def prepare(self, arf, data=None):
"""Fill the fields given the ARF.
Parameters
----------
arf :
The ARF to plot
data : DataPHA instance, optional
The `units` attribute of this object is used
to determine whether the X axis should be
in Angstrom instead of KeV (the default).
"""
self.xlo = arf.energ_lo
self.xhi = arf.energ_hi
self.y = arf.specresp
self.title = arf.name
self.xlabel = arf.get_xlabel()
self.ylabel = arf.get_ylabel()
if data is not None:
if not isinstance(data, DataPHA):
raise PlotErr('notpha', data.name)
if data.units == "wavelength":
self.xlabel = 'Wavelength (Angstrom)'
self.xlo = data._hc/self.xlo
self.xhi = data._hc/self.xhi
def prepare(self, data, src, lo=None, hi=None):
# Note: src is source model before folding
if not isinstance(data, DataPHA):
raise IOErr('notpha', data.name)
lo, hi = bounds_check(lo, hi)
self.units = data.units
if self.units == "channel":
warning("Channel space is unappropriate for the PHA unfolded" +
" source model,\nusing energy.")
self.units = "energy"
self.xlabel = data.get_xlabel()
self.title = f'Source Model of {data.name}'
self.xlo, self.xhi = data._get_indep(filter=False)
# Why do we not apply the mask at the end of prepare?
#
self.mask = filter_bins((lo, ), (hi, ), (self.xlo, ))
# The source model is assumed to not contain an instrument model,
# and so it evaluates the expected number of photons/cm^2/s in
# each bin (or it can be thought of as a 1 second exposure).
#
self.y = src(self.xlo, self.xhi)
prefix_quant = 'E'
quant = 'keV'
if self.units == "wavelength":
# No other labels use the LaTeX forms for lambda and
# Angstrom, so use the text version here too.
# prefix_quant = to_latex('\\lambda')
# quant = to_latex('\\AA')
prefix_quant = 'lambda'
quant = 'Angstrom'
(self.xlo, self.xhi) = (self.xhi, self.xlo)
xmid = abs(self.xhi - self.xlo)
sqr = to_latex('^2')
self.xlabel = f'{self.units.capitalize()} ({quant})'
self.ylabel = f'%s Photons/sec/cm{sqr}%s'
if data.plot_fac == 0:
self.y /= xmid
self.ylabel = self.ylabel % (f'f({prefix_quant})', f'/{quant} ')
elif data.plot_fac == 1:
self.ylabel = self.ylabel % (f'{prefix_quant} f({prefix_quant})',
'')
elif data.plot_fac == 2:
self.y *= xmid
self.ylabel = self.ylabel % (
f'{prefix_quant}{sqr} f({prefix_quant})', f' {quant} ')
else:
raise PlotErr('plotfac', 'Source', data.plot_fac)
def prepare(self, data, src, lo=None, hi=None):
# Note: src is source model before folding
if not isinstance(data, DataPHA):
raise IOErr('notpha', data.name)
lo, hi = bounds_check(lo, hi)
self.units = data.units
if self.units == "channel":
warning("Channel space is unappropriate for the PHA unfolded" +
" source model,\nusing energy.")
self.units = "energy"
self.xlabel = data.get_xlabel()
self.title = 'Source Model of %s' % data.name
self.xlo, self.xhi = data._get_indep(filter=False)
self.mask = filter_bins((lo, ), (hi, ), (self.xlo, ))
self.y = src(self.xlo, self.xhi)
prefix_quant = 'E'
quant = 'keV'
if self.units == "wavelength":
# No other labels use the LaTeX forms for lambda and
# Angstrom, so use the text version here too.
# prefix_quant = to_latex('\\lambda')
# quant = to_latex('\\AA')
prefix_quant = 'lambda'
quant = 'Angstrom'
(self.xlo, self.xhi) = (self.xhi, self.xlo)
xmid = abs(self.xhi - self.xlo)
sqr = to_latex('^2')
self.xlabel = '%s (%s)' % (self.units.capitalize(), quant)
self.ylabel = '%s Photons/sec/cm' + sqr + '%s'
if data.plot_fac == 0:
self.y /= xmid
self.ylabel = self.ylabel % ('f(%s)' % prefix_quant,
'/%s ' % quant)
elif data.plot_fac == 1:
self.ylabel = self.ylabel % ('%s f(%s)' %
(prefix_quant, prefix_quant), '')
elif data.plot_fac == 2:
self.y *= xmid
self.ylabel = self.ylabel % ('%s%s f(%s)' %
(prefix_quant, sqr, prefix_quant),
' %s ' % quant)
else:
raise PlotErr('plotfac', 'Source', data.plot_fac)
def prepare(self, data, model, orders=None, colors=None):
self.orders = data.response_ids
if orders is not None:
if iterable(orders):
self.orders = list(orders)
else:
self.orders = [orders]
if colors is not None:
self.use_default_colors=False
if iterable(colors):
self.colors = list(colors)
else:
self.colors = [colors]
else:
self.colors=[]
top_color = '0xffffff'
bot_color = '0x0000bf'
num = len(self.orders)
jump = (int(top_color, 16) - int(bot_color,16))/(num+1)
for order in self.orders:
self.colors.append(top_color)
top_color = hex(int(top_color,16)-jump)
if not self.use_default_colors and len(colors) != len(orders):
raise PlotErr('ordercolors', len(orders), len(colors))
old_filter = parse_expr(data.get_filter())
old_group = data.grouped
try:
if old_group:
data.ungroup()
for interval in old_filter:
data.notice(*interval)
self.xlo=[]
self.xhi=[]
self.y=[]
(xlo, y, yerr,xerr,
self.xlabel, self.ylabel) = data.to_plot(model)
y = y[1]
if data.units != 'channel':
elo, ehi = data._get_ebins(group=False)
xlo = data.apply_filter(elo, data._min)
xhi = data.apply_filter(ehi, data._max)
if data.units == 'wavelength':
xlo = data._hc/xlo
xhi = data._hc/xhi
else:
xhi = xlo + 1.
for order in self.orders:
self.xlo.append(xlo)
self.xhi.append(xhi)
if len(data.response_ids) > 2:
if order < 1 or order > len(model.rhs.orders):
raise PlotErr('notorder', order)
y = data.apply_filter(model.rhs.orders[order-1])
y = data._fix_y_units(y,True)
if data.exposure:
y = data.exposure * y
self.y.append(y)
finally:
if old_group:
data.ignore()
data.group()
for interval in old_filter:
data.notice(*interval)
self.title = 'Model Orders %s' % str(self.orders)
if len(self.xlo) != len(self.y):
raise PlotErr("orderarrfail")