def fit_coeffs(method='simplex'):
method = method
dummy_data = np.zeros(100)
dummy_times = np.arange(100)
ui.load_arrays(1, dummy_times, dummy_data)
ui.set_method(method)
ui.get_method().config.update(SHERPA_CONFIGS.get(method, {}))
calc_model = CalcModel()
ui.load_user_model(calc_model, 'axo_mod') # sets global axo_mod
parnames = []
for row in range(N_ROWS):
for col in range(N_COLS):
parnames.append('adj_{}_{}'.format(row, col))
ui.add_user_pars('axo_mod', parnames)
ui.set_model(1, 'axo_mod')
calc_stat = CalcStat(axo_mod, M_2d, displ_x)
ui.load_user_stat('axo_stat', calc_stat, lambda x: np.ones_like(x))
ui.set_stat(axo_stat)
calc_model.set_calc_stat(calc_stat)
# Set frozen, min, and max attributes for each axo_mod parameter
for par in axo_mod.pars:
par.val = 0.0
par.min = -5
par.max = 5
ui.fit(1)
coeffs = np.array([(par.val) for pars in axo_mod.pars])
return coeffs
def fit_coeffs(method='simplex'):
method = method
dummy_data = np.zeros(100)
dummy_times = np.arange(100)
ui.load_arrays(1, dummy_times, dummy_data)
ui.set_method(method)
ui.get_method().config.update(SHERPA_CONFIGS.get(method, {}))
calc_model = CalcModel()
ui.load_user_model(calc_model, 'axo_mod') # sets global axo_mod
parnames = []
for row in range(N_ROWS):
for col in range(N_COLS):
parnames.append('adj_{}_{}'.format(row, col))
ui.add_user_pars('axo_mod', parnames)
ui.set_model(1, 'axo_mod')
calc_stat = CalcStat(axo_mod, M_2d, displ_x)
ui.load_user_stat('axo_stat', calc_stat, lambda x: np.ones_like(x))
ui.set_stat(axo_stat)
calc_model.set_calc_stat(calc_stat)
# Set frozen, min, and max attributes for each axo_mod parameter
for par in axo_mod.pars:
par.val = 0.0
par.min = -5
par.max = 5
ui.fit(1)
coeffs = np.array([(par.val) for pars in axo_mod.pars])
return coeffs
def fit_adjuster_set(coeffs, adj_idxs, method='simplex'):
"""
Find best fit parameters for an arbitrary subset of adjustors
specified by the array ``adj_idxs``. The input ``coeffs`` are
the best-fit adjustor coefficients for the last iteration.
"""
import sherpa.astro.ui as ui
dummy_data = np.zeros(100)
dummy_times = np.arange(100)
ui.load_arrays(1, dummy_times, dummy_data)
ui.set_method(method)
ui.get_method().config.update(SHERPA_CONFIGS.get(method, {}))
calc_model = CalcModel()
ui.load_user_model(calc_model, 'axo_mod') # sets global axo_mod
parnames = []
for adj_idx in adj_idxs:
parnames.append('adj_{}'.format(adj_idx))
ui.add_user_pars('axo_mod', parnames)
ui.set_model(1, 'axo_mod')
coeffs = coeffs.copy() # Don't modify input coeffs
coeffs[coeffs < 0] = 0 # Don't allow negative coeffs
# Set frozen, min, and max attributes for each axo_mod parameter
for adj_idx, par in zip(adj_idxs, axo_mod.pars):
par.min = -1000
par.max = 1000
par.val = coeffs[adj_idx]
print 'Setting {} to {}'.format(adj_idx, par.val)
