TNO Intern

Commit a8da3d1d authored by Florian Knappers's avatar Florian Knappers
Browse files

Merge branch '94-finish-the-utc-calculation-4' into 'python-rewrite-of-java-code' 

Resolve "finish the utc calculation"

See merge request !116
parents 9b98434b 88e3ace5

.gitignore

+3 −1
Original line number Diff line number Diff line
@@ -11,3 +11,5 @@ dist 
tests/resources/test_output



pydoubletcalc_install



workdir
 
 No newline at end of file

pixi.lock

+191 −31
Original line number Diff line number Diff line
@@ -52,9 +52,11 @@ environments: 
      - conda: https://conda.anaconda.org/conda-forge/noarch/dask-2025.5.1-pyhd8ed1ab_0.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/dask-core-2025.5.1-pyhd8ed1ab_0.conda

      - conda: https://conda.anaconda.org/conda-forge/linux-64/dbus-1.13.6-h5008d03_3.tar.bz2

      - conda: https://conda.anaconda.org/conda-forge/noarch/deprecated-1.3.1-pyhd8ed1ab_0.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/distlib-0.4.0-pyhd8ed1ab_0.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/distributed-2025.5.1-pyhd8ed1ab_0.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/docutils-0.21.2-pyhd8ed1ab_1.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/donfig-0.8.1.post1-pyhd8ed1ab_1.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/et_xmlfile-2.0.0-pyhd8ed1ab_1.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda

      - conda: https://conda.anaconda.org/conda-forge/linux-64/expat-2.7.0-h5888daf_0.conda
@@ -63,6 +65,7 @@ environments: 
      - conda: https://conda.anaconda.org/conda-forge/linux-64/gflags-2.2.2-h5888daf_1005.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/ghp-import-2.1.0-pyhd8ed1ab_2.conda

      - conda: https://conda.anaconda.org/conda-forge/linux-64/glog-0.7.1-hbabe93e_0.conda

      - conda: https://conda.anaconda.org/conda-forge/linux-64/google-crc32c-1.7.1-py313h74173ec_1.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/griffe-1.7.3-pyhd8ed1ab_0.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/h2-4.2.0-pyhd8ed1ab_0.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/hpack-4.1.0-pyhd8ed1ab_0.conda
@@ -164,6 +167,7 @@ environments: 
      - conda: https://conda.anaconda.org/conda-forge/linux-64/nlohmann_json-3.12.0-h3f2d84a_0.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/nodeenv-1.9.1-pyhd8ed1ab_1.conda

      - conda: https://conda.anaconda.org/conda-forge/linux-64/numba-0.62.1-py313hd8e3f9f_0.conda

      - conda: https://conda.anaconda.org/conda-forge/linux-64/numcodecs-0.16.5-py313h08cd8bf_0.conda

      - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.2.5-py313h17eae1a_0.conda

      - conda: https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.3-h5fbd93e_0.conda

      - conda: https://conda.anaconda.org/conda-forge/linux-64/openpyxl-3.1.5-py313h9c9eb82_1.conda
@@ -231,11 +235,13 @@ environments: 
      - conda: https://conda.anaconda.org/conda-forge/noarch/virtualenv-20.34.0-pyhd8ed1ab_0.conda

      - conda: https://conda.anaconda.org/conda-forge/linux-64/watchdog-6.0.0-py313h78bf25f_0.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.45.1-pyhd8ed1ab_1.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/xarray-2024.9.0-pyhd8ed1ab_1.conda

      - conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-1.17.3-py313h07c4f96_1.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/xarray-2025.12.0-pyhcf101f3_0.conda

      - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.12-hb9d3cd8_0.conda

      - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.5-hb9d3cd8_0.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/xyzservices-2025.4.0-pyhd8ed1ab_0.conda

      - conda: https://conda.anaconda.org/conda-forge/linux-64/yaml-0.2.5-h7f98852_2.tar.bz2

      - conda: https://conda.anaconda.org/conda-forge/noarch/zarr-3.1.5-pyhcf101f3_0.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/zict-3.0.0-pyhd8ed1ab_1.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.21.0-pyhd8ed1ab_1.conda

      - conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-1.3.1-hb9d3cd8_2.conda
@@ -306,14 +312,17 @@ environments: 
      - conda: https://conda.anaconda.org/conda-forge/win-64/cytoolz-1.0.1-py313ha7868ed_0.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/dask-2025.5.1-pyhd8ed1ab_0.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/dask-core-2025.5.1-pyhd8ed1ab_0.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/deprecated-1.3.1-pyhd8ed1ab_0.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/distlib-0.4.0-pyhd8ed1ab_0.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/distributed-2025.5.1-pyhd8ed1ab_0.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/docutils-0.21.2-pyhd8ed1ab_1.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/donfig-0.8.1.post1-pyhd8ed1ab_1.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/et_xmlfile-2.0.0-pyhd8ed1ab_1.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/filelock-3.19.1-pyhd8ed1ab_0.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/fsspec-2025.5.1-pyhd8ed1ab_0.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/ghp-import-2.1.0-pyhd8ed1ab_2.conda

      - conda: https://conda.anaconda.org/conda-forge/win-64/google-crc32c-1.7.1-py313h5327936_1.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/griffe-1.7.3-pyhd8ed1ab_0.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/h2-4.2.0-pyhd8ed1ab_0.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/hpack-4.1.0-pyhd8ed1ab_0.conda
@@ -400,6 +409,7 @@ environments: 
      - conda: https://conda.anaconda.org/conda-forge/win-64/nh3-0.2.21-py39he870945_1.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/nodeenv-1.9.1-pyhd8ed1ab_1.conda

      - conda: https://conda.anaconda.org/conda-forge/win-64/numba-0.62.1-py313h924e429_0.conda

      - conda: https://conda.anaconda.org/conda-forge/win-64/numcodecs-0.16.5-py313hc90dcd4_0.conda

      - conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.2.5-py313hefb8edb_0.conda

      - conda: https://conda.anaconda.org/conda-forge/win-64/openjpeg-2.5.3-h4d64b90_0.conda

      - conda: https://conda.anaconda.org/conda-forge/win-64/openpyxl-3.1.5-py313he57e174_1.conda
@@ -470,11 +480,13 @@ environments: 
      - conda: https://conda.anaconda.org/conda-forge/win-64/watchdog-6.0.0-py313hfa70ccb_0.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.45.1-pyhd8ed1ab_1.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/win_inet_pton-1.1.0-pyh7428d3b_8.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/xarray-2024.9.0-pyhd8ed1ab_1.conda

      - conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-1.17.3-py313h5ea7bf4_1.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/xarray-2025.12.0-pyhcf101f3_0.conda

      - conda: https://conda.anaconda.org/conda-forge/win-64/xorg-libxau-1.0.12-h0e40799_0.conda

      - conda: https://conda.anaconda.org/conda-forge/win-64/xorg-libxdmcp-1.1.5-h0e40799_0.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/xyzservices-2025.4.0-pyhd8ed1ab_0.conda

      - conda: https://conda.anaconda.org/conda-forge/win-64/yaml-0.2.5-h8ffe710_2.tar.bz2

      - conda: https://conda.anaconda.org/conda-forge/noarch/zarr-3.1.5-pyhcf101f3_0.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/zict-3.0.0-pyhd8ed1ab_1.conda

      - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.21.0-pyhd8ed1ab_1.conda

      - conda: https://conda.anaconda.org/conda-forge/win-64/zstandard-0.23.0-py313ha7868ed_2.conda
@@ -1624,6 +1636,18 @@ packages: 
  purls: []

  size: 618596

  timestamp: 1640112124844

- conda: https://conda.anaconda.org/conda-forge/noarch/deprecated-1.3.1-pyhd8ed1ab_0.conda

  sha256: c994a70449d548dd388768090c71c1da81e1e128a281547ab9022908d46878c5

  md5: bf74a83f7a0f2a21b5d709997402cac4

  depends:

  - python >=3.10

  - wrapt <2,>=1.10

  license: MIT

  license_family: MIT

  purls:

