def test_filter_colormaps():
cmap = "Accent"
for example in get_examples():
properties = {}
_ = filter_colormap(example, cmap=cmap, properties=properties)
properties["nan_color"]
properties["inf_color"]
properties["flat_color"]
def test_filter_colormaps():
cmap = 'Accent'
for example in get_examples():
properties = {}
_ = filter_colormap(example, cmap=cmap, properties=properties)
properties['nan_color']
properties['inf_color']
properties['flat_color']
def demo_colormaps(r):
x = get_test_bar()
cmaps = ['Reds', 'jet', 'Accent']
for cmap in cmaps:
f = r.figure(cmap)
properties = {}
rgba = filter_colormap(x, cmap, properties=properties)
f.data_rgb('result', diagflip(rgba))
f.data_rgb('colorbar', diagflip(properties['color_bar']))
f.data_rgb('nan_color', get_solid((20, 20), properties['nan_color']))
f.data_rgb('inf_color', get_solid((20, 20), properties['inf_color']))
f.data_rgb('flat_color', get_solid((20, 20), properties['flat_color']))
def demo_colormaps(r):
x = get_test_bar()
cmaps = ["Reds", "jet", "Accent"]
for cmap in cmaps:
f = r.figure(cmap)
properties = {}
rgba = filter_colormap(x, cmap, properties=properties)
f.data_rgb("result", diagflip(rgba))
f.data_rgb("colorbar", diagflip(properties["color_bar"]))
f.data_rgb("nan_color", get_solid((20, 20), properties["nan_color"]))
f.data_rgb("inf_color", get_solid((20, 20), properties["inf_color"]))
f.data_rgb("flat_color", get_solid((20, 20), properties["flat_color"]))
def report_nmap_distances(nmap, nmap_distances):
r = Report()
timestamps = []
alldists = []
allobs = []
poses = []
for i, (timestamp, (y, distances, pose)) in enumerate(nmap_distances):
timestamps.append(timestamp)
alldists.append(distances)
allobs.append(y)
poses.append(pose)
timestamp = np.array(timestamps)
alldists = np.vstack(alldists).T
print ('ntimestamps: %s' % len(timestamps))
nwaypoints, nmoments = alldists.shape
print('nmoments: %s' % nmoments)
print('nwaypoints: %s' % nwaypoints)
assert len(timestamps) == nmoments
f = r.figure(cols=1)
GREEN = [0, 1, 0]
RED = [1, 0, 0]
alldists_rgb1 = scale(alldists, min_color=GREEN, max_color=RED)
f.data_rgb('alldists', alldists_rgb1)
alldists_rgb2 = filter_colormap(alldists,
cmap='jet',
min_value=None, max_value=None,
nan_color=[1, 0.6, 0.6],
inf_color=[0.6, 1, 0.6],
flat_color=[0.5, 0.5, 0.5])
f.data_rgb('alldists2', alldists_rgb2)
f.data_rgb('observations', _nmapobslist_to_rgb(allobs))
waypoints = range(0, nwaypoints, 5)
with f.plot('distances') as pylab:
for a, i in enumerate(waypoints):
d = alldists[i, :]
d = d - np.min(d)
d = d / np.max(d)
x = timestamps
y = d + a * 1.1
pylab.plot(x, y, 'k-')
return r
def get_rgb(field, colors, **kwargs):
''' Returns a dict with values 'rgb' (rgb in [0,1]), 'colorbar', etc.. '''
properties = {}
if colors == 'posneg':
rgb = posneg(field, properties=properties, **kwargs) / 255.0
elif colors == 'scale':
rgb = scale(field, properties=properties, **kwargs) / 255.0
elif colors == 'cmap':
rgb = filter_colormap(field,
properties=properties, **kwargs) / 255.0
else:
raise ValueError('No known colors %r. ' % colors)
properties['rgb'] = rgb
return properties
