Hi all - As the title states, I’m attempting to perform a Standard Zernike Analysis across multiple files simultaneously using the Zemax Python API and Multiprocessing (running 4 instances of Zemax). My attempt is shown below. Failures I’ve encountered include: 

*** FRU__delta_init(): Attempt to start when running!

and

UnboundLocalError: local variable 'zos' referenced before assignment

I’m not sure if my approach is flawed or if I’m just attempting something that’s not allowed with Zemax or something else. Any insight you can provide would be greatly appreciated. Thanks!

Also, thank you to David Nguyen for his example code on performing a Zernike Standard Analysis here.

if __name__ == '__main__':
    import multiprocessing as mp
    import os
    import glob
    from ShortZernMonolith import ShortZernMonolith

    documents_path = os.path.join(os.path.expanduser("~"), "Documents")
    directory_path = documents_path + "\\SenseCheck"

    # Create list of TSAV*.zmx files found in folder
    TSAV_Files = []
    for files in glob.glob(directory_path + '\\TSAV*.zmx'):
        TSAV_Files.append(files)

    # Start 4 processes of ZernCompute
    with mp.Pool(4) as p:
        p.map(ZernCompute, TSAV_Files)

def ZernCompute(ZemaxFile):
    from PythonStandaloneApplication import PythonStandaloneApplication
    import os
    import pandas as pd

    # Open communication with Zemax
    zos = PythonStandaloneApplication()

    # load local variables
    TheSystem = zos.TheSystem

    # Define folder to store files
    documents_path = os.path.join(os.path.expanduser("~"), "Documents")
    QZ_path = documents_path + "\\QuickZern"
    basefileName = os.path.basename(ZemaxFile)
    fileName = os.path.splitext(basefileName)

    # Specify fields, waves, surface and zernike terms of interest
    field = [1, 3, 7, 11]
    wave = [1, 2, 3]
    surface = 96
    zTerms = [4, 6, 7, 11]
    vertex = False
    sub_x = 0.0
    sub_y = 0.0
    sub_z = 0.0
    eps = 0.0
    maxTerms = max(zTerms)

    # Initialize variable list to store Zernike results
    zernResultsFile = []

    # Load desired ZemaxFile
    TheSystem.LoadFile(ZemaxFile, False)

    # Open a Zernike Standard Analysis
    ZernikeStd = TheSystem.Analyses.New_ZernikeStandardCoefficients()

    # Change settings and compute Zernike Standard Analysis
    for j in range(len(wave)):
        for k in range(len(field)):
            Settings = ZernikeStd.GetSettings() # Get analysis settings

            # Apply Settings
            Settings.Field.SetFieldNumber(field[k])
            Settings.Surface.SetSurfaceNumber(surface)
            Settings.Wavelength.SetWavelengthNumber(wave[j])
            # Settings.SampleSize = Samp
            Settings.ReferenceOBDToVertex = vertex
            Settings.Sx = sub_x
            Settings.Sy = sub_y
            Settings.Sz = sub_z
            Settings.Epsilon = eps
            Settings.MaximumNumberOfTerms = maxTerms

            # Run analysis
            ZernikeStd.ApplyAndWaitForCompletion()

            # Send Zernike results to text file
            tempFile = QZ_path + f'\\ZernTempFile{fileName[0]}-{j}{k}.txt'
            ZernikeStd.GetResults().GetTextFile(tempFile)

            # Open and read the text file
            with open(tempFile, 'r', encoding='utf-16') as F:
                Lines = F.readlines()

            ZernResults = []

            for Index in range(38, len(Lines)):
                SplitLine = Lines[Index].split()
                zCheck = str(SplitLine[0])
                TermCheck = int(SplitLine[1])
                Value = float(SplitLine[2])

                # Filter for desired Zernike Terms
                for i in range(len(zTerms)):
                    if zCheck == 'Z' and TermCheck == zTerms[i]:
                        ZernResults.append(Value)

            #os.remove(tempFile)
            zernResultsFile.extend(ZernResults)

    ZernikeStd.Close() # Close the analysis
    print(f"Analysis of {ZemaxFile} complete.")

    df = pd.DataFrame(zernResultsFile).T #Change list to dataframe

    # Write data from zernResultsFile to CSV
    ZernResultsCSV = os.path.join(os.sep, QZ_path, f'{fileName[0]}-ZernResults.csv')
    df.to_csv(ZernResultsCSV, mode='a', index=False, header=False)

    del zos