I am following the up to date Conjunction example. That example works with a toy satellite but using empheramis data, I am unsure of how to translate the altitude of the satellite to the footprint altitude.
I have initialized the Conjunction class with conj=asilib.Conjunction(asi, (time, (lat_sat,lon_sat,alt_sat)) then using
conj.lla_footprint(alt=110) which returns None and messes up map_azel( see error)
In conjunction_py the comments are out of date so I am unsure what the proper return should be and am quite shocked to see its just one Non. instead an array of length alt_sat of Nones.
from datetime import datetime, timedelta
import string
import matplotlib.pyplot as plt
import matplotlib.dates
import matplotlib.patches
import matplotlib.gridspec
import numpy as np
import pyaurorax.ephemeris as ae
import asilib
import asilib.asi
time_range = (datetime(2021, 3, 18, 8, 10),
datetime(2021, 3, 18, 8, 30))
area_box_km = (20, 20)
#skymap_dict = asilib.load_skymap(asi_array_code, location_code, time_range[0])
print(asilib.config['ASI_DATA_DIR'])
global platforms
platforms = ["swarma"]
sky_map_values = ['trex_nir', 'rabb']
rearth = 6378.1370
def emph():
print(platforms, "test")
def coordinates_extrapolation(array):
n = int((time_range[1] - time_range[0]
).total_seconds() / 3) # 3 second␣
latvelocitytotal, lonvelocitytotal = [], []
cadence = int(60/3)
# every 3 seconds theres a time component
time = np.array([time_range[0] + timedelta(seconds=i*3)
for i in range(n)])
for i in range(len(platforms)):
latvelocity = []
lonvelocity = []
for j in range(len(array[0][0])-1):
latvelocity.append(np.linspace(
array[0][i][j], array[0][i][j+1], cadence))
lonvelocity.append(np.linspace(
array[1][i][j], array[1][i][j+1], cadence))
latvelocitytotal.append(np.reshape(latvelocity, -1))
lonvelocitytotal.append(np.reshape(lonvelocity, -1))
print("ree")
def altitude():
altitude_total = []
altepop = []
altswarma = []
altswarmb = []
altvelocityepop = []
altvelocityswarma = []
altvelocityswarmb = []
altswarmc = []
altvelocityswarmc = []
for i in range(len(platforms)):
if(platforms[i] == "epop"):
for k in range(len(em1.data)):
if(em1.data[k].data_source.platform == "epop"):
altepop.append(
em1.data[k].metadata["radial_distance"]-rearth)
for j in range(len(altepop)-1):
altvelocityepop.append(np.linspace(
altepop[j], altepop[j+1], 20))
altvelocityepop = np.reshape(altvelocityepop, -1)
altitude_total.append(altvelocityepop)
elif(platforms[i] == "swarma"):
for k in range(len(em1.data)):
if(em1.data[k].data_source.platform == "swarma"):
altswarma.append(
em1.data[k].metadata["radial_distance"]-rearth)
for j in range(len(altswarma)-1):
altvelocityswarma.append(np.linspace(
altswarma[j], altswarma[j+1], 20))
altvelocityswarma = np.reshape(altvelocityswarma, -1)
altitude_total.append(altvelocityswarma)
elif(platforms[i] == "swarmb"):
for k in range(len(em1.data)):
if(em1.data[k].data_source.platform == "swarmb"):
altswarmb.append(
em1.data[k].metadata["radial_distance"]-rearth)
for j in range(len(altswarmb)-1):
altvelocityswarmb.append(np.linspace(
altswarmb[j], altswarmb[j+1], 20))
altvelocityswarmb = np.reshape(altvelocityswarmb, -1)
altitude_total.append(altvelocityswarmb)
elif(platforms[i] == 'swarmc'):
for k in range(len(em1.data)):
if(em1.data[k].data_source.platform == "swarmc"):
altswarmc.append(
