<filename>sp3-diffusion/postprocessing.py
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt


def plotcsv_frommoose_multi(file, save, diff=False, fix=False, G=3, dire='x'):
    '''
    Running MOOSE app produces a csv file with the flux values along a line.
    This function plots those values.
    The output is a figure.

    Parameters:
    -----------
    file: [string]
        name of the .csv file
    save: [string]
        name of the figure
    diff: [bool]
        True if plotting the diffusion solution
    fix: [bool]
        True if plotting the solution to a fixed source problem
    G: [int]
        Number of energy groups
    dire: ['x', 'y', 'z']
        direction of the detector
    '''

    file = pd.read_csv(file)

    if dire == 'r':
        x = np.array(file['x'].tolist())
        y = np.array(file['y'].tolist())
        d = np.sqrt(x**2 + y**2)
    else:
        d = file[dire].tolist()

    for g in range(G):
        if diff is True:
            flux = np.array(file['flux'+str(g+1)].tolist())
            legend = 'Diff'
        else:
            flux0 = np.array(file['flux0_'+str(g+1)].tolist())
            flux2 = np.array(file['flux2_'+str(g+1)].tolist())
            flux = flux0 - 2*flux2
            legend = 'SP3'

        if fix is False:
            if g == 0:
                M = max(flux)
            flux /= M
        plt.plot(d, flux, label=legend+', g='+str(g+1))

    plt.legend(loc='upper right')
    plt.xlabel(dire + ' [cm]')
    plt.ylabel(r'$\phi \left[\frac{n}{cm^2s}\right]$')
    return None


if __name__ == "__main__":
    # plots figures of all the 1D test cases

    save = 'output-1g-fixed'
    plt.figure()
    file = 'sp3-1g-fixed_line_0001.csv'
    plotcsv_frommoose_multi(file, save, diff=False, fix=True, G=1, dire='x')
    file = 'diff-1g-fixed_line_0001.csv'
    plotcsv_frommoose_multi(file, save, diff=True, fix=True, G=1, dire='x')
    plt.savefig(save, dpi=300, bbox_inches="tight")

    save = 'output-1g-crit'
    plt.figure()
    file = 'sp3-1g-crit_line_0001.csv'
    plotcsv_frommoose_multi(file, save, diff=False, fix=False, G=1, dire='x')
    file = 'diff-1g-crit_line_0001.csv'
    plotcsv_frommoose_multi(file, save, diff=True, fix=False, G=1, dire='x')
    plt.savefig(save, dpi=300, bbox_inches="tight")

    save = 'output-3g-fixed'
    plt.figure()
    file = 'sp3-3g-fixed_line_0001.csv'
    plotcsv_frommoose_multi(file, save, diff=False, fix=True, G=3, dire='x')
    file = 'diff-3g-fixed_line_0001.csv'
    plotcsv_frommoose_multi(file, save, diff=True, fix=True, G=3, dire='x')
    plt.savefig(save, dpi=300, bbox_inches="tight")

    save = 'output-3g-crit'
    plt.figure()
    file = 'sp3-3g-crit_line_0001.csv'
    plotcsv_frommoose_multi(file, save, diff=False, fix=False, G=3, dire='x')
    file = 'diff-3g-crit_line_0001.csv'
    plotcsv_frommoose_multi(file, save, diff=True, fix=False, G=3, dire='x')
    plt.savefig(save, dpi=300, bbox_inches="tight")

    save = 'output-3g-crit1'
    plt.figure()
    file = 'sp3-3g-crit1_line_0001.csv'
    plotcsv_frommoose_multi(file, save, diff=False, fix=False, G=3, dire='x')
    file = 'diff-3g-crit1_line_0001.csv'
    plotcsv_frommoose_multi(file, save, diff=True, fix=False, G=3, dire='x')
    plt.savefig(save, dpi=300, bbox_inches="tight")

    save = 'output-3g-crit2'
    plt.figure()
    file = 'sp3-3g-crit2_line_0001.csv'
    plotcsv_frommoose_multi(file, save, diff=False, fix=False, G=3, dire='y')
    file = 'diff-3g-crit2_line_0001.csv'
    plotcsv_frommoose_multi(file, save, diff=True, fix=False, G=3, dire='y')
    plt.savefig(save, dpi=300, bbox_inches="tight")

<filename>sp3-diffusion/postprocessing.py
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt


def plotcsv_frommoose_multi(file, save, diff=False, fix=False, G=3, dire='x'):
    '''
    Running MOOSE app produces a csv file with the flux values along a line.
    This function plots those values.
    The output is a figure.

