def update_colormap(attrname, old, new_frequency):
"""
updates the coloring of the plot.
:param attrname: unused, but needed for bokeh callback functions
:param old: unused, but needed for bokeh callback functions
:param new_frequency: new value for the frequency
"""
mandel_iterations = source_mandel_raw.data['its'][0]
frequency = int(
np.mean(mandel_iterations[
mandel_iterations != int(slider_max_iterations.value)]) / new_frequency * 10) # todo magic number?
print "calculating colors."
col = mandel_colormap.iteration_count_to_rgb_color(mandel_iterations, frequency, int(slider_max_iterations.value))
img = mandel_colormap.rgb_color_to_bokeh_rgba(color=col)
print "done."
print "updating image data."
view_data = my_bokeh_utils.get_user_view(plot)
source_view.data = view_data
source_image.data = dict(image=[img],
x0=view_data['x_start'],
y0=view_data['y_start'],
xw=[view_data['x_end'][0]-view_data['x_start'][0]],
yw=[view_data['y_end'][0]-view_data['y_start'][0]],
freq=[new_frequency])
print "data was updated."
def update_colormap(attrname, old, new_frequency):
"""
updates the coloring of the plot.
:param attrname: unused, but needed for bokeh callback functions
:param old: unused, but needed for bokeh callback functions
:param new_frequency: new value for the frequency
"""
mandel_iterations = source_mandel_raw.data['its'][0]
frequency = int(
np.mean(mandel_iterations[
mandel_iterations != int(slider_max_iterations.value)]) /
new_frequency * 10) # todo magic number?
print "calculating colors."
col = mandel_colormap.iteration_count_to_rgb_color(
mandel_iterations, frequency, int(slider_max_iterations.value))
img = mandel_colormap.rgb_color_to_bokeh_rgba(color=col)
print "done."
print "updating image data."
view_data = my_bokeh_utils.get_user_view(plot)
source_view.data = view_data
source_image.data = dict(
image=[img],
x0=view_data['x_start'],
y0=view_data['y_start'],
xw=[view_data['x_end'][0] - view_data['x_start'][0]],
yw=[view_data['y_end'][0] - view_data['y_start'][0]],
freq=[new_frequency])
print "data was updated."
max_iterations = 10000
iteration_bound = 100
median_filtering_size = 0
gauss_filtering_sigma = 0.1
x0 = cx-d*.5
y0 = cy-d*.5
xw = d
yw = d
### main process starts here. ###
# calculate mandelbrot set
it_count = mandel.mandel(x0, y0, xw, yw, x_res, y_res, max_iterations, iteration_bound)
# apply some filters
if median_filtering_size is not 0:
it_count = median_filter(it_count, size=median_filtering_size)
if gauss_filtering_sigma is not 0:
it_count = gaussian_filter(it_count, sigma=gauss_filtering_sigma)
color = mandel_colormap.iteration_count_to_rgb_color(it_count, frequency, max_iterations)
print color.shape
# plot and save picture
img = smp.toimage(color) # Create a PIL image
img.save(name)
img.show()
max_iterations = 10000
iteration_bound = 100
median_filtering_size = 0
gauss_filtering_sigma = 0.1
x0 = cx - d * .5
y0 = cy - d * .5
xw = d
yw = d
### main process starts here. ###
# calculate mandelbrot set
it_count = mandel.mandel(x0, y0, xw, yw, x_res, y_res, max_iterations,
iteration_bound)
# apply some filters
if median_filtering_size is not 0:
it_count = median_filter(it_count, size=median_filtering_size)
if gauss_filtering_sigma is not 0:
it_count = gaussian_filter(it_count, sigma=gauss_filtering_sigma)
color = mandel_colormap.iteration_count_to_rgb_color(it_count, frequency,
max_iterations)
#print(color.shape)
# plot and save picture
img = smp.toimage(color) # Create a PIL image
img.save(name)
img.show()
