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Commit 07f3c0d0 authored by Hen Brett's avatar Hen Brett 🐔
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Testing the speed of the doublet calculations

parent c6a04dba

pythermogis/__init__.py

+1 −1
Original line number Diff line number Diff line 
from thermogis_classes.doublet import calculate_performance_of_single_location, calculate_doublet_performance
 
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from .thermogis_classes.doublet import calculate_performance_of_single_location, calculate_doublet_performance
 
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pythermogis/statistics/calculate_thick_perm_trans.py

+6 −1
Original line number Diff line number Diff line
@@ -20,6 +20,9 @@ def generate_thickness_permeability_transmissivity_for_pvalue(thickness_mean: fl 
    :return:

    thickness, permeability, transmissivity

    """

    if np.isnan(thickness_mean) | np.isnan(thickness_sd) | np.isnan(ln_permeability_mean) | np.isnan(ln_permeability_sd):

        return np.nan, np.nan, np.nan



    p_value_fraction = Pvalue / 100



    thickness_dist = stats.norm(loc=thickness_mean, scale=thickness_sd)
@@ -29,7 +32,9 @@ def generate_thickness_permeability_transmissivity_for_pvalue(thickness_mean: fl 
    permeability_pvalue = np.exp(ln_permeability_dist.ppf(1 - p_value_fraction))



    # Sampling method for transmissivity

    transmissivity_samples = np.sort(np.exp(ln_permeability_dist.rvs(nSamples) + np.log(thickness_dist.rvs(nSamples))))

    thickness_samples = thickness_dist.rvs(nSamples)

    thickness_samples = np.where(thickness_samples < 0.0, 0.1, thickness_samples)   # Ensure no negative thicknesses

    transmissivity_samples = np.sort(np.exp(ln_permeability_dist.rvs(nSamples) + np.log(thickness_samples)))

    transmissivity_pvalue_sampled = transmissivity_samples[int((1 - p_value_fraction) * nSamples)]



    return thickness_pvalue, permeability_pvalue, transmissivity_pvalue_sampled

pythermogis/thermogis_classes/doublet.py

+53 −39
Original line number Diff line number Diff line 
from typing import List



import timeit

import numpy as np

import xarray as xr

from jpype import JClass
@@ -61,6 +62,8 @@ def calculate_doublet_performance(input_data: xr.Dataset, 
    output_data["temperature"] = input_data["temperature"].copy()



    # Calculate Thickness, Permeability and Transmissivity for each P-value



    start = timeit.default_timer()

    output_data["thickness"], output_data["permeability"], output_data["transmissivity"] = xr.apply_ufunc(generate_thickness_permeability_transmissivity_for_pvalue,

                                                             input_data.thickness_mean,

                                                             input_data.thickness_sd,
@@ -71,6 +74,8 @@ def calculate_doublet_performance(input_data: xr.Dataset, 
                                                             output_core_dims=[[], [], []],

                                                             vectorize=True

                                                             )

    stop = timeit.default_timer()

    print('\nTime elapsed: %.1f seconds' % (stop - start))



    # Calculate transmissivity scaled by ntg and converted to Dm

    output_data[f"transmissivity_with_ntg"] = (output_data[f"transmissivity"] * input_data.ntg) / 1e3
@@ -80,6 +85,7 @@ def calculate_doublet_performance(input_data: xr.Dataset, 
    doublet = instantiate_thermogis_doublet(input_params)



    # Calculate the doublet performance across all dimensions

    start = timeit.default_timer()

    output_data_arrays = xr.apply_ufunc(calculate_performance_of_single_location,

                                        input_data.mask,

                                        input_data.depth,
@@ -93,7 +99,11 @@ def calculate_doublet_performance(input_data: xr.Dataset, 
                                        input_core_dims=[[], [], [], [], [], [], [], []],

                                        output_core_dims=[[], [], [], [], [], [], [], [], [], [], [], []],

                                        vectorize=True,

                                        dask="parallelized"

                                        )

    stop = timeit.default_timer()

    print('\nTime elapsed: %.1f seconds' % (stop - start))





    # Assign output DataArrays to the output_data object

    output_data["power"] = output_data_arrays[0]
@@ -154,38 +164,6 @@ def apply_input_params(input_params: dict) -> dict: 
        return {key: input_params.get(key, input_params_basecase[key]) for key in input_params_basecase}





def instantiate_thermogis_doublet(input_params: dict):

    """

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

    :param rng_seed:

    :return:

        doublet, a JPype instantiated object of the ThermoGISDoublet class

    """

    # Start the jvm

    start_jvm()