  - pkg:pypi/deprecated?source=hash-mapping

  size: 15815

  timestamp: 1761813872696

- conda: https://conda.anaconda.org/conda-forge/noarch/distlib-0.4.0-pyhd8ed1ab_0.conda

  sha256: 6d977f0b2fc24fee21a9554389ab83070db341af6d6f09285360b2e09ef8b26e

  md5: 003b8ba0a94e2f1e117d0bd46aebc901
@@ -1674,6 +1698,18 @@ packages: 
  - pkg:pypi/docutils?source=hash-mapping

  size: 402700

  timestamp: 1733217860944

- conda: https://conda.anaconda.org/conda-forge/noarch/donfig-0.8.1.post1-pyhd8ed1ab_1.conda

  sha256: d58e97d418f71703e822c422af5b9c431e3621a0ecdc8b0334c1ca33e076dfe7

  md5: c56a7fa5597ad78b62e1f5d21f7f8b8f

  depends:

  - python >=3.9

  - pyyaml

  license: MIT

  license_family: MIT

  purls:

  - pkg:pypi/donfig?source=hash-mapping

  size: 22491

  timestamp: 1734368817583

- conda: https://conda.anaconda.org/conda-forge/noarch/et_xmlfile-2.0.0-pyhd8ed1ab_1.conda

  sha256: 2209534fbf2f70c20661ff31f57ab6a97b82ee98812e8a2dcb2b36a0d345727c

  md5: 71bf9646cbfabf3022c8da4b6b4da737
@@ -1864,6 +1900,37 @@ packages: 
  purls: []

  size: 143452

  timestamp: 1718284177264

- conda: https://conda.anaconda.org/conda-forge/linux-64/google-crc32c-1.7.1-py313h74173ec_1.conda

  sha256: 81ca7fb5c0756e3a5934fe18b44b90be475a03329bcd715334455aec340e34c0

  md5: 8580f244242b4d6a8c1933d8d0475b9c

  depends:

  - __glibc >=2.17,<3.0.a0

  - libcrc32c >=1.1.2,<1.2.0a0

  - libgcc >=14

  - python >=3.13,<3.14.0a0

  - python_abi 3.13.* *_cp313

  license: Apache-2.0

  license_family: Apache

  purls:

  - pkg:pypi/google-crc32c?source=hash-mapping

  size: 24798

  timestamp: 1755850411927

- conda: https://conda.anaconda.org/conda-forge/win-64/google-crc32c-1.7.1-py313h5327936_1.conda

  sha256: 5d1a3322ab5949c63326bbb779feaf47f95902849a9687da094e8de490250e22

  md5: 35b1131e4ea5eb0f27664eeb68c9a29b

  depends:

  - libcrc32c >=1.1.2,<1.2.0a0

  - python >=3.13,<3.14.0a0

  - python_abi 3.13.* *_cp313

  - ucrt >=10.0.20348.0

  - vc >=14.3,<15

  - vc14_runtime >=14.44.35208

  license: Apache-2.0

  license_family: Apache

  purls:

  - pkg:pypi/google-crc32c?source=hash-mapping

  size: 27799

  timestamp: 1755850484807

- conda: https://conda.anaconda.org/conda-forge/noarch/griffe-1.7.3-pyhd8ed1ab_0.conda

  sha256: c1e4039c9b6d613e8e9feafa21fae58db5eebeaa5f8bece5d8610154ae6ebf80

  md5: aafe052f140a58b1afaf8ab473f5ac0d
@@ -4178,6 +4245,46 @@ packages: 
  - pkg:pypi/numba?source=hash-mapping

  size: 5706597

  timestamp: 1759165298367

- conda: https://conda.anaconda.org/conda-forge/linux-64/numcodecs-0.16.5-py313h08cd8bf_0.conda

  sha256: 7c35d46639f8638849535a22cb7ae1b7210121be0c7b053d8e2ab7ed485e6bff

  md5: 0f394ef25fb81d1dec8ff4fa716f00bd

  depends:

