def plot_data(tempo_results,
xkey,
ykey,
interactive=True,
mark_peri=False,
show_legend=True):
subplot = 1
numsubplots = 2
global axes
axes = []
global ax_types
ax_types = []
global ax_phase_wraps
ax_phase_wraps = []
global ax_jump_ranges
ax_jump_ranges = []
handles = []
labels = []
for usepostfit in [False, True]: # Always use pre, then post
TOAcount = 0
# All subplots are in a single column
if subplot == 1:
axes.append(plt.subplot(numsubplots, 1, subplot))
else:
axes.append(plt.subplot(numsubplots, 1, subplot, sharex=axes[0]))
if usepostfit:
ax_types.append('post')
else:
ax_types.append('pre')
ax_phase_wraps.append([])
ax_jump_ranges.append([])
# set tick formatter to not use scientific notation or an offset
tick_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)
tick_formatter.set_scientific(False)
axes[-1].xaxis.set_major_formatter(tick_formatter)
xmin, xmax = axes[0].get_xlim()
for ii, (lo, hi) in enumerate(tempo_results.freqbands):
freq_label = get_freq_label(lo, hi)
resids = tempo_results.residuals[freq_label]
xlabel, xdata = resids.get_xdata(xkey)
ylabel, ydata, yerr = resids.get_ydata(ykey, usepostfit,
tempo_results.phase_wraps)
if len(xdata):
# Plot the residuals
handle = plt.errorbar(xdata, ydata, yerr=yerr, fmt='.', \
label=freq_label, picker=5,
c=colors[len(tempo_results.freqbands)][ii])
if subplot == 1:
handles.append(handle[0])
labels.append(freq_label)
TOAcount += xdata.size
# Plot phase wraps
text_offset = offset_copy(axes[-1].transData, x=5, y=-10, units='dots')
if usepostfit:
pw = tempo_results.phase_wraps
elif tempo_history.current_index > 0:
pw = tempo_history.tempo_results[tempo_history.current_index -
1].phase_wraps
else:
pw = []
for wrap_index in pw:
wrap_mjd_hi = tempo_results.ordered_MJDs[wrap_index]
if wrap_index > 0:
wrap_mjd_lo = tempo_results.ordered_MJDs[wrap_index - 1]
else:
wrap_mjd_lo = wrap_mjd_hi
if xkey == 'mjd':
wrap_x = 0.5 * (wrap_mjd_hi + wrap_mjd_lo)
elif xkey == 'year':
wrap_x = mjd_to_year(0.5 * (wrap_mjd_hi + wrap_mjd_lo))
elif xkey == 'numtoa':
wrap_x = wrap_index - 0.5
else:
break
wrap_color = {'pre': 'pink', 'post': 'red'}
wrp = plt.axvline(wrap_x,
ls=':',
label='_nolegend_',
color=wrap_color[ax_types[-1]],
lw=1.5)
wrp_txt = plt.text(wrap_x,
axes[-1].get_ylim()[1],
"%+d" % pw[wrap_index],
transform=text_offset,
size='x-small',
color=wrap_color[ax_types[-1]])
ax_phase_wraps[-1].append([wrp, wrp_txt])
# set up span selector for setting new jump ranges
options.jump_spans[ax_types[-1]] = SpanSelector(
axes[-1],
select_jump_range,
'horizontal',
useblit=True,
rectprops=dict(alpha=0.5, facecolor='orange'))
options.jump_spans[ax_types[-1]].visible = options.jump_mode
if subplot > 1:
axes[0].set_xlim((xmin, xmax))
# Finish off the plot
plt.axhline(0, ls='--', label="_nolegend_", c='k', lw=0.5)
axes[-1].ticklabel_format(style='plain', axis='x')
if mark_peri and hasattr(tempo_results.outpar, 'BINARY'):
# Be sure to check if pulsar is in a binary
# Cannot mark passage of periastron if not a binary
if usepostfit:
binpsr = binary_psr.binary_psr(tempo_results.outpar.FILE)
else:
binpsr = binary_psr.binary_psr(tempo_results.inpar.FILE)
xmin, xmax = axes[0].get_xlim()
mjd_min = tempo_results.min_TOA
mjd_max = tempo_results.max_TOA
guess_mjds = np.arange(mjd_max + binpsr.par.PB, \
mjd_min - binpsr.par.PB, -binpsr.par.PB)
for mjd in guess_mjds:
peri_mjd = binpsr.most_recent_peri(float(mjd))
if xkey == 'mjd':
plt.axvline(peri_mjd,
ls=':',
label='_nolegend_',
c='k',
lw=0.5)
elif xkey == 'year':
print "plotting peri passage"
plt.axvline(mjd_to_year(peri_mjd),
ls=':',
label='_nolegend_',
c='k',
lw=0.5)
axes[0].set_xlim((xmin, xmax))
plt.xlabel(xlabel)
plt.ylabel(ylabel)
subplot += 1
# Plot jump ranges
for jstart, jend in tempo_results.jump_ranges:
plot_jump_range((jstart, jend))
# axes[-1].set_ylim((ymin, ymax))
if numsubplots > 1:
# Increase spacing between subplots.
plt.subplots_adjust(hspace=0.25)
# Write name of input files used for timing on figure
if interactive:
fntext = "Solution %d of %d, TOA file: %s, Parameter file: %s" % \
(tempo_history.current_index+1, tempo_history.get_nsolutions(),
tempo_results.intimfn, tempo_results.inparfn)
figure_text = plt.figtext(0.01,
0.01,
fntext,
verticalalignment='bottom',
horizontalalignment='left')
# Make the legend and set its visibility state
leg = plt.figlegend(handles, labels, 'upper right')
leg.set_visible(show_legend)
leg.legendPatch.set_alpha(0.5)
axes[0].xaxis.tick_top()
plt.setp(axes[0].get_yticklabels()[0], visible=False)
plt.setp(axes[1].get_yticklabels()[-1], visible=False)
plt.setp(axes[0].get_yticklabels()[-1], visible=False)
plt.setp(axes[1].get_yticklabels()[0], visible=False)
plt.subplots_adjust(wspace=0.05,
hspace=0.0,
left=0.15,
bottom=0.1,
right=0.8,
top=0.9)
nms = []
fitmes = []
for b in tempo_history.get_parfile():
if tempo_history.get_parfile()[b].fit == None:
tempo_history.get_parfile()[b].fit = 0
if not any(b in s for s in tempy_io.no_fit_pars):
nms.append(b)
fitmes.append(tempo_history.get_parfile()[b].fit)
rax = plt.axes([0.85, 0.1, 0.1, 0.8])
rax.set_frame_on(False)
options.fitcheck = CheckButtons(rax, nms, fitmes)
options.fitcheck.on_clicked(update_fit_flag)
redrawplot()
def plot_data(tempo_results, xkey, ykey, postfit=True, prefit=False, \
interactive=True, mark_peri=False, show_legend=True):
# figure out what should be plotted
# True means to plot postfit
# False means to plot prefit
if postfit and prefit:
to_plot_postfit = [False, True]
elif postfit and not prefit:
to_plot_postfit = [True]
elif not postfit and prefit:
to_plot_postfit = [False]
else:
raise EmptyPlotValueError("At least one of prefit and postfit must be True.")
subplot = 1
numsubplots = len(to_plot_postfit)
global axes
axes = []
handles = []
labels = []
for usepostfit in to_plot_postfit:
TOAcount = 0
# All subplots are in a single column
if subplot == 1:
axes.append(plt.subplot(numsubplots, 1, subplot))
else:
axes.append(plt.subplot(numsubplots, 1, subplot, sharex=axes[0]))
# set tick formatter to not use scientific notation or an offset
tick_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)
tick_formatter.set_scientific(False)
axes[-1].xaxis.set_major_formatter(tick_formatter)
xmin, xmax = axes[0].get_xlim()
for ii,(lo,hi) in enumerate(tempo_results.freqbands):
freq_label = get_freq_label(lo, hi)
resids = tempo_results.residuals[freq_label]
xlabel, xdata = resids.get_xdata(xkey)
ylabel, ydata, yerr = resids.get_ydata(ykey, usepostfit)
if len(xdata):
# Plot the residuals
handle = plt.errorbar(xdata, ydata, yerr=yerr, fmt='.', \
label=freq_label, picker=5,
c=colors[len(tempo_results.freqbands)][ii])
