Module WaveformFittingMultiproc

rfsed.WaveformFittingMultiproc.WaveformPara(rfstream, preonset, HSed, VpSed, VpCrust, rayp, KMoho, HMoho, gaussian)

Calculate the Model Parameters for the Waveform Fitting

Parameters:

• rfstream (RFStream) – RFStream object containing the receiver functions

• preonset (integer) – time in seconds before the P-arrival

• HSed (float) – Sediment Layer Thickness

• VpSed (float) – P-wave velocity in the Sediment

• VpCrust (float) – P-wave velocity in the Crust

• rayp (float) – Ray Parameter (s/km)

• KMoho (numpy array) – numpy array of Vp/Vs ratios for the Moho

• HMoho (numpy array) – numpy array of Moho depths

• gaussian (float) – Gaussian Parameter used in the receiver function calculation (e.g. 1.25)

Returns:

Dictionary of Model Parameters

Example

>>> # Initialize the WaveformPara module:
>>> from rfsed.WaveformFittingMultiproc import WaveformPara
>>> import numpy as np
>>> # Define all the necessary parameters
>>> # rfstream is a RFStream object containing the receiver functions (based on rf)
>>> rfstream = rfstream
>>> preonset = 10
>>> HSed = 0.8
>>> VpSed = 2.5
>>> VpCrust = 6.5
>>> rayp = 0.06
>>> KMoho = np.arange(1.65, 1.95, 201)
>>> HMoho = np.arange(20, 50, 201)
>>> gaussian = 1.25
>>> # Call the WaveformPara function
>>> ModelParams = WaveformPara(rfstream, preonset, HSed, VpSed,
                                VpCrust, rayp, KMoho, HMoho, gaussian)

rfsed.WaveformFittingMultiproc.WaveformFit_multiproc(inputparams)

Calculate the Waveform Fitting for each Moho depth

Parameters:

inputparams (list) – List of input parameters (HMoho, OtherParams)

Returns:

List of Waveform Fitting Results

rfsed.WaveformFittingMultiproc.run_waveformfitting(nproc, HMoho, ModelParams)

Run the Waveform Fitting Function in parallel for each Moho depth

Parameters:

• nproc (int) – Number of processors to use

• HMoho (numpy array) – numpy array of Moho depths

• ModelParams (dict) – Dictionary of Model Parameters from function WaveformPara

Returns:

List of Waveform Fitting Results

Example

>>> # Initialize the run_waveformfitting module:
>>> from rfsed.WaveformFittingMultiproc import run_waveformfitting
>>> # Define all the necessary parameters
>>> import numpy as np
>>> nproc = 20
>>> HMoho = np.arange(20, 50, 201)
>>> # ModelParams is a dictionary of model parameters which is an output from the
>>> # WaveformPara function (see WaveformPara function for more details)
>>> ModelParams = WaveformPara(rfstream, preonset, HSed, VpSed,
                                VpCrust, rayp, KMoho, HMoho, gaussian)
>>> # Call the run_waveformfitting function
>>> WaveformFitResults = run_waveformfitting(nproc, HMoho, ModelParams)

rfsed.WaveformFittingMultiproc.plotbestmodel(WaveformResults, ModelParams, wtCorr, wtRMSE, wtPG, savepath, format)

Plot the best model from the Waveform Fitting Results

Parameters:

• WaveformResults (list) – List of Waveform Fitting Results

• ModelParams (dict) – Dictionary of Model Parameters

• wtCorr (float) – Weighting for the Correlation Coefficient

• wtRMSE (float) – Weighting for the Root-mean-square Error

• wtPG (float) – Weighting for the Phase Goodness of Fit

• savepath (str) – Path to save the plot

• format (str) – Format to save the plot

Returns:

Plot of the best model from the Waveform Fitting Results

Example

>>> # Initialize the plotbestmodel module:
>>> from rfsed.WaveformFittingMultiproc import plotbestmodel
>>> # Define all the necessary parameters
>>> # WaveformResults is a list of Waveform Fitting Results,
>>> # which is an output of the run_waveformfitting function
>>> # (see run_waveformfitting function for more details)
>>> WaveformResults = run_waveformfitting(nproc, HMoho, ModelParams)
>>> # ModelParams is a dictionary of model parameters which is an output from
>>> # the WaveformPara function (see WaveformPara function for more details)
>>> ModelParams = WaveformPara(rfstream, preonset, HSed, VpSed, VpCrust,
                                rayp, KMoho, HMoho, gaussian)
>>> wtCorr, wtRMSE, wtPG = [0.4, 0.4, 0.2]
>>> savepath = 'path/to/save/plot'
>>> format = 'png'
>>> # Call the plotbestmodel function
>>> plotbestmodel(WaveformResults, ModelParams, wtCorr, wtRMSE, wtPG,
                  savepath, format)