gnome.outputters.image

image outputters

These will output images for use in the Web client / OpenLayers

NOTE: doesn’t seem to be tested – and may not be used anyway.

Module Contents

Classes

IceImageOutput

Class that outputs ice data as an image for each ice mover.
class gnome.outputters.image.IceImageOutput(ice_movers=None, image_size=(800, 600), projection=None, viewport=None, **kwargs)

Bases: gnome.outputters.Outputter

Class that outputs ice data as an image for each ice mover.

The image is PNG encoded, then Base64 encoded to include in a JSON response.

Parameters:

ice_movers (An ice_mover object or sequence of ice_mover objects.) – ice_movers associated with this outputter.

Use super to pass optional kwargs to base class __init__ method

_schema
set_gradient_colors(gradient_name, color_range=((0, 0, 127), (0, 255, 255)), scale=(0.0, 10.0), num_colors=16)

Add a color gradient to our palette representing the colors we will use for our ice thickness

Parameters:

  • gradient_name (str) – The name of the gradient.

  • color_range (A 2 element sequence of 3-tuples containing 8-bit RGB values.) – The colors we will build our gradient with.

  • scale (A 2 element sequence of float) – A range of values representing the low and high end of our gradient.

  • num_colors (Number) – The number of colors to use for the gradient.

add_gradient_to_canvas(color_range, color_prefix, num_colors)

Add a color gradient to our palette

NOTE: Probably not the most efficient way to do this.

Parameters:

  • color_range (A sequence of 2 or more 3-tuples) – The colors that we would like to use to generate our gradient

  • color_prefix (str) – The prefix that will be used in the naming of the colors in the gradient

  • num_colors (Number) – The number of gradient colors to generate

lookup_gradient_color(gradient_name, values)
write_output(step_num, islast_step=False)

Generate image from data

get_sample_image()

This returns a base 64 encoded PNG image for testing, just so we have something

This should be removed when we have real functionality

render_images(model_time)

render the actual images This uses the MapCanvas code to do the actual rendering

returns: thickness_image, concentration_image

ice_movers_to_dict()

a dict containing ‘obj_type’ and ‘id’ for each object in list/collection