We know that we can plot 3d graphs in MatPlotLib but in which field does it used and for what purpose
3D graphs are used when you need to establish the relationship between 3 variables(x,y and z).
This application can be used in the following fields:
1)geographical area: In this field X,Y is used as latitude,longitude and Z can be used as Altitude to replicate the geographical area like hills,buildings etc..
2)Geometry: To visualize the 3d objects like plane,sphere,cube etc in three dimensional space we use 3d plotting.
3)Statistics: To compare two variables on third variable we use 3d plots like 3d barchart, Scatter plot etc..
There are many other fields where 3d plotting is used instead of 2d plot, as it provides more information visually.
When you are working with 3 variables and want to plot a graph in between them and identify the relationship between them then you should use 3d graphs.
There can be many use cases of 3d plots:
To describe the position of a point in a plane if the position varies with time, we now need 3 measurements- x-axis distance, y-axis distance, and time elapsed.
To describe the position of a point in 3D space, we need 3 measurements: x distance, y distance and z distance.
To describe the position of a point in 3D space, if the position varies with time, we need 4 measurements: x distance, y distance, z distance and time.
Each of those measurements represents a dimension, and each dimension requires it’s own axis.
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I need to create a 3D Spline (or NURBS, both would be possible as I can convert these internally) based on two 2D Splines (or NURBS). The two base Splines are on orthogonal planes in a 3D space.
Let's say, for simplicity, spline A is on the XY-Plane, spline B on the YZ-Plane.
Both splines start and end on the same Y coordinate but do not share any points in between.
Image above: Two 2D Splines; Black on XY, blue on YZ. Shared Y coordinate for start and end point, different control points
How can I calculate the resulting 3D spline that is a merge of the two 2D splines?
Purple spline is the resulting 3D Spline.
If allways two spline control points of the 2D splines share the same Y coordinate, it is simple, the new point is the compound of the two old points, where
the new X cooridnate is the XY-Plane's spline X coordinate
the new Y cooridnate is any Y coordinate as they are the same
the new Z coordinate is the YZ-Plane's spline Z coordinate
If the spline control points do not share a common Y coordinate, it seems more difficult. Is there a library I can use to achieve that?
Hope I made my problem somewhat clear.
I have a database of the height, weight, and age of 100s of people. Using matplotlib, I've been able to create a 3D scatterplot of these 3 variables with the xyz co-ordinates of each point representing the (height,weight,age) of one person.
Is it possible to create a (i) line (ii) surface of best fit for the data? The meshgrid would be incomplete since I don't have an age (z) value for each pairing of height and weight (x,y) values. Can we draw the line/surface regardless? Do I have to impute the missing z-values in the meshgrid, and if so, how would I do that?
Most other answers I've seen on this topic assume z is a function of x and y, or that the meshgrid is complete, both of which are not the case here.
Can you try using numpy.meshgrid and then fill your unknown z values with numpy.nan? Matplotlib should ignore numpy.nan from the plots.
By 'best fit' did you mean an interpolation? If so you can pass your data through scipy.interpolate.RectBivariateSpline. I think that would suit your problem?
I have a vector field that is defined in a 2D plane. This means that the Z component of the position is constant, but vectors are 3D. I have plotted this using quiver3 as below:
[X,Y] = meshgrid(-pi/2:pi/8:pi/2,-pi/2:pi/8:pi/2);
Z=ones(size(X));
U=rand(size(X));
V=rand(size(X));
W=rand(size(X));
H=quiver3(X,Y,Z,U,V,W,'r');
H.ShowArrowHead = 'off';
The above code does the job, but it does not look good. Is it possible to represent the vector field like the below picture with 3D rods?
this is the graph in question and the dots should appear in the bottom plane, not "above" the plane like i manged to.
bx.scatter(xs,ys,zs, zdir=zs,c=plt.cm.jet(np.linspace(0,1,N))) # scatter points
for i in range(N-1):
bx.plot(xs[i:i+2], ys[i:i+2], zs[i:i+2], color=plt.cm.jet(i/N), alpha=0.5)
#plots the lines between points
bx.scatter(xs,ys,zs=732371.0,zdir="z",c=plt.cm.jet(np.linspace(0,1,N)),depthshade=True)
bx.set_zlim3d(732371.0,) #limit is there so that we can project the points onto the xy-plane
as youll notice the points are drawn above the xy-grid and I had to set a lower limit for the z-axis so that the first projected point will not interfere with the first scatter point
I would prefer the points be in 2d and less hacky since I got 50 other graphs to do like this and fine tune each one would be cumbersome.
Got some simpler method you want to share?
There are many options, and ultimately, it depends on the range of your data in the other plots.
1) Offset the projection point by a fixed amount
You could calculate the minimum Z value, and plot your projection a fixed offset from that minimum value.
zs=min(zs)-offset
2) offset the projection by a relative amount that depends on the range of your data.
You could take into account the range of your data (i.e. the distance from min to max Z) and calculate an offset proportional to that (e.g. 10-15%).
zs=min(zs)-0.15*(max(zs)-min(zs))
I have 3d data produced from mesh points. The structure that was meshed is complex enough that interpolation using griddata is lacking. Specifically, there are regions without data points which are being given values by griddata that are not the fill_value. I need these hollow regions to have the value of 0.0, which I set fill_value to.
A simplified version of this is illustrated below:
The area occupied by the cylinder has no data points but the rest of the cube volume does. There will be data points from interpolation inside the cylinder but I need them to be zero.
Below is a slice parallel to the xy plane of the actual interpolated data with a black oval that approximates the edge 'cylinder'. The red an blue 'bleed' in to the void after interpolation. The fill value of 0.0 can be seen in the upper left corner:
Any ideas on how I can achieve the goal of setting those values to 0.0? Note that the 'cylinder' is not of constant shape.
I thought about going z layer by z layer and finding a polygon that gives the cylinder shape and then setting points inside the polygon to zero.
I also thought about partitioning the volume so a portion of the cylinder ends up in corners of the partion (for each z layer) and hoping that the interpolator would not try to extrapolate into the void region.
The first option seems better, but I would like to know if Python provides some sort of functionality which would work better.
EDIT: Here are some actual points from the data set:
The z scale is much smaller than x or y. You can see that the regions I'm interested in are pretty well defined. But, again, how do I identify them for the purposes of setting grid points to 0.0?