def test_309(make_data_path):
idval = 'bug309'
# have values near unity for the data
ynorm = 1e9
session = Session()
dname = make_data_path('load_template_with_interpolation-bb_data.dat')
session.load_data(idval, dname)
session.get_data(idval).y *= ynorm
indexname = 'bb_index.dat'
datadir = make_data_path('')
# Need to load the data from the same directory as the index
basedir = os.getcwd()
os.chdir(datadir)
try:
session.load_template_model('bbtemp', indexname)
finally:
os.chdir(basedir)
bbtemp = session.get_model_component('bbtemp')
session.set_source(idval, bbtemp * ynorm)
session.set_method('gridsearch')
session.set_method_opt('sequence', None)
session.fit(idval)
def test_309(make_data_path):
idval = 'bug309'
# have values near unity for the data
ynorm = 1e9
session = Session()
dname = make_data_path('load_template_with_interpolation-bb_data.dat')
session.load_data(idval, dname)
session.get_data(idval).y *= ynorm
indexname = 'bb_index.dat'
datadir = make_data_path('')
# Need to load the data from the same directory as the index
basedir = os.getcwd()
os.chdir(datadir)
try:
session.load_template_model('bbtemp', indexname)
finally:
os.chdir(basedir)
bbtemp = session.get_model_component('bbtemp')
session.set_source(idval, bbtemp * ynorm)
session.set_method('gridsearch')
session.set_method_opt('sequence', None)
session.fit(idval)
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, [1, 2, 3], [1, 2, 3])
session.load_arrays("1", [1, 2, 3], [4, 5, 6])
# order of 1 and "1" is not determined
assert {1, "1"} == set(session.list_data_ids())
assert_array_equal([4, 5, 6], session.get_data('1').get_dep())
assert_array_equal([1, 2, 3], 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_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_data_dep_masked_numpyarray_nomask_ui(data_for_load_arrays, Session):
session, args, data = data_for_load_arrays
session = Session()
posy = POS_Y_ARRAY[type(data)]
args = list(args)
args[posy] = numpy.ma.array(args[posy])
session.load_arrays(*args)
new_data = session.get_data(data.name)
# Sherpa's way of saying "mask is not set"
assert new_data.mask is True
assert len(new_data.get_dep(filter=True)) == len(args[posy].flatten())
def test_data_indeperr_masked_numpyarray_ui(arrpos, data_for_load_arrays, Session):
session, args, data = data_for_load_arrays
session = Session()
i = arrpos[type(data)]
mask = numpy.random.rand(*(args[i].shape)) > 0.5
args = list(args)
args[1] = numpy.ma.array(args[i], mask=mask)
with pytest.warns(UserWarning, match="for dependent variables only"):
session.load_arrays(*args)
new_data = session.get_data(data.name)
assert len(new_data.get_dep(filter=True)) == len(args[i])
def test_data_dep_masked_numpyarray_ui(data_for_load_arrays, Session):
session, args, data = data_for_load_arrays
session = Session()
posy = POS_Y_ARRAY[type(data)]
mask = numpy.random.rand(*(args[posy].shape)) > 0.5
args = list(args)
args[posy] = numpy.ma.array(args[posy], mask=mask)
session.load_arrays(*args)
new_data = session.get_data(data.name)
assert new_data.mask.shape == mask.shape
assert numpy.all(new_data.mask == ~mask)
assert len(new_data.get_dep(filter=True)) == (~mask).sum()
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())
from sherpa.ui.utils import Session
import sherpa.models.basic
x = [100, 200, 300, 400]
y = [10, 12, 9, 13]
s = Session()
s._add_model_types(sherpa.models.basic)
s.load_arrays(1, x, y)
print("# list_data_ids")
print(s.list_data_ids())
print("# get_data()")
print(repr(s.get_data()))
print("# get_data()")
print(s.get_data())
print("# get_stat_name/get_method_name")
print(s.get_stat_name())
print(s.get_method_name())
s.set_stat('cash')
s.set_method('simplex')
s.set_source('const1d.mdl')
print("# mdl")
print(mdl)
