def heightErr(height, *cyscan_inputs):
t, traj, stat_pos, sounding = cyscan_inputs
trial_source = traj.findGeo(height)
stat_pos.pos_loc(trial_source)
trial_source.pos_loc(trial_source)
trial_source.z = trial_source.elev
stat_pos.z = stat_pos.elev
### Ray Trace
r, _ = cyscan(np.array([trial_source.x, trial_source.y, trial_source.z]), np.array([stat_pos.x, stat_pos.y, stat_pos.z]), \
sounding, trace=False, plot=False, particle_output=True, debug=False, \
wind=True, h_tol=300, v_tol=3000, print_times=True, processes=1)
T = r[0] + traj.findTime(trial_source.elev)
try:
t_err = (T - t)**2
except:
t_err = 999
print("Height {:.2f} km | Err {:.2f}".format(height[0] / 1000,
np.sqrt(t_err)))
return t_err
def refractiveFactor(S, D, zProfile, D_ANGLE=2):
""" Compare the difference between launch angles of rays in an ideal and a realistic atmosphere going
to the same points on the ground (D_ANGLE away from each other in the realistic atmosphere)
"""
# put into local coordinates
D.pos_loc(S)
S.pos_loc(S)
# Find launch angles in the ideal atmosphere (straight lines)
dx, dy, dz = D.xyz - S.xyz
tf_ideal_n = np.degrees(np.arctan2(dz, np.sqrt((dy)**2 + (dx)**2))) + 90
az_ideal_n = np.degrees(np.arctan2(dx, dy)) % 360
# Find actual angles
_, az_n, tf_n, _ = cyscan(S.xyz, D.xyz, zProfile, wind=True,\
h_tol=330, v_tol=330)
d_angle_ideal = [np.nan]
# Number of angles to try
RANGE = 4
# Go through <RANGE> number of angles <D_ANGLE> away from the actual angles
for i in range(RANGE):
d_az = np.degrees(
np.arctan(
np.cos(i * 2 * np.pi / RANGE) * np.tan(np.radians(D_ANGLE))))
d_tf = np.degrees(
np.arctan(
np.sin(i * 2 * np.pi / RANGE) * np.tan(np.radians(D_ANGLE))))
D_n = anglescan(S.xyz, az_n + d_az, tf_n + d_tf, zProfile, wind=True)
dx, dy, dz = D_n[0:3] - S.xyz
# Find ideal atmosphere angles to get to new launch points
tf = np.degrees(np.arctan2(dz, np.sqrt((dy)**2 + (dx)**2))) + 90
az = (np.degrees(np.arctan2(dx, dy))) % 360
tf = (tf - tf_ideal_n)
az = (az - az_ideal_n)
d_angle_ideal.append(addAngleComp(tf, az, deg=True))
if np.isnan(d_angle_ideal).all():
return np.nan
d_angle_ideal = np.nanmean(d_angle_ideal)
rf = np.sqrt(D_ANGLE / d_angle_ideal)
return rf
def eigenError(S, D, z_profile, n_angle=90):
''' Used to check the error, and verify that the eigenray solution works properly'''
z_profile = zInteg(S[2], D[2], z_profile)
f_time, frag_azimuth, frag_takeoff, frag_err = cyscan(S, D, z_profile, wind=True, \
n_theta=n_angle, n_phi=n_angle, h_tol=1e-15, v_tol=330)
D_new = anglescan(S, frag_azimuth, frag_takeoff, z_profile, wind=True)
d_loc_err = np.sqrt((D[0] - D_new[0])**2 + (D[1] - D_new[1])**2)
d_time_err = D_new[3] - f_time
d_err_err = d_loc_err - frag_err
print("Spatial Error: ", d_loc_err)
print("Temporal Error: ", d_time_err)
print("Error Error: ", d_err_err)
def eigenAngleError(S, az, tf, z_profile, n_angle=90):
D = anglescan(S, az, tf, z_profile, wind=True)
z_profile = zInteg(S[2], D[2], z_profile)
f_time, frag_azimuth, frag_takeoff, frag_err = cyscan(S, D[:3], z_profile, wind=True, \
n_theta=n_angle, n_phi=n_angle, h_tol=1e-15, v_tol=330)
tf_error = tf - frag_takeoff
az_error = az - frag_azimuth
time_err = D[3] - f_time
print("Takeoff Error [deg]: ", tf_error)
print("Azimuth Error [deg]: ", az_error)
print("Temporal Error: ", time_err)
print("Spatial Error: ", frag_err)
def eigenConsistancy(S,
D,
az,
tf,
z_profile,
n_angle=90,
h_tol=None,
v_tol=None):
'''
Checks if a given source-detector, atmosphere, initial launch angle, solution is consistant with itself
by running through both anglescan and cyscan
'''
n_angle = 360
D_new = anglescan(S, az, tf, z_profile, wind=True, debug=True)
if np.isnan(D_new[0]):
print('Inconsistant - bad anglescan')
return 0
T_new, az_new, tf_new, err = cyscan(S, D_new, z_profile, wind=True, \
n_theta=n_angle, n_phi=n_angle, h_tol=330, v_tol=330, debug=True)
d_loc_err = np.sqrt((D[0] - D_new[0])**2 + (D[1] - D_new[1])**2)
d_time_err = D_new[3] - T_new
tf_error = tf - tf_new
az_error = az - az_new
d_err_err = d_loc_err - err
if np.isnan(T_new):
print('Inconsistant - nan solution')
return 0
max_error = np.sqrt(h_tol**2 + v_tol**2)
if not (d_loc_err < max_error and d_time_err < max_error / 310
and tf_error < 1 and az_error < 1):
print("Inconsistant Solution at height {:.2f} km".format(S[2] / 1000))
print('Spatial Error: ', d_loc_err)
print('Temporal Error: ', d_time_err)
print("Takeoff Error [deg]: ", tf_error)
print("Azimuth Error [deg]: ", az_error)
print("Error Error: ", d_err_err)
return 0
return 1
def makeStaff(self, stn, ts, time_err, doc):
# Generate pointes along trajecotry
traj = self.bam.setup.trajectory
points = traj.trajInterp2(div=100)
D = stn.metadata.position
ref_pos = Position(self.bam.setup.lat_centre,
self.bam.setup.lon_centre, 0)
D.pos_loc(ref_pos)
times = []
heights = []
for pt in points:
S = Position(pt[0], pt[1], pt[2])
S.pos_loc(ref_pos)
sounding, perts = self.bam.atmos.getSounding(
lat=[S.lat, D.lat],
lon=[S.lon, D.lon],
heights=[S.elev, D.elev])
f_time, _, _, _ = cyscan(S.xyz, D.xyz, sounding, wind=self.prefs.wind_en, n_theta=self.prefs.pso_theta, n_phi=self.prefs.pso_phi, \
h_tol=self.prefs.pso_min_ang, v_tol=self.prefs.pso_min_dist)
times.append(f_time + pt[3])
heights.append(pt[2] / 1000)
plt.scatter(heights, times)
X = np.linspace(17, 50)
for tt in range(len(ts)):
plt.fill_between(X,
ts[tt] - time_err[tt][0],
y2=ts[tt] + time_err[tt][1])
plt.annotate('F{:}'.format(tt + 1), (18, ts[tt]))
plt.axhline(y=ts[tt], color="black", linestyle="--")
pic_file = os.path.join(self.prefs.workdir,
self.bam.setup.fireball_name, 'staff.png')
plt.savefig(pic_file)
doc.add_picture(pic_file)
os.remove(pic_file)
plt.clf()
def __init__(self, setup, stn_list, position, pert_idx):
QWidget.__init__(self)
self.setWindowTitle('Height Solver')
self.setup = setup
self.stn_list = stn_list
p = self.palette()
p.setColor(self.backgroundRole(), Qt.black)
self.setPalette(p)
menu_bar = QMenuBar(self)
#layout.addWidget(menu_bar, 0, 1)
file_menu = menu_bar.addMenu('&File')
select_menu = menu_bar.addMenu('&Select')
self.selected = False
file_export = QAction("Export Selected Menu", self)
file_export.setShortcut('Ctrl+E')
file_export.setStatusTip(
'Brings up the Export Menu with the selected stations')
file_export.triggered.connect(self.export)
file_menu.addAction(file_export)
select_all = QAction("Select All", self)
select_all.setShortcut('Ctrl+A')
select_all.setStatusTip('Selects all')
select_all.triggered.connect(self.select)
select_menu.addAction(select_all)
theme(self)
self.position = position
point = position
widget = QWidget()
self.layout = QVBoxLayout(widget)
self.layout.setAlignment(Qt.AlignTop)
for point in position:
print('Best Position: {:}'.format(point))
ref_pos = Position(self.setup.lat_centre, self.setup.lon_centre, 0)
count = 0
max_steps = len(stn_list) * len(position) * self.setup.perturb_times
dataset = parseWeather(self.setup)
self.save_button = []
self.stn_view = []
self.stn_canvas = []
self.invert = []
self.waveform_data = [None] * len(stn_list)
A = self.setup.trajectory.pos_i
B = self.setup.trajectory.pos_f
A.pos_loc(B)
B.pos_loc(B)
# Get prediction of time of the meteor, so the timing of each fragmentation can be known
length_of_meteor = np.sqrt((A.x - B.x)**2 + (A.y - B.y)**2 +
(A.z - B.z)**2)
time_of_meteor = length_of_meteor / self.setup.trajectory.v
for index in range(len(stn_list)):
stn = stn_list[index]
station_layout = QGridLayout()
self.layout.addLayout(station_layout)
label_layout = QVBoxLayout()
control_layout = QGridLayout()
waveform_layout = QVBoxLayout()
station_layout.addLayout(label_layout, 0, 100, 1, 100)
station_layout.addLayout(control_layout, 0, 0, 1, 100)
station_layout.addLayout(waveform_layout, 0, 200, 1, 100)
label_layout.addWidget(
QLabel('Station: {:} - {:}'.format(stn_list[index].network,
stn_list[index].code)))
label_layout.addWidget(
QLabel('Channel: {:}'.format(stn_list[index].channel)))
label_layout.addWidget(
QLabel('Name: {:}'.format(stn_list[index].name)))
label_layout.addWidget(
QLabel('Lat: {:}'.format(stn_list[index].position.lat)))
label_layout.addWidget(
QLabel('Lon: {:}'.format(stn_list[index].position.lon)))
label_layout.addWidget(
