def main():
pfdfn = sys.argv[1]
pfd = prepfold.pfd(pfdfn)
cand = candidate.Candidate(pfd.topo_p1, pfd.bary_p1, pfd.bestdm, \
psr_utils.ra_to_rad(pfd.rastr)*psr_utils.RADTODEG, \
psr_utils.dec_to_rad(pfd.decstr)*psr_utils.RADTODEG, \
pfdfn)
print "Loaded %s" % cand.pfdfn
print " Best topo period (s): %f" % cand.topo_period
print " Best bary period (s): %f" % cand.bary_period
print " Best DM (cm^-3/pc): %f" % cand.dm
print " RA (J2000 - deg): %f" % cand.raj_deg
print " Dec (J2000 - deg): %f" % cand.decj_deg
print "-"*10
pprint.pprint(cand.__dict__)
print "-"*10
gauss.add_data(cand)
print "Added gaussian fit to cand"
pprint.pprint(cand.__dict__)
print "-"*10
fit_and_plot(cand)
def main():
parser = OptionParser(usage)
(opts, args) = parser.parse_args()
# Scan files
db = Database(db="local-SBON512")
DBconn = db.conn
DBcursor = db.cursor
QUERY = "SELECT header_id, right_ascension, declination FROM headers"
#QUERY = "SHOW tables"
DBcursor.execute(QUERY)
result_query = [list(row) for row in DBcursor.fetchall()]
print result_query
for header in result_query:
# Convert in degrees
ra_deg = psr_utils.ra_to_rad(header[1]) * RADTODEG
dec_deg = psr_utils.dec_to_rad(header[2]) * RADTODEG
# Convert in galactic coord
galactic_longitude, galactic_latitude = coordconv.eqdeg_to_galdeg(ra_deg, dec_deg)
QUERY = "UPDATE headers SET n_bits=4, galactic_longitude=%f, galactic_latitude=%f, ra_deg=%f, dec_deg=%f WHERE header_id=%d;\n"%(galactic_longitude, galactic_latitude, ra_deg, dec_deg, header[0])
print QUERY
DBcursor.execute(QUERY)
DBconn.close()
def show_known_psrs(self, cand): txt = [] # Convert RA and DEC of the candidate in rad print "known_psrs::show_known_psrs> Cand Positions: ", cand.ra , cand.dec cand_RA = psr_utils.ra_to_rad(cand.ra) cand_DEC = psr_utils.dec_to_rad(cand.dec) for i in range(len(self.psr_list)): #print "cand RA = %s psr RA = %s"%(cand.ra, self.psr_list[i].ra) if abs(cand_RA - self.psr_list[i].ra) < 0.006 and abs(cand_DEC - self.psr_list[i].dec) < 0.006: doc = "%s\n RA : %s\n DEC : %s\n Period : %s\n Ratio : %.4f\n DM : %s\n Survey %s"% \ (self.psr_list[i].psrname, self.psr_list[i].ra_str, \ self.psr_list[i].dec_str, self.psr_list[i].period, \ float(self.psr_list[i].period)/ cand.period, \ self.psr_list[i].dm, self.psr_list[i].survey) txt.append(doc) txt = "\n\n".join(txt) # create a new scrolled window. scrolled_window = gtk.ScrolledWindow() scrolled_window.set_border_width(10) scrolled_window.set_policy(gtk.POLICY_AUTOMATIC, gtk.POLICY_ALWAYS) if txt: label = gtk.Label(txt) scrolled_window.add_with_viewport(label) return scrolled_window
def read_pfd_file(pfdfn):
"""Return a Candidate object for the pfd given.
Input:
pfdfn: PRESTO *.pfd file name.
Output:
cand: Candidate object constructed from the given pfd
file name.
"""
pfd = prepfold.pfd(pfdfn)
cand = Candidate(pfd.topo_p1, pfd.bary_p1, pfd.bestdm, \
psr_utils.ra_to_rad(pfd.rastr)*psr_utils.RADTODEG, \
psr_utils.dec_to_rad(pfd.decstr)*psr_utils.RADTODEG, \
pfdfn)
return cand
def read_pfd_file(pfdfn):
"""Return a Candidate object for the pfd given.
Input:
pfdfn: PRESTO *.pfd file name.
Output:
cand: Candidate object constructed from the given pfd
file name.
"""
pfd = prepfold.pfd(pfdfn)
cand = Candidate(pfd.topo_p1, pfd.bary_p1, pfd.bestdm, \
psr_utils.ra_to_rad(pfd.rastr)*psr_utils.RADTODEG, \
psr_utils.dec_to_rad(pfd.decstr)*psr_utils.RADTODEG, \
pfdfn)
return cand
def main():
pfdfn = sys.argv[1]
pfd = myprepfold.pfd(pfdfn)
cand = candidate.Candidate(pfd.topo_p1, pfd.bary_p1, pfd.bestdm, \
psr_utils.ra_to_rad(pfd.rastr)*psr_utils.RADTODEG, \
psr_utils.dec_to_rad(pfd.decstr)*psr_utils.RADTODEG, \
pfdfn)
print "Loaded %s" % cand.pfdfn
print " Best topo period (s): %f" % cand.topo_period
print " Best bary period (s): %f" % cand.bary_period
print " Best DM (cm^-3/pc): %f" % cand.dm
print " RA (J2000 - deg): %f" % cand.raj_deg
print " Dec (J2000 - deg): %f" % cand.decj_deg
print "-"*10
pprint.pprint(cand.__dict__)
print "-"*10
from rating_classes import time_vs_phase
time_vs_phase.TimeVsPhaseClass().add_data(cand)
tvph = cand.time_vs_phase
print "Added time vs phase data to cand"
pprint.pprint(cand.__dict__)
print "-"*10
pprint.pprint(cand.time_vs_phase.__dict__)
print "DEBUG: tvph.pdelays_bins", tvph.pdelays_bins
print "DEBUG: pfd.pdelays_bins", pfd.pdelays_bins
pfd.dedisperse(doppler=1)
print "DEBUG: tvph.dm, pfd.currdm", tvph.dm, pfd.currdm
if pfd.fold_pow == 1.0:
bestp = pfd.bary_p1
bestpd = pfd.bary_p2
bestpdd = pfd.bary_p3
else:
bestp = pfd.topo_p1
bestpd = pfd.topo_p2
bestpdd = pfd.topo_p3
pfd.adjust_period()
tvph.adjust_period(bestp, bestpd, bestpdd)
mypfd_tvph = pfd.time_vs_phase()
print test_img(tvph.data, mypfd_tvph, 1e-6)
plt.figure()
plt.plot(tvph.data.sum(axis=0), label='TvPh')
plt.plot(mypfd_tvph.sum(axis=0), label='PFD')
plt.title("P=%g s, Pd=%g s/s, Pdd=%g s/s^2" % (bestp, bestpd, bestpdd))
plt.legend(loc='best')
for ii in range(10):
frac_p = 1+(np.random.rand(1)*2-1)/100.0
frac_pd = 1+(np.random.rand(1)*2-1)/100.0
frac_pdd = 1+(np.random.rand(1)*2-1)/100.0
p = frac_p*bestp
pd = frac_pd*bestpd
pdd = frac_pdd*bestpdd
print "Shuffling for the %dth time" % ii
print "frac_p, frac_pd, frac_pdd", frac_p, frac_pd, frac_pdd
tvph.adjust_period(p, pd, pdd)
mypfd_tvph = pfd.time_vs_phase(p, pd, pdd)
print test_img(tvph.data, mypfd_tvph, 1e-9)
plt.figure()
plt.plot(tvph.data.sum(axis=0), label='TvPh')
plt.plot(mypfd_tvph.sum(axis=0), label='PFD')
plt.title("P=%g s, Pd=%g s/s, Pdd=%g s/s^2" % (p, pd, pdd))
plt.legend(loc='best')
plt.show()
sys.exit(1)
print "Check if time_vs_phase rotates out and back to same array"
orig = cand.time_vs_phase.data.copy()
orig_p = cand.time_vs_phase.curr_p
orig_pd = cand.time_vs_phase.curr_pd
orig_pdd = cand.time_vs_phase.curr_pdd
cand.time_vs_phase.adjust_period(p=orig_p-0.00123, pd=0, pdd=0)
mod = cand.time_vs_phase.data.copy()
cand.time_vs_phase.adjust_period(p=orig_p, pd=orig_pd, pdd=orig_pdd)
new = cand.time_vs_phase.data.copy()
print "Compare orig/mod"
print (orig == mod).all()
print "Compare mod/new"
print (mod == new).all()
print "Compare orig/new"
print (orig == new).all()
print "Compare with best prof"
# Create best prof file
print "show_pfd -noxwin %s" % cand.info['pfdfn']
subprocess.call("show_pfd -noxwin %s" % cand.info['pfdfn'], shell=True, stdout=open(os.devnull))
prof = np.loadtxt(os.path.split(cand.pfd.pfd_filename+".bestprof")[-1], usecols=(1,))
pfd = cand.pfd
if pfd.fold_pow == 1.0:
bestp = pfd.bary_p1
bestpd = pfd.bary_p2
bestpdd = pfd.bary_p3
else:
bestp = pfd.topo_p1
bestpd = pfd.topo_p2
bestpdd = pfd.topo_p3
cand.time_vs_phase.adjust_period(bestp, bestpd, bestpdd)
tvph = pfd.time_vs_phase()
print test_img(cand.time_vs_phase.data, tvph, 1e-6)
plt.plot(cand.time_vs_phase.get_profile())
plt.plot(tvph.sum(axis=0).squeeze())
plt.show()
def __init__(self, parfilenm):
self.FILE = parfilenm
pf = open(parfilenm)
for line in pf.readlines():
# Skip comments
if line[0] == "#":
continue
# Convert any 'D-' or 'D+' to 'E-' or 'E+'
line = line.replace("D-", "E-")
line = line.replace("D+", "E+")
splitline = line.split()
# Skip blank lines
if len(splitline) == 0:
continue
key = splitline[0]
if key in str_keys:
setattr(self, key, splitline[1])
