├── .github
    ├── ISSUE_TEMPLATE
    │   ├── bug-report.md
    │   ├── config.yml
    │   ├── documentation.md
    │   ├── feature-request.md
    │   └── issue.md
    ├── dependabot.yml
    ├── labeler.yml
    ├── pull_request_template.md
    └── workflows
    │   ├── ci-citation.yml
    │   ├── ci-label.yml
    │   ├── ci-locks.yml
    │   ├── ci-manifest.yml
    │   ├── ci-template-check.yml
    │   ├── ci-tests.yml
    │   ├── ci-wheels.yml
    │   └── stale.yml
├── .gitignore
├── .pre-commit-config.yaml
├── CITATION.cff
├── Dockerfile
├── LICENSE
├── MANIFEST.in
├── README.md
├── codecov.yml
├── index.ipynb
├── pyproject.toml
├── requirements
    ├── locks
    │   ├── README.md
    │   ├── py310-linux-64.lock
    │   ├── py311-linux-64.lock
    │   └── py312-linux-64.lock
    ├── py310.yml
    ├── py311.yml
    ├── py312.yml
    ├── pypi-core.txt
    └── stratify.yml
├── setup.py
├── src
    └── stratify
    │   ├── __init__.py
    │   ├── _bounded_vinterp.py
    │   ├── _conservative.pyx
    │   ├── _vinterp.pyx
    │   └── tests
    │       ├── __init__.py
    │       ├── performance.py
    │       ├── test_bounded_vinterp.py
    │       └── test_vinterp.py
└── summary.png


/.github/ISSUE_TEMPLATE/bug-report.md:
--------------------------------------------------------------------------------
 1 | ---
 2 | name: "Bug Report \U0001F41B"
 3 | about: Submit a bug report to help us improve Python-Stratify.
 4 | title: ''
 5 | labels: ''
 6 | assignees: ''
 7 | 
 8 | ---
 9 | 
10 | ## 🐛 Bug Report
11 | <!-- Provide a clear description of what the bug is -->
12 | 
13 | ## How To Reproduce
14 | Steps to reproduce the behaviour:
15 | 
16 | 1.
17 | 2.
18 | 3.
19 | 
20 | ## Expected behaviour
21 | <!-- A clear and concise description of what you expected to happen -->
22 | 
23 | ## Screenshots
24 | <!-- If applicable, add screenshots to help explain your problem -->
25 | 
26 | ## Environment
27 |  - OS & Version: [e.g., Ubuntu 20.04 LTS]
28 |  - Stratify Version
29 | 
30 | ## Additional context
31 | <!-- Provide any further information to help us understand -->
32 | <details>
33 | <summary>Click to expand <b>this section...</b></summary>
34 | 
35 | ```
36 | Please add additional verbose information in this section e.g., code, output, tracebacks, screenshots etc
37 | ```
38 | </details>
39 | 


--------------------------------------------------------------------------------
/.github/ISSUE_TEMPLATE/config.yml:
--------------------------------------------------------------------------------
1 | # reference: https://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/configuring-issue-templates-for-your-repository#configuring-the-template-chooser
2 | blank_issues-enabled: false
3 | contact_links:
4 |   - name: 💬 Python-Stratify GitHub Discussions
5 |     url: https://github.com/Scitools/python-stratify/discussions
6 |     about: Engage with the Python-Stratify community to discuss your issue
7 | 


--------------------------------------------------------------------------------
/.github/ISSUE_TEMPLATE/documentation.md:
--------------------------------------------------------------------------------
 1 | ---
 2 | name: "Documentation \U0001F4DA"
 3 | about: Report an issue with the Python-Stratify documentation.
 4 | title: ''
 5 | labels: ''
 6 | assignees: ''
 7 | 
 8 | ---
 9 | 
10 | ## 📚 Documentation
11 | <!-- Describe the issue or provide a suggestion for improving the Python-Stratify documentation -->
12 | 


--------------------------------------------------------------------------------
/.github/ISSUE_TEMPLATE/feature-request.md:
--------------------------------------------------------------------------------
 1 | ---
 2 | name: Feature Request ✨
 3 | about: Submit a request for a new feature in Python-Stratify
 4 | title: ''
 5 | labels: ''
 6 | assignees: ''
 7 | 
 8 | ---
 9 | 
10 | ## ✨ Feature Request
11 | <!-- Provide a clear and concise description of the feature proposal -->
12 | 
13 | ## Motivation
14 | <!-- Is your feature request related to an existing issue? -->
15 | <!-- I'm always frustrated when ... -->
16 | 
17 | ## Additional context
18 | <!-- Provide any further information to help us understand -->
19 | <details>
20 | <summary>Click to expand <b>this section...</b></summary>
21 | 
22 | ```
23 | Please add additional verbose information in this section e.g., references, screenshots, listings etc
24 | ```
25 | </details>
26 | 


--------------------------------------------------------------------------------
/.github/ISSUE_TEMPLATE/issue.md:
--------------------------------------------------------------------------------
 1 | ---
 2 | name: "Custom Issue \U0001F4F0"
 3 | about: Submit a generic issue to help us improve Python-Stratify.
 4 | title: ''
 5 | labels: ''
 6 | assignees: ''
 7 | 
 8 | ---
 9 | 
10 | ## 📰 Custom Issue
11 | <!-- Provide a clear description of what the issue is, and we'll try our best to help 😀 -->
12 | 


--------------------------------------------------------------------------------
/.github/dependabot.yml:
--------------------------------------------------------------------------------
 1 | # Reference:
 2 | #   - https://docs.github.com/en/code-security/supply-chain-security/keeping-your-dependencies-updated-automatically/keeping-your-actions-up-to-date-with-dependabot
 3 | 
 4 | version: 2
 5 | updates:
 6 | 
 7 |   - package-ecosystem: "github-actions"
 8 |     directory: "/"
 9 |     schedule:
10 |       # Check later in the week - the upstream dependabot check in `workflows` runs deliberately early in the week.
11 |       #  Therefore allowing time for the `workflows` update to be merged-and-released first.
12 |       interval: "weekly"
13 |       day: "thursday"
14 |       time: "01:00"
15 |       timezone: "Europe/London"
16 |     groups:
17 |       dependencies:
18 |         patterns:
19 |           - "*"
20 |     labels:
21 |       - "New: Pull Request"
22 |       - "Bot"
23 | 


--------------------------------------------------------------------------------
/.github/labeler.yml:
--------------------------------------------------------------------------------
 1 | 'Type: Dependencies':
 2 | - any:
 3 |     - changed-files:
 4 |         - any-glob-to-any-file: 'requirements/stratify.yml'
 5 |         - any-glob-to-any-file: 'requirements/py*.yml'
 6 |         - any-glob-to-any-file: 'requirements/locks/pypi*.txt'
 7 |         - any-glob-to-any-file: 'requirements/locks/py*.lock'
 8 | 
 9 | 'Type: Documentation':
10 | - any:
11 |     changed-files:
12 |       - any-glob-to-any-file: 'index.ipynb'
13 |       - any-glob-to-any-file: 'README.md'
14 | 
15 | 'Type: GHA':
16 | - any:
17 |     - changed-files:
18 |         - any-glob-to-any-file: '.github/*.yml'
19 |         - any-glob-to-any-file: '.github/**/*.yml'
20 | 
21 | 'Type: Infrastructure':
22 | - any:
23 |     changed-files:
24 |       - any-glob-to-any-file: '*'
25 | 
26 | 'Type: Testing':
27 | - any:
28 |     changed-files:
29 |       - any-glob-to-any-file: 'src/stratify/tests/*'
30 | 


--------------------------------------------------------------------------------
/.github/pull_request_template.md:
--------------------------------------------------------------------------------
1 | ## 🚀 Pull Request
2 | 
3 | ### Description
4 | <!-- Provide a clear description about your awesome pull request -->
5 | <!-- Tell us all about your new feature, improvement, or bug fix -->
6 | 
7 | 
8 | ---
9 | 


--------------------------------------------------------------------------------
/.github/workflows/ci-citation.yml:
--------------------------------------------------------------------------------
 1 | name: ci-citation
 2 | 
 3 | on:
 4 |   pull_request:
 5 |     paths:
 6 |       - "CITATION.cff"
 7 | 
 8 |   push:
 9 |     paths:
10 |       - "CITATION.cff"
11 | 
12 |   workflow_dispatch:
13 | 
14 | concurrency:
15 |   group: ${{ github.workflow }}-${{ github.ref }}
16 |   cancel-in-progress: true
17 | 
18 | jobs:
19 |   validate:
20 |     name: "validate"
21 |     runs-on: ubuntu-latest
22 |     steps:
23 |       - uses: actions/checkout@v4
24 |         with:
25 |           fetch-depth: 0
26 | 
27 |       - name: "check CITATION.cff"
28 |         uses: citation-file-format/cffconvert-github-action@2.0.0
29 |         with:
30 |           args: "--validate"
31 | 


--------------------------------------------------------------------------------
/.github/workflows/ci-label.yml:
--------------------------------------------------------------------------------
 1 | # Reference
 2 | #   - https://github.com/actions/labeler
 3 | 
 4 | name: ci-label
 5 | 
 6 | on:
 7 |   pull_request_target:
 8 |     types: [opened, edited, reopened]
 9 | 
10 | jobs:
11 |   PR_triage:
12 |     runs-on: ubuntu-latest
13 |     steps:
14 |       - uses: actions/labeler@v5
15 |         with:
16 |           configuration-path: .github/labeler.yml
17 |           repo-token: ${{ secrets.GITHUB_TOKEN }}
18 | 


--------------------------------------------------------------------------------
/.github/workflows/ci-locks.yml:
--------------------------------------------------------------------------------
 1 | # Updates the environment lock files. See the called workflow in the
 2 | # scitools/workflows repo for more details.
 3 | 
 4 | name: ci-locks
 5 | 
 6 | 
 7 | on:
 8 |   workflow_dispatch:
 9 |   schedule:
10 |     # Run once a week on a Saturday night
11 |     # N.B. "should" be quoted, according to
12 |     # https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#onschedule
13 |     - cron: "3 0 * * 6"
14 | 
15 | concurrency:
16 |   group: ${{ github.workflow }}-${{ github.ref }}
17 |   cancel-in-progress: true
18 | 
19 | jobs:
20 |   refresh_lockfiles:
21 |     uses: scitools/workflows/.github/workflows/refresh-lockfiles.yml@2025.05.1
22 |     secrets: inherit
23 | 


--------------------------------------------------------------------------------
/.github/workflows/ci-manifest.yml:
--------------------------------------------------------------------------------
 1 | # Reference
 2 | #    - https://github.com/actions/checkout
 3 | 
 4 | name: ci-manifest
 5 | 
 6 | on:
 7 |   pull_request:
 8 | 
 9 |   push:
10 |     branches:
11 |       - "master"
12 |       - "v*x"
13 |       - "!auto-update-lockfiles"
14 |       - "!pre-commit-ci-update-config"
15 |       - "!dependabot/*"
16 |     tags:
17 |       - "v*"
18 | 
19 |   workflow_dispatch:
20 | 
21 | concurrency:
22 |   group: ${{ github.workflow }}-${{ github.ref }}
23 |   cancel-in-progress: true
24 | 
25 | jobs:
26 |   manifest:
27 |     name: "check-manifest"
28 |     uses: scitools/workflows/.github/workflows/ci-manifest.yml@2025.05.1
29 | 


--------------------------------------------------------------------------------
/.github/workflows/ci-template-check.yml:
--------------------------------------------------------------------------------
 1 | # Checks if a PR makes any changes that ought to be shared via templating.
 2 | # See the called workflow in the scitools/workflows repo for more details.
 3 | 
 4 | name: ci-template-check
 5 | 
 6 | on:
 7 |   pull_request_target:
 8 |     branches:
 9 |       - main
10 | 
11 | jobs:
12 |   prompt-share:
13 |     uses: scitools/workflows/.github/workflows/ci-template-check.yml@2025.05.1
14 |     secrets: inherit
15 |     with:
16 |       pr_number: ${{ github.event.pull_request.number }}
17 | 


--------------------------------------------------------------------------------
/.github/workflows/ci-tests.yml:
--------------------------------------------------------------------------------
 1 | # Reference:
 2 | #   - https://github.com/actions/cache
 3 | #   - https://github.com/actions/checkout
 4 | #   - https://github.com/actions/download-artifact
 5 | #   - https://github.com/actions/upload-artifact
 6 | #   - https://github.com/conda-incubator/setup-miniconda
 7 | 
 8 | name: ci-tests
 9 | 
10 | on:
11 |   pull_request:
12 | 
13 |   push:
14 |     branches:
15 |       - "master"
16 |       - "v*x"
17 |       - "!auto-update-lockfiles"
18 |       - "!pre-commit-ci-update-config"
19 |       - "!dependabot/*"
20 |     tags:
21 |       - "v*"
22 | 
23 |   workflow_dispatch:
24 | 
25 | concurrency:
26 |   group: ${{ github.workflow }}-${{ github.ref }}
27 |   cancel-in-progress: true
28 | 
29 | jobs:
30 |   tests:
31 |     name: "tests (py${{ matrix.python-version }} ${{ matrix.os }})"
32 | 
33 |     runs-on: ubuntu-latest
34 | 
35 |     defaults:
36 |       run:
37 |         shell: bash -l {0}
38 | 
39 |     env:
40 |       ENV_NAME: "ci-tests"
41 |       CYTHON_COVERAGE: true
42 |       CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
43 | 
44 |     strategy:
45 |       fail-fast: false
46 |       matrix:
47 |         os: ["ubuntu-latest"]
48 |         python-version: ["310", "311", "312"]
49 |         include:
50 |           - python-version: "312"
51 |             cov-report: "--cov-report=xml --cov"
52 |             codecov: "codecov"
53 | 
54 |     steps:
55 |     - uses: actions/checkout@v4
56 |       with:
57 |         fetch-depth: 0
58 | 
59 |     - name: "mambaforge setup (python ${{ matrix.python-version }})"
60 |       uses: conda-incubator/setup-miniconda@v3
61 |       with:
62 |         miniforge-variant: Miniforge3
63 |         miniforge-version: latest
64 |         channels: conda-forge
65 |         channel-priority: true
66 |         auto-update-conda: true
67 |         environment-file: "requirements/locks/py${{ matrix.python-version }}-linux-64.lock"
68 |         activate-environment: ${{ env.ENV_NAME }}
69 | 
70 |     - name: "stratify tests (py${{ matrix.python-version }})"
71 |       run: |
72 |         python -m pip install --no-deps --editable .
73 |         python setup.py build_ext --inplace
74 |         pytest ${{ matrix.cov-report }}
75 |         ${{ matrix.codecov }}
76 | 


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/.github/workflows/ci-wheels.yml:
--------------------------------------------------------------------------------
  1 | # Reference:
  2 | #   - https://github.com/actions/checkout
  3 | #   - https://github.com/actions/download-artifact
  4 | #   - https://github.com/actions/upload-artifact
  5 | #   - https://github.com/pypa/cibuildwheel
  6 | #   - https://github.com/pypa/build
  7 | #   - https://github.com/pypa/gh-action-pypi-publish
  8 | #   - https://test.pypi.org/help/#apitoken
  9 | 
 10 | name: ci-wheels
 11 | 
 12 | on:
 13 |   pull_request:
 14 | 
 15 |   push:
 16 |     branches:
 17 |       - "main"
 18 |       - "v*x"
 19 |       - "!auto-update-lockfiles"
 20 |       - "!pre-commit-ci-update-config"
 21 |       - "!dependabot/*"
 22 |     tags:
 23 |       - "v*"
 24 | 
 25 | jobs:
 26 |   build_bdist:
 27 |     name: "build ${{ matrix.os }} (${{ matrix.arch }}) wheels"
 28 |     runs-on: ${{ matrix.os }}
 29 |     strategy:
 30 |       fail-fast: false
 31 |       matrix:
 32 |         os: ["ubuntu-latest", "macos-latest", "windows-latest"]
 33 |         arch: ["x86_64", "arm64"]
 34 |         exclude:
 35 |           - os: "ubuntu-latest"
 36 |             arch: "arm64"
 37 |           - os: "windows-latest"
 38 |             arch: "x86_64"
 39 |           - os: "windows-latest"
 40 |             arch: "arm64"
 41 |         include:
 42 |           - os: "windows-latest"
 43 |             arch: "AMD64"
 44 | 
 45 |     steps:
 46 |     - uses: actions/checkout@v4
 47 |       with:
 48 |         fetch-depth: 0
 49 | 
 50 |     - name: "build ${{ matrix.os }} (${{ matrix.arch }}) wheels"
 51 |       uses: pypa/cibuildwheel@v2.23.3
 52 |       env:
 53 |         CIBW_SKIP: "cp39-* cp313-* pp* *-musllinux*"
 54 |         CIBW_ARCHS: ${{ matrix.arch }}
 55 |         CIBW_BUILD_FRONTEND: "build"
 56 |         CIBW_MANYLINUX_X86_64_IMAGE: "manylinux2014"
 57 |         CIBW_TEST_SKIP: "*-macosx_arm64"
 58 |         CIBW_TEST_REQUIRES: "pytest"
 59 |         CIBW_TEST_COMMAND: >
 60 |           python -m pytest --pyargs stratify
 61 | 
 62 |     - uses: actions/upload-artifact@v4
 63 |       with:
 64 |         name: pypi-${{ matrix.os }}-${{ matrix.arch }}-artifact
 65 |         path: ${{ github.workspace }}/wheelhouse/*.whl
 66 | 
 67 | 
 68 |   build_sdist:
 69 |     name: "Build sdist"
 70 |     runs-on: ubuntu-latest
 71 |     steps:
 72 |     - uses: actions/checkout@v4
 73 |       with:
 74 |         fetch-depth: 0
 75 | 
 76 |     - name: "Building sdist"
 77 |       shell: bash
 78 |       run: |
 79 |         pipx run build --sdist
 80 | 
 81 |     - uses: actions/upload-artifact@v4
 82 |       with:
 83 |         name: pypi-sdist
 84 |         path: ${{ github.workspace }}/dist/*.tar.gz
 85 | 
 86 | 
 87 |   show-artifacts:
 88 |     needs: [build_bdist, build_sdist]
 89 |     name: "Show artifacts"
 90 |     runs-on: ubuntu-latest
 91 |     steps:
 92 |     - uses: actions/download-artifact@v4
 93 |       with:
 94 |         merge-multiple: true
 95 |         path: ${{ github.workspace }}/dist
 96 | 
 97 |     - shell: bash
 98 |       run: |
 99 |         ls -l ${{ github.workspace }}/dist
100 | 
101 | 
102 |   publish-artifacts-test-pypi:
103 |     needs: [build_bdist, build_sdist]
104 |     name: "Publish to Test PyPI"
105 |     runs-on: ubuntu-latest
106 |     # upload to Test PyPI for every commit on main branch
107 |     if: github.event_name == 'push' && github.event.ref == 'refs/heads/main'
108 |     steps:
109 |     - uses: actions/download-artifact@v4
110 |       with:
111 |         merge-multiple: true
112 |         path: ${{ github.workspace }}/dist
113 | 
114 |     - uses: pypa/gh-action-pypi-publish@release/v1
115 |       with:
116 |         user: __token__
117 |         password: ${{ secrets.TEST_PYPI_API_TOKEN }}
118 |         repository_url: https://test.pypi.org/legacy/
119 |         skip_existing: true
120 |         print_hash: true
121 | 
122 | 
123 |   publish-artifacts-pypi:
124 |     needs: [build_bdist, build_sdist]
125 |     name: "Publish to PyPI"
126 |     runs-on: ubuntu-latest
127 |     # upload to PyPI for every tag starting with 'v'
128 |     if: github.event_name == 'push' && startsWith(github.event.ref, 'refs/tags/v')
129 |     steps:
130 |     - uses: actions/download-artifact@v4
131 |       with:
132 |         merge-multiple: true
133 |         path: ${{ github.workspace }}/dist
134 | 
135 |     - uses: pypa/gh-action-pypi-publish@release/v1
136 |       with:
137 |         user: __token__
138 |         password: ${{ secrets.PYPI_API_TOKEN }}
139 |         print_hash: true
140 | 


