def Multi_Gaussian_Ratings(pfd, debug=False):
"""
Calculate ratings that depend on a fit of multiple Gaussian profile
components.
Parameters
----------
pfd : class
An instance of the prepfold.pfd class
debug : boolean
If True, plot the profile and best-fit Gaussians
Returns
-------
names : list
A list of ratings names
ratings : list
A list of ratings values
"""
# The ratings names
name1 = "Number_of_Gaussians"
name2 = "Pulse_Width_Rating"
# De-disperse the profile at the best DM
pfd.dedisperse(DM=pfd.bestdm, doppler=1)
# Internally rotate the data cube so that is aligned with best fold values
pfd.adjust_period()
# Get the best fold profile
profile = N.sum(N.sum(pfd.profs, axis=1), axis=0)
# Get the period from bestprof, if it exists
if hasattr(pfd.bestprof, "p0"):
P0 = pfd.bestprof.p0
# Otherwise use the barycentric or topocentric folding periods
else:
if pfd.bary_p1 != 0.0: P0 = pfd.bary_p1
elif pfd.topo_p1 != 0.0: P0 = pfd.topo_p1
# Get the center observing frequency
f_ctr = (pfd.hifreq + pfd.lofreq)/2
# Fit up to MAX_GAUSSIANS Gaussians to the profile
gaussian_params, red_chi2, ngaussians = \
PT.fit_gaussians_presto(profile, pfd.avgprof, N.sqrt(pfd.varprof),
MAX_GAUSSIANS, F_STAT_THRESHOLD)
if debug:
phases = N.arange(pfd.proflen, dtype=N.float)/pfd.proflen
gaussians = PT.make_gaussians_presto(gaussian_params, pfd.proflen)
PL.plot(phases, profile)
PL.plot(phases, gaussians)
PL.show()
# Create a list for storing the FWHMs of the Gaussians
fwhms = []
# If we were able to fit at least one Gaussian...
if ngaussians != 0:
# Get and store the FWHM for each Gaussian
for nn in xrange(ngaussians):
fwhms.append(gaussian_params[1+3*nn+1])
# Calculate the DM smearing time
dm_smear_sec = PU.dm_smear(pfd.bestdm, pfd.chan_wid, f_ctr)
# Calculate the expected minimum pulse width
width_phase = N.sqrt(dm_smear_sec**2 + pfd.dt**2)/P0
# Compare the expected minimum and actual pulse widths
width_ratio = width_phase/min(fwhms)
# If no Gaussians could be fit to the profile, store bogus metrics
else:
width_ratio = -1.0
# Store the ratings
rating1 = ngaussians
rating2 = width_ratio
return [name1,name2],[rating1,rating2]
def Multi_Gaussian_Ratings(pfd, debug=False):
"""
Calculate ratings that depend on a fit of multiple Gaussian profile
components.
Parameters
----------
pfd : class
An instance of the prepfold.pfd class
debug : boolean
If True, plot the profile and best-fit Gaussians
Returns
-------
names : list
A list of ratings names
ratings : list
A list of ratings values
"""
# The ratings names
name1 = "Number_of_Gaussians"
name2 = "Pulse_Width_Rating"
# De-disperse the profile at the best DM
pfd.dedisperse(DM=pfd.bestdm, doppler=1)
# Internally rotate the data cube so that is aligned with best fold values
pfd.adjust_period()
# Get the best fold profile
profile = N.sum(N.sum(pfd.profs, axis=1), axis=0)
# Get the period from bestprof, if it exists
if hasattr(pfd.bestprof, "p0"):
P0 = pfd.bestprof.p0
# Otherwise use the barycentric or topocentric folding periods
else:
if pfd.bary_p1 != 0.0: P0 = pfd.bary_p1
elif pfd.topo_p1 != 0.0: P0 = pfd.topo_p1
# Get the center observing frequency
f_ctr = (pfd.hifreq + pfd.lofreq) / 2
# Fit up to MAX_GAUSSIANS Gaussians to the profile
gaussian_params, red_chi2, ngaussians = \
PT.fit_gaussians_presto(profile, pfd.avgprof, N.sqrt(pfd.varprof),
MAX_GAUSSIANS, F_STAT_THRESHOLD)
if debug:
phases = N.arange(pfd.proflen, dtype=N.float) / pfd.proflen
gaussians = PT.make_gaussians_presto(gaussian_params, pfd.proflen)
PL.plot(phases, profile)
PL.plot(phases, gaussians)
PL.show()
# Create a list for storing the FWHMs of the Gaussians
fwhms = []