elif key in float_keys:
try:
setattr(self, key, float(splitline[1]))
except ValueError:
pass
if len(splitline) == 3: # Some parfiles don't have flags, but do have errors
if splitline[2] not in ["0", "1"]:
setattr(self, key + "_ERR", float(splitline[2]))
if len(splitline) == 4:
setattr(self, key + "_ERR", float(splitline[3]))
# Deal with Ecliptic coords
if hasattr(self, "BETA") and hasattr(self, "LAMBDA"):
setattr(self, "ELAT", self.BETA)
setattr(self, "ELONG", self.LAMBDA)
if slalib and hasattr(self, "ELAT") and hasattr(self, "ELONG"):
if hasattr(self, "POSEPOCH"):
epoch = self.POSEPOCH
else:
epoch = self.PEPOCH
ra_rad, dec_rad = sla_ecleq(self.ELONG * pu.DEGTORAD, self.ELAT * pu.DEGTORAD, epoch)
rstr = pu.coord_to_string(*pu.rad_to_hms(ra_rad))
dstr = pu.coord_to_string(*pu.rad_to_dms(dec_rad))
setattr(self, "RAJ", rstr)
setattr(self, "DECJ", dstr)
if hasattr(self, "RAJ"):
setattr(self, "RA_RAD", pu.ra_to_rad(self.RAJ))
if hasattr(self, "DECJ"):
setattr(self, "DEC_RAD", pu.dec_to_rad(self.DECJ))
# Compute the Galactic coords
if slalib and hasattr(self, "RA_RAD") and hasattr(self, "DEC_RAD"):
l, b = sla_eqgal(self.RA_RAD, self.DEC_RAD)
setattr(self, "GLONG", l * pu.RADTODEG)
setattr(self, "GLAT", b * pu.RADTODEG)
# Compute the Ecliptic coords
if slalib and hasattr(self, "RA_RAD") and hasattr(self, "DEC_RAD"):
if hasattr(self, "POSEPOCH"):
epoch = self.POSEPOCH
else:
epoch = self.PEPOCH
elon, elat = sla_eqecl(self.RA_RAD, self.DEC_RAD, epoch)
setattr(self, "ELONG", elon * pu.RADTODEG)
setattr(self, "ELAT", elat * pu.RADTODEG)
if hasattr(self, "P"):
setattr(self, "P0", self.P)
if hasattr(self, "P0"):
setattr(self, "F0", 1.0 / self.P0)
if hasattr(self, "F0"):
setattr(self, "P0", 1.0 / self.F0)
if hasattr(self, "FB0"):
setattr(self, "PB", (1.0 / self.FB0) / 86400.0)
if hasattr(self, "P0_ERR"):
if hasattr(self, "P1_ERR"):
f, ferr, fd, fderr = pu.pferrs(self.P0, self.P0_ERR, self.P1, self.P1_ERR)
setattr(self, "F0_ERR", ferr)
setattr(self, "F1", fd)
setattr(self, "F1_ERR", fderr)
else:
f, fd, = pu.p_to_f(self.P0, self.P1)
setattr(self, "F0_ERR", self.P0_ERR / (self.P0 * self.P0))
setattr(self, "F1", fd)
if hasattr(self, "F0_ERR"):
if hasattr(self, "F1_ERR"):
p, perr, pd, pderr = pu.pferrs(self.F0, self.F0_ERR, self.F1, self.F1_ERR)
setattr(self, "P0_ERR", perr)
setattr(self, "P1", pd)
setattr(self, "P1_ERR", pderr)
else:
p, pd, = pu.p_to_f(self.F0, self.F1)
setattr(self, "P0_ERR", self.F0_ERR / (self.F0 * self.F0))
setattr(self, "P1", pd)
if hasattr(self, "EPS1") and hasattr(self, "EPS2"):
ecc = math.sqrt(self.EPS1 * self.EPS1 + self.EPS2 * self.EPS2)
omega = math.atan2(self.EPS1, self.EPS2)
setattr(self, "E", ecc)
setattr(self, "OM", omega * pu.RADTODEG)
setattr(self, "T0", self.TASC + self.PB * omega / pu.TWOPI)
if hasattr(self, "PB") and hasattr(self, "A1") and not (hasattr(self, "E") or hasattr(self, "ECC")):
setattr(self, "E", 0.0)
if hasattr(self, "T0") and not hasattr(self, "TASC"):
setattr(self, "TASC", self.T0 - self.PB * self.OM / 360.0)
pf.close()
def read(self, parfilenm):
self.FILE = parfilenm
#print parfilenm
pf = open(parfilenm)
for line in pf.readlines():
# Convert any 'D-' or 'D+' to 'E-' or 'E+'
line = line.replace("D-", "E-")
line = line.replace("D+", "E+")
try:
splitline = line.split()
key = splitline[0]
if key in str_keys:
setattr(self, key, splitline[1])
elif key in float_keys:
try:
setattr(self, key, float(splitline[1]))
except ValueError:
pass
if len(
splitline
) == 3: # Some parfiles don't have flags, but do have errors
if splitline[2] not in ['0', '1']:
setattr(self, key + '_ERR', float(splitline[2]))
if len(splitline) == 4:
setattr(self, key + '_ERR', float(splitline[3]))
except:
if line.strip():
print ' ', line
# Read PSR name
if hasattr(self, 'PSR'):
setattr(self, 'PSR', self.PSR)
if hasattr(self, 'PSRJ'):
setattr(self, 'PSRJ', self.PSRJ)
# Deal with Ecliptic coords
if (hasattr(self, 'BETA') and hasattr(self, 'LAMBDA')):
self.use_eclip = True
setattr(self, 'ELAT', self.BETA)
setattr(self, 'ELONG', self.LAMBDA)
if (slalib and hasattr(self, 'ELAT') and hasattr(self, 'ELONG')):
self.use_eclip = True
if hasattr(self, 'POSEPOCH'):
epoch = self.POSEPOCH
else:
epoch = self.PEPOCH
ra_rad, dec_rad = sla_ecleq(self.ELONG * pu.DEGTORAD,
self.ELAT * pu.DEGTORAD, epoch)
rstr = pu.coord_to_string(*pu.rad_to_hms(ra_rad))
dstr = pu.coord_to_string(*pu.rad_to_dms(dec_rad))
setattr(self, 'RAJ', rstr)
setattr(self, 'DECJ', dstr)
if (slalib and hasattr(self, 'ELAT') and hasattr(self, 'ELONG')):
setattr(self, 'PMELONG', self.PMELONG)
setattr(self, 'PMELAT', self.PMELAT)
if hasattr(self, 'RAJ'):
setattr(self, 'RA_RAD', pu.ra_to_rad(self.RAJ))
if hasattr(self, 'DECJ'):
setattr(self, 'DEC_RAD', pu.dec_to_rad(self.DECJ))
# Compute the Galactic coords
if (slalib and hasattr(self, 'RA_RAD') and hasattr(self, 'DEC_RAD')):
l, b = sla_eqgal(self.RA_RAD, self.DEC_RAD)
setattr(self, 'GLONG', l * pu.RADTODEG)
setattr(self, 'GLAT', b * pu.RADTODEG)
# Compute the Ecliptic coords
if (slalib and hasattr(self, 'RA_RAD') and hasattr(self, 'DEC_RAD')):
if hasattr(self, 'POSEPOCH'):
epoch = self.POSEPOCH
else:
epoch = self.PEPOCH
elon, elat = sla_eqecl(self.RA_RAD, self.DEC_RAD, epoch)
setattr(self, 'ELONG', elon * pu.RADTODEG)
setattr(self, 'ELAT', elat * pu.RADTODEG)
if hasattr(self, 'P'):
setattr(self, 'P0', self.P)
if hasattr(self, 'P0'):
setattr(self, 'F0', 1.0 / self.P0)
if hasattr(self, 'F0'):
setattr(self, 'P0', 1.0 / self.F0)
if hasattr(self, 'F1'):
setattr(self, 'P1', -self.F1 / (self.F0 * self.F0))
if hasattr(self, 'FB0'):
setattr(self, 'PB', (1.0 / self.FB0) / 86400.0)
if hasattr(self, 'P0_ERR'):
if hasattr(self, 'P1_ERR'):
f, ferr, fd, fderr = pu.pferrs(self.P0, self.P0_ERR, self.P1,
self.P1_ERR)
setattr(self, 'F0_ERR', ferr)
setattr(self, 'F1', fd)
setattr(self, 'F1_ERR', fderr)
else:
f, fd, = pu.p_to_f(self.P0, self.P1)
setattr(self, 'F0_ERR', self.P0_ERR / (self.P0 * self.P0))
setattr(self, 'F1', fd)
if hasattr(self, 'F0_ERR'):
if hasattr(self, 'F1_ERR'):
p, perr, pd, pderr = pu.pferrs(self.F0, self.F0_ERR, self.F1,
self.F1_ERR)
setattr(self, 'P0_ERR', perr)
setattr(self, 'P1', pd)
setattr(self, 'P1_ERR', pderr)
else:
p, pd, = pu.p_to_f(self.F0, self.F1)
setattr(self, 'P0_ERR', self.F0_ERR / (self.F0 * self.F0))
setattr(self, 'P1', pd)
if hasattr(self, 'DM'):
setattr(self, 'DM', self.DM)
if hasattr(self, 'DM1'):
setattr(self, 'DM1', self.DM1)
if hasattr(self, 'DM2'):
setattr(self, 'DM2', self.DM2)
if hasattr(self, 'EPS1') and hasattr(self, 'EPS2'):
self.use_ell = True
ecc = math.sqrt(self.EPS1 * self.EPS1 + self.EPS2 * self.EPS2)
omega = math.atan2(self.EPS1, self.EPS2)
if self.EPS1 == 0.0 and self.EPS2 == 0.0:
ecc_err = pow(
pow(self.EPS1_ERR, 2) + pow(self.EPS2_ERR, 2), .5)
else:
ecc_err = pow(
pow(self.EPS1 * self.EPS1_ERR, 2) +
pow(self.EPS2 * self.EPS2_ERR, 2), 0.5) / ecc
setattr(self, 'ECC', ecc)
setattr(self, 'ECC_ERR', ecc_err)
setattr(self, 'OM', omega)
if hasattr(self, 'PB') and hasattr(self,
'A1') and not hasattr(self, 'ECC'):
setattr(self, 'ECC', 0.0)
if hasattr(self, 'BINARY'):
setattr(self, 'BINARY', self.BINARY)
if hasattr(self, 'KIN'):
setattr(self, 'SINI', math.sin(self.KIN * np.pi / 180.))
