def PlotSkeleton(seg_name, plot_type='node', out_res=(30, 48, 48)):
print('Read skeletons')
skeletons = ReadSkeletons(seg_name,
skeleton_algorithm='thinning',
downsample_resolution=out_res,
read_edges=True)
print('Plot skeletons')
node_list = skeletons[1].get_nodes()
nodes = np.stack(node_list).astype(float)
junction_idx = skeletons[1].get_junctions()
junctions = nodes[junction_idx, :]
ends = skeletons[1].get_ends()
jns_ends = np.vstack([junctions, ends])
IX, IY, IZ = 2, 1, 0
ipv.figure()
if plot_type == 'nodes':
nodes = ipv.scatter(nodes[:,IX], nodes[:,IY], nodes[:,IZ], \
size=0.5, marker='sphere', color='blue')
elif plot_type == 'edges':
edges = skeletons[1].get_edges().astype(float)
for e1, e2 in edges:
if not ((e1[IX] == e2[IX]) and (e1[IY] == e2[IY]) and
(e1[IZ] == e2[IZ])):
ipv.plot([e1[IX], e2[IX]], [e1[IY], e2[IY]], [e1[IZ], e2[IZ]], \
color='blue')
jns = ipv.scatter(jns_ends[:,IX], jns_ends[:,IY], jns_ends[:,IZ], \
size=0.85, marker='sphere', color='red')
ipv.pylab.style.axes_off()
ipv.pylab.style.box_off()
ipv.save(seg_name + '_skel.html')
def plot_annulus(
self,
detector1,
detector2,
projection="astro degrees mollweide",
ax=None,
radius=None,
center=None,
threeD=True,
**kwargs,
):
if not threeD:
if ax is None:
skw_dict = create_skw_dict(projection, center, radius)
fig, ax = plt.subplots(subplot_kw=skw_dict)
else:
fig = ax.get_figure()
# compute the annulus for this set of detectors
cart_vec, spherical_vec, theta = self.calculate_annulus(
detector1, detector2)
if not threeD:
circle = SphericalCircle(
spherical_vec,
theta,
vertex_unit=u.deg,
resolution=5000,
# edgecolor=color,
fc="none",
transform=ax.get_transform("icrs"),
**kwargs,
)
ax.add_patch(circle)
return fig
else:
# get all the threeD point
xyz = get_3d_circle(spherical_vec,
theta,
radius=self._grb_radius,
resolution=1000)
ipv.plot(xyz[:, 0], xyz[:, 1], xyz[:, 2], **kwargs)
def spherical_galaxy_orbit(orbit_x,
orbit_y,
orbit_z,
N_stars=100,
sigma_r=1,
orbit_visible=False,
orbit_line_interpolate=5,
N_star_orbits=10,
color=[255, 220, 200],
size_star=1,
scatter_kwargs={}):
"""Create a fake galaxy around the points orbit_x/y/z with N_stars around it"""
if orbit_line_interpolate > 1:
x = np.linspace(0, 1, len(orbit_x))
x_smooth = np.linspace(0, 1, len(orbit_x) * orbit_line_interpolate)
kind = 'quadratic'
orbit_x_line = scipy.interpolate.interp1d(x, orbit_x, kind)(x_smooth)
orbit_y_line = scipy.interpolate.interp1d(x, orbit_y, kind)(x_smooth)
orbit_z_line = scipy.interpolate.interp1d(x, orbit_z, kind)(x_smooth)
else:
orbit_x_line = orbit_x
orbit_y_line = orbit_y
orbit_z_line = orbit_z
line = ipv.plot(orbit_x_line,
orbit_y_line,
orbit_z_line,
visible=orbit_visible)
x = np.repeat(orbit_x, N_stars).reshape((-1, N_stars))
y = np.repeat(orbit_y, N_stars).reshape((-1, N_stars))
z = np.repeat(orbit_z, N_stars).reshape((-1, N_stars))
xr, yr, zr = np.random.normal(0, scale=sigma_r, size=(3, N_stars)) # +
