TNO Intern

Commit a293a735 authored by Hen Brett's avatar Hen Brett 🐔
Browse files

Adding logos and checking the test script used for the latest example added... 

Adding logos and checking the test script used for the latest example added for POS4NPV by Jan-diederik
parent 06726d07

docs/images/GeologischeDienstlogo.svg

0 → 100644
+1 −0
Original line number Diff line number Diff line 
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docs/images/TNOLogo.jpg

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docs/index.md

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# pyThermoGIS



**pyThermoGIS** is a Python package that provides API access to the [ThermoGIS](https://www.thermogis.nl/en) doublet simulations and economic calculations. 

**pyThermoGIS** is a Python package that provides API access to the [ThermoGIS](https://www.thermogis.nl/en) doublet simulations and economic calculations,  ThermoGIS is developed by the [Geological Survey of the Netherlands](https://www.geologischedienst.nl/en/) which is part of [TNO](https://www.tno.nl/en/).



<div style="display: flex; gap: 10px;">

  <img src="../images/pyThermoGIS_transparent.png" style="width: 15%; max-width: 400px; height: auto;">

  <img src="../images/TNOLogo.jpg" alt="Image 1" style="width: 25%; max-width: 400px; height: auto;">

  <img src="../images/GeologischeDienstLogo.svg" style="width: 45%; max-width: 400px; height: auto;">

</div>





This package allows a user to simulate geothermal doublets providing the following parameters:

tests/resources/test_output/example_data/npv.png

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tests/test_doc_examples.py

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import numpy as np

import xarray as xr









def test_example_1():
@@ -59,7 +56,7 @@ def test_example_3(): 




def test_example_4():

    from pythermogis import calculate_doublet_performance, instantiate_utc_properties_builder

    from pythermogis import calculate_doublet_performance

    import xarray as xr

    from matplotlib import pyplot as plt

    from pathlib import Path
@@ -165,9 +162,8 @@ def test_example_5(): 


def test_example_6():

    from pythermogis import calculate_doublet_performance,  calculate_pos4npv_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 pathlib import Path

    from os import path

    input_data_path = Path(path.dirname(__file__), "resources") / "test_input" / "example_data"
@@ -183,7 +179,6 @@ def test_example_6(): 
    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
@@ -204,12 +199,10 @@ def test_example_6(): 
                                                       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 €]")
@@ -224,20 +217,16 @@ def test_example_6(): 
        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()

        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")

        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()
@@ -248,7 +237,6 @@ def test_example_6(): 
        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()