def test_metadata_difficult_types_write_read_round_trip(self):
for version in (3, 4):
s = io.BytesIO()
data_in = numpy.ones((6, 4), numpy.float32)
data_descriptor_in = DataAndMetadata.DataDescriptor(False, 0, 2)
dimensional_calibrations_in = [
Calibration.Calibration(1, 2, "nm"),
Calibration.Calibration(2, 3, u"µm")
]
intensity_calibration_in = Calibration.Calibration(4, 5, "six")
metadata_in = {
"abc": None,
"": "",
"one": [],
"two": {},
"three": [1, None, 2]
}
xdata_in = DataAndMetadata.new_data_and_metadata(
data_in,
data_descriptor=data_descriptor_in,
dimensional_calibrations=dimensional_calibrations_in,
intensity_calibration=intensity_calibration_in,
metadata=metadata_in)
dm3_image_utils.save_image(xdata_in, s, version)
s.seek(0)
xdata = dm3_image_utils.load_image(s)
metadata_expected = {"one": [], "two": {}, "three": [1, 2]}
self.assertEqual(metadata_expected, xdata.metadata)
def test_signal_type_round_trip(self):
for version in (3, 4):
s = io.BytesIO()
data_in = numpy.ones((12, ), numpy.float32)
data_descriptor_in = DataAndMetadata.DataDescriptor(False, 0, 1)
dimensional_calibrations_in = [Calibration.Calibration(1, 2, "eV")]
intensity_calibration_in = Calibration.Calibration(4, 5, "e")
metadata_in = {"hardware_source": {"signal_type": "EELS"}}
xdata_in = DataAndMetadata.new_data_and_metadata(
data_in,
data_descriptor=data_descriptor_in,
dimensional_calibrations=dimensional_calibrations_in,
intensity_calibration=intensity_calibration_in,
metadata=metadata_in)
dm3_image_utils.save_image(xdata_in, s, version)
s.seek(0)
xdata = dm3_image_utils.load_image(s)
metadata_expected = {
'hardware_source': {
'signal_type': 'EELS'
},
'Meta Data': {
'Format': 'Spectrum',
'Signal': 'EELS'
}
}
self.assertEqual(metadata_expected, xdata.metadata)
def test_data_timestamp_write_read_round_trip(self):
for version in (3, 4):
s = io.BytesIO()
data_in = numpy.ones((6, 4), numpy.float32)
data_descriptor_in = DataAndMetadata.DataDescriptor(False, 0, 2)
dimensional_calibrations_in = [
Calibration.Calibration(1.1, 2.1, "nm"),
Calibration.Calibration(2, 3, u"µm")
]
intensity_calibration_in = Calibration.Calibration(4.4, 5.5, "six")
metadata_in = dict()
timestamp_in = datetime.datetime(2013, 11, 18, 14, 5, 4, 0)
timezone_in = "America/Los_Angeles"
timezone_offset_in = "-0700"
xdata_in = DataAndMetadata.new_data_and_metadata(
data_in,
data_descriptor=data_descriptor_in,
dimensional_calibrations=dimensional_calibrations_in,
intensity_calibration=intensity_calibration_in,
metadata=metadata_in,
timestamp=timestamp_in,
timezone=timezone_in,
timezone_offset=timezone_offset_in)
dm3_image_utils.save_image(xdata_in, s, version)
s.seek(0)
xdata = dm3_image_utils.load_image(s)
self.assertEqual(timestamp_in, xdata.timestamp)
self.assertEqual(timezone_in, xdata.timezone)
self.assertEqual(timezone_offset_in, xdata.timezone_offset)
def test_rgb_data_write_read_round_trip(self):
for version in (3, 4):
s = io.BytesIO()
data_in = (numpy.random.randn(6, 4, 3) * 255).astype(numpy.uint8)
data_descriptor_in = DataAndMetadata.DataDescriptor(False, 0, 2)
dimensional_calibrations_in = [
Calibration.Calibration(1, 2, "nm"),
Calibration.Calibration(2, 3, u"µm")
]
intensity_calibration_in = Calibration.Calibration(4, 5, "six")
metadata_in = {
"abc": None,
"": "",
"one": [],
"two": {},
"three": [1, None, 2]
}
xdata_in = DataAndMetadata.new_data_and_metadata(
data_in,
data_descriptor=data_descriptor_in,
dimensional_calibrations=dimensional_calibrations_in,
intensity_calibration=intensity_calibration_in,
metadata=metadata_in)
dm3_image_utils.save_image(xdata_in, s, version)
s.seek(0)
xdata = dm3_image_utils.load_image(s)
self.assertTrue(numpy.array_equal(data_in, xdata.data))
self.assertEqual(data_descriptor_in, xdata.data_descriptor)
def test_metadata_write_read_round_trip(self):
s = io.BytesIO()
data_in = numpy.ones((6, 4), numpy.float32)
data_descriptor_in = DataAndMetadata.DataDescriptor(False, 0, 2)
dimensional_calibrations_in = [
Calibration.Calibration(1, 2, "nm"),
Calibration.Calibration(2, 3, u"µm")
]
intensity_calibration_in = Calibration.Calibration(4, 5, "six")
metadata_in = {
"abc": 1,
"def": "abc",
"efg": {
"one": 1,
"two": "TWO",
"three": [3, 4, 5]
}
}
xdata_in = DataAndMetadata.new_data_and_metadata(
data_in,
data_descriptor=data_descriptor_in,
dimensional_calibrations=dimensional_calibrations_in,
