def test_set_colors():
mc = MapCanvas((400, 300))
colors = [('blue', (0, 0, 255)), ('red', (255, 0, 0))]
mc.add_colors(colors)
assert mc.fore_image.get_color_names() == mc.back_image.get_color_names()
colors = mc.get_color_names()
assert colors == ['transparent', 'black', 'white', 'blue', 'red']
def test_set_colors():
mc = MapCanvas((400, 300))
colors = [('blue', (0, 0, 255)),
('red', (255, 0, 0))]
mc.add_colors(colors)
assert mc.fore_image.get_color_names() == mc.back_image.get_color_names()
colors = mc.get_color_names()
assert colors == ['transparent', 'black', 'white', 'blue', 'red']
def __init__(self,
ice_movers=None,
image_size=(800, 600),
projection=None,
viewport=None,
**kwargs):
'''
:param ice_movers: ice_movers associated with this outputter.
:type ice_movers: An ice_mover object or sequence of ice_mover
objects.
Use super to pass optional \*\*kwargs to base class __init__ method
'''
# this is a place where we store our gradient color infomration
self.gradient_lu = {}
self.map_canvas = MapCanvas(image_size,
projection=projection,
viewport=viewport,
preset_colors='transparent')
self.map_canvas.add_colors([('black', (0, 0, 0))])
self.set_gradient_colors(
'thickness',
color_range=(
(0, 0, 0x7f, 0x7f), # dark blue
(0, 0, 0x7f, 0x3f), # dark blue
(0, 0, 0x7f, 0x00), # dark blue
(0xff, 0, 0, 0x00)), # red
scale=(0.0, 6.0),
num_colors=64)
self.set_gradient_colors(
'concentration',
color_range=(
(0x80, 0xc0, 0xd0, 0x7f), # sky blue
(0, 0x40, 0x60, 0x00)), # dark blue
scale=(0.0, 1.0),
num_colors=64)
super(IceImageOutput, self).__init__(**kwargs)
if (isinstance(ice_movers, collections.Iterable)
and not isinstance(ice_movers, str)):
self.ice_movers = ice_movers
elif ice_movers is not None:
self.ice_movers = (ice_movers, )
else:
self.ice_movers = tuple()
def test_foreground_poly(output_dir):
"""
test drawing polygons to the foreground
"""
mc = MapCanvas((400, 300), preset_colors='web')
mc.background_color = 'transparent'
mc.clear_background()
mc.draw_polygon(((-30, 30), (30, 30), (30, -20), (-30, -20)),
fill_color='white',
line_color='black',
background=False)
mc.save_foreground(os.path.join(output_dir, "foreground.png"))
def test_foreground_polyline(output_dir):
"""
test drawing polygons to the background
"""
mc = MapCanvas((400, 300), preset_colors='web')
mc.background_color = 'transparent'
mc.clear_background()
mc.draw_polyline(((-30, 30), (30, 30), (30, -20), (-30, -20)),
line_color='red',
line_width=5,
background=False)
mc.save_foreground(os.path.join(output_dir, "foreground_polyline.png"))
def test_background_poly(output_dir):
"""
test drawing polygons to the background
"""
mc = MapCanvas((400, 300), preset_colors='web')
mc.background_color = 'transparent'
mc.clear_background()
mc.draw_polygon(((-30, 30),
(30, 30),
(30, -20),
(-30, -20)),
fill_color='blue',
line_color='black',
background=True)
mc.save_background(os.path.join(output_dir, "background.png"))
def test_foreground_polyline(output_dir):
"""
test drawing polygons to the background
"""
mc = MapCanvas((400, 300), preset_colors='web')
mc.background_color = 'transparent'
mc.clear_background()
mc.draw_polyline(((-30, 30),
(30, 30),
(30, -20),
(-30, -20)),
line_color='red',
line_width=5,
background=False)
mc.save_foreground(os.path.join(output_dir, "foreground_polyline.png"))
def __init__(self, ice_movers=None,
image_size=(800, 600),
projection=None,
viewport=None,
**kwargs):
'''
:param ice_movers: ice_movers associated with this outputter.
:type ice_movers: An ice_mover object or sequence of ice_mover
objects.
