Removing unused files.

import h5py

import numpy as np

import os.path

def check_solution(path_to_results):

    # Load reference flux

    ref_flux_filename = os.path.join(path_to_results,'c5g7_3d_flux_ref.h5')

    assert os.path.isfile(ref_flux_filename)

    ref_flux_file = h5py.File(ref_flux_filename,'r')

    ref_flux     = np.array(ref_flux_file['flux_mean'])

    ref_flux_std = np.array(ref_flux_file['flux_std_dev'])

    # Load reference k

    ref_k_filename = os.path.join(path_to_results,'c5g7_3d_output_ref.h5')

    assert os.path.isfile(ref_k_filename)

    ref_k_file = h5py.File(ref_k_filename,'r')

    ref_k_dset     = ref_k_file['/keff/mean']

    ref_k_var_dset = ref_k_file['/keff/variance']

    ref_k = ref_k_dset[0]

    ref_k_std = np.sqrt(ref_k_var_dset[0])

    print('\nReference k = {0:.6f} +/- {1:.3e}'.format(ref_k,ref_k_std))

    # Load flux file for comparison

    flux_filename = os.path.join(path_to_results,'c5g7_3d_flux.h5')

    assert os.path.isfile(flux_filename)

    flux_file = h5py.File(flux_filename,'r')

    flux     = np.array(flux_file['flux_mean'])

    flux_std = np.array(flux_file['flux_std_dev'])

    # Load k file for comparison

    k_filename = os.path.join(path_to_results,'c5g7_3d_output.h5')

    assert os.path.isfile(k_filename)

    k_file = h5py.File(k_filename,'r')

    k_dset     = k_file['/keff/mean']

    k_var_dset = k_file['/keff/variance']

    k = k_dset[0]

    k_std = np.sqrt(k_var_dset[0])

    print('New k = {0:.6f} +/- {1:.3e}'.format(k,k_std))

    passes = True

    # Test to 4 standard deviations, should *almost* never fail

    safety_factor = 4

    # Check k values

    kdiff = np.abs(k - ref_k)

    tol = safety_factor*np.sqrt(k_std*k_std + ref_k_std*ref_k_std)

    print('Difference in k = {0:.2e} with tolerance of {1:.2e}'.format(kdiff,tol))

    passes = kdiff < tol

    if passes:

        'Eigenvalue difference is acceptable'

    else:

        'Eigenvalue difference is outside expected tolerance'

    # Cycle-to-cycle correlations cause reported standard deviation to

    # be under-reported for tally values.  Increase safety factor on flux

    # checks to eliminate false positives.

    safety_factor = 10

    # Check flux

    N = len(ref_flux)

    assert N == len(ref_flux_std)

    assert N == len(flux)

    assert N == len(flux_std)

    failed_indices = []

    max_ratio = 0.0

    for i in range(N):

        ref_val = ref_flux[i]

        ref_std = ref_flux_std[i]

        val     = flux[i]

        std     = flux_std[i]

        tol = safety_factor*np.sqrt(ref_std*ref_std + std*std)

        flux_diff = np.abs(val - ref_val)

        ratio = flux_diff / tol

        max_ratio = max(ratio,max_ratio)

        if flux_diff > tol:

            failed_indices.append(i)

    num_failed = len(failed_indices)

    print('\nMaximum relative error is {0:.3f} times tolerance'.format(max_ratio))

    # Allow one outlier

    if num_failed > 1:

        passes = False

    if num_failed > 0:

        print('\nThe following flux values were outside of expected tolerance:')

        print(' Index   Reference   Value       Difference Tolerance')

        for ind in failed_indices:

            ref_val = ref_flux[ind]

            ref_std = ref_flux_std[ind]

            val     = flux[ind]

            std     = flux_std[ind]

            tol = safety_factor*np.sqrt(ref_std*ref_std + std*std)

            flux_diff = np.abs(val - ref_val)

            print('{0:5}    {1:.4e}  {2:.4e}  {3:.3e}  {4:.3e}'.format(

                ind,ref_val,val,flux_diff,tol))

    # Print pass/fail status

    print('\n'),

    if passes:

        print('Solution PASSED')

        return 0

    else:

        print('Solution FAILED')

        return 1