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 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_does_user_model_get_cleaned(clean_ui):
"""Do user models get removed from the session by clean?"""
mname = "test_model"
with pytest.raises(IdentifierErr):
ui.get_model_component(mname)
ui.load_user_model(um_line, mname)
mdl = ui.get_model_component(mname)
assert mdl.name == "usermodel.{}".format(mname)
assert isinstance(mdl, sherpa.models.basic.UserModel)
ui.clean()
with pytest.raises(IdentifierErr):
ui.get_model_component(mname)
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 example_model():
"""Create an example model."""
ui.create_model_component('const1d', 'cpt')
cpt = ui.get_model_component('cpt')
cpt.c0 = 35
return cpt
def test_does_user_model_get_cleaned():
"""Do user models get removed from the session by clean?"""
mname = "test_model"
with pytest.raises(IdentifierErr):
ui.get_model_component(mname)
ui.load_user_model(um_line, mname)
mdl = ui.get_model_component(mname)
assert mdl.name == "usermodel.{}".format(mname)
assert isinstance(mdl, sherpa.models.basic.UserModel)
ui.clean()
with pytest.raises(IdentifierErr):
ui.get_model_component(mname)
def test_user_model1d_eval_fail():
"""This is expected to fail as the number of pars does not match."""
mname = "test_model"
ui.load_user_model(um_line, mname)
mdl = ui.get_model_component(mname)
with pytest.raises(IndexError):
mdl([2.3, 5.4, 8.7])
def test_user_model1d_eval_fail(clean_ui):
"""This is expected to fail as the number of pars does not match."""
mname = "test_model"
ui.load_user_model(um_line, mname)
mdl = ui.get_model_component(mname)
with pytest.raises(IndexError):
mdl([2.3, 5.4, 8.7])
def test_psf_model2d(self):
ui.dataspace1d(1, 10)
for model in self.models1d:
try:
ui.load_psf("psf1d", model + ".mdl")
ui.set_psf("psf1d")
mdl = ui.get_model_component("mdl")
self.assert_((numpy.array(mdl.get_center()) == numpy.array([4])).all())
except:
print model
raise
def test_psf_model2d(self):
ui.dataspace2d([216, 261])
for model in self.models2d:
try:
ui.load_psf("psf2d", model + ".mdl")
ui.set_psf("psf2d")
mdl = ui.get_model_component("mdl")
self.assert_((numpy.array(mdl.get_center()) == numpy.array([108, 130])).all())
except:
print model
raise
def test_psf_model2d(self):
ui.dataspace2d([216, 261])
for model in self.models2d:
try:
ui.load_psf('psf2d', model + '.mdl')
ui.set_psf('psf2d')
mdl = ui.get_model_component('mdl')
self.assertTrue((numpy.array(mdl.get_center()) ==
numpy.array([108, 130])).all())
except:
print(model)
raise
def test_psf_model2d(self):
ui.dataspace2d([216,261])
for model in self.models2d:
try:
ui.load_psf('psf2d', model+'.mdl')
ui.set_psf('psf2d')
mdl = ui.get_model_component('mdl')
self.assertTrue((numpy.array(mdl.get_center()) ==
numpy.array([108,130])).all())
except:
print model
raise
def test_psf_model1d(self):
ui.dataspace1d(1, 10)
for model in self.models1d:
try:
ui.load_psf('psf1d', model + '.mdl')
ui.set_psf('psf1d')
mdl = ui.get_model_component('mdl')
self.assertTrue(
(numpy.array(mdl.get_center()) == numpy.array([4])).all())
except:
print(model)
raise
def test_psf_model1d(self):
ui.dataspace1d(1, 10)
for model in self.models1d:
try:
ui.load_psf('psf1d', model+'.mdl')
ui.set_psf('psf1d')
mdl = ui.get_model_component('mdl')
self.assertTrue((numpy.array(mdl.get_center()) ==
numpy.array([4])).all())
except:
print model
raise
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_model_create_pars_default(clean_ui):
mname = "test_model"
ui.load_user_model(um_line, mname)
mdl = ui.get_model_component(mname)
assert len(mdl.pars) == 1
par = mdl.pars[0]
assert par.name == "ampl"
assert par.val == pytest.approx(1.0)
assert not par.frozen
assert par.units == ''
assert par.min == pytest.approx(-1 * hugeval)
assert par.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_model_create_pars_default():
mname = "test_model"
ui.load_user_model(um_line, mname)
mdl = ui.get_model_component(mname)
assert len(mdl.pars) == 1
par = mdl.pars[0]
assert par.name == "ampl"
assert par.val == pytest.approx(1.0)
assert not par.frozen
assert par.units == ''
assert par.min == pytest.approx(-1 * hugeval)
assert par.max == pytest.approx(hugeval)
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 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)
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)
#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
# we've defined evaluated at the data x values.
# the model and the data are passed to the user stat/ llh
# function as it is minimized.
ui.fit(data_id)
myfit = ui.get_fit_results()
def test_psf_model2d(model):
ui.dataspace2d([216, 261])
ui.load_psf('psf2d', model + '.mdl')
ui.set_psf('psf2d')
mdl = ui.get_model_component('mdl')
assert mdl.get_center() == (108.0, 130.0)
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()
f = ui.get_fit_results()
if f.rstat > 3:
raise ValueError
ui.confidence()
conf = ui.get_confidence_results()
#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
# we've defined evaluated at the data x values.
# the model and the data are passed to the user stat/ llh
# function as it is minimized.
ui.fit(data_id)
def change_model(idval):
"""Change the example model values (created by setup_model)"""
cpt = ui.get_model_component('cpt')
cpt.c0 = 41
from matplotlib import pyplot as plt
import numpy as np
from sherpa import ui
x1 = np.arange(-5.0, 30, 0.5)
x2 = np.arange(1.0, 29.0, 0.2)
ui.load_arrays(1, x1, x1 * 0, ui.Data1D)
ui.load_arrays(2, x2, x2 * 0, ui.Data1D)
ui.set_source(1, ui.box1d.box)
box = ui.get_model_component('box')
ui.set_source(2, box)
box.xlow = 10.0
box.xhi = 20.0
# Copy all the objects just to make sure
g1 = ui.gauss1d('g1')
g1.fwhm = 3.0
g2 = ui.gauss1d('g2')
g2.fwhm = 3.0
ui.load_psf('psf1', g1)
ui.load_psf('psf2', g2)
ui.set_psf(1, 'psf1')
ui.set_psf(2, 'psf2')
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()
conf = ui.get_confidence_results()
fit_info[range_type][mag][limit] = dict(fit=str(f), conf=str(conf), fmod=fmod, fit_orig=f, conf_orig=conf)
def test_psf_model1d(model, clean_ui):
ui.dataspace1d(1, 10)
ui.load_psf('psf1d', model + '.mdl')
ui.set_psf('psf1d')
mdl = ui.get_model_component('mdl')
assert mdl.get_center() == (4.0, )
# 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")
# 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.set_model("default_interp", bb1)
ui.set_method('gridsearch')
ui.set_method_opt('sequence', ui.get_model_component('bb1').parvals)
ui.fit("default_interp")