Use super to pass optional \*\*kwargs to base class __init__ method
'''
# this is a place where we store our gradient color infomration
self.gradient_lu = {}
self.map_canvas = MapCanvas(image_size,
projection=projection,
viewport=viewport,
preset_colors='transparent')
self.map_canvas.add_colors([('black', (0, 0, 0))])
self.set_gradient_colors('thickness',
color_range=((0, 0, 0x7f, 0x7f), # dark blue
(0, 0, 0x7f, 0x3f), # dark blue
(0, 0, 0x7f, 0x00), # dark blue
(0xff, 0, 0, 0x00)), # red
scale=(0.0, 6.0),
num_colors=64)
self.set_gradient_colors('concentration',
color_range=((0x80, 0xc0, 0xd0, 0x7f), # sky blue
(0, 0x40, 0x60, 0x00)), # dark blue
scale=(0.0, 1.0),
num_colors=64)
super(IceImageOutput, self).__init__(**kwargs)
if (isinstance(ice_movers, collections.Iterable) and
not isinstance(ice_movers, str)):
self.ice_movers = ice_movers
elif ice_movers is not None:
self.ice_movers = (ice_movers,)
else:
self.ice_movers = tuple()
def test_copy_back_to_fore(output_dir):
mc = MapCanvas((400, 300), preset_colors='web')
mc.background_color = 'white'
mc.clear_background()
mc.clear_foreground()
mc.draw_polyline(((-30, 30), (30, 30), (30, -20), (-30, -20)),
line_color='red',
line_width=5,
background=True)
mc.copy_back_to_fore()
mc.draw_polyline(((-20, 20), (20, 20), (20, -10), (-20, -10)),
line_color='blue',
line_width=5,
background=False)
mc.save_foreground(os.path.join(output_dir, "copy_back_to_fore.png"))
class IceImageOutput(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.
'''
_schema = IceImageSchema
def __init__(self,
ice_movers=None,
image_size=(800, 600),
projection=None,
viewport=None,
**kwargs):
'''
:param ice_movers: ice_movers associated with this outputter.
:type ice_movers: An ice_mover object or sequence of ice_mover
objects.
Use super to pass optional \*\*kwargs to base class __init__ method
'''
# this is a place where we store our gradient color infomration
self.gradient_lu = {}
self.map_canvas = MapCanvas(image_size,
projection=projection,
viewport=viewport,
preset_colors='transparent')
self.map_canvas.add_colors([('black', (0, 0, 0))])
self.set_gradient_colors(
'thickness',
color_range=(
(0, 0, 0x7f, 0x7f), # dark blue
(0, 0, 0x7f, 0x3f), # dark blue
(0, 0, 0x7f, 0x00), # dark blue
(0xff, 0, 0, 0x00)), # red
scale=(0.0, 6.0),
num_colors=64)
self.set_gradient_colors(
'concentration',
color_range=(
(0x80, 0xc0, 0xd0, 0x7f), # sky blue
(0, 0x40, 0x60, 0x00)), # dark blue
scale=(0.0, 1.0),
num_colors=64)
super(IceImageOutput, self).__init__(**kwargs)
if (isinstance(ice_movers, collections.Iterable)
and not isinstance(ice_movers, str)):
self.ice_movers = ice_movers
elif ice_movers is not None:
self.ice_movers = (ice_movers, )
else:
self.ice_movers = tuple()
def set_gradient_colors(
self,
gradient_name,
color_range=(
(0, 0, 0x7f), # dark blue
(0, 0xff, 0xff)), # cyan
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
:param gradient_name: The name of the gradient.
:type gradient_name: str
:param color_range: The colors we will build our gradient with.
:type color_range: A 2 element sequence of 3-tuples containing
8-bit RGB values.
:param scale: A range of values representing the low and high end
of our gradient.
:type scale: A 2 element sequence of float
:param num_colors: The number of colors to use for the gradient.
:type num_colors: Number
'''
color_names = self.add_gradient_to_canvas(color_range, gradient_name,
num_colors)
self.gradient_lu[gradient_name] = (scale, np.array(color_names))
def add_gradient_to_canvas(self, color_range, color_prefix, num_colors):
'''
Add a color gradient to our palette
NOTE: Probably not the most efficient way to do this.
:param color_range: The colors that we would like to use to
generate our gradient
:type color_range: A sequence of 2 or more 3-tuples
:param color_prefix: The prefix that will be used in the naming
of the colors in the gradient
:type color_prefix: str
:param num_colors: The number of gradient colors to generate
:type num_colors: Number
'''
color_range_idx = range(len(color_range))
color_space = np.linspace(color_range_idx[0],
color_range_idx[-1],
num=num_colors)
r_grad = np.interp(color_space, color_range_idx,
[c[0] for c in color_range])
g_grad = np.interp(color_space, color_range_idx,
[c[1] for c in color_range])
b_grad = np.interp(color_space, color_range_idx,
[c[2] for c in color_range])
if all([len(c) >= 4 for c in color_range]):
a_grad = np.interp(color_space, color_range_idx,
[c[3] for c in color_range])
else:
a_grad = np.array([0.] * num_colors)
new_colors = []
for i, (r, g, b, a) in enumerate(zip(r_grad, g_grad, b_grad, a_grad)):
new_colors.append(('{}{}'.format(color_prefix, i), (r, g, b, a)))
self.map_canvas.add_colors(new_colors)
return [c[0] for c in new_colors]
def lookup_gradient_color(self, gradient_name, values):
try:
(low_val, high_val), color_names = self.gradient_lu[gradient_name]
except IndexError:
return None
scale_range = high_val - low_val
q_step_range = scale_range / len(color_names)
idx = (np.floor(values / q_step_range).astype(int).clip(
0,
len(color_names) - 1))
return color_names[idx]
def write_output(self, step_num, islast_step=False):
"""
Generate image from data
"""
# I don't think we need this for this outputter:
# - it does stuff with cache initialization
super(IceImageOutput, self).write_output(step_num, islast_step)
if (self.on is False or not self._write_step
or len(self.ice_movers) == 0):
