def visualize_clusters(self, clusters):
"""
Visualization of point cloud data
:param clusters: identified clusters
:return:
"""
trace_1 = go.Scatter3d(x=self.pcd_data.X,
y=self.pcd_data.Y,
z=self.pcd_data.Z,
mode='markers')
# extract points from clusters
cluster_points = pd.DataFrame(columns={"Cluster_id", "X", "Y", "Z"})
for key in clusters:
# Get all the indexes related to the cluster id = key
cluster_point_indexes = clusters[key]
for point_index in cluster_point_indexes:
point = self.get_point(point_index)
cluster_points = cluster_points.append(
{
"Cluster_id": key,
"X": point[0],
"Y": point[1],
"Z": point[2]
},
ignore_index=True)
unique_clusters = []
for cluster_id in cluster_points.Cluster_id.unique():
cluster_traces = [
go.Scatter3d(x=cluster_points[cluster_points["Cluster_id"] ==
cluster_id].X,
y=cluster_points[cluster_points["Cluster_id"] ==
cluster_id].Y,
z=cluster_points[cluster_points["Cluster_id"] ==
cluster_id].Z,
mode='markers')
]
unique_clusters.extend(cluster_traces)
data = [trace_1]
data.extend(unique_clusters)
fig = go.Figure(data)
fig.show()
def test_scatter(X, Y, Z, x_test, y_test, Z_validate, surface_name='MADGE Interpolate Surface', filename='test.html'):
"""
Generates a plot of the interpolated data overlayed with the general data
:param X:
:return: None
"""
trace_surface = go.Surface(x=X, y=Y, z=Z, name=surface_name)
test_scatter = go.Scatter3d(x=x_test, y=y_test, z=Z_validate, mode='markers',
name='Interpolated Values')
data = [trace_surface, test_scatter]
fig = go.Figure(data=data)
py.offline.plot(fig, filename=filename)
def pcshow(xs, ys, zs):
data = [go.Scatter3d(x=xs, y=ys, z=zs, mode='markers')]
fig = visualize_rotate(data)
fig.update_traces(marker=dict(size=2,
line=dict(width=2, color='DarkSlateGrey')),
selector=dict(mode='markers'))
fig.show()
# 文件导出需要用到plotly-orca
# 安装方法: conda install -c plotly plotly-orca
if not os.path.exists("images"):
os.mkdir("images")
fig.write_image("images/fig1.svg")
def add_t(x, y, z, fig, size=.1, color='white'):
fig.add_trace(
go.Scatter3d(
x=x,
y=y,
z=z,
opacity=1,
marker=dict(
size=size,
color=color,
),
))
def _add_camera_trace(
fig: go.Figure,
cameras: CamerasBase,
trace_name: str,
subplot_idx: int,
ncols: int,
camera_scale: float,
): # pragma: no cover
"""
Adds a trace rendering a Cameras object to the passed in figure, with
a given name and in a specific subplot.
Args:
fig: plotly figure to add the trace within.
cameras: the Cameras object to render. It can be batched.
trace_name: name to label the trace with.
subplot_idx: identifies the subplot, with 0 being the top left.
ncols: the number of subplots per row.
camera_scale: the size of the wireframe used to render the Cameras object.
"""
cam_wires = get_camera_wireframe(camera_scale).to(cameras.device)
cam_trans = cameras.get_world_to_view_transform().inverse()
cam_wires_trans = cam_trans.transform_points(cam_wires).detach().cpu()
# if batch size is 1, unsqueeze to add dimension
if len(cam_wires_trans.shape) < 3:
cam_wires_trans = cam_wires_trans.unsqueeze(0)
nan_tensor = torch.Tensor([[float("NaN")] * 3])
all_cam_wires = cam_wires_trans[0]
for wire in cam_wires_trans[1:]:
# We combine camera points into a single tensor to plot them in a
# single trace. The NaNs are inserted between sets of camera
# points so that the lines drawn by Plotly are not drawn between
# points that belong to different cameras.
all_cam_wires = torch.cat((all_cam_wires, nan_tensor, wire))
x, y, z = all_cam_wires.detach().cpu().numpy().T.astype(float)
row, col = subplot_idx // ncols + 1, subplot_idx % ncols + 1
fig.add_trace(
go.Scatter3d(x=x, y=y, z=z, marker={"size": 1}, name=trace_name),
row=row,
col=col,
)
# Access the current subplot's scene configuration
plot_scene = "scene" + str(subplot_idx + 1)
current_layout = fig["layout"][plot_scene]
# flatten for bounds calculations
flattened_wires = cam_wires_trans.flatten(0, 1)
verts_center = flattened_wires.mean(0)
max_expand = (flattened_wires.max(0)[0] - flattened_wires.min(0)[0]).max()
_update_axes_bounds(verts_center, max_expand, current_layout)
def add_line(measurement, p1, p2, line_width, line_color, figure):
midpoint = (p1 + p2) / 2
line = go.Scatter3d(x=[p1[0], p2[0]],
y=[p1[1], p2[1]],
z=[p1[2], p2[2]],
mode="lines",
line={
"width": line_width,
"color": line_color,
"dash": "dot"
})
label = go.Scatter3d(x=[midpoint[0]],
y=[midpoint[1]],
z=[midpoint[2]],
mode="text",
text=measurement,
textposition="middle center")
figure.add_trace(line)
figure.add_trace(label)
def plot_d(dataset, name, htext):
x = [x[0] for x in dataset]
y = [x[1] for x in dataset]
z = [x[2] for x in dataset]
fig.add_trace(go.Scatter3d(
x=x, y=y, z=z,
hovertext=htext, name=name, mode='markers', # hoverinfo='text',
marker=dict(size=8, color=rrgb(), opacity=0.9,
line=dict(width=1, color='black'))
))
return
def production_envelope_3d(self,
dataframe,
grid=None,
width=None,
height=None,
title=None,
points=None,
points_colors=None,
palette=None,
x_axis_label=None,
y_axis_label=None,
z_axis_label=None):
variables = dataframe["strain"].unique()
palette = self.get_option('palette') if palette is None else palette
width = self.get_option('width') if width is None else width
width, height = self.golden_ratio(width, height)
data = []
palette = self._palette(palette, len(variables))
for variable, color in zip_repeat(variables, palette):
_dataframe = dataframe[dataframe["strain"] == variable]
surface = self._make_production_envelope_3d(_dataframe,
variable,
color=color)
data.append(surface)
if points is not None:
x, y, z = zip(*points)
scatter = go.Scatter3d(
x=x,
y=y,
z=z,
mode="markers",
name="Data Points",
marker=dict(
color="green" if points_colors is None else points_colors))
data.append(scatter)
layout = go.Layout(title=title,
scene=go.Scene(xaxis=dict(title=x_axis_label),
yaxis=dict(title=y_axis_label),
zaxis=dict(title=z_axis_label)),
width=width,
height=height)
if grid is not None:
plot = self.Figure(data=data, layout=layout)
grid.append(plot)
return grid
else:
plot = go.Figure(data=data, layout=layout)
return plot
def line_3d_tests(self, env, result):
"""
Example from https://plotly.com/python/3d-line-plots/
"""
rs = np.random.RandomState()
rs.seed(0)
def brownian_motion(T=1, N=100, mu=0.1, sigma=0.01, S0=20):
dt = float(T) / N
t = np.linspace(0, T, N)
W = rs.standard_normal(size=N)
W = np.cumsum(W) * np.sqrt(dt) # standard brownian motion
X = (mu - 0.5 * sigma**2) * t + sigma * W
S = S0 * np.exp(X) # geometric brownian motion
return S
dates = pd.date_range("2012-01-01", "2013-02-22")
