def test_source_component_arbitrary_grid_int():
from sherpa.astro.ui.utils import Session
from sherpa.models import Const1D
from sherpa.data import Data1DInt
ui = Session()
x = numpy.array([1, 2, 3]), numpy.array([2, 3, 4])
y = [1.5, 2.5, 3.5]
re_x = numpy.array([10, 20, 30]), numpy.array([20, 30, 40])
ui.load_arrays(1, x[0], x[1], y, Data1DInt)
model = Const1D('c')
model.c0 = 10
regrid_model = model.regrid(*re_x)
with pytest.warns(UserWarning):
ui.plot_source_component(regrid_model)
x_points = (x[0] + x[1]) / 2
re_x_points = (re_x[0] + re_x[1]) / 2
points = numpy.concatenate((x_points, re_x_points))
numpy.testing.assert_array_equal(ui._compsrcplot.x, points)
numpy.testing.assert_array_equal(ui._compsrcplot.y,
[10, 10, 10, 100, 100, 100])
def test_show_bkg_model():
session = Session()
session.load_arrays(1, [1, 2], [1, 2])
session.show_bkg_model()
session.show_bkg_model('xx')
session.show_bkg_source()
session.show_bkg_source('xx')
def test_source_component_arbitrary_grid_int():
from sherpa.astro.ui.utils import Session
from sherpa.models import Const1D
from sherpa.data import Data1DInt
ui = Session()
x = numpy.array([1, 2, 3]), numpy.array([2, 3, 4])
y = [1.5, 2.5, 3.5]
re_x = numpy.array([10, 20, 30]), numpy.array([20, 30, 40])
ui.load_arrays(1, x[0], x[1], y, Data1DInt)
model = Const1D('c')
model.c0 = 10
regrid_model = model.regrid(*re_x)
with pytest.warns(UserWarning):
ui.plot_source_component(regrid_model)
x_points = (x[0] + x[1])/2
re_x_points = (re_x[0] + re_x[1])/2
points = numpy.concatenate((x_points, re_x_points))
numpy.testing.assert_array_equal(ui._compsrcplot.x, points)
numpy.testing.assert_array_equal(ui._compsrcplot.y, [10, 10, 10, 100, 100, 100])
def test_show_bkg_model():
session = Session()
session.load_arrays(1, [1, 2], [1, 2])
session.show_bkg_model()
session.show_bkg_model('xx')
session.show_bkg_source()
session.show_bkg_source('xx')
def test_list_ids():
session = Session()
session.load_arrays(1, TEST, TEST)
session.load_arrays("1", TEST, TEST2)
# order of 1 and "1" is not determined
assert {1, "1"} == set(session.list_data_ids())
assert_array_equal(TEST2, session.get_data('1').get_dep())
assert_array_equal(TEST, session.get_data(1).get_dep())
def test_list_ids():
session = Session()
session.load_arrays(1, TEST, TEST)
session.load_arrays("1", TEST, TEST2)
# order of 1 and "1" is not determined
assert {1, "1"} == set(session.list_data_ids())
assert_array_equal(TEST2, session.get_data('1').get_dep())
assert_array_equal(TEST, session.get_data(1).get_dep())
def test_save_restore(tmpdir):
outfile = tmpdir.join("sherpa.save")
session = Session()
session.load_arrays(1, TEST, TEST2)
session.save(str(outfile), clobber=True)
session.clean()
assert set() == set(session.list_data_ids())
session.restore(str(outfile))
assert {1, } == set(session.list_data_ids())
assert_array_equal(TEST, session.get_data(1).get_indep()[0])
assert_array_equal(TEST2, session.get_data(1).get_dep())
def test_load_arrays_no_errors(data_no_errors):
from sherpa.astro.ui.utils import Session
session = Session()
data = data_no_errors
data_class = data.__class__
data_args = DATA_NO_ERRORS_ARGS
args = data_args + (data_class, )
session.load_arrays(*args)
new_data = session.get_data(data.name)
assert new_data is not data
# DATA-NOTE: Do we need an equality operator for data classes? These tests are very partial
# Note that when they are created with load_arrays they seem to lose the name, which becomes the ID
numpy.testing.assert_array_equal(new_data.get_indep(), data.get_indep())
numpy.testing.assert_array_equal(new_data.get_dep(), data.get_dep())
def test_save_restore(tmpdir):
outfile = tmpdir.join("sherpa.save")
session = Session()
session.load_arrays(1, TEST, TEST2)
session.save(str(outfile), clobber=True)
session.clean()
assert set() == set(session.list_data_ids())
session.restore(str(outfile))
assert {
1,
} == set(session.list_data_ids())
assert_array_equal(TEST, session.get_data(1).get_indep()[0])
assert_array_equal(TEST2, session.get_data(1).get_dep())
def test_zero_division_calc_stat():
ui = Session()
x = numpy.arange(100)
y = numpy.zeros(100)
ui.load_arrays(1, x, y, DataPHA)
ui.group_counts(1, 100)
ui.set_full_model(1, Const1D("const"))
