diff --git a/.github/workflows/black-check.yml b/.github/workflows/black-check.yml index 5347277..8a83fe6 100644 --- a/.github/workflows/black-check.yml +++ b/.github/workflows/black-check.yml @@ -5,53 +5,29 @@ on: branches: - main +concurrency: + group: black-check-${{ github.ref }} + cancel-in-progress: true + jobs: black-format: runs-on: ubuntu-latest steps: - name: Check out the repo - uses: actions/checkout@v3 + uses: actions/checkout@v7 - name: Set up Python - uses: actions/setup-python@v3 + uses: actions/setup-python@v7 with: - python-version: '3.x' - - - name: Install Black - run: pip install black - - - name: Run Black - run: | - black . --exclude "\.ipynb$" || exit 1 - - - name: Check if Black modified any files - id: check_black_changes - run: | - if [[ $(git status --porcelain) ]]; then - echo "true" > black_changed.txt - else - echo "false" > black_changed.txt - fi - - - name: Setup environment file - run: echo "BLACK_CHANGED=$(cat black_changed.txt)" >> $GITHUB_ENV + python-version: '3.13' + cache: 'pip' + cache-dependency-path: pyproject.toml - - name: setup git config + - name: Install dependencies run: | - git config user.name "GitHub Actions Bot" - git config user.email "<>" - - - name: Fetch all branches - run: git fetch --all + python -m pip install --upgrade pip + pip install -e ".[dev]" - - name: Checkout PR branch - run: git checkout "${{ github.head_ref }}" - - - name: Commit changes if modified by Black - if: env.BLACK_CHANGED == 'true' - run: | - rm black_changed.txt - git add . - git commit -m "files reformatted with black" - git push origin "${{ github.head_ref }}" --quiet --follow-tags \ No newline at end of file + - name: Check formatting with Black + run: black . --check --diff --exclude "\.ipynb$" diff --git a/.github/workflows/pylint.yml b/.github/workflows/pylint.yml index 3b1958b..dc13293 100644 --- a/.github/workflows/pylint.yml +++ b/.github/workflows/pylint.yml @@ -9,30 +9,19 @@ jobs: runs-on: ubuntu-latest steps: - name: Checkout code - uses: actions/checkout@v4 - + uses: actions/checkout@v7 + - name: Set up Python - uses: actions/setup-python@v3 + uses: actions/setup-python@v7 with: - python-version: '3.9' + python-version: '3.10' + cache: 'pip' + cache-dependency-path: pyproject.toml - name: Install dependencies run: | python -m pip install --upgrade pip - pip install -e . - pip install pylint + pip install -e ".[dev]" - name: Run pylint - run: | - pylint_output=$(pylint --rcfile=.pylintrc src/tatc --exit-zero) - if echo "$pylint_output" | grep -q 'Your code has been rated at'; then - score=$(echo "$pylint_output" | grep 'Your code has been rated at' | awk '{print $NF}' | cut -d'/' -f1) # Extract numeric part - if (( $(echo "$score < 9.0" | bc -l) )); then - echo "Found critical errors with score $score. Please fix and try again." - exit 1 - else - echo "No critical errors found. Score: $score" - fi - else - echo "No pylint output found. Passing the build." - fi \ No newline at end of file + run: pylint --rcfile=.pylintrc --fail-under=9.0 src/tatc diff --git a/.github/workflows/pypi-publish.yml b/.github/workflows/pypi-publish.yml new file mode 100644 index 0000000..13b3d29 --- /dev/null +++ b/.github/workflows/pypi-publish.yml @@ -0,0 +1,67 @@ +name: Publish to PyPI + +on: + release: + types: [published] + +jobs: + verify-version: + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v7 + + - name: Check release tag matches package version + env: + TAG: ${{ github.event.release.tag_name }} + run: | + PKG_VERSION=$(grep -oP '(?<=__version__ = ")[^"]+' src/tatc/__init__.py) + TAG_VERSION="${TAG#v}" + echo "Release tag: $TAG (version $TAG_VERSION)" + echo "Package version: $PKG_VERSION" + if [ "$PKG_VERSION" != "$TAG_VERSION" ]; then + echo "::error::Release tag $TAG (version $TAG_VERSION) does not match tatc.__version__ ($PKG_VERSION)" + exit 1 + fi + + build: + needs: verify-version + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v7 + + - name: Set up Python + uses: actions/setup-python@v7 + with: + python-version: '3.13' + + - name: Install build tooling + run: python -m pip install --upgrade build + + - name: Build sdist and wheel + run: python -m build + + - name: Upload distributions + uses: actions/upload-artifact@v4 + with: + name: dist + path: dist/ + + publish: + needs: [verify-version, build] + runs-on: ubuntu-latest + environment: + name: pypi + url: https://pypi.org/project/tatc/ + permissions: + id-token: write + steps: + - name: Download distributions + uses: actions/download-artifact@v4 + with: + name: dist + path: dist/ + + - name: Publish to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 diff --git a/.github/workflows/unit-test.yml b/.github/workflows/unit-test.yml index 26931e0..fcfd5b6 100644 --- a/.github/workflows/unit-test.yml +++ b/.github/workflows/unit-test.yml @@ -1,30 +1,37 @@ name: Run Python Unit Tests on: - push: + pull_request: branches: - - '*' + - main + +concurrency: + group: unit-test-${{ github.ref }} + cancel-in-progress: true jobs: test: runs-on: ubuntu-latest strategy: + fail-fast: false matrix: - python-version: ['3.8', '3.9', '3.10', '3.11', '3.12', '3.13'] + python-version: ['3.10', '3.11', '3.12', '3.13', '3.14'] steps: - name: Checkout code - uses: actions/checkout@v3 + uses: actions/checkout@v7 - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v3 + uses: actions/setup-python@v7 with: python-version: ${{ matrix.python-version }} + cache: 'pip' + cache-dependency-path: pyproject.toml - name: Install project in editable mode run: | pip install -e . - name: Run unit tests - run: python -m unittest \ No newline at end of file + run: python -m unittest diff --git a/.github/workflows/zenodo-doi.yml b/.github/workflows/zenodo-doi.yml new file mode 100644 index 0000000..b3f6869 --- /dev/null +++ b/.github/workflows/zenodo-doi.yml @@ -0,0 +1,134 @@ +name: Update Zenodo DOI + +# Zenodo's GitHub webhook integration automatically archives every published +# GitHub Release and mints a new DOI, but it does not touch this repo. This +# workflow polls Zenodo for that new deposit and opens a PR with the +# resulting version/date/DOI update to CITATION.cff (main is branch +# protected, so it cannot push directly). + +on: + release: + types: [published] + workflow_dispatch: + inputs: + tag: + description: "Release tag to look up (defaults to the latest release)" + required: false + +permissions: + contents: write + pull-requests: write + +jobs: + update-citation: + runs-on: ubuntu-latest + steps: + - name: Checkout main + uses: actions/checkout@v7 + with: + ref: main + + - name: Determine release tag + id: tag + env: + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + INPUT_TAG: ${{ github.event.inputs.tag }} + run: | + if [ -n "$INPUT_TAG" ]; then + TAG="$INPUT_TAG" + elif [ -n "${{ github.event.release.tag_name }}" ]; then + TAG="${{ github.event.release.tag_name }}" + else + TAG=$(gh release view --json tagName -q .tagName) + fi + echo "tag=$TAG" >> "$GITHUB_OUTPUT" + echo "version=${TAG#v}" >> "$GITHUB_OUTPUT" + + - name: Poll Zenodo for the matching deposit + id: zenodo + env: + ZENODO_TOKEN: ${{ secrets.ZENODO_TOKEN }} + TAG: ${{ steps.tag.outputs.tag }} + REPO_URL: https://github.com/${{ github.repository }}/tree/${{ steps.tag.outputs.tag }} + run: | + set +e + for i in $(seq 1 20); do + RESPONSE=$(curl -s -w '\n%{http_code}' -H "Authorization: Bearer ${ZENODO_TOKEN}" \ + "https://zenodo.org/api/deposit/depositions?status=published&sort=mostrecent&size=10") + HTTP_CODE=$(echo "$RESPONSE" | tail -n1) + BODY=$(echo "$RESPONSE" | sed '$d') + + if [ "$HTTP_CODE" = "401" ] || [ "$HTTP_CODE" = "403" ]; then + echo "Zenodo rejected the request (HTTP $HTTP_CODE) - check the ZENODO_TOKEN secret." >&2 + exit 1 + fi + + IS_ARRAY=$(echo "$BODY" | jq -e 'type == "array"' 2>/dev/null) + if [ "$HTTP_CODE" = "200" ] && [ "$IS_ARRAY" = "true" ]; then + MATCH=$(echo "$BODY" | jq -c --arg url "$REPO_URL" --arg tag "$TAG" ' + [.[] | select( + (.metadata.related_identifiers // [] | any(.identifier == $url)) + or (.title // "" | contains($tag)) + )][0]') + if [ "$MATCH" != "null" ] && [ -n "$MATCH" ]; then + echo "Found matching Zenodo deposit for $TAG" + echo "doi=$(echo "$MATCH" | jq -r '.doi')" >> "$GITHUB_OUTPUT" + echo "date=$(echo "$MATCH" | jq -r '.metadata.publication_date')" >> "$GITHUB_OUTPUT" + exit 0 + fi + else + echo "Unexpected response from Zenodo (HTTP $HTTP_CODE), retrying..." >&2 + fi + + echo "No matching Zenodo deposit yet for $TAG, retrying in 30s ($i/20)..." + sleep 30 + done + echo "Timed out waiting for Zenodo to archive $TAG" >&2 + exit 1 + + - name: Update CITATION.cff + env: + VERSION: ${{ steps.tag.outputs.version }} + DOI: ${{ steps.zenodo.outputs.doi }} + DATE: ${{ steps.zenodo.outputs.date }} + run: | + sed -i \ + -e "s/^version: .*/version: ${VERSION}/" \ + -e "s/^date-released: .*/date-released: ${DATE}/" \ + -e "s#^doi: .*#doi: ${DOI}#" \ + CITATION.cff + if ! grep -q "^doi:" CITATION.cff; then + echo "doi: ${DOI}" >> CITATION.cff + fi + cat CITATION.cff + + - name: Open pull request + env: + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + TAG: ${{ steps.tag.outputs.tag }} + VERSION: ${{ steps.tag.outputs.version }} + DOI: ${{ steps.zenodo.outputs.doi }} + run: | + if git diff --quiet -- CITATION.cff; then + echo "CITATION.cff already up to date." + exit 0 + fi + + BRANCH="zenodo-doi-${TAG}" + git config user.name "github-actions[bot]" + git config user.email "github-actions[bot]@users.noreply.github.com" + git checkout -B "$BRANCH" + git add CITATION.cff + git commit -m "chore: update CITATION.cff DOI for ${TAG}" + git push -f origin "$BRANCH" + + EXISTING_PR=$(gh pr list --head "$BRANCH" --state open --json number -q '.[0].number') + if [ -n "$EXISTING_PR" ]; then + echo "Updated existing PR #${EXISTING_PR}" + else + gh pr create \ + --base main \ + --head "$BRANCH" \ + --title "Update CITATION.cff DOI for ${TAG}" \ + --body "Automated update from the Zenodo deposit created for release ${TAG}: DOI \`${DOI}\`, version \`${VERSION}\`." + fi diff --git a/CHANGELOG.md b/CHANGELOG.md index 4402806..94aeb20 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,42 @@ # TAT-C Change Log +## 3.5.0 + +Major refactoring that focused on completing unit tests to approach full code coverage. Drops support for Python < 3.10 to improve compatibility with modern libraries. Replaces the `TwoLineElements` schema with `GeneralPerturbationsOrbit` to support modern orbit specifications including OMM CSV and JSON. Drops the CRS-based buffering approach to determine ground track in favor of geometric projections using the SPICE library. During refactoring, a few bug fixes and breaking changes were also made. + +Added: + - Added `GeosynchronousOrbit` orbit schema. + - Support for Python 3.14. + +Changed: + - Changed the default orbit epoch from `datetime.now()` to `2020-01-01T00:00:00Z`. + - Refactored all orbit schemas to inherit uniform getters from `OrbitBase`. + - Replaced `TwoLineElements` with a more general `GeneralPerturbationsOrbit` to accommodate post-TLE GP data formats. + - Improved `TundraOrbit` and `MolniyaOrbit` schemas to consider J2 perturbations when calculating orbit period. + - Fixed a bug where SPICE projected instrument footprints around the geocentric, rather than geodetic, pointing vector, leading to ~2 km positioning errors. + - Fixed `TrainConstellation` member right ascension of ascending node spacing to be based on a sidereal day, rather than a solar day. + - Fixed `SOCConstellation` member generation to use hexagonal spacing with a non-zero `relative_spacing` value. + - Fixed `MOGConstellation` member generation to use the mean anomaly of the reference orbit. + - Fixed a bug in `config.py` where default configurations (`defaults.yml`) were never provided in wheels. + - Fixed `collect_orbit_track` to only assign the `EPSG:4326` CRS to output when requesting WGS84 coordinates, rather than for all coordinate systems. + - Fixed `collect_orbit_track` to use a proper East/North/Up velocity when requesting WGS84 coordinates. + - Improved `collect_observations` to use the apogee altitude, rather than the initial altitude, to determine the maximum access duration. + - Fixed a bug where `collect_multi_observations` could crash on an empty satellite list. + - Fixed a bug in `grid_observations` and `grid_latencies` where spatial aggregation never actually worked. + - Fixed a bug in `compute_dop` where the latitude and longitude were reversed in output geometry. + - Improved `compute_dop` to use the nearest GP element to each time rather than the first one specified for an orbit. + - Fixed the definition of binormal unit vector in `ro_coverage.py` to accommodate eccentric orbits. + - Improved `collect_ro_observations` to interpolate among samples closest to target elevation. + - Improved performance of `collect_ro_observations` through vectorized profile sampling, a more direct interface to Skyfield for orbit track computation, and making tangent point velocity calculation optional. + - Fixed a bug where `collect_ro_observations` could use incorrect inertial positions for repeat track orbits more than 1 cycle after epoch. + - Added utility methods: `compute_apoapsis_radius` and `geodesic_distance`. + - Requires `setuptools >= 77.0.0` and switches `project.license` to an SPDX expression string to resolve a build metadata deprecation warning (issue #124). + +Removed + - Support for Python < 3.10. + - Removed the `TwoLineElements` orbit schema. + - Removed the legacy `crs` and `method` parameters from `collect_ground_track` and `compute_ground_track`; all instrument projection now uses the SPICE library. + ## 3.4.10 Added: diff --git a/README.md b/README.md index 5a330cf..105be0b 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,12 @@ # Tradespace Analysis Toolkit for Constellations (TAT-C) +[![PyPI](https://img.shields.io/pypi/v/tatc.svg)](https://pypi.org/project/tatc/) +[![Python Versions](https://img.shields.io/pypi/pyversions/tatc.svg)](https://pypi.org/project/tatc/) +[![Unit Tests](https://github.com/code-lab-org/tatc/actions/workflows/unit-test.yml/badge.svg)](https://github.com/code-lab-org/tatc/actions/workflows/unit-test.yml) +[![Documentation](https://readthedocs.org/projects/tatc/badge/?version=latest)](https://tatc.readthedocs.io) +[![License](https://img.shields.io/badge/license-BSD--3--Clause-blue.svg)](LICENSE) +[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.17363628.svg)](https://doi.org/10.5281/zenodo.17363628) + The Tradespace Analysis Toolkit for Constellations (TAT-C) provides low-level data structures and functions for systems engineering analysis and design of Earth-observing space missions suitable for pre-Phase A concept studies. @@ -10,7 +17,14 @@ Repository: [https://github.com/code-lab-org/tatc](https://github.com/code-lab-o ## Installation -TAT-C uses the pip build system to manage dependencies. Install the tatc library in "editable" mode: +TAT-C requires Python 3.10–3.14. Install the latest release from PyPI: +```shell +pip install tatc +``` + +### Development Installation + +To work with the source code, clone the repository and install the tatc library in "editable" mode: ```shell pip install -e . ``` @@ -57,6 +71,21 @@ This project uses the black code style, applied from the project root: black . ``` +Pull requests are also linted with pylint, which must score at least 9.0: +```shell +pylint --rcfile=.pylintrc src/tatc +``` + +## License + +This project is licensed under the BSD 3-Clause License — see +[LICENSE](LICENSE) for details. + +## Citation + +If you use TAT-C in your research, please cite it using the metadata in +[CITATION.cff](CITATION.cff), or the DOI badge above for the latest release. + ## Contact Paul T. Grogan @@ -73,10 +102,10 @@ Financial support is acknowledged under NASA grant numbers: NNX17AE06G, Current Project Team * PI: Paul T. Grogan - * I. Josue Tapia-Tamayo - * Suvan Kumar Project Alumni + * I. Josue Tapia-Tamayo + * Suvan Kumar * Isaac Feldman * Hayden Daly * Lindsay Portelli diff --git a/docs/api_reference/analysis/dop.rst b/docs/api_reference/analysis/dop.rst new file mode 100644 index 0000000..a223ae1 --- /dev/null +++ b/docs/api_reference/analysis/dop.rst @@ -0,0 +1,12 @@ +Dilution of Precision +====================== + +DOP Method +---------- +.. autoclass:: tatc.analysis.DopMethod + :members: + :undoc-members: + +Compute DOP +----------- +.. autofunction:: tatc.analysis.compute_dop diff --git a/docs/api_reference/analysis/index.rst b/docs/api_reference/analysis/index.rst index 4c631a9..f87f758 100644 --- a/docs/api_reference/analysis/index.rst +++ b/docs/api_reference/analysis/index.rst @@ -10,3 +10,5 @@ Analysis functions simulate mission operation to compute key performance metrics coverage track latency + dop + ro_coverage diff --git a/docs/api_reference/analysis/ro_coverage.rst b/docs/api_reference/analysis/ro_coverage.rst new file mode 100644 index 0000000..12d9370 --- /dev/null +++ b/docs/api_reference/analysis/ro_coverage.rst @@ -0,0 +1,6 @@ +Radio Occultation +================== + +Collect RO Observations +------------------------ +.. autofunction:: tatc.analysis.collect_ro_observations diff --git a/docs/api_reference/analysis/track.rst b/docs/api_reference/analysis/track.rst index bf96851..ee4a7a7 100644 --- a/docs/api_reference/analysis/track.rst +++ b/docs/api_reference/analysis/track.rst @@ -1,6 +1,18 @@ Ground and Orbit Track ====================== +Orbit Coordinate +----------------- +.. autoclass:: tatc.analysis.OrbitCoordinate + :members: + :undoc-members: + +Orbit Output +------------- +.. autoclass:: tatc.analysis.OrbitOutput + :members: + :undoc-members: + Collect Orbit Track ------------------- .. autofunction:: tatc.analysis.collect_orbit_track @@ -12,3 +24,7 @@ Collect Ground Track Compute Ground Track -------------------- .. autofunction:: tatc.analysis.compute_ground_track + +Collect Ground Pixels +---------------------- +.. autofunction:: tatc.analysis.collect_ground_pixels diff --git a/docs/api_reference/generation/index.rst b/docs/api_reference/generation/index.rst index 39bcd01..41a7da7 100644 --- a/docs/api_reference/generation/index.rst +++ b/docs/api_reference/generation/index.rst @@ -10,12 +10,17 @@ Points Fibonacci Lattice ----------------- -.. autofunction:: tatc.generation.generate_fibonacci_lattice_points +.. autofunction:: tatc.generation.generate_points_fibonacci_lattice Equally Spaced -------------- -.. autofunction:: tatc.generation.generate_equally_spaced_points +.. autofunction:: tatc.generation.generate_points_uniform_spacing + +Equal Angular Distance +---------------------- + +.. autofunction:: tatc.generation.generate_points_uniform_angular_distance Cells ===== @@ -23,4 +28,9 @@ Cells Equally Spaced -------------- -.. autofunction:: tatc.generation.generate_equally_spaced_cells +.. autofunction:: tatc.generation.generate_cells_uniform_spacing + +Equal Angular Spacing +--------------------- + +.. autofunction:: tatc.generation.generate_cells_uniform_angular_spacing diff --git a/docs/api_reference/schemas/index.rst b/docs/api_reference/schemas/index.rst index b85219a..6d0680b 100644 --- a/docs/api_reference/schemas/index.rst +++ b/docs/api_reference/schemas/index.rst @@ -8,7 +8,7 @@ TAT-C uses pydantic to specify schemas in a format compatible with JavaScript Ob .. toctree:: :maxdepth: 1 - ground + surface orbit space mission diff --git a/docs/api_reference/schemas/orbit.rst b/docs/api_reference/schemas/orbit.rst index c438b15..4e74ef7 100644 --- a/docs/api_reference/schemas/orbit.rst +++ b/docs/api_reference/schemas/orbit.rst @@ -1,11 +1,12 @@ Orbit Models ============ -Two Line Elements ------------------ +General Perturbations Orbit +--------------------------- -.. autopydantic_model:: tatc.schemas.TwoLineElements +.. autopydantic_model:: tatc.schemas.GeneralPerturbationsOrbit :members: + :inherited-members: BaseModel Circular Orbit -------------- @@ -21,6 +22,13 @@ Sun-synchronous Orbit :members: :inherited-members: BaseModel +Geosynchronous Orbit +--------------------- + +.. autopydantic_model:: tatc.schemas.GeosynchronousOrbit + :members: + :inherited-members: BaseModel + Keplerian Orbit --------------- diff --git a/docs/api_reference/schemas/space.rst b/docs/api_reference/schemas/space.rst index ea0a05a..c9f00f8 100644 --- a/docs/api_reference/schemas/space.rst +++ b/docs/api_reference/schemas/space.rst @@ -27,6 +27,13 @@ Train Constellation :members: :inherited-members: BaseModel +Walker Configuration +-------------------- + +.. autoclass:: tatc.schemas.WalkerConfiguration + :members: + :undoc-members: + Walker Constellation -------------------- @@ -35,14 +42,14 @@ Walker Constellation :inherited-members: BaseModel MOG Constellation --------------------- +----------------- .. autopydantic_model:: tatc.schemas.MOGConstellation :members: :inherited-members: BaseModel SOC Constellation --------------------- +----------------- .. autopydantic_model:: tatc.schemas.SOCConstellation :members: diff --git a/docs/api_reference/schemas/ground.rst b/docs/api_reference/schemas/surface.rst similarity index 100% rename from docs/api_reference/schemas/ground.rst rename to docs/api_reference/schemas/surface.rst diff --git a/docs/api_reference/utils/index.rst b/docs/api_reference/utils/index.rst index 8e7f816..9a1c302 100644 --- a/docs/api_reference/utils/index.rst +++ b/docs/api_reference/utils/index.rst @@ -16,18 +16,56 @@ Utility Functions .. autofunction:: tatc.utils.buffer_footprint +.. autofunction:: tatc.utils.buffer_target + .. autofunction:: tatc.utils.compute_footprint +.. autofunction:: tatc.utils.compute_limb + +.. autofunction:: tatc.utils.compute_projected_ray_position + .. autofunction:: tatc.utils.compute_field_of_regard .. autofunction:: tatc.utils.compute_min_elevation_angle -.. autofunction:: tatc.utils.compute_orbit_period +.. autofunction:: tatc.utils.semimajor_axis_to_orbit_period + +.. autofunction:: tatc.utils.semimajor_axis_to_mean_motion + +.. autofunction:: tatc.utils.mean_motion_to_orbit_period + +.. autofunction:: tatc.utils.mean_motion_to_semimajor_axis + +.. autofunction:: tatc.utils.compute_apoapsis_radius + +.. autofunction:: tatc.utils.compute_orbit_inertial_velocity + +.. autofunction:: tatc.utils.compute_ground_inertial_velocity + +.. autofunction:: tatc.utils.compute_ground_surface_velocity + +.. autofunction:: tatc.utils.compute_j2_raan_rate + +.. autofunction:: tatc.utils.compute_j2_aop_rate + +.. autofunction:: tatc.utils.compute_j2_mean_motion_rate .. autofunction:: tatc.utils.compute_max_access_time +.. autofunction:: tatc.utils.compute_max_transit_time + +.. autofunction:: tatc.utils.compute_min_along_track_distance + .. autofunction:: tatc.utils.project_polygon_to_elevation .. autofunction:: tatc.utils.split_polygon .. autofunction:: tatc.utils.normalize_geometry + +.. autofunction:: tatc.utils.geodesic_distance + +.. autofunction:: tatc.utils.get_planar_bounds + +.. autofunction:: tatc.utils.to_datetime64_ns + +.. autofunction:: tatc.utils.zero_pad diff --git a/docs/examples/CollectObservations.ipynb b/docs/examples/CollectObservations.ipynb index c1bdc8d..d1873c0 100644 --- a/docs/examples/CollectObservations.ipynb +++ b/docs/examples/CollectObservations.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "1d35caa8-d57f-4a4a-91c0-54b59d4665d6", "metadata": {}, "outputs": [], @@ -36,19 +36,19 @@ "id": "613e9918-8227-4c1a-a350-ea12a38a4cc2", "metadata": {}, "source": [ - "TAT-C allows several types of orbit specifications, one of which being the `TwoLineElements` specification which requires the TLE as an argument." + "TAT-C allows several types of orbit specifications, one of which being the `GeneralPerturbationsOrbit` specification which can accept the TLE as an argument." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "63634767-5bd9-4765-a4ff-f1f21f705baa", "metadata": {}, "outputs": [], "source": [ - "from tatc.schemas import TwoLineElements\n", + "from tatc.schemas import GeneralPerturbationsOrbit\n", "\n", - "noaa20_orbit = TwoLineElements(tle=noaa20_tle)" + "noaa20_orbit = GeneralPerturbationsOrbit.from_tle(noaa20_tle)" ] }, { @@ -61,18 +61,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "93cce855-971e-4aae-8688-61a205979b62", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "computed field of regard: 111.6 degrees\n" - ] - } - ], + "outputs": [], "source": [ "from tatc import utils\n", "\n", @@ -90,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "1ec7796a-d1eb-4f1e-bdbe-603fc2db8868", "metadata": {}, "outputs": [], @@ -110,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "5bbab563-7c29-4797-b9dd-0b697d431edf", "metadata": {}, "outputs": [], @@ -130,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "bd5d2afb-089b-4901-b9b0-14ed5cc2c435", "metadata": {}, "outputs": [], @@ -150,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "1ea162ff-52e4-4852-a48f-6545b3bd89dd", "metadata": {}, "outputs": [], @@ -176,227 +168,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "6d41f563-cdbc-4d5b-acd7-635c250c39b7", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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point_idgeometrysatelliteinstrumentstartendepochsat_altsat_az
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point_idgeometryaccessrevisitsamples
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point_idgeometrysatelliteinstrumentstartendepochsat_altsat_az
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10POINT Z (-74.02686 40.74259 0)NOAA 20 3VIIRS2022-07-14 16:00:48.305713+00:002022-07-14 16:03:09.455300+00:002022-07-14 16:01:58.880506500+00:0022.391592296.411261
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40POINT Z (-74.02686 40.74259 0)NOAA 20 2VIIRS2022-07-14 20:40:17.146080+00:002022-07-14 20:42:53.973151+00:002022-07-14 20:41:35.559615500+00:0022.80140163.710155
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2370POINT Z (-74.02686 40.74259 0)NOAA 20 3VIIRS2022-08-13 00:58:07.584550+00:002022-08-13 01:03:35.190820+00:002022-08-13 01:00:51.387685+00:0032.87065366.168331
2380POINT Z (-74.02686 40.74259 0)NOAA 20 3VIIRS2022-08-13 02:38:19.726352+00:002022-08-13 02:43:41.909195+00:002022-08-13 02:41:00.817773500+00:0031.910221264.638645
2390POINT Z (-74.02686 40.74259 0)NOAA 20 1VIIRS2022-08-13 06:28:34.773044+00:002022-08-13 06:35:48.839408+00:002022-08-13 06:32:11.806226+00:0061.217019102.685206
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242 rows × 9 columns

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" - ], - "text/plain": [ - " point_id geometry satellite instrument \\\n", - "0 0 POINT Z (-74.02686 40.74259 0) NOAA 20 3 VIIRS \n", - "1 0 POINT Z (-74.02686 40.74259 0) NOAA 20 3 VIIRS \n", - "2 0 POINT Z (-74.02686 40.74259 0) NOAA 20 1 VIIRS \n", - "3 0 POINT Z (-74.02686 40.74259 0) NOAA 20 1 VIIRS \n", - "4 0 POINT Z (-74.02686 40.74259 0) NOAA 20 2 VIIRS \n", - ".. ... ... ... ... \n", - "237 0 POINT Z (-74.02686 40.74259 0) NOAA 20 3 VIIRS \n", - "238 0 POINT Z (-74.02686 40.74259 0) NOAA 20 3 VIIRS \n", - "239 0 POINT Z (-74.02686 40.74259 0) NOAA 20 1 VIIRS \n", - "240 0 POINT Z (-74.02686 40.74259 0) NOAA 20 2 VIIRS \n", - "241 0 POINT Z (-74.02686 40.74259 0) NOAA 20 2 VIIRS \n", - "\n", - " start end \\\n", - "0 2022-07-14 14:18:41.843832+00:00 2022-07-14 14:25:32.984712+00:00 \n", - "1 2022-07-14 16:00:48.305713+00:00 2022-07-14 16:03:09.455300+00:00 \n", - "2 2022-07-14 17:14:31.310369+00:00 2022-07-14 17:21:09.157255+00:00 \n", - "3 2022-07-14 18:56:58.849666+00:00 2022-07-14 18:59:29.309441+00:00 \n", - "4 2022-07-14 20:40:17.146080+00:00 2022-07-14 20:42:53.973151+00:00 \n", - ".. ... ... \n", - "237 2022-08-13 00:58:07.584550+00:00 2022-08-13 01:03:35.190820+00:00 \n", - "238 2022-08-13 02:38:19.726352+00:00 2022-08-13 02:43:41.909195+00:00 \n", - "239 2022-08-13 06:28:34.773044+00:00 2022-08-13 06:35:48.839408+00:00 \n", - "240 2022-08-13 09:53:07.157574+00:00 2022-08-13 09:57:51.075326+00:00 \n", - "241 2022-08-13 11:32:45.365663+00:00 2022-08-13 11:38:40.975563+00:00 \n", - "\n", - " epoch sat_alt sat_az \n", - "0 2022-07-14 14:22:07.414272+00:00 49.134764 99.453987 \n", - "1 2022-07-14 16:01:58.880506500+00:00 22.391592 296.411261 \n", - "2 2022-07-14 17:17:50.233812+00:00 45.832413 69.766369 \n", - "3 2022-07-14 18:58:14.079553500+00:00 22.556721 266.369724 \n", - "4 2022-07-14 20:41:35.559615500+00:00 22.801401 63.710155 \n", - ".. ... ... ... \n", - "237 2022-08-13 01:00:51.387685+00:00 32.870653 66.168331 \n", - "238 2022-08-13 02:41:00.817773500+00:00 31.910221 264.638645 \n", - "239 2022-08-13 06:32:11.806226+00:00 61.217019 102.685206 \n", - "240 2022-08-13 09:55:29.116450+00:00 28.416874 96.047271 \n", - "241 2022-08-13 11:35:43.170613+00:00 36.746860 291.210499 \n", - "\n", - "[242 rows x 9 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from tatc.analysis import collect_multi_observations\n", "\n", @@ -1006,240 +281,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "e886c08b-e5ac-4276-8623-a3cf94063ba2", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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geometrypoint_idsatelliteinstrumentstartepochendaccessrevisit
0POINT Z (-74.02686 40.74259 0)0NOAA 20 3VIIRS2022-07-14 14:18:41.843832+00:002022-07-14 14:22:07.414272+00:002022-07-14 14:25:32.984712+00:000 days 00:06:51.140880NaT
1POINT Z (-74.02686 40.74259 0)0NOAA 20 3VIIRS2022-07-14 16:00:48.305713+00:002022-07-14 16:01:58.880506624+00:002022-07-14 16:03:09.455300+00:000 days 00:02:21.1495870 days 01:35:15.321001
2POINT Z (-74.02686 40.74259 0)0NOAA 20 1VIIRS2022-07-14 17:14:31.310369+00:002022-07-14 17:17:50.233811968+00:002022-07-14 17:21:09.157255+00:000 days 00:06:37.8468860 days 01:11:21.855069
3POINT Z (-74.02686 40.74259 0)0NOAA 20 1VIIRS2022-07-14 18:56:58.849666+00:002022-07-14 18:58:14.079553536+00:002022-07-14 18:59:29.309441+00:000 days 00:02:30.4597750 days 01:35:49.692411
4POINT Z (-74.02686 40.74259 0)0NOAA 20 2VIIRS2022-07-14 20:40:17.146080+00:002022-07-14 20:41:35.559615488+00:002022-07-14 20:42:53.973151+00:000 days 00:02:36.8270710 days 01:40:47.836639
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237POINT Z (-74.02686 40.74259 0)0NOAA 20 3VIIRS2022-08-13 00:58:07.584550+00:002022-08-13 01:00:51.387685120+00:002022-08-13 01:03:35.190820+00:000 days 00:05:27.6062700 days 03:17:15.434013
238POINT Z (-74.02686 40.74259 0)0NOAA 20 3VIIRS2022-08-13 02:38:19.726352+00:002022-08-13 02:41:00.817773568+00:002022-08-13 02:43:41.909195+00:000 days 00:05:22.1828430 days 01:34:44.535532
239POINT Z (-74.02686 40.74259 0)0NOAA 20 1VIIRS2022-08-13 06:28:34.773044+00:002022-08-13 06:32:11.806225920+00:002022-08-13 06:35:48.839408+00:000 days 00:07:14.0663640 days 03:44:52.863849
240POINT Z (-74.02686 40.74259 0)0NOAA 20 2VIIRS2022-08-13 09:53:07.157574+00:002022-08-13 09:55:29.116450048+00:002022-08-13 09:57:51.075326+00:000 days 00:04:43.9177520 days 03:17:18.318166
241POINT Z (-74.02686 40.74259 0)0NOAA 20 2VIIRS2022-08-13 11:32:45.365663+00:002022-08-13 11:35:43.170612992+00:002022-08-13 11:38:40.975563+00:000 days 00:05:55.6099000 days 01:34:54.290337
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242 rows × 9 columns

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" - ], - "text/plain": [ - " geometry point_id satellite instrument \\\n", - "0 POINT Z (-74.02686 40.74259 0) 0 NOAA 20 3 VIIRS \n", - "1 POINT Z (-74.02686 40.74259 0) 0 NOAA 20 3 VIIRS \n", - "2 POINT Z (-74.02686 40.74259 0) 0 NOAA 20 1 VIIRS \n", - "3 POINT Z (-74.02686 40.74259 0) 0 NOAA 20 1 VIIRS \n", - "4 POINT Z (-74.02686 40.74259 0) 0 NOAA 20 2 VIIRS \n", - ".. ... ... ... ... \n", - "237 POINT Z (-74.02686 40.74259 0) 0 NOAA 20 3 VIIRS \n", - "238 POINT Z (-74.02686 40.74259 0) 0 NOAA 20 3 VIIRS \n", - "239 POINT Z (-74.02686 40.74259 0) 0 NOAA 20 1 VIIRS \n", - "240 POINT Z (-74.02686 40.74259 0) 0 NOAA 20 2 VIIRS \n", - "241 POINT Z (-74.02686 40.74259 0) 0 NOAA 20 2 VIIRS \n", - "\n", - " start epoch \\\n", - "0 2022-07-14 14:18:41.843832+00:00 2022-07-14 14:22:07.414272+00:00 \n", - "1 2022-07-14 16:00:48.305713+00:00 2022-07-14 16:01:58.880506624+00:00 \n", - "2 2022-07-14 17:14:31.310369+00:00 2022-07-14 17:17:50.233811968+00:00 \n", - "3 2022-07-14 18:56:58.849666+00:00 2022-07-14 18:58:14.079553536+00:00 \n", - "4 2022-07-14 20:40:17.146080+00:00 2022-07-14 20:41:35.559615488+00:00 \n", - ".. ... ... \n", - "237 2022-08-13 00:58:07.584550+00:00 2022-08-13 01:00:51.387685120+00:00 \n", - "238 2022-08-13 02:38:19.726352+00:00 2022-08-13 02:41:00.817773568+00:00 \n", - "239 2022-08-13 06:28:34.773044+00:00 2022-08-13 06:32:11.806225920+00:00 \n", - "240 2022-08-13 09:53:07.157574+00:00 2022-08-13 09:55:29.116450048+00:00 \n", - "241 2022-08-13 11:32:45.365663+00:00 2022-08-13 11:35:43.170612992+00:00 \n", - "\n", - " end access \\\n", - "0 2022-07-14 14:25:32.984712+00:00 0 days 00:06:51.140880 \n", - "1 2022-07-14 16:03:09.455300+00:00 0 days 00:02:21.149587 \n", - "2 2022-07-14 17:21:09.157255+00:00 0 days 00:06:37.846886 \n", - "3 2022-07-14 18:59:29.309441+00:00 0 days 00:02:30.459775 \n", - "4 2022-07-14 20:42:53.973151+00:00 0 days 00:02:36.827071 \n", - ".. ... ... \n", - "237 2022-08-13 01:03:35.190820+00:00 0 days 00:05:27.606270 \n", - "238 2022-08-13 02:43:41.909195+00:00 0 days 00:05:22.182843 \n", - "239 2022-08-13 06:35:48.839408+00:00 0 days 00:07:14.066364 \n", - "240 2022-08-13 09:57:51.075326+00:00 0 days 00:04:43.917752 \n", - "241 2022-08-13 11:38:40.975563+00:00 0 days 00:05:55.609900 \n", - "\n", - " revisit \n", - "0 NaT \n", - "1 0 days 01:35:15.321001 \n", - "2 0 days 01:11:21.855069 \n", - "3 0 days 01:35:49.692411 \n", - "4 0 days 01:40:47.836639 \n", - ".. ... \n", - "237 0 days 03:17:15.434013 \n", - "238 0 days 01:34:44.535532 \n", - "239 0 days 03:44:52.863849 \n", - "240 0 days 03:17:18.318166 \n", - "241 0 days 01:34:54.290337 \n", - "\n", - "[242 rows x 9 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "aggregated_results = aggregate_observations(results)\n", "display(aggregated_results)" @@ -1255,63 +300,10 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "339198d4-0e32-4b32-96c9-32c006ec3d81", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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point_idgeometryaccessrevisitsamples
00POINT Z (-74.02686 40.74259 0)0 days 00:05:55.5208300 days 02:52:38.353074242
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" - ], - "text/plain": [ - " point_id geometry access \\\n", - "0 0 POINT Z (-74.02686 40.74259 0) 0 days 00:05:55.520830 \n", - "\n", - " revisit samples \n", - "0 0 days 02:52:38.353074 242 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "reduced_results = reduce_observations(aggregated_results)\n", "display(reduced_results)" @@ -1327,21 +319,10 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "d504861d-2c2d-4123-99cf-4686a82c7c50", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import numpy as np\n", "\n", @@ -1358,7 +339,7 @@ " number_satellites=i,\n", " number_planes=i,\n", " configuration=\"star\",\n", - " ),\n", + " ).generate_members(),\n", " start,\n", " end,\n", " )\n", @@ -1396,7 +377,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.11.15" } }, "nbformat": 4, diff --git a/docs/examples/CollectOrbitGroundTrack.ipynb b/docs/examples/CollectOrbitGroundTrack.ipynb index 07d9129..c2027e3 100644 --- a/docs/examples/CollectOrbitGroundTrack.ipynb +++ b/docs/examples/CollectOrbitGroundTrack.ipynb @@ -20,13 +20,13 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "0c1d2907-718f-4f5e-b4f4-7155a3592f29", "metadata": {}, "outputs": [], "source": [ "from tatc import utils\n", - "from tatc.schemas import Instrument, Satellite, TwoLineElements\n", + "from tatc.schemas import GeneralPerturbationsOrbit, Instrument, Satellite\n", "\n", "viirs = Instrument(\n", " name=\"VIIRS\",\n", @@ -35,8 +35,8 @@ ")\n", "noaa20 = Satellite(\n", " name=\"NOAA 20\",\n", - " orbit=TwoLineElements(\n", - " tle=[\n", + " orbit=GeneralPerturbationsOrbit.from_tle(\n", + " [\n", " \"1 43013U 17073A 22195.78278435 .00000038 00000+0 38919-4 0 9996\",\n", " \"2 43013 98.7169 133.9110 0001202 63.8768 296.2532 14.19561306241107\",\n", " ]\n", @@ -55,51 +55,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "86a78faf-dae9-469e-b2c7-89526de6b929", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DatetimeIndex(['2022-07-14 12:00:00+00:00', '2022-07-14 12:02:00+00:00',\n", - " '2022-07-14 12:04:00+00:00', '2022-07-14 12:06:00+00:00',\n", - " '2022-07-14 12:08:00+00:00', '2022-07-14 12:10:00+00:00',\n", - " '2022-07-14 12:12:00+00:00', '2022-07-14 12:14:00+00:00',\n", - " '2022-07-14 12:16:00+00:00', '2022-07-14 12:18:00+00:00',\n", - " '2022-07-14 12:20:00+00:00', '2022-07-14 12:22:00+00:00',\n", - " '2022-07-14 12:24:00+00:00', '2022-07-14 12:26:00+00:00',\n", - " '2022-07-14 12:28:00+00:00', '2022-07-14 12:30:00+00:00',\n", - " '2022-07-14 12:32:00+00:00', '2022-07-14 12:34:00+00:00',\n", - " '2022-07-14 12:36:00+00:00', '2022-07-14 12:38:00+00:00',\n", - " '2022-07-14 12:40:00+00:00', '2022-07-14 12:42:00+00:00',\n", - " '2022-07-14 12:44:00+00:00', '2022-07-14 12:46:00+00:00',\n", - " '2022-07-14 12:48:00+00:00', '2022-07-14 12:50:00+00:00',\n", - " '2022-07-14 12:52:00+00:00', '2022-07-14 12:54:00+00:00',\n", - " '2022-07-14 12:56:00+00:00', '2022-07-14 12:58:00+00:00',\n", - " '2022-07-14 13:00:00+00:00', '2022-07-14 13:02:00+00:00',\n", - " '2022-07-14 13:04:00+00:00', '2022-07-14 13:06:00+00:00',\n", - " '2022-07-14 13:08:00+00:00', '2022-07-14 13:10:00+00:00',\n", - " '2022-07-14 13:12:00+00:00', '2022-07-14 13:14:00+00:00',\n", - " '2022-07-14 13:16:00+00:00', '2022-07-14 13:18:00+00:00',\n", - " '2022-07-14 13:20:00+00:00', '2022-07-14 13:22:00+00:00',\n", - " '2022-07-14 13:24:00+00:00', '2022-07-14 13:26:00+00:00',\n", - " '2022-07-14 13:28:00+00:00', '2022-07-14 13:30:00+00:00',\n", - " '2022-07-14 13:32:00+00:00', '2022-07-14 13:34:00+00:00',\n", - " '2022-07-14 13:36:00+00:00', '2022-07-14 13:38:00+00:00',\n", - " '2022-07-14 13:40:00+00:00', '2022-07-14 13:42:00+00:00',\n", - " '2022-07-14 13:44:00+00:00', '2022-07-14 13:46:00+00:00',\n", - " '2022-07-14 13:48:00+00:00', '2022-07-14 13:50:00+00:00',\n", - " '2022-07-14 13:52:00+00:00', '2022-07-14 13:54:00+00:00',\n", - " '2022-07-14 13:56:00+00:00', '2022-07-14 13:58:00+00:00',\n", - " '2022-07-14 14:00:00+00:00'],\n", - " dtype='datetime64[ns, UTC]', freq='2min')" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from datetime import datetime, timedelta, timezone\n", "import pandas as pd\n", @@ -124,178 +83,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "c45520b9-15b8-40cd-96a6-62d5b0c0041f", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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timesatelliteinstrumentswath_widthvalid_obsgeometry
02022-07-14 12:00:00+00:00NOAA 20VIIRS2.979477e+06TruePOINT Z (37.6979 -4.30588 829604.57272)
12022-07-14 12:02:00+00:00NOAA 20VIIRS2.974766e+06TruePOINT Z (36.12013 2.75031 828593.05309)
22022-07-14 12:04:00+00:00NOAA 20VIIRS2.972688e+06TruePOINT Z (34.52934 9.80636 828146.68837)
32022-07-14 12:06:00+00:00NOAA 20VIIRS2.973169e+06TruePOINT Z (32.89116 16.85703 828250.04018)
42022-07-14 12:08:00+00:00NOAA 20VIIRS2.975986e+06TruePOINT Z (31.16516 23.89678 828855.11416)
.....................
562022-07-14 13:52:00+00:00NOAA 20VIIRS2.982254e+06TruePOINT Z (3.4244 32.68312 830200.45961)
572022-07-14 13:54:00+00:00NOAA 20VIIRS2.988873e+06TruePOINT Z (1.27216 39.67388 831619.33214)
582022-07-14 13:56:00+00:00NOAA 20VIIRS2.996330e+06TruePOINT Z (-1.25039 46.62707 833215.3123)
592022-07-14 13:58:00+00:00NOAA 20VIIRS3.004037e+06TruePOINT Z (-4.366 53.525 834862.31502)
602022-07-14 14:00:00+00:00NOAA 20VIIRS3.011412e+06TruePOINT Z (-8.49191 60.33504 836435.88815)
\n", - "

61 rows × 6 columns

\n", - "
" - ], - "text/plain": [ - " time satellite instrument swath_width valid_obs \\\n", - "0 2022-07-14 12:00:00+00:00 NOAA 20 VIIRS 2.979477e+06 True \n", - "1 2022-07-14 12:02:00+00:00 NOAA 20 VIIRS 2.974766e+06 True \n", - "2 2022-07-14 12:04:00+00:00 NOAA 20 VIIRS 2.972688e+06 True \n", - "3 2022-07-14 12:06:00+00:00 NOAA 20 VIIRS 2.973169e+06 True \n", - "4 2022-07-14 12:08:00+00:00 NOAA 20 VIIRS 2.975986e+06 True \n", - ".. ... ... ... ... ... \n", - "56 2022-07-14 13:52:00+00:00 NOAA 20 VIIRS 2.982254e+06 True \n", - "57 2022-07-14 13:54:00+00:00 NOAA 20 VIIRS 2.988873e+06 True \n", - "58 2022-07-14 13:56:00+00:00 NOAA 20 VIIRS 2.996330e+06 True \n", - "59 2022-07-14 13:58:00+00:00 NOAA 20 VIIRS 3.004037e+06 True \n", - "60 2022-07-14 14:00:00+00:00 NOAA 20 VIIRS 3.011412e+06 True \n", - "\n", - " geometry \n", - "0 POINT Z (37.6979 -4.30588 829604.57272) \n", - "1 POINT Z (36.12013 2.75031 828593.05309) \n", - "2 POINT Z (34.52934 9.80636 828146.68837) \n", - "3 POINT Z (32.89116 16.85703 828250.04018) \n", - "4 POINT Z (31.16516 23.89678 828855.11416) \n", - ".. ... \n", - "56 POINT Z (3.4244 32.68312 830200.45961) \n", - "57 POINT Z (1.27216 39.67388 831619.33214) \n", - "58 POINT Z (-1.25039 46.62707 833215.3123) \n", - "59 POINT Z (-4.366 53.525 834862.31502) \n", - "60 POINT Z (-8.49191 60.33504 836435.88815) \n", - "\n", - "[61 rows x 6 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from tatc.analysis import collect_orbit_track\n", "\n", @@ -314,21 +105,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "52626095-01ee-4cee-a626-5a07bda4dd61", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import cartopy.crs as ccrs\n", @@ -356,183 +136,15 @@ "id": "19477f45-40b9-47d4-82a9-e9121c9478fd", "metadata": {}, "source": [ - "The `collect_ground_track` method projects a ground track using knowledge of the instrument. The default setting applies a buffer equivalent to the half swath width to each point in the EPSG:4087 World Equidistant Cylindrical coordinate system. The resulting Polygon geometry is automatically split into a MultiPolygon when crossing the anti-meridian (+/- 180 degrees longitude) and/or the north/south pole (+/- 90 degrees latitude)." + "The `collect_ground_track` method projects a ground track using knowledge of the instrument. The resulting Polygon geometry is automatically split into a MultiPolygon when crossing the anti-meridian (+/- 180 degrees longitude) and/or the north/south pole (+/- 90 degrees latitude)." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "1f3ac43f-1506-486e-a828-3d3d3c13cabe", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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timesatelliteinstrumentswath_widthvalid_obsgeometry
02022-07-14 12:00:00+00:00NOAA 20VIIRS2.979477e+06TruePOLYGON Z ((51.08044 -4.30588 0, 51.016 -5.617...
12022-07-14 12:02:00+00:00NOAA 20VIIRS2.974766e+06TruePOLYGON Z ((49.48151 2.75031 0, 49.41718 1.440...
22022-07-14 12:04:00+00:00NOAA 20VIIRS2.972688e+06TruePOLYGON Z ((47.88139 9.80636 0, 47.8171 8.4976...
32022-07-14 12:06:00+00:00NOAA 20VIIRS2.973169e+06TruePOLYGON Z ((46.24537 16.85703 0, 46.18107 15.5...
42022-07-14 12:08:00+00:00NOAA 20VIIRS2.975986e+06TruePOLYGON Z ((44.53202 23.89678 0, 44.46766 22.5...
.....................
562022-07-14 13:52:00+00:00NOAA 20VIIRS2.982254e+06TruePOLYGON Z ((16.81942 32.68312 0, 16.75492 31.3...
572022-07-14 13:54:00+00:00NOAA 20VIIRS2.988873e+06TruePOLYGON Z ((14.69691 39.67388 0, 14.63227 38.3...
582022-07-14 13:56:00+00:00NOAA 20VIIRS2.996330e+06TruePOLYGON Z ((12.20786 46.62707 0, 12.14305 45.3...
592022-07-14 13:58:00+00:00NOAA 20VIIRS3.004037e+06TruePOLYGON Z ((9.12686 53.525 0, 9.06189 52.20247...
602022-07-14 14:00:00+00:00NOAA 20VIIRS3.011412e+06TruePOLYGON Z ((5.03408 60.33504 0, 4.96895 59.009...
\n", - "

61 rows × 6 columns

\n", - "
" - ], - "text/plain": [ - " time satellite instrument swath_width valid_obs \\\n", - "0 2022-07-14 12:00:00+00:00 NOAA 20 VIIRS 2.979477e+06 True \n", - "1 2022-07-14 12:02:00+00:00 NOAA 20 VIIRS 2.974766e+06 True \n", - "2 2022-07-14 12:04:00+00:00 NOAA 20 VIIRS 2.972688e+06 True \n", - "3 2022-07-14 12:06:00+00:00 NOAA 20 VIIRS 2.973169e+06 True \n", - "4 2022-07-14 12:08:00+00:00 NOAA 20 VIIRS 2.975986e+06 True \n", - ".. ... ... ... ... ... \n", - "56 2022-07-14 13:52:00+00:00 NOAA 20 VIIRS 2.982254e+06 True \n", - "57 2022-07-14 13:54:00+00:00 NOAA 20 VIIRS 2.988873e+06 True \n", - "58 2022-07-14 13:56:00+00:00 NOAA 20 VIIRS 2.996330e+06 True \n", - "59 2022-07-14 13:58:00+00:00 NOAA 20 VIIRS 3.004037e+06 True \n", - "60 2022-07-14 14:00:00+00:00 NOAA 20 VIIRS 3.011412e+06 True \n", - "\n", - " geometry \n", - "0 POLYGON Z ((51.08044 -4.30588 0, 51.016 -5.617... \n", - "1 POLYGON Z ((49.48151 2.75031 0, 49.41718 1.440... \n", - "2 POLYGON Z ((47.88139 9.80636 0, 47.8171 8.4976... \n", - "3 POLYGON Z ((46.24537 16.85703 0, 46.18107 15.5... \n", - "4 POLYGON Z ((44.53202 23.89678 0, 44.46766 22.5... \n", - ".. ... \n", - "56 POLYGON Z ((16.81942 32.68312 0, 16.75492 31.3... \n", - "57 POLYGON Z ((14.69691 39.67388 0, 14.63227 38.3... \n", - "58 POLYGON Z ((12.20786 46.62707 0, 12.14305 45.3... \n", - "59 POLYGON Z ((9.12686 53.525 0, 9.06189 52.20247... \n", - "60 POLYGON Z ((5.03408 60.33504 0, 4.96895 59.009... \n", - "\n", - "[61 rows x 6 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from tatc.analysis import collect_ground_track\n", "\n", @@ -540,136 +152,17 @@ "display(results)" ] }, - { - "cell_type": "markdown", - "id": "fdef977a-44fe-4663-a74b-9b3722daf8b6", - "metadata": {}, - "source": [ - "While fast, the EPSG:4087 coordinate reference frame is not accurate near the poles, as seen in the plot below." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "7766a927-3763-4584-bb81-4d00f660c8b9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import cartopy.crs as ccrs\n", - "\n", - "results[\"valid\"] = results.apply(\n", - " lambda r: \"Valid\" if r.valid_obs else \"Invalid\", axis=1\n", - ")\n", - "\n", - "fig, ax = plt.subplots(figsize=(8, 5), subplot_kw={\"projection\": ccrs.PlateCarree()})\n", - "\n", - "results.plot(\n", - " column=\"valid\", \n", - " edgecolor=\"none\", \n", - " alpha=0.4, \n", - " legend=True, \n", - " cmap=\"inferno\", \n", - " ax=ax,\n", - " transform=ccrs.PlateCarree()\n", - ")\n", - "ax.stock_img()\n", - "ax.set_global()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "09b12e94-ebc3-4eaa-b393-39d3d37e8aa7", - "metadata": {}, - "source": [ - "Alternatively, setting `crs=\"utm\"` uses the Universal Transverse Mercator (UTM) coordinate reference system to more accurately project swath width near the poles. Note that UTM does not cover the regions above 84 degrees or below -80 degrees latitude. These regions instead use the Unified Polar Stereographic (UPS) CRS; however, with poor performance close to the transition point between zones." - ] - }, { "cell_type": "code", - "execution_count": 7, - "id": "10d1521e-1735-4256-bc2f-4694ba119be4", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import cartopy.crs as ccrs\n", - "\n", - "results = collect_ground_track(noaa20, times, crs=\"utm\")\n", - "\n", - "results[\"valid\"] = results.apply(\n", - " lambda r: \"Valid\" if r.valid_obs else \"Invalid\", axis=1\n", - ")\n", - "\n", - "fig, ax = plt.subplots(figsize=(8, 5), subplot_kw={\"projection\": ccrs.PlateCarree()})\n", - "\n", - "results.plot(\n", - " column=\"valid\", \n", - " edgecolor=\"none\", \n", - " alpha=0.4, \n", - " legend=True, \n", - " cmap=\"inferno\", \n", - " ax=ax,\n", - " transform=ccrs.PlateCarree()\n", - ")\n", - "ax.stock_img()\n", - "ax.set_global()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "a1cfe561", - "metadata": {}, - "source": [ - "As of TAT-C 3.4.0, the option `spice' uses an interace to JPL SPICE to quickly and accurately compute ground tracks." - ] - }, - { - "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "720cf516", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import cartopy.crs as ccrs\n", "\n", - "results = collect_ground_track(noaa20, times, crs=\"spice\")\n", + "results = collect_ground_track(noaa20, times)\n", "\n", "results[\"valid\"] = results.apply(\n", " lambda r: \"Valid\" if r.valid_obs else \"Invalid\", axis=1\n", @@ -701,79 +194,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "9e3c7daa-3ead-4099-b10b-265e50b780d9", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Point method (EPSG:4087) completed in 0.21 seconds\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Point method (UTM CRS) completed in 2.50 seconds\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from tatc.analysis import compute_ground_track\n", "\n", @@ -781,36 +205,7 @@ "import cartopy.crs as ccrs\n", "import time\n", "\n", - "t = time.time()\n", - "results = compute_ground_track(noaa20, times, method=\"point\")\n", - "print(f\"Point method (EPSG:4087) completed in {time.time() - t:.2f} seconds\")\n", - "fig, ax = plt.subplots(figsize=(8, 5), subplot_kw={\"projection\": ccrs.PlateCarree()})\n", - "ax = results.plot(facecolor=\"r\", edgecolor=\"none\", alpha=0.4, zorder=1, ax=ax, transform=ccrs.PlateCarree())\n", - "ax.stock_img()\n", - "ax.set_global()\n", - "plt.show()\n", - "\n", - "t = time.time()\n", - "results = compute_ground_track(noaa20, times, crs=\"utm\", method=\"point\")\n", - "print(f\"Point method (UTM CRS) completed in {time.time() - t:.2f} seconds\")\n", - "fig, ax = plt.subplots(figsize=(8, 5), subplot_kw={\"projection\": ccrs.PlateCarree()})\n", - "ax = results.plot(facecolor=\"r\", edgecolor=\"none\", alpha=0.4, zorder=1, ax=ax, transform=ccrs.PlateCarree())\n", - "ax.stock_img()\n", - "ax.set_global()\n", - "plt.show()\n", - "\n", - "t = time.time()\n", - "results = compute_ground_track(noaa20, times, method=\"line\")\n", - "print(f\"Line method (EPSG:4087) completed in {time.time() - t:.2f} seconds\")\n", - "fig, ax = plt.subplots(figsize=(8, 5), subplot_kw={\"projection\": ccrs.PlateCarree()})\n", - "ax = results.plot(facecolor=\"r\", edgecolor=\"none\", alpha=0.4, zorder=1, ax=ax, transform=ccrs.PlateCarree())\n", - "ax.stock_img()\n", - "ax.set_global()\n", - "plt.show()\n", - "\n", - "t = time.time()\n", - "results = compute_ground_track(noaa20, times, crs=\"spice\", method=\"point\")\n", - "print(f\"Point method (SPICE) completed in {time.time() - t:.2f} seconds\")\n", + "results = compute_ground_track(noaa20, times)\n", "fig, ax = plt.subplots(figsize=(8, 5), subplot_kw={\"projection\": ccrs.PlateCarree()})\n", "ax = results.plot(facecolor=\"r\", edgecolor=\"none\", alpha=0.4, zorder=1, ax=ax, transform=ccrs.PlateCarree())\n", "ax.stock_img()\n", @@ -835,7 +230,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.11.15" } }, "nbformat": 4, diff --git a/docs/examples/CollectRO.ipynb b/docs/examples/CollectRO.ipynb index d2a724c..df03db1 100644 --- a/docs/examples/CollectRO.ipynb +++ b/docs/examples/CollectRO.ipynb @@ -124,12 +124,12 @@ " * `range_elevation` sets the minimum and maximum allowable elevation (in meters) of the tangent point between receiver and transmitter. While realistic GNSS-RO observations sample the atmosphere, a negative minimum value accounts for the lack of refraction considered in this analysis.\n", " * `sample_elevation` sets the elevation (in meters) of the tangent point to represent a GNSS-RO observation as a single point, rather than an arc through space.\n", "\n", - "Other scenario parameters configure the temporal bounds of analysis (`start` and `duration`) and set the temporal resolution of orbital motion (`time_step`)." + "Other scenario parameters configure the temporal bounds of analysis (`start` and `end`) and set the time step (`time_step`) used both to precisely locate valid observation periods (via Skyfield's `find_discrete` search) and to sample tangent point tracks within each period." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -139,12 +139,13 @@ "# occultation validity constraints\n", "max_yaw = 65 # deg\n", "range_elevation = (-200e3, 60e3) # m\n", - "sample_elevation = 0 # m\n", + "sample_elevation = -80e3 # m\n", "\n", "# scenario configuration\n", "start = datetime(2023, 12, 9, tzinfo=timezone.utc)\n", "time_step = timedelta(seconds=10)\n", "duration = timedelta(hours=1)\n", + "end = start + duration\n", "times = np.array([start + i * time_step for i in range(duration // time_step)])" ] }, @@ -157,11 +158,11 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "from tatc.schemas import Satellite, TwoLineElements\n", + "from tatc.schemas import GeneralPerturbationsOrbit, Satellite\n", "\n", "txs = []\n", "\n", @@ -171,14 +172,14 @@ " for i in range(0, len(lines), 3):\n", " txs.append(\n", " Satellite(\n", - " name=lines[i], orbit=TwoLineElements(tle=lines[i + 1 : i + 3])\n", + " name=lines[i], orbit=GeneralPerturbationsOrbit.from_tle(lines[i + 1 : i + 3])\n", " )\n", " )\n", "\n", "rx = Satellite(\n", " name=\"COSMIC-2 FM5\",\n", - " orbit=TwoLineElements(\n", - " tle=[\n", + " orbit=GeneralPerturbationsOrbit.from_tle(\n", + " [\n", " \"1 44358U 19036V 23354.40324524 .00015027 00000-0 91237-3 0 9994\",\n", " \"2 44358 24.0023 0.0734 0003537 251.0816 108.9304 15.08623759245240\",\n", " ]\n", @@ -190,24 +191,24 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "TAT-C simulates GNSS-RO observations using the `collect_ro_observations` script. The function propagates the position of the receiver and transmitters and computes the three conditions required for a valid GNSS-RO observation: 1) the tangent point must between the receiver and transmitter, 2) the acute receiver-transmitter azimuth angle must be less than `max_azimuth`, and 3) the tangent point elevation must be within the `range_elevation` range.\n", + "TAT-C simulates GNSS-RO observations using the `collect_ro_observations` script. The function propagates the position of the receiver and transmitters and, over the analysis period (`start` to `end`), uses Skyfield's `find_discrete` search to precisely identify periods that satisfy the three conditions required for a valid GNSS-RO observation: 1) the tangent point must between the receiver and transmitter, 2) the acute receiver-transmitter azimuth angle must be less than `max_azimuth`, and 3) the tangent point elevation must be within the `range_elevation` range.\n", "\n", "The outputs report each RO observation including the following fields:\n", " * `receiver`: the receiver satellite name\n", " * `transmitter`: the transmitter satellite name\n", " * `is_rising`: true, if the receiver-transmitter azimuth angle is less than `max_azimuth` (false indicates it is greater than 180 - `max_azimuth`)\n", - " * `geometry`: a multipoint geometry that describes the observation arc of points separated by `time_step`\n", - " * `position`: a point geometry that describes the observation closest to the `sample_elevation` value\n", + " * `geometry`: a multipoint geometry that describes the observation arc of points separated by (at most) `time_step`\n", + " * `position`: a point geometry that describes the observation interpolated at the `sample_elevation` value\n", " * `rx_tx_azimuth`: the transmitter azimuth angle as viewed by the receiver\n", " * `tp_tx_azimuth`: the transmitter azimuth angle as viewed by the tangent point (i.e., degrees clockwise from North)\n", " * `start`: start of the RO observation arc\n", " * `end`: end of the RO observation arc\n", - " * `time`: time of the observation closest to the `sample_elevation` value\n" + " * `time`: time of the observation interpolated at the `sample_elevation` value\n" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -248,72 +249,72 @@ " \n", " 0\n", " COSMIC-2 FM5\n", - " BEIDOU-3 M7 (C27)\n", + " BEIDOU-3 G3 (C61)\n", " False\n", - " MULTIPOINT Z (-0.03172 8.43027 -111422.03599, ...\n", - " POINT Z (-0.0317183312985065 8.430274910021483...\n", - " -154.829254\n", - " 174.181601\n", - " 298.666783\n", + " MULTIPOINT Z ((-168.50136 -15.59247 -124560.11...\n", + " POINT Z (-168.5013603513564 -15.59246934270609...\n", + " -152.632620\n", + " -156.364285\n", + " 272.373899\n", + " 2023-12-09 00:00:00+00:00\n", " 2023-12-09 00:00:00+00:00\n", - " 2023-12-09 00:00:30+00:00\n", " 2023-12-09 00:00:00+00:00\n", " \n", " \n", " 1\n", " COSMIC-2 FM5\n", - " COSMOS 2534 (758)\n", + " BEIDOU-2 G7 (C03)\n", " False\n", - " MULTIPOINT Z (8.38669 15.17354 -20482.00391, 8...\n", - " POINT Z (8.386687522755755 15.173539678375606 ...\n", - " -153.251127\n", - " 148.414002\n", - " 323.957256\n", + " MULTIPOINT Z ((-168.77797 -15.03795 -140025.89...\n", + " POINT Z (-168.77796628287317 -15.0379479973095...\n", + " -152.538718\n", + " -157.610317\n", + " 273.570918\n", + " 2023-12-09 00:00:00+00:00\n", " 2023-12-09 00:00:00+00:00\n", - " 2023-12-09 00:01:20+00:00\n", " 2023-12-09 00:00:00+00:00\n", " \n", " \n", " 2\n", " COSMIC-2 FM5\n", - " BEIDOU-3 M12 (C26)\n", + " GSAT0204 (PRN E22)\n", " True\n", - " MULTIPOINT Z (43.67416 -10.25178 12330.4085, 4...\n", - " POINT Z (43.67415791885335 -10.251779478855163...\n", - " -22.664009\n", - " 6.514396\n", - " 104.465665\n", + " MULTIPOINT Z ((-123.08294 -4.46644 57923.5435))\n", + " POINT Z (-123.08293905189406 -4.46644351601891...\n", + " -29.775128\n", + " 46.320778\n", + " 59.395134\n", + " 2023-12-09 00:00:00+00:00\n", " 2023-12-09 00:00:00+00:00\n", - " 2023-12-09 00:00:20+00:00\n", " 2023-12-09 00:00:00+00:00\n", " \n", " \n", " 3\n", " COSMIC-2 FM5\n", - " COSMOS 2557 (706K)\n", - " False\n", - " MULTIPOINT Z (5.13858 7.72295 58877.01276, 5.2...\n", - " POINT Z (5.509687281172558 7.9175282357802645 ...\n", - " -156.574267\n", - " 169.009785\n", - " 304.029604\n", - " 2023-12-09 00:00:20+00:00\n", - " 2023-12-09 00:02:00+00:00\n", - " 2023-12-09 00:00:50+00:00\n", + " GPS BIIF-8  (PRN 03)\n", + " True\n", + " MULTIPOINT Z ((-117.65164 -27.19053 -144220.44...\n", + " POINT Z (-117.23942060857958 -26.9441789094456...\n", + " -25.070771\n", + " -12.674843\n", + " 112.622206\n", + " 2023-12-09 00:00:00+00:00\n", + " 2023-12-09 00:00:55.846369+00:00\n", + " 2023-12-09 00:00:26.428391+00:00\n", " \n", " \n", " 4\n", " COSMIC-2 FM5\n", - " GSAT0215 (PRN E21)\n", - " True\n", - " MULTIPOINT Z (43.4354 -21.61952 -197994.75109,...\n", - " POINT Z (44.94849495030172 -20.81904846996197 ...\n", - " -23.893350\n", - " -17.767677\n", - " 126.837457\n", - " 2023-12-09 00:00:10+00:00\n", - " 2023-12-09 00:01:50+00:00\n", - " 2023-12-09 00:01:30+00:00\n", + " COSMOS 2544 (759)\n", + " False\n", + " MULTIPOINT Z ((-152.24639 5.87018 -11646.86501...\n", + " POINT Z (-150.48323741351425 6.201606650863404...\n", + " -147.249336\n", + " 134.995712\n", + " 334.193139\n", + " 2023-12-09 00:00:00+00:00\n", + " 2023-12-09 00:00:59.312725+00:00\n", + " 2023-12-09 00:00:43.508347+00:00\n", " \n", " \n", " ...\n", @@ -330,147 +331,147 @@ " ...\n", " \n", " \n", - " 133\n", + " 3360\n", " COSMIC-2 FM5\n", - " BEIDOU-3S M1S (C58)\n", + " GSAT0223 (PRN E34)\n", " False\n", - " MULTIPOINT Z (-148.06901 -0.43361 47281.53233,...\n", - " POINT Z (-146.992685046998 -0.8190136846429314...\n", - " -151.471053\n", - " -140.421688\n", - " 212.568932\n", - " 2023-12-09 00:57:50+00:00\n", - " 2023-12-09 00:59:50+00:00\n", - " 2023-12-09 00:58:20+00:00\n", + " MULTIPOINT Z ((-126.63597 -37.51713 49840.00554))\n", + " POINT Z (-126.63596583326387 -37.5171349029685...\n", + " -149.448744\n", + " -133.037701\n", + " 237.261187\n", + " 2023-12-10 00:00:00+00:00\n", + " 2023-12-10 00:00:00+00:00\n", + " 2023-12-10 00:00:00+00:00\n", " \n", " \n", - " 134\n", + " 3361\n", " COSMIC-2 FM5\n", - " BEIDOU-3 M12 (C26)\n", - " False\n", - " MULTIPOINT Z (-157.82214 20.0755 44731.25716, ...\n", - " POINT Z (-157.66151831979104 20.63188433047461...\n", - " -155.288807\n", - " 154.959046\n", - " 271.270420\n", - " 2023-12-09 00:58:10+00:00\n", - " 2023-12-09 00:59:50+00:00\n", - " 2023-12-09 00:58:30+00:00\n", + " BEIDOU-2 M6 (C14)\n", + " True\n", + " MULTIPOINT Z ((-79.69433 -31.15103 -93262.06096))\n", + " POINT Z (-79.69433144679905 -31.15103311351055...\n", + " -26.140112\n", + " -19.067053\n", + " 100.404027\n", + " 2023-12-10 00:00:00+00:00\n", + " 2023-12-10 00:00:00+00:00\n", + " 2023-12-10 00:00:00+00:00\n", " \n", " \n", - " 135\n", + " 3362\n", " COSMIC-2 FM5\n", - " COSMOS 2500 (755)\n", - " True\n", - " MULTIPOINT Z (-126.69412 44.90944 -194723.7506...\n", - " POINT Z (-121.58919915764217 44.59651929331758...\n", - " -47.093233\n", - " 63.730350\n", - " 16.239802\n", - " 2023-12-09 00:58:40+00:00\n", - " 2023-12-09 00:59:50+00:00\n", - " 2023-12-09 00:59:50+00:00\n", + " BEIDOU-3 M1 (C19)\n", + " False\n", + " MULTIPOINT Z ((-125.56756 -10.51066 20051.50999))\n", + " POINT Z (-125.56756338675854 -10.5106631704847...\n", + " -154.711023\n", + " 150.926813\n", + " 308.615986\n", + " 2023-12-10 00:00:00+00:00\n", + " 2023-12-10 00:00:00+00:00\n", + " 2023-12-10 00:00:00+00:00\n", " \n", " \n", - " 136\n", + " 3363\n", " COSMIC-2 FM5\n", - " GSAT0224 (PRN E10)\n", - " True\n", - " MULTIPOINT Z (-123.55537 45.42334 -193679.7174...\n", - " POINT Z (-121.36527296079892 45.17437814841084...\n", - " -47.875649\n", - " 63.751499\n", - " 16.226191\n", - " 2023-12-09 00:59:20+00:00\n", - " 2023-12-09 00:59:50+00:00\n", - " 2023-12-09 00:59:50+00:00\n", + " GPS BIIF-1  (PRN 25)\n", + " False\n", + " MULTIPOINT Z ((-122.98476 -40.86321 54523.9457))\n", + " POINT Z (-122.98476368556415 -40.8632073142474...\n", + " -142.298592\n", + " -121.274281\n", + " 224.042432\n", + " 2023-12-10 00:00:00+00:00\n", + " 2023-12-10 00:00:00+00:00\n", + " 2023-12-10 00:00:00+00:00\n", " \n", " \n", - " 137\n", + " 3364\n", " COSMIC-2 FM5\n", - " GPS BIIF-8 (PRN 03)\n", + " GPS BIIR-8  (PRN 16)\n", " True\n", - " MULTIPOINT Z (-102.06523 18.52248 -194739.9090...\n", - " POINT Z (-101.93398093154444 18.83019958019392...\n", - " -25.946455\n", - " -11.634382\n", - " 96.639069\n", - " 2023-12-09 00:59:30+00:00\n", - " 2023-12-09 00:59:50+00:00\n", - " 2023-12-09 00:59:50+00:00\n", + " MULTIPOINT Z ((-79.23357 -30.96364 -111581.622...\n", + " POINT Z (-79.23357125823694 -30.96364204949102...\n", + " -26.428759\n", + " -18.469231\n", + " 99.625960\n", + " 2023-12-10 00:00:00+00:00\n", + " 2023-12-10 00:00:00+00:00\n", + " 2023-12-10 00:00:00+00:00\n", " \n", " \n", "\n", - "

138 rows × 11 columns

\n", + "

