def plot_comparison(self):
import matplotlib.pyplot as plt
from isp.Gui.Frames import MatplotlibFrame
list_magnitudes = []
labels = []
if len(self.Magnitude_Ws) >= 0:
list_magnitudes.append(self.Magnitude_Ws)
labels.append("Mw")
if len(self.Mss) >= 0:
list_magnitudes.append(self.Mss)
labels.append("Ms")
if len(self.Mcs) >= 0:
list_magnitudes.append(self.Mcs)
labels.append("Mc")
if len(self.MLs) >= 0:
labels.append("ML")
list_magnitudes.append(self.MLs)
fig, ax1 = plt.subplots(figsize=(6, 6))
self.mpf = MatplotlibFrame(fig)
k = 0
for magnitude in list_magnitudes:
label = labels[k]
x = np.arange(len(magnitude)) + 1
ax1.scatter(x, magnitude, s=15, alpha=0.5, label=label)
ax1.tick_params(direction='in', labelsize=10)
ax1.legend(loc='upper right')
plt.ylabel('Magnitudes', fontsize=10)
plt.xlabel('Counts', fontsize=10)
k = k + 1
self.mpf.show()
def plot_scatter(self):
from isp.Gui.Frames import MatplotlibFrame
print("Plotting PDF")
pdf = np.array(self.pdf) / np.max(self.pdf)
left, width = 0.06, 0.65
bottom, height = 0.1, 0.65
spacing = 0.02
rect_scatter = [left, bottom, width, height]
rect_scatterlon = [left, bottom + height + spacing, width, 0.2]
rect_scatterlat = [left + width + spacing, bottom, 0.2, height]
fig = plt.figure(figsize=(10, 8))
self.mpf = MatplotlibFrame(fig)
ax_scatter = plt.axes(rect_scatter)
ax_scatter.tick_params(direction='in',
top=True,
right=True,
labelsize=10)
plt.scatter(self.x,
self.y,
s=10,
c=pdf,
alpha=0.5,
marker=".",
cmap=plt.cm.jet)
plt.xlabel("Longitude", fontsize=10)
plt.ylabel("Latitude", fontsize=10)
ax_scatx = plt.axes(rect_scatterlon)
ax_scatx.tick_params(direction='in', labelbottom=False, labelsize=10)
plt.scatter(self.x,
self.z,
s=10,
c=pdf,
alpha=0.5,
marker=".",
cmap=plt.cm.jet)
plt.ylabel("Depth (km)", fontsize=10)
plt.gca().invert_yaxis()
ax_scatx = plt.axes(rect_scatterlat)
ax_scatx.tick_params(direction='in', labelleft=False, labelsize=10)
ax_scaty = plt.axes(rect_scatterlat)
ax_scaty.tick_params(direction='in')
ax_scaty.tick_params(which='major', labelsize=10)
plt.scatter(self.z,
self.y,
s=10,
c=pdf,
alpha=0.5,
marker=".",
cmap=plt.cm.jet)
ax_scaty = plt.axes(rect_scatterlat)
ax_scaty.tick_params(direction='in', labelsize=10)
plt.xlabel("Depth (km)", fontsize=10)
cax = plt.axes([0.95, 0.1, 0.02, 0.8])
plt.colorbar(cax=cax)
self.mpf.show()
def plot_spectrum(self, freq, spec, jackknife_errors):
import matplotlib.pyplot as plt
from isp.Gui.Frames import MatplotlibFrame
fig, ax1 = plt.subplots(figsize=(6, 6))
self.mpf = MatplotlibFrame(fig)
ax1.loglog(freq, spec, linewidth=1.0, color='steelblue', label=self.__id)
ax1.frequencies = freq
ax1.spectrum = spec
ax1.fill_between(freq, jackknife_errors[:, 0], jackknife_errors[:, 1], facecolor="0.75",
alpha=0.5, edgecolor="0.5")
ax1.set_ylim(spec.min() / 10.0, spec.max() * 100.0)
plt.ylabel('Amplitude')
plt.xlabel('Frequency [Hz]')
plt.grid(True, which="both", ls="-", color='grey')
plt.legend()
self.mpf.show()
def plot_seismograms(self):
parameters = self.get_inversion_parameters()
lat = float(parameters['latitude'])
lon = float(parameters['longitude'])
starttime = convert_qdatetime_utcdatetime(self.starttime_date)
endtime = convert_qdatetime_utcdatetime(self.endtime_date)
diff = endtime - starttime
parameters = self.parameters.getParameters()
all_traces = []
obsfiles = MseedUtil.get_mseed_files(self.root_path_bind.value)
obsfiles.sort()
for file in obsfiles:
sd = SeismogramDataAdvanced(file)
if self.trimCB.isChecked() and diff >= 0:
tr = sd.get_waveform_advanced(
parameters,
self.inventory,
filter_error_callback=self.filter_error_message,
start_time=starttime,
end_time=endtime)
else:
tr = sd.get_waveform_advanced(
parameters,
self.inventory,
filter_error_callback=self.filter_error_message)
all_traces.append(tr)
self.st = Stream(traces=all_traces)
self.stream_frame = MatplotlibFrame(self.st, type='normal')
self.stream_frame.show()
if self.st:
min_dist = self.min_distCB.value()
max_dist = self.max_distCB.value()
mt = MTIManager(self.st, self.inventory, lat, lon, min_dist,
max_dist)
[self.stream, self.deltas,
self.stations_isola_path] = mt.get_stations_index()
def plot_spectrum_all(self, all_items):
import matplotlib.pyplot as plt
from isp.Gui.Frames import MatplotlibFrame
fig, ax1 = plt.subplots(figsize=(6, 6))
self.mpf = MatplotlibFrame(fig)
for key, seismogram in all_items:
data = seismogram[2]
delta = 1 / seismogram[0][6]
sta = seismogram[0][1]
[spec, freq, jackknife_errors] = spectrumelement(data, delta, sta)
info = "{}.{}.{}".format(seismogram[0][0], seismogram[0][1], seismogram[0][3])
ax1.loglog(freq, spec, linewidth=1.0, alpha = 0.5, label=info)
ax1.frequencies = freq
ax1.spectrum = spec
ax1.set_ylim(spec.min() / 10.0, spec.max() * 100.0)
# ax1.set_xlim(freq[0], 1/(2*delta))
plt.ylabel('Amplitude')
plt.xlabel('Frequency [Hz]')
plt.grid(True, which="both", ls="-", color='grey')
plt.legend()
self.mpf.show()
class MagnitudeCalc(pw.QFrame, UiMagnitudeFrame, metaclass=SettingsLoader):
def __init__(self, origin, inventory, chop):
super(MagnitudeCalc, self).__init__()
self.setupUi(self)
self.spectrum_Widget_Canvas = MatplotlibCanvas(self.spectrumWidget,
nrows=2,
ncols=1,
sharex=False,
constrained_layout=True)
self.event = origin
self.chop = chop
self.inventory = inventory
self.runBtn.clicked.connect(self.run_magnitudes)
self.saveBtn.clicked.connect(self.save_results)
self.plotBtn.clicked.connect(self.plot_comparison)
self.Mw = []
self.Mw_std = []
self.Ms = []
self.Ms_std = []
self.Mb = []
self.Mb_std = []
self.ML = []
self.ML_std = []
self.Mc = []
self.Mc_std = []
self.ML = []
self.ML_std = []
self.Magnitude_Ws = []
self.Mss = []
self.Mcs = []
self.Mcs = []
self.MLs = []
def closeEvent(self, ce):
self.save_values()
def __load__(self):
self.load_values()
def moment_magnitude(self):
density = self.densityDB.value()
vp = self.vpDB.value() * 1000
vs = self.vsDB.value() * 1000
if self.phaseCB.currentText() == 'P-Wave':
radiation_pattern = 0.52
velocity = vp
k = 0.32
elif self.phaseCB.currentText() == "S-Wave":
radiation_pattern = 0.63
velocity = vs
k = 0.21
Mws = []
Mws_std = []
Magnitude_Ws = []
origin_time = self.event.time
moments = []
source_radii = []
corner_frequencies = []
corner_freqs = []
self.spectrum_Widget_Canvas.plot([], [], 0)
ax1 = self.spectrum_Widget_Canvas.get_axe(0)
ax1.cla()
for key in self.chop['Body waves']:
magnitude_dict = self.chop['Body waves'][key]
# Calculate distance
coords = get_coordinates_from_metadata(self.inventory,
magnitude_dict[0])
dist, _, _ = gps2dist_azimuth(coords.Latitude, coords.Longitude,
self.event.latitude,
self.event.longitude)
pick_time = UTCDateTime(mdt.num2date(magnitude_dict[1][0]))
data = np.array(magnitude_dict[2])
delta = 1 / magnitude_dict[0][6]
