def hashresponse(band, events, verbose=0):
"""
Given detector xi, eta, return the response at each position.
:param band: The band being used, either 'FUV' or 'NUV'.
:type band: str
:param events: The set of photon events.
:type events: dict
:param verbose: Verbosity level, a value of 0 is minimum verbosity.
:type verbose: int
:returns: dict -- The photon event properties, updated with the response at
each position.
"""
# Hash out the response correction.
if verbose:
mc.print_inline("Applying the response correction.")
flat, _ = cal.flat(band)
events['col'], events['row'] = xieta2colrow(events['xi'], events['eta'],
band)
events['flat'] = flat[np.array(events['col'], dtype='int16'),
np.array(events['row'], dtype='int16')]
events['scale'] = gxt.compute_flat_scale(events['t'], band)
# [Future]: Separately do the binlinearly interpolated response.
events['response'] = (events['flat'] * events['scale'])
return events
def hashresponse(band, events, verbose=0):
"""
Given detector xi, eta, return the response at each position.
:param band: The band being used, either 'FUV' or 'NUV'.
:type band: str
:param events: The set of photon events.
:type events: dict
:param verbose: Verbosity level, a value of 0 is minimum verbosity.
:type verbose: int
:returns: dict -- The photon event properties, updated with the response at
each position.
"""
# Hash out the response correction.
if verbose:
mc.print_inline("Applying the response correction.")
flat, _ = cal.flat(band)
events['col'], events['row'] = xieta2colrow(
events['xi'], events['eta'], band)
events['flat'] = flat[np.array(events['col'], dtype='int16'),
np.array(events['row'], dtype='int16')]
events['scale'] = gxt.compute_flat_scale(events['t'], band)
# [Future]: Separately do the binlinearly interpolated response.
events['response'] = (events['flat']*events['scale'])
return events
def test_compute_flat_scale_NUV_array(self):
band = 'NUV'
ts=[869777733.995,901049156.995]
scales=[0.986709143129,0.994262914909]
out = gt.compute_flat_scale(ts,band,verbose=0)
self.assertEqual(len(ts),len(out))
self.assertAlmostEqual(out[0],scales[0])
self.assertAlmostEqual(out[0],scales[0])
def test_compute_flat_scale_FUV_array(self):
band = 'FUV'
ts=[869777733.995,901049156.995]
scales=[0.991157347104,0.999816178202]
out = gt.compute_flat_scale(ts,band,verbose=0)
self.assertEqual(len(ts),len(out))
self.assertAlmostEqual(out[0],scales[0])
self.assertAlmostEqual(out[0],scales[0])
def create_rr(csvfile,
band,
eclipse,
aspfile=None,
expstart=None,
expend=None,
retries=20,
detsize=1.25,
pltscl=68.754932):
"""
DEPRECATED: Creates a relative response map for an eclipse, given a
photon list.
:param csvfile: Name of CSV file containing photon events.
:type csvfile: str
:param band: The band being used, either 'FUV' or 'NUV'.
:type band: str
:param eclipse: The eclipse number the data are taken from.
:type eclipse: int
:param aspfile: The aspect file to use with the CSV file.
:type aspfile: str
:param expstart: Start of the exposure, in seconds.
:type expstart: float
:param expend: End of the exposure, in seconds.
:type expend: float
:param retries: Number of query retries before giving up.
:type retries: int
:param detsize: Effective size of the detector, in degrees.
:type detsize: float
:param pltscl: The plate scale in arcseconds.
:type pltscl: float
:returns: tuple -- A two-element tuple containing the relative response
and the effective exposure time. The relative response is a 2D array of
values.
"""
aspum = pltscl / 1000.0
print("Loading flat file...")
flat, flatinfo = cal.flat(band)
npixx = flat.shape[0]
npixy = flat.shape[1]
pixsz = flatinfo['CDELT2']
flatfill = detsize / (npixx * pixsz)
print("Retrieving 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 = min(asptime)
maxasp = max(asptime)
print(" trange= ( " + str(minasp) + " , " + str(maxasp) + " )")
ra0, dec0, roll0 = aspheader['RA'], aspheader['DEC'], aspheader['ROLL']
print(" [RA, DEC, ROLL] = [" + str(ra0) + ", " + str(dec0) + ", " +
str(roll0) + "]")
print("Computing aspect vectors...")
print("Calculating aspect solution vectors...")
xi_vec, eta_vec = np.array([]), np.array([])
xi_vec, eta_vec = gnomfwd_simple(ra0, dec0, aspra, aspdec, -asptwist,
1.0 / 36000.0, 0.)
