I am working with azimuth and elevation data in a pandas dataframe. In this reference frame, measurements are specified from -180 to 180 in azimuth and 0 to 90 in elevation. My data points are hovering around the 180 degree mark so it also registers -180 sometimes because in that frame of reference, they are right next to each other.
Here is my dataframe
Az El
-179.90 2.43
179.85 2.30
179.95 2.33
-179.99 2.40
179.98 2.63
-179.92 2.67
I am trying to find the maximum distance between any value in the az column. If I look for the min and max, and find the difference I will get 359.97 even though the true largest difference should be .25.
Does anyone know an algorithm that will work?
Here is my current code:
def findMaxDistanceInColumn(dataframe,dimension):
array = dataframe[dimension].to_numpy().astype(np.float)
min = np.min(array)
max = np.max(array)
difference = max-min
return difference
You seem to have reversed the calculations: you need the lowest absolution value on each side of the 180 split: take the minimum of the positive values, 179.85, and the maximum of the negative values, -179.90. Now do the math:
neg_dist = neg_max + 180
pos_dist = 180 - pos_min
final = neg_dist + pos_dist
Can you finish from there?