QLabel('Elev: {:}'.format(stn_list[index].position.elev)))
self.save_button.append(QPushButton('Select'))
self.save_button[index].clicked.connect(
partial(self.saveButton, index))
control_layout.addWidget(self.save_button[index], 0, 0)
control_layout.addWidget(QPushButton('-'), 0, 1)
control_layout.addWidget(QPushButton('-'), 1, 0)
control_layout.addWidget(QPushButton('-'), 1, 1)
self.stn_view.append(pg.GraphicsLayoutWidget())
self.stn_canvas.append(self.stn_view[index].addPlot())
waveform_layout.addWidget(self.stn_view[index])
self.invert.append(False)
min_point, max_point = self.discountDrawWaveform(
setup, index, self.stn_canvas[index])
# for ptb_n in range(self.setup.perturb_times):
for p, point in enumerate(position):
for ptb_n in range(self.setup.perturb_times):
dataset = self.perturbGenerate(ptb_n, dataset,
self.perturbSetup())
zProfile, _ = getWeather(np.array([point.lat, point.lon, point.elev]), np.array([stn.position.lat, stn.position.lon, stn.position.elev]), self.setup.weather_type, \
ref_pos, dataset, convert=True)
point.pos_loc(ref_pos)
stn.position.pos_loc(ref_pos)
f_time, _, _, _ = cyscan(np.array([point.x, point.y, point.z]), np.array([stn.position.x, stn.position.y, stn.position.z]), zProfile, wind=True, \
n_theta=self.setup.n_theta, n_phi=self.setup.n_phi, h_tol=self.setup.h_tol, v_tol=self.setup.v_tol)
correction = time_of_meteor - A.z / self.setup.trajectory.pos_i.elev * (
time_of_meteor)
nom_time = f_time + correction
with warnings.catch_warnings():
warnings.simplefilter("ignore")
try:
self.stn_canvas[index].plot(
x=[f_time, f_time],
y=[min_point, max_point],
pen=PEN[p % 7],
brush=PEN[p % 7])
except:
pass
count += 1
loadingBar("Generating Plots", count, max_steps)
try:
self.stn_canvas[index].setXRange(nom_time - 25,
nom_time + 25,
padding=1)
except:
avg_time = stn.position.pos_distance(point) / 330
self.stn_canvas[index].setXRange(avg_time - 25,
avg_time + 25,
padding=1)
self.setWidget(widget)
self.setWidgetResizable(True)
def __init__(self, invert, setup, stn_list, position):
self.link = False
self.grid = False
QWidget.__init__(self)
self.selected_stn_view = pg.GraphicsLayoutWidget()
self.stn_canvas = []
self.stn_list = stn_list
self.setup = setup
self.waveform_data = [None]*len(stn_list)
ref_pos = Position(self.setup.lat_centre, self.setup.lon_centre, 0)
count = 0
max_steps = len(position)*len(stn_list)*setup.perturb_times
dataset = parseWeather(self.setup)
theme(self)
for ii, index in enumerate(invert):
if index:
stn = stn_list[ii]
self.stn_canvas.append(self.selected_stn_view.addPlot())
self.selected_stn_view.nextRow()
min_point, max_point = self.discountDrawWaveform(setup, ii, self.stn_canvas[-1])
self.stn_canvas[-1].getAxis('bottom').setPen((0, 0, 0))
self.stn_canvas[-1].getAxis('left').setPen((0, 0, 0))
self.waveform_data[ii].setPen((0, 0, 0))
#self.stn_canvas[-1].addItem(pg.LabelItem(text="{:}-{:}".format(stn.network, stn.code), color=(0, 0, 0)))
self.stn_canvas[-1].setTitle("{:}-{:}".format(stn.network, stn.code), color=(0, 0, 0))
for p, point in enumerate(position):
for ptb_n in range(self.setup.perturb_times):
dataset = self.perturbGenerate(ptb_n, dataset, self.perturbSetup())
zProfile, _ = getWeather(np.array([point.lat, point.lon, point.elev]), np.array([stn.position.lat, stn.position.lon, stn.position.elev]), self.setup.weather_type, \
[ref_pos.lat, ref_pos.lon, ref_pos.elev], dataset, convert=False)
point.pos_loc(ref_pos)
stn.position.pos_loc(ref_pos)
f_time, _, _, _ = cyscan(np.array([point.x, point.y, point.z]), np.array([stn.position.x, stn.position.y, stn.position.z]), zProfile, wind=True, \
n_theta=self.setup.n_theta, n_phi=self.setup.n_phi, h_tol=self.setup.h_tol, v_tol=self.setup.v_tol)
A = self.setup.trajectory.pos_i
B = self.setup.trajectory.pos_f
A.pos_loc(B)
B.pos_loc(B)
# Get prediction of time of the meteor, so the timing of each fragmentation can be known
length_of_meteor = np.sqrt((A.x - B.x)**2 + (A.y - B.y)**2 + (A.z - B.z)**2)
time_of_meteor = length_of_meteor/self.setup.trajectory.v
correction = time_of_meteor - A.z/self.setup.trajectory.pos_i.elev*(time_of_meteor)
nom_time = f_time + correction
with warnings.catch_warnings():
warnings.simplefilter("ignore")
try:
self.stn_canvas[-1].plot(x=[f_time, f_time], y=[min_point, max_point], pen=PEN[p%7], brush=PEN[p%7])
except:
pass
count += 1
loadingBar("Generating Plots", count, max_steps)
try:
self.stn_canvas[-1].setXRange(nom_time-25, nom_time+25, padding=1)
except:
pass
self.selected_stn_view.setBackground((255, 255, 255))
layout = QVBoxLayout()
layout.addWidget(self.selected_stn_view)
self.setLayout(layout)
toggle_group = QGroupBox("Toggles")
layout.addWidget(toggle_group)
toggle_group_layout = QVBoxLayout()
toggle_group.setLayout(toggle_group_layout)
sync_button = QCheckBox('Sync')
toggle_group_layout.addWidget(sync_button)
sync_button.stateChanged.connect(self.sync)
grid_button = QCheckBox('Grid')
toggle_group_layout.addWidget(grid_button)
grid_button.stateChanged.connect(self.grid_toggle)
export_button = QPushButton("Export")
export_button.clicked.connect(self.export)
layout.addWidget(export_button)
def timeConstraints(x, *args):
# Retrieve passed arguments
stns, w, kotc, setup, ref_pos, dataset, v = args
# number of stations total
n_stations = len(stns)
# Residuals to each station
sotc = np.empty(n_stations)
# Initialize variables
# Weight of each station
wn = w
# Mean occurrence time
motc = 0
### multiprocessing
# number of stations total
n_stations = len(stns)
# Station Times
tobs = stns[0:n_stations, 4]
# Station Location
xstn = stns[0:n_stations, 0:3]
# Initialize arrays
# Simulated travel times to each station
time3D = np.empty(n_stations)
# Residuals to each station
sotc = np.empty(n_stations)
for j in range(n_stations):
# if station has weight
if w[j] > 0:
# Create interpolated atmospheric profile for use with cyscan
sounding, points = getWeather([x[0], x[1], x[2]], xstn[j, :], setup.weather_type, ref_pos, copy.copy(dataset))
# Use distance and atmospheric data to find path time
time3D[j], _, _, _ = cyscan(np.array([x[0], x[1], x[2]]), np.array(xstn[j, :]), sounding, \
wind=setup.enable_winds, n_theta=setup.n_theta, n_phi=setup.n_phi, h_tol=setup.h_tol, v_tol=setup.v_tol)
# Residual time for each station
sotc[j] = tobs[j] - time3D[j]
# If station has no weight
else:
sotc[j] = tobs[j]
t_avg = (setup.t_min + setup.t_max)/2
diff = abs(motc - t_avg)
TOL = setup.t_max - t_avg
motc = np.dot(wn, sotc)/sum(wn)
if diff < TOL:
return diff
else:
return -diff
def outputWeather(n_stations, x_opt, stns, setup, ref_pos, atmos, output_name,
s_name, kotc, w, prefs, theo):
""" Function to calculate the results of the optimal Supracenter and export interpolated weather data
from each station.
Arguments:
n_stations: [int] number of stations
x_opt: [list] lat, lon, and height of the optimal supracenter
xstn: [list] lat, lon, and height of all the stations
setup: [Object] object storing user-defined setup
consts: [Object] object storing physical constants
ref_pos: [list] mean position of the stations to act as the origin for the local coordinate system
dataset: [ndarray] atmospheric profile of search area
output_name: [string] folder name to save output files in
s_name: [list] name of all the stations
kotc: [list] user-defined occurence time [HH, MM, SS]
tobs: [list] station arrival times to each station
w: [list] weights of each station
Returns:
time3D: [list] calculated travel time to each station
az: [list] initial azimuth angle to each station
tf: [list] initial takeoff angle to each station
r: [list] residuals to each station
"""
consts = Constants()
# Station Times
tobs = stns[0:n_stations, 4]
# Station Location
xstn = stns[0:n_stations, 0:3]
# Travel time to each station
time3D = np.zeros(n_stations)
# Initial azimuths angles of each station
az = np.zeros(n_stations)
# Initial takeoff angles of each station
tf = np.zeros(n_stations)
# difference in theoretical and simulated travel times
sotc = np.zeros_like(tobs)
#difference in theoretical and simulated travel times
r = np.zeros_like(tobs)
trace = []
# Find parameters of optimal location
print('Exporting weather profiles...')
