TNO Intern

Commit 855fe3fc authored by Florian Knappers's avatar Florian Knappers
Browse files

fix warnings

parent 2b4f9515

pythermogis/doublet.py

+4 −9
Original line number Diff line number Diff line
@@ -100,7 +100,6 @@ def calculate_doublet_performance( 
        - "transmissivity"

        - "transmissivity_with_ntg"

    """

    if print_execution_duration:

    start = timeit.default_timer()



    validate_input_deterministic(reservoir_properties)
@@ -219,7 +218,6 @@ def validate_input_deterministic(reservoir_properties: xr.Dataset): 
def calculate_doublet_performance_stochastic(

    reservoir_properties: xr.Dataset,

    utc_properties=None,

    rng_seed=None,

    p_values: list[float] | None = None,

    chunk_size: int = None,

    print_execution_duration=False,
@@ -264,8 +262,6 @@ def calculate_doublet_performance_stochastic( 
    utc_properties : JClass

        A Java class specifying the properties of the doublet being simulated



    rng_seed : int

        Random seed used for stochastic components of the simulation.



    p_values : list of float, optional

        List of probability values (e.g., [0.1, 0.5, 0.9]) for the performance
@@ -317,7 +313,6 @@ def calculate_doublet_performance_stochastic( 
        - transmissivity

        - transmissivity_with_ntg

    """

    if print_execution_duration:

    start = timeit.default_timer()



    if p_values is None:
@@ -564,7 +559,7 @@ def calculate_performance_of_single_location( 
    doublet = instantiate_thermogis_doublet(utc_properties)



    DoubletInput = JClass("thermogis.calc.utc.doublet.Doublet.DoubletInput")

    input = DoubletInput(

    doublet_input = DoubletInput(

        -9999.0,  # unknowninput

        thickness,

        transmissivity,
@@ -579,7 +574,7 @@ def calculate_performance_of_single_location( 
    # The Java routine which calculates DoubletPerformance,

    # for more detail on the simulation inspect the Java source code

    try:

        results = doublet.calculateDoubletPerformance(input)

        results = doublet.calculateDoubletPerformance(doublet_input)

    except Exception:

        return (mask_value,) * 14

pythermogis/transmissivity.py

+6 −6
Original line number Diff line number Diff line
@@ -9,7 +9,7 @@ def generate_thickness_permeability_transmissivity_for_pvalues( 
    ln_permeability_mean: float,

    ln_permeability_sd: float,

    p_values: xr.DataArray,

    nSamples: int = 10000,

    nsamples: int = 10000,

) -> float:

    """

    Given thickness provided as a normal distribution and ln(permeability)
@@ -30,7 +30,7 @@ def generate_thickness_permeability_transmissivity_for_pvalues( 
    :param ln_permeability_mean:

    :param ln_permeability_sd:

    :param p_values:

    :param nSamples:

    :param nsamples:

    :return:

    thickness, permeability, transmissivity

    """
@@ -67,11 +67,11 @@ def generate_thickness_permeability_transmissivity_for_pvalues( 


    if thickness_sd == 0:

        thickness_pvalues = np.full(len(p_value_fractions), thickness_mean)

        thickness_samples = np.full(nSamples, thickness_mean)

        thickness_samples = np.full(nsamples, thickness_mean)

    else:

        thickness_dist = stats.norm(loc=thickness_mean, scale=thickness_sd)

        thickness_pvalues = thickness_dist.ppf(p_value_fractions)

        thickness_samples = thickness_dist.rvs(nSamples)

        thickness_samples = thickness_dist.rvs(nsamples)

        thickness_samples = np.clip(thickness_samples, a_min=0.01, a_max=None)



    ln_permeability_dist = stats.norm(
@@ -81,10 +81,10 @@ def generate_thickness_permeability_transmissivity_for_pvalues( 


    # Sampling method for transmissivity

    transmissivity_samples = np.sort(

        np.exp(ln_permeability_dist.rvs(nSamples) + np.log(thickness_samples))

        np.exp(ln_permeability_dist.rvs(nsamples) + np.log(thickness_samples))

    )



    sample_indexes = np.array(p_value_fractions * (nSamples - 1))

    sample_indexes = np.array(p_value_fractions * (nsamples - 1))

    transmissivity_pvalues_sampled = transmissivity_samples[sample_indexes.astype(int)]



    return thickness_pvalues, permeability_pvalues, transmissivity_pvalues_sampled

tests/test_benchmarks.py

+5 −5
Original line number Diff line number Diff line
@@ -372,7 +372,7 @@ class PythermoGISDoubletBenchmark(TestCase): 
        )



        results = calculate_doublet_performance_stochastic(

            input_data, utc_properties=utc_properties, rng_seed=0

            input_data, utc_properties=utc_properties

        )



        # Assert
@@ -412,7 +412,7 @@ class PythermoGISDoubletBenchmark(TestCase): 


        # Act

        results = calculate_doublet_performance_stochastic(

            input_data, utc_properties=utc_properties, rng_seed=0

            input_data, utc_properties=utc_properties,

        )



        # Assert
@@ -456,7 +456,7 @@ class PythermoGISDoubletBenchmark(TestCase): 


        # Act

        results = calculate_doublet_performance_stochastic(

            input_data, utc_properties=utc_properties, rng_seed=0

            input_data, utc_properties=utc_properties,

        )



        # Assert
@@ -501,7 +501,7 @@ class PythermoGISDoubletBenchmark(TestCase): 


        # Act

        results = calculate_doublet_performance_stochastic(

            input_data, utc_properties=utc_properties, rng_seed=0

            input_data, utc_properties=utc_properties,

        )



        # Assert
@@ -545,7 +545,7 @@ class PythermoGISDoubletBenchmark(TestCase): 


        # Act

        results = calculate_doublet_performance_stochastic(

            input_data, utc_properties=utc_properties, rng_seed=0

            input_data, utc_properties=utc_properties,

        )

        power_hpelec = results.heat_pump_power / (results.cophp - 1)

        power_ratio = results.power / (results.power + power_hpelec)

tests/test_doublet.py

+2 −4
Original line number Diff line number Diff line
@@ -163,7 +163,7 @@ class PyThermoGISSpeedTest(TestCase): 
        # 43 seconds in the Java code

        start = timeit.default_timer()

        calculate_doublet_performance_stochastic(

            input_grids, chunk_size=5, rng_seed=123, p_values=p_values

            input_grids, chunk_size=5, p_values=p_values

        )

        time_elapsed = timeit.default_timer() - start

        print(f"Python calculation took {time_elapsed:.1f} seconds.")
@@ -248,7 +248,7 @@ class PyThermoGISScenarios(TestCase): 
        # scenario. Run calculation across all dimensions of input_grids,

        # and all provided P_values

        output_grids = calculate_doublet_performance_stochastic(

            self.input_grids, rng_seed=123, p_values=self.p_values

            self.input_grids, p_values=self.p_values

        )



        # Assert values are the same as the benchmark grids generated by the Java code
@@ -272,7 +272,6 @@ class PyThermoGISScenarios(TestCase): 
        output_grids = calculate_doublet_performance_stochastic(

            self.input_grids,

            utc_properties=utc_properties,

            rng_seed=123,

            p_values=self.p_values,

        )
@@ -297,7 +296,6 @@ class PyThermoGISScenarios(TestCase): 
        output_grids = calculate_doublet_performance_stochastic(

            self.input_grids,

            utc_properties=utc_properties,

            rng_seed=123,

            p_values=self.p_values,

        )