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check_traces_single_station.py
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check_traces_single_station.py
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"""
This Python 3 script plots synthetic waveforms from two runs for a single station.
Federico Munch - June, 2021
Modified Utpal Kumar, Oct 2021
"""
# Load python packages
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
from obspy.taup import TauPyModel
import yaml
import glob, os
import pandas as pd
import logging
logger = logging.getLogger(__name__)
## Reads the parameters.yml file
def read_params(inp = "input_params.yml"):
params = {}
with open(inp, "r") as stream:
try:
params = yaml.safe_load(stream)
except yaml.YAMLError as exc:
print(exc)
return params
params = read_params(inp = "input_params.yml")
# --------------------------------- Auxiliar functions ---------------------------
def spectra(data, sampling_rate):
# Plot the spectra of a signal (in variable data).
fourier_transform = np.fft.rfft(data, n=np.size(data) * 3)
abs_fourier_transform = np.abs(fourier_transform)
power_spectrum = np.square(abs_fourier_transform)
frequency = np.linspace(0, sampling_rate/2, len(power_spectrum))
return frequency, power_spectrum
def great_circle_distance(lat1, lon1, lat2, lon2):
# Compute great circle distance between points (lat1,lon1) and (lat2,lon2)
lat1_rad = lat1 * np.pi / 180.
lat2_rad = lat2 * np.pi / 180.
lon1_rad = lon1 * np.pi / 180.
lon2_rad = lon2 * np.pi / 180.
dlon = np.abs(lon1_rad - lon2_rad)
arg = np.sin(lat1_rad) * np.sin(lat2_rad) +\
np.cos(lat1_rad) * np.cos(lat2_rad) * np.cos(dlon)
dist = np.arccos(arg)
dist = dist * 180/np.pi
return dist
def read_waveforms(simulation_type, path):
# read synthetic waveforms
if simulation_type == 'specfem':
sem_wave = np.genfromtxt(
path+stn['code'].decode('utf-8')+'.'+stn['net'].decode('utf-8')+'.MX'+cmpnt+'.sem.ascii')
elif simulation_type == 'regsem':
sem_wave = np.genfromtxt(path+station+'_'+cmpnt)
elif simulation_type == 'dsm':
sem_wave = np.genfromtxt(path+station+'_u'+cmpnt)
elif simulation_type == 'nms':
sem_wave = np.genfromtxt(path+"U"+cmpnt+'_'+station)
return sem_wave
# ------------------------ input ------------------------------------------ #
# Do you want to plot time series of spectra? Options: 'time' and 'frequency'
# plot_in = 'frequency'
for plot_in in ['frequency', 'time']:
# 3D runs
# 1D run
# Indicate path to synthetic files run #2 and label (for plotting):
path1 = params['specfem']['outputLoc']
label1 = params['specfem']['label']
simulation_type1 = 'specfem'
path2 = params['regsem']['outputLoc']
label2 = params['regsem']['label']
simulation_type2 = 'regsem'
path3 = params['nms']['outputLoc']
label3 = params['nms']['label']
simulation_type3 = 'nms'
path4 = params['regsem_no_ocean']['outputLoc']
label4 = params['regsem_no_ocean']['label']
simulation_type4 = 'regsem'
path5 = params['specfem_poly']['outputLoc']
label5 = params['specfem_poly']['label']
simulation_type5 = 'specfem'
path6 = params['specfem_no_ocean']['outputLoc']
label6 = params['specfem_no_ocean']['label']
simulation_type6 = 'specfem'
path7 = params['specfem-ocean-elev-rec']['outputLoc']
label7 = params['specfem-ocean-elev-rec']['label']
simulation_type7 = 'specfem'
path8 = params['specfem-etopo_0']['outputLoc']
label8 = params['specfem-etopo_0']['label']
simulation_type8 = 'specfem'
path9 = params['specfem_rearth_6371']['outputLoc']