setattr(
self, 'SINI_ERR',
math.sin(self.KIN * np.pi / 180.) -
math.sin(self.KIN * np.pi / 180. -
self.KIN_ERR * np.pi / 180.))
pf.close()
def __init__(self, parfilenm):
self.FILE = parfilenm
pf = open(parfilenm)
for line in pf.readlines():
# Convert any 'D-' or 'D+' to 'E-' or 'E+'
line = line.replace("D-", "E-")
line = line.replace("D+", "E+")
splitline = line.split()
key = splitline[0]
if key in str_keys:
setattr(self, key, splitline[1])
elif key in float_keys:
try:
setattr(self, key, float(splitline[1]))
except ValueError:
pass
if len(splitline)==3: # Some parfiles don't have flags, but do have errors
if splitline[2] not in ['0', '1']:
setattr(self, key+'_ERR', float(splitline[2]))
if len(splitline)==4:
setattr(self, key+'_ERR', float(splitline[3]))
# Deal with Ecliptic coords
if (hasattr(self, 'BETA') and hasattr(self, 'LAMBDA')):
setattr(self, 'ELAT', self.BETA)
setattr(self, 'ELONG', self.LAMBDA)
if (slalib and hasattr(self, 'ELAT') and hasattr(self, 'ELONG')):
if hasattr(self, 'POSEPOCH'):
epoch = self.POSEPOCH
else:
epoch = self.PEPOCH
ra_rad, dec_rad = sla_ecleq(self.ELONG*pu.DEGTORAD,
self.ELAT*pu.DEGTORAD, epoch)
rstr = pu.coord_to_string(*pu.rad_to_hms(ra_rad))
dstr = pu.coord_to_string(*pu.rad_to_dms(dec_rad))
setattr(self, 'RAJ', rstr)
setattr(self, 'DECJ', dstr)
if hasattr(self, 'RAJ'):
setattr(self, 'RA_RAD', pu.ra_to_rad(self.RAJ))
if hasattr(self, 'DECJ'):
setattr(self, 'DEC_RAD', pu.dec_to_rad(self.DECJ))
# Compute the Galactic coords
if (slalib and hasattr(self, 'RA_RAD') and hasattr(self, 'DEC_RAD')):
l, b = sla_eqgal(self.RA_RAD, self.DEC_RAD)
setattr(self, 'GLONG', l*pu.RADTODEG)
setattr(self, 'GLAT', b*pu.RADTODEG)
# Compute the Ecliptic coords
if (slalib and hasattr(self, 'RA_RAD') and hasattr(self, 'DEC_RAD')):
if hasattr(self, 'POSEPOCH'):
epoch = self.POSEPOCH
else:
epoch = self.PEPOCH
elon, elat = sla_eqecl(self.RA_RAD, self.DEC_RAD, epoch)
setattr(self, 'ELONG', elon*pu.RADTODEG)
setattr(self, 'ELAT', elat*pu.RADTODEG)
if hasattr(self, 'P'):
setattr(self, 'P0', self.P)
if hasattr(self, 'P0'):
setattr(self, 'F0', 1.0/self.P0)
if hasattr(self, 'F0'):
setattr(self, 'P0', 1.0/self.F0)
if hasattr(self, 'FB0'):
setattr(self, 'PB', (1.0/self.FB0)/86400.0)
if hasattr(self, 'P0_ERR'):
if hasattr(self, 'P1_ERR'):
f, ferr, fd, fderr = pu.pferrs(self.P0, self.P0_ERR,
self.P1, self.P1_ERR)
setattr(self, 'F0_ERR', ferr)
setattr(self, 'F1', fd)
setattr(self, 'F1_ERR', fderr)
else:
f, fd, = pu.p_to_f(self.P0, self.P1)
setattr(self, 'F0_ERR', self.P0_ERR/(self.P0*self.P0))
setattr(self, 'F1', fd)
if hasattr(self, 'F0_ERR'):
if hasattr(self, 'F1_ERR'):
p, perr, pd, pderr = pu.pferrs(self.F0, self.F0_ERR,
self.F1, self.F1_ERR)
setattr(self, 'P0_ERR', perr)
setattr(self, 'P1', pd)
setattr(self, 'P1_ERR', pderr)
else:
p, pd, = pu.p_to_f(self.F0, self.F1)
setattr(self, 'P0_ERR', self.F0_ERR/(self.F0*self.F0))
setattr(self, 'P1', pd)
if hasattr(self, 'EPS1') and hasattr(self, 'EPS2'):
ecc = math.sqrt(self.EPS1 * self.EPS1 + self.EPS2 * self.EPS2)
omega = math.atan2(self.EPS1, self.EPS2)
setattr(self, 'E', ecc)
setattr(self, 'OM', omega * pu.RADTODEG)
setattr(self, 'T0', self.TASC + self.PB * omega/pu.TWOPI)
if hasattr(self, 'PB') and hasattr(self, 'A1') and not \
(hasattr(self, 'E') or hasattr(self, 'ECC')):
setattr(self, 'E', 0.0)
if hasattr(self, 'T0') and hasattr(self, 'PB') and hasattr(self, 'OM') and not hasattr(self, 'TASC'):
setattr(self, 'TASC', self.T0 - self.PB * self.OM/360.0)
pf.close()
def par_diff(params_new, folder):
ar = glob(os.path.join(folder, "*.ar"))[-1]
load_archive = psrchive.Archive_load(ar)
par = load_archive.get_ephemeris()
psr = par.get_value('PSR')
if psr == "": psr = par.get_value('PSRJ')
print "PSR ", psr
print "P0 diff: {:.2e} s".format(params_new['P0'] - float(par.get_value('P0')))
print "DM diff: {:.2e} pc/cc".format(params_new['DM'] - float(par.get_value('DM')))
dist = 60. * np.rad2deg(np.sqrt( (ra_to_rad(par.get_value('RAJ'))-ra_to_rad(params_new['RAJ']))**2 + (dec_to_rad(par.get_value('DECJ'))-dec_to_rad(params_new['DECJ']))**2 ))
print "Distance: {:.3f}'".format(dist)
print ""
return
def main():
pfdfn = sys.argv[1]
pfd = prepfold.pfd(pfdfn)
cand = candidate.Candidate(pfd.topo_p1, pfd.bary_p1, pfd.bestdm, \
psr_utils.ra_to_rad(pfd.rastr)*psr_utils.RADTODEG, \
psr_utils.dec_to_rad(pfd.decstr)*psr_utils.RADTODEG, \
pfdfn)
print "Loaded %s" % cand.pfdfn
print " Best topo period (s): %f" % cand.topo_period
print " Best bary period (s): %f" % cand.bary_period
print " Best DM (cm^-3/pc): %f" % cand.dm
print " RA (J2000 - deg): %f" % cand.raj_deg
print " Dec (J2000 - deg): %f" % cand.decj_deg
print "-"*10
pprint.pprint(cand.__dict__)
print "-"*10
from rating_classes import freq_vs_phase
fvph = freq_vs_phase.FreqVsPhaseClass()
fvph.add_data(cand)
print "Added freq vs phase data to cand"
pprint.pprint(cand.__dict__)
print "-"*10
pprint.pprint(cand.freq_vs_phase.__dict__)
print "Check if freq_vs_phase rotates out and back to same array"
orig = cand.freq_vs_phase.data.copy()
orig_dm = cand.freq_vs_phase.curr_dm
cand.freq_vs_phase.dedisperse(0)
mod = cand.freq_vs_phase.data.copy()
cand.freq_vs_phase.dedisperse(orig_dm)
new = cand.freq_vs_phase.data.copy()
print "Compare orig/mod"
print (orig == mod).all()
print "Compare mod/new"
print (mod == new).all()
print "Compare orig/new"
print (orig == new).all()
print "Compare with best prof"
# Create best prof file
print "show_pfd -noxwin %s" % cand.info['pfdfn']
subprocess.call("show_pfd -noxwin %s" % cand.info['pfdfn'], shell=True, stdout=open(os.devnull))
pfd_bestprof = np.loadtxt(os.path.split(cand.pfd.pfd_filename+".bestprof")[-1], usecols=(1,))
cand.freq_vs_phase.dedisperse(cand.pfd.bestdm)
fvph_prof = cand.freq_vs_phase.data.sum(axis=0).squeeze()
print test_profiles(pfd_bestprof, fvph_prof, 1e-6)
print "Testing dedispersion"
for dm in np.random.randint(0, 1000, size=10):
print "DM: %g" % dm,
cand.freq_vs_phase.dedisperse(dm)
fvph_prof = cand.freq_vs_phase.get_profile()
cand.pfd.dedisperse(dm, doppler=1)
cand.pfd.adjust_period()
pfd_prof = cand.pfd.time_vs_phase().sum(axis=0).squeeze()