# Calculate the spectrum.
spec, freq, jackknife_errors, _, _ = mtspec(data,
delta=delta,
time_bandwidth=3.5,
statistics=True)
spec = spec[1:]
freq = freq[1:]
# go to amplitude and displacement
spec = np.sqrt(spec) / (2 * np.pi * freq)
# Plot spectrum
ax1.loglog(freq,
spec,
'0.1',
linewidth=0.5,
color='black',
alpha=0.5)
ax1.set_ylim(spec.min() / 10.0, spec.max() * 100.0)
ax1.set_xlim(freq[1], freq[len(freq) - 1])
ax1.set_ylabel('Amplitude')
ax1.set_xlabel('Frequency [Hz]')
ax1.grid(True, which="both", ls="-", color='grey')
ax1.legend()
# Finish Plot
tt = pick_time - origin_time
#print("Distance",dist,"Vp",vp,"Q",quality,"Density",density, "Travel Time", tt)
# Q as a function of freq
Qo = self.qualityDB.value()
a = self.coeffDB.value()
quality = (Qo) * (freq)**a
try:
fit = fit_spectrum(spec, freq, tt, spec.max(), 10.0, quality)
except:
continue
if fit is None:
continue
Omega_0, f_c, err, _ = fit
# Second Plot
theoretical_spectrum = calculate_source_spectrum(
freq, Omega_0, f_c, quality, tt)
ax1.loglog(freq,
theoretical_spectrum,
color="red",
alpha=0.5,
lw=0.5)
ax1.set_xlim(freq[1], freq[len(freq) - 1])
ax1.set_ylabel('Amplitude')
ax1.set_xlabel('Frequency [Hz]')
ax1.grid(True, which="both", ls="-", color='grey')
corner_freqs.append(f_c)
M_0 = 4.0 * np.pi * density * velocity**3 * dist * np.sqrt(
Omega_0**2) / radiation_pattern
Magnitude_Ws.append(2.0 / 3.0 * (np.log10(M_0) - 9.1))
r = 3 * k * vs / sum(corner_freqs)
moments.append(M_0)
source_radii.append(r)
corner_frequencies.extend(corner_freqs)
# Calculate the seismic moment via basic statistics.
moments = np.array(moments)
moment = moments.mean()
moment_std = moments.std()
corner_frequencies = np.array(corner_frequencies)
corner_frequency = corner_frequencies.mean()
corner_frequency_std = corner_frequencies.std()
# Calculate the source radius.
source_radii = np.array(source_radii)
source_radius = source_radii.mean()
self.source_radius = source_radius
source_radius_std = source_radii.std()
# Calculate the stress drop of the event based on the average moment and
# source radii.
stress_drop = (7 * moment) / (16 * source_radius**3)
stress_drop_std = np.sqrt((stress_drop ** 2) * \
(((moment_std ** 2) / (moment ** 2)) + \
(9 * source_radius * source_radius_std ** 2)))
if source_radius > 0 and source_radius_std < source_radius:
print("Source radius:", source_radius, " Std:", source_radius_std)
print("Stress drop:", stress_drop / 1E5, " Std:",
stress_drop_std / 1E5)
self.stress_drop = stress_drop
self.Magnitude_Ws = Magnitude_Ws
Mw = 2.0 / 3.0 * (np.log10(moment) - 9.1)
Mw_std = 2.0 / 3.0 * moment_std / (moment * np.log(10))
Mws_std.append(Mw_std)
Mws.append(Mw)
print("Moment Magnitude", Mws, "Moment Magnitude deviation", Mws_std)
label = "Mw"
self.plot_histograms(Magnitude_Ws, label)
self.Mw = Mw
self.Mw_std = Mw_std
def magnitude_surface(self):
Ms = []
for key in self.chop['Surf Waves']:
magnitude_dict = self.chop['Surf Waves'][key]
coords = get_coordinates_from_metadata(self.inventory,
magnitude_dict[0])
dist = locations2degrees(coords.Latitude, coords.Longitude,
self.event.latitude, self.event.longitude)
data = np.array(magnitude_dict[2]) * 1e6 # convert to nm
Ms_value = np.log10(np.max(data) /
(2 * np.pi)) + 1.66 * np.log10(dist) + 3.3
Ms.append(Ms_value)
Ms = np.array(Ms)
self.Mss = Ms
Ms_mean = Ms.mean()
Ms_deviation = Ms.std()
print("Surface Magnitude", Ms_mean, "Variance", Ms_deviation)
label = "Ms"
self.plot_histograms(Ms, label)
self.Ms = Ms_mean
self.Ms_std = Ms_deviation
def magnitude_body(self):
Mb = []
path_to_atenuation_file = os.path.join(ROOT_DIR,
"earthquakeAnalisysis",
"magnitude_atenuation",
"atenuation.txt")
x, y = np.loadtxt(path_to_atenuation_file, skiprows=1, unpack=True)
for key in self.chop['Body waves']:
magnitude_dict = self.chop['Body waves'][key]
coords = get_coordinates_from_metadata(self.inventory,
magnitude_dict[0])
dist = locations2degrees(coords.Latitude, coords.Longitude,
self.event.latitude, self.event.longitude)
dist = np.floor(dist)
if dist < 16:
print(
"Epicentral Distance ", dist,
" is less than 16 degrees, Body-wave Magnitude couldn't be calculated"
)
else:
loc = np.where(x == dist)[0][0]
atenuation = y[loc]
data = np.array(magnitude_dict[2]) * 1e6 # convert to nm
Mb_value = (np.log10(np.max(data)) / (2 * np.pi)) + atenuation
Mb.append(Mb_value)
Mbs = np.array(Mb)
Mb_mean = Mbs.mean()
Mb_deviation = Mb.std()
self.Mb = Mb_mean
self.Mb_std = Mb_deviation
print("Body Magnitude", Mb_mean, "Variance", Mb_deviation)
label = "Mb"
self.Mbs = Mbs
if len(Mb) > 0:
self.plot_histograms(Mb, label)
def magnitude_coda(self):
Mc = []
# values for california
a = 2.0
b = 0.0035
c = -0.87
#
for key in self.chop['Coda']:
magnitude_dict = self.chop['Coda'][key]
coords = get_coordinates_from_metadata(self.inventory,
magnitude_dict[0])
dist, _, _ = gps2dist_azimuth(coords.Latitude, coords.Longitude,
self.event.latitude,
self.event.longitude)
dist = dist / 1000
#data = np.array(magnitude_dict[2])
pick_time = UTCDateTime(mdt.num2date(magnitude_dict[1][0]))
end_time = UTCDateTime(mdt.num2date(magnitude_dict[1][-1]))
t_coda = end_time - pick_time
Mc_value = a * np.log10(t_coda) + b * dist + c
Mc_value = Mc_value
Mc.append(Mc_value)
Mcs = np.array(Mc)
Mc_mean = Mcs.mean()
Mc_deviation = Mcs.std()
print("Coda Magnitude", Mc_mean, "Variance", Mc_deviation)
label = "Mc"
self.plot_histograms(Mc, label)
self.Mcs = Mcs
self.Mc = Mc_mean
self.Mc_std = Mc_deviation
def magnitude_local(self):
ML = []
for key in self.chop['Body waves']:
magnitude_dict = self.chop['Body waves'][key]
coords = get_coordinates_from_metadata(self.inventory,
magnitude_dict[0])
dist, _, _ = gps2dist_azimuth(coords.Latitude, coords.Longitude,
self.event.latitude,
self.event.longitude)
dist = dist / 1000
data = np.array(
magnitude_dict[2]
) # already converted Wood Anderson (Gain in mm 2800 +-60)
max_amplitude = np.max(data) * 1e3 # convert to mm --> nm
ML_value = np.log10(
max_amplitude) + 1.11 * np.log10(dist) + 0.00189 * dist - 2.09
ML.append(ML_value)
MLs = np.array(ML)
ML_mean = MLs.mean()
ML_deviation = MLs.std()
print("Local Magnitude", ML_mean, "Variance", ML_deviation)
label = "ML"
self.MLs = MLs
self.plot_histograms(ML, label)
self.ML = ML_mean
self.ML_std = ML_deviation
def run_magnitudes(self):
self.spectrum_Widget_Canvas.plot([], [], 1)
self.ax2 = self.spectrum_Widget_Canvas.get_axe(1)
self.ax2.cla()
if self.local_magnitudeRB.isChecked():
print("Calculating Local Magnitude")
try:
self.magnitude_local()
except:
pass
if self.magnitudesRB.isChecked():
try:
if self.body_waveCB.isChecked():
print("Calculating Body-Wave Magnitude")
self.magnitude_body()
except:
pass
try:
if self.surface_waveCB.isChecked():
print("Calculating Surface-Wave Magnitude")
self.magnitude_surface()
except:
pass
try:
if self.moment_magnitudeCB.isChecked():
print("Calculating Moment Magnitude")
self.moment_magnitude()
except:
pass
try:
if self.coda_magnitudeCB.isChecked():
print("Coda Moment Magnitude")
self.magnitude_coda()
except:
pass
self.print_magnitudes_results()
def print_magnitudes_results(self):
self.magnitudesText.setPlainText(" Magnitudes Estimation Results ")
try:
if self.Mw:
self.magnitudesText.appendPlainText(
"Moment Magnitude: "
" Mw {Mw:.3f} "
" std {std:.3f} "
"Source Radious {source:.3f} "
"Stress Drop {stress:.3E} ".format(
Mw=self.Mw,
std=self.Mw_std,
source=self.source_radius / 1000,
stress=self.stress_drop))
except:
pass
try:
if self.Ms:
self.magnitudesText.appendPlainText("Surface-Wave Magnitude: "
" Ms {Ms:.3f} "
" std {std:.3f} ".format(
Ms=self.Ms,
std=self.Ms_std))
except:
pass
try:
if self.Mb:
self.magnitudesText.appendPlainText("Body-Wave Magnitude: "
" Mb {Mb:.3f} "