3365 rows × 11 columns

\n", "" ], "text/plain": [ - " receiver transmitter is_rising \\\n", - "0 COSMIC-2 FM5 BEIDOU-3 M7 (C27) False \n", - "1 COSMIC-2 FM5 COSMOS 2534 (758) False \n", - "2 COSMIC-2 FM5 BEIDOU-3 M12 (C26) True \n", - "3 COSMIC-2 FM5 COSMOS 2557 (706K) False \n", - "4 COSMIC-2 FM5 GSAT0215 (PRN E21) True \n", - ".. ... ... ... \n", - "133 COSMIC-2 FM5 BEIDOU-3S M1S (C58) False \n", - "134 COSMIC-2 FM5 BEIDOU-3 M12 (C26) False \n", - "135 COSMIC-2 FM5 COSMOS 2500 (755) True \n", - "136 COSMIC-2 FM5 GSAT0224 (PRN E10) True \n", - "137 COSMIC-2 FM5 GPS BIIF-8 (PRN 03) True \n", + " receiver transmitter is_rising \\\n", + "0 COSMIC-2 FM5 BEIDOU-3 G3 (C61) False \n", + "1 COSMIC-2 FM5 BEIDOU-2 G7 (C03) False \n", + "2 COSMIC-2 FM5 GSAT0204 (PRN E22) True \n", + "3 COSMIC-2 FM5 GPS BIIF-8 (PRN 03) True \n", + "4 COSMIC-2 FM5 COSMOS 2544 (759) False \n", + "... ... ... ... \n", + "3360 COSMIC-2 FM5 GSAT0223 (PRN E34) False \n", + "3361 COSMIC-2 FM5 BEIDOU-2 M6 (C14) True \n", + "3362 COSMIC-2 FM5 BEIDOU-3 M1 (C19) False \n", + "3363 COSMIC-2 FM5 GPS BIIF-1 (PRN 25) False \n", + "3364 COSMIC-2 FM5 GPS BIIR-8 (PRN 16) True \n", "\n", - " geometry \\\n", - "0 MULTIPOINT Z (-0.03172 8.43027 -111422.03599, ... \n", - "1 MULTIPOINT Z (8.38669 15.17354 -20482.00391, 8... \n", - "2 MULTIPOINT Z (43.67416 -10.25178 12330.4085, 4... \n", - "3 MULTIPOINT Z (5.13858 7.72295 58877.01276, 5.2... \n", - "4 MULTIPOINT Z (43.4354 -21.61952 -197994.75109,... \n", - ".. ... \n", - "133 MULTIPOINT Z (-148.06901 -0.43361 47281.53233,... \n", - "134 MULTIPOINT Z (-157.82214 20.0755 44731.25716, ... \n", - "135 MULTIPOINT Z (-126.69412 44.90944 -194723.7506... \n", - "136 MULTIPOINT Z (-123.55537 45.42334 -193679.7174... \n", - "137 MULTIPOINT Z (-102.06523 18.52248 -194739.9090... \n", + " geometry \\\n", + "0 MULTIPOINT Z ((-168.50136 -15.59247 -124560.11... \n", + "1 MULTIPOINT Z ((-168.77797 -15.03795 -140025.89... \n", + "2 MULTIPOINT Z ((-123.08294 -4.46644 57923.5435)) \n", + "3 MULTIPOINT Z ((-117.65164 -27.19053 -144220.44... \n", + "4 MULTIPOINT Z ((-152.24639 5.87018 -11646.86501... \n", + "... ... \n", + "3360 MULTIPOINT Z ((-126.63597 -37.51713 49840.00554)) \n", + "3361 MULTIPOINT Z ((-79.69433 -31.15103 -93262.06096)) \n", + "3362 MULTIPOINT Z ((-125.56756 -10.51066 20051.50999)) \n", + "3363 MULTIPOINT Z ((-122.98476 -40.86321 54523.9457)) \n", + "3364 MULTIPOINT Z ((-79.23357 -30.96364 -111581.622... \n", "\n", - " position rx_tx_pitch \\\n", - "0 POINT Z (-0.0317183312985065 8.430274910021483... -154.829254 \n", - "1 POINT Z (8.386687522755755 15.173539678375606 ... -153.251127 \n", - "2 POINT Z (43.67415791885335 -10.251779478855163... -22.664009 \n", - "3 POINT Z (5.509687281172558 7.9175282357802645 ... -156.574267 \n", - "4 POINT Z (44.94849495030172 -20.81904846996197 ... -23.893350 \n", - ".. ... ... \n", - "133 POINT Z (-146.992685046998 -0.8190136846429314... -151.471053 \n", - "134 POINT Z (-157.66151831979104 20.63188433047461... -155.288807 \n", - "135 POINT Z (-121.58919915764217 44.59651929331758... -47.093233 \n", - "136 POINT Z (-121.36527296079892 45.17437814841084... -47.875649 \n", - "137 POINT Z (-101.93398093154444 18.83019958019392... -25.946455 \n", + " position rx_tx_pitch \\\n", + "0 POINT Z (-168.5013603513564 -15.59246934270609... -152.632620 \n", + "1 POINT Z (-168.77796628287317 -15.0379479973095... -152.538718 \n", + "2 POINT Z (-123.08293905189406 -4.46644351601891... -29.775128 \n", + "3 POINT Z (-117.23942060857958 -26.9441789094456... -25.070771 \n", + "4 POINT Z (-150.48323741351425 6.201606650863404... -147.249336 \n", + "... ... ... \n", + "3360 POINT Z (-126.63596583326387 -37.5171349029685... -149.448744 \n", + "3361 POINT Z (-79.69433144679905 -31.15103311351055... -26.140112 \n", + "3362 POINT Z (-125.56756338675854 -10.5106631704847... -154.711023 \n", + "3363 POINT Z (-122.98476368556415 -40.8632073142474... -142.298592 \n", + "3364 POINT Z (-79.23357125823694 -30.96364204949102... -26.428759 \n", "\n", - " rx_tx_yaw tp_tx_azimuth start \\\n", - "0 174.181601 298.666783 2023-12-09 00:00:00+00:00 \n", - "1 148.414002 323.957256 2023-12-09 00:00:00+00:00 \n", - "2 6.514396 104.465665 2023-12-09 00:00:00+00:00 \n", - "3 169.009785 304.029604 2023-12-09 00:00:20+00:00 \n", - "4 -17.767677 126.837457 2023-12-09 00:00:10+00:00 \n", - ".. ... ... ... \n", - "133 -140.421688 212.568932 2023-12-09 00:57:50+00:00 \n", - "134 154.959046 271.270420 2023-12-09 00:58:10+00:00 \n", - "135 63.730350 16.239802 2023-12-09 00:58:40+00:00 \n", - "136 63.751499 16.226191 2023-12-09 00:59:20+00:00 \n", - "137 -11.634382 96.639069 2023-12-09 00:59:30+00:00 \n", + " rx_tx_yaw tp_tx_azimuth start \\\n", + "0 -156.364285 272.373899 2023-12-09 00:00:00+00:00 \n", + "1 -157.610317 273.570918 2023-12-09 00:00:00+00:00 \n", + "2 46.320778 59.395134 2023-12-09 00:00:00+00:00 \n", + "3 -12.674843 112.622206 2023-12-09 00:00:00+00:00 \n", + "4 134.995712 334.193139 2023-12-09 00:00:00+00:00 \n", + "... ... ... ... \n", + "3360 -133.037701 237.261187 2023-12-10 00:00:00+00:00 \n", + "3361 -19.067053 100.404027 2023-12-10 00:00:00+00:00 \n", + "3362 150.926813 308.615986 2023-12-10 00:00:00+00:00 \n", + "3363 -121.274281 224.042432 2023-12-10 00:00:00+00:00 \n", + "3364 -18.469231 99.625960 2023-12-10 00:00:00+00:00 \n", "\n", - " end time \n", - "0 2023-12-09 00:00:30+00:00 2023-12-09 00:00:00+00:00 \n", - "1 2023-12-09 00:01:20+00:00 2023-12-09 00:00:00+00:00 \n", - "2 2023-12-09 00:00:20+00:00 2023-12-09 00:00:00+00:00 \n", - "3 2023-12-09 00:02:00+00:00 2023-12-09 00:00:50+00:00 \n", - "4 2023-12-09 00:01:50+00:00 2023-12-09 00:01:30+00:00 \n", - ".. ... ... \n", - "133 2023-12-09 00:59:50+00:00 2023-12-09 00:58:20+00:00 \n", - "134 2023-12-09 00:59:50+00:00 2023-12-09 00:58:30+00:00 \n", - "135 2023-12-09 00:59:50+00:00 2023-12-09 00:59:50+00:00 \n", - "136 2023-12-09 00:59:50+00:00 2023-12-09 00:59:50+00:00 \n", - "137 2023-12-09 00:59:50+00:00 2023-12-09 00:59:50+00:00 \n", + " end time \n", + "0 2023-12-09 00:00:00+00:00 2023-12-09 00:00:00+00:00 \n", + "1 2023-12-09 00:00:00+00:00 2023-12-09 00:00:00+00:00 \n", + "2 2023-12-09 00:00:00+00:00 2023-12-09 00:00:00+00:00 \n", + "3 2023-12-09 00:00:55.846369+00:00 2023-12-09 00:00:26.428391+00:00 \n", + "4 2023-12-09 00:00:59.312725+00:00 2023-12-09 00:00:43.508347+00:00 \n", + "... ... ... \n", + "3360 2023-12-10 00:00:00+00:00 2023-12-10 00:00:00+00:00 \n", + "3361 2023-12-10 00:00:00+00:00 2023-12-10 00:00:00+00:00 \n", + "3362 2023-12-10 00:00:00+00:00 2023-12-10 00:00:00+00:00 \n", + "3363 2023-12-10 00:00:00+00:00 2023-12-10 00:00:00+00:00 \n", + "3364 2023-12-10 00:00:00+00:00 2023-12-10 00:00:00+00:00 \n", "\n", - "[138 rows x 11 columns]" + "[3365 rows x 11 columns]" ] }, "metadata": {}, @@ -481,7 +482,7 @@ "from tatc.analysis import collect_ro_observations\n", "\n", "ro_obs = collect_ro_observations(\n", - " rx, txs, times, sample_elevation, max_yaw, range_elevation\n", + " rx, txs, start, end, time_step, sample_elevation, max_yaw, range_elevation\n", ")\n", "display(ro_obs)" ] @@ -495,7 +496,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -513,20 +514,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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22.41505 0)\n", - "22 22 POINT Z (112.27912 22.41505 0)\n", - "23 23 POINT Z (157.24514 22.41505 0)\n", - "24 24 POINT Z (-157.51699 67.38106 0)\n", - "25 25 POINT Z (-112.55097 67.38106 0)\n", - "26 26 POINT Z (-67.58495 67.38106 0)\n", - "27 27 POINT Z (-22.61894 67.38106 0)\n", - "28 28 POINT Z (22.34708 67.38106 0)\n", - "29 29 POINT Z (67.3131 67.38106 0)\n", - "30 30 POINT Z (112.27912 67.38106 0)\n", - "31 31 POINT Z (157.24514 67.38106 0)" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "from tatc.generation import generate_equally_spaced_points\n", + "from tatc.generation import generate_points_uniform_spacing\n", "\n", - "points_df = generate_equally_spaced_points(5000e3)\n", + "points_df = generate_points_uniform_spacing(5000e3)\n", "display(points_df)" ] }, @@ -326,21 +96,10 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "5aca5d4a-93b0-4ddf-8e01-99dfcd4ebb30", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import cartopy.crs as ccrs\n", @@ -362,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "1169501e-9a16-48aa-bcea-3eceae337a6b", "metadata": {}, "outputs": [], @@ -385,18 +144,10 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "dab83f60-5975-405b-afe7-261aba995283", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sequential computation completed in 6.25 seconds\n" - ] - } - ], + "outputs": [], "source": [ "from tatc.analysis import collect_observations\n", "\n", @@ -411,18 +162,10 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "c2fbab13-a087-4ee4-9bf2-429385e20192", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Parallel computation completed in 5.86 seconds\n" - ] - } - ], + "outputs": [], "source": [ "from tatc.analysis import collect_observations\n", "\n", @@ -446,227 +189,10 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "ad62f631-4643-4b1d-a63c-e7a44fe73441", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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point_idgeometrysatelliteinstrumentstartendepochsat_altsat_az
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40POINT Z (-157.51699 -67.51699 0)NOAA 20VIIRS2022-07-15 07:48:30.337424+00:002022-07-15 07:52:58.661577+00:002022-07-15 07:50:44.499500500+00:0027.400167151.767190
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445931POINT Z (157.24514 67.38106 0)NOAA 20VIIRS2022-08-12 18:28:51.557986+00:002022-08-12 18:34:04.353232+00:002022-08-12 18:31:27.955609+00:0031.258697324.067710
446031POINT Z (157.24514 67.38106 0)NOAA 20VIIRS2022-08-12 21:48:45.630367+00:002022-08-12 21:49:49.415577+00:002022-08-12 21:49:17.522972+00:0021.11515512.621024
446131POINT Z (157.24514 67.38106 0)NOAA 20VIIRS2022-08-12 23:25:37.442375+00:002022-08-12 23:31:05.304568+00:002022-08-12 23:28:21.373471500+00:0032.77932636.304442
446231POINT Z (157.24514 67.38106 0)NOAA 20VIIRS2022-08-13 01:04:22.215027+00:002022-08-13 01:11:48.221137+00:002022-08-13 01:08:05.218082+00:0073.79850755.756224
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geometrypoint_idsatelliteinstrumentstartepochendaccessrevisit
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" - ], - "text/plain": [ - " point_id geometry access \\\n", - "0 0 POINT Z (-157.51699 -67.51699 0) 0 days 00:05:30.623220 \n", - "1 1 POINT Z (-112.55097 -67.51699 0) 0 days 00:05:27.458383 \n", - "2 2 POINT Z (-67.58495 -67.51699 0) 0 days 00:05:26.605462 \n", - "3 3 POINT Z (-22.61894 -67.51699 0) 0 days 00:05:30.603801 \n", - "4 4 POINT Z (22.34708 -67.51699 0) 0 days 00:05:30.457130 \n", - "5 5 POINT Z (67.3131 -67.51699 0) 0 days 00:05:26.053693 \n", - "6 6 POINT Z (112.27912 -67.51699 0) 0 days 00:05:28.794415 \n", - "7 7 POINT Z (157.24514 -67.51699 0) 0 days 00:05:28.645462 \n", - "8 8 POINT Z (-157.51699 -22.55097 0) 0 days 00:05:42.864787 \n", - "9 9 POINT Z (-112.55097 -22.55097 0) 0 days 00:05:53.476411 \n", - "10 10 POINT Z (-67.58495 -22.55097 0) 0 days 00:06:05.461238 \n", - "11 11 POINT Z (-22.61894 -22.55097 0) 0 days 00:05:54.254635 \n", - "12 12 POINT Z (22.34708 -22.55097 0) 0 days 00:05:38.389979 \n", - "13 13 POINT Z (67.3131 -22.55097 0) 0 days 00:05:49.560363 \n", - "14 14 POINT Z (112.27912 -22.55097 0) 0 days 00:06:12.242443 \n", - "15 15 POINT Z (157.24514 -22.55097 0) 0 days 00:05:55.908103 \n", - "16 16 POINT Z (-157.51699 22.41505 0) 0 days 00:06:12.222354 \n", - "17 17 POINT Z (-112.55097 22.41505 0) 0 days 00:05:42.024086 \n", - "18 18 POINT Z (-67.58495 22.41505 0) 0 days 00:05:45.371793 \n", - "19 19 POINT Z (-22.61894 22.41505 0) 0 days 00:05:50.506729 \n", - "20 20 POINT Z (22.34708 22.41505 0) 0 days 00:06:04.880874 \n", - "21 21 POINT Z (67.3131 22.41505 0) 0 days 00:05:49.820338 \n", - "22 22 POINT Z (112.27912 22.41505 0) 0 days 00:05:37.878183 \n", - "23 23 POINT Z (157.24514 22.41505 0) 0 days 00:05:51.738942 \n", - "24 24 POINT Z (-157.51699 67.38106 0) 0 days 00:05:32.915323 \n", - "25 25 POINT Z (-112.55097 67.38106 0) 0 days 00:05:29.659118 \n", - "26 26 POINT Z (-67.58495 67.38106 0) 0 days 00:05:34.500849 \n", - "27 27 POINT Z (-22.61894 67.38106 0) 0 days 00:05:29.413149 \n", - "28 28 POINT Z (22.34708 67.38106 0) 0 days 00:05:33.398720 \n", - "29 29 POINT Z (67.3131 67.38106 0) 0 days 00:05:29.335932 \n", - "30 30 POINT Z (112.27912 67.38106 0) 0 days 00:05:26.471776 \n", - "31 31 POINT Z (157.24514 67.38106 0) 0 days 00:05:29.072607 \n", - "\n", - " revisit samples \n", - "0 0 days 03:11:45.894393 216 \n", - "1 0 days 03:08:40.137703 220 \n", - "2 0 days 03:08:13.864107 220 \n", - "3 0 days 03:10:24.345981 218 \n", - "4 0 days 03:11:46.073491 216 \n", - "5 0 days 03:11:05.197080 220 \n", - "6 0 days 03:11:02.062489 220 \n", - "7 0 days 03:12:51.262517 218 \n", - "8 0 days 10:28:27.927617 68 \n", - "9 0 days 10:47:46.979011 66 \n", - "10 0 days 11:06:45.038138 64 \n", - "11 0 days 10:47:46.313110 66 \n", - "12 0 days 10:37:41.709542 66 \n", - "13 0 days 10:29:26.878119 68 \n", - "14 0 days 11:07:50.637346 64 \n", - "15 0 days 10:48:53.594193 66 \n", - "16 0 days 11:18:30.827075 64 \n", - "17 0 days 10:29:35.152544 68 \n", - "18 0 days 10:49:05.422722 66 \n", - "19 0 days 10:48:59.249179 66 \n", - "20 0 days 10:57:34.171803 66 \n", - "21 0 days 10:47:51.108372 66 \n", - "22 0 days 10:28:32.996863 68 \n", - "23 0 days 10:47:49.017594 66 \n", - "24 0 days 03:24:52.485931 206 \n", - "25 0 days 03:22:54.091040 208 \n", - "26 0 days 03:25:56.850045 204 \n", - "27 0 days 03:22:54.329676 208 \n", - "28 0 days 03:24:52.018617 206 \n", - "29 0 days 03:19:53.467370 208 \n", - "30 0 days 03:17:58.496404 210 \n", - "31 0 days 03:19:53.714949 208 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from tatc.analysis import reduce_observations\n", "\n", @@ -1319,21 +252,10 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "c6f33c4d-69cc-4955-94d4-7c8b22486bde", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import cartopy.crs as ccrs\n", @@ -1359,231 +281,15 @@ "id": "f2baca20-d8fb-46c9-9a59-226b75954515", "metadata": {}, "source": [ - "Coverage analyses can also be aggregated to spatial regions for improved visualizations. This example focuses on a smaller spatial region covering the Continental United States (CONUS) defined by a Polygon. Similar to how the `generate_equally_spaced_points` generates uniformly-distributed points (in latitude/longitude), the function `generate_equally_spaced_cells` generates uniformly distributed cells. This example uses twice the characteristic distance (1000 km vs. 500 km) such that each cell covers about four points." + "Coverage analyses can also be aggregated to spatial regions for improved visualizations. This example focuses on a smaller spatial region covering the Continental United States (CONUS) defined by a Polygon. Similar to how the `generate_points_uniform_spacing` generates uniformly-distributed points (in latitude/longitude), the function `generate_cells_uniform_spacing` generates uniformly distributed cells. This example uses twice the characteristic distance (1000 km vs. 500 km) such that each cell covers about four points." ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "bbbf88d8-cba5-4d37-8c46-634f365ff296", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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cell_idgeometry
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DGTt2LHPmzKFr167p6jnLkqLsZRUrVuSTTz5h3bp1fPXVVwwaNIj58+cTFxeHmZkZAQH3gZQux/rNPn7jceo3bU9EWDD7dqaMU5v4Yy++HDCO8lXq8vWQSQz7pj0A9+7eYMrovoycvBgPz7yZ/vqEEEIIkfkeBd5l8ogvcXb14JOu/fAqVIz8BYu+dR+NRk2A/3XOnz7EsYN7gCDu3i3J5MmT6dKlCx4eHunOYWpqSsGCBSlYsCCtW7cmODiY77//nu7du7Nr1y7mzZuX5mNleVHm5OTEmjVruHXrFu3bt6dv377UqVOHhg0b8uzZM65evQpA87YjyJkr31uPVadxG/b8uxb3nHl49uQBC2eOoVT56vT7fgptOn6JpZU1Neo258dvO3Boz2Y+6dovK16iEEIIITJJfHws+3duYPe21Tg6uzFyyhIsLCzf2D4pKZGL545x6dwxLl84QVRkODa2DhQt2YSzJ4byzz/lqVBBfwP/XV1dmTt3LiVKlGD48OFcvHgRX9+0zZ2a5UXZC4ULF+bcuXPcuHGDYsWKoVKpKFOmDDY29sTErKdcJWfg7cs2OLt48FnvISybN0m3rUTpSgA0a/OZblu12k04sn8bHrnyUalaA5keQwghhFCgp48D+W3iQKIjw6lZvxXN23Z9Y0EWFxvDob1b2PfvWsLDgsmdtyA16rWkVLlqFCxcnAf37Dl7ojyZcSNm+fLluX79OgCXL1/m+PHjadrPYEUZgJmZGSVKlNA9jouLez4prD/JSWkrnKrVbsrZkwe49Pzz5OTkZN1zAf7XOHviALnyemFmZs6qRb/w7HEgH3/2jV5fhxBCCCEyV9DTh/w2cSDW1jYMHTsHF7ecr7TRarX437zMycO7OHVsL0mJCVSt1ZhGLT/FM3eBLMu6YMEC6tSpo3tcsmTJNO1n0KLsv/bs2UOfPsPYtasvs6Y5Ur1OI6rUbETeAj5vHSjXuOWnuqLs5rULlK1Yiwf3bzP/t5G6Np9/PYKzJw9w85pfpr8OIYQQQuhPSNATfps4EEtLKwaNnI6Do0uq55MSEzjgu4kDuzcS/OwRTi7u1GvSjrqN2uLo7JrleWvXrk1SUhLdunVj+/btaR6rZlRFWf78+Zk0aTW7do2jZNnxnDi8jT071uDimpOylWpRsVp9vAoVf6VAK1y0DLnzFuRh4B2u+p1ig9l8zp08gHUOW+Jio/HIlY98XkU4uHcL8XHKWcleCCGE+NAlJSUya+pQTE3NGDRyxisF2bXLZ/lz/mQiwkKoXKMh3fr8QOGiZTAx0dtKkhliZmaGt7c3FhYWad8nE/O8Bx8atRhMj696cfPaBc6fPsSZ4/vY+3xQf5WajSlbsRa58xVEpVKhUqkY8OMvnDzqy/pV89Bo1AC6iePMzcyZOfk7wkKDsM5ha8gXJoQQQnxQAu/dZuKwz8mTz5t6TduTK48XnrkLpHl8987Nq3j6JJCRkxbh5Pz/ub9iY6LYs2MN/25eiU+xsgwaMd1oZlnQaDScOXOGuXPn0qpVqzTvZ6RFWYoZk7/j2eNAmrTuzNhflnPvzg1OHN6F7/a/2bpuCfYOzuTz8iFvgcKcP3WQJ4/uA1CsZEVatO3G+VOHOHpgBxaWVsTHx+mOGxkRhr2Dk6FelhBCCPHBsLKyBuDBfX9WLEw9YXz+gkVp/UkvipeuiImJ6Sv7Xr14ip2bV9KkVWfdfKMhQU/YuHoBly6cQK1OpkmrzrT6uGeqieQNJSwsjEGDBrF161ZCQ0PJnTv3GybJfz3Dv4K3uH/3BvFxsaxdMYeNfy+kSPFylChTmU+69uP+3Zvcuu7H/bs3OLJvK/HxcQz48VfcPHLh5pEbgHxeRfio4xcc3ruVlYt+BlJ6z/5a8itfDZpoyJcmhBBCfBDcPHIz/6+D7N+1gUvnj3Hrmh9JSSkz4N+7c51ZU78HoIB3Ueo1aU/lGg1RqUw4eWQ3KxZOo1ipirRo1x2A44d2snrJdKxtbGnY/BNqN/zolY8zDUWr1fLRRx9x5coVvvnmGxo1akSVKlWwsLAgMjJtq58bdVHm7OKBT/FyNGndiVNH93DV7xRrV8xh5+ZVlKtcB1d3T6rUbEKDZh2wss6Bq5sntvYOPAy8Q0RYMMVKVUKlUrFj04pUx71x5byBXpEQQgjx4VGpVNRv2p76TdvrtiXEx3H+9CFWL51OfFwsAf7XWTr3J5bO/QkXt5yEBD2hSs3GdO8zDBNTU7asXcz2DX9SrU4zOnbrnyXTW50/f54ZM2ZQr149unbtiqnpq715Go2GJUuWsGzZMo4ePYqvry8NGzbM0PmMuiiLjo7AxtYOZxcPmrbuQtPWXQh+9ojdW//m8oUThIcG6artF1QqFVqtFoCylWph7+BMaPCTVG1iY6I4d+og5SvXQQghhBBZz9LKmqq1mlC1VhMiI8K4ceUcfy35ldiYaMzMLfhu9O/4FCtLUlIif86dwskju2nbqQ9NWnV+r0W/XycqKop58+bxww8/AODm5oajoyMBAQG4ubmxfPlypkyZQteuXenUqRMFCxYE4OjRo4wbN449e/bQsGFDtmzZkuGCDIy8KMuZKz9PHt5Ptc3VPRedew0GUroKExLiiIoIJzIilKjIMKIiwrF3cCI2JorNaxfr9vP2KYX/zUu6xwumj2L01KWyJqYQQghhYPYOTlSq3oCChUswvH8HmrXugk+xsly/fJa/l80g6NljevcfQ6VqDfR+7ocP71KhQsFU24KCgkhKSqJ3797MmDGDCxcuMGPGDCZNmsTIkSPx8fHBxMSE69evU7RoUbZs2ULLli2BlLUwBw0aRMmSJfnyyy/TlcWoi7ICBYty9MAOYqKjsLG1e+V5lUqFlVUOrKxy4OaR65Xnq9VJWfDcd9vfrFs1N9VzFhaWWD4ffCiEEEIIwztxeBfmFpaUq1yHAP9rzJj0Hd4+Jen17Rjy5i+UKed0dHTlk08+wdzcnP79+2Ntbc2PP/7Iv//+y7x589i9ezfNmzfn999/548//mD79u0cOXIEtVrN1KlTadmyZarpN6ZOncqsWbMoXrx49irK6jVpx5H92/h1/Lf0H/ZLhiaAS0pMwNn11UnbPurYG1f3Vws5IYQQQmS9uNgYDuzeSLVaTTA1NWXFHz+TJ783g0fNyNQ7K21s7FizZk2qbdu3byc0NJSDBw+yd+9eVq1axbZt27h+/TodOnTgk08+Yd++fWzZsoWPPvoIc3Nztm7dip2dHSNGjACgWbNm6c5i2JnV3sHZ1YPvx84hNiaKeb+NQKPRpGt/jUbDrKlDWThzzCvP1W3cTl8xhRBCCPEekpOTWDJnAomJCTRq+SmLZo3n6eNAun051GBTXTg7O9O2bVtmz57NwYMHuXv3LqtXr+bevXs0aNCAhg0bsnXrVgCSkpJo2rRpqkKsW7du6T6nURdlALnyFODzb0YR4H+N86cPpWvfnZtXcuNq6jstbe0cAAw+068QQgghIDExgXm/juDqxdP07jeaXVv+4uK5Y/QZMJ58XkUMHQ9IWbuyVatW9OjRgwIFCnD79m127NiBv78/cXFxPH78mOnTpxMZGUnp0qU5efIkpUuXTvd5jPrjyxd8ipclb4HCXDp/nApV6qZpn6sXT7N5zSIAKlVvyOljewCIjooAIDw0+LUfawohhBAia8TFxjD3lx8JuHOdLweM4/jhnZw9sZ8eX/1IqfLVDB0vleXLl7N8+XKcnJxo1aoVjo6OAFhZWeHi4sLs2bOpXr06Bw4cwNzcPEPnUERRBlCwUAluXD2ne6xSa8h53Y8c4SHEOrrwpGgZtKYpvV9JiQnMnPwdAN2+/IEa9VqQN38hNqyej3UOO+Jio0hOTsqS3G/LaUwkp34pIacSMoLk1DfJqV9KyGmsGQP8r/Hn/CmEhQbxzZDJbFmzmMC7Nxjd9BMquubkiVpjFDlfcHR0pH///q997uzZs/j7+7N8+fIMF2SgoKLMq1AxDu7ZRGREGKVvXKL6spnYhgXpno92cuNYjwEEVK7NpfPHddvX/zWfqrWa0KhlRzb+vYC42CgAgp89xj1nnkzNXODUobfmNBaSU7+UkFMJGUFy6pvk1C8l5DTWjDevXmD6T4PIk78QQ8fO4eHW1dy5dZnDQI2da2HnWqPImVb79+/HxsaGSpUqvddxjKcEfYdS5atjYWmF37IZNJo+EpuXvsEAbMKCaDR9JAVOHcLC0kq3PSY6gq+71md4/466SWVVKhWOTum/kzM9Cpw69M6cxkBy6pcSciohI0hOfZOc+qWEnMaaMTIijEWzxlGoaGl+GD+X0ytms+jwTvoCNYwoZ1rt2LGD8ePH07Zt2/fqJQMFFWW2dg7UrNOc7ScPEAf8dy7fF4+r/TmTUqUq8+uCLRQqUkr3fFjIMwAcHF3oN3QqufJ6ZVpWlVpD9WUzU+V6XU6VOn13k+qb5NQvJeRUQkaQnPomOfVLCTmNNaNGo2HJnAloNBp69xvFjnXL+PfSaeYDs40oZ1qo1Wp+/fVX2rRpQ+PGjfnjjz/e+5iKKcoAPspfiDCtlrNveF4F2IUGkfO6H8FBj7l94/8z+JtbWGJhacXoqUspWbZqpubMed0P27CgV/4hvC6nIUlO/VJCTiVkBMmpb5JTv5SQ0xgzRkWGs3TuT1y/fJZe/UZx5eJptm9ewSSgD68Wj4bKmRahoaGULl2aIUOGUKFCBbp168bjx4/f+7iKGVMGUMQspVuwNrAGaAe8ujQo5AgPwa1MZRq16IhGqyUpIZ4njwNp2b4HtvaOmZ4zR3iIXttlFsmpX0rIqYSM6Tm/5EwbyalfSshpTBk1Gg2H9mxmw+r5mJiY0rJ9T04f38vR/dtpVrwcP/xn6ipD5UyPGzducPXqVQBOnDjBxx9/DMDixYv5/PPPX2m/du3aNB1XUUVZoou77usOz//eA/x3JaxYRxdsbO35+LNvsiraK+fXZ7vMIjn1Swk5lZAxPeeXnGkjOfVLCTmNJWNUZDi/Tx7C/YCbANg7OLNt/TJsbO3o/PlgOnrmQ5WGoszQ7/l/VatWjYsXL+Lr60uNGjVQq9XUqFEDBweHVO20Wi0TJ05k9OjRaTquoj6+fFK0DMOtcugeewAlXnpeC0Q5u/GkaJmsjpbKk6JliHZyQ/uG5yVn+khO/VFCRpCc+iY59UsJOY0hY3RUBKMGddIVZACmZmZ82mMAk2etpU6jNjwtVtbgOTOqVKlSDB48mCpVqnDgwAGsrKxo0qRJqjbLli1j9OjRtGjRIk3HVFRRpjU1waxRGwD+AZ4AOV889/zv490HGHxeE62pCcd6DEj5+r/PPf9bcqad5NQfJWQEyalvklO/lJDTUBk1GjXXLp1h4rBefPdlK+JiYwBwz5mHnl+P4KeZf1O3cVvdLAlKuJZpUaRIEeLj4wkICNBtO3r0KH369KFXr148e/YsTccx7lf5Gi5tu+Fu50BHYMRL26Od3fAdNNFo5jMJqFwb30ETiXFyS7VdcmaM5NQfJWQEyalvklO/lJAzazKqOX/2D77p1pA+nWrTt0s9ZkwaTOC9WwAUKVGeGYt3MGH6X1St1eS161gq4Vq+S+vWrcmVKxeffvopV65cAWDUqFEkJSXxww8/UKZM2nr6FDWmDMDKOgdFKtfm2d6t/GZuQb0vfyDOydVoZih+WUDl2tyrUNMoZ1J+meTULyXkVEJGkJz6Jjn1Swk5MytjXGw0J48sBv7k9MnUz3kXKUX7zn0p4F00zYuJK+Favo25uTlr167l448/5vfff2fBggVYWaX0Bvr4+FCnTp00HUdxRRnA4b0pq7I7uXtyp2YjA6d5O62pCY9LlDN0jHeSnPqlhJxKyAiSU98kp34pIae+MyYmJjDu++6EhaZMSuvhUYYvBg3A1S0n1jlsM3xcJVzLt6levTqNGjXizJkzAGzYsIG7d+/i6+vLgAED0nQMRRZlL2T2MklCCCGESKHVatm9dTUbVs9/aetTPmr/jLz5wwyWy5i0atWK5cuXc+nSJUqVKkWxYsUwNX3d5F2vp4x+wf9QqVKmmKtetzl+Z45w7dIZoiPDSUxMMHAyIYQQInuJi43h6sVTfNW5jq4gK1OhBh267QXc377zB6Z69eoADB8+XLe0o5eXF99++22a9ldcT5lanax7ofN+HfHK8wOH/0axUhWzOpYQQgiRrTwMvMOGv+Zz+cIJ3TZXd09GTV2KlVUOTh7J3DWklShXrlxMnDiRkSNHUqNGDUqUKIGHhwdt2rRh1qxZ79xfcUWZqakZfQf/xLzfXi3IAGZMGgzArwu2ZMns/UIIIUR2k5ycxG8TBhAdFaHb1urjz2nRrrvu0yrxeoMHD+bevXsEBgbi5+fH3bt3+emnn9K0r+KKMoDSFVLWkS9ctAyNW3XCyjoHBQuX4KDvJtYsn0WxkhUxM7cwcEohhBBCeYKePmTkwE66x5VrNKRm/VYUKa7cQfhZydramoULF+oeJyYmsnbtWj777LN37qvIoszExAQzM3MqVKtH6fLVddsbNPuEBs0+MWAyIYQQQrk0GrWuICtYuAT9hk7DxtbOwKmU5cCBA5QuXRpnZ2cALCwsOHXqVJr2VeRAf7U6meTkJCwsrAwdRQghhMg21iyfDYCrey5+GD9PCrJ0io6Opl69eri4uPDw4UMCAwMBGDlyZJr2V2RRplGrAdJ1m6kQQgghXk+r1bJl7WL271oPwKCR0w2cSJly5Pj/+tx58uQhX758nD59GktLyzTtr8iPL80tLLG0siYqQuZFEUIIITJCq9Wya8tf7NmxhuiocN3MBgNHTMfVzdPA6ZRp1KhRAFSoUIGzZ89iZ2dHmTJliI+PT9P+iizKABydXAkPCzZ0DCGEEEKR/lryG4f2bE61relHXShWsoKBEilTQkICU6dOZcWKFdy+fZsSJUpw5coVmjRpwsaNG7GwsMj+RZmZmTnx8XGGjiGEEEIYPbU6mVlTh3L39jXqNm7L44cB+J05kqqNSmVC8zbdDJRQeR49esTy5ctZuHAhgYGBNG/enNu3b3PlyhW6d+/O/PnzdetfppUii7KkpEQePbhLnUZtDR1FCCGEMHoqlYpb1/xITk5i5+aVr23j5pELSyvrLE5mvJKSklizZg3NmjXT3UkJEB8fz5QpU5g8eTImJia0b9+eb775hi5duuDm5kbPnj2pWLEiZ86cwdHRkWfPnnHnzp00nVORRZmZmTkmJqao1UmGjiKEEEIYPRMTU2Yv38Pd21dZNm8STx8HYmJqqrtxzsbOgU97pG3R7A/BgwcPyJs3LwD//vsvTZs2JSkpiU2bNjFy5Eju3r3L0KFDGTJkCI6OjowdO5a7d+9SuHBhpk2bluHzKrIoiwwPQa1OxtnVw9BRhBBCCKOn0Wg4c3wfq5dOJzYmKmWbWk27zn2p3aAV1jlsDZzQuFy+fFn3tb+/P6NGjWLRokU8efKEevXqsXHjRooXL65r06lTJ65evcqBAwcYM2YMgYGBLFmyBIA9e/ZgZmZG3bp133leRRZlT588AMA9Z14DJxFCCCGMW2jIU34dP4DgZ49024oUL0eX3kPw8Pzw/h9NTIhny7olJMQ7ArWAzgDcvHmT6OhogoKC+Prrr3Xt+/Xrh52dHV27duWrr76iVKlSrxyzUKFCtGvXjvv37zNu3DhMTP4/41hgYCC9e/dOUzZFFmXPHj9ApVLh5i637AohhBBvcnjvVlYu+ln3+NMeAyhToeYH/UnT08eB+G77+/mj+Xz//XpiYh5y8uRJXZuGDRuybt06ypUrx8OHD3F2dk41B9nL9u/fz4ABA7h06RKNGjVixYoVNGvWjG3bttGjRw969uyZ5myKLMqePgnE2dUDc4u0TcYmhBBCfGhW/PEzR/ZtBaBmvZZ80rUfVtavLyw+JHnyFyKHjR1lKrTj+KFQHjzwp3TpwgwePJiIiAhcXFxo27atbuH1PHnyvPY4AQEBDBkyhPXr11OtWjVOnDhBlSpVdM9369aNypUrp/qY812Mtii7eMqJoAevX1T85uVnWFl6cfawUxanMh53b6Z8/n/rqr2Bk2QPcj31R66lfsn11K8P6XrmsGyEl3c8Vat9h519bq6c0e/xlXotExNjiI2J4u6tQGAPq1erKF8+7fvHxMQwZcoUfv75Z1xcXFi5ciWdO3fWFXEvqFQqihUrRkREBMePH6dp06bvPLZK+2IK37eIjIzEwcGBiIgI7O0z9+IfPQo1a76rVUmgLjA7U7MIIYQQIrtJAjyBEGAJPXs+ZvDg1pQsWfKte2m1WlavXs3QoUMJDg5myJAhDBs2DFvbd98kkdY6yuh6yqyfT5HSusNdXN1ePwPuykWBlCpnQZkK17IwmXG5fdOeQ765qdcwEK8i0YaOo3hyPfXn7g1b9u/JS+1GDynkE2noOIon35v6JddTf5R2LRPiwwl+dgULSzsO73MiOjKB5OS9LF26ivDwM2zYsOGN+2o0Gtzc3AgNDQVg+/btNG/eXO8Zja4oe6FU2TDyecW89rkta21wdX9KlZof9jJLh3xzU9gnggo1ZQ1QfZDrqR9mWjX79+SlkE/kB/9vVF/ke1O/5HrqjzFfS61Wi0atJiY6kj8XTOHWdT8Snq8E5OzqweffLGXhzE8AyJcv32uPodFo2LhxI/Pnz9cVZAD16tVLcw61Wp1q37cx2qLsbRITEjA3f/14MyGEEEJkb1vWLibo6UN69RuNRqMmMSGB2Ngogp89xqtQcczMzJk56TtuXffD0soarVZLk1adKV+lDpERYeTJ501IkCewnvbtfZk4ceIr53jxkeML3bp1o1GjRnTu3DnVlBdvM3fuXMaOHUtQUFCa2iuuKNNqtURHR2Br5/DuxkIIIYTINpKTk/h30wq2b/gTgI7dB7Dpn4Uc3rsVExNTNBo1Hp55cXHLybXLZ6hepznWOWyoWK0+BQuXAMAzdwEAQoIA2jF8eDv+OyxMq9WmKsgmTJjAyJEj05U1JCSEQYMGUb58+exblCUlpvSSxcW+/qNNIYQQQmRPN69eYNv6ZbrHp4/v5fDelGk/NBo1JiamPH0cyNPHgRQrVZFufX545a7Id4mLi6NYsWK6x4MHD2bEiBHpzhoREUFiYiKffvopQ4YM4eOPP37nPooryi77nSQhPo6K1eobOooQQgghstCTR/dQqUxwdfck6OlD/l46I9XzGo2aRi0/pWDhEpSvXCdD5/jss8+4d+8eLi4u1KxZk19++SXdhR3An3+m9Ob5+fmleQLZtH0oakTCQ4MwN7fAzSO3oaMIIYQQIoskJSawa8tfVKnZiDHTlpEnfyEA7OydKFCwKAA9vx5J+859M1yQAfTv3x9bW1tCQkL46quv0l2QabVa1q1bx/jx46lWrRqTJ09O876K6ymLCA/F3tE5Q1WrEEIIIZTp9s3LhIcF07B5B8wtLHFydkOjUfPdyJnY2juSmBCPhaXVe5+nTp06DB06lN9++40GDRqke//+/fsze/ZsmjZtyrZt2zA1NSUyMm3TAymuKIuKCMXO/sOdyV8IIYTI7p4+DiQ6KpxceQpy7dJpoqMj2btjDU7ObuTMlTJ9Rb+hU1Pto4+C7AWtVou5uTlmZukrk+bNm8fs2bOZNWsW33zzTbo7kBRXlMXHx2FtbWPoGEIIIYTIBEf2b2PFwmmvbM+dtyCDRs7IknWvy5YtS1BQEA8ePCBv3rxp2mf8+PGMGTOG/v37Z6ggAwUWZYkJ8VhY6a8aFkIIIYRhXb14in83ryIpMYG7t6+meq7PwPGULFcNMzPzNM8P9r4qVaoEwLZt2/Dy8qJ27drkyPHmxdxXr17N+PHjGTFiBBMmTMjwECtFFmX2ji6GjiGEEEIIPfh300o2/bMw1TavwiXo8vlgcubKlyU9Y//l6elJ0aJF+frrrwGoWLEix44dw9zcXNfG39+fPXv2cOrUKZYsWUKXLl0YNWrUe415V1RRplYnExz0GHfPPIaOIoQQQoj3dPPqBTb9s5A6jdrQtHUXnjy6T/CzR1Sr08zgK/ds2bKFvn37Ur9+fUaMGMGqVavo0aMHAEeOHKFp06bExMRgY2PDggUL+OKLL977JkRFFWWXzh8nJOgJVWs1NXQUIYQQQryniPAQAFzdPXF29cDZ1cPAif6vcOHC7NmzB4ATJ04wdepUHB0dAWjbti329vZERUVhY2OjtxkhFFWUHTv4L/m8fChUpJShowghhBDiPYQGP2XdqrnkyV+IWvVbGzrOW40ZM4Z69erRtm1b3bZ27dph+9/1md6TYoqy+PhY/M4cASAhPg5LK2sDJxJCCCFERgQHPea3CQMwMzPj26HTsM5h3LMqVKhQgYcPHxIeHs6NGzfw9vbGy8tL7+dRTFG2cfUCAIqUKK/XuUiEEEIIkXU0Gg3zfh2BSmXCd6Nm4ujsauhIaWJnZ4ednV2ap8jICEUUZVqtFr+zR6nVoBWf9f7e0HGEEEIIkUGXL5zgwb3bDBkzy6jGkBkDRax9+fRxIGEhzyhToaahowghhBAig2Kio9j8zx94+5SkUJHSho5jdBTRU3bt0mlMTc3wKVZWt02l1pDzuh85wkOIdXThSdEyaE2Nr8aUnPolOfVHCRlBcuqb5NQvJeQ0pozLF0whNOQp/YZOfeWORWPKaSiKKMpOH99HoaKldYP7C5w6RPVlM7ENC9K1iXZy41iPAQRUrm2omK+QnPolOfVHCRlBcuqb5NQvJeQ0pozx8bFcOn+cjz/7Gm+f1LMoGFNOQzL6EvTZkwf437hE3UZtgJQ3rtH0kdi89MYB2IQF0Wj6SAqcOmSAlK+SnPolOfVHCRlBcuqb5NQvJeQ0towXzx5DrU6mVLnqqbYbW05DMvqi7M6tlDWwvAoVR6XWUH3ZTAD+O03bi8fV/pyJSq3JuoCvITn1S3LqjxIyguTUN8mpX0rIaYwZb147j529E24euf6fxQhzGpLRF2VFn0+BsW/XenJe98M2LOiVN+4FFWAXGkTO635ZGfEVklO/JKf+KCEjSE59k5z6pYScxpixRJkqREWGsX7VPB4F3jXanIZk9EWZo7Mr9Zt+zEHfTZgEPUnTPjmeL9tgKGk9v+RMG8mpP0rImJ7zS860kZz6pYScxpixeKlKAOzetpo1y2el6/yGfs+zitEXZQCly1cjIT4Of406Te1jHV0yOZF+zi8500Zy6o8SMqbn/JIzbSSnfikhp7Fl1Gq1zPt1BABFipejRfvu6Tq/od/zrKKIoiwxMRGAmGLliHZyQ/uGdlogytmNJ0XLZFm213lStIzk1CPJqT9KyAiSU98kp34pIaexZfx30wquXT5D7/5jGDRyBoWfn9fYchqaMoqy+DgAzKytONZjAMArb+CLx8e7DzD4vCZaUxPJqUeSU3+UkBEkp75JTv1SQk5jynji8G42r1lEoxYdqVStQar5yYwppzFQxKt0dHYDIPjZYwIq18Z30ERinNxStYl2dsN30ESjmc9EcuqX5NQfJWQEyalvklO/lJDTWDJGR4UDsGfHWqb/NIgnD+8ZZU5joIjJY/Pk88bS0ppb1/3w9ilJQOXa3KtQ0+hn/pWc+iU59UcJGUFy6pvk1C8l5DSGjA2bd6BUuWrcuHqeXVv+Yum8nxg2YYGux0yr1RpFTmOgiKLM1MyMIiXKcch3M9XrNMfewQmtqQmPS5QzdLR3kpz6JTn1RwkZQXLqm+TULyXkNIaMHp55U/7kzMNvEwdy/vQhyleuQ2R4KN/3bQNAv++noi1fzaA5DU0xJeinPQaSlJTIgumj0GrfNCRQCCGEEMaqcLGUAfvrVs5Bq9ViYWWFpWXKEoqzf/4B/5uXDRnP4BRTlLm45aRFu+7cvnERtTrZ0HGEEEIIkU4qlQkVqtQjJOgJF88dw8oqB78t2kaFqvUAdPOXfagUU5QBREaEkcPGFhMTRcUWQgghPijnTh3kit+pV7arVCq+HDgOF9ecnDm+D3VyMurkZAp4FwUgwP9aVkc1KooYU/ayhPh4/G9e1s1xIoQQQgjjsmzeJBKeT2fVoHkHOnTtB8Cl88e5fOEk1eo0ZfuGP/E7ewS1Wk1yUqJuX7U6GVNTxZUneqGoLqcWbbuRK68XOzevMnQUIYQQQrxGZEQYSS8VWXZ2jiQnJ7Fm+SxmT/uBA7s3UMC7GFVrNSEhPi5VQWbv6JxqHrMPjaJKUVMzM6rWasLG1QtISkzA3MLS0JGEEEII8ZIH926hUf9/WcTDe7dw/PBOgp8+olGLjvhu/4fZ037QjS/L51WY3Pm88fDMi42tAyYmpgZMb1iKKsoAfIqVITk5iXt3blCoaGlDxxFCCCHES0KDn6V+HPKMKjUb0eubUVjb2HL5wkmsrKzp0L0/BQuX0LXTaDQf/JhxxRVlLm6eAER8ICvGCyGEEMYkPj4WCwsr4uPCgN1cu3KU+OQE1Opk/G9exu/sUYqUKE/eAoUpWrw8BX1KYGNrz9WLp5g04gvdcU4d3aMryk4d9WXx7AlMmbMeJ2e3N5w5+1NcURYYcAsAD898Bk4ihBBCKNOdW1dwdvHA0dn1jW3u373BlrVLsHdwQq1Wk5SUSEx0JDevXsDc3IKEhJSB/EcOmXD4oAaVyoS8BQpRo25zmrftjo2tXarjvdyDVrZiLcpVqkVSUiIDP29GcnJSyrH2baPVxz0z4RUrg+KKsutXzmFr50CuvF6GjiKEEEIo0tTRfQH47Y9t2Nja67ar1cmcO3mQYwf/5caVc9jY2ePk7I65uQXm5hZYWlnTttOXPHvyEBOTmhz0bUuvPtGUrxGGWqPG4i1jvWvWb0nlGg2xsLTSbdNo1JSuUINzJw8A4OzinjkvWCEUV5Q9vO9P/oJFP/jPnYUQQoiMqlKzMSeP7Ob65bNUqFqPxIR4jh7cwZ7t/xD87DE+xcry8WffULN+y1SF1ouPGQGatWkPFODZ07+Ji3XA1t7xned9uSADMDExpc/A8fp8aYqmuKLM1MwM9Ut3dQghhBAifQoXLcPJI7uJjork3KmD/LX4V6KjIqlYtR59Bk7A3TMPQU8ecv/ODeLiYggLecaZE/u5ceWc7hj/buoOdGfLRggJa063PsMM94KyCcUVZXGxMZibWxg6hhBCCKFYd29fAeDG1XOcPbEfJ2d38hYoxI1r5wkLDeLe3RskJSbo2puYmKLRvL5DJL9XPdp3+SZLcmd3iirKIsKCuXHlHJ17fWfoKEIIIYQiabVajh7YAcDZE/sBSEpKxNXNk2KlKhIeGkzJslUpWrI81tY2mFlYEBsdyU/D/3/nZK48XngX+ZLDe7+jSbMb2NiGGeS1ZDeKKspGDeqCVqulSPFyho4ihBBCKNKx5wUZgK2dAwULl+Djz77BwzOvbrtGo8H/5iX27VzP2ZP7iY6KwMo6B5VrNKJek/Z45s7PqaNuHN5rboiXkG0ppigLDX6qu/3W9flcZUIIIYRIG61Wy5LZEzh1bI9uW60GrWnethvPHj/g0N4tBNy+SkjwU54+uk9YaBBOzm5Uq9OMUuWq4VWo+FvvrhTvTzFFmd/ZIwCMmfYnpmaKiS2EEEIYhdPH9qYqyAD+3bSCfzetAFLGjeXOVxAHR2fKVKxJpWoNKOhTUmY7yEKKqW7OHN9PqfLVZX4yIYQQIgNOH9sLgMrEhOKlKnH39lXKVaqFT/HyOLu6k9+rCJZW1gZO+WFTTFGWkBBHrjwFDB1DCCGEUKTqdZtzxe8kanUyV/xO0vPrkVSt1djQscRLFNMn6eaeiyeP7hs6hhBCCKFI5SrVYtay3Yz9ZTnWOWwJ8L9m6EjiPxRTlHkXKYX/zctcPHfM0FGEEEIIRTI1M8M9Zx5UKhW2dg6GjiP+QzFFWe2GH1GqfDXm/jKcw3u3GjqOEEIIoUgB/teIjYmiRJnKho4i/kMxRZmFhSV9Bo6ndsPWrFz0M35njxo6khBCCGFUkpOT3tnmit9pAPIV8MnsOCKdFFOUQcrtup/2GEjZirVYMmciTx8HGjqSEEIIYRTW/zWPb7o2IDTk6RvbRIaHsn3DMgCZXsoIKaooAzAxMaFH3+E4ODoz/7eRafqtQAghhMiO1MnJzJ8+kj6darN762oAHJ1c39j+0oUTWRVNZIDiijIA6xw2fDlgPI8fBnDi0C5DxxFCCCEMYlDvFpw/dQgAMzNzRk9bhomJ6Rvb+9+4BICLrIxjlBTbd5knvzc+xctx/vQhatZvaeg4QgghRJY6e2K/bvnBeav2v7UYA4iPj+XEkd0A9Oo3MtPzifRTZE/ZC0VLVuDWdT+0Wq2howghhBCZJjk5iaMHdnDQd5Nu28KZYwAYPGrmOwuy6Mhwls2bhPr5kJ+ChUtmWlaRcYrtKQNwdnEnIT6OpKREWSRVCCFEtrV762o2r1kEQFxcDE1bd9E9lztvwXfuP/b77kRFhgHww/h5qFSqzAkq3ouie8qePg7E1s4Bc3MLQ0cRQggh9O7Ivm306VRbV5ABPHs+88CLyV/XrZzz1k+MkhITiIoMo3T56sxduY+ChUtkbmiRYYouyh4F3iVXHi+p+IUQQmQ7Tx8HsuKPaa9sT0pK+Qjym++nAHD80E4WzxpPWGjQK21vX7/IyEGdAWj7aR9MTRX9AVm2p+h3p2DhEmxdv5SE+DhZ2V4IIUS2EB8fy/BvOxATHZlqe59BE3BydtN9XFmwcAmat+3Gjo3LOX/mMNevnKNNxy8oVLQ0OXPlQ6PRsHLRL1hYWNBv6FRy5fUyxMsR6aDonrJylWuTlJjAFb+Tho4ihBBC6IWlpTWJiQkAVK7REBtbBzxz56d46Up4FSqOhaWVrm2J0ilLJQ0c/ivOrh6s+GMaY4d0Zeu6pRw7sIPHDwP4tMcgSpWrZpDXItJH0UWZe8485MyVj+uXzxk6ihBCCKEXKpWK4qUqAqDRaIiJjuDxw3tcOnf8lbYOTi4AREWG8+PEBfy+dBc+xctx6uhuNqxeQNVaTWSNSwVRdFEG4OzqobujRAghhMgOHj+8B8C5kwd12woVLf1KO1f3XHgXKcWODcvRajVYWFrx4N5tnj15iFqdRLtOfbIss3h/ii/KbO0ceRh4h/j4WENHEUIIIdLt6eNAVi+dnuoOypJlq5DDxg4r6/+Pl3ZydntlX5VKxcddvubBfX92bvkLrVaLpZU1jk6ufDt0Gg5vWXJJGB/FF2UNmn1CRHgIs6f+QEJ8nKHjCCGEEOmyfMFUDuzeSGxMlG5bYkICru658Cr0/+kr3jTtRcHCJWj6URe2rlvCv5tWMOn3NUydu+G1PWvCuCm+KCvgXZT+w37mfsBN5vw8TDc4UgghhDB2z5484PaNiwCppncKDnqMk7Or7kY2Z1ePt3Y8tGrfk9Llq7Nl7WIiw0MyN7TINIovygC8fUrx7Q/TuOt/jRULp8qyS0IIIYzek4f3GPV8DrGylWqRw8YOrVbLjo3L0ajVxMfF0qJdDwBCg58y4POm3Ljy+hvbVCYm3LqWUtzZ2jtmRXyRCRQ7T5lKrSHndT9yhIcQ6+iCqmgZuvX5gUW/j8POwZk2Hb8wiqWX/pvzSdEyaE2NrxaWnPqlhJxKyAiSU98kp369T84Nfy8AoFS5avQd/BMAiQnxutn7HZ1cqdWwdap9IsJDX3usyPBQYqIjALh84SRlKtTQS8aspJScmUmRRVmBU4eovmwmtmH/n7042smN/D0GEN7lazb+vZDL548zdNxc3TIUxpTzWI8BBFSubbBc/yU59UsJOZWQESSnvklO/XrfnH5njgBgZm7O7Gk/8EnXfrjnzKN7PjwsmC1rFmNiYsqMxTveOkm6o7Mr0+ZtZGjftly7dEZXlH0o1zK7UFwJWuDUIRpNH4lNWOrlJGzCgmg0fSRfuOdi5JQlhAY/5fDerQZK+e6cBU4dMlCy1CSnfikhpxIyguTUN8mpX/rMef7UIS6dP87owV2YNuZrOnT7VvdccNBjTExNWb5wKpcvnOTX8f1Zv2rea4+zeNZ4AN0NAx/itVQ6RRVlKrWG6stmpnz93+ee/13tz5nk9sxHlZqN2fTPQk4d9c3SjJD2nCq1Jktz/Zfk1C8l5FRCRpCc+iY59UtfOXt+PQJXd0+KFC+n23bn1hXCQp5RsVp9IGV2/896D+GK3ylmTf2em9cusHvb6leOFfT0Ebeup4wp69h9wAd3LbMLRRVlOa/7YRsW9Mob94IKsAsNIud1Pz7p1g+AxbMnEBryVNcmPDSYyxdOkJgQD0BcbAwajX7f7PTkNCTJqV9KyKmEjCA59U1y6pe+clat1YQJ01eT37toqu2+2//hzPF9AMTFRlPAuyjjfllOszZdAbDOYZuqvVarZdGscWg0agBsbO0+uGuZXShqTFmONN7mmyM8BCurHLrHl8+foHbDj3jy8B4Tf+xFUlIi5haW2Nk7EhqcUrAVKVGexMQEHt73p3ufYbrfUjI7pyFJTv1SQk4lZEzP+SVn2khO/dJnTnVyEru3/r/nq4B3MQL8r6Vqc/7UIZq37UaLdt3xO3uER4F3uX75LEVLVgDgxOFdun1q1m+l94yZSSk5s4qiespiHV3S3C4uNkb3+M7tq6iTk7kfcIukpERy5y1IUmKCriADuHHlHHdvXSExIZ4/fh+bZTkNSXLqlxJyKiFjes4vOdNGcuqXPnOaW1gybd5G3WOf4mWZNncDZSvVAsDU1Ew3aN/c3IKefUcAsOfftURHhpOYEM/aFbN1+3fs3l/vGTOTUnJmFUX1lD0pWoZoJzds3tDVqQWind14UrQMZppkylSsiZ2dI8cO/cuTh/dweP6mPgy8Q9VaTWjcqhNmZuYkxMcRHx+LWq0mX4HCWFhaZVlOQ5Kc+qWEnErICJJT3ySnfuk7p4OjCzMW72Bgr+bs3rqaXHm86PXNKFYvncGJw7vY9M8f9P1uEiYmJsTHpSwpeOncMZb/MY1ipSoSEx0JQOtPeummgvpQr6XSKaqnTGtqwrEeA1K+/u9zz/8+3n0AWlMTzM0t+Pq7SXT9cihDRs9Co9Fw4cxhXXuViQm58xbEwzMv+bx88ClWlmIlK2Bja4+5uUWW5TQkyalfSsiphIwgOfVNcupXZuS0zmHL92NSeryWzZuE39mjdP9qGO069eHiuWMEPX0IpPSkTZmzHgDvwiXZs/0feL4SQPO23TI1Y2ZQSs6sorhXGVC5Nr6DJhLjlHph1mhnN3wHTXztfCbePiUZ/tNCPHPn1207eWQ3V/xOGVVOQ5Cc+qWEnErICJJT3ySnfmVGzkJFS9Or32gAFs0ax4XThwkJfoqjkytuHrkA0GjUzP1lOE7OblhYWRH87DFDx8xm/l8HUy3TlFkZM4NScmYFRX18+UJA5drcq1Az3TP/Vqhan23rlwJga+vAmuW/M+bnPzExMTWqnFlNcuqXEnIqISNITn2TnPqVGTkr12iIg5MLv00YwLzfUsaP1ajXQvf/1JF927h/9wbDJsznryW/UaxkRbyLlMrSjJlBKTkzmyKLMkjp8nxcoty7G77EysoalcqEnLny8fhhAJERoVy7dJYSZSpnUsqM5TQEyalfSsiphIwgOfVNcuqXvnMmJiZQuGgZevQdzrJ5k1LOoUn5IE+r1eK7/R8qVWuAiYkp9+/e5NPnH/1lZcbMopScmemDKkGdXT3QajV0/nwwZSum3Nnyz58zDZxKCCGESDFlZB8G9mpGpeoNmDpnAwDHDu4gJjqKR4F3ePbkAXFx0Uwe+SVOLu4UL515nQoi6ym2pywjylSsiaOTKyeP+tK7/xgO7NqAnYOToWMJIYT4AGm1WsJDg7CwtMLKOgempmaYmJqSEB/H6EFd+OyLIVhYWpGYEM+zJ4FcvXgaSFlwvG7jtnTs3j/Tht8Iw/igijIzM3PKVKzJ7esXMTe3oFHLTw0dSQghxAfK78wR3bgxgEJFStOqfU/m/jqckOAnzJw8BACPXPlw88jNw8B/dG2bfvSZFGTZ0AdVlAHY2jkSGRGaMqv/e059IYQQQmRU8dKVKFaqItcunQHg9o2L3L5x8ZV2IyctwsLSSjdHmbdPSZyc3V5pJ5TvgxpTBlC6fHXi4mJZvmCK3te8FEIIIdLKwtKKgcN/Y8HqQ0ybu4GmH32W6vnaDVpjaWnNP8tnASl3ZgI0btUpy7OKrPHBFWUFvIvSs+9wTh3dw7qVc4gMDzV0JCGEEB84BydX2n76JQtWHyJP/kIAHNq7BVRw9WLKnJpVazVh8uy1uhvVRPbzwRVlABWr1efjz75h779r+b5vG0YM6Mj+XesNHUsIIYSgS6/vsLSyBiAhPo7Q4KfM/XU4W9YuRp2cbOB0IjN9kEUZQKMWHZk8ey29+4+hgHcx/vnzdwL8rxs6lhBCiA9cwcIl6P3tGN3jshVrER8bw7+bV7JgxmgDJhOZ7YMb6P8yZxcPnKt5UKFKXa5dOsPlCyco4F3U0LGEEEJ84EqXr87EGX9jamaKs4sHAKeO7mHx7PFERoRhL9M5ZUsfbE/Zy6KjIklKSsTM3NzQUYQQQggA3Dxy6QoyQDfW7MThnYaKJDKZFGXAkjkTsLCwpGbdFoaOIoQQQrzWhdOHAFi/ap6Bk4jM8sEXZerkZK5dOkPLj3tia+9o6DhCCCHEa0VGhOm+TkpMMGASkVk++KLM1MwMM3MLuaNFCCGEUYuJjtB9fef2VQMmEZnlgy/KAPJ7+XDh9GEpzIQQQhilxw8DaNyqs+7xQd9NhgsjMo0UZUCLdj24c+sKi2aPl8JMCCGEwVw6fzxl0tjntFotVy+eYuyQbkwa/oVu+9kT+w0RT2QyKcqAEmUq8+XAcVw4c5gVf0xDq9UaOpIQQogP0OxpP7Bq0S9otVpCg58ydkhX3cLk1jlsU7V9cM/fEBFFJpKi7LmyFWvRvc8wjh/ayYnDuwwdRwghxAfIzSM3AH8vm8mi2eN58ug+AB6eeZkyZx2DRkzXtf1t4kBDRBSZSIqyl1St1YRqtZuyeul0nj4ONHQcIYQQH5gXi44f2L0B/xuXcHHNiZtHbiLCQ1i+YCqxMVFUrdUESBn4H/T0oSHjCj0z2hn9L55yIuiBRZaft2iRcVy+0J5FMxfSpPksVCpVlmdIi7s3U7qxb121N3CS7EGup/7ceXEtr9hjplUbOI3yyfemfhn79dQm1cDV7RTxceHY2LijRYujkxd2dgmcPraT08f2pGo/cdjXdPv8kEGyGvu1fJfgIGtDR3iFSpuGAVSRkZE4ODgQERGBvX3mXvyjR6FmzUw9RRr8DXQCxgBjDRtFCCGEYCYwEKgLVAHmAZHPn0sGTA2SKjvYvRsaNcrcc6S1jjK6njLr54Vr6w53cXWLN1CKMpw+1otrl6bRvktdbGw93r1LFrt9055Dvrmp1zAQryLRho6jeHI99UeupX7J9dQvpV7PM8cvc/Ec+BRzwdYulit+Kpq22cDmf9pRrFQ3qtUemeWZlHotXwgOsmLLGi9cXAyd5P+Mrih7oVTZMPJ5xRjs/EVLtmTCDxvY9E9zPus9hMo1MrmMzoBDvrkp7BNBhZph724s3kmup/7ItdQvuZ76pcTraWNbBK22GnUbV2X10ul4FylCnYaWbP4HnjzaQ5WaXxkklxKv5Qv379qwZY2XoWOkIgP938DB0YVxv66kdLnq/Dl/Cvfu3DB0JCGEENmQ/81LDO/fgWuXz6LVarnid4qfhvfm6sXTujYly1ah9Sefs2zeT5iYmND580HksEkZ0xUW8sxQ0YWeGW1PmTGwsbWje98fefb0IdPGfkPrT3rRpFUnQ8cSQgiRjVhYWhMS9IQZPw0CwMo6B/FxscyaOpQvB46jeKlKREaE8fuUobi45eTbodOwtXckLjbm+f5Whowv9Eh6yt7B3NyCwaNmUKNOczaunk9YaJChIwkhhMhG8uYvxHejf8fEJGWwfnxcLAAajZr5v41k8BctGTukK9Y5bHQFGUDAnesAVKhazyC5hf5JT1kaWFnloKBPCQ7u2YSFhaWh4wghhMhmfIqVZd6q/QQHPebw3q3s3LySMhVr8sln33DZ7yQJ8XFUr9NcV5DdvHZB17NWvU4zAyYX+iRFWRrlzlsQlUrFlFF9aPNpHypUqWvoSEIIIbKByxdOEBcbQ6XqDXB186RClTrs3LySgoVL4OaRm3qN2wEQEx3J0L5tiQgPwbtIKQBsbO0pVKS0IeMLPZKiLI3yFijM0HFz2b7hT/6YOYY8v67EwzOvoWMJIYRQuG3rl3H39lXu3blOu85fsW/nenLY2NKoRcdU7XZvXU1EeAgAvfuNxsbWHksr45sAVWScFGXpULBwCVq278HlCyeIiY589w5CCCHEO3z93SS+79sG3+3/4Lv9HwBMTc1YPHsCVWo2pnjpSgC0bN+D2g0/wsUtpyHjikwkA/3TKTwsGAAnF3cDJxFCCJEd2Ds68/3YOam2WVnbcOfmZeb+8iP9ujVk3co5mFtYSkGWzUlPWTrlyVcIgAcBt3FydjNwGiGEENlBoSKlmP/XQUKDnxIeFkR0VCTLF0zRPf9iEXKRvUlRlk4vPr9PSk40cBIhhBBK9deS3yhRujL5vHw4dvBfLK2sSU5KJPDebc4c35eq7cAR0/EqVNxASUVWkqIsnczMUi6ZOjnZwEmEEEIo1UHfTRz03YSzqwdREWGYmJhiZm6BhYVFqnZNWnemWMkKBkopspoUZen0YoD/qaO+lK1YE3OZt0wIIUQG2djaM3TsnFTjlCPCglm1+Ddy2NrR9tM+BkwnspoUZenk6p6LHl/9yKrFvzL+h55YWlkTFREGKhW58nhRqXoDChUphUajQaVSybQZQgghdJKTk1izfBYAdRu3o1PPga+0cXBy5eshk7I4mTAGUpSlk0qlolqdZri4e3LyiC8mJibYOzih1Wq5dc2P5QumoNVqde3LVKzJRx16kztvQQOmFkIIYWiR4aEsmj2e29cv8mnPgdRp2MbQkYSRkaIsg3yKlcWnWNlXtkeEBfP44T00Gg0RYcHs2LScn37sTZtPv6BxS1nMXAghPkRxsTH8MqE/sdFRDBz+Gz7Fyxo6kjBCUpTpmYOTKw5OrrrHFas3YN3KuaxfNY/ylevg6p7LgOmEEEJktSt+pziybyvhoUGMmLRIhrWIN5LJY9/Dw8A73L97U/c4MTGBowd2EOB/DY1GQ1RkOObmFnzUoRc2tg78PLYfW9Yu5tGDAMOFFkIIkWW0Wi2/TxnCuVMHafvpl1KQibdSbE+ZSq0h53U/coSHEOvowpOiZdCaZl2NGfT0EeOH9gDAzT0XllbWOLvm5OK5o6na/TBmDjXUySxs34Ml54+z9991bN/wJ86uHmg0GpISE/H2KUGvfqOxss6RZfn/y9DXM60kp/4oISNITn2TnPr1rpxxsdG6rytVa2CIiNnmWn4IFFmUFTh1iOrLZmIbFqTbFu3kxrEeAwioXDtLMjg6u9L0o894cP82l8+fAODBfX/s7B2JigwHwMbSiq7TR1EiMhSAzkCIoyuzG7XhmEaDhaUlJiam/Lt5JTs2Laddp6+yJPt/GcP1TAvJqT9KyAiSU98kp379N2c0sNHGnn+8ixLh6oGpiSmxLxVltvaOBs8IyriWYJw5M5viStACpw7RaPpIbF564wBswoJoNH0kBU4dypIc5uYWtP30S74dOo2W7Xvi6p6LRi06MmnWWsb/tort/ccRlRBP8ecF2QvO4cGM3rKK7wqXoNXHn9OiXXeatOrMnu1riI6KyJLsLzOW6/kuklN/lJARJKe+SU79+m/O7UBhoFtMJHcunuLZlXPcuX2Fh4H+uOfMzbAJ8w2e8QVjv5YvGFvOrKCookyl1lB92cyUr//73PO/q/05E5Vak6W5Wn3ck59m/s3Hn32DhYUlOd1zU3vF7DTn9MydH7U6mXFDu/PbhAEc2rsl1bQamcVYr+d/SU79UUJGkJz6Jjn16+WcCcAXQEugHHALuAKcSUpk1MQ/GDPtTyZMX53lyyQp8Voac86soqiiLOd1P2zDgl55415QAXahQeS87peVsV6R3pyeeQqQM1c+IsND0Wq1rFr0C6uXTEejURtVTkORnPqjhIwgOfVNcurXi5waoB2wClhISm9ZIYwjp9KupbHnzCqKGlOWIzxEr+0yS3pz5s5bkHG/riQpMQFzC0uO7t/Oij+mcdf/Kl16DaGAd1GjyGkoklN/lJAxPeeXnGkjOfXrxfmnATuBf4Emb2lnCEq7lvpqp3SK6imLdXTRa7vMkp6cxw/+y9Z1S4iNidKto1mjXguGjpuLVqPll3H98DtzxOA5DUly6o8SMqbn/JIzbSSnfsU6unAaGA0M4/UF2Yt2hqKka6nPdkqnqKLsSdEyRDu58abRVlogytmNJ0XLZGWsV6Qn5+5tf7Nt/TIG9W7BtUtndG0KFi7BD+PnUrJcVeZPH8Wxg/8aNKchSU79UUJGkJz6Jjn1a9qR3VQGygJjX/O8MeRUyrVUSs6soqiiTGtqwrEeA1K+/u9zz/8+3n2Awec10ZqacLhbPzaS0rWd6rnnf7/I2X/Yz7rnZkwanKqtuYUlXw4YR/W6zflz/mT27Vyv95xKuZ6SUz+UkBEkp75JTv15GHiHIwe2AzAFMP/P88aSUwnXEpSTM6so7lUGVK6N76CJxDi5pdoe7eyG76CJRjOfyQXvYrQHmgF7Xtr+35xOLu58+8M06jVpz8ddvn7lOCYmpnzWewj1m7Zn3co5PAq8q9ecSrmeklN/lJARJKe+SU79MDP7fxkW33+s0eYE47+WLyglZ1ZQ1ED/FwIq1+ZehZpGPfOvi1tOPunaj1tXL3CrUm1UZmZvzFmybFVKlq36xmOpVCradfqKK36nWLn4F4aMnqXXrEq4niA59UkJGUFy6pvkfH+hwU8BqFyjEQ+r1Wd15bpGmfMFY76WL1NKzsymyKIMUro8H5coZ+gYb9WweQcaNu8AgP8b2hzauwVXt5wUL135rccyt7Dk0x4DmDl5CHduXQHc9ZpVCdcTJKc+KSEjSE59k5wZc/XiKU4e8eXkEV+Kl65E1y++B4wv5+soISMoJ2dmUmxRlh2o1cmsWvQLAFNmr8PJ5e2FVtGSFbCxdeDyhRN45q6XFRGFEOKDFxhwi5mTh+CZuwAfdehNk9adMDExNXQskQ19WP2CRsbU1IwW7XoAMKzfx0SGh761vYmJKQULF+fBvdtZkE4IIT5siQnxREaEsXrpdHLY2DJyymKatflMCjKRaaQoM7CmH3XR/QM/c2LfO9vHxcVgZZUjs2MJIcQH7cbV83zbozHff/URz5485Nsffk41yF+IzCAfXxqYhYUls5f7EhbyDBc3z3e2t7Vz4PTxvbi4nwCKZX5AIYT4AKmTkwHwKV6O3t+OxuEDmbxUGJYUZUbA1NQMV/dcaWpbtmItLpw+zMkjU4CemRtMCCE+IOrkZHZuWcVd/2tcu3iafF4+9B/2M+bmFoaOJj4QUpQpTLXaTQkJesLubRt4dao9IYQQaaXVavnnz9/Zv2s9Do4u2Nk78uhBAIWKlqZ1h97Ua9JOCjKRpaQoU6CChYuTEL8EuGLoKEIIoUi3b25n4dxhuLjmBCAiPASvwsXp0L0/RYp/2NMyiIx79OgRT548oXz58hnaX4oyBSpctAymZlaokzcA7QwdRwghFCc05BYAIcFPALB3cKbv4J8MGUko3I0bNyhatCiQ0gubEXL3pQKZW1hSpPjHwEQC7x8xdBwhhFCcoiU+1n2tUqlo2b6H4cIIxYuOjtYVZB06dMjwcaSnTKEqV/+eqxdD2bfnR1p32ijz5gghRDpoNWocnVyxzmHLd6N/x87e0dCRhILZ2tqycOFCHB0dad++fYaPI0WZQpmYmgMjSIivwb07N/AqVNzQkYQQQiFC2bC2AyYmWr4cOE4KMqEXX3zxxXsfQz6+VLTKmFvYsnvb3yQlJhg6jBBCKMR+kpJi+XbYNLx9Shk6jBA6UpQpmhm1ao/k4rljTBvbj9CQpxk6ilar5bcJA1g8ewLJyUl6ziiEEMYhLOQZOzf3AjrikbMshYqUNnQkIVKRokzhCvm04Idxc3j66D47Ni7P0DFUKhU3rp7n1FFfBvVuwfFDO0lMiNdzUiGEMKxnTx7w6MFxoCYtWi9CpVIZOpIQqUhRlg2cP32YhIQ4zMwyPsnhlNnrgJQFeJfNm8S4oT24d+eGviIKIYRBqdXJrF46/fmjIZiZWRo0jxCvI0VZNpDPyweAshVrZvgYTi7uuHnkwtHZjU97DMTWzp5pY77moO9mtFotWq2Wvxb/xrpVcwkLDdJXdCGEyBIxUZE8fngPn+KfAC0NHUeI15K7L7OBfAVSirLwsJAMHyMk6AnBzx7TpdcQajVoRc36LVm3cg5/LfmVC2cO4+zizpH92wDY9+862nz6BY1bdtJLfiGEyGz/bl4BgINDPgMnEeLNpKcsG3B29aB0+er88+dMHgXezdAxHgbeQavVUqpcNQDMzS3o1HMQfQf/RHhYEGdO7KNitfrMWLyDBs0+Yf2qeZw7eUCPr0IIITLP4wf3AHD1kMH9wnhJUZYNqFQqPv9mJM4u7vw+ZQhRkeHpPkZMVAQAtvYOqbaXrVSLMdP+ZOaSnXzRfyzWOWxp36UvDo4uXL5wQu7WFEIYPd/t/3Dt8hlq1G2OZ+5Kho4jxBtJUZZNWOew5dsffiYxMZF/ls1M9/4xMVFYWlljZmaepvYVqtbj6IEdfPdlK6aN/YZ//vydwIBbGV7vSwghMov/zUsAVK3V1MBJhHg7KcqyEUdnVzp2/5bTx/dy89qFdO0bHRWBja19mtt37N6f0dOW0eyjz3B2dufMiX1M/LEXK//4OZ2phRAi81y7fJbzpw4BYGFpZeA0QrydFGXZTOUajbCzd+L65bPp2s/c3ILYmCji42PTvE/uvAVp+tFn9O4/hsmz1mJubsHp43vTG1kIITKN35kjAHzc5WsKeBc1cBoh3k6KsmxGpVJha2dPXGx0uvarVrspyUlJ+G77J0PnjY2JJikpEQsL+U1UCGE8YmIiyZkrPw2adzB0FCHeSYqybChvgcIcO7iTqxdPp3kfZ1cP6jZui+/2v1EnJ6f7nPYOTtjY2lOvSbt07yuEEJlBq9Vy/tQhnjy6R2jwE0PHEeKdpCjLhrr0GkKhIiWZNW0oJw7vSvN+FavVJyE+jnt30z+Tv0ajJiE+DusctuneVwghMkNo8FOSEhMAcHByNXAaId5NirJsyMo6B18PmUy5SrVZu2J2mu+IzFfAB0tLa/xvXEr3OZMSE1GrkzEzk/mIhTBWiQnxRIaHGjpGlrF8aWC/uXnGl6ETIqtIUZZNmZqaUal6A6KjIoiMSNsPYVMzM1zccxIS/DTd57O0sqagT0n8zh5N975CiKwx++dhjBrcmdAM/BtXIhs7B1zcclK6Qg1DRxEiTaQoy8asrW0ASIiPS/M+7jnzEOB/LUPnq1WvJZcvnEj3nZ9CiMwXFRnOjSvniI+LZd/Oda9tc/f2VVYsnMYv477l6ePALE6ofyqViso1GhFwO2M/04TIalKUZWNOLu5AyrqWaVWhSl3u3r5KRHj619GsWrsphYqW5u9lM9Fo1OneXwjxfpISE0h8Pobqv15MoOqRK98be8PXLJ/Fkf3buHXdTzceNSkxgX83reT7vm0Y2Ks5fTrVZvHsCSQlJWbOi9CzXHm8iIwIJSY6ytBRhHgnGQCUjbl55MI6hy3rVs2lY/f++BQr+859LC2tM3w+lUpF+859mTq6L2dPHqBStQYZPpYQH7roqAi2rF1M8DMH4HPA+ZU2EeEhPHoQQGxMFJfOH+fciQMkJSdSuXpD8uTzJjY2mtiYaMzNzbl4/jj2js4ULFSc44d2cuPKOXyKl2P31tXcuu5HqXLVuHPriu7YOzYu5+K5YwQ9eUhSUmKqX7ROHfWlYtV6lKlYMwuuxPvxzJ0fgCeP7uHtU9LAaYR4OynKsjETE1P6DZ3K2hWzmPfrcCb9vhbrHDZv3efF5LFWVjkydM6ChUtQvHRldmxcTsWq9VGpVBk6jhAfMnVyMvN+G8HD+3cwMbEDVnD1yghKVamNhYUlAKEhT5k8oo9uzKhHrnw0btUJS0sr9uxYw/lTh8hha0eOHLbExkaTkBBH6XLVKVmuGscP7eS3iQNp2roLO7esAuDS+eOpMhQvXRlXd0+q1GxE2Yq1OLxvK7u3rtY9f3j/Npxc3MiTvzAHdm/g8cN7tOvUx+juwPbIlQ+VSsWTR/elKBNGT4qybK5QkVJ06jmYySO/5NmTB+QvWOSt7ZMSUz6SSOsamK/ToNnHzJo6lEeBd8idzzvDxxHiQ3XyqC+3r1/k+zGzCQ6qy9K50zhycAJHDqaspFGlVhMO792Cmbk5A4f/Rs5c+XTDFdTJyaxbNReA2o0+okSZKsz4aRAAJw7vSjVNzs4tq8iTz5v8BYtQslw1CngXJYeN3Wt/KWvfuS/tO/clOjKck0d92fvvWn4a/kWqNi3bdTe6oszCwhJ3z7zcuXWFGnWbGzqOEG8lRdkHwPH5/DxpGSfmkSsvAP63Lqfp487XKVKiPFbWOfA7e1SKMiEy4MDujbi6e5I7X0FCgk2AH7Gx3U1M9BMeBt5hw1/zKF66MvWatmPGpMGUKFOF/sNS1p01MTXl2x+msWf7Ggr5lMKnWBk+7TkQtFo8PPMR9PQRzq7uuHnkxtHZNd294rb2jjRo9gl1G7fl/KlDrFr8C7ExKSuIaNI4/Q5ARFgw9+7e5OH9Ozg4uVC1VmNMTEzTlSWtSperxsmjvmi1QzLl+ELoixRlHwAr65QfuvFxMe9s6+1Tilx5vNi2fhkDh/+GiUn67wUxN7cgXwEfHgbeSfe+QhiDuNgY1OpkbO0cDHL+Jq06sXzhVH78tgOWlh7AHTTq/48pK1GmClf8TnL7uh/w/3/bR/dvZ/nCqcz605eSZavq2tdrrP+VNkxNzahYrT5lKtZkw1/z2LdzPRfOHE7TuS6dO87cX4ej0aixsrYhPi6Gg74b6dLrO/J5vb03PyN8ipfDd/s/z6cCcdP78YXQF7n78gPwoofM/+aVd7QEExMT2nfpy82rF1i16Bc0Gk2GzunumZcA/2uo1elfskkIQ1Krk5k88kuG9m1L0NNHBslQoWo9xv68nHpN2pEzdyVgEQ0ap/SE5cnnzRW/kylZNRpatu9Bp88HAxAeFgzAsQM7siyrubkFHbr1p37T9vy9dAbbN/z5xp8banUy1y6f5c8FU3B192TyrLXMWLyD78fOISkxkUkj+rBu5Zw0T3idVgUKpixEntHpfoTIKlKUfQBc3T0pW7EWp47uTtNt7CXLVqV7nx84sn8bJ9OxTNPL6jZqQ2jwM3a9NDBYCCU4fnAnTx8HolYnk5z8/38vWq02XcVCTHQUR/Zv4+rFUxnK4eTizkcdelO9zmjgc3LlrkTbTn0IDnqCnb0jg0ZMR52cRP6CRcmbvxCQsu4tpNy5mZVUKhUduvWnVoNWbFm7mM1rFr3S5tZ1P376sTczfhpEVGQYxUpVxNnVA5VKRaEipRgxaRFtOvbGd/s/HPDdqNd89o7OOLm4E+B/Xa/HFULf5OPLD4CpqRkfdejNuKHdOX/qIJVrNHrnPtXqNOPcqYMsmz+Z2zcu8fFn37zzzs2X5S1QmNoNW7Pv37U0at4B8+d3jAlhKGp1MkmJiRzcs5mY6AhatuuBxUvL8Lxw5MB23ddL504iKSmB5KQkoqPCSUpMJHc+bypVb0Cl6g1wcHR57bkC/K8xa+pQoqMisLC0ok7DjyheuhLFS1d+r9fQtHUXGjXvyNqVc/h9yvcA2Nja6Z5/cQdlwcIl3us8GaFSqXTXY+fmldRv2p7gZ485un8b1y6fJTT4KQW8izF03FzyFiisu4v0BVMzM5p+9BmhIc9Yv3IuRUuUxzN3Ab3l8/Iuxu0bF8mTX2+HFELvpKfsA5Errxely1dn2bzJ7N+9IU2/8ff97ic69RzEmRP7WDJnQro/yqzf9GOioyLZve3vjMYWQm8Wz5rAgM+bsuGveeza8hc3r13QPafVarl68TR//D6Wuy/N1ZUnX0GKlihP2Yq1aNyyM2079cHR2ZWNqxcwYsCnbN/wp24amZet+ONn1Opkun75A3kLFObM8X3M+WX4e8+Sn5SUyORRfdi/az2uHrkY+8tyvH1KASkrdxzasxmVSkWxUhXf6zwZVeal5YyG9m3LtDFfc+roHt2yTt2+GIq3T8lXCrKXfdzla5zdcrJkzkSSk5P0lq10hRrcuXWF6CjDfCQtRFpIT9kH5KtBE1m7cg5/L53B5fMn6N5nGPaOr05I+YKJiSl1G7fF2dWDOT8PY83yWXTo9i0mJiY8ehDA2RP7qFStATlzv/5XTw/PvDRr8xnb1i+jWKmKBvntXYgX1JqU8Y21GrTi8N6t7N+1nsTEBFQqFf9uWsm9O9fJk8+b6nWa45YzN41adHztItYNmn1CTHQUO7esZNuGZeza8hd5CxTG2dUdZ9ec2Nk78uDebQBWLJwKQPW6zbh59QJ/LphCz77DeXj/Dk4u7u+cokatTiYhIVL3+MzxfQQG3KJU+ep8/vUIctj8v5csISGeAt5F+eSzfgabHzCfVxF6fp0yv1poyDPOHN+rGzLx1eCJ5M7/7ruxLSyt6PXNKKaM/oqt65bS9tMv9ZKtbMVamJtbcOfWDkAmthbGSbFFmUqtIed1P3KEhxDr6MKTomXQmhpfx58x5TQ1M+PTHgMoUaYyf86fwsQfe/HtD9PIW6DwW3OWLl+dzp8PZvXS6USGh9Lz6+EcO7gD321/s2/nOr4bNYs8b/hh27JdD65ePM2a5bMYNmH+e78GY7qeb6OEnErICPrL6Zm7ALeu+eHmnhuAyxdOcvlCyoB5n+LlGPDjLxQrVSlNBY2NrR3tO/elXuN2nDrqy8PAu4QFPyXg8jnCoiLw8siNXa78XDx/DIALp4/Qb+gUZkz6jpEDOwFQqnx1+n0/5a3n8d3+DxtXLwDKc/Vyc+q39MHE1JS42GjU6tRLmdk7OPHjxIXvzJ7Z73vVWk0ASE5OwtunJGbm5tSo2xxT07T/d5O/YBE+av85m9YsoompKd7Fy793TuscNpSuUAP/m9uAXzN8nP9Swr8jJWQE5eTMTIosygqcOkT1ZTOxDQvSbYt2cuNYjwEEVK5twGSpGWvOUuWqMWrqEmZPG8a0sd9QvYAPAx/eo1X0/wcH/zdnnUZtcHB0YdGsccyfPgr3nHmwsrbBzsGZjX8v4Nsfpr32XKZmZlSt1Zh//vwdjUaToSk2XjDW6/lfSsiphIyg35x1Gn7Evp3reHDfP9X2oePmZnimd2dXD5p+9Nn/cz6fXZ+nD4lOTOSPDl/wp/9VqtZqgrdPKabN3ciNq+dYNm8y9+/cICY6KtWYsP96cdcgnOPoYT8CAgrw7dBpLJkzgd8mDqBxi0+pULXea8fGvfZ4Wfi+m5mZU79p+wztW+DUIZbu3kgQWiZt+JNdG/6knh5yVqnZmLMnfgTOAa/2gmYkp7H/O1JCRlBOzsymuBK0wKlDNJo+EpuX3jgAm7AgGk0fSYFThwyULDVjz+ng6MJ3o2fSsVx1Qm9cpE10BMteev51OctWqsXXQyZxxe8UyUmJJMTHEvTkITeunmf9qnlvPJeLmydarZblC6a8cbHkdzH26/mCEnIqISPoP6eFpRU2tvacOuqLSqXik679mP/XwfdeeudtOQeu+YNptZtRvkpdIKW3pkyFmvgUL0tEeAi7tq5KtY86OZnTx/eya8tfXL5wkiIlylOk+CeADy0/WsSD+/5sWD0/5SYDBxeWzZ/Md31aM3PydyS949+W0t53+/BgNgL5gbrAeT3kLFm2Cja2OYE3/7xKb05jvp5KyAjKyZkVFFWUqdQaqi+bmfL1f597/ne1P2eiUmdsbi19yUjO0OCnREeGZ0m+F6zNrZh74xLngZ7AV8CB58+9KWfx0pVp1LIjxw7+S7XazdBo1Nja2rN722r27Fjz2vM4OrlSsHAJThzezbZ1S9OdMzu/71lNCRlB/zkjw0P5eVw/3YDzitUa0LB5h/cee5WRnFqthlvXUiZ9PbBrI1cvnkKdnDLe7a+l01n0+zj+3bySWVO/Z8+ONYSG3ATKkdOzPM3adMXMzJx9O9ej0Wr4ceJC6jdpz9WLp1ky9yciw0P1ltMQ/pvTFdgLlAeaADt4v5ympmYUKdEBWJVqrN775kz13PO/DX09lZARlJMzqyiqKMt53Q/bsKBX3rgXVIBdaBA5n89ybSjpzfngnj8/fvsJP3zTXu+TJqYlpwkpvzfWAloBZ96Q84VWH3+Os6sHTx7do3zluro5kR69Zgb/6KgIfhn3LXduXUGr1XD9yrkM58xu77shKCEj6D/n/OkjefwgAIAKVerRq98og+U0MTFl4oy/6di9P86uHsycPITBX7bit4kDObJvK5/2HMj0RdupWqsJ61bOIfjZZaAGKpUJbTp+wbAJ8/nm+yk8uOfPnu3/0ObTL2nbqQ/nTh7g2KF/9ZbTEF6X0x74F2gEtAF2vGdOn2LtgSRuXt+i15wvM4brqYSMoJycWUVRRVmONKzdmJ52mSW9OePiUtaNS05O4sKZw5mW603nB7AENgIlgOZA4BvaQcoCv116fcedW1eo1bAVBZ9//HP+9KtdzAH+10lIiGP0tGWYmJry6MHddE+tkV3fd0NQQsb0nD8t7YKePuLenRt45i5A72/H8OXAcXq7OzGjOW1s7ajf9GPG/PwnIyb9QcPmHdBqNDRp3Zk6DdugUqlo0OxjzM0tcM9ZlpS+7P8rXb46rT7pyenje/mqcx02rl6Anb0jpcpV02vOrPam81sB64FOz/8cO7k/4+ewcQPac/XKP2g06ne2f+0xFHA9lZAxPec3dM6soqiB/rFvmKgxo+0yS3pzevuUwjN3fh4/vEeA/3XKVcqaQY3/zWkLbAUqAu2AQ4D1a9pByuSUJqamnDi0i4bNPiGnZz602leLrQf3bmFlnQPP3AWwsbEnKjKMg3s2pWstvuz6vhuCEjKm5/zvavfsyQNm/zwMZ1d3hoyZpfe1LN83p0qlIp9XkefrPaYuvPJ5FeG3P7Zx7lRuls61fWXfIsXKpXr85YDx5M5bMFNyZpW3nd8MWAZogFmHdjG2fU/sHZwyeKZBRIRX5cTh3VSv00yvOTPSLjMoIWN6zm/onFlFUT1lT4qWIdrJjTd9wKcFopzdeFK0TFbGekV6c5qYmNCweUcAHN4yb5i+vS6nGyk9ZpeBvkCkk+trr6eFpRWlylXj5JHdLJw5hmZtPqNL7yGvtHv0IABnVw8gZSxNfq8irF0+m3MnD7xXzpcp9X03BCVkhPfPqdVqObR3CxOH9UKjVtNv6LRMWVw8s6+nhaXVG3v1PDzz0vWLofw8fzNzVuzFp3hZg+XUl3flVAETHV3QmpmxfcOy9zhTFQp6N2HT3wtJiI/Te05juJ5KyAjKyZlVFFWUaU1NONZjQMrX/33u+d/Huw8w+LwmGclZvW5zBo+aSd109CC9rzflLA8sBP4EfixT5Y3Xs8+A8YyaspSkpESG9fuYW6/5zD9v/kI8CrzLiAEdiY6KoM2nX1Cucm0WzBidakb1jOR8+bFS3/espoSM8H45I8JDmPPLj6xa9AuVajRk5JTFeHjmNbqc78vUzIya9Vti7+CEmZm50eZMj7TkvNlzEKXKVePBPX/eR+EiLYkID8nQiiNKuJ5KyAjKyZlVFPcqAyrXxnfQRGKc3FJtj3Z2w3fQRKOZzyS9OU1MTChSvNx7zeOVEW/K2cbZjXblqzP/0E5OHd3z2n1NzczIk9+bb76fgrOrBwd2v7qIcMMWHen73U/Y2TtRwLsohYuWoVe/0bi652LvjrXvnVPp77shKCEjvD5nfyCnSsWq3kM46OrBk0f3dTfHaLVa9u1cz6iBnQm4fY1vvp9C1y++x8oqR5bnBGVcT1BmzsTE+DTPzfYmp06k3PG3bf1SwkODMyWnoSkhIygnZ1ZQ1JiyFwIq1+ZehZpGP/Ov0nM2RMPjhSkTVaqTk6j2hrEXpctX58G92+zcsoqkxIRUi4+rVCrKVqxF2Yq1Uu3zUYfeLJ49npNHdlOlZuP3yqmU62lMOZWQEV7N6bd/G8+unKProl/euE/dxm1p/UkvbGztDZZTKddTqTmTEhPfun5mWmieL7sFsHnNIrp/NUzvOY2BEjKCcnJmNkUWZZDS5fm4RLl3NzQwJec0xYRufYahViez/q/5VKxWP1XB9bJylWuzec0izpzYT7XaTd95vkrVG3DF7yQrFk7D26ckru65MpzTGCkhpxIyQuqcNT3zcmjEFwA0ad2ZU0d8CQtNmXCySInytGzX461jq7IqpzHLDjmTk5Owsn6/HtDSZbtz+MA4AI4d3EGjFh3JlddLrzmNhRIygnJyZqYPqwQV6WZiYkLL9j2Jjgp/48eYkLKuYPHSlVk2bxIThn3Ow9fMWfYylUpFp88HYWvnwC/jvuWg7yaSk5P0HV9kEy8G7ts7OJEnX8o6q45ObrqCbMSkPxg8cobBCjKRtSLDQ7HOYfNex/DwSD1w/Nb1i+91PCH0QYqyD1RiQjyLZ0/g8N6t72zr4ZkXO3snQoKfvLVdv++n8NXgiUSEhXBk37Z3HtfKKgffjf6dwsXKsHrpdFb+8XOa84sPy7MnD1i16BeG9fuYxw/vAXD14inmrTrAgtWHnk8pIT4EoSFPefTgLkVKlH+v4zg6Fkj1uGK1eu91PCH0QYqyD1TQs0ecOurLykU/v3PNvOjIcCIjQsmV5+1d+6ZmZpSrVBsnF3eePLqXphxuHrnp1W80nXt9x/FDO7lz60qaX4P4MMRER3HF7ySQMkWEWp0yFihP/kJZfmOMMLzLF05iYmJK8VKV3+s4JqbmeBUuoXuclWMQhXgT+Yn2gQp68lD39d3b197a9vaNSwDk8/JJ07HrNWnH1Yun3/kR5stq1mtB3gKF+efPmeme8V9kT48C7/LPnzMZ/EUL/vnzdyClhxdgzLQ/adPxC0PGEwZy8+oF8nsXxcbW7r2P9Wn3AbqvX3xvCWFIUpR9oDb984fu69fNxP+yE0d2kbdAYdxz5knTsd08cr04cJrzmJiY0rF7fwL8r/PvphVp3k9kX77b/2HfzvWpttVr0p55q/ZnaEC2yB7u372Bl3dRvRwrf8H/f+x9+cIJvRxTiPchRdkHqk3HL3BydsPC0uqtPWCPHgRw8ewxqtZqkuZje+YugKWlNWfTMWs/QOGiZWjZvidb1i5mz441JL7jY1WRvZUsWyXV44HDf+PTHgMwMTE1UCJhaHGx0Tx9HEh0VAS7tq5+794tlUql+9m2YMZofUQU4r0odkoM8X7KVqpF6Qo1iIuNwcraBq1W+8pyLkmJCYwf2gOtVoO5edrnBLK1cyB3voKEBL39xoDXadm+B9FREaxdMZstaxbj4OSMqak53j4lKVGmMuUq19HbYtLCOD15eI+Q4KcEPXuk2+ZTvBzFSlU0YCphDO4H3ALg/KlDnDq6h52bV9Cx+4B0/dL4X83bduPE4V0Ar8yzKERWk6LsA2ZiYkIOG1tGDOhISNATHBxdyJ2vIF6FSpDDxpadm1fpPtoMevbwHUdLzcbOgUcPAlAnJ2NqlvZvM5VKRaeeA6nftD27t/1NQlwsOWzsOLhnE0f2b2PYhPl4FSqerixCOSLDQ5kw7HPd9Ch29k64uOXk2x+mGTiZMAZPnt9527pDL9avmkdsTDRL5/5EpeoNMDXN2H9nHp55UalM0Go1rFkxmy69vtNnZCHSRT6+/MCpVCri41IW5C1ToQamZuYc9N3Ipr8Xploc/drF0+kagN+oRUce3L/NqsW/6JbBSY+UxZa/p3f/MXTuNZga9Vrg6p6L/AX1M5ZEGCcbO3vy5E+Zh6xAwaJERYZRokyV9569XWQPRYqnTCy6ftU8ABydXMlboHCGC7IXOnT7FoBDeza/X0Ah3pMUZYKvBk3Azt6R86cP0/bTL/llwRZm/enLqKlL6f7VjwA8uO/P1Yun03zMIsXL0b3Pjxw9sIMVf0x777Ef9+/cpEiJrF8bVGQtU1MzPv9mFCqVioA71wFo0a6bgVMJY5Ezd34+/uwbcubKh6mpGTExUbqfUe+jYrX6uq8zMuxCCH2R/+EEPsXLMnrqMhydXJk+cSCBATd147aq12nG1Dkb+G7U7xQrVSFdx61aqzHd+gzj1NE9TBrxJU8fB2Y4o6WVNQnxcRneXyiHOjlZ17vau/+Y9+4FEdlLoxYdadGuOxqNho869CZv/kLvfUx7Bydy5y0IwL+b5e5vYThSlAkA7B2dGTj8V5xdczJ19Ncc9P1/N76jsys+xctm6D/HGnWbM/ynP4iPj2Xb+mUZzlfAuyi3b1ySOcyyobDQIFYu+pnIiDAATh/fq3uuYtX6b9pNfKCePg5k8ewJaLUa6jdtr7fjNmzeASBNq5wIkVmkKBM6tvaOfD92NjXqtuCvJb/if/OSXo6bK08BylWqzd3bVzN8jIrV6hMeGsTxQzv1kkkYj6iIMA7v3cr3X33EhTOHdeN6WrTrIXfaile458xDpeoNsbK20euErxWq/n+ZpccPA/R2XCHSQ4oykYq5uQWf9hyInb0jV/zSPobsXYoUL0fQ04fcu3MjQ/t7FSpOucq12fTPQoJfmipBKF/eAoUxN7cAYN6vI4iOigBSVoYQ4r9UKhUfd/maxMR4jh38V2/HtbSypmjJlCEaKxbKOrzCMKQoE68wMTGhSInyHN67hTPH9+ll4Gup8tVw88jNxr8XEBMdmaFjdO45GCsra34Z358bV8+j0ajfO5cwPJVKxTdDp1LAuxiQMtjfzt4JO3tHwwYTRsvR2ZUKVeqxf9cGvf4caNyyE4DePiUQIr2kKBOv9fFn3+CZOz9//D6W4f07MH/6yHcuXP42pqZmfPzZ19y5eYUJP/TUzTeUHvaOzgwe9Tu2dg78NmEAQ/u2Zdn8ydy/ezPDuYRxKFayAj9OXEC5yrVRq5OJigwzdCRh5Bo0+5igpw+5fOGk3o5ZrFQFPHPnx8nF/b1+3gmRUVKUiddycnZj0MgZ/DTzb7p/9SOXzp/gj9/Hcuu6X4Z/My1bsRbjfl1BQkI8Rw5sz3Cu4T8t5Puxc6hetzl+Z47wx+9jMnQsYVzi42K5qsePzEX25lWoOF6FirNj0wq0Wi0ajZqI8JAMzYv4gomJKV/0H0tYyDPOnz6sx7RCpI3R3mt+8ZQTQQ8sDB3DaN29aQvArav2mXwmZyxNSlCzth2H9o/G7+xRfIq0pm6Dn1K1unxxFY8fn6VRk9/ecTwnLC09uHzOH093EywsbDAxyci3YW3y56lNXCUfDh8cz4l9lpib58jAcVJk3fXM/jJ6LR/cv0ZCwv+nPTl72EmvuZRKvjffrESJ79i2uRcLfl3JzeubiYl5RvmKfalY+es37vPu61kRF5ciHN5zEVPNJ3rJ6X97J3t3fw+Al3cjPD0rULhIKywtlf2eKv17MzjI2tARXqHSpuHXisjISBwcHIiIiMDePnMv/tGjULNmpp5CZFgisAzoA2wA2gK3gVDgxeLRafktdRfQ9PnXFQBfIKP/Ad8CfIC/gY4ZPIYwDmGA80uPM97jIT4kvYAlLz3eAzR4z2P2AK4B6f1o9BHgCvy3Q6EdsPE17SMBu/SGE3q2ezc0apS550hrHWV0PWXWzwvX1h3u4uqmv9uds5vbN+055Jubeg0D8SoSnYVnroXvtjo8e/o57h4zCbx3MNWzXb88m4Yeq3z4bqvzfN+zOLlUpUmrP8hh45ahRP9uqkJc7AjqN7VBo0nG1NQCByevdB3DcNcz+3mfa7l2RV6iIlMmGe7+lR+mptJbLt+bb6dW9+eKnyPXLv9NTNQjPu1hRg6ba29sn5br6XfGkct+N+nS683H+a+E+AhWLa6GrX0eatWbgFqTREJ8yp3Ewc/sKVj4H2ztc/Ps8Tn2/tsfAGeXSjRrswxLK4d0vGLjofTvzeAgK7as8cLFxdBJ/s/oirIXSpUNI59XjKFjGLVDvrkp7BNBhZpZOyi6ZNnv+eP3sYQE3aZH3+F45s6PRqNh6ui+JCWtpGa9d09l8ORRfgLvgZ29I+rkYO7dmUyPvsMzlCd33q/4edw3bFjdSret65dDqVmvZbqOY6jrmR1l9FreuFKKE4cfo1Yn4+F5FG+fUpmUUFnke/PtipeqyQ/fpAydiI5axt/LlvHD+HkULFzite3fdT2jozRcuhBPlZrBbz3voT2bWbX4V7p+ORQ7h5Tej+jIB/y7uecrba2s7/PlgHFAWVp9vIOBvZoTGnKDE0e+5LtRMxW7coWSvzfv37Vhy5r0/QKf2ZT5XSAMysbWngE//gqQanLPmvVbsfmfRfgUK6tbsuRNqtZqwo6Ny4mKDMfVPRd3bl9FrU7O0A+mPPm9GfvLCh4F3sU6hw3HD+1k5R8/Y25uQZWajdN9PGE4eb18OPZ8guCrF09LUSbS5OXpU16sHOLmkTvDxzMzsyApMfGd7XyeL5C+YuE03bYadZtz9MAO3WNHJ1dy5fWiQpW6um3WOWzpN3Qqs6f9gP+NS0we2Ydvvp+Ck3PGPi0Q2YfcfSkyRKVSvTLbertOfXBxy8nPY/tx8+qFt+7v4ZmXwaNmUrx0JZq07kzQk4fs+3ddhvM4ObtRokxlChYuQaeeg6hSszHLF04jNiYqw8cUWc/FNSfa50tp7dmx1sBphFKcOLwr1eOyFWu91zx3CQmxWFhavvNOzpy58jF66tJU2674naJRi44MmzCf+X8dZOrcDQz48ddUKwYAlCpXjR7PF1MPDLjF2hWzM5xXZB/SUyb0xsbWniGjZzF/+khmTv6Oz78Z+coPopcVKV6OIs9/0zy6fzv3A/Qz35iJiQkt2/fkxOFd3Lp+kTIVaujluCLzvby4dHxcTIZ7T8WHxdunpO5rewdnrl46TVJiAuYWlhk6XvDTx7i550rTMl+583nz08y/OX1sH5558lOqXLU0f8/mK1gESPkl9dnjBxnKKrIX6SkTemWdw4Zvf5hG+Sp1+OP3saxc9DMP7/u/c79S5apx5sR+4uNi9ZLDxS0nAHN/+VF6yxTEycWdQkVLAyn/GQYG3DJsIKEIOXPn5+f5mxk6bg5f9B9LYkI8z55kvMgJDnqMi7tnmtu7uueiWZvPKFuxVrp+iYiLSRkcn5ycRFRUeHpjimxIijKhd2Zm5vT8eiRtP+3DpXPHGf9DTxZMH0XiW2bILlWuGhq1mrv+ab/b6W3Ontyv+/ryhRN6OabIGk1bd+HFdBi3rvkZNoxQDHsHJ7x9SpErb8rA7SeP7mf4WMFBj3F1z6WvaG/0ImNI0BNatX/15gDx4ZGiTGQKExMTmrTuzKTf19D9qx/xO3uUPTvWvLF9Pi8fPHPn55DvJrRaLerk5AyfOzoqgtVLZlC+Sl2GjptD2Uq1M3wskfVKlq1Kzlz5ANi3a72B0wilsbVzwNbOgcB7tzO0v0ajISToCa5uae8py6jo571jpcpXp2b99N0tLrInKcpEpjI1M6N6nWbUbdyWnZtXEhke+tp2KpWKkuWqEXjvFl91rsOkkV+SnJyUoXPu2LgctTqJTj0G4u1TCosMjisRhqFSqWj5vNfAzl5m9RfpV7ZiLY7u3/7W3vk3SYiPIzkpEXuHzP3eO3F4Fxv/XgiAra0yZ8QX+idFmcgSLdp1x9TUlK3rl76xjamJqe429Af3bpMQn7HJg+/fvUmJ0lWwd3R+d2NhlCpWq4+3T0lZmFxkSNOPuhAdFcHy+VNIiI979w4vedHe0jJzl+C5ee2C7uscNjKrv0ghRZnIEja29jRr05Uj+7YRHRn+2jbWOWyIjkqZAbtMxZrksLHN0Lkcnd3kP3OFU6lU5M7njY30IIgMcPPIzeffjOTiuWOMGtSZdavmEhYalKZ9Tx/bA4CZuXlmRqTrF0OpVC1lOajCxcpk6rmEckhRJrJMlZqN0WjUXHrDwHuvQsV1H1l+8e2YNN2O/jpOzm6Eh6X+ARwdFZHu35iFYcVER2Jrq8zlZ4ThVaregBGTF1Guch2O7t/GX4t/TdN+/25eBZDpA/1VKhXdvhrGd6N/p5yMexXPSVEmsoyDowuu7rl49ODua58v6FMSF9eclKtUO8PzCwG4unsSHPSEp49T1lC8d+cG333Zir+XzczwMUXWe/wwAGdXd0PHEArm4ZmXTj0HUq9Je25dv/jGMa0vBPhfIyY6gl79RuPmkfl3X1pYWOJTrGymn0cohxRlIktZWecgLOT1HyOYm1vQukNvzp8+xLXLZzN8jiq1GuPimpNfx/dn8sgvmTTiCwBy5SmQ4WOKrBUa/JRHgXcpUaaKoaOIbKBek/aYmpq+8xezreuWkiuvFxWrvXnS68yWmJiQprkdRfYkRZnIUsVKVeTsif0kveGuqMo1GlKwcAlm/DSIfTszNh2ClVUOBo74jZJlqxDgf123fd2quW+dlkMYj8P7tmJpaU3x0pUMHUVkA3b2jnzc5WvOntxPaMibVw55cO825SrVxsTENAvTpbZuxRzG/9CT29cvGiyDMBwpykSWKuRTCo1GTVxszGufNzExYcjoWRQvXYnzpw9l+Dyubp506zOM0VOXYm5hST4vH0xNzTh6YPsbC0JhHOLjYjmwewM167fEOkfGbvYQ4r8q12iEo7MbVy+ufO3zyclJRISH4OzikcXJ/k+r1XJwzyYAfh7Xj1NHfQ2WRRiGFGUiSz178gBTUzNs37JYsKmZGSXKVOHOzcvvvURS7nzezFq2mxGTFjFw+G88e/KQCcM+5/p7fDwqMtfhfVuJj4+jYYsOho4ishFTMzNKlavKzavrgC9JTkp944/f2aNotVqSMjg/or40a9OVarWbArB49gT6dKrN/bv6WRdYGD8pykSWuX3jEuv/moedgxMmJm//1qtYtT5qtZpzJw++93lf3MXpU7wsIyYtws7Biek/DWLtitloNJr3Pr7Qn+TkJPbsWEPl6g0N2mMhsqf/L/r9Bxf9/gTg6eNA1qyYzdI5EwH4e+l0vZ0v6OmjdE1gq1KpaNPxC3r0Hc6PExeiUqX8nPxpeO/3WjZKKIcUZSLL3L11BUjbRImOzq4ULVGe44d3otVq9ZYhV54CfDfqdz7p2o+9/65l3PfdOOi7maQk/SyELt7PjSvnCQ8NokGzTwwdRWRbKb+knTk1h4nDejHmu64cO7CDpKSUiasHDv9NL2e5fvksIwd+ytC+bbl47li69y/gXZT5fx3go44pNyqN+e4zgoMe6yWbMF5SlIksc/H8cSDtd0HWbdyW29cv8sfvY5k88ksC9LRYuYmJCQ2bd6D/sF948ug+fy35lRULKwFX9HJ8kXEXzhzG1d2TvAUKGzqKyIY+6tgbWztPIB/FS3YkT4FCdOjWj4KFSwApE08XK1VRL+dydHIFUsZILpgxmtDgpwCo1clsWD2faWO/oV+3hpw9eeCtx2nepis16rUAYNeWv/SSTRgvM0MHyCiVWkPO637kCA8h1tGFJ0XLoDU1vhpTcv7fzavnAciZK3+a2petVItaDVpxdP8ONBo1Myd/x/T5W/G8cVEvOYuUKPfSIy1wGKiZoWP9lxLed2PMeOncMcpXrZdq4mBjzPk6klO/MiOnt08pOnTbw5I5xahZ+zIVaoVx784N/vnzdwDGTFumt5weufJhaWVNxWr1Obp/O/63LuPs6kFgwK1UxdXFs0epUKXuW89RokwVju7fnu5s78pobJSSMzMpsigrcOoQ1ZfNxPalWdujndw41mMAAZWNZ2Zkyfl/Ly8uHp7G5U4AOnTrz/lTh4iOiiA2Jpoq37SnTOT/J4B8n5ympmb0H/YLv08ZQp58tXhwvw/66C1TwvtujBnDQp4RFhqET9H/LzljjDlfR3LqV1bk1Gq1xERHMn3iQADG/7Yq3WtQvi3nybxeJMTHUbxUJYKfPWbLmsVUqFKP/AWL0m/oVALv3cbLuxhFSpR/53mOHUgpyAoVKZWufO/K+KG950qguBK0wKlDNJo+Epv/LKNjExZEo+kjKXAq49Mo6JPkTO3lMRUnj+zm6sVTadrPwsKSanWa4ZjDhn5AocjUM3K/b84SZSrTtlMfHtw/DKzN0DFepoT33Vgz3n3+8XQB72Ipfxtpzv+SnPqVGTnV6mTd1yljVG9zaP8YRg3qTPV6LRg5ZQkennn1mnPnnIk4ObtRqlw1PurQi2dPHuB39ggqlYpS5arRvE1XipWq+M6bnnZsXM7lCycBcM+ZR68Zs/N7rlSKKspUag3Vn8/I/N9VEV88rvbnTFRqw95RJzlfZfHSskk5c+dn9rRhnDm+L037xkSE4xYfz++ATSbkbNKqM565qwB/ZGh/XRYFvO/GnDHA/xpOzm44Orsadc5UWSSnXmVGzqePH/D1Z/VZt3ION69dYOncEkBhQkNvU6dRGzp07Ufe/IX0mvMJcMz/GrXrt+J/7d13eJPV38fxd9K9Ny2Ulk5oC5RV9l4F2TIUQbaILAURENkyZFpAtiCizB+iWFkyRPZGNmXP0paWLrrXef4o5LGySps0STmv6+KiTe7xyZ00+ebc5z7HxNQML98KlK9Uk9VLZxAdFf5G+7Kzd1L9/KyvZVZWJn/t3Mz9uzcKnBGK73Ouz/SqKHMJO4dlXPRzT9wzCsAqNhqXsHNFGes5MufzKlSuxSef515ybmFpRbVajVm5cArRUQ9fu+6lM4fokpOtsZwKhQLnklWBo68c7ft19OF51+WMt29cwcMnt5VMl3P+m8ypXurKmZ2dRXxsDAM/bMyEz7sBsHvbRtJUg1YHENwyhPbvfaT2nOeBZ5cKxD7tR6tQKOg3ZAKpKUlvPFJ/7YbvYGllA6BqVTt/+jAbV89n6pd9yUhPe+OMUPye8+JCr/qUmcc/VutymiJzvtizeQzDLp5R3TZuWFfadOpN2859X7peVmYm5vnYfmFyVqjcm7OnDvDbLx9w934DKlaphY2tA2UDqrz29MKb7l+bz7uuZszJyeburTBavdvzjfb/tv0NFdTblnPe9BHcv3ONnOxsAPoOHkdOdjaB1erSd/Blfljkj4XlRSBO7TkzgWdfNZuU8SE9LRUTUzNMTc0AyCnAED9T563n9o3Lqumf/CtWx8LSmuSkRA7t20qTlp3fKGNBltMUfclZVPSqpSzF1kGty2mKzPlixsYmjJy0KM9wB9Y29mzd/CPHDv750vXKlvbkcD62X5icxiZWwElKudbk5JE9/LBoKiHThnPk7+353oY+PO+6mjEy/B7paal4Pu1Ppqs5C7p/mTN/1JWzVv0WNG/dlfmrdrJs/QFq1gumdsN31BHxtfuvBmwg98N18o5NbFyde2ru9o3LAGSkp7503ZcxM7ckILDGv363YNCI6QBsXL2AnJzsN8pYkOU0RV9yFhW9Ksoi/SqRZOfEy75nCOCJvROR/7p6SxtkzpfzKVeRsdNX0K3v5wAkJsRSNqAyqxZP48/QdS8cKNYjqD6HUJDx9PeN/H+X/EwgBYixc1RDTjOatZiT55a7t6/me219eN51NeOz41zGyw/Q3Zz/JXOql7py1m3UitYde2Fqmp829jf3upzvASds7FAolBz+ezsrF37N3Cmf4V2uIkG1m6olg49fIG5lcr/g/rZh+RtnLG7PeXGhV0WZMFBypPdnuT//976n/x/t9ZnWxzWROV9NoVDQsHkHxk7/nvKVatK93xe806EHv65fyqI5Y0hMyHtKoWJQXZIVMA24D3Ql901vOLkDWFgAHR1d1JLTyMicxWv+IrBqHQBuXr2gGun7dfThedfVjLExUVhZ22Jqlvshqqs5/0vmVK/ilPNx3xGErMgdyuLE4T24unthaGDImu9nv7BlqyCGjJoJwK4/1j83n29xOpa6kLOo6N2jvFOjAbuHTyXZzinP7Un2TuwePlVnxjOROV/P3bMcn345G5dS7nR4vz+DvviGOzcu8/Wo3lx4Ovo/gKubF6079eFroNnTPhVVgQXAl0ZGABy8fpG0VPVMlWRgYMjgkTPoPfAroiIfMHfKZyTExeRrXX143nUxY0LcY2yejoD+jC7mfBGZU72KU04zcws6PJ0m6f6d61y9/A9nTx3ku5mj1ZLBxs4BBycXAEKmDc8z9Me/Mz6xdeTflwPo47F8W+hVR/9n7tRowN1q9XR+5F+Z881UqlYXj5mr+GnZDBbOGk27Lv1o3bEXAK3e7UkJF1dWLpwCQKc2H9DSzJzpm1bi5VuBqIj7eQaoVYfaDVriUsqdpd+OY/rYjxk4YppqDK1X0ZXj+Sq6ljEh/jE2L+gzoms5X0bmVK/ilLNR8Lvs3raRcgFVOHv6EDnZ2Vw+f4KkxHgsrW0Ltf+01GQeR0cCYGxiSnZWFgYGeT/Wr1epRd9frElIS2F9z08RJUrq7bF8G+hlUQa5TZ4ReabJ0U0y55uxsXVgyKhZbPt1NaGbVmJr50jdxq1RKpXUqNscK2s7oiLu49C8A44KBY0SYtm/ewuDvvhGddm4Onn6BPDVtOUsCRnP7MlDGTDsa9WpzVfRleP5KrqUMT4uhlKlPV94ny7lfBWZU72KS04zc0ve6zmUVYun8V7PoRgZm7B2xRwePrhD2YDKhdq3mbklMxZtxszMAmMTE9XVmf8W9fA+4fdvAXCnThOMTUwLtU9N0pfnXJPerhJU0gsKhYLWHXvRoGk7fv5+Fn/t3Ky6z79iEI2C31XNjfh+r88ICKzBz9/PIiMjXSN5bOwcGTF+PhUq12T5vAlvPM6Q9HpJT+KxsrHVdgxJ0oha9Vvg6ubF/bs3qF67KUoDAx5FPVDLtu3snTA1M39hQQaQmZn7vli9dlOdLsikXLIok3SSQqHgg76f06zVe2xcPZ8j+3e8cDmlUsn7vT4lMT6WP0PXaiyPkZExHw2ZgKdPAMvmTyAl+YnG9vU2SktNwdTsv/M1SFLxUa58FU4e2UtOTg5KhZJU1UC2muXpE8A3322i96CvimR/UuHIokzSWUqlkk7dB1GvcRvWrpzLmRP7XzhkhnNJN1q068aOLWvyfaVkQRgZm9Bn8FjS01JfeAm6VDBZWZkkJz3B0FBve1NI0muVr1SLrMwMZk8aTFZWJgEVg16/kprYOzpjaGhUZPuTCk4WZZJOUygUdO0zjPKB1VkWMp6Fs0a9cO64ykH1yc7OYvtvP5GakqSxPPYOznR4vz8H9vzOzWsXNLaft4lSaYAQOZiYmGk7iiRpjE+5irzToQce3n4Et/kAV3fvN95GyLTh/LBoKklPEvLcfu3yWSaO+JC0NPVcgS5pjyzKJJ1nZGTMwBHTGThiGhEP7vLNuE+eG6LCw9uPRsHvsuuP9cwY/wmZGupfBrlXU3l4++e+OSbGa2w/b4v0px8kFpbWWk4iSZpjamZOh/f703vgV3TqPvCN109LTSHs4mmOH9rFiI/bEhcbrbrv7q0wIh/eY8V3X6szsqQFsiiT9IJCoaByUH2+mrYcAwMD/ti8Ks/9SqUBH/QZzlfTlhMd9ZC9Oza9ZEuFp1Qa8PFnk0lPS2XZvAlkZ2W9fiXppZKeJAJgoYGrZyWpuDA1M2f8jFU4l3QD4MvBnVRTN9Vr0gaAC2eOaPRMgaR5siiT9IqltS11GrXi1NF9LxzQ1dXdG1//Sty9lf/pkQrCwcmFT4ZP5XrYOQ7+9YdG91XcJSflnoqxlC1lkvRKpct4M2nOT1St0RCA08f/BnKHxqj+dPqmPds194VU0jxZlEl6p1mr9zA0NGL7lp9feH9sTBS29k4vvE+dfPwCqVkvmG2/riY97c0nGZZy/X9LmSzKJOl1lEoDBgyfwtJ1++ncfZDq9vd6DgVg63/OIkj6RRZlkt6xsralUrW6XLty9rn7kpOeEB0VTukyPkWSpW2XvqQkP2HX1vVFsr/iTIFC2xEkPZSWlsLNaxfYv/t3doau5eSRvdy+cZmM9LQ8y2VmpL/w6m199WysxmfEv2aOzO+0cJLukdegS3rJu1xFDv+9jZTkJ5hbWAG5LWQrvpuMkbEJ5QNrFEkOR6eSBLf9gK2bf8Taxp6GzTsUyX6LE1PT3KsuZWuj9CZioiPYu30Th/dtIz09FaXSABNTM1WfKqXSAFd3L2xs7Ul6ksCdm2FUCqqHd9kvgWNcPH+NR49jyc7Ooma9YJycS2n3ARXSsYO7VD+HTPucz8fPx9rGTouJpIKQRZmkl3zKVUQIwa3rl6hQuRaJCXHMmZzbfD/sq2+xtXd8zRbUp12XfqSlprDuh28xM7ekRt1mRbbv4sDkaVGWJosy6QXSUlMIv3+TRxEPiIp8wKPIBzyKeMCDezcwt7CiaasuVK3REBfXMhgZGZOS/IToqIfcvRXG7RtXSEl+gqu7N08S4zl36hDnTuV2ij9+1AQzc1MyMtI58vd2PvtqrqoTvT5q3KIjqSlJ7NjyMxHhdxj5SXsWrPpT9fcl6QdZlEl6ycnZFccSJfl795bcueRWziUzM4Mx05Zh7+BcpFkUCgXv9RxK0pN41nw/G3cPX1xcyxRpBn1momopk2MsSf/v3u1r/PnHOs4c309OTjYAtvZOlHB2pYxXORq37ERQrcbPFR3mFlaU8SpHGa9yNGjWPs99Vy6e5uJZE/Zs60W/AbeoVj+O2Jgo5k75lIkjelA2oDKmpmakJCcREFid4LYfoECBgR4MbGxsbEKH9/vTumMvhvTM/WJ4/p8jqgsAJP2g+680SXoBhUJBx24DWT5vAhfOHMGtjC+DJ35T5AXZv/N0/+gL7t2+xrL5Exg9eQmmZuZayaJvYh49BFCdhpbeXkIIwi6d4c/QtVy5cArHEqXo0mMwvv6VKeHsWuhWH/8K1UiMdwT+/2/T3tGZSbN/4uiBnVz45yiZmZlY29jzxy+r+P1/KwBYvOYvYmOicCxR6rm+XLrGyMiYAcOnsCxkPAlxj/Pcd2BvKM4upSlXvqqW0kmvI4sySW9Vq9mIsdNXkJGehlfZCiiV2r1uxdTUnI+Hfc2siYOYNXEQIyZ8h4WlLDRe5+DePyjp6lFkF2dIuicnJ5t/ThxgZ+g67t2+ipuHLx99OpGqNRpiYKD5jykjYxMaNGufp2Xt3u1rfDt1GKkpSUwb8xHh92/hXNKNMVOXY2au2/O0VqnegB4fj1INnQGQmpLE2hVzAGjRrhsNmnUgNjoSV3dv+T6lQ2RRJuk1d8+y2o6Qh6ubF6MmL2bO5KF8P38iQ0fP0otTH9qSk5PDlYunad76fZ1vgZA04/SxfWzZuJxHkeH4VajGZ2Pm4l8x6IWvByEEu7Zu4Mjf21AolBgYGKA0MECpNMj9WZn7u4WlNbUbtCSwap0C53L3LEvIim2cOvYX638IASAq4j7D+r3D9AX/w8HJpcDb1jSFQkG9xm3y3GZqZkGp0p48fHCbP0PXsX/XFtLSUmjdsRftuvTTUlLpv+SQGJKkZq5uXgwY9jVXL//D37t/03YcnRb3+BGpKUm4efhqO4qkBTtD17J8/kRKunowZupyho8NISCw+gsLsoyMdDb8OJ9f1y3BrYwvAYHV8fGrhIe3P6XdvXEu6Y6DkwvW1nbEPX7EotlfcuroX4XKp1AoqF67KR/2HwlA6afzVd64ev65ZVNTkgm7eJrIh/e4df2Szl1NrFAo+PTL2bTr0g//ikEoDQyA3C9Gku6QX+ElSQP8KlSjSo0GHNjzO01adpatQC/x7JL95P9MsCwVf4f/3s5v65fRumNv2nbu88q/kfjYGEKmDSMmOpKufYbROLjjS5cVQvAo8gEzJwzi7MmDBNVuUuisVao3oEbd5pw8spdO3QdSvc7zV1gvCxnPlYunVL/bOzozJWQdhoZGhd6/utg5lKB1x14ArFw4hROHd3P/zvUXLhv3+BHWNvaypb+IyaMtSRrSoGk7QqYN53rYOcr6V9Z2HJ1kaGSMta09UZEPtB1FKkLh926ybuVc6jVp+9qCLC01hfkzRpCensa4b1ZQ0tXjhctlZKTz67ql/HNiP/FxMZiYmqnmhCwshUJBn0FfYW1jx+a1Szh36jDlK9ckJzubzMwMzMzMqVi1dp6iLDYmisE9mlK/aVu69/tCp76YnT62jxOHdwPQvPX7z90fGxPFmKFdAHBwdKF52640aNquSPr3ve3kEZYkDSlXviolXFw5vG+bLMpeQqFQ4OkToJpYWXo7/LVzM7b2jnTt9ekri5Wb1y7w45LpJCbEMXrykpcWZABRD++x78/NAAwZNRNfv0rPXQGdnZVV4MxKpQFdegyhfKUabPvtJ3Zv3YChkRFGRsYkJSaQnp6KhaU1Ldp2w8PHnx1b1nDlwkkO7v0DExMzOn84WCcKs0vnTrB8/kQAatVvgV+Fas8tE/MoQvXz45hINqyaR9iF0wwcMa3Icr6tZFEmSRqiUCjw8q1ATHTE6xd+i5XxLMfeHXIS5bfJo6hwynj6YWRsorotJyeHjPQ0oqPCuXntIgqFgs3rluDq5sXgkTNxKeX+ym26efjSqftANq9dwvGDu0CAqbk5pqbm2Ds6Y2RkzNDewQTV/hyYW+DsAYE1CPjPjCFpaSncvHqRE4d38+v6pZR296Zjt0+wtrHj+KFd7Nn+P8wtrFSnDrXlwJ7fWbsy97G3bP8hbV6Sp2xAZWYu+pXkpATiYmNY8d1knb6woTiRRZkkaVBOTg5Khbye5lXsHEqQnJRIZmYGRkbG2o4jFQGnEiW5czNM9fvRAztZs2IOWZkZeZarULkm/T+bjKlp/sb8C27zAXb2JVi7ci4nj+5V3W5iYkbNes0BOHX0W2B2nvViH0cRGX73uWIrv0xNzSlfqQblK9WgcYuObFy9gAUzRlK5en28fMtz6/olQjetxNM3gICK1Qu0D3U4e+oQAB9/NplqtRq/cllbe0ds7R1xdffm2++3an3IobeFLMokSYOEEDpxykKX2TuUAODuzTB8/AK1nEbSpJTkJxw7+CcPw+8Sfv8WaWkpmJiY8ccvP+BbLpC6TVpjaWWLh1c5UlOTCzQYdPU6TalcvT7JTxJJS0shNSWJTWsWcWBvKOYWlqQkJwHV+HOHHYcPp5CVmcHVy/8AMGD4lDxjexWEh7c/oyYv5uSRPfy6bikZGek4OLnwODqSxbO/wsPbjw/7j3xty58m9B74FUqFAktr2zdaTxZkRUcWZZKkYenpunVpvK6xd8z94J09eQjL1h/QchpJE54kxrNn+//Y+fsaABQKJTXrBZOZkcHxg7t5HB1J197D8owrZmZumWcb18PO8SQhjlJuXpRwcUWpNHjp/oyMjPPMf/vFhAUkxMdia+fIlg032Bm6i5zshxibmGBuYUXtBi05efQvflo2E09vf+yeflEoKIVCQY26zQmoWJ1500fwKCr3QpbMzHSuh50j/P4trRRlmpmg/DinTqyiTLnGOJbQ70nddYEsyiRJgyoH1eP7BZO4dO4E5SsV7NRIcffvN/Kb1y7gXbaiFtNI6nbr+iVmThiY57YmLTuRlZnJqEHvkpOdjYe33ws7nD8T+fAe86aPUJ3eLOFSmi+nLMv3SPRKpQF29k4AlHKrDfTlnTYXqVY/TrVMlx5DmTK6N/Omf07fweMp41XuDR/p8yytbRk+bh6L54zh1vVLGBkZozQwIPR/K8nKzKBmveBC70MbYqIjSEl6AjgC8zhzagORUbtp36UfXr7lsba113ZEvSXbJCVJg6rVakzZgCps+nkhQghtx9FJSqUSq6enU2ZNHKyafFoqHuIeP1L93PPj0QDs3bGJ44d30eH9j5m1+FfGTF2O8b86/f/Xrj/WYWlpzfQF/2PQF9+Q9CSB3//3vVpzWlhaMXzcPBQKJdPH9mfhrNFcu3y20H+3udsN4Z0OH5Kdk01qShKRD+/yw6KpbN/ys5rSF601y2cz7auP+GFRAJA74fnD+7dY8u1YRg7sQGpKknYD6jFZlElSAe3Ysobl8yeSEBfz0mUUCgWt3+1JRPidF44CLuVq2Pxd1c/fz58kRxkvRqrVaszkuWuYt3I7dRu3plHwu1Sr2ZhJc36mRdsPsLFzfO02bO2dSE1NxtzCkkrV6tK4RScO/vUHSWoedNi5pBvjZ/5A74Ff8TgmkrlTPmXcsA/YsvF7wu/fKvB2DQ2NaNelH1Pnradxi044l3LHuZQ7v2/8nl/WLNK7Isbe8dnpXQVQjZ59D1G3UWsALCxtyMmRX0ALSp6+lKQCOrRvKzGPHmJsYkrvT8a8dLmyAVUo4eLKH7+sYtCI6c+NnSSBd9kKqp/PnNjP0pBxDPx8mrxIopDiYqNJjI9FqVRyZP92Yh8/wqWkOwGB1fEuV1E12nxmRjp3boVx99ZVrlw4RY26zdR6au3f/ac+6DP8jdY9c/xvwu/fIj0tlQN7/6BF2w9o2Lw9239bzdmTB9U2QOwzBgaG1G7Qklr1W3D10hlOHtnL/t1b2LHlZ0qV9nx6IUEDzM0tuXPzCrduXCL83k3KePnRom03TEzNXrptO3snuvb+DIDbNy4zY/wn7N62kd3bNjJ3Wegbd8DXtMyMdAwMjZ7r6P9h/1F06TGU82fK8MMif0xNL9JzwGh6fDyKrKxMeRV1IciiTJIKyMPbj5hHD0lJfvLK5ZRKJZ27D2blwilM+bIvHw2dgKdPQBGl1A9eZctjamaBtY0djyIfcO7UIe7eCsPD21/b0fRS5MN7rF05l2tPryp8ppSbJ7euXWJn6FqMjE3wLluBmvWCOXf6EGdPHlQtd/HsMQwNjV47bEJRuHzhpCpbbEwUyUmJHNgTihCC5Nf87RWGQqHAr0I1/CpU44O+w7l8/iQnj+xh5+9r+f1/K1TL2do5UsrNi11bN3Dz2kU+GzPnlRchPJOdnfc0/ZQx/Rg9ebHqwhdtexT5gPHDu2Fta8+sxb/l+YKkVCoxM7d4bh2FQiELskKSRZkkFVC3vp/jXNKdwKq1X7tspaB6jJ/5A9/NHMXGn77jy6+XFEFC/WFqak6XDwfz8/ezVLf99+o76eWysjKJDL/Hg3s3uHrpH04e2YOdQwn6DRmPSyl3srOzsXcogY2dIzk5OTy4e4Orl85w4ewxflo2I0+/qSYtO5OYEMuqJdNJTU2mXmP1tkS9qXe7DuDo/p1kZWVy6/pFxgzpQnZONs3bdKVJi5fPgalOhoZGBFatQ2DVOmSkp3Hj6gXS01Px8PJTXal55cIp5k3/nFNH91Gj7vNzY/6XT7mKdPzgE35dvxQfv0DiY6OZOXEQAz+fqhNfRm5cvQBAYnysbLEuQrIok6QCsrC0pl2Xvvle3snZFSsbO+zsC3e5fXFVr0mbPEWZk7O8vP6/hBAkxD3mwb2bhN+7qfo/4uFdcp62vJR0LUOLdt1o0bYbxiamz21DqVTi7lkWd8+yNG/TldiYKG5ev0h01EMUKGjcsiNKhZLl8ycS+r+VWi/KLCytmbloM4f37yAi/A6Vg+pTv2k7DQ3v8HrGJqYEBD4/AKx/xSDKBVThyP7t+SrKAJq36Url6vUp4VKaxPjHjBrUkW/GDSCwah36Dh6n1S8mgdXq0rpjb5q16qK1DG8jWZRJUhHKz2mNt1nTd7qwd8cmygZUkccqj3sc/Ptr1v28S9W53cTUDFc3L7zLVaRh8w64unvj6ub5xh/k9o7OLzxl5utXicsXTrF25Vy69xuhlkdRUJbWtrRo+4FWM+RHYLW6/LZhOdlZWRgYvv7jValU4lzSDQAbO0eatOzM3dtXCbt0hlGDOmJr54ibhy+BVetQvXbTfG2zMLKzs1AqDVAoFPy6bgmH922jpGsZqtdpqtH9Sv9PFmWSJOmMlu0/ZO+OTXjJPncAJCc94eSRH4G13L5tQaPgtnh6++Pq7o2Dk4tGR1q3srEjOyuTA3t+p2uvzzReEBQHbmV8yMrMIPrRwwINDvt+r08BuHPzChf+OUpqago3r57nxyXT2fn7Wjp07Y9bGV8sLK0xMjJW23OSmZnB6iXfcOrYPlxKuVG6jC8nj+wBeOVQJZL6yb8ySSoimRnpPIq4j1sZH21H0VnWNnZUq9WYf04dpP37/d/q6V1uXb/EspDxJD1JBr6ka/d3qN0047XrqYuXb3kAmrd+H6WBbLXMj2eFWGT43UKN2O/h7Z+nX9n9uzdYu3IuS+aOVd1maGhE2859aNn+w4IHBhIT4pjweXdSU5J4t+vH3LkVxr3bVzEyNiE7O0tOfVbE3t53PEkqYlt/XU1iYhz1m7bVdhSd1rhFR6Ie3uPy+ZPajqI1d26GMffrT7F3dKZT9+3AJIyNn7/aTdMZAILbdpMdvfPJ2tYBO3sndm/bSFJivNq261bGh9GTF/P1t2sZOno2/T+dRO2GLfltw3Lu37leqG3Hxz4iNSWJNp16E1SnKc1avU/jFh0ROTm0aNsNC0trNT0KKT9kS5kkFYGrl/9h5+9r6PD+x5R09dB2HJ3mUy4Qd8+y7N2xiQqVa2o7jlacOvYXFpbWjJiwgNPHtDNEwrlTBynj5ae1DvX6SKFQ8NHQiSyaM4aJX/TgszFzcfcsq7ZtO5d0U/VBexwdCVDocQ8tLG0A2Lr5R7Zu/hEApYEBZf0r0+rdnoXatvTmZFEmSRoWEx3B/OkjsLC0oUW7btqOo/MUCgVN33mPVYun8vDBHUqV9tB2pCJ35cIp/CsGqQZ3LWrRUQ85e/oQnbsP0sr+9ZmPXyCT565h0ezRzJv+OcPHzVN7l4WYRw/5df1SAFKSCzcbgIOTCzMX/cr9O9cxNDbG0soGl1LucrwxLZFFmSRpWFJiPNnZWSQnJbB47lcMGjH9re4rlR9BtRvz8/KZhF08/dYVZbGPo3hw9wbNW3fVyv5zcnJY98NcrK3tqN9EnmovCGsbOz79ci7zpg9n1sRBBNVqgoWVNVbWdtjZO2FhaY2JqRmlSntgbpG/SdX/7cj+Haqf9+/ZQglnV1AoyMzIwK2MD2W8yqnGT8sPW3tHbO1fP92VpHmyKJMkDfPw9mf2ki1cvnCKVYuncuLwHmrVV98UNsWRoaERbh6+nDr2F41bdHyr+jRdvXgGgErV6mhl/7u2rufy+ZN8NmbOC8c5k/LHwtKKz8fNY/e2jZw9eZDMzHQSE+JIS01RLaNQKPD1q8THw77G6g2mWKpSoyHXw85jYmrGrWuX+OfEAYQQGBgYqIZM8fD2572eQ/NMYSbpPr0tyhTZObiEncM8/jEptg5E+lVCGOhe64PMqV76mDPRypZIWwcy0lMByMhI03K6XLp+LNu/9xHzpn/O7tD19PLx19mcz6jreEY/isDG1kFjA4e+KueOLWvYsnE59Zu05cSRvSya8xU9+o/SypcIXX99PvOqnGbmlrTr0o92Xfqplk9NSSY1NYnU5CRu37jCuh++Zefva+jSY0i+9+lWxocR4+c/d7sQgvjYaG5eu8iurRuYPWkwbTv3pXW7Dyl59bxeH8u3hV4WZR4nDlDnx/lYxkWrbkuyc+JI78+4U6OBFpPlJXOqlz7mvAp0AMKe3vdsomNt04dj6V8xiPerN2DThqUMAJ4NX6lrOUG9xzMzI/2Vk1oXxqty/opgy8bltO7Yi6P7dxL7OAoAI6Oi79emD69PKFhOM3OL3HkjHZxxdfcmLvYRWzf/SGC1upQLqFKoPAqFAjuHEgTVbkLVmg3Z9utPhG5aSY2tG/g4NTnfGbVBX55zTdO7EtTjxAGah4zD4l9PHIBFXDTNQ8bhceKAlpLlJXOql77m/ByIBHYBd4BJ1eppfTBGfTqWa04eoDnQFbj99HZdzKnO42lgaEhWVqY6IwKvzlk3ZBy/rZhDxSq1qViltqog8/QJoEqNhmrPUtCcxfF5r1479+tG3OPo1yz5ZpRKA4a6ezMGGJeazCfkfjmMKkBGTdOX57wo6FVRpsjOoc6PuU22/+1h8uz32qvno8jOKdJc/yVzqpc+5/QD0oEqgDvaz6lvx9IAWAfYAh2BFHQzJ6jveIqcHLVfCPK6nNOBhCcJvP/hkDz993oO+LJIL0rRt9cnFC7nreuXCJk2nBIupamq5tagZxmnAouBNYA/4AI8O/FZnI5lcaFXRZlL2Dks46Kfe+KeUQBWsdG4hJ0ryljPkTnVS59zjgGyyS0sdCGnPh5Le+A34Bow4On9upjzRQqSMyMjDWNj9Xawf1XOWGAOMBIIjIuhdBkfqtZoyEefTizyK1/18fX5Iq/LKYRg365fmTN5KPaOznwxYYHaL6p4llEJDATuA3uBVkBIPjIWFX15zouKXvUpM49/rNblNEXmVC99zukIVAeOAp++Yrmioq/HMhBYDnwItAc6v2S5oqaZ46n+K01ftf8/gQxyP7ivxD/G0NCIAcOnqD1Dfujr6/NNlsvMzODn5bM4fmgXTVp2olP3QRoZj+7Zvh8Ad8k9bfkQOAbUfk3GZ7KyMjmyfwdJifGUDaiCT7mKGsupruX0nV4VZSm2DmpdTlNkTvXS95x1gB/JPY1p8orlioI+H8tuwC/AYKAx4PCS5YqSJo6ngYEB2dlZBY30xvu/Te6Xh1LA6WJ4PDWhMDl3b93AyaN76TdkAjXqNnvl+qkpSTyKDCcmOoKszAwsLG2wsbXH2sYOKxs7lMq8c5Ie/ns7Ht5+WNvYs+LcCT4DLv1nm5WBn/L5WE4d3cfaFXP+f93q9Rn4+bRXZn5T+vKcFxW9Ksoi/SqRZOeExUuaOgWQZO9EpF+loo6Wh8ypXvqesw8wG1gB9NRyTn0+lgpy+8YEAMOBRTqa898KcjyfJMZjYlq4qXP+61U57YF4IMLOsVgeT00oaM7Yx1Hs+H0NTd/pQlDtJsTGRBEV+YDsrCwyM9MJu3iau7eukhD/mOQniaQ/HUbnRcwtrKhWqxFVqjdEoYBHkeGsXxUCgKmZBULk0NHYhEkZ6aq+ZFaAEbl/S686lnGPH3Htylkiwu/g7lmW5CeJPI6J5N6tawghXjluYGJ8LHduhWFubomnbwAKhZK7t8K4ee0iFpbWBFati4Xl/w+Yqy/PeVHRq6JMGCg50vszmoeMQ5C3kV88/f9or8+0Pq6JzKle+p7TH+gLjAdKvPeRVnPq+7EsCXxL7vEMatEJUx3NCQU7nkf27+D4oV207tirSHLmAFeALGBLq/cxKGbHU1MKmvPP0HVkpKdx5O/t/L3rNzIz0vPc71iiJD7lAvGrUA1LKxusbewp4VIaxxIlMTY2IelJAokJsSTEx3L7xmUO/72Ng3v/yLMNExMzatRtRvsu/agQdp7mIeMgnxnjY2NYu3IO588cAcDa1h4HRxcsrGxISXlC7OMoZoz/hNYde71wXtoL/xxl2bwJqsdlZW2HgYEB8XExGBkZk5mZgb2jMx8NnYB32YqFOpbFlV4VZQB3ajRg9/Cpz49nYu/E0V66M56JzKle+p5znK0D/0tNZsHVC/Ro+I4WE+r/sXzXzpEhyU/Ym5VJay3me0Zdx/Pe7Wv8tGwG9Zu0pXXH3kWScwSwAKjs5oWiVedXrl9U9P31+aqc1Wo1xszcEkNDI0zNzHEu6UYJl9IYG5ugUCixsXN4ZSuUvYkp9o65E9RXqlaX1h17kRAXg1JpgJGxyXOzArxpxvWrQrhzM4yeA76kUrW6WFrZqO4TQhB26Qyh/1vBotlfYu/ojHPJpkAlblwHh1K2rFkxB59yFenRfxSJCbGcOb4fIXKoFFQPL9/yxD1+xIrvvmbWxMEYGRljbGJK+/c+guYd9OI5Lwp6V5RB7gvtbrV6Oj/yr8ypXvqes91foaz74Vs8fQKo16SNTmbUl2PpNedLblw9r+14Kuo4nqeP7cPK2o6ufYZpbBiKf+eMuHGJBRu/p9P7Awhu300j+ysofX99vixnWf/KlPWvrLb9GxkZ41iilNoyPnxwmxIupTEzMyfmUQQGBgaqmSUUCgX+FarhV74qd25e4eBff3Dp7DFgK3/tTuSv3WBqZk6n7oNwcHLBwckFT5+APNt3LFGKkZMW8s+JAyQmxHL31lXW/fAt166cxd2jLENdXPEvW54hQfVJfXo6Xdeec03Ty6IMcps8I8oXbvTjoiBzqpc+56zftB3h92/x8/ezQAH1Gmu3MNPnY+nrF8j2LT+TnZWFgaFuvI0V9njGx8Xg5FwKAwPNPp5nOX+7cJIcIbh86RRn/zlM38HjcHQqqdF9vwl9fn3qmvxmfPeDAaxfFcKyeROA3DloGwa/S6sOPVStZgqFAk+fADx9Ajh+yJEfFvnTs+9h7F1O4O5ZLk9/sRcxMDAkqHYT1e++/pX4M3QtZ07sp7S7N79fOUupes2prOPHVFN0491Mkt4CSqWSD/oMJyMjnV/WLCaodhNM1dyh+21RoXJtftuwnDMn9lO9TtPXr6AHEuIeY2PnWGT7q1azEWeO7+Pe7WskJyUSGx2lU0WZVPSq1mhIleoNSH6SwOOYKC6ePcaures5dmAn7/UcSs16wS88vWpqaoN/xaAC7bNe4zbUa9xGdQHB6MGduHvrKpWD6hf24eilt6tdUJK0TKFQ0K5zX9LTU9mz7X/ajqO3SpfxxsPbn/NnDms7itrEx8Vga1d0l/27efhSq35LsjIzcXJ2xcPbr8j2XZSys7P4a+cvPLh7kzs3w8jMTNF2JJ2mUCiwtLaljFc5WnfsxZRv1+HrX4lVi6dx99ZVje4XoFRpT8IunkYI8Zo1iifZUiZJRcze0ZkWbbux/befKOXmSZXqDV7ZuVd6MXfPspw7dYi42Gjs7J20HadQ7t2+SnRUeJH2Nbx7K4zQTSsBaNmuu9pHlNcVUREP2Lh6gep3A0NTcifskvLD2tYen3KBXDp7nJKuZTS+vyYtOrFw9mgWzc6d4utJYjxJT+Ip7e5D934jsPzPxQzFjWwpkyQtaN2xFwGB1VkWMp6QqcO4f+e6tiPpnXfaf4jSwID50z8n6UmCtuMUWMyjh3w3czSly3hTv0nbItvvv1siYqIjimy/Rc3esUSe38t4Fo/T3UXpzs0ruHuVw8TUTOP7qli1Nh/0GU7Mo4ecO32YW9cv8SgynDMn9rN/z+8a37+2yaJMkrTAyMiYwSNnMGTUTBLiHzPtq49YNPtL/jl5gJyct2Pi3cKyd3Rm+NgQ4mJj2Pn7Gm3HKZBrV84SMm04JqZmDB45s0g+9J4p4+WnGg+tQuVaRbbfomZqas53q3cza/GvLF23n0bBs9HEVFbFmampOSlJiUW2v0bB79LknS55bvP1q0S9xrowCI5m6ezpy3u3LbUdQafFROeeaoh4aMG92xlaTqP/tHU8bWyb0XtgQ86f+YN/TvzG0m/H4eVbm7adJ2FpVXSdvtWpaI+lH0G1PmD3thUI4UrNet01vD/1EDk5nD7xC7u2zsHVrQLtu0whPtaV+Njnl9Xk8XwcnYOFpQNGhjW4d7s4f0e3AOxJiJfvnW8qMzON44f2UMYriHu3LfLcp8ljKXK8sLB0IDk5FoQgISGVzes241jCC5dSfpRw8X1umqk3FRGuexdaKUQ+etMlJiZiY2NDQkIC1tbWGg0UEQGlXj3siiQVY38CPYBkcqcwHwXYaTWR7hNAf+APcqdfVv/kzup1AhgEnCZ3GvAFaO/7cW3AC1irpf1LuuccMBpwBVoAnZ/+2wecATyLOE8isBfYDBwB7pD7N+8BfEHu/B6Fa2G+dg18fQu1idfKbx2lc0UZ5BZmEcW3i4PapKaCWdGd7ShSYWH/YGxsgpubL0ZGRfMhqyvHMzExjp9+ms369fOxsbHnm282UqlSHW3HeiNFfSyvXj1Lt25VaN78PaZPX6+xwVcLKzT0RyZP7gPAypWHqFy5br7WU8fxFEJw/Pge3Nx8cHX1JCsriwYNrBk8eBrduw8v3Mb1jK78reuiFSumsmTJeNXvFSvW4ssvFzNqVCcsLKxZsmQvtv+aHLyoj2VaWiqXL59k8+Zl7Ny5jqCgxnzzzQbs7Uu8fuUXsLLSfEEGb1BHiXxISEgQgEhISMjP4pJUKDk5OcLS0lIAYuHChdqOozX3798XdevWFaampuLw4cPajqPT7t27J8j9+iwAsXr1apGdna3tWCoRERGiTZs2onr16qqMSUlJRZph8eLFAhDt2rUTQghx+vRpAYi///67SHNIui0iIkK0b98+z9+Tra2t2Lt3r3BychKVKlUSjx490nZMIYQQx48fFy4uLsLPz0+Eh4drO84r5beO0s2vk9Jbr3793IEDjx07puUk2lO6dGn27NlDjRo1aNu2LVeuXNF2JJ3l5ubGwoULVb/36tULAwMD7Ozs8PDwoEGDBoSFhWlk30IIrl27xoIFCxg2bBj9+vWjUaNGODs74+vry3fffUfJkiXZunUrCQkJLFq0CICNGzdqJM+LXLx4kUGDBgFw584dABYuXIirqyu1a9cushyS9gghOH78OOvWrePixYsvHQfMxcWFLVu2EBoaSt26uS258fHxGBgYsG/fPiIiIqhcuTL//PNPUcZ/oRo1anDgwAGSkpKoV68e168Xg6vY1VnhSZK6ZGZmiv379xd5a4IuiouLExUqVBCenp7iyZMn2o6j02JjY8Xw4cMFID799FMxc+ZM8dVXXwk/Pz/h5OQk9u7dq9YWtJs3b4py5coJQBgbG4uAgABRs2ZN0alTJzF58mShUCiEk5OTAMRHH30k0tLSRGhoqDAyMhJz5sxRW47X+emnnwQgypcvL9q1ayeys7OFqampmDJlSpFlkLSrc+fOeVq//Pz8xNixY8WaNWvEvn37Xvp3cfHiRbF06VKRlZUl7t+/L7Zt2yYAMWjQoCJ+BC93584d4ePjI5o3b67tKC+V3zpKJ/uUSZKU161bt6hYsSJ9+vTJ0yIk5c/jx49p1aoVJ06cwMXFhcmTJ/Pxxx8XeHtCCM6fP8/HH3/MtWvXWL16NU2bNsXC4v+vTktOTsbKygohBJcuXaJs2bKMHTuWWbNmERwczM8//0yJEgXrB/MmcnJyqFChAu7u7pw7d45u3boxatQoVYtI+/btNZ7hbZaVlcX69etZuXIlJUuWxN/fH39/f7y9vbG0tMTd3R1TU80O3Hvx4kUqVqzIggUL6NGjB8ePH2fNmjXs3LmTmJgYAMqVK0fFihVxd3enTJkylClTBnd3dzw8PLCzs2PatGmMGzcOyJ0ybvv27bRo0UKjud/EO++8w5MnTzh06NBrl71+/TpffPEFCQkJ+Pj44OnpiaWlJaamppiZmWFqaoqpqSlOTk5YW1vj7e39xs9RRkYG06dPJzQ0lD///BMTExPZp0ySipMFCxYIQOzcuVPbUfRSVlaWOHDggOjcubMwNjYWN2/efOXyKSkp4scffxTBwcGiUqVKwsvLS5QqVUrUrVtXODo6CkB4enqKQ4cOvXQbK1asEBYWFnlaKObMmSNycnLU/fBeau/evQIQBw4cEE2aNBGmpqZi/vz5AhCnTp0qshxvm0mTJgkrKythYmIiANGoUSNRr1491Wvn2T8LCwvRpUsXMXXqVLFlyxaNvDZGjhwpbGxsREZGxnP3paSkiH379ol+/fqJpk2bCl9fX1XmZ/9cXFwEIIYMGSKOHz+usf5bly5dEkOGDBH+/v7Cw8NDNGjQQPTq1Uu0adNGlC9fXnzwwQdi1qxZYvr06eLOnTuq9UJDQwUg1q5d+9p95OTkiC5dughDQ0Px3nvviapVqwoHBwdhbm4uFApFnsf97J9SqRRBQUFiwYIF4saNG89tMzs7W5w7d05ERkaKffv2CSDP85yQkJDvOkoWZZKkJ7Kzs0VwcLBwdnYWu3fv1qmO7PokKSlJlC5dWrRr1+6FH4BpaWli6dKlqtOOzZo1E4MHDxZffvmlGDdunHj//ffFhAkTxI4dO0Rqaupr9xcdHS12794tli1bJnbv3q2Jh/RKAwYMEIBwdXUVn376qfDx8RGAKFGixAs/pKXC27FjhwBE//79xfz588Xx48fz3B8dHS1Onjwp9u3bJ6ZOnSrq1Kkj7O3tBSD69u0r0tLS1Jpn8eLFQqlUirFjx4r79++/dvns7GwREREhjh07JtatWyfGjBkjli9fXqj3nKioKLF06VLRpEkTERwc/NwFJvPnzxcKhUK4uLiIAQMGiDFjxoguXbqIWrVqiZYtW4pPPvlE1KhRQ1hYWAgLCwthY2Mjjh07JkaPHi0A0apVq3wVtKtWrRKACAkJee6+nJwckZ6eLhISEkRUVJSYMWOGCA4OFsuXLxetWrUSgLCzsxOxsbF51hszZsxzhVzVqlXFn3/+KeLj44UQ+a+jZFEmSXokIiJCBAYGCkB4eXmJ6dOni4iICG3H0jubNm0SgPjll1+EELlvxrdu3RJfffWVqhjr0aOHuH79upaTFt7Zs2fFiBEjROvWrYWVlZX4+uuvBSAmTJig7WjF1qRJk4SJiYk4cOCAiIqKyvd6q1evFoaGhsLX11fMnj1bbS1SGRkZYtiwYcLS0lIYGBiIgQMHqoqF5ORksWLFCtG/f3/RunVr0bZtWzFx4kRx9OjR54r2Bw8eiFWrVolFixaJkJAQMXPmTDFjxgxx/vz5V+6/d+/eqhanFi1aiGrVqglzc3Px9ddfi5EjR6oKnuHDh4v09PTXPp64uDhRqVIlYWdnJwAxfvz4fK0nhBDnz58XgJgxY0a+lv+3jRs3CkCEhYXluf3OnTvC19dX+Pn5CTMzM9GvX7/nCkRZlElSMZWTkyMOHTokevXqJUxNTYWhoaHo1auXuHz5sraj6Y2cnBzRvn17YWlpKYKCglSnGqysrMTQoUPFlStXtB1R7Z613mzatEk0bNhQPHjwQNuRiq2wsDBha2urajX59ttv873uhQsXRJcuXYSJiYlQKpWib9++z7XMFFRCQoKYM2eOsLKyEoGBgeL8+fPC19dXKJVKUaVKFdGuXTvRsmVLVbFjZmYmGjZsKLp16ybq1aunOq1pZGQkLCwshL29vbC0tBQKhUJMmzbthS1V6enpAhCDBw8WDx8+FELkFoIffvihMDIyEl5eXiI4OFjMmzfvjU7dnjhxQtjb24sqVaqIzMzMNzoO7dq1E82aNXujdQ4ePCjMzc2Ft7e3aNq0qXBychJ9+/bNd2uzLMok6S0QFxcn5s6dK1xdXYVCoRBt27YVs2fPFkeOHJGnN18jOjpajB07Vnz00Udi0qRJYvPmzSIxMVHbsTRm4cKFwsDAQHh6egrgtX3qpMJJTU0VFy9eFD179hSWlpZv/KUpLi5OLF++XACic+fOas128eJF4ezsLABhb2//XLbMzExx9OhRMWfOHPHuu++Khg0biq5du4qQkBARFxf33LITJkwQgJg2bdpz+7p27ZoAxJYtW567r7D95zIyMgr0PhccHCw6dOiQ7+WjoqKEm5ubqF+/vpg3b16e05SRkZH52oa8+lKS3iLp6emsXbuW77//ngsXLpCcnIyzszNt27bF09OT6OhoLCws6NOnD97e3tqOK2lBkyZNMDAwYM+ePQBkZmZiaKiz0x8XG48fP6ZKlSpERESwd+9eGjRo8NJlt27dysqVK+nTpw/t2rUD4Mcff6RPnz6cPHmSoKAgteU6efIkrVq1YuTIkYwaNarQ25s0aRKTJ09m1qxZjBw5UnV7ly5dOHToENevX8fSUjfmtC5fvjzR0dH0798fPz8/du7cSVxcHEZGRpiamlKrVi0cHBwwNjbm6tWrLF26lJycHI4dO4aDgwO7du3C0dGRmjVrYpbP6Qz0epolSZIKLjs7m6NHj/L7778TGhpKTEwMTk5OxMTEkJqayu7du6lTR7+mbZIK59GjR5QsWZKhQ4cyf/58mjZtqirOJM1LTU2lWbNmxMTEMHnyZCwtLbGzsyMoKIj09HSGDx+OgYEBa9euJSUlBYBWrVrRunVrYmJimDhxIsOGDSMkJETLj+TVBgwYwG+//UZUVBQKhYJdu3bRokUL1qxZQ/fu3bUdT2XRokV89913xMfHExUVhbu7O1WqVCEjI4MHDx5w6dIlcnJyAHB0dKRRo0YsWLCAkiVLFnifsiiTJCmP5ORk3nnnHa5evcr58+dxdnbWdiSpiKxZs4YePXpw9uxZdu7cydChQzE3N9d2rLfKpUuXaNOmjWpGBcgdPd/NzY2TJ08CUKpUKfbu3cuRI0f49ttvuXLliqp4mzFjBpUrV9ZO+HxatWoVffv25d69e7i6uhIYGIijoyP79u1DoVBoO95zhBAkJydjYWGRJ19OTg5PnjxBoVBgZWWVr+xpaWmsXbuWu3fv0rNnT9zd3TE2NlbdL4sySZKeExUVRcWKFalZsyahoaE6+UYpqd/YsWP54YcfiIiI0HaUt5oQgvj4eNLT0wkPD2f48OEcPHiQ/v37M3r0aNVgpfrq+vXrVKlSBUNDQ4YMGcK0adM4dOiQarqm4mzdunV5WgMbNWrEvn37VL/LokySpBcKDQ2lffv2NG/enHnz5hEQEKDtSJKGVa9eHW9vbzZs2KDtKNK/pKSkMH78eAYMGEDZsmW1HUctHj16ROvWrTl16tRbdZo8MTGR1q1bc/fuXcLDwzlw4ECeYlQWZZIkvdSGDRuYOHEiqampHDp0CHd3d21HkjSoQoUKlCpVil27dgEwffp0LCws+Oyzz7ScTCqO7t69y5IlSxg6dCiurq7ajqMTNFKU3b9/XxZlklRMhIeH06xZMxISEhg/fjz9+/eXV+MVU2PHjmX16tU8ePCA8PBwVetoQkKClpNJ0tshMTERNzc39RRlaWlpeHp6EhkZqdaQkiRJkiRJbwMXFxdu3779ysnN81WUQW5hlpGRobZwkiRJkiRJbwtjY+NXFmTwBkWZJEmSJEmSpDlKbQeQJEmSJEmSZFEmSZIkSZKkE2RRJkmSJEmSpANkUSZJkiRJkqQDZFEmSZIkSZKkA2RRJkmSJEmSpANkUSZJkiRJkqQD/g+8LggCIkuxewAAAABJRU5ErkJggg==", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from shapely.geometry import Polygon\n", "\n", @@ -1593,11 +299,11 @@ "\n", "target_area = gpd.GeoDataFrame({\"geometry\": target}, index=[0], crs=\"EPSG:4326\")\n", "\n", - "from tatc.generation.points import generate_equally_spaced_points\n", - "from tatc.generation.cells import generate_equally_spaced_cells\n", + "from tatc.generation.points import generate_points_uniform_spacing\n", + "from tatc.generation.cells import generate_cells_uniform_spacing\n", "\n", - "points_df = generate_equally_spaced_points(500e3, mask=target)\n", - "cells_df = generate_equally_spaced_cells(1000e3, mask=target)\n", + "points_df = generate_points_uniform_spacing(500e3, mask=target)\n", + "cells_df = generate_cells_uniform_spacing(1000e3, mask=target)\n", "\n", "display(cells_df)\n", "\n", @@ -1622,21 +328,10 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "253b5430-7d7c-45fe-ac0a-095bfe81d909", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from tatc.schemas import Point\n", "\n", @@ -1686,42 +381,21 @@ "id": "fe33784f-d4b7-4af4-969b-2ccbc31f6ccc", "metadata": {}, "source": [ - "In addition, the results can be merged into the cell specification using a spatial join and dissolve operation. Note that, when working on the reduced results, the revisit aggregation function must perform a weighted average based on the number of samples for each point." + "In addition, the results can be spatially aggregated to cells." ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "c2c3db5d-41ce-4c65-befb-8b1bddb92409", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "import numpy as np\n", + "from tatc.analysis.coverage import grid_observations\n", "\n", - "grid_results = (\n", - " cells_df.sjoin(reduced_results, how=\"inner\", predicate=\"contains\")\n", - " .dissolve(\n", - " by=\"cell_id\",\n", - " aggfunc={\n", - " \"samples\": \"sum\",\n", - " \"revisit_hr\": lambda r: np.average(\n", - " r, weights=reduced_results.loc[r.index, \"samples\"]\n", - " ),\n", - " },\n", - " )\n", - " .reset_index()\n", - ")\n", + "grid_results = grid_observations(reduced_results, cells_df)\n", + "\n", + "grid_results[\"revisit_hr\"] = grid_results[\"revisit\"] / timedelta(hours=1)\n", "\n", "import matplotlib.pyplot as plt\n", "import cartopy.crs as ccrs\n", @@ -1757,7 +431,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.15" + "version": "3.11.15" } }, "nbformat": 4, diff --git a/docs/examples/ComputeDOP.ipynb b/docs/examples/ComputeDOP.ipynb index f2a6c46..189740b 100644 --- a/docs/examples/ComputeDOP.ipynb +++ b/docs/examples/ComputeDOP.ipynb @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -72,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -87,7 +87,7 @@ "s1 = Satellite(\n", " name=\"satellite1\",\n", " orbit=CircularOrbit(\n", - " altitude=alt,\n", + " mean_altitude=alt,\n", " inclination=inc,\n", " type=\"circular\",\n", " right_ascension_ascending_node=raan,\n", @@ -115,20 +115,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "from tatc.generation import generate_equally_spaced_points\n", - "\n", - "# define points on ground to measure DOP\n", - "pts_df = generate_equally_spaced_points(5000e3)\n", - "\n", - "points = pts_df.apply(\n", - " lambda r: Point(id=r.point_id, latitude=r.geometry.y, longitude=r.geometry.x),\n", - " axis=1,\n", - ")" - ] + "source": "from tatc.generation import generate_points_uniform_spacing\n\n# define points on ground to measure DOP\npts_df = generate_points_uniform_spacing(5000e3)\n\npoints = pts_df.apply(\n lambda r: Point(id=r.point_id, latitude=r.geometry.y, longitude=r.geometry.x),\n axis=1,\n)" }, { "cell_type": "markdown", @@ -141,19 +131,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 32 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 2 out of 32 | elapsed: 6.0s remaining: 1.5min\n", - "[Parallel(n_jobs=-1)]: Done 32 out of 32 | elapsed: 8.2s finished\n" - ] - } - ], + "outputs": [], "source": [ "from tatc.analysis import compute_dop\n", "from joblib import Parallel, delayed\n", @@ -178,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -202,20 +182,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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49957KSwsxG638/333/Pb3/427DTUoXj11Vf52c9+FnQs3N+xM6qgWV9fH/L7xYsXAwPLGqa2xc0338z8+fNDnjMQ4bEv9DXJRKT7jYGBwfDGEFoMDAwGhBoYri5w4uPjOemkk1i2bBl79+4lLy+vyzWvvfYaTqeTM888MyJ1eOONNygoKOCtt94KWvD8/ve/DzpvMHbfVp9vx44dFBQUaMddLhd79uyJmLDTn7rfcsstQZp41XIzY8YMrFZryI0hy8vLtexqnXn55ZdJSEgIctdSycvLw+fzUVxcHKS137FjR9B5amYxr9fba5vk5eWxdetWhBBBz7179+4er+uO5cuXU1dXx1tvvcXxxx+vHQ+VkW6g78j8+fP59NNPB1RGd6huT939TosXL6awsJCjjjqqy3d5eXl89tlntLa2BllbOv9O6jtstVr79DuF+k3C/Z3Ud2nXrl1MmTJFO15VVUVjY2PIscTAwCB6MGJaDAwMwmbZsmX84Q9/YPz48UHa3TvvvBMhBIsWLQpyUwJ5oXjLLbcwevRorrrqqojUQ9W4BmpYV69ezcqVK4POU+NiukvzGg7z5s0jJiaGRx99NOj+zz77LE1NTSGzaIWDmgmqL3WfOnUq8+bN0/7Nnj0bkF33FixYwDfffBMUx7Ft2za++eabkHt+1NTUsHTpUn7wgx+EjCs644wzAHj00UeDjnfeVd5sNnPeeefx5ptvsnnz5pD3UZk/fz4HDhwISrHrcDj45z//2euzhyLU++FyuXjyySe7nBsfHz8gd7HRo0cHtX04Qmt9fX1QKmaQs7f96U9/IiYmJmSWt3Xr1rFt27Yu6ahVFixYgMfj4amnntKOeb1eHnvssaDzMjMzOfHEE3n66aepqKjoUk7n32nlypVB+83U19fz8ssv9+k5Q9URur47f/vb3wAi1pcMDAxGJoalxcDAoE98+OGHbN++HY/HQ1VVFcuWLePTTz8lLy+P9957D7vdrp17/PHH89e//pUbb7yRmTNnsmjRIkaPHs327du1XeGXLFnSZWPJcDnzzDN56623+MEPfsDChQvZs2cP//jHP5g6daoWfA5yMPbUqVN59dVXmThxIqmpqUyfPp3p06eHfe+MjAxuu+027rnnHk4//XTOPvtsduzYwZNPPsnhhx/e6+aAfUUVPH79618zf/58zGYzF110Ub/Luf/++/nss884+eSTteD5Rx99lNTUVG6//fYu57/66qt4PJ5uXY4OPfRQfvzjH/Pkk0/S1NTE0UcfzWeffRZS2/6nP/2Jzz//nCOPPJIrr7ySqVOnUl9fz/fff8/SpUs1t6errrqKxx9/nB//+Mdcd911jB49mpdffll7x/prDTn66KMZNWoUl112Gb/+9a+RJIn//Oc/Id2IZs+ezauvvsqNN97I4YcfTkJCAmeddRYg7+PyxRdfhO1+1NTUpAkJX3/9NQCPP/44KSkppKSk8Ktf/QqQg/Dvu+8+zj//fMaPH099fT2LFy9m8+bN3H///SFjZFRBobvf6ayzzuKYY47h1ltvpbS0lKlTp/LWW2+FFNCeeOIJjj32WGbMmMGVV15JQUEBVVVVrFy5kv3797NhwwZAtui99NJLnHrqqVx77bVayuPc3Fzq6+v7/TsdcsghXHbZZTzzzDOaS9+3337Liy++yLnnnttjSm4DA4MoYChSlhkYGIwc1PSx6r+YmBiRnZ0tTj31VPHII4+I5ubmbq9dsWKFOOecc0R6erqwWq0iNzdXXHnllaK0tLTLuWr605qamn7X0efzifvvv1/k5eUJm80mZs2aJd5///0uKYaFEOKbb74Rs2fPFjExMb2mP1ZTHr/++uu91uHxxx8XkydPFlarVWRlZYmrr75aNDQ0BJ3TXcrjBx98sEt5nevm8XjEtddeKzIyMoQkSQNKf7x27Voxb948ER8fLxITE8U555wjdu7cGfLco446SmRmZgqPx9NteR0dHeLXv/61SEtLE/Hx8eKss84S+/btC9m+VVVV4pprrhHjxo0TVqtVZGdni1NOOUU888wzQeeVlJSIhQsXitjYWJGRkSFuuukm8eabbwpArFq1qsfnC5Xy+OuvvxZHHXWUiI2NFTk5OeKWW24RH3/8cZcUyq2treLiiy8WKSkpAgj6vWbPni2ys7N7vHdPqL93qH+B9/nuu+/EWWedJcaMGSNiYmJEQkKCOPbYY8Vrr70Wslyv1yvGjBkjDjvssB7vX1dXJ37605+KpKQkkZycLH7605+KdevWdUl5LIQQxcXF4tJLLxXZ2dnCarWKMWPGiDPPPFO88cYbQeetW7dOHHfcccJms4mxY8eKBx54QDz66KMCEJWVlT3Wp3PKYyGEcLvd4p577hHjx48XVqtVjBs3Ttx2221B6ZeFkFMeL1y4sEuZJ5xwgjjhhBN6vK+BgcHIRBLCiFgzMDAwMBj+PPzww9xwww3s37+fMWPGHNR7t7S0kJqaysMPP8w111xzUO890rj++ut5+umnaW1t7TZY3sDAwKC/GEKLgYGBgcGwo6OjI2hvFYfDwaxZs/B6vezcufOg1+eDDz7gmmuuYefOndqeQAZdf6e6ujomTpzIYYcdNmgJCQwMDKITQ2gxMDAwMBh2nHHGGeTm5nLooYfS1NTESy+9xJYtW3j55Ze7DTY3OPgceuihnHjiiUyZMoWqqiqeffZZysvL+eyzz4IytRkYGBgMFCMQ38DAwMBg2DF//nz+9a9/8fLLL+P1epk6dSqvvPIKF1544VBXzSCABQsW8MYbb/DMM88gSRKHHXYYzz77rCGwGBgYRBzD0mJgYGBgYGBgYGBgMKwx9mkxMDAwMDAwMDAwMBjWGEKLgYGBgYGBgYGBgcGw5qDHtPh8PsrLy0lMTOz3xlMGBgYGBgYGBgYGBvpBCEFLSws5OTmYTN3bUw660FJeXs64ceMO9m0NDAwMDAwMDAwMDIYp+/btY+zYsd1+f9CFlsTERECuWFJS0sG+fRBut5tPPvmE0047DavVOqR10SNG+w4uRvsOLkb7Di5G+w4uRvsOLkb7Di5G+w4uw619m5ubGTdunCYjdMdBF1pUl7CkpKRhIbTExcWRlJQ0LH40vWG07+BitO/gYrTv4GK07+BitO/gYrTv4GK07+AyXNu3t7ARIxDfwMDAwMDAwMDAwGBYYwgtBgYGBgYGBgYGBgbDGkNoMTAwMDAwMDAwMDAY1hz0mBYDAwMDAwMDAwOD4YTX68Xtdg91NQ4Kbrcbi8WCw+HA6/UO+v2sVitms3nA5RhCi4GBgYGBgYGBQVQihKCyspLGxsahrspBQwhBdnY2+/btO2h7JqakpJCdnT2g+xlCi4GBgYGBgYGBQVSiCiyZmZnExcVFxcbnPp+P1tZWEhISetzMMRIIIWhvb6e6uhqA0aNHh11WVAstTo+PluiwBBoYjEi8PsE/vijmsNxRzC1MG+rqGBgYdMPb6/ZT2+LiimPHYzLpf9FnoA+8Xq8msKSl6X+O8QlBu8tLnN2Ey+XCbrcPutACEBsbC0B1dTWZmZlhu4pFrdDyze5abn59A+lmExcOdWUMDAxCsq6sgQc/3gHA8ptPJD89fohrZGBgEIqbX9+I1ydwerz86uQJQ10dA4M+ocawxMXFDXFNDg51rS4qmjpIsFlIPcjbs6ht7Ha7wxZaojZ7WGaSjcpmBxvrTWzc3zTU1TEYwbg8Pr7cVUNpbdtQV0V3dLj9AYJPLt89hDUx0AP7G9rZUdmCEGKoq6I7vD65Tf/6yU46XIMf2GugX4QQNHccXDeYaHAJA3Aoc2qr08PB7qaRaOOoFVqKMhM5e6bsV7d4zb4hrs3w5D8rSzn8j0s576lv2FffPtTVGZY43F5+/M9V/PTZbznlb1+wZFPFUFdJt7yzvpxWp2eoqzHsaHG4eeiTHfzxg61UNzuGujrDlsWryzjuL58z/+EV3PXuFkNwGUQ+2Vo51FUYdtS3ufjJv1Yz/fcfc9/7W/H5jPcvFFXNDs545Etm3/85/9xuwunxDXWVdIU5wHWzdQSGR0St0AJwwZyxAHy0pUqTPg1kvtpVy+/e3UJNi5O1exv45cvfa5o0Az//+rKEtXsbAFnT+Ns3N9J0kDVE0YLL42PZ9uqhrsawwucTXPHidzy2bDf//HIPFz2zytByh6Csrp273t2MKqf8Z9VePttmvEudGYggF6hE/d8GQ3nTmVve2MBXu2tpdXr411d7ePnbsqGu0rDk5tc3sL2yBYDNDSaeWF48xDXSL+0jcKqIaqFldm4KyTGCNqeXb/fUD3V1hhV//USOIzh5ciaJdgubDjTxqaE9C8Lh9vLMihIAHvrRIUzMSqDF4eHl1XuHuGbDByEEH22upCpCFoCPNxvvYCDvbSjn2z31xFhMJNotlNS28dzXe4a6WsOOf6woxuMTHFuUzlXHFwDw+OeGu2EgV7ywhgWPfoXHO3DN9le7awxFYADryhpYuq0aq1nih4eNAeCxz3bh9BhtFMjavQ18uasWq1nixnlFALy4soymdkMRqNLicFNc3ar1r8rKSq677jqKioqw2+1kZWVxzDHH8NRTT9HeLnvI5OfnI0kSkiRRODqVM+bO5DdX/4yVX62gLYSS68UXX+Twww8nLi6OxMRETjjhBN5///2gc5YvX66VKUkSWVlZnHfeeZSUlAzq80e10GIySUxOljVLX+6qGeLaDB+2lDexfl8jVrPEX86fyWVz8wFZO2ngZ/mOapodHkYn2zl31hh+fqy8GHrr+wOG64nCv1fu5RcvreXyF9aEdX3nZlxZUme4VQTw4spSAK49qYh7zp4mH/umNCILT73g8vh4f0M5AL88sZArjy/AYpJYv6+R4prWIa7d8OBAYwefba9mW0UzFU3hKRjUvmqSwOH28X1ZQwRrOLL5r2JVOWtmDn/64UyykmxUtzgNa18nXlHa6dxDx/CL48czOlbOdPWB4Xatsae2jTaXh8omByUlJcyaNYtPPvmE+++/n3Xr1rFy5UpuueUW3n//fZYuXapdd++991JRUcGKbzdw39+fIjEpmat+fC5/vO+PQeXffPPNXHXVVVx44YVs3LiRb7/9lmOPPZZzzjmHxx9/vEt9duzYQXl5Oa+//jpbtmzhrLPOGtTNKqNaaAGYnCKPtCt21g5xTYYPqmn/tKnZpCfYuGDOOABWldRT3+YayqoNK9R2OvuQHMwmidNnZBNjMbG7upWdVcZiCOAfX8im/S3lzQMqZ0JmArFWM/VtLnZVG20LsLu6lXVljVhMEhceMY4zZ+YwKs5KdYuTb0sNy7HKl7tqaHZ4yEy0cWRBGukJNo6bkA7A+4YbEwDryxq1z+YBpis+pkhu22921w2oHL3g9vr4cJNsIb7oiFxiLCbOPVS2thgxkH7cXh+fbK0C4IeHjUWSJA7PkJUv764/MJRVGzYEKkMlCX75y19isVj47rvvuOCCC5gyZQoFBQWcc845fPDBB5x11lna+YmJiWRnZzNm3DhmH3UMf33kCW646Tc8+MAf2LFD9qxZtWoVDz30EA8++CA333wzRUVFTJkyhT/+8Y9cf/313HjjjezbFxwDnpmZyejRozn++OO566672Lp1K7t3D54VO+qFlgmKpWVHVYsRi6CwfIes/TltWhYAuWlxTMtJwusTLFUGlWjH6xOsUKxzZ8yQEzok2a0cVSDneTcsdzLham07YzWbmJM/CoCVxYaCAWDZdrkvHlOUTmainRiLidOmZgPGYiiQpYo2+4zp2dqCfP40uZ1WGP0UgPX7ImcVUfdTMlyuZb7f20CL00NqfAxz8uQxTJ0zlm2vxmUEmgPy+9LU4SYtPoYjxqcCcEiavD5bu7fhoCVhEULQ7vIMyb/ePDQC35X2lkY++eQTrrnmGuLjQ28F0FO2LpvFxK9++QuEELz77rsA/Pe//yUhIYGrrrqqy/k33XQTbrebN998s9sy1b1YXK7BU25H7T4tKolWGDcqln0NHWzc38hxEzKGukpDSkVTB9srWzBJcHxAW5wyOZMt5c18XVzLBYePG8IaDg+2lDfR4vCQaLcwY0yydvz4Cems2FnDl7tq+flxBUNYw6HHHeCilJFoG3B5c/JS+XJXLRuMFOWA3zp8wkR/Pz1tWhavfrePL3cZgp3K6hJZ4x84tqvWgPX7Gml2uEmyH+QNC4YZ6/c1RqysWePkhfmmA014vD4s5ujWjS7fKQvGx09I1zbdPGRsMmnxMdS1udiwv5HD81OHsorDgm8UZdQJkzIwmyR8Xki3Q25qLGX1HawqrmPe1KxBr0eH28vUuz4e9PuEYuu984mL6X5Z3h4Qf1JaUowQgkmTJgWdk56ejsMhKwuvueYa/vznP3db3qhRo8jMzKS0tBSAnTt3UlhYSExMTJdzc3JySEpKYufOnSHLqqio4K9//StjxozpUqdIEt2jicKh4+RF57oAE3m08rVi0p85NoVR8f4XV7UgrC6pN+I1gJXFcjsdOT41yJ3iWMXtZPWeuqBFezSyvaJF+5yfNvCNu2Yq/XTj/sYBlzXS6XD5k4ecMMm/GD9CeR/31rVT3tgxVNUbNlQ1OyipbcMkweHj/QvDcalx5KfF4fUJVpdEt0XA7fWx6YBfETDQ0b0wI54Em4UOt5fdRswQX+/2L8ZVJEniyAL5fVxVbLjRAVo/PGp88K70xxbJf3+121DEtPchucW3337L+vXrmTZtGk6ns9fzhRBBFpn+ru/Gjh1LfHw8OTk5tLW18eabb4YUeiJF1FtaQNZ6/G9jZUS1TSOVdUrw5BHjgzU/s3JHEWM2UdnsYG9de9TvTL5S0d7OLUwPOj4xM5Eku4Vmh4cdlS1MD7DCRBvrA4QLifD85AOHT9WiVVLbRovDTWIUa8fX72vE5fUxOtlOQUBfTLRbmT4mmQ37GllVUscPDxs7hLUcelYp/XRaTjLJscHvyxHjUymta2ddWQOnHgQN7nBlV1UrDnfkFCwmk8SMMcmsLKljw75GJmcnRazskYbD7WWrEs93RKfF+NyCNJZsqmRlSR3XnjJhKKo3bHC4vWxQ5gtVmFM5Ij+Vxd/uP2iJHWKtZrbeO/+g3CvUvXuiw+V3kRtfUIgkSVo8ikpBgezhobpq9UR9fT01NTWMHz8egIkTJ/LVV1/hcrm6CB7l5eU0NzczceLEoONffvklSUlJZGZmkpiY2Os9B4phaUEWWgA2GEKLZm2aNS4l6HhsjJlDFE13tAf5CiG0d0X1UVYxmSQOUdpuXZS/T5sj6MYlSZCeYGNMSixCDDywf6SjKlhm5aZ08Vs+Spn0o92CAHI8AXRVwgAcqrgxRbuyakdVZPuSBNoYGO1tu/lAEx6fICPRRk6yPei7IxXvhe/LGqI+29+6skbcXkFWko3c1GCrvOoJs7W8+aCk0ZYkibgYy5D86ykGRQgRpFxITU3j1FNP5fHHH6etrS2MB4V//OMfmEwmzj33XAAuuugiWltbefrpp7uc/te//hWr1cp5550XdHz8+PEUFhYeFIEFDKEFgElZiZgkqGtzUdPSuzlNr7S7PGyvlCewWbmjunx/qDIRbT4Q3TEF+xs6aGh3YzVLTB7dtaOqAt/6KHc33FHV0vtJ/US1tkS7i5gaOH1oJ+UCwGyl726I8jYCNLenmWO7WjzVttu4vymqN85VN/KLJGo/3VoR+bJHEppyYVxX5UJhRgLxMWYcbh/FNWEsOnXEpgONAByWO6pLO+Uk20lPsOHxCbaUR+/aw+Xx4evkuvXkk0/i8XiYM2cOr776Ktu2bWPHjh289NJLbN++HbPZb7lpaWmhsrKSA/v3s3bV1/zmumv461//yn333UdRkbwnzty5c7nuuuv4zW9+w0MPPURxcTHbt2/nzjvv5JFHHuGhhx5i3LihjWk2hBZkK4Lq7rStIno1uBv3N+ETMDrZTnYnrRDAjLEp2nnRjCq0TcxKxGbpas49NDcFgHURzMgz0vD5BLsGQWiZPkZ2NdkWxYshIYRmEVWtBYHMVPrprupWOkJsHBYteLw+tirjeSg3zYlZchrtVqcnqvdr2TkIQsuk7ESt7GjeV0nrp8qcEIjZJDFNeS83RbkicPOB7vupJEmagiGa444dIbLMFRYWsm7dOubNm8dtt93GIYccwpw5c3jssce4+eab+cMf/qCde9dddzF69GiOnT2dO67/Bc3Nzbz77rvccsstQWU+/PDDPPnkk/z3v/9l+vTpzJkzhxUrVvDOO+9w7bXXDvpz9oYR06IwJTuJkpo2tlc2c/zE6MwgpmqFQmlvIVB71ozb68MapVlh1AlmRjfxKocoi8aSmjbanB7ibdHXzQ40doTcaXegTFL843cMwkJrpFDR5KC6xYlZiR3oTFaSjYxEGzUtTrZWNDM7r6tgEw0U17ThcPuIjzEzPq1rDJ7FbGLGmGS+La1n4/4mJmYdHPeG4UbnvhSJRCv5aXHEWEx0uL2U1UdvDGRf5tRv99SzaX8j58+O3vizzYoFpbsY0Fm5KSzdVhXVCtPuXONGjx7NY489xmOPPdbttWp2MIDyxg5qW51kJNqIFaE9iy6//HIuv/zyHutz4oknDklSpuhcdYZgsqIZ2h7FGtxtPWglAfJS40i0WXB5fOyK4s0TVaGlu3ZKS7CRqaT43TkI1oaRQJeFUJg5iToPihOzEgDYXdMatS496sQ9KSuR2Jiulj5JkpipanCj2EVM7afTcpK1VLOdmZqjCsHRaWFvdrgpj8BeSp37qcVsYkKm3FcHw/1sJNDU7uaAksGvu7lCdVvcGMWWllanhz21snvctJzQSRumjjaUVQcjnmckYAgtCpOVTrEtijuFOiBM6kbjaDJJ2uCr+qBGG0IIzT2sO0sL+N0jonXCVuNZbJbIDDGqm/O4UXHYrSZcHh9766LTD1ztp1NGd5+VaYaxGNL6aU8Z/CZHeT9VXcNGJ9sj2FflzqqOgdG60FTHwDEpsd3uA6R5L5Q3R60SZltFM0JAdpIcuxIK9V0qrmnF6YnOxbsahB+tHi4q0f30AaiT1+7qlqjcX8Pt9Wl+3eoAEYopmsYjOi0tNa1OGtrdSFLf2ml7lMZIqQuVyT20UTiYTJLmxhO1Viwl21NPbTstR14MGZbj7rW34O/D0RojpS6sB8M1borqyhnh7GQjBdV619M8kZcWj91qwunxUVbffrCqNqzwKxe676ejk+0k2S14fILi6uhTVvmEwKXEtNh7SYusdwyhRWHsqFgSbBbcXqGZKqOJkpo23F5Bgs3C2FHd5/eelC2b/KN1wViiZHkZOyq2x8FDXVBGq+Vu5yAuhiZkqkJLdArOqlVgYg+LIcONDi0jU5HiphSKiVmJSBLUtjqpbY2+zJGqpaWnhXW4RLu1eXsf2tZskrTxLFotUupc0ZPlWJIkzRtmexS6cjrdPgQCs0nC2o2ra7RgCC0KkiRRmCEHC5ZEYSaZ7QFaoZ5yhU+Ici23KrQUpHe/EAK0DdW2VzQPSbDaUNJXq11fCNVyquA8GCmVhzsOt5dSRanSk6Ul2t3omh1uTQgpyOg+CDzeZiFP2RciGheN6o71EwIEu3CGq1DXqAqLvXXtmpY4muirtXmComAYjGyLIwFVuVCY0fOcOiWKhWDVJc5uMaPu0xxdqwo/htASgNppojFnel+0QuCf3KpbnDS2uwa9XsMNVaDtaSEEUJgZj9kk0ezwUBGBQNeRxP6GDtxegd1qYkyKbLUbqNwm4RekNfewKJy8dle34hOQEmfVkj2EItrd6FTlQkaijcRu4glUVAVDNKa736MqYTLi6UFXFRZZSTbiY8x4fSLqXJ+EEP4Y0V7mVDWGNBqVMBCgCOxlTtXijqOwnzoVoT9ScWcjGaMFAihUFuTF1dFnaemrVijRbtUWotHonqO6Dhb0ohWyWcyMV9J87o6y92lPrfy8+WnxPVrtwkV199lb1x51rk/bA5Jl9Na2qtASjfFnmnKhD6l2VTe7aBPuOlxeLXPY+F4sx/1BfSslSdLGyWjbB+dAYwctTg8Wk9SrVT6alQuBFtHxvfRVtZ2iMXOpKrTEWI0lu9ECAajuYdE2wELvmcMCUf3lo1EzVKIILYV9WAxF6/vUV81ZuOQkx2KzmHB5fexviC4Nrhrc25cEB5OieDHkfwd7X4yrQnBJlFnYSxW3weRYK6PierZGhUtBlI6B6nxamJFATC/acVVoLqlpizo3uv5YRIuUvlzZ7KDV6Rn0ug0nXIp7WKjNrKMNQ2gJINA9LJriENpdHi2f/IS+CC3Z0eme4wrI8NKXxVBhlGoZVWtUb5qzcDGZJK3skihLmrGjSo0V6j5oVWVCFCsX1HewsA+Cc7QqFwL76WBYRME/BkabQKha19U+2BM5yXYSbHJmrNIoiz/rj0U0Oc6qpUSOprhjIYThHhaA0QIB5KbFYZLkzY6qW6Ink0xprbwQT4mzkhof0+v5EzOj0we3rF52R4qLMZOV1H08gYomtERZikbNhS4SLifd6A40oSXKFkPqZN1TRiwV1Zd+T21b1O1tUNzH2DPwv6cN7W7q26InTs/fTweuXOhOxRetlpa+uhGD7EanKRiiTBHYH4soRKeCweMTmht0TJTv0QKG0BKEzWImV8kkE01xLf3VjKsDbLQuGPuqmdRipKJogIWA9ylgwThQu2Xn5lYXQ2r8TDTgcHs1i2hfFuPZSXYSbRa8PsHeuuhxo/MFaKz7IjjHxpi1OL1o6qudx/3AZBeRItDSEk3eCyX9FAiLotQi1R+LKATGHUdPO6kugzFmE6ZO6Y4XLVqEJElIkkRMTAxFRUXce++9eDweli9frn1nMplITk5m1qxZ3HfXHdRUVXa5T319Pddffz15eXnExMSQk5PD5ZdfTllZWZ/veTAwhJZORKNLj99E2zdthzrJ1bY6aXa4B61ew42SfmjP5PPkdqpuiZ52anf5s6VFQoPbHWrgcDRN8nvr2hECEu0W0vpgEZUkSRMco8mdorypA4fbh9Us9bjnVCCFWlxL9LRTKOVCpJEVPNDU4aYuiqxY/Y3r0/ppFClhoH8WUYjO9ZkWhN+Na9jpp59ORUUFu3bt4qabbuLuu+/mwQcf1L7fsWMH5eXlrFmzht/+9rd8+cUyzpt3NNu3bNbOqa+v56ijjmLp0qX84x//YPfu3bzyyivs3r2bww8/nJKSkn7dczAxhJZO+LXj0bMY8puy+zZwJNqtZCSqvqXR00798b8FSLL709JGSzuproaj4qykxMVEPI2qSkFG9LmHqValgn7EIKjvajSNZ+o7kZsah6WP7hR+t5PoaafBij0LfDXt1gArVpR4LwRmxMrvY9sWRKESpr8WUThI7mFCgKttaP6FsEaqrr22bjazttlsZGdnk5eXx9VXX828efN47733tO8zMzPJzs5m4sSJXHTRRbzz0TJGpaXz25uu08654447KC8vZ+nSpZxxxhnk5uZy/PHH8/HHH2O1Wrnmmmv6dc/BxHJQ7jKCiEafyZIwJq+C9HhqWpzsqW3l0HEpg1Sz4UU4WbEKMxKobnFSXB0d7TTYQfgq6mK8stlBm9NDvE3/Q1lxP/2/A8/dE0UJC/x7KfW9nfzxZ9Ex7je2u7T4nfy0we2rhRkJ7G/ooKS2jSML0gb1XsMBdfPX9AQbSb1kxFIp1NxdZTe6wUqMMJwIyyKq9NPS2nY8Xl+flRL9wt0O9+dEvty+cHs5xAT3R1c/g/BjY2Opq6vr8fsf/eRnPHjP7dTU1JCQkMArr7zCJZdcQnZ2dpdzf/nLX3LnnXdSX19PampqWPeMJIalpROq20m0TPJCiKBYjb5SEIU+uFq64/4shjKjy+yvWgM67/sQrj+76CYaJiUuRksaES19NRyBsCAK3cNK+mk5huhzO1HfpewkexeBP5yu2lP/1oLxo0QgDCfBQWASoJooSQIUjkV0TEpguvuOwazesKGvmcOEECxdupSPP/6Yk08+ufvzgPGFEwAoKyujpqaGxsZGpkyZEvL8KVOmIIRg9+7dYd8zkuhfPdlP8tPlQPwDjR04PV7d58VuaHfT7JADqPqjcSuMMvecQM1kfxaN0ZZBLJwFY18IpXccnx5PfZuLkto2po9Jjuj9hiNhCS2q20mUCHbgH5MK+5G9Th3Pyurbo2LcD/UuDZ4rZ3QpAtX3rz/91GYxMy41jr117RTXtJGZZB+s6g0bwrGImkzyhqXbKpoprmnts/tdv7DGyRaPocAaF/RnYLrj7mJa3n//fRISEnC73fh8Pi6++GLuvvtu1qxZ0+1tVGVgoEWvP4rF7u55MDCElk5kJNiIjzHT5vJSVtfep31LRjKqZjwn2U5sTN8n6vHp0eVGp7rmhNJM9oQqtOyOknY6WO5hIGsy1+5tYE+UCM4l/QxaBf/v0Kik8+1LSvORTjjtlJFoI9FmocXpYW9du7b7tl45GEH4KuMVZdieKNmDJNy2LUiPZ29dOyW1rcwt1L8bXbgKrqJMWWjZXd3KKVOyIl8xSeriojVUuL0+zV2wu3THJ510Ek899ZSW8cti6X19UrJrJwC5ublkZGSQkpLCtm3bQp67bds2JEmiqKhoQPeMFIZ7WCfpUpIkTXqPBs1QOH7ygeeX1rXh8+k/lWU4C6HA8/fWtWm51vVM1zSqAyeDRsyiazpFzUUxClzvGtpcNLTLGej6YxENTOcbDS5iHS4v5Wr2un6MaZIkUZAZPXEt/U3J2xeSaCWdpi6pk1XvhX31chyC3imp7V/CFhXNNT1KlDBauuN+7udVEEXrM2dAuuPu4pzi4+MpKioiNze3T8JDR0cHby5+kaOOPpb09HRMJhMXXHABixcvprKyssu5Tz75JPPnzw+KZ+nvPSNJ9AotrTWY3/0Fkyrf6fKVKrREw+604WrGx42KxWqWcLh9VDQ7BqNqw4pwtUI5ybHEWEy4vYIDOvfBbWhz0RjGwron7K1lrLT9iscargZn8GIymjaYVLXU/bX0QXRlWlPHs75ulBuIthiKgnG/dBAsou/F/I5vbb/EvO+boOOBY2B5o77nCiGEJnSEq+CKFlfOcBLbBJ4fDe3U13iWnqiurqayspJdu3bxyiuv8MP5J9JSX8PDf7lPO+f+++8nOzubU089lQ8//JB9+/axYsUK5s+fj9vt5oknnhjws0SK6BVa9n6FafMbTKj6H9QFBxj5JXn9b8i2pzf/W1cb+Lrupm0xm7SNOKNBg9vjXjauNtj6Lhz4vstXJpNEfprcTnpfDKmTSChXw3BtTLEt+7BIPsb4yuHLvwZ9p05epbX637gu3IUQBKQ9jgKLVK9a7oqNUPo1+Lpq/NUMRuWN+lYuCCE04S5UTEB3yS96LBPIN1VhkgTxb/0UvP59qUwmibzU6BgDa1qctLm8mCQYlxrX9QSfF9pCZ1mKpqQZHa7AjXJDzKnuDqjZjiS6rj1Uy3Flk74FYAjIHGYNf6k+adIkcnJymD17Nn/605845bi5bF72GkeOT0Lyyf00LS2NVatWcdJJJ3HVVVdRWFjIBRdcQGFhIWvWrKGgoCAizxMJBiS0/OlPf0KSJK6//voIVecgMvVcfAWnYBYezN88GvSVqiUujQJJvlv/W58X/ncd3D8G/jYF9q7scm00BVh2qxVqq4WnT4DXLoV/ngTfPN7l2kQl7aXD3XUA1hOD7ie/+mlwNGl/qkJzi9Oj+43rSrSsbCHatnobvPwjePEsKFvd5etoyvRX0pO769K74enj4IUFsPgC8Aa7HFpM8nSody/O6hYn7S4vZpPEuFH+hXWk4vAlZ5OsxAlA817Q+VyhKm7Gjorrmsyhais8OgseLJD7ayfLsRr/uK+hQ1us6pUeLaLl6+DhmVifOZYTdvweWquDvjYru8L7dK6ogtCWlsB++sILL/DOO++EvPbEE09ECIEQAp/PR3NzM+vXr+cPv7+N7Mx0AOxu/3yanp7Oo48+SllZGS6Xi8rKSp5//nlyc3ODyu3pngeDsIWWNWvW8PTTTzNz5sxI1ufgIUn4jv+N/HHLG9Ber30VLe5hPp/QNF9dNJPfPAZrXwAEtFbBq5fIC/QACqLEPcfrE+ytk61uXdIdf/hbqNsFkjJBfXoXVG4mFHofY/f0tLCOBO522Pym9qfdaiYnWc6ys1fnfbVbN87Wavj3ObDrE9izQv5cuyvolGjS4HYbe1a8DL76u//v3Z/CyseCTomCrTEA/3g9blRsUEaibGopkMLPmuQTAQ249oWg78ZHSRxCt/3U3SHPoY175b93fQKf3BF0SmainATI6xOU1eu7nbpVwrg74PVF0CYLKskdZZg/uD7olGjYw0ZF3VgyppMAbGEgClB/+8V4W0GMLAE5LKGltbWVSy65hH/+85+MGjUq0nU6aIgxc2iyj0PyumDHh9pxtSNVNDnocOlXO17eJGt0rGZJM7kCsjb7y7/Jn+c/AJnToL0OOlmkCqJkI879De24vD5iLCZyAtuprhg2vyF//vlSmHwmCC989beg6wOSCh6M6g4Z/gnbL9jFuBp43vpnjnF8EWapndps4+tBf+ZHiSunlsa3s9D8xV9kpULaBBh7OHg64OPgxZBqdSiLgkDobgPMP39A/v8RV8E5T8qfv36ki7YbokG5EGJh7fXwmekaltluRnKFN56bpICGK/0SWvxBvZr3QrQqF9a9BPUlkJgD5z8vH/v+3/IcoiBJkmalLta5IlCziHZ2t/7+39BQCok5eC5bgg8zpt2fwL6u6Xv13k99QuAOYWlJctcw1VSGzdM84HtICKQA74WRQFhh/9dccw0LFy5k3rx53HfffT2e63Q6cTr9myU1N8sN7Xa7cbvd3V12UHC73ZSnHE5y5T58m9/CO/0CABKskGS30OzwUFzVxKRsfaa/3Fkpv6y5qXEInxe3ErtiWvtvzM4mRPokPLOvQEoah+X1nyC+ew7PMTdpucRzR8la7pKa1pC/pXpsqH/ngaK2U35qHD6vRwvxMa1+GjPgK5yHN3MGHHsz1u3vI7a8g2fefRCfoZQgj64ejzeibTHc2rdEybqUO8qm1WnKt7eRZd7ASc0bcLt/0+8yvcoiu1WKJ0G0IfatxtNcDbGysiQ3NZZviqG4ujni7TBc2tfnE9pib1yKv21pq8Xy/b+RAM/pf0EkjcHy1JFIuz7GXb4JMiYDkB5rxm414XD72FPTPOg7oPeVSLevEEJToOSm2P3lVm7Euv9bhMmKZ+6vIS4dy4oHkRr24Nn4OuLQnwDgU+JcfL7I9tOhorv2La6W5+C81FjtO+nAd9piwNdag9sdeufrbu/l9aHu/e5NGou5eT+ebR8gZl0KyO8tyLFZemhbCN2+u6taAMhPDXj/hMCy8gkkwHv09fgmnYW5cB6m4qV4Vz6Fb/4D2vX5qXFsPtAsj2cT9Zv2uNt2+vYZpZ2uw5U1i5rUo8mr/xLfqqfwZh8KgNfjUU4XEXmX3G635kLlCxHrNlQ4PT4EYJIkzJJ/fEr0yF5Bca4GfL70MEr2S3teyYIEiIP03D6fT/vdzOZg61Fff8t+Cy2vvPIK33//fY8b1wTywAMPcM8993Q5/sknnxAXFyJQ7SCTmDKHKZVvIUq+4KP338Fnkv0rU8xmmpF469OvOCRNnyL9igoJMBPnaWHJkiXa8eN3PMcoYJP9CPZ8+BEIH/Ni0ol31rLutb9QMeoIAFrcABbKGzt49/0ldBcr9umnnw72owwqy5V2ivU0+9tJ+Dhty2vEAt+KGVQpx0+IzSelo5Stb/6Z0nR5h9j6ejMgsfb77/Hujfy7NFzat6pBfs5t69fQruS2OKfic+37wHesr7Qd2MkcoEKkk2UfRZJjPxvefIgDqUcD0FEt/zarNhezxLWrx7LCZajbt94JDrcFsyTYuGo5WxTTXX7NZxziddIYm88XW5pBauGI5FmMbvqe0nceYOuYC7UyUq1myt0Sr3+0gmmjhtd4Fqn2bXJBm9OChGDbmhXsUsajafsXUwQcSDqMtSvWAjDBPpup7KHhi2f4plxeoO/cL79LZWX7WLJkb0TqNBzo3L5bi02Aier9e1iypASACZXvMVX5fuWqb4nbWkx/8PjgPOXzbutUJrGf2q/+zeoKeVHV6ASwsK++jf+9v4Q+boA+Ighs38175TGwungLS+pkN+GUtmJOaNiDxxTDRxWj8C5ZQqY4hLksxb3+NT72HAOS3CCeBvm3+XLdDsY0h943Qw+sL5bbqaFsB0uWbAdgVNtujq/bjcdk4+OKFDzVnzIq/WRZaNn2Pz42z8drtlPWCmCho6MjrDmlMxaLhezsbFpbW3G5hk9spENRjpoloSn7JZ8XdRtlh7Dga+6/tUXyygU3S0n47KnglsA9cKtNX3C5XHR0dLBixQo8nuCYwvb2vnlL9Eto2bdvH9dddx2ffvopdnvfdmy97bbbuPHGG7W/m5ubGTduHKeddhpJSUn9uX3EcbvdfPrJJ/jiszC3VXHG9DRE/nEALG3bSNnGSlLzJrPg+PFDWs/B4rsPtkNpGUdOK2DB/InywYZSrOtKEJKJKefdxpSETABMsWth5WPMsZfhXXA3IGs6/rTpc1qdHqYecTwTMoNNvW63m08//ZRTTz0Vq9XKSGXVe1uhdD9HTy9kwakTAJAOfI9lfQMiJp7ZP7oZLLIm0TSqGJbdwwzTbqYukLNd/af8W0paGpk1axZnTM+OWL2GW/v+eesKGlwOjj3mGGaMSQafB9bJ33VIsSxYsKDfZa7/wgnVYDabiZ91Dqx8lMOSGjhEKcu2rZp3967HGZPMggVzI/k4w6Z9vy6ug+/XkpeWwFkLj9GOm//zFACJR/+MBUctBEDa5oG3LqfIuZH8M17QAjWWNK2nfGs1GQVTWTA376A/Qygi3b6r99TD2u8YOyqOs8+Ux3GEwPLU7wHIPuVqFkxW3sHG6fDEG6S3bmPBiUdCXBqly0tYsm8348aNY8GCaQOuz1DTXft+8+4WVlYfYNLESSw4Uc4KZH75X9r3c+fOZUxeUZfyesLl8cEG+fPo4y+Dtz8hy7GbBaefBiYLPp/ggU2f4XD7mDH3hGFj7RsIndvX4/Vx0+rPAMGFC07UXIlNS38n/3/yQuaf9QP5Yu+piEeew97RwMJpSYj84+UyN1Tw0Rub8MalsWDB4UPxWIOOEII7vv8c8HD+accxIUteN5g+vw92gmnyGZx21nnK+kzgSxmPpXEPpxeaEFMXsOlAEw9tWo09NpYFC44fcH0cDgf79u0jISGhz+vag4HF5aW6ow2TyUxSkrLvmaMRlKRpktUW1hq6zdUIHjBZrPgkicTExIMWJ+RwOIiNjeX444/v0tbNfRTA+iW0rF27lurqag477DDtmNfrZcWKFTz++OM4nc4uJh+bzYbNZutSltVqHRYLLSQJxh8Hm9/AUvY1TJC144WZiUAl+xocw6Oeg8DeejnlYFFmov8ZSz4DQMo7BuuoMf6Tp/8QVj6GqWQZJhNgls8vyIhn4/4m9jU6mTomdHzTsPmtw6S0TmmnrCT/c+z+CABpwmlYYwOEtennwrJ7MO1bjcnnBFsCJiUrkcViGZR2GG7tqz3nvvXasT3m8UwNo45mpe2QwFx4Aqx8FNO+VZiUsoqy5UF7b107FotlUAbfoW7ffQ3+zRK1erTVQtkqAMwzz8esHp+yAKzxSE37sNZtg9GHAMp4trWasvrhN55Fqn3LGmQ35MLMgHaq2QENe8Acg2XiqaAezyiEzGlI1Vuwln0FM87HogS7SpJp2LXRQOjcvpKi1Teblef0OGH/t9r32vF+IKQA95KcQ8CWjORswlq7DcbI64X8tHi2V7awv9HFhOyU8B9omKG274GmNjw+gc1iYlxaIiYlyxW7PgHANP2H2riF1SrHQK77D5biz2DCKQBMyJLHs9K6dl29g4FUtzhodXowSVCYnYRVDTLf9TEApsln+ttJkhCTTofVT2EpXQ6H/Airxd8ukWgjr9eLJEmYTCZtrh4WBPQprV6d4s3CqW/nGVJ99oOBySRvkhlqzO/rb9mvmp5yyils2rSJ9evXa//mzJnDJZdcwvr167sILCMFn6LlYI8/WDgasp2EzPaktkHRKcEnjz4U4tLkTrP/O+3w+ChIZant/RCYkah4mfz/iWcEnzxqPKTkgs8NZcFpovUeOKii7Yhd+qV2TAxQmBBIMPYI2Y2icS80y1mOxqXGYZKgzeWlptXZSykjk+JQ6bb3fAEIyJoOyWP9x62xshIGoNjvmhcN41nIvZSUBSPjjwdbp6DfIllBpfXlKEMT8MvXgSdye15IJjPkHiX/sde/0aRqXdHrOxgYhK8JLE37ob5Yzi5ZcGLwBYXK+1fi76dqYpHqFietzmD3Gb2gBuEHpYVuLIOabXI7TZgXdL4oUNpp92fRM4l2R5hJMvREv4SWxMREpk+fHvQvPj6etLQ0pk+fPlh1HHREruwfT/l6cMuDtzbA6jTbidPjZb+yQ7u2r4bXA6VfyZ/HnxB8gcnkP1ayXDus94mo1emhqlleDGt7PziaoXKj/Dn/2OALJMk/OSmLRnW5Hs6mbSOJLk93YG1kb2BPguwZ8mdlMWSzmDU3jFKdZhDbEyojVomiXOjcTwEKTlLOiTKhpTaEcKcumkO1U6GimCn+PGgxpPt+2vnxIt1PJQnyFTfGvV9rh/W+lUBJqMxhe1bI/8+ZJY9fgRScCEhQvRVaqgBIjrWSpuxboldFYMg9z0qV92TMbC3JiorInQuWWGipkDecVCZU3csvnZ/P55GtolHOMLKFDSEpeRCXLmvHKzcB/gG2Rqcaj7117QgBiTYLGQmK+17FenA2gz1ZcysJQl2MByyGtD0gdDrAqjuRpyfEkByrmC/3fSvnNh+VD8ljul6ktpM6YUUZmlHlwPeRL1xVMARYsfS+GWzINKqqRbQg1GJcEVr2rpT3PQi4trypQ7ebnHbZo8Xn878necd0vWDckWCyQEs5NJZFzT4tXejUT8NZC3YR9NT23rdaW12OT5cT7+hVcA65R5A6B4wPEXsRl+qfZ6PIy0Ntp6DxrExRLuSFiEu02GGcEt8TMO7rXbmgog1Lylge7QxYaFm+fDkPP/xwBKoyhEgSjJ0jfz4guz4lx/p3atXjYkjVdozPiPe7CajuPPnHgSmEq59qVShfp0n8el8waq5hgS4nqvYw1EIIIFcZeKu3gLM1ejRDgTRXyIvBgdK50cbJmesCF1r5ymJIjxpc2SIqW5A0i2hjmbyXgckCeUd3vSh9IiRkgdcp91UgNT6GJLsFIdA2StUTLo+PfYrlWNvLpmY7dDTIKdpHh9gEOSbOv2jc54/p0Hs/7bLYKw8WWqSwpJZOF2XPAJNV3t+rsQzQ/14tofapYo8yp4YSWsA/p+5brR3Su8u1ZjkO3HNKtYjmhhjPAMYp7oYB/TTqcOlv3A4Hw9KiMkYRWgLiNfLT9LsYCqm9VZ9d9UfuTGqBHNfidXWxSOnVBzdkPIE6wIZaMAIk5UDSGNkaU75Oi/HQ+VooeN3SaSE08IdXJL+cWfL/qzaDR05PqefFUFldO77OFlF14h59CNhC7CElSfJGkwHnSpKkaw1uWX0bXp8gPsZMZqLSTqr2duzhWuKQLow7Uv7/vlVR00+DaK+XNz2MNBYbZClJlBXBWX3/DjTImxrrjS5zanMFNO+X4/BUZUtnVGVpwGI8X8f9FPxeGYVqO7VWQ91uQILcI0NfpPbTslVRpATs9IDuru/DokWLOPfcc7scX758OZIk0djYqH1WA+7HFM1k1mk/5s577qeysrLLtfX19Vx//fXk5eURExNDTk4Ol19+OWVlZV3urZYbExNDUVER9957b5dUxpHGEFpUxs6W/38gQGjRscYjZBC+qr0eMzv0RSEWQ3r3we1i8vd6tElY0/6EQp2M9vdtPyPdofjJu60RTms+Kh/sKbLgXL0VCHSn0J8mqqZFtmhmJdv9FlE1BkFVtIRCXSQFvH96Flr8yoUEfzuVKdrr7pQLECC0rI469zBJwj+WjRqPQ0QmW5WWiCNHyTKqKDAyEm3Ex5jxCdjXoN++mp2spHJVFTeZUyGmmxTPY5V+WrUFXMGxa3p0uXZ5fJTVy7+9ZmlRsiCSObVLPIvGuMMBCRr2YGmvBaJIuaCOS4p7mEcKr5/u2LGD8vJyln/8Dr+9ZhHLV3zF0UcfzaZNm7Rz6uvrOeqoo1i6dCn/+Mc/2L17N6+88gq7d+/m8MMPp6QkWMFx+umnU1FRwa5du7jpppu4++67efDBB8OqX18xhBYVdYBtKIW2OsA/eOhxMVTXKmups5KUAba5XHbnkUyh41lUQizG9bwY0oIGVZN/3W7Z7SYmQbY8dYc6Ge3/LkAzpO9hNsjtRBGAm9NCuOUMBEmCnEPlzxXrAb9yYW9dm+7aWH0ac+CKWhNaulEuQMD7tyYgpkB+h1WFhZ7Q3F0DlTCKNVizzoVCFVqqtmD1yGXo7BXqQkiL6JjDQp47INR2V8YCSZLI07E7cZe+qvXTHto2eQwk5oDwagKknhMWlNW3axbRrCTFIqomtempnezJslADxFV91/15EUAIQbu7fUj+dTt/ed2yog5wmcLbSyYzM5Ps7GwmFBZw0Tnz+fT9t0hPT+eaa67RzrnjjjsoLy9n6dKlnHHGGeTm5nL88cfz8ccfY7Vag84FeUuT7Oxs8vLyuPrqq5k3bx7vvfdeWPXrK/3ap0VvtCxZgm3/fvmP2BR5EVpfIneiwpN0PXioXcPUOWg6QCskvF46NmzEnJKCrUDZYFO1tHRKe/zd3gbdTUQ+nwjwv1UWQ1XyLsdkTpUzqgHelhbaVq3CPmkSMbm58vdaO32LlKLzVVAnJIQ2ATenziCt8qvwC3M5qVybjDc1wOQ8+lA5g135Opi9iHGj5LTH7S4v1S1OvyCuA9Q5TJNZvG6oUHbxCxBahBAgBJKabz/nUDnmpbVKjikYlafFxOhTudDJIupxQu1O+XOWnNlSCIFj82aQTMROVzaPTBotLxpbyklv3QnERlGAryRnzARZabdJXmwI0X/XLSEE9Tvjcbebie1QAobVRWjFBjkpgsnE+PR4tlY06/IdVBec/kQkXZUL7upqnNu3E3vIIZiTlb3Nxx0OW9+VFQz5x2ruro3tbhraXIxSPBn0gBaEHxhLW6nMqWpmSMDb3Ixz3z75vVEZcxhUbyG2dhMwd9CUCx2eDo5c3I2b2iCz+uLVxFnjun7hVhTnFjtCyGN8WLFnAD6Bp8NEXLyNn/3sZ9x+++1UV1eTnp7OK6+8wiWXXEJ2dvAm2LGxsfzyl7/kzjvvpL6+ntTU1JBFx8bGUldXF2bF+kbUWlqa3n2Xqt/eSs6//4NHbWS106jxGnrWCqkDLKG1Qj6Xi7Irfs7eiy+mZMECav/5T+X72YAETWWyLyr69cGtbHbQ4fZiMUmMS1UGElV7my0vhFxlZZSceRYHrv01xQsW0vzhh/L3o2fKOefbakj11g5B7Q8+6iRiaasARyOYLLQlTwAUQSYMTHv207ArHu9qF41vvikfVDW4yoIrxmJi7Ch9ZibqsoCu2iLvqWFPhrRCAFq/+prdJ5zIjsNmU/uPp+W+bY2FbMXKpVhFx6fp13LcJbi3ZrusvbanQFIOwuej4tbbKP3RBZSefz4Vv/udX6upWJbTW7cPQc0PPkFvVNUW+f/Z0we8l1L1+iTqtydQe9OtcttmTJYzPzmbtbgZPSfNUNtVAnmxfUBxvVOElrZVqyg+/Qz2/d9VFC88E+fu3cr3ahIgeQ6OjTGTo7iY6c1FTEtLHpisQFUEKsqFluXL2XXiSew773zGPf00vjalDZR+aq/dfNDqO2xQM4dZYrt89f7775OQkBD074wzzuhynorU7sbdYcZU28KEoiIASktLqampobGxkSlTpoS8bsqUKQgh2K2+twEIIVi6dCkff/wxJ598chgP2Hei1tKScPLJWMePhz17qH/iScb84V5ZaNn6rtaJ1MV4XZuLZoebJLt+dqjVJq5utEK1jz1G+6pV2vk1f/s7cbPnEHfYLEgrgrpdULERJszTrQ+u6nKSmxqH1azI9wEDrBCC8ttvx1NVJVtdPB7K77iT2NmzsWZmQsYkqN5KnrsEKNK924mKvX6b/CFtAj7TALWEXr+mrerPfyFx/umYtWD8LXIwviWG/PR4yurbKa1t46iCtIHdczgT2E8lCVdZGQd+/Wt87bIgUvPww1jHjCH5rDNla0v597LleMb52oKxttWpu/HMvxhSLC2B2ltJovGNN2h6913t/MbX3yBuzhySzzlHXgzt/JD0lu3ArKhxlrd4O2R3aNAWjANB+OTJxLl+I62ff07iySfLFuny76FqE6QXBSgC9Sc4a0jIG0o6m+RFZsYUvC0tHLj5Nwiln3praznwm1sY/8brSKo7dsVGrYj89HjKmxyU1rYxO6+bOI8RSJd0xx0N0LRP/pw1DU9NDeU33ay1U2zpXmr+/BfGPnC/bGEH7LWbkDvp4HTUWEssqy9e3fuJg3TvkCj7B2K1+60uCieddBJPPfVU0LHVq1fzk5/8JGRRUsBCxKQIhFKAwqI/LtaqwOR2u/H5fFx88cXcfffdfb4+HKLW0mJOTCTzrt8B0Pz223hqavyaSUWbnmCzkKFkotGjtQUUmUWIIDcBT3099f95CYCxjz9G8g9+AEJQ+/hj8jmaRUp2U9GrG52W7jgwc5iqmcyaTttXX9Px3Vqk2FgKP/kE+yEzEe3t1D/7nHyO0k75blkzES1uJ/b6HfKHrGkBSeYHjq+5mcbXX4eUXLAly/sqKS5A45VMf3rbDNbvHqZaRIOTZdQ+8QS+9nZiZ88m9bLLAKh+6CGEy+Ufz5TFUKLdqsvxrLHdRX2b7O/dxY0zazrC7ab2UXnsyrzlFjKuvx6AmkcfQ3i9fktLi2xp0XsvVd+ptPZiQEB8JsSnB30XZsnap7pnFMt8luKGp4ybeo5/DGo7tZ+OPgTMFhpeeQVvbS0x+fkULl2KKSkJ57ZttCz9zD+fNu6FjkZAv+3U1d1amU+Tx0FsCrVP/QNfWxv26dPJefZfALS88w7Okj3KfGLC0lFLJo2DpgSUJIk4a9yQ/JO6s3Z6FEuLtatQEx8fT1FRUdC/MWNC7B8Xgl1b5WQ2+fn5ZGRkkJKSwrZt20Keu23bNiRJokixzoAsMK1fv55du3bR0dHBiy++SHx8N0knIkTUCi0AsXPm0JGbCx6PrIVTtU01OzTJdrxOd3wP6vBN+2StkMkKmVNofONNhMOBffp0Ek45hYxfXQNmM23frJRN2qODhbvOPrh6oaSmk8tJW528Ky9A1lQaXn0FgJTzzydm7BgyfvlLABrfeQefw6EtGmVLi/5RXyl7nTLoZU0Fj5faLQlQF5k0iE3vvCM7jXdaDOUq7+D+en1twBXkcgKyxhpg9CG4q6poev8DALJuvZWMG2/AnJGOp7KS5o8+CuinG/3B+Docz9TMYaOT7cTFKM4DmtAyjZZln+OpqcGcnk7qTy4hddFlmJKTcR84QNvXX2tCy6j2EmzoZ/zqjdTWXfKHrKkIIaj8PImSDzMQHveAy+5Yvx5Xaal/Qa70U1XBpedNTiWkoH4qfD4aXnoZgLRfXEXM2DGMuuRiABpeekneZDJZiYVU5lRNaNGZEkadU7W9lAKUgL62Nnl8BzJvupG4I46gdcpkEILGV1+V91XKmAzAdNOeg131g45/7BfavnhYYpGcXhyNFnAPbE7tcDh47vXXOW7uXDIyMjCZTFxwwQUsXry4Syrkjo4OnnzySebPnx8Uz6IKTLm5uVgsB8dxK6qFFoCmw2V/0qb/vS/vrxGbKvtC18gLr1xFg7u/QaeLIUnyDxwZk8BspXnJEgBGXXQhkiRhHTOGhOOOA6Dpgw+6aHBjY8yMVnxw9TTIFqvBvaopW52IRo3H0+6h9fPl8p8XXQhA/LHHYh0zBl9TE63Ll2sTdq6rGNB/ViIVW4MSG5A1HdPbn1GzKQnp8/DeC806FSeB1Ypzxw5ZcNaEFnlxGmuVN0N1e/sfRDycCQru9Xqg2t+2zUs+BK+X2NmziZ0xHZPNxqgLLwKg6b3/QeY0Oa6qvU7ODog+NbhdgvCFCHAPm07TW28BkHLeeUgxMZjsdlLOPQeAxrfflsf9uHRMwstkqUx3Gei6I61N8U3Pmo5z2zYcVTG4mq346uv7X5gQqKK1ddJEQJlT1X6q/B5p8THEWEwIIbsp6oXAd0aSCFiMT6Vj7Vo8VVWYEhNJWrAAgFE/+hEA7d99h7uiIljBQEA/rdFPP21qd1OnKDU197CAGNHmjz7G195OTF4ecUfJ2wk0qf9/912Ex6MpGGZIe3RvEVWJES5AyGO52YrU7ET4JExt/es/1dXVVFZWsru0lNc//JCTL72U2qYmnnj0Ue2c+++/n+zsbE499VQ+/PBD9u3bx4oVK5g/fz5ut5snnngiwk/Xf6JeaGmdNg1MJpw7duAuLw9wfZIH2RiLkiHKp68u4g/EJ2CAnYazpATn9u1gsZA4b552ftKZZwLQvGQJQhVa6ovB2QLoc5DtYmkJWAi1fvEFeL3YpkzBVigHREtmM4nz5wPQ8tky7V3K8laQSLvuhRYhIAY3tkZZSCNrGuYvlCxzjgE+vE0i4ZhjAGj59FP/xnXKXi16xa9cQO5varrtlDxNuZC0cIF2fvKZCwFoW7kST3ObrIgA/2JIhxnEugT3tlRARz1IZnzxebStXAkgx/koqIvHthVfItxubTE03VSq+8WQqgjQLC2ZU2ld8aX/BGlgy4K4U08BoOWzz/xCS1MZOJqQJElLCayn8TDwWeQ5VRmXsqZryVkS583DFCPH+FlzcoidMxuEoPmjj7u4po8PcLnWixBdrLhbZyfZibd1tohOp2XpUgCSzz1Hc5NqmzABU3Iy3sZGOtatC+ine3TTLr0Ro1p/rXZ8gRs3mvvXTydNmkROTg7HnnMef332WU467hi+WbmSaYf5U02npaWxatUqTjrpJK666ioKCwu54IILKCwsZM2aNRQU9LDNw0Ei6oUWX3w89kMPBeSsFZ0ziOmdYK3QNFqXfwFA/FFHYU5J0c5LPOlEsFpx7y3DXdcupwmFLr7Keolrcbi9lDfJ1rVQfvKtyz4HlHYJIHGePGG3fvEFwpoo++oCU6S9g17n4UChVI7k84AtGWHP8H8RgZjvhBNPAKD166/9rpzquxsNBKTbdtfU4Ni0CUwmkk47TTslJj8f+9Sp4PXKC9FOVlE9ZkTsYmlR34n0CbR+uxbhcmEdN44YRbkAYJ8xA3NGOr62Ntq+XaNpuqdJpQez6kOICLC0TKPtm28iVnLs0UeBJOHcvh1PqweSxspfRElfNXXUQWslICHSJ8nrCiDp9PlB5yWdeioAbV+u8K87lH46LjUOs0nS0rjrAb8SMGCj5mrZo8U3aqKmXEgIzD5lNhN/gjzutyxdqo1nU0zBu7PrGZtQhBZLLL5W/x5bwiTxwgsv8I7iUhfIiSeeiBCClJQU7bMQAp/PR+WGtax+4w3+eOstZGVldbk2PT2dRx99lLKyMlwuF5WVlTz//PPkqts5KHR378Em6oUWgHhlMdT25VddNB56JWj/B3UyyZxG2yp54IhXtNoqpvh44mbJWZtav/7ab86uCDZn6yWDWFm9bBlJtFtIU/PkK4tGkTaFtq/kvUcSTgpO76fm3/c1N+PYulV7n6aa9upegwuCSZI/E0z7uvX+r1LMAy5dfSc71m/AGycLg7RUQLvfnUV3baz2U6Qg5YI6wdunTcOSnh50SbziyhkYr6FaWtQFQ0mtfjS4e+vkbDr5nV1OsqbJbppA4sknBQW5SiYTiSeeCEDrii80IXiSaZ+uLAAhEZBBI3Z3I0gmfPHjaF+3LuD7gTWAOSVFFpxR5ops/SsYAlvMrLiWMyofd3UDnvIKsFqJO/zwoGvijz0WgPbv1uIbJbvUUbMd3A6sZhPjRslB1yU68V6oVJSAY5Xnor5ETt9ujaN9RxXC4cAyejS2iRODrtPWZ998o1nuxkq1JAh9tEtv2IQitFrt/vTPUYwhtACxR8i7R7evXYtIlwO9qN4SNHjrdSIze13yLu+ASJ1I+xrZnSd+7lFdzlUH2bavvwkQ7uQMYnpzD1ODRJPsVnmx43XLCRoAR2MMvvZ2eXKeNjXoOslsJu4IeXJq+/ZbTYM2TSrVzSKxJyarGrCsafKiWSXcLGIBbRYzbhxWJXFG+4ZtMCpf/qJqCwPcYmLYorrydLaItq+U05HHH9W1nyYcKwt3bV9/jVAtUopyITc1DkmCFodH8y8f6biUOKb4LkH402n/Vt6jprMSBtD85tu/+84vtEj7IIzNFUcak02KciG1kLZ1m8DtD74XYblCB1+jzRVffR0Q16JfRWDg2G6pCVQuyP009pCZmOKCNw2MKSjAMno0wuWifWeFP55WcXnVW1ZOtYnM6ga4aoxo5lTaVskphhOOP75LBq1YRdhz7tqNp92LO0HOjFUoosPaorqHiU6WlmjFEFoA26RJmOLj8bW04KxH3kna0QTN5ZHM2DqsUBdDiS3F8kAZm0pHSRWiowPzqFFdtB0A8UcfDUD76tWIDGWx3ikrjF58cLs8Qu1O8LrAlkT7jgMAxM6e7d+BPIC4wxUh+Ns1mpZxcpSYsydrlpaptH0b+Vz38UfKOxW3f7dWDjQHfWtwNUsL2nOKTL+lJZRyIfbQQzHFx+NtaMDZJKc4pqkMOhqxW83kJMuaTt3EtXTuq0o7uS1jce/bByYTsQF+2yqq5tu5bTteayZeyUqC5CDVXdnlXL0xWfIrF9rXrBlweUHjpSQRf5TcTzu+/76LK6ceFQxBlpZafzyL5rlw1Nwu10iSRPzR8vH21au7WKT0ljSjy6ogIEa0fa28/1RnaxTIljvbJDk2r33NGlzp8uaHE9G/y7UZH1bkOBbhM8nJCKIcQ2gBJIuF2NnypNb+/Xp580TQ/C31iDrJJLfI+1yQNY02ZfKKO+rIkItx+5TJmOLi8LW24uxIkg9WbwOfl/R4eXHU7vLi0VnSAsA/wGZNkxfMQNycOSFPjTtSFlo61q5FpMmD7QTpAPj0PeAI4RfOfKMm4tga+f6jLj471q/vkkFMj6j9NF60aZuwuRwJeKqrkWJiQi7GJatVO96+dbc/pkAZz9ITZHfH5o6Bp7YdTkgScqr6WjnAvH2/rKG0T56MOSGhy/nWzExi8vJACNrXb6AxQY55yXEWH7Q6DwWCAEtL1jQ6NmyI+D3sM2aCyYS7vBy3abR8sHqrvFO8DgkU2lT3MJE5xW/pU4S4zsTNlueQ9nXr/cKdYmnRNm3WifeCiia0KuO2L3kiDmVvkLjZXcczgDjVG+bbb3GlyULLhCgQWuxqEL45Bp9DH7FNA8UQWhRUCb/9u+/kXXxBdhHTKeogm9Tk3wjQsUF2IYmbFXrgkMxmYg+VfeQ7iqvl3X49DqjfE9FNBIcDXcQuNZ4lYyrt38sbh8XNmR3yWpuS8cTX3o6j0oFDsmOX3CS069vakixayJYaAHDUSkEuJ5EibtahcvmbN+NLVTJjBWQQ04GRLyR5nlL5Q3IuHdvkfX/sM2ZgsttDnh+rtFPH9+v8wp06nulM1R30k9ds0yzH7Vtk4SPu8NDKBUBz5Wz/7jsaEiYAMEbnQgv4LS0idRKOzZGf58wJ8dgmy/2zo7QBLMpO3o2l2jl67KsmfJhr5bTkbpGJt64OrFbsM2aEPF/tp45NmxDqeKZYWpLjZOVCu0sfyq4uv7fynB11NvB6sebkYB09OuS1ah9u/26tJrREg6XFLqlB+HZ87e1DW5lhgiG0KKhB5h0bNwUILX5Nsd52M1efJ6lF1kqKzKl0bJJ9TGNnhh5gAWIPVdpp/QbIDIj/0SmdtUJOdxa+5makuDjsU6aEvsZkIna6rDVzbNlCpVUOGk9o1bfQUqhMIq7EXDq27Az+Mtzu08lqZ83LwzxqFMLlwtmouD5Vb8Mk9LlRnfr0+V5lM7WsaXRslDXjsTNndnudNp6tW+dPD61jNzpQ9CYBLicda+X4vNhuLKLgH88cGzfRGitnREzyhLFPyQhC8rkpkuR9exwtsQiHA1NSEpjUt63/nVWEsKDEKW3bvn49pCvuxtXb9KbfAvzzab5UiaQElzv2yu+RfdIkTDZbyOti8vO18czRrASoR0M/ba+HZtnNun1PI4CcArobYg85FADn7t14YjIBSKNpEGs5PNAsLdZYRIeyV6DOlE79xRBaFOxTp4LJhKeqCrdVDvTSc4CvSlKTvLh0+zLx1teD1Yqtm8U4QOwsdSJaFxBTELxXhh40aF3icpTFUEeN3GViD5mJ1MMOsHZF8OvYtBm3JGvMJKEPjVl3TBCK0JI2RXbfAkRezgBLDQzqkP3AtXewpEbT4Ma37x/gfYYn6nuYr1paAiyisYd0L7QEuedYlExrOuyn0KmvKgs+b/JknLvkBCNxIVzoVOwz/MoFISS1wMGp6DAhw3UAm+TGbY6lo6QakLMe+ue6gT2/Wo7myvn9Or8isNM7qBfUV0aLFcqYTMcm+V3sSbkQNJ6VNgEStNdCa/VgVndICFL8qi69KXm0K+0Ud1j3Qos1KxNLVhb4fHhLFNdGnfdT8AstwmzD53DIn63RvWyP7qcPwBQXh61IjmVxVCudoWYHkl41uALSacLurAUkHBVyh7BPntytVgj8CyX33jI8sePlg3q3tLRWQ1s1IOEslzfTVFN6dkes4g7g2LTRf1Dng2yRks3FmTZZ85P3jR8b8fvEKvsqdWzcpG2emNyyO+L3GQ5olhaPbGnxpUzEsVNWNPS0GApyz6lUSqnequt3UM6wplhEHWkAWDIzsaSldXuNrbAQKS4OX3s7Ur2amUe/bQQwWnF/q48vokMTgA8ZUJlBC1IlHlLtp44dO0K6cuqRSYGxQhvltrX34LkAAa6cG7dAqjKnBlhb9NZlJQlNCSiypmuxjz0pYcDvAeItLpXLGbQaDg+EADuyi7XPIz+tZLWCSe9P3jOG0BKAph0vqQZrHHidpDllE6beBg4BTFIzWqUW0LFFjm2J7cb3VsWclESMsiuqo8lvztabRSro51a1QqkFOHbJE759cvfWKAC74h7m3F2McOvs5ekGNTDSIeXgqaoCsxmRO1BLS1dip8sWPsfWrZoGN0Vxc9TbglMIkPCR6y0FwNFkB48Hc3o6lm78v1XUhaijrB5MVnA2Q9M+3U322i8uhJZW19Eg7wtkmzK5x2slsxn7VLkvm8ob5WM6e4c6M9opx0Q1JEzQlAsDFVpCYR2Tgzk5GdxunB3J8kGdulyr64MpaqxQ2hR5fAJiZ/bctrEz5MW6Y+vWgPizrfrrp4E/tyKUeWLG42tqkj08AjZ/DYV9uiK07JIVOHrvpyafC7Pkw4eEzyl7aXROmx2NGEJLANrgsXkTZMiTXZajZCirNHiI4PS0WjxLL9oOkDe0A3BUKtks6veAS18ZToJQtUKZ03Bul4Ms7YoWuzusmZlYsrNBCESdbK3TQyronkinEQBXtWzSthUVgc06wFK7tplq5XKXleFVMj4lt+rT0gIwTqohVjjAYqejtA6QrSyd9zPojNpOHdu2aRYpvbrnAFjbq8DRCJIZ54FGoHflAkCsshiSFKFF370UEj2NALSRhrtMXmTbFUUAgBTOPjUhrpEkSdvHylGteCzU7cIq6dN7AfzuYU5HihwrlJhITH5ej9eoQrN7/3688UrmUh33U3mjXEW50JoIgK2gACkmpsfrNEvL7lKlHH33VMknW1k8WBCKa5jJHjuUVRoWGEJLALEBcQhCyQWe1aFPoUUg/BlkMqZqWqHuspwEoi6GHLv3QnwGIDDV7QgqWy/IA6wstLjN+bIbSUwMMePH93qtarVShRa9D7Lq03n3yPEl9ilTBkVbaE5JwTpGjjtzNMtpQfXqHgZC096SMVnL9NSTa5hKrKpc2LoNkaEs3gPSQ+vlbVR1AbH1ihY/fSKOHbLlzd6LpQX8cS2mCjnznaRz5YL6dFJNMwCW0aOxjBo1KPfSFFwlB8CWDD4P4ykflHsNJQJBPB3kmmoA6KiQFTexM6aH3D4gEHNSEtZxctyZo01exOsxjbv63pmEB6pl5Z+jVp4b7ZP70E8V7wVfdS0eZ3QtXdV4FilWzhZ53q9+xbmXXR7y3C+//BJJkti4cSOSJGn/EhMTmTZtGjf8/l527+2aea2jo4Pf//73TJw4EZvNRnp6Oj/60Y/YsiXY/f/uu+/WyrRYLOTn53PDDTfQepA2voyuX74XbEVFSDYbvpYW3JK8KNKtpQX/nhouXxbC4UCKjZX3LegFTWjZskVzzzHrbE+boHWLYmlxtMgLZNvEiT0G4auorimiPjosLapQ5iuVLXj2qVMGnumkmybT3sEqeXGQ2LaXGPS17wgoe99oGwFOx7FDVg6oGuyesBUVgdWKr6kJtyVXPqjjmII4RWgRGdNwKnE/fVkMqZn+pKqmUAYD3aH2U3OVnH2pcxbEcIapwGskzNpnTWjZshUy5fsUob8sikLAJMVzwZeQjVNR3PSU1CYQ/3imWKFqtsupu9GXEhAg3VkGXifEJOAslTdy7c2NE8CcmIg1Vx7HnA29z78jHXXmFAKES57n1BT3l/3gByz76mv27++agOb5559nzpw5JCXJe+ktXbqUiooKNmzYwP3338/24mKOPP98Pv/6G+0ap9PJvHnzeO6557jvvvvYuXMnS5YswePxcOSRR7Jq1aqge0ybNo2KigpKS0v585//zDPPPMNNN900CK3QFUNoCUCyWrFNkHP1O5rlYPQshxzDoK9hAySfV97wEP8AYJswAcls7ukywK+9dB84gDdBTmVprtHnYigGN9TKC0WH4vbUF+0tBLimNKhZw/T2FgWjDrK+PfKixNaHBWO4aIuh4v1gS8YkPIyXKnQZe6Zt2Jk6Cdce2Z/bNrFn90QAKSYGuzqeNarxZ1t1F3+mEtcgj0EuxiKcTqS4OG2R0xPWceMwxcUheX24WiyA3iUXuZNIlYrQMpj9VFmMO3fs0DbanagILXrqq4EbdorMaTh2KsqFSb33UwgQWkortf3PEtp0JtwpP3h2h2IVz5yKY7vSTn1w4wR/ezoarYPmuSCEwNfePiT/AhWb2idFjpWsVk1ZuuCEE0hPTeWFF14Iqntrayuvv/46V1xxhXYsLS2N7OxsCgoKOOecc/jg389z+IwZ/PLWO/B65cIffvhhVq5cyfvvv88FF1xAXl4eRxxxBG+++SZTpkzhiiuuCKqbxWIhOzubsWPHcuGFF3LJJZfw3nvvRfy3CIX+xdV+Yp8yGcfmzTiqnCQBqc4D2NRc2Toi21uOXXLjMcfiOCC7RdgnTezTteakJKy5ubjLynC0JxMPmGq2AicMXoUPOnIHzfXtl3eytyfjLK0AwDapr0KLMmE1evHp1407CK9TQtQq+xNMmQJffSB/Ee4KpTtLi+orv3Ur/GgqlK1kkrSPNrrfk2MkIoQ/uNfZkQI+H+ZRo7BkZvTpevu0aTi2bsVR6SAJoHYnlnR9WaRUTXRsveJyoux3YZ84sVfXHJD3VbJNnEjH+vU4GqwoRnbdY6pqBEJoucPpq93sdG8dNw5TYiK+lhac7izsQBH7+l/+MEcIv7u1L2MKzh2fAmDrr9CydRv8cDKUryOxeRfQt34+ksjukBXB3pTJuPctBXqPEVWxTZlMy6ef4my0Yh4soaWjgx09pF8eTCZ9vxapU7C9ulNC4EbCFouFi394Li+88AJ33HGHFt/4+uuv4/V6+fGPf0xDQ0PIe5hMJn55ySVcdP31rF+/npNOOonFixdz6qmnckinhBwmk4kbbriBSy65hA0bNnCokhGwM7GxsbhcB2edbFhaOqFqh53F+yA2FRM+ihSLhJ7Idctub63JE3HulP2/+6K9VdEG2Rp54DDVBGSF0ZEGrchXKn/Imo5DDcLvo6XFMnq0vGmbAFezRV8NEwIJgaNRDry3jh2LOTExkoUHob5/rtJSvEmyNUFLN6ojzJ428iR5zwZnvfz+2CZN6jUIX0UT7naXgT0ZhJexHkUjrKP30YYLW7NshXJWywlC+uJyol2vLJqcjRb0bhFFCIQXTLVK+vY+ujCFgyRJ/rmiTl5uTNCh0AIB7tbmcfhaWuSMWH2IfQR/ML48nsnvYlLTjp4uGXGovUq1tDgd6YA8T5pTUvpUhmoVdDRYdZddrVtUS0uA0ALw0wvOo7i4mC+++EI79vzzz3PeeeeRnJzcY5GTlPeyTEnEsXPnTqZ0Mw6ox3fu3Bny+7Vr17J48WJOPvnk3p8lAhiWlk6oA7hjxw6YOw1Kv9R8VfVEnrLvQ0vyJJyqn3wftR0gLxpbPvpITqeaImFqryWdJmrpubOMFNT1XKFPbidP/AQ8lZ8AfdeeSZKEfdIk2tesURbzOl8MIU8mMLgLIQBLWhqW7Gw8lZU4O1KJAyZK+1k7qHc9+CQ078YkCRpNo3DskZUnfXU5gUAN7lbE0dOQyr5R+n7qYFR3SFDjCSThg7h0HDuUmKo+upwEnqsK3XrH2WJB8vowJSZqSS0iRieB2j5tGu2rV+MobwUTjJWqiacjsvccYgT+mBZXs5wFqy8ZsVSCxjNHGnFAYvNO4Fjd6bqyHbLQ4lDc0vvjnqie62y2EO8dnM2apdhYJn0/NDOJFBsiO1gISwvApMJCjj76aJ577jlOPPFEdu/ezZdffsm9997b6320ZBwBfbU/SqxNmzaRkJCA1+vF5XKxcOFCHn/88T5fPxAMS0snVGuDp7IST5ycTnWSaZ/utORjvHIAV6stF/cBeTFkm9g39zAIFO52QpraTjrzwQWKRCng36zOmpuLOSGhz9er2l5no1V371BXhCa0aFruQQyg8Gvd5DgsPSoXzG45lXizKRnnDlnT1VehWTvXbMZbX4/HLvfTPGXPFz2harnJ7r9FFPwKm2hQLkgB/dQ+eXIXq104Tx94jdRpVaG1bfFeSMgGYIJ0QFetLATEI2d4cpbJGcRsfXS3VtHm1CZZ0Elq2tXT6SMOISCVZhLdtQA4yuXsdf3pp5bRo5Hi40BIeJsHpZpIkoQpLm5I/gX3RSEH4auWlhACzRVXXMGbb75JS0sLzz//PIWFhZxwQu9u+ttLZE+bPCXx0sSJE9m2LXQyJfX4xID14aRJk1i/fj3btm2jo6OD9957j6ysrL4074AxhJZOmBPiseYpGSra5TSQk6SuGRpGOlYh+x/6amSNl2X0aHkjsD6iTkSuvXvxjZIHHS3LkQ4Qyn8L1U396uXBpL9Bq/ZJfnO2/oUWRTgjkpaW7oOiNZeeCnlhn2uqwe5rj9B9hwdqvIYQ9HmPoEBMNhsx4/MBJSYGyPOURrKKQ05g3I8ntghvXR2YTFpSlb5gmzgRAXgdZkwd+or5CYWzs3IhkLDSh/XQT5Ux0LljB0LZ/2yi3lw5hT8rm6tYts73xyIKXcezuLYyYhVBSC9oyoVR43HulGNb+hojCrJAYS6QF9reRp0vXwUIr7zukEwmJGtXK/AFF1yAyWRi8eLF/Pvf/+byyy/v1XXY5/Py1Msvkz9uLDOV1PkXXXQRS5cuZYOy2az/XB9///vfmTp1alC8S0xMDEVFReTn5xPTR2tipND5rx4emquAHE+sS1959bWWypWg6X5YWQDM6emYU1PB58PplXfmnqwzTXcGjaTQDJIJ5wE5046tHwvGwPNlS4vOsxJ5ZbM9DIZ7WNeBWMskU1xKh00OWM1xd80/P6JRFpC+DvA2NYHZTEwvO0d3xq5Yjx31skVKFVr0JEJPNcm/u6MtBYCY/HxMoVwtusEUF4dIlWOwzHX6cl0KheoGF+RCN0hGUdv4fDn1dmsrbks+oD9FoEBozefa3fcMf4Fo41lJGcRnICG0DJ96QCCYIsn9VGRMw7mr73spBWIZHyVCC+BThRa73S+MBAglCQkJXHjhhdx2221UVFSwaNGiLmXU1dVRWVlJSUkJ7733HgsvvZzvNm/myT/dj1nJFnvDDTdwxBFHcNZZZ/H6669TVlbGmjVrOO+889i2bRvPPvtsn+MoBxv9/+phoHYih7JQHS3VY/MMki1yqFC1aZXyDtv9cTkBWeOhmr+dSrYePQl3QsBUVSuUVuTfrK4ffvKg7JUhgddlwtTcFulqDiu8jRIICSkpEYtqKh7EgU7T4O7aRWO8vJDPce4ZtPsNDYqlpUEWeGPG52Oy2fpVgtq3ncqO72m+OlJoiVwVhxrh34DTIQ9nYaXxFdkpAFjq9aXd7oIvwD1s6iDEnnXq8lJMDDZF0Ha2y3tHTNKRVV7FJAl8HnDvU/Zo6ad7mCrkOHft0ja3nmwq05lyQUlWwDiEy4UpPh7r2LH9KsNSIHvCeBqHxyJ6MFEtLT0pYK644goaGhqYP38+OTk5Xb6fN28eo0ePZsaMGdx6661MLizg2zff5IS5R2nn2O12li1bxqWXXsrtt99OUVERp59+OmazmVWrVnHUUUd1KXeoMALxQ6BlENtZTONRWaS4q0hrLwYOH9qKRRAtx3mF7F/a3wEWZA1u+8pV8v4lZjkQ2qSjPQ5UrZAvdSrOku+B/muFTDYbJFug0YO5qi7idRxOeBuUAXZ8boBWZvAmlpi8XCS7HdHRQbNnDKOBMe7SQbvfkKBaWhrlP+39cKVQUd3JnLtL4JQ8aNyr7SmhB7KoJUlqR0gWnPsUJUw/+ymASJcX1KZmfbuHmVpd+NwmhMmEraBgEO4Qyio6Eef27TjqBInAJNN+WnTkLqs+irPZKqclT03FktG/dMUxeblINhuiowO3ZTwxrGCytI/SyFd3yNCUC+pGzZMn9ykteSDmMXJclLdN/zp3n8dvaemOuXPnhgyiz8/PD3m8vXg7UoenizAcFxfHfffdx3333ddjne6++27uvvvuXus+WOj/Vw8DLUNFSQlVVjk1XHpb8VBWaRCQg7wolyf5cDSTmga3rBJhjcMuucmXKiNZySFDCMEURSvk9I4GjwdzcjKW7Oz+lxWjTOIufS+GPIq53qyY7yNCDwsbyWzW4hY6muVBPd1TFbl7DwdUoaXBn+64v2j9tGQPvjRZg6unpAWFXtm65kgpkhOD0H+LKADaxrr6WUyHwlQnW5JEZlLo7Fa+/j+/6GafFhXNKrq/AR8SGVITpo76ft9nuKIuDuWU2bISsL/uNIHjmaNNtUjpp59KXg9Filugo0pOS97fuB8AyRQdGf4gwNISSmjR9zDVLYbQEgJLVpacN9zrpa1VzhQV62ka2kpFGAlwt5qRXG6kmBhi8vq/0NTcw3bsxJsmf54k7dNNvLmaWMDZIm/2ZAuRaadvKNoSnY8yngZVaOl9F/JIob6DUmXjQbvnwUUVWuRFYX+C8FUsWVmYkpPB68XlldPbTpLKdNNPJygZ/toTJ+IqlT+H006agUAn7dIdFiVmx5eVctDuqVn7dhXjRBaUTG79uMsKJV5RixXqZzyLijan1snl6cnlOtVRSozkxWGKx7mnHAjPIopZ6ag676d4PNoYLfXTJbhPDJMYlf5iCC0hkCRJcxEz1eo1KFPgaFIyyBQVIVn67yloKyqS06k2NuKxFwHox+3E66RQkgdWR6WsmQzHGhWEXlaJ3eDrULRCow9O6kPwu0uZKvWjtQ1ECIHPC6I5fEuLJElaog1HqyyAT9FLPwUKhezG2e7MBiEwp6f32zUHCAil1nc/NbXLFl/fqL6nbh8o6nvrKivDNzjbawwpfkuLMqeG0U8hIBj/QAMCiXSpmWRP6J3NRxoZbXJcaFVsobwPHmFaRKNlW0mvkuvYRL9d6PSM0RLdYFc0HqY6OYWq3qYxSYgAU3Z4A2xgOtWODnmzOr2Ys+3Ne7BIPlqIx1kiP1NYWiHQxlidyyx+ggbYwZ1gVM2kSUkooUdrlrPJCgLMKSlYMjPDKkOL06uRF6wTpX26yWaXJ+QMS+p4Fq5yQYvC0t8rFJrB0rSGWGBZ0tIwZ6SDELiU30noqq/K7taB7mHh4A/GL6Y9QbZY53r0kVwk2SW7jtd7s/HW14PZjG1CUb/LGfxoyWGCtgPkkNZi2GEILd2g+uCaamWhRdLdilMEaIXCG2DBbwZ3ahv86SMrjOSV97FpIy5gs7pwM+1Ehwa3R8J99F7861XNpKmxFa9Lh6N7J+VCuGknVSWMs6waF1biJSexbfpIp2oRsureu1/e1K+/yTI0pCjpp73NZeEIs324RrWKuhvNvZw58hACPA4TXpdZXowX9X8xDv652L1vH82xchl6EVq0965WjmeJGZ8fOlajNwyrw8AY4cOb8et3g6bBrevQpYZcQvj9b8O0tEBAkG+lnEI1T6pG+HQQcK4GQLeBr7UVyWrFNn58eGVpayEdvkghkEwBi5IByxGqU2/ob83JyVhGy/sEqUK4vvArF8KK01BQ+6ljx07KzXJazPhWfSyGVOua2CsLYbZw3ThHqI932HT7vGEE4vehWC1eo1F/SUuF8Gkbdsbk5fY7LbmKZdQozZra0iH/P1c3m8HKb4lZUQSH5xoGmOQXLEqmU4NOGEJLN6jxGpLDg6fDpDNTNuAWuFvlxWW47mHytYpmqLgUkHPV62k0UTNixRQVhc600y/00y4hGaLH0/zAmyxDV4lBQgi/cqG/m9UFYisqApMJb309Lqf8Hks6Ci4QPhD75Bi0sGPPpCgJ8B2qfjo52NKio2kChD9G1B6Gy1MgmiJQEe7SvdUDq9twQfnBJSVOOFyLqIT+LHUGfadfQstTTz3FzJkzSUpKIikpiblz5/Lhhx8OVt2GlMB4DWejVXduhaZGNyBBUjyW1NSwy1EXjO7Svfi8EarcsEAeYNW9RwYchI8eXQy7IVDVehC019ok36BDS4vPN+DYM5A3J1MzBKobVeoFCYGr1QxOF5LdTkx+frgFKei9nw6us7zUzbJCC8ZvNMvrVx2NhyLQjXPiwIQW1ZVTUvZQ0xuqy33YFtGoUbXrp39Ekn79/GPHjuVPf/oTa9eu5bvvvuPkk0/mnHPOYcuWLYNVvyFFjddw6NDtRGpQtKxjwgvsVbFkZ2NKSpLTqTarZn8ddDahCi3yn2H7yUPUuYeFJtxn7/061W1K1XTqCVNzi+wnLxFW0GogmtCjCS16eR8D4vMmTkQyD0wTqzcFVX8JtSFdr/SyTwuAbfx4JKsV4THhbtObtjzgHRxwP5XnGqm8ZsC1Gk5ICHxuCalxgNk4o82N0yCIfgktZ511FgsWLGDChAlMnDiRP/7xjyQkJLBq1arBqt+QomXc0aEPrtSomEUGKLQEpVNVBm09rc09jfIAGbZWCIi69GEHeVIJdKeQ+rB4GkmYapRUzgmmsP3kVVQNrmhQ+r6OXkdHgxr3MxDlQpT300HutpLVSowSoK63+DMhBK4WxdJSEGbso4K291R5LULoS4h2NFmQAEtGBpa0tLDKkPC7cYYlYBsAI3f4D3s17vV6ef3112lra2Pu3Lndnud0OnE6ndrfzc3NALjdbtzuoQ3YVu/fXT0shYWAnHJU+HxDXt9IIrXJCxdfWvKAn8s6cSJ8950yEXVov21v7Tuc8Xg8eF0Sok0eIM0FhWE/h0CeeIQQEW2L4dq+Pp9Xq1PgTtnh1NMXkD2su+ulnByExQweoNkTsfYYDu0rvIpF1DzweqjjmWiUfxPvEI9pkWpfCb/CxDpxQvj9VH3VxPDrU+HQbfsK9X+hxyOv19vv53e7/fFRHk/3fTBmQhHObdtwNFrweIZ+DTAQAtvX7XJr74/XbB7Qc5nGjgWLBcnpwt1mRsREdt4YKny+gGQFkyb1+kzdvb8+ETwnhJtRMbAMIQQ+nw/fcFJ6BUgVnesVN2NGj5feddddXHbZZRQqYz5AQkICubm5HDvrEH7140somCYr+9Rn7+jo4M9//jOvvPIKe/fuJTExkRNPPJHf//73TJs2TSvnnnvu4d577wXAbDYzduxYzj33XO69914SEnre+8nn82nrIHMni3hf3/F+Cy2bNm1i7ty5OBwOEhISePvtt5k6dWq35z/wwAPcc889XY5/8sknxMXF9ff2g8Knn34a8rilsYkCwNlsoaGmliVLlhzcig0ihUrHr66tZfcAnyvJ6SAbv0Vq6adLsVr9r1Z37Tucaa4qZpKyEHKPSuHjr78Ku6wCnw8T0NjQMCjv0HBp38nKILtpyxbWuxVXiQPl5AEIwnp2a1UV45EH1p6uHzcqgdiaJrw17RFv46FsX1FZSQry/DXQ57I0NFAAiCYfwgvFxcVUuIZ+TBto+84SftectXX1OMJsp9SmZtIBr8+rq7G+c/uOUwThlpaWoOdUI6Y2b9rM9o7+6WFd7W1MVz5//vlyRGJyyPNSvD4ykS0ta779li079/brPsORTz/9lJYOJ7OVv5d/sQLXqG0DKjM3IwN7RQXOJgvuRI8+3seGBk25sM9qYX0fn6nz+xtTvIV85fMHSz5Uk4mFjcViITs7m9bWVlwu18AKiyRuF6ptXVX2A1iFoOTzz/HZLXhGZfD2229z//33s2bNGu2c+Ph46utlK/0777zD5MmT6ejoYOvWrTz92KMcef75vPbMPzj2rHNoaWnB6XRy9tlns3//fu677z5mz55NTU0Nf//735k7dy5vv/02hx9+OCAbIiZPnsw777yDx+Nh9erVXHvttTQ2NvLwww/3+Egul4uOjg5WrFiBxxOcCKa9vb1PzdJvoWXSpEmsX7+epqYm3njjDS677DK++OKLbgWX2267jRtvvFH7u7m5mXHjxnHaaaeRlJTU39tHFLfbzaeffsqpp56K1drVXC2EYPffHkRyekgXVuYsWDAEtRwcdj1yJwBZ2dkcO8Dncowbx/4339JiCubNO4X4+Phe23c4s2n1Upyfy3VOOXQWCwbQRjsfuVsuJzmZ2RF8h4Zb+5b8/hYAZk6fweh58nNu37tR+z6cNty0TXY9lSSpx+u3vfY81DQR0ywG9FsFMhzad80OeSLq7fn7ghCCPY8/ga+1FWeLhcKjC5g1b+jGtEi1b8XK3+DpMIMkceJll2KKjw+rnG2fvwmA2WSK2Ds0lHTXvlv/dT8AiYlJHBXwnMV3/haA6dOmUXBK/56/qboKNQLjpJNPxp6RFfK89tRUyj/4AEejlcOPOIK8omkhzxsJBLZvbUMTbYqcd+JJJ2HJyR1Q2VVffUXL/97H2WglJt+ii/dxZeXnmtAydcECEk8/vcfzu3t/676OoYH/AHDGGWdgHqDU4nA42LdvHwkJCdjtdoQQeFxDY3GxxJg0y1GH07+ID1wrO+qqyE5PxxdvIzavkMzMTEwmExMmTAgqSxXAxo0bp303c+ZMzp5zKAt/sohrbruD7xecSUpKCv/4xz9Ys2YNa9eu5ZBDDtHKOOGEE5g7dy7XX389GzduRJIkbDYbNptNK3PKlCmsXLmS999/v9c1vcPhIDY2luOPPx57pz16AgWzHtuoT2cFEBMTQ5Hilzp79mzWrFnDI488wtNPPx3yfPUBO2O1WofFQgt6ros3PQ7LgWYsNS3Dpr6RxGQyDfi5zJMngyThdZjxOExYOrXncPqt+4rZZNYG2NipUwdWf2UQkiQGpR2GW/uaLBatPpJkUv4f3rNL6kZiktTj9SJb9o82Nbkj3hZD2b7mgPk4EnWwTZpEx9q1OButmEzmYfHeDLR9fY3y/6XsDGwpKWGXE+hmMhzaJVJ0bl9tR/Fuxn6zpf9zgiXAsm61Wrq9Pl5xM3G3Woh1u3TRzlarVXl+uWWtMTFYBvhcsZOn0PK/9/1ujzpoJykgw1r8tOl9fqbO76/FrLxrQv5uoEKL1+tFkiRMJhMmkwm308u/bvhyQGWGy/89cgJWmxpq7n8uUzcbaqp1DnVO4PHA70wmE7+85BIuuv561q9fz0knncR///tfTj31VGbNmtWljBtuuIFLLrmETZs2ceihh2rjZGCZcXFxuFyubusZWJ6kzOWdf/++vg8DTh7n8/mCYlb0hi9ZlgZNLY4hrkmEUf1CpYHnDzTFxWEZNxbQT9ICgfAH9w4kc1hwoVHBUCR3ETHKgKezNtYCTSPUpmowvp4yIvqUDH+mvLEDK0hVLujsHerMUD6eJTUVyaxs3NvUN83qSEAExRwMfE71JxfRTz81N7QhvCaExURMXviWKMnsb18jED88JikbZZeVlQGwc+dOpkwJvdmnenznzp0hv1+7di2LFy/m5JNPHoSadqVfK8zbbruNM844g9zcXFpaWli8eDHLly/n448/Hqz6DTnSQB0mhzsRWmHGFBXiKduHo9GKTQcrR8nt1lI4DyxzWADRMsAGCMKqVibcR+/rZbrtpVrDReYJ1XSqsnJBH++jV8nwZ8ofN7CClPdWH63SPVIvr1RYfTXgGt3PmSEICpTuRdvcF1TlgqvFDB59vJHmGllI9aXHDTAteYDQMsA6hcISY+L/HjlhEEru2739DN7vrg0BAeu//giAmzZtIiEhAa/Xi8vlYuHChTz++OMRrmVo+iW0VFdXc+mll1JRUUFycjIzZ87k448/5tRTTx2s+g0j9DFwaPjf2ogUFzOhiPZly3E2WhlYYtZhQnUtwieBBaxjxgysLK2NdfYO9YOw37L+rqD01sRCWQxFytIyWX97T/maFKEld4CWFgVJdy9R/5B8YTx/0DV9fFn1pMQRAUJLBOZUS0YGIiEOqbUdGkd+5jAAc1MHAL5RA0zApKUmH2CFui1ewmrT2z5CwWwvKQEgT9lweOLEiWzbFjp5hHp8orK1Bcix7e+99x4Wi4WcnBxiYmIGucZ++iW0PPvss4NVj2GLjobVILSJOWJCixyU5Wi0kqiHycgpB7FJMQw4paKGHtqlJ8IUhGtanGzc34gkwezcVJLj+r+gFnrdcCzC74xN6adehxmaWyNa9pChbDsjxccOrJyo0S2EZ70TQlDR5KDZ4SY3NY64GH24AkcCESi0RWgsEmMykXaUIqn7Ko101CYyD9ASZQq0DgysKD3iE4I2p4f6Njlsw9spXbLP5+Opl18mf9xYZs6cCcBFF13EHXfcwYYNG4IC8X0+H3//+9+ZOnVq0PHA2PaDjTHq9IZO10IaETLl2ybJiyFnswXhHfmDbER9ZZUm9oWjwdQxLQ43972/jTe+349XaRurWeKSI/O45fRJ/VoU6babRnhWNsXFQaIJWnyYKqojWvZQM3DBVb7eJwQuj48Yy8DdfPTC59ur+fNH29le2QKAzWLih4eN4TfzJ5Ma308t6+AqyocESUTWPQzAl5OBaUcpNHh6PXdEEKGxLFCJWNHUQV5aeNkC9Uizw015Qwcur4+qZlloWb9rP/bkNOx42LJlCw89cD/fbd7Mm8/9U9sr5YYbbuDdd9/lrLPO4qGHHuLII4+kqqqK+++/n23btrF06dLIKW8HiDEq94L2M+lNpNfieyPzIpqSEuUPPgmG0wZNYeK3REWiLLmQXVXNvLehfOAFDnOkwEm7m4GussnBOU98zavf7cPrE0zKSqQgIx63V/DCN6Vc+PQq6lrDSfCht34a2UB8AGKUgHO3ThZDCgNtolibLCR7vD4ue+5b2l36ah8NtYv0QWElhOBvn+7kZy+sYXtlC2aTRHKsFafHx3+/3cdZj33F7mqdWOwGgAgQWqRILauy0+Xymke+EhCI2Fhmt/pdt8554ms2H2gaWIEjluC5rrbVSWltGy6vD4vJRKJdHs9+ftE5TC/KZ8bMmdx6661MLizg2zff5IS5R2nX2u12li1bxqWXXsrtt99OUVERp59+OmazmVWrVnHUUUcxXDAsLb0gdKjD9QZq/KMwaLIvBE5CAyXeZqEdWZa7/pV1xJhNnD49O2LlDzuCBJWu71dTu5ufPruakpo2RifbeeSiWRwxPhWAFTtruP7V9Ww60MTlL37HHV6dLhz7SGO7E3mbPqOfdo9Abp+BtVFqXAxVgAmJlSV1XPWftTy/6HAsA3VnGWEI/GPfk8uLefSzXQAsOjqfG+ZNJCnWwrd76rnlzY3srWvnkn+t4t/nj/cX0FdLg44UgaU1LWSof0RqTo3xp/Y18KPtpC6gsd3NJf9azXu/OiZqLS6LFi3i3Asupqxe3tclNT6GnORYTDlT8fl81LW6qGjqQAAZiTaSa/YiOTxdXqu4uDjuu+8+7rvvvh7vd/fdd3P33XcPyrP0hegajQ0A+PunO/EpE0ZSbOTD5nUxxkbSO0yZxPLTYvEJuOm19eyqaoncDUYQQgh+++ZGdlW3kp1k5/VfzNUEFoDjJ2bw2lVzSYmzsmFfI9/srpOv663gYWK6jiQfbqpgW7mcccdijvzzCX30VL/VeKCLRWWxnRJnJdZq5stdtfz1k9BpPkc2coN1sbJ3+vPzHdU8+PEOAO5cOIW7z55GcpwVSZI4siCNt395DEWZCVQ1O7np9Q3dFaN7yhs7+MN7mwOORFsL9JOBjtUB1x8yLoWmDjdX/WctHS6dWKRU+jg8O9xe9jfISQ7SE2yMSYnFZFL3hpNIT7QxVkl+UNPi1NZ+IxVDaIky1u6t54nlu7W0l3G2yBjbgibACFopho5IuofJzByTzNyCNNpcXq5/dT0erx7aqSs9uUe8umYfH22pxGqW+Oelc7TBNJCizASevOQwJAk83pE9wIZLbauT29/ehKRMMIOh7Q8rS9SwJjIxLVazxIM/kgNUn15RzHel9QMsd3jSkxdBbauT3yiCyKVz8/j5cQVdzkmNj+H5RYeTEmelXFk09a8CI//9EwJ+995WmjsCMnxFKKbFf5PIFjdkROr3Dkip/8xPZ5OeYGN7ZQsPL9WjgqFnhBDsq2/HJwQJNgujk+0hY09GxceQkWhTrjnYtYwshtDSC9ruwSP9l0aWyG95YyNCgCVAEo8MOnuVBiEQ34Tg0R/PIjnWypbyZv755Z7I3WM40Ev2sPo2Fw98uB2A38yfxIyxyd0WdXRhOlcdXxjpGo4Y7vnfVhra3VqQc0SDIHWnCFYeaKAb5QZkDztzZg4/mj0WIeCWNzficOtHi9vbPi0A9y/ZRm2ri8nZidy+IPSmcwDjUuP447kzwqvIyJ9S+a5WYsWuOmIsAY0ZKaElAhs/DyekEJ/CKidgLMxKsvOnH8rv3z+/LGHj/sYBlT286L2D1LW56HB7MZskxo2K63GeyEqyE2sd+amc9dUrBgE9xbQ8+9UeimvaSE+wEaNqbnXoVjP8UDdYFGQk2rjrzKkAPLx0JxVNYWgohznBIS3+VEEPfryDpg43U0YnccWxXTW3nbl+3gSt93l7EyJ19B6vLK7jfxvKMZskjitUXOf083iDQKSsouq7Kpd358KpZCTaKKlp498rSwda+IihuKqFt74/AMCfzpsZFPgcioUzRwe5eEbLsqLV6eGdvfKzXnF0nv8LHY1FESVSSUU67dMyb2oWZx+Sg0/Iyp6IZv4cFnS3CyxUNTsAyE6yY+0l26FJkhgzyp8WfqT6eUTH6GJAXauTp5YXA7J/ssYgDLBCD25PkXyETk38w8PGcER+Kk6Pj7/p0Wc+xCslELyypgyAe8+ZhrkP8Qd2q5n507MA/EJ2b7ce4ROWEII/fShv5nXxEbmkJQzepl26mdyF+r/I+coDJMdZ+c18eTPOx5ftprHdNbDyhw0979H1ypp9AFwwZyyHjkvpU4l3nDFZ+9yXvh1UjxHKc1+X0uqWyEuN48eHB25saggtg0qI9/b2BVOwW02s3dvAx1sqwyp22I6H3bxOHp/A6xPEWs19TjseF2PR+qd5CITrSLSxIbT0hk42HHts2W5anR6mj0ni7ENy/F9E6MU1DUKg8NAyCFKL0mElSeK2BfIk/8b3+9lW0RzBew0HQr8LQsDCGaM5PD815PehyFViXmw6MGv3hQ82VbBhfxPxMWZ+fcqEAO2k3vpX5BmwC12I6887bCyTsxNpdnh4fNnugZU/QvD5BPExZm45fXLvJysEanD7/DuM4NjH6hYHz369F4CbTi3CGvDIRlftjvA2Ne1CCLe57GQ7VypxV3/+aEe/4kWtVnkz4/b29oHVa4joLo6lO9SkLoOR3KU31DZW2zwcjJTHUUBdq5OXV8sD7K2nT1EyS2gpd4auYsOayMe0BMbJzModxcIZo/lgUwUPL93J0z+dE7n7DTXdDKCSBNfNm3BQ7znSePGbUgCuPL6AjEQbzcNV+6dDtIk/oM3NJolbz5jMoufX8J9Ve/m/EwrITLQPUQ0jRB/WjouOySc9IfKZJXu770jhtTX7aHd5yUsQnD4tC+r3+b+M2Fikg4YKxYDdw+Q1iwC5ryrtfdUJhSxeXcae2jbeXV/OebPHdl9GAGazmZSUFKqr5c124+J6jg05WDjdHkw+H/gkJIfDf9zrQ/L5cHt92E1ezMKDw9H3mDuX14fP58Pj8eJyuXA4HJginTyiE0II2tvbqa6uJiUlxZ+2OgwMoSUKqGp24vYK0hNiOHaCvGFVb0HTA2IEa9A0IrqpX9fFEMANp05gyeYKPt5SxfbKZiZnJ0XiZsOAwKBUv//xwhmjmZiV2M+yomvR3qRkITpCtUYNoqVl6KflyDLglMfdWNVPmJjBrNwU1pU18q8v9/QYmD6y6Ea5gODnfYg5CyJwbNPbixWCxna5nxYlCSRJQgRuqDwMFrzDEu0ViWA/DRBaEmwWfn5cAX/+aDtPfL6bc2eN6bOrYna2vG+aKrgMB1xNjUht7WACq+RfqjtrqjC5vXgsJkxOL6VN/RMAvLVV+FxeTK0xOG3NxMbGHjQhLSUlRWvrcDGElihA3Y/BFOrFjNTLGmix0cM6M5LPoDVxcKFFmYksmC5bWx5ftpvHLz4sgjcdArSdtv2HvD6h/fnrUwbJyoI+XjkIkbROdSmM5E2UPq+XNvMTmZTHnfevkSSJX588gZ+9sIb/rNzLtScXkWgP371h+BC6vQ4Zl8yoPvrIawS9uH37HYZtDEE4iEEUWnTTTpF6jsD2DS7zp3PzeHpFMSW1bXy0uZKFM0f3rURJYvTo0WRmZuJ2u3u/4CCw5dm/Y3/jU0gxM/6///Mff+QerDuqqR6fzjFPvNhvgaP6qbtp2VRDyinTWD/rdI4//vgBuWv1FavVOiALi4ohtPTKyA9qCams1eIrImMW1J9uKXK/d0+Dyv8dX8AHmypYuq0qYvcbcgKed0xKLNWA2SyFYWXpzy119gZqw44v+G+DrkTKatwpK1EgJ07KIDnWSlOHm/JGB5OyR7LQEtqKLEnyN5fNzT+o1RiJdKl6kKUpMp1V6G1MUxADfSzNlUnqItAl2CycOXM0L60qY9OBpj4LLSpmszkiC+uI0N6KqaICXGbsdr9Laoa7hbaKCiZNTiA2NraHAkJjbajBVFGBtXUMHo8Hu91+UISWSGEENPSGjsaN4A0gB/FGI3gy0hgM7VaIzfyykuTByKujjf4CN5e0KVm/LIPsM6sX/OEGnVwKB8WNUz/vHBDRnba7fiVh1UuykW5+dvXpEiK04bCe6eI9PAiWFt3JLJHaW7KXRZndMkyEjoHSjYu6uieQrZcUx3olOp86ygi17tHGj8FYTOphMRQp/9ugIrtvFz00mZ8QqXTCfb7+NswIb0ehWUCD/zbond4WM70XEDr2rDM99eORRXf7P4TzfOFYGkZuO4rO1qqANou0rKEX2UXqJdV2nzEFzik9zKkj+P0Cel+DjPDHCxdDaOkrI3jxoHZeKfigQoSGxADhZ+S2lB8RyUD8HhZDetKmhXylBvqAff4d9NGQXRI7Dea4M4LHtJAM+iugj3dMo/PjDODxRBjuUSNZID8YlhY9bWwNgzTc6HpODd1gA3WFHsn9DoyYlt7RQQfwW1pCBLANNOMOocrWA4OQ8vjg3G3I6de70FwBm16Dyk3y32Nmw4wfQXx6cJkRrN+wpnNfNfZp6TPSQK3GfbW06Kaz9uOdEgLKVsKeFeBohrRCmHI2JGQMXvVGACGVC0ZfDYnU5UO4BQWkPO5p5hzp/TScgcbngx0fwNb3oGk/xKXChNPkOTUmLujUkbpmM4SWKCDkqz8I7k9+dJTyOCIobbznK2irDVqQj8xhoytCiP4FRAsB3zwGn98Png7/8U2vy8dOvQdm/2xQ6jqc6dKEEU293d3dRjgRGsu0pCQdDfKkP/XsTt8PqPhhg9Tfd6p6G7z3a9j/bfDxj26D426S/w2kHiOQztpqYcS09I7WZgN8MDVQXiC/g6f/CSz+bHcjdTHemX43V8NeePPnXfvp9vfhy4fgB09D3txIVnFIMISWXtCDibZHc2DEYlr05mkYwcViQiZQDc4W+OdJcMkbkDEp+G4jeALvTNCkESrds9cD7/0KNvxX/nvMbFlrK7yw+W2o2gTv3yAvlojv401D3GcE0uU9cMs7CEdyFBIRLm/YMNDFSnqR//NrP4VT7gq5INdNV+2mvYIW4LuXwmuLwNUC1niYvFAez0q/gor1sPx+OPAdnPTH/t9fL+0IsvVJxdhcshcG+Fy2gCyU3z0rzxOXvBZ8nJH/ekm92ZICB6KKDfDSedBWAzEJMOdyyDkU6vfAd89B417499nww38OdrUHHUNoiSK0sXT1M+BxAjFI9uSI30cPk3pEnyFlLLAZYlOhsQz+fQ5c/jGMytPpvCR18xm5YT+4URZYJDOc8Wc4/Of+l/OYG2DlY/Dp7+HbZ6A2J3Q5Pd5z5BKkXNuzAra+DyR2mZANBgFlLBQpeUA9fHYvWGJh7i8Bvbxh/WDvSvjvxeB1Qv5x8MNnIEnpj0LIVtH3fg27PoGGGv91vaXR1+KoR+5EEdRP22rg3Wv9X+pE0x95IrxPi2QGWzKUfQP//TH85E2w2PTTT7ttrk5P2FAKL50vv4dZM+DH/4WUcf7vj7wK3rkatv0P3rwCmjO6ljGC0Jt6POL4f1odDLASULwMPvqt/2mSc0Jf1E+ComXEyHcPkyL4e2uWh6OugYwp0FIBL/8IXG3aOSP37ZIJ3luuhwFx5ePw/YvywuaCf8MRVwafbzLBMdfBOY/Lf7fXK2VGvMrDGlvrfnjtMoTPIx8YlR+xsg9KkP9QEKnFYvpEOPlO+fPHt8vWhgBG8mI7iJ7aq74E/nuRLLBMWgA/ecsvsKjXzrwALn0XrHGy1SWKULuOCS/mt38uK6MU9OKeNGhEsnkufVu2LJR+CR/dGvTVSPde6NM442qDly+AtmpZYPnZkmCBBWSF149ehOnngc+jWe9HqnAd1UJLc20HwtvzOaLLh5GH2nezfLXwxhVyppPYVPlgpF7ckfn+d4uI6L4pSuPY4uGnb0NCNtTugA9uQhrB71W3dPdOla+DpXfLn0//M0w5s/syZv0ETpAnoaak8XQIWy/3VP4/wttTCLDioeDzX0JHPaTkyV9EMjW5zvqqxkCTigSm5z7uZpi9SP7jrf+DpgMjdY7vPz4PvHklOBpl183zng2KGQgi90g4/zkA2uKyaUrK78eNRm5nVReU57S/gWnv17LrXITx79UU8aKHhkjFtATG+42ZLS/IkWQ3qI2v63B86+GBPr5DXkskZMMlr4M9KfR5JjOc+xSMPQIBOGwpCG8vi99hStQKLTX7WnjnoQ3UrYvF5x35loGeEYDgN+4n5YXQ6EMgsa9uN31EbzN6JLU0gVmJkkbLk7xkgg3/JWbn/yJ+u6EgKGV+qAW28MlBgj4PTD1HtrD0xgm3UG8pYu1hN/NZ4R//n72zjpOqev/4+05udyfdDdKCiiBgI6LYiqJi+7W7u8VGRbFRQUVapLsbdtnu7pq4vz/O3Jk7szNbrBI/P68X7O6Nc8899zzPefqQl1Lu+drTZP7JyNym/R3f4n3gHQxnzBQn2vH9TvGp1hjtVVTEKRdLEop1VD+oKYZF99qJ9FSn1WaFx21fijwVY6AQCF2qDjVC90kw6Hp2DriXHYMeZMvizHbt7skIWYb+UjITaheJA+e9JH62J522U2XP0w2NPFldz4WxD4nfF/8P34Zi4HSiUw8oSYUdX4rfL/1YyBZNQWeEqZ+T5ncWG0e8yIbqcZyKQTH/b5UWU50FU72FukIdW/9IO9Hd+Uchy3CZZh3DLLtAaxSWM3tcqGfGmLQtnz8/3Mvq745QUVzr8Tq3Dzxd0My6YWqwsPb7I3zx4Dp+fHErWUdKPV5rd1d3GCUsuYD/qkcJoKq9entSwGnIlPllMUFxMvhHwwXvtGxx12ipO8ORCL30k33U15rbs6snHTpY0rlLt0D8MfkNobhAk/OwpqKBncvS2boolYqi/6d0isoy7QHHdhbww/Nb+ebJTexemdE4fMS1YpveS/BKrQGSlnOedX37d/oEohEJKn+n297zgrcah5p4wrinMRlE3tXOlTmk7Stq9pZTOXxHYzXxqv4zNMhY+0xF7jZRnGiCr9VWNbDup6P88d5u9vyVibVdvfknP1pa8ri6rJ6/vj7Egtd3UX7EgLnBxSPgrjT5mIeEMbaunAlpb7RXl08ONKJT24FaW+j0iDuh89ktaysogcrwYQDkmBOpSPLgQT2J8f9WaYnpGsS460UFp32rsinO9iA4trB2/8kMyVTNk/p54o+zHoGwrs2WUt32ZyrLPz9A2t4iDqzN5udXtlNeWOP5GWqr0Ck8Vg40WwUe2Sqz/LP97FuTTW2liaLMKv54bze5yWXOF7rbFX7MAxDWDU1NIY/pvmvPjp8QCAGk8WSyWiEj7mwqvGLFgUmvidrxLYXKyltd3sCOxWkeLjw9rJK3meZhkCyUJ4wXMcgKPAhDZfk1/PTiVjYtOMa2Ran88PxWclznXyOcHmPVCE2sZknb81n66X6Ks6soL6xlw8/JbPk9xf3Fav4V3k0IRMD9ls8JPB0MDLLyQ5WxKcsc7DCdfb1miNfvNgn6Tm15m1q90waL639KwmJuxox7Ci8TQ4r/oIcmk0rJH8v4F5st915XbeKX13awd1UWGQdLWD8/iZVfHjylFbdWowXhYTUVDfz6xg4Ob8ylMKOKyhQjyz872Hw0jFYHF38AGh09Sv/mXM2OU3l6CXhwgxTJMezpO4sqbTgExsPZj7WqWUm1plamGijL9yzXnYz4f6u0AHToH4ZXpAlZhp3L0k90d/4xGCrSCZKqqcAPRt4tDtoYiLukwZykUrYuSgWg39lxhMb6UVtpYsUXB5vI9TjNBKEWLCaHNuWStq8YrV7DhJt706FfGFaLzF9fHcJiclO3X92mzggXvgfAlbrV9JOOtWfvTyjUU2rzoQiSu0xlT+It0Hkc9LzwuNretyaLumrTcfbw5EW0XABAYa/rbQNpo1M39GUxW1k2Zz/V5Q0ERfoQkeiPqd7Css/2U1d1+o6RR3gQGGsqGlj97REAeo2OYcSlnQHYsTSd/LQK1e0eDFSj7oHwngRRyX26n9u/3ycB8lMryI48k8KIQVQTCpNeaX0jqqph5YW1JO8oaMcenlwIacgGYIvXaPAJpTkNbM33RygvqMUvxMiQ8zug0Uokbcvn0MZczzedJiGvjdDEa6378SgVRXUEhHkxcmonJK1M1uEy9qzKUt3vrGzbEdVXeB2AJ3TfoLU2tHfPTzhkWWY1l1Mc2pujvpNh4stgaF0+lSyrDcwSB9c1MQdPQvy/VloAAjqLiZ20vYDKkroT3Jt/CDbCbpD0wiIhDoofLoxRlmU2/JwMMvQcFc2ZV3Rj8qy+6L205KdWkLzz9F2I3MM9hzU1WNi8UCgawy7qRNchkYy/qRc+gQbKC2vZvza7uSYgcQT1vS4H4HH9t6eP1U3ZsViWScoJAqDKO0Z4Wdq4EAdYSwiN9cPcYOXQhlOLybYF9o0OZfd0CnBgXQ5FmVV4+em55P6BXHL/IIKjfKgpb2DH0rR/r7MnCSQPpXa3LUqlodZMWLwfY6/qzqDzEuk+LApk2PBzkroB2y8udKgziNLcwDXalWirCzktoJpThzbkOI53Gndc1er6jhWx9Xv+ymyap53C/E6pLmmleTrNSykneXsBkkZi0q19GXZhJ4Zd1AmALb+nYKo/NROiW41mcs9ykspI3lGAJMGk2/rSZ2wsQT3rARH5YTdWSU1EdYx5gEp9GB00+fQvWtS+/f+34SYapiC90v67FhP0aKKYTTOICawiqE8dw6d0anMbJwL/75UWQ6CV6C6ByFaZI1vyGp0/LWwddjejGwuFC5PNOFBCQXolOr2G4RcLi2RAqDcDxycAQgBoTrg+HUoeNxc+l7Q1n9pKE/6hXvQ7Ow4Ag5eOM87vCIgFW3Fpe7TgAjXD7gVgoJTU6NypBKeKx7ZBU1ux/evznDfva3XDMv3OEeN8YH22m3yE06PSjuRiTPBEp2aTxa6YDLuoE76BRvRGLaMu7wrAvtXZVJfXN/2w04FOoclvXlvVwKFNQskdNbUrGlsY6/BLOqPRSuQml5OfapunTSnUncZSSgA6yYq2rqS9en5CodBpQ52ZpO0qY9SwW1vfmIoee4+KRqvTUJhRSVFWU+F0pzCxuoY6NaG0bPszDYDuw6OISBTVnfqfE09AmBc15Q1Ne1tE48ff35MJHshs13IR7dJzdAxhcSI/yifOREiML6Y6CwfW2QyBTdGp0Z8joeMACKpvLM+d6lAbFwwxPdtoBBTzKcDYgF+8yc4TTxX8v1daALoPjwTg8KbcRsKQfFqoLUp+huR6CFcOsm+NcMP2HhOLT4AjSav/OfHojFpK82rIOVrW6AmSRtueHT7xaEIxk2WZvX+LCjn9zo5Dq3OQUY/hUXj56aksqSNtb7HrnY0bs20aeDrMMkf1MPE2hzc5Fg0dx2tNlOgyOAKdXkN5QS2FGZXN33IKQrKHgzVtwU3ZVUhNeQN+wUZ6jnRUjUnoFUJkxwAs5iY8UqfFZHMDN56Wg+tzsJishMX7EdstyH7cL9hItzME39+zyrnalSejjIN/ntrKnn2nbdtwpe0rcrb2a/XH1b6Xr44O/UIBOOrGEHg6wXWmuJJWRVEtGQfEOjB4YqL9uFavYcC5whC4f03W6eNlbxKec1rKCmpI2y/GaaBtXMC2HdA4kQ+59++sxrktbsZNlhRZ5NSmU1fF2GK2OoVcypq27g0vOf041fCf0gJ0HBCGzqilvKDWyToMnLIfVg2FIbpVwFTCUFVpPRk2xtH7TOdNJw3eOroNFYv8/nXZnP7wvIgUpFVSnF2NzqBxEhgBdAat/djhzYrQ2LJiDqfyuuXad9kqk7q7PcNoZAxeOjr2DwPg6NZ8t1e156agJwQuip+n1zlos7j1HBXjpDRLkkTfsWKRP7A++/9XhSI3oa4HbYpbv7PjG+Xv9TlLeO5SdxfSUGvGQafumz89DFiNXy91d/OVvloDCYluQ6MAUYGy0Rw8LYbRJhC7etFd5pjiRYnrEUxQhHPp6O7DotA3ZQg8PQZKBc/FbQ5tyAUZEnqHEhTpPE6dB4Xj5aenpryBzMMu1TlP5UWzlcg5WkZ9zeldPbMl+E9pAfRGLR37CsuQZwZ+ChOHO8J2E/50ZEsusgzRXQIJjmqc3NV7tFBkUvcU0VDXBPFYT3ELBzT5uZO2C4G5Y/9wjD6NrZLdh4kFO31/sUiIdg31UUERpE55YVsNSUN+WgU1FepEyPZ5v642YSh5R4HLeJ4eC7zkao10IwyVF9aQfaQMJBopzQCdB0dg9NVRVVJPTlKZ54edZgu+a6REYUYlFYW16AwaugyOaHR9RKI/wVE+mE1Wju0qaHGlyFPdKi7ZWb+ExWQlfb+zR7g93i+xdygGbx3V5Q3ke9pf6VQeR9dIBTd0KltlDttCE3uNdjYCgjAEdh0i5uXR7e6NMM7POsXhIYJOlmX7muqOn2l1GrravKJHNuc5Gx+azJk6rt6ecEgu75ayx8UIeIq/X1vxn9JiQ8cB4QCkuk4MG07pncvdeVrcVA9TXI89RrjfpCg8wZ/ACG8sJmvjOvxqZt0efT7R8BQiYpVJtjFYZcFxRWisH2HxflgtsrMw1Nwj29bTkxIKHTn2mWybUuEqQMX3DEZn1FJdVk9Rpipe/vTQWRzhYfb3aWxcUOg0vkcw/iFejdrQ6bV0tvGz5KaEodMOzpMgaZt49w79wtAbG4evSpJEN5uB4ejW/Mb7tLjgdPG0qJF5uARTvQXfQAOSte1WXFltqJJE+FNiH2EI9LhnyynN8Jy9Bo5IHsccyU+roKq0Hr2X1u4hdkWXIUIYT9lZiMU19On0m25AYzrKT62gsrgOvVFLos147ArFEJi6uxCTujKnu/Cw03DgZKtM6h5BR17a46yKdiobC/hPabEjsXcoGq1EaV4NpXnVjhOnQdlB2W1Oi7PZo6KolqLMKiQJjwxWkiS7xTJ5e4Hryfbt9AmH+0T83GNlVJc3YPDWkdDLPYMF6DxIjJPCaNRNqqEM26nuaZFlq8NHoNWQYvNYJiS0T66T8hl0ei3xPYKBJoShUxrKvLONmxvjgjK2nQa6V5oBugwWwtCxXYXN73FwqsPOyhzLmWyV7cpdV5tg6A5dbHSac7QMk7mFxoVTfNG3Q5Ls/Klj//D240G2udqxn1hHnHjgaYJGHlE3boQUW3hshz6h6PTu+WBstyC8/fXUVZvIdg198lAN71SF1HiIAHXkQhh6g/txikj0JyDMC7PJSlZS0/mMkus3OVWhioYpyKikuqwevVFLhPfpmc/ZUpxeVHEcMHjriLMJQ6cdk20yPEwQuPLO0V2C8PbzvEuqIgxlHCjxHCJ2OizqHt4h1ZZc37F/GFq9Z/JRFL+sw6WYEQlz1ip3lXQ81Jw/hVFRaqEsvwaNViI21vMYybJM5cqVZN11F8cmTiLlwgvJefxxavftU13T+L4ONmEobe9pRqeoZ4NrSKH4u6q0joK0CmjCuAAQ2z0ILz89dVUmst3Ey4vG26PHJxFUwlBRVhVVpfXojFoSenvezDQo0ofgKB+sVpnsfDFX648dc/YcNHrM6TFwsow9SVyhKXGi8bXm0lKK584l6667ybhlJvkvv+xEp+7uSegdgkYjDIFlBe42sDuFx9E1esFlPZVl2a60KFEc7qDRauwGrmO7TpNS2h7R2Hgqy7I9JN9dCKcCSZIcfP9gmf24pbxx6KF8mhgC1VBCOBN6haCVPPMmU24uec+/QPI54zjUtx9JZ59D7pNPUZ+aqrrq1DYw/6e0qNChryCKjIOuVZ/gRDNYWZbbnFSrlCCWnUK4nJmswmA7NcFgAUJjfQkI88JitpJ9xGEZck4aPH2YhSsyD4lyp00JQgAh0Y5xKrCIMS3+9FOK585Ftjgq9SjjdmqzEZtwbfvs+Zminn5050AMevdvZi4sJOP6G8i68y4qV6ykIS2N+qRkyn/5lbTLp5H7zDNYG9y7wZWwk4L0SkdZ35PE09dgtpJTVtvm+x3hYeJ9rNU2r6+dTsUCH9UxEN9Ao8d2NFqNXalpHMqp/HJ60al6Cih0GtctyKOVW4HdwFAojDWW4mLSpk+n/tjps+GrE2wCdq3Vn6rSejQ6iZhuQaiCnZwuL50/n2MTzqPglVepXLGC6nXrKPnqa9Iun0b2/f9zKzgCGH30RHcNAiB9X+M19UQbaepMluMoVOFsTJDNZtVfUJpbQ3lBLRqdRGJvzx55cHik0vcXn/Ax+VegotPygloqS+rQaCXiejS9pnbsL9bR9EPlyLb95o5NnETZgoWemj+1YVeMIeuw4GfxvVRj5DJVyhYs5NjESZR++y2mnBwwmTDn5lI2fz6pF11MyVdfOc2vk2TJbDX+U1pUiO8pJkRucnnTieb/MupMFs55cw1DX1rJi38epLiqmf0XPECxcNTs2IG5QCgpklZLXZWJ3OQyoGnrLQhhKrGPg8m6fc5pwHflRu5/sbN2sW3fgfhmGKwkSXTsZ1NWogdj6CQ2cCp45VWy7rwLa02NciEAGklusQhZVFXPC4sO8vCv+zkZi0PlZQhlI65HiNsVpD41ldSpl1OzdSuSlxehN88gYe5c4j/5mICLLgSg7IcfybxpBnJ94w1ffQONRCSKUtGKpfhk4b83f72dka+s4pw3V/Pb7rZU77Jdr5GoT0mhZM7nAEheQkFRcoU6DWzauAAO5a4xnZ4ee9q4QlaF0yhKi9Mi7wGKMJRTqCXoxhlofHyo27OXtGlXUL1xo6N9u/erZeF2dSYLs1clceWnm0guOPlCOsoahGU7unOgLSyncfW1vOdfIO/Jp7BWVmLs3p2IBx8k6vnnCLjwQtBoqFi8mLTpV2HKdewfoRaGlDmYcfDk2ttma2oJvZ9expmv/c3Ha45RZ2plSXZV6I61poa8p54Wf3qJHDMlaTq+RwgG76ZL08Z0C0Jn0FBdVk9xttobf7JwtXaCa/ECHHQa3TnQbd6ZGtFdAjH66KirNqN/9E2MXbtgra4m99FHyX/1tcZVUlshiKw8mM9lH23kgfl7WnzPvwWzxkh+iqhqq8iorij84ANyH30Uub4e7yGDif/kY7r8tZKELz7Hd/RoZJOJ/JdfoeCVVzjVGf9/SosKgRHe+Id6YbXIjaruFFXWU9NwYhSZjJIaUouqKapq4LN1qZz1+mp+35PT/I0KVCEmNdu2kXHTDOTaWrz69cOrd28yD5cgyxAS40tAmHezzamFodPWMtRYZ7Ez2LB4P7z9PYfQKVD2KsjOtdLx99+JeuYZJIOBqr//JnPWHcKT0Mp16e/DBUx4ey1z1qfy664c8txFXfxLKK818cmaY6QXV9vnmFXSkGfztKgZrLKQmHJzyZgxA3N+PoaOHem44FciHngA3+HD8Bs7ltjXXiN+zhw0fn7UbN9Oybxv3T5bEUYzDznHgZutMltTT5yAdCRPLC4phdXc88Nurp6zher6lvMNezR2fhHp11+PpbwcfVwcYbfdjqneYudLHfs1bVwAITBptBLlBbWU5Z/AifIvQfFamhos5CYL67+nRV6NiA4BePvraaizYL10Bp2WLMZnyBCs1dVk3j6Lmu3bAWdPdXM4ml/JRbPX88byo2xOKWHloYLmb/oH8dP2TDYdc1Zey0xCaWlk4ZaFwlLwyquUfvstSBLh991HxwW/EjrjJoIvv5zY11+jw48/oouKoiElhYybblY14BgnZfxzkkqxmJyVvbSipjae/GexJ7MMi1Umu6yWV5Yc5rx31rZKsbTTqUUm9+57qNm2DcloJOppobxk2pS0Dh4Sy9XQ6bX2b5DmxiN1+qCxrNAa44JWqyHB5rUq8u1MxwULCLvrTgBKvvySwjffFBe2gk6r6s3c9+Nubv56OzvSS/l5R1aL7/0n8POOLJ75/QDV9Wa7bFXm0wWrVSYgzMtJPlNGs+Trryl6fzYAYbNmkfj11/iNHYs+NhbfkSOJ/+xTIh55GCSJkq++pj5FCRU7NZXi/5QWFSRJIsFGPIplSG0oXXHwxFTiUesF3SL9qKw387+fdmNpoRVXmfzmMsi8fRZyfT2+Y8eQOPdLJJ2OLIVxNOM9UBDbLQitXkNVaT0lOdWNLzgZzf+thRtlzB4a1gIGCxDdWVjQaisaKMmrIfjKK0iY+yUaHx9qNm8m99HHnB7TnP739aY0bpy7jZJqR9jUiUyxXrgrm5eXHGbs66tJKxbzoNIvAVO9jNFHR7jNG6LA2tBA1p13Yc7JxdChA4nfzMPYsWOjdv1GjyLhyy+QfNT1+l2qiNnmatbhEmRZtitFkizz7l9H2/Et24bJfUW1m00pxWxNa7kSJSFjqZeof/FdLIVFGLt1o8NPP2Ls1JGcpDKsFhn/EC8CI5o3Lhi8dUR3CQI8e0VPK9jW4NykMixmK37BxkZ7PriDRuMITck8WII+MpL4Lz7H7+yzkevrybx9llNMeHN0uulYMVM+3MjRfIdQbj2Bxp30khoe+nkv0z/bzOJ9ogSvVdJQ1iAUX4di5+hj2Y8/UvLVVwBEv/giYbfORNI4iwveffvQ4Yfv0cVEYykrc/vs0FhfvAMMmBus5LmUPv5pW2aL17D2htqvrZEgvbiG77ZkNnGHmxZkCF97jNotW9D4+JAw90sCzptAQ53Z/q4tEcZBZQh0p7ScBsupO1gsVrJsIeYtMS4AJPZWjFUlSDod4XfcQfQLzwNQPOdzSr//XnV10wNXVFXPFZ9sYsGuk2ffuRf+PMjcjWmc/946LLa8ulKfbgDEKWOk0jWqN28h/5VXAQi/527C776rEZ1KkkToDTcQ9dSTLk87NSfWf0qLC+wWXJvSYlQlW1fXH++u3m2DwmDD/Az8dOsIAEwWueUMX7ZiMUnUrpOwVlXhM2QIce++i8bHB1mW7dbquJ7BLWpOZ9AS111caxeGNKf3VBLjZIuTbyGD1eo1xNrGSVGCfQYNIm72+6DXU/Hnn9Qs+K1FbX21MY2nfjsAwFXDEgjxNdj61arXaFdU1Jrsv183ZwsAJSE9AIjtHoxGo7bkyBS89jp1Bw6gDQoi4fM56EI9WyG9+/Yl7u237H/bw+lsiOoUKBTCShMlOdV4qarOpBS6UaT/JSjf446zu9AnNgAAs6UVH0mWydkSjJxfhD4ujvg5n6ELcSzUIMo+u26U6AmKpTd9/+lXtMAOZXht8y3DPk4hLR6nhN7OY6wxGIh96028BwzAWllJ9n33I7fAYXYkr5Lrv9xKVb2Z4Z1COLen8GacSDqtUoU63/39LswWK5X+CVhkvTAuJDgbF+qTk8l/6WUAwu+/n6Apl3psWx8VRcKcOc4HVUMuSZK92p8yturz5hO0p5fyPaYMimXmmM6t74ssU5rsg19KMeh0xH0wG5+BAwEcxoVQrxZFLoBDaclPLaeu2sZXNaemJbw5KDRZkFqBqc6Cl6+e8Hj/Zu4SUNbewoxKaiuF8S5o6lTC77sPgPyXXkaT37zHzGyxMv3TzRzIqSDMz8BHVw9qy6u0O8pqxLdPK65hZ7qQy0p9hdLialS21tSQ/cADYLUSOGUKobfd1mTbwdOnE3Ljjfa/ZZOpiatPXpzekmYbENc9GEmCsvwaKoprCfUVseQSJy5xybHgSWhVjKyl1jsZmbztgVgrQRcVRez776Gxxd6WFzoS4WJsSZMtgT1W+YCbBMtTVIN3gkvceklONTXlDej0GqI7B7a4GcWClKmK6fYdOZLIBx8AoOz9j6ktERtUNhUr/9m6FABmndWZFy/pg1F34klXrZSU1Ig8q9JgobQ4LGe2hPK6ekq/+QaAmFdfQR8b22z7fmPH4jtSKOnagACnc1q9xj5fMw+VEBXg2K8kPqR56/o/BUdUoYRBK75Ra0IoG45qqMrxAr2OuPfeRR/hqKjTWqUZHHSanVSGqcHZ6HIaUKkLxFzLakXIiQJlvhZkVFJbJYQhjbc3se++izYkhPrDh6neZctNaOJ7rj5SQIPZyoD4IObeONS+fpwsMFuFT7LERqdxjYwLUPDGm8gNDfidfTaht9zsphVnGDt1Ivr11x0HXAxYdh5oK+mrftyJUubUdOplM0y2xhumLa6hYJdYB8Luvw/fESPs57IOObwHLVWa/UO8CInxRZZxKnBzWsFlfDPs/CwYqYUKmm+gkdA4P0DsMaQgdOYt+I0bh2wy4f3HXqwmiaY4XHJhFUkFVXjrtfx06wiGdGg5r/gnoRgjFdQbAqkxRoGE3VCsjFT9kSNYioowdu1K1JNPtGiuRdx/H+iFvKFxWVNPFbRK8nn55Zc544wz8Pf3JyIigksuuYQjR478U307ITD66InsKD5m1qFSp4lwwhisqtCXpg2ak6agiIp0H5Ag9q230AU7PCrKAh/VKRCDV9MJg2rYixaklDcShk4rccg23IrAGNO1+WpEaiihZLnJzuMUfO21+J07DkwmcrcEITfjxFMSRS8eEIsknRyV6BVhfESnUAw6DWaNkfIAEe6llA93RfC11+I3dmyLnxF87XUA6KMbb3gaZw8Rc1nkT+CgqGnVXgGsFf1pSBMs2XDVFLx69bIfry63hWJKLQ/jBFHS1y/YiNUs24ttNOrs6QJJQ3V5PcXZYpw8zUF38A00EhrrB7LzfNJHRhDz2msA1Cdrqc430NQEM9s+dvdIf7z0Wruh60Tm/imPDvMzcEYHMSaKcUGtAKtXFl14ODEvv9RioTtw8iRHOy5Ki0KnhekVNi+CUjHxxI+JJDlyoVpDp4bkEmSrRE1sEIHXXON0ThGmWxrypECZr5mu/Ox0g21OKYa81o6TsqZmHnAoLZIkEfPSi+hiotGU11Kwz79J/qZ4v4N89HQK93MySJ9YWhXPvntcV0CmJLg7ABEJ/nj56RvfoNMR88YbaLxb5tGT9HqCpkwBwNilS7v0+d9Gq5SWNWvWcMcdd7B582ZWrFiByWRiwoQJVFefuHCMfwJ2YehIqRMnP1EeBOW5rt6eFntaakUFJskLfAYNdDqnMMj4FoaGKQiM8HYWhpwWt9NAGHLZH6M1CYNqKEKjxWwlXxXTLUkS0c89hyYokPpyPUWH/JpkloowpHjaFGHiZBjpxFAfPr56EGWBXZA1OvwCtQSGCyaqnha6mGgi7r2nVW0rFjhJ11ihVuZsdlIZFlm1seCJVeUAxcDgfKxFsDnbNB0TnQ4rxoXweA+LlwdIkmQXTBULsMLTTpfgE7XVXD1OTe035Q6KJTPvmHPuhd/oUQRdeQUAuVuDwOw5TkwJ2dXY6dTWxxOpSNtGyKDV8NE1g7FIBvfGBVX1pcgnn0AbFNQuz/cLNhIcfXJ5EdRrqqYt38j2netDfZ0UO7VxQZlPLYU9T08JozspTFPtCNnxo77GJPabog3KnUKnqRVOx7WBgUQ/J/JbSo/6os3zHCZmp1NJUaBV3TyhtCpwUf9ouob7OYwLKkOVI6xLInTGDLy6d2vVMySbp0XSnvhojbagVb1eunQpN9xwA71796Z///7MnTuXjIwMduzY8U/174RAYeRZR0pVlQ1PDquQpi2eHw+xularbF9EWhNyIvoiOcbpcOmpW/TbE1RjazFZybFt0NdaBitJEn7BInSpodbZnaILCSHovrsBKDni1+QHtdgsQzpXN/pJ4lUY0zXcwWA7eTsWctU7RT31FBpf31Y+xPOp0Bg/vP31mOstFNc4YqJP6KJj5xdSmyy4Dgnc+TtnqkJOWgt7ToHNAnyaiECNIUmOfJZWGhcAjL5CMbaYG/PLiAceBJ2MqVoHuXke21CEIQednnjjgoNOJcL8jJiCuiNrdPjqa+3GBfWF+qgoAiZMaN0zmjkf78aLINmS2U8EnD2iyrHW5Z7ZWnA63FbjAojSx5JGorywloqi2tPHquACSXLIVsFRPviHeDV/kwoGH8906jd6FKaukYCEPtVz8RHFCKjTOhsB4eSgVZAYnBBk97Q4GZVV8zTs9qbzWJp5yCmJlscDuUG5bVOpkBDPC0R9fT319Y59RSoqhHZsMpkwneBEIOX5rv0IjfNBqxdVnyo0tneTwWy2nJA+m1WWPfXv9Q0mDJrmJ6BFtZmhuv8FaRXU15gxeGsJjvZu9btFdw3k8KY8Mg+VMGSSI0fB3CDGqaaunuRyqKipI+DEpRk0id2ZZSzYncOZXcI4q1sYOpv1wWpT9GQg80gxZpMV7wA9/uGGVo+TYtUzm82N7tX17S2eZ5EwmUxOOUtqWJSSwlbRhqJEyzSev/8WzLZ5ZbXKmEwNlAYJi09UgmOMtHGxQCq68HC8Ro1qdV/NFjHfPb1nTNcgju0sJKciCCX7Q5bldhkTT/yhKSjeT4vZDE189+Ygy1b7PeoiENFdA1rdVmRnodAVZVZRWeooaGC1OvjZgZwKvPVaOoW3Uqk8DrRmfCvrzHyxIY2OYb6c1ysCozpE08YCLWazPeQkpg3jpNC8xWptfK/RgKQF2Qxmk+d1oMG+yaCYg0qemtny768dyvMca4boU4mX2DMqyrfEaT2R9DqQIfK1V1s/dhaHAGk2m3C9PaprAHv/FtX+YlTHG0wN6KTjEkPaBGVNlGUZuanv7gmqfVrU96Tbcjxjuwe2egwlLUR08Cc/pYL0g0XIVivKLFfaqjdbKa6qJyaoZeFAJwILduWwP6eCSwfE2IuRCNjWLNlRGCS2e5DHcfLEHyxmx7dzd69VyW+0el4H6hts65Pt+5nNjusaGhrscsC/DVm1fpTXedNgDEJjNRGa4Gt/F0O3brC3moBpl2PRarG0cp4pVcmsVitoT5z84IqW9qPN3MJqtXLvvfcyatQo+vTp4/G6l19+mWeffbbR8eXLl+Pjc3JIsitWrGh0TBfgjaVYR3pNKHG2Y/v372dx0b5/t3NAZhWAjvq6OpYtXYry2ZYtX45PC76gJT2dngiWsXjxYvvximQDYEQTUMfSZUta3S9LvQT4UZRZyaLfl9HTdnzz5o0c2ZrCd8kaqsw6dhevZmqnE1mc1zM+O6xhf6mG77ZmEWaUuSDByoBQGUNBPn4IJrJ28XbAiORXw5IlrR+n0lJvQMeOnTs5lOMcWqItzKEzgAzLli1Dr3OfL9NgEpu/rV29mmAj1NY6NoNzN3//DRzNlAAtmRkZLF2ahEkvBN6k1L0kL04HoDZfC/hQ5e3jNPdaitoCcX9ZWZnb+6sb9IAX6UVedqWlpLS0Tc/yhNaMb0OD+C7r1q2lpEQDaNixcxdyRsusWz1slx05msQ+2zuYKjXUVPgiaWR2H93InjZs1K7398FUqWXRj3/T12ZlLC4u5qtfFvNzqobD5Rq8tTIvnWH514sWtWR81+dJzE8VtPGUTmZCnJXRkTI6DXS3XbNp40FqK3sgaWV2HdnI7uTW9UPhh5kZGSxenNTovPKcpKSjpHiYX0fTxTfPSE9j8eIUMjPE30ePHmVx7YnJ/9yyZQugo7a21kYXojiAqSbPiU4sGj+wwPr9+9Gltm6dE7qZUI5XrFiBxsXJYDUB+FFeUEudIRB9rTB4Llu2nGb2FPxHcCRL8K6szExqC2VAS0ZGJottfKs5xJoUPi7Z568sQ+5eX0BDVtlRihYfanW/ajRiDm77+yAxVdkkAsiw6M/F7CiSWJimocoscWcvC10DT06L+XPbtVSZJL7enEG3QCuXJFqJ9YVYkwkjUFVTw9GdOYCG3KpkFi9umi5c+UNDmQbwpbam1i2fj6yrwwiYGho8rgNJ5eL711RXs3jxYmrMoMhVi5cs5UTVuTEp6/zaNXTPFTTiZS5h+Yql9mtKS42AgZTSUorbsM6Vpov7j6WkENj1xMkPrqhxqRDqCW1WWu644w7279/P+vXrm7zu0Ucf5f7777f/XVFRQXx8PBMmTCDgBFcvMJlMrFixgvHjx6PXO3PZ3bpMtv6RRqWXI16wd+/eTB6W8G93k33Z5UTvW0ysl5bJk6byvy0rARg/fjyB3s27oPcWpQLCLTt58mT78T/e3UsF5QwZ24NeZ8Z4ur1JzD+0g9LcGnolDrG7VVeVhvBnoWMl8gqOZPLkge4baAVyy+s4mFvBkMTgFr13S/Bj/nYoFdbZonqJuUlazpRDuTlQeNgkjYTRFEolVQw7tw9dz4hoqjm3+P3oHvJKKxg0cGCjncwrjx0m/433AJgwYQLeXu6rDd2/ZQXIMuPPHUeEv5E3Dq+juL4WGdzO338Dx1YdY0nWMRISE5g4sRNf/bYMgMGDBhM1WNBN2r5ilu88SFBwEJMnn9XqZ6TvK2bZjoMEBbm/v6Kojh/2b6PKEo5ZawTMtmuHHcebCTTFHzzhk10/EG89xlnDbmB1ZS7JFSUMGDCAyf0aFxJwh9QXHgKgR4+exNlode+qbPJJIbZ7CJMvHNOmd9lUl8K+v7OJ8OlozxMqMhl4dZ8eky30sNYiMX7Cec5ejDagss7E+uRiOoX50j3KcynT1oxv9vpUSBWKRLVZYkGalj1Vvrx2mcNgFhbcmzwsxHUPYVIbxmnnsgy2J6UTH5fAmMldG50/9vTDAHTt2oUeKj6qxt6lRyAnnS6dOzH5vG5s+eMgG/Iz6dq1K5PP+XcTX5XxPWPoUNi/Ex9vbyZPHsOSn0Tp9KCgIIar3uPLvzdislgYe9ZZzmFjLYDFbOXzZRsAGD9+AkY31rQFSbspTK+kJKA7keVbkYDxEybgZ/z3PS1pfyeRn7WE3tH9CY5MYFFGErFxcUye7NkAq8aBr0RxBiQH/y3JrebnpTvR6jVcNH08On3rJd+8Y+X8/s5e5EofouOFuVQGvs0NY3t6mf266K59mTwkzn0jLYQsy2xLL0Wn0TAwPrDFRReaw0PbVqIk5x0t1/DGPg23jO5Ioi0vUesbg6VWg0YrcdH0c9F70Fo98YfCjEoWbNqNl5cXkyc3LupyYP7HAOgNBid5R431ycVwcAdBAf5MnjySiloTT29bjozEeRMnnrDqnE/sWgUWM2PHjsV6bB9kipQA9Xusr0zmYGYuXbt2ZcjkxCZac4/1VckczMilc6dOFHH4hMkPrlCisJpDm7jFnXfeyaJFi1i7di1xcU0TjtFoxGhsLIjp9fqTYqDAfV8Seoex9Y80ioi1b1yn0WpPSJ8N5ir+Mj6AT0M98rwvOEtzDqut/dFqdS3qjzppW7neVG8h35bIltg7vM3vFd8zhNLcGnKTK4myHdufXYbOywejXiP2tpGkdhm3m77eSHJBFbFB3mx45Jzjbg+wf9tXpvQlr6KOj1YfY11yMQPySpgMNGh9KRKuLhJ7h7XpPTS2ajruvpdO5/hbr3P/PWXZsSePl0GPPmM9H9c/jNFQxVLrsyeMlpQqQVqNBp1Wh+L50ekc/dHZFiqpjXNAa7tfo3F/f2i0Hv9QLyqL6ygP7EygfKTd5puC1ozva7xNH8Mx+PptZviezzEmt45v2DR/rWou5BwVFreEXqFtfq+EXqHs+zubnKNlRNqeUVpjwmSRGZwYzA7bngBanR79cSots5cm8cUGYShZMGskAxOaTkhuyfhqNKJPlw6MZWjHEN5cfpSUomqmfboFxdZYmCNerK10qrN5OZubq1qNxuN52ZYmatBp0ZelcEPqQzxp3Mkfle+h1/d0e88/DYWGJBsNKblWnmhK54EPNQWN5PCk6/Xu74/vEUJheiWlAd2JZGubn9Ue6FK6lnuMz8ERSC86kz+k80GKbXFf1CnyyvzNSxKJ3zFdg/D2aVup65iuIeiNWuqqTNQ2+KGUktieXoaPQUuNrQKlpDl+WWRdUiHXfr4dgAfP684dZ7ePUq3Xaqg3W/n6pqH8uC2TP/fl8sm6VMbUmzECZVI8ICqW+vg1n8/iyh8cv7ufv5Iqud7TGClrl06nQW+tw2/9S+w2fk6yHINOd8Fx88A2w8ab9Xo9DfaUc9npPTSqdfd45BGNVmt/1skgi7e0D61SJ2VZ5s4772TBggWsWrWKjm52sz5dEJ7gj8Fbhwkjlf7x9NGk4V2bf0L6oqspwEcSeUFS9nbmGl7jLf1HLU8YUxLxVYaU1u6u7Qnuys76GbTMv20Ej5wnAiraq4RgcoFQHrLLatulPXDkm/h56bj33G78efeZ9I8LtA9ViW1jp9BYP3wDj2/PBbfjoIrF8VT1yq6wUI/fsntg3iX0th6liyaHoIbc4+rT8SCwNp15+pfoX7YCkB3zy43Brs1ToAU3KtVkSoO6ESDVMK5maTN3/HMIx1GG89zqP1lhfBBjdRt2XLbNC4vJSk5S25PwFcR0DUKjkagoqqPeIPZukZB57uLefDPD4ZVqj53bCyrr7L9nlLTM5d8clH7ptRLThyaw4r4xXNQ/xl7kwKLRUZQn+NzxjBM0n4jbFD8T8eIyw3PnwUej6Fa5GW+pgeiKfz+s2NEpM+/r32NmwzdgtTS/brRlCqjv8WCxV+i0OKA7MnCpdt0JS3r2r3XwzcTidfxpeJR+5Wta0YIqk98Ge6njVpQkd4VWqyGmWxAAZdWB9kcNSghi2b1jOK93pO3Q8Y9cbrmDTttzQ16FPhJDffjg6kF8dPUggnwcAmmZJKJVjpdOPa0NcjPnwbGm9jUfhA+GYdz6IT5SPf00qcfXp+PE48xhqeFhDEWHmudDbX3IyRlV2GK0Smm54447+Oabb/juu+/w9/cnLy+PvLw8amvbT4g8WaDRSMTamEdpkBC+o4s2npC+OG06OOQmAKZo12M1N7S0AdsvbhhsK3bXdofYrqLiSUVRHbVeggl9c8swBiYEO0pJtrl1z7C0qiSTZyjNKFXZukT48fPtI+lhC2sp8xfW0daWhFZDchhMGp9zIkEPSossE0kJPxmew7jve06WsjJdildzpnY/09Kfhc2fOE78y5XkYhWlxVZp5fzaP/7V57tD8ZnPARAo1RBQ3vpcBkkSVrDCzErMDVa8/fWExrY9Sd7gpSOykwjHrfQX4zS5bzTXjejgvEdBm5+gakPViBJ61l5tKnQa7GvgvekDeX1qP5ChPLAzFgv4BhoIjm5brqS7infOFyidaaKf5gbe0X/AmPTZSiJH8zf9w/AqP8aF2s1ca/4F5t/g8f3+aaqN6hKIRidRbwym1jucszV7kM0nKglYrKkZPr2pNoShlWQ6V+9s+e1uqvzlJpUBrdsfyB0UpaemTvzUayV+unUE8SE+9vnfHsufWvk2Wdov59R1TZ3UN5ql94zBoNVglTSUS6JoT1sq/AH2ieppCKQWzGSzVeYK7d88X/4oVGQj6x0840QW17pSWk4PTSaxP4yD8hz3F7UToZ4cUkTr0Sql5aOPPqK8vJyzzjqL6Oho+78ff/zxn+rfCYXCfJSyc667pP9bUJhLMUFwrqOowZZjRWxJKaagoq5pb4aq0omC8nxhAQ1PPL68IoO3jsgOog1FufM32uqA2xls+3OBqnrPeyW0Bsq4qZOP9VoNo7uEIgOlvraSg21lsICj7Km7cXBwYNnNSlRW08DCXdm8rf+QfppUZO9QuP4PCqRQ7DeeIGjUO2L+9Yw91E4tBP8bjFGh00q/OBo0vpikE+fqlmyfoy52BMkGofCuPlLAF+tT+WNPDpnNeR6Uz2njzMqGpN7+huOOOVcsm6U+wnsY4iOCT9Rl1NuDVtXzfH1S4XG3B6JCHdBoDC4fIkJNSoJtxoVeLd+F3COaNXE2Xgcq6kz8vCOLyh0/cYl2IxZJC+e/yc7QC233nEBJSK08HfodrO3DO1sLvUFLVEfhPVDWCk/l+P9x2D5HkTGBI7GXAZBeXM13WzL4bXc2SfmVzUQIyKr/BUwN4l18Alq3P5ArFH5WWOGLVdKh00r2alYKrbZH9IK6id/3eBCQ2wCFh6jJMCrQi9ggLyr9E7FgxOirIzzBc75bU5CaMx40sT+WxSqz5mghnyzawIu6z9Fhgd5TqL3ZkZt9Ivf5Mqv2G6PwIHBit9s4GdGqnJYTuVPoiYBiwS0P7IylQMcJExAV4VCSVGZ7uP+nXdTbol67Rvhx+ZA4rhmeiI/B+bPKbhZZ5VNqdccvVsb1CCYvpZyS4B7E5G1CGSeFubSTUwSjTsTKAuxIL+GcHpHH3abSN3eCTo1PJHX6ILQ6DTFdgtr8jKaYrMZlPwcFBRV1vLXiKAt2ZVNvtrLNKEKMLNO+RtdxtF1B+CeEoXpbSUmjh0pmCmRPf7gTGtvYz5bc5htoJNDPSnmVhmKfbvjheR+NfxoamwVXkiRkG62mFVby6aKD9msGJwZzx9mdObt7hGcBW6HzJuZnaxHXI4Stf6RS6NWF7qr5plbY28Muo5ZD2y88TPx0X9lMosS2R9BxhZy02DPsuKKyzsR7fyXx7ZYMahos3K4VoXyZMefT4YybkTevbnRPe0GWZUwWGUNzScPqR+u83BoXjrsvqoc01Wxcj2ByksrsXlGZE6O0SO7WVNnKYwscYXydwn25cWQHrjgjofEYu/G0uDMOtgUhMb54BxiorWigPKADPrIqZElZU9thUXVtwmKVPZbcbw1cvaIKJCS7cSGue4hq7Wstmt77SG70ixivX3Zm8e5fSWSV1tJbykNntFKhDSZg6hdQ5lgz3BkP/y1U4kswlVh8I0FWGJIHGmljN091Kf7U3BLzX0JItC9GrQmr1kCJoXOTEpTJYmXe5nSu+mwzOzNKPV7XFihzVgYnpaVzmDeJoT5oJEgqqOKlxYc56/XV/Lk3130Dbvhre9jCldCp0uDuYkF08V6olV1ZlllxMJ95m9NbrQSrmWB7ebOt9r5KrifsDDa6SyA6w/En5rl9XafQHBlZlvluSwbnvLmGH7ZlUm+20i3SD1+duFnnF+58YztzIItVZvxbaxn8/Eqe++MghZX1ni+2vdDO0AvhjJvtfZI07Z/E2JyAFRVu80b69qC5QTmYU8Ed3+7k/h93/2OGGAkNobYk036xAVzQL5r+8UFoJNiRXspNc7dz3RdbyS13H1oruVpU20HAjOjgj95Li0nrS5VfLA7jgntPy5G8Sr7ZnE6dyeLaVJNQC7DNCtQthEc6Ber1/lT5C49L3HHkE0gtpClliNYlFTLuzTV8ti6VmgYLXSL8GNFReJ3jI20hQnaBuP3n2TO/H6DXU0u5+/tdHM7zXHlHMVrlS2Ew7WvHCcmFTv+FsE57KGdQV2RZanJcCivrefq3/Uz9aCNZpe2j/DaGRMdwYfEP8zMwvlckQxKDMeo0pBRW8+RvB5j4zlo3a3rjkGv1Jp7H1SNJcuTp2ZRxBXZPi+qYxSqzI730uOgU2i8iwiOtyjIlIeJ9Eo4jcsFhBPQUxun83JyyWq74dBMP/ryXrNJaAr31TLQZN3x9/UCSnMK029vTsiezjEHPr+Daz7ew5mjTnmfFq1J0wVxkjc34LGlcrmknnKLxYf9+rcFTCJIkEe5dRVZVMMX6zngSxfZklnH/T7s5Zktm+21XNoOaqZjTOiiuaGdPy+K7RoHRn4o6E3/uzeXD1clkltRyx3c7WXU4jhcv7YOXXovklrhtAks7yBSRHQPRGTSY8Kfa11Ha1eHKFn8XVNTxv/l7WJckNpY6o0MwPaJaHp6mZqrtx2CVvjY+pzDY4wsNa0YWUJ2saTDz5KI9LNglvCr944N44vyeDEkMRnpZBgtgY2SyC8fZkFzE+6uSmDmm03F5oCpqTXbr+BcbUpm/I5OHJ/bg6mEJbhZj1byc+Ar8ukgc1qkKFvxLjDEoQPSlVh/s8ZHV9WZe+PMQP2zLsM/Jhyf1IDKgdTsyNwXJTlcSIX5eUAx3ntUJeg8CBA18sSGNLzeksi6piPPeXst70wdyVndbKW1F8FEUP7sgdPx902o1xHYLJm1vkd0zAS6eFoQB5q0VR/lotdgQxteo5dKBLS+vqjZUtpes7i6MU0FpkHiX0Ajd8YXmuDGyuO2LVWb2qiTeXHEUWYaOYb48dUEvzuoejrR6A2SBVmsLUbQLmaLNzJIaHluwj2AfkZNzPNiRUYrZKvP7nhz+3JfLDSM78OB53fFqVPlIsfBI0O088LbtQePf+vLtx4vIDgFIsgWTwZ9qQtC6GWtZlvlmSwavLD5EtS08cuOxYqYNab993WQVYQX7Cn41sVc4Ey8aAggP2q87s3l/VTIpRdVM/WgjD57Xg9vGdhJ80Ha73MI501rE9wwmaVu+CE2vcewNpsx/hcaS8iuZOW8HqUXVXD0sgRcv7dviZ7g6FNpfaXE+bsJIRUAHAOKOI0dUgefuOkIbNiQXcdf3uyipbsDHoOWecV25fmQHvHK3Q4qo6AmOamIAstWK1Srz5cY0dmaU8sqUvvh7tT3keEd6KSXVDaxLKmJdUhHn9Ijg2Yt6Ex/ibj7b1lSDLwyZAXsB7fGFG55u+E9paQYGjW3Xb3ToZGdLhizLzFmXyqtLD2NWcQBzO7sXHQzRWWlRPCgBXnqmD03gskFxvL8qiQ/+TuaXnVkkF1bx2XWD3Vpr29GAK8KnugaRcaBECEMqi5M3dVhlWHu0kPt+3E1xtaN4QHUr81LkdhSGZFnm/VXJVGUdYKZ2J75VEYBD2LdYocy2w/txVzlpKsFXJY3e+MVWDhSa0GokHp7YnZtHd3K40JU4dLvSYmsSmfdXHeP91ceQZQjxNRyX0mJR9bFPbAD7syt4YuF+Nh4r4tXL+jkzb1klUSv/aB8Bu9VwembjcT6YU8Gd3+0kpci5Sk5706rkzsCgcu9HBHjxyKQeXD4kjvt/3M2erHJumruNZy7qzXUjOuCqpbSX9VZBXA+htJQGdwc5y962EUGXWaU1PPnbAfZkltnvqahtLZ2qPavH3+c9mWV8u+EoV2pXE107CnDeS6PEprTEJR5fLlNzefjKHPtlRyZzdUcBmD40nqcv7O1QFCy2/BGN3ukmSbay8mA+t3+7w16c4LWp/dwoGC2HEoanlML9fH0qG5KL+PDqQXQK97Nfp4S72A0dilFB4375b5MA7hQe6vkyrU6DJFuQJS1WWYvG5VnltSYemL+HFQedK3W2V+EVBWZlV3UPdOrvpef6kR24ZGAsT/+2n4W7c3h16WGSC6p4aUof1Ss29s61hyFQ8RhWBiRiynUYVeyJ+FbhkX/2jwP2kOms0tYVRHL9zsdLq1tSivlkbQqDOUwXbTaa+jMAR98LtInIkhZ/TTkBoW2vWNpsQQzb+coaEzd9sRWzVaZ3TAAfXzPYoSgoeV4uxgWA0qp67vxhr90rcumAWM7t1fY1Va0M6rUSqw4XsD2thDcu78+E3lFO19qDxSWNnU6l9phQapziaR7/hYc1B6nRLwCYLSL+9cXFhzBbZSb3jeLm0R2B9k88X75fJMk1WHGrtCgw6DT8b0J3vr15OEE+evZklnHpBxspr1ZCfNxpLe0jDNmTfINt4TklKQzb8QCHvG6if9oX3Dh3G8XVDfSMdnhW1CFeZouVQ7kVrE8qYmdGqdtE+4nSRt7Sf8h4zfYmF1ZZlqmsM2FuIoYsqaCKt1Yc5SHdjzym/57hf54L310J+SL3oLzWG4vWiN5SQ1isn8d2WoKmvNmSyhyVXFBFoLeeb28exswxnZ1jfl2ZrK3VZZkS7/19zN728S7uSqy0RoLf7hjNE+f3RKeRWLwvj6kfbSJPVSbTydMiq5zq7c1kRaMtvs41cXH1kQKmfryRlKJqogO9+P6W4XjZNn5rj9hwBSsP5jt5WhxScON52Dncj59uG8Flg+KwyvDUbwd4d2US9vdsx4RbNZTKRGWBXbBYNFBfBatfYafxVhYbHuWSD9axJ7OMAC8deq3og3pOybJMenE165IK2ZBc5DIfbM+oS+Jd/Wzu1/2ETm4ivBBh7W3uHZ/6bT8TzH/zin4Otx2+ET4bB8l/2c9X+CcCEB1/fBbJ5hVDcb64qh6NBC9P6cvLU1wUDzud6pzu2ZdVxi3ztjtVUzveT6usM59cO5gvbzyDMD8Dh/MqufiDDWxOKW50vd3Q4YH1nwhjgzrfMqeslmkfb2LFwXwMWg1PXtCL8TZhsT3X1NzyWnami+qZFpkmQ/gCvfW8c+VAnru4N1qNxC87s5j59Q6VIbBxuFZLqlc1B/8QLwK865ElLSU+tv1TcnZza8aD7DLO5Jcly3hswT67wqLqih2yLJNTVktKYZVbA6HWVM1Tuq95Svc1MRQ1OR+r680UVNQ1GYL247ZMVh0u4B3DB7ys/5ywD7rC0segRox1pUaENofqi1o4Cu4huXgvPcEqC6PUxQNi+OX2kc6eDRfjgpr2p32y0SmM63gNWwr/nDIolqX3jmFgQhAVdWZmztvB7FVJTvxPcutad3l+OxFqexnC/m3852lpDWyTq7bBwqxvd/D3kUI0Ejx1QS+uH9mBD23hFOpFPr+ijuUH8tiTVU55rYkQHwNDOgRz2aC4Fiei7bAxWNfwME9cZkTnUBbMGsWNX24lrbiGdcWFTHP/Ku22UCmWodKgLsjr34Pkn4i3LeB9NSlYTDJTBsXy0qV9ueD99SQXVGGxymSV1vDp2hQW7Mqmss7BWHUaibO6RzDr7M72ULuXtJ/iJ9UxRbuexcx0er7FKvPHnhx+2ZnFzvRSqhssSBL0iArgvN6RXD0skXB/R9hSrS3swA+VderoEkhaBoNvwGoS1bl01nonxaJNcLGau5y0/+ZnkPju1hGNdxGXZZWnRTBZi+2+GrMYqxGdQ1mXVNSqoge/7Mhi1ZECOof7Mb5nJH1iA5zKVWo1Ejef2YnBicHMnLeDI/mVTPlwA1/PGEqXCH9H2KGkxKafOE+Ls6PFMQg/bcvk0QX7sFhlRnUJZfb0QQT7GlSlQ8W1VqvM1rQSNqcUk19Rh1Yj0SnUB79WhImvTy5iiG2B8dLrm81nMOq0vHF5PxJDfXhrxVHeXnmUScqQal3imNtpTIOjfTCaK6jXBVCUU0TI7CFQmYuvBJ2lXPSymQ6Rocy5fghvLj/Cwt05WKwy1fVm5m5M47stGY32SeodE8D1Iztw2aA4tBqJcyt+ZZRWlIevru8PnOV0fXJBFd9uSefvwwVkFGv535YVJIT4MLprGNcMT2wUMnoor5JhqHIasrfDN1Og8zlw/lvIttwMvbGdlGWP+z+IOS4h8+HVg5jYJ7rxRVbbhLHHo4sPZ7JYkWWYMjCWX23hny0VxNOKqvl4zTFCfA2M6hLG8E6haDWS/X6tJHFm93AW330ms77dyfb0Uq77YitvTRWhQg4l/uQQUtz14mh+Jdd9vpW8ijoi/I18fv0Z9I0LZHuaWPvUxoX92eWsOlxAUkEVDWYLMUHeTB0cR++YwBY9/1hBtX1MDDqtW0+LK64b0YGEEB9u/2Yna44WMqvBjAF10rfa1dKibjSL6OBqKmqNFPl2g4V3wO5v6GFrv7/mGIctCTx4XneiArz43/w9drnjYE4FczemsvxgPmU1jspxfWMDuXxIHFecEY9RpyW2YA1n6cSeVhqsWOWrnZ5fUFnHlxvSWLwvl/RiQX9ajUTf2EAuGxzH1EFxeKtyPSttilEYqvyqzR/A7m/h3Kftnj4Nrcu9cUVz1cMcs13muhGJPHNh78aylp1OlQ1lHbyjpLqe2KAAKutMVNSZW2w4qm2wMGddCgD94oMY0SkUg05jj17QShKdw/34ceYIXl5yiC83pPHG8qPkV9TzzEVCKbYbvSSpWaPG/7fCWAr+U1paCEFwVupMFm75ejvrk4vw0mt478qBdhefUnnDYoWiqnpeW3qYX3dmN9LUf9yeSWywNyM7h7Xo2bFBXlAKvkads/TSBJPtGObLT7eO4Ko5W5CKFYu4GytfOzFY/xChEFi1Rqx7fgJvB7OUgLvP6cJ947shSRJa2zt88Hcy29NLqDOJ9/Az6ogN8qa0poGCynpWHspn5aF8pg6O49mLeuMnOay6VquD8e3MKOWhn/faN59Uv+Oh3AoO5VbwyZoU7hvflZtGdUSndTAShUlYz3oMTcEBOPgbbP8CMnuDfiTtkenu+GSN2zKoKnQtnDWKeFeFBZxLlNosuBYrIloQmY+vHkBxjZl1SUWtYmQvLj5EiS1c772/khicGMz1IzsAODH5gQnB/Hr7SK7/cisphdVc8clmfpg5HDwJQ24k7Dbz1zbcp3zT77Zk2KsBTRkYyyuX9bMnhitz0GLLCXhnxdFGoWMA42M1XNrC5+q1jkUnyMfQImFIkiTuHtcVb72WFxcfUr2DuNdBp+1nXfO2lFGvC8BUmAk+ueAbAdUFAPSK8mPurSMI9Nbb58DKQ/l8sSHVvhmdQauhQ5gPZqtMenENB3IqeOjnvXy1MY13rxyITnaEgPpYHDRZVW/mxT8P8eO2DJVyLfID0oprSCvO4JvNGUwZGMvj5/ck1E/wlOhALzRl4oa6juPwiuwBWz+FY6vgo5HIvGV/t+MbHPHD05TTSBJWYMbojvR3p7BAIwuug/fL3DOuK7PO7mxXWiwtJIp5m9P5YVsmAB+uPkZCiA/3j+9mF1KV7xQR4MU3Nw/j7u93sfxgPnf/uIdrukgkhrmEhzm/brugLeQtIyFbrSTlVzL9080UVzfQJcKPuTeeQVywsIprNA463Z1ZxguLDrI9vbRRW5uOFbP03jEteq5VdoxE18iAFtEpwFndI5h74xncNHebKgCjsUGqvQwM4YG1HMmBKkME7H7X6ZwGK7OvGsgF/WL4bbeYT+W1Jh5fsI/vtjpy9nQaCaNOQ3WDhX3Z5ezLLufTtSm8dlk/tBaHFzRQqrZ/Q6tVZs76FN5acdS+NivvpXyH3ZllfLz6GM9e1NseOmWwGVrMkg4jJpj8Bmz/EgoOwKL7oPY6MP7zRq0AbwMNQJifkWcv6u2eL7hELqh1mthAL36YNZK7v9/FltSSFhsCVx7K580VR+1/h/gamDmmE3U2A6kiHxp0Gp6+sDeJIT48u+gg8zanU1ln4s1pAxrNK/G7y4OOc/xOdVXnP6WlWThmiNls5dZ5O1ifXISvQctXNw1lSAdHvoMiCC07kMdfhx1WjkEJQYzpFk6Yn5EnFu4HoKiqhRtDAnqNRHWeAdlbg8OeDVTmgt4bDO43nYsI8OKHmcP56N4fxJu4cTn+My5CCS77nOqKYnxXPETfGD8mTuhuP1vdIITw9cnCTTysYwh3ndOVkZ1D7YvU0fxKPlubws87s/h5RxY7M0pZpXqCtqEKWZb5bF0Kry49gsUqE+Sj58aRHZnYJ4r4EG8qas1sPFbEVxvT2JNVzkuLD7PmaCHvTx9kt9z56CWwgia8G5z1MKSuhd/uRM50HqfjHQ7wILirLDxxQR4SwqtU8d02C26AtwHq4IpOZsZ2C2fBHlGysTWelhrbd+gXF8iRvEp2pJeywyYQuCo/8SE+/HzbSK77Ygv7syuY/tkWnvZzLHr/NCNsPnLH+YKftmXaFZYZozvyxPk9nea6Ms+u+2KrPRbc30vHOT0i6BTmx6drj1HdYKGi5WSKVYaGfB3Fpb4EFJagV75t2joI7gCBcRAY6/beW8Z0EuFYwvCJXgk5crPnQftBghF3wpn/g9dEaOsPM4di9FYWcvHQLanC2h0X7M2953bjgn7R9pCo0uoGftqeyQd/J3Mgp4IL31/PG9SiVC0Z2LAdEMaD277ZYbfYntszkssGRlNweDvnjDuHowU1/LIziyX78/h1Vzbrk4v46JrBDE4MxmKV7aWkvYJiYOJLMPRm+P1uMbbK4Bx31SbbLx4ms1BaZLpFeAgXrSoQ+6CAXRhKCPWFIji7ezj9xndzClltaXnpmgaHgSbIR09GSQ33/rjbfkxdptZLr+Wjawbz2K/7+HF7JvOSNETpixiAI2ncjhPleFHxltSiKm7/eQ/F1Q30jQ1k3oyhQuG3QZmDLy05bPdW6bUS5/aMZGBCECmF1fywLdNufGkJrLKM1mymJM0H76hqiLHRac5uyNoBvmEQlOB2Pg3rFMrXM4Zh2ar0T3kn1UXtRKyO3HAJQjrDxR9QuuodgtOXctuZHUnsF2Prg3je3qxy9maVA3B+32iuG5HIwIRgDDoNBZV1LN6by0drjpFVWsvVn2/hemMuZ9qecKl2AxWyTHmtiTu+3WlfmwcmBHHz6E6M7hJGgLeO7LJalh/I5/P1qWSX1XLz19u55cyOPDyxh12JNmgRRWO6nCs2w97yCfz1bCOlua2Q3CiKavh76SgGQv087G1lqoOU1eJ3m3FBp9PZ6XHhHSMJDPBq5I1vDhV1DkNtuL+Rwsp6Xlly2H7M1dtzw6iOhPkbufeH3SzcnYNGknhOkcvQ2Nfgk8M/evLgP6WlGUiSY8L+faSANZZCvPVavrzRWWER14qfSj5Gz+gAXrikD4MTg+3XrDpcwKrDBXbtuyXwrqokY7XwypRnXE5UjB7vUBN8PFpcEJgAnc+CQddD3BCne8P8jNw2thNF61fbN6iCli+YbcKta6BTL4zbvgQg2qWij17Vj9cu68flQ+IaMZdukf68fnl/Lh8Sz93f7yKlsJo6ox4vSTCGoKIdPPuHhbkb0wC4qH8Mz1/ch0AfRzKuj0HHlEFxXDIglvk7Mnn2j4NsSC7motnruWdcVwA0yvdVyn92HAO3b4TnHoJi2iU/oymm4/TerpUGDv0B2z4TipTSktZAQ2YmVautlFWEEX2r2fYejfMPmoNy7SfXDkYrSby27Ag/7xDJ2e52Mg/xNfDNjGFc9dkWDuZWkFpXJTiIq9CoYs7HqxS3tvykjES9yczDv+4F4MZRHRopLCCskiCSVw06DXec1YWbz+wovJlAoLeOZ/44SEMr6MQqy5Tt8MZcqaXg0hsIGRFGeKyEZtc82DVPXBSUCP2uEIt5gLO1/oZRHVHsdH62ggf/SASAUo2mz6Vw3i1gdiif6ggrdUjOdSMSeXRST6dwEBA70986tjOXDozlvp92syG5GI3ecV+YpZBNx4qZ+fV2KuvNxAZ58+a0/gzvFIrJZGJxKkQFeBEf6s+4npHssnlNkwqquPLTTbw1bQBWq4xGmQdKVbWQTnDd77D9c/jA9jD98VaBa1oYcig1LpMibT2sf1t4fpRzBl8slZXIS45ydE8kXe4QY9yWjTwVReehid25YWQHPl+XyvurkmmwHXctLavVSLw8pS8Wq5Wfd2bz16ECZhlV79fohZzRpjnn5GloOc3f/NVWymU/ekUHNFJYwBHG22DL3bh0YCyPqCr+pRYJpaW2FeupLENsZiH5O4Ngxyb8BuQRFa9BzxGYc464yDccetpKuUf2drp/cGIw6f5GaqjB39A4v6LdhEx/wR9krQFuWw8GH4J9PwIgMcQx19VKa3SgF29NG8CIzqFOTUX4e3HDqI5MOyOe5xcd5PutmSI/RVW7Iq+0irt+2MeR/Eq89VqeurAXV54R7/Q944J9uGl0R64cGs9by48yZ30qn61LFeHeylIqq2hVo4URs6DreLj3M3FcXV3yeNBcxQzX82WZsP4t2PcL1AvlDoMvsixT9vMCChdGYfA30+EJwWMUpbHldCquO79vNO9eOYAFu7J5dekRiqoE7bvLn7ygXwxaSeLO73fx665snjeqjFTNPfZUd5m0Ef8pLS2FLRTHS6/h8xuGMLRj44pSoXUZzND+yW+W0Vwwsj+Pn9/TSUAH7Iv+J2uPMSgxmC42q9387Zm8sfwIt5zZiRtHdXRiRIY6Rzx33YEDpB0MJ3JgGSHdbMfLM2Dn1+Jf53NgwosQ2ct+j49tA0l3G0e1m6dF3Y6vbS8RReC3Oi8oD/evZ/ehY1xw2fX0iW06DnloxxB+v2sU132+FWupYyxXH85lbn4QAE9e0IsZtiII7qDRSFxxRgID4oOZOW876cU1PPizEGq1SjlQtXJi9BML1nqQPFTYaRXsCdlNnAMHk83ZDX/eD9k7HOdCu0C/K6nauIXsBx7EWmEFDOgKy8Q72ppRGOzW1BKe/v0ACSHefHKto4znhuQijuRVUWe22BUTnUZDuL+RNy7vz7k9I6j6+U4SvGsh2UvMJ1Ufg3wMfHvzMKZ/thnJlk9psuCciP9PoFXTVEaW4drhiTx1Qa/Gc9xcz3XeGymrh6Wa0fxx5+hGeUTKBq27ijXszixjSMcwJEkiuaCSG77cxvBOoTx5fi8nJVmWwWpyPKtkUxHVwVF0uNwbjdYKZRlQlg5rX4NNs2HkXTD6fvfC9j+wuaQdSpK4TSjyVNxjancd3ulHGHjOZUwdkthkkxEBXnx90zCeWLgfaZfjeLVZ4vovt9JgtjK0YwifXTvEacxcMTAhmIV3jOKB+XtYsj+Pe37YhVUGjVYZCFVfNRoYegt88KPtvY5PGGp2/wdXVOSIsJejSx3HInpDwjDqfc4ga+rlNKRnAlpqkvPxwZncrbJMQUUdT/12gKSCSn66dQShfkYsVpntaSXsyy6nrMbEfJshQa/R4GPQcde4rpzbK5KFc99kVN1quuXNgNgrnIRBjUbixYt7kZGZibVIvE+92fZeylA2GoCWvXb7QYQGdo/055ubhzVSWJBlhlt30lW7g7mWCbxx1UjO7+es6HvbPH6V9WZ+2p7JOT0iCLOFFT4wfw/7s8t5+sLeTkK8jIzO7FiTqnanknIwksTJFrwijcKzXV0owoS3fyGMDOOfA39HpSe9jeFq3MnH7TWOWj1gEd/VYEsid5Mn1zM6gC6GYiLjOvPuVUPs7+8OPgYdL0/pR++YQA7+8ZfTuTu/2c7R4gYi/I18ddNQp6I57tp54oJeDE4M5r6fdvP3EUfiuqTkrKhpNawrhHaFepC8g1r0+h7RVOSC6rwd5gbBcze8B0pInF8kxA7BOmQWuQ88SMWffwIaaosN9obVWzbIssz87Vl8vTmN/43vztk9RLnwijoTuzPKyCqt5ZvN6QDotBI6rYbLh8QzrmckT32zknOyPiCmbijUxoN3sFP3JvWN5j0Z7v5hl1N4mKdQ7+OeXqe4svOf0tJSyGKyzLnujMa5KDUlsPIZLt35FZfqYVoPI90vusptM2G+gjEfK6zm3LfWMDAhiAcmdGfZgXzyK+p54c9DLD+Qz+yrBhKh7B+hLsU4/lwqV6yk4EAEwXO3IVlqRGLq3vmw/xdh7fvkTDj7MRh1L2i0Hkoe/4NhJ/bnOYe4UFsKSx9j4p7vmAhgnAQ0rbSAsBL9eOsIdK/JdoJLt+Uf3Dq2U5MKixrdo/yZf+sIrvl8C0fzRay9yWwW4+LiUfknLNzumZBairHAujfh75dEHoveF4bfDoOug+BEyv9YRM7DtztvOW5r07u+iFnaheTWT+CVJYF8vEYUhTiUW0FeeR3v/pXEgl1ZTjHKCox6x7tP7OwNrIRa4JsNED8cJr0KMQPs1wT7CsXlxxc+oSzTG59A5zZPSFUSu8ApavdcNSyhcTyzLMOBX2HZEzwn54ABKu66h4DQxnlEwb4OAeryT7cSH+LN7WO7YLZaySqt5ecdWaw9Wsh70wcyvJMQiKyybKen8FuupfCzedSXStQMeRe/MWNEpa6kZbD5I8jaBmteFd60KZ9BlHMZX8XMJ/PP0anD4q7ynsiymF9bP2Hk0kcYCeDVAWhaaQFhFHnp0j6kZvsILyUi96zBtkHq1zcNbVGJX1+jjg+uGsTjC/fz/dYM0bbkxrhgfw8xOO01Rs2SviyL3Lff74a6MhGyOeh6GHEHhHam9sABMm6agbW83PkeQLKYuFn3JxWyD0v29+GN5UfsYcQ70kvJKKlhzrpU8ioaV2VT02nP6AB6hK1Hyt4NS+6CTa/DhBeg50X2gdBoJKZ3trJT70/VfiN6/5OvWGiQt45vbxlGiK+LwpK7Bxbdz83Z20EPV583mrh+jfOIAr31GLQaGixWHvp5LxpJeN3vPbeb3Ws8/bPNzBzTiYfO645Oq8FqdXgDvOIDMdXqsRQVURZwI1H3PSY8j+kbhcJy6HfY+yMkLYcL34VeF4sHu1ayaaOnqdWwKy02xaAsg46L72KlZjUk3gl+o1rUzDXDEzlYEAc7HcfSiqsBPV/PGNri/dMm9Y0mxNfAjK+22yNMHAVa/pn51pQNUFxg+ynLUHgUfpkBecJISYczYcyD0OFMrLW1ZNx6K7Xbd7g0IFru1HCUWO1uiiu6cvNX2/nrsMj7+31PDnHB3rz7VxLLD+bbvYAK1IbqEF8D7w/IQspdD0nr4e1PYcwDMHyWk5Hh/H7RWGWZ2tk6aiv0eGn+BVo9RePO/lNaWojaEiPTx2rp2tVFYTm6DH67Q1hmbOgejEfccXYX/L307MkqY9OxYnZllHH1nC1O12xNK+Gi2Rv45NrB9I8Pcmys5g3RL71E5YqVyPUNIr/FO0jEjnY5V+RkLHsCjvwJfz0Hx/4WOyC7E5aVQ+3laHF7UDlqFX1ZeLvIw1FgK4Voh8UkjumM4GKNCfTWiwLdNl6tlMpMDHGfz+MJIs9nBGe8uILbpIUMkJLFCddd3OVGv7QZTe7/oGbsP14LWRvE7z0vhPPfAj+bRWfpMnIefhisVgIvvZSqpQux1MpIyEiHFzFp2Q1M0sMvuTn8L2OW0yNGvbrKHgrWKcyXwYnB+HnpyK+oo1d0AAHqvVfUSf86b8jcDJ+dDWMeEszeZqUP9TMyVQ+5W4IxbNmCZXop9lmgngz2d2/jOLbyNotJg/9hKy8808c5hrgyX9Bp8gqn6wN07mPhx3QL4/5zu7B8x1FSq/VkltTac2QUFFTWc82cLTx1YS+uG9FBeLls/fU7cwSVW/dQt2cvstk2pkY/6HMZ9J4CBxfC4geh4CDMGQcXfwB9p6ro0mblU+0L2F5obGFXHSnLgKVXQcYmx7FqlxKlVivUFIkJ7RuuDr5HkkTVNUVpUdAh1LdVe5JobAqQUadh96aVzNAutj2giTaOc4yay2mxt7/9S9hsk/ZiBsKln0K42NOpISvLrrB49++P1lJA1f5cMVb5B2H+9TyhO4pZ1tBz4ZmYVEvwzHkO4SnQW8+ITqFEBXpRVtNArcnCRNc9HVSJ1JRlwE/XCaXlwnfBR0QCaCR4eFAomR8Kxbr22t24pVM12kCqTvTd7HcQ1xbsDuDnn4c7ewYsZvj7RdjwrkMwB+J83cdpehu0zLl+CH8fKWB7Win7sstZuDuHJfvznK77dG0Kh/MqeX/6QKdEfGOUP35nXETRhx+CQqc6I3Q+W/zL3gmL7hVK1E/XCWFz/PONQuycyta215rqrmqlMv+tFjEPlz8BDbZiF4VHWtV+LxfvslJEpENo69bUYZ1C+ebmYbzw4efcoluMRjY799UGB2s7vgGSmlxQcZScLs+Cz86BhkrwDoEL3hZKp82LkXn7LGq370Dj50f0M0+S/cDD4j6LCVY+w7P5b4Mepi+LZJPVESK4YFc2v+3OtuePJoT40C3SD6sMpTUNXDbIeSNeyaJaYxqqYOUzsPt7uGwORPezn7qwfwxJO/ww12jxe+oFGHSJ7X3cv2ebJZNTvOrY/2ulpSWClF3gAMKKVZYzq0Uw13Vv2k52F4m2x/5yFvzqyiF5pQj5qSsjwhjAA7GDYPylFFab+ODvZL7elGYngHvP7cqivbkkF1Rx5aeb+fS6wfRpEFYCSbbQZMnjkE5w5bew+ztY8pBIUv3sHNDZ0u1UzMLhfGl/ddtR8Uj0VZPyN6SuERJYaFcotu3IbDVDfaXo7775YmFQCNwvSsTBDrkRYgfbGnYsXEqMe1SgbcGzmIWydvhPsdDUloJXoIhH7j5JCIq2MJwQXwPbZ8YRPPcnR6ddPS3tNxwtR/oGMBjhgrdgwNVO36vwnXeEwjL1MqKfe47kFQsB6Fi4Et0vn9mvC6ESL72Gl6f05b4f9wAid2VgQhCPTOzB0I4hDqZ/ZAkc+wG2dBVjHdLRMXclLdy1QyyKB36FNa+IbzhtHviJ8D+pxsGMM2fNguCbxPETYMJRpZ5hTjI5Kyzpm2D+DVCVJ/I5znxAvI9sdbyvLEP+AaGklWVilCRmBXWkUzdvxp43nvk7c3lvVZLdKj6uRwR+Xjp+253DU78dIKesjtjKfajiuRy72ltdBC5Jgt6XQuJo+G2WsOL+MkMoMMolLpa2f8J661yZzBZE/elYcczgLxZ7EGNktcDhRYJW0zY4zul9IHEk9J8u3kmjdaFT8XuYqtw4xcdgzw9o09Zzbl4yuvRnxdzrdJYIxbEp6pIk8fSFvSg4ejN+VTbPg7tkPEmyX39caG5vHOV43j4IA0beDeOeUu2dBJUrVmItL8fYrRvxn8+h4K7LxYmCw0I5NYmQXp1kRYeZq0d2Ye3RQnvlugh/I/eP78alg2IxKpUFy7NFbtv+zdBhNET3F31V5tWV30POTlj/jvAMZO+AK+ZBhE0gqnZUxcu8fRaWDlfYHNzO4/Wv0a1tHBuq9ISpk6iqCuDnm8S6BYJnl2dB1lbnNbUkVUQUFB4Gcx1j/CIZM2wqXDiafVnlvLzkEBuPObTmFy/tw/OLDrL2aCHTPt7ETYMDGac5QCEBoi82y7jsSqcAsYNgxkpY/ZLIW9r8IRQdFSFHeLBttfswqhpU1qnlj3u+vDIf9v4AhxdDwSEwVQuhPWYg9JkCvS4Ra6ELLWmwEuClw2irsEhVgQg5P7ZKKESmWvCPhPhhgta7jLcbLAbEBzGn70GCjm5v3Nd/Cs0t0g3Vgk8ljITLv3QK72s4doyarVuR9HoSvvgcfYTDGC3PvQCp4ID97wBqSAz14axu4Xy1SYSAWWWY0CuSu8d1pXdMgOA95gbY+RWUHALjAIgZJMZHmbv9pwset/xJKDoi+MF5L4kQVxvMNYLmqzZspTxHB4nOZaiBf9bQfArg/63SUmuu5fENj+NV78VkJnu8zpCQCAdtFi1lUayvgvnXC2UEYOhM4Zrf/KFNabFCVSGsfhn2fG9fqJxwQxThHUbxzEW9uaBfNDd/vZ2yGhNndg1jxuiOzPp2J+uSipgxdzsrandQg94WxaQOJ/KwgA+8WjCo76+A0lTIzQFC3YaHtdvMVbfjzj0sW2HgNaIM4qdnQ+EhkUSbutbJS2VHVZ5IXt41T3gdJr3mxGQlxIaeY7uGw6FFsOxRYW1Uo6YISo6JhXzF03D2ozDoBtBoCNa5bLbl5RKm1o6VOyS3gc82qL+n3gdu/KNRMQUAS4WofR9y3XU2gVa0FVCbBT5g1vujM1ViRsf8W0fSJzaAV5YcJr+ingfP687tYzs3rlX/+932UrcsATqOhcHX2/qlFZWuLv8Suk8WOTYZm4QSfNWPENnLyaJYf/AQjG7D4DSDMlvCZFJpEnXm3njp3Cdb62NjYVceSGAIVln4tn8Jf/5PWG3DewjPY3h32Pi+WNCsFtj/qzA+5O93alMHdI26GC/9pdw0uiMX9Ivmju92si2tlN6xgdx3ble6R/nz2tIjfLzmGI/r/nAspHovR4iXOzoFofxN/0F4RTe8I/ogKzsvKyWP/y31WfWcqL5wxTfw98tC+ElbJ5SV/H0u90iCtyWvFP/WvgHnv9nIuBDhbxQhnOXZsOwxEVqFjAbwBSgpEHSavBL+eh4G3wDnPA5egUiSRGR9uuORPo1zCe29aS9PiyfIFkArRuriDwWfdb2kXqwVXv36ovXzczCQ2hIxVgkj7F6sxyZ159qxvfnfT3tIKarmzK5hzJ4+qHHOz5aPYeN7jr/Dewivp1K21egP5zwBPc6Hn2eIsfxyMtKFswGD0zphKS1Fjm5FSbw2YG/BXgbHDfR4XtI5xA7ZYttxKm8/fHs5VOaAwQ8uel8I2D9dZ7vQKip7rXrOUflJjaPL4LZ19I0L5Nubh/HN5nSe/O0Agd56pg2Jp39cEDfN3caR/Ep+X7qRccp01+odBgJPRUx0Bjj3GYgeAAtuE/O0OgjwcSi66vdrd3FQbZFR7ZEkaUS//CJhwa3CALjqBdg42/k6EHw+aZn499fzcO7TjXJNNcg8d3EfJFMNrH5FzDuLy1wpSRH/9nwPEb3EutxRGEWDNKqQRknbDoUxPKDF4WEI498F74hvqILVRqfa0FC8+/XDXKiq0Jl/ALwCoF6suRF+el6ZNYpdmaV2peWz64bYNz614+gSWPyA4++AWBEyqnjCNDrof6VQ9n6/E44sFtcXHoaJr4BWj9ZoxVIv5qO1ooJ/Avk1Ys0/XHqEwJZFAZ5U+H+rtCxJXcKKjBVo0DA5bzKj491LXPqIcDgoYmORZREq8e3lwrKl8xbMtZ/Nmqa4Q3d/IwRl26QnrJuoSuUXBX+/II6pwqSGdAhh4yPnkFlSa08InnP9EO79YTdL9udRLXsjYcaiEoQA90qLgshecMvf8OM1oq+ApCxyKvyj6QcGVWnQyW+ISiySJLxPIHJwQHiIht4K3c4T1ZXMtZC1XWxKtW++iPtPW4/VbMVUpcPgb2Z05xCunNIVza8zhCcAwCdUKEYdxwpGXlsirMK7voGKLJE0u+9n4ZZVhR5wyUcQ21hRANpFa1GH2DaCKq5Vvm09UkRnt23IFtFfZcGXVMWvzRd/hFY2w+93cVa3UPRxQgH7/c7RmCxW+54HjVBvs5YHdxAKX+oa8Q+cF6t+l4uclu+miQXr8wlwxdf20/pQH0zFDsVcratabONcr6pQ1RrkVYkwj4qGCh5b/xhvjn3TrUVdFxIE2EJCJMRgr35FeFQA+kwVYTNG25xU6OjzCUK5BZHI3WG0oNctokqPd4PDYhsR4MWPM0dwtKCSrhH+SJLErLO6EOxj4LEF+5zc+JJvqKOfTVV002hh/LNCkfr9LuyzReO8Mv8jRkt1t7xDBL10mwRTv3Ak/oJYXAGMgXDGDGFljegp+lp0BA4sFPumFB6CuecjyzINFTp03ha8vSR+nTWSuIw/hOKrLOBdJ2DuOolNScWMGHYGusKDgh9kb4etnwjF5tKPRYiOXziUVkPfaYJPNHoNJdypfZiZRz3RKwCqqmHym24VFgDZYovr1zovrbIMDLsNxj0NL4ncjGuHijCSFy7pw42jOjgstq5Q+CWINafwsPDMKVCKhcQMFJUbf54BScvQLbiZrtFTIUGsbZIGtOEOQcv1SXWWOoRSdnyK8ozlM1gy7U+i/dzvZaPx8oI6e5yvqL72/VWiolNYN7jiW3u4nX1NVQuDSIJOYwdBaZqYK5WOcDBJkrh2RAfG9YxERuQY9IkNZP5tI7jqsy1oKlTvF95VFb/bTKnA3pcIXvndNJAVQ6Zyq6rN9qJVOwtQfSmlyA3AtQuE5V5ZRzM3i38g5sLAayFhOHgFifE59hfsmCvWwl+Fdd9Uq0GSQOdl5cOrBjA2vAg+nCQKhgDEnQEDrhI/9T7i+NHlYk0tOAhfXSDW9fNecjCpjmNg8I1CmVbBJfK17cPSXFJLSGdgN8QPFWG37h5oVujUNr/U4eG+UXDjH5h+vxd95gaevqAHOl8DY7tF8NVNQ+kdE+C+2EGNKibW4A8VNiONAoVOfUPhyu+EoWrls7BtgCiPEgAAo0JJREFUDhQniygGpQsjh1J0RKypiiFEQZVJeE7buqbmVOVgJJKVGSvp1z2mTW2cSJx8mXn/Ei7tcimTO0zGipVHNzxKUW1RM3dIYlJ+PkEoAd7BcP0fDoUFnCd+fYWwzFy/CO7YKiyQYx+E7ueL8+VZTq37aGW6y6l2RcSo0/L+9IFMGRRLtSwIRNY6Ky3NWmF9w+DahWIhAMF07Pfa3uof1FrkjmPZHzgF0/VLhQtUeZavqhzjhBfF+Ay/TYSIaDRi35lOY2HKp3DbBjGOtaVkrAojZUkE6X+FcVUPLZovxguFRaMTe03cu19UeekyTiQ2dxwjvCv37IbzXhaJ7ekb4JOxYqEEygo7kPr8r5R88y3WOoelyDG27WDpbmqI1eMfGOf5OpNQOO1WSi9hItkZPwO5z+VIyq7gqjipSGshcXrnDTexmMVCX3jEYY27cSncs0cscp4Q1hVu/kskMjZUwrfToDgFAN9uEYTdfZdjcdU66GBlusghKah1401rERzvsyJ9BfOPzm/meklM7kX3OhSWMQ8KRdWoUqIVQbCmSMyLsx6FB47Atb/CpFeEkg14NzjnXWk0Ej00OWjNDiVt+tAE3r1yoHh79crcUmEIhGAw/QeHwGz7vg4Bsj3ptPGctpz/CZbJn8D07x0KiyrsiQFXw927hIU2up84p9WJ8MtzHhc0NvAaQKZovz8pSyI4ujCKTnov4jY/BwtmCoUlbqig6avnIw+8lhK/bsiJo0RZ1JtXCn4V0ll4Wr+ZIkKerBYaKrVkLCincO4PWNQJ7iq0mzDkCT42vhXVz+MlSjixnU6DO4if3SeLghbqcq+2eeFtraaPd3HjohHVRYJOS1PFsXOfhQeOwtmPO/Fyp3XH6C++4fA7AOiV+zOaHV8BoPWViHv3HZS5JOkca8mugl3U2vhBSZ1LrmEb8Mi6R7C4WPIbQxIhh/OmCIUlYSTMWOFQWKCxpb//dMGrblgkeP25z4rj1QUiHEiFGEMtsXKB/e/EUF/m3zZCVIxU1j/vQLuw7dEj6tToALhpmX1/D5O2sUHoHw27GT4LedT9cO8+obCAQxgGYaybNk8YLM+YIegzMBbiBsPYh0TI79lPgEZHbbGe5N8iSVoYRekxH8aaNwr5piwdAuNh+o+CJofcJLyvoZ1FNclJr8B9+4RiAkLonnu+ENKBwsMRZLyzhKp165xllHZ2GntUrm1hYHJUX49MwZVOnYwMt22A8G7obeGZOtuaqtVIjI222IspOaG+EupsRureU+ChY3Dhe8LbouqxHZIEo+8T4fx6X+E9nHu+nf9H3DVThIpjU/KVFmSZ9VlrAUguTXL//s1AoyiXksz8mvkUtnltPjH4f6u0SJLEk8OeJEoTRVl9GS9uftG9EqCe82nrhes9MB5uWg7xZzhfq97kcdxTgnF0PNOZcBTrw8qn4YPhsPZ1kX+x8hlR9WvOOBGHCui0Gl6f2p8wX8EgtVqN88LWEiar90Ie/5z4PUglFLd3eJgKslXGlF9A7n0Pov1kM8ULnQsNMPIeYdG9bQOMvNNZOHJFZC+xkA29VZQjBGqLDcgbZwuLo3803LhEjLfBg0dBqxdC0e3rRUnS6gL4Syx2FUkW6g4cIP+llzg2aTKVf//tdGtLhyetPI2lqUtZm7WWSiXe36UV9zpmy57QSBiyFSqo8rIxRUVwkS2CgS59FN7tJ0LxrFY4+Dt8dwW8HAvv9ocPhjoa1xrEZmoXzxaKeHhP6O+m+p1PCFzzq2DKVhNypcOzEXb77UgG8X00RsFsKxsqWZ62HACr3JwA4+G9Xf5+fdvr5FblurlSNY4NNcKiiCSUj3OeaLx4BXcUP0M6w6yNcNYjzqUojUJpCK86iG72QFjyMBQli9CQD4fBO32Fhdf2US/qH8OEnmGOfkiSw8DQ0hCvruMd/MEmlCr6zj9V5U82mSj66COOTn+A1Ec+c+aB/a4Q8euXfQ6XfOhsbHCFd7Cwak79goZK2xy1StQfTbJ7rRjzENy0tHGlNAWSJDwrt2+AAdeIl1/5NJRnUpnjRfX2fRS9P5vkcedS8s23du8jrfC01Jhq2Jy7mbVZaxsbquxRnB6+V0u+gSudBtji6KP629pQe8otwmL9bn94b6BQUPL2ibDNN7rB650FnSr7NGkNQpkd+5AYo05nC6UovIdzHzRamPgSlgkviUfawh4lwHvAALwHDQLA2KmT/ZbZu2Y7utWSdcUFrmO2s2AnPxz5wf3F6nFc+qgoQ9vjAmEwcC2Hq8pD4Dqb9y040XHMqIpveTkO5l4Ae34UydRfThI88I977EaKmCBv3pmmVjqlRl7NZhHSEbnbJACqvISl2rkQQTsTq63p+pRU0v/3Akce+o3abNUaEzNQKCs9LxR7jPW6yHMf9N7CeDpjBfXlOpSPUVtoEJEI5joRvnTbOug+0XOfvIPhwnfg6p9FaHXWNnuJ/tIVO6lev57MW2aSccONNKSn215DodOWvXZ+TT5Z5ixK60qdjqvr+7hFC8ZfVkpe63WN77EZjBzVT61Qmg7fXQlvdhd7vZjrRcjst9Pg5QQx91Y+La7XGoRxYvD1MGuzCHfV6EVoqCt6nA83LgafMFHhTBHLdDqiX3geAF2Uwzu6NmstRXXCo2NS53m1ETVyDXP2zznudv5N/L8NDwMwao1c5nMZn1R/wsqMlazLXseYuDFur5UlxISK6AXX/AIBbtxqvS4WYTbdJgrXpDv0u1woPxVZIpxi1Quw/l1HYmvOTvj4TDjvRRg6E61GIsrPQBqg0WhaHh7WqPM4NpWjaTnKbDWzKmMV2/O3Y7Fa6B/Rn/M6nIexiX0Q1MpU5fLllH40G2uleKeGJBeLQL/LnT1UzUFngMmvwf1/OI5V5Itdt0fd63msXRHSCW5eAb/OFBY+QK6vBcS4mHNzybp9FoGXXIK1wxmAd7NNZldl8/zm59mQvcF+zFvnzY29b+Tmfjej1+hV1cOaWRWbOG8X0LQuTNZuiLcx2Kwd8OFIsXcPiHn24TCRPKpAaxSLV32FUFDUgkLHMXDHZs991BmE18I7GPb/aO+LJElIegPUO5STeQfnUW22WT8bbcfdMihj5qP3YXDkYHbk7+D17a/z1llvebhDEv+0tn4qJUpdcenHQkgccLVjTNXoMAo5tAtScTJSeaaI797yCfYBrykW8fZ9porF2+hPfJAX9vo9ksYuDMmt2PATDwu7p3U4vSKd1ZmrKasvI8E/gXGJ4wgwtCxQ2VyQT+rl06g/LHZtNuXkIJtMSDalk45nwozlreg70Ocy5E5/2vM2lMRSOo8THpmWQO8tFOiYAaLCGrKTgGKtqiL/hReo+PNPYt98o0VNmqwmvtz/JXP2zbF7FLSSlkkdJ/HgGQ8S4qXKlWn2czVBp4owZLPSNqp0pC568M0URylWEB5gp1wESQiESkhd7CDHqZBOcN1C0a6HyWE9Yya7j2TQs2Su6tmgCw+HrEIkvTAWbc3dyta8rfTkYltXW28SV99za/9b+XD/bD7Y9QETO0wk1Nu9sisr/w2+QYTcuaPD0feLd+07zV4AxAk+IYKGjywWxr+0deLfmleFgRGEASNpJVw+F+LPIMhbR6HSXY1GldPSCmVN44EP4/5zFNYUsiR1CWkVaQQZgzg7/mz6hvdt8hGONVWm6NPPKHr/fWSbx73u4EG8e9sqWgUlwP+OtE5Zih0EE16Gra+J9sp1joTxKZ822kvEI7qOF8bZby+3j7elxhG2VLNlCykXXUzkY44wqeZ6eaD4AK9ufZVdBbsA+PjXjxkdO5oHz3iQToGd7C00T6ZN0aktckGZc+oIFuUJioFhx1fCkKDQ5l/PwdY5Iv/KHdSbkXoFiLDkSa83yquxI2aA4LPzLgFsnkVJg8ZXRAYonharbGX27tmE0NPje7UEyrB0CuyMl6Ga+wbed1zt/dv4f620AETrormqx1V8fehrXt/2OiNiRqDXOCz/agKTvYOFVuyJoL2DhcW/KXQ5F+4/IEr7HlkCmz4AVaUKQCRYLnlIhDJd/KGzubU14WGOKx33u8C1rOLhksM8vPZhUspT7Md+OvoT7+96n1fOfIXBkYObfVrBG2+itarc+u1ledLp7JbM2mIDPhEN7heypmDwhcu/EgLnkT9FnD61RL/0Eg0pxyj+4kvKFy6kNOgADLi3yaYOFR/itpW3UVJXgk7S0SesDyV1JWRUZvDhng/ZmreV9855zzkx0AVOQ+Phe8qyDEpOi90yZD/r3FB9ufgXGA/lmeJY0VFHPkLfqUJR0WiEtUhraP330WhFuOPfObB/j7M11NaV8vpy5h2cRxCxHhppKRxj8ujQR7li0RWsSF/BppxNjIhxWK6cxxER792hicoACcPFP08IjMN822aW//EL53X3RbfnG+dNBEEoivt/htzdInFddoSdIIFk31OhFcKQff8kd8KuA7XmWl7f9jq/JP2CVdX+69tf56EzHuKSLpc0+6iKFSvxKjqMZDQ64qbbYGVvBIMjlr08zZvQnlX2imAthiSJkFLfMJh/A7JN6Q285BK8+/ej4M23qN21i9RLp0CvBx33uEGtuZb/rf4f67JFVapo32h8dD4cKz/GopRFbM3dykfjP0KShJDg2dHi8k3cwOERta0hds+K+h7b73l7RY6K1Sx4vrkWkETO0KBrRbiU3kuEdFoa3HuSm6HdrJCRdDunAyx/D9wUsZBlmQ92f+DUr7bktKjXosu6TuHvnL84VHKI93a9x7Mjn/XYZXnoTLjgVc/vEWhLZvYESRJeQBBVxfb9LAwMisICwhtTkSU8L+e96ByyI+H4Rm2gU0fFOXWXJNVlMl8f/JrZu2bbcoYEPtv3Ged1OI9nRjyDnzr30w2stXUUvvW2y0GXb9SGNVZWRYbUlxqE/qsziByY1iC0swiZmzsZio6i8TJirakj/vM5FM+ZQ82mzeQ9/TT1nS6GhE5NNvVnyp88sf4JzLIZjaTBF18q5UrWZ69ne952Xhj9AqODzrK9QDMe0aamsatH1F0EixKamC5Cye15fyAUFv9oETrX7TzhsZetIsrBnUHbk8KiILSziN75YhwgI/mEQoVz1/7K+IvDJYcZQQ/b67U13k7cF+IVzECf8z0WtzlZ8f82PEyNGb1nEOIVQlpFGj8d+cnzhWc+1KQFQpZlYa1siTLhEyKSOW9bLzRxBdf9LvIvNDoRfjL3fMc+CTaLth0t9rQ0DgWzby6pumxt1lquXXwtKeUpBBmDuKbnNdzY50YifSLJq87j5mU381e68y66nhA6cybhTzzeun42A8UyCGAxScIt3t1z5TeP0OpELOk1v4gSzIDGx4eIBx4g4csv0Yap9uKxuHfBZlZkMnPFTErqSugR0oOFlyxk3uR5LLp0Ea+PeR1fvS/b87czc/nMFsR2NwOTo4CCIxFffDn7Rl4qLxo9LhDufW+b9bjfFXDvHpGPENnbofjqjG4Xu+K5cyl8fzbm0tJG5xwdkSDRtpGZLVzDMesl5h6YS5WpyiG4t0NAc/eQ7lzR/QoA3t7xtpOwrkAGURK2KYUFMJeWYi5qLo8NzFpv5G4TRcW0m5aDr034HnWvCEsMiBNJlJ9PEImuNkhqA0Nr5r8HYUj9lYpri7l+yfXMPzofq2xlRPQIpveYTqfATlQ2VPLkhid5d+e7nvmQak77DBlCpz8XOc61B62qvovWaBWhbkr8e2vR+1K4fZOwpiOU9uDp0+m4cAFevXtjKS93bC7pZl8Li9XCA2seYF32Ory0Xrw0+iWWXbaMhZcs5Pvzv6djYEcKagu4YekN9qo6HudqSwRDi3thyClfQrHS+4TCLX+JsB4QFvPb1omKfZ3PcVRf0urcKizVW7aS/9rr1KekNtklOdYWxmwL71OnXW3K2cTOgp0YNGovfBuUFtWY6TQ6HhsmLOsLkxeSUpbicrUqnG/cU02Oq7Wmhvpjxzyed0JIRxH6dPcukRwOohjM3buEx1UxBi5+0OH5laS2eURlRcFT6LTxvRarhSc2PMEb29+gzlJH37C+3Nb/NiZ1mIRW0rIsbRnXLb2OsrqyZh8nGY1Ev/A8/uPPtT2/PejUzd9jHnKO5mgp/MIFnd6zx25gMCQmkvD554Tff79zmx4+94r0FTy67lHMsplxCeNYcvESHg58mIUXLmR49HDqLHU8tPYhVtjyJD1+rZaEh1mcPaIuwpHtnCqyZNxTznLaOU+K3KqxD4n1xugnvCqBsY2eb6mqFsrbDtdNLF0QEO3IVdNqVS8oYbFa+GCXMC7Yc1KOe0lt5zDGfwn/KS2Av8GfOwYIa87Hez52k5MAIvbVs2OqPiWF9KuuJvmccZTO+6blD9dohHv8gSShsHQcI/IvblgsFrXc3chKSWAXYsi8ZSY5Dz9MydfzaMjKaty2AruCIrkess/bLblbuO/v+6iz1DEqZhSLLl3Ew0Mf5v7B9/P7Jb8zIXECZtnMA2sfYEuuS46Kqh2A6NdeJeL+++xKhtweDNap00DccJFArm8+hMstJAm6nIuseNVsC5fvsKF0/PUXNAGB4jKVoqSg2lTNnavupKy+jF6hvfjivC9IDEi0NSsxseNEvjzvS4KMQewv3s/uol227jfjanFzXrZYqN6y1XG5kuTuaonveKaovz/5DWH19w4WQvXMNa1y91uqqil45VWKPviA5LPPoeTrr5tIUHXvFShvKOPbQ98CMCJa8WYcX3iYcvet/W/FR+fDoZJD9sXLtXm5iRwpuaGBwg8+IGnkKI5NmuxUfKFZJAyD+/YLL87Yh8Xft60TFvH6ClHgQC0R2uZUzsOPkHLhhWTddTelP/yAparK4yMc65SLBdf2d0VDBbetvI1DJYcINgbz2YTP+HTCpzw27DF+vehXZg0QG4vO2TeHOfvcxyorYQe+o0eT8OUX6CIcXpD2KLHs1IbWKJJ5E4a1vcHIXo4cJNuCbYiLI/G7bwmcepn9Mrt3Q4V3dr7D2qy1GLVGPhn/CRd2vtA+X/uE9WHepHn0C+9HZUMlXx2ca3uBZvrjjk6tVqo3bqT0u+9tfXGlU9XFE1+BEXcKoSeyt6DZq3+Gu3aJhOcWovDddyn54gtSJk8m99lnsar2Y3Hpndu+yMDs3SKXZVr3aTgcScentCDBgIgBnBN/DlbZyvu73vd8owfeIssyFUuXkjT2LFLOv4DqjRtb3hmvAOEJvm29yKHyDRPedSW3UxXWI0mSPTys4o8/OHb+BWTMuJmijz/GlJ3t+RkeckLVocDPb36e34/9jlbS8ujQR/l28rfcMeAOXhv7GvMmzSPMO4yk0iRuXXmrPWRRDZ3NeCbp9XT4/juCpk615y62qGhAc3BZl+WLPhBKX1uh1YkcK1vflLENm3kLCZ/PQRksjaGx1+Fg8UEeW/cYMjJTu03lrbPeItxHRFEk+Cfw8bkfc1nXy7DKVl7b9qqtw829n/sL6lNTKZ7zueijjWe4jXgYcpMoPHP9IlHop/skmPIZ3LVT7Gqv8xwur0bFksUUvPEm6VdfQ8bNt7RIVkOS7DQlSbA0bSnHyo/hb/An0T9BXNpGreUfTGf+V/Cf0mLDlK5T6BTYibL6Mr7Y/4XjRAvCd8p++YXUSy6ldpcQTutTWmgZUsMvQlTMUqgnYZhIQA9RlcC1ndOGCAt63cGDlP/2u0giHz+BzDvupD7F1aqlWoTcCMiSJJFSnsLdq+6mwdrAOfHnMHvcbAKNgfZLffQ+vDrmVaG4WM08uOZBD8nQAv7n2qxBikVAZcGyNjRQ9Mmn5D79jNPGnS2CilHLw+9snzrwStiVyhKkj4gg5o3XAcfCocab298kpTyFCO8I3j/nffxV4TAKeob2ZPa42Ri1RpUF1w2aqG5S8u23HDtvIpm3iPKUktFoT3Z3tYZV7ThI6i91VFZ0dLQZ0UPEy7YCcoMjHlmuqyP/pZfJmDHDrdfFMa+cLT/zj8yn1lxL79De9I/oj9PJVkJlbAIgxCuE63tfD4jkYbNrMqIkeXxUQ2YmqZdPo+j92SDLWCsrsZSVta5DOqOwgiuWb58QkVvQ1yVHS6NBH+0IE6hPSqZyxQrynnmW5DFjKZz9gXuFqRGtOhYus9XMvX/fy+GSw4R4hfD1pK8ZHu0IcdNqtNze/3YeOuMhAN7b9R5rMtc0eoQhThTkCJ42DUmv9+i9rfz7b/JeeLFpj5s7qOhdjhnkHOPdRtiFNJU3RWM0Ev3883YDg9bX2YCxKWcTcw/MBeCFUS8wKHIQrgg0BvLhuA/pFNiJ8oZyACyeBEI3tCrLMuW//07KpMlk3OQoQ2z31to/oxiThqws0t5bQ+bv5Y6iC76hIjfAXU5HE1ArKWXf/0DKJZdSd+hQ4wtdQg4Vuk0uTWJf0T68dd7M6Ovoe5uEISedRTznroF3ISGxMmMl+4v2e7iv8bOstbXkPPQw2ffeZ8+LNOV4yB9oClF9HUqgJMGoe0ReixqSBl2U86aD1Rs2UPjOuySPn0D2gw9hys+nMVysfi7GhY/3fswvSb+gkTS8NuY1rup5lROd9Q3vy+cTPifYGMzB4oM8t+m5RsqiLkx4xrz69sWrVy9xUJn/KhqzVFVROn8+prw8WoNGik+fqa263yPstOpYo3xHjCBo2jQADB07Ol1ea67lwTUPCoNp7CgeH/a4w5tgg1aj5ekRTzO542RMOHi+OwXbUxXAukOHyJg5k5RJk6ndKbaB0NlkKVfZSJZlijcVkrm9A5awAbZO6KHfNBHK1Qqo6bR6/XpSL7qY8t9/d3ut/W00GpVxQeajPaKYyQ29b0CvbSbUrFkcv2HqROL/fU6LAp1Gxz2D7uGev+/hm4PfcGX3K4n0VW8eJOH6sWWLhfyXX6H0GxfPisXBDGr37aN8wUJqtm3FlF+AxssLrz59iHn1FbT+jYVdJ4R2ForLW9OAHBFvCsR/9CG1e/ag8fHBVFBAzeYt1GzdStVff1G1di1hM2cSNut2h1XejdKiHKq31PPA3/dTY65hcORgXhv7Gjo3HiWdRseLo18kszKTQyWH+N+a//HVpK/s+T9u2YSdwYrxaMjKIuuuu6m3LaxBU6fi3VdUEjLl5VG1di0NKalY6+vQR0XjO3wYXn372hWK5iyAdQcPUrVmDfVJycimBnRR0fiOGI7v6NFurTuiTYXBap2OS3pbJSyDs/V2S+4We9ndl898mQgfz7H6/cP78/KZL7PosGCQedX5QILnF7C9X+2BA+Q+9jj1R0RatyYwEL9RIwm48EKH50fl3Sj77nuKXn4ZgLIFCx1KYzPIffZZKlesxKt7dwImTSTgggsciqQkEfX0U+S/9jo1mzaTdsWVxH/8kVPFIU/5Fn+k/AF6uGPAHZQds4W2HWcivhrX9bqO7w9/T1pFGr8l/8Zl3S5zvanRPdWbN5N9z71YysvRhoRgKRGxyUritLWmhvI//6RqzRoa0tLAZEYbE4NhWAs8BDqjsMCFdkH++StxTJKIePBB/M89F42vD9aaWur276f8jz9oOHaMotmzKf/jd2LfesuRUKvuu1KGVTXE7+18j2152/DV+/Lp+E/pENjBbXeu7XUtGRUZ/HDkBx5Z9wjzL5xPnL+qcqDrp3Ap7mFtaCD/hRcp+0mEyhq7dyP4cqGUWaurqdq4kbp9+7FUlKMNDsZnwAB8hg1zlOZUC0NuSNZSVUX1unVU7dxF5MEDFO7bj2//fviNHYs2wEMRARtPlVyUdUmSROGA+ganF6s2VfP0xqcBuKL7FUzs6LkSUqAxkI/O/YiH00S1rZzqHKCxguN4J/FSppwcch59jJotwvOs8ffHf/x4fEcMJ2DiRFv/HBXkarZvJ+PmW5Btyqq1rs6pnKknVG/cSO7Tz6CLjMD/rLMIvPRSdKGO5PbQmTMpX/QHpsxM0q6+htjXX8N/3DhHA8r3cKHT1VmrwR+u7HElYd6OyndtUVqsKppTlJYuwV24sPOF/H7sd97d+S6fTfis8Y0utGrKLyDr9tupO3hQhMjYjEoKncpmM5XLl1OxdBm1+/dhrahEGxREyI03EHK1+71znND7UvCPRn7yTqAaJAn/ceNImDtXeB4kifpjx6hcuZKaTZup+OMPqv76i6hnnibwootU3XYN43QYFzZmb+Sj3ULIfGL4E0zoMMFtVzoFdeLNs97kluW3sChlEQMjBgqPl8s4qoVw+3yyfdPavXtJmyZCZgMvvoiYV19VjWU+NVu3YsrLQ2MwYOjSBZ/Bg1V06vKdXZQYa0MDVX+vpnrzJsw5uaDXYezYEb+xY/EePNijcmAfGxda1fj5AaVoXKIX3tv5HhmVGUT4RPDaGPfyhzIOz496nqKSO1UPw7PLQAnha2ig4J13KZk7V7yjJOE7YgQ+I4YTdMkljfpqrakh7/EnqFwqchhrpmzD/5xzPDzEAVNBATkPPoSk0+EzZDABF16EIS7WPoe9Bw0CjUTt9h3kPPQw9UlJhN93nzNPs38Dx0uV1JWQXpFOsDGYq3tezcdrFjjevU2QXH6eWvhPaVHh7PizGRgxkF0Fu/hoz0c8M/IZ591tVQxWNpvJeeRRKhaJePCwu+5E0uoofOcdZKsFU14eec89T9WqVU7PsFZUULVqFTVbtrRMuPQNpaHPncBjWG0Lt3f//nj37++4ZtYs6o8do+CNN6n6+2+KPviAmu3biX3rTbG4Kd12E6s679DXHKs5Rrh3OG+MfaPJCmFeOi/ePvttLv/jcvYV7WPO3jncPuB2z323l3y1OgmNCuSGehqysih8620qli5txDQLAWO3boTffZcYKydhSKUYHjhAwcuvULN9e6MulM6bhy4ykrDbbydo2uUOBchqJf+FF6nbY6ve4xoP72riR5RLVQtCQ6Obr1o2PnE8672E96tcvUmcvXlnRbJ0/nzyn3se2WRCExhI+N13ETRlChpvlzA4221hy5dTlJbuaKOh5btdV/z2O9aaGqqLioR18YMPCb1BeDEknY7gK6/EZ/BgMm+7HVNGBunXXEvCl1/i1d22j4KLdVH5s95aT//w/oyOHc2fKattZ47TuqP6PH4GP27pewuvb3+dj/Z8xPmdzndmvy7zqHLVKrLvuRfZZMKrXz/i3n+PlEmTsdbUgMVM6Q8/Uvjuu1hcPQrp6QQhw4ybWtA/CVP365HNX9k7rPXzxe9MR26N3+hRhN46k8olS8h/9TVM6RmkXzmdqGeeJugym+KlEn7E3+JHYW0RXx74EoDnRz1P95DuTXbnoTMe4nDJYXYX7ubx9Y/zxXlfoHVRzGU3fMGUX0DuU0866AKQa+uw1tRQPGcOJfO+sVu/FRQjvL8hN95A6PXXO4eDqr6FtbaWoo8/ofTbb7HaQuQCgfIdOyn/BiQfH4KnX0nY7beL3eRtqN68haIPbcnWLu+gfhG1DPXW9rfIrc4l1i+W+wff3+RYAcT4xTA8ZjgchnqTh03bVAJq9caNZN//PyxlZUheXoTddhsh116DxtfX7T1Va9ZQMneuvfoTYBdmmkPFihWYMjMxZWZSu30HhR9+RNitt9qLJ/iOGkXojJvIvu8+qjduIuuuu4l59RV8bIqTJ+NCbk0uPsE+3NhbyTeS7e/XWqgVHbUwO2vALBanLmZz7mY2525mePRwF6O24z5TdjbpN9yIKTMTbXAwse+8Q+m331K5fDmy1UL15s3kPf2MvYSuAmtVFSVffNkypQWQowZRvKVa6SySVovvcIdxwnf4cEKuvpra/QfIf+EFanfvJuehh6nZvoOoJ58QhiNXR4tqyB5e97A9zOnybk1XyTwj6gzuG3wfb2x/gze2v8Gw6GH2UGPHGKn+UK2pylqhwFQgPPq1e/dS+P5sqteta/Q8jZ8fQVdMI2zmzEZ8Usnpka1Wyn6aT9GHH2IucI4SqAKKP5uDV+/eRDz8EL5DHWtg5erVFH34kSPJ3UXecLOksiN/hz2U+JkRzzRb+dCgNXBT3xnsXlptb7OR6K1inqb8fLLvuZfa3bsB8J84kYj778OQkODhHsiYcTP1Rx3VNmVTyyJCqtetsxswqjdsoHD2BwRNuxyNj/DKGzp2IPr55yl89z2KP/mE4s/mYC4sIvrFFxoZmCXJ8d1zq0VUy019bsJX74udTtu6prrhl6cS/gsPU0GSJPsCtyB5AcfKnMO8FAYrm0xk/+8BobDodMS+/Rbhd9xhr+xUufIvUi66WCgsWi0BF15I3Aez6bToD3SRwntjKa9ocb8K3hTlXc05nkOyjJ07E//Rh8S8/jqSjw81W7aQNv0qGjIy3AYxKgxqc+5mtJKW18e+brO2NY1Yv1ieGPYEAJ/s/cTh9ndDAIpVqGbDRjJvmYmlvByvvn3tRFz63fekXHQxFYsXg9WK94ABhNx4I6G334b/eeeh8fGh/uhRsu68i6x77nVe5K1WZKuVwvfeJ23aFUJh0evxH38uEQ8+SORTTxJ81VXowsMx5+eT98wzZNx4k52x1yclUfrdd46+NmKwjcfs7R1vk12VTYxvDPcNvq/ZsVKgtY2Du8RxNefIf+Vl8p58Ctlkwu/ss+m8ZDEhV1/dWGHBIRh42xQWfaJgwpLWjVCnfi9Z7KFjys0VQjsQfNV0dFFRmHNzyX9ZbMioJCoau3alw/yf8OrVC0tJCRnXX+8IQXGZV2oB5M6Bdwor+HFaczwx5it6XEGUbxT5Nfl8f/h715e0/1qxdBlZd9+DbDLhP348ifO+Rh8Zad8AM+OmGeQ98wyW0lL08fGE33sPCV98bk941dQ0jjX3hLL5jo0vPS0IkiQRMHkynX5biN/ZZyObTOQ+/gSFH3wgxs+DBfdwqRjz63tdz/jE8c32Ra/V8/KZL+Oj82FnwU6+OviV/ZyrhVSSJPvz0q+6iro9e9EEBqINFnlQNbt2knLppRR9+BHWykr08fEEXXEFYbNmETj1MnRRUVhKSih88y1SL7uM+sP2ws92BaZ2925SLrqY4k8+wVpVhSExkcCrrqJw4kQCr7kGQ+fOyDU1lHz+BSkXXeRkgMh/zWE9dhL6PWBz7mZ+Oiq8RM+NfA4ffeMkdnfQ2eaER2HANkblvy4g4+ZbsJSV4dWrF53++J2w225trLCo7qk/ehTZZMJ3zJmOd2kmL8FSUYEpNxdLaZnoX0w0Xr16IdfUUPj228IjCEhaDdrAQOI/+YTAKVPAaiXnoYep+O0324Oc+6Km06t7Xk2wl3O+W1tEIXWb6vkV6xfLtG7Ce9C4eIbjuobMTNKuvRZTZib6+Hg6zP8J32FD7blBBa+8at/zQxsUROhtt5L4zTziPhTJyebClm+QV2MTYAHkOs/07d2nN4nffkPYnXeCJFH2009kzrpDhPu4JIUq72+WTZTVl9EzpCePDH2kRf25tte1DIseRq25lsfWPeYIeXXHQ2zGtcLZH9jXCjvMFvJfeZW0K64UCosk4dWvH4EXX4T/eeehi4wUCt7nX3Ds/Auo/Mu1qI6MubCQjBtvIu+ZZzAXFKCLiCD4umuJev45Ip96koCLLkTy9qbuwAEyrruevJdesveh/NcF1O1VlfB2NZK66M615lqe2vAUMjKXdrmUM+POpCUIMKoiVNwq2ArNJZE29XJqd+9G4+9P3AeziXvn7cYKi7pTCFrV+Ps7KYjNQZZlp3Bfn2HDwGKh7PsfKPlcpBtIWh2SRkPEffcS8+oroNVSvnAhOQ8/4ohwcGNgqLfUE+YdxhU9rnB6v7bCRd8+5fCfp8UFSgLhqsxVvLvzXaZJdzlOymKhyXnkUSqXLUPS64l99x2H69BmBbTavAleffsS88rLGDs7YiB9hgyh4s8/yX38cSqWLiXwwgsImDSJ2t27KZn3DSHXX4fPYOeywhpvbyxgFyKaQuCFF+DVqyeZt96GKSODtCuniwQ+cBIg6y31tleSuW/wfS0qZaxgUsdJrMpcxbK0ZTy67lF+uvAn9KjCr+wWXJUXwWTCf8IEYl5/jdTLLqMh+RgVf/4pxuSMM4h87FG8ejrXH7eUl1P8xZcUf/45lcuWOZ+rribr7rupWikYb8DkSUQ89BB6VXwyQMQjD1P2ww8UvP2OUOSmXUH8Jx87SrwqXXWjGICDd2zL22bfLO2Zkc/YLB4tg6K8NbdpW/nPvwAQfu+9hM68pZEipUZ9UrL997CHH0IfEEju44/bhURTXh6F772PPiaGsDtmUbNpE6Xz51O9dl2jhN2wO+4g4qGHKProY4o/+QRbZ+3ndSEhJHz5BRk330Ldvn1k3DSDxO++bRQrb7FakNDQL6wvw6KE5fK4GaM78xxij6U7B9zJExue4LN9nzEgfqzqFnFT5erVZP/vf2CxEHDBBcS88rK9qpM9Vj47G8nLi4j77iX46qvt5015+VSuWEnAnj1k3XAjAeeeS9DUy5AbGsh/+RV8hp5B0NSpTt9InR+jc5mHrtAGBRH34QcUvvsuxR9/QtH7s7EUFzcKOzHbqlGZrWYGRQzinsH3tHjo4vzjeGToIzy18Sne3/U+o2JGefbQ2J5rra5GHxdHwudzKPr4E8oXLKByyVL7O0U++ij/1955x0dVpX/4udMzk947Sei9I1U69gUs2BuWdXV11bWsXX/2XV11dde1d1cRFSygYEdReu+hpJPek+nn98dkJjMkgQS4mQTP8/mgmTsz977zvfeU95z3vCds5oyA3y2cTqq/+IKSv/8j4Ln0vAnVn3/heTbtds857rmbsOnTcbpcrFmyhLjTT0en01H3ww8UP/oYjvx8cq6cT9JDDxF59lxs25vXaWgjImgTpSks7JeOzYb6zq3R4aSNfpAf1YsWARAxezaJ//eQbzPV1nA3Nvj+DjvlFJL//iS7hg7zHHC5EHY75a+9hi17L0kP/x/O0lIq33+fmmXLcRYFDlJFX3oZ0VdcTs1nn3Hw4Ud8s1VeB1zR60l65GEUvZ6qDz+k5L77Cb3oQoQ3VKzpmSppKAU0GLUm3/owf45mpsXfGTl0oOLaIdeyKHsR28u3syxnGSh+dacQOEpKyL3iSpyFRRgyMkh/8w1fPe7tEHtnkCMvOJ/4225HG+o5h9dZEXY7u8eNxzJhAlHnzyNk1Cgq33kH255sYv/8Z/QJfskm7M0dfcvEw3eUFa2WuD/fgGnQQApuuZX6FSvIueJKFKOnvfNFvTZJ5sJNmCGMf07552GjFvzRKBoemfAIZy8+m81lm3l1y6tcN/Q6v0+0dAi99Vfs9ddjyMyk8PbbaVizhoY1awBPqFjsn/+MIS2t+SxuN3U//EjJU09h37ePhkOyJ9p27KDgjjtxFhWhhIR46sQLLmheRwlw0UU4KyoofeZZqj76iMq338GenU1KU4RJgHaHJrE5pC73Dwu7bfRt7dIKmgcBA5VpSeOmTQAYevUk7T//ad1ZCbCoCY2G9DffoOTv/6Bh1SrfAF7jlq1ULVhA1MUXYezTh9pvv6Xmiy9p3LAhYEYq/KyzSPnH36lfvZqi++7DkePZM034ZW2MmD0bxRRCwV//Ss0XX6BotSQ9/liA0+JwNT+nVw++mhBdSICtx540pXu6LXKmpRX+MvIvaBQN3+d9T0nTImqhKOB2U/Lk3z2dbZ2O1BeeD4h19E+5GXneeWS8/16AwwIQevIkXyNTv2IFhXfcyd7TzyDvhj9Tu2wZORdfQvHjTwSMoOibZmcSHzjCHjBNGHv2JON/72Ps3x9XRQXlL7/cZGCzfd5QpREJw7lswGXtlabpNAr3jb2PuJA4DtQc4Ln1z7U+ou4XyhExZw4p/3wajdGIs7S5soz7662kv/VmC4cFPB2U+FtuJuODD3wzVF4qXnudum++RTEYSH7yCVL++c8WDgt4MpVEX3YZmZ98jCEzE+fBg+RccimNfuEvcbfc4tsl2odffdDgaOD+Xzzan9vn3ID9QdqDV/ZWF/geMuqd/OQTxF73x8M6LAChTc9d6amnEHnJJaDVeC9CzdfLyJ4xk+pPPqHshRfIveJKcudfRe3SrzwOi1br2fMG0CUkoA0PR9PUcU979VW0UVFYJkwIuJ42IoL011/zpJmtrCTvqqtxlngXqDY1pE0dl/P6zmsebT1kneqhCLegNLeW7HUl7N1QQnlBXUBl7D5Ms3Rm1pn0jupNrb3Wt+BcoICAhg0bKLj5FnC5mmK9n2hOQwu+MgiQ8eGHRF9+ecD7IUMGozQ5stZ16yh58kmyp0wl94orqPniCw7e/wC5V1wZuEi36fsx1157xBkv8JSj+Jtv9pRrRfFknvKW+yb98msLmszV8tTkpwL2kGoPc3rNYWraVJxuJ3f9fJdvsKIttBER9HjvXQw9eiBszSOHodOnk/XZYsJPmdVyXYlOR+ScOWR9+QXmcYF739h27qTw9tsRdjuh06aR9cXnhM+c2eralLCpU8lavIiw004Fh4Oiu++m/M030TclDjCPHk3U+fM4FP+2+5l1z1BYX9jusDB/NK3MRPhj90u9Gz1/PklPPH5YhwXA1M9Tr1kmTSLlqX8EdABte/ey/4ILKH3uX9R8+SX7581j7xlnUvHW280Oi1/Hz9irF4qiEDF7NpkfL8TYpw+a0FAMPZrDiRSNxhNueP75IASJH3xI429N2R6bymJV0w7jwxOGBSRcOdL+FrYGBwf3VZO3s4KKovo2dTp0ljEmJIYrBl0BwPPrn/ddQKDgrqkh79o/4igoQN8j3TMT6lePK4ZmfZOffoqkBx/0OSwA2pgYzKNGAeCqrKTmiy/IufQy8q66iuLHHqfqo4/InjyZmqVLmw1q6lwb+/cn/JTW15scStiUKfR46020kZFYt2yhca03fe2h7Z7g4fEPB64haweJlkTuHutJE/3SppfYVrat1c+56poHnBLuvou4m25s3rsLz7qq1P/8h+QnnwxwWMDzbIRNm0rmok+JuvTSFufO/8vNOIs8jmPmJx8TfdllgQ5LE7roaJIe/j9S//0CitlM/cpfyZ1/Fe4m2wxZWcT/7c4W69P8ZzDXF6/vUFhYwPX917y08gj6OwchQ4eS8e67h3VYANDrMfbtizYulsyPFxIycCBKU5sqHA5K//1vDpx3HlUffUT+X/7C/rlnU3DjTdR+/XWLEDrvtSxjxpD58SeETpkCgKlv4IBR+CmzSH32Gc+My+LFlDz1dED2yIK6/Kbfq+XcPn5JEo7gazTW2tm/uYxtKwrYs7aYmrJDZxO7d3iYnGlphayILOb2msvHez5mXfE6kvEsFi9//XVc5eUAJD/2KKGTJwd8zzJpEsZPPiXiD2cRPX9+qwvVImbPJmzWLOwHDlD7/fdUvvc+jry8gM9UvPUW1m3bSPnXc+iio/0y57Tfx9TFxdHjnbfJu+pq34iD/1PqXTg5MnFUmwvqDkeEMYL/m/B//OmbP/HejveYlHiy7z1vwQsZPgxbQgIJp59O4u23+ToqUeedS9VHC0l8+P8In3XkRiNk0EAyPlpA0V13U/+LZ+d5b2hE3F9uImL27COew5iZScYH/yPvT9fTuH49xY8+Cngq2Ng/Xtvi8/514fMbnie/Lp9ESyJ/HfnXI17rULwzLY17tZTm1hKX3jy9reh0nhC20lJSnv8X4TOPHPoDkPL0U1iLiti9ZYvnPF5H+Oefqf/554DPNqxahaLXEzlvHhFzZmPq1w90OpyFhWgjIwNGxEInTqD3jz/4OuD+aMPCSHv5JXIuuhh7Tg7Viz/z/kDP/5pq06iA9MqtP1sOm4tN3+Wx9Yd86qsD1+GERhsZOi2NwVNSDzvsrdVouXnEzdzw7Q38UvgLU/B03uzZ2eRd9yeE1Ypl8skkPfJICyci/vbbaNy4iYS770If3zKZgrFXLzJ/+J7v/vcBo0JCqF34EbY92QEzCQ2rV7P/nHNJ/ddzmEeMaF7Yqu3YWFDUhReihIRQdNfdLcID7E0bnIXoQnwpQDuCoig8MO4BNpVuYk/lHl7Y8AJZTAUCO+fRl1+GPSeXpMce9WXUMY8ZQ83Sr4i+7FLi77jjiI6YLiqK9JdfpuSpp6l4yxOO5h0hDxk2jNQXnj+iM66xWEh5+mlKU1Mpf+VVSp5oDg2Lv+MO9CmtbFba9DO2lG3hw10fAvDQ+IfaHRbmxbvmx1AZxqZv8xg0JQWt3720TJxI/c8/E3PN1cTdemu76s3I8+dhGXsS+h49Wnw+5+JLAl7bsz1OkWXSJKIuvBDzmDFoQy04y8s9M2B+nVBDjx5kLl6EsFpbzBIrikLi/ffhqKigfvlyKpsGrXzZw7y/p43Z4kPD4wr3VLHuqwPk76jE7bd42xxuYMDEZIZOT2tzTYuXywdczgc7PyC3Nheby453vDTv+huw79uHNjaW9FdfRRcX+IxHXXgBrpoaYq/7Y4sIBPB0xHu8+w7u+nqsO3ZQ/fkXVH/6KfUrfw34XMEtt2LduYu4v9zkGzlXWqnjDkfIkCGkv/0WuZdf0bz+remnOv1Cl1vLUtcezsg8g+9zv2dZzjLu+vku/pH+3xafiTjzDKxbthB3881Enj0XAH2yJ0uhxmz2DFb26nXY62gMBhLvuRtTv76U/ut5nE0DL97kJOlvvekbKD0cYdOnk/HuO+RedTXWrc3Z4aKvuJyoeS0HF7w4hZNHV3rCwub0mtPusDAv/k7LTx/uZsyZmVgimp1bb7KYkGHDSH/t1dbDNg9BURQyF36EEKI5aU9TfVD0t7sCPuudOfGuDwqbPh1DWhpuqxVnaSmmQYN8n9WGWkh98T84CwvRJbfcdDJsxgySHnmEorvuouJ1/6y1GpxNzrVOqwuYtWsr5Lo0r5bVn+0jZ2t5i2YzITOcUadnkDE4tkVa/e6GnGlpg+uHXY9Ja6K0sTlW1uuwxN9+e0AmES/GrCyyFi8i5qqrDtugaUJCMPXvT9z119Nr2ddEXdLceEWcczYai4WGtWs5cMGFnlzx3kq2HaO3/mhDQ0l79RXfon1v41ZeUIexztNx1nUwzaY/E1Mm+jb7e/DXB1u8r0tIIOfWW4i9NTBDRvxtt9F75S/tcli86OPjSX/tVSzjA2c5TAMHtfGNlmgjIkh7+WVChg3zHWszF39TyW5wNvDuDk92uPbsXtwa9jjPrJazSM9Hj6/h54V7cDmbsyFlfvIxvX78sd0OC3jupX8n5lCHNvryy33HTIMGkbV0CYn33UvI4MG+NLf6lJRWK3TFYGizc6mLiSHttVfRxjWvfzp0nYFWaX5OveVA8csednBfNf97aBWrFu+jvtqOwaQlMSuChMxwdHoNdRU2flmYzYLH1mBtmshoqzRNSpnEqIRRuIRfGkyHA3d1NaahQ0h95plW99qJueIKUp99plWHxYvGbMaelEjkRReSuXgxiQ//n+89y4QJGPv0wVVWRu7lV1D7zTe+EVxF6Xi1GjlnDkmPPeZpSHQ6NGYzDTV2Krd5U3IffQMTExLj25X8rW1vUetouQ9Vwl13kfbfF5tTgAJRF1xAnzVrSLjrrnbXPYpeT8JdfyPt1cA9YkwDBx7RYfGdQ6Mh7tZbifnjHwOP61q3wdth9u4HMq/PPE5K6vi+MKYkQYO+Fq3DwM8f7WHBo2uoKGwe2U555p9kff4Z8X/9a7sHehRFwZCR0ebnzaNGNaezBdJeeYX0V14mbNpU34yCLiYGQ3p6q2uR2gxr1WpJfOJxGvxm+u0FnrTBRwor8b5vtzr55o3tfPr0enK3VeB2C0KjjEQlWdAZNDTU2Fm75ADvP7SKnE0VzddupbSa9WZfyJNvTxIF7Pv2obFYSH/5pRYzA1590l95uVWHxR+NxYJ51CiSHnqQrM8/882+gGfNHkD5Sy9ReNvtvvCw9j6P/pj69CH9zTd9odrukBDcbsHWnzyj4m7F3eHZUC/+EQz7q/fz+V7PoJD/7Qo/7TR6//Sjz2EBTxh65qef0Ounn47osPgTec45ngEqP7SxsQH7Nh0J04ABpL/xekDYZpu6Nv2OtQfXkFOTQ7w5nttHd3xfGJPZQEH4HgC2ryjk/Qd+Y9eq5nTPYaeeSs9lX9Pj/ffa5bD47NbrA7KM+of7KiEhWCY1O1cRZ59Nr2+Wk3D77ZhHjEAXF4chLQ3ziBEtMpV629q26oDIuXOIv/0QHRT/ctr697xvu11uVn22jwWPreHAFo/DEp1socfgGOIzwtFoFIr31/Dlvzfz9StbUWy6w5y16yNnWtog3hzPpQMuZcvewKm/6CuvJKY92YTaicZiIfHeewibMZ2GNWuJueZqYq68snlNyiWX+hZMH81OtdqwMNJee5XKd98ldPJk9m8qZdnr2wGo11djjm3/tGxr3DryVn4r+o28Kr8Nk9oRa9lRB8yLu745RjzmT9dhPqn9MevgGflIe/UVdp80Flwu3543h+KtMArrPA39Ob3PYULKhFY/eyQaehWwwLaUixtvwrEnhE3f5FFyoIZTrx2MOdzQYnTxaPAmNwBI/L+HiJo3j5Dhw3BVVRN57jkdHlU8HIbUVNJffZX9s+cA/p3JwBkX/7+9HcvtvxTyw3u7EG5BWLSJk2Zn0WtEPFq959l22l3sWnWQVZ/t83QYC7X+p26BoijcMvIWHtj6z0Abs7JI++9/A3Q5FhSNhqjzzsMybhxVCxcSefbZ6GJiKLjzTuq++Zb8v9zcHCrUwZkWL5Fz52BITcHd0EBVjcKX/15LY4WCU7FTlZpz5BMchilpUzin9zl8vOdj9lXvI44eR/4SBITidAR/J8vYrx8x17aczTzs9xWFuJv/grDZPKlKOczIeFN1U9JYTEpcCn8d1fHZUABDpMJ7wx/idOeF9Nw9lorCej56ci2z5g8gc2gc2rCwI6epbwfaiAhc1dWETp9OyjP/xL53L7XffudJiNFGfXQ0KAYDhZddSq8HHgT81u016aVpoxMlENSUNfL585uoKm5AUaD/xGSGz0gnMsFTnlxON/s3lbH6831UHmxg5fv7m6/bhtN+bu9zeWf7O7gJDJNN/fcLAY7bsWLo0YP0t96k6qOPPDPM55xDyLBhFN5zLzVLlngSv8DR7fwOmPr2IWPBh9SuXMlOdCx7eTu52zxO2674VRi07R98OpRIU6QvguGH/B85jT5H/I6iKK2GVx8NKf/4e4cjL0z9+pH2+mscOMcTwqQJbb2MeHsFW8q3gMUzG9qRsDAvOq2Ozwe8QEptb66qu4uSnFq+eWM7pTm1TDi3F4pGOXI4WDvQJyX5ZpB6vPMOhvQ0Sp99Fsv48e3eVqC9xFw1H2d5uW+2RdHrfeHWLW6H77XAbnWy9L9byN/pmfnrNTKeMWdlEpXYXG/XV9vYuDyXTd/lk72uBBOxh56oWyGdlsNw5aAruXvJs02vFMxjxxJ/e/sXjHUEy9ixWMZ64sGNvXrR4713yb1yPvb9fo3BUVay2tBQYq+7jp2/FfHtW1tAQGVMPosz/8O40H8ck91mvZlHJz7KFV9eeUznaS8RZ5+Ns6yM+Ntv8+2F0FG0oaH0/vEHih9/gtBpUw/7WbvbTpIlidtGHf1912l0VFgKMU4uY+bMM/jm9W0UZVez6JkNzL55WMDU9tFiGT+e2D//GcvYk3yjjEerT3sw9e1Lj/fepfy114k4O3CfFI3fc+pfLW78JpdfFnrCq3qNimfqxf2wa61sKF9PaWMpVqeVcEM4yQOSmTd0FN+/uYvc7Z7OgKGh7RmuIXFDGBo3FLLx1fDpr76Crh2JKzqKITWV+Jtv9r1OffZZiu69j+pFi3yDC0frkINn3UZpbi2Ln16PrcGJLtLNhz2eol9q1pG/fARuH307q4pW+ULO1MQ0cCDGvn0xDRpI4r33tjkjcDgURSH+jttR9Hrs+XkYslrXwJttSeBJB93RsDAvOo0Ol9ZBWeoe7rvwL3z96lYKdlWx9KWtzJw/gN6jjhwy0x6S//k0rrIywv/wB1+H83h1Og/FbTKR8d23lD/+BKHTPevgmnfabr09qStx8Mnb66ivthMaZWTmVQOJzjCRXZXNyn25NDgbMOlMxCfHc/rt/dn6ZQmbvmsOcW4rfEWv1XPryFvZ8H2975NJjz3ma/eOJ4pWS9QFF/heR/zhD2gjIsi/8SZfyOKhC8c7giEtDdPpf6Ds0Z+xV1Wg1St8lfEGe2M3oNM8eEy2eyMYfvvVs67FdQx2toeIOXOw5+WR/NijAeujOkLIwIFkLFxI3Y8/EDplcqufcfotLD+n9zlMTJnY6ueOhE6jAwUKwvcw9/oRrPsyh7VLDrDpuzxsVidTL+mH5hhmpr3EXH0Vurg4Yq6a7wtLTby/feuKj4b42/6KxmxG2GzooqIOMyPa9NvsWj57biPF+2vQGbVMu6QfvUbFU9xQzM8FG6i1e2bUY0wx9D+zF71GJbD0v1uor/Ksa1SOcu+0YCOdlsMQZggjPdzjsRdP6Ev63x45aseho+gTE+nx7jvkXjm/OWd4a3sUtJM9a4v57q0dIGDAhCSeD/031pp69Nqjm8r2Z2jcUK4cdAX85nndamrf40TU+fNaXYzbUXSxsaQ8/VSb71c0esMdBA+Nf+iowsJ812qKwf187+f0Htmbc/82isXPbqSyqJ7Fz2zg7NtGYgo9tvugMRqJ+/MNx3SOjmIeOTIwbKO1EdymP0PrYnwOS/9p8eQOWMeV3z3O5jK/FJl+hOhCGDdgPMm1owjNS6Y++vApTXtF9aQaKOkRTu//rlTFYWkNRacj6bFH0ZhDPAvpaU4XfTSUF9Tx2XMbsTU4ScwKR5yaR+WGYgzafsdsq0Vv4bFJj/HRqtUA5Nfm05vj0xE/FG1EBFmLFx3zeRSNhvi/Hn5Bvc1pQ4eRM7POYHTi6KO+ljesZ3vFdj4vXMTsG+aw4r1sdq06yPLXtqHVacgaduyzoqETjm7G9mjRxcWR+vy/mg+0vm2Lj60feuq+qCQzIbPLeOTA3fz666/NqXj9UFAYGDOQiRPPgp8TadTVtTmDAzA9fTob8YQ9GR+5m8jTprf52eNN6OTJpL38ErlXeAbYXOUVR/hG2zjsLr5+aRv2Ki1Gs47x16Tx79Ub0Gl0LXZzPxpuHXkrf97kSZl8sKFjO913lOQnHj8u5wkZNJCQQQPbfH9f9X7ATKghlNtG3XLU1/EPP35uw7NcMfMKIhMG8O2b29m5sgiNAlMu6XdUa3X9abEfnsooGk1AG+5uK9FF0/+1u2IopgajRcfAS8P5yPYa3y781pc86lAyIzKZdebpuD7IQOc0gMUJtEy00NWRa1qOgLcCskabj2kE9WjQxcSQ/sbrnoV2ioIhvWXMb3torLPzzZvbEU0Oi2FaBYWNnrUcBs3xeWgv6d+cjUTtkaHOoKZplEKv1Xc4W9ihTEmdgl6jZ1flLv74zR+5f/vfGH9dMqFRRioPNrDkxc04Hd1fM29lGrCD8yGjrrYhBfzNPp+n1z3lc1iSLEmMThzNxJSJDIkbQpg+jEZnI98VfMu7KU/yyaB/kjt09RGu7SmnNpOu0xwW37U1GhLu8+wLpOj1mIcPP+pz/fzRHqz1DuJ7hDFmfhJLij4HOOo4+UMZHj/ctwC7xtb+vaK6Mt6m/eS0kw/7uSMxJG4I8SHxVNuqefi3h5m35DzCT6mj37hEhIBlr23j4L7qYze4i3Bo2TQ4m2fDlBg7b/d6mPs33cWKghU43U6iTdGMTBjJlNQpnJR0EimhKQgEW8u38l/X4ywY8iSLBz2Hom27sxhQN2QdXXt2LFjGjiX9jdfRJSYSddFFR32erT8WUJRdg6ITzPpTP75q+BQ4fuXUrDczM8Oz5tPmPHzGv+6C93ekh6cd0yBguCGcMYmesPA3t73JmZ+eybrw75hx1QAUBbb/UsS6pccWTtsVaE5/f+gbzQc0Rlg/ZhHXbrqU/+38HyUNJegUHT0jejI6cTQjE0aSFuYpZ/ur9/PS3n/z5oh7+Lz/v9H2rqc7ImdajoAv48ox58Q+OnQxMfT8ainOiopWU/q2h7pKG26nwBSqY0PfpbzxzRsApIam0iuq/Qv3Dof/btvBUer44ttT5TjMoE7vMZ2vzvmKN7a+wQc7P+CHvB9YVbSKm8+8E8PCOIr2VvPj+7uYfvnxi+sOBl7HwX+k0b+Tkp2wlm/M74AL+kf3Z27vuUxPn068OXDhp1u42V25m6/2f8U729+hJCyHEWGHD5/xje4GqZx6MzbF/+3OFgsxO0JDTVO2rQl1XPjN+VTbqtFr9JyaeRxD/XxSnQglleaZg2MsrMmhyXxx9hd8sucTXt78Mvuq93HV8qs4r9d5DKs5k7xtVSx5cTOXPToenaFzB7COJ80J6gL10rqbuwNv93iEemc18eZ4zu1zLqf0OIXMiMwW3ylpKOHb3G9ZuHshu9lNuCE8YCT8cLhUnJE/HJZx4+j1/XfHNBJfX+3pgGtTarhj501sq/CEcp2ZdeZxsRHAcJwcoC5DG3tudRRFUXh11qv8lP8T/974b3ZU7ODx1Y8zOPYLrpt9F1sWlbLqs33EZ4SRPiDmmM0OFt76+dB6TXE2t6+Lej5PoTUbnaLjlMxTOD3zdE5KOqnFHkHVtmp+KfiFz/Z9xi8Fv1AQuZtw07FHrAQD6bQcgeY0kcFr4BWD4agdFsBXWdQ4q3ljh8dhOb/v+dw68tajjv8+FP+CpWZ4WGdxvO93vDmeO8fcybl9zuWR3x5hbfFaHs9+kDNHXUDqinHsWVPS7Z0WL/6bf5kTtNQZKikK28f3Ge8xJG4Ifx7+Z8YmjW2z06BRNPSL7ke/6H5cP+x61h5cy8DYtsMOoGU612BxLA4LNDvLz2/8F9UR1QyIGcBjEx+jZ2TPI3yz4wRbq+PN8QjLCdGFcHH/izkz60ye3/A8C3Yt4KPsj9iQtIlTsv9MY62D6tJGYlKOfqQ4+ARuCutlR/yvpFb3YUm/l7BEGblz2P9xZtaZhw0hjjfHc2G/C7mg7wVsK9+GRW858n3wOs1H2GxXTY41dMibS2CDYz3bKrYRbgjn/nH3c0rGKcdunJcj7JvT3RCeXbSOS6ZdRVGYnDaZiSkTWbh7Ic+tf44tZVu4TXcVf+z7f1h3GSjYVdm9nRZan2lx966kNM/KupRllEQe4KK+F3HloCtJtLTdR4wwRnB61umcnnU6+6v3s7l0M9NSpvHd7u9U/AXqIJ2WI+AdPe7Oo5JWp2eTOLvbTpg+jIcmPMTMHkef4aQ1AlKydmOtvDSPchxfekb25LVTXuOd7e/w7Ppn+b5+KZcyrnlm5wTAfyF+RkIafx75J0KNoTw8+mHOzDqzQ51Lg9bA+JTxR76m95zd/NkrbyxHixmB4JL+l3DLyFswaNWKO+7eWnnxltHDraXoKBHGCO4dey/T06dzz8/3kF2/mwnuGkI49uxhwcZ71w8th2JyIQsLF3HZwEu5evDVHRrQUhSFQbHtTz8PwZtpOR7kN23851CcDI4ZzFNTniI5tOU+HMdCc+bFEwMhvE7L8SunWo2W8/udz+S0ydy14i7WFq9lVc0vDGUqDlfLNVjdCeEWtNYD6TegB3eU3cGE5Ak8N+ZjsiI6lqQlMyKTzIhMHIdsVdBdkE7LEVBOgFCKojrP7sqKorDgrAUd3q23PQTOtHRfrbx4nQg1EmxoFA2XD7ycMYljuG+ZZ/+M7tyAQ2D50PgtlUsJTWH5vOWEGcJaTFkfT7yOUnd/8qxOGxbMXDrwUi4dc86Rv3AU+AZwu7tYPppSbR+HjEGHMi55HB//4WPu/+V+xFqPYBWNFcTQjWda2tj+4bmpz+F0OzHpTJ1iRneekT9YV4yeeGI00Tw48z7MxuMTseDPsYY7djXEcQoPa41ESyKvznqVN7a9wZocz3qW3ZW7OZljT2ASLDzlQ9tCrdMyT2Ny6uTjFiXT3ZAL8Y+AdzSqOzfwLr/1GWo4LE2n9hHMULrjhVozLf70j+nPk5Obdvzu7pL52a85JMNebEisqg4L+DtK3VzIJvNTjvOobQB+ef5PCJp+RnvXUnSUKFMU/5rWnH2rxt7NExh4s/wd0vzrNLpOc1igezst3vbBrDEft8X3LTixfBa8D97xnBH1R6vRcvXgq+kX7XFU6h0NR/hG18bdRngY8Lt1WEA6Le1G0H0r2OO5qLwt/Kd8u/OslBffBmgqNxzeBu9Eap+CMULYnDCj0y99fGma2tMeQ3rz9nIizIh6aJppUakz5D237xlzd2/dfPu0qDAz1S5OgOiFzhjU8tWj3VemANpKAHG8OVFChf0EC64dXQzptBwBzQnQGWpOQazej/AfXe/OjZGXzvoN3gpc6eZF0V+t47EguqME45pqonbD7qH7l1N/OusZOFFUC1ZXyHtdl+i+ad7VDHXynTlYTqVatJXC93gT3ESSxw13JzjG3ZEO1/I//fQTZ511FsnJySiKwqJFi1Qwqwvhy0rUfWdafIVXzZkWTqyZFt9oqso1hlpT5Z2O3z0PRmPbPLjQvZ89xRu60wmb2HZzqXx0mmZNj3W3T5rhG8ANrqMvunF4WHMHXL1C1NymnhgF1dcNUbnNU04Qr6W7t2Vq0eFaq76+nqFDh/Lvf/9bDXu6HBpOhDUt6o9o+Y9ynghrWjor3jpAt278kPmHGsmZlmOhE8LDThA/uRnPD1L/GRBN/+2+5RT8O49BNaN5rWU3pLNCnZqu0gnXUB9fSJ3aTssJElXnc+pPjNt/3Ohw9rDTTjuN0047TQ1buiTeAnAiLBpUc1TIv2B190YdOnFUyO/8LrcLnbZ7JvTzD/XojPUYh+IdNe72z5531kDN9Rm+S3VzrQ6h08LDuvHgAuDrcQfN0fe2qd06ekH9Z8DTNnTzZ80P0Ql1GxB8b/w40dbmkr93VO8h2Ww2bDab73VNjSfzisPhCHqeaO/1D2tHU0ETQgTd3qPF4fTYLTjCbz0GnO7mnOgOuzPg/nZH3ZxOJ6ABoa79bldzw2232xG69jdSXUlfu83u+9vtdHW6Tb6N6o7j/QqOvs1hbmpd1/uEuV3uoD47x1tfl0vd586rm9Pp7BJl7ki0pa/3dwS7TXN0Ex1bwxs+rCiKar/BE4aoqN4GdRbeTnh7n7ujrh+813F33z4bBM5EqvE7ulL/AdpvhyKOYchAURQ+/fRT5syZ0+ZnHnzwQR566KEWx99//33M5q6ftm3bthIicnuyOfEHxg/tSaQmMtgmdZi9xUUY1/ehJqSUAVPUSWnpFm4Kv4oAIHJKCaEhIapcp7PYnXcQ89belEfkMnR8lGrXqbdaqfw+DoCEUyrRa7rXTEu9u55qdzX1rgZCvhsOQPT0MswGdVMcH8qBggp0m3tQGJaNdnQ+I40jO/X6x4vd3wjMjnB0Y3NJjFLnudv4WzmxlRnUDthF/x4qplbuJHK/MqMRWkwn5xNriVDtOru+c2GxRSLG7CMtJk6166iBTdgod5VjFVbKVptJrulN/aDd9E1L6nRb9nwrCLGH893Q17gseV6nX/94sH59EfHFfajM2sXgvuqUoeyDRZg29KHaUszAk7t+X+lIbFxfTGxxL8qytjOsb5p619lWSGxuX0pSdjFiSPeq31zCxUHXQRpFI/tzKum/Zxpl0QcYdlJMsE1TnYaGBi666CKqq6sJDw9v83Oq95Duuusubr31Vt/rmpoa0tLSmDVr1mEN6wwcDgfLly9n5syZ6PWt51q3Vf9MaW5TTvb+Zk7veXpnmnhc+Hrlz+SsF2i0Gk4/XR37hRC88tXPAEw6+WSS4uLapW9Xxf7195RsBZ1er5pmAOVVVXz8/RYATpl1CqYOdPaDre+Wsi1ctfwqnMKJxq3lWjxOy8zpM4gI69ydwzet3seqzQUA5Iblct+M+475nMHQd8+3SwAYOXIUQ/v1VeUa29ctACA9PZ3TT5+lyjXaw/HS979LfwBg4sQJZCWlHyfrWrL7+y8AGDR4MBOGjVDtOscLr74nTzuZs5ecTZm1DIA/iBsB6JXVi9NnTu50u/79w9cAVLgrmHHKDAxaQ6fbcKzs3PE/399q1Q9f//wzORvUbbc7kz27PgIgLi6uXb/naOuHvKJPIBfCwsK6nW53/nwny3OXA9DfNY7+QIjJpMrvCHb/4VC8UVhHQnWnxWg0YjS27Ijp9fouIRQc3pb08HRKyUFBwY07aDaX5ddSX2XHGKXBEVpHQmhCuyt7T0ykQFFQ1X6BGwUNWp0m4Dpd6V63F2+4t4K6mhkMzffwUN38+a3oN9YeXEtsSCyxIXFYaqNIjfCMIgVL3721e3EKJ0atkRRL86al4SHh6PWdO2MUFRIFFKCg4BKu46pHR/StLm2gcE8VhnANxNqIjAglJqQDo2RN+7QY9Ab17mlTiLSitP28dSbH/vx2gmZ+aDRt61Ztq2ZR9iJ0Gh1xIXGE2aKJcsfRp286miClsK1x1fgclqyILCwGCwB9Y/sE5f5HmiKpb7SjoKBolaDY4Ha5yd1egaJRMMaALkIQGxLb7u/71jxq1Kt/tVot4ASCo5FaHK78tEZH9fVmrzxc2y2EYMGuBVTZqog3xxOtiUVfFk7/rCyi4jp3wM2f7OpsAFJDU0lv8AzApIWnqXr/u0r/rL02dK9YlGDQ1M4MPjgZW3lJUExoqLHxwaOrUISnJ92oq6MgfStP3/63di2mdHVyFooTYdO6zkgT7Tl98wUOp9utP9yKvjKUgcUTyagwYnEorFHKiJncqK6Bh8FaDOduuh1dqp0bL7uYd775NWi2eGVMqu2JbXc5BGmA7f1//YS7tNkRPRi2n0tumUzf5F7t+r73aVCzg+uXnFy1a3Qm3t+jVXtheTs2Rfxg5we8seod+peMo3fZKCKsOqCGPXMPcNYpnT+rAWC3Ozhz+/W4dA5uu/YKfs7eQ1F5NUatOqHCR8Jh9STtmJp9MQ53cOLp167cw5r3Cnyvq02lZM4xcuGUOe07QSfs0+LbNFXFK3QmnZc9TPHcnsNUb1vLtvL4yifpVT6C/iVjSayNAMr5NeQAf31mtqr2HY7+W6Yytuxihk3vQUqfFH7etIdwg3ohr92RDjstdXV1ZGdn+17v37+fjRs3Eh0dTXq6elPzwUJvbM6EZC8Ijo9XVFbqc1gcGhshzlB67htDo70Ri9FyxO97FilrVK/9BCdOBevbi0H1RCct97dxup2UNZZRY6+h0dlIrb2WIbtmMvhgYKdHK3RUNdara+BhsO3XEduQCrvhnXv9HJYgPAQ6Q3M5jSxWL176SDTU2jHR7LQk1mayc0dOu50W70yLqnuOnAA7kvvj3Zi1M/a2gcBMkrX2WqqsVTQ4G2h0NvLruo1ctOF+tCKwrcgvKu4U21qjuthKarUn1PCjx9cGzQ4veqMOe6OL6MYkbHY7dO7yNwB25+8FPE6bS3ERYY2jYGMhTGnf9ztjTMuXuvfEKKadkhkx8HLNwjU6GylrKKPeWU+js5Gv9yzngo33EGYPXDdoagzDLdxBy6yXWNwHgzOE/Usa2M8e4IRJhnbc6HAvfO3atUydOtX32rte5fLLL+fNN988boZ1FQZOSuG3RfuAztnvpDXsLk9mpgZ9LZc9NJ6Fd29BQcM/Vj+FwaDH6XZi0BowaAwYtAbCDeFEh0QTbfL8q7RVAdGdZu/5X5yPOdwALgVNvR5jnpFTs07ttOt3hJL6Ej7bvIQGpQ6nzo5TOHG6nVQedJPBeNXTDWr9OlqXLr2MWmc1ZdayFim2Ly3/PwDSB0UxbFoPPvrvLxjtFlwEb1fprrTJXmrfKCypGurz3cHdCLapnexzjY4NCw9iqYzlgx0fsiFkBQ63A51Gh0FjwKg1YjFYiDHF+MpptCna97xpFfXTRn+y5xNeXfA0QgiMNgsjewzj0cmPqH7do8HhcvDh9gWUNpTi0jh95dTpdhLHyUBnpAb13NwnVz/JswVuihuKqXcEDhoMOzjd47CEO5h53lC+/PYnDAdicbmC90w6Xc2ZHRXFf4+R4Ngz6fzefPXSViDQts7E2dSWV/bYT1RsGKyLZV/lPv6x5h/YXJ5spwatp5yatCaiTFHEmGKIMkURbYrG7e0LdIKGNpeN6R9N9zx+QiHGEMM/ZzxNaljqEb8bDHaU7WD5th9wGmw4tDZcwoXT7cRqi8ZMUqcMBApgb9U+zvnsHIobiqm2VQd8JqY+hfPso3BrXEyY3Yfonka+fGo74OlvmXTBmYVUREtx3O4TxWs9PnTYaZkyZcoJM0LXHkwWPba0Mox5sUHrpNmdTelkFUG0pdn5WLxnMU7tkafX0ysHcDp/VH9396YRz7F7ZxNpTSCyMQGt0LKyfiun3tQ1nZa3Xl+CeVs6WsCtsVNnLqTUkkeIIxQAncp7juiU5iJ4oHo/Lo2z6bo6wg3hhOhCCNGFYGgajpxwTh+ikyy4NZ5GM1iONDQ7LY29ipgzdSYrP87GFKoPmJ3sLLQ6DcnjjexZ0BjUkUlvoxNmCEOr9YST1tnr+XjPx+36/pXicSDQmT3emHWeTEQjCmZRV1pFbH0KRpeZyg0HqT6pmghT1wtHWLr+O8reiMbgTsKhsVEXUkKZuYDCiBymN30mRKduxkLv6KtTOCmoPuA7HqILwawzY9abiTN6soql94umz+hElvzseRiDWU69HfQGUzXzb5/Jig93U5ZfR0xKaFDsSevf3IY5g6SLy+W5rl6rx2I043E9Fd7e/na7vj+j4XIi6aFqk5pgSWAv+cQ2pNB/y1Ti6noQ2RiPUATfxazkspO7Zua1j59ZQ1R5X7SA0NVTZcmnOPQASXWJgPrl1KQz0YBnEGN35W7f8RBdCBa9BbPOTLzSAwBDmIYRp/SgurrO9zm7M3hOi1vxtKnjb0rCud/Elh/yyRjc/rVWvwfkmpZ24A3hCdYGk3ZXk2OiBI6OXdz3EoxmPTpFh8PtwO6yY3VZqbHVUGGtoMJWQUVjBZqm0LIQfeekIe5ZMTzgdcNBF2PfH+txahRPZZIRkcGrs17tUAUmhKCkoYTYkNhj3sBQCMGjqx6lPj8GbzJJvdtAQl0GCXUZvs/1CM9o7evHDc9iSw//OPkpkiMTSTAnEG2KDpiifvm7H3HgQqvzrqL2/C+YG7R51+AoGoVeI+PpOSIORGftEt2S5mciiF5L06X1Oj1JYUlUltmYmT4DQ+9JGLQGXG4XNpcNm8tGnaOOisYKT1m1VlBuLcd7Y9V0HDIjM8nNryDCGkeEtTl1b1RjIjM+mInQuT2LpBWFKGMU/5nxH3pG9uzQNUoaSjBqjUQYj/13fH3ga97+4SOmui8CQO82ElefRlx9Gv1Lx/o+p3b9Fh0STUODk2sGX0uPPnEkmBNIMCdg1jeno1312T7W7jlAhNnzu70ha8Hc/d3lt4dWTEooc24dgXAL34Llzsa/fvDf36sz8c7waDQaekf1ZiMF9I7oRa+BV2LUeQaIbC4bDpeDBmcDFdYKKq2VvrLqJUKjXjn1JAbIB6B/yfjmNwQsWfUd/857GkVR0KBBr9Vz26jbOKvnWR26Rr3DEy7VkSQEbbG9fDtPrH6CYVXn+46ZnBZSq/v6whMB+sX0O+ZrHY4+UX3YSD6jEkZz0YxpJJgTSLQkEqoP9T17RdlVfLJyPRajp+zqtc2LwG1Oe6vn7RyakooYdAw/I5PRZ2QG0ZauiXRa2oG3crc6PZ0No7Zzg3CLaosAHYpCQENz44ibMIYc+RZmbyzm613bSApNVNFK6DkinrK8WlL7RZE2IJq9uwvY830lCKVFGMXm0s1sLdvK6MTR7T7/3T/fzRf7vqB/dH8WnLXgmGwtrC/kw10fcpb4MwADz41m6KDelOXXUbS3mgObyqitsJKYqt4eLYcyJW0KekPrzpjb1bSLtdbTCfK2+w3uRmrsNYRrwgMq3s7A5XahoTlGWVGUoC5q8jotwg119jrMenOnxiY3OBp8szxGnQGzPoRKbJyWcTq9hya06xwvr/oBh8ut6r3MGBRLaU4tiVkRpA+MITY1lI//vg7wjHw7Xc2NdqOzkV8KfumQ0/LJnk94YOUDACw8ayF9o48tdfO7299FIzydC12GlfOvnEJ5YR0lB2rJ2VZOeX4dJoseQzvqwmPBc0+cTEqZRHJSZKuf8W4Wq9E2hfk1OS02p40qaxVmvRm9Rt+pjr2zlVCmYDksEDjwVm2txuV2HfMgVEfZVbGLnoxDo2gw6T3t+dDYYUwbdWG7vv/lfzdyoLyCCK16TktMSihhMSZ0Bi3p/aNJ7hPJNx9vwlGqwS0EDc6G5g874Mt9X3bIacmrzePsxWdjdVm5Y/QdXDrg0mOyd1H2IjaUbGA4FwAw+84hGDQGSnNryd9ZSe62cuxWF4np6rapBp2n7syKyGRiSusOkuuQ9tR/ZruorgijwUCILgS9pnPbVKWp/dBpZde8LaQy7cDbKVtTtJZR7z6DQWMg1BBKuCGcUH0ooYZQwgxhhBnCiDHFcGG/C4kzH7/Nx37O/5keTMGNO6DCF+2MdfSGbandTp567aCA1w01NvZQyUkJJ3HT3LNxCzcCwZ0/3cmOih08s+4Z0sLSCDOEEW4IJ9GSSKIlkSRLEsmhyVj0gUkGtu3ey8CyieQ37iK7MpteUe1b3CyaKvhaey219lqsTit7q/cCzeE8qVHJRCVaiEq00HtUApPm9aahxk5ImLp7CAR0Xtq4nUIIX0y8VueXixn4yfoTCxa+BXimv+856R5m9zq27CcH6w+yoWSDT686R53v/3X25r9jS/szhISgdoD80TXNWjndTsb9bxwKiqds6j1l01dOm14PTxjOqRnHL2zxi31foDR1ri2GUFA8jnpHwmkFfg6gSgyeksrgKc3x8E5Hc4jO4j8sRmfSIBD8Z+N/+GzvZ3yw6wPWFa8j1BBKqD6UeHM8SZYkEi2JJIcmE2+OD3AOt+TuYFDRJGqNlTyx6gneOO2NdtklhKDeUe973uod9TjcDkoaSoh2e5ymHjHpRCaYiUww03N4POPm9qS+2oZGozSXDZXw3ZPD3E6X0/Omd0ZUo9HgBnZX7GbSh5M87ylaJqdO5tmpzx7TfW5wNPBr0a9U26pbllV7HTX2GgpqCwgriWca85t7REHG/zdf/fXV2HVWLHqLp4zqm9rVprIaqg8lNTSVC/tfeNwGC/Nr86lorKQnng6u1572tqfQOWvOzOEGLnt0fMCx9FUJ7C0t5cahN5Ix7l4EgtUHV/PQrw+xuWwzt/5wq69PEhsSS6I50deuJpgTApzDHWU7STs4CAWFf/72LJf0v6Rdz6MQApvL5nnmHJ5nzeaysb96PwB6xdPRDw+zEB4bQnyPcAZOSsHlcuOwujBZOscRONzddDu97WnL+vaypZf5wrQTLYm8e9q7JFjaN+jUqh1CsL5kPXm1eb7206ubt7x6/83gBsATHi5pHalMO4gzx1GB09f5t7vtLaaJ/fGOXBw/PAXKpDO2mm3qSIjOyt97CN5+jFFjIj28ObNcv+h+7KjYwZayLWwp29Lm9+ND4smMyPT9m7D5fEJtUdi1VnJOz6FXVC+EENTYayisK6SwvpCiuiIK6ws9r+sKOVh/kGp7dZuhfd74/kMH5BVFwRLRCTNq/j5LG/dTuEVz5pWmEVyLwYKrHrQ0N0KNzka+yf3mmJwWIQQXL7mYkoYjp/cOd3k6ktEhnZfk4XB4GpYK34JsgfA1BrSSZO1/O//HmMQxRJuOj/2e8C6Pox0VEolG48kY1aGoUrc3LehxMald+Dud8SEJvk7FkNghfLb3M/Jq88irzWvz+yG6EDLCM8iIyCArIgvH6ggm5owDYFfsct/nGhwNvnJaWFdIfk0+6+vX8+HXH1LUUESFtaLNchor+gDNnQx/OqWcBtB2vXvojGhKaDJ51AesXXMJF9/lfUeDs6HFwExHeHz14yzKXnRka62e+Pwus4mj0vJFvaO+xWy8P3HmOM7IOuO4XL7C2lxH9I7u7Xv+O7IWLkhNqs/WCEMEaeGeLIneMlNrr2V5zvI2v2vQGEgPT/e1pzW73EzP9syuRCUn4nA7MGgNONwOiuuLA8qq/98lDSWHTVXtzXR6qDZarQatRf2Zb1/deZj7eWg59bdVhx4XHqflYP1BNpZu5BTLKUdtz+qDq7l62dXt+qx3EDXSFHnU1zvRkU5LO4gJiaaCEu4YfQe9Jj3h85T9R7RqauvZvmsv2/J3smjHZ6wsWIlZb8asMxOib16oadaZSQtL47w+57V7Sty7EN+oMwZ0MLZ874l3dTrcGExazOFGYlJCiUm1oNP7ndubLaaTs/i1NTL5tzF/Y3LqZKrt1b7RwUprJcX1xRTVF1FUX0SNvYaSxhJKGktYdXAVANfZngPA4DKxvmA9L216idza3MM2dv7oFB1hhjDPtK9Wj16jJ84UjwN1R7YPR2tXtdY5yN9VSXlhHdUljTTUNIfreJ2WcGMYlTQw33IVZ14wjaU5X/HYiidodLZ/35b1xetZsn8JVqfVt86i0dmIriicgQ19yUxLQRfnwhyh981Y+M9WlP+kJbegnoyIjGNU4fjgDakaGDOIuy5eQ53DUzb9R7dqrLXUHrSxfNNPlOgKuOCLCwg3hPvKpllv9i2sDteHE+1uv0NjdVp9jY6iKL5nasPyXIoP1OCwudBqFUyheqISzMSlhxOVZA4ciPD+0YmPo6aN2b5z+5xLZkQmZY1l1DnqqHfUU22rprjBU04P1h+kuL6YRmcjOyp2sKNiBwAz667wncNZpXD1sqvZWbGzRQYfH+WBL3UaHWH6MCx6CwatAb1Gz8DGQZDj6fgEi0NT0NqtTvJ3VlKaW0vlwXqsdQ4KdlcBzc5VdEg0edQzf9BVvPiH/6PB2cD0t2dh11ppdDa2y2mptFby1ra3qLRVYnVafWsXs3NzGVg6kdjYSCLiQwiJ1Xhmw5qiAEI0IezYtIPRfSezfVs9sR3Z5FRF/B+3H877AYfBGjDa7PlXR01ZA2u2byG/Jp+/r/o7b2x9o9VyatFbmJUxiwExA9p1favT6kstbtabffbsWnUQS6QRe6MTIQQmi56wGBNx6WHEpIQGzOQFKxmR7xn08+0zIjL44IwP2Fe9r3k23FFLaUMpB+sPUlRfRHFDMXa3neyqbLKrPFtW9CseyxQGAxBqi+Jf6//F1zlfU9JQ0q71uxpF45nV0Ydi1Bk97ak5DkV4sncFazNVv5zugCdksySnlsLsKioPNlBXYSV/ZyXgN9PiZ+vKC1ei6AU3LP8zq4pWtbtNdbqdvLXtLfLr8rE1LSWwuWzkVxfQs2w44cZw0lISMMQJwkKa29NQvadNjTBG8NvaKtxugV6Gh7WJVKY9+HW+vY1CEklUlzaya9NBSraWU51TQ7KIJ5lxrLZ+yfrUZYc9ZXpYOuNTmqd+yxrL+CbnGzSKBove4vtn1plxNLX13tEhRaMg3II1Xx5o9dwanUJq3ygyh8bRa0R80LIpeSsCtxDUV9nYv7mMwt2VFO2txmgO5Zw7prSZaarGXsP+6v0B/5yr7ehcntHCpeu/pzQ01/f5aFO0L6ws2ZJMUmgSyZZkkkOTiTJFEWYIw6Q1BXQQq4obWPDdGsAVxAq2mc3f5bN/cxklOTWtjhIZzTp0eu/IkMdee42GNZ/kUrYhkvk1T7C76me+7vO1r8IcEjukzTUFD/36EPuq9wUc07r0XLnjMXTCAAc8x8LjQkgfEE2f0QkkZkX47uuvIXvJpb7L5JH3d5JNOhMmnYnYkFgcNhf7NpRQs7GMmt2V2BqcTOICnIqdt0z3UqQravOcE4wTmEdzlh6H28GSfUuoc9Rh0VsI1Ydi1ns6TsUVpaS5hzbZAkqTg1meX0d5fl2r57dEGMgYEkvvUQkk94nstA3YAvC7lN3qpGB3Jfm7KincU4XD5uKsGycTldh659rhdlBQW8C+6n2+cmo4EOl7P2F/PxaGf+57HWYI85XLxJBEqnKrmDZ6GmkRacSHxBNmCMOoDZxRtludfP/OTrIpQaNyCFh72L3qIGuXHKBwd1Wb6UhDo5uyDzWZW19pY9OyfPauL+WK3MeoNBWzfOw3hBvDsLlsxIbEcnLqya2ea+Huhby29bUWx0/fdR3p1f3BE5VDSJielL5R9B6ZQHq/aARu3Nvd9IzoyXY2d5kwTv97q9PoCGtK9y3cgsI9VexeU0z9jgoayq0MJJmBwBLNy+yK2tbmOX/M/5FPZ38acGxXxS7WFq/1OTneNnV3xR4GFk9ssqV5IAhg/dc5rZ5fb9KSPiCGnsPjyBoWF/SZFiEEVcUN5G4vp3BPNVXFDYw5cyxZw1sPS3e5XRTVFzW3pzX7qW1s7v71Lh/Jf7f9xfd7jFqjrz1NsiSREppCUqjn/wnmBCKMEZh15hbltCi7mi/cmwJs7XSaLluwp4pv3tzOgc1l2BpaT/gQGuUpp/7V7b6NpRzYVMbgLecw0HU2W1M2YtJ+hdVlRUFhavpUwg3hLc61qmgVz65/tsXxXqUjmZF9mefFVtDpNST2jCBzaCy9RiZ4tofwnoMfgOAlyegOSKelHXhnKITweO17N5Sy5Yd8irJbHzmcnXQuN8w6nwZnAw2OhoD/f7nvSw7UHOBfGzyjGpHGSKKMUTy7/llcovX0j+fvvBsAt9XzIA+alEzu9gqiEs2ERpnQ6jXYrU7qKm2U5tZirXOQu62C3G0V/LxgT3A6QjRXBAU7q3jzrl8COuJ1lTbKC+pIzGp9IWO4IZyhcUMZGjfUd+ylT3/A2bS2w2z3VBq3jbqNeX3ndTiNYn21jf89vAq3szkDVlDwu+yqz5odiOhkCwmZ4UQmmDFZ9Gg0CnE9wnzT2d746+qdJqp3Fvq+pysP47Yfbwu4xBOTnsDmsvlCMLzPozfkZ/6g+cSb4z2j2jYTBasNPhsqi+qpKW1k648FbP2xgLAYE4MnpzJgYlLzDF4X8VqaR8I9hpUX1LHp2zyy15XgsDWXLb1Ji8PqQicMvHjyS7jMNo8mfuV0ffF6fsz/kS32Ldy78l6iQ6KJNEay5uAa38zfoYzMOwXvtpYarcKACck01joIizISEW/GEKLF5RQ01tgpL6yjNLeW+mo721YUsm1FIZEJ5ubnsTN9Fr+LBWwS2kT+zso2nRa9Rk9GREbAbNuSLZvZX1QGgE3nGaWcmjaVRyc+SpghzPc5h8PBkpIlTE+bjl7fdpz7N29sZ/8mz/n8O5idjTekZPsvzU5uRFwIyb0jiU62YIkwotVr0Ju0pPSOBJq13bXqYMC5oqwJ/P3X2wJS1l856Ep6RvT0PYfezE6/Ff0GwLikcUxMmYhRa8SoM1K6LxR7UxOk02torHWQvbaE7LUlGEJ09DkpHqdbaZ4V6CLlNCAk1u2ZWd76Uz7bfymittzqe0+jVXya39j3ZsKHuFuU05KGEt7b8R4Hag5wx093EGWMItIYSYQxgsdXP97q5eNre3C207O/nKJRyBgcy+7VxegMGqITLRhD9Sh47KoqaaAktxZbvZO960vYu74Ek0WPtd5x6E/pFLzt1C8Ls/llYXbAezt+LWrTadFqtKSGpZIalsqkVM/aqq2OAn7cuMv3GY3Qomhg2bnLiAuJ63C9vnbJATYsax5IDHa7UF3SSHWJp/4xmnWk9I0iNjWU8BgTOqMWRVFI6etJCuBv67dvemaMNejQAKu2bubDond97ydvTOamETd52tKm57DeUc+uCo+WWRFZzOk1x7PHj86Edb2FyqZbZbTosNV7Zmjzd1by84I99BgUw5BpaaT2i/Lrq6mtTvdFOi3twDvDsfXHfLb+mE9NmdX7Bmn9o+k9KoG0/tHs/K2IVYv3EamPZExS/1bPZXVaeW3ra2wr38a28tZHjk5KPMnTwXR6CoXD1AhWiE3xNPgnX9h2Nh4hBJUHG9i/qZTsdSWU5bU+wtsZaPV+o6ICErPCSRsQw5ovPEODbr8N1+xWJ7YGJ8ItMJp1GEJ0LSo9t9+UUYg2hAnJE/hDzz+06rAI4VkH0pYzUldpC3BYwmKCk5ddq9VgCtVjrXMQkxrKgAnJ9BwehyXy8HH6IWF6qpo22c4aHovD4SJvayUR+ghGJoxEr9H7Ojt/W/G3Ns9j1Br545A/+tK21lfbeJNfUDQKF95/ErZGJ4V7qti7voR9G0qpLbey8pNsVn+5H2eTI9BlKtgmO8ry6vj8+U3kbmuOOwqPNdHnpER6DIohPj2MV25dgdPmol/kACLiWj4/g2MH82P+j9SIGpYcWNLq5SakTKDe7imn9fZ6IpTmUDJLpJGsKBNZw9pOyOF0uCjYXcW+9SXsWVtCVbFfNqBO7g7pjFrf/QyPNdFjcCzbVxTicrp9HUevzdY6J067C71JiylU3yJky39mV4OGgTEDmT9ofoDDEvj5w08FVxQ1h3/GBmlvEYCwGBPVpY2EhOkZPCWV3qMTiIw3H/Y7/tkdk3pF0HtUAj994Nk7YkjsUPQmLauKPE7wG1sPn7DgjKwzAtarfaBbRTn1/OHmYST3jKT4QA0HNpexZ20xdZU2tv5QCFj4erVn07yuUk796/UVC3ZzYHMZTrunLTCYtPQalUDWsDiSekXw7Vs72LehlFRzKoNSWm6maHVaWZS9iHpHPUv3L231ekPihqBTdL7OZXhDcxbNXiPjCY8N4dw7R7Vpr3ALSnJq2b+plF2rDlJXafN/t4O//tjwD1FTNAopfSKprbBSXdIY0J4CuJxubA1OdHoNeqO2RVt4aOKBtNA0Zvf9A/Hm+Fav7XK6m2amWp/trK1odjiNFl27MpuqQVjTLKeiUeg/LpG+45JIzIo4bDSFRqNgMGmxNyUK6D8+iY3f5yKckBmWQVyikTUH1wCezKOHa1PHJY/jykFX+l6v35fDr+yl37hEpl3an4qD9eRtr2DP2hJKDtRwYEs5B7aUE51s8Ru06iKFtQsinZb20PT8eJ0Vk0XP4CkpDJiYQmhUc+fSWyiES1Bd2khRdhUlB2qoqbBirXOg0ShkGk/mnoghKKPKqHJWUmWrospWRaWtErPOzEPjH2rRuH+Ss46iqmqGTetxZFMVhegkC9FJFkac0oOSnFq2/VRA9roS0vpHHSdB2kdK30gs6XYGDutD35OSCI/xdA73ri+horCeTd/ms2F5nmfEucoW8F2jRUdcWhhp/aPJGhZHZII5II738QmP02ukJ6OH2+X2pCneUk5pTg0VRfXY6p243QKDSUtotIm4tDCS+0SSMTgWc7jB1xELCdMz7+4xAfexM1E0CvPuHo3T7mpzNLs1pl7Sj8K9lewqXMeMOZPIXlNK3tZKRsSN5P5T5wPw+tbX+frA1764b/8wCbPejGF3PMY9CSz/9y5Co4y+FJvQ/CwbQ3RkDoklc0gsjotc7FlTzKZv86go9FtH1EUqWP+KPndbOYriScM9eEoqSb0iAt73/j6HzUnejgpKcmqoKKynsc6By+FGZ9Rzv/E/bDP+RNqwRGocNVTbqqmyVWF32bl+2PUMig3MlrdiwW425+Uz4tQe7Wp0dHotPQbG0GNgDBPO682eNcVsW1GITq/BHNG5i6YnzetN1cEGskbEkZARjqIo2Bud7PrtILtWHaQkp4biAzW+kUsvGp1CTHIoyb0iyRwWS1KvyIDO0PC44Txy5jW+1zVljezfVEbR3irK8uqoqQjlla9+Rm/QEhKmJyYllITMcDIGxxKdbPHsbt10vllXD6TXiNY7VJ3BKdcMoqaskbi0sHbPzA6Zlool0kBSz0giE8y4XG6f0/LS9JcxWfT8WvgrL2x4wRca7L9uw6wzY66PRLMyGfFFLMvCtnlmYXuE+zr6Go2CVq8huXckyb0jGTe3J3k7K9j4TS552ytxWD3OqL/z2VXIXutJ+BGbFsqwGen0HB6Hzi/tu7ecupyC4v01HNxfTVluLfU1dl+H/DbDs7j6l2NNKvOV0SpbFdW2aobGDeWmETcFXDNnazlfbNpEXHpYmzP9/igahYTMcBIywxlzVia52yrY+lMBZQV1GKLat57yeDHo5BQcNhfJvSPpOSKOkFADu1cfZPnr2ynNrWXNl/sp2lvti7jwotEqRCVZSMgIJ2NILGn9olqENn76h0W+cG1rnYMDW8rI21FBaV4dNWWNuBzNjmVkgpm49DDSB8SQNiAavVGLaHq+Rp2eweApqYGDlp1InzEJRCdZiEwwt3ujY0WjMOevI7DWOUjpE4lGq6Ekt4aCXVXcOPwmeo9KoN5Rz+0/3k6VrSqwLW1qX02Y0f+SRuyvCSzbvJWIeDOxaaE0Nt0HjUZB0Xjqy5hkz/NeebCeLT8WsGNl0SFtqhrKnBhIp6UdeD33kDA9o07PpP+EpFb30/CGLuz87SA7fzvY4n1/zhgxjYyhzRs6CSGw1jkwtRIm4e2sd3TdhaIoJGSEk5ARzrTLWp/5URODSUfUQBvDZqYFhH94G/x9G0sDPq/RKChaBZfDHTCF+uune0nuHdgZEgLqq2xs/amAbSsKaKxtPZuJ3eqiorCeisJ6T4iGAql9o4hO9jgIJos+aA6LF+/z1RGiEi2ExhjIbpoE8MU6+2l0UealXNH/ijZHxt5ZuJKaMiu1VLZ4r7X0n3qDlgETkuk/Pon8nZWs+yqHgl2VxKe3PoLe2fjfx0EnpzB0RlqbI+HeBvvDR9Yc9pyZPUZy0YWB4Uv2RmerDfLRllPwlJWBk1IYOCmlw989HgyYkNzimLc+K82tpTS31ndc0SjoDBocNhdup/C9v+m7PMKiTQEjrsLtGaHdu76Ezd/nU7y/5pCreJ1HFw6bi5oyK/s3lfHbon1ExIcwcGIKDdWeRBQRcSFBjfU2WfQdTtdqMOnoP75ZW/+kB94ZptGxY3hr5tsBnXV/Vn2+j7UHDlBAVavvH1q+FY1C+oAYknqHs3jBV0Q6stizpoTEnurtKdJRQqOM1FXaSOoVwejTM0ntH9Wqo++93z9/tOew54ssi+HyhwKziwm3wG5tuZZBHEOGPo1WQ8aQWDKGxHrCG5e0PgurFnHpYcy6amALmwAaax2s/nx/q99zu4Rvbd32nwsxhOiwNwZq411TtPn7PPZtLGszBbTd6qIkp9YzILqiEK1eQ6+R8RTu9cQqhkYZA9ZpdDaKohB3FG1SXFrgd3ybijc5Y2admedOfr7NvdT2byplyY4tFFIDHFrPgdJKOxyVaOHk8/tw0h+y2L6ikI3f5qI3eGawJa0jnZZ2MPqMDDKHxhKbGtpmBxAIaHQUjcdhSMzyrEsICTMghOCrl7YCUJbvWc/hbQTXLc1h1Wf7MIcbSB/kWfCXPiAajVbj62B1dvYvtUjICPesZ8kMJ3NoHIk9I4hJCcVg8sSZOu0uKg82cHBfNfs3lZK/q4rCPVUB59i7voRvXt/u08Zk0ZMxOIbkPlHEpFgwhxvQaDXYGhxUlzRSfKCGnK3lvo2uvNlDgpWk4HjjrWDL8uv4bfFe9m8qo6Kwnti0UM6/ZwzgmZGqq7RhrXfgdLh9I0Djz+6Fw+6iNLeW3K3luN2C0MOEyymKQlr/aNL6R+O0u9rsbHU2UYkWLnrwJEyhekJCD99ohlj01DaFQ4VGGUnqGUFsWhjmCANanYZ1Sw9QXlCPs0FDQ42d8GhPuGLxgRoWPrkWnUFLat8oMgbH0GtkPEaz/pg6Q12RuLQwdlBERFwIPUfEkdIniti0MELC9L4ZkJpyK6W5teRsLWN/04as/tRVWHn3/l+pq/DMpCoKJPeOJH1gDFHJIazb8hszZ01HuBXqK22U5deRv7OCvJ2VVJc0svKT5rj9YK5nOV4EZH/8oYDCPVUU7a1CURQufmgsYdEmz75SNXYaax04rE7yd3jqqowhsST1jKAsv46C3ZU0VNtRNAqWyLafdX2om8mn92HKxf2CmnntUM65YxQOm/OIs8t6U3PdYjBpSeodSXyPcMJjTBhCdJQX1LH68/3UV9uoKKonPNbky5y56JkNFO6pIjYtlB6DYug9OoGY5FC/9rT7P0/gceZRPPVyj0Ex9BgYTVyPcCLiQjCG6HC53DRU2z1la1cl+zeWHhLi5uGrl7eQt6N5ACsmNZTMIbEkZIYTlWjBaNYhhKCx1kF5QR0H91ZzYEsZNWVWdvkN0h6uj9Sd8A4+5e2ooHBPFbnbyqmrtHHyBX18+1y5nG4ammb9ypqSrcSkWOg7NonKonoO7qum8qAn7DfkMI6IMUTH8FnpDJuZhnCLE0ZDNZBOSzvQaDXE92iZLeJQeo2Ip6qkgagEM1nD41rtOPU5KYHdq4pZtXgfqxbvIzzWREJmBOUFnge+ocbOzpVF7FxZhDncQJ+TErF5F/2dIJXslIv6MvG83m1O3eoMWuLSw4hLD2PwlFRPfPZP+Wz7qdC3AHLves8sTWxaKCNPzSBrWGyrBd0cbiAq0ULGkFhO+kMWNWWNbP+5kA3LcnG7xWEb/O6Et0PXUGNn3dLmDDhleXX8snAPhdnVlBfU+ab4/elzUoJvrwuX003x/mpfVpUj0VUcFi/tDbE7+cI+lByoocfgWOJ7hLVcP+USfPPGdqylOt69ZxWmUH1T2BQgwGlzcWBzGQc2l7Hiwz1kDo31paY+Ucrp4Cmp9B6VgNHScn0ZeH5nRFwIEXEh9BoZj9PuYveaYjZ/l++rz+qbZkmMZh1Dp6cxcFKKbxTW4XCweZ/AHGFAr9cTGW8mpW8UQ6enYbc6yV5bwtafCnyzPCbLiVFWvXjX9nkQfPf2DjQahZJDQnu8JPWKYMQsT4iwcAtK82pRNIov7PZwBKTA7wJ4ZkWPPMM9fEY6xhAdyb0jSesX3WKGM6lXBKs/34/D6uJ/D61Co1GITrGQ0ifKN9BVlldHWV4d65bmEN8jDEPTWosTwQkGz+zLZY+O96wFNbXs0uk0WsJjQwiPDSFrWByTzutN7vYKNn+fH7Duz+uw9B+fxNDpacS0sX4sJNRAdJJnE+aJ83pTvL+GbT8V+KJLTpQ21VuP7zokaubXRXspL2xySIrqW4RdRidZGD6zeV+6mrJGSnJq6TH4yCnHFUXxZZ2UtI50Wo4jplA9E8/tfdjPDJyYTGVRA3WVVhprHdSUWZsX9gP9xiWiN+nYs6aYhho7G5f7ZeM4QTpDikZpd6wpeBq4sbN7MuasLD57bgMFu6p8742Y1YNeI9sf5x4eG8LYOT0ZfUYmudvLiU3rGqFNx4p/iFnGkFgyh8by/Ts7Adj4TfPGgFqdhpAwPRqdJ7wnuWdEwFS+VqchuXfnrn0KBhmDY8kYHNvm+2n9o0nqHUFxTiXCocFa5yBna3MDH51soc+YBHavLqaisJ7sdc2bcZ4onSGgQ2EKuqbwwQETkjmwuYwv/7PZ917GkFhGn5HZ7nMZTDoGTExmwMRkDu6vxmlzBT2M83gRmWCmqriBmNRQ+oxJYOM3eTTW2H2zv+CZkTKF6tGbdLgcbvRGbcDzqmiUdg2kdXciE8yMP7tXm++HhBoYMi2V3G0VNFTbsFtdPifFy8TzelOwu5KcLeWU5NS2ea7uTEdCjBVN04zMoBicdhcv3fSj7z2NTmHKJf3aHeKqKAqJWREkZkUw/pxeVJc1kpBxYjyX3rTlOr2GPmMTsdU52LuhFIfVxbafCnyf02gUjBZPV1q4IWt4YH/E6zBKjg/SaelkkntHMe/u0QDYGp2UHKghf2cl+zaWYmtwMPrMTMJjQphwTi9ytpaz7acCcrdXAGAOOzFGMI4WjUbBHN7ccek7NrFdoxetodVryBzadman7kZiVgQXPzSWkDC9J1RJCLb/XEhlUb2ngRocS0JGeNDXBXQXzOEGzrppCEuWLGHWzFOpKbZRuKeK/ZtLKcuvY+SpPegzJpERp/SgLM8TJ75z1UGcNlfAM/p7xT/7XVx6GEOnpR3m04cnMbPrrMU4Hpxzx0gcNpevo+mwulj3VQ7JvSI8IWC9IolJsXS52ZGuyqR5fQDPGqG6ShsH91WTs7WcA5vLsEQaGTI1laHT02istbNr1UE2Ls+lvtp+VGsJTzR0Bq0vhbPJomfsnKyj3rMsJMxAyAnUR5l4Xi/6j0siNj0UrVZDSU4N+7eUYQ4zkDE4lrT+0cSmhxIWbZLZvjoR6bQEEWOIzrc2YNzcngHvaXUasoZ5NrKqPFhPY639qBaXnWhMOLcXPQbFkD4w+ojrFn5vRCY0LzpXFIVz7hjp+1ty9Oj0Gl/2oOGz0gPe8y76nHxRX8bOyaK8oJ6kLrTgOVjEpoVy1o1DCQkzyHrrEA5d0D/mrExGn5nZJTa47c4oikJYtImwaBO9RyW0SHsfEmZg2Ix0hk5LozSvlqik9mdrPJGZfcswGmrspPaNkmsp/NDptSRkNs8axfcI55pnTkar08g2NYhIp6UbEJVo6VA63BMZS4SRviclHvmDElmxdjJGs57kpk0Ff+8oikL6wKObBf29oSjKCZO8oSuhKEqrqWN/L6F17SU2VQ4qtBc5+xl8pFstkUgkEolEIpFIujTSaZFIJBKJRCKRSCRdGum0SCQSiUQikUgkki6NdFokEolEIpFIJBJJl0Y6LRKJRCKRSCQSiaRLI50WiUQikUgkEolE0qWRTotEIpFIJBKJRCLp0nT6Pi1CCABqamo6+9ItcDgcNDQ0UFNTg16vP/IXJB1C6qsuUl91kfqqi9RXXaS+6iL1VRepr7p0NX29PoHXR2iLTndaamtrAUhLS+vsS0skEolEIpFIJJIuSG1tLREREW2+r4gjuTXHGbfbTWFhIWFhYUHfsbumpoa0tDTy8vIID5c75B5vpL7qIvVVF6mvukh91UXqqy5SX3WR+qpLV9NXCEFtbS3JycloNG2vXOn0mRaNRkNqampnX/awhIeHd4mbdqIi9VUXqa+6SH3VReqrLlJfdZH6qovUV126kr6Hm2HxIhfiSyQSiUQikUgkki6NdFokEolEIpFIJBJJl+Z37bQYjUYeeOABjEZjsE05IZH6qovUV12kvuoi9VUXqa+6SH3VReqrLt1V305fiC+RSCQSiUQikUgkHeF3PdMikUgkEolEIpFIuj7SaZFIJBKJRCKRSCRdGum0SCQSiUQikUgkki6NdFokEolEIpFIJBJJl0Y6LRKJRCKRSCRBQuZDkkjah3RaJJLfKSUlJezbty/YZvxukB0TSXeirKws2Cac0LhcLt/fiqLgdruDaM2Jiaxz1eHgwYOsX7+eH3/8EavV2qnXlimPj5L8/Hx27NhBbW0to0aNIj09PdgmnVDk5eXxyy+/UF1dzZgxYxg+fHiwTTqh2Lx5M/PmzeMvf/kL5557LnFxccE26YQiLy+P3bt3U1ZWxogRI+jduzcAbrcbjUaOFR0rhYWF7Ny5k7KyMsaOHSvr3+PMhg0bGDlyJD///DPjx48PtjknHLt37+bpp5+mrq6OmJgY/vWvfwXbpBOK+vp6TCYTWq0WIQSKogTbpBOGzZs3c+aZZxIZGcnWrVuZPHkys2fP5uabb+6U68vW8yjYsmULo0eP5u677+a8887jvPPO45Zbbgm2WScMW7ZsYcKECbz00kvcfvvt3H777WzZsiXYZp0w7N69m6lTp3LqqadyySWXtHBY5IjfsbF582bGjBnD3//+d/70pz9x+eWXc+211wKg0WikvsfIli1bmDRpEvfccw8XXXQR8+fPZ+nSpcE264Rh06ZNTJ48mZtvvlk6LCqwdetWxo8fT11dHYqisHz5cu655x7f+3Ic+djYsWMH5513HgsXLsTpdKIoitT0OFFeXs55553H+eefzxdffMGOHTtIT0/n3Xff5aabbuoUG6TT0kGqq6u55JJLuOCCC1i+fDn79+/njDPOYNmyZcyePTvY5nV7du7cycyZM7n00ktZsmQJmzZtYt26dezatSvYpp0w/Pe//+XUU0/l2WefJTQ0lAULFvCf//yH999/H5Ad62OhuLiY888/nyuvvJLPP/+c7du3M336dF599VXmzp0LSH2PhezsbE4//XQuvPBCPvvsM/bs2UN9fT0fffRRsE07IfB2qG+66Sb++c9/IoRg//79rFy5kpKSkmCb1+2pqqpi/vz5XHHFFbz33nu88sorTJkyJWBXcjkrcPQcOHCAuXPnsmzZMl544QW+/PJL6bgcR/Lz83G73VxzzTWkp6fTt29fnnrqKc4991x+/PFH/va3v6lug3RaOkhlZSVWq5ULLriAyMhI0tPTueWWW7j//vvZsWMHF110UbBN7LY0NDTwj3/8g9mzZ/PQQw9hMpnIzMxk+vTp7N27l8cff1x2To4Dubm5jBs3DoDx48fz/PPP89RTT3HfffcxceJEwNOxlpV8x9m5cychISH85S9/wWAwkJiYyPnnn09ycjI//PADc+bMAZAhYkeBzWbjxRdfZMqUKdxzzz1ER0eTmZnJ7bffzrJly6ioqAi2id0am83GHXfcgd1u55FHHgHgrLPO4txzz2XixImcddZZ3HnnnUG2sntTWlpKXV0dl1xyCQAhISG43W6WLVvGGWecwTnnnENxcTEgZ1w6itPpZMGCBfTt25dVq1ZhMBh49NFHpeNyHAkNDcVqtbJ582bA84zGxcVx3XXXMXfuXL777ju+/vprVW2QLWcHCQ8Px2azsXLlSt+xsLAwZs+ezT333MPWrVt55ZVXgmhh90VRFGbPns3111+PTqdDURQeeeQRPvnkEzZu3Mhnn33Gww8/zB133BFsU7s1DoeDTZs28eKLLxIeHs6nn37K6tWreffddyktLfV1rOWIX8ex2WxUVFRQWFjoO+ZwOEhNTeWBBx5gx44dLFy4MIgWdl+EEBgMBqZNm0ZISAharRaAhIQEGhsbsdvtQbawe6PX67nnnnvIyMjg5JNPZtasWSiKwhNPPMH69euZOXMmX331FU888USwTe22RERE0NDQwIsvvkhlZSUPPPAAb731FrNmzWL69Onk5uYyY8YMXC6XrH87iFarZfr06VxyySWMHDmSL7/8krCwMB599FG++OILHA6HdFyOkcjISDIyMvjkk0+orKz0PaORkZHcdNNNNDY2qh+qKyQdor6+Xlx66aXitNNOE1u3bg14r66uTpx55pni4osvDpJ13Z/6+nrf3xs3bhQhISFi0aJFQgghnE6nuP3228WYMWNEaWlpsEzs9rz++uti5syZYubMmeLBBx8MeO/9998XAwcOFPv37w+Ocd2cvXv3ivT0dHHllVeKBQsWiBUrVoiIiAhxzz33CCGEGDVqlLj77ruDbGX3w+12CyGEKC4u9h1zuVxCCCH2798v+vXrJ6qqqnzvrVmzpnMN7OZ49RVCiNWrV4t+/fqJkSNHioKCAt/x2tpaccEFF4jTTjtNOByOYJjZ7bHb7eLf//63SE5OFrNmzRImk0l88MEHvvezs7NFRESE+Oijj4JoZffFbrcHvK6rqxPTpk0To0ePFp9++qnvuf3888+DYV63o6qqSuzfv1+Ul5eLhoYGIYQQP/zwg9BqteKvf/1rQH9NCCFuu+02MWPGDOF0OlWzSc60HIHy8nI2b95MdnY2NTU1mM1mbr75ZtatW8cjjzwSkDLWYrFw8skns3PnThobG4NodfehNX1F00jI0KFDyc7OZvbs2bjdbrRaLT179qS+vh6DwRBky7sH/vrW1tYCMGnSJKxWK9988w0HDhwI+HxSUhJOp9M3ii05PP76VlVVkZWVxXvvvceaNWu44447uOCCC/jjH//oC7fJzMwkJycnyFZ3HxwOh+9vIQTx8fG+v70hdjabjcrKSl+de99993HttdfKlL3t4FB9AUaNGsX777/PI4884tPb5XIRGhpKnz59KCkpkWuy2olXXyEEQgj0ej3XXnstmzdv5umnn6Znz55MmDDB9xmn00lycrJPd8nhqa2tJTc3l+LiYqxWK3q93vccu1wuLBYLixcvJjw8nMcee4zFixfzpz/9ieuuu46ioqIgW9+12bJlC1OmTGHGjBmMGzeO+fPns2fPHiZPnsw777zDc889x+23387evXt93ykoKCA5OVndWULV3KETgE2bNol+/fqJrKwskZ6eLk466STfCN4vv/wizGazmDdvnvjhhx9837nmmmvE7Nmzhc1mC5bZ3YbW9N2wYUPAZ/xHAIUQ4sYbbxTnn3++aGxs7ERLuyet6bt27VohhBDbt28XQ4cOFdHR0eKJJ54QQghhtVrF/fffLyZMmCAqKyuDaHn34FB9x4wZ46sfDh48KA4cOCC2bdvm+7zdbhennnqqePzxx4Nlcrdi586d4rLLLhObN28WQrSsC7xs3LhRWCwWUVZWJh566CGh1+vlTEs7aE1fr8Yul8s3k+XPFVdcIa666ipVR1JPFA6nrxBClJWViWHDhon333/fd+zBBx8UAwYMCJjhkrTOli1bxMSJE0WfPn1Enz59xG233SbKy8sDPuOdWamrqxMzZ84URqNRWCwWsW7dumCY3G3Izc0VCQkJ4pZbbhErVqwQTz31lJg2bZqIiYnxabd48WIREREhpkyZIk455RRx8cUXi9DQUN/zrhbSaWmDwsJCkZqaKu644w6xdetW8dFHH4m5c+cKo9EoFixYIIQQ4tdffxVDhgwRI0eOFMOHDxdz5swR4eHhYtOmTUG2vuvTlr4mk0l88sknQojATkpDQ4O45557RFxcXIuwPElLDvf8ekMPtm3bJubOnStSU1NFSkqKOPnkk0V0dHQLx1HSksPpu3DhwhafLygo8D2/u3fvDoLF3Yu9e/eK1NRUERkZKc4991yxZcsWIUTrjkt2drYYMWKEuPbaa4XRaPQ55pK26Yi+QghRUVEh7r77bhEfHy+2b9/emaZ2S9qjb1VVlTj33HPFuHHjxJQpU8SFF14oYmJiZP3bDnbs2CFiY2PFLbfcIr777jtx3333idGjR4vFixcLIQJ19jrYf/zjH0V0dLTsP7SDr7/+WowePTpg8HLXrl3i7LPPFmFhYb7nedOmTeIf//iHuOCCC8Stt97aKdpKp6UN1qxZIwYNGiRycnJ8x+rq6sSNN94ojEajWLp0qRBCiD179oiFCxeK66+/Xjz++ONix44dwTK5W3E4fU0mk1i+fLkQwjPit2jRInHFFVeItLQ0sX79+mCZ3K040vP71VdfCSGEKCoqEuvXrxePPvqoeOedd0R2dnawTO5WdOT53bt3r7jrrrtEUlKSfH7bQUNDg7j00kvFeeedJ5577jkxdepUMXfu3DY71tu3bxeKooiIiAipbzvoqL5LliwRl19+uUhJSZH6toP26Oudxdq3b5948sknxbnnnivuuOMOsXPnzmCa3i2oqqoSZ511lrjuuusCjk+dOlWcf/75rX7nueeeE4qiyOe3nfzvf/8Ter1eVFRUBBzPzc0VZ511lujbt6/Iz88PeK+1mVk1kE5LGyxfvlwoiuK7Md4b4nQ6xVVXXSUiIyPF3r17g2lit+ZI+kZFRYkDBw4IIYTIyckRTz31lOxQd4D2PL/79u0Lpondmo48vw0NDWLt2rUiNzc3aPZ2N95++23xyiuvCCE8DeihHT9/CgsLxdy5c+WAUQfoiL55eXnipZdeku1dBziSvv6Oi3cmoLM6fd2dffv2iSuvvFJ8+eWXQojmxffPPvusOPvss4UQLR3vgwcPij179nSuod2YgoICMXLkSHH//fcHLLZ3u91ixYoVYuTIkb4EEp0dKiqdljaw2+1i3Lhx4pJLLvFlpfFWKjk5OWL8+PHi0UcfFUJ0/k07EWivvt5jskLvGB15fqW2Hae9+rYVbiM5Mv7avf/++2Lq1Klizpw5vhAEq9XqyyZmtVqDYmN3piP6yjqi47RHX5kFs+O4XC5fpIAQzTq/9NJLYsaMGQHH6urqOt/AEwCXyyVuvvlmMWrUKPHuu++2WKM9aNAgcdNNNwXFNpk9rA10Oh3nn38+e/bs4fnnn6e+vt6XrSY9PR2LxeLbpV1mWuo47dXXe0xuxtcxOvL8Sm07Tnv1lXstHD2KouByuQC48MILufrqq6murua+++5j48aN3HzzzYwZMwa73Y5erw+ytd2Pjugrn+OO0x59R48ejcPhkHuHtBPRlDXwlFNO8b32Ppv19fVUVVUBHu0ffvhhrr32WpxOZ7DM7ZZ4NX7yySdJTEzk6aef5u233w7INNirVy+SkpKCYp8uKFft4ngLwg033EB2djaLFy+msbGRe++9l5CQEADi4+OJiYnB7XajKIqs1DuA1FddpL7qIvXtPLRaLW63G41Gw0UXXYSiKLz22mvMnDkTh8PB119/LdOfHwNSX3Vpj77S4W4/h9aj/q8jIyMJCwsD4N577+Xvf/87q1atQqeT3dyO4HW2DQYDCxcu5PLLL+fFF1/k008/5ZRTTmHLli189913PP7448GxT0gXv1W8FY3D4eDee+/l+++/p7GxkdmzZ7N//34+++wzVq1axYABA4JtardE6qsuUl91kfp2Lv4jqlOmTGHTpk2sWLGCQYMGBdmyEwOpr7pIfdXn3Xff5ZNPPmHAgAE89dRT/PLLL4wcOTLYZnVp/J/LQ3G5XGi1WhwOB2+//TZLly4lNzeXhIQEHn30UYYMGdLJ1nqQTgtt3zhvx8TlcvHDDz+wYMECDhw4QFxcHHfeeSeDBw8OgrXdD6mvukh91UXqqy6Hazj9cTqd3HXXXfzrX/9izZo1QWs0uxtSX3WR+qpLe/X9z3/+w5///GfMZjM//vijdFgOw549e3j11VfZvXs3J510Eueccw69e/du8TlvG+elsbERrVYb1NnX363Tkp+fT3Z2NlOmTAGO3DHxIpp2tpXrAA6P1FddpL7qIvVVl/bqeyhvvfUWQ4cOZdiwYeoa2M2R+qqL1FddjkbfL7/8kvvvv5933nlHznAfhq1btzJjxgwmTJiA0Whk6dKlzJ07l9dee617hDGrtcK/K7Njxw4RHh4uTjrpJPHNN9/4jh8u04/MAtR+pL7qIvVVF6mvuhyNvpL2I/VVF6mvuhytvk6n05ftTtI6BQUFYvDgweK2227zHfv111+FXq8XK1euDKJl7ed3N9NSVlbG+eefj8VioaCggIiICO6++25mzJgBtH/ERNI6Ul91kfqqi9RXXaS+6iL1VRepr7ocrb6HznhLWiKE4H//+x8ffvghzz33HBkZGTgcDtxuN+PGjePxxx/3ZWXryvzu0iqUlZXRo0cPrrnmGtLT05k9e7YvC8KMGTNQFCWgYMhKqGNIfdVF6qsuUl91kfqqi9RXXaS+6nK0+kqH5cgoikL//v0ZM2YMGRkZAL7MdYqikJ+fH0TrOkAnzeh0GRobG8XOnTt9U405OTli5MiRYtq0aWLZsmW+zzkcjmCZ2K2R+qqL1FddpL7qIvVVF6mvukh91UXqqx5eTb0bxfqH240ePVq8+OKLvtfvv/+++OGHHzrXwHbyu3NahGjewd67y2deXp6vYCxfvlzYbDZx//33i+effz6YZnZbpL7qIvVVF6mvukh91UXqqy5SX3WR+h4/du/eLR544AHxl7/8RbzwwguiurpaCNHssHi1njlzpvjggw+EEELcddddwmAwiOzs7OAYfQRO+DUte/bs4b333qOqqoo+ffpwySWXEB4e7nvfm4s6Pz+fOXPmEBUVhU6n48cff2TVqlUybekRkPqqi9RXXaS+6iL1VRepr7pIfdVF6qse27dvZ9y4cUyfPp3c3FycTifV1dUsWrSIoUOHAs3hdZMmTeKGG25gz549PPHEE/z444+MGjUqyL+gDYLsNKnKtm3bRHh4uJg7d64YOXKkGDp0qMjIyBAbN24M+JzX29y7d68wGAwiKipKbNiwIQgWdy+kvuoi9VUXqa+6SH3VReqrLlJfdZH6qofD4RBz584Vl156qe/19u3bxVlnnSWio6PFTz/9JIRonnGZNm2aSEpKEiaTSaxduzZodreHE9Zpae9N88b3NTY2iuuvv16EhYWJrVu3Bs3u7oLUV12kvuoi9VUXqa+6SH3VReqrLlJfdXE6nWLy5MniiSeeCDheX18v5s2bJ2JjY0VeXp7v2OTJk0V4eLjYsmVLMMztECdsygVFUaioqGDgwIEA6HQ6+vfvzwcffMCMGTM4++yzyc/PR6PRIISgrq6ODRs2sHz5ct93JG0j9VUXqa+6SH3VReqrLlJfdZH6qovUV120Wi0xMTF88cUXvmNutxuz2cyLL77IwIEDmT9/Pna7HbPZzCOPPMKqVasYNGhQEK1uHyf0mpZzzjmHkpISVqxYATTn8q6oqODss8/GYDDw5Zdf+tK+2e12DAZDME3uVkh91UXqqy5SX3WR+qqL1FddpL7qIvVVB9G0TmXx4sU8+OCDXHDBBdx+++1oNBqfxm+//TaPPfYYy5YtIz09Pdgmd4gTcqbF64dddtll1NXV8eSTT/pultvtJjo6mvnz55Obm0txcbHve7JAtA+pr7pIfdVF6qsuUl91kfqqi9RXXaS+6uLdw2b69OmMHTuWTz/9lBdeeAGHw+Hbz6Zv377YbDasVmswTT0qTkinpSM3raGhIZimdkukvuoi9VUXqa+6SH3VReqrLlJfdZH6qo/dbic0NJTHH3+cgQMH8v777/PXv/4Vq9VKaWkpixcvJjQ0lJiYmGCb2mFOSKcFTuyb1hWQ+qqL1FddpL7qIvVVF6mvukh91UXqe3woLS2loKAg4JjL5cJgMJCTk8OKFSt44YUXOPPMM/n++++JiIhg1qxZvPHGG7z99tvdU99OXvh/3CkpKRH5+fkBx7wp8g4cOCA+++wz0dDQIB5++GExaNAgYTAYxLBhw0RiYqJYv359MEzuVkh91UXqqy5SX3WR+qqL1FddpL7qIvVVj61bt4qUlBTxyiuvCCE86Yv9tU1JSRF33HGHEEIIu90uqqurxYIFC8R3330ncnJygmb3sdKtnZbf603rLKS+6iL1VRepr7pIfdVF6qsuUl91kfqqx8aNG0V4eLiIjo4WgwcPFoWFhb73ioqKREJCgrjuuut8+7CcSHRbp+X3fNM6A6mvukh91UXqqy5SX3WR+qqL1FddpL7qsWnTJhESEiLuvvtu8f3334uMjAzx1Vdf+d4vKCgQTzzxxAmrbbd0Wn7vN01tpL7qIvVVF6mvukh91UXqqy5SX3WR+qrHunXrhKIo4p577vEdmzBhgpg6dWoQrepcup3TIm+aukh91UXqqy5SX3WR+qqL1FddpL7qIvVVl3/84x/itttuE0I0rw1avHixyMjIEEuXLg2maZ1Gt3Na5E1TF6mvukh91UXqqy5SX3WR+qqL1FddpL7q0trsVEFBgcjKyhK33HJLECzqfBQhmnb66SaIpt0+/SksLGTSpEnMnj2bf/7zn0Gy7MRA6qsuUl91kfqqi9RXXaS+6iL1VRep7/HH7XbjdrvR6XS+Yy6XC61W69P7lVde4Y477mDZsmWMHj06iNaqT7fYp8XtduN0OoHmjYlcLhfgKSTJycn87W9/44033mDNmjVBs7O7IvVVF6mvukh91UXqqy5SX3WR+qqL1Fc9tm/fzmWXXcapp57Kn/70J7788ksAtFotLpfLp/fYsWNJTU1lxYoVQLP+JyJd3mmRN01dpL7qIvVVF6mvukh91UXqqy5SX3WR+qrHrl27GD9+PC6Xi9GjR/Prr7/y4IMPcssttwDNGgMMHjyY6dOn8+ijj2K1WtFqtcE0XVW6dHjYrl27OOmkkzjttNPIyMhg6dKl6PV6Jk6cyDPPPAM0T5MB3HzzzbzzzjsUFBRgMpmCaXq3QOqrLlJfdZH6qovUV12kvuoi9VUXqa96CCG49957yc7O5sMPPwSgtraWf/3rXyxcuJDRo0fz8ssvA+B0OtHpdKxevZrrr7+eRYsWkZqaGkzz1aUzF9B0BLfbLe6++24xb94837GamhrxyCOPiGHDholrrrnGd9zhcAghhFi1apUYOXKkyMvL63R7uxtSX3WR+qqL1FddpL7qIvVVF6mvukh91eeKK64QJ598csCxmpoa8dRTT4lRo0aJJ554IuA9q9UqysrKOtPEoNBlw8MURaGwsJCDBw/6joWFhXHTTTdxySWXsGHDBp588kkA3wKloUOH8vXXX5/YXuZxQuqrLlJfdZH6qovUV12kvuoi9VUXqa96iKbgpxEjRuByudi1a5fvvbCwMObPn8/w4cP57LPPqK2tBTzrioxGIzExMUGxuTPpkk6LvGnqIvVVF6mvukh91UXqqy5SX3WR+qqL1FddvOuATj/9dHbt2sXf//536urqAI/2UVFR3Hffffz666/88ssvAGg0XbIrrw7BmuJpD9nZ2SI2NlbMnz9f1NbWCiGa81Tn5uYKRVFk7u9jQOqrLlJfdZH6qovUV12kvuoi9VUXqa/6fPfdd8JoNIobbrhBlJaW+o4XFRWJoUOHipUrVwbRuuCgO7JbEzx69uzJggULOO200wgJCeHBBx8kNjYWAL1ez5AhQ4iIiAiyld0Xqa+6SH3VReqrLlJfdZH6qovUV12kvuozdepUPvroI8477zyKioqYN28eQ4YM4e2336akpIS0tLRgm9jpdGmnBeRNUxupr7pIfdVF6qsuUl91kfqqi9RXXaS+6nPWWWexcuVKbr31Vu688050Oh1arZYvv/zyd7k+qEunPPZn/fr13HrrrRw4cMB30z744AOGDx8ebNNOCKS+6iL1VRepr7pIfdVF6qsuUl91kfqqT01NDRUVFdTW1pKUlOSb1fq90W2cFpA3TW2kvuoi9VUXqa+6SH3VReqrLlJfdZH6SjqDbuW0SCQSiUQikUgkkt8fv6M8aRKJRCKRSCQSiaQ7Ip0WiUQikUgkEolE0qWRTotEIpFIJBKJRCLp0kinRSKRSCQSiUQikXRppNMikUgkEolEIpFIujTSaZFIJBKJRCKRSCRdGum0SCQSiUQikUgkki6NdFokEolEogpXXHEFc+bMCbYZEolEIjkB0AXbAIlEIpF0PxRFOez7DzzwAM899xxy/2KJRCKRHA+k0yKRSCSSDlNUVOT7+8MPP+T+++9n165dvmOhoaGEhoYGwzSJRCKRnIDI8DCJRCKRdJjExETfv4iICBRFCTgWGhraIjxsypQp3Hjjjdx8881ERUWRkJDAK6+8Qn19PVdeeSVhYWH06tWLpUuXBlxr69atnHbaaYSGhpKQkMCll15KWVlZJ/9iiUQikQQT6bRIJBKJpNN46623iI2NZfXq1dx444386U9/4rzzzmP8+PGsX7+eWbNmcemll9LQ0ABAVVUV06ZNY/jw4axdu5avvvqK4uJi5s2bF+RfIpFIJJLORDotEolEIuk0hg4dyr333kvv3r256667MJlMxMbGcs0119C7d2/uv/9+ysvL2bx5MwAvvPACw4cP57HHHqNfv34MHz6c119/ne+//57du3cH+ddIJBKJpLOQa1okEolE0mkMGTLE97dWqyUmJobBgwf7jiUkJABQUlICwKZNm/j+++9bXR+zd+9e+vTpo7LFEolEIukKSKdFIpFIJJ2GXq8PeK0oSsAxb1Yyt9sNQF1dHWeddRZPPvlki3MlJSWpaKlEIpFIuhLSaZFIJBJJl2XEiBF8/PHHZGRkoNPJJksikUh+r8g1LRKJRCLpstxwww1UVFRw4YUXsmbNGvbu3cvXX3/NlVdeicvlCrZ5EolEIukkpNMikUgkki5LcnIyv/zyCy6Xi1mzZjF48GBuvvlmIiMj0WhkEyaRSCS/FxQhtyuWSCQSiUQikUgkXRg5TCWRSCQSiUQikUi6NNJpkUgkEolEIpFIJF0a6bRIJBKJRCKRSCSSLo10WiQSiUQikUgkEkmXRjotEolEIpFIJBKJpEsjnRaJRCKRSCQSiUTSpZFOi0QikUgkEolEIunSSKdFIpFIJBKJRCKRdGmk0yKRSCQSiUQikUi6NNJpkUgkEolEIpFIJF0a6bRIJBKJRCKRSCSSLo10WiQSiUQikUgkEkmX5v8BIZukna9PXHsAAAAASUVORK5CYII=", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "\n", @@ -244,20 +213,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Make a copy of the points geodataframe\n", "pts_df_dop = pts_df.copy()\n", @@ -286,41 +244,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from tatc.generation import generate_equally_spaced_cells\n", - "\n", - "# Make a plot with shaded cells\n", - "cells_df = generate_equally_spaced_cells(5000e3)\n", - "cells_df[\"PDOP\"] = [np.mean(p_dops[i][\"dop\"]) for i in range(len(p_dops))]\n", - "\n", - "fig, ax = plt.subplots(figsize=(8, 5), subplot_kw={\"projection\": ccrs.PlateCarree()})\n", - "cells_df.plot(\n", - " ax=ax,\n", - " column=\"PDOP\",\n", - " cmap=\"viridis\",\n", - " edgecolor=\"k\",\n", - " legend=True,\n", - " legend_kwds={\"label\": \"Mean PDOP\", \"orientation\": \"horizontal\"},\n", - " transform=ccrs.PlateCarree()\n", - ")\n", - "ax.coastlines()\n", - "ax.set_global()\n", - "plt.show()" - ] + "outputs": [], + "source": "from tatc.generation import generate_cells_uniform_spacing\n\n# Make a plot with shaded cells\ncells_df = generate_cells_uniform_spacing(5000e3)\ncells_df[\"PDOP\"] = [np.mean(p_dops[i][\"dop\"]) for i in range(len(p_dops))]\n\nfig, ax = plt.subplots(figsize=(8, 5), subplot_kw={\"projection\": ccrs.PlateCarree()})\ncells_df.plot(\n ax=ax,\n column=\"PDOP\",\n cmap=\"viridis\",\n edgecolor=\"k\",\n legend=True,\n legend_kwds={\"label\": \"Mean PDOP\", \"orientation\": \"horizontal\"},\n transform=ccrs.PlateCarree()\n)\nax.coastlines()\nax.set_global()\nplt.show()" }, { "cell_type": "markdown", @@ -342,21 +269,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 POINT (0 0)\n", - "Name: geometry, dtype: geometry\n", - "Mean PDOP: 1.7481994840847743\n", - "Max PDOP: 1.9493918789245415\n", - "Min PDOP: 1.4223627484939771\n" - ] - } - ], + "outputs": [], "source": [ "import geopandas as gpd\n", "import numpy as np\n", @@ -401,20 +316,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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40POINT Z (-157.51699 -67.51699 0)NOAA 20VIIRS2022-08-01 00:50:55.502645+00:002022-08-01 00:58:28.482647+00:002022-08-01 00:54:41.992646+00:0070.12638672.613305
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216931POINT Z (157.24514 67.38106 0)NOAA 20VIIRS2022-08-12 18:28:51.557986+00:002022-08-12 18:34:04.353232+00:002022-08-12 18:31:27.955609+00:0031.258697324.067710
217031POINT Z (157.24514 67.38106 0)NOAA 20VIIRS2022-08-12 21:48:45.630367+00:002022-08-12 21:49:49.415577+00:002022-08-12 21:49:17.522972+00:0021.11515512.621024
217131POINT Z (157.24514 67.38106 0)NOAA 20VIIRS2022-08-12 23:25:37.442375+00:002022-08-12 23:31:05.304568+00:002022-08-12 23:28:21.373471500+00:0032.77932636.304442
217231POINT Z (157.24514 67.38106 0)NOAA 20VIIRS2022-08-13 01:04:22.215027+00:002022-08-13 01:11:48.221137+00:002022-08-13 01:08:05.218082+00:0073.79850755.756224
217331POINT Z (157.24514 67.38106 0)NOAA 20VIIRS2022-08-13 02:45:33.011228+00:002022-08-13 02:51:56.674083+00:002022-08-13 02:48:44.842655500+00:0040.285445264.921794
\n", - "

