TNO Intern

Commit d96db565 authored by Hen Brett's avatar Hen Brett 🐔
Browse files

Allowing the complex logic of the heat pump calculation to occur

parent 3d4be995

pythermogis/pythermogis.py

+1 −2
Original line number Diff line number Diff line
@@ -6,9 +6,8 @@ import xarray as xr 


def calculate_doublet_performance(input_data: xr.Dataset,

                                  input_params: dict = None,

                                  rng_seed: int = 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, rng_seed=rng_seed, p_values=p_values)
 
    return calculate_doublet_performance_across_dimensions(input_data, input_params=input_params, p_values=p_values)

pythermogis/thermogis_classes/doublet.py

+34 −22
Original line number Diff line number Diff line
@@ -85,7 +85,6 @@ def calculate_performance_of_single_location(mask: float, depth: float, thicknes 


def calculate_doublet_performance_across_dimensions(input_data: xr.Dataset,

                                                    input_params: dict = None,

                                  rng_seed: int =None,

                                                    p_values: List[float] = None) -> xr.Dataset:

    """

    Perform a ThermoGIS Doublet performance assessment. This will occur across all dimensions of the input_data (ie. input data can have a single value for each required variable, or it can be 1Dimensional or a 2Dimensional grid)
@@ -131,9 +130,13 @@ def calculate_doublet_performance_across_dimensions(input_data: xr.Dataset, 
                             "stimKhMax": 20,

                             "use_stimulation": False,

                             "use_heat_pump": False,

                             "calculate_cop": True,

                             "hp_application_mode": False,

                             "hp_direct_heat_input_temp": 70.0,

                             "utc_cutoff_shallow": 5.1,

                             "utc_cutoff_deep": 6.5,

                    "utc_cutoff_depth": 4000.0}

                             "utc_cutoff_depth": 4000.0,

                             "rng_seed": np.random.randint(low=0, high=10000)}



    if input_params is None:  # If no input_params provided, use the basecase

        input_params = input_params_basecase
@@ -148,8 +151,14 @@ def calculate_doublet_performance_across_dimensions(input_data: xr.Dataset, 
    if "mask" not in input_data:

        input_data["mask"] = np.nan



    # if (props.useHeatPump() & & props.calculateCop() & & !props.hpApplicationMode() ) {

    #     doublet.doubletCalc1DData.setInjectionTemp(doublet.calculateInjectionTempWithHeatPump(temperature, props.hpDirectHeatInputTemp()));

    # }



    # Instantiate ThermoGIS doublet

    doublet = instantiate_thermogis_doublet(rng_seed=rng_seed)

    doublet = instantiate_thermogis_doublet(input_params)



    # set injectionTemp differently for heat_pump



    # Setup output_data dataset

    output_data = input_data.thickness_mean.copy().to_dataset(name="thickness")
@@ -239,7 +248,7 @@ def calculate_doublet_performance_across_dimensions(input_data: xr.Dataset, 
    return output_data





def instantiate_thermogis_doublet(rng_seed=None):

def instantiate_thermogis_doublet(input_params):

    """

    Instantiate a ThermoGIS Doublet class, with a set random seed if provided

    :param rng_seed:
@@ -257,13 +266,16 @@ def instantiate_thermogis_doublet(rng_seed=None): 


    # Instantiate the UTC properties class

    propsBuilder = UTCPropertiesBuilder()

    utc_properties = propsBuilder.build()

    utc_properties = (propsBuilder

                      .setUseStimulation(input_params["use_stimulation"])

                      .setUseHeatPump(input_params["use_heat_pump"])

                      .setCalculateCop(input_params["calculate_cop"])

                      .setHpApplicationMode(input_params["hp_application_mode"])

                      .setHpDirectHeatInputTemp(input_params["hp_direct_heat_input_temp"])

                      .build())



    # Instantiate random number generator:

    if rng_seed is None:

        rng = RNG()

    else:

        rng = RNG(rng_seed)

    rng = RNG(input_params["rng_seed"])



    # Create an instance of a ThermoGISDoublet

    doublet = ThermoGISDoublet(Mockito.mock(Logger), rng, utc_properties)

tests/tests/test_doublet.py

+16 −4
Original line number Diff line number Diff line
@@ -8,7 +8,7 @@ import xarray as xr 
from jpype.types import *

from pygridsio.pygridsio import read_grid



from pythermogis.thermogis_classes.doublet import calculate_doublet_performance

from pythermogis.pythermogis import calculate_doublet_performance

from pythermogis.thermogis_classes.java_start import start_jvm
@@ -80,7 +80,7 @@ class PyThermoGIS(TestCase): 
        input_grids = self.read_input_grids()



        # Run calculation across all dimensions of input_grids, and all provided P_values

        output_grids = calculate_doublet_performance(input_grids, rng_seed=123, p_values=[10, 20, 30, 40, 50, 60, 70, 80, 90])

        output_grids = calculate_doublet_performance(input_grids, input_params={"rng_seed": 123}, p_values=[10, 20, 30, 40, 50, 60, 70, 80, 90])



        # Assert values are the same as the benchmark grids generated by the Java code

        for p_value in [10, 50, 90]:
@@ -88,6 +88,19 @@ class PyThermoGIS(TestCase): 
            output_grids_p_value = output_grids_p_value.drop_vars("p_value")

            self.assert_output_and_benchmark_is_close(self.test_benchmark_output_basecase, output_grids_p_value, p_value, "")



    def test_doublet_grid_HP(self):

        # Act

        # Read Input grids

        input_grids = self.read_input_grids()



        # Run calculation across all dimensions of input_grids, and all provided P_values

        output_grids = calculate_doublet_performance(input_grids, input_params={"rng_seed": 123, "use_heat_pump": True}, p_values=[10, 20, 30, 40, 50, 60, 70, 80, 90])



        # Assert values are the same as the benchmark grids generated by the Java code

        for p_value in [50]:

            output_grids_p_value = output_grids.sel(p_value=p_value)

            output_grids_p_value = output_grids_p_value.drop_vars("p_value")

            self.assert_output_and_benchmark_is_close(self.test_benchmark_output_HP, output_grids_p_value, p_value, "_HP")



    def test_doublet_grid_STIM(self):

        # Act
@@ -95,7 +108,7 @@ class PyThermoGIS(TestCase): 
        input_grids = self.read_input_grids()



        # Run calculation across all dimensions of input_grids, and all provided P_values

        output_grids = calculate_doublet_performance(input_grids, rng_seed=123, p_values=[10, 20, 30, 40, 50, 60, 70, 80, 90], input_params={"use_stimulation": True})

        output_grids = calculate_doublet_performance(input_grids, input_params={"rng_seed": 123, "use_stimulation": True}, p_values=[10, 20, 30, 40, 50, 60, 70, 80, 90])



        # Assert values are the same as the benchmark grids generated by the Java code

        for p_value in [50]:
@@ -156,4 +169,3 @@ class PyThermoGIS(TestCase): 
        # Act & Assert

        with self.assertRaises(ValueError):

            calculate_doublet_performance(input_data)