mirror of
https://github.com/KrisKennaway/ii-vision.git
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251 lines
6.9 KiB
Python
251 lines
6.9 KiB
Python
"""Computes visual differences between screen image data."""
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import functools
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import numpy as np
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import weighted_levenshtein
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@functools.lru_cache(None)
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def byte_to_nominal_colour_string(b: int, is_odd_offset: bool) -> str:
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"""Compute nominal pixel colours for a byte.
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This ignores any fringing/colour combining effects, as well as
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half-ignoring what happens to the colour pixel that crosses the byte
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boundary.
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A better implementation of this might be to consider neighbouring (even,
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odd) column bytes together since this will allow correctly colouring the
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split pixel in the middle.
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There are also even weirder colour artifacts that happen when
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neighbouring bytes have mismatched colour palettes, which also cross the
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odd/even boundary. But these may not be worth worrying about.
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"""
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pixels = []
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idx = 0
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if is_odd_offset:
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pixels.append("01"[b & 0x01])
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idx += 1
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# K = black
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# G = green
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# V = violet
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# W = white
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palettes = (
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(
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"K", # 0x00
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"V", # 0x01
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"G", # 0x10
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"W" # 0x11
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), (
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"K", # 0x00
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"B", # 0x01
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"O", # 0x10
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"W" # 0x11
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)
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)
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palette = palettes[(b & 0x80) != 0]
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for _ in range(3):
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pixel = palette[(b >> idx) & 0b11]
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pixels.append(pixel)
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idx += 2
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if not is_odd_offset:
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pixels.append("01"[(b & 0x40) != 0])
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idx += 1
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return "".join(pixels)
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@functools.lru_cache(None)
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def byte_to_colour_string_with_white_coalescing(
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b: int, is_odd_offset: bool) -> str:
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"""Model the combining of neighbouring 1 bits to produce white.
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The output is a string of length 7 representing the 7 display dots that now
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have colour.
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Attempt to model the colour artifacting that consecutive runs of
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1 bits are coerced to white. This isn't quite correct since:
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a) it doesn't operate across byte boundaries (see note on
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byte_to_nominal_colour_string)
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b) a sequence like WVV appears more like WWWVVV or WWVVVV rather than WWWKVV
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(at least on the //gs)
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It also ignores other colour fringing e.g. from NTSC artifacts.
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TODO: this needs more work.
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"""
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pixels = []
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fringing = {
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"1V": "WWK", # 110
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"1W": "WWW", # 111
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"1B": "WWB", # 110
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"WV": "WWWK", # 1110
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"WB": "WWWK", # 1110
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"GV": "KWWK", # 0110
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"OB": "KWWK", # 0110
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"GW": "KWWW", # 0111
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"OW": "KWWW", # 0111
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"W1": "WWW", # 111
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"G1": "KWW", # 011
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"O1": "KWW", # 011
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}
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nominal = byte_to_nominal_colour_string(b, is_odd_offset)
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for idx in range(3):
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pair = nominal[idx:idx + 2]
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effective = fringing.get(pair)
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if not effective:
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e = []
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if pair[0] in {"0", "1"}:
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e.append(pair[0])
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else:
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e.extend([pair[0], pair[0]])
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if pair[1] in {"0", "1"}:
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e.append(pair[1])
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else:
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e.extend([pair[1], pair[1]])
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effective = "".join(e)
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if pixels:
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pixels.append(effective[2:])
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else:
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pixels.append(effective)
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return "".join(pixels)
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substitute_costs = np.ones((128, 128), dtype=np.float64)
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# Substitution costs to use when evaluating other potential offsets at which
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# to store a content byte. We penalize more harshly for introducing
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# errors that alter pixel colours, since these tend to be very
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# noticeable as visual noise.
