diff --git a/src/PyMca5/PyMcaGui/pymca/QStackWidget.py b/src/PyMca5/PyMcaGui/pymca/QStackWidget.py
index 6d79f275b..df754e34a 100644
--- a/src/PyMca5/PyMcaGui/pymca/QStackWidget.py
+++ b/src/PyMca5/PyMcaGui/pymca/QStackWidget.py
@@ -246,7 +246,8 @@ def setStack(self, *var, **kw):
         if (1 in self._stack.data.shape) and\
            isinstance(self._stack.data, numpy.ndarray):
             oldshape = self._stack.data.shape
-            dialog = ImageShapeDialog(self, shape=oldshape[0:2])
+            suggested = self._stack.info.get("SuggestedImageShape", oldshape[0:2])
+            dialog = ImageShapeDialog(self, shape=suggested)
             dialog.setModal(True)
             ret = dialog.exec()
             if ret:
diff --git a/src/PyMca5/PyMcaIO/HDF5Stack1D.py b/src/PyMca5/PyMcaIO/HDF5Stack1D.py
index a90df4d3a..6c1ccaa81 100644
--- a/src/PyMca5/PyMcaIO/HDF5Stack1D.py
+++ b/src/PyMca5/PyMcaIO/HDF5Stack1D.py
@@ -330,7 +330,7 @@ def loadFileList(self, filelist, selection, scanlist=None):
                 if self.__dtype0 is None:
                     if (bytefactor == 8) and (neededMegaBytes < (2*physicalMemory)):
                         # try reading as float32
-                        print("Forcing the use of float32 data")
+                        _logger.info("Forcing the use of float32 data")
                         self.__dtype = numpy.float32
                     else:
                         raise MemoryError("Force dynamic loading")
@@ -428,7 +428,7 @@ def loadFileList(self, filelist, selection, scanlist=None):
                         else:
                             del mcaObjectPaths["elapsed_time"]
                     else:
-                        # we have to have as many elpased times as MCA spectra
+                        # we have to have as many elapsed times as MCA spectra
                         _time = numpy.zeros((self.data.shape[0] * self.data.shape[1]),
                                                 numpy.float32)
                 if "calibration" in mcaObjectPaths:
@@ -800,13 +800,46 @@ def loadFileList(self, filelist, selection, scanlist=None):
         # try to get scales
         scaleList = []
         if xSelectionList is not None:
+            # McaIndex not always equal to mcaIndex
+            stackMcaIndex = self.info.get("McaIndex", mcaIndex)
             if len(xDatasetList) == 1:
                 xDataset = xDatasetList[0]
                 if xDataset.size == shape[self.info['McaIndex']]:
                     # assuming providing channels
                     self.x = [xDataset.reshape(-1)]
                 else:
+                    # user could not set coordiantes for one axis only
                     _logger.warning("Ignoring channels selection %s" % xSelectionList)
+
+            # for dataset of N*M self.data.shape = (1, N, M)
+            # if positions are flatten then both X, Y, coordinates should be of size N.
+            elif len(xDatasetList) in (2, 3) and len(xDatasetList[0]) == len(xDatasetList[1]) == self.data.shape[1]:
+                # assuming providing spatial coordinates (and maybe channels)
+                grid_x, grid_y = self.shortenScales(xDatasetList[0], xDatasetList[1])
+                if grid_x.size > 1:
+                    delta_x = numpy.mean(grid_x[1:] - grid_x[:-1], dtype=numpy.float32)
+                else:
+                    delta_x = 1.0
+                xScale = [grid_x[0], delta_x]
+
+                if grid_y.size > 1:
+                    delta_y = numpy.mean(grid_y[1:] - grid_y[:-1], dtype=numpy.float32)
+                else:
+                    delta_y = 1.0
+                yScale = [grid_y[0], delta_y]
+                scaleList = [xScale, yScale]
+                self.info["SuggestedImageShape"] = (len(grid_y), len(grid_x))
+
+                if len(xDatasetList) == 2:
+                    pass 
+                elif len(xDatasetList) == 3 and xDatasetList[2].size == shape[self.info['McaIndex']]:
+                    # assuming providing spatial coordinates and channels
+                    self.x = [xDatasetList[2].reshape(-1)]  
+                else:
+                    _logger.warning("Coordinates were applied, but channels could not be")
+                    _logger.warning("Ignoring channels selection %s" % xSelectionList)
+
+
             elif len(xDatasetList) == len(self.data.shape):
                 # assuming providing spatial coordinates and channels
                 goodScale = 0
@@ -823,7 +856,7 @@ def loadFileList(self, filelist, selection, scanlist=None):
                     for i in range(len(self.data.shape)):
                         dataset = xDatasetList[i].reshape(-1)
                         datasize = self.data.shape[i]
-                        if i == mcaIndex:
+                        if i == stackMcaIndex:
                             self.x = [dataset]
                         else:
                             origin = dataset[0]
@@ -842,8 +875,8 @@ def loadFileList(self, filelist, selection, scanlist=None):
                     _logger.warning("Ignoring dimension selections %s" % xSelectionList)
             elif len(xDatasetList) == (len(self.data.shape) - 1):
                 scaleList = []
-                for i in range(len(self.data.shape)):
-                    if i == mcaIndex:
+                for i in range(len(xDatasetList)):
+                    if i == stackMcaIndex:
                         continue
                     dataset = xDatasetList[i].reshape(-1)
                     datasize = self.data.shape[i]
@@ -901,7 +934,7 @@ def loadFileList(self, filelist, selection, scanlist=None):
             if len(dims) == len(self.data.shape):
                 scaleList = []
                 for i in range(len(self.data.shape)):
-                    if i == mcaIndex:
+                    if i == self.info['McaIndex']:
                         continue
                     dataset = dims[i]
                     origin = dataset[0]
@@ -921,6 +954,43 @@ def loadFileList(self, filelist, selection, scanlist=None):
                 self.info["xScale"] = xScale
                 self.info["yScale"] = yScale
 
+    def shortenScales(self, A, B):
+        A = numpy.asarray(A)
+        B = numpy.asarray(B)
+
+        if len(A) == 0 or len(B) == 0:
+            raise ValueError("Arrays must not be empty")
+
+        # Detect which array is related to the slow motor
+        if len(B) == 1 or (len(A)>1 and abs(A[1] - A[0]) > abs(B[1] - B[0])):
+            fast = A
+            slow = B
+            fast_is_A = True
+        else:
+            fast = B
+            slow = A
+            fast_is_A = False
+
+        # Find repetition length n in the slow array
+        diff_idx = numpy.where(slow != slow[0])[0]
+        # Protection if one motor did not move at all
+        if len(diff_idx) == 0:
+            n = len(slow)
+        else:
+            n = diff_idx[0]
+
+        short_fast = fast[:n]
+        short_slow = slow[::n]
+
+        if fast_is_A:
+            X_sh = short_fast
+            Y_sh = short_slow
+        else:
+            X_sh = short_slow
+            Y_sh = short_fast
+
+        return X_sh, Y_sh
+
     def getDimensions(self, nFiles, nScans, shape, index=None):
         #somebody may want to overwrite this
         """