def test_tf_prep():
pd = polarData.fromPkl(filename)
mic = Microphone(pd, (50, 100))
mic.polar[30].plot(both=True)
mic.self_apply_xy()
mic.polar[30].plot(both=True)
angles, freqs, data = mic.tf_prep()
print(angles)
print(freqs)
print(data)
angles = tf.constant(angles)
freqs = tf.constant(freqs)
data = tf.constant(data)
print(angles)
print(freqs)
print(data)
def test_xy():
filename = "/home/terrasa/UROP/polar-measurement/data/19_Jan15/spv1840.pkl"
pd = polarData.fromPkl(filename)
pd.plotAngle(90)
# position in mm
mic = Microphone(pd, [-500, 2000])
fs = 44.1e3
length = 100000
n = np.arange(length)
f_targ = (fs / length) * 4000 # (2pi/ T)*k
# f_targ_2 = 2300
sin_wave_1 = np.sin(n * (2 * np.pi) * (f_targ / fs))
# sin_wave_2 = np.sin(n*(2*np.pi)*(f_targ_2/fs))
sin_wave = audioSample(sin_wave_1, type="t", Fs=fs)
pad = 10000
# sin_wave.hanning()
sin_wave.zeroPadStart(pad)
sin_wave.zeroPadEnd(pad)
sin_wave.plot(both=True)
for theta in range(0, 20, 15):
sin_shifted = mic.apply_xy(sin_wave, theta)
sin_shifted.plot(both=True)
def simulate_polar_array():
filename = "/home/terrasa/UROP/polar-measurement/data/19_Jan15/spv1840.pkl"
pd = polarData.fromPkl(filename)
fs = 44.1e3
length = 100000
n = np.arange(10000)
f_options = np.int32(np.logspace(2, 4, 4)) * 2 * (fs / length)
# pd.plotAngle(90)
f_1 = f_options[0]
f_2 = f_options[2]
c = 343e3
d_1 = c / (2 * f_1)
d_2 = c / (2 * f_2)
# position in mm
mic_1 = Microphone(pd, [0, 0])
mic_2 = Microphone(pd, [d_1, 0])
mic_3 = Microphone(pd, [0, d_2])
mic_array = MicrophoneArray([mic_1, mic_2])
# mic_array = MicrophoneArray([mic_1, mic_2, mic_3])
plt.figure(2)
for f_test in f_options:
sin_wave = np.sin(n * (2 * np.pi) * (f_test / fs))
sin_wave = 2 / length * audioSample(sin_wave, type="t", Fs=fs)
# sin_wave.hanning()
thetas = np.array(list(range(0, 361, 2)))
mags = []
for theta in thetas:
print(f_test, theta)
result = mic_array.apply(sin_wave, theta)
# sin_wave.plot(both=True)
# result.plot(both=True)
# get magnitude of the closest frequency of the result
result.toDb()
mags.append(result.getFreq([f_test])[0].real)
plt.polar(thetas * np.pi / 180, mags)
plt.legend(f_options, loc="upper left")
mic_array.visualize()
def pd_convert():
"""
Conversion of polarData object to objects compatible
compatible with TensorFlow
"""
filename = "/home/terrasa/UROP/polar-measurement/data/19_Jan15_fixedpkls/spv1840.pkl"
pd = polarData.fromPkl(filename)
print(pd.toTF().shape)
def simulate_polar_1mic():
filename = "/home/terrasa/UROP/polar-measurement/data/19_Jan15/spv1840.pkl"
pd = polarData.fromPkl(filename)
pd.plotAngle(0, both=True)
# position in mm
mic = Microphone(pd, [-500, 2000])
fs = 44.1e3
length = 100000
n = np.arange(10000)
f_options = np.int32(np.logspace(2, 4, 6)) * 2 * (fs / length)
plt.figure(1)
for f_test in f_options:
sin_wave = np.sin(n * (2 * np.pi) * (f_test / fs))
sin_wave = audioSample(sin_wave, type="t", Fs=fs)
# sin_wave.hanning()
thetas = np.array(list(range(0, 361, 1)))
mags = []
for theta in thetas:
print(f_test, theta)
result = mic.apply(sin_wave, theta)
# sin_wave.plot(both=True)
# result.plot(both=True)
# get magnitude of the closest frequency of the result
result.toDb()
mags.append(result.getFreq([f_test])[0].real)
plt.polar(thetas * np.pi / 180, mags)
plt.title("RESULT")
plt.legend(np.int32(f_options), loc="upper left")
pd.plotFreqs(f_options, fig=2)
def test_apply_filter():
"""
Application of a zpk filter to a polarData object
"""
filename = "/home/terrasa/UROP/polar-measurement/data/19_Jan15_fixedpkls/spv1840.pkl"
pd = polarData.fromPkl(filename)
filt = ZPKOptimizableFilter(num_zeros=2,num_poles=1)
worN = len(pd[0].f()) # number of frequencies
fs = pd[0].fs
filt_freqz = filt.freqz(worN, fs) # frequency response, audioSample object
filt_freqz.plot(both=True, fig=3, show=False, figtitle="Filter")
pd.setType("f")
test_frequencies = ([100,1000,1240, 10000])
pd.plotFreqs(test_frequencies, fig=1, show=False, title="BEFORE")
pd.applyFilter(filt_freqz)
pd.plotFreqs(test_frequencies, fig=2, show=True, title="AFTER")
from pythonAudioMeasurements.microphoneArray import MicrophoneArray from pyAudioFilter.polar_optimizer import PolarOptimizer # use to help with GPU tensorflow warnings of you # have not configured or don't have a GPU tf.config.set_visible_devices([], 'GPU') # ------------------------------------------------------------------------------------------ # SET-UP # ------------------------------------------------------------------------------------------ # load in a microphone filename = "/home/terrasa/UROP/polar-measurement/data/19_Jan15_fixedpkls/spv1840.pkl" pd = polarData.fromPkl(filename) # set specific locations for the mics locations = [(0,0), (100,200), (-100, 200), (-100,-200), (100,-200)] # alternate method of generating mics randomly in a box of a specified width num_mics = 20 box_width = 600 # mm locations = box_width*np.random.rand(num_mics,2) # comment to use set microphone locations # create a microphone array mic_array = MicrophoneArray([Microphone(pd, loc) for loc in locations]) # look at where the microphones visualize array geometry mic_array.visualize()
import pickle
from pythonAudioMeasurements.audioSample import audioSample
from pythonAudioMeasurements.polarData import polarData
filename = "/home/terrasa/UROP/polar-measurement/data/19_Jan15_fixedpkls/spv1840.pkl"
filename_2 = "/home/terrasa/UROP/polar-measurement/data/19_Jan15/spv1840.pkl"
with open(filename, "rb") as this_file:
# should be a dictionary
raw_load = pickle.load(this_file)
test_samp = raw_load["measurements"][5]
print(test_samp)
as_asamp = audioSample(test_samp[0], test_samp[1], test_samp[2])
print(as_asamp)
as_asamp.plot(both=True)
pd = polarData.fromPkl(filename)
pd_2 = polarData.fromPkl(filename_2, pickleAsAudioSample=True)
pd.plotFreqs([400, 1000])
pd_2.plotFreqs([400, 1000])