def test_ui_source_methods_with_full_model(clean_ui, setup_ui_full):
ui.load_data('full', setup_ui_full.ascii)
ui.set_full_model('full', 'powlaw1d.p1')
# Test Case 1
with pytest.raises(IdentifierErr) as exc:
ui.get_source('full')
emsg = "Convolved model\n'powlaw1d.p1'\n is set for dataset full. You should use get_model instead."
assert str(exc.value) == emsg
with pytest.raises(IdentifierErr) as exc:
ui.plot_source('full')
emsg = "Convolved model\n'powlaw1d.p1'\n is set for dataset full. You should use plot_model instead."
assert str(exc.value) == emsg
with pytest.raises(IdentifierErr) as exc:
ui.get_source_plot('full')
emsg = "Convolved model\n'powlaw1d.p1'\n is set for dataset full. You should use get_model_plot instead."
assert str(exc.value) == emsg
# Test Case 2
ui.set_source('full', 'powlaw1d.p2')
ui.get_source('full')
# Test Case 3
ui.load_data('not_full', setup_ui_full.ascii)
with pytest.raises(IdentifierErr) as exc:
ui.get_source('not_full')
emsg = 'source not_full has not been set, consider using set_source() or set_model()'
assert emsg == str(exc.value)
def test_source_methods_with_full_model(self):
from sherpa.utils.err import IdentifierErr
ui.load_data('full', self.ascii)
ui.set_full_model('full', 'powlaw1d.p1')
# Test Case 1
try:
ui.get_source('full')
except IdentifierErr as e:
self.assertRegex(
str(e),
"Convolved model\n.*\n is set for dataset full. You should use get_model instead.",
str(e))
try:
ui.plot_source('full')
except IdentifierErr as e:
self.assertRegex(
str(e),
"Convolved model\n.*\n is set for dataset full. You should use plot_model instead.",
str(e))
# Test Case 2
ui.set_source('full', 'powlaw1d.p2')
ui.get_source('full')
# Test Case 3
ui.load_data('not_full', self.ascii)
try:
ui.get_source('not_full')
except IdentifierErr as e:
self.assertEqual(
'source not_full has not been set, consider using set_source() or set_model()',
str(e))
def test_source_methods_with_full_model(self):
from sherpa.utils.err import IdentifierErr
ui.load_data('full', self.ascii)
ui.set_full_model('full', 'powlaw1d.p1')
# Test Case 1
try:
ui.get_source('full')
except IdentifierErr as e:
self.assertRegexpMatches(str(e), "Convolved model\n.*\n is set for dataset full. You should use get_model instead.", str(e))
try:
ui.plot_source('full')
except IdentifierErr as e:
self.assertEquals("Convolved model\n'p1'\n is set for dataset full. You should use plot_model instead.", str(e))
# Test Case 2
ui.set_source('full', 'powlaw1d.p2')
ui.get_source('full')
# Test Case 3
ui.load_data('not_full', self.ascii)
try:
ui.get_source('not_full')
except IdentifierErr as e:
self.assertEquals('source not_full has not been set, consider using set_source() or set_model()', str(e))
def setUp(self):
self._old_logger_level = logger.getEffectiveLevel()
logger.setLevel(logging.ERROR)
ui.clean()
self.ascii = self.make_path('sim.poisson.1.dat')
self.wrong_stat_msg = "Fit statistic must be cash, cstat or wstat, not {}"
self.wstat_err_msg = "No background data has been supplied. Use cstat"
self.no_covar_msg = "covariance has not been performed"
self.fail_msg = "Call should not have succeeded"
self.right_stats = {'cash', 'cstat', 'wstat'}
self.model = PowLaw1D("p1")
ui.load_data(self.ascii)
ui.set_model(self.model)
def setUp(self):
self._old_logger_level = logger.getEffectiveLevel()
logger.setLevel(logging.ERROR)
ui.clean()
self.ascii = self.make_path('sim.poisson.1.dat')
self.wrong_stat_msg = "Fit statistic must be cash, cstat or wstat, not {}"
self.wstat_err_msg = "No background data has been supplied. Use cstat"
self.no_covar_msg = "covariance has not been performed"
self.fail_msg = "Call should not have succeeded"
self.right_stats = {'cash', 'cstat', 'wstat'}
self.model = PowLaw1D("p1")
ui.load_data(self.ascii)
ui.set_model(self.model)
def test_ascii(self):
ui.load_data(1, self.ascii)
ui.load_data(1, self.ascii, 2)
ui.load_data(1, self.ascii, 2, ("col2", "col1"))
# coding: utf-8
import sherpa.ui as ui
ui.load_data("default_interp", "load_template_with_interpolation-bb_data.dat")
ui.load_template_model('bb1', "bb_index.dat")
ui.load_template_model('bb2', "bb_index.dat")
ui.set_model("default_interp", bb1+bb2)
ui.freeze("bb1.dummy")
ui.freeze("bb2.dummy")
ui.fit("default_interp")
def test_reading_strings(self):
ui.load_data(self.template_idx, require_floats=False)
# coding: utf-8
import sherpa.ui as ui
from sherpa.models.template import KNNInterpolator
ui.load_data("custom_interp", "load_template_interpolator-bb_data.dat")
ui.load_template_interpolator('knn', KNNInterpolator, k=2, order=1)
ui.load_template_model('bb1', "bb_index.dat", template_interpolator_name='knn')