return None
# fixme -- doing all this cache stuff just to get the timestep..
# maybe timestep should be passed in.
for sc in self.cache.load_timestep(step_num).items():
model_time = date_to_sec(sc.current_time_stamp)
iso_time = sc.current_time_stamp.isoformat()
thick_image, conc_image, bb = self.render_images(model_time)
# web_mercator = 'EPSG:3857'
equirectangular = 'EPSG:32662'
# info to return to the caller
output_dict = {
'step_num': step_num,
'time_stamp': iso_time,
'thickness_image': thick_image,
'concentration_image': conc_image,
'bounding_box': bb,
'projection': equirectangular,
}
return output_dict
def get_sample_image(self):
"""
This returns a base 64 encoded PNG image for testing,
just so we have something
This should be removed when we have real functionality
"""
# hard-coding the base64 really confused my editor..
image_file_file_path = os.path.join(
os.path.split(__file__)[0], 'sample.b64')
return open(image_file_file_path).read()
def render_images(self, model_time):
"""
render the actual images
This uses the MapCanvas code to do the actual rendering
returns: thickness_image, concentration_image
"""
canvas = self.map_canvas
# We kinda need to figure our our bounding box before doing the
# rendering. We will try to be efficient about it mainly by not
# grabbing our grid data twice.
mover_grid_bb = None
mover_grids = []
for mover in self.ice_movers:
mover_grids.append(mover.get_grid_data())
mover_grid_bb = mover.get_grid_bounding_box(
mover_grids[-1], mover_grid_bb)
canvas.viewport = mover_grid_bb
canvas.clear_background()
# Here is where we draw our grid data....
for mover, mover_grid in zip(self.ice_movers, mover_grids):
mover_grid_bb = mover.get_grid_bounding_box(
mover_grid, mover_grid_bb)
concentration, thickness = mover.get_ice_fields(model_time)
thickness_colors = self.lookup_gradient_color(
'thickness', thickness)
concentration_colors = self.lookup_gradient_color(
'concentration', concentration)
dtype = mover_grid.dtype.descr
unstructured_type = dtype[0][1]
new_shape = mover_grid.shape + (len(dtype), )
mover_grid = (mover_grid.view(dtype=unstructured_type).reshape(
*new_shape))
for poly, tc, cc in zip(mover_grid, thickness_colors,
concentration_colors):
canvas.draw_polygon(poly, fill_color=tc)
canvas.draw_polygon(poly, fill_color=cc, background=True)
# py_gd does not currently have the capability to generate a .png
# formatted buffer in memory. (libgd can be made to do this, but
# the wrapper is yet to be written)
# So we will just write to a tempfile and then read it back.
# If we ever have to do this anywhere else, a context manger would be good.
tempdir = tempfile.mkdtemp()
tempfilename = os.path.join(tempdir, "gnome_temp_image_file.png")
canvas.save_foreground(tempfilename)
thickness_image = open(tempfilename, 'rb').read().encode('base64')
canvas.save_background(tempfilename)
coverage_image = open(tempfilename, 'rb').read().encode('base64')
os.remove(tempfilename)
os.rmdir(tempdir)
return ("data:image/png;base64,{}".format(thickness_image),
"data:image/png;base64,{}".format(coverage_image),
mover_grid_bb)
def ice_movers_to_dict(self):
'''
a dict containing 'obj_type' and 'id' for each object in
list/collection
'''
return self._collection_to_dict(self.ice_movers)
def __init__(
self,
filename,
images_dir,
image_size=(800, 600),
cache=None,
output_timestep=None,
output_zero_step=True,
output_last_step=True,
draw_ontop='forecast',
**kwargs
):
"""
Init the image renderer.
Following args are passed to base class Outputter's init:
:param cache: sets the cache object from which to read data. The model
will automatically set this param
:param output_timestep: default is None in which case everytime the
write_output is called, output is written. If set, then output is
written every output_timestep starting from model_start_time.
:type output_timestep: timedelta object
:param output_zero_step: default is True. If True then output for
initial step (showing initial release conditions) is written
regardless of output_timestep
:type output_zero_step: boolean
:param output_last_step: default is True. If True then output for
final step is written regardless of output_timestep
:type output_last_step: boolean
:param draw_ontop: draw 'forecast' or 'uncertain' LEs on top. Default
is to draw 'forecast' LEs, which are in black on top
:type draw_ontop: str
Remaining kwargs are passed onto baseclass's __init__ with a direct
call: MapCanvas.__init__(..)
Optional parameters (kwargs)
:param projection_class: gnome.utilities.projections class to use.
Default is gnome.utilities.projections.FlatEarthProjection
:param map_BB: map bounding box. Default is to use
land_polygons.bounding_box. If land_polygons is None, then this is
the whole world, defined by ((-180,-90),(180, 90))
:param viewport: viewport of map -- what gets drawn and on what scale.
