Adding extra performance tests to compare more directly with the Java code

import math

from dataclasses import dataclass

from pythermogis.workflow.utc.utc_properties import UTCConfiguration

from numba import njit

from pydoubletcalc import Aquifer, Doublet, Well, WellPipeSegment

from pythermogis.workflow.utc.water import get_salinity

from pythermogis.workflow.utc.rock import get_geothermal_gradient

from numba import njit

from pythermogis.workflow.utc.utc_properties import UTCConfiguration

from pythermogis.workflow.utc.water import get_salinity

INCH_SI = 0.0254

@dataclass

        well_distance=well_distance,

        cooling_fraction=0.1,

        yearly_operating_hours=props.load_hours,

        pressure_balance_tolerance=1e-2,

        pressure_balance_tolerance=1e-1,

        viscosity_mode=props.viscosity_mode,

        heat_exchanger_exit_temperature=injection_temp,

    )

    assert np.isclose(result.var_opex, -7.510325908660889, rtol=rtol)

    assert np.isclose(result.fixed_opex, -11.227973937988281, rtol=rtol)

@pytest.skip("This is for timing performance and comparing to the Java code, not needed to run on the pipeline")

def test_calculate_doublet_performance_approximate():

    # Arrange: instantiate default UTCConfiguration

    props = UTCConfiguration(

    print(f"{n_sims} simulations took: {time_elapsed:.1f} seconds\n"

          f"{n_sims/time_elapsed:.1f} simulations per second")

    # Assert

    rtol = 0.01 # accurate to 1%

    assert np.isclose(result.flow, 227.2757568359375, rtol=rtol)

from pythermogis.workflow.utc.utc_properties import UTCConfiguration

from pythermogis.workflow.utc.doublet import DoubletInput

from pythermogis.workflow.utc.doubletcalc import doubletcalc

import numpy as np

import timeit

from utils.timer import print_time

import numpy as np

import pytest

from pythermogis.workflow.utc.doublet import DoubletInput

from pythermogis.workflow.utc.doubletcalc import doubletcalc

from pythermogis.workflow.utc.utc_properties import UTCConfiguration

def test_doubletcalc():

    assert np.isclose(results.production_temp, 41.36211427733413, rtol=0.01)

    assert np.isclose(results.heat_power_produced[0], 0.027926914290870505, rtol=0.01)

@pytest.skip("This is for timing performance and comparing to the Java code, not needed to run on the pipeline")

def test_doubletcalc_performance():

    props = UTCConfiguration(segment_length=1)

    nsims = 10000

    # one iteration to warm up the JIT compiled functions

    doubletcalc(props, input_data, 600000, 1550, 40)

    start = timeit.default_timer()

    for _ in range(nsims):

        results = doubletcalc(props, input_data, 600000, 1550, 40)