r = np.sqrt(xr**2 + yr**2 + zr**2)
for i in range(N_stars):
a = np.linspace(0, 1, x.shape[0]) * 2 * np.pi * N_star_orbits
xo = r[i] * np.sin(a)
yo = r[i] * np.cos(a)
zo = a * 0
xo, yo, zo = np.dot(_randomSO3(), [xo, yo, zo])
#print(x.shape, xo.shape)
x[:, i] += xo
y[:, i] += yo
z[:, i] += zo
sprite = ipv.scatter(x,
y,
z,
texture=radial_sprite((64, 64), color),
marker='square_2d',
size=size_star,
**scatter_kwargs)
with sprite.material.hold_sync():
sprite.material.blending = pythreejs.BlendingMode.CustomBlending
sprite.material.blendSrc = pythreejs.BlendFactors.SrcColorFactor
sprite.material.blendDst = pythreejs.BlendFactors.OneFactor
sprite.material.blendEquation = 'AddEquation'
sprite.material.transparent = True
sprite.material.depthWrite = False
sprite.material.alphaTest = 0.1
return sprite, line
def hpo_animate(particles,
particleSizes,
particleColors,
paramRanges,
nTimesteps=1):
nParticles = particles.shape[1]
colorStack = np.ones((nTimesteps, nTimesteps * nParticles, 4)) * .9
colorStackLines = np.ones((nTimesteps, nTimesteps * nParticles, 4)) * .5
for iTimestep in range(nTimesteps):
colorStack[iTimestep, :,
0:3] = numpy.matlib.repmat(particleColors[0, :, :],
nTimesteps, 1)
colorStackLines[iTimestep, :,
0:3] = numpy.matlib.repmat(particleColors[0, :, :],
nTimesteps, 1)
colorStackLines[iTimestep, :, 3] = .6 # alpha
ipv.figure()
pplot = ipv.scatter(particles[0:nTimesteps, :, 0],
particles[0:nTimesteps, :, 1],
particles[0:nTimesteps, :, 2],
marker='sphere',
size=particleSizes,
color=colorStack[:, :, :])
for iParticle in range(nParticles):
plines = ipv.plot(particles[0:nTimesteps, iParticle, 0],
particles[0:nTimesteps, iParticle, 1],
particles[0:nTimesteps, iParticle, 2],
color=colorStackLines[:, iParticle, :])
ipv.animation_control([pplot], interval=600)
ipv.xlim(paramRanges[0][1] - .5, paramRanges[0][2] + .5)
ipv.ylim(paramRanges[1][1] - .1, paramRanges[1][2] + .1)
ipv.zlim(paramRanges[2][1] - .1, paramRanges[2][2] + .1)
ipv.show()
def viz_particle_trails(swarm,
topN=None,
globalBestIndicatorFlag=True,
tabbedPlot=False):
startTime = time.time()
targetMedianParticleSize = 3
nTreesDescaler = np.median(swarm.nTreesHistory) / targetMedianParticleSize
minParticleSize = 2.5
maxParticleSize = 10
ipvFig = ipv.figure(width=ipvPlotWidth, height=ipvPlotHeight)
# find top performing particles to show additional detail [ size scaled based on nTrees ]
if topN is None: topN = swarm.nParticles
topN = np.max((1, topN))
particleMaxes = {}
for key, iParticle in swarm.particles.items():
if len(iParticle.testDataPerfHistory):
particleMaxes[key] = max(iParticle.testDataPerfHistory)
else:
particleMaxes[key] = 0
sortedParticles = pd.DataFrame.from_dict(
particleMaxes, orient='index').sort_values(by=0, ascending=False)
topParticles = sortedParticles.index[0:np.min((np.max((topN, 1)),
swarm.nParticles))]
for iParticle in range(swarm.nParticles):
if len(swarm.particles[iParticle].posHistory):