" std {std:.3f} ".format(
Mb=self.Mb,
std=self.Mb_std))
except:
pass
try:
if self.Mc:
self.magnitudesText.appendPlainText("Coda Magnitude: "
" Mc {Mc:.3f} "
" std {std:.3f} ".format(
Mc=self.Mc,
std=self.Mc_std))
except:
pass
try:
if self.ML:
self.magnitudesText.appendPlainText("Coda Magnitude: "
" ML {ML:.3f} "
" std {std:.3f} ".format(
ML=self.ML,
std=self.ML_std))
except:
pass
def save_results(self):
import pandas as pd
path_output = os.path.join(ROOT_DIR, "earthquakeAnalisysis",
"location_output", "loc",
"magnitudes_output.mag")
Magnitude_results = {
'Magnitudes': [
"Mw", "Mw_std", "Ms", "Ms_std", "Mb", "Mb_std", "Mc", "Mc_std",
"ML", "ML_std"
],
'results': [
self.Mw, self.Mw_std, self.Ms, self.Ms_std, self.Mb,
self.Mb_std, self.Mc, self.Mc_std, self.ML, self.ML_std
]
}
df = pd.DataFrame(Magnitude_results, columns=['Magnitudes', 'results'])
print(df)
df.to_csv(path_output, sep=' ', index=False)
def plot_histograms(self, magnitudes, label):
self.ax2.hist(magnitudes,
bins=4 * len(magnitudes),
alpha=0.5,
label=label)
self.ax2.set_xlabel("Magnitude", size=12)
self.ax2.set_ylabel("Count", size=12)
self.ax2.legend(loc='upper right')
def plot_comparison(self):
import matplotlib.pyplot as plt
from isp.Gui.Frames import MatplotlibFrame
list_magnitudes = []
labels = []
if len(self.Magnitude_Ws) >= 0:
list_magnitudes.append(self.Magnitude_Ws)
labels.append("Mw")
if len(self.Mss) >= 0:
list_magnitudes.append(self.Mss)
labels.append("Ms")
if len(self.Mcs) >= 0:
list_magnitudes.append(self.Mcs)
labels.append("Mc")
if len(self.MLs) >= 0:
labels.append("ML")
list_magnitudes.append(self.MLs)
fig, ax1 = plt.subplots(figsize=(6, 6))
self.mpf = MatplotlibFrame(fig)
k = 0
for magnitude in list_magnitudes:
label = labels[k]
x = np.arange(len(magnitude)) + 1
ax1.scatter(x, magnitude, s=15, alpha=0.5, label=label)
ax1.tick_params(direction='in', labelsize=10)
ax1.legend(loc='upper right')
plt.ylabel('Magnitudes', fontsize=10)
plt.xlabel('Counts', fontsize=10)
k = k + 1
self.mpf.show()
def data_availability(cls, files_path: str, only_this=True):
import matplotlib.pyplot as plt
import matplotlib.dates as mdt
from isp.Gui.Frames import MatplotlibFrame
fig, hax = plt.subplots(1, 1, figsize=(12, 6))
cls.mpf = MatplotlibFrame(fig)
starttimes = []
endtimes = []
if only_this:
obsfiles = [
f for f in listdir(files_path) if isfile(join(files_path, f))
]
obsfiles.sort()
else:
obsfiles = []
for top_dir, sub_dir, files in os.walk(files_path):
for file in files:
obsfiles.append(os.path.join(top_dir, file))
obsfiles.sort()
data_map = {}
data_map['nets'] = {}
for i in obsfiles:
paths = os.path.join(files_path, i)
if _is_mseed(paths):
header = read(paths, headlonly=True)
gap = header.get_gaps()
net = header[0].stats.network
sta = header[0].stats.station
chn = header[0].stats.channel
# times
starttimes.append(header[0].stats.starttime)
start = header[0].stats.starttime.matplotlib_date
endtimes.append(header[0].stats.endtime)
end = header[0].stats.endtime.matplotlib_date
name = net + "." + sta + "." + chn
hax.hlines(name,
start,
end,
colors='k',
linestyles='solid',
label=name,
lw=2)
if len(gap) > 0:
for i in range(len(gap)):
starttime_gap = gap[i][4].matplotlib_date
endtime_gap = gap[i][5].matplotlib_date
hax.hlines(name,
starttime_gap,
endtime_gap,
colors='r',
linestyles='solid',
label=name,
lw=2)
start_time = min(starttimes)
end_time = max(endtimes)
formatter = mdt.DateFormatter('%y/%m/%d/%H:%M:%S.%f')
hax.xaxis.set_major_formatter(formatter)
hax.set_xlabel("Date")
hax.set_xlim(start_time.matplotlib_date, end_time.matplotlib_date)
cls.mpf.show()
def plot_seismograms(self, FK=True):
if FK:
starttime = convert_qdatetime_utcdatetime(self.starttime_date)
endtime = convert_qdatetime_utcdatetime(self.endtime_date)
else:
starttime = convert_qdatetime_utcdatetime(self.starttime_date_BP)
endtime = convert_qdatetime_utcdatetime(self.endtime_date_BP)
diff = endtime - starttime
if FK:
file_path = self.root_pathFK_bind.value
else:
file_path = self.root_pathBP_bind.value
obsfiles = []
for dirpath, _, filenames in os.walk(file_path):
for f in filenames:
if f != ".DS_Store":
obsfiles.append(os.path.abspath(os.path.join(dirpath, f)))
obsfiles.sort()
parameters = self.__parameters.getParameters()
all_traces = []
trace_number = 0
for file in obsfiles:
sd = SeismogramDataAdvanced(file)
if FK:
if self.trimCB.isChecked() and diff >= 0:
tr = sd.get_waveform_advanced(
parameters,
self.inventory,
filter_error_callback=self.filter_error_message,
start_time=starttime,
end_time=endtime,
trace_number=trace_number)
else:
tr = sd.get_waveform_advanced(
parameters,
self.inventory,
filter_error_callback=self.filter_error_message,
trace_number=trace_number)
else:
if self.trimCB_BP.isChecked() and diff >= 0:
tr = sd.get_waveform_advanced(
parameters,
self.inventory,
filter_error_callback=self.filter_error_message,
start_time=starttime,
end_time=endtime,
trace_number=trace_number)
else:
tr = sd.get_waveform_advanced(
parameters,
self.inventory,
filter_error_callback=self.filter_error_message,
trace_number=trace_number)
all_traces.append(tr)
trace_number = trace_number + 1
self.st = Stream(traces=all_traces)
if FK:
if self.selectCB.isChecked():
self.st = self.st.select(station=self.stationLE.text(),
channel=self.channelLE.text())
else:
if self.selectCB_BP.isChecked():
self.st = self.st.select(network=self.stationLE_BP.text(),
station=self.stationLE_BP.text(),
channel=self.channelLE_BP.text())
self.stream_frame = MatplotlibFrame(self.st, type='normal')
self.stream_frame.show()
class ArrayAnalysisFrame(BaseFrame, UiArrayAnalysisFrame):
def __init__(self):
super(ArrayAnalysisFrame, self).__init__()
self.setupUi(self)
self.__stations_dir = None
self.stream_frame = None
self.__metadata_manager = None
self.inventory = {}
self._stations_info = {}
self._stations_coords = {}
self.stack = None
self.canvas = MatplotlibCanvas(self.responseMatWidget)
self.canvas_fk = MatplotlibCanvas(self.widget_fk, nrows=4)
self.canvas_slow_map = MatplotlibCanvas(self.widget_slow_map)
self.canvas_fk.on_double_click(self.on_click_matplotlib)
self.canvas_stack = MatplotlibCanvas(self.widget_stack)
self.cartopy_canvas = CartopyCanvas(self.widget_map)
self.canvas.set_new_subplot(1, ncols=1)
#Binding
self.root_pathFK_bind = BindPyqtObject(self.rootPathFormFK)
self.root_pathBP_bind = BindPyqtObject(self.rootPathFormBP)
self.metadata_path_bind = BindPyqtObject(self.datalessPathForm,
self.onChange_metadata_path)
self.metadata_path_bindBP = BindPyqtObject(self.datalessPathFormBP,
self.onChange_metadata_path)
self.output_path_bindBP = BindPyqtObject(self.outputPathFormBP,
self.onChange_metadata_path)
self.fmin_bind = BindPyqtObject(self.fminSB)
self.fmax_bind = BindPyqtObject(self.fmaxSB)
self.grid_bind = BindPyqtObject(self.gridSB)
self.smax_bind = BindPyqtObject(self.smaxSB)
# On select
self.canvas_fk.register_on_select(self.on_select,
rectprops=dict(alpha=0.2,
facecolor='red'))
self.fminFK_bind = BindPyqtObject(self.fminFKSB)
self.fmaxFK_bind = BindPyqtObject(self.fmaxFKSB)
self.overlap_bind = BindPyqtObject(self.overlapSB)
self.timewindow_bind = BindPyqtObject(self.timewindowSB)
self.smaxFK_bind = BindPyqtObject(self.slowFKSB)
self.slow_grid_bind = BindPyqtObject(self.gridFKSB)
# Bind buttons
self.selectDirBtnFK.clicked.connect(
lambda: self.on_click_select_directory(self.root_pathFK_bind))
self.datalessBtn.clicked.connect(
lambda: self.on_click_select_metadata_file(self.metadata_path_bind
))
# Bind buttons BackProjection
self.selectDirBtnBP.clicked.connect(
lambda: self.on_click_select_directory(self.root_pathBP_bind))
self.datalessBtnBP.clicked.connect(
lambda: self.on_click_select_metadata_file(self.
