x, y = scaler.device_to_user(px, py) count = calc(x, y) image[py, px] = count def colorise(counts): counts = np.reshape(counts, (counts.shape[0], counts.shape[1])) colormap = make_npcolormap(MAX_COUNT + 1, [ Color('black'), Color('red'), Color('orange'), Color('yellow'), Color('white') ], [16, 8, 32, 128]) outarray = np.zeros((counts.shape[0], counts.shape[1], 3), dtype=np.uint8) apply_npcolormap(outarray, counts, colormap) return outarray data = make_nparray_data(paint, 600, 600, channels=1) filename = temp_file('tinkerbell.dat') save_nparray(filename, data) data = load_nparray(filename) frame = colorise(data) save_nparray_image('burning-ship-zoom.png', frame)
image[py, px] = MAX_COUNT for i in range(MAX_COUNT): z = iterate(z) if converged(z) > 0: image[py, px] = i break def colorise(counts): counts = np.reshape(counts, (counts.shape[0], counts.shape[1])) max_count = int(np.max(counts)) colormap = make_npcolormap( max_count + 1, [Color(0), Color('darkblue'), Color('yellow'), Color(1)], [0.5, 2, 7]) outarray = np.zeros((counts.shape[0], counts.shape[1], 3), dtype=np.uint8) apply_npcolormap(outarray, counts, colormap) return outarray data = make_nparray_data(paint, 600, 600, channels=1) filename = temp_file('newton-cube-time.dat') save_nparray(filename, data) data = load_nparray(filename) frame = colorise(data) save_nparray_image('newton-cube-time.png', frame)
def colorise(counts): counts = np.reshape(counts, (counts.shape[0], counts.shape[1])) power_counts = np.power(counts, 0.25) maxcount = np.max(power_counts) normalised_counts = (power_counts * 1023 / max(maxcount, 1)).astype( np.uint32) colormap = make_npcolormap(1024, [ Color('black'), Color('red'), Color('orange'), Color('yellow'), Color('white') ]) outarray = np.zeros((counts.shape[0], counts.shape[1], 3), dtype=np.uint8) apply_npcolormap(outarray, normalised_counts, colormap) return outarray data = make_nparray_data(paint, 600, 600, channels=1) filename = temp_file('tinkerbell.dat') save_nparray(filename, data) data = load_nparray(filename) frame = colorise(data) save_nparray_image('tinkerbell.png', frame)
return 0 def paint(image, pixel_width, pixel_height, frame_no, frame_count): scaler = Scaler(pixel_width, pixel_height, width=3.2, startx=-1.6, starty=-1.2) for px in range(pixel_width): for py in range(pixel_height): x, y = scaler.device_to_user(px, py) count = calc(x, y) image[py, px] = count def colorise(counts): counts = np.reshape(counts, (counts.shape[0], counts.shape[1])) colormap = make_npcolormap(MAX_COUNT+1, [Color('black'), Color('darkblue'), Color('green'), Color('cyan'), Color('yellow'), Color('black')], [16, 16, 32, 32, 128]) outarray = np.zeros((counts.shape[0], counts.shape[1], 3), dtype=np.uint8) apply_npcolormap(outarray, counts, colormap) return outarray data = make_nparray_data(paint, 800, 600, channels=1) frame = colorise(data) save_nparray_image('julia.png', frame)
def colorise(counts): counts = np.reshape(counts, (counts.shape[0], counts.shape[1])) power_counts = np.power(counts, 0.25) maxcount = np.max(power_counts) normalised_counts = (power_counts * 1023 / max(maxcount, 1)).astype( np.uint32) colormap = make_npcolormap(1024, [ Color('black'), Color('red'), Color('orange'), Color('yellow'), Color('white') ]) outarray = np.zeros((counts.shape[0], counts.shape[1], 3), dtype=np.uint8) apply_npcolormap(outarray, normalised_counts, colormap) return outarray data = make_nparray_data(paint, 600, 600, channels=1) filename = temp_file('kings-dream.dat') save_nparray(filename, data) data = load_nparray(filename) frame = colorise(data) save_nparray_image('kings-dream.png', frame)
def colorise(counts): counts = np.reshape(counts, (counts.shape[0], counts.shape[1])) colormap = make_npcolormap( int(np.max(counts)) + 1, [ Color('black'), Color('cadetblue'), Color('yellow'), Color('white'), Color('white') ], [50, 100, 100, 102400]) outarray = np.zeros((counts.shape[0], counts.shape[1], 3), dtype=np.uint8) apply_npcolormap(outarray, counts, colormap) return outarray filename = temp_file('popcorn.dat') data = make_nparray_data(paint, WIDTH, WIDTH, channels=1) save_nparray(filename, data) data = load_nparray(filename) print_stats(data) print_histogram(data) frame = colorise(data) save_nparray_image('popcorn.png', frame)
outarray = np.zeros((counts.shape[0], counts.shape[1], 3), dtype=np.uint8) apply_npcolormap(outarray, normalised_counts, colormap) return outarray def paint(image, pixel_width, pixel_height, frame_no, frame_count): scaler = Scaler(pixel_width, pixel_height, width=300, startx=-150, starty=-150) x = -1 y = 0 for i in range(MAX_COUNT): x, y = y - math.sqrt(abs(B * x - C)) * sign(x), A - x px, py = scaler.user_to_device(x, y) if 0 <= px < pixel_width and 0 <= py < pixel_height: image[py, px] += 1 filename = temp_file('hopalong-variant.dat') data = make_nparray_data(paint, 600, 600, channels=1) save_nparray(filename, data) data = load_nparray(filename) frame = colorise(data) save_nparray_image('hopalong-variant.png', frame)
outarray = np.zeros((counts.shape[0], counts.shape[1], 3), dtype=np.uint8) apply_npcolormap(outarray, normalised_counts, colormap) return outarray def paint(image, pixel_width, pixel_height, frame_no, frame_count): scaler = Scaler(pixel_width, pixel_height, width=1000, startx=-500, starty=-500) x = -1 y = 0 for i in range(MAX_COUNT): x, y = y - math.sqrt(abs(B * x - C)) * sign(x), A - x px, py = scaler.user_to_device(x, y) if 0 <= px < pixel_width and 0 <= py < pixel_height: image[py, px] += 1 filename = temp_file('hopalong.dat') data = make_nparray_data(paint, 600, 600, channels=1) save_nparray(filename, data) data = load_nparray(filename) frame = colorise(data) save_nparray_image('hopalong.png', frame)
scaler = Scaler(pixel_width, pixel_height, width=3, startx=-2, starty=-1.5) for px in range(pixel_width): for py in range(pixel_height): x, y = scaler.device_to_user(px, py) count = calc(x, y) image[py, px] = count def colorise(counts): counts = np.reshape(counts, (counts.shape[0], counts.shape[1])) colormap = make_npcolormap(MAX_COUNT + 1, [ Color('black'), Color('darkblue'), Color('green'), Color('cyan'), Color('white') ], [8, 8, 32, 128]) outarray = np.zeros((counts.shape[0], counts.shape[1], 3), dtype=np.uint8) apply_npcolormap(outarray, counts, colormap) return outarray data = make_nparray_data(paint, 600, 600, channels=1) frame = colorise(data) save_nparray_image('mandelbrot.png', frame)