for j in range(n_stations):
#print(np.array(x_opt), np.array(xstn[j, :]))
# get interpolated weather profile
D = Position(0, 0, 0)
D.x, D.y, D.z = xstn[j, 0], xstn[j, 1], xstn[j, 2]
D.pos_geo(ref_pos)
if not theo:
sounding, _ = atmos.getSounding(lat=[x_opt.lat, D.lat],
lon=[x_opt.lon, D.lon],
heights=[x_opt.elev, D.elev],
ref_time=setup.fireball_datetime)
# Rotate winds to match with coordinate system
#sounding[:, 3] = np.radians(angle2NDE(np.degrees(sounding[:, 3])))
# Export the interpolated weather profiles from the optimal Supracenter for each station
# with open(os.path.join(output_name, s_name[j] + '_sounding.txt'), 'w') as f:
# # With winds
# if prefs.wind_en == True:
# if prefs.atm_type == 'custom' or prefs.atm_type == 'none' or prefs.atm_type == 'radio':
# f.write('| Height (m) | Temp (K) | soundSpd (m/s) | wdSpd (m/s) | wdDir (deg fN) |\n')
# else:
# f.write('| Latitude (deg N) | Longitude (deg E) | Height (m) | Temp (K) | soundSpd (m/s) | wdSpd (m/s) | wdDir (deg fN) |\n')
# # No winds
# else:
# if prefs.atm_type == 'custom' or prefs.atm_type == 'none' or prefs.atm_type== 'radio':
# f.write('| Height (m) | Temp (K) | soundSpd (m/s) |\n')
# else:
# f.write('| Latitude (deg N) | Longitude (deg E) | Height (m) | Temp (K) | soundSpd (m/s) |\n')
# for ii in range(len(sounding)):
# if prefs.wind_en == True:
# if prefs.atm_type == 'custom' or prefs.atm_type == 'none' or prefs.atm_type == 'radio':
# f.write('| {:8.2f} | {:7.3f} | {:6.4f} | {:7.3f} | {:6.2f} |\n'\
# .format(sounding[ii, 0], sounding[ii, 1]**2*consts.M_0/consts.GAMMA/consts.R, \
# sounding[ii, 1], sounding[ii, 2], sounding[ii, 3]))
# else:
# f.write('| {:7.4f} | {:7.4f} | {:8.2f} | {:7.3f} | {:6.4f} | {:7.3f} | {:6.2f} |\n'\
# .format(points[ii][0], points[ii][1], sounding[ii, 0], sounding[ii, 1]**2*consts.M_0/consts.GAMMA/consts.R, \
# sounding[ii, 1], sounding[ii, 2], sounding[ii, 3]))
# else:
# if prefs.atm_type == 'custom' or prefs.atm_type == 'none' or prefs.atm_type == 'radio':
# f.write('| {:8.2f} | {:7.3f} | {:6.4f} |\n'\
# .format(sounding[ii, 0], sounding[ii, 1]**2*consts.M_0/consts.GAMMA/consts.R, sounding[ii, 1]))
# else:
# f.write('| {:7.4f} | {:7.4f} | {:8.2f} | {:7.3f} | {:6.4f} |\n'\
# .format(points[ii][0], points[ii][1], sounding[ii, 0], sounding[ii, 1]**2*consts.M_0/consts.GAMMA/consts.R, sounding[ii, 1]))
# ray tracing function
# _, _, _, _, temp_trace = slowscan(x_opt.xyz, np.array(xstn[j, :]), sounding, \
# wind=prefs.wind_en, n_theta=prefs.pso_theta, n_phi=prefs.pso_phi,\
# h_tol=prefs.pso_min_ang, v_tol=prefs.pso_min_dist)
time3D[j], _, _, _ = cyscan(x_opt.xyz, np.array(xstn[j, :]), sounding, \
wind=prefs.wind_en, h_tol=prefs.pso_min_ang, v_tol=prefs.pso_min_dist, processes=1)
else:
time3D[j] = x_opt.pos_distance(D) / prefs.avg_sp_sound
# trace.append(temp_trace)
# find residuals
sotc[j] = tobs[j] - time3D[j]
# for ii, element in enumerate(time3D):
# if np.isnan(element):
# w[ii] = 0
# User defined occurrence time
if kotc != None:
motc = kotc
index = []
# elif setup.manual_fragmentation_search != '' and len(setup.manual_fragmentation_search) > 0:
# motc = setup.manual_fragmentation_search[3]
# Unknown occurrence time
else:
w = np.array([1] * len(sotc))
index = np.isnan(sotc)
sotc[index] = 0
motc = np.dot(w, sotc) / sum(w)
# Station residuals (from average)
r = sotc - motc
r[index] = np.nan
return time3D, az, tf, r, motc, sotc, trace
def scatterPlot(setup, results_arr, n_stations, xstn, s_name, dataset):
""" Outputs a scatter plot of the search data and the optimal supracenter
Arguments:
single_point: [list] lat, lon, and height of a manual search point
n_stations: [int] number of stations
xstn: [list] lat, lon, and height of all the stations
s_name: [list] name of all the stations
r: [list] residuals to each station
x_opt: [list] lat, lon, and height of the optimal supracenter
reported_points: [list] name, lat, lon, and height of extra reference points to be plotted
search: [list] min/max lat, lon, and height of the search boundary
output_name: [string] folder name to save output files in
ref_pos: [list] mean position of the stations to act as the origin for the local coordinate system
sup: [ndarray] array of points searched with the search algorithm used for plotting
errors: [ndarray] array of the errors in each of the points in sup for plotting
Returns:
min_search: [list] min lat, lon and height of the search area
max_search: [list] max lat, lon and height of the search area
"""
single_point = setup.manual_fragmentation_search
r = results_arr[0].r
x_opt = results_arr[0].x_opt
reported_points = setup.reported_points
search = setup.search
output_name = setup.output_name
ref_pos = [setup.ref_pos.lat, setup.ref_pos.lon, setup.ref_pos.elev]
sup = results_arr[0].sup
errors = results_arr[0].errors
run_times = len(results_arr)
### Convert back to lat/lon
# Search Region boundaries
min_search = loc2Geo(ref_pos[0], ref_pos[1], ref_pos[2],
[search[0], search[2], search[4]])
max_search = loc2Geo(ref_pos[0], ref_pos[1], ref_pos[2],
[search[1], search[3], search[5]])
# Set up axes
fig = plt.figure(figsize=plt.figaspect(0.5))
fig.set_size_inches(20.9, 11.7)
# automatic search
if len(single_point) == 0:
ax1 = fig.add_subplot(1, 2, 1, projection='3d')
ax2 = fig.add_subplot(1, 2, 2, projection='3d')
# manual search
else:
ax1 = fig.add_subplot(1, 1, 1, projection='3d')
### Labels
ax1.set_title("Supracenter Locations")
ax1.set_xlabel("Latitude (deg N)", linespacing=3.1)
ax1.set_ylabel("Longitude (deg E)", linespacing=3.1)
ax1.set_zlabel('Elevation (m)', linespacing=3.1)
# plot station names and residuals
for h in range(n_stations):
# Convert station locations to geographic
xstn[h, 0], xstn[h, 1], xstn[h, 2] = loc2Geo(ref_pos[0], ref_pos[1],
ref_pos[2], xstn[h, :])
# Add station names
ax1.text(xstn[h, 0],
xstn[h, 1],
xstn[h, 2],
'%s' % (s_name[h]),
size=10,
zorder=1,
color='k')
if len(single_point) == 0:
ax2.text(xstn[h, 0],
xstn[h, 1],
xstn[h, 2],
'%s' % (s_name[h]),
size=10,
zorder=1,
color='k')
# Add stations with color based off of residual
res = ax1.scatter(xstn[:, 0],
xstn[:, 1],
xstn[:, 2],
c=abs(r),
marker='^',
cmap='viridis_r',
depthshade=False)
# Add point and label
for j in range(run_times):
if j == 0:
ax1.scatter(x_opt[0], x_opt[1], x_opt[2], c='r', marker='*')
ax1.text(x_opt[0],
x_opt[1],
x_opt[2],
'%s' % ('Supracenter'),
zorder=1,
color='k')
else:
ax1.scatter(results_arr[j].x_opt[0],
results_arr[j].x_opt[1],
results_arr[j].x_opt[2],
alpha=0.2,
c='r',
marker='*')
# Get the limits of the plot
x_min, y_min = search[0], search[2]
x_max, y_max = search[1], search[3]
img_dim = 30
x_data = np.linspace(x_min, x_max, img_dim)
y_data = np.linspace(y_min, y_max, img_dim)
xx, yy = np.meshgrid(x_data, y_data)
# Make an array of all plane coordinates
plane_coordinates = np.c_[xx.ravel(),
yy.ravel(),
np.zeros_like(xx.ravel())]
times_of_arrival = np.zeros_like(xx.ravel())
x_opt = geo2Loc(ref_pos[0], ref_pos[1], ref_pos[2], x_opt[0], x_opt[1],
x_opt[2])
print('Creating contour plot...')
