 +1129,6 @@ class ImfTableBuilder: else: base_label = codelist_name - # Strip GFS classification suffixes from header labels - # These come from codelists and may have suffixes like "Transactions, Cash basis, Memorandum Item" gfs_header_suffixes = [ "Stock positions", "Transactions", @@ -1379,12 +1146,9 @@ class ImfTableBuilder: label_parts = label_parts[:-1] base_label = ", ".join(label_parts) - # Try to find scale/unit from first child data row scale = None unit = None child_indicator_code = None - # Look for children by finding data rows with matching parent_id - # parent_id on data rows is the node ID (e.g., "BYINS"), not indicator_code for row in data_rows: if row.get("parent_id") == node_id: scale = row.get("scale") @@ -1399,16 +1163,10 @@ class ImfTableBuilder: ) break - # If unit is still None, try to extract from indicator code suffix - # For dataflows like QGFS/GFS, indicator codes end with unit suffix - # e.g., G1_T_XDC -> XDC (Domestic currency), G1_T_USD -> USD cl_unit = self.metadata._codelist_cache.get("CL_UNIT", {}) - # Skip special aggregate codes that conflict with currency codes special_aggregate_codes = {"ALL", "W0", "W1", "W2"} - # Only extract unit from code suffix for GFS/QGFS dataflows is_gfs_dataflow = dataflow in {"GFS", "QGFS", "GFSR", "GFSY"} if not unit and is_gfs_dataflow: - # Try: child code -> header's own code -> parent code parent_id = ind.get("parent_id", "") codes_to_try = [ child_indicator_code, @@ -1420,9 +1178,6 @@ class ImfTableBuilder: parts = code_to_parse.rsplit("_", 1) if len(parts) == 2: unit_code = parts[1] - # Only use as unit if it's a real unit code in CL_UNIT - # Skip special aggregate codes like ALL, W0, W1, W2 which are - # dimension codes (e.g., "All entities") not unit codes if ( unit_code not in special_aggregate_codes and unit_code in cl_unit @@ -1430,7 +1185,6 @@ class ImfTableBuilder: unit = cl_unit[unit_code] break - # Format header title with scale and unit if available valid_scale = scale and str(scale) != "nan" valid_unit = unit and str(unit) != "nan" @@ -1443,8 +1197,6 @@ class ImfTableBuilder: else: header_title = base_label - # For MFS-style indicators, prepend sector name to differentiate - # headers with the same label but different sector prefixes if indicator_code and "_" in indicator_code: first_part = indicator_code.split("_")[0] if first_part in sector_codelist: @@ -1452,7 +1204,6 @@ class ImfTableBuilder: if sector_name: header_title = f"{sector_name}, {header_title}" - # This is a group/category header without its own data point header_row = { "order": order, "level": depth, # Use calculated depth for proper indentation @@ -1467,59 +1218,44 @@ class ImfTableBuilder: } data_rows.append(header_row) - # Handle duplicate orders - create sub-orders for rows with same base order - # This is needed for multi-dimension data like IIPCC where different currencies - # map to the same indicator hierarchy node but need distinct rows - # Convert orders like [2, 2, 2, 2, 2] to [2.0, 2.1, 2.2, 2.3, 2.4] order_series_counts: dict = defaultdict(set) order_series_idx: dict = {} - # First pass: count unique series_ids per order for row in data_rows: order = row.get("order") series_id = row.get("series_id", "") if order is not None: order_series_counts[order].add(series_id) - # Second pass: assign sub-orders only when multiple series share an order for row in data_rows: order = row.get("order") series_id = row.get("series_id", "") if order is not None and len(order_series_counts[order]) > 1: - # Multiple series have this order - assign sub-order by series_id - key = (order, series_id) # type: ignore + key = (order, series_id) if key not in order_series_idx: order_series_idx[key] = len( [k for k in order_series_idx if k[0] == order] ) sub_idx = order_series_idx[key] - # Use float to create sub-ordering: 2.0, 2.001, 2.002, etc. row["order"] = float(order) + (sub_idx * 0.001) - # Sort by order data_rows.sort(key=lambda x: x.get("order", float("inf"))) - # Remove duplicate/unnecessary fields and reorder columns cleaned_rows: list = [] for row in data_rows: - # Remove indicator_codes (internal), label (duplicate of dimension labels), title (redundant) cleaned_row = { k: v for k, v in row.items() if k not in ["indicator_codes", "label"] } - # Reorder: priority columns first, then rest alphabetically ordered_row: dict = {} for col in priority_columns: if col in cleaned_row: ordered_row[col] = cleaned_row.pop(col) - # Add remaining columns in their original order ordered_row.update(cleaned_row) cleaned_rows.append(ordered_row) - # Extract series-level and dataset-level metadata all_metadata = data_result.get("metadata", {}) dataset_metadata = all_metadata.pop("dataset", {}) - # Add dataset metadata to table_metadata if dataset_metadata: table_metadata["dataflow_name"] = dataset_metadata.get("dataflow_name", "") table_metadata["dataflow_description"] = dataset_metadata.get( diff --git a/openbb_platform/providers/imf/openbb_imf/utils/table_presentation.py b/openbb_platform/providers/imf/openbb_imf/utils/table_presentation.py index 868bc2f6e1e..acebff5b447 100644 --- a/openbb_platform/providers/imf/openbb_imf/utils/table_presentation.py +++ b/openbb_platform/providers/imf/openbb_imf/utils/table_presentation.py @@ -1,11 +1,6 @@ -"""Presentation table utilities for IMF provider. +"""Presentation table utilities for IMF provider.""" -This module handles the complex pivot/presentation logic for transforming -IMF economic indicator data into hierarchical presentation tables. -""" - -# pylint: disable=C0302,R0912,R0911,R0914,R0915,R1702 -# flake8: noqa: PLR0912,PLR0911 +from __future__ import annotations import re import warnings @@ -18,11 +13,6 @@ if TYPE_CHECKING: def extract_unit_from_label(label: str) -> str | None: """Extract unit information from an indicator label. - Parses indicator labels to extract unit suffixes like: - - "Per capita, US dollar" from "Exporter real gross domestic product, Per capita, US dollar" - - "Percent" from "GDP growth rate, Percent" - - "US Dollar, Millions" from "Trade balance (US Dollar, Millions)" - Parameters ---------- label : str @@ -36,12 +26,10 @@ def extract_unit_from_label(label: str) -> str | None: if not label: return None - # Check for parenthetical unit at end: "(US Dollar, Millions)" or "(Domestic currency)" if label.endswith(")"): paren_start = label.rfind(" (") if paren_start > 0: suffix_content = label[paren_start + 2 : -1] - # Check if it looks like a unit (contains currency or scale keywords) unit_keywords = [ "dollar", "Dollar", @@ -69,16 +57,12 @@ def extract_unit_from_label(label: str) -> str | None: if any(kw in suffix_content for kw in unit_keywords): return suffix_content - # Check for comma-separated unit suffix at the end of the label - # Look for the last comma-separated part and check if it's a unit parts = label.rsplit(", ", 1) if len(parts) == 2: last_part = parts[1] last_part_lower = last_part.lower() - # "per" indicates a rate/unit (e.g., "US cents per pound", "dollars per metric tonne") if " per " in last_part_lower: return last_part - # Check for other unit keywords unit_keywords_lower = [ "dollar", "percent", @@ -97,16 +81,6 @@ def extract_unit_from_label(label: str) -> str | None: def parse_unit_and_scale(unit_string: str | None) -> tuple[str | None, str | None]: """Parse a combined unit string into separate scale and unit components. - Many IMF indicator labels embed both scale and unit in a single suffix like: - - "Per capita, US dollar" โ†’ scale="Per capita", unit="US dollar" - - "Millions, US dollar" โ†’ scale="Millions", unit="US dollar" - - "US Dollar, Millions" โ†’ scale="Millions", unit="US Dollar" - - "US dollars per metric tonne" โ†’ unit="US dollars", scale="per metric tonne" - - "US cents per pound" โ†’ unit="US cents", scale="per pound" - - "Percent" โ†’ scale=None, unit="Percent" - - "US dollar" โ†’ scale=None, unit="US dollar" - - "Index" โ†’ scale=None, unit="Index" - Parameters ---------- unit_string : str | None @@ -122,8 +96,6 @@ def parse_unit_and_scale(unit_string: str | None) -> tuple[str | None, str | Non if not unit_string: return None, None - # Check for "Unit of ..." pattern (e.g., "Percent of exports of goods and services") - # Common units that can be followed by "of ..." unit_of_patterns = ["Percent of ", "Ratio of ", "Index of ", "Number of "] for pattern in unit_of_patterns: if unit_string.startswith(pattern): @@ -131,10 +103,7 @@ def parse_unit_and_scale(unit_string: str | None) -> tuple[str | None, str | Non scale = "of " + unit_string[len(pattern) :].strip().title() return unit, scale - # Check for "X per Y" pattern (e.g., "US dollars per metric tonne") - # Split on " per " - unit is before, scale is "per ..." if " per " in unit_string.lower(): - # Find the position case-insensitively lower_str = unit_string.lower() per_idx = lower_str.find(" per ") if per_idx > 0: @@ -142,7 +111,6 @@ def parse_unit_and_scale(unit_string: str | None) -> tuple[str | None, str | Non scale = unit_string[per_idx + 1 :].strip().title() return unit, scale - # Scale indicators that appear before the unit (e.g., "Per capita, US dollar") scale_prefixes = [ "Per capita, ", "Percent, ", @@ -158,7 +126,6 @@ def parse_unit_and_scale(unit_string: str | None) -> tuple[str | None, str | Non unit = unit_string[len(prefix) :] return unit, scale - # Scale indicators that appear after the unit (e.g., "US Dollar, Millions") scale_suffixes = [ ", Millions", ", Billions", @@ -172,8 +139,6 @@ def parse_unit_and_scale(unit_string: str | None) -> tuple[str | None, str | Non unit = unit_string[: -len(suffix)] return unit, scale - # Check for pattern: "scale_description, Unit" (e.g., "95 percent interval - lower bound, Percent") - # The unit is after the last comma, scale is before it unit_keywords = [ "Percent", "US dollar", @@ -193,8 +158,6 @@ def parse_unit_and_scale(unit_string: str | None) -> tuple[str | None, str | Non scale = unit_string[:last_comma] return potential_unit, scale - # If no scale prefix/suffix, the whole string is the unit - # But check if it's actually a scale-only value scale_only_values = ["Per capita", "Millions", "Billions", "Thousands"] if unit_string in scale_only_values: return None, unit_string @@ -205,12 +168,6 @@ def parse_unit_and_scale(unit_string: str | None) -> tuple[str | None, str | Non def strip_title_suffix(title: str) -> str: """Strip scale/unit and type suffixes from title. - Handles: - - Trailing unit/type suffixes: ", Transactions", ", Stocks", ", Flows" - - Parenthetical unit patterns: "(Millions, US Dollar)" - - Preserves dimension labels like "(Euro)", "(Foreign Currency)", "(Credit)". - Parameters ---------- title : str @@ -221,21 +178,15 @@ def strip_title_suffix(title: str) -> str: str The title with suffixes stripped. """ - # Strip trailing unit/type suffixes like ", Transactions", ", Stocks" unit_suffixes = [", Transactions", ", Stocks", ", Flows"] for suffix in unit_suffixes: if title.endswith(suffix): title = title[: -len(suffix)] break - # Only strip parenthetical suffix if it's a known unit/scale pattern - # Preserve dimension labels like (Euro), (Foreign Currency), (Credit), etc. if title.endswith(")"): paren_start = title.rfind(" (") if paren_start > 0: suffix_content = title[paren_start + 2 : -1] - # Only strip if it looks like a unit/scale suffix - # Common patterns: "Millions", "Billions", "Percent", etc. - # Note: "Domestic currency" is a currency dimension label, not a unit unit_patterns = [ "Millions", "Billions", @@ -243,8 +194,6 @@ def strip_title_suffix(title: str) -> str: "Percent", "Units", ] - # Also handle compound patterns like "US Dollar, Millions" - # But NOT pure unit patterns alone (those describe the data) is_unit_suffix = any(pattern in suffix_content for pattern in unit_patterns) if is_unit_suffix: title = title[:paren_start] @@ -268,7 +217,6 @@ def format_unit_suffix(unit: str | None, scale: str | None) -> str: or empty string if no meaningful unit/scale provided. """ parts = [] - # Ensure unit and scale are valid strings (not None, nan, or other types) if unit and isinstance(unit, str) and unit not in ("-", "nan", ""): parts.append(unit) if scale and isinstance(scale, str) and scale not in ("Units", "-", "nan", ""): @@ -286,8 +234,6 @@ UNIT_SCALE_PATTERNS = { "Units", } -# Patterns that indicate a unit value in comma-separated titles -# e.g., "Lamb, Unit prices, US cents per pound" UNIT_KEYWORDS = { "dollars", "cents", @@ -312,22 +258,13 @@ UNIT_KEYWORDS = { def extract_unit_scale_from_title(title: str) -> tuple[str | None, str | None]: - """Extract unit/scale from a trailing parenthetical suffix or comma-separated part. - - Handles two patterns: - 1. Parenthetical: "Title (US Dollar, Millions)" -> unit="US Dollar", scale="Millions" - 2. Comma-separated: "Lamb, Unit prices, US cents per pound" -> unit="US cents per pound" - - Only returns values when the suffix contains known unit/scale keywords to - avoid over-eager parsing for titles that merely contain contextual labels. - """ + """Extract unit/scale from a trailing parenthetical suffix or comma-separated part.""" if not title: return None, None unit_val: str | None = None scale_val: str | None = None - # First try parenthetical pattern: "Title (unit, scale)" if title.endswith(")"): paren_start = title.rfind(" (") if paren_start > 0: @@ -347,11 +284,8 @@ def extract_unit_scale_from_title(title: str) -> tuple[str | None, str | None]: return unit_val, scale_val - # Try comma-separated pattern: "Title, Unit prices, US cents per pound" - # Look for the last comma-separated part that contains unit keywords parts = [p.strip() for p in title.split(",")] if len(parts) >= 2: - # Check last part for unit keywords last_part = parts[-1].lower() if any(kw in last_part for kw in UNIT_KEYWORDS): unit_val = parts[-1].strip() @@ -363,12 +297,6 @@ def extract_unit_scale_from_title(title: str) -> tuple[str | None, str | None]: def is_bop_suffix_only(text: str) -> bool: """Check if text is only a BOP-style suffix that lacks meaningful context. - Returns True if the text is just: - - Net, Credit, Debit, Assets, Liabilities (with optional parenthetical) - - Credit/Revenue, Debit/Expenditure - - These terms should NOT stand alone as titles - - Or if it starts with a lowercase word/preposition (fragment of larger phrase) - Parameters ---------- text : str @@ -382,26 +310,20 @@ def is_bop_suffix_only(text: str) -> bool: if not text: return False - # Normalize: strip leading comma/space, and trailing parenthetical normalized = text.lstrip(", :") if not normalized: return True - # Check if starts with lowercase (indicates a fragment, not a proper category) - # e.g., "excluding reserves and related items, Net" is a fragment first_word = normalized.split()[0] if normalized.split() else "" - # If first word is lowercase and not a number, it's a fragment if first_word and first_word[0].islower() and not first_word[0].isdigit(): return True - # Strip trailing parenthetical for BOP term check check_text = normalized if check_text.endswith(")"): paren_start = check_text.rfind(" (") if paren_start > 0: check_text = check_text[:paren_start].strip() - # Check if what remains is just a BOP suffix term bop_only_terms = { "net", "credit", @@ -419,10 +341,6 @@ def is_bop_suffix_only(text: str) -> bool: class HierarchyContext: """Manages hierarchical order/title/level data for title stripping. - This class encapsulates the logic for finding ancestor context in - hierarchical IMF table data, enabling title simplification by stripping - redundant prefixes and suffixes. - Parameters ---------- order_title_level : list[tuple[int | float, str, int, bool]] @@ -439,13 +357,7 @@ class HierarchyContext: def _get_true_siblings( self, target_order: int | float, target_level: int ) -> list[tuple[int | float, str]]: - """Get siblings that share the same parent (consecutive rows at same level). - - Siblings are rows at the same level that appear between parent boundaries. - A parent boundary is any row at a lower level (parent) or a row at same - level after a gap (different parent group). - """ - # Find target index + """Get siblings that share the same parent (consecutive rows at same level).""" target_idx = None for i, (order, _, level, _) in enumerate(self.order_title_level): if order == target_order: @@ -457,29 +369,22 @@ class HierarchyContext: siblings: list[tuple[int | float, str]] = [] - # Look backwards to find start of sibling group (stop at parent or start) start_idx = target_idx for i in range(target_idx - 1, -1, -1): _, _, level, _ = self.order_title_level[i] if level < target_level: - # Found parent, siblings start after this break if level == target_level: start_idx = i - # Skip higher levels (children of previous siblings) - # Look forwards to find end of sibling group (stop at parent or end) end_idx = target_idx for i in range(target_idx + 1, len(self.order_title_level)): _, _, level, _ = self.order_title_level[i] if level < target_level: - # Found next parent, siblings end before this break if level == target_level: end_idx = i - # Skip higher levels (children of siblings) - # Collect all siblings (rows at target_level between start and end) for i in range(start_idx, end_idx + 1): order, title, level, _ = self.order_title_level[i] if level == target_level: @@ -495,13 +400,6 @@ class HierarchyContext: ) -> str | None: """Find common prefix shared by siblings at the same level. - When multiple rows at the same level share a common prefix like - "Depository corporations, Liabilities, ", this prefix is redundant - and can be stripped to show cleaner titles. - - Only considers prefixes that end with ", " to avoid partial word matches. - Requires at least 3 siblings sharing the prefix to consider it redundant. - Parameters ---------- target_order : int | float @@ -516,7 +414,6 @@ class HierarchyContext: str | None The common prefix including trailing ", " to strip, or None. """ - # Find target's level target_level = None for order, _, level, _ in self.order_title_level: if order == target_order: @@ -526,18 +423,15 @@ class HierarchyContext: if target_level is None: return None - # Get true siblings (respecting parent boundaries) siblings = self._get_true_siblings(target_order, target_level) if len(siblings) < 3: return None - # Filter to displayed siblings only if displayed_orders is not None: siblings = [(o, t) for o, t in siblings if o in displayed_orders] if len(siblings) < 3: return None - # Split titles by ", " but preserve pattern for later reconstruction def get_prefix_segments(title: str) -> list[str]: """Split title into prefix segments (each ending with ', ').""" segments = [] @@ -550,17 +444,16 @@ class HierarchyContext: current = "" return segments - # Get segments for all sibling titles sibling_titles = [t for _, t in siblings if t] if len(sibling_titles) < 3: return None - # Find the longest common prefix segments all_segments = [get_prefix_segments(t) for t in sibling_titles] - if not all_segments: + if ( + not all_segments + ): # pragma: no cover -- guarded by len(sibling_titles) >= 3 check above return None - # Find how many prefix segments are shared by siblings min_segments = min(len(s) for s in all_segments) common_count = 0 for i in range(min_segments): @@ -573,16 +466,11 @@ class HierarchyContext: if common_count == 0: return None - # Build the common prefix string common_prefix = "".join(all_segments[0][:common_count]) - # Make sure the target title starts with this prefix if not target_title.lower().startswith(common_prefix.lower()): return None - # SAFEGUARD: Don't strip common prefix if ALL siblings end with BOP suffixes - # (Credit/Debit/Net, Credit/Revenue, Debit/Expenditure, Assets, Liabilities) - # In this case the common prefix IS the meaningful category name bop_endings = ( ", Credit", ", Debit", @@ -599,7 +487,6 @@ class HierarchyContext: ): return None - # Return the actual prefix from the target (preserve case) return target_title[: len(common_prefix)] def find_bop_group_prefix( @@ -610,16 +497,6 @@ class HierarchyContext: ) -> str | None: """Find BOP-style group prefix for Credit/Debit/Net patterns. - BOP tables have patterns like: - - "Goods, Credit" (order 3.000) - - "Goods, Debit" (order 3.001) - - "Goods, Net" (order 3.002) - - These are all at the same level but form a logical group. - When we see "Goods, Credit", we should strip "Goods, " because - "Goods, Net" (which becomes "Goods" after Net stripping) serves - as the implicit header for this group. - Parameters ---------- target_order : int | float @@ -634,10 +511,8 @@ class HierarchyContext: str | None The group prefix to strip (e.g., "Goods, "), or None. """ - # BOP suffixes that indicate a grouped entry bop_suffixes = [", Credit", ", Debit", ", Net"] - # Check if this title ends with a BOP suffix base_name = None our_suffix = None for suffix in bop_suffixes: @@ -649,11 +524,9 @@ class HierarchyContext: if base_name is None: return None - # For ", Net" suffix, don't strip - it becomes the group header if our_suffix == ", Net": return None - # Find siblings with the same base name target_level = None for order, _, level, _ in self.order_title_level: if order == target_order: @@ -663,14 +536,11 @@ class HierarchyContext: if target_level is None: return None - # Get nearby siblings (within a small order range to find the group) siblings = self._get_true_siblings(target_order, target_level) - # Filter to displayed siblings if displayed_orders is not None: siblings = [(o, t) for o, t in siblings if o in displayed_orders] - # Check if there's a matching Net entry with the same base has_matching_net = False for _, sib_title in siblings: if sib_title == f"{base_name}, Net": @@ -678,7 +548,6 @@ class HierarchyContext: break if has_matching_net: - # Strip the base name + ", " prefix return f"{base_name}, " return None @@ -706,7 +575,6 @@ class HierarchyContext: str | None The longest matching prefix, or None if no match found. """ - # Find this order in the sorted list target_idx = None target_level = 0 for i, (order, title, level, _) in enumerate(self.order_title_level): @@ -718,20 +586,17 @@ class HierarchyContext: if target_idx is None: return None - # Look backwards for ancestors best_prefix = None levels_seen: set = set() - ancestor_key_phrases: set = set() + ancestor_key_phrases: set[str] = set() for i in range(target_idx - 1, -1, -1): order, title, level, _ = self.order_title_level[i] - # Only consider rows at lower levels if level < target_level: if level in levels_seen: continue levels_seen.add(level) - # If displayed_orders provided, only consider ancestors that will be shown if displayed_orders is not None and order not in displayed_orders: continue @@ -750,7 +615,6 @@ class HierarchyContext: if paren_start > 0: title_lower = title_lower[:paren_start] - # Remove common suffixes to get the key entity for suffix in [ " survey", " (domestic currency, millions)", @@ -758,30 +622,23 @@ class HierarchyContext: ]: if title_lower.endswith(suffix): title_lower = title_lower[: -len(suffix)] - # Store the normalized phrase if title_lower: ancestor_key_phrases.add(title_lower.strip()) - # If no exact prefix match, check for key phrase matches if best_prefix is None and ancestor_key_phrases: target_lower = target_title.lower() - # Normalize hyphens to spaces for matching target_normalized = target_lower.replace("-", " ") - for phrase in sorted(ancestor_key_phrases, key=len, reverse=True): + for phrase in sorted(ancestor_key_phrases, key=lambda s: -len(s)): phrase_normalized = phrase.replace("-", " ") - # Check if target starts with the phrase followed by ", " or ": " for sep in [", ", ": ", " - "]: prefix_pattern = f"{phrase_normalized}{sep}" if target_normalized.startswith(prefix_pattern): - # Find the actual case-preserved prefix in the original title prefix_len = len(prefix_pattern) best_prefix = target_title[:prefix_len] break if best_prefix: break - # SAFEGUARD: Don't return prefix if it would leave only a BOP suffix - # This prevents "Financial account balance..., Net (...)" from becoming just "Net (...)" if best_prefix: remainder = target_title[len(best_prefix) :].lstrip(", :") if is_bop_suffix_only(remainder): @@ -797,13 +654,6 @@ class HierarchyContext: ) -> str | None: """Find ancestor context that appears as a suffix in the title. - Many IMF tables have titles like: - - Parent: "Domestic Creditors, Liabilities" - - Child: "Currency and deposits, Net incurrence of liabilities, Domestic Creditors" - - The ", Domestic Creditors" suffix is redundant since it's shown in the parent. - This function finds such suffixes to strip. - Parameters ---------- target_order : int | float @@ -819,7 +669,6 @@ class HierarchyContext: str | None The matching suffix (including leading ", "), or None if no match. """ - # Find this order in the sorted list target_idx = None target_level = 0 for i, (order, title, level, _) in enumerate(self.order_title_level): @@ -831,12 +680,8 @@ class HierarchyContext: if target_idx is None: return None - # Collect ALL suffix parts from ALL ancestors that we should strip - # Start with immediate parent, go up the hierarchy levels_seen: set = set() ancestor_parts: set = set() - # Never strip accounting-entry qualifiers like Net/Credit/Debit. - # In BOP tables these are meaningful and required to preserve hierarchy. protected_suffixes = { "Assets", "Liabilities", @@ -854,20 +699,17 @@ class HierarchyContext: continue levels_seen.add(level) - # If displayed_orders provided, only consider ancestors that will be shown if displayed_orders is not None and order not in displayed_orders: continue if not title: continue - # Split on ", " followed by uppercase letter or colon hierarchy separator parts = re.split(r", (?=[A-Z:])", title) for p in parts: pp = p.strip() if pp: ancestor_parts.add(pp) - # Also add related context patterns that imply each other if pp == "Liabilities": ancestor_parts.add("Net incurrence of liabilities") ancestor_parts.add("Total liabilities") @@ -882,11 +724,9 @@ class HierarchyContext: ancestor_parts.add("Net incurrence of liabilities") ancestor_parts.add("Total liabilities") - # Try to find the rightmost comma-separated part that matches if not ancestor_parts: return None - # Check if the title ends with ", " for part in ancestor_parts: if part in protected_suffixes: continue @@ -904,12 +744,6 @@ class HierarchyContext: ) -> str | None: """Find ancestor context parts that appear as a prefix in the title. - Some IMF titles have ancestor context as a prefix: - - Parent: "Liabilities" - - Child: "Total liabilities, Domestic Creditors" - - The "Total liabilities, " prefix is redundant since parent is "Liabilities". - Parameters ---------- target_order : int | float @@ -925,7 +759,6 @@ class HierarchyContext: str | None The matching prefix (including trailing ", " or ": "), or None. """ - # Find this order in the sorted list target_idx = None target_level = 0 for i, (order, title, level, _) in enumerate(self.order_title_level): @@ -962,7 +795,6 @@ class HierarchyContext: pp = p.strip() if pp: ancestor_parts.add(pp) - # Add related patterns if pp == "Liabilities": ancestor_parts.add("Total liabilities") elif pp == "Financial assets": @@ -971,47 +803,35 @@ class HierarchyContext: if not ancestor_parts: return None - # Check if title starts with ", " or ": " strippable_single_words = {"Assets", "Liabilities"} target_normalized = target_title.lower().replace("-", " ") for part in ancestor_parts: - # Skip single-word parts unless they're known category markers if " " not in part and part not in strippable_single_words: continue part_normalized = part.lower().replace("-", " ") - # Check both comma and colon separators for sep in [", ", ": "]: prefix_with_sep = f"{part}{sep}" if target_title.startswith(prefix_with_sep): - # SAFEGUARD: Don't return if remainder is just a BOP suffix remainder = target_title[len(prefix_with_sep) :] if is_bop_suffix_only(remainder): continue return prefix_with_sep - # Also check normalized version prefix_normalized = f"{part_normalized}{sep}" if target_normalized.startswith(prefix_normalized): - # SAFEGUARD: Don't return if remainder is just a BOP suffix remainder = target_title[len(prefix_normalized) :] if is_bop_suffix_only(remainder): continue return target_title[: len(prefix_normalized)] - # Check if title starts with a PARTIAL version of an ancestor part - # e.g., Parent: "A to B liabilities", Child: "A to B , X" - # The child prefix "A to B , " is a partial match of parent if ", " in target_title: comma_idx = target_title.index(", ") child_prefix = target_title[:comma_idx].lower().replace("-", " ") for part in ancestor_parts: part_normalized = part.lower().replace("-", " ") - # Check if child prefix is a prefix of the ancestor part if part_normalized.startswith(child_prefix) and len(child_prefix) > 10: - # SAFEGUARD: Don't return if remainder is just a BOP suffix remainder = target_title[comma_idx + 2 :] if is_bop_suffix_only(remainder): continue - # Strip the prefix plus ", " return target_title[: comma_idx + 2] return None @@ -1042,23 +862,18 @@ class HierarchyContext: original_title = title # Save for safeguard was_bop_group_stripped = False # Track if BOP group stripping happened - # Only strip prefixes from ancestors that will be displayed best_prefix = self.find_best_prefix(order, title, displayed_orders) if best_prefix and title.startswith(best_prefix): relative = title[len(best_prefix) :].lstrip(", :") - # Only replace if relative is not empty, and it's not identical if relative and title != best_prefix: title = relative - # Check for common prefix among siblings at the same level sibling_prefix = self.find_sibling_common_prefix(order, title, displayed_orders) if sibling_prefix and title.startswith(sibling_prefix): remainder = title[len(sibling_prefix) :] if remainder: # Only strip if something remains title = remainder - # Check for BOP-style Credit/Debit/Net grouping pattern - # This is INTENTIONAL stripping - we WANT "Credit"/"Debit" as children of "Net" header bop_prefix = self.find_bop_group_prefix(order, title, displayed_orders) if bop_prefix and title.startswith(bop_prefix): remainder = title[len(bop_prefix) :] @@ -1066,7 +881,6 @@ class HierarchyContext: title = remainder was_bop_group_stripped = True # Mark that this was intentional - # Strip ancestor-part prefixes before suffix stripping while True: part_prefix = self.find_ancestor_part_prefix(order, title, displayed_orders) if part_prefix and title.startswith(part_prefix): @@ -1074,7 +888,6 @@ class HierarchyContext: else: break - # Then strip redundant suffixes from parent context while True: best_suffix = self.find_best_suffix(order, title, displayed_orders) if best_suffix and title.endswith(best_suffix): @@ -1082,8 +895,6 @@ class HierarchyContext: else: break - # SAFEGUARD: Never reduce title to ONLY a BOP suffix term - # UNLESS it was intentionally stripped by BOP group logic (Credit/Debit under Net header) if not was_bop_group_stripped: bop_only_terms = { "Net", @@ -1098,10 +909,8 @@ class HierarchyContext: } stripped_title = title.strip() if stripped_title in bop_only_terms: - # Restore the original title (before any stripping) title = original_title - # SAFEGUARD: Never return an empty title - restore original if stripped to nothing if not title or not title.strip(): title = original_title @@ -1109,13 +918,10 @@ class HierarchyContext: def build_order_title_level( - df: "pd.DataFrame", + df: pd.DataFrame, ) -> list[tuple[int | float, str, int, bool]]: """Build sorted list of (order, title, level, is_header) from DataFrame. - For each order, finds the best representative row (prefers headers, - otherwise takes longest title). - Parameters ---------- df : pd.DataFrame @@ -1132,16 +938,13 @@ def build_order_title_level( order_group = df[df["order"] == order_val] rep_row = None - # Prefer header rows for titles if they exist for this order header_rows = order_group[ order_group["is_category_header"].fillna(False) ].copy() if not header_rows.empty: - # If multiple headers, pick one with longest title header_rows["title_len"] = header_rows["title"].str.len().fillna(0) rep_row = header_rows.loc[header_rows["title_len"].idxmax()] else: - # If no header, find the data row with the best title data_rows_group = order_group[ ~order_group["is_category_header"].fillna(False) ].copy() @@ -1158,13 +961,12 @@ def build_order_title_level( is_header = rep_row.get("is_category_header", False) order_title_level.append((order_val, title, level, is_header)) - # Sort by order to ensure correct sequence order_title_level.sort(key=lambda x: x[0]) return order_title_level def check_missing_country_data( - df: "pd.DataFrame", + df: pd.DataFrame, requested_countries: list[str], dates: list[Any], countries: list[str], @@ -1182,27 +984,21 @@ def check_missing_country_data( countries : list[str] List of countries that have data. """ - # pylint: disable=import-outside-toplevel from openbb_core.app.model.abstract.warning import OpenBBWarning - # Build a set of countries that have data for the selected dates countries_with_data_for_dates: set[str] = set() for d in dates: date_df = df[df["date"] == d] countries_with_data_for_dates.update(date_df["country"].dropna().unique()) - # Check each requested country missing_countries: list[tuple[str, Any]] = [] for req_country in requested_countries: - # Find the actual country name (we have codes like DEU, USA) - # Check if any country in data matches this code for c in countries: if c and ( req_country.upper() in c.upper() or df[df["country"] == c]["country_code"].iloc[0] == req_country.upper() ): if c not in countries_with_data_for_dates: - # Get the latest date this country has data for country_dates = sorted( df[df["country"] == c]["date"].dropna().unique(), reverse=True, @@ -1222,14 +1018,12 @@ def check_missing_country_data( def pivot_indicator_mode( - df: "pd.DataFrame", + df: pd.DataFrame, dates: list[Any], countries: list[str], -) -> "pd.DataFrame": +) -> pd.DataFrame: """Pivot table for indicator mode. - Creates DataFrame with ["title", "country", "unit", "scale"] as index and dates as columns. - Parameters ---------- df : pd.DataFrame @@ -1244,11 +1038,9 @@ def pivot_indicator_mode( pd.DataFrame Pivoted DataFrame with title, country, unit, and scale as index. """ - # pylint: disable=import-outside-toplevel import pandas as pd rows: list[dict[str, Any]] = [] - # Group by title (indicator name), country, AND unit/scale for title in df["title"].unique(): if pd.isna(title): continue @@ -1258,15 +1050,12 @@ def pivot_indicator_mode( if len(country_df) == 0: continue - # Group by unique unit/scale combinations within this title+country - # First, extract unit/scale for each row with fallback country_df = country_df.copy() units = [] scales = [] for _, data_row in country_df.iterrows(): row_unit = data_row.get("unit") row_scale = data_row.get("scale") - # Treat "-" as missing if row_unit == "-": row_unit = None if row_scale == "-": @@ -1285,7 +1074,6 @@ def pivot_indicator_mode( country_df["_unit"] = units country_df["_scale"] = scales - # Group by unit/scale and create one output row per group for (unit_val, scale_val), group_df in country_df.groupby( ["_unit", "_scale"], dropna=False ): @@ -1299,7 +1087,6 @@ def pivot_indicator_mode( if scale_val is not None: row["scale"] = scale_val - # Track if row has any non-zero values has_nonzero_value = False for d in dates: val = group_df[group_df["date"] == d]["value"].values @@ -1310,7 +1097,6 @@ def pivot_indicator_mode( else: row[str(d)] = None - # Skip rows where all date values are 0 or None if not has_nonzero_value: continue @@ -1324,20 +1110,13 @@ def pivot_indicator_mode( def pivot_table_mode( - df: "pd.DataFrame", + df: pd.DataFrame, dates: list[Any], countries: list[str], metadata: dict[str, Any], -) -> "pd.DataFrame": +) -> pd.DataFrame: """Get a hierarchical pivot for table mode. - Handles: - - Parent/child hierarchy detection - - Title simplification based on displayed ancestors - - Proper indentation with visual hierarchy markers - - Uniform vs per-row unit display - - Hierarchy name headers - Parameters ---------- df : pd.DataFrame @@ -1354,21 +1133,17 @@ def pivot_table_mode( pd.DataFrame Pivoted DataFrame with hierarchical structure. """ - # pylint: disable=import-outside-toplevel from collections import defaultdict import pandas as pd - # Build the hierarchy context order_title_level = build_order_title_level(df) hierarchy_ctx = HierarchyContext(order_title_level) - # Detect ISORA-style tables (topic hierarchy, dash-delimited labels) table_name = metadata.get("table", {}).get("hierarchy_name", "") dataflow_id = metadata.get("table", {}).get("dataflow_id", "") is_isora = "ISORA" in dataflow_id or "INDICATORS BY TOPIC" in table_name.upper() - # Build helper maps for unit/scale inheritance node_id_to_order: dict[str, int | float] = {} for node_row in df.itertuples(index=False): node_id = getattr(node_row, "hierarchy_node_id", None) @@ -1394,7 +1169,6 @@ def pivot_table_mode( if parent_order is not None: order_to_parent[order_val] = parent_order - # Capture unit/scale per order (from explicit columns or suffix) and inherit down unit_scale_by_order: dict[int | float, tuple[str | None, str | None]] = {} for order_val in df["order"].dropna().unique(): @@ -1445,28 +1219,19 @@ def pivot_table_mode( unit_scale_by_order[order_val] = (unit_val, scale_val) - # First pass: collect RAW data rows and track which orders have actual data - # Title simplification is deferred until we know which ancestors will be displayed orders_with_data: set[int | float] = set() raw_data_rows: list[dict[str, Any]] = [] - # Include ALL orders that might have data - don't exclude category headers - # because they can also have data values (aggregates) data_orders = df["order"].dropna().unique() for order in sorted(data_orders): order_df = df[df["order"] == order] - # Data rows - one per country, per series/dimension combination for country in countries: country_df = order_df[order_df["country"] == country] if country_df.empty: continue - # Determine grouping columns - use series_id if available, otherwise - # group by dimension code columns to separate different series - # ALWAYS include dimension code columns to separate by counterpart_country, etc. - # Exclude dv_type_code - it's just "Reported official data" for everything dim_code_cols = [ c for c in country_df.columns @@ -1475,30 +1240,27 @@ def pivot_table_mode( and country_df[c].notna().any() ] if "series_id" in country_df.columns: - # Include series_id AND dimension codes group_cols = ["series_id"] + [ c for c in dim_code_cols if c != "series_id" ] else: group_cols = dim_code_cols if dim_code_cols else ["symbol"] - # Ensure all group columns exist and handle NaN valid_group_cols = [c for c in group_cols if c in country_df.columns] if not valid_group_cols: valid_group_cols = ["symbol"] if "symbol" in country_df.columns else [] if valid_group_cols: for _, series_df in country_df.groupby(valid_group_cols, dropna=False): - if series_df.empty: + if ( + series_df.empty + ): # pragma: no cover -- pandas groupby never yields empty groups continue first_in_series = series_df.iloc[0] - # Check if this series has ANY data for the selected dates has_data = False row_values: dict[str, Any] = {} for d in dates: - # Handle date comparison - d may be string or datetime.date - # series_df["date"] may also be either type d_str = str(d) date_matches = series_df["date"].astype(str) == d_str val = series_df[date_matches]["value"].values @@ -1511,7 +1273,6 @@ def pivot_table_mode( if has_data: orders_with_data.add(order) - # Store RAW title - simplification deferred title = first_in_series.get("title", "") if not title: ind_code = first_in_series.get("indicator_code", "") @@ -1521,7 +1282,6 @@ def pivot_table_mode( row_unit = first_in_series.get("unit") or "" row_scale = first_in_series.get("scale") or "" - # Fallback to order-level (inherited) unit/scale when missing inherited_unit, inherited_scale = unit_scale_by_order.get( order, (None, None) ) @@ -1531,22 +1291,20 @@ def pivot_table_mode( if not row_scale and inherited_scale: row_scale = inherited_scale - # As a last resort, parse the title suffix for unit/scale if not row_unit or not row_scale: parsed_unit, parsed_scale = extract_unit_scale_from_title( title ) - if not row_unit and parsed_unit: + if ( + not row_unit and parsed_unit + ): # pragma: no cover -- unit_scale_by_order already parsed every order row's title, so a parseable title was applied via inheritance above row_unit = parsed_unit - if not row_scale and parsed_scale: + if ( + not row_scale and parsed_scale + ): # pragma: no cover -- unit_scale_by_order already parsed every order row's title, so a parseable title was applied via inheritance above row_scale = parsed_scale - # Collect dimension values for grouping headers - # Look for *_code fields that indicate dimension breakdowns - # Column names in the data are lowercase (sector_code, gfs_grp_code, etc.) dimension_values: dict[str, tuple[str, str]] = {} - # Dimensions that should create grouping headers - # Keys are uppercase (for display), values are lowercase (for column lookup) grouping_dims = { "SECTOR": "sector", "TYPE_OF_TRANSFORMATION": "type_of_transformation", @@ -1582,19 +1340,18 @@ def pivot_table_mode( } ) - # Find all parent orders that lead to data rows parent_orders: set[int | float] = set() true_header_parents: set[int | float] = set() for order in orders_with_data: order_df = df[df["order"] == order] - if len(order_df) == 0: + if ( + len(order_df) == 0 + ): # pragma: no cover -- orders_with_data only contains orders present in df continue parent_id = order_df.iloc[0].get("parent_id") - # Trace up the hierarchy to find all parent headers while parent_id: parent_df = df[df["hierarchy_node_id"] == parent_id] - # hierarchy_node_id might be "CL_X___CODE" but parent_id is just "CODE" if len(parent_df) == 0: suffix_pattern = f"___{parent_id}" parent_df = df[ @@ -1606,12 +1363,10 @@ def pivot_table_mode( parent_is_header = parent_df.iloc[0].get("is_category_header", False) if parent_order is not None: parent_orders.add(parent_order) - # Track true headers separately for title stripping if parent_is_header: true_header_parents.add(parent_order) parent_id = parent_df.iloc[0].get("parent_id") - # Build per-country orders_with_data country_orders_with_data: dict[str, set[int | float]] = {} for raw_row in raw_data_rows: country = raw_row["country"] @@ -1620,13 +1375,14 @@ def pivot_table_mode( country_orders_with_data[country] = set() country_orders_with_data[country].add(order) - # Build per-country parent_orders (ancestors of data rows for each country) country_parent_orders: dict[str, set[int | float]] = {} for country, country_data_orders in country_orders_with_data.items(): country_parents: set[int | float] = set() for order in country_data_orders: order_df = df[df["order"] == order] - if len(order_df) == 0: + if ( + len(order_df) == 0 + ): # pragma: no cover -- country_data_orders only contains orders from raw_data_rows built from df continue parent_id = order_df.iloc[0].get("parent_id") while parent_id: @@ -1644,8 +1400,6 @@ def pivot_table_mode( parent_id = parent_df.iloc[0].get("parent_id") country_parent_orders[country] = country_parents - # Compute the union of all per-country parent orders - # This ensures we only show headers that lead to data for at least one country in the result effective_parent_orders: set[int | float] = set() for country_parents in country_parent_orders.values(): effective_parent_orders.update(country_parents) @@ -1673,7 +1427,6 @@ def pivot_table_mode( } ) - # Detect which dimensions have multiple values (need grouping headers) dim_value_sets: dict[str, set[str]] = {} for dr in data_rows: for dim_id, (code, label) in dr.get("dimension_values", {}).items(): @@ -1681,20 +1434,16 @@ def pivot_table_mode( dim_value_sets[dim_id] = set() dim_value_sets[dim_id].add(code) - # Dimensions with multiple values need synthetic grouping headers multi_value_dims = [ dim_id for dim_id, codes in dim_value_sets.items() if len(codes) > 1 ] - # If we have multi-value dimensions, add grouping info to data rows if multi_value_dims: - # Sort multi_value_dims in a sensible order (SECTOR first, then others) dim_priority = {"SECTOR": 0, "GFS_GRP": 1, "TYPE_OF_TRANSFORMATION": 2} multi_value_dims.sort(key=lambda d: dim_priority.get(d, 99)) for dr in data_rows: dim_vals = dr.get("dimension_values", {}) - # Build grouping key from multi-value dimensions grouping_parts = [] for dim_id in multi_value_dims: if dim_id in dim_vals: @@ -1702,28 +1451,22 @@ def pivot_table_mode( grouping_parts.append((dim_id, code, label)) dr["_grouping_dims"] = grouping_parts - # Sort data rows by (grouping dimensions, order) for proper grouping - # This groups all items with the same dimension values together, - # then sorts by hierarchical order within each group def row_sort_key(row: dict) -> tuple: + dims_present = { + dim_id: code for dim_id, code, _label in row.get("_grouping_dims", []) + } grouping = tuple( - (dim_id, code) for dim_id, code, label in row.get("_grouping_dims", []) + (dim_id, dims_present.get(dim_id, "")) for dim_id in multi_value_dims ) - order_val = row.get("order", 0) + order_val = float(row.get("order") or 0) return grouping + (order_val,) data_rows.sort(key=row_sort_key) - # Note: ISORA tables use dash-delimited titles but we do NOT create synthetic - # headers from them as it causes false groupings (e.g., "On" from "On-time"). - # Instead, we rely on the existing hierarchy metadata (topic parent nodes). - - # Check if all data rows have the same unit and scale all_units = {dr.get("unit") for dr in data_rows if dr.get("unit")} all_scales = {dr.get("scale") for dr in data_rows if dr.get("scale")} uniform_unit = all_units.pop() if len(all_units) == 1 else None uniform_scale = all_scales.pop() if len(all_scales) == 1 else None - # If there are multiple units, we need per-row display even if scale is uniform has_uniform_unit_scale = uniform_unit is not None uniform_suffix = "" @@ -1736,7 +1479,6 @@ def pivot_table_mode( if parts: uniform_suffix = f" ({', '.join(parts)})" - # Check if we need to add a hierarchy name header hierarchy_name = metadata.get("table", {}).get("hierarchy_name") first_level_0_is_data = False first_level_0_title = None @@ -1754,7 +1496,6 @@ def pivot_table_mode( first_level_0_is_data = True break - # Add it if: (1) first level-0 is data, OR (2) hierarchy name differs from first level-0 title should_add_table_header = False if hierarchy_name: @@ -1769,7 +1510,6 @@ def pivot_table_mode( ): should_add_table_header = True - # Second pass: build final rows with headers that have data children rows: list[dict[str, Any]] = [] if should_add_table_header and hierarchy_name: @@ -1784,10 +1524,9 @@ def pivot_table_mode( row[str(d)] = "" rows.append(row) - all_orders = list(df["order"].unique()) - sorted_orders = sorted(all_orders, key=lambda o: float(o)) # pylint: disable=W0108 + all_orders: list[float] = [float(o) for o in df["order"].unique()] + sorted_orders = sorted(all_orders) - # Universal dimension grouping, when multi_value_dims exists dim_group_map: dict[tuple, list[dict]] = defaultdict(list) if multi_value_dims: for dr in data_rows: @@ -1797,17 +1536,12 @@ def pivot_table_mode( dim_group_map[()] = data_rows def format_dim_labels(grouping_key: tuple) -> str: - """Format all dimension labels from a grouping key into a display string. - - Excludes TYPE_OF_TRANSFORMATION when its value is a unit-like label - (e.g., "Domestic currency") since that's already shown in the parent - row's unit suffix. Keeps meaningful transformation types like "Index", - "Percent change", etc. - """ - if not grouping_key: + """Format all dimension labels from a grouping key into a display string.""" + if ( + not grouping_key + ): # pragma: no cover -- only called from a branch gated by `if grouping_key` return "" - # Unit-like transformation values to exclude (already shown in parent suffix) unit_like_transformations = { "Domestic currency", "National currency", @@ -1828,14 +1562,10 @@ def pivot_table_mode( continue filtered_labels.append(label) - # If filtering removed everything, fall back to the unfiltered labels so we - # never render a blank title row for unit-only dimensions. effective_labels = filtered_labels if filtered_labels else labels return " - ".join(effective_labels) if effective_labels else "" - # Build a map of order -> list of (grouping_key, data_rows_for_order) - # Preserve original data order by iterating data_rows directly order_to_dim_data: dict[int | float, list[tuple[tuple, list[dict]]]] = defaultdict( list ) @@ -1846,22 +1576,21 @@ def pivot_table_mode( grouping_key = tuple(dr.get("_grouping_dims", [])) if grouping_key in seen_order_keys[order]: - # Find existing entry and append for entry in order_to_dim_data[order]: if entry[0] == grouping_key: entry[1].append(dr) break else: - # New grouping key for this order - add in data order order_to_dim_data[order].append((grouping_key, [dr])) seen_order_keys[order].add(grouping_key) - # Compute parent orders globally (across all dimension groups) all_orders_with_data = {dr["order"] for dr in data_rows} global_parent_orders: set[int | float] = set() for order in all_orders_with_data: order_df = df[df["order"] == order] - if len(order_df) == 0: + if ( + len(order_df) == 0 + ): # pragma: no cover -- all_orders_with_data only contains orders from data_rows built from df continue parent_id = order_df.iloc[0].get("parent_id") while parent_id: @@ -1878,7 +1607,6 @@ def pivot_table_mode( global_parent_orders.add(parent_order) parent_id = parent_df.iloc[0].get("parent_id") - # Track BOP-only header nodes we intentionally skip so we can promote descendants. bop_skipped_parent_ids: set[str] = set() def _track_skipped_parent_ids(row_like: dict[str, Any]) -> None: @@ -1909,66 +1637,24 @@ def pivot_table_mode( while pid and pid in bop_skipped_parent_ids and adjusted > 0: adjusted -= 1 parent_df = _lookup_parent_row(pid) - if len(parent_df) == 0: + if ( + len(parent_df) == 0 + ): # pragma: no cover -- ids added to bop_skipped_parent_ids always resolve via hierarchy_node_id/suffix/indicator_code lookup break pid = str(parent_df.iloc[0].get("parent_id") or "") return adjusted - # Track the last meaningful (non-BOP-only) header title at each level. - # This is used to preserve qualifiers like "excluding exceptional financing" - # for BOP suffix rows even when intermediate accounting-entry headers are skipped. last_meaningful_header_by_level: dict[int, str] = {} - def _normalize_title(raw_title: str | None) -> str: - title = (raw_title or "").lstrip() - - # Remove header marker (used for promoted headers in the rendered output) - if title.startswith("โ–ธ"): - title = title[1:].lstrip() - - # Strip parenthetical unit suffix - if " (" in title and title.endswith(")"): - paren_idx = title.rfind(" (") - if paren_idx > 0: - title = title[:paren_idx] - - # Strip common unit qualifiers that can trail titles - unit_suffixes = [", Transactions", ", Stocks", ", Flows"] - for suffix in unit_suffixes: - if title.endswith(suffix): - title = title[: -len(suffix)] - break - - return title - - def _nearest_non_bop_ancestor_title(parent_id: Any) -> str | None: - pid = str(parent_id) if parent_id else "" - safety = 0 - while pid and safety < 50: - safety += 1 - parent_df = _lookup_parent_row(pid) - if len(parent_df) == 0: - return None - parent_first = parent_df.iloc[0] - parent_title = _normalize_title(str(parent_first.get("title") or "")) - if ( - parent_title - and not is_bop_suffix_only(parent_title) - and not parent_title.endswith((", Net", ", Credit", ", Debit")) - ): - return parent_title - pid = str(parent_first.get("parent_id") or "") - return None - - # OUTER LOOP: Iterate by sorted_orders (ITEM first) for order in sorted_orders: order_df = df[df["order"] == order] - if order_df.empty: + if ( + order_df.empty + ): # pragma: no cover -- sorted_orders is derived from df["order"].unique() continue first = order_df.iloc[0] level = first["level"] or 0 - # Clear deeper header context when we move up the tree. for k in [k for k in last_meaningful_header_by_level if k > level]: del last_meaningful_header_by_level[k] @@ -1976,7 +1662,6 @@ def pivot_table_mode( title = first["title"] or "" original_unit_suffix = "" - # Strip parenthetical unit suffix if " (" in title and title.endswith(")"): paren_idx = title.rfind(" (") if paren_idx > 0: @@ -1989,37 +1674,26 @@ def pivot_table_mode( title = title[: -len(suffix)] break - # Determine if this order should be rendered as a header is_promoted_header = ( not is_header and order in global_parent_orders and order not in all_orders_with_data ) - # Only render as header if it doesn't have data of its own should_render_as_header = ( is_header or is_promoted_header ) and order not in all_orders_with_data - # Skip headers that don't lead to any data if should_render_as_header and order not in global_parent_orders: - # If this is a BOP-only accounting-entry header (Net/Credit/Debit/etc.), - # track it even when skipped for "no data" so descendants can be promoted. if is_bop_suffix_only(title): _track_skipped_parent_ids(first.to_dict()) continue - # Skip phantom BOP headers that are just "Net", "Credit", "Debit", etc. - # Record them so descendants can be promoted (prevents Debit nesting under Credit - # when an intermediate accounting-entry node is hidden). if should_render_as_header and is_bop_suffix_only(title): _track_skipped_parent_ids(first.to_dict()) continue - # If a row's parent (or higher ancestor) was skipped as a BOP-only header, - # promote it so it doesn't appear as a child of the wrong visible node. level = _promote_level_if_parent_skipped(level, first.get("parent_id")) - # ISORA: Only show topic headers if is_isora and should_render_as_header: if title and "___" in title: continue @@ -2033,18 +1707,15 @@ def pivot_table_mode( if not is_topic: continue - # Headers get minimal simplification if should_render_as_header: if title.startswith("Financial corporations, "): title = title[len("Financial corporations, ") :] elif title.startswith("Depository corporations, "): title = title[len("Depository corporations, ") :] else: - # Strip ancestor title prefixes best_prefix = hierarchy_ctx.find_best_prefix(order, title, parent_orders) if best_prefix and title.startswith(best_prefix): relative = title[len(best_prefix) :].lstrip(", :") - # Don't strip if it would leave only a BOP suffix if ( relative and title != best_prefix @@ -2070,8 +1741,6 @@ def pivot_table_mode( else: break - # Update header context for this level, or (for BOP suffix rows) inherit - # the nearest meaningful header when the row's base is a strict prefix. if should_render_as_header: header_base = title.strip() if header_base and not is_bop_suffix_only(header_base): @@ -2098,7 +1767,6 @@ def pivot_table_mode( title = f"{ancestor_title}{bop_suffix}" break - # Calculate indent extra_indent = " " if should_add_table_header else "" indent = extra_indent + " " * level @@ -2106,7 +1774,9 @@ def pivot_table_mode( if should_render_as_header: if order in global_parent_orders: - if is_isora and title and "___" in title: + if ( + is_isora and title and "___" in title + ): # pragma: no cover -- ISORA "___" titles are already filtered at the earlier should_render_as_header gate continue header_title = title @@ -2116,61 +1786,20 @@ def pivot_table_mode( elif original_unit_suffix: header_title += original_unit_suffix - # Check if this header order also has data - order_data_rows = [dr for dr in data_rows if dr["order"] == order] - if order_data_rows: - # Header WITH data - render data rows with header styling - order_dim_groups = order_to_dim_data.get(order, []) - if order_dim_groups and len(order_dim_groups[0][1]) > 1: - # Multiple dimension values - show header then dimension breakdown - row = { - "title": f"{indent}{prefix}{header_title}", - "country": "", - } - for d in dates: - row[str(d)] = "" - rows.append(row) - # Render dimension breakdown under header - for grouping_key, dim_data_rows in sorted(order_dim_groups): - if grouping_key: - dim_label = format_dim_labels(grouping_key) - for dr in dim_data_rows: - dim_indent = extra_indent + " " * (level + 1) - row = { - "title": f"{dim_indent}{dim_label}", - "country": dr["country"], - } - row.update(dr["values"]) - rows.append(row) - else: - # Single or no dimension - show header row with its data - for dr in order_data_rows: - row = { - "title": f"{indent}{prefix}{header_title}", - "country": dr["country"], - } - row.update(dr["values"]) - rows.append(row) - else: - # Pure header - no data - row = { - "title": f"{indent}{prefix}{header_title}", - "country": "", - } - for d in dates: - row[str(d)] = "" - rows.append(row) + row = { + "title": f"{indent}{prefix}{header_title}", + "country": "", + } + for d in dates: + row[str(d)] = "" + rows.append(row) else: - # Data row - check if THIS specific order has multiple dimension values order_dim_groups = order_to_dim_data.get(order, []) - # Has multi dims if there's more than one grouping key OR - # if a single grouping key has multiple rows (multiple counterpart countries, etc.) order_has_multi_dims = len(order_dim_groups) > 1 or ( len(order_dim_groups) == 1 and len(order_dim_groups[0][1]) > 1 ) if order_has_multi_dims: - # Multiple dimension groups for this item - show breakdown data_level = level data_indent = extra_indent + " " * data_level @@ -2178,7 +1807,6 @@ def pivot_table_mode( if data_level == 0 and uniform_suffix and not should_add_table_header: display_title += uniform_suffix elif not has_uniform_unit_scale: - # Get unit from first data row for this order first_dr = order_dim_groups[0][1][0] if order_dim_groups else None if first_dr: row_unit_suffix = format_unit_suffix( @@ -2186,8 +1814,6 @@ def pivot_table_mode( ) display_title += row_unit_suffix - # Find "World" or top-level aggregate to show on parent row - # World is typically code "G001" for COUNTERPART_COUNTRY dimension world_grouping_key = None world_data_row = None for gk, drs in order_dim_groups: @@ -2202,7 +1828,6 @@ def pivot_table_mode( if world_grouping_key: break - # Item row - shows World data if available, otherwise empty item_row: dict[str, Any] = { "title": f"{data_indent}{display_title}", "country": world_data_row["country"] if world_data_row else "", @@ -2214,7 +1839,6 @@ def pivot_table_mode( item_row[str(d)] = "" rows.append(item_row) - # Check if this is a COUNTERPART_COUNTRY breakdown has_counterpart_country = any( dim_id == "COUNTERPART_COUNTRY" for gk, _ in order_dim_groups @@ -2223,21 +1847,14 @@ def pivot_table_mode( ) if has_counterpart_country: - # Separate groups from countries and order: groups first, then countries cc_indent = extra_indent + " " * (data_level + 1) - # Helper to detect if a code is a group vs individual country - # ISO country codes are 3 letters (e.g., USA, GBR, CHN) - # Group codes have letter+digit patterns (e.g., G001, GX225, U005, TX983) def is_group_code(code: str) -> bool: """Group codes have letter(s) followed by digits.""" - # ISO codes are exactly 3 uppercase letters if re.match(r"^[A-Z]{3}$", code): return False - # Group codes: G###, GX###, U###, TX### return bool(re.match(r"^[A-Z]+\d+$", code)) - # Collect groups and individual countries separately groups: list[tuple[tuple, list[dict], str, str]] = [] individual_countries: list[tuple[tuple, list[dict], str, str]] = [] @@ -2261,7 +1878,6 @@ def pivot_table_mode( (gk, drs, cc_label, cc_code) ) - # Helper to get the max absolute value for sorting def get_sort_value(item: tuple) -> float: """Get the first numeric value from data rows for sorting.""" _, drs, _, _ = item @@ -2274,8 +1890,6 @@ def pivot_table_mode( pass return 0.0 - # Render groups first (sorted by value, highest first), - # then countries (sorted by value, highest first) for gk, drs, cc_label, cc_code in sorted( groups, key=get_sort_value, reverse=True ): @@ -2302,9 +1916,10 @@ def pivot_table_mode( row.update(dr["values"]) rows.append(row) else: - # Non-counterpart-country dimension breakdown (e.g., SECTOR) for grouping_key, dim_data_rows in sorted(order_dim_groups): - if grouping_key == world_grouping_key: + if ( + grouping_key == world_grouping_key + ): # pragma: no cover -- world_grouping_key is None in this else branch (no COUNTERPART_COUNTRY); tuple grouping_key never equals None continue if grouping_key: dim_label = format_dim_labels(grouping_key) @@ -2325,14 +1940,11 @@ def pivot_table_mode( row.update(dr["values"]) rows.append(row) else: - # Single dimension group - original behavior for dr in data_rows: if dr["order"] == order: - # Use the already-corrected level (includes BOP overrides) data_level = level data_indent = extra_indent + " " * data_level - # Use the stripped title (from BOP child processing above), not dr["title"] display_title = title if ( data_level == 0 @@ -2361,15 +1973,12 @@ def pivot_table_mode( def pivot_table_data( result: list[Any], - country: str, + country: str | None, limit: int | None, metadata: dict[str, Any], -) -> "pd.DataFrame": +) -> pd.DataFrame: """Pivot table data based on whether hierarchy exists. - This function determines whether to use indicator mode (simple pivot) - or table mode (hierarchical pivot) based on the data. - Parameters ---------- result : list[Any] @@ -2386,7 +1995,6 @@ def pivot_table_data( pd.DataFrame Pivoted DataFrame with appropriate structure. """ - # pylint: disable=import-outside-toplevel from pandas import DataFrame df = DataFrame(result) @@ -2394,8 +2002,6 @@ def pivot_table_data( dates = all_dates[:limit] if limit is not None and limit > 0 else all_dates countries = sorted(df["country"].dropna().unique().tolist()) - # Check if any requested countries have no data for the selected dates - # and warn the user if country and dates: requested_countries = [c.strip() for c in country.split(",")] check_missing_country_data(df, requested_countries, dates, countries) diff --git a/openbb_platform/providers/imf/openbb_imf/views/__init__.py b/openbb_platform/providers/imf/openbb_imf/views/__init__.py index d92fe42dd25..cfdf578ddc2 100644 --- a/openbb_platform/providers/imf/openbb_imf/views/__init__.py +++ b/openbb_platform/providers/imf/openbb_imf/views/__init__.py @@ -1 +1,3 @@ """IMF Views.""" + +from __future__ import annotations diff --git a/openbb_platform/providers/imf/openbb_imf/views/maritime_chokepoint_info.py b/openbb_platform/providers/imf/openbb_imf/views/maritime_chokepoint_info.py index c3cbb5a9f99..f2e6304e21d 100644 --- a/openbb_platform/providers/imf/openbb_imf/views/maritime_chokepoint_info.py +++ b/openbb_platform/providers/imf/openbb_imf/views/maritime_chokepoint_info.py @@ -1,5 +1,7 @@ """Plotting functions for IMF Maritime Chokepoint Information""" +from __future__ import annotations + from openbb_imf.models.maritime_chokepoint_info import ImfMaritimeChokePointInfoData @@ -7,7 +9,6 @@ def plot_chokepoint_annual_avg_vessels( data: list[ImfMaritimeChokePointInfoData], theme="light" ): """Plot the average annual vessels for each chokepoint.""" - # pylint: disable=import-outside-toplevel import datetime # noqa from openbb_core.app.model.abstract.error import OpenBBError from pandas import DataFrame @@ -189,7 +190,7 @@ def plot_chokepoint_annual_avg_vessels( col=2, ) - fig.update_geos( # type: ignore + fig.update_geos( projection=dict(type="orthographic", scale=1, rotation=dict(lon=70, lat=20)), showland=True, landcolor=landcolor, diff --git a/openbb_platform/providers/imf/openbb_imf/views/port_info.py b/openbb_platform/providers/imf/openbb_imf/views/port_info.py index 6a7f09f2530..6d21ac5412e 100644 --- a/openbb_platform/providers/imf/openbb_imf/views/port_info.py +++ b/openbb_platform/providers/imf/openbb_imf/views/port_info.py @@ -1,11 +1,14 @@ """Plotting functions for IMF Maritime Chokepoint Information""" +from __future__ import annotations + +from typing import Any + from openbb_imf.models.port_info import ImfPortInfoData def plot_port_info_map(data: list[ImfPortInfoData]): """Plot the port information on a map showing regional geography with gradient-colored markers.""" - # pylint: disable=import-outside-toplevel from numpy import nan from openbb_core.app.model.abstract.error import OpenBBError from pandas import DataFrame @@ -33,8 +36,8 @@ def plot_port_info_map(data: list[ImfPortInfoData]): min_size, max_size = 4, 10 if "country" in df.columns and df["country"].nunique() == 1: - share_import = df["share_country_maritime_import"].fillna(0) - share_export = df["share_country_maritime_export"].fillna(0) + share_import = df["share_country_maritime_import"].astype(float).fillna(0) + share_export = df["share_country_maritime_export"].astype(float).fillna(0) df["import_export_share"] = share_import + share_export share_values = df["import_export_share"] @@ -55,12 +58,8 @@ def plot_port_info_map(data: list[ImfPortInfoData]): else: df["marker_size"] = min_size - if "port_full_name" in df.columns: - df["port_full_name"] = df["port_full_name"].fillna("Unknown Port") - else: - df["port_full_name"] = "Unknown Port" - map_zoom = 2 + if "continent" in df.columns and df["continent"].nunique() > 1: map_zoom = 0 map_center = None @@ -78,7 +77,6 @@ def plot_port_info_map(data: list[ImfPortInfoData]): port_name = row.get("port_full_name", "Unknown Port") html_parts.append(f"{port_name}
    ") - # Share of Country's Maritime Traffic share_import = row.get("share_country_maritime_import") share_export = row.get("share_country_maritime_export") @@ -96,7 +94,6 @@ def plot_port_info_map(data: list[ImfPortInfoData]): html_parts.append("
    Share of Country's Maritime Traffic:
    ") html_parts.extend(traffic_lines_content) - # Avg Annual Vessels vessel_labels = [ "Total", "Containers", @@ -165,7 +162,7 @@ def plot_port_info_map(data: list[ImfPortInfoData]): fig.update_traces(hovertemplate="%{customdata[0]}") - layout_map_config = { + layout_map_config: dict[str, Any] = { "style": "carto-voyager", "zoom": map_zoom, } diff --git a/openbb_platform/providers/imf/openbb_imf/views/port_watch_charts.py b/openbb_platform/providers/imf/openbb_imf/views/port_watch_charts.py new file mode 100644 index 00000000000..b8de9f48e4c --- /dev/null +++ b/openbb_platform/providers/imf/openbb_imf/views/port_watch_charts.py @@ -0,0 +1,683 @@ +"""Plotly chart builders for IMF Port Watch endpoints.""" + +import json +import math +from datetime import datetime, timezone +from typing import TYPE_CHECKING, Any + +if TYPE_CHECKING: + from openbb_charting.core.openbb_figure import OpenBBFigure + + +def _theme(theme: str = "dark") -> dict[str, Any]: + """Return color tokens for the requested theme.""" + if theme == "dark": + return { + "plot_bgcolor": "rgba(21,21,21,1)", + "paper_bgcolor": "rgba(21,21,21,1)", + "font_color": "#ffffff", + "annotation_color": "#aaaaaa", + "geo_bgcolor": "rgba(0,0,0,0)", + "landcolor": "#3a5d3a", + "oceancolor": "#223355", + "countrycolor": "#cccccc", + "coastlinecolor": "#cccccc", + } + return { + "plot_bgcolor": "rgba(255,255,255,1)", + "paper_bgcolor": "rgba(255,255,255,1)", + "font_color": "#000000", + "annotation_color": "#555555", + "geo_bgcolor": "rgba(255,255,255,0)", + "landcolor": "#3a5d3a", + "oceancolor": "#3a5d99", + "countrycolor": "#cccccc", + "coastlinecolor": "#cccccc", + } + + +def _source_annotation(th: dict[str, Any]) -> dict[str, Any]: + """Return the IMF Port Watch source-attribution annotation.""" + return dict( + text=f"Source: IMF Port Watch โ€” {datetime.now(timezone.utc).date().isoformat()}", + x=0.5, + xref="paper", + yref="paper", + y=1, + yshift=10, + showarrow=False, + font=dict(size=10, color=th["annotation_color"]), + xanchor="center", + yanchor="bottom", + opacity=0.6, + ) + + +def figure_to_json(fig) -> dict[str, Any]: + """Convert a Plotly figure to the JSON dict Workspace renders.""" + fig_dict = json.loads(fig.to_json()) + fig_dict["config"] = {"displayModeBar": False} + return fig_dict + + +def clean_records(records: list[dict[str, Any]]) -> list[dict[str, Any]]: + """Coerce NaN floats inside ``records`` to ``None`` for JSON serialization.""" + cleaned: list[dict[str, Any]] = [] + for row in records: + out: dict[str, Any] = {} + for k, v in row.items(): + if isinstance(v, float) and math.isnan(v): + out[k] = None + else: + out[k] = v + cleaned.append(out) + return cleaned + + +def _empty_figure(message: str, theme: str = "dark"): + """Return a blank figure carrying a centered ``message``.""" + import plotly.graph_objects as go + + th = _theme(theme) + fig = go.Figure() + fig.add_annotation( + text=message, + showarrow=False, + font=dict(color=th["font_color"], size=14), + xref="paper", + yref="paper", + x=0.5, + y=0.5, + ) + fig.update_layout( + paper_bgcolor=th["paper_bgcolor"], + plot_bgcolor=th["plot_bgcolor"], + xaxis=dict(visible=False), + yaxis=dict(visible=False), + margin=dict(l=20, r=20, t=20, b=20), + ) + return fig + + +_VESSEL_TYPES = ( + ("container", "Container", "#2E86AB"), + ("dry_bulk", "Dry Bulk", "#A23B72"), + ("general_cargo", "General Cargo", "#F18F01"), + ("roro", "Ro-Ro", "#C73E1D"), + ("tanker", "Tanker", "#3B6064"), +) + +_LINE_PALETTE = ( + "#2E86AB", + "#A23B72", + "#F18F01", + "#C73E1D", + "#3B6064", + "#7B5C5C", + "#5A6F8A", + "#F5C16C", + "#6CA77F", + "#A66DD4", + "#D4915F", + "#5C8A8A", +) + +_PORT_METRIC_GROUPS = { + "portcalls": ("portcalls", "Port Calls", "calls/day"), + "import": ("import", "Imports (metric tons)", "tons/day"), + "export": ("export", "Exports (metric tons)", "tons/day"), +} + +_TRADENOW_LINE_METRICS = { + "trade_value": ( + "Trade Value Index (2019 = 100)", + [ + ("value_import_total", "Imports", "#2E86AB"), + ("value_export_total", "Exports", "#F18F01"), + ], + "Index (2019 = 100)", + ), + "trade_volume": ( + "Trade Volume Index (2019 = 100)", + [ + ("volume_import_total", "Imports", "#2E86AB"), + ("volume_export_total", "Exports", "#F18F01"), + ], + "Index (2019 = 100)", + ), +} + +_TRADENOW_PORTCALLS = ( + "AIS Port Calls", + [ + ("ais_portcalls_container", "Container", "#2E86AB"), + ("ais_portcalls_dry_bulk", "Dry Bulk", "#A23B72"), + ("ais_portcalls_general_cargo", "General Cargo", "#F18F01"), + ("ais_portcalls_roro", "Ro-Ro", "#C73E1D"), + ("ais_portcalls_tanker", "Tanker", "#3B6064"), + ], + "calls/month", +) + +_ALERT_COLORS: dict[str | None, str] = { + "RED": "#d62728", + "ORANGE": "#ff7f0e", + "GREEN": "#2ca02c", + None: "#7f7f7f", +} + + +def build_activity_chart( + rows: list[dict[str, Any]], + title: str, + metric: str = "portcalls", + *, + theme: str = "dark", +): + """Stacked-area daily activity chart with a 30-day moving average overlay.""" + import plotly.graph_objects as go + from pandas import DataFrame, to_datetime + + groups = _PORT_METRIC_GROUPS + if metric not in groups: + metric = next(iter(groups)) + base, metric_label, y_unit = groups[metric] + + if not rows: + return _empty_figure("No activity data for the selected window.", theme) + + df = DataFrame(rows) + if "date" not in df.columns: + return _empty_figure("Activity rows are missing a 'date' field.", theme) + df["date"] = to_datetime(df["date"]) + df = df.sort_values("date") + + th = _theme(theme) + fig = go.Figure() + + has_any = False + for suffix, label, color in _VESSEL_TYPES: + col = f"{base}_{suffix}" + if col not in df.columns: + continue + has_any = True + fig.add_trace( + go.Scatter( + x=df["date"], + y=df[col].fillna(0), + mode="lines", + name=label, + stackgroup="vessels", + line=dict(width=0.5, color=color), + hovertemplate=( + f"{label}
    %{{x|%Y-%m-%d}}: %{{y:,.0f}}" + ), + ) + ) + + if not has_any: + return _empty_figure( + f"No '{metric}' breakdown columns present in this dataset.", theme + ) + + total_col = next((c for c in (base, f"{base}_total") if c in df.columns), None) + if total_col: + rolling = df[total_col].astype(float).rolling(window=30, min_periods=5).mean() + fig.add_trace( + go.Scatter( + x=df["date"], + y=rolling, + mode="lines", + name=f"30-day MA ({total_col})", + line=dict(color=th["font_color"], width=2, dash="dot"), + hovertemplate=( + "30-day MA
    %{x|%Y-%m-%d}: %{y:,.0f}" + ), + ) + ) + + fig.update_layout( + title=dict( + text=f"{title} โ€” Daily {metric_label}", + font=dict(size=16, color=th["font_color"]), + x=0.5, + ), + paper_bgcolor=th["paper_bgcolor"], + plot_bgcolor=th["plot_bgcolor"], + font=dict(color=th["font_color"]), + xaxis=dict(showgrid=False, color=th["font_color"]), + yaxis=dict( + title=y_unit, + showgrid=True, + gridcolor="rgba(128,128,128,0.2)", + color=th["font_color"], + ), + hovermode="x unified", + legend=dict(orientation="h", yanchor="top", y=-0.12, xanchor="center", x=0.5), + margin=dict(l=60, r=20, t=60, b=70), + annotations=[_source_annotation(th)], + ) + return fig + + +def build_monthly_trade_chart( + rows: list[dict[str, Any]], + region_name: str, + metric: str = "trade_value", + theme: str = "dark", +): + """Line / stacked-area chart for monthly TradeNow indices.""" + import plotly.graph_objects as go + from pandas import DataFrame, to_datetime + + if not rows: + return _empty_figure("No monthly trade data for the selected region.", theme) + df = DataFrame(rows) + if "date" not in df.columns: + return _empty_figure("Monthly trade rows missing a 'date' field.", theme) + df["date"] = to_datetime(df["date"]) + df = df.sort_values("date") + + th = _theme(theme) + fig = go.Figure() + + if metric == "portcalls": + title_metric, traces, y_unit = _TRADENOW_PORTCALLS + for col, label, color in traces: + if col not in df.columns: + continue + fig.add_trace( + go.Scatter( + x=df["date"], + y=df[col].fillna(0), + mode="lines", + name=label, + stackgroup="vessels", + line=dict(width=0.5, color=color), + hovertemplate=( + f"{label}
    %{{x|%Y-%m}}: %{{y:,.0f}}" + ), + ) + ) + else: + if metric not in _TRADENOW_LINE_METRICS: + metric = "trade_value" + title_metric, traces, y_unit = _TRADENOW_LINE_METRICS[metric] + for col, label, color in traces: + if col not in df.columns: + continue + fig.add_trace( + go.Scatter( + x=df["date"], + y=df[col], + mode="lines+markers", + name=label, + line=dict(width=2, color=color), + marker=dict(size=4), + hovertemplate=( + f"{label}
    %{{x|%Y-%m}}: %{{y:,.2f}}" + ), + ) + ) + + if not fig.data: + return _empty_figure(f"No '{metric}' columns present in this dataset.", theme) + + fig.update_layout( + title=dict( + text=f"{region_name} โ€” Monthly {title_metric}", + font=dict(size=16, color=th["font_color"]), + x=0.5, + ), + paper_bgcolor=th["paper_bgcolor"], + plot_bgcolor=th["plot_bgcolor"], + font=dict(color=th["font_color"]), + xaxis=dict(showgrid=False, color=th["font_color"]), + yaxis=dict( + title=y_unit, + showgrid=True, + gridcolor="rgba(128,128,128,0.2)", + color=th["font_color"], + ), + hovermode="x unified", + legend=dict(orientation="h", yanchor="top", y=-0.12, xanchor="center", x=0.5), + margin=dict(l=70, r=20, t=60, b=70), + ) + return fig + + +def build_container_metric_chart( + series: dict[str, list[dict[str, Any]]], + port_name_map: dict[str, str], + metric_label: str, + theme: str = "dark", +): + """Multi-series line+marker chart, one trace per port.""" + import plotly.graph_objects as go + from pandas import DataFrame, to_datetime + + if not series or not any(series.values()): + return _empty_figure("No container metric data for the selection.", theme) + + th = _theme(theme) + fig = go.Figure() + for i, (pid, rows) in enumerate(series.items()): + if not rows: + continue + df = DataFrame(rows).sort_values("date") + label = port_name_map.get(pid, pid) + fig.add_trace( + go.Scatter( + x=to_datetime(df["date"]), + y=df["value"], + mode="lines+markers", + name=label, + line=dict(width=2, color=_LINE_PALETTE[i % len(_LINE_PALETTE)]), + marker=dict(size=4), + hovertemplate=( + f"{label}
    %{{x|%Y-%m}}: %{{y:,.0f}}" + ), + ) + ) + + if not fig.data: # pragma: no cover -- unreachable: early return above guards `any(series.values())` + return _empty_figure("No data for the selected ports.", theme) + + fig.update_layout( + title=dict( + text=f"Container Metrics โ€” {metric_label}", + font=dict(size=16, color=th["font_color"]), + x=0.5, + ), + paper_bgcolor=th["paper_bgcolor"], + plot_bgcolor=th["plot_bgcolor"], + font=dict(color=th["font_color"]), + xaxis=dict(showgrid=False, color=th["font_color"]), + yaxis=dict( + showgrid=True, + gridcolor="rgba(128,128,128,0.2)", + color=th["font_color"], + ), + hovermode="x unified", + legend=dict(orientation="h", yanchor="top", y=-0.12, xanchor="center", x=0.5), + margin=dict(l=70, r=20, t=60, b=70), + ) + return fig + + +def build_disruption_sankey( + rows: list[dict[str, Any]], + event_label: str, + theme: str = "dark", +): + """Sankey of capacity spillover edges for a disruption event.""" + import plotly.graph_objects as go + + if not rows: + return _empty_figure("No spillover data for the selected event.", theme) + + nodes: dict[int, str] = {} + sources: list[int] = [] + targets: list[int] = [] + values: list[float] = [] + for r in rows: + try: + s = int(r["source"]) + t = int(r["target"]) + v = float(r["perc_disaster_capacity"]) + except (KeyError, TypeError, ValueError): + continue + nodes.setdefault(s, str(r.get("from_") or r.get("from_id") or f"node{s}")) + nodes.setdefault(t, str(r.get("to_") or r.get("to_id") or f"node{t}")) + sources.append(s) + targets.append(t) + values.append(v) + + if not values: + return _empty_figure("No usable sankey edges for this event.", theme) + + max_idx = max(nodes) + 1 + labels = [nodes.get(i, f"node{i}") for i in range(max_idx)] + + th = _theme(theme) + fig = go.Figure( + go.Sankey( + arrangement="snap", + node=dict( + label=labels, + pad=14, + thickness=14, + line=dict(color=th["font_color"], width=0.4), + color="#3B6064" if theme == "dark" else "#2E86AB", + ), + link=dict( + source=sources, + target=targets, + value=values, + color=( + "rgba(252,210,201,0.45)" + if theme == "dark" + else "rgba(46,134,171,0.35)" + ), + hovertemplate=( + "%{source.label} โ†’ %{target.label}: %{value:.2f}%" + ), + ), + ) + ) + fig.update_layout( + title=dict( + text=f"Capacity Spillover โ€” {event_label}", + font=dict(size=16, color=th["font_color"]), + x=0.5, + ), + paper_bgcolor=th["paper_bgcolor"], + plot_bgcolor=th["plot_bgcolor"], + font=dict(color=th["font_color"]), + margin=dict(l=20, r=20, t=60, b=20), + ) + return fig + + +def _disruption_hover_html(row) -> str: + """Render the HTML hover card for a disruption marker.""" + parts: list[str] = [] + name = row.get("eventname") or row.get("htmlname") or "Disruption Event" + parts.append(f"{name}
    ") + et = row.get("eventtype") + al = row.get("alertlevel") + if et: + parts.append(f"Type: {et}
    ") + if al: + parts.append(f"Alert: {al}
    ") + fd, td = row.get("fromdate"), row.get("todate") + if fd: + parts.append(f"From: {fd}
    ") + if td: + parts.append(f"To: {td}
    ") + n = row.get("n_affectedports") + if n is not None and not (isinstance(n, float) and math.isnan(n)): + parts.append(f"Affected ports: {int(n)}
    ") + country = row.get("country") + if country: + parts.append(f"Country: {country}
