def plot(self):
"""
Plots the vp and vs model as html files. Looks nice, but is pretty
costly, so expect some waiting time for bigger models.
Note that vp and vs are flattened values.
Returns
-------
figvp : plotly.graphs_objs._figure.Figure
Figure containg vp model.
figvs : plotly.graphs_objs._figure.Figure
Figure containing vs model.
"""
x, y, z = np.meshgrid(self.lat, self.lon, self.z)
figvp = go.Figure(data=go.Volume(
x=x.flatten(),
y=y.flatten(),
z=z.flatten(),
value=self.vpf.flatten(),
isomin=0,
isomax=10,
opacity=0.3, # needs to be small to see through all surfaces
surface_count=21,
# surface_count
# needs to be a large number for good volume rendering
))
figvs = go.Figure(data=go.Volume(
x=x.flatten(),
y=y.flatten(),
z=z.flatten(),
value=self.vsf.flatten(),
isomin=0,
isomax=10,
opacity=0.3, # needs to be small to see through all surfaces
surface_count=21,
# needs to be a large number for good volume rendering
))
# plot(figvp)
# plot(figvs)
return figvp, figvs
def update_plot(data):
res = cp.parse_dict(data)
_ = [item.to("ppm", "nmr_frequency_ratio") for item in res.x]
x, y, z = [item.coordinates.value for item in res.x]
x_, y_, z_ = np.meshgrid(x, y, z, indexing="ij")
trace = go.Volume(
x=x_.ravel(),
y=y_.ravel(),
z=z_.ravel(),
value=res.y[0].components[0].T.ravel(),
isomin=0.05,
isomax=0.95,
opacity=0.1, # needs to be small to see through all surfaces
surface_count=25, # needs to be a large number for good volume rendering
colorscale="RdBu",
)
return {"data": [trace]}
def plot_volume(array: np.ndarray):
_, x, y, z = array.shape
X, Y, Z = np.mgrid[:x, :y, :z]
fig = go.Figure(data=go.Volume(x=X.flatten(),
y=Y.flatten(),
z=Z.flatten(),
value=array.flatten(),
isomin=0,
isomax=1,
opacity=0.5,
opacityscale="max",
surface_count=5))
f = StringIO()
fig.write_html(f, full_html=False)
f.seek(0)
return f.read()
def _draw_slice(figure, axis, volume, opacity=0.3, step=None, n_steps=0):
if step is None:
height = volume.shape[axis] // 2
visible = True
else:
height = (volume.shape[axis] * step) // n_steps
visible = False
v_min = volume.min()
sf = volume.max() - v_min
if axis == Axes.X:
X, Y, Z = np.mgrid[height:height + 1,
:volume.shape[1], :volume.shape[2]]
values = volume[height, :, :].flatten()
elif axis == Axes.Y:
X, Y, Z = np.mgrid[:volume.shape[0],
height:height + 1, :volume.shape[2]]
values = volume[:, height, :].flatten()
elif axis == Axes.Z:
X, Y, Z = np.mgrid[:volume.shape[0],
:volume.shape[1], height:height + 1]
values = volume[:, :, height].flatten()
values = 1 - (values - v_min) / sf
figure.add_trace(
go.Volume(
x=X.flatten(),
y=Y.flatten(),
z=Z.flatten(),
value=values,
colorscale='greys',
surface_count=1,
showscale=False,
opacity=opacity,
visible=visible,
name=_name_from_enum(axis),
hoverinfo='skip'
)
)
def drawPlotly(self, schematic, title="", ptype="scatter"):
x = []
y = []
z = []
id = []
clrs = []
if type(schematic) is torch.Tensor:
sizes = list(schematic.size())
for i in range(sizes[0]):
for j in range(sizes[1]):
for k in range(sizes[2]):
if schematic[i, j, k] > 0:
x.append(i)
y.append(j)
z.append(k)
id.append(schematic[i, j, k].item())
elif type(schematic) is np.ndarray:
for i in range(schematic.shape[0]):
for j in range(schematic.shape[1]):
for k in range(schematic.shape[2]):
if schematic[i, j, k, 0] > 0:
c = self.bid_to_color.get(
tuple(schematic[i, j, k, :]))
if c:
x.append(i)
y.append(j)
z.append(k)
id.append(i + j + k)
clrs.append(c)
else:
for b in schematic:
if b[1][0] > 0:
c = self.bid_to_color.get(b[1])
if c:
x.append(b[0][0])
y.append(b[0][2])
z.append(b[0][1])
id.append(i + j + k)
clrs.append(c)
# clrs.append(self.bid_to_index[b[1]])
if ptype == "scatter":
X = torch.Tensor([x, y, z]).t()
if len(clrs) == 0:
raise Exception("all 0 input?")