2174 rows × 9 columns

\n", - "
" - ], - "text/plain": [ - " point_id geometry satellite instrument \\\n", - "0 0 POINT Z (-157.51699 -67.51699 0) NOAA 20 VIIRS \n", - "1 0 POINT Z (-157.51699 -67.51699 0) NOAA 20 VIIRS \n", - "2 0 POINT Z (-157.51699 -67.51699 0) NOAA 20 VIIRS \n", - "3 0 POINT Z (-157.51699 -67.51699 0) NOAA 20 VIIRS \n", - "4 0 POINT Z (-157.51699 -67.51699 0) NOAA 20 VIIRS \n", - "... ... ... ... ... \n", - "2169 31 POINT Z (157.24514 67.38106 0) NOAA 20 VIIRS \n", - "2170 31 POINT Z (157.24514 67.38106 0) NOAA 20 VIIRS \n", - "2171 31 POINT Z (157.24514 67.38106 0) NOAA 20 VIIRS \n", - "2172 31 POINT Z (157.24514 67.38106 0) NOAA 20 VIIRS \n", - "2173 31 POINT Z (157.24514 67.38106 0) NOAA 20 VIIRS \n", - "\n", - " start end \\\n", - "0 2022-07-26 23:05:45.258746+00:00 2022-07-26 23:08:44.580526+00:00 \n", - "1 2022-07-30 23:30:00.700453+00:00 2022-07-30 23:35:11.668269+00:00 \n", - "2 2022-07-31 01:09:44.380087+00:00 2022-07-31 01:17:23.069693+00:00 \n", - "3 2022-07-31 23:11:40.551119+00:00 2022-07-31 23:15:24.047070+00:00 \n", - "4 2022-08-01 00:50:55.502645+00:00 2022-08-01 00:58:28.482647+00:00 \n", - "... ... ... \n", - "2169 2022-08-12 18:28:51.557986+00:00 2022-08-12 18:34:04.353232+00:00 \n", - "2170 2022-08-12 21:48:45.630367+00:00 2022-08-12 21:49:49.415577+00:00 \n", - "2171 2022-08-12 23:25:37.442375+00:00 2022-08-12 23:31:05.304568+00:00 \n", - "2172 2022-08-13 01:04:22.215027+00:00 2022-08-13 01:11:48.221137+00:00 \n", - "2173 2022-08-13 02:45:33.011228+00:00 2022-08-13 02:51:56.674083+00:00 \n", - "\n", - " epoch sat_alt sat_az \n", - "0 2022-07-26 23:07:14.919636+00:00 23.232376 96.383771 \n", - "1 2022-07-30 23:32:36.184361+00:00 29.980441 90.571477 \n", - "2 2022-07-31 01:13:33.724890+00:00 83.932517 71.659563 \n", - "3 2022-07-31 23:13:32.299094500+00:00 24.749767 94.883549 \n", - "4 2022-08-01 00:54:41.992646+00:00 70.126386 72.613305 \n", - "... ... ... ... \n", - "2169 2022-08-12 18:31:27.955609+00:00 31.258697 324.067710 \n", - "2170 2022-08-12 21:49:17.522972+00:00 21.115155 12.621024 \n", - "2171 2022-08-12 23:28:21.373471500+00:00 32.779326 36.304442 \n", - "2172 2022-08-13 01:08:05.218082+00:00 73.798507 55.756224 \n", - "2173 2022-08-13 02:48:44.842655500+00:00 40.285445 264.921794 \n", - "\n", - "[2174 rows x 9 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from tatc import utils\n", - "from tatc.schemas import Instrument, Satellite, TwoLineElements\n", + "from tatc.schemas import GeneralPerturbationsOrbit, Instrument, Satellite\n", "\n", "viirs = Instrument(\n", " name=\"VIIRS\",\n", @@ -252,8 +35,8 @@ ")\n", "noaa20 = Satellite(\n", " name=\"NOAA 20\",\n", - " orbit=TwoLineElements(\n", - " tle=[\n", + " orbit=GeneralPerturbationsOrbit.from_tle(\n", + " [\n", " \"1 43013U 17073A 22195.78278435 .00000038 00000+0 38919-4 0 9996\",\n", " \"2 43013 98.7169 133.9110 0001202 63.8768 296.2532 14.19561306241107\",\n", " ]\n", @@ -261,9 +44,9 @@ " instruments=[viirs],\n", ")\n", "\n", - "from tatc.generation import generate_equally_spaced_points\n", + "from tatc.generation import generate_points_uniform_spacing\n", "\n", - "points_df = generate_equally_spaced_points(5000e3)\n", + "points_df = generate_points_uniform_spacing(5000e3)\n", "\n", "from tatc.schemas import Point\n", "\n", @@ -298,196 +81,15 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "cd8c9eea-4f5a-4efa-9c93-047e7a728284", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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stationgeometrysatellitestartendepoch
0HobokenPOINT Z (-74.02686 40.74259 0)NOAA 202022-07-14 17:12:43.763296+00:002022-07-14 17:22:57.456044+00:002022-07-14 17:17:50.609670+00:00
1HobokenPOINT Z (-74.02686 40.74259 0)NOAA 202022-07-14 18:54:04.803537+00:002022-07-14 19:02:25.062501+00:002022-07-14 18:58:14.933019+00:00
2HobokenPOINT Z (-74.02686 40.74259 0)NOAA 202022-07-15 05:32:06.654997+00:002022-07-15 05:39:35.858756+00:002022-07-15 05:35:51.256876500+00:00
3HobokenPOINT Z (-74.02686 40.74259 0)NOAA 202022-07-15 07:11:08.223574+00:002022-07-15 07:21:37.089606+00:002022-07-15 07:16:22.656590+00:00
4HobokenPOINT Z (-74.02686 40.74259 0)NOAA 202022-07-15 16:54:27.877548+00:002022-07-15 17:03:52.170605+00:002022-07-15 16:59:10.024076500+00:00
.....................
115HobokenPOINT Z (-74.02686 40.74259 0)NOAA 202022-08-12 08:28:09.529139+00:002022-08-12 08:32:42.603014+00:002022-08-12 08:30:26.066076500+00:00
116HobokenPOINT Z (-74.02686 40.74259 0)NOAA 202022-08-12 16:30:12.255865+00:002022-08-12 16:37:46.514194+00:002022-08-12 16:33:59.385029500+00:00
117HobokenPOINT Z (-74.02686 40.74259 0)NOAA 202022-08-12 18:08:28.080213+00:002022-08-12 18:19:02.956149+00:002022-08-12 18:13:45.518181+00:00
118HobokenPOINT Z (-74.02686 40.74259 0)NOAA 202022-08-13 06:26:51.204464+00:002022-08-13 06:37:31.285805+00:002022-08-13 06:32:11.245134500+00:00
119HobokenPOINT Z (-74.02686 40.74259 0)NOAA 202022-08-13 08:08:18.072998+00:002022-08-13 08:15:29.539014+00:002022-08-13 08:11:53.806006+00:00
\n", - "

