def run_and_visualize(**kawrgs):
"""Visualize a simulation.
Parameters
----------
kawrgs :
run_multiple_sources kwargs.
"""
wave, speed = run_multiple_sources(**kawrgs)
clim = np.percentile(wave, 99)
wave_cmap = Colormap(
[[0.55, 0, .32, 1], [0, 0, 0, 0], [0.15, 0.4, 0.1, 1]], name='PBlG')
wave_dict = {
'colormap': wave_cmap,
'contrast_limits': [-clim, clim],
'name': 'wave',
'metadata': kawrgs,
'interpolation': 'bilinear'
}
speed_cmap = Colormap([[0, 0, 0, 0], [0.7, 0.5, 0, 1]], name='Orange')
speed_dict = {
'colormap': speed_cmap,
'opacity': 0.5,
'name': 'speed',
'metadata': kawrgs,
'interpolation': 'bilinear'
}
viewer = napari.Viewer()
viewer.add_image(np.atleast_2d(np.squeeze(wave)), **wave_dict)
viewer.add_image(np.atleast_2d(speed[0, 0]), **speed_dict)
napari.run()
def test_colormap_dump(tmp_path):
cmap_list = [Colormap([(0, 0, 0), (1, 1, 1)]), Colormap([(0, 0, 0), (1, 1, 1)], controls=[0, 1])]
with open(tmp_path / "test.json", "w") as f_p:
json.dump(cmap_list, f_p, cls=ProfileEncoder)
with open(tmp_path / "test.json") as f_p:
cmap_list2 = json.load(f_p, object_hook=profile_hook)
assert np.array_equal(cmap_list[0].colors, cmap_list2[0].colors)
assert np.array_equal(cmap_list[0].controls, cmap_list2[0].controls)
assert np.array_equal(cmap_list[1].colors, cmap_list2[1].colors)
assert np.array_equal(cmap_list[1].controls, cmap_list2[1].controls)
cmap_list = [
Colormap([(0, 0, 0), (1, 1, 1)]),
Colormap([(0, 0, 0), (0, 0, 0), (1, 1, 1), (1, 1, 1)], controls=[0, 0.1, 0.8, 1]),
]
with open(tmp_path / "test2.json", "w") as f_p:
json.dump(cmap_list, f_p, cls=ProfileEncoder)
with open(tmp_path / "test2.json") as f_p:
cmap_list2 = json.load(f_p, object_hook=profile_hook)
assert np.array_equal(cmap_list[0].colors, cmap_list2[0].colors)
assert np.array_equal(cmap_list[0].controls, cmap_list2[0].controls)
assert np.array_equal(cmap_list[1].colors, cmap_list2[1].colors)
assert np.array_equal(cmap_list[1].controls, cmap_list2[1].controls)
assert cmap_list2[1].controls[0] == 0
assert cmap_list2[1].controls[-1] == 1
assert np.array_equal(cmap_list[1].colors[0], cmap_list2[1].colors[0])
assert np.array_equal(cmap_list[1].colors[-1], cmap_list2[1].colors[-1])
def test_colormaps():
"""Test setting test_colormaps."""