colors = (256 * torch.Tensor(clrs)[:, 0:3]).long().numpy()
w = self.viz.scatter(
X=X,
opts={
"markercolor": colors,
"markersymbol": "square",
"markersize": 15,
"title": title,
"camera": dict(eye=dict(x=2, y=0.1, z=2)),
},
)
# layout = go.Layout(camera =dict(eye=dict(x=2, y=.1, z=2)))
self.viz._send({
"win": w,
"camera": dict(eye=dict(x=2, y=0.1, z=2))
})
return w
else:
maxid = max(clrs)
clr_set = set(clrs)
cmap = [[
c / maxid,
"rgb({},{},{})".format(
self.index_to_color[c][0],
self.index_to_color[c][1],
self.index_to_color[c][0],
),
] for c in clr_set]
trace1 = go.Volume(
x=np.asarray(x).transpose(),
y=np.asarray(y).transpose(),
z=np.asarray(z).transpose(),
value=np.asarray(clrs).transpose(),
isomin=0.1,
isomax=0.8,
colorscale=cmap,
opacity=0.1, # needs to be small to see through all surfaces
surface_count=
21, # needs to be a large number for good volume rendering
)
data = [trace1]
layout = go.Layout(margin=dict(l=0, r=0, b=0, t=0))
fig = go.Figure(data=data, layout=layout)
self.viz.plotlyplot(fig)
return fig
# In[105]:
fig = go.Figure(data=go.Streamtube(x=[0, 0, 0],
y=[0, 1, 2],
z=[0, 0, 0],
u=[0, 0, 0],
v=[1, 1, 1],
w=[0, 0, 0]))
pyo.plot(fig, filename="streamtube.html")
fig.show()
# In[106]:
X, Y, Z = np.mgrid[-8:8:40j, -8:8:40j, -8:8:40j]
values = np.sin(X * Y * Z) / (X * Y * Z)
fig = go.Figure(data=go.Volume(
x=X.flatten(),
y=Y.flatten(),
z=Z.flatten(),
value=values.flatten(),
isomin=0.1,
isomax=0.8,
opacity=0.1, # needs to be small to see through all surfaces
surface_count=17, # needs to be a large number for good volume rendering
))
pyo.plot(fig, filename="volume.html")
fig.show()
# In[ ]:
def __init__(self, layout, input_data, axes, value_name, cb,
show_variances, volume, volume_sampling):
super().__init__(input_data,
axes,
value_name,
cb,
show_variances,
button_options=['X', 'Y', 'Z'],
volume=volume)
self.cube = None
self.volume = volume
# Initialise Figure and VBox objects
self.fig = None
params = {
"values": {
"cbmin": "min",
"cbmax": "max"
},
"variances": None
}
if self.show_variances:
params["variances"] = {"cbmin": "min_var", "cbmax": "max_var"}
# Set colorbar limits once to keep them constant for slicer
# TODO: should there be auto scaling as slider value is changed?
for i, (key, val) in enumerate(sorted(params.items())):
if val is not None:
arr = getattr(self.input_data, key)
if self.cb[val["cbmin"]] is not None:
val["cmin"] = self.cb[val["cbmin"]]
else:
val["cmin"] = np.amin(arr[np.where(np.isfinite(arr))])
if self.cb[val["cbmax"]] is not None:
val["cmax"] = self.cb[val["cbmax"]]
else:
val["cmax"] = np.amax(arr[np.where(np.isfinite(arr))])
colorbars = [{
"x": 1.0,
"title": value_name,
"thicknessmode": 'fraction',
"thickness": 0.02
}]
# Store min/max for each dimension for invisible scatter
self.xminmax = dict()
for key, var in self.slider_x.items():
self.xminmax[key] = [var.values[0], var.values[-1]]
scatter_x, scatter_y, scatter_z = self.get_outline_as_scatter()
# Make a generic volume trace
if self.volume:
vol_trace = go.Volume(x=[0],
y=[0],
z=[0],
value=[0],
opacity=0.1,
surface_count=volume_sampling,
colorscale=self.cb["name"],
showscale=True)
xyz = "xyz"
if self.show_variances:
self.fig = go.FigureWidget(
make_subplots(rows=1,
cols=2,
horizontal_spacing=0.16,
specs=[[{
"type": "scene"
}, {
"type": "scene"
}]]))
colorbars.append({
"x": 1.0,
"title": "Variances",
"thicknessmode": 'fraction',
"thickness": 0.02
})
colorbars[0]["x"] = -0.1
for i, (key, val) in enumerate(sorted(params.items())):
if self.volume:
vol_trace["isomin"] = val["cmin"]
vol_trace["isomax"] = val["cmax"]
vol_trace["meta"] = key
vol_trace["colorbar"] = colorbars[i]
self.fig.add_trace(vol_trace, row=1, col=i + 1)
else:
for j in range(3):
self.fig.add_trace(go.Surface(
cmin=val["cmin"],
cmax=val["cmax"],
showscale=False,
colorscale=self.cb["name"],
colorbar=colorbars[i],
meta=key,
name="slice_{}".format(xyz[j])),
row=1,
col=i + 1)
self.fig.add_trace(go.Scatter3d(
x=scatter_x,
y=scatter_y,
z=scatter_z,
marker=dict(cmin=val["cmin"],
cmax=val["cmax"],
color=np.linspace(val["cmin"], val["cmax"],
8),
colorbar=colorbars[i],
colorscale=self.cb["name"],
showscale=True,
opacity=1.0e-6),
mode="markers",
hoverinfo="none",
meta=key,
name="scatter"),
row=1,
col=i + 1)
self.fig.update_layout(**layout)
else:
if self.volume:
vol_trace["isomin"] = params["values"]["cmin"]
vol_trace["isomax"] = params["values"]["cmax"]
vol_trace["meta"] = "values"
vol_trace["colorbar"] = colorbars[0]
data = [vol_trace]
else:
data = [
go.Surface(cmin=params["values"]["cmin"],
cmax=params["values"]["cmax"],
colorscale=self.cb["name"],
colorbar=colorbars[0],
showscale=False,
meta="values",
name="slice_{}".format(xyz[j]))
for j in range(3)
]
data += [
go.Scatter3d(x=scatter_x,
y=scatter_y,
z=scatter_z,
marker=dict(cmin=params["values"]["cmin"],
cmax=params["values"]["cmax"],
color=np.linspace(
params["values"]["cmin"],
params["values"]["cmax"], 8),
colorbar=colorbars[0],
colorscale=self.cb["name"],
showscale=True,
opacity=1.0e-6),
mode="markers",
hoverinfo="none",
meta="values",
name="scatter")
]
self.fig = go.FigureWidget(data=data, layout=layout)
# Call update_slice once to make the initial image
self.update_axes()
self.vbox = [self.fig] + self.vbox
self.vbox = widgets.VBox(self.vbox)
self.vbox.layout.align_items = 'center'
return
def activate(self):
filename, _ = compat.getsavefilename(parent=self.viewer,
basedir="plot.html")
# when vispy viewer is in "native aspect ratio" mode, scale axes size by data
if self.viewer.state.native_aspect == True:
width = self.viewer.state.x_max - self.viewer.state.x_min
height = self.viewer.state.y_max - self.viewer.state.y_min
depth = self.viewer.state.z_max - self.viewer.state.z_min
# otherwise, set all axes to be equal size
else:
width = 1200 # this 1200 size is arbitrary, could change to any width; just need to scale rest accordingly
height = 1200
depth = 1200
# set the aspect ratio of the axes, the tick label size, the axis label sizes, and the axes limits
layout = go.Layout(
margin=dict(r=50, l=50, b=50, t=50),
width=1200,
scene=dict(
xaxis=dict(title=self.viewer.state.x_att.label,
titlefont=dict(family=DEFAULT_FONT,
size=20,
color='black'),
showticklabels=True,
backgroundcolor='white',
tickfont=dict(family=DEFAULT_FONT,
size=12,
color='black'),
range=[0, self.viewer.state.resolution]),
yaxis=dict(
title=self.viewer.state.y_att.label,
titlefont=dict(family=DEFAULT_FONT, size=20,
color='black'),
range=[0, self.viewer.state.resolution],
showticklabels=True,
backgroundcolor='white',
tickfont=dict(family=DEFAULT_FONT, size=12, color='black'),
),
zaxis=dict(
title=self.viewer.state.z_att.label,
titlefont=dict(family=DEFAULT_FONT, size=20,
color='black'),
range=[0, self.viewer.state.resolution],
showticklabels=True,
backgroundcolor='white',
tickfont=dict(family=DEFAULT_FONT, size=12, color='black'),
),
aspectratio=dict(
x=1 * self.viewer.state.x_stretch,
y=height / width * self.viewer.state.y_stretch,
z=depth / width * self.viewer.state.z_stretch),
aspectmode='manual',
),
)
#set up function that returns values of cube at different pixel positions in the fixed resolution grid
f = lambda x, y, z, datacube: datacube[z, y, x]
bounds = [(self.viewer.state.z_min, self.viewer.state.z_max,
self.viewer.state.resolution),
(self.viewer.state.y_min, self.viewer.state.y_max,
self.viewer.state.resolution),
(self.viewer.state.x_min, self.viewer.state.x_max,
self.viewer.state.resolution)]
#generate array of vertices at fixed resolution
X, Y, Z = np.mgrid[0:self.viewer.state.resolution,
0:self.viewer.state.resolution,
0:self.viewer.state.resolution]
data = []
for layer_state in self.viewer.state.layers:
#check if subset object
if isinstance(layer_state.layer,
glue.core.subset_group.GroupedSubset):
subcube = layer_state.layer.data.compute_fixed_resolution_buffer(
target_data=self.viewer.state.reference_data,
bounds=bounds,
subset_state=layer_state.layer.subset_state)
datacube = layer_state.layer.data.compute_fixed_resolution_buffer(
target_data=self.viewer.state.reference_data,
bounds=bounds,
target_cid=layer_state.attribute)
datacube = subcube * datacube
for i in range(0, len(self.viewer.state.layers)):
if self.viewer.state.layers[
i].layer is layer_state.layer.data:
isomin = self.viewer.state.layers[i].vmin
isomax = self.viewer.state.layers[i].vmax
#otherwise a data object
else:
datacube = layer_state.layer.compute_fixed_resolution_buffer(
target_data=self.viewer.state.reference_data,
bounds=bounds,
target_cid=layer_state.attribute)
isomin = layer_state.vmin
isomax = layer_state.vmax
#fetch values of cube at different coordinate combination
values = f(X.flatten().astype(int),
Y.flatten().astype(int),
Z.flatten().astype(int), datacube.copy())
voltrace = go.Volume(
x=X.flatten().astype(int),
y=Y.flatten().astype(int),
z=Z.flatten().astype(int),
value=values.flatten(),
flatshading=True,
opacity=0.2,
isomin=isomin,
showscale=False,
isomax=isomax,