print test_profiles(fvph_prof, pfd_prof, 1e-6)
plt.figure(dm)
plt.plot(fvph_prof, label="FvsPh")
plt.plot(pfd_prof, label="PFD")
plt.title("DM: %g" % dm)
plt.legend(loc='best')
plt.show()
def main():
pfdfn = sys.argv[1]
pfd = prepfold.pfd(pfdfn)
cand = candidate.Candidate(pfd.topo_p1, pfd.bary_p1, pfd.bestdm, \
psr_utils.ra_to_rad(pfd.rastr)*psr_utils.RADTODEG, \
psr_utils.dec_to_rad(pfd.decstr)*psr_utils.RADTODEG, \
pfdfn)
print "Loaded %s" % cand.pfdfn
print " Best topo period (s): %f" % cand.topo_period
print " Best bary period (s): %f" % cand.bary_period
print " Best DM (cm^-3/pc): %f" % cand.dm
print " RA (J2000 - deg): %f" % cand.raj_deg
print " Dec (J2000 - deg): %f" % cand.decj_deg
print "-" * 10
pprint.pprint(cand.__dict__)
print "-" * 10
from rating_classes import freq_vs_phase
fvph = freq_vs_phase.FreqVsPhaseClass()
fvph.add_data(cand)
print "Added freq vs phase data to cand"
pprint.pprint(cand.__dict__)
print "-" * 10
pprint.pprint(cand.freq_vs_phase.__dict__)
print "Check if freq_vs_phase rotates out and back to same array"
orig = cand.freq_vs_phase.data.copy()
orig_dm = cand.freq_vs_phase.curr_dm
cand.freq_vs_phase.dedisperse(0)
mod = cand.freq_vs_phase.data.copy()
cand.freq_vs_phase.dedisperse(orig_dm)
new = cand.freq_vs_phase.data.copy()
print "Compare orig/mod"
print(orig == mod).all()
print "Compare mod/new"
print(mod == new).all()
print "Compare orig/new"
print(orig == new).all()
print "Compare with best prof"
# Create best prof file
print "show_pfd -noxwin %s" % cand.info['pfdfn']
subprocess.call("show_pfd -noxwin %s" % cand.info['pfdfn'],
shell=True,
stdout=open(os.devnull))
pfd_bestprof = np.loadtxt(os.path.split(cand.pfd.pfd_filename +
".bestprof")[-1],
usecols=(1, ))
cand.freq_vs_phase.dedisperse(cand.pfd.bestdm)
fvph_prof = cand.freq_vs_phase.data.sum(axis=0).squeeze()
print test_profiles(pfd_bestprof, fvph_prof, 1e-6)
print "Testing dedispersion"
for dm in np.random.randint(0, 1000, size=10):
print "DM: %g" % dm,
cand.freq_vs_phase.dedisperse(dm)
fvph_prof = cand.freq_vs_phase.get_profile()
cand.pfd.dedisperse(dm, doppler=1)
cand.pfd.adjust_period()
pfd_prof = cand.pfd.time_vs_phase().sum(axis=0).squeeze()
print test_profiles(fvph_prof, pfd_prof, 1e-6)
plt.figure(dm)
plt.plot(fvph_prof, label="FvsPh")
plt.plot(pfd_prof, label="PFD")
plt.title("DM: %g" % dm)
plt.legend(loc='best')
plt.show()
def Known_Pulsar_Rating(pfd):
"""
Calculate the probability that a candidate is a known pulsar or a harmonic
of a known pulsar.
Parameters
----------
pfd : class
An instance of the prepfold.pfd class
Returns
-------
names : list
A list of ratings names
ratings : list
A list of ratings values
"""
# The rating name
name1 = "Known_Pulsar_Rating"
# A fudge factor for error calculations
factor = 0.3*pfd.proflen
# Get the candidate RA and DEC (in radians)
cand_ra = PU.ra_to_rad(pfd.rastr)
cand_dec = PU.dec_to_rad(pfd.decstr)
# Get the period and folding epoch from bestprof, if it exists
if hasattr(pfd.bestprof, "p0"):
cand_p = pfd.bestprof.p0
cand_epoch = pfd.bestprof.epochi + pfd.bestprof.epochf
# Get the candidate period error from bestprof, if it exists
if hasattr(pfd.bestprof, "p0err"):
cand_p_err = factor*pfd.bestprof.p0err
# Otherwise, try to estimate it
else:
cand_p_err = factor*cand_p**2/(pfd.proflen*pfd.T)
# Otherwise, use the barycentric or topocentric values
elif pfd.bary_p1 != 0.0:
cand_p = pfd.bary_p1
cand_epoch = pfd.bepoch
cand_p_err = factor*cand_p**2/(pfd.proflen*pfd.T)
elif pfd.topo_p1 != 0.0:
cand_p = pfd.topo_p1
cand_epoch = pfd.tepoch
cand_p_err = factor*cand_p**2/(pfd.proflen*pfd.T)
# Get the min and max frequencies for this observation
f_min = pfd.subfreqs.min()
f_max = pfd.subfreqs.max()
# Get the candidate's best DM
cand_dm = pfd.bestdm
# Try to estimate the DM error
cand_dm_err = factor*cand_p*f_max**2*f_min**2/ \
(4.15e3*pfd.proflen*(f_max**2 - f_min**2))
# Now loop through the catalog of known pulsars and find any that are
# close to the candidate
nearby_psrs = [psr for psr in PSRCAT.psrs \
if (hasattr(psr, "ra") and hasattr(psr, "dec") and \
sla_dsep(cand_ra,cand_dec,psr.ra,psr.dec)<1.3*BEAM_FWHM)]
# If there were no pulsars nearby, return a rating of 0.0 and exit
if len(nearby_psrs) == 0:
rating1 = 0.0
return [name1],[rating1]
# Otherwise, try to estimate the probability that the candidate is a
# known pulsar or its harmonic. This is done by calculting the difference
# between the candidates period and DM and those any nearby known pulsars,
# normalized by the error in the candidate values (i.e., difference in
# "sigmas"). Calculate the probability assuming Gaussian statistics.
else:
# Start off assuming zero probability that the candidate is known
max_prob = 0.0
for psr in nearby_psrs:
# If possible, calculate the known pulsar period at the observing
# epoch
if hasattr(psr,"pepoch"):
delta_t = (cand_epoch - psr.pepoch)*86400.0 # seconds
if hasattr(psr,"pd") and hasattr(psr,"pdd"):
psr_p = psr.p + psr.pd*delta_t + 0.5*psr.pdd*delta_t**2
elif hasattr(psr,"pd"):
psr_p = psr.p + psr.pd*delta_t
else:
psr_p = psr.p
p_prob = 0.0 # The probability that the periods are the same
# Try all harmonic ratios with up to 16 harmonics
for a in xrange(16):
a += 1.0 # Since xrange starts with 0
for b in xrange(16):
b += 1.0 # Since xrange starts with 0
# Difference between candidate and known pulsar harmonic
delta_p = abs(a/b*cand_p - psr_p)
# Calculate the equivalent Gaussian probability
tmp_prob = \
S.special.erfc(delta_p/((a/b)**2*cand_p_err)/ \
N.sqrt(2))
# If this is the highest probability so far, store it
if tmp_prob > p_prob: p_prob = tmp_prob
# Update the total probability
prob = p_prob
# Get the difference in DM
delta_dm = abs(cand_dm - psr.dm)
# Multiply the total probability by the DM probability
prob *= S.special.erfc(delta_dm/cand_dm_err/N.sqrt(2))
# Update max_prob if necessary
if prob > max_prob: max_prob = prob
# Store the rating
rating1 = max_prob
return [name1],[rating1]
def Known_Pulsar_Rating(pfd):
"""
Calculate the probability that a candidate is a known pulsar or a harmonic
of a known pulsar.
