diff options
Diffstat (limited to 'numpy/lib/arraypad.py')
| -rw-r--r-- | numpy/lib/arraypad.py | 143 |
1 files changed, 34 insertions, 109 deletions
diff --git a/numpy/lib/arraypad.py b/numpy/lib/arraypad.py index 97ba12348..600301c56 100644 --- a/numpy/lib/arraypad.py +++ b/numpy/lib/arraypad.py @@ -156,13 +156,9 @@ def _prepend_edge(arr, pad_amt, axis=-1): if pad_amt == 0: return arr - edge_slice = tuple(slice(None) if i != axis else 0 + edge_slice = tuple(slice(None) if i != axis else slice(0, 1) for (i, x) in enumerate(arr.shape)) - - # Shape to restore singleton dimension after slicing - pad_singleton = tuple(x if i != axis else 1 - for (i, x) in enumerate(arr.shape)) - edge_arr = arr[edge_slice].reshape(pad_singleton) + edge_arr = arr[edge_slice] return np.concatenate((edge_arr.repeat(pad_amt, axis=axis), arr), axis=axis) @@ -190,13 +186,9 @@ def _append_edge(arr, pad_amt, axis=-1): if pad_amt == 0: return arr - edge_slice = tuple(slice(None) if i != axis else arr.shape[axis] - 1 + edge_slice = tuple(slice(None) if i != axis else slice(x - 1, x) for (i, x) in enumerate(arr.shape)) - - # Shape to restore singleton dimension after slicing - pad_singleton = tuple(x if i != axis else 1 - for (i, x) in enumerate(arr.shape)) - edge_arr = arr[edge_slice].reshape(pad_singleton) + edge_arr = arr[edge_slice] return np.concatenate((arr, edge_arr.repeat(pad_amt, axis=axis)), axis=axis) @@ -236,15 +228,11 @@ def _prepend_ramp(arr, pad_amt, end, axis=-1): reverse=True).astype(np.float64) # Appropriate slicing to extract n-dimensional edge along `axis` - edge_slice = tuple(slice(None) if i != axis else 0 + edge_slice = tuple(slice(None) if i != axis else slice(0, 1) for (i, x) in enumerate(arr.shape)) - # Shape to restore singleton dimension after slicing - pad_singleton = tuple(x if i != axis else 1 - for (i, x) in enumerate(arr.shape)) - - # Extract edge, reshape to original rank, and extend along `axis` - edge_pad = arr[edge_slice].reshape(pad_singleton).repeat(pad_amt, axis) + # Extract edge, and extend along `axis` + edge_pad = arr[edge_slice].repeat(pad_amt, axis) # Linear ramp slope = (end - edge_pad) / float(pad_amt) @@ -291,15 +279,11 @@ def _append_ramp(arr, pad_amt, end, axis=-1): reverse=False).astype(np.float64) # Slice a chunk from the edge to calculate stats on - edge_slice = tuple(slice(None) if i != axis else -1 + edge_slice = tuple(slice(None) if i != axis else slice(x - 1, x) for (i, x) in enumerate(arr.shape)) - # Shape to restore singleton dimension after slicing - pad_singleton = tuple(x if i != axis else 1 - for (i, x) in enumerate(arr.shape)) - - # Extract edge, reshape to original rank, and extend along `axis` - edge_pad = arr[edge_slice].reshape(pad_singleton).repeat(pad_amt, axis) + # Extract edge, and extend along `axis` + edge_pad = arr[edge_slice].repeat(pad_amt, axis) # Linear ramp slope = (end - edge_pad) / float(pad_amt) @@ -351,12 +335,8 @@ def _prepend_max(arr, pad_amt, num, axis=-1): max_slice = tuple(slice(None) if i != axis else slice(num) for (i, x) in enumerate(arr.shape)) - # Shape to restore singleton dimension after slicing - pad_singleton = tuple(x if i != axis else 1 - for (i, x) in enumerate(arr.shape)) - - # Extract slice, calculate max, reshape to add singleton dimension back - max_chunk = arr[max_slice].max(axis=axis).reshape(pad_singleton) + # Extract slice, calculate max + max_chunk = arr[max_slice].max(axis=axis, keepdims=True) # Concatenate `arr` with `max_chunk`, extended along `axis` by `pad_amt` return np.concatenate((max_chunk.repeat(pad_amt, axis=axis), arr), @@ -407,12 +387,8 @@ def _append_max(arr, pad_amt, num, axis=-1): else: max_slice = tuple(slice(None) for x in arr.shape) - # Shape to restore singleton dimension after slicing - pad_singleton = tuple(x if i != axis else 