stvid/stvid/stio.py

230 lines
7.6 KiB
Python

#!/usr/bin/env python
import numpy as np
from astropy.io import fits
from astropy.time import Time
from astropy import wcs
class observation:
"""Satellite observation"""
def __init__(self, ff, mjd, x0, y0):
"""Define an observation"""
# Store
self.mjd = mjd
self.x0 = x0
self.y0 = y0
# Get times
self.nfd = Time(self.mjd, format='mjd', scale='utc').isot
# Correct for rotation
tobs = Time(ff.mjd+0.5*ff.texp/86400.0, format='mjd', scale='utc')
tobs.delta_ut1_utc = 0
hobs = tobs.sidereal_time("mean", longitude=0.0).degree
tmid = Time(self.mjd, format='mjd', scale='utc')
tmid.delta_ut1_utc = 0
hmid = tmid.sidereal_time("mean", longitude=0.0).degree
# Compute ra/dec
world = ff.w.wcs_pix2world(np.array([[self.x0, self.y0]]), 1)
self.ra = world[0, 0]+hobs-hmid
self.de = world[0, 1]
class satid:
"""Satellite identifications"""
def __init__(self, line):
s = line.split()
self.nfd = s[0]
self.x0 = float(s[1])
self.y0 = float(s[2])
self.t0 = 0.0
self.x1 = float(s[3])
self.y1 = float(s[4])
self.t1 = float(s[5])
self.norad = int(s[6])
self.catalog = s[7]
self.state = s[8]
self.dxdt = (self.x1-self.x0)/(self.t1-self.t0)
self.dydt = (self.y1-self.y0)/(self.t1-self.t0)
def __repr__(self):
return "%s %f %f %f -> %f %f %f %d %s %s" % (self.nfd, self.x0,
self.y0, self.t0,
self.x1, self.y1,
self.t1, self.norad,
self.catalog, self.state)
class fourframe:
"""Four frame class"""
def __init__(self, fname=None):
if fname is None:
# Initialize empty fourframe
self.nx = 0
self.ny = 0
self.nz = 0
self.mjd = -1
self.nfd = None
self.zavg = None
self.zstd = None
self.zmax = None
self.znum = None
self.dt = None
self.site_id = 0
self.observer = None
self.texp = 0.0
self.fname = None
self.crpix = np.array([0.0, 0.0])
self.crval = np.array([0.0, 0.0])
self.cd = np.array([[1.0, 0.0], [0.0, 1.0]])
self.ctype = ["RA---TAN", "DEC--TAN"]
self.cunit = np.array(["deg", "deg"])
self.crres = np.array([0.0, 0.0])
else:
# Read FITS file
hdu = fits.open(fname)
# Read image planes
self.zavg, self.zstd, self.zmax, self.znum = hdu[0].data
# Frame properties
self.ny, self.nx = self.zavg.shape
self.nz = hdu[0].header['NFRAMES']
# Read frame time oselfsets
self.dt = np.array([hdu[0].header['DT%04d' % i]
for i in range(self.nz)])
# Read header
self.mjd = hdu[0].header['MJD-OBS']
self.nfd = hdu[0].header['DATE-OBS']
self.site_id = hdu[0].header['COSPAR']
self.observer = hdu[0].header['OBSERVER']
self.texp = hdu[0].header['EXPTIME']
self.fname = fname
# Astrometry keywords
self.crpix = np.array([hdu[0].header['CRPIX1'],
hdu[0].header['CRPIX2']])
self.crval = np.array([hdu[0].header['CRVAL1'],
hdu[0].header['CRVAL2']])
self.cd = np.array([[hdu[0].header['CD1_1'],
hdu[0].header['CD1_2']],
[hdu[0].header['CD2_1'],
hdu[0].header['CD2_2']]])
self.ctype = [hdu[0].header['CTYPE1'], hdu[0].header['CTYPE2']]
self.cunit = [hdu[0].header['CUNIT1'], hdu[0].header['CUNIT2']]
self.crres = np.array([hdu[0].header['CRRES1'],
hdu[0].header['CRRES2']])
# Compute image properties
self.sx = np.sqrt(self.cd[0, 0]**2+self.cd[1, 0]**2)
self.sy = np.sqrt(self.cd[0, 1]**2+self.cd[1, 1]**2)
self.wx = self.nx*self.sx
self.wy = self.ny*self.sy
self.zmaxmin = np.mean(self.zmax)-2.0*np.std(self.zmax)
self.zmaxmax = np.mean(self.zmax)+6.0*np.std(self.zmax)
self.zavgmin = np.mean(self.zavg)-2.0*np.std(self.zavg)
self.zavgmax = np.mean(self.zavg)+6.0*np.std(self.zavg)
# Setup WCS
self.w = wcs.WCS(naxis=2)
self.w.wcs.crpix = self.crpix
self.w.wcs.crval = self.crval
self.w.wcs.cd = self.cd
self.w.wcs.ctype = self.ctype
self.w.wcs.set_pv([(2, 1, 45.0)])
def significant_pixels_along_track(self, sigma, x0, y0,
dxdt, dydt, rmin=10.0):
"""Extract significant pixels along a track"""
# Generate sigma frame
zsig = (self.zmax-self.zavg)/(self.zstd+1e-9)
# Select
c = (zsig > sigma)
# Positions
xm, ym = np.meshgrid(np.arange(self.nx), np.arange(self.ny))
x, y = np.ravel(xm[c]), np.ravel(ym[c])
inum = np.ravel(self.znum[c]).astype('int')
sig = np.ravel(zsig[c])
t = np.array([self.dt[i] for i in inum])
# Predicted positions
xr = x0+dxdt*t
yr = y0+dydt*t
r = np.sqrt((x-xr)**2+(y-yr)**2)
c = r < rmin
return x[c], y[c], t[c], sig[c]
def significant_pixels(self, sigma):
"""Extract significant pixels"""
# Generate sigma frame
zsig = (self.zmax-self.zavg)/(self.zstd+1e-9)
# Select
c = (zsig > sigma)
# Positions
xm, ym = np.meshgrid(np.arange(self.nx), np.arange(self.ny))
x, y = np.ravel(xm[c]), np.ravel(ym[c])
inum = np.ravel(self.znum[c]).astype('int')
sig = np.ravel(zsig[c])
t = np.array([self.dt[i] for i in inum])
return x, y, t, sig
def track(self, dxdt, dydt, tref):
"""Track and stack"""
# Empty frame
ztrk = np.zeros_like(self.zavg)
# Loop over frames
for i in range(self.nz):
dx = int(np.round(dxdt*(self.dt[i]-tref)))
dy = int(np.round(dydt*(self.dt[i]-tref)))
# Skip if shift larger than image
if np.abs(dx) >= self.nx:
continue
if np.abs(dy) >= self.ny:
continue
# Extract range
if dx >= 0:
i1min, i1max = dx, self.nx-1
i2min, i2max = 0, self.nx-dx-1
else:
i1min, i1max = 0, self.nx+dx-1
i2min, i2max = -dx, self.nx-1
if dy >= 0:
j1min, j1max = dy, self.ny-1
j2min, j2max = 0, self.ny-dy-1
else:
j1min, j1max = 0, self.ny+dy-1
j2min, j2max = -dy, self.ny-1
zsel = np.where(self.znum == i, self.zmax, 0.0)
ztrk[j2min:j2max, i2min:i2max] += zsel[j1min:j1max, i1min:i1max]
return ztrk
def __repr__(self):
return "%s %dx%dx%d %s %.3f %d %s" % (self.fname,
self.nx,
self.ny,
self.nz,
self.nfd,
self.texp,
self.site_id,
self.observer)