wmayner · September 5, 2019 16:15 · Sep 5, 2019
diff --git a/sliding_window.py b/sliding_window.py
@@ -0,0 +1,50 @@
+# from
+# https://gist.github.com/nils-werner/9d321441006b112a4b116a8387c2280c/raw/489137f314c022ca08f0c5089319ded2eb8c5ae1/sliding_window.py
+def sliding_window(size, data, step=1, padded=False, axis=-1, copy=True):
+    """Calculate a sliding window over an array.
+
+    Note:
+        If the array cannot be windowed evenly, it is truncated at the end.
+
+    Args:
+        data (np.ndarray): The array to be windowed.
+        size (int): The window size.
+        step (int): The number of elements between windows. Defaults to 1.
+        axis (int): The axis to slide over. Defaults to the last axis.
+        copy (bool): Return a copy instead of a view.
+
+    Returns:
+        np.ndarray: A matrix where the last dimension corresponds to the
+        window.
+
+    Examples:
+        >>> a = np.array([1, 2, 3, 4, 5])
+        >>> sliding_window(3, a)
+        array([[1, 2, 3],
+               [2, 3, 4],
+               [3, 4, 5]])
+        >>> sliding_window(3, a, step=2)
+        array([[1, 2, 3],
+               [3, 4, 5]])
+    """
+    if axis >= data.ndim:
+        raise ValueError("axis value out of range")
+    if step < 1:
+        raise ValueError("step size must be positive")
+    if size > data.shape[axis]:
+        raise ValueError("window size may not exceed size of selected axis")
+
+    shape = list(data.shape)
+    shape[axis] = np.floor(data.shape[axis] / step - size / step + 1).astype(int)
+    shape.append(size)
+
+    strides = list(data.strides)
+    strides[axis] *= step
+    strides.append(data.strides[axis])
+
+    strided = np.lib.stride_tricks.as_strided(data, shape=shape, strides=strides)
+
+    if copy:
+        return strided.copy()
+    else:
+        return strided
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