def test_array_conversion(self):
"""
Tests the simple array conversion
"""
# Setup two
expected_dset1 = np.zeros([3, 3])
expected_dset2 = np.ones([3, 3])
output_dict = hdf_arrays_to_dict(self.group)
assert isinstance(output_dict, dict)
self.assertIn("DSET1", output_dict)
self.assertIn("DSET2", output_dict)
np.testing.assert_array_almost_equal(output_dict["DSET1"],
expected_dset1)
np.testing.assert_array_almost_equal(output_dict["DSET2"],
expected_dset2)
def test_array_conversion(self):
"""
Tests the simple array conversion
"""
# Setup two
expected_dset1 = np.zeros([3, 3])
expected_dset2 = np.ones([3, 3])
output_dict = hdf_arrays_to_dict(self.group)
assert isinstance(output_dict, dict)
self.assertIn("DSET1", output_dict)
self.assertIn("DSET2", output_dict)
np.testing.assert_array_almost_equal(output_dict["DSET1"],
expected_dset1)
np.testing.assert_array_almost_equal(output_dict["DSET2"],
expected_dset2)
def init(self, fle):
"""
Executes the preprocessing steps at the instantiation stage to read in
the tables from hdf5 and hold them in memory.
"""
self.distance_type = fle["Distances"].attrs["metric"]
self.REQUIRES_DISTANCES = set([self.distance_type])
# Load in magnitude
self.m_w = fle["Mw"][:]
# Load in distances
self.distances = fle["Distances"][:]
# Load intensity measure types and levels
self.imls = hdf_arrays_to_dict(fle["IMLs"])
self.DEFINED_FOR_INTENSITY_MEASURE_TYPES = set(self._supported_imts())
if "SA" in self.imls.keys() and "T" not in self.imls:
raise ValueError("Spectral Acceleration must be accompanied by "
"periods")
# Get the standard deviations
if "Amplification" in fle:
self._setup_amplification(fle)