Parameters
----------
pfd : class
An instance of the prepfold.pfd class
Returns
-------
names : list
A list of ratings names
ratings : list
A list of ratings values
"""
# The rating name
name1 = "Known_Pulsar_Rating"
# A fudge factor for error calculations
factor = 0.3 * pfd.proflen
# Get the candidate RA and DEC (in radians)
cand_ra = PU.ra_to_rad(pfd.rastr)
cand_dec = PU.dec_to_rad(pfd.decstr)
# Get the period and folding epoch from bestprof, if it exists
if hasattr(pfd.bestprof, "p0"):
cand_p = pfd.bestprof.p0
cand_epoch = pfd.bestprof.epochi + pfd.bestprof.epochf
# Get the candidate period error from bestprof, if it exists
if hasattr(pfd.bestprof, "p0err"):
cand_p_err = factor * pfd.bestprof.p0err
# Otherwise, try to estimate it
else:
cand_p_err = factor * cand_p**2 / (pfd.proflen * pfd.T)
# Otherwise, use the barycentric or topocentric values
elif pfd.bary_p1 != 0.0:
cand_p = pfd.bary_p1
cand_epoch = pfd.bepoch
cand_p_err = factor * cand_p**2 / (pfd.proflen * pfd.T)
elif pfd.topo_p1 != 0.0:
cand_p = pfd.topo_p1
cand_epoch = pfd.tepoch
cand_p_err = factor * cand_p**2 / (pfd.proflen * pfd.T)
# Get the min and max frequencies for this observation
f_min = pfd.subfreqs.min()
f_max = pfd.subfreqs.max()
# Get the candidate's best DM
cand_dm = pfd.bestdm
# Try to estimate the DM error
cand_dm_err = factor*cand_p*f_max**2*f_min**2/ \
(4.15e3*pfd.proflen*(f_max**2 - f_min**2))
# Now loop through the catalog of known pulsars and find any that are
# close to the candidate
nearby_psrs = [psr for psr in PSRCAT.psrs \
if (hasattr(psr, "ra") and hasattr(psr, "dec") and \
sla_dsep(cand_ra,cand_dec,psr.ra,psr.dec)<1.3*BEAM_FWHM)]
# If there were no pulsars nearby, return a rating of 0.0 and exit
if len(nearby_psrs) == 0:
rating1 = 0.0
return [name1], [rating1]
# Otherwise, try to estimate the probability that the candidate is a
# known pulsar or its harmonic. This is done by calculting the difference
# between the candidates period and DM and those any nearby known pulsars,
# normalized by the error in the candidate values (i.e., difference in
# "sigmas"). Calculate the probability assuming Gaussian statistics.
else:
# Start off assuming zero probability that the candidate is known
max_prob = 0.0
for psr in nearby_psrs:
# If possible, calculate the known pulsar period at the observing
# epoch
if hasattr(psr, "pepoch"):
delta_t = (cand_epoch - psr.pepoch) * 86400.0 # seconds
if hasattr(psr, "pd") and hasattr(psr, "pdd"):
psr_p = psr.p + psr.pd * delta_t + 0.5 * psr.pdd * delta_t**2
elif hasattr(psr, "pd"):
psr_p = psr.p + psr.pd * delta_t
else:
psr_p = psr.p
p_prob = 0.0 # The probability that the periods are the same
# Try all harmonic ratios with up to 16 harmonics
for a in xrange(16):
a += 1.0 # Since xrange starts with 0
for b in xrange(16):
b += 1.0 # Since xrange starts with 0
# Difference between candidate and known pulsar harmonic
delta_p = abs(a / b * cand_p - psr_p)
# Calculate the equivalent Gaussian probability
tmp_prob = \
S.special.erfc(delta_p/((a/b)**2*cand_p_err)/ \
N.sqrt(2))
# If this is the highest probability so far, store it
if tmp_prob > p_prob: p_prob = tmp_prob
# Update the total probability
prob = p_prob
# Get the difference in DM
delta_dm = abs(cand_dm - psr.dm)
# Multiply the total probability by the DM probability
prob *= S.special.erfc(delta_dm / cand_dm_err / N.sqrt(2))
# Update max_prob if necessary
if prob > max_prob: max_prob = prob
# Store the rating
rating1 = max_prob
return [name1], [rating1]
def main():
pfdfn = sys.argv[1]
pfd = myprepfold.pfd(pfdfn)
cand = candidate.Candidate(pfd.topo_p1, pfd.bary_p1, pfd.bestdm, \
psr_utils.ra_to_rad(pfd.rastr)*psr_utils.RADTODEG, \
psr_utils.dec_to_rad(pfd.decstr)*psr_utils.RADTODEG, \
pfdfn)
print "Loaded %s" % cand.pfdfn
print " Best topo period (s): %f" % cand.topo_period
print " Best bary period (s): %f" % cand.bary_period
print " Best DM (cm^-3/pc): %f" % cand.dm
print " RA (J2000 - deg): %f" % cand.raj_deg
print " Dec (J2000 - deg): %f" % cand.decj_deg
print "-" * 10
pprint.pprint(cand.__dict__)
print "-" * 10
from rating_classes import time_vs_phase
time_vs_phase.TimeVsPhaseClass().add_data(cand)
tvph = cand.time_vs_phase
print "Added time vs phase data to cand"
pprint.pprint(cand.__dict__)
print "-" * 10
pprint.pprint(cand.time_vs_phase.__dict__)
print "DEBUG: tvph.pdelays_bins", tvph.pdelays_bins
print "DEBUG: pfd.pdelays_bins", pfd.pdelays_bins
pfd.dedisperse(doppler=1)
print "DEBUG: tvph.dm, pfd.currdm", tvph.dm, pfd.currdm
if pfd.fold_pow == 1.0:
bestp = pfd.bary_p1
bestpd = pfd.bary_p2
bestpdd = pfd.bary_p3
else:
bestp = pfd.topo_p1
bestpd = pfd.topo_p2
bestpdd = pfd.topo_p3
pfd.adjust_period()
tvph.adjust_period(bestp, bestpd, bestpdd)
mypfd_tvph = pfd.time_vs_phase()
print test_img(tvph.data, mypfd_tvph, 1e-6)
plt.figure()
plt.plot(tvph.data.sum(axis=0), label='TvPh')
plt.plot(mypfd_tvph.sum(axis=0), label='PFD')
plt.title("P=%g s, Pd=%g s/s, Pdd=%g s/s^2" % (bestp, bestpd, bestpdd))
plt.legend(loc='best')
for ii in range(10):
frac_p = 1 + (np.random.rand(1) * 2 - 1) / 100.0
frac_pd = 1 + (np.random.rand(1) * 2 - 1) / 100.0
frac_pdd = 1 + (np.random.rand(1) * 2 - 1) / 100.0
p = frac_p * bestp
pd = frac_pd * bestpd
pdd = frac_pdd * bestpdd
print "Shuffling for the %dth time" % ii
print "frac_p, frac_pd, frac_pdd", frac_p, frac_pd, frac_pdd
tvph.adjust_period(p, pd, pdd)
mypfd_tvph = pfd.time_vs_phase(p, pd, pdd)
print test_img(tvph.data, mypfd_tvph, 1e-9)
plt.figure()
plt.plot(tvph.data.sum(axis=0), label='TvPh')
plt.plot(mypfd_tvph.sum(axis=0), label='PFD')
plt.title("P=%g s, Pd=%g s/s, Pdd=%g s/s^2" % (p, pd, pdd))
plt.legend(loc='best')
plt.show()
sys.exit(1)
print "Check if time_vs_phase rotates out and back to same array"
orig = cand.time_vs_phase.data.copy()
orig_p = cand.time_vs_phase.curr_p
orig_pd = cand.time_vs_phase.curr_pd
orig_pdd = cand.time_vs_phase.curr_pdd
cand.time_vs_phase.adjust_period(p=orig_p - 0.00123, pd=0, pdd=0)
mod = cand.time_vs_phase.data.copy()
cand.time_vs_phase.adjust_period(p=orig_p, pd=orig_pd, pdd=orig_pdd)
new = cand.time_vs_phase.data.copy()
print "Compare orig/mod"
print(orig == mod).all()
print "Compare mod/new"
print(mod == new).all()
print "Compare orig/new"
print(orig == new).all()
print "Compare with best prof"
# Create best prof file
print "show_pfd -noxwin %s" % cand.info['pfdfn']
subprocess.call("show_pfd -noxwin %s" % cand.info['pfdfn'],
shell=True,
stdout=open(os.devnull))
prof = np.loadtxt(os.path.split(cand.pfd.pfd_filename + ".bestprof")[-1],
usecols=(1, ))
pfd = cand.pfd
if pfd.fold_pow == 1.0:
bestp = pfd.bary_p1
bestpd = pfd.bary_p2
bestpdd = pfd.bary_p3
else:
bestp = pfd.topo_p1
bestpd = pfd.topo_p2
bestpdd = pfd.topo_p3
cand.time_vs_phase.adjust_period(bestp, bestpd, bestpdd)
tvph = pfd.time_vs_phase()
print test_img(cand.time_vs_phase.data, tvph, 1e-6)
plt.plot(cand.time_vs_phase.get_profile())
plt.plot(tvph.sum(axis=0).squeeze())
plt.show()
def main():
pfdfn = sys.argv[1]
pfd = prepfold.pfd(pfdfn)
cand = candidate.Candidate(pfd.topo_p1, pfd.bary_p1, pfd.bestdm, \
psr_utils.ra_to_rad(pfd.rastr)*psr_utils.RADTODEG, \
psr_utils.dec_to_rad(pfd.decstr)*psr_utils.RADTODEG, \
pfdfn)
print "Loaded %s" % cand.pfdfn
print " Best topo period (s): %f" % cand.topo_period
print " Best bary period (s): %f" % cand.bary_period
print " Best DM (cm^-3/pc): %f" % cand.dm
print " RA (J2000 - deg): %f" % cand.raj_deg
print " Dec (J2000 - deg): %f" % cand.decj_deg
print "-"*10
pprint.pprint(cand.__dict__)
print "-"*10
intstats.add_data(cand)
print "Added subint stats to cand"
pprint.pprint(cand.__dict__)
print "-"*10
print "Subint stats object:"
pprint.pprint(cand.subint_stats.__dict__)
print "-"*10
print "Sub-int Stats:"
print "On-fraction (area): %g" % cand.subint_stats.get_on_frac()
print "On-fraction (peak): %g" % cand.subint_stats.get_peak_on_frac()
print "Area SNR stddev: %g" % cand.subint_stats.get_snr_stddev()
print "Peak SNR stddev: %g" % cand.subint_stats.get_peak_snr_stddev()
print "Avg correlation coefficient: %g" % cand.subint_stats.get_avg_corrcoef()
mgauss = cand.multigaussfit
tvph = cand.time_vs_phase
onpulse_region = mgauss.get_onpulse_region(tvph.nbin)
offpulse_region = np.bitwise_not(onpulse_region)
m = np.ma.masked_array(onpulse_region, mask=offpulse_region)
onpulse_ranges = np.ma.notmasked_contiguous(m)
print onpulse_ranges
prof = utils.get_scaled_profile(cand.profile, cand.pfd.varprof)
imax = plt.axes([0.1,0.1,0.5,0.7])
plt.imshow(scale2d(tvph.data), interpolation='nearest', \
aspect='auto', origin='lower', cmap=matplotlib.cm.gist_yarg)
for opr in onpulse_ranges:
onpulse_bin = (opr.start, opr.stop)
if onpulse_bin[0] < onpulse_bin[1]:
plt.axvspan(onpulse_bin[0], onpulse_bin[1]+1, fc='g', alpha=0.2, lw=0)
else:
plt.axvspan(onpulse_bin[0], tvph.nbin, fc='g', alpha=0.2, lw=0)
plt.axvspan(-0.5, onpulse_bin[1]+1, fc='g', alpha=0.2, lw=0)
plt.axes([0.1,0.8,0.5,0.15], sharex=imax)
plt.plot(prof, 'k-', label='Profile')
plt.plot(mgauss.make_gaussians(len(prof)), 'r--', label='Fit')
for ii, opr in enumerate(onpulse_ranges):
onpulse_bin = (opr.start, opr.stop)
if ii == 0:
lbl = 'On-pulse'
else:
lbl = '_nolabel_'
if onpulse_bin[0] < onpulse_bin[1]:
plt.axvspan(onpulse_bin[0], onpulse_bin[1]+1, fc='g', alpha=0.2, lw=0, label=lbl)
else:
plt.axvspan(onpulse_bin[0], tvph.nbin, fc='g', alpha=0.2, lw=0, label=lbl)
plt.axvspan(-0.5, onpulse_bin[1]+1, fc='g', alpha=0.2, lw=0, label='_nolabel_')
plt.legend(loc='best', prop=dict(size='xx-small'))
plt.axes([0.6,0.1,0.15,0.7], sharey=imax)
snrs = cand.subint_stats.snrs
plt.plot(snrs, np.arange(len(snrs)), 'mo', label='SNR (area)')
plt.axvline(5.0, c='m', ls='--', lw=2)
peaksnrs = cand.subint_stats.peak_snrs
plt.plot(peaksnrs, np.arange(len(peaksnrs)), 'cD', label='SNR (peak)')
plt.axvline(3.0, c='c', ls='--', lw=2)
plt.legend(loc='best', prop=dict(size='xx-small'))
plt.axes([0.75,0.1,0.15,0.7], sharey=imax)
corrcoefs = cand.subint_stats.corr_coefs
plt.plot(corrcoefs, np.arange(len(corrcoefs)), 'k.')