T = (dates.max() - dates.min()).days / 365
N = dates.size
start_price = 100
y = brownian_motion(T, N, sigma=0.1, S0=start_price)
z = brownian_motion(T, N, sigma=0.1, S0=start_price)
fig = go.Figure(data=go.Scatter3d(
x=dates,
y=y,
z=z,
marker=dict(
size=4,
color=z,
colorscale="Viridis",
),
line=dict(color="darkblue", width=2),
))
fig.update_layout(
width=800,
height=700,
autosize=False,
scene=dict(
camera=dict(
up=dict(x=0, y=0, z=1),
eye=dict(
x=0,
y=1.0707,
z=1,
),
),
aspectratio=dict(x=1, y=1, z=0.7),
aspectmode="manual",
),
)
result.plotly(fig, description="Brownian Motion")
def coordinate(go):
traces = {}
traces["coordinate_x"] = go.Scatter3d(x=[0, 1],
y=[0, 0],
z=[0, 0],
showlegend=False,
mode='lines',
line=dict(color="green", width=5))
traces["coordinate_y"] = go.Scatter3d(x=[0, 0],
y=[0, 1],
z=[0, 0],
showlegend=False,
mode='lines',
line=dict(color="red", width=5))
traces["coordinate_z"] = go.Scatter3d(x=[0, 0],
y=[0, 0],
z=[0, 1],
showlegend=False,
mode='lines',
line=dict(color="blue", width=5))
return traces
def _draw_point(self, _name, _point, _size, _color):
self.fig.add_trace(
go.Scatter3d(name=_name,
x=[_point.x],
y=[_point.y],
z=[_point.z],
mode='markers',
marker={
'size': _size,
'color': _color,
'opacity': 1.0
}))
def generateCombinePlot3(fileAbsOrig, fileAbsNew):
current = Path.cwd()
fileOrig = current.joinpath(fileAbsOrig)
fileNew = current.joinpath(fileAbsNew)
xValOrig = list()
yValOrig = list()
zValOrig = list()
xValNew = list()
yValNew = list()
zValNew = list()
with open(fileOrig, 'r') as f:
lines = f.readlines()
for line in lines:
line = line.rstrip()
splits = line.split(',')
if len(splits) == 3:
xValOrig.append(float(splits[0]))
yValOrig.append(float(splits[1]))
zValOrig.append(float(splits[2]))
with open(fileNew, 'r') as f:
lines = f.readlines()
for line in lines:
line = line.rstrip()
splits = line.split(',')
if len(splits) == 3:
xValNew.append(float(splits[0]))
yValNew.append(float(splits[1]))
zValNew.append(float(splits[2]))
fig = go.Figure()
fig.add_trace(
go.Scatter3d(x=xValOrig, y=yValOrig, z=zValOrig, mode='markers'))
fig.add_trace(go.Scatter3d(x=xValNew, y=yValNew, z=zValNew,
mode='markers'))
fig.show()
fig.write_image(str(Path.joinpath(fileNew.parent, fileNew.stem + ".PNG")))
def draw_pos(self):
if self.pos is None:
self.find_pos()
z_zeros = np.zeros(self.pos.shape[0])
fig = go.Figure(data=[
go.Scatter3d(x=self.pos[:, 0],
y=self.pos[:, 1],
z=self.pos[:, 2],
opacity=0.5,
name='Gesture'),
go.Scatter3d(x=self.pos_proj_3d[:, 0],
y=self.pos_proj_3d[:, 1],
z=self.pos_proj_3d[:, 2],
opacity=0.5,
name='Gesture proj 3d'),
go.Scatter3d(x=self.pos_proj_2d[:, 0],
y=self.pos_proj_2d[:, 1],
z=z_zeros,
opacity=0.5,
name='Gesture proj 2d')
])
size_of_cube = 0.5
fig.update_layout(scene=dict(
xaxis=dict(
nticks=4,
range=[-size_of_cube, size_of_cube],
),
yaxis=dict(
nticks=4,
range=[-size_of_cube, size_of_cube],
),
zaxis=dict(
nticks=4,
range=[-size_of_cube, size_of_cube],
),
),
width=700,
margin=dict(r=20, l=10, b=10, t=10))
fig.show()
def add_line(self,point_start,point_end,row=1,column=1,color='red'):
"""
Definition to add a ray to the figure.
Parameters
----------
point_start : ndarray
Starting point(s).
point_end : ndarray
Ending point(s).
row : int
Row number of the figure.
column : int
Column number of the figure.
color : str
Color of the lune to be drawn.
