Plots Edges 4,0/5 2681 reviews
- Related Questions & Answers
Pointsedges I am looking for a faster / better way to plot them. Currently I have: from matplotlib import pyplot as plt x = points:,0.flatten y = points:,1.flatten plt.plot(xedges.T, yedges.T, 'y-') # Edges plt.plot(x, y, 'ro') # Points plt.savefig('figure.png') I read about lineCollections, but am unsure how to. Definition − A graph (denoted as G = (V, E)) consists of a non-empty set of vertices or nodes V and a set of edges E. A vertex a represents an endpoint of an edge. An edge joins two vertices a, b and is represented by set of vertices it connects. Line and volume plots can be added the same way I added a surface plot in the previous example, by right-clicking the Results node or using the ribbon. Next, I’ll add a new 3D plot group with a line plot. Using the View node again, this time to hide the edges of the channel. I have plotted only the edges of the heat sink. This plot will show.
- Selected Reading
MathematicsComputer EngineeringMCA
A graph is a set of points, called nodes or vertices, which are interconnected by a set of lines called edges. The study of graphs, or graph theory is an important part of a number of disciplines in the fields of mathematics, engineering and computer science.
Graph Theory
Definition − A graph (denoted as G = (V, E)) consists of a non-empty set of vertices or nodes V and a set of edges E. A vertex a represents an endpoint of an edge. An edge joins two vertices a, b and is represented by set of vertices it connects.
Example − Let us consider, a Graph is G = (V, E) where V = {a, b, c, d} and E = {{a, b}, {a, c}, {b, c}, {c, d}}
Here V is verteces and a, b, c, d are various vertex of the graph.
Here E represents edges and {a, b}, {a, c}, {b, c}, {c, d} are various edge of the graph.
Degree of a Vertex − The degree of a vertex V of a graph G (denoted by deg (V)) is the number of edges incident with the vertex V.
| Vertex | Degree | Even / Odd |
|---|---|---|
| a | 2 | even |
| b | 2 | even |
| c | 3 | odd |
| d | 1 | odd |
Even and Odd Vertex − If the degree of a vertex is even, the vertex is called an even vertex and if the degree of a vertex is odd, the vertex is called an odd vertex.
Degree of a Graph − The degree of a graph is the largest vertex degree of that graph. For the above graph the degree of the graph is 3.
The Handshaking Lemma − In a graph, the sum of all the degrees of all the vertices is equal to twice the number of edges. For example, in above case, sum of all the degrees of all vertices is 8 and total edges are 4.
In this section, we detail the general tools to visualizeneuroimaging volumes with nilearn.
Nilearn comes with plotting function to display brain maps coming fromNifti-like images, in the nilearn.plotting module.
Contents
Code examples
Nilearn has a whole section of the example gallery on plotting.
A small tour of the plotting functions can be found in the examplePlotting tools in nilearn.
Finally, note that, as always in the nilearn documentation, clickingon a figure will take you to the code that generates it.
Nilearn has a set of plotting functions to plot brain volumes that arefined tuned to specific applications. Amongst other things, they usedifferent heuristics to find cutting coordinates.
plot_anatPlotting an anatomical image | |
plot_epiPlotting an EPI, or T2* image | |
plot_glass_brainGlass brain visualization. By default plots maximumintensity projection of the absolute values. To plotpositive and negative values set plot_abs parameter toFalse. | |
plot_stat_mapPlotting a statistical map, like a T-map, a Z-map, oran ICA, with an optional background | |
plot_roiPlotting ROIs, or a mask, with an optional background | |
Functions for automatic extraction of coords based onbrain parcellations useful for | |
Function for automatic plotting of network nodes (markers)and color coding them according to provided nodal measure(i.e. connection strength) as demonstrated inExample:Comparing connectomes on different reference atlases | |
plot_prob_atlasPlotting 4D probabilistic atlas maps | |
plot_carpetPlotting voxel intensities across time. | |
| plot_img | plot_imgGeneral-purpose function, with no specific presets |
Warning
Opening too many figures without closing
Each call to a plotting function creates a new figure by default. Whenused in non-interactive settings, such as a script or a program, theseare not displayed, but still accumulate and eventually lead to slowingthe execution and running out of memory.
