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)
