obspy.signal.tf_misfit.plot_tf_gofs¶

plot_tf_gofs(st1, st2, dt=0.01, t0=0.0, fmin=1.0, fmax=10.0, nf=100, w0=6, norm='global', st2_isref=True, a=10.0, k=1.0, left=0.1, bottom=0.1, h_1=0.2, h_2=0.125, h_3=0.2, w_1=0.2, w_2=0.6, w_cb=0.01, d_cb=0.0, show=True, plot_args=['k', 'r', 'b'], ylim=0.0, clim=0.0, cmap=<matplotlib.colors.LinearSegmentedColormap object at 0x43166aac>)[source]¶

Plot all time frequency Goodness-of-Fits and the time series in one plot (per component).

Parameters:

st1 signal 1 of two signals to compare, type numpy.ndarray with shape (number of components, number of time samples) or (number of timesamples, ) for single component data st2 signal 2 of two signals to compare, type and shape as st1 dt time step between two samples in st1 and st2 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 norm ‘global’ or ‘local’ normalization of the misfit st2_isref (bool) True if st2 is a reference signal, False if none is a reference A Maximum value of Goodness-of-Fit for perfect agreement k sensitivity of Goodness-of-Fit to the misfit left plot distance from the left of the figure bottom plot distance from the bottom of the figure h_1 height of the signal axes h_2 height of the TEM and TPM axes h_3 height of the TFEM and TFPM axes w_1 width of the FEM and FPM axes w_2 width of the TFEM, TFPM, signal etc. 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 1/2 and TEM/TPM/FEM/FPM plots ylim limits in misfit for TEM/TPM/FEM/FPM clim limits of the colorbars cmap colormap for TFEM/TFPM, either a string or matplotlib.cm.Colormap instance

Returns:

If show is False, returns a matplotlib.pyplot.figure object (single component data) or a list of figure objects (multi component data)

Example

For a signal with pure amplitude error

>>> import numpy as np
>>> tmax = 6.
>>> dt = 0.01
>>> npts = int(tmax / dt + 1)
>>> t = np.linspace(0., tmax, npts)
>>> A1 = 4.
>>> t1 = 2.
>>> f1 = 2.
>>> phi1 = 0.
>>> phase_shift = 0.1
>>> H1 = (np.sign(t - t1) + 1)/ 2
>>> st1 = (A1 * (t - t1) * np.exp(-2*(t - t1)) *
...       np.cos(2. * np.pi * f1 * (t - t1) + phi1 * np.pi) * H1)
>>> # Reference signal
>>> st2 = st1.copy()
>>> # Distorted signal:
>>> st1 = st1 * 3.
>>> plot_tf_gofs(st1, st2, dt=dt, fmin=1., fmax=10.) 

(Source code, png, hires.png)