"""
if np.__name__ == 'cupy':
point_start = np.asnumpy(point_start)
point_end = np.asnumpy(point_end)
if len(point_start.shape) == 1:
point_start = point_start.reshape((1,3))
if len(point_end.shape) == 1:
point_end = point_end.reshape((1,3))
if point_start.shape != point_end.shape:
print('Size mismatch in line plot. Sizes are {} and {}.'.format(point_start.shape,point_end.shape))
sys.exit()
for point_id in range(0,point_start.shape[0]):
points = np.array(
[
point_start[point_id],
point_end[point_id]
]
)
points = points.reshape((2,3))
if np.__name__ == 'cupy':
points = np.asnumpy(points)
self.fig.add_trace(
go.Scatter3d(
x=points[:,0],
y=points[:,1],
z=points[:,2],
mode='lines',
line=dict(
width=self.settings["line width"],
color=color,
),
opacity=self.settings["opacity"]
),
row=row,
col=column
)
def make_3d_time_map(
times, times_tot_mins, sep_array, Ncolors,
title): # plot standard, scatter-plot time map. Nothing is returned
# import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
print("rendering 3D time map ...")
np.save("sep_array", sep_array)
np.save("times_tot_mins", times_tot_mins)
np.save("times", times)
import sys
sys.exit()
df = pandas.DataFrame()
Xs = sep_array[:, 0]
Ys = sep_array[:, 1]
# Zs = times[1:-1]
Zs = times_tot_mins[1:-1]
df["before"] = Xs
df["after"] = Ys
df["time"] = Zs
df["colors"] = times[1:-1]
# df["colors"] = times_tot_mins[1:-1]
print("plotting")
# fig = make_subplots(
# rows=1, cols=2, specs=[[dict(is_3d=False, type="scatter3d"), dict(is_3d=True, type="scatter3d"),],]
# )
fig = make_subplots(rows=1,
cols=2,
specs=[[{
'type': 'surface'
}, {
'type': 'surface'
}]])
scatter = go.Scatter(
x=Xs,
y=Ys,
# colors=Zs,
mode="markers",
)
scatter3d = go.Scatter3d(
x=Xs,
y=Ys,
z=Zs,
mode="markers",
)
fig.add_trace(scatter3d, row=1, col=1)
fig.add_trace(scatter3d, row=1, col=2)
fig.show()
def Compare_Keywords_with_Assocated_Texts(username, prname, ):
import plotly.graph_objects as go
import plotly.express as px
import numpy as np
from .utils import Read_Arg_, import_dataframe, option_finder
import os
_, input_, output_ = Read_Arg_("3DPlot_for_Pairs(Fit_by_Importance)")
min_freq, max_freq = option_finder("3DPlot_for_Pairs(Fit_by_Importance)")["option_1", "option_2"]
if (username is None) or (prname is None):
cooc = import_dataframe(os.path.join(input_))
else:
input_directory = "/".join(username, prname)
cooc = import_dataframe(os.path.join(input_directory, input_))
cooc = cooc[
(cooc["cooccurrence_count"] >= min_freq*cooc["cooccurrence_count"].max())&
(cooc["cooccurrence_count"] <= max_freq*cooc["cooccurrence_count"].max())
]
# x = cooc[["W_1^%","W_2^%"]].max(axis=1)
# y = cooc["W_1^%"]*cooc["W_2^%"]
z = np.log(cooc["Fit(1|2)"])
fig = px.scatter_3d(cooc,
x="W_1^%",
y="W_2^%",
z=z,
text=cooc["pair"],
opacity=.7,
title="fitplot 3D",
height=1000,
width=1000)
xaxis_min = cooc["W_1^%"].min()
xaxis_max = cooc["W_1^%"].max()
yaxis_min = cooc["W_2^%"].min()
yaxis_max = cooc["W_2^%"].max()
dict_z = {"x": [xaxis_min, xaxis_max, xaxis_max, xaxis_max, xaxis_max, xaxis_min, xaxis_min, xaxis_min],
"y": [yaxis_min, yaxis_min, yaxis_min, yaxis_max, yaxis_max, yaxis_max, yaxis_max, yaxis_min],
"z": [0] * 8}
fig.add_trace(go.Scatter3d(x=dict_z["x"],
y=dict_z["y"],
z=dict_z["z"],
mode="lines",
line=dict(width=10,
color="rgb(000, 000, 000)"),
surfaceaxis=2,
surfacecolor="rgb(204, 204, 153)",
opacity=.5))
fig.write_html(f"{output_}")
fig.show()
def data_plot_clf_3d(h, y_pred_history):
if h.X_raw.shape[1] != 3:
return
y = list(map(lambda x: x[0], h.y))
x_min, x_max = h.X_raw[:, 1].min() - 1, h.X_raw[:, 1].max() + 1
y_min, y_max = h.X_raw[:, 2].min() - 1, h.X_raw[:, 2].max() + 1
xx, yy = np.meshgrid(np.linspace(x_min, x_max, 201),
np.linspace(y_min, y_max, 201))
t = np.c_[xx.ravel(), yy.ravel()]
t = Polynomial(t, h.y, degree=h.degree)
Z = h.hypothesis(X=t.X)