# Compute base adjusted displacements assuming all the fitted actuators
# have zero drive level.
coeffs[adj_idxs] = 0
base_adj_displ = M_2d.dot(coeffs)
m_2d = M_2d[:, adj_idxs].copy()
print m_2d.shape
calc_stat = CalcStat(base_adj_displ, m_2d, DISPL_X)
ui.load_user_stat('axo_stat', calc_stat, lambda x: np.ones_like(x))
ui.set_stat(axo_stat)
calc_model.set_calc_stat(calc_stat)
ui.fit(1)
# Update coeffs with the values determined in fitting
for adj_idx, par in zip(adj_idxs, axo_mod.pars):
coeffs[adj_idx] = abs(par.val)
return coeffs, ui.get_fit_results()
sh.load_table_model("bkg", "analysis_3d/background_2D.fits")
sh.load_psf("psf", "analysis_3d/psf_2D.fits")
# To speed up this tutorial, we change the fit optimazation method to Levenberg-Marquardt and fix a required tolerance. This can make the fitting less robust, and in practise, you can skip this step unless you understand what is going on.
# In[ ]:
sh.set_method("levmar")
sh.set_method_opt("xtol", 1e-5)
sh.set_method_opt("ftol", 1e-5)
sh.set_method_opt("gtol", 1e-5)
sh.set_method_opt("epsfcn", 1e-5)
# In[ ]:
print(sh.get_method())
# In principle one might first want to fit the background amplitude. However the background estimation method already yields the correct normalization, so we freeze the background amplitude to unity instead of adjusting it. The (smoothed) residuals from this background model are then computed and shown.
# In[ ]:
sh.set_full_model(bkg)
bkg.ampl = 1
sh.freeze(bkg)
# In[ ]:
resid = Map.read("analysis_3d/counts_2D.fits")
resid.data = sh.get_data_image().y - sh.get_model_image().y
resid_smooth = resid.smooth(width=4)
resid_smooth.plot(add_cbar=True)
if self.min_fit_stat is None or fit_stat < self.min_fit_stat:
self.min_fit_stat = fit_stat
self.min_parvals = self.parvals
return fit_stat, np.ones_like(self.displ)
# def fit(method='levmar'):
method = "simplex"
dummy_data = np.zeros(100)
dummy_times = np.arange(100)
ui.load_arrays(1, dummy_times, dummy_data)
ui.set_method(method)
ui.get_method().config.update(SHERPA_CONFIGS.get(method, {}))
calc_model = CalcModel()
ui.load_user_model(calc_model, "axo_mod") # sets global axo_mod
parnames = []
for row in range(N_ROWS):
for col in range(N_COLS):
parnames.append("adj_{}_{}".format(row, col))
ui.add_user_pars("axo_mod", parnames)
ui.set_model(1, "axo_mod")
calc_stat = CalcStat(axo_mod, M_2d, displ_x)
ui.load_user_stat("axo_stat", calc_stat, lambda x: np.ones_like(x))
ui.set_stat(axo_stat)
calc_model.set_calc_stat(calc_stat)
if self.min_fit_stat is None or fit_stat < self.min_fit_stat:
self.min_fit_stat = fit_stat
self.min_parvals = self.parvals
return fit_stat, np.ones_like(self.displ)
# def fit(method='levmar'):
method = 'simplex'
dummy_data = np.zeros(100)
dummy_times = np.arange(100)
ui.load_arrays(1, dummy_times, dummy_data)
ui.set_method(method)
ui.get_method().config.update(SHERPA_CONFIGS.get(method, {}))
calc_model = CalcModel()
ui.load_user_model(calc_model, 'axo_mod') # sets global axo_mod
parnames = []
for row in range(N_ROWS):
for col in range(N_COLS):
parnames.append('adj_{}_{}'.format(row, col))
ui.add_user_pars('axo_mod', parnames)
ui.set_model(1, 'axo_mod')
calc_stat = CalcStat(axo_mod, M_2d, displ_x)
ui.load_user_stat('axo_stat', calc_stat, lambda x: np.ones_like(x))
ui.set_stat(axo_stat)
calc_model.set_calc_stat(calc_stat)