np.random.seed(0)
data = np.random.random((10, 15))
layer = Image(data)
assert layer.colormap.name == 'gray'
assert isinstance(layer.colormap, Colormap)
layer.colormap = 'magma'
assert layer.colormap.name == 'magma'
assert isinstance(layer.colormap, Colormap)
cmap = Colormap([[0.0, 0.0, 0.0, 0.0], [0.3, 0.7, 0.2, 1.0]])
layer.colormap = 'custom', cmap
assert layer.colormap.name == 'custom'
assert layer.colormap == cmap
cmap = Colormap([[0.0, 0.0, 0.0, 0.0], [0.7, 0.2, 0.6, 1.0]])
layer.colormap = {'new': cmap}
assert layer.colormap.name == 'new'
assert layer.colormap == cmap
layer = Image(data, colormap='magma')
assert layer.colormap.name == 'magma'
assert isinstance(layer.colormap, Colormap)
cmap = Colormap([[0.0, 0.0, 0.0, 0.0], [0.3, 0.7, 0.2, 1.0]])
layer = Image(data, colormap=('custom', cmap))
assert layer.colormap.name == 'custom'
assert layer.colormap == cmap
cmap = Colormap([[0.0, 0.0, 0.0, 0.0], [0.7, 0.2, 0.6, 1.0]])
layer = Image(data, colormap={'new': cmap})
assert layer.colormap.name == 'new'
assert layer.colormap == cmap
def __init__(self):
super().__init__()
self.color_list: List[Color] = []
self.position_list: List[float] = []
self.move_ind = None
self.image = convert_colormap_to_image(Colormap([(0, 0, 0)]))
self.setMinimumHeight(60)
def colormap(self) -> Colormap:
"""colormap getter"""
if len(self.color_list) == 0:
return Colormap("black")
color_list = self.color_list[:]
position_list = self.position_list[:]
if position_list[0] != 0:
position_list.insert(0, 0)
color_list.insert(0, color_list[0])
if position_list[-1] != 1:
position_list.append(1)
color_list.append(color_list[-1])
return Colormap(colors=color_list, controls=position_list)
def update_colormap(self, name, channel_id):
fixed_range = self._settings.get_from_profile(
f"{name}.lock_{channel_id}", False)
gamma = self._settings.get_from_profile(
f"{name}.gamma_value_{channel_id}", 1)
if fixed_range:
self.range = self._settings.get_from_profile(
f"{name}.range_{channel_id}")
else:
self.range = self._settings.border_val[channel_id]
cmap = self._settings.colormap_dict[
self._settings.get_channel_colormap_name(name, channel_id)][0]
round_factor = self.round_base(self.range[1])
self.round_range = (
int(round(self.range[0] / round_factor) * round_factor),
int(round(self.range[1] / round_factor) * round_factor),
)
if self.round_range[0] < self.range[0]:
self.round_range = self.round_range[
0] + round_factor, self.round_range[1]
if self.round_range[1] > self.range[1]:
self.round_range = self.round_range[
0], self.round_range[1] - round_factor
# print(self.range, self.round_range)
data = np.linspace(0, 1, 512)
interpolated = cmap.map(data)
data = data**gamma
colormap = Colormap(interpolated, controls=data)
self.image = NumpyQImage(
np.array(
make_colorbar(colormap, size=(512, 1),
horizontal=False)[::-1]))
self.repaint()
def clear(self):
"""
Remove color markers. Reset to initial state.
"""
self.color_list = []
self.position_list = []
self.image = convert_colormap_to_image(Colormap([(0, 0, 0), (1, 1, 1)]))
self.repaint()
def load_simulation_dataset(path):
"""Load a simulation dataset.
Parameters
----------
path : str
Path to simulation data to load.
Returns
-------
dataset : zarr.hierarchy.Group
Loaded simulation dataset.
"""
# Load dataset
path = Path(path)
dataset = zarr.open(path.as_posix(), mode='r')
metadata = dataset.attrs.asdict()
# If dataset is a full dataset return it
if dataset.attrs['waver'] and dataset.attrs['dataset']:
# Return simulation wave data
clim = max(dataset['wave'][0].max(), abs(
dataset['wave'][0].min())) / 3**dataset['wave'][0].ndim
wave_cmap = Colormap(
[[0.55, 0, .32, 1], [0, 0, 0, 0], [0.15, 0.4, 0.1, 1]],
name='PBlG')
wave_dict = {
'colormap': wave_cmap,
'contrast_limits': [-clim, clim],
'name': 'wave',
'metadata': metadata
}
speed_cmap = Colormap([[0, 0, 0, 0], [0.7, 0.5, 0, 1]], name='Orange')
speed_dict = {
'colormap': speed_cmap,
'visible': False,
'contrast_limits': (metadata['min_speed'], metadata['max_speed']),
'name': 'speed',
'metadata': metadata
}
return [(dataset['wave'], wave_dict, 'image'),
(dataset['speed'], speed_dict, 'image')]
else:
raise ValueError(f'Dataset at {path} not valid waver simulation')
def test_colormaps():
"""Test setting test_colormaps."""