# Label isn't being set as expected. Use the following
# as a kludgey work-around.
handle[0].set_label(freq_label)
if subplot == 1:
handles.append(handle[0])
labels.append(freq_label)
TOAcount += xdata.size
if subplot > 1:
axes[0].set_xlim((xmin, xmax))
# Finish off the plot
plt.axhline(0, ls='--', label="_nolegend_", c='k', lw=0.5)
axes[-1].ticklabel_format(style='plain', axis='x')
if mark_peri and hasattr(tempo_results.outpar, 'BINARY'):
# Be sure to check if pulsar is in a binary
# Cannot mark passage of periastron if not a binary
if usepostfit:
binpsr = binary_psr.binary_psr(tempo_results.outpar.FILE)
else:
binpsr = binary_psr.binary_psr(tempo_results.inpar.FILE)
xmin, xmax = axes[0].get_xlim()
mjd_min = tempo_results.min_TOA
mjd_max = tempo_results.max_TOA
guess_mjds = np.arange(mjd_max + binpsr.par.PB, \
mjd_min - binpsr.par.PB, -binpsr.par.PB)
for mjd in guess_mjds:
peri_mjd = binpsr.most_recent_peri(float(mjd))
if xkey == 'mjd':
plt.axvline(peri_mjd, ls=':', label='_nolegend_', c='k', lw=0.5)
elif xkey == 'year':
print "plotting peri passage"
plt.axvline(mjd_to_year(peri_mjd), ls=':', label='_nolegend_', c='k', lw=0.5)
axes[0].set_xlim((xmin, xmax))
plt.xlabel(xlabel)
plt.ylabel(ylabel)
if usepostfit:
plt.title("Postfit Residuals (Number of TOAs: %d)" % TOAcount)
else:
plt.title("Prefit Residuals (Number of TOAs: %d)" % TOAcount)
subplot += 1
if numsubplots > 1:
# Increase spacing between subplots.
plt.subplots_adjust(hspace=0.25)
# Write name of input files used for timing on figure
if interactive:
fntext = "TOA file: %s, Parameter file: %s" % \
(tempo_results.intimfn, tempo_results.inparfn)
figure_text = plt.figtext(0.01, 0.01, fntext, verticalalignment='bottom', \
horizontalalignment='left')
# Make the legend and set its visibility state
leg = plt.figlegend(handles, labels, 'upper right')
leg.set_visible(show_legend)
leg.legendPatch.set_alpha(0.5)
def plot_data(tempo_results, xkey, ykey, postfit=True, prefit=False, \
interactive=True, mark_peri=False, show_legend=True):
# figure out what should be plotted
# True means to plot postfit
# False means to plot prefit
if postfit and prefit:
to_plot_postfit = [False, True]
elif postfit and not prefit:
to_plot_postfit = [True]
elif not postfit and prefit:
to_plot_postfit = [False]
else:
raise EmptyPlotValueError(
"At least one of prefit and postfit must be True.")
subplot = 1
numsubplots = len(to_plot_postfit)
global axes
axes = []
handles = []
labels = []
for usepostfit in to_plot_postfit:
TOAcount = 0
# All subplots are in a single column
if subplot == 1:
axes.append(plt.subplot(numsubplots, 1, subplot))
else:
axes.append(plt.subplot(numsubplots, 1, subplot, sharex=axes[0]))
# set tick formatter to not use scientific notation or an offset
tick_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)
tick_formatter.set_scientific(False)
axes[-1].xaxis.set_major_formatter(tick_formatter)
xmin, xmax = axes[0].get_xlim()
for ii, (lo, hi) in enumerate(tempo_results.freqbands):
freq_label = get_freq_label(lo, hi)
resids = tempo_results.residuals[freq_label]
xlabel, xdata = resids.get_xdata(xkey)
ylabel, ydata, yerr = resids.get_ydata(ykey, usepostfit)
if len(xdata):
# Plot the residuals
handle = plt.errorbar(xdata, ydata, yerr=yerr, fmt='.', \
label=freq_label, picker=5,
c=colors[len(tempo_results.freqbands)][ii])
# Label isn't being set as expected. Use the following
# as a kludgey work-around.
handle[0].set_label(freq_label)
if subplot == 1:
handles.append(handle[0])
labels.append(freq_label)
TOAcount += xdata.size
if subplot > 1:
axes[0].set_xlim((xmin, xmax))
# Finish off the plot
plt.axhline(0, ls='--', label="_nolegend_", c='k', lw=0.5)
axes[-1].ticklabel_format(style='plain', axis='x')
if mark_peri and hasattr(tempo_results.outpar, 'BINARY'):
# Be sure to check if pulsar is in a binary
# Cannot mark passage of periastron if not a binary
if usepostfit:
binpsr = binary_psr.binary_psr(tempo_results.outpar.FILE)
else:
binpsr = binary_psr.binary_psr(tempo_results.inpar.FILE)
xmin, xmax = axes[0].get_xlim()
mjd_min = tempo_results.min_TOA
mjd_max = tempo_results.max_TOA
guess_mjds = np.arange(mjd_max + binpsr.par.PB, \
mjd_min - binpsr.par.PB, -binpsr.par.PB)
for mjd in guess_mjds:
peri_mjd = binpsr.most_recent_peri(float(mjd))
if xkey == 'mjd':
plt.axvline(peri_mjd,
ls=':',
label='_nolegend_',
c='k',
lw=0.5)
elif xkey == 'year':
print "plotting peri passage"
plt.axvline(mjd_to_year(peri_mjd),
ls=':',
label='_nolegend_',
c='k',
lw=0.5)
axes[0].set_xlim((xmin, xmax))
plt.xlabel(xlabel)
plt.ylabel(ylabel)
if usepostfit:
plt.title("Postfit Residuals (Number of TOAs: %d)" % TOAcount)
else:
plt.title("Prefit Residuals (Number of TOAs: %d)" % TOAcount)
subplot += 1
if numsubplots > 1:
# Increase spacing between subplots.
plt.subplots_adjust(hspace=0.25)
# Write name of input files used for timing on figure
if interactive:
fntext = "TOA file: %s, Parameter file: %s" % \
(tempo_results.intimfn, tempo_results.inparfn)
figure_text = plt.figtext(0.01, 0.01, fntext, verticalalignment='bottom', \
horizontalalignment='left')
# Make the legend and set its visibility state
leg = plt.figlegend(handles, labels, 'upper right')
leg.set_visible(show_legend)
leg.legendPatch.set_alpha(0.5)
sourcename = initvals['name']
mu_a = float(initvals['pmra'])
mu_d = float(initvals['pmdec'])
px = float(initvals['px'])
Omega = float(initvals['Omega'])
inc = float(initvals['inc'])
ra = initvals['ra']
dec = initvals['dec']
posstring = ra + " " + dec
except KeyError as e:
print "Missing required key ", str(e), " in ", initsfile
sys.exit()
start_pos = coords.SkyCoord(posstring, unit=(u.hourangle, u.deg), frame='icrs')
psr = bp.binary_psr(parfile)
# Get the measurements and convert them to arrays of angles
vlbiepoch, posns, ra_errs, dec_errs, baryMJDs = get_coords(pmparfile)
# This is the Earth position in X, Y, Z (AU) in ICRS wrt SSB
eposns = [solsys.solarsystem(mjd+2400000.5, 3, 0)[0] \
for mjd in baryMJDs]
errs = np.concatenate((ra_errs.to(u.rad).value, dec_errs.to(u.rad).value)) # in radians
ras = np.asarray([pos.ra.rad for pos in posns]) * u.rad
decs = np.asarray([pos.dec.rad for pos in posns]) * u.rad
meas = np.concatenate((ras, decs)) # in radians
try:
# This is going for Figure 1 in Hulse & Taylor 1975
psr1 = presto.psrepoch("B1913+16", 42320.0)
# unfortunatey, OMDOT is not in the binary part of the
# database correctly. So we need to set that:
psr1.orb.w = 179.0
psr1.orb.t = 0.0
Eo = presto.keplers_eqn(psr1.orb.t, psr1.orb.p, psr1.orb.e, 1e-15)
Es = presto.dorbint(Eo, N, 2.0 * psr1.orb.p / N, psr1.orb)
presto.E_to_v(Es, psr1.orb)
plt.plot(ma, Es, 'b-')
plt.xlabel("Orbital Phase")
plt.ylabel("Pulsar Velocity (km/s)")
plt.show()
# This is going for Figure 1 in Champion et al 2008
bpsr = binary_psr.binary_psr("1903+0327.par")
MJDs = bpsr.T0 + ma * bpsr.par.PB
xs, ys = bpsr.position(MJDs)
#cMJDs = bpsr.demodulate_TOAs(MJDs)
#cxs, cys = bpsr.position(cMJDs)
psr2 = presto.psrepoch("1903+0327.par", bpsr.T0)
psr2.orb.t = 0.0
Eo = presto.keplers_eqn(psr2.orb.t, psr2.orb.p, psr2.orb.e, 1e-15)
Es = presto.dorbint(Eo, N, 2.0 * psr2.orb.p / N, psr2.orb)
# bt = Es.copy()
presto.E_to_phib(Es, psr2.orb)
# presto.E_to_phib_BT(bt, psr2.orb)
plt.plot(ma, Es, 'b-')
plt.plot(ma, -xs, 'r-')
def do_demodulate(merging, data_id, par_file, E_trunc, PI1, PI2):
"""
Using do_demodulate in binary_psr.py in Scott Ransom's PRESTO Python library to
demodulate the time series for the merged event file!
merging - True/False - whether to use merged data
data_id - 10-digit ObsID or 6-digit ID for the merged event file
par_file - orbital parameter file for input into binary_psr
E_trunc - True/False - whether to do energy truncation
PI1 - lower bound of PI (not energy in keV!) desired for the energy range
PI2 - upper bound of PI (not energy in keV!) desired for the energy range
"""
TIMEZERO = -1
if type(data_id) != str:
raise TypeError("data_id should be a string!")
if len(data_id) != 6 and len(data_id) != 10:
raise ValueError("data_id should have 6 or 10 digits in the string!")
if E_trunc != True and E_trunc != False:
raise ValueError(
"E_trunc (energy truncation) should either be True or False!")
if merging == True:
print("Doing demodulation, merging is True!")
all_merged_dir = Lv0_dirs.NICERSOFT_DATADIR + 'merged_events/' #directory for the merged events
merged_dir = all_merged_dir + 'merged' + data_id + '/'
if E_trunc == True:
old_file = merged_dir + 'merged' + data_id + '_nicersoft_bary_E' + str(
PI1).zfill(4) + '-' + str(PI2).zfill(4) + '.evt'
else:
old_file = merged_dir + 'merged' + data_id + '_nicersoft_bary.evt'
else:
print("Doing demodulation, merging is False!")
if E_trunc == True:
old_file = Lv0_dirs.NICERSOFT_DATADIR + data_id + '_pipe/ni' + data_id + '_nicersoft_bary_E' + str(
PI1).zfill(4) + '-' + str(PI2).zfill(4) + '.evt'
else:
old_file = Lv0_dirs.NICERSOFT_DATADIR + data_id + '_pipe/ni' + data_id + '_nicersoft_bary.evt'
new_file = old_file[:-4] + '_demod.evt'
subprocess.check_call(['cp', old_file, new_file])
with fits.open(new_file, mode='update') as fitsfile_demod:
MJDREFI = fitsfile_demod[1].header[
'MJDREFI'] #integer for MJD reference
MJDREFF = fitsfile_demod[1].header[
'MJDREFF'] #float decimal for MJD reference
times = fitsfile_demod[1].data['TIME'] #original time series
gtis_start = fitsfile_demod[2].data['START'] #original GTI start times
gtis_stop = fitsfile_demod[2].data['STOP'] #original GTI end times
times_MJD = MJDREFI + MJDREFF + (
times + TIMEZERO) / 86400 #converting METs to MJD
gtis_start_MJD = MJDREFI + MJDREFF + (
gtis_start + TIMEZERO) / 86400 #converting GTIs in METs to MJD
gtis_stop_MJD = MJDREFI + MJDREFF + (
gtis_stop + TIMEZERO) / 86400 #converting GTIs in METs to MJD
try:
times_demod = binary_psr.binary_psr(par_file).demodulate_TOAs(
times_MJD) #demodulated event times
gtis_start_demod = binary_psr.binary_psr(par_file).demodulate_TOAs(
gtis_start_MJD) #demodulated GTI start times
gtis_stop_demod = binary_psr.binary_psr(par_file).demodulate_TOAs(
gtis_stop_MJD) #demodulated GTI end times
fitsfile_demod[1].data['TIME'] = (
times_demod - MJDREFI - MJDREFF) * 86400 #convert back to METs
fitsfile_demod[2].data['START'] = (
gtis_start_demod - MJDREFI -
MJDREFF) * 86400 #convert back to METs
fitsfile_demod[2].data['STOP'] = (
gtis_stop_demod - MJDREFI -
MJDREFF) * 86400 #convert back to METs
fitsfile_demod.flush()
except ValueError:
pass
return
def plot_data(tempo_results, xkey, ykey,
interactive=True, mark_peri=False, show_legend=True):
subplot = 1
numsubplots = 2
global axes
axes = []
global ax_types
ax_types = []
global ax_phase_wraps
ax_phase_wraps = []
global ax_jump_ranges
ax_jump_ranges = []
handles = []
labels = []
for usepostfit in [False, True]:# Always use pre, then post
TOAcount = 0
# All subplots are in a single column
if subplot == 1:
axes.append(plt.subplot(numsubplots, 1, subplot))
else:
axes.append(plt.subplot(numsubplots, 1, subplot, sharex=axes[0]))
if usepostfit:
ax_types.append('post')
else:
ax_types.append('pre')
ax_phase_wraps.append([])
ax_jump_ranges.append([])
# set tick formatter to not use scientific notation or an offset
tick_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)
tick_formatter.set_scientific(False)
axes[-1].xaxis.set_major_formatter(tick_formatter)
xmin, xmax = axes[0].get_xlim()
for ii,(lo,hi) in enumerate(tempo_results.freqbands):
freq_label = get_freq_label(lo, hi)
resids = tempo_results.residuals[freq_label]
xlabel, xdata = resids.get_xdata(xkey)
ylabel, ydata, yerr = resids.get_ydata(ykey, usepostfit,
tempo_results.phase_wraps)
if len(xdata):
# Plot the residuals
handle = plt.errorbar(xdata, ydata, yerr=yerr, fmt='.', \
label=freq_label, picker=5,
c=colors[len(tempo_results.freqbands)][ii])
if subplot == 1:
handles.append(handle[0])
labels.append(freq_label)
TOAcount += xdata.size
# Plot phase wraps
text_offset = offset_copy(axes[-1].transData, x=5, y=-10,
units='dots')
if usepostfit:
pw = tempo_results.phase_wraps
elif tempo_history.current_index > 0:
pw = tempo_history.tempo_results[tempo_history.current_index-1].phase_wraps
else:
pw = []
for wrap_index in pw:
wrap_mjd_hi = tempo_results.ordered_MJDs[wrap_index]
if wrap_index > 0:
wrap_mjd_lo = tempo_results.ordered_MJDs[wrap_index-1]
else:
wrap_mjd_lo = wrap_mjd_hi
if xkey == 'mjd':
wrap_x = 0.5*(wrap_mjd_hi + wrap_mjd_lo)
elif xkey == 'year':
wrap_x = mjd_to_year(0.5*(wrap_mjd_hi + wrap_mjd_lo))
elif xkey == 'numtoa':
wrap_x = wrap_index - 0.5
else:
break
wrap_color = {'pre':'pink', 'post':'red'}
wrp = plt.axvline(wrap_x, ls=':', label='_nolegend_',
color=wrap_color[ax_types[-1]], lw=1.5)
wrp_txt = plt.text(wrap_x, axes[-1].get_ylim()[1],
"%+d" % pw[wrap_index],
transform=text_offset, size='x-small',
color=wrap_color[ax_types[-1]])
ax_phase_wraps[-1].append([wrp, wrp_txt])
# set up span selector for setting new jump ranges
options.jump_spans[ax_types[-1]] = SpanSelector(axes[-1], select_jump_range, 'horizontal', useblit=True, rectprops=dict(alpha=0.5, facecolor='orange'))
options.jump_spans[ax_types[-1]].visible = options.jump_mode
if subplot > 1:
axes[0].set_xlim((xmin, xmax))
# Finish off the plot
plt.axhline(0, ls='--', label="_nolegend_", c='k', lw=0.5)
axes[-1].ticklabel_format(style='plain', axis='x')
if mark_peri and hasattr(tempo_results.outpar, 'BINARY'):
# Be sure to check if pulsar is in a binary
# Cannot mark passage of periastron if not a binary
if usepostfit:
binpsr = binary_psr.binary_psr(tempo_results.outpar.FILE)
else:
binpsr = binary_psr.binary_psr(tempo_results.inpar.FILE)
xmin, xmax = axes[0].get_xlim()
mjd_min = tempo_results.min_TOA
mjd_max = tempo_results.max_TOA
guess_mjds = np.arange(mjd_max + binpsr.par.PB, \
mjd_min - binpsr.par.PB, -binpsr.par.PB)
for mjd in guess_mjds:
peri_mjd = binpsr.most_recent_peri(float(mjd))
if xkey == 'mjd':
plt.axvline(peri_mjd, ls=':', label='_nolegend_', c='k', lw=0.5)
elif xkey == 'year':
print "plotting peri passage"
plt.axvline(mjd_to_year(peri_mjd), ls=':', label='_nolegend_', c='k', lw=0.5)
axes[0].set_xlim((xmin, xmax))
plt.xlabel(xlabel)
plt.ylabel(ylabel)
subplot += 1
# Plot jump ranges
for jstart,jend in tempo_results.jump_ranges:
plot_jump_range((jstart,jend))
# axes[-1].set_ylim((ymin, ymax))
if numsubplots > 1:
# Increase spacing between subplots.
plt.subplots_adjust(hspace=0.25)
# Write name of input files used for timing on figure
if interactive:
fntext = "Solution %d of %d, TOA file: %s, Parameter file: %s" % \
(tempo_history.current_index+1, tempo_history.get_nsolutions(),
tempo_results.intimfn, tempo_results.inparfn)
figure_text = plt.figtext(0.01, 0.01, fntext,
verticalalignment='bottom',
horizontalalignment='left')
# Make the legend and set its visibility state
leg = plt.figlegend(handles, labels, 'upper right')
leg.set_visible(show_legend)
leg.legendPatch.set_alpha(0.5)
axes[0].xaxis.tick_top()
plt.setp(axes[0].get_yticklabels()[0], visible=False)
plt.setp(axes[1].get_yticklabels()[-1], visible=False)
plt.setp(axes[0].get_yticklabels()[-1], visible=False)
plt.setp(axes[1].get_yticklabels()[0], visible=False)
plt.subplots_adjust(wspace=0.05, hspace = 0.0, left=0.15, bottom=0.1, right=0.8, top=0.9)
nms=[]
fitmes=[]
for b in tempo_history.get_parfile():
if tempo_history.get_parfile()[b].fit==None:
tempo_history.get_parfile()[b].fit=0
if not any(b in s for s in tempy_io.no_fit_pars):
nms.append(b)
fitmes.append(tempo_history.get_parfile()[b].fit)
rax = plt.axes([0.85, 0.1, 0.1, 0.8])
rax.set_frame_on(False)
options.fitcheck = CheckButtons(rax, nms, fitmes)
options.fitcheck.on_clicked(update_fit_flag)
redrawplot()
# This is going for Figure 1 in Hulse & Taylor 1975
psr1 = presto.psrepoch("B1913+16", 42320.0)
# unfortunatey, OMDOT is not in the binary part of the
# database correctly. So we need to set that:
psr1.orb.w = 179.0
psr1.orb.t = 0.0
Eo = presto.keplers_eqn(psr1.orb.t, psr1.orb.p, psr1.orb.e, 1e-15)
Es = presto.dorbint(Eo, N, 2.0*psr1.orb.p/N, psr1.orb)
presto.E_to_v(Es, psr1.orb)
plt.plot(ma, Es, 'b-')
plt.xlabel("Orbital Phase")
plt.ylabel("Pulsar Velocity (km/s)")
plt.show()
# This is going for Figure 1 in Champion et al 2008
bpsr = binary_psr.binary_psr("1903+0327.par")
MJDs = bpsr.T0 + ma * bpsr.par.PB
xs, ys = bpsr.position(MJDs)
#cMJDs = bpsr.demodulate_TOAs(MJDs)
#cxs, cys = bpsr.position(cMJDs)
psr2 = presto.psrepoch("1903+0327.par", bpsr.T0)
psr2.orb.t = 0.0
Eo = presto.keplers_eqn(psr2.orb.t, psr2.orb.p, psr2.orb.e, 1e-15)
Es = presto.dorbint(Eo, N, 2.0*psr2.orb.p/N, psr2.orb)
# bt = Es.copy()
presto.E_to_phib(Es, psr2.orb)
# presto.E_to_phib_BT(bt, psr2.orb)
plt.plot(ma, Es, 'b-')
plt.plot(ma, -xs, 'r-')