    # Instantiate doublet class

    Logger = JClass("logging.Logger")

    Mockito = JClass("org.mockito.Mockito")

    RNG = JClass("tno.geoenergy.stochastic.RandomNumberGenerator")

    ThermoGISDoublet = JClass("thermogis.calc.doublet.ThermoGisDoublet")

    UTCPropertiesBuilder = JClass("thermogis.properties.builders.UTCPropertiesBuilder")



    # Instantiate the UTC properties class

    propsBuilder = UTCPropertiesBuilder()

    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:

    rng = RNG(input_params["rng_seed"])



    # Create an instance of a ThermoGISDoublet

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

    return doublet





def calculate_performance_of_single_location(mask: float, depth: float, thickness: float, porosity: float, ntg: float, temperature: float, transmissivity: float, transmissivity_with_ntg: float, doublet=None,
@@ -261,6 +239,46 @@ def calculate_performance_of_single_location(mask: float, depth: float, thicknes 
        "cophp"], output_values["pres"], output_values["flow_rate"], output_values["welld"]





def instantiate_thermogis_doublet(input_params: dict):

    """

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

    :param rng_seed:

    :return:

        doublet, a JPype instantiated object of the ThermoGISDoublet class

    """

    # Start the jvm

    start_jvm()

    # Instantiate doublet class

    Logger = JClass("logging.Logger")

    Mockito = JClass("org.mockito.Mockito")

    RNG = JClass("tno.geoenergy.stochastic.RandomNumberGenerator")

    ThermoGISDoublet = JClass("thermogis.calc.doublet.ThermoGisDoublet")

    UTCPropertiesBuilder = JClass("thermogis.properties.builders.UTCPropertiesBuilder")



    # Instantiate the UTC properties class

    propsBuilder = UTCPropertiesBuilder()

    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:

    rng = RNG(input_params["rng_seed"])



    # Create an instance of a ThermoGISDoublet

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



    # Set parameters that do not change across cells

    doublet.doubletCalc1DData.setSurfaceTemperature(input_params["surface_temperature"])

    doublet.doubletCalc1DData.setUseHeatPump(input_params["use_heat_pump"])

    doublet.doubletCalc1DData.setDhReturnTemp(input_params["return_temperature"])





    return doublet



def set_doublet_parameters(doublet, transmissivity_with_ntg: float, depth: float, porosity: float, ntg: float, temperature: float, input_params: dict):

    """

    For a single location sets the necessary data on the doublet class, to then run a doublet simulation
@@ -285,16 +303,12 @@ def set_doublet_parameters(doublet, transmissivity_with_ntg: float, depth: float 
    doublet.doubletCalc1DData.setDepth(depth)

    doublet.doubletCalc1DData.setPorosity(porosity)

    doublet.doubletCalc1DData.setNtg(ntg)

    doublet.doubletCalc1DData.setSurfaceTemperature(input_params["surface_temperature"])

    doublet.doubletCalc1DData.setReservoirTemp(temperature)

    doublet.doubletCalc1DData.setUseHeatPump(input_params["use_heat_pump"])



    if not input_params["use_heat_pump"]:

        injectionTemp = np.max([temperature - input_params["max_cooling_temperature_range"], input_params["return_temperature"]])

        doublet.doubletCalc1DData.setInjectionTemp(np.max([temperature - input_params["max_cooling_temperature_range"], input_params["return_temperature"]]))

    elif input_params["use_heat_pump"] and input_params["calculate_cop"] and not input_params["hp_application_mode"]:

        injectionTemp = doublet.calculateInjectionTempWithHeatPump(temperature, input_params["hp_direct_heat_input_temp"])

        doublet.doubletCalc1DData.setInjectionTemp(doublet.calculateInjectionTempWithHeatPump(temperature, input_params["hp_direct_heat_input_temp"]))

    else:

        injectionTemp = np.max([temperature - input_params["max_cooling_temperature_range"], input_params["hp_minimum_injection_temperature"]])

        doublet.doubletCalc1DData.setInjectionTemp(np.max([temperature - input_params["max_cooling_temperature_range"], input_params["hp_minimum_injection_temperature"]]))

    doublet.doubletCalc1DData.setInjectionTemp(injectionTemp)

    doublet.doubletCalc1DData.setDhReturnTemp(input_params["return_temperature"])

pythermogis/thermogis_classes/jvm_start.py

+2 −0
Original line number Diff line number Diff line 
import sys

from importlib.resources import files



import jpype

import logging





def get_jvm_path() -> str:

tests/resources/test_input/SLDNA/SLDNA__hc_accum.nc

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