def fit(self, method='simplex'):
"""Initiate a fit of the model using Sherpa.
:param method: Method to be used to fit the model (e.g. simplex, levmar, or moncar)
"""
dummy_data = np.zeros(1)
dummy_times = np.arange(1)
ui.load_arrays(1, dummy_times, dummy_data)
ui.set_method(method)
ui.get_method().config.update(sherpa_configs.get(method, {}))
ui.load_user_model(CalcModel(self.model, self.fit_logger),
'xijamod') # sets global xijamod
ui.add_user_pars('xijamod', self.model.parnames)
ui.set_model(1, 'xijamod')
calc_stat = CalcStat(self.model, self.fit_logger)
ui.load_user_stat('xijastat', calc_stat, lambda x: np.ones_like(x))
ui.set_stat(xijastat)
# Set frozen, min, and max attributes for each xijamod parameter
for par in self.model.pars:
xijamod_par = getattr(xijamod, par.full_name)
xijamod_par.val = par.val
xijamod_par.frozen = par.frozen
xijamod_par.min = par.min
xijamod_par.max = par.max
ui.fit(1)
self.save_snapshot(fit_stat=calc_stat.min_fit_stat, method=method)
def fit_pix_values(t_ccd, esec, id=1):
logger = logging.getLogger("sherpa")
logger.setLevel(logging.WARN)
data_id = id
ui.clean()
ui.set_method('simplex')
ui.load_user_model(dark_scale_model, 'model')
ui.add_user_pars('model', ['scale', 'dark_t_ref'])
ui.set_model(data_id, 'model')
ui.load_arrays(
data_id,
np.array(t_ccd),
np.array(esec),
)
ui.set_staterror(data_id, 30 * np.ones(len(t_ccd)))
model.scale.val = 0.588
model.scale.min = 0.3
model.scale.max = 1.0
model.dark_t_ref.val = 500
ui.freeze(model.scale)
# If more than 5 degrees in the temperature range,
# thaw and fit for model.scale. Else just use/return
# the fit of dark_t_ref
if np.max(t_ccd) - np.min(t_ccd) > 2:
# Fit first for dark_t_ref
ui.fit(data_id)
ui.thaw(model.scale)
ui.fit(data_id)
return ui.get_fit_results(), ui.get_model(data_id)
def fit(self):
dummy_data = np.zeros(1)
dummy_times = np.arange(1)
ui.load_arrays(1, dummy_times, dummy_data)
ui.set_method(self.method)
ui.get_method().config.update(sherpa_configs.get(self.method, {}))
ui.load_user_model(CalcModel(self.model), 'xijamod') # sets global xijamod
ui.add_user_pars('xijamod', self.model.parnames)
ui.set_model(1, 'xijamod')
calc_stat = CalcStat(self.model, self.child_pipe, self.maxiter)
ui.load_user_stat('xijastat', calc_stat, lambda x: np.ones_like(x))
ui.set_stat(xijastat)
# Set frozen, min, and max attributes for each xijamod parameter
for par in self.model.pars:
xijamod_par = getattr(xijamod, par.full_name)
xijamod_par.val = par.val
xijamod_par.frozen = par.frozen
xijamod_par.min = par.min
xijamod_par.max = par.max
if any(not par.frozen for par in self.model.pars):
try:
ui.fit(1)
calc_stat.message['status'] = 'finished'
fit_logger.info('Fit finished normally')
except FitTerminated as err:
calc_stat.message['status'] = 'terminated'
fit_logger.warning('Got FitTerminated exception {}'.format(err))
self.child_pipe.send(calc_stat.message)
def test_user_model_create_pars_names(clean_ui):
mname = "test_model"
ui.load_user_model(um_line, mname)
mdl = ui.get_model_component(mname)
assert len(mdl.pars) == 1
# add user pars doesn't change the existing instance, you have
# to "get" the new version to see the change
#
ui.add_user_pars(mname, ['X1', 'x'])
mdl = ui.get_model_component(mname)
assert len(mdl.pars) == 2
p0 = mdl.pars[0]
p1 = mdl.pars[1]
assert p0.name == 'X1'
assert p0.val == pytest.approx(0.0)
assert p0.units == ''
assert not p0.frozen
assert p0.min == pytest.approx(-1 * hugeval)
assert p0.max == pytest.approx(hugeval)
assert p1.name == 'x'
assert p1.val == pytest.approx(0.0)
assert p1.units == ''
assert not p1.frozen
assert p1.min == pytest.approx(-1 * hugeval)
assert p1.max == pytest.approx(hugeval)
def fit(self):
dummy_data = np.zeros(1)
dummy_times = np.arange(1)
ui.load_arrays(1, dummy_times, dummy_data)
ui.set_method(self.method)
ui.get_method().config.update(sherpa_configs.get(self.method, {}))
ui.load_user_model(CalcModel(self.model), 'xijamod') # sets global xijamod
ui.add_user_pars('xijamod', self.model.parnames)
ui.set_model(1, 'xijamod')
calc_stat = CalcStat(self.model, self.child_pipe)
ui.load_user_stat('xijastat', calc_stat, lambda x: np.ones_like(x))
