def timed(*args, **kw):
ts = time.time()
result = method(*args, **kw)
te = time.time()
sys.stdout.write('{} : {} ms\n'.format(method.__name__,
(te - ts) * 1000))
return result
def make_gui(self):
""" Setups the general structure of the gui, the first function called """
#print(self.config.QD_classes, self.config.QD_class_index)
self.option_window = Toplevel()
self.option_window.protocol("WM_DELETE_WINDOW", self.on_exit)
self.canvas_frame = tk.Frame(self, height=500)
self.option_frame = tk.Frame(self.option_window, height=300)
self.canvas_frame.pack(side=tk.LEFT, fill=tk.BOTH, expand=True)
self.option_frame.pack(side=tk.RIGHT, fill=None, expand=False)
self.make_options_frame()
a = time.time()
self.make_canvas_frame()
print(time.time() - a)
self.disable_singlecolor()
def make_positions():
########################################## PSP
starttime =datetime(2018, 8,13)
endtime = datetime(2025, 8, 31)
psp_time = []
while starttime < endtime:
psp_time.append(starttime)
starttime += timedelta(days=res_in_days)
psp_time_num=mdates.date2num(psp_time)
spice.furnish(spicedata.get_kernel('psp_pred'))
psp=spice.Trajectory('SPP')
psp.generate_positions(psp_time,'Sun',frame)
print('PSP pos')
psp.change_units(astropy.units.AU)
[psp_r, psp_lat, psp_lon]=cart2sphere(psp.x,psp.y,psp.z)
print('PSP conv')
############################################## BepiColombo
starttime =datetime(2018, 10, 21)
endtime = datetime(2025, 11, 2)
bepi_time = []
while starttime < endtime:
bepi_time.append(starttime)
starttime += timedelta(days=res_in_days)
bepi_time_num=mdates.date2num(bepi_time)
spice.furnish(spicedata.get_kernel('bepi_pred'))
bepi=spice.Trajectory('BEPICOLOMBO MPO') # or BEPICOLOMBO MMO
bepi.generate_positions(bepi_time,'Sun',frame)
bepi.change_units(astropy.units.AU)
[bepi_r, bepi_lat, bepi_lon]=cart2sphere(bepi.x,bepi.y,bepi.z)
print('Bepi')
#################################################### Solar Orbiter
starttime = datetime(2020, 3, 1)
endtime = datetime(2026, 1, 1)
solo_time = []
while starttime < endtime:
solo_time.append(starttime)
starttime += timedelta(days=res_in_days)
solo_time_num=mdates.date2num(solo_time)
spice.furnish(spicedata.get_kernel('solo_2020'))
solo=spice.Trajectory('Solar Orbiter')
solo.generate_positions(solo_time, 'Sun',frame)
solo.change_units(astropy.units.AU)
[solo_r, solo_lat, solo_lon]=cart2sphere(solo.x,solo.y,solo.z)
print('Solo')
plt.figure(1, figsize=(12,9))
plt.plot_date(psp_time,psp_r,'-', label='R')
plt.plot_date(psp_time,psp_lat,'-',label='lat')
plt.plot_date(psp_time,psp_lon,'-',label='lon')
plt.ylabel('AU / RAD')
plt.legend()
plt.figure(2, figsize=(12,9))
plt.plot_date(bepi_time,bepi_r,'-', label='R')
plt.plot_date(bepi_time,bepi_lat,'-',label='lat')
plt.plot_date(bepi_time,bepi_lon,'-',label='lon')
plt.title('Bepi Colombo position '+frame)
plt.ylabel('AU / RAD')
plt.legend()
plt.figure(3, figsize=(12,9))
plt.plot_date(solo_time,solo_r,'-', label='R')
plt.plot_date(solo_time,solo_lat,'-',label='lat')
plt.plot_date(solo_time,solo_lon,'-',label='lon')
plt.title('Solar Orbiter position '+frame)
plt.ylabel('AU / RAD')
plt.legend()
########### plots
######## R with all three
plt.figure(4, figsize=(16,10))
plt.plot_date(psp_time,psp.r,'-',label='PSP')
plt.plot_date(bepi_time,bepi.r,'-',label='Bepi Colombo')
