The creation of this program was inspired by the need to include a CQT package with minimal size and dependency for SHARCNET (ComputeCanada) Supercomputer Clusters.
# IMPORTANT DISCLAIMER: All credits for the original Q transform algorithm go to the authors of *GWpy* and *Omega* pipeline.
# See original algorithms at: [Omega Scan] https://gwdetchar.readthedocs.io/en/stable/omega/
# [GWpy] https://gwpy.github.io/docs/stable/
# particularly [GWpy qtransform]
# - https://github.com/gwpy/gwpy/blob/26f63684db17104c5d552c30cdf01248b2ec76c9/gwpy/signal/qtransform.py
#
# The license information does NOT imply this package (constantQ) as the original q transform/q scan algorithm.
# NOTE: Referenced programs are under the GNU license
# for more information on the license visit: https://www.gnu.org/licenses/gpl-faq.en.html
How to use it:
Step 1: Generating a chirp signal
import numpy as np
# Generate np.array chirp signal
dt = 0.001
t = np.arange(0,3,dt)
f0 = 50
f1 = 250
t1 = 2
x = np.cos(2*np.pi*t*(f0 + (f1 - f0)*np.power(t, 2)/(3*t1**2)))
fs = 1/dt
plt.plot(x) # plot the chirp signal
plt.show() # displayStep 2: Generating a TimeSeries object
from constantQ.timeseries import TimeSeries
series = TimeSeries(x, dt = 0.001, unit='m', name='test', t0=0) #np.array --> constantQ.timeseries Step 3: Q Transform
hdata = series
sq = hdata.q_transform(search=None) # q transform
print(len(sq[0])) # freq array length
print(len(sq)) # time array length
plt.imshow(sq.T, origin='lower') # plot the spectrogram
plt.colorbar() # colorbar
plt.show() # displayTo compare the result with a Scipy Spectrogram
from scipy import signal as scisignal
freq, ts, Sxx = scisignal.spectrogram(x) # scipy spectrogram
plt.pcolor(ts, freq, Sxx, shading='auto') # plot the spectrogram
plt.colorbar() # colorbar
plt.show() # displayThis test version 0.0.1 largely follows the GWpy architecture. Changes will be made in future updates if a different structure is better for this package.
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