ui.set_stat(xijastat)
# Set frozen, min, and max attributes for each xijamod parameter
for par in self.model.pars:
xijamod_par = getattr(xijamod, par.full_name)
xijamod_par.val = par.val
xijamod_par.frozen = par.frozen
xijamod_par.min = par.min
xijamod_par.max = par.max
if any(not par.frozen for par in self.model.pars):
try:
ui.fit(1)
calc_stat.message['status'] = 'finished'
logging.debug('Fit finished normally')
except FitTerminated as err:
calc_stat.message['status'] = 'terminated'
logging.debug('Got FitTerminated exception {}'.format(err))
self.child_pipe.send(calc_stat.message)
def test_user_model_create_pars_full(clean_ui):
mname = "test_model"
ui.load_user_model(um_line, mname)
ui.add_user_pars(mname, ['pAr1', '_p'], [23.2, 3.1e2],
parunits=['', 'cm^2 s'],
parfrozen=[True, False],
parmins=[0, -100],
parmaxs=[100, 1e5])
mdl = ui.get_model_component(mname)
assert len(mdl.pars) == 2
p0 = mdl.pars[0]
p1 = mdl.pars[1]
assert p0.name == 'pAr1'
assert p0.val == pytest.approx(23.2)
assert p0.units == ''
assert p0.frozen
assert p0.min == pytest.approx(0)
assert p0.max == pytest.approx(100)
assert p1.name == '_p'
assert p1.val == pytest.approx(3.1e2)
assert p1.units == 'cm^2 s'
assert not p1.frozen
assert p1.min == pytest.approx(-100)
assert p1.max == pytest.approx(1e5)
def test_user_model_create_pars_full():
mname = "test_model"
ui.load_user_model(um_line, mname)
ui.add_user_pars(mname, ['pAr1', '_p'], [23.2, 3.1e2],
parunits=['', 'cm^2 s'],
parfrozen=[True, False],
parmins=[0, -100],
parmaxs=[100, 1e5])
mdl = ui.get_model_component(mname)
assert len(mdl.pars) == 2
p0 = mdl.pars[0]
p1 = mdl.pars[1]
assert p0.name == 'pAr1'
assert p0.val == pytest.approx(23.2)
assert p0.units == ''
assert p0.frozen
assert p0.min == pytest.approx(0)
assert p0.max == pytest.approx(100)
assert p1.name == '_p'
assert p1.val == pytest.approx(3.1e2)
assert p1.units == 'cm^2 s'
assert not p1.frozen
assert p1.min == pytest.approx(-100)
assert p1.max == pytest.approx(1e5)
def test_user_model_create_pars_names():
mname = "test_model"
ui.load_user_model(um_line, mname)
mdl = ui.get_model_component(mname)
assert len(mdl.pars) == 1
# add user pars doesn't change the existing instance, you have
# to "get" the new version to see the change
#
ui.add_user_pars(mname, ['X1', 'x'])
mdl = ui.get_model_component(mname)
assert len(mdl.pars) == 2
p0 = mdl.pars[0]
p1 = mdl.pars[1]
assert p0.name == 'X1'
assert p0.val == pytest.approx(0.0)
assert p0.units == ''
assert not p0.frozen
assert p0.min == pytest.approx(-1 * hugeval)
assert p0.max == pytest.approx(hugeval)
assert p1.name == 'x'
assert p1.val == pytest.approx(0.0)
assert p1.units == ''
assert not p1.frozen
assert p1.min == pytest.approx(-1 * hugeval)
assert p1.max == pytest.approx(hugeval)
def test_user_model1d_fit():
"""Check can use in a fit."""
mname = "test_model"
ui.load_user_model(um_line, mname)
ui.add_user_pars(mname, ["slope", "intercept"],
parvals = [1.0, 1.0])
mdl = ui.get_model_component(mname)
x = numpy.asarray([-2.4, 2.3, 5.4, 8.7, 12.3])
# Set up the data to be scattered around y = -0.2 x + 2.8
# Pick the deltas so that they sum to 0 (except for central
# point)
#
slope = -0.2
intercept = 2.8
dy = numpy.asarray([0.1, -0.2, 0.14, -0.1, 0.2])
ydata = x * slope + intercept + dy
ui.load_arrays(1, x, ydata)
ui.set_source(mname)
ui.ignore(5.0, 6.0) # drop the central bin
ui.set_stat('leastsq')
ui.set_method('simplex')
ui.fit()
fres = ui.get_fit_results()
assert fres.succeeded
assert fres.parnames == ('test_model.slope', 'test_model.intercept')
assert fres.numpoints == 4
assert fres.dof == 2
# Tolerance has been adjusted to get the tests to pass on my
# machine. It's really just to check that the values have chanegd
# from their default values.
#
assert fres.parvals[0] == pytest.approx(slope, abs=0.01)
assert fres.parvals[1] == pytest.approx(intercept, abs=0.05)
# Thse should be the same values, so no need to use pytest.approx
# (unless there's some internal translation between types done
# somewhere?).
#
assert mdl.slope.val == fres.parvals[0]
assert mdl.intercept.val == fres.parvals[1]
def test_user_model1d_fit():
"""Check can use in a fit."""
