def plot_data(self,
data,
outPath="3-D_figure",
marker='sphere',
size=2,
heatmap=None,
inline=False,
plot_type="scatter"):
ipv.figure(width=400, height=500, lighting=True, controls=True)
x, y, z = data[0], data[1], data[2]
#TODO: marker control
if heatmap == None:
if plot_type == "scatter":
ipv.quickscatter(x, y, z, marker=marker,
size=size) #, size=10)
if plot_type == "surface":
ipv.plot_surface(x, y, z)
else:
from matplotlib import cm
colormap = cm.coolwarm
heatmap /= (heatmap - heatmap.min())
color = colormap(heatmap)
if plot_type == "scatter":
ipv.quickscatter(x,
y,
z,
color=color[..., :3],
marker=marker,
size=size) #, size=10)
if plot_type == "surface":
ipv.plot_surface(x, y, z, color=color)
#ipv.plot_surface(data[0], data[1], data[2], color="orange")
#ipv.plot_wireframe(data[0], data[1], data[2], color="red")
ipv.pylab.xlim(self.xlim[0], self.xlim[1])
ipv.pylab.ylim(self.ylim[0], self.ylim[1])
if self.zlim != [None, None]:
ipv.pylab.zlim(self.zlim[0], self.zlim[1])
else:
ipv.pylab.zlim(self.ylim[0], self.ylim[1])
#ipv.style.use(['seaborn-darkgrid', {'axes.x.color':'orange'}])
ipv.pylab.view(azimuth=-45, elevation=25, distance=5)
ipv.pylab.xlabel("%s" % self.axis_labels[0])
ipv.pylab.ylabel("%s" % self.axis_labels[1])
ipv.pylab.zlabel("%s" % self.axis_labels[2])
if inline:
ipv.show()
else:
ipv.pylab.save("%s.html" % outPath,
title=r"%s" % (latex),
offline=None,
template_options=(('extra_script_head', ''),
('body_pre', ''), ('body_post',
'')))
run_cmd('open %s.html' % (str(outPath)))
def test_quick():
x, y, z = ipyvolume.examples.xyz()
p3.volshow(x*y*z)
ipyvolume.quickvolshow(x*y*z, lighting=True)
ipyvolume.quickvolshow(x*y*z, lighting=True, level=1, opacity=1, level_width=1)
x, y, z, u, v, w = np.random.random((6, 100))
ipyvolume.quickscatter(x, y, z)
ipyvolume.quickquiver(x, y, z, u, v, w)
def _get_ipyvolume_app():
# pylint: disable=invalid-name, no-member
x, y, z = np.random.random((3, 1000))
ipv.quickscatter(x, y, z, size=1, marker="sphere")
ipv_plot = ipv.current.figure
ipv_plot.width = 600
ipv_plot.height = 600
def randomize(_):
x, y, z = np.random.random((3, 1000))
scatter = ipv_plot.scatters[0]
with ipv_plot.hold_sync():
scatter.x = x
scatter.y = y
scatter.z = z
ip_randomize = ipw.Button(description="Randomize")
ip_randomize.on_click(randomize)
return ipw.VBox([ip_randomize, ipv_plot])
def render(self, resp: requests.models.Response) -> Widget:
"Return an ipyvolume widget with the data object rendered on it."
f = io.BytesIO(resp.content)
points = pd.read_csv(f, delim_whitespace=True)
# points = points[:: 300] # FIXME: use down-sampling here
x = points.iloc[:, 0].values
y = points.iloc[:, 1].values
z = points.iloc[:, 2].values
res = ipyvolume.quickscatter(x, y, z, size=1, marker="sphere")
return res
def scatter(coords, color='gray'):
"""
Use jupyter notebook to visualize point clouds.
:param coords: [N, 3] numpy array with x, y, z coordinates
:param color: color of points as string, e.g. 'red', '#ff0000' or 'rgb(1, 0, 0)'
:return:
"""
xs = coords[:, 0].astype(np.float)
ys = coords[:, 1].astype(np.float)
zs = coords[:, 2].astype(np.float)
fig = ipv.quickscatter(xs, ys, zs, size=0.1, marker='sphere', color=color)
ipv.pylab.style.use(axis_off)
return fig
def scatter_with_intensities(coords, intensities):
"""
Use jupyter notebook to visualize point clouds.
:param coords: [N, 3] numpy array with x, y, z coordinates
:param intensities [N] numpy array with corresponding intensities
:return:
"""
xs = coords[:, 0].astype(np.float)
ys = coords[:, 1].astype(np.float)
zs = coords[:, 2].astype(np.float)
cs = list(map(lambda i: (1, 1 - i, 0), intensities))
fig = ipv.quickscatter(xs, ys, zs, size=0.1, marker='sphere', color=cs)
ipv.pylab.style.use(axis_off)
ipv.pylab.view(200, 80)
return fig
def scatter_with_target(coords,
target,
color=(0.5, 0.5, 0.5),
color_label=(1, 0, 0)):
"""
Use jupyter notebook to visualize point clouds with an annotated item.
:param coords: [N, 3] numpy array with x, y, z coordinates
:param target: [N] binary numpy array with 0 for background and 1 for label
:param color: color of points as rgb tuple, e.g. (1, 0, 0) for red
:param color_label: color of labelled points as rgb tuple, e.g. (1, 0, 0) for red
:return:
"""
xs = coords[:, 0].astype(np.float)
ys = coords[:, 1].astype(np.float)
zs = coords[:, 2].astype(np.float)
c = np.full_like(target, color, dtype=(float, 3))
c[np.argwhere(target == 1)] = color_label
fig = ipv.quickscatter(xs, ys, zs, size=0.1, marker='sphere', color=c)
ipv.pylab.style.use(axis_off)
ipv.pylab.view(200, 80)
return fig
import ipyvolume as ipv
import ipywidgets as ipw
import numpy as np
from ipywidgets_bokeh import IPyWidget
from bokeh.layouts import column, row
from bokeh.models import Slider
from bokeh.plotting import curdoc
x, y, z = np.random.random((3, 1000))
ipv.quickscatter(x, y, z, size=1, marker="sphere")
plot = ipv.current.figure
x_slider = Slider(start=0, end=359, value=0, step=1, title="X-axis")
y_slider = Slider(start=0, end=359, value=0, step=1, title="Y-axis")
z_slider = Slider(start=0, end=359, value=0, step=1, title="Z-axis")
def randomize(button):
x, y, z = np.random.random((3, 1000))
scatter = plot.scatters[0]
with plot.hold_sync():
scatter.x = x
scatter.y = y
scatter.z = z
randomize_button = ipw.Button(description="Randomize")
randomize_button.on_click(randomize)