plt.plot_date(solo_time,solo.r,'-',label='Solar Orbiter')
plt.legend()
plt.title('Heliocentric distance of heliospheric observatories')
plt.ylabel('AU')
plt.savefig('results/positions_plots/bepi_psp_solo_R.png')
##### Longitude all three
plt.figure(5, figsize=(16,10))
plt.plot_date(psp_time,psp_lon*180/np.pi,'-',label='PSP')
plt.plot_date(bepi_time,bepi_lon*180/np.pi,'-',label='Bepi Colombo')
plt.plot_date(solo_time,solo_lon*180/np.pi,'-',label='Solar Orbiter')
plt.legend()
plt.title(frame+' longitude')
plt.ylabel('DEG')
plt.savefig('results/positions_plots/bepi_psp_solo_longitude_'+frame+'.png')
############# Earth for mercury, venusus, STA
#https://docs.heliopy.org/en/stable/data/spice.html
planet_kernel=spicedata.get_kernel('planet_trajectories')
starttime =datetime(2018, 1, 1)
endtime = datetime(2028, 12, 31)
earth_time = []
while starttime < endtime:
earth_time.append(starttime)
starttime += timedelta(days=res_in_days)
earth_time_num=mdates.date2num(earth_time)
earth=spice.Trajectory('399') #399 for Earth, not barycenter (because of moon)
earth.generate_positions(earth_time,'Sun',frame)
earth.change_units(astropy.units.AU)
[earth_r, earth_lat, earth_lon]=cart2sphere(earth.x,earth.y,earth.z)
print('Earth')
################ mercury
mercury_time_num=earth_time_num
mercury=spice.Trajectory('1') #barycenter
mercury.generate_positions(earth_time,'Sun',frame)
mercury.change_units(astropy.units.AU)
[mercury_r, mercury_lat, mercury_lon]=cart2sphere(mercury.x,mercury.y,mercury.z)
print('mercury')
################# venus
venus_time_num=earth_time_num
venus=spice.Trajectory('2')
venus.generate_positions(earth_time,'Sun',frame)
venus.change_units(astropy.units.AU)
[venus_r, venus_lat, venus_lon]=cart2sphere(venus.x,venus.y,venus.z)
print('venus')
############### Mars
mars_time_num=earth_time_num
mars=spice.Trajectory('4')
mars.generate_positions(earth_time,'Sun',frame)
mars.change_units(astropy.units.AU)
[mars_r, mars_lat, mars_lon]=cart2sphere(mars.x,mars.y,mars.z)
print('mars')
#############stereo-A
sta_time_num=earth_time_num
spice.furnish(spicedata.get_kernel('stereo_a_pred'))
sta=spice.Trajectory('-234')
sta.generate_positions(earth_time,'Sun',frame)
sta.change_units(astropy.units.AU)
[sta_r, sta_lat, sta_lon]=cart2sphere(sta.x,sta.y,sta.z)
print('STEREO-A')
#save positions
if high_res_mode:
pickle.dump([psp_time,psp_time_num,psp_r,psp_lon,psp_lat,bepi_time,bepi_time_num,bepi_r,bepi_lon,bepi_lat,solo_time,solo_time_num,solo_r,solo_lon,solo_lat], open( 'positions_plots/psp_solo_bepi_'+frame+'_1min.p', "wb" ) )
sys.exit()
else:
psp=np.rec.array([psp_time_num,psp_r,psp_lon,psp_lat, psp.x, psp.y,psp.z],dtype=[('time','f8'),('r','f8'),('lon','f8'),('lat','f8'),('x','f8'),('y','f8'),('z','f8')])
bepi=np.rec.array([bepi_time_num,bepi_r,bepi_lon,bepi_lat,bepi.x, bepi.y,bepi.z],dtype=[('time','f8'),('r','f8'),('lon','f8'),('lat','f8'),('x','f8'),('y','f8'),('z','f8')])
solo=np.rec.array([solo_time_num,solo_r,solo_lon,solo_lat,solo.x, solo.y,solo.z],dtype=[('time','f8'),('r','f8'),('lon','f8'),('lat','f8'),('x','f8'),('y','f8'),('z','f8')])
sta=np.rec.array([sta_time_num,sta_r,sta_lon,sta_lat,sta.x, sta.y,sta.z],dtype=[('time','f8'),('r','f8'),('lon','f8'),('lat','f8'),('x','f8'),('y','f8'),('z','f8')])
