def _histogram(xlo, xhi, y, yerr=None, title=None, xlabel=None, ylabel=None,
overplot=False, clearwindow=True,
yerrorbars=False,
errstyle=None,
errcolor=None,
errthickness=None,
fillcolor=None,
fillopacity=None,
fillstyle=None,
xlog=False,
ylog=False,
linestyle=chips.chips_solid,
linecolor=None,
linethickness=None,
symbolangle=None,
symbolcolor=None,
symbolfill=None,
symbolsize=None,
symbolstyle=chips.chips_none):
if (not overplot) and clearwindow:
_clear_window()
if yerrorbars and yerr is not None:
chips.add_histogram(xlo, xhi, y, yerr)
else:
chips.add_histogram(xlo, xhi, y)
for var in ('errstyle', 'errcolor', 'errthickness',
'fillcolor', 'fillopacity', 'fillstyle',
'linestyle', 'linecolor', 'linethickness',
'symbolangle', 'symbolcolor', 'symbolfill', 'symbolsize',
'symbolstyle'):
val = locals()[var]
if val is not None:
if 'color' in var:
val = _check_hex_color(val)
getattr(chips.advanced, 'set_histogram_' + var)(val)
if not overplot:
for log_axis, axis_id in izip((xlog, ylog),
(chips.X_AXIS, chips.Y_AXIS)):
if log_axis:
chips.log_scale(axis_id)
else:
chips.linear_scale(axis_id)
if title:
ttl = title.replace('_', '\\_')
chips.set_plot_title(ttl)
if xlabel:
xlbl = xlabel.replace('_', '\\_')
chips.set_plot_xlabel(xlbl)
if ylabel:
ylbl = ylabel.replace('_', '\\_')
chips.set_plot_ylabel(ylbl)
def _histogram(xlo, xhi, y, yerr=None, title=None, xlabel=None, ylabel=None,
overplot=False, clearwindow=True,
yerrorbars=False,
errstyle=None,
errcolor=None,
errthickness=None,
fillcolor=None,
fillopacity=None,
fillstyle=None,
xlog=False,
ylog=False,
linestyle=chips.chips_solid,
linecolor=None,
linethickness=None,
symbolangle=None,
symbolcolor=None,
symbolfill=None,
symbolsize=None,
symbolstyle=chips.chips_none):
if (not overplot) and clearwindow:
_clear_window()
if yerrorbars and yerr is not None:
chips.add_histogram(xlo, xhi, y, yerr)
else:
chips.add_histogram(xlo, xhi, y)
for var in ('errstyle', 'errcolor', 'errthickness',
'fillcolor', 'fillopacity', 'fillstyle',
'linestyle', 'linecolor', 'linethickness',
'symbolangle', 'symbolcolor', 'symbolfill', 'symbolsize',
'symbolstyle'):
val = locals()[var]
if val is not None:
if 'color' in var:
val = _check_hex_color(val)
getattr(chips.advanced, 'set_histogram_' + var)(val)
if not overplot:
for log_axis, axis_id in zip((xlog, ylog),
(chips.X_AXIS, chips.Y_AXIS)):
if log_axis:
chips.log_scale(axis_id)
else:
chips.linear_scale(axis_id)
if title:
ttl = title.replace('_', '\\_')
chips.set_plot_title(ttl)
if xlabel:
xlbl = xlabel.replace('_', '\\_')
chips.set_plot_xlabel(xlbl)
if ylabel:
ylbl = ylabel.replace('_', '\\_')
chips.set_plot_ylabel(ylbl)
def _contour(x0, x1, y, levels=None, title=None, xlabel=None, ylabel=None,
overcontour=False, clearwindow=True,
xlog=False,
ylog=False,
style=None,
color=None,
thickness=None,
axis_pad=0.05):
if (not overcontour) and clearwindow:
_clear_window()
# Catch NANs before sending to ChIPS
bad = list(numpy.where(numpy.isnan(y)==True)).pop(0)
bad_vals = numpy.array(y[bad])
y[bad] = 0.0
if levels is None:
chips.add_contour(x0, x1, y)
else:
levels = numpy.asarray(levels, numpy.float_)
chips.add_contour(x0, x1, y, levels)
y[bad] = bad_vals
for var in ('style', 'color', 'thickness'):
val = locals()[var]
if val is not None:
if 'color' in var:
val = _check_hex_color(val)
getattr(chips.advanced, 'set_contour_' + var)(val)
chips.advanced.set_axis_pad(axis_pad)
chips.set_data_aspect_ratio()
chips.limits(chips.X_AXIS, x0.min(), x0.max())
chips.limits(chips.Y_AXIS, x1.min(), x1.max())
if not overcontour:
for log_axis, axis_id in izip((xlog, ylog),
(chips.X_AXIS, chips.Y_AXIS)):
if log_axis:
chips.log_scale(axis_id)
else:
chips.linear_scale(axis_id)
if title:
ttl = title.replace('_', '\\_')
chips.set_plot_title(ttl)
if xlabel:
xlbl = xlabel.replace('_', '\\_')
chips.set_plot_xlabel(xlbl)
if ylabel:
ylbl = ylabel.replace('_', '\\_')
chips.set_plot_ylabel(ylbl)
def _contour(x0, x1, y, levels=None, title=None, xlabel=None, ylabel=None,
overcontour=False, clearwindow=True,
xlog=False,
ylog=False,
style=None,
color=None,
thickness=None,
axis_pad=0.05):
if (not overcontour) and clearwindow:
_clear_window()
# Catch NANs before sending to ChIPS
bad = list(numpy.where(numpy.isnan(y)==True)).pop(0)
bad_vals = numpy.array(y[bad])
y[bad] = 0.0
if levels is None:
chips.add_contour(x0, x1, y)
else:
levels = numpy.asarray(levels, numpy.float_)
chips.add_contour(x0, x1, y, levels)
y[bad] = bad_vals
for var in ('style', 'color', 'thickness'):
val = locals()[var]
if val is not None:
if 'color' in var:
val = _check_hex_color(val)
getattr(chips.advanced, 'set_contour_' + var)(val)
chips.advanced.set_axis_pad(axis_pad)
chips.set_data_aspect_ratio()
chips.limits(chips.X_AXIS, x0.min(), x0.max())
chips.limits(chips.Y_AXIS, x1.min(), x1.max())
if not overcontour:
for log_axis, axis_id in zip((xlog, ylog),
(chips.X_AXIS, chips.Y_AXIS)):
if log_axis:
chips.log_scale(axis_id)
else:
chips.linear_scale(axis_id)
if title:
ttl = title.replace('_', '\\_')
chips.set_plot_title(ttl)
if xlabel:
xlbl = xlabel.replace('_', '\\_')
chips.set_plot_xlabel(xlbl)
if ylabel:
ylbl = ylabel.replace('_', '\\_')
chips.set_plot_ylabel(ylbl)
def fit_lines(linelist, id = None, delta_lam = .2, plot = False, outfile = None):
mymodel = filili.multilinemanager.GaussLines('const1d', id = id, baseline = baseline)
linelist['fililiname'] = [''] * len(linelist['linename'])
for i in range(len(linelist['linename'])):
lname = linelist['linename'][i]
lwave = linelist['wave'][i]
previouslines = set(mymodel.line_name_list())
mymodel.add_line(linename = filter(lambda x: x.isalnum(), lname), pos = lwave)
linenamelist = mymodel.line_name_list()
newline = (set(linenamelist) - previouslines).pop()
linelist['fililiname'][i] = newline
if i ==0:
firstline = linenamelist[0]
ui.get_model_component(firstline).pos.max = lwave + delta_lam/10.
ui.get_model_component(firstline).pos.min = lwave - delta_lam/10.
else:
dl = lwave - linelist['wave'][0]
ui.link(ui.get_model_component(newline).pos, ui.get_model_component(firstline).pos + dl)
#ui.set_analysis("wave")
ui.ignore(None, None) #ignores all data
ui.notice(min(linelist['wave'])-delta_lam, max(linelist['wave']) + delta_lam)
ui.fit(id)
if plot:
ui.plot_fit(id)
if has_chips:
pychips.set_curve("crv1",["err.*","true"])
pychips.set_plot_title(linelist['name'])
if outfile is not None:
pychips.print_window(outfile, ['clobber','true'])
elif has_mpl:
plt.title(linelist['name'])
if outfile is not None:
plt.savefig(outfile)
else:
raise NoPlottingSystemError("Neither pychips nor matplotlib are found.")
