reader.py

import numpy as np
import pandas as pd

def parse_vulcan_vdrive(fname,skip_lines,nslice,nrow,ncol,hkl='211',det_bank=1):
    '''
    Function to read VULCAN beam-line generated V-drive files and re-shape them into numpy arrays of strain maps.
    This is useful for strain mapping experiments 
    Input:  fname: Full path to file (txt)
            skip_lines : Number of lines corresponding to test scans or those used to determine d0
            nslice : Number of "X" slices to image - usually the 3rd dimension
            nrow : Number of vertical measurements i.e. y axis
            ncol : Number of horizontal measurments i.e. z axis
            hkl : The hkl peak of interest (string)
            det_bank : Detector bank number
    Output: tot_counts : Total counts at each point 
            fit_quality : Quality of fit for each point
            strain : Estimated d-spacing by fitting
            x,y,z : x, y and z coordinates of each measurement 
    '''
    delimiter='\t'
    count_col = 'TotalCounts'

    temp_str = hkl+'_'+str(det_bank)
    strain_comp='d_'+ temp_str
    quality_flag = 'GoodFit_'+temp_str 

    data = pd.read_csv(fname,sep=delimiter)

    #Read strain pattern 
    strain = data[strain_comp][skip_lines-1:].to_numpy().reshape(nslice,nrow,ncol)

    #Read corresponding quality flag 
    fit_quality = data[quality_flag][skip_lines-1:].to_numpy().reshape(nslice,nrow,ncol)

    #Read counts 
    tot_counts = data[count_col][skip_lines-1:].to_numpy().reshape(nslice,nrow,ncol)

    #Read coordinates for each measurement 
    x = data['X'][skip_lines-1:].to_numpy().reshape(nslice,nrow,ncol)
    y = data['Y'][skip_lines-1:].to_numpy().reshape(nslice,nrow,ncol)
    z = data['Z'][skip_lines-1:].to_numpy().reshape(nslice,nrow,ncol)

    return x,y,z, tot_counts,fit_quality, strain