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_poly(fit_data, evt_times, degree, data_id=0):
"""
Given event data transformed into Y or Z angle positions, and a degree of the desired
fit polynomial, fit a polynomial to the data.
:param fit_data: event y or z angle position data
:param evt_times: times of event/fit_data
:param degree: degree of polynomial to use for the fit model
:param data_id: sherpa dataset id to use for the fit
:returns: (sherpa model plot, sherpa model)
"""
# Set initial value for fit data position error
init_error = 1
ui.clean()
ui.load_arrays(data_id, evt_times - evt_times[0], fit_data,
np.zeros_like(fit_data) + init_error)
v2("Fitting a line to the data to get reduced stat errors")
# First just fit a line to get reduced errors on this set
ui.polynom1d.line
ui.set_model(data_id, 'line')
ui.thaw('line.c1')
ui.fit(data_id)
fit = ui.get_fit_results()
calc_error = init_error * np.sqrt(fit.rstat)
ui.set_staterror(data_id, calc_error)
# Then fit the specified model
v2("Fitting a polynomial of degree {} to the data".format(degree))
ui.polynom1d.fitpoly
ui.freeze('fitpoly')
# Thaw the coefficients requested by the degree of the desired polynomial
ui.thaw('fitpoly.c0')
fitpoly.c0.val = 0
for deg in range(1, 1 + degree):
ui.thaw("fitpoly.c{}".format(deg))
ui.set_model(data_id, 'fitpoly')
ui.fit(data_id)
# Let's screw up Y on purpose
if data_id == 0:
fitpoly.c0.val = 0
fitpoly.c1.val = 7.5e-05
fitpoly.c2.val = -1.0e-09
fitpoly.c3.val = 0
fitpoly.c4.val = 0
mp = ui.get_model_plot(data_id)
model = ui.get_model(data_id)
return mp, model
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 _fit_poly(fit_data, evt_times, degree, data_id=0):
"""
Given event data transformed into Y or Z angle positions, and a degree of the desired
fit polynomial, fit a polynomial to the data.
:param fit_data: event y or z angle position data
:param evt_times: times of event/fit_data
:param degree: degree of polynomial to use for the fit model
:param data_id: sherpa dataset id to use for the fit
:returns: (sherpa model plot, sherpa model)
"""
# Set initial value for fit data position error
init_error = 1
ui.clean()
ui.load_arrays(data_id, evt_times - evt_times[0], fit_data,
np.zeros_like(fit_data) + init_error)
v2("Fitting a line to the data to get reduced stat errors")
# First just fit a line to get reduced errors on this set
ui.polynom1d.line
ui.set_model(data_id, 'line')
ui.thaw('line.c1')
ui.fit(data_id)
fit = ui.get_fit_results()
calc_error = init_error * np.sqrt(fit.rstat)
ui.set_staterror(data_id, calc_error)
# Then fit the specified model
v2("Fitting a polynomial of degree {} to the data".format(degree))
ui.polynom1d.fitpoly
ui.freeze('fitpoly')
# Thaw the coefficients requested by the degree of the desired polynomial
ui.thaw('fitpoly.c0')
fitpoly.c0.val = 0
for deg in range(1, 1 + degree):
ui.thaw("fitpoly.c{}".format(deg))
ui.set_model(data_id, 'fitpoly')
ui.fit(data_id)
mp = ui.get_model_plot(data_id)
model = ui.get_model(data_id)
return mp, model
def test_ui_set_full_model_2d_mismatch_1d(clean_ui, setup_ui_2d):
ui.load_psf('psf1', 'gauss1d.g1')
ui.set_full_model('psf1(gauss1d.g2 ) +const1d.c1')
# Ideally this would fail but it currently does not
ui.get_model()
def test_ui_set_full_model_2d(clean_ui, setup_ui_2d):
ui.load_psf('psf1', 'gauss2d.g1')
ui.set_full_model('psf1(gauss2d.g2 ) +const2d.c1')
ui.get_model()
def test_set_full_model(self):
ui.load_psf("psf1", "gauss2d.g1")
ui.set_full_model("psf1(gauss2d.g2)+const2d.c1")
ui.get_model()
ui.get_source()
def test_set_full_model(self):
ui.load_psf('psf1', 'gauss2d.g1')
ui.set_full_model('psf1(gauss2d.g2)+const2d.c1')
ui.get_model()
def test_set_full_model(self):
ui.load_psf('psf1', 'gauss2d.g1')
ui.set_full_model('psf1(gauss2d.g2)+const2d.c1')
ui.get_model()