--------------------------------------------------------------------------------
/.github/workflows/stale.yml:
--------------------------------------------------------------------------------
 1 | # See https://github.com/actions/stale
 2 | 
 3 | name: Stale issues and pull-requests
 4 | 
 5 | on:
 6 |   schedule:
 7 |     # Run once a day
 8 |     # N.B. "should" be quoted, according to
 9 |     # https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#onschedule
10 |     - cron: "0 0 * * *"
11 | 
12 | jobs:
13 |   stale:
14 |     if: "github.repository == 'SciTools/python-stratify'"
15 |     runs-on: ubuntu-latest
16 | 
17 |     env:
18 |       DAYS_BEFORE_STALE: 500
19 |       DAYS_BEFORE_CLOSE: 28
20 | 
21 |     steps:
22 |       - uses: actions/stale@v9
23 |         with:
24 |           repo-token: ${{ secrets.GITHUB_TOKEN }}
25 | 
26 |           # Idle number of days before marking issues/prs stale.
27 |           days-before-stale: ${{ env.DAYS_BEFORE_STALE }}
28 | 
29 |           # Idle number of days before closing stale issues/prs.
30 |           days-before-close: ${{ env.DAYS_BEFORE_CLOSE }}
31 | 
32 |           # Comment on the staled issues.
33 |           stale-issue-message: |
34 |             In order to maintain a backlog of relevant issues, we automatically label them as stale after ${{ env.DAYS_BEFORE_STALE }} days of inactivity.
35 | 
36 |             If this issue is still important to you, then please comment on this issue and the stale label will be removed.
37 | 
38 |             Otherwise this issue will be automatically closed in ${{ env.DAYS_BEFORE_CLOSE }} days time.
39 | 
40 |           # Comment on the staled prs.
41 |           stale-pr-message: |
42 |             In order to maintain a backlog of relevant PRs, we automatically label them as stale after ${{ env.DAYS_BEFORE_STALE }} days of inactivity.
43 | 
44 |             If this PR is still important to you, then please comment on this PR and the stale label will be removed.
45 | 
46 |             Otherwise this PR will be automatically closed in ${{ env.DAYS_BEFORE_CLOSE }} days time.
47 | 
48 |           # Comment on the staled issues while closed.
49 |           close-issue-message: |
50 |             This stale issue has been automatically closed due to a lack of community activity.
51 | 
52 |             If you still care about this issue, then please either:
53 |               * Re-open this issue, if you have sufficient permissions, or
54 |               * Add a comment pinging `@SciTools/stratify-maintainers` who will re-open on your behalf.
55 | 
56 |           # Comment on the staled prs while closed.
57 |           close-pr-message: |
58 |             This stale PR has been automatically closed due to a lack of community activity.
59 | 
60 |             If you still care about this PR, then please either:
61 |               * Re-open this PR, if you have sufficient permissions, or
62 |               * Add a comment pinging `@SciTools/stratify-maintainers` who will re-open on your behalf.
63 | 
64 |           # Label to apply on staled issues.
65 |           stale-issue-label: Stale
66 | 
67 |           # Label to apply on staled prs.
68 |           stale-pr-label: Stale
69 | 
70 |           # Labels on issues exempted from stale.
71 |           exempt-issue-labels:
72 |             "Status: Blocked,Status: Decision Required"
73 | 
74 |           # Labels on prs exempted from stale.
75 |           exempt-pr-labels:
76 |             "Status: Blocked,Status: Decision Required"
77 | 
78 |           # Max number of operations per run.
79 |           operations-per-run: 300
80 | 
81 |           # Remove stale label from issues/prs on updates/comments.
82 |           remove-stale-when-updated: true
83 | 
84 |           # Order to get issues/PRs.
85 |           ascending: true
86 | 
87 |           # Exempt all issues/prs with milestones from stale.
88 |           exempt-all-milestones: true
89 | 


--------------------------------------------------------------------------------
/.gitignore:
--------------------------------------------------------------------------------
  1 | # setuptools-scm
  2 | _version.py
  3 | 
  4 | # Byte-compiled / optimized / DLL files
  5 | __pycache__/
  6 | *.py[cod]
  7 | *$py.class
  8 | 
  9 | # C extensions
 10 | *.so
 11 | *.c
 12 | 
 13 | # Distribution / packaging
 14 | .Python
 15 | build/
 16 | develop-eggs/
 17 | dist/
 18 | downloads/
 19 | eggs/
 20 | .eggs/
 21 | lib/
 22 | lib64/
 23 | parts/
 24 | sdist/
 25 | var/
 26 | wheels/
 27 | pip-wheel-metadata/
 28 | share/python-wheels/
 29 | *.egg-info/
 30 | .installed.cfg
 31 | *.egg
 32 | MANIFEST
 33 | 
 34 | # PyInstaller
 35 | #  Usually these files are written by a python script from a template
 36 | #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 37 | *.manifest
 38 | *.spec
 39 | 
 40 | # Installer logs
 41 | pip-log.txt
 42 | pip-delete-this-directory.txt
 43 | 
 44 | # Unit test / coverage reports
 45 | htmlcov/
 46 | .tox/
 47 | .nox/
 48 | .coverage
 49 | .coverage.*
 50 | .cache
 51 | nosetests.xml
 52 | coverage.xml
 53 | *.cover
 54 | *.py,cover
 55 | .hypothesis/
 56 | .pytest_cache/
 57 | 
 58 | # Translations
 59 | *.mo
 60 | *.pot
 61 | 
 62 | # Django stuff:
 63 | *.log
 64 | local_settings.py
 65 | db.sqlite3
 66 | db.sqlite3-journal
 67 | 
 68 | # Flask stuff:
 69 | instance/
 70 | .webassets-cache
 71 | 
 72 | # Scrapy stuff:
 73 | .scrapy
 74 | 
 75 | # Sphinx documentation
 76 | docs/_build/
 77 | 
 78 | # PyBuilder
 79 | target/
 80 | 
 81 | # Jupyter Notebook
 82 | .ipynb_checkpoints
 83 | 
 84 | # IPython
 85 | profile_default/
 86 | ipython_config.py
 87 | 
 88 | # pyenv
 89 | .python-version
 90 | 
 91 | # pipenv
 92 | #   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
 93 | #   However, in case of collaboration, if having platform-specific dependencies or dependencies
 94 | #   having no cross-platform support, pipenv may install dependencies that don't work, or not
 95 | #   install all needed dependencies.
 96 | #Pipfile.lock
 97 | 
 98 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow
 99 | __pypackages__/
100 | 
101 | # Celery stuff
102 | celerybeat-schedule
103 | celerybeat.pid
104 | 
105 | # SageMath parsed files
106 | *.sage.py
107 | 
108 | # Environments
109 | .env
110 | .venv
111 | env/
112 | venv/
113 | ENV/
114 | env.bak/
115 | venv.bak/
116 | 
117 | # Spyder project settings
118 | .spyderproject
119 | .spyproject
120 | 
121 | # Rope project settings
122 | .ropeproject
123 | 
124 | # mkdocs documentation
125 | /site
126 | 
127 | # mypy
128 | .mypy_cache/
129 | .dmypy.json
130 | dmypy.json
131 | 
132 | # Pyre type checker
133 | .pyre/
134 | 
135 | # PyCharm
136 | .idea
137 | 


--------------------------------------------------------------------------------
/.pre-commit-config.yaml:
--------------------------------------------------------------------------------
 1 | # See https://pre-commit.com for more information
 2 | # See https://pre-commit.com/hooks.html for more hooks
 3 | # See https://pre-commit.ci/#configuration
 4 | # See https://github.com/scientific-python/cookie#sp-repo-review
 5 | 
 6 | ci:
 7 |     autofix_prs: false
 8 |     autoupdate_commit_msg: "chore: update pre-commit hooks"
 9 | 
10 | repos:
11 | -   repo: https://github.com/pre-commit/pre-commit-hooks
12 |     rev: 'v5.0.0'
13 |     hooks:
14 |         # Prevent giant files from being committed.
15 |     -   id: check-added-large-files
16 |         # Check whether files parse as valid Python.
17 |     -   id: check-ast
18 |         # Check for file name conflicts on case-insensitive filesytems.
19 |     -   id: check-case-conflict
20 |         # Check for files that contain merge conflict strings.
21 |     -   id: check-merge-conflict
22 |         # Check for debugger imports and py37+ `breakpoint()` calls in Python source.
23 |     -   id: debug-statements
24 |         # Check TOML file syntax.
25 |     - id: check-toml
26 |         # Check YAML file syntax.
27 |     - id: check-yaml
28 |         # Makes sure files end in a newline and only a newline.
29 |         # Duplicates Ruff W292 but also works on non-Python files.
30 |     - id: end-of-file-fixer
31 |         # Replaces or checks mixed line ending.
32 |     - id: mixed-line-ending
33 |         # Don't commit to master branch.
34 |     -   id: no-commit-to-branch
35 |       # Trims trailing whitespace.
36 |       # Duplicates Ruff W291 but also works on non-Python files.
37 |     -   id: trailing-whitespace
38 | 
39 | 
40 | -   repo: https://github.com/astral-sh/ruff-pre-commit
41 |     rev: "v0.11.8"
42 |     hooks:
43 |     -   id: ruff
44 |         types: [file, python]
45 |         args: [--fix, --show-fixes]
46 |     -   id: ruff-format
47 |         types: [file, python]
48 | 
49 | -   repo: https://github.com/codespell-project/codespell
50 |     rev: "v2.4.1"
51 |     hooks:
52 |     -   id: codespell
53 |         types_or: [asciidoc, python, markdown, rst]
54 |         additional_dependencies: [tomli]
55 | 
56 | 
57 | 
58 | -   repo: https://github.com/pycqa/isort
59 |     rev: '6.0.1'
60 |     hooks:
61 |     -   id: isort
62 |         name: isort (cython)
63 |         types: [file, cython]
64 |         args: [--filter-files]
65 | 
66 | -   repo: https://github.com/aio-libs/sort-all
67 |     rev: "v1.3.0"
68 |     hooks:
69 |     - id: sort-all
70 |       types: [file, python]
71 | 
72 | -   repo: https://github.com/pre-commit/mirrors-mypy
73 |     rev: 'v1.15.0'
74 |     hooks:
75 |     -   id: mypy
76 |         exclude: 'noxfile\.py|docs/conf\.py'
77 | 
78 | -   repo: https://github.com/abravalheri/validate-pyproject
79 |     # More exhaustive than Ruff RUF200.
80 |     rev: "v0.24.1"
81 |     hooks:
82 |     - id: validate-pyproject
83 | 
84 | -   repo: https://github.com/scientific-python/cookie
85 |     rev: 2025.05.02
86 |     hooks:
87 |     - id: sp-repo-review
88 |       additional_dependencies: ["repo-review[cli]"]
89 |       args: ["--show=errskip"]
90 | 
91 | -   repo: https://github.com/numpy/numpydoc
92 |     rev: v1.8.0
93 |     hooks:
94 |       - id: numpydoc-validation
95 |         types: [file, python]
96 | 


--------------------------------------------------------------------------------
/CITATION.cff:
--------------------------------------------------------------------------------
 1 | cff-version: 1.2.0
 2 | message: "If Python-Stratify played an important part in your research then please add us to your reference list by using the references below."
 3 | authors:
 4 | - family-names: "Elson"
 5 |   given-names: "Phil"
 6 |   orcid: "https://orcid.org/0000-0001-6360-749X"
 7 | - family-names: "Little"
 8 |   given-names: "Bill"
 9 |   orcid: "https://orcid.org/0000-0002-1345-9465"
10 | - family-names: "Andela"
11 |   given-names: "Bouwe"
12 |   orcid: "https://orcid.org/0000-0001-9005-8940"
13 | - family-names: "Dreyer"
14 |   given-names: "Laura"
15 |   orcid: "https://orcid.org/0009-0006-6310-3658"
16 | - family-names: "Fernandes"
17 |   given-names: "Filipe"
18 |   orcid: "https://orcid.org/0000-0003-4165-2913"
19 | - family-names: "Rol"
20 |   given-names: "Evert"
21 |   orcid: "https://orcid.org/0000-0001-8357-4453"
22 | - family-names: "Wright"
23 |   given-names: "Henry"
24 |   orcid: "https://orcid.org/0009-0005-1489-760X"
25 | - family-names: "Yeo"
26 |   given-names: "Martin"
27 |   orcid: "https://orcid.org/0009-0000-6857-560X"
28 | title: "python-stratify"
29 | abstract: "Interpolation for restratification, particularly useful for Nd vertical interpolation of atmospheric and oceanographic datasets"
30 | license: "BSD-3-Clause"
31 | license-url: "https://spdx.org/licenses/BSD-3-Clause.html"
32 | date-released: "2018-06-01"
33 | doi: "10.5281/zenodo.7863857"
34 | repository-code: "https://github.com/SciTools/python-stratify"
35 | type: "software"
36 | 


--------------------------------------------------------------------------------
/Dockerfile:
--------------------------------------------------------------------------------
1 | FROM andrewosh/binder-base
2 | 
3 | LABEL maintainer "bjlittle.pub@gmail.com"
4 | 
5 | USER main
6 | RUN conda install -c conda-forge python-stratify matplotlib
7 | WORKDIR $HOME/notebooks
8 | RUN rm -rf stratify
9 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | BSD 3-Clause License
 2 | Copyright (c) 2015 - 2024, Met Office.
 3 | All rights reserved.
 4 | 
 5 | Redistribution and use in source and binary forms, with or without
 6 | modification, are permitted provided that the following conditions are met:
 7 | 
 8 | * Redistributions of source code must retain the above copyright notice, this
 9 |   list of conditions and the following disclaimer.
10 | 
11 | * Redistributions in binary form must reproduce the above copyright notice,
12 |   this list of conditions and the following disclaimer in the documentation
13 |   and/or other materials provided with the distribution.
14 | 
15 | * Neither the name of conda-buildall nor the names of its
16 |   contributors may be used to endorse or promote products derived from
17 |   this software without specific prior written permission.
18 | 
19 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
20 | AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
21 | IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
22 | DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
23 | FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
24 | DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
25 | SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
26 | CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
27 | OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
28 | OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
29 | 


--------------------------------------------------------------------------------
/MANIFEST.in:
--------------------------------------------------------------------------------
 1 | prune .github
 2 | prune requirements
 3 | recursive-include requirements *.txt
 4 | recursive-include src *.py *.pyx
 5 | include CITATION.cff
 6 | exclude .flake8
 7 | exclude .gitignore
 8 | exclude .pre-commit-config.yaml
 9 | exclude Dockerfile
10 | exclude codecov.yml
11 | exclude index.ipynb
12 | exclude summary.png
13 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # Stratify
 2 | 
 3 | Interpolation for restratification, particularly useful for Nd vertical interpolation of atmospheric and oceanographic datasets
 4 | 
 5 | |                  |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
 6 | |------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
 7 | | ⚙️ CI            | [![ci-locks](https://github.com/SciTools/python-stratify/actions/workflows/ci-locks.yml/badge.svg)](https://github.com/SciTools/python-stratify/actions/workflows/ci-locks.yml) [![ci-manifest](https://github.com/SciTools/python-stratify/actions/workflows/ci-manifest.yml/badge.svg)](https://github.com/SciTools/python-stratify/actions/workflows/ci-manifest.yml) [![ci-tests](https://github.com/SciTools/python-stratify/actions/workflows/ci-tests.yml/badge.svg)](https://github.com/SciTools/python-stratify/actions/workflows/ci-tests.yml) [![ci-wheels](https://github.com/SciTools/python-stratify/actions/workflows/ci-wheels.yml/badge.svg)](https://github.com/SciTools/python-stratify/actions/workflows/ci-wheels.yml) [![pre-commit.ci status](https://results.pre-commit.ci/badge/github/SciTools/python-stratify/master.svg)](https://results.pre-commit.ci/latest/github/SciTools/python-stratify/master) |
 8 | | 💬 Community     | [![GH Discussions](https://img.shields.io/badge/github-discussions%20%F0%9F%92%AC-yellow?logo=github&logoColor=lightgrey)](https://github.com/SciTools/python-stratify/discussions)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
 9 | | 📖 Documentation | [![Binder](http://mybinder.org/badge.svg)](http://mybinder.org:/repo/scitools-incubator/python-stratify)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
10 | | 📈 Health        | [![codecov](https://codecov.io/gh/SciTools/python-stratify/branch/master/graph/badge.svg?token=v1R1bJ4kYr)](https://codecov.io/gh/SciTools/python-stratify)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
11 | | ✨ Meta           | [![code style - black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [![license - bds-3-clause](https://img.shields.io/github/license/SciTools/python-stratify)](https://github.com/python-stratify/python-stratify/blob/main/LICENSE)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
12 | | 📦 Package       | [![DOI](https://img.shields.io/badge/DOI-10.5281%2Fzenodo.7863857-blue)](https://doi.org/10.5281/zenodo.7863857) [![conda-forge](https://img.shields.io/conda/vn/conda-forge/python-stratify?color=orange&label=conda-forge&logo=conda-forge&logoColor=white)](https://anaconda.org/conda-forge/python-stratify) [![pypi](https://img.shields.io/pypi/v/stratify?color=orange&label=pypi&logo=python&logoColor=white)](https://pypi.org/project/stratify/) [![pypi - python version](https://img.shields.io/pypi/pyversions/stratify.svg?color=orange&logo=python&label=python&logoColor=white)](https://pypi.org/project/stratify/)                                                                                                                                                                                                                                                                                                                                |
13 | | 🧰 Repo          | [![contributors](https://img.shields.io/github/contributors/SciTools/python-stratify)](https://github.com/SciTools/python-stratify/graphs/contributors)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
14 | |                  |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
15 | 
16 | 
17 | **Are you a [SciTools/iris](https://github.com/SciTools/iris) user?** Did you know Iris provides a convenience for using Stratify with Iris `Cube`s? Try out [`iris.experimental.stratify`](https://scitools-iris.readthedocs.io/en/latest/generated/api/iris/experimental/stratify.html) and let the devs know what you think!
18 | 
19 | ## Introduction
20 | 
21 | Discover the capabilities of `stratify` with this introductory [Jupyter Notebook](https://github.com/SciTools-incubator/python-stratify/blob/master/index.ipynb).
22 | 
23 | ![](https://SciTools-incubator.github.io/python-stratify/summary.png)
24 | 
25 | ## Installation
26 | 
27 | ```shell
28 | conda install --channel conda-forge python-stratify
29 | ```
30 | ```shell
31 | pip install stratify
32 | ```
33 | 
34 | ## License
35 | Stratify is licenced under a [BSD 3-Clause License](LICENSE).
36 | 


