Updating the dosctring to reflect the new pygridsio convention (a489f81d) · Commits · AGS_public / pythermogis

docs/usage/maprun_analysis.md

+9 −8

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		@@ -33,8 +33,9 @@ directory of the repository.

		```python
		from pythermogis import calculate_doublet_performance_stochastic, calculate_pos
		from pygridsio import read_grid, plot_grid, resample_xarray_grid
		from pygridsio import read_grid, plot_grid, resample_grid
		from matplotlib import pyplot as plt
		import numpy as np
		import xarray as xr
		from pathlib import Path
		from os import path
		@@ -51,13 +52,13 @@ run_simulation = True

		# grids can be in .nc, .asc, .zmap or .tif format: see pygridsio package
		new_cellsize = 5000 # in m, this sets the resolution of the model; to speed up calcualtions you can increase the cellsize
		reservoir_properties = resample_xarray_grid(read_grid(input_data_path / "ROSL_ROSLU__thick.zmap"), new_cellsize=new_cellsize).to_dataset(name="thickness_mean")
		reservoir_properties["thickness_sd"] = resample_xarray_grid(read_grid(input_data_path / "ROSL_ROSLU__thick_sd.zmap"), new_cellsize=new_cellsize)
		reservoir_properties["ntg"] = resample_xarray_grid(read_grid(input_data_path / "ROSL_ROSLU__ntg.zmap"), new_cellsize=new_cellsize)
		reservoir_properties["porosity"] = resample_xarray_grid(read_grid(input_data_path / "ROSL_ROSLU__poro.zmap"), new_cellsize=new_cellsize) / 100
		reservoir_properties["depth"] = resample_xarray_grid(read_grid(input_data_path / "ROSL_ROSLU__top.zmap"), new_cellsize=new_cellsize)
		reservoir_properties["ln_permeability_mean"] = np.log(resample_xarray_grid(read_grid(input_data_path / "ROSL_ROSLU__perm.zmap"), new_cellsize=new_cellsize))
		reservoir_properties["ln_permeability_sd"] = resample_xarray_grid(read_grid(input_data_path / "ROSL_ROSLU__ln_perm_sd.zmap"), new_cellsize=new_cellsize)
		reservoir_properties = resample_grid(read_grid(input_data_path / "ROSL_ROSLU__thick.zmap"), new_cellsize=new_cellsize).to_dataset(name="thickness_mean")
		reservoir_properties["thickness_sd"] = resample_grid(read_grid(input_data_path / "ROSL_ROSLU__thick_sd.zmap"), new_cellsize=new_cellsize)
		reservoir_properties["ntg"] = resample_grid(read_grid(input_data_path / "ROSL_ROSLU__ntg.zmap"), new_cellsize=new_cellsize)
		reservoir_properties["porosity"] = resample_grid(read_grid(input_data_path / "ROSL_ROSLU__poro.zmap"), new_cellsize=new_cellsize) / 100
		reservoir_properties["depth"] = resample_grid(read_grid(input_data_path / "ROSL_ROSLU__top.zmap"), new_cellsize=new_cellsize)
		reservoir_properties["ln_permeability_mean"] = np.log(resample_grid(read_grid(input_data_path / "ROSL_ROSLU__perm.zmap"), new_cellsize=new_cellsize))
		reservoir_properties["ln_permeability_sd"] = resample_grid(read_grid(input_data_path / "ROSL_ROSLU__ln_perm_sd.zmap"), new_cellsize=new_cellsize)

		# simulate
		# if doublet simulation has already been run then read in results, or run the simulation and write results out

pixi.lock

+7 −7

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		@@ -250,7 +250,7 @@ environments:
		- pypi: https://files.pythonhosted.org/packages/f5/64/41c4367bcaecbc03ef0d2a3ecee58a7065d0a36ae1aa817fe573a2da66d4/matplotlib-3.10.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
		- pypi: https://files.pythonhosted.org/packages/d1/80/b9c19f1bb4ac6c5fa6f94a4f278bc68a778473d1814a86a375d7cffa193a/netCDF4-1.7.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
		- pypi: https://files.pythonhosted.org/packages/02/65/ad2bc85f7377f5cfba5d4466d5474423a3fb7f6a97fd807c06f92dd3e721/plotly-6.0.1-py3-none-any.whl
		- pypi: https://files.pythonhosted.org/packages/c2/70/6b5ba59d170fa4a873ae30de74c5d234a12654c282df64b0479413f08ccc/pygridsio-0.3.27-py3-none-any.whl
		- pypi: https://files.pythonhosted.org/packages/c9/58/c3bc54c0fad9a82899e9a2703e04ee6e8eaa76caa90c0689fd1b468a4427/pygridsio-1.0-py3-none-any.whl
		- pypi: https://files.pythonhosted.org/packages/ea/00/d815833441d8c52bf4a6930952e77d3de77d0bf67b3202ccc12dabdae279/pykrige-1.7.2.tar.gz
		- pypi: https://files.pythonhosted.org/packages/9f/bb/e12bebcf2668bcb83736cc76177f36ee300ac8069880fca3a73f8753fc70/pyogrio-0.11.0-cp313-cp313-manylinux_2_28_x86_64.whl
		- pypi: https://files.pythonhosted.org/packages/05/e7/df2285f3d08fee213f2d041540fa4fc9ca6c2d44cf36d3a035bf2a8d2bcc/pyparsing-3.2.3-py3-none-any.whl
		@@ -481,7 +481,7 @@ environments:
		- pypi: https://files.pythonhosted.org/packages/b1/0f/eed564407bd4d935ffabf561ed31099ed609e19287409a27b6d336848653/matplotlib-3.10.3-cp313-cp313-win_amd64.whl
		- pypi: https://files.pythonhosted.org/packages/66/b5/e04550fd53de57001dbd5a87242da7ff784c80790adc48897977b6ccf891/netCDF4-1.7.2-cp313-cp313-win_amd64.whl
		- pypi: https://files.pythonhosted.org/packages/02/65/ad2bc85f7377f5cfba5d4466d5474423a3fb7f6a97fd807c06f92dd3e721/plotly-6.0.1-py3-none-any.whl
		- pypi: https://files.pythonhosted.org/packages/c2/70/6b5ba59d170fa4a873ae30de74c5d234a12654c282df64b0479413f08ccc/pygridsio-0.3.27-py3-none-any.whl
		- pypi: https://files.pythonhosted.org/packages/c9/58/c3bc54c0fad9a82899e9a2703e04ee6e8eaa76caa90c0689fd1b468a4427/pygridsio-1.0-py3-none-any.whl
		- pypi: https://files.pythonhosted.org/packages/ea/00/d815833441d8c52bf4a6930952e77d3de77d0bf67b3202ccc12dabdae279/pykrige-1.7.2.tar.gz
		- pypi: https://files.pythonhosted.org/packages/46/8f/a9d134fbbf213db259b79f5bd5bbe7e3de1ff34fbe2a0b0be9d7d2919323/pyogrio-0.11.0-cp313-cp313-win_amd64.whl
		- pypi: https://files.pythonhosted.org/packages/05/e7/df2285f3d08fee213f2d041540fa4fc9ca6c2d44cf36d3a035bf2a8d2bcc/pyparsing-3.2.3-py3-none-any.whl
		@@ -4672,10 +4672,10 @@ packages:
		- pkg:pypi/pygments?source=hash-mapping
		size: 888600
		timestamp: 1736243563082
		- pypi: https://files.pythonhosted.org/packages/c2/70/6b5ba59d170fa4a873ae30de74c5d234a12654c282df64b0479413f08ccc/pygridsio-0.3.27-py3-none-any.whl
		- pypi: https://files.pythonhosted.org/packages/c9/58/c3bc54c0fad9a82899e9a2703e04ee6e8eaa76caa90c0689fd1b468a4427/pygridsio-1.0-py3-none-any.whl
		name: pygridsio
		version: 0.3.27
		sha256: b8b222455de2a2c447609eb4ac13a11777ce63ce70512feb56ebe5898be7a674
		version: '1.0'
		sha256: 07468ac4df8295421054a38965dbac73b68daf11deee5f3d0fa2565da83ec92d
		requires_dist:
		- numpy
		- pandas
		@@ -4828,14 +4828,14 @@ packages:
		- pypi: ./
		name: pythermogis