  - __glibc >=2.17,<3.0.a0

  - deprecated

  - libgcc >=14

  - libstdcxx >=14

  - msgpack-python

  - numpy >=1.23,<3

  - numpy >=1.24

  - python >=3.13,<3.14.0a0

  - python_abi 3.13.* *_cp313

  - typing_extensions

  license: MIT

  license_family: MIT

  purls:

  - pkg:pypi/numcodecs?source=hash-mapping

  size: 808201

  timestamp: 1764782369322

- conda: https://conda.anaconda.org/conda-forge/win-64/numcodecs-0.16.5-py313hc90dcd4_0.conda

  sha256: 008d11564f2a3bac16ea0e323a02b24ca06fb28f8b74c01cde13098003c35b9b

  md5: 4006d795b35200d0d6e28a1de84dfcc5

  depends:

  - deprecated

  - msgpack-python

  - numpy >=1.23,<3

  - numpy >=1.24

  - python >=3.13,<3.14.0a0

  - python_abi 3.13.* *_cp313

  - typing_extensions

  - ucrt >=10.0.20348.0

  - vc >=14.3,<15

  - vc14_runtime >=14.44.35208

  license: MIT

  license_family: MIT

  purls:

  - pkg:pypi/numcodecs?source=hash-mapping

  size: 516677

  timestamp: 1764782612807

- conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.2.5-py313h17eae1a_0.conda

  sha256: c0a200d0e53a1acbfa1d1e2277e3337ea2aa8cb584448790317a98c62dcaebce

  md5: 6ceeff9ed72e54e4a2f9a1c88f47bdde
@@ -4945,10 +5052,10 @@ packages: 
- pypi: ./

  name: pythermogis

  version: 1.2.0

  sha256: 35de67af8136fb6442b90f5a2956e6ac57f8a6f52f1fbbfad15768e83e08b31f

  sha256: 5307b5b9e674861991952ee2bb4da21dfc517e11259fd4ed7735f28b5fb54408

  requires_dist:

  - jpype1>=1.5.2,<2

  - xarray==2024.9.0.*

  - xarray==2025.12.0.*

  - pandas>=2.2.3,<3

  - pytz>=2024.1,<2025

  - build>=1.2.2.post1,<2
@@ -5949,41 +6056,73 @@ packages: 
  - pkg:pypi/win-inet-pton?source=hash-mapping

  size: 9555

  timestamp: 1733130678956

- conda: https://conda.anaconda.org/conda-forge/noarch/xarray-2024.9.0-pyhd8ed1ab_1.conda

  sha256: 8bb5b522cdf1905d831a9b371a3a3bd2932a9f53398332fbd38ed3442015bbaf

  md5: dc790d427d89b85ae12fc094e264833f

- conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-1.17.3-py313h07c4f96_1.conda

  sha256: 3688598866224e3fbeed8a74f12fd0a3c19dadcb931ce778bdc6cc2e04621b3b

  md5: c2662497e9a9ff2153753682f53989c9

  depends:

  - numpy >=1.24

  - packaging >=23.1

  - pandas >=2.1

  - python >=3.10

  - __glibc >=2.17,<3.0.a0

  - libgcc >=14

  - python >=3.13,<3.14.0a0

  - python_abi 3.13.* *_cp313

  license: BSD-2-Clause

  license_family: BSD

  purls:

  - pkg:pypi/wrapt?source=hash-mapping

  size: 64865

  timestamp: 1756851811052

- conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-1.17.3-py313h5ea7bf4_1.conda

  sha256: 260a3295f39565c28be9232a11ca7ee435af6e9366ffd2569ff29a63e7c144a0

  md5: 3e199c8db04833fe628867462aeaca24

  depends:

  - python >=3.13,<3.14.0a0

  - python_abi 3.13.* *_cp313

  - ucrt >=10.0.20348.0

  - vc >=14.3,<15

  - vc14_runtime >=14.44.35208

  license: BSD-2-Clause

  license_family: BSD

  purls:

  - pkg:pypi/wrapt?source=hash-mapping

  size: 63385

  timestamp: 1756851987645

- conda: https://conda.anaconda.org/conda-forge/noarch/xarray-2025.12.0-pyhcf101f3_0.conda

  sha256: b35f6848f229d65dc6e6d58a232099a5e293405a5e3e369b15110ed255cf9872

  md5: efdb3ef0ff549959650ef070ba2c52d2

  depends:

  - python >=3.11

  - numpy >=1.26

  - packaging >=24.1

  - pandas >=2.2

  - python

  constrains:

  - seaborn-base >=0.12

  - distributed >=2023.9

  - scipy >=1.11

  - bottleneck >=1.4

  - cartopy >=0.23

  - cftime >=1.6

  - dask-core >=2024.6

  - distributed >=2024.6

  - flox >=0.9

  - h5netcdf >=1.3

  - h5py >=3.11

  - hdf5 >=1.14

  - iris >=3.9

  - matplotlib-base >=3.8

  - nc-time-axis >=1.4

  - netcdf4 >=1.6.0

  - numba >=0.60

  - numbagg >=0.8

  - pint >=0.24

  - pydap >=3.5.0

  - scipy >=1.13

  - seaborn-base >=0.13

  - sparse >=0.15

  - toolz >=0.12

  - nc-time-axis >=1.4

  - cftime >=1.6

  - h5netcdf >=1.2

  - matplotlib-base >=3.7

  - h5py >=3.8

  - zarr >=2.16

  - hdf5 >=1.12

  - numba >=0.57

  - iris >=3.7

  - cartopy >=0.22

  - dask-core >=2023.9

  - flox >=0.7

  - bottleneck >=1.3

  - pint >=0.22

  - sparse >=0.14

  - zarr >=2.18

  license: Apache-2.0

  license_family: APACHE

  purls:

  - pkg:pypi/xarray?source=hash-mapping

  size: 801066

  timestamp: 1728453306227

  size: 994025

  timestamp: 1764974555156

- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.12-hb9d3cd8_0.conda

  sha256: ed10c9283974d311855ae08a16dfd7e56241fac632aec3b92e3cfe73cff31038

  md5: f6ebe2cb3f82ba6c057dde5d9debe4f7
@@ -6062,6 +6201,27 @@ packages: 
  purls: []

  size: 63274

  timestamp: 1641347623319

- conda: https://conda.anaconda.org/conda-forge/noarch/zarr-3.1.5-pyhcf101f3_0.conda

  sha256: c36bec7d02d2f227409fcc4cf586cf3a658af068b58374de7f8f2d0b5c1c84f9

  md5: c1844a94b2be61bb03bbb71574a0abfc

  depends:

  - python >=3.11

  - packaging >=22.0

  - numpy >=1.26

  - numcodecs >=0.14

  - typing_extensions >=4.9

  - donfig >=0.8

  - google-crc32c >=1.5

  - python

  constrains:

  - fsspec >=2023.10.0

  - obstore >=0.5.1

  license: MIT

  license_family: MIT

  purls:

  - pkg:pypi/zarr?source=hash-mapping

  size: 305998

  timestamp: 1763742695201

- conda: https://conda.anaconda.org/conda-forge/noarch/zict-3.0.0-pyhd8ed1ab_1.conda

  sha256: 5488542dceeb9f2874e726646548ecc5608060934d6f9ceaa7c6a48c61f9cc8d

  md5: e52c2ef711ccf31bb7f70ca87d144b9e

pyproject.toml

+4 −2
Original line number Diff line number Diff line
@@ -16,7 +16,7 @@ license = { file = "LICENSE" } 
requires-python = ">=3.11"

dependencies = [

    "jpype1>=1.5.2,<2",

    "xarray==2024.9.0.*",

    "xarray==2025.12.0.*",

    "pandas>=2.2.3,<3",

    "pytz>=2024.1,<2025",

    "build>=1.2.2.post1,<2",
@@ -42,6 +42,7 @@ platforms = ["win-64", "linux-64"] 


[tool.pixi.pypi-dependencies]

pythermogis = { path = ".", editable = true }

#pydoubletcalc = { path = 'C:\Users\knappersfy\work\thermogis\pydoubletcalc', editable = true }