120 rows × 6 columns

\n", - "
" - ], - "text/plain": [ - " station geometry satellite \\\n", - "0 Hoboken POINT Z (-74.02686 40.74259 0) NOAA 20 \n", - "1 Hoboken POINT Z (-74.02686 40.74259 0) NOAA 20 \n", - "2 Hoboken POINT Z (-74.02686 40.74259 0) NOAA 20 \n", - "3 Hoboken POINT Z (-74.02686 40.74259 0) NOAA 20 \n", - "4 Hoboken POINT Z (-74.02686 40.74259 0) NOAA 20 \n", - ".. ... ... ... \n", - "115 Hoboken POINT Z (-74.02686 40.74259 0) NOAA 20 \n", - "116 Hoboken POINT Z (-74.02686 40.74259 0) NOAA 20 \n", - "117 Hoboken POINT Z (-74.02686 40.74259 0) NOAA 20 \n", - "118 Hoboken POINT Z (-74.02686 40.74259 0) NOAA 20 \n", - "119 Hoboken POINT Z (-74.02686 40.74259 0) NOAA 20 \n", - "\n", - " start end \\\n", - "0 2022-07-14 17:12:43.763296+00:00 2022-07-14 17:22:57.456044+00:00 \n", - "1 2022-07-14 18:54:04.803537+00:00 2022-07-14 19:02:25.062501+00:00 \n", - "2 2022-07-15 05:32:06.654997+00:00 2022-07-15 05:39:35.858756+00:00 \n", - "3 2022-07-15 07:11:08.223574+00:00 2022-07-15 07:21:37.089606+00:00 \n", - "4 2022-07-15 16:54:27.877548+00:00 2022-07-15 17:03:52.170605+00:00 \n", - ".. ... ... \n", - "115 2022-08-12 08:28:09.529139+00:00 2022-08-12 08:32:42.603014+00:00 \n", - "116 2022-08-12 16:30:12.255865+00:00 2022-08-12 16:37:46.514194+00:00 \n", - "117 2022-08-12 18:08:28.080213+00:00 2022-08-12 18:19:02.956149+00:00 \n", - "118 2022-08-13 06:26:51.204464+00:00 2022-08-13 06:37:31.285805+00:00 \n", - "119 2022-08-13 08:08:18.072998+00:00 2022-08-13 08:15:29.539014+00:00 \n", - "\n", - " epoch \n", - "0 2022-07-14 17:17:50.609670+00:00 \n", - "1 2022-07-14 18:58:14.933019+00:00 \n", - "2 2022-07-15 05:35:51.256876500+00:00 \n", - "3 2022-07-15 07:16:22.656590+00:00 \n", - "4 2022-07-15 16:59:10.024076500+00:00 \n", - ".. ... \n", - "115 2022-08-12 08:30:26.066076500+00:00 \n", - "116 2022-08-12 16:33:59.385029500+00:00 \n", - "117 2022-08-12 18:13:45.518181+00:00 \n", - "118 2022-08-13 06:32:11.245134500+00:00 \n", - "119 2022-08-13 08:11:53.806006+00:00 \n", - "\n", - "[120 rows x 6 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from tatc.schemas import GroundStation\n", "\n", "hoboken = GroundStation(\n", - " name=\"Hoboken\", latitude=40.74259, longitude=-74.02686, min_elevation_angle=10\n", + " id=0, name=\"Hoboken\", latitude=40.74259, longitude=-74.02686, min_elevation_angle=10\n", ")\n", "\n", "from tatc.analysis import collect_downlinks\n", @@ -506,239 +108,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "5446b79a-2c9d-430d-87c8-71a665b4e41a", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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point_idgeometrysatelliteinstrumentsat_altsat_azstationdownlinkedlatencyobserved
020POINT Z (22.34708 22.41505 0)NOAA 20VIIRS46.686116260.777813Hoboken2022-07-14 17:17:50.609670+00:000 days 05:10:34.1449945002022-07-14 12:07:16.464675500+00:00
128POINT Z (22.34708 67.38106 0)NOAA 20VIIRS29.024717271.880297Hoboken2022-07-14 17:17:50.609670+00:000 days 04:57:46.4943202022-07-14 12:20:04.115350+00:00
227POINT Z (-22.61894 67.38106 0)NOAA 20VIIRS48.70455249.346995Hoboken2022-07-14 17:17:50.609670+00:000 days 04:56:13.1297692022-07-14 12:21:37.479901+00:00
325POINT Z (-112.55097 67.38106 0)NOAA 20VIIRS31.323022324.299192Hoboken2022-07-14 17:17:50.609670+00:000 days 04:47:32.6269195002022-07-14 12:30:17.982750500+00:00
424POINT Z (-157.51699 67.38106 0)NOAA 20VIIRS55.694847103.325667Hoboken2022-07-14 17:17:50.609670+00:000 days 04:44:55.8094642022-07-14 12:32:54.800206+00:00
.................................
216926POINT Z (-67.58495 67.38106 0)NOAA 20VIIRS28.848897327.376449NoneNaTNaT2022-08-13 09:44:37.403851500+00:00
21705POINT Z (67.3131 -67.51699 0)NOAA 20VIIRS78.725002245.320118NoneNaTNaT2022-08-13 10:37:41.608547500+00:00
217120POINT Z (22.34708 22.41505 0)NOAA 20VIIRS35.60526570.967792NoneNaTNaT2022-08-13 11:04:24.936363+00:00
217228POINT Z (22.34708 67.38106 0)NOAA 20VIIRS56.654314258.438321NoneNaTNaT2022-08-13 11:16:22.849990500+00:00
217327POINT Z (-22.61894 67.38106 0)NOAA 20VIIRS31.04321033.884307NoneNaTNaT2022-08-13 11:19:02.608528500+00:00
\n", - "

