Box integration

This tutorial demonstrate a primitive method for integrating Bragg peaks from single crystals. Here, we define a range in A3 and 2Theta, and sum the intensities in the region. This means that the detector operates as a point detector. We can plot the integrated intensities and fit it with a Gaussian peak. You shoud use InteractiveViewer and View3D to determin the correct integration parameters, in addition to inspecting the roi. Note that when you use several dataFiles in one dataSet, you must give the dataFile index in the peakDict.

from DMCpy import DataSet,DataFile,_tools
import numpy as np
import pickle


# name for exports
planeFigName = r'sample'

# Give file number and folder the file is stored in.
scanNumbers = '8540'
folder = r'data\SC'
year = 2022

filePath = _tools.fileListGenerator(scanNumbers,folder,year=year)

# # # load dataFiles
dataFiles = [DataFile.loadDataFile(dFP) for dFP in filePath]

# load data files and make data set
ds = DataSet.DataSet(dataFiles)

# use InteractiveViewer to find conditions for integration
if False:
   IA1 = ds[0].InteractiveViewer()
   IA1.set_clim(0,20)
   IA1.set_clim_zIntegrated(0,1000)


peakDict = { }

# stor integration parameters in peakDict
peakDict['111'] = {
              'h' : 1,
              'k' : 1,
              'l' : 1,
              'df' : 0,
              'A3_center' : 113.57,
              'A3_minus' : 10,
              'A3_pluss' : 10,
              'tth' : 49.5,
              'tth_minus' : 2.1,
              'tth_pluss' : 2.5,
              'startZ' : 35,
              'stopZ' : 100,
              'vmin' : 0,
              'vmax' : 0.005,
          }

# add more peaks to the peakDict
peakDict['110'] = {
              'h' : 1,
              'k' : 1,
              'l' : 0,
              'df' : 0,
              'A3_center' : 78.5,
              'A3_minus' : 10,
              'A3_pluss' : 10,
              'tth' : 36.2,
              'tth_minus' : 2.1,
              'tth_pluss' : 2.5,
              'startZ' : 35,
              'stopZ' : 85,
              'vmin' : 0,
              'vmax' : 0.005,
          }

# to integrate all peaks in peakDict
integrationList = None

# to integrate only one or a list of peak, list the peaks you want to integrate in integrationList
integrationList = ['111']

# keywords for box integration
integrationKwargs = {
'roi' : True,
'saveFig' : r'docs/Tutorials/box/box1_',
'title' : r'Integated data',
'integrationList' : integrationList,
'closeFigures' : True,
'plane' : 'HHL'
}

integratedPeakDict = ds.boxIntegration(peakDict,**integrationKwargs)

# print information
if False:
   for peak in integratedPeakDict:
      print(integratedPeakDict[peak]['fit'][1])


# Make hkl file
if False:
   # Specify the file name
   file_name = f"docs/Tutorials/box/{planeFigName}.hkl"

   # Open the file in write mode
   with open(file_name, 'w') as file:
      # Write the column headers with appropriate spacing
      file.write('{:>3} {:>3} {:>3} {:>10} {:>10}\n'.format('h', 'k', 'l', 'Int', 'err'))

      # Loop through the peaks in integratedPeakDict and write the data
      for peak in integratedPeakDict:
          # Format the data with consistent spacing, considering the negative sign
          h = int(integratedPeakDict[peak]['h'])
          k = int(integratedPeakDict[peak]['k'])
          l = int(integratedPeakDict[peak]['l'])
          intensity = np.round(integratedPeakDict[peak]['summed_counts'], 4)
          error = np.round(np.sqrt(integratedPeakDict[peak]['summed_counts'] * np.mean(integratedPeakDict[peak]['monitors'])) / np.mean(integratedPeakDict[peak]['monitors']), 4)

          # Use a modified format string to align numbers to the right
          peak_data = '{:>3} {:>3} {:>3} {:>10} {:>10}\n'.format(h, k, l, intensity, error)

          # Write the formatted data to the file
          file.write(peak_data)

   print(f"Data has been written to {file_name}")


# save dictionary
if False:
   file_name = f"docs/Tutorials/box/{planeFigName}.pickle"
   with open(file_name, 'wb') as file:
      pickle.dump(integratedPeakDict, file)
   print(f"Data has been written to {file_name}")

   # load dictionary
   if False:
      # Load dictionary from file
      file_name = f"docs/Tutorials/box/{planeFigName}.pickle"
      with open(file_name, 'rb') as file:
          loaded_dict = pickle.load(file)

      print(loaded_dict)


# export xy data
if False:
   file_name = f"docs/Tutorials/box/{planeFigName}.txt"
   with open(file_name, 'w') as file:
      for key, values in integratedPeakDict.items():
          # print(key)
          file.write(f'{key}' + '\n')
          file.write(' '.join(map(str, values['peak_cut'][0])) + '\n')
          file.write(' '.join(map(str, values['peak_cut'][1])) + '\n')
          file.write(' '.join(map(str, values['monitors'])) + '\n')
          file.write('\n')  # Add a new line to separate data sets

   print(f"Data has been written to {file_name}")

The above code takes the data from the A3 scan file dmc2022n008540, and select and area in A3 and pixels. It then sums the detector in the given pixel area and extract the intensity as a function of A3. The integration details are given in a dictionary. The A3 range is given in frames, while the tth range is in degrees. startZ and stopZ gives the height on the detector in pixels (0-128). The roi keyword determines if the rois are plotted.

Intensity as a function of A3

Visualization of the pixel area of the detector used