Fixing the doc examples to match the xarray way of working (edd7b295) · Commits · AGS_public / pythermogis

docs/usage/maprun_analysis.md

+23 −24

Original line number	Diff line number	Diff line
		@@ -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, calculate_pos4npv_pvalues, calculate_pos4npv_pvalues_singlelocation
		from pythermogis import calculate_doublet_performance, calculate_pos, calculate_pos_pvalues_singlelocation
		from pygridsio import read_grid, plot_grid, resample_xarray_grid
		from matplotlib import pyplot as plt
		import numpy as np
		@@ -40,7 +40,6 @@ import xarray as xr
		from pathlib import Path
		from os import path


		# the location of the input data: the data can be found in the resources/example_data directory of the repo
		input_data_path = Path(path.dirname(__file__), "resources") / "test_input" / "example_data"

		@@ -83,7 +82,7 @@ else:
		# plot net-present value at a single location as a function of p-value and find the probability of success
		x, y = 125e3, 525e3 # define location
		results_loc = results.sel(x=x, y=y, method="nearest")
		pos = calculate_pos4npv_pvalues_singlelocation(results_loc.npv, results_loc.p_value)
		pos = calculate_pos_pvalues_singlelocation(results_loc.npv, results_loc.p_value)
		# plot npv versus p-value and a map showing the location of interest
		fig, axes = plt.subplots(ncols=2, figsize=(10, 5))
		results_loc.npv.plot(y="p_value", ax=axes[0])
		@@ -94,7 +93,7 @@ axes[0].legend()
		axes[0].set_xlabel("net-present-value [Million €]")
		axes[0].set_ylabel("p-value [%]")

		pos = calculate_pos4npv_pvalues(results)
		pos = calculate_pos(results)
		plot_grid(pos, axes=axes[1], add_netherlands_shapefile=True)
		axes[1].scatter(x, y, marker="x", color="tab:red")

docs/usage/portfoliorun_analysis.md

+92 −94

Original line number	Diff line number	Diff line
		@@ -34,12 +34,13 @@ This example corresponds to test case `test_example6` in `test_doc_examples.py`
		directory of the repository.

		```python
		from pythermogis import calculate_doublet_performance, calculate_pos4npv_pvalues_singlelocation
		from pythermogis import calculate_doublet_performance, calculate_pos_pvalues_singlelocation
		from pygridsio import read_grid, plot_grid, resample_xarray_grid
		from matplotlib import pyplot as plt
		import xarray as xr
		from pathlib import Path
		from os import path

		input_data_path = Path(path.dirname(__file__), "resources") / "test_input" / "example_data"
		# create a directory to write the output files to
		output_data_path = Path(path.dirname(__file__), "resources") / "test_output" / "example_data"
		@@ -57,7 +58,6 @@ directory of the repository.
		portfolioloc = [(125e3, 525e3), (100e3, 525e3), (110e3, 525e3), (125e3, 515e3),
		(125e3, 520e3)] # define location


		fig, axes = plt.subplots(ncols=2, nrows=2, figsize=(10, 10))
		colors = plt.cm.tab10.colors
		ic = 0
		@@ -70,7 +70,7 @@ directory of the repository.
		results_loc['npv'] = results_loc.npv.clip(min=AEC)

		# probability of success is defined as the p-value where npv = 0.0, use interpolation to find pos:
		pos = calculate_pos4npv_pvalues_singlelocation(results_loc.npv,
		pos = calculate_pos_pvalues_singlelocation(results_loc.npv,
		results_loc.p_value) # The order of the npv data has to be reversed as np.interp requires values to increase to function properly

		# plot npv versus p-value and a map showing the location of interest
		@@ -101,8 +101,6 @@ directory of the repository.
		axe.set_xlabel("kH [Dm]")
		axe.set_ylabel("p-value [%]")



		axe = axes[1, 0]
		results_loc.power.plot(y="p_value", ax=axe, color=colors[ic])
		axe.set_title(f"Aquifer: ROSL_ROSLU\n Power")

src/pythermogis/init.py

+2 −1

Original line number	Diff line number	Diff line
		@@ -4,3 +4,4 @@ from pythermogis.thermogis_classes.doublet import *
		from pythermogis.thermogis_classes.utc_properties import *
		from pythermogis.transmissivity.calculate_thick_perm_trans import *
		from pythermogis.tables.utc_properties_table import *
		from pythermogis.postprocessing.pos import *
		No newline at end of file

src/pythermogis/postprocessing/init.py

0 → 100644

+0 −0

Empty file added.

src/pythermogis/postprocessing/pos.py

0 → 100644

+44 −0

Original line number	Diff line number	Diff line
		import xarray as xr
		import numpy as np

		def calculate_pos_pvalues_singlelocation(npv: xr.DataArray, p_values: xr.DataArray) -> float:
		"""
		Calculate the probability of success (POS) from net present value (NPV) and p-values.

		Parameters
		----------
		npv : xr.DataArray
		Net present value.
		p_values : xr.DataArray
		P-values corresponding to the NPV.

		Returns
		-------
		float
		The probability of success (POS) as a percentage.
		"""
		# Ensure that npv and p_values are 1D arrays
		if npv.ndim != 1 or p_values.ndim != 1:
		raise ValueError("Both npv and p_values must be 1D arrays.")

		# Reverse the order of npv and p_values for interpolation
		pos = np.interp(0.0, npv[::-1], p_values[::-1])

		return pos

		def calculate_pos(results:xr.Dataset) -> xr.Dataset:
		"""
		Calculate the probability of success (POS) for each p-value in the results dataset based on the net present value (NPV).
		"""
		# alternatively calculate pos map, by xr.apply_ufunc and plot ma
		pos = xr.apply_ufunc(
		calculate_pos_pvalues_singlelocation,
		results.npv,
		results.p_value,
		input_core_dims=[['p_value'], ['p_value']],
		vectorize=True,
		dask='parallelized',
		output_dtypes=[float]
		)
		pos.name = "POS" # set name for the pos variable
		return pos
		No newline at end of file