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A volume plot with go.Volume shows several partially transparent isosurfaces for volume rendering. The API of go.Volume is close to the one of go.Isosurface. However, whereas isosurface plots show all surfaces with the same opacity, tweaking the opacityscale parameter of go.Volume results in a depth effect and better volume rendering.
In the three examples below, note that the default colormap is different whether isomin and isomax have the same sign or not.
importplotly.graph_objectsasgoimportnumpyasnpX, 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 surfacessurface_count=17, # needs to be a large number for good volume rendering )) fig.show()importplotly.graph_objectsasgoimportnumpyasnpX, Y, Z=np.mgrid[-1:1:30j, -1:1:30j, -1:1:30j] values=np.sin(np.pi*X) *np.cos(np.pi*Z) *np.sin(np.pi*Y) 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 surfacessurface_count=21, # needs to be a large number for good volume rendering )) fig.show()importnumpyasnpimportplotly.graph_objectsasgo# Generate nicely looking random 3D-fieldnp.random.seed(0) l=30X, Y, Z=np.mgrid[:l, :l, :l] vol=np.zeros((l, l, l)) pts= (l*np.random.rand(3, 15)).astype(int) vol[tuple(indicesforindicesinpts)] =1fromscipyimportndimagevol=ndimage.gaussian_filter(vol, 4) vol/=vol.max() fig=go.Figure(data=go.Volume( x=X.flatten(), y=Y.flatten(), z=Z.flatten(), value=vol.flatten(), isomin=0.2, isomax=0.7, opacity=0.1, surface_count=25, )) fig.update_layout(scene_xaxis_showticklabels=False, scene_yaxis_showticklabels=False, scene_zaxis_showticklabels=False) fig.show()In order to see through the volume, the different isosurfaces need to be partially transparent. This transparency is controlled by a global parameter, opacity, as well as an opacity scale mapping scalar values to opacity levels. The figure below shows that changing the opacity scale changes a lot the visualization, so that opacityscale should be chosen carefully (uniform corresponds to a uniform opacity, min/max maps the minimum/maximum value to a maximal opacity, and extremes maps both the minimum and maximum values to maximal opacity, with a dip in between).
importplotly.graph_objectsasgofromplotly.subplotsimportmake_subplotsfig=make_subplots( rows=2, cols=2, specs=[[{'type': 'volume'}, {'type': 'volume'}], [{'type': 'volume'}, {'type': 'volume'}]]) importnumpyasnpX, Y, Z=np.mgrid[-8:8:30j, -8:8:30j, -8:8:30j] values=np.sin(X*Y*Z) / (X*Y*Z) fig.add_trace(go.Volume( opacityscale="uniform", ), row=1, col=1) fig.add_trace(go.Volume( opacityscale="extremes", ), row=1, col=2) fig.add_trace(go.Volume( opacityscale="min", ), row=2, col=1) fig.add_trace(go.Volume( opacityscale="max", ), row=2, col=2) fig.update_traces(x=X.flatten(), y=Y.flatten(), z=Z.flatten(), value=values.flatten(), isomin=0.15, isomax=0.9, opacity=0.1, surface_count=15) fig.show()It is also possible to define a custom opacity scale, mapping scalar values to relative opacity values (between 0 and 1, the maximum opacity is given by the opacity keyword). This is useful to make a range of values completely transparent, as in the example below between -0.2 and 0.2.
importplotly.graph_objectsasgoimportnumpyasnpX, Y, Z=np.mgrid[-1:1:30j, -1:1:30j, -1:1:30j] values=np.sin(np.pi*X) *np.cos(np.pi*Z) *np.sin(np.pi*Y) fig=go.Figure(data=go.Volume( x=X.flatten(), y=Y.flatten(), z=Z.flatten(), value=values.flatten(), isomin=-0.5, isomax=0.5, opacity=0.1, # max opacityopacityscale=[[-0.5, 1], [-0.2, 0], [0.2, 0], [0.5, 1]], surface_count=21, colorscale='RdBu' )) fig.show()For a clearer visualization of internal surfaces, it is possible to remove the caps (color-coded surfaces on the sides of the visualization domain). Caps are visible by default. Compare below with and without caps.
importnumpyasnpimportplotly.graph_objectsasgoX, Y, Z=np.mgrid[:1:20j, :1:20j, :1:20j] vol= (X-1)**2+ (Y-1)**2+Z**2fig=go.Figure(data=go.Volume( x=X.flatten(), y=Y.flatten(), z=Z.flatten(), value=vol.flatten(), isomin=0.2, isomax=0.7, opacity=0.2, surface_count=21, caps=dict(x_show=True, y_show=True, z_show=True, x_fill=1), # with caps (default mode) )) # Change camera view for a better view of the sides, XZ plane# (see https://plotly.com/python/v3/3d-camera-controls/)fig.update_layout(scene_camera=dict( up=dict(x=0, y=0, z=1), center=dict(x=0, y=0, z=0), eye=dict(x=0.1, y=2.5, z=0.1) )) fig.show()importnumpyasnpimportplotly.graph_objectsasgoX, Y, Z=np.mgrid[:1:20j, :1:20j, :1:20j] vol= (X-1)**2+ (Y-1)**2+Z**2fig=go.Figure(data=go.Volume( x=X.flatten(), y=Y.flatten(), z=Z.flatten(), value=vol.flatten(), isomin=0.2, isomax=0.7, opacity=0.2, surface_count=21, caps=dict(x_show=False, y_show=False, z_show=False), # no caps )) fig.update_layout(scene_camera=dict( up=dict(x=0, y=0, z=1), center=dict(x=0, y=0, z=0), eye=dict(x=0.1, y=2.5, z=0.1) )) fig.show()Slices through the volume can be added to the volume plot. In this example the isosurfaces are only partially filled so that the slice is more visible, and the caps were removed for the same purpose.
importnumpyasnpimportplotly.graph_objectsasgoX, Y, Z=np.mgrid[:1:20j, :1:20j, :1:20j] vol= (X-1)**2+ (Y-1)**2+Z**2fig=go.Figure(data=go.Volume( x=X.flatten(), y=Y.flatten(), z=Z.flatten(), value=vol.flatten(), isomin=0.2, isomax=0.7, opacity=0.2, surface_count=21, slices_z=dict(show=True, locations=[0.4]), surface=dict(fill=0.5, pattern='odd'), caps=dict(x_show=False, y_show=False, z_show=False), # no caps )) fig.show()See https://plotly.com/python/reference/volume/ for more information and chart attribute options!