shapes = [(40, 20), (20, 10), (10, 5)]
np.random.seed(0)
data = [np.random.random(s) for s in shapes]
layer = Image(data, multiscale=True)
assert layer.colormap.name == 'gray'
assert isinstance(layer.colormap, Colormap)
layer.colormap = 'magma'
assert layer.colormap.name == 'magma'
assert isinstance(layer.colormap, Colormap)
cmap = Colormap([[0.0, 0.0, 0.0, 0.0], [0.3, 0.7, 0.2, 1.0]])
layer.colormap = 'custom', cmap
assert layer.colormap.name == 'custom'
assert layer.colormap == cmap
cmap = Colormap([[0.0, 0.0, 0.0, 0.0], [0.7, 0.2, 0.6, 1.0]])
layer.colormap = {'new': cmap}
assert layer.colormap.name == 'new'
assert layer.colormap == cmap
layer = Image(data, multiscale=True, colormap='magma')
assert layer.colormap.name == 'magma'
assert isinstance(layer.colormap, Colormap)
cmap = Colormap([[0.0, 0.0, 0.0, 0.0], [0.3, 0.7, 0.2, 1.0]])
layer = Image(data, multiscale=True, colormap=('custom', cmap))
assert layer.colormap.name == 'custom'
assert layer.colormap == cmap
cmap = Colormap([[0.0, 0.0, 0.0, 0.0], [0.7, 0.2, 0.6, 1.0]])
layer = Image(data, multiscale=True, colormap={'new': cmap})
assert layer.colormap.name == 'new'
assert layer.colormap == cmap
def save(self):
if self.show_colormap.colormap:
rand_name = custom_name_generate(self.prohibited_names, self.settings.colormap_dict)
self.prohibited_names.add(rand_name)
colors = list(self.show_colormap.colormap.colors)
positions = list(self.show_colormap.colormap.controls)
if positions[0] != 0:
positions.insert(0, 0)
colors.insert(0, colors[0])
if positions[-1] != 1:
positions.append(1)
colors.append(colors[-1])
self.settings.colormap_dict[rand_name] = Colormap(colors=np.array(colors), controls=np.array(positions))
self.settings.chosen_colormap_change(rand_name, True)
self.colormap_selected.emit(self.settings.colormap_dict[rand_name][0])
def create_colormap(self) -> Colormap:
return Colormap(name='IMC',
colors=[[0., 0., 0., self.color[-1]],
list(self._color)],
interpolation='linear')
def simulation(
ndim: int = 2,
length: float = 12.8,
spacing: float = 100,
min_speed: float = 343,
max_speed: float = 686,
time_step: float = 50,
duration: float = 60,
frequency: float = 500,
method: str = 'flat',
custom: 'napari.layers.Layer' = None,
spatial_downsample: int = 1,
temporal_downsample: int = 1,
boundary: int = 0,
) -> 'napari.types.LayerDataTuple':
"""Run a single simulation.
"""
size = ndim * (length / 1e3, ) # length always same in all dimensions
spacing = spacing / 1e6
time_ste = time_step / 1e9
period = 1 / frequency / 1e3
location = (s / 2 for s in size)
duration = duration / 1e6
# Add randomly sampled speed array
if method == 'custom':
raw_speed = custom.data
if hasattr(custom, 'num_colors'):
raw_speed = raw_speed / raw_speed.max()
else:
import numpy as np
raw_speed = np.clip(raw_speed, custom.contrast_limits[0],
custom.contrast_limits[1])
raw_speed = (raw_speed - custom.contrast_limits[0]) / (
custom.contrast_limits[1] - custom.contrast_limits[0])
speed = raw_speed * (max_speed - min_speed) + min_speed
else:
speed = method
detected_wave, detected_speed = run_single_source(
size=size,
spacing=spacing,
location=location,
duration=duration,
max_speed=max_speed,
min_speed=min_speed,
speed=speed,
period=period,
spatial_downsample=spatial_downsample,
temporal_downsample=temporal_downsample,
boundary=boundary)
# Return simulation wave data
clim = max(detected_wave.max(), abs(detected_wave.min())) / 3**ndim
wave_cmap = Colormap(
[[0.55, 0, .32, 1], [0, 0, 0, 0], [0.15, 0.4, 0.1, 1]], name='PBlG')
wave_dict = {
'colormap': wave_cmap,
'contrast_limits': [-clim, clim],
'name': 'wave'
}
speed_cmap = Colormap([[0, 0, 0, 0], [0.7, 0.5, 0, 1]], name='Orange')
speed_dict = {
'colormap': speed_cmap,
'opacity': 0.5,
'contrast_limits': (min_speed, max_speed),
'name': 'speed'
}
return [(detected_wave, wave_dict, 'Image'),
(detected_speed, speed_dict, 'Image')]