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])) 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)