def render(self, width, height, results):
if not is_gpu_accelerated():
self._logger.error('No GPU acceleration is available.')
return
image = Image.new('RGB', (width, height))
iterations_gpu, z_values_gpu, dc = results
colors = np.empty(width * height, np.int32)
colors_gpu = gpuarray.to_gpu(colors)
dx, mx = divmod(width, self._block_size[0])
dy, my = divmod(height, self._block_size[1])
grid_size = ((dx + (mx > 0)), (dy + (my > 0)))
self._get_pixel_color(colors_gpu,
self._color_scheme_gpu,
iterations_gpu,
z_values_gpu,
np.int32(width),
np.int32(height),
np.float32(dc),
block=self._block_size,
grid=grid_size)
colors = colors_gpu.get()
# This is really slow, must be optimized
image.putdata(colors.tolist())
return image
def get_colors_and_cmap(K):
if K <= 10:
colors = np.array(plt.cm.tab10.colors)[:K]
colors_list = colors.tolist()
elif K <= 20:
colors = np.array(plt.cm.tab20.colors)[:K]
colors_list = colors.tolist()
else:
vals = np.linspace(0, 1, 10 * K)[10 * np.arange(K)]
colors = plt.cm.tab20(vals)
colors_list = colors.tolist()
cvals = np.arange(K)
norm = plt.Normalize(min(cvals), max(cvals))
tuples = list(zip(map(norm, cvals), colors_list))
cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", tuples, K)
return colors, cmap
def for_categories(n, cmap='Spectral', avoid_white=True):
cm = matplotlib.cm.get_cmap(cmap)
colors = cm(numpy.linspace(0, 1, n))
colors_order = []
for i in range(n / 3):
colors_order += [i, i + n / 3, i + 2 * n / 3]
colors = colors[colors_order]
if avoid_white:
hsv_colors = matplotlib.colors.rgb_to_hsv(colors[:, :3])
hsv_colors[:, 2] = numpy.where(hsv_colors[:, 2] > 0.9, 0.9,
hsv_colors[:, 2])
colors = matplotlib.colors.hsv_to_rgb(hsv_colors)
return colors.tolist()
def create(n, cmap='YlGnBu', avoid_white=True):
"""
cmap
"""
cm = matplotlib.cm.get_cmap(cmap)
colors = cm(numpy.linspace(0, 1, cm.N))
gray = grayify(colors)
sign = numpy.sign(gray[1:] - gray[:-1])
if not numpy.all(sign[sign != 0] == sign[sign != 0][0]):
warnings.warn('This cmap ("%s") is not monotonic in grayscale!' % cmap)
colors = cm(numpy.linspace(0, 1, n))
if avoid_white:
hsv_colors = matplotlib.colors.rgb_to_hsv(colors[:, :3])
hsv_colors[:, 2] = numpy.where(hsv_colors[:, 2] > 0.9, 0.9,
hsv_colors[:, 2])
#hsv_colors[:,1] = numpy.where(hsv_colors[:,1] < 0.5, 0.5, hsv_colors[:,1])
colors = matplotlib.colors.hsv_to_rgb(hsv_colors)
#colors = grayify(colors)
return colors.tolist()