seti2.py

# Get sample signal
import ibmseti

sample_data = ibmseti.compamp.SimCompamp(open(raw_signal_files[0],'rb').read())
print('File Name:', raw_signal_files[0])
print('Header:', sample_data.header())
# Output
Out [4]: File Name: ./primary_small_v3/a33c85e3-9316-4871-bcdc-10882a7fe6bd.dat
         Header: {'signal_classification': 'narrowband', 
                  'uuid': 'a33c85e3-9316-4871-bcdc-10882a7fe6bd'}
                  
# Convert raw signal into spectrogram
sample_spec = sample_data.get_spectrogram()
sample_spec.shape
# Output
Out [5]: (384, 512)

# Visualize sample spectrogram
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline

fig, ax = plt.subplots(2, 2, figsize=(10, 6))
p1 = ax[0, 0].imshow(sample_spec, aspect="auto")
t1 = ax[0, 0].set_title('Sample Spectrogram')
p2 = ax[0, 1].hist(sample_spec)
t2 = ax[0, 1].set_title('Sample Spectrogram - Histogram')
p3 = ax[1, 0].imshow(np.log(sample_spec), aspect="auto")
t3 = ax[1, 0].set_title('Sample Spectrogram (log scaled)')
p4 = ax[1, 1].hist(np.log(sample_spec))
t4 = ax[1, 1].set_title('Sample Spectrogram - Histogram (log scaled)')
fig.tight_layout()