label9 = params['specfem_rearth_6371']['label']
simulation_type9 = 'specfem'
# Event coordinates [in deg]
eventdf = pd.read_csv(params['specfem']['cmtFile'], skiprows=1, delimiter=':', names=['params', 'values'], header=None)
eventdf.set_index('params', inplace=True)
# eventdf_inputs = [mi.strip() for mi in eventdf['params'].values]
elon = float(str(eventdf.loc['longitude', 'values']).strip())
elat = float(str(eventdf.loc['latitude', 'values']).strip())
edep = float(str(eventdf.loc['depth', 'values']).strip())
# path to specfem STATION file (for station coordinate information)
specfem_STATION_file = params['specfem']['stationFile']
# Station name (check file "STATIONS" to see the entire list of modelled stations):
# station = 'FFC'
#stnList = params['inputStations'] #['FFC', 'BBB', 'DWPF']
stnList0 = glob.glob(os.path.join(path2,"*_Z"))
stnList = [os.path.basename(stn).split("_")[0] for stn in stnList0]
for station in stnList:
# Time window for plotting the spectra [in sec]
twin = [params['timeWindowSpectra']['min'], params['timeWindowSpectra']['max']]
# make sure that the station name is correct
assert np.any(station == np.array(stnList)),\
print(" This plotting scripts only visualizes stations",",".join(stnList))
# Time window to plot [limits for the plot in time]
xmin = params['timeWindowTimeSeries']['min']
xmax = params['timeWindowTimeSeries']['max']
# Source time function shift [in seconds]
tshift = params['sftshift']
# compute travel times (and plot) travel times for the following phases:
phase_list = np.array(params['phasesToCompute'])
# ------------------------ main ------------------------------------------- #
#print(" >> Plotting traces for "+station+" in "+plot_in+" domain")
# start Taup
model = TauPyModel(model=params['TauPModel'])
# Load station list
stations_list = np.loadtxt(specfem_STATION_file,
dtype=[('code', 'S9'), ('net', 'S2'), ('lat', '<f8'), ('lon', '<f8'),('dum1', '<f8'), ('dum2', '<f8')], ndmin=1)
# Plot traces for a receiver in station
for stn in stations_list:
if (stn['code'].decode('utf-8').split('.')[-1].replace('_', '') == station):
# print(" >> Plotting traces for "+station+" in "+plot_in+" domain")
# Compute epicentral distance
dist = great_circle_distance(lat1=elat, lon1=elon,
lat2=stn['lat'], lon2=stn['lon'])
# Get traveltimes
arrivals = model.get_travel_times(source_depth_in_km=edep,
distance_in_degree=dist)
#print(f"dist: {dist}, arrivals: {len(arrivals)}")
# Create figure frame
if plot_in == 'time':
plt.figure(figsize=(14, 6))
elif plot_in == 'frequency':
# plt.figure(figsize=(6,8))
plt.figure(figsize=(12, 8))
# plot each component
for cmpnt in ['Z', 'E', 'N']:
if cmpnt == 'Z':
plt.subplot(311)
plt.title(station + ' ($\\Delta: $' +
"{:.2f}".format(dist)+')')
elif cmpnt == 'E':
plt.subplot(312)
elif cmpnt == 'N':
plt.subplot(313)
plt.xlabel('Time [s]')
plt.ylabel(cmpnt)
# read synthetics
## specfem
sem_path1 = read_waveforms(simulation_type=simulation_type1,
path=path1)
## regsem_ocean
sem_path2 = read_waveforms(simulation_type=simulation_type2,
path=path2)
## nms
sem_path3 = read_waveforms(simulation_type=simulation_type3,
path=path3)
##regsem_noocean
sem_path4 = read_waveforms(simulation_type=simulation_type4,
path=path4)
##specfem_poly
sem_path5 = read_waveforms(simulation_type=simulation_type5,
path=path5)
##specfem_no_ocean
sem_path6 = read_waveforms(simulation_type=simulation_type6,
path=path6)
##specfem-ocean-elev-rec