2174 rows × 10 columns

\n", - "
" - ], - "text/plain": [ - " point_id geometry satellite instrument \\\n", - "0 20 POINT Z (22.34708 22.41505 0) NOAA 20 VIIRS \n", - "1 28 POINT Z (22.34708 67.38106 0) NOAA 20 VIIRS \n", - "2 27 POINT Z (-22.61894 67.38106 0) NOAA 20 VIIRS \n", - "3 25 POINT Z (-112.55097 67.38106 0) NOAA 20 VIIRS \n", - "4 24 POINT Z (-157.51699 67.38106 0) NOAA 20 VIIRS \n", - "... ... ... ... ... \n", - "2169 26 POINT Z (-67.58495 67.38106 0) NOAA 20 VIIRS \n", - "2170 5 POINT Z (67.3131 -67.51699 0) NOAA 20 VIIRS \n", - "2171 20 POINT Z (22.34708 22.41505 0) NOAA 20 VIIRS \n", - "2172 28 POINT Z (22.34708 67.38106 0) NOAA 20 VIIRS \n", - "2173 27 POINT Z (-22.61894 67.38106 0) NOAA 20 VIIRS \n", - "\n", - " sat_alt sat_az station downlinked \\\n", - "0 46.686116 260.777813 Hoboken 2022-07-14 17:17:50.609670+00:00 \n", - "1 29.024717 271.880297 Hoboken 2022-07-14 17:17:50.609670+00:00 \n", - "2 48.704552 49.346995 Hoboken 2022-07-14 17:17:50.609670+00:00 \n", - "3 31.323022 324.299192 Hoboken 2022-07-14 17:17:50.609670+00:00 \n", - "4 55.694847 103.325667 Hoboken 2022-07-14 17:17:50.609670+00:00 \n", - "... ... ... ... ... \n", - "2169 28.848897 327.376449 None NaT \n", - "2170 78.725002 245.320118 None NaT \n", - "2171 35.605265 70.967792 None NaT \n", - "2172 56.654314 258.438321 None NaT \n", - "2173 31.043210 33.884307 None NaT \n", - "\n", - " latency observed \n", - "0 0 days 05:10:34.144994500 2022-07-14 12:07:16.464675500+00:00 \n", - "1 0 days 04:57:46.494320 2022-07-14 12:20:04.115350+00:00 \n", - "2 0 days 04:56:13.129769 2022-07-14 12:21:37.479901+00:00 \n", - "3 0 days 04:47:32.626919500 2022-07-14 12:30:17.982750500+00:00 \n", - "4 0 days 04:44:55.809464 2022-07-14 12:32:54.800206+00:00 \n", - "... ... ... \n", - "2169 NaT 2022-08-13 09:44:37.403851500+00:00 \n", - "2170 NaT 2022-08-13 10:37:41.608547500+00:00 \n", - "2171 NaT 2022-08-13 11:04:24.936363+00:00 \n", - "2172 NaT 2022-08-13 11:16:22.849990500+00:00 \n", - "2173 NaT 2022-08-13 11:19:02.608528500+00:00 \n", - "\n", - "[2174 rows x 10 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from tatc.analysis import compute_latencies\n", "\n", @@ -756,306 +129,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "7f2306ed-e78e-4401-9124-a0655c8ec299", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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point_idgeometrylatencysamples
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\n", - "
" - ], - "text/plain": [ - " point_id geometry latency samples\n", - "0 0 POINT Z (-157.51699 -67.51699 0) 0 days 05:10:59.268689 31\n", - "1 1 POINT Z (-112.55097 -67.51699 0) 0 days 08:14:18.836280 31\n", - "2 2 POINT Z (-67.58495 -67.51699 0) 0 days 04:21:48.654757 30\n", - "3 3 POINT Z (-22.61894 -67.51699 0) 0 days 01:17:44.394353 31\n", - "4 4 POINT Z (22.34708 -67.51699 0) 0 days 04:08:15.422021 31\n", - "5 5 POINT Z (67.3131 -67.51699 0) 0 days 08:42:06.877307 31\n", - "6 6 POINT Z (112.27912 -67.51699 0) 0 days 02:43:07.838991 33\n", - "7 7 POINT Z (157.24514 -67.51699 0) 0 days 02:19:26.312887 34\n", - "8 8 POINT Z (-157.51699 -22.55097 0) 0 days 05:44:31.317841 34\n", - "9 9 POINT Z (-112.55097 -22.55097 0) 0 days 08:37:46.021444 34\n", - "10 10 POINT Z (-67.58495 -22.55097 0) 0 days 00:17:46.170450 32\n", - "11 11 POINT Z (-22.61894 -22.55097 0) 0 days 01:35:26.242661 32\n", - "12 12 POINT Z (22.34708 -22.55097 0) 0 days 04:33:05.882867 32\n", - "13 13 POINT Z (67.3131 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days 03:01:50.017806 176\n", - "28 28 POINT Z (22.34708 67.38106 0) 0 days 04:34:58.875476 174\n", - "29 29 POINT Z (67.3131 67.38106 0) 0 days 04:11:56.901332 174\n", - "30 30 POINT Z (112.27912 67.38106 0) 0 days 04:35:16.325513 177\n", - "31 31 POINT Z (157.24514 67.38106 0) 0 days 05:24:20.336656 178" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from tatc.analysis import reduce_latencies\n", "\n", @@ -1073,21 +150,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "ea8c0dd6-64df-490c-9c87-1b8946177ce0", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ], - "text/plain": [ - " cell_id geometry samples \\\n", - "0 0 POLYGON Z ((-135.03398 -90 0, -135.03398 -45.0... 31 \n", - "1 1 POLYGON Z ((-90.06796 -90 0, -90.06796 -45.033... 31 \n", - "2 2 POLYGON Z ((-45.10194 -90 0, -45.10194 -45.033... 30 \n", - "3 3 POLYGON Z ((-0.13593 -90 0, -0.13593 -45.03398... 31 \n", - "4 4 POLYGON Z ((44.83009 -90 0, 44.83009 -45.03398... 31 \n", - "5 5 POLYGON Z ((89.79611 -90 0, 89.79611 -45.03398... 31 \n", - "6 6 POLYGON Z ((134.76213 -90 0, 134.76213 -45.033... 33 \n", - "7 7 POLYGON Z ((179.72815 -90 0, 179.72815 -45.033... 34 \n", - "8 8 POLYGON Z ((-135.03398 -45.03398 0, -135.03398... 34 \n", - "9 9 POLYGON Z ((-90.06796 -45.03398 0, -90.06796 -... 34 \n", - "10 10 POLYGON Z ((-45.10194 -45.03398 0, -45.10194 -... 32 \n", - "11 11 POLYGON Z ((-0.13593 -45.03398 0, -0.13593 -0.... 32 \n", - "12 12 POLYGON Z ((44.83009 -45.03398 0, 44.83009 -0.... 32 \n", - "13 13 POLYGON Z ((89.79611 -45.03398 0, 89.79611 -0.... 34 \n", - "14 14 POLYGON Z ((134.76213 -45.03398 0, 134.76213 -... 32 \n", - "15 15 POLYGON Z ((179.72815 -45.03398 0, 179.72815 -... 32 \n", - "16 16 POLYGON Z ((-135.03398 -0.06796 0, -135.03398 ... 32 \n", - "17 17 POLYGON Z ((-90.06796 -0.06796 0, -90.06796 44... 34 \n", - "18 18 POLYGON Z ((-45.10194 -0.06796 0, -45.10194 44... 32 \n", - "19 19 POLYGON Z ((-0.13593 -0.06796 0, -0.13593 44.8... 34 \n", - "20 20 POLYGON Z ((44.83009 -0.06796 0, 44.83009 44.8... 34 \n", - "21 21 POLYGON Z ((89.79611 -0.06796 0, 89.79611 44.8... 32 \n", - "22 22 POLYGON Z ((134.76213 -0.06796 0, 134.76213 44... 34 \n", - "23 23 POLYGON Z ((179.72815 -0.06796 0, 179.72815 44... 32 \n", - "24 24 POLYGON Z ((-135.03398 44.89806 0, -135.03398 ... 173 \n", - "25 25 POLYGON Z ((-90.06796 44.89806 0, -90.06796 89... 173 \n", - "26 26 POLYGON Z ((-45.10194 44.89806 0, -45.10194 89... 171 \n", - "27 27 POLYGON Z ((-0.13593 44.89806 0, -0.13593 89.8... 176 \n", - "28 28 POLYGON Z ((44.83009 44.89806 0, 44.83009 89.8... 174 \n", - "29 29 POLYGON Z ((89.79611 44.89806 0, 89.79611 89.8... 174 \n", - "30 30 POLYGON Z ((134.76213 44.89806 0, 134.76213 89... 177 \n", - "31 31 POLYGON Z ((179.72815 44.89806 0, 179.72815 89... 178 \n", - "\n", - " latency_hr \n", - "0 5.183130 \n", - "1 8.238566 \n", - "2 4.363515 \n", - "3 1.295665 \n", - "4 4.137617 \n", - "5 8.701910 \n", - "6 2.718844 \n", - "7 2.323976 \n", - "8 5.742033 \n", - "9 8.629450 \n", - "10 0.296158 \n", - "11 1.590623 \n", - "12 4.551634 \n", - "13 9.086396 \n", - "14 0.756701 \n", - "15 2.879819 \n", - "16 6.365179 \n", - "17 9.406294 \n", - "18 6.204407 \n", - "19 2.193734 \n", - "20 6.463890 \n", - "21 7.576263 \n", - "22 0.548917 \n", - "23 3.195287 \n", - "24 5.808975 \n", - "25 6.519509 \n", - "26 4.968371 \n", - "27 3.030561 \n", - "28 4.583021 \n", - "29 4.199139 \n", - "30 4.587868 \n", - "31 5.405649 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "from tatc.generation.cells import generate_equally_spaced_cells\n", + "from tatc.generation.cells import generate_cells_uniform_spacing\n", "\n", - "cells_df = generate_equally_spaced_cells(5000e3)\n", + "cells_df = generate_cells_uniform_spacing(5000e3)\n", "\n", - "import numpy as np\n", + "from tatc.analysis.latency import grid_latencies\n", "\n", - "grid_results = (\n", - " cells_df.sjoin(reduced_results, how=\"inner\", predicate=\"contains\")\n", - " .dissolve(\n", - " by=\"cell_id\",\n", - " aggfunc={\n", - " \"samples\": \"sum\",\n", - " \"latency_hr\": lambda r: np.average(\n", - " r, weights=reduced_results.loc[r.index, \"samples\"]\n", - " ),\n", - " },\n", - " )\n", - " .reset_index()\n", - ")\n", + "grid_results = grid_latencies(reduced_results, cells_df)\n", + "grid_results[\"latency_hr\"] = grid_results[\"latency\"] / timedelta(hours=1)\n", "display(grid_results)" ] }, @@ -1484,21 +209,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "acd11c1f-b20d-4a3f-912a-3b922a3fac09", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import cartopy.crs as ccrs\n", @@ -1534,7 +248,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.11.15" } }, "nbformat": 4, diff --git a/pyproject.toml b/pyproject.toml index 5241a12..f78214c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [build-system] requires = [ - "setuptools >= 57.0.0", + "setuptools >= 77.0.0", "wheel" ] build-backend = "setuptools.build_meta" @@ -12,8 +12,9 @@ authors = [ {name = "Paul T. Grogan", email = "paul.grogan@asu.edu"} ] readme = "README.md" -requires-python = ">=3.8, <3.14" -license = {text = "BSD License"} +requires-python = ">=3.10, <3.15" +license = "BSD-3-Clause" +license-files = ["LICENSE"] classifiers = [ "Programming Language :: Python :: 3", "Operating System :: OS Independent", @@ -45,8 +46,22 @@ Issues = "https://github.com/code-lab-org/tatc/issues" [tool.setuptools.packages.find] where = ["src"] +[tool.black] +target-version = ["py310"] + [tool.setuptools.package-data] -"tatc" = ["resources/*.bsp"] +"tatc" = ["resources/*.bsp", "resources/*.yml"] + +[tool.pylint.main] +load-plugins = ["pylint_pydantic"] +extension-pkg-allow-list = ["pydantic"] + +[tool.pytest.ini_options] +minversion = "8.0" +addopts = "-ra -q" +testpaths = [ + "tests", +] [project.optional-dependencies] dev = [ diff --git a/src/tatc/__init__.py b/src/tatc/__init__.py index 226b2ed..ecbbd8a 100644 --- a/src/tatc/__init__.py +++ b/src/tatc/__init__.py @@ -2,12 +2,16 @@ Tradespace Analysis Toolkit for Constellations (TAT-C) """ -from . import analysis -from . import generation -from . import schemas -from . import config -from . import constants -from . import utils -from . import resources +from . import analysis, config, constants, generation, resources, schemas, utils -__version__ = "3.4.10" +__version__ = "3.5.0" + +__all__ = [ + "analysis", + "config", + "constants", + "generation", + "resources", + "schemas", + "utils", +] diff --git a/src/tatc/analysis/__init__.py b/src/tatc/analysis/__init__.py index fca5089..f34210a 100644 --- a/src/tatc/analysis/__init__.py +++ b/src/tatc/analysis/__init__.py @@ -3,27 +3,48 @@ """ from .coverage import ( - collect_observations, - collect_multi_observations, aggregate_observations, - reduce_observations, + collect_multi_observations, + collect_observations, grid_observations, + reduce_observations, +) +from .dop import DopMethod, compute_dop +from .latency import ( + collect_downlinks, + compute_latencies, + grid_latencies, + reduce_latencies, ) from .ro_coverage import ( collect_ro_observations, ) from .track import ( - collect_orbit_track, - collect_ground_track, - collect_ground_pixels, - compute_ground_track, OrbitCoordinate, OrbitOutput, + collect_ground_pixels, + collect_ground_track, + collect_orbit_track, + compute_ground_track, ) -from .latency import ( - collect_downlinks, - compute_latencies, - reduce_latencies, - grid_latencies, -) -from .dop import compute_dop, DopMethod + +__all__ = [ + "DopMethod", + "OrbitCoordinate", + "OrbitOutput", + "aggregate_observations", + "collect_downlinks", + "collect_ground_pixels", + "collect_ground_track", + "collect_multi_observations", + "collect_observations", + "collect_orbit_track", + "collect_ro_observations", + "compute_dop", + "compute_ground_track", + "compute_latencies", + "grid_latencies", + "grid_observations", + "reduce_latencies", + "reduce_observations", +] diff --git a/src/tatc/analysis/coverage.py b/src/tatc/analysis/coverage.py index 856f86f..407c003 100644 --- a/src/tatc/analysis/coverage.py +++ b/src/tatc/analysis/coverage.py @@ -1,35 +1,34 @@ -# -*- coding: utf-8 -*- """ Methods to perform coverage analysis. @author: Paul T. Grogan """ -from typing import List, Union +from __future__ import annotations + from datetime import datetime, timedelta, timezone -import pandas as pd -import numpy as np import geopandas as gpd +import numpy as np +import pandas as pd from shapely import geometry as geo from skyfield.api import wgs84 -from ..schemas.instrument import PointedInstrument -from ..schemas.point import Point -from ..schemas.satellite import Satellite - -from ..utils import ( - compute_min_elevation_angle, +from ..constants import EARTH_MEAN_RADIUS, de421, timescale +from ..schemas import Point, PointedInstrument, Satellite +from ..utils.observation import ( compute_max_access_time, - compute_footprint, + compute_min_elevation_angle, ) -from ..constants import de421, timescale +from ..utils.orbital import compute_apoapsis_radius +from ..utils.projection import compute_footprint def _get_visible_interval_series( point: Point, satellite: Satellite, min_elevation_angle: float, + max_altitude: float, start: datetime, end: datetime, ) -> pd.Series: @@ -40,30 +39,51 @@ def _get_visible_interval_series( point (Point): Point to observe. satellite (Satellite): Satellite doing the observation. min_elevation_angle (float): Minimum elevation angle (degrees) for valid observation. + max_altitude (float): A conservative upper-bound satellite altitude + (meters, e.g. the orbit's apogee altitude), used only to + compute a generously large `max_access_time` bound for + matching rise events to their corresponding set events. start (datetime.datetime): Start of analysis period. end (datetime.datetime): End of analysis period. Returns: pandas.Series: Series of observation intervals. """ - # define starting and ending points - t_0 = timescale.from_datetime(start) - # build skyfield objects - sat = satellite.orbit.to_tle().as_skyfield() - # compute the initial satellite altitude - satellite_altitude = wgs84.geographic_position_of(sat.at(t_0)).elevation.m # compute the maximum access time to filter bad data max_access_time = timedelta( - seconds=compute_max_access_time(satellite_altitude, min_elevation_angle) + seconds=compute_max_access_time(max_altitude, min_elevation_angle) ) # find the set of observation events - times, events = satellite.orbit.to_tle().get_observation_events( + times, events = satellite.orbit.to_gp_orbit().get_observation_events( point, start, end, min_elevation_angle ) # build the observation periods obs_periods = [] - if len(events) > 0 and np.all(events == 1): + if len(events) == 0: + # no rise, culminate, or set event was captured in [start, end]. This + # means the elevation angle never crossed min_elevation_angle and had + # no interior local maximum in this window -- which happens both + # when the point is never visible, and when [start, end] falls + # entirely within a longer visible pass (no rise/set inside the + # window, and the window is too narrow, or off-center, to contain + # the pass's culmination). Disambiguate by sampling the true + # elevation angle at the window's midpoint. + mid = start + (end - start) / 2 + topos = wgs84.latlon(point.latitude, point.longitude, point.elevation) + orbit_track = satellite.orbit.to_gp_orbit().get_orbit_track(mid) + elevation_angle = ( + (orbit_track - topos.at(timescale.from_datetime(mid))).altaz()[0].degrees + ) + if elevation_angle > min_elevation_angle: # type: ignore + # continuously visible for the entire window + obs_periods += [ + pd.Interval( + left=pd.Timestamp(start.astimezone(tz=timezone.utc)), + right=pd.Timestamp(end.astimezone(tz=timezone.utc)), + ) + ] + elif np.all(events == 1): # if all events are type 1 (culminate), create a period from start to end obs_periods += [ pd.Interval( @@ -71,7 +91,7 @@ def _get_visible_interval_series( right=pd.Timestamp(end.astimezone(tz=timezone.utc)), ) ] - elif len(events) > 0: + else: # otherwise, match rise/set events rises = times[events == 0] sets = times[events == 2] @@ -149,12 +169,10 @@ def _get_empty_coverage_frame(omit_solar: bool) -> gpd.GeoDataFrame: if not omit_solar: columns = { **columns, - **{ - "sat_sunlit": pd.Series(dtype="bool"), - "solar_alt": pd.Series(dtype="float"), - "solar_az": pd.Series(dtype="float"), - "solar_time": pd.Series(dtype="float"), - }, + "sat_sunlit": pd.Series(dtype="bool"), + "solar_alt": pd.Series(dtype="float"), + "solar_az": pd.Series(dtype="float"), + "solar_time": pd.Series(dtype="float"), } return gpd.GeoDataFrame(columns, crs="EPSG:4326") @@ -173,7 +191,6 @@ def collect_observations( Args: point (Point): The ground point of interest. satellite (Satellite): The observing satellite. - instrument (Instrument): The observing instrument. start (datetime.datetime): Start of analysis period. end (datetime.datetime): End of analysis period. instrument_index (int): The index of the observing instrument in satellite. @@ -183,13 +200,16 @@ def collect_observations( geopandas.GeoDataFrame: The data frame with recorded observations. """ instrument = satellite.instruments[instrument_index] - # compute the initial satellite altitude - satellite_altitude = wgs84.geographic_position_of( - satellite.orbit.to_tle().get_orbit_track(start) - ).elevation.m + # use the apogee altitude as a conservative upper bound for computing access times + max_altitude = ( + compute_apoapsis_radius( + satellite.orbit.get_semimajor_axis(), satellite.orbit.get_eccentricity() + ) + - EARTH_MEAN_RADIUS + ) # compute the minimum altitude angle required for observation min_elevation_angle = compute_min_elevation_angle( - satellite_altitude, + max_altitude, instrument.field_of_regard, ) records = [ @@ -211,25 +231,35 @@ def collect_observations( "epoch": period.mid, } for period in _get_visible_interval_series( - point, satellite, min_elevation_angle, start, end + point, satellite, min_elevation_angle, max_altitude, start, end ) + # instrument validity (illumination, footprint containment) below is + # only checked at each coarse period's midpoint, as an approximation + # of the whole interval; a more general approach would refine the + # exact observation period boundaries with Skyfield's find_discrete + # using the instrument's own validity condition, but that is out of + # scope for now if ( instrument.min_access_time <= period.right - period.left and instrument.is_valid_observation( - satellite.orbit.to_tle().get_orbit_track(period.mid), + ( + orbit_track := satellite.orbit.to_gp_orbit().get_orbit_track( + period.mid + ) + ), wgs84.latlon(point.latitude, point.longitude, point.elevation), - ) + ).all() and ( not isinstance(instrument, PointedInstrument) or compute_footprint( - orbit_track=satellite.orbit.to_tle().get_orbit_track(period.mid), + orbit_track=orbit_track, cross_track_field_of_view=instrument.cross_track_field_of_view, along_track_field_of_view=instrument.along_track_field_of_view, roll_angle=instrument.roll_angle, pitch_angle=instrument.pitch_angle, is_rectangular=instrument.is_rectangular, elevation=point.elevation, - ).contains(geo.Point(point.longitude, point.latitude)) + )[0].contains(geo.Point(point.longitude, point.latitude)) ) ) ] @@ -239,11 +269,11 @@ def collect_observations( gdf = gpd.GeoDataFrame(records, crs="EPSG:4326") topos = wgs84.latlon(point.latitude, point.longitude, point.elevation) ts = timescale.from_datetimes(gdf.epoch) - orbit_track = satellite.orbit.to_tle().get_orbit_track(gdf.epoch) + orbit_track = satellite.orbit.to_gp_orbit().get_orbit_track(gdf.epoch.tolist()) # append satellite altitude/azimuth columns sat_altaz = (orbit_track - topos.at(ts)).altaz() - gdf["sat_alt"] = sat_altaz[0].degrees - gdf["sat_az"] = sat_altaz[1].degrees + gdf["sat_alt"] = sat_altaz[0].degrees # type: ignore + gdf["sat_az"] = sat_altaz[1].degrees # type: ignore if not omit_solar: # append satellite sunlit column gdf["sat_sunlit"] = orbit_track.is_sunlit(de421) @@ -264,17 +294,20 @@ def collect_observations( def collect_multi_observations( point: Point, - satellites: Union[Satellite, List[Satellite]], + satellites: Satellite | list[Satellite], start: datetime, end: datetime, omit_solar: bool = True, ) -> gpd.GeoDataFrame: """ - Collect multiple satellite observations of a geodetic point of interest. + Collect multiple satellite observations of a geodetic point of interest: + calls `collect_observations` for every instrument on every satellite in + `satellites`, and concatenates the results into one data frame. Args: point (Point): The ground point of interest. - satellites (Satellite or List[Satellite]): The observing satellite(s). + satellites (Satellite | list[Satellite]): The observing satellite(s), + each contributing an observation per instrument it carries. start (datetime.datetime): Start of analysis period. end (datetime.datetime): End of analysis period. omit_solar (bool): `True`, to omit solar angles to improve performance. @@ -284,12 +317,12 @@ def collect_multi_observations( """ gdfs = [ collect_observations(point, satellite, start, end, instrument_index, omit_solar) - for constellation in ( - satellites if isinstance(satellites, list) else [satellites] - ) - for satellite in (constellation.generate_members()) + for satellite in (satellites if isinstance(satellites, list) else [satellites]) for instrument_index in range(len(satellite.instruments)) ] + if len(gdfs) == 0: + # an empty `satellites` list leaves nothing to concatenate + return _get_empty_coverage_frame(omit_solar) # concatenate into one data frame, sort by start time, and re-index return pd.concat(gdfs).sort_values("start").reset_index(drop=True) @@ -318,6 +351,16 @@ def aggregate_observations(observations: gpd.GeoDataFrame) -> gpd.GeoDataFrame: Aggregate constellation observations. Interleaves observations by multiple satellites to compute aggregate performance metrics including access (observation duration) and revisit (duration between observations). + Overlapping (including fully nested) observations for the same point, + possibly from different satellites/instruments, are merged into a single + continuous coverage period; `satellite`/`instrument` record every + contributor to that period, comma-separated. `epoch` is reassigned to + the midpoint of the merged period's `start`/`end` (a representative + instant), not the mean of the constituent observations' own epochs. + Per-observation columns that lose their meaning once merged across + satellites and over a potentially much longer period -- e.g. `sat_alt`, + `sat_az`, `sat_sunlit`, `solar_alt`, `solar_az`, `solar_time` -- are + intentionally dropped, even if present on `observations`. Args: observations (geopandas.GeoDataFrame): The collected observations. @@ -339,13 +382,16 @@ def aggregate_observations(observations: gpd.GeoDataFrame) -> gpd.GeoDataFrame: "obs", aggfunc={ "point_id": "first", - "satellite": ", ".join, - "instrument": ", ".join, + "satellite": ", ".join, # type: ignore + "instrument": ", ".join, # type: ignore "start": "min", - "epoch": "mean", "end": "max", }, ) + # reassign epoch to the midpoint of the merged period, as a single + # representative instant, rather than the mean of the constituent + # observations' own (pre-merge) epochs + gdf["epoch"] = gdf["start"] + (gdf["end"] - gdf["start"]) / 2 # compute access and revisit metrics gdf["access"] = gdf["end"] - gdf["start"] gdf["revisit"] = gdf["start"] - gdf["end"].shift() @@ -374,8 +420,14 @@ def _get_empty_reduce_frame() -> gpd.GeoDataFrame: def reduce_observations(aggregated_observations: gpd.GeoDataFrame) -> gpd.GeoDataFrame: """ - Reduce constellation observations. Computes descriptive statistics for each - geodetic point of interest contained in aggregated observations. + Reduce constellation observations: for each unique point_id in + `aggregated_observations`, computes the mean access period, the mean + revisit period, and the total number of samples (aggregated periods) + over the analysis period. The first sample's revisit is undefined (no + prior observation to measure a gap from) and is excluded from the mean + rather than counted as zero, which would otherwise bias the mean + downward; a point with only one sample accordingly has an undefined + (NaT) mean revisit. Args: aggregated_observations (geopandas.GeoDataFrame): The aggregated observations. @@ -388,8 +440,8 @@ def reduce_observations(aggregated_observations: gpd.GeoDataFrame) -> gpd.GeoDat # operate on a copy of the data frame gdf = aggregated_observations.copy() # convert access and revisit to numeric values before aggregation - gdf["access"] = gdf["access"] / timedelta(seconds=1) - gdf["revisit"] = gdf["revisit"] / timedelta(seconds=1) + gdf["access"] = gdf["access"].dt.total_seconds() + gdf["revisit"] = gdf["revisit"].dt.total_seconds() # assign each record to one observation gdf["samples"] = 1 # perform the aggregation operation @@ -402,10 +454,8 @@ def reduce_observations(aggregated_observations: gpd.GeoDataFrame) -> gpd.GeoDat }, ).reset_index() # convert access and revisit from numeric values after aggregation - gdf["access"] = gdf["access"].apply(lambda t: timedelta(seconds=t)) - gdf["revisit"] = gdf["revisit"].apply( - lambda t: pd.NaT if pd.isna(t) else timedelta(seconds=t) - ) + gdf["access"] = pd.to_timedelta(gdf["access"], unit="s") + gdf["revisit"] = pd.to_timedelta(gdf["revisit"], unit="s") return gdf @@ -413,7 +463,13 @@ def grid_observations( reduced_observations: gpd.GeoDataFrame, cells: gpd.GeoDataFrame ) -> gpd.GeoDataFrame: """ - Grid reduced observations to cells. + Grid reduced observations to cells: for every cell, sums the number of + samples across every point it contains, and combines those points' + access/revisit statistics into a single representative value per cell. + Both access (a per-event duration) and revisit (a time-between-events + duration, i.e. the reciprocal of a sampling rate) use a sample-weighted + mean -- arithmetic for access, harmonic for revisit, since revisit + needs to be averaged as a rate to stay a representative statistic. Args: reduced_observations (geopandas.GeoDataFrame): The reduced observations. @@ -431,23 +487,33 @@ def grid_observations( # operate on a copy of the data frame gdf = reduced_observations.copy() # convert access and revisit to numeric values before aggregation - gdf["access"] = gdf["access"] / timedelta(seconds=1) - gdf["revisit"] = gdf["revisit"] / timedelta(seconds=1) + gdf["access"] = gdf["access"].dt.total_seconds() + gdf["revisit"] = gdf["revisit"].dt.total_seconds() + # pre-transform so the means below reduce to plain sums: groupby().agg() + # with a dict of {column: function} only ever hands a custom callable + # its own column's Series, never a sibling column like "samples" needed + # to compute a weighted statistic within the callable. The weighted + # harmonic mean of revisit is sum(samples) / sum(samples/revisit). + gdf["access_x_samples"] = gdf["access"] * gdf["samples"] + gdf["samples_over_revisit"] = gdf["samples"] / gdf["revisit"] gdf = ( cells.sjoin(gdf, how="inner", predicate="contains") .dissolve( by="cell_id", aggfunc={ "samples": "sum", - "access": lambda r: np.average(r, weights=gdf.loc[r.index, "samples"]), - "revisit": lambda r: np.average(r, weights=gdf.loc[r.index, "samples"]), + "access_x_samples": "sum", + "samples_over_revisit": "sum", }, ) .reset_index() ) + # finish the aggregation: sample-weighted arithmetic mean for access, + # sample-weighted harmonic mean for revisit + gdf["access"] = gdf["access_x_samples"] / gdf["samples"] + gdf["revisit"] = gdf["samples"] / gdf["samples_over_revisit"] + gdf = gdf.drop(columns=["access_x_samples", "samples_over_revisit"]) # convert access and revisit from numeric values after aggregation - gdf["access"] = gdf["access"].apply(lambda t: timedelta(seconds=t)) - gdf["revisit"] = gdf["revisit"].apply( - lambda t: pd.NaT if pd.isna(t) else timedelta(seconds=t) - ) + gdf["access"] = pd.to_timedelta(gdf["access"], unit="s") + gdf["revisit"] = pd.to_timedelta(gdf["revisit"], unit="s") return gdf diff --git a/src/tatc/analysis/dop.py b/src/tatc/analysis/dop.py index fab7deb..12f7f19 100644 --- a/src/tatc/analysis/dop.py +++ b/src/tatc/analysis/dop.py @@ -1,19 +1,20 @@ -# -*- coding: utf-8 -*- """ Methods to analyze dilusion of precision. @author: Michael P. Jones @author: Paul T. Grogan """ + +from __future__ import annotations + import warnings from datetime import datetime from enum import Enum -from typing import List -import pandas as pd -import numpy as np import geopandas as gpd -from skyfield.api import wgs84, EarthSatellite +import numpy as np +import pandas as pd +from skyfield.api import wgs84 from ..constants import timescale from ..schemas import Point, Satellite @@ -32,12 +33,12 @@ class DopMethod(str, Enum): def compute_dop( - times: List[datetime], + times: list[datetime], point: Point, - satellites: List[Satellite], + satellites: list[Satellite], min_elevation: float, dop_method: DopMethod, - min_count_visible: int = 3, + min_count_visible: int = 4, ) -> gpd.GeoDataFrame: """ Calculate the specified dilusion of precision value based on inputs. @@ -48,33 +49,39 @@ def compute_dop( satellites: the list of satellites to be viewed by the ground point min_elevation: the minimum elevation angle (deg) to consider a satellite visible dop_method: dilusion of precision calculation method - min_count_visible: minimum number of visible satellites for a valid measurement + min_count_visible: minimum number of visible satellites, inclusive, + required for a valid measurement (must be at least 4, since + the calculation solves a 4-unknown system: 3D position plus + clock bias); times with fewer visible satellites return NaN Outputs: - geopandas.GeoDataFrame: the dop for the given user location and satellite. """ - # construct skyfield satellites for each satellite - sk_sats = [ - EarthSatellite( - satellite.orbit.to_tle().tle[0], - satellite.orbit.to_tle().tle[1], - satellite.name, - ) - for satellite in satellites - ] - # construct skyfield times for each datetime sk_times = timescale.from_datetimes(times) # construct skyfield geodetic position for user - sk_position = wgs84.latlon(point.latitude, point.longitude) + sk_position = wgs84.latlon(point.latitude, point.longitude, point.elevation) + + # propagate each satellite's orbit at every requested time, using + # per-time nearest-element selection for multi-element orbits (matching + # get_orbit_track_at_time's handling used throughout the rest of the + # codebase), rather than a single element (closest to times[0]) whose + # own propagation is reused for the whole time span regardless of how + # far later times drift from that element's epoch + orbit_tracks = [ + satellite.orbit.to_gp_orbit().get_orbit_track_at_time(sk_times) + for satellite in satellites + ] # compute elevation/azimuth angles and range - altazs = [(sk_sat - sk_position).at(sk_times).altaz() for sk_sat in sk_sats] - el = np.array(list(map(lambda i: i[0].radians, altazs))) - az = np.array(list(map(lambda i: i[1].radians, altazs))) - r = np.array(list(map(lambda i: i[2].m, altazs))) + altazs = [ + (orbit_track - sk_position.at(sk_times)).altaz() for orbit_track in orbit_tracks + ] + el = np.array([i[0].radians for i in altazs]) + az = np.array([i[1].radians for i in altazs]) + r = np.array([i[2].m for i in altazs]) # compute number of visible satellites n = np.sum(el >= np.deg2rad(min_elevation), axis=0) @@ -88,7 +95,7 @@ def _dop(i): """ Compute the dilution of precision value for time index i. """ - if n[i] <= min_count_visible: + if n[i] < min_count_visible: return np.nan mask = el[:, i] >= np.deg2rad(min_elevation) # H is a nx4 matrix where n is the number of visible satellites @@ -126,7 +133,7 @@ def _dop(i): "dop": pd.Series(dop, dtype="float", index=times), "geometry": pd.Series( gpd.points_from_xy( - [point.latitude] * len(dop), [point.longitude] * len(dop) + [point.longitude] * len(dop), [point.latitude] * len(dop) ), dtype="object", index=times, diff --git a/src/tatc/analysis/latency.py b/src/tatc/analysis/latency.py index 50528fa..d38b4e5 100644 --- a/src/tatc/analysis/latency.py +++ b/src/tatc/analysis/latency.py @@ -1,21 +1,21 @@ -# -*- coding: utf-8 -*- """ Methods to perform latency analysis. -@author: Isaac Feldman, Paul T. Grogan +@author: Isaac Feldman +@author: Paul T. Grogan """ -from typing import List, Union -from datetime import datetime, timedelta +from __future__ import annotations + +from datetime import datetime -import numpy as np -import pandas as pd import geopandas as gpd +import pandas as pd from shapely import geometry as geo -from ..schemas.point import GroundStation -from ..schemas.satellite import Satellite - +from ..constants import EARTH_MEAN_RADIUS +from ..schemas import GroundStation, Satellite +from ..utils.orbital import compute_apoapsis_radius from .coverage import _get_visible_interval_series @@ -38,7 +38,7 @@ def _get_empty_downlinks_frame() -> gpd.GeoDataFrame: def collect_downlinks( - stations: Union[GroundStation, List[GroundStation]], + stations: GroundStation | list[GroundStation], satellite: Satellite, start: datetime, end: datetime, @@ -47,7 +47,7 @@ def collect_downlinks( Collect satellite downlink opportunities to ground station(s) of interest. Args: - stations (GroundStation or typing.List[GroundStation]): The ground stations. + stations (GroundStation | list[GroundStation]): The ground stations. satellite (Satellite): The observing satellite. start (datetime.datetime): Start of analysis period. end (datetime.datetime): End of analysis period. @@ -55,6 +55,13 @@ def collect_downlinks( Returns: geopandas.GeoDataFrame: The data frame of collected downlink results. """ + # use the orbit's apogee altitude as a conservative upper bound + max_altitude = ( + compute_apoapsis_radius( + satellite.orbit.get_semimajor_axis(), satellite.orbit.get_eccentricity() + ) + - EARTH_MEAN_RADIUS + ) # collect the records of ground station overpasses records = [ { @@ -67,11 +74,12 @@ def collect_downlinks( "end": period.right, "epoch": period.mid, } - for station in (stations if isinstance(stations, list) else [stations]) + for station in ([stations] if isinstance(stations, GroundStation) else stations) for period in _get_visible_interval_series( station, satellite, station.min_elevation_angle, + max_altitude, start, end, ) @@ -149,15 +157,15 @@ def compute_latencies( # compute latency obs["latency"] = obs["epoch_y"] - obs["epoch_x"] - # rename and select relevant columns + # rename and select relevant columns. Only "epoch" and "geometry" exist + # in both `observations` and `downlinks`, so merge_asof only suffixes + # those two with "_x"/"_y"; "station", "sat_alt", and "sat_az" exist in + # just one of the two frames each and so are never suffixed at all. obs.rename( columns={ - "station_y": "station", "epoch_y": "downlinked", "epoch_x": "observed", "geometry_x": "geometry", - "sat_alt_x": "sat_alt", - "sat_az_x": "sat_az", }, inplace=True, ) @@ -219,8 +227,12 @@ def _get_empty_reduce_frame() -> gpd.GeoDataFrame: def reduce_latencies(latency_observations: gpd.GeoDataFrame) -> gpd.GeoDataFrame: """ - Reduce observation latencies. Computes descriptive statistics for each - pair of observation and first downlink opportunities. + Reduce observation latencies: for each unique point_id in + `latency_observations`, computes the mean latency and the total number + of samples (observation/downlink pairs). An observation with no + matching downlink has an undefined (NaT) latency (see + `compute_latencies`), which is excluded from the mean rather than + counted as zero, while still counting toward `samples`. Args: latency_observations (geopandas.GeoDataFrame): The latency observations. @@ -228,12 +240,12 @@ def reduce_latencies(latency_observations: gpd.GeoDataFrame) -> gpd.GeoDataFrame Returns: geopandas.GeoDataFrame: The data frame with reduced latencies. """ - if latency_observations.notna().empty: + if latency_observations.empty: return _get_empty_reduce_frame() # operate on a copy of the dataframe gdf = latency_observations.copy() # convert latency to a numeric value before aggregation - gdf["latency"] = gdf["latency"] / timedelta(seconds=1) + gdf["latency"] = gdf["latency"].dt.total_seconds() # assign each record to one observation gdf["samples"] = 1 # perform the aggregation operation @@ -245,9 +257,7 @@ def reduce_latencies(latency_observations: gpd.GeoDataFrame) -> gpd.GeoDataFrame }, ).reset_index() # convert latency from numeric values after aggregation - gdf["latency"] = gdf["latency"].apply( - lambda t: pd.NaT if pd.isna(t) else timedelta(seconds=t) - ) + gdf["latency"] = pd.to_timedelta(gdf["latency"], unit="s") return gdf @@ -255,7 +265,9 @@ def grid_latencies( reduced_latencies: gpd.GeoDataFrame, cells: gpd.GeoDataFrame ) -> gpd.GeoDataFrame: """ - Grid reduced latencies to cells. + Grid reduced latencies to cells: for every cell, sums the number of + samples across every point it contains, and combines those points' + latency into a single sample-weighted arithmetic mean per cell. Args: reduced_latencies (geopandas.GeoDataFrame): The reduced latencies. @@ -272,20 +284,26 @@ def grid_latencies( # operate on a copy of the data frame gdf = reduced_latencies.copy() # convert latency to numeric values before aggregation - gdf["latency"] = gdf["latency"] / timedelta(seconds=1) + gdf["latency"] = gdf["latency"].dt.total_seconds() + # pre-multiply so the sample-weighted mean below reduces to a plain sum: + # groupby().agg() with a dict of {column: function} only ever hands a + # custom callable its own column's Series, never a sibling column like + # "samples" needed to compute a weighted statistic within the callable + gdf["latency_x_samples"] = gdf["latency"] * gdf["samples"] gdf = ( cells.sjoin(gdf, how="inner", predicate="contains") .dissolve( by="cell_id", aggfunc={ "samples": "sum", - "latency": lambda r: np.average(r, weights=gdf.loc[r.index, "samples"]), + "latency_x_samples": "sum", }, ) .reset_index() ) + # finish the weighted mean + gdf["latency"] = gdf["latency_x_samples"] / gdf["samples"] + gdf = gdf.drop(columns=["latency_x_samples"]) # convert latency from numeric values after aggregation - gdf["latency"] = gdf["latency"].apply( - lambda t: pd.NaT if pd.isna(t) else timedelta(seconds=t) - ) + gdf["latency"] = pd.to_timedelta(gdf["latency"], unit="s") return gdf diff --git a/src/tatc/analysis/ro_coverage.py b/src/tatc/analysis/ro_coverage.py index f68639b..7b1aa47 100644 --- a/src/tatc/analysis/ro_coverage.py +++ b/src/tatc/analysis/ro_coverage.py @@ -1,109 +1,120 @@ -# -*- coding: utf-8 -*- """ Methods to perform radio occultation (RO) coverage analysis. @author: Paul T. Grogan """ -from typing import List, Tuple, Union -from datetime import datetime +from __future__ import annotations + +from collections.abc import Callable +from datetime import datetime, timedelta from itertools import chain import geopandas as gpd import numpy as np import pandas as pd from shapely.geometry import MultiPoint, Point -from skyfield.api import wgs84, Distance, Velocity +from skyfield.api import Distance, wgs84 from skyfield.positionlib import Geocentric - -from ..schemas.satellite import Satellite +from skyfield.searchlib import find_discrete from ..constants import timescale +from ..schemas import Satellite -def _collect_ro_series( - transmitter: Satellite, - times: List[datetime], +def _tangent_point_geometry( + tx_pv: Geocentric, rx_pv: Geocentric, - rx_v_u: List[float], - rx_n_u: List[float], - rx_b_u: List[float], - max_yaw: float, - range_elevation: Tuple[float], -): - # transmitter position (x_tx), velocity (v_tx) - tx_pv = transmitter.orbit.to_tle().get_orbit_track(times) + rx_v_u: np.ndarray, + rx_n_u: np.ndarray, + rx_b_u: np.ndarray, + compute_velocity: bool = False, +) -> tuple[np.ndarray, np.ndarray | None, np.ndarray, np.ndarray, np.ndarray]: + """ + Computes tangent point position (and, optionally, velocity) and + receiver-frame pitch/yaw angles of the transmitter, as seen from the + receiver, at one or more times. + + Tangent point velocity is not needed for geodetic position or azimuth + computations (Skyfield ignores it there), so it is skipped by default; + pass `compute_velocity=True` to compute it anyway. + """ # relative position, velocity of transmitter from receiver # x_(rx,tx) = x_tx - x_rx; v_(rx,tx) = v_tx - v_rx rx_tx_pv = tx_pv - rx_pv # tangent point position (m) # x_tp = x_tx - x_(rx,tx) . [ x_tx . x_(rx,tx) ] / || x_(rx,tx) || + rx_tx_p_m = np.array(rx_tx_pv.position.m) + rx_p_m = np.array(rx_pv.position.m) + tx_p_m = np.array(tx_pv.position.m) tp_p = tx_pv.position.m - np.einsum( "ij,j->ij", - rx_tx_pv.position.m, + rx_tx_p_m, np.divide( - np.einsum("ij,ij->j", tx_pv.position.m, rx_tx_pv.position.m), - np.einsum("ij,ij->j", rx_tx_pv.position.m, rx_tx_pv.position.m), + np.einsum("ij,ij->j", tx_p_m, rx_tx_p_m), + np.einsum("ij,ij->j", rx_tx_p_m, rx_tx_p_m), ), ) - # tangent point velocity (m/s) - derived using chain rule - # v_tp = v_tx - v_(rx,tx) . [ x_tx . x_(rx,tx) ] / || x_(rx,tx) || - # - x_(rx,tx) . [ - # [ v_tx . x_(rx,tx) ] + [ x_tx . v_(rx,tx) ] ] / || x_(rx,tx) || ] - # - 2 * [ v_(rx,tx) . x_(rx,tx) ] * [ x_tx . x_(rx,tx) ] / || x_(rx,tx) ||^2 - # ] - tp_v = ( - tx_pv.velocity.m_per_s - - np.einsum( - "ij,j->ij", - rx_tx_pv.velocity.m_per_s, - np.divide( - np.einsum("ij,ij->j", tx_pv.position.m, rx_tx_pv.position.m), - np.einsum("ij,ij->j", rx_tx_pv.position.m, rx_tx_pv.position.m), - ), - ) - - np.einsum( - "ij,j->ij", - rx_tx_pv.position.m, - ( + if compute_velocity: + # tangent point velocity (m/s) - derived using chain rule + # v_tp = v_tx - v_(rx,tx) . [ x_tx . x_(rx,tx) ] / || x_(rx,tx) || + # - x_(rx,tx) . [ + # [ v_tx . x_(rx,tx) ] + [ x_tx . v_(rx,tx) ] ] / || x_(rx,tx) || ] + # - 2 * [ v_(rx,tx) . x_(rx,tx) ] * [ x_tx . x_(rx,tx) ] / || x_(rx,tx) ||^2 + # ] + tx_p_m = np.array(tx_pv.position.m) + tx_v_m_per_s = np.array(tx_pv.velocity.m_per_s) + rx_tx_v_m_per_s = np.array(rx_tx_pv.velocity.m_per_s) + tp_v = ( + tx_v_m_per_s + - np.einsum( + "ij,j->ij", + rx_tx_v_m_per_s, np.divide( - ( - np.einsum( - "ij,ij->j", tx_pv.velocity.m_per_s, rx_tx_pv.position.m - ) - + np.einsum( - "ij,ij->j", tx_pv.position.m, rx_tx_pv.velocity.m_per_s - ) - ), - np.einsum("ij,ij->j", rx_tx_pv.position.m, rx_tx_pv.position.m), - ) - - 2 - * np.divide( - np.multiply( - np.einsum( - "ij,ij->j", rx_tx_pv.velocity.m_per_s, rx_tx_pv.position.m + np.einsum("ij,ij->j", tx_p_m, rx_tx_p_m), + np.einsum("ij,ij->j", rx_tx_p_m, rx_tx_p_m), + ), + ) + - np.einsum( + "ij,j->ij", + rx_tx_p_m, + ( + np.divide( + ( + np.einsum("ij,ij->j", tx_v_m_per_s, rx_tx_p_m) + + np.einsum("ij,ij->j", tx_p_m, rx_tx_v_m_per_s) ), - np.einsum("ij,ij->j", tx_pv.position.m, rx_tx_pv.position.m), - ), - np.power( - np.einsum("ij,ij->j", rx_tx_pv.position.m, rx_tx_pv.position.m), - 2, - ), - ) - ), + np.einsum("ij,ij->j", rx_tx_p_m, rx_tx_p_m), + ) + - 2 + * np.divide( + np.multiply( + np.einsum( + "ij,ij->j", + rx_tx_v_m_per_s, + rx_tx_p_m, + ), + np.einsum("ij,ij->j", tx_p_m, rx_tx_p_m), + ), + np.power( + np.einsum("ij,ij->j", rx_tx_p_m, rx_tx_p_m), + 2, + ), + ) + ), + ) ) - ) + else: + tp_v = None # intersecting (-1) or parallel (+1) view of tangent point - tp_sign = np.sign( - np.einsum("ij,ij->j", tp_p - tx_pv.position.m, tp_p - rx_pv.position.m) - ) + tp_sign = np.sign(np.einsum("ij,ij->j", tp_p - tx_p_m, tp_p - rx_p_m)) # relative transmitter position from receiver in plane normal to receiver orbit rx_tx_p_rx_n_plane = rx_tx_pv.position.m - np.einsum( - "ij,j->ij", rx_n_u, np.einsum("ij,ij->j", rx_n_u, rx_tx_pv.position.m) + "ij,j->ij", rx_n_u, np.einsum("ij,ij->j", rx_n_u, rx_tx_p_m) ) # relative transmitter position from receiver in plane binormal to receiver orbit rx_tx_p_rx_t_plane = rx_tx_pv.position.m - np.einsum( - "ij,j->ij", rx_b_u, np.einsum("ij,ij->j", rx_b_u, rx_tx_pv.position.m) + "ij,j->ij", rx_b_u, np.einsum("ij,ij->j", rx_b_u, rx_tx_p_m) ) # transmitter pitch angle in receiver body-fixed frame rx_tx_pitch = np.degrees( @@ -119,56 +130,158 @@ def _collect_ro_series( np.einsum("ij,ij->j", rx_tx_p_rx_t_plane, rx_v_u), ) ) + return tp_p, tp_v, tp_sign, rx_tx_pitch, rx_tx_yaw + + +def _receiver_frame_vectors( + rx_pv: Geocentric, +) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + """ + Computes the receiver body-fixed (VNB) frame unit vectors. + """ + rx_p_m = np.array(rx_pv.position.m) + rx_v_m_per_s = np.array(rx_pv.velocity.m_per_s) + # unit vector tangent to receiver orbit plane (VNB x-axis) + rx_v_u = np.divide(rx_v_m_per_s, np.linalg.norm(rx_v_m_per_s, axis=0)) + # unit vector normal to receiver orbit plane (VNB y-axis) + rx_n_u = np.cross(rx_p_m, rx_v_m_per_s, 0, 0, -1).T + rx_n_u = np.divide(rx_n_u, np.linalg.norm(rx_n_u, axis=0)) + # unit vector completing the right-handed VNB frame (V x N), + # perpendicular to both V and N by construction. This is NOT the same + # as the position unit vector (r-hat): the two coincide only for a + # circular orbit. + rx_b_u = np.cross(rx_v_u, rx_n_u, 0, 0, -1).T + return rx_v_u, rx_n_u, rx_b_u + + +def _make_ro_validity_function( + transmitter: Satellite, receiver: Satellite, max_yaw: float, step_days: float +) -> Callable: + """ + Builds a Skyfield-compatible discrete function of time returning whether the + tangent point intersects the Earth and the transmitter yaw is within bounds, + for use with `skyfield.searchlib.find_discrete`. + """ + + def f(t): + # get_orbit_track_at_time always does true (directly propagated) inertial + # propagation, which is required here since receiver and transmitter + # positions are compared directly in the inertial frame. + rx_pv = receiver.orbit.to_gp_orbit().get_orbit_track_at_time(t) + tx_pv = transmitter.orbit.to_gp_orbit().get_orbit_track_at_time(t) + rx_v_u, rx_n_u, rx_b_u = _receiver_frame_vectors(rx_pv) + _, _, tp_sign, _, rx_tx_yaw = _tangent_point_geometry( + tx_pv, rx_pv, rx_v_u, rx_n_u, rx_b_u + ) + # valid if tangent point intersects and yaw angle below maximum + valid = np.logical_and( + tp_sign < 0, np.abs(rx_tx_yaw) % (180 - max_yaw) < max_yaw + ) + return valid.astype(int) + + f.step_days = step_days # type: ignore + return f + + +def _tangent_point_tx_azimuth( + transmitter: Satellite, + times: list[datetime], + t, + tp_p: np.ndarray, +) -> np.ndarray: + """ + Computes the transmitter azimuth (deg, clockwise from North) as viewed from + each point of a tangent point track, vectorized per distinct TLE element used + across the track (almost always a single element, given how short RO arcs are). + + Only the tangent point position (not velocity) is needed: Skyfield's + geodetic and azimuth computations do not use it. + """ + orbit = transmitter.orbit.to_gp_orbit() + element_indices = np.asarray(orbit.get_closest_element_index(times)) + azimuth = np.empty(len(times)) + for element_index in np.unique(element_indices): + mask = element_indices == element_index + sat = orbit.elements[element_index].to_skyfield() + tpp_geo = wgs84.geographic_position_of( + Geocentric(Distance(m=tp_p[:, mask]).au, None, t[mask]) + ) + azimuth[mask] = (sat - tpp_geo).at(t[mask]).altaz()[1].degrees + return azimuth + + +def _sample_ro_arc( + transmitter: Satellite, + receiver: Satellite, + arc_start: datetime, + arc_end: datetime, + time_step: timedelta, + range_elevation: tuple[float, float], +) -> list[dict]: + """ + Samples tangent point observations across a single valid RO arc, splitting it + into one or more observations if the tangent point elevation leaves the + specified range. + """ + # sample the arc at (at most) the specified time step, including both endpoints + steps = max(int(np.ceil((arc_end - arc_start) / time_step)), 1) + times = [arc_start + i * (arc_end - arc_start) / steps for i in range(steps + 1)] + t = timescale.from_datetimes(times) + + rx_pv = receiver.orbit.to_gp_orbit().get_orbit_track_at_time(t) + rx_v_u, rx_n_u, rx_b_u = _receiver_frame_vectors(rx_pv) + tx_pv = transmitter.orbit.to_gp_orbit().get_orbit_track_at_time(t) + tp_p, _, _, rx_tx_pitch, rx_tx_yaw = _tangent_point_geometry( + tx_pv, rx_pv, rx_v_u, rx_n_u, rx_b_u + ) + + # tangent point geodetic position, computed once for the whole arc + tpp_geo = wgs84.geographic_position_of(Geocentric(Distance(m=tp_p).au, None, t)) + longitude = np.array(tpp_geo.longitude.degrees) + latitude = np.array(tpp_geo.latitude.degrees) + elevation = np.array(tpp_geo.elevation.m) + # azimuth of transmitter from geodetic tangent point (clockwise from North) + tp_tx_azimuth = _tangent_point_tx_azimuth(transmitter, times, t, tp_p) + # tangent point height within elevation range + in_range = np.logical_and( + elevation > range_elevation[0], elevation < range_elevation[1] + ) + # occultation observations occ_obs = [] # occultation arc occ_arc = None - # valid if tangent point intersects and yaw angle below maximum - valid = np.logical_and(tp_sign < 0, np.abs(rx_tx_yaw) % (180 - max_yaw) < max_yaw) - # events occur when validity changes value - is_event = np.diff(valid) - # loop over valid times - for j in np.nonzero(valid)[0]: - # tangent point inertial position - tpp_pv = Geocentric( - Distance(m=tp_p[:, j]).au, - Velocity(km_per_s=tp_v[:, j] / 1000).au_per_d, - timescale.from_datetime(times[j]), - ) - # tangent point geodetic position - tpp_geo = wgs84.geographic_position_of(tpp_pv) - # check if the tangent point height is within elevation range - if ( - tpp_geo.elevation.m > range_elevation[0] - and tpp_geo.elevation.m < range_elevation[1] - ): + for j, time in enumerate(times): + if in_range[j]: if occ_arc is None: - # start of new RO observation + # start of new RO observation. rx_tx_pitch is the + # transmitter's pitch angle relative to the receiver, where + # -90 deg points at the geocenter (never actually reached by a + # real RO profile, since the signal must pass through the + # atmosphere); pitch above -90 deg means the transmitter is + # "ahead" of the receiver (a rising/emersion occultation), + # below -90 deg means "behind" (a setting/immersion one). + # This is an approximation of the more direct (but more + # expensive) definition -- the sign of the tangent point's + # own elevation rate -- using this arc's first sample only. occ_arc = { "tx": transmitter.name, "is_rising": rx_tx_pitch[j] > -90, "points": [], } - - # azimuth of transmitter from geodetic tangent point (clockwise from North) - tp_tx_azmimuth = ( - (transmitter.orbit.to_tle().as_skyfield() - tpp_geo) - .at(timescale.from_datetime(times[j])) - .altaz()[1] - .degrees - ) - occ_arc["points"].append( { - "time": times[j], - "tangent_point": tpp_geo, + "time": time, + "longitude": longitude[j], + "latitude": latitude[j], + "elevation": elevation[j], "rx_tx_pitch": rx_tx_pitch[j], "rx_tx_yaw": rx_tx_yaw[j], - "tp_tx_azimuth": tp_tx_azmimuth, + "tp_tx_azimuth": tp_tx_azimuth[j], } ) - if j + 1 >= len(times) or is_event[j]: - # end of RO observation due to validity or boundary constraint + if j + 1 >= len(times): + # end of RO observation due to arc boundary occ_obs.append(occ_arc) occ_arc = None elif occ_arc is not None: @@ -178,6 +291,97 @@ def _collect_ro_series( return occ_obs +def _collect_ro_series( + transmitter: Satellite, + receiver: Satellite, + start: datetime, + end: datetime, + time_step: timedelta, + max_yaw: float, + range_elevation: tuple[float, float], + min_profile_duration: timedelta, +) -> list[dict]: + # discrete function of time: 1 if the tangent point intersects and the + # transmitter yaw angle is below maximum, 0 otherwise + # scan at half the shortest profile duration we must not skip, decoupled + # from time_step so long mission durations don't blow up the coarse scan + is_valid = _make_ro_validity_function( + transmitter, receiver, max_yaw, (min_profile_duration / 2) / timedelta(days=1) + ) + # find the precise times at which validity changes + transition_times, transition_values = find_discrete( + timescale.from_datetime(start), timescale.from_datetime(end), is_valid + ) + initial_valid = bool(is_valid(timescale.from_datetimes([start]))[0]) + # boundary times delimiting N+1 alternating valid/invalid segments (N = + # number of transitions); each segment's validity is the value that + # becomes active at its start (initial_valid for the first segment, + # else the corresponding transition value) -- the final boundary time + # (`end`) is a pure endpoint with no segment-start value of its own + boundary_times = [start] + list(transition_times.utc_datetime()) + [end] + boundary_values = [initial_valid] + [bool(value) for value in transition_values] + # keep only the segments where validity holds + arcs = [ + (boundary_times[i], boundary_times[i + 1]) + for i in range(len(boundary_times) - 1) + if boundary_values[i] and boundary_times[i + 1] > boundary_times[i] + ] + return list( + chain.from_iterable( + _sample_ro_arc( + transmitter, receiver, arc_start, arc_end, time_step, range_elevation + ) + for arc_start, arc_end in arcs + ) + ) + + +def _interpolate_ro_point(points: list[dict], sample_elevation: float) -> dict: + """ + Interpolates RO observation attributes at the specified tangent point elevation. + + Args: + points (list[dict]): the RO observation points (ordered by time). + sample_elevation (float): the tangent point elevation (m) at which to interpolate. + + Returns: + dict: interpolated longitude (deg), latitude (deg), elevation (m), rx_tx_pitch (deg), + rx_tx_yaw (deg), tp_tx_azimuth (deg), and time. + """ + elevations = np.array([point["elevation"] for point in points]) + diffs = elevations - sample_elevation + # bracketing indices where the tangent point elevation crosses the sample elevation + crossings = np.nonzero(np.diff(np.sign(diffs)))[0] + if len(crossings) > 0: + i = crossings[0] + p0, p1 = points[i], points[i + 1] + denom = diffs[i] - diffs[i + 1] + frac = diffs[i] / denom if denom != 0 else 0.0 + else: + # sample elevation is outside the observed range: clamp to the nearest endpoint + i = 0 if abs(diffs[0]) <= abs(diffs[-1]) else len(points) - 1 + p0 = p1 = points[i] + frac = 0.0 + + def lerp(a, b): + return a + frac * (b - a) + + def lerp_angle(a, b, low=-180.0): + # interpolate along the shortest angular path, then wrap to [low, low + 360) + diff = ((b - a + 180) % 360) - 180 + return (a + frac * diff - low) % 360 + low + + return { + "longitude": lerp_angle(p0["longitude"], p1["longitude"]), + "latitude": lerp(p0["latitude"], p1["latitude"]), + "elevation": lerp(p0["elevation"], p1["elevation"]), + "rx_tx_pitch": lerp_angle(p0["rx_tx_pitch"], p1["rx_tx_pitch"]), + "rx_tx_yaw": lerp_angle(p0["rx_tx_yaw"], p1["rx_tx_yaw"]), + "tp_tx_azimuth": lerp_angle(p0["tp_tx_azimuth"], p1["tp_tx_azimuth"], low=0.0), + "time": p0["time"] + frac * (p1["time"] - p0["time"]), + } + + def _get_empty_ro_frame() -> gpd.GeoDataFrame: """ Gets an empty data frame for ro results. @@ -203,52 +407,53 @@ def _get_empty_ro_frame() -> gpd.GeoDataFrame: def collect_ro_observations( receiver: Satellite, - transmitters: Union[Satellite, List[Satellite]], - times: List[datetime], - sample_elevation: float = 0, + transmitters: Satellite | list[Satellite], + start: datetime, + end: datetime, + time_step: timedelta = timedelta(seconds=10), + sample_elevation: float = -80e3, max_yaw: float = 65, - range_elevation: Tuple[float] = (-200e3, 60e3), + range_elevation: tuple[float, float] = (-200e3, 60e3), + min_profile_duration: timedelta = timedelta(seconds=30), ) -> gpd.GeoDataFrame: """ Collects Radio Occultation (RO) observations. Args: receiver (Satellite): the satellite with a RO receiver. - transmitters (typing.Union[Satellite,typing.List[Satellite]]): the satellite(s) with a RO transmitter. - times (typing.List[datetime.datetime]): The list of datetimes to sample. - sample_elevation: (float): the elevation (m) at which to sample observation attributes. - max_yaw (float): the maximum transmitter yaw angle (from receiver body-fixed frame) for a valid obsevation. - range_elevation: (typing.Tuple[float]): the lower and upper bound on tangent point elevation (m) for a valid observation. + transmitters (Satellite | list[Satellite]]): the satellite(s) with a RO transmitter. + start (datetime.datetime): the start of the analysis period. + end (datetime.datetime): the end of the analysis period. + time_step (datetime.timedelta): the time step used to sample tangent point + tracks within each observation period, once its bounds are found. + sample_elevation: (float): the elevation (m) at which to interpolate observation attributes. + max_yaw (float): the maximum transmitter yaw angle (from receiver body-fixed frame) + for a valid obsevation. + range_elevation: (tuple[float, float]): the lower and upper bound on tangent + point elevation (m) for a valid observation. + min_profile_duration (datetime.timedelta): the shortest RO observation period + guaranteed to be detected. Sets the coarse scan resolution used to search + for observation periods (via `skyfield.searchlib.find_discrete`), + independent of `time_step` and of the overall analysis duration. Set this + no larger than the shortest profile you expect; a smaller value costs more + computation but guards against silently skipping brief observation periods. """ - # receiver position, velocity - rx_pv = receiver.orbit.to_tle().get_orbit_track(times) - # unit vector tangent to receiver orbit plane (VNB x-axis) - rx_v_u = np.divide( - rx_pv.velocity.m_per_s, np.linalg.norm(rx_pv.velocity.m_per_s, axis=0) - ) - # unit vector normal to receiver orbit plane (VNB y-axis) - rx_n_u = np.cross(rx_pv.position.m, rx_pv.velocity.m_per_s, 0, 0, -1).T - rx_n_u = np.divide(rx_n_u, np.linalg.norm(rx_n_u, axis=0)) - # unit vector orthogonal to receiver orbit plane (VNB z-axis) - rx_b_u = np.divide(rx_pv.position.m, np.linalg.norm(rx_pv.position.m, axis=0)) # generate observations obs = list( chain.from_iterable( - [ - _collect_ro_series( - transmitter, - times, - rx_pv, - rx_v_u, - rx_n_u, - rx_b_u, - max_yaw, - range_elevation, - ) - for transmitter in ( - transmitters if isinstance(transmitters, list) else [transmitters] - ) - ] + _collect_ro_series( + transmitter, + receiver, + start, + end, + time_step, + max_yaw, + range_elevation, + min_profile_duration, + ) + for transmitter in ( + transmitters if isinstance(transmitters, list) else [transmitters] + ) ) ) if len(obs) == 0: @@ -262,35 +467,22 @@ def collect_ro_observations( "is_rising": o["is_rising"], "geometry": MultiPoint( [ - [ - point["tangent_point"].longitude.degrees, - point["tangent_point"].latitude.degrees, - point["tangent_point"].elevation.m, - ] + [point["longitude"], point["latitude"], point["elevation"]] for point in o["points"] ] ), "position": Point( - o["points"][sample_index]["tangent_point"].longitude.degrees, - o["points"][sample_index]["tangent_point"].latitude.degrees, - o["points"][sample_index]["tangent_point"].elevation.m, + sample["longitude"], sample["latitude"], sample["elevation"] ), - "rx_tx_pitch": o["points"][sample_index]["rx_tx_pitch"], - "rx_tx_yaw": o["points"][sample_index]["rx_tx_yaw"], - "tp_tx_azimuth": o["points"][sample_index]["tp_tx_azimuth"], + "rx_tx_pitch": sample["rx_tx_pitch"], + "rx_tx_yaw": sample["rx_tx_yaw"], + "tp_tx_azimuth": sample["tp_tx_azimuth"], "start": o["points"][0]["time"], "end": o["points"][-1]["time"], - "time": o["points"][sample_index]["time"], + "time": sample["time"], } for o in obs - for sample_index in [ - min( - range(len(o["points"])), - key=lambda i: abs( - o["points"][i]["tangent_point"].elevation.m - sample_elevation - ), - ) - ] + for sample in [_interpolate_ro_point(o["points"], sample_elevation)] ], crs="EPSG:4326", ).sort_values("time", ignore_index=True) diff --git a/src/tatc/analysis/track.py b/src/tatc/analysis/track.py index 3ff3274..817c12f 100644 --- a/src/tatc/analysis/track.py +++ b/src/tatc/analysis/track.py @@ -1,38 +1,30 @@ -# -*- coding: utf-8 -*- """ Methods to generate coverage statistics. @author: Paul T. Grogan """ -from typing import List, Union, Optional +from __future__ import annotations + from datetime import datetime, timedelta from enum import Enum -import pandas as pd -import numpy as np import geopandas as gpd -from skyfield.api import wgs84 -from skyfield.framelib import itrs -from skyfield.functions import angle_between - +import numpy as np +import pandas as pd from shapely.geometry import ( - Polygon, MultiPolygon, Point, - LineString, -) -from shapely.ops import clip_by_rect, split -import pyproj - -from ..schemas.instrument import PointedInstrument -from ..schemas.satellite import Satellite -from ..utils import ( - buffer_footprint, - buffer_target, - field_of_regard_to_swath_width, + Polygon, ) -from ..constants import de421, EARTH_MEAN_RADIUS, timescale +from skyfield.api import wgs84 +from skyfield.framelib import itrs +from skyfield.functions import angle_between + +from ..constants import de421 +from ..schemas import PointedInstrument, Satellite +from ..utils.observation import field_of_regard_to_swath_width +from ..utils.projection import buffer_target def _get_empty_orbit_track() -> gpd.GeoDataFrame: @@ -74,17 +66,10 @@ class OrbitOutput(str, Enum): def collect_orbit_track( satellite: Satellite, - times: List[datetime], + times: list[datetime], instrument_index: int = 0, elevation: float = 0, - mask: Optional[ - Union[ - Polygon, - MultiPolygon, - gpd.GeoDataFrame, - gpd.GeoSeries, - ] - ] = None, + mask: Polygon | MultiPolygon | gpd.GeoDataFrame | gpd.GeoSeries | None = None, coordinates: OrbitCoordinate = OrbitCoordinate.WGS84, orbit_output: OrbitOutput = OrbitOutput.POSITION, sat_sunlit: bool = False, @@ -92,20 +77,39 @@ def collect_orbit_track( solar_beta: bool = False, ) -> gpd.GeoDataFrame: """ - Collect orbit track points for a satellite of interest. + Collect the satellite's own position (and, optionally, velocity) at each + requested time, in a specified coordinate frame. Note this reports the + satellite's location, not the zero-elevation ground point beneath it; use + `collect_ground_track` for footprint/ground-projected results. Args: satellite (Satellite): The observing satellite. times (typing.List[datetime.datetime]): The list of times to sample. instrument_index (int): The index of the observing instrument in satellite. - elevation (float): The elevation (meters) above the datum in the - WGS 84 coordinate system for which to calculate swath width. - mask (Polygon, MultiPolygon, geopandas.GeoDataFrame, geopandas.GeoSeries): - An optional mask to constrain results. - coordinates (OrbitCoordinate): The coordinate system of orbit track points. - orbit_output (OrbitOutput): The output option. + elevation (float): The elevation (meters) above the WGS 84 datum for + which to project the instrument's field of regard into a + swath width (`swath_width` output column). Does not affect + the reported position itself, which is always the satellite's + true altitude. + mask (shapely.geometry.Polygon | shapely.geometry.MultiPolygon | geopandas.GeoDataFrame | geopandas.GeoSeries | None): + An optional mask, always interpreted in WGS84 (lon/lat) + coordinates, to constrain results to points whose + sub-satellite longitude/latitude falls within the mask. This + filter is applied consistently regardless of the requested + output `coordinates`. + coordinates (OrbitCoordinate): The coordinate system of orbit track + points: `wgs84` (geodetic longitude/latitude/altitude, output + CRS `EPSG:4326`), `ecef` (Earth-fixed Cartesian meters, output + CRS `EPSG:4978`), or `eci` (inertial GCRS Cartesian meters, + no fixed CRS since the frame is time-varying). + orbit_output (OrbitOutput): `position` for position only, or + `velocity` to also include a `velocity` column. Velocity is + expressed in the same frame as `coordinates`, except `wgs84` + velocity is given as local East/North/Up components (m/s) + rather than a rate of change of longitude/latitude/altitude. sat_sunlit (bool): `True` to include whether the satellite is sunlit. - solar_altaz (bool): `True` to include solar altitude/azimuth angles. + solar_altaz (bool): `True` to include the solar altitude/azimuth + angles as seen from the satellite's own position. solar_beta (bool): `True` to include solar beta angles. Returns: @@ -116,50 +120,51 @@ def collect_orbit_track( # select the observing instrument instrument = satellite.instruments[instrument_index] # propagate orbit - orbit_track = satellite.orbit.to_tle().get_orbit_track(times) - ssp = wgs84.geographic_position_of(orbit_track) + orbit_track = satellite.orbit.to_gp_orbit().get_orbit_track(times) + # geodetic (WGS84) position of the satellite itself (not the zero-elevation + # subpoint below it -- elevation here is the satellite's own altitude) + sat_pos = wgs84.geographic_position_of(orbit_track) if mask is not None: - # trim orbit track to provided mask - mask_contains_ssp = [ + # trim orbit track to provided mask; mask is always interpreted in + # WGS84 (lon/lat) coordinates regardless of the requested output + # `coordinates`, so this filter applies consistently to all of them + mask_contains_sat_pos = [ ( any(mask.contains(Point(longitude, latitude))) if isinstance(mask, (gpd.GeoDataFrame, gpd.GeoSeries)) else mask.contains(Point(longitude, latitude)) ) for (longitude, latitude) in zip( - ssp.longitude.degrees, ssp.latitude.degrees + np.array(sat_pos.longitude.degrees), np.array(sat_pos.latitude.degrees) ) ] - if not any(mask_contains_ssp): + if not any(mask_contains_sat_pos): return _get_empty_orbit_track() - orbit_track = orbit_track[mask_contains_ssp] - # recompute ssp - ssp = wgs84.geographic_position_of(orbit_track) + orbit_track = orbit_track[mask_contains_sat_pos] + # recompute sat_pos + sat_pos = wgs84.geographic_position_of(orbit_track) # create shapely points in proper coordinate system if coordinates == OrbitCoordinate.WGS84: points = [ Point(longitude, latitude, elevation) for (longitude, latitude, elevation) in zip( - ssp.longitude.degrees, - ssp.latitude.degrees, - ssp.elevation.m, + np.array(sat_pos.longitude.degrees), + np.array(sat_pos.latitude.degrees), + np.array(sat_pos.elevation.m), ) ] elif coordinates == OrbitCoordinate.ECEF: points = [ Point(position[0], position[1], position[2]) - for position in ssp.itrs_xyz.m.T + for position in np.array(sat_pos.itrs_xyz.m).T ] else: points = [ Point(position[0], position[1], position[2]) - for position in orbit_track.xyz.m.T + for position in np.array(orbit_track.xyz.m).T ] # determine observation validity valid_obs = instrument.is_valid_observation(orbit_track) - if len(orbit_track.t) == 1: - # transform scalar to vector results - valid_obs = np.array([valid_obs]) # create velocity points if needed if orbit_output == OrbitOutput.POSITION: records = [ @@ -168,27 +173,49 @@ def collect_orbit_track( "satellite": satellite.name, "instrument": instrument.name, "swath_width": field_of_regard_to_swath_width( - ssp.elevation.m[i], + np.array(sat_pos.elevation.m)[i], instrument.field_of_regard, elevation, ), "valid_obs": valid_obs[i], "geometry": points[i], } - for i, time in enumerate(orbit_track.t.utc_datetime()) + for i, time in enumerate(orbit_track.t.utc_datetime()) # type: ignore ] else: # compute satellite velocity if coordinates == OrbitCoordinate.ECI: velocities = [ Point(velocity[0], velocity[1], velocity[2]) - for velocity in orbit_track.velocity.m_per_s.T + for velocity in np.array(orbit_track.velocity.m_per_s).T ] - else: + elif coordinates == OrbitCoordinate.ECEF: velocities = [ Point(velocity[0], velocity[1], velocity[2]) - for velocity in orbit_track.frame_xyz_and_velocity(itrs)[1].m_per_s.T + for velocity in np.array( + orbit_track.frame_xyz_and_velocity(itrs)[1].m_per_s + ).T ] + else: + # rotate ECEF velocity into local East/North/Up components at the + # satellite's geodetic longitude/latitude + ecef_velocity = np.array( + orbit_track.frame_xyz_and_velocity(itrs)[1].m_per_s + ) + lon = np.radians(np.array(sat_pos.longitude.degrees)) + lat = np.radians(np.array(sat_pos.latitude.degrees)) + east = -np.sin(lon) * ecef_velocity[0] + np.cos(lon) * ecef_velocity[1] + north = ( + -np.sin(lat) * np.cos(lon) * ecef_velocity[0] + - np.sin(lat) * np.sin(lon) * ecef_velocity[1] + + np.cos(lat) * ecef_velocity[2] + ) + up = ( + np.cos(lat) * np.cos(lon) * ecef_velocity[0] + + np.cos(lat) * np.sin(lon) * ecef_velocity[1] + + np.sin(lat) * ecef_velocity[2] + ) + velocities = [Point(e, n, u) for e, n, u in zip(east, north, up)] records = [ { @@ -196,7 +223,7 @@ def collect_orbit_track( "satellite": satellite.name, "instrument": instrument.name, "swath_width": field_of_regard_to_swath_width( - ssp.elevation.m[i], + np.array(sat_pos.elevation.m)[i], instrument.field_of_regard, elevation, ), @@ -204,35 +231,46 @@ def collect_orbit_track( "geometry": points[i], "velocity": velocities[i], } - for i, time in enumerate(orbit_track.t.utc_datetime()) + for i, time in enumerate(orbit_track.t.utc_datetime()) # type: ignore ] - track = gpd.GeoDataFrame(records, crs="EPSG:4326") + # tag the CRS to match the requested output coordinates: WGS84 is + # geographic degrees, ECEF is geocentric meters, and ECI (GCRS) is an + # inertial, time-varying frame with no fixed EPSG code + track_crs = ( + "EPSG:4326" + if coordinates == OrbitCoordinate.WGS84 + else "EPSG:4978" if coordinates == OrbitCoordinate.ECEF else None + ) + track = gpd.GeoDataFrame(records, crs=track_crs) if sat_sunlit: # append sat_sunlit column track["sat_sunlit"] = orbit_track.is_sunlit(de421) if solar_altaz: # append solar altitude/azimuth columns - solar_altaz = ( - (de421["earth"] + wgs84.geographic_position_of(orbit_track)) + solar_altaz_data = ( + (de421["earth"] + sat_pos) .at(orbit_track.t) .observe(de421["sun"]) .apparent() .altaz() ) - track["solar_alt"] = solar_altaz[0].degrees - track["solar_az"] = solar_altaz[1].degrees + track["solar_alt"] = solar_altaz_data[0].degrees + track["solar_az"] = solar_altaz_data[1].degrees if solar_beta: # append solar beta column # based on https://github.com/skyfielders/python-skyfield/issues/1054 plane_normal = np.cross( - orbit_track.position.m, orbit_track.velocity.m_per_s, axis=0 + np.array(orbit_track.position.m), + np.array(orbit_track.velocity.m_per_s), + axis=0, ) - sun = de421["earth"].at(orbit_track.t).observe(de421["sun"]).position.m + sun = de421["earth"].at(orbit_track.t).observe(de421["sun"]).position.m # type: ignore beta = np.pi / 2 - angle_between(plane_normal, sun) track["solar_beta"] = np.degrees(beta) - if mask is not None: - track = gpd.clip(track, mask).reset_index(drop=True) + # note: no further mask-based clip is needed here -- points outside the + # (WGS84-only) mask were already dropped above, before points were + # projected into the requested `coordinates` frame return track @@ -253,56 +291,20 @@ def _get_empty_ground_track() -> gpd.GeoDataFrame: return gpd.GeoDataFrame(columns, crs="EPSG:4326") -def _get_utm_epsg_code(point: Point, swath_width: float) -> str: - """ - Get the Universal Transverse Mercator (UTM) EPSG code for a ground track. - - Args: - point (Point): the geodetic sub-satellite point - swath_width (float): the observation swath width (meters) - - Returns: - str: the EPSG code - """ - # approximate footprint - polygon = point.buffer(np.degrees(swath_width / 2 / EARTH_MEAN_RADIUS)) - results = pyproj.database.query_utm_crs_info( - datum_name="WGS 84", - area_of_interest=pyproj.aoi.AreaOfInterest( - *clip_by_rect(polygon, -180, -90, 180, 90).bounds - ), - ) - # return 5041 for UPS North; 5042 for UPS South; UTM zone, or 4087 for default - return ( - "EPSG:5041" - if polygon.bounds[3] > 84 - else ( - "EPSG:5042" - if polygon.bounds[1] < -80 - else "EPSG:" + results[0].code if len(results) > 0 else "EPSG:4087" - ) - ) - - def collect_ground_track( satellite: Satellite, - times: List[datetime], + times: list[datetime], instrument_index: int = 0, elevation: float = 0, - mask: Optional[ - Union[ - Polygon, - MultiPolygon, - gpd.GeoDataFrame, - gpd.GeoSeries, - ] - ] = None, - crs: str = "EPSG:4087", + mask: Polygon | MultiPolygon | gpd.GeoDataFrame | gpd.GeoSeries | None = None, sat_altaz: bool = False, solar_altaz: bool = False, ) -> gpd.GeoDataFrame: """ - Collect ground track polygons for a satellite of interest. + Collect the instrument's viewable ground footprint at each requested + time, projected to a specified elevation, using SPICE to compute the + exact ray/WGS-84-geoid intersection (see + `tatc.utils.projection.compute_footprint`). Args: satellite (Satellite): The observing satellite. @@ -310,16 +312,15 @@ def collect_ground_track( times (typing.List[datetime.datetime]): The list of datetimes to sample. elevation (float): The elevation (meters) above the datum in the WGS 84 coordinate system for which to calculate ground track. - mask (Polygon, MultiPolygon, geopandas.GeoDataFrame, geopandas.GeoSeries): - An optional mask to constrain results. - crs (str): The coordinate reference system (CRS) in which to compute - distance (default: World Equidistant Cylindrical `"EPSG:4087"`). - Selecting `crs="utm"` uses Universal Transverse Mercator (UTM) - zones for non-polar regions, and Universal Polar Stereographic - (UPS) systems for polar regions. Selecting `crs="spice"` uses - SPICE to compute observation footprints. - sat_altaz (bool): `True` to include satellite altitude/azimuth angles. - solar_altaz (bool): `True` to include solar altitude/azimuth angles. + mask (shapely.geometry.Polygon | shapely.geometry.MultiPolygon | geopandas.GeoDataFrame | geopandas.GeoSeries | None): + An optional mask, always interpreted in WGS84 (lon/lat) + coordinates, to constrain results. Providing a mask limits + the propagated orbit to the (buffered) region of interest, + improving performance for small areas. + sat_altaz (bool): `True` to include satellite altitude/azimuth angles + for the sub-satellite point. + solar_altaz (bool): `True` to include solar altitude/azimuth angles + for the sub-satellite point. Returns: geopandas.GeoDataFrame: The data frame of collected ground track results. @@ -328,40 +329,37 @@ def collect_ground_track( if len(times) == 0: return _get_empty_ground_track() # propagate orbit - orbit_track = satellite.orbit.to_tle().get_orbit_track(times) + orbit_track = satellite.orbit.to_gp_orbit().get_orbit_track(times) # select the observing instrument instrument = satellite.instruments[instrument_index] if mask is not None and len(times) > 1: - ssp = wgs84.geographic_position_of(orbit_track) + # use the (possibly repeat-cycle-corrected) geodetic position for this + # rough, buffer-tolerant culling step only; final geometry/validity + # below still uses the true orbit_track for consistency. + sat_pos = satellite.orbit.to_gp_orbit().get_geographic_position(times) + if isinstance(mask, (Polygon, MultiPolygon)): + geometry = mask + elif isinstance(mask, gpd.GeoDataFrame): + geometry = mask.dissolve().iloc[0].geometry + else: + geometry = mask.union_all() buffered_mask = buffer_target( - geometry=( - mask - if isinstance(mask, (Polygon, MultiPolygon)) - else ( - mask.dissolve().iloc[0].geometry - if isinstance(mask, gpd.GeoDataFrame) - else mask.iloc[0] - ) - ), - altitude=satellite.orbit.to_tle().get_altitude(), - inclination=satellite.orbit.to_tle().get_inclination(), + geometry=geometry, + altitude=satellite.orbit.to_gp_orbit().get_mean_altitude(), + inclination=satellite.orbit.to_gp_orbit().get_inclination(), field_of_regard=instrument.field_of_regard, - time_step=np.diff(times).mean() / timedelta(seconds=1), + time_step=np.diff(np.array(times)).mean() / timedelta(seconds=1), ) # cull orbit track with buffered mask - buffered_mask_contains_ssp = [ - ( - any(buffered_mask.contains(Point(longitude, latitude))) - if isinstance(buffered_mask, (gpd.GeoDataFrame, gpd.GeoSeries)) - else buffered_mask.contains(Point(longitude, latitude)) - ) + buffered_mask_contains_sat_pos = [ + buffered_mask.contains(Point(longitude, latitude)) for (longitude, latitude) in zip( - ssp.longitude.degrees, ssp.latitude.degrees + sat_pos.longitude.degrees, sat_pos.latitude.degrees ) ] - if not any(buffered_mask_contains_ssp): + if not any(buffered_mask_contains_sat_pos): return _get_empty_ground_track() - orbit_track = orbit_track[buffered_mask_contains_ssp] + orbit_track = orbit_track[buffered_mask_contains_sat_pos] # compute footprint for culling footprint = instrument.compute_footprint(orbit_track, elevation=elevation) # cull orbit track to observable footprint @@ -371,7 +369,7 @@ def collect_ground_track( if isinstance(mask, (gpd.GeoDataFrame, gpd.GeoSeries)) else mask.intersects(f) ) - for f in (footprint if isinstance(footprint, list) else [footprint]) + for f in footprint ] if not any(mask_intersects_footprint): return _get_empty_ground_track() @@ -380,61 +378,12 @@ def collect_ground_track( target = instrument.compute_footprint_center(orbit_track, elevation) # determine observation validity valid_obs = instrument.is_valid_observation(orbit_track, target) - if len(orbit_track.t) == 1: - # transform scalar to vector results - valid_obs = np.array([valid_obs]) - if crs == "spice": - geometries = instrument.compute_footprint( - orbit_track, - None, - elevation, - ) - if len(orbit_track.t) == 1: - geometries = [geometries] - else: - # compute the orbit track of the satellite - gdf = collect_orbit_track( - satellite, orbit_track.t.utc_datetime(), instrument_index, elevation - ) - if crs == "utm": - gdf["utm_crs"] = gdf.apply( - lambda r: _get_utm_epsg_code(r.geometry, r.swath_width), axis=1 - ) - # preload transformers - to_crs = {} - from_crs = {} - for code in gdf.utm_crs.unique(): - to_crs[code] = pyproj.Transformer.from_crs( - gdf.crs, pyproj.CRS(code), always_xy=True - ) - from_crs[code] = pyproj.Transformer.from_crs( - pyproj.CRS(code), gdf.crs, always_xy=True - ) - geometries = gdf.apply( - lambda r: buffer_footprint( - r.geometry, - to_crs[r.utm_crs], - from_crs[r.utm_crs], - r.swath_width, - elevation, - ), - axis=1, - ).values - else: - to_crs = pyproj.Transformer.from_crs( - gdf.crs, pyproj.CRS(crs), always_xy=True - ) - from_crs = pyproj.Transformer.from_crs( - pyproj.CRS(crs), gdf.crs, always_xy=True - ) - # construct polygons based on visible extent of instrument - # project to specified elevation - geometries = gdf.apply( - lambda r: buffer_footprint( - r.geometry, to_crs, from_crs, r.swath_width, elevation - ), - axis=1, - ).values + # compute footprints via SPICE (exact ray/WGS-84-geoid intersection) + geometries = instrument.compute_footprint( + orbit_track, + None, + elevation, + ) records = [ { "time": time, @@ -443,25 +392,25 @@ def collect_ground_track( "valid_obs": valid_obs[i], "geometry": geometries[i], } - for i, time in enumerate(orbit_track.t.utc_datetime()) + for i, time in enumerate(orbit_track.t.utc_datetime()) # type: ignore ] track = gpd.GeoDataFrame(records, crs="EPSG:4326") if sat_altaz: # append satellite altitude/azimuth columns - sat_altaz = (orbit_track - target.at(orbit_track.t)).altaz() - track["sat_alt"] = sat_altaz[0].degrees - track["sat_az"] = sat_altaz[1].degrees + sat_altaz_data = (orbit_track - target.at(orbit_track.t)).altaz() + track["sat_alt"] = sat_altaz_data[0].degrees # type: ignore + track["sat_az"] = sat_altaz_data[1].degrees # type: ignore if solar_altaz: # append solar altitude/azimuth columns - solar_altaz = ( + solar_altaz_data = ( (de421["earth"] + target) .at(orbit_track.t) .observe(de421["sun"]) .apparent() .altaz() ) - track["solar_alt"] = solar_altaz[0].degrees - track["solar_az"] = solar_altaz[1].degrees + track["solar_alt"] = solar_altaz_data[0].degrees # type: ignore + track["solar_az"] = solar_altaz_data[1].degrees # type: ignore if mask is not None: track = gpd.clip(track, mask).reset_index(drop=True) @@ -470,23 +419,18 @@ def collect_ground_track( def compute_ground_track( satellite: Satellite, - times: List[datetime], + times: list[datetime], instrument_index: int = 0, elevation: float = 0, - mask: Optional[ - Union[ - Polygon, - MultiPolygon, - gpd.GeoDataFrame, - gpd.GeoSeries, - ] - ] = None, - crs: str = "EPSG:4087", - method: str = "point", + mask: Polygon | MultiPolygon | gpd.GeoDataFrame | gpd.GeoSeries | None = None, dissolve_orbits: bool = True, ) -> gpd.GeoDataFrame: """ - Compute the aggregated ground track for a satellite of interest. + Compute the aggregated ground track for a satellite of interest: unlike + `collect_ground_track` (one footprint polygon per time step), this + dissolves the valid-observation footprints within each orbit into a + single geometry per orbit, and optionally dissolves across orbits into + one geometry for the entire `times` range. Args: satellite (Satellite): The observing satellite. @@ -494,138 +438,45 @@ def compute_ground_track( times (typing.List[datetime.datetime]): The list of datetimes to sample. elevation (float): The elevation (meters) above the datum in the WGS 84 coordinate system for which to calculate ground track. - mask (Polygon, MultiPolygon, geopandas.GeoDataFrame, geopandas.GeoSeries): - An optional mask to constrain results. - crs (str): The coordinate reference system (CRS) in which to compute - distance (default: World Equidistant Cylindrical `"EPSG:4087"`). - Selecting `crs="utm"` uses Universal Transverse Mercator (UTM) - zones for non-polar regions, and Universal Polar Stereographic - (UPS) systems for polar regions. Selecting `crs="spice"` uses - SPICE to compute footprints. - method (str): The method for computing ground track: `"point"` buffers - individual points and `"line"` buffers a line of points. The line - method is not compatible with the `crs="spice"` option above. + mask (shapely.geometry.Polygon | shapely.geometry.MultiPolygon | geopandas.GeoDataFrame | geopandas.GeoSeries | None): + An optional mask, always interpreted in WGS84 (lon/lat) + coordinates, to constrain results. dissolve_orbits (bool): True, to aggregate multiple orbits in one output. Returns: GeoDataFrame: The data frame of aggregated ground track results. """ - if method not in ["point", "line"]: - raise ValueError("Invalid method: " + str(method)) - if method == "point": - track = collect_ground_track( - satellite, times, instrument_index, elevation, mask, crs - ) - if not track.empty: - # assign orbit identifier - track["orbit_id"] = [ - (time - times[0]) // satellite.orbit.to_tle().get_orbit_period() - for time in track.time - ] - # filter to valid observations and dissolve - track = ( - track[track.valid_obs].dissolve(by="orbit_id").reset_index(drop=True) - ) - if dissolve_orbits: - track = track.dissolve() - return track - if method == "line": - if crs == "spice": - raise ValueError("The line method is not compatible with spice") - track = collect_orbit_track(satellite, times, instrument_index, elevation, None) + track = collect_ground_track(satellite, times, instrument_index, elevation, mask) + if not track.empty: # assign orbit identifier track["orbit_id"] = [ - (time - times[0]) // satellite.orbit.to_tle().get_orbit_period() + (time - times[0]) + // satellite.orbit.to_gp_orbit() + .get_closest_element(time) + .get_orbit_period() for time in track.time ] - # assign track identifiers to group contiguous observation periods - track["track_id"] = ( - (track.valid_obs != track.valid_obs.shift()).astype("int").cumsum() - ) - # filter to valid observations - track = track[track.valid_obs].reset_index(drop=True) - segments = [] - swath_widths = [] - for _, sub_track in track.groupby(["orbit_id", "track_id"]): - # project points to specified elevation - points = sub_track.geometry.apply(lambda p: Point(p.x, p.y, elevation)) - # extract longitudes - lon = sub_track.geometry.apply(lambda p: p.x) - # extract average swath width - swath_widths.append(sub_track.swath_width.mean()) - # no anti-meridian crossings if all absolute longitude differences - # are less than 180 deg - if np.all(np.abs(np.diff(lon)) < 180): - segments.append(LineString(points)) - else: - # find anti-meridian crossings and calculate shift direction - # coords from W -> E (shift < 0) will add 360 degrees to E component - # coords from E -> W (shift > 0) will subtract 360 degrees from W component - shift = np.insert(np.cumsum(np.around(np.diff(lon) / 360)), 0, 0) - points = [ - Point(p.x - 360 * shift[i], p.y, p.z) for i, p in enumerate(points) - ] - # split along the anti-meridian (-180 for shift > 0; 180 for shift < 0) - shift_dir = -180 if shift.max() >= 1 else 180 - collection = split( - LineString(points), - LineString([(shift_dir, -180), (shift_dir, 180)]), - ) - # map longitudes from (-540, -180] to (-180, 180] and from [180, 540) to [-180, 180) - segments.extend( - [ - ( - LineString([(c[0] + 360, c[1], c[2]) for c in line.coords]) - if np.all([c[0] <= -180 for c in line.coords]) - else ( - LineString( - [(c[0] - 360, c[1], c[2]) for c in line.coords] - ) - if np.all([c[0] >= 180 for c in line.coords]) - else LineString( - [(c[0], c[1], c[2]) for c in line.coords] - ) - ) - ) - for line in collection.geoms - ] - ) - to_crs = pyproj.Transformer.from_crs(track.crs, pyproj.CRS(crs), always_xy=True) - from_crs = pyproj.Transformer.from_crs( - pyproj.CRS(crs), track.crs, always_xy=True - ) - polygons = [ - buffer_footprint(segment, to_crs, from_crs, swath_width, elevation) - for segment, swath_width in zip(segments, swath_widths) - ] - # dissolve the original track - track = track.dissolve(by=["orbit_id", "track_id"]).reset_index(drop=True) - # and replace the geometry with the union of computed polygons - track.geometry = polygons - if mask is not None: - track = gpd.clip(track, mask).reset_index(drop=True) - if dissolve_orbits: - track = track.dissolve() - return track + # filter to valid observations and dissolve + track = track[track.valid_obs].dissolve(by="orbit_id").reset_index(drop=True) + if dissolve_orbits: + track = track.dissolve() + return track def collect_ground_pixels( satellite: Satellite, - times: List[datetime], + times: list[datetime], instrument_index: int = 0, elevation: float = 0, - mask: Optional[ - Union[ - Polygon, - MultiPolygon, - gpd.GeoDataFrame, - gpd.GeoSeries, - ] - ] = None, + mask: Polygon | MultiPolygon | gpd.GeoDataFrame | gpd.GeoSeries | None = None, sat_altaz: bool = False, solar_altaz: bool = False, ) -> gpd.GeoDataFrame: """ - Collect ground pixels for a satellite of interest. + Collect the instrument's individual ground sample point (pixel) + locations at each requested time, rather than an aggregate observable + geometry (see `collect_ground_track`). Only supported for a rectangular + `PointedInstrument`, whose `cross_track_pixels` x `along_track_pixels` + grid defines the pixel array. Args: satellite (Satellite): The observing satellite. @@ -633,8 +484,11 @@ def collect_ground_pixels( times (typing.List[datetime.datetime]): The list of datetimes to sample. elevation (float): The elevation (meters) above the datum in the WGS 84 coordinate system for which to calculate ground pixels. - mask (Polygon, MultiPolygon, geopandas.GeoDataFrame, geopandas.GeoSeries): - An optional mask to constrain results. + mask (shapely.geometry.Polygon | shapely.geometry.MultiPolygon | geopandas.GeoDataFrame | geopandas.GeoSeries | None): + An optional mask, always interpreted in WGS84 (lon/lat) + coordinates, to constrain results. Providing a mask limits + the propagated orbit to the (buffered) region of interest, + improving performance for small areas. sat_altaz (bool): `True` to include satellite altitude/azimuth angles. solar_altaz (bool): `True` to include solar altitude/azimuth angles. @@ -645,7 +499,7 @@ def collect_ground_pixels( if len(times) == 0: return _get_empty_ground_track() # propagate orbit - orbit_track = satellite.orbit.to_tle().get_orbit_track(times) + orbit_track = satellite.orbit.to_gp_orbit().get_orbit_track(times) # select the observing instrument instrument = satellite.instruments[instrument_index] if not isinstance(instrument, PointedInstrument) or not instrument.is_rectangular: @@ -653,36 +507,33 @@ def collect_ground_pixels( "Ground pixels are only compatible with rectangular PointedInstrument instances" ) if mask is not None and len(times) > 1: - ssp = wgs84.geographic_position_of(orbit_track) + # use the (possibly repeat-cycle-corrected) geodetic position for this + # rough, buffer-tolerant culling step only; final geometry/validity + # below still uses the true orbit_track for consistency. + sat_pos = satellite.orbit.to_gp_orbit().get_geographic_position(times) + if isinstance(mask, (Polygon, MultiPolygon)): + geometry = mask + elif isinstance(mask, gpd.GeoDataFrame): + geometry = mask.dissolve().iloc[0].geometry + else: + geometry = mask.union_all() buffered_mask = buffer_target( - geometry=( - mask - if isinstance(mask, (Polygon, MultiPolygon)) - else ( - mask.dissolve().iloc[0].geometry - if isinstance(mask, gpd.GeoDataFrame) - else mask.iloc[0] - ) - ), - altitude=satellite.orbit.to_tle().get_altitude(), - inclination=satellite.orbit.to_tle().get_inclination(), + geometry=geometry, + altitude=satellite.orbit.to_gp_orbit().get_mean_altitude(), + inclination=satellite.orbit.to_gp_orbit().get_inclination(), field_of_regard=instrument.field_of_regard, - time_step=np.diff(times).mean() / timedelta(seconds=1), + time_step=np.diff(np.array(times)).mean() / timedelta(seconds=1), ) # cull orbit track with buffered mask - buffered_mask_contains_ssp = [ - ( - any(buffered_mask.contains(Point(longitude, latitude))) - if isinstance(buffered_mask, (gpd.GeoDataFrame, gpd.GeoSeries)) - else buffered_mask.contains(Point(longitude, latitude)) - ) + buffered_mask_contains_sat_pos = [ + buffered_mask.contains(Point(longitude, latitude)) for (longitude, latitude) in zip( - ssp.longitude.degrees, ssp.latitude.degrees + sat_pos.longitude.degrees, sat_pos.latitude.degrees ) ] - if not any(buffered_mask_contains_ssp): + if not any(buffered_mask_contains_sat_pos): return _get_empty_ground_track() - orbit_track = orbit_track[buffered_mask_contains_ssp] + orbit_track = orbit_track[buffered_mask_contains_sat_pos] # compute footprint for culling footprint = instrument.compute_footprint(orbit_track, elevation=elevation) # cull orbit track to observable footprint @@ -692,7 +543,7 @@ def collect_ground_pixels( if isinstance(mask, (gpd.GeoDataFrame, gpd.GeoSeries)) else mask.intersects(f) ) - for f in (footprint if isinstance(footprint, list) else [footprint]) + for f in footprint ] if not any(mask_intersects_footprint): return _get_empty_ground_track() @@ -702,54 +553,60 @@ def collect_ground_pixels( orbit_track, elevation, ) - # construct results as a list of dictionaries - records = [ - { - "time": time, - "satellite": satellite.name, - "instrument": instrument.name, - "valid_obs": instrument.is_valid_observation( - orbit_track[i], wgs84.latlon(point.y, point.x, point.z) - ), - "geometry": point, - } - for i, time in enumerate(orbit_track.t.utc_datetime()) - for point in ( - geometries[i].geoms if len(orbit_track.t) > 1 else geometries.geoms + # construct results as a list of (time index, record) tuples, keeping the + # source orbit_track index alongside each record so satellite/solar altaz + # below can index directly into orbit_track rather than re-deriving it + indexed_records = [ + ( + i, + { + "time": time, + "satellite": satellite.name, + "instrument": instrument.name, + "valid_obs": instrument.is_valid_observation( + orbit_track[i], wgs84.latlon(point.y, point.x, point.z) + ).all(), + "geometry": point, + }, ) + for i, time in enumerate(orbit_track.t.utc_datetime()) # type: ignore + for point in geometries[i].geoms ] + records = [record for _, record in indexed_records] # build geodataframe gdf = gpd.GeoDataFrame(records, crs="EPSG:4326") if sat_altaz: # append satellite altitude/azimuth columns - sat_altaz = [ + sat_altaz_data = [ ( - orbit_track[list(orbit_track.t.utc_datetime()).index(record["time"])] + orbit_track[i] - wgs84.latlon( record["geometry"].y, record["geometry"].x, record["geometry"].z - ).at(timescale.from_datetime(record["time"])) + ).at( + orbit_track.t[i] + ) # type: ignore ).altaz() - for record in records + for i, record in indexed_records ] - gdf["sat_alt"] = list(map(lambda altaz: altaz[0].degrees, sat_altaz)) - gdf["sat_az"] = list(map(lambda altaz: altaz[1].degrees, sat_altaz)) + gdf["sat_alt"] = [altaz[0].degrees for altaz in sat_altaz_data] # type: ignore + gdf["sat_az"] = [altaz[1].degrees for altaz in sat_altaz_data] # type: ignore if solar_altaz: # append solar altitude/azimuth columns - solar_altaz = [ + solar_altaz_data = [ ( de421["earth"] + wgs84.latlon( record["geometry"].y, record["geometry"].x, record["geometry"].z ) ) - .at(timescale.from_datetime(record["time"])) + .at(orbit_track.t[i]) # type: ignore .observe(de421["sun"]) .apparent() .altaz() - for record in records + for i, record in indexed_records ] - gdf["solar_alt"] = list(map(lambda altaz: altaz[0].degrees, solar_altaz)) - gdf["solar_az"] = list(map(lambda altaz: altaz[1].degrees, solar_altaz)) + gdf["solar_alt"] = [altaz[0].degrees for altaz in solar_altaz_data] # type: ignore + gdf["solar_az"] = [altaz[1].degrees for altaz in solar_altaz_data] # type: ignore if mask is not None: gdf = gpd.clip(gdf, mask).reset_index(drop=True) diff --git a/src/tatc/config.py b/src/tatc/config.py index 86aeaee..a66c48b 100644 --- a/src/tatc/config.py +++ b/src/tatc/config.py @@ -1,16 +1,19 @@ -# -*- coding: utf-8 -*- """ Configuration Settings. @author: Paul T. Grogan """ +import functools +import logging import os import pathlib import yaml -from yaml.parser import ParserError from pydantic import BaseModel, Field, ValidationError +from yaml.parser import ParserError + +logger = logging.getLogger(__name__) class ConfigError(Exception): @@ -23,44 +26,57 @@ class RuntimeConfiguration(BaseModel): """ footprint_points_elliptical: int = Field( - 32, description="Number of points for a SPICE elliptical footprint.", ge=4 + default=32, + description="Number of points for a SPICE elliptical footprint.", + ge=4, ) footprint_points_rectangular_side: int = Field( - 8, description="Number of points for a SPICE rectangular footprint side.", ge=1 + default=8, + description="Number of points for a SPICE rectangular footprint side.", + ge=1, ) repeat_cycle_delta_position_m: float = Field( - 10000, + default=10000, description="Maximum difference in position (meters) for a valid repeat.", gt=0, ) repeat_cycle_delta_velocity_m_per_s: float = Field( - 10, + default=3, description="Maximum difference in velocity (meters/second) for a valid repeat.", gt=0, ) - repeat_cycle_search_elevation_deg: float = Field( - 88, description="Minimum elevation angle (degrees) for .", gt=0 - ) repeat_cycle_search_duration_days: float = Field( - 30, description="Maximum duration for which to search for repeat cycles." + default=30, + description="Maximum duration for which to search for repeat cycles.", + ) + repeat_cycle_consistency_threshold_s: float = Field( + default=3600, + description=( + "Maximum allowed difference (seconds) between elements' " + "independently-computed repeat cycles for a multi-element " + "orbit to report one consistent repeat cycle." + ), + gt=0, ) repeat_cycle_lazy_load: bool = Field( - True, description="True, if a previously-computed repeat cycle should be used." + default=True, + description="True, if a previously-computed repeat cycle should be used.", ) repeat_cycle_for_orbit_track: bool = Field( - True, + default=True, description="True, if a repeat cycle should be used to generate orbit tracks.", ) repeat_cycle_for_observation_events: bool = Field( - True, + default=True, description="True, if a repeat cycle should be used to generate observation events.", ) - orbit_tle_lazy_load: bool = Field( - True, description="True, if a previously-computed tle should be used." + gp_orbit_lazy_load: bool = Field( + default=True, + description="True, if a previously-computed general perturbations orbit should be used.", ) -def load_yaml_config(path: pathlib.Path): +def load_yaml_config(path: pathlib.Path) -> RuntimeConfiguration: """ Load configuration settings from a YAML file. @@ -83,11 +99,40 @@ def load_yaml_config(path: pathlib.Path): raise ConfigError(f"Couldn't validate config file - {err}") from err -try: - # try to load default config file - rc = load_yaml_config( - os.path.join(os.path.dirname(__file__), "resources", "defaults.yml") - ) -except ConfigError as e: - # fall back to default constructor - rc = RuntimeConfiguration() +@functools.lru_cache(maxsize=1) +def get_rc() -> RuntimeConfiguration: + """ + Return the process-wide runtime configuration, loading it from the + packaged defaults file on first access and caching it thereafter. + + Returns: + RuntimeConfiguration: The runtime configuration settings. + """ + try: + return load_yaml_config( + pathlib.Path(__file__).parent / "resources" / "defaults.yml" + ) + except ConfigError as err: + # fall back to default constructor, but warn since this masks a broken install + logger.warning( + "Falling back to hard-coded runtime configuration defaults: %s", err + ) + return RuntimeConfiguration() + + +def reset_rc() -> None: + """ + Clear the cached runtime configuration so the next call to get_rc() + reloads it from disk. Intended for tests that need to exercise + load-time behavior (e.g. a missing packaged defaults file); not + needed in normal use. + """ + get_rc.cache_clear() + + +def __getattr__(name: str): + # Preserve `tatc.config.rc` as a read path onto the lazy singleton, for + # backward compatibility with code written against the old eager global. + if name == "rc": + return get_rc() + raise AttributeError(f"module {__name__!r} has no attribute {name!r}") diff --git a/src/tatc/constants.py b/src/tatc/constants.py index 3caf751..02562e6 100644 --- a/src/tatc/constants.py +++ b/src/tatc/constants.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- """ Numerical constants. @@ -8,8 +7,7 @@ import os import numpy as np -from skyfield.api import load, Loader - +from skyfield.api import Loader, load # load ephemeris file resources_dir = os.path.join(os.path.dirname(__file__), "resources") @@ -28,6 +26,10 @@ EARTH_EQUATORIAL_RADIUS = 6378137.0 EARTH_POLAR_RADIUS = 6356752.314245179 EARTH_MU = 3.986004418e14 +# derived from the WGS84/EGM96 fully-normalized zonal coefficient C_bar_20 = +# -0.484165143790e-3 (NIMA TR8350.2, "Department of Defense World Geodetic +# System 1984"), converted to unnormalized form via J2 = -C_bar_20 * sqrt(5) +EARTH_J2 = 1.0826261738504e-3 # derived properties based on wgs84 oblate spheroid EARTH_ECCENTRICITY = np.sqrt(2 * EARTH_FLATTENING - EARTH_FLATTENING**2) @@ -41,3 +43,4 @@ * np.log((1 + EARTH_ECCENTRICITY) / (1 - EARTH_ECCENTRICITY)) ) EARTH_MEAN_RADIUS = (2 * EARTH_EQUATORIAL_RADIUS + EARTH_POLAR_RADIUS) / 3 +EARTH_J2_CRITICAL_INCLINATION = np.degrees(np.arccos(np.sqrt(1 / 5))) diff --git a/src/tatc/generation/__init__.py b/src/tatc/generation/__init__.py index 5a8f5d4..866f625 100644 --- a/src/tatc/generation/__init__.py +++ b/src/tatc/generation/__init__.py @@ -2,9 +2,20 @@ Defines generation functions. """ -from .cells import generate_equally_spaced_cells, _generate_equally_spaced_cells +from .cells import ( + generate_cells_uniform_angular_spacing, + generate_cells_uniform_spacing, +) from .points import ( - generate_fibonacci_lattice_points, - generate_equally_spaced_points, - _generate_equally_spaced_points, + generate_points_fibonacci_lattice, + generate_points_uniform_angular_distance, + generate_points_uniform_spacing, ) + +__all__ = [ + "generate_cells_uniform_angular_spacing", + "generate_cells_uniform_spacing", + "generate_points_fibonacci_lattice", + "generate_points_uniform_angular_distance", + "generate_points_uniform_spacing", +] diff --git a/src/tatc/generation/_grid.py b/src/tatc/generation/_grid.py new file mode 100644 index 0000000..6463e20 --- /dev/null +++ b/src/tatc/generation/_grid.py @@ -0,0 +1,93 @@ +""" +Internal helpers for regular equally-spaced latitude/longitude grids, shared +by the points and cells generation modules. + +@author: Paul T. Grogan +""" + +from __future__ import annotations + +import numpy as np +from numba import njit +from shapely.geometry import MultiPolygon, Polygon + +from ..utils.geometry import get_planar_bounds + + +@njit +def compute_point_id_uniform_spacing(i: int, j: int, theta_i: float) -> int: + """ + Fast method to compute the flattened id for an equally spaced grid point. + Indices increment west-to-east followed by south-to-north with a first + point at -180 degrees latitude and close to -90 degrees latitude. + + Args: + i (int): The zero-based longitude index. + j (int): The zero-based latitude index. + theta_i (float): The angular step in longitude (degrees). + + Returns: + int: The id of this point. + """ + # the row multiplier must use theta_i (longitude step), since that + # determines how many longitude bins actually exist per row; using + # the latitude step here previously caused id collisions whenever the + # longitude and latitude steps differed + return int(j * int(360 / theta_i) + np.mod(i, int(360 / theta_i))) + + +def generate_indices_uniform_spacing( + theta_longitude: float, + theta_latitude: float, + mask: Polygon | MultiPolygon | None = None, + strips: str | None = None, +) -> list: + """ + Generates a list of indices for an equally spaced grid. + + Args: + theta_longitude (float): The angular difference in longitude (degrees) + between points. + theta_latitude (float): The angular difference in latitude (degrees) + between points. + mask (shapely.geometry.Polygon | shapely.geometry.MultiPolygon | None): An optional mask to constrain points + using WGS84 (EPSG:4326) geodetic coordinates in a Polygon or MultiPolygon. + strips (str | None): Option to generate one-dimensional strips along latitude + (`"lat"`), longitude (`"lon"`), or none (`None`). + + Returns: + list: list of indices + """ + # get the bounds of the mask + min_longitude, min_latitude, max_longitude, max_latitude = get_planar_bounds(mask) + + if strips == "lat": + # if latitude strips, only generate indices for variable latitude + return [ + (0, j) + for j in range( + int(np.round((min_latitude + 90) / theta_latitude)), + int(np.round((max_latitude + 90) / theta_latitude)), + ) + ] + if strips == "lon": + # if longitude strips, only generate indices for variable longitude + return [ + (i, 0) + for i in range( + int(np.round((min_longitude + 180) / theta_longitude)), + int(np.round((max_longitude + 180) / theta_longitude)), + ) + ] + # generate indices over the two-dimensional latitude/longitude range + return [ + (i, j) + for j in range( + int(np.round((min_latitude + 90) / theta_latitude)), + int(np.round((max_latitude + 90) / theta_latitude)), + ) + for i in range( + int(np.round((min_longitude + 180) / theta_longitude)), + int(np.round((max_longitude + 180) / theta_longitude)), + ) + ] diff --git a/src/tatc/generation/cells.py b/src/tatc/generation/cells.py index 955993f..a7585a1 100644 --- a/src/tatc/generation/cells.py +++ b/src/tatc/generation/cells.py @@ -1,30 +1,25 @@ -# -*- coding: utf-8 -*- """ Methods to generate geospatial cells to aggregate data. @author: Paul T. Grogan """ -from typing import Optional, Union +from __future__ import annotations -import numpy as np import geopandas as gpd -from shapely.geometry import Polygon, MultiPolygon - -from .points import ( - _compute_equally_spaced_point_id, - _generate_equally_spaced_indices, - _get_bounds, -) +import numpy as np +from shapely.geometry import MultiPolygon, Polygon from ..constants import EARTH_MEAN_RADIUS +from ..utils.geometry import get_planar_bounds +from ._grid import compute_point_id_uniform_spacing, generate_indices_uniform_spacing -def generate_equally_spaced_cells( +def generate_cells_uniform_spacing( distance: float, elevation: float = 0, - mask: Optional[Union[Polygon, MultiPolygon]] = None, - strips: str = None, + mask: Polygon | MultiPolygon | None = None, + strips: str | None = None, ) -> gpd.GeoDataFrame: """ Generates geodetic polygons over a regular equally spaced grid. @@ -37,9 +32,9 @@ def generate_equally_spaced_cells( distance (float): The typical surface distance (meters) between points. elevation (float): The elevation (meters) above the datum in the WGS 84 coordinate system. - mask (Polygon or MultiPolygon): An optional mask to constrain cells + mask (shapely.geometry.Polygon | shapely.geometry.MultiPolygon | None): An optional mask to constrain cells using WGS84 (EPSG:4326) geodetic coordinates in a Polygon or MultiPolygon. - strips (str): Option to generate strip-cells along latitude (`"lat"`), + strips (str | None): Option to generate strip-cells along latitude (`"lat"`), longitude (`"lon"`), or none (`None`). Returns: @@ -48,17 +43,17 @@ def generate_equally_spaced_cells( # compute the angular disance of each sample (assuming sphere) theta_longitude = np.degrees(distance / EARTH_MEAN_RADIUS) theta_latitude = np.degrees(distance / EARTH_MEAN_RADIUS) - return _generate_equally_spaced_cells( + return generate_cells_uniform_angular_spacing( theta_longitude, theta_latitude, elevation, mask, strips ) -def _generate_equally_spaced_cells( +def generate_cells_uniform_angular_spacing( theta_longitude: float, theta_latitude: float, elevation: float = 0, - mask: Optional[Union[Polygon, MultiPolygon]] = None, - strips: str = None, + mask: Polygon | MultiPolygon | None = None, + strips: str | None = None, ) -> gpd.GeoDataFrame: """ Generates geodetic polygons over a regular equally spaced grid. @@ -74,9 +69,9 @@ def _generate_equally_spaced_cells( between cell centroids. elevation (float): The elevation (meters) above the datum in the WGS 84 coordinate system. - mask (Polygon or MultiPolygon): An optional mask to constrain cells + mask (shapely.geometry.Polygon | shapely.geometry.MultiPolygon | None): An optional mask to constrain cells using WGS84 (EPSG:4326) geodetic coordinates in a Polygon or MultiPolygon. - strips (str): Option to generate strip-cells along latitude (`"lat"`), + strips (str | None): Option to generate strip-cells along latitude (`"lat"`), longitude (`"lon"`), or none (`None`). Returns: @@ -84,19 +79,19 @@ def _generate_equally_spaced_cells( """ # generate indices of grid cells over the filtered region - indices = _generate_equally_spaced_indices( + indices = generate_indices_uniform_spacing( theta_longitude, theta_latitude, mask, strips, ) # get the bounds of the mask - min_longitude, min_latitude, max_longitude, max_latitude = _get_bounds(mask) + min_longitude, min_latitude, max_longitude, max_latitude = get_planar_bounds(mask) # create a geodataframe in the WGS84 reference frame gdf = gpd.GeoDataFrame( { "cell_id": [ - _compute_equally_spaced_point_id(i, j, theta_longitude, theta_latitude) + compute_point_id_uniform_spacing(i, j, theta_longitude) for (i, j) in indices ], "geometry": [ diff --git a/src/tatc/generation/points.py b/src/tatc/generation/points.py index 8151d85..8c6750d 100644 --- a/src/tatc/generation/points.py +++ b/src/tatc/generation/points.py @@ -1,19 +1,19 @@ -# -*- coding: utf-8 -*- """ Methods to generate geospatial points to sample data. @author: Paul T. Grogan """ -from typing import Optional, Union +from __future__ import annotations -import numpy as np import geopandas as gpd +import numpy as np from numba import njit -from shapely.geometry import Point, Polygon, MultiPolygon +from shapely.geometry import MultiPolygon, Point, Polygon from ..constants import EARTH_MEAN_RADIUS -from ..utils import compute_number_samples +from ..utils.surface import compute_number_samples +from ._grid import compute_point_id_uniform_spacing, generate_indices_uniform_spacing @njit @@ -52,10 +52,10 @@ def _compute_fibonacci_lattice_point_longitude(index: int) -> float: return longitude -def generate_fibonacci_lattice_points( +def generate_points_fibonacci_lattice( distance: float, elevation: float = 0, - mask: Optional[Union[Polygon, MultiPolygon]] = None, + mask: Polygon | MultiPolygon | None = None, ) -> gpd.GeoDataFrame: """ Generates geodetic points following a Fibonacci lattice. @@ -72,7 +72,7 @@ def generate_fibonacci_lattice_points( distance (float): The typical surface distance (meters) between points. elevation (float): The elevation (meters) above the datum in the WGS 84 coordinate system. - mask (Polygon or MultiPolygon): An optional mask to constrain points + mask (shapely.geometry.Polygon | shapely.geometry.MultiPolygon | None): An optional mask to constrain points using WGS84 (EPSG:4326) geodetic coordinates in a Polygon or MultiPolygon. Returns: @@ -145,10 +145,10 @@ def generate_fibonacci_lattice_points( return gdf -def generate_equally_spaced_points( +def generate_points_uniform_spacing( distance: float, elevation: float = 0, - mask: Optional[Union[Polygon, MultiPolygon]] = None, + mask: Polygon | MultiPolygon | None = None, ) -> gpd.GeoDataFrame: """ Generates geodetic points at the centroid of regular equally spaced grid @@ -162,7 +162,7 @@ def generate_equally_spaced_points( distance (float): The typical surface distance (meters) between points. elevation (float): The elevation (meters) above the datum in the WGS 84 coordinate system. - mask (Polygon or MultiPolygon): An optional mask to constrain points + mask (shapely.geometry.Polygon | shapely.geometry.MultiPolygon | None): An optional mask to constrain points using WGS84 (EPSG:4326) geodetic coordinates in a Polygon or MultiPolygon. Returns: @@ -171,118 +171,16 @@ def generate_equally_spaced_points( # compute the angular disance of each sample (assuming sphere) theta_longitude = np.degrees(distance / EARTH_MEAN_RADIUS) theta_latitude = np.degrees(distance / EARTH_MEAN_RADIUS) - return _generate_equally_spaced_points( + return generate_points_uniform_angular_distance( theta_longitude, theta_latitude, elevation, mask ) -@njit -def _compute_equally_spaced_point_id( - i: int, j: int, theta_i: float, theta_j: float -) -> int: - """ - Fast method to compute the flattened id for an equally spaced grid point. - Indices increment west-to-east followed by south-to-north with a first - point at -180 degrees latitude and close to -90 degrees latitude. - - Args: - i (int): The zero-based longitude index. - j (int): The zero-based latitude index. - theta_i (float): The angular step in longitude (degrees). - theta_j (float): The angular step in latitude (degrees). - - Returns: - int: The id of this point. - """ - return int(j * int(360 / theta_j) + np.mod(i, int(360 / theta_i))) - - -def _get_bounds(mask: Union[Polygon, MultiPolygon]) -> tuple: - """ - Generates a tuple of bounds for a polygon mask. - - Args: - mask (Polygon or MultiPolygon): Geometric shape using WGS84 (EPSG:4326) - geodetic coordinates in a Polygon or MultiPolygon. - - Returns: - tuple: min longitude/latitude, max longitude/latitude (degrees) - """ - if isinstance(mask, (Polygon, MultiPolygon)): - if not mask.is_valid: - raise ValueError("Mask is not a valid Polygon or MultiPolygon.") - total_bounds = mask.bounds - else: - total_bounds = [-180, -90, 180, 90] - min_longitude = total_bounds[0] - min_latitude = total_bounds[1] - max_longitude = 180 if total_bounds[2] == -180 else total_bounds[2] - max_latitude = total_bounds[3] - return (min_longitude, min_latitude, max_longitude, max_latitude) - - -def _generate_equally_spaced_indices( - theta_longitude: float, - theta_latitude: float, - mask: Union[Polygon, MultiPolygon] = None, - strips: str = None, -) -> list: - """ - Generates a list of indices for an equally spaced grid. - - Args: - theta_longitude (float): The angular difference in longitude (degrees) - between points. - theta_latitude (float): The angular difference in latitude (degrees) - between points. - mask (Polygon or MultiPolygon): An optional mask to constrain points - using WGS84 (EPSG:4326) geodetic coordinates in a Polygon or MultiPolygon. - strips (str): Option to generate one-dimensional strips along latitude - (`"lat"`), longitude (`"lon"`), or none (`None`). - - Returns: - list: list of indices - """ - # get the bounds of the mask - min_longitude, min_latitude, max_longitude, max_latitude = _get_bounds(mask) - - if strips == "lat": - # if latitude strips, only generate indices for variable latitude - return [ - (0, j) - for j in range( - int(np.round((min_latitude + 90) / theta_latitude)), - int(np.round((max_latitude + 90) / theta_latitude)), - ) - ] - if strips == "lon": - # if longitude strips, only generate indices for variable longitude - return [ - (i, 0) - for i in range( - int(np.round((min_longitude + 180) / theta_longitude)), - int(np.round((max_longitude + 180) / theta_longitude)), - ) - ] - # generate indices over the two-dimensional latitude/longitude range - return [ - (i, j) - for j in range( - int(np.round((min_latitude + 90) / theta_latitude)), - int(np.round((max_latitude + 90) / theta_latitude)), - ) - for i in range( - int(np.round((min_longitude + 180) / theta_longitude)), - int(np.round((max_longitude + 180) / theta_longitude)), - ) - ] - - -def _generate_equally_spaced_points( +def generate_points_uniform_angular_distance( theta_longitude: float, theta_latitude: float, elevation: float = 0, - mask: Optional[Union[Polygon, MultiPolygon]] = None, + mask: Polygon | MultiPolygon | None = None, ) -> gpd.GeoDataFrame: """ Generates geodetic cells following regular equally spaced grid. @@ -298,7 +196,7 @@ def _generate_equally_spaced_points( between points. elevation (float): The elevation (meters) above the datum in the WGS 84 coordinate system. - mask (Polygon or MultiPolygon): An optional mask to constrain points + mask (shapely.geometry.Polygon | shapely.geometry.MultiPolygon | None): An optional mask to constrain points using WGS84 (EPSG:4326) geodetic coordinates in a Polygon or MultiPolygon. Returns: @@ -306,7 +204,7 @@ def _generate_equally_spaced_points( """ # generate grid cells over the filtered region - indices = _generate_equally_spaced_indices( + indices = generate_indices_uniform_spacing( theta_longitude, theta_latitude, mask, @@ -315,7 +213,7 @@ def _generate_equally_spaced_points( gdf = gpd.GeoDataFrame( { "point_id": [ - _compute_equally_spaced_point_id(i, j, theta_longitude, theta_latitude) + compute_point_id_uniform_spacing(i, j, theta_longitude) for (i, j) in indices ], "geometry": [ diff --git a/src/tatc/resources/defaults.yml b/src/tatc/resources/defaults.yml index 2f51ee2..003c535 100644 --- a/src/tatc/resources/defaults.yml +++ b/src/tatc/resources/defaults.yml @@ -4,12 +4,12 @@ footprint_points_rectangular_side: 8 # orbit repeat cycle-related settings repeat_cycle_delta_position_m: 10000 -repeat_cycle_delta_velocity_m_per_s: 10 -repeat_cycle_search_elevation_deg: 88 +repeat_cycle_delta_velocity_m_per_s: 3 repeat_cycle_search_duration_days: 30 +repeat_cycle_consistency_threshold_s: 3600 repeat_cycle_lazy_load: True repeat_cycle_for_orbit_track: True repeat_cycle_for_observation_events: True # orbit tle-related settings -orbit_tle_lazy_load: True \ No newline at end of file +gp_orbit_lazy_load: True \ No newline at end of file diff --git a/src/tatc/schemas/__init__.py b/src/tatc/schemas/__init__.py index e530b50..ee55a1f 100644 --- a/src/tatc/schemas/__init__.py +++ b/src/tatc/schemas/__init__.py @@ -3,21 +3,49 @@ """ from .architecture import Architecture -from .instrument import Instrument, PointedInstrument +from .instrument import AllInstruments, Instrument, PointedInstrument from .orbit import ( - TwoLineElements, + AllOrbits, CircularOrbit, - SunSynchronousOrbit, + GeneralPerturbationsOrbit, + GeosynchronousOrbit, KeplerianOrbit, MolniyaOrbit, + SunSynchronousOrbit, TundraOrbit, ) -from .point import Point, GroundStation -from .satellite import ( +from .space import ( + AllSpaceObjects, + MOGConstellation, Satellite, + SOCConstellation, TrainConstellation, WalkerConfiguration, WalkerConstellation, - MOGConstellation, - SOCConstellation, ) +from .surface import AllSurfaceObjects, GroundStation, Point + +__all__ = [ + "AllInstruments", + "AllOrbits", + "AllSpaceObjects", + "AllSurfaceObjects", + "Architecture", + "CircularOrbit", + "GeneralPerturbationsOrbit", + "GeosynchronousOrbit", + "GroundStation", + "Instrument", + "KeplerianOrbit", + "MOGConstellation", + "MolniyaOrbit", + "Point", + "PointedInstrument", + "SOCConstellation", + "Satellite", + "SunSynchronousOrbit", + "TrainConstellation", + "TundraOrbit", + "WalkerConfiguration", + "WalkerConstellation", +] diff --git a/src/tatc/schemas/architecture.py b/src/tatc/schemas/architecture.py index e82f4ab..64c2acc 100644 --- a/src/tatc/schemas/architecture.py +++ b/src/tatc/schemas/architecture.py @@ -1,21 +1,15 @@ -# -*- coding: utf-8 -*- """ -Object schemas for architectures. +Object schemas for mission architectures. @author: Paul T. Grogan """ -from typing import List, Union +from __future__ import annotations from pydantic import BaseModel, Field -from .satellite import ( - Satellite, - TrainConstellation, - WalkerConstellation, - MOGConstellation, -) -from .point import GroundStation +from .space import Satellite +from .surface import GroundStation class Architecture(BaseModel): @@ -24,9 +18,7 @@ class Architecture(BaseModel): """ name: str = Field(..., description="Name of this mission.") - satellites: List[ - Union[Satellite, TrainConstellation, WalkerConstellation, MOGConstellation] - ] = Field([], description="List of member space systems.") - stations: List[GroundStation] = Field( + satellites: list[Satellite] = Field([], description="List of member satellites.") + stations: list[GroundStation] = Field( [], description="List of member ground stations." ) diff --git a/src/tatc/schemas/instrument.py b/src/tatc/schemas/instrument.py deleted file mode 100644 index 073633e..0000000 --- a/src/tatc/schemas/instrument.py +++ /dev/null @@ -1,413 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Object schemas for instruments. - -@author: Paul T. Grogan -""" - -from typing import List, Optional, Tuple, Union -from datetime import timedelta - -import numpy as np -import numpy.typing as npt -from pydantic import BaseModel, Field -from shapely import Geometry -from shapely.geometry import MultiPoint, Point -from skyfield.api import wgs84 -from skyfield.positionlib import Geocentric -from skyfield.toposlib import GeographicPosition - -from ..constants import de421 -from ..utils import ( - compute_footprint, - compute_min_elevation_angle, - compute_projected_ray_position, - field_of_regard_to_swath_width, -) - - -class Instrument(BaseModel): - """ - Remote sensing instrument. - """ - - name: str = Field("Default", description="Instrument name.") - field_of_regard: float = Field( - 180, - description="Angular field (degrees) of possible observations (with pointing).", - gt=0, - le=360, - examples=[50], - ) - min_access_time: timedelta = Field( - timedelta(0), - description="Minimum access (integration) time to record an observation.", - examples=[timedelta(seconds=10)], - ) - req_self_sunlit: Optional[bool] = Field( - None, - description="Required instrument sunlit state for valid observation " - + "(`True`: sunlit, `False`: eclipse, `None`: no requirement).", - ) - req_target_sunlit: Optional[bool] = Field( - None, - description="Required target sunlit state for valid observation " - + "(`True`: sunlit, `False`: eclipse, `None`: no requirement).", - ) - access_time_fixed: bool = Field( - False, description="`True`, if access time is fixed to minimum value." - ) - - def get_swath_width(self, height: float) -> float: - """ - Gets the instrument swath width projected to the Earth's surface. - - Args: - height (float): Height (meters) above surface of the observation. - - Returns: - float: The observation diameter (meters). - """ - return field_of_regard_to_swath_width(height, self.field_of_regard) - - def get_min_elevation_angle(self, height: float) -> float: - """ - Get the minimum elevation angle required to observe a point. - - Args: - height (float): Height (meters) above surface of the observation. - - Returns: - float: The minimum elevation angle (degrees) for observation. - """ - return compute_min_elevation_angle(height, self.field_of_regard) - - def compute_footprint( - self, - orbit_track: Geocentric, - number_points: int = None, - elevation: float = 0, - ) -> Union[Geometry, List[Geometry]]: - """ - Compute the instanteous instrument footprint. - - Args: - orbit_track (skyfield.positionlib.Geocentric): The satellite position/velocity. - number_points (int): The required number of polygon points to generate. - elevation (float): The elevation (meters) at which project the footprint. - - Returns: - Union[shapely.Geometry, List[shapely.Geometry]: The instrument footprint(s). - """ - return compute_footprint( - orbit_track=orbit_track, - cross_track_field_of_view=self.field_of_regard, - along_track_field_of_view=self.field_of_regard, - roll_angle=0, - pitch_angle=0, - is_rectangular=False, - number_points=number_points, - elevation=elevation, - ) - - def compute_footprint_center( - self, - orbit_track: Geocentric, - elevation: float = 0, - ) -> GeographicPosition: - """ - Compute the center of an instaneous instrument footprint. - - Args: - orbit_track (skyfield.positionlib.Geocentric): The satellite position/velocity. - elevation (float): The elevation (meters) at which project the footprint. - - Returns: - skyfield.toposlib.GeographicPosition: The instrument footprint center. - """ - return compute_projected_ray_position( - orbit_track=orbit_track, - cross_track_field_of_view=0, - along_track_field_of_view=0, - roll_angle=0, - pitch_angle=0, - is_rectangular=False, - angle=0, - elevation=elevation, - ) - - def is_valid_observation( - self, orbit_track: Geocentric, target: GeographicPosition = None - ) -> Union[bool, npt.NDArray]: - """Determines if an instrument can provide a valid observations. - - Args: - orbit_track (skyfield.positionlib.Geocentric): orbit track position/velocity from Skyfield - target (skyfield.toposlib.GeographicPosition): target position from Skyfield - - Returns: - numpy.typing.NDArray: Array of indicators: `True` if instrument provides a valid observation. - """ - if target is None: - # support backwards compatibility - target = wgs84.subpoint_of(orbit_track) - is_valid = np.ones(np.size(orbit_track.t), dtype=bool) - if self.req_self_sunlit is not None: - # compare requirement to satellite sunlit condition - is_self_sunlit_valid = orbit_track.is_sunlit(de421) == self.req_self_sunlit - is_valid = np.logical_and(is_valid, is_self_sunlit_valid) - if self.req_target_sunlit is not None: - # compute solar altitude angle at sub-satellite points - solar_alt = ( - (de421["earth"] + target) - .at(orbit_track.t) - .observe(de421["sun"]) - .apparent() - .altaz()[0] - .degrees - ) - # compare requirement to sub-satellite point sunlit conditions - is_target_sunlit_valid = (solar_alt > 0) == self.req_target_sunlit - is_valid = np.logical_and(is_valid, is_target_sunlit_valid) - if np.size(orbit_track.t) > 1: - return is_valid - return bool(is_valid[0]) - - -class PointedInstrument(Instrument): - """ - Remote sensing instrument with optional off-nadir orientation. - """ - - cross_track_field_of_view: float = Field( - ..., - description="Angular field (degrees) of view orthogonal to instrument motion.", - gt=0, - le=180, - ) - along_track_field_of_view: float = Field( - ..., - description="Angular field (degrees) of view in direction of instrument motion.", - gt=0, - le=180, - ) - roll_angle: float = Field( - 0, - description="Left/right look angle (degrees) orthogonal to instrument motion.", - ge=-180, - le=180, - ) - pitch_angle: float = Field( - 0, - description="Fore/aft look angle (degrees) in direction of instrument motion.", - ge=-180, - le=180, - ) - is_rectangular: bool = Field( - False, description="True, if this instrument produces a rectangular view." - ) - cross_track_pixels: int = Field( - 1, description="Number of pixels in cross-track direction.", ge=1 - ) - along_track_pixels: int = Field( - 1, description="Number of pixels in along-track direction.", ge=1 - ) - cross_track_oversampling: float = Field( - 0, description="Fraction of pixel overlap in cross-track diraction.", ge=0, lt=1 - ) - along_track_oversampling: float = Field( - 0, description="Fraction of pixel overlap in along-track diraction.", ge=0, lt=1 - ) - - def get_cross_track_instantaneous_field_of_view(self) -> float: - """ - Gets the instananeous field of view (degrees) for cross-track pixels. - - Returns: - float: the cross-track instantaneous pixel field of view (degrees) - """ - return ( - self.cross_track_field_of_view - / self.cross_track_pixels - / (1 - self.cross_track_oversampling) - ) - - def get_along_track_instantaneous_field_of_view(self) -> float: - """ - Gets the instananeous field of view (degrees) for along-track pixels. - - Returns: - float: the along-track instantaneous pixel field of view (degrees) - """ - return ( - self.along_track_field_of_view - / self.along_track_pixels - / (1 - self.along_track_oversampling) - ) - - def compute_footprint( - self, - orbit_track: Geocentric, - number_points: int = None, - elevation: float = 0, - ) -> Union[Geometry, List[Geometry]]: - """ - Compute the instanteous instrument footprint. - - Args: - orbit_track (skyfield.positionlib.Geocentric): The satellite position/velocity. - number_points (int): The required number of polygon points to generate. - elevation (float): The elevation (meters) at which project the footprint. - - Returns: - Union[shapely.Geometry, List[shapely.Geometry]: The instrument footprint(s). - """ - return compute_footprint( - orbit_track=orbit_track, - cross_track_field_of_view=self.cross_track_field_of_view, - along_track_field_of_view=self.along_track_field_of_view, - roll_angle=self.roll_angle, - pitch_angle=self.pitch_angle, - is_rectangular=self.is_rectangular, - number_points=number_points, - elevation=elevation, - ) - - def compute_footprint_center( - self, - orbit_track: Geocentric, - elevation: float = 0, - ) -> GeographicPosition: - """ - Compute the center of an instaneous instrument footprint. - - Args: - orbit_track (skyfield.positionlib.Geocentric): The satellite position/velocity. - elevation (float): The elevation (meters) at which project the footprint. - - Returns: - skyfield.toposlib.GeographicPosition: The instrument footprint center. - """ - return compute_projected_ray_position( - orbit_track=orbit_track, - cross_track_field_of_view=0, - along_track_field_of_view=0, - roll_angle=self.roll_angle, - pitch_angle=self.pitch_angle, - is_rectangular=False, - angle=0, - elevation=elevation, - ) - - def compute_projected_pixel_position( - self, - orbit_track: Geocentric, - cross_track_index: int, - along_track_index: int, - elevation: float = 0, - ) -> GeographicPosition: - """ - Get the location of a projected pixel. - - Args: - orbit_track (skyfield.positionlib.Geocentric): the satellite orbit track. - cross_track_index (int): cross-track pixel index (left-to-right). - along_track_index (int): along-track pixel index (fore-to-aft). - elevation (float): The elevation (meters) at which project the pixel. - - Returns: - (skyfield.toposlib.GeographicPosition): the geographic position of the projected pixel - """ - cone, clock = self.get_pixel_cone_and_clock_angle( - cross_track_index, along_track_index - ) - - return compute_projected_ray_position( - orbit_track=orbit_track, - cross_track_field_of_view=cone, - along_track_field_of_view=cone, - roll_angle=self.roll_angle, - pitch_angle=self.pitch_angle, - is_rectangular=False, - angle=clock, - elevation=elevation, - ) - - def get_pixel_cone_and_clock_angle( - self, cross_track_index: int, along_track_index: int - ) -> Tuple[float, float]: - """ - Gets the cone () and clock angles (degrees) for given pixel. - - Args: - cross_track_index (int): pixel index in cross-track dimension (left to right). - along_track_index (int): pixel index in along-track dimension (fore to aft). - - Returns: - Tuple[float, float]: cone (from nadir-looking) and clock - (counter-clockwise from right-looking) angles (degrees). - """ - cross_track_offset = ( - (0.5 + cross_track_index - self.cross_track_pixels / 2) - * (1 - self.cross_track_oversampling) - * self.cross_track_field_of_view - / self.cross_track_pixels - ) - along_track_offset = ( - (self.along_track_pixels / 2 - 0.5 - along_track_index) - * (1 - self.along_track_oversampling) - * self.along_track_field_of_view - / self.along_track_pixels - ) - return ( - np.sqrt(cross_track_offset**2 + along_track_offset**2), - np.arctan2(along_track_offset, cross_track_offset), - ) - - def compute_footprint_pixel_array( - self, - orbit_track: Geocentric, - elevation: float = 0, - ) -> Union[MultiPoint, List[MultiPoint]]: - """ - Compute the instanteous footprint pixel array. - - Args: - orbit_track (skyfield.positionlib.Geocentric): The satellite position/velocity. - elevation (float): The elevation (meters) at which project the footprint. - - Returns: - Union[shapely.geometry.MultiPoint, List[shapely.geometry.MultiPoint]]: The instrument pixel array(s). - """ - points = [ - self.compute_projected_pixel_position( - orbit_track=orbit_track, - cross_track_index=i, - along_track_index=j, - elevation=elevation, - ) - for i in range(self.cross_track_pixels) - for j in range(self.along_track_pixels) - ] - if np.size(orbit_track.t) > 1: - return [ - MultiPoint( - [ - Point( - point.longitude.degrees[i], - point.latitude.degrees[i], - point.elevation.m[i], - ) - for point in points - ] - ) - for i in range(np.size(orbit_track.t)) - ] - return MultiPoint( - [ - Point( - point.longitude.degrees, point.latitude.degrees, point.elevation.m - ) - for point in points - ] - ) diff --git a/src/tatc/schemas/instrument/__init__.py b/src/tatc/schemas/instrument/__init__.py new file mode 100644 index 0000000..9b1f619 --- /dev/null +++ b/src/tatc/schemas/instrument/__init__.py @@ -0,0 +1,12 @@ +""" +Object schemas for instruments. + +@author: Paul T. Grogan +""" + +from .simple import Instrument +from .pointed import PointedInstrument + +AllInstruments = Instrument | PointedInstrument + +__all__ = ["AllInstruments", "Instrument", "PointedInstrument"] diff --git a/src/tatc/schemas/instrument/pointed.py b/src/tatc/schemas/instrument/pointed.py new file mode 100644 index 0000000..7dcb892 --- /dev/null +++ b/src/tatc/schemas/instrument/pointed.py @@ -0,0 +1,266 @@ +""" +Object schemas for off-nadir pointing instruments. + +@author: Paul T. Grogan +""" + +from __future__ import annotations + +import numpy as np +from pydantic import Field +from shapely import MultiPolygon, Polygon +from shapely.geometry import MultiPoint, Point +from skyfield.positionlib import Geocentric +from skyfield.toposlib import GeographicPosition + +from ...utils.projection import ( + compute_footprint, + compute_projected_ray_position, +) +from .simple import Instrument + + +class PointedInstrument(Instrument): + """ + Remote sensing instrument with an optional off-nadir (roll/pitch) + pointing offset and a rectangular or elliptical pixel array. + """ + + cross_track_field_of_view: float = Field( + ..., + description="Angular field (degrees) of view orthogonal to instrument motion.", + gt=0, + le=180, + ) + along_track_field_of_view: float = Field( + ..., + description="Angular field (degrees) of view in direction of instrument motion.", + gt=0, + le=180, + ) + roll_angle: float = Field( + default=0, + description="Left/right look angle (degrees) orthogonal to instrument motion.", + ge=-180, + le=180, + ) + pitch_angle: float = Field( + default=0, + description="Fore/aft look angle (degrees) in direction of instrument motion.", + ge=-180, + le=180, + ) + is_rectangular: bool = Field( + default=False, + description="True, if this instrument produces a rectangular view.", + ) + cross_track_pixels: int = Field( + default=1, description="Number of pixels in cross-track direction.", ge=1 + ) + along_track_pixels: int = Field( + default=1, description="Number of pixels in along-track direction.", ge=1 + ) + cross_track_oversampling: float = Field( + default=0, + description="Fraction of pixel overlap in cross-track direction.", + ge=0, + lt=1, + ) + along_track_oversampling: float = Field( + default=0, + description="Fraction of pixel overlap in along-track direction.", + ge=0, + lt=1, + ) + + def get_cross_track_instantaneous_field_of_view(self) -> float: + """ + Gets the instananeous field of view (degrees) for cross-track pixels. + + Returns: + float: the cross-track instantaneous pixel field of view (degrees) + """ + return ( + self.cross_track_field_of_view + / self.cross_track_pixels + / (1 - self.cross_track_oversampling) + ) + + def get_along_track_instantaneous_field_of_view(self) -> float: + """ + Gets the instananeous field of view (degrees) for along-track pixels. + + Returns: + float: the along-track instantaneous pixel field of view (degrees) + """ + return ( + self.along_track_field_of_view + / self.along_track_pixels + / (1 - self.along_track_oversampling) + ) + + def compute_footprint( + self, + orbit_track: Geocentric, + number_points: int | None = None, + elevation: float = 0, + ) -> list[Polygon | MultiPolygon]: + """ + Compute the instanteous instrument footprint. + + Args: + orbit_track (skyfield.positionlib.Geocentric): The satellite position/velocity. + number_points (int | None): The required number of polygon points to generate. + elevation (float): The elevation (meters) at which project the footprint. + + Returns: + list[shapely.geometry.Polygon | shapely.geometry.MultiPolygon]: The instrument footprint(s). + """ + return compute_footprint( + orbit_track=orbit_track, + cross_track_field_of_view=self.cross_track_field_of_view, + along_track_field_of_view=self.along_track_field_of_view, + roll_angle=self.roll_angle, + pitch_angle=self.pitch_angle, + is_rectangular=self.is_rectangular, + number_points=number_points, + elevation=elevation, + ) + + def compute_footprint_center( + self, + orbit_track: Geocentric, + elevation: float = 0, + ) -> GeographicPosition: + """ + Compute the center of an instaneous instrument footprint. + + Args: + orbit_track (skyfield.positionlib.Geocentric): The satellite position/velocity. + elevation (float): The elevation (meters) at which project the footprint. + + Returns: + skyfield.toposlib.GeographicPosition: The instrument footprint center. + """ + return compute_projected_ray_position( + orbit_track=orbit_track, + cross_track_field_of_view=0, + along_track_field_of_view=0, + roll_angle=self.roll_angle, + pitch_angle=self.pitch_angle, + is_rectangular=False, + angle=0, + elevation=elevation, + ) + + def compute_projected_pixel_position( + self, + orbit_track: Geocentric, + cross_track_index: int, + along_track_index: int, + elevation: float = 0, + ) -> GeographicPosition: + """ + Get the location of a projected pixel. + + Args: + orbit_track (skyfield.positionlib.Geocentric): the satellite orbit track. + cross_track_index (int): cross-track pixel index (left-to-right). + along_track_index (int): along-track pixel index (fore-to-aft). + elevation (float): The elevation (meters) at which project the pixel. + + Returns: + (skyfield.toposlib.GeographicPosition): the geographic position of the projected pixel + """ + cone, clock = self.get_pixel_cone_and_clock_angle( + cross_track_index, along_track_index + ) + + return compute_projected_ray_position( + orbit_track=orbit_track, + # `cone` is the pixel's angular offset from boresight (not a + # field of view), but compute_projected_ray_position's + # elliptical ray offsets by half of the field of view it is + # given; doubling `cone` here cancels that halving so the + # pixel lands at its true cone-angle offset. + cross_track_field_of_view=2 * cone, + along_track_field_of_view=2 * cone, + roll_angle=self.roll_angle, + pitch_angle=self.pitch_angle, + is_rectangular=False, + angle=clock, + elevation=elevation, + ) + + def get_pixel_cone_and_clock_angle( + self, cross_track_index: int, along_track_index: int + ) -> tuple[float, float]: + """ + Gets the cone and clock angles (degrees) for a given pixel: its + angular offset from the instrument boresight (after roll/pitch), + expressed in polar form. + + Args: + cross_track_index (int): pixel index in cross-track dimension (left to right). + along_track_index (int): pixel index in along-track dimension (fore to aft). + + Returns: + tuple[float, float]: cone (the pixel's total angular + displacement from boresight) and clock (counter-clockwise + from right-looking, about the boresight) angles (degrees). + """ + cross_track_offset = ( + (0.5 + cross_track_index - self.cross_track_pixels / 2) + * (1 - self.cross_track_oversampling) + * self.cross_track_field_of_view + / self.cross_track_pixels + ) + along_track_offset = ( + (self.along_track_pixels / 2 - 0.5 - along_track_index) + * (1 - self.along_track_oversampling) + * self.along_track_field_of_view + / self.along_track_pixels + ) + return ( + np.sqrt(cross_track_offset**2 + along_track_offset**2), + np.degrees(np.arctan2(along_track_offset, cross_track_offset)), + ) + + def compute_footprint_pixel_array( + self, + orbit_track: Geocentric, + elevation: float = 0, + ) -> list[MultiPoint]: + """ + Compute the instanteous footprint pixel array. + + Args: + orbit_track (skyfield.positionlib.Geocentric): The satellite position/velocity. + elevation (float): The elevation (meters) at which project the footprint. + + Returns: + list[shapely.geometry.MultiPoint]: The instrument pixel array(s). + """ + points = [ + self.compute_projected_pixel_position( + orbit_track=orbit_track, + cross_track_index=i, + along_track_index=j, + elevation=elevation, + ) + for i in range(self.cross_track_pixels) + for j in range(self.along_track_pixels) + ] + return [ + MultiPoint( + [ + Point( + point.longitude.degrees[i], + point.latitude.degrees[i], + point.elevation.m[i], + ) + for point in points + ] + ) + for i in range(np.size(orbit_track.t)) # type: ignore + ] diff --git a/src/tatc/schemas/instrument/simple.py b/src/tatc/schemas/instrument/simple.py new file mode 100644 index 0000000..fdf3a73 --- /dev/null +++ b/src/tatc/schemas/instrument/simple.py @@ -0,0 +1,173 @@ +""" +Object schemas for nadir-pointing instruments. + +@author: Paul T. Grogan +""" + +from __future__ import annotations + +from datetime import timedelta + +import numpy as np +import numpy.typing as npt +from pydantic import BaseModel, Field +from shapely import MultiPolygon, Polygon +from skyfield.api import wgs84 +from skyfield.positionlib import Geocentric +from skyfield.toposlib import GeographicPosition + +from ...constants import de421 +from ...utils.observation import ( + compute_min_elevation_angle, + field_of_regard_to_swath_width, +) +from ...utils.projection import ( + compute_footprint, + compute_projected_ray_position, +) + + +class Instrument(BaseModel): + """ + Remote sensing instrument. + """ + + name: str = Field(default="Default", description="Instrument name.") + field_of_regard: float = Field( + default=180, + description="Angular field (degrees) of possible observations (with pointing).", + gt=0, + le=360, + examples=[50], + ) + min_access_time: timedelta = Field( + default=timedelta(0), + description="Minimum access (integration) time to record an observation.", + examples=[timedelta(seconds=10)], + ) + req_self_sunlit: bool | None = Field( + default=None, + description="Required instrument sunlit state for valid observation " + + "(`True`: sunlit, `False`: eclipse, `None`: no requirement).", + ) + req_target_sunlit: bool | None = Field( + default=None, + description="Required target sunlit state for valid observation " + + "(`True`: sunlit, `False`: eclipse, `None`: no requirement).", + ) + access_time_fixed: bool = Field( + default=False, description="`True`, if access time is fixed to minimum value." + ) + + def get_swath_width(self, height: float) -> float: + """ + Gets the instrument swath width projected to the Earth's surface. + + Args: + height (float): Height (meters) above surface of the observation. + + Returns: + float: The observation diameter (meters). + """ + return field_of_regard_to_swath_width(height, self.field_of_regard) + + def get_min_elevation_angle(self, height: float) -> float: + """ + Get the minimum elevation angle required to observe a point. + + Args: + height (float): Height (meters) above surface of the observation. + + Returns: + float: The minimum elevation angle (degrees) for observation. + """ + return compute_min_elevation_angle(height, self.field_of_regard) + + def compute_footprint( + self, + orbit_track: Geocentric, + number_points: int | None = None, + elevation: float = 0, + ) -> list[Polygon | MultiPolygon]: + """ + Compute the instanteous instrument footprint. + + Args: + orbit_track (skyfield.positionlib.Geocentric): The satellite position/velocity. + number_points (int | None): The required number of polygon points to generate. + elevation (float): The elevation (meters) at which project the footprint. + + Returns: + list[shapely.geometry.Polygon | shapely.geometry.MultiPolygon]: The instrument footprint(s). + """ + return compute_footprint( + orbit_track=orbit_track, + cross_track_field_of_view=self.field_of_regard, + along_track_field_of_view=self.field_of_regard, + roll_angle=0, + pitch_angle=0, + is_rectangular=False, + number_points=number_points, + elevation=elevation, + ) + + def compute_footprint_center( + self, + orbit_track: Geocentric, + elevation: float = 0, + ) -> GeographicPosition: + """ + Compute the center of an instaneous instrument footprint. + + Args: + orbit_track (skyfield.positionlib.Geocentric): The satellite position/velocity. + elevation (float): The elevation (meters) at which project the footprint. + + Returns: + skyfield.toposlib.GeographicPosition: The instrument footprint center. + """ + return compute_projected_ray_position( + orbit_track=orbit_track, + cross_track_field_of_view=0, + along_track_field_of_view=0, + roll_angle=0, + pitch_angle=0, + is_rectangular=False, + angle=0, + elevation=elevation, + ) + + def is_valid_observation( + self, orbit_track: Geocentric, target: GeographicPosition | None = None + ) -> npt.NDArray[np.bool_]: + """Determines if an instrument can provide a valid observations. + + Args: + orbit_track (skyfield.positionlib.Geocentric): orbit track position/velocity from Skyfield + target (skyfield.toposlib.GeographicPosition): target position from Skyfield + + Returns: + numpy.typing.NDArray: Array of indicators: `True` if instrument provides a valid observation. + """ + if target is None: + # support backwards compatibility + target = wgs84.subpoint_of(orbit_track) + is_valid = np.ones(np.size(orbit_track.t), dtype=bool) # type: ignore + if self.req_self_sunlit is not None: + # compare requirement to satellite sunlit condition + is_self_sunlit_valid = orbit_track.is_sunlit(de421) == self.req_self_sunlit + is_valid = np.logical_and(is_valid, is_self_sunlit_valid) + if self.req_target_sunlit is not None: + # compute solar altitude angle at sub-satellite points + solar_alt = ( + (de421["earth"] + target) + .at(orbit_track.t) + .observe(de421["sun"]) + .apparent() + .altaz()[0] + .degrees + ) + # compare requirement to sub-satellite point sunlit conditions + is_target_sunlit_valid = (solar_alt > 0) == self.req_target_sunlit + is_valid = np.logical_and(is_valid, is_target_sunlit_valid) + return is_valid diff --git a/src/tatc/schemas/orbit.py b/src/tatc/schemas/orbit.py deleted file mode 100644 index f9f80c1..0000000 --- a/src/tatc/schemas/orbit.py +++ /dev/null @@ -1,1183 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Object schemas for satellite orbits. - -@author: Paul T. Grogan -""" - -from __future__ import annotations - -from datetime import datetime, time, timedelta, timezone -from typing import List, Optional, Tuple, Union - -import numpy as np -from pydantic import AfterValidator, BaseModel, Field -from sgp4.api import Satrec, WGS72 -from sgp4 import exporter -from sgp4.conveniences import sat_epoch_datetime -from skyfield.api import EarthSatellite, Time, wgs84 -from skyfield.positionlib import Geocentric -from skyfield.framelib import itrs -from typing_extensions import Annotated, Literal - -from .point import Point -from .. import config, constants, utils - - -class TwoLineElements(BaseModel): - """ - Orbit defined with standard two line elements. - """ - - type: Literal["tle"] = Field("tle", description="Orbit type discriminator.") - tle: Annotated[ - List[str], - AfterValidator(utils.is_chronological_tle), - AfterValidator(utils.is_valid_tle), - AfterValidator(utils.is_even_length_list), - ] = Field( - ..., - description="Two line elements. Multiple TLEs must be in chronological order.", - min_length=2, - examples=[ - [ - "1 25544U 98067A 21156.30527927 .00003432 00000-0 70541-4 0 9993", - "2 25544 51.6455 41.4969 0003508 68.0432 78.3395 15.48957534286754", - ] - ], - ) - - def get_tle_count(self) -> int: - """ - Gets the number of TLEs specified for this orbit. - - Returns: - int: the number of TLEs - """ - return len(self.tle) // 2 - - def get_catalog_number(self, tle_i=0) -> int: - """ - Gets the TLE catalog number. - - Args: - tle_i (int): the TLE index. - - Returns: - int: the catalog number - """ - # pylint: disable=E1136 - return int(self.tle[2 * tle_i][2:7]) - - def get_classification(self, tle_i=0) -> str: - """ - Gets the TLE classification type (U: unclassified; C: classified). - - Args: - tle_i (int): the TLE index. - - Returns: - str: the classification type - """ - # pylint: disable=E1136 - return self.tle[2 * tle_i][7] - - def get_international_designator(self, tle_i=0) -> str: - """ - Gets the TLE international designator. - - Args: - tle_i (int): the TLE index. - - Returns: - str: the international designator - """ - # pylint: disable=E1136 - year = self.tle[2 * tle_i][9:11] - launch = self.tle[2 * tle_i][11:14] - piece = self.tle[2 * tle_i][14:17] - return str( - ("" if not year.isdigit() else "20" if int(year) < 57 else "19") - + year - + "-" - + launch - + piece - ).strip() - - def get_epoch(self, tle_i=0) -> datetime: - """ - Gets the TLE epoch time. - - Args: - tle_i (int): the TLE index. - - Returns: - datetime: the epoch datetime - """ - # pylint: disable=E1136 - return sat_epoch_datetime( - Satrec.twoline2rv(self.tle[2 * tle_i], self.tle[2 * tle_i + 1]) - ) - - def get_first_derivative_mean_motion(self, tle_i=0) -> float: - """ - Gets the first derivative of mean motion (ballistic coefficient). - - Args: - tle_i (int): the TLE index. - - Returns: - float: the first derivative of mean motion - """ - # pylint: disable=E1136 - return float(self.tle[2 * tle_i][33:43]) - - def get_second_derivative_mean_motion(self, tle_i=0) -> float: - """ - Gets the second derivative of mean motion. - - Args: - tle_i (int): the TLE index. - - Returns: - float: the second derivative of mean motion - """ - # pylint: disable=E1136 - return float("0." + self.tle[2 * tle_i][44:50].strip()) * 10 ** ( - int(self.tle[2 * tle_i][50:52]) - ) - - def get_b_star(self, tle_i=0) -> float: - """ - Gets the b-star term (drag or radiation pressure coefficient). - - Args: - tle_i (int): the TLE index. - - Returns: - float: the b-star term - """ - # pylint: disable=E1136 - return float("0." + self.tle[2 * tle_i][53:59].strip()) * 10 ** ( - int(self.tle[2 * tle_i][59:61]) - ) - - def get_ephemeris_type(self, tle_i=0) -> int: - """ - Gets the TLE ephemeris type. - - Args: - tle_i (int): the TLE index. - - Returns: - int: the ephemeris type - """ - # pylint: disable=E1136 - return int(self.tle[2 * tle_i][62]) - - def get_element_set_number(self, tle_i=0) -> int: - """ - Gets the TLE element set number. - - Args: - tle_i (int): the TLE index. - - Returns: - int: the element set number - """ - # pylint: disable=E1136 - return int(self.tle[2 * tle_i][64:68]) - - def get_inclination(self, tle_i=0) -> float: - """ - Gets the orbit inclination (decimal degrees). - - Args: - tle_i (int): the TLE index. - - Returns: - float: the inclination - """ - # pylint: disable=E1136 - return float(self.tle[2 * tle_i + 1][8:16]) - - def get_right_ascension_ascending_node(self, tle_i=0) -> float: - """ - Gets the right ascension of ascending node (decimal degrees). - - Args: - tle_i (int): the TLE index. - - Returns: - float: the right ascension of ascending node - """ - # pylint: disable=E1136 - return float(self.tle[2 * tle_i + 1][17:25]) - - def get_eccentricity(self, tle_i=0) -> float: - """ - Gets the eccentricity. - - Args: - tle_i (int): the TLE index. - - Returns: - float: the eccentricity - """ - # pylint: disable=E1136 - return float("0." + self.tle[2 * tle_i + 1][26:33].strip()) - - def get_perigee_argument(self, tle_i=0) -> float: - """ - Gets the argument of perigee (decimal degrees). - - Args: - tle_i (int): the TLE index. - - Returns: - float: the argument of perigee - """ - # pylint: disable=E1136 - return float(self.tle[2 * tle_i + 1][34:42]) - - def get_mean_anomaly(self, tle_i=0) -> float: - """ - Gets the mean anomaly (decimal degrees). - - Args: - tle_i (int): the TLE index. - - Returns: - float: the mean anomaly - """ - # pylint: disable=E1136 - return float(self.tle[2 * tle_i + 1][43:51]) - - def get_mean_motion(self, tle_i=0) -> float: - """ - Gets the mean motion (revolutions per day). - - Args: - tle_i (int): the TLE index. - - Returns: - float: the mean motion - """ - # pylint: disable=E1136 - return float(self.tle[2 * tle_i + 1][52:63]) - - def get_orbit_period(self, tle_i=0) -> timedelta: - """ - Gets the approximate orbit period. - - Args: - tle_i (int): the TLE index. - - Returns: - timedelta: the orbit period - """ - return timedelta(days=1 / self.get_mean_motion(tle_i)) - - def get_revolution_number_at_epoch(self, tle_i=0) -> int: - """ - Gets the revolution number at epoch. - - Args: - tle_i (int): the TLE index. - - Returns: - timedelta: the revolution number - """ - # pylint: disable=E1136 - return int(self.tle[2 * tle_i + 1][63:68]) - - def get_semimajor_axis(self, tle_i=0) -> float: - """ - Gets the semimajor axis (meters). - - Args: - tle_i (int): the TLE index. - - Returns: - float: the semimajor axis - """ - mean_motion_rad_s = self.get_mean_motion(tle_i) * 2 * np.pi / 86400 - return np.power( - constants.EARTH_MU / mean_motion_rad_s**2, - 1 / 3, - ) - - def get_altitude(self, tle_i=0) -> float: - """ - Gets the altitude (meters). - - Args: - tle_i (int): the TLE index. - - Returns: - float: the altitude - """ - return self.get_semimajor_axis(tle_i) - constants.EARTH_MEAN_RADIUS - - def get_true_anomaly(self, tle_i=0) -> float: - """ - Gets the true anomaly (decimal degrees). - - Args: - tle_i (int): the TLE index. - - Returns: - float: the true anomaly - """ - return utils.mean_anomaly_to_true_anomaly( - self.get_mean_anomaly(tle_i), self.get_eccentricity(tle_i) - ) - - def get_derived_orbit( - self, delta_mean_anomaly: float, delta_raan: float - ) -> TwoLineElements: - """ - Gets a derived orbit with perturbations to the mean anomaly and right - ascension of ascending node. - - Args: - delta_mean_anomaly (float): Delta mean anomaly (degrees). - delta_raan (float): Delta right ascension of ascending node (degrees). - - Returns: - TwoLineElements: the derived orbit - """ - # pylint: disable=E1101 - tles = self.tle.copy() - for i in range(self.get_tle_count()): - # pylint: disable=E1136 - lead_tle = Satrec.twoline2rv(self.tle[2 * i], self.tle[2 * i + 1]) - epoch = sat_epoch_datetime(lead_tle) - satrec = Satrec() - satrec.sgp4init( - WGS72, - "i", - 0, - (epoch - datetime(1949, 12, 31, tzinfo=timezone.utc)) - / timedelta(days=1), - lead_tle.bstar, - lead_tle.ndot, - lead_tle.nddot, - lead_tle.ecco, - lead_tle.argpo, - lead_tle.inclo, - np.mod(lead_tle.mo + np.radians(delta_mean_anomaly), 2 * np.pi), - lead_tle.no_kozai, - np.mod(lead_tle.nodeo + np.radians(delta_raan), 2 * np.pi), - ) - tle1, tle2 = exporter.export_tle(satrec) - tles[2 * i] = tle1.replace("\x00", "U") - tles[2 * i + 1] = tle2 - return TwoLineElements(tle=tles) - - def as_skyfield(self, tle_i=0): - """ - Converts this orbit to a Skyfield `EarthSatellite`. - - Args: - tle_i (int): the TLE index. - - Returns: - skyfield.api.EarthSatellite: the Skyfield EarthSatellite - """ - # pylint: disable=E1136 - return EarthSatellite(self.tle[2 * tle_i], self.tle[2 * tle_i + 1]) - - def get_closest_tle_index( - self, at_times: Union[datetime, List[datetime]] - ) -> Union[int, List[int]]: - """ - Gets the closest TLE index to specified time(s). - - Args: - at_times (datetime or List[datetime]): specified times - - Returns: - int or List[int]: closest TLE index or indices - """ - - if at_times is None: - return 0 - # lazy-load epochs - tle_epochs = self.__dict__.get("tle_epochs") - if tle_epochs is None: - # extract the orbit epoch time - tle_epochs = np.array( - [self.get_epoch(i) for i in range(self.get_tle_count())] - ) - self.__dict__["tle_epochs"] = tle_epochs - # handle scalar - if isinstance(at_times, datetime): - idx = np.searchsorted(tle_epochs, at_times, side="left") - return ( - int(idx - 1) - if idx > 0 - and ( - idx == len(tle_epochs) - or abs(at_times - tle_epochs[idx - 1]) - < abs(at_times - tle_epochs[idx]) - ) - else int(idx) - ) - # handle vector - indices = np.searchsorted(tle_epochs, at_times, side="left") - return [ - ( - int(idx - 1) - if idx > 0 - and ( - idx == len(tle_epochs) - or abs(at_times[i] - tle_epochs[idx - 1]) - < abs(at_times[i] - tle_epochs[idx]) - ) - else int(idx) - ) - for i, idx in enumerate(indices) - ] - - def partition_by_tle_index( - self, start: datetime, end: datetime - ) -> Tuple[List[datetime], List[int]]: - """ - Partition a timeline based on closest TLE index. - - Args: - start (datetime): Start time. - end (datetime): End time. - - Returns: - Tuple[List[datetime], List[int]]: list of partitioned times and assigned TLE indices - """ - if self.get_tle_count() <= 1: - return [start, end], [0, 0] - epochs = [self.get_epoch(i) for i in range(self.get_tle_count())] - sorted_epochs = np.sort(epochs) - tle_i = np.argsort(epochs) - epoch_midpoints = ( - sorted_epochs[1:] + (sorted_epochs[:-1] - sorted_epochs[1:]) / 2 - ) - midpoint_indices = [i for i, t in enumerate(epoch_midpoints) if start < t < end] - return ( - [start] + list(epoch_midpoints[midpoint_indices]) + [end], - ( - [self.get_closest_tle_index(start)] - + list(map(int, tle_i[midpoint_indices])) - + [self.get_closest_tle_index(end)] - ), - ) - - def get_repeat_cycle( - self, - max_delta_position: float = None, - max_delta_velocity: float = None, - min_elevation_angle: float = None, - max_search_duration: timedelta = None, - lazy_load: bool = None, - ) -> timedelta: - """ - Compute the orbit repeat cycle. Lazy-loads a previously-computed repeat cycle if available. - - Args: - max_delta_position (float): the maximum difference in position (m) allowed for a repeat. - max_delta_velocity (float): the maximum difference in velocity (m/s) allowed for a repeat. - min_elevation_angle (float): the minimum elevation angle (deg) for screening repeats. - max_search_duration (timedelta): the maximum period of time to search for repeats. - lazy_load (bool): True, if the previously-computed repeat cycle should be loaded. - - Returns: - timedelta: the repeat cycle duration (if it exists) - """ - # load defaults - if max_delta_position is None: - max_delta_position = config.rc.repeat_cycle_delta_position_m - if max_delta_velocity is None: - max_delta_velocity = config.rc.repeat_cycle_delta_velocity_m_per_s - if min_elevation_angle is None: - min_elevation_angle = config.rc.repeat_cycle_search_elevation_deg - if max_search_duration is None: - max_search_duration = timedelta( - days=config.rc.repeat_cycle_search_duration_days - ) - if lazy_load is None: - lazy_load = config.rc.repeat_cycle_lazy_load - - if lazy_load: - repeat_cycle = self.__dict__.get("repeat_cycle") - else: - repeat_cycle = None - if repeat_cycle is None: - # extract the orbit epoch time - epoch = self.get_epoch() - # record the initial position and velocity in Earth-centered Earth-fixed frame - datum = wgs84.subpoint_of( - self.as_skyfield().at(constants.timescale.from_datetime(epoch)) - ) - position_0, velocity_0 = ( - self.as_skyfield() - .at(constants.timescale.from_datetime(epoch)) - .frame_xyz_and_velocity(itrs) - ) - # find candidate repeat events - ts, es = self.as_skyfield().find_events( - datum, - constants.timescale.from_datetime(epoch + timedelta(minutes=10)), - constants.timescale.from_datetime(epoch + max_search_duration), - min_elevation_angle, - ) - # compute position and velocity at culmination in Earth-centered Earth-fixed frame - position, velocity = ( - self.as_skyfield().at(ts[es == 1]).frame_xyz_and_velocity(itrs) - ) - # apply validity conditions on position and velocity error norms - is_valid = np.logical_and( - np.linalg.norm((position.m.T - position_0.m.T).T, axis=0) - < max_delta_position, - np.linalg.norm((velocity.m_per_s.T - velocity_0.m_per_s.T).T, axis=0) - < max_delta_velocity, - ) - if np.any(is_valid): - # assign repeat cycle - repeat_cycle = ts[es == 1][is_valid][0].utc_datetime() - epoch - else: - # assign zero repeat cycle value to avoid recalculation - repeat_cycle = timedelta(0) - self.__dict__["repeat_cycle"] = repeat_cycle - if repeat_cycle > timedelta(0): - return repeat_cycle - return None - - def get_orbit_track( - self, times: Union[datetime, List[datetime]], try_repeat: bool = None - ) -> Geocentric: - """ - Gets the orbit track of this orbit using Skyfield. - - Args: - times (Union[datetime, List[datetime]]): time(s) at which to compute position/velocity. - try_repeat (bool): True, if a repeat orbit should be used to improve long-term accuracy. - - Returns: - skyfield.positionlib.Geocentric: the orbit track position/velocity - """ - # load defaults - if try_repeat is None: - try_repeat = config.rc.repeat_cycle_for_orbit_track - - if self.get_tle_count() > 1: - # try to use use multiple TLEs - if isinstance(times, datetime): - return self.as_skyfield(self.get_closest_tle_index(times)).at( - constants.timescale.from_datetime(times) - ) - tle_i = self.get_closest_tle_index(times) - tracks = [ - self.as_skyfield(i).at(constants.timescale.from_datetime(t)) - for i, t in zip(tle_i, times) - ] - return Geocentric( - np.array([track.position.au for track in tracks]).T, - np.array([track.velocity.au_per_d for track in tracks]).T, - constants.timescale.from_datetimes(times), - ) - # create skyfield Time - if isinstance(times, datetime): - ts_times = constants.timescale.from_datetime(times) - else: - ts_times = constants.timescale.from_datetimes(times) - if try_repeat: - # try to compute repeat cycle positions - repeat_cycle = self.get_repeat_cycle() - if repeat_cycle is not None: - epoch = self.get_epoch() - if isinstance(times, datetime): - offset = times - epoch - repeat_times = constants.timescale.from_datetime( - epoch - + np.sign(offset / timedelta(1)) - * np.mod(np.abs(offset), repeat_cycle) - ) - else: - offset = np.array(times) - epoch - repeat_times = constants.timescale.from_datetimes( - epoch - + np.sign(offset / timedelta(1)) - * np.mod(np.abs(offset), repeat_cycle) - ) - repeat_track = self.as_skyfield().at(repeat_times) - return Geocentric( - repeat_track.position.au, repeat_track.velocity.au_per_d, ts_times - ) - # compute satellite positions - return self.as_skyfield().at(ts_times) - - def get_observation_events( - self, - point: Point, - start: datetime, - end: datetime, - min_elevation_angle: float, - try_repeat: bool = None, - ) -> tuple: - """ - Gets the observation events of this orbit using Skyfield. - - Args: - point (Point): Target location to observe. - start (datetime): Start time of the observation period. - end (datetime): End time of the observation period. - min_elevation_angle (float): Minimum elevation angle (deg) to constrain observation. - try_repeat (bool): True, if a repeat orbit should be used to improve long-term accuracy. - - Returns: - skyfield.positionlib.Geocentric: the orbit track position/velocity - """ - # load defaults - if try_repeat is None: - try_repeat = config.rc.repeat_cycle_for_observation_events - topos = wgs84.latlon(point.latitude, point.longitude, point.elevation) - if self.get_tle_count() > 1: - # try to use use multiple TLEs - part_ts, tle_is = self.partition_by_tle_index(start, end) - events = [ - self.as_skyfield(tle_is[i]).find_events( - topos, - constants.timescale.from_datetime(part_ts[i]), - constants.timescale.from_datetime(part_ts[i + 1]), - min_elevation_angle, - ) - for i in range(len(part_ts) - 1) - ] - return ( - constants.timescale.from_datetimes( - [t.utc_datetime() for e in events for t in e[0]] - ), - np.array([v for e in events for v in e[1]]), - ) - # create skyfield Time - t_0 = constants.timescale.from_datetime(start) - if try_repeat: - # try to compute repeat cycle events - repeat_cycle = self.get_repeat_cycle() - if repeat_cycle is not None and repeat_cycle < end - start: - repeat_t_1 = constants.timescale.from_datetime(start + repeat_cycle) - times, events = self.as_skyfield().find_events( - topos, t_0, repeat_t_1, min_elevation_angle - ) - number_cycles = int(np.ceil((end - start) / repeat_cycle)) - if len(times) == 0: - return (Time([], []), np.array([], dtype=int)) - times_py = np.concatenate( - [ - times.utc_datetime() + i * repeat_cycle - for i in range(number_cycles) - ] - ) - events_py = np.concatenate([events for _ in range(number_cycles)]) - if len(times_py) == 0: - return Time([], []), np.array([], dtype=int) - return ( - constants.timescale.from_datetimes(times_py[times_py <= end]), - events_py[times_py <= end], - ) - # compute observation events - t_1 = constants.timescale.from_datetime(end) - # pylint: disable=E1101 - return self.as_skyfield().find_events(topos, t_0, t_1, min_elevation_angle) - - def to_tle(self) -> TwoLineElements: - """ - Converts this orbit to a two line elements representation. - - Returns: - TwoLineElements: the two line elements orbit - """ - return self - - -class OrbitBase(BaseModel): - """ - Base class for orbits. - """ - - altitude: float = Field(..., description="Mean altitude (meters).") - true_anomaly: float = Field(0, description="True anomaly (degrees).", ge=0, lt=360) - epoch: Optional[datetime] = Field( - datetime.now(tz=timezone.utc), - description="Timestamp (epoch) of the initial orbital state.", - ) - - def get_mean_anomaly(self) -> float: - """ - Gets the mean anomaly (decimal degrees). - - Returns: - float: the mean anomaly - """ - return utils.true_anomaly_to_mean_anomaly(self.true_anomaly) - - def get_semimajor_axis(self) -> float: - """ - Gets the semimajor axis (meters). - - Returns: - float: the semimajor axis - """ - return constants.EARTH_MEAN_RADIUS + self.altitude - - def get_mean_motion(self) -> float: - """ - Gets the mean motion (revolutions per day). - - Returns: - float: the mean motion - """ - return 1 / (self.get_orbit_period() / timedelta(days=1)) - - def get_orbit_period(self) -> timedelta: - """ - Gets the approximate orbit period. - - Returns: - timedelta: the orbit period - """ - return timedelta(seconds=utils.compute_orbit_period(self.altitude)) - - -class CircularOrbit(OrbitBase): - """ - Orbit specification using Keplerian elements for elliptical motion -- circular motion case. - """ - - type: Literal["circular"] = Field( - "circular", description="Orbit type discriminator." - ) - inclination: float = Field(0, description="Inclination (degrees).", ge=0, lt=180) - right_ascension_ascending_node: float = Field( - 0, description="Right ascension of ascending node (degrees).", ge=0, lt=360 - ) - - def get_derived_orbit( - self, delta_mean_anomaly: float, delta_raan: float - ) -> CircularOrbit: - """ - Gets a derived orbit with perturbations to the mean anomaly and right - ascension of ascending node. - - Args: - delta_mean_anomaly (float): Delta mean anomaly (degrees). - delta_raan (float): Delta right ascension of ascending node (degrees). - - Returns: - CircularOrbit: the derived orbit - """ - true_anomaly = utils.mean_anomaly_to_true_anomaly( - np.mod(self.get_mean_anomaly() + delta_mean_anomaly, 360) - ) - raan = np.mod(self.right_ascension_ascending_node + delta_raan, 360) - return CircularOrbit( - altitude=self.altitude, - true_anomaly=true_anomaly, - epoch=self.epoch, - inclination=self.inclination, - right_ascension_ascending_node=raan, - ) - - def to_tle(self, lazy_load: bool = None) -> TwoLineElements: - """ - Converts this orbit to a two line elements representation. - - Args: - lazy_load (bool): True, if this tle should be lazy-loaded. - - Returns: - TwoLineElements: the two line elements orbit - """ - if lazy_load is None: - lazy_load = config.rc.orbit_tle_lazy_load - if lazy_load: - tle = self.__dict__.get("tle") - else: - tle = None - if tle is None: - tle = KeplerianOrbit( - altitude=self.altitude, - true_anomaly=self.true_anomaly, - epoch=self.epoch, - inclination=self.inclination, - right_ascension_ascending_node=self.right_ascension_ascending_node, - ).to_tle() - self.__dict__["tle"] = tle - return tle - - -class SunSynchronousOrbit(OrbitBase): - """ - Orbit defined by sun synchronous parameters. - """ - - type: Literal["sso"] = Field("sso", description="Orbit type discriminator.") - altitude: float = Field( - ..., - description="Mean altitude (meters).", - ge=0, - lt=12352000 - constants.EARTH_MEAN_RADIUS, - ) - equator_crossing_time: time = Field( - ..., description="Equator crossing time (local solar time)." - ) - equator_crossing_ascending: bool = Field( - True, - description="True, if the equator crossing time is ascending (south-to-north).", - ) - - def get_inclination(self) -> float: - """ - Gets the inclination (decimal degrees). - - Returns: - float: the inclination - """ - return np.degrees( - np.arccos(-np.power(self.get_semimajor_axis() / 12352000, 7 / 2)) - ) - - def get_right_ascension_ascending_node(self) -> float: - """ - Gets the right ascension of ascending node (decimal degrees). - - Returns: - float: the right ascension of ascending node - """ - # pylint: disable=E1101 - ect_day = timedelta( - hours=self.equator_crossing_time.hour, - minutes=self.equator_crossing_time.minute, - seconds=self.equator_crossing_time.second, - microseconds=self.equator_crossing_time.microsecond, - ) / timedelta(days=1) - epoch_time = constants.timescale.from_datetime(self.epoch) - sun = constants.de421["sun"] - earth = constants.de421["earth"] - right_ascension, _, _ = earth.at(epoch_time).observe(sun).radec() - # pylint: disable=W0212 - return ( - right_ascension._degrees - + 360 * ect_day - + 180 * self.equator_crossing_ascending - ) % 360 - - def get_derived_orbit( - self, delta_mean_anomaly: float, delta_raan: float - ) -> CircularOrbit: - """ - Gets a derived orbit with perturbations to the mean anomaly and right - ascension of ascending node. - - Args: - delta_mean_anomaly (float): Delta mean anomaly (degrees). - delta_raan (float): Delta right ascension of ascending node (degrees). - - Returns: - CircularOrbit: the derived orbit - """ - true_anomaly = utils.mean_anomaly_to_true_anomaly( - np.mod(self.get_mean_anomaly() + delta_mean_anomaly, 360) - ) - raan = np.mod(self.get_right_ascension_ascending_node() + delta_raan, 360) - # TODO the resulting orbit *is* still sun-synchronous; however, with a - # different equator-crossing time. Need to do the math to determine. - # For now, simply return a circular orbit with the correct parameters. - return CircularOrbit( - altitude=self.altitude, - true_anomaly=true_anomaly, - epoch=self.epoch, - inclination=self.get_inclination(), - right_ascension_ascending_node=raan, - ) - - def to_tle(self, lazy_load: bool = None) -> TwoLineElements: - """ - Converts this orbit to a two line elements representation. - - Args: - lazy_load (bool): True, if this tle should be lazy-loaded. - - Returns: - TwoLineElements: the two line elements orbit - """ - if lazy_load is None: - lazy_load = config.rc.orbit_tle_lazy_load - if lazy_load: - tle = self.__dict__.get("tle") - else: - tle = None - if tle is None: - tle = KeplerianOrbit( - altitude=self.altitude, - inclination=self.get_inclination(), - right_ascension_ascending_node=self.get_right_ascension_ascending_node(), - true_anomaly=self.true_anomaly, - epoch=self.epoch, - ).to_tle() - self.__dict__["tle"] = tle - return tle - - -class KeplerianOrbit(CircularOrbit): - """ - Orbit specification using Keplerian elements for elliptical motion. - """ - - type: Literal["keplerian"] = Field( - "keplerian", description="Orbit type discriminator." - ) - eccentricity: float = Field(0, description="Eccentricity.", ge=0) - perigee_argument: float = Field( - 0, description="Perigee argument (degrees).", ge=0, lt=360 - ) - - def get_mean_anomaly(self) -> float: - """ - Gets the mean anomaly (decimal degrees). - - Returns: - float: the mean anomaly - """ - return utils.true_anomaly_to_mean_anomaly(self.true_anomaly, self.eccentricity) - - def get_derived_orbit( - self, delta_mean_anomaly: float, delta_raan: float - ) -> KeplerianOrbit: - """ - Gets a derived orbit with perturbations to the mean anomaly and right - ascension of ascending node. - - Args: - delta_mean_anomaly (float): Delta mean anomaly (degrees). - delta_raan (float): Delta right ascension of ascending node (degrees). - - Returns: - KeplerianOrbit: the derived orbit - """ - true_anomaly = utils.mean_anomaly_to_true_anomaly( - np.mod(self.get_mean_anomaly() + delta_mean_anomaly, 360), - eccentricity=self.eccentricity, - ) - raan = np.mod(self.right_ascension_ascending_node + delta_raan, 360) - return KeplerianOrbit( - altitude=self.altitude, - true_anomaly=true_anomaly, - epoch=self.epoch, - inclination=self.inclination, - right_ascension_ascending_node=raan, - eccentricity=self.eccentricity, - perigee_argument=self.perigee_argument, - ) - - def to_tle(self, lazy_load: bool = None) -> TwoLineElements: - """ - Converts this orbit to a two line elements representation. - - Args: - lazy_load (bool): True, if this tle should be lazy-loaded. - - Returns: - TwoLineElements: the two line elements orbit - """ - if lazy_load is None: - lazy_load = config.rc.orbit_tle_lazy_load - if lazy_load: - tle = self.__dict__.get("tle") - else: - tle = None - if tle is None: - satrec = Satrec() - satrec.sgp4init( - WGS72, - "i", - 0, - (self.epoch - datetime(1949, 12, 31, tzinfo=timezone.utc)) - / timedelta(days=1), - 0, - 0.0, - 0.0, - self.eccentricity, - np.radians(self.perigee_argument), - np.radians(self.inclination), - np.radians(self.get_mean_anomaly()), - self.get_mean_motion() * 2 * np.pi / 1440, - np.radians(self.right_ascension_ascending_node), - ) - tle1, tle2 = exporter.export_tle(satrec) - tle = TwoLineElements(tle=[tle1.replace("\x00", "U"), tle2]) - self.__dict__["tle"] = tle - return tle - - -class MolniyaOrbit(OrbitBase): - """ - Orbit defined by Molniya parameters. The altitude parameter is considered the altitude at perigee. - """ - - type: Literal["molniya"] = Field("molniya", description="Orbit type discriminator.") - inclination: float = Field(0, description="Inclination (degrees).", ge=0, lt=180) - right_ascension_ascending_node: float = Field( - 0, description="Right ascension of ascending node (degrees).", ge=0, lt=360 - ) - orbital_period: float = Field(43080, description="Orbital period (seconds).", gt=0) - perigee_argument: float = Field( - 0, description="Perigee argument (degrees).", ge=0, lt=360 - ) - - def get_semimajor_axis(self) -> float: - gravitational_constant = 6.6743e-11 - earth_mass = 5.9736e24 - - return np.cbrt( - gravitational_constant - * earth_mass - * (self.orbital_period**2) - / (4 * (np.pi**2)) - ) - - def get_eccentricity(self) -> float: - """ - Gets the eccentricity (float between 0 and 1). - - Returns: - float: the eccentricity - """ - semimajor_axis = self.get_semimajor_axis() - return ( - semimajor_axis - (self.altitude + constants.EARTH_EQUATORIAL_RADIUS) - ) / semimajor_axis - - def get_mean_anomaly(self) -> float: - """ - Gets the mean anomaly (decimal degrees). - - Returns: - float: the mean anomaly - """ - return utils.true_anomaly_to_mean_anomaly( - self.true_anomaly, self.get_eccentricity() - ) - - def get_derived_orbit( - self, delta_mean_anomaly: float, delta_raan: float - ) -> MolniyaOrbit: - """ - Gets a derived orbit with perturbations to the mean anomaly and right - ascension of ascending node. - - Args: - delta_mean_anomaly (float): Delta mean anomaly (degrees). - delta_raan (float): Delta right ascension of ascending node (degrees). - - Returns: - Molniya Orbit: the derived orbit - """ - true_anomaly = utils.mean_anomaly_to_true_anomaly( - np.mod(self.get_mean_anomaly() + delta_mean_anomaly, 360), - eccentricity=self.get_eccentricity(), - ) - raan = np.mod(self.right_ascension_ascending_node + delta_raan, 360) - return MolniyaOrbit( - altitude=self.altitude, - true_anomaly=true_anomaly, - epoch=self.epoch, - inclination=self.inclination, - right_ascension_ascending_node=raan, - eccentricity=self.get_eccentricity(), - perigee_argument=self.perigee_argument, - ) - - def to_tle(self, lazy_load: bool = None) -> TwoLineElements: - """ - Converts this orbit to a two line elements representation. - - Args: - lazy_load (bool): True, if this tle should be lazy-loaded. - - Returns: - TwoLineElements: the two line elements orbit - """ - if lazy_load is None: - lazy_load = config.rc.orbit_tle_lazy_load - if lazy_load: - tle = self.__dict__.get("tle") - else: - tle = None - if tle is None: - satrec = Satrec() - satrec.sgp4init( - WGS72, - "i", - 0, - (self.epoch - datetime(1949, 12, 31, tzinfo=timezone.utc)) - / timedelta(days=1), - 0, - 0.0, - 0.0, - self.get_eccentricity(), - np.radians(self.perigee_argument), - np.radians(self.inclination), - np.radians(self.get_mean_anomaly()), - 2 * np.pi / (self.orbital_period / 60), - np.radians(self.right_ascension_ascending_node), - ) - tle1, tle2 = exporter.export_tle(satrec) - tle = TwoLineElements(tle=[tle1.replace("\x00", "U"), tle2]) - self.__dict__["tle"] = tle - return tle - - -class TundraOrbit(MolniyaOrbit): - """ - Orbit defined by Tundra parameters, inherits the Molniya class. - """ - - type: Literal["tundra"] = Field("tundra", description="Orbit type discriminator.") - inclination: float = Field(63.4, description="Inclination (degrees).", ge=0, lt=180) - orbital_period: float = Field(86400, description="Orbital period (seconds).", gt=0) - perigee_argument: float = Field( - 270, description="Perigee argument (degrees).", ge=0, lt=360 - ) - eccentricity: float = Field(0.2, description="Eccentricity.", ge=0) - - def get_eccentricity(self): - return self.eccentricity - - def get_derived_orbit( - self, delta_mean_anomaly: float, delta_raan: float - ) -> TundraOrbit: - """ - Gets a derived orbit with perturbations to the mean anomaly and right - ascension of ascending node. - - Args: - delta_mean_anomaly (float): Delta mean anomaly (degrees). - delta_raan (float): Delta right ascension of ascending node (degrees). - - Returns: - Tundra Orbit: the derived orbit - """ - true_anomaly = utils.mean_anomaly_to_true_anomaly( - np.mod(self.get_mean_anomaly() + delta_mean_anomaly, 360), - eccentricity=self.get_eccentricity(), - ) - raan = np.mod(self.right_ascension_ascending_node + delta_raan, 360) - return TundraOrbit( - altitude=self.altitude, - true_anomaly=true_anomaly, - epoch=self.epoch, - inclination=self.inclination, - right_ascension_ascending_node=raan, - eccentricity=self.eccentricity, - perigee_argument=self.perigee_argument, - ) diff --git a/src/tatc/schemas/orbit/__init__.py b/src/tatc/schemas/orbit/__init__.py new file mode 100644 index 0000000..4718ccb --- /dev/null +++ b/src/tatc/schemas/orbit/__init__.py @@ -0,0 +1,34 @@ +""" +Object schemas for orbits. + +@author: Paul T. Grogan +""" + +from .circular import CircularOrbit +from .geosynchronous import GeosynchronousOrbit +from .gp import GeneralPerturbationsOrbit +from .keplerian import KeplerianOrbit +from .molniya import MolniyaOrbit +from .sun_synchronous import SunSynchronousOrbit +from .tundra import TundraOrbit + +AllOrbits = ( + CircularOrbit + | GeneralPerturbationsOrbit + | GeosynchronousOrbit + | KeplerianOrbit + | MolniyaOrbit + | SunSynchronousOrbit + | TundraOrbit +) + +__all__ = [ + "AllOrbits", + "CircularOrbit", + "GeneralPerturbationsOrbit", + "GeosynchronousOrbit", + "KeplerianOrbit", + "MolniyaOrbit", + "SunSynchronousOrbit", + "TundraOrbit", +] diff --git a/src/tatc/schemas/orbit/base.py b/src/tatc/schemas/orbit/base.py new file mode 100644 index 0000000..c28d6fb --- /dev/null +++ b/src/tatc/schemas/orbit/base.py @@ -0,0 +1,210 @@ +""" +Object schemas for orbits. + +@author: Paul T. Grogan +""" + +from __future__ import annotations + +from datetime import datetime, timedelta, timezone + +from pydantic import BaseModel, Field + +from ... import config, constants, utils +from .gp import GeneralPerturbationsOrbit + + +class OrbitBase(BaseModel): + """ + Base class for orbits. + """ + + true_anomaly: float = Field( + default=0, description="True anomaly (degrees).", ge=0, lt=360 + ) + epoch: datetime = Field( + default=datetime(2020, 1, 1, tzinfo=timezone.utc), + description="Timestamp (epoch) of the initial orbital state.", + ) + + def get_true_anomaly(self) -> float: + """ + Gets the true anomaly (decimal degrees). + + Returns: + float: the true anomaly + """ + return self.true_anomaly + + def get_epoch(self) -> datetime: + """ + Gets the timestamp (epoch) of the initial orbital state. + + Returns: + datetime: the epoch + """ + return self.epoch + + def get_mean_anomaly(self) -> float: + """ + Gets the mean anomaly (decimal degrees). + + Returns: + float: the mean anomaly + """ + return utils.orbital.true_anomaly_to_mean_anomaly(self.true_anomaly) + + def get_semimajor_axis(self) -> float: + """ + Gets the semimajor axis (meters). Must be implemented by + subclasses, since OrbitBase has no universal way to derive it. + + Returns: + float: the semimajor axis + """ + raise NotImplementedError( + "get_semimajor_axis() must be implemented in subclasses." + ) + + def get_mean_altitude(self) -> float: + """ + Gets the mean altitude (meters) above the WGS 84 mean radius. + Derived from the semimajor axis by default; subclasses that store + altitude directly (e.g. circular orbits) may override this for + efficiency. + + Returns: + float: the mean altitude + """ + return self.get_semimajor_axis() - constants.EARTH_MEAN_RADIUS + + def get_mean_motion(self) -> float: + """ + Gets the mean motion (revolutions per day). Derived from the + semimajor axis by default. + + Returns: + float: the mean motion + """ + return utils.orbital.semimajor_axis_to_mean_motion(self.get_semimajor_axis()) + + def get_orbit_period(self) -> timedelta: + """ + Gets the approximate orbit period. Derived from the semimajor axis + by default. Subclasses for which the orbit period is instead the + defining (independent) quantity -- with semimajor axis derived + from it, rather than the other way around -- must override this + method (and cannot rely on this default, which would be + circular). + + Returns: + timedelta: the orbit period + """ + return timedelta( + seconds=utils.orbital.semimajor_axis_to_orbit_period( + self.get_semimajor_axis() + ) + ) + + def get_inclination(self) -> float: + """ + Gets the inclination (degrees). Must be implemented by subclasses, + since OrbitBase has no universal way to derive it. + + Returns: + float: the inclination + """ + raise NotImplementedError( + "get_inclination() must be implemented in subclasses." + ) + + def get_right_ascension_ascending_node(self) -> float: + """ + Gets the right ascension of ascending node (degrees). Must be + implemented by subclasses, since OrbitBase has no universal way to + derive it. + + Returns: + float: the right ascension of ascending node + """ + raise NotImplementedError( + "get_right_ascension_ascending_node() must be implemented in subclasses." + ) + + def get_eccentricity(self) -> float: + """ + Gets the eccentricity (float between 0 and 1). Must be + implemented by subclasses, since OrbitBase has no universal way to + derive it. + + Returns: + float: the eccentricity + """ + raise NotImplementedError( + "get_eccentricity() must be implemented in subclasses." + ) + + def get_perigee_argument(self) -> float: + """ + Gets the perigee argument (degrees). Must be implemented by + subclasses, since OrbitBase has no universal way to derive it. + + Returns: + float: the perigee argument + """ + raise NotImplementedError( + "get_perigee_argument() must be implemented in subclasses." + ) + + def to_gp_orbit(self, lazy_load: bool | None = None) -> GeneralPerturbationsOrbit: + """ + Converts this orbit to a general perturbations orbit representation. + Lazy-loads a previously-computed conversion if available, since + `_compute_gp_orbit()` can be expensive (e.g. requires SGP4 fitting). + Subclasses must implement `_compute_gp_orbit()` rather than + overriding this method directly. + + Args: + lazy_load (bool | None): True, if this gp orbit should be lazy-loaded. + + Returns: + GeneralPerturbationsOrbit: the general perturbations orbit + """ + if lazy_load is None: + lazy_load = config.get_rc().gp_orbit_lazy_load + if lazy_load: + gp_orbit = self.__dict__.get("gp_orbit") + else: + gp_orbit = None + if gp_orbit is None: + gp_orbit = self._compute_gp_orbit() + self.__dict__["gp_orbit"] = gp_orbit # type: ignore + return gp_orbit + + def _compute_gp_orbit(self) -> GeneralPerturbationsOrbit: + """ + Computes a general perturbations orbit representation of this + orbit, without caching, by constructing an intermediate + KeplerianOrbit from this orbit's getter methods. Subclasses for + which this generic Keplerian-element construction does not apply + (e.g. KeplerianOrbit itself, which would otherwise recurse + infinitely) must override this method. + + Returns: + GeneralPerturbationsOrbit: the general perturbations orbit + """ + # deferred import to avoid a circular import (keplerian.py imports + # OrbitBase from this module); the cycle is real but harmless since + # this import only runs after both modules have finished loading + # pylint: disable-next=import-outside-toplevel,cyclic-import + from .keplerian import KeplerianOrbit + + return KeplerianOrbit( + semimajor_axis=self.get_semimajor_axis(), + inclination=self.get_inclination(), + right_ascension_ascending_node=self.get_right_ascension_ascending_node(), + eccentricity=self.get_eccentricity(), + perigee_argument=self.get_perigee_argument(), + true_anomaly=self.get_true_anomaly(), + epoch=self.get_epoch(), + ).to_gp_orbit() diff --git a/src/tatc/schemas/orbit/base_circular.py b/src/tatc/schemas/orbit/base_circular.py new file mode 100644 index 0000000..7554869 --- /dev/null +++ b/src/tatc/schemas/orbit/base_circular.py @@ -0,0 +1,57 @@ +""" +Base object schemas for circular orbits. + +@author: Paul T. Grogan +""" + +from __future__ import annotations + +from pydantic import Field + +from ... import constants +from .base import OrbitBase + + +class CircularOrbitBase(OrbitBase): + """ + Base class for circular orbits. + """ + + mean_altitude: float = Field(..., description="Mean altitude (meters).", ge=0) + + def get_semimajor_axis(self) -> float: + """ + Gets the semimajor axis. + + Returns: + float: the semimajor axis (meters) + """ + return constants.EARTH_MEAN_RADIUS + self.mean_altitude + + def get_mean_altitude(self) -> float: + """ + Gets the mean altitude. + + Returns: + float: the mean altitude (meters) + """ + return self.mean_altitude + + def get_eccentricity(self) -> float: + """ + Gets the eccentricity, always 0 for a circular orbit. + + Returns: + float: the eccentricity + """ + return 0 + + def get_perigee_argument(self) -> float: + """ + Gets the perigee argument, always 0 for a circular orbit (there is + no perigee to reference). + + Returns: + float: the perigee argument + """ + return 0 diff --git a/src/tatc/schemas/orbit/base_molniya_tundra.py b/src/tatc/schemas/orbit/base_molniya_tundra.py new file mode 100644 index 0000000..b303de9 --- /dev/null +++ b/src/tatc/schemas/orbit/base_molniya_tundra.py @@ -0,0 +1,175 @@ +""" +Base object schemas for Molniya and Tundra orbits. + +@author: Paul T. Grogan +""" + +from __future__ import annotations + +from datetime import timedelta + +import numpy as np +from pydantic import Field, model_validator + +from ... import constants, utils +from .base import OrbitBase + + +class MolniyaTundraOrbitBase(OrbitBase): + """ + Base class for Molniya and Tundra orbits. + """ + + northern_coverage: bool = Field( + default=True, + description="True, if the orbit generates northern hemisphere coverage.", + ) + perigee_altitude: float = Field(..., description="Perigee altitude (meters).", ge=0) + right_ascension_ascending_node: float = Field( + default=0, + description="Right ascension of ascending node (degrees).", + ge=0, + lt=360, + ) + + def get_inclination(self) -> float: + """ + Gets the inclination (degrees) of the frozen orbit. + + Returns: + float: the inclination + """ + return constants.EARTH_J2_CRITICAL_INCLINATION + + def get_right_ascension_ascending_node(self) -> float: + """ + Gets the right ascension of ascending node. + + Returns: + float: the right ascension of ascending node (degrees) + """ + return self.right_ascension_ascending_node + + def get_perigee_argument(self) -> float: + """ + Gets the perigee argument (degrees) of the frozen orbit. + + Returns: + float: the perigee argument + """ + return 270 if self.northern_coverage else 90 + + def get_orbit_period(self) -> timedelta: + """ + Gets the orbit period (seconds). + + Returns: + float: the orbit period + """ + raise NotImplementedError( + "get_orbit_period() must be implemented in subclasses." + ) + + def _compute_j2_corrected_orbit_period(self, target_period_s: float) -> timedelta: + """ + Corrects a nominal repeat-ground-track target period (e.g. exactly + half or one full sidereal day, ignoring perturbations) to account + for Earth's J2 oblateness perturbation to the true rate at which + mean anomaly advances. Subclasses call this from get_orbit_period() + with their own target (half a sidereal day for Molniya, one + sidereal day for Tundra). + + The correction is computed in a single pass (not iteratively): + the target period first implies a "naive" semimajor axis and + eccentricity via simple two-body Kepler's third law, which are + then used to evaluate the J2 mean anomaly rate correction + (compute_j2_mean_motion_rate). Since that correction is itself + tiny (on the order of 1e-4 relative to the mean motion), using + the naive semimajor axis/eccentricity to evaluate it introduces + only a negligible (second-order, ~1e-8 relative) residual error, + rather than requiring a full iterative solve. + + The returned period, when passed through get_semimajor_axis()'s + existing (unmodified) two-body Kepler's third law formula, + yields a semimajor axis whose true (J2-corrected) mean anomaly + rate matches the target repeat-ground-track requirement. + + Args: + target_period_s (float): The nominal, uncorrected target orbit + period (seconds), ignoring J2 perturbation. + + Returns: + timedelta: The J2-corrected orbit period. + """ + naive_semimajor_axis = np.cbrt( + constants.EARTH_MU * target_period_s**2 / (4 * np.pi**2) + ) + naive_eccentricity = ( + 1 + - (constants.EARTH_MEAN_RADIUS + self.perigee_altitude) + / naive_semimajor_axis + ) + mean_motion_correction = utils.orbital.compute_j2_mean_motion_rate( + naive_semimajor_axis, self.get_inclination(), naive_eccentricity + ) + target_mean_motion = 360 / target_period_s + return timedelta(seconds=360 / (target_mean_motion - mean_motion_correction)) + + def get_semimajor_axis(self) -> float: + """ + Gets the semimajor axis (meters). + + Returns: + float: the semimajor axis + """ + return np.cbrt( + (constants.EARTH_MU * self.get_orbit_period().total_seconds() ** 2) + / (4 * np.pi**2) + ) + + def get_eccentricity(self) -> float: + """ + Gets the eccentricity (float between 0 and 1). + + Returns: + float: the eccentricity + """ + return ( + 1 + - (constants.EARTH_MEAN_RADIUS + self.perigee_altitude) + / self.get_semimajor_axis() + ) + + def get_mean_anomaly(self) -> float: + """ + Gets the mean anomaly (decimal degrees). + + Returns: + float: the mean anomaly + """ + return utils.orbital.true_anomaly_to_mean_anomaly( + self.true_anomaly, self.get_eccentricity() + ) + + @model_validator(mode="after") + def _validate_eccentricity(self) -> MolniyaTundraOrbitBase: + """ + Validates that perigee_altitude, combined with this orbit's fixed + orbit period, yields a physically valid elliptical eccentricity in + [0, 1). A perigee_altitude above the Kepler-derived ceiling + implied by the fixed period would otherwise silently produce a + negative eccentricity. Not implemented on the bare + MolniyaTundraOrbitBase class (get_orbit_period is abstract there), + so this is a no-op until a concrete subclass defines the period. + """ + try: + eccentricity = self.get_eccentricity() + except NotImplementedError: + return self + if not 0 <= eccentricity < 1: + raise ValueError( + f"perigee_altitude={self.perigee_altitude} is invalid for this " + f"orbit's fixed period: implies eccentricity={eccentricity}, " + "which is outside the valid elliptical range [0, 1)." + ) + return self diff --git a/src/tatc/schemas/orbit/circular.py b/src/tatc/schemas/orbit/circular.py new file mode 100644 index 0000000..37b6141 --- /dev/null +++ b/src/tatc/schemas/orbit/circular.py @@ -0,0 +1,78 @@ +""" +Object schemas for circular orbits. + +@author: Paul T. Grogan +""" + +from __future__ import annotations + +from typing import Literal + +import numpy as np +from pydantic import Field + +from ... import utils +from .base_circular import CircularOrbitBase + + +class CircularOrbit(CircularOrbitBase): + """ + Orbit specification using Keplerian elements for elliptical motion -- circular motion case. + """ + + type: Literal["circular"] = Field( + default="circular", description="Orbit type discriminator." + ) + inclination: float = Field( + default=0, description="Inclination (degrees).", ge=0, lt=180 + ) + right_ascension_ascending_node: float = Field( + default=0, + description="Right ascension of ascending node (degrees).", + ge=0, + lt=360, + ) + + def get_inclination(self) -> float: + """ + Gets the inclination. + + Returns: + float: the inclination (degrees) + """ + return self.inclination + + def get_right_ascension_ascending_node(self) -> float: + """ + Gets the right ascension of ascending node. + + Returns: + float: the right ascension of ascending node (degrees) + """ + return self.right_ascension_ascending_node + + def get_derived_orbit( + self, delta_mean_anomaly: float, delta_raan: float + ) -> CircularOrbit: + """ + Gets a derived orbit with perturbations to the mean anomaly and right + ascension of ascending node. + + Args: + delta_mean_anomaly (float): Delta mean anomaly (degrees). + delta_raan (float): Delta right ascension of ascending node (degrees). + + Returns: + CircularOrbit: the derived orbit + """ + true_anomaly = utils.orbital.mean_anomaly_to_true_anomaly( + np.mod(self.get_mean_anomaly() + delta_mean_anomaly, 360) + ) + raan = np.mod(self.right_ascension_ascending_node + delta_raan, 360) + return CircularOrbit( + mean_altitude=self.mean_altitude, + inclination=self.inclination, + right_ascension_ascending_node=raan, + true_anomaly=true_anomaly, + epoch=self.epoch, + ) diff --git a/src/tatc/schemas/orbit/geosynchronous.py b/src/tatc/schemas/orbit/geosynchronous.py new file mode 100644 index 0000000..eedbe7a --- /dev/null +++ b/src/tatc/schemas/orbit/geosynchronous.py @@ -0,0 +1,102 @@ +""" +Object schemas for geosynchronous orbits. + +@author: Paul T. Grogan +""" + +from __future__ import annotations + +from typing import Literal + +import numpy as np +from pydantic import Field + +from ... import constants, utils +from .base_circular import CircularOrbitBase + +# mean altitude (meters) yielding an orbit period of exactly one sidereal +# day, via Kepler's third law +_GEOSYNCHRONOUS_MEAN_ALTITUDE = ( + utils.orbital.mean_motion_to_semimajor_axis(360 / constants.EARTH_SIDEREAL_DAY_S) + - constants.EARTH_MEAN_RADIUS +) + + +class GeosynchronousOrbit(CircularOrbitBase): + """ + Orbit defined by geosynchronous parameters: a fixed longitude rather + than a right ascension of ascending node. + """ + + type: Literal["geosynchronous"] = Field( + default="geosynchronous", description="Orbit type discriminator." + ) + mean_altitude: float = Field( + default=_GEOSYNCHRONOUS_MEAN_ALTITUDE, + description="Mean altitude (meters).", + ge=0, + ) + inclination: float = Field( + default=0, description="Inclination (degrees).", ge=0, lt=180 + ) + longitude: float = Field( + ..., + description="Longitude (decimal degrees) of the sub-satellite point " + + "in the WGS 84 coordinate system.", + ge=-180, + le=180, + ) + + def get_inclination(self) -> float: + """ + Gets the inclination. + + Returns: + float: the inclination (degrees) + """ + return self.inclination + + def get_right_ascension_ascending_node(self) -> float: + """ + Gets the right ascension of ascending node, derived from this + orbit's fixed longitude and Earth's rotation angle (Greenwich + Apparent Sidereal Time) at epoch, since a geosynchronous satellite + remains above a fixed Earth-relative longitude rather than a + fixed inertial-frame RAAN. As with SunSynchronousOrbit's + equator-crossing-time convention, this is computed independently + of true_anomaly. + + Returns: + float: the right ascension of ascending node (degrees) + """ + epoch_time = constants.timescale.from_datetime(self.epoch) + earth_rotation_angle = epoch_time.gast * 15 # hours -> degrees + return (earth_rotation_angle + self.longitude) % 360 + + def get_derived_orbit( + self, delta_mean_anomaly: float, delta_raan: float + ) -> GeosynchronousOrbit: + """ + Gets a derived orbit with perturbations to the mean anomaly and + longitude (equivalent, for a geosynchronous orbit, to a right + ascension of ascending node perturbation, since both epoch and + Earth's rotation angle stay fixed). + + Args: + delta_mean_anomaly (float): Delta mean anomaly (degrees). + delta_raan (float): Delta right ascension of ascending node (degrees). + + Returns: + GeosynchronousOrbit: the derived orbit + """ + true_anomaly = utils.orbital.mean_anomaly_to_true_anomaly( + np.mod(self.get_mean_anomaly() + delta_mean_anomaly, 360) + ) + longitude = ((self.longitude + delta_raan + 180) % 360) - 180 + return GeosynchronousOrbit( + mean_altitude=self.mean_altitude, + inclination=self.inclination, + longitude=longitude, + true_anomaly=true_anomaly, + epoch=self.epoch, + ) diff --git a/src/tatc/schemas/orbit/gp.py b/src/tatc/schemas/orbit/gp.py new file mode 100644 index 0000000..0598394 --- /dev/null +++ b/src/tatc/schemas/orbit/gp.py @@ -0,0 +1,820 @@ +""" +Object schema for general perturbations orbits. + +@author: Paul T. Grogan +""" + +from __future__ import annotations + +import csv +import json +from datetime import datetime, timedelta +from typing import Literal, overload + +import numpy as np +import numpy.typing as npt +from pydantic import BaseModel, Field, model_validator +from skyfield.api import Time, wgs84 +from skyfield.positionlib import Geocentric +from skyfield.toposlib import GeographicPosition + +from ... import config, constants, utils +from ..surface import Point +from .gp_elements import GeneralPerturbationsElements + + +class GeneralPerturbationsOrbit(BaseModel): + """ + Orbit defined with general perturbations (GP) elements. + """ + + type: Literal["gp"] = Field(default="gp", description="Orbit type discriminator.") + elements: list[GeneralPerturbationsElements] = Field( + ..., description="General perturbations elements.", min_length=1 + ) + + @model_validator(mode="after") + def _sort_elements_by_epoch(self) -> GeneralPerturbationsOrbit: + """ + Sorts elements by epoch (ascending) after construction. + get_closest_element_index relies on np.searchsorted, which + silently returns incorrect results if its input is not sorted, so + this guarantees that precondition holds regardless of the order + elements were provided in. This only covers elements provided at + construction time; directly mutating self.elements in place + afterward (e.g. via .append()) bypasses this validator and can + reintroduce unsorted order. + """ + self.elements.sort(key=lambda el: el.epoch) + return self + + def get_semimajor_axis(self, index: int = 0) -> float: + """ + Gets the semimajor axis of the specified element. + + Args: + index (int): the index of the element + + Returns: + float: the semimajor axis (meters) + """ + return self.elements[index].get_semimajor_axis() + + def get_mean_altitude(self, index: int = 0) -> float: + """ + Gets the mean altitude of the specified element. + + Args: + index (int): the index of the element + + Returns: + float: the mean altitude (meters) + """ + return self.elements[index].get_mean_altitude() + + def get_inclination(self, index: int = 0) -> float: + """ + Gets the inclination of the specified element. + + Args: + index (int): the index of the element + + Returns: + float: the inclination (degrees) + """ + return self.elements[index].inclination + + def get_eccentricity(self, index: int = 0) -> float: + """ + Gets the eccentricity of the specified element. + + Args: + index (int): the index of the element + + Returns: + float: the eccentricity + """ + return self.elements[index].eccentricity + + def get_epoch(self, index: int = 0) -> datetime: + """ + Gets the epoch of the specified element. + + Args: + index (int): the index of the element + + Returns: + datetime: the epoch + """ + return self.elements[index].epoch + + def get_mean_motion(self, index: int = 0) -> float: + """ + Gets the mean motion of the specified element. + + Args: + index (int): the index of the element + + Returns: + float: the mean motion (degrees/second) + """ + return self.elements[index].mean_motion + + def get_mean_anomaly(self, index: int = 0) -> float: + """ + Gets the mean anomaly of the specified element. + + Args: + index (int): the index of the element + + Returns: + float: the mean anomaly (degrees) + """ + return self.elements[index].mean_anomaly + + def get_orbit_period(self, index: int = 0) -> timedelta: + """ + Gets the approximate orbit period of the specified element. + + Args: + index (int): the index of the element + + Returns: + timedelta: the orbit period + """ + return self.elements[index].get_orbit_period() + + def get_true_anomaly(self, index: int = 0) -> float: + """ + Gets the true anomaly of the specified element. + + Args: + index (int): the index of the element + + Returns: + float: the true anomaly (degrees) + """ + return self.elements[index].get_true_anomaly() + + def get_right_ascension_ascending_node(self, index: int = 0) -> float: + """ + Gets the right ascension of ascending node of the specified element. + + Args: + index (int): the index of the element + + Returns: + float: the right ascension of ascending node (degrees) + """ + return self.elements[index].ra_of_asc_node + + def get_perigee_argument(self, index: int = 0) -> float: + """ + Gets the argument of perigee of the specified element. + + Args: + index (int): the index of the element + + Returns: + float: the argument of perigee (degrees) + """ + return self.elements[index].arg_of_pericenter + + def get_catalog_number(self, index: int = 0) -> int: + """ + Gets the NORAD catalog number of the specified element. + + Args: + index (int): the index of the element + + Returns: + int: the NORAD catalog number + """ + return self.elements[index].norad_cat_id + + def get_bstar(self, index: int = 0) -> float: + """ + Gets the starred ballistic coefficient of the specified element. + + Args: + index (int): the index of the element + + Returns: + float: the starred ballistic coefficient + """ + return self.elements[index].bstar + + def get_mean_motion_dot(self, index: int = 0) -> float: + """ + Gets the first derivative of mean motion of the specified element. + + Args: + index (int): the index of the element + + Returns: + float: the first derivative of mean motion (degrees/second^2) + """ + return self.elements[index].mean_motion_dot + + def get_mean_motion_ddot(self, index: int = 0) -> float: + """ + Gets the second derivative of mean motion of the specified element. + + Args: + index (int): the index of the element + + Returns: + float: the second derivative of mean motion (degrees/second^3) + """ + return self.elements[index].mean_motion_ddot + + @classmethod + def from_tle(cls, tle_lines: list[str]) -> GeneralPerturbationsOrbit: + """ + Creates a GP orbit from two line element (TLE) lines. Multiple + TLEs (e.g. a history of element sets for one satellite) may be + concatenated into a single flat list of lines, two per element + set, to construct an orbit with multiple elements. + + Args: + tle_lines (list[str]): the two line element lines + + Returns: + GeneralPerturbationsOrbit: the GP orbit + """ + if len(tle_lines) % 2 != 0: + raise ValueError( + f"Expected an even number of TLE lines (two per element set), " + f"got {len(tle_lines)}." + ) + return GeneralPerturbationsOrbit( + elements=[ + GeneralPerturbationsElements.from_tle((tle_lines[i], tle_lines[i + 1])) + for i in range(0, len(tle_lines), 2) + ] + ) + + @classmethod + def from_omm_csv(cls, omm_csv: list[str]) -> GeneralPerturbationsOrbit: + """ + Creates a GP orbit from OMM CSV lines, using every data row + (unlike GeneralPerturbationsElements.from_omm_csv, which only + uses the first) to build one element per row. + + Args: + omm_csv (list[str]): The OMM CSV lines, including a header row. + + Returns: + GeneralPerturbationsOrbit: the GP orbit + """ + return GeneralPerturbationsOrbit( + elements=[ + GeneralPerturbationsElements.from_omm_dict(fields) + for fields in csv.DictReader(omm_csv) + ] + ) + + @classmethod + def from_omm_json(cls, omm_json: str) -> GeneralPerturbationsOrbit: + """ + Creates a GP orbit from an OMM JSON string, using every entry + (unlike GeneralPerturbationsElements.from_omm_json, which only + uses the first) to build one element per entry. + + Args: + omm_json (str): The OMM JSON string, encoding a list of OMM + records. + + Returns: + GeneralPerturbationsOrbit: the GP orbit + """ + return GeneralPerturbationsOrbit( + elements=[ + GeneralPerturbationsElements.from_omm_dict(fields) + for fields in json.loads(omm_json) + ] + ) + + def get_element_epochs(self) -> list[datetime]: + """ + Lazy-loads the epoch times for all elements in this orbit. The + cache is invalidated (and recomputed) if the number of elements + has changed since it was last computed (e.g. after appending or + removing an element), but not if an element is replaced in place + at the same list position with a different epoch -- such a + same-length in-place replacement is unusual/unsupported usage + that this lightweight invalidation check cannot detect without + recomputing on every call, which would defeat the purpose of + caching. + + Returns: + list[datetime]: the epoch times + """ + # lazy-load epochs, invalidating the cache if the element count changed + element_epochs = self.__dict__.get("element_epochs") + if element_epochs is None or len(element_epochs) != len(self.elements): + # extract the element epoch times + element_epochs = [el.epoch for el in self.elements] + self.__dict__["element_epochs"] = element_epochs # type: ignore + return element_epochs # type: ignore + + def get_derived_orbit( + self, delta_mean_anomaly: float, delta_raan: float + ) -> GeneralPerturbationsOrbit: + """ + Gets a derived orbit with perturbations to the mean anomaly and right + ascension of ascending node. + + Args: + delta_mean_anomaly (float): Delta mean anomaly (degrees). + delta_raan (float): Delta right ascension of ascending node (degrees). + + Returns: + GeneralPerturbationsOrbit: the derived orbit + """ + derived_elements = [] + for original_el in self.elements: + derived_el = original_el.model_copy(deep=True) + derived_el.mean_anomaly = np.mod( + derived_el.mean_anomaly + delta_mean_anomaly, 360 + ) + derived_el.ra_of_asc_node = np.mod( + derived_el.ra_of_asc_node + delta_raan, 360 + ) + derived_elements.append(derived_el) + return GeneralPerturbationsOrbit(elements=derived_elements) + + @overload + def get_closest_element_index(self, at_times: None) -> int: ... + + @overload + def get_closest_element_index(self, at_times: datetime) -> int: ... + + @overload + def get_closest_element_index(self, at_times: list[datetime]) -> list[int]: ... + + @overload + def get_closest_element_index( + self, at_times: npt.NDArray[np.datetime64] + ) -> list[int]: ... + + def get_closest_element_index( + self, at_times: datetime | list[datetime] | npt.NDArray[np.datetime64] | None + ) -> int | list[int]: + """ + Gets the closest element index to specified time(s), assuming + elements are sorted by epoch (guaranteed for any orbit built + through the constructor, since elements are sorted at + construction time; see _sort_elements_by_epoch). + + Args: + at_times (datetime | list[datetime] | npt.NDArray[np.datetime64] | None): + specified times, or None to always select the first element (index 0) + + Returns: + int | list[int]: closest element index or indices + """ + + if at_times is None: + return 0 + # lazy-load element epochs + element_epochs = utils.to_datetime64_ns(self.get_element_epochs()) + # handle scalar + if isinstance(at_times, datetime): + at_time = utils.to_datetime64_ns(at_times) + idx = np.searchsorted(element_epochs, at_time, side="left") + return ( + int(idx - 1) + if idx > 0 + and ( + idx == len(element_epochs) + or abs(at_time - element_epochs[idx - 1]) + < abs(at_time - element_epochs[idx]) + ) + else int(idx) + ) + # handle vector + at_time_array = utils.to_datetime64_ns(at_times) + indices = np.searchsorted(element_epochs, at_time_array, side="left") + return [ + ( + int(idx - 1) + if idx > 0 + and ( + idx == len(element_epochs) + or abs(at_time_array[i] - element_epochs[idx - 1]) + < abs(at_time_array[i] - element_epochs[idx]) + ) + else int(idx) + ) + for i, idx in enumerate(indices) + ] + + @overload + def get_closest_element(self, at_times: None) -> GeneralPerturbationsElements: ... + + @overload + def get_closest_element( + self, at_times: datetime + ) -> GeneralPerturbationsElements: ... + + @overload + def get_closest_element( + self, at_times: list[datetime] + ) -> list[GeneralPerturbationsElements]: ... + + @overload + def get_closest_element( + self, at_times: npt.NDArray[np.datetime64] + ) -> list[GeneralPerturbationsElements]: ... + + def get_closest_element( + self, at_times: datetime | list[datetime] | npt.NDArray[np.datetime64] | None + ) -> GeneralPerturbationsElements | list[GeneralPerturbationsElements]: + """ + Gets the closest element to specified time(s). + + Args: + at_times (datetime | list[datetime] | npt.NDArray[np.datetime64] | None): + specified times, or None to always select the first element (index 0) + + Returns: + GeneralPerturbationsElements | list[GeneralPerturbationsElements]: closest element or elements + """ + indices = self.get_closest_element_index(at_times) + if isinstance(indices, int): + return self.elements[indices] + return [self.elements[i] for i in indices] + + def partition_by_element_index( + self, start: datetime, end: datetime + ) -> tuple[list[datetime], list[int]]: + """ + Partitions the time range [start, end] into consecutive segments, + each assigned the index of whichever element is closest + throughout that segment. Uses get_element_epochs() (benefiting + from its lazy-load cache) rather than re-reading each element's + epoch directly. The midpoint between each pair of consecutive + elements' epochs is where the closest element switches from one + to the next (elements are guaranteed sorted by epoch by the + constructor's _sort_elements_by_epoch validator), so only + midpoints strictly inside (start, end) become segment boundaries. + Each segment's element index is determined by querying + get_closest_element_index at that segment's own midpoint, so it + is correct by construction rather than tracked separately. + + Args: + start (datetime): Start time. + end (datetime): End time. + + Returns: + tuple[list[datetime], list[int]]: segment boundary times + (length N+1, including start and end) and the element + index for each of the N segments between consecutive + boundaries (length N). + """ + epochs = self.get_element_epochs() + epoch_midpoints = [ + epochs[i] + (epochs[i + 1] - epochs[i]) / 2 for i in range(len(epochs) - 1) + ] + boundary_times = ( + [start] + [t for t in epoch_midpoints if start < t < end] + [end] + ) + segment_midpoints = [ + boundary_times[i] + (boundary_times[i + 1] - boundary_times[i]) / 2 + for i in range(len(boundary_times) - 1) + ] + return boundary_times, self.get_closest_element_index(segment_midpoints) + + def get_repeat_cycle( + self, + max_delta_position: float | None = None, + max_delta_velocity: float | None = None, + max_search_duration: timedelta | None = None, + lazy_load: bool | None = None, + consistency_threshold: timedelta | None = None, + ) -> timedelta | None: + """ + Compute the orbit's repeat cycle, if every element agrees on one. + Lazy-loads a previously-computed repeat cycle if available. + + Each element's own repeat cycle is computed independently (see + `GeneralPerturbationsElements.get_repeat_cycle` for how), since a + `GeneralPerturbationsOrbit` with multiple elements may span a + significant maneuver (altitude change, plane change, etc.) + partway through its history -- in which case there may be no + single repeat cycle that legitimately describes the whole orbit. + This method reports a repeat cycle for the orbit only if every + element has one and they all agree within `consistency_threshold` + of each other; otherwise it returns None. For a single-element + orbit (the common case) this is equivalent to just asking that + one element, since there is nothing to compare against. + + Args: + max_delta_position (float | None): the maximum difference in position (m) allowed for a repeat. + max_delta_velocity (float | None): the maximum difference in velocity (m/s) allowed for a repeat. + max_search_duration (timedelta | None): the maximum period of time to search for repeats. + lazy_load (bool | None): True, if the previously-computed repeat cycle should be loaded. + consistency_threshold (timedelta | None): the maximum allowed spread between elements' repeat cycles. + + Returns: + timedelta: the repeat cycle duration (if every element agrees on one) + """ + if lazy_load is None: + lazy_load = config.get_rc().repeat_cycle_lazy_load + if consistency_threshold is None: + consistency_threshold = timedelta( + seconds=config.get_rc().repeat_cycle_consistency_threshold_s + ) + + if lazy_load: + repeat_cycle = self.__dict__.get("repeat_cycle") + else: + repeat_cycle = None + if repeat_cycle is None: + repeat_cycle = timedelta(0) + min_cycle = max_cycle = None + for element in self.elements: + cycle = element.get_repeat_cycle( + max_delta_position, + max_delta_velocity, + max_search_duration, + lazy_load, + ) + if cycle is None: + repeat_cycle = timedelta(0) + break + min_cycle = cycle if min_cycle is None else min(min_cycle, cycle) + max_cycle = cycle if max_cycle is None else max(max_cycle, cycle) + if max_cycle - min_cycle > consistency_threshold: + repeat_cycle = timedelta(0) + break + # keep the most recent element's cycle as the orbit's + # representative value, once every element seen so far agrees + repeat_cycle = cycle + self.