particlePosHistory = np.matrix(
swarm.particles[iParticle].posHistory)
# plot markers along the particle's journey over the epoch sequence
if iParticle in topParticles:
# draw top particles with their unique color and scaled by the number of trees
sortedIndex = np.where(topParticles == iParticle)[0][0]
particleColor = rapidsColorsHex[sortedIndex % nRapidsColors]
particleSizes = {}
for iEval in range(swarm.particles[iParticle].nEvals):
particleSizes[iEval] = np.clip(
swarm.particles[iParticle].nTreesHistory[iEval] /
nTreesDescaler, minParticleSize, maxParticleSize)
#import pdb; pdb.set_trace()
ipv.scatter(particlePosHistory[:, 0].squeeze(),
particlePosHistory[:, 1].squeeze(),
particlePosHistory[:, 2].squeeze(),
size=np.array(list(particleSizes.values())),
marker='sphere',
color=particleColor,
grow_limits=False)
else:
# draw non-top particle locations in gray
particleColor = (.7, .7, .7, .9)
ipv.scatter(particlePosHistory[:, 0].squeeze(),
particlePosHistory[:, 1].squeeze(),
particlePosHistory[:, 2].squeeze(),
size=1.5,
marker='sphere',
color=particleColor,
grow_limits=False)
# plot line trajectory [ applies to both top and non-top particles ]
ipv.plot(particlePosHistory[:, 0].squeeze(),
particlePosHistory[:, 1].squeeze(),
particlePosHistory[:, 2].squeeze(),
color=particleColor)
# draw an intersecting set of lines/volumes to indicate the location of the best particle / parameter-set
if globalBestIndicatorFlag:
bestXYZ = np.matrix(swarm.globalBest['params'])
ipv.scatter(bestXYZ[:, 0],
bestXYZ[:, 1],
bestXYZ[:, 2],
color=rapidsColorsHex[1],
size=3,
marker='box')
data = np.ones((100, 2, 2))
xSpan = np.clip(
(swarm.paramRanges[0][2] - swarm.paramRanges[0][1]) / 200, .007, 1)
ySpan = np.clip(
(swarm.paramRanges[1][2] - swarm.paramRanges[1][1]) / 200, .007, 1)
zSpan = np.clip(
(swarm.paramRanges[2][2] - swarm.paramRanges[2][1]) / 200, .007, 1)
xMin = swarm.globalBest['params'][0] - xSpan
xMax = swarm.globalBest['params'][0] + xSpan
yMin = swarm.globalBest['params'][1] - ySpan
yMax = swarm.globalBest['params'][1] + ySpan
zMin = swarm.globalBest['params'][2] - zSpan
zMax = swarm.globalBest['params'][2] + zSpan
ipv.volshow(data=data.T,
opacity=.15,
level=[0.25, 0., 0.25],
extent=[[xMin, xMax], [yMin, yMax],
[swarm.paramRanges[2][1],
swarm.paramRanges[2][2]]],
controls=False)
ipv.volshow(data=data.T,
opacity=.15,
level=[0.25, 0., 0.25],
extent=[[swarm.paramRanges[0][1], swarm.paramRanges[0][2]],
[yMin, yMax], [zMin, zMax]],
controls=False)
comboBox = append_label_buttons(swarm, ipvFig)
if tabbedPlot:
return comboBox
display(comboBox)
return None
def plot_in_space(
position_interpolator,
time,
show_detector_pointing=False,
show_earth=True,
show_sun=False,
show_moon=False,
background_color="#01000F",
detector_scaling_factor=20000.0,
show_stars=False,
show_orbit=True,
realistic=True,
earth_time="night",
sky_points=None,
min_distance=8000,
):
"""
Plot Fermi in Space!
