This project is for Time Series Analysis and Frequency Spectral Estimation. It allows for convenient computation of periodograms and spectrograms (aka Dynamic Power Spectra) as well as enabling plotting of multivariate time series and interactive Time-Frequency Representations of data.
pip install https://github.com/astromancer/tsaAs an example, let's generate a harmonic signal:
import numpy as np
from tsa import TimeSeries
# generate data
np.random.seed(54321)
n = 1000 # number of points
A = 5 # amplitude
ω = 1.35 * 2 * np.pi # angular frequency [radians/s]
t = np.linspace(0, 6, n) # time
signal = A * np.sin(ω * t) + np.random.randn(n)
errors = np.random.rand(n) # uniformly distributed uncertainties
# create time series
ts = TimeSeries(t, signal, errors)
tsp = ts.plot()
As an example, we generate a multi-tone harmonic signal using the built in
Harmonic signal generator. We compute the periodogram using the
TimeSeries.periodogram method, which returns a plottable Periodogram object.
import matplotlib.pyplot as plt
from tsa.ts.generate import Harmonic
# generate the signal
harmonic = Harmonic(amplitudes=[5, 4.3, 2.7],
frequencies=[1.35, 20.27, 51.3])
ts = TimeSeries(t, harmonic(t))
# compute the periodogram
pg = ts.periodogram(normalize='rms')
# plot
fig, (ax0, ax1) = plt.subplots(2, 1)
ts.plot(ax=ax0)
pg.plot(ax=ax1)
To demonstrate the spectrogram, we generate a multi-component signal consisting of two superposed time series:
- A harmonic signal with constant tone at 10 Hz
- An amplitude- and frequency modulated signal.
We compute the spectrogram using
TimeSeries.spectrogram, and plot a Time-Frequency Representation of the data.
fs = 100 # sampling frequency
fc = 25 # carier signal
fm = 0.1 # modulation frequency
Δf = 10 # frequency deviation
duration = 60
t = np.linspace(0, duration, duration * fs)
a = Harmonic(5, 0.05, np.pi / 4)(t) # amplitude (modulated)
signalA = Harmonic(2, 10)(t)
signalB = a * np.cos(2 * np.pi * fc * t + (Δf / fm) * np.sin(2 * np.pi * fm * t))
ts = TimeSeries(t, signalA + signalB)
sg = ts.spectrogram(nwindow=128, noverlap='50%', normalize='rms')
tfr = sg.plot()
To activate the interactive features of the map:
tfr.connect() Contributions are welcome!
- Fork it!
- Create your feature branch
git checkout -b feature/rad - Commit your changes
git commit -am 'Add some cool feature 😎' - Push to the branch
git push origin feature/rad - Create a new Pull Request
- e-mail: hannes@saao.ac.za
- see LICENSE