__dict__["repeat_cycle"] = repeat_cycle # type: ignore + if repeat_cycle is not None and repeat_cycle > timedelta(0): + return repeat_cycle + return None + + def get_orbit_track_at_time(self, t: Time) -> Geocentric: + """ + Gets the true (directly propagated) orbit track of this orbit at given + Skyfield time(s), in the inertial (GCRS) frame. + + Prefer this method over `get_orbit_track` when a Skyfield `Time` is + already in hand (e.g. while iterating a Skyfield search such as + `skyfield.searchlib.find_discrete`). Converting a `Time` to Python + `datetime` objects and back (as `get_orbit_track` must, since it only + accepts `datetime`) builds a new `Time` instance that starts without any + of the per-instant quantities Skyfield caches on a `Time` object (such as + nutation angles), forcing Skyfield to recompute them from scratch. + + If this orbit has multiple elements (e.g. built from a historical + archive of TLEs via `from_tle` with multiple pairs), each query time + is independently propagated using whichever element's epoch is + closest to it (see `get_closest_element_index`), not always the + first or most recent element. A vectorized `t` may therefore draw + from different elements for different entries. + + Args: + t (skyfield.timelib.Time): time(s) at which to compute position/velocity. + + Returns: + skyfield.positionlib.Geocentric: the orbit track position/velocity + """ + if len(self.elements) > 1: + # try to use multiple TLEs + nearest_indices = self.get_closest_element_index(t.utc_datetime()) + if isinstance(nearest_indices, int): + return self.elements[nearest_indices].to_skyfield().at(t) # type: ignore + nearest_indices = np.asarray(nearest_indices) + position_au = np.empty((3,) + t.shape) + velocity_au_per_d = np.empty((3,) + t.shape) + for element_index in np.unique(nearest_indices): + # propagate each distinct nearest TLE across all its assigned + # times in one vectorized call, rather than one time at a time + mask = nearest_indices == element_index + track = self.elements[element_index].to_skyfield().at(t[mask]) + position_au[:, mask] = track.position.au + velocity_au_per_d[:, mask] = track.velocity.au_per_d + return Geocentric(position_au, velocity_au_per_d, t) + # compute satellite positions directly at the given time(s) + return self.elements[0].to_skyfield().at(t) # type: ignore + + def get_orbit_track(self, times: datetime | list[datetime]) -> Geocentric: + """ + Gets the true (directly propagated) orbit track of this orbit using + Skyfield, in the inertial (GCRS) frame. + + Accepts plain Python `datetime`(s) for convenience; builds a Skyfield + `Time` and delegates to `get_orbit_track_at_time`, which documents + the multi-element selection behavior that also applies here. Prefer + calling `get_orbit_track_at_time` directly when a Skyfield `Time` is + already in hand, to avoid rebuilding one (see that method's + docstring for why that matters). + + Args: + times (datetime | list[datetime]): time(s) at which to compute position/velocity. + + Returns: + skyfield.positionlib.Geocentric: the orbit track position/velocity + """ + t = ( + constants.timescale.from_datetime(times) + if isinstance(times, datetime) + else constants.timescale.from_datetimes(times) + ) + return self.get_orbit_track_at_time(t) + + def get_geographic_position_at_time( + self, t: Time, try_repeat: bool | None = None + ) -> GeographicPosition: + """ + Gets the geodetic (WGS84) position of this orbit at given Skyfield + time(s), in an Earth-fixed frame. + + Unlike `get_orbit_track_at_time`, this method may substitute a detected + repeat cycle to improve long-term accuracy: rather than directly + propagating to a possibly-distant `t`, it propagates near this orbit's + epoch (reducing `t`'s offset from epoch modulo the repeat cycle) and + relies on the orbit's ground track repeating with that period. Because + the result is a `GeographicPosition` -- a location descriptor, not a + frozen inertial state vector -- it can be freely reused afterward (e.g. + `.at(some_time)` for a look angle or Sun angle at any moment) without + carrying forward any inaccuracy from the substitution. + + Args: + t (skyfield.timelib.Time): time(s) at which to compute geodetic position. + try_repeat (bool | None): True, if a repeat orbit should be used to improve long-term accuracy. + + Returns: + skyfield.toposlib.GeographicPosition: the geodetic position + """ + if try_repeat is None: + try_repeat = config.get_rc().repeat_cycle_for_orbit_track + if try_repeat and len(self.elements) == 1: + repeat_cycle = self.get_repeat_cycle() + if repeat_cycle is not None: + epoch = self.get_epoch() + offset = t.utc_datetime() - epoch + repeat_offset = np.multiply( + np.sign(offset / timedelta(1)), + np.mod(np.abs(offset / timedelta(1)), repeat_cycle / timedelta(1)), + ) * timedelta(1) + repeat_times = ( + constants.timescale.from_datetime(epoch + repeat_offset) + if t.shape == () + else constants.timescale.from_datetimes(epoch + repeat_offset) + ) + return wgs84.geographic_position_of( + self.elements[0].to_skyfield().at(repeat_times) + ) + # compute geodetic position from a true, directly propagated orbit track + return wgs84.geographic_position_of(self.get_orbit_track_at_time(t)) + + def get_geographic_position( + self, times: datetime | list[datetime], try_repeat: bool | None = None + ) -> GeographicPosition: + """ + Gets the geodetic (WGS84) position of this orbit at given time(s), in + an Earth-fixed frame. + + Args: + times (datetime | list[datetime]): time(s) at which to compute geodetic position. + try_repeat (bool | None): True, if a repeat orbit should be used to improve long-term accuracy. + + Returns: + skyfield.toposlib.GeographicPosition: the geodetic position + """ + t = ( + constants.timescale.from_datetime(times) + if isinstance(times, datetime) + else constants.timescale.from_datetimes(times) + ) + return self.get_geographic_position_at_time(t, try_repeat) + + def get_observation_events( + self, + point: Point, + start: datetime, + end: datetime, + min_elevation_angle: float, + try_repeat: bool | None = None, + ) -> tuple: + """ + Gets the observation events (rise/culminate/set) of this orbit + with respect to a ground point, between `start` and `end`, using + Skyfield's `find_events`. + + Tries three strategies, in order, and uses the first that applies: + + 1. If `try_repeat` and this orbit has a repeat cycle shorter than + `end - start` (see `get_repeat_cycle`, which validates that + *every* element agrees on the same cycle if there are several), + events are computed once over a single repeat cycle starting at + `start` and then copy-pasted forward for as many cycles as + needed to cover the full period, rather than propagating the + whole span directly. Because a validated repeat cycle means the + whole orbit -- not just one element -- repeats identically, + this does not need to consider which element is closest to + each time the way strategy 2 does; the single element closest + to `start` is enough to compute the one cycle's worth of events + that every subsequent cycle repeats. + 2. Otherwise, if this orbit has multiple elements, the requested + period is partitioned by whichever element's epoch is closest + at each point in time (`partition_by_element_index`), and + events are computed separately over each segment using its + assigned element. + 3. Otherwise (a single element with no usable repeat cycle), + events are computed directly over the whole period. + + Args: + point (Point): Target location to observe. + start (datetime): Start time of the observation period. + end (datetime): End time of the observation period. + min_elevation_angle (float): Minimum elevation angle (deg) to constrain observation. + try_repeat (bool | None): True, if a repeat orbit should be used to improve long-term accuracy. + + Returns: + tuple[skyfield.timelib.Time, numpy.ndarray]: event times and their rise (0) / culminate (1) / set (2) codes + """ + # load defaults + if try_repeat is None: + try_repeat = config.get_rc().repeat_cycle_for_observation_events + topos = wgs84.latlon(point.latitude, point.longitude, point.elevation) + t_0 = constants.timescale.from_datetime(start) + if try_repeat: + # try to compute repeat cycle events + repeat_cycle = self.get_repeat_cycle() + if repeat_cycle is not None and repeat_cycle < end - start: + repeat_t_1 = constants.timescale.from_datetime(start + repeat_cycle) + times, events = ( + self.get_closest_element(start) + .to_skyfield() + .find_events(topos, t_0, repeat_t_1, min_elevation_angle) + ) + number_cycles = int(np.ceil((end - start) / repeat_cycle)) + if len(times) == 0: + return (Time([], []), np.array([], dtype=int)) + times_py = np.concatenate( + [ + times.utc_datetime() + i * repeat_cycle + for i in range(number_cycles) + ] + ) + events_py = np.concatenate([events for _ in range(number_cycles)]) + if len(times_py) == 0: + return Time([], []), np.array([], dtype=int) + return ( + constants.timescale.from_datetimes(times_py[times_py <= end]), + events_py[times_py <= end], + ) + if len(self.elements) > 1: + # partition the period by whichever element is closest at each time + part_ts, element_is = self.partition_by_element_index(start, end) + events = [ + self.elements[element_is[i]] + .to_skyfield() + .find_events( + topos, + constants.timescale.from_datetime(part_ts[i]), + constants.timescale.from_datetime(part_ts[i + 1]), + min_elevation_angle, + ) + for i in range(len(part_ts) - 1) + ] + return ( + constants.timescale.from_datetimes( + [t.utc_datetime() for e in events for t in e[0]] + ), + np.array([v for e in events for v in e[1]]), + ) + # compute observation events directly, over the whole period + t_1 = constants.timescale.from_datetime(end) + return ( + self.elements[0] + .to_skyfield() + .find_events(topos, t_0, t_1, min_elevation_angle) + ) + + def to_gp_orbit(self, lazy_load: bool | None = None) -> GeneralPerturbationsOrbit: + """ + Converts this orbit to a general perturbations orbit representation. + Since this orbit already is one, this is always just `self` -- no + computation or caching is needed, unlike `OrbitBase.to_gp_orbit`, + which fits a `GeneralPerturbationsOrbit` from other orbit + representations (e.g. via SGP4 fitting) and so benefits from + lazy-loading a previously-computed result. + + Args: + lazy_load (bool | None): accepted, but has no effect, for + interface parity with `OrbitBase.to_gp_orbit`: callers + that only know an orbit as `AllOrbits` (e.g. + `Satellite.orbit`) can call `to_gp_orbit(lazy_load=...)` + uniformly without checking which concrete type it is. + + Returns: + GeneralPerturbationsOrbit: this orbit, unchanged + """ + return self diff --git a/src/tatc/schemas/orbit/gp_elements.py b/src/tatc/schemas/orbit/gp_elements.py new file mode 100644 index 0000000..814ef4c --- /dev/null +++ b/src/tatc/schemas/orbit/gp_elements.py @@ -0,0 +1,397 @@ +""" +Object schema for general perturbations orbital elements. + +@author: Paul T. Grogan +""" + +from __future__ import annotations + +import csv +import json +from datetime import datetime, timedelta, timezone + +import numpy as np +import numpy.typing as npt +from pydantic import BaseModel, Field +from sgp4 import exporter, omm +from sgp4.api import WGS72, Satrec +from sgp4.conveniences import sat_epoch_datetime +from skyfield.api import EarthSatellite, Time +from skyfield.framelib import itrs +from skyfield.searchlib import find_minima + +from ... import config, constants, utils + + +class GeneralPerturbationsElements(BaseModel): + """General perturbations orbital elements for a satellite.""" + + object_name: str | None = Field(default=None, description="Object name.") + epoch: datetime = Field(..., description="Epoch.") + mean_motion: float = Field(..., description="Mean motion (degrees/second).", gt=0) + eccentricity: float = Field(..., description="Eccentricity.", ge=0, le=1) + inclination: float = Field(..., description="Inclination (degrees).", ge=0, le=180) + ra_of_asc_node: float = Field( + ..., description="Right ascension of ascending node (degrees).", ge=0, lt=360 + ) + arg_of_pericenter: float = Field( + ..., description="Argument of pericenter (degrees).", ge=0, lt=360 + ) + mean_anomaly: float = Field( + ..., description="Mean anomaly (degrees).", ge=0, lt=360 + ) + norad_cat_id: int = Field(default=0, description="NORAD catalog identifier.", ge=0) + bstar: float = Field(default=0, description="Starred ballistic coefficient.") + mean_motion_dot: float = Field( + default=0, description="First derivative of mean motion (degrees/second^2)." + ) + mean_motion_ddot: float = Field( + default=0, description="Second derivative of mean motion (degrees/second^3)." + ) + classification: str = Field(default="U", description="Classification type.") + international_designator: str = Field( + default="00000A", description="International designator." + ) + ephemeris_type: int = Field(default=0, description="Ephemeris type.") + element_set_num: int = Field(default=0, description="Element set number.") + revolution_num: int = Field(default=0, description="Revolution number at epoch.") + + @classmethod + def from_satrec(cls, satrec: Satrec) -> GeneralPerturbationsElements: + """ + Creates a GP elements object from a Satrec object. + + Args: + satrec (Satrec): The Satrec object. + + Returns: + GeneralPerturbationsElements: the GP elements + """ + return GeneralPerturbationsElements( + epoch=sat_epoch_datetime(satrec), + mean_motion=np.degrees(satrec.no_kozai) / 60, + eccentricity=satrec.ecco, + inclination=np.degrees(satrec.inclo), + ra_of_asc_node=np.degrees(satrec.nodeo), + arg_of_pericenter=np.degrees(satrec.argpo), + mean_anomaly=np.degrees(satrec.mo), + norad_cat_id=satrec.satnum, + bstar=satrec.bstar, + mean_motion_dot=np.degrees(satrec.ndot) / 60**2, + mean_motion_ddot=np.degrees(satrec.nddot) / 60**3, + classification=satrec.classification, + international_designator=satrec.intldesg, + ephemeris_type=satrec.ephtype, + element_set_num=satrec.elnum, + revolution_num=satrec.revnum, + ) + + def to_satrec(self) -> Satrec: + """ + Converts this GP elements object to a Satrec object. + + Returns: + Satrec: the Satrec object + """ + satrec = Satrec() + satrec.classification = self.classification + satrec.intldesg = self.international_designator + satrec.ephtype = self.ephemeris_type + satrec.elnum = self.element_set_num + satrec.revnum = self.revolution_num + satrec.sgp4init( + WGS72, + "i", + self.norad_cat_id, + (self.epoch - datetime(1949, 12, 31, tzinfo=timezone.utc)) + / timedelta(days=1), + self.bstar, + np.radians(self.mean_motion_dot) * 60**2, + np.radians(self.mean_motion_ddot) * 60**3, + self.eccentricity, + np.radians(self.arg_of_pericenter), + np.radians(self.inclination), + np.radians(self.mean_anomaly), + np.radians(self.mean_motion) * 60, + np.radians(self.ra_of_asc_node), + ) + return satrec + + def get_orbit_period(self) -> timedelta: + """ + Gets the approximate orbit period. + + Returns: + timedelta: the orbit period + """ + return timedelta( + seconds=utils.orbital.mean_motion_to_orbit_period(self.mean_motion) + ) + + def get_semimajor_axis(self) -> float: + """ + Gets the semimajor axis. + + Returns: + float: the semimajor axis (meters) + """ + + return utils.orbital.mean_motion_to_semimajor_axis(self.mean_motion) + + def get_mean_altitude(self) -> float: + """ + Gets the mean altitude. + + Returns: + float: the mean altitude (meters) + """ + return self.get_semimajor_axis() - constants.EARTH_MEAN_RADIUS + + def get_true_anomaly(self) -> float: + """ + Gets the true anomaly. + + Returns: + float: the true anomaly (degrees) + """ + return utils.orbital.mean_anomaly_to_true_anomaly( + self.mean_anomaly, self.eccentricity + ) + + @classmethod + def from_tle(cls, tle_lines: tuple[str, str]) -> GeneralPerturbationsElements: + """ + Creates a GP elements object from two line element (TLE) lines. + + Args: + tle_lines (tuple[str, str]): The two TLE lines. + + Returns: + GeneralPerturbationsElements: the GP elements + """ + return GeneralPerturbationsElements.from_satrec( + Satrec.twoline2rv(tle_lines[0], tle_lines[1]) + ) + + def to_tle(self) -> tuple[str, str]: + """ + Converts this GP elements object to a two line element (TLE) representation. + + Returns: + tuple[str, str]: the two line elements + """ + return exporter.export_tle(self.to_satrec()) + + @classmethod + def from_omm_dict(cls, omm_dict: dict) -> GeneralPerturbationsElements: + """ + Creates a GP elements object from an OMM dictionary. + + Args: + omm_dict (dict): The OMM dictionary. + + Returns: + GeneralPerturbationsElements: the GP elements + """ + satrec = Satrec() + omm.initialize(satrec, omm_dict) + elements = GeneralPerturbationsElements.from_satrec(satrec) + # object_name has no equivalent on Satrec, so from_satrec can never + # recover it; restore it directly from the OMM dictionary + return elements.model_copy(update={"object_name": omm_dict.get("OBJECT_NAME")}) + + def to_omm_dict(self) -> dict: + """ + Converts this GP elements object to an OMM dictionary. + + Returns: + dict: the OMM dictionary + """ + return exporter.export_omm(self.to_satrec(), self.object_name) + + @classmethod + def from_omm_csv(cls, omm_csv: list[str]) -> GeneralPerturbationsElements: + """ + Creates a GP elements object from OMM CSV lines. Only the first + data row is used; all subsequent rows are ignored. + + Args: + omm_csv (list[str]): The OMM CSV lines, including a header row. + + Returns: + GeneralPerturbationsElements: the GP elements + """ + for fields in csv.DictReader(omm_csv): + return GeneralPerturbationsElements.from_omm_dict(fields) + raise ValueError("No OMM CSV lines found.") + + @classmethod + def from_omm_json(cls, omm_json: str) -> GeneralPerturbationsElements: + """ + Creates a GP elements object from an OMM JSON string. Only the + first entry in the JSON array is used; all subsequent entries + are ignored. + + Args: + omm_json (str): The OMM JSON string, encoding a list of OMM + records. + + Returns: + GeneralPerturbationsElements: the GP elements + """ + for fields in json.loads(omm_json): + return GeneralPerturbationsElements.from_omm_dict(fields) + raise ValueError("No OMM JSON lines found.") + + def to_skyfield(self) -> EarthSatellite: + """ + Converts this GP elements object to a Skyfield `EarthSatellite`, + which can be used to propagate this orbital state via SGP4. + + Returns: + skyfield.api.EarthSatellite: the Skyfield EarthSatellite + """ + return EarthSatellite.from_omm(constants.timescale, self.to_omm_dict()) + + def get_repeat_cycle( + self, + max_delta_position: float | None = None, + max_delta_velocity: float | None = None, + max_search_duration: timedelta | None = None, + lazy_load: bool | None = None, + ) -> timedelta | None: + """ + Compute this element's repeat cycle. Lazy-loads a previously-computed + repeat cycle if available. + + Uses the classical repeat-ground-track condition: the orbit + repeats once a whole number of orbits (paced by the nodal period) + fits a whole number of nodal days (paced by the node precession + rate) -- the same rational-commensurability principle behind + published repeat cycles like Landsat-8's 233 orbits/16 days. The + nodal period and nodal day are read directly from the underlying + SGP4 model's own secular rates (mdot, argpdot, nodedot), rather + than re-derived independently, since SGP4 initialization applies a + Kozai-to-Brouwer mean element correction that a from-scratch J2 + calculation (starting from the TLE's mean motion converted to a + semimajor axis via plain Kepler's third law) would otherwise miss + -- a small (~0.1%) but real discrepancy that is enough to make a + genuine multi-week repeat cycle miss its tolerance entirely. Each + analytically-predicted candidate is confirmed by directly + propagating the real orbit and checking that both position and + velocity match the initial state within tolerance; the first + (shortest) candidate that does so is the reported repeat cycle. + + This is scoped to a single element on purpose: a + `GeneralPerturbationsOrbit` with multiple elements may span a + significant maneuver (altitude change, plane change, etc.) + partway through its history, after which this element's repeat + cycle (if any) may no longer apply. See + `GeneralPerturbationsOrbit.get_repeat_cycle`, which checks every + element's own repeat cycle for mutual consistency before + reporting one for the whole orbit. + + Args: + max_delta_position (float | None): the maximum difference in position (m) allowed for a repeat. + max_delta_velocity (float | None): the maximum difference in velocity (m/s) allowed for a repeat. + max_search_duration (timedelta | None): the maximum period of time to search for repeats. + lazy_load (bool | None): True, if the previously-computed repeat cycle should be loaded. + + Returns: + timedelta: the repeat cycle duration (if it exists) + """ + # load defaults + if max_delta_position is None: + max_delta_position = config.get_rc().repeat_cycle_delta_position_m + if max_delta_velocity is None: + max_delta_velocity = config.get_rc().repeat_cycle_delta_velocity_m_per_s + if max_search_duration is None: + max_search_duration = timedelta( + days=config.get_rc().repeat_cycle_search_duration_days + ) + if lazy_load is None: + lazy_load = config.get_rc().repeat_cycle_lazy_load + + if lazy_load: + repeat_cycle = self.__dict__.get("repeat_cycle") + else: + repeat_cycle = None + if repeat_cycle is None: + epoch = self.epoch + satellite = self.to_skyfield() + # record the initial position and velocity in Earth-centered Earth-fixed frame + position_0, velocity_0 = satellite.at( + constants.timescale.from_datetime(epoch) + ).frame_xyz_and_velocity(itrs) + p_0_m = np.array(position_0.m) + v_0_m_per_s = np.array(velocity_0.m_per_s) + + # analytic repeat ground track candidates: how many nodal days + # (D) are needed for a whole number of orbits (C) to elapse. + # mdot/argpdot/nodedot (rad/minute) are SGP4's own secular + # rates for mean anomaly, argument of perigee, and RAAN. + model = satellite.model + nodal_period = 2 * np.pi / (model.mdot + model.argpdot) * 60 + earth_rotation_rate = 2 * np.pi / constants.EARTH_SIDEREAL_DAY_S * 60 + nodal_day = 2 * np.pi / (earth_rotation_rate - model.nodedot) * 60 + orbits_per_day = nodal_day / nodal_period + max_days = int(max_search_duration.total_seconds() / nodal_day) + days_range = np.arange(1, max_days + 1) + orbit_counts = np.round(orbits_per_day * days_range) + residual_orbits = np.abs(orbits_per_day * days_range - orbit_counts) + # approximate ground-track drift (m) implied by missing a whole + # orbit count by residual_orbits, at the equator; only a coarse + # heuristic to shortlist candidates worth verifying by direct + # propagation below, not itself a pass/fail criterion + ground_track_spacing = ( + 2 * np.pi * constants.EARTH_MEAN_RADIUS / orbits_per_day + ) + approx_drift = residual_orbits * ground_track_spacing + # generous margin: this estimate is J2-only, while the actual + # verification below propagates the real (e.g. SGP4) orbit + candidate_days = days_range[approx_drift < 3 * max_delta_position] + + def position_error(t: Time) -> npt.NDArray[np.float64]: + position, _ = satellite.at(t).frame_xyz_and_velocity(itrs) + return np.linalg.norm((np.array(position.m).T - p_0_m.T).T, axis=0) + + position_error.rough_period = nodal_period / 86400 # type: ignore + + def find_closest_approach( + center: datetime, + ) -> tuple[datetime, float, float] | None: + window = timedelta(seconds=nodal_period / 2) + times, errors = find_minima( + constants.timescale.from_datetime(center - window), + constants.timescale.from_datetime(center + window), + position_error, + ) + if len(times) == 0: + return None + t_min = times[np.argmin(errors)] + position, velocity = satellite.at(t_min).frame_xyz_and_velocity(itrs) + delta_position = float(np.linalg.norm(np.array(position.m) - p_0_m)) + delta_velocity = float( + np.linalg.norm(np.array(velocity.m_per_s) - v_0_m_per_s) + ) + return t_min.utc_datetime(), delta_position, delta_velocity + + # assign zero repeat cycle value to avoid recalculation, unless + # a candidate below is confirmed + repeat_cycle = timedelta(0) + for days in candidate_days: + predicted = epoch + timedelta(seconds=int(days) * nodal_day) + result = find_closest_approach(predicted) + if result is None: + continue + t_min, delta_position, delta_velocity = result + if ( + delta_position < max_delta_position + and delta_velocity < max_delta_velocity + ): + repeat_cycle = t_min - epoch + break + self.__dict__["repeat_cycle"] = repeat_cycle # type: ignore + if repeat_cycle is not None and repeat_cycle > timedelta(0): + return repeat_cycle + return None diff --git a/src/tatc/schemas/orbit/keplerian.py b/src/tatc/schemas/orbit/keplerian.py new file mode 100644 index 0000000..33ed12e --- /dev/null +++ b/src/tatc/schemas/orbit/keplerian.py @@ -0,0 +1,143 @@ +""" +Object schemas for Keplerian orbits. + +@author: Paul T. Grogan +""" + +from __future__ import annotations + +from typing import Literal + +import numpy as np +from pydantic import Field + +from ... import utils +from .base import OrbitBase +from .gp import GeneralPerturbationsOrbit +from .gp_elements import GeneralPerturbationsElements + + +class KeplerianOrbit(OrbitBase): + """ + Orbit specification using Keplerian elements for elliptical motion. + """ + + type: Literal["keplerian"] = Field( + default="keplerian", description="Orbit type discriminator." + ) + semimajor_axis: float = Field(..., description="Semimajor axis (meters).", gt=0) + inclination: float = Field(0, description="Inclination (degrees).", ge=0, lt=180) + right_ascension_ascending_node: float = Field( + 0, description="Right ascension of ascending node (degrees).", ge=0, lt=360 + ) + eccentricity: float = Field(0, description="Eccentricity.", ge=0, lt=1) + perigee_argument: float = Field( + 0, description="Perigee argument (degrees).", ge=0, lt=360 + ) + + def get_semimajor_axis(self) -> float: + """ + Gets the semimajor axis. + + Returns: + float: the semimajor axis (meters) + """ + return self.semimajor_axis + + def get_inclination(self) -> float: + """ + Gets the inclination. + + Returns: + float: the inclination (degrees) + """ + return self.inclination + + def get_right_ascension_ascending_node(self) -> float: + """ + Gets the right ascension of ascending node. + + Returns: + float: the right ascension of ascending node (degrees) + """ + return self.right_ascension_ascending_node + + def get_eccentricity(self) -> float: + """ + Gets the eccentricity. + + Returns: + float: the eccentricity + """ + return self.eccentricity + + def get_perigee_argument(self) -> float: + """ + Gets the perigee argument. + + Returns: + float: the perigee argument (degrees) + """ + return self.perigee_argument + + def get_mean_anomaly(self) -> float: + """ + Gets the mean anomaly (decimal degrees). + + Returns: + float: the mean anomaly + """ + return utils.orbital.true_anomaly_to_mean_anomaly( + self.true_anomaly, self.eccentricity + ) + + def get_derived_orbit( + self, delta_mean_anomaly: float, delta_raan: float + ) -> KeplerianOrbit: + """ + Gets a derived orbit with perturbations to the mean anomaly and right + ascension of ascending node. + + Args: + delta_mean_anomaly (float): Delta mean anomaly (degrees). + delta_raan (float): Delta right ascension of ascending node (degrees). + + Returns: + KeplerianOrbit: the derived orbit + """ + true_anomaly = utils.orbital.mean_anomaly_to_true_anomaly( + np.mod(self.get_mean_anomaly() + delta_mean_anomaly, 360), + eccentricity=self.eccentricity, + ) + raan = np.mod(self.right_ascension_ascending_node + delta_raan, 360) + return KeplerianOrbit( + semimajor_axis=self.semimajor_axis, + true_anomaly=true_anomaly, + epoch=self.epoch, + inclination=self.inclination, + right_ascension_ascending_node=raan, + eccentricity=self.eccentricity, + perigee_argument=self.perigee_argument, + ) + + def _compute_gp_orbit(self) -> GeneralPerturbationsOrbit: + """ + Computes a general perturbations orbit representation of this + orbit. + + Returns: + GeneralPerturbationsOrbit: the general perturbations orbit + """ + return GeneralPerturbationsOrbit( + elements=[ + GeneralPerturbationsElements( + epoch=self.get_epoch(), + mean_motion=self.get_mean_motion(), + eccentricity=self.get_eccentricity(), + inclination=self.get_inclination(), + ra_of_asc_node=self.get_right_ascension_ascending_node(), + arg_of_pericenter=self.get_perigee_argument(), + mean_anomaly=self.get_mean_anomaly(), + ) + ] + ) diff --git a/src/tatc/schemas/orbit/molniya.py b/src/tatc/schemas/orbit/molniya.py new file mode 100644 index 0000000..38a0481 --- /dev/null +++ b/src/tatc/schemas/orbit/molniya.py @@ -0,0 +1,66 @@ +""" +Object schemas for Molniya orbits. + +@author: Paul T. Grogan +""" + +from __future__ import annotations + +from datetime import timedelta +from typing import Literal + +import numpy as np +from pydantic import Field + +from ... import constants, utils +from .base_molniya_tundra import MolniyaTundraOrbitBase + + +class MolniyaOrbit(MolniyaTundraOrbitBase): + """ + Orbit defined by Molniya parameters. + """ + + type: Literal["molniya"] = Field( + default="molniya", description="Orbit type discriminator." + ) + + def get_orbit_period(self) -> timedelta: + """ + Gets the orbit period, targeting half a sidereal day (so the + ground track repeats twice daily) and corrected for Earth's J2 + oblateness perturbation to the true rate of mean anomaly advance. + + Returns: + timedelta: the orbit period + """ + return self._compute_j2_corrected_orbit_period( + constants.EARTH_SIDEREAL_DAY_S / 2 + ) + + def get_derived_orbit( + self, delta_mean_anomaly: float, delta_raan: float + ) -> MolniyaOrbit: + """ + Gets a derived orbit with perturbations to the mean anomaly and right + ascension of ascending node. + + Args: + delta_mean_anomaly (float): Delta mean anomaly (degrees). + delta_raan (float): Delta right ascension of ascending node (degrees). + + Returns: + Molniya Orbit: the derived orbit + """ + true_anomaly = utils.orbital.mean_anomaly_to_true_anomaly( + np.mod(self.get_mean_anomaly() + delta_mean_anomaly, 360), + eccentricity=self.get_eccentricity(), + ) + raan = np.mod(self.right_ascension_ascending_node + delta_raan, 360) + return MolniyaOrbit( + true_anomaly=true_anomaly, + epoch=self.epoch, + perigee_altitude=self.perigee_altitude, + right_ascension_ascending_node=raan, + northern_coverage=self.northern_coverage, + ) diff --git a/src/tatc/schemas/orbit/sun_synchronous.py b/src/tatc/schemas/orbit/sun_synchronous.py new file mode 100644 index 0000000..d25902e --- /dev/null +++ b/src/tatc/schemas/orbit/sun_synchronous.py @@ -0,0 +1,102 @@ +""" +Object schemas for sunsynchronous orbits. + +@author: Paul T. Grogan +""" + +from __future__ import annotations + +from datetime import date, datetime, time, timedelta +from typing import Literal + +import numpy as np +from pydantic import Field + +from ... import constants, utils +from .base_circular import CircularOrbitBase + + +class SunSynchronousOrbit(CircularOrbitBase): + """ + Orbit defined by sun synchronous parameters. + """ + + type: Literal["sso"] = Field(default="sso", description="Orbit type discriminator.") + mean_altitude: float = Field( + ..., + description="Mean altitude (meters).", + ge=0, + lt=12352000 - constants.EARTH_MEAN_RADIUS, + ) + equator_crossing_time: time = Field( + ..., description="Equator crossing time (local solar time)." + ) + equator_crossing_ascending: bool = Field( + default=True, + description="True, if the equator crossing time is ascending (south-to-north).", + ) + + def get_inclination(self) -> float: + """ + Gets the inclination (decimal degrees). + + Returns: + float: the inclination + """ + return np.degrees( + np.arccos(-np.power(self.get_semimajor_axis() / 12352000, 7 / 2)) + ) + + def get_right_ascension_ascending_node(self) -> float: + """ + Gets the right ascension of ascending node (decimal degrees). + + Returns: + float: the right ascension of ascending node + """ + ect_day = timedelta( + hours=self.equator_crossing_time.hour, + minutes=self.equator_crossing_time.minute, + seconds=self.equator_crossing_time.second, + microseconds=self.equator_crossing_time.microsecond, + ) / timedelta(days=1) + epoch_time = constants.timescale.from_datetime(self.epoch) + sun = constants.de421["sun"] + earth = constants.de421["earth"] + right_ascension, _, _ = earth.at(epoch_time).observe(sun).radec() # type: ignore + # pylint: disable=W0212 + return ( + right_ascension._degrees + + 360 * ect_day + + 180 * self.equator_crossing_ascending + ) % 360 + + def get_derived_orbit( + self, delta_mean_anomaly: float, delta_raan: float + ) -> SunSynchronousOrbit: + """ + Gets a derived orbit with perturbations to the mean anomaly and right + ascension of ascending node. + + Args: + delta_mean_anomaly (float): Delta mean anomaly (degrees). + delta_raan (float): Delta right ascension of ascending node (degrees). + + Returns: + SunSynchronousOrbit: the derived orbit + """ + true_anomaly = utils.orbital.mean_anomaly_to_true_anomaly( + np.mod(self.get_mean_anomaly() + delta_mean_anomaly, 360) + ) + # every 15 degrees of raan shift ect by 1 hour + equator_crossing_time = ( + datetime.combine(date(2000, 1, 1), self.equator_crossing_time) + + timedelta(hours=delta_raan / 15) + ).time() + return SunSynchronousOrbit( + mean_altitude=self.mean_altitude, + equator_crossing_time=equator_crossing_time, + equator_crossing_ascending=self.equator_crossing_ascending, + true_anomaly=true_anomaly, + epoch=self.epoch, + ) diff --git a/src/tatc/schemas/orbit/tundra.py b/src/tatc/schemas/orbit/tundra.py new file mode 100644 index 0000000..6608a28 --- /dev/null +++ b/src/tatc/schemas/orbit/tundra.py @@ -0,0 +1,64 @@ +""" +Object schemas for satellite orbits. + +@author: Paul T. Grogan +""" + +from __future__ import annotations + +from datetime import timedelta +from typing import Literal + +import numpy as np +from pydantic import Field + +from ... import constants, utils +from .base_molniya_tundra import MolniyaTundraOrbitBase + + +class TundraOrbit(MolniyaTundraOrbitBase): + """ + Orbit defined by Tundra parameters. + """ + + type: Literal["tundra"] = Field( + default="tundra", description="Orbit type discriminator." + ) + + def get_orbit_period(self) -> timedelta: + """ + Gets the orbit period, targeting one sidereal day (so the ground + track repeats once daily) and corrected for Earth's J2 oblateness + perturbation to the true rate of mean anomaly advance. + + Returns: + timedelta: the orbit period + """ + return self._compute_j2_corrected_orbit_period(constants.EARTH_SIDEREAL_DAY_S) + + def get_derived_orbit( + self, delta_mean_anomaly: float, delta_raan: float + ) -> TundraOrbit: + """ + Gets a derived orbit with perturbations to the mean anomaly and right + ascension of ascending node. + + Args: + delta_mean_anomaly (float): Delta mean anomaly (degrees). + delta_raan (float): Delta right ascension of ascending node (degrees). + + Returns: + Tundra Orbit: the derived orbit + """ + true_anomaly = utils.orbital.mean_anomaly_to_true_anomaly( + np.mod(self.get_mean_anomaly() + delta_mean_anomaly, 360), + eccentricity=self.get_eccentricity(), + ) + raan = np.mod(self.right_ascension_ascending_node + delta_raan, 360) + return TundraOrbit( + true_anomaly=true_anomaly, + epoch=self.epoch, + perigee_altitude=self.perigee_altitude, + right_ascension_ascending_node=raan, + northern_coverage=self.northern_coverage, + ) diff --git a/src/tatc/schemas/point.py b/src/tatc/schemas/point.py deleted file mode 100644 index 6e87b8c..0000000 --- a/src/tatc/schemas/point.py +++ /dev/null @@ -1,74 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Object schemas for sampling points. - -@author: Paul T. Grogan -""" - -from datetime import timedelta - -from pydantic import BaseModel, Field, NonNegativeInt - - -class Point(BaseModel): - """ - Geodetic point in the WGS 84 coordinate system. - """ - - id: NonNegativeInt = Field(..., description="Unique point identifier.") - latitude: float = Field( - ..., - description="Latitude (decimal degrees) in the WGS 84 coordinate system.", - ge=-90, - le=90, - examples=[40.74259], - ) - longitude: float = Field( - ..., - description="Longitude (decimal degrees) in the WGS 84 coordinate system.", - ge=-180, - le=180, - examples=[-74.02686], - ) - elevation: float = Field( - 0, - description="Elevation (meters) above datum in the WGS 84 coordinate system.", - ) - - -class GroundStation(BaseModel): - """ - Ground station in the WGS 84 coordinate system. - """ - - name: str = Field(..., description="Ground station name", examples=["station 1"]) - latitude: float = Field( - ..., - description="Latitude (decimal degrees) in the WGS 84 coordinate system.", - ge=-90, - le=90, - examples=[40.74259], - ) - longitude: float = Field( - ..., - description="Longitude (decimal degrees) in the WGS 84 coordinate system.", - ge=-180, - le=180, - examples=[-74.02686], - ) - elevation: float = Field( - 0, - description="Elevation (meters) above datum in the WGS 84 coordinate system.", - ) - min_elevation_angle: float = Field( - 0, - description="The minimum elevation angle (decimal degrees) required " - + "for satellite communication.", - ge=0, - le=90, - ) - min_access_time: timedelta = Field( - timedelta(0), - description="Minimum access (integration) time required for satellite communication.", - examples=[timedelta(seconds=10)], - ) diff --git a/src/tatc/schemas/satellite.py b/src/tatc/schemas/satellite.py deleted file mode 100644 index 66e8146..0000000 --- a/src/tatc/schemas/satellite.py +++ /dev/null @@ -1,482 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Object schemas for satellites. - -@author: Paul T. Grogan -""" -from __future__ import annotations - -import copy -from datetime import timedelta -from enum import Enum -import math -from typing import List, Union - -import numpy as np -from pydantic import BaseModel, Field, model_validator -from typing_extensions import Literal - -from tatc.utils import ( - zero_pad, - swath_width_to_field_of_regard, - compute_min_elevation_angle, -) -from ..constants import EARTH_MEAN_RADIUS -from .instrument import Instrument -from .orbit import TwoLineElements, CircularOrbit, SunSynchronousOrbit, KeplerianOrbit - - -class SpaceSystem(BaseModel): - """ - Base class for space systems. - """ - - name: str = Field( - ..., - description="Space system name.", - examples=["International Space Station"], - ) - orbit: Union[ - TwoLineElements, CircularOrbit, SunSynchronousOrbit, KeplerianOrbit - ] = Field(..., description="Orbit specification.") - instruments: List[Instrument] = Field( - [Instrument()], min_length=1, description="List of assigned instruments." - ) - - -class Satellite(SpaceSystem): - """ - Single satellite. - """ - - type: Literal["satellite"] = Field( - "satellite", description="Space system type discriminator." - ) - - def generate_members(self) -> List[Satellite]: - """ - Generate space system member satellites (returns a list containing this satellite). - - Returns: - List[Satellite]: the member satellites - """ - return [self] - - -class TrainConstellation(Satellite): - """ - A constellation that arranges member satellites in sequence. - """ - - type: Literal["train"] = Field( - "train", description="Space system type discriminator." - ) - orbit: Union[ - TwoLineElements, SunSynchronousOrbit, CircularOrbit, KeplerianOrbit - ] = Field(..., description="Lead orbit for this constellation.") - number_satellites: int = Field( - 1, description="The count of the number of satellites.", ge=1 - ) - interval: timedelta = Field( - ..., - description="The local time interval between satellites in a train constellation.", - ) - repeat_ground_track: bool = Field( - True, - description="True, if the train satellites should repeat the same ground track.", - ) - - def get_delta_mean_anomaly(self) -> float: - """ - Gets the difference in mean anomaly (decimal degrees) for adjacent - member satellites. - - Returns: - float: the difference in mean anomaly - """ - # pylint: disable=E1101 - return -360 * self.orbit.get_mean_motion() * (self.interval / timedelta(days=1)) - - def get_delta_raan(self) -> float: - """ - Gets the difference in right ascension of ascending node (decimal - degrees) for adjacent member satellites. - - Returns: - float: the difference in right ascension of ascending node - """ - if self.repeat_ground_track: - return 360 * (self.interval / timedelta(days=1)) - return 0 - - def generate_members(self) -> List[Satellite]: - """ - Generate space system member satellites. - - Returns: - List[Satellite]: the member satellites - """ - # pylint: disable=E1101 - return [ - Satellite( - name=zero_pad(self.name, self.number_satellites, i + 1), - orbit=self.orbit.get_derived_orbit( - i * self.get_delta_mean_anomaly(), i * self.get_delta_raan() - ), - instruments=copy.deepcopy(self.instruments), - ) - for i in range(self.number_satellites) - ] - - -class WalkerConfiguration(str, Enum): - """ - Enumeration of different Walker constellation configurations. - """ - - DELTA = "delta" - STAR = "star" - - -class WalkerConstellation(Satellite): - """ - A constellation that arranges member satellites following the Walker pattern. - """ - - type: Literal["walker"] = Field( - "walker", description="Space system type discriminator." - ) - configuration: WalkerConfiguration = Field( - WalkerConfiguration.DELTA, description="Walker configuration." - ) - orbit: Union[ - TwoLineElements, SunSynchronousOrbit, CircularOrbit, KeplerianOrbit - ] = Field(..., description="Lead orbit for this constellation.") - number_satellites: int = Field( - 1, description="Number of satellites in the constellation.", ge=1 - ) - number_planes: int = Field( - 1, - description="The number of equally-spaced planes in a Walker Delta " - + "constellation. Ranges from 1 to (number of satellites).", - ge=1, - ) - relative_spacing: int = Field( - 0, - description="Relative spacing of satellites between plans for a Walker Delta " - + "constellation. Ranges from 0 for equal true anomaly to " - + "(number of planes) - 1. For example, `relative_spacing=1` " - + "means the true anomaly is shifted by `360/number_satellites` " - + "between adjacent planes.", - ge=0, - ) - - @model_validator(mode="after") - def number_planes_le_number_satellites(self) -> "WalkerConstellation": - """ - Validates the number of planes given the number of satellites. - """ - if ( - self.number_planes is not None - and self.number_satellites is not None - and self.number_planes > self.number_satellites - ): - raise ValueError("number planes exceeds number satellites") - return self - - @model_validator(mode="after") - def relative_spacing_lt_number_planes(self) -> "WalkerConstellation": - """ - Validates the relative spacing given the number of planes. - """ - if ( - self.relative_spacing is not None - and self.number_planes is not None - and self.relative_spacing >= self.number_planes - ): - raise ValueError("relative spacing exceeds number planes - 1") - return self - - def get_satellites_per_plane(self) -> int: - """ - Gets the (max) number of satellites per plane. - - Returns: - int: number of satellites per plane - """ - return math.ceil(self.number_satellites / self.number_planes) - - def get_delta_mean_anomaly_within_planes(self) -> float: - """ - Gets the difference in mean anomaly (decimal degrees) for adjacent - member satellites within a single plane. - - Returns: - float: difference in mean anomaly - """ - return 360 / self.get_satellites_per_plane() - - def get_delta_mean_anomaly_between_planes(self) -> float: - """ - Gets the difference in mean anomaly (decimal degrees) for adjacent - member satellites between adjacent planes. - - Returns: - float: difference in mean anomaly - """ - return 360 * self.relative_spacing / self.number_satellites - - def get_delta_raan_between_planes(self) -> float: - """ - Gets the difference in right ascension of ascending node (decimal - degrees) for adjacent planes of member satellites. - - Returns: - float: difference in right ascension of ascending node - """ - if self.configuration == WalkerConfiguration.DELTA: - return 360 / self.number_planes - return 180 / self.number_planes - - def generate_members(self) -> List[Satellite]: - """ - Generate space system member satellites. - - Returns: - List[Satellite]: the member satellites - """ - # pylint: disable=E1101 - return [ - Satellite( - name=zero_pad(self.name, self.number_satellites, i + 1), - orbit=self.orbit.get_derived_orbit( - np.mod(i, self.get_satellites_per_plane()) - * self.get_delta_mean_anomaly_within_planes() - + (i // self.get_satellites_per_plane()) - * self.get_delta_mean_anomaly_between_planes(), - (i // self.get_satellites_per_plane()) - * self.get_delta_raan_between_planes(), - ), - instruments=copy.deepcopy(self.instruments), - ) - for i in range(self.number_satellites) - ] - - -class MOGConstellation(Satellite): - """ - A constellation that arranges member satellites following the mutual orbiting group pattern. - - Based on Stephen Leroy, Riley Fitzgerald, Kerri Cahoy, James Abel, and James Clark (2020). - "Orbital Maintenance of a Constellation of CubeSats for Internal Gravity Wave Tomography," - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, - pp. 307-317. doi: 10.1109/JSTARS.2019.2961084 - """ - - type: Literal["mog"] = Field("mog", description="Space system type discriminator.") - orbit: CircularOrbit = Field( - ..., description="Reference circular orbit for this constellation." - ) - parallel_axis: float = Field( - ..., - description="Mutual orbit axis length (m) parallel to velocity vector.", - gt=0, - ) - transverse_axis: float = Field( - ..., - description="Mutual orbit axis length (m) transverse to velocity vector.", - gt=0, - ) - clockwise: bool = Field(True, description="True, if the mutual orbit is clockwise.") - number_satellites: int = Field( - 2, description="Number of equally-spaced mutually orbiting satellites.", gt=0 - ) - - def generate_members(self) -> List[Satellite]: - """ - Generate space system member satellites. - Returns: - List[Satellite]: the member satellites - """ - - orbits = [] - - # semimajor axis (m) - # pylint: disable=E1101 - a = self.orbit.altitude + EARTH_MEAN_RADIUS - - # angle of separation (radians) of angular momentum vectors for reference and mutual orbiter - delta = self.transverse_axis / (2 * a) - - # eccentricity of the mutual orbit - e = self.parallel_axis / (4 * a) - - # direction of the mutual orbit (1: clockwise, -1: counter-clockwise) - s = 1 if self.clockwise else -1 - - # inclination (radians) of reference orbit - # pylint: disable=E1101 - i_0 = np.radians(self.orbit.inclination) - - # right ascension of ascending node (radians) of reference orbit - # pylint: disable=E1101 - omega_0 = np.radians(self.orbit.right_ascension_ascending_node) - - # direction of angular momentum for reference orbit [Eq. (21) in Leroy et al. (2020)] - l_0 = np.cos(i_0) * np.array([0, 0, 1]) - np.sin(i_0) * np.array([0, 1, 0]) - - # angle describing position of the mutual orbiter w.r.t. reference orbiter at ascending node - for theta in np.linspace(0, 2 * np.pi, self.number_satellites, endpoint=False): - - # direction of angular momentum of mutual orbit [Eq. (22) in Leroy et al. (2020)] - l = ( - np.cos(delta) * l_0 - + np.sin(delta) * np.cos(theta) * np.cross(l_0, np.array([1, 0, 0])) - + np.sin(delta) * np.sin(theta) * np.array([1, 0, 0]) - ) - - # inclination (radians) of mutual orbiting satellite [Eq. (23) in Leroy et al. (2020)] - i = np.arccos(np.dot(l, np.array([0, 0, 1]))) - - # raan (radians) of mutual orbiting satellite [Eq. (24) in Leroy et al. (2020)] - omega = omega_0 + np.arctan2( - np.dot(l, np.array([1, 0, 0])), np.dot(-l, np.array([0, 1, 0])) - ) - - # direction of mutual orbit ascending node w.r.t. Earth's center of mass [Eq. (25) in Leroy et al. (2020)] - p_node = np.cos(omega - omega_0) * np.array([1, 0, 0]) + np.sin( - omega - omega_0 - ) * np.array([0, 1, 0]) - - # direction of mutual and reference orbit intersection ([Eq. (26) in Leroy et al. (2020)]) - t = np.cross(l, l_0) / np.sin(delta) - - # perigee direction [Eq. (27) in Leroy et al. (2020)] - p_peri = s * np.cross(t, l) - - # argument of perigee [Eq. (28) in Leroy et al. (2020)] - w = np.arctan2(np.dot(p_peri, np.cross(l, p_node)), np.dot(p_peri, p_node)) - - # time from mutual orbiter passing through its perigee to time when circular orbiter - # passes through its ascending node [Eq. (31) in Leroy et al. (2020)] - n = np.arctan2( - s * np.dot(t, np.array([1, 0, 0])), - s * np.dot(t, np.cross(l_0, np.array([1, 0, 0]))), - ) - - # eccentric anomaly (radians) implicit equation [Eq. (32) in Leroy et al. (2020)] - psi_ = 0 - psi = 0.1 # initial guess - while np.abs(psi - psi_) > 1e-6: # convergence criterion - psi_ = psi - psi = n + e * np.sin(psi_) - - # true anomaly (radians) [Eq. (33a) in Leroy et al. (2020)] - nu = np.arctan2(np.sin(psi) * np.sqrt(1 - e**2), np.cos(psi) - e) - - # pylint: disable=E1101 - orbits.append( - KeplerianOrbit( - altitude=self.orbit.altitude, - inclination=(360 + np.degrees(i)) % 360, - eccentricity=e, - right_ascension_ascending_node=(360 + np.degrees(omega)) % 360, - perigee_argument=(360 + np.degrees(w)) % 360, - true_anomaly=(360 + np.degrees(nu)) % 360, - epoch=self.orbit.epoch, - ) - ) - - return [ - Satellite( - name=zero_pad(self.name, self.number_satellites, i + 1), - orbit=orbit, - instruments=copy.deepcopy(self.instruments), - ) - for i, orbit in enumerate(orbits) - ] - - -class SOCConstellation(Satellite): - """ - A constellation that arranges member satellites following the streets of coverage pattern. - - Based on Joshua F. Anderson, Michel-Alexandre Cardin, and Paul T. Grogan (2022). - "Design and analysis of flexible multi-layer staged deployment for satellite - mega-constellations under demand uncertainty" - Acta Astronautica, vol. 198, - pp. 179-193. doi: 10.1016/j.actaastro.2022.05.022 - """ - - type: Literal["soc"] = Field("soc", description="Space system type discriminator.") - orbit: CircularOrbit = Field( - ..., description="Reference circular orbit for this constellation." - ) - swath_width: float = Field( - ..., description="Observation diameter (meters) at specified elevation.", gt=0 - ) - packing_distance: float = Field( - ..., description="Relative distance between footprint centers", gt=0, le=1 - ) - - def generate_walker(self) -> WalkerConstellation: - """ - Generate a WalkerConstellation fitting the Streets of Coverage description. - - Returns: - WalkerConstellation: the member satellites following the Walker pattern. - """ - # compute min elevation angle - # pylint: disable=E1101 - e = compute_min_elevation_angle( - altitude=self.orbit.altitude, - field_of_regard=swath_width_to_field_of_regard( - altitude=self.orbit.altitude, swath_width=self.swath_width - ), - ) - - # nadir angle (degrees) [Eq. (19) in Anderson et al. (2022)] - # pylint: disable=E1101 - eta = math.degrees( - math.asin( - (EARTH_MEAN_RADIUS / (EARTH_MEAN_RADIUS + self.orbit.altitude)) - * math.cos(math.radians(e)) - ) - ) - - # compute gamma (earth central angle) [Eq. (20) in Anderson et al. (2022)] - gamma = 90 - e - eta - - # satellite footprint radius (km) [Eq. (21) in Anderson et al. (2022)] - r_foot = EARTH_MEAN_RADIUS * math.sin(math.radians(gamma)) - - # distance between adjacent footprint centers (km) [Eq. (23) in Anderson et al. (2022)] - d_f = 2 * r_foot * self.packing_distance - - # distance between adjacent planes (km) [Eq. (24) in Anderson et al. (2022)] - d_p = math.sqrt(3) * r_foot * self.packing_distance - - # angle (degrees) between footprint centers [Eq. (25) in Anderson et al. (2022)] - gamma_f = 2 * math.asin((0.5 * d_f) / (EARTH_MEAN_RADIUS)) - - # number of satellites per plane [Eq. (26) in Anderson et al. (2022)] - satellites_per_plane = math.ceil((2 * math.pi) / gamma_f) - - # angle (degrees) between adjacent planes [Eq. (27) in Anderson et al. (2022)] - gamma_p = 2 * math.asin((0.5 * d_p) / (EARTH_MEAN_RADIUS)) - - # number of planes [Eq. (28) in Anderson et al. (2022)] - number_planes = math.ceil((2 * math.pi) / gamma_p) - - number_satellites = satellites_per_plane * number_planes - - return WalkerConstellation( - name=self.name, - orbit=self.orbit, - instruments=self.instruments, - number_satellites=number_satellites, - number_planes=number_planes, - ) - - def generate_members(self) -> List[Satellite]: - return self.generate_walker().generate_members() diff --git a/src/tatc/schemas/space/__init__.py b/src/tatc/schemas/space/__init__.py new file mode 100644 index 0000000..274727d --- /dev/null +++ b/src/tatc/schemas/space/__init__.py @@ -0,0 +1,29 @@ +""" +Object schemas for space objects. + +@author: Paul T. Grogan +""" + +from .mog import MOGConstellation +from .satellite import Satellite +from .soc import SOCConstellation +from .train import TrainConstellation +from .walker import WalkerConfiguration, WalkerConstellation + +AllSpaceObjects = ( + MOGConstellation + | Satellite + | SOCConstellation + | TrainConstellation + | WalkerConstellation +) + +__all__ = [ + "AllSpaceObjects", + "MOGConstellation", + "SOCConstellation", + "Satellite", + "TrainConstellation", + "WalkerConfiguration", + "WalkerConstellation", +] diff --git a/src/tatc/schemas/space/base.py b/src/tatc/schemas/space/base.py new file mode 100644 index 0000000..9f1c08d --- /dev/null +++ b/src/tatc/schemas/space/base.py @@ -0,0 +1,28 @@ +""" +Base object schemas for space systems. + +@author: Paul T. Grogan +""" + +from __future__ import annotations + +from pydantic import BaseModel, Field + +from ..instrument import AllInstruments, Instrument + + +class SpaceSystem(BaseModel): + """ + Base class for space systems. + """ + + name: str = Field( + ..., + description="Space system name.", + examples=["International Space Station"], + ) + instruments: list[AllInstruments] = Field( + default=[Instrument()], + min_length=1, + description="List of assigned instruments.", + ) diff --git a/src/tatc/schemas/space/base_constellation.py b/src/tatc/schemas/space/base_constellation.py new file mode 100644 index 0000000..c0c9ddd --- /dev/null +++ b/src/tatc/schemas/space/base_constellation.py @@ -0,0 +1,27 @@ +""" +Base object schemas for constellations. + +@author: Paul T. Grogan +""" + +from __future__ import annotations + +from .base import SpaceSystem +from .satellite import Satellite + + +class BaseConstellation(SpaceSystem): + """ + Base class for constellations. + """ + + def generate_members(self) -> list[Satellite]: + """ + Generates the members of the constellation. + + Returns: + list[Satellite]: The list of generated members. + """ + raise NotImplementedError( + "generate_members() must be implemented in subclasses." + ) diff --git a/src/tatc/schemas/space/mog.py b/src/tatc/schemas/space/mog.py new file mode 100644 index 0000000..8ebdf15 --- /dev/null +++ b/src/tatc/schemas/space/mog.py @@ -0,0 +1,163 @@ +""" +Object schema for mutual orbiting group (MOG) constellations. + +@author: Paul T. Grogan +""" + +from __future__ import annotations + +import copy +from typing import Literal + +import numpy as np +from pydantic import Field + +from tatc.utils.formatting import zero_pad + +from ..orbit import CircularOrbit, KeplerianOrbit +from .base_constellation import BaseConstellation +from .satellite import Satellite + + +class MOGConstellation(BaseConstellation): + """ + A constellation that arranges member satellites following the mutual orbiting group pattern. + + Based on Stephen Leroy, Riley Fitzgerald, Kerri Cahoy, James Abel, and James Clark (2020). + "Orbital Maintenance of a Constellation of CubeSats for Internal Gravity Wave Tomography," + IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, + pp. 307-317. doi: 10.1109/JSTARS.2019.2961084 + """ + + type: Literal["mog"] = Field( + default="mog", description="Space system type discriminator." + ) + orbit: CircularOrbit = Field( + ..., description="Reference circular orbit for this constellation." + ) + parallel_axis: float = Field( + ..., + description="Mutual orbit axis length (m) parallel to velocity vector.", + gt=0, + ) + transverse_axis: float = Field( + ..., + description="Mutual orbit axis length (m) transverse to velocity vector.", + gt=0, + ) + clockwise: bool = Field(True, description="True, if the mutual orbit is clockwise.") + number_satellites: int = Field( + 2, description="Number of equally-spaced mutually orbiting satellites.", gt=0 + ) + + def generate_members(self) -> list[Satellite]: + """ + Generate space system member satellites. + + Returns: + list[Satellite]: the member satellites + """ + + orbits = [] + + # semimajor axis (m) + a = self.orbit.get_semimajor_axis() + + # angle of separation (radians) of angular momentum vectors for reference and mutual orbiter + delta = self.transverse_axis / (2 * a) + + # eccentricity of the mutual orbit + e = self.parallel_axis / (4 * a) + + # direction of the mutual orbit (1: clockwise, -1: counter-clockwise) + s = 1 if self.clockwise else -1 + + # inclination (radians) of reference orbit + i_0 = np.radians(self.orbit.inclination) + + # right ascension of ascending node (radians) of reference orbit + omega_0 = np.radians(self.orbit.right_ascension_ascending_node) + + # direction of angular momentum for reference orbit [Eq. (21) in Leroy et al. (2020)] + l_0 = np.cos(i_0) * np.array([0, 0, 1]) - np.sin(i_0) * np.array([0, 1, 0]) + + # angle describing position of the mutual orbiter w.r.t. reference orbiter at ascending node + for theta in np.linspace(0, 2 * np.pi, self.number_satellites, endpoint=False): + # direction of angular momentum of mutual orbit [Eq. (22) in Leroy et al. (2020)] + l = ( + np.cos(delta) * l_0 + + np.sin(delta) * np.cos(theta) * np.cross(l_0, np.array([1, 0, 0])) + + np.sin(delta) * np.sin(theta) * np.array([1, 0, 0]) + ) + + # inclination (radians) of mutual orbiting satellite [Eq. (23) in Leroy et al. (2020)] + i = np.arccos(np.dot(l, np.array([0, 0, 1]))) + + # raan (radians) of mutual orbiting satellite [Eq. (24) in Leroy et al. (2020)] + omega = omega_0 + np.arctan2( + np.dot(l, np.array([1, 0, 0])), np.dot(-l, np.array([0, 1, 0])) + ) + + # direction of mutual orbit ascending node w.r.t. + # Earth's center of mass [Eq. (25) in Leroy et al. (2020)] + p_node = np.cos(omega - omega_0) * np.array([1, 0, 0]) + np.sin( + omega - omega_0 + ) * np.array([0, 1, 0]) + + # direction of mutual and reference orbit intersection + # ([Eq. (26) in Leroy et al. (2020)]) + t = np.cross(l, l_0) / np.sin(delta) + + # perigee direction [Eq. (27) in Leroy et al. (2020)] + p_peri = s * np.cross(t, l) + + # argument of perigee [Eq. (28) in Leroy et al. (2020)] + w = np.arctan2(np.dot(p_peri, np.cross(l, p_node)), np.dot(p_peri, p_node)) + + # time from mutual orbiter passing through its perigee to time when circular orbiter + # passes through its ascending node [Eq. (31) in Leroy et al. (2020)] + n = np.arctan2( + s * np.dot(t, np.array([1, 0, 0])), + s * np.dot(t, np.cross(l_0, np.array([1, 0, 0]))), + ) + + # mean anomaly (radians) of the mutual orbiter at the reference + # orbit's actual epoch. `n` above [Eq. (31)] is only the mutual + # orbiter's mean anomaly at the instant the reference orbiter + # crosses its ascending node; since both orbits share the same + # semimajor axis (and thus mean motion, independent of + # eccentricity), advancing by the reference orbiter's own mean + # anomaly at epoch gives the mutual orbiter's mean anomaly at + # that same epoch. + m = n + np.radians(self.orbit.get_mean_anomaly()) + + # eccentric anomaly (radians) implicit equation [Eq. (32) in Leroy et al. (2020)] + psi_ = 0 + psi = 0.1 # initial guess + while np.abs(psi - psi_) > 1e-6: # convergence criterion + psi_ = psi + psi = m + e * np.sin(psi_) + + # true anomaly (radians) [Eq. (33a) in Leroy et al. (2020)] + nu = np.arctan2(np.sin(psi) * np.sqrt(1 - e**2), np.cos(psi) - e) + + orbits.append( + KeplerianOrbit( + semimajor_axis=self.orbit.get_semimajor_axis(), + inclination=(360 + np.degrees(i)) % 360, + eccentricity=e, + right_ascension_ascending_node=(360 + np.degrees(omega)) % 360, + perigee_argument=(360 + np.degrees(w)) % 360, + true_anomaly=(360 + np.degrees(nu)) % 360, + epoch=self.orbit.epoch, + ) + ) + + return [ + Satellite( + name=zero_pad(self.name, self.number_satellites, i + 1), + orbit=orbit, + instruments=copy.deepcopy(self.instruments), + ) + for i, orbit in enumerate(orbits) + ] diff --git a/src/tatc/schemas/space/satellite.py b/src/tatc/schemas/space/satellite.py new file mode 100644 index 0000000..3b2a834 --- /dev/null +++ b/src/tatc/schemas/space/satellite.py @@ -0,0 +1,25 @@ +""" +Object schema for satellites. + +@author: Paul T. Grogan +""" + +from __future__ import annotations + +from typing import Literal + +from pydantic import Field + +from ..orbit import AllOrbits +from .base import SpaceSystem + + +class Satellite(SpaceSystem): + """ + Single satellite. + """ + + type: Literal["satellite"] = Field( + default="satellite", description="Space system type discriminator." + ) + orbit: AllOrbits = Field(..., description="Orbit specification.") diff --git a/src/tatc/schemas/space/soc.py b/src/tatc/schemas/space/soc.py new file mode 100644 index 0000000..b85624f --- /dev/null +++ b/src/tatc/schemas/space/soc.py @@ -0,0 +1,120 @@ +""" +Object schema for streets-of-coverage (SOC) constellations. + +@author: Paul T. Grogan +""" + +from __future__ import annotations + +import math +from typing import Literal + +from pydantic import Field + +from tatc.utils.observation import ( + compute_min_elevation_angle, + swath_width_to_field_of_regard, +) + +from ...constants import EARTH_MEAN_RADIUS +from ..orbit import CircularOrbit +from .base_constellation import BaseConstellation +from .satellite import Satellite +from .walker import WalkerConstellation + + +class SOCConstellation(BaseConstellation): + """ + A constellation that arranges member satellites following the streets of coverage pattern. + + Based on Joshua F. Anderson, Michel-Alexandre Cardin, and Paul T. Grogan (2022). + "Design and analysis of flexible multi-layer staged deployment for satellite + mega-constellations under demand uncertainty" + Acta Astronautica, vol. 198, + pp. 179-193. doi: 10.1016/j.actaastro.2022.05.022 + """ + + type: Literal["soc"] = Field( + default="soc", description="Space system type discriminator." + ) + orbit: CircularOrbit = Field( + ..., description="Reference circular orbit for this constellation." + ) + swath_width: float = Field( + ..., description="Observation diameter (meters) at specified elevation.", gt=0 + ) + packing_distance: float = Field( + ..., description="Relative distance between footprint centers", gt=0, le=1 + ) + + def generate_walker(self) -> WalkerConstellation: + """ + Generate a WalkerConstellation fitting the Streets of Coverage description. + + Returns: + WalkerConstellation: the member satellites following the Walker pattern. + """ + # compute min elevation angle + e = compute_min_elevation_angle( + altitude=self.orbit.mean_altitude, + field_of_regard=swath_width_to_field_of_regard( + altitude=self.orbit.mean_altitude, swath_width=self.swath_width + ), + ) + + # nadir angle (degrees) [Eq. (19) in Anderson et al. (2022)] + eta = math.degrees( + math.asin( + (EARTH_MEAN_RADIUS / (EARTH_MEAN_RADIUS + self.orbit.mean_altitude)) + * math.cos(math.radians(e)) + ) + ) + + # compute gamma (earth central angle) [Eq. (20) in Anderson et al. (2022)] + gamma = 90 - e - eta + + # satellite footprint radius (m) [Eq. (21) in Anderson et al. (2022)] + r_foot = EARTH_MEAN_RADIUS * math.sin(math.radians(gamma)) + + # distance between adjacent footprint centers (m) [Eq. (23) in Anderson et al. (2022)] + d_f = 2 * r_foot * self.packing_distance + + # distance between adjacent planes (m) [Eq. (24) in Anderson et al. (2022)] + d_p = math.sqrt(3) * r_foot * self.packing_distance + + # angle (radians) between footprint centers [Eq. (25) in Anderson et al. (2022)] + gamma_f = 2 * math.asin((0.5 * d_f) / (EARTH_MEAN_RADIUS)) + + # number of satellites per plane [Eq. (26) in Anderson et al. (2022)] + satellites_per_plane = math.ceil((2 * math.pi) / gamma_f) + + # angle (radians) between adjacent planes [Eq. (27) in Anderson et al. (2022)] + gamma_p = 2 * math.asin((0.5 * d_p) / (EARTH_MEAN_RADIUS)) + + # number of planes [Eq. (28) in Anderson et al. (2022)] + number_planes = math.ceil((2 * math.pi) / gamma_p) + + number_satellites = satellites_per_plane * number_planes + + return WalkerConstellation( + name=self.name, + orbit=self.orbit, + instruments=self.instruments, + number_satellites=number_satellites, + number_planes=number_planes, + # offset adjacent planes by half a within-plane satellite + # spacing, so the d_p row spacing (derived above via the + # hexagonal-packing sqrt(3) factor) actually yields a + # staggered hex/brick layout rather than a plain rectangular + # grid of planes + relative_spacing=number_planes // 2, + ) + + def generate_members(self) -> list[Satellite]: + """ + Generate member satellites for this streets-of-coverage constellation. + + Returns: + list[Satellite]: the member satellites + """ + return self.generate_walker().generate_members() diff --git a/src/tatc/schemas/space/train.py b/src/tatc/schemas/space/train.py new file mode 100644 index 0000000..3bee6a2 --- /dev/null +++ b/src/tatc/schemas/space/train.py @@ -0,0 +1,87 @@ +""" +Object schema for train constellations. + +@author: Paul T. Grogan +""" + +from __future__ import annotations + +import copy +from datetime import timedelta +from typing import Literal + +from pydantic import Field + +from ... import constants +from ...utils.formatting import zero_pad +from ..orbit import AllOrbits +from .base_constellation import BaseConstellation +from .satellite import Satellite + + +class TrainConstellation(BaseConstellation): + """ + A constellation that arranges member satellites in sequence. + """ + + type: Literal["train"] = Field( + default="train", description="Space system type discriminator." + ) + orbit: AllOrbits = Field(..., description="Lead orbit for this constellation.") + number_satellites: int = Field( + 1, description="The count of the number of satellites.", ge=1 + ) + interval: timedelta = Field( + ..., + description="The local time interval between satellites in a train constellation.", + ) + repeat_ground_track: bool = Field( + True, + description="True, if the train satellites should repeat the same ground track.", + ) + + def get_delta_mean_anomaly(self) -> float: + """ + Gets the difference in mean anomaly (decimal degrees) for adjacent + member satellites. + + Returns: + float: the difference in mean anomaly + """ + return -360 * self.interval / self.orbit.get_orbit_period() + + def get_delta_raan(self) -> float: + """ + Gets the difference in right ascension of ascending node (decimal + degrees) for adjacent member satellites. When repeating the ground + track, this compensates for the Earth's rotation (relative to + inertial space, i.e. the sidereal day, not the 24-hour solar day) + during the trailing interval, so each satellite's ascending node + lands at the same Earth-fixed longitude as the one ahead of it. + + Returns: + float: the difference in right ascension of ascending node + """ + if self.repeat_ground_track: + return 360 * ( + self.interval.total_seconds() / constants.EARTH_SIDEREAL_DAY_S + ) + return 0 + + def generate_members(self) -> list[Satellite]: + """ + Generate space system member satellites. + + Returns: + list[Satellite]: the member satellites + """ + return [ + Satellite( + name=zero_pad(self.name, self.number_satellites, i + 1), + orbit=self.orbit.get_derived_orbit( + i * self.get_delta_mean_anomaly(), i * self.get_delta_raan() + ), + instruments=copy.deepcopy(self.instruments), + ) + for i in range(self.number_satellites) + ] diff --git a/src/tatc/schemas/space/walker.py b/src/tatc/schemas/space/walker.py new file mode 100644 index 0000000..9dafb18 --- /dev/null +++ b/src/tatc/schemas/space/walker.py @@ -0,0 +1,152 @@ +""" +Object schemas for Walker constellations. + +@author: Paul T. Grogan +""" + +from __future__ import annotations + +import copy +import math +from enum import Enum +from typing import Literal + +import numpy as np +from pydantic import Field, model_validator + +from tatc.utils.formatting import zero_pad + +from ..orbit import AllOrbits +from .base_constellation import BaseConstellation +from .satellite import Satellite + + +class WalkerConfiguration(str, Enum): + """ + Enumeration of different Walker constellation configurations. + """ + + DELTA = "delta" + STAR = "star" + + +class WalkerConstellation(BaseConstellation): + """ + A constellation that arranges member satellites following the Walker pattern. + """ + + type: Literal["walker"] = Field( + default="walker", description="Space system type discriminator." + ) + configuration: WalkerConfiguration = Field( + default=WalkerConfiguration.DELTA, description="Walker configuration." + ) + orbit: AllOrbits = Field(..., description="Lead orbit for this constellation.") + number_satellites: int = Field( + default=1, description="Number of satellites in the constellation.", ge=1 + ) + number_planes: int = Field( + default=1, + description="The number of equally-spaced planes in a Walker Delta " + + "constellation. Ranges from 1 to (number of satellites).", + ge=1, + ) + relative_spacing: int = Field( + default=0, + description="Relative spacing of satellites between planes for a Walker Delta " + + "constellation. Ranges from 0 for equal mean anomaly to " + + "(number of planes) - 1. For example, `relative_spacing=1` " + + "means the mean anomaly is shifted by `360/number_satellites` " + + "between adjacent planes.", + ge=0, + ) + + @model_validator(mode="after") + def number_planes_le_number_satellites(self) -> WalkerConstellation: + """ + Validates the number of planes given the number of satellites. + """ + if ( + self.number_planes is not None + and self.number_satellites is not None + and self.number_planes > self.number_satellites + ): + raise ValueError("number planes exceeds number satellites") + return self + + @model_validator(mode="after") + def relative_spacing_lt_number_planes(self) -> WalkerConstellation: + """ + Validates the relative spacing given the number of planes. + """ + if ( + self.relative_spacing is not None + and self.number_planes is not None + and self.relative_spacing >= self.number_planes + ): + raise ValueError("relative spacing exceeds number planes - 1") + return self + + def get_satellites_per_plane(self) -> int: + """ + Gets the (max) number of satellites per plane. + + Returns: + int: number of satellites per plane + """ + return math.ceil(self.number_satellites / self.number_planes) + + def get_delta_mean_anomaly_within_planes(self) -> float: + """ + Gets the difference in mean anomaly (decimal degrees) for adjacent + member satellites within a single plane. + + Returns: + float: difference in mean anomaly + """ + return 360 / self.get_satellites_per_plane() + + def get_delta_mean_anomaly_between_planes(self) -> float: + """ + Gets the difference in mean anomaly (decimal degrees) for adjacent + member satellites between adjacent planes. + + Returns: + float: difference in mean anomaly + """ + return 360 * self.relative_spacing / self.number_satellites + + def get_delta_raan_between_planes(self) -> float: + """ + Gets the difference in right ascension of ascending node (decimal + degrees) for adjacent planes of member satellites. + + Returns: + float: difference in right ascension of ascending node + """ + if self.configuration == WalkerConfiguration.DELTA: + return 360 / self.number_planes + return 180 / self.number_planes + + def generate_members(self) -> list[Satellite]: + """ + Generate space system member satellites. + + Returns: + list[Satellite]: the member satellites + """ + return [ + Satellite( + name=zero_pad(self.name, self.number_satellites, i + 1), + orbit=self.orbit.get_derived_orbit( + np.mod(i, self.get_satellites_per_plane()) + * self.get_delta_mean_anomaly_within_planes() + + (i // self.get_satellites_per_plane()) + * self.get_delta_mean_anomaly_between_planes(), + (i // self.get_satellites_per_plane()) + * self.get_delta_raan_between_planes(), + ), + instruments=copy.deepcopy(self.instruments), + ) + for i in range(self.number_satellites) + ] diff --git a/src/tatc/schemas/surface/__init__.py b/src/tatc/schemas/surface/__init__.py new file mode 100644 index 0000000..4450e02 --- /dev/null +++ b/src/tatc/schemas/surface/__init__.py @@ -0,0 +1,16 @@ +""" +Object schemas for surface objects. + +@author: Paul T. Grogan +""" + +from .point import Point +from .station import GroundStation + +AllSurfaceObjects = Point | GroundStation + +__all__ = [ + "AllSurfaceObjects", + "GroundStation", + "Point", +] diff --git a/src/tatc/schemas/surface/point.py b/src/tatc/schemas/surface/point.py new file mode 100644 index 0000000..aa036b2 --- /dev/null +++ b/src/tatc/schemas/surface/point.py @@ -0,0 +1,33 @@ +""" +Base classes for surface objects. + +@author Paul T. Grogan +""" + +from pydantic import BaseModel, Field, NonNegativeInt + + +class Point(BaseModel): + """ + Surface point in the WGS 84 coordinate system. + """ + + id: NonNegativeInt = Field(default=0, description="Unique point identifier.") + latitude: float = Field( + ..., + description="Latitude (decimal degrees) in the WGS 84 coordinate system.", + ge=-90, + le=90, + examples=[40.74259], + ) + longitude: float = Field( + ..., + description="Longitude (decimal degrees) in the WGS 84 coordinate system.", + ge=-180, + le=180, + examples=[-74.02686], + ) + elevation: float = Field( + default=0, + description="Elevation (meters) above datum in the WGS 84 coordinate system.", + ) diff --git a/src/tatc/schemas/surface/station.py b/src/tatc/schemas/surface/station.py new file mode 100644 index 0000000..5ee4d55 --- /dev/null +++ b/src/tatc/schemas/surface/station.py @@ -0,0 +1,31 @@ +""" +Object schemas for ground stations. + +@author: Paul T. Grogan +""" + +from datetime import timedelta + +from pydantic import Field + +from .point import Point + + +class GroundStation(Point): + """ + Ground station in the WGS 84 coordinate system. + """ + + name: str = Field(..., description="Ground station name", examples=["station 1"]) + min_elevation_angle: float = Field( + default=0, + description="The minimum elevation angle (decimal degrees) required " + + "for satellite communication.", + ge=0, + le=90, + ) + min_access_time: timedelta = Field( + default=timedelta(0), + description="Minimum access (integration) time required for satellite communication.", + examples=[timedelta(seconds=10)], + ) diff --git a/src/tatc/utils.py b/src/tatc/utils.py deleted file mode 100644 index 04f2202..0000000 --- a/src/tatc/utils.py +++ /dev/null @@ -1,1273 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Utility functions. - -@author: Paul T. Grogan -""" -import re -from typing import List, Union - -import numpy as np -from numba import njit -import geopandas as gpd -from pyproj import Transformer -from sgp4.conveniences import sat_epoch_datetime -from sgp4.api import Satrec -from shapely import Geometry, make_valid, simplify -from shapely.geometry import ( - Point, - Polygon, - MultiPolygon, - GeometryCollection, - LineString, -) -from shapely.ops import split, transform -from skyfield.api import wgs84 -from skyfield.framelib import itrs -from skyfield.toposlib import GeographicPosition -from skyfield.positionlib import Geocentric -from spiceypy.spiceypy import edlimb, inelpl, nvp2pl, recgeo, surfpt -from spiceypy.utils.exceptions import NotFoundError - -from . import constants -from . import config - - -@njit -def mean_anomaly_to_true_anomaly(mean_anomaly: float, eccentricity: float = 0) -> float: - """ - Converts mean anomaly to true anomaly. - - Args: - mean_anomaly (float): The mean anomaly (degrees). - true_anomaly (float): The orbit eccentricity. - - Returns: - float: The true anomaly (degrees). - """ - mean_anomaly_rad = np.radians(mean_anomaly) - true_anomaly_rad = ( - mean_anomaly_rad - + (2 * eccentricity - (1 / 4) * eccentricity**3) * np.sin(mean_anomaly_rad) - + (5 / 4) * eccentricity**2 * np.sin(2 * mean_anomaly_rad) - + (13 / 12) * eccentricity**3 * np.sin(3 * mean_anomaly_rad) - ) - return np.degrees(true_anomaly_rad) - - -@njit -def true_anomaly_to_mean_anomaly(true_anomaly: float, eccentricity: float = 0) -> float: - """ - Converts true anomaly to mean anomaly. - - Args: - true_anomaly (float): The true anomaly (degrees). - eccentricity (float): The orbit eccentricity. - - Returns: - float: The mean anomaly (degrees). - """ - true_anomaly_rad = np.radians(true_anomaly) - mean_anomaly_rad = ( - true_anomaly_rad - - 2 * eccentricity * np.sin(true_anomaly_rad) - + ((3 / 4) * eccentricity**2 + (1 / 8) * eccentricity**4) - * np.sin(2 * true_anomaly_rad) - - (1 / 3) * eccentricity**3 * np.sin(3 * true_anomaly_rad) - + (5 / 32) * eccentricity**4 * np.sin(4 * true_anomaly_rad) - ) - return np.degrees(mean_anomaly_rad) - - -@njit -def compute_number_samples(distance: float) -> int: - """ - Compute the number of global samples required to achieve a typical - sample distance (meters) assuming equal spacing. - - Args: - distance (float): The typical distance between samples (meters). - - Returns: - int: The number of global samples. - """ - # compute the angular distance of each sample (assuming mean sphere) - theta = distance / constants.EARTH_MEAN_RADIUS - # compute the distance from the center of earth to conic plane (assuming sphere) - radius = constants.EARTH_MEAN_RADIUS * np.cos(theta / 2) - # compute the distance from the conic plane to the surface (assuming sphere) - height = constants.EARTH_MEAN_RADIUS - radius - # compute the sperical cap area covered by the sample (assuming sphere) - # https://en.wikipedia.org/wiki/Spherical_cap - sample_area = 2 * np.pi * constants.EARTH_MEAN_RADIUS * height - # return the fraction of earth-to-sample area - return int(constants.EARTH_SURFACE_AREA / sample_area) - - -@njit -def swath_width_to_field_of_regard( - altitude: float, swath_width: float, elevation: float = 0 -) -> float: - """ - Fast conversion from swath width to field of regard. - - Args: - altitude (float): Altitude (meters) above WGS 84 datum for the observing instrument. - swath_width (float): Observation diameter (meters) at specified elevation. - elevation (float): Elevation (meters) above WGS 84 datum to observe. - - Returns: - float: The field of regard (degrees). - """ - # rho is the angular radius of the earth viewed by the satellite - sin_rho = (constants.EARTH_MEAN_RADIUS + elevation) / ( - constants.EARTH_MEAN_RADIUS + altitude - ) - # lambda is the Earth central angle - sin_lambda = np.sin((swath_width / 2) / (constants.EARTH_MEAN_RADIUS + elevation)) - # eta is the angular radius of the region viewable by the satellite - tan_eta = sin_rho * sin_lambda / (1 - sin_rho * np.cos(np.arcsin(sin_lambda))) - return np.degrees(2 * np.arctan(tan_eta)) - - -@njit -def swath_width_to_field_of_view( - altitude: float, swath_width: float, look_angle: float = 0, elevation: float = 0 -) -> float: - """ - Fast conversion from swath width to field of view considering off-nadir pointing. - - Args: - altitude (float): Altitude (meters) above WGS 84 datum for the observing instrument. - swath_width (float): Observation diameter (meters) at specified elevation. - look_angle (float): Off-nadir look angle (degrees) to observation center. - elevation (float): Elevation (meters) above WGS 84 datum to observe. - - Returns: - float: The field of view (degrees). - """ - # rho is the angular radius of the earth viewed by the satellite - sin_rho = (constants.EARTH_MEAN_RADIUS + elevation) / ( - constants.EARTH_MEAN_RADIUS + altitude - ) - # eta is the angular radius from sub-satellite point to center of view - sin_eta = min(sin_rho, np.sin(np.radians(look_angle) / 2)) - # epsilon is the satellite elevation from the center of view - cos_epsilon = sin_eta / sin_rho - # lambda is the Earth central angle to the center of view - _lambda = np.pi / 2 - np.arcsin(sin_eta) - np.arccos(cos_epsilon) - sin_lambda_1 = np.sin( - _lambda - (swath_width / 2) / (constants.EARTH_MEAN_RADIUS + elevation) - ) - sin_lambda_2 = np.sin( - _lambda + (swath_width / 2) / (constants.EARTH_MEAN_RADIUS + elevation) - ) - # eta is the angular radius of the region viewable by the satellite - tan_eta_1 = sin_rho * sin_lambda_1 / (1 - sin_rho * np.cos(np.arcsin(sin_lambda_1))) - tan_eta_2 = sin_rho * sin_lambda_2 / (1 - sin_rho * np.cos(np.arcsin(sin_lambda_2))) - return np.degrees(np.arctan(tan_eta_2) - np.arctan(tan_eta_1)) - - -@njit -def field_of_regard_to_swath_width( - altitude: float, field_of_regard: float, elevation: float = 0 -) -> float: - """ - Fast conversion from field of regard to swath width. - - Args: - altitude (float): Altitude (meters) above WGS 84 datum for the observing instrument. - field_of_regard (float): Angular width (degrees) of observation. - elevation (float): Elevation (meters) above WGS 84 datum to observe. - - Returns: - float: The observation diameter (meters) at the specified elevation. - """ - # rho is the angular radius of the earth viewed by the satellite - sin_rho = (constants.EARTH_MEAN_RADIUS + elevation) / ( - constants.EARTH_MEAN_RADIUS + altitude - ) - # eta is the angular radius of the region viewable by the satellite - sin_eta = min(sin_rho, np.sin(np.radians(field_of_regard) / 2)) - # epsilon is the min satellite elevation for obs (grazing angle) - cos_epsilon = sin_eta / sin_rho - # lambda is the Earth central angle - _lambda = np.pi / 2 - np.arcsin(sin_eta) - np.arccos(cos_epsilon) - return 2 * (constants.EARTH_MEAN_RADIUS + elevation) * _lambda - - -@njit -def compute_field_of_regard( - altitude: float, min_elevation_angle: float, elevation: float = 0 -) -> float: - """ - Fast computation of field of regard for observation with a minimum altitude angle. - - Args: - altitude (float): Altitude (meters) above WGS 84 datum for the observing instrument. - min_elevation_angle (float): The minimum elevation angle (degrees) for observation. - elevation (float): Elevation (meters) above WGS 84 datum to observe. - - Returns: - float: Angular width (degrees) of observation. - """ - # rho is the angular radius of the earth viewed by the satellite - sin_rho = (constants.EARTH_MEAN_RADIUS + elevation) / ( - constants.EARTH_MEAN_RADIUS + altitude - ) - # epsilon is the min satellite elevation for obs (grazing angle) - cos_epsilon = np.cos(np.radians(min_elevation_angle)) - # eta is the angular radius of the region viewable by the satellite - sin_eta = sin_rho * cos_epsilon - return np.degrees(np.arcsin(sin_eta) * 2) - - -@njit -def compute_min_elevation_angle( - altitude: float, field_of_regard: float, elevation: float = 0 -) -> float: - """ - Fast computation of minimum elevation angle required to observe a point. - - Args: - altitude (float): Altitude (meters) above WGS 84 datum for the observing instrument. - field_of_regard (float): Angular width (degrees) of observation. - elevation (float): Elevation (meters) above WGS 84 datum to observe. - - Returns: - float: The minimum elevation angle (degrees) for observation. - """ - # eta is the angular radius of the region viewable by the satellite - sin_eta = np.sin(np.radians(field_of_regard) / 2) - # rho is the angular radius of the earth viewed by the satellite - sin_rho = (constants.EARTH_MEAN_RADIUS + elevation) / ( - constants.EARTH_MEAN_RADIUS + altitude - ) - # epsilon is the min satellite elevation for obs (grazing angle) - cos_epsilon = sin_eta / sin_rho - if cos_epsilon > 1: - return 0 - return np.degrees(np.arccos(cos_epsilon)) - - -@njit -def compute_orbit_period(altitude: float) -> float: - """ - Fast computation of approximate orbital period. - - Args: - altitude (float): Altitude (meters) above WGS 84 datum for the observing instrument. - - Returns: - float: The orbital period (seconds). - """ - semimajor_axis = constants.EARTH_MEAN_RADIUS + altitude - mean_motion_rad_s = np.sqrt(constants.EARTH_MU / semimajor_axis**3) - return 2 * np.pi / mean_motion_rad_s - - -@njit -def compute_max_access_time(altitude: float, min_elevation_angle: float) -> float: - """ - Fast computation of maximum access time to observe a point. - - Args: - altitude (float): Altitude (meters) above WGS 84 datum for the observing instrument. - min_elevation_angle (float): Minimum elevation angle (degrees) for observation. - - Returns: - float: The maximum access time (seconds) for observation. - """ - orbital_distance = (constants.EARTH_MEAN_RADIUS + altitude) * ( - np.pi - 2 * np.radians(min_elevation_angle) - ) - orbital_velocity = np.sqrt( - constants.EARTH_MU / (constants.EARTH_MEAN_RADIUS + altitude) - ) - return orbital_distance / orbital_velocity - - -@njit -def compute_ground_velocity(altitude: float, inclination: float) -> float: - """ - Fast computation of mean ground velocity for a nadir-pointing instrument. - - Args: - altitude (float): Altitude (meters) above WGS 84 datum for the observing instrument. - inclination (float): Inclination (degrees) of the observing instrument orbit. - - Returns: - float: The access time (seconds) for observation. - """ - semimajor_axis = constants.EARTH_MEAN_RADIUS + altitude - mean_motion_rad_s = np.sqrt(constants.EARTH_MU / semimajor_axis**3) - return constants.EARTH_MEAN_RADIUS * ( - mean_motion_rad_s - - (2 * np.pi * np.cos(np.degrees(inclination)) / constants.EARTH_SIDEREAL_DAY_S) - ) - - -@njit -def along_track_distance_to_access_time( - altitude: float, inclination: float, along_track: float -) -> float: - """ - Fast computation of mean access time for a specified along track distance. - - Args: - altitude (float): Altitude (meters) above WGS 84 datum for the observing instrument. - inclination (float): Inclination (degrees) of the observing instrument orbit. - along_track (float): Along track distance (meters) observed during access. - - Returns: - float: The access time (seconds) for observation. - """ - semimajor_axis = constants.EARTH_MEAN_RADIUS + altitude - mean_motion_rad_s = np.sqrt(constants.EARTH_MU / semimajor_axis**3) - ground_velocity = constants.EARTH_MEAN_RADIUS * ( - mean_motion_rad_s - - (2 * np.pi * np.cos(np.degrees(inclination)) / constants.EARTH_SIDEREAL_DAY_S) - ) - return along_track / ground_velocity - - -@njit -def access_time_to_along_track_distance( - altitude: float, inclination: float, access_time: float -) -> float: - """ - Fast computation of along track distance for a specified access time. - - Args: - altitude (float): Altitude (meters) above WGS 84 datum for the observing instrument. - inclination (float): Inclination (degrees) of the observing instrument orbit. - access_time (float): Access time (seconds) during observation. - - Returns: - float: The observation along track distance (meters). - """ - semimajor_axis = constants.EARTH_MEAN_RADIUS + altitude - mean_motion_rad_s = np.sqrt(constants.EARTH_MU / semimajor_axis**3) - ground_velocity = constants.EARTH_MEAN_RADIUS * ( - mean_motion_rad_s - - (2 * np.pi * np.cos(np.degrees(inclination)) / constants.EARTH_SIDEREAL_DAY_S) - ) - return ground_velocity * access_time - - -def compute_projected_ray_position( - orbit_track: Geocentric, - cross_track_field_of_view: float, - along_track_field_of_view: float, - roll_angle: float = 0, - pitch_angle: float = 0, - is_rectangular: bool = False, - angle: float = 0, - elevation: float = 0, -) -> GeographicPosition: - """ - Get the location of a projected ray from an instrument. - - Args: - orbit_track (skyfield.positionlib.Geocentric): the satellite orbit track. - cross_track_field_of_view (float): the instrument cross-track - (orthogonal to velocity vector) field of view (degrees). - along_track_field_of_view (float): the instrument along-track - (parallel to velocity vector) field of view (degrees). - roll_angle (float): the instrument roll (right-hand about - velocity vector) angle (degrees). - pitch_angle (float): the instrument pitch (right-hand about - orbit normal vector) angle (degrees). - is_rectangular (bool): `True` if the instrument view has a rectangular - shape (otherwise elliptical). - angle (float): ray angle (degrees) counterclockwise from right-hand cross-track - direction about the instrument field of view. - elevation (float): The elevation (meters) at which project the footprint. - - Returns: - (skyfield.toposlib.GeographicPosition): the geographic position of the projected ray - """ - # extract earth-fixed position and velocity - position, velocity = orbit_track.frame_xyz_and_velocity(itrs) - # velocity unit vector - v = np.divide(velocity.m_per_s, np.linalg.norm(velocity.m_per_s, axis=0)) - # binormal unit vector - b = np.divide(-position.m, np.linalg.norm(position.m, axis=0)) - # normal unit vector - if len(np.shape(position.m)) > 1: - n = np.cross(v, b, 0, 0, -1).T - else: - n = np.cross(v, b) - # construct projected ray - if is_rectangular: - # find orientation of rectangle corner - theta = np.arctan(along_track_field_of_view / cross_track_field_of_view) - # along track half width - tan_a_2 = np.tan(np.radians(along_track_field_of_view / 2)) - # cross track half width - tan_c_2 = np.tan(np.radians(cross_track_field_of_view / 2)) - # compose the ray with different equations for each side - ray = ( - b - + v * np.tan(np.radians(pitch_angle)) - + n * np.tan(np.radians(roll_angle)) - + v - * ( - tan_a_2 - if theta <= angle <= np.pi - theta - else ( - -tan_a_2 - if np.pi + theta <= angle <= 2 * np.pi - theta - else ( - tan_c_2 * np.tan(angle) - if angle < theta - else ( - tan_c_2 * np.tan(np.pi - angle) - if angle < np.pi - else ( - -tan_c_2 * np.tan(angle - np.pi) - if angle < np.pi + theta - else -tan_c_2 * np.tan(2 * np.pi - angle) - ) - ) - ) - ) - ) - + n - * ( - tan_c_2 - if angle <= theta or angle >= 2 * np.pi - theta - else ( - -tan_c_2 - if np.pi - theta <= angle <= np.pi + theta - else ( - tan_a_2 * np.tan(np.pi / 2 - angle) - if angle < np.pi / 2 - else ( - -tan_a_2 * np.tan(angle - np.pi / 2) - if angle < np.pi - theta - else ( - -tan_a_2 * np.tan(3 * np.pi / 2 - angle) - if angle < 3 * np.pi / 2 - else tan_a_2 * np.tan(angle - 3 * np.pi / 2) - ) - ) - ) - ) - ) - ) - else: - ray = ( - b - + v * np.tan(np.radians(pitch_angle)) - + n * np.tan(np.radians(roll_angle)) - + v * np.sin(angle) * np.tan(np.radians(along_track_field_of_view / 2)) - + n * np.cos(angle) * np.tan(np.radians(cross_track_field_of_view / 2)) - ) - geos = np.zeros_like(position.m) - for i in range(np.size(geos, axis=1)) if geos.ndim > 1 else [-1]: - _position = position.m[:, i].copy() if i >= 0 else position.m - _ray = ray[:, i].copy() if i >= 0 else ray - # find the intersection of the ray and the WGS 84 geoid - try: - pt = surfpt( - _position, - _ray, - constants.EARTH_EQUATORIAL_RADIUS + elevation, - constants.EARTH_EQUATORIAL_RADIUS + elevation, - constants.EARTH_POLAR_RADIUS + elevation, - ) - geo = recgeo( - pt, - constants.EARTH_EQUATORIAL_RADIUS, - constants.EARTH_FLATTENING, - ) - if i >= 0: - geos[:, i] = geo - else: - geos[:] = geo - except NotFoundError: - # projected point does not fall on the WGS 84 geoid surface - # compute the observable limb ellipse for WGS 84 geoid - limb = edlimb( - constants.EARTH_EQUATORIAL_RADIUS + elevation, - constants.EARTH_EQUATORIAL_RADIUS + elevation, - constants.EARTH_POLAR_RADIUS + elevation, - _position, - ) - # find the two intersection points between orthogonal plane and limb ellipse - _v = v[:, i].copy() if i >= 0 else v - _n = n[:, i].copy() if i >= 0 else n - _, pt_1, pt_2 = inelpl( - limb, - nvp2pl( - _v * np.sin(np.pi / 2 + angle) + _n * np.cos(np.pi / 2 + angle), - _position, - ), - ) - # compute the angles between the ray and limb intersection points - angle_1 = np.arccos( - np.dot(_ray, pt_1) / np.linalg.norm(_ray) / np.linalg.norm(pt_1) - ) - angle_2 = np.arccos( - np.dot(_ray, pt_2) / np.linalg.norm(_ray) / np.linalg.norm(pt_2) - ) - # use the limb intersection point closer to the ray - limb_pt = pt_1 if angle_1 <= angle_2 else pt_2 - limb_geo = recgeo( - limb_pt, - constants.EARTH_EQUATORIAL_RADIUS, - constants.EARTH_FLATTENING, - ) - if i >= 0: - geos[:, i] = limb_geo - else: - geos[:] = limb_geo - # return resulting geographic position - if len(np.shape(geos)) > 1: - return wgs84.latlon(np.degrees(geos[1, :]), np.degrees(geos[0, :]), geos[2, :]) - return wgs84.latlon(np.degrees(geos[1]), np.degrees(geos[0]), geos[2]) - - -def compute_footprint( - orbit_track: Geocentric, - cross_track_field_of_view: float, - along_track_field_of_view: float, - roll_angle: float = 0, - pitch_angle: float = 0, - is_rectangular: bool = False, - number_points: int = None, - elevation: float = 0, -) -> Union[Geometry, List[Geometry]]: - """ - Compute the instanteous instrument footprint. - - Args: - orbit_track (skyfield.positionlib.Geocentric): The satellite position/velocity. - cross_track_field_of_view (float): The angular (degrees) view orthogonal to velocity. - along_track_field_of_view (float): The angular (degrees) view in direction of velocity. - pitch_angle (float): The fore/aft look angle (degrees); right-hand - rotation about orbit normal vector. - roll_angle (float): The left/right look angle (degrees); right-hand - rotation about orbit velocity vector. - is_rectangular (float): True, if this is a rectangular sensor. - number_points (int): The required number of polygon points to generate. - elevation (float): The elevation (meters) at which project the footprint. - - Returns: - Union[shapely.Geometry, List[shapely.Geometry]: The instrument footprint(s). - """ - if number_points is None: - # default number of points - if is_rectangular: - number_points = config.rc.footprint_points_rectangular_side - else: - number_points = config.rc.footprint_points_elliptical - if is_rectangular: - theta = np.arctan(along_track_field_of_view / cross_track_field_of_view) - angles = np.concatenate( - ( - np.linspace(-theta, theta, number_points, endpoint=False), - np.linspace(theta, np.pi - theta, number_points, endpoint=False), - np.linspace( - np.pi - theta, np.pi + theta, number_points, endpoint=False - ), - np.linspace( - np.pi + theta, 2 * np.pi - theta, number_points, endpoint=False - ), - ) - ) - else: - angles = np.linspace(0, 2 * np.pi, number_points) - points = [ - compute_projected_ray_position( - orbit_track, - cross_track_field_of_view, - along_track_field_of_view, - roll_angle, - pitch_angle, - is_rectangular, - angle, - elevation, - ) - for angle in angles - ] - if np.size(orbit_track.t) > 1: - return [ - project_polygon_to_elevation( - split_polygon( - Polygon( - [ - (point.longitude.degrees[i], point.latitude.degrees[i]) - for point in points - ] - ) - ), - elevation, - ) - for i in range(np.size(orbit_track.t)) - ] - return project_polygon_to_elevation( - split_polygon( - Polygon( - [(point.longitude.degrees, point.latitude.degrees) for point in points] - ) - ), - elevation, - ) - - -def compute_limb( - orbit_track: Geocentric, - number_points: int = 16, - elevation: float = 0, -) -> Union[Geometry, List[Geometry]]: - """ - Compute the instanteous limb. - - Args: - orbit_track (skyfield.positionlib.Geocentric): The satellite position/velocity. - number_points (int): The required number of polygon points to generate. - elevation (float): The elevation (meters) at which project the limb. - - Returns: - Union[shapely.Geometry, List[shapely.Geometry]: The limb(s). - """ - position, _ = orbit_track.frame_xyz_and_velocity(itrs) - polygons = [None] * np.size(position.m, axis=1) if position.m.ndim > 1 else None - for i in range(len(polygons)) if position.m.ndim > 1 else [-1]: - _position = position.m[:, i].copy() if i >= 0 else position.m - limb = edlimb( - constants.EARTH_EQUATORIAL_RADIUS + elevation, - constants.EARTH_EQUATORIAL_RADIUS + elevation, - constants.EARTH_POLAR_RADIUS + elevation, - _position, - ) - polygon = project_polygon_to_elevation( - split_polygon( - Polygon( - [ - Point(np.degrees(g[0]), np.degrees(g[1])) - for p in [ - limb.center - + np.cos(i) * limb.semi_major - + np.sin(i) * limb.semi_minor - for i in np.linspace(0, np.pi * 2, number_points) - ] - for g in [ - recgeo( - p, - constants.EARTH_EQUATORIAL_RADIUS, - constants.EARTH_FLATTENING, - ) - ] - ] - ) - ), - elevation, - ) - if i >= 0: - polygons[i] = polygon - else: - polygons = polygon - return polygons - - -def buffer_footprint( - geometry: Geometry, - to_crs: Transformer, - from_crs: Transformer, - swath_width: float, - elevation: float, -) -> Polygon: - """ - Buffers a ground track point to create a footprint. - - Args: - geometry (shapely.Geometry): The geometry to buffer. - origin_crs (str): The origin coordinate reference system (CRS). - buffer_crs (str): The buffering coordinate reference system (CRS). - swath_width (float): The swath width (meters) to buffer. - elevation (float): The elevation (meters) at which project the buffered polygon. - - Returns: - Union[shapely.geometry.Polygon, shapely.geometry.MultiPolygon]: The buffered footprint. - """ - # do the swath projection in the specified coordinate reference system - # split polygons to wrap over the anti-meridian and poles - # reproject to specified elevation (lost during buffer) - return project_polygon_to_elevation( - split_polygon( - transform( - from_crs.transform, - transform(to_crs.transform, geometry).buffer(swath_width / 2), - ) - ), - elevation, - ) - - -def buffer_target( - geometry: Geometry, - altitude: float, - inclination: float, - field_of_regard: float, - time_step: float, - distance_crs: str = "EPSG:4087", - distance_scaling: float = 1.0, -): - """ - Buffers a target geometry to support culling operations. Selects a buffer distance - equal to the distance traveled in one time step plus half of the field of regard - swath width. Simplifies geometries to distance tolerances within 5% of the buffer distance. - - Args: - geometry (shapely.Geometry): The target geometry (with EPSG:4326 coordinates) to buffer. - altitude (float): The spacecraft orbit altitude (meters). - inclination (float): The spacecraft orbit inclination (degrees). - field_of_regard (float): The spacecraft instrument field of regard (degrees). - time_step (float): The simulation time step (seconds). - distance_crs (str): The coordinate reference system in which to perform - distance calculations (default: EPSG:4087). - distance_scaling (float): A multiplicative scaling factor to adjust the buffer - distance (default: 1.0). - - Returns: - Union[shapely.geometry.Polygon, shapely.geometry.MultiPolygon]: The buffered geometry. - """ - to_crs = Transformer.from_crs("EPSG:4326", distance_crs, always_xy=True) - from_crs = Transformer.from_crs(distance_crs, "EPSG:4326", always_xy=True) - swath_width = field_of_regard_to_swath_width(altitude, field_of_regard) - ground_distance = compute_ground_velocity(altitude, inclination) * time_step - distance = (ground_distance + swath_width / 2) * distance_scaling - return split_polygon( - transform( - from_crs.transform, transform(to_crs.transform, geometry).buffer(distance) - ) - ) - - -def project_polygon_to_elevation( - polygon: Union[Polygon, MultiPolygon], elevation: float -) -> Union[Polygon, MultiPolygon]: - """ - Projects a polygon to a specified elevation (z-coordinate). - - Args: - polygon (Polygon or MultiPolygon): The polygon to project. - elevation (float): The elevation (meters) above the WGS 84 geoid. - - Returns: - Polygon or MultiPolygon: The projected polygon. - """ - if isinstance(polygon, Polygon): - return Polygon( - [(p[0], p[1], elevation) for p in polygon.exterior.coords], - [[(p[0], p[1], elevation) for p in i.coords] for i in polygon.interiors], - ) - return MultiPolygon( - [project_polygon_to_elevation(g, elevation) for g in polygon.geoms] - ) - - -def _wrap_polygon_over_north_pole( - polygon: Union[Polygon, MultiPolygon], -) -> Union[Polygon, MultiPolygon]: - """ - Wraps polygon coordinates over the North pole. Due to buffering and projection, - sometimes latitudes exceed 90 degrees. This method wraps them to the correct - latitude between -90 and 90 degrees and adjusts the longitude by 180 degrees. - This method requires a polygon above 90 degrees latitude to be only on one - side of the prime meridian. - - Note: this method only changes coordinates: it does not create a MultiPolygon. - - Args: - polygon (Polygon or MultiPolygon): The polygon to wrap. - - Returns: - Polygon, or MultiPolygon: The wrapped polygon. - """ - if isinstance(polygon, Polygon): - if all(c[1] <= 90 for c in polygon.exterior.coords): - # no wrapping necessary - return polygon - # map latitudes from [90, 180) to [90, -90), adjusting longitude by 180 degrees - lat_shift = 180 if all(c[0] <= 0 for c in polygon.exterior.coords) else -180 - pgon = Polygon( - [ - [ - c[0] + lat_shift if c[1] >= 90 else c[0], - 180 - c[1] if c[1] >= 90 else c[1], - ] - for c in polygon.exterior.coords - ], - [ - [ - [ - c[0] + lat_shift if c[1] >= 90 else c[0], - 180 - c[1] if c[1] >= 90 else c[1], - ] - for c in i.coords - ] - for i in polygon.interiors - ], - ) - # give up and return original polygon if invalid - if not pgon.is_valid: - return polygon - return pgon - if isinstance(polygon, MultiPolygon): - # recursive call for each polygon - polygons = [_wrap_polygon_over_north_pole(p) for p in polygon.geoms] - return MultiPolygon( - [ - g - for p in polygons - for g in (p.geoms if isinstance(p, MultiPolygon) else [p]) - ] - ) - raise ValueError("Unknown geometry: " + str(type(polygon))) - - -def _split_polygon_north_pole( - polygon: Union[Polygon, MultiPolygon], -) -> Union[Polygon, MultiPolygon]: - """ - Splits a Polygon into a MultiPolygon if it crosses north pole. - - Args: - polygon (Polygon or MultiPolygon): The polygon to split. - - Returns: - Polygon, or MultiPolygon: The split polygon. - """ - if isinstance(polygon, Polygon): - if all(c[1] <= 90 for c in polygon.exterior.coords): - # no splitting necessary - return polygon - # split polygon along north pole - parts = split(polygon, LineString([(-360, 90), (360, 90)])) - # check and split part over prime meridian if necessary - for part in parts.geoms: - if part.crosses(LineString([(0, 90), (0, 180)])): - parts = GeometryCollection( - [g for g in parts.geoms if g != part] - + [g for g in split(part, LineString([(0, 90), (0, 180)])).geoms] - ) - # convert to a multi polygon - if isinstance(parts, GeometryCollection): - parts = _convert_collection_to_polygon(parts) - # return polygon with components wrapped over north pole - return _wrap_polygon_over_north_pole(parts) - if isinstance(polygon, MultiPolygon): - # recursive call for each polygon - pgons = [_split_polygon_north_pole(p) for p in polygon.geoms] - return MultiPolygon( - [ - g - for p in pgons - for g in (p.geoms if isinstance(p, MultiPolygon) else [p]) - ] - ) - raise ValueError("Unknown geometry: " + str(type(polygon))) - - -def _wrap_polygon_over_south_pole( - polygon: Union[Polygon, MultiPolygon], -) -> Union[Polygon, MultiPolygon]: - """ - Wraps polygon coordinates over the South pole. Due to buffering and projection, - sometimes latitudes exceed -90 degrees. This method wraps them to the correct - latitude between -90 and 90 degrees and adjusts the longitude by 180 degrees. - This method requires a polygon above 90 degrees latitude to be only on one - side of the prime meridian. - - Note: this method only changes coordinates: it does not create a MultiPolygon. - - Args: - polygon (Polygon or MultiPolygon): The polygon to wrap. - - Returns: - Polygon, or MultiPolygon: The wrapped polygon. - """ - if isinstance(polygon, Polygon): - if all(c[1] >= -90 for c in polygon.exterior.coords): - # no splitting necessary - return polygon - # map latitudes from [-90, -180) to [-90, 90), adjusting longitude by 180 degrees - lat_shift = 180 if all(c[0] <= 0 for c in polygon.exterior.coords) else -180 - pgon = Polygon( - [ - [ - c[0] + lat_shift if c[1] <= -90 else c[0], - -180 - c[1] if c[1] <= -90 else c[1], - ] - for c in polygon.exterior.coords - ], - [ - [ - [ - (c[0] + lat_shift if c[1] <= -90 else c[0],), - -180 - c[1] if c[1] <= -90 else c[1], - ] - for c in i.coords - ] - for i in polygon.interiors - ], - ) - # give up and return original polygon if invalid - if not pgon.is_valid: - return polygon - return pgon - if isinstance(polygon, MultiPolygon): - # recursive call for each polygon - polygons = [_wrap_polygon_over_south_pole(p) for p in polygon.geoms] - return MultiPolygon( - [ - g - for p in polygons - for g in (p.geoms if isinstance(p, MultiPolygon) else [p]) - ] - ) - raise ValueError("Unknown geometry: " + str(type(polygon))) - - -def _split_polygon_south_pole( - polygon: Union[Polygon, MultiPolygon], -) -> Union[Polygon, MultiPolygon]: - """ - Splits a Polygon into a MultiPolygon if it crosses south pole. - - Args: - polygon (Polygon or MultiPolygon): The polygon to split. - - Returns: - Polygon, or MultiPolygon: The split polygon. - """ - if isinstance(polygon, Polygon): - lat = np.array([c[1] for c in polygon.exterior.coords]) - if np.all(lat >= -90): - return polygon - # split polygon along south pole - parts = split(polygon, LineString([(-360, -90), (360, -90)])) - # check and split part over prime meridian if necessary - for part in parts.geoms: - if part.crosses(LineString([(0, -90), (0, -180)])): - parts = GeometryCollection( - [g for g in parts.geoms if g != part] - + [g for g in split(part, LineString([(0, -90), (0, -180)])).geoms] - ) - # convert to a multi polygon - if isinstance(parts, GeometryCollection): - parts = _convert_collection_to_polygon(parts) - # return polygon with components wrapped over south pole - return _wrap_polygon_over_south_pole(parts) - if isinstance(polygon, MultiPolygon): - # recursive call for each polygon - pgons = [_split_polygon_south_pole(p) for p in polygon.geoms] - return MultiPolygon( - [ - g - for p in pgons - for g in (p.geoms if isinstance(p, MultiPolygon) else [p]) - ] - ) - raise ValueError("Unknown geometry: " + str(type(polygon))) - - -def _wrap_polygon_over_antimeridian( - polygon: Union[Polygon, MultiPolygon], -) -> Union[Polygon, MultiPolygon]: - """ - Wraps polygon coordinates over the antimeridian. Due to buffering and projection, - sometimes longitudes exceed 180 degrees. This method wraps them to - the correct longitude between -180 and 180 degrees. - - Note: this method only changes coordinates: it does not create a MultiPolygon. - - Args: - polygon (Polygon or MultiPolygon): The polygon to wrap. - - Returns: - Polygon, or MultiPolygon: The wrapped polygon. - """ - if isinstance(polygon, Polygon): - if all(c[0] >= -180 and c[0] <= 180 for c in polygon.exterior.coords): - # no wrapping necessary - return polygon - if all(c[0] <= -180 for c in polygon.exterior.coords): - # map longitudes from (-540, -180] to (-180, 180] - pgon = Polygon( - [[c[0] + 360, c[1]] for c in polygon.exterior.coords], - [[[c[0] + 360, c[1]] for c in i.coords] for i in polygon.interiors], - ) - if all(c[0] >= 180 for c in polygon.exterior.coords): - # map longitudes from [180, 540) to [-180, 180) - pgon = Polygon( - [[c[0] - 360, c[1]] for c in polygon.exterior.coords], - [[[c[0] - 360, c[1]] for c in i.coords] for i in polygon.interiors], - ) - # give up and return original polygon if invalid - if not pgon.is_valid: - return polygon - return pgon - if isinstance(polygon, MultiPolygon): - # recursive call for each polygon - pgons = [_wrap_polygon_over_antimeridian(p) for p in polygon.geoms] - return MultiPolygon( - [ - g - for p in pgons - for g in (p.geoms if isinstance(p, MultiPolygon) else [p]) - ] - ) - raise ValueError("Unknown geometry: " + str(type(polygon))) - - -def _convert_collection_to_polygon( - collection: GeometryCollection, -) -> Union[Polygon, MultiPolygon]: - """ - Converts a GeometryCollection to a Polygon or MultiPolygon. Quick clipping - can create dirty results with points or lines on boundaries. This method - drops and lines or points from a GeometryCollection to return only the - Polygon or MultiPolygon geometry. - - Args: - polygon (Polygon or MultiPolygon): The polygon to convert. - - Returns: - Polygon, or MultiPolygon: The converted polygon. - """ - pgons = [p for p in collection.geoms if isinstance(p, Polygon)] + [ - p for g in collection.geoms if isinstance(g, MultiPolygon) for p in g.geoms - ] - if len(pgons) == 1: - return pgons[0] - return MultiPolygon(pgons) - - -def _split_polygon_antimeridian( - polygon: Union[Polygon, MultiPolygon], -) -> Union[Polygon, MultiPolygon]: - """ - Splits a Polygon into a MultiPolygon if it crosses the anti-meridian after - wrapping its coordinates using `wrap_coordinates_antimeridian`. Note: this - function only supports polygons that span LESS than 360 degrees longitude. - - Args: - polygon (Polygon or MultiPolygon): The polygon to split. - - Returns: - Polygon, or MultiPolygon: The split polygon. - """ - if isinstance(polygon, Polygon): - lon = np.array([c[0] for c in polygon.exterior.coords]) - # check if any longitudes cross the anti-meridian - # (adjacent coordinate longitude differs by more than 180 degrees) - if all(np.abs(np.diff(lon)) < 180): - return polygon - # check if this polygon contains a pole - if Polygon(zip(np.cos(np.radians(lon)), np.sin(np.radians(lon)))).contains( - Point(0, 0) - ): - # extract and sort coords by longitude - coords = polygon.exterior.coords[0:-1] - coords.sort(key=lambda r: r[0]) - # determine if contains north or south pole based on sign of mean latitude - n_s = 1 if np.array(coords)[:, 1].mean() > 0 else -1 - # interpolate latitude at antimeridian - lat = np.interp( - 180, [coords[-1][0], coords[0][0] + 180], [coords[-1][1], coords[0][1]] - ) - # reconstruct polygon (ccw) with added coords on antimeridian - # TODO potential problem if provided polygon has z-dimension - pgon = Polygon( - [(-180, 90 * n_s), (-180, lat)] - + coords - + [(180, lat), (180, 90 * n_s), (-180, 90 * n_s)], - polygon.interiors, - ) - # return polygon split down prime meridian to improve handling - parts = split(pgon, LineString([(0, -180), (0, 180)])) - # convert to multi polygon - if isinstance(parts, GeometryCollection): - parts = _convert_collection_to_polygon(parts) - return parts - # find anti-meridian crossings and calculate shift direction - # coords from W -> E (shift < 0) will add 360 degrees to E component - # coords from E -> W (shift > 0) will subtract 360 degrees from W component - shift = np.insert(np.cumsum(np.around(np.diff(lon) / 360)), 0, 0) - pgon = Polygon( - [ - (c[0] - 360 * shift[i], c[1]) - for i, c in enumerate(polygon.exterior.coords) - ], - [ - [ - ( - ic[0] - - 360 * np.interp(ic[0], np.sort(lon), shift[np.argsort(lon)]), - ic[1], - ) - for ic in i.coords - ] - for i in polygon.interiors - ], - ) - # split along the anti-meridian (-180 for shift > 0; 180 for shift < 0) - shift_dir = -180 if shift.max() >= 1 else 180 - parts = split(pgon, LineString([(shift_dir, -180), (shift_dir, 180)])) - # convert to multi polygon - if isinstance(parts, GeometryCollection): - parts = _convert_collection_to_polygon(parts) - # return polygon with components wrapped over anti-meridian - return _wrap_polygon_over_antimeridian(parts) - if isinstance(polygon, MultiPolygon): - # recursive call for each polygon - pgons = [_split_polygon_antimeridian(p) for p in polygon.geoms] - return MultiPolygon( - [ - g - for p in pgons - for g in (p.geoms if isinstance(p, MultiPolygon) else [p]) - ] - ) - raise ValueError("Unknown geometry: " + str(type(polygon))) - - -def split_polygon( - polygon: Union[Polygon, MultiPolygon], -) -> Union[Polygon, MultiPolygon]: - """ - Splits a Polygon into a MultiPolygon if it crosses the anti-meridian - (180 degrees longitude), exceeds the north pole (90 degrees latitude), or - exceeds the south pole (-90 degrees latitude). Note: this function - only supports polygons that span LESS than 360 degrees longitude. - - Args: - polygon (Polygon or MultiPolygon): The polygon to split. - - Returns: - Polygon, or MultiPolygon: The split polygon. - """ - polygon = _split_polygon_north_pole( - _split_polygon_south_pole(_split_polygon_antimeridian(polygon)) - ) - # invalid polygons can arise from narrow sensor geometries in polar regions - if not polygon.is_valid: - # try to fix geometry - polygon = make_valid(polygon) - if isinstance(polygon, GeometryCollection): - polygon = _convert_collection_to_polygon(polygon) - return polygon - - -def normalize_geometry( - geometry: Union[Polygon, MultiPolygon, gpd.GeoDataFrame], -) -> gpd.GeoDataFrame: - """ - Normalize geometry to a GeoDataFrame with antimeridian wrapping. - - Args: - geometry (geopandas.GeoDataFrame, geopandas.GeoSeries, Polygon, or MultiPolygon): The geometry to normalize. - - Returns: - geopandas.GeoDataFrame: The normalized geometry. - """ - if isinstance(geometry, (Polygon, MultiPolygon)): - if not geometry.is_valid: - raise ValueError("Geometry is not a valid Polygon or MultiPolygon.") - geometry = gpd.GeoDataFrame(geometry=gpd.GeoSeries([geometry]), crs="EPSG:4326") - elif isinstance(geometry, gpd.GeoSeries): - geometry = gpd.GeoDataFrame(geometry=geometry, crs="EPSG:4326") - if isinstance(geometry, gpd.GeoDataFrame): - geometry["geometry"] = geometry.apply( - lambda r: split_polygon(r.geometry), - axis=1, - ) - return geometry - - -def zero_pad(object_name: str, max_number: int, current_number: int) -> str: - """ - Uses length of max_number to zero pad allowing for alphanumeric sorting. - - Args: - object_name (str): Object name, to be concatenated with zero padded number. - max_number (int): Maximum number, utilized as reference for zero padding. - current_number (int): Index number to be zero padded. - - Returns: - str: The object name with zero padded number appended. - """ - max_length = len(str(max_number)) - return object_name + " " + str(current_number).zfill(max_length) - - -def is_even_length_list(v: List) -> List: - """ - Validator to check for even-length lists. - """ - if len(v) % 2 == 1: - raise ValueError(f"{v} does not have even length") - return v - - -def is_valid_tle(v: List[str]) -> List[str]: - """ - Validate the two line element set. - """ - for i in range(0, len(v), 2): - # based on orekit's TLE.isFormatOK function - if len(v[i]) != 69: - raise ValueError(f"Invalid tle: line {i+1} incorrect length.") - if len(v[i + 1]) != 69: - raise ValueError(f"Invalid tle: line {i+2} incorrect length.") - - line_1_pattern = ( - r"1 [ 0-9A-HJ-NP-Z][ 0-9]{4}[A-Z] [ 0-9]{5}[ A-Z]{3} " - + r"[ 0-9]{5}[.][ 0-9]{8} (?:(?:[ 0+-][.][ 0-9]{8})|(?: " - + r"[ +-][.][ 0-9]{7})) [ +-][ 0-9]{5}[+-][ 0-9] " - + r"[ +-][ 0-9]{5}[+-][ 0-9] [ 0-9] [ 0-9]{4}[ 0-9]" - ) - if re.match(line_1_pattern, v[i]) is None: - raise ValueError(f"Invalid tle: line {i+1} does not match pattern.") - line_2_pattern = ( - r"2 [ 0-9A-HJ-NP-Z][ 0-9]{4} [ 0-9]{3}[.][ 0-9]{4} " - + r"[ 0-9]{3}[.][ 0-9]{4} [ 0-9]{7} [ 0-9]{3}[.][ 0-9]{4} " - + r"[ 0-9]{3}[.][ 0-9]{4} [ 0-9]{2}[.][ 0-9]{13}[ 0-9]" - ) - if re.match(line_2_pattern, v[i + 1]) is None: - raise ValueError(f"Invalid tle: line {i+2} does not match pattern.") - - def checksum(line): - the_sum = 0 - for j in range(68): - if line[j].isdigit(): - the_sum += int(line[j]) - elif line[j] == "-": - the_sum += 1 - return the_sum % 10 - - if int(v[i][68]) != checksum(v[i]): - raise ValueError(f"Invalid tle: line {i+1} checksum failed.") - if int(v[i + 1][68]) != checksum(v[i + 1]): - raise ValueError(f"Invalid tle: line {1+2} checksum failed.") - return v - - -def is_chronological_tle(v: List[str]) -> List[str]: - """ - Validator to check for chronological TLEs. - """ - epochs = np.array( - [ - sat_epoch_datetime(Satrec.twoline2rv(v[i], v[i + 1])) - for i in range(0, len(v), 2) - ] - ) - if not all(epochs[:-1] <= epochs[1:]): - raise ValueError(f"Invalid multi-tle: not in chronological order.") - return v diff --git a/src/tatc/utils/__init__.py b/src/tatc/utils/__init__.py new file mode 100644 index 0000000..b07aece --- /dev/null +++ b/src/tatc/utils/__init__.py @@ -0,0 +1,85 @@ +""" +Utility functions for the TATC library. + +@author: Paul T. Grogan +""" + +from .formatting import zero_pad +from .geometry import ( + geodesic_distance, + get_planar_bounds, + normalize_geometry, + project_polygon_to_elevation, + split_polygon, +) +from .observation import ( + compute_field_of_regard, + compute_max_access_time, + compute_max_transit_time, + compute_min_along_track_distance, + compute_min_elevation_angle, + field_of_regard_to_swath_width, + swath_width_to_field_of_regard, + swath_width_to_field_of_view, +) +from .orbital import ( + compute_apoapsis_radius, + compute_ground_inertial_velocity, + compute_ground_surface_velocity, + compute_j2_aop_rate, + compute_j2_mean_motion_rate, + compute_j2_raan_rate, + compute_orbit_inertial_velocity, + mean_anomaly_to_true_anomaly, + mean_motion_to_orbit_period, + mean_motion_to_semimajor_axis, + semimajor_axis_to_mean_motion, + semimajor_axis_to_orbit_period, + true_anomaly_to_mean_anomaly, +) +from .projection import ( + buffer_footprint, + buffer_target, + compute_footprint, + compute_limb, + compute_projected_ray_position, +) +from .surface import compute_number_samples +from .time import to_datetime64_ns + +__all__ = [ + "buffer_footprint", + "buffer_target", + "compute_apoapsis_radius", + "compute_field_of_regard", + "compute_footprint", + "compute_ground_inertial_velocity", + "compute_ground_surface_velocity", + "compute_j2_aop_rate", + "compute_j2_mean_motion_rate", + "compute_j2_raan_rate", + "compute_limb", + "compute_max_access_time", + "compute_max_transit_time", + "compute_min_along_track_distance", + "compute_min_elevation_angle", + "compute_number_samples", + "compute_orbit_inertial_velocity", + "compute_projected_ray_position", + "field_of_regard_to_swath_width", + "geodesic_distance", + "get_planar_bounds", + "mean_anomaly_to_true_anomaly", + "mean_motion_to_orbit_period", + "mean_motion_to_semimajor_axis", + "normalize_geometry", + "project_polygon_to_elevation", + "semimajor_axis_to_mean_motion", + "semimajor_axis_to_orbit_period", + "split_polygon", + "swath_width_to_field_of_regard", + "swath_width_to_field_of_view", + "to_datetime64_ns", + "true_anomaly_to_mean_anomaly", + "zero_pad", +] diff --git a/src/tatc/utils/formatting.py b/src/tatc/utils/formatting.py new file mode 100644 index 0000000..82d8cb8 --- /dev/null +++ b/src/tatc/utils/formatting.py @@ -0,0 +1,30 @@ +""" +Formatting utility functions. + +@author: Paul T. Grogan +""" + + +def zero_pad(object_name: str, max_number: int, current_number: int) -> str: + """ + Appends a zero-padded number to an object name to create a unique, + sortable label for a member of a numbered collection (e.g. a satellite + in a constellation). The padding width is derived from `max_number` so + every generated label consumes the same number of characters, which + keeps lexicographic (string) sort order consistent with numeric order. + + Note: + If `current_number` requires more digits than `max_number`, it is + left at its natural width rather than truncated, so the + fixed-width guarantee no longer holds for that entry. + + Args: + object_name (str): Base name to prefix the generated label (e.g. a constellation name). + max_number (int): The largest number that will be padded; determines the padding width. + current_number (int): The number to zero-pad and append to `object_name`. + + Returns: + str: `object_name`, a single space, and `current_number` zero-padded to the width of `max_number`. + """ + max_length = len(str(max_number)) + return object_name + " " + str(current_number).zfill(max_length) diff --git a/src/tatc/utils/geometry.py b/src/tatc/utils/geometry.py new file mode 100644 index 0000000..9c61bee --- /dev/null +++ b/src/tatc/utils/geometry.py @@ -0,0 +1,481 @@ +""" +Geometry utility functions. + +@author: Paul T. Grogan +""" + +from __future__ import annotations + +from typing import overload + +import geopandas as gpd +import numpy as np +from pyproj import Geod +from shapely import make_valid +from shapely.geometry import ( + GeometryCollection, + LineString, + MultiPolygon, + Point, + Polygon, +) +from shapely.ops import split + +# WGS 84 ellipsoid geodesic solver, shared across calls +_WGS84_GEOD = Geod(ellps="WGS84") + + +def geodesic_distance( + longitude_1: float, latitude_1: float, longitude_2: float, latitude_2: float +) -> float: + """ + Computes the geodesic surface distance between two longitude/latitude + points on the WGS 84 ellipsoid: the length of the shortest path + between them that stays on the ellipsoid surface. Unlike a + longitude/latitude-based (planar) distance, this accounts for the + Earth's oblateness and the convergence of meridians toward the poles, + so it remains accurate at any latitude or longitude separation + (including antipodal-ish or antimeridian-spanning point pairs). + + Args: + longitude_1 (float): Longitude (degrees) of the first point. + latitude_1 (float): Latitude (degrees) of the first point. + longitude_2 (float): Longitude (degrees) of the second point. + latitude_2 (float): Latitude (degrees) of the second point. + + Returns: + float: The geodesic distance (meters) between the two points. + """ + _, _, distance = _WGS84_GEOD.inv(longitude_1, latitude_1, longitude_2, latitude_2) + return distance + + +@overload +def project_polygon_to_elevation(polygon: Polygon, elevation: float) -> Polygon: ... + + +@overload +def project_polygon_to_elevation( + polygon: MultiPolygon, elevation: float +) -> MultiPolygon: ... + + +def project_polygon_to_elevation( + polygon: Polygon | MultiPolygon, elevation: float +) -> Polygon | MultiPolygon: + """ + Assigns a fixed z-coordinate (elevation) to every coordinate of a + polygon or multipolygon, including exterior and interior (hole) rings. + Any existing z-coordinate is overwritten, not offset. + + Args: + polygon (shapely.geometry.Polygon | shapely.geometry.MultiPolygon): The polygon to project. + elevation (float): The elevation (meters) above the WGS 84 geoid to + assign to every coordinate. + + Returns: + shapely.geometry.Polygon | shapely.geometry.MultiPolygon: The projected + polygon, matching the input type. + """ + if isinstance(polygon, Polygon): + return Polygon( + [(p[0], p[1], elevation) for p in polygon.exterior.coords], + [[(p[0], p[1], elevation) for p in i.coords] for i in polygon.interiors], + ) + return MultiPolygon( + [project_polygon_to_elevation(g, elevation) for g in polygon.geoms] + ) + + +def _flatten_polygons(pgons: list[Polygon | MultiPolygon]) -> list[Polygon]: + """ + Flattens a list of Polygon/MultiPolygon geometries into a flat list of + Polygon, unpacking any MultiPolygon into its constituent polygons. + + Args: + pgons (list[shapely.geometry.Polygon | shapely.geometry.MultiPolygon]): + The geometries to flatten. + + Returns: + list[shapely.geometry.Polygon]: The flattened list of polygons. + """ + return [g for p in pgons for g in (p.geoms if isinstance(p, MultiPolygon) else [p])] + + +def _wrap_polygon_over_pole( + polygon: Polygon | MultiPolygon, pole: int +) -> Polygon | MultiPolygon: + """ + Wraps polygon coordinates over a pole (`pole` = 1 for the North pole, + -1 for the South pole). Due to buffering and projection, sometimes + latitudes exceed the pole (i.e. exceed 90 * pole degrees). This method + wraps them to the correct latitude between -90 and 90 degrees and + adjusts the longitude by 180 degrees. Only coordinates exceeding the + pole are shifted; other coordinates are left unchanged. + + This method requires a polygon exceeding the pole to be confined to one + side of the prime meridian: this is guaranteed by `_split_polygon_over_pole`, + which is the only caller, since it splits off any piece straddling the + prime meridian before wrapping. A polygon violating this precondition + would produce a self-intersecting (invalid) ring. + + Note: this method only changes coordinates: it does not create a MultiPolygon. + + Args: + polygon (shapely.geometry.Polygon | shapely.geometry.MultiPolygon): The polygon to wrap. + pole (int): 1 for the North pole, -1 for the South pole. + + Returns: + shapely.geometry.Polygon | shapely.geometry.MultiPolygon: The wrapped polygon. + """ + if isinstance(polygon, Polygon): + if all(c[1] * pole <= 90 for c in polygon.exterior.coords): + # no wrapping necessary + return polygon + # map latitudes beyond the pole back between -90 and 90, adjusting longitude by 180 degrees + lat_shift = 180 if all(c[0] <= 0 for c in polygon.exterior.coords) else -180 + return Polygon( + [ + [ + c[0] + lat_shift if c[1] * pole >= 90 else c[0], + pole * 180 - c[1] if c[1] * pole >= 90 else c[1], + ] + for c in polygon.exterior.coords + ], + [ + [ + [ + c[0] + lat_shift if c[1] * pole >= 90 else c[0], + pole * 180 - c[1] if c[1] * pole >= 90 else c[1], + ] + for c in i.coords + ] + for i in polygon.interiors + ], + ) + # recursive call for each polygon + return MultiPolygon( + _flatten_polygons([_wrap_polygon_over_pole(p, pole) for p in polygon.geoms]) + ) + + +def _split_polygon_over_pole( + polygon: Polygon | MultiPolygon, pole: int +) -> Polygon | MultiPolygon: + """ + Splits a polygon that encompasses a pole (`pole` = 1 for the North + pole, -1 for the South pole; i.e. exceeds 90 * pole degrees latitude) + into a valid MultiPolygon on the standard (-180, -90, 180, 90) plane. + The polygon is first split along the pole latitude; any resulting + piece that also straddles the prime meridian (0 degrees longitude) + while beyond the pole is split again there, since such a piece cannot + be wrapped to one side in a single step. Each piece exceeding the pole + latitude is then wrapped to its correct latitude/longitude. A polygon + that does not exceed the pole latitude is returned unchanged. + + Args: + polygon (shapely.geometry.Polygon | shapely.geometry.MultiPolygon): The polygon to split. + pole (int): 1 for the North pole, -1 for the South pole. + + Returns: + shapely.geometry.Polygon | shapely.geometry.MultiPolygon: The split polygon. + """ + if isinstance(polygon, Polygon): + if all(c[1] * pole <= 90 for c in polygon.exterior.coords): + # no splitting necessary + return polygon + # split polygon along the pole + pole_line = LineString([(-360, 90 * pole), (360, 90 * pole)]) + parts = split(polygon, pole_line) + # check and split part over prime meridian if necessary + meridian_line = LineString([(0, 90 * pole), (0, 180 * pole)]) + for part in parts.geoms: + if part.crosses(meridian_line): + parts = GeometryCollection( + [g for g in parts.geoms if g != part] + + list(split(part, meridian_line).geoms) + ) + # convert to a multi polygon + if isinstance(parts, GeometryCollection): + parts = _convert_collection_to_polygon(parts) + # return polygon with components wrapped over the pole + return _wrap_polygon_over_pole(parts, pole) + # recursive call for each polygon + return MultiPolygon( + _flatten_polygons([_split_polygon_over_pole(p, pole) for p in polygon.geoms]) + ) + + +@overload +def _wrap_polygon_over_antimeridian(polygon: Polygon) -> Polygon: ... + + +@overload +def _wrap_polygon_over_antimeridian(polygon: MultiPolygon) -> MultiPolygon: ... + + +def _wrap_polygon_over_antimeridian( + polygon: Polygon | MultiPolygon, +) -> Polygon | MultiPolygon: + """ + Wraps polygon coordinates over the antimeridian. Due to buffering and projection, + sometimes longitudes exceed 180 degrees. This method wraps them to + the correct longitude between -180 and 180 degrees by adding or + subtracting 360 degrees to every coordinate. + + This method requires all coordinates to be at or beyond the same side + of the antimeridian (all at or below -180 degrees, or all at or above + 180 degrees): this is guaranteed by `_split_polygon_antimeridian`, which + is the only caller, since it splits along a single meridian line before + wrapping, confining each resulting piece to one side. + + Note: this method only changes coordinates: it does not create a MultiPolygon. + + Args: + polygon (shapely.geometry.Polygon | shapely.geometry.MultiPolygon): The polygon to wrap. + + Returns: + shapely.geometry.Polygon | shapely.geometry.MultiPolygon: The wrapped polygon. + """ + if isinstance(polygon, Polygon): + if all(c[0] >= -180 and c[0] <= 180 for c in polygon.exterior.coords): + # no wrapping necessary + return polygon + if all(c[0] <= -180 for c in polygon.exterior.coords): + # map longitudes from (-540, -180] to (-180, 180] + return Polygon( + [[c[0] + 360, c[1]] for c in polygon.exterior.coords], + [[[c[0] + 360, c[1]] for c in i.coords] for i in polygon.interiors], + ) + # map longitudes from [180, 540) to [-180, 180) + return Polygon( + [[c[0] - 360, c[1]] for c in polygon.exterior.coords], + [[[c[0] - 360, c[1]] for c in i.coords] for i in polygon.interiors], + ) + # recursive call for each polygon + return MultiPolygon( + _flatten_polygons([_wrap_polygon_over_antimeridian(p) for p in polygon.geoms]) + ) + + +def _convert_collection_to_polygon( + collection: GeometryCollection, +) -> Polygon | MultiPolygon: + """ + Converts a GeometryCollection to a Polygon or MultiPolygon. Quick clipping + can create dirty results with points or lines on boundaries. This method + drops any points or lines from a GeometryCollection, flattens any + MultiPolygon members into their constituent polygons, and returns a + single Polygon if only one remains, or a MultiPolygon otherwise + (empty if no polygons remain). + + Args: + collection (shapely.geometry.GeometryCollection): The geometry collection to convert. + + Returns: + shapely.geometry.Polygon | shapely.geometry.MultiPolygon: The converted polygon. + """ + pgons = [p for p in collection.geoms if isinstance(p, Polygon)] + [ + p for g in collection.geoms if isinstance(g, MultiPolygon) for p in g.geoms + ] + if len(pgons) == 1: + return pgons[0] + return MultiPolygon(pgons) + + +def _split_polygon_antimeridian( + polygon: Polygon | MultiPolygon, +) -> Polygon | MultiPolygon: + """ + Splits a polygon that crosses the antimeridian (180 degrees longitude) + into a valid MultiPolygon on the standard (-180, 180) longitude range. + A crossing is detected when adjacent exterior vertices jump by 180 + degrees of longitude or more. + + Two cases are handled differently: + + - If the polygon's vertex longitudes wrap all the way around the globe + (e.g. a polar cap that does not itself exceed +/-90 degrees + latitude), it is reconstructed with a flattened edge at the pole and + split along the prime meridian instead of the antimeridian. Note: + the raw result of this case may be reported as invalid (the two + pieces touch along the shared prime-meridian cut edge); the public + `split_polygon` function repairs this via `shapely.make_valid`. + - Otherwise, coordinates are "unrolled" past +/-180 degrees according to + the cumulative crossing direction, split along the antimeridian, and + wrapped back with `_wrap_polygon_over_antimeridian`. + + A polygon with no detected crossing is returned unchanged. Note: this + function only supports polygons that span LESS than 360 degrees longitude. + + Args: + polygon (shapely.geometry.Polygon | shapely.geometry.MultiPolygon): The polygon to split. + + Returns: + shapely.geometry.Polygon | shapely.geometry.MultiPolygon: The split polygon. + """ + if isinstance(polygon, Polygon): + lon = np.array([c[0] for c in polygon.exterior.coords]) + # check if any longitudes cross the anti-meridian + # (adjacent coordinate longitude differs by more than 180 degrees) + if all(np.abs(np.diff(lon)) < 180): + return polygon + # check if this polygon contains a pole + if Polygon(zip(np.cos(np.radians(lon)), np.sin(np.radians(lon)))).contains( + Point(0, 0) + ): + # extract (lon, lat) only, discarding any z-dimension, and sort by longitude + coords = [(c[0], c[1]) for c in polygon.exterior.coords[0:-1]] + coords.sort(key=lambda r: r[0]) + # determine if contains north or south pole based on sign of mean latitude + n_s = 1 if np.array(coords)[:, 1].mean() > 0 else -1 + # interpolate latitude at antimeridian + lat = np.interp( + 180, [coords[-1][0], coords[0][0] + 180], [coords[-1][1], coords[0][1]] + ) + # reconstruct polygon (ccw) with added coords on antimeridian + pgon = Polygon( + [(-180, 90 * n_s), (-180, lat)] + + coords + + [(180, lat), (180, 90 * n_s), (-180, 90 * n_s)], + [ + [(c[0], c[1]) for c in interior.coords] + for interior in polygon.interiors + ], + ) + # return polygon split down prime meridian to improve handling + parts = split(pgon, LineString([(0, -180), (0, 180)])) + # convert to multi polygon + if isinstance(parts, GeometryCollection): + parts = _convert_collection_to_polygon(parts) + return parts + # find anti-meridian crossings and calculate shift direction + # coords from W -> E (shift < 0) will add 360 degrees to E component + # coords from E -> W (shift > 0) will subtract 360 degrees from W component + shift = np.insert(np.cumsum(np.around(np.diff(lon) / 360)), 0, 0) + pgon = Polygon( + [ + (c[0] - 360 * shift[i], c[1]) + for i, c in enumerate(polygon.exterior.coords) + ], + [ + [ + ( + ic[0] + - 360 * np.interp(ic[0], np.sort(lon), shift[np.argsort(lon)]), + ic[1], + ) + for ic in i.coords + ] + for i in polygon.interiors + ], + ) + # split along the anti-meridian (-180 for shift > 0; 180 for shift < 0) + shift_dir = -180 if shift.max() >= 1 else 180 + parts = split(pgon, LineString([(shift_dir, -180), (shift_dir, 180)])) + # convert to multi polygon + if isinstance(parts, GeometryCollection): + parts = _convert_collection_to_polygon(parts) + # return polygon with components wrapped over anti-meridian + return _wrap_polygon_over_antimeridian(parts) + if isinstance(polygon, MultiPolygon): + # recursive call for each polygon + return MultiPolygon( + _flatten_polygons([_split_polygon_antimeridian(p) for p in polygon.geoms]) + ) + raise ValueError("Unknown geometry: " + str(type(polygon))) + + +def split_polygon( + polygon: Polygon | MultiPolygon, +) -> Polygon | MultiPolygon: + """ + Splits a Polygon into a MultiPolygon if it crosses the anti-meridian + (180 degrees longitude), exceeds the north pole (90 degrees latitude), or + exceeds the south pole (-90 degrees latitude). Note: this function + only supports polygons that span LESS than 360 degrees longitude. + Operates on (longitude, latitude) only: any z-dimension on the input + is discarded. Use `project_polygon_to_elevation` to add elevation back + after splitting. + + Args: + polygon (shapely.geometry.Polygon | shapely.geometry.MultiPolygon): The polygon to split. + + Returns: + shapely.geometry.Polygon | shapely.geometry.MultiPolygon: The split polygon. + """ + polygon = _split_polygon_over_pole( + _split_polygon_over_pole(_split_polygon_antimeridian(polygon), pole=-1), + pole=1, + ) + # invalid polygons can arise from narrow sensor geometries in polar regions + if not polygon.is_valid: + # try to fix geometry + polygon = make_valid(polygon) # type: ignore + if isinstance(polygon, GeometryCollection): + polygon = _convert_collection_to_polygon(polygon) + return polygon + + +def get_planar_bounds( + mask: Polygon | MultiPolygon | None, +) -> tuple[float, float, float, float]: + """ + Generates a tuple of bounds for a polygon mask. + + Known limitation: this method assumes `mask` lies on the planar + (non-antimeridian-crossing) longitude domain. A mask crossing the + antimeridian with longitude below -180 (e.g. bounds spanning + -190 to -180, representing the same region as 170 to 180) only has + its max_longitude corrected to 180; min_longitude is left unadjusted, + so the resulting bounds do not coherently describe such a mask. + Fully supporting antimeridian-crossing masks can be achieved by + pre-processing the geometry (e.g. via `split_polygon`). + + Args: + mask (shapely.geometry.Polygon | shapely.geometry.MultiPolygon | None): + Geometric shape using WGS84 (EPSG:4326) + geodetic coordinates in a Polygon or MultiPolygon. + + Returns: + tuple[float, float, float, float]: min longitude (degrees), + min latitude (degrees), max longitude (degrees), max latitude (degrees) + """ + if isinstance(mask, (Polygon, MultiPolygon)): + if not mask.is_valid: + raise ValueError("Mask is not a valid Polygon or MultiPolygon.") + total_bounds = mask.bounds + else: + total_bounds = [-180, -90, 180, 90] + min_longitude = total_bounds[0] + min_latitude = total_bounds[1] + max_longitude = 180 if total_bounds[2] == -180 else total_bounds[2] + max_latitude = total_bounds[3] + return (min_longitude, min_latitude, max_longitude, max_latitude) + + +def normalize_geometry( + geometry: Polygon | MultiPolygon | gpd.GeoDataFrame, +) -> gpd.GeoDataFrame: + """ + Normalize geometry to a GeoDataFrame with antimeridian wrapping. + + Args: + geometry (shapely.geometry.Polygon | shapely.geometry.MultiPolygon | + geopandas.GeoDataFrame): The geometry to normalize. + + Returns: + geopandas.GeoDataFrame: The normalized geometry. + """ + if isinstance(geometry, (Polygon, MultiPolygon)): + if not geometry.is_valid: + raise ValueError("Geometry is not a valid Polygon or MultiPolygon.") + geometry = gpd.GeoDataFrame(geometry=gpd.GeoSeries([geometry]), crs="EPSG:4326") + elif isinstance(geometry, gpd.GeoSeries): + geometry = gpd.GeoDataFrame(geometry=geometry, crs="EPSG:4326") + if isinstance(geometry, gpd.GeoDataFrame): + geometry["geometry"] = geometry.apply( + lambda r: split_polygon(r.geometry), + axis=1, + ) + return geometry diff --git a/src/tatc/utils/observation.py b/src/tatc/utils/observation.py new file mode 100644 index 0000000..891ab08 --- /dev/null +++ b/src/tatc/utils/observation.py @@ -0,0 +1,266 @@ +""" +Observation utility functions. + +@author: Paul T. Grogan +""" + +from __future__ import annotations + +import numpy as np +from numba import njit + +from .. import constants +from .orbital import compute_ground_surface_velocity, semimajor_axis_to_mean_motion + + +@njit +def swath_width_to_field_of_regard( + altitude: float, swath_width: float, elevation: float = 0 +) -> float: + """ + Fast computation of the field of regard (the total angular width an + instrument must be able to point or scan across) required to observe a + specified ground swath width, assuming a spherical Earth (using the + mean Earth radius) and a circular orbit at constant altitude. + + Args: + altitude (float): Altitude (meters) above the mean Earth radius for the + observing instrument. + swath_width (float): Ground swath width (meters): the cross-track distance, + measured along the Earth's surface, at the specified elevation. + elevation (float): Elevation (meters) above the mean Earth radius of the observed swath. + + Returns: + float: The field of regard (degrees): the full angular width, centered on + nadir, that the instrument must be able to point across to observe the swath. + """ + # rho is the angular radius of the earth viewed by the satellite + sin_rho = (constants.EARTH_MEAN_RADIUS + elevation) / ( + constants.EARTH_MEAN_RADIUS + altitude + ) + # lambda is the Earth central angle + sin_lambda = np.sin((swath_width / 2) / (constants.EARTH_MEAN_RADIUS + elevation)) + # eta is the angular radius of the region viewable by the satellite + tan_eta = sin_rho * sin_lambda / (1 - sin_rho * np.cos(np.arcsin(sin_lambda))) + return np.degrees(2 * np.arctan(tan_eta)) + + +@njit +def swath_width_to_field_of_view( + altitude: float, swath_width: float, look_angle: float = 0, elevation: float = 0 +) -> float: + """ + Fast computation of the field of view (the angular extent, as seen + from the satellite, spanning the near to far edge of a swath) required + to observe a specified ground swath width centered on a given off-nadir + look angle, assuming a spherical Earth (using the mean Earth radius) + and a circular orbit at constant altitude. Unlike + `swath_width_to_field_of_regard`, the swath is not centered on nadir, + so the field of view spans asymmetrically between the near and far + edges of the swath. + + Args: + altitude (float): Altitude (meters) above the mean Earth radius for the + observing instrument. + swath_width (float): Ground swath width (meters): the cross-track distance, + measured along the Earth's surface, centered on the look angle direction. + look_angle (float): Off-nadir look angle (degrees), measured at the satellite, + to the center of the swath. Saturates at the horizon-limited maximum. + elevation (float): Elevation (meters) above the mean Earth radius of the observed swath. + + Returns: + float: The field of view (degrees): the angular extent, as seen from the + satellite, spanning the near to far edge of the swath. + """ + # rho is the angular radius of the earth viewed by the satellite + sin_rho = (constants.EARTH_MEAN_RADIUS + elevation) / ( + constants.EARTH_MEAN_RADIUS + altitude + ) + # eta is the angular radius from sub-satellite point to center of view + sin_eta = min(sin_rho, np.sin(np.radians(look_angle))) + # epsilon is the satellite elevation from the center of view + cos_epsilon = sin_eta / sin_rho + # lambda is the Earth central angle to the center of view + _lambda = np.pi / 2 - np.arcsin(sin_eta) - np.arccos(cos_epsilon) + sin_lambda_1 = np.sin( + _lambda - (swath_width / 2) / (constants.EARTH_MEAN_RADIUS + elevation) + ) + sin_lambda_2 = np.sin( + _lambda + (swath_width / 2) / (constants.EARTH_MEAN_RADIUS + elevation) + ) + # eta is the angular radius of the region viewable by the satellite + tan_eta_1 = sin_rho * sin_lambda_1 / (1 - sin_rho * np.cos(np.arcsin(sin_lambda_1))) + tan_eta_2 = sin_rho * sin_lambda_2 / (1 - sin_rho * np.cos(np.arcsin(sin_lambda_2))) + return np.degrees(np.arctan(tan_eta_2) - np.arctan(tan_eta_1)) + + +@njit +def field_of_regard_to_swath_width( + altitude: float, field_of_regard: float, elevation: float = 0 +) -> float: + """ + Fast computation of the ground swath width observable for a specified + field of regard, assuming a spherical Earth (using the mean Earth + radius) and a circular orbit at constant altitude. This is the inverse + of `swath_width_to_field_of_regard`. A `field_of_regard` at or beyond + the horizon-limited maximum (i.e. pointing to the horizon) saturates + to the maximum observable swath width rather than growing without bound. + + Args: + altitude (float): Altitude (meters) above the mean Earth radius for the + observing instrument. + field_of_regard (float): The full angular width (degrees), centered on + nadir, that the instrument points or scans across. + elevation (float): Elevation (meters) above the mean Earth radius of the observed swath. + + Returns: + float: The ground swath width (meters): the cross-track distance, + measured along the Earth's surface, at the specified elevation. + """ + # rho is the angular radius of the earth viewed by the satellite + sin_rho = (constants.EARTH_MEAN_RADIUS + elevation) / ( + constants.EARTH_MEAN_RADIUS + altitude + ) + # eta is the angular radius of the region viewable by the satellite + sin_eta = min(sin_rho, np.sin(np.radians(field_of_regard) / 2)) + # epsilon is the min satellite elevation for obs (grazing angle) + cos_epsilon = sin_eta / sin_rho + # lambda is the Earth central angle + _lambda = np.pi / 2 - np.arcsin(sin_eta) - np.arccos(cos_epsilon) + return 2 * (constants.EARTH_MEAN_RADIUS + elevation) * _lambda + + +@njit +def compute_field_of_regard( + altitude: float, min_elevation_angle: float, elevation: float = 0 +) -> float: + """ + Fast computation of field of regard for observation with a minimum altitude angle. + + Args: + altitude (float): Altitude (meters) above WGS 84 datum for the observing instrument. + min_elevation_angle (float): The minimum elevation angle (degrees) for observation. + elevation (float): Elevation (meters) above WGS 84 datum to observe. + + Returns: + float: Angular width (degrees) of observation. + """ + # rho is the angular radius of the earth viewed by the satellite + sin_rho = (constants.EARTH_MEAN_RADIUS + elevation) / ( + constants.EARTH_MEAN_RADIUS + altitude + ) + # epsilon is the min satellite elevation for obs (grazing angle) + cos_epsilon = np.cos(np.radians(min_elevation_angle)) + # eta is the angular radius of the region viewable by the satellite + sin_eta = sin_rho * cos_epsilon + return np.degrees(np.arcsin(sin_eta) * 2) + + +@njit +def compute_min_elevation_angle( + altitude: float, field_of_regard: float, elevation: float = 0 +) -> float: + """ + Fast computation of minimum elevation angle required to observe a point. + + Args: + altitude (float): Altitude (meters) above WGS 84 datum for the observing instrument. + field_of_regard (float): Angular width (degrees) of observation. + elevation (float): Elevation (meters) above WGS 84 datum to observe. + + Returns: + float: The minimum elevation angle (degrees) for observation. + """ + # eta is the angular radius of the region viewable by the satellite + sin_eta = np.sin(np.radians(field_of_regard) / 2) + # rho is the angular radius of the earth viewed by the satellite + sin_rho = (constants.EARTH_MEAN_RADIUS + elevation) / ( + constants.EARTH_MEAN_RADIUS + altitude + ) + # epsilon is the min satellite elevation for obs (grazing angle) + cos_epsilon = sin_eta / sin_rho + if cos_epsilon > 1: + return 0 + return np.degrees(np.arccos(cos_epsilon)) + + +@njit +def compute_max_access_time(altitude: float, min_elevation_angle: float) -> float: + """ + Fast computation of maximum access time to observe a point. + + Args: + altitude (float): Orbit altitude (meters). + min_elevation_angle (float): Minimum elevation angle (degrees) for observation. + + Returns: + float: The maximum access time (seconds) for observation. + """ + # angular distance from sub-satellite point to edge of viewable region + earth_angle = np.degrees( + np.arccos( + constants.EARTH_MEAN_RADIUS + / (constants.EARTH_MEAN_RADIUS + altitude) + * np.cos(np.radians(min_elevation_angle)) + ) + - np.radians(min_elevation_angle) + ) + # max access time is twice the earth central angle divided by the mean motion of the orbit + return ( + 2 + * earth_angle + / semimajor_axis_to_mean_motion(constants.EARTH_MEAN_RADIUS + altitude) + ) + + +@njit +def compute_max_transit_time( + mean_altitude: float, inclination: float, along_track: float +) -> float: + """ + Fast computation of the maximum (conservative, worst-case) transit time + to cover a specified along-track distance, using the slowest ground + track velocity attained over the orbit, which occurs at the orbit's + extreme latitude (min(inclination, 180 - inclination)), not the equator. + + Args: + mean_altitude (float): The mean orbit altitude (meters) above WGS 84 datum. + inclination (float): The orbit inclination (degrees). + along_track (float): The along track distance (meters) observed during access. + + Returns: + float: The maximum access time (seconds) to traverse the along track distance. + """ + # slowest velocity occurs at the orbit's extreme latitude + extreme_latitude = min(inclination, 180 - inclination) + v_slowest = compute_ground_surface_velocity( + mean_altitude, 0, inclination, extreme_latitude + ) + return along_track / v_slowest + + +@njit +def compute_min_along_track_distance( + mean_altitude: float, inclination: float, access_time: float +) -> float: + """ + Fast computation of the minimum (conservative, worst-case) along-track + distance observed in a specified access time, using the slowest ground + track velocity attained over the orbit, which occurs at the orbit's + extreme latitude (min(inclination, 180 - inclination)), not the + equator. This is the inverse of `compute_max_transit_time`. + + Args: + mean_altitude (float): The mean orbit altitude (meters) above WGS 84 datum. + inclination (float): The orbit inclination (degrees). + access_time (float): The access time (seconds) during observation. + + Returns: + float: The minimum along track distance (meters) observed during the access time. + """ + # slowest velocity occurs at the orbit's extreme latitude + extreme_latitude = min(inclination, 180 - inclination) + v_slowest = compute_ground_surface_velocity( + mean_altitude, 0, inclination, extreme_latitude + ) + return access_time * v_slowest diff --git a/src/tatc/utils/orbital.py b/src/tatc/utils/orbital.py new file mode 100644 index 0000000..95708ef --- /dev/null +++ b/src/tatc/utils/orbital.py @@ -0,0 +1,318 @@ +""" +Orbital utility functions. + +@author: Paul T. Grogan +""" + +from __future__ import annotations + +import numpy as np +from numba import njit + +from .. import constants + + +@njit +def compute_orbit_inertial_velocity(mean_altitude: float) -> float: + """ + Fast computation of orbit inertial velocity (the orbital speed relative + to a non-rotating, Earth-centered frame) assuming a circular orbit + around a spherical Earth (using the mean Earth radius). + + Args: + mean_altitude (float): Orbit mean altitude (meters) above the mean Earth radius. + + Returns: + float: Inertial orbit velocity (meters/second). + """ + return np.sqrt(constants.EARTH_MU / (constants.EARTH_MEAN_RADIUS + mean_altitude)) + + +@njit +def compute_ground_inertial_velocity( + mean_altitude: float, elevation: float = 0 +) -> float: + """ + Fast computation of the inertial velocity (relative to a non-rotating, + Earth-centered frame) of the sub-satellite point projected onto a + sphere at the specified elevation, assuming a circular orbit around a + spherical Earth (using the mean Earth radius). Since the projected + point shares the satellite's angular velocity, its linear velocity + scales with its (smaller, or larger if elevation exceeds mean_altitude) + radius relative to the orbit's radius. + + Args: + mean_altitude (float): Orbit mean altitude (meters) above the mean Earth radius. + elevation (float): Surface elevation (meters) above the mean Earth + radius at which to project the ground point. + + Returns: + float: Ground point inertial velocity (meters/second). + """ + v_orbital = compute_orbit_inertial_velocity(mean_altitude) + return ( + v_orbital + * (constants.EARTH_MEAN_RADIUS + elevation) + / (constants.EARTH_MEAN_RADIUS + mean_altitude) + ) + + +@njit +def compute_ground_surface_velocity( + mean_altitude: float, + elevation: float = 0, + inclination: float = 0, + latitude: float = 0, +) -> float: + """ + Fast computation of ground surface velocity (relative to the rotating + Earth's surface) assuming a circular orbit around a spherical Earth + (using the mean Earth radius). + + Args: + mean_altitude (float): Orbit mean altitude (meters) above the mean Earth radius. + elevation (float): Surface elevation (meters) above the mean Earth + radius at which to project the ground point. + inclination (float): Orbit inclination (degrees). + latitude (float): Surface latitude (degrees) at which to evaluate the ground velocity. + + Returns: + float: Ground surface velocity (meters/second), relative to the rotating Earth. + """ + v_inertial = compute_ground_inertial_velocity(mean_altitude, elevation) + # compute flight path angle beta relative to due North + sin_beta = np.cos(np.deg2rad(inclination)) / np.cos(np.deg2rad(latitude)) + sin_beta = max(-1.0, min(1.0, sin_beta)) + beta = np.arcsin(sin_beta) + v_surface_north = v_inertial * np.cos(beta) + v_surface_east = v_inertial * np.sin(beta) + v_earth_west = ( + 2 + * np.pi + / constants.EARTH_SIDEREAL_DAY_S + * constants.EARTH_MEAN_RADIUS + * np.cos(np.deg2rad(latitude)) + ) + return np.sqrt((v_surface_east - v_earth_west) ** 2 + v_surface_north**2) + + +@njit +def semimajor_axis_to_mean_motion(semimajor_axis: float) -> float: + """ + Fast computation of mean motion (average angular rate) from Kepler's + third law, assuming a circular orbit around a spherical Earth. + + Args: + semimajor_axis (float): Orbit semimajor axis (meters). + + Returns: + float: Orbit mean motion (degrees/second). + """ + return np.degrees(np.sqrt(constants.EARTH_MU / semimajor_axis**3)) + + +@njit +def mean_motion_to_orbit_period(mean_motion: float) -> float: + """ + Fast computation of orbital period: the time (360 degrees of mean + motion) to complete one revolution. This function is its own inverse: + calling it again on a period (seconds) recovers the mean motion + (degrees/second), since both are 360 divided by the other. + + Args: + mean_motion (float): Orbit mean motion (degrees/second). + + Returns: + float: Orbital period (seconds). + """ + return 360 / mean_motion + + +@njit +def mean_motion_to_semimajor_axis(mean_motion: float) -> float: + """ + Fast computation of semimajor axis from Kepler's third law, assuming a + circular orbit around a spherical Earth. This is the inverse of + `semimajor_axis_to_mean_motion`. + + Args: + mean_motion (float): Orbit mean motion (degrees/second). + + Returns: + float: The semimajor axis (meters). + """ + return np.cbrt(constants.EARTH_MU / (np.radians(mean_motion) ** 2)) + + +@njit +def semimajor_axis_to_orbit_period(semimajor_axis: float) -> float: + """ + Fast computation of orbital period from Kepler's third law, assuming a + circular orbit around a spherical Earth. + + Args: + semimajor_axis (float): Orbit semimajor axis (meters). + + Returns: + float: Orbital period (seconds). + """ + return mean_motion_to_orbit_period(semimajor_axis_to_mean_motion(semimajor_axis)) + + +@njit +def mean_anomaly_to_true_anomaly(mean_anomaly: float, eccentricity: float = 0) -> float: + """ + Approximates orbit true anomaly using a third-order series expansion. + + Args: + mean_anomaly (float): Orbit mean anomaly (degrees). + eccentricity (float): Orbit eccentricity. + + Returns: + float: Orbit true anomaly (degrees). + """ + mean_anomaly_rad = np.radians(mean_anomaly) + true_anomaly_rad = ( + mean_anomaly_rad + + (2 * eccentricity - (1 / 4) * eccentricity**3) * np.sin(mean_anomaly_rad) + + (5 / 4) * eccentricity**2 * np.sin(2 * mean_anomaly_rad) + + (13 / 12) * eccentricity**3 * np.sin(3 * mean_anomaly_rad) + ) + return np.degrees(true_anomaly_rad) + + +@njit +def true_anomaly_to_mean_anomaly(true_anomaly: float, eccentricity: float = 0) -> float: + """ + Approximates orbit mean anomaly using a third-order series expansion. + + Args: + true_anomaly (float): Orbit true anomaly (degrees). + eccentricity (float): Orbit eccentricity. + + Returns: + float: Orbit mean anomaly (degrees). + """ + true_anomaly_rad = np.radians(true_anomaly) + mean_anomaly_rad = ( + true_anomaly_rad + - 2 * eccentricity * np.sin(true_anomaly_rad) + + ((3 / 4) * eccentricity**2 + (1 / 8) * eccentricity**4) + * np.sin(2 * true_anomaly_rad) + - (1 / 3) * eccentricity**3 * np.sin(3 * true_anomaly_rad) + + (5 / 32) * eccentricity**4 * np.sin(4 * true_anomaly_rad) + ) + return np.degrees(mean_anomaly_rad) + + +@njit +def compute_apoapsis_radius(semimajor_axis: float, eccentricity: float) -> float: + """ + Fast computation of the apoapsis radius: the maximum orbit-to-Earth's- + center distance, at the far point of the ellipse from the focus. + + Args: + semimajor_axis (float): Orbit semimajor axis (meters). + eccentricity (float): Orbit eccentricity. + + Returns: + float: The apoapsis radius (meters). + """ + return semimajor_axis * (1 + eccentricity) + + +@njit +def compute_j2_raan_rate( + semimajor_axis: float, inclination: float, eccentricity: float +) -> float: + """ + Fast computation of the secular precession rate of the right ascension + of the ascending node (nodal regression) due to Earth's J2 oblateness + perturbation. The rate is negative (westward regression) for prograde + orbits (inclination < 90 degrees), zero for polar orbits (cos(90) = 0), + and positive for retrograde orbits. Sun-synchronous orbits are defined + by choosing an inclination that makes this rate equal to the Earth's + mean motion around the Sun (about 360/365.2422 degrees/day). + + Args: + semimajor_axis (float): Orbit semimajor axis (meters). + inclination (float): Orbit inclination (degrees). + eccentricity (float): Orbit eccentricity. + + Returns: + float: The right ascension of ascending node precession rate (degrees/second). + """ + return ( + -3 + / 2 + * constants.EARTH_J2 + * semimajor_axis_to_mean_motion(semimajor_axis) + * (constants.EARTH_MEAN_RADIUS / (semimajor_axis * (1 - eccentricity**2))) ** 2 + * np.cos(np.radians(inclination)) + ) + + +@njit +def compute_j2_aop_rate( + semimajor_axis: float, inclination: float, eccentricity: float +) -> float: + """ + Fast computation of the secular precession rate of the argument of + periapsis due to Earth's J2 oblateness perturbation. The rate is + positive below the critical inclination (~63.43 degrees, where + 5*cos^2(inclination) - 1 = 0), negative above it, and exactly zero at + the critical inclination itself. Molniya-type orbits use this + critical inclination specifically so their periapsis (and apoapsis) + location does not drift over time. + + Args: + semimajor_axis (float): Orbit semimajor axis (meters). + inclination (float): Orbit inclination (degrees). + eccentricity (float): Orbit eccentricity. + + Returns: + float: The argument of periapsis rate (degrees/second). + """ + return ( + 3 + / 4 + * constants.EARTH_J2 + * semimajor_axis_to_mean_motion(semimajor_axis) + * (constants.EARTH_MEAN_RADIUS / (semimajor_axis * (1 - eccentricity**2))) ** 2 + * (5 * np.cos(np.radians(inclination)) ** 2 - 1) + ) + + +@njit +def compute_j2_mean_motion_rate( + semimajor_axis: float, inclination: float, eccentricity: float +) -> float: + """ + Fast computation of the secular correction to mean anomaly's rate of + advance (beyond the unperturbed two-body mean motion) due to Earth's + J2 oblateness perturbation. Unlike the argument of periapsis rate, + this correction is generally nonzero even at the critical inclination + (~63.43 degrees), since its trigonometric factor + (3*cos^2(inclination) - 1) only vanishes near ~54.7 degrees, not + ~63.43 degrees. The true ("anomalistic") mean motion is the + unperturbed mean motion plus this correction; the anomalistic period + (time from periapsis to periapsis) is 360 degrees divided by that + sum, rather than by the unperturbed mean motion alone. + + Args: + semimajor_axis (float): Orbit semimajor axis (meters). + inclination (float): Orbit inclination (degrees). + eccentricity (float): Orbit eccentricity. + + Returns: + float: The correction to the mean anomaly rate (degrees/second). + """ + return ( + 3 + / 4 + * constants.EARTH_J2 + * semimajor_axis_to_mean_motion(semimajor_axis) + * (constants.EARTH_MEAN_RADIUS / (semimajor_axis * (1 - eccentricity**2))) ** 2 + * np.sqrt(1 - eccentricity**2) + * (3 * np.cos(np.radians(inclination)) ** 2 - 1) + ) diff --git a/src/tatc/utils/projection.py b/src/tatc/utils/projection.py new file mode 100644 index 0000000..8ae663d --- /dev/null +++ b/src/tatc/utils/projection.py @@ -0,0 +1,474 @@ +""" +Projection utility functions. + +@author: Paul T. Grogan +""" + +from __future__ import annotations + +from collections.abc import Iterable + +import numpy as np +from pyproj import Transformer +from shapely import Geometry +from shapely.geometry import ( + MultiPolygon, + Point, + Polygon, +) +from shapely.ops import transform +from skyfield.api import wgs84 +from skyfield.framelib import itrs +from skyfield.positionlib import Geocentric +from skyfield.toposlib import GeographicPosition +from spiceypy.spiceypy import edlimb, inelpl, nvp2pl, recgeo, surfpt +from spiceypy.utils.exceptions import NotFoundError + +from .. import config, constants +from .geometry import project_polygon_to_elevation, split_polygon +from .observation import field_of_regard_to_swath_width +from .orbital import compute_ground_surface_velocity + + +def compute_projected_ray_position( # pylint: disable=too-many-branches,too-many-statements + orbit_track: Geocentric, + cross_track_field_of_view: float, + along_track_field_of_view: float, + roll_angle: float = 0, + pitch_angle: float = 0, + is_rectangular: bool = False, + angle: float = 0, + elevation: float = 0, +) -> GeographicPosition: + """ + Get the location of a projected ray from an instrument. The ray is cast + from the satellite position toward the WGS 84 geoid at the specified + elevation; if it misses the geoid entirely (e.g. an off-nadir angle + pointing past the horizon), the projected position instead falls back + to the nearest point on the visible Earth limb. Zero roll, pitch, and + field of view center on the geodetic nadir (the WGS 84 ellipsoid + surface normal through the satellite), matching Skyfield's + `wgs84.subpoint_of`/`wgs84.geographic_position_of`. + + Args: + orbit_track (skyfield.positionlib.Geocentric): the satellite orbit track. + cross_track_field_of_view (float): the instrument cross-track + (orthogonal to velocity vector) field of view (degrees). + along_track_field_of_view (float): the instrument along-track + (parallel to velocity vector) field of view (degrees). + roll_angle (float): the instrument roll (right-hand about + velocity vector) angle (degrees). + pitch_angle (float): the instrument pitch (right-hand about + orbit normal vector) angle (degrees). + is_rectangular (bool): `True` if the instrument view has a rectangular + shape (otherwise elliptical). + angle (float): ray angle (degrees) counterclockwise from right-hand cross-track + direction about the instrument field of view. + elevation (float): The elevation (meters) at which project the footprint. + + Returns: + (skyfield.toposlib.GeographicPosition): the geographic position of the projected ray + """ + # convert to radians for internal use + angle = np.radians(angle) + # extract earth-fixed position and velocity + position, velocity = orbit_track.frame_xyz_and_velocity(itrs) + v_m_per_s = np.array(velocity.m_per_s) + p_m = np.array(position.m) + # velocity unit vector + v = np.divide(v_m_per_s, np.linalg.norm(v_m_per_s, axis=0)) + # nadir unit vector: the geodetic vertical, i.e. the WGS 84 + # ellipsoid surface normal at the sub-satellite point, pointed inward + # (toward the Earth). This is NOT simply the geocentric direction + # (-position, toward the Earth's center): the two coincide only at the + # equator and poles, and otherwise differ by up to the WGS 84 + # geodetic/geocentric latitude discrepancy (~0.19 degrees), which can + # shift a projected nadir point by kilometers at typical LEO altitudes. + subpoint = wgs84.geographic_position_of(orbit_track) + lat = np.array(subpoint.latitude.radians) + lon = np.array(subpoint.longitude.radians) + n = -np.array([np.cos(lat) * np.cos(lon), np.cos(lat) * np.sin(lon), np.sin(lat)]) + # whether orbit_track represents a single time or a vector of times + is_vectorized = len(np.shape(p_m)) > 1 + # cross-track unit vector + if is_vectorized: + c = np.cross(v, n, 0, 0, -1).T + else: + c = np.cross(v, n) + # ray pointed at the field of view center (before adding the field of view extent) + base_ray = ( + n + v * np.tan(np.radians(pitch_angle)) + c * np.tan(np.radians(roll_angle)) + ) + # construct projected ray + if is_rectangular: + # find orientation of rectangle corner + theta = np.arctan(along_track_field_of_view / cross_track_field_of_view) + # along track half width + tan_a_2 = np.tan(np.radians(along_track_field_of_view / 2)) + # cross track half width + tan_c_2 = np.tan(np.radians(cross_track_field_of_view / 2)) + # compose the ray by walking around the rectangle boundary: the (v, c) + # coefficients are determined together, one segment at a time, rather + # than by two independently re-derived branch chains. Corners are at + # theta, pi - theta, pi + theta, and 2*pi - theta; the pi/2, pi, and + # 3*pi/2 splits are just internal subdivisions of a single flat edge + # (each formula is continuous across them) chosen to keep every + # tan() argument close to zero. + if angle <= theta: + # right edge, upper half + v_coef, c_coef = tan_c_2 * np.tan(angle), tan_c_2 + elif angle < np.pi / 2: + # top edge, right half + v_coef, c_coef = tan_a_2, tan_a_2 * np.tan(np.pi / 2 - angle) + elif angle <= np.pi - theta: + # top edge, left half + v_coef, c_coef = tan_a_2, -tan_a_2 * np.tan(angle - np.pi / 2) + elif angle < np.pi: + # left edge, upper half + v_coef, c_coef = tan_c_2 * np.tan(np.pi - angle), -tan_c_2 + elif angle < np.pi + theta: + # left edge, lower half + v_coef, c_coef = -tan_c_2 * np.tan(angle - np.pi), -tan_c_2 + elif angle < 3 * np.pi / 2: + # bottom edge, right half + v_coef, c_coef = -tan_a_2, -tan_a_2 * np.tan(3 * np.pi / 2 - angle) + elif angle <= 2 * np.pi - theta: + # bottom edge, left half + v_coef, c_coef = -tan_a_2, tan_a_2 * np.tan(angle - 3 * np.pi / 2) + else: + # right edge, lower half + v_coef, c_coef = -tan_c_2 * np.tan(2 * np.pi - angle), tan_c_2 + ray = base_ray + v * v_coef + c * c_coef + else: + ray = ( + base_ray + + v * np.sin(angle) * np.tan(np.radians(along_track_field_of_view / 2)) + + c * np.cos(angle) * np.tan(np.radians(cross_track_field_of_view / 2)) + ) + geos = np.zeros_like(p_m) + for i in range(np.size(geos, axis=1)) if is_vectorized else [-1]: + _position = p_m[:, i].copy() if is_vectorized else p_m + _ray = ray[:, i].copy() if is_vectorized else ray + # find the intersection of the ray and the WGS 84 geoid + try: + pt = surfpt( + _position, + _ray, + constants.EARTH_EQUATORIAL_RADIUS + elevation, + constants.EARTH_EQUATORIAL_RADIUS + elevation, + constants.EARTH_POLAR_RADIUS + elevation, + ) + geo = recgeo( + pt, + constants.EARTH_EQUATORIAL_RADIUS, + constants.EARTH_FLATTENING, + ) + if is_vectorized: + geos[:, i] = geo + else: + geos[:] = geo + except NotFoundError: + # projected point does not fall on the WGS 84 geoid surface + # compute the observable limb ellipse for WGS 84 geoid + limb = edlimb( + constants.EARTH_EQUATORIAL_RADIUS + elevation, + constants.EARTH_EQUATORIAL_RADIUS + elevation, + constants.EARTH_POLAR_RADIUS + elevation, + _position, + ) + # find the two intersection points between orthogonal plane and limb ellipse + _v = v[:, i].copy() if is_vectorized else v + _c = c[:, i].copy() if is_vectorized else c + _, pt_1, pt_2 = inelpl( + limb, + nvp2pl( + _v * np.sin(np.pi / 2 + angle) + _c * np.cos(np.pi / 2 + angle), + _position, + ), + ) + # compute the angles between the ray and limb intersection points + angle_1 = np.arccos( + np.dot(_ray, pt_1) / np.linalg.norm(_ray) / np.linalg.norm(pt_1) + ) + angle_2 = np.arccos( + np.dot(_ray, pt_2) / np.linalg.norm(_ray) / np.linalg.norm(pt_2) + ) + # use the limb intersection point closer to the ray + limb_pt = pt_1 if angle_1 <= angle_2 else pt_2 + limb_geo = recgeo( + limb_pt, + constants.EARTH_EQUATORIAL_RADIUS, + constants.EARTH_FLATTENING, + ) + if is_vectorized: + geos[:, i] = limb_geo + else: + geos[:] = limb_geo + # return resulting geographic position + return wgs84.latlon(np.degrees(geos[1]), np.degrees(geos[0]), geos[2]) + + +def compute_footprint( + orbit_track: Geocentric, + cross_track_field_of_view: float, + along_track_field_of_view: float, + roll_angle: float = 0, + pitch_angle: float = 0, + is_rectangular: bool = False, + number_points: int | None = None, + elevation: float = 0, +) -> list[Polygon | MultiPolygon]: + """ + Compute the instantaneous instrument footprint. Supports both a scalar + (single-time) and vectorized (multi-time) `orbit_track`; the result is + always a list, with one footprint per time (length 1 for a scalar + `orbit_track`). + + Args: + orbit_track (skyfield.positionlib.Geocentric): The satellite position/velocity. + cross_track_field_of_view (float): The angular (degrees) view orthogonal to velocity. + along_track_field_of_view (float): The angular (degrees) view in direction of velocity. + roll_angle (float): The left/right look angle (degrees); right-hand + rotation about orbit velocity vector. + pitch_angle (float): The fore/aft look angle (degrees); right-hand + rotation about orbit normal vector. + is_rectangular (bool): True, if this is a rectangular sensor. + number_points (int | None): The required number of polygon points to + generate: per side for a rectangular sensor, or total for an + elliptical sensor. Defaults to the runtime configuration. + elevation (float): The elevation (meters) at which project the footprint. + + Returns: + list[shapely.geometry.Polygon | shapely.geometry.MultiPolygon]: The instrument footprint(s). + """ + if number_points is None: + # default number of points + if is_rectangular: + number_points = config.get_rc().footprint_points_rectangular_side + else: + number_points = config.get_rc().footprint_points_elliptical + if is_rectangular: + theta = np.degrees( + np.arctan(along_track_field_of_view / cross_track_field_of_view) + ) + angles = np.concatenate( + ( + np.linspace(-theta, theta, number_points, endpoint=False), + np.linspace(theta, 180 - theta, number_points, endpoint=False), + np.linspace(180 - theta, 180 + theta, number_points, endpoint=False), + np.linspace(180 + theta, 360 - theta, number_points, endpoint=False), + ) + ) + else: + angles = np.linspace(0, 360, number_points) + points = [ + compute_projected_ray_position( + orbit_track, + cross_track_field_of_view, + along_track_field_of_view, + roll_angle, + pitch_angle, + is_rectangular, + angle, + elevation, + ) + for angle in angles + ] + is_vectorized = len(np.shape(orbit_track.t)) > 0 # type: ignore + return [ + project_polygon_to_elevation( + split_polygon( + Polygon( + [ + ( + ( + point.longitude.degrees[i] + if is_vectorized + else point.longitude.degrees + ), + ( + point.latitude.degrees[i] + if is_vectorized + else point.latitude.degrees + ), + ) + for point in points + ] + ) + ), + elevation, + ) + for i in (range(np.size(orbit_track.t)) if is_vectorized else [None]) # type: ignore + ] + + +def _compute_limb_for_position( + position: Iterable[float], + number_points: int = 16, + elevation: float = 0, +) -> Polygon | MultiPolygon: + """ + Compute the visible Earth limb (the outline of the Earth's disk, at the + specified elevation, as seen from a single observer position) as a + polygon sampled at evenly spaced angles around the limb ellipse + returned by SPICE's `edlimb`. + + Args: + position (Iterable[float]): The observer position (meters), in an + Earth-fixed frame, from which the limb is visible. + number_points (int): The required number of polygon points to generate. + elevation (float): The elevation (meters) at which to project the limb. + + Returns: + shapely.geometry.Polygon | shapely.geometry.MultiPolygon: The limb. + """ + limb = edlimb( + constants.EARTH_EQUATORIAL_RADIUS + elevation, + constants.EARTH_EQUATORIAL_RADIUS + elevation, + constants.EARTH_POLAR_RADIUS + elevation, + position, + ) + return project_polygon_to_elevation( + split_polygon( + Polygon( + [ + Point(np.degrees(g[0]), np.degrees(g[1])) + for p in [ + limb.center + + np.cos(i) * limb.semi_major + + np.sin(i) * limb.semi_minor + for i in np.linspace(0, np.pi * 2, number_points) + ] + for g in [ + recgeo( + p, + constants.EARTH_EQUATORIAL_RADIUS, + constants.EARTH_FLATTENING, + ) + ] + ] + ) + ), + elevation, + ) + + +def compute_limb( + orbit_track: Geocentric, + number_points: int = 16, + elevation: float = 0, +) -> list[Polygon | MultiPolygon]: + """ + Compute the instantaneous limb (the outline of the visible Earth disk). + Supports both a scalar (single-time) and vectorized (multi-time) + `orbit_track`; the result is always a list, with one limb per time + (length 1 for a scalar `orbit_track`). + + Args: + orbit_track (skyfield.positionlib.Geocentric): The satellite position/velocity. + number_points (int): The required number of polygon points to generate. + elevation (float): The elevation (meters) at which project the limb. + + Returns: + list[shapely.geometry.Polygon | shapely.geometry.MultiPolygon]: The limb(s). + """ + position, _ = orbit_track.frame_xyz_and_velocity(itrs) + p_m = np.array(position.m) + if len(np.shape(p_m)) > 1: + return [ + _compute_limb_for_position(p_m[:, i].copy(), number_points, elevation) + for i in range(np.size(p_m, axis=1)) + ] + return [_compute_limb_for_position(p_m, number_points, elevation)] + + +def buffer_footprint( + geometry: Geometry, + to_crs: Transformer, + from_crs: Transformer, + swath_width: float, + elevation: float, +) -> Polygon | MultiPolygon: + """ + Buffers a ground track point (in EPSG:4326 coordinates) to create a + footprint, by reprojecting to a distance-preserving CRS, buffering by + half the swath width, and reprojecting back. + + Args: + geometry (shapely.Geometry): The geometry (with EPSG:4326 coordinates) to buffer. + to_crs (pyproj.Transformer): Transformer from EPSG:4326 to the CRS + in which to perform the buffer (must use meters). + from_crs (pyproj.Transformer): Transformer back from the buffering + CRS to EPSG:4326. + swath_width (float): The swath width (meters) to buffer. + elevation (float): The elevation (meters) at which project the buffered polygon. + + Returns: + shapely.geometry.Polygon | shapely.geometry.MultiPolygon: The buffered footprint. + """ + # do the swath projection in the specified coordinate reference system + # split polygons to wrap over the anti-meridian and poles + # reproject to specified elevation (lost during buffer) + return project_polygon_to_elevation( + split_polygon( + transform( + from_crs.transform, + transform(to_crs.transform, geometry).buffer(swath_width / 2), # type: ignore + ) + ), + elevation, + ) + + +def buffer_target( + geometry: Geometry, + altitude: float, + inclination: float, + field_of_regard: float, + time_step: float, + distance_crs: str | None = None, + distance_scaling: float = 1.0, +) -> Polygon | MultiPolygon: + """ + Buffers a target geometry to support culling operations. Selects a + buffer distance equal to the distance traveled in one time step plus + half of the field of regard swath width, so that no point still + reachable within the time step is incorrectly excluded. The ground + distance traveled uses the ground velocity at the equator, which is + the fastest (most conservative, largest-distance) point for any given + inclination. + + Args: + geometry (shapely.Geometry): The target geometry (with EPSG:4326 coordinates) to buffer. + altitude (float): The spacecraft orbit altitude (meters). + inclination (float): The spacecraft orbit inclination (degrees). + field_of_regard (float): The spacecraft instrument field of regard (degrees). + time_step (float): The simulation time step (seconds). + distance_crs (str | None): The coordinate reference system in which to + perform distance calculations. Defaults to `None`, which builds an + equidistant cylindrical projection whose true-scale parallel + (`lat_ts`) is set to `geometry`'s own most poleward latitude. This + keeps the buffer conservative (true ground distance >= the + requested distance) at every latitude within `geometry`. + distance_scaling (float): A multiplicative scaling factor to adjust the buffer + distance (default: 1.0). + + Returns: + shapely.geometry.Polygon | shapely.geometry.MultiPolygon: The buffered geometry. + """ + if distance_crs is None: + _, min_lat, _, max_lat = geometry.bounds + lat_ts = min(max(abs(min_lat), abs(max_lat)), 89.9) + distance_crs = f"+proj=eqc +lat_ts={lat_ts} +datum=WGS84 +units=m" + to_crs = Transformer.from_crs("EPSG:4326", distance_crs, always_xy=True) + from_crs = Transformer.from_crs(distance_crs, "EPSG:4326", always_xy=True) + swath_width = field_of_regard_to_swath_width(altitude, field_of_regard) + ground_distance = ( + compute_ground_surface_velocity(altitude, 0, inclination) * time_step + ) + distance = (ground_distance + swath_width / 2) * distance_scaling + return split_polygon( + transform( + from_crs.transform, transform(to_crs.transform, geometry).buffer(distance) # type: ignore + ) + ) diff --git a/src/tatc/utils/surface.py b/src/tatc/utils/surface.py new file mode 100644 index 0000000..e27244e --- /dev/null +++ b/src/tatc/utils/surface.py @@ -0,0 +1,42 @@ +""" +Surface utility functions. + +@author: Paul T. Grogan +""" + +import numpy as np +from numba import njit + +from .. import constants + + +@njit +def compute_number_samples(distance: float) -> int: + """ + Compute the number of global samples required to achieve a typical + sample distance (meters) assuming equal spacing, by dividing the + Earth's surface area (assuming a mean sphere) by the area of a + spherical cap whose angular radius corresponds to half the sample + distance. The result is truncated (not rounded), so the achieved + average sample spacing is always at least the requested distance, + never less. Requires distance > 0. + + Args: + distance (float): The typical distance between samples (meters). + + Returns: + int: The number of global samples. + """ + if distance <= 0: + raise ValueError("distance must be positive, got " + str(distance)) + # compute the angular distance of each sample (assuming mean sphere) + theta = distance / constants.EARTH_MEAN_RADIUS + # compute the distance from the center of earth to conic plane (assuming sphere) + radius = constants.EARTH_MEAN_RADIUS * np.cos(theta / 2) + # compute the distance from the conic plane to the surface (assuming sphere) + height = constants.EARTH_MEAN_RADIUS - radius + # compute the sperical cap area covered by the sample (assuming sphere) + # https://en.wikipedia.org/wiki/Spherical_cap + sample_area = 2 * np.pi * constants.EARTH_MEAN_RADIUS * height + # return the fraction of earth-to-sample area + return int(constants.EARTH_SURFACE_AREA / sample_area) diff --git a/src/tatc/utils/time.py b/src/tatc/utils/time.py new file mode 100644 index 0000000..008dad7 --- /dev/null +++ b/src/tatc/utils/time.py @@ -0,0 +1,54 @@ +""" +Time conversion utility functions. + +@author: Paul T. Grogan +""" + +from __future__ import annotations + +from datetime import datetime, timezone +from typing import overload + +import numpy as np +import numpy.typing as npt + + +@overload +def to_datetime64_ns(value: datetime) -> np.datetime64: ... + + +@overload +def to_datetime64_ns( + value: list[datetime] | npt.NDArray[np.datetime64], +) -> npt.NDArray[np.datetime64]: ... + + +def to_datetime64_ns( + value: datetime | list[datetime] | npt.NDArray[np.datetime64], +) -> np.datetime64 | npt.NDArray[np.datetime64]: + """ + Converts a datetime (or a list/array of datetimes) to numpy + datetime64[ns]. Timezone-aware datetimes are normalized to naive UTC + first because numpy deprecates implicit timezone-aware conversion. + + Args: + value (datetime | list[datetime] | npt.NDArray[np.datetime64]): the datetime(s) to convert + + Returns: + np.datetime64 | npt.NDArray[np.datetime64]: the converted datetime64 value(s) + """ + if isinstance(value, datetime): + return np.datetime64(value.astimezone(timezone.utc).replace(tzinfo=None), "ns") + if isinstance(value, np.ndarray) and np.issubdtype(value.dtype, np.datetime64): + return value.astype("datetime64[ns]") + return np.array( + [ + ( + v.astimezone(timezone.utc).replace(tzinfo=None) + if isinstance(v, datetime) + else v + ) + for v in value + ], + dtype="datetime64[ns]", + ) diff --git a/tests/analysis/common.py b/tests/analysis/common.py new file mode 100644 index 0000000..318a28c --- /dev/null +++ b/tests/analysis/common.py @@ -0,0 +1,40 @@ +""" +Shared fixtures for tatc.analysis unit tests. + +@author Paul T. Grogan +""" + +import unittest + +from tatc.schemas import ( + GeneralPerturbationsOrbit, + Instrument, + Satellite, + WalkerConstellation, +) + +ISS_TLE = [ + "1 25544U 98067A 22171.11255782 .00008307 00000+0 15444-3 0 9992", + "2 25544 51.6448 322.0970 0003980 282.3738 231.6559 15.49798078345636", +] + + +class IssConstellationTestCase(unittest.TestCase): + """ + Base test case providing a satellite and constellation in an ISS-like + orbit with a single wide-field instrument. + """ + + def setUp(self): + self.instrument = Instrument(name="Test", field_of_regard=180.0) + self.orbit = GeneralPerturbationsOrbit.from_tle(ISS_TLE) + self.satellite = Satellite( + name="Test", orbit=self.orbit, instruments=[self.instrument] + ) + self.constellation = WalkerConstellation( + name="Test", + orbit=self.orbit, + instruments=[self.instrument], + number_satellites=4, + number_planes=2, + ) diff --git a/tests/analysis/test_coverage.py b/tests/analysis/test_coverage.py index 06666da..7434919 100644 --- a/tests/analysis/test_coverage.py +++ b/tests/analysis/test_coverage.py @@ -1,45 +1,42 @@ -import unittest +""" +Unit tests for the coverage analysis functions in tatc.analysis. + +@author Paul T. Grogan +""" from datetime import datetime, timedelta, timezone +import geopandas as gpd +import pandas as pd +from shapely.geometry import Point as ShapelyPoint +from shapely.geometry import box + from tatc.analysis import ( - collect_observations, - collect_multi_observations, aggregate_observations, + collect_multi_observations, + collect_observations, + grid_observations, reduce_observations, ) -from tatc.schemas import ( - Point, - Satellite, - Instrument, - TwoLineElements, - WalkerConstellation, -) +from tatc.schemas import Point + +from .common import IssConstellationTestCase -class TestCoverageAnalysis(unittest.TestCase): +class TestCoverageAnalysis(IssConstellationTestCase): + """ + Unit tests for the coverage analysis functions in tatc.analysis. + """ + def setUp(self): + super().setUp() self.point = Point(id=0, latitude=0, longitude=0) - self.instrument = Instrument(name="Test", field_of_regard=180.0) - self.orbit = TwoLineElements( - tle=[ - "1 25544U 98067A 22171.11255782 .00008307 00000+0 15444-3 0 9992", - "2 25544 51.6448 322.0970 0003980 282.3738 231.6559 15.49798078345636", - ] - ) - self.satellite = Satellite( - name="Test", orbit=self.orbit, instruments=[self.instrument] - ) - self.constellation = WalkerConstellation( - name="Test", - orbit=self.orbit, - instruments=[self.instrument], - number_satellites=4, - number_planes=2, - ) def test_collect_observations(self): - results = collect_observations( + """ + Test that observations can be collected for a single satellite and point. + """ + collect_observations( self.point, self.satellite, datetime(2022, 6, 1, tzinfo=timezone.utc), @@ -49,7 +46,10 @@ def test_collect_observations(self): ) def test_collect_observations_with_solar(self): - results = collect_observations( + """ + Test that observations can be collected for a single satellite and point with solar constraints. + """ + collect_observations( self.point, self.satellite, datetime(2022, 6, 1, tzinfo=timezone.utc), @@ -59,6 +59,10 @@ def test_collect_observations_with_solar(self): ) def test_collect_observations_all_culminate(self): + """ + Test that observations can be collected for a single satellite + and point when all observations culminate. + """ start = datetime(2022, 6, 1, 0, 43, tzinfo=timezone.utc) end = datetime(2022, 6, 1, 0, 45, tzinfo=timezone.utc) results = collect_observations( @@ -73,6 +77,10 @@ def test_collect_observations_all_culminate(self): self.assertEqual(results.iloc[0].end, end) def test_collect_observations_all_culminate_no_instrument_index(self): + """ + Test that observations can be collected for a single satellite and p + oint when all observations culminate and no instrument index is provided. + """ start = datetime(2022, 6, 1, 0, 43, tzinfo=timezone.utc) end = datetime(2022, 6, 1, 0, 45, tzinfo=timezone.utc) results = collect_observations( @@ -86,6 +94,10 @@ def test_collect_observations_all_culminate_no_instrument_index(self): self.assertEqual(results.iloc[0].end, end) def test_collect_observations_miss_first_rise(self): + """ + Test that observations can be collected for a single satellite and point + when the first observation is missed due to the start time. + """ start = datetime(2022, 6, 1, 0, 43, tzinfo=timezone.utc) end = datetime(2022, 6, 1, 1, tzinfo=timezone.utc) results = collect_observations( @@ -99,6 +111,10 @@ def test_collect_observations_miss_first_rise(self): self.assertEqual(results.iloc[0].start, start) def test_collect_observations_miss_last_set(self): + """ + Test that observations can be collected for a single satellite and point + when the last observation is missed due to the end time. + """ start = datetime(2022, 6, 1, tzinfo=timezone.utc) end = datetime(2022, 6, 1, 0, 45, tzinfo=timezone.utc) results = collect_observations( @@ -111,7 +127,52 @@ def test_collect_observations_miss_last_set(self): self.assertEqual(len(results), 1) self.assertEqual(results.iloc[0].end, end) + def test_collect_observations_narrow_window_fully_visible(self): + """ + Regression test: a narrow window entirely contained within a longer + visible pass, with no rise, set, or culmination event inside it + (elevation angle stays continuously above the threshold and never + reaches a local maximum in-window), must still be reported as one + continuous observation spanning the whole window -- not as no + observation at all. + """ + start = datetime(2022, 6, 1, 0, 43, 0, tzinfo=timezone.utc) + end = datetime(2022, 6, 1, 0, 43, 30, tzinfo=timezone.utc) + results = collect_observations( + self.point, + self.satellite, + start, + end, + instrument_index=0, + ) + self.assertEqual(len(results), 1) + self.assertEqual(results.iloc[0].start, start) + self.assertEqual(results.iloc[0].end, end) + + def test_collect_observations_narrow_window_fully_invisible(self): + """ + Test that a narrow window entirely outside any visible pass (also + producing no rise/set/culmination events) correctly yields no + observations, distinguishing this from the fully-visible case in + `test_collect_observations_narrow_window_fully_visible` (both + produce zero events, but only one is a true miss). + """ + start = datetime(2022, 6, 1, 0, 30, 0, tzinfo=timezone.utc) + end = datetime(2022, 6, 1, 0, 30, 30, tzinfo=timezone.utc) + results = collect_observations( + self.point, + self.satellite, + start, + end, + instrument_index=0, + ) + self.assertTrue(results.empty) + def test_collect_observations_null(self): + """ + Test that no observations are collected for a single satellite and point + when the time window does not overlap with any observations. + """ start = datetime(2022, 6, 1, tzinfo=timezone.utc) end = datetime(2022, 6, 1, 0, 30, tzinfo=timezone.utc) results = collect_observations( @@ -124,6 +185,10 @@ def test_collect_observations_null(self): self.assertTrue(results.empty) def test_collect_observations_null_with_solar(self): + """ + Test that no observations are collected for a single satellite and point + when the time window does not overlap with any observations, even with solar constraints. + """ start = datetime(2022, 6, 1, tzinfo=timezone.utc) end = datetime(2022, 6, 1, 0, 30, tzinfo=timezone.utc) results = collect_observations( @@ -132,7 +197,10 @@ def test_collect_observations_null_with_solar(self): self.assertTrue(results.empty) def test_collect_multi_observations(self): - results = collect_multi_observations( + """ + Test that observations can be collected for multiple satellites and a single point. + """ + collect_multi_observations( self.point, self.constellation.generate_members(), datetime(2022, 6, 1, tzinfo=timezone.utc), @@ -140,6 +208,10 @@ def test_collect_multi_observations(self): ) def test_collect_multi_observations_null(self): + """ + Test that no observations are collected for multiple satellites and a single point + when the time window does not overlap with any observations. + """ start = datetime(2022, 6, 1, 0, 10, tzinfo=timezone.utc) end = datetime(2022, 6, 1, 0, 30, tzinfo=timezone.utc) results = collect_multi_observations( @@ -150,7 +222,151 @@ def test_collect_multi_observations_null(self): ) self.assertTrue(results.empty) + def test_collect_multi_observations_no_satellites(self): + """ + Regression test: an empty `satellites` list must return an empty + DataFrame with the expected columns, not raise (there is nothing to + concatenate when no satellite/instrument pair ever runs). + """ + results = collect_multi_observations( + self.point, + [], + datetime(2022, 6, 1, tzinfo=timezone.utc), + datetime(2022, 6, 2, tzinfo=timezone.utc), + ) + self.assertTrue(results.empty) + self.assertIn("start", results.columns) + + @staticmethod + def _make_observation(point_id, satellite, instrument, start, end): + """ + Build a synthetic observation record matching the schema + `collect_observations` produces, for direct, deterministic control + over `aggregate_observations` inputs (bypassing orbital propagation). + """ + return { + "point_id": point_id, + "geometry": ShapelyPoint(0, 0), + "satellite": satellite, + "instrument": instrument, + "start": pd.Timestamp(start), + "epoch": pd.Timestamp(start) + + (pd.Timestamp(end) - pd.Timestamp(start)) / 2, + "end": pd.Timestamp(end), + } + + def test_aggregate_observations_merges_overlapping_and_nested_intervals(self): + """ + Test the core interval-merging algorithm directly with synthetic, + deliberately overlapping/nested/gapped observations: satellite B's + window is fully nested inside A's, and C's window starts before A + ends but after B ends (a case a naive "compare only to the previous + row" check would wrongly split, since C.start > B.end even though + C still overlaps A's still-ongoing window -- the running max via + `.cummax()` is what keeps this correct). D is fully separate. + """ + t0 = datetime(2022, 6, 1, tzinfo=timezone.utc) + observations = gpd.GeoDataFrame( + [ + self._make_observation(0, "A", "Test", t0, t0 + timedelta(minutes=10)), + self._make_observation( + 0, "B", "Test", t0 + timedelta(minutes=2), t0 + timedelta(minutes=4) + ), + self._make_observation( + 0, + "C", + "Test", + t0 + timedelta(minutes=9), + t0 + timedelta(minutes=15), + ), + self._make_observation( + 0, + "D", + "Test", + t0 + timedelta(minutes=20), + t0 + timedelta(minutes=25), + ), + ], + crs="EPSG:4326", + ) + results = aggregate_observations(observations) + self.assertEqual(len(results.index), 2) + self.assertEqual(results.iloc[0].satellite, "A, B, C") + self.assertEqual(results.iloc[0].start, t0) + self.assertEqual(results.iloc[0].end, t0 + timedelta(minutes=15)) + self.assertTrue(pd.isna(results.iloc[0].revisit)) + self.assertEqual(results.iloc[1].satellite, "D") + self.assertEqual(results.iloc[1].start, t0 + timedelta(minutes=20)) + self.assertEqual(results.iloc[1].end, t0 + timedelta(minutes=25)) + self.assertEqual(results.iloc[1].revisit, timedelta(minutes=5)) + + def test_aggregate_observations_isolates_point_ids(self): + """ + Test that merging and revisit computation are scoped per point_id: + a point_id=1 observation must not be merged with, or treated as a + revisit predecessor for, a point_id=0 observation, even if their + windows would otherwise overlap/abut. + """ + t0 = datetime(2022, 6, 1, tzinfo=timezone.utc) + observations = gpd.GeoDataFrame( + [ + self._make_observation(0, "A", "Test", t0, t0 + timedelta(minutes=10)), + self._make_observation( + 1, + "B", + "Test", + t0 + timedelta(minutes=5), + t0 + timedelta(minutes=15), + ), + ], + crs="EPSG:4326", + ) + results = aggregate_observations(observations) + self.assertEqual(len(results.index), 2) + for i in range(len(results.index)): + self.assertTrue(pd.isna(results.iloc[i].revisit)) + + def test_aggregate_observations_epoch_is_merged_midpoint(self): + """ + Test that the merged group's epoch is the midpoint of its (merged) + start/end, not the mean of the constituent observations' own + (pre-merge) epochs -- these differ here since B's epoch sits much + earlier than the midpoint of the full A+B merged window. + """ + t0 = datetime(2022, 6, 1, tzinfo=timezone.utc) + observations = gpd.GeoDataFrame( + [ + self._make_observation(0, "A", "Test", t0, t0 + timedelta(minutes=10)), + self._make_observation( + 0, "B", "Test", t0 + timedelta(minutes=1), t0 + timedelta(minutes=2) + ), + ], + crs="EPSG:4326", + ) + results = aggregate_observations(observations) + self.assertEqual(len(results.index), 1) + self.assertEqual(results.iloc[0].epoch, t0 + timedelta(minutes=5)) + + def test_aggregate_observations_drops_per_observation_columns(self): + """ + Test that per-observation columns that lose their meaning once + merged across satellites (sat_alt, sat_az, sat_sunlit, solar_alt, + solar_az, solar_time) are dropped, even if present on the input. + """ + t0 = datetime(2022, 6, 1, tzinfo=timezone.utc) + record = self._make_observation(0, "A", "Test", t0, t0 + timedelta(minutes=10)) + record["sat_alt"] = 45.0 + record["sat_az"] = 180.0 + record["solar_alt"] = 10.0 + observations = gpd.GeoDataFrame([record], crs="EPSG:4326") + results = aggregate_observations(observations) + for column in ("sat_alt", "sat_az", "solar_alt"): + self.assertNotIn(column, results.columns) + def test_aggregate_observations(self): + """ + Test that observations can be aggregated for multiple satellites and a single point. + """ results = collect_multi_observations( self.point, self.constellation.generate_members(), @@ -168,6 +384,10 @@ def test_aggregate_observations(self): ) def test_aggregate_observations_null(self): + """ + Test that no observations are aggregated for multiple satellites and a single point + when the time window does not overlap with any observations. + """ results = collect_multi_observations( self.point, self.constellation.generate_members(), @@ -177,7 +397,75 @@ def test_aggregate_observations_null(self): results = aggregate_observations(results) self.assertTrue(results.empty) + @staticmethod + def _make_aggregated_observation(point_id, access_minutes, revisit_minutes): + """ + Build a synthetic aggregated-observation record (matching + `aggregate_observations`'s output schema) for direct, deterministic + control over `reduce_observations` inputs. `revisit_minutes=None` + produces `pandas.NaT`, matching the first observation for a point. + """ + return { + "point_id": point_id, + "geometry": ShapelyPoint(0, 0), + "access": pd.Timedelta(minutes=access_minutes), + "revisit": ( + pd.NaT + if revisit_minutes is None + else pd.Timedelta(minutes=revisit_minutes) + ), + } + + def test_reduce_observations_computes_mean_access_and_skips_first_revisit(self): + """ + Test that access is averaged over every sample, but revisit is + averaged only over the samples with a defined revisit -- the first + sample's revisit is undefined (NaT, no prior observation), and must + be skipped rather than treated as a zero-minute revisit (which + would otherwise skew the mean down substantially). + """ + observations = gpd.GeoDataFrame( + [ + self._make_aggregated_observation(0, 5, None), + self._make_aggregated_observation(0, 10, 55), + self._make_aggregated_observation(0, 15, 50), + ], + crs="EPSG:4326", + ) + results = reduce_observations(observations) + self.assertEqual(len(results.index), 1) + self.assertEqual(results.iloc[0].samples, 3) + self.assertEqual(results.iloc[0].access, timedelta(minutes=10)) + self.assertEqual(results.iloc[0].revisit, timedelta(minutes=52.5)) + + def test_reduce_observations_isolates_point_ids(self): + """ + Test that statistics are computed independently per point_id, not + pooled across points. + """ + observations = gpd.GeoDataFrame( + [ + self._make_aggregated_observation(0, 5, None), + self._make_aggregated_observation(1, 5, None), + self._make_aggregated_observation(1, 7, 30), + ], + crs="EPSG:4326", + ) + results = reduce_observations(observations) + self.assertEqual(len(results.index), 2) + point_0 = results[results.point_id == 0].iloc[0] + point_1 = results[results.point_id == 1].iloc[0] + self.assertEqual(point_0.samples, 1) + self.assertEqual(point_0.access, timedelta(minutes=5)) + self.assertTrue(pd.isna(point_0.revisit)) + self.assertEqual(point_1.samples, 2) + self.assertEqual(point_1.access, timedelta(minutes=6)) + self.assertEqual(point_1.revisit, timedelta(minutes=30)) + def test_reduce_observations(self): + """ + Test that observations can be reduced for multiple satellites and a single point. + """ results = collect_multi_observations( self.point, self.constellation.generate_members(), @@ -203,6 +491,10 @@ def test_reduce_observations(self): ) def test_reduce_observations_null(self): + """ + Test that no observations are reduced for multiple satellites and a single point + when the time window does not overlap with any observations. + """ results = collect_multi_observations( self.point, self.constellation.generate_members(), @@ -212,3 +504,164 @@ def test_reduce_observations_null(self): aggregated_results = aggregate_observations(results) reduced_results = reduce_observations(aggregated_results) self.assertTrue(reduced_results.empty) + + @staticmethod + def _make_cell(cell_id, min_lon, min_lat, max_lon, max_lat): + """ + Build a synthetic cell record matching the schema expected by + `grid_observations` (a `cell_id` plus a polygon `geometry`). + """ + return {"cell_id": cell_id, "geometry": box(min_lon, min_lat, max_lon, max_lat)} + + @staticmethod + def _make_reduced_observation( + point_id, lon, lat, access_seconds, revisit_seconds, samples + ): + """ + Build a synthetic reduced-observation record (matching + `reduce_observations`'s output schema) for direct, deterministic + control over `grid_observations` inputs. + """ + return { + "point_id": point_id, + "geometry": ShapelyPoint(lon, lat), + "access": pd.Timedelta(seconds=access_seconds), + "revisit": pd.Timedelta(seconds=revisit_seconds), + "samples": samples, + } + + def test_grid_observations_empty_reduced_observations(self): + """ + Test that an empty `reduced_observations` yields every cell with + zero samples and undefined access/revisit, rather than an empty + result. + """ + cells = gpd.GeoDataFrame( + [self._make_cell(0, 0, 0, 1, 1), self._make_cell(1, 2, 2, 3, 3)], + crs="EPSG:4326", + ) + reduced = gpd.GeoDataFrame( + columns=["point_id", "geometry", "access", "revisit", "samples"], + crs="EPSG:4326", + ) + result = grid_observations(reduced, cells) + self.assertEqual(len(result.index), 2) + self.assertTrue((result.samples == 0).all()) + self.assertTrue(result.access.isna().all()) + self.assertTrue(result.revisit.isna().all()) + + def test_grid_observations_single_point_passes_through_unchanged(self): + """ + Test that a single point within a cell yields that point's own + access/revisit/samples unchanged (the trivial case of a weighted + mean over one value). + """ + cells = gpd.GeoDataFrame([self._make_cell(0, 0, 0, 1, 1)], crs="EPSG:4326") + reduced = gpd.GeoDataFrame( + [self._make_reduced_observation(0, 0.5, 0.5, 5, 10, 100)], + crs="EPSG:4326", + ) + result = grid_observations(reduced, cells) + self.assertEqual(len(result.index), 1) + self.assertEqual(result.iloc[0].samples, 100) + self.assertEqual(result.iloc[0].access, timedelta(seconds=5)) + self.assertEqual(result.iloc[0].revisit, timedelta(seconds=10)) + + def test_grid_observations_uses_weighted_arithmetic_mean_for_access(self): + """ + Test that access is combined across points in the same cell using + a sample-weighted arithmetic mean. + """ + cells = gpd.GeoDataFrame([self._make_cell(0, 0, 0, 1, 1)], crs="EPSG:4326") + reduced = gpd.GeoDataFrame( + [ + self._make_reduced_observation(0, 0.4, 0.4, 5, 10, 100), + self._make_reduced_observation(1, 0.6, 0.6, 50, 100, 10), + ], + crs="EPSG:4326", + ) + result = grid_observations(reduced, cells) + self.assertEqual(len(result.index), 1) + self.assertEqual(result.iloc[0].samples, 110) + expected_access = (5 * 100 + 50 * 10) / 110 + self.assertAlmostEqual( + result.iloc[0].access.total_seconds(), expected_access, places=6 + ) + + def test_grid_observations_uses_weighted_harmonic_mean_for_revisit(self): + """ + Test that revisit is combined across points in the same cell using + a sample-weighted harmonic mean, not an arithmetic mean: revisit is + a time-between-events (reciprocal-of-rate) quantity, so a naive + arithmetic mean would under-weight the more frequently sampled + point. The two means give clearly different results here (~10.9s + harmonic vs. 55s arithmetic), so this distinguishes them concretely. + """ + cells = gpd.GeoDataFrame([self._make_cell(0, 0, 0, 1, 1)], crs="EPSG:4326") + reduced = gpd.GeoDataFrame( + [ + self._make_reduced_observation(0, 0.4, 0.4, 5, 10, 100), + self._make_reduced_observation(1, 0.6, 0.6, 50, 100, 10), + ], + crs="EPSG:4326", + ) + result = grid_observations(reduced, cells) + expected_harmonic_revisit = (100 + 10) / (100 / 10 + 10 / 100) + naive_arithmetic_revisit = (10 * 100 + 100 * 10) / 110 + self.assertAlmostEqual( + result.iloc[0].revisit.total_seconds(), expected_harmonic_revisit, places=6 + ) + self.assertNotAlmostEqual( + result.iloc[0].revisit.total_seconds(), naive_arithmetic_revisit, places=1 + ) + + def test_grid_observations_revisit_is_invariant_to_point_density(self): + """ + Regression test: a cell's gridded revisit must not depend on how + many (near-identical) points happen to fall inside it -- it should + represent a typical point's revisit, not shrink just because the + input point grid happened to be sampled more finely there. This + specifically distinguishes the (correct) sample-weighted harmonic + mean from summing raw per-point rates (1/revisit) unweighted by + sample count, which would make revisit shrink roughly in + proportion to the number of pooled points instead of staying + constant. + """ + cells = gpd.GeoDataFrame([self._make_cell(0, 0, 0, 1, 1)], crs="EPSG:4326") + sparse = gpd.GeoDataFrame( + [self._make_reduced_observation(0, 0.5, 0.5, 5, 100, 20)], + crs="EPSG:4326", + ) + dense = gpd.GeoDataFrame( + [ + self._make_reduced_observation(i, 0.1 * i, 0.1 * i, 5, 100, 20) + for i in range(1, 10) + ], + crs="EPSG:4326", + ) + sparse_result = grid_observations(sparse, cells) + dense_result = grid_observations(dense, cells) + self.assertAlmostEqual( + sparse_result.iloc[0].revisit.total_seconds(), + dense_result.iloc[0].revisit.total_seconds(), + places=6, + ) + + def test_grid_observations_cell_without_points_is_omitted(self): + """ + Documents current behavior: unlike the fully-empty + `reduced_observations` case (which zero-fills every cell), a cell + with no points inside it is simply absent from a non-empty result, + since the point-in-cell join is an inner join. + """ + cells = gpd.GeoDataFrame( + [self._make_cell(0, 0, 0, 1, 1), self._make_cell(1, 2, 2, 3, 3)], + crs="EPSG:4326", + ) + reduced = gpd.GeoDataFrame( + [self._make_reduced_observation(0, 0.5, 0.5, 5, 10, 100)], + crs="EPSG:4326", + ) + result = grid_observations(reduced, cells) + self.assertEqual(len(result.index), 1) + self.assertEqual(result.iloc[0].cell_id, 0) diff --git a/tests/analysis/test_dop.py b/tests/analysis/test_dop.py index 07bc000..f798c98 100644 --- a/tests/analysis/test_dop.py +++ b/tests/analysis/test_dop.py @@ -1,21 +1,34 @@ -import unittest +""" +Unit tests for the DOP analysis functions in the tatc.analysis module. + +@author Paul T. Grogan +""" +import unittest from datetime import datetime, timezone import numpy as np -from tatc.analysis import compute_dop, DopMethod + +from tatc.analysis import DopMethod, compute_dop from tatc.schemas import ( - Point, CircularOrbit, + GeneralPerturbationsOrbit, + Instrument, + Point, + Satellite, WalkerConstellation, ) class TestDopAnalysis(unittest.TestCase): + """ + Unit tests for the DOP analysis functions in the tatc.analysis module. + """ + def setUp(self): self.null_island = Point(id=0, latitude=0, longitude=0) self.orbit = CircularOrbit( - altitude=20180e3, + mean_altitude=20180e3, inclination=55, epoch=datetime(2024, 1, 1, tzinfo=timezone.utc), ) @@ -32,6 +45,9 @@ def setUp(self): ) def test_compute_gdop(self): + """ + Test that GDOP computation works for a single point, constellation, and time. + """ results = compute_dop( self.times, self.null_island, @@ -43,6 +59,9 @@ def test_compute_gdop(self): self.assertNotIn(np.nan, results.dop.values) def test_compute_pdop(self): + """ + Test that PDOP computation works for a single point, constellation, and time. + """ results = compute_dop( self.times, self.null_island, @@ -54,6 +73,9 @@ def test_compute_pdop(self): self.assertNotIn(np.nan, results.dop.values) def test_compute_hdop(self): + """ + Test that HDOP computation works for a single point, constellation, and time. + """ results = compute_dop( self.times, self.null_island, @@ -65,6 +87,9 @@ def test_compute_hdop(self): self.assertNotIn(np.nan, results.dop.values) def test_compute_vdop(self): + """ + Test that VDOP computation works for a single point, constellation, and time. + """ results = compute_dop( self.times, self.null_island, @@ -76,6 +101,9 @@ def test_compute_vdop(self): self.assertNotIn(np.nan, results.dop.values) def test_compute_tdop(self): + """ + Test that TDOP computation works for a single point, constellation, and time. + """ results = compute_dop( self.times, self.null_island, @@ -87,6 +115,9 @@ def test_compute_tdop(self): self.assertNotIn(np.nan, results.dop.values) def test_compute_gdop_nan(self): + """ + Test that GDOP computation returns NaN when there are not enough visible satellites. + """ results = compute_dop( self.times, self.null_island, @@ -97,3 +128,178 @@ def test_compute_gdop_nan(self): self.assertEqual(len(results), len(self.times)) # minimum elevation angle of 80 is too high to yield enough visible satellites self.assertTrue(np.all(np.isnan(results.dop.values))) + + def test_compute_dop_geometry_uses_lon_lat_order(self): + """ + Regression test: the output geometry must be (longitude, latitude), + matching `geopandas.points_from_xy`'s documented (x=lon, y=lat) + convention and every other geometry construction in this codebase. + A point at (latitude=10, longitude=50) was previously reported as + (50, 10) interpreted backwards -- undetectable with a symmetric + point like (0, 0), so this uses distinct lat/lon values instead. + """ + point = Point(id=0, latitude=10, longitude=50) + results = compute_dop( + self.times, + point, + self.gps_constellation.generate_members(), + 10, + DopMethod.GDOP, + ) + self.assertEqual(results.geometry.iloc[0].x, 50) + self.assertEqual(results.geometry.iloc[0].y, 10) + + def test_compute_dop_honors_point_elevation(self): + """ + Regression test: `point.elevation` must actually affect the + computed geometry (range/angles to each satellite), not be + silently ignored. The effect is small at GNSS-scale ranges, so a + large elevation is used to make the difference unambiguous. + """ + point_sea_level = Point(id=0, latitude=10, longitude=50, elevation=0) + point_elevated = Point(id=0, latitude=10, longitude=50, elevation=8000) + satellites = self.gps_constellation.generate_members() + dop_sea_level = compute_dop( + self.times, point_sea_level, satellites, 10, DopMethod.GDOP + ).dop.iloc[0] + dop_elevated = compute_dop( + self.times, point_elevated, satellites, 10, DopMethod.GDOP + ).dop.iloc[0] + self.assertNotEqual(dop_sea_level, dop_elevated) + + def test_compute_dop_min_count_visible_boundary_is_inclusive(self): + """ + Regression test: `min_count_visible` must be an inclusive lower + bound (as its docstring states) -- exactly `min_count_visible` + visible satellites still yields a valid (non-NaN) DOP value, and + one fewer returns NaN. The true visible count depends on the + specific constellation geometry and test time, so it is found by + scanning `min_count_visible` (the DOP value itself is unaffected by + `min_count_visible` below the true count, since it only gates + whether NaN is returned, not which satellites are used). + """ + satellites = self.gps_constellation.generate_members() + time = self.times[0] + last_valid = None + for candidate in range(1, len(satellites) + 1): + value = compute_dop( + [time], self.null_island, satellites, 10, DopMethod.GDOP, candidate + ).dop.iloc[0] + if np.isnan(value): + break + last_valid = candidate + self.assertIsNotNone(last_valid) + value_at_boundary = compute_dop( + [time], self.null_island, satellites, 10, DopMethod.GDOP, last_valid + ).dop.iloc[0] + self.assertFalse(np.isnan(value_at_boundary)) + value_past_boundary = compute_dop( + [time], self.null_island, satellites, 10, DopMethod.GDOP, last_valid + 1 + ).dop.iloc[0] + self.assertTrue(np.isnan(value_past_boundary)) + + def test_compute_dop_multi_element_orbit_uses_per_time_nearest_element(self): + """ + Regression test: a satellite built from a multi-element orbit (e.g. + a history of TLEs) must be propagated with whichever element is + closest to *each* requested time, not a single element chosen once + (from the first requested time) and reused for the whole span. + + `compute_dop`'s output only exposes a `dop` value (which needs >= 4 + simultaneously "visible" satellites to be non-NaN) and `geometry` + (which is just the ground point, independent of the satellites) -- + neither directly exposes a single satellite's own propagated + position. So this uses `min_elevation=-90` (every satellite counts + as "visible" regardless of true geometry, since elevation is always + >= -90) plus 3 arbitrary "filler" satellites to reach the 4-visible + threshold, isolating the comparison to whether the 4th (test + subject) satellite's own contribution to the DOP design matrix + differs between a multi-element orbit and a single-element orbit + built directly from the second TLE, at a two-time query where the + first time is near the first element's epoch and the second time is + near the second element's epoch. If per-time selection works, the + multi-element satellite must select the second element for the + second query time, exactly matching the single-element-2 satellite; + if it doesn't (the element closest to the *first* query time is + reused throughout, as directly verified below to diverge by + thousands of km at the second time), the DOP values would differ. + """ + tle_1 = [ + "1 25544U 98067A 22171.11255782 .00008307 00000+0 15444-3 0 9992", + "2 25544 51.6448 322.0970 0003980 282.3738 231.6559 15.49798078345636", + ] + tle_2 = [ + "1 25544U 98067A 22200.50000000 .00008307 00000+0 15444-3 0 9994", + "2 25544 51.6448 10.0000 0003980 100.0000 50.0000 15.49798078350000", + ] + multi_element_orbit = GeneralPerturbationsOrbit.from_tle(tle_1 + tle_2) + single_element_orbit = GeneralPerturbationsOrbit.from_tle(tle_2) + instrument = Instrument(name="Test", field_of_regard=10.0) + multi_satellite = Satellite( + name="Multi", orbit=multi_element_orbit, instruments=[instrument] + ) + single_satellite = Satellite( + name="Single", orbit=single_element_orbit, instruments=[instrument] + ) + filler_satellites = self.gps_constellation.generate_members()[:3] + # first time near the first element's epoch, second near the second + times = [ + datetime(2022, 6, 20, 12, tzinfo=timezone.utc), + datetime(2022, 7, 19, 12, tzinfo=timezone.utc), + ] + multi_result = compute_dop( + times, + self.null_island, + filler_satellites + [multi_satellite], + -90, + DopMethod.GDOP, + min_count_visible=4, + ) + single_result = compute_dop( + times, + self.null_island, + filler_satellites + [single_satellite], + -90, + DopMethod.GDOP, + min_count_visible=4, + ) + self.assertAlmostEqual( + multi_result.dop.iloc[1], single_result.dop.iloc[1], places=6 + ) + + def test_compute_dop_singular_matrix_returns_nan_with_warning(self): + """ + Test that a degenerate geometry (satellites sharing the exact same + position/velocity, making the DOP design matrix singular) returns + NaN with a warning, rather than raising. + """ + instrument = Instrument(name="I", field_of_regard=180.0) + satellites = [ + Satellite(name=f"S{i}", orbit=self.orbit, instruments=[instrument]) + for i in range(4) + ] + with self.assertWarns(UserWarning): + results = compute_dop( + [self.times[0]], + self.null_island, + satellites, + -90, + DopMethod.GDOP, + min_count_visible=1, + ) + self.assertTrue(np.isnan(results.dop.iloc[0])) + + def test_compute_dop_invalid_method_raises(self): + """ + Test that an unrecognized `dop_method` value raises a ValueError, + for a geometry with enough visible satellites to actually reach + the dop_method dispatch. + """ + with self.assertRaises(ValueError): + compute_dop( + [self.times[0]], + self.null_island, + self.gps_constellation.generate_members(), + 10, + "not_a_real_method", + ) diff --git a/tests/analysis/test_latency.py b/tests/analysis/test_latency.py index 0480239..ca6a383 100644 --- a/tests/analysis/test_latency.py +++ b/tests/analysis/test_latency.py @@ -1,25 +1,35 @@ -import unittest +""" +Unit tests for latency analysis functions. -from datetime import datetime, timezone +@author Paul T. Grogan +""" + +from datetime import datetime, timedelta, timezone + +import geopandas as gpd +import pandas as pd +from shapely.geometry import Point as ShapelyPoint +from shapely.geometry import box from tatc.analysis import ( - collect_observations, collect_downlinks, + collect_observations, compute_latencies, + grid_latencies, reduce_latencies, ) -from tatc.schemas import ( - Point, - GroundStation, - Satellite, - Instrument, - TwoLineElements, - WalkerConstellation, -) +from tatc.schemas import GroundStation, Point +from .common import IssConstellationTestCase + + +class TestLatencyAnalysis(IssConstellationTestCase): + """ + Unit tests for latency analysis functions. + """ -class TestLatencyAnalysis(unittest.TestCase): def setUp(self): + super().setUp() self.point = Point(id=0, latitude=0, longitude=0, min_elevation_angle=10) self.station = GroundStation( name="Station 1", latitude=0, longitude=180, min_elevation_angle=10 @@ -36,26 +46,12 @@ def setUp(self): name="Station 4", latitude=50, longitude=-90, min_elevation_angle=10 ), ] - self.instrument = Instrument(name="Test", field_of_regard=180.0) - self.orbit = TwoLineElements( - tle=[ - "1 25544U 98067A 22171.11255782 .00008307 00000+0 15444-3 0 9992", - "2 25544 51.6448 322.0970 0003980 282.3738 231.6559 15.49798078345636", - ] - ) - self.satellite = Satellite( - name="Test", orbit=self.orbit, instruments=[self.instrument] - ) - self.constellation = WalkerConstellation( - name="Test", - orbit=self.orbit, - instruments=[self.instrument], - number_satellites=4, - number_planes=2, - ) def test_collect_downlinks(self): - results = collect_downlinks( + """ + Test that downlink collection works for a single station and satellite. + """ + collect_downlinks( self.station, self.satellite, datetime(2022, 6, 1, tzinfo=timezone.utc), @@ -63,6 +59,9 @@ def test_collect_downlinks(self): ) def test_collect_downlinks_empty(self): + """ + Test that downlink collection returns an empty DataFrame when no downlinks are available. + """ results = collect_downlinks( self.station, self.satellite, @@ -72,7 +71,10 @@ def test_collect_downlinks_empty(self): self.assertTrue(results.empty) def test_collect_multi_downlinks(self): - results = collect_downlinks( + """ + Test that downlink collection works for multiple stations and a single satellite. + """ + collect_downlinks( self.stations, self.satellite, datetime(2022, 6, 1, tzinfo=timezone.utc), @@ -80,6 +82,9 @@ def test_collect_multi_downlinks(self): ) def test_collect_multi_downlinks_empty(self): + """ + Test that downlink collection returns an empty DataFrame when no downlinks are available. + """ results = collect_downlinks( self.stations, self.satellite, @@ -89,7 +94,10 @@ def test_collect_multi_downlinks_empty(self): self.assertTrue(results.empty) def test_compute_latency(self): - results = compute_latencies( + """ + Test that latency computation works for a single point, satellite, and station. + """ + compute_latencies( collect_observations( self.point, self.satellite, @@ -106,6 +114,9 @@ def test_compute_latency(self): ) def test_compute_latency_empty(self): + """ + Test that latency computation returns an empty DataFrame when no latencies are available. + """ results = compute_latencies( collect_observations( self.point, @@ -124,6 +135,9 @@ def test_compute_latency_empty(self): self.assertTrue(results.empty) def test_compute_latency_no_downlinks(self): + """ + Test that latency computation returns an empty DataFrame when no downlinks are available. + """ results = compute_latencies( collect_observations( self.point, @@ -142,6 +156,9 @@ def test_compute_latency_no_downlinks(self): self.assertTrue(results.empty) def test_compute_latency_multi_station(self): + """ + Test that latency computation works for a single point, satellite, and multiple stations. + """ results = compute_latencies( collect_observations( self.point, @@ -159,7 +176,10 @@ def test_compute_latency_multi_station(self): ) def test_reduce_latency(self): - results = reduce_latencies( + """ + Test that latency reduction works for a single point, satellite, and multiple stations. + """ + reduce_latencies( compute_latencies( collect_observations( self.point, @@ -178,6 +198,9 @@ def test_reduce_latency(self): ) def test_reduce_latency_empty(self): + """ + Test that latency reduction returns an empty DataFrame when no latencies are available. + """ results = reduce_latencies( compute_latencies( collect_observations( @@ -196,3 +219,164 @@ def test_reduce_latency_empty(self): ) ) self.assertTrue(results.empty) + + def test_compute_latency_columns_present_and_correct(self): + """ + Regression test: `sat_alt`/`sat_az`/`station` must survive + `compute_latencies` with their real values -- these columns are + never suffixed by the underlying `merge_asof` (each exists in only + one of `observations`/`downlinks`), unlike `epoch`/`geometry` + (which exist in both, and are suffixed and explicitly renamed). + """ + observations = collect_observations( + self.point, + self.satellite, + datetime(2022, 6, 1, tzinfo=timezone.utc), + datetime(2022, 6, 10, tzinfo=timezone.utc), + instrument_index=0, + ) + downlinks = collect_downlinks( + self.station, + self.satellite, + datetime(2022, 6, 1, tzinfo=timezone.utc), + datetime(2022, 6, 10, tzinfo=timezone.utc), + ) + results = compute_latencies(observations, downlinks) + self.assertIn("sat_alt", results.columns) + self.assertIn("sat_az", results.columns) + self.assertIn("station", results.columns) + for column in ("sat_alt", "sat_az"): + self.assertTrue((results[column] == observations[column]).all()) + matched = results.station.notna() + self.assertTrue(matched.any()) + self.assertTrue((results.station[matched] == self.station.name).all()) + + def test_reduce_latencies_all_unmatched(self): + """ + Test that observations with no matching downlink (NaT latency) + still count toward `samples`, but are excluded from the mean + latency (which stays undefined/NaT), rather than being treated as + a zero-latency sample or crashing. + """ + observations = gpd.GeoDataFrame( + [ + { + "point_id": 0, + "geometry": ShapelyPoint(0, 0), + "satellite": "A", + "instrument": "I", + "start": pd.Timestamp("2022-06-01T10:00", tz="UTC"), + "epoch": pd.Timestamp("2022-06-01T10:01", tz="UTC"), + "end": pd.Timestamp("2022-06-01T10:02", tz="UTC"), + "sat_alt": 45.0, + "sat_az": 90.0, + }, + { + "point_id": 0, + "geometry": ShapelyPoint(0, 0), + "satellite": "A", + "instrument": "I", + "start": pd.Timestamp("2022-06-01T20:00", tz="UTC"), + "epoch": pd.Timestamp("2022-06-01T20:01", tz="UTC"), + "end": pd.Timestamp("2022-06-01T20:02", tz="UTC"), + "sat_alt": 30.0, + "sat_az": 100.0, + }, + ], + crs="EPSG:4326", + ) + downlinks = gpd.GeoDataFrame( + [ + { + "station": "S1", + "geometry": ShapelyPoint(1, 1), + "satellite": "A", + "start": pd.Timestamp("2022-06-01T00:00", tz="UTC"), + "epoch": pd.Timestamp("2022-06-01T00:01", tz="UTC"), + "end": pd.Timestamp("2022-06-01T00:02", tz="UTC"), + } + ], + crs="EPSG:4326", + ) + latencies = compute_latencies(observations, downlinks) + self.assertTrue(latencies.latency.isna().all()) + result = reduce_latencies(latencies) + self.assertEqual(len(result.index), 1) + self.assertEqual(result.iloc[0].samples, 2) + self.assertTrue(pd.isna(result.iloc[0].latency)) + + @staticmethod + def _make_cell(cell_id, min_lon, min_lat, max_lon, max_lat): + """ + Build a synthetic cell record matching the schema expected by + `grid_latencies` (a `cell_id` plus a polygon `geometry`). + """ + return {"cell_id": cell_id, "geometry": box(min_lon, min_lat, max_lon, max_lat)} + + @staticmethod + def _make_reduced_latency(point_id, lon, lat, latency_seconds, samples): + """ + Build a synthetic reduced-latency record (matching + `reduce_latencies`'s output schema) for direct, deterministic + control over `grid_latencies` inputs. + """ + return { + "point_id": point_id, + "geometry": ShapelyPoint(lon, lat), + "latency": pd.Timedelta(seconds=latency_seconds), + "samples": samples, + } + + def test_grid_latencies_empty_reduced_latencies(self): + """ + Test that an empty `reduced_latencies` yields every cell with zero + samples and undefined latency, rather than an empty result. + """ + cells = gpd.GeoDataFrame( + [self._make_cell(0, 0, 0, 1, 1), self._make_cell(1, 2, 2, 3, 3)], + crs="EPSG:4326", + ) + reduced = gpd.GeoDataFrame( + columns=["point_id", "geometry", "latency", "samples"], crs="EPSG:4326" + ) + result = grid_latencies(reduced, cells) + self.assertEqual(len(result.index), 2) + self.assertTrue((result.samples == 0).all()) + self.assertTrue(result.latency.isna().all()) + + def test_grid_latencies_single_point_passes_through_unchanged(self): + """ + Test that a single point within a cell yields that point's own + latency/samples unchanged (the trivial case of a weighted mean + over one value). + """ + cells = gpd.GeoDataFrame([self._make_cell(0, 0, 0, 1, 1)], crs="EPSG:4326") + reduced = gpd.GeoDataFrame( + [self._make_reduced_latency(0, 0.5, 0.5, 5, 100)], crs="EPSG:4326" + ) + result = grid_latencies(reduced, cells) + self.assertEqual(len(result.index), 1) + self.assertEqual(result.iloc[0].samples, 100) + self.assertEqual(result.iloc[0].latency, timedelta(seconds=5)) + + def test_grid_latencies_uses_weighted_arithmetic_mean(self): + """ + Test that latency is combined across points in the same cell using + a sample-weighted arithmetic mean (latency is a per-observation + duration, unlike revisit, so no harmonic-mean treatment applies). + """ + cells = gpd.GeoDataFrame([self._make_cell(0, 0, 0, 1, 1)], crs="EPSG:4326") + reduced = gpd.GeoDataFrame( + [ + self._make_reduced_latency(0, 0.4, 0.4, 5, 100), + self._make_reduced_latency(1, 0.6, 0.6, 50, 10), + ], + crs="EPSG:4326", + ) + result = grid_latencies(reduced, cells) + self.assertEqual(len(result.index), 1) + self.assertEqual(result.iloc[0].samples, 110) + expected_latency = (5 * 100 + 50 * 10) / 110 + self.assertAlmostEqual( + result.iloc[0].latency.total_seconds(), expected_latency, places=6 + ) diff --git a/tests/analysis/test_ro_coverage.py b/tests/analysis/test_ro_coverage.py new file mode 100644 index 0000000..7f8cf96 --- /dev/null +++ b/tests/analysis/test_ro_coverage.py @@ -0,0 +1,446 @@ +""" +Unit tests for the radio occultation (RO) coverage analysis functions in tatc.analysis. + +@author Paul T. Grogan +""" + +import unittest +from datetime import datetime, timedelta, timezone + +import numpy as np +from shapely.geometry import MultiPoint +from shapely.geometry import Point as ShapelyPoint +from skyfield.positionlib import Geocentric +from skyfield.units import Distance, Velocity + +from tatc.analysis import collect_ro_observations +from tatc.analysis.ro_coverage import ( + _interpolate_ro_point, + _receiver_frame_vectors, + _sample_ro_arc, + _tangent_point_geometry, +) +from tatc.constants import timescale +from tatc.schemas import GeneralPerturbationsOrbit, Instrument, Satellite + + +def _make_geocentric(position_m, velocity_m_per_s, t): + """ + Builds a synthetic Geocentric position/velocity for direct, + deterministic control over the geometry helper functions, bypassing + real orbit propagation. + """ + position_m = np.array(position_m, dtype=float) + velocity_m_per_s = np.array(velocity_m_per_s, dtype=float) + return Geocentric( + Distance(m=position_m).au, + Velocity(km_per_s=velocity_m_per_s / 1000.0).au_per_d, + t, + ) + + +class TestReceiverFrameVectors(unittest.TestCase): + """ + Unit tests for `_receiver_frame_vectors`. + """ + + def setUp(self): + self.t = timescale.from_datetime(datetime(2024, 1, 1, tzinfo=timezone.utc)) + + def test_circular_orbit_matches_hand_computed_frame(self): + """ + Test the VNB frame for a simple circular-orbit-like configuration + (position along +x, velocity along +y): V should point along the + velocity (+y), N (orbit normal, r x v) along +z, and B (V x N, + completing the right-handed frame) along +x -- coinciding with the + position direction exactly in this case, since velocity is purely + tangential. + """ + rx = _make_geocentric([[7000e3], [0], [0]], [[0], [7.5e3], [0]], self.t) + v_u, n_u, b_u = _receiver_frame_vectors(rx) + np.testing.assert_allclose(v_u.ravel(), [0, 1, 0], atol=1e-12) + np.testing.assert_allclose(n_u.ravel(), [0, 0, 1], atol=1e-12) + np.testing.assert_allclose(b_u.ravel(), [1, 0, 0], atol=1e-12) + + def test_frame_is_exactly_orthonormal_for_eccentric_orbit(self): + """ + Regression test: V, N, B must be an exact orthonormal frame, even + when velocity has a radial component (i.e. is not purely + tangential) -- unlike a previous implementation, which used the + position unit vector (r-hat) in place of B, only exactly + orthogonal to V for a circular orbit (for an eccentric orbit the + two can differ by tens of degrees). + """ + orbit = GeneralPerturbationsOrbit.from_tle( + [ + "1 25544U 98067A 22171.11255782 .00008307 00000+0 15444-3 0 9992", + "2 25544 51.6448 322.0970 0003980 282.3738 231.6559 15.49798078345636", + ] + ) + t = timescale.from_datetimes( + [datetime(2022, 6, 20, i, tzinfo=timezone.utc) for i in range(5)] + ) + track = orbit.to_gp_orbit().get_orbit_track_at_time(t) + v_u, n_u, b_u = _receiver_frame_vectors(track) + np.testing.assert_allclose(np.einsum("ij,ij->j", v_u, n_u), 0, atol=1e-9) + np.testing.assert_allclose(np.einsum("ij,ij->j", v_u, b_u), 0, atol=1e-9) + np.testing.assert_allclose(np.einsum("ij,ij->j", n_u, b_u), 0, atol=1e-9) + np.testing.assert_allclose(np.linalg.norm(b_u, axis=0), 1, atol=1e-9) + + +class TestTangentPointGeometry(unittest.TestCase): + """ + Unit tests for `_tangent_point_geometry`. + """ + + def setUp(self): + self.t = timescale.from_datetime(datetime(2024, 1, 1, tzinfo=timezone.utc)) + + def test_collinear_through_earth_center_is_intersecting(self): + """ + Test that a receiver and transmitter on exactly opposite sides of + the Earth (collinear through the center) produce a tangent point + at the origin, correctly flagged as "intersecting" (sign < 0). + """ + rx = _make_geocentric([[7000e3], [0], [0]], [[0], [7.5e3], [0]], self.t) + tx = _make_geocentric([[-7000e3], [0], [0]], [[0], [-3.0e3], [0]], self.t) + v_u, n_u, b_u = _receiver_frame_vectors(rx) + tp_p, tp_v, tp_sign, _, _ = _tangent_point_geometry(tx, rx, v_u, n_u, b_u) + np.testing.assert_allclose(tp_p.ravel(), [0, 0, 0], atol=1e-6) + self.assertEqual(tp_sign[0], -1) + self.assertIsNone(tp_v) + + def test_same_side_is_not_intersecting(self): + """ + Test that a receiver and transmitter on the same side of the Earth + (tangent point not between them) are correctly flagged as + "parallel"/non-intersecting (sign > 0). + """ + rx = _make_geocentric([[7000e3], [0], [0]], [[0], [7.5e3], [0]], self.t) + tx = _make_geocentric([[8000e3], [2000e3], [0]], [[0], [3.0e3], [0]], self.t) + v_u, n_u, b_u = _receiver_frame_vectors(rx) + _, _, tp_sign, _, _ = _tangent_point_geometry(tx, rx, v_u, n_u, b_u) + self.assertEqual(tp_sign[0], 1) + + def test_tangent_point_matches_independent_closest_point_formula(self): + """ + Cross-checks the tangent point position against an independently + (re-)implemented closest-point-on-a-line-to-the-origin formula: + for the line through the transmitter with direction d = tx - rx, + the point closest to the origin is tx - d*(tx.d)/(d.d). + """ + rx = _make_geocentric([[7000e3], [500e3], [0]], [[0], [7.5e3], [1e3]], self.t) + tx = _make_geocentric( + [[-6000e3], [-1000e3], [3000e3]], [[1e3], [-3.0e3], [0]], self.t + ) + v_u, n_u, b_u = _receiver_frame_vectors(rx) + tp_p, _, _, _, _ = _tangent_point_geometry(tx, rx, v_u, n_u, b_u) + tx_p = np.array(tx.position.m).ravel() + rx_p = np.array(rx.position.m).ravel() + d = tx_p - rx_p + expected_tp = tx_p - d * np.dot(tx_p, d) / np.dot(d, d) + np.testing.assert_allclose(tp_p.ravel(), expected_tp, rtol=1e-9) + + def test_tangent_point_velocity_matches_finite_difference(self): + """ + Cross-checks the analytic tangent point velocity + (`compute_velocity=True`, otherwise unused by any caller in the + codebase) against a central finite difference of the tangent point + position, using synthetic constant-velocity (straight-line) motion + so the finite difference is essentially exact for a small enough + time step. + """ + dt = 0.001 # seconds + rx_p = np.array([7000e3, 500e3, 0.0]) + rx_v = np.array([0.0, 7.5e3, 1e3]) + tx_p = np.array([-6000e3, -1000e3, 3000e3]) + tx_v = np.array([1e3, -3.0e3, 0.0]) + + def tangent_point_position(offset): + rx = _make_geocentric( + (rx_p + rx_v * offset).reshape(3, 1), rx_v.reshape(3, 1), self.t + ) + tx = _make_geocentric( + (tx_p + tx_v * offset).reshape(3, 1), tx_v.reshape(3, 1), self.t + ) + v_u, n_u, b_u = _receiver_frame_vectors(rx) + return _tangent_point_geometry(tx, rx, v_u, n_u, b_u)[0] + + finite_diff_velocity = ( + tangent_point_position(dt) - tangent_point_position(-dt) + ) / (2 * dt) + + rx0 = _make_geocentric(rx_p.reshape(3, 1), rx_v.reshape(3, 1), self.t) + tx0 = _make_geocentric(tx_p.reshape(3, 1), tx_v.reshape(3, 1), self.t) + v_u, n_u, b_u = _receiver_frame_vectors(rx0) + _, tp_v, _, _, _ = _tangent_point_geometry( + tx0, rx0, v_u, n_u, b_u, compute_velocity=True + ) + np.testing.assert_allclose( + tp_v.ravel(), finite_diff_velocity.ravel(), rtol=1e-6 + ) + + +class TestInterpolateRoPoint(unittest.TestCase): + """ + Unit tests for `_interpolate_ro_point`. + """ + + def test_interpolates_at_exact_crossing(self): + """ + Test that the interpolated point falls exactly halfway between two + samples when the sample elevation is exactly halfway between their + elevations. + """ + points = [ + { + "time": datetime(2024, 1, 1, tzinfo=timezone.utc), + "longitude": 0.0, + "latitude": 0.0, + "elevation": 100.0, + "rx_tx_pitch": 0.0, + "rx_tx_yaw": 0.0, + "tp_tx_azimuth": 0.0, + }, + { + "time": datetime(2024, 1, 1, 0, 0, 10, tzinfo=timezone.utc), + "longitude": 10.0, + "latitude": 10.0, + "elevation": 0.0, + "rx_tx_pitch": 10.0, + "rx_tx_yaw": 10.0, + "tp_tx_azimuth": 10.0, + }, + ] + result = _interpolate_ro_point(points, 50.0) + self.assertAlmostEqual(result["longitude"], 5.0) + self.assertAlmostEqual(result["latitude"], 5.0) + self.assertAlmostEqual(result["elevation"], 50.0) + self.assertEqual( + result["time"], datetime(2024, 1, 1, 0, 0, 5, tzinfo=timezone.utc) + ) + + def test_clamps_to_nearest_endpoint_when_never_crossed(self): + """ + Test that when the sample elevation is never crossed by the + profile, the result clamps to the nearest endpoint rather than + extrapolating. + """ + points = [ + { + "time": datetime(2024, 1, 1, tzinfo=timezone.utc), + "longitude": 0.0, + "latitude": 0.0, + "elevation": 100.0, + "rx_tx_pitch": 0.0, + "rx_tx_yaw": 0.0, + "tp_tx_azimuth": 0.0, + }, + { + "time": datetime(2024, 1, 1, 0, 0, 10, tzinfo=timezone.utc), + "longitude": 10.0, + "latitude": 10.0, + "elevation": 80.0, + "rx_tx_pitch": 10.0, + "rx_tx_yaw": 10.0, + "tp_tx_azimuth": 10.0, + }, + ] + # both elevations (100, 80) are above sample_elevation=50, so the + # sign of (elevation - sample_elevation) never changes -- a + # genuine non-crossing case (unlike sample_elevation=80, which + # would exactly touch the second point's elevation and register as + # a crossing via the sign-based diff detection, not exercising + # this fallback at all). 