--------------------------------------------------------------------------------
/codecov.yml:
--------------------------------------------------------------------------------
 1 | coverage:
 2 |   # see https://docs.codecov.com/docs/commit-status
 3 |   status:
 4 |     project:
 5 |       default:
 6 |         target: auto
 7 |         # coverage can drop by up to <threshold>% while still posting success
 8 |         threshold: 3%
 9 |     patch: off
10 | 


--------------------------------------------------------------------------------
/pyproject.toml:
--------------------------------------------------------------------------------
  1 | [build-system]
  2 | # Defined by PEP 518
  3 | requires = [
  4 |   "Cython<3.1.0",
  5 |   "numpy>=2",
  6 |   "setuptools>=77.0.3",
  7 |   "setuptools_scm[toml]>=8",
  8 |   "wheel",
  9 | ]
 10 | # Defined by PEP 517
 11 | build-backend = "setuptools.build_meta"
 12 | 
 13 | [project]
 14 | authors = [
 15 |     {name = "Stratify Contributors", email = "scitools.pub@gmail.com"}
 16 | ]
 17 | classifiers = [
 18 |     "Development Status :: 5 - Production/Stable",
 19 |     "Intended Audience :: Science/Research",
 20 |     "Operating System :: POSIX",
 21 |     "Operating System :: POSIX :: Linux",
 22 |     "Operating System :: Unix",
 23 |     "Programming Language :: Python",
 24 |     "Programming Language :: Python :: 3 :: Only",
 25 |     "Programming Language :: Python :: 3.10",
 26 |     "Programming Language :: Python :: 3.11",
 27 |     "Programming Language :: Python :: 3.12",
 28 |     "Programming Language :: Python :: Implementation :: CPython",
 29 |     "Topic :: Scientific/Engineering",
 30 |     "Topic :: Scientific/Engineering :: Atmospheric Science",
 31 | ]
 32 | description = "Vectorized interpolators for Nd atmospheric and oceanographic data"
 33 | dynamic = [
 34 |     "dependencies",
 35 |     "readme",
 36 |     "version",
 37 | ]
 38 | keywords = [
 39 |     "atmospheric-science",
 40 |     "cython",
 41 |     "interpolation",
 42 |     "numpy",
 43 |     "python",
 44 |     "ocean-science",
 45 | ]
 46 | license = "BSD-3-Clause"
 47 | license-files = ["LICENSE"]
 48 | name = "stratify"
 49 | requires-python = ">=3.10"
 50 | 
 51 | [project.urls]
 52 | Code = "https://github.com/SciTools-incubator/python-stratify"
 53 | Issues = "https://github.com/SciTools-incubator/python-stratify/issues"
 54 | 
 55 | [tool.setuptools]
 56 | zip-safe = false
 57 | 
 58 | [tool.setuptools.dynamic]
 59 | dependencies = {file = "requirements/pypi-core.txt"}
 60 | readme = {file = "README.md", content-type = "text/markdown"}
 61 | 
 62 | [tool.setuptools.packages.find]
 63 | include = ["stratify*"]
 64 | where = ["src"]
 65 | 
 66 | [tool.setuptools_scm]
 67 | write_to = "src/stratify/_version.py"
 68 | local_scheme = "dirty-tag"
 69 | 
 70 | [tool.check-manifest]
 71 | ignore = [
 72 |     "src/stratify/_conservative.c",
 73 |     "src/stratify/_version.py",
 74 |     "src/stratify/_vinterp.c",
 75 | ]
 76 | 
 77 | [tool.pytest.ini_options]
 78 | addopts = "-ra -v --doctest-modules"
 79 | testpaths = ["src/stratify"]
 80 | 
 81 | [tool.mypy]
 82 | disable_error_code = ["call-arg", "no-untyped-def", "no-untyped-call", "attr-defined", "misc", "index", "var-annotated", "assignment"]
 83 | enable_error_code = ["ignore-without-code", "truthy-bool", "redundant-expr"]
 84 | warn_unreachable = true
 85 | strict = true
 86 | 
 87 | [tool.numpydoc_validation]
 88 | checks = [
 89 |     "all",  # Enable all numpydoc validation rules, apart from the following:
 90 | 
 91 |     # -> Docstring text (summary) should start in the line immediately
 92 |     #    after the opening quotes (not in the same line, or leaving a
 93 |     #    blank line in between)
 94 |     "GL01",  # Permit summary line on same line as docstring opening quotes.
 95 | 
 96 |     # -> Closing quotes should be placed in the line after the last text
 97 |     #    in the docstring (do not close the quotes in the same line as
 98 |     #    the text, or leave a blank line between the last text and the
 99 |     #    quotes)
100 |     "GL02",  # Permit a blank line before docstring closing quotes.
101 | 
102 |     # -> Double line break found; please use only one blank line to
103 |     #    separate sections or paragraphs, and do not leave blank lines
104 |     #    at the end of docstrings
105 |     "GL03",  # Ignoring.
106 | 
107 |     "GL08", # The object does not have a docstring
108 | 
109 |     "PR01", # Parameters {missing_params} not documented
110 |     "PR02", # Unknown parameters {unknown_params}
111 |     "PR10", # Parameter "{param_name}" requires a space before the colon separating the parameter name and type
112 | 
113 |     "RT04", # Return value description should start with a capital letter
114 | 
115 |     "SS06", # Summary should fit in a single line
116 | 
117 | 
118 |     # -> See Also section not found
119 |     "SA01",  # Not all docstrings require a "See Also" section.
120 | 
121 |     # -> No extended summary found
122 |     "ES01",  # Not all docstrings require an "Extended Summary" section.
123 | 
124 |     # -> No examples section found
125 |     "EX01",  # Not all docstrings require an "Examples" section.
126 | 
127 |     # -> No Yields section found
128 |     "YD01",  # Not all docstrings require a "Yields" section.
129 | 
130 |     # Record temporarily ignored checks below; will be reviewed at a later date:
131 | 
132 | ]
133 | exclude = [
134 |     '\.__eq__$',
135 |     '\.__ne__$',
136 |     '\.__repr__$',
137 | ]
138 | 
139 | [tool.ruff]
140 | line-length = 88
141 | 
142 | [tool.ruff.format]
143 | preview = false
144 | 
145 | [tool.ruff.lint]
146 | ignore = [
147 | 
148 |     # flake8-annotations (ANN)
149 |     # https://docs.astral.sh/ruff/rules/#flake8-annotations-ann
150 |     "ANN001", # Missing type annotation for function argument {name}
151 |     "ANN002", # Missing type annotation for *{name}
152 |     "ANN003", # Missing type annotation for **{name}
153 |     "ANN201", # Missing return type annotation for public function {name}
154 |     "ANN202", # Missing return type annotation for private function {name}
155 |     "ANN204", # Missing return type annotation for special method {name}
156 | 
157 |     "ARG002", # Unused method argument: {name}
158 |     "ARG003", # Unused class method argument: {name}
159 | 
160 |     # flake8-bugbear (B)
161 |     # https://docs.astral.sh/ruff/rules/#flake8-bugbear-b
162 |     "B028", # No explicit stacklevel keyword argument found
163 | 
164 |     # flake8-comprehensions (C4)
165 |     # https://docs.astral.sh/ruff/rules/#flake8-comprehensions-c4
166 |     "C405", # Unnecessary {obj_type} literal (rewrite as a set literal)
167 |     "C419", # Unnecessary list comprehension
168 | 
169 |     # flake8-commas (COM)
170 |     # https://docs.astral.sh/ruff/rules/#flake8-commas-com
171 |     "COM812",  # Trailing comma missing.
172 |     "COM819",  # Trailing comma prohibited.
173 | 
174 |     # pydocstyle (D)
175 |     # https://docs.astral.sh/ruff/rules/#pydocstyle-d
176 |     "D100", # Missing docstring in public module
177 |     "D101", # Missing docstring in public class
178 |     "D102", # Missing docstring in public method
179 |     "D103", # Missing docstring in public function
180 |     "D104", # Missing docstring in public package
181 |     "D106", # Missing docstring in public nested class
182 |     "D205", # 1 blank line required between summary line and description
183 | 
184 |     # https://docs.astral.sh/ruff/rules/#eradicate-era
185 |     "ERA001", # Found commented-out code
186 | 
187 |     # flake8-boolean-trap (FBT)
188 |     # https://docs.astral.sh/ruff/rules/#flake8-boolean-trap-fbt
189 |     "FBT002", # Boolean default positional argument in function definition
190 | 
191 |     # flake8-implicit-str-concat (ISC)
192 |     # https://docs.astral.sh/ruff/rules/single-line-implicit-string-concatenation/
193 |     # NOTE: This rule may cause conflicts when used with "ruff format".
194 |     "ISC001",  # Implicitly concatenate string literals on one line.
195 | 
196 |     # pep8-naming (N)
197 |     # https://docs.astral.sh/ruff/rules/#pep8-naming-n
198 |     "N801", # Class name {name} should use CapWords convention
199 | 
200 |     # Refactor (R)
201 |     # https://docs.astral.sh/ruff/rules/#refactor-r
202 |     "PLR2004", # Magic value used in comparison, consider replacing {value} with a constant variable
203 | 
204 |     # flake8-pytest-style (PT)
205 |     # https://docs.astral.sh/ruff/rules/#flake8-pytest-style-pt
206 |     "PT009", # Use a regular assert instead of unittest-style {assertion}
207 |     "PT027", # Use pytest.raises instead of unittest-style {assertion}
208 | 
209 |     # flake8-return (RET)
210 |     # https://docs.astral.sh/ruff/rules/#flake8-return-ret
211 |     "RET504", # Unnecessary assignment to {name} before return statement
212 | 
213 |     # Ruff-specific rules (RUF)
214 |     # https://docs.astral.sh/ruff/rules/#ruff-specific-rules-ruf
215 |     "RUF005", # Consider {expression} instead of concatenation
216 |     "RUF012", # Mutable class attributes should be annotated with typing.ClassVar
217 | 
218 |     # flake8-self (SLF)
219 |     # https://docs.astral.sh/ruff/rules/#flake8-self-slf
220 |     "SLF001", # Private member accessed: {access}
221 | 
222 |     # flake8-print (T20)
223 |     # https://docs.astral.sh/ruff/rules/#flake8-print-t20
224 |     "T201", # print found
225 | 
226 |     ]
227 |     preview = false
228 |     select = [
229 |         "ALL",
230 |         # list specific rules to include that is skipped using numpy convention.
231 |         "D212",  # Multi-line docstring summary should start at the first line
232 |     ]
233 | 
234 | [tool.ruff.lint.isort]
235 | force-sort-within-sections = true
236 | # Change to match specific package name:
237 | known-first-party = ["iris"]
238 | 
239 | [tool.ruff.lint.per-file-ignores]
240 | # All test scripts
241 | 
242 | # Change to match specific package path:
243 | "lib/iris/tests/*.py" = [
244 |     # https://docs.astral.sh/ruff/rules/undocumented-public-module/
245 |     "D100",  # Missing docstring in public module
246 |     "D205",  # 1 blank line required between summary line and description
247 |     "D401",  # 1 First line of docstring should be in imperative mood
248 | ]
249 | 
250 | [tool.ruff.lint.pydocstyle]
251 | convention = "numpy"
252 | 
253 | [tool.codespell]
254 | ignore-words-list = "ND,Nd"
255 | 
256 | [tool.repo-review]
257 | # These are a list of the currently failing tests to be fixed later:
258 | ignore = [
259 |     "PY004", # Has docs folder
260 |     "PP302", # Sets a minimum pytest to at least 6
261 |     "PP304", # Sets the log level in pytest
262 |     "PP305", # Specifies xfail_strict
263 |     "PP306", # Specifies strict config
264 |     "PP307", # Specifies strict markers
265 |     "PP309", # Filter warnings specified
266 |     "PY007", # Supports an easy task runner (nox or tox)
267 |     "PP003", # Does not list wheel as a build-dep
268 |     "PC111", # Uses blacken-docs
269 |     "PC170", # Uses PyGrep hooks (only needed if rST present)
270 |     "PC180", # Uses a markdown formatter
271 |     "RTD100" # Uses ReadTheDocs (pyproject config)
272 | ]
273 | 


--------------------------------------------------------------------------------
/requirements/locks/README.md:
--------------------------------------------------------------------------------
1 | # ⚠️
2 | 
3 | This directory contains auto-generated `conda-lock` environment files for each `python` distribution supported by `python-stratify`.
4 | 
5 | Please **do not** manually edit these files as they will be overwritten by the GHA CI workflow.
6 | 


--------------------------------------------------------------------------------
/requirements/locks/py310-linux-64.lock:
--------------------------------------------------------------------------------
  1 | # Generated by conda-lock.
  2 | # platform: linux-64
  3 | # input_hash: 526538341013c7b9d1904478a3fdd47179375908e4675950d963788dc53c5125
  4 | @EXPLICIT
  5 | https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81
  6 | https://conda.anaconda.org/conda-forge/linux-64/libopentelemetry-cpp-headers-1.21.0-ha770c72_0.conda#11b1bed92c943d3b741e8a1e1a815ed1
  7 | https://conda.anaconda.org/conda-forge/linux-64/nlohmann_json-3.12.0-h3f2d84a_0.conda#d76872d096d063e226482c99337209dc
  8 | https://conda.anaconda.org/conda-forge/noarch/python_abi-3.10-7_cp310.conda#44e871cba2b162368476a84b8d040b6c
  9 | https://conda.anaconda.org/conda-forge/noarch/tzdata-2025b-h78e105d_0.conda#4222072737ccff51314b5ece9c7d6f5a
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143 | https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.71.0-h8e591d7_1.conda#c3cfd72cbb14113abee7bbd86f44ad69
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148 | https://conda.anaconda.org/conda-forge/noarch/pip-25.1.1-pyh8b19718_0.conda#32d0781ace05105cc99af55d36cbec7c
149 | https://conda.anaconda.org/conda-forge/linux-64/prometheus-cpp-1.3.0-ha5d0236_0.conda#a83f6a2fdc079e643237887a37460668
150 | https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhff2d567_1.conda#5ba79d7c71f03c678c8ead841f347d6e
151 | https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.14.0-h32cad80_0.conda#a1cdd40fc962e2f7944bc19e01c7e584
152 | https://conda.anaconda.org/conda-forge/noarch/virtualenv-20.31.2-pyhd8ed1ab_0.conda#c0600c1b374efa7a1ff444befee108ca
153 | https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.8.0-h670bddd_2.conda#9c9ec5641de50fff32aa71e5296e627e
154 | https://conda.anaconda.org/conda-forge/linux-64/azure-identity-cpp-1.10.0-h113e628_0.conda#73f73f60854f325a55f1d31459f2ab73
155 | https://conda.anaconda.org/conda-forge/linux-64/azure-storage-common-cpp-12.8.0-h736e048_1.conda#13de36be8de3ae3f05ba127631599213
156 | https://conda.anaconda.org/conda-forge/noarch/dask-core-2025.5.1-pyhd8ed1ab_0.conda#8f0ef561cd615a17df3256742a3457c4
157 | https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.36.0-hc4361e1_1.conda#ae36e6296a8dd8e8a9a8375965bf6398
158 | https://conda.anaconda.org/conda-forge/linux-64/libopentelemetry-cpp-1.21.0-hd1b1c89_0.conda#4b25cd8720fd8d5319206e4f899f2707
159 | https://conda.anaconda.org/conda-forge/linux-64/numpy-2.2.6-py312h72c5963_0.conda#17fac9db62daa5c810091c2882b28f45
160 | https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.0-pyhd8ed1ab_0.conda#516d31f063ce7e49ced17f105b63a1f1
161 | https://conda.anaconda.org/conda-forge/noarch/setuptools-scm-8.3.1-pyhd8ed1ab_0.conda#996376098e3648237b3efb0e0ad460c1
162 | https://conda.anaconda.org/conda-forge/linux-64/ukkonen-1.0.1-py312h68727a3_5.conda#f9664ee31aed96c85b7319ab0a693341
163 | https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py312h66e93f0_2.conda#630db208bc7bbb96725ce9832c7423bb
164 | https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.32.8-h0ead740_2.conda#df290cfb1fc024a1dc9e30f6167579a5
165 | https://conda.anaconda.org/conda-forge/linux-64/azure-storage-blobs-cpp-12.13.0-h3cf044e_1.conda#7eb66060455c7a47d9dcdbfa9f46579b
166 | https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.2-py312h68727a3_0.conda#e688276449452cdfe9f8f5d3e74c23f6
167 | https://conda.anaconda.org/conda-forge/noarch/identify-2.6.12-pyhd8ed1ab_0.conda#84463b10c1eb198541cd54125c7efe90
168 | https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.36.0-h0121fbd_1.conda#a0f7588c1f0a26d550e7bae4fb49427a
169 | https://conda.anaconda.org/conda-forge/linux-64/pandas-2.3.0-py312hf9745cd_0.conda#ac82ac336dbe61106e21fb2e11704459
170 | https://conda.anaconda.org/conda-forge/noarch/pytest-cov-6.1.1-pyhd8ed1ab_0.conda#1e35d8f975bc0e984a19819aa91c440a
171 | https://conda.anaconda.org/conda-forge/noarch/urllib3-2.4.0-pyhd8ed1ab_0.conda#c1e349028e0052c4eea844e94f773065
172 | https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.11.510-h4607db7_10.conda#96f240f245fe2e031ec59dbb3044bd6c
173 | https://conda.anaconda.org/conda-forge/linux-64/azure-storage-files-datalake-cpp-12.12.0-ha633028_1.conda#7c1980f89dd41b097549782121a73490
174 | https://conda.anaconda.org/conda-forge/noarch/bokeh-3.7.3-pyhd8ed1ab_0.conda#708d2f99b8a2c833ff164a225a265e76
175 | https://conda.anaconda.org/conda-forge/noarch/distributed-2025.5.1-pyhd8ed1ab_0.conda#d2949f56a1479507e36e847681903376
176 | https://conda.anaconda.org/conda-forge/noarch/pre-commit-4.2.0-pyha770c72_0.conda#17e487cc8b5507cd3abc09398cf27949
177 | https://conda.anaconda.org/conda-forge/noarch/requests-2.32.3-pyhd8ed1ab_1.conda#a9b9368f3701a417eac9edbcae7cb737
178 | https://conda.anaconda.org/conda-forge/noarch/codecov-2.1.13-pyhd8ed1ab_1.conda#d924fe46139596ebc3d4d424ec39ed51
179 | https://conda.anaconda.org/conda-forge/linux-64/libarrow-20.0.0-h314c690_6_cpu.conda#36f91dcd71682a790981f59b1f25e81a
180 | https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-20.0.0-hcb10f89_6_cpu.conda#d30eef9d28672b910decb5c11b00e09e
181 | https://conda.anaconda.org/conda-forge/linux-64/libparquet-20.0.0-h081d1f1_6_cpu.conda#640b79f7a2c691ab4101345c5d87a540
182 | https://conda.anaconda.org/conda-forge/linux-64/pyarrow-core-20.0.0-py312h01725c0_0_cpu.conda#9b1b453cdb91a2f24fb0257bbec798af
183 | https://conda.anaconda.org/conda-forge/linux-64/libarrow-dataset-20.0.0-hcb10f89_6_cpu.conda#528a0f333effc7e8b1ef1c40c6437bae
184 | https://conda.anaconda.org/conda-forge/linux-64/libarrow-substrait-20.0.0-h1bed206_6_cpu.conda#b051b8e680398c103567e331ce3a339c
185 | https://conda.anaconda.org/conda-forge/linux-64/pyarrow-20.0.0-py312h7900ff3_0.conda#57b626b4232b77ee6410c7c03a99774d
186 | https://conda.anaconda.org/conda-forge/noarch/dask-2025.5.1-pyhd8ed1ab_0.conda#97ee3885b8de1d729c40f28c2dddb335
187 | 


--------------------------------------------------------------------------------
/requirements/py310.yml:
--------------------------------------------------------------------------------
 1 | name: stratify-dev
 2 | 
 3 | channels:
 4 |   - conda-forge
 5 | 
 6 | dependencies:
 7 |   - python=3.10
 8 | 
 9 | # Setup dependencies.
10 |   - setuptools>=77.0.3
11 |   - setuptools-scm>=8
12 | 
13 | # Core dependencies.
14 |   - numpy>=2
15 |   - cython<3.1.0
16 | 
17 | # Optional dependencies.
18 |   - dask
19 | 
20 | # Developer dependencies.
21 |   - pip
22 |   - pre-commit
23 | 
24 | # Test dependencies.
25 |   - codecov
26 |   - pytest
27 |   - pytest-cov
28 | 


--------------------------------------------------------------------------------
/requirements/py311.yml:
--------------------------------------------------------------------------------
 1 | name: stratify-dev
 2 | 
 3 | channels:
 4 |   - conda-forge
 5 | 
 6 | dependencies:
 7 |   - python=3.11
 8 | 
 9 | # Setup dependencies.
10 |   - setuptools>=77.0.3
11 |   - setuptools-scm>=8
12 | 
13 | # Core dependencies.
14 |   - numpy>=2
15 |   - cython<3.1.0
16 | 
17 | # Optional dependencies.
18 |   - dask
19 | 
20 | # Developer dependencies.
21 |   - pip
22 |   - pre-commit
23 | 
24 | # Test dependencies.
25 |   - codecov
26 |   - pytest
27 |   - pytest-cov
28 | 


--------------------------------------------------------------------------------
/requirements/py312.yml:
--------------------------------------------------------------------------------
 1 | name: stratify-dev
 2 | 
 3 | channels:
 4 |   - conda-forge
 5 | 
 6 | dependencies:
 7 |   - python=3.12
 8 | 
 9 | # Setup dependencies.
10 |   - setuptools>=77.0.3
11 |   - setuptools-scm>=8
12 | 
13 | # Core dependencies.
14 |   - numpy>=2
15 |   - cython<3.1.0
16 | 
17 | # Optional dependencies.
18 |   - dask
19 | 
20 | # Developer dependencies.
21 |   - pip
22 |   - pre-commit
23 | 
24 | # Test dependencies.
25 |   - codecov
26 |   - pytest
27 |   - pytest-cov
28 | 


--------------------------------------------------------------------------------
/requirements/pypi-core.txt:
--------------------------------------------------------------------------------
1 | Cython<3.1.0
2 | dask[array]
3 | numpy>=2
4 | 


--------------------------------------------------------------------------------
/requirements/stratify.yml:
--------------------------------------------------------------------------------
1 | py311.yml


--------------------------------------------------------------------------------
/setup.py:
--------------------------------------------------------------------------------
 1 | import os
 2 | from pathlib import Path
 3 | import sys
 4 | import warnings
 5 | 
 6 | # safe to import numpy here thanks to pep518
 7 | import numpy as np
 8 | from setuptools import Command, Extension, setup
 9 | 
10 | try:
11 |     from Cython.Build import cythonize  # isort:skip
12 | except ImportError:
13 |     wmsg = "Cython unavailable, unable to build stratify extensions!"
14 |     warnings.warn(wmsg)
15 |     cythonize = None
16 | 
17 | 
18 | BASE_DIR = Path(__file__).resolve().parent
19 | PACKAGE_NAME = "stratify"
20 | SRC_DIR = BASE_DIR / "src"
21 | STRATIFY_DIR = SRC_DIR / PACKAGE_NAME
22 | CMDS_NOCYTHONIZE = ["clean", "sdist"]
23 | FLAG_COVERAGE = "--cython-coverage"  # custom flag enabling Cython line tracing
24 | 
25 | 
26 | class CleanCython(Command):
27 |     description = "Purge artifacts built by Cython"
28 |     user_options = []
29 | 
30 |     def initialize_options(self):
31 |         pass
32 | 
33 |     def finalize_options(self):
34 |         pass
35 | 
36 |     def run(self):
37 |         for path in STRATIFY_DIR.rglob("*"):
38 |             if path.suffix in (".pyc", ".pyo", ".c", ".so"):
39 |                 msg = f"clean: removing file {path}"
40 |                 print(msg)
41 |                 path.unlink()
42 | 
43 | 
44 | cython_coverage_enabled = (
45 |     os.environ.get("CYTHON_COVERAGE", None) or FLAG_COVERAGE in sys.argv
46 | )
47 | cython_directives = {}
48 | define_macros = []
49 | extensions = []
50 | 
51 | if cythonize and cython_coverage_enabled:
52 |     define_macros.extend(
53 |         [
54 |             ("CYTHON_TRACE", "1"),
55 |             ("CYTHON_TRACE_NOGIL", "1"),
56 |         ]
57 |     )
58 |     cython_directives.update({"linetrace": True})
59 |     if FLAG_COVERAGE in sys.argv:
60 |         sys.argv.remove(FLAG_COVERAGE)
61 |     print('enabled "linetrace" Cython compiler directive')
62 | 
63 | for fname in SRC_DIR.glob(f"{PACKAGE_NAME}/*.pyx"):
64 |     # ref: https://setuptools.pypa.io/en/latest/userguide/ext_modules.html
65 |     extension = Extension(
66 |         f"{PACKAGE_NAME}.{fname.stem}",
67 |         sources=[str(fname.relative_to(BASE_DIR))],
68 |         include_dirs=[np.get_include()],
69 |         define_macros=define_macros,
70 |     )
71 |     extensions.append(extension)
72 | 
73 | if cythonize and not any([arg in CMDS_NOCYTHONIZE for arg in sys.argv]):
74 |     extensions = cythonize(
75 |         extensions, compiler_directives=cython_directives, language_level=3
76 |     )
77 | 
78 | cmdclass = {"clean_cython": CleanCython}
79 | kwargs = {"cmdclass": cmdclass, "ext_modules": extensions}
80 | setup(**kwargs)
81 | 


--------------------------------------------------------------------------------
/src/stratify/__init__.py:
--------------------------------------------------------------------------------
1 | from ._bounded_vinterp import interpolate_conservative  # noqa: F401
2 | from ._version import version as __version__  # noqa: F401
3 | from ._vinterp import *  # noqa: F403
4 | 


--------------------------------------------------------------------------------
/src/stratify/_bounded_vinterp.py:
--------------------------------------------------------------------------------
  1 | import numpy as np
  2 | 
  3 | from ._conservative import conservative_interpolation
  4 | 
  5 | 
  6 | def interpolate_conservative(z_target, z_src, fz_src, axis=-1):
  7 |     """1d conservative interpolation across multiple dimensions.
  8 | 
  9 |     This function provides the ability to perform 1d interpolation on datasets
 10 |     with more than one dimension. For instance, this function can be used to
 11 |     interpolate a set of vertical levels, even if the interpolation coordinate
 12 |     depends upon other dimensions.
 13 | 
 14 |     A good use case might be when wanting to interpolate at a specific height
 15 |     for height data which also depends on x and y - e.g. extract 1000hPa level
 16 |     from a 3d dataset and associated pressure field. In the case of this
 17 |     example, pressure would be the `z` coordinate, and the dataset
 18 |     (e.g. geopotential height / temperature etc.) would be `f(z)`.
 19 | 
 20 |     Parameters
 21 |     ----------
 22 |     z_target: :class:`np.ndarray`
 23 |         Target coordinate.
 24 |         This coordinate defines the levels to interpolate the source data
 25 |         ``fz_src`` to.  ``z_target`` must have the same dimensionality as the
 26 |         source coordinate ``z_src``, and the shape of ``z_target`` must match
 27 |         the shape of ``z_src``, although the axis of interpolation may differ
 28 |         in dimension size.
 29 |     z_src: :class:`np.ndarray`
 30 |         Source coordinate.
 31 |         This coordinate defines the levels that the source data ``fz_src`` is
 32 |         interpolated from.
 33 |     fz_src: :class:`np.ndarray`
 34 |         The source data; the phenomenon data values to be interpolated from
 35 |         ``z_src`` to ``z_target``.
 36 |         The data array must be at least ``z_src.ndim``, and its trailing
 37 |         dimensions (i.e. those on its right hand side) must be exactly
 38 |         the same as the shape of ``z_src``.
 39 |     axis: int (default -1)
 40 |         The ``fz_src`` axis to perform the interpolation over.
 41 | 
 42 |     Returns
 43 |     -------
 44 |     : :class:`np.ndarray`
 45 |         fz_src interpolated from z_src to z_target.
 46 | 
 47 |     Note
 48 |     ----
 49 |     - Support for 1D z_target and corresponding ND z_src will be provided in
 50 |     future as driven by user requirement.
 51 |     - Those cells, where 'nan' values in the source data contribute, a 'nan'
 52 |       value is returned.
 53 | 
 54 |     """
 55 |     if z_src.ndim != z_target.ndim:
 56 |         msg = (
 57 |             "Expecting source and target levels dimensionality to be "
 58 |             "identical.  {} != {}."
 59 |         )
 60 |         raise ValueError(msg.format(z_src.ndim, z_target.ndim))
 61 | 
 62 |     # Relative axis
 63 |     axis = axis % fz_src.ndim
 64 |     axis_relative = axis - (fz_src.ndim - (z_target.ndim - 1))
 65 | 
 66 |     src_shape = list(z_src.shape)
 67 |     src_shape.pop(axis_relative)
 68 |     tgt_shape = list(z_target.shape)
 69 |     tgt_shape.pop(axis_relative)
 70 | 
 71 |     if src_shape != tgt_shape:
 72 |         src_shape = list(z_src.shape)
 73 |         src_shape[axis_relative] = "-"
 74 |         tgt_shape = list(z_target.shape)
 75 |         src_shape[axis_relative] = "-"
 76 |         msg = (
 77 |             "Expecting the shape of the source and target levels except "
 78 |             "the axis of interpolation to be identical.  {} != {}"
 79 |         )
 80 |         raise ValueError(msg.format(tuple(src_shape), tuple(tgt_shape)))
 81 | 
 82 |     dat_shape = list(fz_src.shape)
 83 |     dat_shape = dat_shape[-(z_src.ndim - 1) :]
 84 |     src_shape = list(z_src.shape[:-1])
 85 |     if dat_shape != src_shape:
 86 |         dat_shape = list(fz_src.shape)
 87 |         dat_shape[: -(z_src.ndim - 1)] = "-"
 88 |         msg = (
 89 |             "The provided data is not of compatible shape with the "
 90 |             "provided source bounds. {} != {}"
 91 |         )
 92 |         raise ValueError(msg.format(tuple(dat_shape), tuple(src_shape)))
 93 | 
 94 |     if z_src.shape[-1] != 2:
 95 |         msg = "Unexpected source and target bounds shape. shape[-1] != 2"
 96 |         raise ValueError(msg)
 97 | 
 98 |     # Define our source in a consistent way.
 99 |     # [broadcasting_dims, axis_interpolation, z_varying]
100 | 
101 |     # src_data
102 |     bdims = list(range(fz_src.ndim - (z_src.ndim - 1)))
103 |     data_vdims = [ind for ind in range(fz_src.ndim) if ind not in (bdims + [axis])]
104 |     data_transpose = bdims + [axis] + data_vdims
105 |     fz_src_reshaped = np.transpose(fz_src, data_transpose)
106 |     fz_src_orig = list(fz_src_reshaped.shape)
107 |     shape = (
108 |         int(np.prod(fz_src_reshaped.shape[: len(bdims)])),
109 |         fz_src_reshaped.shape[len(bdims)],
110 |         int(np.prod(fz_src_reshaped.shape[len(bdims) + 1 :])),
111 |     )
112 |     fz_src_reshaped = fz_src_reshaped.reshape(shape)
113 | 
114 |     # Define our src and target bounds in a consistent way.
115 |     # [axis_interpolation, z_varying, 2]
116 |     vdims = list(set(range(z_src.ndim)) - set([axis_relative]))
117 |     z_src_reshaped = np.transpose(z_src, [axis_relative] + vdims)
118 |     z_target_reshaped = np.transpose(z_target, [axis_relative] + vdims)
119 | 
120 |     shape = int(np.prod(z_src_reshaped.shape[1:-1]))
121 |     z_src_reshaped = z_src_reshaped.reshape(
122 |         [z_src_reshaped.shape[0], shape, z_src_reshaped.shape[-1]]
123 |     )
124 |     shape = int(np.prod(z_target_reshaped.shape[1:-1]))
125 |     z_target_reshaped = z_target_reshaped.reshape(
126 |         [z_target_reshaped.shape[0], shape, z_target_reshaped.shape[-1]]
127 |     )
128 | 
129 |     result = conservative_interpolation(
130 |         z_src_reshaped, z_target_reshaped, fz_src_reshaped
131 |     )
132 | 
133 |     # Turn the result into a shape consistent with the source.
134 |     # First reshape, then reverse transpose.
135 |     shape = fz_src_orig
136 |     shape[len(bdims)] = z_target.shape[axis_relative]
137 |     result = result.reshape(shape)
138 |     invert_transpose = [data_transpose.index(ind) for ind in list(range(result.ndim))]
139 |     result = result.transpose(invert_transpose)
140 |     return result
141 | 


--------------------------------------------------------------------------------
/src/stratify/_conservative.pyx:
--------------------------------------------------------------------------------
  1 | import numpy as np
  2 | cimport numpy as np
  3 | 
  4 | # Must be called to use the C-API with Numpy.
  5 | np.import_array()
  6 | 
  7 | cdef calculate_weights(np.ndarray[np.float64_t, ndim=2] src_point,
  8 |                        np.ndarray[np.float64_t, ndim=2] tgt_point):
  9 |     """
 10 |     Calculate weights for a given point.
 11 | 
 12 |     The following visually illustrates the calculation::
 13 | 
 14 |         src_min    src_max
 15 |            |----------|                 : Source
 16 |               tgt_min     tgt_max
 17 |                  |------------|         : Target
 18 |            |----------|                 : Delta (src_max - src_min)
 19 |                  |----|                 : Overlap (between src & tgt)
 20 |            weight = overlap / delta
 21 | 
 22 |     Parameters
 23 |     ----------
 24 |     src_point (2d double array) - Source point (at a specific location).
 25 |     tgt_point (2d double array) - Target point (at a specific location).
 26 | 
 27 |     Returns
 28 |     -------
 29 |     2d double array - Weights corresponding to shape [src_point.shape[0],
 30 |         tgt_point.shape[0]].
 31 | 
 32 |     """
 33 |     cdef Py_ssize_t src_ind, tgt_ind
 34 |     cdef np.float64_t delta, weight
 35 |     cdef np.ndarray[np.float64_t, ndim=2] weights
 36 |     cdef np.ndarray[np.float64_t, ndim=1] src_cell, tgt_cell
 37 | 
 38 |     weights = np.zeros([src_point.shape[0], tgt_point.shape[0]],
 39 |                        dtype=np.float64)
 40 |     for src_ind, src_cell in enumerate(src_point):
 41 |         delta = src_cell.max() - src_cell.min()
 42 |         for tgt_ind, tgt_cell in enumerate(tgt_point):
 43 |             weight = (min(src_cell.max(), tgt_cell.max()) -
 44 |                       max(src_cell.min(), tgt_cell.min())) / float(delta)
 45 |             if weight > 0:
 46 |                 weights[src_ind, tgt_ind] = weight
 47 |     return weights
 48 | 
 49 | 
 50 | cdef apply_weights(np.ndarray[np.float64_t, ndim=3] src_point,
 51 |                    np.ndarray[np.float64_t, ndim=3] tgt_point,
 52 |                    np.ndarray[np.float64_t, ndim=3] src_data):
 53 |     """
 54 |     Perform conservative interpolation.
 55 | 
 56 |     Conservation interpolation of a dataset between a provided source
 57 |     coordinate and a target coordinate.  Where no source cells contribute to a
 58 |     target cell, a np.nan value is returned.
 59 | 
 60 |     Parameters
 61 |     ----------
 62 |     src_points (3d double array) - Source coordinate, taking the form
 63 |         [axis_interpolation, z_varying, 2].
 64 |     tgt_points (3d double array) - Target coordinate, taking the form
 65 |         [axis_interpolation, z_varying, 2].
 66 |     src_data (3d double array) - The source data, the phenomenon data to be
 67 |         interpolated from ``src_points`` to ``tgt_points``.  Taking the form
 68 |         [broadcasting_dims, axis_interpolation, z_varying].
 69 | 
 70 |     Returns
 71 |     -------
 72 |     3d double array - Interpolated result over target levels (``tgt_points``).
 73 |         Taking the form [broadcasting_dims, axis_interpolation, z_varying].
 74 | 
 75 |     """
 76 |     cdef Py_ssize_t ind
 77 |     cdef np.ndarray[np.float64_t, ndim=3] results, weighted_contrib
 78 |     cdef np.ndarray[np.float64_t, ndim=2] weights
 79 |     results = np.zeros(
 80 |         [src_data.shape[0], tgt_point.shape[0], src_data.shape[2]],
 81 |         dtype='float64')
 82 |     # Calculate and apply weights
 83 |     for ind in range(src_data.shape[2]):
 84 |         weights = calculate_weights(src_point[:, ind], tgt_point[:, ind])
 85 |         if not (weights.sum(axis=1) == 1).all():
 86 |             msg = ('Weights calculation yields a less than conservative '
 87 |                    'result.  Aborting.')
 88 |             raise ValueError(msg)
 89 |         weighted_contrib = weights * src_data[..., ind][..., None]
 90 |         results[..., ind] = (
 91 |             np.nansum(weighted_contrib, axis=1))
 92 |         # Return nan values for those target cells, where there is any
 93 |         # contribution of nan data from the source data.
 94 |         results[..., ind][
 95 |             ((weights > 0) * np.isnan(weighted_contrib)).any(axis=1)] = np.nan
 96 | 
 97 |         # Return np.nan for those target cells where no source contributes.
 98 |         results[:, weights.sum(axis=0) == 0, ind] = np.nan
 99 |     return results
100 | 
101 | 
102 | def conservative_interpolation(src_points, tgt_points, src_data):
103 |     """
104 |     Perform conservative interpolation.
105 | 
106 |     Conservation interpolation of a dataset between a provided source
107 |     coordinate and a target coordinate.  All inputs are recast to 64-bit float
108 |     arrays before calculation.
109 | 
110 |     Parameters
111 |     ----------
112 |     src_points (3d array) - Source coordinate, taking the form
113 |         [axis_interpolation, z_varying, 2].
114 |     tgt_points (3d array) - Target coordinate, taking the form
115 |         [axis_interpolation, z_varying, 2].
116 |     src_data (3d array) - The source data, the phenonenon data to be
117 |         interpolated from ``src_points`` to ``tgt_points``.  Taking the form
118 |         [broadcasting_dims, axis_interpolation, z_varying].
119 | 
120 |     Returns
121 |     -------
122 |     3d double array - Interpolated result over target levels (``tgt_points``).
123 |         Taking the form [broadcasting_dims, axis_interpolation, z_varying].
124 | 
125 |     """
126 |     return apply_weights(src_points.astype('float64'),
127 |                          tgt_points.astype('float64'),
128 |                          src_data.astype('float64'))
129 | 