Default is to set viewport = map_BB
:param image_mode: Image mode ('P' for palette or 'L' for Black and
White image). BW_MapCanvas inherits from MapCanvas and sets the
mode to 'L'. Default image_mode is 'P'.
:param id: unique identifier for a instance of this class (UUID given
as a string). This is used when loading an object from a persisted
model.
"""
# set up the canvas
self._filename = filename
polygons = haz_files.ReadBNA(filename, 'PolygonSet')
Outputter.__init__(self, cache, output_timestep, output_zero_step,
output_last_step)
MapCanvas.__init__(self, image_size, land_polygons=polygons,
**kwargs)
self.images_dir = images_dir
self.last_filename = ''
self.draw_ontop = draw_ontop
def test_projection(output_dir):
"""
draw the "same sized" rectangle at three latitudes to see how the look
"""
mc = MapCanvas((400, 400), preset_colors='web')
mc.viewport = ((-20, 25), (20, 65))
mc.draw_polygon(((-15, 60), (15, 60), (15, 30), (-15, 30)),
fill_color='maroon',
line_color='black')
mc.save_foreground(os.path.join(output_dir, "image_projection_north.png"))
mc.viewport = ((-20, -20), (20, 20))
mc.draw_polygon(((-15, 15), (15, 15), (15, -15), (-15, -15)),
fill_color='maroon',
line_color='black')
mc.save_foreground(os.path.join(output_dir,
"image_projection_equator.png"))
mc.viewport = ((-20, -45), (20, -90))
mc.draw_polygon(((-15, -80), (15, -80), (15, -50), (-15, -50)),
fill_color='maroon',
line_color='black')
mc.save_foreground(os.path.join(output_dir, "image_projection_south.png"))
def test_init():
"""
can we even initialize one
"""
mc = MapCanvas((400, 300))
def test_copy_back_to_fore(output_dir):
mc = MapCanvas((400, 300), preset_colors='web')
mc.background_color = 'white'
mc.clear_background()
mc.clear_foreground()
mc.draw_polyline(((-30, 30),
(30, 30),
(30, -20),
(-30, -20)),
line_color='red',
line_width=5,
background=True)
mc.copy_back_to_fore()
mc.draw_polyline(((-20, 20),
(20, 20),
(20, -10),
(-20, -10)),
line_color='blue',
line_width=5,
background=False)
mc.save_foreground(os.path.join(output_dir, "copy_back_to_fore.png"))
class IceImageOutput(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.
'''
_state = copy.deepcopy(Outputter._state)
# need a schema and also need to override save so output_dir
# is saved correctly - maybe point it to saveloc
_state.add_field(Field('ice_movers',
save=True, update=True, iscollection=True))
_schema = IceImageSchema
def __init__(self, ice_movers=None,
image_size=(800, 600),
projection=None,
viewport=None,
**kwargs):
'''
:param ice_movers: ice_movers associated with this outputter.
:type ice_movers: An ice_mover object or sequence of ice_mover
objects.
Use super to pass optional \*\*kwargs to base class __init__ method
'''
# this is a place where we store our gradient color infomration
self.gradient_lu = {}
self.map_canvas = MapCanvas(image_size,
projection=projection,
viewport=viewport,
preset_colors='transparent')
self.map_canvas.add_colors([('black', (0, 0, 0))])
self.set_gradient_colors('thickness',
color_range=((0, 0, 0x7f, 0x7f), # dark blue
(0, 0, 0x7f, 0x3f), # dark blue
(0, 0, 0x7f, 0x00), # dark blue
(0xff, 0, 0, 0x00)), # red
scale=(0.0, 6.0),
num_colors=64)
self.set_gradient_colors('concentration',
color_range=((0x80, 0xc0, 0xd0, 0x7f), # sky blue
(0, 0x40, 0x60, 0x00)), # dark blue
scale=(0.0, 1.0),
num_colors=64)
super(IceImageOutput, self).__init__(**kwargs)
if (isinstance(ice_movers, collections.Iterable) and
not isinstance(ice_movers, str)):
self.ice_movers = ice_movers
elif ice_movers is not None:
self.ice_movers = (ice_movers,)
else:
self.ice_movers = tuple()
def set_gradient_colors(self, gradient_name,
color_range=((0, 0, 0x7f), # dark blue
(0, 0xff, 0xff)), # cyan
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
:param gradient_name: The name of the gradient.
:type gradient_name: str
:param color_range: The colors we will build our gradient with.
:type color_range: A 2 element sequence of 3-tuples containing
8-bit RGB values.
:param scale: A range of values representing the low and high end
of our gradient.
:type scale: A 2 element sequence of float
:param num_colors: The number of colors to use for the gradient.
:type num_colors: Number
'''
color_names = self.add_gradient_to_canvas(color_range,
gradient_name, num_colors)
self.gradient_lu[gradient_name] = (scale, np.array(color_names))
def add_gradient_to_canvas(self, color_range, color_prefix, num_colors):
'''
Add a color gradient to our palette
NOTE: Probably not the most efficient way to do this.