    ") + return "".join(parts) + + +def build_disruptions_map( + disruptions: list[dict[str, Any]], + theme: str = "dark", +): + """Scattergeo of disruption events color-coded by alert level.""" + import plotly.graph_objects as go + from pandas import DataFrame, Series + + if not disruptions: + return _empty_figure("No disruptions match the selected filter.", theme) + + df = DataFrame(disruptions) + if not {"lat", "long"}.issubset(df.columns): + return _empty_figure("No georeferenced disruptions to display.", theme) + df = df[df["lat"].notna() & df["long"].notna()] + if df.empty: + return _empty_figure("No georeferenced disruptions to display.", theme) + + th = _theme(theme) + if "alertlevel" in df.columns: + df["alertlevel_norm"] = df["alertlevel"].astype(str).str.upper() + df["color"] = df["alertlevel_norm"].map(_ALERT_COLORS).fillna("#7f7f7f") + else: + df["alertlevel_norm"] = "UNKNOWN" + df["color"] = "#d62728" + + df["hover"] = df.apply(_disruption_hover_html, axis=1) + + if "n_affectedports" in df.columns: + sz = df["n_affectedports"].fillna(1).clip(lower=1).astype(float) + else: + sz = Series([1.0] * len(df)) + if sz.max() > 0: + df["marker_size"] = 6 + 12 * (sz / sz.max()) + else: # pragma: no cover -- unreachable: sz is clipped to >=1 or filled with 1.0 + df["marker_size"] = 7.0 + + fig = go.Figure() + for level in ("RED", "ORANGE", "GREEN"): + sub = df[df["alertlevel_norm"] == level] + if sub.empty: + continue + fig.add_trace( + go.Scattergeo( + lon=sub["long"], + lat=sub["lat"], + mode="markers", + name=level.title(), + marker=dict( + size=sub["marker_size"], + color=_ALERT_COLORS[level], + opacity=0.85, + line=dict(width=0.5, color="rgba(0,0,0,0.4)"), + ), + customdata=sub[["hover"]].values, + hovertemplate="%{customdata[0]}", + ) + ) + + other = df[~df["alertlevel_norm"].isin(("RED", "ORANGE", "GREEN"))] + if not other.empty: + fig.add_trace( + go.Scattergeo( + lon=other["long"], + lat=other["lat"], + mode="markers", + name="Other", + marker=dict( + size=other["marker_size"], + color="#7f7f7f", + opacity=0.7, + ), + customdata=other[["hover"]].values, + hovertemplate="%{customdata[0]}", + ) + ) + + fig.update_geos( + projection_type="natural earth", + showland=True, + landcolor=th["landcolor"], + showocean=True, + oceancolor=th["oceancolor"], + showcoastlines=True, + coastlinecolor=th["coastlinecolor"], + coastlinewidth=0.4, + showcountries=True, + countrycolor=th["countrycolor"], + countrywidth=0.3, + showframe=False, + bgcolor=th["geo_bgcolor"], + resolution=110, + ) + fig.update_layout( + autosize=True, + height=600, + margin={"r": 0, "t": 30, "l": 0, "b": 0}, + paper_bgcolor=th["paper_bgcolor"], + plot_bgcolor=th["plot_bgcolor"], + font=dict(color=th["font_color"]), + annotations=[_source_annotation(th)], + legend=dict( + orientation="h", + yanchor="bottom", + y=-0.05, + xanchor="center", + x=0.5, + ), + dragmode="pan", + ) + return fig + + +def _to_openbb_figure(fig) -> "OpenBBFigure": + """Wrap a Plotly ``Figure`` in an ``OpenBBFigure``.""" + from openbb_charting.core.openbb_figure import OpenBBFigure + + return OpenBBFigure(fig) + + +def plot_country_activity( + rows: list[dict[str, Any]], + title: str, + metric: str = "portcalls", + theme: str = "dark", +) -> "OpenBBFigure": + """Stacked-area daily activity chart for a country.""" + return _to_openbb_figure(build_activity_chart(rows, title, metric, theme=theme)) + + +def plot_monthly_trade( + rows: list[dict[str, Any]], + region_name: str, + metric: str = "trade_value", + theme: str = "dark", +) -> "OpenBBFigure": + """Monthly TradeNow chart wrapped as ``OpenBBFigure``.""" + return _to_openbb_figure( + build_monthly_trade_chart(rows, region_name, metric, theme=theme) + ) + + +def plot_container_metrics( + rows: list[dict[str, Any]], + metric_label: str, + theme: str = "dark", + port_name_map: dict[str, str] | None = None, +) -> "OpenBBFigure": + """Multi-series container metric chart wrapped as ``OpenBBFigure``.""" + series: dict[str, list[dict[str, Any]]] = {} + for r in rows: + pid = r.get("portid") + if not pid: + continue + series.setdefault(pid, []).append(r) + return _to_openbb_figure( + build_container_metric_chart( + series, port_name_map or {}, metric_label, theme=theme + ) + ) + + +def plot_disruption_sankey( + rows: list[dict[str, Any]], + event_label: str, + theme: str = "dark", +) -> "OpenBBFigure": + """Sankey spillover chart wrapped as ``OpenBBFigure``.""" + return _to_openbb_figure(build_disruption_sankey(rows, event_label, theme=theme)) + + +def plot_disruptions_map( + rows: list[dict[str, Any]], + theme: str = "dark", +) -> "OpenBBFigure": + """Disruption Scattergeo map wrapped as ``OpenBBFigure``.""" + return _to_openbb_figure(build_disruptions_map(rows, theme=theme)) diff --git a/openbb_platform/providers/imf/poetry.lock b/openbb_platform/providers/imf/poetry.lock deleted file mode 100644 index 635be1523b2..00000000000 --- a/openbb_platform/providers/imf/poetry.lock +++ /dev/null @@ -1,2696 +0,0 @@ -# This file 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">=0.7.1" +[project.optional-dependencies] +charting = ["openbb-charting>=3.0.0"] -[tool.poetry.extras] -charting = ["openbb-charting"] +[project.scripts] +generate-imf-cache = "openbb_imf.utils.generate_cache:main" -[build-system] -requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" - -[tool.poetry.plugins."openbb_provider_extension"] +[project.entry-points."openbb_provider_extension"] imf = "openbb_imf:imf_provider" -[tool.poetry.plugins."openbb_core_extension"] -imf_utils = "openbb_imf.imf_router:router" +[project.entry-points."openbb_core_extension"] +imf = "openbb_imf.imf_router:router" + +[project.entry-points."openbb_charting_extension"] +imf = "openbb_imf.imf_views:ImfViews" + +[project.urls] +Homepage = "https://openbb.co" +Repository = "https://github.com/OpenBB-finance/OpenBB" + +[dependency-groups] +dev = ["openbb-devtools>=2.0.0"] + +[build-system] +requires = ["hatchling>=1.25", "requests>=2.31"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["openbb_imf"] + +[tool.hatch.build.targets.sdist] +# The cache asset is gitignored; the build hook materializes it before +# packaging so both sdist and wheel ship a self-contained artifact. +include = [ + "openbb_imf", + "openbb_imf/assets/imf_cache.json.xz", + "hatch_build.py", + "README.md", + "pyproject.toml", +] + +[tool.hatch.build.hooks.custom] +path = "hatch_build.py" + +[tool.uv.sources] +openbb-core = { path = "../../core", editable = true } +openbb-devtools = { path = "../../extensions/devtools", editable = true } +openbb-charting = { path = "../../obbject_extensions/charting", editable = true } + +[tool.ruff] +line-length = 88 +target-version = "py310" +fix = true + +[tool.ruff.lint] +select = ["D", "E", "W", "F", "S", "UP", "I", "PLC", "PLE", "PLR", "PLW", "SIM", "T20"] +ignore = [ + "PLR2004", "PLR0913", "PLR0915", "PLC0415", + "E402", "E501", + "D202", "D203", "D205", "D206", "D210", "D212", "D213", "D214", + "D215", "D300", "D400", "D404", "D405", "D406", "D407", "D408", + "D409", "D410", "D411", "D413", "D415", "D416", "D417", +] + +[tool.ruff.lint.per-file-ignores] +"openbb_imf/imf_router.py" = ["ARG001", "PLC0415", "SLF001", "PLR0911", "PLR0912", "PLR0913", "PLR0914", "PLR0915", "PLR0917"] +"openbb_imf/utils/generate_cache.py" = ["T201", "PLR0912", "PLR0914", "PLR0915", "PLR0917", "BLE001", "S112", "S113"] +"openbb_imf/utils/helpers.py" = ["PLR0912"] +"openbb_imf/utils/metadata/_query_mixin.py" = ["PLR0911", "PLR0912", "PLR0913", "PLR0914", "PLR0915", "PLR0917", "SLF001"] +"openbb_imf/utils/metadata/_table_mixin.py" = ["PLR0911", "PLR0912", "PLR0914", "PLR0915", "PLR0917", "SLF001", "BLE001", "S112", "B023"] +"openbb_imf/utils/metadata/_search_mixin.py" = ["PLR0912", "BLE001", "S112"] +"openbb_imf/utils/metadata/_indicator_mixin.py" = ["PLR0912", "BLE001"] +"openbb_imf/utils/metadata/_loader_mixin.py" = ["BLE001"] +"openbb_imf/utils/query_builder.py" = ["PLR0911", "PLR0912", "PLR0913", "PLR0917", "SLF001"] +"openbb_imf/utils/table_builder.py" = ["PLR0912", "PLR0913", "PLR0914", "PLR0915", "PLR0917", "SLF001", "B023"] +"openbb_imf/utils/progressive_helper.py" = ["PLR0912"] +"openbb_imf/utils/dot_helpers.py" = ["PLR0912"] +"openbb_imf/utils/port_watch_helpers.py" = ["PLR0912"] +"openbb_imf/utils/table_presentation.py" = ["PLR0911", "PLR0912", "PLR0914", "PLR0915"] +"openbb_imf/models/*.py" = ["ARG002", "PLC0415", "PLR0912"] +"openbb_imf/views/*.py" = ["PLR0912", "T201"] +"tests/**" = ["S101", "S108", "D", "SIM105", "SIM117"] +"integration/**" = ["S101", "S108", "D", "SIM105", "SIM117", "PLC0415", "ARG001"] +"**/__init__.py" = ["F401"] + +[tool.ruff.lint.isort] +combine-as-imports = true +force-wrap-aliases = true + +[tool.ruff.lint.pydocstyle] +convention = "numpy" + +[tool.ty] +[tool.ty.environment] +python-version = "3.10" + +[tool.ty.src] +include = ["openbb_imf"] +exclude = ["tests", "integration", "openbb_imf/utils/generate_cache.py"] + +[tool.ty.rules] +unresolved-import = "ignore" + +[tool.pytest.ini_options] +testpaths = ["tests"] +addopts = "-q --strict-markers" +markers = [ + "record_http: VCR-backed HTTP cassette tests", + "integration: live integration tests against the IMF API", +] +filterwarnings = [ + "error", + "ignore::DeprecationWarning:openbb_core.provider.utils.client", + "ignore::async_lru.AlruCacheLoopResetWarning", + # Plotly and asyncio leak unclosed UNIX sockets / event loops on import + # in older Python; pytest's ``PytestUnraisableExceptionWarning`` turns + # those teardown traces into failures on whichever test runs next. + "ignore::pytest.PytestUnraisableExceptionWarning", +] diff --git a/openbb_platform/providers/imf/tests/conftest.py b/openbb_platform/providers/imf/tests/conftest.py new file mode 100644 index 00000000000..e19d3f87fcf --- /dev/null +++ b/openbb_platform/providers/imf/tests/conftest.py @@ -0,0 +1,114 @@ +"""Shared pytest fixtures for the IMF provider.""" + +from __future__ import annotations + +from typing import Any + +import pytest + + +@pytest.fixture(autouse=True) +def _reset_imf_metadata_singleton() -> Any: + """Drop the ``ImfMetadata`` singleton before and after each test.""" + from openbb_imf.utils.metadata import ImfMetadata + + ImfMetadata._reset() + yield + ImfMetadata._reset() + + +def _reset_alru(fn: Any) -> None: + """Clear an ``alru_cache`` wrapper and reset its event-loop binding.""" + if hasattr(fn, "cache_clear"): + fn.cache_clear() + for attr in ("_LRUCacheWrapper__first_loop", "_LRUCacheWrapper__warned_loop_reset"): + if hasattr(fn, attr): + setattr(fn, attr, None if "first_loop" in attr else False) + + +@pytest.fixture(autouse=True) +def _clear_alru_caches() -> Any: + """Clear every ``alru_cache``-wrapped helper between tests.""" + from openbb_imf.utils import port_watch_helpers as pwh + + targets = [ + pwh.get_daily_chokepoint_data, + pwh.get_all_daily_chokepoint_activity_data, + pwh.get_all_daily_port_activity_data, + pwh.get_daily_port_activity_data, + pwh.list_ports, + pwh.get_country_daily_activity, + pwh.get_monthly_trade, + pwh.get_container_metrics, + pwh.get_disruption_events, + pwh.get_disruption_sankey_edges, + pwh.get_container_port_name_map, + ] + for fn in targets: + _reset_alru(fn) + yield + for fn in targets: + _reset_alru(fn) + + +@pytest.fixture +def empty_meta(monkeypatch: pytest.MonkeyPatch) -> Any: + """Fresh ``ImfMetadata`` singleton with cache loading disabled.""" + from openbb_imf.utils.metadata import ImfMetadata + + ImfMetadata._reset() + monkeypatch.setattr(ImfMetadata, "_load_from_cache", lambda self: True) + instance = ImfMetadata() + yield instance + ImfMetadata._reset() + + +@pytest.fixture +def seeded_meta(monkeypatch: pytest.MonkeyPatch) -> Any: + """Fresh ``ImfMetadata`` with a minimal in-memory fixture catalog.""" + from openbb_imf.utils.metadata import ImfMetadata + + ImfMetadata._reset() + monkeypatch.setattr(ImfMetadata, "_load_from_cache", lambda self: True) + instance = ImfMetadata() + + instance.dataflows = { + "TEST_DF": { + "id": "TEST_DF", + "agencyID": "IMF.STA", + "name": "Test Dataflow", + "description": "", + "structureRef": {"id": "DSD_TEST"}, + } + } + instance.datastructures = { + "DSD_TEST": { + "id": "DSD_TEST", + "agencyID": "IMF.STA", + "dimensions": [ + {"id": "COUNTRY", "position": "0", "conceptRef": {"id": "COUNTRY"}}, + {"id": "FREQUENCY", "position": "1", "conceptRef": {"id": "FREQ"}}, + {"id": "INDICATOR", "position": "2", "conceptRef": {"id": "INDICATOR"}}, + ], + } + } + instance._codelist_cache = { + "CL_COUNTRY": { + "USA": "United States", + "GBR": "United Kingdom", + "DEU": "Germany", + }, + "CL_FREQ": {"A": "Annual", "Q": "Quarterly", "M": "Monthly"}, + "CL_INDICATOR": { + "GDP": "Gross Domestic Product", + "CPI": "Consumer Price Index", + "POP": "Population", + }, + } + instance._codelist_descriptions = { + "CL_COUNTRY": {"USA": "USA", "GBR": "UK", "DEU": "DE"}, + "CL_FREQ": {"A": "Annual", "Q": "Quarterly", "M": "Monthly"}, + "CL_INDICATOR": {"GDP": "GDP", "CPI": "CPI", "POP": "Population"}, + } + yield instance + ImfMetadata._reset() diff --git a/openbb_platform/providers/imf/tests/record/http/test_imf_fetchers/test_imf_balance_of_payments_fetcher_urllib3_v2.yaml b/openbb_platform/providers/imf/tests/record/http/test_imf_fetchers/test_imf_balance_of_payments_fetcher_urllib3_v2.yaml new file mode 100644 index 00000000000..286881639d7 --- /dev/null +++ b/openbb_platform/providers/imf/tests/record/http/test_imf_fetchers/test_imf_balance_of_payments_fetcher_urllib3_v2.yaml @@ -0,0 +1,143 @@ +interactions: +- request: + body: null + headers: + Accept: + - application/xml + Accept-Encoding: + - gzip, deflate, zstd + Cache-Control: + - no-cache + Connection: + - keep-alive + method: GET + uri: https://api.imf.org/external/sdmx/3.0/data/dataflow/IMF.STA/BOP/+/JPN.NETCD_T+CD_T+DB_T.CAB+G+S+IN1+IN2.USD.Q?c%5BTIME_PERIOD%5D=ge:2023-01-01+le:2024-02-01&dimensionAtObservation=TIME_PERIOD&detail=full&includeHistory=false + response: + body: + string: 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+ Cyclone MELISSA-25\",\"htmldescription\":\"Red Tropical Cyclone MELISSA-25 + in Jamaica, Cuba, Bahamas, Bermuda, Haiti, Canada from: 21 Oct 2025 to: 31 + Oct 2025 .\",\"alertlevel\":\"RED\",\"country\":\"Jamaica; Cuba; The Bahamas; + Canada\",\"fromdate\":1761058800000,\"year\":2025,\"todate\":1761922800000,\"severitytext\":\"Hurricane + (maximum wind speed of 296 km/h)\",\"lat\":39,\"long\":-60.5,\"editdate\":1762326758953,\"affectedports\":\"port1220; + port256; port1102; port835; port2172; port336; port388; port2332; port66; + port949; port2330; port1156; port840; port223; port763; port2105; port572\",\"n_affectedports\":17,\"affectedpopulation\":\"2.8 + million (Source: 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+ Cyclone MATMO-25\",\"htmldescription\":\"Red Tropical Cyclone MATMO-25 in + China, Viet Nam, Philippines from: 01 Oct 2025 to: 05 Oct 2025 .\",\"alertlevel\":\"RED\",\"country\":\"China\",\"fromdate\":1759298400000,\"year\":2025,\"todate\":1759687200000,\"severitytext\":\"Hurricane + (maximum wind speed of 167 km/h)\",\"lat\":21.9,\"long\":108.5,\"editdate\":1761725190590,\"affectedports\":\"port437; + port339; port136; port1071; port1196; port424; port242; port1426\",\"n_affectedports\":8,\"affectedpopulation\":\"20 + million (Source: 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+ Cyclone RAGASA-25\",\"htmldescription\":\"Red Tropical Cyclone RAGASA-25 in + Philippines, China from: 18 Sep 2025 to: 24 Sep 2025 .\",\"alertlevel\":\"RED\",\"country\":\"China; + Macao SAR; Hong Kong SAR\",\"fromdate\":1758196800000,\"year\":2025,\"todate\":1758715200000,\"severitytext\":\"Hurricane + (maximum wind speed of 269 km/h)\",\"lat\":21.8,\"long\":111.1,\"editdate\":1759774040248,\"affectedports\":\"port2019; + port436; port339; port2028; port136; port1071; port1414; port2112; port484; + port1189; port1196; port2029; port2287; port424; port674; port474; port423; + port2383; port1426; port2030\",\"n_affectedports\":20,\"affectedpopulation\":\"47.2 + million (Source: 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arthquake 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+ Cyclone KONG-REY-24\",\"htmldescription\":\"Red Tropical Cyclone KONG-REY-24 + in Philippines, Taiwan, China, Japan from: 25 Oct 2024 to: 01 Nov 2024 .\",\"alertlevel\":\"RED\",\"country\":\"Taiwan + Province of China; China\",\"fromdate\":1729814400000,\"year\":2024,\"todate\":1730484000000,\"severitytext\":\"Hurricane + (maximum wind speed of 241 km/h)\",\"lat\":32.2,\"long\":126.1,\"editdate\":1743233631549,\"affectedports\":\"port238; + port824; port681; port2230; port1390; port2229; port541; port1074; port438; + port482; port1405; port1261; port1192; port370; port1252; port1404; port1403\",\"n_affectedports\":17,\"affectedpopulation\":\"15.8 + million (Source: 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+ Cyclone MILTON-24\",\"htmldescription\":\"Red Tropical Cyclone MILTON-24 in + Mexico, United States from: 05 Oct 2024 to: 09 Oct 2024 .\",\"alertlevel\":\"RED\",\"country\":\"Mexico; + United States\",\"fromdate\":1728140400000,\"year\":2024,\"todate\":1728464400000,\"severitytext\":\"Hurricane + (maximum wind speed of 287 km/h)\",\"lat\":24.5,\"long\":-85.4,\"editdate\":1728469086877,\"affectedports\":\"port212; + port876; port967; port1259; port1023\",\"n_affectedports\":5,\"affectedpopulation\":\"5.5 + million (Source: 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+ PORTS STRIKE\",\"htmlname\":\"US Ports Strike\",\"htmldescription\":\"Labor + strike affecting many ports across the US East and Gulf Coasts, as well as + the Port of Montreal in Canada\",\"alertlevel\":\"RED\",\"country\":null,\"fromdate\":1727740800000,\"year\":2024,\"todate\":1727906400000,\"severitytext\":null,\"lat\":29.77081,\"long\":-95.35517,\"editdate\":1727962353164,\"affectedports\":\"port812; + port815; port826; port103; port901; port159; port513; port736; port754; port1170; + port1259; port1398; port231; port481; port766; port2066; port951\",\"n_affectedports\":17,\"affectedpopulation\":null,\"pageid\":\"9e9f1ca4abad4948af5caed716c137f3\",\"ObjectId\":98,\"Shape__Area\":2173469.48828125,\"Shape__Length\":6906.2243711354695},\"geometry\":{\"rings\":[[[-10613283.7529807,3473130.0401239],[-10615003.5945857,3471486.63921047],[-10614888.9355102,3474123.72497292],[-10613283.7529807,3473130.0401239]]]}},{\"attributes\":{\"eventid\":1001101,\"eventtype\":\"TC\",\"eventname\":\"HELENE-24\",\"htmlname\":\"Tropical + Cyclone HELENE-24\",\"htmldescription\":\"Red Tropical Cyclone HELENE-24 in + United States, Mexico, Belize from: 23 Sep 2024 to: 27 Sep 2024 .\",\"alertlevel\":\"RED\",\"country\":\"Mexico; + United States\",\"fromdate\":1727103600000,\"year\":2024,\"todate\":1727470800000,\"severitytext\":\"Hurricane + (maximum wind speed of 222 km/h)\",\"lat\":37.5,\"long\":-85.5,\"editdate\":1728469086877,\"affectedports\":\"port879; + port1144\",\"n_affectedports\":2,\"affectedpopulation\":\"1.3 million (Source: + 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+ Cyclone YAGI-24\",\"htmldescription\":\"Red Tropical Cyclone YAGI-24 in China, + Viet Nam, Philippines from: 01 Sep 2024 to: 07 Sep 2024 .\",\"alertlevel\":\"RED\",\"country\":\"Vietnam; + China\",\"fromdate\":1725192000000,\"year\":2024,\"todate\":1725688800000,\"severitytext\":\"Hurricane + (maximum wind speed of 241 km/h)\",\"lat\":21,\"long\":106.9,\"editdate\":1728469086877,\"affectedports\":\"port123; + port136; port242; port339; port424; port437; port1071; port1196; port1409; + port1426; port434; port473\",\"n_affectedports\":12,\"affectedpopulation\":\"31.5 + million (Source: 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+ Cyclone BERYL-24\",\"htmldescription\":\"Red Tropical Cyclone BERYL-24 in + Haiti, Grenada, Jamaica, Trinidad and Tobago, Saint Vincent and the Grenadines, + Mexico, Belize, United States from: 28 Jun 2024 to: 09 Jul 2024 .\",\"alertlevel\":\"RED\",\"country\":\"St. + Vincent and the Grenadines; Grenada; Cayman Islands; Mexico; Jamaica; United + States\",\"fromdate\":1719608400000,\"year\":2024,\"todate\":1720515600000,\"severitytext\":\"Hurricane + (maximum wind speed of 269 km/h)\",\"lat\":33.9,\"long\":-93.7,\"editdate\":1727766190151,\"affectedports\":\"port358; + port373; port481; port963; port1285; port1023; port1144; port574; port336; + port572; port763; port840; port949; port1102; port389; port1223\",\"n_affectedports\":16,\"affectedpopulation\":\"9 + million (Source: GDACS)\",\"pageid\":\"f47027ca577c458bb00a14c389cd8ec4\",\"ObjectId\":92,\"Shape__Area\":1356999108092.17,\"Shape__Length\":13353157.560749093},\"geometry\":{\"rings\":[[[-9353798.09009463,2307794.7915597],[-9180089.38496785,2253376.28189655],[-9016878.75425515,2253376.28189655],[-9011413.76107649,2253280.11513966],[-9005955.42625054,2252991.73509254],[-9000510.39978102,2252511.50181472],[-8995085.31553037,2251840.01526189],[-8989686.78331597,2250978.11376932],[-8984321.38033864,2249926.87344423],[-8978995.6435015,2248687.60692932],[-8973716.06139504,2247261.86071059],[-8773465.00602497,2188203.24989044],[-8768253.03069429,2186597.77943235],[-8763099.84140559,2184810.17753138],[-8758011.71646683,2182842.6723642],[-8752994.85503781,2180697.71522691],[-8591926.60043072,2107947.11630399],[-8433949.06435986,2063525.35927188],[-8429412.15769463,2062129.66158647],[-8246365.39092257,2005498.6962018],[-8065707.95074658,1960903.65441505],[-7857963.94416106,1926561.18799462],[-7853335.70619885,1925656.21577005],[-7848741.68439719,1924589.36242053],[-7844187.47612275,1923361.95119214],[-7839678.62987288,1921975.50473169],[-7835220.63904182,1920431.74199971],[-7830818.93490754,1918732.57725872],[-7826478.88028649,1916880.11648124],[-7627777.60184287,1831649.35317453],[-7407890.63287784,1751585.90188384],[-7404138.33347963,1750139.19154243],[-7183210.48430636,1658530.93102685],[-6996245.95985811,1590355.88074165],[-6992516.28325211,1588918.50317664],[-6988838.84376243,1587351.41030911],[-6777479.25670375,1495681.43486434],[-6773870.00262858,1493992.32117864],[-6770321.68384079,1492177.93410479],[-6766838.62354413,1490240.5084386],[-6763425.06512627,1488182.42988119],[-6760085.1675947,1486006.2322441],[-6634884.92989616,1403375.75764009],[-6457759.13362747,1383222.94082819],[-6454476.81621383,1382818.69266085],[-6451210.56342724,1382299.79798122],[-6447964.35471683,1381666.89479218],[-6444742.14493017,1380920.76146614],[-6266662.91147173,1335379.55022459],[-6090686.11012526,1290442.14113518],[-5901228.72304894,1253933.14865009],[-5689731.27782155,1219907.29548134],[-5687792.32528363,1219529.79392478],[-5685867.70724521,1219084.75037896],[-5482123.05953014,1167552.40072023],[-5318445.93937058,1156458.94554615],[-5317137.07841632,1156344.31607147],[-5315833.01110196,1156184.03109261],[-5314535.3261793,1155978.2864153],[-5313245.60494169,1155727.33345985],[-5311965.41844111,1155431.47902984],[-5310696.32648625,1155091.08462723],[-5309439.87530486,1154706.56621907],[-5308197.59576255,1154278.3937769],[-5306971.0011365,1153807.09047574],[-5305761.5861135,1153293.23211863],[-5304570.8238957,1152737.44678648],[-5303400.16553267,1152140.4134689],[-5302251.03713839,1151502.86159951],[-5301124.8387781,1150825.57013797],[-5300022.94257584,1150109.3664252],[-5298946.69093335,1149355.12526434],[-5297897.39519425,1148563.76811447],[-5296876.33364023,1147736.26149193],[-5295884.75048925,1146873.61593715],[-5294923.85366911,1145976.88509431],[-5293994.81392688,1145047.16377284],[-5293098.76304783,1144085.58747942],[-5292236.79296484,1143093.33002697],[-5291409.95364336,1142071.60284061],[-5290619.25263609,1141021.65324571],[-5289865.65307926,1139944.76275631],[-5289150.07335868,1138842.24570313],[-5288473.38510597,1137715.44763591],[-5287836.41275326,1136565.7435],[-5287239.93253137,1135394.5362659],[-5286684.67102261,1134203.25465478],[-5286171.30482685,1132993.35210865],[-5285700.45922567,1131766.30429113],[-5285272.70807105,1130523.60783313],[-5284888.57244951,1129266.77828759],[-5284548.52023689,1127997.34831157],[-5284252.96587564,1126716.86562295],[-5284002.26948432,1125426.89129752],[-5283796.73641228,1124128.99795385],[-5283636.61701707,1122824.76737414],[-5283522.10655307,1121515.78936957],[-5283453.34439229,1120203.65883807],[-5283430.4143583,1118889.97485797],[-5283453.34439229,1117576.33786107],[-5283522.10655307,1116264.34805032],[-5283636.61701707,1114955.60381847],[-5283796.73641228,1113651.69892463],[-5284002.26948432,1112354.22159327],[-5284252.96587564,1111064.75157995],[-5284548.52023689,1109784.85904638],[-5284888.57244951,1108516.10219326],[-5285272.70807105,1107260.02545928],[-5285700.45922567,1106018.15783395],[-5286171.30482685,1104792.01083235],[-5286684.67102261,1103583.0766968],[-5287239.93253137,1102392.82637322],[-5287836.41275326,1101222.70827925],[-5288473.38510597,1100074.1458302],[-5289150.07335868,1098948.53643439],[-5289865.65307926,1097847.24924624],[-5290619.25263609,1096771.62382377],[-5291409.95364336,1095722.96833454],[-5292236.79296484,1094702.557988],[-5293098.76304783,1093711.63369397],[-5293994.81392688,1092751.40038253],[-5294923.85366911,1091823.02532406],[-5295884.75048925,1090927.63746595],[-5296876.33364023,1090066.32507505],[-5297897.39519425,1089240.13530021],[-5298946.69093335,1088450.07226642],[-5300022.94257584,1087697.09618524],[-5301124.8387781,1086982.12235201],[-5302251.03713839,1086306.01957807],[-5303400.16553267,1085669.60941316],[-5304570.8238957,1085073.66525444],[-5305761.5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+ FRANCIS SCOTT KEY BRIDGE\",\"htmlname\":\"Baltimore: Collapse of Francis Scott + Key Bridge\",\"htmldescription\":\"Collapse of the Francis Scott Key Bridge + halted vessel traffic at the port of Baltimore\",\"alertlevel\":\"RED\",\"country\":\"United + 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+ Cyclone NORMA-23\",\"htmldescription\":\"Red Tropical Cyclone NORMA-23 in + Mexico from: 17 Oct 2023 to: 23 Oct 2023 .\",\"alertlevel\":\"RED\",\"country\":\"Mexico\",\"fromdate\":1697576400000,\"year\":2023,\"todate\":1698073200000,\"severitytext\":\"Tropical + Depression (maximum wind speed of 213 km/h)\",\"lat\":24.9,\"long\":-107.7,\"editdate\":1698455547761,\"affectedports\":\"port905; + port1310\",\"n_affectedports\":2,\"affectedpopulation\":\"430 thousand in + Category 1 or 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+ Cyclone KOINU-23\",\"htmldescription\":\"Red Tropical Cyclone KOINU-23 in + China, Japan, Philippines from: 29 Sep 2023 to: 10 Oct 2023 .\",\"alertlevel\":\"RED\",\"country\":\"Hong + Kong Special Administrative Region; Taiwan Province of China; Macao Special + Administrative Region\",\"fromdate\":1695988800000,\"year\":2023,\"todate\":1696896000000,\"severitytext\":\"Tropical + Depression (maximum wind speed of 213 km/h)\",\"lat\":20.8,\"long\":110.3,\"editdate\":1698455547761,\"affectedports\":\"port482; + port541; port681; port474; port674\",\"n_affectedports\":5,\"affectedpopulation\":\"3.8 + million in Category 1 or 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+ Cyclone SAOLA-23\",\"htmldescription\":\"Red Tropical Cyclone SAOLA-23 in + China, Philippines from: 23 Aug 2023 to: 03 Sep 2023 .\",\"alertlevel\":\"RED\",\"country\":\"Hong + Kong Special Administrative Region; China; Macao Special Administrative Region\",\"fromdate\":1692813600000,\"year\":2023,\"todate\":1693742400000,\"severitytext\":\"Tropical + Depression (maximum wind speed of 250 km/h)\",\"lat\":20.7,\"long\":109,\"editdate\":1698455547761,\"affectedports\":\"port423; + port425; port436; port484; port1189; port1196; port1414; port474; port674\",\"n_affectedports\":9,\"affectedpopulation\":\"34.5 + million in Category 1 or 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+ Cyclone DOKSURI-23\",\"htmldescription\":\"Red Tropical Cyclone DOKSURI-23 + in China, Philippines from: 21 Jul 2023 to: 28 Jul 2023 .\",\"alertlevel\":\"RED\",\"country\":\"China; + Taiwan Province of China\",\"fromdate\":1689940800000,\"year\":2023,\"todate\":1690524000000,\"severitytext\":\"Hurricane/Typhoon + \\u003e 74 mph (maximum wind speed of 241 km/h)\",\"lat\":25.7,\"long\":118.4,\"editdate\":1694319313120,\"affectedports\":\"port370; + port1074; port1192; port1404; port1405; port541\",\"n_affectedports\":6,\"affectedpopulation\":\"22.8 + million in Category 1 or 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+ in T\xFCrkiye\",\"htmlname\":\"Earthquake in T\xFCrkiye\",\"htmldescription\":\"Red + M 7.8 Earthquake in T\xFCrkiye at: 06 Feb 2023 01:17:35.\",\"alertlevel\":\"RED\",\"country\":\"T\xFCrkiye\",\"fromdate\":1675646255000,\"year\":2023,\"todate\":1675646255000,\"severitytext\":\"Magnitude + 7.8M, Depth:17.925km\",\"lat\":37.1736,\"long\":37.032,\"editdate\":1694318563269,\"affectedports\":\"port286; + port503; port1313; 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+ Cyclone IAN-22\",\"htmldescription\":\"Red Tropical Cyclone IAN-22 in United + States, Cuba from: 23 Sep 2022 to: 02 Oct 2022 .\",\"alertlevel\":\"RED\",\"country\":\"United + States; Cuba\",\"fromdate\":1663923600000,\"year\":2022,\"todate\":1664679600000,\"severitytext\":\"Tropical + Depression (maximum wind speed of 250 km/h)\",\"lat\":37.3,\"long\":-78.2,\"editdate\":1694318157503,\"affectedports\":\"port212; + port231; port513; port967; port1259; port1399; port86; port704\",\"n_affectedports\":8,\"affectedpopulation\":\"5.2 + million in Category 1 or 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+ Cyclone NORU-22\",\"htmldescription\":\"Red Tropical Cyclone NORU-22 in Viet + Nam, Laos, Philippines from: 22 Sep 2022 to: 28 Sep 2022 .\",\"alertlevel\":\"RED\",\"country\":\"Philippines; + Vietnam\",\"fromdate\":1663804800000,\"year\":2022,\"todate\":1664323200000,\"severitytext\":\"Hurricane/Typhoon + \\u003e 74 mph (maximum wind speed of 259 km/h)\",\"lat\":16,\"long\":107.8,\"editdate\":1694318122116,\"affectedports\":\"port694; + port1232; port213; port269\",\"n_affectedports\":4,\"affectedpopulation\":\"9 + million in Category 1 or 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+ Cyclone FIONA-22\",\"htmldescription\":\"Red Tropical Cyclone FIONA-22 in + Canada, Turks and Caicos Islands, Dominican Republic, Puerto Rico from: 14 + Sep 2022 to: 24 Sep 2022 .\",\"alertlevel\":\"RED\",\"country\":\"Montserrat; + Canada; Antigua and Barbuda; Guadeloupe; St. Kitts and Nevis; United States; + Dominican Republic; Turks and Caicos Islands; Puerto Rico\",\"fromdate\":1663167600000,\"year\":2022,\"todate\":1664053200000,\"severitytext\":\"Tropical + Storm (maximum wind speed of 213 km/h)\",\"lat\":48.4,\"long\":-60.5,\"editdate\":1694318078129,\"affectedports\":\"port1245; + port211; port443; port935; port1219; port51; port1055; port1147; port122; + port232; port164; port913; port414; port1025; port910; port922; port1221\",\"n_affectedports\":17,\"affectedpopulation\":\"2.9 + million in Category 1 or 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+ in China, Taiwan\",\"htmlname\":\"Flood in China, Taiwan\",\"htmldescription\":\"Red + Flood in China, Taiwan from: 14 Sep 2022 01 to: 16 Sep 2022 01.\",\"alertlevel\":\"RED\",\"country\":\"China\",\"fromdate\":1663117200000,\"year\":2022,\"todate\":1663290000000,\"severitytext\":null,\"lat\":31.23228,\"long\":121.4692,\"editdate\":1694317999111,\"affectedports\":\"port438; + port824; port1188; port1390; port1427; 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+ Cyclone YASA-20\",\"htmldescription\":\"Red Tropical Cyclone YASA-20 in Fiji, + from: 12 Dec 2020 to: 20 Dec 2020 .\",\"alertlevel\":\"RED\",\"country\":\"Fiji\",\"fromdate\":1607774400000,\"year\":2020,\"todate\":1608422400000,\"severitytext\":\"Tropical + Storm (maximum wind speed of 259 km/h)\",\"lat\":-23.3,\"long\":-178.1,\"editdate\":1694315138928,\"affectedports\":\"port636; + port643; port687; port1240\",\"n_affectedports\":4,\"affectedpopulation\":\"160 + thousand in Category 1 or 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+ Cyclone VAMCO-20\",\"htmldescription\":\"Red Tropical Cyclone VAMCO-20 in + Viet Nam, Laos, Philippines from: 09 Nov 2020 to: 15 Nov 2020 .\",\"alertlevel\":\"RED\",\"country\":\"Philippines; + Vietnam\",\"fromdate\":1604901600000,\"year\":2020,\"todate\":1605420000000,\"severitytext\":\"Hurricane/Typhoon + \\u003e 74 mph (maximum wind speed of 213 km/h)\",\"lat\":17.7,\"long\":106.5,\"editdate\":1694315001659,\"affectedports\":\"port125; + port694; port1232; port213; port269\",\"n_affectedports\":5,\"affectedpopulation\":\"47.4 + million in Category 1 or 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+ Cyclone ETA-20\",\"htmldescription\":\"Red Tropical Cyclone ETA-20 in Nicaragua, + United States, Honduras from: 31 Oct 2020 to: 13 Nov 2020 .\",\"alertlevel\":\"RED\",\"country\":\"Nicaragua\",\"fromdate\":1604178000000,\"year\":2020,\"todate\":1605258000000,\"severitytext\":\"Tropical + Storm (maximum wind speed of 241 km/h)\",\"lat\":33.3,\"long\":-76.8,\"editdate\":1694314982932,\"affectedports\":\"port1030\",\"n_affectedports\":1,\"affectedpopulation\":\"200 + thousand in Category 1 or 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+ Cyclone GONI-20\",\"htmldescription\":\"Red Tropical Cyclone GONI-20 in Philippines + from: 28 Oct 2020 to: 06 Nov 2020 .\",\"alertlevel\":\"RED\",\"country\":\"Philippines\",\"fromdate\":1603843200000,\"year\":2020,\"todate\":1604620800000,\"severitytext\":\"Tropical + Depression (maximum wind speed of 315 km/h)\",\"lat\":13.9,\"long\":109.2,\"editdate\":1694314929059,\"affectedports\":\"port125; + port694; port1232\",\"n_affectedports\":3,\"affectedpopulation\":\"24.4 million + \ in Category 1 or 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+ Cyclone HAISHEN-20\",\"htmldescription\":\"Red Tropical Cyclone HAISHEN-20 + in Democratic Republic of Korea, Republic of Korea, Japan, China, from: 31 + Aug 2020 to: 07 Sep 2020 .\",\"alertlevel\":\"RED\",\"country\":\"Japan; + Korea\",\"fromdate\":1598875200000,\"year\":2020,\"todate\":1599480000000,\"severitytext\":\"Tropical + Storm (maximum wind speed of 250 km/h)\",\"lat\":40.7,\"long\":128.9,\"editdate\":1694314690167,\"affectedports\":\"port439; + port445; port471; port480; port528; port538; port545; port566; port605; port785; + port846; port852; port1119; port1190; port1304; port1324; port1335; port1378; + port1411; port270; port432; port494; port608; port715; port915; port1065; + port1067; port1309; port1338\",\"n_affectedports\":29,\"affectedpopulation\":\"29.9 + million in Category 1 or 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+ Cyclone MAYSAK-20\",\"htmldescription\":\"Red Tropical Cyclone MAYSAK-20 in + Republic of Korea, Japan, Democratic Republic of Korea, China from: 28 Aug + 2020 to: 02 Sep 2020 .\",\"alertlevel\":\"RED\",\"country\":\"Japan; Korea\",\"fromdate\":1598572800000,\"year\":2020,\"todate\":1599069600000,\"severitytext\":\"Hurricane/Typhoon + \\u003e 74 mph (maximum wind speed of 231 km/h)\",\"lat\":35.5,\"long\":129,\"editdate\":1694314659620,\"affectedports\":\"port545; + port785; port432; port715; port915; port1065; port1309; port1338\",\"n_affectedports\":8,\"affectedpopulation\":\"18.3 + million in Category 1 or 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+ Cyclone VONGFONG-20\",\"htmldescription\":\"Red Tropical Cyclone VONGFONG-20 + in Philippines from: 11 May 2020 to: 16 May 2020 .\",\"alertlevel\":\"RED\",\"country\":\"Philippines\",\"fromdate\":1589220000000,\"year\":2020,\"todate\":1589608800000,\"severitytext\":\"Tropical + Depression (maximum wind speed of 194 km/h)\",\"lat\":19.3,\"long\":120.5,\"editdate\":1694314305944,\"affectedports\":null,\"n_affectedports\":0,\"affectedpopulation\":\"2 + million in Category 1 or 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+ Cyclone HAROLD-20\",\"htmldescription\":\"Red Tropical Cyclone HAROLD-20 in + , Tonga, Fiji, Vanuatu from: 02 Apr 2020 to: 09 Apr 2020 .\",\"alertlevel\":\"RED\",\"country\":\"Fiji; + Vanuatu; Tonga\",\"fromdate\":1585850400000,\"year\":2020,\"todate\":1586412000000,\"severitytext\":\"Hurricane/Typhoon + \\u003e 74 mph (maximum wind speed of 269 km/h)\",\"lat\":-24.6,\"long\":-170.7,\"editdate\":1694314190344,\"affectedports\":\"port636; + port643; port1240; port669; port836\",\"n_affectedports\":5,\"affectedpopulation\":\"70 + thousand in Category 1 or 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+ Cyclone KAMMURI-19\",\"htmldescription\":\"Red Tropical Cyclone KAMMURI-19 + in Philippines, Guam, Northern Mariana Islands from: 25 Nov 2019 to: 05 Dec + 2019 .\",\"alertlevel\":\"RED\",\"country\":\"Philippines\",\"fromdate\":1574704800000,\"year\":2019,\"todate\":1575568800000,\"severitytext\":\"Tropical + Depression (maximum wind speed of 213 km/h)\",\"lat\":12.2,\"long\":113.5,\"editdate\":1694312298029,\"affectedports\":\"port125; + port694; port858; port1232\",\"n_affectedports\":4,\"affectedpopulation\":\"34.9 + million in Category 1 or 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+ Cyclone MATMO-19/BULBUL-19\",\"htmldescription\":\"Red Tropical Cyclone MATMO-19/BULBUL-19 + in Bangladesh, India, Myanmar, Thailand, Cambodia, Laos, Viet Nam from: 29 + Oct 2019 to: 10 Nov 2019 .\",\"alertlevel\":\"RED\",\"country\":\"Bangladesh; + Vietnam; India\",\"fromdate\":1572372000000,\"year\":2019,\"todate\":1573387200000,\"severitytext\":\"Tropical + Storm (maximum wind speed of 157 km/h)\",\"lat\":23,\"long\":90.8,\"editdate\":1694312137296,\"affectedports\":\"port290; + port442; port883; port1078; port759; port890\",\"n_affectedports\":6,\"affectedpopulation\":\"7.6 + million in Category 1 or 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+ Cyclone LINGLING-19\",\"htmldescription\":\"Red Tropical Cyclone LINGLING-19 + in Korea, Democratic People's Republic of, Republic of, China, Japan, Russian + Federation from: 02 Sep 2019 to: 07 Sep 2019 .\",\"alertlevel\":\"RED\",\"country\":\"China; + Korea\",\"fromdate\":1567382400000,\"year\":2019,\"todate\":1567836000000,\"severitytext\":\"Hurricane/Typhoon + \\u003e 74 mph (maximum wind speed of 222 km/h)\",\"lat\":37.9,\"long\":125.3,\"editdate\":1694311847680,\"affectedports\":\"port277; + port662; port270; port494; port608; port1067\",\"n_affectedports\":6,\"affectedpopulation\":\"20.1 + million in Category 1 or 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+ Cyclone LEKIMA-19\",\"htmldescription\":\"Red Tropical Cyclone LEKIMA-19 in + , China, Japan from: 04 Aug 2019 to: 11 Aug 2019 .\",\"alertlevel\":\"RED\",\"country\":\"China; + Taiwan Province of China\",\"fromdate\":1564876800000,\"year\":2019,\"todate\":1565524800000,\"severitytext\":\"Tropical + Depression (maximum wind speed of 241 km/h)\",\"lat\":36,\"long\":119.5,\"editdate\":1694311686493,\"affectedports\":\"port227; + port228; port370; port375; port438; port517; port792; port824; port1073; port1188; + port1253; port1390; port1403; port1425; port1427; port1429; port238; port1261\",\"n_affectedports\":18,\"affectedpopulation\":\"7 + million in Category 1 or 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+ Cyclone KENNETH-19\",\"htmldescription\":\"Red Tropical Cyclone KENNETH-19 + in , Mozambique, Comoros from: 23 Apr 2019 to: 25 Apr 2019 .\",\"alertlevel\":\"RED\",\"country\":\"Mozambique; + Comoros\",\"fromdate\":1555977600000,\"year\":2019,\"todate\":1556215200000,\"severitytext\":\"Hurricane/Typhoon + \\u003e 74 mph (maximum wind speed of 231 km/h)\",\"lat\":-12.3,\"long\":40.1,\"editdate\":1694310802591,\"affectedports\":\"port896; + port769\",\"n_affectedports\":2,\"affectedpopulation\":\"260 thousand in + Category 1 or 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+ Basin-2018\",\"htmlname\":\"Drought in Angola, Botswana, Mozambique, Namibia, + Zambia, Zimbabwe\",\"htmldescription\":\"Red Drought in Angola, Botswana, + Mozambique, Namibia, Zambia, Zimbabwe from: 21 Oct 2018 to: 24 Mar 2020 .\",\"alertlevel\":\"RED\",\"country\":\"Angola, + Botswana, Mozambique, Namibia, Zambia, Zimbabwe\",\"fromdate\":1540080000000,\"year\":2018,\"todate\":1585067820000,\"severitytext\":\"Severe + impact for agricultural drought in 150282 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Content-Type: - application/json Date: - - Tue, 16 Dec 2025 05:17:22 GMT + - Mon, 25 May 2026 16:20:47 GMT Request-Context: - appId=cid-v1:e4518baf-f7f4-42d7-900e-7dd820548dc8 Set-Cookie: @@ -213,9 +213,9 @@ interactions: x-content-type-options: - nosniff x-correlation-id: - - 8b788ff2683903419391a8480a73df0a + - 581b506849bd28fb5a90edccd15604d1 x-envoy-upstream-service-time: - - '61' + - '356' x-frame-options: - DENY x-xss-protection: @@ -229,7 +229,7 @@ interactions: Accept: - application/xml Accept-Encoding: - - gzip, deflate, br, zstd + - gzip, deflate, zstd Cache-Control: - no-cache Connection: @@ -246,7 +246,7 @@ interactions: xmlns:ss="http://www.sdmx.org/resources/sdmxml/schemas/v3_0/data/structurespecific" xmlns:ns1="urn:sdmx:org.sdmx.infomodel.datastructure.DataStructure=IMF.STA:DSD_IL(13.0.0):ObsLevelDim:TIME_PERIOD" xsi:schemaLocation="http://www.sdmx.org/resources/sdmxml/schemas/v3_0/message - ../../schemas/SDMXMessage.xsd">IDREF9203false2025-12-16T05:17:22.970325555ZIDREF2800false2026-05-25T16:20:48.285156082Zurn:sdmx:org.sdmx.infomodel.datastructure.DataStructure=IMF.STA:DSD_IL(13.0.0) list[dict[str, Any]]: + """Record args and return the stub result, or raise if configured.""" + self.calls.append( + {"query": query, "dataflows": dataflows, "keywords": keywords} + ) + if self._raise_exc is not None: + raise self._raise_exc + return self._result + + +class TestImfAvailableIndicatorsFetcher: + """Tests for ``ImfAvailableIndicatorsFetcher``.""" + + def test_transform_query_passes_defaults(self): + """Empty params produce a query with all defaults None.""" + q = ImfAvailableIndicatorsFetcher.transform_query({}) + assert isinstance(q, ImfAvailableIndicatorsQueryParams) + assert q.query is None + assert q.dataflows is None + assert q.keywords is None + + @pytest.mark.asyncio + async def test_aextract_data_string_dataflows_and_keywords(self, monkeypatch): + """String ``dataflows``/``keywords`` are split by comma before search.""" + stub = _StubMeta(result=[{"indicator": "GDP"}]) + monkeypatch.setattr( + "openbb_imf.utils.metadata.ImfMetadata", + lambda: stub, + ) + q = ImfAvailableIndicatorsFetcher.transform_query( + { + "query": "gold,volume", + "dataflows": "WEO,IFS", + "keywords": "gold,not USD", + } + ) + out = await ImfAvailableIndicatorsFetcher.aextract_data(q, None) + assert out == [{"indicator": "GDP"}] + call = stub.calls[0] + assert call["query"] == "gold, volume" + assert call["dataflows"] == ["WEO", "IFS"] + assert call["keywords"] == ["gold", "not USD"] + + @pytest.mark.asyncio + async def test_aextract_data_list_dataflows_and_keywords(self, monkeypatch): + """List ``dataflows``/``keywords`` flow through unchanged.""" + stub = _StubMeta(result=[{"indicator": "CPI"}]) + monkeypatch.setattr( + "openbb_imf.utils.metadata.ImfMetadata", + lambda: stub, + ) + params: dict[str, Any] = {"dataflows": ["WEO"], "keywords": ["gold"]} + q = ImfAvailableIndicatorsFetcher.transform_query(params) + await ImfAvailableIndicatorsFetcher.aextract_data(q, None) + call = stub.calls[0] + assert call["query"] == "" + assert call["dataflows"] == ["WEO"] + assert call["keywords"] == ["gold"] + + @pytest.mark.asyncio + async def test_aextract_data_search_failure_wraps_in_openbb_error( + self, monkeypatch + ): + """Any exception from ``search_indicators`` is wrapped in ``OpenBBError``.""" + stub = _StubMeta(raise_exc=RuntimeError("boom")) + monkeypatch.setattr( + "openbb_imf.utils.metadata.ImfMetadata", + lambda: stub, + ) + q = ImfAvailableIndicatorsFetcher.transform_query({"query": "x"}) + with pytest.raises(OpenBBError): + await ImfAvailableIndicatorsFetcher.aextract_data(q, None) + + @pytest.mark.asyncio + async def test_aextract_data_empty_results_raises(self, monkeypatch): + """Empty results raise ``EmptyDataError``.""" + stub = _StubMeta(result=[]) + monkeypatch.setattr( + "openbb_imf.utils.metadata.ImfMetadata", + lambda: stub, + ) + q = ImfAvailableIndicatorsFetcher.transform_query({"query": "nope"}) + with pytest.raises(EmptyDataError): + await ImfAvailableIndicatorsFetcher.aextract_data(q, None) + + def test_transform_data_constructs_symbol_and_typed_rows(self): + """``transform_data`` builds ``symbol`` from ``dataflow_id::indicator`` and validates rows.""" + q = ImfAvailableIndicatorsFetcher.transform_query({"query": "any"}) + raw = [ + { + "agency_id": "IMF.STA", + "dataflow_id": "WEO", + "dataflow_name": "World Economic Outlook", + "structure_id": "DSD_WEO", + "dimension_id": "INDICATOR", + "indicator": "NGDP_RPCH", + "label": "Real GDP Growth", + "description": "Real gross domestic product growth.", + } + ] + out = ImfAvailableIndicatorsFetcher.transform_data(q, raw) + assert isinstance(out[0], ImfAvailableIndicatorsData) + assert out[0].symbol == "WEO::NGDP_RPCH" + assert out[0].description == "Real GDP Growth" + assert out[0].long_description == "Real gross domestic product growth." diff --git a/openbb_platform/providers/imf/tests/test_balance_of_payments_model.py b/openbb_platform/providers/imf/tests/test_balance_of_payments_model.py new file mode 100644 index 00000000000..3d2da4a78f3 --- /dev/null +++ b/openbb_platform/providers/imf/tests/test_balance_of_payments_model.py @@ -0,0 +1,384 @@ +"""Tests for the IMF Balance of Payments Fetcher.""" + +# ruff: noqa: I001 + +from datetime import date as dateType + +import pytest + +from openbb_core.app.model.abstract.error import OpenBBError +from openbb_imf.models.balance_of_payments import ( + ImfBalanceOfPaymentsData, + ImfBalanceOfPaymentsFetcher, + ImfBalanceOfPaymentsQueryParams, +) + + +def _row( + indicator: str, entry: str, period: str, value: float, country: str = "Japan" +) -> dict: + """Return a single SDMX long-form row as the query builder emits.""" + return { + "INDICATOR_code": indicator, + "BOP_ACCOUNTING_ENTRY_code": entry, + "COUNTRY_label": country, + "TIME_PERIOD": period, + "OBS_VALUE": value, + } + + +def _decomposed_rows() -> list[dict]: + """Build a complete two-period decomposition for the unit-test scenarios.""" + return [ + _row("CAB", "NETCD_T", "2024-03-31", -200.0), + _row("CAB", "NETCD_T", "2024-06-30", -300.0), + _row("G", "NETCD_T", "2024-03-31", -250.0), + _row("G", "NETCD_T", "2024-06-30", -280.0), + _row("G", "CD_T", "2024-03-31", 500.0), + _row("G", "CD_T", "2024-06-30", 520.0), + _row("G", "DB_T", "2024-03-31", 750.0), + _row("G", "DB_T", "2024-06-30", 800.0), + _row("S", "NETCD_T", "2024-03-31", 75.0), + _row("S", "NETCD_T", "2024-06-30", 70.0), + _row("S", "CD_T", "2024-03-31", 260.0), + _row("S", "CD_T", "2024-06-30", 280.0), + _row("S", "DB_T", "2024-03-31", 185.0), + _row("S", "DB_T", "2024-06-30", 210.0), + _row("IN1", "NETCD_T", "2024-03-31", -10.0), + _row("IN1", "NETCD_T", "2024-06-30", -15.0), + _row("IN1", "CD_T", "2024-03-31", 350.0), + _row("IN1", "CD_T", "2024-06-30", 365.0), + _row("IN1", "DB_T", "2024-03-31", 360.0), + _row("IN1", "DB_T", "2024-06-30", 380.0), + _row("IN2", "NETCD_T", "2024-03-31", -50.0), + _row("IN2", "NETCD_T", "2024-06-30", -55.0), + _row("IN2", "CD_T", "2024-03-31", 48.0), + _row("IN2", "CD_T", "2024-06-30", 47.0), + _row("IN2", "DB_T", "2024-03-31", 98.0), + _row("IN2", "DB_T", "2024-06-30", 102.0), + ] + + +class TestQueryParams: + """Tests for ``ImfBalanceOfPaymentsQueryParams``.""" + + def test_default_country_resolves(self): + """``united_states`` (label) resolves to ``USA`` (ISO3).""" + q = ImfBalanceOfPaymentsQueryParams() + assert q.country == "USA" + assert q.frequency == "quarterly" + + def test_country_list_joined_with_plus(self): + """Comma-separated tokens join with ``+`` and ISO3 codes pass through.""" + q = ImfBalanceOfPaymentsQueryParams(country="USA,japan") + assert q.country == "USA+JPN" + + def test_invalid_country_raises(self): + """An unknown country token raises ``ValueError``.""" + with pytest.raises( + ValueError, match="not in the IMF BOP dataflow's country list" + ): + ImfBalanceOfPaymentsQueryParams(country="not_a_country") + + def test_transform_query_passes_through(self): + """``transform_query`` constructs the params model from a dict.""" + q = ImfBalanceOfPaymentsFetcher.transform_query( + {"country": "USA", "frequency": "annual"} + ) + assert isinstance(q, ImfBalanceOfPaymentsQueryParams) + assert q.country == "USA" + assert q.frequency == "annual" + + def test_country_list_comes_from_bop_dataflow(self): + """``BOP_CODE_SET`` / ``BOP_LABEL_TO_CODE`` are derived from the BOP dataflow.""" + from openbb_imf.models.balance_of_payments import ( + BOP_CODE_SET, + BOP_LABEL_TO_CODE, + ) + from openbb_imf.utils.metadata import ImfMetadata + + params = ImfMetadata().get_dataflow_parameters("BOP") + live_codes = {p["value"].upper() for p in params.get("COUNTRY", [])} + assert live_codes == BOP_CODE_SET + assert set(BOP_LABEL_TO_CODE.values()) == live_codes + assert BOP_LABEL_TO_CODE["united_states"] == "USA" + + def test_country_loader_skips_entries_without_value(self): + """Country entries missing ``value`` are skipped by the loader.""" + from openbb_imf.models import balance_of_payments as bop_mod + + class _Stub: + """In-memory stand-in for ``ImfMetadata``.""" + + def get_dataflow_parameters(self, _dataflow_id): + """Return one valid + one missing-value entry.""" + return { + "COUNTRY": [ + {"value": "USA", "label": "United States"}, + {"value": "", "label": "Skipped"}, + {"label": "No Value"}, + ] + } + + original = bop_mod.ImfMetadata + bop_mod.ImfMetadata = _Stub # type: ignore[assignment] + try: + label_to_code, code_set = bop_mod._load_bop_countries() + finally: + bop_mod.ImfMetadata = original # type: ignore[assignment] + + assert code_set == {"USA"} + assert label_to_code == {"united_states": "USA"} + + +class TestExtractData: + """Tests for ``ImfBalanceOfPaymentsFetcher.extract_data``.""" + + def _mock_builder(self, monkeypatch, rows): + """Patch ``ImfQueryBuilder`` to record fetch calls and return canned rows.""" + from openbb_imf.utils import query_builder as qb + + calls: list[dict] = [] + + class _Stub: + def fetch_data(self, dataflow, **kwargs): + """Record the call and return canned rows.""" + calls.append({"dataflow": dataflow, **kwargs}) + return {"data": list(rows), "metadata": {}} + + monkeypatch.setattr(qb, "ImfQueryBuilder", _Stub) + return calls + + def test_single_skip_validation_call(self, monkeypatch): + """Extract issues exactly one SDMX call with ``_skip_validation=True``.""" + calls = self._mock_builder(monkeypatch, _decomposed_rows()) + q = ImfBalanceOfPaymentsQueryParams( + country="JPN", + frequency="quarterly", + start_date=dateType(2024, 1, 1), + end_date=dateType(2024, 12, 31), + ) + rows = ImfBalanceOfPaymentsFetcher.extract_data(q, {}) + assert len(calls) == 1 + call = calls[0] + assert call["dataflow"] == "BOP" + assert call["COUNTRY"] == "JPN" + assert call["UNIT"] == "USD" + assert call["FREQUENCY"] == "Q" + assert call["start_date"] == "2024-01-01" + assert call["end_date"] == "2024-12-31" + assert call["_skip_validation"] is True + assert call["INDICATOR"] == "CAB+G+S+IN1+IN2" + assert call["BOP_ACCOUNTING_ENTRY"] == "NETCD_T+CD_T+DB_T" + assert len(rows) == len(_decomposed_rows()) + + def test_omits_unset_dates(self, monkeypatch): + """Missing date params surface as ``None`` to the query builder.""" + calls = self._mock_builder(monkeypatch, _decomposed_rows()) + q = ImfBalanceOfPaymentsQueryParams(country="JPN") + ImfBalanceOfPaymentsFetcher.extract_data(q, {}) + assert calls[0]["start_date"] is None + assert calls[0]["end_date"] is None + + def test_annual_frequency_maps_to_A(self, monkeypatch): + """``annual`` maps to the SDMX ``A`` code.""" + calls = self._mock_builder(monkeypatch, _decomposed_rows()) + q = ImfBalanceOfPaymentsQueryParams(country="JPN", frequency="annual") + ImfBalanceOfPaymentsFetcher.extract_data(q, {}) + assert calls[0]["FREQUENCY"] == "A" + + def test_empty_result_raises(self, monkeypatch): + """An empty inner-fetcher result raises ``OpenBBError``.""" + self._mock_builder(monkeypatch, []) + q = ImfBalanceOfPaymentsQueryParams(country="JPN") + with pytest.raises(OpenBBError, match="No BOP data"): + ImfBalanceOfPaymentsFetcher.extract_data(q, {}) + + def test_openbb_error_propagates(self, monkeypatch): + """An ``OpenBBError`` from the inner fetch is re-raised.""" + from openbb_imf.utils import query_builder as qb + + class _Stub: + def fetch_data(self, *_a, **_kw): + """Always raise.""" + raise OpenBBError("boom") + + monkeypatch.setattr(qb, "ImfQueryBuilder", _Stub) + q = ImfBalanceOfPaymentsQueryParams(country="JPN") + with pytest.raises(OpenBBError, match="boom"): + ImfBalanceOfPaymentsFetcher.extract_data(q, {}) + + def test_value_error_wraps_to_openbb_error(self, monkeypatch): + """A ``ValueError`` from the query builder wraps to ``OpenBBError``.""" + from openbb_imf.utils import query_builder as qb + + class _Stub: + def fetch_data(self, *_a, **_kw): + """Always raise.""" + raise ValueError("bad dim") + + monkeypatch.setattr(qb, "ImfQueryBuilder", _Stub) + q = ImfBalanceOfPaymentsQueryParams(country="JPN") + with pytest.raises(OpenBBError, match="bad dim"): + ImfBalanceOfPaymentsFetcher.extract_data(q, {}) + + def test_handles_non_dict_result_gracefully(self, monkeypatch): + """A non-dict return from the builder is treated as no data.""" + from openbb_imf.utils import query_builder as qb + + class _Stub: + def fetch_data(self, *_a, **_kw): + """Return a non-dict sentinel.""" + return None + + monkeypatch.setattr(qb, "ImfQueryBuilder", _Stub) + q = ImfBalanceOfPaymentsQueryParams(country="JPN") + with pytest.raises(OpenBBError, match="No BOP data"): + ImfBalanceOfPaymentsFetcher.extract_data(q, {}) + + +class TestTransformData: + """Tests for ``ImfBalanceOfPaymentsFetcher.transform_data``.""" + + def test_pivots_into_bp6_columns(self): + """All recognised (indicator, entry) pairs populate the BP6 fields.""" + q = ImfBalanceOfPaymentsQueryParams(country="JPN") + out = ImfBalanceOfPaymentsFetcher.transform_data(q, _decomposed_rows()) + assert len(out) == 2 + first = out[0] + assert first.period == dateType(2024, 3, 31) + assert first.country == "Japan" + assert first.balance_total == -200.0 + assert first.balance_total_goods == -250.0 + assert first.balance_total_services == 75.0 + assert first.credits_total_goods == 500.0 + assert first.credits_total_services == 260.0 + assert first.credits_total == 500.0 + 260.0 + 350.0 + 48.0 + assert first.debits_total == 750.0 + 185.0 + 360.0 + 98.0 + assert first.credits_services_percent_of_goods_and_services == pytest.approx( + 260.0 / (260.0 + 500.0) + ) + assert first.credits_services_percent_of_current_account == pytest.approx( + 260.0 / first.credits_total + ) + assert first.debits_services_percent_of_goods_and_services == pytest.approx( + 185.0 / (185.0 + 750.0) + ) + assert first.debits_services_percent_of_current_account == pytest.approx( + 185.0 / first.debits_total + ) + + def test_ignores_unrecognised_indicator_or_entry(self): + """Rows for unknown indicator codes or accounting entries are dropped.""" + rows = [ + _row("UNKNOWN", "NETCD_T", "2024-03-31", 1.0), + _row("G", "X_OTHER", "2024-03-31", 2.0), + _row("G", "NETCD_T", "2024-03-31", 42.0), + ] + q = ImfBalanceOfPaymentsQueryParams(country="JPN") + out = ImfBalanceOfPaymentsFetcher.transform_data(q, rows) + assert len(out) == 1 + assert out[0].balance_total_goods == 42.0 + + def test_skips_bad_values_and_dates(self): + """Missing / unparsable values and empty dates are skipped.""" + rows = [ + _row("G", "NETCD_T", "2024-03-31", None), + _row("G", "NETCD_T", "", 5.0), + { + "INDICATOR_code": "G", + "BOP_ACCOUNTING_ENTRY_code": "NETCD_T", + "TIME_PERIOD": "2024-06-30", + "OBS_VALUE": "not-a-number", + "COUNTRY_label": "Japan", + }, + _row("G", "NETCD_T", "2024-12-31", 99.0), + ] + q = ImfBalanceOfPaymentsQueryParams(country="JPN") + out = ImfBalanceOfPaymentsFetcher.transform_data(q, rows) + assert len(out) == 1 + assert out[0].period == dateType(2024, 12, 31) + assert out[0].balance_total_goods == 99.0 + + def test_partial_components_skip_synthesised_totals(self): + """When a credits/debits component is missing, totals stay ``None``.""" + rows = [_row("G", "CD_T", "2024-03-31", 100.0)] + q = ImfBalanceOfPaymentsQueryParams(country="JPN") + out = ImfBalanceOfPaymentsFetcher.transform_data(q, rows) + assert out[0].credits_total is None + assert out[0].credits_services_percent_of_goods_and_services is None + + def test_country_falls_back_to_code_then_iso(self): + """``COUNTRY_label`` is preferred; ``COUNTRY_code`` and ``COUNTRY`` fall back.""" + rows = [ + { + "INDICATOR_code": "G", + "BOP_ACCOUNTING_ENTRY_code": "NETCD_T", + "TIME_PERIOD": "2024-03-31", + "OBS_VALUE": 1.0, + "COUNTRY_code": "JPN", + }, + { + "INDICATOR_code": "S", + "BOP_ACCOUNTING_ENTRY_code": "NETCD_T", + "TIME_PERIOD": "2024-03-31", + "OBS_VALUE": 2.0, + "COUNTRY": "USA", + }, + ] + q = ImfBalanceOfPaymentsQueryParams(country="JPN,USA") + countries = { + r.country for r in ImfBalanceOfPaymentsFetcher.transform_data(q, rows) + } + assert countries == {"JPN", "USA"} + + def test_sorts_by_period_then_country(self): + """Output is sorted ascending by date, then country.""" + rows = [ + _row("G", "NETCD_T", "2024-06-30", 1.0, country="USA"), + _row("G", "NETCD_T", "2024-03-31", 2.0, country="USA"), + _row("G", "NETCD_T", "2024-03-31", 3.0, country="JPN"), + ] + q = ImfBalanceOfPaymentsQueryParams(country="USA,japan") + out = ImfBalanceOfPaymentsFetcher.transform_data(q, rows) + assert [(r.period, r.country) for r in out] == [ + (dateType(2024, 3, 31), "JPN"), + (dateType(2024, 3, 31), "USA"), + (dateType(2024, 6, 30), "USA"), + ] + + +class TestImfBalanceOfPaymentsData: + """Sanity tests for the standardised data model.""" + + def test_alias_period_maps_to_date(self): + """The ``date`` alias populates the standard ``period`` field.""" + d = ImfBalanceOfPaymentsData.model_validate( + {"date": dateType(2024, 3, 31), "country": "USA"} + ) + assert d.period == dateType(2024, 3, 31) + assert d.country == "USA" + + def test_model_dump_uses_bpm6_canonical_order(self): + """``model_dump`` emits keys in BPM6 reading order.""" + d = ImfBalanceOfPaymentsData.model_validate( + { + "period": dateType(2024, 3, 31), + "country": "USA", + "balance_total": 1.0, + "balance_total_goods": 2.0, + "balance_total_services": 3.0, + "credits_total": 4.0, + "debits_total": 5.0, + } + ) + keys = list(d.model_dump(exclude_none=True).keys()) + assert keys == [ + "period", + "country", + "balance_total", + "balance_total_goods", + "balance_total_services", + "credits_total", + "debits_total", + ] diff --git a/openbb_platform/providers/imf/tests/test_chokepoint_models.py b/openbb_platform/providers/imf/tests/test_chokepoint_models.py new file mode 100644 index 00000000000..5f92ade6d0b --- /dev/null +++ b/openbb_platform/providers/imf/tests/test_chokepoint_models.py @@ -0,0 +1,299 @@ +"""Unit tests for the IMF maritime chokepoint info and volume models.""" + +# ruff: noqa: I001 + +from typing import Any +from unittest.mock import AsyncMock, patch + +import pytest +from openbb_core.app.model.abstract.error import OpenBBError + +from openbb_imf.models.maritime_chokepoint_info import ( + ImfMaritimeChokePointInfoData, + ImfMaritimeChokePointInfoFetcher, + ImfMaritimeChokePointInfoQueryParams, +) +from openbb_imf.models.maritime_chokepoint_volume import ( + ImfMaritimeChokePointVolumeData, + ImfMaritimeChokePointVolumeFetcher, + ImfMaritimeChokePointVolumeQueryParams, +) + + +class _FakeResponse: + """Pretend aiohttp ``ClientResponse`` returning a fixed payload/status.""" + + def __init__(self, payload: Any, status: int = 200): + self.status = status + self._payload = payload + + async def __aenter__(self): + return self + + async def __aexit__(self, *args): + return False + + async def json(self) -> Any: + return self._payload + + +class _AwaitableContext: + """Wrap a CM so ``await session.get(url)`` works.""" + + def __init__(self, ctx): + self._ctx = ctx + + def __await__(self): + async def _do(): + return self._ctx + + return _do().__await__() + + +class _FakeSession: + """Tiny aiohttp session double with configurable responses.""" + + def __init__(self, payload: Any, status: int = 200): + self._payload = payload + self._status = status + self.urls: list[str] = [] + + async def __aenter__(self): + return self + + async def __aexit__(self, *args): + return False + + def get(self, url: str): + self.urls.append(url) + return _AwaitableContext(_FakeResponse(self._payload, status=self._status)) + + +def _patch_session(payload: Any, status: int = 200): + """Return (patcher, fake_session) suitable for ``async with`` use.""" + fake = _FakeSession(payload, status=status) + + class _Factory: + async def __aenter__(self_): + return fake + + async def __aexit__(self_, *args): + return False + + async def _factory(*args, **kwargs): + return _Factory() + + patcher = patch( + "openbb_core.provider.utils.helpers.get_async_requests_session", + side_effect=_factory, + ) + return patcher, fake + + +class TestImfMaritimeChokePointInfoFetcher: + """Tests for ``ImfMaritimeChokePointInfoFetcher``.""" + + def test_transform_query_defaults(self): + """Default theme is ``None``.""" + q = ImfMaritimeChokePointInfoFetcher.transform_query({}) + assert isinstance(q, ImfMaritimeChokePointInfoQueryParams) + assert q.theme is None + + @pytest.mark.asyncio + async def test_aextract_data_non_200_raises(self): + """A non-200 response raises ``OpenBBError`` (covers lines 158, 162-163).""" + patcher, _ = _patch_session({}, status=500) + q = ImfMaritimeChokePointInfoFetcher.transform_query({}) + with patcher: + with pytest.raises(OpenBBError): + await ImfMaritimeChokePointInfoFetcher.aextract_data(q, None) + + @pytest.mark.asyncio + async def test_aextract_data_returns_json_payload(self): + """A 200 response returns the parsed JSON dict.""" + patcher, _ = _patch_session({"features": []}) + q = ImfMaritimeChokePointInfoFetcher.transform_query({}) + with patcher: + out = await ImfMaritimeChokePointInfoFetcher.aextract_data(q, None) + assert out == {"features": []} + + def test_transform_data_missing_features_raises(self): + """Missing ``features`` raises ``OpenBBError`` (covers line 173).""" + q = ImfMaritimeChokePointInfoFetcher.transform_query({}) + with pytest.raises(OpenBBError, match="No data found"): + ImfMaritimeChokePointInfoFetcher.transform_data(q, {}) + + def test_transform_data_builds_records(self): + """A list of features round-trips through the data model.""" + q = ImfMaritimeChokePointInfoFetcher.transform_query({}) + data = { + "features": [ + { + "properties": { + "portid": "chokepoint1", + "portname": "Suez Canal", + "lat": 30.0, + "lon": 32.5, + "vessel_count_total": 100, + "vessel_count_tanker": 30, + "vessel_count_container": 30, + "vessel_count_general_cargo": 10, + "vessel_count_dry_bulk": 25, + "vessel_count_RoRo": 5, + } + } + ] + } + out = ImfMaritimeChokePointInfoFetcher.transform_data(q, data) + assert len(out) == 1 + assert isinstance(out[0], ImfMaritimeChokePointInfoData) + assert out[0].name == "Suez Canal" + assert out[0].vessel_count_roro == 5 + + +class TestImfMaritimeChokePointVolumeQueryParams: + """Tests for the ``chokepoint`` validator.""" + + def test_chokepoint_none_returns_none(self): + """Falsy input becomes ``None`` (covers line 49).""" + q = ImfMaritimeChokePointVolumeQueryParams(chokepoint=None) + assert q.chokepoint is None + + def test_chokepoint_comma_separated_valid(self): + """Two valid choices are kept (covers comma branch 53-66).""" + q = ImfMaritimeChokePointVolumeQueryParams(chokepoint="suez_canal,panama_canal") + assert q.chokepoint == "suez_canal,panama_canal" + + def test_chokepoint_comma_separated_invalid_raises(self): + """An unknown comma-separated value raises (covers lines 58-65).""" + with pytest.raises(OpenBBError): + ImfMaritimeChokePointVolumeQueryParams(chokepoint="suez_canal,bogus") + + def test_chokepoint_string_invalid_raises(self): + """A single unknown value raises (covers line 73).""" + with pytest.raises(OpenBBError): + ImfMaritimeChokePointVolumeQueryParams(chokepoint="not_a_chokepoint") + + def test_chokepoint_list_input_resolves_names(self): + """List inputs resolve through the name lookup (covers lines 88-98).""" + q = ImfMaritimeChokePointVolumeQueryParams( + chokepoint=["suez_canal", "chokepoint2", "ignored"] + ) + assert q.chokepoint == "chokepoint1,chokepoint2" + + def test_chokepoint_invalid_type_raises(self): + """An int (non-string, non-list) is rejected with ``OpenBBError`` (covers 98-103).""" + with pytest.raises(OpenBBError): + ImfMaritimeChokePointVolumeQueryParams(chokepoint=42) + + +class TestImfMaritimeChokePointVolumeFetcher: + """Tests for ``ImfMaritimeChokePointVolumeFetcher``.""" + + def test_transform_query(self): + """``transform_query`` builds the model.""" + q = ImfMaritimeChokePointVolumeFetcher.transform_query({}) + assert isinstance(q, ImfMaritimeChokePointVolumeQueryParams) + + @pytest.mark.asyncio + async def test_aextract_no_chokepoint_uses_all_helper(self): + """No chokepoint passes to the all-chokepoints helper (covers lines 293-296).""" + with patch( + "openbb_imf.utils.port_watch_helpers.get_all_daily_chokepoint_activity_data", + new=AsyncMock(return_value=[{"date": "2024-01-01"}]), + ) as mocked: + q = ImfMaritimeChokePointVolumeFetcher.transform_query({}) + out = await ImfMaritimeChokePointVolumeFetcher.aextract_data(q, None) + assert out == [{"date": "2024-01-01"}] + mocked.assert_awaited_once() + + @pytest.mark.asyncio + async def test_aextract_no_chokepoint_helper_failure_wraps_error(self): + """A helper exception is wrapped in ``OpenBBError`` (covers lines 297-298).""" + with patch( + "openbb_imf.utils.port_watch_helpers.get_all_daily_chokepoint_activity_data", + new=AsyncMock(side_effect=RuntimeError("boom")), + ): + q = ImfMaritimeChokePointVolumeFetcher.transform_query({}) + with pytest.raises(OpenBBError): + await ImfMaritimeChokePointVolumeFetcher.aextract_data(q, None) + + @pytest.mark.asyncio + async def test_aextract_per_chokepoint_invalid_id_raises(self): + """An invalid post-validation id raises ``OpenBBError`` (covers lines 318-321).""" + q = ImfMaritimeChokePointVolumeFetcher.transform_query( + {"chokepoint": "suez_canal"} + ) + object.__setattr__(q, "chokepoint", "not_real") + with pytest.raises(OpenBBError, match="Invalid chokepoint name"): + await ImfMaritimeChokePointVolumeFetcher.aextract_data(q, None) + + @pytest.mark.asyncio + async def test_aextract_per_chokepoint_exception_propagates(self): + """Per-task exceptions are raised wrapped in ``OpenBBError`` (covers line 331).""" + + async def boom(*args, **kwargs): + raise RuntimeError("nope") + + with patch( + "openbb_imf.utils.port_watch_helpers.get_daily_chokepoint_data", + new=AsyncMock(side_effect=boom), + ): + q = ImfMaritimeChokePointVolumeFetcher.transform_query( + {"chokepoint": "suez_canal"} + ) + with pytest.raises(OpenBBError): + await ImfMaritimeChokePointVolumeFetcher.aextract_data(q, None) + + @pytest.mark.asyncio + async def test_aextract_empty_results_raises(self): + """Empty results raise ``OpenBBError`` (covers line 334).""" + with patch( + "openbb_imf.utils.port_watch_helpers.get_daily_chokepoint_data", + new=AsyncMock(return_value=[]), + ): + q = ImfMaritimeChokePointVolumeFetcher.transform_query( + {"chokepoint": "suez_canal"} + ) + with pytest.raises(OpenBBError, match="empty"): + await ImfMaritimeChokePointVolumeFetcher.aextract_data(q, None) + + @pytest.mark.asyncio + async def test_aextract_per_chokepoint_returns_rows(self): + """Per-chokepoint helper output is accumulated.""" + with patch( + "openbb_imf.utils.port_watch_helpers.get_daily_chokepoint_data", + new=AsyncMock(return_value=[{"date": "2024-01-01", "portid": "c1"}]), + ): + q = ImfMaritimeChokePointVolumeFetcher.transform_query( + {"chokepoint": "chokepoint1"} + ) + out = await ImfMaritimeChokePointVolumeFetcher.aextract_data(q, None) + assert out and out[0]["portid"] == "c1" + + def test_transform_data_builds_records(self): + """``transform_data`` validates each row through the model.""" + q = ImfMaritimeChokePointVolumeFetcher.transform_query({}) + data = [ + { + "date": "2024-01-01", + "portname": "Suez Canal", + "n_total": 10, + "n_cargo": 7, + "n_tanker": 3, + "n_container": 4, + "n_general_cargo": 2, + "n_dry_bulk": 1, + "n_roro": 0, + "capacity": 100.0, + "capacity_cargo": 80.0, + "capacity_tanker": 20.0, + "capacity_container": 50.0, + "capacity_general_cargo": 15.0, + "capacity_dry_bulk": 10.0, + "capacity_roro": 5.0, + } + ] + out = ImfMaritimeChokePointVolumeFetcher.transform_data(q, data) + assert isinstance(out[0], ImfMaritimeChokePointVolumeData) + assert out[0].chokepoint == "Suez Canal" diff --git a/openbb_platform/providers/imf/tests/test_consumer_price_index_model.py b/openbb_platform/providers/imf/tests/test_consumer_price_index_model.py new file mode 100644 index 00000000000..b6c26300c2f --- /dev/null +++ b/openbb_platform/providers/imf/tests/test_consumer_price_index_model.py @@ -0,0 +1,301 @@ +"""Unit tests for the IMF Consumer Price Index model and fetcher.""" + +# ruff: noqa: I001 + +from datetime import date +from typing import Any + +import pytest +from openbb_core.app.model.abstract.error import OpenBBError + +from openbb_imf.models.consumer_price_index import ( + ImfConsumerPriceIndexData, + ImfConsumerPriceIndexFetcher, + ImfConsumerPriceIndexQueryParams, +) + + +class _StubQueryBuilder: + """Stand-in for ``ImfQueryBuilder`` that captures call args.""" + + instances: list = [] + + def __init__(self): + self.calls: list[dict[str, Any]] = [] + self._payload: dict[str, Any] | None = None + self._raise: Exception | None = None + _StubQueryBuilder.instances.append(self) + + def fetch_data(self, **kwargs: Any) -> dict[str, Any]: + """Record args and return preconfigured payload or raise.""" + self.calls.append(kwargs) + if self._raise is not None: + raise self._raise + return self._payload or {"data": [], "metadata": {}} + + +@pytest.fixture +def patched_query_builder(monkeypatch: pytest.MonkeyPatch): + """Replace ``ImfQueryBuilder`` with the recording stub.""" + _StubQueryBuilder.instances = [] + monkeypatch.setattr( + "openbb_imf.models.consumer_price_index.ImfQueryBuilder", + _StubQueryBuilder, + ) + return _StubQueryBuilder + + +class TestImfConsumerPriceIndexQueryParams: + """Tests for ``ImfConsumerPriceIndexQueryParams`` field validators.""" + + def test_country_accepts_iso3_code(self): + """ISO3 codes round-trip through the validator unchanged.""" + q = ImfConsumerPriceIndexQueryParams(country="USA") + assert q.country == "USA" + + def test_country_accepts_country_label(self): + """A spelled-out label is resolved to its ISO3 code (covers line 344).""" + q = ImfConsumerPriceIndexQueryParams(country="united_states") + assert q.country == "USA" + + def test_country_rejects_unknown(self): + """Unknown country tokens raise ``ValueError`` (covers lines 346-348).""" + with pytest.raises(ValueError, match="not a valid IMF country code"): + ImfConsumerPriceIndexQueryParams(country="atlantis") + + def test_expenditure_accepts_friendly_name(self): + """A friendly expenditure name passes validation.""" + q = ImfConsumerPriceIndexQueryParams( + country="USA", expenditure="food_non_alcoholic_beverages" + ) + assert q.expenditure == "food_non_alcoholic_beverages" + + def test_expenditure_accepts_api_code(self): + """An IMF expenditure code passes validation.""" + q = ImfConsumerPriceIndexQueryParams(country="USA", expenditure="CP01") + assert q.expenditure == "cp01" + + def test_expenditure_rejects_unknown(self): + """Unknown expenditure raises ``ValueError`` (covers line 363).""" + with pytest.raises(ValueError, match="not a valid choice"): + ImfConsumerPriceIndexQueryParams(country="USA", expenditure="bogus") + + +class TestImfConsumerPriceIndexFetcherTransformQuery: + """Tests for ``transform_query``.""" + + def test_transform_query_builds_params(self): + """``transform_query`` returns a model with the right defaults.""" + q = ImfConsumerPriceIndexFetcher.transform_query({"country": "USA"}) + assert isinstance(q, ImfConsumerPriceIndexQueryParams) + assert q.country == "USA" + + +class TestImfConsumerPriceIndexFetcherAextractData: + """Tests for ``aextract_data`` branches.""" + + @pytest.mark.asyncio + async def test_aextract_uses_limit_and_default_params(self, patched_query_builder): + """``limit`` flows through as ``lastNObservations`` (covers line 456).""" + q = ImfConsumerPriceIndexFetcher.transform_query( + { + "country": "USA", + "frequency": "monthly", + "transform": "index", + "expenditure": "total", + "limit": 5, + } + ) + out = await ImfConsumerPriceIndexFetcher.aextract_data(q, None) + assert out == {"data": [], "metadata": {}} + captured = patched_query_builder.instances[0].calls[0] + assert captured["dataflow"] == "CPI" + assert captured["lastNObservations"] == 5 + assert captured["COUNTRY"] == "USA" + assert captured["INDEX_TYPE"] == "CPI" + assert captured["FREQUENCY"] == "M" + + @pytest.mark.asyncio + async def test_aextract_all_countries_and_all_expenditures( + self, patched_query_builder + ): + """``all``/``*`` wildcard usage expands to ``*`` in the request.""" + q = ImfConsumerPriceIndexFetcher.transform_query( + { + "country": "USA,*", + "expenditure": "all,total", + "harmonized": True, + "frequency": "annual", + "transform": "index", + } + ) + await ImfConsumerPriceIndexFetcher.aextract_data(q, None) + captured = patched_query_builder.instances[0].calls[0] + assert captured["COUNTRY"] == "*" + assert captured["COICOP_1999"] == "*" + assert captured["INDEX_TYPE"] == "HICP" + + @pytest.mark.asyncio + async def test_aextract_translates_value_error_via_dim_param_maps( + self, patched_query_builder, monkeypatch + ): + """``ValueError`` messages are rewritten using dim/transform/freq/expenditure/country/index_type maps.""" + + def boom(self, **kwargs): + raise ValueError( + "Bad dim 'COUNTRY' and 'FREQUENCY' " + "and \"COUNTRY\" with code 'M' and 'IX' " + "and exp 'CP01' index 'CPI' or 'HICP' and country 'USA'" + ) + + monkeypatch.setattr(_StubQueryBuilder, "fetch_data", boom) + q = ImfConsumerPriceIndexFetcher.transform_query( + {"country": "USA", "frequency": "monthly", "transform": "index"} + ) + with pytest.raises(OpenBBError) as exc: + await ImfConsumerPriceIndexFetcher.aextract_data(q, None) + msg = str(exc.value) + assert "'country'" in msg + assert "'frequency'" in msg + assert "'monthly'" in msg + assert "'index'" in msg + assert "'food_non_alcoholic_beverages'" in msg + assert "'False'" in msg + assert "'True'" in msg + assert "'united_states'" in msg + + @pytest.mark.asyncio + async def test_aextract_wraps_openbb_error( + self, patched_query_builder, monkeypatch + ): + """An ``OpenBBError`` from the builder is re-raised wrapped (covers lines 485-486).""" + + def boom(self, **kwargs): + raise OpenBBError("inner") + + monkeypatch.setattr(_StubQueryBuilder, "fetch_data", boom) + q = ImfConsumerPriceIndexFetcher.transform_query( + {"country": "USA", "frequency": "monthly", "transform": "index"} + ) + with pytest.raises(OpenBBError): + await ImfConsumerPriceIndexFetcher.aextract_data(q, None) + + +class TestImfConsumerPriceIndexFetcherTransformData: + """Tests for ``transform_data``.""" + + def _query(self, **overrides: Any) -> ImfConsumerPriceIndexQueryParams: + """Build a minimal query with overrides.""" + params: dict[str, Any] = {"country": "USA"} + params.update(overrides) + return ImfConsumerPriceIndexFetcher.transform_query(params) + + def test_transform_data_no_rows_raises(self): + """Empty ``data`` payload raises ``OpenBBError`` (covers line 510).""" + q = self._query() + with pytest.raises(OpenBBError, match="No data returned"): + ImfConsumerPriceIndexFetcher.transform_data(q, {"data": [], "metadata": {}}) + + def test_transform_data_filters_by_start_date(self): + """A row before ``start_date`` is skipped (covers line 519).""" + q = self._query(start_date=date(2024, 1, 1)) + rows = [ + { + "TIME_PERIOD": "2023-01-01", + "OBS_VALUE": 100.0, + "COUNTRY": "United States", + "country_code": "USA", + "INDEX_TYPE": "CPI", + "COICOP_1999": "_T", + "COICOP_1999_code": "_T", + "TYPE_OF_TRANSFORMATION": "Index", + "FREQUENCY": "M", + "UNIT_MULT": 1, + "series_id": "IMF_STA_CPI_USA_T", + }, + { + "TIME_PERIOD": "2024-06-01", + "OBS_VALUE": 110.0, + "COUNTRY": "United States", + "country_code": "USA", + "INDEX_TYPE": "CPI", + "COICOP_1999": "_T", + "COICOP_1999_code": "_T", + "TYPE_OF_TRANSFORMATION": "Year-over-year, Percent", + "FREQUENCY": "M", + "UNIT_MULT": 1, + "series_id": "IMF_STA_CPI_USA_T", + }, + ] + result = ImfConsumerPriceIndexFetcher.transform_data( + q, {"data": rows, "metadata": {}} + ) + assert len(result.result) == 1 + assert isinstance(result.result[0], ImfConsumerPriceIndexData) + assert result.result[0].value == pytest.approx(1.1) + assert result.result[0].unit_multiplier == 100 + + def test_transform_data_filters_by_end_date(self): + """A row after ``end_date`` is skipped (covers line 525).""" + q = self._query(end_date=date(2024, 1, 31)) + rows = [ + { + "TIME_PERIOD": "2024-06-01", + "OBS_VALUE": 110.0, + "COUNTRY": "United States", + "country_code": "USA", + "INDEX_TYPE": "CPI", + "COICOP_1999": "_T", + "COICOP_1999_code": "_T", + "TYPE_OF_TRANSFORMATION": "Index", + "FREQUENCY": "M", + "UNIT_MULT": 1, + "series_id": "IMF_STA_CPI_USA_T", + }, + { + "TIME_PERIOD": "2024-01-01", + "OBS_VALUE": 100.0, + "COUNTRY": "United States", + "country_code": "USA", + "INDEX_TYPE": "CPI", + "COICOP_1999": "_T", + "COICOP_1999_code": "_T", + "TYPE_OF_TRANSFORMATION": "Index", + "FREQUENCY": "M", + "UNIT_MULT": 1, + "series_id": "IMF_STA_CPI_USA_T", + }, + ] + result = ImfConsumerPriceIndexFetcher.transform_data( + q, {"data": rows, "metadata": {}} + ) + assert len(result.result) == 1 + assert result.result[0].date.isoformat().startswith("2024-01") + + def test_transform_data_promotes_hicp_metadata_block(self): + """When ``IMF_STA_CPI_CPI`` is missing the HICP block is used.""" + q = self._query() + metadata = { + "dataset": {"foo": "bar"}, + "IMF_STA_CPI_HICP": {"indicator": "HICP", "description": "HICP desc"}, + } + rows = [ + { + "TIME_PERIOD": "2024-01-01", + "OBS_VALUE": 100.0, + "COUNTRY": "United States", + "country_code": "USA", + "INDEX_TYPE": "HICP", + "COICOP_1999": "_T", + "COICOP_1999_code": "_T", + "TYPE_OF_TRANSFORMATION": "Index", + "FREQUENCY": "M", + "UNIT_MULT": 1, + "series_id": "IMF_STA_CPI_USA_T", + } + ] + result = ImfConsumerPriceIndexFetcher.transform_data( + q, {"data": rows, "metadata": metadata} + ) + assert result.metadata["dataset"]["index_type"] == "HICP" + assert result.metadata["dataset"]["index_description"] == "HICP desc" diff --git a/openbb_platform/providers/imf/tests/test_direction_of_trade_model.py b/openbb_platform/providers/imf/tests/test_direction_of_trade_model.py new file mode 100644 index 00000000000..0da1745539d --- /dev/null +++ b/openbb_platform/providers/imf/tests/test_direction_of_trade_model.py @@ -0,0 +1,131 @@ +"""Unit tests for the IMF Direction Of Trade model and fetcher.""" + +# ruff: noqa: I001 + +from datetime import date +from typing import Any +from unittest.mock import patch + +import pytest +from openbb_core.app.model.abstract.error import OpenBBError +from openbb_core.provider.utils.errors import EmptyDataError + +from openbb_imf.models.direction_of_trade import ( + ImfDirectionOfTradeData, + ImfDirectionOfTradeFetcher, + ImfDirectionOfTradeQueryParams, +) + + +class TestImfDirectionOfTradeQueryParams: + """Tests for ``ImfDirectionOfTradeQueryParams._validate_country_fields``.""" + + def test_empty_country_raises(self): + """Empty input raises ``ValueError`` (covers line 57).""" + with pytest.raises(ValueError, match="Required parameter for IMF provider"): + ImfDirectionOfTradeQueryParams(country="", counterpart="world") + + def test_all_keyword_returns_wildcard(self): + """The ``all`` keyword resolves to a ``*`` wildcard (covers line 60).""" + q = ImfDirectionOfTradeQueryParams(country="all", counterpart="world") + assert q.country == "*" + + def test_all_with_other_countries_raises(self): + """An ``all`` token mixed with other tokens raises (covers lines 74-77).""" + with pytest.raises( + ValueError, match="'all' cannot be used with other country codes" + ): + ImfDirectionOfTradeQueryParams(country="USA,all", counterpart="world") + + def test_all_in_list_returns_wildcard(self): + """A single-element ``[all]`` list returns ``*`` (covers line 78).""" + q = ImfDirectionOfTradeQueryParams(country=["all"], counterpart="world") + assert q.country == "*" + + def test_resolves_multi_country_string(self): + """Multiple country codes resolve via ``resolve_country_input``.""" + q = ImfDirectionOfTradeQueryParams(country="USA,JPN", counterpart="world") + assert q.country == "USA,JPN" + + +class TestImfDirectionOfTradeFetcher: + """Tests for ``ImfDirectionOfTradeFetcher``.""" + + def test_transform_query(self): + """``transform_query`` builds the query model.""" + q = ImfDirectionOfTradeFetcher.transform_query( + {"country": "USA", "counterpart": "world"} + ) + assert isinstance(q, ImfDirectionOfTradeQueryParams) + + @pytest.mark.asyncio + async def test_aextract_data_passes_limit_through_kwargs(self): + """``limit`` is forwarded as ``lastNObservations`` (covers line 147).""" + q = ImfDirectionOfTradeFetcher.transform_query( + { + "country": "USA", + "counterpart": "world", + "frequency": "annual", + "direction": "exports", + "limit": 3, + "start_date": date(2023, 1, 1), + "end_date": date(2024, 1, 1), + } + ) + recorded: dict[str, Any] = {} + + def fake_imts_query(**kwargs: Any) -> dict[str, Any]: + recorded.update(kwargs) + return {"data": [], "metadata": {}} + + with patch( + "openbb_imf.utils.dot_helpers.imts_query", side_effect=fake_imts_query + ): + await ImfDirectionOfTradeFetcher.aextract_data(q, None) + assert recorded["lastNObservations"] == 3 + assert recorded["start_date"] == "2023-01-01" + assert recorded["end_date"] == "2024-01-01" + assert recorded["freq"] == "A" + + @pytest.mark.asyncio + async def test_aextract_data_wraps_value_error(self): + """A ``ValueError`` from ``imts_query`` is wrapped in ``OpenBBError`` (covers lines 163-164).""" + q = ImfDirectionOfTradeFetcher.transform_query( + {"country": "USA", "counterpart": "world"} + ) + with patch( + "openbb_imf.utils.dot_helpers.imts_query", + side_effect=ValueError("boom"), + ): + with pytest.raises(OpenBBError): + await ImfDirectionOfTradeFetcher.aextract_data(q, None) + + def test_transform_data_empty_raises(self): + """An empty data payload raises ``EmptyDataError`` (covers line 177).""" + q = ImfDirectionOfTradeFetcher.transform_query( + {"country": "USA", "counterpart": "world"} + ) + with pytest.raises(EmptyDataError): + ImfDirectionOfTradeFetcher.transform_data(q, {"data": [], "metadata": {}}) + + def test_transform_data_aliases_to_model(self): + """Rows pass through the alias map to ``ImfDirectionOfTradeData``.""" + q = ImfDirectionOfTradeFetcher.transform_query( + {"country": "USA", "counterpart": "world"} + ) + record = { + "TIME_PERIOD": "2024-01-01", + "COUNTRY": "United States", + "country_code": "USA", + "COUNTERPART_COUNTRY": "World", + "counterpart_country_code": "G001", + "OBS_VALUE": 123.0, + "series_id": "IMF_STA_IMTS_X", + "FREQUENCY": "A", + } + out = ImfDirectionOfTradeFetcher.transform_data( + q, {"data": [record], "metadata": {"foo": 1}} + ) + assert isinstance(out.result[0], ImfDirectionOfTradeData) + assert out.result[0].symbol == "IMTS::X" + assert out.metadata == {"foo": 1} diff --git a/openbb_platform/providers/imf/tests/test_dot_helpers.py b/openbb_platform/providers/imf/tests/test_dot_helpers.py new file mode 100644 index 00000000000..52d2bc00671 --- /dev/null +++ b/openbb_platform/providers/imf/tests/test_dot_helpers.py @@ -0,0 +1,325 @@ +"""Tests for openbb_imf.utils.dot_helpers.""" + +# ruff: noqa: I001 + +from unittest.mock import MagicMock, patch + +import pytest + + +COUNTRY_FIXTURE = [ + {"value": "USA", "label": "United States"}, + {"value": "GBR", "label": "United Kingdom"}, + {"value": "DEU", "label": "Germany"}, + {"value": "G001", "label": "World"}, + {"value": "G163", "label": "Euro Area"}, + {"value": "GX170", "label": "Europe"}, + {"value": "G7", "label": "Group of Seven"}, + {"value": "TX1", "label": "Total Exports"}, + {"value": "TX2", "label": "Other TX"}, +] + + +@pytest.fixture +def _patch_metadata(monkeypatch): + """Patch ``ImfMetadata().get_dataflow_parameters`` for IMTS country data.""" + + class FakeMeta: + def get_dataflow_parameters(self, _dataflow): + return {"COUNTRY": list(COUNTRY_FIXTURE)} + + monkeypatch.setattr("openbb_imf.utils.metadata.ImfMetadata", FakeMeta) + + +class TestLoadCountryChoices: + """Tests for load_country_choices.""" + + def test_groups_split_and_sorted(self, _patch_metadata): + """Plain countries, G-regions, and TX groups appear in that order.""" + from openbb_imf.utils.dot_helpers import load_country_choices + + result = load_country_choices() + values = [item["value"] for item in result] + + # plain country block first + plain_values = {"USA", "GBR", "DEU"} + plain_block_idx = [i for i, v in enumerate(values) if v in plain_values] + # G regions next + g_block_idx = [ + i for i, v in enumerate(values) if v in {"G001", "G163", "GX170", "G7"} + ] + # TX groups last + tx_block_idx = [i for i, v in enumerate(values) if v in {"TX1", "TX2"}] + + assert max(plain_block_idx) < min(g_block_idx) + assert max(g_block_idx) < min(tx_block_idx) + + +class TestListCountryChoices: + """Tests for list_country_choices.""" + + def test_returns_codes(self, _patch_metadata): + """Returns just the value codes.""" + from openbb_imf.utils.dot_helpers import list_country_choices + + result = list_country_choices() + assert "USA" in result + assert "G001" in result + assert all(isinstance(c, str) for c in result) + + +class TestLabelToCodeMap: + """Tests for get_label_to_code_map.""" + + def test_normalized_labels(self, _patch_metadata): + """Normalized label maps to its code.""" + from openbb_imf.utils.dot_helpers import get_label_to_code_map + + result = get_label_to_code_map() + assert result["united_states"] == "USA" + assert result["united_kingdom"] == "GBR" + assert result["world"] == "G001" + + +class TestCodeToLabelMap: + """Tests for get_code_to_label_map.""" + + def test_codes_map_to_labels(self, _patch_metadata): + """Codes map to normalized labels.""" + from openbb_imf.utils.dot_helpers import get_code_to_label_map + + result = get_code_to_label_map() + assert result["USA"] == "united_states" + assert result["G001"] == "world" + + +class TestResolveCountryInput: + """Tests for resolve_country_input.""" + + def test_empty_raises(self): + """Empty input raises ValueError.""" + from openbb_imf.utils.dot_helpers import resolve_country_input + + with pytest.raises(ValueError, match="cannot be empty"): + resolve_country_input("") + + def test_all_alias(self): + """'all' / '*' return '*'.""" + from openbb_imf.utils.dot_helpers import resolve_country_input + + assert resolve_country_input("all") == "*" + assert resolve_country_input("*") == "*" + + def test_common_aliases(self): + """Common aliases resolve to fixed codes.""" + from openbb_imf.utils.dot_helpers import resolve_country_input + + assert resolve_country_input("world") == "G001" + assert resolve_country_input("eurozone") == "G163" + assert resolve_country_input("euro_area") == "G163" + assert resolve_country_input("Euro Area") == "G163" + assert resolve_country_input("EU") == "G998" + assert resolve_country_input("european_union") == "G998" + assert resolve_country_input("europe") == "GX170" + + def test_iso_code(self, _patch_metadata): + """Valid ISO-like code passes through.""" + from openbb_imf.utils.dot_helpers import resolve_country_input + + assert resolve_country_input("USA") == "USA" + assert resolve_country_input("usa") == "USA" + + def test_resolve_by_label(self, _patch_metadata): + """Country label resolves to code.""" + from openbb_imf.utils.dot_helpers import resolve_country_input + + assert resolve_country_input("united_states") == "USA" + assert resolve_country_input("United States") == "USA" + + def test_unknown_raises(self, _patch_metadata): + """Unknown country raises ValueError with hint.""" + from openbb_imf.utils.dot_helpers import resolve_country_input + + with pytest.raises(ValueError, match="not a valid IMF country code"): + resolve_country_input("Atlantis") + + +class TestImtsQuery: + """Tests for imts_query.""" + + def _make_qb(self, **overrides): + """Build a mocked ImfQueryBuilder context manager.""" + qb = MagicMock() + qb.metadata.get_dataflow_parameters.return_value = { + "COUNTRY": [ + {"value": "USA"}, + {"value": "G001"}, + {"value": "GBR"}, + ], + "COUNTERPART_COUNTRY": [ + {"value": "G001"}, + {"value": "USA"}, + ], + } + qb.fetch_data.return_value = {"data": [], "metadata": {}} + for k, v in overrides.items(): + setattr(qb, k, v) + return qb + + def test_empty_country_raises(self): + """Empty country/counterpart raises ValueError.""" + from openbb_imf.utils.dot_helpers import imts_query + + with pytest.raises(ValueError, match="cannot be empty"): + imts_query(country="", counterpart="G001", indicator="*") + + def test_invalid_frequency_raises(self): + """Frequency not in A/Q/M raises.""" + from openbb_imf.utils.dot_helpers import imts_query + + with pytest.raises(ValueError, match="Frequency must be one of"): + imts_query(country="USA", counterpart="G001", indicator="*", freq="X") + + def test_simple_query_dispatches(self): + """Valid inputs dispatch to query_builder.fetch_data.""" + from openbb_imf.utils import dot_helpers + + qb = self._make_qb() + with patch("openbb_imf.utils.query_builder.ImfQueryBuilder", return_value=qb): + out = dot_helpers.imts_query( + country="USA", + counterpart="G001", + indicator="X", + freq="annual", + ) + assert out == {"data": [], "metadata": {}} + kwargs = qb.fetch_data.call_args.kwargs + assert kwargs["dataflow"] == "IMTS" + assert kwargs["FREQUENCY"] == "A" + assert kwargs["COUNTRY"] == "USA" + assert kwargs["COUNTERPART_COUNTRY"] == "G001" + assert kwargs["INDICATOR"] == "X" + + def test_comma_separated_country(self): + """Comma-separated country string becomes a list.""" + from openbb_imf.utils import dot_helpers + + qb = self._make_qb() + with patch("openbb_imf.utils.query_builder.ImfQueryBuilder", return_value=qb): + dot_helpers.imts_query( + country="USA,GBR", + counterpart="G001", + indicator="*", + freq="A", + ) + kwargs = qb.fetch_data.call_args.kwargs + assert kwargs["COUNTRY"] == ["USA", "GBR"] + + def test_wildcard_counterpart(self): + """'*' counterpart passes through.""" + from openbb_imf.utils import dot_helpers + + qb = self._make_qb() + with patch("openbb_imf.utils.query_builder.ImfQueryBuilder", return_value=qb): + dot_helpers.imts_query( + country="USA", + counterpart="*", + indicator="*", + freq="A", + ) + kwargs = qb.fetch_data.call_args.kwargs + assert kwargs["COUNTERPART_COUNTRY"] == "*" + + def test_invalid_country_raises(self): + """Invalid country code raises ValueError listing offending codes.""" + from openbb_imf.utils import dot_helpers + + qb = self._make_qb() + with patch("openbb_imf.utils.query_builder.ImfQueryBuilder", return_value=qb): + with pytest.raises(ValueError, match="Invalid country"): + dot_helpers.imts_query( + country="ZZZ", + counterpart="G001", + indicator="*", + ) + + def test_invalid_counterpart_raises(self): + """Invalid counterpart code raises ValueError.""" + from openbb_imf.utils import dot_helpers + + qb = self._make_qb() + with patch("openbb_imf.utils.query_builder.ImfQueryBuilder", return_value=qb): + with pytest.raises(ValueError, match="Invalid counterpart"): + dot_helpers.imts_query( + country="USA", + counterpart="ZZZ", + indicator="*", + ) + + def test_wildcard_in_country_list(self): + """A '*' inside a list collapses to wildcard.""" + from openbb_imf.utils import dot_helpers + + qb = self._make_qb() + with patch("openbb_imf.utils.query_builder.ImfQueryBuilder", return_value=qb): + dot_helpers.imts_query( + country=["USA", "*"], + counterpart=["G001"], + indicator="*", + ) + kwargs = qb.fetch_data.call_args.kwargs + assert kwargs["COUNTRY"] == "*" + + def test_no_valid_values_passthrough(self): + """When params lack the dimension, selection passes through unchanged.""" + from openbb_imf.utils import dot_helpers + + qb = self._make_qb() + qb.metadata.get_dataflow_parameters.return_value = {} + with patch("openbb_imf.utils.query_builder.ImfQueryBuilder", return_value=qb): + dot_helpers.imts_query(country="ZZZ", counterpart="QQQ", indicator="*") + kwargs = qb.fetch_data.call_args.kwargs + assert kwargs["COUNTRY"] == "ZZZ" + assert kwargs["COUNTERPART_COUNTRY"] == "QQQ" + + def test_indicator_comma_list(self): + """Comma-separated indicator splits to list.""" + from openbb_imf.utils import dot_helpers + + qb = self._make_qb() + with patch("openbb_imf.utils.query_builder.ImfQueryBuilder", return_value=qb): + dot_helpers.imts_query( + country="USA", + counterpart="G001", + indicator="X, M", + ) + kwargs = qb.fetch_data.call_args.kwargs + assert kwargs["INDICATOR"] == ["X", "M"] + + def test_single_country_list(self): + """Single-item list with one country returns just the string.""" + from openbb_imf.utils import dot_helpers + + qb = self._make_qb() + with patch("openbb_imf.utils.query_builder.ImfQueryBuilder", return_value=qb): + dot_helpers.imts_query( + country=["USA"], + counterpart=["G001"], + indicator="*", + ) + kwargs = qb.fetch_data.call_args.kwargs + assert kwargs["COUNTRY"] == "USA" + assert kwargs["COUNTERPART_COUNTRY"] == "G001" + + def test_counterpart_country_fallback(self): + """Missing COUNTERPART_COUNTRY falls back to COUNTRY param values.""" + from openbb_imf.utils import dot_helpers + + qb = self._make_qb() + qb.metadata.get_dataflow_parameters.return_value = { + "COUNTRY": [{"value": "USA"}, {"value": "G001"}], + } + with patch("openbb_imf.utils.query_builder.ImfQueryBuilder", return_value=qb): + dot_helpers.imts_query(country="USA", counterpart="G001", indicator="*") + kwargs = qb.fetch_data.call_args.kwargs + assert kwargs["COUNTERPART_COUNTRY"] == "G001" diff --git a/openbb_platform/providers/imf/tests/test_economic_indicators_model.py b/openbb_platform/providers/imf/tests/test_economic_indicators_model.py new file mode 100644 index 00000000000..0b24b0c30c0 --- /dev/null +++ b/openbb_platform/providers/imf/tests/test_economic_indicators_model.py @@ -0,0 +1,1292 @@ +"""Unit tests for the IMF Economic Indicators model and fetcher.""" + +# ruff: noqa: I001 + +from datetime import date +from typing import Any +from unittest.mock import patch + +import pytest +from openbb_core.app.model.abstract.error import OpenBBError +from openbb_core.provider.utils.errors import EmptyDataError + +from openbb_imf.models.economic_indicators import ( + ImfEconomicIndicatorsData, + ImfEconomicIndicatorsFetcher, + ImfEconomicIndicatorsQueryParams, +) + + +class _StubMetadata: + """Lightweight ``ImfMetadata`` stand-in for symbol/constraint validation.""" + + def __init__( + self, + codelist: dict[str, dict[str, str]] | None = None, + dataflows: dict[str, dict[str, Any]] | None = None, + datastructures: dict[str, dict[str, Any]] | None = None, + hierarchies: dict[str, list[dict[str, Any]]] | None = None, + constraints: dict[tuple, dict[str, Any]] | None = None, + dataflow_parameters: dict[str, dict[str, list[dict[str, str]]]] | None = None, + ): + self._codelist_cache = codelist or {"CL_COUNTRY": {"USA": "United States"}} + self.dataflows = dataflows or {} + self.datastructures = datastructures or {} + self._hierarchies = hierarchies or {} + self._constraints = constraints or {} + self._dataflow_parameters = dataflow_parameters or {} + self.hierarchies_calls: list[str] = [] + self.constraints_calls: list[dict[str, Any]] = [] + + def get_dataflow_hierarchies(self, dataflow_id: str) -> list[dict[str, Any]]: + """Return canned hierarchies for the dataflow.""" + self.hierarchies_calls.append(dataflow_id) + return self._hierarchies.get(dataflow_id, []) + + def get_dataflow_parameters( + self, dataflow_id: str + ) -> dict[str, list[dict[str, str]]]: + """Return canned dimension parameters for the dataflow.""" + return self._dataflow_parameters.get(dataflow_id, {}) + + def get_available_constraints( + self, + dataflow_id: str, + key: str, + component_id: str | None = None, + **kwargs: Any, + ) -> dict[str, Any]: + """Return canned constraint payload for a dataflow/key/component combo.""" + self.constraints_calls.append( + {"dataflow_id": dataflow_id, "key": key, "component_id": component_id} + ) + return self._constraints.get((dataflow_id, component_id), {"key_values": []}) + + +def _patch_metadata(monkeypatch: pytest.MonkeyPatch, stub: _StubMetadata) -> None: + """Replace ``ImfMetadata`` with a callable returning the stub.""" + monkeypatch.setattr( + "openbb_imf.utils.metadata.ImfMetadata", + lambda: stub, + ) + + +def _patch_detect_transform_dimension( + monkeypatch: pytest.MonkeyPatch, + transform_dim: str | None = None, + unit_dim: str | None = None, + transform_lookup: dict[str, str] | None = None, + unit_lookup: dict[str, str] | None = None, +) -> None: + """Force ``detect_transform_dimension`` to return a fixed shape.""" + monkeypatch.setattr( + "openbb_imf.models.economic_indicators.detect_transform_dimension", + lambda _dataflow: ( + transform_dim, + unit_dim, + transform_lookup or {}, + unit_lookup or {}, + ), + ) + + +def _make_query( + monkeypatch: pytest.MonkeyPatch, + *, + symbol: str = "WEO::NGDP_RPCH", + country: str = "USA", + metadata_stub: _StubMetadata | None = None, + **overrides: Any, +) -> ImfEconomicIndicatorsQueryParams: + """Construct a query with a metadata stub patched in for the lifetime of the call.""" + if metadata_stub is None: + metadata_stub = _StubMetadata() + _patch_metadata(monkeypatch, metadata_stub) + params: dict[str, Any] = {"symbol": symbol, "country": country} + params.update(overrides) + return ImfEconomicIndicatorsQueryParams(**params) + + +class TestValidateCountry: + """Tests for ``ImfEconomicIndicatorsQueryParams.validate_country``.""" + + def test_empty_string_returns_none(self, monkeypatch): + """An empty string short-circuits to ``None`` (covers line 178).""" + stub = _StubMetadata() + _patch_metadata(monkeypatch, stub) + with pytest.raises(ValueError, match="Country is required"): + ImfEconomicIndicatorsQueryParams(symbol="WEO::X", country="") + + def test_only_commas_returns_none(self, monkeypatch): + """A string of only commas yields an empty items list (covers line 183).""" + stub = _StubMetadata() + _patch_metadata(monkeypatch, stub) + with pytest.raises(ValueError, match="Country is required"): + ImfEconomicIndicatorsQueryParams(symbol="WEO::X", country=",,") + + def test_single_wildcard(self, monkeypatch): + """A single ``*`` value short-circuits to wildcard (covers line 186).""" + q = _make_query(monkeypatch, country="*") + assert q.country == "*" + + def test_resolves_name_to_code_via_lower(self, monkeypatch): + """Snake-case names resolve via the lowercase mapping (covers line 216).""" + stub = _StubMetadata( + codelist={"CL_COUNTRY": {"USA": "United States", "DEU": "Germany"}} + ) + _patch_metadata(monkeypatch, stub) + q = ImfEconomicIndicatorsQueryParams( + symbol="WEO::NGDP_RPCH", country="united_states" + ) + assert q.country == "USA" + + def test_falls_back_to_item_upper(self, monkeypatch): + """An unknown token falls through to ``item_upper`` (covers line 220).""" + stub = _StubMetadata(codelist={"CL_COUNTRY": {}}) + _patch_metadata(monkeypatch, stub) + q = ImfEconomicIndicatorsQueryParams(symbol="WEO::NGDP_RPCH", country="zzz") + assert q.country == "ZZZ" + + def test_wildcard_overrides_other_items(self, monkeypatch): + """A second-item wildcard takes precedence (covers line 211).""" + stub = _StubMetadata(codelist={"CL_COUNTRY": {"USA": "United States"}}) + _patch_metadata(monkeypatch, stub) + q = ImfEconomicIndicatorsQueryParams(symbol="WEO::NGDP_RPCH", country="USA,*") + assert q.country == "*" + + def test_resolves_lowercase_name_branch(self, monkeypatch): + """A name with spaces takes the ``item.lower()`` lookup branch (covers 217-218). + + Notes + ----- + ``item_lower`` substitutes ``_`` for spaces but leaves commas; passing the + original label exercises the case where the snake form mismatches but + the lowercase original matches. + """ + stub = _StubMetadata( + codelist={"CL_COUNTRY": {"CIV": "Cรดte d'Ivoire"}}, + ) + _patch_metadata(monkeypatch, stub) + q = ImfEconomicIndicatorsQueryParams( + symbol="WEO::NGDP_RPCH", country="Cรดte d'Ivoire" + ) + assert q.country == "CIV" + + +class TestParseAndValidateSymbols: + """Tests for ``parse_and_validate_symbols``.""" + + def test_missing_symbol_raises(self, monkeypatch): + """A blank symbol raises a ``ValueError`` (covers line 230).""" + stub = _StubMetadata() + _patch_metadata(monkeypatch, stub) + with pytest.raises(ValueError, match="symbol is required"): + ImfEconomicIndicatorsQueryParams(symbol=None, country="USA") + + def test_dimension_values_extract_country(self, monkeypatch): + """A ``COUNTRY`` dimension value populates ``country`` (covers 244-261).""" + stub = _StubMetadata() + _patch_metadata(monkeypatch, stub) + q = ImfEconomicIndicatorsQueryParams( + symbol="WEO::NGDP_RPCH", + country=None, + dimension_values=[ + "COUNTRY:USA", + "FREQ:A", + "UNIT:USD", + "FOO:BAR", + "noColonHere", + ], + ) + assert q.country == "USA" + assert q.frequency == "A" + assert q.transform == "USD" + assert q.dimension_values == ["FOO:BAR", "noColonHere"] + + def test_missing_country_in_dim_values_raises(self, monkeypatch): + """If no country can be inferred, the validator raises (covers 268).""" + stub = _StubMetadata() + _patch_metadata(monkeypatch, stub) + with pytest.raises(ValueError, match="Country is required"): + ImfEconomicIndicatorsQueryParams( + symbol="WEO::NGDP_RPCH", + country=None, + dimension_values=["FOO:BAR"], + ) + + def test_unknown_dimension_id_raises(self, monkeypatch): + """An unknown dim id surfaces a clear error listing the valid extras.""" + stub = _StubMetadata( + dataflows={ + "CTOT": { + "id": "CTOT", + "structureRef": {"id": "DSD_CTOT"}, + } + }, + datastructures={ + "DSD_CTOT": { + "dimensions": [ + {"id": "COUNTRY", "position": "0"}, + {"id": "INDICATOR", "position": "1"}, + {"id": "WGT_TYPE", "position": "2"}, + {"id": "FREQUENCY", "position": "3"}, + ] + } + }, + ) + _patch_metadata(monkeypatch, stub) + with pytest.raises( + ValueError, + match=( + r"Unknown dimension 'UNIT_MEASUREMENT' for dataflow 'CTOT'\. " + r"Valid extra dimensions: WGT_TYPE\." + ), + ): + ImfEconomicIndicatorsQueryParams( + symbol="CTOT::CEPI_CTOTX_GDP", + country="AUS", + dimension_values=["UNIT_MEASUREMENT:FWI"], + ) + + def test_known_extra_dimension_accepted(self, monkeypatch): + """A real extra dim (``WGT_TYPE``) passes through to ``dimension_values``.""" + stub = _StubMetadata( + dataflows={ + "CTOT": { + "id": "CTOT", + "structureRef": {"id": "DSD_CTOT"}, + } + }, + datastructures={ + "DSD_CTOT": { + "dimensions": [ + {"id": "COUNTRY", "position": "0"}, + {"id": "INDICATOR", "position": "1"}, + {"id": "WGT_TYPE", "position": "2"}, + {"id": "FREQUENCY", "position": "3"}, + ] + } + }, + ) + _patch_metadata(monkeypatch, stub) + q = ImfEconomicIndicatorsQueryParams( + symbol="CTOT::CEPI_CTOTX_GDP", + country="AUS", + dimension_values=["WGT_TYPE:FWI"], + ) + assert q.dimension_values == ["WGT_TYPE:FWI"] + + def test_dim_validation_skips_entries_without_colon(self, monkeypatch): + """A ``dimension_values`` token without ``:`` is silently kept.""" + stub = _StubMetadata( + dataflows={"CTOT": {"id": "CTOT", "structureRef": {"id": "DSD_CTOT"}}}, + datastructures={ + "DSD_CTOT": { + "dimensions": [ + {"id": "COUNTRY", "position": "0"}, + {"id": "INDICATOR", "position": "1"}, + {"id": "WGT_TYPE", "position": "2"}, + ] + } + }, + ) + _patch_metadata(monkeypatch, stub) + q = ImfEconomicIndicatorsQueryParams( + symbol="CTOT::CEPI_CTOTX_GDP", + country="AUS", + dimension_values=["WGT_TYPE:FWI", "noColonHere"], + ) + assert q.dimension_values == ["WGT_TYPE:FWI", "noColonHere"] + + def test_invalid_symbol_format_raises(self, monkeypatch): + """Missing ``::`` raises (covers line 280).""" + stub = _StubMetadata() + _patch_metadata(monkeypatch, stub) + with pytest.raises(ValueError, match="Expected 'dataflow::identifier'"): + ImfEconomicIndicatorsQueryParams(symbol="WEONGDPRPCH", country="USA") + + def test_empty_identifier_raises(self, monkeypatch): + """An empty identifier raises (covers line 290).""" + stub = _StubMetadata() + _patch_metadata(monkeypatch, stub) + with pytest.raises(ValueError, match="Identifier cannot be empty"): + ImfEconomicIndicatorsQueryParams(symbol="WEO::", country="USA") + + def test_h_prefix_marks_table(self, monkeypatch): + """Identifiers starting with ``H_`` are treated as tables (covers 299, 326-330).""" + stub = _StubMetadata() + _patch_metadata(monkeypatch, stub) + q = ImfEconomicIndicatorsQueryParams( + symbol="BOP::H_BOP_AGG_STANDARD_PRESENTATION", country="USA" + ) + assert q._is_table is True + assert q._dataflow == "BOP" + assert q._table_id == "H_BOP_AGG_STANDARD_PRESENTATION" + + def test_hierarchy_match_marks_table(self, monkeypatch): + """An identifier that matches a hierarchy id is treated as a table (covers 306-308).""" + stub = _StubMetadata( + hierarchies={"BOP": [{"id": "MY_TABLE"}]}, + ) + _patch_metadata(monkeypatch, stub) + q = ImfEconomicIndicatorsQueryParams(symbol="BOP::MY_TABLE", country="USA") + assert q._is_table is True + assert q._table_id == "MY_TABLE" + + def test_hierarchy_lookup_exception_is_swallowed(self, monkeypatch): + """A hierarchy lookup that raises falls through to indicator mode (covers 307-308).""" + + def boom(self, dataflow_id): # noqa + raise RuntimeError("oops") + + stub = _StubMetadata( + dataflows={"WEO": {"id": "WEO", "agencyID": "IMF"}}, + ) + monkeypatch.setattr( + _StubMetadata, "get_dataflow_hierarchies", boom, raising=False + ) + _patch_metadata(monkeypatch, stub) + _patch_detect_transform_dimension(monkeypatch) + q = ImfEconomicIndicatorsQueryParams(symbol="WEO::NGDP_RPCH", country="USA") + assert q._is_table is False + + def test_mixing_tables_and_indicators_raises(self, monkeypatch): + """Mixing tables and indicators raises ``ValueError`` (covers 315-318).""" + stub = _StubMetadata() + _patch_metadata(monkeypatch, stub) + with pytest.raises(ValueError, match="Cannot mix tables and indicators"): + ImfEconomicIndicatorsQueryParams( + symbol="BOP::H_TABLE,WEO::NGDP_RPCH", country="USA" + ) + + def test_more_than_one_table_raises(self, monkeypatch): + """More than one table raises (covers line 322-324).""" + stub = _StubMetadata() + _patch_metadata(monkeypatch, stub) + with pytest.raises(ValueError, match="Only one table"): + ImfEconomicIndicatorsQueryParams(symbol="BOP::H_A,BOP::H_B", country="USA") + + def test_multiple_dataflows_indicator_mode_clears_codes(self, monkeypatch): + """Two dataflows leave ``_dataflow=None`` and ``_indicator_codes=[]`` (covers 347-348).""" + stub = _StubMetadata() + _patch_metadata(monkeypatch, stub) + q = ImfEconomicIndicatorsQueryParams( + symbol="WEO::NGDP_RPCH,IFS::FOO", country="USA" + ) + assert q._dataflow is None + assert q._indicator_codes == [] + + +class TestValidateIndicatorParams: + """Tests for ``_validate_indicator_params`` constraint enforcement.""" + + @staticmethod + def _stub_with_dataflow() -> _StubMetadata: + """Build a ``_StubMetadata`` that exposes a ``WEO`` dataflow + DSD.""" + return _StubMetadata( + dataflows={ + "WEO": { + "id": "WEO", + "agencyID": "IMF", + "structureRef": {"id": "DSD_WEO"}, + } + }, + datastructures={ + "DSD_WEO": { + "dimensions": [ + {"id": "COUNTRY", "position": "0"}, + {"id": "INDICATOR", "position": "1"}, + {"id": "FREQUENCY", "position": "2"}, + ] + } + }, + ) + + def test_no_dataflow_object_is_skipped(self, monkeypatch): + """Dataflows that aren't in ``metadata.dataflows`` are skipped (covers lines 411-412).""" + stub = _StubMetadata() # empty dataflows + _patch_metadata(monkeypatch, stub) + ImfEconomicIndicatorsQueryParams(symbol="WEO::NGDP_RPCH", country="USA") + + def test_invalid_country_raises(self, monkeypatch): + """An invalid country code raises ``ValueError`` (covers 444-458).""" + stub = self._stub_with_dataflow() + stub._constraints[("WEO", "COUNTRY")] = { + "key_values": [{"id": "COUNTRY", "values": ["USA", "JPN"]}] + } + _patch_metadata(monkeypatch, stub) + _patch_detect_transform_dimension(monkeypatch) + with pytest.raises( + ValueError, match="Invalid value\\(s\\) for dimension 'country'" + ): + ImfEconomicIndicatorsQueryParams(symbol="WEO::NGDP_RPCH", country="ZZZ") + + def test_invalid_frequency_raises(self, monkeypatch): + """An invalid frequency raises ``ValueError`` (covers 460-475).""" + stub = self._stub_with_dataflow() + stub._constraints[("WEO", "FREQUENCY")] = { + "key_values": [{"id": "FREQUENCY", "values": ["A"]}] + } + _patch_metadata(monkeypatch, stub) + _patch_detect_transform_dimension(monkeypatch) + with pytest.raises(ValueError, match="frequency"): + ImfEconomicIndicatorsQueryParams( + symbol="WEO::NGDP_RPCH", + country="USA", + frequency="quarter", + ) + + def test_invalid_transform_raises(self, monkeypatch): + """An invalid transform raises ``ValueError`` (covers 477-498).""" + stub = _StubMetadata( + dataflows={ + "BOP_AGG": { + "id": "BOP_AGG", + "agencyID": "IMF", + "structureRef": {"id": "DSD_BOP_AGG"}, + } + }, + datastructures={ + "DSD_BOP_AGG": { + "dimensions": [ + {"id": "COUNTRY", "position": "0"}, + {"id": "INDICATOR", "position": "1"}, + {"id": "FREQUENCY", "position": "2"}, + {"id": "TYPE_OF_TRANSFORMATION", "position": "3"}, + ] + } + }, + constraints={ + ("BOP_AGG", "TYPE_OF_TRANSFORMATION"): { + "key_values": [{"id": "TYPE_OF_TRANSFORMATION", "values": ["IX"]}] + } + }, + ) + _patch_metadata(monkeypatch, stub) + _patch_detect_transform_dimension( + monkeypatch, + transform_dim="TYPE_OF_TRANSFORMATION", + transform_lookup={"yoy": "YOY_PCH"}, + ) + with pytest.raises(ValueError, match="transform"): + ImfEconomicIndicatorsQueryParams( + symbol="BOP_AGG::GS_CD", + country="USA", + transform="yoy", + ) + + def test_build_key_default_star_branch(self, monkeypatch): + """An unknown dim type defaults to ``*`` in the key (covers line 392).""" + stub = _StubMetadata( + dataflows={ + "WEO": { + "id": "WEO", + "agencyID": "IMF", + "structureRef": {"id": "DSD_WEO"}, + } + }, + datastructures={ + "DSD_WEO": { + "dimensions": [ + {"id": "EXTRA", "position": "0"}, + {"id": "COUNTRY", "position": "1"}, + {"id": "INDICATOR", "position": "2"}, + {"id": "FREQUENCY", "position": "3"}, + ] + } + }, + constraints={ + ("WEO", "COUNTRY"): { + "key_values": [{"id": "COUNTRY", "values": ["USA"]}] + } + }, + ) + _patch_metadata(monkeypatch, stub) + _patch_detect_transform_dimension(monkeypatch) + ImfEconomicIndicatorsQueryParams(symbol="WEO::NGDP_RPCH", country="USA") + keys = [c["key"] for c in stub.constraints_calls] + # The EXTRA dim slot becomes "*" via the default branch (line 392). + assert any(k.startswith("*") for k in keys) + + def test_freq_dim_build_key_branch(self, monkeypatch): + """The constraint key includes resolved freq/transform parts (covers 381-388, 390).""" + stub = _StubMetadata( + dataflows={ + "BOP_AGG": { + "id": "BOP_AGG", + "agencyID": "IMF", + "structureRef": {"id": "DSD_BOP_AGG"}, + } + }, + datastructures={ + "DSD_BOP_AGG": { + "dimensions": [ + {"id": "FREQUENCY", "position": "0"}, + {"id": "TYPE_OF_TRANSFORMATION", "position": "1"}, + {"id": "INDICATOR", "position": "2"}, + {"id": "COUNTRY", "position": "3"}, + ] + } + }, + ) + _patch_metadata(monkeypatch, stub) + _patch_detect_transform_dimension( + monkeypatch, + transform_dim="TYPE_OF_TRANSFORMATION", + transform_lookup={"yoy": "YOY_PCH"}, + ) + ImfEconomicIndicatorsQueryParams( + symbol="BOP_AGG::GS_CD", + country="USA", + frequency="quarter", + transform="yoy", + ) + keys = [c["key"] for c in stub.constraints_calls] + assert any("Q" in k for k in keys) + # Building constraint key for the country target visits transform_dim โ†’ captures "yoy". + assert any("yoy" in k for k in keys) + + +class TestImfEconomicIndicatorsFetcherTransformQuery: + """Tests for ``transform_query`` exception handling.""" + + def test_transform_query_wraps_exception(self, monkeypatch): + """Any pydantic error is wrapped in ``OpenBBError`` (covers 603-604).""" + stub = _StubMetadata() + _patch_metadata(monkeypatch, stub) + with pytest.raises(OpenBBError): + ImfEconomicIndicatorsFetcher.transform_query({"symbol": None}) + + +class _StubQueryBuilder: + """Minimal ``ImfQueryBuilder`` substitute capturing args.""" + + instances: list = [] + shared_payload: dict[str, Any] | None = None + shared_raise: Exception | None = None + + def __init__(self): + self.metadata: Any = None + self.calls: list[dict[str, Any]] = [] + _StubQueryBuilder.instances.append(self) + + def fetch_data(self, **kwargs: Any) -> dict[str, Any]: + """Return preconfigured payload or raise.""" + self.calls.append(kwargs) + if _StubQueryBuilder.shared_raise is not None: + raise _StubQueryBuilder.shared_raise + return _StubQueryBuilder.shared_payload or {"data": [], "metadata": {}} + + +class _StubTableBuilder: + """Minimal ``ImfTableBuilder`` substitute capturing args.""" + + instances: list = [] + shared_payload: dict[str, Any] | None = None + shared_raise: Exception | None = None + + def __init__(self): + self.calls: list[dict[str, Any]] = [] + _StubTableBuilder.instances.append(self) + + def get_table(self, **kwargs: Any) -> dict[str, Any]: + """Return preconfigured payload or raise.""" + self.calls.append(kwargs) + if _StubTableBuilder.shared_raise is not None: + raise _StubTableBuilder.shared_raise + return _StubTableBuilder.shared_payload or { + "data": [], + "table_metadata": {}, + "series_metadata": {}, + } + + +@pytest.fixture +def patched_builders(monkeypatch: pytest.MonkeyPatch): + """Swap in stub query/table builders for the model module.""" + _StubQueryBuilder.instances = [] + _StubQueryBuilder.shared_payload = None + _StubQueryBuilder.shared_raise = None + _StubTableBuilder.instances = [] + _StubTableBuilder.shared_payload = None + _StubTableBuilder.shared_raise = None + monkeypatch.setattr( + "openbb_imf.utils.query_builder.ImfQueryBuilder", + _StubQueryBuilder, + ) + monkeypatch.setattr( + "openbb_imf.utils.table_builder.ImfTableBuilder", + _StubTableBuilder, + ) + return _StubQueryBuilder, _StubTableBuilder + + +class TestImfEconomicIndicatorsFetcherAextractData: + """Tests for ``aextract_data`` branches.""" + + @pytest.mark.asyncio + async def test_table_mode_basic(self, monkeypatch, patched_builders): + """Table-mode dispatch returns table-shaped payload (covers happy path of 637-739).""" + stub_meta = _StubMetadata() + _patch_metadata(monkeypatch, stub_meta) + q = ImfEconomicIndicatorsFetcher.transform_query( + {"symbol": "BOP::H_AGG", "country": "USA"} + ) + _, _ = patched_builders + out = await ImfEconomicIndicatorsFetcher.aextract_data(q, None) + assert out["mode"] == "table" + captured = _StubTableBuilder.instances[0].calls[0] + assert captured["TYPE_OF_TRANSFORMATION"] == "*" + assert captured["dataflow"] == "BOP" + + @pytest.mark.asyncio + async def test_table_mode_gfs_branch(self, monkeypatch, patched_builders): + """GFS dataflows set SECTOR/GFS_GRP defaults (covers line 647-649).""" + stub_meta = _StubMetadata() + _patch_metadata(monkeypatch, stub_meta) + q = ImfEconomicIndicatorsFetcher.transform_query( + {"symbol": "GFS_R::H_T1", "country": "USA"} + ) + await ImfEconomicIndicatorsFetcher.aextract_data(q, None) + captured = _StubTableBuilder.instances[0].calls[0] + assert captured["SECTOR"] == "*" + assert captured["GFS_GRP"] == "*" + + @pytest.mark.asyncio + async def test_table_mode_fsic_branch(self, monkeypatch, patched_builders): + """FSIC/IRFCL set the ``SECTOR`` default (covers 650-651).""" + stub_meta = _StubMetadata() + _patch_metadata(monkeypatch, stub_meta) + q = ImfEconomicIndicatorsFetcher.transform_query( + {"symbol": "FSIC::H_T1", "country": "USA"} + ) + await ImfEconomicIndicatorsFetcher.aextract_data(q, None) + captured = _StubTableBuilder.instances[0].calls[0] + assert captured["SECTOR"] == "*" + + @pytest.mark.asyncio + async def test_table_mode_isora_branch(self, monkeypatch, patched_builders): + """ISORA_LATEST_DATA_PUB sets the ``INDICATOR`` default (covers 654-655).""" + stub_meta = _StubMetadata() + _patch_metadata(monkeypatch, stub_meta) + q = ImfEconomicIndicatorsFetcher.transform_query( + {"symbol": "ISORA_LATEST_DATA_PUB::H_T1", "country": "USA"} + ) + await ImfEconomicIndicatorsFetcher.aextract_data(q, None) + captured = _StubTableBuilder.instances[0].calls[0] + assert captured["INDICATOR"] == "*" + + @pytest.mark.asyncio + async def test_aextract_skips_non_string_dimension_values( + self, monkeypatch, patched_builders + ): + """Non-string entries in ``dimension_values`` are skipped (covers line 628).""" + stub_meta = _StubMetadata() + _patch_metadata(monkeypatch, stub_meta) + q = ImfEconomicIndicatorsFetcher.transform_query( + {"symbol": "BOP::H_AGG", "country": "USA"} + ) + object.__setattr__(q, "dimension_values", [None, 42, "FOO:bar"]) + await ImfEconomicIndicatorsFetcher.aextract_data(q, None) + captured = _StubTableBuilder.instances[0].calls[0] + assert captured["FOO"] == "BAR" + + @pytest.mark.asyncio + async def test_table_mode_extra_dimensions(self, monkeypatch, patched_builders): + """``dimension_values`` are merged into the query params (covers 626-633, 657-658).""" + stub_meta = _StubMetadata() + _patch_metadata(monkeypatch, stub_meta) + q = ImfEconomicIndicatorsFetcher.transform_query( + { + "symbol": "BOP::H_AGG", + "country": "USA", + "dimension_values": ["FOO:bar"], + } + ) + await ImfEconomicIndicatorsFetcher.aextract_data(q, None) + captured = _StubTableBuilder.instances[0].calls[0] + assert captured["FOO"] == "BAR" + + @pytest.mark.asyncio + async def test_table_mode_transform_resolved_via_transform_lookup( + self, monkeypatch, patched_builders + ): + """A friendly transform name routes through ``transform_lookup`` (covers 668-675).""" + stub_meta = _StubMetadata() + _patch_metadata(monkeypatch, stub_meta) + _patch_detect_transform_dimension( + monkeypatch, + transform_dim="TYPE_OF_TRANSFORMATION", + transform_lookup={"yoy": "YOY_PCH"}, + ) + q = ImfEconomicIndicatorsFetcher.transform_query( + { + "symbol": "BOP::H_AGG", + "country": "USA", + "transform": "yoy", + } + ) + await ImfEconomicIndicatorsFetcher.aextract_data(q, None) + captured = _StubTableBuilder.instances[0].calls[0] + assert captured["TYPE_OF_TRANSFORMATION"] == "YOY_PCH" + + @pytest.mark.asyncio + async def test_table_mode_transform_all_wildcard( + self, monkeypatch, patched_builders + ): + """``transform='all'`` is converted to ``*`` (covers 669-671).""" + stub_meta = _StubMetadata() + _patch_metadata(monkeypatch, stub_meta) + _patch_detect_transform_dimension( + monkeypatch, transform_dim="TYPE_OF_TRANSFORMATION" + ) + q = ImfEconomicIndicatorsFetcher.transform_query( + { + "symbol": "BOP::H_AGG", + "country": "USA", + "transform": "all", + } + ) + await ImfEconomicIndicatorsFetcher.aextract_data(q, None) + captured = _StubTableBuilder.instances[0].calls[0] + assert captured["TYPE_OF_TRANSFORMATION"] == "*" + + @pytest.mark.asyncio + async def test_table_mode_transform_via_unit_dim( + self, monkeypatch, patched_builders + ): + """``transform`` falling back to ``unit_dim`` resolves via ``unit_lookup`` (covers 677-684).""" + stub_meta = _StubMetadata() + _patch_metadata(monkeypatch, stub_meta) + _patch_detect_transform_dimension( + monkeypatch, + unit_dim="UNIT", + unit_lookup={"usd": "USD"}, + ) + q = ImfEconomicIndicatorsFetcher.transform_query( + { + "symbol": "IFS::IND", + "country": "USA", + "transform": "usd", + } + ) + # Force into table mode without H_ prefix. + object.__setattr__(q, "_is_table", True) + object.__setattr__(q, "_dataflow", "IFS") + object.__setattr__(q, "_table_id", "T1") + await ImfEconomicIndicatorsFetcher.aextract_data(q, None) + captured = _StubTableBuilder.instances[0].calls[0] + assert captured["UNIT"] == "USD" + + @pytest.mark.asyncio + async def test_table_mode_transform_via_unit_dim_wildcard( + self, monkeypatch, patched_builders + ): + """``transform='*'`` with a unit dim sets ``UNIT='*'`` (covers 678-680).""" + stub_meta = _StubMetadata() + _patch_metadata(monkeypatch, stub_meta) + _patch_detect_transform_dimension(monkeypatch, unit_dim="UNIT") + q = ImfEconomicIndicatorsFetcher.transform_query( + { + "symbol": "IFS::IND", + "country": "USA", + "transform": "*", + } + ) + object.__setattr__(q, "_is_table", True) + object.__setattr__(q, "_dataflow", "IFS") + object.__setattr__(q, "_table_id", "T1") + await ImfEconomicIndicatorsFetcher.aextract_data(q, None) + captured = _StubTableBuilder.instances[0].calls[0] + assert captured["UNIT"] == "*" + + @pytest.mark.asyncio + async def test_table_mode_transform_unrecognized_raises( + self, monkeypatch, patched_builders + ): + """An unknown transform with neither dim raises (covers 686-706).""" + stub_meta = _StubMetadata() + _patch_metadata(monkeypatch, stub_meta) + _patch_detect_transform_dimension(monkeypatch) + q = ImfEconomicIndicatorsFetcher.transform_query( + { + "symbol": "BOP::H_AGG", + "country": "USA", + "transform": "weird", + } + ) + with pytest.raises(OpenBBError, match="does not support transform"): + await ImfEconomicIndicatorsFetcher.aextract_data(q, None) + + @pytest.mark.asyncio + async def test_table_mode_transform_invalid_with_available( + self, monkeypatch, patched_builders + ): + """An invalid value when options exist surfaces available choices (covers 686-706).""" + stub_meta = _StubMetadata() + _patch_metadata(monkeypatch, stub_meta) + _patch_detect_transform_dimension( + monkeypatch, + transform_dim="TYPE_OF_TRANSFORMATION", + transform_lookup={"yoy": "YOY", "yoy_lc": "yoy_lc"}, + ) + q = ImfEconomicIndicatorsFetcher.transform_query( + { + "symbol": "BOP::H_AGG", + "country": "USA", + "transform": "weird", + } + ) + with pytest.raises(OpenBBError, match="Invalid transform value"): + await ImfEconomicIndicatorsFetcher.aextract_data(q, None) + + @pytest.mark.asyncio + async def test_table_mode_transform_invalid_only_unit_lookup( + self, monkeypatch, patched_builders + ): + """The unit_lookup branch of the error message is taken (covers line 696).""" + stub_meta = _StubMetadata() + _patch_metadata(monkeypatch, stub_meta) + _patch_detect_transform_dimension( + monkeypatch, + unit_dim="UNIT", + unit_lookup={"usd": "USD"}, + ) + q = ImfEconomicIndicatorsFetcher.transform_query( + { + "symbol": "BOP::H_AGG", + "country": "USA", + "transform": "weird", + } + ) + with pytest.raises(OpenBBError, match="Invalid transform value"): + await ImfEconomicIndicatorsFetcher.aextract_data(q, None) + + @pytest.mark.asyncio + async def test_table_mode_limit_with_annual_frequency( + self, monkeypatch, patched_builders + ): + """``limit`` without a ``start_date`` derives a year boundary (covers 708-720).""" + stub_meta = _StubMetadata() + _patch_metadata(monkeypatch, stub_meta) + q = ImfEconomicIndicatorsFetcher.transform_query( + { + "symbol": "BOP::H_AGG", + "country": "USA", + "frequency": "annual", + "limit": 5, + } + ) + await ImfEconomicIndicatorsFetcher.aextract_data(q, None) + captured = _StubTableBuilder.instances[0].calls[0] + assert captured["start_date"] is not None + assert len(captured["start_date"]) == 4 + + @pytest.mark.asyncio + async def test_table_mode_limit_with_quarterly_frequency( + self, monkeypatch, patched_builders + ): + """``limit`` with ``quarter`` frequency derives ``start_date`` via 713-716.""" + stub_meta = _StubMetadata() + _patch_metadata(monkeypatch, stub_meta) + q = ImfEconomicIndicatorsFetcher.transform_query( + { + "symbol": "BOP::H_AGG", + "country": "USA", + "frequency": "quarter", + "limit": 5, + } + ) + await ImfEconomicIndicatorsFetcher.aextract_data(q, None) + captured = _StubTableBuilder.instances[0].calls[0] + assert captured["start_date"] is not None + + @pytest.mark.asyncio + async def test_table_mode_limit_with_monthly_frequency( + self, monkeypatch, patched_builders + ): + """``limit`` with ``month`` frequency derives ``start_date`` via 717-720.""" + stub_meta = _StubMetadata() + _patch_metadata(monkeypatch, stub_meta) + q = ImfEconomicIndicatorsFetcher.transform_query( + { + "symbol": "BOP::H_AGG", + "country": "USA", + "frequency": "month", + "limit": 5, + } + ) + await ImfEconomicIndicatorsFetcher.aextract_data(q, None) + captured = _StubTableBuilder.instances[0].calls[0] + assert captured["start_date"] is not None + + @pytest.mark.asyncio + async def test_table_mode_get_table_value_error_translates( + self, monkeypatch, patched_builders + ): + """A ``ValueError`` from ``get_table`` is wrapped in ``OpenBBError`` (covers 738-739).""" + stub_meta = _StubMetadata() + _patch_metadata(monkeypatch, stub_meta) + q = ImfEconomicIndicatorsFetcher.transform_query( + {"symbol": "BOP::H_AGG", "country": "USA"} + ) + + def boom(self, **kwargs): + raise ValueError("table boom") + + monkeypatch.setattr(_StubTableBuilder, "get_table", boom) + with pytest.raises(OpenBBError): + await ImfEconomicIndicatorsFetcher.aextract_data(q, None) + + @pytest.mark.asyncio + async def test_table_mode_missing_dataflow_raises( + self, monkeypatch, patched_builders + ): + """Table mode with no ``_dataflow`` raises ``OpenBBError`` (covers 639-640).""" + stub_meta = _StubMetadata() + _patch_metadata(monkeypatch, stub_meta) + q = ImfEconomicIndicatorsFetcher.transform_query( + {"symbol": "BOP::H_AGG", "country": "USA"} + ) + object.__setattr__(q, "_dataflow", None) + with pytest.raises(OpenBBError, match="Could not determine dataflow"): + await ImfEconomicIndicatorsFetcher.aextract_data(q, None) + + @pytest.mark.asyncio + async def test_indicator_mode_no_indicators_raises( + self, monkeypatch, patched_builders + ): + """An empty ``_indicators_by_dataflow`` raises (covers line 748).""" + stub_meta = _StubMetadata() + _patch_metadata(monkeypatch, stub_meta) + q = ImfEconomicIndicatorsFetcher.transform_query( + {"symbol": "WEO::NGDP_RPCH", "country": "USA"} + ) + object.__setattr__(q, "_is_table", False) + object.__setattr__(q, "_indicators_by_dataflow", {}) + with pytest.raises(OpenBBError, match="No indicators specified"): + await ImfEconomicIndicatorsFetcher.aextract_data(q, None) + + @pytest.mark.asyncio + async def test_indicator_mode_full_path(self, monkeypatch, patched_builders): + """Indicator mode happy path exercises transform/limit/dimension_codes paths.""" + stub_meta = _StubMetadata() + _patch_metadata(monkeypatch, stub_meta) + _patch_detect_transform_dimension( + monkeypatch, + transform_dim="TYPE_OF_TRANSFORMATION", + transform_lookup={"yoy": "YOY"}, + ) + monkeypatch.setattr( + "openbb_imf.models.economic_indicators.detect_indicator_dimensions", + lambda dataflow, codes, meta: {"INDICATOR": codes}, + ) + q = ImfEconomicIndicatorsFetcher.transform_query( + { + "symbol": "WEO::NGDP_RPCH", + "country": "USA", + "transform": "yoy", + "limit": 3, + "dimension_values": ["EXTRA:val"], + } + ) + _StubQueryBuilder.shared_payload = { + "data": [{"OBS_VALUE": 1.0}], + "metadata": {"foo": "bar"}, + } + out = await ImfEconomicIndicatorsFetcher.aextract_data(q, None) + assert out["mode"] == "indicator" + captured = _StubQueryBuilder.instances[0].calls[0] + assert captured["lastNObservations"] == 3 + assert captured["EXTRA"] == "VAL" + assert captured["INDICATOR"] == "NGDP_RPCH" + assert captured["TYPE_OF_TRANSFORMATION"] == "YOY" + + @pytest.mark.asyncio + async def test_indicator_mode_transform_wildcard_for_transform_dim( + self, monkeypatch, patched_builders + ): + """``transform='*'`` with transform_dim sets ``*`` (covers 768-770).""" + stub_meta = _StubMetadata() + _patch_metadata(monkeypatch, stub_meta) + _patch_detect_transform_dimension( + monkeypatch, transform_dim="TYPE_OF_TRANSFORMATION" + ) + monkeypatch.setattr( + "openbb_imf.models.economic_indicators.detect_indicator_dimensions", + lambda *a, **kw: {"INDICATOR": ["NGDP_RPCH"]}, + ) + q = ImfEconomicIndicatorsFetcher.transform_query( + {"symbol": "WEO::NGDP_RPCH", "country": "USA", "transform": "*"} + ) + await ImfEconomicIndicatorsFetcher.aextract_data(q, None) + captured = _StubQueryBuilder.instances[0].calls[0] + assert captured["TYPE_OF_TRANSFORMATION"] == "*" + + @pytest.mark.asyncio + async def test_indicator_mode_transform_unit_dim_lookup( + self, monkeypatch, patched_builders + ): + """``unit_dim`` matches a unit_lookup entry (covers 776-783).""" + stub_meta = _StubMetadata() + _patch_metadata(monkeypatch, stub_meta) + _patch_detect_transform_dimension( + monkeypatch, + unit_dim="UNIT", + unit_lookup={"usd": "USD"}, + ) + monkeypatch.setattr( + "openbb_imf.models.economic_indicators.detect_indicator_dimensions", + lambda *a, **kw: {"INDICATOR": ["NGDP_RPCH"]}, + ) + q = ImfEconomicIndicatorsFetcher.transform_query( + {"symbol": "WEO::NGDP_RPCH", "country": "USA", "transform": "usd"} + ) + await ImfEconomicIndicatorsFetcher.aextract_data(q, None) + captured = _StubQueryBuilder.instances[0].calls[0] + assert captured["UNIT"] == "USD" + + @pytest.mark.asyncio + async def test_indicator_mode_transform_unit_dim_wildcard( + self, monkeypatch, patched_builders + ): + """``unit_dim`` wildcard branch is reached (covers 777-780).""" + stub_meta = _StubMetadata() + _patch_metadata(monkeypatch, stub_meta) + _patch_detect_transform_dimension(monkeypatch, unit_dim="UNIT") + monkeypatch.setattr( + "openbb_imf.models.economic_indicators.detect_indicator_dimensions", + lambda *a, **kw: {"INDICATOR": ["NGDP_RPCH"]}, + ) + q = ImfEconomicIndicatorsFetcher.transform_query( + {"symbol": "WEO::NGDP_RPCH", "country": "USA", "transform": "*"} + ) + await ImfEconomicIndicatorsFetcher.aextract_data(q, None) + captured = _StubQueryBuilder.instances[0].calls[0] + assert captured["UNIT"] == "*" + + @pytest.mark.asyncio + async def test_indicator_mode_transform_unrecognized_dataflow_raises( + self, monkeypatch, patched_builders + ): + """Unrecognized transform on a dataflow without dims raises (covers 800-807).""" + stub_meta = _StubMetadata() + _patch_metadata(monkeypatch, stub_meta) + _patch_detect_transform_dimension(monkeypatch) + monkeypatch.setattr( + "openbb_imf.models.economic_indicators.detect_indicator_dimensions", + lambda *a, **kw: {"INDICATOR": ["NGDP_RPCH"]}, + ) + q = ImfEconomicIndicatorsFetcher.transform_query( + {"symbol": "WEO::NGDP_RPCH", "country": "USA", "transform": "weird"} + ) + with pytest.raises(OpenBBError): + await ImfEconomicIndicatorsFetcher.aextract_data(q, None) + + @pytest.mark.asyncio + async def test_indicator_mode_transform_unrecognized_with_lookups( + self, monkeypatch, patched_builders + ): + """Unknown transform with options raises with available list (covers 804-807).""" + stub_meta = _StubMetadata() + _patch_metadata(monkeypatch, stub_meta) + _patch_detect_transform_dimension( + monkeypatch, + transform_dim="TYPE_OF_TRANSFORMATION", + transform_lookup={"yoy": "YOY"}, + unit_dim="UNIT", + unit_lookup={"usd": "USD"}, + ) + monkeypatch.setattr( + "openbb_imf.models.economic_indicators.detect_indicator_dimensions", + lambda *a, **kw: {"INDICATOR": ["NGDP_RPCH"]}, + ) + q = ImfEconomicIndicatorsFetcher.transform_query( + {"symbol": "WEO::NGDP_RPCH", "country": "USA", "transform": "weird"} + ) + with pytest.raises(OpenBBError, match="Invalid transform"): + await ImfEconomicIndicatorsFetcher.aextract_data(q, None) + + @pytest.mark.asyncio + async def test_indicator_mode_query_builder_value_error_translates( + self, monkeypatch, patched_builders + ): + """A ``ValueError`` from ``fetch_data`` is wrapped in ``OpenBBError`` (covers 830-831).""" + stub_meta = _StubMetadata() + _patch_metadata(monkeypatch, stub_meta) + monkeypatch.setattr( + "openbb_imf.models.economic_indicators.detect_indicator_dimensions", + lambda *a, **kw: {"INDICATOR": ["NGDP_RPCH"]}, + ) + q = ImfEconomicIndicatorsFetcher.transform_query( + {"symbol": "WEO::NGDP_RPCH", "country": "USA"} + ) + + def boom(self, **kwargs): + raise ValueError("translate me") + + monkeypatch.setattr(_StubQueryBuilder, "fetch_data", boom) + with pytest.raises(OpenBBError): + await ImfEconomicIndicatorsFetcher.aextract_data(q, None) + + +class TestImfEconomicIndicatorsFetcherTransformData: + """Tests for ``transform_data`` branches.""" + + def _query(self, monkeypatch, **overrides: Any) -> ImfEconomicIndicatorsQueryParams: + """Build a query bypassing constraint validation.""" + stub_meta = _StubMetadata() + _patch_metadata(monkeypatch, stub_meta) + params: dict[str, Any] = {"symbol": "WEO::NGDP_RPCH", "country": "USA"} + params.update(overrides) + return ImfEconomicIndicatorsFetcher.transform_query(params) + + def test_empty_payload_raises(self, monkeypatch): + """An empty ``data`` raises ``EmptyDataError`` (covers line 850).""" + q = self._query(monkeypatch) + with pytest.raises(EmptyDataError): + ImfEconomicIndicatorsFetcher.transform_data(q, {"data": []}) + + def test_table_mode_metadata_routing(self, monkeypatch): + """Table mode pulls table_metadata/series_metadata (covers lines 855-859).""" + q = self._query(monkeypatch) + rows = [ + { + "TIME_PERIOD": "2024-01-01", + "OBS_VALUE": 1.0, + "COUNTRY": "United States", + "country_code": "USA", + } + ] + result = ImfEconomicIndicatorsFetcher.transform_data( + q, + { + "mode": "table", + "data": rows, + "table_metadata": {"foo": 1}, + "series_metadata": {"bar": 2}, + }, + ) + assert result.metadata == {"table": {"foo": 1}, "series": {"bar": 2}} + + def test_start_and_end_date_filter(self, monkeypatch): + """``start_date`` and ``end_date`` skip rows (covers lines 874, 880).""" + q = self._query( + monkeypatch, start_date=date(2024, 2, 1), end_date=date(2024, 4, 1) + ) + rows = [ + {"TIME_PERIOD": "2024-01-01", "OBS_VALUE": 1.0}, + {"TIME_PERIOD": "2024-03-01", "OBS_VALUE": 2.0}, + {"TIME_PERIOD": "2024-05-01", "OBS_VALUE": 3.0}, + ] + result = ImfEconomicIndicatorsFetcher.transform_data( + q, {"data": rows, "metadata": {}} + ) + assert len(result.result) == 1 + assert result.result[0].value == 2.0 + + def test_all_rows_filtered_raises(self, monkeypatch): + """All rows being filtered raises ``EmptyDataError`` (covers 973-977).""" + q = self._query(monkeypatch, start_date=date(2024, 12, 1)) + rows = [{"TIME_PERIOD": "2024-01-01", "OBS_VALUE": 1.0}] + with pytest.raises(EmptyDataError, match="No data remaining"): + ImfEconomicIndicatorsFetcher.transform_data( + q, {"data": rows, "metadata": {}} + ) + + def test_row_without_date_is_skipped(self, monkeypatch): + """Rows without a ``date`` are dropped (covers line 998).""" + q = self._query(monkeypatch) + rows = [ + {"TIME_PERIOD": "2024-01-01", "OBS_VALUE": 1.0}, + {"OBS_VALUE": 2.0}, + ] + result = ImfEconomicIndicatorsFetcher.transform_data( + q, {"data": rows, "metadata": {}} + ) + # Only the dated row survives. + assert len(result.result) == 1 + + def test_value_falls_back_to_value_field(self, monkeypatch): + """Missing ``OBS_VALUE`` falls back to ``value`` (covers line 907).""" + q = self._query(monkeypatch) + rows = [{"TIME_PERIOD": "2024-01-01", "value": 7.0}] + result = ImfEconomicIndicatorsFetcher.transform_data( + q, {"data": rows, "metadata": {}} + ) + assert result.result[0].value == 7.0 + + def test_nan_scale_and_unit_are_normalised(self, monkeypatch): + """``nan`` scale/unit become ``None`` (covers 911-914 and 922-925).""" + q = self._query(monkeypatch) + rows = [ + { + "TIME_PERIOD": "2024-01-01", + "OBS_VALUE": 1.0, + "SCALE": "nan", + "UNIT": "nan", + } + ] + result = ImfEconomicIndicatorsFetcher.transform_data( + q, {"data": rows, "metadata": {}} + ) + assert result.result[0].scale is None + assert result.result[0].unit is None + + def test_non_string_scale_and_unit_are_stringified(self, monkeypatch): + """Numeric scale/unit values are coerced to strings (covers 913-914, 924-925).""" + q = self._query(monkeypatch) + rows = [ + { + "TIME_PERIOD": "2024-01-01", + "OBS_VALUE": 1.0, + "scale": 1.5, + "unit": 9, + } + ] + result = ImfEconomicIndicatorsFetcher.transform_data( + q, {"data": rows, "metadata": {}} + ) + assert result.result[0].scale == "1.5" + assert result.result[0].unit == "9" + + def test_pivot_path_exercises_pivot_table_data(self, monkeypatch): + """``pivot=True`` builds a DataFrame via ``pivot_table_data`` (covers 1009-1019).""" + q = self._query(monkeypatch, pivot=True) + rows = [ + { + "TIME_PERIOD": "2024-01-01", + "OBS_VALUE": 1.0, + "COUNTRY": "United States", + "country_code": "USA", + "INDICATOR": "NGDP_RPCH", + "series_id": "WEO::NGDP_RPCH", + } + ] + + class _FakeDF: + def fillna(self, _): + return self + + def reset_index(self): + return self + + def to_dict(self, orient: str): # noqa: ARG002 + return [ + { + "date": "2024-01-01", + "country": "United States", + "country_code": "USA", + "value": 1.0, + "symbol": "WEO::NGDP_RPCH", + } + ] + + with patch( + "openbb_imf.utils.table_presentation.pivot_table_data", + return_value=_FakeDF(), + ): + out = ImfEconomicIndicatorsFetcher.transform_data( + q, {"data": rows, "metadata": {}} + ) + assert isinstance(out.result[0], ImfEconomicIndicatorsData) diff --git a/openbb_platform/providers/imf/tests/test_generate_cache.py b/openbb_platform/providers/imf/tests/test_generate_cache.py new file mode 100644 index 00000000000..2ac47cb4783 --- /dev/null +++ b/openbb_platform/providers/imf/tests/test_generate_cache.py @@ -0,0 +1,850 @@ +"""Tests for openbb_imf.utils.generate_cache.""" + +# ruff: noqa: I001 + +import json +import lzma + +import pytest +import requests + +from openbb_imf.utils import generate_cache as gc + + +class _FakeResp: + """Minimal stand-in for ``requests.Response``.""" + + def __init__( + self, + status_code: int = 200, + json_data: dict | None = None, + json_exc: Exception | None = None, + ): + self.status_code = status_code + self._json_data = json_data or {} + self._json_exc = json_exc + + def json(self): + """Return JSON payload or raise the seeded exception.""" + if self._json_exc is not None: + raise self._json_exc + return self._json_data + + def raise_for_status(self): + """Mirror ``requests.Response.raise_for_status``.""" + if self.status_code >= 400: + raise requests.HTTPError(f"HTTP {self.status_code}") + + +class TestGet: + """Tests for ``_get``.""" + + def test_success(self, monkeypatch): + """Returns parsed JSON on 200.""" + resp = _FakeResp(200, {"ok": True}) + monkeypatch.setattr(gc._session, "get", lambda *a, **kw: resp) + assert gc._get("http://x") == {"ok": True} + + def test_400_returns_empty(self, monkeypatch): + """HTTP 400 returns empty dict without raising.""" + resp = _FakeResp(400, {}) + monkeypatch.setattr(gc._session, "get", lambda *a, **kw: resp) + assert gc._get("http://x") == {} + + def test_404_returns_empty(self, monkeypatch): + """HTTP 404 returns empty dict.""" + resp = _FakeResp(404, {}) + monkeypatch.setattr(gc._session, "get", lambda *a, **kw: resp) + assert gc._get("http://x") == {} + + def test_429_retries_then_succeeds(self, monkeypatch): + """429 backs off then returns the next 200 response.""" + responses = [_FakeResp(429, {}), _FakeResp(200, {"ok": True})] + calls = {"n": 0} + + def fake_get(*a, **kw): + r = responses[calls["n"]] + calls["n"] += 1 + return r + + monkeypatch.setattr(gc._session, "get", fake_get) + monkeypatch.setattr(gc.time, "sleep", lambda *_a, **_kw: None) + assert gc._get("http://x") == {"ok": True} + assert calls["n"] == 2 + + def test_request_exception_retried_then_raises(self, monkeypatch): + """RequestException retries up to ``retries`` then re-raises.""" + + def fake_get(*a, **kw): + raise requests.ConnectionError("nope") + + monkeypatch.setattr(gc._session, "get", fake_get) + monkeypatch.setattr(gc.time, "sleep", lambda *_a, **_kw: None) + with pytest.raises(requests.ConnectionError): + gc._get("http://x", retries=2, backoff=0.0) + + def test_request_exception_then_success(self, monkeypatch): + """RequestException followed by success returns parsed JSON.""" + calls = {"n": 0} + + def fake_get(*a, **kw): + calls["n"] += 1 + if calls["n"] == 1: + raise requests.ConnectionError("flaky") + return _FakeResp(200, {"ok": True}) + + monkeypatch.setattr(gc._session, "get", fake_get) + monkeypatch.setattr(gc.time, "sleep", lambda *_a, **_kw: None) + assert gc._get("http://x", retries=3, backoff=0.0) == {"ok": True} + + def test_json_decode_error_retries(self, monkeypatch): + """``json.JSONDecodeError`` triggers retry path.""" + calls = {"n": 0} + + def fake_get(*a, **kw): + calls["n"] += 1 + if calls["n"] == 1: + return _FakeResp(200, json_exc=json.JSONDecodeError("bad", "doc", 0)) + return _FakeResp(200, {"ok": True}) + + monkeypatch.setattr(gc._session, "get", fake_get) + monkeypatch.setattr(gc.time, "sleep", lambda *_a, **_kw: None) + assert gc._get("http://x", retries=3, backoff=0.0) == {"ok": True} + + +class TestNormalizeVersion: + """Tests for ``_normalize_version``.""" + + def test_strips_plus(self): + """SDMX 3 ``+`` wildcard is removed.""" + assert gc._normalize_version("1.0+.0") == "1.0.0" + + def test_no_plus(self): + """Plain version is returned unchanged.""" + assert gc._normalize_version("1.0.0") == "1.0.0" + + +class TestEnglish: + """Tests for ``_english``.""" + + def test_none(self): + """None returns empty string.""" + assert gc._english(None) == "" + + def test_string(self): + """A bare string returns itself.""" + assert gc._english("foo") == "foo" + + def test_dict_with_en(self): + """An ``en`` key wins.""" + assert gc._english({"en": "Hello", "fr": "Bonjour"}) == "Hello" + + def test_dict_without_en(self): + """First value is used when ``en`` missing.""" + assert gc._english({"fr": "Bonjour"}) == "Bonjour" + + def test_dict_empty(self): + """Empty dict returns empty string.""" + assert gc._english({}) == "" + + def test_non_string_value(self): + """Non-string returns empty.""" + assert gc._english(123) == "" + + def test_dict_non_string_first(self): + """Non-string fallback returns empty.""" + assert gc._english({"x": 1}) == "" + + +class TestParseStructureRef: + """Tests for ``_parse_structure_ref``.""" + + def test_valid(self): + """Valid DSD URN parses fully.""" + urn = ( + "urn:sdmx:org.sdmx.infomodel.datastructure.DataStructure=" + "IMF.STA:DSD_X(1.0+.0)" + ) + ref = gc._parse_structure_ref(urn) + assert ref == { + "agencyID": "IMF.STA", + "id": "DSD_X", + "version": "1.0.0", + "package": "datastructure", + "class": "DataStructure", + } + + def test_invalid_returns_empty(self): + """Unparsable URN yields empty dict.""" + assert gc._parse_structure_ref("garbage") == {} + + def test_none(self): + """``None`` URN yields empty dict.""" + assert gc._parse_structure_ref(None) == {} + + +class TestParseConceptRef: + """Tests for ``_parse_concept_ref``.""" + + def test_valid(self): + """Concept URN parses fully.""" + urn = ( + "urn:sdmx:org.sdmx.infomodel.conceptscheme.Concept=IMF.STA:CS_X(1.0+.0).GDP" + ) + ref = gc._parse_concept_ref(urn) + assert ref == { + "maintainableParentID": "CS_X", + "maintainableParentVersion": "1.0.0", + "agencyID": "IMF.STA", + "id": "GDP", + "package": "conceptscheme", + "class": "Concept", + } + + def test_invalid(self): + """Unparsable URN yields empty dict.""" + assert gc._parse_concept_ref("garbage") == {} + + +class TestParsePresentations: + """Tests for ``_parse_presentations``.""" + + def test_no_annotations(self): + """Empty list returns empty list.""" + assert gc._parse_presentations([]) == [] + + def test_skips_non_presentation(self): + """Other annotation types are ignored.""" + out = gc._parse_presentations( + [{"type": "OTHER", "text": "presentation_title=foo"}] + ) + assert out == [] + + def test_empty_text(self): + """Empty text annotation is skipped.""" + out = gc._parse_presentations([{"type": "DATAFLOW_PRESENTATION"}]) + assert out == [] + + def test_parses_entry(self): + """Plain ``text`` is split on ';' and '='.""" + text = "presentation_title=Hello;presentation_description=Desc;extra=ignored" + out = gc._parse_presentations([{"type": "DATAFLOW_PRESENTATION", "text": text}]) + assert out == [ + {"presentation_title": "Hello", "presentation_description": "Desc"} + ] + + def test_uses_english_texts_when_text_missing(self): + """Falls back to ``texts`` dict via ``_english``.""" + out = gc._parse_presentations( + [ + { + "type": "DATAFLOW_PRESENTATION", + "texts": {"en": "presentation_title=From Texts"}, + } + ] + ) + assert out == [{"presentation_title": "From Texts"}] + + def test_part_without_equals_skipped(self): + """Parts without '=' are silently skipped.""" + out = gc._parse_presentations( + [ + { + "type": "DATAFLOW_PRESENTATION", + "text": "presentation_title=Foo;loose_part", + } + ] + ) + assert out == [{"presentation_title": "Foo"}] + + def test_entry_with_no_known_keys(self): + """Annotation with no known keys yields empty list.""" + out = gc._parse_presentations( + [{"type": "DATAFLOW_PRESENTATION", "text": "random=value"}] + ) + assert out == [] + + +class TestFetchDataflows: + """Tests for ``fetch_dataflows``.""" + + def test_filters_vintage_and_normalizes(self, monkeypatch): + """VINTAGE entries are filtered; non-VINTAGE entries normalized.""" + raw = { + "data": { + "dataflows": [ + { + "id": "DF1", + "agencyID": "IMF.STA", + "version": "1.0+.0", + "name": "Flow One", + "description": "Desc One", + "structure": ("DataStructure=IMF.STA:DSD1(1.0+.0)"), + "annotations": [], + }, + { + "id": "DF_2024_JAN_VINTAGE", + "agencyID": "IMF.STA", + "version": "1.0", + }, + {"id": "", "agencyID": "IMF"}, + { + "id": "DF2", + "agencyID": "IMF", + "version": "1.0", + "names": {"en": "Name two"}, + "descriptions": {"en": "D2"}, + }, + ] + } + } + monkeypatch.setattr(gc, "_get", lambda *_a, **_kw: raw) + out = gc.fetch_dataflows() + assert set(out.keys()) == {"DF1", "DF2"} + assert out["DF1"]["name"] == "Flow One" + assert out["DF1"]["description"] == "Desc One" + assert out["DF1"]["version"] == "1.0.0" + assert out["DF1"]["structureRef"]["id"] == "DSD1" + assert out["DF2"]["name"] == "Name two" + assert out["DF2"]["description"] == "D2" + + +class TestConvertComponents: + """Tests for ``_convert_components``.""" + + def test_basic(self): + """A component with position and conceptIdentity is fully populated.""" + comps = [ + { + "id": "FREQ", + "position": 1, + "conceptIdentity": ( + "urn:sdmx:org.sdmx.infomodel.conceptscheme.Concept=" + "IMF:CS_X(1.0+.0).FREQ" + ), + }, + {"id": "OTHER"}, + ] + out = gc._convert_components(comps) + assert out[0]["id"] == "FREQ" + assert out[0]["position"] == "1" + assert out[0]["conceptRef"]["id"] == "FREQ" + assert out[1] == {"id": "OTHER"} + + def test_empty(self): + """An empty list returns empty list.""" + assert gc._convert_components([]) == [] + + def test_none(self): + """None returns empty list.""" + assert gc._convert_components(None) == [] + + +class TestFetchDatastructures: + """Tests for ``fetch_datastructures``.""" + + def test_extract_dims_and_attrs(self, monkeypatch): + """Dimensions and attributes are extracted and normalized.""" + raw = { + "data": { + "dataStructures": [ + { + "id": "DSD1", + "agencyID": "IMF.STA", + "version": "1.0+.0", + "name": "X", + "dataStructureComponents": { + "dimensionList": {"dimensions": [{"id": "FREQ"}]}, + "attributeList": {"attributes": [{"id": "UNIT"}]}, + }, + }, + {"id": ""}, + { + "id": "DSD2", + "agencyID": "IMF", + "version": "1.0", + "names": {"en": "From names"}, + }, + ] + } + } + monkeypatch.setattr(gc, "_get", lambda *_a, **_kw: raw) + out = gc.fetch_datastructures() + assert set(out.keys()) == {"DSD1", "DSD2"} + assert out["DSD1"]["dimensions"][0]["id"] == "FREQ" + assert out["DSD1"]["attributes"][0]["id"] == "UNIT" + assert out["DSD2"]["name"] == "From names" + + +class TestFetchConceptschemes: + """Tests for ``fetch_conceptschemes``.""" + + def test_concepts_extracted(self, monkeypatch): + """Concept schemes and their concepts come through normalized.""" + raw = { + "data": { + "conceptSchemes": [ + { + "id": "CS1", + "agencyID": "IMF", + "version": "1.0", + "name": "CS One", + "concepts": [ + {"id": "GDP", "name": "GDP concept"}, + {"id": "", "name": "skipped"}, + {"id": "POP", "names": {"en": "Pop"}}, + ], + }, + {"id": ""}, + { + "id": "CS2", + "agencyID": "IMF", + "version": "1.0", + "names": {"en": "CS2 Name"}, + }, + ] + } + } + monkeypatch.setattr(gc, "_get", lambda *_a, **_kw: raw) + out = gc.fetch_conceptschemes() + assert set(out.keys()) == {"CS1", "CS2"} + cs1_concepts = {c["id"]: c["name"] for c in out["CS1"]["concepts"]} + assert cs1_concepts == {"GDP": "GDP concept", "POP": "Pop"} + assert out["CS2"]["name"] == "CS2 Name" + + def test_concept_codelist_urn_extracted(self, monkeypatch): + """A concept's ``coreRepresentation.enumeration`` URN is parsed into id+agency.""" + raw = { + "data": { + "conceptSchemes": [ + { + "id": "CS_GS", + "agencyID": "IMF.STA", + "version": "4.0", + "name": "GS", + "concepts": [ + { + "id": "GS_MS", + "name": "Marital Status", + "coreRepresentation": { + "enumeration": ( + "urn:sdmx:org.sdmx.infomodel.codelist." + "Codelist=IMF.STA:CL_GS_LI_MS(1.0+.0)" + ) + }, + }, + { + "id": "NO_ENUM", + "name": "No Enumeration", + "coreRepresentation": {}, + }, + { + "id": "BAD_URN", + "name": "Bad URN", + "coreRepresentation": { + "enumeration": "not-a-codelist-urn" + }, + }, + { + "id": "ENUM_NOT_STR", + "name": "Enumeration is not a string", + "coreRepresentation": {"enumeration": {"x": "y"}}, + }, + ], + } + ] + } + } + monkeypatch.setattr(gc, "_get", lambda *_a, **_kw: raw) + out = gc.fetch_conceptschemes() + by_id = {c["id"]: c for c in out["CS_GS"]["concepts"]} + assert by_id["GS_MS"]["codelist_id"] == "CL_GS_LI_MS" + assert by_id["GS_MS"]["codelist_agency"] == "IMF.STA" + assert "codelist_id" not in by_id["NO_ENUM"] + assert "codelist_id" not in by_id["BAD_URN"] + assert "codelist_id" not in by_id["ENUM_NOT_STR"] + + +class TestFetchCodelists: + """Tests for ``fetch_codelists``.""" + + def test_labels_and_descriptions(self, monkeypatch): + """Codelists yield labels and descriptions per code.""" + raw = { + "data": { + "codelists": [ + { + "id": "CL_X", + "codes": [ + { + "id": "USA", + "names": {"en": "United States"}, + "descriptions": {"en": "USA Desc"}, + }, + { + "id": "GBR", + "name": "United Kingdom", + "description": "UK Desc", + }, + {"id": "OTHER"}, + {"id": ""}, + ], + }, + {"id": ""}, + ] + } + } + monkeypatch.setattr(gc, "_get", lambda *_a, **_kw: raw) + codes_by_id, descs_by_id = gc.fetch_codelists() + assert codes_by_id["CL_X"]["USA"] == "United States" + assert codes_by_id["CL_X"]["GBR"] == "United Kingdom" + # fallback: code_id becomes own label, label is fallback description + assert codes_by_id["CL_X"]["OTHER"] == "OTHER" + assert descs_by_id["CL_X"]["USA"] == "USA Desc" + assert descs_by_id["CL_X"]["OTHER"] == "OTHER" + + +class TestFetchHierarchies: + """Tests for ``fetch_hierarchies``.""" + + def test_hierarchies_returned(self, monkeypatch): + """Hierarchies are passed through, keyed by id.""" + raw = { + "data": { + "hierarchies": [ + {"id": "H1", "extra": "stuff"}, + {"id": ""}, + ] + } + } + monkeypatch.setattr(gc, "_get", lambda *_a, **_kw: raw) + out = gc.fetch_hierarchies() + assert out == {"H1": {"id": "H1", "extra": "stuff"}} + + +class TestFetchOneConstraint: + """Tests for ``_fetch_one_constraint``.""" + + def test_extracts_keyvalues(self, monkeypatch): + """Extracts ``keyValues`` entries from the response.""" + payload = { + "data": { + "dataConstraints": [ + { + "cubeRegions": [ + { + "keyValues": [ + { + "id": "REF_AREA", + "values": [ + {"value": "USA"}, + "GBR", + ], + }, + {"values": ["skipped"]}, + ], + "components": [], + } + ] + } + ] + } + } + monkeypatch.setattr(gc, "_get", lambda *_a, **_kw: payload) + key, out = gc._fetch_one_constraint("DF1", "IMF.STA") + assert key == "DF1:all:all:available:all:()" + kv = {x["id"]: set(x["values"]) for x in out["key_values"]} + assert kv["REF_AREA"] == {"USA", "GBR"} + + def test_extracts_components(self, monkeypatch): + """Extracts ``components`` entries from the response.""" + payload = { + "data": { + "dataConstraints": [ + { + "cubeRegions": [ + { + "keyValues": [], + "components": [ + { + "id": "FREQ", + "values": [ + {"value": "A"}, + "Q", + ], + }, + {"values": ["skip"]}, + ], + } + ] + } + ] + } + } + monkeypatch.setattr(gc, "_get", lambda *_a, **_kw: payload) + _key, out = gc._fetch_one_constraint("DF1", "IMF.STA") + kv = {x["id"]: set(x["values"]) for x in out["key_values"]} + assert kv["FREQ"] == {"A", "Q"} + + def test_returns_empty_on_get_exception(self, monkeypatch): + """Exception from ``_get`` yields empty payload.""" + + def fake_get(*_a, **_kw): + raise requests.ConnectionError("boom") + + monkeypatch.setattr(gc, "_get", fake_get) + key, out = gc._fetch_one_constraint("DF1", "IMF.STA") + assert key == "DF1:all:all:available:all:()" + assert out == {} + + def test_returns_empty_on_empty_response(self, monkeypatch): + """Empty body produces empty result.""" + monkeypatch.setattr(gc, "_get", lambda *_a, **_kw: {}) + _key, out = gc._fetch_one_constraint("DF1", "IMF.STA") + assert out == {} + + +class TestFetchConstraints: + """Tests for ``fetch_constraints``.""" + + def test_parallel_aggregates(self, monkeypatch): + """Per-dataflow constraints are gathered in parallel.""" + + def fake_fetch_one(df_id, _agency): + if df_id == "FAIL": + return f"{df_id}:all:all:available:all:()", {} + return ( + f"{df_id}:all:all:available:all:()", + {"key_values": [], "full_response": {}}, + ) + + monkeypatch.setattr(gc, "_fetch_one_constraint", fake_fetch_one) + + dataflows = { + "DF1": {"agencyID": "IMF"}, + "DF2": {"agencyID": "IMF"}, + "FAIL": {"agencyID": "IMF"}, + } + out = gc.fetch_constraints(dataflows) + assert "DF1:all:all:available:all:()" in out + assert "DF2:all:all:available:all:()" in out + assert "FAIL:all:all:available:all:()" not in out + + def test_exception_in_worker_counted(self, monkeypatch): + """Future raising an exception is counted as a failure.""" + + def fake_fetch_one(df_id, _agency): + if df_id == "BOOM": + raise RuntimeError("explode") + return ( + f"{df_id}:all:all:available:all:()", + {"key_values": [], "full_response": {}}, + ) + + monkeypatch.setattr(gc, "_fetch_one_constraint", fake_fetch_one) + dataflows = {"BOOM": {"agencyID": "IMF"}, "OK": {"agencyID": "IMF"}} + out = gc.fetch_constraints(dataflows) + assert "OK:all:all:available:all:()" in out + assert "BOOM:all:all:available:all:()" not in out + + def test_progress_log_at_25_boundary(self, monkeypatch): + """Logs every 25 dataflows and on the final entry.""" + + def fake_fetch_one(df_id, _agency): + return ( + f"{df_id}:all:all:available:all:()", + {"key_values": [], "full_response": {}}, + ) + + monkeypatch.setattr(gc, "_fetch_one_constraint", fake_fetch_one) + dataflows = {f"DF{i}": {"agencyID": "IMF"} for i in range(26)} + out = gc.fetch_constraints(dataflows) + assert len(out) == 26 + + +class TestDeriveDataflowGroups: + """Tests for ``derive_dataflow_groups``.""" + + def test_groups_and_sorts(self): + """Dataflows are grouped by agency and sorted by id.""" + dataflows = { + "DF2": { + "urn": "u2", + "agencyID": "A", + "id": "DF2", + "version": "1.0", + "name": "n2", + "description": "d2", + }, + "DF1": { + "urn": "u1", + "agencyID": "A", + "id": "DF1", + "version": "1.0", + "name": "n1", + "description": "d1", + }, + "DFX": { + "urn": "ux", + "agencyID": "B", + "id": "DFX", + "version": "1.0", + "name": "nx", + "description": "dx", + }, + } + groups = gc.derive_dataflow_groups(dataflows) + assert [d["id"] for d in groups["A"]] == ["DF1", "DF2"] + assert [d["id"] for d in groups["B"]] == ["DFX"] + + +class TestMain: + """Tests for ``main``.""" + + def test_main_writes_blob(self, monkeypatch, tmp_path): + """``main`` collects fetchers and writes a compressed blob.""" + cache_file = tmp_path / "imf_cache.json.xz" + assets_dir = tmp_path + monkeypatch.setattr(gc, "CACHE_FILE", cache_file) + monkeypatch.setattr(gc, "ASSETS_DIR", assets_dir) + + monkeypatch.setattr( + gc, + "fetch_dataflows", + lambda: { + "DF1": { + "agencyID": "IMF", + "id": "DF1", + "urn": "u", + "version": "1.0", + "name": "N", + "description": "D", + } + }, + ) + monkeypatch.setattr(gc, "fetch_datastructures", lambda: {"DSD": {}}) + monkeypatch.setattr(gc, "fetch_conceptschemes", lambda: {"CS": {}}) + monkeypatch.setattr( + gc, "fetch_codelists", lambda: ({"CL": {"A": "Annual"}}, {"CL": {"A": "A"}}) + ) + monkeypatch.setattr(gc, "fetch_hierarchies", lambda: {"H": {}}) + monkeypatch.setattr(gc, "fetch_constraints", lambda _d: {"k": {}}) + + result = gc.main() + assert result is None + assert cache_file.exists() + + with lzma.open(cache_file, "rb") as fh: + blob = json.loads(fh.read().decode("utf-8")) + + assert set(blob.keys()) == { + "dataflows", + "datastructures", + "conceptschemes", + "dataflow_groups", + "metadata_cache", + "constraints_cache", + "codelist_cache", + "codelist_descriptions", + "dataflow_parameters", + "dataflow_indicators", + "hierarchies", + } + assert blob["dataflows"]["DF1"]["id"] == "DF1" + assert blob["dataflow_groups"]["IMF"][0]["id"] == "DF1" + assert blob["constraints_cache"] == {"k": {}} + assert blob["metadata_cache"] == {} + + +class TestModuleEntrypoint: + """Tests to exercise the ``__main__`` guard.""" + + def test_session_headers(self): + """Module-level session has IMF-specific headers.""" + assert gc._session.headers["Accept"] == "application/json" + assert "openbb-imf" in gc._session.headers["User-Agent"] + + def test_get_raises_unreachable_runtime_branch(self, monkeypatch): + """Verify the unreachable ``raise`` after the loop is wired.""" + # We invoke _get with retries=0 โ€” the for loop body never runs, + # so we hit the final ``raise requests.RequestException`` line. + monkeypatch.setattr(gc.time, "sleep", lambda *_a, **_kw: None) + with pytest.raises(requests.RequestException, match="failed after"): + gc._get("http://x", retries=0, backoff=0.0) + + +class TestSessionGetCalled: + """Smoke test: confirm ``_session.get`` is wired in fetchers.""" + + def test_fetch_dataflows_calls_session(self, monkeypatch): + """The ``_get`` helper is hit and uses the seeded URL.""" + captured = {} + + def fake_session_get(url, timeout=300): + captured["url"] = url + return _FakeResp(200, {"data": {"dataflows": []}}) + + monkeypatch.setattr(gc._session, "get", fake_session_get) + gc.fetch_dataflows() + assert "dataflow" in captured["url"] + + +class TestScriptEntrypoint: + """Tests for the ``__main__`` guard.""" + + @pytest.mark.filterwarnings("ignore::RuntimeWarning") + def test_script_run_invokes_main_and_exits(self, monkeypatch, tmp_path): + """Running the module as a script triggers ``sys.exit(main())``.""" + import runpy + + cache_file = tmp_path / "imf_cache.json.xz" + + def fake_session_get(self, *_a, **_kw): # noqa: ARG001 + return _FakeResp(200, {"data": {}}) + + monkeypatch.setattr("requests.sessions.Session.get", fake_session_get) + monkeypatch.setattr(gc.time, "sleep", lambda *_a, **_kw: None) + monkeypatch.setattr( + "openbb_imf.utils.generate_cache.CACHE_FILE", + cache_file, + raising=True, + ) + monkeypatch.setattr( + "openbb_imf.utils.generate_cache.ASSETS_DIR", + tmp_path, + raising=True, + ) + + # runpy re-executes the module body in a fresh namespace; module-level + # names (CACHE_FILE/ASSETS_DIR) get recomputed from the script path, + # so we also need to redirect lzma.open and Path.mkdir for safety. + real_open = lzma.open + + def safe_lzma_open(*_a, **kw): + return real_open(cache_file, "wb", format=lzma.FORMAT_XZ, preset=6) + + monkeypatch.setattr(lzma, "open", safe_lzma_open) + + from pathlib import Path as _P + + real_mkdir = _P.mkdir + + def fake_mkdir(self, *_a, **_kw): + return real_mkdir(tmp_path, parents=True, exist_ok=True) + + monkeypatch.setattr(_P, "mkdir", fake_mkdir) + + # Stub stat so the post-write size lookup hits our tmp file. + # Pass-through for any other path (pathlib's ``is_dir``/``mkdir`` + # internals also call ``Path.stat``; redirecting those breaks the + # ``exist_ok=True`` recovery path on Python 3.10). + real_stat = _P.stat + + def fake_stat(self, *args, **kw): + target = cache_file if str(self).endswith("imf_cache.json.xz") else self + return real_stat(target, *args, **kw) + + monkeypatch.setattr(_P, "stat", fake_stat) + + with pytest.raises(SystemExit): + runpy.run_module("openbb_imf.utils.generate_cache", run_name="__main__") diff --git a/openbb_platform/providers/imf/tests/test_helpers.py b/openbb_platform/providers/imf/tests/test_helpers.py index dba004e1748..7274d38afe4 100644 --- a/openbb_platform/providers/imf/tests/test_helpers.py +++ b/openbb_platform/providers/imf/tests/test_helpers.py @@ -1,17 +1,29 @@ """Tests for IMF helper utilities.""" # ruff: noqa: I001 -# pylint: disable=W0621,W0613,W0612,W0212,R0903,C0302,C0415 from unittest.mock import MagicMock, patch import pytest from openbb_core.app.model.abstract.error import OpenBBError from openbb_imf.utils.helpers import ( + _build_dimension_lookups, + _parse_compound_code, + build_codelist_to_hierarchies_map, + build_hierarchy_to_codelist_map, + build_time_period_params, detect_indicator_dimensions, detect_transform_dimension, + extract_all_codelists_from_hierarchy, normalize_country_label, + parse_agency_from_urn, + parse_codelist_id_from_urn, + parse_codelist_urn, + parse_indicator_code_from_urn, + parse_search_query, + parse_time_period, resolve_country_code, + translate_error_message, ) @@ -188,7 +200,6 @@ class TestDetectTransformDimension: detect_transform_dimension("CPI") ) - # Direct codes should be accessible (case-insensitive) assert "ix" in transform_lookup assert "pc_pa" in transform_lookup assert transform_lookup["ix"] == "IX" @@ -222,7 +233,6 @@ class TestDetectIndicatorDimensions: def test_indicator_dimension_mapping(self): """Test that indicators are mapped to correct dimensions.""" mock_metadata = MagicMock() - # Mock get_dataflow_parameters to return dimension -> codes mapping mock_metadata.get_dataflow_parameters.return_value = { "INDICATOR": [ {"value": "GDP", "label": "GDP"}, @@ -254,7 +264,6 @@ class TestDetectIndicatorDimensions: mock_metadata.dataflows = {} mock_metadata.datastructures = {} - # "UNKNOWN" is not in any dimension - raises OpenBBError with pytest.raises(OpenBBError): detect_indicator_dimensions("TEST", ["GDP", "UNKNOWN"], mock_metadata) @@ -268,7 +277,6 @@ class TestDetectIndicatorDimensions: mock_metadata.dataflows = {} mock_metadata.datastructures = {} - # Compound code: S1_I1 (underscore separator) result = detect_indicator_dimensions("TEST", ["S1_I1"], mock_metadata) assert "SECTOR" in result @@ -295,7 +303,6 @@ class TestDetectIndicatorDimensions: } } - # Compound code with missing dimension: S1 (implies wildcard for INDICATOR) result = detect_indicator_dimensions("TEST", ["S1"], mock_metadata) assert "SECTOR" in result @@ -324,6 +331,632 @@ class TestDetectIndicatorDimensions: mock_metadata.dataflows = {} mock_metadata.datastructures = {} - # When metadata fails, the function should raise an error for invalid codes with pytest.raises(OpenBBError): detect_indicator_dimensions("TEST", ["GDP", "CPI"], mock_metadata) + + +class TestBuildDimensionLookups: + """Tests for _build_dimension_lookups.""" + + def test_params_exception_handled(self): + """Exception from get_dataflow_parameters is swallowed.""" + meta = MagicMock() + meta.get_dataflow_parameters.side_effect = RuntimeError("boom") + meta.dataflows = {} + meta.datastructures = {} + code_to_dim, codes_by_dim, dim_order = _build_dimension_lookups("X", meta) + assert code_to_dim == {} + assert codes_by_dim == {} + assert dim_order == [] + + def test_dim_order_skips_trailing_and_transform(self): + """TIME_PERIOD/FREQUENCY/TRANSFORMATION-like dims are filtered out.""" + meta = MagicMock() + meta.get_dataflow_parameters.return_value = {} + meta.dataflows = {"DF": {"structureRef": {"id": "DSD"}}} + meta.datastructures = { + "DSD": { + "dimensions": [ + {"id": "SECTOR"}, + {"id": "FREQUENCY"}, + {"id": "TIME_PERIOD"}, + {"id": "TYPE_OF_TRANSFORMATION"}, + {"id": "MY_TRANSFORM"}, + {"id": "INDICATOR"}, + {"id": ""}, + ] + } + } + _ct, _cb, dim_order = _build_dimension_lookups("DF", meta) + assert dim_order == ["SECTOR", "INDICATOR"] + + def test_dsd_exception_handled(self): + """Exception inside DSD inspection is swallowed.""" + meta = MagicMock() + meta.get_dataflow_parameters.return_value = {} + + class _DataflowsRaises: + def get(self, *_, **__): + raise RuntimeError("nope") + + meta.dataflows = _DataflowsRaises() + meta.datastructures = {} + _ct, _cb, dim_order = _build_dimension_lookups("DF", meta) + assert dim_order == [] + + +class TestParseCompoundCode: + """Tests for _parse_compound_code.""" + + def test_unmatched_parts(self): + """Unmatched parts are returned as-is.""" + code_to_dim = {"S1": "SECTOR"} + matched, unmatched = _parse_compound_code("S1_X9_T", code_to_dim) + assert matched == [("SECTOR", "S1")] + assert unmatched == ["X9", "T"] + + def test_duplicate_dimension_skipped(self): + """A second match for the same dimension is skipped.""" + code_to_dim = {"S1": "SECTOR", "S2": "SECTOR", "I1": "INDICATOR"} + matched, unmatched = _parse_compound_code("S1_S2_I1", code_to_dim) + # First match for SECTOR wins; S2 becomes unmatched + matched_dims = [m[0] for m in matched] + assert matched_dims.count("SECTOR") == 1 + assert "S2" in unmatched + + +class TestDetectIndicatorDimensionsExtra: + """Extra branches for detect_indicator_dimensions.""" + + def test_wildcard_with_indicator(self): + """Wildcard maps to INDICATOR when available.""" + meta = MagicMock() + meta.get_dataflow_parameters.return_value = { + "INDICATOR": [{"value": "GDP", "label": "GDP"}] + } + meta.dataflows = {} + meta.datastructures = {} + result = detect_indicator_dimensions("X", ["*"], meta) + assert result["INDICATOR"] == ["*"] + + def test_wildcard_with_no_indicator_dim_uses_first(self): + """Wildcard falls back to first known dimension when no INDICATOR.""" + meta = MagicMock() + meta.get_dataflow_parameters.return_value = { + "SECTOR": [{"value": "S1", "label": "Sector 1"}] + } + meta.dataflows = {} + meta.datastructures = {} + result = detect_indicator_dimensions("X", ["*"], meta) + assert "*" in result.get("SECTOR", []) + + def test_wildcard_with_empty_lookups(self): + """Wildcard falls back to INDICATOR when no codes mapped.""" + meta = MagicMock() + meta.get_dataflow_parameters.return_value = {} + meta.dataflows = {} + meta.datastructures = {} + result = detect_indicator_dimensions("X", ["*"], meta) + assert result["INDICATOR"] == ["*"] + + def test_compound_skips_duplicate_dimension_match(self): + """Compound code dedups same code per dimension list.""" + meta = MagicMock() + meta.get_dataflow_parameters.return_value = { + "SECTOR": [{"value": "S1", "label": "S1"}], + "INDICATOR": [{"value": "I1", "label": "I1"}], + } + meta.dataflows = {} + meta.datastructures = {} + result = detect_indicator_dimensions("X", ["S1_I1", "S1_I1"], meta) + assert result["SECTOR"] == ["S1"] + assert result["INDICATOR"] == ["I1"] + + def test_error_with_country_match_uses_full_order(self): + """Error message uses full dim order when COUNTRY/REF_AREA was matched.""" + meta = MagicMock() + meta.get_dataflow_parameters.return_value = { + "REF_AREA": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "GDP", "label": "GDP"}], + } + meta.dataflows = {"X": {"structureRef": {"id": "DSD"}}} + meta.datastructures = { + "DSD": { + "dimensions": [ + {"id": "REF_AREA"}, + {"id": "INDICATOR"}, + ] + } + } + with pytest.raises(OpenBBError) as exc_info: + detect_indicator_dimensions("X", ["USA_BAD"], meta) + assert "BAD" in str(exc_info.value) + + def test_error_message_with_expected_dim(self): + """Error message lists expected dimension samples.""" + meta = MagicMock() + meta.get_dataflow_parameters.return_value = { + "SECTOR": [{"value": "S1", "label": "S1"}], + "INDICATOR": [{"value": "GDP", "label": "GDP"}], + } + meta.dataflows = {"X": {"structureRef": {"id": "DSD"}}} + meta.datastructures = { + "DSD": {"dimensions": [{"id": "SECTOR"}, {"id": "INDICATOR"}]} + } + with pytest.raises(OpenBBError) as exc_info: + detect_indicator_dimensions("X", ["S1_BAD"], meta) + msg = str(exc_info.value) + assert "BAD" in msg + assert "INDICATOR" in msg or "unrecognized" in msg + + def test_unmatched_beyond_dim_order(self): + """Unmatched segment beyond dimension order falls to 'unrecognized'.""" + meta = MagicMock() + meta.get_dataflow_parameters.return_value = { + "SECTOR": [{"value": "S1", "label": "S1"}], + } + meta.dataflows = {"X": {"structureRef": {"id": "DSD"}}} + meta.datastructures = {"DSD": {"dimensions": [{"id": "SECTOR"}]}} + with pytest.raises(OpenBBError) as exc_info: + detect_indicator_dimensions("X", ["S1_BAD_EXTRA"], meta) + assert "unrecognized" in str(exc_info.value) or "EXTRA" in str(exc_info.value) + + def test_unexpected_exception_falls_back(self): + """Unexpected error inside try-block falls back to plain INDICATOR mapping.""" + meta = MagicMock() + meta.get_dataflow_parameters.return_value = { + "INDICATOR": [{"value": "GDP", "label": "GDP"}], + } + meta.dataflows = {} + meta.datastructures = {} + with patch( + "openbb_imf.utils.helpers._parse_compound_code", + side_effect=RuntimeError("kaboom"), + ): + result = detect_indicator_dimensions("X", ["UNKNOWN"], meta) + assert result == {"INDICATOR": ["UNKNOWN"]} + + +class TestDetectTransformDimensionExtra: + """Branches in detect_transform_dimension not covered elsewhere.""" + + def test_currency_and_percent_gdp(self, monkeypatch): + """Labels matching currency/percent_gdp map correctly.""" + from openbb_imf.utils import helpers as helpers_mod + + class FakeMeta: + def get_dataflow_parameters(self, _dataflow): + return { + "TRANSFORMATION": [ + {"value": "PG", "label": "Percent of GDP"}, + {"value": "DC", "label": "Domestic currency"}, + ] + } + + monkeypatch.setattr("openbb_imf.utils.metadata.ImfMetadata", FakeMeta) + _td, _ud, t_lookup, _ul = helpers_mod.detect_transform_dimension("CPI") + assert t_lookup.get("percent_gdp") == "PG" + assert t_lookup.get("currency") == "DC" + + def test_unit_percent_label(self, monkeypatch): + """UNIT dimension picks up percent variants.""" + from openbb_imf.utils import helpers as helpers_mod + + class FakeMeta: + def get_dataflow_parameters(self, _dataflow): + return { + "UNIT": [ + {"value": "PCT", "label": "Percent change"}, + ] + } + + monkeypatch.setattr("openbb_imf.utils.metadata.ImfMetadata", FakeMeta) + _td, ud, _tl, u_lookup = helpers_mod.detect_transform_dimension("X") + assert ud == "UNIT" + assert u_lookup.get("percent") == "PCT" + + def test_metadata_keyerror_swallowed(self, monkeypatch): + """KeyError from metadata results in empty lookups.""" + from openbb_imf.utils import helpers as helpers_mod + + class FakeMeta: + def get_dataflow_parameters(self, _dataflow): + raise KeyError("missing") + + monkeypatch.setattr("openbb_imf.utils.metadata.ImfMetadata", FakeMeta) + td, ud, tl, ul = helpers_mod.detect_transform_dimension("X") + assert td is None + assert ud is None + assert tl == {} + assert ul == {} + + +class TestParseTimePeriod: + """Tests for parse_time_period.""" + + def test_empty_string(self): + """Empty string returns empty.""" + assert parse_time_period("") == "" + + def test_monthly(self): + """YYYY-Mm becomes period end of month.""" + assert parse_time_period("2023-M02") == "2023-02-28" + assert parse_time_period("2024-M02") == "2024-02-29" + + def test_monthly_malformed(self): + """Extra split parts return input untouched.""" + assert parse_time_period("2023-M01-M02") == "2023-M01-M02" + + def test_quarterly(self): + """YYYY-Qn becomes quarter-end date.""" + assert parse_time_period("2023-Q1") == "2023-03-31" + assert parse_time_period("2023-Q4") == "2023-12-31" + + def test_quarterly_malformed(self): + """Multi-Q strings return input untouched.""" + assert parse_time_period("2023-Q1-Q2") == "2023-Q1-Q2" + + def test_quarterly_unknown_quarter(self): + """Unknown quarter falls back to December.""" + assert parse_time_period("2023-Q9") == "2023-12-31" + + def test_yearly(self): + """Bare year becomes Dec 31.""" + assert parse_time_period("2024") == "2024-12-31" + + def test_passthrough(self): + """Strings not matching known formats pass through.""" + assert parse_time_period("2024-01-15") == "2024-01-15" + assert parse_time_period("abc") == "abc" + + def test_invalid_month_returns_input(self): + """Invalid month (13) triggers ValueError and returns input.""" + assert parse_time_period("2023-M13") == "2023-M13" + + +class TestParseAgencyFromUrn: + """Tests for parse_agency_from_urn.""" + + def test_simple(self): + """Parses agency from URN.""" + urn = "urn:sdmx:org.sdmx.infomodel.codelist.Code=ISORA:CL_X(1.0+.0).A" + assert parse_agency_from_urn(urn) == "ISORA" + + def test_compound_agency(self): + """Parses compound agency.""" + urn = "urn:sdmx:org.sdmx.infomodel.codelist.Code=IMF.STA:CL_X(1.0+.0).A" + assert parse_agency_from_urn(urn) == "IMF.STA" + + def test_no_equals(self): + """Returns None for URN without '='.""" + assert parse_agency_from_urn("no-equals-here:foo") is None + + def test_no_colon(self): + """Returns None for URN without ':'.""" + assert parse_agency_from_urn("foo=bar") is None + + def test_empty(self): + """Empty input returns None.""" + assert parse_agency_from_urn("") is None + + def test_malformed_after_equals(self): + """Returns None when no colon after equals.""" + # has ":" elsewhere but not after "=" โ€” split would fail + assert parse_agency_from_urn("urn:sdmx=onlypart") is None + + def test_string_method_raises_returns_none(self): + """Defensive except: rsplit raising returns None.""" + + class WeirdStr(str): + def rsplit(self, *_a, **_kw): + raise RuntimeError("boom") + + # Passes early "=" and ":" checks because __contains__ inherited + assert parse_agency_from_urn(WeirdStr("a=b:c")) is None + + +class TestParseIndicatorCodeFromUrn: + """Tests for parse_indicator_code_from_urn.""" + + def test_simple(self): + """Parses indicator code from URN.""" + urn = "urn:sdmx:org.sdmx.infomodel.codelist.Code=IMF.STA:CL_X(4.0+.0).INTINC" + assert parse_indicator_code_from_urn(urn) == "INTINC" + + def test_no_dot(self): + """No dot returns None.""" + assert parse_indicator_code_from_urn("nodothere") is None + + def test_empty(self): + """Empty returns None.""" + assert parse_indicator_code_from_urn("") is None + + +class TestParseCodelistIdFromUrn: + """Tests for parse_codelist_id_from_urn.""" + + def test_simple(self): + """Parses codelist ID.""" + urn = "urn:sdmx:org.sdmx.infomodel.codelist.Code=IMF.STA:CL_BOP(10.0+.0).CAB" + assert parse_codelist_id_from_urn(urn) == "CL_BOP" + + def test_no_colon(self): + """Missing colon returns None.""" + assert parse_codelist_id_from_urn("nocolon(x)") is None + + def test_no_paren(self): + """Missing paren returns None.""" + assert parse_codelist_id_from_urn("a:b") is None + + def test_empty(self): + """Empty returns None.""" + assert parse_codelist_id_from_urn("") is None + + def test_string_method_raises_returns_none(self): + """Defensive except: rsplit raising returns None.""" + + class WeirdStr(str): + def rsplit(self, *_a, **_kw): + raise RuntimeError("boom") + + assert parse_codelist_id_from_urn(WeirdStr("a:b(c)")) is None + + +class TestParseSearchQuery: + """Tests for parse_search_query.""" + + def test_or_groups(self): + """OR groups separated by pipe.""" + result = parse_search_query('inflation | "consumer price"') + assert result == [["inflation"], ["consumer price"]] + + def test_simple_and(self): + """Whitespace creates AND terms.""" + assert parse_search_query("gdp growth") == [["gdp", "growth"]] + + def test_plus_treated_as_space(self): + """Plus separates AND terms.""" + assert parse_search_query("gdp+growth") == [["gdp", "growth"]] + + def test_stop_words_filtered(self): + """Stop words like 'of' / 'the' are dropped.""" + assert parse_search_query("of the gdp") == [["gdp"]] + + def test_quoted_with_leading_term(self): + """Bare term before quote is processed.""" + result = parse_search_query('gdp "consumer price"') + assert result == [["gdp", "consumer price"]] + + def test_quoted_with_attached_leading_term(self): + """Term butted up against opening quote is processed then quote starts.""" + result = parse_search_query('gdp"consumer price"') + assert result == [["gdp", "consumer price"]] + + def test_quoted_attached_stop_word_dropped(self): + """Stop-word attached to opening quote is dropped from output.""" + result = parse_search_query('the"price"') + assert result == [["price"]] + + def test_quoted_with_empty_close(self): + """An empty quoted region is skipped.""" + result = parse_search_query('"" inflation') + assert result == [["inflation"]] + + def test_empty_or_part_skipped(self): + """Empty OR groups are skipped.""" + assert parse_search_query("inflation | | gdp") == [["inflation"], ["gdp"]] + + def test_only_stop_words(self): + """A part containing only stop words produces no group.""" + assert parse_search_query("of the") == [] + + def test_punctuation_strip(self): + """Trailing punctuation is stripped.""" + result = parse_search_query("gdp.") + assert result == [["gdp"]] + + +class TestBuildTimePeriodParams: + """Tests for build_time_period_params.""" + + def test_none_input(self): + """None constraints return empties.""" + assert build_time_period_params(None) == ([], None) + + def test_empty_dict(self): + """Empty dict returns empties.""" + opts, count = build_time_period_params({}) + assert opts == [] + assert count is None + + def test_full_payload(self): + """Full payload yields start/end/series_count.""" + resp = { + "full_response": { + "data": { + "contentConstraints": [ + { + "annotations": [ + {"id": "time_period_start", "title": "2000-01"}, + {"id": "time_period_end", "title": "2024-12"}, + {"id": "series_count", "title": "42"}, + {"id": "other", "title": "ignored"}, + ] + } + ] + } + } + } + opts, count = build_time_period_params(resp) + assert {"label": "Start Date: 2000-01", "value": "2000-01"} in opts + assert {"label": "End Date: 2024-12", "value": "2024-12"} in opts + assert count == "42" + + def test_no_content_constraints(self): + """Empty contentConstraints returns empty options.""" + resp = {"full_response": {"data": {"contentConstraints": []}}} + opts, count = build_time_period_params(resp) + assert opts == [] + assert count is None + + +class TestParseCodelistUrn: + """Tests for parse_codelist_urn.""" + + def test_simple(self): + """Parses codelist URN.""" + urn = ( + "urn:sdmx:com.epam.quanthub.sdmxplus.infomodel.Glossary=" + "IMF.STA:CL_FSIBSIS(4.0+.0)" + ) + assert parse_codelist_urn(urn) == "CL_FSIBSIS" + + def test_empty(self): + """Empty returns None.""" + assert parse_codelist_urn("") is None + + def test_no_equals(self): + """No '=' returns None.""" + assert parse_codelist_urn("noequals") is None + + def test_string_method_raises_returns_none(self): + """Defensive except: rsplit raising returns None.""" + + class WeirdStr(str): + def rsplit(self, *_a, **_kw): + raise RuntimeError("boom") + + assert parse_codelist_urn(WeirdStr("a=b")) is None + + +class TestExtractAllCodelistsFromHierarchy: + """Tests for extract_all_codelists_from_hierarchy.""" + + def test_empty(self): + """Hierarchy with no codes returns empty set.""" + assert extract_all_codelists_from_hierarchy({}) == set() + + def test_recursive(self): + """Nested hierarchies are scanned.""" + urn = "urn:sdmx:org.sdmx.infomodel.codelist.Code=IMF.STA:CL_TOP(1.0+.0).A" + sub_urn = "urn:sdmx:org.sdmx.infomodel.codelist.Code=IMF.STA:CL_SUB(1.0+.0).B" + hier = { + "hierarchicalCodes": [ + { + "code": urn, + "hierarchicalCodes": [{"code": sub_urn}], + } + ] + } + result = extract_all_codelists_from_hierarchy(hier) + assert "CL_TOP" in result + assert "CL_SUB" in result + + def test_empty_code(self): + """Entry with empty code is skipped.""" + hier = {"hierarchicalCodes": [{"code": ""}]} + assert extract_all_codelists_from_hierarchy(hier) == set() + + def test_non_list_hcodes(self): + """Non-list hierarchicalCodes returns empty set.""" + assert extract_all_codelists_from_hierarchy({"hierarchicalCodes": "x"}) == set() + + +class TestBuildHierarchyToCodelistMap: + """Tests for build_hierarchy_to_codelist_map.""" + + def test_via_annotation(self): + """Mapping is derived from owningCodelistUrn annotation.""" + urn = ( + "urn:sdmx:com.epam.quanthub.sdmxplus.infomodel.Glossary=" + "IMF.STA:CL_X(1.0+.0)" + ) + hierarchies = { + "H1": {"annotations": [{"id": "owningCodelistUrn", "text": urn}]} + } + mapping = build_hierarchy_to_codelist_map(hierarchies) + assert mapping == {"H1": "CL_X"} + + def test_fallback_to_first_code(self): + """Falls back to first hierarchicalCode when no annotation.""" + code_urn = "urn:sdmx:org.sdmx.infomodel.codelist.Code=IMF:CL_FIRST(1.0+.0).A" + hierarchies = {"H1": {"hierarchicalCodes": [{"code": code_urn}]}} + mapping = build_hierarchy_to_codelist_map(hierarchies) + assert mapping == {"H1": "CL_FIRST"} + + def test_no_match(self): + """Empty hierarchy yields nothing.""" + assert build_hierarchy_to_codelist_map({"H1": {}}) == {} + + def test_annotation_without_urn(self): + """Annotation with empty URN falls back to hierarchicalCodes.""" + hierarchies = { + "H1": { + "annotations": [{"id": "owningCodelistUrn", "text": ""}], + "hierarchicalCodes": [ + { + "code": ( + "urn:sdmx:org.sdmx.infomodel.codelist.Code=" + "IMF:CL_FALLBACK(1.0+.0).A" + ) + } + ], + } + } + mapping = build_hierarchy_to_codelist_map(hierarchies) + assert mapping == {"H1": "CL_FALLBACK"} + + +class TestBuildCodelistToHierarchiesMap: + """Tests for build_codelist_to_hierarchies_map.""" + + def test_basic(self): + """Reverse mapping covers all codelists.""" + urn1 = "urn:sdmx:org.sdmx.infomodel.codelist.Code=IMF:CL_A(1.0+.0).X" + urn2 = "urn:sdmx:org.sdmx.infomodel.codelist.Code=IMF:CL_B(1.0+.0).Y" + hierarchies = { + "H1": {"hierarchicalCodes": [{"code": urn1}, {"code": urn2}]}, + "H2": {"hierarchicalCodes": [{"code": urn1}]}, + } + mapping = build_codelist_to_hierarchies_map(hierarchies) + assert set(mapping["CL_A"]) == {"H1", "H2"} + assert mapping["CL_B"] == ["H1"] + + def test_dedupes_hierarchy(self): + """Same hierarchy ID is not duplicated.""" + urn = "urn:sdmx:org.sdmx.infomodel.codelist.Code=IMF:CL_A(1.0+.0).X" + hierarchies = {"H1": {"hierarchicalCodes": [{"code": urn}, {"code": urn}]}} + mapping = build_codelist_to_hierarchies_map(hierarchies) + assert mapping["CL_A"] == ["H1"] + + +class TestTranslateErrorMessage: + """Tests for translate_error_message.""" + + def test_dim_translation(self): + """COUNTRY/INDICATOR mapped to user-friendly names.""" + msg = "Invalid value for 'COUNTRY'" + assert "'country'" in translate_error_message(msg) + + def test_frequency_value_translation(self): + """Frequency code 'A' maps to 'annual'.""" + msg = "Invalid value 'A'" + out = translate_error_message(msg) + assert "'annual'" in out + + def test_dimension_phrase_replacement(self): + """`'X'` quoted dimensions are translated.""" + msg = "Invalid dimension 'BOP_ACCOUNTING_ENTRY'" + out = translate_error_message(msg) + assert "'accounting_entry'" in out + + def test_codes_phrase_replacement(self): + """`X codes` becomes ` codes`.""" + msg = "Available SECTOR codes: x" + out = translate_error_message(msg) + assert "sector codes" in out + + def test_double_quote_variant(self): + """Double-quoted dimension name is also translated.""" + msg = 'Invalid value for "COUNTRY"' + assert '"country"' in translate_error_message(msg) diff --git a/openbb_platform/providers/imf/tests/test_imf_fetchers.py b/openbb_platform/providers/imf/tests/test_imf_fetchers.py index ae3c8911978..d0ba0c7c265 100644 --- a/openbb_platform/providers/imf/tests/test_imf_fetchers.py +++ b/openbb_platform/providers/imf/tests/test_imf_fetchers.py @@ -4,14 +4,21 @@ from datetime import date import pytest from openbb_core.app.service.user_service import UserService + from openbb_imf.models.available_indicators import ImfAvailableIndicatorsFetcher +from openbb_imf.models.balance_of_payments import ImfBalanceOfPaymentsFetcher from openbb_imf.models.consumer_price_index import ImfConsumerPriceIndexFetcher +from openbb_imf.models.container_metrics import ImfContainerMetricsFetcher +from openbb_imf.models.country_activity import ImfCountryActivityFetcher from openbb_imf.models.direction_of_trade import ImfDirectionOfTradeFetcher +from openbb_imf.models.disruption_events import ImfDisruptionEventsFetcher +from openbb_imf.models.disruption_sankey import ImfDisruptionSankeyFetcher from openbb_imf.models.economic_indicators import ImfEconomicIndicatorsFetcher from openbb_imf.models.maritime_chokepoint_info import ImfMaritimeChokePointInfoFetcher from openbb_imf.models.maritime_chokepoint_volume import ( ImfMaritimeChokePointVolumeFetcher, ) +from openbb_imf.models.monthly_trade import ImfMonthlyTradeFetcher from openbb_imf.models.port_info import ImfPortInfoFetcher from openbb_imf.models.port_volume import ImfPortVolumeFetcher @@ -25,6 +32,7 @@ def vcr_config(): """VCR configuration.""" return { "filter_headers": [("User-Agent", None)], + "decode_compressed_response": True, } @@ -66,13 +74,25 @@ def test_imf_economic_indicators_fetcher(credentials=test_credentials): assert result is None -# The data for this request are local files, so we can't record them. def test_imf_available_indicators_fetcher(credentials=test_credentials): - """Test the IMF Available Indicators fetcher.""" + """Test the IMF Available Indicators fetcher. + + The IMF SDMX catalog references a handful of codelists + (``CL_NA_MAIN_REF_SECTOR``, ``CL_NA_MAIN_EXPENDITURE``, + ``CL_GS_LI_GS_MS``, ``CL_SDG_COMPOSITE_BREAKDOWN``) that aren't + published as standalone resources and one dataflow (``FFS``) with + no clear indicator dimension; the fetcher warns on these by design. + """ + import warnings as _warnings + + from openbb_core.app.model.abstract.warning import OpenBBWarning + params = {"query": "gold+volume"} fetcher = ImfAvailableIndicatorsFetcher() - result = fetcher.test(params, credentials) + with _warnings.catch_warnings(): + _warnings.simplefilter("ignore", OpenBBWarning) + result = fetcher.test(params, credentials) assert result is None @@ -140,3 +160,83 @@ def test_imf_maritime_chokepoint_volume_fetcher(credentials=test_credentials): fetcher = ImfMaritimeChokePointVolumeFetcher() result = fetcher.test(params, credentials) assert result is None + + +@pytest.mark.record_http +def test_imf_balance_of_payments_fetcher(credentials=test_credentials): + """Test the ImfBalanceOfPayments fetcher.""" + params = { + "country": "JPN", + "frequency": "quarterly", + "start_date": date(2023, 1, 1), + "end_date": date(2024, 1, 1), + } + + fetcher = ImfBalanceOfPaymentsFetcher() + result = fetcher.test(params, credentials) + assert result is None + + +@pytest.mark.record_http +def test_imf_container_metrics_fetcher(credentials=test_credentials): + """Test the ImfContainerMetrics fetcher.""" + params = { + "metric": "portcalls", + "port_ids": "TOP10", + "start_date": "2024-01-01", + "end_date": "2024-03-31", + } + + fetcher = ImfContainerMetricsFetcher() + result = fetcher.test(params, credentials) + assert result is None + + +@pytest.mark.record_http +def test_imf_country_activity_fetcher(credentials=test_credentials): + """Test the ImfCountryActivity fetcher.""" + params = { + "country_code": "USA", + "metric": "portcalls", + "start_date": "2024-01-01", + "end_date": "2024-01-31", + } + + fetcher = ImfCountryActivityFetcher() + result = fetcher.test(params, credentials) + assert result is None + + +@pytest.mark.record_http +def test_imf_disruption_events_fetcher(credentials=test_credentials): + """Test the ImfDisruptionEvents fetcher.""" + params = {"alertlevel": "ALL", "active_only": False} + + fetcher = ImfDisruptionEventsFetcher() + result = fetcher.test(params, credentials) + assert result is None + + +@pytest.mark.record_http +def test_imf_disruption_sankey_fetcher(credentials=test_credentials): + """Test the ImfDisruptionSankey fetcher.""" + params = {"event_id": "1001234"} + + fetcher = ImfDisruptionSankeyFetcher() + result = fetcher.test(params, credentials) + assert result is None + + +@pytest.mark.record_http +def test_imf_monthly_trade_fetcher(credentials=test_credentials): + """Test the ImfMonthlyTrade fetcher.""" + params = { + "code": "USA", + "metric": "trade_value", + "start_date": "2024-01-01", + "end_date": "2024-06-30", + } + + fetcher = ImfMonthlyTradeFetcher() + result = fetcher.test(params, credentials) + assert result is None diff --git a/openbb_platform/providers/imf/tests/test_imf_router.py b/openbb_platform/providers/imf/tests/test_imf_router.py new file mode 100644 index 00000000000..763c57b747e --- /dev/null +++ b/openbb_platform/providers/imf/tests/test_imf_router.py @@ -0,0 +1,1638 @@ +"""Tests for the IMF utilities router.""" + +# ruff: noqa: I001 + +from types import SimpleNamespace +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest + +from openbb_imf.imf_router import ( + _APPS_WIDGET_ID_FALLBACK_MAP, + _INDENT_UNIT, + _dump_economic_indicator_rows, + _indent_title, + _rewrap_indent, + _render_dataflows_markdown, + _render_parameters_markdown, + _rewrite_widget_ids, + get_dataflow_dimensions, + get_imf_apps_json, + indicator_choices, + list_bop_country_choices, + list_cpi_country_choices, + list_dataflow_choices, + list_dataflows, + list_table_choices, + list_tables, + presentation_table, + presentation_table_choices, + router, +) +from openbb_imf.portwatch_router import ( + container_metrics, + country_activity, + disruption_events, + disruption_sankey, + disruptions_map, + list_container_port_choices, + list_country_choices, + list_disruption_event_choices, + list_port_id_choices, + list_tradenow_region_choices, + monthly_trade, +) + + +class TestIndentHelpers: + """Tests for ``_INDENT_UNIT`` and ``_indent_title``.""" + + def test_indent_unit_starts_with_gt(self): + """The unit string starts with ``>`` then eight non-breaking spaces.""" + assert _INDENT_UNIT[0] == ">" + assert _INDENT_UNIT[1:] == "ย " * 8 + + def test_indent_zero_level_returns_title_unchanged(self): + """Level 0 (or below) leaves the title untouched.""" + assert _indent_title("Total", 0) == "Total" + assert _indent_title("Total", -1) == "Total" + + def test_indent_positive_level_prefixes_unit(self): + """Each positive level prepends one ``_INDENT_UNIT``.""" + assert _indent_title("Total", 1) == _INDENT_UNIT + "Total" + assert _indent_title("Sub", 2) == _INDENT_UNIT * 2 + "Sub" + + def test_indent_non_string_passes_through(self): + """Non-string titles short-circuit and return as-is.""" + assert _indent_title(None, 3) is None + assert _indent_title(7, 3) == 7 + + +class TestRewrapIndent: + """Tests for ``_rewrap_indent``.""" + + def test_no_leading_spaces_returns_unchanged(self): + """A title without leading whitespace is returned as-is.""" + assert _rewrap_indent("โ–ธ Header") == "โ–ธ Header" + assert _rewrap_indent("") == "" + + def test_three_spaces_becomes_one_indent_unit(self): + """Three leading ASCII spaces map to ``_INDENT_UNIT`` (level 1).""" + assert ( + _rewrap_indent(" Financial assets") == _INDENT_UNIT + "Financial assets" + ) + + def test_six_spaces_becomes_two_indent_units(self): + """Six leading ASCII spaces map to two ``_INDENT_UNIT`` markers.""" + assert ( + _rewrap_indent(" Monetary gold and SDRs") + == _INDENT_UNIT * 2 + "Monetary gold and SDRs" + ) + + def test_eight_spaces_floor_divides_to_two(self): + """Non-multiple leading spaces floor-divide by 3 to a level integer.""" + assert ( + _rewrap_indent(" General government") + == _INDENT_UNIT * 2 + "General government" + ) + + def test_eleven_spaces_floor_divides_to_three(self): + """Eleven leading spaces collapse to three ``_INDENT_UNIT`` markers.""" + assert ( + _rewrap_indent(" General government") + == _INDENT_UNIT * 3 + "General government" + ) + + def test_non_string_passes_through(self): + """Non-string inputs short-circuit.""" + assert _rewrap_indent(None) is None + assert _rewrap_indent(7) == 7 + + def test_first_character_is_gt_when_indented(self): + """Every indented title's first character is the literal ``>``.""" + for raw in (" A", " B", " C", " D"): + out = _rewrap_indent(raw) + assert out[0] == ">" + + +class TestDumpEconomicIndicatorRows: + """Tests for ``_dump_economic_indicator_rows``.""" + + def test_handles_results_attribute(self): + """The helper unwraps ``.result`` and serialises each model.""" + item = SimpleNamespace(model_dump=lambda **kw: {"k": 1}) + results = SimpleNamespace(result=[item]) + assert _dump_economic_indicator_rows(results) == [{"k": 1}] + + def test_handles_plain_list(self): + """A plain list is iterated directly.""" + item = SimpleNamespace(model_dump=lambda **kw: {"k": 2}) + assert _dump_economic_indicator_rows([item]) == [{"k": 2}] + + def test_handles_dict_rows(self): + """Dict rows fall through the ``dict(r)`` branch.""" + + class _Mapping: + """A mapping-like object lacking ``model_dump``.""" + + def keys(self): + """Expose the single ``k`` key.""" + return ["k"] + + def __getitem__(self, key): + """Return the value for ``k``.""" + return 3 if key == "k" else None + + assert _dump_economic_indicator_rows([_Mapping()]) == [{"k": 3}] + + def test_handles_empty(self): + """Empty or falsy input returns an empty list.""" + assert _dump_economic_indicator_rows(None) == [] + assert _dump_economic_indicator_rows([]) == [] + assert _dump_economic_indicator_rows(SimpleNamespace(result=[])) == [] + + +class TestRenderMarkdownHelpers: + """Tests for ``_render_dataflows_markdown`` and ``_render_parameters_markdown``.""" + + def test_render_parameters_emits_dim_headers(self): + """Each dimension produces a level-3 markdown header and a code list.""" + params = {"COUNTRY": [{"value": "USA", "label": "United States"}]} + md = _render_parameters_markdown(params) + assert "### `COUNTRY`" in md + assert "- `USA` : United States" in md + assert "---" in md + + def test_render_dataflows_walks_dataflows(self): + """The dataflow renderer pulls indicators, params, and presentations.""" + metadata = MagicMock() + metadata.dataflows = {"TF": {"name": "Test", "description": "Desc"}} + metadata.list_all_dataflow_tables.return_value = { + "TF": [ + { + "name": "T1", + "description": "d1", + "id": "P1", + "friendly_name": "Friendly", + } + ] + } + metadata.get_indicators_in.return_value = [{"code": "X"}] + metadata.get_dataflow_parameters.return_value = {"COUNTRY": []} + + md = _render_dataflows_markdown(metadata) + assert "## `TF` - Test" in md + assert "**Number of Series:** 1" in md + assert "**Dimensions:** `COUNTRY`" in md + assert "#### T1" in md + assert "**Symbol:** `TF::P1`" in md + assert "**Friendly Name:** `Friendly`" in md + assert "d1" in md + + def test_render_dataflows_drops_duplicate_presentation_names(self): + """Duplicate presentation names are emitted only once.""" + metadata = MagicMock() + metadata.dataflows = {"TF": {"name": "X", "description": ""}} + metadata.list_all_dataflow_tables.return_value = { + "TF": [ + {"name": "Same", "id": "P1", "description": "Same"}, + {"name": "Same", "id": "P2", "description": "Same"}, + ] + } + metadata.get_indicators_in.return_value = [] + metadata.get_dataflow_parameters.return_value = {} + + md = _render_dataflows_markdown(metadata) + assert md.count("#### Same") == 1 + + def test_render_dataflows_tolerates_missing_indicator_dim(self): + """A dataflow whose ``get_indicators_in`` raises ``KeyError`` is skipped.""" + metadata = MagicMock() + metadata.dataflows = { + "FFS": {"name": "Financial Soundness", "description": "desc"}, + "OK": {"name": "Other", "description": ""}, + } + metadata.list_all_dataflow_tables.return_value = {} + + def _indicators(df_id): + """Raise ``KeyError`` for ``FFS`` only.""" + if df_id == "FFS": + raise KeyError( + "Could not find an indicator-like dimension for dataflow 'FFS'." + ) + return [{"indicator": "X"}] + + metadata.get_indicators_in.side_effect = _indicators + metadata.get_dataflow_parameters.return_value = {} + + md = _render_dataflows_markdown(metadata) + assert "## `FFS` - Financial Soundness" in md + assert "## `OK` - Other" in md + assert "**Number of Series:** 1" in md + + +class TestChoiceEndpoints: + """Tests for the small ``list_*_choices`` async endpoints.""" + + @patch("openbb_imf.utils.port_watch_helpers.list_countries") + @pytest.mark.asyncio + async def test_list_country_choices_delegates(self, mock_list): + """``list_country_choices`` defers to ``list_countries``.""" + mock_list.return_value = [{"label": "USA", "value": "USA"}] + assert await list_country_choices() == [{"label": "USA", "value": "USA"}] + mock_list.assert_called_once() + + @patch("openbb_imf.utils.port_watch_helpers.get_tradenow_region_choices") + @pytest.mark.asyncio + async def test_list_tradenow_region_choices(self, mock_get): + """The endpoint surfaces the helper's choices unchanged.""" + mock_get.return_value = [{"label": "WLD", "value": "WLD"}] + assert await list_tradenow_region_choices() == [ + {"label": "WLD", "value": "WLD"} + ] + + @patch("openbb_imf.utils.port_watch_helpers.get_container_port_choices") + @pytest.mark.asyncio + async def test_list_container_port_choices_prepends_top10(self, mock_get): + """The container-port endpoint prepends the ``TOP10`` sentinel.""" + mock_get.return_value = [{"label": "Port A", "value": "1"}] + choices = await list_container_port_choices() + assert choices[0] == {"label": "Top 10 by Metric", "value": "TOP10"} + assert choices[1] == {"label": "Port A", "value": "1"} + + @patch("openbb_imf.utils.port_watch_helpers.get_sankey_event_choices") + @pytest.mark.asyncio + async def test_list_disruption_event_choices_prepends_latest(self, mock_get): + """The disruption endpoint prepends the ``LATEST`` sentinel.""" + mock_get.return_value = [{"label": "Event A", "value": "1"}] + choices = await list_disruption_event_choices() + assert choices[0] == {"label": "Latest Disruption", "value": "LATEST"} + assert choices[1] == {"label": "Event A", "value": "1"} + + +class TestPresentationTableIndent: + """Tests for the ``presentation_table`` post-processing indent logic.""" + + def test_indent_rows_via_level_string(self): + """A string ``level`` is parsed back into an int before indenting.""" + rows = [ + {"title": "A", "level": "0"}, + {"title": "B", "level": "1"}, + {"title": "C", "level": "2"}, + ] + for row in rows: + level = row.get("level") + try: + lvl = int(level) if level is not None else 0 + except (TypeError, ValueError): + lvl = 0 + row["title"] = _indent_title(row.get("title", ""), lvl) + + assert rows[0]["title"] == "A" + assert rows[1]["title"] == _INDENT_UNIT + "B" + assert rows[2]["title"] == _INDENT_UNIT * 2 + "C" + + def test_indent_rows_with_invalid_level_treats_as_zero(self): + """A non-numeric level falls back to level 0 (no indent).""" + row = {"title": "X", "level": "bogus"} + try: + lvl = int(row.get("level")) + except (TypeError, ValueError): + lvl = 0 + row["title"] = _indent_title(row.get("title", ""), lvl) + assert row["title"] == "X" + + +class TestAppsJson: + """Tests for ``get_imf_apps_json``.""" + + @pytest.mark.asyncio + async def test_returns_apps_json_when_present(self, tmp_path, monkeypatch): + """A valid ``apps.json`` next to the router yields a parsed list.""" + from openbb_imf import imf_router as router_module + + apps_path = tmp_path / "apps.json" + apps_path.write_text('[{"name": "Demo"}]', encoding="utf-8") + + monkeypatch.setattr(router_module, "__file__", str(tmp_path / "imf_router.py")) + + assert await get_imf_apps_json() == [{"name": "Demo"}] + + @pytest.mark.asyncio + async def test_returns_empty_when_missing(self, tmp_path, monkeypatch): + """A missing ``apps.json`` returns the empty fallback list.""" + from openbb_imf import imf_router as router_module + + monkeypatch.setattr(router_module, "__file__", str(tmp_path / "imf_router.py")) + assert await get_imf_apps_json() == [] + + +class TestRouterRegistration: + """Tests confirming the new fetcher endpoints are registered on the router.""" + + def test_country_activity_registered(self): + """``country_activity`` is reachable through the router's api routes.""" + paths = {r.path for r in router.api_router.routes} + assert any(p.endswith("/country_activity") for p in paths) + + def test_monthly_trade_registered(self): + """``monthly_trade`` is registered.""" + paths = {r.path for r in router.api_router.routes} + assert any(p.endswith("/monthly_trade") for p in paths) + + def test_container_metrics_registered(self): + """``container_metrics`` is registered.""" + paths = {r.path for r in router.api_router.routes} + assert any(p.endswith("/container_metrics") for p in paths) + + def test_disruption_events_registered(self): + """``disruption_events`` is registered.""" + paths = {r.path for r in router.api_router.routes} + assert any(p.endswith("/disruption_events") for p in paths) + + def test_disruptions_map_registered(self): + """``disruptions_map`` is registered.""" + paths = {r.path for r in router.api_router.routes} + assert any(p.endswith("/disruptions_map") for p in paths) + + def test_disruption_sankey_registered(self): + """``disruption_sankey`` is registered.""" + paths = {r.path for r in router.api_router.routes} + assert any(p.endswith("/disruption_sankey") for p in paths) + + def test_choice_endpoints_registered(self): + """The 4 ``list_*_choices`` endpoints are registered.""" + paths = {r.path for r in router.api_router.routes} + assert any(p.endswith("/list_country_choices") for p in paths) + assert any(p.endswith("/list_tradenow_region_choices") for p in paths) + assert any(p.endswith("/list_container_port_choices") for p in paths) + assert any(p.endswith("/list_disruption_event_choices") for p in paths) + + +def _patched_query_and_obbject(sentinel): + """Return contextmanagers patching ``OBBQuery`` and ``OBBject.from_query``.""" + return ( + patch("openbb_imf.portwatch_router.OBBQuery", new=MagicMock()), + patch( + "openbb_imf.portwatch_router.OBBject.from_query", + new=AsyncMock(return_value=sentinel), + ), + ) + + +class TestFetcherEndpointBodies: + """Invocation tests for the 6 new fetcher-backed endpoint bodies.""" + + @pytest.mark.asyncio + async def test_country_activity_calls_from_query(self): + """``country_activity`` delegates to ``OBBject.from_query``.""" + + sentinel = SimpleNamespace(result=[]) + q_patch, o_patch = _patched_query_and_obbject(sentinel) + with q_patch, o_patch as mock_from_query: + out = await country_activity( + cc=MagicMock(), + provider_choices=MagicMock(), + standard_params=MagicMock(), + extra_params=MagicMock(), + ) + assert out is sentinel + mock_from_query.assert_awaited_once() + + @pytest.mark.asyncio + async def test_monthly_trade_calls_from_query(self): + """``monthly_trade`` delegates to ``OBBject.from_query``.""" + + sentinel = SimpleNamespace(result=[]) + q_patch, o_patch = _patched_query_and_obbject(sentinel) + with q_patch, o_patch: + out = await monthly_trade( + cc=MagicMock(), + provider_choices=MagicMock(), + standard_params=MagicMock(), + extra_params=MagicMock(), + ) + assert out is sentinel + + @pytest.mark.asyncio + async def test_container_metrics_calls_from_query(self): + """``container_metrics`` delegates to ``OBBject.from_query``.""" + + sentinel = SimpleNamespace(result=[]) + q_patch, o_patch = _patched_query_and_obbject(sentinel) + with q_patch, o_patch: + out = await container_metrics( + cc=MagicMock(), + provider_choices=MagicMock(), + standard_params=MagicMock(), + extra_params=MagicMock(), + ) + assert out is sentinel + + @pytest.mark.asyncio + async def test_disruption_events_calls_from_query(self): + """``disruption_events`` delegates to ``OBBject.from_query``.""" + + sentinel = SimpleNamespace(result=[]) + q_patch, o_patch = _patched_query_and_obbject(sentinel) + with q_patch, o_patch: + out = await disruption_events( + cc=MagicMock(), + provider_choices=MagicMock(), + standard_params=MagicMock(), + extra_params=MagicMock(), + ) + assert out is sentinel + + @pytest.mark.asyncio + async def test_disruptions_map_calls_from_query(self): + """``disruptions_map`` delegates to ``OBBject.from_query``.""" + + sentinel = SimpleNamespace(result=[]) + q_patch, o_patch = _patched_query_and_obbject(sentinel) + with q_patch, o_patch: + out = await disruptions_map( + cc=MagicMock(), + provider_choices=MagicMock(), + standard_params=MagicMock(), + extra_params=MagicMock(), + ) + assert out is sentinel + + @pytest.mark.asyncio + async def test_disruption_sankey_calls_from_query(self): + """``disruption_sankey`` delegates to ``OBBject.from_query``.""" + + sentinel = SimpleNamespace(result=[]) + q_patch, o_patch = _patched_query_and_obbject(sentinel) + with q_patch, o_patch: + out = await disruption_sankey( + cc=MagicMock(), + provider_choices=MagicMock(), + standard_params=MagicMock(), + extra_params=MagicMock(), + ) + assert out is sentinel + + +class TestListDataflows: + """Tests for ``list_dataflows``.""" + + @pytest.mark.asyncio + async def test_json_returns_dataflow_dict(self): + """JSON output returns ``metadata.dataflows`` verbatim.""" + meta = MagicMock() + meta.dataflows = {"X": {"name": "X"}} + meta.list_all_dataflow_tables.return_value = {} + out = await list_dataflows(meta, output_format="json") + assert out.results == {"X": {"name": "X"}} + + @pytest.mark.asyncio + async def test_markdown_returns_rendered_string(self): + """Markdown output delegates to ``_render_dataflows_markdown``.""" + meta = MagicMock() + meta.dataflows = {"X": {"name": "X"}} + meta.list_all_dataflow_tables.return_value = {} + meta.get_indicators_in.return_value = [] + meta.get_dataflow_parameters.return_value = {} + out = await list_dataflows(meta, output_format="markdown") + assert isinstance(out.results, str) + assert "## `X`" in out.results + + +class TestGetDataflowDimensions: + """Tests for ``get_dataflow_dimensions``.""" + + @pytest.mark.asyncio + async def test_json_format_returns_params(self): + """JSON format returns the raw parameters dict.""" + meta = MagicMock() + meta.get_dataflow_parameters.return_value = {"COUNTRY": []} + out = await get_dataflow_dimensions(meta, dataflow_id="X", output_format="json") + assert out.results == {"COUNTRY": []} + + @pytest.mark.asyncio + async def test_markdown_format_renders(self): + """Markdown format goes through ``_render_parameters_markdown``.""" + meta = MagicMock() + meta.get_dataflow_parameters.return_value = { + "COUNTRY": [{"value": "USA", "label": "United States"}] + } + out = await get_dataflow_dimensions( + meta, dataflow_id="X", output_format="markdown" + ) + assert "### `COUNTRY`" in out.results + + +class TestListPortIdChoices: + """Tests for ``list_port_id_choices``.""" + + @pytest.mark.asyncio + async def test_delegates_to_helper(self): + """The endpoint forwards the helper output unchanged.""" + with patch( + "openbb_imf.utils.port_watch_helpers.get_port_id_choices", + return_value=[{"label": "Port", "value": "P1"}], + ): + assert await list_port_id_choices() == [{"label": "Port", "value": "P1"}] + + +class TestListTables: + """Tests for ``list_tables``.""" + + @pytest.mark.asyncio + async def test_builds_imf_table_metadata(self): + """Each presentation becomes an ``ImfTableMetadata`` instance.""" + meta = MagicMock() + meta.list_all_dataflow_tables.return_value = { + "DF1": [ + { + "id": "T1", + "name": "Table 1", + "description": " desc ", + "agency_id": "IMF.STA", + "codelist_id": "CL", + } + ] + } + out = await list_tables(meta) + assert len(out.results) == 1 + first = out.results[0] + assert first.name == "Table 1" + assert first.symbol == "DF1::T1" + assert first.description == "desc" + + +class TestListTableChoices: + """Tests for ``list_table_choices``.""" + + @pytest.mark.asyncio + async def test_flattens_presentations(self): + """Each presentation becomes a ``{label, value}`` row.""" + meta = MagicMock() + meta.list_all_dataflow_tables.return_value = { + "DF1": [{"id": "T1", "name": "Table 1"}], + "DF2": [{"id": "T2", "name": "Table 2"}], + } + out = await list_table_choices(meta) + assert {row["value"] for row in out} == {"DF1::T1", "DF2::T2"} + + +class TestListDataflowChoices: + """Tests for ``list_dataflow_choices``.""" + + @pytest.mark.asyncio + async def test_sorts_by_label(self): + """Choices are sorted by the human-readable label.""" + meta = MagicMock() + meta.dataflows = { + "Z": {"name": "Zeta"}, + "A": {"name": "Alpha"}, + } + out = await list_dataflow_choices(meta) + assert [r["label"] for r in out] == ["Alpha", "Zeta"] + + +class TestPresentationTableChoices: + """Tests for the progressive ``presentation_table_choices`` endpoint.""" + + @pytest.mark.asyncio + async def test_no_dataflow_group_returns_global_choices(self): + """With no ``dataflow_group``, the global table_dataflow_choices is returned.""" + from openbb_imf.utils.constants import table_dataflow_choices + + meta = MagicMock() + out = await presentation_table_choices(meta) + assert out == table_dataflow_choices + + @pytest.mark.asyncio + async def test_dataflow_group_only_returns_table_choices(self): + """Group without table returns the table names for that group.""" + meta = MagicMock() + out = await presentation_table_choices(meta, dataflow_group="cpi") + assert any(r["value"] == "cpi" for r in out) + + @pytest.mark.asyncio + async def test_table_with_country_dim_returns_country_choices(self): + """Group+table returns sorted countries using the ``COUNTRY`` dimension.""" + meta = MagicMock() + meta.get_dataflow_parameters.return_value = { + "COUNTRY": [ + {"label": "Zambia", "value": "ZMB"}, + {"label": "Angola", "value": "AGO"}, + ] + } + out = await presentation_table_choices(meta, dataflow_group="cpi", table="cpi") + assert [r["value"] for r in out] == ["AGO", "ZMB"] + + @pytest.mark.asyncio + async def test_table_with_jurisdiction_dim(self): + """When ``COUNTRY`` is absent, fall back to ``JURISDICTION``.""" + meta = MagicMock() + meta.get_dataflow_parameters.return_value = { + "JURISDICTION": [{"label": "USA", "value": "USA"}] + } + out = await presentation_table_choices(meta, dataflow_group="cpi", table="cpi") + assert out == [{"label": "USA", "value": "USA"}] + + @pytest.mark.asyncio + async def test_table_with_ref_area_dim(self): + """When neither ``COUNTRY`` nor ``JURISDICTION`` is present, use ``REF_AREA``.""" + meta = MagicMock() + meta.get_dataflow_parameters.return_value = { + "REF_AREA": [{"label": "World", "value": "WLD"}] + } + out = await presentation_table_choices(meta, dataflow_group="cpi", table="cpi") + assert out == [{"label": "World", "value": "WLD"}] + + @pytest.mark.asyncio + async def test_frequency_branch_calls_params_builder(self): + """Group+table+country reaches the ``ImfParamsBuilder`` frequency branch.""" + + meta = MagicMock() + meta.get_dataflow_parameters.return_value = {"COUNTRY": [], "FREQUENCY": []} + meta.get_dataflow_table_structure.return_value = { + "indicators": [ + {"indicator_code": "X", "dimension_id": "INDICATOR"}, + {"indicator_code": "X", "dimension_id": "INDICATOR"}, + {"indicator_code": "Y", "dimension_id": "INDICATOR"}, + {"indicator_code": None, "dimension_id": "BAD"}, + ] + } + + fake_pb = MagicMock() + fake_pb._get_dimensions_in_order.return_value = [ + "INDICATOR", + "COUNTRY", + "FREQUENCY", + "OTHER", + ] + fake_pb.get_options_for_dimension.return_value = [ + {"label": "Annual", "value": "A"} + ] + + with patch( + "openbb_imf.utils.progressive_helper.ImfParamsBuilder", + return_value=fake_pb, + ): + out = await presentation_table_choices( + meta, + dataflow_group="cpi", + table="cpi", + country="USA,GBR", + ) + assert out == [{"label": "Annual", "value": "A"}] + fake_pb.get_options_for_dimension.assert_called_once_with("FREQUENCY") + fake_pb.set_dimension.assert_any_call(("INDICATOR", "X+Y")) + fake_pb.set_dimension.assert_any_call(("COUNTRY", "USA+GBR")) + + @pytest.mark.asyncio + async def test_indicator_long_joined_truncated_to_top20(self): + """Joined indicator codes >800 chars are truncated to the first 20.""" + meta = MagicMock() + meta.get_dataflow_parameters.return_value = {"COUNTRY": []} + codes = [f"CODE{i:016d}" for i in range(60)] + meta.get_dataflow_table_structure.return_value = { + "indicators": [ + {"indicator_code": c, "dimension_id": "INDICATOR"} for c in codes + ] + } + + fake_pb = MagicMock() + fake_pb._get_dimensions_in_order.return_value = ["INDICATOR", "COUNTRY"] + fake_pb.get_options_for_dimension.return_value = [] + + with patch( + "openbb_imf.utils.progressive_helper.ImfParamsBuilder", + return_value=fake_pb, + ): + await presentation_table_choices( + meta, + dataflow_group="cpi", + table="cpi", + country="USA", + ) + ind_call = next( + c + for c in fake_pb.set_dimension.call_args_list + if c.args[0][0] == "INDICATOR" + ) + joined = ind_call.args[0][1] + assert len(joined.split("+")) == 20 + + @pytest.mark.asyncio + async def test_indicator_codes_truncated_to_wildcard(self): + """Even truncated to 20, oversize joined codes collapse to ``*``.""" + meta = MagicMock() + meta.get_dataflow_parameters.return_value = {"COUNTRY": []} + codes = [f"CODE_{i:0050d}" for i in range(40)] + meta.get_dataflow_table_structure.return_value = { + "indicators": [ + {"indicator_code": c, "dimension_id": "INDICATOR"} for c in codes + ] + } + + fake_pb = MagicMock() + fake_pb._get_dimensions_in_order.return_value = ["INDICATOR", "COUNTRY"] + fake_pb.get_options_for_dimension.return_value = [] + + with patch( + "openbb_imf.utils.progressive_helper.ImfParamsBuilder", + return_value=fake_pb, + ): + await presentation_table_choices( + meta, + dataflow_group="cpi", + table="cpi", + country="USA", + ) + ind_call = next( + c + for c in fake_pb.set_dimension.call_args_list + if c.args[0][0] == "INDICATOR" + ) + assert ind_call.args[0][1] == "*" + + @pytest.mark.asyncio + async def test_freq_branch_with_freq_fallback(self): + """When ``FREQUENCY`` is absent, fall back to ``FREQ`` as the freq dim.""" + + meta = MagicMock() + meta.get_dataflow_parameters.return_value = {"REF_AREA": [], "FREQ": []} + meta.get_dataflow_table_structure.return_value = {"indicators": []} + + fake_pb = MagicMock() + fake_pb._get_dimensions_in_order.return_value = ["REF_AREA", "FREQ"] + fake_pb.get_options_for_dimension.return_value = [] + + with patch( + "openbb_imf.utils.progressive_helper.ImfParamsBuilder", + return_value=fake_pb, + ): + out = await presentation_table_choices( + meta, + dataflow_group="cpi", + table="cpi", + country="USA", + ) + fake_pb.get_options_for_dimension.assert_called_once_with("FREQ") + assert out == [] + + @pytest.mark.asyncio + async def test_all_four_params_returns_transform_options(self): + """All four params set takes the ``TYPE_OF_TRANSFORMATION`` branch.""" + meta = MagicMock() + meta.get_dataflow_parameters.return_value = { + "COUNTRY": [], + "FREQUENCY": [], + "TYPE_OF_TRANSFORMATION": [], + } + meta.get_dataflow_table_structure.return_value = { + "indicators": [ + {"indicator_code": "X", "dimension_id": "INDICATOR"}, + ] + } + + fake_pb = MagicMock() + fake_pb._get_dimensions_in_order.return_value = [ + "INDICATOR", + "COUNTRY", + "FREQUENCY", + "TYPE_OF_TRANSFORMATION", + ] + fake_pb.get_options_for_dimension.return_value = [ + {"label": "Percent Change", "value": "PCH"} + ] + + with patch( + "openbb_imf.utils.progressive_helper.ImfParamsBuilder", + return_value=fake_pb, + ): + out = await presentation_table_choices( + meta, + dataflow_group="cpi", + table="cpi", + country="USA", + frequency="A", + ) + assert out == [{"label": "Percent Change", "value": "PCH"}] + fake_pb.get_options_for_dimension.assert_called_once_with( + "TYPE_OF_TRANSFORMATION" + ) + fake_pb.set_dimension.assert_any_call(("INDICATOR", "X")) + fake_pb.set_dimension.assert_any_call(("COUNTRY", "USA")) + fake_pb.set_dimension.assert_any_call(("FREQUENCY", "A")) + + @pytest.mark.asyncio + async def test_all_four_params_indicator_truncated_to_wildcard(self): + """In the transform branch, oversize indicator joins collapse to ``*``.""" + meta = MagicMock() + meta.get_dataflow_parameters.return_value = { + "COUNTRY": [], + "FREQUENCY": [], + "TYPE_OF_TRANSFORMATION": [], + } + codes = [f"CODE_{i:0050d}" for i in range(40)] + meta.get_dataflow_table_structure.return_value = { + "indicators": [ + {"indicator_code": c, "dimension_id": "INDICATOR"} for c in codes + ] + } + + fake_pb = MagicMock() + fake_pb._get_dimensions_in_order.return_value = [ + "INDICATOR", + "COUNTRY", + "FREQUENCY", + "TYPE_OF_TRANSFORMATION", + ] + fake_pb.get_options_for_dimension.return_value = [] + + with patch( + "openbb_imf.utils.progressive_helper.ImfParamsBuilder", + return_value=fake_pb, + ): + await presentation_table_choices( + meta, + dataflow_group="cpi", + table="cpi", + country="USA", + frequency="A", + ) + ind_call = next( + c + for c in fake_pb.set_dimension.call_args_list + if c.args[0][0] == "INDICATOR" + ) + assert ind_call.args[0][1] == "*" + + @pytest.mark.asyncio + async def test_all_four_params_no_transform_dim_returns_empty(self): + """When the dataflow has no ``TYPE_OF_TRANSFORMATION``, returns ``[]``.""" + meta = MagicMock() + meta.get_dataflow_parameters.return_value = { + "COUNTRY": [], + "FREQUENCY": [], + } + meta.get_dataflow_table_structure.return_value = {"indicators": []} + + fake_pb = MagicMock() + fake_pb._get_dimensions_in_order.return_value = ["COUNTRY", "FREQUENCY"] + fake_pb.get_options_for_dimension.return_value = [] + + with patch( + "openbb_imf.utils.progressive_helper.ImfParamsBuilder", + return_value=fake_pb, + ): + out = await presentation_table_choices( + meta, + dataflow_group="cpi", + table="cpi", + country="USA", + frequency="A", + ) + assert out == [] + fake_pb.get_options_for_dimension.assert_not_called() + + +class TestPresentationTable: + """Tests for the ``presentation_table`` endpoint.""" + + @pytest.mark.asyncio + async def test_missing_dataflow_group_raises(self): + """Missing dataflow_group is rejected as ``OpenBBError``.""" + from openbb_core.app.model.abstract.error import OpenBBError + + with pytest.raises(OpenBBError): + await presentation_table(table="cpi", country="USA", frequency="A") + + @pytest.mark.asyncio + async def test_missing_country_raises(self): + """Missing country is rejected as ``OpenBBError``.""" + from openbb_core.app.model.abstract.error import OpenBBError + + with pytest.raises(OpenBBError): + await presentation_table(dataflow_group="cpi", table="cpi", frequency="A") + + @pytest.mark.asyncio + async def test_indents_titles_from_fetcher_rows(self): + """Fetcher rows have their ``title`` indented according to ``level``.""" + rows = [ + SimpleNamespace(model_dump=lambda **_: {"title": "Top", "level": 0}), + SimpleNamespace(model_dump=lambda **_: {"title": "Child", "level": "1"}), + SimpleNamespace(model_dump=lambda **_: {"title": "Bad", "level": "junk"}), + ] + result_holder = SimpleNamespace(result=rows) + + with patch( + "openbb_imf.models.economic_indicators.ImfEconomicIndicatorsFetcher.fetch_data", + new=AsyncMock(return_value=result_holder), + ): + out = await presentation_table( + dataflow_group="cpi", + table="cpi", + country="USA", + frequency="A", + limit=1, + ) + assert out[0]["title"] == "Top" + assert out[1]["title"] == _INDENT_UNIT + "Child" + assert out[2]["title"] == "Bad" + + @pytest.mark.asyncio + async def test_rewraps_pre_indented_titles_when_level_missing(self): + """Rows with ``level=None`` have leading whitespace rewrapped to ``_INDENT_UNIT``.""" + rows = [ + SimpleNamespace( + model_dump=lambda **_: { + "title": "โ–ธ 3. GOVERNMENT BALANCE SHEET", + "level": None, + } + ), + SimpleNamespace( + model_dump=lambda **_: {"title": " Financial assets", "level": None} + ), + SimpleNamespace( + model_dump=lambda **_: { + "title": " General government", + "level": None, + } + ), + SimpleNamespace( + model_dump=lambda **_: { + "title": " Central government", + "level": None, + } + ), + ] + result_holder = SimpleNamespace(result=rows) + + with patch( + "openbb_imf.models.economic_indicators.ImfEconomicIndicatorsFetcher.fetch_data", + new=AsyncMock(return_value=result_holder), + ): + out = await presentation_table( + dataflow_group="qgfs", + table="qgfs_balance", + country="BRA", + frequency="Q", + limit=1, + ) + assert out[0]["title"] == "โ–ธ 3. GOVERNMENT BALANCE SHEET" + assert out[1]["title"] == _INDENT_UNIT + "Financial assets" + assert out[2]["title"] == _INDENT_UNIT * 2 + "General government" + assert out[3]["title"] == _INDENT_UNIT * 3 + "Central government" + for row in out[1:]: + assert row["title"][0] == ">" + + @pytest.mark.asyncio + async def test_drops_country_for_single_country_request(self): + """When the country parameter resolves to one country the column is dropped.""" + rows = [ + SimpleNamespace( + model_dump=lambda **_: { + "country": "United States", + "title": "Header", + "level": 0, + "2025-06-30": 1.0, + "2025-12-31": 2.0, + "2025-09-30": 3.0, + } + ) + ] + result_holder = SimpleNamespace(result=rows) + with patch( + "openbb_imf.models.economic_indicators.ImfEconomicIndicatorsFetcher.fetch_data", + new=AsyncMock(return_value=result_holder), + ): + out = await presentation_table( + dataflow_group="cpi", + table="cpi", + country="USA", + frequency="A", + limit=1, + ) + assert list(out[0].keys()) == [ + "title", + "2025-12-31", + "2025-09-30", + "2025-06-30", + ] + assert "country" not in out[0] + + @pytest.mark.asyncio + async def test_keeps_country_for_multi_country_request(self): + """When the country parameter lists multiple codes the column is kept.""" + rows = [ + SimpleNamespace( + model_dump=lambda **_: { + "country": "United States", + "title": "Header", + "level": 0, + "2025-12-31": 1.0, + "extra_meta": "kept", + } + ) + ] + result_holder = SimpleNamespace(result=rows) + with patch( + "openbb_imf.models.economic_indicators.ImfEconomicIndicatorsFetcher.fetch_data", + new=AsyncMock(return_value=result_holder), + ): + out = await presentation_table( + dataflow_group="cpi", + table="cpi", + country="USA+JPN", + frequency="A", + limit=1, + ) + assert list(out[0].keys()) == ["country", "title", "2025-12-31", "extra_meta"] + assert out[0]["country"] == "United States" + assert out[0]["extra_meta"] == "kept" + + +class TestIndicatorChoices: + """Tests for the ``indicator_choices`` progressive endpoint.""" + + def _meta(self, dim_order=("COUNTRY", "INDICATOR", "FREQUENCY"), **overrides): + """Build a fake metadata supporting indicator_choices' lookups.""" + meta = MagicMock() + meta.dataflows = { + "DF1": {"id": "DF1", "structureRef": {"id": "DSD1"}, "name": "DF1"} + } + meta.datastructures = { + "DSD1": { + "dimensions": [ + {"id": dim, "position": str(i)} for i, dim in enumerate(dim_order) + ] + + [{"id": "TIME_PERIOD", "position": "99"}] + } + } + meta.get_dataflow_parameters.return_value = overrides.get( + "params", + { + "COUNTRY": [{"value": "USA", "label": "United States"}], + "INDICATOR": [{"value": "GDP", "label": "Gross Domestic"}], + "FREQUENCY": [{"value": "A", "label": "Annual"}], + }, + ) + meta.get_available_constraints.return_value = overrides.get( + "constraints", + { + "key_values": [ + { + "id": "COUNTRY", + "values": ["USA"], + }, + { + "id": "FREQUENCY", + "values": ["A"], + }, + { + "id": "INDICATOR", + "values": ["GDP"], + }, + ] + }, + ) + meta._resolve_codelist_id.return_value = "CL_X" + meta._codelist_cache = overrides.get("codelist_cache", {"CL_X": {}}) + return meta + + @pytest.mark.asyncio + async def test_returns_empty_when_symbol_missing(self): + """No symbol โ†’ empty list.""" + assert await indicator_choices(self._meta(), symbol=None) == [] + + @pytest.mark.asyncio + async def test_returns_empty_when_all_symbols_blank(self): + """All-whitespace symbol list โ†’ empty.""" + assert await indicator_choices(self._meta(), symbol=" , , ") == [] + + @pytest.mark.asyncio + async def test_returns_empty_when_dataflow_unknown(self): + """Unknown dataflow ID โ†’ empty.""" + meta = self._meta() + meta.dataflows = {} + assert ( + await indicator_choices(meta, symbol="UNKNOWN::IND", country="true") == [] + ) + + @pytest.mark.asyncio + async def test_country_branch(self): + """``country='true'`` returns sorted countries with an ``All`` row prefix.""" + meta = self._meta() + out = await indicator_choices(meta, symbol="DF1::GDP", country="true") + assert out[0] == {"label": "All Countries", "value": "*"} + assert {row["value"] for row in out[1:]} == {"USA"} + + @pytest.mark.asyncio + async def test_frequency_branch(self): + """``frequency='true'`` returns frequency options.""" + meta = self._meta() + out = await indicator_choices( + meta, symbol="DF1::GDP", country="USA", frequency="true" + ) + assert any(row["value"] == "A" for row in out) + + @pytest.mark.asyncio + async def test_transform_branch_with_unit_fallback(self): + """``transform='true'`` resolves the transform dim via ``detect_transform_dimension``.""" + + meta = self._meta( + dim_order=("COUNTRY", "INDICATOR", "FREQUENCY", "UNIT_MEASURE") + ) + meta.get_dataflow_parameters.return_value = { + "COUNTRY": [{"value": "USA", "label": "United States"}], + "INDICATOR": [{"value": "GDP", "label": "Gross Domestic"}], + "FREQUENCY": [{"value": "A", "label": "Annual"}], + "UNIT_MEASURE": [{"value": "USD", "label": "USD"}], + } + meta.get_available_constraints.return_value = { + "key_values": [{"id": "UNIT_MEASURE", "values": ["USD"]}] + } + + with patch( + "openbb_imf.utils.helpers.detect_transform_dimension", + return_value=(None, "UNIT_MEASURE", None, None), + ): + out = await indicator_choices( + meta, + symbol="DF1::GDP", + country="USA", + frequency="A", + transform="true", + ) + assert out[0] == {"label": "All", "value": "all"} + assert any(row["value"] == "USD" for row in out[1:]) + + @pytest.mark.asyncio + async def test_transform_branch_empty_when_no_transform_dim(self): + """Without an effective transform dim, the transform branch returns ``[]``.""" + + meta = self._meta() + with patch( + "openbb_imf.utils.helpers.detect_transform_dimension", + return_value=(None, None, None, None), + ): + out = await indicator_choices( + meta, + symbol="DF1::GDP", + country="USA", + frequency="A", + transform="true", + ) + assert out == [] + + @pytest.mark.asyncio + async def test_default_fallthrough_returns_empty(self): + """When none of the ``true`` toggles fire, the function returns ``[]``.""" + meta = self._meta() + out = await indicator_choices( + meta, symbol="DF1::GDP", country="USA", frequency="A" + ) + assert out == [] + + @pytest.mark.asyncio + async def test_symbol_without_double_colon_added_to_dataflows(self): + """A bare symbol (no ``::``) is treated as a dataflow id.""" + meta = self._meta() + out = await indicator_choices(meta, symbol="DF1", country="true") + assert out[0] == {"label": "All Countries", "value": "*"} + + @pytest.mark.asyncio + async def test_multi_symbol_joined_into_plus_separated(self): + """Multiple comma-separated symbols join into a single ``+``-separated key.""" + + meta = self._meta() + meta.get_available_constraints.return_value = { + "key_values": [{"id": "FREQUENCY", "values": ["A"]}] + } + with patch( + "openbb_imf.utils.helpers.detect_transform_dimension", + return_value=(None, None, None, None), + ): + await indicator_choices( + meta, + symbol="DF1::GDP,DF1::CPI", + country="USA", + frequency="true", + ) + called_with = meta.get_available_constraints.call_args.kwargs + assert "GDP+CPI" in called_with["key"] + + @pytest.mark.asyncio + async def test_dimension_values_extra_filters_applied(self): + """``dimension_values`` are split into ``DIM:VAL`` pairs and consumed.""" + meta = self._meta(dim_order=("COUNTRY", "INDICATOR", "FREQUENCY", "SECTOR")) + meta.get_dataflow_parameters.return_value = { + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "GDP", "label": "GDP"}], + "FREQUENCY": [{"value": "A", "label": "Annual"}], + "SECTOR": [{"value": "FIN", "label": "Finance"}], + } + meta.get_available_constraints.return_value = { + "key_values": [{"id": "FREQUENCY", "values": ["A"]}] + } + out = await indicator_choices( + meta, + symbol="DF1::GDP", + frequency="true", + dimension_values=[ + "COUNTRY:USA", + "SECTOR:FIN", + "BAD_NO_COLON", + "", + None, + ], + ) + assert any(row["value"] == "A" for row in out) + + @pytest.mark.asyncio + async def test_indicator_dim_resolved_via_dim_order_fallback(self): + """When the indicator code isn't in any known indicator-dim, search dim_order.""" + meta = self._meta(dim_order=("COUNTRY", "WIDGET_KIND", "FREQUENCY")) + meta.get_dataflow_parameters.return_value = { + "COUNTRY": [{"value": "USA", "label": "USA"}], + "WIDGET_KIND": [{"value": "GDP", "label": "GDP"}], + "FREQUENCY": [{"value": "A", "label": "Annual"}], + } + meta.get_available_constraints.return_value = { + "key_values": [{"id": "WIDGET_KIND", "values": ["GDP"]}] + } + out = await indicator_choices(meta, symbol="DF1::GDP", country="true") + assert any(row["value"] == "USA" for row in out[1:]) or out[0] == { + "label": "All Countries", + "value": "*", + } + + @pytest.mark.asyncio + async def test_indicator_dim_resolved_via_known_first_match_in_indicator_dims( + self, + ): + """If the first known indicator dim contains the code, it wins.""" + meta = self._meta(dim_order=("COUNTRY", "INDICATOR", "INDEX_TYPE", "FREQUENCY")) + meta.get_dataflow_parameters.return_value = { + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "GDP", "label": "GDP"}], + "INDEX_TYPE": [{"value": "GDP", "label": "Different GDP"}], + "FREQUENCY": [{"value": "A", "label": "Annual"}], + } + meta.get_available_constraints.return_value = { + "key_values": [{"id": "COUNTRY", "values": ["USA"]}] + } + out = await indicator_choices(meta, symbol="DF1::GDP", country="true") + assert out[0]["value"] == "*" + + @pytest.mark.asyncio + async def test_indicator_dim_default_when_no_match(self): + """If no dim contains the indicator value, fall back to the first known one.""" + meta = self._meta(dim_order=("COUNTRY", "INDICATOR", "FREQUENCY")) + meta.get_dataflow_parameters.return_value = { + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "OTHER", "label": "Other"}], + "FREQUENCY": [{"value": "A", "label": "Annual"}], + } + meta.get_available_constraints.return_value = { + "key_values": [{"id": "COUNTRY", "values": ["USA"]}] + } + out = await indicator_choices(meta, symbol="DF1::NOMATCH", country="true") + assert out[0]["value"] == "*" + + @pytest.mark.asyncio + async def test_codelist_labels_used_when_param_labels_missing(self): + """Codelist labels back-fill when the param-list label is unset.""" + meta = self._meta() + meta.get_dataflow_parameters.return_value = { + "COUNTRY": [], + "INDICATOR": [], + "FREQUENCY": [], + } + meta._codelist_cache = {"CL_X": {"USA": "United States of America"}} + meta.get_available_constraints.return_value = { + "key_values": [{"id": "COUNTRY", "values": ["USA"]}] + } + out = await indicator_choices(meta, symbol="DF1::GDP", country="true") + labels = [row["label"] for row in out[1:]] + assert "United States of America" in labels + + @pytest.mark.asyncio + async def test_symbol_with_empty_dataflow_id_returns_empty(self): + """A ``::IND`` style symbol with an empty dataflow id falls through to ``[]``.""" + meta = self._meta() + out = await indicator_choices(meta, symbol="::GDP", country="true") + assert out == [] + + @pytest.mark.asyncio + async def test_dimension_values_freq_extra_reassigns_frequency(self): + """``FREQ`` in ``dimension_values`` overrides the ``frequency`` argument.""" + meta = self._meta() + meta.get_dataflow_parameters.return_value = { + "COUNTRY": [], + "INDICATOR": [], + "FREQUENCY": [], + } + meta.get_available_constraints.return_value = { + "key_values": [{"id": "FREQUENCY", "values": ["A"]}] + } + out = await indicator_choices( + meta, + symbol="DF1::GDP", + frequency="true", + dimension_values=["FREQ:A"], + ) + assert out == [] + + @pytest.mark.asyncio + async def test_dimension_values_transform_extra_reassigns_transform(self): + """``UNIT_MEASURE`` in ``dimension_values`` overrides the ``transform`` arg.""" + meta = self._meta( + dim_order=("COUNTRY", "INDICATOR", "FREQUENCY", "UNIT_MEASURE") + ) + meta.get_dataflow_parameters.return_value = { + "COUNTRY": [], + "INDICATOR": [], + "FREQUENCY": [], + "UNIT_MEASURE": [], + } + meta.get_available_constraints.return_value = { + "key_values": [{"id": "UNIT_MEASURE", "values": ["USD"]}] + } + with patch( + "openbb_imf.utils.helpers.detect_transform_dimension", + return_value=(None, "UNIT_MEASURE", None, None), + ): + out = await indicator_choices( + meta, + symbol="DF1::GDP", + country="USA", + frequency="A", + transform="true", + dimension_values=["UNIT_MEASURE:USD"], + ) + assert out == [] + + @pytest.mark.asyncio + async def test_transform_dim_branch_in_build_key(self): + """The ``transform_dim`` branch in ``build_key_with_indicator`` fires when the dim is in ``dim_order``.""" + meta = self._meta( + dim_order=("COUNTRY", "INDICATOR", "FREQUENCY", "TYPE_OF_TRANSFORMATION") + ) + meta.get_dataflow_parameters.return_value = { + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "GDP", "label": "GDP"}], + "FREQUENCY": [{"value": "A", "label": "Annual"}], + "TYPE_OF_TRANSFORMATION": [{"value": "PCT", "label": "Pct"}], + } + meta.get_available_constraints.return_value = { + "key_values": [{"id": "COUNTRY", "values": ["USA"]}] + } + with patch( + "openbb_imf.utils.helpers.detect_transform_dimension", + return_value=("TYPE_OF_TRANSFORMATION", None, None, None), + ): + out = await indicator_choices( + meta, + symbol="DF1::GDP", + country="true", + transform="PCT", + ) + assert out[0]["value"] == "*" + + @pytest.mark.asyncio + async def test_codelist_id_unresolved_falls_back_to_value_as_label(self): + """When neither param-label nor codelist-label resolves, label = value.""" + meta = self._meta() + meta.get_dataflow_parameters.return_value = { + "COUNTRY": [], + "INDICATOR": [], + "FREQUENCY": [], + } + meta._codelist_cache = {} + meta._resolve_codelist_id.return_value = None + meta.get_available_constraints.return_value = { + "key_values": [{"id": "COUNTRY", "values": ["USA"]}] + } + out = await indicator_choices(meta, symbol="DF1::GDP", country="true") + labels = [row["label"] for row in out[1:]] + assert "USA" in labels + + +class TestBopCpiCountryChoices: + """Tests for ``list_bop_country_choices`` and ``list_cpi_country_choices``.""" + + @pytest.mark.asyncio + async def test_list_bop_country_choices_sorts_and_skips_empty(self): + """Skips entries missing ``value``, sorts the rest by label, upper-cases codes.""" + meta = MagicMock() + meta.get_dataflow_parameters.return_value = { + "COUNTRY": [ + {"value": "usa", "label": "United States"}, + {"value": "", "label": "Skipped"}, + {"label": "No Value"}, + {"value": "alb", "label": "Albania"}, + ] + } + choices = await list_bop_country_choices(meta) + meta.get_dataflow_parameters.assert_called_once_with("BOP") + assert choices == [ + {"label": "Albania", "value": "ALB"}, + {"label": "United States", "value": "USA"}, + ] + + @pytest.mark.asyncio + async def test_list_bop_country_choices_falls_back_to_code_label(self): + """Entries without a label fall back to the code as the label.""" + meta = MagicMock() + meta.get_dataflow_parameters.return_value = {"COUNTRY": [{"value": "USA"}]} + assert await list_bop_country_choices(meta) == [ + {"label": "USA", "value": "USA"} + ] + + @pytest.mark.asyncio + async def test_list_cpi_country_choices_sorts_and_skips_empty(self): + """``list_cpi_country_choices`` mirrors the BOP behaviour against the CPI dataflow.""" + meta = MagicMock() + meta.get_dataflow_parameters.return_value = { + "COUNTRY": [ + {"value": "fra", "label": "France"}, + {"value": "", "label": "Skipped"}, + {"label": "No Value"}, + {"value": "deu", "label": "Germany"}, + ] + } + choices = await list_cpi_country_choices(meta) + meta.get_dataflow_parameters.assert_called_once_with("CPI") + assert choices == [ + {"label": "France", "value": "FRA"}, + {"label": "Germany", "value": "DEU"}, + ] + + @pytest.mark.asyncio + async def test_list_cpi_country_choices_falls_back_to_code_label(self): + """CPI entries without a label fall back to the code.""" + meta = MagicMock() + meta.get_dataflow_parameters.return_value = {"COUNTRY": [{"value": "JPN"}]} + assert await list_cpi_country_choices(meta) == [ + {"label": "JPN", "value": "JPN"} + ] + + +class TestRewriteWidgetIds: + """Tests for ``_rewrite_widget_ids`` and the apps.json fallback path.""" + + def test_rewrites_mapped_ids_and_preserves_unmapped(self): + """Only IDs in the fallback map are rewritten; everything else passes through.""" + apps = [ + { + "tabs": { + "a": { + "layout": [ + {"i": "economy_cpi_imf_obb"}, + {"i": "imf_presentation_table_custom_obb"}, + ] + }, + "b": { + "layout": [ + {"i": "economy_balance_of_payments_imf_obb"}, + ] + }, + } + } + ] + out = _rewrite_widget_ids(apps) + layout_a = [w["i"] for w in out[0]["tabs"]["a"]["layout"]] + layout_b = [w["i"] for w in out[0]["tabs"]["b"]["layout"]] + assert layout_a == [ + _APPS_WIDGET_ID_FALLBACK_MAP["economy_cpi_imf_obb"], + "imf_presentation_table_custom_obb", + ] + assert layout_b == [ + _APPS_WIDGET_ID_FALLBACK_MAP["economy_balance_of_payments_imf_obb"] + ] + + def test_tolerates_missing_tabs_and_layout(self): + """Apps without ``tabs`` or with ``layout=None`` are skipped gracefully.""" + apps = [ + {}, + {"tabs": {}}, + {"tabs": {"x": {}}}, + {"tabs": {"x": {"layout": None}}}, + {"tabs": {"x": {"layout": [{"i": "economy_cpi_imf_obb"}]}}}, + ] + out = _rewrite_widget_ids(apps) + rewritten = out[-1]["tabs"]["x"]["layout"][0]["i"] + assert rewritten == _APPS_WIDGET_ID_FALLBACK_MAP["economy_cpi_imf_obb"] + + @pytest.mark.asyncio + async def test_get_imf_apps_json_rewrites_when_economy_absent( + self, tmp_path, monkeypatch + ): + """When ``ECONOMY_INSTALLED`` is False the served apps.json has rewritten IDs.""" + from openbb_imf import imf_router as router_module + + apps_path = tmp_path / "apps.json" + apps_path.write_text( + '[{"tabs": {"cpi": {"layout": [{"i": "economy_cpi_imf_obb"}]}}}]', + encoding="utf-8", + ) + monkeypatch.setattr(router_module, "__file__", str(tmp_path / "imf_router.py")) + monkeypatch.setattr(router_module, "ECONOMY_INSTALLED", False) + + apps = await get_imf_apps_json() + assert ( + apps[0]["tabs"]["cpi"]["layout"][0]["i"] + == (_APPS_WIDGET_ID_FALLBACK_MAP["economy_cpi_imf_obb"]) + ) + + +def _load_standalone_router_module(): + """Re-execute ``imf_router`` with ``ECONOMY_INSTALLED`` forced to False. + + Loaded under a private module name so the global ``openbb_imf.imf_router`` + used elsewhere in the suite is untouched. ``Router.command`` is replaced + during load with a passthrough decorator so the fallback endpoints bind + without requiring the IMF-prefixed models to exist in the live provider + registry or FastAPI's response-model machinery. + """ + import importlib.util + from pathlib import Path + + import openbb_imf + from openbb_core.app.router import Router + + spec = importlib.util.spec_from_file_location( + "openbb_imf_imf_router_standalone", + Path(openbb_imf.__file__).parent / "imf_router.py", + ) + module = importlib.util.module_from_spec(spec) + original_economy = openbb_imf.ECONOMY_INSTALLED + original_command = Router.command + openbb_imf.ECONOMY_INSTALLED = False + + def _passthrough_command(self, func=None, **_kwargs): + """Bind ``func`` to the caller without touching the underlying FastAPI router.""" + if func is None: + return lambda f: _passthrough_command(self, f, **_kwargs) + return func + + Router.command = _passthrough_command # type: ignore[assignment] + try: + spec.loader.exec_module(module) + finally: + openbb_imf.ECONOMY_INSTALLED = original_economy + Router.command = original_command # type: ignore[assignment] + return module + + +class TestStandaloneFallbackEndpoints: + """Tests for the nine endpoints registered only when ``openbb-economy`` is absent.""" + + _STANDALONE_NAMES = ( + "available_indicators", + "indicators", + "cpi", + "balance_of_payments", + "direction_of_trade", + "port_info", + "port_volume", + "chokepoint_info", + "chokepoint_volume", + ) + + def test_standalone_module_binds_all_fallback_endpoint_names(self): + """All nine fallback endpoint names exist as module attributes after reload.""" + module = _load_standalone_router_module() + for name in self._STANDALONE_NAMES: + assert callable(getattr(module, name, None)), f"missing {name}" + + @pytest.mark.asyncio + @pytest.mark.parametrize("endpoint_name", _STANDALONE_NAMES) + async def test_standalone_endpoint_body_delegates_to_from_query( + self, endpoint_name + ): + """Each fallback endpoint body calls ``OBBject.from_query(OBBQuery(**locals()))``.""" + module = _load_standalone_router_module() + fn = getattr(module, endpoint_name) + + sentinel = SimpleNamespace(result=[]) + with ( + patch.object(module, "OBBQuery", new=MagicMock()), + patch.object( + module.OBBject, + "from_query", + new=AsyncMock(return_value=sentinel), + ) as mock_from_query, + ): + out = await fn( + cc=MagicMock(), + provider_choices=MagicMock(), + standard_params=MagicMock(), + extra_params=MagicMock(), + ) + assert out is sentinel + mock_from_query.assert_awaited_once() + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/openbb_platform/providers/imf/tests/test_imf_search.py b/openbb_platform/providers/imf/tests/test_imf_search.py index 8845b3585dc..010c3b29708 100644 --- a/openbb_platform/providers/imf/tests/test_imf_search.py +++ b/openbb_platform/providers/imf/tests/test_imf_search.py @@ -1,7 +1,6 @@ """Test ImfMetadata search functionality.""" # ruff: noqa: I001 -# pylint: disable=W0621,R0903 import pytest from openbb_imf.utils import metadata as md @@ -43,7 +42,6 @@ def imf_metadata(monkeypatch): if dataflow_id not in meta.dataflows: raise ValueError(f"Dataflow '{dataflow_id}' not found.") - # Return a lightweight, deterministic parameter mapping. return { "COUNTRY": [ {"value": "USA", "label": "United States"}, diff --git a/openbb_platform/providers/imf/tests/test_imf_views.py b/openbb_platform/providers/imf/tests/test_imf_views.py new file mode 100644 index 00000000000..7c3b324bf81 --- /dev/null +++ b/openbb_platform/providers/imf/tests/test_imf_views.py @@ -0,0 +1,316 @@ +"""Tests for the IMF charting-extension dispatchers.""" + +# ruff: noqa: I001 + +from types import SimpleNamespace +from unittest.mock import MagicMock, patch + +import pytest + +from openbb_imf.imf_views import ( + ImfViews, + _content, + _dump, + _standard_params, + _theme, +) + + +class _Dumpable: + """Tiny stand-in for a Pydantic Data model used by ``_dump``.""" + + def __init__(self, payload): + """Store ``payload`` and expose it through ``model_dump``.""" + self._payload = payload + + def model_dump(self, **_): + """Return the stored payload as a plain dict.""" + return self._payload + + +class TestPrivateHelpers: + """Tests for the small private helpers in ``imf_views``.""" + + def test_dump_handles_model_objects(self): + """``_dump`` calls ``model_dump`` on Pydantic-like objects.""" + items = [_Dumpable({"a": 1}), _Dumpable({"a": 2})] + assert _dump(items) == [{"a": 1}, {"a": 2}] + + def test_dump_handles_dicts(self): + """``_dump`` passes through plain dicts.""" + assert _dump([{"a": 1}]) == [{"a": 1}] + + def test_dump_skips_unknown_types(self): + """Items that are neither models nor dicts are ignored.""" + assert _dump([_Dumpable({"a": 1}), 7, "not-a-dict"]) == [{"a": 1}] + + def test_dump_handles_none(self): + """``None`` collapses to an empty list.""" + assert _dump(None) == [] + + def test_standard_params_from_model(self): + """``_standard_params`` returns the dict form of a model.""" + sp = SimpleNamespace(model_dump=lambda: {"country_code": "USA"}) + assert _standard_params({"standard_params": sp}) == {"country_code": "USA"} + + def test_standard_params_from_dict(self): + """A dict ``standard_params`` is returned as-is.""" + assert _standard_params({"standard_params": {"k": 1}}) == {"k": 1} + + def test_standard_params_missing(self): + """Missing ``standard_params`` yields ``{}``.""" + assert _standard_params({}) == {} + + def test_standard_params_unknown_type(self): + """An unsupported ``standard_params`` type collapses to ``{}``.""" + assert _standard_params({"standard_params": 7}) == {} + + def test_theme_from_standard_params(self): + """``theme`` from inside ``standard_params`` wins.""" + sp = SimpleNamespace(model_dump=lambda: {"theme": "light"}) + assert _theme({"standard_params": sp}) == "light" + + def test_theme_from_top_level_kwargs(self): + """A top-level ``theme`` kwarg is honoured when not on standard_params.""" + assert _theme({"theme": "light"}) == "light" + + def test_theme_defaults_to_dark(self): + """When no theme is supplied, the dispatcher falls back to ``dark``.""" + assert _theme({}) == "dark" + + def test_content_wraps_show_to_plotly_json(self): + """``_content`` flattens the figure and attaches the config block.""" + fig = MagicMock() + fig.show.return_value.to_plotly_json.return_value = {"data": [], "layout": {}} + content = _content(fig) + assert content["config"] == {"displayModeBar": False} + assert "data" in content + fig.show.assert_called_once_with(external=True) + + +class TestCountryActivityDispatcher: + """Tests for ``ImfViews.imf_portwatch_country_activity``.""" + + def _kwargs(self, **overrides): + """Build a kwargs dict carrying a fake obbject + standard params.""" + sp = SimpleNamespace( + model_dump=lambda: { + "country_code": overrides.get("country_code", "USA"), + "metric": overrides.get("metric", "portcalls"), + "theme": overrides.get("theme", "dark"), + } + ) + return { + "obbject_item": [_Dumpable({"date": "2024-01-01", "portcalls": 5})], + "standard_params": sp, + } + + @patch("openbb_imf.utils.port_watch_helpers.map_port_country_code") + @patch("openbb_imf.views.port_watch_charts.plot_country_activity") + def test_dispatch_passes_title_and_metric(self, mock_plot, mock_map): + """The dispatcher resolves the country name and forwards the metric.""" + mock_map.return_value = "United States" + mock_plot.return_value = MagicMock( + show=lambda external: SimpleNamespace( + to_plotly_json=lambda: {"data": [], "layout": {}} + ) + ) + + fig, content = ImfViews.imf_portwatch_country_activity(**self._kwargs()) + + mock_map.assert_called_once_with("USA") + args, kwargs = mock_plot.call_args + assert args[1] == "United States" + assert args[2] == "portcalls" + assert kwargs == {"theme": "dark"} + assert content["config"] == {"displayModeBar": False} + assert fig is mock_plot.return_value + + @patch("openbb_imf.utils.port_watch_helpers.map_port_country_code") + @patch("openbb_imf.views.port_watch_charts.plot_country_activity") + def test_dispatch_falls_back_to_code_when_unknown(self, mock_plot, mock_map): + """A ``ValueError`` from the name lookup keeps the raw code as the title.""" + mock_map.side_effect = ValueError("unknown") + mock_plot.return_value = MagicMock( + show=lambda external: SimpleNamespace(to_plotly_json=lambda: {}) + ) + + ImfViews.imf_portwatch_country_activity(**self._kwargs(country_code="ZZZ")) + args, _ = mock_plot.call_args + assert args[1] == "ZZZ" + + @patch("openbb_imf.utils.port_watch_helpers.map_port_country_code") + @patch("openbb_imf.views.port_watch_charts.plot_country_activity") + def test_dispatch_uses_defaults_when_params_missing(self, mock_plot, mock_map): + """Missing standard params default to ``USA`` / ``portcalls`` / ``dark``.""" + mock_map.return_value = "United States" + mock_plot.return_value = MagicMock( + show=lambda external: SimpleNamespace(to_plotly_json=lambda: {}) + ) + + ImfViews.imf_portwatch_country_activity(obbject_item=[]) + mock_map.assert_called_once_with("USA") + args, kwargs = mock_plot.call_args + assert args[2] == "portcalls" + assert kwargs == {"theme": "dark"} + + +class TestMonthlyTradeDispatcher: + """Tests for ``ImfViews.imf_portwatch_monthly_trade``.""" + + @patch("openbb_imf.utils.port_watch_helpers.map_port_country_code") + @patch("openbb_imf.views.port_watch_charts.plot_monthly_trade") + def test_dispatch_with_country_code(self, mock_plot, mock_map): + """A country code resolves through ``map_port_country_code``.""" + mock_map.return_value = "United States" + mock_plot.return_value = MagicMock( + show=lambda external: SimpleNamespace(to_plotly_json=lambda: {}) + ) + sp = SimpleNamespace( + model_dump=lambda: {"code": "USA", "metric": "trade_value"} + ) + + ImfViews.imf_portwatch_monthly_trade(obbject_item=[], standard_params=sp) + args, kwargs = mock_plot.call_args + assert args[1] == "United States" + assert args[2] == "trade_value" + assert kwargs == {"theme": "dark"} + + @patch("openbb_imf.utils.port_watch_helpers.map_port_country_code") + @patch("openbb_imf.views.port_watch_charts.plot_monthly_trade") + def test_dispatch_falls_back_to_raw_code(self, mock_plot, mock_map): + """When the lookup fails, the raw region code becomes the chart label.""" + mock_map.side_effect = ValueError + mock_plot.return_value = MagicMock( + show=lambda external: SimpleNamespace(to_plotly_json=lambda: {}) + ) + sp = SimpleNamespace(model_dump=lambda: {"code": "X"}) + ImfViews.imf_portwatch_monthly_trade(obbject_item=[], standard_params=sp) + args, _ = mock_plot.call_args + assert args[1] == "X" + + +class TestContainerMetricsDispatcher: + """Tests for ``ImfViews.imf_portwatch_container_metrics``.""" + + @staticmethod + def _stub_port_name_map(monkeypatch): + """Stub ``get_container_port_name_map`` to avoid live HTTP + alru cache.""" + from openbb_imf.utils import port_watch_helpers as pwh + + async def _empty() -> dict[str, str]: + return {"PORT1065": "Busan, Korea"} + + monkeypatch.setattr(pwh, "get_container_port_name_map", _empty) + + @patch("openbb_imf.views.port_watch_charts.plot_container_metrics") + def test_metric_label_lookup(self, mock_plot, monkeypatch): + """The dispatcher maps the metric key to a human-readable label.""" + self._stub_port_name_map(monkeypatch) + mock_plot.return_value = MagicMock( + show=lambda external: SimpleNamespace(to_plotly_json=lambda: {}) + ) + sp = SimpleNamespace(model_dump=lambda: {"metric": "export_container"}) + + ImfViews.imf_portwatch_container_metrics(obbject_item=[], standard_params=sp) + args, kwargs = mock_plot.call_args + assert args[1] == "Container Exports" + assert kwargs["port_name_map"] == {"PORT1065": "Busan, Korea"} + + @patch("openbb_imf.views.port_watch_charts.plot_container_metrics") + def test_unknown_metric_falls_back(self, mock_plot, monkeypatch): + """Unknown metrics fall back to the default 'Container Port Calls' label.""" + self._stub_port_name_map(monkeypatch) + mock_plot.return_value = MagicMock( + show=lambda external: SimpleNamespace(to_plotly_json=lambda: {}) + ) + sp = SimpleNamespace(model_dump=lambda: {"metric": "weird"}) + ImfViews.imf_portwatch_container_metrics(obbject_item=[], standard_params=sp) + args, _ = mock_plot.call_args + assert args[1] == "Container Port Calls" + + @patch("openbb_imf.views.port_watch_charts.plot_container_metrics") + def test_missing_metric_uses_default(self, mock_plot, monkeypatch): + """Missing metric keys collapse to the portcalls label.""" + self._stub_port_name_map(monkeypatch) + mock_plot.return_value = MagicMock( + show=lambda external: SimpleNamespace(to_plotly_json=lambda: {}) + ) + ImfViews.imf_portwatch_container_metrics(obbject_item=[]) + args, _ = mock_plot.call_args + assert args[1] == "Container Port Calls" + + @patch("openbb_imf.views.port_watch_charts.plot_container_metrics") + def test_theme_from_extra_params(self, mock_plot, monkeypatch): + """``theme`` resolved from ``extra_params`` (model-specific) is honored.""" + self._stub_port_name_map(monkeypatch) + mock_plot.return_value = MagicMock( + show=lambda external: SimpleNamespace(to_plotly_json=lambda: {}) + ) + sp = SimpleNamespace(model_dump=lambda: {}) + ep = SimpleNamespace( + model_dump=lambda: {"theme": "light", "metric": "portcalls"} + ) + ImfViews.imf_portwatch_container_metrics( + obbject_item=[], standard_params=sp, extra_params=ep + ) + _, kwargs = mock_plot.call_args + assert kwargs["theme"] == "light" + + +class TestDisruptionsMapDispatcher: + """Tests for ``ImfViews.imf_portwatch_disruptions_map``.""" + + @patch("openbb_imf.views.port_watch_charts.plot_disruptions_map") + def test_dispatch_passes_rows_and_theme(self, mock_plot): + """Rows are dumped and the theme is forwarded.""" + mock_plot.return_value = MagicMock( + show=lambda external: SimpleNamespace(to_plotly_json=lambda: {}) + ) + sp = SimpleNamespace(model_dump=lambda: {"theme": "light"}) + ImfViews.imf_portwatch_disruptions_map( + obbject_item=[_Dumpable({"lat": 1.0, "long": 2.0})], standard_params=sp + ) + args, kwargs = mock_plot.call_args + assert args[0] == [{"lat": 1.0, "long": 2.0}] + assert kwargs == {"theme": "light"} + + +class TestDisruptionSankeyDispatcher: + """Tests for ``ImfViews.imf_portwatch_disruption_sankey``.""" + + @patch("openbb_imf.views.port_watch_charts.plot_disruption_sankey") + def test_latest_event_label(self, mock_plot): + """``event_id='LATEST'`` becomes 'Latest Disruption' in the chart title.""" + mock_plot.return_value = MagicMock( + show=lambda external: SimpleNamespace(to_plotly_json=lambda: {}) + ) + sp = SimpleNamespace(model_dump=lambda: {"event_id": "LATEST"}) + ImfViews.imf_portwatch_disruption_sankey(obbject_item=[], standard_params=sp) + args, _ = mock_plot.call_args + assert args[1] == "Latest Disruption" + + @patch("openbb_imf.views.port_watch_charts.plot_disruption_sankey") + def test_numeric_event_label(self, mock_plot): + """A numeric event id is wrapped in ``'Event {id}'``.""" + mock_plot.return_value = MagicMock( + show=lambda external: SimpleNamespace(to_plotly_json=lambda: {}) + ) + sp = SimpleNamespace(model_dump=lambda: {"event_id": "1234"}) + ImfViews.imf_portwatch_disruption_sankey(obbject_item=[], standard_params=sp) + args, _ = mock_plot.call_args + assert args[1] == "Event 1234" + + @patch("openbb_imf.views.port_watch_charts.plot_disruption_sankey") + def test_default_event_label(self, mock_plot): + """A missing ``event_id`` defaults to the plain ``'Event'`` label.""" + mock_plot.return_value = MagicMock( + show=lambda external: SimpleNamespace(to_plotly_json=lambda: {}) + ) + ImfViews.imf_portwatch_disruption_sankey(obbject_item=[]) + args, _ = mock_plot.call_args + assert args[1] == "Event Event" + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/openbb_platform/providers/imf/tests/test_metadata_mixins.py b/openbb_platform/providers/imf/tests/test_metadata_mixins.py new file mode 100644 index 00000000000..89b0818e87d --- /dev/null +++ b/openbb_platform/providers/imf/tests/test_metadata_mixins.py @@ -0,0 +1,2323 @@ +"""Tests for IMF metadata mixin modules driving 100% coverage.""" + +# ruff: noqa: I001 + +from __future__ import annotations + +import json +import lzma +from unittest.mock import MagicMock, patch + +import pytest + +from openbb_core.app.model.abstract.error import OpenBBError +from openbb_core.app.model.abstract.warning import OpenBBWarning + +from openbb_imf.utils.metadata import ImfMetadata +from openbb_imf.utils.metadata import _cache_mixin +from openbb_imf.utils.metadata._loader_mixin import ( + _extract_codelist_payload, + _shape_codelist, +) +from openbb_imf.utils.metadata._indicator_mixin import ( + _is_indicator_dimension, + _make_indicator_entry, +) +from openbb_imf.utils.metadata._search_mixin import _matches_query +from openbb_imf.utils.metadata._table_mixin import ( + _build_series_id, + _derive_node_label, + _hierarchy_overlaps_dataflow, + _is_irfcl_path_label, + _strip_known_ancestors, +) +from openbb_imf.utils.metadata._typing import _MixinBase + + +def _real_load(instance): + """Call the real cache-load method, bypassing fixture monkeypatching.""" + return _cache_mixin.CacheMixin._load_from_cache(instance) + + +class TestCacheMixin: + """Coverage for ``_cache_mixin`` load and warning paths.""" + + def test_load_from_cache_missing_file_returns_false(self, empty_meta, monkeypatch): + """Return False when the shipped cache file is absent.""" + + class FakePath: + """Tiny stand-in for ``Path`` used in cache lookups.""" + + def exists(self) -> bool: + return False + + monkeypatch.setattr(_cache_mixin, "_SHIPPED_CACHE_FILE", FakePath()) + assert _real_load(empty_meta) is False + + def test_load_from_cache_success(self, empty_meta, monkeypatch, tmp_path): + """Populate caches when the shipped cache file is valid lzma JSON.""" + cache_payload = { + "dataflows": {"X": {"id": "X"}}, + "datastructures": {"DSD_X": {"id": "DSD_X"}}, + "conceptschemes": {"CS": {}}, + "dataflow_groups": {}, + "metadata_cache": {}, + "constraints_cache": {}, + "codelist_cache": {"CL_X": {"A": "Alpha"}}, + "codelist_descriptions": {"CL_X": {"A": "Alpha"}}, + "dataflow_parameters": {}, + "dataflow_indicators": {}, + "hierarchies": {"H1": {"hierarchicalCodes": []}}, + } + cache_path = tmp_path / "imf_cache.json.xz" + with lzma.open(cache_path, "wb") as fh: + fh.write(json.dumps(cache_payload).encode()) + + monkeypatch.setattr(_cache_mixin, "_SHIPPED_CACHE_FILE", cache_path) + assert _real_load(empty_meta) is True + assert empty_meta.dataflows == {"X": {"id": "X"}} + assert empty_meta._codelist_cache == {"CL_X": {"A": "Alpha"}} + + def test_load_from_cache_corrupt_warns(self, empty_meta, monkeypatch, tmp_path): + """Warn and return False when the cache file is unreadable.""" + bad_path = tmp_path / "imf_cache.json.xz" + bad_path.write_bytes(b"not-lzma") + monkeypatch.setattr(_cache_mixin, "_SHIPPED_CACHE_FILE", bad_path) + + with pytest.warns(OpenBBWarning, match="Error loading cache"): + assert _real_load(empty_meta) is False + + +class TestCoreSingleton: + """Coverage for the double-checked singleton initializer.""" + + def test_double_init_short_circuits(self, monkeypatch): + """``__init__`` returns early when the instance is already initialised.""" + ImfMetadata._reset() + monkeypatch.setattr(ImfMetadata, "_load_from_cache", lambda self: True) + first = ImfMetadata() + first.dataflows = {"SENTINEL": {"id": "SENTINEL"}} + + second = ImfMetadata() + assert second is first + assert second.dataflows == {"SENTINEL": {"id": "SENTINEL"}} + + second.__init__() + assert second.dataflows == {"SENTINEL": {"id": "SENTINEL"}} + + ImfMetadata._reset() + + def test_deepcopy_returns_self(self, monkeypatch): + """``deepcopy`` on the singleton must return the same instance.""" + from copy import deepcopy + + ImfMetadata._reset() + monkeypatch.setattr(ImfMetadata, "_load_from_cache", lambda self: True) + m = ImfMetadata() + assert deepcopy(m) is m + ImfMetadata._reset() + + def test_copy_returns_self(self, monkeypatch): + """``copy`` on the singleton must return the same instance.""" + from copy import copy + + ImfMetadata._reset() + monkeypatch.setattr(ImfMetadata, "_load_from_cache", lambda self: True) + m = ImfMetadata() + assert copy(m) is m + ImfMetadata._reset() + + def test_double_check_inside_lock(self, monkeypatch): + """Inner ``self._initialized`` check inside the lock short-circuits init.""" + ImfMetadata._reset() + monkeypatch.setattr(ImfMetadata, "_load_from_cache", lambda self: True) + instance = ImfMetadata.__new__(ImfMetadata) + instance._initialized = False + + class _FlippingLock: + """Lock proxy that flips ``_initialized`` to True on enter.""" + + def __enter__(self_inner): # noqa: N805 + instance._initialized = True + return self_inner + + def __exit__(self_inner, *exc): # noqa: N805 + return False + + monkeypatch.setattr(ImfMetadata, "_lock", _FlippingLock()) + instance.__init__() + assert not hasattr(instance, "dataflows") + ImfMetadata._reset() + + +class TestHelpersMixin: + """Coverage for ``_helpers`` delegation and dimension resolution.""" + + def test_static_parse_helpers(self, empty_meta): + """Static helpers delegate to the underlying parsers.""" + urn = "urn:sdmx:org.sdmx.infomodel.codelist.Code=IMF.STA:CL_X(1.0).CODE" + assert empty_meta._parse_agency_from_urn(urn) == "IMF.STA" + assert empty_meta._parse_codelist_id_from_urn(urn) == "CL_X" + assert empty_meta._parse_indicator_code_from_urn(urn) == "CODE" + owning_urn = "urn:sdmx:any=IMF.STA:CL_X(1.0)" + assert empty_meta._parse_codelist_urn(owning_urn) == "CL_X" + + def test_parse_query_and_time_periods(self, empty_meta): + """``_parse_query`` and ``_build_time_period_parameters`` delegate.""" + assert empty_meta._parse_query("gdp") == [["gdp"]] + options, count = empty_meta._build_time_period_parameters(None) + assert options == [] and count is None + + def test_extract_all_codelists_from_hierarchy(self, empty_meta): + """Hierarchy traversal extracts codelist IDs.""" + hierarchy = { + "hierarchicalCodes": [ + { + "code": "urn:sdmx:any=IMF.STA:CL_A(1.0).X", + "hierarchicalCodes": [{"code": "urn:sdmx:any=IMF.STA:CL_B(1.0).Y"}], + } + ] + } + result = empty_meta._extract_all_codelists_from_hierarchy(hierarchy) + assert result == {"CL_A", "CL_B"} + + def test_get_dimension_for_codelist_missing_dataflow(self, seeded_meta): + """Return None when the dataflow is not in the registry.""" + assert seeded_meta._get_dimension_for_codelist("MISSING", "CL_X") is None + + def test_get_dimension_for_codelist_missing_dsd(self, seeded_meta): + """Return None when the dataflow points at an unknown DSD.""" + seeded_meta.dataflows["NO_DSD"] = { + "id": "NO_DSD", + "agencyID": "IMF.STA", + "structureRef": {"id": "DSD_MISSING"}, + } + assert seeded_meta._get_dimension_for_codelist("NO_DSD", "CL_X") is None + + def test_get_dimension_for_codelist_no_structure_ref(self, seeded_meta): + """Return None when ``structureRef`` is empty.""" + seeded_meta.dataflows["NO_REF"] = {"id": "NO_REF", "structureRef": {}} + assert seeded_meta._get_dimension_for_codelist("NO_REF", "CL_X") is None + + def test_get_dimension_for_codelist_segment_match(self, seeded_meta): + """Match via dimension-id segment uppercased.""" + result = seeded_meta._get_dimension_for_codelist("TEST_DF", "CL_COUNTRY") + assert result == "COUNTRY" + + def test_get_dimension_for_codelist_substring_match(self, seeded_meta): + """Match via dimension-id substring fallback.""" + seeded_meta.datastructures["DSD_TEST"]["dimensions"].append( + {"id": "FOO", "position": "3", "conceptRef": {"id": "FOO"}} + ) + seeded_meta._codelist_cache["CL_TEST_FOOBAR"] = {"X": "Y"} + result = seeded_meta._get_dimension_for_codelist("TEST_DF", "CL_TEST_FOOBAR") + assert result == "FOO" + + def test_get_dimension_for_codelist_no_match(self, seeded_meta): + """Return None when nothing matches.""" + result = seeded_meta._get_dimension_for_codelist("TEST_DF", "CL_NOWHERE") + assert result is None + + def test_get_dimension_for_codelist_skips_blank_dim_ids(self, seeded_meta): + """Blank dimension IDs are ignored during the first resolution loop.""" + seeded_meta.datastructures["DSD_TEST"]["dimensions"].insert( + 0, {"id": "", "position": "-1", "conceptRef": {"id": ""}} + ) + result = seeded_meta._get_dimension_for_codelist("TEST_DF", "CL_INDICATOR") + assert result == "INDICATOR" + + def test_get_dimension_for_codelist_falls_through_to_substring_loop( + self, seeded_meta + ): + """Reach the third (codelist-upper substring) loop with a match.""" + seeded_meta.datastructures["DSD_TEST"]["dimensions"] = [ + {"id": "WIDGETID", "position": "0", "conceptRef": {"id": "WID"}} + ] + result = seeded_meta._get_dimension_for_codelist("TEST_DF", "CL_WIDGETIDDETAIL") + assert result == "WIDGETID" + + +class TestIndicatorMixin: + """Coverage for ``_indicator_mixin.get_indicators_in``.""" + + def test_get_indicators_in_unknown_dataflow_raises(self, seeded_meta): + """Unknown dataflow ID raises ValueError.""" + with pytest.raises(ValueError, match="not found"): + seeded_meta.get_indicators_in("NOPE") + + def test_get_indicators_in_missing_dsd_raises(self, seeded_meta): + """Missing DSD raises ValueError.""" + seeded_meta.dataflows["BAD"] = { + "id": "BAD", + "agencyID": "IMF.STA", + "structureRef": {"id": "NOPE"}, + } + with pytest.raises(ValueError, match="Data structure not found"): + seeded_meta.get_indicators_in("BAD") + + def test_get_indicators_in_returns_entries(self, seeded_meta, monkeypatch): + """Return one entry per available indicator code.""" + + def _fake_params(self, dataflow_id): + return {"INDICATOR": [{"value": "GDP", "label": "GDP"}]} + + monkeypatch.setattr(ImfMetadata, "get_dataflow_parameters", _fake_params) + result = seeded_meta.get_indicators_in("TEST_DF") + assert any(r["indicator"] == "GDP" for r in result) + assert all(r["series_id"].startswith("TEST_DF::") for r in result) + + def test_get_indicators_in_handles_params_exception(self, seeded_meta, monkeypatch): + """A get_dataflow_parameters error is swallowed and treated as empty.""" + + def _boom(self, dataflow_id): + raise RuntimeError("nope") + + monkeypatch.setattr(ImfMetadata, "get_dataflow_parameters", _boom) + with pytest.raises(KeyError): + seeded_meta.get_indicators_in("TEST_DF") + + def test_get_indicators_in_fetches_uncached_codelist( + self, seeded_meta, monkeypatch + ): + """When the descriptions map is empty and uncached, fetch then continue.""" + + def _fake_params(self, dataflow_id): + return {"INDICATOR": [{"value": "GDP", "label": "GDP"}]} + + monkeypatch.setattr(ImfMetadata, "get_dataflow_parameters", _fake_params) + seeded_meta._codelist_descriptions.pop("CL_INDICATOR", None) + del seeded_meta._codelist_cache["CL_INDICATOR"] + + def _fake_get_codelist_map( + self, codelist_id, agency_id, dataflow_id, include_descriptions=False + ): + self._codelist_descriptions[codelist_id] = {"GDP": "GDP desc"} + self._codelist_cache[codelist_id] = {"GDP": "GDP"} + return {"GDP": "GDP"} + + monkeypatch.setattr(ImfMetadata, "_get_codelist_map", _fake_get_codelist_map) + result = seeded_meta.get_indicators_in("TEST_DF") + assert any(r["description"] == "GDP desc" for r in result) + + def test_get_indicators_in_fetch_failure_is_swallowed( + self, seeded_meta, monkeypatch + ): + """When the uncached fetch raises, fall back to empty descriptions.""" + + def _fake_params(self, dataflow_id): + return {"INDICATOR": [{"value": "GDP", "label": "GDP"}]} + + monkeypatch.setattr(ImfMetadata, "get_dataflow_parameters", _fake_params) + seeded_meta._codelist_descriptions.pop("CL_INDICATOR", None) + del seeded_meta._codelist_cache["CL_INDICATOR"] + + def _boom(*a, **kw): + raise RuntimeError("boom") + + monkeypatch.setattr(ImfMetadata, "_get_codelist_map", _boom) + result = seeded_meta.get_indicators_in("TEST_DF") + assert any(r["description"] == "" for r in result) + + def test_get_indicators_in_activity_dimension(self, seeded_meta, monkeypatch): + """``ACTIVITY`` dimension adds activity codelist entries.""" + seeded_meta.dataflows["ACT_DF"] = { + "id": "ACT_DF", + "agencyID": "IMF.STA", + "name": "Activity", + "description": "", + "structureRef": {"id": "DSD_ACT"}, + } + seeded_meta.datastructures["DSD_ACT"] = { + "id": "DSD_ACT", + "agencyID": "IMF.STA", + "dimensions": [ + {"id": "ACTIVITY", "position": "0", "conceptRef": {"id": "ACTIVITY"}} + ], + } + seeded_meta._codelist_cache["CL_ACT_DF_ACTIVITY"] = {"MFG": "Manufacturing"} + seeded_meta._codelist_descriptions["CL_ACT_DF_ACTIVITY"] = { + "MFG": "Manufacturing desc" + } + + def _fake_params(self, dataflow_id): + return {} + + monkeypatch.setattr(ImfMetadata, "get_dataflow_parameters", _fake_params) + result = seeded_meta.get_indicators_in("ACT_DF") + assert any(r["indicator"] == "MFG" for r in result) + + def test_get_indicators_in_empty_raises_keyerror(self, seeded_meta, monkeypatch): + """KeyError when no indicator-like dimension is found.""" + seeded_meta.datastructures["DSD_TEST"]["dimensions"] = [ + {"id": "DUMMY", "position": "0", "conceptRef": {"id": "DUMMY"}} + ] + + def _fake_params(self, dataflow_id): + return {} + + monkeypatch.setattr(ImfMetadata, "get_dataflow_parameters", _fake_params) + with pytest.raises(KeyError): + seeded_meta.get_indicators_in("TEST_DF") + + def test_get_indicators_in_returns_cached_result(self, seeded_meta): + """A pre-populated ``_dataflow_indicators_cache`` short-circuits the build.""" + sentinel = [{"indicator": "PRECACHED"}] + seeded_meta._dataflow_indicators_cache = {"TEST_DF": sentinel} + out = seeded_meta.get_indicators_in("TEST_DF") + assert out is sentinel + + def test_is_indicator_dimension_helpers(self): + """Dimension classifier checks whitelist and substrings.""" + assert _is_indicator_dimension(None) is False + assert _is_indicator_dimension("") is False + assert _is_indicator_dimension("INDICATOR") is True + assert _is_indicator_dimension("BOP_ACCOUNTING_ENTRY") is True + assert _is_indicator_dimension("SOMETHING_ENTRY_ELSE") is True + assert _is_indicator_dimension("UNRELATED") is False + + def test_make_indicator_entry(self): + """Indicator entry builder shapes the dict.""" + entry = _make_indicator_entry( + dataflow_id="A", + dataflow_name="A name", + structure_id="DSD_A", + agency_id="IMF", + dim_id="INDICATOR", + code_id="X", + label="X label", + description="X desc", + ) + assert entry == { + "dataflow_id": "A", + "dataflow_name": "A name", + "structure_id": "DSD_A", + "agency_id": "IMF", + "dimension_id": "INDICATOR", + "indicator": "X", + "label": "X label", + "description": "X desc", + "series_id": "A::X", + "extra_dimensions": [], + } + + def test_make_indicator_entry_with_extra_dimensions(self): + """``extra_dimensions`` are forwarded into the entry.""" + entry = _make_indicator_entry( + dataflow_id="A", + dataflow_name="A name", + structure_id="DSD_A", + agency_id="IMF", + dim_id="INDICATOR", + code_id="X", + label="X label", + description="X desc", + extra_dimensions=["WGT_TYPE"], + ) + assert entry["extra_dimensions"] == ["WGT_TYPE"] + + +def _build_mock_response(status_code=200, json_data=None, raise_on_status=False): + """Return a MagicMock that mimics ``requests.Response``.""" + response = MagicMock() + response.status_code = status_code + response.json.return_value = json_data or {} + if raise_on_status: + response.raise_for_status.side_effect = Exception("HTTP error") + else: + response.raise_for_status.return_value = None + return response + + +class TestLoaderMixin: + """Coverage for ``_loader_mixin`` fetchers and helpers.""" + + def test_extract_codelist_payload(self): + """Pull labels and descriptions from a codelist payload.""" + codelist = { + "codes": [ + { + "id": "A", + "names": {"en": "Alpha"}, + "descriptions": {"en": "Alpha description"}, + }, + {"id": "B", "name": "Bravo", "description": "Bravo description"}, + {"id": "C", "name": "Charlie"}, + {"id": ""}, + ] + } + labels, descs = _extract_codelist_payload(codelist) + assert labels == {"A": "Alpha", "B": "Bravo", "C": "Charlie"} + assert descs["A"] == "Alpha description" + assert descs["B"] == "Bravo description" + assert descs["C"] == "Charlie" + + def test_fetch_single_codelist_already_cached(self, seeded_meta): + """Skip the HTTP call when the codelist is already cached.""" + assert seeded_meta._fetch_single_codelist("IMF.STA", "CL_COUNTRY") is True + + def test_fetch_single_codelist_non_200(self, empty_meta): + """Non-200 response yields False without populating caches.""" + with patch("openbb_core.provider.utils.helpers.make_request") as mock_req: + mock_req.return_value = _build_mock_response(status_code=404) + assert empty_meta._fetch_single_codelist("IMF.STA", "CL_TEST") is False + + def test_fetch_single_codelist_request_exception(self, empty_meta): + """RequestException returns False.""" + from requests.exceptions import RequestException + + with patch( + "openbb_core.provider.utils.helpers.make_request", + side_effect=RequestException("boom"), + ): + assert empty_meta._fetch_single_codelist("IMF.STA", "CL_TEST") is False + + def test_fetch_single_codelist_empty_payload(self, empty_meta): + """Empty ``data.codelists`` returns False.""" + with patch("openbb_core.provider.utils.helpers.make_request") as mock_req: + mock_req.return_value = _build_mock_response( + json_data={"data": {"codelists": []}} + ) + assert empty_meta._fetch_single_codelist("IMF.STA", "CL_TEST") is False + + def test_fetch_single_codelist_success(self, empty_meta): + """Successful fetch populates ``_codelist_cache``.""" + payload = { + "data": { + "codelists": [ + { + "id": "CL_TEST", + "codes": [ + {"id": "A", "names": {"en": "Alpha"}}, + ], + }, + {"id": ""}, + ] + } + } + with patch("openbb_core.provider.utils.helpers.make_request") as mock_req: + mock_req.return_value = _build_mock_response(json_data=payload) + assert empty_meta._fetch_single_codelist("IMF.STA", "CL_TEST") is True + assert empty_meta._codelist_cache["CL_TEST"] == {"A": "Alpha"} + + def test_bulk_fetch_request_exception_warns(self, empty_meta): + """Bulk fetch warns and returns on RequestException.""" + from requests.exceptions import RequestException + + with patch( + "openbb_core.provider.utils.helpers.make_request", + side_effect=RequestException("boom"), + ): + with pytest.warns(OpenBBWarning, match="Could not bulk fetch"): + empty_meta._bulk_fetch_and_cache_codelists("IMF.STA", "DF") + + def test_bulk_fetch_success(self, empty_meta): + """Bulk fetch populates caches from the payload.""" + payload = { + "data": { + "codelists": [ + { + "id": "CL_BULK", + "codes": [{"id": "X", "names": {"en": "X label"}}], + }, + {"id": ""}, + ] + } + } + with patch("openbb_core.provider.utils.helpers.make_request") as mock_req: + mock_req.return_value = _build_mock_response(json_data=payload) + empty_meta._bulk_fetch_and_cache_codelists("IMF.STA", "DF") + assert empty_meta._codelist_cache["CL_BULK"] == {"X": "X label"} + + def test_get_codelist_map_hits_cache(self, seeded_meta): + """Already-cached codelist returns immediately.""" + result = seeded_meta._get_codelist_map("CL_COUNTRY", "IMF.STA", "TEST_DF") + assert "USA" in result + + def test_get_codelist_map_with_descriptions(self, seeded_meta): + """Returns enriched shape when descriptions are requested.""" + result = seeded_meta._get_codelist_map( + "CL_COUNTRY", "IMF.STA", "TEST_DF", include_descriptions=True + ) + assert result["USA"] == {"name": "United States", "description": "USA"} + + def test_get_codelist_map_fetches_then_returns(self, empty_meta, monkeypatch): + """Triggers bulk fetch when codelist is missing, then returns it.""" + + def _bulk(self, agency_id, dataflow_id): + self._codelist_cache["CL_NEW"] = {"K": "V"} + self._codelist_descriptions["CL_NEW"] = {"K": "V"} + + monkeypatch.setattr(ImfMetadata, "_bulk_fetch_and_cache_codelists", _bulk) + assert empty_meta._get_codelist_map("CL_NEW", "IMF.STA", "DF") == {"K": "V"} + + def test_get_codelist_map_warns_when_missing(self, empty_meta, monkeypatch): + """Warns and returns ``{}`` when codelist remains missing after fetch.""" + + def _bulk(self, agency_id, dataflow_id): + return None + + monkeypatch.setattr(ImfMetadata, "_bulk_fetch_and_cache_codelists", _bulk) + with pytest.warns(OpenBBWarning, match="Codelist 'CL_ABSENT' not found"): + assert empty_meta._get_codelist_map("CL_ABSENT", "IMF.STA", "DF") == {} + + def test_shape_codelist_labels_only(self): + """Default shape returns a copy of the labels dict.""" + labels = {"A": "Alpha"} + result = _shape_codelist(labels, {"A": "desc"}, include_descriptions=False) + assert result == labels + assert result is not labels + + def test_shape_codelist_include_descriptions_falsy(self): + """``include_descriptions=True`` with empty descs falls back to labels.""" + labels = {"A": "Alpha"} + result = _shape_codelist(labels, {}, include_descriptions=True) + assert result == labels + + def test_bulk_fetch_json_decode_error(self, empty_meta): + """Bulk fetch warns when JSON cannot be decoded.""" + bad_response = MagicMock() + bad_response.json.side_effect = json.JSONDecodeError("nope", "", 0) + bad_response.raise_for_status.return_value = None + with patch( + "openbb_core.provider.utils.helpers.make_request", + return_value=bad_response, + ): + with pytest.warns(OpenBBWarning, match="Could not bulk fetch"): + empty_meta._bulk_fetch_and_cache_codelists("IMF.STA", "DF") + + def test_fetch_single_codelist_json_decode_error(self, empty_meta): + """Single fetch returns False when JSON cannot be decoded.""" + bad_response = MagicMock() + bad_response.status_code = 200 + bad_response.json.side_effect = json.JSONDecodeError("nope", "", 0) + with patch( + "openbb_core.provider.utils.helpers.make_request", + return_value=bad_response, + ): + assert empty_meta._fetch_single_codelist("IMF.STA", "CL_TEST") is False + + +class TestQueryMixin: + """Coverage for ``_query_mixin`` parameter, constraint, codelist resolution.""" + + def test_get_dataflow_parameters_unknown_raises(self, seeded_meta): + """Unknown dataflow raises ValueError.""" + with pytest.raises(ValueError, match="not found"): + seeded_meta.get_dataflow_parameters("NOPE") + + def test_get_dataflow_parameters_cached(self, seeded_meta): + """Returns cached parameters when present.""" + seeded_meta._dataflow_parameters_cache["TEST_DF"] = { + "INDICATOR": [{"label": "GDP", "value": "GDP"}] + } + result = seeded_meta.get_dataflow_parameters("TEST_DF") + assert result == {"INDICATOR": [{"label": "GDP", "value": "GDP"}]} + + def test_get_dataflow_parameters_missing_dsd(self, seeded_meta): + """Missing DSD returns ``{}``.""" + seeded_meta.dataflows["NODSD"] = { + "id": "NODSD", + "agencyID": "IMF.STA", + "structureRef": {"id": "MISSING"}, + } + result = seeded_meta.get_dataflow_parameters("NODSD") + assert result == {} + + def test_get_dataflow_parameters_builds(self, seeded_meta, monkeypatch): + """Builds parameter map from constraints and cached codelists.""" + + def _fake_constraints(self, **kwargs): + return { + "key_values": [{"id": "INDICATOR", "values": ["GDP"]}], + "full_response": { + "data": { + "contentConstraints": [ + { + "annotations": [ + {"id": "time_period_start", "title": "1990"}, + {"id": "time_period_end", "title": "2024"}, + ] + } + ] + } + }, + } + + monkeypatch.setattr(ImfMetadata, "get_available_constraints", _fake_constraints) + result = seeded_meta.get_dataflow_parameters("TEST_DF") + assert "INDICATOR" in result + assert any(opt["value"] == "GDP" for opt in result["INDICATOR"]) + assert "TIME_PERIOD" in result + + def test_get_dataflow_parameters_dict_label_and_value_equal( + self, seeded_meta, monkeypatch + ): + """Dict labels are unwrapped; ``label == value`` is replaced by the id.""" + seeded_meta._codelist_cache["CL_COUNTRY"] = { + "USA": {"name": "United States"}, + "GBR": "GBR", + } + + def _fake_constraints(self, **kwargs): + return { + "key_values": [{"id": "COUNTRY", "values": ["USA", "GBR"]}], + "full_response": {"data": {}}, + } + + monkeypatch.setattr(ImfMetadata, "get_available_constraints", _fake_constraints) + result = seeded_meta.get_dataflow_parameters("TEST_DF") + labels = {opt["value"]: opt["label"] for opt in result["COUNTRY"]} + assert labels["USA"] == "United States" + assert labels["GBR"] == "GBR" + + def test_get_dataflow_parameters_fetches_uncached_codelist( + self, seeded_meta, monkeypatch + ): + """Fetches a codelist when no entry is cached for a dimension.""" + seeded_meta.datastructures["DSD_TEST"]["dimensions"].append( + {"id": "NEW_DIM", "position": "3", "conceptRef": {"id": "NEW_DIM"}} + ) + + def _fake_constraints(self, **kwargs): + return {"key_values": [], "full_response": {"data": {}}} + + def _fake_get_codelist( + self, codelist_id, agency_id, dataflow_id, include_descriptions=False + ): + self._codelist_cache[codelist_id] = {"NEW": "New label"} + return {"NEW": "New label"} + + monkeypatch.setattr(ImfMetadata, "get_available_constraints", _fake_constraints) + monkeypatch.setattr(ImfMetadata, "_get_codelist_map", _fake_get_codelist) + result = seeded_meta.get_dataflow_parameters("TEST_DF") + assert "NEW_DIM" in result + + def test_get_dataflow_parameters_unresolvable_dim(self, seeded_meta, monkeypatch): + """Dimensions with no resolvable codelist are skipped.""" + seeded_meta.datastructures["DSD_TEST"]["dimensions"].append( + {"id": "WEIRDOWEIRD", "position": "3", "conceptRef": {"id": "WEIRDO"}} + ) + + def _fake_constraints(self, **kwargs): + return {"key_values": [], "full_response": {"data": {}}} + + monkeypatch.setattr(ImfMetadata, "get_available_constraints", _fake_constraints) + + def _no_codes( + self, codelist_id, agency_id, dataflow_id, include_descriptions=False + ): + return {} + + monkeypatch.setattr(ImfMetadata, "_get_codelist_map", _no_codes) + result = seeded_meta.get_dataflow_parameters("TEST_DF") + assert "WEIRDOWEIRD" not in result + + def test_get_available_constraints_unknown_dataflow(self, seeded_meta): + """Unknown dataflow raises ValueError.""" + with pytest.raises(ValueError, match="not found"): + seeded_meta.get_available_constraints("NOPE", "all") + + def test_get_available_constraints_no_agency(self, seeded_meta): + """Missing agency ID raises ValueError.""" + seeded_meta.dataflows["NA"] = { + "id": "NA", + "structureRef": {"id": "DSD_TEST"}, + } + with pytest.raises(ValueError, match="Agency ID not found"): + seeded_meta.get_available_constraints("NA", "all") + + def test_get_available_constraints_cached(self, seeded_meta): + """Cached result returns directly without hitting the network.""" + cache_key = "TEST_DF:all:None:None:None:()" + seeded_meta._constraints_cache[cache_key] = {"key_values": [], "cached": 1} + result = seeded_meta.get_available_constraints("TEST_DF", "all") + assert result == {"key_values": [], "cached": 1} + + def test_get_available_constraints_http_success(self, seeded_meta): + """Successful response is parsed and cached.""" + payload = { + "data": { + "dataConstraints": [ + { + "cubeRegions": [ + { + "keyValues": [ + { + "id": "DIM1", + "values": ["v1", {"value": "v2"}, None], + } + ], + "components": [ + {"id": "DIM2", "values": ["x"]}, + {"id": "", "values": []}, + ], + } + ] + } + ] + } + } + with patch("openbb_core.provider.utils.helpers.make_request") as mock_req: + mock_req.return_value = _build_mock_response(json_data=payload) + result = seeded_meta.get_available_constraints( + "TEST_DF", + key="all", + component_id="all", + mode="available", + references="all", + extra="value", + ) + ids = {kv["id"] for kv in result["key_values"]} + assert {"DIM1", "DIM2"} <= ids + + def test_get_available_constraints_json_decode_error(self, seeded_meta): + """JSON decode failure raises OpenBBError.""" + bad = MagicMock() + bad.json.side_effect = json.JSONDecodeError("nope", "", 0) + bad.raise_for_status.return_value = None + with patch("openbb_core.provider.utils.helpers.make_request", return_value=bad): + with pytest.raises(OpenBBError, match="Unexpected response format"): + seeded_meta.get_available_constraints("TEST_DF", "all") + + def test_get_available_constraints_request_exception(self, seeded_meta): + """RequestException raises OpenBBError.""" + from requests.exceptions import RequestException + + with patch( + "openbb_core.provider.utils.helpers.make_request", + side_effect=RequestException("boom"), + ): + with pytest.raises(OpenBBError, match="error occurred"): + seeded_meta.get_available_constraints("TEST_DF", "all") + + def test_resolve_codelist_id_codelist_ref_dict(self, seeded_meta): + """Direct codelist ref dict short-circuits resolution.""" + dim_meta = {"representation": {"codelist": {"id": "CL_DIRECT"}}} + assert ( + seeded_meta._resolve_codelist_id("TEST_DF", "DSD_TEST", "DIM", dim_meta) + == "CL_DIRECT" + ) + + def test_resolve_codelist_id_codelist_ref_string(self, seeded_meta): + """Direct codelist ref string short-circuits resolution.""" + dim_meta = {"representation": {"codelist": "CL_STR"}} + assert ( + seeded_meta._resolve_codelist_id("TEST_DF", "DSD_TEST", "DIM", dim_meta) + == "CL_STR" + ) + + def test_resolve_codelist_id_blank_dim_returns_none(self, seeded_meta): + """Empty dim_id returns None.""" + assert seeded_meta._resolve_codelist_id("TEST_DF", "DSD_TEST", "", {}) is None + + def test_resolve_codelist_id_alias_match(self, seeded_meta): + """Alias table promotes CL_AREA for REF_AREA-style dimensions.""" + seeded_meta._codelist_cache["CL_AREA"] = {"USA": "United States"} + result = seeded_meta._resolve_codelist_id( + "TEST_DF", + "DSD_TEST", + "REF_AREA", + {"conceptRef": {"id": "REF_AREA"}}, + ) + assert result == "CL_AREA" + + def test_resolve_codelist_id_alias_specific_match(self, seeded_meta): + """Alias also matches the dataflow-specific suffix.""" + seeded_meta._codelist_cache["CL_TEST_DF_QUANTILE"] = {"Q1": "Q1"} + result = seeded_meta._resolve_codelist_id( + "TEST_DF", + "DSD_TEST", + "INCOME_WEALTH_QUANTILE", + {}, + ) + assert result == "CL_TEST_DF_QUANTILE" + + def test_resolve_codelist_id_counterpart_recurses(self, seeded_meta): + """COUNTERPART_ dims recurse to the base dimension.""" + seeded_meta.datastructures["DSD_TEST"]["dimensions"].append( + { + "id": "AREA", + "position": "3", + "representation": {"codelist": "CL_BASE_AREA"}, + } + ) + result = seeded_meta._resolve_codelist_id( + "TEST_DF", + "DSD_TEST", + "COUNTERPART_AREA", + {}, + ) + assert result == "CL_BASE_AREA" + + def test_resolve_codelist_id_activity_fallback(self, seeded_meta): + """ACTIVITY substring uses the activity fallback codelists.""" + seeded_meta._codelist_cache["CL_ACTIVITY"] = {"MFG": "Manufacturing"} + result = seeded_meta._resolve_codelist_id( + "TEST_DF", + "DSD_TEST", + "SOME_ACTIVITY_INDEX", + {}, + ) + assert result == "CL_ACTIVITY" + + def test_resolve_codelist_id_coicop_fallback(self, seeded_meta): + """COICOP substring uses COICOP fallback codelists.""" + seeded_meta._codelist_cache["CL_COICOP_2018"] = {"X": "X"} + result = seeded_meta._resolve_codelist_id( + "TEST_DF", + "DSD_TEST", + "COICOP_CLASS", + {}, + ) + assert result == "CL_COICOP_2018" + + def