earth=np.rec.array([earth_time_num,earth_r,earth_lon,earth_lat, earth.x, earth.y,earth.z],dtype=[('time','f8'),('r','f8'),('lon','f8'),('lat','f8'),('x','f8'),('y','f8'),('z','f8')])
venus=np.rec.array([venus_time_num,venus_r,venus_lon,venus_lat, venus.x, venus.y,venus.z],dtype=[('time','f8'),('r','f8'),('lon','f8'),('lat','f8'),('x','f8'),('y','f8'),('z','f8')])
mars=np.rec.array([mars_time_num,mars_r,mars_lon,mars_lat, mars.x, mars.y,mars.z],dtype=[('time','f8'),('r','f8'),('lon','f8'),('lat','f8'),('x','f8'),('y','f8'),('z','f8')])
mercury=np.rec.array([mercury_time_num,mercury_r,mercury_lon,mercury_lat,mercury.x, mercury.y,mercury.z],dtype=[('time','f8'),('r','f8'),('lon','f8'),('lat','f8'),('x','f8'),('y','f8'),('z','f8')])
pickle.dump([psp, bepi, solo, sta, earth, venus, mars, mercury,frame], open( 'results/positions_psp_solo_bepi_sta_planets_'+frame+'_2hours.p', "wb" ) )
#load with [psp, bepi, solo, sta, earth, venus, mars, mercury,frame]=pickle.load( open( 'positions_psp_solo_bepi_sta_planets_HCI_6hours_2018_2025.p', "rb" ) )
end=time.time()
print( 'generate position took time in seconds:', round((end-start),1) )
##################################################### MAIN ############################### start=time.time() ########## MAKE TRAJECTORIES ############ #make_positions() file=data_path+'psp_2018_2019_merged.p' [p,ph]=pickle.load(open(file, "rb" ) ) file=data_path+'wind_2018_now.p' [w,wh]=pickle.load(open(file, "rb" ) ) file=data_path+'sta_2018_now_beacon.p'
1.0, # b_s (magnetic field scaling parameter)
15, # b_1au (magnetic field strength at 1au)
1.5, # Gamma (solar wind drag coefficient)
400, # v_sw (solar wind speed)
0 # sigma (measurement noise)
]],
dtype=np.float32)
model_obj = py3dcore.models.ThinTorusGH3DCOREModel(t_launch,
runs=1,
use_gpu=False)
model_obj.update_iparams(iparams_arr, seed=42)
############################### measure magnetic field
print()
start = time.time()
#18 is middle
satposindex = 20
print('current satpos measured is ', satposindex)
#t0, btot0, bxyz0, os = measure(model_obj, satpos[6], tm1 - datetime.timedelta(days=3), tm1 + datetime.timedelta(days=20))
t1, btot1, bxyz1, os1 = measure(model_obj, satpos[satposindex],
tm1 - datetime.timedelta(days=3),
tm1 + datetime.timedelta(days=15))
#t2, btot2, bxyz2, os = measure(model_obj, satpos[30], tm1 - datetime.timedelta(days=3), tm1 + datetime.timedelta(days=20))
print('took ', np.round(time.time() - start, 3), ' seconds')
print()
#print(t1)
#print(os1)
sc_time_num = mdates.date2num(sc_time)
sc.generate_positions(sc_time, 'Sun', frame)
sc.change_units(astropy.units.AU)
sc_r, sc_lat, sc_lon = cart2sphere(sc.x, sc.y, sc.z)
screc = np.rec.array([sc_time_num, sc_r, sc_lon, sc_lat, sc.x, sc.y, sc.z],
dtype=[('time', 'f8'), ('r', 'f8'), ('lon', 'f8'),
('lat', 'f8'), ('x', 'f8'), ('y', 'f8'),
('z', 'f8')])
return screc
##################################################### MAIN ###############################
start = time.time()
############################################ SETTINGS
#Coordinate System
frame = 'HCI'
#frame='HEEQ'
print(frame)
#sidereal solar rotation rate
if frame == 'HCI': sun_rot = 24.47
#synodic
if frame == 'HEEQ': sun_rot = 26.24
#black background on or off
back = True
def make_canvas_frame(self):
""" Create the data and thematic map images for the first time """
