reconditioning of lidar readings and practice with numPy

findValidScanPoints.py

# the Lidar returns some error values when it can't get a measurement.
# any value below 16mm indicates a bad reading or other problem.
# this code is practice for trimming the bad readings out of the array
# containing distances & angles.

import numpy as np
np.set_printoptions(suppress=True)

# create an array with some distances which returned invalid (ie 0, 0.002)
scan= np.array([ \
 [  1.471, -24.875],\
 [  1.357, -19.87 ],\
 [  0.,    -14.864],\
 [  0.268,  -9.858],\
 [  0.258,  -4.853],\
 [  0.002,   0.153],\
 [  0.002,   5.159],\
 [  0.002,  10.164]])
print("\n scan \n", scan) # verify the values stored properly

dist = scan[:,0] # store just first column
print("\n distances:\n", dist)

angles = scan[:,1] # store just 2nd column
print("\n angles:\n", angles)

valid = np.where(dist > 0.016) # find values 16mm
print("\n valid:\n", valid )

myNums = dist[valid] # get valid distances
print("\n myNums:\n", myNums)

myAng = angles[valid] # get corresponding valid angles
print("\n myAng:\n", myAng)

output = np.vstack((myNums,myAng)) # recombine columns 
print("\n output:\n",output)

n = output.T # transpose the matrix
print("\n final output: \n", n)