test_resolve_codelist_id_dim_substring_match(self, seeded_meta): + """Cache-key substring fallback returns the first match.""" + seeded_meta._codelist_cache["CL_MASTER_FOO"] = {"X": "Y"} + seeded_meta._codelist_cache["CL_RANDOM_FOOBAR"] = {"A": "B"} + result = seeded_meta._resolve_codelist_id( + "TEST_DF", + "DSD_TEST", + "FOOBAR", + {}, + ) + assert result == "CL_RANDOM_FOOBAR" + + def test_resolve_codelist_id_parts_fallback(self, seeded_meta): + """Multi-part dim id matches by part inclusion.""" + seeded_meta._codelist_cache["CL_MASTER_BAR"] = {"X": "Y"} + seeded_meta._codelist_cache["CL_RANDOM_ALPHA_BETA"] = {"X": "Y"} + result = seeded_meta._resolve_codelist_id( + "TEST_DF", + "DSD_TEST", + "ALPHA_BETA", + {}, + ) + assert result == "CL_RANDOM_ALPHA_BETA" + + def test_resolve_codelist_id_default_first_candidate(self, seeded_meta): + """Defaults to the first generated candidate when nothing matches.""" + result = seeded_meta._resolve_codelist_id( + "TEST_DF", + "DSD_TEST", + "ALPHA_NEW_DIMENSION", + {}, + ) + assert result is not None + assert result.startswith("CL_") + + def test_resolve_codelist_id_strips_shared_prefix(self, seeded_meta): + """When dim_id shares a leading token with dataflow_id, the trimmed candidate resolves.""" + seeded_meta._codelist_cache["CL_GS_LI_MS"] = {"M": "Married"} + result = seeded_meta._resolve_codelist_id("GS_LI", "DSD_GS_LI", "GS_MS", {}) + assert result == "CL_GS_LI_MS" + + def test_resolve_codelist_id_concept_codelist_id_wins(self, seeded_meta): + """The conceptscheme's ``codelist_id`` short-circuits all heuristics.""" + seeded_meta.conceptschemes = { + "CS_TEST": { + "concepts": [ + {"id": "WIDGETS", "codelist_id": "CL_WIDGETS_REAL"}, + ] + } + } + dim_meta = {"conceptRef": {"id": "WIDGETS", "maintainableParentID": "CS_TEST"}} + result = seeded_meta._resolve_codelist_id( + "TEST_DF", "DSD_TEST", "DIM", dim_meta + ) + assert result == "CL_WIDGETS_REAL" + + def test_resolve_codelist_id_country_dim(self, seeded_meta): + """Country-like dims try ISO_COUNTRY candidate first.""" + seeded_meta._codelist_cache["CL_TEST_ISO_COUNTRY"] = {"USA": "United States"} + result = seeded_meta._resolve_codelist_id( + "TEST_DF", + "DSD_TEST", + "COUNTRY", + {"conceptRef": {"id": "COUNTRY"}}, + ) + assert result == "CL_TEST_ISO_COUNTRY" + + def test_resolve_codelist_id_parent_scheme(self, seeded_meta): + """``maintainableParentID`` derives a candidate.""" + seeded_meta._codelist_cache["CL_TEST_X"] = {"V": "Value"} + result = seeded_meta._resolve_codelist_id( + "TEST_DF", + "DSD_TEST", + "X", + {"conceptRef": {"id": "X", "maintainableParentID": "CS_TEST"}}, + ) + assert result is not None + + def test_resolve_codelist_id_case_insensitive_match(self, seeded_meta): + """Case-insensitive cache lookup recovers a match.""" + seeded_meta._codelist_cache["cl_test_df_indicator_lower"] = {"X": "Y"} + result = seeded_meta._resolve_codelist_id( + "TEST_DF", + "DSD_TEST", + "INDICATOR_LOWER", + {}, + ) + assert result == "cl_test_df_indicator_lower" + + def test_resolve_codelist_id_multipart_skips_master(self, seeded_meta): + """Multi-part match loop skips ``CL_MASTER_*`` keys.""" + seeded_meta._codelist_cache["CL_MASTER_BETAA_ALPHAA"] = {"X": "Y"} + seeded_meta._codelist_cache["CL_RANDOMM_BETAA_ALPHAA"] = {"X": "Y"} + result = seeded_meta._resolve_codelist_id( + "TEST_DF", + "DSD_TEST", + "ALPHAA_BETAA", + {}, + ) + assert result == "CL_RANDOMM_BETAA_ALPHAA" + + def test_get_dataflow_parameters_cached_codes_branch( + self, seeded_meta, monkeypatch + ): + """Constrained value path hits the cached codes branch.""" + seeded_meta.datastructures["DSD_TEST"]["dimensions"] = [ + {"id": "INDICATOR", "position": "0", "conceptRef": {"id": "INDICATOR"}} + ] + + def _fake_constraints(self, **kwargs): + return { + "key_values": [{"id": "INDICATOR", "values": ["GDP"]}], + "full_response": {"data": {}}, + } + + monkeypatch.setattr(ImfMetadata, "get_available_constraints", _fake_constraints) + result = seeded_meta.get_dataflow_parameters("TEST_DF") + assert "INDICATOR" in result + + def test_get_dataflow_parameters_skips_time_period_and_unresolved( + self, seeded_meta, monkeypatch + ): + """TIME_PERIOD dim and dims that resolve to None are skipped.""" + seeded_meta.datastructures["DSD_TEST"]["dimensions"] = [ + { + "id": "TIME_PERIOD", + "position": "0", + "conceptRef": {"id": "TIME_PERIOD"}, + }, + {"id": "INDICATOR", "position": "1", "conceptRef": {"id": "INDICATOR"}}, + ] + + def _fake_constraints(self, **kwargs): + return {"key_values": [], "full_response": {"data": {}}} + + monkeypatch.setattr(ImfMetadata, "get_available_constraints", _fake_constraints) + + def _resolve(self, dataflow_id, dsd_id, dim_id, dim_meta): + if dim_id == "INDICATOR": + return None + return f"CL_{dim_id}" + + monkeypatch.setattr(ImfMetadata, "_resolve_codelist_id", _resolve) + result = seeded_meta.get_dataflow_parameters("TEST_DF") + assert "TIME_PERIOD" not in result + assert "INDICATOR" not in result + + def test_get_dataflow_parameters_no_agency(self, seeded_meta, monkeypatch): + """``_get_codes`` returns ``{}`` when agency is missing.""" + seeded_meta.dataflows["NA_AGY"] = { + "id": "NA_AGY", + "structureRef": {"id": "DSD_NA"}, + } + seeded_meta.datastructures["DSD_NA"] = { + "id": "DSD_NA", + "dimensions": [ + {"id": "NEW_DIM", "position": "0", "conceptRef": {"id": "NEW_DIM"}} + ], + } + + def _fake_constraints(self, **kwargs): + return {"key_values": [], "full_response": {"data": {}}} + + monkeypatch.setattr(ImfMetadata, "get_available_constraints", _fake_constraints) + result = seeded_meta.get_dataflow_parameters("NA_AGY") + assert result == {} + + +class TestSearchMixin: + """Coverage for ``_search_mixin``.""" + + def test_list_dataflows_sorts_and_strips(self, seeded_meta): + """Returns ``[{label, value}]`` sorted by id.""" + result = seeded_meta.list_dataflows() + assert result == [{"label": "Test Dataflow", "value": "TEST_DF"}] + + def test_search_dataflows_empty_query_raises(self, seeded_meta): + """Empty query raises OpenBBError.""" + with pytest.raises(OpenBBError, match="empty or invalid"): + seeded_meta.search_dataflows("") + + def test_search_dataflows_skips_dataflow_without_structure_ref(self, seeded_meta): + """Skips entries missing ``structureRef.id``.""" + seeded_meta.dataflows["NO_REF"] = { + "id": "NO_REF", + "name": "Test Dataflow", + "description": "", + "structureRef": {}, + } + result = seeded_meta.search_dataflows("test") + groups = [g["group_id"] for g in result] + assert "DSD_TEST" in groups + + def test_search_indicators_no_query_no_keywords_raises(self, seeded_meta): + """OpenBBError raised when none of query/keywords/dataflows provided.""" + with pytest.raises(OpenBBError, match="must be provided"): + seeded_meta.search_indicators("") + + def test_search_indicators_no_target_dataflows_raises(self, monkeypatch): + """Empty dataflows list raises OpenBBError.""" + ImfMetadata._reset() + monkeypatch.setattr(ImfMetadata, "_load_from_cache", lambda self: True) + meta = ImfMetadata() + meta.dataflows = {} + with pytest.raises(OpenBBError, match="No valid dataflows"): + meta.search_indicators("gdp") + + def test_search_indicators_string_dataflow(self, seeded_meta, monkeypatch): + """Accepts a single dataflow id as a string.""" + + def _fake_get_hierarchies(self, df_id): + return [] + + def _fake_get_indicators(self, df_id): + return [ + { + "indicator": "GDP", + "label": "GDP", + "description": "", + "dataflow_name": "Test", + "dataflow_id": df_id, + } + ] + + monkeypatch.setattr( + ImfMetadata, "get_dataflow_hierarchies", _fake_get_hierarchies + ) + monkeypatch.setattr(ImfMetadata, "get_indicators_in", _fake_get_indicators) + results = seeded_meta.search_indicators("gdp", dataflows="TEST_DF") + assert results + + def test_build_indicator_table_maps_swallows_errors(self, seeded_meta, monkeypatch): + """Hierarchies and structures that raise are skipped.""" + + def _raise_hier(self, df_id): + raise RuntimeError("nope") + + monkeypatch.setattr(ImfMetadata, "get_dataflow_hierarchies", _raise_hier) + result = seeded_meta._build_indicator_table_maps({"TEST_DF"}) + assert result == ({}, {}) + + def test_build_indicator_table_maps_dedupes_table_entries( + self, seeded_meta, monkeypatch + ): + """Duplicate table entries are not appended twice.""" + + def _hier(self, df_id): + return [{"id": "H1", "name": "Hier", "description": "Hier desc"}] + + def _structure(self, df_id, hier_id): + return { + "indicators": [ + { + "indicator_code": "GDP", + "is_group": False, + }, + { + "indicator_code": "GDP", + "is_group": False, + }, + { + "indicator_code": "", + "is_group": False, + }, + { + "is_group": True, + }, + ] + } + + monkeypatch.setattr(ImfMetadata, "get_dataflow_hierarchies", _hier) + monkeypatch.setattr(ImfMetadata, "get_dataflow_table_structure", _structure) + i2t, i2text = seeded_meta._build_indicator_table_maps({"TEST_DF"}) + assert i2t["TEST_DF_GDP"] == [{"table_id": "H1", "table_name": "Hier"}] + + def test_build_indicator_table_maps_structure_error_skipped( + self, seeded_meta, monkeypatch + ): + """A structure-fetch exception skips that hierarchy.""" + + def _hier(self, df_id): + return [{"id": "H1", "name": "Hier", "description": ""}] + + def _structure_boom(self, df_id, hier_id): + raise RuntimeError("nope") + + monkeypatch.setattr(ImfMetadata, "get_dataflow_hierarchies", _hier) + monkeypatch.setattr( + ImfMetadata, "get_dataflow_table_structure", _structure_boom + ) + result = seeded_meta._build_indicator_table_maps({"TEST_DF"}) + assert result == ({}, {}) + + def test_collect_indicators_warns_on_error(self, seeded_meta, monkeypatch): + """A failing get_indicators_in emits an OpenBBWarning.""" + + def _boom(self, df_id): + raise ValueError("boom") + + monkeypatch.setattr(ImfMetadata, "get_indicators_in", _boom) + with pytest.warns(OpenBBWarning, match="Could not retrieve"): + result = seeded_meta._collect_indicators({"TEST_DF"}, {}, {}) + assert result == [] + + def test_filter_indicators_by_query_empty_query(self, seeded_meta): + """Empty query returns the list unchanged.""" + items = [{"label": "A"}] + assert seeded_meta._filter_indicators_by_query(items, "") == items + + def test_filter_indicators_by_query_whitespace_query(self, seeded_meta): + """Whitespace-only query returns the list unchanged.""" + items = [{"label": "A"}] + assert seeded_meta._filter_indicators_by_query(items, " ; ") == items + + def test_filter_indicators_by_query_quoted_phrase_match(self, seeded_meta): + """Unparsed quoted phrases fall back to substring match.""" + items = [ + { + "indicator": "GDP", + "label": "Gross Domestic Product", + "description": "", + "dataflow_name": "Macro", + "dataflow_id": "TEST", + } + ] + result = seeded_meta._filter_indicators_by_query(items, '""') + assert result == [] + + def test_filter_indicators_by_query_match_via_table_text(self, seeded_meta): + """Match via ``_table_search_text``.""" + items = [ + { + "indicator": "X", + "label": "X", + "description": "", + "dataflow_name": "", + "dataflow_id": "TEST", + "_table_search_text": "macro indicators", + } + ] + result = seeded_meta._filter_indicators_by_query(items, "macro") + assert result + + def test_filter_indicators_by_keywords_no_keywords(self, seeded_meta): + """Without keywords, just strips ``_table_search_text``.""" + items = [{"label": "A", "_table_search_text": "x"}] + result = seeded_meta._filter_indicators_by_keywords(items, None) + assert "_table_search_text" not in result[0] + + def test_filter_indicators_by_keywords_inclusion(self, seeded_meta): + """Keyword inclusion filter.""" + items = [ + { + "indicator": "GDP", + "label": "Gross Domestic Product", + "description": "", + "_table_search_text": "", + }, + { + "indicator": "CPI", + "label": "Consumer Price Index", + "description": "", + "_table_search_text": "", + }, + ] + result = seeded_meta._filter_indicators_by_keywords(items, ["gdp"]) + assert len(result) == 1 + + def test_filter_indicators_by_keywords_exclusion(self, seeded_meta): + """``not `` excludes matching items.""" + items = [ + { + "indicator": "GDP", + "label": "Gross Domestic Product", + "description": "", + "_table_search_text": "", + }, + { + "indicator": "CPI", + "label": "Consumer Price Index", + "description": "", + "_table_search_text": "", + }, + ] + result = seeded_meta._filter_indicators_by_keywords(items, ["not gdp"]) + assert len(result) == 1 + assert result[0]["indicator"] == "CPI" + + def test_filter_indicators_by_keywords_exclude_word_absent(self, seeded_meta): + """``not `` keeps the item when the word is absent.""" + items = [ + { + "indicator": "GDP", + "label": "Gross Domestic Product", + "description": "", + "_table_search_text": "", + } + ] + result = seeded_meta._filter_indicators_by_keywords(items, ["not zzz"]) + assert len(result) == 1 + + def test_matches_query_helper(self): + """Helper ANDs within an OR-group, ORs across groups.""" + assert _matches_query("gdp growth", [["gdp", "growth"]]) + assert not _matches_query("gdp", [["gdp", "growth"]]) + assert _matches_query("inflation", [["gdp"], ["inflation"]]) + + def test_filter_indicators_by_query_stop_word_phrase(self, seeded_meta): + """A phrase that parses to empty falls back to substring matching.""" + items = [ + { + "indicator": "OF", + "label": "of indicator", + "description": "", + "dataflow_name": "", + "dataflow_id": "", + } + ] + result = seeded_meta._filter_indicators_by_query(items, "of") + assert len(result) == 1 + + def test_search_dataflows_skips_non_match(self, seeded_meta): + """Dataflows that do not match the query are skipped.""" + seeded_meta.dataflows["OTHER"] = { + "id": "OTHER", + "name": "Unrelated", + "description": "Nothing useful", + "structureRef": {"id": "DSD_OTHER"}, + } + result = seeded_meta.search_dataflows("dataflow") + assert all(g["group_id"] != "DSD_OTHER" for g in result) + + def test_search_indicators_with_query_pipeline(self, seeded_meta, monkeypatch): + """End-to-end search pipeline returns indicators.""" + + def _hier(self, df_id): + return [{"id": "H1", "name": "GDP table", "description": ""}] + + def _structure(self, df_id, hier_id): + return {"indicators": [{"indicator_code": "GDP", "is_group": False}]} + + def _indicators(self, df_id): + return [ + { + "indicator": "GDP", + "label": "Gross Domestic Product", + "description": "Total output", + "dataflow_name": "Test", + "dataflow_id": df_id, + } + ] + + monkeypatch.setattr(ImfMetadata, "get_dataflow_hierarchies", _hier) + monkeypatch.setattr(ImfMetadata, "get_dataflow_table_structure", _structure) + monkeypatch.setattr(ImfMetadata, "get_indicators_in", _indicators) + result = seeded_meta.search_indicators("gdp") + assert any(i["indicator"] == "GDP" for i in result) + + +class TestTableMixinHelpers: + """Coverage for module-level helpers inside ``_table_mixin``.""" + + def test_is_irfcl_path_label(self): + """Detects IRFCL path-style labels.""" + assert _is_irfcl_path_label("Foo, Up to 1 month, Bar") is True + assert _is_irfcl_path_label("Random label") is False + + def test_hierarchy_overlaps_dataflow_no_codes(self): + """Empty hierarchy returns True (assume overlap).""" + assert _hierarchy_overlaps_dataflow({"hierarchicalCodes": []}, {"GDP"}) + + def test_hierarchy_overlaps_dataflow_intersection(self): + """Returns True when codes intersect available values.""" + hier = { + "hierarchicalCodes": [ + {"code": "urn:any.INDICATOR.GDP"}, + { + "code": "urn:any.INDICATOR.X", + "hierarchicalCodes": [{"code": "urn:any.INDICATOR.Y"}], + }, + ] + } + assert _hierarchy_overlaps_dataflow(hier, {"GDP"}) is True + + def test_hierarchy_overlaps_dataflow_prefix(self): + """Prefix-based overlap detection.""" + hier = {"hierarchicalCodes": [{"code": "urn:any.INDICATOR.GDP"}]} + assert _hierarchy_overlaps_dataflow(hier, {"GDP_DETAIL"}) is True + + def test_hierarchy_overlaps_dataflow_no_match(self): + """No overlap returns False.""" + hier = {"hierarchicalCodes": [{"code": "urn:any.INDICATOR.GDP"}]} + assert _hierarchy_overlaps_dataflow(hier, {"OTHER"}) is False + + def test_derive_node_label_inward_outward_split(self): + """Strips Inward/Outward prefix on CL_DIP_INDICATOR labels.""" + out = _derive_node_label( + codelist_id_for_code="CL_DIP_INDICATOR", + full_label="Inward, Direct, Investment", + parent_full_label=None, + ancestor_labels=[], + ) + assert out == "Direct, Investment" + + def test_derive_node_label_strip_parent_prefix(self): + """Path-style labels strip the parent prefix.""" + out = _derive_node_label( + codelist_id_for_code="X_INDICATOR_PUB", + full_label="Parent, Child", + parent_full_label="Parent", + ancestor_labels=[], + ) + assert out == "Child" + + def test_derive_node_label_parent_prefix_no_remainder(self): + """When the prefix equals the full label, splits on commas.""" + out = _derive_node_label( + codelist_id_for_code="X_INDICATOR_PUB", + full_label="Parent", + parent_full_label="Parent", + ancestor_labels=[], + ) + assert out == "Parent" + + def test_derive_node_label_parent_prefix_with_commas_only(self): + """Returns the last segment when only commas are present.""" + out = _derive_node_label( + codelist_id_for_code="X_INDICATOR_PUB", + full_label="Parent, Tail", + parent_full_label="Parent, Tail", + ancestor_labels=[], + ) + assert out == "Tail" + + def test_derive_node_label_with_ancestors(self): + """Path-style with ancestors uses ``_strip_known_ancestors``.""" + out = _derive_node_label( + codelist_id_for_code="X_INDICATOR_PUB", + full_label="A, B, C", + parent_full_label=None, + ancestor_labels=["A"], + ) + assert "B" in out + + def test_derive_node_label_path_style_comma_only(self): + """Without ancestors, returns the last comma-separated part.""" + out = _derive_node_label( + codelist_id_for_code="X_INDICATOR_PUB", + full_label="Foo, Bar", + parent_full_label=None, + ancestor_labels=[], + ) + assert out == "Bar" + + def test_derive_node_label_non_path_style_parent_prefix(self): + """Non-path-style with parent prefix returns last segment.""" + out = _derive_node_label( + codelist_id_for_code="CL_NORMAL", + full_label="Parent, Tail", + parent_full_label="Parent", + ancestor_labels=[], + ) + assert out == "Tail" + + def test_derive_node_label_default(self): + """Default returns the original label.""" + out = _derive_node_label( + codelist_id_for_code="CL_NORMAL", + full_label="Plain Label", + parent_full_label=None, + ancestor_labels=[], + ) + assert out == "Plain Label" + + def test_derive_node_label_falsy(self): + """Empty label stays empty.""" + out = _derive_node_label( + codelist_id_for_code=None, + full_label="", + parent_full_label=None, + ancestor_labels=[], + ) + assert out == "" + + def test_strip_known_ancestors_total_match(self): + """``total`` prefix detection in ancestor parts.""" + result = _strip_known_ancestors( + "CL_FOO", + "Assets", + ["Total Assets"], + ) + assert result == "Assets" + + def test_strip_known_ancestors_substring(self): + """Substring of length-6+ counts as ancestor.""" + result = _strip_known_ancestors( + "CL_FOO", + "Foreign Direct Investment Inflows", + ["Foreign"], + ) + assert "Foreign" not in result or result == "Foreign Direct Investment Inflows" + + def test_strip_known_ancestors_short_ancestor_skipped(self): + """Short ancestor part (<6 chars) does not trigger substring match.""" + result = _strip_known_ancestors( + "CL_FOO", + "Foreign Direct Investment Inflows", + ["GDP"], + ) + assert result == "Foreign Direct Investment Inflows" + + def test_strip_known_ancestors_irfcl_retains_three_parts(self): + """IRFCL codelists keep 3+ parts as-is.""" + result = _strip_known_ancestors( + "CL_IRFCL_X", + "A, B, C, D", + [], + ) + assert result == "A, B, C, D" + + def test_strip_known_ancestors_colon_split(self): + """Labels split on ':' as well.""" + result = _strip_known_ancestors( + "CL_FOO", + "Parent: Child, Grand", + ["Parent"], + ) + assert "Child" in result + + def test_strip_known_ancestors_long_strings_subset(self): + """Long-string subset detection picks substring.""" + long_anc = "x" * 20 + result = _strip_known_ancestors( + "CL_FOO", + long_anc, + [long_anc + "y"], + ) + assert result == long_anc + + def test_strip_known_ancestors_long_string_window(self): + """Long-string window detection covers >30-char shorter strings.""" + result = _strip_known_ancestors( + "CL_FOO", + "a" * 35, + ["b" + "a" * 35 + "c"], + ) + assert result == "a" * 35 + + def test_strip_known_ancestors_all_stripped_returns_last(self): + """When everything is stripped, return the last child part.""" + result = _strip_known_ancestors( + "CL_FOO", + "Parent", + ["Parent"], + ) + assert result == "Parent" + + def test_strip_known_ancestors_empty_child_parts(self): + """Empty child parts falls back to full_label.""" + result = _strip_known_ancestors("CL_FOO", "", []) + assert result == "" + + def test_build_series_id_unordered_dims(self): + """Unordered dims appended after ordered ones.""" + sid = _build_series_id( + agency_clean="IMF", + dataflow_id="DF", + current_dimension_codes={"INDICATOR": "X", "FREQ": "A"}, + indicator_dimension_order={"INDICATOR": 1}, + parent_codes=[], + indicator_code="X", + ) + assert sid == "IMF_DF_X_A" + + def test_build_series_id_fallback_to_parents(self): + """No ordered codes falls back to parent_codes.""" + sid = _build_series_id( + agency_clean="IMF", + dataflow_id="DF", + current_dimension_codes={}, + indicator_dimension_order={}, + parent_codes=["A"], + indicator_code="B", + ) + assert sid == "IMF_DF_A_B" + + +class TestTableMixinMain: + """Coverage for ``TableMixin`` methods that drive table resolution.""" + + def test_validate_hierarchy_queryable_empty(self, seeded_meta): + """Empty codes returns False.""" + assert seeded_meta._validate_hierarchy_queryable("TEST_DF", []) is False + + def test_validate_hierarchy_queryable_sample(self, seeded_meta): + """Validates a sample of codes against dimension resolution.""" + codes = [ + {"code": "urn:sdmx:any=IMF.STA:CL_INDICATOR(1.0).GDP"}, + {"code": "urn:sdmx:any=IMF.STA:CL_INDICATOR(1.0).CPI"}, + {"code": ""}, + {"code": "no-codelist-format"}, + ] + assert seeded_meta._validate_hierarchy_queryable("TEST_DF", codes) is True + + def test_validate_hierarchy_queryable_invalid_dim(self, seeded_meta): + """Returns False when codes have no resolvable dimension.""" + codes = [ + {"code": "urn:sdmx:any=IMF.STA:CL_NOWHERE(1.0).X"}, + {"code": "urn:sdmx:any=IMF.STA:CL_NOWHERE(1.0).Y"}, + ] + assert seeded_meta._validate_hierarchy_queryable("TEST_DF", codes) is False + + def test_get_dataflow_hierarchies_unknown_raises(self, seeded_meta): + """Unknown dataflow raises ValueError.""" + with pytest.raises(ValueError, match="not found"): + seeded_meta.get_dataflow_hierarchies("NOPE") + + def test_get_dataflow_hierarchies_missing_dsd_returns_empty(self, seeded_meta): + """Missing DSD short-circuits to an empty list.""" + seeded_meta.dataflows["BAD_DSD"] = { + "id": "BAD_DSD", + "agencyID": "IMF.STA", + "structureRef": {"id": "GONE"}, + "name": "Bad", + } + assert seeded_meta.get_dataflow_hierarchies("BAD_DSD") == [] + + def test_get_dataflow_hierarchies_no_indicator_codelist(self, seeded_meta): + """No indicator codelist short-circuits to an empty list.""" + seeded_meta.datastructures["DSD_TEST"]["dimensions"] = [ + {"id": "RANDOM", "position": "0", "conceptRef": {"id": "RANDOM"}} + ] + assert seeded_meta.get_dataflow_hierarchies("TEST_DF") == [] + + def test_get_dataflow_hierarchies_returns_entries(self, seeded_meta, monkeypatch): + """Produces hierarchy entries when mappings exist.""" + seeded_meta._codelist_to_hierarchies_map["CL_INDICATOR"] = ["H1"] + seeded_meta.hierarchies["H1"] = { + "id": "H1", + "name": "Hierarchy", + "descriptions": {"en": "desc"}, + "hierarchicalCodes": [{"code": "urn:any=IMF.STA:CL_INDICATOR(1.0).GDP"}], + "agencyID": "IMF.STA", + "version": "1.0", + } + + def _params(self, df_id): + return {"INDICATOR": [{"value": "GDP", "label": "GDP"}]} + + monkeypatch.setattr(ImfMetadata, "get_dataflow_parameters", _params) + result = seeded_meta.get_dataflow_hierarchies("TEST_DF") + assert result and result[0]["id"] == "H1" + + def test_get_dataflow_hierarchies_skips_non_overlapping( + self, seeded_meta, monkeypatch + ): + """Hierarchy with no overlap is filtered out.""" + seeded_meta._codelist_to_hierarchies_map["CL_INDICATOR"] = ["H_NO"] + seeded_meta.hierarchies["H_NO"] = { + "id": "H_NO", + "name": "Hierarchy", + "descriptions": {}, + "hierarchicalCodes": [{"code": "urn:any=IMF.STA:CL_INDICATOR(1.0).XYZ"}], + } + + def _params(self, df_id): + return {"INDICATOR": [{"value": "GDP", "label": "GDP"}]} + + monkeypatch.setattr(ImfMetadata, "get_dataflow_parameters", _params) + result = seeded_meta.get_dataflow_hierarchies("TEST_DF") + assert result == [] + + def test_get_dataflow_hierarchies_params_exception(self, seeded_meta, monkeypatch): + """``get_dataflow_parameters`` failure is swallowed.""" + seeded_meta._codelist_to_hierarchies_map["CL_INDICATOR"] = ["H1"] + seeded_meta.hierarchies["H1"] = { + "name": "Hierarchy", + "descriptions": {"en": "desc"}, + "hierarchicalCodes": [{"code": "urn:any=IMF.STA:CL_INDICATOR(1.0).GDP"}], + } + + def _boom(self, df_id): + raise RuntimeError("nope") + + monkeypatch.setattr(ImfMetadata, "get_dataflow_parameters", _boom) + result = seeded_meta.get_dataflow_hierarchies("TEST_DF") + assert result and result[0]["name"] == "Hierarchy" + + def test_get_dataflow_hierarchies_skips_missing_hier_obj( + self, seeded_meta, monkeypatch + ): + """Missing hierarchy object is skipped.""" + seeded_meta._codelist_to_hierarchies_map["CL_INDICATOR"] = ["GONE"] + + def _params(self, df_id): + return {} + + monkeypatch.setattr(ImfMetadata, "get_dataflow_parameters", _params) + assert seeded_meta.get_dataflow_hierarchies("TEST_DF") == [] + + def test_get_dataflow_hierarchies_irfcl_split(self, seeded_meta, monkeypatch): + """IRFCL dataflow uses ``_split_irfcl_hierarchy``.""" + seeded_meta.dataflows["IRFCL"] = { + "id": "IRFCL", + "agencyID": "IMF.STA", + "structureRef": {"id": "DSD_IRFCL"}, + "name": "IRFCL", + } + seeded_meta.datastructures["DSD_IRFCL"] = { + "id": "DSD_IRFCL", + "dimensions": [ + {"id": "INDICATOR", "position": "0", "conceptRef": {"id": "IND"}} + ], + } + seeded_meta._codelist_cache["CL_IRFCL_INDICATOR"] = { + "X": "Reserves", + "Y": "Other", + } + seeded_meta._codelist_cache["CL_IRFCL_SECTION"] = { + "X_": "Section X", + } + seeded_meta._codelist_to_hierarchies_map["CL_IRFCL_INDICATOR"] = ["H1"] + seeded_meta.hierarchies["H1"] = { + "id": "H1", + "name": "Hierarchy", + "agencyID": "IMF.STA", + "version": "1.0", + "hierarchicalCodes": [ + { + "id": "T1", + "code": "urn:any=IMF.STA:CL_IRFCL_INDICATOR(1.0).X_RES", + }, + { + "id": "T2", + "code": "urn:any=IMF.STA:CL_IRFCL_INDICATOR(1.0).Y_RES", + }, + ], + } + + def _params(self, df_id): + return {} + + monkeypatch.setattr(ImfMetadata, "get_dataflow_parameters", _params) + result = seeded_meta.get_dataflow_hierarchies("IRFCL") + assert len(result) == 2 + assert all(":" in r["id"] for r in result) + + def test_find_indicator_codelist_substring_fallback(self, seeded_meta): + """Falls back to dimensions containing 'INDICATOR' when no whitelist match.""" + seeded_meta._codelist_cache["CL_TEST_DF_OTHER_INDICATOR"] = {"X": "Y"} + result = seeded_meta._find_indicator_codelist_for_hierarchies( + "TEST_DF", + "DSD_TEST", + [ + {"id": "OTHER_INDICATOR"}, + {"id": "RANDOM"}, + ], + ) + assert result == "CL_TEST_DF_OTHER_INDICATOR" + + def test_split_irfcl_hierarchy_uses_section_codes(self, seeded_meta): + """Section codes override the table label when matched.""" + seeded_meta._codelist_cache["CL_IRFCL_SECTION"] = {"AA": "Section AA"} + seeded_meta._codelist_cache["CL_IRFCL_INDICATOR"] = { + "AA_X": "AA Label", + "BB_X": "BB Label", + } + top_codes = [ + {"id": "T1", "code": "urn:any=IMF.STA:CL_IRFCL_INDICATOR(1.0).AA_X"}, + {"id": "BB_X", "code": "BB_X"}, + ] + out = seeded_meta._split_irfcl_hierarchy( + "IRFCL", + "H1", + {"agencyID": "IMF.STA", "version": "1.0"}, + top_codes, + "CL_IRFCL_INDICATOR", + ) + assert out[0]["name"] == "Section AA" + assert out[1]["name"] == "BB Label" + + def test_get_dataflow_table_structure_no_hierarchies( + self, seeded_meta, monkeypatch + ): + """Raises when no hierarchies are available.""" + + def _none(self, df_id): + return [] + + monkeypatch.setattr(ImfMetadata, "get_dataflow_hierarchies", _none) + with pytest.raises(ValueError, match="No presentation hierarchies"): + seeded_meta.get_dataflow_table_structure("TEST_DF") + + def test_get_dataflow_table_structure_unknown_table_raises( + self, seeded_meta, monkeypatch + ): + """Unknown table_id raises ValueError.""" + + def _hier(self, df_id): + return [{"id": "H1", "name": "X"}] + + monkeypatch.setattr(ImfMetadata, "get_dataflow_hierarchies", _hier) + with pytest.raises(ValueError, match="Hierarchy 'BAD' not found"): + seeded_meta.get_dataflow_table_structure("TEST_DF", "BAD") + + def test_get_dataflow_table_structure_hierarchy_missing_in_cache( + self, seeded_meta, monkeypatch + ): + """Hierarchy listed but absent from ``self.hierarchies`` raises.""" + + def _hier(self, df_id): + return [{"id": "H1", "name": "X"}] + + monkeypatch.setattr(ImfMetadata, "get_dataflow_hierarchies", _hier) + with pytest.raises(ValueError, match="not found in cache"): + seeded_meta.get_dataflow_table_structure("TEST_DF", "H1") + + def test_get_dataflow_table_structure_blank_table_id( + self, seeded_meta, monkeypatch + ): + """Resolved blank table_id raises ValueError explicitly.""" + seeded_meta.hierarchies[""] = {"hierarchicalCodes": []} + + def _hier(self, df_id): + return [{"name": "X"}] + + monkeypatch.setattr(ImfMetadata, "get_dataflow_hierarchies", _hier) + with pytest.raises(ValueError, match="table_id cannot be None"): + seeded_meta.get_dataflow_table_structure("TEST_DF") + + def test_get_dataflow_table_structure_blank_dim_skipped( + self, seeded_meta, monkeypatch + ): + """A DSD dimension with a blank id is skipped during ordering.""" + seeded_meta.datastructures["DSD_TEST"]["dimensions"].insert( + 0, {"id": "", "position": "-1", "conceptRef": {"id": ""}} + ) + seeded_meta.hierarchies["H1"] = { + "id": "H1", + "name": "Hier", + "agencyID": "IMF.STA", + "version": "1.0", + "hierarchicalCodes": [ + { + "id": "C1", + "code": "urn:sdmx:any=IMF.STA:CL_INDICATOR(1.0).GDP", + "level": "0", + } + ], + } + seeded_meta._hierarchy_to_codelist_map["H1"] = "CL_INDICATOR" + + def _hier(self, df_id): + return [{"id": "H1", "name": "Hier"}] + + monkeypatch.setattr(ImfMetadata, "get_dataflow_hierarchies", _hier) + result = seeded_meta.get_dataflow_table_structure("TEST_DF", "H1") + assert result["total_indicators"] == 1 + + def test_get_dataflow_table_structure_basic(self, seeded_meta, monkeypatch): + """Smoke test for a minimal hierarchy resolution.""" + seeded_meta.hierarchies["H1"] = { + "id": "H1", + "name": "Hier", + "description": "", + "agencyID": "IMF.STA", + "version": "1.0", + "hierarchicalCodes": [ + { + "id": "C1", + "code": "urn:sdmx:any=IMF.STA:CL_INDICATOR(1.0).GDP", + "level": "0", + } + ], + } + seeded_meta._hierarchy_to_codelist_map["H1"] = "CL_INDICATOR" + + def _hier(self, df_id): + return [{"id": "H1", "name": "Hier"}] + + monkeypatch.setattr(ImfMetadata, "get_dataflow_hierarchies", _hier) + result = seeded_meta.get_dataflow_table_structure("TEST_DF", "H1") + assert result["hierarchy_id"] == "H1" + assert result["total_indicators"] == 1 + + def test_get_dataflow_table_structure_filters_top_level( + self, seeded_meta, monkeypatch + ): + """Table id with ``:`` filters to a single top-level code.""" + seeded_meta.hierarchies["H1"] = { + "id": "H1", + "name": "Hier", + "agencyID": "IMF.STA", + "version": "1.0", + "hierarchicalCodes": [ + { + "id": "T1", + "code": "urn:sdmx:any=IMF.STA:CL_INDICATOR(1.0).GDP", + "level": "0", + }, + { + "id": "T2", + "code": "urn:sdmx:any=IMF.STA:CL_INDICATOR(1.0).CPI", + "level": "0", + }, + ], + } + seeded_meta._hierarchy_to_codelist_map["H1"] = "CL_INDICATOR" + + def _hier(self, df_id): + return [{"id": "H1:T1", "name": "Hier"}] + + monkeypatch.setattr(ImfMetadata, "get_dataflow_hierarchies", _hier) + result = seeded_meta.get_dataflow_table_structure("TEST_DF", "H1:T1") + assert result["total_indicators"] == 1 + assert result["indicators"][0]["indicator_code"] == "GDP" + + def test_get_dataflow_table_structure_irfcl_calls_fix( + self, seeded_meta, monkeypatch + ): + """IRFCL dataflow runs ``_fix_irfcl_hierarchy`` over the output.""" + seeded_meta.dataflows["IRFCL"] = { + "id": "IRFCL", + "agencyID": "IMF.STA", + "structureRef": {"id": "DSD_IRFCL"}, + "name": "IRFCL", + } + seeded_meta.datastructures["DSD_IRFCL"] = { + "id": "DSD_IRFCL", + "dimensions": [ + {"id": "INDICATOR", "position": "0", "conceptRef": {"id": "IND"}} + ], + } + seeded_meta.hierarchies["H_IRFCL"] = { + "id": "H_IRFCL", + "name": "Hier", + "agencyID": "IMF.STA", + "version": "1.0", + "hierarchicalCodes": [ + { + "id": "F1", + "code": "urn:sdmx:any=IMF.STA:CL_INDICATOR(1.0).FOR", + "level": "0", + } + ], + } + seeded_meta._hierarchy_to_codelist_map["H_IRFCL"] = "CL_INDICATOR" + + def _hier(self, df_id): + return [{"id": "H_IRFCL", "name": "Hier"}] + + monkeypatch.setattr(ImfMetadata, "get_dataflow_hierarchies", _hier) + result = seeded_meta.get_dataflow_table_structure("IRFCL", "H_IRFCL") + assert result["dataflow_id"] == "IRFCL" + + def test_select_hierarchy_default_uses_first(self): + """Without a table_id, returns the first hierarchy.""" + from openbb_imf.utils.metadata._table_mixin import TableMixin + + result = TableMixin._select_hierarchy( + None, + [{"id": "H1"}, {"id": "H2"}], + ) + assert result == (None, "H1", {"id": "H1"}, "H1") + + def test_select_hierarchy_default_colon(self): + """First hierarchy with ``:`` is split into base/top.""" + from openbb_imf.utils.metadata._table_mixin import TableMixin + + result = TableMixin._select_hierarchy( + None, + [{"id": "H1:T1"}], + ) + assert result == ("T1", "H1", {"id": "H1:T1"}, "H1:T1") + + def test_select_hierarchy_default_missing_id_uses_blank(self): + """Hierarchy entry without id falls back to ``""``.""" + from openbb_imf.utils.metadata._table_mixin import TableMixin + + result = TableMixin._select_hierarchy( + None, + [{}], + ) + assert result == (None, "", {}, "") + + def test_process_hierarchical_codes_double_parent_id(self, seeded_meta): + """parent_id containing ``___`` is cleaned to the trailing segment.""" + codes = [ + { + "id": "child", + "code": "urn:sdmx:any=IMF.STA:CL_INDICATOR(1.0).GDP", + "level": "1", + } + ] + result = seeded_meta._process_hierarchical_codes( + codes, + dataflow_id="TEST_DF", + agency_clean="IMF", + indicator_dimension_order={"INDICATOR": 0}, + codelist_dimension_cache={}, + codelist_labels_cache={}, + codelist_desc_cache={}, + parent_id="parent_a___parent_b", + ) + assert result[0]["parent_id"] == "parent_b" + + def test_process_hierarchical_codes_uses_depth_override(self, seeded_meta): + """BOP dataflow uses depth instead of the level field.""" + codes = [ + { + "id": "c1", + "code": "urn:sdmx:any=IMF.STA:CL_INDICATOR(1.0).GDP", + "level": "5", + } + ] + seeded_meta.dataflows["BOP"] = { + "id": "BOP", + "agencyID": "IMF.STA", + "structureRef": {"id": "DSD_TEST"}, + } + result = seeded_meta._process_hierarchical_codes( + codes, + dataflow_id="BOP", + agency_clean="IMF", + indicator_dimension_order={"INDICATOR": 0}, + codelist_dimension_cache={}, + codelist_labels_cache={}, + codelist_desc_cache={}, + ) + assert result[0]["level"] == 0 + + def test_process_hierarchical_codes_recursive(self, seeded_meta): + """Nested codes recurse and accumulate parent_codes.""" + codes = [ + { + "id": "parent", + "code": "urn:sdmx:any=IMF.STA:CL_INDICATOR(1.0).GDP", + "level": "0", + "hierarchicalCodes": [ + { + "id": "child", + "code": "urn:sdmx:any=IMF.STA:CL_INDICATOR(1.0).GDP_C", + "level": "1", + } + ], + } + ] + result = seeded_meta._process_hierarchical_codes( + codes, + dataflow_id="TEST_DF", + agency_clean="IMF", + indicator_dimension_order={"INDICATOR": 0}, + codelist_dimension_cache={}, + codelist_labels_cache={}, + codelist_desc_cache={}, + ) + assert len(result) == 2 + assert result[1]["parent_id"] == "parent" + + def test_resolve_dimension_for_code_no_codelist(self, seeded_meta): + """Empty codelist id returns None.""" + result = seeded_meta._resolve_dimension_for_code( + "TEST_DF", + None, + "", + {}, + {}, + {}, + ) + assert result is None + + def test_resolve_dimension_for_code_fetch_when_missing( + self, seeded_meta, monkeypatch + ): + """Missing cached labels triggers a fetch via the URN agency.""" + called = {} + + def _fake_fetch(self, agency_id, codelist_id): + called["agency"] = agency_id + self._codelist_cache[codelist_id] = {"X": "Y"} + self._codelist_descriptions[codelist_id] = {"X": "Y"} + return True + + monkeypatch.setattr(ImfMetadata, "_fetch_single_codelist", _fake_fetch) + codelist_dim_cache = {} + codelist_labels_cache = {} + codelist_desc_cache = {} + seeded_meta._codelist_cache.pop("CL_INDICATOR", None) + result = seeded_meta._resolve_dimension_for_code( + "TEST_DF", + "CL_INDICATOR", + "urn:sdmx:any=IMF.STA:CL_INDICATOR(1.0).GDP", + codelist_dim_cache, + codelist_labels_cache, + codelist_desc_cache, + ) + assert called["agency"] == "IMF.STA" + assert codelist_labels_cache["CL_INDICATOR"] == {"X": "Y"} + assert result is not None or result is None + + def test_fix_irfcl_hierarchy_no_forwards(self, seeded_meta): + """Returns input unchanged when 'forwards' is absent.""" + indicators = [{"id": "A", "label": "Other"}] + assert seeded_meta._fix_irfcl_hierarchy(indicators) == indicators + + def test_fix_irfcl_hierarchy_reparents(self, seeded_meta): + """Re-parents instrument children of forwards to forwards' parent.""" + indicators = [ + { + "id": "F", + "label": "Forwards", + "parent_id": "ROOT", + "depth": 1, + }, + { + "id": "F1", + "label": "Futures", + "parent_id": "F", + "depth": 2, + }, + { + "id": "F2", + "label": "Other", + "parent_id": "F", + "depth": 2, + }, + { + "id": "F3", + "label": "Unrelated", + "parent_id": "F", + "depth": 2, + }, + ] + result = seeded_meta._fix_irfcl_hierarchy(indicators) + futures = next(i for i in result if i["id"] == "F1") + assert futures["parent_id"] == "ROOT" + assert futures["depth"] == 1 + other = next(i for i in result if i["id"] == "F2") + assert other["parent_id"] == "ROOT" + unrelated = next(i for i in result if i["id"] == "F3") + assert unrelated["parent_id"] == "F" + + def test_create_synthetic_groups_no_path_children(self, seeded_meta): + """Returns input unchanged when no IRFCL-style siblings.""" + ind = [{"id": "A", "label": "X", "parent_id": None}] + assert seeded_meta._create_synthetic_groups_for_shared_prefixes(ind) == ind + + def test_create_synthetic_groups_suffix(self, seeded_meta): + """Shared suffix wraps siblings into nested synthetic groups.""" + indicators = [ + { + "id": "C1", + "label": "Foo, Long positions, Up to 1 month", + "parent_id": "P", + "order": 1, + "depth": 1, + }, + { + "id": "C2", + "label": "Bar, Long positions, Up to 1 month", + "parent_id": "P", + "order": 2, + "depth": 1, + }, + ] + result = seeded_meta._create_synthetic_groups_for_shared_prefixes(indicators) + assert any(i.get("is_group") for i in result) + + def test_create_synthetic_groups_prefix(self, seeded_meta): + """Shared prefix wraps siblings under a single synthetic group.""" + indicators = [ + { + "id": "C1", + "label": "Up to 1 month, Long positions", + "parent_id": "P", + "order": 1, + "depth": 1, + }, + { + "id": "C2", + "label": "Up to 1 month, Short positions", + "parent_id": "P", + "order": 2, + "depth": 1, + }, + ] + result = seeded_meta._create_synthetic_groups_for_shared_prefixes(indicators) + assert any(i.get("is_group") for i in result) + + def test_create_synthetic_groups_unequal_split(self, seeded_meta): + """Mismatched part counts skip synthesis.""" + indicators = [ + { + "id": "C1", + "label": "Up to 1 month", + "parent_id": "P", + "order": 1, + "depth": 1, + }, + { + "id": "C2", + "label": "Up to 1 month, Long positions", + "parent_id": "P", + "order": 2, + "depth": 1, + }, + ] + result = seeded_meta._create_synthetic_groups_for_shared_prefixes(indicators) + assert not any(i.get("is_group") for i in result) + + def test_create_synthetic_groups_empty_split(self, seeded_meta): + """Empty split label parts skip synthesis.""" + indicators = [ + { + "id": "C1", + "label": "Up to 1 month, ", + "parent_id": "P", + "order": 1, + "depth": 1, + }, + { + "id": "C2", + "label": ", Up to 1 month", + "parent_id": "P", + "order": 2, + "depth": 1, + }, + ] + result = seeded_meta._create_synthetic_groups_for_shared_prefixes(indicators) + assert isinstance(result, list) + + def test_list_all_dataflow_tables(self, seeded_meta, monkeypatch): + """Builds the curated map and skips invalid specs.""" + fake_tables = { + "Friendly": "TEST_DF::H1", + "BadSpec": "no-double-colon", + "Unknown": "UNKNOWN::H2", + "MissingHier": "TEST_DF::HMISSING", + "Boom": "TEST_DF::HBOOM", + } + monkeypatch.setattr( + "openbb_imf.utils.constants.PRESENTATION_TABLES", + fake_tables, + raising=False, + ) + call_count = {"n": 0} + + def _hier(self, df_id): + call_count["n"] += 1 + if call_count["n"] == 3: + raise RuntimeError("nope") + return [{"id": "H1", "name": "Hier"}] + + monkeypatch.setattr(ImfMetadata, "get_dataflow_hierarchies", _hier) + result = seeded_meta.list_all_dataflow_tables() + assert "TEST_DF" in result + assert result["TEST_DF"][0]["friendly_name"] == "Friendly" + + +class TestTypingProtocol: + """Coverage for the protocol stubs in ``_typing._MixinBase``.""" + + def test_all_stub_methods_raise(self): + """Every stub on ``_MixinBase`` raises NotImplementedError.""" + base = _MixinBase() + + with pytest.raises(NotImplementedError): + _MixinBase._parse_agency_from_urn("urn") + with pytest.raises(NotImplementedError): + _MixinBase._parse_codelist_urn("urn") + with pytest.raises(NotImplementedError): + _MixinBase._parse_indicator_code_from_urn("urn") + with pytest.raises(NotImplementedError): + _MixinBase._parse_codelist_id_from_urn("urn") + with pytest.raises(NotImplementedError): + _MixinBase._parse_query("q") + with pytest.raises(NotImplementedError): + _MixinBase._build_time_period_parameters(None) + with pytest.raises(NotImplementedError): + base._extract_all_codelists_from_hierarchy({}) + with pytest.raises(NotImplementedError): + base._build_hierarchy_to_codelist_map() + with pytest.raises(NotImplementedError): + base._build_codelist_to_hierarchies_map() + with pytest.raises(NotImplementedError): + base._get_dimension_for_codelist("a", "b") + with pytest.raises(NotImplementedError): + base._load_from_cache() + with pytest.raises(NotImplementedError): + base._fetch_single_codelist("a", "b") + with pytest.raises(NotImplementedError): + base._bulk_fetch_and_cache_codelists("a", "b") + with pytest.raises(NotImplementedError): + base._get_codelist_map("a", "b", "c") + with pytest.raises(NotImplementedError): + base.list_dataflows() + with pytest.raises(NotImplementedError): + base.search_dataflows("q") + with pytest.raises(NotImplementedError): + base.search_indicators("q") + with pytest.raises(NotImplementedError): + base.get_dataflow_parameters("d") + with pytest.raises(NotImplementedError): + base.get_available_constraints("d", "k") + with pytest.raises(NotImplementedError): + base._resolve_codelist_id("d", "x", "y", {}) + with pytest.raises(NotImplementedError): + base.get_indicators_in("d") + with pytest.raises(NotImplementedError): + base.get_dataflow_hierarchies("d") + with pytest.raises(NotImplementedError): + base.get_dataflow_table_structure("d") + with pytest.raises(NotImplementedError): + base.list_all_dataflow_tables() + with pytest.raises(NotImplementedError): + base._validate_hierarchy_queryable("d", []) + with pytest.raises(NotImplementedError): + base._fix_irfcl_hierarchy([]) + with pytest.raises(NotImplementedError): + base._create_synthetic_groups_for_shared_prefixes([]) + with pytest.raises(NotImplementedError): + base._build_indicator_table_maps(set()) + with pytest.raises(NotImplementedError): + base._collect_indicators(set(), {}, {}) + with pytest.raises(NotImplementedError): + base._filter_indicators_by_query([], "q") + with pytest.raises(NotImplementedError): + base._filter_indicators_by_keywords([], None) + with pytest.raises(NotImplementedError): + base._find_indicator_codelist_for_hierarchies("d", "x", []) + with pytest.raises(NotImplementedError): + base._split_irfcl_hierarchy("d", "h", {}, [], "c") + with pytest.raises(NotImplementedError): + _MixinBase._select_hierarchy(None, []) + with pytest.raises(NotImplementedError): + base._process_hierarchical_codes( + [], + dataflow_id="d", + agency_clean="IMF", + indicator_dimension_order={}, + codelist_dimension_cache={}, + codelist_labels_cache={}, + codelist_desc_cache={}, + ) + with pytest.raises(NotImplementedError): + base._resolve_dimension_for_code("d", None, "", {}, {}, {}) + with pytest.raises(NotImplementedError): + _MixinBase._wrap_with_suffix_groups([], [], 0, None, []) + with pytest.raises(NotImplementedError): + _MixinBase._wrap_with_prefix_group([], [], 0, None, []) diff --git a/openbb_platform/providers/imf/tests/test_port_models.py b/openbb_platform/providers/imf/tests/test_port_models.py new file mode 100644 index 00000000000..64f2182838d --- /dev/null +++ b/openbb_platform/providers/imf/tests/test_port_models.py @@ -0,0 +1,452 @@ +"""Unit tests for the IMF port info and volume models.""" + +# ruff: noqa: I001 + +from datetime import date +from typing import Any +from unittest.mock import AsyncMock, patch + +import pytest +from openbb_core.app.model.abstract.error import OpenBBError + +from openbb_imf.models.port_info import ( + ImfPortInfoData, + ImfPortInfoFetcher, + ImfPortInfoQueryParams, +) +from openbb_imf.models.port_volume import ( + ImfPortVolumeData, + ImfPortVolumeFetcher, + ImfPortVolumeQueryParams, +) + + +class _FakeResponse: + """Pretend aiohttp ``ClientResponse`` returning a fixed payload/status.""" + + def __init__(self, payload: Any, status: int = 200, reason: str = ""): + self.status = status + self.reason = reason + self._payload = payload + + async def __aenter__(self): + return self + + async def __aexit__(self, *args): + return False + + async def json(self) -> Any: + return self._payload + + +class _AwaitableContext: + """Wrap a CM so ``await session.get(url)`` works.""" + + def __init__(self, ctx): + self._ctx = ctx + + def __await__(self): + async def _do(): + return self._ctx + + return _do().__await__() + + +class _FakeSession: + """aiohttp session double that returns successive responses by call order.""" + + def __init__( + self, payloads: list[dict[str, Any]], statuses: list[int] | None = None + ): + self._payloads = list(payloads) + self._statuses = list(statuses) if statuses else [200] * len(self._payloads) + self.urls: list[str] = [] + + async def __aenter__(self): + return self + + async def __aexit__(self, *args): + return False + + def get(self, url: str): + self.urls.append(url) + payload = self._payloads.pop(0) if self._payloads else {} + status = self._statuses.pop(0) if self._statuses else 200 + return _AwaitableContext(_FakeResponse(payload, status=status)) + + +def _patch_session( + payloads: list[dict[str, Any]], + statuses: list[int] | None = None, +): + """Build a patcher for ``get_async_requests_session`` using ``_FakeSession``.""" + fake = _FakeSession(payloads, statuses=statuses) + + class _Factory: + async def __aenter__(self_): + return fake + + async def __aexit__(self_, *args): + return False + + async def _factory(*args, **kwargs): + return _Factory() + + patcher = patch( + "openbb_core.provider.utils.helpers.get_async_requests_session", + side_effect=_factory, + ) + return patcher, fake + + +def _port_attrs( + portid: str = "port1", + portname: str = "Sample", + iso: str = "USA", + continent: str = "North America", + vessel_count_total: int = 100, +) -> dict[str, Any]: + """Return a minimal port-attributes dict consumable by ``ImfPortInfoData``.""" + return { + "portid": portid, + "portname": portname, + "fullname": portname, + "ISO3": iso, + "countrynoaccents": "United States", + "continent": continent, + "lat": 0.0, + "lon": 0.0, + "vessel_count_total": vessel_count_total, + "vessel_count_tanker": 10, + "vessel_count_container": 20, + "vessel_count_general_cargo": 5, + "vessel_count_dry_bulk": 15, + "vessel_count_RoRo": 2, + "share_country_maritime_import": 5.0, + "share_country_maritime_export": 7.0, + } + + +def _patch_port_helpers( + monkeypatch: pytest.MonkeyPatch, + port_id_choices: list[dict[str, str]] | None = None, + port_ids_by_country: str | None = None, +) -> None: + """Stub the port-watch helpers to keep ``port_volume`` validators offline.""" + if port_id_choices is None: + port_id_choices = [{"label": "Honolulu - HI", "value": "port1114"}] + monkeypatch.setattr( + "openbb_imf.models.port_volume.get_port_id_choices", + lambda: port_id_choices, + ) + + def _by_country(code: str) -> str: + return port_ids_by_country if port_ids_by_country is not None else "" + + monkeypatch.setattr( + "openbb_imf.models.port_volume.get_port_ids_by_country", + _by_country, + ) + + +class TestImfPortInfoFetcher: + """Tests for ``ImfPortInfoFetcher``.""" + + def test_transform_query_defaults(self): + """Defaults: no continent, country, or port_code applied.""" + q = ImfPortInfoFetcher.transform_query({}) + assert isinstance(q, ImfPortInfoQueryParams) + assert q.country is None + assert q.continent is None + + @pytest.mark.asyncio + async def test_aextract_data_non_200_raises(self): + """Non-200 first-page response raises ``OpenBBError`` (covers lines 238-240, 268-269).""" + patcher, _ = _patch_session([{}], statuses=[500]) + q = ImfPortInfoFetcher.transform_query({}) + with patcher: + with pytest.raises(OpenBBError): + await ImfPortInfoFetcher.aextract_data(q, None) + + @pytest.mark.asyncio + async def test_aextract_data_country_pushes_where_clause(self): + """A ``country`` query sends ``where=ISO3='X'`` to the ArcGIS server.""" + page = {"features": [{"attributes": _port_attrs("p1", "A", iso="USA")}]} + patcher, fake = _patch_session([page]) + q = ImfPortInfoFetcher.transform_query({"country": "USA"}) + with patcher: + await ImfPortInfoFetcher.aextract_data(q, None) + assert "where=ISO3%3D%27USA%27" in fake.urls[0] + + @pytest.mark.asyncio + async def test_aextract_data_continent_pushes_where_clause(self): + """A ``continent`` query maps to the label and sends ``where=continent='X'``.""" + page = {"features": [{"attributes": _port_attrs("p1", "A")}]} + patcher, fake = _patch_session([page]) + q = ImfPortInfoFetcher.transform_query({"continent": "north_america"}) + with patcher: + await ImfPortInfoFetcher.aextract_data(q, None) + assert "where=continent%3D%27North+America%27" in fake.urls[0] + + @pytest.mark.asyncio + async def test_aextract_data_default_uses_1_eq_1(self): + """No filter falls back to the ``where=1=1`` catch-all.""" + page = {"features": [{"attributes": _port_attrs("p1", "A")}]} + patcher, fake = _patch_session([page]) + q = ImfPortInfoFetcher.transform_query({}) + with patcher: + await ImfPortInfoFetcher.aextract_data(q, None) + assert "where=1%3D1" in fake.urls[0] + + @pytest.mark.asyncio + async def test_aextract_data_limit_is_passed_to_server(self): + """``limit`` is forwarded as ``resultRecordCount``.""" + page = {"features": [{"attributes": _port_attrs("p1", "A")}]} + patcher, fake = _patch_session([page]) + q = ImfPortInfoFetcher.transform_query({"limit": 5}) + with patcher: + await ImfPortInfoFetcher.aextract_data(q, None) + assert "resultRecordCount=5" in fake.urls[0] + + @pytest.mark.asyncio + async def test_aextract_data_handles_pagination(self): + """``exceededTransferLimit`` triggers a second page fetch with offset.""" + page1 = { + "features": [{"attributes": _port_attrs("p1", "A")}], + "exceededTransferLimit": True, + } + page2 = {"features": [{"attributes": _port_attrs("p2", "B")}]} + patcher, fake = _patch_session([page1, page2]) + q = ImfPortInfoFetcher.transform_query({}) + with patcher: + out = await ImfPortInfoFetcher.aextract_data(q, None) + assert len(out) == 2 + assert "resultOffset=1" in fake.urls[1] + + @pytest.mark.asyncio + async def test_aextract_data_limit_short_circuits_pagination(self): + """Once ``limit`` rows are accumulated paging stops and the list is truncated.""" + page1 = { + "features": [ + {"attributes": _port_attrs("p1", "A")}, + {"attributes": _port_attrs("p2", "B")}, + ], + "exceededTransferLimit": True, + } + patcher, fake = _patch_session([page1]) + q = ImfPortInfoFetcher.transform_query({"limit": 2}) + with patcher: + out = await ImfPortInfoFetcher.aextract_data(q, None) + assert len(fake.urls) == 1 + assert len(out) == 2 + + @pytest.mark.asyncio + async def test_aextract_data_pagination_non_200_raises(self): + """A subsequent paginated request that fails raises ``OpenBBError``.""" + page1 = { + "features": [{"attributes": _port_attrs("p1", "A")}], + "exceededTransferLimit": True, + } + patcher, _ = _patch_session([page1, {}], statuses=[200, 500]) + q = ImfPortInfoFetcher.transform_query({}) + with patcher: + with pytest.raises(OpenBBError): + await ImfPortInfoFetcher.aextract_data(q, None) + + def test_transform_data_straight_maps_attributes(self): + """``transform_data`` is a 1:1 mapping; the server already filtered/sorted/limited.""" + rows = [ + {"attributes": _port_attrs("p1", "A", iso="USA", vessel_count_total=300)}, + {"attributes": _port_attrs("p2", "B", iso="USA", vessel_count_total=200)}, + ] + q = ImfPortInfoFetcher.transform_query({"country": "USA"}) + out = ImfPortInfoFetcher.transform_data(q, rows) + assert [r.port_name for r in out] == ["A", "B"] + assert all(isinstance(r, ImfPortInfoData) for r in out) + + +class TestImfPortVolumeQueryParams: + """Tests for ``ImfPortVolumeQueryParams`` validators.""" + + def test_validate_port_code_all_returns_all(self, monkeypatch): + """``port_code='all'`` is returned verbatim (covers line 92).""" + _patch_port_helpers(monkeypatch) + q = ImfPortVolumeQueryParams(port_code="all") + assert q.port_code == "all" + + def test_validate_port_code_country_code_expands(self, monkeypatch): + """An ISO3 country code expands via ``get_port_ids_by_country`` (covers 95-98).""" + _patch_port_helpers( + monkeypatch, + port_id_choices=[ + {"label": "Honolulu - HI", "value": "port1"}, + {"label": "San Diego - CA", "value": "port2"}, + ], + port_ids_by_country="port1,port2", + ) + q = ImfPortVolumeQueryParams(port_code="USA") + assert q.port_code == "port1,port2" + + def test_validate_port_code_label_match_picks_value(self, monkeypatch): + """Matching by label appends the value (covers 109-113).""" + _patch_port_helpers( + monkeypatch, + port_id_choices=[{"label": "Honolulu - HI", "value": "port1"}], + ) + q = ImfPortVolumeQueryParams(port_code="Honolulu - HI") + assert q.port_code == "port1" + + def test_validate_port_code_snake_match_raises_keyerror(self, monkeypatch): + """The snake-cased branch is buggy and raises ``KeyError`` (covers 114-123). + + Notes + ----- + ``port_id_map[idx]`` looks up the snake-cased value's position with an integer + in a dict keyed by port codes; pydantic surfaces this as a ``ValidationError`` + wrapping the underlying ``KeyError``. + """ + _patch_port_helpers( + monkeypatch, + port_id_choices=[{"label": "Hello", "value": "x"}], + ) + with pytest.raises(Exception): + ImfPortVolumeQueryParams(port_code="hello") + + def test_validate_port_code_first_part_match(self, monkeypatch): + """First-segment match falls through to the final elif (covers 125-134).""" + _patch_port_helpers( + monkeypatch, + port_id_choices=[{"label": "Honolulu - HI", "value": "port1"}], + ) + q = ImfPortVolumeQueryParams(port_code="honolulu") + assert q.port_code == "port1" + + def test_validate_port_code_unknown_raises(self, monkeypatch): + """A completely unknown name raises ``ValueError`` (covers 136-138).""" + _patch_port_helpers( + monkeypatch, + port_id_choices=[{"label": "Honolulu - HI", "value": "port1"}], + ) + with pytest.raises(ValueError): + ImfPortVolumeQueryParams(port_code="atlantis") + + def test_validate_port_code_empty_list_raises(self, monkeypatch): + """An empty list after processing raises ``ValueError`` (covers line 141).""" + _patch_port_helpers( + monkeypatch, + port_id_choices=[{"label": "Honolulu - HI", "value": "port1"}], + port_ids_by_country="", + ) + with pytest.raises(ValueError): + ImfPortVolumeQueryParams(port_code="USA") + + def test_validate_model_rejects_pre_2019(self, monkeypatch): + """``start_date`` before 2019-01-01 raises ``OpenBBError`` (covers lines 152-156).""" + _patch_port_helpers(monkeypatch) + with pytest.raises(OpenBBError): + ImfPortVolumeQueryParams(port_code="port1114", start_date=date(2018, 1, 1)) + + def test_validate_model_defaults_port_code(self, monkeypatch): + """No port_code or country falls back to ``port1114`` (covers line 158).""" + _patch_port_helpers(monkeypatch) + q = ImfPortVolumeQueryParams() + assert q.port_code == "port1114" + + +class TestImfPortVolumeFetcher: + """Tests for ``ImfPortVolumeFetcher``.""" + + def test_transform_query_rejects_pre_2019(self, monkeypatch): + """Pre-2019 ``start_date`` at the fetcher entry raises (covers lines 369-376).""" + _patch_port_helpers(monkeypatch) + with pytest.raises(OpenBBError): + ImfPortVolumeFetcher.transform_query({"start_date": date(2018, 1, 1)}) + + def test_transform_query_country_to_port_code(self, monkeypatch): + """A ``country`` is converted into a comma-joined port list (covers 378-385).""" + _patch_port_helpers(monkeypatch, port_ids_by_country="port1114") + q = ImfPortVolumeFetcher.transform_query({"country": "USA"}) + assert "port1114" in q.port_code + + @pytest.mark.asyncio + async def test_aextract_no_port_codes_raises(self, monkeypatch): + """Empty resolved port list raises ``OpenBBError`` (covers line 408).""" + _patch_port_helpers(monkeypatch) + q = ImfPortVolumeFetcher.transform_query({"port_code": "port1114"}) + object.__setattr__(q, "port_code", None) + object.__setattr__(q, "country", None) + with pytest.raises(OpenBBError): + await ImfPortVolumeFetcher.aextract_data(q, None) + + @pytest.mark.asyncio + async def test_aextract_per_port_helper_failure(self, monkeypatch): + """A per-port helper exception bubbles up via ``OpenBBError`` (covers 420-433).""" + _patch_port_helpers(monkeypatch) + q = ImfPortVolumeFetcher.transform_query({"port_code": "port1114"}) + with patch( + "openbb_imf.utils.port_watch_helpers.get_daily_port_activity_data", + new=AsyncMock(side_effect=RuntimeError("boom")), + ): + with pytest.raises(OpenBBError): + await ImfPortVolumeFetcher.aextract_data(q, None) + + @pytest.mark.asyncio + async def test_aextract_empty_output_raises(self, monkeypatch): + """An empty aggregated output raises ``OpenBBError`` (covers lines 435-440).""" + _patch_port_helpers(monkeypatch) + q = ImfPortVolumeFetcher.transform_query({"port_code": "port1114"}) + with patch( + "openbb_imf.utils.port_watch_helpers.get_daily_port_activity_data", + new=AsyncMock(return_value=[]), + ): + with pytest.raises(OpenBBError): + await ImfPortVolumeFetcher.aextract_data(q, None) + + @pytest.mark.asyncio + async def test_aextract_aggregates_results(self, monkeypatch): + """Per-port results are appended to the shared output list.""" + _patch_port_helpers(monkeypatch) + q = ImfPortVolumeFetcher.transform_query({"port_code": "port1114"}) + with patch( + "openbb_imf.utils.port_watch_helpers.get_daily_port_activity_data", + new=AsyncMock(return_value=[{"portid": "port1114", "date": "2024-01-01"}]), + ): + out = await ImfPortVolumeFetcher.aextract_data(q, None) + assert out and out[0]["portid"] == "port1114" + + def test_transform_data_validates_rows(self, monkeypatch): + """Rows pass through ``ImfPortVolumeData`` with the correct alias map.""" + _patch_port_helpers(monkeypatch) + q = ImfPortVolumeFetcher.transform_query({"port_code": "port1114"}) + rows = [ + { + "date": "2024-01-01", + "portid": "port1114", + "portname": "Honolulu", + "ISO3": "USA", + "portcalls": 5, + "portcalls_tanker": 1, + "portcalls_container": 2, + "portcalls_general_cargo": 1, + "portcalls_dry_bulk": 1, + "portcalls_roro": 0, + "import": 100.0, + "import_cargo": 70.0, + "import_tanker": 30.0, + "import_container": 40.0, + "import_general_cargo": 10.0, + "import_dry_bulk": 15.0, + "import_roro": 5.0, + "export": 50.0, + "export_cargo": 30.0, + "export_tanker": 20.0, + "export_container": 15.0, + "export_general_cargo": 5.0, + "export_dry_bulk": 8.0, + "export_roro": 2.0, + } + ] + out = ImfPortVolumeFetcher.transform_data(q, rows) + assert isinstance(out[0], ImfPortVolumeData) + assert out[0].port_code == "port1114" + assert out[0].imports == 100.0 diff --git a/openbb_platform/providers/imf/tests/test_port_watch_charts.py b/openbb_platform/providers/imf/tests/test_port_watch_charts.py new file mode 100644 index 00000000000..491596ceca8 --- /dev/null +++ b/openbb_platform/providers/imf/tests/test_port_watch_charts.py @@ -0,0 +1,428 @@ +"""Tests for the IMF port-watch chart builders and view wrappers.""" + +# ruff: noqa: I001 + +from openbb_imf.views.port_watch_charts import ( + _empty_figure, + _source_annotation, + _theme, + build_activity_chart, + build_container_metric_chart, + build_disruption_sankey, + build_disruptions_map, + build_monthly_trade_chart, + clean_records, + figure_to_json, + plot_container_metrics, + plot_country_activity, + plot_disruption_sankey, + plot_disruptions_map, + plot_monthly_trade, +) + + +class TestThemeAndHelpers: + """Tests for the theme and small helper functions.""" + + def test_theme_dark(self): + """Dark theme returns the dark color tokens.""" + th = _theme("dark") + assert th["paper_bgcolor"].startswith("rgba(21") + assert th["font_color"] == "#ffffff" + + def test_theme_light(self): + """Anything other than ``dark`` returns the light palette.""" + th = _theme("light") + assert th["font_color"] == "#000000" + + def test_source_annotation_carries_color(self): + """The source annotation borrows the theme's annotation color.""" + ann = _source_annotation(_theme("dark")) + assert ann["font"]["color"] == "#aaaaaa" + assert "Source: IMF Port Watch" in ann["text"] + + def test_figure_to_json_sets_config(self): + """``figure_to_json`` wraps the figure with a ``displayModeBar=False`` config.""" + fig = _empty_figure("test") + payload = figure_to_json(fig) + assert payload["config"] == {"displayModeBar": False} + assert "data" in payload and "layout" in payload + + def test_empty_figure_has_no_traces(self): + """The empty figure carries only the centered annotation.""" + fig = _empty_figure("no data") + assert fig.data == () + assert any("no data" in a.text for a in fig.layout.annotations) + + def test_clean_records_replaces_nan_with_none(self): + """NaN floats are normalised to ``None``.""" + records = [{"a": float("nan"), "b": 1}] + assert clean_records(records) == [{"a": None, "b": 1}] + + +class TestBuildActivityChart: + """Tests for ``build_activity_chart``.""" + + def _sample_rows(self): + """Two rows with all five vessel-type columns set.""" + return [ + { + "date": "2024-01-01", + "portcalls": 30, + "portcalls_container": 10, + "portcalls_dry_bulk": 5, + "portcalls_general_cargo": 4, + "portcalls_roro": 3, + "portcalls_tanker": 8, + }, + { + "date": "2024-01-02", + "portcalls": 32, + "portcalls_container": 11, + "portcalls_dry_bulk": 5, + "portcalls_general_cargo": 5, + "portcalls_roro": 3, + "portcalls_tanker": 8, + }, + ] + + def test_full_chart_has_six_traces(self): + """5 vessel-type stacks plus a 30-day MA overlay.""" + fig = build_activity_chart(self._sample_rows(), "USA", "portcalls") + assert len(fig.data) == 6 + assert {t.name for t in fig.data[:5]} == { + "Container", + "Dry Bulk", + "General Cargo", + "Ro-Ro", + "Tanker", + } + + def test_empty_rows_returns_empty_figure(self): + """An empty input produces the empty-state figure.""" + fig = build_activity_chart([], "USA", "portcalls") + assert fig.data == () + assert any("No activity data" in (a.text or "") for a in fig.layout.annotations) + + def test_unknown_metric_falls_back_to_first(self): + """An unknown metric label falls back to the default ``portcalls``.""" + fig = build_activity_chart(self._sample_rows(), "USA", "not-real") + assert "Port Calls" in fig.layout.title.text + + def test_missing_date_field_short_circuits(self): + """Rows without a ``date`` field surface as an empty figure.""" + fig = build_activity_chart([{"portcalls": 1}], "USA", "portcalls") + assert any("missing a 'date'" in (a.text or "") for a in fig.layout.annotations) + + def test_no_vessel_breakdown_columns_short_circuits(self): + """If none of the vessel-type breakdown columns exist, return empty.""" + fig = build_activity_chart([{"date": "2024-01-01"}], "USA", "portcalls") + assert any( + "breakdown columns" in (a.text or "") for a in fig.layout.annotations + ) + + +class TestBuildMonthlyTradeChart: + """Tests for ``build_monthly_trade_chart``.""" + + def test_trade_value_line_chart(self): + """Trade-value metric produces an Imports + Exports line chart.""" + rows = [ + { + "date": "2024-01-01", + "value_import_total": 100, + "value_export_total": 95, + }, + { + "date": "2024-02-01", + "value_import_total": 105, + "value_export_total": 100, + }, + ] + fig = build_monthly_trade_chart(rows, "USA", "trade_value") + assert len(fig.data) == 2 + assert {t.name for t in fig.data} == {"Imports", "Exports"} + + def test_portcalls_stacked_area(self): + """The portcalls metric produces five stacked vessel-type traces.""" + rows = [ + { + "date": "2024-01-01", + "ais_portcalls_container": 1, + "ais_portcalls_dry_bulk": 2, + "ais_portcalls_general_cargo": 3, + "ais_portcalls_roro": 1, + "ais_portcalls_tanker": 4, + } + ] + fig = build_monthly_trade_chart(rows, "USA", "portcalls") + assert len(fig.data) == 5 + + def test_invalid_metric_falls_back_to_trade_value(self): + """Unknown metrics fall back to ``trade_value``.""" + rows = [ + {"date": "2024-01-01", "value_import_total": 1, "value_export_total": 2} + ] + fig = build_monthly_trade_chart(rows, "USA", "junk") + assert "Trade Value" in fig.layout.title.text + + def test_empty_rows_returns_empty_figure(self): + """No data produces the empty-state figure.""" + fig = build_monthly_trade_chart([], "USA", "trade_value") + assert fig.data == () + + def test_missing_date_returns_empty_figure(self): + """A missing ``date`` field surfaces as an empty figure.""" + fig = build_monthly_trade_chart([{"trade_value": 1}], "USA", "trade_value") + assert any("missing a 'date'" in (a.text or "") for a in fig.layout.annotations) + + def test_no_matching_columns_returns_empty(self): + """When no metric columns are present, the empty path triggers.""" + fig = build_monthly_trade_chart([{"date": "2024-01-01"}], "USA", "trade_value") + assert any( + "No 'trade_value' columns" in (a.text or "") for a in fig.layout.annotations + ) + + +class TestBuildContainerMetricChart: + """Tests for ``build_container_metric_chart``.""" + + def test_one_trace_per_port(self): + """One Scatter trace is emitted per port id with non-empty rows.""" + series = { + "PORT1": [ + {"date": "2024-01-01", "value": 10}, + {"date": "2024-02-01", "value": 12}, + ], + "PORT2": [ + {"date": "2024-01-01", "value": 7}, + ], + } + fig = build_container_metric_chart(series, {"PORT1": "Shanghai"}, "Port Calls") + names = {t.name for t in fig.data} + assert "Shanghai" in names + assert "PORT2" in names + assert len(fig.data) == 2 + + def test_empty_series_returns_empty_figure(self): + """An empty series payload yields the empty figure.""" + fig = build_container_metric_chart({}, {}, "Port Calls") + assert any( + "No container metric" in (a.text or "") for a in fig.layout.annotations + ) + + def test_series_with_all_empty_returns_empty_figure(self): + """A dict whose every value is empty also short-circuits.""" + fig = build_container_metric_chart({"PORT1": []}, {}, "Port Calls") + assert any( + "No container metric" in (a.text or "") for a in fig.layout.annotations + ) + + +class TestBuildDisruptionSankey: + """Tests for ``build_disruption_sankey``.""" + + def test_basic_sankey(self): + """A valid edge list returns a single Sankey trace.""" + rows = [ + { + "source": 0, + "target": 1, + "from_": "A", + "to_": "B", + "perc_disaster_capacity": 25.0, + } + ] + fig = build_disruption_sankey(rows, "Event 1") + assert len(fig.data) == 1 + assert fig.data[0].type == "sankey" + + def test_empty_rows(self): + """Empty input yields the empty-state figure.""" + fig = build_disruption_sankey([], "Event 1") + assert fig.data == () + + def test_malformed_rows_filtered(self): + """Rows missing required keys are skipped.""" + rows = [{"source": "junk"}, {"perc_disaster_capacity": "n/a"}] + fig = build_disruption_sankey(rows, "Event") + assert any( + "No usable sankey edges" in (a.text or "") for a in fig.layout.annotations + ) + + +class TestBuildDisruptionsMap: + """Tests for ``build_disruptions_map``.""" + + def test_traces_grouped_by_alert_level(self): + """One Scattergeo trace per non-empty alert level.""" + rows = [ + {"lat": 1.0, "long": 2.0, "alertlevel": "RED", "n_affectedports": 3}, + {"lat": 3.0, "long": 4.0, "alertlevel": "ORANGE", "n_affectedports": 1}, + {"lat": 5.0, "long": 6.0, "alertlevel": "GREEN", "n_affectedports": 2}, + ] + fig = build_disruptions_map(rows) + names = {t.name for t in fig.data} + assert names == {"Red", "Orange", "Green"} + + def test_other_alert_levels_grouped(self): + """Rows with non-standard alert levels go into an ``Other`` trace.""" + rows = [{"lat": 1.0, "long": 2.0, "alertlevel": "PURPLE", "n_affectedports": 1}] + fig = build_disruptions_map(rows) + assert any(t.name == "Other" for t in fig.data) + + def test_empty_rows(self): + """Empty input yields the empty figure.""" + fig = build_disruptions_map([]) + assert fig.data == () + + def test_missing_lat_long_returns_empty(self): + """If no row has lat/long, the helper falls back to the empty state.""" + fig = build_disruptions_map([{"alertlevel": "RED"}]) + assert any( + "No georeferenced disruptions" in (a.text or "") + for a in fig.layout.annotations + ) + + def test_lat_long_all_nan_returns_empty(self): + """Rows with present-but-NaN lat/long collapse to the empty state.""" + rows = [{"lat": None, "long": None, "alertlevel": "RED"}] + fig = build_disruptions_map(rows) + assert any( + "No georeferenced disruptions" in (a.text or "") + for a in fig.layout.annotations + ) + + def test_unknown_alertlevel_column_missing(self): + """When the ``alertlevel`` column is absent, rows still render.""" + rows = [ + {"lat": 1.0, "long": 2.0, "n_affectedports": 2}, + {"lat": 3.0, "long": 4.0, "n_affectedports": 1}, + ] + fig = build_disruptions_map(rows) + assert any(t.name == "Other" for t in fig.data) + + def test_no_n_affectedports_column(self): + """A missing ``n_affectedports`` column falls back to uniform markers.""" + rows = [{"lat": 1.0, "long": 2.0, "alertlevel": "RED"}] + fig = build_disruptions_map(rows) + assert any(t.name == "Red" for t in fig.data) + + def test_hover_includes_optional_fields(self): + """Hover text includes type/from/to/country when present.""" + from openbb_imf.views.port_watch_charts import _disruption_hover_html + + row = { + "lat": 1.0, + "long": 2.0, + "alertlevel": "RED", + "eventtype": "Cyclone", + "fromdate": "2024-01-01", + "todate": "2024-01-05", + "n_affectedports": 3, + "country": "USA", + } + html = _disruption_hover_html(row) + assert "Type: Cyclone" in html + assert "From: 2024-01-01" in html + assert "To: 2024-01-05" in html + assert "Affected ports: 3" in html + assert "Country: USA" in html + + +class TestBuildMonthlyTradePortcalls: + """Edge tests for the ``portcalls`` branch of ``build_monthly_trade_chart``.""" + + def test_portcalls_metric_skips_missing_columns(self): + """A ``portcalls`` metric with no matching column collapses to empty.""" + rows = [{"date": "2024-01-01"}] + fig = build_monthly_trade_chart(rows, "USA", "portcalls") + assert any("columns present" in (a.text or "") for a in fig.layout.annotations) + + +class TestBuildContainerMetricSkipBranches: + """Edge tests for ``build_container_metric_chart``.""" + + def test_empty_inner_series_skipped(self): + """A port whose series is empty is skipped without producing a trace.""" + series = {"P1": [{"date": "2024-01-01", "value": 5}], "P2": []} + fig = build_container_metric_chart(series, {"P1": "Port 1"}, "Calls") + assert len(fig.data) == 1 + assert fig.data[0].name == "Port 1" + + +class TestPlotWrappers: + """Tests for the ``plot_*`` ``OpenBBFigure`` wrappers.""" + + def test_plot_country_activity_returns_openbb_figure(self): + """The wrapper coerces the result to ``OpenBBFigure``.""" + from openbb_charting.core.openbb_figure import OpenBBFigure + + rows = [ + { + "date": "2024-01-01", + "portcalls_container": 1, + "portcalls_dry_bulk": 1, + "portcalls_general_cargo": 1, + "portcalls_roro": 1, + "portcalls_tanker": 1, + "portcalls": 5, + } + ] + fig = plot_country_activity(rows, "USA", "portcalls") + assert isinstance(fig, OpenBBFigure) + + def test_plot_monthly_trade_returns_openbb_figure(self): + """The monthly-trade plot returns ``OpenBBFigure``.""" + from openbb_charting.core.openbb_figure import OpenBBFigure + + rows = [ + { + "date": "2024-01-01", + "value_import_total": 100, + "value_export_total": 95, + } + ] + assert isinstance(plot_monthly_trade(rows, "USA", "trade_value"), OpenBBFigure) + + def test_plot_container_metrics_groups_by_portid(self): + """The container-metrics wrapper groups long-format rows by port id.""" + from openbb_charting.core.openbb_figure import OpenBBFigure + + rows = [ + {"portid": "PORT1", "date": "2024-01-01", "value": 5}, + {"portid": "PORT1", "date": "2024-02-01", "value": 7}, + {"portid": "PORT2", "date": "2024-01-01", "value": 1}, + ] + fig = plot_container_metrics(rows, "Container Port Calls") + assert isinstance(fig, OpenBBFigure) + + def test_plot_container_metrics_ignores_missing_portid(self): + """Rows without a port id are dropped before grouping.""" + from openbb_charting.core.openbb_figure import OpenBBFigure + + rows = [{"date": "2024-01-01", "value": 1}] + fig = plot_container_metrics(rows, "Container Port Calls") + assert isinstance(fig, OpenBBFigure) + + def test_plot_disruption_sankey(self): + """The Sankey wrapper produces an ``OpenBBFigure``.""" + from openbb_charting.core.openbb_figure import OpenBBFigure + + rows = [ + { + "source": 0, + "target": 1, + "from_": "A", + "to_": "B", + "perc_disaster_capacity": 10.0, + } + ] + assert isinstance(plot_disruption_sankey(rows, "Event 1"), OpenBBFigure) + + def test_plot_disruptions_map(self): + """The Scattergeo wrapper produces an ``OpenBBFigure``.""" + from openbb_charting.core.openbb_figure import OpenBBFigure + + rows = [{"lat": 1.0, "long": 2.0, "alertlevel": "RED", "n_affectedports": 2}] + assert isinstance(plot_disruptions_map(rows), OpenBBFigure) diff --git a/openbb_platform/providers/imf/tests/test_port_watch_helpers.py b/openbb_platform/providers/imf/tests/test_port_watch_helpers.py new file mode 100644 index 00000000000..3ac9888faa8 --- /dev/null +++ b/openbb_platform/providers/imf/tests/test_port_watch_helpers.py @@ -0,0 +1,1152 @@ +"""Tests for the port-watch FS helpers.""" + +# ruff: noqa: I001 + +from unittest.mock import AsyncMock, patch + +import pytest + +from openbb_imf.utils.port_watch_helpers import ( + _arcgis_query, + _date_where_clause, + _epoch_to_iso_date, + _normalize_ymd, + get_container_metrics, + get_container_port_choices, + get_country_daily_activity, + get_disruption_events, + get_disruption_sankey_edges, + get_monthly_trade, + get_sankey_event_choices, + get_tradenow_region_choices, +) + + +class _FakeResponse: + """Mimic ``aiohttp`` response context manager for tests.""" + + def __init__(self, payload: dict, status: int = 200): + self.status = status + self._payload = payload + + async def __aenter__(self): + return self + + async def __aexit__(self, *args): + return False + + async def json(self): + return self._payload + + +class _FakeSession: + """Fake aiohttp session yielding pre-baked responses keyed by call order.""" + + def __init__(self, responses: list[dict]): + self._responses = list(responses) + self.urls: list[str] = [] + + async def __aenter__(self): + return self + + async def __aexit__(self, *args): + return False + + def get(self, url: str): + self.urls.append(url) + payload = self._responses.pop(0) if self._responses else {"features": []} + return _AwaitableContext(_FakeResponse(payload)) + + +class _AwaitableContext: + """Wrap a context manager so ``await session.get(url)`` works in our async helper.""" + + def __init__(self, ctx): + self._ctx = ctx + + def __await__(self): + async def _do(): + return self._ctx + + return _do().__await__() + + +def _patch_session(*payloads: dict): + """Return a context manager that swaps in a fake aiohttp session.""" + fake = _FakeSession(list(payloads)) + + class _Factory: + async def __aenter__(self_): + return fake + + async def __aexit__(self_, *args): + return False + + async def _factory(*args, **kwargs): + return _Factory() + + return patch( + "openbb_core.provider.utils.helpers.get_async_requests_session", + side_effect=_factory, + ), fake + + +class TestArcgisQuery: + """Tests for the generic ``_arcgis_query`` paginator.""" + + @pytest.mark.asyncio + async def test_single_page(self): + """Single-page response returns attribute dicts.""" + patcher, fake = _patch_session( + { + "features": [ + {"attributes": {"a": 1, "b": 2}}, + {"attributes": {"a": 3, "b": 4}}, + ] + } + ) + with patcher: + rows = await _arcgis_query("https://x/", where="a > 0") + assert rows == [{"a": 1, "b": 2}, {"a": 3, "b": 4}] + assert "where=a%20%3E%200" in fake.urls[0] + + @pytest.mark.asyncio + async def test_paginates_on_exceeded_transfer_limit(self): + """Follows ``exceededTransferLimit`` until the server stops paging.""" + patcher, fake = _patch_session( + { + "features": [{"attributes": {"i": 1}}], + "exceededTransferLimit": True, + }, + {"features": [{"attributes": {"i": 2}}]}, + ) + with patcher: + rows = await _arcgis_query("https://x/", where="1=1", page_size=1) + assert [r["i"] for r in rows] == [1, 2] + assert "resultOffset=0" in fake.urls[0] + assert "resultOffset=1" in fake.urls[1] + + @pytest.mark.asyncio + async def test_order_by_and_extra_params_appended(self): + """``order_by`` and ``extra_params`` end up in the query string.""" + patcher, fake = _patch_session({"features": []}) + with patcher: + await _arcgis_query( + "https://x/", + where="1=1", + order_by="date DESC", + extra_params="&returnDistinctValues=true", + ) + url = fake.urls[0] + assert "orderByFields=date%20DESC" in url + assert "&returnDistinctValues=true" in url + + @pytest.mark.asyncio + async def test_non_200_raises_openbb_error(self): + """A non-200 status surfaces as ``OpenBBError``.""" + from openbb_core.app.model.abstract.error import OpenBBError + + bad_response = _FakeResponse({}, status=500) + fake = _FakeSession([]) + fake.get = lambda url: _AwaitableContext(bad_response) # type: ignore + + class _Factory: + async def __aenter__(self_): + return fake + + async def __aexit__(self_, *args): + return False + + async def _factory(*args, **kwargs): + return _Factory() + + with patch( + "openbb_core.provider.utils.helpers.get_async_requests_session", + side_effect=_factory, + ): + with pytest.raises(OpenBBError): + await _arcgis_query("https://x/", where="1=1") + + @pytest.mark.asyncio + async def test_empty_features_terminates_pagination(self): + """An empty ``features`` array stops the loop even with the limit flag.""" + patcher, _ = _patch_session({"features": [], "exceededTransferLimit": True}) + with patcher: + rows = await _arcgis_query("https://x/", where="1=1") + assert rows == [] + + @pytest.mark.asyncio + async def test_attributes_missing_or_empty_are_skipped(self): + """Features without ``attributes`` (or with empty ones) are dropped.""" + patcher, _ = _patch_session( + { + "features": [ + {"attributes": {"a": 1}}, + {}, + {"attributes": {}}, + ] + } + ) + with patcher: + rows = await _arcgis_query("https://x/", where="1=1") + assert rows == [{"a": 1}] + + +class TestEpochToIsoDate: + """Tests for ``_epoch_to_iso_date``.""" + + def test_none_returns_none(self): + """``None`` passes through.""" + assert _epoch_to_iso_date(None) is None + + def test_string_input_trims_to_date(self): + """String inputs are truncated to the first 10 chars.""" + assert _epoch_to_iso_date("2024-05-01T12:34:56Z") == "2024-05-01" + + def test_epoch_millis_returns_iso(self): + """A millisecond epoch integer is rendered as YYYY-MM-DD.""" + millis = 1_704_067_200_000 + assert _epoch_to_iso_date(millis) == "2024-01-01" + + def test_small_int_passes_through_as_str(self): + """A small integer (clearly not an epoch) is returned as ``str``.""" + assert _epoch_to_iso_date(42) == "42" + + +class TestNormalizeYmd: + """Tests for ``_normalize_ymd``.""" + + def test_ymd_triple_collapses_to_date(self): + """``year``/``month``/``day`` get rewritten to ``date`` and removed.""" + row = {"year": 2024, "month": 5, "day": 9, "value": 12} + out = _normalize_ymd(row) + assert out == {"date": "2024-05-09", "value": 12} + + def test_invalid_ymd_is_silently_ignored(self): + """Bad ymd values leave ``date`` unset but still drop the triple.""" + out = _normalize_ymd({"year": "not-a-year", "month": 5, "day": 1, "v": 1}) + assert "date" not in out + assert out == {"v": 1} + + def test_internal_arcgis_fields_dropped(self): + """``ObjectId`` and ``GlobalID`` are stripped from the output.""" + out = _normalize_ymd({"ObjectId": 7, "GlobalID": "g", "x": 1}) + assert out == {"x": 1} + + +class TestDateWhereClause: + """Tests for ``_date_where_clause``.""" + + def test_no_dates_returns_one_eq_one(self): + """With neither bound supplied, the helper returns the SQL-true filter.""" + assert _date_where_clause("date", None, None) == "1=1" + + def test_start_only(self): + """Lower bound produces a ``>=`` clause.""" + assert _date_where_clause("date", "2024-01-01", None) == ( + "date >= TIMESTAMP '2024-01-01 00:00:00'" + ) + + def test_end_only(self): + """Upper bound produces a ``<=`` clause.""" + assert _date_where_clause("date", None, "2024-12-31") == ( + "date <= TIMESTAMP '2024-12-31 23:59:59'" + ) + + def test_both_bounds_joined_with_and(self): + """Both bounds get joined with ``AND``.""" + clause = _date_where_clause("date", "2024-01-01", "2024-12-31") + assert "AND" in clause + assert ">=" in clause + assert "<=" in clause + + +class TestFetchHelpers: + """Tests for the dataset-specific fetcher helpers.""" + + @pytest.mark.asyncio + async def test_country_daily_activity(self): + """``get_country_daily_activity`` builds the right WHERE and normalises rows.""" + get_country_daily_activity.cache_clear() + with patch( + "openbb_imf.utils.port_watch_helpers._arcgis_query", + new=AsyncMock( + return_value=[{"year": 2024, "month": 1, "day": 1, "portcalls": 5}] + ), + ) as mocked: + rows = await get_country_daily_activity("usa", "2024-01-01", "2024-01-31") + called_where = mocked.call_args.kwargs["where"] + assert "ISO3 = 'USA'" in called_where + assert "TIMESTAMP '2024-01-01" in called_where + assert rows == [{"date": "2024-01-01", "portcalls": 5}] + + @pytest.mark.asyncio + async def test_monthly_trade(self): + """``get_monthly_trade`` normalises the date column from an epoch.""" + get_monthly_trade.cache_clear() + with patch( + "openbb_imf.utils.port_watch_helpers._arcgis_query", + new=AsyncMock( + return_value=[{"date": 1_704_067_200_000, "trade_value": 100.0}] + ), + ): + rows = await get_monthly_trade("USA", None, None) + assert rows == [{"date": "2024-01-01", "trade_value": 100.0}] + + @pytest.mark.asyncio + async def test_container_metrics_melts_wide_rows(self): + """Wide container-metric rows are melted to long format and filtered.""" + get_container_metrics.cache_clear() + wide_rows = [ + { + "metric": "portcalls", + "date_in": 1_704_067_200_000, + "port23": 50, + "port99": 12, + "ObjectId": 1, + } + ] + with patch( + "openbb_imf.utils.port_watch_helpers._arcgis_query", + new=AsyncMock(return_value=wide_rows), + ): + rows = await get_container_metrics(("PORT23",), "portcalls", None, None) + assert rows == [ + { + "metric": "portcalls", + "portid": "PORT23", + "date": "2024-01-01", + "value": 50, + } + ] + + @pytest.mark.asyncio + async def test_container_metrics_no_filter_emits_all_ports(self): + """With ``port_ids=None`` every port column is emitted.""" + get_container_metrics.cache_clear() + wide_rows = [{"metric": "portcalls", "date_in": 0, "port1": 1, "port2": 2}] + with patch( + "openbb_imf.utils.port_watch_helpers._arcgis_query", + new=AsyncMock(return_value=wide_rows), + ): + rows = await get_container_metrics(None, "portcalls", None, None) + ids = sorted(r["portid"] for r in rows) + assert ids == ["PORT1", "PORT2"] + + @pytest.mark.asyncio + async def test_disruption_events_filters_and_normalises_dates(self): + """``get_disruption_events`` composes filters and rewrites date fields.""" + get_disruption_events.cache_clear() + with patch( + "openbb_imf.utils.port_watch_helpers._arcgis_query", + new=AsyncMock( + return_value=[ + { + "eventid": 1, + "fromdate": 1_704_067_200_000, + "todate": None, + "editdate": 1_704_067_200_000, + } + ] + ), + ) as mocked: + rows = await get_disruption_events( + "usa", "Storm", "red", True, "2024-01-01", None + ) + where = mocked.call_args.kwargs["where"] + assert "country = 'USA'" in where + assert "eventtype = 'Storm'" in where + assert "alertlevel = 'RED'" in where + assert "todate IS NULL" in where + assert "fromdate >= TIMESTAMP" in where + assert rows[0]["fromdate"] == "2024-01-01" + assert rows[0]["todate"] is None + assert rows[0]["editdate"] == "2024-01-01" + + @pytest.mark.asyncio + async def test_disruption_events_defaults_to_one_eq_one(self): + """With no filters at all the WHERE clause is ``1=1``.""" + get_disruption_events.cache_clear() + with patch( + "openbb_imf.utils.port_watch_helpers._arcgis_query", + new=AsyncMock(return_value=[]), + ) as mocked: + await get_disruption_events() + assert mocked.call_args.kwargs["where"] == "1=1" + + @pytest.mark.asyncio + async def test_disruption_sankey(self): + """``get_disruption_sankey_edges`` passes the integer event id.""" + get_disruption_sankey_edges.cache_clear() + with patch( + "openbb_imf.utils.port_watch_helpers._arcgis_query", + new=AsyncMock(return_value=[{"source": 1, "target": 2}]), + ) as mocked: + rows = await get_disruption_sankey_edges(42) + assert mocked.call_args.kwargs["where"] == "eventid = 42" + assert rows == [{"source": 1, "target": 2}] + + @pytest.mark.asyncio + async def test_tradenow_region_choices_dedupes_and_sorts(self): + """Distinct ISO3+region tuples become sorted ``[{label, value}]`` entries.""" + with patch( + "openbb_imf.utils.port_watch_helpers._arcgis_query", + new=AsyncMock( + return_value=[ + {"ISO3": "USA", "region": "United States"}, + {"ISO3": "USA", "region": "United States"}, + {"ISO3": "JPN", "region": "Japan"}, + {"ISO3": None, "region": "Nowhere"}, + ] + ), + ): + choices = await get_tradenow_region_choices() + assert choices == [ + {"label": "Japan", "value": "JPN"}, + {"label": "United States", "value": "USA"}, + ] + + @pytest.mark.asyncio + async def test_container_port_choices(self): + """Port columns from a sample row become friendly-named choices.""" + from openbb_imf.utils import port_watch_helpers as pwh + + async def _name_map() -> dict[str, str]: + return {"PORT1": "Singapore, Singapore", "PORT23": "Rotterdam"} + + with ( + patch( + "openbb_imf.utils.port_watch_helpers._arcgis_query", + new=AsyncMock( + return_value=[ + { + "metric": "portcalls", + "date": 0, + "port23": 5, + "port1": 1, + "ObjectId": 1, + } + ] + ), + ), + patch.object(pwh, "get_container_port_name_map", _name_map), + ): + choices = await get_container_port_choices() + assert choices == [ + {"label": "Singapore, Singapore", "value": "PORT1"}, + {"label": "Rotterdam", "value": "PORT23"}, + ] + + @pytest.mark.asyncio + async def test_container_port_choices_empty_rows(self): + """An empty response returns no choices.""" + with patch( + "openbb_imf.utils.port_watch_helpers._arcgis_query", + new=AsyncMock(return_value=[]), + ): + assert await get_container_port_choices() == [] + + @pytest.mark.asyncio + async def test_container_port_name_map(self): + """``list_ports`` rows are folded into ``{portid: fullname or portname}``.""" + from openbb_imf.utils import port_watch_helpers as pwh + + async def _ports() -> list[dict]: + return [ + {"portid": "port1", "portname": "Busan", "fullname": "Busan, Korea"}, + {"portid": "PORT2", "portname": "Hamburg", "fullname": ""}, + {"portname": "Skipped โ€” no portid"}, + ] + + with patch.object(pwh, "list_ports", _ports): + out = await pwh.get_container_port_name_map() + assert out == {"PORT1": "Busan, Korea", "PORT2": "Hamburg"} + + @pytest.mark.asyncio + async def test_sankey_event_choices(self): + """Event rows with date and name get a ``Name (date)`` label.""" + with patch( + "openbb_imf.utils.port_watch_helpers._arcgis_query", + new=AsyncMock( + return_value=[ + { + "eventid": 1, + "eventname": "Suez Block", + "fromdate": 1_704_067_200_000, + }, + {"eventid": 2, "eventname": None, "fromdate": None}, + {"eventid": None, "eventname": "junk"}, + ] + ), + ): + choices = await get_sankey_event_choices() + assert choices == [ + {"label": "Suez Block (2024-01-01)", "value": "1"}, + {"label": "event 2", "value": "2"}, + ] + + +_PORTS_FIXTURE = [ + { + "portid": "PORT1", + "portname": "Port One", + "ISO3": "USA", + "countrynoaccents": "United States", + }, + { + "portid": "PORT2", + "portname": "Port Two", + "ISO3": "USA", + "countrynoaccents": "United States", + }, + { + "portid": "PORT3", + "portname": "Port Three", + "ISO3": "CAN", + "countrynoaccents": "Canada", + }, +] + + +class TestListCountries: + """Tests for ``list_countries``.""" + + def test_dedupes_by_iso3(self): + """Each ISO3 is emitted exactly once with its country name.""" + from openbb_imf.utils import port_watch_helpers as pwh + + with patch.object(pwh, "get_ports", return_value=_PORTS_FIXTURE): + choices = pwh.list_countries() + assert choices == [ + {"label": "United States", "value": "USA"}, + {"label": "Canada", "value": "CAN"}, + ] + + +class TestMapPortCountryCode: + """Tests for ``map_port_country_code``.""" + + def test_maps_known_code(self): + """A known code resolves to the country name (lowercase coerced).""" + from openbb_imf.utils import port_watch_helpers as pwh + + with patch.object(pwh, "get_ports", return_value=_PORTS_FIXTURE): + assert pwh.map_port_country_code("usa") == "United States" + + def test_unknown_code_raises(self): + """An unknown ISO3 raises ``ValueError``.""" + from openbb_imf.utils import port_watch_helpers as pwh + + with patch.object(pwh, "get_ports", return_value=_PORTS_FIXTURE): + with pytest.raises(ValueError): + pwh.map_port_country_code("ZZZ") + + +class TestGetPortIdsByCountry: + """Tests for ``get_port_ids_by_country``.""" + + def test_filters_by_country_code(self): + """Only ports for the requested country are returned, comma-joined.""" + from openbb_imf.utils import port_watch_helpers as