ui.set_model("custom_interp", "bb1")
ui.freeze("bb1.dummy")
ui.fit("custom_interp")
def setup_covar(make_data_path):
print("A")
ui.load_data(make_data_path('sim.poisson.1.dat'))
ui.set_model(PowLaw1D("p1"))
def test_ui_ascii(clean_ui, setup_ui_full):
ui.load_data(1, setup_ui_full.ascii)
ui.load_data(1, setup_ui_full.ascii, 2)
ui.load_data(1, setup_ui_full.ascii, 2, ("col2", "col1"))
def test_not_reading_header_without_comment(make_data_path):
with pytest.raises(ValueError):
ui.load_data(make_data_path('agn2'))
# coding: utf-8
import sherpa.ui as ui
ui.load_data("load_template_without_interpolation-bb_data.dat")
ui.load_template_model('bb1', "bb_index.dat", template_interpolator_name=None)
ui.set_source('bb1')
assert ui.get_source().is_discrete
ui.set_source('bb1*const1d.c1+gauss1d.g2**const1d.c2')
assert ui.get_source().is_discrete
# coding: utf-8
import sherpa.ui as ui
ui.load_data("bb_data.dat")
ui.load_template_model('bb1', "bb_index.dat", template_interpolator_name=None)
ui.set_method('gridsearch')
ui.set_method_opt('sequence', [[2234, 0],[3512,0]])
ui.set_source('bb1')
ui.fit()
# coding: utf-8
import sherpa.ui as ui
ui.load_data("default_interp", "bb_data.dat")
ui.load_template_model('bb1', "bb_index.dat")
ui.load_template_model('bb2', "bb_index.dat")
ui.set_model("default_interp", bb1+bb2)
ui.freeze("bb1.dummy")
ui.freeze("bb2.dummy")
ui.fit("default_interp")
def test_ascii(self):
ui.load_data(1, self.ascii)
ui.load_data(1, self.ascii, 2)
ui.load_data(1, self.ascii, 2, ("col2", "col1"))
def test_require_float(make_data_path):
with pytest.raises(ValueError):
ui.load_data(make_data_path('agn2'))
def test_reading_floats(make_data_path):
ui.load_data(make_data_path('agn2_fixed'))
def test_reading_strings(make_data_path):
ui.load_data(make_data_path('table.txt'), require_floats=False)
# coding: utf-8
import sherpa.ui as ui
from sherpa.models.template import KNNInterpolator
ui.load_data("custom_interp", "bb_data.dat")
ui.load_template_interpolator('knn', KNNInterpolator, k=2, order=1)
ui.load_template_model('bb1', "bb_index.dat", template_interpolator_name='knn')
ui.set_model("custom_interp", "bb1")
ui.freeze("bb1.dummy")
ui.fit("custom_interp")
def test_reading_floats(self):
ui.load_data(self.agn2_fixed)
def test_reading_floats(self):
ui.load_data(self.agn2_fixed)
def test_reading_strings(self):
ui.load_data(self.template_idx, require_floats=False)
# coding: utf-8
import sherpa.ui as ui
ui.load_data("default_interp", "load_template_with_interpolation-bb_data.dat")
ui.load_template_model('bb1', "bb_index.dat")
ui.set_model("default_interp", bb1)
ui.set_method('gridsearch')
ui.set_method_opt('sequence', ui.get_model_component('bb1').parvals)
ui.fit("default_interp")
def fit_evol(dateglob='20?????', rootdir='darkhist_peaknorm', outroot='', xmin=25.0, xmax=4000,
conf=True, gauss=False):
results = {}
fileglob = os.path.join(rootdir, '{}.dat'.format(dateglob))
for i, filename in enumerate(glob(fileglob)):
filedate = re.search(r'(\d{7})', filename).group(1)
print "\n\n*************** {} *****************".format(filename)
plt.figure(1)
ui.load_data(1, filename, 2)
data = ui.get_data()
ui.ignore(None, xmin)
ui.ignore(xmax, None)
dark_models.xall = data.x
# dark_models.imin = np.where(xall > xmin)[0][0]
# dark_models.imax = np.where(xall > xmax)[0][0]
sbp.gamma1 = 0.05
sbp.gamma2 = 3.15
sbp.gamma2.min = 2.
sbp.gamma2.max = 4.
sbp.x_b = 130.
sbp.x_b.min = 100.
sbp.x_b.max = 160.
sbp.x_r = 50.
ok = (data.x > 40) & (data.x < 60)
sbp.ampl1 = np.mean(data.y[ok])
if gauss:
fit_gauss_sbp()
else:
fit_sbp()
pars = (sbp.gamma1.val, sbp.gamma2.val, sbp.x_b.val, sbp.x_r.val, sbp.ampl1.val)
fit_y = dark_models.smooth_broken_pow(pars, data.x)
if conf:
ui.set_conf_opt('numcores', 1)
ui.conf()
res = ui.get_conf_results()
result = dict((x, getattr(res, x))
for x in ('parnames', 'parmins', 'parvals', 'parmaxes'))
result['x'] = data.x
result['y'] = data.y
result['y_fit'] = fit_y
results[filedate] = result
if outroot is not None:
ui.notice(0, xmax)
ui.set_xlog()
ui.set_ylog()
ui.plot_fit()
plt.xlim(1, 1e4)
plt.ylim(0.5, 1e5)
plt.grid(True)
plt.xlabel('Dark current (e-/sec)')
outfile = os.path.join(rootdir, '{}{}.png'.format(outroot, filedate))
print 'Writing', outfile
plt.savefig(outfile)
if conf:
outfile = os.path.join(rootdir, '{}{}.pkl'.format(outroot, filedate))
print 'Writing', outfile
pickle.dump(result, open(outfile, 'w'), protocol=-1)
if outroot is not None:
outfile = os.path.join(rootdir, '{}fits.pkl'.format(outroot))
print 'Writing', outfile
pickle.dump(results, open(outfile, 'w'), protocol=-1)
return results