:param color_range: The colors that we would like to use to
generate our gradient
:type color_range: A sequence of 2 or more 3-tuples
:param color_prefix: The prefix that will be used in the naming
of the colors in the gradient
:type color_prefix: str
:param num_colors: The number of gradient colors to generate
:type num_colors: Number
'''
color_range_idx = range(len(color_range))
color_space = np.linspace(color_range_idx[0], color_range_idx[-1],
num=num_colors)
r_grad = np.interp(color_space, color_range_idx,
[c[0] for c in color_range])
g_grad = np.interp(color_space, color_range_idx,
[c[1] for c in color_range])
b_grad = np.interp(color_space, color_range_idx,
[c[2] for c in color_range])
if all([len(c) >= 4 for c in color_range]):
a_grad = np.interp(color_space, color_range_idx,
[c[3] for c in color_range])
else:
a_grad = np.array([0.] * num_colors)
new_colors = []
for i, (r, g, b, a) in enumerate(zip(r_grad, g_grad, b_grad, a_grad)):
new_colors.append(('{}{}'.format(color_prefix, i), (r, g, b, a)))
self.map_canvas.add_colors(new_colors)
return [c[0] for c in new_colors]
def lookup_gradient_color(self, gradient_name, values):
try:
(low_val, high_val), color_names = self.gradient_lu[gradient_name]
except IndexError:
return None
scale_range = high_val - low_val
q_step_range = scale_range / len(color_names)
idx = (np.floor(values / q_step_range)
.astype(int)
.clip(0, len(color_names) - 1))
return color_names[idx]
def write_output(self, step_num, islast_step=False):
"""
Generate image from data
"""
# I don't think we need this for this outputter:
# - it does stuff with cache initialization
super(IceImageOutput, self).write_output(step_num, islast_step)
if (self.on is False or
not self._write_step or
len(self.ice_movers) == 0):
return None
# fixme -- doing all this cache stuff just to get the timestep..
# maybe timestep should be passed in.
for sc in self.cache.load_timestep(step_num).items():
model_time = date_to_sec(sc.current_time_stamp)
iso_time = sc.current_time_stamp.isoformat()
thick_image, conc_image, bb = self.render_images(model_time)
# info to return to the caller
web_mercator = 'EPSG:3857'
equirectangular = 'EPSG:32662'
output_dict = {'step_num': step_num,
'time_stamp': iso_time,
'thickness_image': thick_image,
'concentration_image': conc_image,
'bounding_box': bb,
'projection': equirectangular,
}
return output_dict
def get_sample_image(self):
"""
This returns a base 64 encoded PNG image for testing,
just so we have something
This should be removed when we have real functionality
"""
# hard-coding the base64 really confused my editor..
image_file_file_path = os.path.join(os.path.split(__file__)[0],
'sample.b64')
return open(image_file_file_path).read()
def render_images(self, model_time):
"""
render the actual images
This uses the MapCanvas code to do the actual rendering
returns: thickness_image, concentration_image
"""
canvas = self.map_canvas
# We kinda need to figure our our bounding box before doing the
# rendering. We will try to be efficient about it mainly by not
# grabbing our grid data twice.
mover_grid_bb = None
mover_grids = []
for mover in self.ice_movers:
mover_grids.append(mover.get_grid_data())
mover_grid_bb = mover.get_grid_bounding_box(mover_grids[-1],
mover_grid_bb)
canvas.viewport = mover_grid_bb
canvas.clear_background()
# Here is where we draw our grid data....
for mover, mover_grid in zip(self.ice_movers, mover_grids):
mover_grid_bb = mover.get_grid_bounding_box(mover_grid,
mover_grid_bb)
concentration, thickness = mover.get_ice_fields(model_time)
thickness_colors = self.lookup_gradient_color('thickness',
thickness)
concentration_colors = self.lookup_gradient_color('concentration',
concentration)
dtype = mover_grid.dtype.descr
unstructured_type = dtype[0][1]
new_shape = mover_grid.shape + (len(dtype),)
mover_grid = (mover_grid
.view(dtype=unstructured_type)
.reshape(*new_shape))
for poly, tc, cc in zip(mover_grid,
thickness_colors, concentration_colors):
canvas.draw_polygon(poly, fill_color=tc)
canvas.draw_polygon(poly, fill_color=cc, background=True)