--------------------------------------------------------------------------------
/src/stratify/_vinterp.pyx:
--------------------------------------------------------------------------------
  1 | # Terminology:
  2 | # Z - the coordinate over which we are interpolating.
  3 | # z_src - the values of Z where fz_src is defined
  4 | # z_target - the desired values of Z to generate new data for.
  5 | # fz_src - the data, defined at each z_src
  6 | import functools
  7 | 
  8 | import numpy as np
  9 | 
 10 | cimport cython
 11 | cimport numpy as np
 12 | 
 13 | # Must be called to use the C-API with Numpy.
 14 | np.import_array()
 15 | 
 16 | cdef extern from "numpy/npy_math.h" nogil:
 17 |     bint isnan "npy_isnan"(long double)
 18 |     float NAN "NPY_NAN"
 19 |     float INFINITY "NPY_INFINITY"
 20 | 
 21 | 
 22 | cdef extern from "math.h" nogil:
 23 |     double fabs(double z)
 24 | 
 25 | 
 26 | __all__ = ['interpolate', 'interp_schemes', 'extrap_schemes',
 27 |            'INTERPOLATE_LINEAR', 'INTERPOLATE_NEAREST',
 28 |            'EXTRAPOLATE_NAN', 'EXTRAPOLATE_NEAREST', 'EXTRAPOLATE_LINEAR',
 29 |            'PyFuncExtrapolator', 'PyFuncInterpolator']
 30 | 
 31 | 
 32 | cdef inline int relative_sign(double z, double z_base) nogil:
 33 |     """
 34 |     Return the sign of z relative to z_base.
 35 | 
 36 |     Parameters
 37 |     ----------
 38 |     z - the value to compare to z_base
 39 |     z_base - the other one
 40 | 
 41 |     Returns
 42 |     -------
 43 |     +1 if z > z_base, 0 if z == z_base, and -1 if z < z_base
 44 | 
 45 |     """
 46 |     cdef double delta
 47 | 
 48 |     delta = z - z_base
 49 |     # 1, -1, or 0. http://stackoverflow.com/a/1903975/741316
 50 |     return (delta > 0) - (delta < 0)
 51 | 
 52 | 
 53 | @cython.boundscheck(False)
 54 | @cython.wraparound(False)
 55 | cdef long gridwise_interpolation(double[:] z_target, double[:] z_src,
 56 |                                  double[:, :] fz_src, bint increasing,
 57 |                                  Interpolator interpolation,
 58 |                                  Extrapolator extrapolation,
 59 |                                  double [:, :] fz_target) nogil except -1:
 60 |     """
 61 |     Computes the interpolation of multiple levels of a single column.
 62 | 
 63 |     Parameters
 64 |     ----------
 65 |     z_target - the levels to interpolate the source data ``fz_src`` to.
 66 |     z_src - the levels that the source data ``fz_src`` is interpolated from.
 67 |     fz_src - the source data to be interpolated.
 68 |     increasing - true when increasing Z index generally implies increasing Z values
 69 |     interpolation - the inner interpolation functionality. See the definition of
 70 |                     Interpolator.
 71 |     extrapolation - the inner extrapolation functionality. See the definition of
 72 |                     Extrapolator.
 73 |     fz_target - the pre-allocated array to be used for the interpolated result
 74 | 
 75 |     Note: This algorithm is not symmetric. It does not make assumptions about monotonicity
 76 |           of z_src nor z_target. Instead, the algorithm marches forwards from the last
 77 |           z_target found. To visualise this, imagine a single vertical column of temperature
 78 |           values being our non-monotonic Z *coordinate*, with our fx data being height.
 79 |           At low indices of Z (i.e. at the bottom of the column) the temperature is high, and
 80 |           decreases as we ascend. As some point, the trend reverses, and the temperature
 81 |           again begins to rise, before finally trailing off again as we reach the very top
 82 |           of our column. Algorithmically, we march the column looking for the next z_target,
 83 |           when a crossing is detected we invoke the interpolation for fx at our current index.
 84 |           We then continue from this index, only looking for the crossing of the next z_target.
 85 | 
 86 |           For this reason, the order that the levels are provided is important.
 87 |           If z_src = [2, 4, 6], fz_src = [2, 4, 6] and z_target = [3, 5], fz_target will be
 88 |           [3, 5]. But if z_target = [5, 3], fz_target will be [5, <extrapolation value>].
 89 | 
 90 |     """
 91 |     cdef unsigned int i_src, i_target, n_src, n_target, i, m
 92 |     cdef bint all_nans = True
 93 |     cdef double z_before, z_current, z_after, z_last
 94 |     cdef int sign_after, sign_before, extrapolating
 95 | 
 96 |     n_src = z_src.shape[0]
 97 |     n_target = z_target.shape[0]
 98 | 
 99 |     # Check for a source coordinate that has only NaN values.
100 |     if n_target and isnan(z_src[0]):
101 |         for i in range(n_src):
102 |             all_nans = isnan(z_src[i])
103 |             if not all_nans:
104 |                 break
105 |         if all_nans:
106 |             # The result is also only NaN values.
107 |             m = fz_target.shape[0]
108 |             for i in range(m):
109 |                 for i_target in range(n_target):
110 |                     fz_target[i, i_target] = NAN
111 |             return 0
112 | 
113 |     interpolation.prepare_column(z_target, z_src, fz_src, increasing)
114 |     extrapolation.prepare_column(z_target, z_src, fz_src, increasing)
115 | 
116 |     if increasing:
117 |         z_before = -INFINITY
118 |     else:
119 |         z_before = INFINITY
120 | 
121 |     z_last = -z_before
122 | 
123 |     i_src = 0
124 |     # The first window for which we are looking for a crossing is between the
125 |     # first window value (typically -inf, but may be +inf) and the first z_src.
126 |     # This search window will be moved along until a crossing is detected, at
127 |     # which point we will do an interpolation.
128 |     z_after = z_src[0]
129 | 
130 |     # We start in extrapolation mode. This will be turned off as soon as we
131 |     # start increasing i_src.
132 |     extrapolating = -1
133 | 
134 |     for i_target in range(n_target):
135 |         # Move the level we are looking for forwards one.
136 |         z_current = z_target[i_target]
137 | 
138 |         if isnan(z_current):
139 |             with gil:
140 |                 raise ValueError('The target coordinate may not contain NaN values.')
141 | 
142 |         # Determine if the z_current has a crossing within
143 |         # the current window.
144 |         sign_before = relative_sign(z_before, z_current)
145 |         sign_after = relative_sign(z_after, z_current)
146 | 
147 |         # Move the window forwards until a crossing *is* found for this level.
148 |         # If we run out of z_src to check, we go back to extrapolation, and put the
149 |         # upper edge of the window at z_last (typically +inf).
150 |         while sign_before == sign_after:
151 |             i_src += 1
152 |             if i_src < n_src:
153 |                 extrapolating = 0
154 |                 z_after = z_src[i_src]
155 |                 if isnan(z_after):
156 |                     with gil:
157 |                         raise ValueError('The source coordinate may not contain NaN values.')
158 |                 sign_after = relative_sign(z_after, z_current)
159 |             else:
160 |                 extrapolating = 1
161 |                 z_after = z_last
162 |                 sign_after = relative_sign(z_after, z_current)
163 |                 break
164 | 
165 |         if extrapolating == 0 or sign_after == 0:
166 |             interpolation.kernel(i_src, z_src, fz_src, z_current,
167 |                                  fz_target[:, i_target])
168 |         else:
169 |             extrapolation.kernel(extrapolating, z_src, fz_src, z_current,
170 |                                  fz_target[:, i_target])
171 | 
172 |         # Move the lower edge of the window forwards to the level we've just computed,
173 |         # thus preventing the levels from stepping back within a single index.
174 |         z_before = z_current
175 | 
176 | 
177 | cdef class Interpolator(object):
178 |     cdef long kernel(self, unsigned int index,
179 |                      double[:] z_src, double[:, :] fz_src,
180 |                      double level, double[:] fz_level
181 |                      ) nogil except -1:
182 |         """
183 |         The inner part of an interpolation operation.
184 | 
185 |         Parameters:
186 |         ----------
187 |         i (unsigned int) - the current (upper) index along z_src. 0 <= i < z_src.size[0]
188 |                   i will only ever be 0 if z_src[i] == current_level.
189 |                   the interpolation value may lie on exactly i, but will never lie on exactly i-1.
190 |         z_src (double array) - the 1d column of z_src values.
191 |         fz_src (2d double array) - the m 1d columns of fz_src values.
192 |                                fz_src.shape[1] == z_src.shape[0].
193 |                                fz_src.shape[0] may be 1 (common).
194 |         current_level (double) - the value that we are interpolating for
195 |         fz_target (double array) - the pre-allocated array to put the resulting
196 |                                    interpolated values into.
197 | 
198 |         """
199 |         with gil:
200 |             raise RuntimeError('Interpolator subclasses should implement '
201 |                                'the kernel function.')
202 | 
203 |     cdef bint prepare_column(self, double[:] z_target, double[:] z_src,
204 |                       double[:, :] fz_src, bint increasing) nogil except -1:
205 |         # Called before all levels are interpolated.
206 |         pass
207 | 
208 | 
209 | cdef class LinearInterpolator(Interpolator):
210 |     @cython.boundscheck(False)
211 |     @cython.wraparound(False)
212 |     cdef long kernel(self, unsigned int index, double[:] z_src, double[:, :] fz_src, double level,
213 |                      double[:] fz_target) nogil except -1:
214 |         """
215 |         Compute a linear interpolation.
216 | 
217 |         The behaviour is as follows:
218 |             * if we've found a crossing and it is exactly on the level, use the
219 |               exact value from the original data (i.e. level == z_src[index])
220 |             * otherwise, compute the distance of the level from i and i-1, and
221 |               use these as proportions for linearly combining the fz_src values at
222 |               those indices.
223 | 
224 |         """
225 |         cdef unsigned int m = fz_src.shape[0]
226 |         cdef double frac
227 |         cdef unsigned int i
228 | 
229 |         if level == z_src[index]:
230 |             for i in range(m):
231 |                 fz_target[i] = fz_src[i, index]
232 |         else:
233 |             frac = ((level - z_src[index - 1]) /
234 |                     (z_src[index] - z_src[index - 1]))
235 | 
236 |             for i in range(m):
237 |                fz_target[i] = fz_src[i, index - 1] + \
238 |                                 frac * (fz_src[i, index] - fz_src[i, index - 1])
239 | 
240 | 
241 | cdef class NearestNInterpolator(Interpolator):
242 |     @cython.boundscheck(False)
243 |     @cython.wraparound(False)
244 |     cdef long kernel(self, unsigned int index, double[:] z_src, double[:, :] fz_src, double level,
245 |                      double[:] fz_target) nogil except -1:
246 |         """Compute a nearest-neighbour interpolation."""
247 |         cdef unsigned int m = fz_src.shape[0]
248 |         cdef unsigned int nearest_index, i
249 | 
250 |         if index != 0 and fabs(level - z_src[index - 1]) <= fabs(level - z_src[index]):
251 |             nearest_index = index - 1
252 |         else:
253 |             nearest_index = index
254 | 
255 |         for i in range(m):
256 |             fz_target[i] = fz_src[i, nearest_index]
257 | 
258 | 
259 | cdef class PyFuncInterpolator(Interpolator):
260 |     cdef bint use_column_prep
261 | 
262 |     def __init__(self, use_column_prep=True):
263 |         self.use_column_prep = use_column_prep
264 | 
265 |     def column_prep(self, z_target, z_src, fz_src, increasing):
266 |         """
267 |         Called each time this interpolator sees a new data array.
268 |         This method may be used for validation of a column, or for column
269 |         based pre-interpolation calculations (e.g. spline gradients).
270 | 
271 |         Note: This method is not called if :attr:`.call_column_prep` is False.
272 | 
273 |         """
274 |         pass
275 | 
276 |     cdef bint prepare_column(self, double[:] z_target, double[:] z_src,
277 |                       double[:, :] fz_src, bint increasing) nogil except -1:
278 |         if self.use_column_prep:
279 |             with gil:
280 |                 self.column_prep(z_target, z_src, fz_src, increasing)
281 | 
282 |     def interp_kernel(self, index, z_src, fz_src, level, output_array):
283 |         # Fill the output array with the fz_src data at the given index.
284 |         # This is nealy equivalent to nearest neighbour, but doesn't take
285 |         # into account which neighbour is nearest.
286 |         output_array[:] = fz_src[:, index]
287 | 
288 |     @cython.boundscheck(False)
289 |     @cython.wraparound(False)
290 |     cdef long kernel(self,
291 |                      unsigned int index, double[:] z_src,
292 |                      double[:, :] fz_src, double level,
293 |                      double[:] fz_target) nogil except -1:
294 |         with gil:
295 |             self.interp_kernel(index, z_src, fz_src, level, fz_target)
296 | 
297 | 
298 | cdef class Extrapolator(object):
299 |     cdef long kernel(self, int direction,
300 |                      double[:] z_src, double[:, :] fz_src,
301 |                      double current_level, double[:] fz_target
302 |                     ) nogil except -1:
303 |         """
304 |         Defines the inner part of an extrapolation operation.
305 | 
306 |         Parameters:
307 |         ----------
308 |         direction (int) - -1 for the bottom edge, +1 for the top edge
309 |         z_src (double array) - the 1d column of z_src values.
310 |         fz_src (2d double array) - the m 1d columns of fz_src values.
311 |                                fz_src.shape[1] == z_src.shape[0].
312 |                                fz_src.shape[0] may be 1 (common).
313 |         current_level (double) - the value that we are interpolating for
314 |         fz_target (double array) - the pre-allocated array to put the resulting
315 |                                    extrapolated values into.
316 |         """
317 |         with gil:
318 |             raise RuntimeError('Interpolator subclasses should implement '
319 |                                'the kernel function.')
320 | 
321 |     cdef bint prepare_column(self, double[:] z_target, double[:] z_src,
322 |                       double[:, :] fz_src, bint increasing) nogil except -1:
323 |         pass
324 | 
325 | 
326 | cdef class NaNExtrapolator(Extrapolator):
327 |     @cython.boundscheck(False)
328 |     @cython.wraparound(False)
329 |     cdef long kernel(self, int direction, double[:] z_src,
330 |                      double[:, :] fz_src, double level,
331 |                      double[:] fz_target) nogil except -1:
332 |         """NaN values for extrapolation."""
333 |         cdef unsigned int m = fz_src.shape[0]
334 |         cdef unsigned int i
335 | 
336 |         for i in range(m):
337 |             fz_target[i] = NAN
338 | 
339 | 
340 | cdef class NearestNExtrapolator(Extrapolator):
341 |     @cython.boundscheck(False)
342 |     @cython.wraparound(False)
343 |     cdef long kernel(self,
344 |                      int direction, double[:] z_src,
345 |                      double[:, :] fz_src, double level,
346 |                      double[:] fz_target) nogil except -1:
347 |         """Nearest-neighbour/edge extrapolation."""
348 |         cdef unsigned int m = fz_src.shape[0]
349 |         cdef unsigned int index, i
350 | 
351 |         if direction < 0:
352 |             index = 0
353 |         else:
354 |             index = fz_src.shape[1] - 1
355 | 
356 |         for i in range(m):
357 |             fz_target[i] = fz_src[i, index]
358 | 
359 | 
360 | cdef class LinearExtrapolator(Extrapolator):
361 |     cdef bint prepare_column(self, double[:] z_target, double[:] z_src,
362 |                       double[:, :] fz_src, bint increasing) nogil except -1:
363 |         cdef unsigned int n_src_pts = z_src.shape[0]
364 | 
365 |         if n_src_pts < 2:
366 |             with gil:
367 |                 raise ValueError('Linear extrapolation requires at least '
368 |                                  '2 source points. Got {}.'.format(n_src_pts))
369 | 
370 |     @cython.boundscheck(False)
371 |     @cython.wraparound(False)
372 |     cdef long kernel(self, int direction, double[:] z_src,
373 |                      double[:, :] fz_src, double level,
374 |                      double[:] fz_target) nogil except -1:
375 |         """Linear extrapolation using either the first or last 2 values."""
376 |         cdef unsigned int m = fz_src.shape[0]
377 |         cdef unsigned int n_src_pts = fz_src.shape[1]
378 |         cdef unsigned int p0, p1, i
379 |         cdef double frac, z_step
380 | 
381 |         if direction < 0:
382 |             p0, p1 = 0, 1
383 |         else:
384 |             p0, p1 = n_src_pts - 2, n_src_pts - 1
385 | 
386 |         # Compute the normalised distance of the target point between p0 and p1
387 |         z_step = z_src[p1] - z_src[p0]
388 |         if z_step == 0:
389 |             # If there is nothing between the last two points then we
390 |             # extrapolate using a 0 gradient.
391 |             frac = 0
392 |         else:
393 |             frac = ((level - z_src[p0]) / z_step)
394 | 
395 |         for i in range(m):
396 |            fz_target[i] = fz_src[i, p0] + frac * (fz_src[i, p1] - fz_src[i, p0])
397 | 
398 | 
399 | cdef class PyFuncExtrapolator(Extrapolator):
400 |     cdef bint use_column_prep
401 | 
402 |     def __init__(self, use_column_prep=True):
403 |         self.use_column_prep = use_column_prep
404 | 
405 |     def column_prep(self, z_target, z_src, fz_src, increasing):
406 |         """
407 |         Called each time this extrapolator sees a new data array.
408 |         This method may be used for validation of a column, or for column
409 |         based pre-interpolation calculations (e.g. spline gradients).
410 | 
411 |         Note: This method is not called if :attr:`.call_column_prep` is False.
412 | 
413 |         """
414 |         pass
415 | 
416 |     cdef bint prepare_column(self, double[:] z_target, double[:] z_src,
417 |                       double[:, :] fz_src, bint increasing) nogil except -1:
418 |         if self.use_column_prep:
419 |             with gil:
420 |                 self.column_prep(z_target, z_src, fz_src, increasing)
421 | 
422 |     def extrap_kernel(self, direction, z_src, fz_src, level, output_array):
423 |         # Fill the output array with nans.
424 |         output_array[:] = np.nan
425 | 
426 |     @cython.boundscheck(False)
427 |     @cython.wraparound(False)
428 |     cdef long kernel(self,
429 |                      int direction, double[:] z_src,
430 |                      double[:, :] fz_src, double level,
431 |                      double[:] fz_target) nogil except -1:
432 |         with gil:
433 |             self.extrap_kernel(direction, z_src, fz_src, level, fz_target)
434 | 
435 | 
436 | interp_schemes = {'nearest': NearestNInterpolator,
437 |                   'linear': LinearInterpolator}
438 | 
439 | extrap_schemes = {'nearest': NearestNExtrapolator,
440 |                   'linear': LinearExtrapolator,
441 |                   'nan': NaNExtrapolator}
442 | 
443 | 
444 | # Construct interp/extrap constants exposed to the user.
445 | INTERPOLATE_LINEAR = interp_schemes['linear']()
446 | INTERPOLATE_NEAREST = interp_schemes['nearest']()
447 | EXTRAPOLATE_NAN = extrap_schemes['nan']()
448 | EXTRAPOLATE_NEAREST = extrap_schemes['nearest']()
449 | EXTRAPOLATE_LINEAR = extrap_schemes['linear']()
450 | 
451 | 
452 | def interpolate(z_target, z_src, fz_src, axis=-1, rising=None,
453 |                 interpolation='linear', extrapolation='nan'):
454 |     """
455 |     Interface for optimised 1d interpolation across multiple dimensions.
456 | 
457 |     This function provides the ability to perform 1d interpolation on datasets
458 |     with more than one dimension. For instance, this function can be used to
459 |     interpolate a set of vertical levels, even if the interpolation coordinate
460 |     depends upon other dimensions.
461 | 
462 |     A good use case might be when wanting to interpolate at a specific height
463 |     for height data which also depends on x and y - e.g. extract 1000hPa level
464 |     from a 3d dataset and associated pressure field. In the case of this
465 |     example, pressure would be the `z` coordinate, and the dataset
466 |     (e.g. geopotential height / temperature etc.) would be `f(z)`.
467 | 
468 |     Parameters
469 |     ----------
470 |     z_target: 1d or nd array
471 |         Target coordinate.
472 |         This coordinate defines the levels to interpolate the source data
473 |         ``fz_src`` to. If ``z_target`` is an nd array, it must have the same
474 |         dimensionality as the source coordinate ``z_src``, and the shape of
475 |         ``z_target`` must match the shape of ``z_src``, although the axis
476 |         of interpolation may differ in dimension size.
477 |     z_src: nd array
478 |         Source coordinate.
479 |         This coordinate defines the levels that the source data ``fz_src`` is
480 |         interpolated from.
481 |     fz_src: nd array
482 |         The source data; the phenomenon data values to be interpolated from
483 |         ``z_src`` to ``z_target``.
484 |         The data array must be at least ``z_src.ndim``, and its trailing
485 |         dimensions (i.e. those on its right hand side) must be exactly
486 |         the same as the shape of ``z_src``.
487 |     axis: int (default -1)
488 |         The ``fz_src`` axis to perform the interpolation over.
489 |     rising: bool (default None)
490 |         Whether the values of the source's interpolation coordinate values
491 |         are generally rising or generally falling. For example, values of
492 |         pressure levels will be generally falling as the z coordinate
493 |         increases.
494 |         This will determine whether extrapolation needs to occur for
495 |         ``z_target`` below the first and above the last ``z_src``.
496 |         If rising is None, the first two interpolation coordinate values
497 |         will be used to determine the general direction. In most cases,
498 |         this is a good option.
499 |     interpolation: :class:`.Interpolator` instance or valid scheme name
500 |         The core interpolation operation to use. :attr:`.INTERPOLATE_LINEAR`
501 |         and :attr:`_INTERPOLATE_NEAREST` are provided for convenient
502 |         iterpolation modes. Linear interpolation is the default.
503 |     extrapolation: :class:`.Extrapolator` instance or valid scheme name
504 |         The core extrapolation operation to use. :attr:`.EXTRAPOLATE_NAN` and
505 |         :attr:`.EXTRAPOLATE_NEAREST` are provided for convenient extrapolation
506 |         modes. NaN extrapolation is the default.
507 | 
508 |     """
509 |     func = functools.partial(
510 |         _interpolate,
511 |         axis=axis,
512 |         rising=rising,
513 |         interpolation=interpolation,
514 |         extrapolation=extrapolation
515 |     )
516 |     if not hasattr(fz_src, 'compute'):
517 |         # Numpy array
518 |         return func(z_target, z_src, fz_src)
519 | 
520 |     # Dask array
521 |     import dask.array as da
522 | 
523 |     # Ensure z_target is an array.
524 |     if not isinstance(z_target, (np.ndarray, da.Array)):
525 |       z_target = np.array(z_target)
526 | 
527 |     # Compute chunk sizes
528 |     if axis < 0:
529 |       axis += fz_src.ndim
530 |     in_chunks = list(fz_src.chunks)
531 |     in_chunks[axis] = fz_src.shape[axis]
532 | 
533 |     out_chunks = list(in_chunks)
534 |     if z_target.ndim == 1:
535 |         out_chunks[axis] = z_target.shape[0]
536 |     else:
537 |         out_chunks[axis] = z_target.shape[axis]
538 | 
539 |     # Ensure `fz_src` is not chunked along `axis`.
540 |     fz_src = fz_src.rechunk(in_chunks)
541 | 
542 |     # Ensure z_src is a dask array with the correct chunks.
543 |     if isinstance(z_src, da.Array):
544 |         z_src = z_src.rechunk(in_chunks)
545 |     else:
546 |         z_src = da.asarray(z_src, chunks=in_chunks)
547 | 
548 |     # Compute with 1-dimensional target array.
549 |     if z_target.ndim == 1:
550 |         func = functools.partial(func, z_target)
551 |         return da.map_blocks(func, z_src, fz_src,
552 |                              chunks=out_chunks, dtype=fz_src.dtype,
553 |                              meta=np.array((), dtype=fz_src.dtype))
554 | 
555 |     # Ensure z_target is a dask array with the correct chunks
556 |     if isinstance(z_target, da.Array):
557 |        z_target = z_target.rechunk(out_chunks)
558 |     else:
559 |        z_target = da.asarray(z_target, chunks=out_chunks)
560 | 
561 |     # Compute with multi-dimensional target array.
562 |     return da.map_blocks(func, z_target, z_src, fz_src,
563 |                          chunks=out_chunks, dtype=fz_src.dtype,
564 |                          meta=np.array((), dtype=fz_src.dtype))
565 | 
566 | 
567 | def _interpolate(z_target, z_src, fz_src, axis=-1, rising=None,
568 |                  interpolation='linear', extrapolation='nan'):
569 |     if interpolation in interp_schemes:
570 |         interpolation = interp_schemes[interpolation]()
571 |     if extrapolation in extrap_schemes:
572 |         extrapolation = extrap_schemes[extrapolation]()
573 | 
574 |     interp = _Interpolation(z_target, z_src, fz_src, rising=rising, axis=axis,
575 |                             interpolation=interpolation,
576 |                             extrapolation=extrapolation)
577 |     if interp.z_target.ndim == 1:
578 |         return interp.interpolate()
579 |     else:
580 |         return interp.interpolate_z_target_nd()
581 | 
582 | 
583 | cdef class _Interpolation(object):
584 |     """
585 |     Where the magic happens for gridwise_interp. The work of this __init__ is
586 |     mostly for putting the input nd arrays into a 3 and 4 dimensional form for
587 |     convenient (read: efficient) Cython form. Inline comments should help with
588 |     understanding.
589 | 
590 |    """
591 |     cdef Interpolator interpolation
592 |     cdef Extrapolator extrapolation
593 | 
594 |     cdef public np.dtype _target_dtype
595 |     cdef int rising
596 |     cdef public z_target, orig_shape, axis, _zp_reshaped, _fp_reshaped
597 |     cdef public _result_working_shape, result_shape
598 | 
599 |     def __init__(self, z_target, z_src, fz_src, axis=-1,
600 |                  rising=None,
601 |                  Interpolator interpolation=INTERPOLATE_LINEAR,
602 |                  Extrapolator extrapolation=EXTRAPOLATE_NAN):
603 |         # Cast data to numpy arrays if not already.
604 |         z_target = np.array(z_target, dtype=np.float64)
605 |         z_src = np.array(z_src, dtype=np.float64)
606 |         fz_src = np.array(fz_src)
607 |         #: The result data dtype.
608 |         if np.issubdtype(fz_src.dtype, np.signedinteger):
609 |             self._target_dtype = np.dtype('f8')
610 |         else:
611 |             self._target_dtype = fz_src.dtype
612 |         fz_src = fz_src.astype(np.float64)
613 | 
614 |         # Compute the axis in absolute terms.
615 |         fp_axis = (axis + fz_src.ndim) % fz_src.ndim
616 |         zp_axis = fp_axis - (fz_src.ndim - z_src.ndim)
617 |         if (not 0 <= zp_axis < z_src.ndim) or (axis >= fz_src.ndim):
618 |             raise ValueError('Axis {} out of range.'.format(axis))
619 | 
620 |         # Ensure that fz_src's shape is a superset of z_src's.
621 |         if z_src.shape != fz_src.shape[-z_src.ndim:]:
622 |             emsg = 'Shape for z_src {} is not a subset of fz_src {}.'
623 |             raise ValueError(emsg.format(z_src.shape, fz_src.shape))
624 | 
625 |         if z_target.ndim == 1:
626 |             z_target_size = z_target.shape[0]
627 |         else:
628 |             # Ensure z_target and z_src have same ndims.
629 |             if z_target.ndim != z_src.ndim:
630 |                 emsg = ('z_target and z_src must have the same number '
631 |                         'of dimensions, got {} != {}.')
632 |                 raise ValueError(emsg.format(z_target.ndim, z_src.ndim))
633 |             # Ensure z_target and z_src have same shape over their
634 |             # non-interpolated axes i.e. we need to ignore the axis of
635 |             # interpolation when comparing the shapes of z_target and z_src.
636 |             # E.g a z_target.shape=(3, 4, 5) and z_src.shape=(3, 10, 5),
637 |             # interpolating over zp_axis=1 is fine as (3, :, 5) == (3, :, 5).
638 |             # However, a z_target.shape=(3, 4, 6) and z_src.shape=(3, 10, 5),
639 |             # interpolating over zp_axis=1 must fail as (3, :, 6) != (3, :, 5)
640 |             zts, zss = z_target.shape, z_src.shape
641 |             ztsp, zssp = zip(*[(str(j), str(k)) if i!=zp_axis else (':', ':')
642 |                                for i, (j, k) in enumerate(zip(zts, zss))])
643 |             if ztsp != zssp:
644 |                 sep, emsg = ', ', ('z_target and z_src have different shapes, '
645 |                                    'got ({}) != ({}).')
646 |                 raise ValueError(emsg.format(sep.join(ztsp), sep.join(zssp)))
647 |             z_target_size = zts[zp_axis]
648 | 
649 |         # We are going to put the source coordinate into a 3d shape for convenience of
650 |         # Cython interface. Writing generic, fast, n-dimensional Cython code
651 |         # is not possible, but it is possible to always support a 3d array with
652 |         # the middle dimensions being from the axis argument.
653 | 
654 |         # Work out the shape of the left hand side of the 3d array.
655 |         lh_dim_size = ([1] + list(np.cumprod(z_src.shape)))[zp_axis]
656 | 
657 |         # The coordinate shape will therefore be (size of lhs, size of axis, the rest).
658 |         new_shape = (lh_dim_size, z_src.shape[zp_axis], -1)
659 | 
660 |         #: The levels to interpolate onto.
661 |         self.z_target = z_target
662 |         #: The shape of the input data (fz_src).
663 |         self.orig_shape = fz_src.shape
664 |         #: The fz_src axis over which to do the interpolation.
665 |         self.axis = axis
666 | 
667 |         #: The source z coordinate data reshaped into 3d working shape form.
668 |         self._zp_reshaped = z_src.reshape(new_shape)
669 |         #: The fz_src data reshaped into 4d working shape form. The left-most
670 |         #: dimension is the dimension broadcast dimension - these are the
671 |         #: values which are not dependent on z, and come from the fact that
672 |         #: fz_src may be higher dimensional than z_src.
673 |         self._fp_reshaped = fz_src.reshape((-1, ) + self._zp_reshaped.shape)
674 | 
675 |         # Figure out the normalised 4d shape of the working result array.
676 |         # This will be the same as _fp_reshaped, but the length of the
677 |         # interpolation axis will change.
678 |         result_working_shape = list(self._fp_reshaped.shape)
679 |         # Update the axis to be of the size of the levels.
680 |         result_working_shape[2] = z_target_size
681 | 
682 |         #: The shape of result while the interpolation is being calculated.
683 |         self._result_working_shape = tuple(result_working_shape)
684 | 
685 |         # Figure out the nd shape of the result array.
686 |         # This will be the same as fz_src, but the length of the
687 |         # interpolation axis will change.
688 |         result_shape = list(self.orig_shape)
689 |         # Update the axis to be of the size of the levels.
690 |         result_shape[fp_axis] = z_target_size
691 | 
692 |         #: The shape of the interpolated data.
693 |         self.result_shape = tuple(result_shape)
694 | 
695 |         if rising is None:
696 |             if z_src.shape[zp_axis] < 2:
697 |                 raise ValueError('The rising keyword must be defined when '
698 |                                  'the size of the source array is <2 in '
699 |                                  'the interpolation axis.')
700 |             z_src_indexer = [0] * z_src.ndim
701 |             z_src_indexer[zp_axis] = slice(0, 2)
702 |             first_two = z_src[tuple(z_src_indexer)]
703 |             rising = first_two[0] <= first_two[1]
704 | 
705 |         self.rising = bool(rising)
706 | 
707 |         # Sometimes we want to add additional constraints on our interpolation
708 |         # and extrapolation - for example, linear extrapolation requires there
709 |         # to be two coordinates to interpolate from.
710 |         if hasattr(self.interpolation, 'validate_data'):
711 |             self.interpolation.validate_data(self)
712 | 
713 |         if hasattr(self.extrapolation, 'validate_data'):
714 |             self.extrapolation.validate_data(self)
715 | 
716 |         self.interpolation = interpolation
717 |         self.extrapolation = extrapolation
718 | 
719 |     def interpolate(self):
720 |         # Construct the output array for the interpolation to fill in.
721 |         fz_target = np.empty(self._result_working_shape, dtype=np.float64)
722 | 
723 |         cdef unsigned int i, j, ni, nj
724 | 
725 |         ni = fz_target.shape[1]
726 |         nj = fz_target.shape[3]
727 | 
728 |         # Pull in our pre-formed z_target, z_src, and fz_src arrays.
729 |         cdef double[:] z_target = self.z_target
730 |         cdef double[:, :, :] z_src = self._zp_reshaped
731 |         cdef double[:, :, :, :] fz_src = self._fp_reshaped
732 | 
733 |         # Construct a memory view of the fz_target array.
734 |         cdef double[:, :, :, :] fz_target_view = fz_target
735 | 
736 |         # Release the GIL and do the for loop over the left-hand, and
737 |         # right-hand dimensions. The loop optimised for row-major data (C).
738 |         with nogil:
739 |             for j in range(nj):
740 |                 for i in range(ni):
741 |                     gridwise_interpolation(z_target, z_src[i, :, j], fz_src[:, i, :, j],
742 |                                            self.rising,
743 |                                            self.interpolation,
744 |                                            self.extrapolation,
745 |                                            fz_target_view[:, i, :, j])
746 |         return fz_target.reshape(self.result_shape).astype(self._target_dtype)
747 | 
748 |     def interpolate_z_target_nd(self):
749 |         """
750 |         Using the exact same functionality as found in interpolate, only without the assumption
751 |         that the target z is 1d.
752 | 
753 |         """
754 |         # Construct the output array for the interpolation to fill in.
755 |         fz_target = np.empty(self._result_working_shape, dtype=np.float64)
756 | 
757 |         cdef unsigned int i, j, ni, nj
758 | 
759 |         ni = fz_target.shape[1]
760 |         nj = fz_target.shape[3]
761 | 
762 |         z_target_reshaped = self.z_target.reshape(self._result_working_shape[1:])
763 |         cdef double[:, :, :] z_target = z_target_reshaped
764 | 
765 |         # Pull in our pre-formed z_src, and fz_src arrays.
766 |         cdef double[:, :, :] z_src = self._zp_reshaped
767 |         cdef double[:, :, :, :] fz_src = self._fp_reshaped
768 | 
769 |         # Construct a memory view of the fz_target array.
770 |         cdef double[:, :, :, :] fz_target_view = fz_target
771 | 
772 |         # Release the GIL and do the for loop over the left-hand, and
773 |         # right-hand dimensions. The loop optimised for row-major data (C).
774 |         with nogil:
775 |             for j in range(nj):
776 |                 for i in range(ni):
777 |                     gridwise_interpolation(z_target[i, :, j], z_src[i, :, j], fz_src[:, i, :, j],
778 |                                            self.rising,
779 |                                            self.interpolation,
780 |                                            self.extrapolation,
781 |                                            fz_target_view[:, i, :, j])
782 | 
783 |         return fz_target.reshape(self.result_shape).astype(self._target_dtype)
784 | 