mname = "test_model"
ui.load_user_model(um_line, mname)
ui.add_user_pars(mname, ["slope", "intercept"],
parvals = [1.0, 1.0])
mdl = ui.get_model_component(mname)
x = numpy.asarray([-2.4, 2.3, 5.4, 8.7, 12.3])
# Set up the data to be scattered around y = -0.2 x + 2.8
# Pick the deltas so that they sum to 0 (except for central
# point)
#
slope = -0.2
intercept = 2.8
dy = numpy.asarray([0.1, -0.2, 0.14, -0.1, 0.2])
ydata = x * slope + intercept + dy
ui.load_arrays(1, x, ydata)
ui.set_source(mname)
ui.ignore(5.0, 6.0) # drop the central bin
ui.set_stat('leastsq')
ui.set_method('simplex')
ui.fit()
fres = ui.get_fit_results()
assert fres.succeeded
assert fres.parnames == ('test_model.slope', 'test_model.intercept')
assert fres.numpoints == 4
assert fres.dof == 2
# Tolerance has been adjusted to get the tests to pass on my
# machine. It's really just to check that the values have chanegd
# from their default values.
#
assert fres.parvals[0] == pytest.approx(slope, abs=0.01)
assert fres.parvals[1] == pytest.approx(intercept, abs=0.05)
# Thse should be the same values, so no need to use pytest.approx
# (unless there's some internal translation between types done
# somewhere?).
#
assert mdl.slope.val == fres.parvals[0]
assert mdl.intercept.val == fres.parvals[1]
def fit_model(
model,
comm=None,
method='simplex',
config=None,
nofit=None,
freeze_pars=freeze_pars,
thaw_pars=[],
):
dummy_data = np.zeros(1)
dummy_times = np.arange(1)
ui.load_arrays(1, dummy_times, dummy_data)
ui.set_method(method)
ui.get_method().config.update(config or sherpa_configs.get(method, {}))
ui.load_user_model(CalcModel(model, comm), 'xijamod')
ui.add_user_pars('xijamod', model.parnames)
ui.set_model(1, 'xijamod')
fit_parnames = set()
for parname, parval in zip(model.parnames, model.parvals):
getattr(xijamod, parname).val = parval
fit_parnames.add(parname)
if any([re.match(x + '$', parname) for x in freeze_pars]):
fit_logger.info('Freezing ' + parname)
ui.freeze(getattr(xijamod, parname))
fit_parnames.remove(parname)
if any([re.match(x + '$', parname) for x in thaw_pars]):
fit_logger.info('Thawing ' + parname)
ui.thaw(getattr(xijamod, parname))
fit_parnames.add(parname)
if 'tau' in parname:
getattr(xijamod, parname).min = 0.1
calc_stat = CalcStat(model, comm)
ui.load_user_stat('xijastat', calc_stat, lambda x: np.ones_like(x))
ui.set_stat(xijastat)
if fit_parnames and not nofit:
ui.fit(1)
else:
model.calc()
def fit_model(model,
comm=None,
method='simplex',
config=None,
nofit=None,
freeze_pars=freeze_pars,
thaw_pars=[],
):
dummy_data = np.zeros(1)
dummy_times = np.arange(1)
ui.load_arrays(1, dummy_times, dummy_data)
ui.set_method(method)
ui.get_method().config.update(config or sherpa_configs.get(method, {}))
ui.load_user_model(CalcModel(model, comm), 'xijamod')
ui.add_user_pars('xijamod', model.parnames)
ui.set_model(1, 'xijamod')
fit_parnames = set()
for parname, parval in zip(model.parnames, model.parvals):
getattr(xijamod, parname).val = parval
fit_parnames.add(parname)
if any([re.match(x + '$', parname) for x in freeze_pars]):
fit_logger.info('Freezing ' + parname)
ui.freeze(getattr(xijamod, parname))
fit_parnames.remove(parname)
if any([re.match(x + '$', parname) for x in thaw_pars]):
fit_logger.info('Thawing ' + parname)
ui.thaw(getattr(xijamod, parname))
fit_parnames.add(parname)
if 'tau' in parname:
getattr(xijamod, parname).min = 0.1
calc_stat = CalcStat(model, comm)
ui.load_user_stat('xijastat', calc_stat, lambda x: np.ones_like(x))
ui.set_stat(xijastat)
if fit_parnames and not nofit:
ui.fit(1)
else:
model.calc()
def test_user_model_change_par(clean_ui):
mname = "test_model"
ui.load_user_model(um_line, mname)
ui.add_user_pars(mname, ['xXx', 'Y2'])
mdl = ui.get_model_component(mname)
assert len(mdl.pars) == 2
p0 = mdl.pars[0]
p1 = mdl.pars[1]
assert p0.name == 'xXx'
assert p1.name == 'Y2'
assert p0.val == pytest.approx(0.0)
