Astrid - 1 year ago 155
Python Question

From scatter plot to 2D array

My mind has gone completely blank on this one.

I want to do what I think is very simple.

Suppose I have some test data:

``````import pandas as pd
import numpy as np
k=10
df = pd.DataFrame(np.array([range(k),
[x + 1 for x in range(k)],
[x + 4 for x in range(k)],
[x + 9 for x in range(k)]]).T,columns=list('abcd'))
``````

where rows correspond to time and columns to angles, and it looks like this:

``````   a   b   c   d
0  0   1   4   9
1  1   2   5  10
2  2   3   6  11
3  3   4   7  12
4  4   5   8  13
5  5   6   9  14
6  6   7  10  15
7  7   8  11  16
8  8   9  12  17
9  9  10  13  18
``````

Then for reasons I convert it to and ordered dictionary:

``````def highDimDF2Array(df):
from collections import OrderedDict # Need to preserve order

vels = [1.42,1.11,0.81,0.50]

# Get dataframe shapes
cols = df.columns

trajectories = OrderedDict()
for i,j in enumerate(cols):
x = df[j].values
x = x[~np.isnan(x)]

maxTimeSteps = len(x)
tmpTraj = np.empty((maxTimeSteps,3))
# This should be fast
tmpTraj[:,0] = range(maxTimeSteps)
# Remove construction nans
tmpTraj[:,1] = x
tmpTraj[:,2].fill(vels[i])

trajectories[j] = tmpTraj

return trajectories
``````

Then I plot it all

``````import matplotlib.pyplot as plt
m = highDimDF2Array(df)
M = np.vstack(m.values())
plt.scatter(M[:,0],M[:,1],15,M[:,2])
plt.title('Angle \$[^\circ]\$ vs. Time \$[s]\$')
plt.colorbar()
plt.show()
``````

Now all I want to do is to put all of that into a 2D numpy array with the properties:

• Time is mapped to the x-axis (or y doesn't matter)

• Angle is mapped to the y-axis

• The entries in the matrix correspond to the values of the coloured dots in the scatter plot

• All other entries are treated as
`NaNs`
(i.e. those that are undefined by a point in the scatter plot)

In 3D the colour would correspond to the height.

I was thinking of using something like this: 3d Numpy array to 2d but am not quite sure how.

You can convert the values in M[:,1] and M[:,2] to integers and use them as indices to a 2D numpy array. Here's an example using the value for M you defined.

``````out = np.empty((20,10))
out[:] = np.NAN
N = M[:,[0,1]].astype(int)
out[N[:,1], N[:,0]] = M[:,2]
plt.scatter(M[:,0],M[:,1],15,M[:,2])
plt.scatter(M[:,0],M[:,1],15,M[:,2])
plt.title('Angle \$[^\circ]\$ vs. Time \$[s]\$')
plt.colorbar()
plt.imshow(out, interpolation='none', origin = 'lower')
``````

Here you can convert M to integers directly but you might have to come up with a function to map the columns of M to integers depending on the resolution of the array you are creating.

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