def examine_draws(draws, parname, params=None, plot=True, verbose=True):
"""Use the cumulative-distribution of the draws to get mode and sigma values for a parameter.
The return value is (median, lower 1-sigma value, upper 1-sigma value).
If params is not None then it should be the return value of
get_parameter_info().
If plot is True then the cumulative distribution function will
be displayed.
If verbose is True then a quick comparison of the fit
results will be displayed.
"""
if parname not in draws["parnames"]:
raise RuntimeError, "Unknown parameter '%s'" % parname
# Exclude any point with an iteration number of 0
#
idx = draws["iteration"] > 0
x = np.sort(draws[parname][idx])
sigfrac = 0.682689
median = _quantile(x, 0.5)
lval = _quantile(x, (1 - sigfrac) / 2.0)
hval = _quantile(x, (1 + sigfrac) / 2.0)
y = (np.arange(x.size) + 1) * 1.0 / x.size
# Get the best-fit value if available
if params != None:
idx = params["parnames"] == parname
p0 = params["parvals"][idx][0]
psigma = params["parmaxes"][idx][0]
if plot:
chips.lock()
try:
chips.open_undo_buffer()
chips.erase()
chips.add_curve(x, y, ["symbol.style", "none", "line.style", "solid"])
chips.add_vline(median, ["style", "dot"])
chips.add_vline(lval, ["style", "dot"])
chips.add_vline(hval, ["style", "dot"])
if params != None:
chips.add_vline(p0, ["line.color", "green", "line.style", "longdash"])
chips.add_vline(p0 - psigma, ["line.color", "green", "line.style", "longdash"])
chips.add_vline(p0 + psigma, ["line.color", "green", "line.style", "longdash"])
chips.set_plot_xlabel(parname)
chips.set_plot_ylabel(r"p(\leq x)")
except:
chips.discard_undo_buffer()
chips.unlock()
raise
chips.close_undo_buffer()
chips.unlock()
if verbose:
print ""
print "CDF of draws for parameter %s gives" % parname
print " median = %g" % median
print " sigma = %g , %g" % (lval - median, hval - median)
print ""
if params != None:
print " best fit = %g" % p0
print " covar sigma = %g" % psigma
print ""
return (median, lval, hval)
def examine_draws(draws, parname, params=None, plot=True, verbose=True):
"""Use the cumulative-distribution of the draws to get mode and sigma values for a parameter.
The return value is (median, lower 1-sigma value, upper 1-sigma value).
If params is not None then it should be the return value of
get_parameter_info().
If plot is True then the cumulative distribution function will
be displayed.
If verbose is True then a quick comparison of the fit
results will be displayed.
"""
if parname not in draws["parnames"]:
raise RuntimeError, "Unknown parameter '%s'" % parname
# Exclude any point with an iteration number of 0
#
idx = draws["iteration"] > 0
x = np.sort(draws[parname][idx])
sigfrac = 0.682689
median = _quantile(x, 0.5)
lval = _quantile(x, (1 - sigfrac) / 2.0)
hval = _quantile(x, (1 + sigfrac) / 2.0)
y = (np.arange(x.size) + 1) * 1.0 / x.size
# Get the best-fit value if available
if params != None:
idx = params["parnames"] == parname
p0 = params["parvals"][idx][0]
psigma = params["parmaxes"][idx][0]
if plot:
chips.lock()
try:
chips.open_undo_buffer()
chips.erase()
chips.add_curve(x, y,
["symbol.style", "none", "line.style", "solid"])
chips.add_vline(median, ["style", "dot"])
chips.add_vline(lval, ["style", "dot"])
chips.add_vline(hval, ["style", "dot"])
if params != None:
chips.add_vline(
p0, ["line.color", "green", "line.style", "longdash"])
chips.add_vline(
p0 - psigma,
["line.color", "green", "line.style", "longdash"])
chips.add_vline(
p0 + psigma,
["line.color", "green", "line.style", "longdash"])
chips.set_plot_xlabel(parname)
chips.set_plot_ylabel(r"p(\leq x)")
except:
chips.discard_undo_buffer()
chips.unlock()
raise
chips.close_undo_buffer()
chips.unlock()
if verbose:
print ""
print "CDF of draws for parameter %s gives" % parname
print " median = %g" % median
print " sigma = %g , %g" % (lval - median, median - hval)
print ""
if params != None:
print " best fit = %g" % p0
print " covar sigma = %g" % psigma
print ""
return (median, lval, hval)
def fit_draws(draws, parname, nbins=50, params=None, plot=True, verbose=True):
"""Fit a gaussian to the histogram of the given parameter.
Before using this routine you should use get_parameter_info()
to extract the parameter info for use by get_draws(). This is
because using this routine will invalidate the internal
data structures that get_draws() uses when its params argument
is None.
If params is not None then it should be the return value of
get_parameter_info().
If plot is True then a plot of the histogram and fit will be
made.
If verbose is True then a quick comparison of the fit
results will be displayed.