sem_path7 = read_waveforms(simulation_type=simulation_type7,
path=path7)
##specfem-etopo_0
sem_path8 = read_waveforms(simulation_type=simulation_type8,
path=path8)
##specfem_rearth_6371
sem_path9 = read_waveforms(simulation_type=simulation_type9,
path=path9)
if plot_in == 'time':
sem_path_dict = {}
# plot signals
if params['plot_toggles']['specfem']:
plt.plot(sem_path1[:, 0]-tshift,
sem_path1[:, 1], label=label1, color=params['specfem']['color'], ls=params['specfem']['lineStyle'])
sem_path_dict['specPath'] = sem_path1
sem_path_dict['specLab'] = label1
if params['plot_toggles']['specfem_poly']:
plt.plot(sem_path5[:, 0]-tshift,
sem_path5[:, 1], label=label5, color=params['specfem_poly']['color'], ls=params['specfem_poly']['lineStyle'])
sem_path_dict['specPath'] = sem_path5
sem_path_dict['specLab'] = label5
if params['plot_toggles']['specfem_no_ocean']:
plt.plot(sem_path6[:, 0]-tshift,
sem_path6[:, 1], label=label6, color=params['specfem_no_ocean']['color'], ls=params['specfem_no_ocean']['lineStyle'])
sem_path_dict['specPath'] = sem_path6
sem_path_dict['specLab'] = label6
if params['plot_toggles']['specfem-ocean-elev-rec']:
plt.plot(sem_path7[:, 0]-tshift,
sem_path7[:, 1], label=label7, color=params['specfem-ocean-elev-rec']['color'], ls=params['specfem-ocean-elev-rec']['lineStyle'])
sem_path_dict['specPath'] = sem_path7
sem_path_dict['specLab'] = label7
if params['plot_toggles']['specfem-etopo_0']:
plt.plot(sem_path8[:, 0]-tshift,
sem_path8[:, 1], label=label8, color=params['specfem-etopo_0']['color'], ls=params['specfem-etopo_0']['lineStyle'])
sem_path_dict['specPath'] = sem_path8
sem_path_dict['specLab'] = label8
if params['plot_toggles']['specfem_rearth_6371']:
plt.plot(sem_path9[:, 0]-tshift,
sem_path9[:, 1], label=label9, color=params['specfem_rearth_6371']['color'], ls=params['specfem_rearth_6371']['lineStyle'])
sem_path_dict['specPath'] = sem_path9
sem_path_dict['specLab'] = label9
if params['plot_toggles']['regsem']:
plt.plot(sem_path2[:, 0]-tshift,
sem_path2[:, 1], label=label2, color=params['regsem']['color'], ls=params['regsem']['lineStyle'])
sem_path_dict['regPath'] = sem_path2
sem_path_dict['regLab'] = label2
if params['plot_toggles']['regsem_no_ocean']:
plt.plot(sem_path4[:, 0]-tshift,
sem_path4[:, 1], label=label4, color=params['regsem_no_ocean']['color'], ls=params['regsem_no_ocean']['lineStyle'])
sem_path_dict['regPath'] = sem_path4
sem_path_dict['regLab'] = label4
if params['plot_toggles']['nms']:
plt.plot(sem_path3[:, 0]-tshift,
sem_path3[:, 1], label=label3, color=params['nms']['color'], ls=params['nms']['lineStyle'])
sem_path_dict['nmsPath'] = sem_path3
sem_path_dict['nmsLab'] = label3
# plt.plot(sem_path4[:, 0]-tshift,
# sem_path4[:, 1], label=label4, color=params['regsem4']['color'], ls=params['regsem_no_ocean']['lineStyle'])
if params['plot_toggles']['diff']:
diff_sem_path1 = sem_path_dict['specPath']
diff_sem_path2 = sem_path_dict['regPath']
# plot differences
if np.size(diff_sem_path1[:, 1]) == np.size(diff_sem_path2[:, 1]):
diff = (diff_sem_path1[:, 1] - diff_sem_path2[:, 1])
else:
resampled = np.interp(
diff_sem_path1[:, 0], diff_sem_path2[:, 0], diff_sem_path2[:, 1])
diff = resampled - diff_sem_path1[:, 1]
if params['diffAmp'] != 1:
diffLabel = f"{params['diffAmp']} x Diff."
else:
diffLabel = f"Diff. [{sem_path_dict['regLab']} - {sem_path_dict['specLab']}]"
plt.plot(diff_sem_path1[:, 0]-tshift, diff*params['diffAmp'],
'k', label=diffLabel, linewidth=1.)