Z = Z.reshape(xx.shape)
Z = -np.log((1 / Z) - 1)
del xx, yy, t
gc.collect()
fig = go.Figure()
fig.add_trace(
go.Scatter3d(
x=h.X_raw[:, 1],
y=h.X_raw[:, 2],
z=y,
mode='markers',
marker=dict(
size=3,
color=y, # set color to an array/list of desired values
colorscale=[[0, "rgb(166,206,227)"], [0.25, "rgb(31,120,180)"],
[0.45,
"rgb(178,223,138)"], [0.65, "rgb(51,160,44)"],
[0.85, "rgb(251,154,153)"],
[1, "rgb(227,26,28)"]], # choose a colorscale
opacity=0.8)))
fig.add_trace(
go.Surface(
z=Z,
x=np.linspace(x_min, x_max, 201),
y=np.linspace(y_min, y_max, 201),
showscale=False,
))
fig.update_layout(title='Data scatter plot and Decision boundary',
autosize=False,
width=900,
height=600)
st.plotly_chart(fig)
def cost_function_plot_3d(h, properties, weights_history, loss_history):
if h.weight.shape[0] != 2 and len(weights_history) > 2:
return
y = list(map(lambda x: x[0], h.y))
theta = [np.array([i[0][0], i[1][0]]) for i in weights_history]
theta0 = np.array([i[0][0] for i in weights_history])
theta1 = np.array([i[1][0] for i in weights_history])
thetha0_min = np.min(theta0) - 3 * np.std(theta0)
thetha0_max = np.max(theta0) + 3 * np.std(theta0)
thetha1_min = np.min(theta1) - 3 * np.std(theta1)
thetha1_max = np.max(theta1) + 3 * np.std(theta1)
theta0_grid = np.linspace(thetha0_min, thetha0_max, 101)
theta1_grid = np.linspace(thetha1_min, thetha1_max, 101)
J_grid = compute_j_grid(h,
theta0_grid,
theta1_grid,
properties.cost_function,
C=properties.reg_coef,
regularization=properties.regularization)
X, Y = np.meshgrid(theta0_grid, theta1_grid)
fig = go.Figure()
fig.add_trace(go.Surface(x=X, y=Y, z=J_grid, showscale=False))
fig.add_trace(
go.Scatter3d(
x=theta0[1:],
y=theta1[1:],
z=loss_history[1:],
mode='lines+markers',
marker=dict(
size=3,
color='red',
#color=y, # set color to an array/list of desired values
# colorscale='greens', # choose a colorscale
opacity=0.8)))
fig.update_layout(title='Cost function surface plot',
autosize=False,
width=900,
height=600)
st.plotly_chart(fig)
def drawParticles(self, label, colortype):
print("Drawing particles", label)
if not self.particles or "particles" not in self.data or not label in self.data["particles"]:
return []
df = self.data["particles"][label]
mom = self.momentumVectors(label)
vtx = self.makeVertices(label)
charge = self.data["particles"][label][label+"_charge"]
ids = df[label+"_pdgId"] if label+"_pdgId" in df else np.zeros_like(charge)
if colortype == "PFTruthPartIdx":
ids = df[label+"_PFTruthPartIdx"]
end = self.trajectoryEndPoint(label)
# Should make this array based
ptEtaPhi = self.PtEtaPhiVectors(label)
traces = []
isPU = np.zeros(len(charge), dtype=int)
if label in ["Track", "TrackDisp"] and all([x in self.data["particles"] for x in ["TrackMatch", "TrackingPart"]]):
tpdf = self.data["particles"]["TrackingPart"]
tpisPU = (tpdf["TrackingPart_eventId"] != 0) | (tpdf["TrackingPart_bunchCrossing"] != 0)
tpid = tpdf["TrackingPart_pdgId"]
bestmatch = np.cumsum(df["Track_TrackingPartNumMatch"])-df["Track_TrackingPartNumMatch"][0]
matchdf = self.data["particles"]["TrackMatch"]
if "Track_TrackingPart_MatchIdx" in matchdf:
tpidx = np.where(bestmatch >= 0, matchdf["Track_TrackingPart_MatchIdx"][bestmatch], -1)
ids = np.where(tpidx >= 0, tpid[tpidx], -1)
isPU = np.where(tpidx >= 0, tpisPU[tpidx], -1)
elif label == "CaloPart":
isPU = (df["CaloPart_eventId"] != 0) | (df["CaloPart_bunchCrossing"] != 0)
for i, (v,m,e,q) in enumerate(zip(vtx, mom, end, charge)):
pt,eta,phi = ptEtaPhi[i]
if pt < self.trackPtCut:
continue
pid = ids[i]
points = self.trajectory(v, m, e, q)
#lightenPileup = self.removePU and isPU[i]
#color = self.mapColor(i if colortype == "Index" else pid, 0.25 if lightenPileup else 1.0)
color = self.mapColor(i if colortype == "Index" else pid)
if self.removePU and isPU[i]:
color = self.mapColor(-2, 1.)