imax.set_xlim(-0.5,tvph.nbin-0.5)
imax.set_ylim(-0.5,tvph.nsubint-0.5)
plt.show()
def read_parfiles(self):
"""
Read in all parfiles and store relevant fields.
"""
### Useful cluster parameters. This should be updated to reflect individual clusters being examined.
cluster = {"Ter5": ["Terzan5", "17:48:04.85", "-24:46:44.6", 5.9, 7.9/60.0, 0.83, 13.27, 7.60, 8.97, 20.33, 5.06, 3.8, 1.7, 17.0, 21.72, 5.0], \
"M28": ['M28', "18:24:32.89", "-24:52:11.4", 7.0, 7.9/60.0, 0.83, 13.27, 7.60, 8.97, 20.33, 5.06, 8.14, -6.18, 17.0, 21.72, 5.0], \
"47TUC": ['47TUC', "00:24:5.29", "-72:04:52.3", 7.0, 7.9/60.0, 0.83, 13.27, 7.60, 8.97, 20.33, 5.06, 305.89, -44.88, 17.0, 21.72, 5.0]}
### Create an easy to use dictionary with simple attributes
params = self.DataBunch(R=[],ACCEL=[],ACCEL_ERR=[],P0=[],P0_ERR=[],P1=[],P1_ERR=[],P2=[],P2_ERR=[],P3=[],P3_ERR=[],A1=[],A1_ERR=[], \
PB=[],PB_ERR=[],PBDOT=[],PBDOT_ERR=[],ACCEL_BINARY=[],ACCEL_BINARY_ERR=[],FNAME=[],PSRNAME=[],ACCEL_CLUSTER=[],ACCEL_CLUSTER_ERR=[], \
RA_OFFSET=[],DEC_OFFSET=[],DM=[])
for ipar in self.files:
psr = psr_par(ipar,unit_flag=True)
psr.cluster = self.cluster
### Calculate the Surface Density and its Error
psr.D = cluster[psr.cluster][3]
psr.l = (cluster[psr.cluster][11]*units.deg).to(units.rad)
psr.b = (cluster[psr.cluster][12]*units.deg).to(units.rad)
psr.cluster_RA = ra_to_rad(cluster[psr.cluster][1])*units.rad
psr.cluster_DEC = dec_to_rad(cluster[psr.cluster][2])*units.rad
psr.core_ang_asec = (sphere_ang_diff(psr.RA_RAD.value, psr.DEC_RAD.value, psr.cluster_RA.value, psr.cluster_DEC.value)*units.rad).to(units.arcsec)+self.r_c_offset_error
R0 = (8.34*units.kpc).to(units.m)
R0_error = (.16*units.kpc).to(units.m)
Omega0 = (240.0*units.km/units.s).to(units.m/units.s)
Omega0_error = (8.0*units.km/units.s).to(units.m/units.s)
d = (5.9*units.kpc).to(units.m)
d_error = (.5*units.kpc).to(units.m)
beta = (d/R0)*np.cos(psr.b)-np.cos(psr.l)
beta_error = np.sqrt(((d_error/R0)*np.cos(psr.b))**2.+((R0_error*d/R0**2.)*np.cos(psr.b))**2.)
psr.abyc_gal = (-np.cos(psr.b)*((Omega0**2.)/R0)*(np.cos(psr.l)+beta/(np.sin(psr.l)**2.+beta**2.)))/constants.c
psr.abyc_gal_error = np.sqrt((((-np.cos(psr.b)*((Omega0**2.)/R0))/constants.c)*((np.sin(psr.l)**2.-beta**2.)/(np.sin(psr.l)**2.+beta**2.)**2.)*beta_error)**2.
+((-np.cos(psr.b)*((Omega0_error*Omega0)/R0)*(np.cos(psr.l)+beta/(np.sin(psr.l)**2.+beta**2.)))/constants.c)**2.
+((-np.cos(psr.b)*((R0_error*Omega0**2.)/R0**2.)*(np.cos(psr.l)+beta/(np.sin(psr.l)**2.+beta**2.)))/constants.c)**2.)
psr.abyc_meas = psr.P1/psr.P0
psr.abyc_meas_error = np.sqrt((psr.P1_ERR/psr.P0)**2.+(psr.P0_ERR*psr.P1/psr.P0**2.)**2.)
psr.abyc_clust = psr.abyc_meas - psr.abyc_gal
psr.abyc_clust_error = np.sqrt(psr.abyc_meas_error**2.+psr.abyc_gal_error**2.)
### Store important attributes to params
params.R.append((psr.core_ang_asec)*psr.D*1000/206265)
params.ACCEL.append(psr.abyc_clust*self.c)
params.ACCEL_ERR.append(psr.abyc_clust_error*self.c)
params.P0.append(psr.P0)
params.P0_ERR.append(psr.P0_ERR)
params.P1.append(psr.P1)
params.P1_ERR.append(psr.P1_ERR)
params.FNAME.append(psr.FILE.split('.par')[0])
params.PSRNAME.append(psr.FILE.split('.par')[0].split('-2446')[-1])
params.ACCEL_CLUSTER.append(0.*units.m/(units.s**2.))
params.ACCEL_CLUSTER_ERR.append(0.*units.m/(units.s**2.))
params.RA_OFFSET.append((psr.RA_RAD-psr.cluster_RA).to(units.arcsec))
params.DEC_OFFSET.append((psr.DEC_RAD-psr.cluster_DEC).to(units.arcsec))
params.DM.append(psr.DM)
if hasattr(psr, 'F2'):
params.P2.append(-1.0*((psr.F2/psr.F0**2)-2.0*psr.F1**2/psr.F0**3))
params.P2_ERR.append(((psr.P0_ERR*(2*psr.P0*psr.F2+2*(psr.P1**2)/(psr.P0**2)))**2+(psr.F2_ERR*psr.P0**2)**2+(4*psr.P1*psr.P1_ERR/psr.P0)**2)**.5)
else:
unit = 1./units.s
params.P2.append(0*unit)
params.P2_ERR.append(0*unit)
if hasattr(psr, 'F3'):
dF0 = 2.0*(psr.F3*psr.F0**2+12.0*psr.F1**3-9.0*psr.F0*psr.F1*psr.F2)/psr.F0**5
dF1 = 6.0*(psr.F0*psr.F2-3.0*psr.F1**2)/psr.F0**4
dF2 = 6.0*psr.F1/psr.F0**3
dF3 = -1.0/psr.F0**2
err = np.sqrt((dF0*psr.F0_ERR)**2+(dF1*psr.F1_ERR)**2+(dF2*psr.F2_ERR)**2+(dF3*psr.F3_ERR)**2)
params.P3.append(-1.0*(psr.F3*psr.F0**2+6.0*psr.F1**3-6.0*psr.F0*psr.F1*psr.F2)/psr.F0**4)
params.P3_ERR.append(err)
else:
unit = 1./units.s**2.
params.P3.append(-999*unit)
params.P3_ERR.append(-999*unit)
if hasattr(psr, 'PBDOT'):
params.A1.append(psr.A1*self.c)
params.A1_ERR.append(psr.A1_ERR*self.c)
params.PB.append(psr.PB.to(units.s))
params.PB_ERR.append(psr.PB_ERR.to(units.s))
params.PBDOT.append(psr.PBDOT*1e-12)
params.PBDOT_ERR.append(psr.PBDOT_ERR*1e-12)
params.ACCEL_BINARY.append((psr.PBDOT*1e-12/(psr.PB.to(units.s))-psr.abyc_gal)*self.c)
params.ACCEL_BINARY_ERR.append(np.sqrt((psr.PBDOT_ERR*1e-12/(psr.PB.to(units.s)))**2+(psr.PBDOT*1e-12*psr.PB_ERR.to(units.s)/(psr.PB.to(units.s))**2)**2)*self.c)
else:
unit = units.m
params.A1.append(0*unit)
params.A1_ERR.append(0*unit)
unit = units.s
params.PB.append(0*unit)
params.PB_ERR.append(0*unit)
unit = units.s/units.s
params.PBDOT.append(0*unit)
params.PBDOT_ERR.append(0*unit)
params.ACCEL_BINARY.append(0.*units.m/(units.s**2.))
params.ACCEL_BINARY_ERR.append(0.*units.m/(units.s**2.))