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/src/stratify/tests/__init__.py:
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https://raw.githubusercontent.com/SciTools/python-stratify/b09c836f1667d6c05258b42fbba64e2fbf1bd687/src/stratify/tests/__init__.py


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/src/stratify/tests/performance.py:
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 1 | """Functions that may be used to measure performance of a component."""
 2 | 
 3 | import dask.array as da
 4 | import numpy as np
 5 | 
 6 | import stratify
 7 | 
 8 | 
 9 | def src_data(shape=(400, 500, 100), lazy=False):
10 |     z = np.tile(np.linspace(0, 100, shape[-1]), np.prod(shape[:2])).reshape(shape)
11 |     if lazy:
12 |         fz = da.arange(np.prod(shape), dtype=np.float64).reshape(shape)
13 |     else:
14 |         fz = np.arange(np.prod(shape), dtype=np.float64).reshape(shape)
15 |     return z, fz
16 | 
17 | 
18 | def interp_and_extrap(
19 |     shape,
20 |     lazy,
21 |     interp=stratify.INTERPOLATE_LINEAR,
22 |     extrap=stratify.EXTRAPOLATE_NEAREST,
23 | ):
24 |     z, fz = src_data(shape, lazy)
25 |     tgt = np.linspace(-20, 120, 50)
26 |     result = stratify.interpolate(
27 |         tgt,
28 |         z,
29 |         fz,
30 |         interpolation=interp,
31 |         extrapolation=extrap,
32 |     )
33 |     if isinstance(result, da.Array):
34 |         print("lazy calculation")
35 |         print(result.chunks)
36 |         result.compute()
37 |     else:
38 |         print("non-lazy calculation")
39 | 
40 | 
41 | if __name__ == "__main__":
42 |     import sys
43 | 
44 |     lazy = "lazy" in sys.argv[1:]
45 |     interp_and_extrap(shape=(500, 600, 100), lazy=lazy)
46 | 