def main(opt):
# Use verbose option to control sherpa output
logger = logging.getLogger("sherpa")
logger.setLevel(LOGLEVELS[opt['verbose']])
events = extract_events(opt['evtfile'],
opt['x'], opt['y'], opt['radius'])
evt_ra_pnt = events.get_key('RA_PNT').value
evt_dec_pnt = events.get_key('DEC_PNT').value
evt_roll_pnt = events.get_key('ROLL_PNT').value
asol = pycrates.read_file(opt['infile'])
asol_times = asol.get_column('time').values
# Sanity check the two input files
asol_obsid = asol.get_key('OBS_ID').value
evt_obsid = events.get_key('OBS_ID').value
if asol_obsid != evt_obsid:
v1("Error Aspect solution obsid {} != event file obsid {}".format(asol_obsid, evt_obsid))
# Extract event RA, Dec, and times from event file
# Do the WCS transformation directly instead of using the pycrates RA/Dec properties to
# work around intermittent bug https://icxc.harvard.edu/pipe/ascds_help/2013a/0315.html
wcs = events.get_transform("eqpos")
evt_x = events.get_column("x").values
evt_y = events.get_column("y").values
rd = wcs.apply(np.column_stack([evt_x, evt_y]))
evt_ra = rd[:, 0]
evt_dec = rd[:, 1]
evt_times = events.get_column('Time').values
# Limit to only using events contained within the range of the aspect solution
ok_times = (evt_times > asol_times[0]) & (evt_times < asol_times[-1])
if not np.any(ok_times):
raise ValueError("No events in region are contained within time range of aspect solution.")
# Limit this *in place*
evt_ra = evt_ra[ok_times]
evt_dec = evt_dec[ok_times]
evt_times = evt_times[ok_times]
if len(evt_times) < opt['src_min_counts']:
v1("Warning only {} counts in src region. {} minimum suggested 'src_min_counts'".format(
len(evt_times), opt['src_min_counts']))
ax_data = {}
ax_map = {'yag': 'dy',
'zag': 'dz'}
ax_data['yag'], ax_data['zag'] = get_event_yag_zag(evt_ra, evt_dec,
evt_ra_pnt, evt_dec_pnt, evt_roll_pnt)
# Store comments to print in block after all of the sherpa fit output
fit_comments = []
plot_list = []
for data_id, ax in enumerate(['yag', 'zag']):
fit_data = ax_data[ax] - np.mean(ax_data[ax])
mp, model = _fit_poly(fit_data, evt_times, opt['corr_poly_degree'], data_id=data_id)
bin_centers, bin_mean, bin_std = time_bins(evt_times, fit_data)
add_window(6, 4, "inches")
add_curve((bin_centers - evt_times[0]) / 1000., bin_mean, [bin_std, +bin_std],
["line.style", "none", "symbol.style", "none", "err.style", "cap"])
add_curve(mp.x / 1000., mp.y, ["symbol.style", "none"])
# set minimum limit on fit plot in arcsecs and set this explicitly as a symmetric limit
fit_ymax = max(0.3, np.max(np.abs(bin_mean - bin_std)), np.max(np.abs(bin_mean + bin_std)))
limits(Y_AXIS, -1 * fit_ymax, fit_ymax)
set_plot_xlabel("Observation elapsed/delta time (ks)")
set_plot_ylabel("Position offset from mean, {} (arcsec)".format(ax))
set_plot_title("Fit of {} data (with time-binned event offsets)".format(ax))
fit_plot = "{}_fit_{}.png".format(opt['corr_plot_root'], ax)
if os.path.exists(fit_plot) and opt['clobber'] == 'yes':
os.unlink(fit_plot)
plot_list.append(fit_plot)
print_window(fit_plot)
add_window(6, 4, "inches")
data_plot = "{}_data_{}.png".format(opt['corr_plot_root'], ax)
ui.get_data_plot_prefs()['yerrorbars'] = False
ui.plot_fit(data_id)
if os.path.exists(data_plot) and opt['clobber'] == 'yes':
os.unlink(data_plot)
# set minimum limit on data plot in arcsecs and set this explicitly as a symmetric limit
data_ymax = max(2.0, np.max(np.abs(fit_data)) + .2)
limits(Y_AXIS, -1 * data_ymax, data_ymax)
set_plot_xlabel("Observation elapsed/delta time (s)")
set_plot_ylabel("Position offset from mean, {} (arcsec)".format(ax))
set_plot_title("Raw data and fit in {}".format(ax))
plot_list.append(data_plot)
print_window(data_plot)
asol_corr = np.interp(asol_times, mp.x + evt_times[0], mp.y)
asol_col_to_fix = asol.get_column(ax_map[ax])
fit_comments.append("Events show drift range of {:.2f} arcsec in {} axis".format(
np.max(asol_corr) - np.min(asol_corr), ax))
fit_comments.append("Max absolute correction of {:.2f} arcsec for {} axis".format(
np.max(np.abs(asol_corr)), ax))
