new filestructure (6efd2b75) · Commits · AGS_public / pythermogis

.gitignore

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		@@ -10,4 +10,4 @@ dist

		tests/resources/test_output

		/src/pythermogis/jvm/JVM17
		No newline at end of file
		/src/pythermogis/resources/JVM17
		No newline at end of file

docs/usage/customised_stochastic_simulations.md

+32 −26

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		@@ -11,16 +11,18 @@ Luckily, it is easy (and insightful) to use pythermogis to generate samples and
		Here is a simple example where you define probability distributions on your input parameters and then run simulations across random combinations of those input parameters before deriving statistics from those simulations:

		```python
		from pythermogis import calculate_doublet_performance, calculate_pos, plot_exceedance
		from pythermogis.doublet import calculate_doublet_performance
		from pythermogis.plot import plot_exceedance
		from pythermogis.postprocess import calculate_pos
		import xarray as xr
		from matplotlib import pyplot as plt
		from pathlib import Path
		from os import path
		import numpy as np


		# output directory to write output to
		output_data_path = Path(path.dirname(__file__), "resources") / "test_output" / "exceedance_example"
		output_data_path = Path(path.dirname(__file__),
		"resources") / "test_output" / "exceedance_example"
		output_data_path.mkdir(parents=True, exist_ok=True)

		# generate simulation samples across desired reservoir properties
		@@ -42,13 +44,16 @@ reservoir_properties = xr.Dataset(
		)

		# For every sample, run a doublet simulation store the output values
		simulations = calculate_doublet_performance(reservoir_properties, print_execution_duration=True)
		simulations = calculate_doublet_performance(reservoir_properties,
		print_execution_duration=True)

		# post process the samples to calculate the percentiles of each variable
		percentiles = np.arange(1, 99)
		results = simulations.quantile(q=percentiles / 100, dim="sample")
		results = results.assign_coords(quantile=("quantile", percentiles)) # Overwrite the 'quantile' coordinate with integer percentiles
		results = results.rename({"quantile": "p_value"}) # Rename dimension to 'p_value' to match nomenclature in the rest of pythermogis
		results = results.assign_coords(quantile=("quantile",
		percentiles)) # Overwrite the 'quantile' coordinate with integer percentiles
		results = results.rename({
		"quantile": "p_value"}) # Rename dimension to 'p_value' to match nomenclature in the rest of pythermogis
		results["pos"] = calculate_pos(results)

		# plotting
		@@ -76,7 +81,8 @@ plot_exceedance(results["permeability"], ax=axes[0,0])
		plot_exceedance(results["utc"], ax=axes[0, 1])
		plot_exceedance(results["power"], ax=axes[1, 0])
		plot_exceedance(results["npv"], ax=axes[1, 1])
		axes[1, 1].axhline(results["pos"], ls="--", c="tab:orange", label=f"POS: {results["pos"]:.1f}%") # add the probability of success
		axes[1, 1].axhline(results["pos"], ls="--", c="tab:orange",
		label=f"POS: {results["pos"]:.1f}%") # add the probability of success
		axes[1, 1].axvline(0, ls="--", c="tab:orange") # add the probability of success
		axes[1, 1].legend()
		plt.savefig(output_data_path / "exceedance_probabilities.png")

docs/usage/customized_props.md

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		@@ -39,7 +39,8 @@ Here is an example, where the default utc_properties is used, but the UseHeatpum
		`utc_properties` themselves, with the `.setupUTCParameters()` method of the `thermogis_properties`.

		```python
		from pythermogis import calculate_doublet_performance, instantiate_thermogis_parameters
		from pythermogis.doublet import calculate_doublet_performance
		from pythermogis.properties import instantiate_thermogis_parameters
		import xarray as xr

		input_data = xr.Dataset({
		@@ -67,7 +68,8 @@ 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_stochastic, instantiate_thermogis_properties_from_file
		from pythermogis.doublet import calculate_doublet_performance_stochastic
		from pythermogis.properties import instantiate_thermogis_properties_from_file
		import xarray as xr

		input_data = xr.Dataset({

docs/usage/depth_optimization.md

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		@@ -25,7 +25,7 @@ import numpy as np
		import xarray as xr
		from matplotlib import pyplot as plt

		from pythermogis import calculate_doublet_performance
		from pythermogis.doublet import calculate_doublet_performance

		# output directory to write output to
		output_data_path = Path(__file__).parent.parent / "resources" / "test_output" / "example9"

docs/usage/deterministic_doublet.md

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		@@ -15,7 +15,7 @@ This example demonstrates how to run a deterministic doublet simulation using th
		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 pythermogis import calculate_doublet_performance
		from pythermogis.doublet import calculate_doublet_performance
		import xarray as xr

		input_data = xr.Dataset({
		@@ -39,7 +39,7 @@ 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 pythermogis import calculate_doublet_performance
		from pythermogis.doublet import calculate_doublet_performance
		import xarray as xr
		import numpy as np