# If we were able to fit at least one Gaussian...
if ngaussians != 0:
# Get and store the FWHM for each Gaussian
for nn in xrange(ngaussians):
fwhms.append(gaussian_params[1 + 3 * nn + 1])
# Calculate the DM smearing time
dm_smear_sec = PU.dm_smear(pfd.bestdm, pfd.chan_wid, f_ctr)
# Calculate the expected minimum pulse width
width_phase = N.sqrt(dm_smear_sec**2 + pfd.dt**2) / P0
# Compare the expected minimum and actual pulse widths
width_ratio = width_phase / min(fwhms)
# If no Gaussians could be fit to the profile, store bogus metrics
else:
width_ratio = -1.0
# Store the ratings
rating1 = ngaussians
rating2 = width_ratio
return [name1, name2], [rating1, rating2]
def Single_Gaussian_Ratings(pfd, debug=False):
"""
Calculate ratings that depend on the fit of a single Gaussian profile
component.
Parameters
----------
pfd : class
An instance of the prepfold.pfd class
debug : boolean
If True, plot the profile and best-fit Gaussian
Returns
-------
names : list
A list of ratings names
ratings : list
A list of ratings values
"""
# The rating names
name1 = "Gaussian_Amplitude"
name2 = "Gaussian_Width"
name3 = "Gaussian_Phase"
name4 = "Gaussian_GoF"
name5 = "Fraction_Good_Subints"
name6 = "Subints_SNR_StdDev"
name7 = "Fraction_Good_Subbands"
name8 = "Subbands_SNR_StdDev"
# De-disperse the profile at the best DM
pfd.dedisperse(DM=pfd.bestdm, doppler=1)
# Internally rotate the data cube so that is aligned with best fold values
pfd.adjust_period()
# Get the subints, subbands, and profile
subints = N.sum(pfd.profs, axis=1)
subbands = N.sum(pfd.profs, axis=0)
profile = N.sum(subints, axis=0)
# Fit a gaussian to the profile
gaussian_params, red_chi2, ngaussians = \
PT.fit_gaussians_presto(profile, pfd.avgprof, N.sqrt(pfd.varprof),
1, F_STAT_THRESHOLD)
# If we were able to fit a Gaussian...
if ngaussians != 0:
# Parse the Gaussian parameters
amplitude = gaussian_params[1]
fwhm = gaussian_params[2]
phase = gaussian_params[3]
# Get the on-pulse and off-pulse bins
on_pulse_bins,off_pulse_bins = \
PT.calc_on_pulse_region(profile,gaussian_params)
# Get the persistence metrics
frac_good_subints,subints_snr_std = \
calc_persistence_metrics(subints,on_pulse_bins,off_pulse_bins)
# Get the broadbandedness metrics
frac_good_subbands,subbands_snr_std = \
calc_broadbandedness_metrics(subbands,on_pulse_bins,off_pulse_bins)
if debug:
phases = N.arange(pfd.proflen, dtype=N.float)/pfd.proflen
gaussians = PT.make_gaussians_presto(gaussian_params, pfd.proflen)
print len(on_pulse_bins)/float(pfd.proflen)
PL.plot(phases, profile, "k")
PL.plot(phases, gaussians, "b")
PL.axvline(on_pulse_bins[0]/float(pfd.proflen), c="g")
PL.axvline(on_pulse_bins[-1]/float(pfd.proflen), c="r")
PL.show()
# If no Gaussians could be fit to the profile, store bogus metrics
else:
amplitude = 0.0
fwhm = 0.0
phase = -1.0
frac_good_subints = -1.0
frac_good_subbands = -1.0
subints_snr_std = -1.0
subbands_snr_std = -1.0
# Store the ratings values
rating1 = amplitude
rating2 = fwhm
rating3 = phase
rating4 = red_chi2
rating5 = frac_good_subints
rating6 = subints_snr_std
rating7 = frac_good_subbands
rating8 = subbands_snr_std
return [name1,name2,name3,name4,name5,name6,name7,name8],\
[rating1,rating2,rating3,rating4,rating5,rating6,rating7,rating8]
def Single_Gaussian_Ratings(pfd, debug=False):
"""
Calculate ratings that depend on the fit of a single Gaussian profile
component.