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/src/stratify/tests/test_bounded_vinterp.py:
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  1 | import unittest
  2 | 
  3 | import numpy as np
  4 | from numpy.testing import assert_array_equal
  5 | 
  6 | import stratify._bounded_vinterp as bounded_vinterp
  7 | 
  8 | 
  9 | class Test1D(unittest.TestCase):
 10 |     # 1D cases represent the situation where the vertical coordinate is
 11 |     # 1-dimensional and as such, there are no dimension which the vertical
 12 |     # coordinates vary over other than the 'axis of interpolation'.
 13 |     # The common approach in solving these problems is to reshape and
 14 |     # transpose arrays into the form:
 15 |     # [broadcasting_dims, axis_interpolation, z_varying].
 16 |     # This this will take the form:
 17 |     # [broadcasting_dims, axis_interpolation, 1].
 18 |     def setUp(self):
 19 |         self.data = np.ones((4, 6))
 20 |         self.bounds = self.gen_bounds(0, 7, 1)
 21 | 
 22 |     def gen_bounds(self, start, stop, step):
 23 |         bounds = np.vstack(
 24 |             [
 25 |                 np.arange(start, stop - step, step),
 26 |                 np.arange(start + step, stop, step),
 27 |             ]
 28 |         )
 29 |         bounds = bounds.transpose((1, 0))
 30 |         return bounds.copy()
 31 | 
 32 |     def test_target_half_resolution(self):
 33 |         target_bounds = self.gen_bounds(0, 7, 2)
 34 |         res = bounded_vinterp.interpolate_conservative(
 35 |             target_bounds, self.bounds, self.data, axis=1
 36 |         )
 37 |         target_data = np.ones((4, 3)) * 2
 38 |         assert_array_equal(res, target_data)
 39 | 
 40 |     def test_target_double_resolution(self):
 41 |         target_bounds = self.gen_bounds(0, 6.5, 0.5)
 42 |         res = bounded_vinterp.interpolate_conservative(
 43 |             target_bounds, self.bounds, self.data, axis=1
 44 |         )
 45 |         target_data = np.ones((4, 12)) * 0.5
 46 |         assert_array_equal(res, target_data)
 47 | 
 48 |     def test_no_broadcasting(self):
 49 |         # In the case of no broadcasting, transposed and reshaped array of form
 50 |         # [broadcasting_dims, axis_interpolation, z_varying] should end up
 51 |         # [1, axis_interpolation, 1].
 52 |         data = self.data[0]
 53 |         target_bounds = self.gen_bounds(0, 7, 2)
 54 |         res = bounded_vinterp.interpolate_conservative(
 55 |             target_bounds, self.bounds, data, axis=0
 56 |         )
 57 |         target_data = np.ones(3) * 2
 58 |         assert_array_equal(res, target_data)
 59 | 
 60 |     def test_source_with_nans(self):
 61 |         # In the case of no broadcasting, transposed and reshaped array of form
 62 |         # [broadcasting_dims, axis_interpolation, z_varying] should end up
 63 |         # [1, axis_interpolation, 1].
 64 |         data = self.data[0]
 65 |         data[0] = data[-2:] = np.nan
 66 |         target_bounds = self.gen_bounds(0, 6.5, 0.5)
 67 |         res = bounded_vinterp.interpolate_conservative(
 68 |             target_bounds, self.bounds, data, axis=0
 69 |         )
 70 |         target_data = np.ones(12) * 0.5
 71 |         target_data[:2] = np.nan
 72 |         target_data[-4:] = np.nan
 73 |         assert_array_equal(res, target_data)
 74 | 
 75 |     def test_target_extends_above_source(self):
 76 |         # |-|-|-|-|-|-|-|   - Source
 77 |         # |-|-|-|-|-|-|-|-| - Target
 78 |         source_bounds = self.gen_bounds(0, 7, 1)
 79 |         target_bounds = self.gen_bounds(0, 8, 1)
 80 |         res = bounded_vinterp.interpolate_conservative(
 81 |             target_bounds, source_bounds, self.data, axis=1
 82 |         )
 83 |         target_data = np.ones((4, 7))
 84 |         target_data[:, -1] = np.nan
 85 |         assert_array_equal(res, target_data)
 86 | 
 87 |     def test_target_extends_above_source_non_equally_spaced_coords(self):
 88 |         # |--|--|-------||  - Source
 89 |         # |-|-|-|-|-|-|-|-| - Target
 90 |         source_bounds = np.array([[0, 1.5], [1.5, 2], [2, 6], [6, 6.5]])
 91 |         target_bounds = self.gen_bounds(0, 8, 1)
 92 |         data = np.ones((4, 4))
 93 |         res = bounded_vinterp.interpolate_conservative(
 94 |             target_bounds, source_bounds, data, axis=1
 95 |         )
 96 |         target_data = np.array(
 97 |             [1 / 1.5, 1 + ((1 / 3.0) / 1), 0.25, 0.25, 0.25, 0.25, 1.0]
 98 |         )[None]
 99 |         target_data = np.repeat(target_data, 4, 0)
100 |         assert_array_equal(res, target_data)
101 | 
102 |     def test_target_extends_below_source(self):
103 |         #   |-|-|-|-|-|-|-|   - Source
104 |         # |-|-|-|-|-|-|-|-|   - Target
105 |         source_bounds = self.gen_bounds(0, 7, 1)
106 |         target_bounds = self.gen_bounds(-1, 7, 1)
107 |         res = bounded_vinterp.interpolate_conservative(
108 |             target_bounds, source_bounds, self.data, axis=1
109 |         )
110 |         target_data = np.ones((4, 7))
111 |         target_data[:, 0] = np.nan
112 |         assert_array_equal(res, target_data)
113 | 
114 | 
115 | class TestND(unittest.TestCase):
116 |     # ND cases represent the situation where the vertical coordinate varies
117 |     # over dimensions other than the axis of interpolation.
118 |     def setUp(self):
119 |         self.data = np.ones((2, 6, 4, 3))
120 |         self.bounds = self.gen_bounds(0, 7, 1)
121 | 
122 |     def gen_bounds(self, start, stop, step):
123 |         bounds = np.vstack(
124 |             [
125 |                 np.arange(start, stop - step, step),
126 |                 np.arange(start + step, stop, step),
127 |             ]
128 |         )
129 |         bounds = bounds.transpose((1, 0))
130 |         bounds = bounds[..., None, :].repeat(4, -2)
131 |         bounds = bounds[..., None, :].repeat(3, -2)
132 |         return bounds.copy()
133 | 
134 |     def test_target_half_resolution(self):
135 |         target_bounds = self.gen_bounds(0, 7, 2)
136 |         res = bounded_vinterp.interpolate_conservative(
137 |             target_bounds, self.bounds, self.data, axis=1
138 |         )
139 | 
140 |         target_data = np.ones((2, 3, 4, 3)) * 2
141 |         assert_array_equal(res, target_data)
142 | 
143 |     def test_target_half_resolution_alt_axis(self):
144 |         # Ensure results as expected with an alternative axis of interpolation.
145 |         data = self.data.transpose((0, 2, 1, 3))
146 |         bounds = self.bounds.transpose((1, 0, 2, 3))
147 |         target_bounds = self.gen_bounds(0, 7, 2)
148 |         target_bounds = target_bounds.transpose((1, 0, 2, 3))
149 | 
150 |         res = bounded_vinterp.interpolate_conservative(
151 |             target_bounds, bounds, data, axis=2
152 |         )
153 |         target_data = np.ones((2, 4, 3, 3)) * 2
154 |         assert_array_equal(res, target_data)
155 | 
156 |     def test_target_double_resolution(self):
157 |         target_bounds = self.gen_bounds(0, 6.5, 0.5)
158 |         res = bounded_vinterp.interpolate_conservative(
159 |             target_bounds, self.bounds, self.data, axis=1
160 |         )
161 |         target_data = np.ones((2, 12, 4, 3)) * 0.5
162 |         assert_array_equal(res, target_data)
163 | 
164 | 
165 | class TestExceptions(unittest.TestCase):
166 |     def test_mismatch_source_target_level_dimensionality(self):
167 |         source_bounds = np.zeros((3, 4, 2))
168 |         target_bounds = np.zeros((4, 2))
169 |         data = np.zeros((3, 4))
170 | 
171 |         msg = "Expecting source and target levels dimensionality"
172 |         with self.assertRaisesRegex(ValueError, msg):
173 |             bounded_vinterp.interpolate_conservative(target_bounds, source_bounds, data)
174 | 
175 |     def test_mismatch_source_target_level_shape(self):
176 |         # The source and target levels should have identical shape, other than
177 |         # the axis of interpolation.
178 |         source_bounds = np.zeros((3, 4, 2))
179 |         target_bounds = np.zeros((2, 5, 2))
180 |         data = np.zeros((3, 4))
181 | 
182 |         msg = (
183 |             "Expecting the shape of the source and target levels except "
184 |             "the axis of interpolation to be identical.  "
185 |             r"\('-', 4, 2\) != \(2, 5, 2\)"
186 |         )
187 |         with self.assertRaisesRegex(ValueError, msg):
188 |             bounded_vinterp.interpolate_conservative(
189 |                 target_bounds, source_bounds, data, axis=0
190 |             )
191 | 
192 |     def test_mismatch_between_source_levels_source_data(self):
193 |         # The source levels should reflect the shape of the data.
194 |         source_bounds = np.zeros((2, 4, 2))
195 |         target_bounds = np.zeros((2, 4, 2))
196 |         data = np.zeros((3, 4))
197 | 
198 |         msg = (
199 |             "The provided data is not of compatible shape with the "
200 |             r"provided source bounds. \('-', 3, 4\) != \(2, 4\)"
201 |         )
202 |         with self.assertRaisesRegex(ValueError, msg):
203 |             bounded_vinterp.interpolate_conservative(
204 |                 target_bounds, source_bounds, data, axis=0
205 |             )
206 | 
207 |     def test_unexpected_bounds_shape(self):
208 |         # Expecting bounds of size 2 (that is the upper and lower).
209 |         # The source levels should reflect the shape of the data.
210 |         source_bounds = np.zeros((3, 4, 4))
211 |         target_bounds = np.zeros((4, 4, 4))
212 |         data = np.zeros((3, 4))
213 | 
214 |         msg = r"Unexpected source and target bounds shape. shape\[-1\] != 2"
215 |         with self.assertRaisesRegex(ValueError, msg):
216 |             bounded_vinterp.interpolate_conservative(
217 |                 target_bounds, source_bounds, data, axis=0
218 |             )
219 | 
220 |     def test_not_conservative(self):
221 |         # Where the target does not cover the full extent of the source.
222 |         # |-|-|-|-|-|-|  - Source
223 |         #   |-|-|-|-|    - Target
224 |         def gen_bounds(start, stop, step):
225 |             bounds = np.vstack(
226 |                 [
227 |                     np.arange(start, stop - step, step),
228 |                     np.arange(start + step, stop, step),
229 |                 ]
230 |             )
231 |             bounds = bounds.transpose((1, 0))
232 |             return bounds.copy()
233 | 
234 |         source_bounds = gen_bounds(0, 7, 1)
235 |         target_bounds = gen_bounds(1, 6, 1)
236 |         data = np.ones((4, 6))
237 | 
238 |         msg = "Weights calculation yields a less than conservative result."
239 |         with self.assertRaisesRegex(ValueError, msg):
240 |             bounded_vinterp.interpolate_conservative(
241 |                 target_bounds, source_bounds, data, axis=1
242 |             )
243 | 
244 | 
245 | if __name__ == "__main__":
246 |     unittest.main()
247 | 