# diagnostic so we can see what we have rendered.
# print '\ndrawing reference objects...'
# canvas.draw_graticule(False)
# canvas.draw_tags(False)
# canvas.save_background('background.png')
# canvas.save_foreground('foreground.png')
# py_gd does not currently have the capability to generate a .png
# formatted buffer in memory. (libgd can be made to do this, but
# the wrapper is yet to be written)
# So we will just write to a tempfile and then read it back.
with NamedTemporaryFile() as fp:
canvas.save_foreground(fp.name)
fp.seek(0)
thickness_image = fp.read().encode('base64')
with NamedTemporaryFile() as fp:
canvas.save_background(fp.name)
fp.seek(0)
coverage_image = fp.read().encode('base64')
return ("data:image/png;base64,{}".format(thickness_image),
"data:image/png;base64,{}".format(coverage_image),
mover_grid_bb)
def rewind(self):
'remove previously written files'
super(IceImageOutput, self).rewind()
def ice_movers_to_dict(self):
'''
a dict containing 'obj_type' and 'id' for each object in
list/collection
'''
return self._collection_to_dict(self.ice_movers)
@classmethod
def deserialize(cls, json_):
"""
append correct schema for current mover
"""
schema = cls._schema()
_to_dict = schema.deserialize(json_)
if 'ice_movers' in json_:
_to_dict['ice_movers'] = []
for i, cm in enumerate(json_['ice_movers']):
cm_cls = class_from_objtype(cm['obj_type'])
cm_dict = cm_cls.deserialize(json_['ice_movers'][i])
_to_dict['ice_movers'].append(cm_dict)
return _to_dict
def __init__(self,
map_filename=None,
output_dir='./',
image_size=(800, 600),
projection=None,
viewport=None,
map_BB=None,
land_polygons=None,
draw_back_to_fore=True,
draw_map_bounds=False,
draw_spillable_area=False,
formats=['png', 'gif'],
draw_ontop='forecast',
cache=None,
output_timestep=None,
output_zero_step=True,
output_last_step=True,
output_start_time=None,
on=True,
timestamp_attrib={},
**kwargs):
"""
Init the image renderer.
:param str map_filename=None: name of file for basemap (BNA)
:type map_filename: str
:param str output_dir='./': directory to output the images
:param 2-tuple image_size=(800, 600): size of images to output
:param projection=None: projection instance to use: If None,
set to projections.FlatEarthProjection()
:type projection: a gnome.utilities.projection.Projection instance
:param viewport: viewport of map -- what gets drawn and on what scale.
Default is full globe: (((-180, -90), (180, 90)))
If not specifies, it will be set to the map's bounds.
:type viewport: pair of (lon, lat) tuples ( lower_left, upper right )
:param map_BB=None: bounding box of map if None, it will use the
bounding box of the mapfile.
:param draw_back_to_fore=True: draw the background (map) to the
foregound image when outputting
the images each time step.
:type draw_back_to_fore: boolean
:param formats=['gif']: list of formats to output.
:type formats: string or list of strings. Options are:
['bmp', 'jpg', 'jpeg', 'gif', 'png']
:param draw_ontop: draw 'forecast' or 'uncertain' LEs on top. Default
is to draw 'forecast' LEs, which are in black on top
:type draw_ontop: str
Following args are passed to base class Outputter's init:
:param cache: sets the cache object from which to read prop. The model
will automatically set this param
:param output_timestep: default is None in which case everytime the
write_output is called, output is written. If set, then output is
written every output_timestep starting from model_start_time.
:type output_timestep: timedelta object
:param output_zero_step: default is True. If True then output for
initial step (showing initial release conditions) is written
regardless of output_timestep
:type output_zero_step: boolean
:param output_last_step: default is True. If True then output for
final step is written regardless of output_timestep
:type output_last_step: boolean
Remaining kwargs are passed onto baseclass's __init__ with a direct
call: Outputter.__init__(..)
"""
projection = (projections.FlatEarthProjection()
if projection is None else projection)
# set up the canvas
self.map_filename = map_filename
self.output_dir = output_dir
if map_filename is not None and land_polygons is None:
self.land_polygons = haz_files.ReadBNA(map_filename, 'PolygonSet')
elif land_polygons is not None:
self.land_polygons = land_polygons
else:
self.land_polygons = [] # empty list so we can loop thru it
self.last_filename = ''
self.draw_ontop = draw_ontop
self.draw_back_to_fore = draw_back_to_fore
Outputter.__init__(self, cache, on, output_timestep, output_zero_step,
output_last_step, output_start_time, output_dir,
**kwargs)
if map_BB is None:
if not self.land_polygons:
map_BB = ((-180, -90), (180, 90))
else:
map_BB = self.land_polygons.bounding_box
self.map_BB = map_BB
viewport = self.map_BB if viewport is None else viewport
MapCanvas.__init__(self,
image_size,
projection=projection,
viewport=viewport)
# assorted rendering flags:
self.draw_map_bounds = draw_map_bounds
self.draw_spillable_area = draw_spillable_area
self.raster_map = None
self.raster_map_fill = True
self.raster_map_outline = False
# initilize the images:
self.add_colors(self.map_colors)
self.background_color = 'background'
if self.map_filename is not None:
file_prefix = os.path.splitext(self.map_filename)[0]
sep = '_'
else:
file_prefix = sep = ''
fn = '{}{}anim.gif'.format(file_prefix, sep)
self.anim_filename = os.path.join(output_dir, fn)
self.formats = formats
self.delay = 50
self.repeat = True
self.timestamp_attribs = {}
self.set_timestamp_attrib(**timestamp_attrib)
self.grids = []
self.props = []
def __init__(
self,
filename=None,
images_dir="./",
image_size=(800, 600),
cache=None,
output_timestep=None,
output_zero_step=True,
output_last_step=True,
draw_ontop="forecast",
draw_back_to_fore=True,
**kwargs
):
"""
Init the image renderer.