# Convert the correction from arcsecs to mm (divide by 20) and add the correction
# to the dy and dz columns in the file.
asol_col_to_fix.values += (asol_corr / 20)
# Add header keys saving the axis-specific parts of this correction
write_key(asol, "ADC{}MN".format(ax.upper()), np.mean(ax_data[ax]),
"Aspect Drift Corr. Mean of uncorr {} data".format(ax))
for deg in range(0, 1 + opt['corr_poly_degree']):
write_key(asol, "ADC{}C{}".format(ax.upper(), deg),
getattr(model, 'c{}'.format(deg)).val,
"Aspect Drift Corr. {} model c{}".format(ax, deg))
# Add header keywords about fit
write_key(asol, "ADCTIME0", evt_times[0],
"Aspect Drift Corr. reference time")
write_key(asol, "ADCSRCX", opt['x'],
"Aspect Drift Corr. input src x")
write_key(asol, "ADCSRCY", opt['y'],
"Aspect Drift Corr. input src y")
write_key(asol, "ADCSRCR", opt['radius'],
"Aspect Drift Corr. input src radius", units='pix')
write_key(asol, "ADCORDR", opt['corr_poly_degree'],
"Aspect Drift Corr. model poly degree")
write_key(asol, "ADCVER", VERSION,
"Aspect Drift Corr. tool version")
v2("-" * 60)
v2("Fit results")
for c in fit_comments:
v2("\t{}".format(c))
v2("-" * 60)
v2("Writing out corrected aspect solution file to {}".format(opt['outfile']))
v2("\tTo review fit see correction plots in:")
for p in plot_list:
v2("\t\t{}".format(p))
# Actually write out the new aspect solution file
asol.write(opt['outfile'], clobber=opt['clobber'])
# Add history
add_tool_history(opt['outfile'], TOOLNAME, params=opt, toolversion=VERSION)
def main(opt):
# Use verbose option to control sherpa output
logger = logging.getLogger("sherpa")
logger.setLevel(LOGLEVELS[opt['verbose']])
events = extract_events(opt['evtfile'], opt['x'], opt['y'], opt['radius'])
evt_ra_pnt = events.get_key('RA_PNT').value
evt_dec_pnt = events.get_key('DEC_PNT').value
evt_roll_pnt = events.get_key('ROLL_PNT').value
asol = pycrates.read_file(opt['infile'])
asol_times = asol.get_column('time').values
# Sanity check the two input files
asol_obsid = asol.get_key('OBS_ID').value
evt_obsid = events.get_key('OBS_ID').value
if asol_obsid != evt_obsid:
v1("Error Aspect solution obsid {} != event file obsid {}".format(
asol_obsid, evt_obsid))
# Extract event RA, Dec, and times from event file
# Do the WCS transformation directly instead of using the pycrates RA/Dec properties to
# work around intermittent bug https://icxc.harvard.edu/pipe/ascds_help/2013a/0315.html
wcs = events.get_transform("eqpos")
evt_x = events.get_column("x").values
evt_y = events.get_column("y").values
rd = wcs.apply(np.column_stack([evt_x, evt_y]))
evt_ra = rd[:, 0]
evt_dec = rd[:, 1]
evt_times = events.get_column('Time').values
# Limit to only using events contained within the range of the aspect solution
ok_times = (evt_times > asol_times[0]) & (evt_times < asol_times[-1])
if not np.any(ok_times):
raise ValueError(
"No events in region are contained within time range of aspect solution."
