def set_legend_to_bw(leg, style, colormap, line_style='continuous'):
"""
Takes the figure legend and converts it to black and white. Note that
it currently only converts lines to black and white, other artist
intances are currently not being supported, and might cause errors or
other unexpected behavior.
Parameters
----------
leg : legend
style : {GREYSCALE, HATCHING}
colormap : dict
mapping of color to B&W rendering
line_style: str
linestyle to use for converting, can be continuous, black
or None
# TODO:: None is strange as a value, and should be field based, see
# set_ax_lines_bw
"""
color_converter = ColorConverter()
if leg:
if isinstance(leg, list):
leg = leg[0]
for element in leg.legendHandles:
if isinstance(element, mpl.collections.PathCollection):
rgb_orig = color_converter.to_rgb(element._facecolors[0])
new_color = color_converter.to_rgba(colormap[rgb_orig]['fill'])
element._facecolors = np.array((new_color,))
elif isinstance(element, mpl.patches.Rectangle):
rgb_orig = color_converter.to_rgb(element._facecolor)
if style==HATCHING:
element.update({'alpha':1})
element.update({'facecolor':'none'})
element.update({'edgecolor':'black'})
element.update({'hatch':colormap[rgb_orig]['hatch']})
elif style==GREYSCALE:
ema_logging.info(colormap.keys())
element.update({'facecolor':colormap[rgb_orig]['fill']})
element.update({'edgecolor':colormap[rgb_orig]['fill']})
else:
line = element
orig_color = line.get_color()
line.set_color('black')
if not line_style=='continuous':
line.set_dashes(colormap[orig_color]['dash'])
line.set_marker(colormap[orig_color]['marker'])
line.set_markersize(MARKERSIZE)
def set_legend_to_bw(leg, style, colormap, line_style='continuous'):
"""
Takes the figure legend and converts it to black and white. Note that
it currently only converts lines to black and white, other artist
intances are currently not being supported, and might cause errors or
other unexpected behavior.
Parameters
----------
leg : legend
style : {GREYSCALE, HATCHING}
colormap : dict
mapping of color to B&W rendering
line_style: str
linestyle to use for converting, can be continuous, black
or None
# TODO:: None is strange as a value, and should be field based, see
# set_ax_lines_bw
"""
color_converter = ColorConverter()
if leg:
if isinstance(leg, list):
leg = leg[0]
for element in leg.legendHandles:
if isinstance(element, mpl.collections.PathCollection):
rgb_orig = color_converter.to_rgb(element._facecolors[0])
new_color = color_converter.to_rgba(colormap[rgb_orig]['fill'])
element._facecolors = np.array((new_color, ))
elif isinstance(element, mpl.patches.Rectangle):
rgb_orig = color_converter.to_rgb(element._facecolor)
if style == HATCHING:
element.update({'alpha': 1})
element.update({'facecolor': 'none'})
element.update({'edgecolor': 'black'})
element.update({'hatch': colormap[rgb_orig]['hatch']})
elif style == GREYSCALE:
ema_logging.info(colormap.keys())
element.update({'facecolor': colormap[rgb_orig]['fill']})
element.update({'edgecolor': colormap[rgb_orig]['fill']})
else:
line = element
orig_color = line.get_color()
line.set_color('black')
if not line_style == 'continuous':
line.set_dashes(colormap[orig_color]['dash'])
line.set_marker(colormap[orig_color]['marker'])
line.set_markersize(MARKERSIZE)
def test_log_messages(self):
ema_logging.log_to_stderr(ema_logging.DEBUG)
with mock.patch('ema_workbench.util.ema_logging._logger') as mocked_logger:
message = 'test message'
ema_logging.debug(message)
mocked_logger.debug.assert_called_with(message)
ema_logging.info(message)
mocked_logger.info.assert_called_with(message)
ema_logging.warning(message)
mocked_logger.warning.assert_called_with(message)
ema_logging.error(message)
mocked_logger.error.assert_called_with(message)
ema_logging.exception(message)
mocked_logger.exception.assert_called_with(message)
ema_logging.critical(message)
mocked_logger.critical.assert_called_with(message)
def test_log_messages(self):
ema_logging.log_to_stderr(ema_logging.DEBUG)
with mock.patch(
'ema_workbench.util.ema_logging._logger') as mocked_logger:
message = 'test message'
ema_logging.debug(message)
mocked_logger.debug.assert_called_with(message)
ema_logging.info(message)
mocked_logger.info.assert_called_with(message)
ema_logging.warning(message)
mocked_logger.warning.assert_called_with(message)
ema_logging.error(message)
mocked_logger.error.assert_called_with(message)
ema_logging.exception(message)
mocked_logger.exception.assert_called_with(message)
ema_logging.critical(message)
mocked_logger.critical.assert_called_with(message)
ParameterUncertainty((1, 20), "wolf-reproduce"),
CategoricalUncertainty(("true", "true"), "grass?")
]
outcomes = [Outcome('sheep', time=True),
Outcome('wolves', time=True),
Outcome('grass', time=True) # TODO patches not working in reporting
]
if __name__ == "__main__":
import multiprocessing
ema_logging.LOG_FORMAT = multiprocessing.util.DEFAULT_LOGGING_FORMAT
#turn on logging
ema_logging.log_to_stderr(ema_logging.DEBUG)
ema_logging.info('in main')
#instantiate a model
fh = r"./models/predatorPreyNetlogo"
model = PredatorPrey(fh, "simpleModel")
#instantiate an ensemble
ensemble = ModelEnsemble()
#set the model on the ensemble
ensemble.model_structure = model
#run in parallel, if not set, FALSE is assumed
ensemble.parallel = True
ensemble.processes = 2