# Calculate times of arrival for each point on the reference plane
for i, plane_coords in enumerate(plane_coordinates):
#plane_coords = geo2Loc(ref_pos[0], ref_pos[1], ref_pos[2], plane_coords[0], plane_coords[1], plane_coords[2])
# Create interpolated atmospheric profile for use with cyscan
sounding, points = getWeather(x_opt, plane_coords, setup.weather_type,
ref_pos, copy.copy(dataset))
# Use distance and atmospheric data to find path time
ti, _, _ = cyscan(np.array(x_opt), np.array(plane_coords), sounding, \
wind=setup.enable_winds, n_theta=setup.n_theta, n_phi=setup.n_phi, \
precision=setup.angle_precision)
if np.isnan(ti):
#Make blank contour
ti = -1
times_of_arrival[i] = ti
times_of_arrival = times_of_arrival.reshape(img_dim, img_dim)
# Determine range and number of contour levels, so they are always centred around 0
toa_abs_max = np.max(
[np.abs(np.min(times_of_arrival)),
np.max(times_of_arrival)])
toa_abs_min = np.min(
[np.abs(np.min(times_of_arrival)),
np.max(times_of_arrival)])
levels = np.linspace(toa_abs_min, toa_abs_max, 50)
# Plot colorcoded times of arrival on the surface
toa_conture = ax1.contourf(xx,
yy,
times_of_arrival,
levels,
cmap='inferno',
alpha=1.0)
# Add a color bar which maps values to colors
fig.colorbar(toa_conture, label='Time of arrival (s)', ax=ax1)
# plot given reference points
for q in range(len(reported_points[:])):
ax1.scatter(reported_points[q][1],
reported_points[q][2],
reported_points[q][3],
c='k',
marker='X')
ax1.text(reported_points[q][1],
reported_points[q][2],
reported_points[q][3],
'%s' % (reported_points[q][0]),
size=7,
zorder=1,
color='k')
if len(single_point) == 0:
# potential Supracenter points with color based on error
for i in range(len(sup)):
sup[i, 0], sup[i, 1], sup[i, 2] = loc2Geo(ref_pos[0], ref_pos[1],
ref_pos[2], sup[i, :])
sc = ax2.scatter(sup[:, 0],
sup[:, 1],
sup[:, 2],
c=errors,
cmap='inferno_r',
depthshade=False)
a = plt.colorbar(sc, ax=ax2)
a.set_label("Error in Supracenter (s)")
ax2.scatter(xstn[:, 0],
xstn[:, 1],
xstn[:, 2],
c=abs(r),
marker='^',
cmap='viridis_r',
depthshade=False)
ax2.set_title("Fits of Potential Supracenters")
ax2.set_xlabel("Latitude (deg N)")
ax2.set_ylabel("Longitude (deg E)")
ax2.set_zlabel('Elevation (m)')
if len(single_point) == 0 and setup.restrict_to_trajectory:
# A = [0, 0, 0]
# B = [0, 0, 0]
# A[0], A[1], A[2] = loc2Geo(ref_pos[0], ref_pos[1], ref_pos[2], [setup.lat_i, setup.lon_i, setup.elev_i])
# B[0], B[1], B[2] = loc2Geo(ref_pos[0], ref_pos[1], ref_pos[2], [setup.lat_f, setup.lon_f, setup.elev_f])
ax1.plot([setup.lat_i, setup.lat_f], [setup.lon_i, setup.lon_f],
[setup.elev_i * 1000, setup.elev_f * 1000],
c='g')
ax2.plot([setup.lat_i, setup.lat_f], [setup.lon_i, setup.lon_f],
[setup.elev_i * 1000, setup.elev_f * 1000],
c='g')
elif setup.restrict_to_trajectory:
ax1.plot([setup.lat_i, setup.lat_f], [setup.lon_i, setup.lon_f],
[setup.elev_i * 1000, setup.elev_f * 1000],
c='g')
# colorbars
b = plt.colorbar(res, ax=ax1)
b.set_label("Station Residuals (s)")
# automatic search
if len(single_point) == 0:
ax1.set_xlim3d(min_search[0], max_search[0])
ax2.set_xlim3d(min_search[0], max_search[0])
ax1.set_ylim3d(min_search[1], max_search[1])
ax2.set_ylim3d(min_search[1], max_search[1])
ax1.set_zlim3d(0, max_search[2])
ax2.set_zlim3d(0, max_search[2])
# manual search, don't pull up pso graph
else:
ax1.set_xlim3d(min_search[0], max_search[0])
ax1.set_ylim3d(min_search[1], max_search[1])
ax1.set_zlim3d(0, max_search[2])
plt.tight_layout()
plt.savefig(os.path.join(output_name, '1output_graph.png'))
plt.show()
plt.clf()
plt.close()
return min_search, max_search
def calcAllTimes(obj, bam, prefs):
''' Calculates all arrivals to all stations
'''
time_step = -999
#######################################
# Check if times need to be calculated
#######################################
if checkSkip(bam, prefs):
if prefs.debug:
printStatus(bam, prefs)
return bam.stn_list
qm = QMessageBox()
ret = qm.question(obj, '', "No arrival times detected, calculate?",
qm.Yes | qm.No)
if ret == qm.No:
return bam.stn_list
####################
# Times Calculation
####################
ref_pos = Position(bam.setup.lat_centre, bam.setup.lon_centre, 0)
if not hasattr(bam.setup, "fragmentation_point"):
no_of_frags = 0
else:
no_of_frags = len(bam.setup.fragmentation_point)
total_steps = 1 + (
prefs.frag_en * no_of_frags *
(1 + prefs.pert_en * prefs.pert_num) + prefs.ballistic_en *
(1 + prefs.pert_en * prefs.pert_num)) * len(bam.stn_list)
step = 0
for ii, stn in enumerate(bam.stn_list):
if not hasattr(stn, 'times'):
stn.times = Times()
stn.times.ballistic = []
stn.times.fragmentation = []
################
# Fragmentation
################
if prefs.frag_en:
for i, frag in enumerate(bam.setup.fragmentation_point):
if i == 0 and ii == 0:
t1 = time.time()
elif i == 1 and ii == 0:
time_step = time.time() - t1
step += 1
time_hour = time_step * (total_steps - step) // 3600
time_minute = time_step * (total_steps - step) % 3600 // 60
time_second = time_minute * (total_steps - step) % 60
time_string = "{:02d}:{:02d}:{:02d}".format(
int(time_hour), int(time_minute), int(time_second))
loadingBar(
'Calculating Station Times - ETA {:}: '.format(
time_string), step, total_steps)
offset = frag.time
a = []
supra = frag.position
# convert to local coordinates based off of the ref_pos
stn.metadata.position.pos_loc(supra)
supra.pos_loc(supra)
lats = [supra.lat, stn.metadata.position.lat]
lons = [supra.lon, stn.metadata.position.lon]
heights = [supra.elev, stn.metadata.position.elev]
sounding, perturbations = bam.atmos.getSounding(
lats,
lons,
heights,
ref_time=bam.setup.fireball_datetime,
spline=1000)
# Travel time of the fragmentation wave
f_time, frag_azimuth, frag_takeoff, frag_err = cyscan(np.array([supra.x, supra.y, supra.z]), np.array([stn.metadata.position.x, stn.metadata.position.y, stn.metadata.position.z]), sounding, \
wind=prefs.wind_en, h_tol=prefs.pso_min_ang, v_tol=prefs.pso_min_dist)
results = []
if perturbations is not None:
for pert in perturbations:
step += 1
loadingBar('Calculating Station Times: ', step,
total_steps)
temp = cyscan(np.array([supra.x, supra.y, supra.z]), \
np.array([stn.metadata.position.x, stn.metadata.position.y, stn.metadata.position.z]), \
sounding, wind=prefs.wind_en, h_tol=prefs.pso_min_ang, v_tol=prefs.pso_min_dist)
temp[0] += offset
results.append(temp)
a.append(
[f_time + offset, frag_azimuth, frag_takeoff, frag_err])
a.append(results)
stn.times.fragmentation.append(a)
############
# Ballistic
############
if prefs.ballistic_en and bam.setup.trajectory is not None:
step += 1
loadingBar('Calculating Station Times: ', step, total_steps)
a = []
# define line bottom boundary
max_height = bam.setup.trajectory.pos_i.elev
min_height = bam.setup.trajectory.pos_f.elev
points = bam.setup.trajectory.trajInterp2(div=1,
min_p=min_height,
max_p=max_height)
u = np.array([
bam.setup.trajectory.vector.x, bam.setup.trajectory.vector.y,
bam.setup.trajectory.vector.z
])
angle_off = []
X = []
for pt in points:
S = Position(pt[0], pt[1], pt[2])
lats = [S.lat, stn.metadata.position.lat]
lons = [S.lon, stn.metadata.position.lon]
heights = [S.elev, stn.metadata.position.elev]
S.pos_loc(S)
stn.metadata.position.pos_loc(S)
sounding, perturbations = bam.atmos.getSounding(
lats, lons, heights, ref_time=bam.setup.fireball_datetime)
# Travel time of the fragmentation wave
_, az, tf, _ = cyscan(np.array([S.x, S.y, S.z]), \
np.array([stn.metadata.position.x, stn.metadata.position.y, stn.metadata.position.z]), \
sounding, wind=prefs.wind_en, h_tol=prefs.pso_min_ang, v_tol=prefs.pso_min_dist)
az = np.radians(az)
tf = np.radians(180 - tf)
v = np.array([
np.sin(az) * np.sin(tf),
np.cos(az) * np.sin(tf), -np.cos(tf)
])
angle_off.append(
np.degrees(
np.arccos(
np.dot(u / np.sqrt(u.dot(u)),
v / np.sqrt(v.dot(v))))))
X.append(S.elev)
angle_off = np.array(angle_off)
try:
best_indx = np.nanargmin(abs(angle_off - 90))
except ValueError:
best_indx = None
a.append([np.nan, np.nan, np.nan, np.nan])
results = []
if perturbations is not None:
for pert in perturbations:
results.append(
np.array([np.nan, np.nan, np.nan, np.nan]))
a.append(results)
stn.times.ballistic.append(a)
continue
supra = points[best_indx]
ref_time = supra[3]
supra = Position(supra[0], supra[1], supra[2])
supra.pos_loc(supra)
lats = [supra.lat, stn.metadata.position.lat]
lons = [supra.lon, stn.metadata.position.lon]
heights = [supra.elev, stn.metadata.position.elev]
sounding, perturbations = bam.atmos.getSounding(
lats, lons, heights, ref_time=bam.setup.fireball_datetime)
# Travel time of the fragmentation wave
f_time, frag_azimuth, frag_takeoff, frag_err = cyscan(np.array([supra.x, supra.y, supra.z]), \
np.array([stn.metadata.position.x, stn.metadata.position.y, stn.metadata.position.z]),\
sounding, wind=prefs.wind_en, h_tol=prefs.pso_min_ang, v_tol=prefs.pso_min_dist)
speed = bam.setup.trajectory.v
distance = supra.pos_distance(bam.setup.trajectory.pos_f)
timing = distance / speed
results = []
if perturbations is not None:
for pert in perturbations:
step += 1
loadingBar('Calculating Station Times: ', step,
total_steps)
e, b, c, d = cyscan(np.array([supra.x, supra.y, supra.z]), \
np.array([stn.metadata.position.x, stn.metadata.position.y, stn.metadata.position.z]),\
sounding, wind=prefs.wind_en, h_tol=prefs.pso_min_ang, v_tol=prefs.pso_min_dist)
e += timing
results.append(np.array([e, b, c, d]))
a.append([ref_time + f_time, frag_azimuth, frag_takeoff, frag_err])
a.append(results)
stn.times.ballistic.append(a)
step += 1
loadingBar('Calculating Station Times: ', step, total_steps)
if prefs.debug:
printStatus(bam, prefs)
return bam.stn_list
def plotAllWaveforms(dir_path, stn_list, setup, sounding, ax=None, waveform_window=None,\
difference_filter_all=False):
""" Bandpass filter and plot all waveforms from the given data list.
Keyword arguments:
waveform_window: [int] If given, the waveforms will be cut around the modelled time of arrival line
with +/- waveform_window/2 seconds. None by default, which means the whole waveform will be
plotted.
difference_filter_all: [bool] If True, the Kalenda et al. (2014) difference filter will be applied
on the data plotted in the overview plot of all waveforms.