colorscale=[[0, 'white'], [1., layer_state.color]],
opacityscale='max',
reversescale=False,
surface=dict(show=True, count=25),
spaceframe=dict(show=True), #,
contour=dict(show=False, width=4))
data.append(voltrace)
fig = go.Figure(data=data, layout=layout)
plot(fig, filename=filename, auto_open=False)
def volume(self,
x=None, y=None, z=None, vol=None,
w=700, h=700, res=50, grid=True, pane_fill=None, prune=None,
x_slice=True, x_center=None, x_scale=1, x_lim=None,
y_slice=True, y_center=-3.5, y_scale=1, y_lim=None,
z_slice=True, z_center=None, z_scale=1, z_lim=None,
norm=None, cb_labelpad=10, cb_nticks=10, cb_x=None, cb_y=None,
cb_title=None, cb_length=0.75, cb_outlinecolor=None, cb_outlinewidth=1, cb_tickfontsize=10,
plot_title='Airstream', title_bold=True, title_size=20, title_pad=5, title_y=0.85,
x_label='x', xaxis_bold=False, xaxis_labelpad=5, xlabel_rotation=None, x_tick_number=10,
y_label='y', yaxis_bold=False, yaxis_labelpad=5, ylabel_rotation=None, y_tick_number=10,
z_label='z', zaxis_bold=False, zaxis_labelpad=5, zlabel_rotation=None, z_tick_number=10,
axis_label_size=20, tick_color=None, tick_label_size=12, tick_label_pad=5,
xtick_rotation=None, ytick_rotation=None, ztick_rotation=None,
floating_text=None,
filename=None):
# Colorbar parameters
self.cb_outlinecolor = cb_outlinecolor if cb_outlinecolor is not None else self.color2
self.cb_outlinewidth = cb_outlinewidth
self.cb_length = cb_length
self.cb_title = cb_title
self.cb_x = cb_x
self.cb_y = cb_y
self.cb_xpad = cb_labelpad
self.cb_nticks = cb_nticks
self.cb_tickfontsize = cb_tickfontsize
# Mock plot
if isinstance(x, type(None)) and isinstance(y, type(None)) and isinstance(z, type(None)) and isinstance(vol, type(None)):
np.random.seed(0)
def f(x, y, z):
return x+y+z
x, y, z, vol = Data3D().volumetric(f, u0=0, un=1, v0=0, vn=1, w0=0, wn=1, n=100)
# Individual axis setup
axis = dict(showbackground=True,
backgroundcolor=pane_fill,
showgrid=grid,
gridcolor=self.color2,
zerolinecolor=self.color2,
tickfont={'family': self.font,
'size': tick_label_size,
'color': tick_color},
titlefont={'family': self.font,
'size': axis_label_size},
)
# Layout
layout = go.Layout(
width=w,
height=h,
scene=dict(xaxis=dict(axis, range=x_lim, tickangle=xtick_rotation, nticks=x_tick_number, title='<b>'+x_label+'</b>' if xaxis_bold is True else x_label),
yaxis=dict(axis, range=y_lim, tickangle=ytick_rotation, nticks=y_tick_number, title='<b>'+y_label+'</b>' if yaxis_bold is True else y_label),
zaxis=dict(axis, range=z_lim, tickangle=ztick_rotation, nticks=z_tick_number, title='<b>'+z_label+'</b>' if zaxis_bold is True else z_label),
aspectratio=dict(x=x_scale, y=y_scale, z=z_scale)
),
template=self.template
)
# Plot
fig = go.Figure(data=go.Volume(x=x, y=y, z=z,
value=vol,
isomin=0.2,
isomax=0.7,
opacity=0.1,
surface_count=25,
colorbar=dict(len=self.cb_length,
x=self.cb_x,
xpad=self.cb_xpad,
y=self.cb_y,
outlinecolor=self.cb_outlinecolor,
outlinewidth=self.cb_outlinewidth,
tick0=0,
dtick=0.5,
ticklen=5,
tickwidth=1,
tickfont=dict(size=self.cb_tickfontsize,
color=self.color,
family=self.font),
title=self.cb_title)
),
layout=layout)
# Makeup
fig.update_layout(
title={'text': '<b>'+plot_title+'<b>' if title_bold is True else plot_title,
'x': 0.5,
'y': title_y,
'xanchor': 'center',
'yanchor': 'top'},
font=dict(
family=self.font,
size=title_size,
color=self.color,
)
)
fig.show()
if not isinstance(filename, type(None)):
self.save(fig=fig, filename=filename)