50 is closer to the second point's diff + # (30) than the first's (50), so it should clamp to the second. + result = _interpolate_ro_point(points, 50.0) + self.assertEqual(result["longitude"], 10.0) + self.assertEqual(result["elevation"], 80.0) + + def test_longitude_interpolates_along_shortest_path_across_antimeridian(self): + """ + Test that longitude interpolation wraps across the antimeridian + along the shortest path, rather than the long way around. + """ + points = [ + { + "time": datetime(2024, 1, 1, tzinfo=timezone.utc), + "longitude": 170.0, + "latitude": 0.0, + "elevation": 100.0, + "rx_tx_pitch": 0.0, + "rx_tx_yaw": 0.0, + "tp_tx_azimuth": 0.0, + }, + { + "time": datetime(2024, 1, 1, 0, 0, 10, tzinfo=timezone.utc), + "longitude": -170.0, + "latitude": 0.0, + "elevation": 0.0, + "rx_tx_pitch": 0.0, + "rx_tx_yaw": 0.0, + "tp_tx_azimuth": 0.0, + }, + ] + result = _interpolate_ro_point(points, 50.0) + # shortest path from 170 to -170 crosses the antimeridian (180), + # so the midpoint should be +/-180, not 0 + self.assertAlmostEqual(abs(result["longitude"]), 180.0) + + +class TestCollectRoObservations(unittest.TestCase): + """ + Integration tests for `collect_ro_observations`. + """ + + def setUp(self): + self.receiver = Satellite( + name="RX", + orbit=GeneralPerturbationsOrbit.from_tle( + [ + "1 25544U 98067A 22171.11255782 .00008307 00000+0 15444-3 0 9992", + "2 25544 51.6448 322.0970 0003980 282.3738 231.6559 15.49798078345636", + ] + ), + instruments=[Instrument(name="RO Receiver", field_of_regard=180.0)], + ) + self.transmitter = Satellite( + name="TX", + orbit=GeneralPerturbationsOrbit.from_tle( + [ + "1 24876U 97035A 22171.50000000 .00000015 00000-0 00000-0 0 9990", + "2 24876 55.0000 100.0000 0100000 90.0000 270.0000 2.00561000123456", + ] + ), + instruments=[Instrument(name="RO Transmitter", field_of_regard=180.0)], + ) + self.start = datetime(2022, 6, 20, tzinfo=timezone.utc) + self.end = self.start + timedelta(hours=6) + + def test_collect_ro_observations_returns_results(self): + """ + Test that RO observations are found for a several-hour analysis + period between a LEO receiver and a MEO transmitter. + """ + results = collect_ro_observations( + self.receiver, self.transmitter, self.start, self.end + ) + self.assertFalse(results.empty) + self.assertTrue((results.receiver == "RX").all()) + self.assertTrue((results.transmitter == "TX").all()) + + def test_collect_ro_observations_empty_for_short_period(self): + """ + Test that no observations are found for a period too short to + contain any RO profile. + """ + results = collect_ro_observations( + self.receiver, + self.transmitter, + self.start, + self.start + timedelta(seconds=1), + ) + self.assertTrue(results.empty) + + def test_collect_ro_observations_geometry_types(self): + """ + Test that each observation's `geometry` is a MultiPoint (the full + sampled profile) and `position` is a single Point (the + interpolated representative sample), both 3D (lon/lat/elevation). + """ + results = collect_ro_observations( + self.receiver, self.transmitter, self.start, self.end + ) + self.assertGreater(len(results), 0) + for geometry in results.geometry: + self.assertIsInstance(geometry, MultiPoint) + for position in results.position: + self.assertIsInstance(position, ShapelyPoint) + self.assertTrue(position.has_z) + + def test_collect_ro_observations_smaller_max_yaw_is_more_restrictive(self): + """ + Test that decreasing `max_yaw` never increases the number of + observations found (a tighter yaw bound can only exclude + profiles, not add new ones). + """ + permissive = collect_ro_observations( + self.receiver, self.transmitter, self.start, self.end, max_yaw=65 + ) + restrictive = collect_ro_observations( + self.receiver, self.transmitter, self.start, self.end, max_yaw=5 + ) + self.assertLessEqual(len(restrictive), len(permissive)) + + def test_collect_ro_observations_respects_range_elevation(self): + """ + Test that every returned profile's sampled tangent point track + stays within the requested elevation range. + """ + range_elevation = (-100e3, 40e3) + results = collect_ro_observations( + self.receiver, + self.transmitter, + self.start, + self.end, + range_elevation=range_elevation, + ) + self.assertGreater(len(results), 0) + for geometry in results.geometry: + for point in geometry.geoms: + self.assertGreaterEqual(point.z, range_elevation[0]) + self.assertLessEqual(point.z, range_elevation[1]) + + def test_collect_ro_observations_multiple_transmitters(self): + """ + Test that observations from multiple transmitters are combined + and sorted by time. + """ + transmitter_2 = Satellite( + name="TX2", + orbit=GeneralPerturbationsOrbit.from_tle( + [ + "1 24876U 97035A 22171.50000000 .00000015 00000-0 00000-0 0 9990", + "2 24876 55.0000 50.0000 0100000 200.0000 90.0000 2.00561000123456", + ] + ), + instruments=[Instrument(name="RO Transmitter", field_of_regard=180.0)], + ) + results = collect_ro_observations( + self.receiver, [self.transmitter, transmitter_2], self.start, self.end + ) + self.assertTrue(set(results.transmitter.unique()) <= {"TX", "TX2"}) + self.assertTrue((results.time.values == np.sort(results.time.values)).all()) + + def test_collect_ro_observations_is_rising_is_boolean(self): + """ + Test that `is_rising` is a boolean flag on every observation. + """ + results = collect_ro_observations( + self.receiver, self.transmitter, self.start, self.end + ) + self.assertGreater(len(results), 0) + self.assertTrue(results.is_rising.isin([True, False]).all()) + + def test_sample_ro_arc_closes_at_arc_boundary_when_still_in_range(self): + """ + Test that an observation correctly closes at the sampled arc's own + boundary, not just via exiting the elevation range -- using an + effectively unbounded elevation range to guarantee the tangent + point never leaves it, so the only way the sampled window's single + observation can end is by reaching the last sample. + """ + observations = _sample_ro_arc( + self.transmitter, + self.receiver, + self.start, + self.start + timedelta(minutes=5), + timedelta(seconds=30), + (-1e9, 1e9), + ) + self.assertEqual(len(observations), 1) + self.assertEqual(len(observations[0]["points"]), 11) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/analysis/test_track.py b/tests/analysis/test_track.py index 71d53fb..1fdb87b 100644 --- a/tests/analysis/test_track.py +++ b/tests/analysis/test_track.py @@ -1,49 +1,45 @@ -import unittest +""" +Unit tests for the track analysis functions. -from datetime import datetime, timezone, timedelta -from shapely.geometry import Polygon, MultiPolygon +@author Paul T. Grogan +""" + +from datetime import datetime, timedelta, timezone + +import geopandas as gpd +import numpy as np +from pyproj import Transformer +from shapely.geometry import MultiPolygon, Point as ShapelyPoint, Polygon +from skyfield.api import wgs84 from tatc.analysis import ( - collect_orbit_track, + OrbitCoordinate, + OrbitOutput, + collect_ground_pixels, collect_ground_track, + collect_orbit_track, compute_ground_track, ) -from tatc.schemas import ( - Point, - GroundStation, - Satellite, - Instrument, - TwoLineElements, - WalkerConstellation, -) +from tatc.schemas import GroundStation, Instrument, Point, PointedInstrument, Satellite +from tatc.utils.geometry import geodesic_distance +from tatc.utils.observation import field_of_regard_to_swath_width + +from .common import IssConstellationTestCase -class TestGroundTrackAnalysis(unittest.TestCase): +class TestGroundTrackAnalysis(IssConstellationTestCase): def setUp(self): + super().setUp() self.point = Point(id=0, latitude=0, longitude=0, min_elevation_angle=10) self.station = GroundStation( name="Station 1", latitude=0, longitude=180, min_elevation_angle=10 ) - self.instrument = Instrument(name="Test", field_of_regard=180.0) - self.orbit = TwoLineElements( - tle=[ - "1 25544U 98067A 22171.11255782 .00008307 00000+0 15444-3 0 9992", - "2 25544 51.6448 322.0970 0003980 282.3738 231.6559 15.49798078345636", - ] - ) - self.satellite = Satellite( - name="Test", orbit=self.orbit, instruments=[self.instrument] - ) - self.constellation = WalkerConstellation( - name="Test", - orbit=self.orbit, - instruments=[self.instrument], - number_satellites=4, - number_planes=2, - ) def test_collect_orbit_track(self): - results = collect_orbit_track( + """ + Test that orbit track collection works for a single satellite and a list of times. + """ + collect_orbit_track( self.satellite, [ datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(minutes=i) @@ -52,14 +48,20 @@ def test_collect_orbit_track(self): ) def test_collect_orbit_track_empty(self): - results = collect_orbit_track( + """ + Test that orbit track collection returns an empty DataFrame when no times are provided. + """ + collect_orbit_track( self.satellite, [], ) def test_collect_orbit_track_with_mask(self): + """ + Test that orbit track collection works for a single satellite, a list of times, and a mask. + """ mask = Polygon([[-90, 45], [-90, 45], [90, 45], [90, -45], [-90, -45]]) - results = collect_orbit_track( + collect_orbit_track( self.satellite, [ datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(minutes=5 * i) @@ -68,113 +70,805 @@ def test_collect_orbit_track_with_mask(self): mask=mask, ) - def test_collect_ground_track(self): - results = collect_ground_track( + def test_collect_orbit_track_wgs84_altitude_matches_published_iss_range(self): + """ + Test that the WGS84 orbit track altitude falls within the ISS's + publicly reported operating altitude range (~330-460 km), confirming + the reported height is the satellite's own altitude rather than a + zero-elevation ground point or the `elevation` parameter. + """ + results = collect_orbit_track( self.satellite, - times=[ + [ datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(minutes=i) for i in range(10) ], ) + for point in results.geometry: + self.assertGreater(point.z, 330e3) + self.assertLess(point.z, 460e3) - def test_collect_ground_track_empty(self): - results = collect_ground_track( - self.satellite, - [], + def test_collect_orbit_track_elevation_only_affects_swath_width(self): + """ + Test that the `elevation` parameter changes the computed swath width + but does not change the reported WGS84 position, which always + reflects the satellite's true altitude. + """ + times = [ + datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(minutes=i) + for i in range(10) + ] + results_low = collect_orbit_track(self.satellite, times, elevation=0) + results_high = collect_orbit_track(self.satellite, times, elevation=100e3) + for p_low, p_high in zip(results_low.geometry, results_high.geometry): + self.assertAlmostEqual(p_low.x, p_high.x) + self.assertAlmostEqual(p_low.y, p_high.y) + self.assertAlmostEqual(p_low.z, p_high.z) + self.assertTrue( + (results_low.swath_width.values != results_high.swath_width.values).all() + ) + + def test_collect_orbit_track_ecef_roundtrips_to_wgs84(self): + """ + Test that the ECEF coordinate output, converted back to geodetic + longitude/latitude/height using an independent pyproj transform, + matches the directly-computed WGS84 output for the same times. + """ + times = [ + datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(minutes=i) + for i in range(10) + ] + wgs84_results = collect_orbit_track( + self.satellite, times, coordinates=OrbitCoordinate.WGS84 + ) + ecef_results = collect_orbit_track( + self.satellite, times, coordinates=OrbitCoordinate.ECEF ) + to_wgs84 = Transformer.from_crs("EPSG:4978", "EPSG:4326", always_xy=True) + for wgs84_point, ecef_point in zip( + wgs84_results.geometry, ecef_results.geometry + ): + lon, lat, height = to_wgs84.transform( + ecef_point.x, ecef_point.y, ecef_point.z + ) + self.assertAlmostEqual(lon, wgs84_point.x, places=6) + self.assertAlmostEqual(lat, wgs84_point.y, places=6) + self.assertAlmostEqual(height, wgs84_point.z, places=2) - def test_collect_ground_track_utm(self): - results = collect_ground_track( + def test_collect_orbit_track_crs_by_coordinates(self): + """ + Test that the returned GeoDataFrame's CRS matches the requested + coordinate system: geographic degrees for WGS84, geocentric meters + for ECEF, and unset for ECI (an inertial, time-varying frame with + no fixed EPSG code). + """ + times = [datetime(2022, 6, 1, tzinfo=timezone.utc)] + self.assertEqual( + collect_orbit_track( + self.satellite, times, coordinates=OrbitCoordinate.WGS84 + ).crs.to_string(), + "EPSG:4326", + ) + self.assertEqual( + collect_orbit_track( + self.satellite, times, coordinates=OrbitCoordinate.ECEF + ).crs.to_string(), + "EPSG:4978", + ) + self.assertIsNone( + collect_orbit_track( + self.satellite, times, coordinates=OrbitCoordinate.ECI + ).crs + ) + + def test_collect_orbit_track_mask_filters_consistently_across_coordinates(self): + """ + Test that a mask (always interpreted in WGS84 lon/lat) filters the + same set of times regardless of the requested output `coordinates`. + """ + times = [ + datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(minutes=5 * i) + for i in range(20) + ] + mask = Polygon([[-90, 45], [90, 45], [90, -45], [-90, -45]]) + wgs84_times = list( + collect_orbit_track( + self.satellite, times, mask=mask, coordinates=OrbitCoordinate.WGS84 + ).time + ) + ecef_times = list( + collect_orbit_track( + self.satellite, times, mask=mask, coordinates=OrbitCoordinate.ECEF + ).time + ) + eci_times = list( + collect_orbit_track( + self.satellite, times, mask=mask, coordinates=OrbitCoordinate.ECI + ).time + ) + self.assertTrue(0 < len(wgs84_times) < len(times)) + self.assertEqual(wgs84_times, ecef_times) + self.assertEqual(wgs84_times, eci_times) + + def test_collect_orbit_track_velocity_eci_matches_published_iss_speed(self): + """ + Test that the ECI velocity magnitude matches the ISS's publicly + reported orbital speed of approximately 7.66 km/s. + """ + results = collect_orbit_track( self.satellite, [ datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(minutes=i) for i in range(10) ], - crs="utm", + coordinates=OrbitCoordinate.ECI, + orbit_output=OrbitOutput.POSITION_VELOCITY, ) + for velocity in results.velocity: + speed = np.linalg.norm([velocity.x, velocity.y, velocity.z]) + self.assertGreater(speed, 7.5e3) + self.assertLess(speed, 7.8e3) - def test_collect_ground_track_with_mask(self): - mask = Polygon([[-90, 45], [-90, 45], [90, 45], [90, -45], [-90, -45]]) - results = collect_ground_track( + def test_collect_orbit_track_velocity_eci_faster_than_ecef(self): + """ + Test that the inertial (ECI) speed exceeds the Earth-fixed (ECEF) + speed, as expected for this prograde (51.6 deg inclination) orbit + where Earth's rotation partially cancels the satellite's ground- + relative motion. + """ + times = [ + datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(minutes=i) + for i in range(10) + ] + eci_results = collect_orbit_track( + self.satellite, + times, + coordinates=OrbitCoordinate.ECI, + orbit_output=OrbitOutput.POSITION_VELOCITY, + ) + ecef_results = collect_orbit_track( + self.satellite, + times, + coordinates=OrbitCoordinate.ECEF, + orbit_output=OrbitOutput.POSITION_VELOCITY, + ) + for eci_velocity, ecef_velocity in zip( + eci_results.velocity, ecef_results.velocity + ): + eci_speed = np.linalg.norm([eci_velocity.x, eci_velocity.y, eci_velocity.z]) + ecef_speed = np.linalg.norm( + [ecef_velocity.x, ecef_velocity.y, ecef_velocity.z] + ) + self.assertGreater(eci_speed, ecef_speed) + + def test_collect_orbit_track_velocity_wgs84_enu_matches_ecef_magnitude(self): + """ + Test that the WGS84 East/North/Up velocity has the same magnitude as + the ECEF velocity it is rotated from, since a rotation preserves + vector length. + """ + times = [ + datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(minutes=i) + for i in range(10) + ] + wgs84_results = collect_orbit_track( + self.satellite, + times, + coordinates=OrbitCoordinate.WGS84, + orbit_output=OrbitOutput.POSITION_VELOCITY, + ) + ecef_results = collect_orbit_track( + self.satellite, + times, + coordinates=OrbitCoordinate.ECEF, + orbit_output=OrbitOutput.POSITION_VELOCITY, + ) + for enu_velocity, ecef_velocity in zip( + wgs84_results.velocity, ecef_results.velocity + ): + enu_speed = np.linalg.norm([enu_velocity.x, enu_velocity.y, enu_velocity.z]) + ecef_speed = np.linalg.norm( + [ecef_velocity.x, ecef_velocity.y, ecef_velocity.z] + ) + self.assertAlmostEqual(enu_speed, ecef_speed, places=3) + + def test_collect_orbit_track_swath_width_independent_of_coordinates(self): + """ + Test that `swath_width` is the same regardless of the requested + output `coordinates`, since it depends only on the satellite's + altitude and the `elevation` parameter, not the output frame. + """ + times = [ + datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(minutes=i) + for i in range(10) + ] + wgs84_widths = collect_orbit_track( + self.satellite, times, coordinates=OrbitCoordinate.WGS84 + ).swath_width.values + ecef_widths = collect_orbit_track( + self.satellite, times, coordinates=OrbitCoordinate.ECEF + ).swath_width.values + eci_widths = collect_orbit_track( + self.satellite, times, coordinates=OrbitCoordinate.ECI + ).swath_width.values + np.testing.assert_allclose(wgs84_widths, ecef_widths) + np.testing.assert_allclose(wgs84_widths, eci_widths) + + def test_collect_orbit_track_mask_excludes_all_points_returns_empty(self): + """ + Test that a mask entirely disjoint from the orbit track returns an + empty DataFrame, rather than one with zero rows but a different + schema. + """ + mask = Polygon([[10, 89], [11, 89], [11, 89.5], [10, 89.5]]) + results = collect_orbit_track( self.satellite, [ - datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(minutes=5 * i) + datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(minutes=i) for i in range(10) ], mask=mask, ) + self.assertTrue(results.empty) - def test_compute_ground_track_point(self): - results = compute_ground_track( + def test_collect_orbit_track_sat_sunlit(self): + """ + Test that `sat_sunlit` appends a boolean column. + """ + results = collect_orbit_track( self.satellite, [ - datetime(2022, 6, 1, 1, tzinfo=timezone.utc) + timedelta(minutes=i) + datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(minutes=i) for i in range(10) ], - method="point", + sat_sunlit=True, ) - self.assertEqual(len(results.index), 1) - self.assertEqual(type(results.iloc[0].geometry), Polygon) + self.assertIn("sat_sunlit", results.columns) + self.assertTrue(results.sat_sunlit.isin([True, False]).all()) - def test_compute_ground_track_point_no_instr_index(self): - results = compute_ground_track( + def test_collect_orbit_track_solar_altaz_valid_range(self): + """ + Test that `solar_altaz` appends solar altitude/azimuth columns + within their valid ranges. + """ + results = collect_orbit_track( self.satellite, [ - datetime(2022, 6, 1, 1, tzinfo=timezone.utc) + timedelta(minutes=i) + datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(minutes=i) for i in range(10) ], - method="point", + solar_altaz=True, ) - self.assertEqual(len(results.index), 1) - self.assertEqual(type(results.iloc[0].geometry), Polygon) + self.assertIn("solar_alt", results.columns) + self.assertIn("solar_az", results.columns) + for solar_alt, solar_az in zip(results.solar_alt, results.solar_az): + self.assertGreaterEqual(solar_alt, -90.0) + self.assertLessEqual(solar_alt, 90.0) + self.assertGreaterEqual(solar_az, 0.0) + self.assertLess(solar_az, 360.0) - def test_compute_ground_track_point_multipolygon(self): - results = compute_ground_track( + def test_collect_orbit_track_solar_beta_valid_range(self): + """ + Test that `solar_beta` appends a column within its valid range + (+/- 90 deg). + """ + results = collect_orbit_track( self.satellite, [ - datetime(2022, 6, 1, 1, 40, tzinfo=timezone.utc) + timedelta(minutes=i) + datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(minutes=i) for i in range(10) ], - method="point", + solar_beta=True, ) - self.assertEqual(len(results.index), 1) - self.assertEqual(type(results.iloc[0].geometry), MultiPolygon) + self.assertIn("solar_beta", results.columns) + for solar_beta in results.solar_beta: + self.assertGreaterEqual(solar_beta, -90.0) + self.assertLessEqual(solar_beta, 90.0) + + def test_collect_ground_track(self): + """ + Test that ground track collection works for a single satellite and a list of times. + """ + collect_ground_track( + self.satellite, + times=[ + datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(minutes=i) + for i in range(10) + ], + ) + + def test_collect_ground_track_empty(self): + """ + Test that ground track collection returns an empty DataFrame when + no times are provided. + """ + collect_ground_track( + self.satellite, + [], + ) + + def test_collect_ground_track_with_mask(self): + """ + Test that ground track collection works for a single satellite, a list + of times, and a mask. + """ + mask = Polygon([[-90, 45], [-90, 45], [90, 45], [90, -45], [-90, -45]]) + collect_ground_track( + self.satellite, + [ + datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(minutes=5 * i) + for i in range(10) + ], + mask=mask, + ) + + def test_collect_ground_track_returns_one_row_per_time(self): + """ + Test that ground track collection returns one polygon per requested + time, each a valid, non-empty Polygon or MultiPolygon. + """ + times = [ + datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(minutes=i) + for i in range(10) + ] + results = collect_ground_track(self.satellite, times) + self.assertEqual(len(results.index), len(times)) + for geometry in results.geometry: + self.assertIsInstance(geometry, (Polygon, MultiPolygon)) + self.assertFalse(geometry.is_empty) + + def test_collect_ground_track_footprint_contains_subsatellite_point(self): + """ + Test that the (nadir-pointing) instrument's footprint contains the + satellite's true sub-satellite point. + """ + times = [ + datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(minutes=i) + for i in range(10) + ] + results = collect_ground_track(self.satellite, times) + orbit_track = self.orbit.to_gp_orbit().get_orbit_track(times) + for i, geometry in enumerate(results.geometry): + subpoint = wgs84.subpoint_of(orbit_track[i]) + self.assertTrue( + geometry.contains( + ShapelyPoint(subpoint.longitude.degrees, subpoint.latitude.degrees) + ) + ) + + def test_collect_ground_track_footprint_radius_matches_swath_width(self): + """ + Test that the SPICE-computed footprint radius (geodesic distance + from the sub-satellite point to the farthest footprint vertex) + matches the analytic swath width formula from + `field_of_regard_to_swath_width`, for a narrow field of regard + (which keeps the footprint a single, near-circular polygon). + """ + instrument = Instrument(name="Narrow", field_of_regard=10.0) + satellite = Satellite(name="Narrow", orbit=self.orbit, instruments=[instrument]) + times = [ + datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(minutes=i) + for i in range(5) + ] + results = collect_ground_track(satellite, times) + orbit_track = self.orbit.to_gp_orbit().get_orbit_track(times) + gp_orbit = self.orbit.to_gp_orbit() + expected_radius = ( + field_of_regard_to_swath_width( + gp_orbit.get_mean_altitude(), instrument.field_of_regard + ) + / 2 + ) + for i, geometry in enumerate(results.geometry): + self.assertIsInstance(geometry, Polygon) + subpoint = wgs84.subpoint_of(orbit_track[i]) + max_distance = max( + geodesic_distance( + subpoint.longitude.degrees, subpoint.latitude.degrees, x, y + ) + for x, y, *_ in geometry.exterior.coords + ) + self.assertAlmostEqual( + max_distance, expected_radius, delta=expected_radius * 0.05 + ) + + def test_collect_ground_track_sat_altaz_is_zenith_at_footprint_center(self): + """ + Test that the satellite appears at zenith (90 deg altitude) as seen + from its own footprint center, since this is a nadir-pointing + instrument. + """ + times = [ + datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(minutes=i) + for i in range(10) + ] + results = collect_ground_track(self.satellite, times, sat_altaz=True) + for sat_alt in results.sat_alt: + self.assertAlmostEqual(sat_alt, 90.0, places=1) - def test_compute_ground_track_line_short(self): + def test_collect_ground_track_solar_altaz_valid_range(self): + """ + Test that the solar altitude/azimuth angles fall within their valid + ranges. + """ + times = [ + datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(minutes=i) + for i in range(10) + ] + results = collect_ground_track(self.satellite, times, solar_altaz=True) + for solar_alt, solar_az in zip(results.solar_alt, results.solar_az): + self.assertGreaterEqual(solar_alt, -90.0) + self.assertLessEqual(solar_alt, 90.0) + self.assertGreaterEqual(solar_az, 0.0) + self.assertLess(solar_az, 360.0) + + def test_collect_ground_track_mask_limits_footprints_to_region(self): + """ + Test that every returned footprint intersects the provided mask, + and that the mask actually excludes some of the requested times. + """ + times = [ + datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(minutes=5 * i) + for i in range(10) + ] + mask = Polygon([[-90, 45], [90, 45], [90, -45], [-90, -45]]) + results = collect_ground_track(self.satellite, times, mask=mask) + self.assertTrue(0 < len(results.index) < len(times)) + for geometry in results.geometry: + self.assertTrue(mask.intersects(geometry)) + + def test_collect_ground_track_mask_as_geodataframe(self): + """ + Test that a mask passed as a GeoDataFrame (rather than a bare + Polygon) works the same way. + """ + times = [ + datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(minutes=5 * i) + for i in range(10) + ] + polygon = Polygon([[-90, 45], [90, 45], [90, -45], [-90, -45]]) + mask = gpd.GeoDataFrame(geometry=[polygon], crs="EPSG:4326") + results = collect_ground_track(self.satellite, times, mask=mask) + self.assertTrue(0 < len(results.index) < len(times)) + for geometry in results.geometry: + self.assertTrue(polygon.intersects(geometry)) + + def test_collect_ground_track_mask_as_geoseries(self): + """ + Test that a mask passed as a GeoSeries (rather than a bare + Polygon) works the same way. + """ + times = [ + datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(minutes=5 * i) + for i in range(10) + ] + polygon = Polygon([[-90, 45], [90, 45], [90, -45], [-90, -45]]) + mask = gpd.GeoSeries([polygon], crs="EPSG:4326") + results = collect_ground_track(self.satellite, times, mask=mask) + self.assertTrue(0 < len(results.index) < len(times)) + for geometry in results.geometry: + self.assertTrue(polygon.intersects(geometry)) + + def test_collect_ground_track_mask_excludes_everything_at_coarse_stage(self): + """ + Test that a mask far enough away to exclude every point even at + the coarse, buffer-tolerant culling stage returns an empty result. + """ + times = [ + datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(seconds=i) + for i in range(10) + ] + mask = Polygon([[10, 89], [11, 89], [11, 89.5], [10, 89.5]]) + results = collect_ground_track(self.satellite, times, mask=mask) + self.assertTrue(results.empty) + + def test_collect_ground_track_mask_excludes_everything_at_footprint_stage(self): + """ + Test that a mask close enough to pass the coarse, buffer-tolerant + culling stage, but too far for any actual instrument footprint to + intersect, still returns an empty result (rather than incorrectly + returning the coarsely-culled, unfiltered points). + """ + instrument = Instrument(name="Narrow", field_of_regard=10.0) + satellite = Satellite(name="Narrow", orbit=self.orbit, instruments=[instrument]) + times = [ + datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(seconds=i) + for i in range(10) + ] + subpoint = collect_orbit_track(satellite, [times[0]]).geometry.iloc[0] + # 0.5 deg (~55 km) away: within the coarse buffer's tolerance + # (ground distance traveled in one second, plus half the narrow + # instrument's swath width), but well beyond the true footprint + mask = ShapelyPoint(subpoint.x + 0.5, subpoint.y).buffer(0.01) + results = collect_ground_track(satellite, times, mask=mask) + self.assertTrue(results.empty) + + def test_compute_ground_track(self): + """ + Test that ground track computation works for a single satellite and a list + of times. + """ results = compute_ground_track( self.satellite, [ datetime(2022, 6, 1, 1, tzinfo=timezone.utc) + timedelta(minutes=i) for i in range(10) ], - method="line", - crs="EPSG:4087", ) self.assertEqual(len(results.index), 1) self.assertEqual(type(results.iloc[0].geometry), Polygon) - def test_compute_ground_track_line_long(self): + def test_compute_ground_track_no_instr_index(self): + """ + Test that ground track computation works for a single satellite and a list + of times with no instrument index. + """ results = compute_ground_track( self.satellite, [ - datetime(2022, 6, 1, 1, tzinfo=timezone.utc) + timedelta(minutes=5 * i) - for i in range(12) + datetime(2022, 6, 1, 1, tzinfo=timezone.utc) + timedelta(minutes=i) + for i in range(10) ], - method="line", - crs="EPSG:4087", ) self.assertEqual(len(results.index), 1) - self.assertEqual(type(results.iloc[0].geometry), MultiPolygon) + self.assertEqual(type(results.iloc[0].geometry), Polygon) - def test_compute_ground_track_line_multipolygon(self): + def test_compute_ground_track_multipolygon(self): + """ + Test that ground track computation works for a single satellite and a list + of times with a multipolygon result. + """ results = compute_ground_track( self.satellite, [ datetime(2022, 6, 1, 1, 40, tzinfo=timezone.utc) + timedelta(minutes=i) for i in range(10) ], - method="line", - crs="EPSG:4087", ) self.assertEqual(len(results.index), 1) self.assertEqual(type(results.iloc[0].geometry), MultiPolygon) + + def test_collect_ground_pixels_requires_rectangular_pointed_instrument(self): + """ + Test that ground pixel collection raises a ValueError for an + instrument that is not a rectangular PointedInstrument. + """ + with self.assertRaises(ValueError): + collect_ground_pixels( + self.satellite, + [datetime(2022, 6, 1, tzinfo=timezone.utc)], + ) + + def test_collect_ground_pixels_returns_grid_per_time(self): + """ + Test that ground pixel collection returns one point per pixel per + requested time, for a rectangular pixel array. + """ + instrument = PointedInstrument( + name="Pixels", + cross_track_field_of_view=10.0, + along_track_field_of_view=10.0, + is_rectangular=True, + cross_track_pixels=3, + along_track_pixels=3, + ) + satellite = Satellite(name="Pixels", orbit=self.orbit, instruments=[instrument]) + times = [ + datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(minutes=i) + for i in range(3) + ] + results = collect_ground_pixels(satellite, times) + self.assertEqual(len(results.index), len(times) * 9) + self.assertTrue((results.groupby("time").size() == 9).all()) + + def test_collect_ground_pixels_center_pixel_matches_subpoint(self): + """ + Test that, for a nadir-pointing instrument with an odd-sized pixel + grid, one pixel coincides with the satellite's true sub-satellite + point (the exact center of the grid has zero cone-angle offset from + boresight). + """ + instrument = PointedInstrument( + name="Pixels", + cross_track_field_of_view=10.0, + along_track_field_of_view=10.0, + is_rectangular=True, + cross_track_pixels=3, + along_track_pixels=3, + ) + satellite = Satellite(name="Pixels", orbit=self.orbit, instruments=[instrument]) + times = [ + datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(minutes=i) + for i in range(3) + ] + results = collect_ground_pixels(satellite, times) + orbit_track = self.orbit.to_gp_orbit().get_orbit_track(times) + for i, time in enumerate(results.time.unique()): + subpoint = wgs84.subpoint_of(orbit_track[i]) + rows = results[results.time == time] + min_distance = min( + geodesic_distance( + subpoint.longitude.degrees, subpoint.latitude.degrees, p.x, p.y + ) + for p in rows.geometry + ) + self.assertAlmostEqual(min_distance, 0.0, delta=1.0) + + def test_collect_ground_pixels_sat_altaz_center_pixel_is_zenith(self): + """ + Test that the center pixel's satellite altitude is at zenith + (90 deg), since it coincides with the nadir sub-satellite point. + """ + instrument = PointedInstrument( + name="Pixels", + cross_track_field_of_view=10.0, + along_track_field_of_view=10.0, + is_rectangular=True, + cross_track_pixels=3, + along_track_pixels=3, + ) + satellite = Satellite(name="Pixels", orbit=self.orbit, instruments=[instrument]) + times = [ + datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(minutes=i) + for i in range(3) + ] + results = collect_ground_pixels(satellite, times, sat_altaz=True) + for _, rows in results.groupby("time"): + self.assertAlmostEqual(rows.sat_alt.max(), 90.0, places=1) + + def test_collect_ground_pixels_solar_altaz_valid_range(self): + """ + Test that the solar altitude/azimuth angles fall within their valid + ranges. + """ + instrument = PointedInstrument( + name="Pixels", + cross_track_field_of_view=10.0, + along_track_field_of_view=10.0, + is_rectangular=True, + cross_track_pixels=2, + along_track_pixels=2, + ) + satellite = Satellite(name="Pixels", orbit=self.orbit, instruments=[instrument]) + times = [datetime(2022, 6, 1, tzinfo=timezone.utc)] + results = collect_ground_pixels(satellite, times, solar_altaz=True) + for solar_alt, solar_az in zip(results.solar_alt, results.solar_az): + self.assertGreaterEqual(solar_alt, -90.0) + self.assertLessEqual(solar_alt, 90.0) + self.assertGreaterEqual(solar_az, 0.0) + self.assertLess(solar_az, 360.0) + + def test_collect_ground_pixels_mask_limits_pixels_to_region(self): + """ + Test that every returned pixel falls within the provided mask, and + that the mask actually excludes some of the requested times. + """ + instrument = PointedInstrument( + name="Pixels", + cross_track_field_of_view=10.0, + along_track_field_of_view=10.0, + is_rectangular=True, + cross_track_pixels=3, + along_track_pixels=3, + ) + satellite = Satellite(name="Pixels", orbit=self.orbit, instruments=[instrument]) + times = [ + datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(minutes=5 * i) + for i in range(10) + ] + mask = Polygon([[-90, 45], [90, 45], [90, -45], [-90, -45]]) + results = collect_ground_pixels(satellite, times, mask=mask) + self.assertTrue(0 < results.time.nunique() < len(times)) + for geometry in results.geometry: + self.assertTrue(mask.contains(geometry)) + + def test_collect_ground_pixels_mask_as_geodataframe(self): + """ + Test that a mask passed as a GeoDataFrame (rather than a bare + Polygon) works the same way. + """ + instrument = PointedInstrument( + name="Pixels", + cross_track_field_of_view=10.0, + along_track_field_of_view=10.0, + is_rectangular=True, + cross_track_pixels=3, + along_track_pixels=3, + ) + satellite = Satellite(name="Pixels", orbit=self.orbit, instruments=[instrument]) + times = [ + datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(minutes=5 * i) + for i in range(10) + ] + polygon = Polygon([[-90, 45], [90, 45], [90, -45], [-90, -45]]) + mask = gpd.GeoDataFrame(geometry=[polygon], crs="EPSG:4326") + results = collect_ground_pixels(satellite, times, mask=mask) + self.assertTrue(0 < results.time.nunique() < len(times)) + for geometry in results.geometry: + self.assertTrue(polygon.contains(geometry)) + + def test_collect_ground_pixels_mask_as_geoseries(self): + """ + Test that a mask passed as a GeoSeries (rather than a bare + Polygon) works the same way. + """ + instrument = PointedInstrument( + name="Pixels", + cross_track_field_of_view=10.0, + along_track_field_of_view=10.0, + is_rectangular=True, + cross_track_pixels=3, + along_track_pixels=3, + ) + satellite = Satellite(name="Pixels", orbit=self.orbit, instruments=[instrument]) + times = [ + datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(minutes=5 * i) + for i in range(10) + ] + polygon = Polygon([[-90, 45], [90, 45], [90, -45], [-90, -45]]) + mask = gpd.GeoSeries([polygon], crs="EPSG:4326") + results = collect_ground_pixels(satellite, times, mask=mask) + self.assertTrue(0 < results.time.nunique() < len(times)) + for geometry in results.geometry: + self.assertTrue(polygon.contains(geometry)) + + def test_collect_ground_pixels_mask_excludes_everything_at_coarse_stage(self): + """ + Test that a mask far enough away to exclude every point even at + the coarse, buffer-tolerant culling stage returns an empty result. + """ + instrument = PointedInstrument( + name="Pixels", + cross_track_field_of_view=10.0, + along_track_field_of_view=10.0, + is_rectangular=True, + cross_track_pixels=3, + along_track_pixels=3, + ) + satellite = Satellite(name="Pixels", orbit=self.orbit, instruments=[instrument]) + times = [ + datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(seconds=i) + for i in range(10) + ] + mask = Polygon([[10, 89], [11, 89], [11, 89.5], [10, 89.5]]) + results = collect_ground_pixels(satellite, times, mask=mask) + self.assertTrue(results.empty) + + def test_collect_ground_pixels_mask_excludes_everything_at_footprint_stage(self): + """ + Test that a mask close enough to pass the coarse, buffer-tolerant + culling stage, but too far for any actual pixel footprint to + intersect, still returns an empty result. + """ + instrument = PointedInstrument( + name="Pixels", + cross_track_field_of_view=10.0, + along_track_field_of_view=10.0, + is_rectangular=True, + cross_track_pixels=3, + along_track_pixels=3, + ) + satellite = Satellite(name="Pixels", orbit=self.orbit, instruments=[instrument]) + times = [ + datetime(2022, 6, 1, tzinfo=timezone.utc) + timedelta(seconds=i) + for i in range(10) + ] + subpoint = collect_orbit_track(satellite, [times[0]]).geometry.iloc[0] + mask = ShapelyPoint(subpoint.x + 0.5, subpoint.y).buffer(0.01) + results = collect_ground_pixels(satellite, times, mask=mask) + self.assertTrue(results.empty) + + def test_collect_ground_pixels_empty(self): + """ + Test that ground pixel collection returns an empty DataFrame when + no times are provided. + """ + instrument = PointedInstrument( + name="Pixels", + cross_track_field_of_view=10.0, + along_track_field_of_view=10.0, + is_rectangular=True, + ) + satellite = Satellite(name="Pixels", orbit=self.orbit, instruments=[instrument]) + results = collect_ground_pixels(satellite, []) + self.assertTrue(results.empty) diff --git a/tests/generation/test_cells.py b/tests/generation/test_cells.py index 5fbada6..21dc322 100644 --- a/tests/generation/test_cells.py +++ b/tests/generation/test_cells.py @@ -1,42 +1,75 @@ +""" +Unit tests for the cell generation functions. + +@author: Paul T. Grogan """ + import unittest +from shapely.geometry import Polygon + from tatc.generation import ( - generate_equally_spaced_cells, - _generate_equally_spaced_cells, + generate_cells_uniform_angular_spacing, + generate_cells_uniform_spacing, ) -from shapely.geometry import Polygon class TestCellGenerators(unittest.TestCase): + """ + Unit tests for the cell generation functions. + """ + def test_generate_equally_spaced_cells_no_mask_count(self): - points = _generate_equally_spaced_cells(10, 10) + """ + Test that the number of cells generated by the equally spaced generator is correct. + """ + points = generate_cells_uniform_angular_spacing(10, 10) num_samples = (360 / 10) * (180 / 10) self.assertEqual(len(points), num_samples) - def test_generate_equally_spaced_cells_mask_contains(self): + def test_generate_cells_uniform_spacing_mask_contains(self): + """ + Test that the cells generated by the equally spaced generator + are contained within the mask. + """ distance = 2000000 mask = Polygon([[-100, 25], [-50, 25], [-50, -25], [-100, -25], [-100, 25]]) - points = generate_equally_spaced_cells(distance, mask=mask) + points = generate_cells_uniform_spacing(distance, mask=mask) points.apply(lambda r: self.assertTrue(mask.contains(r.geometry)), axis=1) - def test_generate_equally_spaced_cells_mask_small(self): + def test_generate_cells_uniform_spacing_mask_small(self): + """ + Test that the cells generated by the equally spaced generator + are empty when the mask is smaller than the cell size. + """ distance = 2000000 mask = Polygon([[0, 0], [1, 0], [1, 1], [0, 1], [0, 0]]) - points = generate_equally_spaced_cells(distance, mask=mask) + points = generate_cells_uniform_spacing(distance, mask=mask) self.assertEqual(len(points), 0) - def test_generate_equally_spaced_cells_mask_invalid(self): + def test_generate_cells_uniform_spacing_mask_invalid(self): + """ + Test that the cells generated by the equally spaced generator + raise a ValueError when the mask is invalid. + """ distance = 2000000 mask = Polygon([[-100, 25], [-50, 25], [-100, -25], [-50, -25], [-100, 25]]) with self.assertRaises(ValueError): - generate_equally_spaced_cells(distance, mask=mask) + generate_cells_uniform_spacing(distance, mask=mask) def test_generate_equally_spaced_cells_no_mask_lat(self): - points = _generate_equally_spaced_cells(10, 10, strips="lat") + """ + Test that the number of cells generated by the equally spaced + generator is correct when using latitude strips. + """ + points = generate_cells_uniform_angular_spacing(10, 10, strips="lat") num_samples = 180 / 10 self.assertEqual(len(points), num_samples) def test_generate_equally_spaced_cells_no_mask_lon(self): - points = _generate_equally_spaced_cells(10, 10, strips="lon") + """ + Test that the number of cells generated by the equally spaced + generator is correct when using longitude strips. + """ + points = generate_cells_uniform_angular_spacing(10, 10, strips="lon") num_samples = 360 / 10 self.assertEqual(len(points), num_samples) diff --git a/tests/generation/test_grid.py b/tests/generation/test_grid.py new file mode 100644 index 0000000..61522fd --- /dev/null +++ b/tests/generation/test_grid.py @@ -0,0 +1,136 @@ +""" +Unit tests for the tatc.generation._grid module. + +@author Paul T. Grogan +""" + +import unittest + +from shapely.geometry import Polygon + +from tatc.generation._grid import ( + compute_point_id_uniform_spacing, + generate_indices_uniform_spacing, +) + + +class TestComputeEquallySpacedPointId(unittest.TestCase): + """ + Unit tests for the tatc.generation._grid.compute_point_id_uniform_spacing + function. + """ + + def test_first_point_has_id_zero(self): + """ + Test that the first grid point (i=0, j=0) is assigned id 0. + """ + self.assertEqual(compute_point_id_uniform_spacing(0, 0, 10), 0) + + def test_ids_increment_west_to_east_within_a_latitude_row(self): + """ + Test that the longitude index i increments the id by 1 within a + fixed latitude row j, matching the documented west-to-east + ordering. + """ + ids = [compute_point_id_uniform_spacing(i, 0, 10) for i in range(5)] + self.assertEqual(ids, [0, 1, 2, 3, 4]) + + def test_ids_increment_by_full_row_width_south_to_north(self): + """ + Test that the latitude index j increments the id by a full row's + worth of longitude bins (360/theta_i), matching the documented + south-to-north ordering. + """ + row_width = int(360 / 10) + self.assertEqual(compute_point_id_uniform_spacing(0, 1, 10), row_width) + + def test_longitude_index_wraps_around_row_width(self): + """ + Test that a longitude index at the row width wraps back to the + start of the row (np.mod behavior) rather than overflowing into + the next latitude row's id range. + """ + row_width = int(360 / 10) + self.assertEqual(compute_point_id_uniform_spacing(row_width, 0, 10), 0) + + def test_row_width_scales_with_theta_i(self): + """ + Test the id formula with a longitude step that doesn't evenly + divide into the default 10-degree examples above, confirming the + row width (360/theta_i) scales correctly. + """ + # 360/20 = 18 longitude bins per row + self.assertEqual(compute_point_id_uniform_spacing(0, 1, 20), 18) + self.assertEqual(compute_point_id_uniform_spacing(5, 1, 20), 23) + + def test_ids_are_unique_for_unequal_longitude_and_latitude_steps(self): + """ + Regression test: ensure no two distinct grid points collide on the + same id when the grid's longitude and latitude angular steps + differ (compute_point_id_uniform_spacing only takes the longitude + step; a prior bug used the latitude step for the row multiplier + instead, causing collisions in exactly this scenario). + """ + theta_longitude, theta_latitude = 10, 20 + indices = generate_indices_uniform_spacing(theta_longitude, theta_latitude) + ids = [ + compute_point_id_uniform_spacing(i, j, theta_longitude) + for (i, j) in indices + ] + self.assertEqual(len(ids), len(set(ids))) + + +class TestGenerateEquallySpacedIndices(unittest.TestCase): + """ + Unit tests for the tatc.generation._grid.generate_indices_uniform_spacing + function. + """ + + def test_global_grid_index_count(self): + """ + Test that a global (no mask) grid produces the expected number of + two-dimensional (longitude, latitude) index pairs. + """ + indices = generate_indices_uniform_spacing(10, 10) + self.assertEqual(len(indices), (360 / 10) * (180 / 10)) + + def test_latitude_strips_produce_one_dimensional_indices(self): + """ + Test that strips="lat" produces one index per latitude row, each + with longitude index fixed at 0. + """ + indices = generate_indices_uniform_spacing(10, 10, strips="lat") + self.assertEqual(len(indices), 180 / 10) + self.assertTrue(all(i == 0 for i, j in indices)) + + def test_longitude_strips_produce_one_dimensional_indices(self): + """ + Test that strips="lon" produces one index per longitude column, + each with latitude index fixed at 0. + """ + indices = generate_indices_uniform_spacing(10, 10, strips="lon") + self.assertEqual(len(indices), 360 / 10) + self.assertTrue(all(j == 0 for i, j in indices)) + + def test_mask_restricts_indices_to_its_bounds(self): + """ + Test that supplying a mask restricts generated indices to the + mask's bounding box rather than the full globe. Uses bounds that + land on exact grid-index boundaries (rather than a half-step + offset) to avoid np.round's round-half-to-even tie-breaking. + """ + mask = Polygon([[-100, 20], [-50, 20], [-50, -20], [-100, -20], [-100, 20]]) + indices = generate_indices_uniform_spacing(10, 10, mask=mask) + expected_count = ((-50) - (-100)) / 10 * (20 - (-20)) / 10 + self.assertEqual(len(indices), expected_count) + + def test_indices_are_unique(self): + """ + Test that a global grid produces no duplicate (i, j) index pairs. + """ + indices = generate_indices_uniform_spacing(10, 10) + self.assertEqual(len(indices), len(set(indices))) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/generation/test_points.py b/tests/generation/test_points.py index de15a42..03c65a6 100644 --- a/tests/generation/test_points.py +++ b/tests/generation/test_points.py @@ -1,34 +1,54 @@ +""" +Unit tests for the point generation functions in tatc.generation. + +@author Paul T. Grogan +""" + import unittest import numpy as np +import pyproj from shapely.geometry import Polygon from shapely.ops import transform -import pyproj +from tatc import constants, utils from tatc.generation import ( - generate_fibonacci_lattice_points, - generate_equally_spaced_points, - _generate_equally_spaced_points, + generate_points_fibonacci_lattice, + generate_points_uniform_angular_distance, + generate_points_uniform_spacing, ) -from tatc import constants, utils +from tatc.generation._grid import compute_point_id_uniform_spacing class TestPointGenerators(unittest.TestCase): - def test_generate_fibonacci_lattice_points_no_mask_count(self): + """ + Unit tests for the point generation functions in tatc.generation. + """ + + def test_generate_points_fibonacci_lattice_no_mask_count(self): + """ + Test that the number of points generated by the Fibonacci lattice generator is correct. + """ distance = 2000000 - points = generate_fibonacci_lattice_points(distance) + points = generate_points_fibonacci_lattice(distance) num_samples = utils.compute_number_samples(distance) self.assertEqual(len(points), num_samples) def test_generate_equally_spaced_points_no_mask_count(self): - points = _generate_equally_spaced_points(10, 10) + """ + Test that the number of points generated by the equally spaced generator is correct. + """ + points = generate_points_uniform_angular_distance(10, 10) num_samples = (360 / 10) * (180 / 10) self.assertEqual(len(points), num_samples) - def test_generate_fibonacci_lattice_points_no_mask_distance(self): + def test_generate_points_fibonacci_lattice_no_mask_distance(self): + """ + Test that the mean distance between points generated by the + Fibonacci lattice generator is correct. + """ distance = 2000000 - points = generate_fibonacci_lattice_points(distance) - num_samples = utils.compute_number_samples(distance) + points = generate_points_fibonacci_lattice(distance) test_points_m = points.to_crs("EPSG:32663") test_points_m["closest_neighbor"] = test_points_m.apply( lambda r: test_points_m[test_points_m.point_id != r.point_id] @@ -43,10 +63,14 @@ def test_generate_fibonacci_lattice_points_no_mask_distance(self): delta=0.10 * distance, ) - def test_generate_fibonacci_lattice_points_mask_count(self): + def test_generate_points_fibonacci_lattice_mask_count(self): + """ + Test that the number of points generated by the Fibonacci + lattice generator with a mask is correct. + """ distance = 2000000 mask = Polygon([[-100, 25], [-50, 25], [-50, -25], [-100, -25], [-100, 25]]) - points = generate_fibonacci_lattice_points(distance, mask=mask) + points = generate_points_fibonacci_lattice(distance, mask=mask) num_samples = utils.compute_number_samples(distance) transformer = pyproj.Transformer.from_crs( pyproj.CRS("EPSG:4326"), pyproj.CRS("EPSG:32663"), always_xy=True @@ -58,20 +82,24 @@ def test_generate_fibonacci_lattice_points_mask_count(self): delta=1, ) - def test_generate_fibonacci_lattice_points_mask_contains(self): + def test_generate_points_fibonacci_lattice_mask_contains(self): + """ + Test that all points generated by the Fibonacci lattice generator + with a mask are contained within the mask. + """ distance = 2000000 mask = Polygon([[-100, 25], [-50, 25], [-50, -25], [-100, -25], [-100, 25]]) - points = generate_fibonacci_lattice_points(distance, mask=mask) + points = generate_points_fibonacci_lattice(distance, mask=mask) points.apply(lambda r: self.assertTrue(mask.contains(r.geometry)), axis=1) - def test_generate_fibonacci_lattice_points_mask_distance(self): + def test_generate_points_fibonacci_lattice_mask_distance(self): + """ + Test that the mean distance between points generated by the + Fibonacci lattice generator with a mask is correct. + """ distance = 2000000 mask = Polygon([[-100, 25], [-50, 25], [-50, -25], [-100, -25], [-100, 25]]) - points = generate_fibonacci_lattice_points(distance, mask=mask) - transformer = pyproj.Transformer.from_crs( - pyproj.CRS("EPSG:4326"), pyproj.CRS("EPSG:32663"), always_xy=True - ).transform - mask_m = transform(transformer, mask) + points = generate_points_fibonacci_lattice(distance, mask=mask) test_points_m = points.to_crs("EPSG:32663") test_points_m["closest_neighbor"] = test_points_m.apply( lambda r: test_points_m[test_points_m.point_id != r.point_id] @@ -86,10 +114,13 @@ def test_generate_fibonacci_lattice_points_mask_distance(self): delta=0.20 * distance, ) - def test_generate_equally_spaced_points_no_mask_distance(self): + def test_generate_points_uniform_spacing_no_mask_distance(self): + """ + Test that the mean distance between points generated by the + equally spaced generator is correct. + """ distance = 2000000 - points = generate_equally_spaced_points(distance) - num_samples = utils.compute_number_samples(distance) + points = generate_points_uniform_spacing(distance) test_points_m = points.to_crs("EPSG:32663") test_points_m["closest_neighbor"] = test_points_m.apply( lambda r: test_points_m[test_points_m.point_id != r.point_id] @@ -104,20 +135,24 @@ def test_generate_equally_spaced_points_no_mask_distance(self): delta=0.10 * distance, ) - def test_generate_equally_spaced_points_mask_contains(self): + def test_generate_points_uniform_spacing_mask_contains(self): + """ + Test that all points generated by the equally spaced generator + with a mask are contained within the mask. + """ distance = 2000000 mask = Polygon([[-100, 25], [-50, 25], [-50, -25], [-100, -25], [-100, 25]]) - points = generate_equally_spaced_points(distance, mask=mask) + points = generate_points_uniform_spacing(distance, mask=mask) points.apply(lambda r: self.assertTrue(mask.contains(r.geometry)), axis=1) - def test_generate_equally_spaced_points_mask_distance(self): + def test_generate_points_uniform_spacing_mask_distance(self): + """ + Test that the mean distance between points generated by the + equally spaced generator with a mask is correct. + """ distance = 2000000 mask = Polygon([[-100, 25], [-50, 25], [-50, -25], [-100, -25], [-100, 25]]) - points = generate_equally_spaced_points(distance, mask=mask) - transformer = pyproj.Transformer.from_crs( - pyproj.CRS("EPSG:4326"), pyproj.CRS("EPSG:32663"), always_xy=True - ).transform - mask_m = transform(transformer, mask) + points = generate_points_uniform_spacing(distance, mask=mask) test_points_m = points.to_crs("EPSG:32663") test_points_m["closest_neighbor"] = test_points_m.apply( lambda r: test_points_m[test_points_m.point_id != r.point_id] @@ -132,26 +167,126 @@ def test_generate_equally_spaced_points_mask_distance(self): delta=0.20 * distance, ) - def test_generate_fibonacci_lattice_points_mask_small(self): + def test_generate_points_fibonacci_lattice_mask_small(self): + """ + Test that the number of points generated by the Fibonacci + lattice generator with a small mask is zero. + """ distance = 2000000 mask = Polygon([[0, 0], [1, 0], [1, 1], [0, 1], [0, 0]]) - points = generate_fibonacci_lattice_points(distance, mask=mask) + points = generate_points_fibonacci_lattice(distance, mask=mask) self.assertEqual(len(points), 0) - def test_generate_equally_spaced_points_mask_small(self): + def test_generate_points_uniform_spacing_mask_small(self): + """ + Test that the number of points generated by the equally + spaced generator with a small mask is zero. + """ distance = 2000000 mask = Polygon([[0, 0], [1, 0], [1, 1], [0, 1], [0, 0]]) - points = generate_equally_spaced_points(distance, mask=mask) + points = generate_points_uniform_spacing(distance, mask=mask) self.assertEqual(len(points), 0) - def test_generate_fibonacci_lattice_points_mask_invalid(self): + def test_generate_points_fibonacci_lattice_mask_invalid(self): + """ + Test that the Fibonacci lattice generator raises a ValueError + when given an invalid mask. + """ distance = 2000000 mask = Polygon([[-100, 25], [-50, 25], [-100, -25], [-50, -25], [-100, 25]]) with self.assertRaises(ValueError): - generate_fibonacci_lattice_points(distance, mask=mask) + generate_points_fibonacci_lattice(distance, mask=mask) - def test_generate_equally_spaced_points_mask_invalid(self): + def test_generate_points_uniform_spacing_mask_invalid(self): + """ + Test that the equally spaced generator raises a ValueError + when given an invalid mask. + """ distance = 2000000 mask = Polygon([[-100, 25], [-50, 25], [-100, -25], [-50, -25], [-100, 25]]) with self.assertRaises(ValueError): - generate_equally_spaced_points(distance, mask=mask) + generate_points_uniform_spacing(distance, mask=mask) + + def test_generate_points_fibonacci_lattice_elevation(self): + """ + Test that the elevation argument is propagated to the z-coordinate + of every generated Fibonacci lattice point. + """ + points = generate_points_fibonacci_lattice(3000000, elevation=500) + self.assertTrue((points.geometry.apply(lambda g: g.z) == 500).all()) + + def test_generate_points_uniform_spacing_elevation(self): + """ + Test that the elevation argument is propagated to the z-coordinate + of every generated equally spaced point. + """ + points = generate_points_uniform_spacing(3000000, elevation=-200) + self.assertTrue((points.geometry.apply(lambda g: g.z) == -200).all()) + + def test_generate_points_fibonacci_lattice_no_poles(self): + """ + Test that no generated Fibonacci lattice point sits exactly at + either pole, per Gonzalez (2010)'s construction (referenced in the + function's docstring). + """ + points = generate_points_fibonacci_lattice(2000000) + self.assertTrue((points.geometry.y.abs() < 90).all()) + + def test_generate_points_fibonacci_lattice_matches_published_formula(self): + """ + Test the first and last (by index) Fibonacci lattice point + latitudes against Gonzalez (2010)'s closed-form placement formula, + computed independently here rather than by importing the + implementation's private helper. + """ + distance = 2000000 + points = generate_points_fibonacci_lattice(distance) + n = len(points) + expected_first_latitude = np.degrees(np.arcsin(2 * 1 / (n + 2) - 1)) + expected_last_latitude = np.degrees(np.arcsin(2 * n / (n + 2) - 1)) + points_by_id = points.set_index("point_id") + self.assertAlmostEqual( + points_by_id.loc[0].geometry.y, expected_first_latitude, places=9 + ) + self.assertAlmostEqual( + points_by_id.loc[n - 1].geometry.y, expected_last_latitude, places=9 + ) + + def test_generate_points_fibonacci_lattice_antimeridian_crossing_mask(self): + """ + Test the Fibonacci lattice generator with a mask crossing the + antimeridian expressed in unwrapped longitude (170 to 190 degrees, + rather than 170 to -170). All returned points should fall within + the mask's own [170, 190] longitude convention -- including points + whose natural longitude is negative (e.g. -175), which the + implementation shifts by +360 to match the mask's unwrapped frame + -- rather than being wrapped back to the standard [-180, 180] + range. + """ + mask = Polygon([[170, -10], [190, -10], [190, 10], [170, 10], [170, -10]]) + points = generate_points_fibonacci_lattice(500000, mask=mask) + self.assertGreater(len(points), 0) + self.assertTrue((points.geometry.x >= 170).all()) + self.assertTrue((points.geometry.x <= 190).all()) + # confirms points originally at negative longitude were included + # via the +360 shift, not dropped + self.assertTrue((points.geometry.x > 180).any()) + + def test_generate_points_uniform_angular_distance_point_id_matches_grid_formula( + self, + ): + """ + Test that every point_id returned by the equally spaced generator + matches compute_point_id_uniform_spacing for that point's + recovered (i, j) grid index, confirming point_id is a stable, + dense (no gaps or collisions) enumeration for a global grid. + """ + theta = 10 + points = generate_points_uniform_angular_distance(theta, theta) + self.assertEqual(sorted(points.point_id), list(range(len(points)))) + for _, row in points.iterrows(): + i = round((row.geometry.x - (-180 + 0.5 * theta)) / theta) + j = round((row.geometry.y - (-90 + 0.5 * theta)) / theta) + self.assertEqual( + compute_point_id_uniform_spacing(i, j, theta), row.point_id + ) diff --git a/tests/schemas/instrument/__init__.py b/tests/schemas/instrument/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/schemas/instrument/test_pointed.py b/tests/schemas/instrument/test_pointed.py new file mode 100644 index 0000000..7a26c5c --- /dev/null +++ b/tests/schemas/instrument/test_pointed.py @@ -0,0 +1,411 @@ +""" +Unit tests for the PointedInstrument schema. + +@author: Paul T. Grogan +""" + +import unittest +from datetime import datetime, timezone + +from pydantic import ValidationError +from skyfield.api import EarthSatellite, wgs84 + +from tatc.constants import timescale +from tatc.schemas import CircularOrbit, PointedInstrument +from tatc.utils import field_of_regard_to_swath_width, geodesic_distance +from tatc.utils.projection import compute_projected_ray_position + + +class TestPointedInstrument(unittest.TestCase): + """ + Unit tests for the PointedInstrument schema. + """ + + def setUp(self): + noon_utc = datetime(2020, 3, 20, 12, tzinfo=timezone.utc) + self.test_time = timescale.from_datetime(noon_utc) + self.test_sat = EarthSatellite.from_satrec( + CircularOrbit( + mean_altitude=500000, + true_anomaly=0, + epoch=noon_utc, + inclination=0.0, + right_ascension_ascending_node=0.0, + ) + .to_gp_orbit() + .elements[0] + .to_satrec(), + timescale, + ) + self.orbit_track = self.test_sat.at(self.test_time) # type: ignore + + def test_good_data(self): + """ + Test that a PointedInstrument can be created with valid data. + """ + good_data = { + "name": "Test Instrument", + "cross_track_field_of_view": 20.0, + "along_track_field_of_view": 10.0, + "roll_angle": 5.0, + "pitch_angle": -5.0, + "is_rectangular": True, + "cross_track_pixels": 4, + "along_track_pixels": 2, + "cross_track_oversampling": 0.1, + "along_track_oversampling": 0.2, + } + o = PointedInstrument(**good_data) + self.assertEqual( + o.cross_track_field_of_view, good_data["cross_track_field_of_view"] + ) + self.assertEqual( + o.along_track_field_of_view, good_data["along_track_field_of_view"] + ) + self.assertEqual(o.roll_angle, good_data["roll_angle"]) + self.assertEqual(o.pitch_angle, good_data["pitch_angle"]) + self.assertEqual(o.is_rectangular, good_data["is_rectangular"]) + self.assertEqual(o.cross_track_pixels, good_data["cross_track_pixels"]) + self.assertEqual(o.along_track_pixels, good_data["along_track_pixels"]) + self.assertEqual( + o.cross_track_oversampling, good_data["cross_track_oversampling"] + ) + self.assertEqual( + o.along_track_oversampling, good_data["along_track_oversampling"] + ) + + def test_defaults(self): + """ + Test that optional fields default to a single non-rectangular, + boresight-pointed pixel with no oversampling. + """ + o = PointedInstrument( + name="Test Instrument", + cross_track_field_of_view=20.0, + along_track_field_of_view=10.0, + ) + self.assertEqual(o.roll_angle, 0) + self.assertEqual(o.pitch_angle, 0) + self.assertFalse(o.is_rectangular) + self.assertEqual(o.cross_track_pixels, 1) + self.assertEqual(o.along_track_pixels, 1) + self.assertEqual(o.cross_track_oversampling, 0) + self.assertEqual(o.along_track_oversampling, 0) + + def test_field_of_view_bounds(self): + """ + Test that cross/along track field of view must be in (0, 180]. + """ + PointedInstrument( + name="t", cross_track_field_of_view=180.0, along_track_field_of_view=180.0 + ) + with self.assertRaises(ValidationError): + PointedInstrument( + name="t", cross_track_field_of_view=0.0, along_track_field_of_view=10.0 + ) + with self.assertRaises(ValidationError): + PointedInstrument( + name="t", + cross_track_field_of_view=10.0, + along_track_field_of_view=180.1, + ) + + def test_angle_bounds(self): + """ + Test that roll/pitch angle must be in [-180, 180]. + """ + PointedInstrument( + name="t", + cross_track_field_of_view=10.0, + along_track_field_of_view=10.0, + roll_angle=180.0, + pitch_angle=-180.0, + ) + with self.assertRaises(ValidationError): + PointedInstrument( + name="t", + cross_track_field_of_view=10.0, + along_track_field_of_view=10.0, + roll_angle=180.1, + ) + with self.assertRaises(ValidationError): + PointedInstrument( + name="t", + cross_track_field_of_view=10.0, + along_track_field_of_view=10.0, + pitch_angle=-180.1, + ) + + def test_pixel_count_bounds(self): + """ + Test that cross/along track pixel counts must be at least 1. + """ + with self.assertRaises(ValidationError): + PointedInstrument( + name="t", + cross_track_field_of_view=10.0, + along_track_field_of_view=10.0, + cross_track_pixels=0, + ) + with self.assertRaises(ValidationError): + PointedInstrument( + name="t", + cross_track_field_of_view=10.0, + along_track_field_of_view=10.0, + along_track_pixels=0, + ) + + def test_oversampling_bounds(self): + """ + Test that cross/along track oversampling must be in [0, 1). + """ + with self.assertRaises(ValidationError): + PointedInstrument( + name="t", + cross_track_field_of_view=10.0, + along_track_field_of_view=10.0, + cross_track_oversampling=-0.1, + ) + with self.assertRaises(ValidationError): + PointedInstrument( + name="t", + cross_track_field_of_view=10.0, + along_track_field_of_view=10.0, + along_track_oversampling=1.0, + ) + + def test_get_cross_track_instantaneous_field_of_view_single_pixel(self): + """ + Test that a single pixel's instantaneous field of view equals the + full cross track field of view when there is no oversampling. + """ + o = PointedInstrument( + name="t", cross_track_field_of_view=20.0, along_track_field_of_view=10.0 + ) + self.assertAlmostEqual(o.get_cross_track_instantaneous_field_of_view(), 20.0) + + def test_get_cross_track_instantaneous_field_of_view_with_oversampling(self): + """ + Test that oversampling (fractional pixel overlap) inflates each + pixel's instantaneous field of view beyond the non-overlapping + pixel pitch (field of view / pixel count), consistent with the + pixel footprints overlapping by the requested fraction. + """ + o = PointedInstrument( + name="t", + cross_track_field_of_view=20.0, + along_track_field_of_view=10.0, + cross_track_pixels=4, + cross_track_oversampling=0.5, + ) + # pitch = 20 / 4 = 5 degrees; ifov = pitch / (1 - 0.5) = 10 degrees + self.assertAlmostEqual(o.get_cross_track_instantaneous_field_of_view(), 10.0) + + def test_get_along_track_instantaneous_field_of_view_with_oversampling(self): + """ + Test that oversampling inflates the along track instantaneous + field of view analogously to the cross track case. + """ + o = PointedInstrument( + name="t", + cross_track_field_of_view=20.0, + along_track_field_of_view=10.0, + along_track_pixels=2, + along_track_oversampling=0.5, + ) + # pitch = 10 / 2 = 5 degrees; ifov = pitch / (1 - 0.5) = 10 degrees + self.assertAlmostEqual(o.get_along_track_instantaneous_field_of_view(), 10.0) + + def test_get_pixel_cone_and_clock_angle_single_pixel(self): + """ + Test that a single pixel (spanning the full field of view) is + centered on boresight: zero cone angle. + """ + o = PointedInstrument( + name="t", cross_track_field_of_view=20.0, along_track_field_of_view=10.0 + ) + cone, clock = o.get_pixel_cone_and_clock_angle(0, 0) + self.assertAlmostEqual(cone, 0.0) + + def test_get_pixel_cone_and_clock_angle_cross_track(self): + """ + Test the cone and clock angles for a 2-pixel cross-track-only + array: each pixel is offset a quarter of the full field of view + from boresight, in opposite cross-track (clock 0/180) directions. + """ + o = PointedInstrument( + name="t", + cross_track_field_of_view=20.0, + along_track_field_of_view=10.0, + cross_track_pixels=2, + ) + cone_0, clock_0 = o.get_pixel_cone_and_clock_angle(0, 0) + cone_1, clock_1 = o.get_pixel_cone_and_clock_angle(1, 0) + self.assertAlmostEqual(cone_0, 5.0) + self.assertAlmostEqual(cone_1, 5.0) + self.assertAlmostEqual(clock_0, 180.0) + self.assertAlmostEqual(clock_1, 0.0) + + def test_get_pixel_cone_and_clock_angle_along_track(self): + """ + Test the cone and clock angles for a 2-pixel along-track-only + array: each pixel is offset a quarter of the full field of view + from boresight, in opposite along-track (clock 90/-90) directions. + """ + o = PointedInstrument( + name="t", + cross_track_field_of_view=20.0, + along_track_field_of_view=10.0, + along_track_pixels=2, + ) + cone_0, clock_0 = o.get_pixel_cone_and_clock_angle(0, 0) + cone_1, clock_1 = o.get_pixel_cone_and_clock_angle(0, 1) + self.assertAlmostEqual(cone_0, 2.5) + self.assertAlmostEqual(cone_1, 2.5) + self.assertAlmostEqual(clock_0, 90.0) + self.assertAlmostEqual(clock_1, -90.0) + + def test_compute_footprint_center_matches_subpoint_when_unpointed(self): + """ + Test that with zero roll/pitch, the footprint center coincides + with Skyfield's own WGS 84 sub-satellite point (same as a plain + nadir Instrument). + """ + o = PointedInstrument( + name="t", cross_track_field_of_view=20.0, along_track_field_of_view=10.0 + ) + center = o.compute_footprint_center(self.orbit_track) + subpoint = wgs84.subpoint_of(self.orbit_track) + self.assertAlmostEqual( + geodesic_distance( + center.longitude.degrees, + center.latitude.degrees, + subpoint.longitude.degrees, + subpoint.latitude.degrees, + ), + 0, + delta=1e-3, + ) + + def test_compute_footprint_center_moves_with_roll(self): + """ + Test that increasing the magnitude of the roll angle moves the + footprint center monotonically farther from the sub-satellite + point, in both directions. + """ + subpoint = wgs84.subpoint_of(self.orbit_track) + + def distance_for_roll(roll): + o = PointedInstrument( + name="t", + cross_track_field_of_view=1.0, + along_track_field_of_view=1.0, + roll_angle=roll, + ) + center = o.compute_footprint_center(self.orbit_track) + return geodesic_distance( + subpoint.longitude.degrees, + subpoint.latitude.degrees, + center.longitude.degrees, + center.latitude.degrees, + ) + + distances = [distance_for_roll(r) for r in (0, 5, 10, 20)] + self.assertEqual(distances, sorted(distances)) + distances_negative = [distance_for_roll(r) for r in (0, -5, -10, -20)] + self.assertEqual(distances_negative, sorted(distances_negative)) + + def test_compute_projected_pixel_position_matches_direct_cone_offset(self): + """ + Test that a pixel's projected position matches an independent + computation using its cone angle as a direct roll offset (the + angular displacement convention used elsewhere, e.g. `roll_angle`, + which is not halved). This is a regression test for a bug where + `compute_projected_pixel_position` passed the pixel's cone angle + directly as a field of view, which `compute_projected_ray_position` + halves internally, landing pixels at roughly half their intended + offset from boresight. + """ + o = PointedInstrument( + name="t", + cross_track_field_of_view=20.0, + along_track_field_of_view=20.0, + cross_track_pixels=2, + ) + cone, _ = o.get_pixel_cone_and_clock_angle(1, 0) + pixel_position = o.compute_projected_pixel_position(self.orbit_track, 1, 0) + # clock = 0 for this pixel, so its offset is purely in the roll direction + direct_position = compute_projected_ray_position( + self.orbit_track, + cross_track_field_of_view=0, + along_track_field_of_view=0, + roll_angle=cone, + pitch_angle=0, + is_rectangular=False, + angle=0, + elevation=0, + ) + self.assertAlmostEqual( + geodesic_distance( + pixel_position.longitude.degrees, + pixel_position.latitude.degrees, + direct_position.longitude.degrees, + direct_position.latitude.degrees, + ), + 0, + delta=50.0, + ) + + def test_compute_footprint_pixel_array_shape(self): + """ + Test that the pixel array contains one point per cross/along + track pixel, for each orbit track time. + """ + o = PointedInstrument( + name="t", + cross_track_field_of_view=20.0, + along_track_field_of_view=10.0, + cross_track_pixels=3, + along_track_pixels=2, + ) + times = timescale.utc(2020, 3, 20, 12, 0, [0, 1, 2]) # type: ignore + orbit_track = self.test_sat.at(times) # type: ignore + pixel_arrays = o.compute_footprint_pixel_array(orbit_track) + self.assertEqual(len(pixel_arrays), 3) + for pixel_array in pixel_arrays: + self.assertEqual(len(pixel_array.geoms), 6) + + def test_compute_footprint_cross_track_extent_matches_swath_width(self): + """ + Test that the cross-track and along-track extents of a computed + footprint match `field_of_regard_to_swath_width` evaluated at the + satellite's actual height above the WGS 84 ellipsoid, cross-checking + the closed-form swath width formula against the WGS 84 ellipsoid + footprint geometry (same validation as for the nadir Instrument, + extended to distinct cross/along track fields of view). + """ + o = PointedInstrument( + name="t", cross_track_field_of_view=30.0, along_track_field_of_view=10.0 + ) + height = wgs84.geographic_position_of(self.orbit_track).elevation.m + expected_cross_track = field_of_regard_to_swath_width( + height, o.cross_track_field_of_view + ) + expected_along_track = field_of_regard_to_swath_width( + height, o.along_track_field_of_view + ) + # angle = [0, 90, 180, 270, 360] degrees, sampling both axes exactly + footprint = o.compute_footprint(self.orbit_track, number_points=5) + coords = list(footprint[0].exterior.coords) + cross_track_extent = geodesic_distance( + coords[0][0], coords[0][1], coords[2][0], coords[2][1] + ) + along_track_extent = geodesic_distance( + coords[1][0], coords[1][1], coords[3][0], coords[3][1] + ) + self.assertAlmostEqual(cross_track_extent, expected_cross_track, delta=50.0) + self.assertAlmostEqual(along_track_extent, expected_along_track, delta=50.0) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/schemas/instrument/test_simple.py b/tests/schemas/instrument/test_simple.py new file mode 100644 index 0000000..1129be1 --- /dev/null +++ b/tests/schemas/instrument/test_simple.py @@ -0,0 +1,350 @@ +""" +Unit tests for the Instrument schema. + +@author: Paul T. Grogan +""" + +import unittest +from datetime import datetime, timedelta, timezone + +from pydantic import ValidationError +from skyfield.api import EarthSatellite, wgs84 + +from tatc.constants import timescale +from tatc.schemas import CircularOrbit, Instrument +from tatc.utils import geodesic_distance + + +class TestInstrument(unittest.TestCase): + """ + Unit tests for the Instrument schema. + """ + + def setUp(self): + noon_utc = datetime(2020, 3, 20, 12, tzinfo=timezone.utc) + self.test_time = timescale.from_datetime(noon_utc) + self.test_sat_1 = EarthSatellite.from_satrec( + CircularOrbit( + mean_altitude=400000, + true_anomaly=0, + epoch=noon_utc, + inclination=0.0, + right_ascension_ascending_node=0.0, + ) + .to_gp_orbit() + .elements[0] + .to_satrec(), + timescale, + ) + self.test_sat_2 = EarthSatellite.from_satrec( + CircularOrbit( + mean_altitude=400000, + true_anomaly=0, + epoch=noon_utc, + inclination=0.0, + right_ascension_ascending_node=80.0, + ) + .to_gp_orbit() + .elements[0] + .to_satrec(), + timescale, + ) + self.test_sat_3 = EarthSatellite.from_satrec( + CircularOrbit( + mean_altitude=400000, + true_anomaly=0, + epoch=noon_utc, + inclination=0.0, + right_ascension_ascending_node=100.0, + ) + .to_gp_orbit() + .elements[0] + .to_satrec(), + timescale, + ) + self.test_sat_4 = EarthSatellite.from_satrec( + CircularOrbit( + mean_altitude=400000, + true_anomaly=0, + epoch=noon_utc, + inclination=0.0, + right_ascension_ascending_node=180.0, + ) + .to_gp_orbit() + .elements[0] + .to_satrec(), + timescale, + ) + self.test_sat_5 = EarthSatellite.from_satrec( + CircularOrbit( + mean_altitude=400000, + true_anomaly=0, + epoch=noon_utc, + inclination=45.0, + right_ascension_ascending_node=0.0, + ) + .to_gp_orbit() + .elements[0] + .to_satrec(), + timescale, + ) + + def test_good_data(self): + """ + Test that an Instrument can be created with valid data. + """ + good_data = { + "name": "Test Instrument", + "field_of_regard": 20.0, + "min_access_time": timedelta(seconds=10), + "req_self_sunlit": None, + "req_target_sunlit": None, + } + o = Instrument(**good_data) + self.assertEqual(o.name, good_data.get("name")) + self.assertEqual(o.field_of_regard, good_data.get("field_of_regard")) + self.assertEqual(o.min_access_time, good_data.get("min_access_time")) + self.assertEqual(o.req_self_sunlit, good_data.get("req_self_sunlit")) + self.assertEqual(o.req_target_sunlit, good_data.get("req_target_sunlit")) + + def test_field_of_regard_bounds(self): + """ + Test that field_of_regard must be in the interval (0, 360]. + """ + Instrument(name="Test Instrument", field_of_regard=360.0) + with self.assertRaises(ValidationError): + Instrument(name="Test Instrument", field_of_regard=0.0) + with self.assertRaises(ValidationError): + Instrument(name="Test Instrument", field_of_regard=360.1) + with self.assertRaises(ValidationError): + Instrument(name="Test Instrument", field_of_regard=-10.0) + + def test_access_time_fixed_default(self): + """ + Test that access_time_fixed defaults to False. + """ + o = Instrument(name="Test Instrument") + self.assertFalse(o.access_time_fixed) + o = Instrument(name="Test Instrument", access_time_fixed=True) + self.assertTrue(o.access_time_fixed) + + def test_get_swath_width(self): + """ + Test that the swath width can be computed from the field of regard. + """ + o = Instrument(name="GMI", field_of_regard=15.0) + self.assertAlmostEqual(o.get_swath_width(705000), 185815, delta=1.0) + + def test_get_min_elevation_angle(self): + """ + Test that the minimum elevation angle can be computed from the field of regard. + """ + o = Instrument(name="GMI", field_of_regard=15.0) + self.assertAlmostEqual(o.get_min_elevation_angle(705000), 81.66446, delta=0.01) + + def test_valid_observation_no_constraints(self): + """ + Test that an observation is valid when there are no constraints + on sunlit conditions. + """ + o = Instrument(name="Test Instrument") + self.assertTrue(o.is_valid_observation(self.test_sat_1.at(self.test_time)).all()) # type: ignore + self.assertTrue(o.is_valid_observation(self.test_sat_2.at(self.test_time)).all()) # type: ignore + self.assertTrue(o.is_valid_observation(self.test_sat_3.at(self.test_time)).all()) # type: ignore + self.assertTrue(o.is_valid_observation(self.test_sat_4.at(self.test_time)).all()) # type: ignore + + def test_valid_observation_self_sunlit(self): + """ + Test that an observation is valid when the instrument requires + self-sunlit conditions. + """ + o = Instrument(name="Test Instrument", req_self_sunlit=True) + self.assertTrue(o.is_valid_observation(self.test_sat_1.at(self.test_time)).all()) # type: ignore + self.assertTrue(o.is_valid_observation(self.test_sat_2.at(self.test_time)).all()) # type: ignore + self.assertTrue(o.is_valid_observation(self.test_sat_3.at(self.test_time)).all()) # type: ignore + self.assertFalse(o.is_valid_observation(self.test_sat_4.at(self.test_time)).all()) # type: ignore + + def test_valid_observation_self_not_sunlit(self): + """ + Test that an observation is valid when the instrument requires + self-not-sunlit conditions. + """ + o = Instrument(name="Test Instrument", req_self_sunlit=False) + self.assertFalse(o.is_valid_observation(self.test_sat_1.at(self.test_time)).all()) # type: ignore + self.assertFalse(o.is_valid_observation(self.test_sat_2.at(self.test_time)).all()) # type: ignore + self.assertFalse(o.is_valid_observation(self.test_sat_3.at(self.test_time)).all()) # type: ignore + self.assertTrue(o.is_valid_observation(self.test_sat_4.at(self.test_time)).all()) # type: ignore + + def test_valid_observation_target_sunlit(self): + """ + Test that an observation is valid when the instrument requires + target-sunlit conditions. + """ + o = Instrument(name="Test Instrument", req_target_sunlit=True) + self.assertTrue(o.is_valid_observation(self.test_sat_1.at(self.test_time)).all()) # type: ignore + self.assertTrue(o.is_valid_observation(self.test_sat_2.at(self.test_time)).all()) # type: ignore + self.assertFalse(o.is_valid_observation(self.test_sat_3.at(self.test_time)).all()) # type: ignore + self.assertFalse(o.is_valid_observation(self.test_sat_4.at(self.test_time)).all()) # type: ignore + + def test_valid_observation_target_not_sunlit(self): + """ + Test that an observation is valid when the instrument requires + target-not-sunlit conditions. + """ + o = Instrument(name="Test Instrument", req_target_sunlit=False) + self.assertFalse(o.is_valid_observation(self.test_sat_1.at(self.test_time)).all()) # type: ignore + self.assertFalse(o.is_valid_observation(self.test_sat_2.at(self.test_time)).all()) # type: ignore + self.assertTrue(o.is_valid_observation(self.test_sat_3.at(self.test_time)).all()) # type: ignore + self.assertTrue(o.is_valid_observation(self.test_sat_4.at(self.test_time)).all()) # type: ignore + + def test_valid_observation_self_sunlit_target_sunlit(self): + """ + Test that an observation is valid when the instrument requires + both self-sunlit and target-sunlit conditions.""" + o = Instrument( + name="Test Instrument", req_self_sunlit=True, req_target_sunlit=True + ) + self.assertTrue(o.is_valid_observation(self.test_sat_1.at(self.test_time)).all()) # type: ignore + self.assertTrue(o.is_valid_observation(self.test_sat_2.at(self.test_time)).all()) # type: ignore + self.assertFalse(o.is_valid_observation(self.test_sat_3.at(self.test_time)).all()) # type: ignore + self.assertFalse(o.is_valid_observation(self.test_sat_4.at(self.test_time)).all()) # type: ignore + + def test_valid_observation_self_not_sunlit_target_sunlit(self): + """ + Test that an observation is valid when the instrument requires + self-not-sunlit and target-sunlit conditions. + """ + o = Instrument( + name="Test Instrument", req_self_sunlit=False, req_target_sunlit=True + ) + self.assertFalse(o.is_valid_observation(self.test_sat_1.at(self.test_time)).all()) # type: ignore + self.assertFalse(o.is_valid_observation(self.test_sat_2.at(self.test_time)).all()) # type: ignore + self.assertFalse(o.is_valid_observation(self.test_sat_3.at(self.test_time)).all()) # type: ignore + self.assertFalse(o.is_valid_observation(self.test_sat_4.at(self.test_time)).all()) # type: ignore + + def test_valid_observation_self_sunlit_target_not_sunlit(self): + """ + Test that an observation is valid when the instrument requires + self-sunlit and target-not-sunlit conditions. + """ + o = Instrument( + name="Test Instrument", req_self_sunlit=True, req_target_sunlit=False + ) + self.assertFalse(o.is_valid_observation(self.test_sat_1.at(self.test_time)).all()) # type: ignore + self.assertFalse(o.is_valid_observation(self.test_sat_2.at(self.test_time)).all()) # type: ignore + self.assertTrue(o.is_valid_observation(self.test_sat_3.at(self.test_time)).all()) # type: ignore + self.assertFalse(o.is_valid_observation(self.test_sat_4.at(self.test_time)).all()) # type: ignore + + def test_valid_observation_self_not_sunlit_target_not_sunlit(self): + """ + Test that an observation is valid when the instrument requires + both self-not-sunlit and target-not-sunlit conditions. + """ + o = Instrument( + name="Test Instrument", req_self_sunlit=False, req_target_sunlit=False + ) + self.assertFalse(o.is_valid_observation(self.test_sat_1.at(self.test_time)).all()) # type: ignore + self.assertFalse(o.is_valid_observation(self.test_sat_2.at(self.test_time)).all()) # type: ignore + self.assertFalse(o.is_valid_observation(self.test_sat_3.at(self.test_time)).all()) # type: ignore + self.assertTrue(o.is_valid_observation(self.test_sat_4.at(self.test_time)).all()) # type: ignore + + def test_valid_observation_self_sunlit_vector(self): + """ + Test that an observation is valid when the instrument requires + self-sunlit conditions for a vector of times. + """ + o = Instrument(name="Test Instrument", req_self_sunlit=True) + times = timescale.utc(2020, 3, 20, [11, 12, 13]) # type: ignore + results = o.is_valid_observation(self.test_sat_1.at(times)) # type: ignore + self.assertEqual(len(results), 3) + self.assertFalse(results[0]) + self.assertTrue(results[1]) + self.assertFalse(results[2]) + + def test_valid_observation_target_sunlit_vector(self): + """ + Test that an observation is valid when the instrument requires + target-sunlit conditions for a vector of times. + """ + o = Instrument(name="Test Instrument", req_target_sunlit=True) + times = timescale.utc(2020, 3, 20, [11, 12, 13]) # type: ignore + results = o.is_valid_observation(self.test_sat_1.at(times)) # type: ignore + self.assertEqual(len(results), 3) + self.assertFalse(results[0]) + self.assertTrue(results[1]) + self.assertFalse(results[2]) + + def test_valid_observation_self_sunlit_vector_inclined(self): + """ + Test that an observation is valid when the instrument requires self-sunlit conditions for a vector of times with an inclined orbit. + """ + o = Instrument(name="Test Instrument", req_self_sunlit=True) + times = timescale.utc(2020, 3, 20, [11, 12, 13]) # type: ignore + results = o.is_valid_observation(self.test_sat_5.at(times)) # type: ignore + self.assertEqual(len(results), 3) + self.assertFalse(results[0]) + self.assertTrue(results[1]) + self.assertFalse(results[2]) + + def test_valid_observation_target_sunlit_vector_inclined(self): + """ + Test that an observation is valid when the instrument requires target-sunlit conditions for a vector of times with an inclined orbit. + """ + o = Instrument(name="Test Instrument", req_target_sunlit=True) + times = timescale.utc(2020, 3, 20, [11, 12, 13]) # type: ignore + results = o.is_valid_observation(self.test_sat_5.at(times)) # type: ignore + self.assertEqual(len(results), 3) + self.assertFalse(results[0]) + self.assertTrue(results[1]) + self.assertFalse(results[2]) + + def test_compute_footprint_center_matches_subpoint(self): + """ + Test that the footprint center of a nadir-pointing instrument + (zero field of view, roll, and pitch) coincides with Skyfield's + own WGS 84 sub-satellite point, for both equatorial and inclined + orbits. + """ + o = Instrument(name="Test Instrument") + for sat in (self.test_sat_1, self.test_sat_2, self.test_sat_5): + orbit_track = sat.at(self.test_time) # type: ignore + center = o.compute_footprint_center(orbit_track) + subpoint = wgs84.subpoint_of(orbit_track) + self.assertAlmostEqual( + geodesic_distance( + center.longitude.degrees, + center.latitude.degrees, + subpoint.longitude.degrees, + subpoint.latitude.degrees, + ), + 0, + delta=1e-3, + ) + self.assertAlmostEqual(center.elevation.m, subpoint.elevation.m, delta=1e-3) + + def test_compute_footprint_cross_track_extent_matches_swath_width(self): + """ + Test that the cross-track extent of a computed footprint (the + geodesic distance between the footprint edge points directly + left and right of nadir) matches `get_swath_width`, evaluated at + the satellite's actual height above the WGS 84 ellipsoid (which, + due to Earth's oblateness, differs slightly from the orbit's + nominal mean altitude away from the equator). This cross-checks + the closed-form swath width formula (assumes a spherical Earth) + against the WGS 84 ellipsoid footprint geometry. + """ + o = Instrument(name="Test Instrument", field_of_regard=30.0) + for sat in (self.test_sat_1, self.test_sat_5): + orbit_track = sat.at(self.test_time) # type: ignore + height = wgs84.geographic_position_of(orbit_track).elevation.m + expected_swath_width = o.get_swath_width(height) + # request 5 points so the polygon samples exactly the + # cross-track edges (angle=0 and angle=180 degrees) + footprint = o.compute_footprint(orbit_track, number_points=5) + coords = list(footprint[0].exterior.coords) + # coords are sampled at angle = [0, 90, 180, 270, 360] degrees; + # index 0 (angle=0) and index 2 (angle=180) are the cross-track edges + cross_track_extent = geodesic_distance( + coords[0][0], coords[0][1], coords[2][0], coords[2][1] + ) + self.assertAlmostEqual(cross_track_extent, expected_swath_width, delta=50.0) diff --git a/tests/schemas/orbit/__init__.py b/tests/schemas/orbit/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/schemas/orbit/test_base.py b/tests/schemas/orbit/test_base.py new file mode 100644 index 0000000..c2172d4 --- /dev/null +++ b/tests/schemas/orbit/test_base.py @@ -0,0 +1,235 @@ +""" +Unit tests for the tatc.schemas.orbit.base module. + +@author Paul T. Grogan +""" + +import unittest +from datetime import datetime, timezone + +from tatc import config +from tatc.constants import EARTH_MEAN_RADIUS +from tatc.schemas import CircularOrbit +from tatc.schemas.orbit.base import OrbitBase +from tatc.utils.orbital import ( + semimajor_axis_to_mean_motion, + semimajor_axis_to_orbit_period, +) + + +class TestOrbitBase(unittest.TestCase): + """ + Unit tests for the tatc.schemas.orbit.base.OrbitBase schema. + """ + + def test_epoch_defaults_to_fixed_reference_timestamp(self): + """ + Test that omitting epoch defaults to the fixed reference timestamp + 2020-01-01T00:00:00Z, not the current time. A prior version used + datetime.now(), which pydantic evaluates once at class-definition + time (module import), freezing every instance that omits epoch to + the same, increasingly stale timestamp. + """ + self.assertEqual(OrbitBase().epoch, datetime(2020, 1, 1, tzinfo=timezone.utc)) + + def test_true_anomaly_defaults_to_zero(self): + """ + Test that omitting true_anomaly defaults to 0 degrees. + """ + self.assertEqual(OrbitBase().true_anomaly, 0) + + def test_get_mean_anomaly_treats_orbit_as_circular(self): + """ + Test that get_mean_anomaly assumes zero eccentricity by default, + so mean anomaly equals true anomaly exactly. + """ + self.assertEqual(OrbitBase(true_anomaly=123.4).get_mean_anomaly(), 123.4) + + def test_get_semimajor_axis_not_implemented_on_base(self): + """ + Test that get_semimajor_axis raises NotImplementedError on the + bare base class, since OrbitBase has no universal way to derive + it. + """ + with self.assertRaises(NotImplementedError): + OrbitBase().get_semimajor_axis() + + def test_get_inclination_not_implemented_on_base(self): + """ + Test that get_inclination raises NotImplementedError on the bare + base class. + """ + with self.assertRaises(NotImplementedError): + OrbitBase().get_inclination() + + def test_get_right_ascension_ascending_node_not_implemented_on_base(self): + """ + Test that get_right_ascension_ascending_node raises + NotImplementedError on the bare base class. + """ + with self.assertRaises(NotImplementedError): + OrbitBase().get_right_ascension_ascending_node() + + def test_get_eccentricity_not_implemented_on_base(self): + """ + Test that get_eccentricity raises NotImplementedError on the bare + base class. + """ + with self.assertRaises(NotImplementedError): + OrbitBase().get_eccentricity() + + def test_get_perigee_argument_not_implemented_on_base(self): + """ + Test that get_perigee_argument raises NotImplementedError on the + bare base class. + """ + with self.assertRaises(NotImplementedError): + OrbitBase().get_perigee_argument() + + def test_compute_gp_orbit_not_implemented_on_base(self): + """ + Test that _compute_gp_orbit raises NotImplementedError on the bare + base class, since concrete orbit subclasses must supply their own + conversion logic. + """ + with self.assertRaises(NotImplementedError): + OrbitBase()._compute_gp_orbit() + + def test_to_gp_orbit_surfaces_not_implemented_on_base(self): + """ + Test that to_gp_orbit() on the bare base class surfaces the same + NotImplementedError, since it only adds caching around whatever + _compute_gp_orbit produces. + """ + with self.assertRaises(NotImplementedError): + OrbitBase().to_gp_orbit() + + +class _OrbitWithFixedSemimajorAxis(OrbitBase): + """ + Minimal OrbitBase subclass implementing only get_semimajor_axis, used + to test OrbitBase's generic get_mean_altitude/get_mean_motion/ + get_orbit_period defaults in isolation from any production subclass's + own overrides. + """ + + semimajor_axis: float + + def get_semimajor_axis(self) -> float: + return self.semimajor_axis + + +class TestOrbitBaseDerivedDefaults(unittest.TestCase): + """ + Unit tests for OrbitBase's generic get_mean_altitude/get_mean_motion/ + get_orbit_period defaults, each derived solely from get_semimajor_axis. + """ + + def setUp(self): + self.orbit = _OrbitWithFixedSemimajorAxis(semimajor_axis=7000000) + + def test_get_mean_altitude_derived_from_semimajor_axis(self): + """ + Test that get_mean_altitude defaults to semimajor axis minus + Earth's mean radius. + """ + self.assertAlmostEqual( + self.orbit.get_mean_altitude(), 7000000 - EARTH_MEAN_RADIUS, delta=0.01 + ) + + def test_get_mean_motion_derived_from_semimajor_axis(self): + """ + Test that get_mean_motion defaults to the standard + semimajor-axis-to-mean-motion conversion. + """ + self.assertAlmostEqual( + self.orbit.get_mean_motion(), + semimajor_axis_to_mean_motion(7000000), + delta=1e-9, + ) + + def test_get_orbit_period_derived_from_semimajor_axis(self): + """ + Test that get_orbit_period defaults to the standard + semimajor-axis-to-orbit-period conversion. + """ + self.assertAlmostEqual( + self.orbit.get_orbit_period().total_seconds(), + semimajor_axis_to_orbit_period(7000000), + delta=1e-6, + ) + + +class TestOrbitBaseToGpOrbitCaching(unittest.TestCase): + """ + Unit tests for the lazy-load caching behavior of + OrbitBase.to_gp_orbit, shared by every concrete orbit subclass + (CircularOrbit, KeplerianOrbit, SunSynchronousOrbit, MolniyaOrbit, + TundraOrbit). Uses CircularOrbit as a concrete vehicle to exercise the + shared implementation, since OrbitBase itself has no _compute_gp_orbit + to cache. + """ + + def setUp(self): + self.orbit = CircularOrbit(mean_altitude=500000) + self._original_lazy_load = config.rc.gp_orbit_lazy_load + + def tearDown(self): + config.rc.gp_orbit_lazy_load = self._original_lazy_load + + def test_repeat_call_reuses_cached_result(self): + """ + Test that calling to_gp_orbit() twice with lazy_load=True (the + default) returns the identical cached object rather than + recomputing. + """ + first = self.orbit.to_gp_orbit() + second = self.orbit.to_gp_orbit() + self.assertIs(first, second) + + def test_lazy_load_false_forces_recomputation(self): + """ + Test that lazy_load=False always recomputes a fresh gp orbit, even + if a cached result already exists. + """ + first = self.orbit.to_gp_orbit() + second = self.orbit.to_gp_orbit(lazy_load=False) + self.assertIsNot(first, second) + + def test_lazy_load_false_still_updates_the_cache(self): + """ + Test that a lazy_load=False call still stores its result in the + cache, so a subsequent lazy_load=True call reuses that new result + rather than the original. + """ + first = self.orbit.to_gp_orbit() + second = self.orbit.to_gp_orbit(lazy_load=False) + third = self.orbit.to_gp_orbit() + self.assertIs(third, second) + self.assertIsNot(third, first) + + def test_lazy_load_none_follows_config_rc_true(self): + """ + Test that lazy_load=None (the default) follows config.rc's + gp_orbit_lazy_load setting when it is True: repeat calls reuse the + cached result. + """ + config.rc.gp_orbit_lazy_load = True + first = self.orbit.to_gp_orbit() + second = self.orbit.to_gp_orbit() + self.assertIs(first, second) + + def test_lazy_load_none_follows_config_rc_false(self): + """ + Test that lazy_load=None (the default) follows config.rc's + gp_orbit_lazy_load setting when it is False: repeat calls always + recompute. + """ + config.rc.gp_orbit_lazy_load = False + first = self.orbit.to_gp_orbit() + second = self.orbit.to_gp_orbit() + self.assertIsNot(first, second) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/schemas/orbit/test_base_molniya_tundra.py b/tests/schemas/orbit/test_base_molniya_tundra.py new file mode 100644 index 0000000..45fb8fa --- /dev/null +++ b/tests/schemas/orbit/test_base_molniya_tundra.py @@ -0,0 +1,90 @@ +""" +Unit tests for the tatc.schemas.orbit.base_molniya_tundra module. + +@author Paul T. Grogan +""" + +import unittest + +from tatc.constants import EARTH_J2_CRITICAL_INCLINATION +from tatc.schemas.orbit.base_molniya_tundra import MolniyaTundraOrbitBase + + +class TestMolniyaTundraOrbitBase(unittest.TestCase): + """ + Unit tests for the + tatc.schemas.orbit.base_molniya_tundra.MolniyaTundraOrbitBase schema. + """ + + def test_get_inclination_is_the_j2_critical_inclination(self): + """ + Test that get_inclination returns Earth's J2 critical inclination + (~63.4 degrees), the inclination at which J2 perturbation of the + argument of perigee vanishes -- the defining property of a frozen + Molniya/Tundra orbit. + """ + o = MolniyaTundraOrbitBase(perigee_altitude=1000000) + self.assertEqual(o.get_inclination(), EARTH_J2_CRITICAL_INCLINATION) + + def test_get_perigee_argument_northern_coverage_true(self): + """ + Test that get_perigee_argument returns 270 degrees (apogee over + the northern hemisphere) when northern_coverage is True (the + default). + """ + o = MolniyaTundraOrbitBase(perigee_altitude=1000000, northern_coverage=True) + self.assertEqual(o.get_perigee_argument(), 270) + + def test_get_perigee_argument_northern_coverage_false(self): + """ + Test that get_perigee_argument returns 90 degrees (apogee over the + southern hemisphere) when northern_coverage is False. + """ + o = MolniyaTundraOrbitBase(perigee_altitude=1000000, northern_coverage=False) + self.assertEqual(o.get_perigee_argument(), 90) + + def test_get_right_ascension_ascending_node(self): + """ + Test that get_right_ascension_ascending_node returns the + right_ascension_ascending_node field directly. + """ + o = MolniyaTundraOrbitBase( + perigee_altitude=1000000, right_ascension_ascending_node=123.4 + ) + self.assertEqual(o.get_right_ascension_ascending_node(), 123.4) + + def test_get_orbit_period_not_implemented_on_base(self): + """ + Test that get_orbit_period raises NotImplementedError on the base + class, since concrete Molniya/Tundra subclasses must each define + their own fixed orbit period (half vs. full sidereal day). + """ + o = MolniyaTundraOrbitBase(perigee_altitude=1000000) + with self.assertRaises(NotImplementedError): + o.get_orbit_period() + + def test_get_semimajor_axis_not_implemented_on_base(self): + """ + Test that get_semimajor_axis also surfaces NotImplementedError on + the base class, since it depends on get_orbit_period(). + """ + o = MolniyaTundraOrbitBase(perigee_altitude=1000000) + with self.assertRaises(NotImplementedError): + o.get_semimajor_axis() + + def test_eccentricity_validator_is_a_no_op_on_bare_base(self): + """ + Test that the shared eccentricity model_validator does not raise + on the bare base class, even though get_eccentricity() itself + would raise NotImplementedError there (since get_orbit_period is + abstract) -- the validator must catch and skip this case rather + than blocking construction of the bare base class, which the + other tests in this file rely on. + """ + # construction succeeding at all is the assertion; no concrete + # period exists yet for the validator to check against + MolniyaTundraOrbitBase(perigee_altitude=1000000) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/schemas/orbit/test_circular.py b/tests/schemas/orbit/test_circular.py new file mode 100644 index 0000000..2507ebb --- /dev/null +++ b/tests/schemas/orbit/test_circular.py @@ -0,0 +1,299 @@ +""" +Unit tests for the CircularOrbit schema. + +@author Paul T. Grogan +""" + +import unittest +from datetime import datetime, timedelta, timezone + +import numpy as np +from pydantic import ValidationError + +from tatc.constants import EARTH_MEAN_RADIUS, EARTH_MU +from tatc.schemas import CircularOrbit +from tatc.utils.orbital import semimajor_axis_to_mean_motion + + +class TestCircularOrbit(unittest.TestCase): + """ + Unit tests for the CircularOrbit schema. + """ + + def setUp(self): + self.test_data = { + "mean_altitude": 400000, + "true_anomaly": 10.0, + "epoch": datetime(2022, 1, 1, 12, tzinfo=timezone.utc), + "inclination": 45.0, + "right_ascension_ascending_node": 50.0, + } + self.test_orbit = CircularOrbit(**self.test_data) + + def test_good_data(self): + """ + Test that the CircularOrbit schema correctly initializes with valid data. + """ + self.assertEqual( + self.test_orbit.mean_altitude, self.test_data.get("mean_altitude") + ) + self.assertEqual( + self.test_orbit.true_anomaly, self.test_data.get("true_anomaly") + ) + self.assertEqual(self.test_orbit.epoch, self.test_data.get("epoch")) + self.assertEqual(self.test_orbit.inclination, self.test_data.get("inclination")) + self.assertEqual( + self.test_orbit.right_ascension_ascending_node, + self.test_data.get("right_ascension_ascending_node"), + ) + + def test_good_data_iso8601_datetime(self): + """ + Test that the CircularOrbit schema correctly initializes with valid data + when the epoch is provided as an ISO 8601 string. + """ + good_data = { + "mean_altitude": 400000, + "true_anomaly": 10.0, + "epoch": "2022-01-01T12:00:00Z", + "inclination": 45.0, + "right_ascension_ascending_node": 50.0, + } + o = CircularOrbit(**good_data) + self.assertEqual(o.mean_altitude, good_data.get("mean_altitude")) + self.assertEqual(o.true_anomaly, good_data.get("true_anomaly")) + self.assertEqual(o.epoch, datetime(2022, 1, 1, 12, tzinfo=timezone.utc)) + self.assertEqual(o.inclination, good_data.get("inclination")) + self.assertEqual( + o.right_ascension_ascending_node, + good_data.get("right_ascension_ascending_node"), + ) + + def test_defaults(self): + """ + Test that inclination, right_ascension_ascending_node, and the + type discriminator each default correctly when omitted. + """ + o = CircularOrbit(mean_altitude=500000) + self.assertEqual(o.inclination, 0) + self.assertEqual(o.right_ascension_ascending_node, 0) + self.assertEqual(o.type, "circular") + + def test_bad_mean_altitude_negative(self): + """ + Test that a negative mean_altitude is rejected, since a circular + orbit cannot have a semimajor axis below Earth's mean radius. + """ + with self.assertRaises(ValidationError): + CircularOrbit(mean_altitude=-1) + + def test_mean_altitude_boundary_zero(self): + """ + Test that a mean_altitude of exactly 0 (the ge=0 boundary) is + accepted. + """ + self.assertEqual(CircularOrbit(mean_altitude=0).mean_altitude, 0) + + def test_bad_mean_altitude_missing(self): + """ + Test that the CircularOrbit schema raises a ValidationError when + the required mean_altitude field is missing. + """ + with self.assertRaises(ValidationError): + CircularOrbit() + + def test_bad_inclination_negative(self): + """ + Test that a negative inclination is rejected. + """ + with self.assertRaises(ValidationError): + CircularOrbit(mean_altitude=500000, inclination=-0.1) + + def test_bad_inclination_too_large(self): + """ + Test that an inclination of 180 degrees or more is rejected + (inclination is conventionally bounded to [0, 180)). + """ + with self.assertRaises(ValidationError): + CircularOrbit(mean_altitude=500000, inclination=180) + + def test_inclination_boundary_zero(self): + """ + Test that an inclination of exactly 0 degrees (the ge=0 boundary) + is accepted. + """ + self.assertEqual( + CircularOrbit(mean_altitude=500000, inclination=0).inclination, 0 + ) + + def test_bad_right_ascension_ascending_node_negative(self): + """ + Test that a negative right_ascension_ascending_node is rejected. + """ + with self.assertRaises(ValidationError): + CircularOrbit(mean_altitude=500000, right_ascension_ascending_node=-0.1) + + def test_bad_right_ascension_ascending_node_too_large(self): + """ + Test that a right_ascension_ascending_node of 360 degrees or more + is rejected. + """ + with self.assertRaises(ValidationError): + CircularOrbit(mean_altitude=500000, right_ascension_ascending_node=360) + + def test_get_semimajor_axis(self): + """ + Test that the CircularOrbit class correctly calculates the semimajor axis. + """ + self.assertEqual( + self.test_orbit.get_semimajor_axis(), + self.test_data.get("mean_altitude") + EARTH_MEAN_RADIUS, + ) + + def test_get_mean_altitude(self): + """ + Test that get_mean_altitude returns the mean_altitude field + directly. + """ + self.assertEqual( + self.test_orbit.get_mean_altitude(), self.test_data.get("mean_altitude") + ) + + def test_get_inclination(self): + """ + Test that get_inclination returns the inclination field directly. + """ + self.assertEqual( + self.test_orbit.get_inclination(), self.test_data.get("inclination") + ) + + def test_get_right_ascension_ascending_node(self): + """ + Test that get_right_ascension_ascending_node returns the + right_ascension_ascending_node field directly. + """ + self.assertEqual( + self.test_orbit.get_right_ascension_ascending_node(), + self.test_data.get("right_ascension_ascending_node"), + ) + + def test_get_eccentricity(self): + """ + Test that get_eccentricity returns 0, since a CircularOrbit has no + eccentricity. + """ + self.assertEqual(self.test_orbit.get_eccentricity(), 0) + + def test_get_perigee_argument(self): + """ + Test that get_perigee_argument returns 0, since a CircularOrbit + has no perigee to reference. + """ + self.assertEqual(self.test_orbit.get_perigee_argument(), 0) + + def test_get_mean_anomaly(self): + """ + Test that the CircularOrbit class correctly calculates the mean anomaly. + """ + self.assertEqual( + self.test_orbit.get_mean_anomaly(), self.test_data.get("true_anomaly") + ) + + def test_get_mean_motion(self): + """ + Test that the CircularOrbit class correctly calculates the mean motion. + """ + self.assertAlmostEqual( + self.test_orbit.get_mean_motion(), + semimajor_axis_to_mean_motion( + EARTH_MEAN_RADIUS + self.test_orbit.mean_altitude + ), + delta=0.001, + ) + + def test_get_orbit_period(self): + """ + Test that the CircularOrbit class correctly calculates the orbit period. + """ + orbit_period = ( + 2 + * np.pi + * np.sqrt( + np.power(EARTH_MEAN_RADIUS + self.test_orbit.mean_altitude, 3) + / EARTH_MU + ) + ) + self.assertAlmostEqual( + self.test_orbit.get_orbit_period(), + timedelta(seconds=orbit_period), + delta=1.0, + ) + + def test_get_derived_orbit(self): + """ + Test that the CircularOrbit schema correctly derives a new orbit. + """ + derived_orbit = self.test_orbit.get_derived_orbit(20, 10) + self.assertAlmostEqual( + derived_orbit.get_mean_anomaly(), + self.test_orbit.get_mean_anomaly() + 20, + delta=0.001, + ) + self.assertAlmostEqual( + derived_orbit.right_ascension_ascending_node, + self.test_orbit.right_ascension_ascending_node + 10, + delta=0.001, + ) + + def test_get_derived_orbit_preserves_other_fields(self): + """ + Test that get_derived_orbit preserves mean_altitude, inclination, + and epoch unchanged, only perturbing mean anomaly and RAAN. + """ + derived_orbit = self.test_orbit.get_derived_orbit(20, 10) + self.assertEqual(derived_orbit.mean_altitude, self.test_orbit.mean_altitude) + self.assertEqual(derived_orbit.inclination, self.test_orbit.inclination) + self.assertEqual(derived_orbit.epoch, self.test_orbit.epoch) + + def test_get_derived_orbit_wraps_past_360_degrees(self): + """ + Test that get_derived_orbit wraps both mean anomaly and RAAN back + into [0, 360) when the perturbation pushes them past 360 degrees. + """ + orbit = CircularOrbit( + mean_altitude=400000, + true_anomaly=350.0, + right_ascension_ascending_node=350.0, + ) + derived_orbit = orbit.get_derived_orbit(20, 20) + self.assertAlmostEqual(derived_orbit.get_mean_anomaly(), 10.0, delta=0.001) + self.assertAlmostEqual( + derived_orbit.right_ascension_ascending_node, 10.0, delta=0.001 + ) + + def test_to_gp_orbit(self): + """ + Test that the CircularOrbit schema correctly converts to a general perturbations orbit. + """ + gp_orbit = self.test_orbit.to_gp_orbit() + self.assertAlmostEqual( + gp_orbit.get_mean_altitude(), self.test_data.get("mean_altitude"), delta=1.0 + ) + self.assertAlmostEqual( + gp_orbit.get_true_anomaly(), self.test_data.get("true_anomaly"), delta=0.001 + ) + self.assertAlmostEqual( + gp_orbit.get_epoch().timestamp(), + self.test_data.get("epoch").timestamp(), + delta=1, + ) + self.assertEqual( + gp_orbit.get_inclination(), + self.test_data.get("inclination"), + ) + self.assertAlmostEqual( + gp_orbit.get_right_ascension_ascending_node(), + self.test_data.get("right_ascension_ascending_node"), + ) + self.assertAlmostEqual(gp_orbit.get_eccentricity(), 0, delta=0.001) + self.assertAlmostEqual(gp_orbit.get_perigee_argument(), 0, delta=0.001) diff --git a/tests/schemas/orbit/test_geosynchronous.py b/tests/schemas/orbit/test_geosynchronous.py new file mode 100644 index 0000000..46a08fc --- /dev/null +++ b/tests/schemas/orbit/test_geosynchronous.py @@ -0,0 +1,219 @@ +""" +Unit tests for the GeosynchronousOrbit schema. + +@author: Paul T. Grogan +""" + +import unittest +from datetime import datetime, timedelta, timezone + +from pydantic import ValidationError + +from tatc import constants +from tatc.constants import EARTH_SIDEREAL_DAY_S +from tatc.schemas import GeosynchronousOrbit + + +class TestGeosynchronousOrbit(unittest.TestCase): + """ + Unit tests for the GeosynchronousOrbit schema. + """ + + def setUp(self): + self.test_data = { + "longitude": -75.0, + "epoch": datetime(2022, 1, 1, 12, 0, 0, tzinfo=timezone.utc), + } + self.test_orbit = GeosynchronousOrbit(**self.test_data) + + def test_good_data(self): + """ + Test that the GeosynchronousOrbit schema correctly initializes + with valid data. + """ + self.assertEqual(self.test_orbit.longitude, self.test_data.get("longitude")) + self.assertEqual(self.test_orbit.epoch, self.test_data.get("epoch")) + self.assertEqual(self.test_orbit.type, "geosynchronous") + + def test_defaults(self): + """ + Test that mean_altitude defaults to the geosynchronous altitude + (the value yielding an orbit period of exactly one sidereal day) + and inclination defaults to 0, when omitted. + """ + o = GeosynchronousOrbit(longitude=0) + self.assertAlmostEqual(o.mean_altitude, 35786000, delta=10000) + self.assertEqual(o.inclination, 0) + + def test_bad_longitude_missing(self): + """ + Test that the GeosynchronousOrbit schema raises a ValidationError + when the required longitude field is missing. + """ + with self.assertRaises(ValidationError): + GeosynchronousOrbit() + + def test_bad_longitude_too_large(self): + """ + Test that a longitude above 180 degrees is rejected. + """ + with self.assertRaises(ValidationError): + GeosynchronousOrbit(longitude=180.1) + + def test_bad_longitude_too_small(self): + """ + Test that a longitude below -180 degrees is rejected. + """ + with self.assertRaises(ValidationError): + GeosynchronousOrbit(longitude=-180.1) + + def test_longitude_boundary_values(self): + """ + Test that longitude values exactly at the antimeridian (-180, 180 + degrees) are accepted. + """ + self.assertEqual(GeosynchronousOrbit(longitude=180).longitude, 180) + self.assertEqual(GeosynchronousOrbit(longitude=-180).longitude, -180) + + def test_bad_inclination_negative(self): + """ + Test that a negative inclination is rejected. + """ + with self.assertRaises(ValidationError): + GeosynchronousOrbit(longitude=0, inclination=-0.1) + + def test_bad_inclination_too_large(self): + """ + Test that an inclination of 180 degrees or more is rejected. + """ + with self.assertRaises(ValidationError): + GeosynchronousOrbit(longitude=0, inclination=180) + + def test_get_eccentricity_and_perigee_argument_inherited_from_circular_base(self): + """ + Test that get_eccentricity and get_perigee_argument are inherited + from CircularOrbitBase (both 0, since a geosynchronous orbit is + circular). + """ + self.assertEqual(self.test_orbit.get_eccentricity(), 0) + self.assertEqual(self.test_orbit.get_perigee_argument(), 0) + + def test_get_semimajor_axis_matches_published_geosynchronous_altitude(self): + """ + Test get_semimajor_axis against the well-known published + geosynchronous altitude of ~35,786 km (semimajor axis ~42,164 km). + """ + self.assertAlmostEqual( + self.test_orbit.get_semimajor_axis(), 42164000, delta=10000 + ) + + def test_get_orbit_period_is_one_sidereal_day(self): + """ + Test that get_orbit_period matches Earth's sidereal day, the + defining property of a geosynchronous orbit. + """ + self.assertAlmostEqual( + self.test_orbit.get_orbit_period().total_seconds(), + EARTH_SIDEREAL_DAY_S, + delta=1.0, + ) + + def test_get_right_ascension_ascending_node_at_greenwich_matches_gast(self): + """ + Test that a satellite at longitude 0 (the Greenwich meridian) has + a right ascension of ascending node exactly equal to Earth's + rotation angle (Greenwich Apparent Sidereal Time) at epoch -- the + defining relationship between Earth-fixed longitude and inertial + right ascension. + """ + epoch = datetime(2022, 6, 15, 6, 0, 0, tzinfo=timezone.utc) + o = GeosynchronousOrbit(longitude=0, epoch=epoch) + expected_raan = constants.timescale.from_datetime(epoch).gast * 15 % 360 + self.assertAlmostEqual( + o.get_right_ascension_ascending_node(), expected_raan, delta=1e-6 + ) + + def test_get_right_ascension_ascending_node_wraps_to_0_360(self): + """ + Test that the right ascension of ascending node always falls + within [0, 360), even when the raw longitude+rotation-angle sum + would otherwise exceed that range. + """ + raan = self.test_orbit.get_right_ascension_ascending_node() + self.assertGreaterEqual(raan, 0) + self.assertLess(raan, 360) + + def test_get_right_ascension_ascending_node_tracks_earth_rotation(self): + """ + Test that a satellite parked at a fixed longitude has nearly the + same right ascension of ascending node exactly one sidereal day + later, since it remains above the same Earth-fixed point (the + defining behavior of a geosynchronous orbit). + """ + later_orbit = GeosynchronousOrbit( + longitude=self.test_orbit.longitude, + epoch=self.test_orbit.epoch + timedelta(seconds=EARTH_SIDEREAL_DAY_S), + ) + self.assertAlmostEqual( + later_orbit.get_right_ascension_ascending_node(), + self.test_orbit.get_right_ascension_ascending_node(), + delta=0.01, + ) + + def test_get_derived_orbit_shifts_longitude(self): + """ + Test that get_derived_orbit shifts longitude by delta_raan + degrees directly, since Earth's rotation angle at a fixed epoch + does not change. + """ + derived_orbit = self.test_orbit.get_derived_orbit(0, 10) + self.assertAlmostEqual(derived_orbit.longitude, -65.0, delta=1e-9) + + def test_get_derived_orbit_wraps_longitude_across_antimeridian(self): + """ + Test that get_derived_orbit wraps longitude correctly when the + shift crosses the +/-180 degree antimeridian. + """ + o = GeosynchronousOrbit(longitude=170) + derived_orbit = o.get_derived_orbit(0, 20) + self.assertAlmostEqual(derived_orbit.longitude, -170.0, delta=1e-9) + + def test_get_derived_orbit_preserves_other_fields(self): + """ + Test that get_derived_orbit preserves mean_altitude, inclination, + and epoch unchanged. + """ + derived_orbit = self.test_orbit.get_derived_orbit(20, 10) + self.assertEqual(derived_orbit.mean_altitude, self.test_orbit.mean_altitude) + self.assertEqual(derived_orbit.inclination, self.test_orbit.inclination) + self.assertEqual(derived_orbit.epoch, self.test_orbit.epoch) + + def test_to_gp_orbit(self): + """ + Test that the GeosynchronousOrbit schema correctly converts to a + general perturbations orbit. + """ + gp_orbit = self.test_orbit.to_gp_orbit() + self.assertAlmostEqual( + gp_orbit.get_mean_altitude(), + self.test_orbit.mean_altitude, + delta=1.0, + ) + self.assertAlmostEqual( + gp_orbit.get_inclination(), self.test_orbit.get_inclination(), delta=0.001 + ) + self.assertAlmostEqual( + gp_orbit.get_right_ascension_ascending_node(), + self.test_orbit.get_right_ascension_ascending_node(), + delta=0.001, + ) + self.assertAlmostEqual(gp_orbit.get_eccentricity(), 0, delta=0.001) + self.assertAlmostEqual( + gp_orbit.get_orbit_period().total_seconds(), + EARTH_SIDEREAL_DAY_S, + delta=1.0, + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/schemas/orbit/test_gp.py b/tests/schemas/orbit/test_gp.py new file mode 100644 index 0000000..86605b7 --- /dev/null +++ b/tests/schemas/orbit/test_gp.py @@ -0,0 +1,1583 @@ +""" +Unit tests for the GeneralPerturbationsOrbit schema. + +@author Paul T. Grogan +""" + +import csv +import io +import json +import unittest +from datetime import datetime, timedelta, timezone + +import numpy as np +from pydantic import ValidationError +from skyfield.api import wgs84 + +from tatc import config, constants +from tatc.schemas import GeneralPerturbationsOrbit, Point + + +class TestGPOrbit(unittest.TestCase): + """ + Unit tests for the GeneralPerturbationsOrbit schema. + """ + + def setUp(self): + self.test_tle = [ + "1 25544U 98067A 21156.30527927 .00003432 00000-0 70541-4 0 9993", + "2 25544 51.6455 41.4969 0003508 68.0432 78.3395 15.48957534286754", + ] + self.test_orbit = GeneralPerturbationsOrbit.from_tle(self.test_tle) + # a clearly-distinguishable second element, for testing index-based + # access on a multi-element orbit + self.second_element = self.test_orbit.elements[0].model_copy( + update={ + "norad_cat_id": 99999, + "epoch": datetime(2021, 6, 6, 7, 19, 36, tzinfo=timezone.utc), + "inclination": 45.0, + "eccentricity": 0.001, + "ra_of_asc_node": 100.0, + "arg_of_pericenter": 10.0, + "mean_anomaly": 20.0, + "bstar": 0.0001, + "mean_motion_dot": 0.00001, + "mean_motion_ddot": 0.000001, + } + ) + self.multi_element_orbit = GeneralPerturbationsOrbit( + elements=[self.test_orbit.elements[0], self.second_element] + ) + + def test_bad_elements_empty(self): + """ + Test that an empty elements list is rejected, since every getter + assumes at least one element exists. + """ + with self.assertRaises(ValidationError): + GeneralPerturbationsOrbit(elements=[]) + + def test_bad_elements_missing(self): + """ + Test that the required elements field must be provided. + """ + with self.assertRaises(ValidationError): + GeneralPerturbationsOrbit() + + def test_elements_sorted_by_epoch_on_construction(self): + """ + Regression test: elements provided out of epoch order must be + sorted ascending by epoch at construction time, since + get_closest_element_index relies on np.searchsorted, which + silently produces incorrect results if its input is not sorted. + """ + base = self.test_orbit.elements[0] + e_jan = base.model_copy( + update={"epoch": datetime(2022, 1, 1, tzinfo=timezone.utc)} + ) + e_mar = base.model_copy( + update={"epoch": datetime(2022, 3, 1, tzinfo=timezone.utc)} + ) + e_feb = base.model_copy( + update={"epoch": datetime(2022, 2, 1, tzinfo=timezone.utc)} + ) + o = GeneralPerturbationsOrbit(elements=[e_jan, e_mar, e_feb]) + self.assertEqual( + [el.epoch for el in o.elements], [e_jan.epoch, e_feb.epoch, e_mar.epoch] + ) + + def test_get_catalog_number(self): + """ + Test that the catalog number can be retrieved from the GeneralPerturbationsOrbit object. + """ + self.assertEqual(self.test_orbit.get_catalog_number(), 25544) + + def test_get_epoch(self): + """ + Test that the epoch can be retrieved from the GeneralPerturbationsOrbit object. + """ + self.assertEqual( + self.test_orbit.get_epoch(), + datetime(2021, 6, 5, 7, 19, 36, 128928, tzinfo=timezone.utc), + ) + + def test_get_mean_motion_dot(self): + """ + Test that the first derivative of the mean motion can be retrieved from the GeneralPerturbationsOrbit object. + """ + self.assertEqual( + self.test_orbit.get_mean_motion_dot() * (24 * 60 * 60) ** 2 / 360, + 0.00003432, + ) + + def test_get_mean_motion_ddot(self): + """ + Test that the second derivative of the mean motion can be retrieved from the GeneralPerturbationsOrbit object. + """ + self.assertEqual( + self.test_orbit.get_mean_motion_ddot() * (24 * 60 * 60) ** 3 / 360, 0.0 + ) + + def test_get_bstar(self): + """ + Test that the B* drag term can be retrieved from the GeneralPerturbationsOrbit object. + """ + self.assertEqual(self.test_orbit.get_bstar(), 0.000070541) + + def test_get_inclination(self): + """ + Test that the inclination can be retrieved from the GeneralPerturbationsOrbit object. + """ + self.assertEqual(self.test_orbit.get_inclination(), 51.6455) + + def test_get_right_ascension_ascending_node(self): + """ + Test that the right ascension of the ascending node can be retrieved from the GeneralPerturbationsOrbit object. + """ + self.assertEqual(self.test_orbit.get_right_ascension_ascending_node(), 41.4969) + + def test_get_eccentricity(self): + """ + Test that the eccentricity can be retrieved from the GeneralPerturbationsOrbit object. + """ + self.assertEqual(self.test_orbit.get_eccentricity(), 0.0003508) + + def test_get_perigee_argument(self): + """ + Test that the argument of perigee can be retrieved from the GeneralPerturbationsOrbit object. + """ + self.assertEqual(self.test_orbit.get_perigee_argument(), 68.0432) + + def test_get_mean_anomaly(self): + """ + Test that the mean anomaly can be retrieved from the GeneralPerturbationsOrbit object. + """ + self.assertEqual(self.test_orbit.get_mean_anomaly(), 78.3395) + + def test_get_mean_motion(self): + """ + Test that the mean motion can be retrieved from the GeneralPerturbationsOrbit object. + """ + self.assertAlmostEqual( + self.test_orbit.get_mean_motion() * (24 * 60 * 60) / 360, 15.48957534 + ) + + def test_get_orbit_period(self): + """ + Test that the orbit period can be retrieved from the GeneralPerturbationsOrbit object + using the ISS's well-known published orbital period of approximately 93 minutes. + """ + self.assertAlmostEqual( + self.test_orbit.get_orbit_period().total_seconds() / 60, 93, delta=1.0 + ) + + def test_get_semimajor_axis(self): + """ + Test that the semi-major axis can be retrieved from the GeneralPerturbationsOrbit object. + """ + self.assertAlmostEqual(self.test_orbit.get_semimajor_axis(), 6797911, delta=1.0) + + def test_get_mean_altitude(self): + """ + Test that the mean altitude can be retrieved from the GeneralPerturbationsOrbit object. + """ + self.assertAlmostEqual(self.test_orbit.get_mean_altitude(), 426902, delta=1.0) + + def test_get_true_anomaly(self): + """ + Test that the true anomaly can be retrieved from the GeneralPerturbationsOrbit object. + """ + self.assertAlmostEqual( + self.test_orbit.elements[0].get_true_anomaly(), 78.3788725993742 + ) + + def test_getters_default_to_first_element(self): + """ + Test that every index-aware getter defaults to index=0 (the first + element), matching the design intent that a single-element orbit + should be usable without ever passing an explicit index. + """ + self.assertEqual( + self.multi_element_orbit.get_catalog_number(), + self.multi_element_orbit.get_catalog_number(0), + ) + self.assertEqual( + self.multi_element_orbit.get_inclination(), + self.multi_element_orbit.get_inclination(0), + ) + + def test_getters_select_specified_element_by_index(self): + """ + Test that passing index=1 on a multi-element orbit retrieves + values from the second element, not the first -- the core + multi-element design intent of this class. + """ + o = self.multi_element_orbit + self.assertEqual(o.get_catalog_number(1), 99999) + self.assertEqual(o.get_inclination(1), 45.0) + self.assertEqual(o.get_eccentricity(1), 0.001) + self.assertEqual(o.get_right_ascension_ascending_node(1), 100.0) + self.assertEqual(o.get_perigee_argument(1), 10.0) + self.assertEqual(o.get_mean_anomaly(1), 20.0) + self.assertEqual(o.get_bstar(1), 0.0001) + self.assertEqual(o.get_mean_motion_dot(1), 0.00001) + self.assertEqual(o.get_mean_motion_ddot(1), 0.000001) + self.assertEqual( + o.get_epoch(1), datetime(2021, 6, 6, 7, 19, 36, tzinfo=timezone.utc) + ) + # first element (index 0) is unaffected + self.assertEqual(o.get_catalog_number(0), 25544) + + def test_from_tle_multiple_elements(self): + """ + Test that from_tle builds one element per TLE pair when given a + flat list of multiple concatenated TLEs. + """ + combined_tle_lines = self.test_tle + self.test_tle + o = GeneralPerturbationsOrbit.from_tle(combined_tle_lines) + self.assertEqual(len(o.elements), 2) + + def test_from_tle_odd_number_of_lines_raises_value_error(self): + """ + Test that from_tle raises a clear ValueError (rather than an + unhelpful IndexError) when given an odd number of TLE lines. + """ + with self.assertRaises(ValueError): + GeneralPerturbationsOrbit.from_tle(self.test_tle + [self.test_tle[0]]) + + def test_from_omm_csv_multiple_elements(self): + """ + Test that from_omm_csv builds one element per CSV row, unlike + GeneralPerturbationsElements.from_omm_csv, which only uses the + first row. + """ + omm_dict_1 = self.test_orbit.elements[0].to_omm_dict() + omm_dict_2 = self.second_element.to_omm_dict() + buf = io.StringIO() + writer = csv.DictWriter(buf, fieldnames=list(omm_dict_1.keys())) + writer.writeheader() + writer.writerow(omm_dict_1) + writer.writerow(omm_dict_2) + o = GeneralPerturbationsOrbit.from_omm_csv(buf.getvalue().splitlines()) + self.assertEqual(len(o.elements), 2) + self.assertEqual(o.get_catalog_number(0), 25544) + self.assertEqual(o.get_catalog_number(1), 99999) + + def test_from_omm_csv_empty_rejected(self): + """ + Test that from_omm_csv with no data rows is rejected via the + elements field's min_length constraint (rather than silently + building a zero-element orbit). + """ + with self.assertRaises(ValidationError): + GeneralPerturbationsOrbit.from_omm_csv([]) + + def test_from_omm_json_multiple_elements(self): + """ + Test that from_omm_json builds one element per JSON entry, unlike + GeneralPerturbationsElements.from_omm_json, which only uses the + first entry. + """ + omm_json = json.dumps( + [ + self.test_orbit.elements[0].to_omm_dict(), + self.second_element.to_omm_dict(), + ] + ) + o = GeneralPerturbationsOrbit.from_omm_json(omm_json) + self.assertEqual(len(o.elements), 2) + self.assertEqual(o.get_catalog_number(0), 25544) + self.assertEqual(o.get_catalog_number(1), 99999) + + def test_from_omm_json_empty_rejected(self): + """ + Test that from_omm_json with an empty JSON array is rejected via + the elements field's min_length constraint. + """ + with self.assertRaises(ValidationError): + GeneralPerturbationsOrbit.from_omm_json("[]") + + def test_get_element_epochs(self): + """ + Test that get_element_epochs returns the epoch of every element, + in element order. + """ + epochs = self.multi_element_orbit.get_element_epochs() + self.assertEqual(len(epochs), 2) + self.assertEqual(epochs[0], self.test_orbit.elements[0].epoch) + self.assertEqual(epochs[1], self.second_element.epoch) + + def test_get_derived_orbit(self): + """ + Test that a derived orbit can be created from the GeneralPerturbationsOrbit object. + """ + derived_orbit = self.test_orbit.get_derived_orbit(20, 10) + self.assertAlmostEqual( + derived_orbit.get_mean_anomaly(), + self.test_orbit.get_mean_anomaly() + 20, + delta=0.001, + ) + self.assertAlmostEqual( + derived_orbit.get_right_ascension_ascending_node(), + self.test_orbit.get_right_ascension_ascending_node() + 10, + delta=0.001, + ) + + def test_get_derived_orbit_shifts_every_element(self): + """ + Test that get_derived_orbit applies the same mean anomaly and + RAAN perturbation to every element in a multi-element orbit, not + just the first. + """ + derived_orbit = self.multi_element_orbit.get_derived_orbit(20, 10) + self.assertEqual(len(derived_orbit.elements), 2) + for i in range(2): + with self.subTest(index=i): + self.assertAlmostEqual( + derived_orbit.get_mean_anomaly(i), + self.multi_element_orbit.get_mean_anomaly(i) + 20, + delta=0.001, + ) + self.assertAlmostEqual( + derived_orbit.get_right_ascension_ascending_node(i), + self.multi_element_orbit.get_right_ascension_ascending_node(i) + 10, + delta=0.001, + ) + + def test_get_derived_orbit_does_not_mutate_original(self): + """ + Test that get_derived_orbit does not mutate the original orbit's + elements (since it deep-copies each element before modifying it). + """ + original_mean_anomaly = self.test_orbit.get_mean_anomaly() + self.test_orbit.get_derived_orbit(20, 10) + self.assertEqual(self.test_orbit.get_mean_anomaly(), original_mean_anomaly) + + +class TestGetClosestElementIndex(unittest.TestCase): + """ + Unit tests for GeneralPerturbationsOrbit.get_closest_element_index. + """ + + def setUp(self): + tle = [ + "1 25544U 98067A 21156.30527927 .00003432 00000-0 70541-4 0 9993", + "2 25544 51.6455 41.4969 0003508 68.0432 78.3395 15.48957534286754", + ] + base = GeneralPerturbationsOrbit.from_tle(tle).elements[0] + self.epoch_0 = datetime(2022, 1, 1, tzinfo=timezone.utc) + self.epoch_1 = datetime(2022, 1, 3, tzinfo=timezone.utc) + self.epoch_2 = datetime(2022, 1, 5, tzinfo=timezone.utc) + self.multi_element_orbit = GeneralPerturbationsOrbit( + elements=[ + base.model_copy(update={"epoch": self.epoch_0}), + base.model_copy(update={"epoch": self.epoch_1}), + base.model_copy(update={"epoch": self.epoch_2}), + ] + ) + self.single_element_orbit = GeneralPerturbationsOrbit( + elements=[base.model_copy(update={"epoch": self.epoch_0})] + ) + + def test_none_always_returns_first_index(self): + """ + Test that at_times=None always selects index 0, regardless of + how many elements exist. + """ + self.assertEqual(self.single_element_orbit.get_closest_element_index(None), 0) + self.assertEqual(self.multi_element_orbit.get_closest_element_index(None), 0) + + def test_single_element_orbit_always_returns_zero(self): + """ + Test that a single-element orbit always returns index 0 for any + query time, since there is only one element to choose from. + """ + far_future = self.epoch_0 + timedelta(days=3650) + self.assertEqual( + self.single_element_orbit.get_closest_element_index(far_future), 0 + ) + + def test_query_exactly_at_an_epoch_returns_that_index(self): + """ + Test that querying exactly at an element's epoch returns that + element's index. + """ + o = self.multi_element_orbit + self.assertEqual(o.get_closest_element_index(self.epoch_0), 0) + self.assertEqual(o.get_closest_element_index(self.epoch_1), 1) + self.assertEqual(o.get_closest_element_index(self.epoch_2), 2) + + def test_query_before_first_epoch_clamps_to_first_index(self): + """ + Test that a query time before every element's epoch returns the + first (nearest) index. + """ + query = self.epoch_0 - timedelta(days=365) + self.assertEqual(self.multi_element_orbit.get_closest_element_index(query), 0) + + def test_query_after_last_epoch_clamps_to_last_index(self): + """ + Test that a query time after every element's epoch returns the + last (nearest) index. + """ + query = self.epoch_2 + timedelta(days=365) + self.assertEqual(self.multi_element_orbit.get_closest_element_index(query), 2) + + def test_query_closer_to_earlier_epoch(self): + """ + Test that a query time closer to an earlier epoch than the next + one returns the earlier index. + """ + query = self.epoch_0 + timedelta(hours=1) # much closer to epoch_0 than epoch_1 + self.assertEqual(self.multi_element_orbit.get_closest_element_index(query), 0) + + def test_query_closer_to_later_epoch(self): + """ + Test that a query time closer to a later epoch than the previous + one returns the later index. + """ + query = self.epoch_1 - timedelta(hours=1) # much closer to epoch_1 than epoch_0 + self.assertEqual(self.multi_element_orbit.get_closest_element_index(query), 1) + + def test_query_at_exact_midpoint_breaks_tie_toward_later_index(self): + """ + Test the documented tie-breaking convention: a query exactly + equidistant between two epochs resolves to the later index + (since the implementation only prefers the earlier neighbor when + it is strictly closer). + """ + midpoint = self.epoch_0 + (self.epoch_1 - self.epoch_0) / 2 + self.assertEqual( + self.multi_element_orbit.get_closest_element_index(midpoint), 1 + ) + + def test_list_input_returns_list_of_indices(self): + """ + Test that a list of query times returns a list of indices, one + per query, matching each query's closest element independently. + """ + queries = [ + self.epoch_0 - timedelta(days=365), + self.epoch_0 + (self.epoch_1 - self.epoch_0) / 2, + self.epoch_2 + timedelta(days=365), + ] + self.assertEqual( + self.multi_element_orbit.get_closest_element_index(queries), [0, 1, 2] + ) + + def test_ndarray_input_returns_list_of_indices(self): + """ + Test that a numpy datetime64 array of query times is accepted + and returns the same result as an equivalent list of datetimes. + """ + # strip tzinfo (all test epochs are UTC) before building the + # array, since numpy warns when directly casting timezone-aware + # datetimes to datetime64 + queries = np.array( + [ + self.epoch_0.replace(tzinfo=None), + self.epoch_1.replace(tzinfo=None), + self.epoch_2.replace(tzinfo=None), + ], + dtype="datetime64[ns]", + ) + self.assertEqual( + self.multi_element_orbit.get_closest_element_index(queries), [0, 1, 2] + ) + + def test_empty_list_input_returns_empty_list(self): + """ + Test that an empty list of query times returns an empty list of + indices, rather than raising an error. + """ + self.assertEqual(self.multi_element_orbit.get_closest_element_index([]), []) + + +class TestGetClosestElement(unittest.TestCase): + """ + Unit tests for GeneralPerturbationsOrbit.get_closest_element. Since + this is a thin wrapper mapping get_closest_element_index's result + onto self.elements, index-selection edge cases (ties, boundaries, + sorting) are covered by TestGetClosestElementIndex; these tests focus + on the element-mapping behavior itself. + """ + + def setUp(self): + tle = [ + "1 25544U 98067A 21156.30527927 .00003432 00000-0 70541-4 0 9993", + "2 25544 51.6455 41.4969 0003508 68.0432 78.3395 15.48957534286754", + ] + base = GeneralPerturbationsOrbit.from_tle(tle).elements[0] + self.epoch_0 = datetime(2022, 1, 1, tzinfo=timezone.utc) + self.epoch_1 = datetime(2022, 1, 3, tzinfo=timezone.utc) + self.element_0 = base.model_copy( + update={"norad_cat_id": 11111, "epoch": self.epoch_0} + ) + self.element_1 = base.model_copy( + update={"norad_cat_id": 22222, "epoch": self.epoch_1} + ) + self.multi_element_orbit = GeneralPerturbationsOrbit( + elements=[self.element_0, self.element_1] + ) + + def test_none_returns_first_element(self): + """ + Test that at_times=None returns the first element. + """ + result = self.multi_element_orbit.get_closest_element(None) + self.assertIs(result, self.element_0) + + def test_scalar_returns_matching_element(self): + """ + Test that a scalar query time returns the actual closest element + object (by identity), not merely its index. + """ + near_epoch_1 = self.epoch_1 - timedelta(hours=1) + result = self.multi_element_orbit.get_closest_element(near_epoch_1) + self.assertIs(result, self.element_1) + self.assertEqual(result.norad_cat_id, 22222) + + def test_list_input_returns_list_of_matching_elements(self): + """ + Test that a list of query times returns a list of the + corresponding closest element objects, in query order. + """ + queries = [ + self.epoch_0 + timedelta(hours=1), + self.epoch_1 - timedelta(hours=1), + ] + result = self.multi_element_orbit.get_closest_element(queries) + self.assertEqual(len(result), 2) + self.assertIs(result[0], self.element_0) + self.assertIs(result[1], self.element_1) + + def test_empty_list_input_returns_empty_list(self): + """ + Test that an empty list of query times returns an empty list of + elements, rather than raising an error. + """ + self.assertEqual(self.multi_element_orbit.get_closest_element([]), []) + + def test_single_element_orbit_always_returns_that_element(self): + """ + Test that a single-element orbit always returns its one element, + regardless of the query time. + """ + o = GeneralPerturbationsOrbit(elements=[self.element_0]) + far_future = self.epoch_0 + timedelta(days=3650) + self.assertIs(o.get_closest_element(far_future), self.element_0) + + +class TestGetOrbitTrackAtTime(unittest.TestCase): + """ + Unit tests for GeneralPerturbationsOrbit.get_orbit_track_at_time. This + method is mostly a thin wrapper around Skyfield's own SGP4 propagation + (EarthSatellite.at); the specific behavior worth validating here is + that a multi-element orbit selects and propagates the correct element + per query time, both for a single scalar time and for a vectorized + batch spanning multiple elements' regions. + """ + + def setUp(self): + tle = [ + "1 25544U 98067A 21156.30527927 .00003432 00000-0 70541-4 0 9993", + "2 25544 51.6455 41.4969 0003508 68.0432 78.3395 15.48957534286754", + ] + base = GeneralPerturbationsOrbit.from_tle(tle).elements[0] + self.epoch_0 = datetime(2022, 1, 1, tzinfo=timezone.utc) + self.epoch_1 = datetime(2022, 1, 3, tzinfo=timezone.utc) + self.epoch_2 = datetime(2022, 1, 5, tzinfo=timezone.utc) + self.element_0 = base.model_copy(update={"epoch": self.epoch_0}) + self.element_1 = base.model_copy(update={"epoch": self.epoch_1}) + self.element_2 = base.model_copy(update={"epoch": self.epoch_2}) + self.multi_element_orbit = GeneralPerturbationsOrbit( + elements=[self.element_0, self.element_1, self.element_2] + ) + self.single_element_orbit = GeneralPerturbationsOrbit(elements=[self.element_0]) + + def test_single_element_scalar_time_matches_direct_propagation(self): + """ + Test that a single-element orbit's result at a scalar time is + identical to propagating that element directly. + """ + t = constants.timescale.from_datetime(self.epoch_0 + timedelta(hours=5)) + expected = self.element_0.to_skyfield().at(t) + actual = self.single_element_orbit.get_orbit_track_at_time(t) + self.assertTrue(np.array_equal(actual.position.km, expected.position.km)) + self.assertTrue( + np.array_equal(actual.velocity.km_per_s, expected.velocity.km_per_s) + ) + + def test_single_element_vector_time_matches_direct_propagation(self): + """ + Test that a single-element orbit's result at a vector of times is + identical to propagating that element directly. + """ + times = [self.epoch_0 + timedelta(hours=h) for h in (1, 5, 10)] + t = constants.timescale.from_datetimes(times) + expected = self.element_0.to_skyfield().at(t) + actual = self.single_element_orbit.get_orbit_track_at_time(t) + self.assertTrue(np.array_equal(actual.position.km, expected.position.km)) + + def test_multi_element_scalar_time_uses_nearest_element(self): + """ + Test that a scalar query time near epoch_1 is propagated using + element_1, not element_0 -- verified both by matching element_1's + own direct propagation, and by confirming element_0 would have + given a different (wrong) answer, since the two elements carry + different epochs and so a different elapsed-time offset from the + same absolute query time. + """ + t = constants.timescale.from_datetime(self.epoch_1 - timedelta(hours=1)) + actual = self.multi_element_orbit.get_orbit_track_at_time(t) + expected = self.element_1.to_skyfield().at(t) + self.assertTrue(np.array_equal(actual.position.km, expected.position.km)) + wrong = self.element_0.to_skyfield().at(t) + self.assertFalse(np.array_equal(actual.position.km, wrong.position.km)) + + def test_multi_element_vector_time_selects_nearest_element_per_time(self): + """ + Test that a vectorized query spanning all three elements' regions + propagates each time with its own nearest element, rather than + applying a single element to the whole batch -- exercising the + implementation's grouped-by-unique-index vectorization. + """ + times = [ + self.epoch_0 + timedelta(hours=1), # nearest element_0 + self.epoch_1 - timedelta(hours=1), # nearest element_1 + self.epoch_1 + timedelta(hours=1), # nearest element_1 + self.epoch_2 + timedelta(hours=1), # nearest element_2 + ] + expected_elements = [ + self.element_0, + self.element_1, + self.element_1, + self.element_2, + ] + t = constants.timescale.from_datetimes(times) + actual = self.multi_element_orbit.get_orbit_track_at_time(t) + for i, (time, element) in enumerate(zip(times, expected_elements)): + expected = element.to_skyfield().at(constants.timescale.from_datetime(time)) + self.assertTrue( + np.array_equal(actual.position.km[:, i], expected.position.km), + f"time index {i} did not match its nearest element", + ) + + def test_multi_element_vector_time_all_same_nearest_element(self): + """ + Test the edge case where every time in a vectorized query shares + the same nearest element, so the grouped-by-unique-index loop + runs exactly once, still returning correct per-time results. + """ + times = [self.epoch_1 + timedelta(minutes=m) for m in (10, 20, 30)] + t = constants.timescale.from_datetimes(times) + expected = self.element_1.to_skyfield().at(t) + actual = self.multi_element_orbit.get_orbit_track_at_time(t) + self.assertTrue(np.array_equal(actual.position.km, expected.position.km)) + + +class TestGetOrbitTrack(unittest.TestCase): + """ + Unit tests for GeneralPerturbationsOrbit.get_orbit_track. This method + only builds a Skyfield Time from the given datetime(s) and delegates + to get_orbit_track_at_time (see TestGetOrbitTrackAtTime for + propagation and multi-element selection coverage), so these tests + focus on the datetime-to-Time dispatch itself: a scalar datetime vs. + a list, including the list-of-one edge case. + """ + + def setUp(self): + tle = [ + "1 25544U 98067A 21156.30527927 .00003432 00000-0 70541-4 0 9993", + "2 25544 51.6455 41.4969 0003508 68.0432 78.3395 15.48957534286754", + ] + self.orbit = GeneralPerturbationsOrbit.from_tle(tle) + self.epoch = self.orbit.elements[0].epoch + + def test_scalar_datetime_matches_get_orbit_track_at_time(self): + """ + Test that a single datetime produces the same result as calling + get_orbit_track_at_time with the equivalent scalar Skyfield Time. + """ + time = self.epoch + timedelta(hours=2) + expected = self.orbit.get_orbit_track_at_time( + constants.timescale.from_datetime(time) + ) + actual = self.orbit.get_orbit_track(time) + self.assertTrue(np.array_equal(actual.position.km, expected.position.km)) + + def test_list_of_datetimes_matches_get_orbit_track_at_time(self): + """ + Test that a list of datetimes produces the same result as calling + get_orbit_track_at_time with the equivalent vector Skyfield Time. + """ + times = [self.epoch + timedelta(hours=h) for h in (1, 2, 3)] + expected = self.orbit.get_orbit_track_at_time( + constants.timescale.from_datetimes(times) + ) + actual = self.orbit.get_orbit_track(times) + self.assertTrue(np.array_equal(actual.position.km, expected.position.km)) + + def test_scalar_datetime_returns_scalar_shaped_result(self): + """ + Test that a scalar datetime returns a scalar (non-vectorized) + position, distinguishing it from an equal-valued single-item list + (see test_list_of_one_datetime_returns_vector_shaped_result). + """ + time = self.epoch + timedelta(hours=1) + result = self.orbit.get_orbit_track(time) + self.assertEqual(result.position.km.shape, (3,)) + + def test_list_of_one_datetime_returns_vector_shaped_result(self): + """ + Test that a single-item list is still treated as a vector query + (shape (3, 1)), not collapsed to the scalar shape a bare datetime + would produce -- the dispatch is based on the input's type + (datetime vs. list), not its length. + """ + time = self.epoch + timedelta(hours=1) + result = self.orbit.get_orbit_track([time]) + self.assertEqual(result.position.km.shape, (3, 1)) + # and the single entry's value matches the scalar-input case + scalar_result = self.orbit.get_orbit_track(time) + self.assertTrue( + np.array_equal(result.position.km[:, 0], scalar_result.position.km) + ) + + +class TestGetGeographicPositionAtTime(unittest.TestCase): + """ + Unit tests for GeneralPerturbationsOrbit.get_geographic_position_at_time. + This method is almost the same as get_orbit_track_at_time (already + covered elsewhere), but optionally substitutes a nearby, epoch-relative + time for a distant one when a repeat cycle is known, trading a little + ground-track drift for much less accumulated SGP4 propagation error. + These tests focus on that substitution: when it applies, whether it + preserves the sign of the time offset from epoch, and when it falls + back to direct propagation. + """ + + def setUp(self): + # Landsat-8: real, single-element, actively repeating orbit + landsat_8_tle = [ + "1 39084U 13008A 26213.27824675 .00000294 00000+0 75333-4 0 9990", + "2 39084 98.2277 282.8718 0001275 92.4910 267.6434 14.57104473704466", + ] + self.repeat_orbit = GeneralPerturbationsOrbit.from_tle(landsat_8_tle) + self.epoch = self.repeat_orbit.get_epoch() + self.repeat_cycle = self.repeat_orbit.get_repeat_cycle() + self.assertIsNotNone(self.repeat_cycle) + + # ISS: single-element, not designed for a repeat ground track + iss_tle = [ + "1 25544U 98067A 21156.30527927 .00003432 00000-0 70541-4 0 9993", + "2 25544 51.6455 41.4969 0003508 68.0432 78.3395 15.48957534286754", + ] + self.non_repeat_orbit = GeneralPerturbationsOrbit.from_tle(iss_tle) + + # a multi-element orbit (elements otherwise identical to Landsat-8, + # just re-epoched), to confirm the substitution never applies + base = self.repeat_orbit.elements[0] + self.multi_element_orbit = GeneralPerturbationsOrbit( + elements=[ + base.model_copy(update={"epoch": self.epoch}), + base.model_copy(update={"epoch": self.epoch + timedelta(days=1)}), + ] + ) + + def test_try_repeat_false_matches_direct_propagation(self): + """ + Test that try_repeat=False always returns the true, directly + propagated position, even for an orbit with a known repeat cycle. + """ + t = constants.timescale.from_datetime(self.epoch + self.repeat_cycle * 3) + actual = self.repeat_orbit.get_geographic_position_at_time(t, try_repeat=False) + expected = wgs84.geographic_position_of( + self.repeat_orbit.get_orbit_track_at_time(t) + ) + self.assertEqual(actual.latitude.degrees, expected.latitude.degrees) + self.assertEqual(actual.longitude.degrees, expected.longitude.degrees) + + def test_try_repeat_true_within_first_cycle_matches_direct_propagation(self): + """ + Test that, for a time less than one repeat cycle after epoch, the + substitution is a no-op (the wrapped offset equals the original + offset), so try_repeat=True and try_repeat=False agree exactly. + """ + t = constants.timescale.from_datetime(self.epoch + timedelta(hours=5)) + with_repeat = self.repeat_orbit.get_geographic_position_at_time( + t, try_repeat=True + ) + direct = self.repeat_orbit.get_geographic_position_at_time(t, try_repeat=False) + self.assertEqual(with_repeat.latitude.degrees, direct.latitude.degrees) + self.assertEqual(with_repeat.longitude.degrees, direct.longitude.degrees) + + def test_try_repeat_true_substitutes_epoch_relative_time_for_far_future(self): + """ + Test that a time several repeat cycles in the future is + substituted with the equivalent epoch-relative offset (t's offset + from epoch, wrapped modulo the repeat cycle) rather than + propagated directly -- verified against a manual replica of that + formula, and confirmed to differ from true direct propagation + (which accumulates more SGP4 error over the longer elapsed time). + """ + far_future = self.epoch + self.repeat_cycle * 3 + timedelta(hours=5) + t = constants.timescale.from_datetime(far_future) + actual = self.repeat_orbit.get_geographic_position_at_time(t, try_repeat=True) + + offset_days = (far_future - self.epoch) / timedelta(days=1) + cycle_days = self.repeat_cycle / timedelta(days=1) + wrapped_offset = timedelta(days=float(np.mod(offset_days, cycle_days))) + expected = wgs84.geographic_position_of( + self.repeat_orbit.elements[0] + .to_skyfield() + .at(constants.timescale.from_datetime(self.epoch + wrapped_offset)) + ) + self.assertAlmostEqual( + actual.latitude.degrees, expected.latitude.degrees, places=9 + ) + self.assertAlmostEqual( + actual.longitude.degrees, expected.longitude.degrees, places=9 + ) + + direct = wgs84.geographic_position_of( + self.repeat_orbit.get_orbit_track_at_time(t) + ) + self.assertNotEqual(actual.latitude.degrees, direct.latitude.degrees) + + def test_try_repeat_true_preserves_sign_for_time_before_epoch(self): + """ + Regression test: a query 2.5 repeat cycles *before* epoch must + wrap to -0.5 cycles (epoch minus half a cycle), not +0.5 cycles. + A naive numpy np.mod() on the raw (negative) offset always + returns a non-negative result, which would silently wrap to the + wrong side of the repeat cycle -- a real bug this implementation + avoids by explicitly reapplying the offset's original sign. + """ + query_time = self.epoch - self.repeat_cycle * 2.5 + t = constants.timescale.from_datetime(query_time) + actual = self.repeat_orbit.get_geographic_position_at_time(t, try_repeat=True) + + correct = wgs84.geographic_position_of( + self.repeat_orbit.elements[0] + .to_skyfield() + .at(constants.timescale.from_datetime(self.epoch - self.repeat_cycle * 0.5)) + ) + wrong = wgs84.geographic_position_of( + self.repeat_orbit.elements[0] + .to_skyfield() + .at(constants.timescale.from_datetime(self.epoch + self.repeat_cycle * 0.5)) + ) + self.assertAlmostEqual( + actual.latitude.degrees, correct.latitude.degrees, places=9 + ) + self.assertNotAlmostEqual( + actual.latitude.degrees, wrong.latitude.degrees, places=2 + ) + + def test_try_repeat_true_ignored_for_multi_element_orbit(self): + """ + Test that the repeat-cycle substitution never applies to a + multi-element orbit, even with try_repeat=True and a detectable + single-element repeat cycle -- it always falls back to direct, + per-time nearest-element propagation (get_orbit_track_at_time). + """ + t = constants.timescale.from_datetime(self.epoch + timedelta(days=40)) + actual = self.multi_element_orbit.get_geographic_position_at_time( + t, try_repeat=True + ) + expected = wgs84.geographic_position_of( + self.multi_element_orbit.get_orbit_track_at_time(t) + ) + self.assertEqual(actual.latitude.degrees, expected.latitude.degrees) + self.assertEqual(actual.longitude.degrees, expected.longitude.degrees) + + def test_try_repeat_true_falls_back_when_no_repeat_cycle_found(self): + """ + Test that try_repeat=True falls back to direct propagation for an + orbit (the ISS) with no detectable repeat cycle. + """ + t = constants.timescale.from_datetime( + self.non_repeat_orbit.get_epoch() + timedelta(days=10) + ) + actual = self.non_repeat_orbit.get_geographic_position_at_time( + t, try_repeat=True + ) + expected = wgs84.geographic_position_of( + self.non_repeat_orbit.get_orbit_track_at_time(t) + ) + self.assertEqual(actual.latitude.degrees, expected.latitude.degrees) + self.assertEqual(actual.longitude.degrees, expected.longitude.degrees) + + def test_try_repeat_none_uses_config_default(self): + """ + Test that omitting try_repeat follows + config.get_rc().repeat_cycle_for_orbit_track. + """ + t = constants.timescale.from_datetime( + self.epoch + self.repeat_cycle * 3 + timedelta(hours=5) + ) + original = config.get_rc().repeat_cycle_for_orbit_track + try: + config.get_rc().repeat_cycle_for_orbit_track = True + with_default_true = self.repeat_orbit.get_geographic_position_at_time(t) + config.get_rc().repeat_cycle_for_orbit_track = False + with_default_false = self.repeat_orbit.get_geographic_position_at_time(t) + finally: + config.get_rc().repeat_cycle_for_orbit_track = original + expected_true = self.repeat_orbit.get_geographic_position_at_time( + t, try_repeat=True + ) + expected_false = self.repeat_orbit.get_geographic_position_at_time( + t, try_repeat=False + ) + self.assertEqual( + with_default_true.latitude.degrees, expected_true.latitude.degrees + ) + self.assertEqual( + with_default_false.latitude.degrees, expected_false.latitude.degrees + ) + + def test_vectorized_time_substitution_matches_per_time_computation(self): + """ + Test that a vectorized query spanning multiple repeat cycles, on + both sides of epoch, produces the same result as computing each + time individually -- exercising the array branch of the epoch- + relative substitution (the tests above use the scalar branch). + """ + query_times = [ + self.epoch + timedelta(hours=5), + self.epoch + self.repeat_cycle * 3 + timedelta(hours=5), + self.epoch - self.repeat_cycle * 2.5, + ] + t_vector = constants.timescale.from_datetimes(query_times) + actual = self.repeat_orbit.get_geographic_position_at_time( + t_vector, try_repeat=True + ) + for i, query_time in enumerate(query_times): + expected = self.repeat_orbit.get_geographic_position_at_time( + constants.timescale.from_datetime(query_time), try_repeat=True + ) + self.assertAlmostEqual( + actual.latitude.degrees[i], expected.latitude.degrees, places=9 + ) + self.assertAlmostEqual( + actual.longitude.degrees[i], expected.longitude.degrees, places=9 + ) + + +class TestPartitionByElementIndex(unittest.TestCase): + """ + Unit tests for GeneralPerturbationsOrbit.partition_by_element_index. + """ + + def setUp(self): + tle = [ + "1 25544U 98067A 21156.30527927 .00003432 00000-0 70541-4 0 9993", + "2 25544 51.6455 41.4969 0003508 68.0432 78.3395 15.48957534286754", + ] + base = GeneralPerturbationsOrbit.from_tle(tle).elements[0] + self.epoch_0 = datetime(2022, 1, 1, tzinfo=timezone.utc) + self.epoch_1 = datetime(2022, 1, 3, tzinfo=timezone.utc) + self.epoch_2 = datetime(2022, 1, 5, tzinfo=timezone.utc) + self.multi_element_orbit = GeneralPerturbationsOrbit( + elements=[ + base.model_copy(update={"epoch": self.epoch_0}), + base.model_copy(update={"epoch": self.epoch_1}), + base.model_copy(update={"epoch": self.epoch_2}), + ] + ) + + def test_uses_get_element_epochs_cache(self): + """ + Test that partition_by_element_index reads epochs through + get_element_epochs (populating its lazy-load cache) rather than + re-deriving them independently, so a subsequent call reuses the + same cached list. + """ + self.assertIsNone(self.multi_element_orbit.__dict__.get("element_epochs")) + start = self.epoch_0 - timedelta(days=365) + end = self.epoch_2 + timedelta(days=365) + self.multi_element_orbit.partition_by_element_index(start, end) + cached = self.multi_element_orbit.__dict__.get("element_epochs") + self.assertIsNotNone(cached) + self.assertEqual(cached, [self.epoch_0, self.epoch_1, self.epoch_2]) + + def test_single_element_orbit(self): + """ + Test that a single-element orbit returns exactly one segment + covering the whole window, assigned to element 0. + """ + o = GeneralPerturbationsOrbit( + elements=[ + self.multi_element_orbit.elements[0].model_copy( + update={"epoch": self.epoch_0} + ) + ] + ) + start = self.epoch_0 - timedelta(days=1) + end = self.epoch_0 + timedelta(days=1) + boundary_times, segment_indices = o.partition_by_element_index(start, end) + self.assertEqual(boundary_times, [start, end]) + self.assertEqual(segment_indices, [0]) + + def test_window_spanning_all_elements(self): + """ + Regression test for a bug where this method previously crashed + with a TypeError comparing Python datetime against numpy + datetime64 whenever called with more than one element -- the + exact scenario this method exists for. A window spanning well + before the first and well after the last epoch should produce + one segment per element, in order. + """ + start = self.epoch_0 - timedelta(days=365) + end = self.epoch_2 + timedelta(days=365) + boundary_times, segment_indices = ( + self.multi_element_orbit.partition_by_element_index(start, end) + ) + self.assertEqual(len(boundary_times), 4) + self.assertEqual(boundary_times[0], start) + self.assertEqual(boundary_times[-1], end) + self.assertEqual(segment_indices, [0, 1, 2]) + + def test_boundary_times_are_plain_datetimes(self): + """ + Regression test: every boundary time (including the internally + computed midpoints, not just start/end) must be a plain Python + datetime, not a numpy.datetime64 -- the downstream caller + (get_observation_events) passes each one directly to + skyfield's Timescale.from_datetime(), which requires a real + datetime object. + """ + start = self.epoch_0 - timedelta(days=365) + end = self.epoch_2 + timedelta(days=365) + boundary_times, _ = self.multi_element_orbit.partition_by_element_index( + start, end + ) + for t in boundary_times: + with self.subTest(t=t): + self.assertIsInstance(t, datetime) + + def test_window_within_single_elements_region(self): + """ + Test that a narrow window falling entirely within one element's + closest-region (no epoch midpoint inside the window) returns a + single segment for that element, not one per element. + """ + start = self.epoch_1 - timedelta(hours=1) + end = self.epoch_1 + timedelta(hours=1) + boundary_times, segment_indices = ( + self.multi_element_orbit.partition_by_element_index(start, end) + ) + self.assertEqual(boundary_times, [start, end]) + self.assertEqual(segment_indices, [1]) + + def test_window_spanning_only_first_midpoint(self): + """ + Test a window that includes the first epoch midpoint but not the + second, producing two segments (elements 0 and 1). + """ + start = self.epoch_0 + end = self.epoch_1 + timedelta(hours=12) + boundary_times, segment_indices = ( + self.multi_element_orbit.partition_by_element_index(start, end) + ) + self.assertEqual(len(boundary_times), 3) + self.assertEqual(boundary_times[0], start) + self.assertEqual(boundary_times[-1], end) + self.assertEqual(segment_indices, [0, 1]) + + def test_number_of_indices_is_one_less_than_boundary_times(self): + """ + Test the length contract: N+1 boundary times always yield + exactly N segment indices, for a variety of window sizes. + """ + cases = [ + (self.epoch_0 - timedelta(days=365), self.epoch_2 + timedelta(days=365)), + (self.epoch_1 - timedelta(hours=1), self.epoch_1 + timedelta(hours=1)), + (self.epoch_0, self.epoch_1 + timedelta(hours=12)), + ] + for start, end in cases: + with self.subTest(start=start, end=end): + boundary_times, segment_indices = ( + self.multi_element_orbit.partition_by_element_index(start, end) + ) + self.assertEqual(len(segment_indices), len(boundary_times) - 1) + + def test_get_observation_events_does_not_crash_for_multi_element_orbit(self): + """ + Smoke test for the underlying bug: get_observation_events, the + sole caller of partition_by_element_index, must not crash when + given a multi-element orbit. Full coverage of + get_observation_events itself is a separate, later piece of work. + """ + point = Point(id=0, latitude=40.0, longitude=-74.0) + start = self.epoch_0 + end = self.epoch_0 + timedelta(hours=12) + _, events = self.multi_element_orbit.get_observation_events( + point, start, end, min_elevation_angle=10, try_repeat=False + ) + self.assertGreater(len(events), 0) + + +class TestGetRepeatCycle(unittest.TestCase): + """ + Unit tests for GeneralPerturbationsOrbit.get_repeat_cycle. + """ + + def setUp(self): + # a real Landsat-8 element set (NORAD 39084, fetched from + # Celestrak), whose published repeat ground track is exactly 233 + # orbits every 16 days (USGS) + self.landsat_8_tle = [ + "1 39084U 13008A 26213.27824675 .00000294 00000+0 75333-4 0 9990", + "2 39084 98.2277 282.8718 0001275 92.4910 267.6434 14.57104473704466", + ] + # a real Sentinel-2A element set (NORAD 40697), whose published + # repeat ground track is exactly 143 orbits every 10 days (ESA) + self.sentinel_2a_tle = [ + "1 40697U 15028A 26213.24967738 .00000092 00000+0 51774-4 0 9990", + "2 40697 98.5671 287.4570 0001329 92.6662 267.4673 14.30818788580177", + ] + # a real Sentinel-1A element set (NORAD 39634) from early 2026, + # while the satellite was still actively operated (its mission + # concluded 2026-06-29): published repeat ground track is exactly + # 175 orbits every 12 days (ESA) + self.sentinel_1a_tle = [ + "1 39634U 14016A 26001.19041520 .00000521 00000-0 12012-3 0 9995", + "2 39634 98.1805 11.1453 0001276 85.2963 274.8383 14.59199668625660", + ] + # a real GPS BIIR-5 element set (NORAD 26407): GPS orbits are + # designed to repeat their ground track every sidereal day (two + # ~12-hour orbits per day), a much shorter cycle than the + # sun-synchronous imaging orbits above, at MEO altitude (~20,200 km) + self.gps_tle = [ + "1 26407U 00040A 26213.32131914 .00000072 00000+0 00000+0 0 9998", + "2 26407 54.8467 213.3697 0120005 302.9740 169.1529 2.00558010190856", + ] + # a real Molniya 3-8 element set (NORAD 10455): a highly eccentric, + # critical-inclination (~63.4 degree) orbit that, like GPS, repeats + # every sidereal day by design, but is geometrically nothing like + # the near-circular orbits above + self.molniya_tle = [ + "1 10455U 77105A 26212.97315042 .00000728 00000+0 00000+0 0 9994", + "2 10455 63.8024 172.4301 6701249 276.1687 17.0849 2.00778403357346", + ] + # the ISS is not designed for a repeat ground track + self.iss_tle = [ + "1 25544U 98067A 21156.30527927 .00003432 00000-0 70541-4 0 9993", + "2 25544 51.6455 41.4969 0003508 68.0432 78.3395 15.48957534286754", + ] + + def test_landsat_8_repeat_cycle_matches_published_16_days(self): + """ + Test against Landsat-8's published repeat ground track of 233 + orbits every 16 days (USGS). A small tolerance accounts for the + real orbit's minor drift between station-keeping maneuvers. + """ + orbit = GeneralPerturbationsOrbit.from_tle(self.landsat_8_tle) + repeat_cycle = orbit.get_repeat_cycle() + self.assertIsNotNone(repeat_cycle) + self.assertAlmostEqual(repeat_cycle.total_seconds() / 86400, 16, delta=0.1) + + def test_sentinel_2a_repeat_cycle_matches_published_10_days(self): + """ + Test against Sentinel-2A's published repeat ground track of 143 + orbits every 10 days (ESA), at a different altitude/inclination + than Landsat-8, using the default tolerances. + """ + orbit = GeneralPerturbationsOrbit.from_tle(self.sentinel_2a_tle) + repeat_cycle = orbit.get_repeat_cycle() + self.assertIsNotNone(repeat_cycle) + self.assertAlmostEqual(repeat_cycle.total_seconds() / 86400, 10, delta=0.1) + + def test_sentinel_1a_repeat_cycle_matches_published_12_days(self): + """ + Test against Sentinel-1A's published repeat ground track of 175 + orbits every 12 days (ESA), at a different altitude/inclination + than Landsat-8, using the default tolerances. + """ + orbit = GeneralPerturbationsOrbit.from_tle(self.sentinel_1a_tle) + repeat_cycle = orbit.get_repeat_cycle() + self.assertIsNotNone(repeat_cycle) + self.assertAlmostEqual(repeat_cycle.total_seconds() / 86400, 12, delta=0.1) + + def test_gps_repeat_cycle_matches_sidereal_day(self): + """ + Test against GPS's designed repeat ground track of one sidereal + day (~23h56m), using the default tolerances. Confirms the search + also correctly resolves a very short repeat cycle (day 1), not + just the multi-week cycles above, and at a completely different + (MEO) altitude regime. + """ + orbit = GeneralPerturbationsOrbit.from_tle(self.gps_tle) + repeat_cycle = orbit.get_repeat_cycle() + self.assertIsNotNone(repeat_cycle) + self.assertAlmostEqual( + repeat_cycle.total_seconds() / 86400, + constants.EARTH_SIDEREAL_DAY_S / 86400, + delta=0.01, + ) + + def test_molniya_repeat_cycle_matches_sidereal_day(self): + """ + Test against a Molniya orbit's designed repeat ground track of + one sidereal day, at a highly eccentric, critical-inclination + geometry very different from every other case here. Public + Molniya TLEs are published with an epoch near perigee (where + ground radar tracking is easiest), and perigee is where a highly + eccentric orbit moves fastest (~10 km/s here, vs ~1.5 km/s at + apogee) -- so a given timing imprecision in the analytic + prediction translates into a much larger position/velocity error + than for the near-circular orbits above. This is a property of + the epoch's orbital phase, not of the orbit's true repeatability, + so this test widens the tolerance rather than the global default. + """ + orbit = GeneralPerturbationsOrbit.from_tle(self.molniya_tle) + repeat_cycle = orbit.get_repeat_cycle( + max_delta_position=80000, max_delta_velocity=25 + ) + self.assertIsNotNone(repeat_cycle) + self.assertAlmostEqual( + repeat_cycle.total_seconds() / 86400, + constants.EARTH_SIDEREAL_DAY_S / 86400, + delta=0.01, + ) + + def test_non_repeating_orbit_returns_none(self): + """ + Test that the ISS's orbit, which is not designed for a repeat + ground track, finds no repeat at the default tolerances (10 km, + 3 m/s). The ISS does have a coincidental ~4-day near-repeat + (delta position 10.6 km, delta velocity 8.0 m/s) -- narrowly + outside the position tolerance, and well outside the velocity + tolerance, so both criteria independently reject it. A looser + position tolerance alone (e.g. 30 km, to accommodate a less + precisely maintained real repeat orbit) would accept this + incidental match on position, which is exactly why velocity is + checked too rather than relying on position alone. + """ + orbit = GeneralPerturbationsOrbit.from_tle(self.iss_tle) + self.assertIsNone(orbit.get_repeat_cycle()) + + def test_lazy_load_reuses_cached_result(self): + """ + Test that calling get_repeat_cycle() twice with lazy_load=True + (the default) returns the identical cached timedelta rather than + recomputing. + """ + orbit = GeneralPerturbationsOrbit.from_tle(self.landsat_8_tle) + first = orbit.get_repeat_cycle() + second = orbit.get_repeat_cycle() + self.assertIs(first, second) + + def test_lazy_load_false_forces_recomputation(self): + """ + Test that lazy_load=False recomputes rather than reusing the + cached result (a fresh but equal timedelta). + """ + orbit = GeneralPerturbationsOrbit.from_tle(self.landsat_8_tle) + first = orbit.get_repeat_cycle() + second = orbit.get_repeat_cycle(lazy_load=False) + self.assertEqual(first, second) + self.assertIsNot(first, second) + + def test_too_short_search_duration_returns_none(self): + """ + Test that a max_search_duration shorter than the true repeat + cycle (16 days) cannot find it. + """ + orbit = GeneralPerturbationsOrbit.from_tle(self.landsat_8_tle) + repeat_cycle = orbit.get_repeat_cycle( + max_search_duration=timedelta(days=10), lazy_load=False + ) + self.assertIsNone(repeat_cycle) + + def test_too_tight_tolerance_returns_none(self): + """ + Test that an unrealistically tight position/velocity tolerance + rejects even the real repeat cycle. + """ + orbit = GeneralPerturbationsOrbit.from_tle(self.landsat_8_tle) + repeat_cycle = orbit.get_repeat_cycle( + max_delta_position=1, max_delta_velocity=0.001, lazy_load=False + ) + self.assertIsNone(repeat_cycle) + + def test_multi_element_consistent_repeat_cycles_are_combined(self): + """ + Test that a multi-element orbit reports a repeat cycle when every + element's own repeat cycle agrees within the consistency + threshold. The second element here is a near-identical copy (mean + motion nudged by 1e-6, a negligible fitting-noise-scale change) + of the real Landsat-8 element, so both independently resolve to + ~16 days, well within the default 1-hour threshold. + """ + base = GeneralPerturbationsOrbit.from_tle(self.landsat_8_tle).elements[0] + nearly_identical = base.model_copy( + update={"mean_motion": base.mean_motion * (1 + 1e-6)} + ) + orbit = GeneralPerturbationsOrbit(elements=[base, nearly_identical]) + repeat_cycle = orbit.get_repeat_cycle() + self.assertIsNotNone(repeat_cycle) + self.assertAlmostEqual(repeat_cycle.total_seconds() / 86400, 16, delta=0.1) + + def test_multi_element_valid_but_inconsistent_repeat_cycles_return_none(self): + """ + Test that a multi-element orbit returns None when its elements + each have a valid repeat cycle, but they disagree well beyond the + consistency threshold -- simulating a maneuver partway through + the orbit's history that changed its fundamental repeat behavior. + A 0.05% mean motion change shifts the second element's best + (smallest-drift) candidate from 16 days to 7 days, at a loosened + but still fairly tight tolerance (40 km) chosen so that the + original element still resolves cleanly to 16 days too (its own + other candidate days all drift well over 100 km, so 40 km can't + accidentally admit any of them) -- isolating the consistency + check from the tolerance itself. + """ + base = GeneralPerturbationsOrbit.from_tle(self.landsat_8_tle).elements[0] + maneuvered = base.model_copy(update={"mean_motion": base.mean_motion * 1.0005}) + orbit = GeneralPerturbationsOrbit(elements=[base, maneuvered]) + max_delta_position = 40000 + max_delta_velocity = 5 + base_cycle = base.get_repeat_cycle(max_delta_position, max_delta_velocity) + maneuvered_cycle = maneuvered.get_repeat_cycle( + max_delta_position, max_delta_velocity + ) + self.assertIsNotNone(base_cycle) + self.assertIsNotNone(maneuvered_cycle) + self.assertNotEqual(base_cycle, maneuvered_cycle) + self.assertIsNone( + orbit.get_repeat_cycle(max_delta_position, max_delta_velocity) + ) + + def test_multi_element_one_element_without_repeat_cycle_returns_none(self): + """ + Test that a multi-element orbit returns None if any element has + no repeat cycle at all (here, a 0.1% mean motion change from the + real Landsat-8 element, which at default tolerances finds no + commensurate day within the search duration), even though the + other element's own repeat cycle is perfectly valid. + """ + base = GeneralPerturbationsOrbit.from_tle(self.landsat_8_tle).elements[0] + maneuvered = base.model_copy(update={"mean_motion": base.mean_motion * 1.001}) + orbit = GeneralPerturbationsOrbit(elements=[base, maneuvered]) + self.assertIsNotNone(base.get_repeat_cycle()) + self.assertIsNone(maneuvered.get_repeat_cycle()) + self.assertIsNone(orbit.get_repeat_cycle()) + + def test_consistency_threshold_override_changes_accept_reject_boundary(self): + """ + Test that consistency_threshold controls the accept/reject + boundary directly: the same pair of elements (differing by + ~1.59 seconds in their independently-computed repeat cycles) is + rejected under a 1-second threshold and accepted under a + 2-second threshold. + """ + base = GeneralPerturbationsOrbit.from_tle(self.landsat_8_tle).elements[0] + nearly_identical = base.model_copy( + update={"mean_motion": base.mean_motion * (1 + 1e-6)} + ) + orbit = GeneralPerturbationsOrbit(elements=[base, nearly_identical]) + self.assertIsNone( + orbit.get_repeat_cycle( + consistency_threshold=timedelta(seconds=1), lazy_load=False + ) + ) + self.assertIsNotNone( + orbit.get_repeat_cycle( + consistency_threshold=timedelta(seconds=2), lazy_load=False + ) + ) + + +class TestGetObservationEvents(unittest.TestCase): + """ + Unit tests for GeneralPerturbationsOrbit.get_observation_events. + Focuses on which of the three strategies (repeat-cycle tiling, + multi-element partitioning, direct propagation) gets used and when, + since each one is separately exercised elsewhere (find_events itself + is Skyfield's; partition_by_element_index has its own tests in + TestPartitionByElementIndex; get_repeat_cycle has its own tests in + TestGetRepeatCycle). + """ + + def setUp(self): + landsat_8_tle = [ + "1 39084U 13008A 26213.27824675 .00000294 00000+0 75333-4 0 9990", + "2 39084 98.2277 282.8718 0001275 92.4910 267.6434 14.57104473704466", + ] + self.base = GeneralPerturbationsOrbit.from_tle(landsat_8_tle).elements[0] + self.point = Point(id=0, latitude=40.0, longitude=-105.0) + + def test_repeat_cycle_tiles_events_across_cycles(self): + """ + Test that, when a repeat cycle applies, consecutive cycles' + events are separated by exactly the repeat cycle duration -- + the structural signature of copy-pasting one cycle's events + forward, rather than directly propagating the whole period (which + would not produce exactly period-spaced events, since real + orbital dynamics drift slightly cycle to cycle). Uses a + two-element orbit (a near-identical copy, mean motion nudged by + 1e-6) to also confirm this applies even with multiple elements, + as long as they agree on one repeat cycle. + """ + nearly_identical = self.base.model_copy( + update={"mean_motion": self.base.mean_motion * (1 + 1e-6)} + ) + orbit = GeneralPerturbationsOrbit(elements=[self.base, nearly_identical]) + repeat_cycle = orbit.get_repeat_cycle() + self.assertIsNotNone(repeat_cycle) + start = self.base.epoch + end = start + repeat_cycle * 2 + timedelta(hours=1) + times, _ = orbit.get_observation_events( + self.point, start, end, min_elevation_angle=10, try_repeat=True + ) + utc_times = times.utc_datetime() + events_per_cycle = int(np.sum(utc_times < start + repeat_cycle)) + self.assertGreater(events_per_cycle, 0) + self.assertGreater(len(utc_times), events_per_cycle) + for i in range(len(utc_times) - events_per_cycle): + self.assertEqual( + utc_times[i + events_per_cycle] - utc_times[i], repeat_cycle + ) + + def test_repeat_cycle_uses_element_closest_to_start(self): + """ + Test that the repeat-cycle strategy propagates from whichever + element is closest to `start`, not always the first (earliest) + element -- important once a multi-element orbit's repeat cycle is + trusted regardless of element count. The second element here + (epoch 20 days after the first, but otherwise the same orbit) is + closest to a `start` 25 days after the first element's epoch. + """ + second = self.base.model_copy( + update={ + "epoch": self.base.epoch + timedelta(days=20), + "mean_motion": self.base.mean_motion * (1 + 1e-6), + } + ) + orbit = GeneralPerturbationsOrbit(elements=[self.base, second]) + self.assertIs( + orbit.get_closest_element(self.base.epoch + timedelta(days=25)), second + ) + repeat_cycle = orbit.get_repeat_cycle() + self.assertIsNotNone(repeat_cycle) + start = self.base.epoch + timedelta(days=25) + end = start + repeat_cycle + timedelta(hours=1) + + actual_times, _ = orbit.get_observation_events( + self.point, start, end, min_elevation_angle=10, try_repeat=True + ) + topos = wgs84.latlon( + self.point.latitude, self.point.longitude, self.point.elevation + ) + t_0 = constants.timescale.from_datetime(start) + repeat_t_1 = constants.timescale.from_datetime(start + repeat_cycle) + expected_times, _ = second.to_skyfield().find_events(topos, t_0, repeat_t_1, 10) + wrong_times, _ = self.base.to_skyfield().find_events(topos, t_0, repeat_t_1, 10) + # sanity check that using the wrong (first) element would actually + # have given a different answer, so this test is a meaningful + # discriminator, not a coincidence + self.assertFalse( + np.array_equal(expected_times.utc_datetime(), wrong_times.utc_datetime()) + ) + self.assertTrue( + np.array_equal( + actual_times.utc_datetime()[: len(expected_times)], + expected_times.utc_datetime(), + ) + ) + + def test_repeat_cycle_ignored_when_shorter_period_requested(self): + """ + Test that a period shorter than the repeat cycle skips the + tiling strategy entirely and falls through to direct propagation + -- verified by exact equality with try_repeat=False, since both + end up on the same code path in that case. + """ + orbit = GeneralPerturbationsOrbit(elements=[self.base]) + repeat_cycle = orbit.get_repeat_cycle() + self.assertIsNotNone(repeat_cycle) + start = self.base.epoch + end = start + timedelta(days=5) + self.assertLess(end - start, repeat_cycle) + with_repeat = orbit.get_observation_events( + self.point, start, end, min_elevation_angle=10, try_repeat=True + ) + without_repeat = orbit.get_observation_events( + self.point, start, end, min_elevation_angle=10, try_repeat=False + ) + self.assertTrue( + np.array_equal( + with_repeat[0].utc_datetime(), without_repeat[0].utc_datetime() + ) + ) + self.assertTrue(np.array_equal(with_repeat[1], without_repeat[1])) + + def test_multi_element_partition_used_when_no_consistent_repeat_cycle(self): + """ + Test that a multi-element orbit whose elements do not agree on a + repeat cycle (simulating a maneuver) falls through to per-time + nearest-element partitioning even with try_repeat=True -- + verified by exact equality with try_repeat=False, since both end + up on the same partition_by_element_index-based code path when + get_repeat_cycle() returns None. + """ + maneuvered = self.base.model_copy( + update={"mean_motion": self.base.mean_motion * 1.001} + ) + orbit = GeneralPerturbationsOrbit(elements=[self.base, maneuvered]) + self.assertIsNone(orbit.get_repeat_cycle()) + start = self.base.epoch + end = start + timedelta(days=5) + with_repeat = orbit.get_observation_events( + self.point, start, end, min_elevation_angle=10, try_repeat=True + ) + without_repeat = orbit.get_observation_events( + self.point, start, end, min_elevation_angle=10, try_repeat=False + ) + self.assertGreater(len(with_repeat[1]), 0) + self.assertTrue( + np.array_equal( + with_repeat[0].utc_datetime(), without_repeat[0].utc_datetime() + ) + ) + self.assertTrue(np.array_equal(with_repeat[1], without_repeat[1])) + + +class TestToGpOrbit(unittest.TestCase): + """ + Unit tests for GeneralPerturbationsOrbit.to_gp_orbit. + """ + + def setUp(self): + tle = [ + "1 25544U 98067A 21156.30527927 .00003432 00000-0 70541-4 0 9993", + "2 25544 51.6455 41.4969 0003508 68.0432 78.3395 15.48957534286754", + ] + self.orbit = GeneralPerturbationsOrbit.from_tle(tle) + + def test_returns_self(self): + """ + Test that to_gp_orbit() returns this exact instance (identity, + not merely an equal copy), since a GeneralPerturbationsOrbit + already is its own general perturbations representation. + """ + self.assertIs(self.orbit.to_gp_orbit(), self.orbit) + + def test_accepts_lazy_load_without_effect(self): + """ + Test that to_gp_orbit() accepts the lazy_load parameter (for + interface parity with OrbitBase.to_gp_orbit, used polymorphically + across the AllOrbits union) without error, and that it has no + effect on the result -- still just this instance, regardless of + the value passed. + """ + self.assertIs(self.orbit.to_gp_orbit(lazy_load=True), self.orbit) + self.assertIs(self.orbit.to_gp_orbit(lazy_load=False), self.orbit) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/schemas/orbit/test_gp_elements.py b/tests/schemas/orbit/test_gp_elements.py new file mode 100644 index 0000000..fb70abc --- /dev/null +++ b/tests/schemas/orbit/test_gp_elements.py @@ -0,0 +1,551 @@ +""" +Unit tests for the GeneralPerturbationsElements schema. + +@author Paul T. Grogan +""" + +import csv +import io +import json +import unittest +from datetime import datetime, timedelta, timezone + +from pydantic import ValidationError +from skyfield.api import EarthSatellite + +from tatc import constants +from tatc.schemas.orbit.gp_elements import GeneralPerturbationsElements + + +class TestGeneralPerturbationsElements(unittest.TestCase): + """ + Unit tests for the GeneralPerturbationsElements schema. + """ + + def setUp(self): + self.test_tle = ( + "1 25544U 98067A 21156.30527927 .00003432 00000-0 70541-4 0 9993", + "2 25544 51.6455 41.4969 0003508 68.0432 78.3395 15.48957534286754", + ) + self.test_elements = GeneralPerturbationsElements.from_tle(self.test_tle) + + def test_good_data(self): + """ + Test that the GeneralPerturbationsElements schema correctly + initializes with valid data. + """ + good_data = { + "epoch": datetime(2022, 1, 1, tzinfo=timezone.utc), + "mean_motion": 0.06, + "eccentricity": 0.001, + "inclination": 51.6, + "ra_of_asc_node": 40, + "arg_of_pericenter": 68, + "mean_anomaly": 78, + } + e = GeneralPerturbationsElements(**good_data) + for field, value in good_data.items(): + self.assertEqual(getattr(e, field), value) + + def test_defaults(self): + """ + Test that all optional fields default correctly when omitted. + """ + e = GeneralPerturbationsElements( + epoch=datetime(2022, 1, 1, tzinfo=timezone.utc), + mean_motion=0.06, + eccentricity=0.001, + inclination=51.6, + ra_of_asc_node=40, + arg_of_pericenter=68, + mean_anomaly=78, + ) + self.assertIsNone(e.object_name) + self.assertEqual(e.norad_cat_id, 0) + self.assertEqual(e.bstar, 0) + self.assertEqual(e.mean_motion_dot, 0) + self.assertEqual(e.mean_motion_ddot, 0) + self.assertEqual(e.classification, "U") + self.assertEqual(e.international_designator, "00000A") + self.assertEqual(e.ephemeris_type, 0) + self.assertEqual(e.element_set_num, 0) + self.assertEqual(e.revolution_num, 0) + + def test_bad_required_field_missing(self): + """ + Test that omitting any required field raises a ValidationError. + """ + required = { + "epoch": datetime(2022, 1, 1, tzinfo=timezone.utc), + "mean_motion": 0.06, + "eccentricity": 0.001, + "inclination": 51.6, + "ra_of_asc_node": 40, + "arg_of_pericenter": 68, + "mean_anomaly": 78, + } + for field in required: + with self.subTest(field=field): + data = {k: v for k, v in required.items() if k != field} + with self.assertRaises(ValidationError): + GeneralPerturbationsElements(**data) + + def test_bad_mean_motion_non_positive(self): + """ + Test that a zero or negative mean_motion is rejected. + """ + with self.assertRaises(ValidationError): + GeneralPerturbationsElements( + epoch=datetime(2022, 1, 1, tzinfo=timezone.utc), + mean_motion=0, + eccentricity=0, + inclination=0, + ra_of_asc_node=0, + arg_of_pericenter=0, + mean_anomaly=0, + ) + + def test_bad_eccentricity_out_of_range(self): + """ + Test that an eccentricity outside [0, 1] is rejected. + """ + base = dict( + epoch=datetime(2022, 1, 1, tzinfo=timezone.utc), + mean_motion=1, + inclination=0, + ra_of_asc_node=0, + arg_of_pericenter=0, + mean_anomaly=0, + ) + with self.assertRaises(ValidationError): + GeneralPerturbationsElements(eccentricity=-0.1, **base) + with self.assertRaises(ValidationError): + GeneralPerturbationsElements(eccentricity=1.1, **base) + + def test_bad_angle_fields_out_of_range(self): + """ + Test that inclination, ra_of_asc_node, arg_of_pericenter, and + mean_anomaly each reject values outside their documented ranges. + """ + base = dict( + epoch=datetime(2022, 1, 1, tzinfo=timezone.utc), + mean_motion=1, + eccentricity=0, + inclination=0, + ra_of_asc_node=0, + arg_of_pericenter=0, + mean_anomaly=0, + ) + with self.assertRaises(ValidationError): + GeneralPerturbationsElements(**{**base, "inclination": 180.1}) + with self.assertRaises(ValidationError): + GeneralPerturbationsElements(**{**base, "inclination": -0.1}) + with self.assertRaises(ValidationError): + GeneralPerturbationsElements(**{**base, "ra_of_asc_node": 360}) + with self.assertRaises(ValidationError): + GeneralPerturbationsElements(**{**base, "arg_of_pericenter": 360}) + with self.assertRaises(ValidationError): + GeneralPerturbationsElements(**{**base, "mean_anomaly": 360}) + + def test_get_orbit_period_matches_published_iss_period(self): + """ + Test get_orbit_period against the ISS's well-known published + orbital period of approximately 93 minutes. + """ + self.assertAlmostEqual( + self.test_elements.get_orbit_period().total_seconds() / 60, + 93, + delta=1.0, + ) + + def test_get_semimajor_axis(self): + """ + Test get_semimajor_axis against the ISS's well-known published + semimajor axis of approximately 6,798 km. + """ + self.assertAlmostEqual( + self.test_elements.get_semimajor_axis(), 6797911, delta=1.0 + ) + + def test_get_mean_altitude(self): + """ + Test get_mean_altitude against the ISS's well-known published + mean altitude of approximately 427 km. + """ + self.assertAlmostEqual( + self.test_elements.get_mean_altitude(), 426902, delta=1.0 + ) + + def test_get_true_anomaly(self): + """ + Test that get_true_anomaly converts mean anomaly to true anomaly + using the class's own eccentricity. + """ + self.assertAlmostEqual( + self.test_elements.get_true_anomaly(), 78.3788725993742, delta=1e-6 + ) + + def test_from_tle(self): + """ + Test that from_tle correctly parses all fields from a real TLE. + """ + e = self.test_elements + self.assertEqual(e.norad_cat_id, 25544) + self.assertEqual( + e.epoch, datetime(2021, 6, 5, 7, 19, 36, 128928, tzinfo=timezone.utc) + ) + self.assertEqual(e.inclination, 51.6455) + self.assertEqual(e.ra_of_asc_node, 41.4969) + self.assertEqual(e.eccentricity, 0.0003508) + self.assertEqual(e.arg_of_pericenter, 68.0432) + self.assertEqual(e.mean_anomaly, 78.3395) + self.assertAlmostEqual(e.mean_motion * 86400 / 360, 15.48957534, delta=1e-9) + self.assertEqual(e.bstar, 0.000070541) + self.assertEqual(e.international_designator, "98067A") + self.assertEqual(e.element_set_num, 999) + self.assertEqual(e.revolution_num, 28675) + + def test_to_tle_round_trip(self): + """ + Test that converting to TLE and back reproduces the original TLE + lines exactly. + """ + tle_out = self.test_elements.to_tle() + self.assertEqual(tle_out[0], self.test_tle[0]) + self.assertEqual(tle_out[1], self.test_tle[1]) + + def test_from_satrec_to_satrec_round_trip(self): + """ + Test that converting to a Satrec and back preserves every field + (to within floating-point precision from the radian/degree and + per-minute/per-second unit conversions). + """ + satrec = self.test_elements.to_satrec() + round_tripped = GeneralPerturbationsElements.from_satrec(satrec) + for field in type(self.test_elements).model_fields: + with self.subTest(field=field): + original = getattr(self.test_elements, field) + restored = getattr(round_tripped, field) + if isinstance(original, float): + self.assertAlmostEqual(original, restored, delta=1e-9) + else: + self.assertEqual(original, restored) + + def test_to_omm_dict_contents(self): + """ + Test that to_omm_dict produces the expected OMM field names and + values, including the object name and mean motion in the OMM + convention of revolutions/day (rather than this class's own + degrees/second). + """ + elements = self.test_elements.model_copy(update={"object_name": "ISS (ZARYA)"}) + omm_dict = elements.to_omm_dict() + self.assertEqual(omm_dict["OBJECT_NAME"], "ISS (ZARYA)") + self.assertEqual(omm_dict["NORAD_CAT_ID"], 25544) + self.assertEqual(omm_dict["INCLINATION"], 51.6455) + self.assertAlmostEqual(omm_dict["MEAN_MOTION"], 15.48957534286754, delta=1e-6) + + def test_from_omm_dict_round_trip_including_object_name(self): + """ + Regression test: from_omm_dict must preserve object_name, not + just the orbital elements. object_name has no equivalent on the + Satrec object from_omm_dict constructs internally, so it must be + restored directly from the OMM dictionary rather than being lost. + """ + elements = self.test_elements.model_copy(update={"object_name": "ISS (ZARYA)"}) + omm_dict = elements.to_omm_dict() + round_tripped = GeneralPerturbationsElements.from_omm_dict(omm_dict) + self.assertEqual(round_tripped.object_name, "ISS (ZARYA)") + self.assertEqual(round_tripped.norad_cat_id, elements.norad_cat_id) + self.assertEqual(round_tripped.inclination, elements.inclination) + + def test_from_omm_dict_object_name_none_when_absent(self): + """ + Test that from_omm_dict leaves object_name as None when the OMM + dictionary has no OBJECT_NAME key, rather than raising an error. + """ + omm_dict = self.test_elements.to_omm_dict() + del omm_dict["OBJECT_NAME"] + round_tripped = GeneralPerturbationsElements.from_omm_dict(omm_dict) + self.assertIsNone(round_tripped.object_name) + + def test_from_omm_csv_round_trip_including_object_name(self): + """ + Test that from_omm_csv preserves object_name through a CSV + round trip, via the same from_omm_dict path. + """ + elements = self.test_elements.model_copy(update={"object_name": "ISS (ZARYA)"}) + omm_dict = elements.to_omm_dict() + buf = io.StringIO() + writer = csv.DictWriter(buf, fieldnames=list(omm_dict.keys())) + writer.writeheader() + writer.writerow(omm_dict) + round_tripped = GeneralPerturbationsElements.from_omm_csv( + buf.getvalue().splitlines() + ) + self.assertEqual(round_tripped.object_name, "ISS (ZARYA)") + self.assertEqual(round_tripped.norad_cat_id, elements.norad_cat_id) + + def test_from_omm_csv_only_uses_first_row(self): + """ + Test that from_omm_csv uses only the first data row when given + multiple rows, since it returns a single GeneralPerturbationsElements + rather than a list. + """ + omm_dict = self.test_elements.to_omm_dict() + other_dict = dict(omm_dict, NORAD_CAT_ID=99999) + buf = io.StringIO() + writer = csv.DictWriter(buf, fieldnames=list(omm_dict.keys())) + writer.writeheader() + writer.writerow(omm_dict) + writer.writerow(other_dict) + round_tripped = GeneralPerturbationsElements.from_omm_csv( + buf.getvalue().splitlines() + ) + self.assertEqual(round_tripped.norad_cat_id, 25544) + + def test_from_omm_csv_empty_raises_value_error(self): + """ + Test that from_omm_csv raises a ValueError when given no lines. + """ + with self.assertRaises(ValueError): + GeneralPerturbationsElements.from_omm_csv([]) + + def test_from_omm_json_round_trip_including_object_name(self): + """ + Test that from_omm_json preserves object_name through a JSON + round trip, via the same from_omm_dict path. + """ + elements = self.test_elements.model_copy(update={"object_name": "ISS (ZARYA)"}) + omm_json = json.dumps([elements.to_omm_dict()]) + round_tripped = GeneralPerturbationsElements.from_omm_json(omm_json) + self.assertEqual(round_tripped.object_name, "ISS (ZARYA)") + self.assertEqual(round_tripped.norad_cat_id, elements.norad_cat_id) + + def test_from_omm_json_only_uses_first_entry(self): + """ + Test that from_omm_json uses only the first entry when given + multiple, since it returns a single GeneralPerturbationsElements + rather than a list. + """ + omm_dict = self.test_elements.to_omm_dict() + other_dict = dict(omm_dict, NORAD_CAT_ID=99999) + omm_json = json.dumps([omm_dict, other_dict]) + round_tripped = GeneralPerturbationsElements.from_omm_json(omm_json) + self.assertEqual(round_tripped.norad_cat_id, 25544) + + def test_from_omm_json_empty_raises_value_error(self): + """ + Test that from_omm_json raises a ValueError when given an empty + JSON array. + """ + with self.assertRaises(ValueError): + GeneralPerturbationsElements.from_omm_json("[]") + + def test_to_skyfield_returns_configured_earth_satellite(self): + """ + Test that to_skyfield returns a Skyfield EarthSatellite whose + catalog number and epoch match this element set, confirming it + is usable to propagate this orbital state via SGP4. + """ + satellite = self.test_elements.to_skyfield() + self.assertIsInstance(satellite, EarthSatellite) + self.assertEqual(satellite.model.satnum, 25544) + self.assertAlmostEqual( + satellite.epoch.utc_datetime().timestamp(), + self.test_elements.epoch.timestamp(), + delta=1e-3, + ) + + +class TestGetRepeatCycle(unittest.TestCase): + """ + Unit tests for GeneralPerturbationsElements.get_repeat_cycle. This is + the single-element repeat-cycle computation itself; see + tests/schemas/orbit/test_gp.py's TestGetRepeatCycle for + GeneralPerturbationsOrbit's multi-element consistency-checking + aggregation on top of this. + """ + + def setUp(self): + # a real Landsat-8 element set (NORAD 39084, fetched from + # Celestrak), whose published repeat ground track is exactly 233 + # orbits every 16 days (USGS) + self.landsat_8_tle = ( + "1 39084U 13008A 26213.27824675 .00000294 00000+0 75333-4 0 9990", + "2 39084 98.2277 282.8718 0001275 92.4910 267.6434 14.57104473704466", + ) + # a real Sentinel-2A element set (NORAD 40697), whose published + # repeat ground track is exactly 143 orbits every 10 days (ESA) + self.sentinel_2a_tle = ( + "1 40697U 15028A 26213.24967738 .00000092 00000+0 51774-4 0 9990", + "2 40697 98.5671 287.4570 0001329 92.6662 267.4673 14.30818788580177", + ) + # a real Sentinel-1A element set (NORAD 39634) from early 2026, + # while the satellite was still actively operated (its mission + # concluded 2026-06-29): published repeat ground track is exactly + # 175 orbits every 12 days (ESA) + self.sentinel_1a_tle = ( + "1 39634U 14016A 26001.19041520 .00000521 00000-0 12012-3 0 9995", + "2 39634 98.1805 11.1453 0001276 85.2963 274.8383 14.59199668625660", + ) + # a real GPS BIIR-5 element set (NORAD 26407): GPS orbits are + # designed to repeat their ground track every sidereal day (two + # ~12-hour orbits per day), a much shorter cycle than the + # sun-synchronous imaging orbits above, at MEO altitude (~20,200 km) + self.gps_tle = ( + "1 26407U 00040A 26213.32131914 .00000072 00000+0 00000+0 0 9998", + "2 26407 54.8467 213.3697 0120005 302.9740 169.1529 2.00558010190856", + ) + # a real Molniya 3-8 element set (NORAD 10455): a highly eccentric, + # critical-inclination (~63.4 degree) orbit that, like GPS, repeats + # every sidereal day by design, but is geometrically nothing like + # the near-circular orbits above + self.molniya_tle = ( + "1 10455U 77105A 26212.97315042 .00000728 00000+0 00000+0 0 9994", + "2 10455 63.8024 172.4301 6701249 276.1687 17.0849 2.00778403357346", + ) + # the ISS is not designed for a repeat ground track + self.iss_tle = ( + "1 25544U 98067A 21156.30527927 .00003432 00000-0 70541-4 0 9993", + "2 25544 51.6455 41.4969 0003508 68.0432 78.3395 15.48957534286754", + ) + + def test_landsat_8_repeat_cycle_matches_published_16_days(self): + """ + Test against Landsat-8's published repeat ground track of 233 + orbits every 16 days (USGS). A small tolerance accounts for the + real orbit's minor drift between station-keeping maneuvers. + """ + elements = GeneralPerturbationsElements.from_tle(self.landsat_8_tle) + repeat_cycle = elements.get_repeat_cycle() + self.assertIsNotNone(repeat_cycle) + self.assertAlmostEqual(repeat_cycle.total_seconds() / 86400, 16, delta=0.1) + + def test_sentinel_2a_repeat_cycle_matches_published_10_days(self): + """ + Test against Sentinel-2A's published repeat ground track of 143 + orbits every 10 days (ESA), at a different altitude/inclination + than Landsat-8, using the default tolerances. + """ + elements = GeneralPerturbationsElements.from_tle(self.sentinel_2a_tle) + repeat_cycle = elements.get_repeat_cycle() + self.assertIsNotNone(repeat_cycle) + self.assertAlmostEqual(repeat_cycle.total_seconds() / 86400, 10, delta=0.1) + + def test_sentinel_1a_repeat_cycle_matches_published_12_days(self): + """ + Test against Sentinel-1A's published repeat ground track of 175 + orbits every 12 days (ESA), at a different altitude/inclination + than Landsat-8, using the default tolerances. + """ + elements = GeneralPerturbationsElements.from_tle(self.sentinel_1a_tle) + repeat_cycle = elements.get_repeat_cycle() + self.assertIsNotNone(repeat_cycle) + self.assertAlmostEqual(repeat_cycle.total_seconds() / 86400, 12, delta=0.1) + + def test_gps_repeat_cycle_matches_sidereal_day(self): + """ + Test against GPS's designed repeat ground track of one sidereal + day (~23h56m), using the default tolerances. Confirms the search + also correctly resolves a very short repeat cycle (day 1), not + just the multi-week cycles above, and at a completely different + (MEO) altitude regime. + """ + elements = GeneralPerturbationsElements.from_tle(self.gps_tle) + repeat_cycle = elements.get_repeat_cycle() + self.assertIsNotNone(repeat_cycle) + self.assertAlmostEqual( + repeat_cycle.total_seconds() / 86400, + constants.EARTH_SIDEREAL_DAY_S / 86400, + delta=0.01, + ) + + def test_molniya_repeat_cycle_matches_sidereal_day(self): + """ + Test against a Molniya orbit's designed repeat ground track of + one sidereal day, at a highly eccentric, critical-inclination + geometry very different from every other case here. Public + Molniya TLEs are published with an epoch near perigee (where + ground radar tracking is easiest), and perigee is where a highly + eccentric orbit moves fastest (~10 km/s here, vs ~1.5 km/s at + apogee) -- so a given timing imprecision in the analytic + prediction translates into a much larger position/velocity error + than for the near-circular orbits above. This is a property of + the epoch's orbital phase, not of the orbit's true repeatability, + so this test widens the tolerance rather than the global default. + """ + elements = GeneralPerturbationsElements.from_tle(self.molniya_tle) + repeat_cycle = elements.get_repeat_cycle( + max_delta_position=80000, max_delta_velocity=25 + ) + self.assertIsNotNone(repeat_cycle) + self.assertAlmostEqual( + repeat_cycle.total_seconds() / 86400, + constants.EARTH_SIDEREAL_DAY_S / 86400, + delta=0.01, + ) + + def test_non_repeating_orbit_returns_none(self): + """ + Test that the ISS's orbit, which is not designed for a repeat + ground track, finds no repeat at the default tolerances (10 km, + 3 m/s). The ISS does have a coincidental ~4-day near-repeat + (delta position 10.6 km, delta velocity 8.0 m/s) -- narrowly + outside the position tolerance, and well outside the velocity + tolerance, so both criteria independently reject it. A looser + position tolerance alone (e.g. 30 km, to accommodate a less + precisely maintained real repeat orbit) would accept this + incidental match on position, which is exactly why velocity is + checked too rather than relying on position alone. + """ + elements = GeneralPerturbationsElements.from_tle(self.iss_tle) + self.assertIsNone(elements.get_repeat_cycle()) + + def test_lazy_load_reuses_cached_result(self): + """ + Test that calling get_repeat_cycle() twice with lazy_load=True + (the default) returns the identical cached timedelta rather than + recomputing. + """ + elements = GeneralPerturbationsElements.from_tle(self.landsat_8_tle) + first = elements.get_repeat_cycle() + second = elements.get_repeat_cycle() + self.assertIs(first, second) + + def test_lazy_load_false_forces_recomputation(self): + """ + Test that lazy_load=False recomputes rather than reusing the + cached result (a fresh but equal timedelta). + """ + elements = GeneralPerturbationsElements.from_tle(self.landsat_8_tle) + first = elements.get_repeat_cycle() + second = elements.get_repeat_cycle(lazy_load=False) + self.assertEqual(first, second) + self.assertIsNot(first, second) + + def test_too_short_search_duration_returns_none(self): + """ + Test that a max_search_duration shorter than the true repeat + cycle (16 days) cannot find it. + """ + elements = GeneralPerturbationsElements.from_tle(self.landsat_8_tle) + repeat_cycle = elements.get_repeat_cycle( + max_search_duration=timedelta(days=10), lazy_load=False + ) + self.assertIsNone(repeat_cycle) + + def test_too_tight_tolerance_returns_none(self): + """ + Test that an unrealistically tight position/velocity tolerance + rejects even the real repeat cycle. + """ + elements = GeneralPerturbationsElements.from_tle(self.landsat_8_tle) + repeat_cycle = elements.get_repeat_cycle( + max_delta_position=1, max_delta_velocity=0.001, lazy_load=False + ) + self.assertIsNone(repeat_cycle) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/schemas/orbit/test_keplerian.py b/tests/schemas/orbit/test_keplerian.py new file mode 100644 index 0000000..ee5d1f7 --- /dev/null +++ b/tests/schemas/orbit/test_keplerian.py @@ -0,0 +1,223 @@ +""" +Unit tests for the KeplerianOrbit schema. +@author: Paul T. Grogan +""" + +import unittest +from datetime import datetime, timedelta, timezone + +from pydantic import ValidationError + +from tatc.schemas import KeplerianOrbit + + +class TestKeplerianOrbit(unittest.TestCase): + """ + Unit tests for the KeplerianOrbit schema. + """ + + def setUp(self): + self.test_data = { + "semimajor_axis": 400000 + 6371000, # 400 km mean altitude + "true_anomaly": 10.0, + "epoch": datetime(2022, 1, 1, 12, 0, 0, tzinfo=timezone.utc), + "inclination": 45.0, + "right_ascension_ascending_node": 50.0, + "eccentricity": 0.01, + "perigee_argument": 100.0, + } + self.test_orbit = KeplerianOrbit(**self.test_data) + + def test_good_data(self): + """ + Test that the KeplerianOrbit schema correctly initializes with valid data. + """ + self.assertEqual( + self.test_orbit.semimajor_axis, self.test_data.get("semimajor_axis") + ) + self.assertEqual( + self.test_orbit.true_anomaly, self.test_data.get("true_anomaly") + ) + self.assertEqual(self.test_orbit.epoch, self.test_data.get("epoch")) + self.assertEqual(self.test_orbit.inclination, self.test_data.get("inclination")) + self.assertEqual( + self.test_orbit.right_ascension_ascending_node, + self.test_data.get("right_ascension_ascending_node"), + ) + self.assertEqual( + self.test_orbit.eccentricity, self.test_data.get("eccentricity") + ) + self.assertEqual( + self.test_orbit.perigee_argument, self.test_data.get("perigee_argument") + ) + + def test_defaults(self): + """ + Test that inclination, right_ascension_ascending_node, + eccentricity, and perigee_argument each default to 0 when + omitted, leaving semimajor_axis as the only required field. + """ + o = KeplerianOrbit(semimajor_axis=7000000) + self.assertEqual(o.inclination, 0) + self.assertEqual(o.right_ascension_ascending_node, 0) + self.assertEqual(o.eccentricity, 0) + self.assertEqual(o.perigee_argument, 0) + + def test_bad_semimajor_axis_missing(self): + """ + Test that the KeplerianOrbit schema raises a ValidationError when + the required semimajor_axis field is missing. + """ + with self.assertRaises(ValidationError): + KeplerianOrbit() + + def test_bad_semimajor_axis_negative(self): + """ + Test