"""
# Initialize variables
v_sound = setup.v_sound
t0 = setup.t0
lat_centre = setup.lat_centre
lon_centre = setup.lon_centre
if ax is None:
ax = plt.gca()
max_wave_value = 0
min_wave_value = np.inf
min_time = np.inf
max_time = 0
# # Add extra stations from the config file
# if setup.stations is not None:
# for line in setup.stations:
# # Prevent adding duplicates
# if line in data_list:
# continue
# data_list.append(line)
lats = []
lons = []
for i in range(len(stn_list)):
lats.append(stn_list[i].position.lat)
lons.append(stn_list[i].position.lon)
# Azimuth from source point to station (degrees +N of due E)
az = np.arctan2(lat_centre - np.array(lats), lon_centre - np.array(lons))
# az - normalized values for color-coding,
# az_n - original values for text
az_n = copy.copy(az)
#az_n = (90 - np.degrees(az_n)%360)%360
# normalize azimuths
for i in range(len(az)):
az[i] += abs(min(az))
az[i] /= (max(az) + abs(min(az)))
# Go though all stations and waveforms
bad_stats = []
for idx, stn in enumerate(stn_list):
sys.stdout.write('\rPlotting: {:} {:} '.format(stn.network, stn.code))
sys.stdout.flush()
time.sleep(0.001)
mseed_file_path = os.path.join(dir_path, stn.file_name)
try:
# Read the miniSEED file
if os.path.isfile(mseed_file_path):
mseed = obspy.read(mseed_file_path)
else:
bad_stats.append(idx)
print('File {:s} does not exist!'.format(mseed_file_path))
continue
except TypeError as e:
bad_stats.append(idx)
print('Opening file {:} failed with error: {:}'.format(mseed_file_path, e))
continue
# Find channel with BHZ, HHZ, or BDF
for i in range(len(mseed)):
if mseed[i].stats.channel == 'BDF':
stn.channel = 'BDF'
stream = i
for i in range(len(mseed)):
if mseed[i].stats.channel == 'BHZ':
stn.channel = 'BHZ'
stream = i
for i in range(len(mseed)):
if mseed[i].stats.channel == 'HHZ':
stn.channel = 'HHZ'
stream = i
for i in range(len(mseed)):
if mseed[i].stats.channel == 'EHZ':
stn.channel = 'EHZ'
stream = i
for i in range(len(mseed)):
if mseed[i].stats.channel == 'SHZ':
stn.channel = 'SHZ'
stream = i
# Unpack miniSEED data
delta = mseed[stream].stats.delta
waveform_data = mseed[stream].data
# Extract time
start_datetime = mseed[stream].stats.starttime.datetime
end_datetime = mseed[stream].stats.endtime.datetime
stn.offset = (start_datetime - setup.fireball_datetime - datetime.timedelta(minutes=5)).total_seconds()
# Skip stations with no data
if len(waveform_data) == 0:
continue
# Apply the Kalenda et al. (2014) difference filter instead of Butterworth
if difference_filter_all:
waveform_data = convolutionDifferenceFilter(waveform_data)
else:
### BANDPASS FILTERING ###
# Init the butterworth bandpass filter
butter_b, butter_a = butterworthBandpassFilter(0.8, 5.0, 1.0/delta, order=6)
# Filter the data
waveform_data = scipy.signal.filtfilt(butter_b, butter_a, waveform_data)
# Average and subsample the array for quicker plotting (reduces 40Hz to 10Hz)
waveform_data = subsampleAverage(waveform_data, 4)
delta *= 4
##########################
# Calculate the distance from the source point to this station (kilometers)
station_dist = greatCircleDistance(np.radians(lat_centre), np.radians(lon_centre), stn.position.lat_r, stn.position.lon_r)
# Construct time array, 0 is at start_datetime
time_data = np.arange(0, (end_datetime - start_datetime).total_seconds(), delta)
# Cut the waveform data length to match the time data
waveform_data = waveform_data[:len(time_data)]
time_data = time_data[:len(waveform_data)] + stn.offset
# Detrend the waveform and normalize to fixed width
waveform_data = waveform_data - np.mean(waveform_data)
#waveform_data = waveform_data/np.percentile(waveform_data, 99)*2
waveform_data = waveform_data/np.max(waveform_data)*10
# Add the distance to the waveform
waveform_data += station_dist
# Cut the waveforms around the time of arrival, if the window for cutting was given.
if waveform_window is not None:
# Time of arrival
toa = station_dist/(v_sound/1000) + t0
# Cut the waveform around the time of arrival
crop_indices = (time_data >= toa - waveform_window/2) & (time_data <= toa + waveform_window/2)
time_data = time_data[crop_indices]
waveform_data = waveform_data[crop_indices]
# Skip plotting if array empty
if len(time_data) == 0:
continue
# Replace all NaNs with 0s
waveform_data = np.nan_to_num(waveform_data, 0)
max_time = np.max([max_time, np.max(time_data)])
min_time = np.min([min_time, np.min(time_data)])
# Keep track of minimum and maximum waveform values (used for plotting)
max_wave_value = np.max([max_wave_value, np.max(waveform_data)])
min_wave_value = np.min([min_wave_value, np.min(waveform_data)])
if setup.colortoggle:
c = plt.cm.plasma(az[idx])
else:
c = None
#if data_list[idx][1].strip() not in setup.rm_stat:
# Plot the waveform on the the time vs. distance graph
ax.plot(waveform_data, time_data, c=c, alpha=0.7, linewidth=0.2, zorder=2)
if stn.code in setup.rm_stat:
print('Excluding station: {:}'.format(stn.network + '-' + stn.code))
else:
# Print the name of the station
# Fragmentation
if stn.code in setup.high_f:
ax.text(np.mean(waveform_data), np.max(time_data), "{:} - {:} \n Az: {:5.1f}".format(stn.network, stn.code, az_n[idx]), \
rotation=270, va='bottom', ha='center', size=7, zorder=2, color="g")
# Ballistic
elif stn.code in setup.high_b:
ax.text(np.mean(waveform_data), np.max(time_data), "{:} - {:} \n Az: {:5.1f}".format(stn.network, stn.code, az_n[idx]), \
rotation=270, va='bottom', ha='center', size=7, zorder=2, color="b")
else:
ax.text(np.mean(waveform_data), np.max(time_data), "{:} - {:} \n Az: {:5.1f}".format(stn.network, stn.code, az_n[idx]), \
rotation=270, va='bottom', ha='center', size=7, zorder=2, color="w")
toa_line_time = np.linspace(0, max_time, 10)
# Plot the constant sound speed line (assumption is that the release happened at t = 0)
ax.plot((toa_line_time)*v_sound/1000, (toa_line_time + t0), color='r', alpha=0.25, linewidth=1, \
zorder=2, label="$V_s = " + "{:d}".format(int(v_sound)) + r" \rm{ms^{-1}}$")
# Reference location for the local coordinate system
ref_pos = position(lat_centre, lon_centre, 0)
# Ballistic Prediction
b_time = [0]*len(stn_list)
b_dist = [0]*len(stn_list)
rb_dist = [0]*len(stn_list)
good_stats = (x for x in range(len(stn_list)) if x not in bad_stats)
print('')
if setup.perturb_times <= 0 and setup.perturb:
print("ERROR: perturb_times must be greater than 0")
# for ptb_n in range(setup.perturb_times):
# # Manual search for ballistic wave
# if ptb_n > 0:
# print("STATUS: Perturbation: {:}".format(ptb_n))
# sounding_p = perturb(sounding, setup.perturb_method)
# else:
# sounding_p = sounding
sounding_p = sounding
if setup.show_ballistic_waveform:
# Input coordinate type. True - coordinates are given as lat/lon. False - coordinates are given in local
# coordinates in reference to the source center
# Convert to local coordinates
setup.traj_f.pos_loc(ref_pos)
for stn in good_stats:
if stn_list[stn].code.strip() not in setup.rm_stat:
# Station location in local coordinates
stn_list[stn].position.pos_loc(ref_pos)
# Time to travel from trajectory to station
b_time[i] = timeOfArrival([stn_list[stn].position.x, stn_list[stn].position.y, stn_list[stn].position.z], setup.traj_f.x/1000, setup.traj_f.y/1000, setup.t0, 1000*setup.v, \
np.radians(setup.azim), np.radians(setup.zangle), setup, sounding=sounding_p, fast=True)# - setup.t + setup.t0
# Point on trajectory where wave is released
bx, by, bz = waveReleasePoint([stn_list[stn].position.x, stn_list[stn].position.y, stn_list[stn].position.z], setup.traj_f.x, setup.traj_f.y, setup.t0, 1000*setup.v, \
np.radians(setup.azim), np.radians(setup.zangle), setup.v_sound)
# Distance from source center to station
b_dist[i] = ((stn_list[stn].position.x)**2 + (stn_list[stn].position.y)**2)**0.5
# Distance from ballistic wave to station
rb_dist[i] = ((stn_list[stn].position.x - bx)**2 + (stn_list[stn].position.y - by)**2 + (stn_list[stn].position.z - bz)**2)**0.5
# Convert to km
b_dist[i] /= 1000
rb_dist[i] /= 1000
else:
b_dist[i], b_time[i], rb_dist[i] = np.nan, np.nan, np.nan
# Plot Ballistic Prediction
# if ptb_n == 0:
ax.scatter(b_dist, b_time, c='b', marker='_', s=100, label='Ballistic', zorder=3)
# else:
# ax.scatter(b_dist, b_time, c='b', marker='_', s=100, alpha=0.3, zorder=3)
# Fragmentation Prediction
f_time = [0]*len(stn_list)
f_dist = [0]*len(stn_list)
rf_dist = [0]*len(stn_list)
# Manual search for fragmentation waves
if setup.show_fragmentation_waveform:
if len(setup.fragmentation_point) == 0:
print("ERROR: Cannot plot fragmentation if there is no fragmentation point. Set show_fragmentation_waveform = False if not using.")