b = time.time()
self.fig, (self.imageax, self.previewax) = plt.subplots(ncols=2,
figsize=self.canvas_size,
sharex=True, sharey=True,
gridspec_kw=self.subplot_grid_spec)
print(time.time() - b)
self.canvas = FigureCanvasTkAgg(self.fig, master=self.canvas_frame)
self.canvas.mpl_connect('button_press_event', self.onclick)
self.canvas.mpl_connect('key_press_event', self.onpress)
# set up the channel data view
b = time.time()
#self.configure_threecolor_image()
self.configure_image()
print(time.time()-b)
self.imageplot = self.imageax.imshow(self.image)
self.imageax.set_xlim([0, self.shape[1]])
self.imageax.set_ylim([0, self.shape[0]])
# self.imageax.set_aspect("equal")
self.imageax.set_axis_off()
# set up the thematic map view
self.selection_array = np.zeros(self.shape, dtype=np.uint8)
if self.blank:
self.selection_array += self.config.QD_class_index['unlabeled']
else:
self.draw_default()
self.history.append(self.selection_array)
colortable = [self.config.QD_colors[self.config.QD_class_name[i]]
for i in range(len(self.config.QD_classes))]
cmap = matplotlib.colors.ListedColormap(colortable)
self.mask = self.previewax.imshow(self.selection_array,
origin='lower',
interpolation='nearest',
cmap=cmap,
vmin=-1, vmax=len(colortable))
self.previewax.set_xlim([0, self.shape[1]])
self.previewax.set_ylim([0, self.shape[0]])
self.previewax.set_aspect("equal")
self.previewax.set_axis_off()
# add selection layer for lasso
#self.pix = np.arange(self.shape[0]) # assumes square image
#xv, yv = np.meshgrid(self.pix, self.pix)
self.pix = np.asarray(list(list(reversed(list(elem))) for elem in
itertools.product(list(range(self.shape[0])), list(range(self.shape[1])))))
lineprops = dict(color=self.config.default['lasso_color'],
linewidth=self.config.default['lasso_width'])
self.lasso = LassoSelector(self.imageax, self.onlasso, lineprops=lineprops)
# display everything
self.canvas.show()
self.canvas.get_tk_widget().pack(side=tk.TOP, fill=tk.BOTH, expand=True)
self.canvas._tkcanvas.pack(side=tk.TOP, fill=tk.BOTH, expand=True)
# add the tool bar
self.toolbarcenterframe = tk.LabelFrame(self.canvas_frame,
borderwidth=0,
text="Draw: unlabeled",
relief=tk.FLAT,
labelanchor=tk.N,
background='red')
toolbarframe = tk.Frame(self.toolbarcenterframe)
toolbar = CustomToolbar(self.canvas, toolbarframe, self.toolbarcenterframe, self)
toolbar.update()
self.fig.canvas.toolbar.set_message = lambda x: "" # remove state reporting
toolbarframe.pack()
self.toolbarcenterframe.pack(side=tk.BOTTOM, fill=tk.X)
#go through all wind data and check for each hour whether this is inside a MFR or sheath or background wind
#3 labels: background wind, sheath, MFR
#data: about 1e5 training instances (without test data) with 4 features: average btot, std btot, vtot, vstd
#get the features and labels for ICME classification
get_fl_classify=False
#get_fl_classify=True
interval_hours=1
#takes 22 minutes for full data
if get_fl_classify:
start = time.time()
print('extract features for classification')
#test CME extraction April 2010
#win=win[1690000:1720000]
#win_time=win_time[1690000:1720000]
#win=win[1500000:2000000]
#win_time=win_time[1500000:2000000]
#win=win[0:2500000]
#win_time=win_time[0:2500000]
hour_int_size=round(np.size(win)/(60*interval_hours))-1