assert p1.val == pytest.approx(0.0)
# Use the user-supplied names:
#
mdl.xXx = 2.0
assert p0.val == pytest.approx(2.0)
mdl.Y2 = 3.0
assert p1.val == pytest.approx(3.0)
# Now all lower case
#
mdl.xxx = 4.0
assert p0.val == pytest.approx(4.0)
mdl.y2 = 12.0
assert p1.val == pytest.approx(12.0)
# Try with the set_par function
#
ui.set_par('test_model.xxx', 12.2)
assert p0.val == pytest.approx(12.2)
ui.set_par('test_model.y2', 14.0, frozen=True)
assert p1.val == pytest.approx(14.0)
assert p1.frozen
ui.clean()
def test_user_model_change_par():
mname = "test_model"
ui.load_user_model(um_line, mname)
ui.add_user_pars(mname, ['xXx', 'Y2'])
mdl = ui.get_model_component(mname)
assert len(mdl.pars) == 2
p0 = mdl.pars[0]
p1 = mdl.pars[1]
assert p0.name == 'xXx'
assert p1.name == 'Y2'
assert p0.val == pytest.approx(0.0)
assert p1.val == pytest.approx(0.0)
# Use the user-supplied names:
#
mdl.xXx = 2.0
assert p0.val == pytest.approx(2.0)
mdl.Y2 = 3.0
assert p1.val == pytest.approx(3.0)
# Now all lower case
#
mdl.xxx = 4.0
assert p0.val == pytest.approx(4.0)
mdl.y2 = 12.0
assert p1.val == pytest.approx(12.0)
# Try with the set_par function
#
ui.set_par('test_model.xxx', 12.2)
assert p0.val == pytest.approx(12.2)
ui.set_par('test_model.y2', 14.0, frozen=True)
assert p1.val == pytest.approx(14.0)
assert p1.frozen
ui.clean()
def fit_pix_values(t_ccd, esec, id=1):
logger = logging.getLogger("sherpa")
logger.setLevel(logging.WARN)
data_id = id
ui.clean()
ui.set_method("simplex")
ui.load_user_model(dark_scale_model, "model")
ui.add_user_pars("model", ["scale", "dark_t_ref"])
ui.set_model(data_id, "model")
ui.load_arrays(data_id, np.array(t_ccd), np.array(esec), 0.1 * np.ones(len(t_ccd)))
model.scale.val = 0.70
model.dark_t_ref.val = 500
ui.freeze(model.scale)
# If more than 5 degrees in the temperature range,
# thaw and fit for model.scale. Else just use/return
# the fit of dark_t_ref
ui.fit(data_id)
ui.thaw(model.scale)
ui.fit(data_id)
return ui.get_fit_results(), ui.get_model(data_id)
def test_user_model1d_eval(clean_ui):
"""Simple evaluation check for 1D case."""
mname = "test_model"
ui.load_user_model(um_line, mname)
ui.add_user_pars(mname, ["slope", "intercept"])
m = 2.1
c = -4.8
mdl = ui.get_model_component(mname)
mdl.slope = m
mdl.intercept = c
x = np.asarray([2.3, 5.4, 8.7])
y = mdl(x)
yexp = x * m + c
# This check require pytest >= 3.2.0
#
assert y == pytest.approx(yexp)
def test_user_model1d_eval():
"""Simple evaluation check for 1D case."""
mname = "test_model"
ui.load_user_model(um_line, mname)
ui.add_user_pars(mname, ["slope", "intercept"])
m = 2.1
c = -4.8
mdl = ui.get_model_component(mname)
mdl.slope = m
mdl.intercept = c
x = numpy.asarray([2.3, 5.4, 8.7])
y = mdl(x)
yexp = x * m + c
# This check require pytest >= 3.2.0
#
assert y == pytest.approx(yexp)
line = pars[0] * x + pars[1]
line[line <= 0] = 1e-7
line[line >= 1] = 1 - 1e-7
return line
#axplot = {}
#ftype = 'obc_bad'
for ftype in failures:
fail_mask = failures[ftype]
data_id = figmap[ftype]
ui.set_method('simplex')
ui.load_user_model(lim_line, '%s_mod' % ftype)
ui.add_user_pars('%s_mod' % ftype, ['m', 'b'])
ui.set_model(data_id, '%s_mod' % ftype)
ui.load_arrays(data_id, times, failures[ftype])
fmod = ui.get_model_component('%s_mod' % ftype)
fmod.b.min = 0
fmod.b.max = 1
fmod.m.min = 0
fmod.m.max = 0.5
fmod.b.val = 1e-7
ui.load_user_stat("loglike", llh, my_err)
ui.set_stat(loglike)
# the tricky part here is that the "model" is the probability polynomial
def run_fits(obsids, ax, user_pars=None,
fixed_pars=None, guess_pars=None, label='model',
per_obs_dir='per_obs_nfits',
outdir=None, redo=False):
if len(obsids) == 0:
print "No obsids, nothing to fit"
return None
if user_pars is None:
user_pars = USER_PARS
if not os.path.exists(per_obs_dir):
os.makedirs(per_obs_dir)
obsfits = []
for obsid in obsids:
outdir = os.path.join(per_obs_dir, 'obs{:05d}'.format(obsid))
