def write_movie(photon_file,
band,
tranges,
stepsz=30,
overwrite=False,
pixsz=0.000416666666666667):
"""This function creates FITS cubes based on a photon event .csv file."""
fitsfilename = photon_file.replace('.csv',
'-{s}s.fits'.format(s=int(stepsz)))
if not os.path.exists(fitsfilename) or overwrite:
events = calibrate_photons(photon_file, band)
for trange in tranges:
print_inline(trange)
image, wcs = make_image(photon_file,
band,
trange=trange,
events=events,
pixsz=pixsz)
try:
movie = np.append(movie, [image], axis=0)
except:
movie = [image]
hdu = pyfits.PrimaryHDU(movie)
hdulist = pyfits.HDUList([hdu])
hdulist.writeto(fitsfilename)
def globalcount_shuttered(band, trange, verbose=0, timestamplist=False):
"""
Global event counts over the time range, exluding shuttered periods (due to
no non-NULL data).
:param band: The band to use, either 'FUV' or 'NUV'.
:type band: str
:param trange: Minimum and maximum time (in GALEX time) to consider.
:type trange: list
:param verbose: Verbosity level, a value of 0 is minimum verbosity.
:type verbose: int
:param timestamplist: Global event time stamps.
:type timestamplist: list
:returns: int -- Total global counts excluding shuttered periods.
"""
try:
t = (timestamplist if np.array(timestamplist).any() else
np.array(gQuery.getArray(gQuery.uniquetimes(
band, trange[0], trange[1], flag=True),
verbose=verbose),
dtype='float64')[:, 0] / gQuery.tscale)
except IndexError: # Shutter this whole time range.
if verbose:
print_inline('No data in {t0},{t1}'.format(t0=trange[0],
t1=trange[1]))
return 0
times = np.sort(np.unique(np.append(t, trange)))
tranges = distinct_tranges(times, maxgap=0.05)
nonnullevents, nullevents = 0, 0
for trange in tranges:
nullevents += gQuery.getValue(gQuery.deadtime2(band, trange[0],
trange[1]),
verbose=verbose)
nonnullevents += gQuery.getValue(gQuery.deadtime1(
band, trange[0], trange[1]),
verbose=verbose)
return nullevents + nonnullevents
def globalcount_shuttered(band, trange, verbose=0, timestamplist=False):
"""
Global event counts over the time range, exluding shuttered periods (due to
no non-NULL data).
:param band: The band to use, either 'FUV' or 'NUV'.
:type band: str
:param trange: Minimum and maximum time (in GALEX time) to consider.
:type trange: list
:param verbose: Verbosity level, a value of 0 is minimum verbosity.
:type verbose: int
:param timestamplist: Global event time stamps.
:type timestamplist: list
:returns: int -- Total global counts excluding shuttered periods.
"""
try:
t = (timestamplist if np.array(timestamplist).any() else
np.array(gQuery.getArray(gQuery.uniquetimes(band, trange[0],
trange[1], flag=True),
verbose=verbose),
dtype='float64')[:, 0]/gQuery.tscale)
except IndexError: # Shutter this whole time range.
if verbose:
print_inline('No data in {t0},{t1}'.format(t0=trange[0], t1=trange[1]))
return 0
times = np.sort(np.unique(np.append(t, trange)))
tranges = distinct_tranges(times, maxgap=0.05)
nonnullevents, nullevents = 0, 0
for trange in tranges:
nullevents += gQuery.getValue(
gQuery.deadtime2(band, trange[0], trange[1]), verbose=verbose)
nonnullevents += gQuery.getValue(gQuery.deadtime1(band, trange[0],
trange[1]),
verbose=verbose)
return nullevents+nonnullevents
def stimcount_shuttered(band, trange, verbose=0, timestamplist=False):
"""
Returns the stim count over a time range, excluding periods that the
detector is considered shuttered (because of no non-NULL data).
:param band: The band to use, either 'FUV' or 'NUV'.
:type band: str
:param trange: Minimum and maximum time (in GALEX time) to consider.
:type trange: list
:param verbose: Verbosity level, a value of 0 is minimum verbosity.
:type verbose: int
:param timestamplist: Global detector event timestamps.
:type timestamplist: list
:returns: int -- Total stim counts excluding shuttered time ranges.
"""
try:
t = (timestamplist if np.array(timestamplist).any() else
np.array(gQuery.getArray(gQuery.uniquetimes(band, trange[0],
trange[1]),
verbose=verbose),
dtype='float64')[:, 0]/gQuery.tscale)
except IndexError: # Shutter this whole time range.
if verbose:
print_inline('No data in {t0},{t1}'.format(t0=trange[0], t1=trange[1]))
return 0
times = np.sort(np.unique(np.append(t, trange)))
tranges = distinct_tranges(times, maxgap=0.05)
stimcount = 0
for trange in tranges:
stimcount += (gQuery.getValue(gQuery.stimcount(band, trange[0],
trange[1]),
verbose=verbose) +
gQuery.getValue(gQuery.stimcount(band, trange[0],
trange[1], null=False),
verbose=verbose))
return stimcount
def stimcount_shuttered(band, trange, verbose=0, timestamplist=False):
"""
Returns the stim count over a time range, excluding periods that the
detector is considered shuttered (because of no non-NULL data).
:param band: The band to use, either 'FUV' or 'NUV'.
:type band: str
:param trange: Minimum and maximum time (in GALEX time) to consider.
:type trange: list
:param verbose: Verbosity level, a value of 0 is minimum verbosity.
:type verbose: int
:param timestamplist: Global detector event timestamps.
:type timestamplist: list
:returns: int -- Total stim counts excluding shuttered time ranges.
"""
try:
t = (timestamplist if np.array(timestamplist).any() else np.array(
gQuery.getArray(gQuery.uniquetimes(band, trange[0], trange[1]),
verbose=verbose),
dtype='float64')[:, 0] / gQuery.tscale)
except IndexError: # Shutter this whole time range.
if verbose:
print_inline('No data in {t0},{t1}'.format(t0=trange[0],
t1=trange[1]))
return 0
times = np.sort(np.unique(np.append(t, trange)))
tranges = distinct_tranges(times, maxgap=0.05)
stimcount = 0
for trange in tranges:
stimcount += (gQuery.getValue(
gQuery.stimcount(band, trange[0], trange[1]), verbose=verbose) +
gQuery.getValue(gQuery.stimcount(
band, trange[0], trange[1], null=False),
verbose=verbose))
return stimcount
def exposure(band, trange, verbose=0):
"""
Calculate the effective exposure time in a period, in seconds, accounting
for shutter and deadtime. Does not account for actual sky coverage of
the telescope during the time period queried (see: compute_exptime()
below).
:param band: The band to use, either 'FUV' or 'NUV'.
:type band: str
:param trange: Minimum and maximum time (in GALEX time) to consider.
:type trange: list
:param verbose: Verbosity level, a value of 0 is minimum verbosity.
:type verbose: int
:returns: float -- The effective exposure time, in seconds.
"""
rawexpt = trange[1] - trange[0]
if rawexpt == 0.:
return 0.
try:
t = (np.array(gQuery.getArray(gQuery.uniquetimes(
band, trange[0], trange[1], flag=True),
verbose=verbose),
dtype='float64')[:, 0] / gQuery.tscale)
except IndexError: # Shutter this whole time range.
if verbose:
print_inline('No data in {t0},{t1}'.format(t0=trange[0],
t1=trange[1]))
return 0.
shutter = compute_shutter(band, trange, verbose=verbose, timestamplist=t)
deadtime = empirical_deadtime(band,
trange,
verbose=verbose,
timestamplist=t)
return (rawexpt - shutter) * (1. - deadtime)
def exposure(band, trange, verbose=0):
"""
Calculate the effective exposure time in a period, in seconds, accounting
for shutter and deadtime. Does not account for actual sky coverage of
the telescope during the time period queried (see: compute_exptime()
below).
:param band: The band to use, either 'FUV' or 'NUV'.
:type band: str
:param trange: Minimum and maximum time (in GALEX time) to consider.
:type trange: list
:param verbose: Verbosity level, a value of 0 is minimum verbosity.
:type verbose: int
:returns: float -- The effective exposure time, in seconds.
"""
rawexpt = trange[1]-trange[0]
if rawexpt == 0.:
return 0.
try:
t = (np.array(gQuery.getArray(
gQuery.uniquetimes(band, trange[0], trange[1], flag=True),
verbose=verbose), dtype='float64')[:, 0]/gQuery.tscale)
except IndexError: # Shutter this whole time range.
if verbose:
print_inline('No data in {t0},{t1}'.format(t0=trange[0], t1=trange[1]))
return 0.
shutter = compute_shutter(band, trange, verbose=verbose, timestamplist=t)
deadtime = empirical_deadtime(band, trange, verbose=verbose, timestamplist=t)
return (rawexpt-shutter)*(1.-deadtime)
def mcat_skybg(band, skypos, radius, verbose=0, trange=None, mcat=None,
searchradius=0.1):
"""
Estimate the sky background using the MCAT 'skybg' for nearby sources.
:param band: The band to use, either 'FUV' or 'NUV'.
:type band: str
:param skypos: The right ascension and declination, in degrees.
:type skypos: list
:param radius: The radius in which to search for MCAT sources in degrees.
:type radius: float
:param verbose: Verbosity level, a value of 0 is minimum verbosity.
:type verbose: int
:param trange: Minimum and maximum time (in GALEX time) to consider.
:type trange: list
:returns: float -- The estimated sky background in the photometric
aperture, in counts per second.
"""
# Search the visit-level MCAT for nearby detections.
# Unless the MCAT data has already been handed off for detection purposes.
if not mcat:
mcat = get_mcat_data(skypos, searchradius)
try:
# Find the distance to each source.
dist = np.array([angularSeparation(skypos[0], skypos[1], a[0], a[1])
for a in zip(mcat[band]['ra'], mcat[band]['dec'])])
except TypeError:
print_inline(
'No {b} MCAT sources within {r} degrees of {p}'.format(
b=band, r=searchradius, p=skypos))
return np.nan
# Find visits that overlap in time.
if not trange:
tix = (np.array(list(range(len(mcat[band]['mag']))), dtype='int32'),)
else:
tix = np.where(
((trange[0] >= mcat[band]['t0']) & (trange[0] <= mcat[band]['t1'])) |
((trange[1] >= mcat[band]['t0']) & (trange[1] <= mcat[band]['t1'])) |
((trange[0] <= mcat[band]['t0']) & (trange[1] >= mcat[band]['t1'])))
if not len(tix[0]):
print_inline('No concurrent {b} MCAT source nearby.'.format(b=band))
return np.nan # Might not be the preferred behavior here.
ix = np.where(dist[tix] == min(dist[tix]))
skybg = mcat[band]['skybg'][tix][ix]
# This should rarely happen, but sometimes there's a duplicate entry in
# the visit-level MCAT.
if len(skybg) > 1:
# If the skybg array is all the same value, it's a duplicate.
if np.all(skybg == skybg[0]):
skybg = np.asarray([skybg[0]])
else:
skybg = np.asarray([np.median(skybg)])
return skybg[0]*area(radius*60.*60.)
def fGetTimeRanges(band, skypos, trange=None, detsize=1.1, verbose=0,
maxgap=1., minexp=1., skyrange=None, maxgap_override=False,
minexp_override = False):
"""
Find the contiguous time ranges within a time range at a specific location.
:param band: The band to use, either 'FUV' or 'NUV'.
:type band: str
:param skypos: The right ascension and declination, in degrees.
:type skypos: list
:param trange: Minimum and maximum time (in GALEX time) to consider.
:type trange: list
:param detsize: The effective detector diameter, in degrees.
:type detsize: float
:param verbose: Verbosity level, a value of 0 is minimum verbosity.
:type verbose: int
:param maxgap: Maximum gap size, in seconds, for data to be considered
contiguous.
:type maxgap: float
:param minexp: Minimum gap size, in seconds, for data to be considered
contiguous.
:type minexp: float
:param skyrange: Values in degrees RA and Dec of a box around skypos that
defines the extent of the region of interest.
:type skyrange: list
:param maxgap_override: Enables an experimental feature where maxgap
can be less than one second.
:type maxgap_override: bool
:returns: numpy.ndarray -- A valid set of time ranges, accounting for
minimum exposure lengths and maximum gaps.
"""
if trange is not None:
trange_buffer = (trange[0]-1,trange[1]+1)
times = get_valid_times(band, skypos, trange=trange_buffer,
detsize=detsize, verbose=verbose, skyrange=skyrange)
if len(times):
times = np.append(times,times[-1]+1)
if times[0]<trange[0]:
times[0]=trange[0]
if times[-1]>trange[1]:
times[-1]=trange[1]
else:
times = get_valid_times(band, skypos, trange=None, detsize=detsize,
verbose=verbose, skyrange=skyrange)
if len(times):
times = np.append(times,times[-1]+1)
if not len(times):
return np.array([[]], dtype='float64')
if verbose:
print_inline('Parsing ~'+str(len(times)-1)+' seconds of raw exposure.')
# NOTE: The minimum meaningful maxgap is 1 second.
if maxgap < 1 and not maxgap_override:
raise ValueError('maxgap must be >=1 second')
tranges = distinct_tranges(times, maxgap=maxgap)
if not minexp_override:
ix = np.where(np.array(tranges)[:, 1]-np.array(tranges)[:, 0] >= minexp)
tranges = np.array(tranges)[ix].tolist()
return np.array(tranges, dtype='float64')
def photonpipe(outbase,
band,
raw6file=None,
scstfile=None,
aspfile=None,
ssdfile=None,
nullfile=None,
verbose=0,
retries=20,
eclipse=None):
"""
Apply static and sky calibrations to -raw6 GALEX data, producing fully
aspect-corrected and time-tagged photon list files.
:param raw6file: Name of the raw6 file to use.
:type raw6file: str
:param scstfile: Spacecraft state file to use.
:type scstfile: str
:param band: Name of the band to use, either 'FUV' or 'NUV'.
:type band: str
:param outbase: Base of the output file names.
:type outbase: str
:param aspfile: Name of aspect file to use.
:type aspfile: int
:param ssdfile: Name of Stim Separation Data file to use.
:type ssdfile: int
:param nullfile: Name of output file to record NULL lines.
:type nullfile: int
:param verbose: Note used. Included for consistency with other tools.
:type verbose: int
:param retries: Number of query retries to attempt before giving up.
:type retries: int
"""
startt = time.time()
# Scale factor for the time column in the output csv so that it
# can be recorded as an int in the database.
dbscale = 1000
# This number determines the size of the chunks that gPhoton reads
# in from the raw6 for processing. Even if your machine has a lot
# of memory, making this number bigger is unlikely to improve the
# processing time much because so much is eaten up by the .csv write.
chunksz = 1000000
# These are just constants for the mission.
detsize = 1.25 # Detector size in degrees
pltscl = 68.754932 # Plate scale
aspum = pltscl / 1000.0
arcsecperpixel = 1.5
xi_xsc, xi_ysc, eta_xsc, eta_ysc = 0., 1., 1., 0.
# Determine the eclipse number from the raw6 header.
if not raw6file:
if not eclipse:
raise ValueError('Must specifiy eclipse if no raw6file.')
else:
raw6file = download_data(eclipse,
band,
'raw6',
datadir=os.path.dirname(outbase))
if raw6file == None:
print('Unable to retrieve raw6 file for this eclipse.')
return
hdulist = pyfits.open(raw6file)
hdr = hdulist[0].header
hdulist.close()
if eclipse and (eclipse != hdr['eclipse']): # just a consistency check
print("Warning: eclipse mismatch {e0} vs. {e1} (header)".format(
e0=eclipse, e1=hdr['eclipse']))
eclipse = hdr['eclipse']
print("Processing eclipse " + str(eclipse) + ".")
# Returns detector constants.
print("Band is " + band + ".")
(xclk, yclk, xcen, ycen, xscl, yscl, xslp,
yslp) = clk_cen_scl_slp(band, eclipse)
# This determines the values for the post-CSP detector stim scaling
# and detector constant corrections.
Mx, Bx, My, By, stimsep = 1, 0, 1, 0, 0
if eclipse > 37460:
(Mx, Bx, My, By, stimsep,
yactbl) = compute_stimstats(raw6file, band, eclipse)
wig2, wig2data, wlk2, wlk2data, clk2, clk2data = post_csp_caldata()
print("Loading wiggle files...")
wiggle_x, _ = cal.wiggle(band, 'x')
wiggle_y, _ = cal.wiggle(band, 'y')
print("Loading walk files...")
walk_x, _ = cal.walk(band, 'x')
walk_y, _ = cal.walk(band, 'y')
print("Loading linearity files...")
linearity_x, _ = cal.linearity(band, 'x')
linearity_y, _ = cal.linearity(band, 'y')
# This is for the post-CSP stim distortion corrections.
print("Loading distortion files...")
if eclipse > 37460:
print(" Using stim separation of :" + str(stimsep))
distortion_x, disthead = cal.distortion(band, 'x', eclipse, stimsep)
distortion_y, _ = cal.distortion(band, 'y', eclipse, stimsep)
(cube_x0, cube_dx, cube_y0, cube_dy, cube_d0, cube_dd, cube_nd, cube_nc,
cube_nr) = (disthead['DC_X0'], disthead['DC_DX'], disthead['DC_Y0'],
disthead['DC_DY'], disthead['DC_D0'], disthead['DC_DD'],
disthead['NAXIS3'], disthead['NAXIS1'], disthead['NAXIS2'])
if band == 'FUV':
if not scstfile:
if not eclipse:
raise ValueError('Must specifiy eclipse if no scstfile.')
else:
scstfile = download_data(eclipse,
band,
'scst',
datadir=os.path.dirname(outbase))
if scstfile == None:
print('Unable to retrieve SCST file for this eclipse.')
return
xoffset, yoffset = find_fuv_offset(scstfile)
else:
xoffset, yoffset = 0., 0.
if os.path.isfile(str(ssdfile)):
print("SSD file provided: " + str(ssdfile))
stim_coef0, stim_coef1 = get_stim_coefs(ssdfile)
elif ssdfile:
print("SSD file requested: " + str(ssdfile))
stim_coef0, stim_coef1 = create_ssd(raw6file, band, eclipse, ssdfile)
else:
print("No SSD file provided or requested.")
stim_coef0, stim_coef1 = create_ssd(raw6file, band, eclipse)
print(" stim_coef0, stim_coef1 = " + str(stim_coef0) + ", " +
str(stim_coef1))
print("Loading mask file...")
mask, maskinfo = cal.mask(band)
npixx = mask.shape[0]
npixy = mask.shape[1]
pixsz = maskinfo['CDELT2']
maskfill = detsize / (npixx * pixsz)
print("Loading aspect data...")
if aspfile:
(aspra, aspdec, asptwist, asptime, aspheader,
aspflags) = load_aspect(aspfile)
else:
(aspra, aspdec, asptwist, asptime, aspheader,
aspflags) = web_query_aspect(eclipse, retries=retries)
minasp, maxasp = min(asptime), max(asptime)
trange = [minasp, maxasp]
print(" trange= ( {t0} , {t1} )".format(t0=trange[0], t1=trange[1]))
ra0, dec0, roll0 = aspheader['RA'], aspheader['DEC'], aspheader['ROLL']
print(" [avgRA, avgDEC, avgROLL] = [{RA}, {DEC}, {ROLL}]".format(
RA=aspra.mean(), DEC=aspdec.mean(), ROLL=asptwist.mean()))
# This projects the aspect solutions onto the MPS field centers.
print("Computing aspect vectors...")
(xi_vec, eta_vec) = gnomfwd_simple(aspra, aspdec, ra0, dec0, -asptwist,
1.0 / 36000.0, 0.)
print("Loading raw6 file...")
raw6hdulist = pyfits.open(raw6file, memmap=1)
raw6htab = raw6hdulist[1].header
nphots = raw6htab['NAXIS2']
print(" " + str(nphots) + " events")
cnt = 0
outfile = outbase + '.csv'
print("Preparing output file " + outfile)
spreadsheet = csv.writer(open(outfile, 'w'),
delimiter=',',
quotechar='|',
quoting=csv.QUOTE_MINIMAL)
# If specified, dump lines with NULLS into a separate csv file.
if nullfile:
nullfile = outbase + '_NULL.csv'
print("Preparing output file " + nullfile)
NULLspreadsheet = csv.writer(open(nullfile, 'w'),
delimiter=',',
quotechar='|',
quoting=csv.QUOTE_MINIMAL)
print("")
for i in range(int(nphots / chunksz) + 1):
a = time.time()
csvrows = []
chunkbeg, chunkend = i * chunksz, (i + 1) * chunksz
if chunkend > nphots:
chunkend = nphots
chunkid = " " + str(i + 1) + " of " + str(int(nphots / chunksz) +
1) + ": "
print_inline(chunkid + "Unpacking raw6 data...")
t = np.array(raw6hdulist[1].data.field('t')[chunkbeg:chunkend])
phb1 = np.array(raw6hdulist[1].data.field('phb1')[chunkbeg:chunkend],
dtype='int64')
phb2 = np.array(raw6hdulist[1].data.field('phb2')[chunkbeg:chunkend],
dtype='int64')
phb3 = np.array(raw6hdulist[1].data.field('phb3')[chunkbeg:chunkend],
dtype='int64')
phb4 = np.array(raw6hdulist[1].data.field('phb4')[chunkbeg:chunkend],
dtype='int64')
phb5 = np.array(raw6hdulist[1].data.field('phb5')[chunkbeg:chunkend],
dtype='int64')
# Bitwise "decoding" of the raw6 telemetry.
q = ((phb4 & 3) << 3) + ((phb5 & 224) >> 5)
xb = phb1 >> 5
xamc = (np.array(((phb1 & 31) << 7), dtype='int16') + np.array(
((phb2 & 254) >> 1), dtype='int16') - np.array(
((phb1 & 16) << 8), dtype='int16'))
yb = ((phb2 & 1) << 2) + ((phb3 & 192) >> 6)
yamc = (np.array(((phb3 & 63) << 6), dtype='int16') + np.array(
((phb4 & 252) >> 2), dtype='int16') - np.array(
((phb3 & 32) << 7), dtype='int16'))
xa = ((phb5 & 16) >> 4) + ((phb5 & 3) << 3) + ((phb5 & 12) >> 1)
xraw0 = xb * xclk + xamc
yraw0 = yb * yclk + yamc
ya = np.array(
((((yraw0 / (2 * yclk) - xraw0 / (2 * xclk)) + 10) * 32) + xa),
dtype='int64') % 32
xraw = xraw0 + np.array((((xa + 7) % 32) - 16), dtype='int64') * xslp
yraw = yraw0 + np.array((((ya + 7) % 32) - 16), dtype='int64') * yslp
# Centering and scaling.
x = (xraw - xcen) * xscl
y = (yraw - ycen) * yscl
# Post-CSP 'yac' corrections.
if eclipse > 37460:
x = Mx * x + Bx
y = My * y + By
yac = rtaph_yac(yactbl, ya, yb, yamc, eclipse)
y = y - yac
yac = rtaph_yac2(q, xb, yb, ya, y, aspum, wig2, wig2data, wlk2,
wlk2data, clk2, clk2data)
y = y + yac
# [Future] This and other ugly lines like it below are for the purpose
# of memory management. There is likely a more Pythonic way.
(phb1, phb2, phb3, phb4, phb5, xb, xamc, yb, yamc, xraw0, yraw0, xraw,
yraw) = ([], [], [], [], [], [], [], [], [], [], [], [], [])
flags = np.zeros(len(t))
print_inline(chunkid + "Applying wiggle correction...")
x_as = x * aspum
y_as = y * aspum
fptrx = x_as / 10. + 240.
fptry = y_as / 10. + 240.
x_as, y_as = [], []
# This and other lines like it below are to verify that the
# event is still on the detector.
cut = ((fptrx > 0.) & (fptrx < 479.) & (fptry > 0.) & (fptry < 479.) &
(flags == 0))
flags[np.where(cut == False)[0]] = 8
ix = np.where(cut == True)[0]
blt = fptrx - np.array(fptrx, dtype='int64')
blu = fptry - np.array(fptry, dtype='int64')
wigx, wigy = np.zeros(len(t)), np.zeros(len(t))
wigx[ix] = (
(1 - blt[ix]) *
(wiggle_x[xa[ix], np.array(fptrx[ix], dtype='int64')]) +
(blt[ix]) *
(wiggle_x[xa[ix], np.array(fptrx[ix], dtype='int64') + 1]))
wigy[ix] = (
(1 - blu[ix]) *
(wiggle_y[ya[ix], np.array(fptry[ix], dtype='int64')]) +
(blu[ix]) *
(wiggle_y[ya[ix], np.array(fptry[ix], dtype='int64') + 1]))
xdig = x + wigx / (10. * aspum)
ydig = y + wigy / (10. * aspum)
wigx, wigy = [], []
print_inline(chunkid + "Applying walk correction...")
xdig_as = xdig * aspum
ydig_as = ydig * aspum
fptrx = xdig_as / 10. + 240.
fptry = ydig_as / 10. + 240.
xdig_as, ydig_as = [], []
cut = ((fptrx > 0.) & (fptrx < 479.) & (fptry > 0.) & (fptry < 479.) &
(flags == 0))
flags[np.where(cut == False)[0]] = 9
ix = np.where(cut == True)[0]
cut[ix] = ((walk_x[q[ix],
np.array(fptry[ix], dtype='int64'),
np.array(fptrx[ix], dtype='int64')] != -999) |
(walk_x[q[ix],
np.array(fptry[ix], dtype='int64'),
np.array(fptrx[ix], dtype='int64') + 1] != -999) |
(walk_x[q[ix],
np.array(fptry[ix], dtype='int64') + 1,
np.array(fptrx[ix], dtype='int64')] != -999) |
(walk_x[q[ix],
np.array(fptry[ix], dtype='int64') + 1,
np.array(fptrx[ix], dtype='int64') + 1] != -999) |
(walk_y[q[ix],
np.array(fptry[ix], dtype='int64'),
np.array(fptrx[ix], dtype='int64')] != -999) |
(walk_y[q[ix],
np.array(fptry[ix], dtype='int64'),
np.array(fptrx[ix], dtype='int64') + 1] != -999) |
(walk_y[q[ix],
np.array(fptry[ix], dtype='int64') + 1,
np.array(fptrx[ix], dtype='int64')] != -999) |
(walk_y[q[ix],
np.array(fptry[ix], dtype='int64') + 1,
np.array(fptrx[ix], dtype='int64') + 1] != -999))
flags[np.where(cut == False)[0]] = 9
ix = np.where(cut == True)[0]
blt = fptrx - np.array(fptrx, dtype='int64')
blu = fptry - np.array(fptry, dtype='int64')
walkx, walky = np.zeros(len(t)), np.zeros(len(t))
walkx[ix] = ((1 - blt[ix]) * (1 - blu[ix]) *
(walk_x[q[ix],
np.array(fptry[ix], dtype='int64'),
np.array(fptrx[ix], dtype='int64')]) + (blt[ix]) *
(1 - blu[ix]) *
(walk_x[q[ix],
np.array(fptry[ix], dtype='int64'),
np.array(fptrx[ix], dtype='int64') + 1]) +
(1 - blt[ix]) * (blu[ix]) *
(walk_x[q[ix],
np.array(fptry[ix], dtype='int64') + 1,
np.array(fptrx[ix], dtype='int64')]) + (blt[ix]) *
(blu[ix]) *
(walk_x[q[ix],
np.array(fptry[ix], dtype='int64') + 1,
np.array(fptrx[ix], dtype='int64') + 1]))
walky[ix] = ((1 - blt[ix]) * (1 - blu[ix]) *
(walk_y[q[ix],
np.array(fptry[ix], dtype='int64'),
np.array(fptrx[ix], dtype='int64')]) + (blt[ix]) *
(1 - blu[ix]) *
(walk_y[q[ix],
np.array(fptry[ix], dtype='int64'),
np.array(fptrx[ix], dtype='int64') + 1]) +
(1 - blt[ix]) * (blu[ix]) *
(walk_y[q[ix],
np.array(fptry[ix], dtype='int64') + 1,
np.array(fptrx[ix], dtype='int64')]) + (blt[ix]) *
(blu[ix]) *
(walk_y[q[ix],
np.array(fptry[ix], dtype='int64') + 1,
np.array(fptrx[ix], dtype='int64') + 1]))
print_inline(chunkid + "Applying spatial non-linearity correction...")
xp = xdig - walkx
yp = ydig - walky
xp_as = xp * aspum
yp_as = yp * aspum
fptrx = xp_as / 10. + 240.
fptry = yp_as / 10. + 240.
xp, yp = [], []
walkx, walky = [], []
cut = ((fptrx > 0.) & (fptrx < 479.) & (fptry > 0.) & (fptry < 479.) &
(flags == 0))
flags[np.where(cut == False)[0]] = 10
ix = np.where(cut == True)[0]
blt = fptrx - np.array(fptrx, dtype='int64')
blu = fptry - np.array(fptry, dtype='int64')
dx, dy = np.zeros(len(t)), np.zeros(len(t))
dx[ix] = (
(1 - blt[ix]) *
(1 - blu[ix]) * linearity_x[np.array(fptry[ix], dtype='int64'),
np.array(fptrx[ix], dtype='int64')] +
(blt[ix]) *
(1 - blu[ix]) * linearity_x[np.array(fptry[ix], dtype='int64'),
np.array(fptrx[ix], dtype='int64') + 1]
+ (1 - blt[ix]) *
(blu[ix]) * linearity_x[np.array(fptry[ix], dtype='int64') + 1,
np.array(fptrx[ix], dtype='int64')] +
(blt[ix]) *
(blu[ix]) * linearity_x[np.array(fptry[ix], dtype='int64') + 1,
np.array(fptrx[ix], dtype='int64') + 1])
dy[ix] = (
(1 - blt[ix]) *
(1 - blu[ix]) * linearity_y[np.array(fptry[ix], dtype='int64'),
np.array(fptrx[ix], dtype='int64')] +
(blt[ix]) *
(1 - blu[ix]) * linearity_y[np.array(fptry[ix], dtype='int64'),
np.array(fptrx[ix], dtype='int64') + 1]
+ (1 - blt[ix]) *
(blu[ix]) * linearity_y[np.array(fptry[ix], dtype='int64') + 1,
np.array(fptrx[ix], dtype='int64')] +
(blt[ix]) *
(blu[ix]) * linearity_y[np.array(fptry[ix], dtype='int64') + 1,
np.array(fptrx[ix], dtype='int64') + 1])
print_inline(chunkid + "Applying stim distortion correction...")
ss = stim_coef0 + (t * stim_coef1) # stim separation
col, row, depth = np.zeros(len(t)), np.zeros(len(t)), np.zeros(len(t))
col[ix] = (xp_as[ix] - cube_x0) / cube_dx
row[ix] = (yp_as[ix] - cube_y0) / cube_dy
depth[ix] = (ss[ix] - cube_d0) / cube_dd
# [Future]: This throws an error sometimes like the following, may need
# fixing...
"""PhotonPipe.py:262: RuntimeWarning: invalid value encountered in
less depth[((depth < 0)).nonzero()[0]] = 0.
PhotonPipe.py:263: RuntimeWarning: invalid value encountered in
greater_equal depth[((depth >= cube_nd)).nonzero()[0]] = -1.
ERROR: IndexError: index -9223372036854775808 is out of bounds for
axis 0 with size 17 [PhotonPipe]
depth[((depth < 0)).nonzero()[0]] = 0.
depth[((depth >= cube_nd)).nonzero()[0]] = -1.
"""
cut = ((col > -1) & (col < cube_nc) & (row > -1) & (row < cube_nr) &
(flags == 0) & (np.array(depth, dtype='int64') < 18))
flags[np.where(cut == False)[0]] = 11
ix = np.where(cut == True)[0]
xshift, yshift = np.zeros(len(t)), np.zeros(len(t))
xshift[ix] = distortion_x[np.array(depth[ix], dtype='int64'),
np.array(row[ix], dtype='int64'),
np.array(col[ix], dtype='int64')]
yshift[ix] = distortion_y[np.array(depth[ix], dtype='int64'),
np.array(row[ix], dtype='int64'),
np.array(col[ix], dtype='int64')]
xshift = (xshift * arcsecperpixel) + xoffset
yshift = (yshift * arcsecperpixel) + yoffset
print_inline(chunkid + "Applying hotspot mask...")
# The detectors aren't oriented the same way.
flip = 1.
if band == 'FUV':
flip = -1.
xi = (xi_xsc * (flip * (yp_as + dy + yshift) * 10.) + xi_ysc *
(flip * (xp_as + dx + xshift) * 10.))
eta = (eta_xsc * (flip * (yp_as + dy + yshift) * 10.) + eta_ysc *
(flip * (xp_as + dx + xshift) * 10.))
xp_as, yp_as, depth, ss = [], [], [], []
dx, dy, xshift, yshift = [], [], [], []
col = (((xi / 36000.) / (detsize / 2.) * maskfill + 1.) / 2. * npixx)
row = (((eta / 36000.) / (detsize / 2.) * maskfill + 1.) / 2. * npixy)
# This mask out data that is not on the detector at all
cut = ((col > 0.) & (col < 799.) & (row > 0.) & (row < 799.) &
(flags == 0))
flags[np.where(cut == False)[0]] = 6
ix = np.where(cut == True)[0]
# This masks out hotspot regions and is experimentally deprecated.
#cut[ix] = (
# (mask[np.array(col[ix], dtype='int64'),
# np.array(row[ix], dtype='int64')] == 1.))
#flags[np.where(cut == False)[0]] = 6
#ix = np.where(cut == True)[0]
col, row = [], []
# This gives the index of the aspect time that comes _before_
# each photon time. Without the '-1' it will give the index
# of the aspect time _after_ the photon time.
print_inline(chunkid + "Mapping photon times to aspect times...")
aspix = np.digitize(t, asptime) - 1
print_inline(chunkid + "Applying dither correction...")
# Use only photons that are bracketed by valid aspect solutions
# and have been not themselves been flagged as invalid.
cut = ((aspix > 0) & (aspix < (len(asptime) - 1)) & ((flags == 0) |
(flags == 6)))
flags[np.where(cut == False)[0]] = 7
ix = np.where(cut == True)[0]
print_inline(chunkid + "Interpolating aspect solutions...")
dxi, deta = np.zeros(len(t)), np.zeros(len(t))
dxi[ix] = ((xi_vec[aspix[ix] + 1] - xi_vec[aspix[ix]]) *
(t[ix] - asptime[aspix[ix]]) /
(asptime[aspix[ix] + 1] - asptime[aspix[ix]]))
deta[ix] = ((eta_vec[aspix[ix] + 1] - eta_vec[aspix[ix]]) *
(t[ix] - asptime[aspix[ix]]) /
(asptime[aspix[ix] + 1] - asptime[aspix[ix]]))
print_inline(chunkid + "Mapping to sky...")
ra, dec = np.zeros(len(t)), np.zeros(len(t))
ra[ix], dec[ix] = gnomrev_simple(xi[ix] + dxi[ix], eta[ix] + deta[ix],
aspra[aspix[ix]], aspdec[aspix[ix]],
-asptwist[aspix[ix]], 1 / 36000., 0.)
cut = (((asptime[aspix[ix] + 1] - asptime[aspix[ix]]) == 1) &
(aspflags[aspix[ix]] % 2 == 0) &
(aspflags[aspix[ix] + 1] % 2 == 0) &
(aspflags[aspix[ix] - 1] % 2 == 0) & (flags[ix] == 0) &
(flags[ix] != 7))
flags[np.where(cut == False)[0]] = 12
# NOTE: If you wish to add a hook that filters the gPhoton output
# (like perhaps by sky position or time range) then add it here.
# I reccomend that you use the "ix = np.where" technique used above.
# [Future]: Preprogram a (commented out) filter on RA/Dec.
print_inline(chunkid + "Writing to spreadsheet...")
# The issue is that we need to recombine the data into rows to
# feed to csv.writerow, hence the loop.
# It might be possible to do without a loop. One way would be
# with numpy.column_stack except that this requires stupid
# amounts of memory and therefore takes longer to run than the
# loop iffen it manages to complete at all without a memory
# error or segmentation fault.
for i in range(len(t)):
cnt += 1
# To avoid repeat indexing of flags...
thisflag = flags[i]
# This substitutes empty strings for RA and Dec
# values so that when they're dumped into the database
# they are correctly recorded as NULL
if (thisflag == 2 or thisflag == 5 or thisflag == 7
or thisflag == 8 or thisflag == 9 or thisflag == 10
or thisflag == 11 or thisflag == 12):
# Should be:
#if ((thisflag == 3) or (thisflag == 6) or (thisflag == 8) or
# (thisflag == 9) or (thisflag == 10) or
# (thisflag == 11) or (thisflag = 12):)
if nullfile:
NULLspreadsheet.writerow([
int(t[i] * dbscale), x[i], y[i], xa[i], ya[i], q[i],
xi[i], eta[i], "", "", flags[i]
])
else:
spreadsheet.writerow([
int(t[i] * dbscale), x[i], y[i], xa[i], ya[i], q[i],
xi[i], eta[i], "", "", flags[i]
])
else:
spreadsheet.writerow([
int(t[i] * dbscale), x[i], y[i], xa[i], ya[i], q[i], xi[i],
eta[i], ra[i], dec[i], flags[i]
])
raw6hdulist.close()
stopt = time.time()
print_inline("")
print("")
print("Runtime statistics:")
print(" runtime = {seconds} sec. = ({minutes} min.)".format(
seconds=stopt - startt, minutes=(stopt - startt) / 60.))
print(" processed = " + str(cnt) + " of " + str(nphots) + " events.")
if cnt < nphots:
print(" WARNING: MISSING EVENTS!")
print(" rate = " + str(nphots / (stopt - startt)) + " photons/sec.")
print("")
return
def photonpipe(outbase, band, raw6file=None, scstfile=None, aspfile=None,
ssdfile=None, nullfile=None, verbose=0, retries=20,
eclipse=None):
"""
Apply static and sky calibrations to -raw6 GALEX data, producing fully
aspect-corrected and time-tagged photon list files.
:param raw6file: Name of the raw6 file to use.
:type raw6file: str
:param scstfile: Spacecraft state file to use.
:type scstfile: str
:param band: Name of the band to use, either 'FUV' or 'NUV'.
:type band: str
:param outbase: Base of the output file names.
:type outbase: str
:param aspfile: Name of aspect file to use.
:type aspfile: int
:param ssdfile: Name of Stim Separation Data file to use.
:type ssdfile: int
:param nullfile: Name of output file to record NULL lines.
:type nullfile: int
:param verbose: Note used. Included for consistency with other tools.
:type verbose: int
:param retries: Number of query retries to attempt before giving up.
:type retries: int
"""
startt = time.time()
# Scale factor for the time column in the output csv so that it
# can be recorded as an int in the database.
dbscale = 1000
# This number determines the size of the chunks that gPhoton reads
# in from the raw6 for processing. Even if your machine has a lot
# of memory, making this number bigger is unlikely to improve the
# processing time much because so much is eaten up by the .csv write.
chunksz = 1000000
# These are just constants for the mission.
detsize = 1.25 # Detector size in degrees
pltscl = 68.754932 # Plate scale
aspum = pltscl/1000.0
arcsecperpixel = 1.5
xi_xsc, xi_ysc, eta_xsc, eta_ysc = 0., 1., 1., 0.
# Determine the eclipse number from the raw6 header.
if not raw6file:
if not eclipse:
raise ValueError('Must specifiy eclipse if no raw6file.')
else:
raw6file = download_data(
eclipse,band,'raw6',datadir=os.path.dirname(outbase))
if raw6file==None:
print('Unable to retrieve raw6 file for this eclipse.')
return
hdulist = pyfits.open(raw6file)
hdr = hdulist[0].header
hdulist.close()
if eclipse and (eclipse!=hdr['eclipse']): # just a consistency check
print("Warning: eclipse mismatch {e0} vs. {e1} (header)".format(
e0=eclipse,e1=hdr['eclipse']))
eclipse = hdr['eclipse']
print("Processing eclipse "+str(eclipse)+".")
# Returns detector constants.
print("Band is "+band+".")
(xclk, yclk, xcen, ycen, xscl, yscl, xslp,
yslp) = clk_cen_scl_slp(band, eclipse)
# This determines the values for the post-CSP detector stim scaling
# and detector constant corrections.
Mx, Bx, My, By, stimsep = 1, 0, 1, 0, 0
if eclipse > 37460:
(Mx, Bx, My, By, stimsep, yactbl) = compute_stimstats(raw6file, band,
eclipse)
wig2, wig2data, wlk2, wlk2data, clk2, clk2data = post_csp_caldata()
print("Loading wiggle files...")
wiggle_x, _ = cal.wiggle(band, 'x')
wiggle_y, _ = cal.wiggle(band, 'y')
print("Loading walk files...")
walk_x, _ = cal.walk(band, 'x')
walk_y, _ = cal.walk(band, 'y')
print("Loading linearity files...")
linearity_x, _ = cal.linearity(band, 'x')
linearity_y, _ = cal.linearity(band, 'y')
# This is for the post-CSP stim distortion corrections.
print("Loading distortion files...")
if eclipse > 37460:
print(" Using stim separation of :"+str(stimsep))
distortion_x, disthead = cal.distortion(band, 'x', eclipse, stimsep)
distortion_y, _ = cal.distortion(band, 'y', eclipse, stimsep)
(cube_x0, cube_dx, cube_y0, cube_dy, cube_d0, cube_dd, cube_nd, cube_nc,
cube_nr) = (disthead['DC_X0'], disthead['DC_DX'], disthead['DC_Y0'],
disthead['DC_DY'], disthead['DC_D0'], disthead['DC_DD'],
disthead['NAXIS3'], disthead['NAXIS1'], disthead['NAXIS2'])
if band == 'FUV':
if not scstfile:
if not eclipse:
raise ValueError('Must specifiy eclipse if no scstfile.')
else:
scstfile = download_data(
eclipse,band,'scst',datadir=os.path.dirname(outbase))
if scstfile==None:
print('Unable to retrieve SCST file for this eclipse.')
return
xoffset, yoffset = find_fuv_offset(scstfile)
else:
xoffset, yoffset = 0., 0.
if os.path.isfile(str(ssdfile)):
print("SSD file provided: "+str(ssdfile))
stim_coef0, stim_coef1 = get_stim_coefs(ssdfile)
elif ssdfile:
print("SSD file requested: "+str(ssdfile))
stim_coef0, stim_coef1 = create_ssd(raw6file, band, eclipse, ssdfile)
else:
print("No SSD file provided or requested.")
stim_coef0, stim_coef1 = create_ssd(raw6file, band, eclipse)
print(" stim_coef0, stim_coef1 = "+str(stim_coef0)+", "+str(stim_coef1))
print("Loading mask file...")
mask, maskinfo = cal.mask(band)
npixx = mask.shape[0]
npixy = mask.shape[1]
pixsz = maskinfo['CDELT2']
maskfill = detsize/(npixx*pixsz)
print("Loading aspect data...")
if aspfile:
(aspra, aspdec, asptwist, asptime, aspheader,
aspflags) = load_aspect(aspfile)
else:
(aspra, aspdec, asptwist, asptime, aspheader,
aspflags) = web_query_aspect(eclipse, retries=retries)
minasp, maxasp = min(asptime), max(asptime)
trange = [minasp, maxasp]
print(" trange= ( {t0} , {t1} )".format(t0=trange[0], t1=trange[1]))
ra0, dec0, roll0 = aspheader['RA'], aspheader['DEC'], aspheader['ROLL']
print(" [avgRA, avgDEC, avgROLL] = [{RA}, {DEC}, {ROLL}]".format(
RA=aspra.mean(), DEC=aspdec.mean(), ROLL=asptwist.mean()))
# This projects the aspect solutions onto the MPS field centers.
print("Computing aspect vectors...")
(xi_vec, eta_vec) = gnomfwd_simple(aspra, aspdec, ra0, dec0, -asptwist,
1.0/36000.0, 0.)
print("Loading raw6 file...")
raw6hdulist = pyfits.open(raw6file, memmap=1)
raw6htab = raw6hdulist[1].header
nphots = raw6htab['NAXIS2']
print(" "+str(nphots)+" events")
cnt = 0
outfile = outbase+'.csv'
print("Preparing output file "+outfile)
spreadsheet = csv.writer(open(outfile, 'w'), delimiter=',',
quotechar='|', quoting=csv.QUOTE_MINIMAL)
# If specified, dump lines with NULLS into a separate csv file.
if nullfile:
nullfile = outbase+'_NULL.csv'
print("Preparing output file "+nullfile)
NULLspreadsheet = csv.writer(open(nullfile, 'w'), delimiter=',',
quotechar='|', quoting=csv.QUOTE_MINIMAL)
print("")
for i in range(int(nphots/chunksz)+1):
a = time.time() # Start the timer.
csvrows = []
chunkbeg, chunkend = i*chunksz, min(nphots,(i+1)*chunksz)
chunkid = " "+str(i+1)+" of "+str(int(nphots/chunksz)+1)+": "
print_inline(chunkid+"Unpacking raw6 data...")
t = np.array(raw6hdulist[1].data.field('t')[chunkbeg:chunkend])
phb1 = np.array(
raw6hdulist[1].data.field('phb1')[chunkbeg:chunkend],dtype='int64')
phb2 = np.array(
raw6hdulist[1].data.field('phb2')[chunkbeg:chunkend],dtype='int64')
phb3 = np.array(
raw6hdulist[1].data.field('phb3')[chunkbeg:chunkend],dtype='int64')
phb4 = np.array(
raw6hdulist[1].data.field('phb4')[chunkbeg:chunkend],dtype='int64')
phb5 = np.array(
raw6hdulist[1].data.field('phb5')[chunkbeg:chunkend],dtype='int64')
# Bitwise "decoding" of the raw6 telemetry.
q = ((phb4 & 3) << 3) + ((phb5 & 224) >> 5)
xb = phb1 >> 5
xamc = (
np.array(((phb1 & 31) << 7), dtype='int16') +
np.array(((phb2 & 254) >> 1), dtype='int16') -
np.array(((phb1 & 16) << 8), dtype='int16'))
yb = ((phb2 & 1) << 2) + ((phb3 & 192) >> 6)
yamc = (
np.array(((phb3 & 63) << 6), dtype='int16') +
np.array(((phb4 & 252) >> 2), dtype='int16') -
np.array(((phb3 & 32) << 7), dtype='int16'))
xa = ((phb5 & 16) >> 4) + ((phb5 & 3) << 3) + ((phb5 & 12) >> 1)
xraw0 = xb*xclk + xamc
yraw0 = yb*yclk + yamc
ya = np.array(((((yraw0/(2*yclk) - xraw0/(2*xclk)) + 10)*32) + xa),
dtype='int64') % 32
xraw = xraw0 + np.array((((xa+7) % 32) - 16), dtype='int64') * xslp
yraw = yraw0 + np.array((((ya+7) % 32) - 16), dtype='int64') * yslp
# Centering and scaling.
x = (xraw - xcen)*xscl
y = (yraw - ycen)*yscl
# Post-CSP 'yac' corrections.
if eclipse > 37460:
x = Mx*x+Bx
y = My*y+By
yac = rtaph_yac(yactbl, ya, yb, yamc, eclipse)
y = y-yac
yac = rtaph_yac2(q, xb, yb, ya, y, aspum, wig2, wig2data, wlk2,
wlk2data, clk2, clk2data)
y = y + yac
# Empty these variables for memory management purposes.
phb1,phb2,phb3,phb4,phb5,xb,xamc,yb,yamc,xraw0,yraw0,xraw,yraw=[[]]*13
flags = np.zeros(len(t)) # initialized
print_inline(chunkid+"Applying wiggle correction...")
x_as = x*aspum
y_as = y*aspum
fptrx = x_as/10. + 240.
fptry = y_as/10. + 240.
x_as, y_as = [[]]*2 # Memory management.
# This and other lines like it below are to verify that the
# event is still on the detector.
cut = ((fptrx > 0.) & (fptrx < 479.) & (fptry > 0.) & (fptry < 479.))
flags[np.where((cut == False) & (flags == 0))[0]] = 8
ix = np.where(cut == True)[0]
fptrx_ix = np.array(fptrx, dtype='int64')
fptry_ix = np.array(fptry, dtype='int64')
blt = fptrx-fptrx_ix
blu = fptry-fptry_ix
wigx, wigy = np.zeros(len(t)), np.zeros(len(t))
wigx[ix] = (
(1-blt[ix])*(wiggle_x[xa[ix], fptrx_ix[ix]]) +
(blt[ix])*(wiggle_x[xa[ix], fptrx_ix[ix]+1]))
wigy[ix] = (
(1-blu[ix])*(wiggle_y[ya[ix], fptry_ix[ix]]) +
(blu[ix])*(wiggle_y[ya[ix], fptry_ix[ix]+1]))
xdig = x + wigx/(10.*aspum)
ydig = y + wigy/(10.*aspum)
wigx, wigy = [[]]*2 # Memory management.
print_inline(chunkid+"Applying walk correction...")
xdig_as = xdig*aspum
ydig_as = ydig*aspum
fptrx = xdig_as/10. + 240.
fptry = ydig_as/10. + 240.
xdig_as, ydig_as = [[]]*2 # Memory management.
cut = ((fptrx > 0.) & (fptrx < 479.) & (fptry > 0.) & (fptry < 479.))
flags[np.where((cut == False) & (flags == 0))[0]] = 9
ix = np.where(cut == True)[0]
fptrx_ix = np.array(fptrx, dtype='int64')
fptry_ix = np.array(fptry, dtype='int64')
cut[ix] = ((walk_x[q[ix],fptry_ix[ix],fptrx_ix[ix]] != -999) |
(walk_x[q[ix],fptry_ix[ix],fptrx_ix[ix]+1] != -999) |
(walk_x[q[ix],fptry_ix[ix]+1,fptrx_ix[ix]] != -999) |
(walk_x[q[ix],fptry_ix[ix]+1,fptrx_ix[ix]+1] != -999) |
(walk_y[q[ix],fptry_ix[ix],fptrx_ix[ix]] != -999) |
(walk_y[q[ix],fptry_ix[ix],fptrx_ix[ix]+1] != -999) |
(walk_y[q[ix],fptry_ix[ix]+1,fptrx_ix[ix]] != -999) |
(walk_y[q[ix],fptry_ix[ix]+1,fptrx_ix[ix]+1] != -999))
flags[np.where((cut == False) & (flags == 0))] = 9
ix = np.where(cut == True)[0]
fptrx_ix = np.array(fptrx, dtype='int64')
fptry_ix = np.array(fptry, dtype='int64')
blt = fptrx-fptrx_ix
blu = fptry-fptry_ix
walkx, walky = np.zeros(len(t)), np.zeros(len(t))
walkx[ix] = (
(1-blt[ix])*(1-blu[ix])*(walk_x[q[ix],fptry_ix[ix],fptrx_ix[ix]])+
(blt[ix])*(1-blu[ix])*(walk_x[q[ix],fptry_ix[ix],fptrx_ix[ix]+1])+
(1-blt[ix])*(blu[ix])*(walk_x[q[ix],fptry_ix[ix]+1,fptrx_ix[ix]]) +
(blt[ix])*(blu[ix])*(walk_x[q[ix],fptry_ix[ix]+1,fptrx_ix[ix]+1]))
walky[ix] = (
(1-blt[ix])*(1-blu[ix])*(walk_y[q[ix],fptry_ix[ix],fptrx_ix[ix]]) +
(blt[ix])*(1-blu[ix])*(walk_y[q[ix],fptry_ix[ix],fptrx_ix[ix]+1]) +
(1-blt[ix])*(blu[ix])*(walk_y[q[ix],fptry_ix[ix]+1,fptrx_ix[ix]]) +
(blt[ix])*(blu[ix])*(walk_y[q[ix],fptry_ix[ix]+1,fptrx_ix[ix]+1]))
print_inline(chunkid+"Applying spatial non-linearity correction...")
xp = xdig - walkx
yp = ydig - walky
xp_as = xp*aspum
yp_as = yp*aspum
fptrx = xp_as/10. + 240.
fptry = yp_as/10. + 240.
xp, yp, walkx, walky = [[]]*4 # Memory management.
cut = ((fptrx > 0.) & (fptrx < 479.) & (fptry > 0.) & (fptry < 479.))
flags[np.where((cut == False) & (flags == 0))] = 10
ix = np.where(cut == True)[0]
fptrx_ix = np.array(fptrx, dtype='int64')
fptry_ix = np.array(fptry, dtype='int64')
blt = fptrx-fptrx_ix
blu = fptry-fptry_ix
dx, dy = np.zeros(len(t)), np.zeros(len(t))
dx[ix] = (
(1-blt[ix])*(1-blu[ix])*linearity_x[fptry_ix[ix],fptrx_ix[ix]]+
(blt[ix])*(1-blu[ix])*linearity_x[fptry_ix[ix],fptrx_ix[ix]+1]+
(1-blt[ix])*(blu[ix])*linearity_x[fptry_ix[ix]+1,fptrx_ix[ix]]+
(blt[ix])*(blu[ix])*linearity_x[fptry_ix[ix]+1,fptrx_ix[ix]+1])
dy[ix] = (
(1-blt[ix])*(1-blu[ix])*linearity_y[fptry_ix[ix],fptrx_ix[ix]]+
(blt[ix])*(1-blu[ix])*linearity_y[fptry_ix[ix],fptrx_ix[ix]+1]+
(1-blt[ix])*(blu[ix])*linearity_y[fptry_ix[ix]+1,fptrx_ix[ix]]+
(blt[ix])*(blu[ix])*linearity_y[fptry_ix[ix]+1,fptrx_ix[ix]+1])
print_inline(chunkid+"Applying stim distortion correction...")
ss = stim_coef0 + (t * stim_coef1) # stim separation
col, row, depth = np.zeros(len(t)), np.zeros(len(t)), np.zeros(len(t))
col[ix] = (xp_as[ix] - cube_x0) / cube_dx
row[ix] = (yp_as[ix] - cube_y0) / cube_dy
depth[ix] = (ss[ix] - cube_d0) / cube_dd
# [Future]: This throws an error sometimes like the following, may need
# fixing...
"""PhotonPipe.py:262: RuntimeWarning: invalid value encountered in
less depth[((depth < 0)).nonzero()[0]] = 0.
PhotonPipe.py:263: RuntimeWarning: invalid value encountered in
greater_equal depth[((depth >= cube_nd)).nonzero()[0]] = -1.
ERROR: IndexError: index -9223372036854775808 is out of bounds for
axis 0 with size 17 [PhotonPipe]
depth[((depth < 0)).nonzero()[0]] = 0.
depth[((depth >= cube_nd)).nonzero()[0]] = -1.
"""
cut = ((col > -1) & (col < cube_nc) & (row > -1) & (row < cube_nr) &
(flags == 0) & (np.array(depth,dtype='int64')<18))
flags[np.where(cut == False)[0]] = 11
ix = np.where(cut == True)[0]
xshift, yshift = np.zeros(len(t)), np.zeros(len(t))
xshift[ix] = distortion_x[np.array(depth[ix], dtype='int64'),
np.array(row[ix], dtype='int64'),np.array(col[ix], dtype='int64')]
yshift[ix] = distortion_y[np.array(depth[ix], dtype='int64'),
np.array(row[ix], dtype='int64'),np.array(col[ix], dtype='int64')]
xshift = (xshift*arcsecperpixel)+xoffset
yshift = (yshift*arcsecperpixel)+yoffset
print_inline(chunkid+"Applying hotspot mask...")
# The detectors aren't oriented the same way.
flip = -1. if band=='FUV' else 1.
xi = (
xi_xsc*(flip*(yp_as + dy + yshift)*10.) +
xi_ysc*(flip*(xp_as + dx + xshift)*10.))
eta = (
eta_xsc*(flip*(yp_as + dy + yshift)*10.) +
eta_ysc*(flip*(xp_as + dx + xshift)*10.))
xp_as,yp_as,depth,ss,dx,dy,xshift,yshift=[[]]*8 # Memory management.
col = (((xi/36000.)/(detsize/2.)*maskfill + 1.)/2. * npixx)
row = (((eta/36000.)/(detsize/2.)*maskfill + 1.)/2. * npixy)
# Off detector at hotspot mask. Should mostly be stims.
cut = ((col > 0.) & (col < 799.) & (row > 0.) & (row < 799.))
flags[np.where((cut == False))] = 5 # Clobbers some earlier flags.
ix = np.where(cut == True)[0]
# Hotspot masks. Must use ix to prevent out-of-bounds error.
cut[ix] = (mask[np.array(col, dtype='int64')[ix],
np.array(row, dtype='int64')[ix]] == 1.)
flags[np.where((cut == False) & (flags == 0))] = 6
ix = np.where(cut == True)[0]
col, row = [[]]*2 # Memory management.
# This gives the index of the aspect time that comes _before_
# each photon time. Without the '-1' it will give the index
# of the aspect time _after_ the photon time.
print_inline(chunkid+"Mapping photon times to aspect times...")
aspix = np.digitize(t, asptime)-1
print_inline(chunkid+"Applying dither correction...")
# Sky-project all data that are bracketed by aspect timestamps.
cut = ((aspix > 0) & (aspix < (len(asptime)-1)))
flags[np.where((cut == False) & ((flags==0) | (flags == 6)))] = 7
ix = np.where(cut == True)[0]
print_inline(chunkid+"Interpolating aspect solutions...")
dxi, deta = np.zeros(len(t)), np.zeros(len(t))
dxi[ix] = (
(xi_vec[aspix[ix]+1]-xi_vec[aspix[ix]])*
(t[ix]-asptime[aspix[ix]])/
(asptime[aspix[ix]+1]-asptime[aspix[ix]]))
deta[ix] = (
(eta_vec[aspix[ix]+1]-eta_vec[aspix[ix]])*
(t[ix]-asptime[aspix[ix]])/
(asptime[aspix[ix]+1]-asptime[aspix[ix]]))
print_inline(chunkid+"Mapping to sky...")
ra, dec = np.zeros(len(t)), np.zeros(len(t))
ra[ix], dec[ix] = gnomrev_simple(xi[ix]+dxi[ix], eta[ix]+deta[ix],
aspra[aspix[ix]],aspdec[aspix[ix]],
-asptwist[aspix[ix]], 1/36000.,0.)
# Flag data that do not have "good" aspect flags on both sides.
# "Good" aspect solutions are divisible by 2.
cut[ix] = (
((asptime[aspix[ix]+1]-asptime[aspix[ix]]) == 1) &
(aspflags[aspix[ix]]%2 == 0) & (aspflags[aspix[ix]+1]%2 == 0) &
(aspflags[aspix[ix]-1]%2 == 0) & (flags[ix] == 0) &
(flags[ix] != 7) & (flags[ix] !=6))
flags[np.where((cut == False) & (flags == 0))[0]] = 12
# NOTE: If you wish to add a hook that filters the gPhoton output
# (like perhaps by sky position or time range) then add it here.
# [Future]: Preprogram a (commented out) filter on RA/Dec.
print_inline(chunkid+"Writing to spreadsheet...")
# There is a loop here because:
# we need to recombine the data into rows to feed to csv.writerow...
# numpy.column_stack requires a ton of memory and therefore takes
# longer to run than the loop...
# so there might be a more Pythonic way, but I don't know it. (cm)
for i in range(len(t)):
cnt += 1
# To avoid repeat indexing of flags...
thisflag = flags[i]
# This substitutes empty strings for RA and Dec
# values so that when they're dumped into the database
# they are correctly recorded as NULL
if thisflag in [2,5,7,8,9,10,11,12]:
if nullfile:
NULLspreadsheet.writerow(
[int(t[i]*dbscale), x[i], y[i],xa[i], ya[i], q[i],
xi[i], eta[i], "", "", flags[i]])
else:
spreadsheet.writerow(
[int(t[i]*dbscale), x[i], y[i], xa[i], ya[i], q[i],
xi[i], eta[i], "", "", flags[i]])
else:
spreadsheet.writerow(
[int(t[i]*dbscale), x[i], y[i], xa[i], ya[i], q[i], xi[i],
eta[i], ra[i], dec[i], flags[i]])
raw6hdulist.close()
stopt = time.time()
print_inline("")
print("")
print("Runtime statistics:")
print(" runtime = {seconds} sec. = ({minutes} min.)".format(
seconds=stopt-startt, minutes=(stopt-startt)/60.))
print(" processed = "+str(cnt)+" of "+str(nphots)+" events.")
if cnt < nphots:
print(" WARNING: MISSING EVENTS!") # This should never happen.
print(" rate = "+str(nphots/(stopt-startt))+" photons/sec.")
print("")
return
def mcat_skybg(band,
skypos,
radius,
verbose=0,
trange=None,
mcat=None,
searchradius=0.1):
"""
Estimate the sky background using the MCAT 'skybg' for nearby sources.
:param band: The band to use, either 'FUV' or 'NUV'.
:type band: str
:param skypos: The right ascension and declination, in degrees.
:type skypos: list
:param radius: The radius in which to search for MCAT sources in degrees.
:type radius: float
:param verbose: Verbosity level, a value of 0 is minimum verbosity.
:type verbose: int
:param trange: Minimum and maximum time (in GALEX time) to consider.
:type trange: list
:returns: float -- The estimated sky background in the photometric
aperture, in counts per second.
"""
# Search the visit-level MCAT for nearby detections.
# Unless the MCAT data has already been handed off for detection purposes.
if not mcat:
mcat = get_mcat_data(skypos, searchradius)
try:
# Find the distance to each source.
dist = np.array([
angularSeparation(skypos[0], skypos[1], a[0], a[1])
for a in zip(mcat[band]['ra'], mcat[band]['dec'])
])
except TypeError:
print_inline('No {b} MCAT sources within {r} degrees of {p}'.format(
b=band, r=searchradius, p=skypos))
return np.nan
# Find visits that overlap in time.
if not trange:
tix = (np.array(list(range(len(mcat[band]['mag']))), dtype='int32'), )
else:
tix = np.where((
(trange[0] >= mcat[band]['t0']) & (trange[0] <= mcat[band]['t1']))
| ((trange[1] >= mcat[band]['t0'])
& (trange[1] <= mcat[band]['t1']))
| ((trange[0] <= mcat[band]['t0'])
& (trange[1] >= mcat[band]['t1'])))
if not len(tix[0]):
print_inline('No concurrent {b} MCAT source nearby.'.format(b=band))
return np.nan # Might not be the preferred behavior here.
ix = np.where(dist[tix] == min(dist[tix]))
skybg = mcat[band]['skybg'][tix][ix]
# This should rarely happen, but sometimes there's a duplicate entry in
# the visit-level MCAT.
if len(skybg) > 1:
# If the skybg array is all the same value, it's a duplicate.
if np.all(skybg == skybg[0]):
skybg = np.asarray([skybg[0]])
else:
skybg = np.asarray([np.median(skybg)])
return skybg[0] * area(radius * 60. * 60.)
def fGetTimeRanges(band,
skypos,
trange=None,
detsize=1.1,
verbose=0,
maxgap=1.,
minexp=1.,
skyrange=None,
maxgap_override=False,
minexp_override=False):
"""
Find the contiguous time ranges within a time range at a specific location.
:param band: The band to use, either 'FUV' or 'NUV'.
:type band: str
:param skypos: The right ascension and declination, in degrees.
:type skypos: list
:param trange: Minimum and maximum time (in GALEX time) to consider.
:type trange: list
:param detsize: The effective detector diameter, in degrees.
:type detsize: float
:param verbose: Verbosity level, a value of 0 is minimum verbosity.
:type verbose: int
:param maxgap: Maximum gap size, in seconds, for data to be considered
contiguous.
:type maxgap: float
:param minexp: Minimum gap size, in seconds, for data to be considered
contiguous.
:type minexp: float
:param skyrange: Values in degrees RA and Dec of a box around skypos that
defines the extent of the region of interest.
:type skyrange: list
:param maxgap_override: Enables an experimental feature where maxgap
can be less than one second.
:type maxgap_override: bool
:returns: numpy.ndarray -- A valid set of time ranges, accounting for
minimum exposure lengths and maximum gaps.
"""
if trange is not None:
trange_buffer = (trange[0] - 1, trange[1] + 1)
times = get_valid_times(band,
skypos,
trange=trange_buffer,
detsize=detsize,
verbose=verbose,
skyrange=skyrange)
if len(times):
times = np.append(times, times[-1] + 1)
if times[0] < trange[0]:
times[0] = trange[0]
if times[-1] > trange[1]:
times[-1] = trange[1]
else:
times = get_valid_times(band,
skypos,
trange=None,
detsize=detsize,
verbose=verbose,
skyrange=skyrange)
if len(times):
times = np.append(times, times[-1] + 1)
if not len(times):
return np.array([[]], dtype='float64')
if verbose:
print_inline('Parsing ~' + str(len(times) - 1) +
' seconds of raw exposure.')
# NOTE: The minimum meaningful maxgap is 1 second.
if maxgap < 1 and not maxgap_override:
raise ValueError('maxgap must be >=1 second')
tranges = distinct_tranges(times, maxgap=maxgap)
if not minexp_override:
ix = np.where(
np.array(tranges)[:, 1] - np.array(tranges)[:, 0] >= minexp)
tranges = np.array(tranges)[ix].tolist()
return np.array(tranges, dtype='float64')
def raw6_to_stims(raw6file, band, eclipse, margin=90.001):
"""
Extracts stim events from a raw6 file.
:param raw6file: The name of the raw6 FITS file to read.
:type raw6file: str
:param band: The band to return the stim data for, either 'FUV' or 'NUV'.
:type band: str
:param eclipse: The eclipse number to return the stim data for.
:type eclipse: int
:param margin: +/- extent in arcseconds defining stim search box
:type margin: float
:returns: tuple -- A four-element tuple containing data from each stim. The
data from each stim are stored in dicts.
"""
print("Extracting stim data from ", raw6file, " ...")
print(" Using a search box with sides of ", margin, " arcseconds.")
