Updating main (4ceb56e5) · Commits · AGS_public / pythermogis

docs/usage/customized_props.md

+4 −4

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		@@ -39,7 +39,7 @@ 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_probabilistic, instantiate_utc_properties_builder
		from src.pythermogis import calculate_doublet_performance_stochastic, instantiate_utc_properties_builder
		import xarray as xr

		input_data = xr.Dataset({
		@@ -53,7 +53,7 @@ input_data = xr.Dataset({
		})

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

		@@ -67,7 +67,7 @@ The vast majority of the parameters in the xml are not used by this python API;
		unfortunately they are still needed in the xml file to enable parsing.

		```python
		from src.pythermogis import calculate_doublet_performance_probabilistic, instantiate_utc_properties_from_xml
		from src.pythermogis import calculate_doublet_performance_stochastic, instantiate_utc_properties_from_xml
		import xarray as xr

		input_data = xr.Dataset({
		@@ -81,7 +81,7 @@ input_data = xr.Dataset({
		})

		utc_properties = instantiate_utc_properties_from_xml("path/to/valid/xml/file")
		results = calculate_doublet_performance_probabilistic(input_data, utc_properties=utc_properties)
		results = calculate_doublet_performance_stochastic(input_data, utc_properties=utc_properties)
		print(results)
		```

docs/usage/deterministic_doublet.md

+6 −6

Original line number	Diff line number	Diff line
		@@ -16,7 +16,7 @@ for a single location. The outcomes correspond to the mean values of the input p
		and the results are printed to the console.

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

		input_data = xr.Dataset({
		@@ -29,7 +29,7 @@ input_data = xr.Dataset({
		"ln_permeability_sd": ((), 0.5),
		})

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

		@@ -42,7 +42,7 @@ for a user defined 2-d grid of locations. The outcomes correspond to the mean va
		and the results are printed to the console

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

		@@ -56,7 +56,7 @@ input_data = xr.Dataset({
		"ln_permeability_sd": (("x", "y"), np.array([[0.5, 0.5], [0.75, 0.75]])),
		}, coords={"x": [0, 1], "y": [10, 20]})

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

		@@ -71,7 +71,7 @@ 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_probabilistic
		from src.pythermogis import calculate_doublet_performance_stochastic
		from pygridsio import read_grid

		input_grids = read_grid("thickness.zmap").to_dataset(name="thickness_mean")
		@@ -83,7 +83,7 @@ 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")

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

docs/usage/maprun_analysis.md

+2 −2

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		@@ -32,7 +32,7 @@ This example corresponds to test case `test_example5` in `test_doc_examples.py`
		directory of the repository.

		```python
		from pythermogis import calculate_doublet_performance_probabilistic, calculate_pos
		from pythermogis import calculate_doublet_performance_stochastic, calculate_pos
		from pygridsio import read_grid, plot_grid, resample_xarray_grid
		from matplotlib import pyplot as plt
		import xarray as xr
		@@ -64,7 +64,7 @@ reservoir_properties["ln_permeability_sd"] = resample_xarray_grid(read_grid(inpu
		if (output_data_path / "output_results.nc").exists and not run_simulation:
		results = xr.load_dataset(output_data_path / "output_results.nc")
		else:
		results = calculate_doublet_performance_probabilistic(reservoir_properties, p_values=[10, 20, 30, 40, 50, 60, 70, 80, 90])
		results = calculate_doublet_performance_stochastic(reservoir_properties, p_values=[10, 20, 30, 40, 50, 60, 70, 80, 90])
		results.to_netcdf(output_data_path / "output_results.nc") # write doublet simulation results to file

		# plot results as maps

docs/usage/portfoliorun_analysis.md

+2 −2

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		@@ -34,7 +34,7 @@ This example corresponds to test case `test_example6` in `test_doc_examples.py`
		directory of the repository.

		```python
		from pythermogis import calculate_doublet_performance_probabilistic, calculate_pos
		from pythermogis import calculate_doublet_performance_stochastic, calculate_pos
		from pygridsio import read_grid
		from matplotlib import pyplot as plt
		import xarray as xr
		@@ -65,7 +65,7 @@ for i, (x, y) in enumerate(portfolio_coords):
		locations.append(point)
		portfolio_reservoir_properties = xr.concat(locations, dim="location")

		results_portfolio = calculate_doublet_performance_probabilistic(portfolio_reservoir_properties, p_values=[10, 20, 30, 40, 50, 60, 70, 80, 90])
		results_portfolio = calculate_doublet_performance_stochastic(portfolio_reservoir_properties, p_values=[10, 20, 30, 40, 50, 60, 70, 80, 90])

		# post process: clip the npv and calculate the probability of success
		AEC = -3.5

docs/usage/pvalues_doublet.md

+2 −2

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		@@ -17,7 +17,7 @@ This example corresponds to test case `test_example4` in `test_doc_examples.py`
		directory of the repository.

		```python
		from pythermogis import calculate_doublet_performance_probabilistic
		from pythermogis import calculate_doublet_performance_stochastic
		from matplotlib import pyplot as plt
		from pathlib import Path
		from os import path
		@@ -34,7 +34,7 @@ input_data = xr.Dataset({
		"ln_permeability_mean": ((), 5),
		"ln_permeability_sd": ((), 0.5),
		})
		results = calculate_doublet_performance_probabilistic(input_data, p_values=[1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 99])
		results = calculate_doublet_performance_stochastic(input_data, p_values=[1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 99])
		fig, axe = plt.subplots(figsize=(10, 5))
		kh = results.transmissivity_with_ntg * 1.0
		kh.plot(y="p_value", ax=axe)