exit()
for j, line in enumerate(setup.fragmentation_point):
# Supracenter location in local coordinates
supra = position(float(line[0]), float(line[1]), float(line[2]))
supra.pos_loc(ref_pos)
for i, stn in enumerate(stn_list):
if stn.code.strip() not in setup.rm_stat:
if stn in bad_stats:
f_dist[i], f_time[i], rf_dist[i] = np.nan, np.nan, np.nan
# Station location in local coordinates
stn.position.pos_loc(ref_pos)
###### DIFFERENT WEATHERS HERE ######
if setup.weather_type == 'none':
zProfile = np.array([[0, setup.v_sound, 0, 0], [10000, setup.v_sound, 0, 0]])
else:
# Cut down atmospheric profile to the correct heights, and interp
zProfile, _ = getWeather(np.array([supra.x, supra.y, supra.z]), np.array([stn.position.x, stn.position.y, stn.position.z]), setup.weather_type, \
[ref_pos.lat, ref_pos.lon, ref_pos.elev], sounding_p, convert=True)
# Time to travel from Supracenter to station
f_time[i], _, _ = cyscan(np.array([supra.x, supra.y, supra.z]), np.array([stn.position.x, stn.position.y, stn.position.z]), zProfile, wind=True)
# Add reference time
f_time[i] += float(line[3])
# Distance from source center to station
f_dist[i] = ((stn.position.x)**2 + (stn.position.y)**2)**0.5
# Distance from Supracenter to station
rf_dist[i] = ((stn.position.x - supra.x)**2 + (stn.position.y - supra.y)**2 + (stn.position.z - supra.z)**2)**0.5
# Convert to km
f_dist[i] /= 1000
rf_dist[i] /= 1000
else:
f_dist[i], f_time[i], rf_dist[i] = np.nan, np.nan, np.nan
# Plot Fragmentation Prediction
# if ptb_n == 0:
#ax.scatter(f_dist, f_time, c=C[(j+1)%4], marker='_', s=100, label='Fragmentation {:}'.format(j+1), zorder=3)
else:
ax.scatter(f_dist, f_time, c=C[(j+1)%4], marker='_', s=100, alpha=0.3, zorder=3)
ax.scatter(f_dist[i], f_time[i], c=C[(j+1)%4], marker='_', s=100, label='Fragmentation', zorder=3)
ax.set_xlabel('Distance (km)')
ax.set_ylabel('Time (s)')
#ax.set_ylim(min_time - 200, max_time + 500)
ax.set_xlim(0, max_wave_value)
ax.grid(color='#ADD8E6', linestyle='dashed', linewidth=0.5, alpha=0.7)
# Export station distance file
with open(os.path.join(dir_path, 'output.txt'), 'w') as f:
f.write('Station Lat(deg N) Lon(deg E) Elev(m) Az(+E dN) Ball_d(km) Ball_t(s) Frag_d(km) Frag_t(s)\n')
for i, stn in enumerate(stn_list):
f.write('{:8}, {:8.4f}, {:8.4f}, {:7.2f}, {:8.3f}, {:7.2f}, {:7.2f}, {:7.2f}, {:7.2f}\n'\
.format(str(stn.network) + '-' + str(stn.code), stn.position.lat, stn.position.lon, \
stn.position.elev, az_n[i], rb_dist[i], b_time[i], rf_dist[i], f_time[i]))
def rayTraceFromSource(self, source, clean_mode=False, debug=False):
traj = self.bam.setup.trajectory
### Set up parameters of source
stat_idx = self.station_combo.currentIndex()
stat = self.bam.stn_list[stat_idx]
stat_pos = stat.metadata.position
lat = [source.lat, stat_pos.lat]
lon = [source.lon, stat_pos.lon]
elev = [source.elev, stat_pos.elev]
if not clean_mode:
print("Source Location")
print(source)
print("Station Location")
print(stat_pos)
sounding, perturbations = self.bam.atmos.getSounding(
lat=lat,
lon=lon,
heights=elev,
spline=1000,
ref_time=self.bam.setup.fireball_datetime)
ref_pos = Position(self.bam.setup.lat_centre,
self.bam.setup.lon_centre, 0)
stat_pos.pos_loc(source)
source.pos_loc(source)
source.z = source.elev
stat_pos.z = stat_pos.elev
h_tol = float(self.horizontal_tol.text())
v_tol = float(self.vertical_tol.text())
### Ray Trace
r, tr, f_particle = cyscan(np.array([source.x, source.y, source.z]), np.array([stat_pos.x, stat_pos.y, stat_pos.z]), \
sounding, trace=True, plot=False, particle_output=True, debug=False, \
wind=True, h_tol=h_tol, v_tol=v_tol, print_times=True, processes=1)
if not clean_mode:
az = np.radians(r[1])
tf = np.radians(180 - r[2])
u = np.array([traj.vector.x, traj.vector.y, traj.vector.z])
v = np.array([
np.sin(az) * np.sin(tf),
np.cos(az) * np.sin(tf), -np.cos(tf)
])
angle_off = abs(
np.degrees(
np.arccos(
np.dot(u / np.sqrt(u.dot(u)), v /
np.sqrt(v.dot(v))))) - 90)
if not self.pertstog.isChecked():
dx = np.abs(stat_pos.x - source.x)
dy = np.abs(stat_pos.y - source.y)
dz = np.abs(stat_pos.z - source.z)
time_along_trajectory = traj.findTime(source.elev)
print("###### RESULTS ######")
print("Time Along Trajectory (wrt Reference): {:.4f} s".format(
time_along_trajectory))
print("Acoustic Path Time: {:.4f} s".format(r[0]))
print("Total Time from Reference: {:.4f} s".format(
r[0] + time_along_trajectory))
print("Launch Angle {:.2f} deg".format(angle_off))
print("###### EXTRAS #######")
print("Time: {:.4f} s".format(r[0]))
print("Azimuth: {:.2f} deg from North".format(r[1]))
print("Takeoff: {:.2f} deg from up".format(r[2]))
print("Error in Solution {:.2f} m".format(r[3]))
print("Distance in x: {:.2f} m".format(dx))
print("Distance in y: {:.2f} m".format(dy))
print("Distance in z: {:.2f} m".format(dz))
print("Horizontal Distance: {:.2f} m".format(
np.sqrt(dx**2 + dy**2)))
print("Total Distance: {:.2f} m".format(
np.sqrt(dx**2 + dy**2 + dz**2)))
print("No Winds Time: {:.2f} s".format(
np.sqrt(dx**2 + dy**2 + dz**2) / 330))
print("Time Difference: {:.2f} s".format(
r[0] - np.sqrt(dx**2 + dy**2 + dz**2) / 330))
print("Time Along Trajectory: {:.4f} s".format(
time_along_trajectory))
print("Total Time from Reference: {:.4f} s".format(
r[0] + time_along_trajectory))
else:
t_array = []
az_array = []
tk_array = []
err_array = []
angle_off_array = []
t_array.append(r[0])
az_array.append(r[1])
tk_array.append(r[2])
err_array.append(r[3])
angle_off_array.append(angle_off)
try:
N_LAYERS = 1
for i in range(N_LAYERS):
try:
ba, tf = determineBackAz(tr[-(i + 2), :], tr[-1, :],
sounding[0, 2],
np.degrees(sounding[0, 3]))
if hasattr(self, "plot_ba_data"):
self.plot_ba_data.append([source.elev / 1000, ba, tf])
except IndexError:
pass
except TypeError:
pass
### Plot begin and end points of ray-trace
self.rtv_graph.ax.scatter(source.lon,
source.lat,
source.elev,
c='r',
marker='*',
s=200)
self.rtv_graph.ax.scatter(stat_pos.lon,
stat_pos.lat,
stat_pos.elev,
c='b',
marker='^',
s=200)
positions = []
### Plot trace of eigen-ray
try:
path_len = 0
for i in range(len(tr[:, 0])):
A = Position(0, 0, 0)
A.x, A.y, A.z = tr[i, 0], tr[i, 1], tr[i, 2]
if i > 0:
path_len += np.sqrt((tr[i, 0] - tr[i - 1, 0])**2 +
(tr[i, 1] - tr[i - 1, 1])**2 +
(tr[i, 2] - tr[i - 1, 2])**2)
else:
path_len += np.sqrt((tr[i, 0] - 0)**2 + (tr[i, 1] - 0)**2 +
(tr[i, 2] - 0)**2)
A.pos_geo(source)
positions.append([A.lon, A.lat, A.elev])
if not clean_mode:
print("Total Path Length: {:.2f} m".format(path_len))
print("Approximate Ray Time: {:.2f} s".format(path_len / 330))
positions = np.array(positions)
if not clean_mode:
self.rtv_graph.ax.scatter(positions[:, 0],
positions[:, 1],
positions[:, 2],
c='b',
alpha=0.5)
self.rtv_graph.ax.plot(positions[:, 0],
positions[:, 1],
positions[:, 2],
c='k')
self.current_eigen = [positions, tr[:, -1]]
err = np.sqrt((stat_pos.x - tr[-1, 0])**2 +
(stat_pos.y - tr[-1, 1])**2 +
(stat_pos.z - tr[-1, 2])**2)
h_err = np.sqrt((stat_pos.x - tr[-1, 0])**2 +
(stat_pos.y - tr[-1, 1])**2)
v_err = np.sqrt((stat_pos.z - tr[-1, 2])**2)
if debug:
print(
"Source Height: {:.2f} km - Final Error: {:.2f} m (v) {:.2f} m (h)"
.format(source.elev / 1000, v_err, h_err))
self.rtv_graph.ax.scatter(positions[-1, 0],
positions[-1, 1],
positions[-1, 2],
c='g')
self.current_loaded_rays.append([positions, tr[:, -1]])
self.plothvt()
except:
pass
if not clean_mode:
for sol in f_particle:
r = anglescan(np.array([source.x, source.y, source.z]),
sol[0],
sol[1],
sounding,
trace=True,
debug=False,
wind=True)
tr = np.array(r[1])
positions = []
for i in range(len(tr[:, 0])):
A = Position(0, 0, 0)
A.x, A.y, A.z = tr[i, 0], tr[i, 1], tr[i, 2]
A.pos_geo(source)
positions.append([A.lon, A.lat, A.elev])
positions = np.array(positions)
# self.rtv_graph.ax.plot(positions[:, 0], positions[:, 1], positions[:, 2], alpha=0.3)
err = np.sqrt((stat_pos.x - tr[-1, 0])**2 +
(stat_pos.y - tr[-1, 1])**2 +
(stat_pos.z - tr[-1, 2])**2)
if err <= 1000:
self.rtv_graph.ax.scatter(positions[-1, 0],
positions[-1, 1],
positions[-1, 2],
c='g')
else:
self.rtv_graph.ax.scatter(positions[-1, 0],
positions[-1, 1],
positions[-1, 2],
c='r')
if self.pertstog.isChecked() and len(perturbations) > 0:
if clean_mode:
t_array = []
az_array = []
tk_array = []
err_array = []
angle_off_array = []
for pert_idx, pert in enumerate(perturbations):
sys.stdout.write("\r Working on Perturbation {:}/{:}".format(
pert_idx + 1, len(perturbations)))
sys.stdout.flush()
