Enh integration

xpdView/azimuthal.py

@@ -15,12 +15,71 @@
 '''
 This file will handle the pyFai integration for quick azimuthal integration of the data
 '''
+import os
+
+from tifffile import imread
 
 import pyFAI
 import pyFAI.calibrant
 
-
-class Azimuthal(object):
+# init at top to save time
+ai = pyFAI.AzimuthalIntegrator()
+
+
+def _npt_cal(config_dict):
+    """ config_dict should be a PyFAI calibration dict """
+    try:
+        x_0, y_0 = (config_dict['centerX'], config_dict['centerY'])
+    except KeyError:
+        print("Not a pyFAI calibration dictionary, default "
+              "beam center will be set")
+        x_0, y_0 = (1024, 1024)
+    dist = np.sqrt((2048-x_0)**2 + (2048-y_0)**2)
+    return dist
+
+
+def xpdView_integrate(input_dir):
+    """ function to operate on file-based """
+    tif_list = [os.path.join(input_dir, f) for f in os.listdir(input_dir)
+                if os.path.splitext(f)[1] == '.tif']
+    # calibration dict
+    cfg_list = []
+    cfg_inuse = None # init
+
+    for tif in tif_list:
+        stem, ext = os.path.splitext(tif)
+        cfg_name = os.path.join(input_dir, stem + '.yaml')
+        if os.path.isfile(cfg_name):
+            with open(cfg_name, 'r') as f:
+                cfg_inuse = yaml.load(f)
+        cfg_list.append(cfg_inuse)
+
+    while any(x is None for x in cfg_list):
+        ind = cfg_list.index(None)
+        cfg_list.remove(None)
+        cfg_list.insert(ind, cfg_name)
+        print("WARNING: calibration paramters corresponding to {} is "
+              "missing, use alternative set of paramters from {}\n"
+              "integration results might be incorrect"
+              .format(tif_list[ind], cfg_name))
+              # use the last one, user's liability
+
+    # integration
+    if len(cfg_list) == len(tif_list):
+        for i in range(len(tif_list)):
+            w_name = tif_list[i].replace('.tif', '.chi')
+            cfg_dict = cfg_list[i]
+            npt = _npt_cal(cfg_dict)
+            img = imread(os.path.join(input_dir, tif_list[i]))
+            ai.setPyFAI(**config_dict)
+            integration_dict = {'filename':w_name,
+                                'polarization_factor': 0.99}
+            rv = ai.integrate1d(img, npt, **integration_dict)
+    else:
+        raise ValueError("Number of images is not equal to number "
+                         "of calibration parameter sets")
+
+class Azimuthal:
     """
     This class handles all of the azimuthal integration of the data so that it can be done automatically as long
     as the data comes in as a list of 2D numpy arrays
@@ -58,14 +117,18 @@ def get_right_names(self, file_names, data_list):
 
         Returns
         -------
-        None
-
+        file_data_dict : dict
+            a dictionary that mapps file name to 2D numpy array
         """
-        for file in file_names:
-            if file[-4:] == '.tif':
-                self.file_names.append(file[:-4] + '.chi')
+
+        file_data_dict = {}
+        for f in file_names:
+            stem, ext = os.path.splitext(f)
+            if ext == '.tif':
+                self.file_names.append(stem + '.chi')
             else:
-                self.file_names.append(file)
+                #FIXME: need to thinkg if should leave it unchanged
+                self.file_names.append(f)
 
         self.integration_time(data_list)
 
@@ -83,17 +146,11 @@ def integration_time(self, data_list):
         None
 
         """
-        # Insert pyFAI calibration parameters, can be changed if better detector parameters found
-        det = pyFAI.detectors.Perkin()
-        wl = 0.184320e-10
-        ni = pyFAI.calibrant.ALL_CALIBRANTS("Ni")
-        ni.set_wavelength(wl)
-        poni1 = .1006793 * 2
-        poni2 = .1000774 * 2
-        ai = pyFAI.AzimuthalIntegrator(dist=0.2418217, poni1=poni1, poni2=poni2, rot1=0, rot2=0, detector=det)
-        ai.set_wavelength(wl)
+
 
         for data in data_list:
+            ai.setPyFAI(**config_dict)
+            npt = _npt_cal(config_dict)
             x, y = ai.integrate1d(data, 1000, unit='q_A^-1')
             self.x_lists.append(x)
             self.y_lists.append(y)