updating the examples and the documentation

directory of the repository.

```python

from pythermogis import calculate_doublet_performance, calculate_pos, calculate_pos_pvalues_singlelocation

from pythermogis import calculate_doublet_performance, calculate_pos

from pygridsio import read_grid, plot_grid, resample_xarray_grid

from matplotlib import pyplot as plt

import numpy as np

import xarray as xr

from pathlib import Path

from os import path

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 = True

run_simulation = False

# 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

plt.savefig(output_data_path / "maps.png")

# plot net-present value at a single location as a function of p-value and find the probability of success

results["pos"] = calculate_pos(results) # calculate probability of success across whole aquifer

x, y = 125e3, 525e3  # define location

results_loc = results.sel(x=x, y=y, method="nearest")

pos = calculate_pos_pvalues_singlelocation(results_loc.npv, results_loc.p_value)

results_loc = results.sel(x=x,y=y,method="nearest") # select only the location of interest

pos = results_loc.pos.data   # get probability of success at location of interest as a single 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])

axes[0].set_xlabel("net-present-value [Million €]")

axes[0].set_ylabel("p-value [%]")

pos = calculate_pos(results)

plot_grid(pos, axes=axes[1], add_netherlands_shapefile=True)

plot_grid(results.pos,axes=axes[1], add_netherlands_shapefile=True)

axes[1].scatter(x,y,marker="x",color="tab:red")

plt.tight_layout()  # ensure there is enough spacing

plt.savefig(output_data_path / "pos.png")

```

---

```python

from pythermogis import calculate_doublet_performance, calculate_pos_pvalues_singlelocation

from pygridsio import read_grid, plot_grid, resample_xarray_grid

from pygridsio import read_grid

from matplotlib import pyplot as plt

import xarray as xr

from numpy import np

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"

reservoir_properties["ln_permeability_mean"] = np.log( read_grid(input_data_path / "ROSL_ROSLU__perm.zmap"))

reservoir_properties["ln_permeability_sd"] =  read_grid(input_data_path / "ROSL_ROSLU__ln_perm_sd.zmap")

x, y = 125e3, 525e3  # define location

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

                                                 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

   axe = axes[0, 0]

   results_loc.npv.plot(y="p_value", ax=axe, color=colors[ic])

   axe.set_title(f"Aquifer: ROSL_ROSLU\n  POS ")

   axe.axhline(pos, label=f"POS: {pos:.1f}% [ {x:.0f}m, {y:.0f}m]", ls="--", c=colors[ic])

    # axes.axhline(pos, label=f"probability of success: {pos:.1f}%",ls="--", c="tab:orange")

   axe.axvline(0.0, ls="--", c=colors[ic])

   axe.legend()

   axe.set_xlabel("net-present-value [Million €]")

   prd_temp = results_loc.prd_temp.sel(p_value=50, method="nearest")

   axe.axhline(50.0, label=f"POS: {pos:.1f}%  TEMP res: {temp:.1f} inj: {inj_temp:.1f} prd: {prd_temp:.1f} ",

              ls="--", c=colors[ic])

    # axes.axhline(pos, label=f"probability of success: {pos:.1f}%",ls="--", c="tab:orange")

   kh50 = kh.sel(p_value=50, method="nearest")

   axe.axvline(kh50, ls="--", c=colors[ic])

   axe.legend()

   results_loc.power.plot(y="p_value", ax=axe, color=colors[ic])

   axe.set_title(f"Aquifer: ROSL_ROSLU\n Power")

   axe.axhline(pos, label=f"POS: {pos:.1f}% [ {x:.0f}m, {y:.0f}m]", ls="--", c=colors[ic])

    # axes.axhline(pos, label=f"probability of success: {pos:.1f}%",ls="--", c="tab:orange")

   valp50 = results_loc.power.sel(p_value=50, method="nearest")

   axe.axvline(valp50, ls="--", c=colors[ic])

   axe.legend()

   results_loc.cop.plot(y="p_value", ax=axe, color=colors[ic])

   axe.set_title(f"Aquifer: ROSL_ROSLU\n COP")

   axe.axhline(pos, label=f"POS: {pos:.1f}% [ {x:.0f}m, {y:.0f}m]", ls="--", c=colors[ic])

    # axes.axhline(pos, label=f"probability of success: {pos:.1f}%",ls="--", c="tab:orange")

   valp50 = results_loc.cop.sel(p_value=50, method="nearest")

   axe.axvline(valp50, ls="--", c=colors[ic])

   axe.legend()