)
# Limit this *in place*
evt_ra = evt_ra[ok_times]
evt_dec = evt_dec[ok_times]
evt_times = evt_times[ok_times]
if len(evt_times) < opt['src_min_counts']:
v1("Warning only {} counts in src region. {} minimum suggested 'src_min_counts'"
.format(len(evt_times), opt['src_min_counts']))
ax_data = {}
ax_map = {'yag': 'dy', 'zag': 'dz'}
ax_data['yag'], ax_data['zag'] = get_event_yag_zag(evt_ra, evt_dec,
evt_ra_pnt, evt_dec_pnt,
evt_roll_pnt)
# Store comments to print in block after all of the sherpa fit output
fit_comments = []
plot_list = []
for data_id, ax in enumerate(['yag', 'zag']):
fit_data = ax_data[ax] - np.mean(ax_data[ax])
mp, model = _fit_poly(fit_data,
evt_times,
opt['corr_poly_degree'],
data_id=data_id)
bin_centers, bin_mean, bin_std = time_bins(evt_times, fit_data)
add_window(6, 4, "inches")
add_curve(
(bin_centers - evt_times[0]) / 1000., bin_mean,
[bin_std, +bin_std],
["line.style", "none", "symbol.style", "none", "err.style", "cap"])
add_curve(mp.x / 1000., mp.y, ["symbol.style", "none"])
# set minimum limit on fit plot in arcsecs and set this explicitly as a symmetric limit
fit_ymax = max(0.3, np.max(np.abs(bin_mean - bin_std)),
np.max(np.abs(bin_mean + bin_std)))
#limits(Y_AXIS, -1 * fit_ymax, fit_ymax)
set_plot_xlabel("Observation elapsed/delta time (ks)")
set_plot_ylabel("Position offset from mean, {} (arcsec)".format(ax))
set_plot_title(
"Fit of {} data (with time-binned event offsets)".format(ax))
fit_plot = "{}_fit_{}.png".format(opt['corr_plot_root'], ax)
if os.path.exists(fit_plot) and opt['clobber']:
os.unlink(fit_plot)
plot_list.append(fit_plot)
print_window(fit_plot)
add_window(6, 4, "inches")
data_plot = "{}_data_{}.png".format(opt['corr_plot_root'], ax)
ui.get_data_plot_prefs()['yerrorbars'] = False
ui.plot_fit(data_id)
if os.path.exists(data_plot) and opt['clobber']:
os.unlink(data_plot)
# set minimum limit on data plot in arcsecs and set this explicitly as a symmetric limit
data_ymax = max(2.0, np.max(np.abs(fit_data)) + .2)
#limits(Y_AXIS, -1 * data_ymax, data_ymax)
set_plot_xlabel("Observation elapsed/delta time (s)")
set_plot_ylabel("Position offset from mean, {} (arcsec)".format(ax))
set_plot_title("Raw data and fit in {}".format(ax))
plot_list.append(data_plot)
print_window(data_plot)
asol_corr = np.interp(asol_times, mp.x + evt_times[0], mp.y)
asol_col_to_fix = asol.get_column(ax_map[ax])
fit_comments.append(
"Events show drift range of {:.2f} arcsec in {} axis".format(
np.max(asol_corr) - np.min(asol_corr), ax))
fit_comments.append(
"Max absolute correction of {:.2f} arcsec for {} axis".format(
np.max(np.abs(asol_corr)), ax))