To avoid this, you must close the plot as follow:
See also
| display_mode=’ortho’, cut_coords=[36, -27, 60] Ortho slicer: 3 cuts along the x, y, z directions |
| display_mode=’z’, cut_coords=5 Cutting in the z direction, specifying the number ofcuts |
| display_mode=’x’, cut_coords=[-36, 36] Cutting in the x direction, specifying the exactcuts |
| display_mode=’y’, cut_coords=1 Cutting in the y direction, with only 1 cut, that isautomatically positionned |
| display_mode=’z’, cut_coords=1, colorbar=False Cutting in the z direction, with only 1 cut, that isautomatically positionned |
| display_mode=’xz’, cut_coords=[36, 60] Cutting in the x and z direction, with cuts manuallypositionned |
| display_mode=’yx’, cut_coords=[-27, 36] Cutting in the y and x direction, with cuts manuallypositionned |
| display_mode=’yz’, cut_coords=[-27, 60] Cutting in the y and z direction, with cuts manuallypositionned |
| display_mode=’tiled’, cut_coords=[36, -27, 60] Tiled slicer: 3 cuts along the x, y, z directions,arranged in a 2x2 grid |
| Glass brain display_mode=’lzr’ Glass brain and Connectome provide additional display modesdue to the possibility of doing hemispheric projections.Check out: ‘l’, ‘r’, ‘lr’, ‘lzr’, ‘lyr’, ‘lzry’, ‘lyrz’. |
| Glass brain display_mode=’lyrz’ Glass brain and Connectome provide additional display modesdue to the possibility of doing hemispheric projections.Check out: ‘l’, ‘r’, ‘lr’, ‘lzr’, ‘lyr’, ‘lzry’, ‘lyrz’. |
Nilearn plotting library ships with a set of extra colormaps, as seen in theimage below
These colormaps can be used as any other matplotlib colormap.
Plots Edges Pictures
To add overlays, contours, or edges, use the return value of the plottingfunctions. Indeed, these return a display object, such as thenilearn.plotting.displays.OrthoSlicer. This object represents theplot, and has methods to add overlays, contours or edge maps:
| display.add_edges(img) Add a plot of the edges of img, where edges areextracted using a Canny edge-detection routine. Thisis typically useful to check registration. Note thatimg should have some visible sharp edges. Typicallyan EPI img does not, but a T1 does. |
| display.add_contours(img, levels=[.5], colors=’r’) Add a plot of the contours of img, where contoursare computed for constant values, specified in‘levels’. This is typically useful to outline a mask,or ROI on top of another map. Example:Plot Haxby masks |
| display.add_contours(img, filled=True, alpha=0.7, levels=[0.5], colors=’b’) Add a plot of img with contours filled with colors |
| display.add_overlay(img, cmap=plotting.cm.purple_green, threshold=3) Add a new overlay on the existing figure Example:Visualizing a probablistic atlas: the default mode in the MSDL atlas |
| display.add_markers(coords, marker_color=’y’, marker_size=100) Add seed based MNI coordinates as spheres on top ofstatistical image or EPI image. This is useful for seedbased regions specific interpretation of brain images. Example:Producing single subject maps of seed-to-voxel correlation |
| display.annotate(scalebar=True) Adds annotations such as a scale bar, or the cross ofthe cut coordinates Example:More plotting tools from nilearn |
To display the figure when running a script, you need to callnilearn.plotting.show: (this is just an alias tomatplotlib.pyplot.show):
The simplest way to output an image file from the plotting functions isto specify the output_file argument:
In this case, the display is closed automatically and the plottingfunction returns None.
The display object returned by the plotting function has a savefig methodthat can be used to save the plot to an image file:
Plotting functions required to plot surface data or statistical mapson a brain surface.
plot_surf_roiPlotting surface atlases on a brain surface Example:Loading and plotting of a cortical surface atlas |
plot_surf_stat_mapPlotting statistical maps onto a brain surface Example:Seed-based connectivity on the surface |
Nilearn also has functions for making interactive plots that can beseen in a web browser.
New in version 0.5: Interactive plotting is new in nilearn 0.5
Pilots Edges
For 3D surface plots of statistical maps or surface atlases, useview_img_on_surf and view_surf. Both produce a 3D plot on thecortical surface. The difference is that view_surf takes as input asurface map and a cortical mesh, whereas view_img_on_surf takes as inputa volume statistical map, and projects it on the cortical surface before makingthe plot.
For 3D plots of a connectome, use view_connectome. To see only markers,use view_markers.
4.7.1. 3D Plots of statistical maps or atlases on the cortical surface¶
view_img_on_surf: Surface plot using a 3D statistical map:
If you are running a notebook, displaying view will embed an interactiveplot (this is the case for all interactive plots produced by nilearn’s “view”functions):
If you are not using a notebook, you can open the plot in a browser like this:
This will open this 3D plot in your web browser:
Or you can save it to an html file:
view_surf: Surface plot using a surface map and a cortical mesh:
4.7.2. 3D Plots of connectomes¶
Plots Edges Shapes
view_connectome: 3D plot of a connectome:
4.7.3. 3D Plots of markers¶
view_markers: showing markers (e.g. seed locations) in 3D:
4.7.4. Interactive visualization of statistical map slices¶
view_img: open stat map in a Brainsprite viewer (https://github.com/simexp/brainsprite.js):
in a Jupyter notebook, if html_view is not requested, the viewer will be inserted in the notebook:
Or you can open a viewer in your web browser if you are not in a notebook:
Plots Edges Definition
Finally, you can also save the viewer as a stand-alone html file: