How to use the presto.Pgplot.plotxy function in presto
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scottransom / presto / python / presto / psr_utils.py View on Github 
Num.fmod(Num.argmax(profile) / float(len(profile)) + 0.5, 1.0),
min(profile)], args=(profile))
if (output):
phases = Num.arange(0.0, 1.0,
1.0 / len(profile)) + 0.5 / len(profile)
Pgplot.plotxy(profile, phases, rangex=[0.0, 1.0],
labx='Pulse Phase', laby='Pulse Intensity')
bestfit = yfunct(ret[0], len(profile))
if (output):
Pgplot.plotxy(bestfit, phases, color='red')
Pgplot.closeplot()
residuals = bestfit - profile
resid_avg = residuals.mean()
resid_std = residuals.std()
if (output):
Pgplot.plotxy(residuals, phases, rangex=[0.0, 1.0],
rangey=[min(residuals) - 2 * resid_std,
max(residuals) + 2 * resid_std],
labx='Pulse Phase', laby='Residuals',
line=None, symbol=3)
ppgplot.pgerrb(6, phases, residuals,
Num.zeros(len(residuals), 'd') + \
resid_std, 2)
Pgplot.plotxy([resid_avg, resid_avg], [0.0, 1.0], line=2)
Pgplot.closeplot()
print("")
print(" Best-fit gaussian integrated 'flux' = ", ret[0][0])
print(" Best-fit gaussian FWHM = ", ret[0][1])
print(" Best-fit gaussian phase (0.0-1.0) = ", ret[0][2])
print(" Best-fit gaussian integrated 'flux' = ", ret[0][3])
print(" Best-fit gaussian FWHM = ", ret[0][4])
print(" Best-fit gaussian phase (0.0-1.0) = ", ret[0][5])afpo[3] * gaussian_profile(n, afpo[5], afpo[4]) + afpo[6]
def min_funct(afpo, profile):
return yfunct(afpo, len(profile)) - profile
ret = leastsq(min_funct, [max(profile) - min(profile),
0.05,
Num.argmax(profile) / float(len(profile)),
0.2 * max(profile) - min(profile),
0.1,
Num.fmod(Num.argmax(profile) / float(len(profile)) + 0.5, 1.0),
min(profile)], args=(profile))
if (output):
phases = Num.arange(0.0, 1.0,
1.0 / len(profile)) + 0.5 / len(profile)
Pgplot.plotxy(profile, phases, rangex=[0.0, 1.0],
labx='Pulse Phase', laby='Pulse Intensity')
bestfit = yfunct(ret[0], len(profile))
if (output):
Pgplot.plotxy(bestfit, phases, color='red')
Pgplot.closeplot()
residuals = bestfit - profile
resid_avg = residuals.mean()
resid_std = residuals.std()
if (output):
Pgplot.plotxy(residuals, phases, rangex=[0.0, 1.0],
rangey=[min(residuals) - 2 * resid_std,
max(residuals) + 2 * resid_std],
labx='Pulse Phase', laby='Residuals',
line=None, symbol=3)
ppgplot.pgerrb(6, phases, residuals,
Num.zeros(len(residuals), 'd') + \vars = []
for sub in range(self.nsub):
var = 0.0
if sub in self.killed_subbands:
vars.append(var)
continue
for part in range(self.npart):
if part in self.killed_intervals:
continue
var += self.stats[part][sub][5] # foldstats prof_var
vars.append(var)
chis = Num.zeros(self.nsub, dtype='f')
for ii in range(self.nsub):
chis[ii] = self.calc_redchi2(prof=profs[ii], avg=avgs[ii], var=vars[ii])
# Now plot it
Pgplot.plotxy(chis, labx="Subband Number", laby=r"Reduced-\gx\u2\d",
rangey=[0.0, max(chis)*1.1], device=device)
return chisdef bins_to_accel(z, T, f=[1.0, 1000.0], device="/XWIN"):
"""
bins_to_accel(z, T, f=[1.0, 1000.0], device="/XWIN"):
Make a plot showing the acceleration which corresponds
to a certain number of Fourier bins drifted 'z' during
an observation of length 'T'.
"""
fs = span(Num.log10(f[0]), Num.log10(f[1]), 1000)
accels = z_to_accel(z, T, 10.0 ** fs)
if (device):
Pgplot.plotxy(Num.log10(accels), fs, logx=1, logy=1,
labx="Frequency (Hz)",
laby=r"Acceleration (m/s\u2\d)", device=device)
ppgplot.pgmtxt("T", -2.0, 0.75, 0.0, "T = %.0f sec" % T)
ppgplot.pgmtxt("T", -3.5, 0.75, 0.0, r"r\B\u\.\d = %.1f bins" % z)
if (device != '/XWIN'):
Pgplot.closeplot()
else:
return accelsif (output):
Pgplot.plotxy(bestfit, phases, color='red')
Pgplot.closeplot()
residuals = bestfit - profile
resid_avg = residuals.mean()
resid_std = residuals.std()
if (output):
Pgplot.plotxy(residuals, phases, rangex=[0.0, 1.0],
rangey=[min(residuals) - 2 * resid_std,
max(residuals) + 2 * resid_std],
labx='Pulse Phase', laby='Residuals',
line=None, symbol=3)
ppgplot.pgerrb(6, phases, residuals,
Num.zeros(len(residuals), 'd') + \
resid_std, 2)
Pgplot.plotxy([resid_avg, resid_avg], [0.0, 1.0], line=2)
Pgplot.closeplot()
print("")
print(" Best-fit gaussian integrated 'flux' = ", ret[0][0])
print(" Best-fit gaussian FWHM = ", ret[0][1])
print(" Best-fit gaussian phase (0.0-1.0) = ", ret[0][2])
print(" Baseline (i.e. noise) average = ", ret[0][3])
print(" Residuals average = ", resid_avg)
print(" Residuals standard deviation = ", resid_std)
print("")
return (ret[0][0], ret[0][1], ret[0][2], ret[0][3], resid_avg, resid_std)return afpo[0] * gaussian_profile(len(profile), afpo[2], afpo[1]) \
+ afpo[3] - profile
ret = leastsq(funct, [profile.max() - profile.min(),
0.25, profile.argmax() / float(len(profile)),
profile.min()], args=(profile))
if (output):
phases = Num.arange(0.0, 1.0,
1.0 / len(profile)) + 0.5 / len(profile)
Pgplot.plotxy(profile, phases, rangex=[0.0, 1.0],
labx='Pulse Phase', laby='Pulse Intensity')
bestfit = ret[0][0] * gaussian_profile(len(profile),
ret[0][2], ret[0][1]) \
+ ret[0][3]
if (output):
Pgplot.plotxy(bestfit, phases, color='red')
Pgplot.closeplot()
residuals = bestfit - profile
resid_avg = residuals.mean()
resid_std = residuals.std()
if (output):
Pgplot.plotxy(residuals, phases, rangex=[0.0, 1.0],
rangey=[min(residuals) - 2 * resid_std,
max(residuals) + 2 * resid_std],
labx='Pulse Phase', laby='Residuals',
line=None, symbol=3)
ppgplot.pgerrb(6, phases, residuals,
Num.zeros(len(residuals), 'd') + \
resid_std, 2)
Pgplot.plotxy([resid_avg, resid_avg], [0.0, 1.0], line=2)
Pgplot.closeplot()
print("")ppgplot.pgsch(0.8)
ppgplot.pgmtxt("t", 1.5, 1.0 / 12.0, 0.5, r"\(2156)\dcenter\u = %gMHz" % freq)
ppgplot.pgmtxt("t", 1.5, 3.0 / 12.0, 0.5, r"N\dchan\u = %d" % numchan)
ppgplot.pgmtxt("t", 1.5, 5.0 / 12.0, 0.5, r"N\dsub\u = %d" % numsub)
ppgplot.pgmtxt("t", 1.5, 7.0 / 12.0, 0.5, r"BW\dchan\u = %gMHz" % chanwidth)
ppgplot.pgmtxt("t", 1.5, 9.0 / 12.0, 0.5, r"\gDDM = %g" % dmstep)
ppgplot.pgmtxt("t", 1.5, 11.0 / 12.0, 0.5, r"\gDDM\dsub\u = %g" % subdmstep)
ppgplot.pgsch(1.0)
ppgplot.pgmtxt("b", -7.5, 0.95, 1.0, "Total")
Pgplot.plotxy(Num.log10(dts), ldms, color="green",
logx=1, logy=1)
ppgplot.pgmtxt("b", -6.0, 0.95, 1.0, "Sample Rate")
Pgplot.plotxy(Num.log10(chan_smear), ldms, color="purple",
logx=1, logy=1)
ppgplot.pgmtxt("b", -4.5, 0.95, 1.0, "Channel")
Pgplot.plotxy(Num.log10(BW_smear), ldms, color="red",
logx=1, logy=1)
ppgplot.pgmtxt("b", -3.0, 0.95, 1.0, "Full BW")
Pgplot.plotxy(Num.log10(subband_smear), ldms, color="blue",
logx=1, logy=1)
ppgplot.pgmtxt("b", -1.5, 0.95, 1.0, "Subband")
ppgplot.pgsci(1)If 'output' is true, the fit will be plotted and
the return values will be printed.
"""
profile = Num.asarray(profile)
def funct(afpo, profile):
return afpo[0] * gaussian_profile(len(profile), afpo[2], afpo[1]) \
+ afpo[3] - profile
ret = leastsq(funct, [profile.max() - profile.min(),
0.25, profile.argmax() / float(len(profile)),
profile.min()], args=(profile))
if (output):
phases = Num.arange(0.0, 1.0,
1.0 / len(profile)) + 0.5 / len(profile)
Pgplot.plotxy(profile, phases, rangex=[0.0, 1.0],
labx='Pulse Phase', laby='Pulse Intensity')
bestfit = ret[0][0] * gaussian_profile(len(profile),
ret[0][2], ret[0][1]) \
+ ret[0][3]
if (output):
Pgplot.plotxy(bestfit, phases, color='red')
Pgplot.closeplot()
residuals = bestfit - profile
resid_avg = residuals.mean()
resid_std = residuals.std()
if (output):
Pgplot.plotxy(residuals, phases, rangex=[0.0, 1.0],
rangey=[min(residuals) - 2 * resid_std,
max(residuals) + 2 * resid_std],
labx='Pulse Phase', laby='Residuals',
line=None, symbol=3)for jj in range(self.nsub):
profs[jj] = psr_utils.interp_rotate(sumprofs[jj], delaybins[jj],
zoomfact=interp_factor)
# Note: Since the interpolation process slightly changes the values of the
# profs, we need to re-calculate the average profile value
avgprof = (profs/self.proflen).sum()
else:
new_subdelays_bins = Num.floor(delaybins+0.5)
for jj in range(self.nsub):
profs[jj] = psr_utils.rotate(profs[jj], int(new_subdelays_bins[jj]))
subdelays_bins += new_subdelays_bins
avgprof = self.avgprof
sumprof = profs.sum(0)
chis[ii] = self.calc_redchi2(prof=sumprof, avg=avgprof)
# Now plot it
Pgplot.plotxy(chis, DMs, labx="DM", laby=r"Reduced-\gx\u2\d", device=device)
return (chis, DMs)presto
A bioinformatics toolkit for processing high-throughput lymphocyte receptor sequencing data.
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50 / 100Popular presto functions
- presto.filterbank.create_filterbank_file
- presto.filterbank.FilterbankFile
- presto.infodata.infodata
- presto.Pgplot.plotxy
- presto.ppgplot
- presto.ppgplot.pgmtxt
- presto.ppgplot.pgsci
- presto.ppgplot.pgscr
- presto.psr_constants
- presto.psr_utils
- presto.psr_utils.delay_from_DM
- presto.psr_utils.gaussian_profile
- presto.psr_utils.p_to_f
- presto.singlepulse.sp_pgplot.ppgplot.pgbox
- presto.singlepulse.sp_pgplot.ppgplot.pgline
- presto.singlepulse.sp_pgplot.ppgplot.pgmtxt
- presto.singlepulse.sp_pgplot.ppgplot.pgsch
- presto.singlepulse.sp_pgplot.ppgplot.pgslw
- presto.singlepulse.sp_pgplot.ppgplot.pgsvp
- presto.singlepulse.sp_pgplot.ppgplot.pgswin