metadata_path_bindBP))
self.outputBtn.clicked.connect(
lambda: self.on_click_select_directory(self.output_path_bindBP))
#Action Buttons
self.arfBtn.clicked.connect(lambda: self.arf())
self.runFKBtn.clicked.connect(lambda: self.FK_plot())
self.plotBtn.clicked.connect(lambda: self.plot_seismograms())
self.plotBtnBP.clicked.connect(lambda: self.plot_seismograms(FK=False))
self.actionSettings.triggered.connect(
lambda: self.open_parameters_settings())
self.actionProcessed_Seimograms.triggered.connect(self.write)
self.actionStacked_Seismograms.triggered.connect(self.write_stack)
self.stationsBtn.clicked.connect(lambda: self.stationsInfo())
self.stationsBtnBP.clicked.connect(lambda: self.stationsInfo(FK=False))
self.mapBtn.clicked.connect(self.stations_map)
self.actionCreate_Stations_File.triggered.connect(
self.stations_coordinates)
self.actionLoad_Stations_File.triggered.connect(self.load_path)
self.actionRunVespagram.triggered.connect(self.open_vespagram)
self.shortcut_open = pw.QShortcut(pqg.QKeySequence('Ctrl+O'), self)
self.shortcut_open.activated.connect(self.open_solutions)
self.create_gridBtn.clicked.connect(self.create_grid)
self.actionOpen_Help.triggered.connect(lambda: self.open_help())
self.load_videoBtn.clicked.connect(self.loadvideoBP)
# help Documentation
self.help = HelpDoc()
# Parameters settings
self.__parameters = ParametersSettings()
# Stations Coordinates
self.__stations_coords = StationsCoords()
# picks
self.picks = {
'Time': [],
'Phase': [],
'BackAzimuth': [],
'Slowness': [],
'Power': []
}
# video
self.player = QMediaPlayer(None, QMediaPlayer.VideoSurface)
self.player.setVideoOutput(self.backprojection_widget)
self.player.stateChanged.connect(self.mediaStateChanged)
self.player.positionChanged.connect(self.positionChanged)
self.player.durationChanged.connect(self.durationChanged)
self.playButton.setIcon(self.style().standardIcon(QStyle.SP_MediaPlay))
self.playButton.clicked.connect(self.play_bp)
self.positionSlider.sliderMoved.connect(self.setPosition)
def mediaStateChanged(self):
if self.player.state() == QMediaPlayer.PlayingState:
self.playButton.setIcon(self.style().standardIcon(
QStyle.SP_MediaPause))
else:
self.playButton.setIcon(self.style().standardIcon(
QStyle.SP_MediaPlay))
def positionChanged(self, position):
self.positionSlider.setValue(position)
def durationChanged(self, duration):
self.positionSlider.setRange(0, duration)
def setPosition(self, position):
self.player.setPosition(position)
def open_parameters_settings(self):
self.__parameters.show()
def stations_coordinates(self):
self.__stations_coords.show()
def open_vespagram(self):
if self.st and self.inventory and self.t1 and self.t2:
self.__vespagram = Vespagram(self.st, self.inventory, self.t1,
self.t2)
self.__vespagram.show()
def on_click_select_directory(self, bind: BindPyqtObject):
if "darwin" == platform:
dir_path = pw.QFileDialog.getExistingDirectory(
self, 'Select Directory', bind.value)
else:
dir_path = pw.QFileDialog.getExistingDirectory(
self, 'Select Directory', bind.value,
pw.QFileDialog.DontUseNativeDialog)
if dir_path:
bind.value = dir_path
def onChange_metadata_path(self, value):
md = MessageDialog(self)
try:
self.__metadata_manager = MetadataManager(value)
self.inventory = self.__metadata_manager.get_inventory()
print(self.inventory)
md.set_info_message(
"Loaded Metadata, please check your terminal for further details"
)
except:
md.set_error_message(
"Something went wrong. Please check your metada file is a correct one"
)
def on_click_select_metadata_file(self, bind: BindPyqtObject):
selected = pw.QFileDialog.getOpenFileName(self, "Select metadata file")
if isinstance(selected[0], str) and os.path.isfile(selected[0]):
bind.value = selected[0]
def load_path(self):
selected_file = pw.QFileDialog.getOpenFileName(
self, "Select Stations Coordinates file")
self.path_file = selected_file[0]
df = pd.read_csv(self.path_file, delim_whitespace=True)
n = len(df)
self.coords = np.zeros([n, 3])
for i in range(n):
#coords[i]=data[i]
self.coords[i] = np.array(
[df['Lon'][i], df['Lat'][i], df['Depth'][i]])
def arf(self):
try:
if self.coords.all():
wavenumber = array_analysis.array()
arf = wavenumber.arf(self.coords, self.fmin_bind.value,
self.fmax_bind.value,
self.smax_bind.value,
self.grid_bind.value)
slim = self.smax_bind.value
x = y = np.linspace(-1 * slim, slim, len(arf))
self.canvas.plot_contour(x,
y,
arf,
axes_index=0,
clabel="Power [dB]",
cmap=plt.get_cmap("jet"))
self.canvas.set_xlabel(0, "Sx (s/km)")
self.canvas.set_ylabel(0, "Sy (s/km)")
except:
md = MessageDialog(self)
md.set_error_message(
"Couldn't compute ARF, please check if you have loaded stations coords"
)
def stations_map(self):
coords = {}
if self.path_file:
df = pd.read_csv(self.path_file, delim_whitespace=True)
n = len(df)
self.coords = np.zeros([n, 3])
for i in range(n):
coords[df['Name'][i]] = [
df['Lat'][i],
df['Lon'][i],
]
#try:
self.cartopy_canvas.plot_map(df['Lat'][0],
df['Lon'][0],
0,
0,
0,
0,
resolution="low",
stations=coords)
#except:
# pass
def FK_plot(self):
self.canvas_stack.set_new_subplot(nrows=1, ncols=1)
starttime = convert_qdatetime_utcdatetime(self.starttime_date)
endtime = convert_qdatetime_utcdatetime(self.endtime_date)
selection = self.inventory.select(station=self.stationLE.text(),
channel=self.channelLE.text())
if self.trimCB.isChecked():
wavenumber = array_analysis.array()
relpower, abspower, AZ, Slowness, T = wavenumber.FK(
self.st, selection, starttime, endtime, self.fminFK_bind.value,
self.fmaxFK_bind.value, self.smaxFK_bind.value,
self.slow_grid_bind.value, self.timewindow_bind.value,
self.overlap_bind.value)
self.canvas_fk.scatter3d(T,
relpower,
relpower,
axes_index=0,
clabel="Power [dB]")
self.canvas_fk.scatter3d(T,
abspower,
relpower,
axes_index=1,
clabel="Power [dB]")
self.canvas_fk.scatter3d(T,
AZ,
relpower,
axes_index=2,
clabel="Power [dB]")
self.canvas_fk.scatter3d(T,
Slowness,
relpower,
axes_index=3,
clabel="Power [dB]")
self.canvas_fk.set_ylabel(0, " Rel Power ")
self.canvas_fk.set_ylabel(1, " Absolute Power ")
self.canvas_fk.set_ylabel(2, " Back Azimuth ")
self.canvas_fk.set_ylabel(3, " Slowness [s/km] ")
self.canvas_fk.set_xlabel(3, " Time [s] ")
ax = self.canvas_fk.get_axe(3)
formatter = mdt.DateFormatter('%H:%M:%S')
ax.xaxis.set_major_formatter(formatter)
ax.xaxis.set_tick_params(rotation=30)
else:
md = MessageDialog(self)
md.set_info_message("Please select dates and then check Trim box")
def on_click_matplotlib(self, event, canvas):
output_path = os.path.join(ROOT_DIR, 'arrayanalysis', 'dataframe.csv')
if isinstance(canvas, MatplotlibCanvas):
st = self.st.copy()
wavenumber = array_analysis.array()
selection = self.inventory.select(station=self.stationLE.text(),
channel=self.channelLE.text())
x1, y1 = event.xdata, event.ydata
DT = x1
Z, Sxpow, Sypow, coord = wavenumber.FKCoherence(
st, selection, DT, self.fminFK_bind.value,
self.fmaxFK_bind.value, self.smaxFK_bind.value,