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error_substitute_costs = np.ones((128, 128), dtype=np.float64)
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# Penalty for turning on/off a black bit
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for c in "01GVWOB":
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substitute_costs[(ord('K'), ord(c))] = 1
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substitute_costs[(ord(c), ord('K'))] = 1
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error_substitute_costs[(ord('K'), ord(c))] = 5
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error_substitute_costs[(ord(c), ord('K'))] = 5
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# Penalty for changing colour
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for c in "01GVWOB":
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for d in "01GVWOB":
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substitute_costs[(ord(c), ord(d))] = 1
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substitute_costs[(ord(d), ord(c))] = 1
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error_substitute_costs[(ord(c), ord(d))] = 5
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error_substitute_costs[(ord(d), ord(c))] = 5
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insert_costs = np.ones(128, dtype=np.float64) * 1000
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delete_costs = np.ones(128, dtype=np.float64) * 1000
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def _edit_weight(a: int, b: int, is_odd_offset: bool, error: bool):
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a_pixels = byte_to_colour_string_with_white_coalescing(a, is_odd_offset)
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b_pixels = byte_to_colour_string_with_white_coalescing(b, is_odd_offset)
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dist = weighted_levenshtein.dam_lev(
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a_pixels, b_pixels,
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insert_costs=insert_costs,
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delete_costs=delete_costs,
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substitute_costs=error_substitute_costs if error else substitute_costs,
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)
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return np.int64(dist)
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def edit_weight_matrixes(error: bool) -> np.array:
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ewm = np.zeros(shape=(256, 256, 2), dtype=np.int64)
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for a in range(256):
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for b in range(256):
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for is_odd_offset in (False, True):
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ewm[a, b, int(is_odd_offset)] = _edit_weight(
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a, b, is_odd_offset, error)
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return ewm
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_ewm = edit_weight_matrixes(False)
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_error_ewm = edit_weight_matrixes(True)
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@functools.lru_cache(None)
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def edit_weight(a: int, b: int, is_odd_offset: bool, error: bool):
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e = _error_ewm if error else _ewm
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return e[a, b, int(is_odd_offset)]
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_even_ewm = {}
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_odd_ewm = {}
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_even_error_ewm = {}
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_odd_error_ewm = {}
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for a in range(256):
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for b in range(256):
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_even_ewm[(a << 8) + b] = edit_weight(a, b, False, False)
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_odd_ewm[(a << 8) + b] = edit_weight(a, b, True, False)
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_even_error_ewm[(a << 8) + b] = edit_weight(a, b, False, True)
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_odd_error_ewm[(a << 8) + b] = edit_weight(a, b, True, True)
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@functools.lru_cache(None)
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def _content_a_array(content: int, shape) -> np.array:
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return (np.ones(shape, dtype=np.uint16) * content) << 8
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def content_edit_weight(content: int, b: np.array) -> np.array:
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assert b.shape == (32, 256), b.shape
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# Extract even and off column offsets (128,)
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even_b = b[:, ::2]
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odd_b = b[:, 1::2]
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a = _content_a_array(content, even_b.shape)
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even = a + even_b
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odd = a + odd_b
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even_weights = np.vectorize(_even_error_ewm.__getitem__)(even)
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odd_weights = np.vectorize(_odd_error_ewm.__getitem__)(odd)
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res = np.ndarray(shape=b.shape, dtype=np.int64)
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res[:, ::2] = even_weights
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res[:, 1::2] = odd_weights
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return res
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def array_edit_weight(a: np.array, b: np.array) -> np.array:
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# Extract even and off column offsets (32, 128)
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even_a = a[:, ::2]
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odd_a = a[:, 1::2]
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even_b = b[:, ::2]
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odd_b = b[:, 1::2]
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even = (even_a.astype(np.uint16) << 8) + even_b
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odd = (odd_a.astype(np.uint16) << 8) + odd_b
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even_weights = np.vectorize(_even_ewm.__getitem__)(even)
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odd_weights = np.vectorize(_odd_ewm.__getitem__)(odd)
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res = np.ndarray(shape=a.shape, dtype=np.int64)
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res[:, ::2] = even_weights
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res[:, 1::2] = odd_weights
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return res
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