--------------------------------------------------------------------------------
/src/stratify/tests/test_vinterp.py:
--------------------------------------------------------------------------------
  1 | import unittest
  2 | 
  3 | import dask.array as da
  4 | import numpy as np
  5 | from numpy.testing import assert_array_almost_equal, assert_array_equal
  6 | 
  7 | import stratify
  8 | import stratify._vinterp as vinterp
  9 | 
 10 | 
 11 | class IndexInterpolator(vinterp.PyFuncInterpolator):
 12 |     def interp_kernel(self, index, z_src, fz_src, level, output_array):
 13 |         output_array[:] = index
 14 | 
 15 | 
 16 | class DirectionExtrapolator(vinterp.PyFuncExtrapolator):
 17 |     def extrap_kernel(self, direction, z_src, fz_src, level, output_array):
 18 |         output_array[:] = np.inf if direction > 0 else -np.inf
 19 | 
 20 | 
 21 | class TestColumnInterpolation(unittest.TestCase):
 22 |     def interpolate(self, x_target, x_src, rising=None):
 23 |         x_target = np.array(x_target)
 24 |         x_src = np.array(x_src)
 25 |         fx_src = np.empty(x_src.shape)
 26 | 
 27 |         index_interp = IndexInterpolator()
 28 |         extrap_direct = DirectionExtrapolator()
 29 | 
 30 |         r1 = stratify.interpolate(
 31 |             x_target,
 32 |             x_src,
 33 |             fx_src,
 34 |             rising=rising,
 35 |             interpolation=index_interp,
 36 |             extrapolation=extrap_direct,
 37 |         )
 38 | 
 39 |         if rising is not None:
 40 |             r2 = stratify.interpolate(
 41 |                 -1 * x_target,
 42 |                 -1 * x_src,
 43 |                 fx_src,
 44 |                 rising=not rising,
 45 |                 interpolation=index_interp,
 46 |                 extrapolation=extrap_direct,
 47 |             )
 48 |             assert_array_equal(r1, r2)
 49 | 
 50 |         lazy_fx_src = da.asarray(fx_src, chunks=tuple(range(1, x_src.ndim + 1)))
 51 |         r3 = stratify.interpolate(
 52 |             x_target,
 53 |             x_src,
 54 |             lazy_fx_src,
 55 |             rising=rising,
 56 |             interpolation=index_interp,
 57 |             extrapolation=extrap_direct,
 58 |         )
 59 |         assert_array_equal(r1, r3.compute())
 60 | 
 61 |         return r1
 62 | 
 63 |     def test_interp_only(self):
 64 |         r = self.interpolate([1, 2, 3], [1, 3])
 65 |         assert_array_equal(r, [0, 1, 1])
 66 | 
 67 |     def test_interp_multi_level_single_source(self):
 68 |         r = self.interpolate([1.5, 2, 2.5], [1, 3])
 69 |         assert_array_equal(r, [1, 1, 1])
 70 | 
 71 |     def test_interp_single_level_multiple_source(self):
 72 |         r = self.interpolate([3.5], [1, 2, 3, 3, 4])
 73 |         assert_array_equal(r, [4])
 74 | 
 75 |     def test_lower_extrap_only(self):
 76 |         r = self.interpolate([1, 2, 3], [4, 5])
 77 |         assert_array_equal(r, [-np.inf, -np.inf, -np.inf])
 78 | 
 79 |     def test_upper_extrap_only(self):
 80 |         r = self.interpolate([1, 2, 3], [-4, -5], rising=True)
 81 |         assert_array_equal(r, [np.inf, np.inf, np.inf])
 82 | 
 83 |     def test_extrap_on_both_sides_only(self):
 84 |         r = self.interpolate([1, 2, 5, 6], [3, 4])
 85 |         assert_array_equal(r, [-np.inf, -np.inf, np.inf, np.inf])
 86 | 
 87 |     def test_interp_and_extrap(self):
 88 |         r = self.interpolate([1, 2, 3, 5, 6], [2, 4, 5])
 89 |         assert_array_equal(r, [-np.inf, 0, 1, 2, np.inf])
 90 | 
 91 |     def test_nan_in_target(self):
 92 |         msg = "The target coordinate .* NaN"
 93 |         with self.assertRaisesRegex(ValueError, msg):
 94 |             self.interpolate([1, np.nan], [2, 4, 5])
 95 | 
 96 |     def test_nan_in_src(self):
 97 |         msg = "The source coordinate .* NaN"
 98 |         with self.assertRaisesRegex(ValueError, msg):
 99 |             self.interpolate([1], [0, np.nan], rising=True)
100 | 
101 |     def test_all_nan_in_src(self):
102 |         r = self.interpolate([1, 2, 3, 4], [np.nan, np.nan, np.nan])
103 |         assert_array_equal(r, [np.nan, np.nan, np.nan, np.nan])
104 | 
105 |     def test_nan_in_src_not_a_problem(self):
106 |         # If we pick levels low enough, we can get away with having NaNs
107 |         # in the source.
108 |         r = self.interpolate([1, 3], [2, 4, np.nan])
109 |         assert_array_equal(r, [-np.inf, 1])
110 | 
111 |     def test_no_levels(self):
112 |         r = self.interpolate([], [2, 4, np.nan])
113 |         assert_array_equal(r, [])
114 | 
115 |     def test_wrong_rising_target(self):
116 |         r = self.interpolate([2, 1], [1, 2])
117 |         assert_array_equal(r, [1, np.inf])
118 | 
119 |     def test_wrong_rising_source(self):
120 |         r = self.interpolate([1, 2], [2, 1], rising=True)
121 |         assert_array_equal(r, [-np.inf, 0])
122 | 
123 |     def test_wrong_rising_source_and_target(self):
124 |         # If we overshoot the first level, there is no hope,
125 |         # so we end up extrapolating.
126 |         r = self.interpolate([3, 2, 1, 0], [2, 1], rising=True)
127 |         assert_array_equal(r, [np.inf, np.inf, np.inf, np.inf])
128 | 
129 |     def test_non_monotonic_coordinate_interp(self):
130 |         result = self.interpolate([15, 5, 15.0], [10.0, 20, 0, 20])
131 |         assert_array_equal(result, [1, 2, 3])
132 | 
133 |     def test_non_monotonic_coordinate_extrap(self):
134 |         result = self.interpolate([0, 15, 16, 17, 5, 15.0, 25], [10.0, 40, 0, 20])
135 |         assert_array_equal(result, [-np.inf, 1, 1, 1, 2, 3, np.inf])
136 | 
137 |     def test_length_one_interp(self):
138 |         r = self.interpolate([1], [2], rising=True)
139 |         assert_array_equal(r, [-np.inf])
140 | 
141 |     def test_auto_rising_not_enough_values(self):
142 |         with self.assertRaises(ValueError):
143 |             _ = self.interpolate([1], [2])
144 | 
145 |     def test_auto_rising_equal_values(self):
146 |         # The code checks whether the first value is <= or equal to
147 |         # the second. If it didn't, we'd end up with +inf, not -inf.
148 |         r = self.interpolate([1], [2, 2])
149 |         assert_array_equal(r, [-np.inf])
150 | 
151 | 
152 | class Test_INTERPOLATE_LINEAR(unittest.TestCase):
153 |     def interpolate(self, x_target):
154 |         interpolation = stratify.INTERPOLATE_LINEAR
155 |         extrapolation = DirectionExtrapolator()
156 | 
157 |         x_src = np.arange(5)
158 |         fx_src = 10 * x_src
159 | 
160 |         # Use -2 to test negative number support.
161 |         return stratify.interpolate(
162 |             np.array(x_target) - 2,
163 |             x_src - 2,
164 |             fx_src,
165 |             interpolation=interpolation,
166 |             extrapolation=extrapolation,
167 |         )
168 | 
169 |     def test_on_the_mark(self):
170 |         assert_array_equal(self.interpolate([0, 1, 2, 3, 4]), [0, 10, 20, 30, 40])
171 | 
172 |     def test_zero_gradient(self):
173 |         assert_array_equal(
174 |             stratify.interpolate(
175 |                 [1], [0, 1, 1, 2], [10, 20, 30, 40], interpolation="linear"
176 |             ),
177 |             [20],
178 |         )
179 | 
180 |     def test_inbetween(self):
181 |         assert_array_equal(
182 |             self.interpolate([0.5, 1.25, 2.5, 3.75]), [5, 12.5, 25, 37.5]
183 |         )
184 | 
185 |     def test_high_precision(self):
186 |         assert_array_almost_equal(
187 |             self.interpolate([1.123456789]), [11.23456789], decimal=6
188 |         )
189 | 
190 |     def test_single_point(self):
191 |         # Test that a single input point that falls exactly on the target
192 |         # level triggers a shortcut that avoids the expectation of >=2 source
193 |         # points.
194 |         interpolation = stratify.INTERPOLATE_LINEAR
195 |         extrapolation = DirectionExtrapolator()
196 | 
197 |         r = stratify.interpolate(
198 |             [2],
199 |             [2],
200 |             [20],
201 |             interpolation=interpolation,
202 |             extrapolation=extrapolation,
203 |             rising=True,
204 |         )
205 |         self.assertEqual(r, 20)
206 | 
207 | 
208 | class Test_INTERPOLATE_NEAREST(unittest.TestCase):
209 |     def interpolate(self, x_target):
210 |         interpolation = stratify.INTERPOLATE_NEAREST
211 |         extrapolation = DirectionExtrapolator()
212 | 
213 |         x_src = np.arange(5)
214 |         fx_src = 10 * x_src
215 | 
216 |         # Use -2 to test negative number support.
217 |         return stratify.interpolate(
218 |             np.array(x_target) - 2,
219 |             x_src - 2,
220 |             fx_src,
221 |             interpolation=interpolation,
222 |             extrapolation=extrapolation,
223 |         )
224 | 
225 |     def test_on_the_mark(self):
226 |         assert_array_equal(self.interpolate([0, 1, 2, 3, 4]), [0, 10, 20, 30, 40])
227 | 
228 |     def test_inbetween(self):
229 |         # Nearest rounds down for exactly half way.
230 |         assert_array_equal(self.interpolate([0.5, 1.25, 2.5, 3.75]), [0, 10, 20, 40])
231 | 
232 |     def test_high_precision(self):
233 |         assert_array_equal(self.interpolate([1.123456789]), [10])
234 | 
235 | 
236 | class Test_EXTRAPOLATE_NAN(unittest.TestCase):
237 |     def interpolate(self, x_target):
238 |         interpolation = IndexInterpolator()
239 |         extrapolation = stratify.EXTRAPOLATE_NAN
240 | 
241 |         x_src = np.arange(5)
242 |         fx_src = 10 * x_src
243 | 
244 |         # Use -2 to test negative number support.
245 |         return stratify.interpolate(
246 |             np.array(x_target) - 2,
247 |             x_src - 2,
248 |             fx_src,
249 |             interpolation=interpolation,
250 |             extrapolation=extrapolation,
251 |         )
252 | 
253 |     def test_below(self):
254 |         assert_array_equal(self.interpolate([-1]), [np.nan])
255 | 
256 |     def test_above(self):
257 |         assert_array_equal(self.interpolate([5]), [np.nan])
258 | 
259 | 
260 | class Test_EXTRAPOLATE_NEAREST(unittest.TestCase):
261 |     def interpolate(self, x_target):
262 |         interpolation = IndexInterpolator()
263 |         extrapolation = stratify.EXTRAPOLATE_NEAREST
264 | 
265 |         x_src = np.arange(5)
266 |         fx_src = 10 * x_src
267 | 
268 |         # Use -2 to test negative number support.
269 |         return stratify.interpolate(
270 |             np.array(x_target) - 2,
271 |             x_src - 2,
272 |             fx_src,
273 |             interpolation=interpolation,
274 |             extrapolation=extrapolation,
275 |         )
276 | 
277 |     def test_below(self):
278 |         assert_array_equal(self.interpolate([-1]), [0.0])
279 | 
280 |     def test_above(self):
281 |         assert_array_equal(self.interpolate([5]), [40])
282 | 
283 | 
284 | class Test_EXTRAPOLATE_LINEAR(unittest.TestCase):
285 |     def interpolate(self, x_target):
286 |         interpolation = IndexInterpolator()
287 |         extrapolation = stratify.EXTRAPOLATE_LINEAR
288 | 
289 |         x_src = np.arange(5)
290 |         # To spice things up a bit, let's make x_src non-equal distance.
291 |         x_src[4] = 9
292 |         fx_src = 10 * x_src
293 | 
294 |         # Use -2 to test negative number support.
295 |         return stratify.interpolate(
296 |             np.array(x_target) - 2,
297 |             x_src - 2,
298 |             fx_src,
299 |             interpolation=interpolation,
300 |             extrapolation=extrapolation,
301 |         )
302 | 
303 |     def test_below(self):
304 |         assert_array_equal(self.interpolate([-1]), [-10.0])
305 | 
306 |     def test_above(self):
307 |         assert_array_almost_equal(self.interpolate([15.123]), [151.23])
308 | 
309 |     def test_zero_gradient(self):
310 |         assert_array_almost_equal(
311 |             stratify.interpolate([2], [0, 0], [1, 1], extrapolation="linear"),
312 |             [1],
313 |         )
314 | 
315 |     def test_npts(self):
316 |         interpolation = IndexInterpolator()
317 |         extrapolation = stratify.EXTRAPOLATE_LINEAR
318 | 
319 |         msg = r"Linear extrapolation requires at least 2 " r"source points. Got 1."
320 |         with self.assertRaisesRegex(ValueError, msg):
321 |             stratify.interpolate(
322 |                 [1, 3.0],
323 |                 [2],
324 |                 [20],
325 |                 interpolation=interpolation,
326 |                 extrapolation=extrapolation,
327 |                 rising=True,
328 |             )
329 | 
330 | 
331 | class Test_custom_extrap_kernel(unittest.TestCase):
332 |     class my_kernel(vinterp.PyFuncExtrapolator):
333 |         def __init__(self, *args, **kwargs):
334 |             super(Test_custom_extrap_kernel.my_kernel, self).__init__(*args, **kwargs)
335 | 
336 |         def extrap_kernel(self, direction, z_src, fz_src, level, output_array):
337 |             output_array[:] = -10
338 | 
339 |     def test(self):
340 |         interpolation = IndexInterpolator()
341 |         extrapolation = Test_custom_extrap_kernel.my_kernel()
342 | 
343 |         r = stratify.interpolate(
344 |             [1, 3.0],
345 |             [1, 2],
346 |             [10, 20],
347 |             interpolation=interpolation,
348 |             extrapolation=extrapolation,
349 |             rising=True,
350 |         )
351 |         assert_array_equal(r, [0, -10])
352 | 
353 | 
354 | class Test_Interpolation(unittest.TestCase):
355 |     def test_axis_m1(self):
356 |         data = np.empty([5, 4, 23, 7, 3])
357 |         zdata = np.empty([5, 4, 23, 7, 3])
358 |         i = vinterp._Interpolation([1, 3], zdata, data)
359 |         # 1288 == 5 * 4 * 23 * 7
360 |         self.assertEqual(i._result_working_shape, (1, 3220, 2, 1))
361 |         self.assertEqual(i.result_shape, (5, 4, 23, 7, 2))
362 |         self.assertEqual(i._zp_reshaped.shape, (3220, 3, 1))
363 |         self.assertEqual(i._fp_reshaped.shape, (1, 3220, 3, 1))
364 |         self.assertEqual(i.axis, -1)
365 |         self.assertEqual(i.orig_shape, data.shape)
366 |         self.assertIsInstance(i.z_target, np.ndarray)
367 |         self.assertEqual(list(i.z_target), [1, 3])
368 | 
369 |     def test_axis_0(self):
370 |         data = zdata = np.empty([5, 4, 23, 7, 3])
371 |         i = vinterp._Interpolation([1, 3], data, zdata, axis=0)
372 |         # 1932 == 4 * 23 * 7 *3
373 |         self.assertEqual(i._result_working_shape, (1, 1, 2, 1932))
374 |         self.assertEqual(i.result_shape, (2, 4, 23, 7, 3))
375 |         self.assertEqual(i._zp_reshaped.shape, (1, 5, 1932))
376 | 
377 |     def test_axis_2(self):
378 |         data = zdata = np.empty([5, 4, 23, 7, 3])
379 |         i = vinterp._Interpolation([1, 3], data, zdata, axis=2)
380 |         # 1932 == 4 * 23 * 7 *3
381 |         self.assertEqual(i._result_working_shape, (1, 20, 2, 21))
382 |         self.assertEqual(i.result_shape, (5, 4, 2, 7, 3))
383 |         self.assertEqual(i._zp_reshaped.shape, (20, 23, 21))
384 | 
385 |     def test_inconsistent_shape(self):
386 |         data = np.empty([5, 4, 23, 7, 3])
387 |         zdata = np.empty([5, 4, 3, 7, 3])
388 |         emsg = "z_src .* is not a subset of fz_src"
389 |         with self.assertRaisesRegex(ValueError, emsg):
390 |             vinterp._Interpolation([1, 3], data, zdata, axis=2)
391 | 
392 |     def test_axis_out_of_bounds_fz_src_relative(self):
393 |         # axis is out of bounds as identified by the absolute axis with z_src.
394 |         data = np.empty((5, 4))
395 |         zdata = np.empty((5, 4))
396 |         axis = 4
397 |         emsg = "Axis {} out of range"
398 |         with self.assertRaisesRegex(ValueError, emsg.format(axis)):
399 |             vinterp._Interpolation([1, 3], data, zdata, axis=axis)
400 | 
401 |     def test_axis_out_of_bounds_z_src_absolute(self):
402 |         # axis is out of bounds as identified by the relative axis with fz_src.
403 |         data = np.empty((5, 4))
404 |         zdata = np.empty((3, 5, 4))
405 |         axis = 0
406 |         emsg = "Axis {} out of range"
407 |         with self.assertRaisesRegex(ValueError, emsg.format(axis)):
408 |             vinterp._Interpolation([1, 3], data, zdata, axis=axis)
409 | 
410 |     def test_axis_greater_than_z_src_ndim(self):
411 |         # Ensure that axis is not unnecessarily constrained to the dimensions
412 |         # of z_src.
413 |         data = np.empty(4)
414 |         zdata = np.empty((3, 5, 4))
415 |         axis = 2
416 |         result = vinterp._Interpolation(data.copy(), data, zdata, axis=axis)
417 |         self.assertEqual(result.result_shape, (3, 5, 4))
418 | 
419 |     def test_nd_inconsistent_ndims(self):
420 |         z_target = np.empty((2, 3, 4))
421 |         z_src = np.empty((3, 4))
422 |         fz_src = np.empty((2, 3, 4))
423 |         emsg = "z_target and z_src must have the same number of dimensions"
424 |         with self.assertRaisesRegex(ValueError, emsg):
425 |             vinterp._Interpolation(z_target, z_src, fz_src)
426 | 
427 |     def test_nd_inconsistent_shape(self):
428 |         z_target = np.empty((3, 2, 6))
429 |         z_src = np.empty((3, 4, 5))
430 |         fz_src = np.empty((2, 3, 4, 5))
431 |         emsg = (
432 |             "z_target and z_src have different shapes, "
433 |             r"got \(3, :, 6\) != \(3, :, 5\)"
434 |         )
435 |         with self.assertRaisesRegex(ValueError, emsg):
436 |             vinterp._Interpolation(z_target, z_src, fz_src, axis=2)
437 | 
438 |     def test_result_dtype_f4(self):
439 |         interp = vinterp._Interpolation(
440 |             [17.5], np.arange(4) * 10, np.arange(4, dtype="f4")
441 |         )
442 |         result = interp.interpolate()
443 | 
444 |         self.assertEqual(interp._target_dtype, np.dtype("f4"))
445 |         self.assertEqual(result.dtype, np.dtype("f4"))
446 | 
447 |     def test_result_dtype_f8(self):
448 |         interp = vinterp._Interpolation(
449 |             [17.5], np.arange(4) * 10, np.arange(4, dtype="f8")
450 |         )
451 |         result = interp.interpolate()
452 | 
453 |         self.assertEqual(interp._target_dtype, np.dtype("f8"))
454 |         self.assertEqual(result.dtype, np.dtype("f8"))
455 | 
456 | 
457 | class Test__Interpolation_interpolate_z_target_nd(unittest.TestCase):
458 |     def test_target_z_3d_on_axis_0(self):
459 |         z_target = z_source = f_source = np.arange(3) * np.ones([4, 2, 3])
460 |         interp = vinterp._Interpolation(
461 |             z_target,
462 |             z_source,
463 |             f_source,
464 |             axis=0,
465 |             extrapolation=stratify.EXTRAPOLATE_NEAREST,
466 |         )
467 |         result = interp.interpolate_z_target_nd()
468 |         assert_array_equal(result, f_source)
469 | 
470 |     def test_target_z_3d_on_axis_m1(self):
471 |         z_target = z_source = f_source = np.arange(3) * np.ones([4, 2, 3])
472 |         interp = vinterp._Interpolation(
473 |             z_target,
474 |             z_source,
475 |             f_source,
476 |             axis=-1,
477 |             extrapolation=stratify.EXTRAPOLATE_NEAREST,
478 |         )
479 |         result = interp.interpolate_z_target_nd()
480 |         assert_array_equal(result, f_source)
481 | 
482 |     def test_target_z_2d_over_3d_on_axis_1(self):
483 |         """Test the case where z_target(2, 4) and z_src(3, 4) are 2d, but the
484 |         source data fz_src(3, 3, 4) is 3d. z_target and z_src cover the last
485 |         2 dimensions of fz_src. The axis of interpolation is axis=1 wrt fz_src.
486 | 
487 |         """
488 |         # Generate the 3d source data fz_src(3, 3, 4)
489 |         base = np.arange(3).reshape(1, 3, 1) * 2
490 |         data = np.broadcast_to(base, (3, 3, 4))
491 |         fz_src = data * np.arange(1, 4).reshape(3, 1, 1) * 10
492 |         # Generate the 2d target coordinate z_target(2, 4)
493 |         # The target coordinate is configured to request the interpolated
494 |         # mid-points over axis=1 of fz_src.
495 |         z_target = np.repeat(np.arange(1, 4, 2).reshape(2, 1), 4, axis=1) * 10
496 |         # Generate the 2d source coordinate z_src(3, 4)
497 |         z_src = np.repeat(np.arange(3).reshape(3, 1), 4, axis=1) * 20
498 |         # Configure the vertical interpolator.
499 |         interp = vinterp._Interpolation(z_target, z_src, fz_src, axis=1)
500 |         # Perform the vertical interpolation.
501 |         result = interp.interpolate_z_target_nd()
502 |         # Generate the 3d expected interpolated result(3, 2, 4).
503 |         expected = np.repeat(z_target[np.newaxis, ...], 3, axis=0)
504 |         expected = expected * np.arange(1, 4).reshape(3, 1, 1)
505 |         assert_array_equal(result, expected)
506 | 
507 |     def test_target_z_2d_over_3d_on_axis_m1(self):
508 |         """Test the case where z_target(3, 3) and z_src(3, 4) are 2d, but the
509 |         source data fz_src(3, 3, 4) is 3d. z_target and z_src cover the last
510 |         2 dimensions of fz_src. The axis of interpolation is the default last
511 |         dimension, axis=-1, wrt fx_src.
512 | 
513 |         """
514 |         # Generate the 3d source data fz_src(3, 3, 4)
515 |         base = np.arange(4) * 2
516 |         data = np.broadcast_to(base, (3, 3, 4))
517 |         fz_src = data * np.arange(1, 4).reshape(3, 1, 1) * 10
518 |         # Generate the 2d target coordinate z_target(3, 3)
519 |         # The target coordinate is configured to request the interpolated
520 |         # mid-points over axis=-1 (aka axis=2) of fz_src.
521 |         z_target = np.repeat(np.arange(1, 6, 2).reshape(1, 3), 3, axis=0) * 10
522 |         # Generate the 2d source coordinate z_src(3, 4)
523 |         z_src = np.repeat(np.arange(4).reshape(1, 4), 3, axis=0) * 20
524 |         # Configure the vertical interpolator.
525 |         interp = vinterp._Interpolation(
526 |             z_target,
527 |             z_src,
528 |             fz_src,
529 |         )
530 |         # Perform the vertical interpolation.
531 |         result = interp.interpolate_z_target_nd()
532 |         # Generate the 3d expected interpolated result(3, 3, 3)
533 |         expected = np.repeat(z_target[np.newaxis, ...], 3, axis=0)
534 |         expected = expected * np.arange(1, 4).reshape(3, 1, 1)
535 |         assert_array_equal(result, expected)
536 | 
537 | 
538 | class Test_interpolate(unittest.TestCase):
539 |     def test_target_z_3d_axis_0(self):
540 |         z_target = z_source = f_source = np.arange(3) * np.ones([4, 2, 3])
541 |         result = vinterp.interpolate(
542 |             z_target, z_source, f_source, extrapolation="linear"
543 |         )
544 |         assert_array_equal(result, f_source)
545 | 
546 |     def test_dask(self):
547 |         z_target = z_source = f_source = np.arange(3) * np.ones([4, 2, 3])
548 |         reference = vinterp.interpolate(
549 |             z_target, z_source, f_source, extrapolation="linear"
550 |         )
551 |         # Test with various combinations of lazy input
552 |         f_src = da.asarray(f_source, chunks=(2, 1, 2))
553 |         for z_tgt in (z_target, z_target.tolist(), da.asarray(z_target)):
554 |             for z_src in (z_source, da.asarray(z_source)):
555 |                 result = vinterp.interpolate(
556 |                     z_tgt, z_src, f_src, extrapolation="linear"
557 |                 )
558 |                 assert_array_equal(reference, result.compute())
559 | 
560 |     def test_dask_1d_target(self):
561 |         z_target = np.array([0.5])
562 |         z_source = f_source = np.arange(3) * np.ones([4, 2, 3])
563 |         reference = vinterp.interpolate(
564 |             z_target, z_source, f_source, axis=1, extrapolation="linear"
565 |         )
566 |         # Test with various combinations of lazy input
567 |         f_src = da.asarray(f_source, chunks=(2, 1, 2))
568 |         for z_tgt in (z_target, z_target.tolist(), da.asarray(z_target)):
569 |             for z_src in (z_source, da.asarray(z_source)):
570 |                 result = vinterp.interpolate(
571 |                     z_tgt, z_src, f_src, axis=1, extrapolation="linear"
572 |                 )
573 |                 assert_array_equal(reference, result.compute())
574 | 
575 | 
576 | if __name__ == "__main__":
577 |     unittest.main()
578 | 


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