How to polar plot contour a specific value from a 2D array?

Question

How can I highlight a specific contour value in a polar plot?

azi is polar azimuth degrees (with 360 values).
dev is deviation degrees from horizontal (with 90 values).
W is a 2D array shaped (90,360) with values that range 0 to 110.

I need to highlight and contour the W-values that equal 90.
Below is the code I've tried:

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a=np.radians(np.linspace(0,360,360).round()) # borehole azimuth - degrees converted to radians
d=np.radians(np.linspace(0,90,90).round())     # borehole deviation - degrees converted to radians
azi,dev=np.meshgrid(a,d)  # create numpy array 90x360 for borehole deviation
W=np.random.randint(80,100, size=(90,360))

ax2 = plt.subplot(111, projection= 'polar')  
ax2.set_theta_direction(-1)  
ax2.set_theta_zero_location("N")  
data=ax2.contourf(azi,dev, W)  
plt.contour(azi, dev, np.where(W == 90))  
plt.colorbar(data)  
plt.show()

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However, I get the error:

TypeError: Input z must be at least a 2x2 array.  

I can't figure out how to contour and index the 2D- 'W' array values that equal 90.

Another solution might be to create a new 2D array of W==90 values and contour those values?

Answer 1

import numpy as np
import matplotlib.pyplot as plt

a=np.radians(np.linspace(0,360,360).round()) # borehole azimuth - degrees converted to radians
    d=np.radians(np.linspace(0,90,90).round())     # borehole deviation - degrees converted to radians
    azi,dip=np.meshgrid(a,d)  # create numpy array 90x360 for borehole deviation
    W=np.random.randint(80,100, size=(90,360))

# first we need to create a new 2D Numpy array (Z) from W where W=90
Z = np.zeros_like(W)
mask = np.isclose(W, 90)
Z[mask] = W[mask]

ax2 = plt.subplot(111, projection= 'polar')  
ax2.set_theta_direction(-1)  
ax2.set_theta_zero_location("N")  
data=ax2.contourf(azi,dip, W)  
plt.contour(azi, dip, Z)  # then plot contour using the new Z array
plt.colorbar(data)  
plt.show()