def relative_position(obj, reference, ettemp, frame='J2000'):
kernels = load_kernels()
if isinstance(ettemp, Time):
et = spice.str2et(ettemp.isot)
elif isinstance(ettemp, float):
et = ettemp
else:
et = [
spice.str2et(e.isot) if type(e) == type(Time.now()) else e
for e in ettemp
]
abcor = 'LT+S'
if isinstance(et, float):
posvel, lt = spice.spkezr(obj, et, frame, abcor, reference)
pos, vel = posvel[:3], posvel[3:]
else:
pos = np.zeros((len(et), 3))
vel = np.zeros((len(et), 3))
for i, t in enumerate(et):
posvel, lt = spice.spkezr(obj, t, frame, abcor, reference)
pos[i, :] = np.array(posvel[:3])
vel[i, :] = np.array(posvel[3:])
pos *= u.km
vel *= u.km / u.s
for k in kernels:
spice.unload(k)
return pos, vel
def __exit__(self, exc_type, exc_val, exc_tb):
"""
Called when the context goes out of scope. Once
this is done, the object is out of scope and the
kernels can be unloaded.
"""
spice.unload(self.metakernel)
def cal2et(time, format='UTC', support_ker=False, unload=False):
"""
Converts UTC or Calendar TDB (CAL) time to Ephemeris Time (ET). Accepts
a single time or a lists of times. This function assumes that the support
kernels (meta-kernel or leapseconds) kernel has been loaded.
:param time: Input UTC or CAL time
:type time: Union[float, list]
:param format: Input format; 'UTC' or 'CAL'
:type format: str
:param unload: If True it will unload the input meta-kernel
:type unload: bool
:return: Output ET
:rtype: Union[str, list]
"""
out_list = []
if isinstance(time, str):
time = [time]
#
# We need to specify that is Calendar format in TDB. If it is UTC we need
# to load the support kernels
#
if support_ker:
spiceypy.furnsh(support_ker)
if format == 'CAL':
time[:] = [x.replace('T', ' ') for x in time]
time[:] = [x + ' TDB' for x in time]
for element in time:
try:
if format == 'UTC':
out_elm = spiceypy.utc2et(element)
elif format == 'CAL':
out_elm = spiceypy.str2et(element)
else:
out_elm = element
except:
out_elm = spiceypy.str2et(element)
out_list.append(out_elm)
if len(out_list) == 1:
out_time = out_list[0]
else:
out_time = out_list
if unload:
spiceypy.unload(support_ker)
return out_time
def simulate(self, starttime, stoptime, camera, threshold, obscode):
"""
"""
# loading all SPICE kernels required for simulation
sp.furnsh(camera.ikfile)
sp.furnsh(obscode + ".bsp")
sp.furnsh(camera.ckfile)
sp.furnsh(camera.fkfile)
sp.furnsh(camera.sclkfile)
count = 0
#Print header
# head="#AstID "
head = "ObjID "
head = head + "FieldID "
head = head + "FieldMJD "
head = head + "AstRange(km) "
head = head + "AstRangeRate(km/s) "
head = head + "AstRA(deg) "
head = head + "AstRARate(deg/day) "
head = head + "AstDec(deg) "
head = head + "AstDecRate(deg/day) "
head = head + "Ast-Sun(J2000x)(km) "
head = head + "Ast-Sun(J2000y)(km) "
head = head + "Ast-Sun(J2000z)(km) "
head = head + "Ast-Sun(J2000vx)(km/s) "
head = head + "Ast-Sun(J2000vy)(km/s) "
head = head + "Ast-Sun(J2000vz)(km/s) "
head = head + "Obs-Sun(J2000x)(km) "
head = head + "Obs-Sun(J2000y)(km) "
head = head + "Obs-Sun(J2000z)(km) "
head = head + "Obs-Sun(J2000vx)(km/s) "
head = head + "Obs-Sun(J2000vy)(km/s) "
head = head + "Obs-Sun(J2000vz)(km/s) "
head = head + "Sun-Ast-Obs(deg) "
head = head + "V "
head = head + "V(H=0) "
print(head)
while self.asteroids:
i = self.asteroids[0]
i.nightlystates([starttime, stoptime])
[times, ids] = i.shortlist(camera, threshold)
i.checkvisspice(obscode, camera, times, ids)
del i
del self.asteroids[0]
count = count + 1
# Unloading all SPICE kernels required for simulation
sp.unload(camera.ikfile)
sp.unload(camera.ckfile)
sp.unload(camera.fkfile)
sp.unload(camera.sclkfile)
sp.unload(obscode + ".bsp")
def unload_kernels(self):
self.sub_count()
if self.get_count() <= 0:
spice.unload(self.meta_kernel_path)
msg = 'Kernels in the file {:s} are unloaded.'.format(
self.meta_kernel_path)
self.logger.info(msg)
self.logging_kernel_names()
else:
msg = '{:s} is still used by {:d} objects.'.format(
self.meta_kernel_path, self.get_count())
self.logger.info(msg)
def getsta(target, TIME, mode="LT+S", observer="SOLAR SYSTEM BARYCENTER"):
# https://spiceypy.readthedocs.io/en/master/remote_sensing.html
#
# Local parameters
#
METAKR = 'getsta.tm'
#
# Load the kernels that this program requires. We
# will need a leapseconds kernel to convert input
# UTC time strings into ET. We also will need the
# necessary SPK files with coverage for the bodies
# in which we are interested.
#
spiceypy.furnsh(METAKR)
#
#Prompt the user for the input time string.
#
utctim = parseDate(TIME)
#print( 'Converting UTC Time: {:s}'.format(utctim) )
#
#Convert utctim to ET.
#
et = spiceypy.str2et(utctim)
#print( ' ET seconds past J2000: {:16.3f}'.format(et) )
#
# Compute the apparent state of target as seem from
# observer in the J2000 frame. All of the ephemeris
# readers return states in units of kilometers and
# kilometers per second.
#
[state, ltime] = spiceypy.spkezr(target, et, 'J2000', mode, observer)
#print( ' Apparent state of MARS BARYCENTER (4) as seen '
#'from SSB (0) in the J2000\n'
#' frame (km, km/s):' )
#print( ' X = {:16.3f}'.format(state[0]) )
#print( ' Y = {:16.3f}'.format(state[1]) )
#print( ' Z = {:16.3f}'.format(state[2]) )
#print( ' VX = {:16.3f}'.format(state[3]) )
#print( ' VY = {:16.3f}'.format(state[4]) )
#print( ' VZ = {:16.3f}'.format(state[5]) )
spiceypy.unload(METAKR)
return state
def set_meta_kernel(self, kernel):
if kernel == self.meta_kernel:
return
if self.meta_kernel is not None:
spiceypy.unload(self.meta_kernel)
if kernel is not None:
s3manager.get_meta_kernel(kernel)
spiceypy.furnsh(kernel)
self.meta_kernel = kernel
def UTC2ET(time, target):
#converting from UTC time to ephemeris seconds since J2000 -- need leapseconds kernel -- 'kernels/lsk/naif0008.tls'
#get mission named from target
mission = getMissionFromTarget(target)
#get metakernel
metakernel = getKernels(mission, target, 'UTC2ET',time)
#load kernels from metakernel
spice.furnsh(metakernel)
#convert time from UTC to ET
ET = spice.str2et(time)
#unload kernels
spice.unload(metakernel)
return(ET)
def CarringtonLongitude(Date, ut):
'''
Get MESSENGER's Carrington longitude
'''
n = np.size(ut)
et = np.zeros((n, ), dtype='float64')
lon = np.zeros(n, dtype='float64')
sp.furnsh(lsk_path)
sp.furnsh(spk_kernel)
sp.furnsh(spk_kernel2)
sp.furnsh(pck_kernel)
if np.size(Date) == 1:
et[0] = utc2et(Date, 0.0)
et = et[0] + ut * 3600.0
else:
ud = np.unique(Date)
for i in range(0, ud.size):
use = np.where(Date == ud[i])[0]
tmp = utc2et(ud[i], 0.0)
et[use] = tmp + ut[use] * 3600.0
pos, lt = sp.spkpos('MESSENGER', et, 'IAU_SUN', 'NONE', 'SUN')
pos = np.array(pos)
lon = np.arctan2(pos.T[1], pos.T[0]) * 180 / np.pi
sp.unload(lsk_path)
sp.unload(spk_kernel)
sp.unload(spk_kernel2)
sp.unload(pck_kernel)
return (lon)
def _generate_solar_panel_kernel(self,
observations,
metakernel_file_path,
ck_file_path,
output_folder_path,
extra_time_hours: float = None,
step_size_s: float = None):
if extra_time_hours is None:
extra_time_hours = self.juice_config.get_solar_panel_ck_span_days(
) * 24.0 / 2
if step_size_s is None:
step_size_s = self.juice_config.get_solar_panel_ck_sampling_seconds(
)
spp = SolarPanelProcessor("JUICE")
spy.furnsh(metakernel_file_path)
spy.furnsh(ck_file_path)
td = timedelta(hours=extra_time_hours)
start_time = self._find_first_start_time(observations) - td
end_time = self._find_last_end_time(observations) + td
spp.create_panel_ck(
start_time, end_time, step_size_s,
os.path.abspath(
os.path.join(output_folder_path, 'spacecraft',
'solar_panel_kernel.ck')))
spy.unload(ck_file_path)
spy.unload(metakernel_file_path)
spacecraft_json_path = os.path.join(output_folder_path, "spacecraft",
"spacecraft",
"JUICE_panel_def.json")
with open(spacecraft_json_path) as f:
spacecraft_json = json.load(f)
during = spacecraft_json["items"][0]
before = spacecraft_json["items"][1]
after = spacecraft_json["items"][2]
def format_time_str(time: datetime) -> str:
return time.strftime("%Y-%m-%d %H:%M:%S.000 UTC")
during["startTime"] = format_time_str(start_time)
during["endTime"] = format_time_str(end_time)
before["endTime"] = format_time_str(start_time)
after["startTime"] = format_time_str(end_time)
with open(spacecraft_json_path, 'w') as json_file:
json.dump(spacecraft_json, json_file, indent=2)
return
def f_manage_kernels(kernel_meta):
#Unload all kernels
spiceypy.unload( kernel_meta )
#Load the necessary kernels from meta file
spiceypy.kclear()
spiceypy.furnsh(kernel_meta)
spiceypy.furnsh(r'E:\Data Science Projects\Space Science\SpaceScience-P2-SSBandSunWobbling\data\external\_kernels\lsk\naif0012.tls')
#List loaded kernels
count = spiceypy.ktotal( 'ALL' )
for i in range(0, count):
[ file, type, source, handle] = spiceypy.kdata(i, 'ALL');
print( 'File {0}'.format(file) )
print( 'Type {0}'.format(type) )
print( 'Source {0}\n'.format(source) )
def PosVSO(Date, ut):
'''
VEX position in VSO coordinates
'''
#create the output arrays
n = np.size(ut)
et = np.zeros((n, ), dtype='float64')
x = np.zeros(n, dtype='float64')
y = np.zeros(n, dtype='float64')
z = np.zeros(n, dtype='float64')
#load the relevant kernels
sp.furnsh(lsk_path)
sp.furnsh(spk_kernel)
VEXspk = ListVenusSPK(Date)
for vk in VEXspk:
sp.furnsh(vk)
sp.furnsh(pck_kernel)
sp.furnsh(vso_kernel)
#do each unique date to speed things up
if np.size(Date) == 1:
et[0] = utc2et(Date, 0.0)
et = et[0] + ut * 3600.0
else:
ud = np.unique(Date)
for i in range(0, ud.size):
use = np.where(Date == ud[i])[0]
tmp = utc2et(ud[i], 0.0)
et[use] = tmp + ut[use] * 3600.0
#get the positions for each date/time
pos, lt = sp.spkpos('VEX', et, 'VENUSVSO', 'NONE', 'VENUS')
x = pos.T[0]
y = pos.T[1]
z = pos.T[2]
#unload kernels
sp.unload(lsk_path)
sp.unload(spk_kernel)
for vk in VEXspk:
sp.unload(vk)
sp.unload(pck_kernel)
sp.unload(vso_kernel)
return (x, y, z)
def removeKernel(cls, kern, rmDupsOnly=False):
# Determine number of times kernel is loaded
nDups = cls.checkDuplicates(kern)
if rmDupsOnly:
# only want to remove duplicates
if nDups > 0:
# recursively remove
# NOTE: I wouldn't like to say where in the full kernel last
# the last one remaining will be
spice.unload(kern)
cls.removeKernel(kern, rmDupsOnly=rmDupsOnly)
else:
if nDups >= 0:
# recursively remove
spice.unload(kern)
cls.removeKernel(kern)
def et2cal(time, format='UTC', support_ker=False, unload=False):
"""
Converts Ephemeris Time (ET) into UTC or Calendar TDB (CAL) time. Accepts
a single time or a lists of times. This function assumes that the support
kernels (meta-kernel or leapseconds kernel) has been loaded.
:param time: Input ET time
:type time: Union[float, list]
:param format: Desired output format; 'UTC' or 'CAL'
:type format: str
:param unload: If True it will unload the input meta-kernel
:type unload: bool
:return: Output time in 'UTC', 'CAL' or 'TDB'
:rtype: Union[str, list]
"""
timlen = 62
out_list = []
if support_ker:
spiceypy.furnsh(support_ker)
if isinstance(time, float) or isinstance(time, str):
time = [time]
for element in time:
if format == 'UTC':
out_elm = spiceypy.et2utc(element, 'ISOC', 3)
elif format == 'CAL':
out_elm = spiceypy.timout(element, "YYYY-MM-DDTHR:MN:SC.###::TDB",
timlen)
else:
out_elm = element
out_list.append(out_elm)
if len(out_list) == 1:
out_time = out_list[0]
else:
out_time = out_list
if unload:
spiceypy.unload(support_ker)
return out_time
def computeTimeWidths(self, leapsecondspath=None):
#note we need SPICE to compute time widths because we have to convert from string ET to seconds-past-J2000
if not leapsecondspath == None:
try:
import spiceypy
except:
print('spiceypy not available')
return
spiceypy.furnsh(leapsecondspath)
self.__data__[-1]['timeWidth'] = 0.0
for recordIndex in range(0, self.getLength() - 1):
self.__data__[recordIndex]['timeWidth'] = spiceypy.str2et(
self.getValue(recordIndex + 1, 'epoch')) - spiceypy.str2et(
self.getValue(recordIndex, 'epoch'))
spiceypy.unload(leapsecondspath)
def CarringtonLongitude(Date, ut):
#create output array
n = np.size(ut)
et = np.zeros((n, ), dtype='float64')
lon = np.zeros(n, dtype='float64')
if n == 1:
ut = np.array([ut])
#load kernels
sp.furnsh(lsk_path)
sp.furnsh(spk_kernel)
VEXspk = ListVenusSPK(Date)
for vk in VEXspk:
sp.furnsh(vk)
sp.furnsh(pck_kernel)
sp.furnsh(hci_kernel)
#do each unique date to speed things up
if np.size(Date) == 1:
et[0] = utc2et(Date, 0.0)
et = et[0] + ut * 3600.0
else:
ud = np.unique(Date)
for i in range(0, ud.size):
use = np.where(Date == ud[i])[0]
tmp = utc2et(ud[i], 0.0)
et[use] = tmp + ut[use] * 3600.0
#get the longitudes
pos, lt = sp.spkpos('VEX', et, 'IAU_SUN', 'NONE', 'SUN')
pos = np.array(pos)
lon = np.arctan2(pos.T[1], pos.T[0]) * 180 / np.pi
#unload kernels
sp.unload(lsk_path)
sp.unload(spk_kernel)
for vk in VEXspk:
sp.unload(vk)
sp.unload(pck_kernel)
sp.unload(hci_kernel)
return lon
def PosHAE(date, ut):
'''
Messenger position in HAE coords (km)
'''
n = np.size(ut)
if np.size(ut) == 1:
ut = np.array([ut])
et = np.zeros((n, ), dtype='float64')
x = np.zeros(n, dtype='float64')
y = np.zeros(n, dtype='float64')
z = np.zeros(n, dtype='float64')
sp.furnsh(lsk_path)
sp.furnsh(spk_kernel)
sp.furnsh(spk_kernel2)
sp.furnsh(pck_kernel)
sp.furnsh(hci_kernel)
if np.size(Date) == 1:
et[0] = utc2et(Date, 0.0)
et = et[0] + ut * 3600.0
else:
ud = np.unique(Date)
for i in range(0, ud.size):
use = np.where(Date == ud[i])[0]
tmp = utc2et(ud[i], 0.0)
et[use] = tmp + ut[use] * 3600.0
pos, lt = sp.spkpos('MESSENGER', et, 'ECLIPDATE', 'NONE', 'SUN')
x = pos.T[0]
y = pos.T[1]
z = pos.T[2]
sp.unload(lsk_path)
sp.unload(spk_kernel)
sp.unload(spk_kernel2)
sp.unload(pck_kernel)
sp.unload(hci_kernel)
return (x, y, z)
def PosMSM(Date, ut):
'''
Messenger position in MSM coords (km)
'''
n = np.size(ut)
et = np.zeros((n, ), dtype='float64')
x = np.zeros(n, dtype='float64')
y = np.zeros(n, dtype='float64')
z = np.zeros(n, dtype='float64')
sp.furnsh(lsk_path)
sp.furnsh(spk_kernel)
sp.furnsh(spk_kernel2)
sp.furnsh(pck_kernel)
sp.furnsh(mso_kernel)
if np.size(Date) == 1:
et[0] = utc2et(Date, 0.0)
et = et[0] + ut * 3600.0
else:
ud = np.unique(Date)
for i in range(0, ud.size):
use = np.where(Date == ud[i])[0]
tmp = utc2et(ud[i], 0.0)
et[use] = tmp + ut[use] * 3600.0
for i in range(0, n):
pos, lt = sp.spkpos('MESSENGER', et[i], 'MERCURYMSO', 'NONE',
'MERCURY')
x[i] = pos[0]
y[i] = pos[1]
z[i] = pos[2] - 478.0
sp.unload(lsk_path)
sp.unload(spk_kernel)
sp.unload(spk_kernel2)
sp.unload(pck_kernel)
sp.unload(mso_kernel)
return (x, y, z)
def PosHCIDates(Date0, Date1):
'''
Get the postion of Mercury in HCI coordinates between two dates
Inputs
======
Date0 : int
Start date in format yyyymmdd
Date1 : int
End date
Returns
=======
x : float64
x-position in HCI coords (km)
y : float64
y-position in HCI coords (km)
z : float64
z-position in HCI coords (km)
'''
#list the dates
dates = ListDates(Date0, Date1)
n = dates.size
#load kernels
sp.furnsh(lsk_path)
sp.furnsh(spk_kernel)
sp.furnsh(pck_kernel)
sp.furnsh(hci_kernel)
#get the ephemeris times
et = utc2et(dates, np.zeros(n, dtype='float32'))
#positions
x = np.zeros(n, dtype='float64')
y = np.zeros(n, dtype='float64')
z = np.zeros(n, dtype='float64')
for i in range(0, n):
pos, lt = sp.spkpos('MERCURY', et[i], 'HCI', 'NONE', 'SUN')
x[i] = pos[0]
y[i] = pos[1]
z[i] = pos[2]
#unload kernels
sp.unload(lsk_path)
sp.unload(spk_kernel)
sp.unload(pck_kernel)
sp.unload(hci_kernel)
return (x, y, z)
def HAEtoHCI(Date, ut, xi, yi, zi):
'''
Convert HAE to HCI coordinates
'''
#create some arrays
n = np.size(xi)
if np.size(ut) == 1:
ut = np.zeros(n, dtype='float32') + ut
if np.size(xi) == 1:
xi = np.array([xi])
yi = np.array([yi])
zi = np.array([zi])
et = np.zeros((n, ), dtype='float64')
x = np.zeros(n, dtype='float64')
y = np.zeros(n, dtype='float64')
z = np.zeros(n, dtype='float64')
#load kernels
sp.furnsh(lsk_path)
sp.furnsh(spk_kernel)
sp.furnsh(pck_kernel)
sp.furnsh(hci_kernel)
#do each unique date to speed things up
if np.size(Date) == 1:
et[0] = utc2et(Date, 0.0)
et = et[0] + ut * 3600.0
else:
ud = np.unique(Date)
for i in range(0, ud.size):
use = np.where(Date == ud[i])[0]
tmp = utc2et(ud[i], 0.0)
et[use] = tmp + ut[use] * 3600.0
for i in range(0, n):
rot = sp.pxform('ECLIPDATE', 'HCI', et[i])
x[i], y[i], z[i] = np.sum(rot * np.array([xi[i], yi[i], zi[i]]),
axis=1)
#free kernels
sp.unload(lsk_path)
sp.unload(spk_kernel)
sp.unload(pck_kernel)
sp.unload(hci_kernel)
return (x, y, z)
def mjd20002et(mjd2000, support_ker=False, unload=False):
"""
Given a date in MJD2000 (Modified Julian Date 2000) returns the Ephemeris
time (ET which in SPICE is equivalent to TDB). Accepts a single time entry
or a list of times.
:param mjd2000: Date in MJD200
:type mjd2000: Union[float, list]
:param support_ker: Support kernels required to run the function. At least it should be a leapseconds kernel (LSK) and optionally a meta-kernel (MK)
:type support_ker: Union[str, list]
:param unload: If True it will unload the input support kernel
:type unload: bool
:return: Date in ET/TDB
:rtype: Union[float, list]
"""
tdb = []
if support_ker:
spiceypy.furnsh(support_ker)
if not isinstance(mjd2000, list):
mjd2000 = [mjd2000]
for time in mjd2000:
mjd2000 = float(time)
mjd = mjd2000 + 51544
jd = mjd + 2400000.5
jd = str(jd) + ' JD'
tdb.append(spiceypy.str2et(jd))
if unload:
spiceypy.unload(support_ker)
if len(tdb) == 1:
return tdb[0]
else:
return tdb
def PosIAU_SUN(Date,ut):
'''
Get Venus' position in IAU_SUN coordinates, wher Z is along the
Sun's rotational axis, X and Y rotate with the Sun.
'''
#get output arrays
n = np.size(ut)
if np.size(ut) == 1:
ut = np.array([ut])
et = np.zeros((n,),dtype='float64')
x = np.zeros(n,dtype='float64')
y = np.zeros(n,dtype='float64')
z = np.zeros(n,dtype='float64')
#load kernels
sp.furnsh(lsk_path)
sp.furnsh(spk_kernel)
sp.furnsh(pck_kernel)
sp.furnsh(hci_kernel)
#do each unique date to speed things up
if np.size(Date) == 1 :
et[0] = utc2et(Date,0.0)
et = et[0] + ut*3600.0
else:
ud = np.unique(Date)
for i in range(0,ud.size):
use = np.where(Date == ud[i])[0]
tmp = utc2et(ud[i],0.0)
et[use] = tmp + ut[use]*3600.0
#get the positions
pos,lt = sp.spkpos('VENUS',et,'IAU_SUN','NONE','SUN')
pos = np.array(pos)
x = pos.T[0]
y = pos.T[1]
z = pos.T[2]
#unload kernels
sp.unload(lsk_path)
sp.unload(spk_kernel)
sp.unload(pck_kernel)
sp.unload(hci_kernel)
return (x,y,z)
def PosHCI(Date,ut):
'''
HCI position at set times
'''
#get output arrays
n = np.size(ut)
if np.size(ut) == 1:
ut = np.array([ut])
et = np.zeros((n,),dtype='float64')
x = np.zeros(n,dtype='float64')
y = np.zeros(n,dtype='float64')
z = np.zeros(n,dtype='float64')
#load kernels
sp.furnsh(lsk_path)
sp.furnsh(spk_kernel)
sp.furnsh(pck_kernel)
sp.furnsh(hci_kernel)
#do each unique date to speed things up
if np.size(Date) == 1 :
et[0] = utc2et(Date,0.0)
et = et[0] + ut*3600.0
else:
ud = np.unique(Date)
for i in range(0,ud.size):
use = np.where(Date == ud[i])[0]
tmp = utc2et(ud[i],0.0)
et[use] = tmp + ut[use]*3600.0
#get the positions
pos,lt = sp.spkpos('VENUS',et,'HCI','NONE','SUN')
pos = np.array(pos)
x = pos.T[0]
y = pos.T[1]
z = pos.T[2]
#unload kernels
sp.unload(lsk_path)
sp.unload(spk_kernel)
sp.unload(pck_kernel)
sp.unload(hci_kernel)
return (x,y,z)
# =============================================================================
# End of function
# =============================================================================
# =============================================================================
# Run the function if requested
# When run, this program regenerates all of the backplanes
# It uses multiprocessing to run these in parallel
# =============================================================================
#%%%
if (__name__ == '__main__'):
file_tm = 'kernels_kem_prime.tm'
sp.unload(file_tm)
sp.furnsh(file_tm)
# NB: This would be an ideal candidate for multi-processing
# NB: Done!
# Set paramters here
do_tuna = False
digit_filter = None
# Run code here
digit_filters = ['12', '34', '56', '78', '90']
frames = ['2014_MU69_SUNFLOWER_ROT'] #, '2014_MU69_TUNACAN_ROT']
def unload(file_name):
mk_path = path(file_name)
spiceypy.unload(mk_path)
def OrientationSUN(date, ut):
n = np.size(ut)
et = np.zeros((n, ), dtype='float64')
x = np.zeros(n, dtype='float64')
y = np.zeros(n, dtype='float64')
z = np.zeros(n, dtype='float64')
ck = 'msgr_{:04d}_v*.bc'
yymm = (Date // 100) % 10000
uyymm = np.unique(yymm)
nck = np.size(uyymm)
ck_kernel = []
for i in range(0, nck):
files = FileSearch(ck_path, ck.format(uyymm[i]))
if files.size > 0:
ck_kernel.append(ck_path + files[-1])
nck = np.size(ck_kernel)
#load kernels
sp.furnsh(lsk_path)
sp.furnsh(spk_kernel)
sp.furnsh(spk_kernel2)
sp.furnsh(sclk_kernel)
sp.furnsh(pck_kernel)
sp.furnsh(mso_kernel)
sp.furnsh(ik_kernel)
sp.furnsh(fk_kernel)
for i in range(0, nck):
sp.furnsh(ck_kernel[i])
if np.size(Date) == 1:
et[0] = utc2et(Date, 0.0)
et = et[0] + ut * 3600.0
else:
ud = np.unique(Date)
for i in range(0, ud.size):
use = np.where(Date == ud[i])[0]
tmp = utc2et(ud[i], 0.0)
et[use] = tmp + ut[use] * 3600.0
#m = sp.pxform('J2000','MSGR_SPACECRAFT',et[0])
for i in range(0, n):
pos, lt = sp.spkpos('SUN', et[i], 'MSGR_SPACECRAFT', 'NONE',
'MESSENGER')
x[i] = pos[0]
y[i] = pos[1]
z[i] = pos[2]
sp.unload(lsk_path)
sp.unload(spk_kernel)
sp.unload(spk_kernel2)
sp.unload(sclk_kernel)
sp.unload(pck_kernel)
sp.unload(mso_kernel)
sp.unload(ik_kernel)
sp.unload(fk_kernel)
for i in range(0, nck):
sp.unload(ck_kernel[i])
return (x, y, z)
def NSOrientationMSO(Date, ut):
'''
Get the orientation of MESSENGER NS?
'''
n = np.size(ut)
et = np.zeros((n, ), dtype='float64')
x = np.zeros(n, dtype='float64')
y = np.zeros(n, dtype='float64')
z = np.zeros(n, dtype='float64')
ck = 'msgr_{:04d}_v*.bc'
yymm = (Date // 100) % 10000
uyymm = np.unique(yymm)
nck = np.size(uyymm)
ck_kernel = []
for i in range(0, nck):
files = FileSearch(ck_path, ck.format(uyymm[i]))
if files.size > 0:
ck_kernel.append(ck_path + files[-1])
nck = np.size(ck_kernel)
#load kernels
sp.furnsh(lsk_path)
sp.furnsh(spk_kernel)
sp.furnsh(spk_kernel2)
sp.furnsh(sclk_kernel)
sp.furnsh(pck_kernel)
sp.furnsh(mso_kernel)
sp.furnsh(ik_kernel)
sp.furnsh(fk_kernel)
for i in range(0, nck):
sp.furnsh(ck_kernel[i])
if np.size(Date) == 1:
et[0] = utc2et(Date, 0.0)
et = et[0] + ut * 3600.0
else:
ud = np.unique(Date)
for i in range(0, ud.size):
use = np.where(Date == ud[i])[0]
tmp = utc2et(ud[i], 0.0)
et[use] = tmp + ut[use] * 3600.0
#calculate positions
m = sp.pxform('MERCURYMSO', 'MSGR_GRNS_NS', et)
#unload kernels
sp.unload(lsk_path)
sp.unload(spk_kernel)
sp.unload(spk_kernel2)
sp.unload(sclk_kernel)
sp.unload(pck_kernel)
sp.unload(mso_kernel)
sp.unload(ik_kernel)
sp.unload(fk_kernel)
for i in range(0, nck):
sp.unload(ck_kernel[i])
return m
def unloadSpiceFiles(self):
# unload spice files
for spiceFile in self.loadedSpiceFiles:
spice.unload(spiceFile)
spice.kclear()
def MMOPosMSM(Date, ut):
'''
Position of MMO in MSM coords
'''
#create the output arrays
if np.size(np.shape(ut)) == 0:
ut = np.array([ut])
n = np.size(ut)
et = np.zeros((n, ), dtype='float64')
x = np.zeros(n, dtype='float64')
y = np.zeros(n, dtype='float64')
z = np.zeros(n, dtype='float64')
#load kernels
sp.furnsh(lsk_path)
sp.furnsh(sclk_kernel)
sp.furnsh(de430_kernel)
sp.furnsh(mpo_kernel)
sp.furnsh(pck_kernel)
sp.furnsh(mso_kernel)
#do each unique date to speed things up
if np.size(Date) == 1:
et[0] = utc2et(Date, 0.0)
et = et[0] + ut * 3600.0
else:
ud = np.unique(Date)
for i in range(0, ud.size):
use = np.where(Date == ud[i])[0]
tmp = utc2et(ud[i], 0.0)
et[use] = tmp + ut[use] * 3600.0
#get positions
pos, lt = sp.spkpos('MMO', et, 'MERCURYMSO', 'NONE', 'MERCURY')
x = pos.T[0]
y = pos.T[1]
z = pos.T[2] - 478.0
#unload kernels
sp.unload(lsk_path)
sp.unload(sclk_kernel)
sp.unload(de430_kernel)
sp.unload(mpo_kernel)
sp.unload(pck_kernel)
sp.unload(mso_kernel)
return (x, y, z)
def OrientationMSO(Date, ut, Verbose=False):
'''
This should return the direction in which MESSENGER is oriented
no idea if this is right
'''
n = np.size(ut)
et = np.zeros((n, ), dtype='float64')
x = np.zeros(n, dtype='float64')
y = np.zeros(n, dtype='float64')
z = np.zeros(n, dtype='float64')
#find the ck kernels
ck = 'msgr_{:04d}_v*.bc'
yymm = (Date // 100) % 10000
uyymm = np.unique(yymm)
nck = np.size(uyymm)
ck_kernel = []
for i in range(0, nck):
files = FileSearch(ck_path, ck.format(uyymm[i]))
if files.size > 0:
ck_kernel.append(ck_path + files[-1])
nck = np.size(ck_kernel)
#load all the kernels
sp.furnsh(lsk_path)
sp.furnsh(spk_kernel)
sp.furnsh(spk_kernel2)
sp.furnsh(sclk_kernel)
sp.furnsh(pck_kernel)
sp.furnsh(mso_kernel)
sp.furnsh(ik_kernel)
sp.furnsh(fk_kernel)
for i in range(0, nck):
sp.furnsh(ck_kernel[i])
if np.size(Date) == 1:
et[0] = utc2et(Date, 0.0)
et = et[0] + ut * 3600.0
else:
ud = np.unique(Date)
for i in range(0, ud.size):
use = np.where(Date == ud[i])[0]
tmp = utc2et(ud[i], 0.0)
et[use] = tmp + ut[use] * 3600.0
#get the positions
if Verbose:
for i in range(0, n):
print('\rVector {0} of {1}'.format(i + 1, n), end='')
pos, lt = sp.spkpos('MERCURY', et[i], 'MSGR_SPACECRAFT', 'NONE',
'MESSENGER')
x[i] = pos[0]
y[i] = pos[1]
z[i] = pos[2]
print()
else:
for i in range(0, n):
pos, lt = sp.spkpos('MERCURY', et[i], 'MSGR_SPACECRAFT', 'NONE',
'MESSENGER')
x[i] = pos[0]
y[i] = pos[1]
z[i] = pos[2]
#unload kernels
sp.unload(lsk_path)
sp.unload(spk_kernel)
sp.unload(spk_kernel2)
sp.unload(sclk_kernel)
sp.unload(pck_kernel)
sp.unload(mso_kernel)
sp.unload(ik_kernel)
sp.unload(fk_kernel)
for i in range(0, nck):
sp.unload(ck_kernel[i])
return (x, y, z)
def nh_ort_track4_flyby(dir_in=None, dir_out=None, name_trajectory = 'prime'):
#%%%
# dir_in = '/Users/throop/
# dir_in = '/Users/throop/data/ORT4/throop/ort4_bc3_10cbr2_dph/'
stretch_percent = 99
stretch = astropy.visualization.PercentileInterval(stretch_percent)
# dir_data = os.path.expanduser('~/Data/')
# dir_in git
do_compress = False # Do we use .gzip compression on the Track-4 input grids?
# If we used compression on the track4_calibrate routine, we must use it here too.
# dir_track4 = os.path.join(dir_data, name_ort, 'throop', 'track4')
if do_compress:
files = glob.glob(os.path.join(dir_in, '*.grid4d.gz'))
else:
files = glob.glob(os.path.join(dir_in, '*.grid4d'))
files = glob.glob(os.path.join(dir_in, '*.dust.pkl'))
# Alphabetize file list
files = sorted(files)
plt.set_cmap('plasma')
utc_ca = '2019 1 Jan 05:33:00'
dt_before = 1*u.hour
dt_after = 1*u.hour
# area_sc = (1*u.m)**2
frame = '2014_MU69_SUNFLOWER_ROT'
name_target = 'MU69'
origin = 'lower' # Required plotting order for imshow
name_observer = 'New Horizons'
hbt.figsize((8,6))
hbt.set_fontsize(12)
dt = 1*u.s # Sampling time through the flyby. Astropy units.
# Create an output table, Astropy format
t = Table(names = ['trajectory', 'speed', 'q_dust', 'albedo', 'rho',
'tau_max', 'tau_typical', 'iof_max', 'iof_typical'],
dtype = ['U30', float, float, float, float,
float, float, float, float] )
# Start up SPICE if needed. Unload old kernels just as a safety precaution.
sp.unload('kernels_kem_prime.tm')
sp.unload('kernels_kem_alternate.tm')
sp.furnsh(f'kernels_kem_{name_trajectory}.tm')
do_short = False
if do_short:
files = files[0:4]
i=3
file = files[i]
num_files = len(files)
name_run = dir_out.split('/')[-2]
#%%%
for i,file in enumerate(files):
#%%%
print(f'Starting file {i}/{len(files)}')
grid = nh_ort_track4_grid(file) # Load the grid from disk. Uses gzip, so it is quite slow (10 sec/file)
print(f'Loading file {file}')
# Load the trajectory parameters
et_ca = int( sp.utc2et(utc_ca) ) # Force this to be an integer, just to make output cleaner.
et_start = et_ca - dt_before.to('s').value
et_end = et_ca + dt_after.to('s').value
grid.frame = frame
grid.name_target = name_target
grid.name_trajectory = name_trajectory
# And call the method to fly through it!
# The returned density values etc are available within the instance variables, not returned explicitly.
grid.fly_trajectory(name_observer, et_start, et_end, dt)
# If the first time thru loop, make plot of our path through the system
do_plots_geometry = True
if (do_plots_geometry and (i==0)):
grid.plot_trajectory_geometry()
# Make slice plots thru the grid
do_plot_slices_xyz = False
if do_plot_slices_xyz:
hbt.fontsize(8)
hbt.figsize((20,5))
grid.plot(axis_sum=0)
grid.plot(axis_sum=1)
grid.plot(axis_sum=2)
hbt.fontsize(10)
# Make a plot of optical depth
do_plot_tau = True
if do_plot_tau:
grid.plot_tau()
# =============================================================================
# Make some plots of count rate vs. time!
# =============================================================================
# Make a plot of the instantaneous count rate
hbt.figsize((10,15))
# Make a plot of the actual density that we give to Doug Mehoke
# Define a list of colors. This is so we can use colors= argument to set
# a marker to show grain size, rather than let plot() auto-assign.
# colors = plt.rcParams['axes.prop_cycle'].by_key()['color'] # This is the default color iterator.
colors = ['antiquewhite',
'tomato',
'blueviolet',
'skyblue',
'gold',
'darkcyan',
'thistle',
'olive',
'red',
'sienna',
'deepskyblue',
'lightsalmon',
'pink',
]
# 'aqua']
# 'antiquewhite4', 'aqua', 'aquamarine4', 'black', 'blue', 'blueviolet',
# 'brown1', 'chartreuse1', 'darkgreen', 'darkorange1', 'dodgerblue1', 'lightpink', 'magenta']
# Make a plot of dust number density. This is straight out of the grid, and for comparison with MRS.
vals_fiducial = [1e-10, 1e-8, 1e-6,1e-4, 1e-2, 1e-0]
plt.subplot(3,1,1)
for j,s in enumerate(grid.s):
plt.plot(grid.delta_et_t, grid.number_t[j],
label = 's={:.2f} mm'.format(s),
color=colors[j])
plt.legend()
plt.title('Dust number density'.format(grid.area_sc))
plt.xlabel('ET from C/A')
plt.yscale('log')
plt.ylim((1e-10, 1e2)) # Match to MRS.
plt.axvline(0, color='black', alpha=0.05)
plt.ylabel(r'Dust,, # km$^{-3}$')
for val in vals_fiducial:
plt.axhline(val, color='black', alpha=0.05)
# Make a plot of impact rate. This assumes a s/c area.
plt.subplot(3,1,2)
for j,s in enumerate(grid.s): # 's' is dust size
plt.plot(grid.delta_et_t, grid.number_sc_t[j],
label = f's={s:.2f} mm',
color=colors[j])
plt.title('Impact rate, A={}'.format(grid.area_sc))
plt.yscale('log')
plt.xlabel('ET from C/A')
plt.legend()
plt.ylabel(r'Dust, # Impacts sec$^{{-1}}$')
# Make a plot of the cumulative count rate. Mark grain sizes here too.
plt.subplot(3,1,3)
for j,s in enumerate(grid.s): # Loop over size
plt.plot(grid.delta_et_t, grid.number_sc_cum_t[j], # Main plot line
label = 's={:.2f} mm'.format(s), color=colors[j])
plt.plot([grid.delta_et_t[-1]], [grid.number_sc_cum_t[j,-1].value], # Circle to indicate grain size
markersize=(7-j)*2, marker = 'o', # Use same color as prev line!
color=colors[j])
hbt.figsize(5,5)
plt.legend()
plt.title('Number of impacts (cumulative), A={}'.format(grid.area_sc))
plt.xlabel('ET from C/A')
plt.yscale('log')
plt.ylabel('# of Impacts')
plt.axhline(y = 1, linestyle = '--', alpha = 0.1)
plt.tight_layout()
plt.show()
# Make a plot of size distibution.
# Make two curves: one for n(r) for the entire grid, and one for n(r) that hits s/c
# Now add an entry to the table. This is a table that lists all of the results --
# e.g., max_tau, count rate etc
# One line per grid.
t.add_row(vals=[grid.name_trajectory, grid.speed, grid.q, grid.albedo, grid.rho,
grid.tau_max, grid.tau_typ,
grid.iof_max, grid.iof_typ])
# Get size dist along path
number_path = grid.number_sc_cum_t[:,-1].value
# Take the full particle grid, and sum along all spatial axes, leaving just the size axis left.
number_grid = np.sum(np.sum(np.sum(grid.density, axis=1), axis=1), axis=1)
# Normalize the size dists both
number_grid = hbt.normalize(number_grid)
number_path = hbt.normalize(number_path)
plt.plot(grid.s, number_path, label = 'Along s/c path')
plt.plot(grid.s, number_grid, label = 'In grid, total')
plt.yscale('log')
plt.xscale('log')
plt.ylim( (hbt.minval(np.array([number_grid, number_path]))/2, 1) )
plt.xlabel('Radius [mm]')
plt.ylabel('Particle number [arbitrary]')
plt.legend(loc = 'lower right')
plt.show()
# Output the dust population for this run to a file. This is the file that Doug Mehoke will read.
grid.output_trajectory(name_run=name_run, do_positions=False, dir_out=dir_out)
print('---')
#%%%
# Print the table
t.pprint(max_width=-1)
# Save the table as output
file_out = os.path.join(dir_out, f'nh_{name_trajectory}_track4_table.pkl')
lun = open(file_out, 'wb')
pickle.dump(t,lun)
lun.close()
print(f'Wrote: {file_out}')
#%%%
# Now that all files have been created, compress results into an archive (.tar.gz) for Doug Mehoke
inits_track4 = 'hbt'
# if 'hamilton' in files[0]:
# inits_track3 = 'dph'
# if 'kauf' in files[0]:
# inits_track3 = 'dk'
file_out = f'{name_trajectory}_{name_run}_{inits_track4}_n{num_files}.tgz'
str = f'cd {dir_out}; tar -czf {file_out} *{name_trajectory}*.dust'
_ = subprocess.Popen(str, shell=True)
print(str)
print(f'Wrote: {dir_out}/{file_out}')
def tearDown(self):
spice.unload(self.updated_kernels)
for kern in self.binary_kernels:
os.remove(kern)
def nh_ort_find_ring_pole():
file_superstack = '/Users/throop/Data/ORT4/superstack_ORT4_z4_mean_wcs_sm_hbt.fits'
file_tm = 'kernels_kem_prime.tm'
sp.unload(file_tm)
sp.furnsh(file_tm)
f = fits.open(file_superstack)
img = f[0].data
# plt.imshow(stretch(img))
# plt.show()
wcs = WCS(file)
num_pts = 200
ra_pole = 275 * hbt.d2r
# dec_pole = -56 * hbt.d2r
dec_pole = 13 * hbt.d2r
radius_ring = 9000 # Radius in km
vec_pole_j2k = sp.radrec(1, ra_pole, dec_pole)
et = float(f[0].header['SPCSCET'])
utc = sp.et2utc(et, 'C', 0)
# Get position from NH to UT
(st, lt) = sp.spkezr('MU69', et, 'J2000', 'LT', 'New Horizons')
vec_nh_ut = st[0:3]
# Get position from Sun, to NH
(st, lt) = sp.spkezr('New Horizons', et, 'J2000', 'LT', 'Sun')
vec_sun_nh = st[0:3]
vec_sun_ut = vec_sun_nh + vec_nh_ut
# Define a 'ring plane', based on a pole vector, and a point
# This ring plane should be in J2K space -- that is, centered on Sun.
plane_ring = sp.nvp2pl(vec_pole_j2k, vec_sun_ut) # Pole position is variable. Point is UT in J2K.
# Get the point and spanning vectors that define this plane
# XXX for some reason, these values from pl2psv do not depend on value of vec_pol_j2k
(pt_pl, vec1_pl, vec2_pl) = sp.pl2psv(plane_ring)
# Now take a bunch of linear combinations of these spanning vectors
# Plot UT's position on the plot
(st, lt) = sp.spkezr('MU69', et, 'J2000', 'LT', 'New Horizons')
(_, ra, dec) = sp.recrad(vec_nh_ut)
(x, y) = wcs.wcs_world2pix(ra*hbt.r2d, dec*hbt.r2d, 0)
# plt.plot(x, y, marker = 'o', ms = 10, alpha=0.3, color='purple')
# Set an offset to the WCS values, in case UT is not in the right position (ie, not centered properly)
dy = 0 # Large value moves up
dx = 0
# Draw the ring image
plt.imshow(stretch(img), origin='lower')
# Calculate and draw all of the ring points
for i in range(num_pts):
angle_azimuth = 2*math.pi * (i / num_pts) # Put in range 0 .. 2 pi
vec_i = vec1_pl * math.sin(angle_azimuth) + vec2_pl * math.cos(angle_azimuth)
vec_i = vec_i * radius_ring
# Now get the point in space, J2K
pt_ring_i_j2k = vec_i + vec_sun_ut
vec_sun_ring_i = pt_ring_i_j2k
vec_nh_ring_i = vec_sun_ring_i- vec_sun_nh
#
(_, ra_i, dec_i) = sp.recrad(vec_nh_ring_i)
(x, y) = wcs.wcs_world2pix(ra_i*hbt.r2d, dec_i*hbt.r2d, 0)
plt.plot(x+dx, y+dy, marker = 'o', ms = 1, color='red', alpha = 0.15)
# print(f'{i}, {ra_i*hbt.r2d}, {dec_i*hbt.r2d}, {x}, {y}')
plt.title(f'ORT4 Superstack, Ring Pole = ({ra_pole*hbt.r2d},{dec_pole*hbt.r2d}) deg')
plt.show()
return
