Add scalar sum and average functions. Add window functions.

nion/data/Core.py

@@ -908,6 +908,44 @@ def calculate_data() -> _ImageDataType:
     return DataAndMetadata.new_data_and_metadata(data=calculate_data(), intensity_calibration=data_and_metadata.intensity_calibration, dimensional_calibrations=data_and_metadata.dimensional_calibrations)
 
 
+def function_gaussian_window(data_shape: DataAndMetadata.ShapeType, sigma: float) -> DataAndMetadata.DataAndMetadata:
+    sigma = sigma if sigma > 0.0 else 1.0  # clamp sigma to avoid divide by zero
+    if len(data_shape) == 1:
+        w = data_shape[0]
+        return DataAndMetadata.new_data_and_metadata(scipy.signal.windows.gaussian(w, std=sigma))
+    elif len(data_shape) == 2:
+        # uses circularly rotated approach of generating 2D filter from 1D
+        h, w = data_shape
+        y, x = numpy.meshgrid(numpy.arange(0, h) - (h - 1) / 2, numpy.arange(0, w) - (w - 1) / 2, indexing='ij')
+        r_squared = y * y + x * x
+        return DataAndMetadata.new_data_and_metadata(numpy.exp(-0.5 * r_squared / (sigma * sigma)))
+    raise ValueError("Window input data must be 1D or 2D")
+
+
+def function_hamming_window(data_shape: DataAndMetadata.ShapeType) -> DataAndMetadata.DataAndMetadata:
+    if len(data_shape) == 1:
+        return DataAndMetadata.new_data_and_metadata(scipy.signal.windows.hamming(data_shape[0]))
+    elif len(data_shape) == 2:
+        # uses outer product approach of generating 2D filter from 1D
+        h, w = data_shape
+        w0 = numpy.reshape(scipy.signal.windows.hamming(w), (1, w))
+        w1 = numpy.reshape(scipy.signal.windows.hamming(h), (h, 1))
+        return DataAndMetadata.new_data_and_metadata(w0 * w1)
+    raise ValueError("Window input data must be 1D or 2D")
+
+
+def function_hann_window(data_shape: DataAndMetadata.ShapeType) -> DataAndMetadata.DataAndMetadata:
+    if len(data_shape) == 1:
+        return DataAndMetadata.new_data_and_metadata(scipy.signal.windows.hann(data_shape[0]))
+    elif len(data_shape) == 2:
+        # uses outer product approach of generating 2D filter from 1D
+        h, w = data_shape
+        w0 = numpy.reshape(scipy.signal.windows.hann(w), (1, w))
+        w1 = numpy.reshape(scipy.signal.windows.hann(h), (h, 1))
+        return DataAndMetadata.new_data_and_metadata(w0 * w1)
+    raise ValueError("Window input data must be 1D or 2D")
+
+
 def function_transpose_flip(data_and_metadata_in: _DataAndMetadataLike, transpose: bool = False, flip_v: bool = False, flip_h: bool = False) -> DataAndMetadata.DataAndMetadata:
     data_and_metadata = DataAndMetadata.promote_ndarray(data_and_metadata_in)
 

nion/data/test/Core_test.py

@@ -3,7 +3,6 @@
 import io
 import logging
 import math
-import os
 import typing
 import unittest
 
@@ -1429,6 +1428,18 @@ def test_warp_rgba(self) -> None:
         dst = Core.function_warp(src, coords)
         self._validate_warp_shape(src, dst, coords, is_channel_data=True)
 
+    def test_gaussian_window(self) -> None:
+        sigma = 8.0
+        size = 17  # use an odd size so the center is well-defined
+        reference_data = scipy.signal.windows.gaussian(size, std=sigma)
+        data_1d = Core.function_gaussian_window((size,), sigma).data
+        data_2d = Core.function_gaussian_window((size, size), sigma).data
+        for i in range(size):
+            self.assertAlmostEqual(reference_data[i], data_1d[i])
+            # check that center column and center row are close
+            self.assertAlmostEqual(reference_data[i], data_2d[i, size//2])
+            self.assertAlmostEqual(reference_data[i], data_2d[size//2, i])
+
 
 if __name__ == '__main__':
     logging.getLogger().setLevel(logging.DEBUG)

nion/data/xdata_1_0.py

@@ -257,11 +257,32 @@ def median_filter(data_and_metadata: _DataAndMetadataLike, size: int) -> DataAnd
 def uniform_filter(data_and_metadata: _DataAndMetadataLike, size: int) -> DataAndMetadata.DataAndMetadata:
     return Core.function_uniform_filter(data_and_metadata, size)
 
-def transpose_flip(data_and_metadata: _DataAndMetadataLike, transpose: bool=False, flip_v: bool=False, flip_h: bool=False) -> DataAndMetadata.DataAndMetadata:
-    return Core.function_transpose_flip(data_and_metadata, transpose, flip_v, flip_h)
+# windows
+
+def gaussian_window(data_shape: DataAndMetadata.Shape2dType, sigma: float) -> DataAndMetadata.DataAndMetadata:
+    return Core.function_gaussian_window(data_shape, sigma)
+
+def hamming_window(data_shape: DataAndMetadata.Shape2dType) -> DataAndMetadata.DataAndMetadata:
+    return Core.function_hamming_window(data_shape)
+
+def hann_window(data_shape: DataAndMetadata.Shape2dType) -> DataAndMetadata.DataAndMetadata:
+    return Core.function_hann_window(data_shape)
+
+# scalar functions
+
+def sum_scalar(data_and_metadata_in: _DataAndMetadataLike) -> DataAndMetadata.ScalarAndMetadata:
+    data_and_metadata = DataAndMetadata.promote_ndarray(data_and_metadata_in)
+    return DataAndMetadata.ScalarAndMetadata.from_value(numpy.sum(data_and_metadata), data_and_metadata.intensity_calibration)
+
+def mean_scalar(data_and_metadata_in: _DataAndMetadataLike) -> DataAndMetadata.ScalarAndMetadata:
+    data_and_metadata = DataAndMetadata.promote_ndarray(data_and_metadata_in)
+    return DataAndMetadata.ScalarAndMetadata.from_value(numpy.average(data_and_metadata), data_and_metadata.intensity_calibration)
 
 # miscellaneous
 
+def transpose_flip(data_and_metadata: _DataAndMetadataLike, transpose: bool=False, flip_v: bool=False, flip_h: bool=False) -> DataAndMetadata.DataAndMetadata:
+    return Core.function_transpose_flip(data_and_metadata, transpose, flip_v, flip_h)
+
 def histogram(data_and_metadata: _DataAndMetadataLike, bins: int) -> DataAndMetadata.DataAndMetadata:
     return Core.function_histogram(data_and_metadata, bins)