[core][dataset] Modify shift_detection attributes

darfix/core/dataset.py

@@ -737,7 +737,7 @@ class Dataset():
         return Dataset(_dir=roi_dir, data=new_data, dims=self.__dims, transformation=transformation,
                        in_memory=self._in_memory)
 
-    def find_shift(self, dimension=None, h_max=0.5, steps=50, indices=None):
+    def find_shift(self, dimension=None, steps=100, indices=None):
         """
         Find shift of the data or part of it.
 
@@ -751,7 +751,7 @@ class Dataset():
         :type indices: Union[None, array_like]
         :returns: Array with shift per frame.
         """
-        return shift_detection(self.get_data(indices, dimension), h_max, steps)
+        return shift_detection(self.get_data(indices, dimension), steps)
 
     def apply_shift(self, shift, dimension=None, shift_approach="fft", indices=None,
                     callback=None, _dir=None):
@@ -846,7 +846,7 @@ class Dataset():
         return Dataset(_dir=_dir, data=data, dims=self.__dims, transformation=self.transformation,
                        in_memory=self._in_memory)
 
-    def find_and_apply_shift(self, dimension=None, h_max=0.5, steps=50, shift_approach="fft",
+    def find_and_apply_shift(self, dimension=None, steps=100, shift_approach="fft",
                              indices=None, callback=None):
         """
         Find the shift of the data or part of it and apply it.
@@ -863,7 +863,7 @@ class Dataset():
         :param Union[function, None] callback: Callback
         :returns: Dataset with the new data.
         """
-        shift = self.find_shift(dimension, h_max, steps, indices=indices)
+        shift = self.find_shift(dimension, steps, indices=indices)
         return self.apply_shift(shift, dimension, indices=indices)
 
     def _cascade_nmf(self, num_components, iterations, vstep=None, hstep=None, indices=None):

darfix/core/test/test_image_registration.py

@@ -104,7 +104,7 @@ class TestImageRegistration(unittest.TestCase):
         for i in range(9):
             data += [numpy.fft.ifftn(scipy.ndimage.fourier_shift(numpy.fft.fftn(data[-1]), shift)).real]
         data = numpy.asanyarray(data, dtype=numpy.int16)
-        optimal_shift = imageRegistration.shift_detection(data, 2, shift_approach="fft")
+        optimal_shift = imageRegistration.shift_detection(data, 100, shift_approach="fft")
 
         shift = [[0, -1, -2, -3, -4, -5, -6, -7, -8, -9],
                  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
@@ -122,7 +122,7 @@ class TestImageRegistration(unittest.TestCase):
         for i in range(9):
             data += [numpy.fft.ifftn(scipy.ndimage.fourier_shift(numpy.fft.fftn(data[-1]), shift)).real]
         data = numpy.asanyarray(data, dtype=numpy.int16)
-        optimal_shift = imageRegistration.shift_detection(data, 2, shift_approach="fft")
+        optimal_shift = imageRegistration.shift_detection(data, 100, shift_approach="fft")
 
         shift = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                  [0, -1, -2, -3, -4, -5, -6, -7, -8, -9]]
@@ -141,7 +141,7 @@ class TestImageRegistration(unittest.TestCase):
             data += [numpy.fft.ifftn(scipy.ndimage.fourier_shift(numpy.fft.fftn(data[-1]), shift)).real]
         data = numpy.asanyarray(data, dtype=numpy.int16)
 
-        optimal_shift = imageRegistration.shift_detection(data, 2)
+        optimal_shift = imageRegistration.shift_detection(data, 100)
 
         shift = [[0, -1, -2, -3, -4, -5, -6, -7, -8, -9],
                  [0, -1, -2, -3, -4, -5, -6, -7, -8, -9]]
@@ -159,8 +159,7 @@ class TestImageRegistration(unittest.TestCase):
         for i in range(9):
             data += [numpy.fft.ifftn(scipy.ndimage.fourier_shift(numpy.fft.fftn(data[-1]), shift)).real]
         data = numpy.asanyarray(data, dtype=numpy.int16)
-        optimal_shift = imageRegistration.shift_detection(data, 1)
-
+        optimal_shift = imageRegistration.shift_detection(data, 100)
         shift = [[0, -0.5, -1, -1.5, -2, -2.5, -3, -3.5, -4, -4.5],
                  [0, -0.2, -0.4, -0.6, -0.8, -1, -1.2, -1.4, -1.6, -1.8]]
 
@@ -313,7 +312,7 @@ class TestReshapedShift(unittest.TestCase):
         dataset = self.dataset.reshape_data()
 
         # Detects shift using only images where value 1 of dimension 1 is fixed
-        optimal_shift = dataset.find_shift(dimension=[1, 0], h_max=1)
+        optimal_shift = dataset.find_shift(dimension=[1, 0])
 
         shift = [[0, -0.5, -1, -1.5, -2],
                  [0, -0.2, -0.4, -0.6, -0.8]]
@@ -334,7 +333,7 @@ class TestReshapedShift(unittest.TestCase):
         dataset = self.dataset.reshape_data()
 
         # Detects shift using only images where value 1 of dimension 1 is fixed
-        optimal_shift = dataset.find_shift(dimension=[0, 0], h_max=3)
+        optimal_shift = dataset.find_shift(dimension=[0, 0])
 
         shift = [[0, -2.5],
                  [0, -1]]
@@ -358,7 +357,7 @@ class TestReshapedShift(unittest.TestCase):
         self.dataset.find_dimensions(POSITIONER_METADATA)
         dataset = self.dataset.reshape_data()
 
-        dataset = dataset.find_and_apply_shift(dimension=[1, 0], h_max=1)
+        dataset = dataset.find_and_apply_shift(dimension=[1, 0])
 
         for frame in dataset.data.take(0, 0):
             print(numpy.max(abs(data[0] - frame)))