that a negative semimajor_axis is rejected. + """ + with self.assertRaises(ValidationError): + KeplerianOrbit(semimajor_axis=-1) + + def test_bad_semimajor_axis_zero(self): + """ + Test that a zero semimajor_axis is rejected, since it is + physically meaningless (and previously produced nan downstream in + get_mean_motion/get_orbit_period). + """ + with self.assertRaises(ValidationError): + KeplerianOrbit(semimajor_axis=0) + + def test_bad_eccentricity_parabolic(self): + """ + Test that an eccentricity of exactly 1 (parabolic trajectory) is + rejected, since KeplerianOrbit represents elliptical motion only + (per its own docstring). + """ + with self.assertRaises(ValidationError): + KeplerianOrbit(semimajor_axis=7000000, eccentricity=1.0) + + def test_bad_eccentricity_hyperbolic(self): + """ + Test that an eccentricity greater than 1 (hyperbolic escape + trajectory) is rejected. + """ + with self.assertRaises(ValidationError): + KeplerianOrbit(semimajor_axis=7000000, eccentricity=1.5) + + def test_eccentricity_boundary_zero(self): + """ + Test that an eccentricity of exactly 0 (the ge=0 boundary, + a circular orbit) is accepted. + """ + self.assertEqual( + KeplerianOrbit(semimajor_axis=7000000, eccentricity=0).eccentricity, 0 + ) + + def test_get_orbit_period_and_mean_motion_match_published_gps_orbit(self): + """ + Test get_orbit_period and get_mean_motion against the well-known + GPS constellation orbit: a semimajor axis of about 26,560 km + gives an orbital period of half a sidereal day (~11h58m, by + design, so that ground tracks repeat daily) and a mean motion of + about 2 revolutions/day. + """ + gps_orbit = KeplerianOrbit( + semimajor_axis=26560000, inclination=55, eccentricity=0.01 + ) + self.assertAlmostEqual( + gps_orbit.get_orbit_period(), + timedelta(hours=11, minutes=58), + delta=timedelta(minutes=1), + ) + self.assertAlmostEqual( + gps_orbit.get_mean_motion() * 86400 / 360, 2.0, delta=0.01 + ) + + def test_to_gp_orbit_period_matches_published_gps_orbit(self): + """ + Test that converting the same GPS-like orbit to a general + perturbations representation preserves the published ~11h58m + orbital period end-to-end. + """ + gps_orbit = KeplerianOrbit( + semimajor_axis=26560000, inclination=55, eccentricity=0.01 + ) + self.assertAlmostEqual( + gps_orbit.to_gp_orbit().get_orbit_period(), + timedelta(hours=11, minutes=58), + delta=timedelta(minutes=1), + ) + + def test_getters_return_the_underlying_fields(self): + """ + Test that the OrbitBase-interface getter methods each return the + corresponding field value directly. + """ + self.assertEqual( + self.test_orbit.get_semimajor_axis(), self.test_data.get("semimajor_axis") + ) + self.assertEqual( + self.test_orbit.get_inclination(), self.test_data.get("inclination") + ) + self.assertEqual( + self.test_orbit.get_right_ascension_ascending_node(), + self.test_data.get("right_ascension_ascending_node"), + ) + self.assertEqual( + self.test_orbit.get_eccentricity(), self.test_data.get("eccentricity") + ) + self.assertEqual( + self.test_orbit.get_perigee_argument(), + self.test_data.get("perigee_argument"), + ) + + def test_get_derived_orbit(self): + """ + Test that a derived orbit can be computed from the base orbit. + """ + derived_orbit = self.test_orbit.get_derived_orbit(20, 10) + self.assertAlmostEqual( + derived_orbit.get_mean_anomaly(), + self.test_orbit.get_mean_anomaly() + 20, + delta=0.001, + ) + self.assertAlmostEqual( + derived_orbit.right_ascension_ascending_node, + self.test_orbit.right_ascension_ascending_node + 10, + delta=0.001, + ) + + def test_to_gp_orbit(self): + """ + Test that the KeplerianOrbit can be converted to a GP orbit. + """ + gp_orbit = self.test_orbit.to_gp_orbit() + self.assertAlmostEqual( + gp_orbit.get_mean_altitude(), + self.test_data.get("semimajor_axis") - 6371000, + delta=10, + ) + self.assertAlmostEqual( + gp_orbit.get_true_anomaly(), self.test_data.get("true_anomaly"), delta=0.001 + ) + self.assertAlmostEqual( + gp_orbit.get_epoch().timestamp(), + self.test_data.get("epoch").timestamp(), + delta=1, + ) + self.assertEqual( + gp_orbit.get_inclination(), + self.test_data.get("inclination"), + ) + self.assertAlmostEqual( + gp_orbit.get_right_ascension_ascending_node(), + self.test_data.get("right_ascension_ascending_node"), + ) + self.assertAlmostEqual( + gp_orbit.get_eccentricity(), + self.test_data.get("eccentricity"), + ) + self.assertAlmostEqual( + gp_orbit.get_perigee_argument(), + self.test_data.get("perigee_argument"), + ) diff --git a/tests/schemas/orbit/test_molniya.py b/tests/schemas/orbit/test_molniya.py new file mode 100644 index 0000000..80b5031 --- /dev/null +++ b/tests/schemas/orbit/test_molniya.py @@ -0,0 +1,175 @@ +""" +Unit tests for the MolniyaOrbit schema. + +@author: Paul T. Grogan +""" + +import unittest + +from pydantic import ValidationError + +from tatc.constants import EARTH_MEAN_RADIUS, EARTH_SIDEREAL_DAY_S +from tatc.schemas import MolniyaOrbit + + +class TestMolniyaOrbit(unittest.TestCase): + """ + Unit tests for the MolniyaOrbit schema. + """ + + def setUp(self): + self.test_data = { + "perigee_altitude": 1199100, + "right_ascension_ascending_node": 11.0394, + } + self.test_orbit = MolniyaOrbit(**self.test_data) + + def test_good_data(self): + """ + Test that the MolniyaOrbit schema correctly initializes with valid data. + """ + self.assertEqual( + self.test_orbit.perigee_altitude, self.test_data.get("perigee_altitude") + ) + self.assertEqual( + self.test_orbit.right_ascension_ascending_node, + self.test_data.get("right_ascension_ascending_node"), + ) + self.assertAlmostEqual(self.test_orbit.get_inclination(), 63.4, delta=0.1) + self.assertAlmostEqual( + self.test_orbit.get_orbit_period().total_seconds(), 718 * 60, delta=60 + ) + self.assertEqual(self.test_orbit.get_perigee_argument(), 270) + self.assertAlmostEqual(self.test_orbit.get_eccentricity(), 0.74, delta=0.1) + + def test_defaults(self): + """ + Test that northern_coverage and right_ascension_ascending_node + default correctly when omitted. + """ + o = MolniyaOrbit(perigee_altitude=550000) + self.assertTrue(o.northern_coverage) + self.assertEqual(o.right_ascension_ascending_node, 0) + + def test_bad_perigee_altitude_missing(self): + """ + Test that the MolniyaOrbit schema raises a ValidationError when + the required perigee_altitude field is missing. + """ + with self.assertRaises(ValidationError): + MolniyaOrbit() + + def test_bad_perigee_altitude_negative(self): + """ + Test that a negative perigee_altitude is rejected. + """ + with self.assertRaises(ValidationError): + MolniyaOrbit(perigee_altitude=-1) + + def test_bad_perigee_altitude_exceeds_implied_apogee(self): + """ + Test that a perigee_altitude above the Kepler-derived ceiling + implied by Molniya's fixed (half sidereal day) orbit period is + rejected, since it would otherwise silently produce a negative + (physically invalid) eccentricity. + """ + with self.assertRaises(ValidationError): + MolniyaOrbit(perigee_altitude=21190753) + + def test_get_inclination_and_apogee_altitude_match_published_molniya_orbit(self): + """ + Test get_inclination and the implied apogee altitude against a + published classic Molniya orbit: a ~550 km perigee altitude with + the 63.4 degree critical inclination yields an apogee altitude of + approximately 40,000 km and a ~12 hour (half sidereal day) period. + + See: https://en.wikipedia.org/wiki/Molniya_orbit + """ + o = MolniyaOrbit(perigee_altitude=550000) + self.assertAlmostEqual(o.get_inclination(), 63.4, delta=0.1) + apogee_altitude = ( + 2 * o.get_semimajor_axis() + - (EARTH_MEAN_RADIUS + o.perigee_altitude) + - EARTH_MEAN_RADIUS + ) + self.assertAlmostEqual(apogee_altitude, 40000000, delta=500000) + + def test_get_orbit_period_is_j2_corrected(self): + """ + Regression test: get_orbit_period must not simply return the + naive, uncorrected half sidereal day -- it should be shifted by a + small (order 1-10 second), nonzero correction accounting for + Earth's J2 oblateness perturbation to the true rate of mean + anomaly advance. This exercises the fix for the historical + "TODO this needs to be corrected to account for J2 effects". + """ + naive_period_s = EARTH_SIDEREAL_DAY_S / 2 + corrected_period_s = self.test_orbit.get_orbit_period().total_seconds() + self.assertNotAlmostEqual(corrected_period_s, naive_period_s, delta=1e-6) + self.assertAlmostEqual(corrected_period_s, naive_period_s, delta=10) + + def test_get_semimajor_axis_and_mean_motion(self): + """ + Test get_semimajor_axis and get_mean_motion are self-consistent + with the fixed half-sidereal-day orbit period via Kepler's third + law and the standard mean-motion relation. + """ + self.assertAlmostEqual( + self.test_orbit.get_mean_motion() * 86400 / 360, 2.0, delta=0.01 + ) + + def test_get_mean_anomaly_at_perigee_is_zero(self): + """ + Test that a true anomaly of 0 (perigee) always maps to a mean + anomaly of 0, regardless of eccentricity -- a basic invariant of + the true-to-mean anomaly relationship. + """ + o = MolniyaOrbit(perigee_altitude=550000, true_anomaly=0) + self.assertAlmostEqual(o.get_mean_anomaly(), 0, delta=1e-6) + + def test_get_derived_orbit_preserves_other_fields(self): + """ + Test that get_derived_orbit preserves perigee_altitude, + northern_coverage, and epoch unchanged. + """ + derived_orbit = self.test_orbit.get_derived_orbit(20, 10) + self.assertEqual( + derived_orbit.perigee_altitude, self.test_orbit.perigee_altitude + ) + self.assertEqual( + derived_orbit.northern_coverage, self.test_orbit.northern_coverage + ) + self.assertEqual(derived_orbit.epoch, self.test_orbit.epoch) + + def test_get_derived_orbit(self): + """ + Test that the MolniyaOrbit schema correctly derives a new orbit with the specified parameters. + """ + derived_orbit = self.test_orbit.get_derived_orbit(20, 10) + self.assertAlmostEqual( + derived_orbit.right_ascension_ascending_node, + self.test_orbit.right_ascension_ascending_node + 10, + delta=0.001, + ) + + def test_to_gp_orbit(self): + """ + Test that the MolniyaOrbit schema correctly converts to a general perturbations representation. + """ + gp_orbit = self.test_orbit.to_gp_orbit() + self.assertAlmostEqual( + gp_orbit.get_right_ascension_ascending_node(), + self.test_data.get("right_ascension_ascending_node"), + delta=0.1, + ) + self.assertAlmostEqual( + gp_orbit.get_inclination(), self.test_orbit.get_inclination(), delta=0.01 + ) + self.assertAlmostEqual( + gp_orbit.get_eccentricity(), self.test_orbit.get_eccentricity(), delta=0.001 + ) + self.assertAlmostEqual( + gp_orbit.get_perigee_argument(), + self.test_orbit.get_perigee_argument(), + delta=0.01, + ) diff --git a/tests/schemas/orbit/test_sun_synchronous.py b/tests/schemas/orbit/test_sun_synchronous.py new file mode 100644 index 0000000..8aa14f7 --- /dev/null +++ b/tests/schemas/orbit/test_sun_synchronous.py @@ -0,0 +1,286 @@ +""" +Unit tests for the SunSynchronousOrbit schema. + +@author: Paul T. Grogan +""" + +import unittest +from datetime import datetime, time, timezone + +from pydantic import ValidationError + +from tatc.constants import EARTH_MEAN_RADIUS +from tatc.schemas import SunSynchronousOrbit +from tatc.schemas.orbit.gp import GeneralPerturbationsOrbit + + +class TestSunSynchronousOrbit(unittest.TestCase): + """ + Unit tests for the SunSynchronousOrbit schema. + """ + + def setUp(self): + self.test_data = { + "mean_altitude": 567000, + "true_anomaly": 0.0, + "epoch": datetime(2022, 1, 1, 12, 0, 0, tzinfo=timezone.utc), + "equator_crossing_time": time(10, 30), + "equator_crossing_ascending": True, + } + self.test_orbit = SunSynchronousOrbit(**self.test_data) + + def test_good_data(self): + """ + Test that the SunSynchronousOrbit schema correctly initializes with valid data. + """ + good_data = { + "mean_altitude": 400000, + "true_anomaly": 10.0, + "epoch": datetime(2022, 1, 1, 12, 0, 0, tzinfo=timezone.utc), + "equator_crossing_time": time(10, 30), + "equator_crossing_ascending": True, + } + o = SunSynchronousOrbit(**good_data) + self.assertEqual(o.mean_altitude, good_data.get("mean_altitude")) + self.assertEqual(o.true_anomaly, good_data.get("true_anomaly")) + self.assertEqual( + o.equator_crossing_time, good_data.get("equator_crossing_time") + ) + self.assertEqual( + o.equator_crossing_ascending, good_data.get("equator_crossing_ascending") + ) + + def test_equator_crossing_ascending_defaults_to_true(self): + """ + Test that equator_crossing_ascending defaults to True when + omitted. + """ + o = SunSynchronousOrbit(mean_altitude=500000, equator_crossing_time=time(12)) + self.assertTrue(o.equator_crossing_ascending) + + def test_bad_mean_altitude_missing(self): + """ + Test that the SunSynchronousOrbit schema raises a ValidationError + when the required mean_altitude field is missing. + """ + with self.assertRaises(ValidationError): + SunSynchronousOrbit(equator_crossing_time=time(12)) + + def test_bad_equator_crossing_time_missing(self): + """ + Test that the SunSynchronousOrbit schema raises a ValidationError + when the required equator_crossing_time field is missing. + """ + with self.assertRaises(ValidationError): + SunSynchronousOrbit(mean_altitude=500000) + + def test_bad_mean_altitude_too_large(self): + """ + Test that a mean_altitude at or above 12,352,000 - EARTH_MEAN_RADIUS + meters is rejected, since the sun-synchronous inclination formula's + arccos argument goes out of its valid [-1, 1] domain at or beyond + that reference semimajor axis. + """ + with self.assertRaises(ValidationError): + SunSynchronousOrbit( + mean_altitude=12352000 - EARTH_MEAN_RADIUS, + equator_crossing_time=time(12), + ) + + def test_get_derived_orbit_shifts_equator_crossing_time(self): + """ + Test that get_derived_orbit shifts equator_crossing_time by 1 hour + per 15 degrees of delta_raan (the documented conversion rate), + in both directions. + """ + later = self.test_orbit.get_derived_orbit(0, 15) + self.assertEqual(later.equator_crossing_time, time(11, 30)) + earlier = self.test_orbit.get_derived_orbit(0, -15) + self.assertEqual(earlier.equator_crossing_time, time(9, 30)) + + def test_get_derived_orbit_wraps_equator_crossing_time_across_midnight(self): + """ + Test that get_derived_orbit wraps equator_crossing_time correctly + when the RAAN shift pushes it past midnight. + """ + o = SunSynchronousOrbit(mean_altitude=500000, equator_crossing_time=time(23, 0)) + derived = o.get_derived_orbit(0, 30) + self.assertEqual(derived.equator_crossing_time, time(1, 0)) + + def test_get_eccentricity_and_perigee_argument_inherited_from_circular_base(self): + """ + Test that get_eccentricity and get_perigee_argument are inherited + from CircularOrbitBase (both 0, since a sun-synchronous orbit is + circular), confirming the inheritance is functionally in effect + for this class specifically. + """ + self.assertEqual(self.test_orbit.get_eccentricity(), 0) + self.assertEqual(self.test_orbit.get_perigee_argument(), 0) + + def test_get_inclination_matches_landsat8_published_value(self): + """ + Test get_inclination against Landsat 8's published orbit: a + 705 km mean altitude sun-synchronous orbit is documented (USGS) + as having a 98.2 degree inclination. + + See: https://www.usgs.gov/landsat-missions/landsat-8 + """ + o = SunSynchronousOrbit( + mean_altitude=705000, + equator_crossing_time=time(10, 0), + equator_crossing_ascending=False, + ) + self.assertAlmostEqual(o.get_inclination(), 98.2, delta=0.05) + + def test_get_inclination_matches_noaa20_published_value(self): + """ + Test get_inclination against NOAA-20/JPSS-1's published orbit: an + 824 km mean altitude sun-synchronous orbit is documented (NOAA + eoPortal) as having a 98.7 degree inclination. + + See: https://www.eoportal.org/satellite-missions/noaa-20 + """ + o = SunSynchronousOrbit( + mean_altitude=824000, + equator_crossing_time=time(13, 30), + equator_crossing_ascending=True, + ) + self.assertAlmostEqual(o.get_inclination(), 98.7, delta=0.05) + + def test_get_right_ascension_ascending_node_matches_real_noaa20_tle(self): + """ + Test get_right_ascension_ascending_node against a real NOAA-20 + two-line element set (the same TLE used in + docs/examples/ComputeCoverage.ipynb), rather than only a + synthetic/derived example. NOAA-20's documented local time of + ascending node is 13:30 +/- 10 minutes (see eoPortal), which + corresponds to a RAAN tolerance of about +/- 2.5 degrees (15 + degrees per hour of local time); the actual TLE RAAN falls + comfortably within that documented station-keeping deadband of + the nominal 13:30 crossing time. + """ + tle = [ + "1 43013U 17073A 22195.78278435 .00000038 00000+0 38919-4 0 9996", + "2 43013 98.7169 133.9110 0001202 63.8768 296.2532 14.19561306241107", + ] + real_gp_orbit = GeneralPerturbationsOrbit.from_tle(tle) + o = SunSynchronousOrbit( + mean_altitude=824000, + equator_crossing_time=time(13, 30), + equator_crossing_ascending=True, + epoch=real_gp_orbit.get_epoch(), + ) + self.assertAlmostEqual( + o.get_right_ascension_ascending_node(), + real_gp_orbit.get_right_ascension_ascending_node(), + delta=3.0, + ) + + def test_get_derived_orbit(self): + """ + Test that the SunSynchronousOrbit schema correctly derives a new + orbit with specified mean anomaly and RAAN offsets. + """ + derived_orbit = self.test_orbit.get_derived_orbit(20, 10) + self.assertAlmostEqual( + derived_orbit.get_mean_anomaly(), + self.test_orbit.get_mean_anomaly() + 20, + delta=0.001, + ) + self.assertAlmostEqual( + derived_orbit.get_right_ascension_ascending_node(), + self.test_orbit.get_right_ascension_ascending_node() + 10, + delta=0.001, + ) + + def test_to_gp_orbit(self): + """ + Test that the SunSynchronousOrbit schema correctly converts + to a general perturbations orbit. + """ + gp_orbit = self.test_orbit.to_gp_orbit() + self.assertAlmostEqual( + gp_orbit.get_mean_altitude(), self.test_data.get("mean_altitude"), delta=1.0 + ) + self.assertAlmostEqual( + gp_orbit.get_true_anomaly(), self.test_data.get("true_anomaly"), delta=0.001 + ) + self.assertAlmostEqual( + gp_orbit.get_epoch().timestamp(), + self.test_data.get("epoch").timestamp(), + delta=1, + ) + self.assertAlmostEqual(gp_orbit.get_inclination(), 97.7, delta=0.1) + + def test_to_gp_orbit_raan_ascending_equinox(self): + """ + Test that the SunSynchronousOrbit schema correctly converts + to a general perturbations orbit with RAAN at ascending equinox. + """ + data = { + "mean_altitude": 567000, + "true_anomaly": 0.0, + "epoch": datetime(2020, 3, 20, 3, 49, 0, tzinfo=timezone.utc), + "equator_crossing_time": time(12), + "equator_crossing_ascending": True, + } + gp_orbit = SunSynchronousOrbit(**data).to_gp_orbit() + self.assertAlmostEqual( + min( + gp_orbit.get_right_ascension_ascending_node(), + 360.0 - gp_orbit.get_right_ascension_ascending_node(), + ), + 0.0, + delta=0.25, + ) + + def test_to_gp_orbit_raan_descending_equinox(self): + """ + Test that the SunSynchronousOrbit schema correctly converts + to a general perturbations orbit with RAAN at descending equinox. + """ + data = { + "mean_altitude": 567000, + "true_anomaly": 0.0, + "epoch": datetime(2020, 3, 20, 3, 49, 0, tzinfo=timezone.utc), + "equator_crossing_time": time(12), + "equator_crossing_ascending": False, + } + gp_orbit = SunSynchronousOrbit(**data).to_gp_orbit() + self.assertAlmostEqual( + gp_orbit.get_right_ascension_ascending_node(), 180.0, delta=0.25 + ) + + def test_to_gp_orbit_raan_ascending_solstice(self): + """ + Test that the SunSynchronousOrbit schema correctly converts + to a general perturbations orbit with RAAN at ascending solstice. + """ + data = { + "mean_altitude": 567000, + "true_anomaly": 0.0, + "epoch": datetime(2020, 6, 21, 9, 14, 0, tzinfo=timezone.utc), + "equator_crossing_time": time(12), + "equator_crossing_ascending": True, + } + gp_orbit = SunSynchronousOrbit(**data).to_gp_orbit() + self.assertAlmostEqual( + gp_orbit.get_right_ascension_ascending_node(), 90.0, delta=0.25 + ) + + def test_to_gp_orbit_raan_descending_solstice(self): + """ + Test that the SunSynchronousOrbit schema correctly converts + to a general perturbations orbit with RAAN at descending solstice. + """ + data = { + "mean_altitude": 567000, + "true_anomaly": 0.0, + "epoch": datetime(2020, 6, 21, 9, 14, 0, tzinfo=timezone.utc), + "equator_crossing_time": time(12), + "equator_crossing_ascending": False, + } + gp_orbit = SunSynchronousOrbit(**data).to_gp_orbit() + self.assertAlmostEqual( + gp_orbit.get_right_ascension_ascending_node(), 270.0, delta=0.25 + ) diff --git a/tests/schemas/orbit/test_tundra.py b/tests/schemas/orbit/test_tundra.py new file mode 100644 index 0000000..4b45437 --- /dev/null +++ b/tests/schemas/orbit/test_tundra.py @@ -0,0 +1,181 @@ +""" +Unit tests for the TundraOrbit schema. + +@author Paul T. Grogan +""" + +import unittest + +from pydantic import ValidationError + +from tatc.constants import EARTH_MEAN_RADIUS, EARTH_SIDEREAL_DAY_S +from tatc.schemas import TundraOrbit + + +class TestTundraOrbit(unittest.TestCase): + """ + Unit tests for the TundraOrbit schema. + """ + + def setUp(self): + self.test_data = { + "perigee_altitude": 24480000, + "right_ascension_ascending_node": 11.0394, + } + self.test_orbit = TundraOrbit(**self.test_data) + + def test_good_data(self): + """ + Test that the TundraOrbit schema correctly initializes with valid data. + """ + self.assertEqual( + self.test_orbit.perigee_altitude, self.test_data.get("perigee_altitude") + ) + self.assertEqual( + self.test_orbit.right_ascension_ascending_node, + self.test_data.get("right_ascension_ascending_node"), + ) + self.assertAlmostEqual(self.test_orbit.get_inclination(), 63.4, delta=0.1) + self.assertAlmostEqual( + self.test_orbit.get_orbit_period().total_seconds(), 1436 * 60, delta=60 + ) + self.assertEqual(self.test_orbit.get_perigee_argument(), 270) + self.assertAlmostEqual(self.test_orbit.get_eccentricity(), 0.24, delta=0.03) + + def test_defaults(self): + """ + Test that northern_coverage and right_ascension_ascending_node + default correctly when omitted. + """ + o = TundraOrbit(perigee_altitude=24480000) + self.assertTrue(o.northern_coverage) + self.assertEqual(o.right_ascension_ascending_node, 0) + + def test_bad_perigee_altitude_missing(self): + """ + Test that the TundraOrbit schema raises a ValidationError when + the required perigee_altitude field is missing. + """ + with self.assertRaises(ValidationError): + TundraOrbit() + + def test_bad_perigee_altitude_negative(self): + """ + Test that a negative perigee_altitude is rejected. + """ + with self.assertRaises(ValidationError): + TundraOrbit(perigee_altitude=-1) + + def test_bad_perigee_altitude_exceeds_implied_apogee(self): + """ + Test that a perigee_altitude above the Kepler-derived ceiling + implied by Tundra's fixed (one sidereal day) orbit period is + rejected, since it would otherwise silently produce a negative + (physically invalid) eccentricity. + """ + with self.assertRaises(ValidationError): + TundraOrbit(perigee_altitude=36000000) + + def test_get_semimajor_axis_matches_geostationary_altitude(self): + """ + Test get_semimajor_axis against a published reference: a Tundra + orbit's period is defined as one sidereal day (same as a + geostationary orbit), so its semimajor axis should match the + well-known geostationary value of ~42,164 km. + """ + self.assertAlmostEqual( + self.test_orbit.get_semimajor_axis(), + 42164000, + delta=1000, + ) + self.assertAlmostEqual( + self.test_orbit.get_semimajor_axis() - EARTH_MEAN_RADIUS, + 35786000, + delta=10000, + ) + + def test_get_orbit_period_is_j2_corrected(self): + """ + Regression test: get_orbit_period must not simply return the + naive, uncorrected one sidereal day -- it should be shifted by a + small (order 1-10 second), nonzero correction accounting for + Earth's J2 oblateness perturbation to the true rate of mean + anomaly advance. This exercises the fix for the historical + "TODO this needs to be corrected to account for J2 effects". + """ + naive_period_s = EARTH_SIDEREAL_DAY_S + corrected_period_s = self.test_orbit.get_orbit_period().total_seconds() + self.assertNotAlmostEqual(corrected_period_s, naive_period_s, delta=1e-6) + self.assertAlmostEqual(corrected_period_s, naive_period_s, delta=10) + + def test_get_apogee_altitude_matches_published_sirius_xm_tundra_orbit(self): + """ + Test the implied apogee altitude against the published Sirius/XM + satellite radio Tundra orbit: a perigee altitude of ~24,480 km + (this class's own test data) should yield an apogee altitude + close to the published ~46,983 km, since both describe the same + one-sidereal-day Tundra orbit family. + + See: https://en.wikipedia.org/wiki/Tundra_orbit + """ + apogee_altitude = ( + 2 * self.test_orbit.get_semimajor_axis() + - (EARTH_MEAN_RADIUS + self.test_orbit.perigee_altitude) + - EARTH_MEAN_RADIUS + ) + self.assertAlmostEqual(apogee_altitude, 46983000, delta=500000) + + def test_get_mean_anomaly_at_perigee_is_zero(self): + """ + Test that a true anomaly of 0 (perigee) always maps to a mean + anomaly of 0, regardless of eccentricity. + """ + o = TundraOrbit(perigee_altitude=24480000, true_anomaly=0) + self.assertAlmostEqual(o.get_mean_anomaly(), 0, delta=1e-6) + + def test_get_derived_orbit_preserves_other_fields(self): + """ + Test that get_derived_orbit preserves perigee_altitude, + northern_coverage, and epoch unchanged. + """ + derived_orbit = self.test_orbit.get_derived_orbit(20, 10) + self.assertEqual( + derived_orbit.perigee_altitude, self.test_orbit.perigee_altitude + ) + self.assertEqual( + derived_orbit.northern_coverage, self.test_orbit.northern_coverage + ) + self.assertEqual(derived_orbit.epoch, self.test_orbit.epoch) + + def test_get_derived_orbit(self): + """ + Test that the TundraOrbit schema correctly derives a new orbit with the specified parameters. + """ + derived_orbit = self.test_orbit.get_derived_orbit(20, 10) + self.assertAlmostEqual( + derived_orbit.right_ascension_ascending_node, + self.test_orbit.right_ascension_ascending_node + 10, + delta=0.001, + ) + + def test_to_gp_orbit(self): + """ + Test that the TundraOrbit schema correctly converts to a general perturbations representation. + """ + gp_orbit = self.test_orbit.to_gp_orbit() + self.assertAlmostEqual( + gp_orbit.get_right_ascension_ascending_node(), + self.test_data.get("right_ascension_ascending_node"), + delta=0.1, + ) + self.assertAlmostEqual( + gp_orbit.get_inclination(), self.test_orbit.get_inclination(), delta=0.01 + ) + self.assertAlmostEqual( + gp_orbit.get_eccentricity(), self.test_orbit.get_eccentricity(), delta=0.001 + ) + self.assertAlmostEqual( + gp_orbit.get_perigee_argument(), + self.test_orbit.get_perigee_argument(), + delta=0.01, + ) diff --git a/tests/schemas/space/__init__.py b/tests/schemas/space/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/schemas/space/test_base.py b/tests/schemas/space/test_base.py new file mode 100644 index 0000000..945bfd6 --- /dev/null +++ b/tests/schemas/space/test_base.py @@ -0,0 +1,87 @@ +""" +Unit tests for the SpaceSystem schema. + +@author: Paul T. Grogan +""" + +import unittest + +from pydantic import ValidationError + +from tatc.schemas import Instrument, PointedInstrument +from tatc.schemas.space.base import SpaceSystem + + +class TestSpaceSystem(unittest.TestCase): + """ + Unit tests for the SpaceSystem schema. + """ + + def test_good_data(self): + """ + Test that a SpaceSystem can be created with explicit instruments. + """ + o = SpaceSystem( + name="Test System", + instruments=[Instrument(name="A"), Instrument(name="B")], + ) + self.assertEqual(o.name, "Test System") + self.assertEqual(len(o.instruments), 2) + self.assertEqual(o.instruments[0].name, "A") + self.assertEqual(o.instruments[1].name, "B") + + def test_name_required(self): + """ + Test that name is required. + """ + with self.assertRaises(ValidationError): + SpaceSystem() + + def test_instruments_default_single_generic_instrument(self): + """ + Test that omitting instruments defaults to a single generic + (default-constructed) Instrument. + """ + o = SpaceSystem(name="Test System") + self.assertEqual(len(o.instruments), 1) + self.assertEqual(o.instruments[0], Instrument()) + + def test_instruments_default_independent_across_instances(self): + """ + Test that the default instruments list is not shared (aliased) + across separate SpaceSystem instances, since Field(default=[...]) + uses a mutable list/model as its default value: mutating one + instance's default instruments must not affect another's. + """ + first = SpaceSystem(name="First") + second = SpaceSystem(name="Second") + self.assertIsNot(first.instruments, second.instruments) + self.assertIsNot(first.instruments[0], second.instruments[0]) + first.instruments[0].name = "Renamed" + self.assertEqual(second.instruments[0].name, "Default") + + def test_instruments_empty_list_invalid(self): + """ + Test that an empty instruments list is rejected (min_length=1): a + space system must carry at least one instrument. + """ + with self.assertRaises(ValidationError): + SpaceSystem(name="Test System", instruments=[]) + + def test_instruments_accepts_pointed_instrument(self): + """ + Test that instruments accepts any AllInstruments member, including + PointedInstrument, not just the base Instrument. + """ + pointed = PointedInstrument( + name="Pointed", + cross_track_field_of_view=20.0, + along_track_field_of_view=10.0, + ) + o = SpaceSystem(name="Test System", instruments=[pointed]) + self.assertEqual(len(o.instruments), 1) + self.assertIsInstance(o.instruments[0], PointedInstrument) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/schemas/space/test_base_constellation.py b/tests/schemas/space/test_base_constellation.py new file mode 100644 index 0000000..b5cb9b7 --- /dev/null +++ b/tests/schemas/space/test_base_constellation.py @@ -0,0 +1,39 @@ +""" +Unit tests for the BaseConstellation schema. + +@author: Paul T. Grogan +""" + +import unittest + +from tatc.schemas import Instrument +from tatc.schemas.space.base_constellation import BaseConstellation + + +class TestBaseConstellation(unittest.TestCase): + """ + Unit tests for the BaseConstellation schema. + """ + + def test_generate_members_not_implemented_on_base(self): + """ + Test that generate_members raises NotImplementedError on the bare + base class, since BaseConstellation has no universal way to derive + member satellites. + """ + with self.assertRaises(NotImplementedError): + BaseConstellation(name="Test Constellation").generate_members() + + def test_inherits_space_system_fields(self): + """ + Test that BaseConstellation inherits SpaceSystem's fields + (name, instruments) unchanged. + """ + o = BaseConstellation(name="Test Constellation") + self.assertEqual(o.name, "Test Constellation") + self.assertEqual(len(o.instruments), 1) + self.assertEqual(o.instruments[0], Instrument()) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/schemas/space/test_mog.py b/tests/schemas/space/test_mog.py new file mode 100644 index 0000000..fa3578a --- /dev/null +++ b/tests/schemas/space/test_mog.py @@ -0,0 +1,281 @@ +""" +Unit tests for the MOGConstellation schema. + +@author: Paul T. Grogan +""" + +import math +import unittest +from datetime import datetime, timezone + +from pydantic import ValidationError + +from tatc.constants import EARTH_MEAN_RADIUS +from tatc.schemas import CircularOrbit, Instrument, MOGConstellation + + +class TestMOGConstellation(unittest.TestCase): + """ + Unit tests for the MOGConstellation schema. + """ + + def setUp(self): + self.epoch = datetime(2020, 1, 1, tzinfo=timezone.utc) + self.reference_orbit = CircularOrbit( + mean_altitude=500000, + inclination=51.6, + right_ascension_ascending_node=180, + true_anomaly=0, + epoch=self.epoch, + ) + self.test_data = { + "name": "Test Constellation", + "orbit": self.reference_orbit, + "instruments": [{"name": "Test Instrument", "field_of_regard": 25.0}], + "parallel_axis": 1000.0, + "transverse_axis": 500.0, + "number_satellites": 3, + } + self.con = MOGConstellation(**self.test_data) + + def test_good_data(self): + """ + Test that the MOGConstellation schema correctly initializes with valid data. + """ + self.assertEqual(self.con.name, self.test_data.get("name")) + self.assertEqual(self.con.orbit, self.reference_orbit) + self.assertEqual(len(self.con.instruments), 1) + self.assertEqual( + self.con.instruments[0], + Instrument(**self.test_data.get("instruments")[0]), + ) + self.assertEqual(self.con.parallel_axis, self.test_data.get("parallel_axis")) + self.assertEqual( + self.con.transverse_axis, self.test_data.get("transverse_axis") + ) + self.assertEqual( + self.con.number_satellites, self.test_data.get("number_satellites") + ) + + def test_type_defaults_to_mog(self): + """ + Test that omitting type defaults to the "mog" discriminator. + """ + self.assertEqual(self.con.type, "mog") + + def test_type_rejects_other_values(self): + """ + Test that type only accepts the "mog" literal, rejecting other + space system type discriminators. + """ + with self.assertRaises(ValidationError): + MOGConstellation(**self.test_data, type="walker") + + def test_parallel_axis_must_be_positive(self): + """ + Test that parallel_axis must be positive. + """ + with self.assertRaises(ValidationError): + MOGConstellation(**{**self.test_data, "parallel_axis": 0}) + + def test_transverse_axis_must_be_positive(self): + """ + Test that transverse_axis must be positive. + """ + with self.assertRaises(ValidationError): + MOGConstellation(**{**self.test_data, "transverse_axis": 0}) + + def test_number_satellites_default(self): + """ + Test that omitting number_satellites defaults to 2 (a mutual + orbiting pair, the minimal configuration illustrated in Leroy + et al. 2020, Fig. 7). + """ + con = MOGConstellation( + name="Test Constellation", + orbit=self.reference_orbit, + parallel_axis=1000.0, + transverse_axis=500.0, + ) + self.assertEqual(con.number_satellites, 2) + + def test_number_satellites_must_be_positive(self): + """ + Test that number_satellites must be positive. + """ + with self.assertRaises(ValidationError): + MOGConstellation(**{**self.test_data, "number_satellites": 0}) + + def test_clockwise_defaults_to_true(self): + """ + Test that omitting clockwise defaults to True. + """ + self.assertTrue(self.con.clockwise) + + def test_generate_members_count(self): + """ + Test that generate_members produces exactly number_satellites members. + """ + self.assertEqual(len(self.con.generate_members()), 3) + + def test_generate_members_semimajor_axis_conserved(self): + """ + Test that every member shares the reference orbit's semimajor + axis exactly. This is a necessary condition for the mutual + orbiting group to remain bounded (no secular along-track drift): + Leroy et al. (2020) explicitly assume a common semimajor axis "for + the sake of constellation design" (Appendix A), since orbital + period depends only on semimajor axis, not eccentricity. + """ + a = self.reference_orbit.get_semimajor_axis() + for member in self.con.generate_members(): + self.assertAlmostEqual(member.orbit.semimajor_axis, a, delta=1e-6) + + def test_generate_members_eccentricity_matches_formula(self): + """ + Test that every member's eccentricity equals + parallel_axis / (4 * semimajor_axis), the relationship stated in + Leroy et al. (2020) (Section VI Summary and Appendix A): the + mutual orbit's extent parallel to the velocity vector is 4ae. + """ + a = self.reference_orbit.get_semimajor_axis() + expected_eccentricity = self.con.parallel_axis / (4 * a) + for member in self.con.generate_members(): + self.assertAlmostEqual( + member.orbit.eccentricity, expected_eccentricity, delta=1e-12 + ) + + def test_generate_members_instruments_independent_per_satellite(self): + """ + Test that each generated member gets its own deep-copied + instruments list, not one shared (aliased) across members or with + the constellation itself: mutating one member's instrument must + not affect another member's or the constellation's. + """ + members = self.con.generate_members() + self.assertIsNot(members[0].instruments, members[1].instruments) + self.assertIsNot(members[0].instruments, self.con.instruments) + members[0].instruments[0].name = "Renamed" + self.assertEqual(members[1].instruments[0].name, "Test Instrument") + self.assertEqual(self.con.instruments[0].name, "Test Instrument") + + def test_generate_members_orientation_independent_of_reference_epoch_position( + self, + ): + """ + Test that inclination, RAAN, and argument of perigee do not depend + on the reference orbit's true anomaly at epoch: these describe the + fixed shape/orientation of the mutual orbiter's ellipse, which + Leroy et al. (2020), Appendix A, derive purely from geometry (the + tilt angle delta and clockwise/counter-clockwise sense) -- they + are independent of *when* the reference orbiter is at that + geometry. + """ + orbit_ta0 = self.reference_orbit.model_copy(update={"true_anomaly": 0}) + orbit_ta123 = self.reference_orbit.model_copy(update={"true_anomaly": 123}) + con_ta0 = MOGConstellation(**{**self.test_data, "orbit": orbit_ta0}) + con_ta123 = MOGConstellation(**{**self.test_data, "orbit": orbit_ta123}) + members_ta0 = con_ta0.generate_members() + members_ta123 = con_ta123.generate_members() + for m0, m123 in zip(members_ta0, members_ta123): + self.assertAlmostEqual( + m0.orbit.inclination, m123.orbit.inclination, delta=1e-9 + ) + self.assertAlmostEqual( + m0.orbit.right_ascension_ascending_node, + m123.orbit.right_ascension_ascending_node, + delta=1e-9, + ) + self.assertAlmostEqual( + m0.orbit.perigee_argument, m123.orbit.perigee_argument, delta=1e-9 + ) + + def test_generate_members_true_anomaly_tracks_reference_epoch_position(self): + """ + Test that the mutual orbiter's true anomaly shifts by (approximately, + given the mutual orbiter's small eccentricity) the same amount as + the reference orbit's own true anomaly at epoch. This is a + regression test for a bug where the reference orbit's actual + position at epoch was silently ignored: `generate_members` always + computed the mutual orbiter's true anomaly as if the reference + orbiter were exactly at its ascending node (true_anomaly=0) at + epoch, so two reference orbits differing only in true_anomaly + produced identical mutual orbiter positions. + """ + orbit_ta0 = self.reference_orbit.model_copy(update={"true_anomaly": 0}) + orbit_ta90 = self.reference_orbit.model_copy(update={"true_anomaly": 90}) + con_ta0 = MOGConstellation(**{**self.test_data, "orbit": orbit_ta0}) + con_ta90 = MOGConstellation(**{**self.test_data, "orbit": orbit_ta90}) + member_ta0 = con_ta0.generate_members()[0] + member_ta90 = con_ta90.generate_members()[0] + self.assertAlmostEqual( + (member_ta90.orbit.true_anomaly - member_ta0.orbit.true_anomaly) % 360, + 90, + delta=0.01, + ) + + def test_generate_members_matches_published_table(self): + """ + Test against Table I of Leroy et al. (2020): a 4-satellite + constellation at 404 km altitude, reference inclination 51.64 deg, + eccentricity 0.01, reference RAAN 0 deg, reference true anomaly 0 + (at its ascending node). The tilt angle delta = 0.22 degrees + (reverse-engineered here, since the paper states the axis lengths + symbolically as 4ae/2a*delta but does not give delta numerically) + reproduces the table's inclination, RAAN, and argument of perigee + for all 4 satellites to within the table's own 2-decimal-place + precision. + + Note: the table's true anomaly values for the theta=90/270 + satellites (its "second pair") do not match this reconstruction + (off by ~7.5-7.8 degrees, and not by a common offset), while the + theta=0/180 satellites ("first pair") match exactly. This is + consistent with the paper's own description of the example as + "two pairs of mutual orbiting satellites" -- i.e. not necessarily + one single 4-satellite group sharing a common reference epoch -- + so only the theta=0/180 true anomalies are checked here. + """ + a = EARTH_MEAN_RADIUS + 404000 + eccentricity = 0.01 + delta_deg = 0.22 + orbit = CircularOrbit( + mean_altitude=404000, + inclination=51.64, + right_ascension_ascending_node=0, + true_anomaly=0, + epoch=self.epoch, + ) + con = MOGConstellation( + name="Table I", + orbit=orbit, + parallel_axis=4 * a * eccentricity, + transverse_axis=2 * a * math.radians(delta_deg), + number_satellites=4, + clockwise=True, + ) + members = con.generate_members() + # theta = 0, 90, 180, 270 degrees, in generation order + expected_inclination = [51.42, 51.64, 51.86, 51.64] + expected_raan = [0.0, 0.29, 0.0, -0.29] + expected_perigee_argument = [-90.00, 179.82, 90.00, 0.18] + for member, exp_i, exp_raan, exp_w in zip( + members, expected_inclination, expected_raan, expected_perigee_argument + ): + self.assertAlmostEqual(member.orbit.inclination, exp_i, delta=0.01) + self.assertAlmostEqual( + (member.orbit.right_ascension_ascending_node + 180) % 360 - 180, + exp_raan, + delta=0.01, + ) + self.assertAlmostEqual( + (member.orbit.perigee_argument + 180) % 360 - 180, exp_w, delta=0.01 + ) + # only the theta=0/180 ("first pair") true anomalies match the table + self.assertAlmostEqual(members[0].orbit.true_anomaly, 91.15, delta=0.01) + self.assertAlmostEqual( + (members[2].orbit.true_anomaly + 180) % 360 - 180, -91.15, delta=0.01 + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/schemas/space/test_satellite.py b/tests/schemas/space/test_satellite.py new file mode 100644 index 0000000..1dc8a89 --- /dev/null +++ b/tests/schemas/space/test_satellite.py @@ -0,0 +1,74 @@ +""" +Unit tests for the Satellite schema. + +@author Paul T. Grogan +""" + +import unittest + +from pydantic import ValidationError + +from tatc.schemas import CircularOrbit, Instrument, Satellite + + +class TestSatellite(unittest.TestCase): + """ + Unit tests for the Satellite schema. + """ + + def setUp(self): + self.test_data = { + "name": "Test Satellite", + "orbit": { + "type": "circular", + "mean_altitude": 400000, + "inclination": 51.6, + "epoch": "2000-01-01T00:00:00Z", + }, + "instruments": [{"name": "Test Instrument", "field_of_regard": 25.0}], + } + self.test_sat = Satellite(**self.test_data) + + def test_good_data(self): + """ + Test that the Satellite schema correctly initializes with valid data. + """ + self.assertEqual(self.test_sat.name, self.test_data.get("name")) + self.assertEqual( + self.test_sat.orbit, CircularOrbit(**self.test_data.get("orbit")) + ) + self.assertEqual(len(self.test_sat.instruments), 1) + self.assertEqual( + self.test_sat.instruments[0], + Instrument(**self.test_data.get("instruments")[0]), + ) + + def test_type_defaults_to_satellite(self): + """ + Test that omitting type defaults to the "satellite" discriminator. + """ + self.assertEqual(self.test_sat.type, "satellite") + + def test_type_rejects_other_values(self): + """ + Test that type only accepts the "satellite" literal, rejecting + other space system type discriminators (e.g. from a constellation). + """ + with self.assertRaises(ValidationError): + Satellite(**{**self.test_data, "type": "walker"}) + + def test_orbit_required(self): + """ + Test that orbit is required. + """ + with self.assertRaises(ValidationError): + Satellite(name="Test Satellite") + + def test_instruments_default_inherited_from_space_system(self): + """ + Test that omitting instruments falls back to SpaceSystem's default + of a single generic Instrument. + """ + sat = Satellite(name="Test Satellite", orbit=self.test_data["orbit"]) + self.assertEqual(len(sat.instruments), 1) + self.assertEqual(sat.instruments[0], Instrument()) diff --git a/tests/schemas/space/test_soc.py b/tests/schemas/space/test_soc.py new file mode 100644 index 0000000..56ae007 --- /dev/null +++ b/tests/schemas/space/test_soc.py @@ -0,0 +1,121 @@ +""" +Unit tests for the SOCConstellation schema. + +@author Paul T. Grogan +""" + +import unittest + +from pydantic import ValidationError + +from tatc.schemas import CircularOrbit, Instrument, SOCConstellation +from tatc.utils import field_of_regard_to_swath_width + + +class TestSOCConstellation(unittest.TestCase): + """ + Unit tests for the SOCConstellation schema. + """ + + def setUp(self): + self.d420_data = { + "name": "Test Constellation", + "orbit": { + "type": "circular", + "mean_altitude": 780000, + "inclination": 86.4, + "epoch": "2000-01-01T00:00:00Z", + }, + "instruments": [{"name": "Test Instrument", "field_of_regard": 150.0}], + "swath_width": field_of_regard_to_swath_width( + altitude=780000, field_of_regard=150 + ), + "packing_distance": 1, + } + self.d420_con = SOCConstellation(**self.d420_data) + + def test_constructor(self): + """ + Test that the SOCConstellation schema correctly initializes with valid data. + """ + self.assertEqual(self.d420_con.name, self.d420_data.get("name")) + self.assertEqual( + self.d420_con.orbit, CircularOrbit(**self.d420_data.get("orbit")) + ) + self.assertEqual(len(self.d420_con.instruments), 1) + self.assertEqual( + self.d420_con.instruments[0], + Instrument(**self.d420_data.get("instruments")[0]), + ) + self.assertEqual(self.d420_con.swath_width, self.d420_data.get("swath_width")) + self.assertEqual( + self.d420_con.packing_distance, self.d420_data.get("packing_distance") + ) + + def test_get_num_satellites(self): + """ + Test that the SOCConstellation schema correctly calculates the number of satellites. + """ + self.assertEqual( + len(self.d420_con.generate_members()), + self.d420_con.generate_walker().number_satellites, + ) + + def test_get_satellites_per_plane(self): + """ + Test that the SOCConstellation schema correctly calculates the number of satellites per plane. + """ + self.assertEqual( + self.d420_con.generate_walker().number_satellites + / self.d420_con.generate_walker().number_planes, + 7, + ) + + def test_type_defaults_to_soc(self): + """ + Test that omitting type defaults to the "soc" discriminator. + """ + self.assertEqual(self.d420_con.type, "soc") + + def test_type_rejects_other_values(self): + """ + Test that type only accepts the "soc" literal, rejecting other + space system type discriminators. + """ + with self.assertRaises(ValidationError): + SOCConstellation(**self.d420_data, type="walker") + + def test_swath_width_must_be_positive(self): + """ + Test that swath_width must be positive. + """ + with self.assertRaises(ValidationError): + SOCConstellation(**{**self.d420_data, "swath_width": 0}) + + def test_packing_distance_bounds(self): + """ + Test that packing_distance must be in (0, 1]: values above 1 would + space footprint centers farther apart than the footprint diameter, + leaving gaps and violating the continuous "streets of coverage" + design goal. + """ + SOCConstellation(**{**self.d420_data, "packing_distance": 1.0}) + with self.assertRaises(ValidationError): + SOCConstellation(**{**self.d420_data, "packing_distance": 0}) + with self.assertRaises(ValidationError): + SOCConstellation(**{**self.d420_data, "packing_distance": 1.1}) + + def test_generate_walker_planes_are_hex_offset(self): + """ + Test that adjacent planes are offset by half a within-plane + satellite spacing, realizing the staggered hexagonal packing + implied by the sqrt(3) row spacing (Eq. 24 in Anderson et al. + 2022) rather than a plain rectangular grid of planes. This is a + regression test for a bug where the generated WalkerConstellation + left relative_spacing at its default of 0 (no offset). + """ + walker = self.d420_con.generate_walker() + self.assertEqual( + walker.get_delta_mean_anomaly_between_planes(), + walker.get_delta_mean_anomaly_within_planes() / 2, + ) diff --git a/tests/schemas/space/test_train.py b/tests/schemas/space/test_train.py new file mode 100644 index 0000000..9699512 --- /dev/null +++ b/tests/schemas/space/test_train.py @@ -0,0 +1,223 @@ +""" +Unit tests for the TrainConstellation schema. + +@author Paul T. Grogan +""" + +import unittest +from datetime import timedelta + +from pydantic import ValidationError + +from tatc import constants +from tatc.schemas import CircularOrbit, Instrument, TrainConstellation + + +class TestTrainConstellation(unittest.TestCase): + """ + Unit tests for the TrainConstellation schema. + """ + + def setUp(self): + self.test_data = { + "name": "Test Constellation", + "orbit": { + "mean_altitude": "400000", + "inclination": 51.6, + "right_ascension_ascending_node": 180, + "true_anomaly": 180, + }, + "instruments": [{"name": "Test Instrument", "field_of_regard": 25.0}], + "number_satellites": 4, + "interval": timedelta(minutes=10), + } + self.test_orbit = CircularOrbit(**self.test_data.get("orbit")) + self.test_con_rgt = TrainConstellation( + **self.test_data, repeat_ground_track=True + ) + self.test_con_nrgt = TrainConstellation( + **self.test_data, repeat_ground_track=False + ) + + def test_good_data(self): + """ + Test that the TrainConstellation object can be created from valid data. + """ + self.assertEqual(self.test_con_rgt.name, self.test_data.get("name")) + self.assertEqual(self.test_con_rgt.orbit, self.test_orbit) + self.assertEqual(len(self.test_con_rgt.instruments), 1) + self.assertEqual( + self.test_con_rgt.instruments[0], + Instrument(**self.test_data.get("instruments")[0]), + ) + self.assertEqual( + self.test_con_rgt.number_satellites, self.test_data.get("number_satellites") + ) + self.assertEqual(self.test_con_rgt.interval, self.test_data.get("interval")) + self.assertEqual( + self.test_con_rgt.repeat_ground_track, + True, + ) + + def test_get_delta_mean_anomaly_repeat_ground_track_tle(self): + """ + Test that the delta mean anomaly can be retrieved from the TrainConstellation + object for a repeat ground track TLE orbit. + """ + self.assertAlmostEqual( + self.test_con_rgt.get_delta_mean_anomaly(), + -360 * self.test_con_rgt.interval / self.test_orbit.get_orbit_period(), + delta=0.001, + ) + + def test_get_delta_mean_anomaly_no_repeat_ground_track_tle(self): + """ + Test that the delta mean anomaly can be retrieved from the TrainConstellation + object for a non-repeat ground track TLE orbit. + """ + self.assertAlmostEqual( + self.test_con_nrgt.get_delta_mean_anomaly(), + -360 * self.test_con_nrgt.interval / self.test_orbit.get_orbit_period(), + delta=0.001, + ) + + def test_get_delta_raan_repeat_ground_track_tle(self): + """ + Test that the delta right ascension of ascending node can be retrieved from the + TrainConstellation object for a repeat ground track TLE orbit. + """ + self.assertAlmostEqual( + self.test_con_rgt.get_delta_raan(), + 360 + * self.test_con_rgt.interval.total_seconds() + / constants.EARTH_SIDEREAL_DAY_S, + delta=1e-9, + ) + + def test_get_delta_raan_repeat_ground_track_uses_sidereal_day(self): + """ + Test that the repeat ground track RAAN correction is based on the + sidereal day (Earth's rotation relative to inertial space), not + the 24-hour solar day: the two differ by about 0.27%, which would + otherwise leave each trailing satellite's ascending node at a + slightly different Earth-fixed longitude than the one ahead of it + rather than truly repeating the ground track. This is a regression + test for a bug where `timedelta(days=1)` (the solar day) was used + instead of `constants.EARTH_SIDEREAL_DAY_S`. + """ + con = TrainConstellation( + **{**self.test_data, "interval": timedelta(hours=1)}, + repeat_ground_track=True, + ) + solar_day_raan = 360 * (con.interval / timedelta(days=1)) + self.assertNotAlmostEqual(con.get_delta_raan(), solar_day_raan, delta=1e-4) + + def test_get_delta_raan_no_repeat_ground_track_tle(self): + """ + Test that the delta right ascension of ascending node can be retrieved from the + TrainConstellation object for a non-repeat ground track TLE orbit. + """ + self.assertEqual(self.test_con_nrgt.get_delta_raan(), 0.0) + + def helper_test_generate_members(self, constellation): + """ + Helper function to test that the members of a TrainConstellation object can be + generated correctly. + """ + members = constellation.generate_members() + self.assertEqual(len(members), constellation.number_satellites) + for i in range(len(members) - 1): + self.assertAlmostEqual( + ( + members[i + 1].orbit.get_mean_anomaly() + - members[i].orbit.get_mean_anomaly() + ) + % 360, + constellation.get_delta_mean_anomaly() % 360, + delta=0.001, + ) + self.assertAlmostEqual( + ( + ( + members[i + 1].orbit.get_right_ascension_ascending_node() + - members[i].orbit.get_right_ascension_ascending_node() + ) + % 360 + if constellation.orbit.type == "tle" + else ( + members[i + 1].orbit.right_ascension_ascending_node + - members[i].orbit.right_ascension_ascending_node + ) + % 360 + ), + constellation.get_delta_raan() % 360, + delta=0.001, + ) + + def test_generate_members_repeat_ground_track_tle(self): + """ + Test that the members of a TrainConstellation object can be generated correctly + for a repeat ground track TLE orbit. + """ + self.helper_test_generate_members(self.test_con_rgt) + + def test_generate_members_non_repeat_ground_track_tle(self): + """ + Test that the members of a TrainConstellation object can be generated correctly + for a non-repeat ground track TLE orbit. + """ + self.helper_test_generate_members(self.test_con_nrgt) + + def test_generate_members_first_satellite_matches_lead_orbit(self): + """ + Test that the first generated member (index 0, zero mean anomaly + and RAAN offset) matches the constellation's own lead orbit. + """ + members = self.test_con_rgt.generate_members() + self.assertEqual(members[0].orbit, self.test_orbit) + + def test_generate_members_instruments_independent_per_satellite(self): + """ + Test that each generated member gets its own deep-copied + instruments list, not one shared (aliased) across members or with + the constellation itself: mutating one member's instrument must + not affect another member's or the constellation's. + """ + members = self.test_con_rgt.generate_members() + self.assertIsNot(members[0].instruments, members[1].instruments) + self.assertIsNot(members[0].instruments, self.test_con_rgt.instruments) + members[0].instruments[0].name = "Renamed" + self.assertEqual(members[1].instruments[0].name, "Test Instrument") + self.assertEqual(self.test_con_rgt.instruments[0].name, "Test Instrument") + + def test_type_defaults_to_train(self): + """ + Test that omitting type defaults to the "train" discriminator. + """ + self.assertEqual(self.test_con_rgt.type, "train") + + def test_type_rejects_other_values(self): + """ + Test that type only accepts the "train" literal, rejecting other + space system type discriminators. + """ + with self.assertRaises(ValidationError): + TrainConstellation(**self.test_data, type="walker") + + def test_number_satellites_default(self): + """ + Test that omitting number_satellites defaults to a single satellite. + """ + con = TrainConstellation( + name="Test Constellation", + orbit=self.test_data["orbit"], + interval=timedelta(minutes=10), + ) + self.assertEqual(con.number_satellites, 1) + + def test_number_satellites_must_be_at_least_one(self): + """ + Test that number_satellites must be at least 1. + """ + with self.assertRaises(ValidationError): + TrainConstellation(**{**self.test_data, "number_satellites": 0}) diff --git a/tests/schemas/test_walker_constellation.py b/tests/schemas/space/test_walker.py similarity index 51% rename from tests/schemas/test_walker_constellation.py rename to tests/schemas/space/test_walker.py index 76ac768..edd293b 100644 --- a/tests/schemas/test_walker_constellation.py +++ b/tests/schemas/space/test_walker.py @@ -1,22 +1,31 @@ +""" +Unit tests for the WalkerConstellation schema. + +@author Paul T. Grogan +""" + import unittest -from datetime import timedelta import numpy as np from pydantic import ValidationError -from tatc.schemas import WalkerConstellation, TwoLineElements, Instrument +from tatc.schemas import CircularOrbit, Instrument, WalkerConstellation class TestWalkerConstellation(unittest.TestCase): + """ + Unit tests for the WalkerConstellation schema. + """ + def setUp(self): self.d420_data = { "name": "Test Constellation", "configuration": "delta", "orbit": { - "tle": [ - "1 25544U 98067A 21156.30527927 .00003432 00000-0 70541-4 0 9993", - "2 25544 51.6455 41.4969 0003508 68.0432 78.3395 15.48957534286754", - ] + "mean_altitude": "400000", + "inclination": 51.6, + "right_ascension_ascending_node": 180, + "true_anomaly": 180, }, "instruments": [{"name": "Test Instrument", "field_of_regard": 25.0}], "number_satellites": 4, @@ -28,42 +37,33 @@ def setUp(self): "name": "Test Constellation", "configuration": "star", "orbit": { - "tle": [ - "1 25544U 98067A 21156.30527927 .00003432 00000-0 70541-4 0 9993", - "2 25544 51.6455 41.4969 0003508 68.0432 78.3395 15.48957534286754", - ] - }, - "instruments": [{"name": "Test Instrument", "field_of_regard": 25.0}], - "number_satellites": 4, - "number_planes": 2, - "relative_spacing": 0, - } - self.s420_con = WalkerConstellation(**self.s420_data) - self.d520_data = { - "name": "Test Constellation", - "configuration": "delta", - "orbit": { - "altitude": "400000", + "mean_altitude": "400000", "inclination": 51.6, "right_ascension_ascending_node": 180, "true_anomaly": 180, }, "instruments": [{"name": "Test Instrument", "field_of_regard": 25.0}], - "number_satellites": 5, + "number_satellites": 4, "number_planes": 2, - "relative_spacing": 1, + "relative_spacing": 0, } - self.d520_con = WalkerConstellation(**self.d520_data) + self.s420_con = WalkerConstellation(**self.s420_data) def normalize_angle(self, angle): + """ + Normalize an angle to the range [0, 360) degrees. + """ return np.mod(360 + angle, 360) def test_invalid_planes(self): + """ + Test that the WalkerConstellation schema raises a ValidationError when the number of planes is invalid. + """ bad_data = { "name": "Test Constellation", "configuration": "delta", "orbit": { - "altitude": "400000", + "mean_altitude": "400000", "inclination": 51.6, "right_ascension_ascending_node": 180, "true_anomaly": 180, @@ -77,11 +77,14 @@ def test_invalid_planes(self): WalkerConstellation(**bad_data) def test_invalid_relative_spacing(self): + """ + Test that the WalkerConstellation schema raises a ValidationError when the relative spacing is invalid. + """ bad_data = { "name": "Test Constellation", "configuration": "delta", "orbit": { - "altitude": "400000", + "mean_altitude": "400000", "inclination": 51.6, "right_ascension_ascending_node": 180, "true_anomaly": 180, @@ -94,10 +97,57 @@ def test_invalid_relative_spacing(self): with self.assertRaises(ValidationError): WalkerConstellation(**bad_data) + def test_number_planes_equal_to_number_satellites_is_valid(self): + """ + Test that number_planes equal to number_satellites (the boundary + of number_planes_le_number_satellites) is valid, not rejected. + """ + con = WalkerConstellation(**{**self.d420_data, "number_planes": 4}) + self.assertEqual(con.number_planes, 4) + + def test_relative_spacing_equal_to_number_planes_minus_one_is_valid(self): + """ + Test that relative_spacing equal to number_planes - 1 (the + boundary of relative_spacing_lt_number_planes) is valid, not + rejected. + """ + con = WalkerConstellation(**{**self.d420_data, "relative_spacing": 1}) + self.assertEqual(con.relative_spacing, 1) + + def test_defaults(self): + """ + Test that optional fields default to a single-satellite, + single-plane Walker Delta constellation. + """ + con = WalkerConstellation( + name="Test Constellation", orbit=self.d420_data["orbit"] + ) + self.assertEqual(con.configuration, "delta") + self.assertEqual(con.number_satellites, 1) + self.assertEqual(con.number_planes, 1) + self.assertEqual(con.relative_spacing, 0) + + def test_type_defaults_to_walker(self): + """ + Test that omitting type defaults to the "walker" discriminator. + """ + self.assertEqual(self.d420_con.type, "walker") + + def test_type_rejects_other_values(self): + """ + Test that type only accepts the "walker" literal, rejecting other + space system type discriminators. + """ + with self.assertRaises(ValidationError): + WalkerConstellation(**self.d420_data, type="train") + def test_constructor(self): + """ + Test that the WalkerConstellation schema correctly initializes with valid data. + """ self.assertEqual(self.d420_con.name, self.d420_data.get("name")) self.assertEqual( - self.d420_con.orbit, TwoLineElements(**self.d420_data.get("orbit")) + self.d420_con.orbit, CircularOrbit(**self.d420_data.get("orbit")) ) self.assertEqual(len(self.d420_con.instruments), 1) self.assertEqual( @@ -118,64 +168,67 @@ def test_constructor(self): ) def test_get_satellites_per_plane(self): + """ + Test that the number of satellites per plane can be calculated correctly. + """ self.assertEqual( self.d420_con.get_satellites_per_plane(), np.ceil(self.d420_con.number_satellites / self.d420_con.number_planes), ) - self.assertEqual( - self.d520_con.get_satellites_per_plane(), - np.ceil(self.d520_con.number_satellites / self.d520_con.number_planes), - ) self.assertEqual( self.s420_con.get_satellites_per_plane(), np.ceil(self.s420_con.number_satellites / self.s420_con.number_planes), ) def test_get_delta_mean_anomaly_within_planes(self): + """ + Test that the delta mean anomaly within planes can be calculated correctly. + """ self.assertEqual( self.d420_con.get_delta_mean_anomaly_within_planes(), 360 / self.d420_con.get_satellites_per_plane(), ) - self.assertEqual( - self.d520_con.get_delta_mean_anomaly_within_planes(), - 360 / self.d520_con.get_satellites_per_plane(), - ) self.assertEqual( self.s420_con.get_delta_mean_anomaly_within_planes(), 360 / self.s420_con.get_satellites_per_plane(), ) def test_get_delta_mean_anomaly_between_planes(self): + """ + Test that the delta mean anomaly between planes can be calculated correctly. + """ self.assertEqual( self.d420_con.get_delta_mean_anomaly_between_planes(), self.d420_con.relative_spacing * 360 / self.d420_con.number_satellites, ) - self.assertEqual( - self.d520_con.get_delta_mean_anomaly_between_planes(), - self.d520_con.relative_spacing * 360 / self.d520_con.number_satellites, - ) self.assertEqual( self.s420_con.get_delta_mean_anomaly_between_planes(), self.s420_con.relative_spacing * 360 / self.s420_con.number_satellites, ) def test_get_delta_raan_between_planes_delta(self): + """ + Test that the delta right ascension of ascending node between planes can be calculated correctly for delta configuration. + """ self.assertEqual( self.d420_con.get_delta_raan_between_planes(), 360 / self.d420_con.number_planes, ) - self.assertEqual( - self.d520_con.get_delta_raan_between_planes(), - 360 / self.d520_con.number_planes, - ) def test_get_delta_raan_between_planes_star(self): + """ + Test that the delta right ascension of ascending node between planes can be calculated correctly for star configuration. + """ self.assertEqual( self.s420_con.get_delta_raan_between_planes(), 180 / self.s420_con.number_planes, ) def helper_test_generate_members(self, constellation): + """ + Helper function to test that the WalkerConstellation schema correctly + generates constellation members with the specified parameters + """ members = constellation.generate_members() self.assertEqual(len(members), constellation.number_satellites) for i in range(len(members) - 1): @@ -230,11 +283,81 @@ def helper_test_generate_members(self, constellation): delta=0.001, ) - def test_generate_members_delta_tle(self): + def test_generate_members_delta(self): + """ + Test that the WalkerConstellation schema correctly generates + constellation members for delta configuration with TLE orbit. + """ self.helper_test_generate_members(self.d420_con) - def test_generate_members_delta_circular(self): - self.helper_test_generate_members(self.d520_con) - - def test_generate_members_star_tle(self): + def test_generate_members_star(self): + """ + Test that the WalkerConstellation schema correctly generates + constellation members for star configuration with TLE orbit. + """ self.helper_test_generate_members(self.s420_con) + + def test_generate_members_first_satellite_matches_lead_orbit(self): + """ + Test that the first generated member (index 0, zero mean anomaly + and RAAN offset) matches the constellation's own lead orbit. + """ + members = self.d420_con.generate_members() + self.assertEqual(members[0].orbit, CircularOrbit(**self.d420_data["orbit"])) + + def test_generate_members_instruments_independent_per_satellite(self): + """ + Test that each generated member gets its own deep-copied + instruments list, not one shared (aliased) across members or with + the constellation itself: mutating one member's instrument must + not affect another member's or the constellation's. + """ + members = self.d420_con.generate_members() + self.assertIsNot(members[0].instruments, members[1].instruments) + self.assertIsNot(members[0].instruments, self.d420_con.instruments) + members[0].instruments[0].name = "Renamed" + self.assertEqual(members[1].instruments[0].name, "Test Instrument") + self.assertEqual(self.d420_con.instruments[0].name, "Test Instrument") + + def test_generate_members_uneven_plane_distribution(self): + """ + Test the documented "(max) number of satellites per plane" + generalization beyond the strict Walker definition: when + number_satellites does not divide evenly by number_planes, + get_satellites_per_plane() ceils, so some planes get fewer + satellites than others (5 satellites / 2 planes -> 3 + 2, not a + ValidationError), and every plane shares the same mean-anomaly + slot spacing (360 / 3 degrees here) rather than each plane + re-spreading its own satellites evenly across the full 360 + degrees. The under-full plane's satellites are therefore + clustered within a portion of the plane, not evenly spaced + around it. + """ + con = WalkerConstellation(**{**self.d420_data, "number_satellites": 5}) + self.assertEqual(con.get_satellites_per_plane(), 3) + members = con.generate_members() + self.assertEqual(len(members), 5) + plane_0 = [m.orbit.right_ascension_ascending_node for m in members[0:3]] + plane_1 = [m.orbit.right_ascension_ascending_node for m in members[3:5]] + self.assertEqual(len(set(plane_0)), 1) + self.assertEqual(len(set(plane_1)), 1) + self.assertNotEqual(plane_0[0], plane_1[0]) + # plane 1's 2 satellites are spaced by a full plane's slot spacing + # (120 degrees, the same as within plane 0), not by 180 degrees + # (360 / 2, which would evenly spread only 2 satellites) + self.assertAlmostEqual( + self.normalize_angle( + members[4].orbit.get_mean_anomaly() + - members[3].orbit.get_mean_anomaly() + ), + con.get_delta_mean_anomaly_within_planes(), + delta=0.001, + ) + self.assertNotAlmostEqual( + self.normalize_angle( + members[4].orbit.get_mean_anomaly() + - members[3].orbit.get_mean_anomaly() + ), + 180.0, + delta=0.001, + ) diff --git a/tests/schemas/surface/__init__.py b/tests/schemas/surface/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/schemas/surface/test_point.py b/tests/schemas/surface/test_point.py new file mode 100644 index 0000000..7575937 --- /dev/null +++ b/tests/schemas/surface/test_point.py @@ -0,0 +1,129 @@ +""" +Unit tests for the Point schema. + +@author Paul T. Grogan +""" + +import unittest + +from pydantic import ValidationError + +from tatc.schemas import Point + + +class TestPoint(unittest.TestCase): + """ + Unit tests for the Point schema. + """ + + def test_good_data(self): + """ + Test that the Point schema correctly initializes with valid data. + """ + good_data = {"id": 42, "latitude": 40.74259, "longitude": -74.02686} + o = Point(**good_data) + self.assertEqual(o.id, good_data.get("id")) + self.assertEqual(o.latitude, good_data.get("latitude")) + self.assertEqual(o.longitude, good_data.get("longitude")) + + def test_bad_latitude_too_big(self): + """ + Test that the Point schema raises a ValidationError when the latitude is too large. + """ + bad_data = {"id": 0, "latitude": 100.0, "longitude": -74.02686} + with self.assertRaises(ValidationError): + Point(**bad_data) + + def test_bad_latitude_too_small(self): + """ + Test that the Point schema raises a ValidationError when the latitude is too small. + """ + bad_data = {"id": 0, "latitude": -90.1, "longitude": -74.02686} + with self.assertRaises(ValidationError): + Point(**bad_data) + + def test_bad_latitude_missing(self): + """ + Test that the Point schema raises a ValidationError when the latitude is missing. + """ + bad_data = {"id": 0, "longitude": -74.02686} + with self.assertRaises(ValidationError): + Point(**bad_data) + + def test_bad_longitude_too_big(self): + """ + Test that the Point schema raises a ValidationError when the longitude is too large. + """ + bad_data = {"id": 0, "latitude": 40.74259, "longitude": 180.1} + with self.assertRaises(ValidationError): + Point(**bad_data) + + def test_bad_longitude_too_small(self): + """ + Test that the Point schema raises a ValidationError when the longitude is too small. + """ + bad_data = {"id": 0, "latitude": 40.74259, "longitude": -180.1} + with self.assertRaises(ValidationError): + Point(**bad_data) + + def test_bad_longitude_missing(self): + """ + Test that the Point schema raises a ValidationError when the longitude is missing. + """ + bad_data = {"id": 0, "latitude": 40.74259} + with self.assertRaises(ValidationError): + Point(**bad_data) + + def test_latitude_boundary_values(self): + """ + Test that latitude values exactly at the poles (-90, 90 degrees) + are accepted rather than rejected by an off-by-one bound. + """ + self.assertEqual(Point(latitude=90, longitude=0).latitude, 90) + self.assertEqual(Point(latitude=-90, longitude=0).latitude, -90) + + def test_longitude_boundary_values(self): + """ + Test that longitude values exactly at the antimeridian (-180, 180 + degrees) are accepted rather than rejected by an off-by-one bound. + """ + self.assertEqual(Point(latitude=0, longitude=180).longitude, 180) + self.assertEqual(Point(latitude=0, longitude=-180).longitude, -180) + + def test_id_defaults_to_zero(self): + """ + Test that omitting id defaults to 0. + """ + self.assertEqual(Point(latitude=0, longitude=0).id, 0) + + def test_id_rejects_negative(self): + """ + Test that a negative id is rejected, since id is a + NonNegativeInt. + """ + with self.assertRaises(ValidationError): + Point(id=-1, latitude=0, longitude=0) + + def test_id_rejects_non_integer(self): + """ + Test that a fractional id (not cleanly convertible to int) is + rejected rather than silently truncated. + """ + with self.assertRaises(ValidationError): + Point(id=5.5, latitude=0, longitude=0) + + def test_elevation_defaults_to_zero(self): + """ + Test that omitting elevation defaults to 0. + """ + self.assertEqual(Point(latitude=0, longitude=0).elevation, 0) + + def test_elevation_accepts_negative_values(self): + """ + Test that elevation accepts negative values (e.g. below-sea-level + locations like Death Valley or the Dead Sea), since it has no + lower-bound constraint. + """ + self.assertEqual( + Point(latitude=0, longitude=0, elevation=-430.5).elevation, -430.5 + ) diff --git a/tests/schemas/surface/test_station.py b/tests/schemas/surface/test_station.py new file mode 100644 index 0000000..5986ae9 --- /dev/null +++ b/tests/schemas/surface/test_station.py @@ -0,0 +1,121 @@ +""" +Unit tests for the GroundStation schema. + +@author Paul T. Grogan +""" + +import unittest +from datetime import timedelta + +from pydantic import ValidationError + +from tatc.schemas import GroundStation + + +class TestGroundStation(unittest.TestCase): + """ + Unit tests for the GroundStation schema. + """ + + def test_good_data(self): + """ + Test that the GroundStation schema correctly initializes with valid data. + """ + good_data = { + "name": "test", + "latitude": 40.74259, + "longitude": -74.02686, + "min_elevation_angle": 20.0, + "min_access_time": timedelta(20), + } + o = GroundStation(**good_data) + self.assertEqual(o.name, good_data.get("name")) + self.assertEqual(o.latitude, good_data.get("latitude")) + self.assertEqual(o.longitude, good_data.get("longitude")) + self.assertEqual(o.min_elevation_angle, good_data.get("min_elevation_angle")) + self.assertEqual(o.min_access_time, good_data.get("min_access_time")) + + def test_good_data_timedelta_seconds(self): + """ + Test that the GroundStation schema correctly initializes with valid data + when min_access_time is provided as seconds. + """ + good_data = { + "name": "test", + "latitude": 40.74259, + "longitude": -74.02686, + "min_elevation_angle": 20.0, + "min_access_time": 20, + } + o = GroundStation(**good_data) + self.assertEqual(o.name, good_data.get("name")) + self.assertEqual(o.latitude, good_data.get("latitude")) + self.assertEqual(o.longitude, good_data.get("longitude")) + self.assertEqual(o.min_elevation_angle, good_data.get("min_elevation_angle")) + self.assertEqual( + o.min_access_time, timedelta(seconds=good_data.get("min_access_time")) + ) + + def test_defaults(self): + """ + Test that min_elevation_angle defaults to 0 and min_access_time + defaults to a zero timedelta when omitted. + """ + o = GroundStation(name="test", latitude=40.74259, longitude=-74.02686) + self.assertEqual(o.min_elevation_angle, 0) + self.assertEqual(o.min_access_time, timedelta(0)) + + def test_bad_name_missing(self): + """ + Test that the GroundStation schema raises a ValidationError when + the required name field is missing. + """ + bad_data = {"latitude": 40.74259, "longitude": -74.02686} + with self.assertRaises(ValidationError): + GroundStation(**bad_data) + + def test_bad_min_elevation_angle_negative(self): + """ + Test that a negative min_elevation_angle is rejected. + """ + with self.assertRaises(ValidationError): + GroundStation( + name="test", latitude=0, longitude=0, min_elevation_angle=-0.1 + ) + + def test_bad_min_elevation_angle_too_large(self): + """ + Test that a min_elevation_angle above 90 degrees is rejected. + """ + with self.assertRaises(ValidationError): + GroundStation( + name="test", latitude=0, longitude=0, min_elevation_angle=90.1 + ) + + def test_min_elevation_angle_boundary_values(self): + """ + Test that min_elevation_angle values exactly at its bounds (0 and + 90 degrees) are accepted. + """ + self.assertEqual( + GroundStation( + name="test", latitude=0, longitude=0, min_elevation_angle=0 + ).min_elevation_angle, + 0, + ) + self.assertEqual( + GroundStation( + name="test", latitude=0, longitude=0, min_elevation_angle=90 + ).min_elevation_angle, + 90, + ) + + def test_inherits_point_latitude_validation(self): + """ + Test that GroundStation inherits Point's latitude validation + (out-of-range latitude is still rejected), confirming the + inheritance relationship is functionally in effect and not just + structural. + """ + with self.assertRaises(ValidationError): + GroundStation(name="test", latitude=100, longitude=0) diff --git a/tests/schemas/test_circular_orbit.py b/tests/schemas/test_circular_orbit.py deleted file mode 100644 index cc0847a..0000000 --- a/tests/schemas/test_circular_orbit.py +++ /dev/null @@ -1,82 +0,0 @@ -import unittest - -from datetime import datetime, timezone - -from tatc.schemas import CircularOrbit - - -class TestCircularOrbit(unittest.TestCase): - def setUp(self): - self.test_data = { - "altitude": 400000, - "true_anomaly": 10.0, - "epoch": datetime(2022, 1, 1, 12, tzinfo=timezone.utc), - "inclination": 45.0, - "right_ascension_ascending_node": 50.0, - } - self.test_orbit = CircularOrbit(**self.test_data) - - def test_good_data(self): - self.assertEqual(self.test_orbit.altitude, self.test_data.get("altitude")) - self.assertEqual( - self.test_orbit.true_anomaly, self.test_data.get("true_anomaly") - ) - self.assertEqual(self.test_orbit.epoch, self.test_data.get("epoch")) - self.assertEqual(self.test_orbit.inclination, self.test_data.get("inclination")) - self.assertEqual( - self.test_orbit.right_ascension_ascending_node, - self.test_data.get("right_ascension_ascending_node"), - ) - - def test_good_data_iso8601_datetime(self): - good_data = { - "altitude": 400000, - "true_anomaly": 10.0, - "epoch": "2022-01-01T12:00:00Z", - "inclination": 45.0, - "right_ascension_ascending_node": 50.0, - } - o = CircularOrbit(**good_data) - self.assertEqual(o.altitude, good_data.get("altitude")) - self.assertEqual(o.true_anomaly, good_data.get("true_anomaly")) - self.assertEqual(o.epoch, datetime(2022, 1, 1, 12, tzinfo=timezone.utc)) - self.assertEqual(o.inclination, good_data.get("inclination")) - self.assertEqual( - o.right_ascension_ascending_node, - good_data.get("right_ascension_ascending_node"), - ) - - def test_get_derived_orbit(self): - derived_orbit = self.test_orbit.get_derived_orbit(20, 10) - self.assertAlmostEqual( - derived_orbit.get_mean_anomaly(), - self.test_orbit.get_mean_anomaly() + 20, - delta=0.001, - ) - self.assertAlmostEqual( - derived_orbit.right_ascension_ascending_node, - self.test_orbit.right_ascension_ascending_node + 10, - delta=0.001, - ) - - def test_to_tle(self): - tle = self.test_orbit.to_tle() - self.assertAlmostEqual( - tle.get_altitude(), self.test_data.get("altitude"), delta=1.0 - ) - self.assertAlmostEqual( - tle.get_true_anomaly(), self.test_data.get("true_anomaly"), delta=0.001 - ) - self.assertAlmostEqual( - tle.get_epoch().timestamp(), - self.test_data.get("epoch").timestamp(), - delta=1, - ) - self.assertEqual( - tle.get_inclination(), - self.test_data.get("inclination"), - ) - self.assertAlmostEqual( - tle.get_right_ascension_ascending_node(), - self.test_data.get("right_ascension_ascending_node"), - ) diff --git a/tests/schemas/test_ground.py b/tests/schemas/test_ground.py deleted file mode 100644 index 807410a..0000000 --- a/tests/schemas/test_ground.py +++ /dev/null @@ -1,39 +0,0 @@ -import unittest - -from datetime import timedelta - -from tatc.schemas import GroundStation - - -class TestGroundStation(unittest.TestCase): - def test_good_data(self): - good_data = { - "name": "test", - "latitude": 40.74259, - "longitude": -74.02686, - "min_elevation_angle": 20.0, - "min_access_time": timedelta(20), - } - o = GroundStation(**good_data) - self.assertEqual(o.name, good_data.get("name")) - self.assertEqual(o.latitude, good_data.get("latitude")) - self.assertEqual(o.longitude, good_data.get("longitude")) - self.assertEqual(o.min_elevation_angle, good_data.get("min_elevation_angle")) - self.assertEqual(o.min_access_time, good_data.get("min_access_time")) - - def test_good_data_timedelta_seconds(self): - good_data = { - "name": "test", - "latitude": 40.74259, - "longitude": -74.02686, - "min_elevation_angle": 20.0, - "min_access_time": 20, - } - o = GroundStation(**good_data) - self.assertEqual(o.name, good_data.get("name")) - self.assertEqual(o.latitude, good_data.get("latitude")) - self.assertEqual(o.longitude, good_data.get("longitude")) - self.assertEqual(o.min_elevation_angle, good_data.get("min_elevation_angle")) - self.assertEqual( - o.min_access_time, timedelta(seconds=good_data.get("min_access_time")) - ) diff --git a/tests/schemas/test_instrument.py b/tests/schemas/test_instrument.py deleted file mode 100644 index 13bfeef..0000000 --- a/tests/schemas/test_instrument.py +++ /dev/null @@ -1,209 +0,0 @@ -import unittest - -from datetime import datetime, timedelta, timezone -from sgp4.api import Satrec, WGS72 -from skyfield.api import EarthSatellite - -from tatc.schemas import Instrument, CircularOrbit -from tatc.constants import timescale - - -class TestInstrument(unittest.TestCase): - def setUp(self): - noon_utc = datetime(2020, 3, 20, 12, tzinfo=timezone.utc) - self.test_time = timescale.from_datetime(noon_utc) - test_orbit_1 = CircularOrbit( - altitude=400000, - true_anomaly=0, - epoch=noon_utc, - inclination=0.0, - right_ascension_ascending_node=0.0, - ).to_tle() - self.test_sat_1 = EarthSatellite.from_satrec( - Satrec.twoline2rv(test_orbit_1.tle[0], test_orbit_1.tle[1], WGS72), - timescale, - ) - test_orbit_2 = CircularOrbit( - altitude=400000, - true_anomaly=0, - epoch=noon_utc, - inclination=0.0, - right_ascension_ascending_node=80.0, - ).to_tle() - self.test_sat_2 = EarthSatellite.from_satrec( - Satrec.twoline2rv(test_orbit_2.tle[0], test_orbit_2.tle[1], WGS72), - timescale, - ) - test_orbit_3 = CircularOrbit( - altitude=400000, - true_anomaly=0, - epoch=noon_utc, - inclination=0.0, - right_ascension_ascending_node=100.0, - ).to_tle() - self.test_sat_3 = EarthSatellite.from_satrec( - Satrec.twoline2rv(test_orbit_3.tle[0], test_orbit_3.tle[1], WGS72), - timescale, - ) - test_orbit_4 = CircularOrbit( - altitude=400000, - true_anomaly=0, - epoch=noon_utc, - inclination=0.0, - right_ascension_ascending_node=180.0, - ).to_tle() - self.test_sat_4 = EarthSatellite.from_satrec( - Satrec.twoline2rv(test_orbit_4.tle[0], test_orbit_4.tle[1], WGS72), - timescale, - ) - test_orbit_5 = CircularOrbit( - altitude=400000, - true_anomaly=0, - epoch=noon_utc, - inclination=45.0, - right_ascension_ascending_node=0.0, - ).to_tle() - self.test_sat_5 = EarthSatellite.from_satrec( - Satrec.twoline2rv(test_orbit_5.tle[0], test_orbit_5.tle[1], WGS72), - timescale, - ) - - def test_good_data(self): - good_data = { - "name": "Test Instrument", - "field_of_regard": 20.0, - "min_access_time": timedelta(seconds=10), - "req_self_sunlit": None, - "req_target_sunlit": None, - } - o = Instrument(**good_data) - self.assertEqual(o.name, good_data.get("name")) - self.assertEqual(o.field_of_regard, good_data.get("field_of_regard")) - self.assertEqual(o.min_access_time, good_data.get("min_access_time")) - self.assertEqual(o.req_self_sunlit, good_data.get("req_self_sunlit")) - self.assertEqual(o.req_target_sunlit, good_data.get("req_target_sunlit")) - - def test_get_swath_width(self): - o = Instrument( - **{ - "name": "GMI", - "field_of_regard": 15.0, - } - ) - self.assertAlmostEqual(o.get_swath_width(705000), 185815, delta=1.0) - - def test_get_min_elevation_angle(self): - o = Instrument( - **{ - "name": "GMI", - "field_of_regard": 15.0, - } - ) - self.assertAlmostEqual(o.get_min_elevation_angle(705000), 81.66446, delta=0.01) - - def test_valid_observation_no_constraints(self): - o = Instrument(name="Test Instrument") - self.assertTrue(o.is_valid_observation(self.test_sat_1.at(self.test_time))) - self.assertTrue(o.is_valid_observation(self.test_sat_2.at(self.test_time))) - self.assertTrue(o.is_valid_observation(self.test_sat_3.at(self.test_time))) - self.assertTrue(o.is_valid_observation(self.test_sat_4.at(self.test_time))) - - def test_valid_observation_self_sunlit(self): - o = Instrument(name="Test Instrument", req_self_sunlit=True) - self.assertTrue(o.is_valid_observation(self.test_sat_1.at(self.test_time))) - self.assertTrue(o.is_valid_observation(self.test_sat_2.at(self.test_time))) - self.assertTrue(o.is_valid_observation(self.test_sat_3.at(self.test_time))) - self.assertFalse(o.is_valid_observation(self.test_sat_4.at(self.test_time))) - - def test_valid_observation_self_not_sunlit(self): - o = Instrument(name="Test Instrument", req_self_sunlit=False) - self.assertFalse(o.is_valid_observation(self.test_sat_1.at(self.test_time))) - self.assertFalse(o.is_valid_observation(self.test_sat_2.at(self.test_time))) - self.assertFalse(o.is_valid_observation(self.test_sat_3.at(self.test_time))) - self.assertTrue(o.is_valid_observation(self.test_sat_4.at(self.test_time))) - - def test_valid_observation_target_sunlit(self): - o = Instrument(name="Test Instrument", req_target_sunlit=True) - self.assertTrue(o.is_valid_observation(self.test_sat_1.at(self.test_time))) - self.assertTrue(o.is_valid_observation(self.test_sat_2.at(self.test_time))) - self.assertFalse(o.is_valid_observation(self.test_sat_3.at(self.test_time))) - self.assertFalse(o.is_valid_observation(self.test_sat_4.at(self.test_time))) - - def test_valid_observation_target_not_sunlit(self): - o = Instrument(name="Test Instrument", req_target_sunlit=False) - self.assertFalse(o.is_valid_observation(self.test_sat_1.at(self.test_time))) - self.assertFalse(o.is_valid_observation(self.test_sat_2.at(self.test_time))) - self.assertTrue(o.is_valid_observation(self.test_sat_3.at(self.test_time))) - self.assertTrue(o.is_valid_observation(self.test_sat_4.at(self.test_time))) - - def test_valid_observation_self_sunlit_target_sunlit(self): - o = Instrument( - name="Test Instrument", req_self_sunlit=True, req_target_sunlit=True - ) - self.assertTrue(o.is_valid_observation(self.test_sat_1.at(self.test_time))) - self.assertTrue(o.is_valid_observation(self.test_sat_2.at(self.test_time))) - self.assertFalse(o.is_valid_observation(self.test_sat_3.at(self.test_time))) - self.assertFalse(o.is_valid_observation(self.test_sat_4.at(self.test_time))) - - def test_valid_observation_self_not_sunlit_target_sunlit(self): - o = Instrument( - name="Test Instrument", req_self_sunlit=False, req_target_sunlit=True - ) - self.assertFalse(o.is_valid_observation(self.test_sat_1.at(self.test_time))) - self.assertFalse(o.is_valid_observation(self.test_sat_2.at(self.test_time))) - self.assertFalse(o.is_valid_observation(self.test_sat_3.at(self.test_time))) - self.assertFalse(o.is_valid_observation(self.test_sat_4.at(self.test_time))) - - def test_valid_observation_self_sunlit_target_not_sunlit(self): - o = Instrument( - name="Test Instrument", req_self_sunlit=True, req_target_sunlit=False - ) - self.assertFalse(o.is_valid_observation(self.test_sat_1.at(self.test_time))) - self.assertFalse(o.is_valid_observation(self.test_sat_2.at(self.test_time))) - self.assertTrue(o.is_valid_observation(self.test_sat_3.at(self.test_time))) - self.assertFalse(o.is_valid_observation(self.test_sat_4.at(self.test_time))) - - def test_valid_observation_self_not_sunlit_target_not_sunlit(self): - o = Instrument( - name="Test Instrument", req_self_sunlit=False, req_target_sunlit=False - ) - self.assertFalse(o.is_valid_observation(self.test_sat_1.at(self.test_time))) - self.assertFalse(o.is_valid_observation(self.test_sat_2.at(self.test_time))) - self.assertFalse(o.is_valid_observation(self.test_sat_3.at(self.test_time))) - self.assertTrue(o.is_valid_observation(self.test_sat_4.at(self.test_time))) - - def test_valid_observation_self_sunlit_vector(self): - o = Instrument(name="Test Instrument", req_self_sunlit=True) - times = timescale.utc(2020, 3, 20, [11, 12, 13]) - results = o.is_valid_observation(self.test_sat_1.at(times)) - self.assertEqual(len(results), 3) - self.assertFalse(results[0]) - self.assertTrue(results[1]) - self.assertFalse(results[2]) - - def test_valid_observation_target_sunlit_vector(self): - o = Instrument(name="Test Instrument", req_target_sunlit=True) - times = timescale.utc(2020, 3, 20, [11, 12, 13]) - results = o.is_valid_observation(self.test_sat_1.at(times)) - self.assertEqual(len(results), 3) - self.assertFalse(results[0]) - self.assertTrue(results[1]) - self.assertFalse(results[2]) - - def test_valid_observation_self_sunlit_vector_inclined(self): - o = Instrument(name="Test Instrument", req_self_sunlit=True) - times = timescale.utc(2020, 3, 20, [11, 12, 13]) - results = o.is_valid_observation(self.test_sat_5.at(times)) - self.assertEqual(len(results), 3) - self.assertFalse(results[0]) - self.assertTrue(results[1]) - self.assertFalse(results[2]) - - def test_valid_observation_target_sunlit_vector_inclined(self): - o = Instrument(name="Test Instrument", req_target_sunlit=True) - times = timescale.utc(2020, 3, 20, [11, 12, 13]) - results = o.is_valid_observation(self.test_sat_5.at(times)) - self.assertEqual(len(results), 3) - self.assertFalse(results[0]) - self.assertTrue(results[1]) - self.assertFalse(results[2]) diff --git a/tests/schemas/test_keplerian_orbit.py b/tests/schemas/test_keplerian_orbit.py deleted file mode 100644 index 381ead2..0000000 --- a/tests/schemas/test_keplerian_orbit.py +++ /dev/null @@ -1,80 +0,0 @@ -import unittest - -from datetime import datetime, timezone - -from tatc.schemas import KeplerianOrbit - - -class TestKeplerianOrbit(unittest.TestCase): - def setUp(self): - self.test_data = { - "altitude": 400000, - "true_anomaly": 10.0, - "epoch": datetime(2022, 1, 1, 12, 0, 0, tzinfo=timezone.utc), - "inclination": 45.0, - "right_ascension_ascending_node": 50.0, - "eccentricity": 0.01, - "perigee_argument": 100.0, - } - self.test_orbit = KeplerianOrbit(**self.test_data) - - def test_good_data(self): - self.assertEqual(self.test_orbit.altitude, self.test_data.get("altitude")) - self.assertEqual( - self.test_orbit.true_anomaly, self.test_data.get("true_anomaly") - ) - self.assertEqual(self.test_orbit.epoch, self.test_data.get("epoch")) - self.assertEqual(self.test_orbit.inclination, self.test_data.get("inclination")) - self.assertEqual( - self.test_orbit.right_ascension_ascending_node, - self.test_data.get("right_ascension_ascending_node"), - ) - self.assertEqual( - self.test_orbit.eccentricity, self.test_data.get("eccentricity") - ) - self.assertEqual( - self.test_orbit.perigee_argument, self.test_data.get("perigee_argument") - ) - - def test_get_derived_orbit(self): - derived_orbit = self.test_orbit.get_derived_orbit(20, 10) - self.assertAlmostEqual( - derived_orbit.get_mean_anomaly(), - self.test_orbit.get_mean_anomaly() + 20, - delta=0.001, - ) - self.assertAlmostEqual( - derived_orbit.right_ascension_ascending_node, - self.test_orbit.right_ascension_ascending_node + 10, - delta=0.001, - ) - - def test_to_tle(self): - tle = self.test_orbit.to_tle() - self.assertAlmostEqual( - tle.get_altitude(), self.test_data.get("altitude"), delta=1.0 - ) - self.assertAlmostEqual( - tle.get_true_anomaly(), self.test_data.get("true_anomaly"), delta=0.001 - ) - self.assertAlmostEqual( - tle.get_epoch().timestamp(), - self.test_data.get("epoch").timestamp(), - delta=1, - ) - self.assertEqual( - tle.get_inclination(), - self.test_data.get("inclination"), - ) - self.assertAlmostEqual( - tle.get_right_ascension_ascending_node(), - self.test_data.get("right_ascension_ascending_node"), - ) - self.assertAlmostEqual( - tle.get_eccentricity(), - self.test_data.get("eccentricity"), - ) - self.assertAlmostEqual( - tle.get_perigee_argument(), - self.test_data.get("perigee_argument"), - ) diff --git a/tests/schemas/test_molniya_orbit.py b/tests/schemas/test_molniya_orbit.py deleted file mode 100644 index 2838c01..0000000 --- a/tests/schemas/test_molniya_orbit.py +++ /dev/null @@ -1,62 +0,0 @@ -import unittest - -from datetime import datetime, timezone - -from tatc.schemas import MolniyaOrbit - - -class TestMolniyaOrbit(unittest.TestCase): - def setUp(self): - self.test_data = { - "altitude": 1199100, - "true_anomaly": 53.42, - "epoch": datetime(2024, 11, 20, 7, 11, 20, 742432, tzinfo=timezone.utc), - "inclination": 64.2583, - "right_ascension_ascending_node": 11.0394, - "perigee_argument": 289.3942, - "orbit_period": 86400, - } - self.test_orbit = MolniyaOrbit(**self.test_data) - - def test_good_data(self): - self.assertEqual(self.test_orbit.altitude, self.test_data.get("altitude")) - self.assertEqual( - self.test_orbit.true_anomaly, self.test_data.get("true_anomaly") - ) - self.assertEqual(self.test_orbit.epoch, self.test_data.get("epoch")) - self.assertEqual(self.test_orbit.inclination, self.test_data.get("inclination")) - self.assertEqual( - self.test_orbit.right_ascension_ascending_node, - self.test_data.get("right_ascension_ascending_node"), - ) - self.assertEqual( - self.test_orbit.perigee_argument, self.test_data.get("perigee_argument") - ) - - def test_get_derived_orbit(self): - derived_orbit = self.test_orbit.get_derived_orbit(20, 10) - self.assertAlmostEqual( - derived_orbit.right_ascension_ascending_node, - self.test_orbit.right_ascension_ascending_node + 10, - delta=0.001, - ) - - def test_to_tle(self): - tle = self.test_orbit.to_tle() - self.assertAlmostEqual( - tle.get_epoch().timestamp(), - self.test_data.get("epoch").timestamp(), - delta=1, - ) - self.assertEqual( - tle.get_inclination(), - self.test_data.get("inclination"), - ) - self.assertAlmostEqual( - tle.get_right_ascension_ascending_node(), - self.test_data.get("right_ascension_ascending_node"), - ) - self.assertAlmostEqual( - tle.get_perigee_argument(), - self.test_data.get("perigee_argument"), - ) diff --git a/tests/schemas/test_orbit_base.py b/tests/schemas/test_orbit_base.py deleted file mode 100644 index dda5237..0000000 --- a/tests/schemas/test_orbit_base.py +++ /dev/null @@ -1,54 +0,0 @@ -import unittest - -from datetime import datetime, timedelta, timezone -import numpy as np - -from tatc.schemas import CircularOrbit -from tatc.constants import EARTH_MEAN_RADIUS, EARTH_MU - - -class TestOrbitBase(unittest.TestCase): - def setUp(self): - self.test_data = { - "altitude": 400000, - "true_anomaly": 10.0, - "epoch": datetime(2022, 1, 1, 12, 0, 0, tzinfo=timezone.utc), - } - self.test_orbit = CircularOrbit(**self.test_data) - - def test_get_semimajor_axis(self): - self.assertEqual( - self.test_orbit.get_semimajor_axis(), - self.test_data.get("altitude") + EARTH_MEAN_RADIUS, - ) - - def test_get_mean_anomaly(self): - self.assertEqual( - self.test_orbit.get_mean_anomaly(), self.test_data.get("true_anomaly") - ) - - def test_get_mean_motion(self): - orbit_period = ( - 2 - * np.pi - * np.sqrt( - np.power(EARTH_MEAN_RADIUS + self.test_orbit.altitude, 3) / EARTH_MU - ) - ) - self.assertAlmostEqual( - self.test_orbit.get_mean_motion(), 1 / (orbit_period / 86400), delta=0.001 - ) - - def test_get_orbit_period(self): - orbit_period = ( - 2 - * np.pi - * np.sqrt( - np.power(EARTH_MEAN_RADIUS + self.test_orbit.altitude, 3) / EARTH_MU - ) - ) - self.assertAlmostEqual( - self.test_orbit.get_orbit_period(), - timedelta(seconds=orbit_period), - delta=1.0, - ) diff --git a/tests/schemas/test_point.py b/tests/schemas/test_point.py deleted file mode 100644 index ffb7d40..0000000 --- a/tests/schemas/test_point.py +++ /dev/null @@ -1,44 +0,0 @@ -import unittest - -from pydantic import ValidationError - -from tatc.schemas import Point - - -class TestPoint(unittest.TestCase): - def test_good_data(self): - good_data = {"id": 42, "latitude": 40.74259, "longitude": -74.02686} - o = Point(**good_data) - self.assertEqual(o.id, good_data.get("id")) - self.assertEqual(o.latitude, good_data.get("latitude")) - self.assertEqual(o.longitude, good_data.get("longitude")) - - def test_bad_latitude_too_big(self): - bad_data = {"id": 0, "latitude": 100.0, "longitude": -74.02686} - with self.assertRaises(ValidationError): - Point(**bad_data) - - def test_bad_latitude_too_small(self): - bad_data = {"id": 0, "latitude": -90.1, "longitude": -74.02686} - with self.assertRaises(ValidationError): - Point(**bad_data) - - def test_bad_latitude_missing(self): - bad_data = {"id": 0, "longitude": -74.02686} - with self.assertRaises(ValidationError): - Point(**bad_data) - - def test_bad_longitude_too_big(self): - bad_data = {"id": 0, "latitude": 40.74259, "longitude": 180.1} - with self.assertRaises(ValidationError): - Point(**bad_data) - - def test_bad_longitude_too_small(self): - bad_data = {"id": 0, "latitude": 40.74259, "longitude": -180.1} - with self.assertRaises(ValidationError): - Point(**bad_data) - - def test_bad_longitude_missing(self): - bad_data = {"id": 0, "latitude": 40.74259} - with self.assertRaises(ValidationError): - Point(**bad_data) diff --git a/tests/schemas/test_satellite.py b/tests/schemas/test_satellite.py deleted file mode 100644 index a3fc35c..0000000 --- a/tests/schemas/test_satellite.py +++ /dev/null @@ -1,34 +0,0 @@ -import unittest - -from tatc.schemas import Satellite, TwoLineElements, Instrument - - -class TestSatellite(unittest.TestCase): - def setUp(self): - self.test_data = { - "name": "Test Satellite", - "orbit": { - "tle": [ - "1 25544U 98067A 21156.30527927 .00003432 00000-0 70541-4 0 9993", - "2 25544 51.6455 41.4969 0003508 68.0432 78.3395 15.48957534286754", - ] - }, - "instruments": [{"name": "Test Instrument", "field_of_regard": 25.0}], - } - self.test_sat = Satellite(**self.test_data) - - def test_good_data(self): - self.assertEqual(self.test_sat.name, self.test_data.get("name")) - self.assertEqual( - self.test_sat.orbit, TwoLineElements(**self.test_data.get("orbit")) - ) - self.assertEqual(len(self.test_sat.instruments), 1) - self.assertEqual( - self.test_sat.instruments[0], - Instrument(**self.test_data.get("instruments")[0]), - ) - - def test_generate_members(self): - members = self.test_sat.generate_members() - self.assertEqual(len(members), 1) - self.assertEqual(members[0], self.test_sat) diff --git a/tests/schemas/test_soc_constellation.py b/tests/schemas/test_soc_constellation.py deleted file mode 100644 index c9ccd99..0000000 --- a/tests/schemas/test_soc_constellation.py +++ /dev/null @@ -1,57 +0,0 @@ -import unittest - -from datetime import timedelta -import numpy as np -from pydantic import ValidationError - -from tatc.utils import field_of_regard_to_swath_width -from tatc.schemas import SOCConstellation, CircularOrbit, Instrument - - -class TestSOCConstellation(unittest.TestCase): - def setUp(self): - self.d420_data = { - "name": "Test Constellation", - "orbit": { - "tle": [ - "1 25544U 98067A 21156.30527927 .00003432 00000-0 70541-4 0 9993", - "2 25544 51.6455 41.4969 0003508 68.0432 78.3395 15.48957534286754", - ], - "altitude": 780000, - "inclination": 86.4, - }, - "instruments": [{"name": "Test Instrument", "field_of_regard": 180.0}], - "swath_width": field_of_regard_to_swath_width( - altitude=780000, field_of_regard=150 - ), - "packing_distance": 1, - } - self.d420_con = SOCConstellation(**self.d420_data) - - def test_constructor(self): - self.assertEqual(self.d420_con.name, self.d420_data.get("name")) - self.assertEqual( - self.d420_con.orbit, CircularOrbit(**self.d420_data.get("orbit")) - ) - self.assertEqual(len(self.d420_con.instruments), 1) - self.assertEqual( - self.d420_con.instruments[0], - Instrument(**self.d420_data.get("instruments")[0]), - ) - self.assertEqual(self.d420_con.swath_width, self.d420_data.get("swath_width")) - self.assertEqual( - self.d420_con.packing_distance, self.d420_data.get("packing_distance") - ) - - def test_get_num_satellites(self): - self.assertEqual( - len(self.d420_con.generate_members()), - self.d420_con.generate_walker().number_satellites, - ) - - def test_get_satellites_per_plane(self): - self.assertEqual( - self.d420_con.generate_walker().number_satellites - / self.d420_con.generate_walker().number_planes, - 7, - ) diff --git a/tests/schemas/test_sun_synchronous_orbit.py b/tests/schemas/test_sun_synchronous_orbit.py deleted file mode 100644 index 4865f68..0000000 --- a/tests/schemas/test_sun_synchronous_orbit.py +++ /dev/null @@ -1,120 +0,0 @@ -import unittest - -from datetime import datetime, time, timezone - -from tatc.schemas import SunSynchronousOrbit - - -class TestSunSynchronousOrbit(unittest.TestCase): - def setUp(self): - self.test_data = { - "altitude": 567000, - "true_anomaly": 0.0, - "epoch": datetime(2022, 1, 1, 12, 0, 0, tzinfo=timezone.utc), - "equator_crossing_time": time(10, 30), - "equator_crossing_ascending": True, - } - self.test_orbit = SunSynchronousOrbit(**self.test_data) - - def test_good_data(self): - good_data = { - "altitude": 400000, - "true_anomaly": 10.0, - "epoch": datetime(2022, 1, 1, 12, 0, 0, tzinfo=timezone.utc), - "equator_crossing_time": time(10, 30), - "equator_crossing_ascending": True, - } - o = SunSynchronousOrbit(**good_data) - self.assertEqual(o.altitude, good_data.get("altitude")) - self.assertEqual(o.true_anomaly, good_data.get("true_anomaly")) - self.assertEqual( - o.equator_crossing_time, good_data.get("equator_crossing_time") - ) - self.assertEqual( - o.equator_crossing_ascending, good_data.get("equator_crossing_ascending") - ) - - def test_get_derived_orbit(self): - derived_orbit = self.test_orbit.get_derived_orbit(20, 10) - self.assertAlmostEqual( - derived_orbit.get_mean_anomaly(), - self.test_orbit.get_mean_anomaly() + 20, - delta=0.001, - ) - self.assertAlmostEqual( - derived_orbit.right_ascension_ascending_node, - self.test_orbit.get_right_ascension_ascending_node() + 10, - delta=0.001, - ) - - def test_to_tle(self): - tle = self.test_orbit.to_tle() - self.assertAlmostEqual( - tle.get_altitude(), self.test_data.get("altitude"), delta=1.0 - ) - self.assertAlmostEqual( - tle.get_true_anomaly(), self.test_data.get("true_anomaly"), delta=0.001 - ) - self.assertAlmostEqual( - tle.get_epoch().timestamp(), - self.test_data.get("epoch").timestamp(), - delta=1, - ) - self.assertAlmostEqual(tle.get_inclination(), 97.7, delta=0.1) - - def test_to_tle_raan_ascending_equinox(self): - data = { - "altitude": 567000, - "true_anomaly": 0.0, - "epoch": datetime(2020, 3, 20, 3, 49, 0, tzinfo=timezone.utc), - "equator_crossing_time": time(12), - "equator_crossing_ascending": True, - } - tle = SunSynchronousOrbit(**data).to_tle() - self.assertAlmostEqual( - min( - tle.get_right_ascension_ascending_node(), - 360.0 - tle.get_right_ascension_ascending_node(), - ), - 0.0, - delta=0.25, - ) - - def test_to_tle_raan_descending_equinox(self): - data = { - "altitude": 567000, - "true_anomaly": 0.0, - "epoch": datetime(2020, 3, 20, 3, 49, 0, tzinfo=timezone.utc), - "equator_crossing_time": time(12), - "equator_crossing_ascending": False, - } - tle = SunSynchronousOrbit(**data).to_tle() - self.assertAlmostEqual( - tle.get_right_ascension_ascending_node(), 180.0, delta=0.25 - ) - - def test_to_tle_raan_ascending_solstice(self): - data = { - "altitude": 567000, - "true_anomaly": 0.0, - "epoch": datetime(2020, 6, 21, 9, 14, 0, tzinfo=timezone.utc), - "equator_crossing_time": time(12), - "equator_crossing_ascending": True, - } - tle = SunSynchronousOrbit(**data).to_tle() - self.assertAlmostEqual( - tle.get_right_ascension_ascending_node(), 90.0, delta=0.25 - ) - - def test_to_tle_raan_descending_solstice(self): - data = { - "altitude": 567000, - "true_anomaly": 0.0, - "epoch": datetime(2020, 6, 21, 9, 14, 0, tzinfo=timezone.utc), - "equator_crossing_time": time(12), - "equator_crossing_ascending": False, - } - tle = SunSynchronousOrbit(**data).to_tle() - self.assertAlmostEqual( - tle.get_right_ascension_ascending_node(), 270.0, delta=0.25 - ) diff --git a/tests/schemas/test_tle.py b/tests/schemas/test_tle.py deleted file mode 100644 index 1ec7d0f..0000000 --- a/tests/schemas/test_tle.py +++ /dev/null @@ -1,163 +0,0 @@ -import unittest - -from pydantic import ValidationError -from datetime import datetime, timezone - -from tatc.schemas import TwoLineElements - - -class TestTLE(unittest.TestCase): - def setUp(self): - self.test_tle = TwoLineElements( - tle=[ - "1 25544U 98067A 21156.30527927 .00003432 00000-0 70541-4 0 9993", - "2 25544 51.6455 41.4969 0003508 68.0432 78.3395 15.48957534286754", - ] - ) - - def test_good_data(self): - good_data = { - "tle": [ - "1 25544U 98067A 21156.30527927 .00003432 00000-0 70541-4 0 9993", - "2 25544 51.6455 41.4969 0003508 68.0432 78.3395 15.48957534286754", - ] - } - o = TwoLineElements(**good_data) - self.assertEqual(o.tle[0], good_data.get("tle")[0]) - self.assertEqual(o.tle[1], good_data.get("tle")[1]) - - def test_bad_data_line_1_length(self): - bad_data = { - "tle": [ - "1 25544U 98067A 21156.30527927 .00003432 00000-0 70541-4 0 999", - "2 25544 51.6455 41.4969 0003508 68.0432 78.3395 15.48957534286754", - ] - } - with self.assertRaises(ValidationError): - TwoLineElements(**bad_data) - - def test_bad_data_line_2_length(self): - bad_data = { - "tle": [ - "1 25544U 98067A 21156.30527927 .00003432 00000-0 70541-4 0 9993", - "2 25544 51.6455 41.4969 0003508 68.0432 78.3395 15.4895753428675", - ] - } - with self.assertRaises(ValidationError): - TwoLineElements(**bad_data) - - def test_bad_data_line_1_format(self): - bad_data = { - "tle": [ - "1 25544U 98067A 21156.30527927 .00003432 00000-0 70541-4 0 99Q3", - "2 25544 51.6455 41.4969 0003508 68.0432 78.3395 15.48957534286754", - ] - } - with self.assertRaises(ValidationError): - TwoLineElements(**bad_data) - - def test_bad_data_line_2_format(self): - bad_data = { - "tle": [ - "1 25544U 98067A 21156.30527927 .00003432 00000-0 70541-4 0 9993", - "2 25544 51.6455 41.4969 0003508 68.0432 78.3395 15.489575342867Q4", - ] - } - with self.assertRaises(ValidationError): - TwoLineElements(**bad_data) - - def test_bad_data_line_1_checksums(self): - bad_data = { - "tle": [ - "1 25544U 98067A 21156.30527927 .00003432 00000-0 70541-4 0 9994", - "2 25544 51.6455 41.4969 0003508 68.0432 78.3395 15.48957534286754", - ] - } - with self.assertRaises(ValidationError): - TwoLineElements(**bad_data) - - def test_bad_data_line_2_checksums(self): - bad_data = { - "tle": [ - "1 25544U 98067A 21156.30527927 .00003432 00000-0 70541-4 0 9993", - "2 25544 51.6455 41.4969 0003508 68.0432 78.3395 15.48957534286753", - ] - } - with self.assertRaises(ValidationError): - TwoLineElements(**bad_data) - - def test_get_catalog_number(self): - self.assertEqual(self.test_tle.get_catalog_number(), 25544) - - def test_get_classification(self): - self.assertEqual(self.test_tle.get_classification(), "U") - - def test_get_international_designator(self): - self.assertEqual(self.test_tle.get_international_designator(), "1998-067A") - - def test_get_epoch(self): - self.assertEqual( - self.test_tle.get_epoch(), - datetime(2021, 6, 5, 7, 19, 36, 128928, tzinfo=timezone.utc), - ) - - def test_get_first_derivative_mean_motion(self): - self.assertEqual(self.test_tle.get_first_derivative_mean_motion(), 0.00003432) - - def test_get_second_derivative_mean_motion(self): - self.assertEqual(self.test_tle.get_second_derivative_mean_motion(), 0.0) - - def test_get_b_star(self): - self.assertEqual(self.test_tle.get_b_star(), 0.000070541) - - def test_get_ephemeris_type(self): - self.assertEqual(self.test_tle.get_ephemeris_type(), 0) - - def test_get_element_set_number(self): - self.assertEqual(self.test_tle.get_element_set_number(), 999) - - def test_get_inclination(self): - self.assertEqual(self.test_tle.get_inclination(), 51.6455) - - def test_get_right_ascension_ascending_node(self): - self.assertEqual(self.test_tle.get_right_ascension_ascending_node(), 41.4969) - - def test_get_eccentricity(self): - self.assertEqual(self.test_tle.get_eccentricity(), 0.0003508) - - def test_get_perigee_argument(self): - self.assertEqual(self.test_tle.get_perigee_argument(), 68.0432) - - def test_get_mean_anomaly(self): - self.assertEqual(self.test_tle.get_mean_anomaly(), 78.3395) - - def test_get_mean_motion(self): - self.assertAlmostEqual(self.test_tle.get_mean_motion(), 15.48957534) - - def test_get_revolution_number_at_epoch(self): - self.assertEqual(self.test_tle.get_revolution_number_at_epoch(), 28675) - - def test_get_semimajor_axis(self): - self.assertAlmostEqual(self.test_tle.get_semimajor_axis(), 6797911, delta=1.0) - - def test_get_altitude(self): - self.assertAlmostEqual(self.test_tle.get_altitude(), 426902, delta=1.0) - - def test_get_true_anomaly(self): - self.assertAlmostEqual(self.test_tle.get_true_anomaly(), 78.3788725993742) - - def test_get_derived_orbit(self): - derived_tle = self.test_tle.get_derived_orbit(20, 10) - self.assertAlmostEqual( - derived_tle.get_mean_anomaly(), - self.test_tle.get_mean_anomaly() + 20, - delta=0.001, - ) - self.assertAlmostEqual( - derived_tle.get_right_ascension_ascending_node(), - self.test_tle.get_right_ascension_ascending_node() + 10, - delta=0.001, - ) - - def test_get_tle(self): - self.assertAlmostEqual(self.test_tle.to_tle(), self.test_tle) diff --git a/tests/schemas/test_train_constellation.py b/tests/schemas/test_train_constellation.py deleted file mode 100644 index a375d2c..0000000 --- a/tests/schemas/test_train_constellation.py +++ /dev/null @@ -1,146 +0,0 @@ -import unittest - -from datetime import timedelta - -from tatc.schemas import TrainConstellation, TwoLineElements, Instrument - - -class TestTrainConstellation(unittest.TestCase): - def setUp(self): - self.test_data_1 = { - "name": "Test Constellation", - "orbit": { - "tle": [ - "1 25544U 98067A 21156.30527927 .00003432 00000-0 70541-4 0 9993", - "2 25544 51.6455 41.4969 0003508 68.0432 78.3395 15.48957534286754", - ] - }, - "instruments": [{"name": "Test Instrument", "field_of_regard": 25.0}], - "number_satellites": 4, - "interval": timedelta(minutes=10), - "repeat_ground_track": True, - } - self.test_con_1 = TrainConstellation(**self.test_data_1) - self.test_data_2 = { - "name": "Test Constellation", - "orbit": { - "tle": [ - "1 25544U 98067A 21156.30527927 .00003432 00000-0 70541-4 0 9993", - "2 25544 51.6455 41.4969 0003508 68.0432 78.3395 15.48957534286754", - ] - }, - "instruments": [{"name": "Test Instrument", "field_of_regard": 25.0}], - "number_satellites": 4, - "interval": timedelta(minutes=10), - "repeat_ground_track": False, - } - self.test_con_2 = TrainConstellation(**self.test_data_2) - self.test_data_3 = { - "name": "Test Constellation", - "orbit": { - "altitude": "400000", - "inclination": 51.6, - "right_ascension_ascending_node": 180, - "true_anomaly": 180, - }, - "instruments": [{"name": "Test Instrument", "field_of_regard": 25.0}], - "number_satellites": 4, - "interval": timedelta(minutes=10), - "repeat_ground_track": True, - } - self.test_con_3 = TrainConstellation(**self.test_data_3) - - def test_good_data(self): - self.assertEqual(self.test_con_1.name, self.test_data_1.get("name")) - self.assertEqual( - self.test_con_1.orbit, TwoLineElements(**self.test_data_1.get("orbit")) - ) - self.assertEqual(len(self.test_con_1.instruments), 1) - self.assertEqual( - self.test_con_1.instruments[0], - Instrument(**self.test_data_1.get("instruments")[0]), - ) - self.assertEqual( - self.test_con_1.number_satellites, self.test_data_1.get("number_satellites") - ) - self.assertEqual(self.test_con_1.interval, self.test_data_1.get("interval")) - self.assertEqual( - self.test_con_1.repeat_ground_track, - self.test_data_1.get("repeat_ground_track"), - ) - - def test_get_delta_mean_anomaly_repeat_ground_track_tle(self): - self.assertAlmostEqual( - self.test_con_1.get_delta_mean_anomaly(), - -360 * self.test_con_1.interval / self.test_con_1.orbit.get_orbit_period(), - delta=0.001, - ) - - def test_get_delta_mean_anomaly_no_repeat_ground_track_tle(self): - self.assertAlmostEqual( - self.test_con_2.get_delta_mean_anomaly(), - -360 * self.test_con_2.interval / self.test_con_2.orbit.get_orbit_period(), - delta=0.001, - ) - - def test_get_delta_mean_anomaly_circular(self): - self.assertAlmostEqual( - self.test_con_3.get_delta_mean_anomaly(), - -360 * self.test_con_3.interval / self.test_con_3.orbit.get_orbit_period(), - delta=0.001, - ) - - def test_get_delta_raan_repeat_ground_track_tle(self): - self.assertEqual( - self.test_con_1.get_delta_raan(), - 360 * self.test_con_1.interval / timedelta(days=1), - ) - - def test_get_delta_raan_repeat_ground_track_circular(self): - self.assertEqual( - self.test_con_3.get_delta_raan(), - 360 * self.test_con_1.interval / timedelta(days=1), - ) - - def test_get_delta_raan_no_repeat_ground_track_tle(self): - self.assertEqual(self.test_con_2.get_delta_raan(), 0.0) - - def helper_test_generate_members(self, constellation): - members = constellation.generate_members() - self.assertEqual(len(members), constellation.number_satellites) - for i in range(len(members) - 1): - self.assertAlmostEqual( - ( - members[i + 1].orbit.get_mean_anomaly() - - members[i].orbit.get_mean_anomaly() - ) - % 360, - constellation.get_delta_mean_anomaly() % 360, - delta=0.001, - ) - self.assertAlmostEqual( - ( - ( - members[i + 1].orbit.get_right_ascension_ascending_node() - - members[i].orbit.get_right_ascension_ascending_node() - ) - % 360 - if constellation.orbit.type == "tle" - else ( - members[i + 1].orbit.right_ascension_ascending_node - - members[i].orbit.right_ascension_ascending_node - ) - % 360 - ), - constellation.get_delta_raan() % 360, - delta=0.001, - ) - - def test_generate_members_repeat_ground_track_tle(self): - self.helper_test_generate_members(self.test_con_1) - - def test_generate_members_non_repeat_ground_track_tle(self): - self.helper_test_generate_members(self.test_con_2) - - def test_generate_members_repeat_ground_track_circular(self): - self.helper_test_generate_members(self.test_con_3) diff --git a/tests/schemas/test_tundra_orbit.py b/tests/schemas/test_tundra_orbit.py deleted file mode 100644 index 597610c..0000000 --- a/tests/schemas/test_tundra_orbit.py +++ /dev/null @@ -1,54 +0,0 @@ -import unittest - -from datetime import datetime, timezone - -from tatc.schemas import TundraOrbit - - -class TestTundraOrbit(unittest.TestCase): - def setUp(self): - self.test_data = { - "altitude": 1199100, - "true_anomaly": 10.0, - "epoch": datetime(2024, 11, 20, 7, 11, 20, 742432, tzinfo=timezone.utc), - "inclination": 63.4, - "right_ascension_ascending_node": 11.04, - "eccentricity": 0.3, - } - self.test_orbit = TundraOrbit(**self.test_data) - - def test_good_data(self): - self.assertEqual(self.test_orbit.altitude, self.test_data.get("altitude")) - self.assertEqual( - self.test_orbit.true_anomaly, self.test_data.get("true_anomaly") - ) - self.assertEqual(self.test_orbit.epoch, self.test_data.get("epoch")) - self.assertEqual(self.test_orbit.inclination, self.test_data.get("inclination")) - self.assertEqual( - self.test_orbit.right_ascension_ascending_node, - self.test_data.get("right_ascension_ascending_node"), - ) - - def test_get_derived_orbit(self): - derived_orbit = self.test_orbit.get_derived_orbit(20, 10) - self.assertAlmostEqual( - derived_orbit.right_ascension_ascending_node, - self.test_orbit.right_ascension_ascending_node + 10, - delta=0.001, - ) - - def test_to_tle(self): - tle = self.test_orbit.to_tle() - self.assertAlmostEqual( - tle.get_epoch().timestamp(), - self.test_data.get("epoch").timestamp(), - delta=1, - ) - self.assertEqual( - tle.get_inclination(), - self.test_data.get("inclination"), - ) - self.assertAlmostEqual( - tle.get_right_ascension_ascending_node(), - self.test_data.get("right_ascension_ascending_node"), - ) diff --git a/tests/test_config.py b/tests/test_config.py new file mode 100644 index 0000000..9890065 --- /dev/null +++ b/tests/test_config.py @@ -0,0 +1,336 @@ +""" +Unit tests for the tatc.config module. + +@author Paul T. Grogan +""" + +import shutil +import tempfile +import textwrap +import unittest +from pathlib import Path + +import yaml +from pydantic import ValidationError + +from tatc import config as tatc_config +from tatc.config import ConfigError, RuntimeConfiguration, load_yaml_config + + +class TestRuntimeConfiguration(unittest.TestCase): + """ + Unit tests for the tatc.config.RuntimeConfiguration schema. + """ + + def test_defaults(self): + """ + Test that constructing with no arguments produces the hard-coded + default value documented for every field. + """ + rc = RuntimeConfiguration() + self.assertEqual(rc.footprint_points_elliptical, 32) + self.assertEqual(rc.footprint_points_rectangular_side, 8) + self.assertEqual(rc.repeat_cycle_delta_position_m, 10000) + self.assertEqual(rc.repeat_cycle_delta_velocity_m_per_s, 3) + self.assertEqual(rc.repeat_cycle_search_duration_days, 30) + self.assertEqual(rc.repeat_cycle_consistency_threshold_s, 3600) + self.assertTrue(rc.repeat_cycle_lazy_load) + self.assertTrue(rc.repeat_cycle_for_orbit_track) + self.assertTrue(rc.repeat_cycle_for_observation_events) + self.assertTrue(rc.gp_orbit_lazy_load) + + def test_footprint_points_elliptical_accepts_minimum(self): + """ + Test that exactly 4 elliptical footprint points is accepted (the + boundary of the ge=4 constraint). + """ + rc = RuntimeConfiguration(footprint_points_elliptical=4) + self.assertEqual(rc.footprint_points_elliptical, 4) + + def test_footprint_points_elliptical_rejects_below_minimum(self): + """ + Test that fewer than 4 elliptical footprint points is rejected, + since a closed elliptical footprint polygon needs at least 4 + vertices. + """ + with self.assertRaises(ValidationError): + RuntimeConfiguration(footprint_points_elliptical=3) + + def test_footprint_points_rectangular_side_accepts_minimum(self): + """ + Test that exactly 1 rectangular footprint side point is accepted + (the boundary of the ge=1 constraint). + """ + rc = RuntimeConfiguration(footprint_points_rectangular_side=1) + self.assertEqual(rc.footprint_points_rectangular_side, 1) + + def test_footprint_points_rectangular_side_rejects_below_minimum(self): + """ + Test that fewer than 1 rectangular footprint side point is + rejected. + """ + with self.assertRaises(ValidationError): + RuntimeConfiguration(footprint_points_rectangular_side=0) + + def test_repeat_cycle_delta_position_m_rejects_non_positive(self): + """ + Test that a zero or negative position delta tolerance is rejected, + since a non-positive distance tolerance cannot discriminate + repeated positions. + """ + with self.assertRaises(ValidationError): + RuntimeConfiguration(repeat_cycle_delta_position_m=0) + with self.assertRaises(ValidationError): + RuntimeConfiguration(repeat_cycle_delta_position_m=-1) + + def test_repeat_cycle_delta_velocity_m_per_s_rejects_non_positive(self): + """ + Test that a zero or negative velocity delta tolerance is rejected. + """ + with self.assertRaises(ValidationError): + RuntimeConfiguration(repeat_cycle_delta_velocity_m_per_s=0) + with self.assertRaises(ValidationError): + RuntimeConfiguration(repeat_cycle_delta_velocity_m_per_s=-1) + + def test_repeat_cycle_consistency_threshold_s_rejects_non_positive(self): + """ + Test that a zero or negative consistency threshold is rejected, + since a non-positive threshold could never treat any two distinct + repeat cycle values as consistent. + """ + with self.assertRaises(ValidationError): + RuntimeConfiguration(repeat_cycle_consistency_threshold_s=0) + with self.assertRaises(ValidationError): + RuntimeConfiguration(repeat_cycle_consistency_threshold_s=-1) + + +class TestLoadYamlConfig(unittest.TestCase): + """ + Unit tests for the tatc.config.load_yaml_config function. + """ + + def setUp(self): + self.tmp_dir = Path(tempfile.mkdtemp()) + + def tearDown(self): + shutil.rmtree(self.tmp_dir, ignore_errors=True) + + def _write(self, name: str, content: str) -> Path: + path = self.tmp_dir / name + path.write_text(textwrap.dedent(content), encoding="utf-8") + return path + + def test_missing_file_raises_config_error(self): + """ + Test that a nonexistent path raises a ConfigError rather than an + OS-level FileNotFoundError. + """ + with self.assertRaises(ConfigError): + load_yaml_config(self.tmp_dir / "does_not_exist.yml") + + def test_valid_yaml_overrides_all_fields(self): + """ + Test that a fully-specified YAML file is loaded into a matching + RuntimeConfiguration. + """ + path = self._write( + "config.yml", + """\ + footprint_points_elliptical: 16 + footprint_points_rectangular_side: 4 + repeat_cycle_delta_position_m: 500 + repeat_cycle_delta_velocity_m_per_s: 1 + repeat_cycle_search_duration_days: 7 + repeat_cycle_consistency_threshold_s: 60 + repeat_cycle_lazy_load: false + repeat_cycle_for_orbit_track: false + repeat_cycle_for_observation_events: false + gp_orbit_lazy_load: false + """, + ) + rc = load_yaml_config(path) + self.assertIsInstance(rc, RuntimeConfiguration) + self.assertEqual(rc.footprint_points_elliptical, 16) + self.assertEqual(rc.footprint_points_rectangular_side, 4) + self.assertEqual(rc.repeat_cycle_delta_position_m, 500) + self.assertEqual(rc.repeat_cycle_delta_velocity_m_per_s, 1) + self.assertEqual(rc.repeat_cycle_search_duration_days, 7) + self.assertEqual(rc.repeat_cycle_consistency_threshold_s, 60) + self.assertFalse(rc.repeat_cycle_lazy_load) + self.assertFalse(rc.repeat_cycle_for_orbit_track) + self.assertFalse(rc.repeat_cycle_for_observation_events) + self.assertFalse(rc.gp_orbit_lazy_load) + + def test_partial_yaml_falls_back_to_defaults_for_missing_fields(self): + """ + Test that fields omitted from the YAML file retain their + RuntimeConfiguration default rather than raising a validation + error. + """ + path = self._write("config.yml", "footprint_points_elliptical: 16\n") + rc = load_yaml_config(path) + self.assertEqual(rc.footprint_points_elliptical, 16) + self.assertEqual(rc.footprint_points_rectangular_side, 8) + + def test_unrecognized_key_is_silently_ignored(self): + """ + Test that an unrecognized top-level key in the YAML file does not + raise an error. RuntimeConfiguration relies on pydantic's default + "ignore" behavior for unknown fields, so a typo'd setting name is + currently dropped without any indication to the user. + """ + path = self._write("config.yml", "not_a_real_setting: 123\n") + rc = load_yaml_config(path) + self.assertEqual(rc, RuntimeConfiguration()) + + def test_yaml_value_violating_field_constraint_raises_config_error(self): + """ + Test that a value violating a field's constraint (here, fewer than + 4 elliptical footprint points) surfaces as a ConfigError rather + than a raw pydantic ValidationError. + """ + path = self._write("config.yml", "footprint_points_elliptical: 2\n") + with self.assertRaises(ConfigError): + load_yaml_config(path) + + def test_malformed_yaml_parser_error_raises_config_error(self): + """ + Test that YAML content triggering PyYAML's ParserError (here, an + unclosed flow sequence) is caught and re-raised as a ConfigError. + """ + path = self._write("config.yml", "footprint_points_elliptical: [1, 2\n") + with self.assertRaises(ConfigError): + load_yaml_config(path) + + def test_malformed_yaml_scanner_error_is_not_converted_to_config_error(self): + """ + Known gap (flagged during review, fix deferred): load_yaml_config + only catches yaml.parser.ParserError, not the broader + yaml.YAMLError hierarchy. Malformed YAML that instead raises a + ScannerError (e.g. inconsistent indentation) currently propagates + uncaught rather than becoming a ConfigError. This test pins down + that actual current behavior so a future fix is a deliberate, + visible change rather than a silent one. + """ + path = self._write("config.yml", "a: 1\n b: 2\n") + with self.assertRaises(yaml.YAMLError) as ctx: + load_yaml_config(path) + self.assertNotIsInstance(ctx.exception, ConfigError) + + def test_empty_yaml_file_raises_type_error(self): + """ + Known gap (flagged during review, fix deferred): an empty YAML + file parses to None via yaml.safe_load, and + RuntimeConfiguration(**None) raises a TypeError rather than a + pydantic ValidationError, so load_yaml_config does not convert it + to a ConfigError. This test pins down that actual current + behavior so a future fix is a deliberate, visible change rather + than a silent one. + """ + path = self._write("config.yml", "") + with self.assertRaises(TypeError): + load_yaml_config(path) + + +class TestPackagedDefaults(unittest.TestCase): + """ + Unit tests for the packaged resources/defaults.yml file and the + lazily-cached singleton get_rc() populates from it. + """ + + def test_defaults_yaml_ships_with_the_package(self): + """ + Test that resources/defaults.yml is present on disk relative to + the installed tatc.config module. Regression test for a packaging + bug where pyproject.toml's package-data only listed + "resources/*.bsp", so defaults.yml was silently omitted from + built wheels/sdists and every real (non-editable) install fell + back to hard-coded defaults regardless of this file's contents. + """ + resources_path = ( + Path(tatc_config.__file__).parent / "resources" / "defaults.yml" + ) + self.assertTrue( + resources_path.exists(), + "resources/defaults.yml must ship with the package (see " + "pyproject.toml [tool.setuptools.package-data])", + ) + + def test_defaults_yaml_matches_expected_values(self): + """ + Test that resources/defaults.yml contains the exact values + documented in this repository, guarding against silent drift + between the shipped defaults file and RuntimeConfiguration's own + hard-coded defaults. + """ + resources_path = ( + Path(tatc_config.__file__).parent / "resources" / "defaults.yml" + ) + with open(resources_path, "r", encoding="utf-8") as f: + raw = yaml.safe_load(f) + self.assertEqual( + raw, + { + "footprint_points_elliptical": 32, + "footprint_points_rectangular_side": 8, + "repeat_cycle_delta_position_m": 10000, + "repeat_cycle_delta_velocity_m_per_s": 3, + "repeat_cycle_search_duration_days": 30, + "repeat_cycle_consistency_threshold_s": 3600, + "repeat_cycle_lazy_load": True, + "repeat_cycle_for_orbit_track": True, + "repeat_cycle_for_observation_events": True, + "gp_orbit_lazy_load": True, + }, + ) + + def test_module_level_rc_is_a_runtime_configuration(self): + """ + Test that get_rc() (consumed elsewhere in the codebase as + tatc.config.get_rc(), and via the tatc.config.rc backward-compat + accessor) returns a valid RuntimeConfiguration instance matching + the packaged defaults. + """ + self.assertIsInstance(tatc_config.get_rc(), RuntimeConfiguration) + self.assertEqual(tatc_config.get_rc(), RuntimeConfiguration()) + self.assertIsInstance(tatc_config.rc, RuntimeConfiguration) + + +class TestImportTimeFallback(unittest.TestCase): + """ + Unit tests for the fallback behavior of get_rc() when the packaged + resources/defaults.yml cannot be loaded. + """ + + def test_fallback_logs_warning_and_still_produces_valid_rc(self): + """ + Test that if the packaged resources/defaults.yml is missing (e.g. + the packaging regression this module was patched to fix), + get_rc() still succeeds by falling back to hard-coded defaults, + and now logs a warning rather than failing silently. + """ + resources_path = ( + Path(tatc_config.__file__).parent / "resources" / "defaults.yml" + ) + backup_dir = Path(tempfile.mkdtemp()) + backup_path = backup_dir / "defaults.yml" + shutil.copy2(resources_path, backup_path) + # get_rc() is cached, so force it to reload from disk within this + # test, and again afterward to pick the real file back up. This + # only busts the cache, it does not touch class/module identity, + # so it's safe regardless of test execution order. + tatc_config.reset_rc() + try: + resources_path.unlink() + with self.assertLogs("tatc.config", level="WARNING") as ctx: + rc = tatc_config.get_rc() + self.assertTrue(any("hard-coded" in message for message in ctx.output)) + self.assertEqual(rc, RuntimeConfiguration()) + finally: + shutil.copy2(backup_path, resources_path) + shutil.rmtree(backup_dir, ignore_errors=True) + tatc_config.reset_rc() + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_utils.py b/tests/test_utils.py deleted file mode 100644 index 1e411c9..0000000 --- a/tests/test_utils.py +++ /dev/null @@ -1,343 +0,0 @@ -import unittest - -import numpy as np -from shapely.geometry import Polygon, MultiPolygon -import geopandas as gpd - -from tatc.utils import ( - mean_anomaly_to_true_anomaly, - true_anomaly_to_mean_anomaly, - compute_number_samples, - swath_width_to_field_of_regard, - field_of_regard_to_swath_width, - compute_field_of_regard, - compute_min_elevation_angle, - compute_max_access_time, - split_polygon, - normalize_geometry, -) - -from tatc import constants - - -class TestUtils(unittest.TestCase): - def test_mean_anomaly_to_true_anomaly(self): - self.assertAlmostEqual( - mean_anomaly_to_true_anomaly(78.940629, 0.0001492), 78.95065818, delta=0.01 - ) - - def test_true_anomaly_to_mean_anomaly(self): - self.assertAlmostEqual( - true_anomaly_to_mean_anomaly(78.95065818, 0.0001492), 78.940629, delta=0.01 - ) - - def test_compute_number_samples(self): - # rough approximation based on flat sample areas - sample_distance = 10000 - num_samples = int( - constants.EARTH_SURFACE_AREA / (np.pi * (sample_distance / 2) ** 2) - ) - self.assertEqual(compute_number_samples(sample_distance), num_samples) - - def test_swath_width_to_field_of_regard(self): - self.assertAlmostEqual( - swath_width_to_field_of_regard(705000, 185815), 15.0, delta=0.001 - ) - - def test_field_of_regard_to_swath_width(self): - self.assertAlmostEqual( - field_of_regard_to_swath_width(705000, 15.0), 185815, delta=1.0 - ) - - def test_compute_field_of_regard(self): - self.assertAlmostEqual( - compute_field_of_regard(705000, 81.66446), 15.0, delta=0.001 - ) - - def test_compute_min_elevation_angle(self): - self.assertAlmostEqual( - compute_min_elevation_angle(705000, 15.0), 81.66446, delta=0.001 - ) - - def test_compute_min_elevation_angle_saturated(self): - self.assertEqual(compute_min_elevation_angle(30000000, 180.0), 0.0) - - def test_compute_max_access_time(self): - self.assertAlmostEqual( - compute_max_access_time(705000, 81.66446), 274.31828, delta=0.001 - ) - - def test_split_polygon_nominal_small(self): - polygon = Polygon([(-10, 10), (10, 10), (10, -10), (-10, -10), (-10, 10)]) - self.assertEqual(split_polygon(polygon), polygon) - - def test_split_polygon_nominal_large(self): - polygon = Polygon([(-90, 10), (90, 10), (90, -10), (-90, -10), (-90, 10)]) - self.assertEqual(split_polygon(polygon), polygon) - - def test_normalize_geometry_polygon(self): - polygon = Polygon([(-50, 10), (50, 10), (50, -10), (-50, -10), (-50, 10)]) - result = normalize_geometry(polygon) - self.assertIsInstance(result, gpd.GeoDataFrame) - self.assertEqual(result.crs, "EPSG:4326") - self.assertEqual(len(result.index), 1) - self.assertEqual(result.iloc[0].geometry, split_polygon(polygon)) - - def test_normalize_geometry_polygon_invalid(self): - polygon = Polygon([(-150, 0), (-50, 0), (0, 0), (50, 0), (150, 0)]) - with self.assertRaises(ValueError): - normalize_geometry(polygon) - - def test_normalize_geometry_multipolygon(self): - geometries = [ - [(-50, 10), (50, 10), (50, -10), (-50, -10), (-50, 10)], - [(-50, 30), (50, 30), (50, 20), (-50, 20), (-50, 30)], - ] - multipolygon = MultiPolygon([[geometry, []] for geometry in geometries]) - result = normalize_geometry(multipolygon) - self.assertIsInstance(result, gpd.GeoDataFrame) - self.assertEqual(result.crs, "EPSG:4326") - self.assertEqual(len(result.index), 1) - self.assertEqual( - result.iloc[0].geometry, - split_polygon(multipolygon), - ) - - def test_normalize_geometry_geoseries(self): - polygon = Polygon([(-50, 10), (50, 10), (50, -10), (-50, -10), (-50, 10)]) - gs = gpd.GeoSeries(polygon, crs="EPSG:4326") - result = normalize_geometry(gs) - self.assertIsInstance(result, gpd.GeoDataFrame) - self.assertEqual(result.crs, "EPSG:4326") - self.assertEqual(len(result.index), 1) - self.assertEqual( - result.iloc[0].geometry, - split_polygon(polygon), - ) - - def test_normalize_geometry_geodataframe(self): - polygon = Polygon([(-50, 10), (50, 10), (50, -10), (-50, -10), (-50, 10)]) - df = gpd.GeoDataFrame(geometry=[polygon], index=[0], crs="EPSG:4326") - result = normalize_geometry(df) - self.assertIsInstance(result, gpd.GeoDataFrame) - self.assertEqual(result.crs, "EPSG:4326") - self.assertEqual(len(result.index), 1) - self.assertEqual( - result.iloc[0].geometry, - split_polygon(polygon), - ) - - def test_split_polygon_north_pole(self): - polygon = Polygon([(-50, 95), (-20, 95), (-20, 85), (-50, 85), (-50, 95)]) - result = MultiPolygon( - [ - Polygon([(130, 85), (160, 85), (160, 90), (130, 90), (130, 85)]), - Polygon([(-20, 90), (-20, 85), (-50, 85), (-50, 90), (-20, 90)]), - ] - ) - self.assertTrue(split_polygon(polygon).equals(result)) - - def test_split_polygon_south_pole(self): - polygon = Polygon( - [(-150, -95), (-120, -95), (-120, -85), (-150, -85), (-150, -95)] - ) - result = MultiPolygon( - [ - Polygon( - [(-150, -90), (-150, -85), (-120, -85), (-120, -90), (-150, -90)] - ), - Polygon([(30, -85), (30, -90), (60, -90), (60, -85), (30, -85)]), - ] - ) - self.assertTrue(split_polygon(polygon).equals(result)) - - def test_split_polygon_antimeridian_short_cw(self): - polygon = Polygon([(170, 10), (-170, 10), (-170, -10), (170, -10), (170, 10)]) - result = MultiPolygon( - [ - Polygon([(170, -10), (170, 10), (180, 10), (180, -10), (170, -10)]), - Polygon( - [(-180, -10), (-180, 10), (-170, 10), (-170, -10), (-180, -10)] - ), - ] - ) - self.assertTrue(split_polygon(polygon).equals(result)) - - def test_split_polygon_antimeridian_short_ccw(self): - polygon = Polygon([(-170, 10), (170, 10), (170, -10), (-170, -10), (-170, 10)]) - result = MultiPolygon( - [ - Polygon([(-180, 10), (-170, 10), (-170, -10), (-180, -10), (-180, 10)]), - Polygon([(180, -10), (170, -10), (170, 10), (180, 10), (180, -10)]), - ] - ) - self.assertTrue(split_polygon(polygon).equals(result)) - - def test_split_polygon_antimeridian_long(self): - polygon = Polygon( - [ - (170, 10), - (-170, 10), - (-70, 10), - (30, 10), - (30, -10), - (-70, -10), - (-170, -10), - (170, -10), - (170, 10), - ] - ) - result = MultiPolygon( - [ - Polygon([(170, 10), (180, 10), (180, -10), (170, -10), (170, 10)]), - Polygon( - [ - (-180, 10), - (-170, 10), - (-70, 10), - (30, 10), - (30, -10), - (-70, -10), - (-170, -10), - (-180, -10), - (-180, 10), - ] - ), - ] - ) - self.assertTrue(split_polygon(polygon).equals(result)) - - def test_split_polygon_north_pole_multipolygon(self): - polygon = MultiPolygon( - [ - Polygon([(-150, 95), (-120, 95), (-120, 85), (-150, 85), (-150, 95)]), - Polygon([(150, 95), (120, 95), (120, 85), (150, 85), (150, 95)]), - ] - ) - result = MultiPolygon( - [ - Polygon([(-150, 85), (-150, 90), (-120, 90), (-120, 85), (-150, 85)]), - Polygon([(30, 90), (30, 85), (60, 85), (60, 90), (30, 90)]), - Polygon([(120, 85), (120, 90), (150, 90), (150, 85), (120, 85)]), - Polygon([(-60, 90), (-60, 85), (-30, 85), (-30, 90), (-60, 90)]), - ] - ) - self.assertTrue(split_polygon(polygon).equals(result)) - - def test_split_polygon_south_pole_multipolygon(self): - polygon = MultiPolygon( - [ - Polygon( - [(-150, -95), (-120, -95), (-120, -85), (-150, -85), (-150, -95)] - ), - Polygon([(150, -95), (120, -95), (120, -85), (150, -85), (150, -95)]), - ] - ) - result = MultiPolygon( - [ - Polygon( - [(-150, -90), (-150, -85), (-120, -85), (-120, -90), (-150, -90)] - ), - Polygon([(30, -85), (30, -90), (60, -90), (60, -85), (30, -85)]), - Polygon([(120, -90), (120, -85), (150, -85), (150, -90), (120, -90)]), - Polygon([(-60, -85), (-60, -90), (-30, -90), (-30, -85), (-60, -85)]), - ] - ) - self.assertTrue(split_polygon(polygon).equals(result)) - - def test_split_polygon_antimeridian_multipolygon(self): - polygon = MultiPolygon( - [ - Polygon([(170, 10), (-170, 10), (-170, -10), (170, -10), (170, 10)]), - Polygon([(170, 50), (-170, 50), (-170, 30), (170, 30), (170, 50)]), - ] - ) - result = MultiPolygon( - [ - Polygon([(170, -10), (170, 10), (180, 10), (180, -10), (170, -10)]), - Polygon( - [(-180, -10), (-180, 10), (-170, 10), (-170, -10), (-180, -10)] - ), - Polygon([(170, 30), (170, 50), (180, 50), (180, 30), (170, 30)]), - Polygon([(-180, 30), (-180, 50), (-170, 50), (-170, 30), (-180, 30)]), - ] - ) - self.assertTrue(split_polygon(polygon).equals(result)) - - def test_split_polygon_north_pole_top_multipolygon(self): - polygon = Polygon( - [ - (-150, 95), - (-140, 95), - (-140, 85), - (-130, 85), - (-130, 95), - (-120, 95), - (-120, 85), - (-150, 85), - (-150, 95), - ] - ) - result = MultiPolygon( - [ - Polygon([(50, 90), (60, 90), (60, 85), (50, 85), (50, 90)]), - Polygon([(30, 90), (40, 90), (40, 85), (30, 85), (30, 90)]), - Polygon([(-150, 85), (-150, 90), (-140, 90), (-140, 85), (-150, 85)]), - Polygon([(-130, 85), (-130, 90), (-120, 90), (-120, 85), (-130, 85)]), - ] - ) - self.assertTrue(split_polygon(polygon).equals(result)) - - def test_split_polygon_south_pole_bottom_multipolygon(self): - polygon = Polygon( - [ - (-150, -95), - (-140, -95), - (-140, -85), - (-130, -85), - (-130, -95), - (-120, -95), - (-120, -85), - (-150, -85), - (-150, -95), - ] - ) - result = MultiPolygon( - [ - Polygon([(50, -85), (60, -85), (60, -90), (50, -90), (50, -85)]), - Polygon([(30, -85), (40, -85), (40, -90), (30, -90), (30, -85)]), - Polygon( - [(-150, -90), (-150, -85), (-140, -85), (-140, -90), (-150, -90)] - ), - Polygon( - [(-130, -85), (-120, -85), (-120, -90), (-130, -90), (-130, -85)] - ), - ] - ) - self.assertTrue(split_polygon(polygon).equals(result)) - - def test_split_polygon_antimeridian_right_multipolygon(self): - polygon = Polygon( - [ - (170, 10), - (-170, 10), - (-170, 5), - (170, 5), - (170, -5), - (-170, -5), - (-170, -10), - (170, -10), - (170, 10), - ] - ) - result = MultiPolygon( - [ - Polygon([(170, -10), (170, -5), (180, -5), (180, -10), (170, -10)]), - Polygon([(170, 5), (170, 10), (180, 10), (180, 5), (170, 5)]), - Polygon( - [(-180, -10), (-180, -5), (-170, -5), (-170, -10), (-180, -10)] - ), - Polygon([(-180, 5), (-180, 10), (-170, 10), (-170, 5), (-180, 5)]), - ] - ) - self.assertTrue(split_polygon(polygon).equals(result)) diff --git a/tests/utils/__init__.py b/tests/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/utils/test_formatting.py b/tests/utils/test_formatting.py new file mode 100644 index 0000000..e19eac6 --- /dev/null +++ b/tests/utils/test_formatting.py @@ -0,0 +1,51 @@ +""" +Unit tests for the tatc.utils.formatting module. + +@author Paul T. Grogan +""" + +import unittest + +from tatc.utils import zero_pad + + +class TestFormatting(unittest.TestCase): + """ + Unit tests for the tatc.utils.formatting module. + """ + + def test_zero_pad_pads_to_max_number_width(self): + """ + Test that the current number is zero-padded to the digit width of the max number. + """ + self.assertEqual(zero_pad("Sat", 12, 3), "Sat 03") + + def test_zero_pad_current_equals_max(self): + """ + Test that no padding is added when the current number already has the + same digit width as the max number. + """ + self.assertEqual(zero_pad("Sat", 12, 12), "Sat 12") + + def test_zero_pad_single_digit_max(self): + """ + Test that no padding is added when the max number is a single digit. + """ + self.assertEqual(zero_pad("Sat", 5, 3), "Sat 3") + + def test_zero_pad_current_number_exceeds_max_number_width(self): + """ + Test that a current number with more digits than the max number is + left at its natural width rather than truncated. + """ + self.assertEqual(zero_pad("Sat", 9, 15), "Sat 15") + + def test_zero_pad_preserves_numeric_sort_order(self): + """ + Test that labels generated in increasing numeric order also sort in + increasing lexicographic (string) order, which is the purpose of + the zero padding. + """ + count = 12 + labels = [zero_pad("Sat", count, i) for i in range(1, count + 1)] + self.assertEqual(sorted(labels), labels) diff --git a/tests/utils/test_geometry.py b/tests/utils/test_geometry.py new file mode 100644 index 0000000..7a84f20 --- /dev/null +++ b/tests/utils/test_geometry.py @@ -0,0 +1,650 @@ +""" +Unit tests for the tatc.utils.geometry module. + +@author Paul T. Grogan +""" + +import unittest + +import geopandas as gpd +from shapely.geometry import MultiPolygon, Point, Polygon + +from tatc.utils import ( + geodesic_distance, + get_planar_bounds, + normalize_geometry, + project_polygon_to_elevation, + split_polygon, +) + + +class TestGeometry(unittest.TestCase): # pylint: disable=too-many-public-methods + """ + Unit tests for the tatc.utils.geometry module. + """ + + def test_geodesic_distance_same_point(self): + """ + Test that the geodesic distance between a point and itself is zero. + """ + self.assertAlmostEqual(geodesic_distance(10, 20, 10, 20), 0, delta=1e-6) + + def test_geodesic_distance_one_degree_at_equator(self): + """ + Test the geodesic distance for one degree of longitude along the + equator against the known WGS 84 equatorial circumference (the + equator is itself a geodesic, so this distance is exact): + 2 * pi * EARTH_EQUATORIAL_RADIUS / 360 = 111319.4908 meters. + """ + self.assertAlmostEqual(geodesic_distance(0, 0, 1, 0), 111319.4908, delta=0.01) + + def test_geodesic_distance_pole_to_equator(self): + """ + Test the geodesic distance from the North pole to the equator + against the published WGS 84 meridian quadrant length (a meridian + is itself a geodesic, so this distance is exact): 10001965.7293 + meters. + """ + self.assertAlmostEqual( + geodesic_distance(0, 90, 0, 0), 10001965.7293, delta=0.01 + ) + + def test_geodesic_distance_is_symmetric(self): + """ + Test that the geodesic distance does not depend on point order. + """ + self.assertAlmostEqual( + geodesic_distance(-73.9857, 40.7484, -0.1278, 51.5074), + geodesic_distance(-0.1278, 51.5074, -73.9857, 40.7484), + delta=1e-6, + ) + + def test_geodesic_distance_across_antimeridian(self): + """ + Test that the geodesic distance between points on either side of + the antimeridian takes the short way across it, rather than the + long way around through the prime meridian. + """ + self.assertAlmostEqual( + geodesic_distance(179, 0, -179, 0), 222638.9816, delta=0.01 + ) + + def test_project_polygon_to_elevation_polygon(self): + """ + Test that all exterior coordinates of a polygon are assigned the + specified elevation as a z-coordinate, leaving x/y unchanged. + """ + polygon = Polygon([(0, 0), (10, 0), (10, 10), (0, 10), (0, 0)]) + result = project_polygon_to_elevation(polygon, 500) + self.assertIsInstance(result, Polygon) + self.assertEqual( + list(result.exterior.coords), + [(x, y, 500) for x, y in polygon.exterior.coords], + ) + + def test_project_polygon_to_elevation_polygon_with_hole(self): + """ + Test that both exterior and interior ring coordinates are assigned + the specified elevation. + """ + exterior = [(0, 0), (10, 0), (10, 10), (0, 10), (0, 0)] + interior = [(2, 2), (2, 4), (4, 4), (4, 2), (2, 2)] + polygon = Polygon(exterior, [interior]) + result = project_polygon_to_elevation(polygon, 250) + self.assertEqual( + list(result.exterior.coords), [(x, y, 250) for x, y in exterior] + ) + self.assertEqual(len(list(result.interiors)), 1) + self.assertEqual( + list(result.interiors[0].coords), [(x, y, 250) for x, y in interior] + ) + + def test_project_polygon_to_elevation_overwrites_existing_z(self): + """ + Test that an existing z-coordinate is replaced (not offset) by the + specified elevation. + """ + polygon = Polygon( + [(0, 0, 100), (10, 0, 100), (10, 10, 100), (0, 10, 100), (0, 0, 100)] + ) + result = project_polygon_to_elevation(polygon, 50) + self.assertEqual( + list(result.exterior.coords), + [(x, y, 50) for x, y, _ in polygon.exterior.coords], + ) + + def test_project_polygon_to_elevation_negative(self): + """ + Test that a negative elevation (below the WGS 84 geoid) is applied as-is. + """ + polygon = Polygon([(0, 0), (10, 0), (10, 10), (0, 10), (0, 0)]) + result = project_polygon_to_elevation(polygon, -500) + self.assertEqual( + list(result.exterior.coords), + [(x, y, -500) for x, y in polygon.exterior.coords], + ) + + def test_project_polygon_to_elevation_multipolygon(self): + """ + Test that a multipolygon projects each constituent polygon to the + specified elevation and preserves the number of geometries. + """ + polygon_a = Polygon([(0, 0), (10, 0), (10, 10), (0, 10), (0, 0)]) + polygon_b = Polygon([(20, 0), (30, 0), (30, 10), (20, 10), (20, 0)]) + multipolygon = MultiPolygon([polygon_a, polygon_b]) + result = project_polygon_to_elevation(multipolygon, 1000) + self.assertIsInstance(result, MultiPolygon) + self.assertEqual(len(result.geoms), 2) + for original, projected in zip(multipolygon.geoms, result.geoms): + self.assertEqual( + list(projected.exterior.coords), + [(x, y, 1000) for x, y in original.exterior.coords], + ) + + def test_split_polygon_nominal_small(self): + """ + Test that a polygon that does not cross the antimeridian or poles is not split. + """ + polygon = Polygon([(-10, 10), (10, 10), (10, -10), (-10, -10), (-10, 10)]) + self.assertEqual(split_polygon(polygon), polygon) + + def test_split_polygon_nominal_large(self): + """ + Test that a polygon that does not cross the antimeridian or poles is not split. + """ + polygon = Polygon([(-90, 10), (90, 10), (90, -10), (-90, -10), (-90, 10)]) + self.assertEqual(split_polygon(polygon), polygon) + + def test_split_polygon_nominal_boundary(self): + """ + Test that a polygon touching (but not exceeding) 90 degrees + latitude is not split. + """ + polygon = Polygon([(-10, 90), (10, 90), (10, 80), (-10, 80), (-10, 90)]) + self.assertEqual(split_polygon(polygon), polygon) + + def test_split_polygon_north_pole(self): + """ + Test that a polygon that crosses the north pole is split into two polygons. + """ + polygon = Polygon([(-50, 95), (-20, 95), (-20, 85), (-50, 85), (-50, 95)]) + result = MultiPolygon( + [ + Polygon([(130, 85), (160, 85), (160, 90), (130, 90), (130, 85)]), + Polygon([(-20, 90), (-20, 85), (-50, 85), (-50, 90), (-20, 90)]), + ] + ) + self.assertTrue(split_polygon(polygon).equals(result)) + + def test_split_polygon_north_pole_crosses_prime_meridian(self): + """ + Test that a polygon crossing the north pole AND straddling the + prime meridian is split into three valid polygons: the part below + the pole, and the two halves above it wrapped to either side. + """ + polygon = Polygon([(-10, 95), (10, 95), (10, 85), (-10, 85), (-10, 95)]) + result = MultiPolygon( + [ + Polygon([(10, 90), (10, 85), (-10, 85), (-10, 90), (10, 90)]), + Polygon([(170, 85), (180, 85), (180, 90), (170, 90), (170, 85)]), + Polygon([(-180, 85), (-170, 85), (-170, 90), (-180, 90), (-180, 85)]), + ] + ) + self.assertTrue(split_polygon(polygon).equals(result)) + + def test_split_polygon_north_pole_with_hole(self): + """ + Test that an interior ring (hole) survives the split, wrapped + along with the piece that contains it. + """ + polygon = Polygon( + [(-50, 95), (-20, 95), (-20, 85), (-50, 85), (-50, 95)], + [[(-45, 91), (-45, 93), (-40, 93), (-40, 91), (-45, 91)]], + ) + result = split_polygon(polygon) + self.assertIsInstance(result, MultiPolygon) + self.assertTrue(result.is_valid) + wrapped_part = next(g for g in result.geoms if len(g.interiors) > 0) + self.assertEqual( + list(wrapped_part.interiors[0].coords), + [(135, 89), (140, 89), (140, 87), (135, 87), (135, 89)], + ) + + def test_split_polygon_north_pole_multipolygon(self): + """ + Test that a multipolygon that crosses the north pole is split into multiple polygons. + """ + polygon = MultiPolygon( + [ + Polygon([(-150, 95), (-120, 95), (-120, 85), (-150, 85), (-150, 95)]), + Polygon([(150, 95), (120, 95), (120, 85), (150, 85), (150, 95)]), + ] + ) + result = MultiPolygon( + [ + Polygon([(-150, 85), (-150, 90), (-120, 90), (-120, 85), (-150, 85)]), + Polygon([(30, 90), (30, 85), (60, 85), (60, 90), (30, 90)]), + Polygon([(120, 85), (120, 90), (150, 90), (150, 85), (120, 85)]), + Polygon([(-60, 90), (-60, 85), (-30, 85), (-30, 90), (-60, 90)]), + ] + ) + self.assertTrue(split_polygon(polygon).equals(result)) + + def test_split_polygon_north_pole_top_multipolygon(self): + """ + Test that a polygon that crosses the north pole is split into two polygons. + """ + polygon = Polygon( + [ + (-150, 95), + (-140, 95), + (-140, 85), + (-130, 85), + (-130, 95), + (-120, 95), + (-120, 85), + (-150, 85), + (-150, 95), + ] + ) + result = MultiPolygon( + [ + Polygon([(50, 90), (60, 90), (60, 85), (50, 85), (50, 90)]), + Polygon([(30, 90), (40, 90), (40, 85), (30, 85), (30, 90)]), + Polygon([(-150, 85), (-150, 90), (-140, 90), (-140, 85), (-150, 85)]), + Polygon([(-130, 85), (-130, 90), (-120, 90), (-120, 85), (-130, 85)]), + ] + ) + self.assertTrue(split_polygon(polygon).equals(result)) + + def test_split_polygon_south_pole(self): + """ + Test that a polygon that crosses the south pole is split into two polygons. + """ + polygon = Polygon( + [(-150, -95), (-120, -95), (-120, -85), (-150, -85), (-150, -95)] + ) + result = MultiPolygon( + [ + Polygon( + [(-150, -90), (-150, -85), (-120, -85), (-120, -90), (-150, -90)] + ), + Polygon([(30, -85), (30, -90), (60, -90), (60, -85), (30, -85)]), + ] + ) + self.assertTrue(split_polygon(polygon).equals(result)) + + def test_split_polygon_south_pole_crosses_prime_meridian(self): + """ + Test that a polygon crossing the south pole AND straddling the + prime meridian is split into three valid polygons: the part above + the pole, and the two halves below it wrapped to either side. + """ + polygon = Polygon([(-10, -95), (10, -95), (10, -85), (-10, -85), (-10, -95)]) + result = MultiPolygon( + [ + Polygon([(-10, -90), (-10, -85), (10, -85), (10, -90), (-10, -90)]), + Polygon( + [ + (-170, -90), + (-170, -85), + (-180, -85), + (-180, -90), + (-170, -90), + ] + ), + Polygon([(180, -85), (170, -85), (170, -90), (180, -90), (180, -85)]), + ] + ) + self.assertTrue(split_polygon(polygon).equals(result)) + + def test_split_polygon_south_pole_multipolygon(self): + """ + Test that a multipolygon that crosses the south pole is split into multiple polygons. + """ + polygon = MultiPolygon( + [ + Polygon( + [(-150, -95), (-120, -95), (-120, -85), (-150, -85), (-150, -95)] + ), + Polygon([(150, -95), (120, -95), (120, -85), (150, -85), (150, -95)]), + ] + ) + result = MultiPolygon( + [ + Polygon( + [(-150, -90), (-150, -85), (-120, -85), (-120, -90), (-150, -90)] + ), + Polygon([(30, -85), (30, -90), (60, -90), (60, -85), (30, -85)]), + Polygon([(120, -90), (120, -85), (150, -85), (150, -90), (120, -90)]), + Polygon([(-60, -85), (-60, -90), (-30, -90), (-30, -85), (-60, -85)]), + ] + ) + self.assertTrue(split_polygon(polygon).equals(result)) + + def test_split_polygon_south_pole_bottom_multipolygon(self): + """ + Test that a polygon that crosses the south pole is split into two polygons. + """ + polygon = Polygon( + [ + (-150, -95), + (-140, -95), + (-140, -85), + (-130, -85), + (-130, -95), + (-120, -95), + (-120, -85), + (-150, -85), + (-150, -95), + ] + ) + result = MultiPolygon( + [ + Polygon([(50, -85), (60, -85), (60, -90), (50, -90), (50, -85)]), + Polygon([(30, -85), (40, -85), (40, -90), (30, -90), (30, -85)]), + Polygon( + [(-150, -90), (-150, -85), (-140, -85), (-140, -90), (-150, -90)] + ), + Polygon( + [(-130, -85), (-120, -85), (-120, -90), (-130, -90), (-130, -85)] + ), + ] + ) + self.assertTrue(split_polygon(polygon).equals(result)) + + def test_split_polygon_antimeridian_short_cw(self): + """ + Test that a polygon that crosses the antimeridian is split into two polygons. + """ + polygon = Polygon([(170, 10), (-170, 10), (-170, -10), (170, -10), (170, 10)]) + result = MultiPolygon( + [ + Polygon([(170, -10), (170, 10), (180, 10), (180, -10), (170, -10)]), + Polygon( + [(-180, -10), (-180, 10), (-170, 10), (-170, -10), (-180, -10)] + ), + ] + ) + self.assertTrue(split_polygon(polygon).equals(result)) + + def test_split_polygon_antimeridian_short_ccw(self): + """ + Test that a polygon that crosses the antimeridian in a + counter-clockwise direction is split into two polygons. + """ + polygon = Polygon([(-170, 10), (170, 10), (170, -10), (-170, -10), (-170, 10)]) + result = MultiPolygon( + [ + Polygon([(-180, 10), (-170, 10), (-170, -10), (-180, -10), (-180, 10)]), + Polygon([(180, -10), (170, -10), (170, 10), (180, 10), (180, -10)]), + ] + ) + self.assertTrue(split_polygon(polygon).equals(result)) + + def test_split_polygon_antimeridian_long(self): + """ + Test that a polygon that crosses the antimeridian multiple times is + split into multiple polygons. + """ + polygon = Polygon( + [ + (170, 10), + (-170, 10), + (-70, 10), + (30, 10), + (30, -10), + (-70, -10), + (-170, -10), + (170, -10), + (170, 10), + ] + ) + result = MultiPolygon( + [ + Polygon([(170, 10), (180, 10), (180, -10), (170, -10), (170, 10)]), + Polygon( + [ + (-180, 10), + (-170, 10), + (-70, 10), + (30, 10), + (30, -10), + (-70, -10), + (-170, -10), + (-180, -10), + (-180, 10), + ] + ), + ] + ) + self.assertTrue(split_polygon(polygon).equals(result)) + + def test_split_polygon_antimeridian_with_hole(self): + """ + Test that an interior ring (hole) survives the antimeridian split, + remaining in the piece that contains it. + """ + polygon = Polygon( + [(170, 10), (-170, 10), (-170, -10), (170, -10), (170, 10)], + [[(175, 2), (175, 4), (177, 4), (177, 2), (175, 2)]], + ) + result = split_polygon(polygon) + self.assertIsInstance(result, MultiPolygon) + self.assertTrue(result.is_valid) + wrapped_part = next(g for g in result.geoms if len(g.interiors) > 0) + self.assertEqual( + list(wrapped_part.interiors[0].coords), + [(175, 2), (177, 2), (177, 4), (175, 4), (175, 2)], + ) + + def test_split_polygon_antimeridian_multipolygon(self): + """ + Test that a multipolygon that crosses the antimeridian is split into multiple polygons. + """ + polygon = MultiPolygon( + [ + Polygon([(170, 10), (-170, 10), (-170, -10), (170, -10), (170, 10)]), + Polygon([(170, 50), (-170, 50), (-170, 30), (170, 30), (170, 50)]), + ] + ) + result = MultiPolygon( + [ + Polygon([(170, -10), (170, 10), (180, 10), (180, -10), (170, -10)]), + Polygon( + [(-180, -10), (-180, 10), (-170, 10), (-170, -10), (-180, -10)] + ), + Polygon([(170, 30), (170, 50), (180, 50), (180, 30), (170, 30)]), + Polygon([(-180, 30), (-180, 50), (-170, 50), (-170, 30), (-180, 30)]), + ] + ) + self.assertTrue(split_polygon(polygon).equals(result)) + + def test_split_polygon_antimeridian_right_multipolygon(self): + """ + Test that a polygon that crosses the antimeridian is split into two polygons. + """ + polygon = Polygon( + [ + (170, 10), + (-170, 10), + (-170, 5), + (170, 5), + (170, -5), + (-170, -5), + (-170, -10), + (170, -10), + (170, 10), + ] + ) + result = MultiPolygon( + [ + Polygon([(170, -10), (170, -5), (180, -5), (180, -10), (170, -10)]), + Polygon([(170, 5), (170, 10), (180, 10), (180, 5), (170, 5)]), + Polygon( + [(-180, -10), (-180, -5), (-170, -5), (-170, -10), (-180, -10)] + ), + Polygon([(-180, 5), (-180, 10), (-170, 10), (-170, 5), (-180, 5)]), + ] + ) + self.assertTrue(split_polygon(polygon).equals(result)) + + def test_split_polygon_polar_cap_repairs_invalid_antimeridian_split(self): + """ + Test that a polygon encircling a pole (all longitudes, without + exceeding +/-90 degrees latitude) is split into a valid geometry. + The antimeridian split's raw result touches itself along the + prime-meridian cut, so this exercises the `make_valid` repair. + """ + polygon = Polygon([(45, 80), (135, 80), (-135, 80), (-45, 80), (45, 80)]) + result = split_polygon(polygon) + self.assertTrue(result.is_valid) + self.assertTrue(result.contains(Point(100, 85))) + self.assertFalse(result.contains(Point(100, 75))) + + def test_split_polygon_polar_cap_drops_z_dimension(self): + """ + Test that a polar cap polygon with a z-dimension does not raise an + error: the antimeridian split's pole-containing case reconstructs + the ring using synthetic 2D corner points, so any input z must be + discarded rather than mixed in. + """ + polygon = Polygon( + [ + (45, 80, 100), + (135, 80, 100), + (-135, 80, 100), + (-45, 80, 100), + (45, 80, 100), + ] + ) + result = split_polygon(polygon) + self.assertTrue(result.is_valid) + self.assertFalse(result.has_z) + + def test_split_polygon_unknown_geometry(self): + """ + Test that an unsupported geometry type raises a ValueError. + """ + with self.assertRaises(ValueError): + split_polygon(Point(0, 0)) + + def test_normalize_geometry_polygon(self): + """ + Test that a polygon is normalized to a GeoDataFrame with the correct CRS and geometry. + """ + polygon = Polygon([(-50, 10), (50, 10), (50, -10), (-50, -10), (-50, 10)]) + result = normalize_geometry(polygon) + self.assertIsInstance(result, gpd.GeoDataFrame) + self.assertEqual(result.crs, "EPSG:4326") + self.assertEqual(len(result.index), 1) + self.assertEqual(result.iloc[0].geometry, split_polygon(polygon)) + + def test_normalize_geometry_polygon_invalid(self): + """ + Test that an invalid polygon raises a ValueError when normalized. + """ + polygon = Polygon([(-150, 0), (-50, 0), (0, 0), (50, 0), (150, 0)]) + with self.assertRaises(ValueError): + normalize_geometry(polygon) + + def test_normalize_geometry_multipolygon(self): + """ + Test that a multipolygon is normalized to a GeoDataFrame with the correct CRS and geometry. + """ + geometries = [ + [(-50, 10), (50, 10), (50, -10), (-50, -10), (-50, 10)], + [(-50, 30), (50, 30), (50, 20), (-50, 20), (-50, 30)], + ] + multipolygon = MultiPolygon([[geometry, []] for geometry in geometries]) + result = normalize_geometry(multipolygon) + self.assertIsInstance(result, gpd.GeoDataFrame) + self.assertEqual(result.crs, "EPSG:4326") + self.assertEqual(len(result.index), 1) + self.assertEqual( + result.iloc[0].geometry, + split_polygon(multipolygon), + ) + + def test_normalize_geometry_geoseries(self): + """ + Test that a GeoSeries is normalized to a GeoDataFrame with the correct CRS and geometry. + """ + polygon = Polygon([(-50, 10), (50, 10), (50, -10), (-50, -10), (-50, 10)]) + gs = gpd.GeoSeries(polygon, crs="EPSG:4326") + result = normalize_geometry(gs) + self.assertIsInstance(result, gpd.GeoDataFrame) + self.assertEqual(result.crs, "EPSG:4326") + self.assertEqual(len(result.index), 1) + self.assertEqual( + result.iloc[0].geometry, + split_polygon(polygon), + ) + + def test_get_planar_bounds_no_mask_returns_global_extent(self): + """ + Test that omitting a mask returns the full global longitude/latitude + extent. + """ + self.assertEqual(get_planar_bounds(None), (-180, -90, 180, 90)) + + def test_get_planar_bounds_polygon_mask_returns_its_bounds(self): + """ + Test that a Polygon mask's bounds are returned directly. + """ + mask = Polygon([[-100, 25], [-50, 25], [-50, -25], [-100, -25], [-100, 25]]) + self.assertEqual(get_planar_bounds(mask), (-100, -25, -50, 25)) + + def test_get_planar_bounds_multipolygon_mask_returns_combined_bounds(self): + """ + Test that a MultiPolygon mask's bounds span all its constituent + polygons. + """ + mask = MultiPolygon( + [ + Polygon([[-100, 0], [-90, 0], [-90, 10], [-100, 10], [-100, 0]]), + Polygon([[50, -20], [60, -20], [60, -10], [50, -10], [50, -20]]), + ] + ) + self.assertEqual(get_planar_bounds(mask), (-100, -20, 60, 10)) + + def test_get_planar_bounds_mask_with_max_longitude_exactly_negative_180_expands_to_180( + self, + ): + """ + Known-limitation regression test (not a full antimeridian fix, see + get_planar_bounds's docstring): when a mask's raw bounding box has + its maximum longitude exactly -180 (e.g. a mask expressed with + unwrapped-negative longitude spanning -190 to -180), max_longitude + is expanded to 180. This only patches max_longitude; min_longitude + (-190 here) is left as-is, so the resulting bounds do not + coherently describe the mask's actual extent. + """ + mask = Polygon([[-190, -10], [-180, -10], [-180, 10], [-190, 10], [-190, -10]]) + min_longitude, _, max_longitude, _ = get_planar_bounds(mask) + self.assertEqual(max_longitude, 180) + self.assertEqual(min_longitude, -190) + + def test_get_planar_bounds_invalid_polygon_raises_value_error(self): + """ + Test that a self-intersecting (invalid) Polygon mask raises a + ValueError. + """ + mask = Polygon([[-100, 25], [-50, 25], [-100, -25], [-50, -25], [-100, 25]]) + with self.assertRaises(ValueError): + get_planar_bounds(mask) + + def test_normalize_geometry_geodataframe(self): + """ + Test that a GeoDataFrame is normalized to a GeoDataFrame with the correct CRS and geometry. + """ + polygon = Polygon([(-50, 10), (50, 10), (50, -10), (-50, -10), (-50, 10)]) + df = gpd.GeoDataFrame(geometry=[polygon], index=[0], crs="EPSG:4326") + result = normalize_geometry(df) + self.assertIsInstance(result, gpd.GeoDataFrame) + self.assertEqual(result.crs, "EPSG:4326") + self.assertEqual(len(result.index), 1) + self.assertEqual( + result.iloc[0].geometry, + split_polygon(polygon), + ) diff --git a/tests/utils/test_observation.py b/tests/utils/test_observation.py new file mode 100644 index 0000000..aaac618 --- /dev/null +++ b/tests/utils/test_observation.py @@ -0,0 +1,337 @@ +""" +Unit tests for the tatc.utils.observation module. + +@author Paul T. Grogan +""" + +import unittest + +from tatc.utils import ( + compute_field_of_regard, + compute_max_access_time, + compute_max_transit_time, + compute_min_along_track_distance, + compute_min_elevation_angle, + field_of_regard_to_swath_width, + swath_width_to_field_of_regard, + swath_width_to_field_of_view, +) + + +class TestObservation(unittest.TestCase): # pylint: disable=too-many-public-methods + """ + Unit tests for the tatc.utils.observation module. + """ + + def test_swath_width_to_field_of_regard(self): + """ + Test that the swath width can be converted to field of regard. + """ + self.assertAlmostEqual( + swath_width_to_field_of_regard(705000, 185815), 15.0, delta=0.001 + ) + + def test_swath_width_to_field_of_regard_zero_swath(self): + """ + Test that a zero swath width (nadir-only observation) requires no + field of regard. + """ + self.assertEqual(swath_width_to_field_of_regard(705000, 0), 0.0) + + def test_swath_width_to_field_of_regard_elevation(self): + """ + Test that a positive elevation (observing a point above the mean + Earth radius) changes the field of regard required for the same + nominal swath width and altitude. + """ + self.assertAlmostEqual( + swath_width_to_field_of_regard(705000, 185815, 5000), + 15.105822, + delta=1e-5, + ) + + def test_swath_width_to_field_of_regard_inverts_field_of_regard_to_swath_width( + self, + ): + """ + Test that swath_width_to_field_of_regard is the inverse of + field_of_regard_to_swath_width across a range of altitudes and + field of regard values (as fractions of the horizon-limited maximum + field of regard at each altitude, so every combination stays within + the physically achievable range). + """ + for altitude in (500000, 705000, 800000, 20000000): + max_field_of_regard = compute_field_of_regard(altitude, 0) + for fraction in (0.05, 0.2, 0.5, 0.9): + field_of_regard = max_field_of_regard * fraction + swath_width = field_of_regard_to_swath_width(altitude, field_of_regard) + self.assertAlmostEqual( + swath_width_to_field_of_regard(altitude, swath_width), + field_of_regard, + delta=1e-6, + ) + + def test_swath_width_to_field_of_view_matches_field_of_regard_at_nadir(self): + """ + Test that, at a look angle of zero (swath centered on nadir), the + field of view matches the symmetric field of regard exactly. + """ + self.assertAlmostEqual( + swath_width_to_field_of_view(705000, 185815, 0), + swath_width_to_field_of_regard(705000, 185815), + delta=1e-9, + ) + + def test_swath_width_to_field_of_view_zero_swath(self): + """ + Test that a zero swath width requires no field of view, regardless + of look angle. + """ + self.assertEqual(swath_width_to_field_of_view(705000, 0, 20), 0.0) + + def test_swath_width_to_field_of_view_decreases_with_look_angle(self): + """ + Test that, for a fixed altitude and swath width, the field of view + decreases as the off-nadir look angle increases (a fixed ground + swath subtends a smaller angle as viewing becomes more oblique). + """ + field_of_views = [ + swath_width_to_field_of_view(705000, 185815, look_angle) + for look_angle in (0, 5, 10, 20, 30) + ] + self.assertEqual(field_of_views, sorted(field_of_views, reverse=True)) + + def test_swath_width_to_field_of_view_off_nadir(self): + """ + Test that the field of view can be computed for a specified + off-nadir look angle. + """ + self.assertAlmostEqual( + swath_width_to_field_of_view(705000, 185815, 20), + 12.992488, + delta=1e-5, + ) + + def test_swath_width_to_field_of_view_saturates_beyond_horizon(self): + """ + Test that a look angle at or beyond the horizon-limited maximum + saturates to the same field of view rather than raising an error. + """ + max_look_angle = compute_field_of_regard(705000, 0) / 2 + self.assertAlmostEqual( + swath_width_to_field_of_view(705000, 185815, max_look_angle), + swath_width_to_field_of_view(705000, 185815, max_look_angle + 10), + delta=1e-12, + ) + + def test_field_of_regard_to_swath_width(self): + """ + Test that the field of regard can be converted to swath width. + """ + self.assertAlmostEqual( + field_of_regard_to_swath_width(705000, 15.0), 185815, delta=1.0 + ) + + def test_field_of_regard_to_swath_width_modis(self): + """ + Test against the published MODIS instrument specification (Terra/ + Aqua, 705 km altitude, +/-55 degree scan angle for a 110 degree + field of regard, 2330 km swath width). + """ + self.assertAlmostEqual( + field_of_regard_to_swath_width(705000, 110.0), + 2330000, + delta=100, + ) + + def test_field_of_regard_to_swath_width_zero(self): + """ + Test that a zero field of regard (nadir-only observation) yields a + zero-width swath. + """ + self.assertEqual(field_of_regard_to_swath_width(705000, 0), 0.0) + + def test_field_of_regard_to_swath_width_elevation(self): + """ + Test that a positive elevation (observing a point above the mean + Earth radius) changes the swath width for the same field of regard + and altitude. + """ + self.assertAlmostEqual( + field_of_regard_to_swath_width(705000, 15.0, 5000), + 184495.638, + delta=1e-3, + ) + + def test_field_of_regard_to_swath_width_increases_with_field_of_regard(self): + """ + Test that, for a fixed altitude, the swath width increases + monotonically with the field of regard. + """ + swath_widths = [ + field_of_regard_to_swath_width(705000, field_of_regard) + for field_of_regard in (1, 5, 10, 15, 20, 30, 50) + ] + self.assertEqual(swath_widths, sorted(swath_widths)) + + def test_field_of_regard_to_swath_width_saturates_beyond_horizon(self): + """ + Test that a field of regard at or beyond the horizon-limited + maximum saturates to the maximum observable swath width rather + than raising an error or growing without bound. + """ + max_field_of_regard = compute_field_of_regard(705000, 0) + self.assertEqual( + field_of_regard_to_swath_width(705000, max_field_of_regard), + field_of_regard_to_swath_width(705000, max_field_of_regard + 50), + ) + self.assertEqual( + field_of_regard_to_swath_width(705000, max_field_of_regard), + field_of_regard_to_swath_width(705000, 180), + ) + + def test_compute_field_of_regard(self): + """ + Test that the field of regard can be computed for a given altitude + and minimum elevation angle. + """ + self.assertAlmostEqual( + compute_field_of_regard(705000, 81.66446), 15.0, delta=0.001 + ) + + def test_compute_min_elevation_angle(self): + """ + Test that the minimum elevation angle can be computed for a given + altitude and field of regard. + """ + self.assertAlmostEqual( + compute_min_elevation_angle(705000, 15.0), 81.66446, delta=0.001 + ) + + def test_compute_min_elevation_angle_modis(self): + """ + Test against the published MODIS instrument specification + (Terra/Aqua, 705 km altitude, 110 degree field of regard). The + elevation angle at the swath edge should be roughly consistent + with MODIS's documented maximum view zenith angle of about 65 + degrees (elevation = 90 - view zenith angle = ~25 degrees). + """ + self.assertAlmostEqual( + compute_min_elevation_angle(705000, 110.0), 25.0, delta=1.0 + ) + + def test_compute_min_elevation_angle_saturated(self): + """ + Test that the minimum elevation angle is saturated at 0 degrees for + a given altitude and field of regard. + """ + self.assertEqual(compute_min_elevation_angle(30000000, 180.0), 0.0) + + def test_compute_max_access_time(self): + """ + Test that the maximum access time can be computed for a given + altitude and minimum elevation angle. + """ + self.assertAlmostEqual(compute_max_access_time(705000, 81.66446), 28, delta=1) + + def test_compute_max_access_time_iss(self): + """ + Test against the commonly cited maximum ISS visibility duration: + at its typical ~408 km altitude, a directly overhead pass (0 + degree minimum elevation, horizon-to-horizon) lasts up to about + 10 minutes (per NASA's Spot The Station and similar references). + """ + self.assertAlmostEqual(compute_max_access_time(408000, 0), 600, delta=30) + + def test_compute_max_transit_time(self): + """ + Test that the maximum transit time can be computed for a given + altitude, inclination, and along track distance. + """ + self.assertAlmostEqual( + compute_max_transit_time(705000, 51.6, 100000), + 15.458200, + delta=1e-5, + ) + + def test_compute_max_transit_time_zero_along_track(self): + """ + Test that a zero along track distance requires zero transit time. + """ + self.assertEqual(compute_max_transit_time(705000, 51.6, 0), 0.0) + + def test_compute_max_transit_time_linear_in_along_track(self): + """ + Test that the maximum transit time scales linearly with the along + track distance, for a fixed altitude and inclination. + """ + self.assertAlmostEqual( + compute_max_transit_time(705000, 51.6, 200000), + 2 * compute_max_transit_time(705000, 51.6, 100000), + delta=1e-9, + ) + + def test_compute_max_transit_time_equatorial_slower_than_polar(self): + """ + Test that, for the same along track distance and altitude, an + equatorial orbit requires more transit time than a polar orbit. + Each orbit's slowest (worst-case) ground velocity is evaluated at + its own extreme latitude: for the equatorial orbit this is the + equator itself, where Earth's eastward rotation partially cancels + the ground-relative velocity (a subtraction); for the polar orbit + this is the pole, where rotation contributes nothing and the + ground-relative velocity equals the full inertial ground speed. + The equatorial case remains slower overall. + """ + self.assertGreater( + compute_max_transit_time(705000, 0, 100000), + compute_max_transit_time(705000, 90, 100000), + ) + + def test_compute_min_along_track_distance(self): + """ + Test that the minimum along track distance can be computed for a + given altitude, inclination, and access time. + """ + self.assertAlmostEqual( + compute_min_along_track_distance(705000, 51.6, 20), + 129381.170, + delta=1e-3, + ) + + def test_compute_min_along_track_distance_zero_access_time(self): + """ + Test that a zero access time yields zero along track distance. + """ + self.assertEqual(compute_min_along_track_distance(705000, 51.6, 0), 0.0) + + def test_compute_min_along_track_distance_linear_in_access_time(self): + """ + Test that the minimum along track distance scales linearly with + the access time, for a fixed altitude and inclination. + """ + self.assertAlmostEqual( + compute_min_along_track_distance(705000, 51.6, 40), + 2 * compute_min_along_track_distance(705000, 51.6, 20), + delta=1e-9, + ) + + def test_compute_min_along_track_distance_inverts_compute_max_transit_time(self): + """ + Test that compute_min_along_track_distance is the exact inverse of + compute_max_transit_time across a range of altitudes, inclinations, + and along track distances. + """ + for altitude in (500000, 705000, 800000): + for inclination in (0, 30, 51.6, 90, 98): + for along_track in (1000, 50000, 200000): + transit_time = compute_max_transit_time( + altitude, inclination, along_track + ) + self.assertAlmostEqual( + compute_min_along_track_distance( + altitude, inclination, transit_time + ), + along_track, + delta=1e-6, + ) diff --git a/tests/utils/test_orbital.py b/tests/utils/test_orbital.py new file mode 100644 index 0000000..82fa64e --- /dev/null +++ b/tests/utils/test_orbital.py @@ -0,0 +1,421 @@ +""" +Unit tests for the tatc.utils.orbital module. + +@author Paul T. Grogan +""" + +import unittest + +import numpy as np + +from tatc import constants +from tatc.utils import mean_anomaly_to_true_anomaly, true_anomaly_to_mean_anomaly +from tatc.utils.orbital import ( + compute_apoapsis_radius, + compute_ground_inertial_velocity, + compute_ground_surface_velocity, + compute_j2_aop_rate, + compute_j2_mean_motion_rate, + compute_j2_raan_rate, + compute_orbit_inertial_velocity, + mean_motion_to_orbit_period, + mean_motion_to_semimajor_axis, + semimajor_axis_to_mean_motion, + semimajor_axis_to_orbit_period, +) + + +class TestOrbital(unittest.TestCase): # pylint: disable=too-many-public-methods + """ + Unit tests for the tatc.utils.orbital module. + """ + + def test_compute_orbit_inertial_velocity_iss(self): + """ + Test against the commonly cited ISS orbital speed of + approximately 7.66 km/s at its typical ~408 km altitude + (per NASA ISS fact sheets). + """ + self.assertAlmostEqual(compute_orbit_inertial_velocity(408000), 7660, delta=10) + + def test_compute_orbit_inertial_velocity_geo(self): + """ + Test against the well-known geostationary orbital speed of + approximately 3.07 km/s at ~35,786 km altitude. + """ + self.assertAlmostEqual(compute_orbit_inertial_velocity(35786000), 3075, delta=5) + + def test_compute_orbit_inertial_velocity_surface(self): + """ + Test against the classic "first cosmic velocity" of + approximately 7.9 km/s for a circular orbit at the Earth's + surface (zero altitude). + """ + self.assertAlmostEqual(compute_orbit_inertial_velocity(0), 7910, delta=5) + + def test_compute_orbit_inertial_velocity_decreases_with_altitude(self): + """ + Test that the inertial orbit velocity decreases monotonically as + altitude increases (higher, slower orbits). + """ + velocities = [ + compute_orbit_inertial_velocity(altitude) + for altitude in (0, 400000, 705000, 20000000, 35786000) + ] + self.assertEqual(velocities, sorted(velocities, reverse=True)) + + def test_compute_ground_inertial_velocity_matches_orbit_at_same_radius(self): + """ + Test that the ground point velocity equals the orbital velocity + exactly when projected to the satellite's own altitude (the + projected point coincides with the satellite). + """ + self.assertAlmostEqual( + compute_ground_inertial_velocity(408000, 408000), + compute_orbit_inertial_velocity(408000), + delta=1e-9, + ) + + def test_compute_ground_inertial_velocity_slower_than_orbit_at_sea_level(self): + """ + Test that the ground point velocity projected to sea level (zero + elevation) is slower than the satellite's own orbital velocity, + since it shares the same angular velocity at a smaller radius. + """ + self.assertLess( + compute_ground_inertial_velocity(408000, 0), + compute_orbit_inertial_velocity(408000), + ) + + def test_compute_ground_inertial_velocity_increases_with_elevation(self): + """ + Test that, for a fixed altitude, the ground point velocity + increases monotonically with elevation. + """ + velocities = [ + compute_ground_inertial_velocity(408000, elevation) + for elevation in (-1000, 0, 100000, 300000, 408000) + ] + self.assertEqual(velocities, sorted(velocities)) + + def test_compute_ground_inertial_velocity_decreases_with_altitude(self): + """ + Test that, for a fixed (sea-level) elevation, the ground point + velocity decreases monotonically as altitude increases. + """ + velocities = [ + compute_ground_inertial_velocity(altitude, 0) + for altitude in (200000, 408000, 705000, 20000000) + ] + self.assertEqual(velocities, sorted(velocities, reverse=True)) + + def test_compute_ground_surface_velocity_equatorial_orbit_at_equator(self): + """ + Test that an equatorial orbit's ground track, which moves purely + eastward, has its ground-relative speed reduced by exactly Earth's + eastward rotation speed at the equator. + """ + v_inertial = compute_ground_inertial_velocity(408000, 0) + v_earth_equator = ( + 2 * np.pi / constants.EARTH_SIDEREAL_DAY_S * constants.EARTH_MEAN_RADIUS + ) + self.assertAlmostEqual( + compute_ground_surface_velocity(408000, 0, 0, 0), + v_inertial - v_earth_equator, + delta=1.0, + ) + + def test_compute_ground_surface_velocity_polar_orbit_at_equator(self): + """ + Test that a polar orbit's ground track, which moves purely + northward as it crosses the equator, combines with Earth's + (perpendicular) eastward rotation speed in quadrature rather than + by simple addition or subtraction. + """ + v_inertial = compute_ground_inertial_velocity(408000, 0) + v_earth_equator = ( + 2 * np.pi / constants.EARTH_SIDEREAL_DAY_S * constants.EARTH_MEAN_RADIUS + ) + self.assertAlmostEqual( + compute_ground_surface_velocity(408000, 0, 90, 0), + (v_inertial**2 + v_earth_equator**2) ** 0.5, + delta=1.0, + ) + + def test_compute_ground_surface_velocity_landsat(self): + """ + Test against the commonly cited Landsat ground track velocity of + approximately 6.75 km/s (per USGS Landsat documentation), using + Landsat's published altitude (705 km) and inclination (98.2 + degrees, sun-synchronous). + """ + self.assertAlmostEqual( + compute_ground_surface_velocity(705000, 0, 98.2, 0), 6750, delta=100 + ) + + def test_compute_ground_surface_velocity_increases_with_inclination(self): + """ + Test that, at the equator, the ground surface velocity increases + monotonically with inclination from the equatorial (slowest) to + the polar (fastest) case. + """ + velocities = [ + compute_ground_surface_velocity(408000, 0, inclination, 0) + for inclination in (0, 15, 30, 45, 60, 75, 90) + ] + self.assertEqual(velocities, sorted(velocities)) + + def test_compute_ground_surface_velocity_pole_does_not_raise(self): + """ + Test that evaluating at latitude = 90 degrees (a pole, where the + azimuth formula's denominator approaches zero) does not raise an + error, and instead saturates via clipping. + """ + compute_ground_surface_velocity(408000, 0, 45, 90) + + def test_semimajor_axis_to_mean_motion_geo(self): + """ + Test against the geostationary mean motion: 360 degrees per + sidereal day, at the well-known geostationary geocentric + semimajor axis of approximately 42,164 km. + """ + self.assertAlmostEqual( + semimajor_axis_to_mean_motion(42164000), + 360 / constants.EARTH_SIDEREAL_DAY_S, + delta=1e-6, + ) + + def test_semimajor_axis_to_mean_motion_inverts_mean_motion_to_semimajor_axis( + self, + ): + """ + Test that semimajor_axis_to_mean_motion is the exact inverse of + mean_motion_to_semimajor_axis across a range of orbit altitudes. + """ + for semimajor_axis in (6779008, 7076008, 8071008, 42164000): + mean_motion = semimajor_axis_to_mean_motion(semimajor_axis) + self.assertAlmostEqual( + mean_motion_to_semimajor_axis(mean_motion), + semimajor_axis, + delta=1e-3, + ) + + def test_mean_motion_to_orbit_period_is_its_own_inverse(self): + """ + Test that mean_motion_to_orbit_period is an involution: applying + it twice recovers the original value, since a mean motion + (degrees/second) and its orbital period (seconds) are each 360 + divided by the other. + """ + mean_motion = 0.0648 + self.assertAlmostEqual( + mean_motion_to_orbit_period(mean_motion_to_orbit_period(mean_motion)), + mean_motion, + delta=1e-12, + ) + + def test_semimajor_axis_to_orbit_period_geo(self): + """ + Test against the geostationary orbital period, which by + definition equals Earth's sidereal rotation period, at the + well-known geostationary geocentric semimajor axis of + approximately 42,164 km. + """ + self.assertAlmostEqual( + semimajor_axis_to_orbit_period(42164000), + constants.EARTH_SIDEREAL_DAY_S, + delta=1.0, + ) + + def test_semimajor_axis_to_orbit_period_iss(self): + """ + Test against the commonly cited ISS orbital period of + approximately 92.68 minutes, at its typical ~408 km altitude. + """ + self.assertAlmostEqual( + semimajor_axis_to_orbit_period(constants.EARTH_MEAN_RADIUS + 408000), + 92.68 * 60, + delta=30, + ) + + def test_semimajor_axis_to_orbit_period_increases_with_semimajor_axis(self): + """ + Test that the orbital period increases monotonically with the + semimajor axis. + """ + periods = [ + semimajor_axis_to_orbit_period(semimajor_axis) + for semimajor_axis in (6779008, 7076008, 8071008, 42164000) + ] + self.assertEqual(periods, sorted(periods)) + + def test_compute_apoapsis_radius_circular_orbit(self): + """ + Test that a circular orbit's (zero eccentricity) apoapsis radius + equals its semimajor axis exactly. + """ + semimajor_axis = constants.EARTH_MEAN_RADIUS + 705000 + self.assertEqual(compute_apoapsis_radius(semimajor_axis, 0), semimajor_axis) + + def test_compute_apoapsis_radius_matches_perigee_apogee_definition(self): + """ + Test that compute_apoapsis_radius recovers the apogee radius that + defines the semimajor axis and eccentricity in the first place: + semimajor axis is the mean of the perigee/apogee radii, and + eccentricity is their normalized difference. + """ + perigee_radius = constants.EARTH_MEAN_RADIUS + 550000 + apogee_radius = constants.EARTH_MEAN_RADIUS + 39900000 + semimajor_axis = (perigee_radius + apogee_radius) / 2 + eccentricity = (apogee_radius - perigee_radius) / ( + apogee_radius + perigee_radius + ) + self.assertAlmostEqual( + compute_apoapsis_radius(semimajor_axis, eccentricity), + apogee_radius, + delta=1e-6, + ) + + def test_compute_apoapsis_radius_molniya_orbit(self): + """ + Test against the published classic Molniya orbit: a semimajor axis + of approximately 26,600 km and eccentricity of approximately 0.74 + yields an apogee altitude of approximately 40,000 km. + + See: https://en.wikipedia.org/wiki/Molniya_orbit + """ + apogee_altitude = ( + compute_apoapsis_radius(26600000, 0.74) - constants.EARTH_MEAN_RADIUS + ) + self.assertAlmostEqual(apogee_altitude, 40000000, delta=500000) + + def test_compute_j2_raan_rate_sun_synchronous(self): + """ + Test against the sun-synchronous condition: at Landsat/Terra/Aqua's + published altitude (705 km) and inclination (98.2 degrees), the + RAAN precession rate should match the Earth's mean motion around + the Sun (360 degrees per 365.2422-day year) -- this is exactly why + that inclination was chosen for those missions. + """ + semimajor_axis = constants.EARTH_MEAN_RADIUS + 705000 + sun_synchronous_rate = 360 / 365.2422 / 86400 + self.assertAlmostEqual( + compute_j2_raan_rate(semimajor_axis, 98.2, 0), + sun_synchronous_rate, + delta=1e-7, + ) + + def test_compute_j2_raan_rate_zero_for_polar_orbit(self): + """ + Test that a polar orbit (inclination = 90 degrees) has zero RAAN + precession rate. + """ + semimajor_axis = constants.EARTH_MEAN_RADIUS + 705000 + self.assertAlmostEqual( + compute_j2_raan_rate(semimajor_axis, 90, 0), 0.0, delta=1e-15 + ) + + def test_compute_j2_raan_rate_sign_by_inclination(self): + """ + Test that the RAAN precession rate is negative (westward + regression) for prograde orbits and positive for retrograde + orbits. + """ + semimajor_axis = constants.EARTH_MEAN_RADIUS + 705000 + self.assertLess(compute_j2_raan_rate(semimajor_axis, 51.6, 0), 0.0) + self.assertGreater(compute_j2_raan_rate(semimajor_axis, 98.2, 0), 0.0) + + def test_compute_j2_raan_rate_increases_with_eccentricity(self): + """ + Test that the magnitude of the RAAN precession rate increases + monotonically with eccentricity, for a fixed semimajor axis and + inclination. + """ + semimajor_axis = constants.EARTH_MEAN_RADIUS + 705000 + magnitudes = [ + abs(compute_j2_raan_rate(semimajor_axis, 51.6, eccentricity)) + for eccentricity in (0, 0.1, 0.3, 0.5) + ] + self.assertEqual(magnitudes, sorted(magnitudes)) + + def test_compute_j2_aop_rate_zero_at_critical_inclination(self): + """ + Test against the well-known critical inclination (~63.43 degrees, + arccos(1/sqrt(5))) at which the argument of periapsis rate is + exactly zero -- the reason Molniya-type orbits use this + inclination to keep their periapsis location "frozen". + """ + critical_inclination = np.degrees(np.arccos(1 / np.sqrt(5))) + semimajor_axis = 26600000 + self.assertAlmostEqual( + compute_j2_aop_rate(semimajor_axis, critical_inclination, 0.74), + 0.0, + delta=1e-15, + ) + + def test_compute_j2_aop_rate_sign_by_inclination(self): + """ + Test that the argument of periapsis rate is positive below the + critical inclination (~63.43 degrees) and negative above it. + """ + semimajor_axis = constants.EARTH_MEAN_RADIUS + 705000 + self.assertGreater(compute_j2_aop_rate(semimajor_axis, 45, 0), 0.0) + self.assertLess(compute_j2_aop_rate(semimajor_axis, 90, 0), 0.0) + + def test_compute_j2_mean_motion_rate_nonzero_at_critical_inclination(self): + """ + Test that, unlike compute_j2_aop_rate, the correction to mean + anomaly's rate of advance is NOT zero at the critical inclination + (~63.43 degrees). This is the key distinction that makes this + correction (not the argument of periapsis rate) the correct one + for computing a J2-corrected orbital period for Molniya/Tundra + orbits, which always use the critical inclination. + """ + critical_inclination = np.degrees(np.arccos(1 / np.sqrt(5))) + semimajor_axis = 26600000 + self.assertNotAlmostEqual( + compute_j2_mean_motion_rate(semimajor_axis, critical_inclination, 0.74), + 0.0, + delta=1e-8, + ) + + def test_compute_j2_mean_motion_rate_zero_crossing(self): + """ + Test that the correction vanishes at its own zero-crossing + inclination (~54.74 degrees, arccos(1/sqrt(3)), where + 3*cos^2(inclination) - 1 = 0) -- a different inclination than + compute_j2_aop_rate's zero crossing, confirming this is a + genuinely distinct correction term. + """ + zero_crossing_inclination = np.degrees(np.arccos(1 / np.sqrt(3))) + semimajor_axis = constants.EARTH_MEAN_RADIUS + 705000 + self.assertAlmostEqual( + compute_j2_mean_motion_rate(semimajor_axis, zero_crossing_inclination, 0), + 0.0, + delta=1e-9, + ) + + def test_compute_j2_mean_motion_rate_sign_by_inclination(self): + """ + Test that the correction is positive below the ~54.74 degree + zero-crossing inclination, and negative above it. + """ + semimajor_axis = constants.EARTH_MEAN_RADIUS + 705000 + self.assertGreater(compute_j2_mean_motion_rate(semimajor_axis, 0, 0), 0.0) + self.assertLess(compute_j2_mean_motion_rate(semimajor_axis, 90, 0), 0.0) + + def test_mean_anomaly_to_true_anomaly(self): + """ + Test that the mean anomaly can be converted to true anomaly. + """ + self.assertAlmostEqual( + mean_anomaly_to_true_anomaly(78.940629, 0.0001492), 78.95065818, delta=0.01 + ) + + def test_true_anomaly_to_mean_anomaly(self): + """ + Test that the true anomaly can be converted to mean anomaly. + """ + self.assertAlmostEqual( + true_anomaly_to_mean_anomaly(78.95065818, 0.0001492), 78.940629, delta=0.01 + ) diff --git a/tests/utils/test_projection.py b/tests/utils/test_projection.py new file mode 100644 index 0000000..fe810de --- /dev/null +++ b/tests/utils/test_projection.py @@ -0,0 +1,634 @@ +""" +Unit tests for the tatc.utils.projection module. + +@author Paul T. Grogan +""" + +import unittest +from datetime import datetime, timezone + +import numpy as np +from pyproj import Transformer +from shapely.geometry import Point +from skyfield.api import EarthSatellite + +from tatc import config, constants +from tatc.constants import timescale +from tatc.schemas import CircularOrbit +from tatc.utils.observation import ( + compute_field_of_regard, + field_of_regard_to_swath_width, +) +from tatc.utils.orbital import compute_ground_surface_velocity +from tatc.utils.projection import ( + buffer_footprint, + buffer_target, + compute_footprint, + compute_limb, + compute_projected_ray_position, +) + + +def _great_circle_distance(lat1, lon1, lat2, lon2): + earth_radius = 6371000 + phi1, phi2 = np.radians(lat1), np.radians(lat2) + dphi = np.radians(lat2 - lat1) + dlambda = np.radians(lon2 - lon1) + a = np.sin(dphi / 2) ** 2 + np.cos(phi1) * np.cos(phi2) * np.sin(dlambda / 2) ** 2 + return 2 * earth_radius * np.arcsin(np.sqrt(a)) + + +class TestProjection(unittest.TestCase): # pylint: disable=too-many-public-methods + """ + Unit tests for the tatc.utils.projection module. + """ + + def setUp(self): + noon_utc = datetime(2020, 3, 20, 12, tzinfo=timezone.utc) + self.orbit = CircularOrbit( + mean_altitude=705000, + true_anomaly=0, + epoch=noon_utc, + inclination=51.6, + right_ascension_ascending_node=0.0, + ) + self.satellite = EarthSatellite.from_satrec( + self.orbit.to_gp_orbit().elements[0].to_satrec(), timescale + ) + self.orbit_track = self.satellite.at(timescale.from_datetime(noon_utc)) + self.subpoint = self.orbit_track.subpoint() + + def test_compute_projected_ray_position_nadir_matches_subpoint(self): + """ + Test that a pure nadir ray (zero field of view, roll, and pitch) + lands at essentially the same location as Skyfield's own + independently-computed sub-satellite point. The nadir ray must + follow the geodetic vertical (the WGS 84 ellipsoid surface normal), + not the geocentric direction (straight toward the Earth's center): + those two only coincide at the equator and poles, and this test + point (true_anomaly=0, near the ascending node) sits close to the + equator, where the two would be hard to tell apart. See + `test_compute_projected_ray_position_nadir_matches_subpoint_off_equator` + for a test point where the distinction actually matters. + """ + position = compute_projected_ray_position( + self.orbit_track, 0, 0, 0, 0, False, 0, 0 + ) + distance = _great_circle_distance( + self.subpoint.latitude.degrees, + self.subpoint.longitude.degrees, + position.latitude.degrees, + position.longitude.degrees, + ) + self.assertLess(distance, 1) + + def test_compute_projected_ray_position_nadir_matches_subpoint_off_equator(self): + """ + Test that a pure nadir ray still matches Skyfield's sub-satellite + point away from the equator (near maximum latitude for this + orbit's inclination), where the geocentric and geodetic nadir + directions diverge by kilometers if conflated. This is the + regression test for a bug where the nadir ray pointed toward the + Earth's center (geocentric) instead of along the local WGS 84 + ellipsoid normal (geodetic), which agreed with Skyfield's subpoint + only near the equator/poles and was off by ~2 km at 45 degrees + latitude for a 700 km altitude orbit. + """ + noon_utc = datetime(2020, 3, 20, 12, tzinfo=timezone.utc) + orbit = CircularOrbit( + mean_altitude=705000, + true_anomaly=90, + epoch=noon_utc, + inclination=51.6, + right_ascension_ascending_node=0.0, + ) + satellite = EarthSatellite.from_satrec( + orbit.to_gp_orbit().elements[0].to_satrec(), timescale + ) + orbit_track = satellite.at(timescale.from_datetime(noon_utc)) + subpoint = orbit_track.subpoint() + position = compute_projected_ray_position(orbit_track, 0, 0, 0, 0, False, 0, 0) + # confirm this test point is actually away from the equator + self.assertGreater(abs(subpoint.latitude.degrees), 45) + distance = _great_circle_distance( + subpoint.latitude.degrees, + subpoint.longitude.degrees, + position.latitude.degrees, + position.longitude.degrees, + ) + self.assertLess(distance, 1) + + def test_compute_projected_ray_position_elevation(self): + """ + Test that the projected position's elevation matches the + requested elevation. + """ + position = compute_projected_ray_position( + self.orbit_track, 0, 0, 0, 0, False, 0, 1000 + ) + self.assertAlmostEqual(position.elevation.m, 1000, delta=1e-3) + + def test_compute_projected_ray_position_roll_moves_away_from_subpoint(self): + """ + Test that increasing the magnitude of the roll angle moves the + projected position monotonically farther from the sub-satellite + point, in both directions. + """ + distances = [ + _great_circle_distance( + self.subpoint.latitude.degrees, + self.subpoint.longitude.degrees, + *self._latlon(roll_angle=roll), + ) + for roll in (0, 5, 10, 20) + ] + self.assertEqual(distances, sorted(distances)) + distances_negative = [ + _great_circle_distance( + self.subpoint.latitude.degrees, + self.subpoint.longitude.degrees, + *self._latlon(roll_angle=roll), + ) + for roll in (0, -5, -10, -20) + ] + self.assertEqual(distances_negative, sorted(distances_negative)) + + def test_compute_projected_ray_position_pitch_moves_away_from_subpoint(self): + """ + Test that increasing the magnitude of the pitch angle moves the + projected position monotonically farther from the sub-satellite + point, in both directions. + """ + distances = [ + _great_circle_distance( + self.subpoint.latitude.degrees, + self.subpoint.longitude.degrees, + *self._latlon(pitch_angle=pitch), + ) + for pitch in (0, 5, 10, 20) + ] + self.assertEqual(distances, sorted(distances)) + + def test_compute_projected_ray_position_rectangular_matches_elliptical_at_principal_angles( + self, + ): + """ + Test that rectangular and elliptical field of view shapes produce + identical rays at the four axis-aligned angles (0, 90, 180, 270 + degrees), where an ellipse touches its bounding rectangle. + """ + for angle in (0, 90, 180, 270): + rectangular = compute_projected_ray_position( + self.orbit_track, 10, 4, 0, 0, True, angle, 0 + ) + elliptical = compute_projected_ray_position( + self.orbit_track, 10, 4, 0, 0, False, angle, 0 + ) + self.assertAlmostEqual( + rectangular.latitude.degrees, elliptical.latitude.degrees, delta=1e-9 + ) + self.assertAlmostEqual( + rectangular.longitude.degrees, elliptical.longitude.degrees, delta=1e-9 + ) + + def test_compute_projected_ray_position_rectangular_differs_off_axis(self): + """ + Test that rectangular and elliptical field of view shapes produce + different rays at an off-axis angle, confirming the rectangular + shape is not silently ignored. + """ + rectangular = compute_projected_ray_position( + self.orbit_track, 10, 4, 0, 0, True, 45, 0 + ) + elliptical = compute_projected_ray_position( + self.orbit_track, 10, 4, 0, 0, False, 45, 0 + ) + self.assertNotAlmostEqual( + rectangular.latitude.degrees, elliptical.latitude.degrees, delta=1e-6 + ) + + def test_compute_projected_ray_position_rectangular_all_edge_segments(self): + """ + Test that sampling one angle from each of the rectangle's 8 + boundary segments (split at the 4 corners and, within the top and + bottom edges, at the axes) produces 8 distinct, well-defined + positions, none of which collapse to the sub-satellite point. + """ + theta = np.degrees(np.arctan(4 / 10)) + sample_angles = [ + theta / 2, # right edge, upper half + 45, # top edge, right half + (theta + (180 - theta)) / 2, # top edge, left half + 179, # left edge, upper half + 180 + theta / 2, # left edge, lower half + 225, # bottom edge, right half + 304, # bottom edge, left half + 350, # right edge, lower half + ] + positions = [ + compute_projected_ray_position(self.orbit_track, 10, 4, 0, 0, True, a, 0) + for a in sample_angles + ] + latlons = [(p.latitude.degrees, p.longitude.degrees) for p in positions] + self.assertEqual(len(set(latlons)), len(latlons)) + for lat, lon in latlons: + self.assertLess( + _great_circle_distance( + self.subpoint.latitude.degrees, + self.subpoint.longitude.degrees, + lat, + lon, + ), + 200000, + ) + + def test_compute_projected_ray_position_saturates_beyond_horizon(self): + """ + Test that a roll angle beyond the horizon-limited maximum (where + the ray misses the WGS 84 geoid entirely) falls back to a stable + limb-edge position rather than raising an error, and that this + fallback position no longer changes with further increases in + roll angle. + """ + max_look_angle = compute_field_of_regard(705000, 0) / 2 + beyond_horizon = compute_projected_ray_position( + self.orbit_track, 0, 0, max_look_angle + 5, 0, False, 0, 0 + ) + further_beyond = compute_projected_ray_position( + self.orbit_track, 0, 0, max_look_angle + 20, 0, False, 0, 0 + ) + self.assertAlmostEqual( + beyond_horizon.latitude.degrees, further_beyond.latitude.degrees, delta=1e-9 + ) + self.assertAlmostEqual( + beyond_horizon.longitude.degrees, + further_beyond.longitude.degrees, + delta=1e-9, + ) + + def test_compute_projected_ray_position_vectorized_matches_scalar(self): + """ + Test that passing a vector of times produces the same results as + calling the function separately for each individual time. + """ + times = timescale.utc(2020, 3, 20, 12, [0, 1, 2]) + orbit_track = self.satellite.at(times) + vectorized = compute_projected_ray_position( + orbit_track, 0, 0, 5, 0, False, 0, 0 + ) + for i in range(3): + scalar = compute_projected_ray_position( + self.satellite.at(times[i]), 0, 0, 5, 0, False, 0, 0 + ) + self.assertAlmostEqual( + vectorized.latitude.degrees[i], scalar.latitude.degrees, delta=1e-9 + ) + self.assertAlmostEqual( + vectorized.longitude.degrees[i], scalar.longitude.degrees, delta=1e-9 + ) + + def test_compute_footprint_scalar_orbit_track(self): + """ + Test that a scalar (single-time) orbit_track produces a single + valid footprint, rather than raising an error. + """ + footprints = compute_footprint(self.orbit_track, 10, 10, 0, 0, False) + self.assertEqual(len(footprints), 1) + self.assertTrue(footprints[0].is_valid) + + def test_compute_footprint_scalar_matches_single_element_vector(self): + """ + Test that a scalar orbit_track produces the same footprint as a + vectorized orbit_track containing that single time. + """ + vector_track = self.satellite.at(timescale.utc(2020, 3, 20, 12, [0])) + scalar_footprint = compute_footprint(self.orbit_track, 10, 10, 0, 0, False) + vector_footprint = compute_footprint(vector_track, 10, 10, 0, 0, False) + self.assertTrue(scalar_footprint[0].equals(vector_footprint[0])) + + def test_compute_footprint_vectorized_multiple_times(self): + """ + Test that a vectorized orbit_track produces one valid footprint per time. + """ + times = timescale.utc(2020, 3, 20, 12, [0, 1, 2]) + orbit_track = self.satellite.at(times) + footprints = compute_footprint(orbit_track, 10, 10, 0, 0, False) + self.assertEqual(len(footprints), 3) + for footprint in footprints: + self.assertTrue(footprint.is_valid) + + def test_compute_footprint_default_number_points_elliptical(self): + """ + Test that the default number of points for an elliptical footprint + matches the runtime configuration. + """ + footprints = compute_footprint(self.orbit_track, 10, 10, 0, 0, False) + self.assertEqual( + len(footprints[0].exterior.coords) - 1, + config.rc.footprint_points_elliptical, + ) + + def test_compute_footprint_default_number_points_rectangular(self): + """ + Test that the default number of points for a rectangular footprint + is 4 times the per-side runtime configuration (one segment per side). + """ + footprints = compute_footprint(self.orbit_track, 10, 4, 0, 0, True) + self.assertEqual( + len(footprints[0].exterior.coords) - 1, + 4 * config.rc.footprint_points_rectangular_side, + ) + + def test_compute_footprint_explicit_number_points(self): + """ + Test that an explicit number_points overrides the runtime + configuration default. + """ + footprints = compute_footprint( + self.orbit_track, 10, 10, 0, 0, False, number_points=10 + ) + self.assertEqual(len(footprints[0].exterior.coords) - 1, 10) + + def test_compute_footprint_contains_subpoint(self): + """ + Test that both elliptical and rectangular nadir footprints contain + the sub-satellite point. + """ + subpoint_geom = Point( + self.subpoint.longitude.degrees, self.subpoint.latitude.degrees + ) + elliptical = compute_footprint(self.orbit_track, 10, 10, 0, 0, False) + rectangular = compute_footprint(self.orbit_track, 10, 4, 0, 0, True) + self.assertTrue(elliptical[0].contains(subpoint_geom)) + self.assertTrue(rectangular[0].contains(subpoint_geom)) + + def test_compute_limb_scalar_orbit_track(self): + """ + Test that a scalar (single-time) orbit_track produces a single + valid limb, rather than raising an error. + """ + limbs = compute_limb(self.orbit_track) + self.assertEqual(len(limbs), 1) + self.assertTrue(limbs[0].is_valid) + + def test_compute_limb_scalar_matches_single_element_vector(self): + """ + Test that a scalar orbit_track produces the same limb as a + vectorized orbit_track containing that single time. + """ + vector_track = self.satellite.at(timescale.utc(2020, 3, 20, 12, [0])) + scalar_limb = compute_limb(self.orbit_track) + vector_limb = compute_limb(vector_track) + self.assertTrue(scalar_limb[0].equals(vector_limb[0])) + + def test_compute_limb_vectorized_multiple_times(self): + """ + Test that a vectorized orbit_track produces one valid limb per time. + """ + times = timescale.utc(2020, 3, 20, 12, [0, 1, 2]) + orbit_track = self.satellite.at(times) + limbs = compute_limb(orbit_track) + self.assertEqual(len(limbs), 3) + for limb in limbs: + self.assertTrue(limb.is_valid) + + def test_compute_limb_matches_expected_horizon_angle(self): + """ + Test that every point on the limb boundary is at approximately the + expected Earth central angle (subpoint to horizon, for a zero + minimum elevation angle) from the sub-satellite point. + """ + altitude = self.orbit.mean_altitude + expected_angle = np.degrees( + np.arccos( + constants.EARTH_MEAN_RADIUS / (constants.EARTH_MEAN_RADIUS + altitude) + ) + ) + limb = compute_limb(self.orbit_track)[0] + for x, y, _ in limb.exterior.coords: + distance = np.degrees( + _great_circle_distance( + self.subpoint.latitude.degrees, + self.subpoint.longitude.degrees, + y, + x, + ) + / constants.EARTH_MEAN_RADIUS + ) + self.assertAlmostEqual(distance, expected_angle, delta=0.5) + + def test_compute_limb_contains_subpoint(self): + """ + Test that the limb contains the sub-satellite point (the closest, + and definitely visible, point on the Earth's surface). + """ + subpoint_geom = Point( + self.subpoint.longitude.degrees, self.subpoint.latitude.degrees + ) + limb = compute_limb(self.orbit_track)[0] + self.assertTrue(limb.contains(subpoint_geom)) + + def test_compute_limb_number_points(self): + """ + Test that the number_points parameter controls the number of + vertices in the limb polygon. + """ + limb = compute_limb(self.orbit_track, number_points=8)[0] + self.assertEqual(len(limb.exterior.coords) - 1, 8) + + def test_buffer_footprint_contains_center(self): + """ + Test that buffering a point produces a polygon containing that + original point. + """ + point = Point(0, 0) + to_crs = Transformer.from_crs("EPSG:4326", "EPSG:4087", always_xy=True) + from_crs = Transformer.from_crs("EPSG:4087", "EPSG:4326", always_xy=True) + result = buffer_footprint(point, to_crs, from_crs, 100000, 0) + self.assertTrue(result.contains(point)) + + def test_buffer_footprint_excludes_far_point(self): + """ + Test that a point well outside the swath width is not contained + in the buffered footprint. + """ + point = Point(0, 0) + to_crs = Transformer.from_crs("EPSG:4326", "EPSG:4087", always_xy=True) + from_crs = Transformer.from_crs("EPSG:4087", "EPSG:4326", always_xy=True) + result = buffer_footprint(point, to_crs, from_crs, 100000, 0) + self.assertFalse(result.contains(Point(5, 5))) + + def test_buffer_footprint_matches_expected_radius(self): + """ + Test that the buffered polygon's boundary is approximately + swath_width / 2 (great-circle distance) from the center point. + """ + point = Point(0, 0) + swath_width = 200000 + to_crs = Transformer.from_crs("EPSG:4326", "EPSG:4087", always_xy=True) + from_crs = Transformer.from_crs("EPSG:4087", "EPSG:4326", always_xy=True) + result = buffer_footprint(point, to_crs, from_crs, swath_width, 0) + for x, y, _ in result.exterior.coords: + distance = _great_circle_distance(0, 0, y, x) + self.assertAlmostEqual(distance, swath_width / 2, delta=1000) + + def test_buffer_footprint_increases_with_swath_width(self): + """ + Test that the buffered footprint's area increases monotonically + with the swath width. + """ + point = Point(0, 0) + to_crs = Transformer.from_crs("EPSG:4326", "EPSG:4087", always_xy=True) + from_crs = Transformer.from_crs("EPSG:4087", "EPSG:4326", always_xy=True) + smaller = buffer_footprint(point, to_crs, from_crs, 100000, 0) + larger = buffer_footprint(point, to_crs, from_crs, 200000, 0) + self.assertGreater(larger.area, smaller.area) + + def test_buffer_footprint_elevation(self): + """ + Test that the buffered footprint is projected to the requested elevation. + """ + point = Point(0, 0) + to_crs = Transformer.from_crs("EPSG:4326", "EPSG:4087", always_xy=True) + from_crs = Transformer.from_crs("EPSG:4087", "EPSG:4326", always_xy=True) + result = buffer_footprint(point, to_crs, from_crs, 100000, 500) + self.assertAlmostEqual(next(iter(result.exterior.coords))[2], 500, delta=1e-6) + + def test_buffer_footprint_zero_swath_width_is_empty(self): + """ + Test that a zero swath width produces an empty geometry rather + than raising an error (buffering a point by zero distance). + """ + point = Point(0, 0) + to_crs = Transformer.from_crs("EPSG:4326", "EPSG:4087", always_xy=True) + from_crs = Transformer.from_crs("EPSG:4087", "EPSG:4326", always_xy=True) + result = buffer_footprint(point, to_crs, from_crs, 0, 0) + self.assertTrue(result.is_empty) + + def test_buffer_target_contains_original_geometry(self): + """ + Test that the buffered target contains the original geometry. + """ + point = Point(0, 0) + result = buffer_target(point, 705000, 51.6, 20, 60) + self.assertTrue(result.contains(point)) + + def test_buffer_target_matches_expected_distance(self): + """ + Test that the buffer distance matches the independently-computed + expected distance: half the swath width (from the field of + regard) plus the ground distance traveled in one time step (using + the ground velocity at the orbit's extreme latitude, the fastest, + most conservative point). + """ + point = Point(0, 0) + altitude, inclination, field_of_regard, time_step = 705000, 51.6, 20, 60 + swath_width = field_of_regard_to_swath_width(altitude, field_of_regard) + extreme_latitude = min(inclination, 180 - inclination) + ground_velocity = compute_ground_surface_velocity( + altitude, 0, inclination, extreme_latitude + ) + expected_distance = ground_velocity * time_step + swath_width / 2 + result = buffer_target(point, altitude, inclination, field_of_regard, time_step) + for x, y, *_ in result.exterior.coords: + distance = _great_circle_distance(0, 0, y, x) + self.assertAlmostEqual(distance, expected_distance, delta=1000) + + def test_buffer_target_is_conservative_at_high_latitude(self): + """ + Test that the default `distance_crs` (an equidistant cylindrical + projection whose standard parallel tracks the target's own + latitude) keeps the buffer close to the intended distance in every + direction, even far from the equator -- unlike a fixed low-latitude + standard parallel (e.g. `EPSG:4087`), whose east-west ground + distance for a fixed buffer shrinks by `cos(latitude)` away from + the equator (see + `test_buffer_target_fixed_low_latitude_crs_under_buffers_at_high_latitude`). + """ + altitude, inclination, field_of_regard, time_step = 705000, 51.6, 20, 60 + swath_width = field_of_regard_to_swath_width(altitude, field_of_regard) + extreme_latitude = min(inclination, 180 - inclination) + ground_velocity = compute_ground_surface_velocity( + altitude, 0, inclination, extreme_latitude + ) + expected_distance = ground_velocity * time_step + swath_width / 2 + point = Point(0, 51.6) + result = buffer_target(point, altitude, inclination, field_of_regard, time_step) + distances = [ + _great_circle_distance(51.6, 0, y, x) for x, y, *_ in result.exterior.coords + ] + # every direction (including the worst-case, most-compressed one) + # should still reach at least the intended distance + self.assertGreater(min(distances), expected_distance * 0.95) + + def test_buffer_target_fixed_low_latitude_crs_under_buffers_at_high_latitude(self): + """ + Regression test documenting why `distance_crs` now defaults to a + latitude-tracking projection instead of a fixed `EPSG:4087` (true + scale only at the equator): forcing `EPSG:4087` at a high latitude + under-buffers in the east-west direction by roughly `cos(latitude)`, + which could silently exclude a target that is still within reach. + """ + altitude, inclination, field_of_regard, time_step = 705000, 51.6, 20, 60 + swath_width = field_of_regard_to_swath_width(altitude, field_of_regard) + extreme_latitude = min(inclination, 180 - inclination) + ground_velocity = compute_ground_surface_velocity( + altitude, 0, inclination, extreme_latitude + ) + expected_distance = ground_velocity * time_step + swath_width / 2 + point = Point(0, 51.6) + result = buffer_target( + point, + altitude, + inclination, + field_of_regard, + time_step, + distance_crs="EPSG:4087", + ) + distances = [ + _great_circle_distance(51.6, 0, y, x) for x, y, *_ in result.exterior.coords + ] + # the east-west (worst-case) direction falls well short of the + # intended distance -- roughly cos(51.6 deg) = 0.62x + self.assertLess(min(distances), expected_distance * 0.7) + + def test_buffer_target_increases_with_time_step(self): + """ + Test that the buffered target's area increases monotonically with + the time step (more distance traveled). + """ + point = Point(0, 0) + smaller = buffer_target(point, 705000, 51.6, 20, 10) + larger = buffer_target(point, 705000, 51.6, 20, 200) + self.assertGreater(larger.area, smaller.area) + + def test_buffer_target_increases_with_field_of_regard(self): + """ + Test that the buffered target's area increases monotonically with + the field of regard (wider swath). + """ + point = Point(0, 0) + smaller = buffer_target(point, 705000, 51.6, 5, 60) + larger = buffer_target(point, 705000, 51.6, 60, 60) + self.assertGreater(larger.area, smaller.area) + + def test_buffer_target_distance_scaling(self): + """ + Test that distance_scaling scales the buffer distance: doubling + it should roughly double how far the boundary extends beyond the + original point. + """ + point = Point(0, 0) + result_1x = buffer_target(point, 705000, 51.6, 20, 60, distance_scaling=1.0) + result_2x = buffer_target(point, 705000, 51.6, 20, 60, distance_scaling=2.0) + coords_1x = list(result_1x.exterior.coords) + coords_2x = list(result_2x.exterior.coords) + max_dist_1x = max(_great_circle_distance(0, 0, y, x) for x, y in coords_1x) + max_dist_2x = max(_great_circle_distance(0, 0, y, x) for x, y in coords_2x) + self.assertAlmostEqual(max_dist_2x / max_dist_1x, 2.0, delta=0.05) + + def _latlon(self, roll_angle=0, pitch_angle=0): + position = compute_projected_ray_position( + self.orbit_track, 0, 0, roll_angle, pitch_angle, False, 0, 0 + ) + return position.latitude.degrees, position.longitude.degrees diff --git a/tests/utils/test_surface.py b/tests/utils/test_surface.py new file mode 100644 index 0000000..300a4c8 --- /dev/null +++ b/tests/utils/test_surface.py @@ -0,0 +1,86 @@ +""" +Unit tests for the tatc.utils.surface module. + +@author Paul T. Grogan +""" + +import unittest + +import numpy as np + +from tatc import constants +from tatc.utils import compute_number_samples + + +class TestSurface(unittest.TestCase): + """ + Unit tests for the tatc.utils.surface module. + """ + + def test_compute_number_samples(self): + """ + Test that the number of samples can be computed for a given sample distance. + """ + # rough approximation based on flat sample areas + sample_distance = 10000 + num_samples = int( + constants.EARTH_SURFACE_AREA / (np.pi * (sample_distance / 2) ** 2) + ) + self.assertEqual(compute_number_samples(sample_distance), num_samples) + + def test_compute_number_samples_matches_flat_approximation_at_small_scale(self): + """ + Test that, for sample distances much smaller than the Earth's + radius, the spherical-cap-based sample count closely approximates + the flat-plane (circular disk area) approximation. + """ + for sample_distance in (1000, 50000, 200000): + spherical = compute_number_samples(sample_distance) + flat = constants.EARTH_SURFACE_AREA / (np.pi * (sample_distance / 2) ** 2) + self.assertAlmostEqual(spherical / flat, 1.0, delta=1e-4) + + def test_compute_number_samples_exceeds_flat_approximation_at_large_scale(self): + """ + Test that, at a sample distance large enough for Earth's curvature + to matter, the spherical-cap-based sample count is greater than + the flat-plane approximation: a spherical cap covers less area + than a flat disk of the same angular radius, so more of them are + needed to cover the same total surface area. + """ + sample_distance = 5000000 + spherical = compute_number_samples(sample_distance) + flat = int(constants.EARTH_SURFACE_AREA / (np.pi * (sample_distance / 2) ** 2)) + self.assertGreater(spherical, flat) + + def test_compute_number_samples_decreases_with_distance(self): + """ + Test that the number of samples decreases monotonically as the + sample distance increases. + """ + counts = [compute_number_samples(d) for d in (1000, 10000, 100000, 1000000)] + self.assertEqual(counts, sorted(counts, reverse=True)) + + def test_compute_number_samples_zero_distance_raises(self): + """ + Test that a zero sample distance (a meaningless, infinitely dense + request) raises a clear ValueError rather than returning a + misleading value. (Previously this reached an unguarded division + by zero deep in the geometry, which happened to raise + OverflowError -- via a RuntimeWarning-then-inf-then-int(inf) + chain -- purely by numeric coincidence, and did not extend to + negative distances at all; see + test_compute_number_samples_negative_distance_raises.) + """ + with self.assertRaises(ValueError): + compute_number_samples(0) + + def test_compute_number_samples_negative_distance_raises(self): + """ + Regression test: a negative sample distance must also be + rejected. Previously this was silently accepted and treated as + if it were the corresponding positive distance, since the + geometry's cos(theta/2) term is an even function of distance and + so cannot distinguish the sign on its own. + """ + with self.assertRaises(ValueError): + compute_number_samples(-2000000) diff --git a/tests/utils/test_time.py b/tests/utils/test_time.py new file mode 100644 index 0000000..eff7b80 --- /dev/null +++ b/tests/utils/test_time.py @@ -0,0 +1,61 @@ +""" +Unit tests for the tatc.utils.time module. + +@author Paul T. Grogan +""" + +import unittest +from datetime import datetime, timedelta, timezone + +import numpy as np + +from tatc.utils import to_datetime64_ns + + +class TestTime(unittest.TestCase): + """ + Unit tests for the tatc.utils.time module. + """ + + def test_to_datetime64_ns_scalar_utc(self): + """ + Test that a UTC datetime is converted to a naive datetime64[ns]. + """ + value = datetime(2022, 6, 1, 12, tzinfo=timezone.utc) + result = to_datetime64_ns(value) + self.assertEqual(result, np.datetime64("2022-06-01T12:00:00", "ns")) + + def test_to_datetime64_ns_scalar_non_utc(self): + """ + Test that a non-UTC timezone-aware datetime is normalized to UTC + before conversion to datetime64[ns]. + """ + offset_tz = timezone(timedelta(hours=-5)) + value = datetime(2022, 6, 1, 7, tzinfo=offset_tz) + result = to_datetime64_ns(value) + self.assertEqual(result, np.datetime64("2022-06-01T12:00:00", "ns")) + + def test_to_datetime64_ns_list(self): + """ + Test that a list of UTC datetimes is converted to an array of + naive datetime64[ns] values. + """ + values = [ + datetime(2022, 6, 1, tzinfo=timezone.utc), + datetime(2022, 6, 2, tzinfo=timezone.utc), + ] + result = to_datetime64_ns(values) + expected = np.array( + ["2022-06-01T00:00:00", "2022-06-02T00:00:00"], dtype="datetime64[ns]" + ) + np.testing.assert_array_equal(result, expected) + + def test_to_datetime64_ns_ndarray_passthrough(self): + """ + Test that an existing datetime64 array is cast to datetime64[ns] + without modification. + """ + values = np.array(["2022-06-01", "2022-06-02"], dtype="datetime64[D]") + result = to_datetime64_ns(values) + expected = values.astype("datetime64[ns]") + np.testing.assert_array_equal(result, expected)