removing redundant transmissivity generation

from scipy import stats

import xarray as xr

def generate_thickness_permeability_transmissivity_for_pvalue(thickness_mean: float, thickness_sd: float, ln_permeability_mean: float, ln_permeability_sd: float, p_values: float, nSamples: int = 10000) -> float:

    """

    Given thickness provided as a normal distribution and ln(permeability) provided as a normal distribution, and a specific p-value, then generate the values for that p-value of the

    -thickness

    -permeability

    -transmissivity

    Note: Transmissivity is a function of ln(permeability) * thickness, and so no analytical solution exists to combine these two probability distributions and so the transmissivity distribution has to be sampled.

    :param thickness_mean:

    :param thickness_sd:

    :param ln_permeability_mean:

    :param ln_permeability_sd:

    :param Pvalue:

    :param nSamples:

    :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_fractions = p_values / 100

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

    thickness_pvalues = thickness_dist.ppf(1 - p_value_fractions)

    ln_permeability_dist = stats.norm(loc=ln_permeability_mean, scale=ln_permeability_sd)

    permeability_pvalues = np.exp(ln_permeability_dist.ppf(1 - p_value_fractions))

    # Sampling method for transmissivity

    thickness_samples = thickness_dist.rvs(nSamples)

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

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

    transmissivity_pvalues_sampled = transmissivity_samples[int((1 - p_value_fractions) * nSamples)]

    return thickness_pvalues, permeability_pvalues, transmissivity_pvalues_sampled

def generate_thickness_permeability_transmissivity_for_pvalues(thickness_mean: float, thickness_sd: float, ln_permeability_mean: float, ln_permeability_sd: float, p_values: xr.DataArray, nSamples: int = 10000) -> float:

    """

    Given thickness provided as a normal distribution and ln(permeability) provided as a normal distribution, and a specific p-value, then generate the values for that p-value of the

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

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

    sample_indexes = p_value_fractions * nSamples

    sample_indexes = np.array(p_value_fractions * nSamples)

    transmissivity_pvalues_sampled = transmissivity_samples[sample_indexes.astype(int)]

    return thickness_pvalues, permeability_pvalues, transmissivity_pvalues_sampled

 No newline at end of file

import xarray as xr

from jpype.types import *

from pythermogis.statistics.calculate_thick_perm_trans import generate_thickness_permeability_transmissivity_for_pvalue

from pythermogis.statistics.calculate_thick_perm_trans import generate_thickness_permeability_transmissivity_for_pvalues

from pythermogis.thermogis_classes.jvm_start import start_jvm

            ))

        # Calculate Thickness, Permeability and Transmissivity for each P-value using the Python implementation

        thickness, permeability, transmissivity = xr.apply_ufunc(generate_thickness_permeability_transmissivity_for_pvalue,

        thickness, permeability, transmissivity = xr.apply_ufunc(generate_thickness_permeability_transmissivity_for_pvalues,

                                                                 thickness_mean,

                                                                 thickness_sd,

                                                                 ln_perm_mean,