r, tr, f_particle = cyscan(np.array([source.x, source.y, source.z]), np.array([stat_pos.x, stat_pos.y, stat_pos.z]), \
pert, trace=True, plot=False, particle_output=True, debug=False, \
wind=True, h_tol=h_tol, v_tol=v_tol, print_times=True)
t_array.append(r[0])
az_array.append(r[1])
tk_array.append(r[2])
err_array.append(r[3])
az = np.radians(r[1])
tf = np.radians(180 - r[2])
u = np.array([traj.vector.x, traj.vector.y, traj.vector.z])
v = np.array([
np.sin(az) * np.sin(tf),
np.cos(az) * np.sin(tf), -np.cos(tf)
])
angle_off = abs(
np.degrees(
np.arccos(
np.dot(u / np.sqrt(u.dot(u)), v /
np.sqrt(v.dot(v))))) - 90)
angle_off_array.append(angle_off)
try:
ba = determineBackAz(tr[-2, :], tr[-1, :], pert[0, 2],
np.degrees(pert[0, 3]))
# print("Height: {:.2f} km".format(source.elev/1000))
# print("Back Azimuth: {:.2f} deg".format(ba))
# print("Travel Time: {:.2f} s".format(r[0]))
# print("Approx: {:.2f} deg".format(last_azimuth))
# print("Pure Winds: {:.2f} deg".format(np.degrees(sounding[0, 3])%360))
self.plot_ba_data.append([source.elev / 1000, ba, r[0]])
except TypeError:
pass
print()
print("###### NOMINAL RESULTS ######")
print("Time: {:.4f} s".format(t_array[0]))
print("Azimuth: {:.2f} deg from North".format(az_array[1]))
print("Takeoff: {:.2f} deg from up".format(tk_array[2]))
print("Error in Solution {:.2f} m".format(err_array[3]))
print("### UNCERTAINTIES ###")
print("Time: {:.4f} - {:.4f} s ({:.4f} s)".format(
np.nanmin(t_array), np.nanmax(t_array),
np.nanmax(t_array) - np.nanmin(t_array)))
print(
"Azimuth: {:.2f} - {:.2f} deg from North ({:.2f} deg)".format(
np.nanmin(az_array), np.nanmax(az_array),
np.nanmax(az_array) - np.nanmin(az_array)))
print("Takeoff: {:.2f} - {:.2f} deg from up ({:.2f} deg)".format(
np.nanmin(tk_array), np.nanmax(tk_array),
np.nanmax(tk_array) - np.nanmin(tk_array)))
print("Error in Solution {:.2f} - {:.2f} m ({:.2f} m)".format(
np.nanmin(err_array), np.nanmax(err_array),
np.nanmax(err_array) - np.nanmin(err_array)))
print("Angle Off {:.2f} - {:.2f} deg ({:.2f} deg)".format(
np.nanmin(angle_off_array), np.nanmax(angle_off_array),
np.nanmax(angle_off_array) - np.nanmin(angle_off_array)))
print("Saving CSV of Perturbations")
file_name = saveFile("csv", note="Perturbations")
with open(file_name, "w+") as f:
f.write(
"Height [km], Time [s], Azimuth [deg from North], Takeoff [deg from Up], Error [m], Angle Off [deg]\n"
)
for ll, line in enumerate(t_array):
if ll == len(t_array):
f.write("{:}, {:}, {:}, {:}, {:}, {:}".format(
source.elev / 1000, t_array[ll], az_array[ll],
tk_array[ll], err_array[ll], angle_off_array[ll]))
else:
f.write("{:}, {:}, {:}, {:}, {:}, {:}\n".format(
source.elev / 1000, t_array[ll], az_array[ll],
tk_array[ll], err_array[ll], angle_off_array[ll]))
def waveReleasePointWindsContour(bam,
traj,
ref_loc,
points,
div=37,
mode='ballistic'):
setup = bam.setup
atmos = bam.atmos
steps = 90
alpha = np.linspace(0, 360 * ((steps - 1) / steps), steps)
alpha = np.radians(alpha)
beta = np.linspace(0, 90 * ((steps - 1) / steps), steps)
beta = np.radians(beta)
theta = setup.trajectory.azimuth.rad
phi = setup.trajectory.zenith.rad
tol = 25 #deg
# tol_fact = np.radians(np.linspace(-tol, tol, 10))
WIND = True
u = traj.getTrajVect()
v_list = []
for i in range(steps):
for j in range(steps):
v = np.array([np.sin(alpha[i])*np.sin(beta[j]),\
np.cos(alpha[i])*np.sin(beta[j]),\
-np.cos(beta[j])])
if np.abs(90 - np.degrees(np.arccos(np.dot(u, v)))) <= tol:
v_list.append([alpha[i], beta[j]])
v_list = np.array(v_list)
results = []
# temp hotfix
if mode == 'ballistic_old':
grid_space = 25
p1 = Position(43.8, 13.1, 0)
p2 = Position(47.8, 17.1, 0)
p1.pos_loc(ref_loc)
p2.pos_loc(ref_loc)
xs = np.linspace(p1.x, p2.x, grid_space)
ys = np.linspace(p1.y, p2.y, grid_space)
n_steps = grid_space**2 * len(points)
for xnum, xx in enumerate(xs):
for ynum, yy in enumerate(ys):
angle_list = []
time_list = []
D = [xx, yy, 0]
for pnum, pp in enumerate(points):
step = pnum + ynum * len(points) + xnum * grid_space * len(
points)
loadingBar("Contour Calculation", step, n_steps)
P = Position(pp[0], pp[1], pp[2])
P.pos_loc(ref_loc)
S = P.xyz
R = Position(0, 0, 0)
R.x = D[0]
R.y = D[1]
R.z = D[2]
R.pos_geo(ref_loc)
lats = [P.lat, R.lat]
lons = [P.lon, R.lon]
elev = [P.elev, R.elev]
z_profile, _ = atmos.getSounding(
lats,
lons,
elev,
ref_time=setup.fireball_datetime,
spline=100)
res = cyscan(np.array(S),
np.array(D),
z_profile,
wind=True,
h_tol=330,
v_tol=3000,
debug=False)
alpha = np.radians(res[1])
beta = np.radians(180 - res[2])
res_vector = np.array([np.sin(alpha)*np.sin(beta),\
np.cos(alpha)*np.sin(beta),\
-np.cos(beta)])
angle_list.append(
np.abs(90 - np.degrees(
np.arccos(
np.dot(
u / np.sqrt(u.dot(u)), res_vector /
np.sqrt(res_vector.dot(res_vector)))))))
time_list.append(res[0])
if np.nanmin(angle_list) <= tol:
best_angle_index = np.nanargmin(angle_list)
best_time = time_list[best_angle_index]
if not np.isnan(best_time):
res = [xx, yy, 0, best_time]
results.append(res)
# u = np.array([bam.setup.trajectory.vector.x,
# bam.setup.trajectory.vector.y,
# bam.setup.trajectory.vector.z])
# angle_off = []
# X = []
# for i in range(len(bam.setup.fragmentation_point)):
# az = stn.times.fragmentation[i][0][1]
# tf = stn.times.fragmentation[i][0][2]
# az = np.radians(az)
# tf = np.radians(180 - tf)
# v = np.array([np.sin(az)*np.sin(tf), np.cos(az)*np.sin(tf), -np.cos(tf)])
# angle_off.append(np.degrees(np.arccos(np.dot(u/np.sqrt(u.dot(u)), v/np.sqrt(v.dot(v))))))
# X.append(bam.setup.fragmentation_point[i].position.elev)
# angle_off = np.array(angle_off)
# try:
# best_indx = np.nanargmin(abs(angle_off - 90))
# except ValueError:
# best_indx = None
# a.append(np.array([np.nan, np.nan, np.nan, np.nan]))
# for pert in perturbations:
# a.append(np.array([np.nan, np.nan, np.nan, np.nan]))
# stn.times.ballistic.append(a)
# continue
# np.array([t_arrival, azimuth, takeoff, E[k, l]])
elif mode == 'ballistic':
n_steps = len(v_list) * len(points)
WIND = True
for pp, p in enumerate(points):
for vv, v in enumerate(v_list):
step = vv + pp * len(v_list)
loadingBar("Contour Calculation", step, n_steps)
# v[i] = np.array([np.sin(alpha[i])*np.sin(beta[i]),\
# np.cos(alpha[i])*np.sin(beta[i]),\
# -np.cos(beta[i])])
P = Position(p[0], p[1], p[2])
S = Position(p[0], p[1], 0)
P.pos_loc(ref_loc)
pt = P.xyz
# s = p + p[2]/np.cos(beta[i])*v[i]
# S = Position(0, 0, 0)
# P = Position(0, 0, 0)
# S.x, S.y, S.z = s[0], s[1], s[2]
# P.x, P.y, P.z = p[0], p[1], p[2]
# S.pos_geo(ref_loc)
# P.pos_geo(ref_loc)
lats = [P.lat, S.lat]
lons = [P.lon, S.lon]
elev = [P.elev, S.elev]
# z_profile, _ = supra.Supracenter.cyweatherInterp.getWeather(p, s, setup.weather_type, \
# ref_loc, copy.copy(sounding), convert=True)
if WIND:
z_profile, _ = atmos.getSounding(
lats,
lons,
elev,
ref_time=setup.fireball_datetime,
spline=200)
res = anglescan(pt,
np.degrees(v[0]),
np.degrees(v[1]) + 90,
z_profile,
wind=True,
debug=False)
else:
# if no wind, just draw a straight line
vect = np.array([np.sin(v[0])*np.sin(v[1]),\
np.cos(v[0])*np.sin(v[1]),\
-np.cos(v[1])])
s = -pt[2] / vect[2]
ground_point = pt + s * vect
ground_time = s / 330
res = [
ground_point[0], ground_point[1], ground_point[2],
ground_time
]
# This is the limit in distance from the trajectory (hardcoded)
if res[-1] <= 1000:
# if np.sqrt(res[0]**2 + res[1]**2) <= 150000:
results.append(res)
else:
# n_steps = len(tol_fact)*len(points)*steps
beta = np.linspace(90 + 0.01, 180, steps)
beta = np.radians(beta)
p = points
for ii, i in enumerate(range(steps)):
for jj, j in enumerate(range(steps)):
# print(np.degrees(beta[i]))
step = jj + ii * steps
loadingBar("Contour Calculation", step, n_steps)
v[i*steps + j] = np.array([np.sin(alpha[i])*np.sin(beta[j]),\
np.cos(alpha[i])*np.sin(beta[j]),\
-np.cos(beta[j])])
s = p + p[2] / np.cos(beta[j]) * v[i * steps + j]
S = Position(0, 0, 0)
P = Position(0, 0, 0)
S.x, S.y, S.z = s[0], s[1], s[2]
P.x, P.y, P.z = p[0], p[1], p[2]
S.pos_geo(ref_loc)
P.pos_geo(ref_loc)
lats = [P.lat, S.lat]
lons = [P.lon, S.lon]
elev = [P.elev, S.elev]
z_profile, _ = atmos.getSounding(
lats,
lons,
elev,
ref_time=setup.fireball_datetime,
spline=100)
res = anglescan(p,
np.degrees(alpha[i]),
np.degrees(beta[j]),
z_profile,
wind=True,
debug=False)
# if np.sqrt(res[0]**2 + res[1]**2) <= 200000:
results.append(res)
return results
def timeFunction(x, *args):
''' Helper function for PSO
Takes in supracenter ranges, and calculates the travel time, which is used to find the error.