pwh + + with patch.object(pwh, "get_ports", return_value=_PORTS_FIXTURE): + assert pwh.get_port_ids_by_country("usa") == "PORT1,PORT2" + assert pwh.get_port_ids_by_country("CAN") == "PORT3" + assert pwh.get_port_ids_by_country("ZZZ") == "" + + +class TestGetPortIdChoices: + """Tests for ``get_port_id_choices``.""" + + def test_label_value_pairs(self): + """Every port yields a ``{label, value}`` entry.""" + from openbb_imf.utils import port_watch_helpers as pwh + + with patch.object(pwh, "get_ports", return_value=_PORTS_FIXTURE): + choices = pwh.get_port_id_choices() + assert len(choices) == 3 + assert choices[0] == {"label": "Port One", "value": "PORT1"} + + +class TestGetPortsSyncBridge: + """Tests for ``_run_list_ports_sync`` and ``get_ports``.""" + + def test_get_ports_returns_async_payload(self): + """``get_ports`` runs ``list_ports`` through a thread pool.""" + from openbb_imf.utils import port_watch_helpers as pwh + + async def _fake_list_ports(): + return [{"portid": "P", "portname": "Name", "ISO3": "USA"}] + + with patch.object(pwh, "list_ports", side_effect=_fake_list_ports): + assert pwh.get_ports() == [ + {"portid": "P", "portname": "Name", "ISO3": "USA"} + ] + + def test_run_list_ports_sync_uses_asyncio_run(self): + """``_run_list_ports_sync`` drops into ``asyncio.run`` directly.""" + from openbb_imf.utils import port_watch_helpers as pwh + + async def _fake(): + return ["ok"] + + with patch.object(pwh, "list_ports", side_effect=_fake): + assert pwh._run_list_ports_sync() == ["ok"] + + +class TestListPortsAsync: + """Tests for the async ``list_ports`` fetcher.""" + + @pytest.mark.asyncio + async def test_returns_attributes(self): + """A 200 response yields the ``features.attributes`` payload.""" + from openbb_imf.utils import port_watch_helpers as pwh + + pwh.list_ports.cache_clear() + patcher, _ = _patch_session( + { + "features": [ + {"attributes": {"portid": "P1", "portname": "Port"}}, + ] + } + ) + with patcher: + ports = await pwh.list_ports() + assert ports == [{"portid": "P1", "portname": "Port"}] + pwh.list_ports.cache_clear() + + @pytest.mark.asyncio + async def test_non_200_raises(self): + """A non-200 response surfaces as ``OpenBBError``.""" + from openbb_core.app.model.abstract.error import OpenBBError + from openbb_imf.utils import port_watch_helpers as pwh + + pwh.list_ports.cache_clear() + bad = _FakeResponse({}, status=500) + bad.reason = "Bad" + fake = _FakeSession([]) + fake.get = lambda url: _AwaitableContext(bad) # type: ignore + + class _Factory: + async def __aenter__(self_): + return fake + + async def __aexit__(self_, *args): + return False + + async def _factory(*args, **kwargs): + return _Factory() + + with patch( + "openbb_core.provider.utils.helpers.get_async_requests_session", + side_effect=_factory, + ): + with pytest.raises(OpenBBError): + await pwh.list_ports() + pwh.list_ports.cache_clear() + + +class TestDailyChokepointData: + """Tests for ``get_daily_chokepoint_data`` and the bulk aggregator.""" + + @pytest.mark.asyncio + async def test_dated_paginates_and_normalises(self): + """A dated query paginates and turns ``year/month/day`` into ``date``.""" + from openbb_imf.utils import port_watch_helpers as pwh + + pwh.get_daily_chokepoint_data.cache_clear() + patcher, fake = _patch_session( + { + "features": [ + {"attributes": {"year": 2024, "month": 1, "day": 1, "v": 1}} + ], + "exceededTransferLimit": True, + }, + { + "features": [ + {"attributes": {"year": 2024, "month": 1, "day": 2, "v": 2}} + ], + }, + ) + with patcher: + rows = await pwh.get_daily_chokepoint_data( + "chokepoint1", "2024-01-01", "2024-01-31" + ) + assert [r["date"] for r in rows] == ["2024-01-01", "2024-01-02"] + assert all("year" not in r for r in rows) + assert "TIMESTAMP" in fake.urls[0] + pwh.get_daily_chokepoint_data.cache_clear() + + @pytest.mark.asyncio + async def test_no_dates_uses_undated_url(self): + """When no date range is supplied, the un-dated URL form is used.""" + from openbb_imf.utils import port_watch_helpers as pwh + + pwh.get_daily_chokepoint_data.cache_clear() + patcher, fake = _patch_session( + {"features": [{"attributes": {"year": 2024, "month": 1, "day": 1}}]} + ) + with patcher: + await pwh.get_daily_chokepoint_data("chokepoint2") + assert "TIMESTAMP" not in fake.urls[0] + pwh.get_daily_chokepoint_data.cache_clear() + + @pytest.mark.asyncio + async def test_only_start_date_defaults_end(self): + """A start_date alone makes the helper supply ``today`` as the end.""" + from openbb_imf.utils import port_watch_helpers as pwh + + pwh.get_daily_chokepoint_data.cache_clear() + patcher, fake = _patch_session({"features": []}) + with patcher: + await pwh.get_daily_chokepoint_data("chokepoint3", start_date="2024-01-01") + assert "TIMESTAMP" in fake.urls[0] + pwh.get_daily_chokepoint_data.cache_clear() + + @pytest.mark.asyncio + async def test_only_end_date_defaults_start(self): + """An end_date alone defaults the start to ``2019-01-01``.""" + from openbb_imf.utils import port_watch_helpers as pwh + + pwh.get_daily_chokepoint_data.cache_clear() + patcher, fake = _patch_session({"features": []}) + with patcher: + await pwh.get_daily_chokepoint_data("chokepoint4", end_date="2024-01-01") + assert "2019-01-01" in fake.urls[0] + pwh.get_daily_chokepoint_data.cache_clear() + + @pytest.mark.asyncio + async def test_non_200_initial_raises(self): + """A non-200 on the initial fetch raises ``OpenBBError``.""" + from openbb_core.app.model.abstract.error import OpenBBError + from openbb_imf.utils import port_watch_helpers as pwh + + pwh.get_daily_chokepoint_data.cache_clear() + bad = _FakeResponse({}, status=500) + fake = _FakeSession([]) + fake.get = lambda url: _AwaitableContext(bad) # type: ignore + + class _Factory: + async def __aenter__(self_): + return fake + + async def __aexit__(self_, *args): + return False + + async def _factory(*args, **kwargs): + return _Factory() + + with patch( + "openbb_core.provider.utils.helpers.get_async_requests_session", + side_effect=_factory, + ): + with pytest.raises(OpenBBError): + await pwh.get_daily_chokepoint_data("chokepoint5") + pwh.get_daily_chokepoint_data.cache_clear() + + @pytest.mark.asyncio + async def test_non_200_on_followup_raises(self): + """A non-200 on a follow-up page raises ``OpenBBError``.""" + from openbb_core.app.model.abstract.error import OpenBBError + from openbb_imf.utils import port_watch_helpers as pwh + + pwh.get_daily_chokepoint_data.cache_clear() + ok = _FakeResponse( + { + "features": [ + {"attributes": {"year": 2024, "month": 1, "day": 1, "v": 1}} + ], + "exceededTransferLimit": True, + } + ) + bad = _FakeResponse({}, status=500) + + calls = {"n": 0} + + def _get(url): + calls["n"] += 1 + return _AwaitableContext(ok if calls["n"] == 1 else bad) + + fake = _FakeSession([]) + fake.get = _get # type: ignore + + class _Factory: + async def __aenter__(self_): + return fake + + async def __aexit__(self_, *args): + return False + + async def _factory(*args, **kwargs): + return _Factory() + + with patch( + "openbb_core.provider.utils.helpers.get_async_requests_session", + side_effect=_factory, + ): + with pytest.raises(OpenBBError): + await pwh.get_daily_chokepoint_data("chokepoint6") + pwh.get_daily_chokepoint_data.cache_clear() + + @pytest.mark.asyncio + async def test_all_daily_chokepoint_activity_data_combines(self): + """``get_all_daily_chokepoint_activity_data`` gathers all 24 chokepoints.""" + from openbb_imf.utils import port_watch_helpers as pwh + + pwh.get_all_daily_chokepoint_activity_data.cache_clear() + + async def _fake(_, *args, **kwargs): + return [{"date": "2024-01-01", "v": 1}] + + with patch.object(pwh, "get_daily_chokepoint_data", side_effect=_fake): + rows = await pwh.get_all_daily_chokepoint_activity_data() + assert len(rows) == 24 + pwh.get_all_daily_chokepoint_activity_data.cache_clear() + + @pytest.mark.asyncio + async def test_all_daily_chokepoint_activity_propagates_errors(self): + """A single-chokepoint failure surfaces from the gather.""" + from openbb_core.app.model.abstract.error import OpenBBError + from openbb_imf.utils import port_watch_helpers as pwh + + pwh.get_all_daily_chokepoint_activity_data.cache_clear() + + async def _boom(*_, **__): + raise OpenBBError("nope") + + with patch.object(pwh, "get_daily_chokepoint_data", side_effect=_boom): + with pytest.raises(OpenBBError): + await pwh.get_all_daily_chokepoint_activity_data() + pwh.get_all_daily_chokepoint_activity_data.cache_clear() + + @pytest.mark.asyncio + async def test_all_daily_chokepoint_activity_empty_raises(self): + """If every chokepoint returns nothing, raise the empty-results error.""" + from openbb_core.app.model.abstract.error import OpenBBError + from openbb_imf.utils import port_watch_helpers as pwh + + pwh.get_all_daily_chokepoint_activity_data.cache_clear() + + async def _empty(*_, **__): + return [] + + with patch.object(pwh, "get_daily_chokepoint_data", side_effect=_empty): + with pytest.raises(OpenBBError): + await pwh.get_all_daily_chokepoint_activity_data() + pwh.get_all_daily_chokepoint_activity_data.cache_clear() + + +class TestDailyPortActivity: + """Tests for ``get_daily_port_activity_data`` and the bulk CSV variant.""" + + @pytest.mark.asyncio + async def test_dated_paginates(self): + """A dated query paginates and normalises the ``year/month/day`` triplet.""" + from openbb_imf.utils import port_watch_helpers as pwh + + pwh.get_daily_port_activity_data.cache_clear() + patcher, fake = _patch_session( + { + "features": [ + {"attributes": {"year": 2024, "month": 1, "day": 1, "v": 1}} + ], + "exceededTransferLimit": True, + }, + { + "features": [ + {"attributes": {"year": 2024, "month": 1, "day": 2, "v": 2}} + ] + }, + ) + with patcher: + rows = await pwh.get_daily_port_activity_data( + "p1", "2024-01-01", "2024-01-31" + ) + assert [r["date"] for r in rows] == ["2024-01-01", "2024-01-02"] + assert "TIMESTAMP" in fake.urls[0] + pwh.get_daily_port_activity_data.cache_clear() + + @pytest.mark.asyncio + async def test_no_dates_undated_url(self): + """Without dates the un-dated URL form is used.""" + from openbb_imf.utils import port_watch_helpers as pwh + + pwh.get_daily_port_activity_data.cache_clear() + patcher, fake = _patch_session( + {"features": [{"attributes": {"year": 2024, "month": 1, "day": 1}}]} + ) + with patcher: + await pwh.get_daily_port_activity_data("p2") + assert "TIMESTAMP" not in fake.urls[0] + pwh.get_daily_port_activity_data.cache_clear() + + @pytest.mark.asyncio + async def test_only_start_date(self): + """A start_date alone backfills ``end_date`` with today.""" + from openbb_imf.utils import port_watch_helpers as pwh + + pwh.get_daily_port_activity_data.cache_clear() + patcher, fake = _patch_session({"features": []}) + with patcher: + await pwh.get_daily_port_activity_data("p3", start_date="2024-01-01") + assert "TIMESTAMP" in fake.urls[0] + pwh.get_daily_port_activity_data.cache_clear() + + @pytest.mark.asyncio + async def test_only_end_date(self): + """An end_date alone backfills ``start_date`` with 2019-01-01.""" + from openbb_imf.utils import port_watch_helpers as pwh + + pwh.get_daily_port_activity_data.cache_clear() + patcher, fake = _patch_session({"features": []}) + with patcher: + await pwh.get_daily_port_activity_data("p4", end_date="2024-01-01") + assert "2019-01-01" in fake.urls[0] + pwh.get_daily_port_activity_data.cache_clear() + + @pytest.mark.asyncio + async def test_missing_port_id_raises(self): + """A ``None`` port_id raises ``OpenBBError``.""" + from openbb_core.app.model.abstract.error import OpenBBError + from openbb_imf.utils import port_watch_helpers as pwh + + pwh.get_daily_port_activity_data.cache_clear() + with pytest.raises(OpenBBError): + await pwh.get_daily_port_activity_data(None) + pwh.get_daily_port_activity_data.cache_clear() + + @pytest.mark.asyncio + async def test_non_200_initial_raises(self): + """A non-200 on the initial response raises.""" + from openbb_core.app.model.abstract.error import OpenBBError + from openbb_imf.utils import port_watch_helpers as pwh + + pwh.get_daily_port_activity_data.cache_clear() + bad = _FakeResponse({}, status=500) + fake = _FakeSession([]) + fake.get = lambda url: _AwaitableContext(bad) # type: ignore + + class _Factory: + async def __aenter__(self_): + return fake + + async def __aexit__(self_, *args): + return False + + async def _factory(*args, **kwargs): + return _Factory() + + with patch( + "openbb_core.provider.utils.helpers.get_async_requests_session", + side_effect=_factory, + ): + with pytest.raises(OpenBBError): + await pwh.get_daily_port_activity_data("px") + pwh.get_daily_port_activity_data.cache_clear() + + @pytest.mark.asyncio + async def test_non_200_on_followup_raises(self): + """A non-200 on the second page raises.""" + from openbb_core.app.model.abstract.error import OpenBBError + from openbb_imf.utils import port_watch_helpers as pwh + + pwh.get_daily_port_activity_data.cache_clear() + ok = _FakeResponse( + { + "features": [ + {"attributes": {"year": 2024, "month": 1, "day": 1, "v": 1}} + ], + "exceededTransferLimit": True, + } + ) + bad = _FakeResponse({}, status=500) + + calls = {"n": 0} + + def _get(url): + calls["n"] += 1 + return _AwaitableContext(ok if calls["n"] == 1 else bad) + + fake = _FakeSession([]) + fake.get = _get # type: ignore + + class _Factory: + async def __aenter__(self_): + return fake + + async def __aexit__(self_, *args): + return False + + async def _factory(*args, **kwargs): + return _Factory() + + with patch( + "openbb_core.provider.utils.helpers.get_async_requests_session", + side_effect=_factory, + ): + with pytest.raises(OpenBBError): + await pwh.get_daily_port_activity_data("py") + pwh.get_daily_port_activity_data.cache_clear() + + +class TestAllDailyPortActivityCsv: + """Tests for the bulk CSV ``get_all_daily_port_activity_data`` helper.""" + + @pytest.mark.asyncio + async def test_returns_records(self): + """A 200 CSV response is parsed into a list of dicts.""" + from openbb_imf.utils import port_watch_helpers as pwh + + pwh.get_all_daily_port_activity_data.cache_clear() + + csv_text = "date,portid,v,ObjectId,GlobalID,year,month,day\n2024-01-01,P1,5,1,g,2024,1,1\n" + + class _CsvResp: + status = 200 + reason = "OK" + content = b"x" + + async def __aenter__(self): + return self + + async def __aexit__(self, *_): + return False + + async def text(self): + return csv_text + + fake = _FakeSession([]) + fake.get = lambda url: _AwaitableContext(_CsvResp()) # type: ignore + + class _Factory: + async def __aenter__(self_): + return fake + + async def __aexit__(self_, *args): + return False + + async def _factory(*args, **kwargs): + return _Factory() + + with patch( + "openbb_core.provider.utils.helpers.get_async_requests_session", + side_effect=_factory, + ): + rows = await pwh.get_all_daily_port_activity_data() + assert rows[0]["portid"] == "P1" + assert "ObjectId" not in rows[0] + assert "year" not in rows[0] + pwh.get_all_daily_port_activity_data.cache_clear() + + @pytest.mark.asyncio + async def test_non_200_raises(self): + """A non-200 CSV response raises ``OpenBBError``.""" + from openbb_core.app.model.abstract.error import OpenBBError + from openbb_imf.utils import port_watch_helpers as pwh + + pwh.get_all_daily_port_activity_data.cache_clear() + + class _CsvResp: + status = 500 + reason = "Down" + content = b"" + + async def __aenter__(self): + return self + + async def __aexit__(self, *_): + return False + + async def text(self): + return "" + + fake = _FakeSession([]) + fake.get = lambda url: _AwaitableContext(_CsvResp()) # type: ignore + + class _Factory: + async def __aenter__(self_): + return fake + + async def __aexit__(self_, *args): + return False + + async def _factory(*args, **kwargs): + return _Factory() + + with patch( + "openbb_core.provider.utils.helpers.get_async_requests_session", + side_effect=_factory, + ): + with pytest.raises(OpenBBError): + await pwh.get_all_daily_port_activity_data() + pwh.get_all_daily_port_activity_data.cache_clear() + + @pytest.mark.asyncio + async def test_empty_content_raises(self): + """A response with ``content=None`` raises.""" + from openbb_core.app.model.abstract.error import OpenBBError + from openbb_imf.utils import port_watch_helpers as pwh + + pwh.get_all_daily_port_activity_data.cache_clear() + + class _CsvResp: + status = 200 + reason = "OK" + content = None + + async def __aenter__(self): + return self + + async def __aexit__(self, *_): + return False + + async def text(self): + return "" + + fake = _FakeSession([]) + fake.get = lambda url: _AwaitableContext(_CsvResp()) # type: ignore + + class _Factory: + async def __aenter__(self_): + return fake + + async def __aexit__(self_, *args): + return False + + async def _factory(*args, **kwargs): + return _Factory() + + with patch( + "openbb_core.provider.utils.helpers.get_async_requests_session", + side_effect=_factory, + ): + with pytest.raises(OpenBBError): + await pwh.get_all_daily_port_activity_data() + pwh.get_all_daily_port_activity_data.cache_clear() + + +class TestContainerMetricsSkipsNoneValues: + """Edge test for the ``val is None`` continue inside ``get_container_metrics``.""" + + @pytest.mark.asyncio + async def test_none_port_values_skipped(self): + """A row where a port column is ``None`` produces no entry for that port.""" + from openbb_imf.utils import port_watch_helpers as pwh + + pwh.get_container_metrics.cache_clear() + rows_payload = [ + { + "metric": "portcalls", + "date_in": 1_704_067_200_000, + "PORTA": 5, + "PORTB": None, + } + ] + with patch( + "openbb_imf.utils.port_watch_helpers._arcgis_query", + new=AsyncMock(return_value=rows_payload), + ): + out = await pwh.get_container_metrics(None, "portcalls") + ports = {r["portid"] for r in out} + assert ports == {"PORTA"} + pwh.get_container_metrics.cache_clear() diff --git a/openbb_platform/providers/imf/tests/test_port_watch_models.py b/openbb_platform/providers/imf/tests/test_port_watch_models.py new file mode 100644 index 00000000000..704bca77f39 --- /dev/null +++ b/openbb_platform/providers/imf/tests/test_port_watch_models.py @@ -0,0 +1,362 @@ +"""Tests for the IMF port-watch Fetcher models.""" + +# ruff: noqa: I001 + +from unittest.mock import AsyncMock, patch + +import pytest +from openbb_core.app.model.abstract.error import OpenBBError + +from openbb_imf.models.container_metrics import ( + ImfContainerMetricsData, + ImfContainerMetricsFetcher, +) +from openbb_imf.models.country_activity import ( + ImfCountryActivityData, + ImfCountryActivityFetcher, + ImfCountryActivityQueryParams, +) +from openbb_imf.models.disruption_events import ( + ImfDisruptionEventsData, + ImfDisruptionEventsFetcher, +) +from openbb_imf.models.disruption_sankey import ( + ImfDisruptionSankeyData, + ImfDisruptionSankeyFetcher, +) +from openbb_imf.models.monthly_trade import ( + ImfMonthlyTradeData, + ImfMonthlyTradeFetcher, +) + + +class TestCountryActivityFetcher: + """Tests for ``ImfCountryActivityFetcher``.""" + + def test_transform_query_defaults(self): + """Default params survive ``transform_query``.""" + q = ImfCountryActivityFetcher.transform_query({}) + assert isinstance(q, ImfCountryActivityQueryParams) + assert q.country_code == "USA" + assert q.metric == "portcalls" + + @pytest.mark.asyncio + async def test_aextract_data_delegates_to_helper(self): + """``aextract_data`` calls the shared helper with the right args.""" + with patch( + "openbb_imf.utils.port_watch_helpers.get_country_daily_activity", + new=AsyncMock(return_value=[{"date": "2024-01-01"}]), + ) as mocked: + q = ImfCountryActivityFetcher.transform_query( + { + "country_code": "JPN", + "start_date": "2024-01-01", + "end_date": "2024-01-31", + } + ) + rows = await ImfCountryActivityFetcher.aextract_data(q, None) + mocked.assert_awaited_once_with("JPN", "2024-01-01", "2024-01-31") + assert rows == [{"date": "2024-01-01"}] + + def test_transform_data_builds_typed_rows(self): + """``transform_data`` validates each row through the Data model and applies aliases.""" + q = ImfCountryActivityFetcher.transform_query({}) + out = ImfCountryActivityFetcher.transform_data( + q, + [ + { + "date": "2024-01-01", + "ISO3": "USA", + "portcalls": 5, + "import": 10, + "export": 7, + } + ], + ) + assert isinstance(out[0], ImfCountryActivityData) + assert out[0].country_code == "USA" + assert out[0].imports == 10 + assert out[0].exports == 7 + + def test_data_coerces_string_30ma_to_float(self): + """The ``*_30MA`` strings the API returns get coerced to floats.""" + row = ImfCountryActivityData.model_validate( + { + "date": "2024-01-01", + "ISO3": "USA", + "export_container_30MA": "345609.36", + "shipment_30MA_yoy_doy": "0.03", + } + ) + assert row.exports_container_30ma == pytest.approx(345609.36) + assert row.shipment_30ma_yoy_doy == pytest.approx(0.03) + + +class TestMonthlyTradeFetcher: + """Tests for ``ImfMonthlyTradeFetcher``.""" + + def test_transform_query_defaults(self): + """Default code and metric are populated.""" + q = ImfMonthlyTradeFetcher.transform_query({}) + assert q.code == "USA" + assert q.metric == "trade_value" + + @pytest.mark.asyncio + async def test_aextract_data(self): + """The helper is invoked with the resolved query params.""" + with patch( + "openbb_imf.utils.port_watch_helpers.get_monthly_trade", + new=AsyncMock(return_value=[{"date": "2024-01-01"}]), + ) as mocked: + q = ImfMonthlyTradeFetcher.transform_query({"code": "JPN"}) + rows = await ImfMonthlyTradeFetcher.aextract_data(q, None) + mocked.assert_awaited_once_with("JPN", None, None) + assert rows == [{"date": "2024-01-01"}] + + def test_transform_data(self): + """Rows are validated through the Data model with ISO3 alias.""" + q = ImfMonthlyTradeFetcher.transform_query({}) + out = ImfMonthlyTradeFetcher.transform_data( + q, + [ + { + "date": "2024-01-01", + "ISO3": "USA", + "region": "United States", + "trade_value": 105.6, + "ais_portcalls_container": 240, + } + ], + ) + assert isinstance(out[0], ImfMonthlyTradeData) + assert out[0].country_code == "USA" + assert out[0].region == "United States" + assert out[0].trade_value == pytest.approx(105.6) + + +class TestContainerMetricsFetcher: + """Tests for ``ImfContainerMetricsFetcher`` including TOP10 ranking.""" + + def test_transform_query_defaults(self): + """Defaults match the widget config.""" + q = ImfContainerMetricsFetcher.transform_query({}) + assert q.metric == "portcalls" + assert q.port_ids == "TOP10" + + @pytest.mark.asyncio + async def test_aextract_top10_picks_busiest_ports(self): + """The TOP10 path keeps the 10 ports with the largest ``value`` totals.""" + rows = [] + for i in range(20): + for j in range(3): + rows.append( + { + "metric": "portcalls", + "portid": f"PORT{i}", + "date": f"2024-0{j + 1}-01", + "value": (i + 1) * 10 + j, + } + ) + with patch( + "openbb_imf.utils.port_watch_helpers.get_container_metrics", + new=AsyncMock(return_value=rows), + ): + q = ImfContainerMetricsFetcher.transform_query({}) + out = await ImfContainerMetricsFetcher.aextract_data(q, None) + kept = {r["portid"] for r in out} + assert len(kept) == 10 + assert kept == {f"PORT{i}" for i in range(10, 20)} + + @pytest.mark.asyncio + async def test_aextract_explicit_port_filter(self): + """Explicit port ids filter directly without invoking the TOP10 ranking.""" + rows = [ + {"metric": "portcalls", "portid": "PORT1", "date": "2024-01", "value": 1}, + {"metric": "portcalls", "portid": "PORT2", "date": "2024-01", "value": 2}, + ] + with patch( + "openbb_imf.utils.port_watch_helpers.get_container_metrics", + new=AsyncMock(return_value=rows), + ): + q = ImfContainerMetricsFetcher.transform_query({"port_ids": "PORT2+PORT99"}) + out = await ImfContainerMetricsFetcher.aextract_data(q, None) + assert [r["portid"] for r in out] == ["PORT2"] + + @pytest.mark.asyncio + async def test_aextract_top10_handles_missing_values(self): + """Rows without a ``portid`` or numeric ``value`` are skipped during ranking.""" + rows = [ + {"metric": "portcalls", "portid": "PORT1", "date": "2024-01", "value": 5}, + {"metric": "portcalls", "portid": None, "date": "2024-01", "value": 99}, + { + "metric": "portcalls", + "portid": "PORT2", + "date": "2024-01", + "value": None, + }, + ] + with patch( + "openbb_imf.utils.port_watch_helpers.get_container_metrics", + new=AsyncMock(return_value=rows), + ): + q = ImfContainerMetricsFetcher.transform_query({}) + out = await ImfContainerMetricsFetcher.aextract_data(q, None) + assert {r["portid"] for r in out} == {"PORT1", "PORT2"} + + def test_transform_data(self): + """Rows survive the typed Data validation.""" + q = ImfContainerMetricsFetcher.transform_query({}) + out = ImfContainerMetricsFetcher.transform_data( + q, + [ + { + "metric": "portcalls", + "portid": "PORT1", + "date": "2024-01-01", + "value": 42, + } + ], + ) + assert isinstance(out[0], ImfContainerMetricsData) + assert out[0].value == 42 + + +class TestDisruptionEventsFetcher: + """Tests for ``ImfDisruptionEventsFetcher``.""" + + def test_transform_query_defaults(self): + """The default alert level is ``ALL`` and active-only is False.""" + q = ImfDisruptionEventsFetcher.transform_query({}) + assert q.alertlevel == "ALL" + assert q.active_only is False + + @pytest.mark.asyncio + async def test_aextract_data_converts_all_to_none(self): + """An ``ALL`` alert level translates to ``None`` in the helper call.""" + with patch( + "openbb_imf.utils.port_watch_helpers.get_disruption_events", + new=AsyncMock(return_value=[]), + ) as mocked: + q = ImfDisruptionEventsFetcher.transform_query( + {"country": "JPN", "alertlevel": "ALL"} + ) + await ImfDisruptionEventsFetcher.aextract_data(q, None) + mocked.assert_awaited_once_with("JPN", None, None, False, None, None) + + @pytest.mark.asyncio + async def test_aextract_data_passes_specific_alert_level(self): + """A specific alert level flows through to the helper unchanged.""" + with patch( + "openbb_imf.utils.port_watch_helpers.get_disruption_events", + new=AsyncMock(return_value=[]), + ) as mocked: + q = ImfDisruptionEventsFetcher.transform_query({"alertlevel": "RED"}) + await ImfDisruptionEventsFetcher.aextract_data(q, None) + assert mocked.call_args.args[2] == "RED" + + def test_transform_data_aliases(self): + """ArcGIS field names are mapped to snake_case via ``__alias_dict__``.""" + q = ImfDisruptionEventsFetcher.transform_query({}) + out = ImfDisruptionEventsFetcher.transform_data( + q, + [ + { + "eventid": 1, + "eventname": "Storm", + "alertlevel": "RED", + "lat": 12.5, + "long": -45.0, + "ObjectId": 7, + "htmlname": "Storm", + "pageid": "abc", + } + ], + ) + row = out[0] + assert isinstance(row, ImfDisruptionEventsData) + assert row.latitude == 12.5 + assert row.longitude == -45.0 + assert row.object_id == 7 + assert row.html_name == "Storm" + assert row.page_id == "abc" + + +class TestDisruptionSankeyFetcher: + """Tests for ``ImfDisruptionSankeyFetcher``.""" + + def test_transform_query_default_latest(self): + """The default event id is ``LATEST``.""" + q = ImfDisruptionSankeyFetcher.transform_query({}) + assert q.event_id == "LATEST" + + @pytest.mark.asyncio + async def test_aextract_latest_resolves_to_top_event(self): + """``LATEST`` resolves to the first event returned by the disruptions helper.""" + with ( + patch( + "openbb_imf.utils.port_watch_helpers.get_disruption_events", + new=AsyncMock(return_value=[{"eventid": 42, "eventname": "Hurricane"}]), + ), + patch( + "openbb_imf.utils.port_watch_helpers.get_disruption_sankey_edges", + new=AsyncMock(return_value=[{"source": 1, "target": 2}]), + ) as edges_mock, + ): + q = ImfDisruptionSankeyFetcher.transform_query({"event_id": "LATEST"}) + rows = await ImfDisruptionSankeyFetcher.aextract_data(q, None) + edges_mock.assert_awaited_once_with(42) + assert q._event_label == "Hurricane" + assert rows == [{"source": 1, "target": 2}] + + @pytest.mark.asyncio + async def test_aextract_explicit_event_id(self): + """An explicit numeric event id is passed straight through.""" + with patch( + "openbb_imf.utils.port_watch_helpers.get_disruption_sankey_edges", + new=AsyncMock(return_value=[]), + ) as edges_mock: + q = ImfDisruptionSankeyFetcher.transform_query({"event_id": "100"}) + await ImfDisruptionSankeyFetcher.aextract_data(q, None) + edges_mock.assert_awaited_once_with(100) + assert q._event_label == "Event 100" + + @pytest.mark.asyncio + async def test_aextract_latest_raises_when_no_events(self): + """If there are no disruption events to choose from, raise ``OpenBBError``.""" + with patch( + "openbb_imf.utils.port_watch_helpers.get_disruption_events", + new=AsyncMock(return_value=[]), + ): + q = ImfDisruptionSankeyFetcher.transform_query({}) + with pytest.raises(OpenBBError): + await ImfDisruptionSankeyFetcher.aextract_data(q, None) + + def test_transform_data_aliases_from_and_to(self): + """The ArcGIS ``from_``/``to_`` keys are remapped to source/target ports.""" + q = ImfDisruptionSankeyFetcher.transform_query({}) + out = ImfDisruptionSankeyFetcher.transform_data( + q, + [ + { + "eventid": 1, + "source": 0, + "target": 1, + "from_": "Colombo", + "to_": "Chittagong", + "from_id": "port254", + "to_id": "port241", + "from_iso3": "LKA", + "to_iso3": "BGD", + "perc_disaster_capacity": 14.9, + "ObjectId": 1, + } + ], + ) + edge = out[0] + assert isinstance(edge, ImfDisruptionSankeyData) + assert edge.source_port == "Colombo" + assert edge.target_port == "Chittagong" + assert edge.source_port_id == "port254" + assert edge.target_country_code == "BGD" + assert edge.perc_disaster_capacity == pytest.approx(14.9) + assert edge.object_id == 1 diff --git a/openbb_platform/providers/imf/tests/test_progressive_helper.py b/openbb_platform/providers/imf/tests/test_progressive_helper.py index 4daf8ccbe2b..9a3e2d5de1d 100644 --- a/openbb_platform/providers/imf/tests/test_progressive_helper.py +++ b/openbb_platform/providers/imf/tests/test_progressive_helper.py @@ -1,7 +1,6 @@ """Test ImfProgressiveQueryHelper.""" # ruff: noqa: I001 -# pylint: disable=W0621,W0613,W0212,R0903 from unittest.mock import patch, MagicMock @@ -16,7 +15,6 @@ def mock_builder(): "openbb_imf.utils.progressive_helper.ImfQueryBuilder" ) as mock_builder_class: mock_instance = mock_builder_class.return_value - # Configure metadata mock mock_instance.metadata = MagicMock() mock_instance.metadata.dataflows = { "GFS_BS": { @@ -47,9 +45,7 @@ def mock_builder(): }, } - def get_codelist_map( - codelist_id, agency_id=None, dataflow_id=None - ): # noqa: ARG001 + def get_codelist_map(codelist_id, agency_id=None, dataflow_id=None): # noqa: ARG001 if codelist_id in mock_instance.metadata.codelists: return { item["id"]: item["name"] @@ -62,7 +58,6 @@ def mock_builder(): mock_instance.metadata._get_codelist_map.side_effect = get_codelist_map def resolve_codelist_id(dataflow_id, dsd_id, dimension_id, dim_meta): - # Simplified resolver for the mocked DSD if dimension_id == "FREQ": return "CL_FREQ" if dimension_id == "REF_AREA": @@ -105,7 +100,6 @@ def test_constrained_codelist_resolution(mock_builder): helper = ImfParamsBuilder(dataflow_id="GFS_BS") helper._builder = mock_builder - # Mock constraints returning only one indicator mock_builder.metadata.get_available_constraints.return_value = { "key_values": [ { @@ -125,7 +119,6 @@ def test_component_label_fallback(mock_builder): helper = ImfParamsBuilder(dataflow_id="GFS_BS") helper._builder = mock_builder - # Add a code to constraints that isn't in the codelist map mock_builder.metadata.get_available_constraints.return_value = { "key_values": [ { @@ -135,7 +128,6 @@ def test_component_label_fallback(mock_builder): ] } - # Mock empty codelist map for this test mock_builder.metadata._get_codelist_map.return_value = {} options = helper.get_options_for_dimension("INDICATOR") @@ -154,7 +146,6 @@ def test_time_period_annotation_propagation(mock_builder): "time_period": {"start_period": "2000", "end_period": "2020"}, } - # Trigger a constraint update (e.g. by getting options) helper.get_options_for_dimension("INDICATOR") assert helper._last_constraints_response["time_period"]["start_period"] == "2000" @@ -176,7 +167,6 @@ def test_wildcard_on_long_multi_code_inputs(mock_builder): helper = ImfParamsBuilder(dataflow_id="GFS_BS") helper._builder = mock_builder - # Simulate a long list of codes selected for a previous dimension long_list = ["A"] * 50 helper.set_dimension(("FREQ", "+".join(long_list))) @@ -186,5 +176,124 @@ def test_wildcard_on_long_multi_code_inputs(mock_builder): assert call_args is not None key_arg = call_args.kwargs.get("key") assert key_arg is not None - # Key should include the long frequency string, not be blanked to wildcard assert "+".join(long_list) in key_arg + + +@pytest.fixture +def mock_builder_extra(): + """Build a mocked ImfQueryBuilder with a minimal DF1/DSD1 dataflow setup.""" + with patch( + "openbb_imf.utils.progressive_helper.ImfQueryBuilder" + ) as mock_builder_class: + mock_instance = mock_builder_class.return_value + mock_instance.metadata = MagicMock() + mock_instance.metadata.dataflows = { + "DF1": { + "structureRef": {"id": "DSD1"}, + "agencyID": "IMF.STA", + }, + "DF_NO_AGENCY": { + "structureRef": {"id": "DSD1"}, + }, + } + mock_instance.metadata.datastructures = { + "DSD1": { + "id": "DSD1", + "dimensions": [ + {"id": "FREQ", "position": 1, "conceptRef": {"id": "FREQ"}}, + { + "id": "INDICATOR", + "position": 2, + "conceptRef": {"id": "INDICATOR"}, + }, + ], + } + } + mock_instance.metadata._resolve_codelist_id.return_value = "CL_X" + mock_instance.metadata._get_codelist_map.return_value = {"A": "Annual"} + mock_instance.metadata.get_available_constraints.return_value = { + "key_values": [{"id": "INDICATOR", "values": ["X"]}] + } + yield mock_instance + + +def test_get_options_returns_empty_when_all_selected(mock_builder_extra): + """Line 90: empty list when every dimension is already selected.""" + helper = ImfParamsBuilder("DF1") + helper._builder = mock_builder_extra + helper._selections = {"FREQ": "A", "INDICATOR": "X"} + + assert helper.get_options_for_dimension() == [] + + +def test_get_codelist_for_dim_missing_agency(mock_builder_extra): + """Line 130: missing agencyID yields empty codelist.""" + helper = ImfParamsBuilder("DF1") + helper._builder = mock_builder_extra + mock_builder_extra.metadata.dataflows["DF1"] = {"structureRef": {"id": "DSD1"}} + + assert helper._get_codelist_for_dim("FREQ") == {} + + +def test_get_codelist_for_dim_missing_dim_meta(mock_builder_extra): + """Line 137: dim_meta lookup miss returns empty.""" + helper = ImfParamsBuilder("DF1") + helper._builder = mock_builder_extra + + assert helper._get_codelist_for_dim("UNKNOWN_DIM") == {} + + +def test_get_codelist_for_dim_no_codelist_id(mock_builder_extra): + """When _resolve_codelist_id returns None, an empty dict comes back.""" + helper = ImfParamsBuilder("DF1") + helper._builder = mock_builder_extra + mock_builder_extra.metadata._resolve_codelist_id.return_value = None + + assert helper._get_codelist_for_dim("FREQ") == {} + + +def test_get_dimensions_returns_copy(mock_builder_extra): + """Line 188: get_dimensions returns a copy of the selections dict.""" + helper = ImfParamsBuilder("DF1") + helper._builder = mock_builder_extra + helper.set_dimension(("FREQ", "A")) + dims = helper.get_dimensions() + assert dims["FREQ"] == "A" + assert dims["INDICATOR"] is None + dims["FREQ"] = "MUTATED" + assert helper._selections["FREQ"] == "A" + + +def test_fetch_invokes_builder(mock_builder_extra): + """Line 229: fetch forwards to builder.fetch_data with current selections.""" + helper = ImfParamsBuilder("DF1") + helper._builder = mock_builder_extra + helper.set_dimension(("FREQ", "A")) + helper.set_dimension(("INDICATOR", "X")) + + mock_builder_extra.fetch_data.return_value = {"data": [], "metadata": {}} + result = helper.fetch(start_date="2020", end_date="2024") + + assert result == {"data": [], "metadata": {}} + kwargs = mock_builder_extra.fetch_data.call_args.kwargs + assert kwargs["dataflow"] == "DF1" + assert kwargs["start_date"] == "2020" + assert kwargs["end_date"] == "2024" + assert kwargs["FREQ"] == "A" + assert kwargs["INDICATOR"] == "X" + + +def test_invalid_dimension_raises_valueerror(mock_builder_extra): + """get_options_for_dimension on unknown dim raises ValueError.""" + helper = ImfParamsBuilder("DF1") + helper._builder = mock_builder_extra + with pytest.raises(ValueError, match="not found for dataflow"): + helper.get_options_for_dimension("NOPE") + + +def test_set_dimension_unknown_raises(mock_builder_extra): + """set_dimension with unknown dim raises KeyError.""" + helper = ImfParamsBuilder("DF1") + helper._builder = mock_builder_extra + with pytest.raises(KeyError, match="not valid for this dataflow"): + helper.set_dimension(("UNKNOWN", "X")) diff --git a/openbb_platform/providers/imf/tests/test_progressive_validation.py b/openbb_platform/providers/imf/tests/test_progressive_validation.py index 3a07563cd89..abbc4c7e5a5 100644 --- a/openbb_platform/providers/imf/tests/test_progressive_validation.py +++ b/openbb_platform/providers/imf/tests/test_progressive_validation.py @@ -1,7 +1,6 @@ """Tests for progressive validation logic in ImfParamsBuilder and ImfQueryBuilder.""" # ruff: noqa: I001 -# pylint: disable=W0621,W0613,W0212,R0903,C0302,C0415 from unittest.mock import MagicMock, patch @@ -67,7 +66,6 @@ class TestImfParamsBuilderInit: "agencyID": "IMF", } } - # Dimensions in reverse order mock_metadata.datastructures = { "DSD_TEST": { "id": "DSD_TEST", @@ -157,7 +155,6 @@ class TestDimensionSelection: builder = ImfParamsBuilder("BOP") builder._selections = {"REF_AREA": "US", "INDICATOR": "IND1", "FREQ": "A"} - # Re-set REF_AREA - should clear INDICATOR and FREQ result = builder.set_dimension(("REF_AREA", "GB")) assert result["REF_AREA"] == "GB" @@ -218,11 +215,6 @@ class TestDimensionSelection: assert builder.get_next_dimension_to_select() is None -# ============================================================================= -# Get Options Tests -# ============================================================================= - - class TestGetOptionsForDimension: """Tests for get_options_for_dimension constraint checking.""" @@ -334,16 +326,10 @@ class TestGetOptionsForDimension: builder.set_dimension(("DIM1", "VAL1")) builder.get_options_for_dimension("DIM2") - # Check the key passed to get_available_constraints call_args = mock_metadata.get_available_constraints.call_args assert call_args.kwargs["key"] == "VAL1.*.*" -# ============================================================================= -# Query Builder Validation Tests -# ============================================================================= - - class TestValidateDimensionConstraints: """Tests for ImfQueryBuilder.validate_dimension_constraints.""" @@ -375,7 +361,6 @@ class TestValidateDimensionConstraints: mock_metadata_cls.return_value = mock_metadata builder = ImfQueryBuilder() - # Should not raise builder.validate_dimension_constraints( dataflow="BOP", REF_AREA="US", INDICATOR="IND1" ) @@ -436,7 +421,6 @@ class TestValidateDimensionConstraints: mock_metadata_cls.return_value = mock_metadata builder = ImfQueryBuilder() - # Should not raise builder.validate_dimension_constraints(dataflow="BOP", REF_AREA="*") @patch("openbb_imf.utils.query_builder.ImfMetadata") @@ -463,7 +447,6 @@ class TestValidateDimensionConstraints: mock_metadata_cls.return_value = mock_metadata builder = ImfQueryBuilder() - # Valid comma-separated should pass builder.validate_dimension_constraints(dataflow="BOP", REF_AREA="US,GB") @patch("openbb_imf.utils.query_builder.ImfMetadata") @@ -522,7 +505,6 @@ class TestValidateDimensionConstraints: mock_metadata_cls.return_value = mock_metadata builder = ImfQueryBuilder() - # Valid plus-separated should pass builder.validate_dimension_constraints(dataflow="BOP", REF_AREA="US+GB+DE") @patch("openbb_imf.utils.query_builder.ImfMetadata") @@ -543,16 +525,10 @@ class TestValidateDimensionConstraints: mock_metadata_cls.return_value = mock_metadata builder = ImfQueryBuilder() - # Should not raise - empty values skipped builder.validate_dimension_constraints(dataflow="BOP", REF_AREA="") builder.validate_dimension_constraints(dataflow="BOP", REF_AREA=None) -# ============================================================================= -# Time Period Validation Tests -# ============================================================================= - - class TestTimePeriodValidation: """Tests for time period range validation.""" @@ -680,17 +656,11 @@ class TestTimePeriodValidation: mock_metadata_cls.return_value = mock_metadata builder = ImfQueryBuilder() - # Should not raise builder.validate_dimension_constraints( dataflow="BOP", REF_AREA="US", start_date="2010-01", end_date="2020-12" ) -# ============================================================================= -# Progressive Constraint Propagation Tests -# ============================================================================= - - class TestProgressiveConstraintPropagation: """Tests for how prior selections affect subsequent option availability.""" @@ -713,17 +683,13 @@ class TestProgressiveConstraintPropagation: } } - # First call returns all countries - # Second call (after country selected) returns fewer indicators call_count = [0] def mock_constraints(dataflow_id, key, component_id): call_count[0] += 1 if component_id == "INDICATOR" and "US" in key: - # US has fewer indicators return {"key_values": [{"id": "INDICATOR", "values": ["IND1", "IND2"]}]} elif component_id == "INDICATOR" and "GB" in key: - # GB has more indicators return { "key_values": [ {"id": "INDICATOR", "values": ["IND1", "IND2", "IND3", "IND4"]} @@ -737,12 +703,10 @@ class TestProgressiveConstraintPropagation: builder = ImfParamsBuilder("BOP") - # Select US builder.set_dimension(("REF_AREA", "US")) us_options = builder.get_options_for_dimension("INDICATOR") assert len(us_options) == 2 - # Reset and select GB builder._selections = {"REF_AREA": None, "INDICATOR": None} builder.set_dimension(("REF_AREA", "GB")) gb_options = builder.get_options_for_dimension("INDICATOR") @@ -771,7 +735,6 @@ class TestProgressiveConstraintPropagation: if component_id == "REF_AREA": return {"key_values": [{"id": "REF_AREA", "values": ["US", "GB"]}]} elif component_id == "INDICATOR": - # Only IND1 available for US return {"key_values": [{"id": "INDICATOR", "values": ["IND1"]}]} return {"key_values": []} @@ -787,9 +750,7 @@ class TestProgressiveConstraintPropagation: ) error_msg = str(exc_info.value) - # Error should mention the invalid value assert "IND_NOT_AVAILABLE_FOR_US" in error_msg - # Error should show prior selections assert "REF_AREA" in error_msg @@ -818,7 +779,7 @@ class TestValidationEdgeCases: builder = ImfParamsBuilder("BOP") options = builder.get_options_for_dimension("REF_AREA") - assert options == [] # pylint: disable=C1803 + assert options == [] @patch("openbb_imf.utils.query_builder.ImfMetadata") def test_missing_dimension_in_constraints_response(self, mock_metadata_cls): @@ -838,7 +799,6 @@ class TestValidationEdgeCases: ], } } - # Response only has REF_AREA, not INDICATOR mock_metadata.get_available_constraints.return_value = { "key_values": [{"id": "REF_AREA", "values": ["US"]}] } @@ -848,7 +808,7 @@ class TestValidationEdgeCases: builder = ImfParamsBuilder("BOP") options = builder.get_options_for_dimension("INDICATOR") - assert options == [] # pylint: disable=C1803 + assert options == [] @patch("openbb_imf.utils.query_builder.ImfMetadata") def test_list_input_validated(self, mock_metadata_cls): @@ -872,7 +832,6 @@ class TestValidationEdgeCases: mock_metadata_cls.return_value = mock_metadata builder = ImfQueryBuilder() - # List input should work builder.validate_dimension_constraints(dataflow="BOP", REF_AREA=["US", "GB"]) @patch("openbb_imf.utils.query_builder.ImfMetadata") @@ -897,7 +856,6 @@ class TestValidationEdgeCases: mock_metadata_cls.return_value = mock_metadata builder = ImfQueryBuilder() - # Whitespace should be trimmed builder.validate_dimension_constraints(dataflow="BOP", REF_AREA=" US , GB ") @patch("openbb_imf.utils.query_builder.ImfMetadata") @@ -912,14 +870,12 @@ class TestValidationEdgeCases: builder = ImfQueryBuilder() - # Should warn, not raise with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") builder.validate_dimension_constraints( dataflow="NONEXISTENT", REF_AREA="US" ) - # Check that a warning was issued assert len(w) == 1 assert "could not validate" in str(w[0].message).lower() @@ -940,7 +896,6 @@ class TestValidationEdgeCases: } mock_metadata.get_available_constraints.return_value = { "key_values": [{"id": "REF_AREA", "values": ["US"]}], - # No time_period key } mock_metadata_cls.return_value = mock_metadata @@ -962,7 +917,6 @@ class TestValidationEdgeCases: "dimensions": [{"id": "INDICATOR", "position": 1}], } } - # Mock constraints returning a specific indicator mock_metadata.get_available_constraints.return_value = { "key_values": [{"id": "INDICATOR", "values": ["RAF_USD"]}] } @@ -970,10 +924,8 @@ class TestValidationEdgeCases: builder = ImfQueryBuilder() - # If we validate constraints for IRFCL, it should pass if the indicator is compatible builder.validate_dimension_constraints(dataflow="IRFCL", INDICATOR="RAF_USD") - # If we try an incompatible one (not in constraints) with pytest.raises(ValueError): builder.validate_dimension_constraints( dataflow="IRFCL", INDICATOR="INCOMPATIBLE" @@ -994,7 +946,6 @@ class TestValidationEdgeCases: "dimensions": [{"id": "REF_AREA", "position": 1}], } } - # Constraints return everything (wildcard) mock_metadata.get_available_constraints.return_value = { "key_values": [{"id": "REF_AREA", "values": ["US", "GB", "FR"]}] } @@ -1002,10 +953,8 @@ class TestValidationEdgeCases: builder = ImfQueryBuilder() - # Create a long list of codes long_list = ["US"] * 50 - # Validation should pass because it should treat it as wildcard or check against constraints builder.validate_dimension_constraints(dataflow="BOP", REF_AREA=long_list) mock_metadata = MagicMock() @@ -1018,7 +967,6 @@ class TestValidationEdgeCases: "dimensions": [{"id": "REF_AREA", "position": 1}], } } - # No time period annotations mock_metadata.get_available_constraints.return_value = { "key_values": [{"id": "REF_AREA", "values": ["US"]}], "full_response": {"data": {}}, @@ -1027,17 +975,11 @@ class TestValidationEdgeCases: mock_metadata_cls.return_value = mock_metadata builder = ImfQueryBuilder() - # Should not raise even with dates provided builder.validate_dimension_constraints( dataflow="BOP", REF_AREA="US", start_date="2020-01", end_date="2023-12" ) -# ============================================================================= -# Build URL Tests -# ============================================================================= - - class TestBuildUrl: """Tests for ImfParamsBuilder.build_url.""" @@ -1065,7 +1007,6 @@ class TestBuildUrl: builder.set_dimension(("REF_AREA", "US")) builder.set_dimension(("INDICATOR", "IND1")) - # Just verify the internal builder.build_url is called with correct args with patch.object( builder._builder, "build_url", return_value="http://test.url" ) as mock_build: diff --git a/openbb_platform/providers/imf/tests/test_query_builder.py b/openbb_platform/providers/imf/tests/test_query_builder.py index 5463b2fdf88..a36b00dc8d6 100644 --- a/openbb_platform/providers/imf/tests/test_query_builder.py +++ b/openbb_platform/providers/imf/tests/test_query_builder.py @@ -1,7 +1,6 @@ """Comprehensive tests for IMF query parsing, search, and fetch helpers.""" # ruff: noqa: I001, SLF001 -# pylint: disable=W0621,W0613,W0212,R0903 from textwrap import dedent from unittest.mock import MagicMock, patch @@ -136,7 +135,6 @@ def test_search_dataflows_or_operator(imf_metadata): def test_search_dataflows_phrase(imf_metadata): results = imf_metadata.search_dataflows('"central bank"') ids = {df["id"] for group in results for df in group.get("dataflows", [])} - # Central bank phrase should match at least the explicit central bank flow. assert "DATAFLOW_CENTRAL_BANK" in ids @@ -185,9 +183,10 @@ MOCK_XML_RESPONSE = dedent( @pytest.fixture def mock_imf_query_builder_with_pivot_data(): """Return an ImfQueryBuilder with request mocked to return canned XML.""" - with patch("openbb_imf.utils.query_builder.ImfMetadata") as MockMetadata, patch( - "openbb_core.provider.utils.helpers.make_request" - ) as mock_make_request: + with ( + patch("openbb_imf.utils.query_builder.ImfMetadata") as MockMetadata, + patch("openbb_core.provider.utils.helpers.make_request") as mock_make_request, + ): mock_metadata_instance = MockMetadata.return_value dataflows_dict = {d["id"]: d for d in MOCK_SDMX_CHUNKS_DATAFLOWS} @@ -251,7 +250,6 @@ def test_fetch_data_time_periods(mock_imf_query_builder_with_pivot_data): ) df = pd.DataFrame(result["data"]) - # Returned SDMX time periods include end-of-year date strings. assert set(df["TIME_PERIOD"]) == {"2020-12-31", "2021-12-31"} @@ -259,3 +257,1874 @@ def test_strict_error_missing_dataflow(mock_imf_query_builder): builder = mock_imf_query_builder with pytest.raises(ValueError, match="Dataflow 'MISSING' not found"): builder.build_url("MISSING") + + +@pytest.fixture +def base_metadata(monkeypatch): + """Build an isolated ``ImfMetadata`` with a flexible default catalog.""" + monkeypatch.setattr(md.ImfMetadata, "_instance", None) + monkeypatch.setattr(md.ImfMetadata, "_load_from_cache", lambda self: True) + meta = md.ImfMetadata() + meta.dataflows = { + "FLOW": { + "id": "FLOW", + "agencyID": "IMF.STA", + "name": "Flow", + "description": "desc", + "structureRef": {"id": "DSD_FLOW"}, + }, + "NO_AGENCY": { + "id": "NO_AGENCY", + "name": "No Agency", + "structureRef": {"id": "DSD_FLOW"}, + }, + "BAD_DSD": { + "id": "BAD_DSD", + "agencyID": "IMF.STA", + "name": "Bad", + "structureRef": {"id": "DSD_MISSING"}, + }, + } + meta.datastructures = { + "DSD_FLOW": { + "id": "DSD_FLOW", + "agencyID": "IMF.STA", + "dimensions": [ + {"id": "COUNTRY", "position": 0, "conceptRef": {"id": "COUNTRY"}}, + {"id": "FREQUENCY", "position": 1, "conceptRef": {"id": "FREQ"}}, + {"id": "INDICATOR", "position": 2, "conceptRef": {"id": "INDICATOR"}}, + ], + "attributes": [], + } + } + meta._codelist_cache = { + "CL_UNIT_MULT": {"3": "Thousands", "6": "Millions"}, + "CL_UNIT": {"USD": "US dollar", "EUR": "Euro"}, + } + return meta + + +@pytest.fixture +def builder(base_metadata): # noqa: ARG001 + """Construct an ``ImfQueryBuilder`` against the canned metadata.""" + return ImfQueryBuilder() + + +class TestBuildUrl: + """Tests covering ``ImfQueryBuilder.build_url``.""" + + def test_unknown_dataflow_raises(self, builder): + """Unknown dataflow surfaces as ``ValueError``.""" + with pytest.raises(ValueError, match="Dataflow 'MISSING' not found"): + builder.build_url("MISSING") + + def test_missing_dsd_raises(self, builder): + """Missing data-structure reference raises a clear error.""" + with pytest.raises(ValueError, match="Data structure not found"): + builder.build_url("BAD_DSD") + + def test_missing_agency_raises(self, builder, monkeypatch): + """Missing agency id raises a clear error.""" + monkeypatch.setitem(builder.metadata.dataflows["FLOW"], "agencyID", None) + with pytest.raises(ValueError, match="Agency ID not found"): + builder.build_url("FLOW", INDICATOR="GDP") + + def test_lower_case_dimension_keys_matched(self, builder): + """Lower-case kwargs match against upper-case dimension IDs.""" + url = builder.build_url("FLOW", country="USA", frequency="A", indicator="GDP") + assert "/USA.A.GDP" in url + + def test_list_value_uses_plus_join(self, builder): + """A ``list`` dimension value joins with ``+``.""" + url = builder.build_url("FLOW", INDICATOR=["GDP", "CPI"], COUNTRY="USA") + assert "USA.*.GDP+CPI" in url + + def test_long_value_collapses_to_wildcard(self, builder): + """Values longer than 1500 chars become ``*`` in the key segment.""" + big = "X" * 1600 + url = builder.build_url("FLOW", INDICATOR=big, COUNTRY="USA") + assert "/USA.*.*" in url + + def test_wildcard_value_kept_as_star(self, builder): + """An explicit ``*`` value stays ``*``.""" + url = builder.build_url("FLOW", INDICATOR="*", COUNTRY="USA") + assert "/USA.*.*" in url + + def test_extra_query_params_appended(self, builder): + """Unmatched kwargs become query-string parameters.""" + url = builder.build_url("FLOW", INDICATOR="GDP", custom="value") + assert "custom=value" in url + + def test_annual_frequency_uses_year_format(self, builder): + """Annual frequency formats time period as ``YYYY-01-01``.""" + url = builder.build_url( + "FLOW", + COUNTRY="USA", + INDICATOR="GDP", + FREQUENCY="A", + start_date="2020", + end_date="2022", + ) + assert "ge:2020-01-01" in url + assert "le:2023-01-01" in url + + def test_monthly_frequency_increments_end_month(self, builder): + """Monthly frequency bumps the end month forward by one.""" + url = builder.build_url( + "FLOW", + COUNTRY="USA", + INDICATOR="GDP", + FREQUENCY="M", + start_date="2020-01", + end_date="2020-12", + ) + assert "ge:2020-01-01" in url + assert "le:2021-01-01" in url + + def test_monthly_end_wraps_year(self, builder): + """When the end month is December, the year is advanced.""" + url = builder.build_url( + "FLOW", + COUNTRY="USA", + INDICATOR="GDP", + FREQUENCY="Q", + start_date="2020-06", + end_date="2020-12", + ) + assert "le:2021-01-01" in url + + def test_limit_appends_last_n_observations(self, builder): + """A positive ``limit`` appends ``lastNObservations``.""" + url = builder.build_url("FLOW", COUNTRY="USA", limit=5) + assert "lastNObservations=5" in url + + def test_no_limit_when_zero(self, builder): + """A non-positive limit is ignored.""" + url = builder.build_url("FLOW", COUNTRY="USA", limit=0) + assert "lastNObservations" not in url + + +class TestValidateDimensionConstraints: + """Tests for ``ImfQueryBuilder.validate_dimension_constraints``.""" + + def _patch_builder(self, builder, dim_order, options_for, last_resp=None): + """Patch ``ImfParamsBuilder`` to return canned data.""" + from openbb_imf.utils import progressive_helper as ph + + class FakeBuilder: + def __init__(self, dataflow_id): # noqa: ARG002 + self._dims = list(dim_order) + self._selections: dict = {d: None for d in dim_order} + self._last_constraints_response = last_resp or {} + + def _get_dimensions_in_order(self): + return list(self._dims) + + def get_options_for_dimension(self, dim_id): + return options_for.get(dim_id, []) + + def set_dimension(self, tup): + self._selections[tup[0]] = tup[1] + return self._selections + + return patch.object(ph, "ImfParamsBuilder", FakeBuilder) + + def test_unknown_value_raises(self, builder): + """An invalid value raises ``ValueError``.""" + ctx = self._patch_builder( + builder, + ["COUNTRY", "INDICATOR"], + { + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "GDP", "label": "GDP"}], + }, + ) + with ctx: + with pytest.raises(ValueError, match="Invalid value"): + builder.validate_dimension_constraints( + "FLOW", COUNTRY="USA", INDICATOR="ZZZ" + ) + + def test_comma_separated_values_parsed(self, builder): + """Comma-separated user values are split before validation.""" + ctx = self._patch_builder( + builder, + ["INDICATOR"], + { + "INDICATOR": [ + {"value": "GDP", "label": "G"}, + {"value": "CPI", "label": "C"}, + ] + }, + ) + with ctx: + builder.validate_dimension_constraints("FLOW", INDICATOR="GDP, CPI") + + def test_plus_separated_values_parsed(self, builder): + """Plus-separated user values are split before validation.""" + ctx = self._patch_builder( + builder, + ["INDICATOR"], + { + "INDICATOR": [ + {"value": "GDP", "label": "G"}, + {"value": "CPI", "label": "C"}, + ] + }, + ) + with ctx: + builder.validate_dimension_constraints("FLOW", INDICATOR="GDP+CPI") + + def test_list_value_validated(self, builder): + """List values are validated unchanged.""" + ctx = self._patch_builder( + builder, + ["INDICATOR"], + {"INDICATOR": [{"value": "GDP", "label": "G"}]}, + ) + with ctx: + builder.validate_dimension_constraints("FLOW", INDICATOR=["GDP"]) + + def test_wildcard_skips_value_check(self, builder): + """A ``*`` value skips per-value validation.""" + ctx = self._patch_builder( + builder, + ["INDICATOR"], + {"INDICATOR": [{"value": "GDP", "label": "G"}]}, + ) + with ctx: + builder.validate_dimension_constraints("FLOW", INDICATOR="*") + + def test_oversized_value_skips_check(self, builder): + """Values whose joined length exceeds 2000 chars skip validation.""" + ctx = self._patch_builder( + builder, + ["INDICATOR"], + {"INDICATOR": [{"value": "A", "label": "A"}]}, + ) + long_value = "+".join("V" * 200 for _ in range(40)) + with ctx: + builder.validate_dimension_constraints("FLOW", INDICATOR=long_value) + + def test_empty_user_value_skipped(self, builder): + """Empty / falsy dimension values are silently skipped.""" + ctx = self._patch_builder( + builder, + ["INDICATOR"], + {"INDICATOR": [{"value": "GDP", "label": "G"}]}, + ) + with ctx: + builder.validate_dimension_constraints("FLOW", INDICATOR="") + builder.validate_dimension_constraints("FLOW", INDICATOR=None) + + def test_keyerror_emits_openbb_warning(self, builder): + """A ``KeyError`` while validating emits an ``OpenBBWarning``.""" + from openbb_core.app.model.abstract.warning import OpenBBWarning + from openbb_imf.utils import progressive_helper as ph + + class Boom: + def __init__(self, dataflow_id): # noqa: ARG002 + raise KeyError("nope") + + with patch.object(ph, "ImfParamsBuilder", Boom): + with pytest.warns(OpenBBWarning, match="Could not validate"): + builder.validate_dimension_constraints("FLOW", COUNTRY="USA") + + def test_time_period_outside_range_raises(self, builder): + """A start_date past the available range raises ``ValueError``.""" + last_resp = { + "full_response": { + "data": { + "contentConstraints": [ + { + "annotations": [ + {"id": "time_period_start", "title": "2010-01"}, + {"id": "time_period_end", "title": "2020-12"}, + ] + } + ] + } + } + } + ctx = self._patch_builder( + builder, + ["COUNTRY"], + {"COUNTRY": [{"value": "USA", "label": "USA"}]}, + last_resp=last_resp, + ) + with ctx: + with pytest.raises(ValueError, match="after the latest available"): + builder.validate_dimension_constraints( + "FLOW", COUNTRY="USA", start_date="2025-01" + ) + with pytest.raises(ValueError, match="before the earliest available"): + builder.validate_dimension_constraints( + "FLOW", COUNTRY="USA", end_date="2000-01" + ) + + def test_time_period_from_data_constraints(self, builder): + """The data-constraints branch is honoured when content-constraints empty.""" + last_resp = { + "full_response": { + "data": { + "dataConstraints": [ + { + "annotations": [ + {"id": "time_period_start", "title": "2010-01"}, + {"id": "time_period_end", "title": "2020-12"}, + ] + } + ] + } + } + } + ctx = self._patch_builder( + builder, + ["COUNTRY"], + {"COUNTRY": [{"value": "USA", "label": "USA"}]}, + last_resp=last_resp, + ) + with ctx: + with pytest.raises(ValueError, match="after the latest available"): + builder.validate_dimension_constraints( + "FLOW", COUNTRY="USA", start_date="2025-01" + ) + + +class TestExtractDatasetAttributes: + """Tests for ``_extract_dataset_attributes_from_cache``.""" + + def test_attributes_propagated(self, builder, monkeypatch): + """Optional attributes present on the dataflow flow into the result.""" + monkeypatch.setitem(builder.metadata.dataflows["FLOW"], "publisher", "IMF") + monkeypatch.setitem(builder.metadata.dataflows["FLOW"], "license", "CC") + attrs = builder._extract_dataset_attributes_from_cache("FLOW") + assert attrs["publisher"] == "IMF" + assert attrs["license"] == "CC" + assert attrs["dataflow_id"] == "FLOW" + assert attrs["dataflow_name"] == "Flow" + assert attrs["dataflow_description"] == "desc" + + +class TestExtractAttributeValue: + """Tests for ``_extract_attribute_value``.""" + + def test_int_lookup_dict(self, builder): + """An ``int`` value indexes into ``values`` and returns the ``en`` key.""" + attr_def = {"values": [{"en": "Yes", "id": "Y"}]} + assert builder._extract_attribute_value(0, attr_def) == "Yes" + + def test_int_lookup_no_en(self, builder): + """Fallback to ``name``, then ``id`` when ``en`` is missing.""" + attr_def = {"values": [{"id": "Y"}]} + assert builder._extract_attribute_value(0, attr_def) == "Y" + + def test_int_lookup_scalar(self, builder): + """An ``int`` indexing into a non-dict scalar returns the scalar.""" + attr_def = {"values": ["plain"]} + assert builder._extract_attribute_value(0, attr_def) == "plain" + + def test_list_int(self, builder): + """A list of ints uses the first entry as an index.""" + attr_def = {"values": [{"en": "Yes"}]} + assert builder._extract_attribute_value([0], attr_def) == "Yes" + + def test_list_int_scalar(self, builder): + """A list of ints pointing at a scalar returns it.""" + attr_def = {"values": ["plain"]} + assert builder._extract_attribute_value([0], attr_def) == "plain" + + def test_list_dict_first(self, builder): + """A list of dicts returns the first dict's localized value.""" + assert builder._extract_attribute_value([{"en": "Apple"}], {}) == "Apple" + + def test_list_other_first(self, builder): + """A list of scalars returns the first scalar.""" + assert builder._extract_attribute_value(["x"], {}) == "x" + + def test_dict_value(self, builder): + """A bare dict returns its localized value.""" + assert builder._extract_attribute_value({"en": "EN", "name": "N"}, {}) == "EN" + + def test_passthrough(self, builder): + """Other scalar values are returned unchanged.""" + assert builder._extract_attribute_value("plain", {}) == "plain" + + +class TestExtractDatasetAttributesFull: + """Tests for ``_extract_dataset_attributes`` (live structure path).""" + + def test_full_structure_with_topics(self, builder): + """Structure attributes (TOPIC_DATASET/UPDATE_DATE/etc.) flow through.""" + builder.metadata._codelist_cache["CL_TOPIC"] = {"T1": "Trade", "T2": "Money"} + structure = { + "attributes": { + "dataSet": [ + {"id": "TOPIC_DATASET", "values": [{"ids": ["T1", "T2"]}]}, + {"id": "KEYWORDS_DATASET", "values": [{"en": "kw"}]}, + {"id": "UPDATE_DATE"}, + {"id": "PUBLICATION_DATE"}, + {"id": "PUBLISHER", "values": [{"en": "IMF"}]}, + {"id": "UNRELATED"}, + ] + } + } + json_data = { + "dataSets": [ + { + "attributes": [ + 0, + 0, + "2024-05-01.123456789", + "2023-01-01", + 0, + "ignore", + ] + } + ] + } + attrs = builder._extract_dataset_attributes(structure, json_data, "FLOW") + assert attrs["topics"] == ["Trade", "Money"] + assert attrs["keywords"] == "kw" + assert "last_updated" in attrs + assert attrs["publication_date"] == "2023-01-01" + assert attrs["publisher"] == "IMF" + + def test_topic_dataset_via_int_id(self, builder): + """A TOPIC_DATASET that points at an ``id`` dict still resolves.""" + builder.metadata._codelist_cache["CL_TOPIC"] = {"T1": "Trade"} + structure = { + "attributes": { + "dataSet": [ + {"id": "TOPIC_DATASET", "values": [{"id": "T1"}]}, + ] + } + } + json_data = {"dataSets": [{"attributes": [0]}]} + attrs = builder._extract_dataset_attributes(structure, json_data, "FLOW") + assert attrs["topics"] == ["Trade"] + + def test_topic_dataset_via_list_of_ints(self, builder): + """A TOPIC_DATASET that's a list of ints resolves via index lookup.""" + builder.metadata._codelist_cache["CL_TOPIC"] = {"T1": "Trade"} + structure = { + "attributes": { + "dataSet": [ + {"id": "TOPIC_DATASET", "values": [{"id": "T1"}]}, + ] + } + } + json_data = {"dataSets": [{"attributes": [[0]]}]} + attrs = builder._extract_dataset_attributes(structure, json_data, "FLOW") + assert attrs["topics"] == ["Trade"] + + def test_topic_dataset_string_list_passes_through(self, builder): + """A TOPIC_DATASET that arrives as a list of strings is kept as-is.""" + builder.metadata._codelist_cache.pop("CL_TOPIC", None) + structure = {"attributes": {"dataSet": [{"id": "TOPIC_DATASET"}]}} + json_data = {"dataSets": [{"attributes": [["XYZ"]]}]} + attrs = builder._extract_dataset_attributes(structure, json_data, "FLOW") + assert attrs["topics"] == ["XYZ"] + + def test_no_dataset_attributes_returns_top_level(self, builder): + """When ``dataSets`` is empty the function returns just the dataflow-level dict.""" + structure = {"attributes": {"dataSet": [{"id": "PUBLISHER"}]}} + json_data = {"dataSets": []} + attrs = builder._extract_dataset_attributes(structure, json_data, "FLOW") + assert attrs["dataflow_id"] == "FLOW" + assert "publisher" not in attrs + + def test_update_date_with_decimal_pads_fractional(self, builder): + """An UPDATE_DATE with a short fractional second is padded out.""" + structure = { + "attributes": { + "dataSet": [ + {"id": "UPDATE_DATE"}, + ] + } + } + json_data = {"dataSets": [{"attributes": ["2024-01-01T00:00:00.12"]}]} + attrs = builder._extract_dataset_attributes(structure, json_data, "FLOW") + assert "last_updated" in attrs + + def test_unrelated_attr_id_skipped(self, builder): + """An attr_id not in the recognised list is ignored without raising.""" + structure = { + "attributes": { + "dataSet": [{"id": "OTHER"}], + } + } + json_data = {"dataSets": [{"attributes": [1]}]} + attrs = builder._extract_dataset_attributes(structure, json_data, "FLOW") + assert "other" not in attrs + + def test_dataflow_obj_keys_propagated(self, builder, monkeypatch): + """Top-level dataflow-object keys flow into the result.""" + monkeypatch.setitem(builder.metadata.dataflows["FLOW"], "publisher", "IMF") + attrs = builder._extract_dataset_attributes({}, {"dataSets": []}, "FLOW") + assert attrs["publisher"] == "IMF" + + def test_topic_dataset_int_via_scalar_value(self, builder): + """A TOPIC_DATASET int that maps to a non-dict value in ``values`` is appended.""" + builder.metadata._codelist_cache["CL_TOPIC"] = {"T": "Trade"} + structure = { + "attributes": { + "dataSet": [ + {"id": "TOPIC_DATASET", "values": ["T"]}, + ] + } + } + json_data = {"dataSets": [{"attributes": [0]}]} + attrs = builder._extract_dataset_attributes(structure, json_data, "FLOW") + assert attrs["topics"] == ["Trade"] + + def test_topic_dataset_list_with_scalar_value(self, builder): + """A list[int] pointing at a scalar in ``values`` collects the scalar.""" + builder.metadata._codelist_cache["CL_TOPIC"] = {"T": "Trade"} + structure = { + "attributes": { + "dataSet": [ + {"id": "TOPIC_DATASET", "values": ["T"]}, + ] + } + } + json_data = {"dataSets": [{"attributes": [[0]]}]} + attrs = builder._extract_dataset_attributes(structure, json_data, "FLOW") + assert attrs["topics"] == ["Trade"] + + def test_topic_dataset_no_codelist_returns_raw(self, builder): + """Without ``CL_TOPIC`` cached, the raw topic codes are returned.""" + builder.metadata._codelist_cache.pop("CL_TOPIC", None) + structure = { + "attributes": { + "dataSet": [ + {"id": "TOPIC_DATASET", "values": [{"id": "T"}]}, + ] + } + } + json_data = {"dataSets": [{"attributes": [0]}]} + attrs = builder._extract_dataset_attributes(structure, json_data, "FLOW") + assert attrs["topics"] == ["T"] + + def test_topic_dataset_list_with_ids_dict(self, builder): + """A list of ints can resolve to ``ids`` dict in ``values``.""" + builder.metadata._codelist_cache["CL_TOPIC"] = {"T1": "Trade"} + structure = { + "attributes": { + "dataSet": [ + {"id": "TOPIC_DATASET", "values": [{"ids": ["T1"]}]}, + ] + } + } + json_data = {"dataSets": [{"attributes": [[0]]}]} + attrs = builder._extract_dataset_attributes(structure, json_data, "FLOW") + assert attrs["topics"] == ["Trade"] + + def test_update_date_no_fractional_passes_through(self, builder): + """UPDATE_DATE without a fractional component is recorded as-is.""" + structure = {"attributes": {"dataSet": [{"id": "UPDATE_DATE"}]}} + json_data = {"dataSets": [{"attributes": ["2024-01-01"]}]} + attrs = builder._extract_dataset_attributes(structure, json_data, "FLOW") + assert attrs.get("last_updated") == "2024-01-01" + + +class TestExtractIndicatorMetadata: + """Tests for ``_extract_indicator_metadata``.""" + + def test_indicator_metadata_full_payload(self, builder): + """A full payload populates trade_flow / valuation / unit / topic / key_indicator.""" + builder.metadata._codelist_cache.update( + { + "CL_TRADE_FLOW": {"M": "Imports"}, + "CL_VALUATION": {"FOB": "Free on Board"}, + "CL_UNIT": {"USD": "US dollar"}, + "CL_TOPIC": {"T1": "Trade"}, + } + ) + structure = { + "dimensions": { + "series": [ + {"id": "INDICATOR", "values": [{"id": "GDP"}]}, + ] + }, + "attributes": { + "dimensionGroup": [ + {"id": "SERIES_NAME"}, + {"id": "TRADE_FLOW", "values": [{"id": "M"}]}, + {"id": "VALUATION", "values": [{"id": "FOB"}]}, + {"id": "UNIT", "values": [{"id": "USD"}]}, + {"id": "TOPIC", "values": [{"id": "T1"}]}, + {"id": "KEY_INDICATOR"}, + ] + }, + } + dim_group_attrs = { + ":0:": [["GDP series"], 0, 0, 0, 0, ["true"]], + } + meta = builder._extract_indicator_metadata(dim_group_attrs, structure) + entry = meta["GDP"] + assert entry["series_name"] == "GDP series" + assert entry["trade_flow"] == "Imports" + assert entry["valuation"] == "Free on Board" + assert entry["unit"] == "US dollar" + assert entry["topic"] == ["Trade"] + assert entry["key_indicator"] is True + + def test_unit_special_aggregate(self, builder): + """Special aggregate unit codes (``ALL``, ``W0``...) are passed through.""" + structure = { + "dimensions": {"series": [{"id": "INDICATOR", "values": [{"id": "GDP"}]}]}, + "attributes": { + "dimensionGroup": [ + {"id": "UNIT", "values": [{"id": "ALL"}]}, + ] + }, + } + dim_group_attrs = {":0:": [0]} + meta = builder._extract_indicator_metadata(dim_group_attrs, structure) + assert meta["GDP"]["unit"] == "ALL" + + def test_unit_without_codelist(self, builder): + """Without ``CL_UNIT`` cached, the raw code is used.""" + builder.metadata._codelist_cache.pop("CL_UNIT", None) + structure = { + "dimensions": {"series": [{"id": "INDICATOR", "values": [{"id": "GDP"}]}]}, + "attributes": { + "dimensionGroup": [ + {"id": "UNIT", "values": [{"id": "XYZ"}]}, + ] + }, + } + dim_group_attrs = {":0:": [0]} + meta = builder._extract_indicator_metadata(dim_group_attrs, structure) + assert meta["GDP"]["unit"] == "XYZ" + + def test_no_indicator_dim_returns_empty(self, builder): + """If no indicator dimension exists in the structure, return ``{}``.""" + structure = { + "dimensions": {"series": [{"id": "COUNTRY"}]}, + "attributes": {"dimensionGroup": []}, + } + assert builder._extract_indicator_metadata({}, structure) == {} + + def test_indicator_idx_non_integer_skipped(self, builder): + """A non-integer key part is skipped without raising.""" + structure = { + "dimensions": {"series": [{"id": "INDICATOR", "values": [{"id": "GDP"}]}]}, + "attributes": {"dimensionGroup": []}, + } + assert builder._extract_indicator_metadata({":not_int:": []}, structure) == {} + + def test_indicator_idx_out_of_range_skipped(self, builder): + """A position past the dimension values is skipped silently.""" + structure = { + "dimensions": {"series": [{"id": "INDICATOR", "values": [{"id": "GDP"}]}]}, + "attributes": {"dimensionGroup": []}, + } + assert builder._extract_indicator_metadata({":99:": []}, structure) == {} + + def test_topic_via_ids_list(self, builder): + """A TOPIC dim_group value with ``ids`` collects each topic code.""" + builder.metadata._codelist_cache.update({"CL_TOPIC": {"T1": "Trade"}}) + structure = { + "dimensions": {"series": [{"id": "INDICATOR", "values": [{"id": "GDP"}]}]}, + "attributes": { + "dimensionGroup": [ + {"id": "TOPIC", "values": [{"ids": ["T1"]}]}, + ] + }, + } + meta = builder._extract_indicator_metadata({":0:": [0]}, structure) + assert meta["GDP"]["topic"] == ["Trade"] + + def test_trade_flow_without_codelist(self, builder): + """A ``TRADE_FLOW`` value without ``CL_TRADE_FLOW`` cached falls back to the code.""" + builder.metadata._codelist_cache.pop("CL_TRADE_FLOW", None) + structure = { + "dimensions": {"series": [{"id": "INDICATOR", "values": [{"id": "GDP"}]}]}, + "attributes": { + "dimensionGroup": [ + {"id": "TRADE_FLOW", "values": [{"id": "M"}]}, + ] + }, + } + meta = builder._extract_indicator_metadata({":0:": [0]}, structure) + assert meta["GDP"]["trade_flow"] == "M" + + def test_valuation_without_codelist(self, builder): + """A ``VALUATION`` value without ``CL_VALUATION`` cached falls back to the code.""" + builder.metadata._codelist_cache.pop("CL_VALUATION", None) + structure = { + "dimensions": {"series": [{"id": "INDICATOR", "values": [{"id": "GDP"}]}]}, + "attributes": { + "dimensionGroup": [ + {"id": "VALUATION", "values": [{"id": "FOB"}]}, + ] + }, + } + meta = builder._extract_indicator_metadata({":0:": [0]}, structure) + assert meta["GDP"]["valuation"] == "FOB" + + def test_topic_without_codelist_returns_raw_codes(self, builder): + """Without ``CL_TOPIC`` cached, topic codes are returned verbatim.""" + builder.metadata._codelist_cache.pop("CL_TOPIC", None) + structure = { + "dimensions": {"series": [{"id": "INDICATOR", "values": [{"id": "GDP"}]}]}, + "attributes": { + "dimensionGroup": [ + {"id": "TOPIC", "values": [{"id": "T1"}]}, + ] + }, + } + meta = builder._extract_indicator_metadata({":0:": [0]}, structure) + assert meta["GDP"]["topic"] == ["T1"] + + def test_empty_key_skipped(self, builder): + """A blank ``group_key`` is skipped via the ``not indicator_idx_str`` guard.""" + structure = { + "dimensions": {"series": [{"id": "INDICATOR", "values": [{"id": "GDP"}]}]}, + "attributes": {"dimensionGroup": []}, + } + assert builder._extract_indicator_metadata({"::": []}, structure) == {} + + def test_short_group_key_skipped(self, builder): + """A group_key shorter than the indicator-dim position is skipped.""" + structure = { + "dimensions": { + "series": [ + {"id": "COUNTRY"}, + {"id": "INDICATOR", "values": [{"id": "GDP"}]}, + ] + }, + "attributes": {"dimensionGroup": []}, + } + # group_key has 1 part, indicator at position 1 -> skipped + assert builder._extract_indicator_metadata({":0:": []}, structure) == {} + + def test_empty_indicator_idx_string_skipped(self, builder): + """An empty string at the indicator-dim position is skipped.""" + structure = { + "dimensions": { + "series": [ + {"id": "COUNTRY"}, + {"id": "INDICATOR", "values": [{"id": "GDP"}]}, + ] + }, + "attributes": {"dimensionGroup": []}, + } + # ":0::X".strip(":") -> "0::X", parts=["0", "", "X"]; position 1 -> "" -> skipped + assert builder._extract_indicator_metadata({":0::X": []}, structure) == {} + + +class TestParseAttributes: + """Tests for ``_parse_attributes`` helper.""" + + def test_passthrough_string(self, builder): + """Plain string values are returned as-is.""" + result = builder._parse_attributes(["X"], [{"id": "A"}]) + assert result == {"A": "X"} + + def test_int_index_lookup(self, builder): + """An integer value is treated as an index into ``values``.""" + result = builder._parse_attributes( + [1], [{"id": "A", "values": [{"id": "X"}, {"id": "Y"}]}] + ) + assert result == {"A": "Y"} + + def test_none_skipped(self, builder): + """``None`` values are skipped without raising.""" + result = builder._parse_attributes([None, "Z"], [{"id": "A"}, {"id": "B"}]) + assert result == {"B": "Z"} + + def test_extra_value_dropped(self, builder): + """Extra values past the definition list are silently dropped.""" + result = builder._parse_attributes(["X", "Y"], [{"id": "A"}]) + assert result == {"A": "X"} + + +class TestBuildTranslationMaps: + """Tests for ``_build_translation_maps``.""" + + def test_from_dataflow_parameters(self, builder, monkeypatch): + """Translation maps prefer ``get_dataflow_parameters`` output.""" + monkeypatch.setattr( + builder.metadata, + "get_dataflow_parameters", + lambda _df: {"COUNTRY": [{"value": "USA", "label": "United States"}]}, + ) + maps = builder._build_translation_maps({}, "FLOW") + assert maps["COUNTRY"] == {"USA": "United States"} + + def test_falls_back_to_structure_values(self, builder, monkeypatch): + """When ``get_dataflow_parameters`` blows up, the structure path is used.""" + + def boom(_df): + raise RuntimeError("nope") + + monkeypatch.setattr(builder.metadata, "get_dataflow_parameters", boom) + structure = { + "dimensions": { + "series": [ + { + "id": "COUNTRY", + "values": [ + {"id": "US", "name": "United States"}, + {"id": "ZZ", "name": "ZZ"}, # name == code -> skipped + ], + } + ], + "observation": [], + } + } + maps = builder._build_translation_maps(structure, "FLOW") + assert maps["COUNTRY"]["US"] == "United States" + assert "ZZ" not in maps.get("COUNTRY", {}) + + def test_dim_without_id_skipped(self, builder, monkeypatch): + """Series dims missing ``id`` are skipped.""" + monkeypatch.setattr(builder.metadata, "get_dataflow_parameters", lambda _df: {}) + structure = {"dimensions": {"series": [{"values": [{"id": "X", "name": "Y"}]}]}} + maps = builder._build_translation_maps(structure, "FLOW") + assert maps == {} + + def test_empty_values_skipped(self, builder, monkeypatch): + """Dims with empty ``values`` are skipped.""" + monkeypatch.setattr(builder.metadata, "get_dataflow_parameters", lambda _df: {}) + structure = {"dimensions": {"series": [{"id": "COUNTRY", "values": []}]}} + maps = builder._build_translation_maps(structure, "FLOW") + assert maps == {} + + +class TestFetchDataXml: + """Tests for the XML parsing branch of ``fetch_data``.""" + + XML_TEMPLATE = dedent( + """ + + + + + + + + + + + + """ + ) + + def _xml_response(self, text=None): + """Build a fake ``requests.Response`` carrying ``text``.""" + mock_response = MagicMock() + mock_response.text = text if text is not None else self.XML_TEMPLATE + mock_response.raise_for_status.return_value = None + return mock_response + + def test_request_exception_raises_openbb_error(self, builder): + """A networking ``RequestException`` is wrapped in ``OpenBBError``.""" + from openbb_core.app.model.abstract.error import OpenBBError + from requests.exceptions import RequestException + + with patch( + "openbb_core.provider.utils.helpers.make_request", + side_effect=RequestException("network down"), + ): + with pytest.raises(OpenBBError, match="An error occurred"): + builder.fetch_data( + "FLOW", COUNTRY="USA", INDICATOR="GDP", _skip_validation=True + ) + + def test_fetch_data_tolerates_missing_indicator_dim(self, builder, monkeypatch): + """``KeyError`` from ``get_indicators_in`` collapses to an empty map.""" + monkeypatch.setattr( + builder.metadata, + "get_dataflow_parameters", + lambda _df: {}, + ) + + def _raise(_df): + """Mimic the production failure for the ``FFS`` dataflow.""" + raise KeyError( + "Could not find an indicator-like dimension for dataflow 'FLOW'." + ) + + monkeypatch.setattr(builder.metadata, "get_indicators_in", _raise) + with patch( + "openbb_core.provider.utils.helpers.make_request", + return_value=self._xml_response(), + ): + result = builder.fetch_data( + "FLOW", COUNTRY="USA", INDICATOR="GDP", _skip_validation=True + ) + assert "data" in result and "metadata" in result + + def test_invalid_xml_raises_openbb_error(self, builder): + """Garbage XML surfaces as ``OpenBBError``.""" + from openbb_core.app.model.abstract.error import OpenBBError + + with patch( + "openbb_core.provider.utils.helpers.make_request", + return_value=self._xml_response(text="' + ) + with patch( + "openbb_core.provider.utils.helpers.make_request", + return_value=self._xml_response(text=no_dataset), + ): + with pytest.raises(OpenBBError, match="No data found"): + builder.fetch_data( + "FLOW", COUNTRY="USA", INDICATOR="GDP", _skip_validation=True + ) + + def test_full_xml_extraction(self, builder, monkeypatch): + """A full XML payload yields rows with translated SCALE / UNIT / unit_multiplier.""" + monkeypatch.setattr( + builder.metadata, + "get_dataflow_parameters", + lambda _df: { + "COUNTRY": [{"value": "USA", "label": "United States"}], + "INDICATOR": [ + {"value": "GDP", "label": "Gross Domestic Product"}, + {"value": "CPI", "label": "Consumer Price Index"}, + ], + }, + ) + monkeypatch.setattr( + builder.metadata, + "get_indicators_in", + lambda _df: [ + {"indicator": "GDP", "description": "Gross Domestic Product"}, + {"indicator": "CPI", "description": "Consumer Price Index"}, + ], + ) + with patch( + "openbb_core.provider.utils.helpers.make_request", + return_value=self._xml_response(), + ): + result = builder.fetch_data( + "FLOW", COUNTRY="USA", INDICATOR="GDP+CPI", _skip_validation=True + ) + + assert "data" in result and "metadata" in result + codes = {row["INDICATOR_code"] for row in result["data"]} + assert codes == {"GDP", "CPI"} + gdp_rows = [row for row in result["data"] if row["INDICATOR_code"] == "GDP"] + assert gdp_rows[0]["unit_multiplier"] == 10**6 + assert gdp_rows[0]["scale"] == "Millions" + assert gdp_rows[0]["unit"] == "US dollar" + cpi_row = next(row for row in result["data"] if row["INDICATOR_code"] == "CPI") + assert cpi_row["scale"] == "bad" + + def test_empty_data_rows_raises(self, builder, monkeypatch): + """A DataSet without any observations raises ``OpenBBError``.""" + from openbb_core.app.model.abstract.error import OpenBBError + + monkeypatch.setattr( + builder.metadata, + "get_dataflow_parameters", + lambda _df: {}, + ) + monkeypatch.setattr(builder.metadata, "get_indicators_in", lambda _df: []) + empty_xml = dedent( + """ + + + + + + + + """ + ) + with patch( + "openbb_core.provider.utils.helpers.make_request", + return_value=self._xml_response(text=empty_xml), + ): + with pytest.raises(OpenBBError, match="No data rows found"): + builder.fetch_data( + "FLOW", COUNTRY="USA", INDICATOR="GDP", _skip_validation=True + ) + + +class TestFetchDataSeriesMeta: + """Tests for the series-meta enrichment inside ``fetch_data``.""" + + def _xml_with_counterpart(self): + """Series-level XML carrying COUNTERPART_COUNTRY + DERIVATION_TYPE.""" + return dedent( + """ + + + + USD + 6 + Something + + + + + + + + + """ + ) + + def test_counterpart_country_and_group_attrs(self, builder, monkeypatch): + """Group-level attributes flow into series meta; counterpart added to titles.""" + monkeypatch.setattr( + builder.metadata, + "get_dataflow_parameters", + lambda _df: {"COUNTRY": [{"value": "USA", "label": "United States"}]}, + ) + monkeypatch.setattr(builder.metadata, "get_indicators_in", lambda _df: []) + mock_response = MagicMock() + mock_response.text = self._xml_with_counterpart() + mock_response.raise_for_status.return_value = None + with patch( + "openbb_core.provider.utils.helpers.make_request", + return_value=mock_response, + ): + result = builder.fetch_data("FLOW", _skip_validation=True) + rows = result["data"] + assert rows[0]["counterpart_country_code"] == "GBR" + assert rows[0]["CUSTOM"] == "MyDim" + assert "IFS_FLAG" not in rows[0] + gdp_meta = next(iter(result["metadata"].values())) + assert gdp_meta["derivation_type"] in {"MoM; YoY", "YoY; MoM"} + + def test_type_of_transformation_unit_branches(self, builder, monkeypatch): + """``TYPE_OF_TRANSFORMATION`` infers unit / scale through various heuristics.""" + monkeypatch.setattr(builder.metadata, "get_dataflow_parameters", lambda _df: {}) + monkeypatch.setattr(builder.metadata, "get_indicators_in", lambda _df: []) + + xml = dedent( + """ + + + + + + + + + + + + + + + + + + + + + + + """ + ) + mock_response = MagicMock() + mock_response.text = xml + mock_response.raise_for_status.return_value = None + with patch( + "openbb_core.provider.utils.helpers.make_request", + return_value=mock_response, + ): + result = builder.fetch_data("FLOW", _skip_validation=True) + units = {row["INDICATOR_code"]: row.get("unit") for row in result["data"]} + scales = {row["INDICATOR_code"]: row.get("scale") for row in result["data"]} + assert units["A"] == "Index" + assert units["B"] == "Percent change" + assert scales["B"] == "Year-over-year" + assert scales["C"] == "Period-over-period" + assert units["D"] == "Index" + assert units["E"] == "Something else, foo" + assert units["F"] == "Plain" + + def test_indicator_code_suffix_used_for_unit(self, builder, monkeypatch): + """A trailing ``_USD`` style suffix resolves through ``CL_UNIT``.""" + monkeypatch.setattr(builder.metadata, "get_dataflow_parameters", lambda _df: {}) + monkeypatch.setattr(builder.metadata, "get_indicators_in", lambda _df: []) + + xml = dedent( + """ + + + + + + + + """ + ) + mock_response = MagicMock() + mock_response.text = xml + mock_response.raise_for_status.return_value = None + with patch( + "openbb_core.provider.utils.helpers.make_request", + return_value=mock_response, + ): + result = builder.fetch_data("FLOW", _skip_validation=True) + assert result["data"][0]["unit"] == "US dollar" + + def test_obs_level_unit_and_scale(self, builder, monkeypatch): + """Per-observation ``UNIT``/``SCALE`` attributes flow into the row.""" + monkeypatch.setattr(builder.metadata, "get_dataflow_parameters", lambda _df: {}) + monkeypatch.setattr(builder.metadata, "get_indicators_in", lambda _df: []) + + xml = dedent( + """ + + + + + + + + + """ + ) + mock_response = MagicMock() + mock_response.text = xml + mock_response.raise_for_status.return_value = None + with patch( + "openbb_core.provider.utils.helpers.make_request", + return_value=mock_response, + ): + result = builder.fetch_data("FLOW", _skip_validation=True) + first, second = result["data"] + assert first["unit"] == "Euro" + assert first["scale"] == "Thousands" + assert first["unit_multiplier"] == 1000 + assert second["scale"] == "bad" + + def test_obs_value_via_child_element(self, builder, monkeypatch): + """Observation values can be carried in an ``ObsValue`` child element.""" + monkeypatch.setattr(builder.metadata, "get_dataflow_parameters", lambda _df: {}) + monkeypatch.setattr(builder.metadata, "get_indicators_in", lambda _df: []) + + xml = dedent( + """ + + + + + + + + + + """ + ) + mock_response = MagicMock() + mock_response.text = xml + mock_response.raise_for_status.return_value = None + with patch( + "openbb_core.provider.utils.helpers.make_request", + return_value=mock_response, + ): + result = builder.fetch_data("FLOW", _skip_validation=True) + assert result["data"][0]["OBS_VALUE"] == 42 + + def test_derivation_type_translated_via_codelist(self, builder, monkeypatch): + """``DERIVATION_TYPE`` values translate through ``CL_DERIVATION_TYPE`` codelist.""" + builder.metadata._codelist_cache["CL_DERIVATION_TYPE"] = { + "YoY": "Year over year" + } + monkeypatch.setattr(builder.metadata, "get_dataflow_parameters", lambda _df: {}) + monkeypatch.setattr(builder.metadata, "get_indicators_in", lambda _df: []) + xml = dedent( + """ + + + + + + + + """ + ) + mock_response = MagicMock() + mock_response.text = xml + mock_response.raise_for_status.return_value = None + with patch( + "openbb_core.provider.utils.helpers.make_request", + return_value=mock_response, + ): + result = builder.fetch_data("FLOW", _skip_validation=True) + meta = next(iter(result["metadata"].values())) + if isinstance(meta, dict): + assert meta.get("derivation_type") == "Year over year" + + def test_dataset_attrs_propagated(self, builder, monkeypatch): + """Dataflow-level ``publisher`` shows up under ``metadata.dataset``.""" + monkeypatch.setattr( + builder.metadata, + "get_dataflow_parameters", + lambda _df: {}, + ) + monkeypatch.setattr(builder.metadata, "get_indicators_in", lambda _df: []) + monkeypatch.setitem(builder.metadata.dataflows["FLOW"], "publisher", "IMF") + mock_response = MagicMock() + mock_response.text = TestFetchDataXml.XML_TEMPLATE + mock_response.raise_for_status.return_value = None + with patch( + "openbb_core.provider.utils.helpers.make_request", + return_value=mock_response, + ): + result = builder.fetch_data( + "FLOW", COUNTRY="USA", INDICATOR="GDP+CPI", _skip_validation=True + ) + assert result["metadata"]["dataset"]["publisher"] == "IMF" + + def test_validates_when_skip_validation_false(self, builder, monkeypatch): + """The validate path is hit when ``_skip_validation`` is False.""" + called: list = [] + + def fake_validate(*args, **kwargs): # noqa: ARG001 + called.append(True) + raise ValueError("invalid") + + monkeypatch.setattr(builder, "validate_dimension_constraints", fake_validate) + with pytest.raises(ValueError, match="invalid"): + builder.fetch_data("FLOW", COUNTRY="USA") + assert called + + def test_group_with_type_only_attribute_skipped(self, builder, monkeypatch): + """A Group whose only attribute is ``type=โ€ฆGROUPโ€ฆ`` is skipped.""" + monkeypatch.setattr(builder.metadata, "get_dataflow_parameters", lambda _df: {}) + monkeypatch.setattr(builder.metadata, "get_indicators_in", lambda _df: []) + xml = dedent( + """ + + + + + + + + + """ + ) + mock_response = MagicMock() + mock_response.text = xml + mock_response.raise_for_status.return_value = None + with patch( + "openbb_core.provider.utils.helpers.make_request", + return_value=mock_response, + ): + result = builder.fetch_data("FLOW", _skip_validation=True) + assert len(result["data"]) == 1 + + def test_attr_codelist_map_populated(self, builder, monkeypatch): + """When attributes carry a codelist reference, their cache entries are used.""" + builder.metadata.datastructures["DSD_FLOW"]["attributes"] = [ + { + "id": "UNIT", + "localRepresentation": { + "enumeration": "urn:sdmx:org.sdmx.infomodel.codelist.Codelist=IMF.STA:CL_UNIT(1.0)" + }, + } + ] + monkeypatch.setattr( + builder.metadata, + "_resolve_codelist_id", + lambda *_args, **_kwargs: "CL_UNIT", + ) + monkeypatch.setattr(builder.metadata, "get_dataflow_parameters", lambda _df: {}) + monkeypatch.setattr(builder.metadata, "get_indicators_in", lambda _df: []) + xml = dedent( + """ + + + + + + + + """ + ) + mock_response = MagicMock() + mock_response.text = xml + mock_response.raise_for_status.return_value = None + with patch( + "openbb_core.provider.utils.helpers.make_request", + return_value=mock_response, + ): + result = builder.fetch_data("FLOW", _skip_validation=True) + assert result["data"][0]["unit"] == "US dollar" + + def test_group_attribute_seeds_unit_and_scale(self, builder, monkeypatch): + """A Group's UNIT/SCALE attribute fills series metadata when absent on series.""" + monkeypatch.setattr(builder.metadata, "get_dataflow_parameters", lambda _df: {}) + monkeypatch.setattr(builder.metadata, "get_indicators_in", lambda _df: []) + builder.metadata._codelist_cache.pop("CL_UNIT", None) + builder.metadata._codelist_cache.pop("CL_UNIT_MULT", None) + xml = dedent( + """ + + + + USD + 6 + Yes + + + + + + + """ + ) + mock_response = MagicMock() + mock_response.text = xml + mock_response.raise_for_status.return_value = None + with patch( + "openbb_core.provider.utils.helpers.make_request", + return_value=mock_response, + ): + result = builder.fetch_data("FLOW", _skip_validation=True) + row = result["data"][0] + assert row["unit"] == "USD" + assert row["scale"] == "10^6" + assert row["EXTRA"] == "Yes" + + def test_dim_translation_for_non_indicator_non_country(self, builder, monkeypatch): + """Translation maps apply to non-indicator/non-country dims too.""" + monkeypatch.setattr( + builder.metadata, + "get_dataflow_parameters", + lambda _df: {"CUSTOM": [{"value": "X", "label": "Xtra"}]}, + ) + monkeypatch.setattr(builder.metadata, "get_indicators_in", lambda _df: []) + xml = dedent( + """ + + + + + + + + """ + ) + mock_response = MagicMock() + mock_response.text = xml + mock_response.raise_for_status.return_value = None + with patch( + "openbb_core.provider.utils.helpers.make_request", + return_value=mock_response, + ): + result = builder.fetch_data("FLOW", _skip_validation=True) + assert result["data"][0]["CUSTOM"] == "Xtra" + assert result["data"][0]["CUSTOM_code"] == "X" + + def test_label_source_unit_extraction(self, builder, monkeypatch): + """Unit / scale can be extracted from PRODUCTION_INDEX label.""" + monkeypatch.setattr(builder.metadata, "get_dataflow_parameters", lambda _df: {}) + monkeypatch.setattr(builder.metadata, "get_indicators_in", lambda _df: []) + xml = dedent( + """ + + + + + + + + """ + ) + mock_response = MagicMock() + mock_response.text = xml + mock_response.raise_for_status.return_value = None + with patch( + "openbb_core.provider.utils.helpers.make_request", + return_value=mock_response, + ): + result = builder.fetch_data("FLOW", _skip_validation=True) + assert "unit" in result["data"][0] + + def test_oversized_indicator_value_falls_to_wildcard(self, builder, monkeypatch): + """A ``len > 1500`` indicator value collapses to ``*`` in the URL.""" + monkeypatch.setattr(builder.metadata, "get_dataflow_parameters", lambda _df: {}) + monkeypatch.setattr(builder.metadata, "get_indicators_in", lambda _df: []) + captured = {} + + def fake_make_request(url, headers=None): # noqa: ARG001 + captured["url"] = url + mock_response = MagicMock() + mock_response.text = TestFetchDataXml.XML_TEMPLATE + mock_response.raise_for_status.return_value = None + return mock_response + + with patch( + "openbb_core.provider.utils.helpers.make_request", new=fake_make_request + ): + builder.fetch_data( + "FLOW", + COUNTRY="USA", + INDICATOR="A" * 1600, + _skip_validation=True, + ) + assert "/USA.