em1.data[k].metadata["radial_distance"]-rearth)
for j in range(len(altswarmc)-1):
altvelocityswarmc.append(np.linspace(
altswarmc[j], altswarmc[j+1], 20))
altvelocityswarmc = np.reshape(altvelocityswarmc, -1)
altitude_total.append(altvelocityswarmc)
else:
pass
return altitude_total
# for i in range(len(platforms)):
# for j in range(len(platforms)):
altitude_array = altitude()
return [time, latvelocitytotal, lonvelocitytotal, altitude_array]
# image_bounds = imager_bounds()
global em1
em1 = ae.search(time_range[0], time_range[1],
platforms=platforms)
lattotal, lontotal = [], []
for k in range(len(platforms)):
latstarts, lonstarts = [], [],
for i in range(len(em1.data)): # 3 spacecraft
# sees if the data corresponds to the correct space-craft
if(em1.data[i].data_source.platform == platforms[k]):
latstarts.append(em1.data[i].location_geo.lat) # starting
lonstarts.append((em1.data[i].location_geo.lon)) # ending
lattotal.append(latstarts)
lontotal.append(lonstarts)
return coordinates_extrapolation(
np.array([lattotal, lontotal]))
# [time, satellite_lattitute,.] #add everything in main
def main():
print(__name__)
save_file = []
asi_array_code = sky_map_values[0]
location_code = sky_map_values[1]
fig, ax = plt.subplots(
3, 1, figsize=(7, 10), gridspec_kw={'height_ratios': [4, 1, 1]}, constrained_layout=True
)
# retrieves emphermaris data in the format [time, [sattelite_longitudes], [satellite_lattitudes], [satelliite_alitiudes]]
data = emph()
sat_time = data[0]
# Finds the footprint of selected region with each selected spacecraft
def animator():
for i, (time, image, _, im) in enumerate(movie_generator):
# Plot the entire satellite track, its current location, and a 20x20 km box
# around its location.
ax[1].clear()
ax[2].clear()
for j in range(len(sat_azel_pixels_total)):
ax[0].plot(sat_azel_pixels_total[j][:, 0],
sat_azel_pixels_total[j][:, 1], 'blue')
ax[0].scatter(sat_azel_pixels_total[j][i, 0], sat_azel_pixels_total[j][i, 1],
c='red', marker='o', s=50)
ax[0].contour(area_mask_total[j][i, :, :],
levels=[0.99], colors=['yellow'])
ax[1].plot(sat_time, nearest_pixel_intensity_total[j])
ax[2].plot(sat_time, area_intensity_total[j])
# Plot the ASI intensity along the satellite path
vline1 = ax[1].axvline(time, c='b')
vline2 = ax[2].axvline(time, c='b')
# Annotate the location_code and satellite info in the top-left corner.
location_code_str = (
f'{asi_array_code}/{location_code} '
f'LLA=({asi.meta["lat"]:.2f}, '
f'{asi.meta["lon"]:.2f}, {asi.meta["alt"]:.2f})'
)
text_obj = ax[0].text(
0,
1,
location_code_str,
va='top',
transform=ax[0].transAxes,
color='red',
)
ax[1].set(ylabel='ASI intensity\nnearest pixel [counts]')
ax[2].set(xlabel='Time',
ylabel='ASI intensity\n10x10 km area [counts]')
def ASI_logic():
alt = 110 # footprint value
if(asi_array_code.lower() == 'themis'):
asi = asilib.asi.themis(
location_code, time_range=time_range, alt=alt)
elif(asi_array_code.lower() == 'rego'):
asi = asilib.asi.rego(
location_code, time_range=time_range, alt=alt)
print("test")
elif(asi_array_code.lower() == 'trex_nir'):
asi = asilib.asi.trex.trex_nir(
location_code, time_range=time_range, alt=alt)
return asi
asi = ASI_logic()
# Initiate the movie generator function. Any errors with the data will be␣
movie_generator = asi.animate_fisheye_gen( # initaliziation
ax=ax[0], azel_contours=True, overwrite=True, cardinal_directions='NE'
)
# Use the generator to get the images and time stamps to estimate mean the ASI
# brightness along the satellite path and in a (20x20 km) box.
sat_azel_pixels_total, nearest_pixel_intensity_total, area_intensity_total, area_mask_total = [], [
], [], [] # Creates empty arrays for data from satellites to go, which is there rendered in animator
def Animation_logic_generator():
nonlocal sat_azel_pixels_total, nearest_pixel_intensity_total, area_intensity_total, area_mask_total
for i in range(len(platforms)): # length of spacecraft
conjunction_obj = asilib.Conjunction(asi, (sat_time, np.array( # lat_sattelite, long, alt
[data[1][i], data[2][i], data[3][i]]).T))
print(data[3][i])
blah = conjunction_obj.lla_footprint(alt=110)
print(blah)
# Normally the satellite time stamps are not the same as the ASI.
# You may need to call Conjunction.interp_sat() to find the LLA coordinates
# at the ASI timestamps.
# Map the satellite track to the imager's azimuth and elevation coordinates and
# image pixels. NOTE: the mapping is not along the magnetic field lines! You need
# to install IRBEM and then use conjunction.lla_footprint() before
# calling conjunction_obj.map_azel.
sat_azel, sat_azel_pixels = conjunction_obj.map_azel()
nearest_pixel_intensity = conjunction_obj.intensity(box=None)
area_intensity = conjunction_obj.intensity(box=(10, 10))
area_mask = conjunction_obj.equal_area(box=(10, 10))
# Need to change masked NaNs to 0s so we can plot the rectangular area contours.
area_mask[np.where(np.isnan(area_mask))] = 0
sat_azel_pixels_total.append(sat_azel_pixels)
nearest_pixel_intensity_total.append(nearest_pixel_intensity)
area_intensity_total.append(area_intensity)
area_mask_total.append(area_mask)
# sat_azel_pixels, area_box_mask_2, asi_brightness_2 for each satellite
Animation_logic_generator()
animator()
print(
f'Movie saved in {asilib.config["ASI_DATA_DIR"] / "animations"}', )
movie_container = 'mp4'
movie_address = f'{time_range[0].strftime("%Y%m%d_%H%M%S")}_' \
f'{time_range[1].strftime("%H%M%S")}_' \
f'{asi_array_code.lower()}_{location_code.lower()}_fisheye.{movie_container}'
movie_address_total = asilib.config["ASI_DATA_DIR"] / \
'animations'/movie_address
print(movie_address_total)
save_file.append(movie_address_total)
return save_file
if __name__ == '__main__': # arg parse look up
test = main()Traceback (most recent call last):
File "c:/Users/1101w/Clone/Programming-8/Summer 2023/AnimationSattelite.py", line 262, in <module>
test = main()
File "c:/Users/1101w/Clone/Programming-8/Summer 2023/AnimationSattelite.py", line 243, in main
Animation_logic_generator()
File "c:/Users/1101w/Clone/Programming-8/Summer 2023/AnimationSattelite.py", line 231, in Animation_logic_generator
sat_azel, sat_azel_pixels = conjunction_obj.map_azel()
File "C:\Users\1101w\anaconda3\lib\site-packages\asilib\conjunction.py", line 315, in map_azel
az, el, _ = pymap3d.geodetic2aer(
File "C:\Users\1101w\anaconda3\lib\site-packages\pymap3d\aer.py", line 110, in geodetic2aer
e, n, u = geodetic2enu(lat, lon, h, lat0, lon0, h0, ell, deg=deg)
File "C:\Users\1101w\anaconda3\lib\site-packages\pymap3d\enu.py", line 199, in geodetic2enu
x1, y1, z1 = geodetic2ecef(lat, lon, h, ell, deg=deg)
File "C:\Users\1101w\anaconda3\lib\site-packages\pymap3d\ecef.py", line 68, in geodetic2ecef
lat, ell = sanitize(lat, ell, deg)
File "C:\Users\1101w\anaconda3\lib\site-packages\pymap3d\utils.py", line 62, in sanitize
raise ValueError("-pi/2 <= latitude <= pi/2")
ValueError: -pi/2 <= latitude <= pi/2
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