Following args are passed to base class Outputter's init:
:param filename: the name of the image file
:param images_dir: the folder in which to write the image
:param image_size: the width and height of the image
:param cache: sets the cache object from which to read data. The model
will automatically set this param
:param output_timestep: default is None in which case everytime the
write_output is called, output is written. If set, then output is
written every output_timestep starting from model_start_time.
:type output_timestep: timedelta object
:param output_zero_step: default is True. If True then output for
initial step (showing initial release conditions) is written
regardless of output_timestep
:type output_zero_step: boolean
:param output_last_step: default is True. If True then output for
final step is written regardless of output_timestep
:type output_last_step: boolean
:param draw_ontop: draw 'forecast' or 'uncertain' LEs on top. Default
is to draw 'forecast' LEs, which are in black on top
:type draw_ontop: str
:param draw_back_to_fore=True: draw the background (map) to the
foregound image when drawing Elements.
:type draw_ontop: boolean
Remaining kwargs are passed onto baseclass's __init__ with a direct
call: MapCanvas.__init__(..)
Optional parameters (kwargs)
:param projection_class: gnome.utilities.projections class to use.
Default is gnome.utilities.projections.FlatEarthProjection
:param map_BB: map bounding box. Default is to use
land_polygons.bounding_box. If land_polygons is None, then this is
the whole world, defined by ((-180,-90),(180, 90))
:param viewport: viewport of map -- what gets drawn and on what scale.
Default is to set viewport = map_BB
:param image_mode: Image mode ('P' for palette or 'L' for Black and
White image). BW_MapCanvas inherits from MapCanvas and sets the
mode to 'L'. Default image_mode is 'P'.
"""
# set up the canvas
self._filename = filename
if filename is not None:
polygons = haz_files.ReadBNA(filename, "PolygonSet")
else:
polygons = None
self.images_dir = images_dir
self.last_filename = ""
self.draw_ontop = draw_ontop
self.draw_back_to_fore = draw_back_to_fore
Outputter.__init__(
self,
cache,
kwargs.pop("on", True),
output_timestep,
output_zero_step,
output_last_step,
kwargs.pop("name", None),
)
MapCanvas.__init__(self, image_size, land_polygons=polygons, **kwargs)
def test_projection(output_dir):
"""
draw the "same sized" rectangle at three latitudes to see how the look
"""
mc = MapCanvas((400, 400), preset_colors='web')
mc.viewport = ((-20, 25), (20, 65))
mc.draw_polygon(((-15, 60),
(15, 60),
(15, 30),
(-15, 30)),
fill_color='maroon',
line_color='black')
mc.save_foreground(os.path.join(output_dir, "image_projection_north.png"))
mc.viewport = ((-20, -20), (20, 20))
mc.draw_polygon(((-15, 15),
(15, 15),
(15, -15),
(-15, -15)
),
fill_color='maroon',
line_color='black')
mc.save_foreground(os.path.join(output_dir,
"image_projection_equator.png"))
mc.viewport = ((-20, -45), (20, -90))
mc.draw_polygon(((-15, -80),
(15, -80),
(15, -50),
(-15, -50)),
fill_color='maroon',
line_color='black')
mc.save_foreground(os.path.join(output_dir, "image_projection_south.png"))
def __init__(self,
map_filename=None,
output_dir='./',
image_size=(800, 600),
projection=None,
viewport=None,
map_BB=None,
land_polygons=None,
draw_back_to_fore=True,
draw_map_bounds=False,
draw_spillable_area=False,
formats=['png', 'gif'],
draw_ontop='forecast',
cache=None,
output_timestep=None,
output_zero_step=True,
output_last_step=True,
output_start_time=None,
on=True,
timestamp_attrib={},
**kwargs
):
"""
Init the image renderer.
:param str map_filename=None: name of file for basemap (BNA)
:type map_filename: str
:param str output_dir='./': directory to output the images
:param 2-tuple image_size=(800, 600): size of images to output
:param projection=None: projection instance to use: If None,
set to projections.FlatEarthProjection()
:type projection: a gnome.utilities.projection.Projection instance
:param viewport: viewport of map -- what gets drawn and on what scale.
Default is full globe: (((-180, -90), (180, 90)))
If not specifies, it will be set to the map's bounds.
:type viewport: pair of (lon, lat) tuples ( lower_left, upper right )
:param map_BB=None: bounding box of map if None, it will use the
bounding box of the mapfile.
:param draw_back_to_fore=True: draw the background (map) to the
foregound image when outputting
the images each time step.
:type draw_back_to_fore: boolean
:param formats=['gif']: list of formats to output.
