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