    Parameters:
    -----------
    file: [string]
        name of the .csv file
    save: [string]
        name of the figure
    diff: [bool]
        True if plotting the diffusion solution
    fix: [bool]
        True if plotting the solution to a fixed source problem
    G: [int]
        Number of energy groups
    dire: ['x', 'y', 'z']
        direction of the detector
    '''

    file = pd.read_csv(file)

    if dire == 'r':
        x = np.array(file['x'].tolist())
        y = np.array(file['y'].tolist())
        d = np.sqrt(x**2 + y**2)
    else:
        d = file[dire].tolist()

    for g in range(G):
        if diff is True:
            flux = np.array(file['flux'+str(g+1)].tolist())
            legend = 'Diff'
        else:
            flux0 = np.array(file['flux0_'+str(g+1)].tolist())
            flux2 = np.array(file['flux2_'+str(g+1)].tolist())
            flux = flux0 - 2*flux2
            legend = 'SP3'

        if fix is False:
            if g == 0:
                M = max(flux)
            flux /= M
        plt.plot(d, flux, label=legend+', g='+str(g+1))

    plt.legend(loc='upper right')
    plt.xlabel(dire + ' [cm]')
    plt.ylabel(r'$\phi \left[\frac{n}{cm^2s}\right]$')
    return None


if __name__ == "__main__":
    # plots figures of all the 1D test cases

    save = 'output-1g-fixed'
    plt.figure()
    file = 'sp3-1g-fixed_line_0001.csv'
    plotcsv_frommoose_multi(file, save, diff=False, fix=True, G=1, dire='x')
    file = 'diff-1g-fixed_line_0001.csv'
    plotcsv_frommoose_multi(file, save, diff=True, fix=True, G=1, dire='x')
    plt.savefig(save, dpi=300, bbox_inches="tight")

    save = 'output-1g-crit'
    plt.figure()
    file = 'sp3-1g-crit_line_0001.csv'
    plotcsv_frommoose_multi(file, save, diff=False, fix=False, G=1, dire='x')
    file = 'diff-1g-crit_line_0001.csv'
    plotcsv_frommoose_multi(file, save, diff=True, fix=False, G=1, dire='x')
    plt.savefig(save, dpi=300, bbox_inches="tight")

    save = 'output-3g-fixed'
    plt.figure()
    file = 'sp3-3g-fixed_line_0001.csv'
    plotcsv_frommoose_multi(file, save, diff=False, fix=True, G=3, dire='x')
    file = 'diff-3g-fixed_line_0001.csv'
    plotcsv_frommoose_multi(file, save, diff=True, fix=True, G=3, dire='x')
    plt.savefig(save, dpi=300, bbox_inches="tight")

    save = 'output-3g-crit'
    plt.figure()
    file = 'sp3-3g-crit_line_0001.csv'
    plotcsv_frommoose_multi(file, save, diff=False, fix=False, G=3, dire='x')
    file = 'diff-3g-crit_line_0001.csv'
    plotcsv_frommoose_multi(file, save, diff=True, fix=False, G=3, dire='x')
    plt.savefig(save, dpi=300, bbox_inches="tight")

    save = 'output-3g-crit1'
    plt.figure()
    file = 'sp3-3g-crit1_line_0001.csv'
    plotcsv_frommoose_multi(file, save, diff=False, fix=False, G=3, dire='x')
    file = 'diff-3g-crit1_line_0001.csv'
    plotcsv_frommoose_multi(file, save, diff=True, fix=False, G=3, dire='x')
    plt.savefig(save, dpi=300, bbox_inches="tight")

    save = 'output-3g-crit2'
    plt.figure()
    file = 'sp3-3g-crit2_line_0001.csv'
    plotcsv_frommoose_multi(file, save, diff=False, fix=False, G=3, dire='y')
    file = 'diff-3g-crit2_line_0001.csv'
    plotcsv_frommoose_multi(file, save, diff=True, fix=False, G=3, dire='y')
    plt.savefig(save, dpi=300, bbox_inches="tight")

Running MOOSE app produces a csv file with the flux values along a line.     This function plots those values.     The output is a figure.      Parameters:     -----------     file: [string]         name of the .csv file     save: [string]         name of the figure     diff: [bool]         True if plotting the diffusion solution     fix: [bool]         True if plotting the solution to a fixed source problem     G: [int]         Number of energy groups     dire: ['x', 'y', 'z']         direction of the detector # plots figures of all the 1D test cases