# Clean up the units a bit. Not good to have them IN the list.
for ikey in params.keys():
try:
unit = params[ikey][0].unit
tmp = []
for ivalue in params[ikey]:
tmp.append(ivalue.value)
params[ikey] = np.asarray(tmp)*unit
except AttributeError:
params[ikey] = np.asarray(params[ikey])
### Return values in params sorted by projected distance from the core. (Returns as numpy array).
idx = np.argsort(params.R)
for i in params.keys():
params[i] = params[i][idx]
return params
def __init__(self, parfilenm):
self.FILE = parfilenm
pf = open(parfilenm)
for line in pf.readlines():
# Skip comments
if line[0] == '#':
continue
# Convert any 'D-' or 'D+' to 'E-' or 'E+'
line = line.replace("D-", "E-")
line = line.replace("D+", "E+")
splitline = line.split()
# Skip blank lines
if len(splitline) == 0:
continue
key = splitline[0]
# Regex checks for non-digit chars, followed by digit chars
m1 = re.search(r'(\D+)(\d+)$', key)
# This one looks for the DMX[RF][12]_* params
m2 = re.search(r'(\D+\d+_)(\d+)$', key)
if key == "JUMP":
if splitline[3] not in ['0', '1']:
setattr(self, key + '_%s' % splitline[2],
float(splitline[3]))
if len(splitline) == 5:
if splitline[4] not in ['0', '1']:
setattr(self, key + '_%s' % splitline[2] + '_ERR',
float(splitline[4]))
elif len(splitline) == 6:
setattr(self, key + '_%s' % splitline[2] + '_ERR',
float(splitline[5]))
if key in str_keys:
setattr(self, key, splitline[1])
elif key in float_keys:
try:
setattr(self, key, float(splitline[1]))
except ValueError:
pass
elif m1 is not None:
m = m1
if m2 is not None:
m = m2
if m.group(1) in floatn_keys:
try:
setattr(self, key, float(splitline[1]))
except ValueError:
pass
if len(
splitline
) == 3: # Some parfiles don't have flags, but do have errors
if splitline[2] not in ['0', '1']:
setattr(self, key + '_ERR', float(splitline[2]))
if len(splitline) == 4:
setattr(self, key + '_ERR', float(splitline[3]))
# Deal with Ecliptic coords
if (hasattr(self, 'BETA') and hasattr(self, 'LAMBDA')):
setattr(self, 'ELAT', self.BETA)
setattr(self, 'ELONG', self.LAMBDA)
if (slalib and hasattr(self, 'ELAT') and hasattr(self, 'ELONG')):
# TEMPO's ecliptic coords are always based on J2000 epoch
ra_rad, dec_rad = sla_ecleq(self.ELONG * pu.DEGTORAD,
self.ELAT * pu.DEGTORAD, J2000)
rstr = pu.coord_to_string(*pu.rad_to_hms(ra_rad))
dstr = pu.coord_to_string(*pu.rad_to_dms(dec_rad))
setattr(self, 'RAJ', rstr)
setattr(self, 'DECJ', dstr)
if hasattr(self, 'RAJ'):
setattr(self, 'RA_RAD', pu.ra_to_rad(self.RAJ))
if hasattr(self, 'DECJ'):
setattr(self, 'DEC_RAD', pu.dec_to_rad(self.DECJ))
# Compute the Galactic coords
if (slalib and hasattr(self, 'RA_RAD') and hasattr(self, 'DEC_RAD')):
l, b = sla_eqgal(self.RA_RAD, self.DEC_RAD)
setattr(self, 'GLONG', l * pu.RADTODEG)
setattr(self, 'GLAT', b * pu.RADTODEG)
# Compute the Ecliptic coords
if (slalib and hasattr(self, 'RA_RAD') and hasattr(self, 'DEC_RAD')):
# TEMPO's ecliptic coords are always based on J2000 epoch
elon, elat = sla_eqecl(self.RA_RAD, self.DEC_RAD, J2000)
setattr(self, 'ELONG', elon * pu.RADTODEG)
setattr(self, 'ELAT', elat * pu.RADTODEG)
if hasattr(self, 'P'):
setattr(self, 'P0', self.P)
if hasattr(self, 'P0'):
setattr(self, 'F0', 1.0 / self.P0)
if hasattr(self, 'F0'):
setattr(self, 'P0', 1.0 / self.F0)
if hasattr(self, 'FB0'):
setattr(self, 'PB', (1.0 / self.FB0) / 86400.0)
if hasattr(self, 'P0_ERR'):
if hasattr(self, 'P1_ERR'):
f, ferr, fd, fderr = pu.pferrs(self.P0, self.P0_ERR, self.P1,
self.P1_ERR)
setattr(self, 'F0_ERR', ferr)
setattr(self, 'F1', fd)
setattr(self, 'F1_ERR', fderr)
else:
f, fd, = pu.p_to_f(self.P0, self.P1)
setattr(self, 'F0_ERR', self.P0_ERR / (self.P0 * self.P0))
setattr(self, 'F1', fd)
else:
if hasattr(self, 'P1'):
f, fd, = pu.p_to_f(self.P0, self.P1)
setattr(self, 'F1', fd)
elif hasattr(self, 'F1'):
p, pd, = pu.p_to_f(self.F0, self.F1)
setattr(self, 'P1', pd)
if (hasattr(self, 'F0_ERR') and hasattr(self, 'F1_ERR')):
p, perr, pd, pderr = pu.pferrs(self.F0, self.F0_ERR, self.F1,
self.F1_ERR)
setattr(self, 'P0_ERR', perr)
setattr(self, 'P1', pd)
setattr(self, 'P1_ERR', pderr)
elif (hasattr(self, 'F0') and hasattr(self, 'F0_ERR')):
setattr(self, 'P0_ERR', self.F0_ERR / (self.F0 * self.F0))
if hasattr(self, 'EPS1') and hasattr(self, 'EPS2'):
ecc = math.sqrt(self.EPS1 * self.EPS1 + self.EPS2 * self.EPS2)
omega = math.atan2(self.EPS1, self.EPS2)
setattr(self, 'E', ecc)
setattr(self, 'OM', omega * pu.RADTODEG)
setattr(self, 'T0', self.TASC + self.PB * omega / pu.TWOPI)
if hasattr(self, 'PB') and hasattr(self, 'A1') and not \
(hasattr(self, 'E') or hasattr(self, 'ECC')):
setattr(self, 'E', 0.0)
if hasattr(self, 'T0') and not hasattr(self, 'TASC'):
setattr(self, 'TASC', self.T0 - self.PB * self.OM / 360.0)
if hasattr(self, 'E') and not hasattr(self, 'ECC'):
setattr(self, 'ECC', self.E)
if not hasattr(self, 'EPS1'):
setattr(self, 'ECC_ERR', self.E_ERR)
if hasattr(self, 'ECC') and not hasattr(self, 'E'):
setattr(self, 'E', self.ECC)
setattr(self, 'E_ERR', self.ECC_ERR)
pf.close()
def __init__(self, parfilenm):
self.FILE = parfilenm
pf = open(parfilenm)
for line in pf.readlines():
# Skip comments
if line[0] == '#':
continue
# Convert any 'D-' or 'D+' to 'E-' or 'E+'
line = line.replace("D-", "E-")
line = line.replace("D+", "E+")
splitline = line.split()
# Skip blank lines
if len(splitline) == 0:
continue
key = splitline[0]
if key in str_keys:
setattr(self, key, splitline[1])
elif key in float_keys:
try:
setattr(self, key, float(splitline[1]))
except ValueError:
pass
if len(
splitline
) == 3: # Some parfiles don't have flags, but do have errors
if splitline[2] not in ['0', '1']:
setattr(self, key + '_ERR', float(splitline[2]))
if len(splitline) == 4:
setattr(self, key + '_ERR', float(splitline[3]))
# Deal with Ecliptic coords
if (hasattr(self, 'BETA') and hasattr(self, 'LAMBDA')):
setattr(self, 'ELAT', self.BETA)
setattr(self, 'ELONG', self.LAMBDA)
if (slalib and hasattr(self, 'ELAT') and hasattr(self, 'ELONG')):
if hasattr(self, 'POSEPOCH'):
epoch = self.POSEPOCH
else:
epoch = self.PEPOCH
ra_rad, dec_rad = sla_ecleq(self.ELONG * pu.DEGTORAD,
self.ELAT * pu.DEGTORAD, epoch)
rstr = pu.coord_to_string(*pu.rad_to_hms(ra_rad))
dstr = pu.coord_to_string(*pu.rad_to_dms(dec_rad))
setattr(self, 'RAJ', rstr)
setattr(self, 'DECJ', dstr)
if hasattr(self, 'RAJ'):
setattr(self, 'RA_RAD', pu.ra_to_rad(self.RAJ))
