Loading examples/06_example_maps_p10_90.ipynb +1 −0 Original line number Diff line number Diff line Loading @@ -186,6 +186,7 @@ " doublet = ThermoGISDoublet(aquifer=aquifer)\n", " results = doublet.simulate(\n", " p_values=[10, 20, 30, 40, 50, 60, 70, 80, 90],\n", " chunk_size=100_000\n", " ).to_dataset()\n", " results.to_netcdf(\n", " output_data_path / \"output_results.nc\"\n", tests/test_doc_examples.py +1 −2 Original line number Diff line number Diff line Loading @@ -112,7 +112,6 @@ def test_example_5(): plt.savefig(output_data_path / "kh.png") @pytest.mark.skip("This test is computationally expensive, skip on the pipeline") def test_example_6(): # the location of the input data: # the data can be found in the resources/example_data directory of the repo Loading Loading @@ -213,7 +212,7 @@ def test_example_6(): else: results = ( ThermoGISDoublet(aquifer=aquifer) .simulate(p_values=[10, 20, 30, 40, 50, 60, 70, 80, 90]) .simulate(p_values=[10, 20, 30, 40, 50, 60, 70, 80, 90], chunk_size=100_000) .to_dataset() ) results.to_netcdf( Loading Loading
examples/06_example_maps_p10_90.ipynb +1 −0 Original line number Diff line number Diff line Loading @@ -186,6 +186,7 @@ " doublet = ThermoGISDoublet(aquifer=aquifer)\n", " results = doublet.simulate(\n", " p_values=[10, 20, 30, 40, 50, 60, 70, 80, 90],\n", " chunk_size=100_000\n", " ).to_dataset()\n", " results.to_netcdf(\n", " output_data_path / \"output_results.nc\"\n",
tests/test_doc_examples.py +1 −2 Original line number Diff line number Diff line Loading @@ -112,7 +112,6 @@ def test_example_5(): plt.savefig(output_data_path / "kh.png") @pytest.mark.skip("This test is computationally expensive, skip on the pipeline") def test_example_6(): # the location of the input data: # the data can be found in the resources/example_data directory of the repo Loading Loading @@ -213,7 +212,7 @@ def test_example_6(): else: results = ( ThermoGISDoublet(aquifer=aquifer) .simulate(p_values=[10, 20, 30, 40, 50, 60, 70, 80, 90]) .simulate(p_values=[10, 20, 30, 40, 50, 60, 70, 80, 90], chunk_size=100_000) .to_dataset() ) results.to_netcdf( Loading