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)