:type formats: string or list of strings. Options are:
['bmp', 'jpg', 'jpeg', 'gif', 'png']
:param draw_ontop: draw 'forecast' or 'uncertain' LEs on top. Default
is to draw 'forecast' LEs, which are in black on top
:type draw_ontop: str
Following args are passed to base class Outputter's init:
:param cache: sets the cache object from which to read prop. The model
will automatically set this param
:param output_timestep: default is None in which case everytime the
write_output is called, output is written. If set, then output is
written every output_timestep starting from model_start_time.
:type output_timestep: timedelta object
:param output_zero_step: default is True. If True then output for
initial step (showing initial release conditions) is written
regardless of output_timestep
:type output_zero_step: boolean
:param output_last_step: default is True. If True then output for
final step is written regardless of output_timestep
:type output_last_step: boolean
Remaining kwargs are passed onto baseclass's __init__ with a direct
call: Outputter.__init__(..)
"""
projection = (projections.FlatEarthProjection()
if projection is None
else projection)
# set up the canvas
self.map_filename = map_filename
self.output_dir = output_dir
if map_filename is not None and land_polygons is None:
self.land_polygons = haz_files.ReadBNA(map_filename, 'PolygonSet')
elif land_polygons is not None:
self.land_polygons = land_polygons
else:
self.land_polygons = [] # empty list so we can loop thru it
self.last_filename = ''
self.draw_ontop = draw_ontop
self.draw_back_to_fore = draw_back_to_fore
Outputter.__init__(self,
cache,
on,
output_timestep,
output_zero_step,
output_last_step,
output_start_time,
output_dir,
**kwargs)
if map_BB is None:
if not self.land_polygons:
map_BB = ((-180, -90), (180, 90))
else:
map_BB = self.land_polygons.bounding_box
self.map_BB = map_BB
viewport = self.map_BB if viewport is None else viewport
MapCanvas.__init__(self, image_size, projection=projection,
viewport=viewport)
# assorted rendering flags:
self.draw_map_bounds = draw_map_bounds
self.draw_spillable_area = draw_spillable_area
self.raster_map = None
self.raster_map_fill = True
self.raster_map_outline = False
# initilize the images:
self.add_colors(self.map_colors)
self.background_color = 'background'
if self.map_filename is not None:
file_prefix = os.path.splitext(self.map_filename)[0]
sep = '_'
else:
file_prefix = sep = ''
fn = '{}{}anim.gif'.format(file_prefix, sep)
self.anim_filename = os.path.join(output_dir, fn)
self.formats = formats
self.delay = 50
self.repeat = True
self.timestamp_attribs = {}
self.set_timestamp_attrib(**timestamp_attrib)
self.grids = []
self.props = []
def __init__(self,
filename=None,
images_dir='./',
image_size=(800, 600),
cache=None,
output_timestep=None,
output_zero_step=True,
output_last_step=True,
draw_ontop='forecast',
draw_back_to_fore=True,
**kwargs):
"""
Init the image renderer.
Following args are passed to base class Outputter's init:
:param filename: the name of the image file
:param images_dir: the folder in which to write the image
:param image_size: the width and height of the image
:param cache: sets the cache object from which to read data. The model
will automatically set this param
:param output_timestep: default is None in which case everytime the
write_output is called, output is written. If set, then output is
written every output_timestep starting from model_start_time.
:type output_timestep: timedelta object
:param output_zero_step: default is True. If True then output for
initial step (showing initial release conditions) is written
regardless of output_timestep
:type output_zero_step: boolean
:param output_last_step: default is True. If True then output for
final step is written regardless of output_timestep
:type output_last_step: boolean
:param draw_ontop: draw 'forecast' or 'uncertain' LEs on top. Default
is to draw 'forecast' LEs, which are in black on top
:type draw_ontop: str
:param draw_back_to_fore=True: draw the background (map) to the
foregound image when drawing Elements.
:type draw_ontop: boolean
Remaining kwargs are passed onto baseclass's __init__ with a direct
call: MapCanvas.__init__(..)
Optional parameters (kwargs)
:param projection_class: gnome.utilities.projections class to use.
Default is gnome.utilities.projections.FlatEarthProjection
:param map_BB: map bounding box. Default is to use
land_polygons.bounding_box. If land_polygons is None, then this is
the whole world, defined by ((-180,-90),(180, 90))
:param viewport: viewport of map -- what gets drawn and on what scale.
Default is to set viewport = map_BB
:param image_mode: Image mode ('P' for palette or 'L' for Black and
White image). BW_MapCanvas inherits from MapCanvas and sets the
mode to 'L'. Default image_mode is 'P'.
"""
# set up the canvas
self._filename = filename
if filename is not None:
polygons = haz_files.ReadBNA(filename, 'PolygonSet')
else:
polygons = None
self.images_dir = images_dir
self.last_filename = ''
self.draw_ontop = draw_ontop
self.draw_back_to_fore = draw_back_to_fore
Outputter.__init__(self,
cache,
kwargs.pop('on', True),
output_timestep,
output_zero_step,
output_last_step,
kwargs.pop('name', None))
MapCanvas.__init__(self,
image_size,
land_polygons=polygons,
**kwargs)