Parameters
----------
pfd : class
An instance of the prepfold.pfd class
debug : boolean
If True, plot the profile and best-fit Gaussian
Returns
-------
names : list
A list of ratings names
ratings : list
A list of ratings values
"""
# The rating names
name1 = "Gaussian_Amplitude"
name2 = "Gaussian_Width"
name3 = "Gaussian_Phase"
name4 = "Gaussian_GoF"
name5 = "Fraction_Good_Subints"
name6 = "Subints_SNR_StdDev"
name7 = "Fraction_Good_Subbands"
name8 = "Subbands_SNR_StdDev"
# De-disperse the profile at the best DM
pfd.dedisperse(DM=pfd.bestdm, doppler=1)
# Internally rotate the data cube so that is aligned with best fold values
pfd.adjust_period()
# Get the subints, subbands, and profile
subints = N.sum(pfd.profs, axis=1)
subbands = N.sum(pfd.profs, axis=0)
profile = N.sum(subints, axis=0)
# Fit a gaussian to the profile
gaussian_params, red_chi2, ngaussians = \
PT.fit_gaussians_presto(profile, pfd.avgprof, N.sqrt(pfd.varprof),
1, F_STAT_THRESHOLD)
# If we were able to fit a Gaussian...
if ngaussians != 0:
# Parse the Gaussian parameters
amplitude = gaussian_params[1]
fwhm = gaussian_params[2]
phase = gaussian_params[3]
# Get the on-pulse and off-pulse bins
on_pulse_bins,off_pulse_bins = \
PT.calc_on_pulse_region(profile,gaussian_params)
# Get the persistence metrics
frac_good_subints,subints_snr_std = \
calc_persistence_metrics(subints,on_pulse_bins,off_pulse_bins)
# Get the broadbandedness metrics
frac_good_subbands,subbands_snr_std = \
calc_broadbandedness_metrics(subbands,on_pulse_bins,off_pulse_bins)
if debug:
phases = N.arange(pfd.proflen, dtype=N.float) / pfd.proflen
gaussians = PT.make_gaussians_presto(gaussian_params, pfd.proflen)
print len(on_pulse_bins) / float(pfd.proflen)
PL.plot(phases, profile, "k")
PL.plot(phases, gaussians, "b")
PL.axvline(on_pulse_bins[0] / float(pfd.proflen), c="g")
PL.axvline(on_pulse_bins[-1] / float(pfd.proflen), c="r")
PL.show()
# If no Gaussians could be fit to the profile, store bogus metrics
else:
amplitude = 0.0
fwhm = 0.0
phase = -1.0
frac_good_subints = -1.0
frac_good_subbands = -1.0
subints_snr_std = -1.0
subbands_snr_std = -1.0
# Store the ratings values
rating1 = amplitude
rating2 = fwhm
rating3 = phase
rating4 = red_chi2
rating5 = frac_good_subints
rating6 = subints_snr_std
rating7 = frac_good_subbands
rating8 = subbands_snr_std
return [name1,name2,name3,name4,name5,name6,name7,name8],\
[rating1,rating2,rating3,rating4,rating5,rating6,rating7,rating8]
