set default data type to np.float32

nabu/preproc/alignment.py

@@ -49,7 +49,7 @@ except ImportError:
 
 
 class AlignmentBase(object):
-    def __init__(self, horz_fft_width=False, verbose=False):
+    def __init__(self, horz_fft_width=False, verbose=False, data_type=np.float32):
         """
         Alignment basic functions.
 
@@ -62,15 +62,16 @@ class AlignmentBase(object):
         verbose: boolean, optional
             When True it will produce verbose output, including plots.
         """
-        self._init_parameters(horz_fft_width, verbose)
+        self._init_parameters(horz_fft_width, verbose, data_type)
 
-    def _init_parameters(self, horz_fft_width, verbose):
+    def _init_parameters(self, horz_fft_width, verbose, data_type):
         self.truncate_horz_pow2 = horz_fft_width
 
         if verbose and not __have_matplotlib__:
             logging.getLogger(__name__).warning("Matplotlib not available. Plotting disabled, despite being activated by user")
             verbose = False
         self.verbose = verbose
+        self.data_type = data_type
 
     @staticmethod
     def refine_max_position_2d(f_vals: np.ndarray, fy=None, fx=None):
@@ -319,7 +320,7 @@ class AlignmentBase(object):
 
     @staticmethod
     def _prepare_image(
-        img, invalid_val=1e-5, roi_yxhw=None, median_filt_shape=None, low_pass=None, high_pass=None,
+        img, invalid_val=1e-5, roi_yxhw=None, median_filt_shape=None, low_pass=None, high_pass=None, data_type=None
     ):
         """
         Prepare and returns  a cropped  and filtered image, or array of filtered images if the input is an  array of images.
@@ -343,7 +344,7 @@ class AlignmentBase(object):
             The computed filter
         """
         img = np.squeeze(img)  # Removes singleton dimensions, but does a shallow copy
-        img = np.ascontiguousarray(img)
+        img = np.ascontiguousarray(img, dtype=data_type)
 
         if roi_yxhw is not None:
             img = img[
@@ -533,8 +534,8 @@ class CenterOfRotation(AlignmentBase):
         img_shape = img_2.shape
         roi_yxhw = self._determine_roi(img_shape, roi_yxhw)
 
-        img_1 = self._prepare_image(img_1, roi_yxhw=roi_yxhw, median_filt_shape=median_filt_shape)
-        img_2 = self._prepare_image(img_2, roi_yxhw=roi_yxhw, median_filt_shape=median_filt_shape)
+        img_1 = self._prepare_image(img_1, roi_yxhw=roi_yxhw, median_filt_shape=median_filt_shape, data_type=self.data_type)
+        img_2 = self._prepare_image(img_2, roi_yxhw=roi_yxhw, median_filt_shape=median_filt_shape, data_type=self.data_type)
 
         cc = self._compute_correlation_fft(img_1, img_2, padding_mode, high_pass=high_pass, low_pass=low_pass)
 
@@ -680,7 +681,9 @@ class DetectorTranslationAlongBeam(AlignmentBase):
         img_shape = img_stack.shape[-2:]
         roi_yxhw = self._determine_roi(img_shape, roi_yxhw)
 
-        img_stack = self._prepare_image(img_stack, roi_yxhw=roi_yxhw, median_filt_shape=median_filt_shape)
+        img_stack = self._prepare_image(
+            img_stack, roi_yxhw=roi_yxhw, median_filt_shape=median_filt_shape, data_type=self.data_type
+        )
 
         # do correlations
         ccs = [