if hasattr(self, 'DECJ'):
setattr(self, 'DEC_RAD', pu.dec_to_rad(self.DECJ))
# Compute the Galactic coords
if (slalib and hasattr(self, 'RA_RAD') and hasattr(self, 'DEC_RAD')):
l, b = sla_eqgal(self.RA_RAD, self.DEC_RAD)
setattr(self, 'GLONG', l * pu.RADTODEG)
setattr(self, 'GLAT', b * pu.RADTODEG)
# Compute the Ecliptic coords
if (slalib and hasattr(self, 'RA_RAD') and hasattr(self, 'DEC_RAD')):
if hasattr(self, 'POSEPOCH'):
epoch = self.POSEPOCH
else:
epoch = self.PEPOCH
elon, elat = sla_eqecl(self.RA_RAD, self.DEC_RAD, epoch)
setattr(self, 'ELONG', elon * pu.RADTODEG)
setattr(self, 'ELAT', elat * pu.RADTODEG)
if hasattr(self, 'P'):
setattr(self, 'P0', self.P)
if hasattr(self, 'P0'):
setattr(self, 'F0', 1.0 / self.P0)
if hasattr(self, 'F0'):
setattr(self, 'P0', 1.0 / self.F0)
if hasattr(self, 'FB0'):
setattr(self, 'PB', (1.0 / self.FB0) / 86400.0)
if hasattr(self, 'P0_ERR'):
if hasattr(self, 'P1_ERR'):
f, ferr, fd, fderr = pu.pferrs(self.P0, self.P0_ERR, self.P1,
self.P1_ERR)
setattr(self, 'F0_ERR', ferr)
setattr(self, 'F1', fd)
setattr(self, 'F1_ERR', fderr)
else:
f, fd, = pu.p_to_f(self.P0, self.P1)
setattr(self, 'F0_ERR', self.P0_ERR / (self.P0 * self.P0))
setattr(self, 'F1', fd)
if (hasattr(self, 'F0_ERR') and hasattr(self, 'F1_ERR')):
p, perr, pd, pderr = pu.pferrs(self.F0, self.F0_ERR, self.F1,
self.F1_ERR)
setattr(self, 'P0_ERR', perr)
setattr(self, 'P1', pd)
setattr(self, 'P1_ERR', pderr)
elif (hasattr(self, 'F0') and hasattr(self, 'F0_ERR')):
setattr(self, 'P0_ERR', self.F0_ERR / (self.F0 * self.F0))
if hasattr(self, 'EPS1') and hasattr(self, 'EPS2'):
ecc = math.sqrt(self.EPS1 * self.EPS1 + self.EPS2 * self.EPS2)
omega = math.atan2(self.EPS1, self.EPS2)
setattr(self, 'E', ecc)
setattr(self, 'OM', omega * pu.RADTODEG)
setattr(self, 'T0', self.TASC + self.PB * omega / pu.TWOPI)
if hasattr(self, 'PB') and hasattr(self, 'A1') and not \
(hasattr(self, 'E') or hasattr(self, 'ECC')):
setattr(self, 'E', 0.0)
if hasattr(self, 'T0') and not hasattr(self, 'TASC'):
setattr(self, 'TASC', self.T0 - self.PB * self.OM / 360.0)
pf.close()
def __init__(self, parfilenm):
self.FILE = parfilenm
pf = open(parfilenm)
for line in pf.readlines():
# Skip comments
if line[0]=='#':
continue
# Convert any 'D-' or 'D+' to 'E-' or 'E+'
line = line.replace("D-", "E-")
line = line.replace("D+", "E+")
splitline = line.split()
# Skip blank lines
if len(splitline)==0:
continue
key = splitline[0]
# Regex checks for non-digit chars, followed by digit chars
m1 = re.search(r'(\D+)(\d+)$', key)
# This one looks for the DMX[RF][12]_* params
m2 = re.search(r'(\D+\d+_)(\d+)$', key)
if key == "JUMP":
if splitline[3] not in ['0', '1']:
setattr(self, key+'_%s'%splitline[2], float(splitline[3]))
if len(splitline)==5:
if splitline[4] not in ['0', '1']:
setattr(self, key+'_%s'%splitline[2]+'_ERR',
float(splitline[4]))
elif len(splitline)==6:
setattr(self, key+'_%s'%splitline[2]+'_ERR',
float(splitline[5]))
if key in str_keys:
setattr(self, key, splitline[1])
elif key in float_keys:
try:
setattr(self, key, float(splitline[1]))
except ValueError:
pass
elif m1 is not None:
m = m1
if m2 is not None:
m = m2
if m.group(1) in floatn_keys:
try:
setattr(self, key, float(splitline[1]))
except ValueError:
pass
if len(splitline)==3: # Some parfiles don't have flags, but do have errors
if splitline[2] not in ['0', '1']:
setattr(self, key+'_ERR', float(splitline[2]))
if len(splitline)==4:
setattr(self, key+'_ERR', float(splitline[3]))
# Deal with Ecliptic coords
if (hasattr(self, 'BETA') and hasattr(self, 'LAMBDA')):
setattr(self, 'ELAT', self.BETA)
setattr(self, 'ELONG', self.LAMBDA)
if (slalib and hasattr(self, 'ELAT') and hasattr(self, 'ELONG')):
if hasattr(self, 'POSEPOCH'):
epoch = self.POSEPOCH
else:
epoch = self.PEPOCH
ra_rad, dec_rad = sla_ecleq(self.ELONG*pu.DEGTORAD,
self.ELAT*pu.DEGTORAD, epoch)
rstr = pu.coord_to_string(*pu.rad_to_hms(ra_rad))
dstr = pu.coord_to_string(*pu.rad_to_dms(dec_rad))
setattr(self, 'RAJ', rstr)
setattr(self, 'DECJ', dstr)
if hasattr(self, 'RAJ'):
setattr(self, 'RA_RAD', pu.ra_to_rad(self.RAJ))
if hasattr(self, 'DECJ'):
setattr(self, 'DEC_RAD', pu.dec_to_rad(self.DECJ))
# Compute the Galactic coords
if (slalib and hasattr(self, 'RA_RAD') and hasattr(self, 'DEC_RAD')):
l, b = sla_eqgal(self.RA_RAD, self.DEC_RAD)
setattr(self, 'GLONG', l*pu.RADTODEG)
setattr(self, 'GLAT', b*pu.RADTODEG)
# Compute the Ecliptic coords
if (slalib and hasattr(self, 'RA_RAD') and hasattr(self, 'DEC_RAD')):
if hasattr(self, 'POSEPOCH'):
epoch = self.POSEPOCH
else:
epoch = self.PEPOCH
elon, elat = sla_eqecl(self.RA_RAD, self.DEC_RAD, epoch)
setattr(self, 'ELONG', elon*pu.RADTODEG)
setattr(self, 'ELAT', elat*pu.RADTODEG)
if hasattr(self, 'P'):
setattr(self, 'P0', self.P)
if hasattr(self, 'P0'):
setattr(self, 'F0', 1.0/self.P0)
if hasattr(self, 'F0'):
setattr(self, 'P0', 1.0/self.F0)
if hasattr(self, 'FB0'):
setattr(self, 'PB', (1.0/self.FB0)/86400.0)
if hasattr(self, 'P0_ERR'):
if hasattr(self, 'P1_ERR'):
f, ferr, fd, fderr = pu.pferrs(self.P0, self.P0_ERR,
self.P1, self.P1_ERR)
setattr(self, 'F0_ERR', ferr)
setattr(self, 'F1', fd)
setattr(self, 'F1_ERR', fderr)
else:
f, fd, = pu.p_to_f(self.P0, self.P1)
setattr(self, 'F0_ERR', self.P0_ERR/(self.P0*self.P0))
setattr(self, 'F1', fd)
else:
if hasattr(self, 'P1'):
f, fd, = pu.p_to_f(self.P0, self.P1)
setattr(self, 'F1', fd)
elif hasattr(self, 'F1'):
p, pd, = pu.p_to_f(self.F0, self.F1)
setattr(self, 'P1', pd)
if (hasattr(self, 'F0_ERR') and hasattr(self, 'F1_ERR')):
p, perr, pd, pderr = pu.pferrs(self.F0, self.F0_ERR,
self.F1, self.F1_ERR)
setattr(self, 'P0_ERR', perr)
setattr(self, 'P1', pd)
setattr(self, 'P1_ERR', pderr)
elif (hasattr(self, 'F0') and hasattr(self, 'F0_ERR')):
setattr(self, 'P0_ERR', self.F0_ERR/(self.F0*self.F0))
if hasattr(self, 'EPS1') and hasattr(self, 'EPS2'):
ecc = math.sqrt(self.EPS1 * self.EPS1 + self.EPS2 * self.EPS2)
omega = math.atan2(self.EPS1, self.EPS2)
setattr(self, 'E', ecc)
setattr(self, 'OM', omega * pu.RADTODEG)
setattr(self, 'T0', self.TASC + self.PB * omega/pu.TWOPI)
if hasattr(self, 'PB') and hasattr(self, 'A1') and not \
(hasattr(self, 'E') or hasattr(self, 'ECC')):
setattr(self, 'E', 0.0)
if hasattr(self, 'T0') and not hasattr(self, 'TASC'):
setattr(self, 'TASC', self.T0 - self.PB * self.OM/360.0)
pf.close()
