RaphaelMeudec

import numpy as np
import tensorflow as tf

# Create function to apply a grey patch on an image
def apply_grey_patch(image, top_left_x, top_left_y, patch_size):
    patched_image = np.array(image, copy=True)
    patched_image[top_left_y:top_left_y + patch_size, top_left_x:top_left_x + patch_size, :] = 127.5

    return patched_image

nornagon

import numpy as np

def smith_waterman(a: str, b: str, alignment_score: float = 1, gap_cost: float = 1) -> float:
  """
  Compute the Smith-Waterman alignment score for two strings.

  See https://en.wikipedia.org/wiki/Smith%E2%80%93Waterman_algorithm#Algorithm
  This implementation has a fixed gap cost (i.e. extending a gap is considered
  free). In the terminology of the Wikipedia description, W_k = {c, c, c, ...}.
  This implementation also has a fixed alignment score, awarded if the relevant

henrik

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