1 - for (i, x) in enumerate(arr.shape)) - - # Extract slice, calculate max, reshape to add singleton dimension back - max_chunk = arr[max_slice].max(axis=axis).reshape(pad_singleton) + # Extract slice, calculate max + max_chunk = arr[max_slice].max(axis=axis, keepdims=True) # Concatenate `arr` with `max_chunk`, extended along `axis` by `pad_amt` return np.concatenate((arr, max_chunk.repeat(pad_amt, axis=axis)), @@ -458,12 +434,8 @@ def _prepend_mean(arr, pad_amt, num, axis=-1): mean_slice = tuple(slice(None) if i != axis else slice(num) for (i, x) in enumerate(arr.shape)) - # Shape to restore singleton dimension after slicing - pad_singleton = tuple(x if i != axis else 1 - for (i, x) in enumerate(arr.shape)) - - # Extract slice, calculate mean, reshape to add singleton dimension back - mean_chunk = arr[mean_slice].mean(axis).reshape(pad_singleton) + # Extract slice, calculate mean + mean_chunk = arr[mean_slice].mean(axis, keepdims=True) _round_ifneeded(mean_chunk, arr.dtype) # Concatenate `arr` with `mean_chunk`, extended along `axis` by `pad_amt` @@ -515,12 +487,8 @@ def _append_mean(arr, pad_amt, num, axis=-1): else: mean_slice = tuple(slice(None) for x in arr.shape) - # Shape to restore singleton dimension after slicing - pad_singleton = tuple(x if i != axis else 1 - for (i, x) in enumerate(arr.shape)) - - # Extract slice, calculate mean, reshape to add singleton dimension back - mean_chunk = arr[mean_slice].mean(axis=axis).reshape(pad_singleton) + # Extract slice, calculate mean + mean_chunk = arr[mean_slice].mean(axis=axis, keepdims=True) _round_ifneeded(mean_chunk, arr.dtype) # Concatenate `arr` with `mean_chunk`, extended along `axis` by `pad_amt` @@ -567,12 +535,8 @@ def _prepend_med(arr, pad_amt, num, axis=-1): med_slice = tuple(slice(None) if i != axis else slice(num) for (i, x) in enumerate(arr.shape)) - # Shape to restore singleton dimension after slicing - pad_singleton = tuple(x if i != axis else 1 - for (i, x) in enumerate(arr.shape)) - - # Extract slice, calculate median, reshape to add singleton dimension back - med_chunk = np.median(arr[med_slice], axis=axis).reshape(pad_singleton) + # Extract slice, calculate median + med_chunk = np.median(arr[med_slice], axis=axis, keepdims=True) _round_ifneeded(med_chunk, arr.dtype) # Concatenate `arr` with `med_chunk`, extended along `axis` by `pad_amt` @@ -624,12 +588,8 @@ def _append_med(arr, pad_amt, num, axis=-1): else: med_slice = tuple(slice(None) for x in arr.shape) - # Shape to restore singleton dimension after slicing - pad_singleton = tuple(x if i != axis else 1 - for (i, x) in enumerate(arr.shape)) - - # Extract slice, calculate median, reshape to add singleton dimension back - med_chunk = np.median(arr[med_slice], axis=axis).reshape(pad_singleton) + # Extract slice, calculate median + med_chunk = np.median(arr[med_slice], axis=axis, keepdims=True) _round_ifneeded(med_chunk, arr.dtype) # Concatenate `arr` with `med_chunk`, extended along `axis` by `pad_amt` @@ -677,12 +637,8 @@ def _prepend_min(arr, pad_amt, num, axis=-1): min_slice = tuple(slice(None) if i != axis else slice(num) for (i, x) in enumerate(arr.shape)) - # Shape to restore singleton dimension after slicing - pad_singleton = tuple(x if i != axis else 1 - for (i, x) in enumerate(arr.shape)) - - # Extract slice, calculate min, reshape to add singleton dimension back - min_chunk = arr[min_slice].min(axis=axis).reshape(pad_singleton) + # Extract slice, calculate min + min_chunk = arr[min_slice].min(axis=axis, keepdims=True) # Concatenate `arr` with `min_chunk`, extended along `axis` by `pad_amt` return np.concatenate((min_chunk.repeat(pad_amt, axis=axis), arr), @@ -733,12 +689,8 @@ def _append_min(arr, pad_amt, num, axis=-1): else: min_slice = tuple(slice(None) for x in arr.shape) - # Shape to restore