updating the docs to reflect this new way of working (d89cb025) · Commits · AGS_public / pythermogis

docs/reference/doublet_simulation_deterministic.md

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		# API Reference

		::: pythermogis.doublet_simulation.deterministic_doublet
		No newline at end of file

docs/reference/doublet_simulation_stochastic.md

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		# API Reference

		::: pythermogis.doublet_simulation.stochastic_doublet
		No newline at end of file

docs/usage/customized_props.md

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		@@ -39,21 +39,19 @@ Here is an example, where the default utc_properties is used, but the UseHeatPum
		`utc_properties` themselves, with the `.build()` method of the `utc_properties_builder`.

		```python
		from src.pythermogis import calculate_doublet_performance_stochastic, instantiate_utc_properties_builder
		from pythermogis import calculate_doublet_performance, instantiate_utc_properties_builder
		import xarray as xr

		input_data = xr.Dataset({
		"thickness_mean": ((), 300),
		"thickness_sd": ((), 50),
		"ntg": ((), 0.5),
		"porosity": ((), 0.5),
		"depth": ((), 5000),
		"ln_permeability_mean": ((), 5),
		"ln_permeability_sd": ((), 0.5),
		"thickness": 300,
		"ntg": 0.5,
		"porosity": 0.5,
		"depth": 2000,
		"permeability": 300,
		})

		utc_properties = instantiate_utc_properties_builder().setUseHeatPump(True).build()
		results = calculate_doublet_performance_stochastic(input_data, utc_properties=utc_properties)
		results = calculate_doublet_performance(input_data, utc_properties=utc_properties)
		print(results)
		```

docs/usage/deterministic_doublet.md

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		## Detmerninistic Doublet Performance Calculations
		## Deterministic Doublet Performance Calculations

		This is a very simple example of how to use the `calculate_doublet_performance` function from
		the `pythermogis` package to run a deterministic (without uncertainty quatification) to run a doublet simulation.
		This is a simple example of how to use the `calculate_doublet_performance` function from
		the `pythermogis` package to run a deterministic (without uncertainty quantification) doublet simulation.

		!!! note "xarray is at the heart of data handling in pythermogis"
		The `calculate_doublet_performance` function expects an xarray Dataset as input, which contains the necessary reservoir properties.
		@@ -11,25 +11,22 @@ the `pythermogis` package to run a deterministic (without uncertainty quatificat

		### 🧪 Basic Example

		This example demonstrates how to run a deterministic doublet simulation using the `calculate_doublet_performance` function
		for a single location. The outcomes correspond to the mean values of the input parameters,
		and the results are printed to the console.
		This example demonstrates how to run a deterministic doublet simulation using the `calculate_doublet_performance` function for a single location.
		The outcomes are deterministic, meaning there is no stochastic sampling or probabilities associated with this simulation, the results are printed to the console.

		```python
		from src.pythermogis import calculate_doublet_performance_stochastic
		from pythermogis import calculate_doublet_performance
		import xarray as xr

		input_data = xr.Dataset({
		"thickness_mean": ((), 300),
		"thickness_sd": ((), 50),
		"ntg": ((), 0.5),
		"porosity": ((), 0.5),
		"depth": ((), 2000),
		"ln_permeability_mean": ((), 5),
		"ln_permeability_sd": ((), 0.5),
		"thickness": 300,
		"ntg": 0.5,
		"porosity": 0.5,
		"depth": 2000,
		"permeability": 300,
		})

		results = calculate_doublet_performance_stochastic(input_data)
		results = calculate_doublet_performance(input_data)
		print(results)
		```