[tool.pixi.tasks]

test = "PYTHONPATH=src/pythermogis pytest -s tests/"
@@ -53,7 +54,7 @@ pytg = "PYTHONPATH=src pixi run python src/pythermogis/main.py" 
[tool.pixi.dependencies]

python = ">=3.13.2,<3.14"

jpype1 = ">=1.5.2,<2"

xarray = "2024.9.0.*"

xarray = "2025.12.0.*"

pandas = ">=2.2.3,<3"

setuptools = ">=75.8.2,<76"

wheel = ">=0.45.1,<0.46"
@@ -71,6 +72,7 @@ pre-commit = ">=4.3.0,<5" 
python-dotenv = ">=1.2.1,<2"

numba = ">=0.62.1,<0.63"

ruff = ">=0.14.8,<0.15"

zarr = ">=3.1.5,<4"



[tool.ruff]

line-length = 88

src/pythermogis/thermogis_classes/doublet.py

+1 −1
Original line number Diff line number Diff line
@@ -2,7 +2,7 @@ import numpy as np 
import xarray as xr

from jpype import JClass

from pythermogis.workflow.utc.doublet import calculate_doublet_performance, DoubletInput, DoubletOutput

from pythermogis.workflow.utc.utc_properties import UTCConfiguration

from pythermogis.workflow.utc.configuration import UTCConfiguration





def simulate_doublet(

src/pythermogis/transmissivity/calculate_thick_perm_trans.py

+66 −1
Original line number Diff line number Diff line 
import numpy as np

from scipy import stats

import xarray as xr

from numba import njit, prange

from numpy.typing import NDArray





def generate_thickness_permeability_transmissivity_for_pvalues(thickness_mean: float, thickness_sd: float, ln_permeability_mean: float, ln_permeability_sd: float, p_values: xr.DataArray, nSamples: int = 10000) -> float:

    """
@@ -53,3 +56,65 @@ def generate_thickness_permeability_transmissivity_for_pvalues(thickness_mean: f 
    transmissivity_pvalues_sampled = transmissivity_samples[sample_indexes.astype(int)]



    return thickness_pvalues, permeability_pvalues, transmissivity_pvalues_sampled





@njit(parallel=True)

def calculate_transmissivity(

        thickness_mean: NDArray[np.float64],

        thickness_sd: NDArray[np.float64],

        ln_perm_mean: NDArray[np.float64],

        ln_perm_sd: NDArray[np.float64],

        quantile: float,

        z_score: float,

        n_samples: int,

):

    shape = thickness_mean.shape

    tm_flat = thickness_mean.ravel()

    ts_flat = thickness_sd.ravel()

    lpm_flat = ln_perm_mean.ravel()

    lps_flat = ln_perm_sd.ravel()



    n_cells = tm_flat.size

    idx = int(quantile * n_samples)



    thickness_p = np.empty(n_cells)

    permeability_p = np.empty(n_cells)

    transmissivity_p = np.empty(n_cells)



    for i in prange(n_cells):

        tm = tm_flat[i]

        ts = ts_flat[i]

        lpm = lpm_flat[i]

        lps = lps_flat[i]



        if np.isnan(tm) or np.isnan(ts) or np.isnan(lpm) or np.isnan(lps):

            thickness_p[i] = np.nan

            permeability_p[i] = np.nan

            transmissivity_p[i] = np.nan

            continue



        # Generate samples for this cell

        thickness_samples = np.empty(n_samples)

        ln_perm_samples = np.empty(n_samples)



        for j in range(n_samples):

            if ts == 0:

                thickness_samples[j] = tm

            else:

                thickness_samples[j] = max(0.01, np.random.normal(tm, ts))

            ln_perm_samples[j] = np.random.normal(lpm, lps)



        # Transmissivity samples

        tr_samples = np.exp(ln_perm_samples) * thickness_samples

        tr_samples.sort()

        transmissivity_p[i] = tr_samples[idx]



        # Analytical percentiles

        thickness_p[i] = tm + z_score * ts if ts > 0 else tm

        permeability_p[i] = np.exp(lpm + z_score * lps)



    return (

        thickness_p.reshape(shape),

        permeability_p.reshape(shape),

        transmissivity_p.reshape(shape),

    )
 
 No newline at end of file