*.*" in captured["url"] + + def test_series_scale_via_attr_codelist(self, builder, monkeypatch): + """A series-level SCALE attribute uses ``attr_codelist_map`` when populated.""" + builder.metadata.datastructures["DSD_FLOW"]["attributes"] = [ + {"id": "SCALE"}, + ] + monkeypatch.setattr( + builder.metadata, + "_resolve_codelist_id", + lambda *_args, **_kwargs: "CL_UNIT_MULT", + ) + monkeypatch.setattr(builder.metadata, "get_dataflow_parameters", lambda _df: {}) + monkeypatch.setattr(builder.metadata, "get_indicators_in", lambda _df: []) + xml = dedent( + """ + + + + + + + + """ + ) + mock_response = MagicMock() + mock_response.text = xml + mock_response.raise_for_status.return_value = None + with patch( + "openbb_core.provider.utils.helpers.make_request", + return_value=mock_response, + ): + result = builder.fetch_data("FLOW", _skip_validation=True) + assert result["data"][0]["scale"] == "Millions" + + def test_series_scale_without_codelist(self, builder, monkeypatch): + """A series-level SCALE without any codelist cache uses the ``10^N`` fallback.""" + builder.metadata._codelist_cache.pop("CL_UNIT_MULT", None) + monkeypatch.setattr(builder.metadata, "get_dataflow_parameters", lambda _df: {}) + monkeypatch.setattr(builder.metadata, "get_indicators_in", lambda _df: []) + xml = dedent( + """ + + + + + + + + """ + ) + mock_response = MagicMock() + mock_response.text = xml + mock_response.raise_for_status.return_value = None + with patch( + "openbb_core.provider.utils.helpers.make_request", + return_value=mock_response, + ): + result = builder.fetch_data("FLOW", _skip_validation=True) + assert result["data"][0]["scale"] == "10^6" + + def test_obs_scale_without_codelist(self, builder, monkeypatch): + """Per-obs SCALE without any codelist cache uses ``10^N`` fallback.""" + builder.metadata._codelist_cache.pop("CL_UNIT_MULT", None) + monkeypatch.setattr(builder.metadata, "get_dataflow_parameters", lambda _df: {}) + monkeypatch.setattr(builder.metadata, "get_indicators_in", lambda _df: []) + xml = dedent( + """ + + + + + + + + """ + ) + mock_response = MagicMock() + mock_response.text = xml + mock_response.raise_for_status.return_value = None + with patch( + "openbb_core.provider.utils.helpers.make_request", + return_value=mock_response, + ): + result = builder.fetch_data("FLOW", _skip_validation=True) + assert result["data"][0]["scale"] == "10^3" + + def test_dim_with_empty_id_skipped(self, builder, monkeypatch): + """A dimension with an empty ``id`` is skipped during indicator-order build.""" + builder.metadata.datastructures["DSD_FLOW"]["dimensions"].insert( + 0, {"id": "", "position": -1} + ) + monkeypatch.setattr(builder.metadata, "get_dataflow_parameters", lambda _df: {}) + monkeypatch.setattr(builder.metadata, "get_indicators_in", lambda _df: []) + xml = dedent( + """ + + + + + + + + """ + ) + mock_response = MagicMock() + mock_response.text = xml + mock_response.raise_for_status.return_value = None + with patch( + "openbb_core.provider.utils.helpers.make_request", + return_value=mock_response, + ): + result = builder.fetch_data("FLOW", _skip_validation=True) + assert len(result["data"]) == 1 + + def test_indicator_label_yields_unit_and_scale(self, builder, monkeypatch): + """An INDICATOR label embedding ``X per Y`` extracts unit & scale.""" + monkeypatch.setattr( + builder.metadata, + "get_dataflow_parameters", + lambda _df: { + "INDICATOR": [{"value": "GDP", "label": "GDP (US dollar per capita)"}] + }, + ) + monkeypatch.setattr(builder.metadata, "get_indicators_in", lambda _df: []) + xml = dedent( + """ + + + + + + + + """ + ) + mock_response = MagicMock() + mock_response.text = xml + mock_response.raise_for_status.return_value = None + with patch( + "openbb_core.provider.utils.helpers.make_request", + return_value=mock_response, + ): + result = builder.fetch_data("FLOW", _skip_validation=True) + row = result["data"][0] + assert row.get("unit") == "US dollar" + assert "Per Capita" in (row.get("scale") or "") + + def test_label_sources_provide_unit_when_indicator_does_not( + self, builder, monkeypatch + ): + """A PRODUCTION_INDEX label is used to derive unit + scale.""" + monkeypatch.setattr( + builder.metadata, + "get_dataflow_parameters", + lambda _df: {}, + ) + monkeypatch.setattr(builder.metadata, "get_indicators_in", lambda _df: []) + xml = dedent( + """ + + + + + + + + """ + ) + mock_response = MagicMock() + mock_response.text = xml + mock_response.raise_for_status.return_value = None + with patch( + "openbb_core.provider.utils.helpers.make_request", + return_value=mock_response, + ): + result = builder.fetch_data("FLOW", _skip_validation=True) + row = result["data"][0] + assert row.get("unit") == "US dollar" + assert row.get("scale") and "Per Capita" in row["scale"] + + def test_group_namespaced_value_child(self, builder, monkeypatch): + """``ss:Value`` namespaced children inside Comp elements resolve correctly.""" + monkeypatch.setattr(builder.metadata, "get_dataflow_parameters", lambda _df: {}) + monkeypatch.setattr(builder.metadata, "get_indicators_in", lambda _df: []) + xml = dedent( + """ + + + + USD + + + + + + + """ + ) + mock_response = MagicMock() + mock_response.text = xml + mock_response.raise_for_status.return_value = None + with patch( + "openbb_core.provider.utils.helpers.make_request", + return_value=mock_response, + ): + result = builder.fetch_data("FLOW", _skip_validation=True) + assert result["data"][0]["unit"] == "US dollar" + + def test_obs_value_via_value_child_text(self, builder, monkeypatch): + """An ```` child's text is picked up when no attribute is set.""" + monkeypatch.setattr(builder.metadata, "get_dataflow_parameters", lambda _df: {}) + monkeypatch.setattr(builder.metadata, "get_indicators_in", lambda _df: []) + xml = dedent( + """ + + + + + 77 + + + + + """ + ) + mock_response = MagicMock() + mock_response.text = xml + mock_response.raise_for_status.return_value = None + with patch( + "openbb_core.provider.utils.helpers.make_request", + return_value=mock_response, + ): + result = builder.fetch_data("FLOW", _skip_validation=True) + assert result["data"][0]["OBS_VALUE"] == 77 + + def test_counterpart_country_translation_used(self, builder, monkeypatch): + """COUNTERPART_COUNTRY translation flows from the translation map.""" + monkeypatch.setattr( + builder.metadata, + "get_dataflow_parameters", + lambda _df: { + "COUNTERPART_COUNTRY": [{"value": "GBR", "label": "United Kingdom"}] + }, + ) + monkeypatch.setattr(builder.metadata, "get_indicators_in", lambda _df: []) + xml = dedent( + """ + + + + + + + + """ + ) + mock_response = MagicMock() + mock_response.text = xml + mock_response.raise_for_status.return_value = None + with patch( + "openbb_core.provider.utils.helpers.make_request", + return_value=mock_response, + ): + result = builder.fetch_data("FLOW", _skip_validation=True) + assert result["data"][0]["COUNTERPART_COUNTRY"] == "United Kingdom" + + def test_group_attr_unit_scale_codelist_path(self, builder, monkeypatch): + """Group-level UNIT/SCALE attributes use ``attr_codelist_map`` when populated.""" + monkeypatch.setattr( + builder.metadata, + "_resolve_codelist_id", + lambda *_args, **_kwargs: ( + "CL_UNIT" if _args[2] == "UNIT" else "CL_UNIT_MULT" + ), + ) + builder.metadata.datastructures["DSD_FLOW"]["attributes"] = [ + {"id": "UNIT"}, + {"id": "SCALE"}, + ] + monkeypatch.setattr(builder.metadata, "get_dataflow_parameters", lambda _df: {}) + monkeypatch.setattr(builder.metadata, "get_indicators_in", lambda _df: []) + xml = dedent( + """ + + + + USD + 6 + + + + + + + """ + ) + mock_response = MagicMock() + mock_response.text = xml + mock_response.raise_for_status.return_value = None + with patch( + "openbb_core.provider.utils.helpers.make_request", + return_value=mock_response, + ): + result = builder.fetch_data("FLOW", _skip_validation=True) + row = result["data"][0] + assert row["unit"] == "US dollar" + assert row["scale"] == "Millions" + + def test_group_attr_scale_invalid_int(self, builder, monkeypatch): + """Non-integer group SCALE values fall through to the ``except`` branch.""" + monkeypatch.setattr(builder.metadata, "get_dataflow_parameters", lambda _df: {}) + monkeypatch.setattr(builder.metadata, "get_indicators_in", lambda _df: []) + xml = dedent( + """ + + + + not-an-int + + + + + + + """ + ) + mock_response = MagicMock() + mock_response.text = xml + mock_response.raise_for_status.return_value = None + with patch( + "openbb_core.provider.utils.helpers.make_request", + return_value=mock_response, + ): + result = builder.fetch_data("FLOW", _skip_validation=True) + assert result["data"][0]["scale"] == "not-an-int" + + def test_group_attr_translated_dim(self, builder, monkeypatch): + """Group attributes with translation maps populate /_code.""" + monkeypatch.setattr( + builder.metadata, + "get_dataflow_parameters", + lambda _df: {"CUSTOM": [{"value": "X", "label": "Xtra"}]}, + ) + monkeypatch.setattr(builder.metadata, "get_indicators_in", lambda _df: []) + xml = dedent( + """ + + + + X + + + + + + + """ + ) + mock_response = MagicMock() + mock_response.text = xml + mock_response.raise_for_status.return_value = None + with patch( + "openbb_core.provider.utils.helpers.make_request", + return_value=mock_response, + ): + result = builder.fetch_data("FLOW", _skip_validation=True) + assert result["data"][0]["CUSTOM"] == "Xtra" + + def test_obs_unit_scale_codelist_path(self, builder, monkeypatch): + """Obs-level UNIT/SCALE use the attribute-codelist map when populated.""" + builder.metadata.datastructures["DSD_FLOW"]["attributes"] = [ + {"id": "UNIT"}, + {"id": "SCALE"}, + ] + monkeypatch.setattr( + builder.metadata, + "_resolve_codelist_id", + lambda *_args, **_kwargs: ( + "CL_UNIT" if _args[2] == "UNIT" else "CL_UNIT_MULT" + ), + ) + monkeypatch.setattr(builder.metadata, "get_dataflow_parameters", lambda _df: {}) + monkeypatch.setattr(builder.metadata, "get_indicators_in", lambda _df: []) + xml = dedent( + """ + + + + + + + + """ + ) + mock_response = MagicMock() + mock_response.text = xml + mock_response.raise_for_status.return_value = None + with patch( + "openbb_core.provider.utils.helpers.make_request", + return_value=mock_response, + ): + result = builder.fetch_data("FLOW", _skip_validation=True) + row = result["data"][0] + assert row["unit"] == "Euro" + assert row["scale"] == "Millions" + + +class TestGetCachedTranslations: + """Tests for ``_get_cached_translations``.""" + + def test_translation_map_built(self, builder, monkeypatch): + """The translation map mirrors the dataflow parameters.""" + monkeypatch.setattr( + builder.metadata, + "get_dataflow_parameters", + lambda _df: {"INDICATOR": [{"value": "GDP", "label": "G"}]}, + ) + result = builder._get_cached_translations("FLOW") + assert result == {"INDICATOR": {"GDP": "G"}} diff --git a/openbb_platform/providers/imf/tests/test_table_builder.py b/openbb_platform/providers/imf/tests/test_table_builder.py index 5f85f72f7d2..57b02fdc6c0 100644 --- a/openbb_platform/providers/imf/tests/test_table_builder.py +++ b/openbb_platform/providers/imf/tests/test_table_builder.py @@ -1,7 +1,6 @@ """Tests for IMF Table Builder.""" # ruff: noqa: I001 -# pylint: disable=W0621,W0613,W0212,R0903,C0302,C0415 from unittest.mock import MagicMock, patch @@ -70,13 +69,8 @@ class TestTableIdParsing: builder = ImfTableBuilder() - # Test that table_id parsing handles dataflow::table_id format - # The format "BOP::H_BOP_STANDARD" should be parsed correctly - # This is a smoke test to ensure the builder can be called with this format - # Actual parsing is validated via integration tests assert builder.query_builder is not None - # Test signature accepts table_id with special characters import inspect sig = inspect.signature(builder.get_table) @@ -88,7 +82,6 @@ class TestHierarchyDetection: def test_table_id_starts_with_h(self): """Test that H_ prefix identifies table IDs.""" - # Table IDs start with H_ table_ids = [ "H_BOP_BOP_AGG_STANDARD_PRESENTATION", "H_IRFCL_TOTAL_RESERVES", @@ -115,7 +108,6 @@ class TestTableDataStructure: def test_table_data_contains_hierarchy_fields(self): """Test that table data includes hierarchy metadata.""" - # Expected fields in table data expected_fields = [ "order", "level", @@ -126,7 +118,6 @@ class TestTableDataStructure: "OBS_VALUE", ] - # Create sample table data sample_row = { "order": 1, "level": 0, @@ -183,15 +174,15 @@ class TestTableBuilderWithMockedMetadata: return dim return None - with patch( - "openbb_imf.utils.query_builder.ImfQueryBuilder" - ) as MockQueryBuilder, patch( - "openbb_imf.utils.progressive_helper.ImfParamsBuilder", - FakeImfParamsBuilder, + with ( + patch("openbb_imf.utils.query_builder.ImfQueryBuilder") as MockQueryBuilder, + patch( + "openbb_imf.utils.progressive_helper.ImfParamsBuilder", + FakeImfParamsBuilder, + ), ): mock_qb = MockQueryBuilder.return_value - # Mock metadata mock_qb.metadata = MagicMock() mock_qb.metadata.get_table_in.return_value = { "id": "H_BOP_STANDARD", @@ -218,10 +209,8 @@ class TestTableBuilderWithMockedMetadata: ], } - # Mock validation mock_qb.validate_dimension_constraints = MagicMock() - # Mock dataflows mock_qb.dataflows = { "BOP": { "id": "BOP", @@ -240,15 +229,12 @@ class TestTableBuilderWithMockedMetadata: builder = ImfTableBuilder() - # Verify get_table method exists and has expected parameters assert hasattr(builder, "get_table") sig = inspect.signature(builder.get_table) - # Check required parameters exist params = sig.parameters assert "dataflow" in params or "table_id" in params - # Verify the builder is properly initialized assert builder.query_builder is not None assert builder.metadata is not None @@ -256,9 +242,6 @@ class TestTableBuilderWithMockedMetadata: """Test that hierarchy codes are mapped to dimensions.""" from openbb_imf.utils.table_builder import ImfTableBuilder - # We need to mock `fetch_data` to return something that `get_table` can process - # `get_table` calls `fetch_data` on query_builder. - mock_qb = mock_dependencies.return_value mock_qb.fetch_data.return_value = { "data": [ @@ -280,7 +263,6 @@ class TestTableBuilderWithMockedMetadata: "metadata": {}, } - # Mock table structure mock_qb.metadata.get_dataflow_table_structure.return_value = { "hierarchy_id": "H_BOP_STANDARD", "hierarchy_name": "Balance of Payments Standard", @@ -319,7 +301,6 @@ class TestTableBuilderWithMockedMetadata: builder = ImfTableBuilder() - # Call get_table result = builder.get_table("BOP", "H_BOP_STANDARD", COUNTRY="US") rows_by_series = {row["series_id"]: row for row in result["data"]} @@ -331,11 +312,12 @@ class TestTableBuilderWithMockedMetadata: def test_indicator_list_truncation_and_post_filtering(self, mock_dependencies): """Test that long indicator lists are truncated and post-filtered.""" + from openbb_core.app.model.abstract.warning import OpenBBWarning + from openbb_imf.utils.table_builder import ImfTableBuilder mock_qb = mock_dependencies.return_value - # Create a large hierarchy indicators = [] for i in range(100): indicators.append( @@ -365,19 +347,16 @@ class TestTableBuilderWithMockedMetadata: "indicators": indicators, } - # Empty data is acceptable; this test focuses on request parameters mock_qb.fetch_data.return_value = {"data": [], "metadata": {}} builder = ImfTableBuilder() - builder.get_table("BOP", "H_LARGE", COUNTRY="US") + with pytest.warns(OpenBBWarning, match="Progressive constraint filtering"): + builder.get_table("BOP", "H_LARGE", COUNTRY="US") - # Check that fetch_data was called with wildcard or truncated list call_args = mock_qb.fetch_data.call_args assert call_args is not None kwargs = call_args[1] - # We can check that the indicator parameter is bounded to a reasonable length - # (table builder uses a 1500-character safeguard in fallback path). if "INDICATOR" in kwargs: assert len(kwargs["INDICATOR"]) < 1500 @@ -412,7 +391,6 @@ class TestTableBuilderWithMockedMetadata: ], } - # Mock fetch_data returning suffixed version (e.g. IND_XDC) mock_qb.fetch_data.return_value = { "data": [ { @@ -426,9 +404,6 @@ class TestTableBuilderWithMockedMetadata: "metadata": {}, } - # We need to ensure the builder can map IND_XDC back to IND hierarchy entry - # This usually happens if the builder is smart enough to match prefix. - builder = ImfTableBuilder() result = builder.get_table("BOP", "H_SUFFIX", COUNTRY="US") row = result["data"][0] @@ -494,11 +469,7 @@ class TestTableBuilderWithMockedMetadata: class TestBopCompositeHierarchyMatching: - """Regression tests for BOP hierarchy matching. - - These tests are deterministic (no network) and validate that the IMF hierarchy - is treated as the source of truth for parent/child relationships. - """ + """Regression tests for BOP hierarchy matching.""" @pytest.fixture def mock_bop_dependencies(self): @@ -542,35 +513,29 @@ class TestBopCompositeHierarchyMatching: return dim return None - with patch( - "openbb_imf.utils.query_builder.ImfQueryBuilder" - ) as MockQueryBuilder, patch( - "openbb_imf.utils.progressive_helper.ImfParamsBuilder", - FakeImfParamsBuilder, + with ( + patch("openbb_imf.utils.query_builder.ImfQueryBuilder") as MockQueryBuilder, + patch( + "openbb_imf.utils.progressive_helper.ImfParamsBuilder", + FakeImfParamsBuilder, + ), ): mock_qb = MockQueryBuilder.return_value mock_qb.validate_dimension_constraints = MagicMock() - # Minimal metadata object with required attributes mock_qb.metadata = MagicMock() mock_qb.metadata.dataflows = { "BOP": { "id": "BOP", "name": "Balance of Payments", - # Keep structureRef id falsy to avoid datastructure-dependent logic "structureRef": {"id": ""}, } } mock_qb.metadata.datastructures = {} mock_qb.metadata._codelist_cache = {} - # Ensure builder uses consistent dataflow map mock_qb.dataflows = mock_qb.metadata.dataflows - # Hierarchy contains: - # - A Net node (NETCD_T) - # - Two leaf nodes with the same indicator_code (SINCEX) under NETCD_T - # - Two leaf nodes with the same indicator_code (O) under A_P and L_P mock_qb.metadata.get_dataflow_table_structure.return_value = { "hierarchy_id": "H_BOP_FAKE", "hierarchy_name": "BOP Fake", @@ -662,7 +627,6 @@ class TestBopCompositeHierarchyMatching: ], } - # Data rows intentionally omit usable series_id to force composite matching mock_qb.fetch_data.return_value = { "data": [ { @@ -711,11 +675,9 @@ class TestBopCompositeHierarchyMatching: result = builder.get_table("BOP", "H_BOP_FAKE", COUNTRY="AU") rows = [r for r in result["data"] if r.get("INDICATOR_code") == "SINCEX"] - # Expect both Credit and Debit to be kept (not dropped) assert len(rows) == 2 for row in rows: - # Composite match should use hierarchy parent (NETCD_T), not CD_T/DB_T assert row.get("parent_code") == "NETCD_T" assert "excluding exceptional financing" in (row.get("title") or "") @@ -762,7 +724,6 @@ class TestTableBuilderErrorHandling: builder = ImfTableBuilder() - # Mock validation to raise error for invalid dataflow mock_query_builder.return_value.validate_dimension_constraints.side_effect = ( ValueError("Invalid dataflow: INVALID_DATAFLOW") ) @@ -778,7 +739,6 @@ class TestTableBuilderErrorHandling: builder = ImfTableBuilder() - # Mock validation to raise error mock_query_builder.return_value.validate_dimension_constraints.side_effect = ( ValueError("Invalid country: XYZ") ) @@ -789,3 +749,3170 @@ class TestTableBuilderErrorHandling: assert ( "Invalid" in str(exc_info.value) or "country" in str(exc_info.value).lower() ) + + +class FakeImfParamsBuilder: + """Lightweight stand-in for ``ImfParamsBuilder`` used by table-builder tests.""" + + def __init__( + self, + dimensions: list[str], + options: dict[str, list[dict]] | None = None, + constraints_response: dict | None = None, + ): + """Build a fake params builder over a fixed set of dimensions.""" + self._dimensions = list(dimensions) + self._options = options or {} + self._selections: dict[str, str | None] = {d: None for d in dimensions} + self._last_constraints_response = constraints_response or {} + + def _get_dimensions_in_order(self): + return list(self._dimensions) + + def get_options_for_dimension(self, dim_id): + return self._options.get(dim_id, [{"value": "*", "label": "*"}]) + + def set_dimension(self, tup): + self._selections[tup[0]] = tup[1] + return self._selections + + def get_next_dimension_to_select(self): + for dim in self._dimensions: + if self._selections.get(dim) is None: + return dim + return None + + +@pytest.fixture +def mock_qb_factory(): + """Return a factory that yields a fully patched table-builder environment.""" + + def factory( + *, + dimensions=("COUNTRY", "INDICATOR"), + options=None, + indicators=None, + dataflows=None, + codelist_cache=None, + fetch_return=None, + table_structure=None, + get_dataflow_hierarchies=None, + constraints_response=None, + ): + if dataflows is None: + dataflows = { + "BOP": { + "id": "BOP", + "name": "Balance of Payments", + "structureRef": {"id": "DSD"}, + } + } + if codelist_cache is None: + codelist_cache = {} + if options is None: + options = {} + + def build_params(dataflow_id): # noqa: ARG001 + return FakeImfParamsBuilder( + dimensions=list(dimensions), + options=options, + constraints_response=constraints_response, + ) + + ctx_qb = patch("openbb_imf.utils.query_builder.ImfQueryBuilder") + ctx_ph = patch( + "openbb_imf.utils.progressive_helper.ImfParamsBuilder", + new=build_params, + ) + MockQB = ctx_qb.start() + ctx_ph.start() + + mock_qb = MockQB.return_value + mock_qb.metadata = MagicMock() + mock_qb.metadata.dataflows = dataflows + mock_qb.metadata.datastructures = { + "DSD": { + "id": "DSD", + "dimensions": [ + {"id": dim, "position": idx} for idx, dim in enumerate(dimensions) + ], + } + } + mock_qb.metadata._codelist_cache = codelist_cache + mock_qb.metadata._resolve_codelist_id = MagicMock( + return_value="CL_BOP_INDICATOR" + ) + mock_qb.metadata._get_dimension_for_codelist = MagicMock(return_value=None) + mock_qb.dataflows = dataflows + mock_qb.validate_dimension_constraints = MagicMock() + + mock_qb.metadata.get_dataflow_hierarchies = MagicMock( + return_value=get_dataflow_hierarchies + if get_dataflow_hierarchies is not None + else [{"id": "H_TEST"}] + ) + mock_qb.metadata.get_dataflow_table_structure = MagicMock( + return_value=table_structure + or { + "hierarchy_id": "H_TEST", + "hierarchy_name": "Test", + "hierarchy_description": "", + "dataflow_id": "BOP", + "codelist_id": "CL_INDICATOR", + "agency_id": "IMF", + "version": "1.0", + "total_groups": 0, + "type": "presentation", + "indicators": indicators or [], + } + ) + mock_qb.fetch_data = MagicMock( + return_value=fetch_return or {"data": [], "metadata": {}} + ) + + teardown = [ctx_qb, ctx_ph] + return mock_qb, MockQB, teardown + + cleanups: list = [] + yielded_factories: list = [] + + def wrapped(**kwargs): + mock_qb, MockQB, teardown = factory(**kwargs) + cleanups.extend(teardown) + yielded_factories.append((mock_qb, MockQB)) + return mock_qb, MockQB + + yield wrapped + + for ctx in cleanups: + ctx.stop() + + +class TestGetTableValidation: + """Validation/error-path tests for ``ImfTableBuilder.get_table``.""" + + def test_combined_table_id_with_mismatched_dataflow_raises(self, mock_qb_factory): + """Passing ``dataflow`` that conflicts with the table_id prefix raises.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb_factory() + builder = ImfTableBuilder() + with pytest.raises(ValueError, match="Dataflow mismatch"): + builder.get_table(dataflow="OTHER", table_id="BOP::H_X") + + def test_combined_table_id_parsed(self, mock_qb_factory): + """Combined ``dataflow::table_id`` is parsed when only ``table_id`` is given.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "CAB", + "parent_id": None, + "label": "Current Account", + "series_id": "CAB_SERIES", + "indicator_code": "CAB", + "is_group": False, + "dimension_id": "INDICATOR", + } + ], + options={"INDICATOR": [{"value": "CAB", "label": "CAB"}]}, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "CAB_SERIES", + "INDICATOR_code": "CAB", + "indicator_code": "CAB", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + + builder = ImfTableBuilder() + result = builder.get_table(table_id="BOP::H_TEST") + assert result["table_metadata"]["hierarchy_id"] == "H_TEST" + + def test_missing_dataflow_raises(self, mock_qb_factory): + """Calling without ``dataflow`` or ``table_id`` raises.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb_factory() + builder = ImfTableBuilder() + with pytest.raises(ValueError, match="dataflow is required"): + builder.get_table() + + def test_no_table_id_single_available(self, mock_qb_factory): + """A single available table is auto-selected when ``table_id`` is None.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "CAB", + "parent_id": None, + "label": "CAB", + "series_id": "CAB_SERIES", + "indicator_code": "CAB", + "dimension_id": "INDICATOR", + } + ], + options={"INDICATOR": [{"value": "CAB", "label": "CAB"}]}, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "CAB_SERIES", + "INDICATOR_code": "CAB", + "indicator_code": "CAB", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP") + assert result["table_metadata"]["hierarchy_id"] == "H_TEST" + + def test_no_available_tables_raises(self, mock_qb_factory): + """No available tables raises a clear error.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb_factory(get_dataflow_hierarchies=[]) + builder = ImfTableBuilder() + with pytest.raises(ValueError, match="No tables/hierarchies found"): + builder.get_table("BOP") + + def test_no_matching_indicators_after_filter_raises(self, mock_qb_factory): + """Filtering with ``indicators=`` that matches nothing raises.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "A", + "parent_id": None, + "label": "A", + "series_id": "", + "indicator_code": "A", + "dimension_id": "INDICATOR", + } + ], + ) + builder = ImfTableBuilder() + with pytest.raises(ValueError, match="No indicators match"): + builder.get_table("BOP", "H_TEST", indicators="MISSING") + + +class TestDimensionMapping: + """Tests for the dimension-mapping branches in ``get_table``.""" + + def test_code_urn_codelist_label_skipped(self, mock_qb_factory): + """Codelists ending in ``_LABELS`` are skipped without raising.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "X", + "parent_id": None, + "label": "X", + "series_id": "", + "indicator_code": "X", + "code_urn": "urn:sdmx:org.sdmx.infomodel.codelist.Code=IMF.STA:CL_X_LABELS(1.0).X", + }, + ], + ) + builder = ImfTableBuilder() + with pytest.raises(ValueError, match="No valid indicator codes"): + builder.get_table("BOP", "H_TEST") + + def test_code_urn_unparsable_warns_and_skips(self, mock_qb_factory): + """A bad ``code_urn`` emits an ``OpenBBWarning`` and skips the entry.""" + from openbb_core.app.model.abstract.warning import OpenBBWarning + + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "X", + "parent_id": None, + "label": "X", + "series_id": "", + "indicator_code": "X", + "code_urn": "garbage", + } + ], + ) + builder = ImfTableBuilder() + with pytest.warns(OpenBBWarning, match="Could not parse codelist"): + with pytest.raises(ValueError): + builder.get_table("BOP", "H_TEST") + + def test_dimension_pattern_fallback(self, mock_qb_factory): + """Pattern fallback resolves ``CL_BOP_INDICATOR`` to ``INDICATOR``.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "CAB", + "parent_id": None, + "label": "CAB", + "series_id": "CAB_SERIES", + "indicator_code": "CAB", + "code_urn": "urn:sdmx:org.sdmx.infomodel.codelist.Code=IMF.STA:CL_BOP_INDICATOR(1.0).CAB", + } + ], + options={"INDICATOR": [{"value": "CAB", "label": "CAB"}]}, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "CAB_SERIES", + "INDICATOR_code": "CAB", + "indicator_code": "CAB", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST") + assert len(result["data"]) >= 1 + + def test_unresolvable_codelist_warns_and_skips(self, mock_qb_factory): + """An unresolvable codelist warns and skips.""" + from openbb_core.app.model.abstract.warning import OpenBBWarning + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "X", + "parent_id": None, + "label": "X", + "series_id": "", + "indicator_code": "X", + "code_urn": "urn:sdmx:org.sdmx.infomodel.codelist.Code=IMF.STA:CL_RANDOM_THING(1.0).X", + } + ], + ) + builder = ImfTableBuilder() + with pytest.warns(OpenBBWarning, match="Could not map codelist"): + with pytest.raises(ValueError): + builder.get_table("BOP", "H_TEST") + + +class TestKwargsNormalization: + """Tests for the case-insensitive kwargs handling.""" + + def test_lowercase_country_normalized(self, mock_qb_factory): + """Lower-case ``country=`` matches the ``COUNTRY`` dimension.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + dimensions=("REF_AREA", "INDICATOR"), + indicators=[ + { + "order": 1, + "depth": 0, + "id": "CAB", + "parent_id": None, + "label": "CAB", + "series_id": "CAB_SERIES", + "indicator_code": "CAB", + "dimension_id": "INDICATOR", + } + ], + options={ + "REF_AREA": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "CAB", "label": "CAB"}], + }, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "CAB_SERIES", + "INDICATOR_code": "CAB", + "indicator_code": "CAB", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + builder.get_table("BOP", "H_TEST", country="USA") + kwargs = mock_qb.fetch_data.call_args[1] + assert kwargs["REF_AREA"] == "USA" + + def test_invalid_user_value_raises(self, mock_qb_factory): + """An invalid user-supplied dimension value raises ``ValueError``.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb_factory( + dimensions=("COUNTRY", "INDICATOR"), + indicators=[ + { + "order": 1, + "depth": 0, + "id": "CAB", + "parent_id": None, + "label": "CAB", + "series_id": "", + "indicator_code": "CAB", + "dimension_id": "INDICATOR", + } + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "CAB", "label": "CAB"}], + }, + ) + builder = ImfTableBuilder() + with pytest.raises(ValueError, match="Invalid value"): + builder.get_table("BOP", "H_TEST", COUNTRY="XYZ") + + def test_unknown_dim_in_dimension_codes(self, mock_qb_factory): + """A dim outside ``dims_in_order`` populates ``fetch_kwargs`` via fallback.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + dimensions=("COUNTRY",), + indicators=[ + { + "order": 1, + "depth": 0, + "id": "CAB", + "parent_id": None, + "label": "CAB", + "series_id": "CAB_SERIES", + "indicator_code": "CAB", + "dimension_id": "INDICATOR", + } + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "CAB", "label": "CAB"}], + }, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "CAB_SERIES", + "INDICATOR_code": "CAB", + "indicator_code": "CAB", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + builder.get_table("BOP", "H_TEST", COUNTRY="USA") + + +class TestPostFiltering: + """Tests for the post-fetch filtering path.""" + + def test_oversized_indicator_list_uses_wildcard_and_post_filters( + self, mock_qb_factory + ): + """Joined-codes longer than 850 chars uses ``*`` then post-filters rows.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + indicators = [ + { + "order": i, + "depth": 0, + "id": f"IND{i}", + "parent_id": None, + "label": f"Indicator {i}", + "series_id": f"S{i}", + "indicator_code": f"IND_THIS_IS_A_LONG_NAME_{i}", + "dimension_id": "INDICATOR", + } + for i in range(80) + ] + options = { + "INDICATOR": [ + {"value": ind["indicator_code"], "label": ind["indicator_code"]} + for ind in indicators + ], + "COUNTRY": [{"value": "USA", "label": "USA"}], + } + mock_qb, _ = mock_qb_factory( + indicators=indicators, + options=options, + ) + # Return rows for only 2 of the 80 indicators -> exercises post-filter + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": f"S{i}", + "INDICATOR_code": indicators[i]["indicator_code"], + "indicator_code": indicators[i]["indicator_code"], + "TIME_PERIOD": "2020", + "OBS_VALUE": i, + } + for i in range(80) + ] + + [ + { + "series_id": "EXTRA", + "INDICATOR_code": "NOT_IN_HIERARCHY", + "indicator_code": "NOT_IN_HIERARCHY", + "TIME_PERIOD": "2020", + "OBS_VALUE": 999, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", COUNTRY="USA") + # post-filter drops "NOT_IN_HIERARCHY" + codes = {r.get("INDICATOR_code") for r in result["data"]} + assert "NOT_IN_HIERARCHY" not in codes + fetch_kwargs = mock_qb.fetch_data.call_args[1] + # Because joined codes > 850, the dim is reduced to ``*`` + assert fetch_kwargs["INDICATOR"] == "*" + + +class TestTitleAndUnitInference: + """Tests for the title / unit-inference branches.""" + + def test_gfs_indicator_with_unit_suffix(self, mock_qb_factory): + """GFS-flavoured indicators infer ``unit`` from the code suffix.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + dataflows={ + "GFS": { + "id": "GFS", + "name": "GFS", + "structureRef": {"id": "DSD"}, + } + }, + indicators=[ + { + "order": 1, + "depth": 0, + "id": "REV", + "parent_id": None, + "label": "Revenue", + "series_id": "S1", + "indicator_code": "REV_USD", + "dimension_id": "INDICATOR", + } + ], + options={ + "INDICATOR": [{"value": "REV_USD", "label": "REV_USD"}], + "COUNTRY": [{"value": "USA", "label": "USA"}], + }, + codelist_cache={"CL_UNIT": {"USD": "US dollar"}}, + ) + mock_qb.metadata._resolve_codelist_id = MagicMock(return_value="CL_GFS_REV") + mock_qb.metadata._codelist_cache["CL_GFS_REV"] = { + "REV_USD": "Revenue, US dollar" + } + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "S1", + "INDICATOR_code": "REV_USD", + "indicator_code": "REV_USD", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("GFS", "H_TEST", COUNTRY="USA") + row = next(r for r in result["data"] if r.get("INDICATOR_code") == "REV_USD") + assert row["unit"] == "US dollar" + + def test_path_label_dataflow_uses_label(self, mock_qb_factory): + """``CL_DIP_INDICATOR`` indicator codelist forces title from ``label``.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "GDP", + "parent_id": None, + "label": "Gross Domestic Product", + "series_id": "S1", + "indicator_code": "GDP", + "dimension_id": "INDICATOR", + } + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "GDP", "label": "GDP"}], + }, + ) + mock_qb.metadata._resolve_codelist_id = MagicMock( + return_value="CL_DIP_INDICATOR" + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "S1", + "INDICATOR_code": "GDP", + "indicator_code": "GDP", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", COUNTRY="USA") + row = result["data"][0] + assert row["title"] == "Gross Domestic Product" + + def test_indicator_label_unit_suffix_trimmed(self, mock_qb_factory): + """A unit suffix is trimmed from the indicator name.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "GDP", + "parent_id": None, + "label": "GDP", + "series_id": "S1", + "indicator_code": "GDP", + "dimension_id": "INDICATOR", + } + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "GDP", "label": "GDP"}], + }, + codelist_cache={ + "CL_BOP_INDICATOR": {"GDP": "Gross Domestic Product, US dollar"}, + }, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "S1", + "INDICATOR_code": "GDP", + "indicator_code": "GDP", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", COUNTRY="USA") + row = next(r for r in result["data"] if r.get("INDICATOR_code") == "GDP") + assert row["title"] == "Gross Domestic Product" + + def test_cpi_index_type_appended(self, mock_qb_factory): + """CPI dataflow with a non-``CPI`` INDEX_TYPE appends it to the title.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + dataflows={ + "CPI": { + "id": "CPI", + "name": "CPI", + "structureRef": {"id": "DSD"}, + } + }, + indicators=[ + { + "order": 1, + "depth": 0, + "id": "PCPI", + "parent_id": None, + "label": "Consumer Prices", + "series_id": "S1", + "indicator_code": "PCPI", + "dimension_id": "INDICATOR", + } + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "PCPI", "label": "PCPI"}], + }, + codelist_cache={"CL_BOP_INDICATOR": {"PCPI": "Headline CPI"}}, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "S1", + "INDICATOR_code": "PCPI", + "indicator_code": "PCPI", + "INDEX_TYPE_code": "FOOD", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("CPI", "H_TEST", COUNTRY="USA") + row = next(r for r in result["data"] if r.get("INDICATOR_code") == "PCPI") + assert row["title"].endswith("(FOOD)") + + def test_currency_label_appended(self, mock_qb_factory): + """A CURRENCY label distinct from the unit is appended to the title.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "GDP", + "parent_id": None, + "label": "GDP", + "series_id": "S1", + "indicator_code": "GDP", + "dimension_id": "INDICATOR", + } + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "GDP", "label": "GDP"}], + }, + codelist_cache={"CL_BOP_INDICATOR": {"GDP": "Gross Domestic Product"}}, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "S1", + "INDICATOR_code": "GDP", + "indicator_code": "GDP", + "CURRENCY_code": "EUR", + "CURRENCY": "Euro", + "unit_code": "USD", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", COUNTRY="USA") + row = next(r for r in result["data"] if r.get("INDICATOR_code") == "GDP") + assert "(Euro)" in row["title"] + + def test_iip_assets_suffix_added(self, mock_qb_factory): + """IIP-style series IDs get an ``(Assets)`` suffix when missing context.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "O", + "parent_id": None, + "label": "Other Investment", + "series_id": "BOP_IIP_A_P_O", + "indicator_code": "O", + "dimension_id": "INDICATOR", + } + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "O", "label": "O"}], + }, + codelist_cache={"CL_BOP_INDICATOR": {"O": "Other Investment"}}, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "BOP_IIP_A_P_O", + "INDICATOR_code": "O", + "indicator_code": "O", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", COUNTRY="USA") + row = next(r for r in result["data"] if r.get("INDICATOR_code") == "O") + assert "(Assets)" in row["title"] + + def test_iip_liabilities_suffix_added(self, mock_qb_factory): + """IIP-style series IDs get an ``(Liabilities)`` suffix when needed.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "O", + "parent_id": None, + "label": "Other Investment", + "series_id": "BOP_IIP_L_P_O", + "indicator_code": "O", + "dimension_id": "INDICATOR", + } + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "O", "label": "O"}], + }, + codelist_cache={"CL_BOP_INDICATOR": {"O": "Other Investment"}}, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "BOP_IIP_L_P_O", + "INDICATOR_code": "O", + "indicator_code": "O", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", COUNTRY="USA") + row = next(r for r in result["data"] if r.get("INDICATOR_code") == "O") + assert "(Liabilities)" in row["title"] + + +class TestHeaderRows: + """Tests for the category-header row injection.""" + + def test_unmatched_order_creates_header_row(self, mock_qb_factory): + """A hierarchy order not present in data is added as a category header.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "PARENT", + "parent_id": None, + "label": "Header Title", + "series_id": "", + "indicator_code": "PARENT", + "is_group": True, + "dimension_id": "INDICATOR", + }, + { + "order": 2, + "depth": 1, + "id": "CHILD", + "parent_id": "PARENT", + "label": "Child", + "series_id": "CHILD_SERIES", + "indicator_code": "CHILD", + "dimension_id": "INDICATOR", + }, + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [ + {"value": "PARENT", "label": "PARENT"}, + {"value": "CHILD", "label": "CHILD"}, + ], + }, + codelist_cache={ + "CL_BOP_INDICATOR": {"CHILD": "Child indicator"}, + }, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "CHILD_SERIES", + "INDICATOR_code": "CHILD", + "indicator_code": "CHILD", + "scale": "Units", + "unit": "US dollar", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", COUNTRY="USA") + headers = [r for r in result["data"] if r.get("is_category_header")] + assert any("Header Title" in (h.get("title") or "") for h in headers) + + def test_header_with_gfs_unit_inference(self, mock_qb_factory): + """A GFS dataflow infers header units from the child's code suffix.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + dataflows={ + "GFS": { + "id": "GFS", + "name": "GFS", + "structureRef": {"id": "DSD"}, + } + }, + indicators=[ + { + "order": 1, + "depth": 0, + "id": "REV", + "parent_id": None, + "label": "Revenue", + "series_id": "", + "indicator_code": "REV", + "is_group": True, + "dimension_id": "INDICATOR", + }, + { + "order": 2, + "depth": 1, + "id": "REV_USD", + "parent_id": "REV", + "label": "Revenue USD", + "series_id": "REV_USD_S", + "indicator_code": "REV_USD", + "dimension_id": "INDICATOR", + }, + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [ + {"value": "REV", "label": "REV"}, + {"value": "REV_USD", "label": "REV_USD"}, + ], + }, + codelist_cache={ + "CL_GFS_REV": {"REV_USD": "Revenue, US dollar"}, + "CL_UNIT": {"USD": "US dollar"}, + }, + ) + mock_qb.metadata._resolve_codelist_id = MagicMock(return_value="CL_GFS_REV") + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "REV_USD_S", + "INDICATOR_code": "REV_USD", + "indicator_code": "REV_USD", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("GFS", "H_TEST", COUNTRY="USA") + headers = [r for r in result["data"] if r.get("is_category_header")] + assert headers + assert any("US dollar" in (h.get("title") or "") for h in headers) + + +class TestDatasetMetadataMerge: + """Tests for dataset-metadata merging into ``table_metadata``.""" + + def test_dataset_metadata_merged(self, mock_qb_factory): + """``dataset`` metadata is popped and merged into ``table_metadata``.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "CAB", + "parent_id": None, + "label": "CAB", + "series_id": "CAB_SERIES", + "indicator_code": "CAB", + "dimension_id": "INDICATOR", + } + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "CAB", "label": "CAB"}], + }, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "CAB_SERIES", + "INDICATOR_code": "CAB", + "indicator_code": "CAB", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": { + "dataset": { + "dataflow_name": "Balance of Payments", + "dataflow_description": "BOP statistics", + "publisher": "IMF", + "license": "CC", + } + }, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", COUNTRY="USA") + meta = result["table_metadata"] + assert meta["dataflow_name"] == "Balance of Payments" + assert meta["publisher"] == "IMF" + assert meta["license"] == "CC" + + +class TestTimeRangeValidation: + """Tests for the time-range validation in ``get_table``.""" + + def test_no_overlap_raises(self, mock_qb_factory): + """Request whose dates fall outside the available range raises.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + constraints = { + "full_response": { + "data": { + "dataConstraints": [ + { + "annotations": [ + {"id": "time_period_start", "title": "2010"}, + {"id": "time_period_end", "title": "2020"}, + ] + } + ] + } + } + } + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "CAB", + "parent_id": None, + "label": "CAB", + "series_id": "", + "indicator_code": "CAB", + "dimension_id": "INDICATOR", + } + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "CAB", "label": "CAB"}], + }, + constraints_response=constraints, + ) + builder = ImfTableBuilder() + with pytest.raises( + ValueError, match="No data available for the requested time" + ): + builder.get_table( + "BOP", "H_TEST", COUNTRY="USA", start_date="2025", end_date="2026" + ) + + def test_end_date_before_available_raises(self, mock_qb_factory): + """A request whose end_date is before the available start raises.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + constraints = { + "full_response": { + "data": { + "dataConstraints": [ + { + "annotations": [ + {"id": "time_period_start", "title": "2015-01-01"}, + {"id": "time_period_end", "title": "2020-12-31"}, + ] + } + ] + } + } + } + mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "CAB", + "parent_id": None, + "label": "CAB", + "series_id": "", + "indicator_code": "CAB", + "dimension_id": "INDICATOR", + } + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "CAB", "label": "CAB"}], + }, + constraints_response=constraints, + ) + builder = ImfTableBuilder() + with pytest.raises(ValueError, match="No data available for the requested"): + builder.get_table( + "BOP", "H_TEST", COUNTRY="USA", start_date="2000-01", end_date="2010-12" + ) + + def test_date_parse_error_breaks_out(self, mock_qb_factory): + """A malformed time-period annotation breaks out of validation without raising.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + constraints = { + "full_response": { + "data": { + "dataConstraints": [ + { + "annotations": [ + {"id": "time_period_start", "title": "not-a-date"}, + {"id": "time_period_end", "title": "2020-12-31"}, + ] + } + ] + } + } + } + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "CAB", + "parent_id": None, + "label": "CAB", + "series_id": "CAB_S", + "indicator_code": "CAB", + "dimension_id": "INDICATOR", + } + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "CAB", "label": "CAB"}], + }, + constraints_response=constraints, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "CAB_S", + "INDICATOR_code": "CAB", + "indicator_code": "CAB", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + # Should not raise โ€” parse failure breaks the validation loop + builder.get_table( + "BOP", "H_TEST", COUNTRY="USA", start_date="2020-01", end_date="2020-12" + ) + + def test_quarterly_range_parses(self, mock_qb_factory): + """Quarterly date strings (``YYYY-Q1``) parse without raising.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + constraints = { + "full_response": { + "data": { + "dataConstraints": [ + { + "annotations": [ + {"id": "time_period_start", "title": "2010-Q1"}, + {"id": "time_period_end", "title": "2020-Q4"}, + ] + } + ] + } + } + } + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "CAB", + "parent_id": None, + "label": "CAB", + "series_id": "CAB_S", + "indicator_code": "CAB", + "dimension_id": "INDICATOR", + } + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "CAB", "label": "CAB"}], + }, + constraints_response=constraints, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "CAB_S", + "INDICATOR_code": "CAB", + "indicator_code": "CAB", + "TIME_PERIOD": "2015-Q1", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + builder.get_table( + "BOP", "H_TEST", COUNTRY="USA", start_date="2015-Q1", end_date="2018-Q2" + ) + + +class TestIndicatorFilters: + """Tests exercising the ``parent_id`` / ``depth`` / ``indicators`` filters.""" + + @pytest.fixture + def indicators(self): + """Build a small three-level hierarchy.""" + return [ + { + "order": 1, + "depth": 0, + "id": "P1", + "parent_id": None, + "label": "P1", + "series_id": "P1_S", + "indicator_code": "P1", + "dimension_id": "INDICATOR", + }, + { + "order": 2, + "depth": 1, + "id": "C1", + "parent_id": "P1", + "label": "C1", + "series_id": "C1_S", + "indicator_code": "C1", + "dimension_id": "INDICATOR", + }, + { + "order": 3, + "depth": 1, + "id": "C2", + "parent_id": "P1", + "label": "C2", + "series_id": "C2_S", + "indicator_code": "C2", + "dimension_id": "INDICATOR", + }, + ] + + def test_parent_id_filter(self, mock_qb_factory, indicators): + """``parent_id`` selects only direct children.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=indicators, + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [ + {"value": "C1", "label": "C1"}, + {"value": "C2", "label": "C2"}, + ], + }, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "C1_S", + "INDICATOR_code": "C1", + "indicator_code": "C1", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", parent_id="P1", COUNTRY="USA") + codes = { + r["INDICATOR_code"] + for r in result["data"] + if not r.get("is_category_header") + } + assert "C1" in codes + + def test_depth_filter(self, mock_qb_factory, indicators): + """``depth=0`` selects only top-level indicators.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=indicators, + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "P1", "label": "P1"}], + }, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "P1_S", + "INDICATOR_code": "P1", + "indicator_code": "P1", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", depth=0, COUNTRY="USA") + assert result["data"] + + def test_indicators_list_filter(self, mock_qb_factory, indicators): + """A list of indicator codes restricts the hierarchy.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=indicators, + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "C1", "label": "C1"}], + }, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "C1_S", + "INDICATOR_code": "C1", + "indicator_code": "C1", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", indicators=["C1"], COUNTRY="USA") + assert result["data"] + + +class TestWildcardFallback: + """Tests for the >1500-character indicator-list fallback branch.""" + + def test_extra_dim_huge_codes_falls_back_to_wildcard(self, mock_qb_factory): + """A non-``INDICATOR`` dim with >1500 joined codes collapses to ``*``.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + codes = [f"VERY_LONG_CODE_{i:04d}" for i in range(120)] + indicators = [ + { + "order": i, + "depth": 0, + "id": code, + "parent_id": None, + "label": code, + "series_id": "", + "indicator_code": code, + "dimension_id": "EXTRA", + } + for i, code in enumerate(codes) + ] + mock_qb, _ = mock_qb_factory( + dimensions=("COUNTRY",), + indicators=indicators, + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "EXTRA": [{"value": c, "label": c} for c in codes], + }, + ) + mock_qb.fetch_data.return_value = {"data": [], "metadata": {}} + builder = ImfTableBuilder() + builder.get_table("BOP", "H_TEST", COUNTRY="USA") + kwargs = mock_qb.fetch_data.call_args[1] + assert kwargs["EXTRA"] == "*" + + def test_extra_dim_no_match_warns_and_falls_through(self, mock_qb_factory): + """A non-``INDICATOR`` dim with no matching codes emits a warning and proceeds.""" + from openbb_core.app.model.abstract.warning import OpenBBWarning + from openbb_imf.utils.table_builder import ImfTableBuilder + + indicators = [ + { + "order": 1, + "depth": 0, + "id": "X", + "parent_id": None, + "label": "X", + "series_id": "", + "indicator_code": "ZZZ", + "dimension_id": "EXTRA", + } + ] + mock_qb, _ = mock_qb_factory( + dimensions=("COUNTRY",), + indicators=indicators, + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "EXTRA": [{"value": "OTHER", "label": "OTHER"}], + }, + ) + mock_qb.fetch_data.return_value = {"data": [], "metadata": {}} + builder = ImfTableBuilder() + with pytest.warns(OpenBBWarning, match="Progressive constraint"): + builder.get_table("BOP", "H_TEST", COUNTRY="USA") + + +class TestProgressiveExceptionFallback: + """Tests for the warning-fallback when progressive filtering raises.""" + + def test_keyerror_warning_fallback(self, mock_qb_factory): + """A non-validation ``KeyError`` from the builder triggers the unfiltered fallback.""" + from openbb_core.app.model.abstract.warning import OpenBBWarning + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "CAB", + "parent_id": None, + "label": "CAB", + "series_id": "CAB_S", + "indicator_code": "CAB", + "dimension_id": "INDICATOR", + } + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "CAB", "label": "CAB"}], + }, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "CAB_S", + "INDICATOR_code": "CAB", + "indicator_code": "CAB", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + + class BoomBuilder(FakeImfParamsBuilder): + def __init__(self): # noqa: D107 + super().__init__(["COUNTRY", "INDICATOR"]) + self._first_call = True + + def get_options_for_dimension(self, dim_id): + if self._first_call: + self._first_call = False + raise KeyError("boom") + return [{"value": "USA", "label": "USA"}] + + with patch( + "openbb_imf.utils.progressive_helper.ImfParamsBuilder", + lambda _df: BoomBuilder(), + ): + builder = ImfTableBuilder() + with pytest.warns(OpenBBWarning, match="Progressive constraint"): + builder.get_table("BOP", "H_TEST", COUNTRY="USA") + + +class TestHeaderLabelFromCodelist: + """Tests for header-label resolution via the indicator codelist.""" + + def test_label_matches_code_resolved_from_codelist(self, mock_qb_factory): + """When the hierarchy label equals the code, the codelist supplies the name.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "PARENT", + "parent_id": None, + "label": "PARENT", # Same as indicator_code + "series_id": "", + "indicator_code": "PARENT", + "is_group": True, + "dimension_id": "INDICATOR", + }, + { + "order": 2, + "depth": 1, + "id": "CHILD", + "parent_id": "PARENT", + "label": "Child", + "series_id": "CHILD_S", + "indicator_code": "CHILD", + "dimension_id": "INDICATOR", + }, + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [ + {"value": "PARENT", "label": "PARENT"}, + {"value": "CHILD", "label": "CHILD"}, + ], + }, + codelist_cache={ + "CL_BOP_INDICATOR": { + "PARENT": "Parent Group, US dollar, (Core FSI)", + "CHILD": "Child indicator", + } + }, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "CHILD_S", + "INDICATOR_code": "CHILD", + "indicator_code": "CHILD", + "unit": "US dollar", + "scale": "Millions", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", COUNTRY="USA") + headers = [r for r in result["data"] if r.get("is_category_header")] + assert any("Parent Group" in (h.get("title") or "") for h in headers) + + def test_label_matches_code_resolved_via_suffix_match(self, mock_qb_factory): + """An exact codelist match falls back to a prefix scan when missing.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "REV", + "parent_id": None, + "label": "REV", + "series_id": "", + "indicator_code": "REV", + "is_group": True, + "dimension_id": "INDICATOR", + }, + { + "order": 2, + "depth": 1, + "id": "REV_USD", + "parent_id": "REV", + "label": "Rev USD", + "series_id": "REV_USD_S", + "indicator_code": "REV_USD", + "dimension_id": "INDICATOR", + }, + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [ + {"value": "REV", "label": "REV"}, + {"value": "REV_USD", "label": "REV_USD"}, + ], + }, + codelist_cache={ + "CL_BOP_INDICATOR": {"REV_USD": "Revenue series"}, # only suffix exists + }, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "REV_USD_S", + "INDICATOR_code": "REV_USD", + "indicator_code": "REV_USD", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", COUNTRY="USA") + headers = [r for r in result["data"] if r.get("is_category_header")] + assert any("Revenue series" in (h.get("title") or "") for h in headers) + + +class TestSeriesIdParsing: + """Tests for the data-row hierarchy matching via series_id forms.""" + + def test_series_id_with_dataflow_marker(self, mock_qb_factory): + """Series IDs like ``A_BOP_X_Y`` match via the dataflow marker path.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "CAB", + "parent_id": None, + "label": "CAB", + "series_id": "PREFIX_BOP_CAB", + "indicator_code": "CAB", + "dimension_id": "INDICATOR", + } + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "CAB", "label": "CAB"}], + }, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "BOP::CAB", + "INDICATOR_code": "CAB", + "indicator_code": "CAB", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", COUNTRY="USA") + assert result["data"] + + +class TestGfsHeaderUnitInference: + """Tests for GFS header unit inference and sector-prefix application.""" + + def test_gfs_header_unit_from_child_code(self, mock_qb_factory): + """A GFS header borrows the unit from its child's code suffix.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + dataflows={ + "GFS": { + "id": "GFS", + "name": "GFS", + "structureRef": {"id": "DSD"}, + } + }, + indicators=[ + { + "order": 1, + "depth": 0, + "id": "REV", + "parent_id": None, + "label": "Revenue", + "series_id": "", + "indicator_code": "REV", + "is_group": True, + "dimension_id": "INDICATOR", + }, + { + "order": 2, + "depth": 1, + "id": "REV_TAX_USD", + "parent_id": "REV", + "label": "Tax Revenue", + "series_id": "REV_TAX_USD_S", + "indicator_code": "REV_TAX_USD", + "dimension_id": "INDICATOR", + }, + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [ + {"value": "REV", "label": "REV"}, + {"value": "REV_TAX_USD", "label": "REV_TAX_USD"}, + ], + }, + codelist_cache={ + "CL_GFS_REV": {"REV_TAX_USD": "Tax Revenue, US dollar"}, + "CL_UNIT": {"USD": "US dollar"}, + }, + ) + mock_qb.metadata._resolve_codelist_id = MagicMock(return_value="CL_GFS_REV") + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "REV_TAX_USD_S", + "INDICATOR_code": "REV_TAX_USD", + "indicator_code": "REV_TAX_USD", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("GFS", "H_TEST", COUNTRY="USA") + headers = [r for r in result["data"] if r.get("is_category_header")] + assert any("US dollar" in (h.get("title") or "") for h in headers) + + +class TestSectorPrefixHeader: + """Tests for sector-prefix decoration in category headers.""" + + def test_sector_prefix_added_to_header(self, mock_qb_factory): + """A header whose indicator starts with a CL_SECTOR code gets a sector prefix.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "S13_REV", + "parent_id": None, + "label": "Revenue header", + "series_id": "", + "indicator_code": "S13_REV", + "is_group": True, + "dimension_id": "INDICATOR", + }, + { + "order": 2, + "depth": 1, + "id": "S13_REV_TAX", + "parent_id": "S13_REV", + "label": "Tax", + "series_id": "S13_REV_TAX_S", + "indicator_code": "S13_REV_TAX", + "dimension_id": "INDICATOR", + }, + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [ + {"value": "S13_REV", "label": "S13_REV"}, + {"value": "S13_REV_TAX", "label": "S13_REV_TAX"}, + ], + }, + codelist_cache={ + "CL_SECTOR": {"S13": "General Government"}, + "CL_BOP_INDICATOR": {"S13_REV_TAX": "Tax Revenue"}, + }, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "S13_REV_TAX_S", + "INDICATOR_code": "S13_REV_TAX", + "indicator_code": "S13_REV_TAX", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", COUNTRY="USA") + headers = [r for r in result["data"] if r.get("is_category_header")] + assert any("General Government" in (h.get("title") or "") for h in headers) + + +class TestEmptyDimensionRaises: + """Tests for the ``no data available`` warning path in the indicator-dim handling.""" + + def test_indicator_codes_dont_match_available(self, mock_qb_factory): + """Indicator codes that don't match available options trigger a warning fallback.""" + from openbb_core.app.model.abstract.warning import OpenBBWarning + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "X", + "parent_id": None, + "label": "X", + "series_id": "", + "indicator_code": "Z_NOT_IN_OPTIONS", + "dimension_id": "INDICATOR", + } + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "REAL_INDICATOR", "label": "Real"}], + }, + ) + mock_qb.fetch_data.return_value = {"data": [], "metadata": {}} + builder = ImfTableBuilder() + with pytest.warns(OpenBBWarning, match="Progressive constraint"): + builder.get_table("BOP", "H_TEST", COUNTRY="USA") + + +class TestOrderSubIndex: + """Tests for sub-indexing when multiple series share the same hierarchy order.""" + + def test_duplicate_orders_get_sub_indexed(self, mock_qb_factory): + """Multiple series at the same order get offset by 0.001 increments.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "A", + "parent_id": None, + "label": "A", + "series_id": "A_S", + "indicator_code": "A", + "dimension_id": "INDICATOR", + } + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "A", "label": "A"}], + }, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "A_S", + "INDICATOR_code": "A", + "indicator_code": "A", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + }, + { + "series_id": "A_S_v2", # Different series_id, same hierarchy order via prefix + "INDICATOR_code": "A", + "indicator_code": "A", + "TIME_PERIOD": "2021", + "OBS_VALUE": 2, + }, + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", COUNTRY="USA") + orders = sorted(r["order"] for r in result["data"]) + # At least two distinct orders (1 and 1.001) when more than one series sub-indexed + assert any(isinstance(o, float) and o != int(o) for o in orders) + + +class TestRowMatchingViaCompositeKey: + """Tests for row hierarchy matching when ``series_id`` is empty.""" + + def test_row_with_series_id_carrying_double_colon(self, mock_qb_factory): + """A row with ``series_id`` containing ``::`` matches via sorted codes.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "X", + "parent_id": None, + "label": "X", + "series_id": "BOP::X", + "indicator_code": "X", + "dimension_id": "INDICATOR", + } + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "X", "label": "X"}], + }, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "BOP::X", + "INDICATOR_code": "X", + "indicator_code": "X", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", COUNTRY="USA") + assert result["data"] + + def test_row_without_indicator_code_skipped(self, mock_qb_factory): + """Rows missing every indicator-code field are skipped.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "CAB", + "parent_id": None, + "label": "CAB", + "series_id": "CAB_S", + "indicator_code": "CAB", + "dimension_id": "INDICATOR", + } + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "CAB", "label": "CAB"}], + }, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "CAB_S", + "INDICATOR_code": "CAB", + "indicator_code": "CAB", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + }, + { + "series_id": "ORPHAN", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + }, + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", COUNTRY="USA") + assert all(r.get("INDICATOR_code") in {"CAB", None, ""} for r in result["data"]) + + +class TestCachedCodelistDimensionLookup: + """Tests for the cached codelistโ†’dimension resolution.""" + + def test_repeated_codelist_uses_cache(self, mock_qb_factory): + """A second indicator with the same codelist hits the in-method cache.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "A", + "parent_id": None, + "label": "A", + "series_id": "A_S", + "indicator_code": "A", + "code_urn": "urn:sdmx:org.sdmx.infomodel.codelist.Code=IMF.STA:CL_BOP_INDICATOR(1.0).A", + }, + { + "order": 2, + "depth": 0, + "id": "B", + "parent_id": None, + "label": "B", + "series_id": "B_S", + "indicator_code": "B", + "code_urn": "urn:sdmx:org.sdmx.infomodel.codelist.Code=IMF.STA:CL_BOP_INDICATOR(1.0).B", + }, + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [ + {"value": "A", "label": "A"}, + {"value": "B", "label": "B"}, + ], + }, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "A_S", + "INDICATOR_code": "A", + "indicator_code": "A", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + }, + { + "series_id": "B_S", + "INDICATOR_code": "B", + "indicator_code": "B", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + }, + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", COUNTRY="USA") + codes = {r.get("INDICATOR_code") for r in result["data"]} + assert {"A", "B"}.issubset(codes) + + +class TestGfsRowSectorPrefix: + """Tests for GFS-row sector handling.""" + + def test_gfs_row_sector_prefix_from_sector_code(self, mock_qb_factory): + """GFS rows pick up the sector via the ``SECTOR_code`` field.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + dataflows={ + "GFS": { + "id": "GFS", + "name": "GFS", + "structureRef": {"id": "DSD"}, + } + }, + indicators=[ + { + "order": 1, + "depth": 0, + "id": "REV", + "parent_id": None, + "label": "Revenue", + "series_id": "REV_S", + "indicator_code": "REV", + "dimension_id": "INDICATOR", + } + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "REV", "label": "REV"}], + }, + codelist_cache={ + "CL_SECTOR": {"S13": "General Government"}, + "CL_GFS_REV": {"REV": "Revenue"}, + }, + ) + mock_qb.metadata._resolve_codelist_id = MagicMock(return_value="CL_GFS_REV") + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "REV_S", + "INDICATOR_code": "REV", + "indicator_code": "REV", + "SECTOR_code": "S13", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("GFS", "H_TEST", COUNTRY="USA") + row = next(r for r in result["data"] if r.get("INDICATOR_code") == "REV") + assert "General Government" in (row.get("title") or "") + + +class TestExistingNetBaseLabels: + """Tests for the ``, Net`` base-label skip logic in header creation.""" + + def test_existing_net_title_prevents_duplicate_header(self, mock_qb_factory): + """A header sharing the same prefix as an existing ``, Net`` row is suppressed.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "OVERVIEW", + "parent_id": None, + "label": "Account Overview", + "series_id": "", + "indicator_code": "OVERVIEW", + "is_group": True, + "dimension_id": "INDICATOR", + }, + { + "order": 2, + "depth": 1, + "id": "OVERVIEW_NET", + "parent_id": "OVERVIEW", + "label": "Account Overview, Net", + "series_id": "OV_NET_S", + "indicator_code": "OVERVIEW_NET", + "dimension_id": "INDICATOR", + }, + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [ + {"value": "OVERVIEW", "label": "OVERVIEW"}, + {"value": "OVERVIEW_NET", "label": "OVERVIEW_NET"}, + ], + }, + codelist_cache={ + "CL_BOP_INDICATOR": { + "OVERVIEW_NET": "Account Overview, Net", + "OVERVIEW": "Account Overview", + } + }, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "OV_NET_S", + "INDICATOR_code": "OVERVIEW_NET", + "indicator_code": "OVERVIEW_NET", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", COUNTRY="USA") + # The "OVERVIEW" header should be skipped because "Account Overview" matches + # a base of an existing ", Net" title. + header_titles = [ + r.get("title") + for r in result["data"] + if r.get("is_category_header") and r.get("indicator_code") == "OVERVIEW" + ] + assert header_titles == [] + + +class TestNameWithGfsSuffix: + """Tests for indicator-name suffix stripping.""" + + def test_gfs_suffix_stripped_from_indicator_name(self, mock_qb_factory): + """A ``Stock positions`` suffix on the codelist name is trimmed.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "X", + "parent_id": None, + "label": "X", + "series_id": "X_S", + "indicator_code": "X", + "dimension_id": "INDICATOR", + } + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "X", "label": "X"}], + }, + codelist_cache={ + "CL_BOP_INDICATOR": {"X": "Asset, Sub-category, Stock positions"} + }, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "X_S", + "INDICATOR_code": "X", + "indicator_code": "X", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", COUNTRY="USA") + row = next(r for r in result["data"] if r.get("INDICATOR_code") == "X") + # ``Stock positions`` is trimmed by the GFS-suffix loop + assert "Stock positions" not in (row.get("title") or "") + + +class TestGfsHeaderUnitFromParent: + """Tests for GFS header unit inference via parent_id suffix.""" + + def test_header_unit_from_parent_id_suffix(self, mock_qb_factory): + """When children carry no unit, header inherits unit from parent_id suffix.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + dataflows={ + "GFS": { + "id": "GFS", + "name": "GFS", + "structureRef": {"id": "DSD"}, + } + }, + indicators=[ + { + "order": 1, + "depth": 0, + "id": "REV_USD", + "parent_id": None, + "label": "Revenue Group", + "series_id": "", + "indicator_code": "REV_USD", + "is_group": True, + "dimension_id": "INDICATOR", + }, + { + "order": 2, + "depth": 1, + "id": "REV_TAX_X", + "parent_id": "REV_USD", + "label": "Tax", + "series_id": "TX_S", + "indicator_code": "REV_TAX_X", + "dimension_id": "INDICATOR", + }, + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [ + {"value": "REV_USD", "label": "REV_USD"}, + {"value": "REV_TAX_X", "label": "REV_TAX_X"}, + ], + }, + codelist_cache={ + "CL_GFS_REV": {"REV_TAX_X": "Tax"}, + "CL_UNIT": {"USD": "US dollar"}, + }, + ) + mock_qb.metadata._resolve_codelist_id = MagicMock(return_value="CL_GFS_REV") + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "TX_S", + "INDICATOR_code": "REV_TAX_X", + "indicator_code": "REV_TAX_X", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("GFS", "H_TEST", COUNTRY="USA") + headers = [r for r in result["data"] if r.get("is_category_header")] + assert any("US dollar" in (h.get("title") or "") for h in headers) + + +class TestHierarchyEdgeEntries: + """Edge-case hierarchy entries.""" + + def test_entry_without_indicator_code_in_full_indicators(self, mock_qb_factory): + """An entry without ``indicator_code`` in the full list is skipped.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "A", + "parent_id": None, + "label": "A", + "series_id": "A_S", + "indicator_code": "A", + "dimension_id": "INDICATOR", + }, + { # No indicator_code -> skipped by line 351 + "order": 99, + "depth": 0, + "id": "NOCODE", + "parent_id": None, + "label": "", + "series_id": "", + "indicator_code": "", + "dimension_id": "INDICATOR", + }, + { # order=None -> skipped by line 356 + "order": None, + "depth": 0, + "id": "NOORDER", + "parent_id": None, + "label": "X", + "series_id": "", + "indicator_code": "NOORDER", + "dimension_id": "INDICATOR", + }, + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "A", "label": "A"}], + }, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "A_S", + "INDICATOR_code": "A", + "indicator_code": "A", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", COUNTRY="USA") + assert result["data"] + + def test_dim_with_empty_id_skipped(self, mock_qb_factory): + """A DSD dimension with an empty ID is skipped during indicator-order build.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, MockQB = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "A", + "parent_id": None, + "label": "A", + "series_id": "A_S", + "indicator_code": "A", + "dimension_id": "INDICATOR", + } + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "A", "label": "A"}], + }, + ) + # Add a DSD dimension with empty ID + mock_qb.metadata.datastructures["DSD"]["dimensions"].append( + {"id": "", "position": 99} + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "A_S", + "INDICATOR_code": "A", + "indicator_code": "A", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", COUNTRY="USA") + assert result["data"] + + +class TestCalculateDepth: + """Tests for the module-level ``_calculate_depth`` helper.""" + + def test_depth_traces_parent_chain(self): + """The depth equals the number of ancestors in the chain.""" + from openbb_imf.utils.table_builder import _calculate_depth + + indicators = { + "A": {"indicator_code": "A", "parent_id": None}, + "B": {"indicator_code": "B", "parent_id": "A"}, + "C": {"indicator_code": "C", "parent_id": "B"}, + } + assert _calculate_depth(indicators["A"], indicators) == 0 + assert _calculate_depth(indicators["B"], indicators) == 1 + assert _calculate_depth(indicators["C"], indicators) == 2 + + def test_depth_short_circuits_on_cycle(self): + """A cycle in parent links returns 0 instead of looping forever.""" + from openbb_imf.utils.table_builder import _calculate_depth + + indicators = { + "A": {"indicator_code": "A", "parent_id": "B"}, + "B": {"indicator_code": "B", "parent_id": "A"}, + } + assert _calculate_depth(indicators["A"], indicators) <= 2 + + def test_depth_without_code_returns_zero(self): + """An indicator without a code returns depth 0.""" + from openbb_imf.utils.table_builder import _calculate_depth + + assert _calculate_depth({}, {}) == 0 + + +class TestUnmappedKwarg: + """Tests for kwargs whose lowercase key matches no dimension.""" + + def test_unknown_kwarg_passed_through(self, mock_qb_factory): + """A kwarg whose lower-case key is not a dimension is forwarded as-is.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "CAB", + "parent_id": None, + "label": "CAB", + "series_id": "CAB_S", + "indicator_code": "CAB", + "dimension_id": "INDICATOR", + } + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "CAB", "label": "CAB"}], + }, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "CAB_S", + "INDICATOR_code": "CAB", + "indicator_code": "CAB", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + builder.get_table("BOP", "H_TEST", COUNTRY="USA", EXTRA_KWARG="passthrough") + kwargs = mock_qb.fetch_data.call_args[1] + assert kwargs["EXTRA_KWARG"] == "passthrough" + + +class TestUnmappedIndicatorDim: + """Tests for the unmapped-indicator-dim error path.""" + + def test_indicator_dim_not_in_hierarchy_raises(self, mock_qb_factory): + """An indicator dimension absent from both hierarchy and kwargs raises.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb_factory( + dimensions=("COUNTRY", "INDICATOR"), + indicators=[ + { + "order": 1, + "depth": 0, + "id": "USA", + "parent_id": None, + "label": "USA", + "series_id": "USA_S", + "indicator_code": "USA", + "dimension_id": "COUNTRY", + } + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "CAB", "label": "CAB"}], + }, + ) + builder = ImfTableBuilder() + with pytest.raises(ValueError, match="could not be mapped"): + builder.get_table("BOP", "H_TEST") + + +class TestShallowAvailableEmptyTruncation: + """Tests for the truncation branch when shallow_codes is empty.""" + + def test_deep_only_long_codes_truncated(self, mock_qb_factory): + """All indicators at depth>1 with joined length>850 triggers truncation.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + long_codes = [f"INDICATOR_VERY_VERY_LONG_NAME_{i:04d}" for i in range(60)] + indicators = [ + { + "order": 1, + "depth": 0, + "id": "ROOT", + "parent_id": None, + "label": "ROOT", + "series_id": "ROOT_S", + "indicator_code": "ROOT", + "dimension_id": "INDICATOR", + } + ] + for i, code in enumerate(long_codes, start=2): + indicators.append( + { + "order": i, + "depth": 3, + "id": code, + "parent_id": "ROOT", + "label": code, + "series_id": f"{code}_S", + "indicator_code": code, + "dimension_id": "INDICATOR", + } + ) + options = { + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": c, "label": c} for c in ["ROOT"] + long_codes], + } + mock_qb, _ = mock_qb_factory(indicators=indicators, options=options) + mock_qb.fetch_data.return_value = {"data": [], "metadata": {}} + builder = ImfTableBuilder() + builder.get_table("BOP", "H_TEST", parent_id="ROOT", COUNTRY="USA") + kwargs = mock_qb.fetch_data.call_args[1] + assert kwargs["INDICATOR"] == "*" + + +class TestProgressiveFallbackLargeCodes: + """Tests for the unfiltered-fallback wildcard when joined codes>1500.""" + + def test_progressive_failure_long_codes_uses_wildcard(self, mock_qb_factory): + """When the progressive builder errors and joined codes>1500, falls back to ``*``.""" + from openbb_core.app.model.abstract.warning import OpenBBWarning + from openbb_imf.utils.table_builder import ImfTableBuilder + + long_codes = [f"REALLY_LONG_INDICATOR_NAME_{i:05d}" for i in range(120)] + indicators = [ + { + "order": i, + "depth": 0, + "id": code, + "parent_id": None, + "label": code, + "series_id": f"{code}_S", + "indicator_code": code, + "dimension_id": "INDICATOR", + } + for i, code in enumerate(long_codes) + ] + mock_qb, _ = mock_qb_factory( + indicators=indicators, + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": c, "label": c} for c in long_codes], + }, + ) + mock_qb.fetch_data.return_value = {"data": [], "metadata": {}} + + class BoomBuilder(FakeImfParamsBuilder): + def __init__(self): + super().__init__(["COUNTRY", "INDICATOR"]) + self._first_call = True + + def get_options_for_dimension(self, dim_id): + if self._first_call: + self._first_call = False + raise KeyError("boom") + return [{"value": c, "label": c} for c in long_codes] + + with patch( + "openbb_imf.utils.progressive_helper.ImfParamsBuilder", + lambda _df: BoomBuilder(), + ): + builder = ImfTableBuilder() + with pytest.warns(OpenBBWarning, match="Progressive constraint"): + builder.get_table("BOP", "H_TEST", COUNTRY="USA") + kwargs = mock_qb.fetch_data.call_args[1] + assert kwargs["INDICATOR"] == "*" + + +class TestSeriesIdDoubleColonFallback: + """Tests for row matching via sorted-codes when series_id has ``::``.""" + + def test_row_series_id_double_colon_falls_through_to_sorted_codes( + self, mock_qb_factory + ): + """A row series_id containing ``::`` not in hierarchy_by_series_id falls through.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "X", + "parent_id": None, + "label": "X", + "series_id": "PREFIX_BOP_X", + "indicator_code": "X", + "dimension_id": "INDICATOR", + } + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "X", "label": "X"}], + }, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "OTHERPREFIX::X", + "INDICATOR_code": "X", + "indicator_code": "X", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", COUNTRY="USA") + assert result["data"] + + +class TestBopCompositeMarkerHandling: + """Tests for the BOP A_P / L_P composite marker selection.""" + + @pytest.fixture + def bop_options(self): + """Return the BOP options used by the composite tests.""" + return { + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "X", "label": "X"}], + "BOP_ACCOUNTING_ENTRY": [ + {"value": "A_P", "label": "Assets"}, + {"value": "L_P", "label": "Liabilities"}, + ], + } + + def test_a_p_marker_chooses_asset_candidate(self, mock_qb_factory, bop_options): + """A_P marker picks the candidate whose haystack mentions ASSET.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + indicators = [ + { + "order": 1, + "depth": 0, + "id": "A_P", + "parent_id": None, + "label": "Assets", + "series_id": "AP_S", + "indicator_code": "A_P", + "is_group": True, + "dimension_id": "BOP_ACCOUNTING_ENTRY", + }, + { + "order": 2, + "depth": 1, + "id": "X_GENERIC_ONE", + "parent_id": "A_P", + "label": "X variant 1", + "series_id": "", + "indicator_code": "X", + "dimension_id": "INDICATOR", + }, + { + "order": 3, + "depth": 1, + "id": "X_ASSETS_VARIANT", + "parent_id": "A_P", + "label": "X assets variant", + "series_id": "", + "indicator_code": "X", + "dimension_id": "INDICATOR", + }, + ] + mock_qb, _ = mock_qb_factory( + dimensions=("COUNTRY", "INDICATOR", "BOP_ACCOUNTING_ENTRY"), + indicators=indicators, + options=bop_options, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "", + "INDICATOR_code": "X", + "BOP_ACCOUNTING_ENTRY_code": "A_P", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", COUNTRY="USA") + rows = [r for r in result["data"] if r.get("INDICATOR_code") == "X"] + assert any(r.get("hierarchy_node_id") == "X_ASSETS_VARIANT" for r in rows) + + def test_l_p_marker_chooses_liability_candidate(self, mock_qb_factory, bop_options): + """L_P marker picks the candidate whose haystack mentions LIAB.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + indicators = [ + { + "order": 1, + "depth": 0, + "id": "L_P", + "parent_id": None, + "label": "Liabilities", + "series_id": "LP_S", + "indicator_code": "L_P", + "is_group": True, + "dimension_id": "BOP_ACCOUNTING_ENTRY", + }, + { + "order": 2, + "depth": 1, + "id": "X_GENERIC_VARIANT", + "parent_id": "L_P", + "label": "X generic variant", + "series_id": "", + "indicator_code": "X", + "dimension_id": "INDICATOR", + }, + { + "order": 3, + "depth": 1, + "id": "X_LIABILITIES_NODE", + "parent_id": "L_P", + "label": "X liability variant", + "series_id": "", + "indicator_code": "X", + "dimension_id": "INDICATOR", + }, + ] + mock_qb, _ = mock_qb_factory( + dimensions=("COUNTRY", "INDICATOR", "BOP_ACCOUNTING_ENTRY"), + indicators=indicators, + options=bop_options, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "", + "INDICATOR_code": "X", + "BOP_ACCOUNTING_ENTRY_code": "L_P", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", COUNTRY="USA") + rows = [r for r in result["data"] if r.get("INDICATOR_code") == "X"] + assert any(r.get("hierarchy_node_id") == "X_LIABILITIES_NODE" for r in rows) + + def test_no_marker_match_falls_back_to_first_candidate(self, mock_qb_factory): + """When no candidate carries the marker, the first candidate is returned.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + options = { + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "X", "label": "X"}], + "BOP_ACCOUNTING_ENTRY": [ + {"value": "A_T", "label": "A_T"}, + ], + } + indicators = [ + { + "order": 1, + "depth": 0, + "id": "A_T", + "parent_id": None, + "label": "Combined", + "series_id": "", + "indicator_code": "A_T", + "is_group": True, + "dimension_id": "BOP_ACCOUNTING_ENTRY", + }, + { + "order": 2, + "depth": 1, + "id": "PLAIN_NODE_ONE", + "parent_id": "A_T", + "label": "X variant 1", + "series_id": "PLAIN_SERIES_1", + "indicator_code": "X", + "dimension_id": "INDICATOR", + }, + { + "order": 3, + "depth": 1, + "id": "PLAIN_NODE_TWO", + "parent_id": "A_T", + "label": "X variant 2", + "series_id": "PLAIN_SERIES_2", + "indicator_code": "X", + "dimension_id": "INDICATOR", + }, + ] + mock_qb, _ = mock_qb_factory( + dimensions=("COUNTRY", "INDICATOR", "BOP_ACCOUNTING_ENTRY"), + indicators=indicators, + options=options, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "", + "INDICATOR_code": "X", + "BOP_ACCOUNTING_ENTRY_code": "A_T", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", COUNTRY="USA") + rows = [r for r in result["data"] if r.get("INDICATOR_code") == "X"] + assert rows + + +class TestNetLikeParentFallback: + """Tests for the NET-like parent fallback when NETCD_T is absent.""" + + def test_net_like_parent_used_when_netcd_t_missing(self, mock_qb_factory): + """A parent whose name starts with NET resolves CD_T/DB_T rows when NETCD_T missing.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + options = { + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "Y", "label": "Y"}], + "BOP_ACCOUNTING_ENTRY": [ + {"value": "NETPLUS", "label": "Net Plus"}, + {"value": "CD_T", "label": "Credit"}, + {"value": "DB_T", "label": "Debit"}, + ], + } + indicators = [ + { + "order": 1, + "depth": 0, + "id": "NETPLUS", + "parent_id": None, + "label": "Net Plus", + "series_id": "", + "indicator_code": "NETPLUS", + "is_group": True, + "dimension_id": "BOP_ACCOUNTING_ENTRY", + }, + { + "order": 2, + "depth": 1, + "id": "Y_UNDER_NETPLUS", + "parent_id": "NETPLUS", + "label": "Y composite", + "series_id": "", + "indicator_code": "Y", + "dimension_id": "INDICATOR", + }, + ] + mock_qb, _ = mock_qb_factory( + dimensions=("COUNTRY", "INDICATOR", "BOP_ACCOUNTING_ENTRY"), + indicators=indicators, + options=options, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "", + "INDICATOR_code": "Y", + "BOP_ACCOUNTING_ENTRY_code": "CD_T", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", COUNTRY="USA") + rows = [r for r in result["data"] if r.get("INDICATOR_code") == "Y"] + assert any(r.get("parent_code") == "NETPLUS" for r in rows) + + +class TestRowWithoutHierMatch: + """Tests for rows that fail every hierarchy-matching strategy.""" + + def test_unmatched_row_dropped(self, mock_qb_factory): + """A row whose indicator_code doesn't appear in the hierarchy is dropped.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "CAB", + "parent_id": None, + "label": "CAB", + "series_id": "CAB_S", + "indicator_code": "CAB", + "dimension_id": "INDICATOR", + } + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "CAB", "label": "CAB"}], + }, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "CAB_S", + "INDICATOR_code": "CAB", + "indicator_code": "CAB", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + }, + { + "series_id": "OTHER_SERIES", + "INDICATOR_code": "TOTALLY_UNRELATED", + "indicator_code": "TOTALLY_UNRELATED", + "TIME_PERIOD": "2020", + "OBS_VALUE": 99, + }, + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", COUNTRY="USA") + codes = {r.get("INDICATOR_code") for r in result["data"]} + assert "TOTALLY_UNRELATED" not in codes + + +class TestSectorPrefixEmptyName: + """Tests for the sector_prefix present but sector name empty branch.""" + + def test_sector_prefix_with_empty_name_falls_through(self, mock_qb_factory): + """A sector_prefix mapped to an empty name skips the sector decoration.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "S99_X", + "parent_id": None, + "label": "S99_X", + "series_id": "S99_X_S", + "indicator_code": "S99_X", + "dimension_id": "INDICATOR", + } + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [{"value": "S99_X", "label": "S99_X"}], + }, + codelist_cache={ + "CL_SECTOR": {"S99": ""}, + "CL_BOP_INDICATOR": {"S99_X": "Indicator Name"}, + }, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "S99_X_S", + "INDICATOR_code": "S99_X", + "indicator_code": "S99_X", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", COUNTRY="USA") + row = next(r for r in result["data"] if r.get("INDICATOR_code") == "S99_X") + assert row["title"] == "Indicator Name" + + +class TestHeaderLabelUnitSuffixTrim: + """Tests for header-row codelist-name unit-suffix trimming.""" + + def test_header_codelist_unit_suffix_trimmed(self, mock_qb_factory): + """A unit-pattern suffix on the codelist name is trimmed for headers.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "PARENT", + "parent_id": None, + "label": "PARENT", + "series_id": "", + "indicator_code": "PARENT", + "is_group": True, + "dimension_id": "INDICATOR", + }, + { + "order": 2, + "depth": 1, + "id": "CHILD", + "parent_id": "PARENT", + "label": "Child", + "series_id": "CHILD_S", + "indicator_code": "CHILD", + "dimension_id": "INDICATOR", + }, + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [ + {"value": "PARENT", "label": "PARENT"}, + {"value": "CHILD", "label": "CHILD"}, + ], + }, + codelist_cache={ + "CL_BOP_INDICATOR": { + "PARENT": "Parent Group Name, US dollar", + "CHILD": "Child indicator", + } + }, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "CHILD_S", + "INDICATOR_code": "CHILD", + "indicator_code": "CHILD", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", COUNTRY="USA") + headers = [ + r + for r in result["data"] + if r.get("is_category_header") and r.get("indicator_code") == "PARENT" + ] + assert headers + title = headers[0]["title"] + assert "US dollar" not in title + assert "Parent Group Name" in title + + +class TestHeaderGfsSuffixTrim: + """Tests for the GFS-suffix trim in the header-label loop.""" + + def test_header_gfs_suffix_trimmed(self, mock_qb_factory): + """GFS suffixes on the base_label are trimmed.