		@@ -38,25 +35,23 @@ print(results)
		### 🌍 2D Grid Example

		This example demonstrates how to run a deterministic doublet simulation using the `calculate_doublet_performance` function
		for a user defined 2-d grid of locations. The outcomes correspond to the mean values of the input parameters,
		and the results are printed to the console
		for a user defined 2-d grid of locations.
		The outcomes are deterministic, meaning there is no stochastic sampling or probabilities associated with this simulation, the results are printed to the console.

		```python
		from src.pythermogis import calculate_doublet_performance_stochastic
		from pythermogis import calculate_doublet_performance
		import xarray as xr
		import numpy as np

		input_data = xr.Dataset({
		"thickness_mean": (("x", "y"), np.array([[300, 300], [200, 200]])),
		"thickness_sd": (("x", "y"), np.array([[50, 50], [25, 25]])),
		"thickness": (("x", "y"), np.array([[300, 300], [200, 200]])),
		"ntg": (("x", "y"), np.array([[0.5, 0.5], [0.25, 0.25]])),
		"porosity": (("x", "y"), np.array([[0.5, 0.5], [0.75, 0.7]])),
		"depth": (("x", "y"), np.array([[5000, 5000], [4500, 4500]])),
		"ln_permeability_mean": (("x", "y"), np.array([[5, 5], [4.5, 4.5]])),
		"ln_permeability_sd": (("x", "y"), np.array([[0.5, 0.5], [0.75, 0.75]])),
		"permeability": (("x", "y"), np.array([[300, 300], [450, 450]])),
		}, coords={"x": [0, 1], "y": [10, 20]})

		results = calculate_doublet_performance_stochastic(input_data)
		results = calculate_doublet_performance(input_data)
		print(results)
		```

		@@ -71,23 +66,23 @@ run a deterministic doublet simulation using the `calculate_doublet_performance`
		Example input data for some of these examples is available in the `/resources/example_data` directory

		```python
		from src.pythermogis import calculate_doublet_performance_stochastic
		from pythermogis import calculate_doublet_performance
		from pygridsio import read_grid
		import numpy as np

		input_grids = read_grid("thickness.zmap").to_dataset(name="thickness_mean")
		input_grids["thickness_sd"] = read_grid("thickness_sd.zmap")
		input_grids = read_grid("thickness.zmap").to_dataset(name="thickness")
		input_grids["ntg"] = read_grid("ntg.zmap")
		input_grids["porosity"] = read_grid("porosity.zmap")
		input_grids["depth"] = read_grid("top_depth.zmap")
		input_grids["mask"] = read_grid("hydrocarbons.zmap")
		input_grids["ln_permeability_mean"] = read_grid("ln_perm.zmap")
		input_grids["ln_permeability_sd"] = read_grid("ln_perm_sd.zmap")
		input_grids["permeability"] = np.exp(read_grid("ln_perm.zmap")) # convert to natural space permeability

		results = calculate_doublet_performance_stochastic(input_grids)
		results = calculate_doublet_performance(input_grids)
		print(results)
		x, y = 125e3, 525e3 # define location
		results_loc = results.sel(x=x, y=y, method="nearest")
		print(results_loc)
		```

		note: you get the results for a specific location by using xarray function `sel` to select the desired location from the results Dataset
		!!! info "Selecting values from a dataset"
		You get the results for a specific location by using xarray function `sel` to select the desired location from the results Dataset, see [here](https://docs.xarray.dev/en/stable/user-guide/indexing.html) for more details.

docs/usage/maprun_analysis.md

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		@@ -28,7 +28,7 @@ This example demonstrates the following steps:

		Example input data for this workflow is available in the `/resources/example_data` directory of the repository.

		This example corresponds to test case `test_example5` in `test_doc_examples.py` in the `tests`
		This example corresponds to test case `test_example6` in `test_doc_examples.py` in the `tests`
		directory of the repository.

		```python
		@@ -47,7 +47,7 @@ output_data_path = Path(path.dirname(__file__), "resources") / "test_output" / "
		output_data_path.mkdir(parents=True, exist_ok=True)

		# if set to True then simulation is always run, otherwise pre-calculated results are read (if available)
		run_simulation = False
		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