# Convert the correction from arcsecs to mm (divide by 20) and add the correction
# to the dy and dz columns in the file.
asol_col_to_fix.values += (asol_corr / 20)
# Add header keys saving the axis-specific parts of this correction
write_key(asol, "ADC{}MN".format(ax.upper()), np.mean(ax_data[ax]),
"Aspect Drift Corr. Mean of uncorr {} data".format(ax))
for deg in range(0, 1 + opt['corr_poly_degree']):
write_key(asol, "ADC{}C{}".format(ax.upper(), deg),
getattr(model, 'c{}'.format(deg)).val,
"Aspect Drift Corr. {} model c{}".format(ax, deg))
# Add header keywords about fit
write_key(asol, "ADCTIME0", evt_times[0],
"Aspect Drift Corr. reference time")
write_key(asol, "ADCSRCX", opt['x'], "Aspect Drift Corr. input src x")
write_key(asol, "ADCSRCY", opt['y'], "Aspect Drift Corr. input src y")
write_key(asol,
"ADCSRCR",
opt['radius'],
"Aspect Drift Corr. input src radius",
units='pix')
write_key(asol, "ADCORDR", opt['corr_poly_degree'],
"Aspect Drift Corr. model poly degree")
write_key(asol, "ADCVER", VERSION, "Aspect Drift Corr. tool version")
v2("-" * 60)
v2("Fit results")
for c in fit_comments:
v2("\t{}".format(c))
v2("-" * 60)
v2("Writing out corrected aspect solution file to {}".format(
opt['outfile']))
v2("\tTo review fit see correction plots in:")
for p in plot_list:
v2("\t\t{}".format(p))
# Actually write out the new aspect solution file
asol.write(opt['outfile'], clobber=opt['clobber'])
def _plot(x, y, yerr=None, xerr=None, title=None, xlabel=None, ylabel=None,
overplot=False, clearwindow=True,
xerrorbars=False,
yerrorbars=False,
errstyle=None,
errcolor=None,
errthickness=None,
xlog=False,
ylog=False,
linestyle=chips.chips_solid,
linecolor=None,
symbolstyle=chips.chips_none,
symbolcolor=None,
symbolsize=None,
symbolfill=True,
linethickness=None,
xaxis=False,
ratioline=False):
if (not overplot) and clearwindow:
_clear_window()
if yerrorbars and (yerr is not None) and xerrorbars and (xerr is not None):
xerr = xerr / 2.
chips.add_curve(x, y, (yerr, yerr, xerr, xerr) )
elif yerrorbars and (yerr is not None):
chips.add_curve(x, y, yerr)
else:
chips.add_curve(x, y)
for var in ('errstyle', 'errcolor', 'errthickness', 'linestyle',
'linecolor', 'symbolstyle', 'symbolcolor', 'symbolsize',
'symbolfill', 'linethickness'):
val = locals()[var]
if val is not None:
if 'color' in var:
val = _check_hex_color(val)
getattr(chips.advanced, 'set_curve_' + var)(val)
if not overplot:
for log_axis, axis_id in izip((xlog, ylog),
(chips.X_AXIS, chips.Y_AXIS)):
if log_axis:
chips.log_scale(axis_id)
else:
chips.linear_scale(axis_id)
if title:
ttl = title.replace('_', '\\_')
chips.set_plot_title(ttl)
if xlabel:
xlbl = xlabel.replace('_', '\\_')
chips.set_plot_xlabel(xlbl)
if ylabel:
ylbl = ylabel.replace('_', '\\_')
chips.set_plot_ylabel(ylbl)
if xaxis:
chips.add_hline(0);
if ratioline:
chips.add_hline(1);
def _plot(x,
y,
yerr=None,
xerr=None,
title=None,
xlabel=None,
ylabel=None,
overplot=False,
clearwindow=True,
xerrorbars=False,
yerrorbars=False,
errstyle=None,
errcolor=None,
errthickness=None,
xlog=False,
ylog=False,
linestyle=chips.chips_solid,
linecolor=None,
symbolstyle=chips.chips_none,
symbolcolor=None,
symbolsize=None,
symbolfill=True,
linethickness=None,
xaxis=False,
ratioline=False):
if (not overplot) and clearwindow:
_clear_window()
if yerrorbars and (yerr is not None) and xerrorbars and (xerr is not None):
xerr = xerr / 2.
chips.add_curve(x, y, (yerr, yerr, xerr, xerr))
elif yerrorbars and (yerr is not None):
chips.add_curve(x, y, yerr)
else:
chips.add_curve(x, y)
for var in ('errstyle', 'errcolor', 'errthickness', 'linestyle',
'linecolor', 'symbolstyle', 'symbolcolor', 'symbolsize',
'symbolfill', 'linethickness'):
val = locals()[var]
if val is not None:
if 'color' in var:
val = _check_hex_color(val)
getattr(chips.advanced, 'set_curve_' + var)(val)
if not overplot:
for log_axis, axis_id in izip((xlog, ylog),
(chips.X_AXIS, chips.Y_AXIS)):
if log_axis:
chips.log_scale(axis_id)
else:
chips.linear_scale(axis_id)
if title:
ttl = title.replace('_', '\\_')
chips.set_plot_title(ttl)
if xlabel:
xlbl = xlabel.replace('_', '\\_')
chips.set_plot_xlabel(xlbl)
if ylabel:
ylbl = ylabel.replace('_', '\\_')
chips.set_plot_ylabel(ylbl)
if xaxis:
chips.add_hline(0)
if ratioline:
chips.add_hline(1)