self.timewindow_bind.value, self.slow_grid_bind.value,
self.methodSB.currentText())
backacimuth = wavenumber.azimuth2mathangle(
np.arctan2(Sypow, Sxpow) * 180 / np.pi)
slowness = np.abs(Sxpow, Sypow)
if self.methodSB.currentText() == "FK":
clabel = "Power"
elif self.methodSB.currentText() == "MTP.COHERENCE":
clabel = "Magnitude Coherence"
Sx = np.arange(-1 * self.smaxFK_bind.value, self.smaxFK_bind.value,
self.slow_grid_bind.value)[np.newaxis]
nx = len(Sx[0])
x = y = np.linspace(-1 * self.smaxFK_bind.value,
self.smaxFK_bind.value, nx)
X, Y = np.meshgrid(x, y)
self.canvas_slow_map.plot_contour(X,
Y,
Z,
axes_index=0,
clabel=clabel,
cmap=plt.get_cmap("jet"))
self.canvas_slow_map.set_xlabel(0, "Sx [s/km]")
self.canvas_slow_map.set_ylabel(0, "Sy [s/km]")
# Save in a dataframe the pick value
x1 = wavenumber.gregorian2date(x1)
self.picks['Time'].append(x1.isoformat())
self.picks['Phase'].append(self.phaseCB.currentText())
self.picks['BackAzimuth'].append(backacimuth[0])
self.picks['Slowness'].append(slowness[0])
self.picks['Power'].append(np.max(Z))
df = pd.DataFrame(self.picks)
df.to_csv(output_path, index=False, header=True)
# Call Stack and Plot###
#stream_stack, time = wavenumber.stack_stream(self.root_pathFK_bind.value, Sxpow, Sypow, coord)
if st:
st2 = self.st.copy()
# Align for the maximum power and give the data of the traces
stream_stack, self.time, self.stats = wavenumber.stack_stream(
st2, Sxpow, Sypow, coord)
# stack the traces
self.stack = wavenumber.stack(
stream_stack, stack_type=self.stackCB.currentText())
self.canvas_stack.plot(self.time,
self.stack,
axes_index=0,
linewidth=0.75)
self.canvas_stack.set_xlabel(0, " Time [s] ")
self.canvas_stack.set_ylabel(0, "Stack Amplitude")
def filter_error_message(self, msg):
md = MessageDialog(self)
md.set_info_message(msg)
def plot_seismograms(self, FK=True):
if FK:
starttime = convert_qdatetime_utcdatetime(self.starttime_date)
endtime = convert_qdatetime_utcdatetime(self.endtime_date)
else:
starttime = convert_qdatetime_utcdatetime(self.starttime_date_BP)
endtime = convert_qdatetime_utcdatetime(self.endtime_date_BP)
diff = endtime - starttime
if FK:
file_path = self.root_pathFK_bind.value
else:
file_path = self.root_pathBP_bind.value
obsfiles = []
for dirpath, _, filenames in os.walk(file_path):
for f in filenames:
if f != ".DS_Store":
obsfiles.append(os.path.abspath(os.path.join(dirpath, f)))
obsfiles.sort()
parameters = self.__parameters.getParameters()
all_traces = []
trace_number = 0
for file in obsfiles:
sd = SeismogramDataAdvanced(file)
if FK:
if self.trimCB.isChecked() and diff >= 0:
tr = sd.get_waveform_advanced(
parameters,
self.inventory,
filter_error_callback=self.filter_error_message,
start_time=starttime,
end_time=endtime,
trace_number=trace_number)
else:
tr = sd.get_waveform_advanced(
parameters,
self.inventory,
filter_error_callback=self.filter_error_message,
trace_number=trace_number)
else:
if self.trimCB_BP.isChecked() and diff >= 0:
tr = sd.get_waveform_advanced(
parameters,
self.inventory,
filter_error_callback=self.filter_error_message,
start_time=starttime,
end_time=endtime,
trace_number=trace_number)
else:
tr = sd.get_waveform_advanced(
parameters,
self.inventory,
filter_error_callback=self.filter_error_message,
trace_number=trace_number)
all_traces.append(tr)
trace_number = trace_number + 1
self.st = Stream(traces=all_traces)
if FK:
if self.selectCB.isChecked():
self.st = self.st.select(station=self.stationLE.text(),
channel=self.channelLE.text())
else:
if self.selectCB_BP.isChecked():
self.st = self.st.select(network=self.stationLE_BP.text(),
station=self.stationLE_BP.text(),
channel=self.channelLE_BP.text())
self.stream_frame = MatplotlibFrame(self.st, type='normal')
self.stream_frame.show()
def stationsInfo(self, FK=True):
if FK:
obsfiles = MseedUtil.get_mseed_files(self.root_pathFK_bind.value)
else:
obsfiles = MseedUtil.get_mseed_files(self.root_pathBP_bind.value)
obsfiles.sort()
sd = []
for file in obsfiles:
st = SeismogramDataAdvanced(file)
station = [
st.stats.Network, st.stats.Station, st.stats.Location,
st.stats.Channel, st.stats.StartTime, st.stats.EndTime,
st.stats.Sampling_rate, st.stats.Npts
]
sd.append(station)
self._stations_info = StationsInfo(sd, check=True)
self._stations_info.show()
def write(self):
root_path = os.path.dirname(os.path.abspath(__file__))
if "darwin" == platform:
dir_path = pw.QFileDialog.getExistingDirectory(
self, 'Select Directory', root_path)
else:
dir_path = pw.QFileDialog.getExistingDirectory(
self, 'Select Directory', root_path,
pw.QFileDialog.DontUseNativeDialog)
if dir_path:
n = len(self.st)
try:
if len(n) > 0:
for j in range(n):
tr = self.st[j]
t1 = tr.stats.starttime
id = tr.id + "." + "D" + "." + str(
t1.year) + "." + str(t1.julday)
print(tr.id, "Writing data processed")
path_output = os.path.join(dir_path, id)
tr.write(path_output, format="MSEED")
else:
md = MessageDialog(self)
md.set_info_message("Nothing to write")
except:
pass
def write_stack(self):
if self.stack is not None and len(self.stack) > 0:
root_path = os.path.dirname(os.path.abspath(__file__))
if "darwin" == platform:
dir_path = pw.QFileDialog.getExistingDirectory(
self, 'Select Directory', root_path)
else:
dir_path = pw.QFileDialog.getExistingDirectory(
self, 'Select Directory', root_path,
pw.QFileDialog.DontUseNativeDialog)
if dir_path:
tr = Trace(data=self.stack, header=self.stats)
file = os.path.join(dir_path, tr.id)
tr.write(file, format="MSEED")
else:
md = MessageDialog(self)
md.set_info_message("Nothing to write")
def __to_UTC(self, DT):
# Convert start from Greogorian to actual date
Time = DT
Time = Time - int(Time)
d = date.fromordinal(int(DT))
date1 = d.isoformat()
H = (Time * 24)
H1 = int(H) # Horas
minutes = (H - int(H)) * 60
minutes1 = int(minutes)
seconds = (minutes - int(minutes)) * 60
H1 = str(H1).zfill(2)
minutes1 = str(minutes1).zfill(2)
seconds = "%.2f" % seconds
seconds = str(seconds).zfill(2)
DATE = date1 + "T" + str(H1) + minutes1 + seconds
t1 = UTCDateTime(DATE)
return t1
def on_select(self, ax_index, xmin, xmax):
self.t1 = self.__to_UTC(xmin)
self.t2 = self.__to_UTC(xmax)
def open_solutions(self):
output_path = os.path.join(ROOT_DIR, 'arrayanalysis', 'dataframe.csv')
try:
command = "{} {}".format('open', output_path)
exc_cmd(command, cwd=ROOT_DIR)
except:
md = MessageDialog(self)
md.set_error_message("Coundn't open solutions file")
### New part back-projection
def create_grid(self):
area_coords = [
self.minLonBP, self.maxLonBP, self.minLatBP, self.maxLatBP
]
bp = back_proj_organize(self, self.rootPathFormBP,
self.datalessPathFormBP, area_coords,
self.sxSB.value, self.sxSB.value,
self.depthSB.value)
mapping = bp.create_dict()
try:
self.path_file = os.path.join(self.output_path_bindBP.value,
"mapping.pkl")
file_to_store = open(self.path_file, "wb")
pickle.dump(mapping, file_to_store)
md = MessageDialog(self)
md.set_info_message("BackProjection grid created succesfully!!!")