# This is unscoped for some reason... so I'm just coding it.
(xclk, yclk, xcen, ycen, xscl, yscl, xslp, yslp) = clk_cen_scl_slp(band,
eclipse)
chunksz = 1000000
print("Loading raw6 file...")
raw6hdulist = pyfits.open(raw6file, memmap=1)
raw6htab = raw6hdulist[1].header
nphots = raw6htab['NAXIS2']
stim1 = (
{'t':np.array([]), 'q':np.array([]), 'xb':np.array([]),
'xamc':np.array([]), 'yamc':np.array([]), 'xa':np.array([]),
'ya':np.array([]), 'x':np.array([]), 'y':np.array([]),
'yb':np.array([]), 'yap':np.array([])}
)
stim2 = (
{'t':np.array([]), 'q':np.array([]), 'xb':np.array([]),
'xamc':np.array([]), 'yamc':np.array([]), 'xa':np.array([]),
'ya':np.array([]), 'x':np.array([]), 'y':np.array([]),
'yb':np.array([]), 'yap':np.array([])}
)
stim3 = (
{'t':np.array([]), 'q':np.array([]), 'xb':np.array([]),
'xamc':np.array([]), 'yamc':np.array([]), 'xa':np.array([]),
'ya':np.array([]), 'x':np.array([]), 'y':np.array([]),
'yb':np.array([]), 'yap':np.array([])}
)
stim4 = (
{'t':np.array([]), 'q':np.array([]), 'xb':np.array([]),
'xamc':np.array([]), 'yamc':np.array([]), 'xa':np.array([]),
'ya':np.array([]), 'x':np.array([]), 'y':np.array([]),
'yb':np.array([]), 'yap':np.array([])}
)
print("")
for i in range(int(nphots/chunksz)+1):
csvrows = []
chunkbeg, chunkend = i*chunksz, (i+1)*chunksz-1
if chunkend > nphots:
chunkend = nphots-1
chunkid = " "+str(i+1)+" of "+str(int(nphots/chunksz)+1)+": "
print_inline(chunkid+"Unpacking raw6 data...")
t = np.array(raw6hdulist[1].data.field('t')[chunkbeg:chunkend])
phb1 = np.array(
raw6hdulist[1].data.field('phb1')[chunkbeg:chunkend], dtype='int64')
phb2 = np.array(
raw6hdulist[1].data.field('phb2')[chunkbeg:chunkend], dtype='int64')
phb3 = np.array(
raw6hdulist[1].data.field('phb3')[chunkbeg:chunkend], dtype='int64')
phb4 = np.array(
raw6hdulist[1].data.field('phb4')[chunkbeg:chunkend], dtype='int64')
phb5 = np.array(
raw6hdulist[1].data.field('phb5')[chunkbeg:chunkend], dtype='int64')
q = ((phb4 & 3) << 3) + ((phb5 & 224) >> 5)
xb = phb1 >> 5
xamc = (np.array(((phb1 & 31) << 7), dtype='int16') +
np.array(((phb2 & 254) >> 1), dtype='int16') -
np.array(((phb1 & 16) << 8), dtype='int16'))
yb = ((phb2 & 1) << 2) + ((phb3 & 192) >> 6)
yamc = (np.array(((phb3 & 63) << 6), dtype='int16') +
np.array(((phb4 & 252) >> 2), dtype='int16') -
np.array(((phb3 & 32) << 7), dtype='int16'))
xa = ((phb5 & 16) >> 4) + ((phb5 & 3) << 3) + ((phb5 & 12) >> 1)
xraw0 = xb*xclk + xamc
yraw0 = yb*yclk + yamc
ya = (np.array(((((yraw0/(2*yclk) - xraw0/(2*xclk)) + 10)*32) + xa),
dtype='int64') % 32)
xraw = (xraw0 + np.array((((xa+7) % 32) - 16), dtype='int64') * xslp)
yraw = (yraw0 + np.array((((ya+7) % 32) - 16), dtype='int64') * yslp)
x = (xraw - xcen)*xscl
y = (yraw - ycen)*yscl
index1, index2, index3, index4 = find_stims_index(x, y, band, eclipse,
margin)
# [Future] There may well be a better way to do these assignments.
stim1['t'] = np.append(stim1['t'], t[index1])
stim1['x'] = np.append(stim1['x'], x[index1])
stim1['y'] = np.append(stim1['y'], y[index1])
stim1['q'] = np.append(stim1['q'], q[index1])
stim1['xa'] = np.append(stim1['xa'], xa[index1])
stim1['xb'] = np.append(stim1['xb'], ya[index1])
stim1['ya'] = np.append(stim1['ya'], ya[index1])
stim1['yb'] = np.append(stim1['yb'], yb[index1])
stim1['xamc'] = np.append(stim1['xamc'], xamc[index1])
stim1['yamc'] = np.append(stim1['yamc'], yamc[index1])
stim1['yap'] = np.append(stim1['yap'], rtaph_yap(ya[index1],
yb[index1],
yamc[index1]))
stim2['t'] = np.append(stim2['t'], t[index2])
stim2['x'] = np.append(stim2['x'], x[index2])
stim2['y'] = np.append(stim2['y'], y[index2])
stim2['q'] = np.append(stim2['q'], q[index2])
stim2['xa'] = np.append(stim2['xa'], xa[index2])
stim2['xb'] = np.append(stim2['xb'], ya[index2])
stim2['ya'] = np.append(stim2['ya'], ya[index2])
stim2['yb'] = np.append(stim2['yb'], yb[index2])
stim2['xamc'] = np.append(stim2['xamc'], xamc[index2])
stim2['yamc'] = np.append(stim2['yamc'], yamc[index2])
stim2['yap'] = np.append(stim2['yap'], rtaph_yap(ya[index2],
yb[index2],
yamc[index2]))
stim3['t'] = np.append(stim3['t'], t[index3])
stim3['x'] = np.append(stim3['x'], x[index3])
stim3['y'] = np.append(stim3['y'], y[index3])
stim3['q'] = np.append(stim3['q'], q[index3])
stim3['xa'] = np.append(stim3['xa'], xa[index3])
stim3['xb'] = np.append(stim3['xb'], ya[index3])
stim3['ya'] = np.append(stim3['ya'], ya[index3])
stim3['yb'] = np.append(stim3['yb'], yb[index3])
stim3['xamc'] = np.append(stim3['xamc'], xamc[index3])
stim3['yamc'] = np.append(stim3['yamc'], yamc[index3])
stim3['yap'] = np.append(stim3['yap'], rtaph_yap(ya[index3],
yb[index3],
yamc[index3]))
stim4['t'] = np.append(stim4['t'], t[index4])
stim4['x'] = np.append(stim4['x'], x[index4])
stim4['y'] = np.append(stim4['y'], y[index4])
stim4['q'] = np.append(stim4['q'], q[index4])
stim4['xa'] = np.append(stim4['xa'], xa[index4])
stim4['xb'] = np.append(stim4['xb'], ya[index4])
stim4['ya'] = np.append(stim4['ya'], ya[index4])
stim4['yb'] = np.append(stim4['yb'], yb[index4])
stim4['xamc'] = np.append(stim4['xamc'], xamc[index4])
stim4['yamc'] = np.append(stim4['yamc'], yamc[index4])
stim4['yap'] = np.append(stim4['yap'], rtaph_yap(ya[index4],
yb[index4],
yamc[index4]))
print_inline(" Done.")
return stim1, stim2, stim3, stim4
def make_lightcurve(photon_file,
band,
stepsz=30.,
skypos=(24.76279, -17.94948),
aper=gt.aper2deg(7),
fixed_t0=False,
makefile=False,
quiet=False,
filetag='',
lc_filename=None):
""" Generate a light curve of a specific target. """
if lc_filename is None:
lc_filename = photon_file.replace(
'.csv', '-{stepsz}s{filetag}.csv'.format(stepsz=int(stepsz),
filetag=filetag))
if os.path.exists(lc_filename) and not makefile:
if not quiet:
print_inline(' Pre-exists, reading in file...')
return pd.read_csv(lc_filename)
else:
if not quiet:
print_inline('Generating {fn}'.format(fn=lc_filename))
events = calibrate_photons(photon_file, band)
# Below is a calculation of the re-centering, if desired.
skypos_recentered = recenter(events, skypos=skypos)
c1 = SkyCoord(ra=skypos[0] * u.degree, dec=skypos[1] * u.degree)
c2 = SkyCoord(ra=skypos_recentered[0] * u.degree,
dec=skypos_recentered[1] * u.degree)
if not quiet:
print('Recentering aperture on [{ra}, {dec}]'.format(
ra=skypos_recentered[0], dec=skypos_recentered[1]))
print("Recenter shift (arcsec): " + str(c1.separation(c2).arcsec))
ix = aper_photons(events, skypos=skypos_recentered, aper=aper)
if len(ix[0]) == 0:
return [], [], [], []
trange = [
np.floor(np.array(events['t'])[ix].min()),
np.ceil(np.array(events['t'])[ix].max())
]
if fixed_t0:
# Use this to force NUV and FUV to have the same bins
trange[0] = fixed_t0
expt = compute_exptime_array(np.array(events['t'].values), band, trange,
stepsz, np.array(events['flags'].values))
counts, tbins, detrads = [], [], []
col, row = np.array(events['col']), np.array(events['row'])
detrad = np.sqrt((col - 400)**2 + (row - 400)**2)
for t0 in np.arange(trange[0], trange[1], stepsz):
tix = np.where((np.array(events['t'])[ix] >= t0)
& (np.array(events['t']) < t0 + stepsz)[ix]
& (np.array(events['flags'], dtype='int16')[ix] == 0))
tbins += [t0]
detrads += [detrad[ix][tix].mean()]
if len(tix[0]) == 0:
counts += [0.]
else:
counts += [np.array(events['response'])[ix][tix].sum()]
cps = np.array(counts) / np.array(expt)
cps_err = np.sqrt(counts) / np.array(expt)
lc = pd.DataFrame({
't0': tbins,
't1': list(np.array(tbins) + stepsz),
'cps': cps,
'cps_err': cps_err,
'flux': gt.counts2flux(cps, band),
'flux_err': gt.counts2flux(cps_err, band),
'counts': counts,
'expt': expt,
'detrad': detrads
})
lc['cps_apcorrected'] = apcorrect_cps(lc, band, aper=aper)
lc['flux_apcorrected'] = gt.counts2flux(lc['cps_apcorrected'], band)
lc.to_csv(lc_filename)
return lc