Reputation: 421
I have a 2D numpy array that's created like this:
data = np.empty((number_of_elements, 7))
Each row with 7 (or whatever) floats represents an object's properties. The first two for example are the x
and y
position of the object, the others are various properties that could even be used to apply color information to the plot.
I want to do a scatter plot from data
, so that if p = data[i]
, an object is plotted as a point with p[:2]
as its 2D position and with say p[2:4]
as a color information (the length of that vector should determine a color for the point). Other columns should not matter to the plot at all.
How should I go about this?
Upvotes: 24
Views: 120798
Reputation: 879421
Setting up a basic matplotlib figure is easy:
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
Picking off the columns for x
, y
and color
might look something like this:
N = 100
data = np.random.random((N, 7))
x = data[:,0]
y = data[:,1]
points = data[:,2:4]
# color is the length of each vector in `points`
color = np.sqrt((points**2).sum(axis = 1))/np.sqrt(2.0)
rgb = plt.get_cmap('jet')(color)
The last line retrieves the jet
colormap and maps each of the float values (between 0 and 1) in the array color
to a 3-tuple RGB value.
There is a list of colormaps to choose from here. There is also a way to define custom colormaps.
Making a scatter plot is now straight-forward:
ax.scatter(x, y, color = rgb)
plt.show()
# plt.savefig('/tmp/out.png') # to save the figure to a file
Upvotes: 29
Reputation: 19547
Not sure exactly what you are looking for in the plot, but you can slice 2D arrays like this:
>>> a
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
>>> a[:,1]
array([1, 4, 7])
>>> a[:,1:3]
array([[1, 2],
[4, 5],
[7, 8]])
Then some matplot to take care of the plotting. If you find what you are looking for at the Matplotlib Gallery I can help you more.
Upvotes: 11