""" .. _styling_3d_gallery: Styling in 3D ================= This tutorial shows how to adjust the layout of 3D plots in Plotly. For more advanced usage, see the Plotly documentation: https://plotly.com/python/reference/layout/. """ # %% import oat_python as oat import networkx as nx import numpy as np import plotly import plotly.express as px import plotly.graph_objects as go from plotly.subplots import make_subplots # %% # Setup # --------------------------------------- # # Let's generate a random simplicial complex to work with. # We'll sample random points in a 30-dimensional ambient space, # then construct a Vietoris-Rips complex. # Sample points np.random.seed(0) # Set random seed for reproducibility n_points = 10 ambient_dimension = 30 points_array = np.random.rand(n_points, ambient_dimension) # Generate a Vietoris-Rips complex on these points epsilon = 2.2 dissimilarity_matrix = oat.dissimilarity.sparse_matrix_for_points( points_array, max_dissimilarity = 2.2, ) vietoris_rips_complex = oat.core.vietoris_rips.VietorisRipsComplex( dissimilarity_matrix, ) # List the simplices in the complex simplices = vietoris_rips_complex.simplices_for_dimensions([0,1,2]) simplices[30:35] # %% # Generate 3D coordinates for the vertices of the complex, based on its combinatorial structure points = oat.plot.vertex_embedding_for_simplices( simplices.simplex, dimension = 3, iterations = 1000, seed = 11, method = "spring", ) # %% # Generate an initial plot, using :func:`oat_python.plot.fig_3d_for_simplices` fig = oat.plot.fig_3d_for_simplices( simplices = simplices.simplex, points = points, ) fig # %% # Canvas size # --------------------------------------- # %% # Set a fixed width and height (in pixels) fig.update_layout( width = 300, height = 300, ) # %% # Reset the width to automatic sizing fig.update_layout( width = None, ) # %% # Increase the height fig.update_layout( height = 600, ) # %% # Aspect ratio # --------------------------------------- # # Enforce a 1:1:1 aspect ratio (compare with the plot above): fig.update_layout( scene = dict( aspectratio=go.layout.scene.Aspectratio(x=1, y=1, z=1), ) ) fig # %% # Axis limits # --------------------------------------- # # Set explicit axis limits. fig.update_layout( scene = dict( xaxis = dict(range=[-1, 1],), # x axis limits yaxis = dict(range=[-1.5, 1],), # y axis limits zaxis = dict(range=[-1, 1],), # z axis limits ), ) fig # %% # Grid lines # --------------------------------------- # # Grid lines can be removed with ``oat_python.plot.blank_background``. oat.plot.blank_background(fig) fig # %% # Zoom # --------------------------------------- # # The camera position can be initialized with greater/lesser zoom by adjusting the `eye` parameter. fig.update_layout(scene_camera=dict( eye=dict(x=0.6, y=0.6, z=0.6) )) fig # %% # Background color # --------------------------------------- # # Use a solid white background. oat.plot.set_background_color(fig, 'white') fig # %% # Color # --------------------------------------- # # Colors for vertices, edges, and triangles can be adjusted either by modifying the traces in ``fig.data``, or # by passing keyword arguments to the relevant constructor functions. # The traces for vertices, edges, and triangles are stored in ``fig.data[0]``, ``fig.data[1]``, and ``fig.data[2]``, respectively. # These plots are generated, internally, using the functions # # - :func:`plotly.graph_objects.Scatter3d` (for vertices) # - :func:`oat_python.plot.trace_3d_for_edges` (for edges) # - :func:`oat_python.plot.trace_3d_for_triangles` (for triangles) # # Check out the documentation for these functions to see what parameters can be adjusted. # %% [markdown] # .. _see_also_style_3d_color: # # See also # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # # - Color lists # # - `The list of built-in Plotly color sequences (high contrast) and color scales (smooth gradients) `_. # - `The named CSS colors are listed here `_. # # - The :ref:`triangles_2d_gallery` gallery shows how to color triangles in 2D. # - The :ref:`edges_2d_gallery` and :ref:`edges_3d_gallery` galleries show how to color edges in 2D and 3D. # - The :ref:`triangles_3d_gallery` gallery shows how to color triangles in 3D based on scalar values assigned to vertices or faces. # %% # Choosing good triangle colors # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # %% # You can use any color format that Plotly understands to update colors, # including named colors ("white","red","brown"), hex codes (#RRGGBB), and RGB/RGBA values (rgb(255, 0, 0)). # Change simplex color fig.data[2].update(color="red", opacity=0.7) # triangles fig.data[1].update(line=dict(color="black", width=4)) # edges fig.data[0].update(marker=dict(size=7, color="black")) # vertices # Re-set the background color oat.plot.set_background_color(fig, 