singleton dimension after slicing - pad_singleton = tuple(x if i != axis else 1 - for (i, x) in enumerate(arr.shape)) - - # Extract slice, calculate min, reshape to add singleton dimension back - min_chunk = arr[min_slice].min(axis=axis).reshape(pad_singleton) + # Extract slice, calculate min + min_chunk = arr[min_slice].min(axis=axis, keepdims=True) # Concatenate `arr` with `min_chunk`, extended along `axis` by `pad_amt` return np.concatenate((arr, min_chunk.repeat(pad_amt, axis=axis)), @@ -790,17 +742,11 @@ def _pad_ref(arr, pad_amt, method, axis=-1): ref_chunk1 = arr[ref_slice] - # Shape to restore singleton dimension after slicing - pad_singleton = tuple(x if i != axis else 1 - for (i, x) in enumerate(arr.shape)) - if pad_amt[0] == 1: - ref_chunk1 = ref_chunk1.reshape(pad_singleton) - # Memory/computationally more expensive, only do this if `method='odd'` if 'odd' in method and pad_amt[0] > 0: - edge_slice1 = tuple(slice(None) if i != axis else 0 + edge_slice1 = tuple(slice(None) if i != axis else slice(0, 1) for (i, x) in enumerate(arr.shape)) - edge_chunk = arr[edge_slice1].reshape(pad_singleton) + edge_chunk = arr[edge_slice1] ref_chunk1 = 2 * edge_chunk - ref_chunk1 del edge_chunk @@ -816,13 +762,10 @@ def _pad_ref(arr, pad_amt, method, axis=-1): for (i, x) in enumerate(arr.shape)) ref_chunk2 = arr[ref_slice][rev_idx] - if pad_amt[1] == 1: - ref_chunk2 = ref_chunk2.reshape(pad_singleton) - if 'odd' in method: - edge_slice2 = tuple(slice(None) if i != axis else -1 + edge_slice2 = tuple(slice(None) if i != axis else slice(x - 1, x) for (i, x) in enumerate(arr.shape)) - edge_chunk = arr[edge_slice2].reshape(pad_singleton) + edge_chunk = arr[edge_slice2] ref_chunk2 = 2 * edge_chunk - ref_chunk2 del edge_chunk @@ -876,17 +819,11 @@ def _pad_sym(arr, pad_amt, method, axis=-1): for (i, x) in enumerate(arr.shape)) sym_chunk1 = arr[sym_slice][rev_idx] - # Shape to restore singleton dimension after slicing - pad_singleton = tuple(x if i != axis else 1 - for (i, x) in enumerate(arr.shape)) - if pad_amt[0] == 1: - sym_chunk1 = sym_chunk1.reshape(pad_singleton) - # Memory/computationally more expensive, only do this if `method='odd'` if 'odd' in method and pad_amt[0] > 0: - edge_slice1 = tuple(slice(None) if i != axis else 0 + edge_slice1 = tuple(slice(None) if i != axis else slice(0, 1) for (i, x) in enumerate(arr.shape)) - edge_chunk = arr[edge_slice1].reshape(pad_singleton) + edge_chunk = arr[edge_slice1] sym_chunk1 = 2 * edge_chunk - sym_chunk1 del edge_chunk @@ -900,13 +837,10 @@ def _pad_sym(arr, pad_amt, method, axis=-1): for (i, x) in enumerate(arr.shape)) sym_chunk2 = arr[sym_slice][rev_idx] - if pad_amt[1] == 1: - sym_chunk2 = sym_chunk2.reshape(pad_singleton) - if 'odd' in method: - edge_slice2 = tuple(slice(None) if i != axis else -1 + edge_slice2 = tuple(slice(None) if i != axis else slice(x - 1, x) for (i, x) in enumerate(arr.shape)) - edge_chunk = arr[edge_slice2].reshape(pad_singleton) + edge_chunk = arr[edge_slice2] sym_chunk2 = 2 * edge_chunk - sym_chunk2 del edge_chunk @@ -957,12 +891,6 @@ def _pad_wrap(arr, pad_amt, axis=-1): for (i, x) in enumerate(arr.shape)) wrap_chunk1 = arr[wrap_slice] - # Shape to restore singleton dimension after slicing - pad_singleton = tuple(x if i != axis else 1 - for (i, x) in enumerate(arr.shape)) - if pad_amt[0] == 1: - wrap_chunk1 = wrap_chunk1.reshape(pad_singleton) - ########################################################################## # Appended region @@ -971,9 +899,6 @@ def _pad_wrap(arr, pad_amt, axis=-1): for (i, x) in enumerate(arr.shape)) wrap_chunk2 = arr[wrap_slice] - if pad_amt[1] == 1: - wrap_chunk2 = wrap_chunk2.reshape(pad_singleton) - # Concatenate `arr` with both chunks, extending along `axis` return np.concatenate((wrap_chunk1, arr, wrap_chunk2), axis=axis) |