if not os.path.exists(outdir):
os.makedirs(outdir)
model_file = os.path.join(outdir, '{}.pkl'.format(label))
if os.path.exists(model_file) and not redo:
#logger.warn('Using previous fit found in %s' % model_file)
print model_file
mod_pick = open(model_file, 'r')
modelfit = cPickle.load( mod_pick )
mod_pick.close()
obsfits.append(modelfit)
continue
modelfit = {'label': obsid}
ui.clean()
data_id = 0
obsdir = "%s/obs%05d" % (DATADIR, obsid)
tf = open(os.path.join(obsdir,'tilt.pkl'), 'r')
tilt = cPickle.load(tf)
tf.close()
pf = open(os.path.join(obsdir, 'pos.pkl'), 'r')
pos = cPickle.load(pf)
pf.close()
pos_data = pos[ax]
point_error = 5
pos_data_mean = np.mean(pos_data)
ui.set_method('simplex')
# Fit a line to get more reasonable errors
init_staterror = np.zeros(len(pos_data))+point_error
ui.load_arrays(data_id,
pos['time']-pos['time'][0],
pos_data-np.mean(pos_data),
init_staterror)
ui.polynom1d.ypoly
ui.set_model(data_id, 'ypoly')
ui.thaw(ypoly.c0, ypoly.c1)
ui.fit(data_id)
fit = ui.get_fit_results()
calc_staterror = init_staterror * np.sqrt(fit.rstat)
ui.set_staterror(data_id, calc_staterror)
# Confirm those errors
ui.fit(data_id)
fit = ui.get_fit_results()
if ( abs(fit.rstat-1) > .2):
raise ValueError('Reduced statistic not close to 1 for error calc')
# Load up data to do the real model fit
fit_times = pos['time']
tm_func = tilt_model(tilt,
fit_times,
user_pars=user_pars)
ui.get_data(data_id).name = str(obsid)
ui.load_user_model(tm_func, 'tiltm%d' % data_id)
ui.add_user_pars('tiltm%d' % data_id, user_pars)
ui.set_method('simplex')
ui.set_model(data_id, 'tiltm%d' % (data_id))
ui.set_par('tiltm%d.diam' % data_id, 0)
if fixed_pars is not None and ax in fixed_pars:
for par in fixed_pars[ax]:
ui.set_par('tiltm{}.{}'.format(0, par), fixed_pars[ax][par])
ui.freeze('tiltm{}.{}'.format(0, par))
if guess_pars is not None and ax in guess_pars:
for par in guess_pars[ax]:
ui.set_par('tiltm{}.{}'.format(0, par), guess_pars[ax][par])
ui.show_all()
# Fit the tilt model
ui.fit(data_id)
fitres = ui.get_fit_results()
ui.confidence(data_id)
myconf = ui.get_confidence_results()
# save_fits(ax=ax, fit=fitres, conf=myconf, outdir=outdir)
# plot_fits(ids,outdir=os.path.join(outdir,'fit_plots'))
axmod = dict(fit=fitres, conf=myconf)
for idx, modpar in enumerate(myconf.parnames):
par = modpar.lstrip('tiltm0.')
axmod[par] = ui.get_par('tiltm0.%s' % par).val
axmod["{}_parmax".format(par)] = myconf.parmaxes[idx]
axmod["{}_parmin".format(par)] = myconf.parmins[idx]
modelfit[ax] = axmod
mod_pick = open(model_file, 'w')
cPickle.dump( modelfit, mod_pick)
mod_pick.close()
obsfits.append(modelfit)
plot_fits([dict(obsid=obsid, data_id=data_id, ax=ax)],
posdir=obsdir,
outdir=outdir)
return obsfits
print "ftype {}".format(limit)
warm_frac = data[range_type][mag][ok]['n{}'.format(limit)]
extent = np.max(warm_frac) - np.min(warm_frac)
wp_min = np.min(warm_frac)
warm_frac = warm_frac - wp_min
def scaled_warm_frac(pars, x):
scaled = pars[1] + warm_frac * pars[0]
return scaled
data_id = 1
ui.set_method('simplex')
ui.set_stat('chi2datavar')
#ui.set_stat('leastsq')
#ui.load_user_stat("chi2custom", my_chi2, my_err)
#ui.set_stat(chi2custom)
ui.load_user_model(scaled_warm_frac, 'model')
ui.add_user_pars('model', ['scale', 'offset'])
ui.set_model(data_id, 'model')
ui.load_arrays(data_id,
np.array(times),
np.array(bad_frac))
fmod = ui.get_model_component('model')
fmod.scale.min = 1e-9
fmod.offset.val = 0
ui.freeze(fmod.offset)
max_err = np.max([err_high, err_low], axis=0)
ui.set_staterror(data_id, max_err)
ui.fit(data_id)
f = ui.get_fit_results()
scale = f.rstat ** .5
ui.set_staterror(data_id, max_err * scale)
ui.fit()
def fitne(ne_data, nemodeltype, tspec_data=None):
'''
Fits gas number density profile according to selected profile model.
The fit is performed using python sherpa with the Levenberg-Marquardt
method of minimizing chi-squared .