'white') fig # %% # Dark colors often fade away against a black background oat.plot.set_background_color(fig, 'black') fig # %% # White edges and vertices contrast better fig.data[0].update(marker=dict(size=7, color="white")) # vertices fig.data[1].update(line=dict(color="white", width=6)) # edges oat.plot.set_background_color(fig, 'black') fig # %% # Shades of grey # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # # %% # Shades of grey can give a clean look # Color all simplices white fig.data[2].update(color="grey", opacity=1.0) # triangles fig.data[1].update(line=dict(color="white", width=8)) # edges fig.data[0].update(marker=dict(size=10, color='white')) # vertices # Re-set the background color oat.plot.set_background_color(fig, 'black') fig # %% # With transparency, the effect is luminous fig.data[2].update( color="white", facecolor=None, # we previously assigned an explicit value to facecolor, which takes precedence over color; so to use the color parameter, we need to reset facecolor to None opacity=0.4, ) oat.plot.set_background_color(fig, 'black') fig # %% # Mosaic # --------------------------------------- # # Plotly provides predefined color sequences that can be used to assign different colors to different simplices. # These "discrete color sequences" are designed to create visual contrast between objects. See # the :ref:`color_mapping_gallery` gallery for a complete list of color pallettes. # %% # Load a Plotly sequence of high-contrast pastel colors pastel_sequence = px.colors.qualitative.Plotly pastel_sequence # %% # Shrink/expand this sequence to match the number of triangles triangles (wrapping around cyclicly, if necessary) num_triangles = len( [s for s in simplices.simplex if len(s) == 3] ) # count triangles in the plot triangle_colors = [pastel_sequence[i % len(pastel_sequence)] for i in range(num_triangles)] # match the length of the sequence to the number of triangles # %% # Assign these colors to the triangles in the plot fig.data[2].update( facecolor=triangle_colors ) fig # %% # Smooth color gradients # --------------------------------------- # %% # Color gradients can be used to convey additional information about simplices. # Here we color triangles based on the distance of their vertices from the origin. # # - See the documentation for :func:`oat_python.plot.trace_3d_for_triangles` for usage instructions. # - See the `Plotly documentation on colorscales `_ for a list of built-in colorscales. triangles = [s for s in simplices.simplex if len(s) == 3] vertices = oat.simplex.vertices_incident_to_simplices(triangles) pastel_sequence = [np.linalg.norm(points[v]) for v in vertices] # distance of each vertex from the origin fig.data[2].update( intensity = pastel_sequence, # assign scalar color values to vertices colorscale = 'Jet', # choose a colorscale intensitymode = 'vertex', # specify that colors are assigned to vertices (not faces) showscale = False, # hide the colorbar ) fig # %% # Color values for vertices can also be passed directly to the :func:`oat_python.plot.trace_3d_for_triangles` function. # Here we color triangles based on their z-coordinate. # Again, see the documentation for :func:`oat_python.plot.trace_3d_for_triangles` for usage instructions. trace = oat.plot.trace_3d_for_triangles( triangles = triangles, points = points, opacity = 1.0, intensity = [ point[2] for point in points.values() ], # color triangles based on the z-coordinate of their vertices colorscale = 'Pinkyl', intensitymode = 'vertex', ) triangles_fig = go.Figure(trace) triangles_fig # %% # Opacity # --------------------------------------- # # %% # Opaque simplices can make make 3d structure easier to perceive. fig.data[2].update(opacity=1.0) # triangles fig # %% # Multiple static views (alternative to video) # ----------------------------------------------- # # %% # If you're unable to make a video of a rotating object, you can # often convey the same depth information by showing multiple # static views of the object from different angles. # Create a new figure with 3 3D subplots subfig = make_subplots( rows=3, cols=1, specs=[[{'type': 'scene'}], [{'type': 'scene'}], [{'type': 'scene'}]], subplot_titles=["View 1", "View 2", "View 3"], vertical_spacing=0.05 # Adjust this value to control spacing ) # Add the same traces to each subplot for trace in fig.data: subfig.add_trace(trace, row=1, col=1) subfig.add_trace(trace, row=2, col=1) subfig.add_trace(trace, row=3, col=1) # Set different camera angles for each subplot subfig.update_layout( scene=dict(camera=dict(eye=dict(x=0.8, y=0.8, z=0.8))), scene2=dict(camera=dict(eye=dict(x=0., y=0., z=1.6))), scene3=dict(camera=dict(eye=dict(x=-0.8, y=-0.8, z=0.8))), height=400, width=1200 ) subfig.update_layout( showlegend = False, height = 1400, width = None, template = "plotly_dark", ) subfig