		version: 1.1.1
		sha256: 6144a7e98f661d174d6aeae7b3695e7d78fa2ad50a26133e22e6c0239ec33186
		sha256: 77448402bd1f149d14b6ef577dd4e1b1f200c2b6f0e20c782f73fd5f55824804
		requires_dist:
		- jpype1>=1.5.2,<2
		- xarray==2024.9.0.*
		- pandas>=2.2.3,<3
		- pytz>=2024.1,<2025
		- build>=1.2.2.post1,<2
		- pygridsio>=0.3.27,<0.4
		- pygridsio>=1.0,<2.0
		requires_python: '>=3.11'
		editable: true
		- conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.13.3-hf636f53_101_cp313.conda

pyproject.toml

+1 −1

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		@@ -20,7 +20,7 @@ dependencies = [
		"pandas>=2.2.3,<3",
		"pytz>=2024.1,<2025",
		"build>=1.2.2.post1,<2",
		"pygridsio>=0.3.27,<0.4"]
		"pygridsio>=1.0,<2.0"]


		[tool.pytest.ini_options]

tests/test_doc_examples.py

+8 −8

Original line number	Diff line number	Diff line
		@@ -100,7 +100,7 @@ def test_example_5():

		def test_example_6():
		from pythermogis import calculate_doublet_performance_stochastic, calculate_pos
		from pygridsio import read_grid, plot_grid, resample_xarray_grid
		from pygridsio import read_grid, plot_grid, resample_grid
		from matplotlib import pyplot as plt
		import numpy as np
		import xarray as xr
		@@ -119,13 +119,13 @@ def test_example_6():

		# grids can be in .nc, .asc, .zmap or .tif format: see pygridsio package
		new_cellsize = 20000 # in m, this sets the resolution of the model; to speed up calcualtions you can increase the cellsize
		reservoir_properties = resample_xarray_grid(read_grid(input_data_path / "ROSL_ROSLU__thick.zmap"), new_cellsize=new_cellsize).to_dataset(name="thickness_mean")
		reservoir_properties["thickness_sd"] = resample_xarray_grid(read_grid(input_data_path / "ROSL_ROSLU__thick_sd.zmap"), new_cellsize=new_cellsize)
		reservoir_properties["ntg"] = resample_xarray_grid(read_grid(input_data_path / "ROSL_ROSLU__ntg.zmap"), new_cellsize=new_cellsize)
		reservoir_properties["porosity"] = resample_xarray_grid(read_grid(input_data_path / "ROSL_ROSLU__poro.zmap"), new_cellsize=new_cellsize) / 100
		reservoir_properties["depth"] = resample_xarray_grid(read_grid(input_data_path / "ROSL_ROSLU__top.zmap"), new_cellsize=new_cellsize)
		reservoir_properties["ln_permeability_mean"] = np.log(resample_xarray_grid(read_grid(input_data_path / "ROSL_ROSLU__perm.zmap"), new_cellsize=new_cellsize))
		reservoir_properties["ln_permeability_sd"] = resample_xarray_grid(read_grid(input_data_path / "ROSL_ROSLU__ln_perm_sd.zmap"), new_cellsize=new_cellsize)
		reservoir_properties = resample_grid(read_grid(input_data_path / "ROSL_ROSLU__thick.zmap"), new_cellsize=new_cellsize).to_dataset(name="thickness_mean")
		reservoir_properties["thickness_sd"] = resample_grid(read_grid(input_data_path / "ROSL_ROSLU__thick_sd.zmap"), new_cellsize=new_cellsize)
		reservoir_properties["ntg"] = resample_grid(read_grid(input_data_path / "ROSL_ROSLU__ntg.zmap"), new_cellsize=new_cellsize)
		reservoir_properties["porosity"] = resample_grid(read_grid(input_data_path / "ROSL_ROSLU__poro.zmap"), new_cellsize=new_cellsize) / 100
		reservoir_properties["depth"] = resample_grid(read_grid(input_data_path / "ROSL_ROSLU__top.zmap"), new_cellsize=new_cellsize)