# in principle I wouldn't need to call calc_stat_info(), I could just
# use _get_stat_info to reproduce the issue, However, _get_stat_info is not a public
# method, so I want to double check that calc_stat_info does not throw an exception.
# So, first we try to run calc_stat_info and make sure there are no exceptions.
# Then, since calc_stat_info only logs something and doesn't return anything, we use
# a white box approach to get the result from _get_stat_info.
ui.calc_stat_info()
assert ui._get_stat_info()[0].rstat is numpy.nan
def test_zero_division_calc_stat():
ui = AstroSession()
x = numpy.arange(100)
y = numpy.zeros(100)
ui.load_arrays(1, x, y, DataPHA)
ui.group_counts(1, 100)
ui.set_full_model(1, Const1D("const"))
# in principle I wouldn't need to call calc_stat_info(), I could just
# use _get_stat_info to reproduce the issue, However, _get_stat_info is not a public
# method, so I want to double check that calc_stat_info does not throw an exception.
# So, first we try to run calc_stat_info and make sure there are no exceptions.
# Then, since calc_stat_info only logs something and doesn't return anything, we use
# a white box approach to get the result from _get_stat_info.
ui.calc_stat_info()
assert ui._get_stat_info()[0].rstat is numpy.nan
def test_source_component_arbitrary_grid_int():
ui = Session()
x = numpy.array([1, 2, 3]), numpy.array([2, 3, 4])
y = [1.5, 2.5, 3.5]
re_x = numpy.array([10, 20, 30]), numpy.array([20, 30, 40])
ui.load_arrays(1, x[0], x[1], y, Data1DInt)
model = Const1D('c')
model.c0 = 10
regrid_model = model.regrid(*re_x)
ui.plot_source_component(regrid_model)
x_points = (x[0] + x[1]) / 2.0
numpy.testing.assert_array_equal(ui._compsrcplot.x, x_points)
numpy.testing.assert_array_equal(ui._compsrcplot.y, [0., 0., 0.])
def test_plot_model_arbitrary_grid_integrated():
ui = Session()
x = [1, 2, 3], [2, 3, 4]
y = [1, 2, 3]
re_x = [10, 20, 30], [20, 30, 40]
ui.load_arrays(1, x[0], x[1], y, Data1DInt)
model = Const1D('c')
model.c0 = 10
regrid_model = model.regrid(*re_x)
ui.set_model(regrid_model)
with pytest.warns(UserWarning):
ui.plot_model()
numpy.testing.assert_array_equal(ui._modelplot.x, [1.5, 2.5, 3.5])
numpy.testing.assert_array_equal(ui._modelplot.y, [10, 10, 10])
def test_source_component_arbitrary_grid():
ui = Session()
x = [1, 2, 3]
y = [1, 2, 3]
re_x = [10, 20, 30]
ui.load_arrays(1, x, y)
model = Const1D('c')
model.c0 = 10
regrid_model = model.regrid(re_x)
with pytest.warns(UserWarning):
ui.plot_source_component(regrid_model)
numpy.testing.assert_array_equal(ui._compsrcplot.x, x + re_x)
numpy.testing.assert_array_equal(ui._compsrcplot.y, [
10,
] * 6)
def test_source_component_arbitrary_grid():
from sherpa.astro.ui.utils import Session
from sherpa.models import Const1D
ui = Session()
x = [1, 2, 3]
y = [1, 2, 3]
re_x = [10, 20, 30]
ui.load_arrays(1, x, y)
model = Const1D('c')
model.c0 = 10
regrid_model = model.regrid(re_x)
with pytest.warns(UserWarning):
ui.plot_source_component(regrid_model)
numpy.testing.assert_array_equal(ui._compsrcplot.x, x + re_x)
numpy.testing.assert_array_equal(ui._compsrcplot.y, [10, ]*6)
def test_plot_model_arbitrary_grid_integrated():
from sherpa.astro.ui.utils import Session
from sherpa.models import Const1D
from sherpa.data import Data1DInt
ui = Session()
x = [1, 2, 3], [2, 3, 4]
y = [1, 2, 3]
re_x = [10, 20, 30], [20, 30, 40]
ui.load_arrays(1, x[0], x[1], y, Data1DInt)
model = Const1D('c')
model.c0 = 10
regrid_model = model.regrid(*re_x)
ui.set_model(regrid_model)
with pytest.warns(UserWarning):
ui.plot_model()
numpy.testing.assert_array_equal(ui._modelplot.x, [1.5, 2.5, 3.5])
numpy.testing.assert_array_equal(ui._modelplot.y, [10, 10, 10])