obspy.signal.tf_misfit.plot_tfr¶

plot_tfr(st, dt=0.01, t0=0.0, fmin=1.0, fmax=10.0, nf=100, w0=6, left=0.1, bottom=0.1, h_1=0.2, h_2=0.6, w_1=0.2, w_2=0.6, w_cb=0.01, d_cb=0.0, show=True, plot_args=['k', 'k'], clim=0.0, cmap=<matplotlib.colors.LinearSegmentedColormap object at 0x4348f7ec>, mode='absolute', fft_zero_pad_fac=0)[source]

Plot time frequency representation, spectrum and time series of the signal.

Parameters: st signal, type numpy.ndarray with shape (number of components, number of time samples) or (number of timesamples, ) for single component data dt time step between two samples in st t0 starting time for plotting fmin minimal frequency to be analyzed fmax maximal frequency to be analyzed nf number of frequencies (will be chosen with logarithmic spacing) w0 parameter for the wavelet, tradeoff between time and frequency resolution left plot distance from the left of the figure bottom plot distance from the bottom of the figure h_1 height of the signal axis h_2 height of the TFR/spectrum axis w_1 width of the spectrum axis w_2 width of the TFR/signal axes w_cb width of the colorbar axes d_cb distance of the colorbar axes to the other axes show show figure or return plot_args list of plot arguments passed to the signal and spectrum plots clim limits of the colorbars cmap colormap for TFEM/TFPM, either a string or matplotlib.cm.Colormap instance mode ‘absolute’ for absolute value of TFR, ‘power’ for |TFR|^2 fft_zero_pad_fac integer, if > 0, the signal is zero padded to nfft = next_pow_2(len(st)) * fft_zero_pad_fac to get smoother spectrum in the low frequencies (has no effect on the TFR and might make demeaning/tapering necessary to avoid artifacts) If show is False, returns a matplotlib.pyplot.figure object (single component data) or a list of figure objects (multi component data)

Example

>>> from obspy import read