except:
md = MessageDialog(self)
md.set_error_message("Coundn't create a BackProjection grid")
def run_bp(self):
try:
if os.path.exists(self.path_file):
with open(self.path_file, 'rb') as handle:
mapping = pickle.load(handle)
except:
md = MessageDialog(self)
md.set_error_message(
"Please you need try to previously create a BackProjection grid"
)
power = backproj.run_back(self.st,
mapping,
self.time_winBP.value,
self.stepBP.value,
window=self.slide_winBP.value,
multichannel=self.mcccCB.isChecked(),
stack_process=self.methodBP.currentText())
#plot_cum(power, mapping['area_coords'], self.cum_sumBP.value, self.st)
plot_bp(power,
mapping['area_coords'],
self.cum_sumBP.value,
self.st,
output=self.output_path_bindBP.value)
fname = os.path.join(self.output_path_bindBP.value, "power.pkl")
file_to_store = open(fname, "wb")
pickle.dump(power, file_to_store)
def loadvideoBP(self):
self.path_video, _ = pw.QFileDialog.getOpenFileName(
self, "Choose your BackProjection", ".",
"Video Files (*.mp4 *.flv *.ts *.mts *.avi)")
if self.path_video != '':
self.player.setVideoOutput(self.backprojection_widget)
self.player.setMedia(
QMediaContent(pyc.QUrl.fromLocalFile(self.path_video)))
md = MessageDialog(self)
md.set_info_message(
"Video containing BackProjection succesfully loaded")
else:
md = MessageDialog(self)
md.set_error_message(
"Video containing BackProjection couldn't be loaded")
def play_bp(self):
if self.player.state() == QMediaPlayer.PlayingState:
self.player.pause()
else:
self.player.play()
def open_help(self):
self.help.show()
def plot_day_view(self):
st = read(self.file_selector.file_path)
self.dayplot_frame = MatplotlibFrame(st, type='dayplot')
self.dayplot_frame.show()
class TimeFrequencyFrame(BaseFrame, UiTimeFrequencyFrame):
def __init__(self, ):
super(TimeFrequencyFrame, self).__init__()
self.setupUi(self)
self.dayplot_frame = None
# Bind buttons
self.selectDirBtn.clicked.connect(self.on_click_select_directory)
self.dayPlotBtn.clicked.connect(self.on_click_dayplot)
# Bind qt objects
self.root_path_bind = BindPyqtObject(self.rootPathForm,
self.onChange_root_path)
self.event_info = EventInfoBox(self.mainToolsWidget, None)
self.event_info.set_buttons_visibility(False)
# Add file selector to the widget
self.file_selector = FilesView(
self.root_path_bind.value,
parent=self.fileSelectorWidget,
on_change_file_callback=lambda file_path: self.onChange_file(
file_path))
self.time_analysis_widget = TimeAnalysisWidget(
self.canvasWidget, parent_name="time_analysis_0")
self.time_analysis_widget2 = TimeAnalysisWidget(
self.canvasWidget, parent_name="time_analysis_1")
self.time_analysis_windows = [
self.time_analysis_widget, self.time_analysis_widget2
]
for taw in self.time_analysis_windows:
taw.register_file_selector(self.file_selector)
taw.set_event_info(self.event_info)
self.actionPlotEnvelope.toggled.connect(self.on_envelop_toggle)
def onChange_root_path(self, value):
"""
Fired every time the root_path is changed
:param value: The path of the new directory.
:return:
"""
self.file_selector.set_new_rootPath(value)
def onChange_file(self, file_path):
# Called every time user select a different file
pass
def on_envelop_toggle(self, checked: bool):
for taw in self.time_analysis_windows:
taw.is_envelop_checked = checked
def validate_file(self):
if not MseedUtil.is_valid_mseed(self.file_selector.file_path):
msg = "The file {} is not a valid mseed. Please, choose a valid format". \
format(self.file_selector.file_name)
raise InvalidFile(msg)
def on_click_select_directory(self):
if "darwin" == platform:
dir_path = pw.QFileDialog.getExistingDirectory(
self, 'Select Directory', self.root_path_bind.value)
else:
dir_path = pw.QFileDialog.getExistingDirectory(
self, 'Select Directory', self.root_path_bind.value,
pw.QFileDialog.DontUseNativeDialog)
if dir_path:
self.root_path_bind.value = dir_path
def plot_day_view(self):
st = read(self.file_selector.file_path)
self.dayplot_frame = MatplotlibFrame(st, type='dayplot')
self.dayplot_frame.show()
def on_click_dayplot(self):
try:
self.validate_file()
self.plot_day_view()
except InvalidFile as e:
md = MessageDialog(self)
md.set_info_message(e.message)
class MTIFrame(BaseFrame, UiMomentTensor):
def __init__(self):
super(MTIFrame, self).__init__()
self.setupUi(self)
#super(MTIFrame, self).__init__()
self.setupUi(self)
self.__stations_dir = None
self.__metadata_manager = None
self.inventory = {}
self._stations_info = {}
self.stream = None
# Binding
self.root_path_bind = BindPyqtObject(self.rootPathForm)
#self.dataless_path_bind = BindPyqtObject(self.datalessPathForm)
self.metadata_path_bind = BindPyqtObject(self.datalessPathForm,
self.onChange_metadata_path)
self.earth_path_bind = BindPyqtObject(self.earth_modelPathForm)
# Binds
self.selectDirBtn.clicked.connect(
lambda: self.on_click_select_directory(self.root_path_bind))
self.datalessBtn.clicked.connect(
lambda: self.on_click_select_metadata_file(self.metadata_path_bind
))
self.earthmodelBtn.clicked.connect(
lambda: self.on_click_select_file(self.earth_path_bind))
# Action Buttons
self.actionSettings.triggered.connect(
lambda: self.open_parameters_settings())
self.plotBtn.clicked.connect(self.plot_seismograms)
self.actionSettings.triggered.connect(
lambda: self.open_parameters_settings())
self.actionWrite.triggered.connect(self.write)
self.actionEarth_Model.triggered.connect(
lambda: self.open_earth_model())
self.actionFrom_File.triggered.connect(
lambda: self.load_event_from_isolapath())
self.actionOpen_Help.triggered.connect(lambda: self.open_help())
self.stationsBtn.clicked.connect(self.stationsInfo)
self.run_inversionBtn.clicked.connect(lambda: self.run_inversion())
self.stations_mapBtn.clicked.connect(lambda: self.plot_map_stations())
self.plot_solutionBtn.clicked.connect(lambda: self.plot_solution())
#self.earthmodelBtn.clicked.connect(self.read_earth_model)
# Parameters settings
self.parameters = ParametersSettings()
self.earth_model = CrustalModelParametersFrame()
# help Documentation
self.help = HelpDoc()
def open_parameters_settings(self):
self.parameters.show()
def open_earth_model(self):
self.earth_model.show()
def filter_error_message(self, msg):
md = MessageDialog(self)
md.set_info_message(msg)
def message_dataless_not_found(self):
if len(self.dataless_not_found) > 1:
md = MessageDialog(self)
md.set_info_message("Metadata not found.")
else:
for file in self.dataless_not_found:
md = MessageDialog(self)
md.set_info_message("Metadata for {} not found.".format(file))
def validate_file(self):
if not MseedUtil.is_valid_mseed(self.file_selector.file_path):
msg = "The file {} is not a valid mseed. Please, choose a valid format". \
format(self.file_selector.file_name)
raise InvalidFile(msg)
def on_click_select_directory(self, bind: BindPyqtObject):
if "darwin" == platform:
dir_path = pw.QFileDialog.getExistingDirectory(
self, 'Select Directory', bind.value)
else:
dir_path = pw.QFileDialog.getExistingDirectory(
self, 'Select Directory', bind.value,
pw.QFileDialog.DontUseNativeDialog)
if dir_path:
bind.value = dir_path
def on_click_select_file(self, bind: BindPyqtObject):
file_path = pw.QFileDialog.getOpenFileName(self, 'Select Directory',
bind.value)
file_path = file_path[0]
if file_path:
bind.value = file_path
def on_click_select_metadata_file(self, bind: BindPyqtObject):
selected = pw.QFileDialog.getOpenFileName(self, "Select metadata file")
if isinstance(selected[0], str) and os.path.isfile(selected[0]):
bind.value = selected[0]
def onChange_metadata_path(self, value):
md = MessageDialog(self)
try:
self.__metadata_manager = MetadataManager(value)
self.inventory = self.__metadata_manager.get_inventory()
print(self.inventory)
md.set_info_message(
"Loaded Metadata, please check your terminal for further details"
)
except:
md.set_error_message(
"Something went wrong. Please check your metada file is a correct one"
)
# def read_earth_model(self):
# model = self.earth_model.getParametersWithFormat()
# print(model)
def plot_seismograms(self):
parameters = self.get_inversion_parameters()
lat = float(parameters['latitude'])
lon = float(parameters['longitude'])
starttime = convert_qdatetime_utcdatetime(self.starttime_date)
endtime = convert_qdatetime_utcdatetime(self.endtime_date)
diff = endtime - starttime
parameters = self.parameters.getParameters()
all_traces = []
obsfiles = MseedUtil.get_mseed_files(self.root_path_bind.value)
obsfiles.sort()
for file in obsfiles:
sd = SeismogramDataAdvanced(file)
if self.trimCB.isChecked() and diff >= 0:
tr = sd.get_waveform_advanced(
parameters,
self.inventory,
filter_error_callback=self.filter_error_message,
start_time=starttime,
end_time=endtime)
else:
tr = sd.get_waveform_advanced(
parameters,
self.inventory,
filter_error_callback=self.filter_error_message)
all_traces.append(tr)
self.st = Stream(traces=all_traces)
self.stream_frame = MatplotlibFrame(self.st, type='normal')
self.stream_frame.show()
if self.st:
min_dist = self.min_distCB.value()
max_dist = self.max_distCB.value()
mt = MTIManager(self.st, self.inventory, lat, lon, min_dist,
max_dist)
[self.stream, self.deltas,
self.stations_isola_path] = mt.get_stations_index()
def stationsInfo(self):
file_path = self.root_path_bind.value
obsfiles = MseedUtil.get_mseed_files(self.root_path_bind.value)
obsfiles.sort()
sd = []
for file in obsfiles:
st = SeismogramDataAdvanced(file)
station = [
st.stats.Network, st.stats.Station, st.stats.Location,
st.stats.Channel, st.stats.StartTime, st.stats.EndTime,