Returns the residual error with each guess of a supracenter from pso()
Arguments:
x: [ndarray] position to search with
args: list of passed arguments
stns: [list] list of station positions and arrival times
w: [list] list of station weights
kotc: [float] user-defined occurence time
tweaks: [Object] user-defined option
ref_pos: [list] mean station location used for converting to/from local coordinates
dataset: [ndarray] atmospheric profile for the entire search area
pool: [multiprocessing pool] pool of workers for multiprocessing
Returns:
err: [float] error in the current position, x, searched
'''
# Retrieve passed arguments
stns, w, kotc, setup, ref_pos, atmos, prefs, v, pert_num, theo = args
# number of stations total
n_stations = len(stns)
# Residuals to each station
sotc = np.empty(n_stations)
# Initialize variables
# Weight of each station
wn = w
# total weight
nwn = sum(w)
# Mean occurrence time
motc = 0
S = Position(0, 0, 0)
S.x, S.y, S.z = x[0], x[1], x[2]
S.pos_geo(ref_pos)
# number of stations total
n_stations = len(stns)
# Station Times
tobs = stns[0:n_stations, 4]
# Station Location
xstn = stns[0:n_stations, 0:3]
# Initialize arrays
# Simulated travel times to each station
time3D = np.empty(n_stations)
# Residuals to each station
sotc = np.empty(n_stations)
for j in range(n_stations):
# if station has weight
if w[j] > 0:
D = Position(0, 0, 0)
D.x, D.y, D.z = xstn[j, 0], xstn[j, 1], xstn[j, 2]
D.pos_geo(ref_pos)
if not theo:
if pert_num == 0:
# No perturbations used here
sounding, _ = atmos.getSounding(lat=[S.lat, D.lat], lon=[S.lon, D.lon], heights=[S.elev, D.elev], ref_time=setup.fireball_datetime)
else:
# Sounding is a specfic perturbation number
# TODO: Go back and fix how this is done, not all perts need to be generated, just one here
nom, sounding = atmos.getSounding(lat=[S.lat, D.lat], lon=[S.lon, D.lon], heights=[S.elev, D.elev], ref_time=setup.fireball_datetime)
# sounding is none when there is an error in getting sounding
if sounding is not None:
sounding = sounding[pert_num - 1]
else:
sounding = nom
# Use distance and atmospheric data to find path time
time3D[j], _, _, _ = cyscan(S.xyz, D.xyz, sounding, \
wind=prefs.wind_en, h_tol=prefs.pso_min_ang, v_tol=prefs.pso_min_dist, processes=1)
# Residual time for each station
else:
distance = np.sqrt((S.x - D.x)**2 + (S.y - D.y)**2 + (S.z - D.z)**2)
time3D[j] = distance/prefs.avg_sp_sound
sotc[j] = tobs[j] - time3D[j]
# If station has no weight
else:
sotc[j] = tobs[j]
motc = np.nanmean(sotc)
##########
N_s = np.count_nonzero(~np.isnan(sotc))
# User defined occurrence time
if kotc != None:
# make kOTC a list
err = np.dot(wn, np.absolute(sotc - np.array([kotc]*n_stations)))/nwn
# Unknown occurrence time
else:
#err = np.dot(wn, np.absolute(sotc - motc))/nwn
N_s = 0
err = 0
for s in sotc:
if np.isnan(s):
continue
else:
err += (1 + (s - motc)**2)**0.5 - 1
N_s += 1
if N_s == 0:
err = np.inf
else:
err = err/N_s
# if setup.debug:
# # print out current search location
# print("Supracenter: {:10.2f} m x {:10.2f} m y {:10.2f} m z Time: {:8.2f} Error: {:25.2f}".format(x[0], x[1], x[2], motc, err))
# variable to be minimized by the particle swarm optimization
perc_fail = 100 - (n_stations-N_s)/n_stations*100
# temporary adjustment to try and get the most stations
if N_s >= 3:
total_error = err# + 2*max(error_list)*(failed_stats)
else:
total_error = np.inf
if np.isnan(total_error):
total_error = np.inf
if prefs.debug:
print("Error {:10.4f} | Solution {:10.4f}N {:10.4f}E {:8.2f} km {:8.2f} s | Failed Stats {:3} {:}".format(total_error, S.lat, S.lon, S.elev/1000, motc, n_stations-N_s, printPercent(perc_fail, N_s)))
# Quick adjustment to try and better include stations
return total_error
def waveReleasePointWinds(stat_coord, bam, prefs, ref_loc, points, u):
#azim = (np.pi - azim)%(2*np.pi)
# Break up the trajectory into points
# Trajectory vector
#u = np.array([-np.cos(azim)*np.sin(zangle), np.sin(azim)*np.sin(zangle), -np.cos(zangle)])
# Cut down atmospheric profile to the correct heights, and interp
u = np.array([u.x, u.y, u.z])
a = (len(points))
D = np.array(stat_coord)
cyscan_res = []
import time
# Compute time of flight residuals for all stations
for i in range(a):
S = np.array(points[i][0:3])
traj_point = angle2Geo(points[i][0:3], ref_loc)
stat_point = angle2Geo(stat_coord, ref_loc)
lats = [traj_point.lat, stat_point.lat]
lons = [traj_point.lon, stat_point.lon]
heights = [traj_point.elev, stat_point.elev]
if traj_point.elev < 17000 or traj_point.elev > 50000:
continue
sounding, perturbations = bam.atmos.getSounding(
lats,
lons,
heights,
ref_time=bam.setup.fireball_datetime,
perturbations=0,
spline=50)
if prefs.wind_en:
A = cyscan(S, D, sounding, \
wind=prefs.wind_en, h_tol=prefs.pso_min_ang, v_tol=prefs.pso_min_dist, processes=1)
else:
dx, dy, dz = D[0] - S[0], D[1] - S[1], D[2] - S[2]
r = (dx**2 + dy**2 + dz**2)**0.5
h = (dx**2 + dy**2)**0.5
T = r / 330
az = np.arctan2(dx, dy)
tf = np.pi / 2 + np.arctan2(dz, h)
A = np.array([T, az, tf, np.nan])
az = np.radians(A[1])
tf = np.radians(A[2])
v = np.array(
[np.sin(az) * np.sin(tf),
np.cos(az) * np.sin(tf), -np.cos(tf)])
mag_u = u / np.sqrt(u.dot(u))
mag_v = v / np.sqrt(v.dot(v))
angle = np.abs(90 - np.degrees(np.arccos(np.dot(mag_u, mag_v))))
print("\tHeight: {:.2f} km | Angle: {:.2f} deg".format(
traj_point.elev / 1000, angle))
cyscan_res.append(A)
T_nom = getTimes(np.array(cyscan_res), u, a)
T_pert = []
# if perturbations is not None:
# for p in range(len(perturbations)):
# cyscan_res = []
# for i in range(a):
# S = np.array(points[i])
# traj_point = angle2Geo(points[i][0:3], ref_loc)
# stat_point = angle2Geo(stat_coord, ref_loc)
# lats = [traj_point.lat, stat_point.lat]
# lons = [traj_point.lon, stat_point.lon]
# heights = [traj_point.elev, stat_point.elev]
# sounding, perturbations = bam.atmos.getSounding(lats, lons, heights)
# A_p = cyscan(S, D, perturbations[p], \
# wind=prefs.wind_en, n_theta=prefs.pso_theta, n_phi=prefs.pso_phi,
# h_tol=prefs.pso_min_ang, v_tol=prefs.pso_min_dist)
# cyscan_res.append(A_p)
# T_pert.append(getTimes(np.array(cyscan_res), u, a))
return T_nom, T_pert
D = np.array([6.41420602e-13, 1.04751934e+04, 0.00000000e+00])
theta = 135
phi = 0
a2 = anglescan(S,
phi,
theta,
sounding,
wind=True,
debug=True,
trace=False,
plot=False)
print("A2: ", a2)
c5 = cyscan(S, D, sounding, wind=True, h_tol=330, v_tol=330)
print("C5: ", c5)
c2 = cy2(S,
D,
sounding,
wind=True,
h_tol=330,
v_tol=330,
n_theta=1080,
n_phi=1080)
print("C2: ", c2)
tt = np.sqrt(2) * 7000 / 300 + np.sqrt(2) * 3000 / 330
def integrate(self, height, D_ANGLE=1.5, tf=1, az=1):
ref_pos = Position(self.setup.lat_centre, self.setup.lon_centre, 0)
try:
point = self.setup.trajectory.findGeo(height)
except AttributeError:
print("STATUS: No trajectory given, assuming lat/lon center")
point = Position(self.setup.lat_centre, self.setup.lon_centre,
height)
point.pos_loc(ref_pos)
stn = self.stn_list[self.current_station]
stn.metadata.position.pos_loc(ref_pos)
lats = [point.lat, stn.metadata.position.lat]
lons = [point.lon, stn.metadata.position.lon]
elevs = [point.elev, stn.metadata.position.elev]
# make the spline lower to save time here
sounding, perturbations = self.bam.atmos.getSounding(
lats,
lons,
elevs,
spline=N_LAYERS,
ref_time=self.setup.fireball_datetime)
trans = []
ints = []
ts = []
ps = []
rfs = []
if perturbations is None:
ptb_len = 1
else:
ptb_len = len(perturbations) + 1
for ptb_n in range(ptb_len):
# Temporary adjustment to remove randomness from perts
if ptb_n == 0:
zProfile = sounding
else:
zProfile = perturbations[ptb_n - 1]
S = np.array([point.x, point.y, point.z])
D = np.array([
stn.metadata.position.x, stn.metadata.position.y,
stn.metadata.position.z
])
_, az_n, tf_n, _ = cyscan(S, D, zProfile, wind=True,\
h_tol=30, v_tol=30)
self.T_d = tryFloat(self.freq_edits.text())
self.v = 1 / self.T_d
f, g, T, P, path_length, pdr, reed_attenuation = intscan(S,
az_n,
tf_n,
zProfile,
self.v,
wind=True)
self.reed_attenuation = reed_attenuation
rf = refractiveFactor(point,
stn.metadata.position,
zProfile,
D_ANGLE=D_ANGLE)
trans.append(f)
ints.append(g)
ts.append(T)
ps = P
rfs.append(rf)
return trans, ints, ts, ps, rfs, path_length, pdr