def test_calc_rad_loss_errors():
# Define input variables
temp = 11.0 * Quantity(np.ones(6), unit="MK")
em = 4.0e+48 * Quantity(np.ones(6), unit="1/cm**3")
obstime = np.array([datetime.datetime(2014, 1, 1, 0, 0, 0),
datetime.datetime(2014, 1, 1, 0, 0, 2),
datetime.datetime(2014, 1, 1, 0, 0, 4),
datetime.datetime(2014, 1, 1, 0, 0, 6),
datetime.datetime(2014, 1, 1, 0, 0, 8),
datetime.datetime(2014, 1, 1, 0, 0, 10)], dtype=object)
temp_toolong = Quantity(np.append(temp.value, 0), unit="MK")
obstime_toolong = np.array([datetime.datetime(2014, 1, 1, 0, 0, 0),
datetime.datetime(2014, 1, 1, 0, 0, 2),
datetime.datetime(2014, 1, 1, 0, 0, 4),
datetime.datetime(2014, 1, 1, 0, 0, 6),
datetime.datetime(2014, 1, 1, 0, 0, 8),
datetime.datetime(2014, 1, 1, 0, 0, 10),
datetime.datetime(2014, 1, 1, 0, 0, 12)], dtype=object)
obstime_nonchrono = copy.deepcopy(obstime)
obstime_nonchrono[1] = obstime[-1]
obstime_nonchrono[-1] = obstime[1]
obstime_notdatetime = copy.deepcopy(obstime)
obstime_notdatetime[0] = 1
temp_outofrange = Quantity([101, 11.0, 11.0, 11.0, 11.0, 11.0], unit="MK")
# Ensure correct exceptions are raised.
with pytest.raises(ValueError):
rad_loss_test = goes._calc_rad_loss(temp_toolong, em, obstime)
with pytest.raises(ValueError):
rad_loss_test = goes._calc_rad_loss(temp_outofrange, em, obstime)
with pytest.raises(IOError):
rad_loss_test = goes._calc_rad_loss(temp, em, obstime_toolong)
with pytest.raises(TypeError):
lx_test = goes._calc_rad_loss(temp, em, obstime_notdatetime)
with pytest.raises(ValueError):
rad_loss_test = goes._calc_rad_loss(temp, em, obstime_nonchrono)
def test_calc_rad_loss_obstime():
# Define input variables
temp = Quantity([11.0, 11.0, 11.0, 11.0, 11.0, 11.0], unit="MK")
em = Quantity([4.0e+48, 4.0e+48, 4.0e+48, 4.0e+48, 4.0e+48, 4.0e+48],
unit="1/cm**3")
obstime = np.array([datetime.datetime(2014, 1, 1, 0, 0, 0),
datetime.datetime(2014, 1, 1, 0, 0, 2),
datetime.datetime(2014, 1, 1, 0, 0, 4),
datetime.datetime(2014, 1, 1, 0, 0, 6),
datetime.datetime(2014, 1, 1, 0, 0, 8),
datetime.datetime(2014, 1, 1, 0, 0, 10)], dtype=object)
# Test output is correct when obstime and cumulative kwargs are set.
rad_loss_test = goes._calc_rad_loss(temp, em, obstime)
rad_loss_expected = {
"rad_loss_rate": 3.01851392e+19 * Quantity(np.ones(6), unit="J/s"),
"rad_loss_int": Quantity(3.01851392e+20, unit="J"),
"rad_loss_cumul": Quantity([6.03702783e+19, 1.20740557e+20,
1.81110835e+20, 2.41481113e+20,
3.01851392e+20], unit="J")
}
assert sorted(rad_loss_test.keys()) == sorted(rad_loss_expected.keys())
assert_quantity_allclose(rad_loss_test["rad_loss_rate"],
rad_loss_expected["rad_loss_rate"], rtol=0.0001)
assert_quantity_allclose(rad_loss_test["rad_loss_int"],
rad_loss_expected["rad_loss_int"], rtol=0.0001)
assert_quantity_allclose(rad_loss_test["rad_loss_cumul"],
rad_loss_expected["rad_loss_cumul"], rtol=0.0001)
def test_calc_rad_loss_nokwags():
# Define input variables
temp = Quantity([11.0, 11.0, 11.0, 11.0, 11.0, 11.0], unit="MK")
em = Quantity([4.0e+48, 4.0e+48, 4.0e+48, 4.0e+48, 4.0e+48, 4.0e+48],
unit="1/cm**3")
# Test output is correct when no kwags are set.
rad_loss_test = goes._calc_rad_loss(temp[:2], em[:2])
rad_loss_expected = {"rad_loss_rate":
3.01851392e+19 * Quantity(np.ones(2), unit="J/s")}
assert sorted(rad_loss_test.keys()) == sorted(rad_loss_expected.keys())
assert_quantity_allclose(rad_loss_test["rad_loss_rate"],
rad_loss_expected["rad_loss_rate"], rtol=0.01)
def test_calc_rad_loss_nokwags():
# Define input variables
temp = Quantity([11.0, 11.0, 11.0, 11.0, 11.0, 11.0], unit="MK")
em = Quantity([4.0e+48, 4.0e+48, 4.0e+48, 4.0e+48, 4.0e+48, 4.0e+48],
unit="1/cm**3")
obstime = np.array([datetime.datetime(2014, 1, 1, 0, 0, 0),
datetime.datetime(2014, 1, 1, 0, 0, 2),
datetime.datetime(2014, 1, 1, 0, 0, 4),
datetime.datetime(2014, 1, 1, 0, 0, 6),
datetime.datetime(2014, 1, 1, 0, 0, 8),
datetime.datetime(2014, 1, 1, 0, 0, 10)], dtype=object)
# Test output is correct when no kwags are set.
rad_loss_test = goes._calc_rad_loss(temp[:2], em[:2])
rad_loss_expected = {"rad_loss_rate":
3.01851392e+19 * Quantity(np.ones(2), unit="J/s")}
assert sorted(rad_loss_test.keys()) == sorted(rad_loss_expected.keys())
assert_quantity_allclose(rad_loss_test["rad_loss_rate"],
rad_loss_expected["rad_loss_rate"], rtol=0.01)
def test_calc_rad_loss_nokwags():
# Define input variables
temp = Quantity([11.0, 11.0, 11.0, 11.0, 11.0, 11.0], unit="MK")
em = Quantity([4.0e+48, 4.0e+48, 4.0e+48, 4.0e+48, 4.0e+48, 4.0e+48],
unit="1/cm**3")
obstime = np.array([
parse_time((2014, 1, 1, 0, 0, 0)),
parse_time((2014, 1, 1, 0, 0, 2)),
parse_time((2014, 1, 1, 0, 0, 4)),
parse_time((2014, 1, 1, 0, 0, 6)),
parse_time((2014, 1, 1, 0, 0, 8)),
parse_time((2014, 1, 1, 0, 0, 10))
],
dtype=object)
# Test output is correct when no kwags are set.
rad_loss_test = goes._calc_rad_loss(temp[:2], em[:2])
rad_loss_expected = {
"rad_loss_rate": 3.01851392e+19 * Quantity(np.ones(2), unit="J/s")
}
assert sorted(rad_loss_test.keys()) == sorted(rad_loss_expected.keys())
assert_quantity_allclose(rad_loss_test["rad_loss_rate"],
rad_loss_expected["rad_loss_rate"],
rtol=0.01)