		reservoir_properties["ln_permeability_mean"] = np.log(resample_grid(read_grid(input_data_path / "ROSL_ROSLU__perm.zmap"), new_cellsize=new_cellsize))
		reservoir_properties["ln_permeability_sd"] = resample_grid(read_grid(input_data_path / "ROSL_ROSLU__ln_perm_sd.zmap"), new_cellsize=new_cellsize)

		# simulate
		# if doublet simulation has already been run then read in results, or run the simulation and write results out

tests/test_doublet_speed.py

+11 −10

Original line number	Diff line number	Diff line
		import shutil
		import timeit
		from os import path
		from unittest.case import TestCase, skip
		from pygridsio import read_grid, resample_xarray_grid
		from unittest.case import TestCase

		from pygridsio import read_grid, resample_grid

		from pythermogis import *


		class PyThermoGIS(TestCase):
		test_files_path = Path(path.dirname(__file__), "resources") / "test_input" / "example_data"
		test_files_out_path = Path(path.dirname(__file__), "resources") / "test_output" / "example_data"
		@@ -35,11 +36,11 @@ class PyThermoGIS(TestCase):

		def read_input_grids(self):
		new_cellsize=5000 # in m
		input_grids = resample_xarray_grid(read_grid(self.test_files_out_path / "ROSL_ROSLU__thick.zmap"), new_cellsize=new_cellsize).to_dataset(name="thickness_mean")
		input_grids["thickness_sd"] = resample_xarray_grid(read_grid(self.test_files_out_path / "ROSL_ROSLU__thick_sd.zmap"), new_cellsize=new_cellsize)
		input_grids["ntg"] = resample_xarray_grid(read_grid(self.test_files_out_path / "ROSL_ROSLU__ntg.zmap"), new_cellsize=new_cellsize)
		input_grids["porosity"] = resample_xarray_grid(read_grid(self.test_files_out_path / "ROSL_ROSLU__poro.zmap"), new_cellsize=new_cellsize) / 100
		input_grids["depth"] = resample_xarray_grid(read_grid(self.test_files_out_path / "ROSL_ROSLU__top.zmap"), new_cellsize=new_cellsize)
		input_grids["ln_permeability_mean"] = np.log(resample_xarray_grid(read_grid(self.test_files_out_path / "ROSL_ROSLU__perm.zmap"), new_cellsize=new_cellsize))
		input_grids["ln_permeability_sd"] = resample_xarray_grid(read_grid(self.test_files_out_path / "ROSL_ROSLU__ln_perm_sd.zmap"), new_cellsize=new_cellsize)
		input_grids = resample_grid(read_grid(self.test_files_out_path / "ROSL_ROSLU__thick.zmap"), new_cellsize=new_cellsize).to_dataset(name="thickness_mean")
		input_grids["thickness_sd"] = resample_grid(read_grid(self.test_files_out_path / "ROSL_ROSLU__thick_sd.zmap"), new_cellsize=new_cellsize)
		input_grids["ntg"] = resample_grid(read_grid(self.test_files_out_path / "ROSL_ROSLU__ntg.zmap"), new_cellsize=new_cellsize)
		input_grids["porosity"] = resample_grid(read_grid(self.test_files_out_path / "ROSL_ROSLU__poro.zmap"), new_cellsize=new_cellsize) / 100
		input_grids["depth"] = resample_grid(read_grid(self.test_files_out_path / "ROSL_ROSLU__top.zmap"), new_cellsize=new_cellsize)
		input_grids["ln_permeability_mean"] = np.log(resample_grid(read_grid(self.test_files_out_path / "ROSL_ROSLU__perm.zmap"), new_cellsize=new_cellsize))
		input_grids["ln_permeability_sd"] = resample_grid(read_grid(self.test_files_out_path / "ROSL_ROSLU__ln_perm_sd.zmap"), new_cellsize=new_cellsize)
		return input_grids
		No newline at end of file