idtext = 'pdgId' if colortype != 'PFTruthPartIdx' else "PFTruthPartIdx"
text = [f'{idtext}: {pid}<br>isPU: {isPU[i]}<br>p<sub>T</sub>, η, phi: ({pt:0.2f} GeV, {eta:0.2f}, {phi:0.2f})'
for p in points]
traces.append(go.Scatter3d(x=points[:,2], y=points[:,0], z=points[:,1],
mode='lines', name=f"{label}Idx{i} (pdgId={pid})",
hovertemplate="x: %{y}<br>y: %{z}<br>z: %{x}<br>%{text}<br>",
text=text,
line=dict(color=color)
)
)
return traces
def plotPointCloud(object, model=None):
"""
Retrieve 4 random point clouds and plot interactive graphs
"""
# Layout for plot
fig = make_subplots(rows=2,
cols=2,
vertical_spacing=0.05,
horizontal_spacing=0.05,
specs=[[{
'type': 'scatter3d'
}, {
'type': 'scatter3d'
}], [{
'type': 'scatter3d'
}, {
'type': 'scatter3d'
}]])
objlst = Data(object)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
for i in range(4):
point_cloud = objlst[np.random.randint(len(objlst))]
if model is not None and 'Autoencoder' in model.name:
point_cloud = model(point_cloud[None, :].to(device))
point_cloud = torch.squeeze(point_cloud, 0)
if model is not None and 'Generator' in model.name:
noise = noiseFunc(0, 0.2, 1, device)
point_cloud = model(noise)
point_cloud = torch.squeeze(point_cloud, 0)
np_point_cloud = point_cloud.detach().cpu().numpy()
fig.add_trace(go.Scatter3d(x=np_point_cloud[:, 0],
y=np_point_cloud[:, 1],
z=np_point_cloud[:, 2],
mode='markers',
marker=dict(size=1,
color=np_point_cloud[:, 2],
colorscale='Viridis',
opacity=0.8)),
row=i // 2 + 1,
col=i % 2 + 1)
fig.update_layout(showlegend=False, height=800, width=1500)
fig.show()
def scatter_plot_3d(input_data,
hover_info=[],
marker_size=6,
title="",
plot_save_path=""):
"""Generate and open an interactive 3D scatter plot as html.
Args:
input_data (dict): Dictionary of (user: feature_vector).
hover_info (list): List of labels/info to display for each
user in input_data in 3D scatter plot.
May be Hastags or Mentions.
marker_size (int, optional): Point size in plot. Defaults to 6.
title (str, optional): Title of the plot. Defaults to "".
plot_save_path (str, optional): Save path for plot html file. Defaults to "".
TODO:
Add marker labels.