st.stats.Sampling_rate, st.stats.Npts
]
sd.append(station)
self._stations_info = StationsInfo(sd, check=True)
self._stations_info.show()
def write(self):
root_path = os.path.dirname(os.path.abspath(__file__))
if "darwin" == platform:
dir_path = pw.QFileDialog.getExistingDirectory(
self, 'Select Directory', root_path)
else:
dir_path = pw.QFileDialog.getExistingDirectory(
self, 'Select Directory', root_path,
pw.QFileDialog.DontUseNativeDialog)
if not dir_path:
return
n = len(self.st)
for j in range(n):
tr = self.st[j]
print(tr.id, "Writing data processed")
path_output = os.path.join(dir_path, tr.id)
tr.write(path_output, format="MSEED")
##In progress##
def load_event_from_isolapath(self):
root_path = os.path.dirname(os.path.abspath(__file__))
file_path = pw.QFileDialog.getOpenFileName(self, 'Select Directory',
root_path)
file = file_path[0]
frame = pd.read_csv(file, sep='\s+', header=None)
time = frame.iloc[3][0]
year = time[0:4]
mm = time[4:6]
dd = time[6:8]
hour = frame.iloc[4][0]
minute = frame.iloc[5][0]
sec = frame.iloc[6][0]
sec = float(sec)
dec = sec - int(sec)
dec = int(sec)
time = UTCDateTime(int(year), int(mm), int(dd), int(hour), int(minute),
int(sec), dec)
event = {
'lat': frame.iloc[0][0],
'lon': frame.iloc[0][1],
'depth': frame.iloc[1][0],
'mag': frame.iloc[2][0],
'time': time,
'istitution': frame.iloc[7][0]
}
return event
@AsycTime.run_async()
def run_inversion(self):
parameters = self.get_inversion_parameters()
try:
stations_map = self._stations_info.get_stations_map()
except:
md = MessageDialog(self)
md.set_info_message("Press Stations info and check your selection")
if len(self.stream) and len(stations_map) > 0:
isola = ISOLA(self.stream,
self.deltas,
location_unc=parameters['location_unc'],
depth_unc=parameters['depth_unc'],
time_unc=parameters['time_unc'],
deviatoric=parameters['deviatoric'],
threads=8,
circle_shape=parameters['circle_shape'],
use_precalculated_Green=parameters['GFs'])
#
isola.set_event_info(parameters['latitude'],
parameters['longitude'], parameters['depth'],
parameters['magnitude'],
parameters['origin_time'])
#
print(isola.event)
#
#
if self.stations_isola_path:
isola.read_network_coordinates(self.stations_isola_path)
isola.set_use_components(stations_map)
#print(isola.stations)
isola.read_crust(self.earth_path_bind.value)
isola.set_parameters(parameters['freq_max'],
parameters['freq_min'])
self.infoTx.setPlainText("Calculated GFs")
if not isola.calculate_or_verify_Green():
exit()
self.infoTx.appendPlainText("Filtered and trim")
isola.trim_filter_data()
try:
if parameters['covariance']:
self.infoTx.appendPlainText(
"Calculating Covariance Matrix")
isola.covariance_matrix(crosscovariance=True,
save_non_inverted=True,
save_covariance_function=True)
except:
md = MessageDialog(self)
md.set_error_message(
"No Possible calculate covariance matrix, "
"please try increasing the noise time window")
#
self.infoTx.appendPlainText("decimate and shift")
isola.decimate_shift()
self.infoTx.appendPlainText("Run inversion")
isola.run_inversion()
self.infoTx.appendPlainText("Finished Inversion")
isola.find_best_grid_point()
isola.print_solution()
isola.print_fault_planes()
self.infoTx.appendPlainText("Plotting Solutions")
if len(isola.grid) > len(isola.depths):
isola.plot_maps()
self.infoTx.appendPlainText("plot_maps")
if len(isola.depths) > 1:
isola.plot_slices()
self.infoTx.appendPlainText("plot_slices")
if len(isola.grid) > len(isola.depths) and len(
isola.depths) > 1:
isola.plot_maps_sum()
self.infoTx.appendPlainText("plot_maps_sum")
try:
isola.plot_MT()
self.infoTx.appendPlainText("plot_MT")
isola.plot_uncertainty(n=400)
self.infoTx.appendPlainText("plot_uncertainty")
#plot_MT_uncertainty_centroid()
isola.plot_seismo('seismo.png')
isola.plot_seismo('seismo_sharey.png', sharey=True)
self.infoTx.appendPlainText("plot_seismo")
if self.covarianceCB.isChecked():
isola.plot_seismo('plot_seismo.png', cholesky=True)
self.infoTx.appendPlainText("plot_seismo_cova")
isola.plot_noise()
self.infoTx.appendPlainText("plot_noise")
isola.plot_spectra()
self.infoTx.appendPlainText("plot_spectra")
isola.plot_stations()
self.infoTx.appendPlainText("plot_stations")
except:
print("Couldn't Plot")
try:
if self.covarianceCB.isChecked():
isola.plot_covariance_matrix(colorbar=True)
#isola.plot_3D()
except:
pass
try:
isola.html_log(
h1='ISP Moment Tensor inversion',
plot_MT='centroid.png',
plot_uncertainty='uncertainty.png',
plot_stations='stations.png',
plot_seismo_cova='seismo_cova.png',
plot_seismo_sharey='seismo_sharey.png',
plot_spectra='spectra.png',
plot_noise='noise.png',
plot_covariance_matrix='covariance_matrix.png',
plot_maps='map.png',
plot_slices='slice.png',
plot_maps_sum='map_sum.png')
except:
self.infoTx.appendPlainText("Couldn't load url")
try:
isola.html_log(h1='ISP Moment Tensor inversion',
plot_MT='centroid.png',
plot_uncertainty='uncertainty.png',
plot_stations='stations.png',
plot_seismo_sharey='seismo_sharey.png',
plot_maps='map.png',
plot_slices='slice.png')
except:
self.infoTx.appendPlainText("Couldn't load url")
self.infoTx.appendPlainText(
"Moment Tensor Inversion Successfully done !!!, please plot last solution"
)
else:
pass
def plot_solution(self):
path = os.path.join(ROOT_DIR, 'mti/output/index.html')
url = pyc.QUrl.fromLocalFile(path)
self.widget.load(url)
def get_inversion_parameters(self):
parameters = {
'latitude': self.latDB.value(),
'longitude': self.lonDB.value(),
'depth': self.depthDB.value(),
'origin_time': convert_qdatetime_utcdatetime(self.origin_time),
'location_unc': self.location_uncDB.value(),
'time_unc': self.timeDB.value(),
'magnitude': self.magnitudeDB.value(),
'depth_unc': self.depth_uncDB.value(),
'freq_min': self.freq_min_DB.value(),
'freq_max': self.freq_max_DB.value(),
'deviatoric': self.deviatoricCB.isChecked(),
'circle_shape': self.circle_shapeCB.isChecked(),
'GFs': self.gfCB.isChecked(),
'covariance': self.covarianceCB.isChecked()
}
return parameters
def plot_map_stations(self):
md = MessageDialog(self)
md.hide()
try:
stations = []
obsfiles = MseedUtil.get_mseed_files(self.root_path_bind.value)
obsfiles.sort()
try:
if len(self.stream) > 0:
stations = ObspyUtil.get_stations_from_stream(self.stream)
except:
pass
map_dict = {}
sd = []
for file in obsfiles:
if len(stations) == 0:
st = SeismogramDataAdvanced(file)
name = st.stats.Network + "." + st.stats.Station
sd.append(name)
st_coordinates = self.__metadata_manager.extract_coordinates(
self.inventory, file)
map_dict[name] = [
st_coordinates.Latitude, st_coordinates.Longitude
]
else:
st = SeismogramDataAdvanced(file)
if st.stats.Station in stations:
name = st.stats.Network + "." + st.stats.Station
sd.append(name)
st_coordinates = self.__metadata_manager.extract_coordinates(
self.inventory, file)
map_dict[name] = [
st_coordinates.Latitude, st_coordinates.Longitude
]
else:
pass
self.map_stations = StationsMap(map_dict)
self.map_stations.plot_stations_map(latitude=self.latDB.value(),
longitude=self.lonDB.value())
md.set_info_message("Station Map OK !!! ")
except:
md.set_error_message(
" Please check you have process and plot seismograms and opened stations info,"
"Please additionally check that your metada fits with your mseed files"
)
md.show()
def open_help(self):
self.help.show()
progname = os.path.basename(sys.argv[0])
progversion = "0.1"
qApp = QtWidgets.QApplication(sys.argv)
def on_select(ax_index, xmin, xmax):
mpf.canvas.plot(xmax, 2, ax_index, clear_plot=False, marker="o")
mpf.canvas.draw()
def on_click(event, canvas):
print(event, canvas)
def on_dlb_click(event, canvas):
print(event, canvas)
mpf = MatplotlibFrame(None)
mpf.canvas.set_new_subplot(2, 1)
mpf.canvas.on_double_click(on_dlb_click)
mpf.canvas.register_on_select(on_select)
mpf.canvas.plot([1, 2, 3], [1, 2, 3], 0)
mpf.canvas.plot([1, 2, 3], [1, 2, 3], 1)
mpf.setWindowTitle("%s" % progname)
mpf.show()
sys.exit(qApp.exec_())
#matplotlib.use('Qt5Agg')
from PyQt5 import QtWidgets
import sys
qApp = QtWidgets.QApplication(sys.argv)
# Fixing random state for reproducibility
np.random.seed(19680801)
dt = 0.01
t = np.arange(0, 30, dt)
nse1 = np.random.randn(len(t)) # white noise 1
nse2 = np.random.randn(len(t)) # white noise 2
# Two signals with a coherent part at 10Hz and a random part
s1 = np.sin(2 * np.pi * 10 * t) + nse1
s2 = np.sin(2 * np.pi * 10 * t) + nse2
fig, axs = plt.subplots(2, 1)
mpf = MatplotlibFrame(fig)
axs[0].plot(t, s1, t, s2)
axs[0].set_xlim(0, 2)
axs[0].set_xlabel('time')
axs[0].set_ylabel('s1 and s2')
axs[0].grid(True)
cxy, f = axs[1].cohere(s1, s2, 256, 1. / dt)
axs[1].set_ylabel('coherence')
mpf.show()
#sys.exit(qApp.exec_())
class StationsMap:
def __init__(self, stations_dict):
"""
Plot stations map fro dictionary (key = stations name, coordinates)
:param
"""
self.__stations_dict = stations_dict
def plot_stations_map(self, **kwargs):
from matplotlib.transforms import offset_copy
import cartopy.crs as ccrs
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
from owslib.wms import WebMapService
from matplotlib.patheffects import Stroke
import cartopy.feature as cfeature
import shapely.geometry as sgeom
from matplotlib import pyplot as plt
from isp import ROOT_DIR
import os
os.environ["CARTOPY_USER_BACKGROUNDS"] = os.path.join(ROOT_DIR, "maps")
# MAP_SERVICE_URL = 'https://gis.ngdc.noaa.gov/arcgis/services/gebco08_hillshade/MapServer/WMSServer'
MAP_SERVICE_URL = 'https://www.gebco.net/data_and_products/gebco_web_services/2020/mapserv?'
