small bug fixes

XRStools/roifinder_and_gui.py

@@ -1975,7 +1975,7 @@ class roi_finder:
         ax1.legend(['Row No. %02d / %02d' %(1, rw_data[roi_keys[0]].shape[1])],
                        frameon=False, loc=1, fontsize='x-small')
         # plot the very first mask image
-        ax2.plot([-0.5, data_dict_norm['ROI00'].shape[0]+0.5, data_dict_norm['ROI00'].shape[0]+0.5, -0.5],
+        ax2.plot([-0.5, data_dict_norm['ROI00'].shape[2]+0.5, data_dict_norm['ROI00'].shape[2]+0.5, -0.5],
                      [-0.5, -0.5, 0.5, 0.5 ], 'k-')
         ax2.imshow( np.sum(data_dict_norm['ROI00'], axis=0), interpolation=interpolation, cmap=cmap )
         ax2.imshow( red_rois_copy[roi_keys[0]][1], interpolation=interpolation, cmap='Greys', alpha=0.5 )

XRStools/xrs_calctools.py

@@ -2371,11 +2371,11 @@ def vaspBoxParser(filename):
         for ind in range(num_atoms[zz]):
             # print(ind, kind, cou)
             rel_coords = np.array([float(ii) for ii in all_lines[coor_start+cou].split()])
-            abs_coords = cell.dot(rel_coords)
+            abs_coords = cell.T.dot(rel_coords)
             atoms.append(xyzAtom(kind, abs_coords, cou ))
             cou += 1
     box = xyzBox(atoms,boxLength=0.0)
-    box.lattice = np.array([cell1, cell2, cell3])
+    box.lattice = np.array([cell1, cell2, cell3]).T
     box.lattice_inv = np.linalg.inv(box.lattice)
     return box
 
@@ -2409,7 +2409,7 @@ def vaspTrajecParser(filename, min_boxes=0, max_boxes=1000):
                             atoms.append(xyzAtom( ([atom_kinds[ii_kind]]*num_atoms[ii_kind])[jj_num] ,coordinates[coorcou],counter))
                             coorcou += 1
                     box = xyzBox(atoms,boxLength=0.0)
-                    box.lattice = np.array([cell1, cell2, cell3])
+                    box.lattice = np.array([cell1, cell2, cell3]).T
                     box.lattice_inv = np.linalg.inv(box.lattice)
                     boxes.append(box)
                 # if first box, start here
@@ -2440,7 +2440,7 @@ def vaspTrajecParser(filename, min_boxes=0, max_boxes=1000):
                 #print(counter, line.split())
                 cell = np.array([cell1, cell2, cell3])
                 rel_coords = np.array([float(ii) for ii in line.split()])
-                abs_coords = cell.dot(rel_coords)
+                abs_coords = cell.T.dot(rel_coords)
                 coordinates.append(abs_coords)
         counter += 1
     return boxes
@@ -3181,7 +3181,7 @@ def parseVaspFile(fname):
         for jj in range(ntypat[ii]):
             atomType.append(elements[ii])
     # get absolute atom positions
-    xabs  = [np.dot(lattice, xred[ii]) for ii in range(len(xred))]
+    xabs  = [np.dot(lattice.T, xred[ii]) for ii in range(len(xred))]
     # create the box
     xyzAtoms = []
     for ii in range(len(xabs)):
@@ -3295,7 +3295,7 @@ def parseOCEANinputFile(fname):
     for at in typat:
         atoms.append(atoms_name[at-1])
 
-    xabs  = [np.dot(lattice, xred[ii]) for ii in range(len(xred))]
+    xabs  = [np.dot(lattice.T, xred[ii]) for ii in range(len(xred))]
 
     xyzAtoms = []
     for ii in range(len(xabs)):
@@ -3303,8 +3303,8 @@ def parseOCEANinputFile(fname):
 
     box = xyzBox( xyzAtoms )
 
-    box.lattice = lattice
-    box.lattice_inv = np.linalg.inv(lattice)
+    box.lattice = lattice.T
+    box.lattice_inv = np.linalg.inv(lattice.T)
     box.relAtoms     = xred
 
     return box

XRStools/xrs_extraction.py

@@ -381,6 +381,8 @@ class edge_extraction:
     def removeCorePearsonAv(self,element,edge,range1,range2,weights=[2,1],HFcore_shift=0.0,guess=None,scaling=None,return_background=False):
         """
         **removeCorePearsonAv**
+
+        guess (list): [position, FWHM, shape, intensity, ax, b, scale  ]
         """
         # check that there are averaged signals available
         if not np.any(self.avsignals):

XRStools/xrs_utilities.py

@@ -359,6 +359,9 @@ def get_gnuplot_rgb( start=None, end=None, length=None ):
     for ii in range(len(rgb)):
         print( 'set style line %d lt -1 lc rgb \'#%02x%02x%02x\' lw 1.5'%(ii+1, rgb[ii][0]*255., rgb[ii][1]*255., rgb[ii][2]*255.) )
 
+def hex2rgb( hex_val ):
+    return tuple( int(hex_val.lstrip('#')[i:i+2], 16) for i in (0, 2, 4) )
+
 class maxipix_det:
     """
     Class to store some useful values from the detectors used. To be used for arranging the ROIs.