The result i want to achieve is shown below. Obviously it is scatter plot created with matplotlib. The problem is that i dont know how to create these lines, which indicate the partioning done by the Isolation Trees/Decision Trees.
I looked around but nowhere is explained how to achieve this kind of plot. Right now i am a bit confused if it is even possible. Maybe the lines are hand drawn afterwards?
What would be a good way to draw a figure such as the one shown in Figure 1 of the following paper:
here
It seems to be plotted with python, using matplotlib. But, maybe a tensorflow library or similar has been used, which I don't know.
Maybe try a hexbin plot from matplotlib.
In summary, I'd like to plot a model I made using sympy.
I'm currently trying to animate the evolution of a bicycle model along a curve in 2-space.
So far I have three matplotlib patches that represent the body and two tires of the bicycle, as well as the dynamic equations created with the sympy mechanics module.
I'm currently able to numerically integrate the equations using KanesMethod, however plotting these objects is a different challenge.
This is what I've come up with so far, and I feel like there has to be a better way to go about all this.
position_vector.subs(...).to_matrix(inertial_reference_frame)
Then I'd have to plot each of the two coordinates over time.
I don't even know where to start with plotting reference frames. I guess I'd somehow turn the reference frame into a set of 3 unit vectors, substitute numerical values, and plot them using matplotlib's quiver command.
Any insight would be greatly appreciated!
I have two sets of sampled points in 2d space[x ,y], each set represents one class. When I plot all points, it's mess and one can't see anything on it. I need somehow plot distribution of each set (if it's possible on same canvas with different colours, then better). Does anybody know about some good library for it?
Matplotlib is a very good library for that task. You can plot histograms, scatter plots and lot of other things. You just have to know what you want and then you can probably do it with that. I use that for similar tasks a lot.
[UPDATE]
As I said, you can do that with matplotlib. Here is an example from their gallery: http://matplotlib.org/examples/pylab_examples/scatter_hist.html
It's not so pretty as with the answer in the comment of #lejlot, but still correct.
I need to plot some data in various forms. Currently I'm using Matplotlib and I'm fairly happy with the plots I'm able to produce.
This question is on how to plot the last one. The data is similar to the "distance table", like this (just bigger, my table is 128x128 and still have 3 or more number per element).
Now, my data is much better "structured" than a distance table (my data doesn't varies "randomly" like in a alphabetically sorted distance table), thus a 3D barchart, or maybe 3 of them, would be perfect. My understanding is that such a chart is missing in Matplotlib.
I could use a (colored) Countor3d like these or something in 2D like imshow, but it isn't really well representative of what the data is (the data has meaning just in my 128 points, there isn't anything between two points). And the height of bars is more readable than color, IMO.
Thus the questions:
is it possible to create 3D barchart in Matplotlib? It should be clear that I mean with a 2D domain, not just a 2D barchart with a "fake" 3D rendering for aesthetics purposes
if the answer to the previous question is no, then is there some other library able to do that? I strongly prefer something Python-based, but I'm OK with other Linux-friendly possibilities
if the answer to the previous question is no, then do you have any suggestions on how to show that data? E.g. create a table with the values, superimposed to the imshow or other colored way?
For some time now, matplotlib had no 3D support, but it has been added back recently. You will need to use the svn version, since no release has been made since, and the documentation is a little sparse (see examples/mplot3d/demo.py). I don't know if mplot3d supports real 3D bar charts, but one of the demos looks a little like it could be extended to something like that.
Edit: The source code for the demo is in the examples but for some reason the result is not. I mean the test_polys function, and here's how it looks like:
example figure http://www.iki.fi/jks/tmp/poly3d.png
The test_bar2D function would be even better, but it's commented out in the demo as it causes an error with the current svn version. Might be some trivial problem, or something that's harder to fix.
MyavaVi2 can make 3D barcharts (scroll down a bit). Once you have MayaVi/VTK/ETS/etc. installed it all works beautifully, but it can be some work getting it all installed. Ubuntu has all of it packaged, but they're the only Linux distribution I know that does.
One more possibility is Gnuplot, which can draw some kind of pseudo 3D bar charts, and gnuplot.py allows interfacing to Gnuplot from Python. I have not tried it myself, though.
This is my code for a simple Bar-3d using matplotlib.
import mpl_toolkits
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
%matplotlib inline
## The value you want to plot
zval=[0.020752244,0.078514652,0.170302899,0.29543857,0.45358061,0.021255922,0.079022499,\
0.171294169,0.29749654,0.457114286,0.020009631,0.073154019,0.158043498,0.273889264,0.419618287]
fig = plt.figure(figsize=(12,9))
ax = fig.add_subplot(111,projection='3d')
col=["#ccebc5","#b3cde3","#fbb4ae"]*5
xpos=[1,2,3]*5
ypos=range(1,6,1)*5
zpos=[0]*15
dx=[0.4]*15
dy=[0.5]*15
dz=zval
for i in range(0,15,1):
ax.bar3d(ypos[i], xpos[i], zpos[i], dx[i], dy[i], dz[i],
color=col[i],alpha=0.75)
ax.view_init(azim=120)
plt.show()
http://i8.tietuku.com/ea79b55837914ab2.png
You might check out Chart Director:
http://www.advsofteng.com
It has a pretty wide variety of charts and graphs and has a nice Python (and several other languages) API.
There are two editions: The free version puts a blurb on the generated image, and the
pay version is pretty reasonably priced.
Here's one of the more interesting looking 3d stacked bar charts:
(source: advsofteng.com)