flat_scale = compute_flat_scale(asptime.mean(), band)
if not expstart:
expstart = asptime.min() + GPSSECS
if not expend:
expend = asptime.max() + GPSSECS
flatbuff = np.zeros([960, 960])
# Rotate the flat into the correct orientation to start
flatbuff[80:960 - 80, 80:960 - 80] = np.flipud(np.rot90(flat))
expt = 0
rr = np.zeros([960, 960])
col = (((xi_vec / 36000.) /
(detsize / 2.) * flatfill + 1.) / 2. * npixx) - 400.
row = (((eta_vec / 36000.) /
(detsize / 2.) * flatfill + 1.) / 2. * npixy) - 400.
for i in range(len(asptime) - 1):
if (asptime[i] + GPSSECS) < expstart or (asptime[i] +
GPSSECS) > expend:
print(" ", asptime[i] + GPSSECS, " out of range.")
continue
elif (aspflags[i] % 2 != 0) or (aspflags[i + 1] % 2 != 0):
print(" ", asptime[i] + GPSSECS, " flagged.")
continue
else:
rr += scipy.ndimage.interpolation.shift(
scipy.ndimage.interpolation.rotate(flatbuff,
-asptwist[i],
reshape=False,
order=0,
prefilter=False),
[col[i], row[i]],
order=0,
prefilter=False)
expt += 1
# Need to modify this to handle NUV files better.
t, x, y, flags = load_txy(csvfile)
exp = compute_exposure(t, x, y, flags, band, eclipse)
deadt = compute_deadtime(t, x, y, band, eclipse)
return rr * flat_scale * (1 - deadt), exp
def test_compute_flat_scale_NUV(self):
band = 'NUV'
ts=[869777733.995,901049156.995]
scales=[0.986709143129,0.994262914909]
for t,scl in zip(ts,scales):
self.assertAlmostEqual(gt.compute_flat_scale(t,band,verbose=0),scl)
def test_compute_flat_scale_FUV(self):
band = 'FUV'
ts=[869777733.995,901049156.995]
scales=[0.991157347104,0.999816178202]
for t,scl in zip(ts,scales):
self.assertAlmostEqual(gt.compute_flat_scale(t,band,verbose=0),scl)
def create_rr(csvfile, band, eclipse, aspfile=None, expstart=None, expend=None,
retries=20, detsize=1.25, pltscl=68.754932):
"""
DEPRECATED: Creates a relative response map for an eclipse, given a
photon list.
:param csvfile: Name of CSV file containing photon events.
:type csvfile: str
:param band: The band being used, either 'FUV' or 'NUV'.
:type band: str
:param eclipse: The eclipse number the data are taken from.
:type eclipse: int
:param aspfile: The aspect file to use with the CSV file.
:type aspfile: str
:param expstart: Start of the exposure, in seconds.
:type expstart: float
:param expend: End of the exposure, in seconds.
:type expend: float
:param retries: Number of query retries before giving up.
:type retries: int
:param detsize: Effective size of the detector, in degrees.
:type detsize: float
:param pltscl: The plate scale in arcseconds.
:type pltscl: float
:returns: tuple -- A two-element tuple containing the relative response
and the effective exposure time. The relative response is a 2D array of
values.
"""
aspum = pltscl/1000.0
print("Loading flat file...")
flat, flatinfo = cal.flat(band)
npixx = flat.shape[0]
npixy = flat.shape[1]
pixsz = flatinfo['CDELT2']
flatfill = detsize/(npixx*pixsz)
print("Retrieving 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 = min(asptime)
maxasp = max(asptime)
print(" trange= ( "+str(minasp)+" , "+str(maxasp)+" )")
ra0, dec0, roll0 = aspheader['RA'], aspheader['DEC'], aspheader['ROLL']
print(" [RA, DEC, ROLL] = ["+str(ra0)+", "+str(dec0)+
", "+str(roll0)+"]")
print("Computing aspect vectors...")
print("Calculating aspect solution vectors...")
xi_vec, eta_vec = np.array([]), np.array([])
xi_vec, eta_vec = gnomfwd_simple(ra0, dec0, aspra, aspdec, -asptwist,
1.0/36000.0, 0.)