:param position_interpolator:
:param time:
:param show_detector_pointing:
:param show_earth:
:param show_sun:
:param show_moon:
:param background_color:
:param detector_scaling_factor:
:returns:
:rtype:
"""
fig = ipv.figure(width=800, height=600)
ipv.pylab.style.box_off()
ipv.pylab.style.axes_off()
ipv.pylab.style.set_style_dark()
ipv.pylab.style.background_color(background_color)
distances = [min_distance]
if sky_points is not None:
sky_points = np.atleast_1d(sky_points)
if show_orbit:
tmin, tmax = position_interpolator.minmax_time()
tt = np.linspace(tmin, tmax, 500)
sc_pos = position_interpolator.sc_pos(tt)
ipv.plot(sc_pos[:, 0], sc_pos[:, 1], sc_pos[:, 2], lw=0.5)
if show_earth:
earth = Earth(
earth_time=earth_time,
realistic=realistic,
astro_time=position_interpolator.astro_time(time),
)
earth.plot()
if show_sun:
sun_pos = position_interpolator.sun_position(time)
x, y, z = sun_pos.cartesian.xyz.to("km").value
sol = Sol(x, y, z)
distances.append(compute_distance(x, y, z, sol.radius))
sol.plot()
if show_moon:
moon_pos = position_interpolator.moon_position(time)
x, y, z = moon_pos.cartesian.xyz.to("km").value
moon = Moon(x, y, z, realistic=True)
distances.append(compute_distance(x, y, z, moon.radius))
moon.plot()
# now get fermi position
sx, sy, sz = position_interpolator.sc_pos(time)
fermi_real = Fermi(
position_interpolator.quaternion(time),
sc_pos=position_interpolator.sc_pos(time),
transform_to_space=True,
)
fermi_real.plot_fermi_ipy()
if show_detector_pointing:
distances.append(detector_scaling_factor)
gbm = GBM(
position_interpolator.quaternion(time), position_interpolator.sc_pos(time),
)
for k, v in gbm.detectors.items():
x, y, z = v.center_icrs.cartesian.xyz.value * max(distances)
# x_line = np.array([sx, sx + x])
# y_line = np.array([sy, sy + y])
# z_line = np.array([sz, sz + z])
color = _det_colors[k]
cone = Cone(sx, sy, sz, sx + x, sy + y, sz + z, _open_angle)
cone.plot(color=color)
# ipv.pylab.plot(x_line, y_line, z_line, color=color)
if sky_points is not None:
for sp in sky_points:
sp.plot(sx, sy, sz, max(distances))
# distances.append(sp.distance)
if show_stars:
sf = StarField(n_stars=100, distance=max(distances) - 2)
sf.plot()
# move the camera here
fig.camera.position = tuple(
position_interpolator.sc_pos(time)
/ np.linalg.norm(position_interpolator.sc_pos(time))
)
ipv.xyzlim(max(distances))
ipv.show()
return fig
def plot_brain(brain,
surface='dots',
nodes=True,
node_colors=False,
node_groups=None,
surface_color=None,
node_color='red',
network=None,
dot_size=0.1,
dot_color='gray',
min_fibers=10,
max_fibers=500,
lowest_cmap_color=0.2,
highest_cmap_color=0.7,
cmap='rocket',
background='light'):
import ipyvolume as ipv
if surface_color is None:
if surface == 'dots': surface_color = 'gray'
elif surface == 'full': surface_color = 'orange'
if isinstance(node_colors, bool) and node_colors:
node_colors = ['red', 'green', 'blue', 'violet', 'yellow']
if node_colors and node_groups is None:
node_groups = brain['nodes']['label']
fig = ipv.figure()
# plot surface
x, y, z = [brain['surface'][key] for key in list('xyz')]
if surface == 'dots':
ipv.scatter(x, y, z, marker='box', color=dot_color, size=dot_size)
elif surface == 'full':
ipv.plot_trisurf(x,
y,
z,
triangles=brain['surface']['tri'],
color=surface_color)
# plot nodes
if nodes:
xyz = brain['nodes']['xyz']
