diff --git a/defdap/file_readers.py b/defdap/file_readers.py
index 8b9539e..1c3cecb 100644
--- a/defdap/file_readers.py
+++ b/defdap/file_readers.py
@@ -984,12 +984,12 @@ def load(self, file_name: pathlib.Path) -> None:
         """
         if not file_name.is_file():
             raise FileNotFoundError(f"Cannot open file {file_name}")
-        
+
         int_type = 'int32'
         double_type = 'double'
         data_format = np.dtype([
-            ('x', int_type),
-            ('y', int_type),
+            ('x', double_type),
+            ('y', double_type),
             ('u', double_type),
             ('v', double_type),
             ('displacement_mag', double_type),
@@ -1026,12 +1026,12 @@ def load(self, file_name: pathlib.Path) -> None:
         yx_max = yx_array.max(axis=0)
         shape = yx_max - yx_min
         assert np.allclose(shape % binning, 0.)
+        shape = np.rint(shape).astype(int)
         shape = tuple((shape // binning + 1).tolist())
         self.loaded_metadata['shape'] = shape
 
         self.checkMetadata()
-
-        index_array = (yx_array - yx_min) // binning
+        index_array = np.rint((yx_array - yx_min) / binning).astype(int)
         disp_dense = np.zeros(shape + (2,))
         disp_dense[index_array[:, 0], index_array[:, 1]] = (
             structured_to_unstructured(data[['u', 'v']]))
diff --git a/defdap/hrdic.py b/defdap/hrdic.py
index 81b9230..5de3c85 100755
--- a/defdap/hrdic.py
+++ b/defdap/hrdic.py
@@ -534,12 +534,27 @@ def load_pattern(self):
             raise FileNotFoundError("First set path to pattern image.")
 
         img = imread(path)
-        exp_shape = tuple(v * binning for v in self.original_shape)
-        if img.shape != exp_shape:
+
+        bounds = {
+            'DaVis': (-1, 0),    # Davis rounds up to nearest subwindow
+            'PyVale': (0, 1),    # PyVale rounds down to nearest subwindow
+            'OpenPIV': (0, 1)    # OpenPIV rounds down to nearest subwindow
+        }
+        lower, upper = bounds[self.format]
+
+        img_shape = np.array(img.shape)
+        dic_shape = np.array(self.original_shape)
+
+        min_shape = np.array((dic_shape + lower) * binning, dtype=int)
+        max_shape = np.array((dic_shape + upper) * binning, dtype=int)
+
+        if not np.all((min_shape <= img_shape) & (img_shape <= max_shape)):
             raise ValueError(
                 f'Incorrect size of pattern image. For binning of {binning} '
-                f'expected size {exp_shape[::-1]} but got {img.shape[::-1]}'
+                f'expected size {min_shape[::-1]} to {max_shape[::-1]} '
+                f'but got {img_shape[::-1]}'
             )
+
         return img
 
     def plot_grain_av_max_shear(self, **kwargs):