Args:
-----
ne_data (astropy table): observed gas density profile
in the form established by set_prof_data()
tspec_data (astropy table): observed temperature profile
in the form established by set_prof_data()
Returns:
--------
nemodel (dictionary): stores relevant information about the model gas
density profile
nemodel['type']: ne model type; one of the following:
['single_beta','cusped_beta','double_beta_tied','double_beta']
nemodel['parnames']: names of the stored ne model parameters
nemodel['parvals']: parameter values of fitted gas density model
nemodel['parmins']: lower error bound on parvals
nemodel['parmaxes']: upper error bound on parvals
nemodel['chisq']: chi-squared of fit
nemodel['dof']: degrees of freedom
nemodel['rchisq']: reduced chi-squared of fit
nemodel['nefit']: ne model values at radial values matching
tspec_data (the observed temperature profile)
References:
-----------
python sherpa: https://github.com/sherpa/
'''
# remove any existing models and data
ui.clean()
# load data
ui.load_arrays(1, np.array(ne_data['radius']), np.array(ne_data['ne']),
np.array(ne_data['ne_err']))
# set guess and boundaries on params given selected model
if nemodeltype == 'single_beta':
# param estimate
betaguess = 0.6
rcguess = 20. # units?????
ne0guess = max(ne_data['ne'])
# beta model
ui.load_user_model(betamodel, "beta1d")
ui.add_user_pars("beta1d", ["ne0", "rc", "beta"])
ui.set_source(beta1d) # creates model
ui.set_full_model(beta1d)
# set parameter values
ui.set_par(beta1d.ne0, ne0guess, min=0, max=10. * max(ne_data['ne']))
ui.set_par(beta1d.rc, rcguess, min=0.1, max=max(ne_data['radius']))
ui.set_par(beta1d.beta, betaguess, min=0.1, max=1.)
if nemodeltype == 'cusped_beta':
# param estimate
betaguess = 0.7
rcguess = 5. # [kpc]
ne0guess = max(ne_data['ne'])
alphaguess = 10. # ????
# beta model
ui.load_user_model(cuspedbetamodel, "cuspedbeta1d")
ui.add_user_pars("cuspedbeta1d", ["ne0", "rc", "beta", "alpha"])
ui.set_source(cuspedbeta1d) # creates model
ui.set_full_model(cuspedbeta1d)
# set parameter values
ui.set_par(cuspedbeta1d.ne0,
ne0guess,
min=0.001 * max(ne_data['ne']),
max=10. * max(ne_data['ne']))
ui.set_par(cuspedbeta1d.rc,
rcguess,
min=0.1,
max=max(ne_data['radius']))
ui.set_par(cuspedbeta1d.beta, betaguess, min=0.1, max=1.)
ui.set_par(cuspedbeta1d.alpha, alphaguess, min=0., max=100.)
if nemodeltype == 'double_beta':
# param estimate
ne0guess1 = max(ne_data['ne']) # [cm^-3]
rcguess1 = 10. # [kpc]
betaguess1 = 0.6
ne0guess2 = 0.01 * max(ne_data['ne']) # [cm^-3]
rcguess2 = 100. # [kpc]
betaguess2 = 0.6
# double beta model
ui.load_user_model(doublebetamodel, "doublebeta1d")
ui.add_user_pars("doublebeta1d",
["ne01", "rc1", "beta1", "ne02", "rc2", "beta2"])
ui.set_source(doublebeta1d) # creates model
ui.set_full_model(doublebeta1d)
# set parameter values
ui.set_par(doublebeta1d.ne01,
ne0guess1,
min=0.0001 * max(ne_data['ne']),
max=100. * max(ne_data['ne']))
ui.set_par(doublebeta1d.rc1,
rcguess1,
min=0.1,
max=max(ne_data['radius']))
ui.set_par(doublebeta1d.beta1, betaguess1, min=0.1, max=1.)
ui.set_par(doublebeta1d.ne02,
ne0guess2,
min=0.0001 * max(ne_data['ne']),
max=100. * max(ne_data['ne']))
ui.set_par(doublebeta1d.rc2,
rcguess2,
min=10.,
max=max(ne_data['radius']))
ui.set_par(doublebeta1d.beta2, betaguess2, min=0.1, max=1.)
if nemodeltype == 'double_beta_tied':
# param estimate
ne0guess1 = max(ne_data['ne'])
rcguess1 = 10.
betaguess1 = 0.6
ne0guess2 = 0.01 * max(ne_data['ne'])
rcguess2 = 100.
# double beta model
ui.load_user_model(doublebetamodel_tied, "doublebeta1d_tied")
ui.add_user_pars("doublebeta1d_tied",
["ne01", "rc1", "beta1", "ne02", "rc2"])
ui.set_source(doublebeta1d_tied) # creates model
ui.set_full_model(doublebeta1d_tied)
# set parameter values
ui.set_par(doublebeta1d_tied.ne01,
ne0guess1,
min=0.00001 * max(ne_data['ne']),
max=100. * max(ne_data['ne']))
ui.set_par(doublebeta1d_tied.rc1,
rcguess1,
min=0.1,
max=max(ne_data['radius']))
ui.set_par(doublebeta1d_tied.beta1, betaguess1, min=0.1, max=1.)
