Astrid - 4 months ago 36

Python Question

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 (i.e. those that are undefined by a point in the scatter plot)
`NaNs`

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.

Answer

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.