flat_scale = compute_flat_scale(asptime.mean(), band)
if not expstart:
expstart = asptime.min()+GPSSECS
if not expend:
expend = asptime.max()+GPSSECS
flatbuff = np.zeros([960, 960])
# Rotate the flat into the correct orientation to start
flatbuff[80:960-80, 80:960-80] = np.flipud(np.rot90(flat))
expt = 0
rr = np.zeros([960, 960])
col = (((xi_vec/36000.)/(detsize/2.)*flatfill + 1.)/2. * npixx)-400.
row = (((eta_vec/36000.)/(detsize/2.)*flatfill + 1.)/2. * npixy)-400.
for i in range(len(asptime)-1):
if (asptime[i]+GPSSECS) < expstart or (asptime[i]+GPSSECS) > expend:
print(" ", asptime[i]+GPSSECS, " out of range.")
continue
elif (aspflags[i]%2 != 0) or (aspflags[i+1]%2 != 0):
print(" ", asptime[i]+GPSSECS, " flagged.")
continue
else:
rr += scipy.ndimage.interpolation.shift(
scipy.ndimage.interpolation.rotate(flatbuff, -asptwist[i],
reshape=False, order=0,
prefilter=False), [col[i],
row[i]],
order=0, prefilter=False)
expt += 1
# Need to modify this to handle NUV files better.
t, x, y, flags = load_txy(csvfile)
exp = compute_exposure(t, x, y, flags, band, eclipse)
deadt = compute_deadtime(t, x, y, band, eclipse)
return rr*flat_scale*(1-deadt), exp
def calibrate_photons(photon_file, band, overwrite=False):
"""This function takes the raw photon events and produces calibrated
photon events."""
xfilename = '{d}{base}-x.csv'.format(d=photon_file[:-13],
base=photon_file[-13:-4])
if os.path.exists(xfilename) and not overwrite:
return pd.read_csv(xfilename, index_col=None)
data = pd.read_csv(photon_file,
names=[
't', 'x', 'y', 'xa', 'ya', 'q', 'xi', 'eta', 'ra',
'dec', 'flags'
])
events = pd.DataFrame()
flat, _ = gPhoton.cal.flat(band)
col, row = gPhoton.curvetools.xieta2colrow(np.array(data['xi']),
np.array(data['eta']), band)
# Use only data that is on the detector.
ix = np.where((col > 0) & (col < 800) & (row > 0) & (row < 800))
events['t'] = pd.Series(
(np.array(data.iloc[ix]['t']) / 1000.).byteswap().newbyteorder())
events['ra'] = pd.Series(
(np.array(data.iloc[ix]['ra'])).byteswap().newbyteorder())
events['dec'] = pd.Series(
(np.array(data.iloc[ix]['dec'])).byteswap().newbyteorder())
events['flags'] = pd.Series(
(np.array(data.iloc[ix]['flags'])).byteswap().newbyteorder())
events['col'] = pd.Series((col[ix]).byteswap().newbyteorder())
events['row'] = pd.Series((row[ix]).byteswap().newbyteorder())
flat = flat[np.array(events['col'], dtype='int16'),
np.array(events['row'], dtype='int16')]
events['flat'] = pd.Series((flat).byteswap().newbyteorder())
scale = gt.compute_flat_scale(np.array(data.iloc[ix]['t']) / 1000., band)
events['scale'] = pd.Series((scale).byteswap().newbyteorder())
response = np.array(events['flat']) * np.array(events['scale'])
events['response'] = pd.Series((response).byteswap().newbyteorder())
# Define the hotspot mask
mask, maskinfo = gPhoton.cal.mask(band)
events['mask'] = pd.Series(((mask[np.array(col[ix], dtype='int64'),
np.array(row[ix], dtype='int64')] == 0)
).byteswap().newbyteorder())
# Add the remaining photon list parameters back in for completeness.
events['x'] = pd.Series(
(np.array(data.iloc[ix]['x'])).byteswap().newbyteorder())
events['y'] = pd.Series(
(np.array(data.iloc[ix]['y'])).byteswap().newbyteorder())
events['xa'] = pd.Series(
(np.array(data.iloc[ix]['xa'])).byteswap().newbyteorder())
events['ya'] = pd.Series(
(np.array(data.iloc[ix]['ya'])).byteswap().newbyteorder())
events['q'] = pd.Series(
(np.array(data.iloc[ix]['q'])).byteswap().newbyteorder())
events['xi'] = pd.Series(
(np.array(data.iloc[ix]['xi'])).byteswap().newbyteorder())
events['eta'] = pd.Series(
(np.array(data.iloc[ix]['eta'])).byteswap().newbyteorder())
print('Writing {xf}'.format(xf=xfilename))
events.to_csv(xfilename, index=None)
return events
