This seems like it would be very straight forward but I can't seem to figure out how to map angles between -Pi and Pi to the range 0 to 2Pi. I tried using np.select but it freezes my program for some reason. I need the angles in this range since they will be used as training data for a neural net which can not output negative numbers.

audio = wav.read('/home/chase/Desktop/ge.wav')[1].astype(np.float32)
audio = np.mean(audio, 1)
audio /= np.max(np.abs(audio))
audio = np.array([np.fft.rfft(audio[i:i + FRAME_SIZE]) for i in range(0, len(audio) - len(audio) % FRAME_SIZE, FRAME_SIZE)])
audio /= FRAME_SIZE
audio_mag = np.abs(audio)
audio_phase = np.angle(audio)

#this line freezes the program
audio_phase = np.select(audio_phase < 0 , 2 * np.pi + audio_phase, audio_phase)

I need the audio

Let us say you have these angles:

>>> angles = np.linspace(-np.pi, np.pi, 10)
>>> angles
array([-3.14159265, -2.44346095, -1.74532925, -1.04719755, -0.34906585,
    0.34906585,  1.04719755,  1.74532925,  2.44346095,  3.14159265])

There are two possible ways of doing this ...

1. Use numpy expressions for finding the negative values ...

The ones less than zero should be converted to the right value. Something like this:

>>> (2*np.pi + angles) * (angles < 0) + angles*(angles > 0)
array([ 3.14159265,  3.83972435,  4.53785606,  5.23598776,  5.93411946,
    0.34906585,  1.04719755,  1.74532925,  2.44346095,  3.14159265])

Remember that in numpy, you can do logical tests ... angles < 0 is a boolean array. However, 1*(angles < 0) is a numeric array, where True values are mapped to 1 and False values are mapped to 0. You can combine the two concepts to get your answer.

2. You can simply recognize that this is a mathematical expression you are trying to solve:

So for that, simply add 2*np.pi to everything and take the mod. This is exactly like finding the "units place", or converting a number into an octal number etc ...

>>> (angles + 2 * np.pi) % (2 * np.pi)
array([ 3.14159265,  3.83972435,  4.53785606,  5.23598776,  5.93411946,
    0.34906585,  1.04719755,  1.74532925,  2.44346095,  3.14159265])

I hope this helps.