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. 

.. code-block:: python
   :linenos:

   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 

.. figure:: box1_111.png 
  :width: 50%
  :align: center

 

Visualization of the pixel area of the detector used 

.. figure:: box1_111_roi.png 
  :width: 50%
  :align: center