Refactor generate_gradients.py
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ZyX
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d1c2980ca4
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ca0a8a2659
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@ -45,19 +45,6 @@ def nums(s):
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return [int(i) for i in s.split()]
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p = argparse.ArgumentParser(description=__doc__)
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p.add_argument('gradient', nargs='*', metavar='COLOR', type=color, help='List of colors (either indexes from 8-bit palette or 24-bit RGB in hexadecimal notation)')
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p.add_argument('-n', '--num_items', metavar='INT', type=int, help='Number of items in resulting list', default=101)
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p.add_argument('-N', '--num_output', metavar='INT', type=int, help='Number of characters in sample', default=101)
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p.add_argument('-r', '--range', metavar='V1 V2', type=num2, help='Use this range when outputting scale')
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p.add_argument('-s', '--show', action='store_true', help='If present output gradient sample')
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p.add_argument('-p', '--palette', choices=('16', '256'), help='Use this palette. Defaults to 240-color palette (256 colors without first 16)')
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p.add_argument('-w', '--weights', metavar='INT INT ...', type=nums, help='Adjust weights of colors. Number of weights must be equal to number of colors')
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p.add_argument('-C', '--omit-terminal', action='store_true', help='If present do not compute values for terminal')
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args = p.parse_args()
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def linear_gradient(start_value, stop_value, start_offset, stop_offset, offset):
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return start_value + ((offset - start_offset) * (stop_value - start_value) / (stop_offset - start_offset))
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@ -133,62 +120,91 @@ def dec_scale_generator(num):
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return r
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m = args.num_items
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def compute_steps(gradient, weights):
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maxweight = len(gradient) - 1
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if weights:
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weight_sum = sum(weights)
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norm_weights = [100.0 * weight / weight_sum for weight in weights]
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steps = [0]
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for weight in norm_weights:
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steps.append(steps[-1] + weight)
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steps.pop(0)
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steps.pop(0)
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else:
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step = m / maxweight
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steps = [i * step for i in range(1, maxweight + 1)]
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return steps
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maxweight = len(args.gradient) - 1
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if args.weights:
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weight_sum = sum(args.weights)
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norm_weights = [100.0 * weight / weight_sum for weight in args.weights]
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steps = [0]
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for weight in norm_weights:
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steps.append(steps[-1] + weight)
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steps.pop(0)
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steps.pop(0)
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else:
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step = m / maxweight
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steps = [i * step for i in range(1, maxweight + 1)]
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data = [(weight, args.gradient[i - 1], args.gradient[i]) for weight, i in zip(steps, range(1, len(args.gradient)))]
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gr_func = generate_gradient_function(data)
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gradient = [gr_func(y) for y in range(0, m)]
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palettes = {
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'16': (cterm_to_lab[:16], lambda c: c),
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'256': (cterm_to_lab, lambda c: c),
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None: (cterm_to_lab[16:], lambda c: c + 16),
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}
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r = [get_rgb(lab) for lab in gradient]
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if not args.omit_terminal:
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r2 = [find_color(lab, *palettes[args.palette])[0] for lab in gradient]
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r3 = [i[0] for i in groupby(r2)]
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print(json.dumps(r))
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if not args.omit_terminal:
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print(json.dumps(r2))
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print(json.dumps(r3))
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if args.show:
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print_colors(args.gradient, args.num_output)
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print_colors(gradient, args.num_output)
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if not args.omit_terminal:
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print_colors(r2, args.num_output)
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print_colors(r3, args.num_output)
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if not args.range and args.num_output >= 32 and (args.num_output - 1) // 10 >= 4 and (args.num_output - 1) % 10 == 0:
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def show_scale(rng, num_output):
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if not rng and num_output >= 32 and (num_output - 1) // 10 >= 4 and (num_output - 1) % 10 == 0:
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sys.stdout.write('0')
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sys.stdout.write(''.join(('%*u' % (args.num_output // 10, i) for i in range(10, 101, 10))))
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sys.stdout.write(''.join(('%*u' % (num_output // 10, i) for i in range(10, 101, 10))))
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sys.stdout.write('\n')
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else:
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if args.range:
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vmin, vmax = args.range[1]
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isint = args.range[0]
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if rng:
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vmin, vmax = rng[1]
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isint = rng[0]
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else:
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isint = True
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vmin = 0
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vmax = 100
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s = ''
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lasts = ' ' + str(vmax)
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while len(s) + len(lasts) < args.num_output:
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while len(s) + len(lasts) < num_output:
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curpc = len(s) + 1 if s else 0
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curval = vmin + curpc * (vmax - vmin) / args.num_output
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curval = vmin + curpc * (vmax - vmin) / num_output
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if isint:
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curval = int(round(curval))
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s += str(curval) + ' '
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sys.stdout.write(s[:-1] + lasts + '\n')
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sys.stdout.write(dec_scale_generator(args.num_output) + '\n')
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sys.stdout.write(dec_scale_generator(num_output) + '\n')
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if __name__ == '__main__':
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p = argparse.ArgumentParser(description=__doc__)
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p.add_argument('gradient', nargs='*', metavar='COLOR', type=color, help='List of colors (either indexes from 8-bit palette or 24-bit RGB in hexadecimal notation)')
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p.add_argument('-n', '--num_items', metavar='INT', type=int, help='Number of items in resulting list', default=101)
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p.add_argument('-N', '--num_output', metavar='INT', type=int, help='Number of characters in sample', default=101)
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p.add_argument('-r', '--range', metavar='V1 V2', type=num2, help='Use this range when outputting scale')
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p.add_argument('-s', '--show', action='store_true', help='If present output gradient sample')
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p.add_argument('-p', '--palette', choices=('16', '256'), help='Use this palette. Defaults to 240-color palette (256 colors without first 16)')
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p.add_argument('-w', '--weights', metavar='INT INT ...', type=nums, help='Adjust weights of colors. Number of weights must be equal to number of colors')
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p.add_argument('-C', '--omit-terminal', action='store_true', help='If present do not compute values for terminal')
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args = p.parse_args()
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m = args.num_items
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steps = compute_steps(args.gradient, args.weights)
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data = [(weight, args.gradient[i - 1], args.gradient[i])
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for weight, i in zip(steps, range(1, len(args.gradient)))]
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gr_func = generate_gradient_function(data)
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gradient = [gr_func(y) for y in range(0, m)]
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r = [get_rgb(lab) for lab in gradient]
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if not args.omit_terminal:
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r2 = [find_color(lab, *palettes[args.palette])[0] for lab in gradient]
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r3 = [i[0] for i in groupby(r2)]
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print(json.dumps(r))
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if not args.omit_terminal:
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print(json.dumps(r2))
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print(json.dumps(r3))
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if args.show:
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print_colors(args.gradient, args.num_output)
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print_colors(gradient, args.num_output)
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if not args.omit_terminal:
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print_colors(r2, args.num_output)
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print_colors(r3, args.num_output)
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show_scale(args.range, args.num_output)
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