ui.set_par(doublebeta1d_tied.ne02,
ne0guess2,
min=0.00001 * max(ne_data['ne']),
max=100. * max(ne_data['ne']))
ui.set_par(doublebeta1d_tied.rc2,
rcguess2,
min=10.,
max=max(ne_data['radius']))
# fit model
ui.fit()
# fit statistics
chisq = ui.get_fit_results().statval
dof = ui.get_fit_results().dof
rchisq = ui.get_fit_results().rstat
# error analysis
ui.set_conf_opt("max_rstat", 1e9)
ui.conf()
parvals = np.array(ui.get_conf_results().parvals)
parmins = np.array(ui.get_conf_results().parmins)
parmaxes = np.array(ui.get_conf_results().parmaxes)
parnames = [
str(x).split('.')[1] for x in list(ui.get_conf_results().parnames)
]
# where errors are stuck on a hard limit, change error to Inf
if None in list(parmins):
ind = np.where(parmins == np.array(None))[0]
parmins[ind] = float('Inf')
if None in list(parmaxes):
ind = np.where(parmaxes == np.array(None))[0]
parmaxes[ind] = float('Inf')
# set up a dictionary to contain useful results of fit
nemodel = {}
nemodel['type'] = nemodeltype
nemodel['parnames'] = parnames
nemodel['parvals'] = parvals
nemodel['parmins'] = parmins
nemodel['parmaxes'] = parmaxes
nemodel['chisq'] = chisq
nemodel['dof'] = dof
nemodel['rchisq'] = rchisq
# if tspec_data included, calculate value of ne model at the same radius
# positions as temperature profile
if tspec_data is not None:
if nemodeltype == 'double_beta':
nefit_arr = doublebetamodel(nemodel['parvals'],
np.array(tspec_data['radius']))
# [cm-3]
if nemodeltype == 'single_beta':
nefit_arr = betamodel(nemodel['parvals'],
np.array(tspec_data['radius']))
# [cm-3]
if nemodeltype == 'cusped_beta':
nefit_arr = cuspedbetamodel(nemodel['parvals'],
np.array(tspec_data['radius']))
# [cm-3]
if nemodeltype == 'double_beta_tied':
nefit_arr = doublebetamodel_tied(nemodel['parvals'],
np.array(tspec_data['radius']))
# [cm-3]
nemodel['nefit'] = nefit_arr
return nemodel
import pickle
import matplotlib.pyplot as plt
import numpy as np
from sherpa import ui
import dark_models
sbp = None # for pychecker
g1 = None
method = 'levmar'
ui.set_stat('cash')
ui.set_method('simplex')
ui.load_user_model(dark_models.smooth_broken_pow, 'sbp')
ui.add_user_pars('sbp', ('gamma1', 'gamma2', 'x_b', 'x_r', 'ampl1'))
def fit_gauss_sbp():
g1 = ui.gauss1d.g1
ui.set_model(sbp + g1)
ui.set_method('simplex')
g1.fwhm = 5.0
g1.pos = 7.0
g1.ampl = 30000.
ui.freeze(sbp.gamma1)
ui.freeze(sbp.gamma2)
ui.freeze(sbp.x_b)
ui.freeze(sbp.x_r)
ui.freeze(g1.fwhm)
extent = np.max(warm_frac) - np.min(warm_frac)
wp_min = np.min(warm_frac)
warm_frac = warm_frac - wp_min
def scaled_warm_frac(pars, x):
scaled = pars[1] + warm_frac * pars[0]
return scaled
data_id = 1
ui.set_method("simplex")
ui.set_stat("chi2datavar")
# ui.set_stat('leastsq')
# ui.load_user_stat("chi2custom", my_chi2, my_err)
# ui.set_stat(chi2custom)
ui.load_user_model(scaled_warm_frac, "model")
ui.add_user_pars("model", ["scale", "offset"])
ui.set_model(data_id, "model")
ui.load_arrays(data_id, np.array(times), np.array(bad_frac))
fmod = ui.get_model_component("model")
fmod.scale.min = 1e-9
max_err = np.max([data[range_type][mag][ok]["err_high"], data[range_type][mag][ok]["err_low"]], axis=0)
ui.set_staterror(data_id, max_err)
ui.fit(data_id)
f = ui.get_fit_results()
scale = f.rstat ** 0.5
ui.set_staterror(data_id, max_err * scale)
ui.fit()
f = ui.get_fit_results()
if f.rstat > 3:
raise ValueError
ui.confidence()
class AstropyToSherpa(object):
def __init__(self, model):
self.model = model
def __call__(self, pars, x):
self.model.parameters[:] = pars
return self.model(x)
ap_model = (models.Gaussian1D(amplitude=1.2, mean=0.9, stddev=0.5) +
models.Gaussian1D(amplitude=2.0, mean=-0.9, stddev=0.75))
err = 0.02
x = np.arange(-3, 3, .1)
y = ap_model(x) + err * np.random.uniform(size=len(x))
sh_model = AstropyToSherpa(ap_model)
ui.load_arrays(1, x, y, err * np.ones_like(x))
ui.load_user_model(sh_model, 'sherpa_model')