# MAP_SERVICE_URL = 'https://gis.ngdc.noaa.gov/arcgis/services/etopo1/MapServer/WMSServer'
geodetic = ccrs.Geodetic(globe=ccrs.Globe(datum='WGS84'))
#layer = 'GEBCO_08 Hillshade'
layer ='GEBCO_2020_Grid'
#layer = 'shaded_relief'
epi_lat = kwargs.pop('latitude')
epi_lon = kwargs.pop('longitude')
name_stations = []
lat = []
lon = []
for name, coords in self.__stations_dict.items():
name_stations.append(name)
lat.append(float(coords[0]))
lon.append(float(coords[1]))
#
proj = ccrs.PlateCarree()
fig, ax = plt.subplots(1, 1, subplot_kw=dict(projection=proj), figsize=(16, 12))
self.mpf = MatplotlibFrame(fig)
xmin = min(lon)-4
xmax = max(lon)+4
ymin = min(lat)-4
ymax = max(lat)+4
extent = [xmin, xmax, ymin, ymax]
ax.set_extent(extent, crs=ccrs.PlateCarree())
try:
wms = WebMapService(MAP_SERVICE_URL)
ax.add_wms(wms, layer)
except:
ax.background_img(name='ne_shaded', resolution="high")
#geodetic_transform = ccrs.Geodetic()._as_mpl_transform(ax)
geodetic_transform = ccrs.PlateCarree()._as_mpl_transform(ax)
text_transform = offset_copy(geodetic_transform, units='dots', x=-25)
ax.scatter(lon, lat, s=12, marker="^", color='red', alpha=0.7, transform=ccrs.PlateCarree())
ax.plot(epi_lon, epi_lat, color='black', marker='*', markersize=8)
N=len(name_stations)
for n in range(N):
lon1=lon[n]
lat1 = lat[n]
name = name_stations[n]
ax.text(lon1, lat1, name, verticalalignment='center', horizontalalignment='right', transform=text_transform,
bbox=dict(facecolor='sandybrown', alpha=0.5, boxstyle='round'))
# Create an inset GeoAxes showing the Global location
#sub_ax = self.mpf.canvas.figure.add_axes([0.70, 0.75, 0.28, 0.28],
# projection=ccrs.PlateCarree())
sub_ax = self.mpf.canvas.figure.add_axes([0.70, 0.73, 0.28, 0.28], projection=ccrs.PlateCarree())
sub_ax.set_extent([-179.9, 180, -89.9, 90], geodetic)
# Make a nice border around the inset axes.
effect = Stroke(linewidth=4, foreground='wheat', alpha=0.5)
sub_ax.outline_patch.set_path_effects([effect])
# Add the land, coastlines and the extent .
sub_ax.add_feature(cfeature.LAND)
sub_ax.coastlines()
extent_box = sgeom.box(extent[0], extent[2], extent[1], extent[3])
sub_ax.add_geometries([extent_box], ccrs.PlateCarree(), facecolor='none',
edgecolor='blue', linewidth=1.0)
gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
linewidth=0.2, color='gray', alpha=0.2, linestyle='-')
gl.top_labels = False
gl.left_labels = False
gl.xlines = False
gl.ylines = False
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
#plt.savefig("/Users/robertocabieces/Documents/ISPshare/isp/mti/output/stations.png", bbox_inches='tight')
self.mpf.show()
class PlotToolsManager:
def __init__(self, id):
"""
Manage Plot signal analysis in Earthquake Frame.
:param obs_file_path: The file path of pick observations.
"""
self.__id = id
def plot_spectrum(self, freq, spec, jackknife_errors):
import matplotlib.pyplot as plt
from isp.Gui.Frames import MatplotlibFrame
fig, ax1 = plt.subplots(figsize=(6, 6))
self.mpf = MatplotlibFrame(fig)
ax1.loglog(freq, spec, linewidth=1.0, color='steelblue', label=self.__id)
ax1.frequencies = freq
ax1.spectrum = spec
ax1.fill_between(freq, jackknife_errors[:, 0], jackknife_errors[:, 1], facecolor="0.75",
alpha=0.5, edgecolor="0.5")
ax1.set_ylim(spec.min() / 10.0, spec.max() * 100.0)
plt.ylabel('Amplitude')
plt.xlabel('Frequency [Hz]')
plt.grid(True, which="both", ls="-", color='grey')
plt.legend()
self.mpf.show()
def plot_spectrum_all(self, all_items):
import matplotlib.pyplot as plt
from isp.Gui.Frames import MatplotlibFrame
fig, ax1 = plt.subplots(figsize=(6, 6))
self.mpf = MatplotlibFrame(fig)
for key, seismogram in all_items:
data = seismogram[2]
delta = 1 / seismogram[0][6]
sta = seismogram[0][1]
[spec, freq, jackknife_errors] = spectrumelement(data, delta, sta)
info = "{}.{}.{}".format(seismogram[0][0], seismogram[0][1], seismogram[0][3])
ax1.loglog(freq, spec, linewidth=1.0, alpha = 0.5, label=info)
ax1.frequencies = freq
ax1.spectrum = spec
ax1.set_ylim(spec.min() / 10.0, spec.max() * 100.0)
# ax1.set_xlim(freq[0], 1/(2*delta))
plt.ylabel('Amplitude')
plt.xlabel('Frequency [Hz]')
plt.grid(True, which="both", ls="-", color='grey')
plt.legend()
self.mpf.show()
def find_nearest(self,array, value):
idx, val = min(enumerate(array), key=lambda x: abs(x[1] - value))
return idx, val
# def __compute_spectrogram(self, tr):
# npts = len(tr)
# t = np.linspace(0, (tr.stats.delta * npts), npts - self.win)
# mt_spectrum = self.MTspectrum(tr.data, self.win, tr.stats.delta, self.tbp, self.ntapers, self.f_min, self.f_max)
# log_spectrogram = 10. * np.log(mt_spectrum / np.max(mt_spectrum))
# x, y = np.meshgrid(t, np.linspace(self.f_min, self.f_max, log_spectrogram.shape[0]))
# return x, y, log_spectrogram
def MTspectrum_plot(self, data, win, dt, tbp, ntapers, linf, lsup):
if (win % 2) == 0:
nfft = win / 2 + 1
else:
nfft = (win + 1) / 2
lim = len(data) - win
S = np.zeros([int(nfft), int(lim)])
data2 = np.zeros(2 ** math.ceil(math.log2(win)))
for n in range(lim):
data1 = data[n:win + n]
data1 = data1 - np.mean(data1)
data2[0:win] = data1
spec, freq = mtspec(data2, delta=dt, time_bandwidth=tbp, number_of_tapers=ntapers)
spec = spec[0:int(nfft)]
S[:, n] = spec
value1, freq1 = self.find_nearest(freq, linf)
value2, freq2 = self.find_nearest(freq, lsup)
S = S[value1:value2]
return S
def compute_spectrogram_plot(self, data, win, delta, tbp, ntapers, f_min, f_max, t):
npts = len(data)
x = np.linspace(0, (delta * npts), npts - win)
t = t[0:len(x)]
mt_spectrum = self.MTspectrum_plot(data, win, delta, tbp, ntapers, f_min, f_max)
log_spectrogram = 10. * np.log(mt_spectrum / np.max(mt_spectrum))
x, y = np.meshgrid(t, np.linspace(f_min, f_max, log_spectrogram.shape[0]))
return x, y, log_spectrogram