# Set boundaries x-axis
plt.xlim([xmin, xmax])
plt.gca().yaxis.set_major_formatter(mtick.FormatStrFormatter('%.2e'))
for ar in arrivals:
if np.any(phase_list == ar.name) & (ar.time < 1800):
plt.vlines(x=ar.time, ymin=np.min(sem_path1[:, 1]),
ymax=np.max(sem_path1[:, 1]), color='k', linestyle='--', alpha=0.5)
if cmpnt == 'N':
plt.text(ar.time, np.min(
sem_path1[:, 1]), s=ar.name)
elif plot_in == 'frequency':
dt = np.mean(np.diff(sem_path1[:, 0]))
# Find time window of interest
idx = np.where((sem_path1[:, 0] > twin[0]) & (
sem_path1[:, 0] <= twin[1]))[0]
freq, power_spectrum1 = spectra(
data=sem_path1[idx, 1], sampling_rate=1/dt)
if params['plot_toggles']['specfem']:
plt.plot(freq, power_spectrum1, color=params['specfem']['color'],
label=label1, linewidth=1.5)
#specfem_poly
if params['plot_toggles']['specfem_poly']:
resampled_specfem_poly = np.interp(
sem_path1[:, 0], sem_path5[:, 0], sem_path5[:, 1])
freq_specfem_poly, power_spectrum2_specfem_poly = spectra(
data=resampled_specfem_poly[idx], sampling_rate=1/dt)
plt.plot(freq_specfem_poly, power_spectrum2_specfem_poly, color=params['specfem_poly']['color'],
label=label5, linewidth=1.5)
#specfem_no_ocean
if params['plot_toggles']['specfem_no_ocean']:
resampled_specfem_noocean = np.interp(
sem_path1[:, 0], sem_path6[:, 0], sem_path6[:, 1])
freq_specfem_noocean, power_spectrum2_specfem_noocean = spectra(
data=resampled_specfem_noocean[idx], sampling_rate=1/dt)
plt.plot(freq_specfem_noocean, power_spectrum2_specfem_noocean, color=params['specfem_no_ocean']['color'],
label=label6, linewidth=1.5)
#specfem-ocean-elev-rec
if params['plot_toggles']['specfem-ocean-elev-rec']:
resampled_specfem_ocean_elev_rec = np.interp(
sem_path1[:, 0], sem_path7[:, 0], sem_path7[:, 1])
freq_specfem_ocean_elev_rec, power_spectrum2_specfem_ocean_elev_rec = spectra(
data=resampled_specfem_ocean_elev_rec[idx], sampling_rate=1/dt)
plt.plot(freq_specfem_ocean_elev_rec, power_spectrum2_specfem_ocean_elev_rec, color=params['specfem-ocean-elev-rec']['color'],
label=label7, linewidth=1.5)
#specfem-etopo_0
if params['plot_toggles']['specfem-etopo_0']:
resampled_specfem_etopo_0 = np.interp(
sem_path1[:, 0], sem_path8[:, 0], sem_path8[:, 1])
freq_specfem_etopo_0, power_spectrum2_specfem_etopo_0 = spectra(
data=resampled_specfem_etopo_0[idx], sampling_rate=1/dt)
plt.plot(freq_specfem_etopo_0, power_spectrum2_specfem_etopo_0, color=params['specfem-etopo_0']['color'],
label=label8, linewidth=1.5)
#specfem_rearth_6371
if params['plot_toggles']['specfem_rearth_6371']:
resampled_specfem_rearth_6371 = np.interp(
sem_path1[:, 0], sem_path9[:, 0], sem_path9[:, 1])
freq_specfem_rearth_6371, power_spectrum2_specfem_rearth_6371 = spectra(
data=resampled_specfem_rearth_6371[idx], sampling_rate=1/dt)
plt.plot(freq_specfem_rearth_6371, power_spectrum2_specfem_rearth_6371, color=params['specfem_rearth_6371']['color'],
label=label9, linewidth=1.5)
## regsem_with ocean
if params['plot_toggles']['regsem']:
resampled = np.interp(
sem_path1[:, 0], sem_path2[:, 0], sem_path2[:, 1])
freq, power_spectrum2 = spectra(
data=resampled[idx], sampling_rate=1/dt)
plt.plot(freq, power_spectrum2, color=params['regsem']['color'],
label=label2, linewidth=1.5)
## regsem_no_ocean
if params['plot_toggles']['regsem_no_ocean']:
resampled = np.interp(
sem_path1[:, 0], sem_path4[:, 0], sem_path4[:, 1])
freq, power_spectrum4 = spectra(
data=resampled[idx], sampling_rate=1/dt)
plt.plot(freq, power_spectrum4, color=params['regsem_no_ocean']['color'],
label=label4, linewidth=1.5)
#nms
if params['plot_toggles']['nms']:
resampled_nms = np.interp(
sem_path1[:, 0], sem_path3[:, 0], sem_path3[:, 1])
freq_nms, power_spectrum2_nms = spectra(
data=resampled_nms[idx], sampling_rate=1/dt)
plt.plot(freq_nms, power_spectrum2_nms, color=params['nms']['color'],
label=label3, linewidth=1.5)
## Plot difference
# plt.plot(freq, (power_spectrum1-power_spectrum2)*10, color='k',
# label='10 x Diff.', linewidth=1.5)
# set boundaries x-axis
plt.xlim([0, 0.04])
plt.xlabel('Frequency [Hz]')
plt.ylabel('Power Spectra '+cmpnt)
# Plot legend
if cmpnt == 'N':
plt.legend(loc='lower right', ncol=1, fontsize=10)
plt.tight_layout()
outputFig = f"figures/{station}_{plot_in}.png"
plt.savefig(outputFig, dpi=params['outputImageDPI'])
print(f" >>>> Output figure is {outputFig}")
plt.close()
# plt.show()