Add XRD functionality

beamtime/xrd/io.py

@@ -1,4 +1,5 @@
 import fabio, pyFAI
+import pandas as pd
 import numpy as np
 import os
 
@@ -11,11 +12,29 @@ def get_image_array(path):
     return image_array
 
 
-def integrate_1d(path, calibrant, bins, options):
+def integrate_1d(calibrant, bins, path=None, image=None, options=None):
+    ''' Integrates an image file to a 1D diffractogram. 
 
-    required_options = ['unit', 'extension']
+    Input:
+    calibrant: path to .poni-file
+    bins: Number of bins to divide image into
+    path (optional): path to image file - either this or image must be specified. If both is passed, image is prioritsed
+    image (optional): image array (Numpy) as extracted from get_image_array
+    options (optional): dictionary of options
 
-    default_options = {'unit': '2th_deg', 'extension': '_integrated.dat'}
+    Output:
+    df: DataFrame contianing 1D diffractogram if option 'return' is True
+    ''' 
+
+    required_options = ['unit', 'extension', 'filename', 'save_folder', 'overwrite', 'return']
+
+    default_options = {
+        'unit': '2th_deg', 
+        'extension': '_integrated.dat',
+        'filename': None,
+        'save_folder': '.',
+        'overwrite': False,
+        'return': False}
 
     if not options:
         options = default_options
@@ -26,15 +45,85 @@ def integrate_1d(path, calibrant, bins, options):
                 options[option] = default_options[option]
     
     
-    image = get_image_array(path)
+    if not image:
+        image = get_image_array(path)
+    
     ai = pyFAI.load(calibrant)
 
-    filename = os.path.split(path)[-1].split('.')[0] + options['extension']
+
+    if not options['filename']:
+        if path:
+            filename = os.path.join(options['save_folder'], os.path.split(path)[-1].split('.')[0] + options['extension'])
+        else:
+            filename = os.path.join(options['save_folder'], 'integrated.dat')
+
+
+    if not options['overwrite']:
+        trunk = os.path.join(options['save_folder'], filename.split('\\')[-1].split('.')[0])
+        extension = filename.split('.')[-1]
+        counter = 0
+
+        while os.path.isfile(filename):
+            counter_string = str(counter)
+            filename = trunk + '_' + counter_string.zfill(4) + '.' + extension
+            counter += 1
+
+
+    if not os.path.isdir(options['save_folder']):
+        os.makedirs(options['save_folder'])
+
 
     res = ai.integrate1d(image, bins, unit=options['unit'], filename=filename)
 
+    if options['return']:
+        return open_1d_data(filename)
     
 
+def open_1d_data(path, options=None):
+
+    with open(path, 'r') as f:
+        position = 0
+    
+        current_line = f.readline()
+        
+        while current_line[0] == '#':
+            position = f.tell()
+            current_line = f.readline()
+            
+        f.seek(position)
+
+        df = pd.read_csv(f, header=None, delim_whitespace=True)
+
+    df.columns = ['2th', 'I']
+
+
+    return df
+    
+
+
+def average_images(images):
+    ''' Takes a list of path to image files, reads them and averages them before returning the average image'''
+
+
+    image_arrays = []
+
+    for image in images:
+        image_array = xrd.io.get_image_array(os.path.join(root, image))
+        image_arrays.append(image_array)
+    
+    
+    image_arrays = np.array(image_arrays)
+
+    image_average = image_arrays.mean(axis=0)
+
+
+    return image_average
+
+
+def subtract_dark(image, dark):
+
+    return image - dark
+
 
 
 def view_integrator(calibrant):