"""
if parname not in draws["parnames"]:
raise RuntimeError, "Unknown parameter '%s'" % parname
# Exclude any point with an iteration number of 0
#
idx = draws["iteration"] > 0
parvals = draws[parname][idx]
(hy, hx) = np.histogram(parvals, bins=nbins, new=True)
xlo = hx[:-1]
xhi = hx[1:]
id = parname
ui.load_arrays(id, 0.5 * (xlo + xhi), hy)
# We can guess the amplitude and position fairly reliably;
# for the FWHM we just use the inter-quartile range of the
# X axis.
#
ui.set_source(id, ui.gauss1d.gparam)
gparam.pos = xlo[xlo.size // 2]
gparam.ampl = hy[xlo.size // 2]
gparam.fwhm = xlo[xlo.size * 3 // 4] - xlo[xlo.size // 4]
# Get the best-fit value if available
if params != None:
p0 = dict(zip(params["parnames"], params["parvals"]))[parname]
logger = logging.getLogger("sherpa")
olvl = logger.level
logger.setLevel(40)
ostat = ui.get_stat_name()
ui.set_stat("leastsq")
ui.fit(id)
ui.set_stat(ostat)
logger.setLevel(olvl)
if plot:
# We manually create the plot since we want to use a histogram for the
# data and the Sherpa plots use curves.
#
##dplot = ui.get_data_plot(id)
mplot = ui.get_model_plot(id)
chips.lock()
try:
chips.open_undo_buffer()
chips.erase()
chips.add_histogram(xlo, xhi, hy)
##chips.add_histogram(xlo, xhi, mplot.y, ["line.color", "red", "line.style", "dot"])
chips.add_curve(mplot.x, mplot.y, ["line.color", "red", "symbol.style", "none"])
if params != None:
chips.add_vline(p0, ["line.color", "green", "line.style", "longdash"])
chips.set_plot_xlabel(parname)
except:
chips.discard_undo_buffer()
chips.unlock()
raise
chips.close_undo_buffer()
chips.unlock()
sigma = gparam.fwhm.val / (2.0 * np.sqrt(2 * np.log(2)))
if verbose:
print ""
print "Fit to histogram of draws for parameter %s gives" % parname
print " mean = %g" % gparam.pos.val
print " sigma = %g" % sigma
print ""
if params != None:
idx = params["parnames"] == parname
print " best fit = %g" % p0
print " covar sigma = %g" % params["parmaxes"][idx][0]
print ""
return (gparam.pos.val, sigma, gparam.ampl.val)
def fit_draws(draws, parname, nbins=50, params=None, plot=True, verbose=True):
"""Fit a gaussian to the histogram of the given parameter.
Before using this routine you should use get_parameter_info()
to extract the parameter info for use by get_draws(). This is
because using this routine will invalidate the internal
data structures that get_draws() uses when its params argument
is None.
If params is not None then it should be the return value of
get_parameter_info().
If plot is True then a plot of the histogram and fit will be
made.
If verbose is True then a quick comparison of the fit
results will be displayed.
"""
if parname not in draws["parnames"]:
raise RuntimeError, "Unknown parameter '%s'" % parname
# Exclude any point with an iteration number of 0
#
idx = draws["iteration"] > 0
parvals = draws[parname][idx]
(hy, hx) = np.histogram(parvals, bins=nbins, new=True)
xlo = hx[:-1]
xhi = hx[1:]
id = parname
ui.load_arrays(id, 0.5 * (xlo + xhi), hy)
# We can guess the amplitude and position fairly reliably;
# for the FWHM we just use the inter-quartile range of the
# X axis.
#
ui.set_source(id, ui.gauss1d.gparam)
gparam.pos = xlo[xlo.size // 2]
gparam.ampl = hy[xlo.size // 2]
gparam.fwhm = xlo[xlo.size * 3 // 4] - xlo[xlo.size // 4]
# Get the best-fit value if available
if params != None:
p0 = dict(zip(params["parnames"], params["parvals"]))[parname]
logger = logging.getLogger("sherpa")
olvl = logger.level
logger.setLevel(40)
ostat = ui.get_stat_name()
ui.set_stat("leastsq")
ui.fit(id)
ui.set_stat(ostat)
logger.setLevel(olvl)
if plot:
# We manually create the plot since we want to use a histogram for the
# data and the Sherpa plots use curves.
#
##dplot = ui.get_data_plot(id)
mplot = ui.get_model_plot(id)
chips.lock()
try:
chips.open_undo_buffer()
chips.erase()
chips.add_histogram(xlo, xhi, hy)
##chips.add_histogram(xlo, xhi, mplot.y, ["line.color", "red", "line.style", "dot"])
chips.add_curve(mplot.x, mplot.y,
["line.color", "red", "symbol.style", "none"])
if params != None:
chips.add_vline(
p0, ["line.color", "green", "line.style", "longdash"])
chips.set_plot_xlabel(parname)
except:
chips.discard_undo_buffer()
chips.unlock()
raise
chips.close_undo_buffer()
chips.unlock()
sigma = gparam.fwhm.val / (2.0 * np.sqrt(2 * np.log(2)))
if verbose:
print ""
print "Fit to histogram of draws for parameter %s gives" % parname
print " mean = %g" % gparam.pos.val
print " sigma = %g" % sigma
print ""
if params != None:
idx = params["parnames"] == parname
print " best fit = %g" % p0
print " covar sigma = %g" % params["parmaxes"][idx][0]
print ""
return (gparam.pos.val, sigma, gparam.ampl.val)