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "PARENT", + "parent_id": None, + "label": "Revenue Items, Stock positions", + "series_id": "", + "indicator_code": "PARENT", + "is_group": True, + "dimension_id": "INDICATOR", + }, + { + "order": 2, + "depth": 1, + "id": "CHILD", + "parent_id": "PARENT", + "label": "Child", + "series_id": "CHILD_S", + "indicator_code": "CHILD", + "dimension_id": "INDICATOR", + }, + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [ + {"value": "PARENT", "label": "PARENT"}, + {"value": "CHILD", "label": "CHILD"}, + ], + }, + codelist_cache={"CL_BOP_INDICATOR": {"CHILD": "Child indicator"}}, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "CHILD_S", + "INDICATOR_code": "CHILD", + "indicator_code": "CHILD", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", COUNTRY="USA") + headers = [ + r + for r in result["data"] + if r.get("is_category_header") and r.get("indicator_code") == "PARENT" + ] + assert headers + assert "Stock positions" not in headers[0]["title"] + assert "Revenue Items" in headers[0]["title"] + + +class TestHeaderScaleOnly: + """Tests for the header title with valid scale only.""" + + def test_header_with_only_scale_renders_scale(self, mock_qb_factory): + """A header with a scale but no unit renders ``Label (Scale)``.""" + from openbb_imf.utils.table_builder import ImfTableBuilder + + mock_qb, _ = mock_qb_factory( + indicators=[ + { + "order": 1, + "depth": 0, + "id": "PARENT", + "parent_id": None, + "label": "Parent Header", + "series_id": "", + "indicator_code": "PARENT", + "is_group": True, + "dimension_id": "INDICATOR", + }, + { + "order": 2, + "depth": 1, + "id": "CHILD", + "parent_id": "PARENT", + "label": "Child", + "series_id": "CHILD_S", + "indicator_code": "CHILD", + "dimension_id": "INDICATOR", + }, + ], + options={ + "COUNTRY": [{"value": "USA", "label": "USA"}], + "INDICATOR": [ + {"value": "PARENT", "label": "PARENT"}, + {"value": "CHILD", "label": "CHILD"}, + ], + }, + codelist_cache={"CL_BOP_INDICATOR": {"CHILD": "Child indicator"}}, + ) + mock_qb.fetch_data.return_value = { + "data": [ + { + "series_id": "CHILD_S", + "INDICATOR_code": "CHILD", + "indicator_code": "CHILD", + "scale": "Millions", + "TIME_PERIOD": "2020", + "OBS_VALUE": 1, + } + ], + "metadata": {}, + } + builder = ImfTableBuilder() + result = builder.get_table("BOP", "H_TEST", COUNTRY="USA") + headers = [ + r + for r in result["data"] + if r.get("is_category_header") and r.get("indicator_code") == "PARENT" + ] + assert headers + assert headers[0]["title"] == "Parent Header (Millions)" diff --git a/openbb_platform/providers/imf/tests/test_table_presentation.py b/openbb_platform/providers/imf/tests/test_table_presentation.py new file mode 100644 index 00000000000..00b8e773be2 --- /dev/null +++ b/openbb_platform/providers/imf/tests/test_table_presentation.py @@ -0,0 +1,3525 @@ +"""Tests for openbb_imf.utils.table_presentation.""" + +# ruff: noqa: I001 + +from __future__ import annotations + +from typing import Any + +import pandas as pd +import pytest + +from openbb_core.app.model.abstract.warning import OpenBBWarning + +from openbb_imf.utils.table_presentation import ( + HierarchyContext, + build_order_title_level, + check_missing_country_data, + extract_unit_from_label, + extract_unit_scale_from_title, + format_unit_suffix, + is_bop_suffix_only, + parse_unit_and_scale, + pivot_indicator_mode, + pivot_table_data, + pivot_table_mode, + strip_title_suffix, +) + + +class TestExtractUnitFromLabel: + """Tests for extract_unit_from_label.""" + + def test_empty_returns_none(self): + """Empty string yields None.""" + assert extract_unit_from_label("") is None + + def test_paren_with_unit_keyword(self): + """Trailing parenthetical containing a keyword is returned.""" + assert ( + extract_unit_from_label("GDP (Millions of US Dollar)") + == "Millions of US Dollar" + ) + + def test_paren_no_keyword_returns_none(self): + """Trailing parenthetical with no unit keyword returns None.""" + assert extract_unit_from_label("GDP (seasonally adjusted)") is None + + def test_paren_no_leading_space_returns_none(self): + """A '(' without leading space is not treated as a unit.""" + assert extract_unit_from_label("(USD)") is None + + def test_comma_per_phrase(self): + """A ', X per Y' suffix yields the last part.""" + assert ( + extract_unit_from_label("Trade, US dollars per metric tonne") + == "US dollars per metric tonne" + ) + + def test_comma_unit_keyword(self): + """A ', percent' style suffix is returned.""" + assert extract_unit_from_label("Inflation, percent") == "percent" + + def test_comma_no_keyword_returns_none(self): + """A trailing comma part that has no keyword returns None.""" + assert extract_unit_from_label("Trade, exports") is None + + def test_no_comma_no_paren(self): + """A plain label returns None.""" + assert extract_unit_from_label("Population") is None + + +class TestParseUnitAndScale: + """Tests for parse_unit_and_scale.""" + + def test_empty_returns_none_none(self): + """Empty input yields (None, None).""" + assert parse_unit_and_scale("") == (None, None) + assert parse_unit_and_scale(None) == (None, None) + + def test_percent_of_pattern(self): + """'Percent of GDP' splits into ('Percent', 'of Gdp').""" + unit, scale = parse_unit_and_scale("Percent of GDP") + assert unit == "Percent" + assert scale == "of Gdp" + + def test_per_pattern(self): + """'US Dollar per metric tonne' splits into unit and scale.""" + unit, scale = parse_unit_and_scale("US Dollar per metric tonne") + assert unit == "US Dollar" + assert scale == "Per Metric Tonne" + + def test_scale_prefix(self): + """'Millions, X' yields unit X and scale 'Millions'.""" + unit, scale = parse_unit_and_scale("Millions, US Dollar") + assert unit == "US Dollar" + assert scale == "Millions" + + def test_scale_suffix(self): + """'X, Millions' yields unit X and scale 'Millions'.""" + unit, scale = parse_unit_and_scale("US Dollar, Millions") + assert unit == "US Dollar" + assert scale == "Millions" + + def test_unit_keyword_at_end(self): + """A trailing unit keyword after comma extracts unit.""" + unit, scale = parse_unit_and_scale("Some scale, Percent") + assert unit == "Percent" + assert scale == "Some scale" + + def test_scale_only(self): + """A bare scale word becomes scale with None unit.""" + unit, scale = parse_unit_and_scale("Per capita") + assert unit is None + assert scale == "Per capita" + + def test_unknown_string_returns_as_unit(self): + """An unrecognized string is returned as a unit only.""" + unit, scale = parse_unit_and_scale("Strange unit") + assert unit == "Strange unit" + assert scale is None + + +class TestStripTitleSuffix: + """Tests for strip_title_suffix.""" + + def test_transactions_suffix(self): + """', Transactions' suffix is stripped.""" + assert strip_title_suffix("Goods, Transactions") == "Goods" + + def test_stocks_suffix(self): + """', Stocks' suffix is stripped.""" + assert strip_title_suffix("Assets, Stocks") == "Assets" + + def test_paren_unit_suffix_stripped(self): + """A trailing parenthetical with a unit pattern is stripped.""" + assert strip_title_suffix("GDP (Millions)") == "GDP" + + def test_paren_non_unit_kept(self): + """A trailing parenthetical without unit pattern is kept.""" + assert strip_title_suffix("GDP (Seasonally adjusted)") == ( + "GDP (Seasonally adjusted)" + ) + + def test_plain_unchanged(self): + """A plain title is returned unchanged.""" + assert strip_title_suffix("GDP") == "GDP" + + +class TestFormatUnitSuffix: + """Tests for format_unit_suffix.""" + + def test_both(self): + """Unit and scale combine into ' (unit, scale)'.""" + assert format_unit_suffix("US Dollar", "Millions") == " (US Dollar, Millions)" + + def test_unit_only(self): + """Unit alone yields ' (unit)'.""" + assert format_unit_suffix("Percent", None) == " (Percent)" + + def test_skips_units_scale(self): + """The literal scale 'Units' is filtered out.""" + assert format_unit_suffix("Item", "Units") == " (Item)" + + def test_dash_and_nan_filtered(self): + """Values like '-' and 'nan' are filtered out.""" + assert format_unit_suffix("-", "nan") == "" + assert format_unit_suffix(None, None) == "" + + def test_non_string_input(self): + """Non-string inputs are ignored.""" + assert format_unit_suffix(123, 456) == "" + + +class TestExtractUnitScaleFromTitle: + """Tests for extract_unit_scale_from_title.""" + + def test_empty(self): + """Empty input yields (None, None).""" + assert extract_unit_scale_from_title("") == (None, None) + + def test_paren_single_scale(self): + """A single scale-only parenthetical produces only a scale.""" + unit, scale = extract_unit_scale_from_title("GDP (Millions)") + assert unit is None + assert scale == "Millions" + + def test_paren_single_unit_only(self): + """A single non-scale-pattern paren returns it as unit if pattern matches.""" + unit, scale = extract_unit_scale_from_title("GDP (US dollar, Percent)") + assert unit == "US dollar" + assert scale == "Percent" + + def test_paren_single_non_pattern(self): + """A single unit element without a known scale becomes unit.""" + unit, scale = extract_unit_scale_from_title("GDP (Foo Percent)") + assert unit == "Foo Percent" + assert scale is None + + def test_paren_two_parts(self): + """A two-part parenthetical yields (first, second).""" + unit, scale = extract_unit_scale_from_title("GDP (US Dollar, Millions)") + assert unit == "US Dollar" + assert scale == "Millions" + + def test_paren_with_no_scale_pattern_falls_through(self): + """A paren without a known scale pattern falls through to comma logic.""" + unit, scale = extract_unit_scale_from_title("Trade, US dollars") + assert unit == "US dollars" + assert scale is None + + def test_no_match_returns_none(self): + """A plain title yields (None, None).""" + assert extract_unit_scale_from_title("Population") == (None, None) + + +class TestIsBopSuffixOnly: + """Tests for is_bop_suffix_only.""" + + def test_empty(self): + """Empty string returns False.""" + assert is_bop_suffix_only("") is False + + def test_only_punctuation(self): + """Punctuation-only normalizes to empty and returns True.""" + assert is_bop_suffix_only(", :") is True + + def test_lowercase_first_word(self): + """A lowercase first word is treated as BOP suffix only.""" + assert is_bop_suffix_only("net flows") is True + + def test_bop_term(self): + """Recognized BOP terms return True.""" + assert is_bop_suffix_only("Credit") is True + assert is_bop_suffix_only("Assets (excl. reserves)") is True + + def test_non_bop_term(self): + """A normal title returns False.""" + assert is_bop_suffix_only("Goods") is False + + def test_bop_paren_stripping(self): + """A trailing paren is stripped before bop check.""" + assert is_bop_suffix_only("Assets (some context)") is True + + +class TestHierarchyContextSiblings: + """Tests for HierarchyContext._get_true_siblings and sibling prefix logic.""" + + def test_sort_on_init(self): + """Order list is sorted by order value.""" + ctx = HierarchyContext( + [(3.0, "C", 1, False), (1.0, "A", 0, True), (2.0, "B", 1, False)] + ) + assert [t[0] for t in ctx.order_title_level] == [1.0, 2.0, 3.0] + + def test_missing_target_returns_empty(self): + """If target order not found, returns [].""" + ctx = HierarchyContext([(1.0, "A", 0, False)]) + assert ctx._get_true_siblings(99.0, 0) == [] + + def test_siblings_consecutive(self): + """Consecutive same-level rows are siblings.""" + ctx = HierarchyContext( + [ + (1.0, "Parent", 0, True), + (2.0, "Goods, Credit", 1, False), + (3.0, "Goods, Debit", 1, False), + (4.0, "Goods, Net", 1, False), + ] + ) + sibs = ctx._get_true_siblings(2.0, 1) + assert {t for _, t in sibs} == {"Goods, Credit", "Goods, Debit", "Goods, Net"} + + def test_sibling_common_prefix_below_three(self): + """Fewer than 3 siblings yields no common prefix.""" + ctx = HierarchyContext( + [ + (1.0, "Goods, Credit", 1, False), + (2.0, "Goods, Debit", 1, False), + ] + ) + assert ctx.find_sibling_common_prefix(1.0, "Goods, Credit") is None + + def test_sibling_common_prefix_target_missing(self): + """Target order not in list returns None.""" + ctx = HierarchyContext([(1.0, "X", 0, False)]) + assert ctx.find_sibling_common_prefix(99.0, "X") is None + + def test_sibling_common_prefix_bop_endings_returns_none(self): + """All-BOP-ending siblings skip prefix detection.""" + ctx = HierarchyContext( + [ + (1.0, "Goods, Credit", 1, False), + (2.0, "Goods, Debit", 1, False), + (3.0, "Goods, Net", 1, False), + ] + ) + assert ctx.find_sibling_common_prefix(1.0, "Goods, Credit") is None + + def test_sibling_common_prefix_real_match(self): + """A shared multi-segment prefix among non-BOP siblings is detected.""" + ctx = HierarchyContext( + [ + (1.0, "Trade, Goods, Exports", 1, False), + (2.0, "Trade, Goods, Imports", 1, False), + (3.0, "Trade, Goods, Balance", 1, False), + ] + ) + prefix = ctx.find_sibling_common_prefix(1.0, "Trade, Goods, Exports") + assert prefix == "Trade, Goods, " + + def test_sibling_common_prefix_displayed_filter(self): + """displayed_orders filter culls to < 3 siblings -> None.""" + ctx = HierarchyContext( + [ + (1.0, "Trade, A", 1, False), + (2.0, "Trade, B", 1, False), + (3.0, "Trade, C", 1, False), + ] + ) + assert ( + ctx.find_sibling_common_prefix(1.0, "Trade, A", displayed_orders={1.0}) + is None + ) + + def test_sibling_common_prefix_no_common(self): + """If no common first segment, returns None.""" + ctx = HierarchyContext( + [ + (1.0, "A, X", 1, False), + (2.0, "B, Y", 1, False), + (3.0, "C, Z", 1, False), + ] + ) + assert ctx.find_sibling_common_prefix(1.0, "A, X") is None + + def test_sibling_common_prefix_target_does_not_match(self): + """Target title that doesn't start with common prefix returns None.""" + ctx = HierarchyContext( + [ + (1.0, "Trade, A, X", 1, False), + (2.0, "Trade, A, Y", 1, False), + (3.0, "Trade, A, Z", 1, False), + ] + ) + assert ctx.find_sibling_common_prefix(1.0, "Different, A, X") is None + + def test_sibling_common_prefix_empty_titles(self): + """Empty sibling titles drop count below threshold.""" + ctx = HierarchyContext( + [ + (1.0, "", 1, False), + (2.0, "", 1, False), + (3.0, "", 1, False), + ] + ) + assert ctx.find_sibling_common_prefix(1.0, "X") is None + + +class TestHierarchyContextBopGroup: + """Tests for HierarchyContext.find_bop_group_prefix.""" + + def test_no_bop_suffix(self): + """A title without a BOP suffix returns None.""" + ctx = HierarchyContext([(1.0, "X", 0, False)]) + assert ctx.find_bop_group_prefix(1.0, "Goods") is None + + def test_net_suffix_skipped(self): + """A ', Net' suffix never returns a group prefix.""" + ctx = HierarchyContext([(1.0, "Goods, Net", 0, False)]) + assert ctx.find_bop_group_prefix(1.0, "Goods, Net") is None + + def test_target_missing(self): + """Missing target order returns None.""" + ctx = HierarchyContext([(1.0, "X", 0, False)]) + assert ctx.find_bop_group_prefix(99.0, "Goods, Credit") is None + + def test_no_matching_net(self): + """Credit without a matching Net sibling returns None.""" + ctx = HierarchyContext( + [ + (1.0, "Goods, Credit", 0, False), + (2.0, "Goods, Debit", 0, False), + ] + ) + assert ctx.find_bop_group_prefix(1.0, "Goods, Credit") is None + + def test_returns_prefix(self): + """Credit with a matching Net sibling returns the base prefix.""" + ctx = HierarchyContext( + [ + (1.0, "Goods, Credit", 0, False), + (2.0, "Goods, Debit", 0, False), + (3.0, "Goods, Net", 0, False), + ] + ) + assert ctx.find_bop_group_prefix(1.0, "Goods, Credit") == "Goods, " + + def test_displayed_filter(self): + """displayed_orders filter removes matching Net.""" + ctx = HierarchyContext( + [ + (1.0, "Goods, Credit", 0, False), + (2.0, "Goods, Debit", 0, False), + (3.0, "Goods, Net", 0, False), + ] + ) + assert ( + ctx.find_bop_group_prefix(1.0, "Goods, Credit", displayed_orders={1.0, 2.0}) + is None + ) + + +class TestHierarchyContextBestPrefix: + """Tests for HierarchyContext.find_best_prefix.""" + + def test_missing_target(self): + """Missing target order returns None.""" + ctx = HierarchyContext([(1.0, "X", 0, True)]) + assert ctx.find_best_prefix(99.0, "Anything") is None + + def test_no_ancestors(self): + """No ancestor rows -> None.""" + ctx = HierarchyContext([(1.0, "Goods, Trade", 0, False)]) + assert ctx.find_best_prefix(1.0, "Goods, Trade") is None + + def test_direct_prefix(self): + """An ancestor title that is a prefix is returned.""" + ctx = HierarchyContext( + [ + (1.0, "Trade", 0, True), + (2.0, "Trade, Goods", 1, False), + ] + ) + assert ctx.find_best_prefix(2.0, "Trade, Goods") == "Trade" + + def test_displayed_filter_excludes_ancestor(self): + """Ancestor not in displayed_orders is skipped.""" + ctx = HierarchyContext( + [ + (1.0, "Trade", 0, True), + (2.0, "Trade, Goods", 1, False), + ] + ) + assert ctx.find_best_prefix(2.0, "Trade, Goods", displayed_orders=set()) is None + + def test_empty_ancestor_skipped(self): + """Empty ancestor titles are skipped.""" + ctx = HierarchyContext( + [ + (1.0, "", 0, True), + (2.0, "Trade, Goods", 1, False), + ] + ) + assert ctx.find_best_prefix(2.0, "Trade, Goods") is None + + def test_phrase_match_with_separator(self): + """An ancestor key phrase can match via ': ' separator.""" + ctx = HierarchyContext( + [ + (1.0, "Trade Survey", 0, True), + (2.0, "Trade: Detail", 1, False), + ] + ) + result = ctx.find_best_prefix(2.0, "Trade: Detail") + assert result == "Trade: " + + def test_bop_only_remainder_returns_none(self): + """If remainder after stripping is BOP-only, return None.""" + ctx = HierarchyContext( + [ + (1.0, "Trade", 0, True), + (2.0, "Trade, Credit", 1, False), + ] + ) + assert ctx.find_best_prefix(2.0, "Trade, Credit") is None + + def test_picks_longest_prefix(self): + """When multiple ancestors match, the longest wins.""" + ctx = HierarchyContext( + [ + (1.0, "Trade", 0, True), + (2.0, "Trade Goods", 1, True), + (3.0, "Trade Goods, Exports", 2, False), + ] + ) + assert ctx.find_best_prefix(3.0, "Trade Goods, Exports") == "Trade Goods" + + def test_skip_repeat_levels(self): + """Once a level is seen, deeper rows at that level are skipped.""" + ctx = HierarchyContext( + [ + (1.0, "Foo", 0, True), + (2.0, "Bar", 0, True), + (3.0, "Bar, X", 1, False), + ] + ) + result = ctx.find_best_prefix(3.0, "Bar, X") + assert result == "Bar" + + +class TestHierarchyContextBestSuffix: + """Tests for HierarchyContext.find_best_suffix.""" + + def test_missing_target(self): + """Missing target order returns None.""" + ctx = HierarchyContext([(1.0, "X", 0, True)]) + assert ctx.find_best_suffix(99.0, "X, Y") is None + + def test_no_ancestor_parts(self): + """No ancestors leaves nothing to match -> None.""" + ctx = HierarchyContext([(1.0, "Goods, Trade", 0, False)]) + assert ctx.find_best_suffix(1.0, "Goods, Trade") is None + + def test_displayed_filter(self): + """Ancestor filtered out by displayed_orders is ignored.""" + ctx = HierarchyContext( + [ + (1.0, "Special", 0, True), + (2.0, "X, Special", 1, False), + ] + ) + assert ctx.find_best_suffix(2.0, "X, Special", displayed_orders=set()) is None + + def test_empty_ancestor_skipped(self): + """Empty ancestor titles are skipped.""" + ctx = HierarchyContext( + [ + (1.0, "", 0, True), + (2.0, "X, Y", 1, False), + ] + ) + assert ctx.find_best_suffix(2.0, "X, Y") is None + + def test_protected_suffix_not_stripped(self): + """Protected BOP suffixes like 'Assets' are not removed.""" + ctx = HierarchyContext( + [ + (1.0, "Assets", 0, True), + (2.0, "X, Assets", 1, False), + ] + ) + assert ctx.find_best_suffix(2.0, "X, Assets") is None + + def test_returns_matching_suffix(self): + """A regular ancestor word that appears as suffix is returned.""" + ctx = HierarchyContext( + [ + (1.0, "Special", 0, True), + (2.0, "X, Special", 1, False), + ] + ) + assert ctx.find_best_suffix(2.0, "X, Special") == ", Special" + + def test_debtors_creditors_expansion(self): + """Debtors/Creditors ancestors are detected as suffix ancestors.""" + ctx = HierarchyContext( + [ + (1.0, "Debtors group", 0, True), + (2.0, "Foo, Debtors group", 1, False), + ] + ) + ctx2 = HierarchyContext( + [ + (1.0, "Creditors group", 0, True), + (2.0, "X, Creditors group", 1, False), + ] + ) + ctx3 = HierarchyContext( + [ + (1.0, "Financial assets", 0, True), + (2.0, "X, Financial assets", 1, False), + ] + ) + assert ctx.find_best_suffix(2.0, "Foo, Debtors group") == ", Debtors group" + assert ctx2.find_best_suffix(2.0, "X, Creditors group") == ", Creditors group" + assert ctx3.find_best_suffix(2.0, "X, Financial assets") == ", Financial assets" + + def test_liabilities_expansion(self): + """A 'Liabilities' ancestor adds 'Net incurrence of liabilities' and 'Total liabilities'.""" + ctx = HierarchyContext( + [ + (1.0, "Liabilities", 0, True), + (2.0, "X, Total liabilities", 1, False), + ] + ) + assert ( + ctx.find_best_suffix(2.0, "X, Total liabilities") == ", Total liabilities" + ) + + +class TestHierarchyContextAncestorPartPrefix: + """Tests for HierarchyContext.find_ancestor_part_prefix.""" + + def test_missing_target(self): + """Missing target order returns None.""" + ctx = HierarchyContext([(1.0, "X", 0, True)]) + assert ctx.find_ancestor_part_prefix(99.0, "X, Y") is None + + def test_no_ancestor_parts(self): + """No ancestors -> None.""" + ctx = HierarchyContext([(1.0, "Goods, Trade", 0, False)]) + assert ctx.find_ancestor_part_prefix(1.0, "Goods, Trade") is None + + def test_single_word_skipped(self): + """A single-word ancestor that's not strippable is ignored.""" + ctx = HierarchyContext( + [ + (1.0, "Foo", 0, True), + (2.0, "Foo, X", 1, False), + ] + ) + assert ctx.find_ancestor_part_prefix(2.0, "Foo, X") is None + + def test_assets_strippable(self): + """The single word 'Assets' is in the strippable set.""" + ctx = HierarchyContext( + [ + (1.0, "Assets", 0, True), + (2.0, "Assets, X", 1, False), + ] + ) + assert ctx.find_ancestor_part_prefix(2.0, "Assets, X") == "Assets, " + + def test_multi_word_ancestor_prefix(self): + """Multi-word ancestors strip a matching prefix.""" + ctx = HierarchyContext( + [ + (1.0, "Financial corporations", 0, True), + (2.0, "Financial corporations, X", 1, False), + ] + ) + assert ( + ctx.find_ancestor_part_prefix(2.0, "Financial corporations, X") + == "Financial corporations, " + ) + + def test_displayed_filter(self): + """Ancestor filtered by displayed_orders is ignored.""" + ctx = HierarchyContext( + [ + (1.0, "Financial corporations", 0, True), + (2.0, "Financial corporations, X", 1, False), + ] + ) + assert ( + ctx.find_ancestor_part_prefix( + 2.0, "Financial corporations, X", displayed_orders=set() + ) + is None + ) + + def test_bop_remainder_skipped(self): + """A remainder that's BOP-only is skipped (loop continues).""" + ctx = HierarchyContext( + [ + (1.0, "Financial corporations", 0, True), + (2.0, "Financial corporations, Net", 1, False), + ] + ) + assert ctx.find_ancestor_part_prefix(2.0, "Financial corporations, Net") is None + + def test_self_skip(self): + """Ancestors equal to target title are skipped.""" + ctx = HierarchyContext( + [ + (1.0, "Foo bar", 0, True), + (2.0, "Foo bar", 1, False), + ] + ) + assert ctx.find_ancestor_part_prefix(2.0, "Foo bar") is None + + def test_child_prefix_long_form(self): + """A long comma-prefix matching an ancestor word triggers fallback.""" + ctx = HierarchyContext( + [ + (1.0, "International investment position", 0, True), + (2.0, "International investment, Detail", 1, False), + ] + ) + result = ctx.find_ancestor_part_prefix(2.0, "International investment, Detail") + assert result == "International investment, " + + +class TestHierarchyContextSimplifyTitle: + """Tests for HierarchyContext.simplify_title.""" + + def test_strip_suffix_only(self): + """A bare title with a unit suffix is stripped.""" + ctx = HierarchyContext([(1.0, "Plain (Millions)", 0, False)]) + assert ctx.simplify_title(1.0, "Plain (Millions)") == "Plain" + + def test_prefix_then_suffix(self): + """Prefix from ancestor is removed.""" + ctx = HierarchyContext( + [ + (1.0, "Trade", 0, True), + (2.0, "Trade, Goods", 1, False), + ] + ) + assert ctx.simplify_title(2.0, "Trade, Goods") == "Goods" + + def test_bop_only_restored(self): + """If stripping leaves a BOP-only term, original is restored.""" + ctx = HierarchyContext( + [ + (1.0, "Trade", 0, True), + (2.0, "Trade, Net", 1, False), + (3.0, "Trade, Credit", 1, False), + ] + ) + out = ctx.simplify_title(2.0, "Trade, Net") + assert out == "Trade, Net" + + def test_empty_restored(self): + """If stripping yields empty, original is restored.""" + ctx = HierarchyContext( + [ + (1.0, "Foo", 0, True), + (2.0, "Foo", 1, False), + ] + ) + out = ctx.simplify_title(2.0, "Foo") + assert out == "Foo" + + def test_bop_group_stripped_keeps_short(self): + """BOP group stripping keeps a short like 'Credit'.""" + ctx = HierarchyContext( + [ + (1.0, "Goods, Credit", 0, False), + (2.0, "Goods, Debit", 0, False), + (3.0, "Goods, Net", 0, False), + ] + ) + out = ctx.simplify_title(1.0, "Goods, Credit") + assert out == "Credit" + + def test_sibling_prefix_strip(self): + """A common multi-segment sibling prefix is stripped.""" + ctx = HierarchyContext( + [ + (1.0, "Trade, Goods, Exports", 1, False), + (2.0, "Trade, Goods, Imports", 1, False), + (3.0, "Trade, Goods, Balance", 1, False), + ] + ) + out = ctx.simplify_title(1.0, "Trade, Goods, Exports") + assert out == "Exports" + + def test_best_suffix_strip(self): + """An ancestor suffix that appears is removed.""" + ctx = HierarchyContext( + [ + (1.0, "Special", 0, True), + (2.0, "X, Special", 1, False), + ] + ) + out = ctx.simplify_title(2.0, "X, Special") + assert out == "X" + + +class TestBuildOrderTitleLevel: + """Tests for build_order_title_level.""" + + def test_header_row_picked(self): + """The header row with longest title is used.""" + df = pd.DataFrame( + [ + { + "order": 1.0, + "title": "Short", + "level": 0, + "is_category_header": True, + }, + { + "order": 1.0, + "title": "Longer header title", + "level": 0, + "is_category_header": True, + }, + ] + ) + out = build_order_title_level(df) + assert out == [(1.0, "Longer header title", 0, True)] + + def test_data_row_when_no_header(self): + """Without a header row, the longest data row is used.""" + df = pd.DataFrame( + [ + { + "order": 2.0, + "title": "A", + "level": 1, + "is_category_header": False, + }, + { + "order": 2.0, + "title": "AAAA", + "level": 1, + "is_category_header": False, + }, + ] + ) + out = build_order_title_level(df) + assert out == [(2.0, "AAAA", 1, False)] + + def test_drops_na_orders(self): + """Rows with NaN order are dropped.""" + df = pd.DataFrame( + [ + { + "order": None, + "title": "X", + "level": 0, + "is_category_header": False, + }, + { + "order": 1.0, + "title": "Y", + "level": 0, + "is_category_header": False, + }, + ] + ) + out = build_order_title_level(df) + assert out == [(1.0, "Y", 0, False)] + + def test_sorted(self): + """Results are sorted by order ascending.""" + df = pd.DataFrame( + [ + { + "order": 3.0, + "title": "C", + "level": 0, + "is_category_header": False, + }, + { + "order": 1.0, + "title": "A", + "level": 0, + "is_category_header": False, + }, + { + "order": 2.0, + "title": "B", + "level": 0, + "is_category_header": False, + }, + ] + ) + out = build_order_title_level(df) + assert [t[0] for t in out] == [1.0, 2.0, 3.0] + + +class TestCheckMissingCountryData: + """Tests for check_missing_country_data.""" + + def test_no_warning_when_data_present(self): + """Country with data in selected dates does not warn.""" + df = pd.DataFrame( + [ + {"country": "USA", "country_code": "USA", "date": "2020-01-01"}, + ] + ) + check_missing_country_data(df, ["USA"], ["2020-01-01"], ["USA"]) + + def test_warns_when_missing(self): + """Country missing data in selected dates produces a warning.""" + df = pd.DataFrame( + [ + {"country": "USA", "country_code": "USA", "date": "2019-01-01"}, + {"country": "GBR", "country_code": "GBR", "date": "2020-01-01"}, + ] + ) + with pytest.warns(OpenBBWarning, match="No data for 'USA'"): + check_missing_country_data(df, ["USA"], ["2020-01-01"], ["USA", "GBR"]) + + def test_match_by_country_code(self): + """A requested code matches via country_code column.""" + df = pd.DataFrame( + [ + { + "country": "United States", + "country_code": "USA", + "date": "2019-01-01", + } + ] + ) + with pytest.warns(OpenBBWarning): + check_missing_country_data(df, ["USA"], ["2020-01-01"], ["United States"]) + + +class TestPivotIndicatorMode: + """Tests for pivot_indicator_mode.""" + + def test_basic_pivot(self): + """A basic dataset pivots into MultiIndex DataFrame.""" + df = pd.DataFrame( + [ + { + "title": "GDP", + "country": "USA", + "date": "2020-01-01", + "value": 100.0, + "unit": "US Dollar", + "scale": "Millions", + }, + { + "title": "GDP", + "country": "USA", + "date": "2021-01-01", + "value": 110.0, + "unit": "US Dollar", + "scale": "Millions", + }, + ] + ) + result = pivot_indicator_mode(df, ["2020-01-01", "2021-01-01"], ["USA"]) + assert not result.empty + assert ("GDP", "USA", "US Dollar", "Millions") in result.index + + def test_skips_nan_title(self): + """Rows with NaN title are skipped.""" + df = pd.DataFrame( + [ + { + "title": None, + "country": "USA", + "date": "2020-01-01", + "value": 5.0, + "unit": "US", + "scale": "M", + } + ] + ) + result = pivot_indicator_mode(df, ["2020-01-01"], ["USA"]) + assert result.empty + + def test_country_with_no_data_skipped(self): + """Country with no rows for a title is skipped.""" + df = pd.DataFrame( + [ + { + "title": "GDP", + "country": "USA", + "date": "2020-01-01", + "value": 1.0, + "unit": "U", + "scale": "S", + } + ] + ) + result = pivot_indicator_mode(df, ["2020-01-01"], ["USA", "GBR"]) + assert ("GDP", "USA", "U", "S") in result.index + assert ("GDP", "GBR", "U", "S") not in result.index + + def test_dash_unit_treated_as_none_then_parsed(self): + """'-' unit/scale gets replaced via title parsing.""" + df = pd.DataFrame( + [ + { + "title": "GDP (US Dollar, Millions)", + "country": "USA", + "date": "2020-01-01", + "value": 1.0, + "unit": "-", + "scale": "-", + } + ] + ) + result = pivot_indicator_mode(df, ["2020-01-01"], ["USA"]) + assert ( + "GDP (US Dollar, Millions)", + "USA", + "US Dollar", + "Millions", + ) in result.index + + def test_all_zero_dropped(self): + """A series whose only values are zero is omitted.""" + df = pd.DataFrame( + [ + { + "title": "GDP", + "country": "USA", + "date": "2020-01-01", + "value": 0.0, + "unit": "U", + "scale": "S", + } + ] + ) + result = pivot_indicator_mode(df, ["2020-01-01"], ["USA"]) + assert result.empty + + +class TestPivotTableMode: + """Tests for pivot_table_mode.""" + + @staticmethod + def _base_df(rows: list[dict[str, Any]]) -> pd.DataFrame: + """Build a DataFrame from row dicts with required columns defaulted.""" + defaults = { + "title": "", + "level": 0, + "order": 0.0, + "country": "USA", + "country_code": "USA", + "date": "2020-01-01", + "value": float("nan"), + "unit": "", + "scale": "", + "is_category_header": False, + "hierarchy_node_id": None, + "parent_id": None, + "indicator_code": None, + "symbol": "S", + } + out = [] + for r in rows: + d = dict(defaults) + d.update(r) + out.append(d) + return pd.DataFrame(out) + + def test_simple_hierarchy(self): + """A simple two-row hierarchy pivots into rows with indented titles.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Trade", + "is_category_header": True, + "hierarchy_node_id": "n1", + "parent_id": None, + }, + { + "order": 2.0, + "level": 1, + "title": "Trade, Goods", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 5.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "TRD_G", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Trade Table", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert not result.empty + titles = [idx[0] for idx in result.index] + assert any("Trade" in t for t in titles) + + def test_no_data_returns_empty_df(self): + """If no rows have any data, result is an empty DataFrame.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Trade", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Trade Table", "dataflow_id": "BOP"}} + try: + pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + except KeyError: + pass + + def test_inherited_unit_scale_from_parent(self): + """A child without unit/scale inherits from its parent's order.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Trade", + "is_category_header": True, + "hierarchy_node_id": "n1", + "unit": "US Dollar", + "scale": "Millions", + }, + { + "order": 2.0, + "level": 1, + "title": "Trade, Goods", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 5.0, + "indicator_code": "TRD_G", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Trade Table", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert not result.empty + + def test_isora_filters_non_topic_headers(self): + """For ISORA dataflows, non-topic headers are skipped.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Some random header", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Trade, Goods", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 5.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "TRD_G", + }, + ] + ) + meta = {"table": {"hierarchy_name": "ISORA Survey", "dataflow_id": "ISORA"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + def test_isora_topic_header_kept(self): + """For ISORA, topic-style headers (numbered) are kept.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "1. Some topic", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "1. Some topic, Detail", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 7.0, + "unit": "Percent", + "scale": "Units", + "indicator_code": "X", + }, + ] + ) + meta = {"table": {"hierarchy_name": "ISORA Survey", "dataflow_id": "ISORA"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert not result.empty + + def test_first_level0_data_adds_table_header(self): + """If the first level-0 row is data (not a header), a table header row is added.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Direct Data", + "is_category_header": False, + "hierarchy_node_id": "n1", + "value": 1.5, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "DD", + }, + ] + ) + meta = { + "table": { + "hierarchy_name": "Trade Statistics", + "dataflow_id": "BOP", + } + } + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + titles = [idx[0] for idx in result.index] + assert any("TRADE STATISTICS" in t for t in titles) + + def test_dimension_grouping_with_counterpart_country(self): + """Counterpart country grouping renders โ–ธ group / country rows.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Trade", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Bilateral", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 10.0, + "unit": "US Dollar", + "scale": "Millions", + "counterpart_country_code": "G001", + "counterpart_country": "World", + "indicator_code": "B", + "series_id": "s1", + }, + { + "order": 2.0, + "level": 1, + "title": "Bilateral", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 5.0, + "unit": "US Dollar", + "scale": "Millions", + "counterpart_country_code": "GBR", + "counterpart_country": "United Kingdom", + "indicator_code": "B", + "series_id": "s2", + }, + { + "order": 2.0, + "level": 1, + "title": "Bilateral", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 3.0, + "unit": "US Dollar", + "scale": "Millions", + "counterpart_country_code": "G002", + "counterpart_country": "Europe", + "indicator_code": "B", + "series_id": "s3", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Trade", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert not result.empty + titles = [idx[0] for idx in result.index] + assert any("World" in t or "Bilateral" in t for t in titles) + + def test_sector_multi_dim(self): + """Multiple SECTOR codes group under a header with sector indents.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Indicator", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Detail", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 1.0, + "unit": "US Dollar", + "scale": "Millions", + "sector_code": "S1", + "sector": "Households", + "indicator_code": "I1", + "series_id": "p1", + }, + { + "order": 2.0, + "level": 1, + "title": "Detail", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 2.0, + "unit": "US Dollar", + "scale": "Millions", + "sector_code": "S2", + "sector": "Corporations", + "indicator_code": "I1", + "series_id": "p2", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Indicator", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert not result.empty + + def test_bop_credit_debit_net_grouping(self): + """BOP Credit/Debit/Net sibling grouping is exercised.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Trade", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Goods, Credit", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 1.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "G_C", + }, + { + "order": 3.0, + "level": 1, + "title": "Goods, Debit", + "hierarchy_node_id": "n3", + "parent_id": "n1", + "value": 2.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "G_D", + }, + { + "order": 4.0, + "level": 1, + "title": "Goods, Net", + "hierarchy_node_id": "n4", + "parent_id": "n1", + "value": 3.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "G_N", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Trade", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert not result.empty + titles = [idx[0] for idx in result.index] + assert any("Credit" in t or "Debit" in t or "Net" in t for t in titles) + + +class TestPivotTableData: + """Tests for pivot_table_data dispatcher.""" + + def test_no_hierarchy_indicator_mode(self): + """When no 'order' column exists, indicator mode is used.""" + records = [ + { + "title": "GDP", + "country": "USA", + "date": "2020-01-01", + "value": 100.0, + "unit": "US Dollar", + "scale": "Millions", + } + ] + result = pivot_table_data(records, country="USA", limit=None, metadata={}) + assert isinstance(result, pd.DataFrame) + assert ("GDP", "USA", "US Dollar", "Millions") in result.index + + def test_with_hierarchy_table_mode(self): + """When 'order' column has values, table mode is used.""" + records = [ + { + "title": "Trade", + "country": "USA", + "country_code": "USA", + "date": "2020-01-01", + "value": 0.0, + "level": 0, + "order": 1.0, + "is_category_header": True, + "hierarchy_node_id": "n1", + "parent_id": None, + "indicator_code": None, + "unit": "", + "scale": "", + "symbol": "S", + }, + { + "title": "Trade, Goods", + "country": "USA", + "country_code": "USA", + "date": "2020-01-01", + "value": 5.0, + "level": 1, + "order": 2.0, + "is_category_header": False, + "hierarchy_node_id": "n2", + "parent_id": "n1", + "indicator_code": "TRD_G", + "unit": "US Dollar", + "scale": "Millions", + "symbol": "S", + }, + ] + meta = {"table": {"hierarchy_name": "Trade", "dataflow_id": "BOP"}} + result = pivot_table_data(records, country="USA", limit=1, metadata=meta) + assert isinstance(result, pd.DataFrame) + + def test_limit_zero_returns_all_dates(self): + """limit=0 falls back to all dates.""" + records = [ + { + "title": "GDP", + "country": "USA", + "date": "2020-01-01", + "value": 100.0, + "unit": "US Dollar", + "scale": "Millions", + } + ] + result = pivot_table_data(records, country=None, limit=0, metadata={}) + assert isinstance(result, pd.DataFrame) + + +class TestPivotTableModeExtras: + """Tests targeting specific branches in pivot_table_mode.""" + + @staticmethod + def _base_df(rows: list[dict[str, Any]]) -> pd.DataFrame: + """Same defaults as TestPivotTableMode._base_df.""" + defaults = { + "title": "", + "level": 0, + "order": 0.0, + "country": "USA", + "country_code": "USA", + "date": "2020-01-01", + "value": float("nan"), + "unit": "", + "scale": "", + "is_category_header": False, + "hierarchy_node_id": None, + "parent_id": None, + "indicator_code": None, + "symbol": "S", + } + out = [] + for r in rows: + d = dict(defaults) + d.update(r) + out.append(d) + return pd.DataFrame(out) + + def test_parent_lookup_via_suffix_pattern(self): + """Parent_id matched via '___pid' suffix lookup branch.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Parent", + "is_category_header": True, + "hierarchy_node_id": "PFX___N1", + "parent_id": None, + }, + { + "order": 2.0, + "level": 1, + "title": "Child", + "hierarchy_node_id": "PFX___N2", + "parent_id": "N1", # No direct match; suffix lookup matches PFX___N1 + "value": 1.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "C", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Parent", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + def test_indicator_code_fallback_title(self): + """When title is empty, indicator_code drives the raw_title path.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Parent", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 5.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "gross_domestic_product", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Parent", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + def test_parent_unit_scale_inheritance_via_traversal(self): + """A grandchild inherits unit/scale from grandparent via traversal.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Root", + "is_category_header": True, + "hierarchy_node_id": "r", + "unit": "US Dollar", + "scale": "Millions", + }, + { + "order": 2.0, + "level": 1, + "title": "Mid", + "is_category_header": True, + "hierarchy_node_id": "m", + "parent_id": "r", + }, + { + "order": 3.0, + "level": 2, + "title": "Leaf", + "hierarchy_node_id": "leaf", + "parent_id": "m", + "value": 7.0, + "indicator_code": "L", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Root", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + def test_first_level0_data_matches_hierarchy_name(self): + """If first level-0 title matches hierarchy_name, no extra table header is added.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Trade", + "is_category_header": False, + "hierarchy_node_id": "n1", + "value": 3.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "TR", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Trade", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + titles = [idx[0] for idx in result.index] + assert not any(t.strip() == "TRADE" for t in titles) + + def test_bop_suffix_only_header_skipped(self): + """Headers whose title is a BOP-only suffix are skipped.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Real Header", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Credit", + "is_category_header": True, + "hierarchy_node_id": "n2", + "parent_id": "n1", + }, + { + "order": 3.0, + "level": 2, + "title": "Real Header, Goods", + "hierarchy_node_id": "n3", + "parent_id": "n2", + "value": 5.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "X", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Real Header", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + titles = [idx[0] for idx in result.index] + assert not any(t.strip().endswith("Credit") and "โ–ธ" in t for t in titles) + + def test_financial_corporations_prefix_strip(self): + """Header titles starting with 'Financial corporations, ' have that prefix stripped.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Financial corporations, Survey", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Financial corporations, Survey, Detail", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 2.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "X", + }, + ] + ) + meta = { + "table": {"hierarchy_name": "Financial corporations", "dataflow_id": "BOP"} + } + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + titles = [idx[0] for idx in result.index] + assert any("Survey" in t for t in titles) + + def test_depository_corporations_prefix_strip(self): + """Header titles starting with 'Depository corporations, ' have prefix stripped.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Depository corporations, Items", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Depository corporations, Items, Specific", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 2.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "X", + }, + ] + ) + meta = { + "table": {"hierarchy_name": "Depository corporations", "dataflow_id": "BOP"} + } + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + titles = [idx[0] for idx in result.index] + assert any("Items" in t for t in titles) + + def test_paren_unit_suffix_stripped_in_pivot(self): + """A trailing parenthetical unit suffix is captured separately.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Indicator (US Dollar)", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Detail, Transactions", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 1.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "D", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Indicator", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + def test_bop_credit_promotion_to_ancestor_title(self): + """A child Credit row is rewritten to use its ancestor title as base.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Goods Total", + "is_category_header": True, + "hierarchy_node_id": "h0", + }, + { + "order": 2.0, + "level": 1, + "title": "Goods Total, Credit", + "hierarchy_node_id": "h1", + "parent_id": "h0", + "value": 1.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "X", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Goods Total", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + def test_two_dates_with_limit(self): + """Two dates with limit pivots correctly through pivot_table_data.""" + records = [ + { + "title": "Trade", + "country": "USA", + "country_code": "USA", + "date": "2020-01-01", + "value": 0.0, + "level": 0, + "order": 1.0, + "is_category_header": True, + "hierarchy_node_id": "n1", + "parent_id": None, + "indicator_code": None, + "unit": "", + "scale": "", + "symbol": "S", + }, + { + "title": "Trade, Goods", + "country": "USA", + "country_code": "USA", + "date": "2020-01-01", + "value": 5.0, + "level": 1, + "order": 2.0, + "is_category_header": False, + "hierarchy_node_id": "n2", + "parent_id": "n1", + "indicator_code": "TRD_G", + "unit": "US Dollar", + "scale": "Millions", + "symbol": "S", + }, + { + "title": "Trade, Goods", + "country": "USA", + "country_code": "USA", + "date": "2021-01-01", + "value": 6.0, + "level": 1, + "order": 2.0, + "is_category_header": False, + "hierarchy_node_id": "n2", + "parent_id": "n1", + "indicator_code": "TRD_G", + "unit": "US Dollar", + "scale": "Millions", + "symbol": "S", + }, + ] + meta = {"table": {"hierarchy_name": "Trade", "dataflow_id": "BOP"}} + result = pivot_table_data(records, country="USA", limit=2, metadata=meta) + assert isinstance(result, pd.DataFrame) + + def test_counterpart_country_individual_only(self): + """All counterpart countries are individual (no group code).""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Trade", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Bilateral", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 5.0, + "unit": "US Dollar", + "scale": "Millions", + "counterpart_country_code": "GBR", + "counterpart_country": "United Kingdom", + "indicator_code": "B", + "series_id": "s1", + }, + { + "order": 2.0, + "level": 1, + "title": "Bilateral", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 3.0, + "unit": "US Dollar", + "scale": "Millions", + "counterpart_country_code": "DEU", + "counterpart_country": "Germany", + "indicator_code": "B", + "series_id": "s2", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Trade", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + titles = [idx[0] for idx in result.index] + assert any("United Kingdom" in t or "Germany" in t for t in titles) + + def test_counterpart_country_all_zero_skipped(self): + """Zero-only counterpart country values are skipped.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Trade", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Bilateral", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 0.0, + "unit": "US Dollar", + "scale": "Millions", + "counterpart_country_code": "G001", + "counterpart_country": "World", + "indicator_code": "B", + "series_id": "s1", + }, + { + "order": 2.0, + "level": 1, + "title": "Bilateral", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 0.0, + "unit": "US Dollar", + "scale": "Millions", + "counterpart_country_code": "G002", + "counterpart_country": "Europe", + "indicator_code": "B", + "series_id": "s2", + }, + { + "order": 2.0, + "level": 1, + "title": "Bilateral", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 0.0, + "unit": "US Dollar", + "scale": "Millions", + "counterpart_country_code": "GBR", + "counterpart_country": "United Kingdom", + "indicator_code": "B", + "series_id": "s3", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Trade", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + titles = [idx[0] for idx in result.index] + assert all("Europe" not in t for t in titles) + + def test_no_symbol_column_fallback(self): + """When no dim_code columns and no symbol, the loop just skips.""" + df = pd.DataFrame( + [ + { + "title": "GDP", + "level": 0, + "order": 1.0, + "country": "USA", + "country_code": "USA", + "date": "2020-01-01", + "value": 5.0, + "unit": "", + "scale": "", + "is_category_header": False, + "hierarchy_node_id": "n1", + "parent_id": None, + "indicator_code": "GDP", + "symbol": "S", + } + ] + ) + meta = {"table": {"hierarchy_name": "GDP", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + def test_isora_topic_indicator(self): + """ISORA dataflow with 'INDICATORS BY TOPIC' in title keeps the header.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "INDICATORS BY TOPIC", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Detail value", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 7.0, + "unit": "Percent", + "scale": "Units", + "indicator_code": "X", + }, + ] + ) + meta = { + "table": { + "hierarchy_name": "INDICATORS BY TOPIC", + "dataflow_id": "OTHER", + } + } + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + titles = [idx[0] for idx in result.index] + assert any("INDICATORS BY TOPIC" in t for t in titles) + + def test_dim_value_in_title_only_uses_iterrows_path(self): + """Test that title without unit/scale parses from title for an order with NaN unit.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Population (Millions)", + "is_category_header": False, + "hierarchy_node_id": "n1", + "value": 100.0, + "indicator_code": "POP", + "unit": "", + "scale": "", + } + ] + ) + meta = {"table": {"hierarchy_name": "Population", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + +class TestPivotIndicatorModeExtras: + """Extra tests for pivot_indicator_mode branches.""" + + def test_no_unit_no_scale_parsed_from_title(self): + """When unit/scale are missing, they get parsed from title.""" + df = pd.DataFrame( + [ + { + "title": "GDP (US Dollar, Millions)", + "country": "USA", + "date": "2020-01-01", + "value": 1.0, + "unit": None, + "scale": None, + } + ] + ) + result = pivot_indicator_mode(df, ["2020-01-01"], ["USA"]) + assert not result.empty + + def test_missing_date_value(self): + """A date with no value is stored as None.""" + df = pd.DataFrame( + [ + { + "title": "GDP", + "country": "USA", + "date": "2020-01-01", + "value": 5.0, + "unit": "U", + "scale": "S", + }, + ] + ) + result = pivot_indicator_mode(df, ["2020-01-01", "2021-01-01"], ["USA"]) + assert not result.empty + + +class TestExtractUnitFromLabelExtra: + """Additional tests for extract_unit_from_label edge cases.""" + + def test_paren_with_no_leading_space_at_zero(self): + """A label that is exactly '(X)' has paren_start == 0 and returns None via the path.""" + assert extract_unit_from_label("(Percent)") is None + + def test_comma_split_no_second_part(self): + """No comma at all means rsplit returns one part.""" + assert extract_unit_from_label("Population") is None + + +class TestPivotTableModeMoreBranches: + """More branch tests for pivot_table_mode.""" + + @staticmethod + def _base_df(rows: list[dict[str, Any]]) -> pd.DataFrame: + """Default row builder with NaN value default.""" + defaults = { + "title": "", + "level": 0, + "order": 0.0, + "country": "USA", + "country_code": "USA", + "date": "2020-01-01", + "value": float("nan"), + "unit": "", + "scale": "", + "is_category_header": False, + "hierarchy_node_id": None, + "parent_id": None, + "indicator_code": None, + "symbol": "S", + } + out = [] + for r in rows: + d = dict(defaults) + d.update(r) + out.append(d) + return pd.DataFrame(out) + + def test_header_with_own_data_and_dim_groups(self): + """A parent order that has own data AND multiple sector dim groups.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Indicator", + "is_category_header": True, + "hierarchy_node_id": "n1", + "value": 100.0, + "unit": "US Dollar", + "scale": "Millions", + "sector_code": "S1", + "sector": "Households", + "indicator_code": "X", + "series_id": "a", + }, + { + "order": 1.0, + "level": 0, + "title": "Indicator", + "is_category_header": True, + "hierarchy_node_id": "n1", + "value": 200.0, + "unit": "US Dollar", + "scale": "Millions", + "sector_code": "S2", + "sector": "Firms", + "indicator_code": "X", + "series_id": "b", + }, + { + "order": 2.0, + "level": 1, + "title": "Indicator, Sub", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 50.0, + "unit": "US Dollar", + "scale": "Millions", + "sector_code": "S1", + "sector": "Households", + "indicator_code": "Y", + "series_id": "c", + }, + { + "order": 2.0, + "level": 1, + "title": "Indicator, Sub", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 60.0, + "unit": "US Dollar", + "scale": "Millions", + "sector_code": "S2", + "sector": "Firms", + "indicator_code": "Y", + "series_id": "d", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Indicator", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + def test_multi_dim_non_uniform_unit_scale(self): + """A multi-dim order with mixed units/scales pulls suffix from first dr.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Parent", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Item", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 1.0, + "unit": "Percent", + "scale": "Units", + "sector_code": "S1", + "sector": "A", + "indicator_code": "X", + "series_id": "p1", + }, + { + "order": 2.0, + "level": 1, + "title": "Item", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 2.0, + "unit": "US Dollar", + "scale": "Millions", + "sector_code": "S2", + "sector": "B", + "indicator_code": "X", + "series_id": "p2", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Parent", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + def test_isora_underscored_promoted_title_continues(self): + """An ISORA title with '___' is skipped.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "1. INDICATORS BY TOPIC", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "internal___thing", + "is_category_header": True, + "hierarchy_node_id": "n2", + "parent_id": "n1", + }, + { + "order": 3.0, + "level": 2, + "title": "Detail", + "hierarchy_node_id": "n3", + "parent_id": "n2", + "value": 7.0, + "unit": "Percent", + "scale": "Units", + "indicator_code": "Y", + }, + ] + ) + meta = { + "table": { + "hierarchy_name": "1. INDICATORS BY TOPIC", + "dataflow_id": "ISORA", + } + } + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + def test_bop_header_skipped_via_skipped_parent(self): + """A BOP-only header gets tracked as skipped parent.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Trade", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Credit", # BOP-only header + "is_category_header": True, + "hierarchy_node_id": "n2", + "parent_id": "n1", + "indicator_code": "CRD", + }, + { + "order": 3.0, + "level": 2, + "title": "Trade, Credit, Goods", + "hierarchy_node_id": "n3", + "parent_id": "n2", + "value": 5.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "X", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Trade", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + def test_bop_credit_promotion_with_meaningful_ancestor(self): + """A child Credit row picks up the ancestor header that starts with its base.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Goods Trade", + "is_category_header": True, + "hierarchy_node_id": "h0", + }, + { + "order": 2.0, + "level": 1, + "title": "Goods, Credit", + "hierarchy_node_id": "h1", + "parent_id": "h0", + "value": 1.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "X", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Goods Trade", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + def test_counterpart_country_individual_zero_skipped(self): + """Individual counterpart with all-zero values is skipped.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Trade", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Bilateral", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 1.0, + "unit": "US Dollar", + "scale": "Millions", + "counterpart_country_code": "GBR", + "counterpart_country": "United Kingdom", + "indicator_code": "B", + "series_id": "s1", + }, + { + "order": 2.0, + "level": 1, + "title": "Bilateral", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 0.0, + "unit": "US Dollar", + "scale": "Millions", + "counterpart_country_code": "DEU", + "counterpart_country": "Germany", + "indicator_code": "B", + "series_id": "s2", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Trade", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + titles = [idx[0] for idx in result.index] + assert any("United Kingdom" in t for t in titles) + + def test_bop_skipped_parent_with_underscores(self): + """Skipped BOP parent with '___' in node_id triggers the suffix-split branch.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Trade", + "is_category_header": True, + "hierarchy_node_id": "prefix___n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Credit", + "is_category_header": True, + "hierarchy_node_id": "PFX___N2", + "parent_id": "prefix___n1", + "indicator_code": None, + }, + { + "order": 3.0, + "level": 2, + "title": "Trade, Credit, Goods", + "hierarchy_node_id": "X___N3", + "parent_id": "PFX___N2", + "value": 5.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "X", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Trade", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + def test_bop_skipped_parent_only_indicator_code(self): + """A skipped header with only indicator_code (no node id) is tracked.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Trade", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Credit", + "is_category_header": True, + "hierarchy_node_id": None, + "parent_id": "n1", + "indicator_code": "CRED_IND", + }, + { + "order": 3.0, + "level": 2, + "title": "Trade, Credit, Goods", + "hierarchy_node_id": "n3", + "parent_id": "CRED_IND", + "value": 5.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "X", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Trade", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + def test_get_sort_value_non_numeric(self): + """get_sort_value catches non-numeric values via try/except.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Trade", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Bilateral", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": "not-a-number", + "unit": "US Dollar", + "scale": "Millions", + "counterpart_country_code": "GBR", + "counterpart_country": "United Kingdom", + "indicator_code": "B", + "series_id": "s1", + }, + { + "order": 2.0, + "level": 1, + "title": "Bilateral", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": "also-not-numeric", + "unit": "US Dollar", + "scale": "Millions", + "counterpart_country_code": "G001", + "counterpart_country": "World", + "indicator_code": "B", + "series_id": "s2", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Trade", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + +class TestHierarchyContextMore: + """Targeted branch coverage for HierarchyContext methods.""" + + def test_break_on_lower_level_sibling_lookup(self): + """_get_true_siblings breaks once a lower-level row is encountered after target.""" + ctx = HierarchyContext( + [ + (1.0, "ParentA", 0, True), + (2.0, "A1", 1, False), + (3.0, "A2", 1, False), + (4.0, "ParentB", 0, True), + (5.0, "B1", 1, False), + ] + ) + sibs = ctx._get_true_siblings(2.0, 1) + titles = {t for _, t in sibs} + assert titles == {"A1", "A2"} + + def test_best_suffix_repeats_level_skipped(self): + """A second ancestor at the same level is skipped (line 697).""" + ctx = HierarchyContext( + [ + (1.0, "Older Special", 0, True), + (2.0, "Closer Special", 0, True), + (3.0, "X, Closer Special", 1, False), + ] + ) + result = ctx.find_best_suffix(3.0, "X, Closer Special") + assert result == ", Closer Special" + + def test_best_suffix_net_acq_branch(self): + """'Net acquisition of financial assets' ancestor is preserved.""" + ctx = HierarchyContext( + [ + (1.0, "Net acquisition of financial assets", 0, True), + (2.0, "X, Net acquisition of financial assets", 1, False), + ] + ) + result = ctx.find_best_suffix(2.0, "X, Net acquisition of financial assets") + assert result == ", Net acquisition of financial assets" + + def test_ancestor_part_prefix_repeats_level_skipped(self): + """Repeat ancestor levels are skipped via levels_seen (line 778).""" + ctx = HierarchyContext( + [ + (1.0, "Older corporations", 0, True), + (2.0, "Financial corporations", 0, True), + (3.0, "Financial corporations, X", 1, False), + ] + ) + result = ctx.find_ancestor_part_prefix(3.0, "Financial corporations, X") + assert result == "Financial corporations, " + + def test_ancestor_part_prefix_liabilities_expansion(self): + """Liabilities ancestor expansion is exercised (lines 797-799).""" + ctx = HierarchyContext( + [ + (1.0, "Liabilities", 0, True), + (2.0, "Total liabilities, X", 1, False), + ] + ) + result = ctx.find_ancestor_part_prefix(2.0, "Total liabilities, X") + assert result == "Total liabilities, " + + def test_ancestor_part_prefix_normalized_dash(self): + """A normalized dash mismatch falls through to the normalized branch.""" + ctx = HierarchyContext( + [ + (1.0, "Financial-Corporations", 0, True), + (2.0, "Financial corporations, Detail", 1, False), + ] + ) + result = ctx.find_ancestor_part_prefix(2.0, "Financial corporations, Detail") + assert result == "Financial corporations, " + + def test_simplify_title_only_paren_strip(self): + """A title with only a unit paren -- no prefix/suffix to find.""" + ctx = HierarchyContext([(1.0, "Plain (Percent)", 0, False)]) + assert ctx.simplify_title(1.0, "Plain (Percent)") == "Plain" + + +class TestExtractUnitFromLabelMore: + """A few more targeted unit-extraction tests.""" + + def test_paren_with_percent_sign(self): + """A trailing '(%)' is detected.""" + assert extract_unit_from_label("Inflation (%)") == "%" + + +class TestPivotTableModeMoreEdgeCases: + """Additional pivot_table_mode edge cases.""" + + @staticmethod + def _base_df(rows: list[dict[str, Any]]) -> pd.DataFrame: + """Default builder with NaN value.""" + defaults = { + "title": "", + "level": 0, + "order": 0.0, + "country": "USA", + "country_code": "USA", + "date": "2020-01-01", + "value": float("nan"), + "unit": "", + "scale": "", + "is_category_header": False, + "hierarchy_node_id": None, + "parent_id": None, + "indicator_code": None, + "symbol": "S", + } + out = [] + for r in rows: + d = dict(defaults) + d.update(r) + out.append(d) + return pd.DataFrame(out) + + def test_parent_lookup_break_when_missing(self): + """An unresolvable parent_id chain breaks the parent traversal loop.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Trade", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Trade, Goods", + "hierarchy_node_id": "n2", + "parent_id": "completely_missing_id", + "value": 5.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "X", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Trade", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + def test_unit_like_transformation_filtered(self): + """A TYPE_OF_TRANSFORMATION dim with a unit-like value is filtered from label.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Trade", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Detail", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 5.0, + "unit": "US Dollar", + "scale": "Millions", + "type_of_transformation_code": "USD", + "type_of_transformation": "US dollar", + "sector_code": "S1", + "sector": "Households", + "indicator_code": "X", + "series_id": "p1", + }, + { + "order": 2.0, + "level": 1, + "title": "Detail", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 7.0, + "unit": "US Dollar", + "scale": "Millions", + "type_of_transformation_code": "USD", + "type_of_transformation": "US dollar", + "sector_code": "S2", + "sector": "Firms", + "indicator_code": "X", + "series_id": "p2", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Trade", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + def test_isora_underscored_promoted_title(self): + """An ISORA promoted header with '___' in title is skipped (line 1714).""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "1. Topic", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Abc___Xyz", + "is_category_header": True, + "hierarchy_node_id": "n2", + "parent_id": "n1", + }, + { + "order": 3.0, + "level": 2, + "title": "Detail", + "hierarchy_node_id": "n3", + "parent_id": "n2", + "value": 7.0, + "unit": "Percent", + "scale": "Units", + "indicator_code": "Y", + }, + ] + ) + meta = { + "table": { + "hierarchy_name": "1. INDICATORS BY TOPIC", + "dataflow_id": "ISORA", + } + } + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + def test_track_skipped_parent_continues_for_empty(self): + """_track_skipped_parent_ids continues when a value is empty (line 1598).""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Trade", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Credit", + "is_category_header": True, + "hierarchy_node_id": None, + "parent_id": "n1", + "indicator_code": None, + }, + { + "order": 3.0, + "level": 2, + "title": "Trade, Credit, Goods", + "hierarchy_node_id": "n3", + "parent_id": "n2", + "value": 5.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "X", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Trade", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + def test_promote_level_break_on_missing_parent(self): + """_promote_level_if_parent_skipped breaks when lookup fails (line 1622).""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Trade", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Credit", + "is_category_header": True, + "hierarchy_node_id": "n2", + "parent_id": "missing_parent", + "indicator_code": "C", + }, + { + "order": 3.0, + "level": 2, + "title": "Trade, Credit, Goods", + "hierarchy_node_id": "n3", + "parent_id": "n2", + "value": 5.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "X", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Trade", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + def test_prune_deeper_headers(self): + """When level drops, deeper levels' last_meaningful_header is pruned.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Root", + "is_category_header": True, + "hierarchy_node_id": "r", + }, + { + "order": 2.0, + "level": 1, + "title": "Mid", + "is_category_header": True, + "hierarchy_node_id": "m", + "parent_id": "r", + }, + { + "order": 3.0, + "level": 2, + "title": "Mid leaf", + "hierarchy_node_id": "ml", + "parent_id": "m", + "value": 1.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "X", + }, + { + "order": 4.0, + "level": 0, + "title": "OtherRoot", + "is_category_header": True, + "hierarchy_node_id": "r2", + }, + { + "order": 5.0, + "level": 1, + "title": "OtherRoot, sub", + "hierarchy_node_id": "r2s", + "parent_id": "r2", + "value": 2.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "Y", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Root", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + def test_paren_unit_suffix_kept_for_promoted_header(self): + """A promoted header carrying parenthetical unit applies original_unit_suffix.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Indicator (Percent of GDP)", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Indicator, X", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 5.0, + "unit": "Percent", + "scale": "Units", + "indicator_code": "X", + }, + { + "order": 3.0, + "level": 1, + "title": "Indicator, Y", + "hierarchy_node_id": "n3", + "parent_id": "n1", + "value": 10.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "Y", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Indicator", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + def test_dim_dedup_same_grouping_key(self): + """When two raw rows share the same grouping_key, the later is appended (1562-1565).""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Parent", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Item", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 1.0, + "unit": "US Dollar", + "scale": "Millions", + "sector_code": "S1", + "sector": "A", + "type_of_transformation_code": "T1", + "type_of_transformation": "Type1", + "indicator_code": "X", + "series_id": "p1", + }, + { + "order": 2.0, + "level": 1, + "title": "Item", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 2.0, + "unit": "US Dollar", + "scale": "Millions", + "sector_code": "S1", + "sector": "A", + "type_of_transformation_code": "T1", + "type_of_transformation": "Type1", + "indicator_code": "X", + "series_id": "p2", + }, + { + "order": 2.0, + "level": 1, + "title": "Item", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 3.0, + "unit": "US Dollar", + "scale": "Millions", + "sector_code": "S2", + "sector": "B", + "type_of_transformation_code": "T2", + "type_of_transformation": "Type2", + "indicator_code": "X", + "series_id": "p3", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Parent", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + def test_bop_promotion_to_ancestor_via_meaningful_header(self): + """BOP suffix child finds a non-BOP ancestor header and rewrites itself.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Goods Trade Account", + "is_category_header": True, + "hierarchy_node_id": "h0", + }, + { + "order": 2.0, + "level": 1, + "title": "Goods, Credit", + "hierarchy_node_id": "h1", + "parent_id": "h0", + "value": 1.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "X", + }, + { + "order": 3.0, + "level": 1, + "title": "Goods, Debit", + "hierarchy_node_id": "h2", + "parent_id": "h0", + "value": 2.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "Y", + }, + ] + ) + meta = { + "table": {"hierarchy_name": "Goods Trade Account", "dataflow_id": "BOP"} + } + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + def test_raw_row_unit_scale_parsed_from_title(self): + """A row missing unit/scale picks them up from title parsing.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Trade", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Goods (US Dollar, Millions)", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 5.0, + "unit": "", + "scale": "", + "indicator_code": "X", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Trade", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + def test_empty_order_df_skipped(self): + """If sorted_orders iterates a value not in df, the loop continues (line 1669).""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Trade", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Trade, Goods", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 5.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "TRD_G", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Trade", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + +class TestParseUnitAndScaleExtra: + """Additional tests for parse_unit_and_scale edge cases.""" + + def test_ratio_of_pattern(self): + """'Ratio of X' splits into unit Ratio and scale 'of X'.""" + unit, scale = parse_unit_and_scale("Ratio of GDP") + assert unit == "Ratio" + assert scale == "of Gdp" + + def test_number_of_pattern(self): + """'Number of X' splits into unit Number.""" + unit, scale = parse_unit_and_scale("Number of People") + assert unit == "Number" + assert scale == "of People" + + def test_index_of_pattern(self): + """'Index of X' splits into unit Index.""" + unit, scale = parse_unit_and_scale("Index of Prices") + assert unit == "Index" + assert scale == "of Prices" + + +class TestHierarchyContextEmptyAncestor: + """Edge cases for ancestor traversal with empty titles.""" + + def test_ancestor_part_prefix_skips_empty_title(self): + """An ancestor row with an empty title is skipped via continue.""" + ctx = HierarchyContext( + [ + (1.0, "", 0, True), + (2.0, "Top context", 1, True), + (3.0, "Top context, Detail", 2, False), + ] + ) + out = ctx.find_ancestor_part_prefix(3.0, "Top context, Detail") + assert out == "Top context, " + + def test_ancestor_part_prefix_financial_assets_alias(self): + """An ancestor containing 'Financial assets' adds the alias to ancestor_parts.""" + ctx = HierarchyContext( + [ + (1.0, "Holdings, Financial assets", 0, True), + (2.0, "Financial assets, Bonds", 1, False), + ] + ) + out = ctx.find_ancestor_part_prefix(2.0, "Financial assets, Bonds") + assert out == "Financial assets, " + + def test_ancestor_part_prefix_case_insensitive_match(self): + """A case-insensitive match returns the matched slice from target_title.""" + ctx = HierarchyContext( + [ + (1.0, "Other Investment", 0, True), + (2.0, "other investment, Loans", 1, False), + ] + ) + out = ctx.find_ancestor_part_prefix(2.0, "other investment, Loans") + assert out == "other investment, " + + def test_ancestor_part_prefix_case_insensitive_bop_remainder_skipped(self): + """A case-insensitive match whose remainder is BOP-only continues the loop.""" + ctx = HierarchyContext( + [ + (1.0, "Other Investment", 0, True), + (2.0, "other investment, Net", 1, False), + ] + ) + out = ctx.find_ancestor_part_prefix(2.0, "other investment, Net") + assert out is None + + +class TestSimplifyTitleExtraPaths: + """Additional paths through simplify_title.""" + + def test_part_prefix_strip_via_ancestor_part(self): + """An ancestor whose part matches strips via the part_prefix loop.""" + ctx = HierarchyContext( + [ + (1.0, "Z, Assets", 0, True), + (2.0, "Assets, X", 1, False), + ] + ) + assert ctx.simplify_title(2.0, "Assets, X") == "X" + + def test_sibling_strip_leaves_bop_only_restores(self): + """A sibling-prefix strip that leaves only 'Net' restores original title.""" + ctx = HierarchyContext( + [ + (1.0, "Foo, Bar, Net", 0, False), + (2.0, "Foo, Bar, X", 0, False), + (3.0, "Foo, Bar, Y", 0, False), + ] + ) + assert ctx.simplify_title(1.0, "Foo, Bar, Net") == "Foo, Bar, Net" + + def test_part_prefix_consumes_full_title_restores(self): + """A part_prefix strip that empties the title restores original.""" + ctx = HierarchyContext( + [ + (1.0, "Z, Assets", 0, True), + (2.0, "Assets, ", 1, False), + ] + ) + assert ctx.simplify_title(2.0, "Assets, ") == "Assets, " + + +class TestPivotTableModeCoverageGaps: + """Targeted pivot_table_mode coverage gaps.""" + + @staticmethod + def _base_df(rows: list[dict[str, Any]]) -> pd.DataFrame: + """Default row builder. + + Parameters + ---------- + rows : list[dict[str, Any]] + List of partial row dicts merged with defaults. + + Returns + ------- + pd.DataFrame + Built DataFrame. + """ + defaults = { + "title": "", + "level": 0, + "order": 0.0, + "country": "USA", + "country_code": "USA", + "date": "2020-01-01", + "value": float("nan"), + "unit": "", + "scale": "", + "is_category_header": False, + "hierarchy_node_id": None, + "parent_id": None, + "indicator_code": None, + "symbol": "S", + } + out = [] + for r in rows: + d = dict(defaults) + d.update(r) + out.append(d) + return pd.DataFrame(out) + + def test_country_without_data_for_order_skipped(self): + """A country lacking rows at a given order hits the country_df.empty continue.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Trade", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Trade, Goods", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 5.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "G", + "country": "USA", + "country_code": "USA", + }, + { + "order": 2.0, + "level": 1, + "title": "Trade, Goods", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 6.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "G", + "country": "CAN", + "country_code": "CAN", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Trade", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA", "CAN", "MEX"], meta) + assert isinstance(result, pd.DataFrame) + + def test_no_symbol_column_falls_back_to_empty(self): + """A DataFrame with neither dim_code cols nor symbol triggers the symbol-fallback line.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "GDP (Millions of US Dollar)", + "is_category_header": False, + "hierarchy_node_id": "n1", + "value": 10.0, + "indicator_code": "G", + }, + ] + ).drop(columns=["symbol", "indicator_code"]) + meta = {"table": {"hierarchy_name": "GDP", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + def test_unit_scale_parsed_from_title_fills_missing(self): + """A row without unit/scale parses them from the title.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Trade", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Trade, Goods (Millions, US Dollar)", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 5.0, + "indicator_code": "G", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Trade", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + def test_format_dim_labels_empty_grouping_key_returns_empty(self): + """An empty grouping_key yields an empty dim label string.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Top", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Top, A", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 1.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "A", + "sector_code": "S1", + "sector": "AA", + "series_id": "a", + }, + { + "order": 2.0, + "level": 1, + "title": "Top, B", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 2.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "B", + "sector_code": "S2", + "sector": "BB", + "series_id": "b", + }, + { + "order": 2.0, + "level": 1, + "title": "Top, C", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 3.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "C", + "series_id": "c", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Top", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + def test_unit_like_transformation_filtered_from_labels(self): + """A unit-like TYPE_OF_TRANSFORMATION value is filtered from dim labels.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Top", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Top, Item", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 1.0, + "unit": "US Dollar", + "scale": "Millions", + "type_of_transformation_code": "T1", + "type_of_transformation": "US dollar", + "indicator_code": "A", + "series_id": "a", + }, + { + "order": 2.0, + "level": 1, + "title": "Top, Item", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 2.0, + "unit": "US Dollar", + "scale": "Millions", + "type_of_transformation_code": "T2", + "type_of_transformation": "Euro", + "indicator_code": "A", + "series_id": "b", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Top", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + def test_track_skipped_with_empty_string_node_id(self): + """A bop-only header with empty hierarchy_node_id continues past the empty value.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Trade", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Credit", + "is_category_header": True, + "hierarchy_node_id": "", + "parent_id": "n1", + "indicator_code": "CRED", + }, + { + "order": 3.0, + "level": 2, + "title": "Trade, Credit, Goods", + "hierarchy_node_id": "n3", + "parent_id": "CRED", + "value": 5.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "X", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Trade", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + def test_data_row_best_suffix_stripped(self): + """A data row whose title ends with an ancestor's name strips the suffix.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Survey Metric", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Detail, Survey Metric", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 5.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "D", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Survey Metric", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + def test_data_row_part_prefix_stripped(self): + """A data row whose title starts with an ancestor part strips the prefix.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Z, Trade total", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Trade total, Detail", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 5.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "D", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Z, Trade total", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + def test_bop_promotion_skips_empty_and_bop_ancestor_levels(self): + """BOP promotion walks past empty and bop-ending ancestor levels to a meaningful one.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Top", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Top, Sub, Net", + "is_category_header": True, + "hierarchy_node_id": "n2", + "parent_id": "n1", + }, + { + "order": 3.0, + "level": 2, + "title": "Top, Sub, Net, Detail", + "hierarchy_node_id": "n3", + "parent_id": "n2", + "value": 1.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "D", + }, + { + "order": 4.0, + "level": 3, + "title": "Top, Sub, Net, Detail, Credit", + "hierarchy_node_id": "n4", + "parent_id": "n3", + "value": 2.0, + "unit": "US Dollar", + "scale": "Millions", + "indicator_code": "E", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Top", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) + + def test_multi_dim_no_counterpart_with_empty_grouping_row(self): + """A row at a multi-dim order with no dim values falls into the empty-grouping branch.""" + df = self._base_df( + [ + { + "order": 1.0, + "level": 0, + "title": "Top", + "is_category_header": True, + "hierarchy_node_id": "n1", + }, + { + "order": 2.0, + "level": 1, + "title": "Top, Item", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 1.0, + "unit": "US Dollar", + "scale": "Millions", + "sector_code": "S1", + "sector": "A", + "indicator_code": "X", + "series_id": "a", + }, + { + "order": 2.0, + "level": 1, + "title": "Top, Item", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 2.0, + "unit": "US Dollar", + "scale": "Millions", + "sector_code": "S2", + "sector": "B", + "indicator_code": "X", + "series_id": "b", + }, + { + "order": 2.0, + "level": 1, + "title": "Top, Item", + "hierarchy_node_id": "n2", + "parent_id": "n1", + "value": 3.0, + "unit": "US Dollar", + "scale": "Millions", + "sector_code": None, + "sector": None, + "indicator_code": "X", + "series_id": "c", + }, + ] + ) + meta = {"table": {"hierarchy_name": "Top", "dataflow_id": "BOP"}} + result = pivot_table_mode(df, ["2020-01-01"], ["USA"], meta) + assert isinstance(result, pd.DataFrame) diff --git a/openbb_platform/providers/imf/tests/test_views_maritime_chokepoint_info.py b/openbb_platform/providers/imf/tests/test_views_maritime_chokepoint_info.py new file mode 100644 index 00000000000..5fdce0fae9d --- /dev/null +++ b/openbb_platform/providers/imf/tests/test_views_maritime_chokepoint_info.py @@ -0,0 +1,98 @@ +"""Tests for ``openbb_imf.views.maritime_chokepoint_info``.""" + +# ruff: noqa: I001 + +import pytest + +from openbb_imf.models.maritime_chokepoint_info import ImfMaritimeChokePointInfoData +from openbb_imf.views.maritime_chokepoint_info import plot_chokepoint_annual_avg_vessels + + +def _row( + name: str, total: int, lat: float = 0.0, lon: float = 0.0 +) -> ImfMaritimeChokePointInfoData: + """Build a populated chokepoint data row.""" + return ImfMaritimeChokePointInfoData.model_validate( + { + "chokepoint_code": name, + "name": name, + "latitude": lat, + "longitude": lon, + "vessel_count_total": total, + "vessel_count_tanker": total // 5, + "vessel_count_container": total // 4, + "vessel_count_general_cargo": total // 6, + "vessel_count_dry_bulk": total // 3, + "vessel_count_roro": total // 10, + "industry_top1": "Trade", + "industry_top2": "Energy", + "industry_top3": "Manufacturing", + } + ) + + +class TestPlotChokepointAnnualAvgVessels: + """Tests for ``plot_chokepoint_annual_avg_vessels``.""" + + def test_light_theme_returns_figure_with_two_traces(self): + """Default light theme builds a 2-trace (Scattergeo + Table) figure.""" + data = [ + _row("Suez", 500, lat=30.0, lon=32.0), + _row("Panama", 300, lat=9.0, lon=-79.0), + ] + fig = plot_chokepoint_annual_avg_vessels(data) + assert len(fig.data) == 2 + types = {t.type for t in fig.data} + assert types == {"scattergeo", "table"} + assert "Annual Average Vessels" in fig.layout.title.text + + def test_dark_theme_changes_paper_color(self): + """Dark theme switches the paper background to the dark palette.""" + data = [_row("Suez", 500), _row("Hormuz", 250)] + fig = plot_chokepoint_annual_avg_vessels(data, theme="dark") + assert fig.layout.paper_bgcolor.startswith("rgba(21") + + def test_odd_number_of_rows_uses_alternating_fill(self): + """An odd-length df still gets alternating row colors.""" + data = [_row("A", 100), _row("B", 90), _row("C", 80)] + fig = plot_chokepoint_annual_avg_vessels(data) + table_trace = next(t for t in fig.data if t.type == "table") + assert table_trace.cells.fill.color is not None + + def test_source_annotation_is_present(self): + """An ``IMF Port Watch`` source annotation appears on the layout.""" + data = [_row("Suez", 200)] + fig = plot_chokepoint_annual_avg_vessels(data) + annotations = [a.text for a in fig.layout.annotations] + assert any("IMF Port Watch" in (t or "") for t in annotations) + + def test_empty_data_raises(self): + """An empty list raises ``OpenBBError``.""" + from openbb_core.app.model.abstract.error import OpenBBError + + with pytest.raises(OpenBBError, match="No data to plot"): + plot_chokepoint_annual_avg_vessels([]) + + def test_wrong_type_raises(self): + """Non-list/non-model input raises ``OpenBBError``.""" + from openbb_core.app.model.abstract.error import OpenBBError + + with pytest.raises(OpenBBError, match="Invalid data format"): + plot_chokepoint_annual_avg_vessels([{"not": "a model"}]) # type: ignore[list-item] + + def test_charting_import_failure_raises_openbb_error(self, monkeypatch): + """A failure importing ``openbb_charting`` is wrapped in ``OpenBBError``.""" + import builtins + + from openbb_core.app.model.abstract.error import OpenBBError + + real_import = builtins.__import__ + + def fake_import(name, globals=None, locals=None, fromlist=(), level=0): + if name == "openbb_charting.core.openbb_figure": + raise ImportError("forced") + return real_import(name, globals, locals, fromlist, level) + + monkeypatch.setattr(builtins, "__import__", fake_import) + with pytest.raises(OpenBBError, match="Could not import Charting modules"): + plot_chokepoint_annual_avg_vessels([_row("Suez", 100)]) diff --git a/openbb_platform/providers/imf/tests/test_views_port_info.py b/openbb_platform/providers/imf/tests/test_views_port_info.py new file mode 100644 index 00000000000..18f014a2103 --- /dev/null +++ b/openbb_platform/providers/imf/tests/test_views_port_info.py @@ -0,0 +1,236 @@ +"""Tests for ``openbb_imf.views.port_info``.""" + +# ruff: noqa: I001 + +import pytest + +from openbb_imf.models.port_info import ImfPortInfoData +from openbb_imf.views.port_info import plot_port_info_map + + +def _row( + port_code: str, + *, + country: str = "United States", + continent: str = "North America", + lat: float = 35.0, + lon: float = -120.0, + total: int = 100, + share_import: float = 0.1, + share_export: float = 0.2, + industries: int = 3, +) -> ImfPortInfoData: + """Build a populated port-info row.""" + payload = { + "port_code": port_code, + "port_name": port_code, + "port_full_name": f"Port of {port_code}", + "country": country, + "country_code": "USA", + "continent": continent, + "latitude": lat, + "longitude": lon, + "vessel_count_total": total, + "vessel_count_tanker": max(total // 5, 1), + "vessel_count_container": max(total // 4, 1), + "vessel_count_general_cargo": max(total // 6, 1), + "vessel_count_dry_bulk": max(total // 3, 1), + "vessel_count_roro": max(total // 10, 1), + "share_country_maritime_import": share_import, + "share_country_maritime_export": share_export, + "industry_top1": "Energy" if industries >= 1 else None, + "industry_top2": "Trade" if industries >= 2 else None, + "industry_top3": "Manufacturing" if industries >= 3 else None, + } + return ImfPortInfoData.model_validate(payload) + + +class TestPlotPortInfoMap: + """Tests for ``plot_port_info_map``.""" + + def test_multi_continent_zooms_out(self): + """Multiple continents produce a global map (zoom = 0).""" + data = [ + _row("USNYC", continent="North America", lat=40.0, lon=-74.0, total=500), + _row( + "CNSHA", + country="China", + continent="Asia", + lat=31.0, + lon=121.0, + total=900, + ), + ] + fig = plot_port_info_map(data) + assert fig.layout.map.zoom == 0 + assert len(fig.data) == 1 + + def test_single_country_uses_share_for_marker_size(self): + """Single-country input uses import+export share for sizing.""" + data = [ + _row( + "USA1", + total=300, + share_import=0.05, + share_export=0.10, + lat=35.0, + lon=-120.0, + ), + _row( + "USA2", + total=500, + share_import=0.15, + share_export=0.25, + lat=33.0, + lon=-118.0, + ), + ] + fig = plot_port_info_map(data) + assert fig.layout.map.center.lat == pytest.approx(34.0) + assert fig.layout.map.zoom == 2 + + def test_single_country_uniform_share_falls_back_to_midsize(self): + """When all rows share identical import/export, the midsize marker is used.""" + data = [ + _row("US1", share_import=0.1, share_export=0.2, total=100), + _row("US2", share_import=0.1, share_export=0.2, total=200), + ] + fig = plot_port_info_map(data) + assert fig.data[0].marker is not None + + def test_single_continent_one_country_one_row(self): + """A 1-row input still renders successfully.""" + data = [_row("US1", total=100)] + fig = plot_port_info_map(data) + assert fig.data[0].lat[0] == pytest.approx(35.0) + assert fig.layout.map.center is not None + + def test_empty_list_raises(self): + """An empty list raises ``OpenBBError``.""" + from openbb_core.app.model.abstract.error import OpenBBError + + with pytest.raises(OpenBBError, match="No data to plot"): + plot_port_info_map([]) + + def test_wrong_type_raises(self): + """A non-model entry raises ``OpenBBError``.""" + from openbb_core.app.model.abstract.error import OpenBBError + + with pytest.raises(OpenBBError, match="Invalid data format"): + plot_port_info_map([{"x": 1}]) # type: ignore[list-item] + + def test_charting_import_failure_raises(self, monkeypatch): + """A failure importing the charting modules surfaces as ``OpenBBError``.""" + import builtins + + from openbb_core.app.model.abstract.error import OpenBBError + + real_import = builtins.__import__ + + def fake_import(name, globals=None, locals=None, fromlist=(), level=0): + if name == "openbb_charting.core.openbb_figure": + raise ImportError("forced") + return real_import(name, globals, locals, fromlist, level) + + monkeypatch.setattr(builtins, "__import__", fake_import) + with pytest.raises(OpenBBError, match="Could not import Charting modules"): + plot_port_info_map([_row("Test")]) + + def test_hover_html_includes_all_sections(self): + """The hover HTML carries name, traffic shares, vessel counts, industries.""" + data = [_row("MAIN", industries=3, share_import=0.1, share_export=0.2)] + fig = plot_port_info_map(data) + hover = fig.data[0].customdata[0][0] + assert "Port of MAIN" in hover + assert "Imports" in hover and "Exports" in hover + assert "Containers" in hover and "Tankers" in hover + assert "Top Industries" in hover + + def test_hover_html_skips_missing_shares(self): + """When share fields are absent, the traffic block is suppressed.""" + from numpy import nan + + from openbb_imf.views.port_info import plot_port_info_map # noqa: F401 + + rows = [ + _row("US1", share_import=nan, share_export=nan, total=200), + _row("US2", share_import=nan, share_export=nan, total=100), + ] + fig = plot_port_info_map(rows) + hover = fig.data[0].customdata[0][0] + assert "Imports" not in hover + assert "Exports" not in hover + + def test_hover_html_no_industries(self): + """When all industry fields are blank, the industries block is suppressed.""" + rows = [ + _row("US1", industries=0, share_import=0.0, share_export=0.0, total=300), + _row("US2", industries=0, share_import=0.0, share_export=0.0, total=400), + ] + fig = plot_port_info_map(rows) + hover = fig.data[0].customdata[0][0] + assert "Top Industries" not in hover + + def test_single_country_uniform_vessel_count_falls_back_to_min_size(self): + """Single country with identical share *and* vessel counts falls back to min size.""" + rows = [ + _row("US1", share_import=0.0, share_export=0.0, total=100), + _row("US2", share_import=0.0, share_export=0.0, total=100), + ] + fig = plot_port_info_map(rows) + assert fig.data[0].marker is not None + + def test_multi_country_uses_vessel_count_for_sizing(self): + """When ``country.nunique() > 1`` the helper uses vessel-count sizing.""" + data = [ + _row( + "US1", + country="United States", + continent="North America", + total=100, + lat=35.0, + lon=-120.0, + ), + _row( + "MX1", + country="Mexico", + continent="North America", + total=900, + lat=20.0, + lon=-100.0, + ), + ] + fig = plot_port_info_map(data) + assert fig.layout.map.zoom == 2 + + def test_zero_vessel_count_filtered_out(self): + """Rows with ``vessel_count_total == 0`` are filtered out by the ``query``.""" + data = [ + _row("LIVE", total=100, lat=35.0, lon=-120.0), + _row("DEAD", total=1, lat=10.0, lon=-50.0), + ] + fig = plot_port_info_map(data) + assert len(fig.data[0].lat) == 2 + + def test_multi_country_uniform_vessel_count_uses_min_size(self): + """Multi-country input with identical vessel counts falls into the min-size branch.""" + data = [ + _row( + "US1", + country="United States", + continent="North America", + total=100, + lat=35.0, + lon=-120.0, + ), + _row( + "MX1", + country="Mexico", + continent="North America", + total=100, + lat=20.0, + lon=-100.0, + ), + ] + fig = plot_port_info_map(data) + assert fig.data[0].marker is not None diff --git a/openbb_platform/providers/imf/uv.lock b/openbb_platform/providers/imf/uv.lock new file mode 100644 index 00000000000..4697a56e426 --- /dev/null +++ b/openbb_platform/providers/imf/uv.lock @@ -0,0 +1,3714 @@ +version = 1 +revision = 3 +requires-python = ">=3.10, <4" +resolution-markers = [ + "python_full_version >= '3.15' and sys_platform == 'win32'", + "python_full_version == '3.14.*' and sys_platform == 'win32'", + "python_full_version >= '3.15' and sys_platform == 'emscripten'", + "python_full_version == '3.14.*' and sys_platform == 'emscripten'", + "python_full_version >= '3.15' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.14.*' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.12' and python_full_version < '3.14' and 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