data utils

+import numpy
+
+
+
+def find_closest(value, array):
+    array = numpy.asarray(array)
+    idx = (numpy.abs(array - value)).argmin()
+    return array[idx]
+
+
+def frame_drop(scan, frame_keep_style="random", frame_keep_fraction=1, frame_dim=1, impute="drop"):
+    if frame_keep_fraction >= 1:
+        return scan
+
+    n = scan.shape[frame_dim]
+    if frame_keep_style == "random":
+        frames_to_keep = int(numpy.floor(n * frame_keep_fraction))
+        indices = numpy.random.permutation(numpy.arange(n))[:frames_to_keep]
+    elif frame_keep_style == "ordered":
+        k = int(numpy.ceil(1 / frame_keep_fraction))
+        indices = numpy.arange(0, n, k)
+        # pick every k'th frame
+    else:
+        raise Exception("Wrong frame drop style")
+
+    if impute == "zeros":
+        if frame_dim == 1:
+            t = numpy.zeros((1, n, 1, 1))
+            t[:, indices] = 1
+            return scan * t
+        if frame_dim == 2:
+            t = numpy.zeros((1, 1, n, 1))
+            t[:, :, indices] = 1
+            return scan * t
+        if frame_dim == 3:
+            t = numpy.zeros((1, 1, 1, n))
+            t[:, :, :, indices] = 1
+            return scan * t
+    elif impute == "fill":
+        # fill with nearest available frame
+        if frame_dim == 1:
+            for i in range(scan.shape[1]):
+                if i in indices:
+                    pass
+                scan[:, i, :, :] = scan[:, find_closest(i, indices), :, :]
+            return scan
+
+        if frame_dim == 2:
+            for i in range(scan.shape[1]):
+                if i in indices:
+                    pass
+                scan[:, :, i, :] = scan[:, :, find_closest(i, indices), :]
+            return scan
+
+        if frame_dim == 3:
+            for i in range(scan.shape[1]):
+                if i in indices:
+                    pass
+                scan[:, :, :, i] = scan[:, :, :, find_closest(i, indices)]
+            return scan
+    elif impute == "noise":
+        noise = numpy.random.uniform(high=scan.max(), low=scan.min(), size=scan.shape)
+
+        if frame_dim == 1:
+            t = numpy.zeros((1, n, 1, 1))
+            t[:, indices] = 1
+            return scan + noise * (1 - t)
+        if frame_dim == 2:
+            t = numpy.zeros((1, 1, n, 1))
+            t[:, :, indices] = 1
+            return scan + noise * (1 - t)
+        if frame_dim == 3:
+            t = numpy.zeros((1, 1, 1, n))
+            t[:, :, :, indices] = 1
+            return scan + noise * (1 - t)
+    else: # drop
+        if frame_dim == 1:
+            return scan[:, indices, :, :]
+        if frame_dim == 2:
+            return scan[:, :, indices, :]
+        if frame_dim == 3:
+            return scan[:, :, :, indices]
+    return scan
+
+
+
+
+def gaussian_noise(scan, sigma=0.0):
+    return scan + sigma * numpy.random.randn(*scan.shape)
+
+
+def intensity_scaling(scan):
+    scale = 2 ** (2 * numpy.random.rand() - 1)  # 2**(-1,1)
+    return scan * scale