if node_colors:
for label_idx, color in zip(np.unique(node_groups), node_colors):
mask = node_groups == label_idx
ipv.scatter(xyz[mask, 0],
xyz[mask, 1],
xyz[mask, 2],
marker='sphere',
color=color,
size=1.5)
else:
ipv.scatter(xyz[:, 0],
xyz[:, 1],
xyz[:, 2],
marker='sphere',
color=node_color,
size=1.5)
# plot connections
if network is not None:
x, y, z = brain['nodes']['xyz'].T
scaling = highest_cmap_color - lowest_cmap_color
min_fibers_log = np.log(min_fibers)
max_fibers_log = np.log(max_fibers)
if hasattr(plt.cm, cmap):
cmp = getattr(plt.cm, cmap)
else:
import seaborn as sns
cmp = getattr(sns.cm, cmap)
with fig.hold_sync():
for ii in range(89):
for jj in range(ii, 90):
if network[ii, jj] > min_fibers:
float_color = (min(
np.log((network[ii, jj] - min_fibers)) /
(max_fibers_log - min_fibers_log), 1.) * scaling +
lowest_cmap_color)
line_color = cmp(float_color)
ipv.plot(x[[ii, jj]],
y[[ii, jj]],
z[[ii, jj]],
color=line_color[:3])
ipv.squarelim()
ipv.style.use([background, 'minimal'])
ipv.show()
return fig
def plot_all_annuli(
self,
projection="astro degrees mollweide",
radius=None,
center=None,
cmap="Set1",
threeD=True,
use_all=False,
**kwargs,
):
if not threeD:
assert projection in [
"astro degrees aitoff",
"astro degrees mollweide",
"astro hours aitoff",
"astro hours mollweide",
"astro globe",
"astro zoom",
]
skw_dict = dict(projection=projection)
if projection in ["astro globe", "astro zoom"]:
assert center is not None, "you must specify a center"
skw_dict = dict(projection=projection, center=center)
if projection == "astro zoom":
assert radius is not None, "you must specify a radius"
skw_dict = dict(projection=projection,
center=center,
radius=radius)
fig, ax = plt.subplots(subplot_kw=skw_dict)
else:
fig = ipv.figure()
ipv.pylab.style.box_off()
ipv.pylab.style.axes_off()
ax = None
# get the colors to use
if use_all:
n_verts = self._n_detectors * (self._n_detectors - 1) / 2
colors = mpl_color.colors_from_cmap(int(n_verts), cmap=cmap)
for i, (d1,
d2) in enumerate(combinations(self._detectors.keys(), 2)):
_ = self.plot_annulus(
d1,
d2,
projection=projection,
center=center,
radius=radius,
ax=ax,
edgecolor=colors[i],
threeD=threeD,
color=colors[i],
**kwargs,
)
if threeD:
loc1 = self._detectors[d1].location.get_cartesian_coord(
).xyz.value
loc2 = self._detectors[d2].location.get_cartesian_coord(
).xyz.value
ipv.plot(
np.array([loc1[0], loc2[0]]),
np.array([loc1[1], loc2[1]]),
np.array([loc1[2], loc2[2]]),
color=colors[i],
)
else:
colors = mpl_color.colors_from_cmap(len(self._detectors) - 1,
cmap=cmap)
det_list = list(self._detectors.keys())
d1 = det_list[0]
for i, d2 in enumerate(det_list[1:]):
_ = self.plot_annulus(
d1,
d2,
projection=projection,
center=center,
radius=radius,
ax=ax,
edgecolor=colors[i],
threeD=threeD,
color=colors[i],
**kwargs,
)
if threeD:
loc1 = self._detectors[d1].location.get_cartesian_coord(
).xyz.value
loc2 = self._detectors[d2].location.get_cartesian_coord(
).xyz.value
ipv.plot(
np.array([loc1[0], loc2[0]]),
np.array([loc1[1], loc2[1]]),
np.array([loc1[2], loc2[2]]),
color=colors[i],
)
if threeD:
ipv.scatter(
*(self._grb_radius *
self._grb.location.get_cartesian_coord("gcrs").xyz.value /
np.linalg.norm(
self._grb.location.get_cartesian_coord("gcrs").xyz.value)
)[np.newaxis].T,
marker="sphere",
color="green",
)
ipv.show()
return fig