Adding some extra usage information (a6f495dd) · Commits · AGS_public / pythermogis

README.md

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		@@ -8,8 +8,10 @@ It uses xarray (https://docs.xarray.dev/en/stable/index.html) Datasets as input
		## Installation for Usage

		### Install from the TNO GitLab
		This repository is currently hosted on ci.tno.nl and is currently not available for the public.
		Therefore, to install `pyThermoGIS` from the TNO GitLab repository authentication is required for now.

		To install `pyThermoGIS` from the GitLab repository for now; authentication is required. Follow these steps:
		Follow these steps:

		### 1. Create a Personal Access Token (PAT)

		@@ -29,7 +31,7 @@ username = __token__
		password = <your_personal_token>
		```

		Replace `<your personal access token>` with your actual GitLab PAT. Ensure the token has the necessary permissions to access the repository.
		Replace `<your_personal_token>` with your actual GitLab PAT. Ensure the token has the necessary permissions to access the repository.

		### 3. Install the Package

		@@ -73,6 +75,58 @@ doublet_simulation_results = calculate_doublet_performance(input_data)
		print(doublet_simulation_results)
		```

		The API utilizes xarray's ability to efficiently calculate over multiple dimensions; so you can also provide a grid of values in the input_data and the calculate_doublet_performance method will honour the input
		dimensions:

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

		# Initialize an input Dataset with the required input variables:
		input_data = xr.Dataset({
		"thickness_mean": (("x", "y"), np.array([[300, 300], [200, 200]])),
		"thickness_sd": (("x", "y"), np.array([[50, 50], [25, 25]])),
		"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]]))},
		coords=dict(
		x=("x", [0, 1]),
		y=("y", [10, 20]),
		)
		)

		# Simulate a Geothermal doublet using the ThermoGIS methodology
		doublet_simulation_results = calculate_doublet_performance(input_data)

		# Display results
		print(doublet_simulation_results)
		```

		Consider utilizing the pypi package pygridsio to read in and write out 2D raster grids (with either .asc, .zmap, .nc, .tif file formats) to a xarray dataset, before applying the calculate_doublet_performance code:
		```python
		from pythermogis.pythermogis import calculate_doublet_performance
		from pygridsio.pygridsio import read_grid

		# Initialize an input Dataset with the required input variables:
		input_grids = read_grid("thickness.zmap").to_dataset(name="thickness_mean")
		input_grids["thickness_sd"] = read_grid("thickness_sd.zmap")
		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")

		# Simulate a Geothermal doublet using the ThermoGIS methodology
		doublet_simulation_results = calculate_doublet_performance(input_grids)

		# Display results
		print(doublet_simulation_results)
		```

		## Contributing

		Contributions are welcome! Feel free to open issues or submit pull requests.

build/lib/pythermogis/init.py

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build/lib/pythermogis/physics/init.py

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build/lib/pythermogis/physics/temperature_grid_calculation.py

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def calculate_temperature_from_gradient(depth: float, thickness: float, degrees_per_km: float, surface_temperature: float):

    return surface_temperature + (depth + thickness / 2) * (degrees_per_km * 0.001)

build/lib/pythermogis/pythermogis.py

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		from typing import List

		from pythermogis.thermogis_classes.doublet import calculate_doublet_performance_across_dimensions
		import xarray as xr


		def calculate_doublet_performance(input_data: xr.Dataset,
		input_params: dict = None,
		p_values: List[float] = None) -> xr.Dataset:
		"""
		An access point to the calculate_doublet_performance_across_dimensions function in pythermogis.thermogis_classes.doublet, see that method for more detailed instruction
		"""
		return calculate_doublet_performance_across_dimensions(input_data, input_params=input_params, p_values=p_values)