ui.add_user_pars('sherpa_model', ap_model.param_names, ap_model.parameters)
ui.set_model(1, 'sherpa_model')
ui.fit(1)
ui.plot_fit(1)
print()
print('Params from astropy model: {}'.format(ap_model.parameters))
plt.show()
def lim_line(pars, x):
line = pars[0] * x + pars[1]
line[line <= 0] = 1e-7
line[line >= 1] = 1 - 1e-7
return line
#axplot = {}
#ftype = 'obc_bad'
for ftype in failures:
fail_mask = failures[ftype]
data_id = figmap[ftype]
ui.set_method('simplex')
ui.load_user_model(lim_line, '%s_mod' % ftype)
ui.add_user_pars('%s_mod' % ftype, ['m', 'b'])
ui.set_model(data_id, '%s_mod' % ftype)
ui.load_arrays(data_id,
times,
failures[ftype])
fmod = ui.get_model_component('%s_mod' % ftype)
fmod.b.min = 0
fmod.b.max = 1
fmod.m.min = 0
fmod.m.max = 0.5
fmod.b.val=1e-7
def run_fits(obsids,
ax,
user_pars=None,
fixed_pars=None,
guess_pars=None,
label='model',
per_obs_dir='per_obs_nfits',
outdir=None,
redo=False):
if len(obsids) == 0:
print "No obsids, nothing to fit"
return None
if user_pars is None:
user_pars = USER_PARS
if not os.path.exists(per_obs_dir):
os.makedirs(per_obs_dir)
obsfits = []
for obsid in obsids:
outdir = os.path.join(per_obs_dir, 'obs{:05d}'.format(obsid))
if not os.path.exists(outdir):
os.makedirs(outdir)
model_file = os.path.join(outdir, '{}.pkl'.format(label))
if os.path.exists(model_file) and not redo:
#logger.warn('Using previous fit found in %s' % model_file)
print model_file
mod_pick = open(model_file, 'r')
modelfit = cPickle.load(mod_pick)
mod_pick.close()
obsfits.append(modelfit)
continue
modelfit = {'label': obsid}
ui.clean()
data_id = 0
obsdir = "%s/obs%05d" % (DATADIR, obsid)
tf = open(os.path.join(obsdir, 'tilt.pkl'), 'r')
tilt = cPickle.load(tf)
tf.close()
pf = open(os.path.join(obsdir, 'pos.pkl'), 'r')
pos = cPickle.load(pf)
pf.close()
pos_data = pos[ax]
point_error = 5
pos_data_mean = np.mean(pos_data)
ui.set_method('simplex')
# Fit a line to get more reasonable errors
init_staterror = np.zeros(len(pos_data)) + point_error
ui.load_arrays(data_id, pos['time'] - pos['time'][0],
pos_data - np.mean(pos_data), init_staterror)
ui.polynom1d.ypoly
ui.set_model(data_id, 'ypoly')
ui.thaw(ypoly.c0, ypoly.c1)
ui.fit(data_id)
fit = ui.get_fit_results()
calc_staterror = init_staterror * np.sqrt(fit.rstat)
ui.set_staterror(data_id, calc_staterror)
# Confirm those errors
ui.fit(data_id)
fit = ui.get_fit_results()
if (abs(fit.rstat - 1) > .2):
raise ValueError('Reduced statistic not close to 1 for error calc')
# Load up data to do the real model fit
fit_times = pos['time']
tm_func = tilt_model(tilt, fit_times, user_pars=user_pars)
ui.get_data(data_id).name = str(obsid)
ui.load_user_model(tm_func, 'tiltm%d' % data_id)
ui.add_user_pars('tiltm%d' % data_id, user_pars)
ui.set_method('simplex')
ui.set_model(data_id, 'tiltm%d' % (data_id))
ui.set_par('tiltm%d.diam' % data_id, 0)
if fixed_pars is not None and ax in fixed_pars:
for par in fixed_pars[ax]:
ui.set_par('tiltm{}.{}'.format(0, par), fixed_pars[ax][par])
ui.freeze('tiltm{}.{}'.format(0, par))
if guess_pars is not None and ax in guess_pars:
for par in guess_pars[ax]:
ui.set_par('tiltm{}.{}'.format(0, par), guess_pars[ax][par])
ui.show_all()
# Fit the tilt model
ui.fit(data_id)
fitres = ui.get_fit_results()
ui.confidence(data_id)
myconf = ui.get_confidence_results()
# save_fits(ax=ax, fit=fitres, conf=myconf, outdir=outdir)
# plot_fits(ids,outdir=os.path.join(outdir,'fit_plots'))
axmod = dict(fit=fitres, conf=myconf)
for idx, modpar in enumerate(myconf.parnames):
par = modpar.lstrip('tiltm0.')
axmod[par] = ui.get_par('tiltm0.%s' % par).val
axmod["{}_parmax".format(par)] = myconf.parmaxes[idx]
axmod["{}_parmin".format(par)] = myconf.parmins[idx]
modelfit[ax] = axmod
mod_pick = open(model_file, 'w')
cPickle.dump(modelfit, mod_pick)
mod_pick.close()
obsfits.append(modelfit)
plot_fits([dict(obsid=obsid, data_id=data_id, ax=ax)],
posdir=obsdir,
outdir=outdir)
return obsfits