"""
# Get data to plot from input_data dict
feature_vectors, labels = zip(*list(input_data.values()))
x, y, z = zip(*[tuple(x) for x in feature_vectors])
trace = [
go.Scatter3d(x=x,
y=y,
z=z,
mode='markers',
hovertemplate='%{text}',
text=[" ".join(x for x in item) for item in hover_info],
marker=dict(size=marker_size,
color=labels,
colorscale='Viridis',
opacity=0.8))
]
fig = go.Figure(data=trace)
title += INFO.LOAD_PARAMS_USED + INFO.DIM_RED_USED + INFO.CLUSTERING_USED
fig.update_layout(title=title,
xaxis_title="",
yaxis_title="",
legend_title="")
if plot_save_path:
extra_info = INFO.LOAD_PARAMS_USED + INFO.DIM_RED_USED + INFO.CLUSTERING_USED
plot_save_path = unique_filename(plot_save_path, extra_info)
fig.write_html(plot_save_path)
fig.show()
return
def drawCSCME1(self):
x, y, z = boundary_circle(275, 580)
return [
go.Scatter3d(x=z,
y=x,
z=y,
mode='lines',
surfaceaxis=0,
line=dict(color='#f5ebd7'),
opacity=0.25,
hoverinfo='skip',
name='CSC ME1/1'),
go.Scatter3d(x=-1 * z,
y=x,
z=y,
mode='lines',
surfaceaxis=0,
line=dict(color='#f5ebd7'),
opacity=0.25,
hoverinfo='skip',
name='CSC ME-1/1'),
]
def manoeuvretraces(seq):
traces = []
for name, manoeuvre in seq.split_manoeuvres().items():
traces.append(
go.Scatter3d(x=manoeuvre.x,
y=manoeuvre.y,
z=manoeuvre.z,
mode='lines',
text=manoeuvre.element,
hoverinfo="text",
name=name))
return traces
def plot(pc):
'''
plots the Nx6 point cloud pc in 3D
assumes (1,0,0), (0,1,0), (0,0,-1) as basis
'''
fig = go.Figure(data=[
go.Scatter3d(x=pc[:, 0],
y=pc[:, 1],
z=-pc[:, 2],
mode='markers',
marker=dict(size=2, color=pc[:, 3:], opacity=1))
])
fig.show()
def create_ellipse(a, b, R, x_c=0, y_c=0, z_c=0, N=100):
y_c -= a - b
t = np.linspace(0, 2 * pi, N)
xs = a * cos(t) + x_c
ys = b * sin(t) + y_c
zs = np.zeros(N) + z_c
# coordinate of the ellipse points with respect to the system of axes [1, 0], [0,1] with origin (0,0)
xp, yp, zp = np.dot(R, [xs, ys, zs])
x = [x for x in np.array(xp).flat]
y = [y for y in np.array(yp).flat]
z = [z for z in np.array(zp).flat]
trace = go.Scatter3d(x=x, y=y, z=z, mode='lines')
return trace
def elementtraces(seq):
traces = []
for name, element in seq.split_elements().items():
traces.append(
go.Scatter3d(x=element.x,
y=element.y,
z=element.z,
mode='lines',
text=element.manoeuvre,
hoverinfo="text",
name=name))
return traces
def graph_reflections(v_set):
vx = np.array([])
vy = np.array([])
vz = np.array([])
for v in v_set:
vx = np.append(vx, v[0])
vy = np.append(vy, v[1])
vz = np.append(vz, v[2])
fig = go.Figure(data=[go.Scatter3d(x=vx, y=vy, z=vz, mode='markers')])
fig.write_html('first_figure.html', auto_open=True)
def make_scatter(cls,
points: Union[np.ndarray, Sequence[Vec3f]],
offset: Optional[Vec3f] = None,
**kwargs):
kwargs.setdefault("marker", {})
kwargs["marker"].setdefault("size", 0.1)
kwargs.setdefault("mode", "markers")
pts = np.asarray(points)
if offset is not None:
pts += offset
x, y, z = pts.T
return go.Scatter3d(x=x, y=y, z=z, **kwargs)
def plot_data(coord, label):
subfig = go.Scatter3d(x=coord[:, 0],
y=coord[:, 1],
z=coord[:, 2],
mode='markers',
marker=dict(size=2,
color=label,
colorscale='Viridis',
opacity=0.8))
fig = make_subplots(rows=1, cols=1, specs=[[{'type': 'surface'}]])
fig.add_trace(subfig)
fig.update_layout(scene_aspectmode='data', )
fig.show()
def plotly_block_edges(origin, sizes):
x = origin[0] + np.array([0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0
]) * sizes[0]
y = origin[1] + np.array([0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1
]) * sizes[1]
z = origin[2] + np.array([0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0
]) * sizes[2]
return go.Scatter3d(x=x,
y=z,
z=y,
mode="lines",
line=dict(width=1.0, color="black"),
showlegend=False)