intensity_calibration=intensity_calibration_in,
metadata=metadata_in)
dm3_image_utils.save_image(xdata_in, s)
s.seek(0)
xdata = dm3_image_utils.load_image(s)
self.assertEqual(metadata_in, xdata.metadata)
def test_metadata_export_large_integer(self):
s = io.BytesIO()
data_in = numpy.ones((6, 4), numpy.float32)
data_descriptor_in = DataAndMetadata.DataDescriptor(False, 0, 2)
dimensional_calibrations_in = [
Calibration.Calibration(1, 2, "nm"),
Calibration.Calibration(2, 3, u"µm")
]
intensity_calibration_in = Calibration.Calibration(4, 5, "six")
metadata_in = {"abc": 999999999999}
xdata_in = DataAndMetadata.new_data_and_metadata(
data_in,
data_descriptor=data_descriptor_in,
dimensional_calibrations=dimensional_calibrations_in,
intensity_calibration=intensity_calibration_in,
metadata=metadata_in)
dm3_image_utils.save_image(xdata_in, s)
s.seek(0)
xdata = dm3_image_utils.load_image(s)
metadata_expected = {"abc": 999999999999}
self.assertEqual(metadata_expected, xdata.metadata)
def test_metadata_write_read_round_trip(self):
for version in (3, 4):
s = io.BytesIO()
data_in = numpy.ones((6, 4), numpy.float32)
data_descriptor_in = DataAndMetadata.DataDescriptor(False, 0, 2)
dimensional_calibrations_in = [
Calibration.Calibration(1, 2, "nm"),
Calibration.Calibration(2, 3, u"µm")
]
intensity_calibration_in = Calibration.Calibration(4, 5, "six")
metadata_in = {
"abc": 1,
"def": "abc",
"efg": {
"one": 1,
"two": "TWO",
"three": [3, 4, 5],
"threef": [3.0, 4.0, 5.0],
"four": (32, 32),
"fourf": (33.0, 34.0),
"six": ((1, 2), (3, 4)),
"sixf": ((1.0, 2.0), (3.0, 4.0)),
"seven": [[1, 2], [3, 4]],
"sevenf": [[1.0, 2.0], [3.0, 4.0]],
# the following will not work until there is a schema or other type hinting to distinguish
# this from the "six" case.
# "eight": (1, 2, 3, 4), "eightf": (1.0, 2.0, 3.0, 4.0),
}
}
xdata_in = DataAndMetadata.new_data_and_metadata(
data_in,
data_descriptor=data_descriptor_in,
dimensional_calibrations=dimensional_calibrations_in,
intensity_calibration=intensity_calibration_in,
metadata=metadata_in)
dm3_image_utils.save_image(xdata_in, s, version)
s.seek(0)
xdata = dm3_image_utils.load_image(s)
self.assertEqual(metadata_in, xdata.metadata)
def test_calibrations_write_read_round_trip(self):
s = io.BytesIO()
data_in = numpy.ones((6, 4), numpy.float32)
data_descriptor_in = DataAndMetadata.DataDescriptor(False, 0, 2)
dimensional_calibrations_in = [
Calibration.Calibration(1.1, 2.1, "nm"),
Calibration.Calibration(2, 3, u"µm")
]
intensity_calibration_in = Calibration.Calibration(4.4, 5.5, "six")
metadata_in = dict()
xdata_in = DataAndMetadata.new_data_and_metadata(
data_in,
data_descriptor=data_descriptor_in,
dimensional_calibrations=dimensional_calibrations_in,
intensity_calibration=intensity_calibration_in,
metadata=metadata_in)
dm3_image_utils.save_image(xdata_in, s)
s.seek(0)
xdata = dm3_image_utils.load_image(s)
self.assertEqual(dimensional_calibrations_in,
xdata.dimensional_calibrations)
self.assertEqual(intensity_calibration_in, xdata.intensity_calibration)
def test_data_write_read_round_trip(self):
def db_make_directory_if_needed(directory_path):
if os.path.exists(directory_path):
if not os.path.isdir(directory_path):
raise OSError("Path is not a directory:", directory_path)
else:
os.makedirs(directory_path)
class numpy_array_type:
def __init__(self, shape, dtype):
self.data = numpy.ones(shape, dtype)
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
pass
class h5py_array_type:
def __init__(self, shape, dtype):
current_working_directory = os.getcwd()
self.__workspace_dir = os.path.join(current_working_directory,
"__Test")
db_make_directory_if_needed(self.__workspace_dir)
self.f = h5py.File(
os.path.join(self.__workspace_dir, "file.h5"), "a")
self.data = self.f.create_dataset("data",
data=numpy.ones(
shape, dtype))
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
self.f.close()
shutil.rmtree(self.__workspace_dir)
array_types = numpy_array_type, h5py_array_type
dtypes = (numpy.float32, numpy.float64, numpy.complex64,
numpy.complex128, numpy.int16, numpy.uint16, numpy.int32,
numpy.uint32)
shape_data_descriptors = (
((6, ), DataAndMetadata.DataDescriptor(False, 0, 1)), # spectrum
((6, 4),
DataAndMetadata.DataDescriptor(False, 1,
1)), # 1d collection of spectra
((6, 8, 10),
DataAndMetadata.DataDescriptor(False, 2,
1)), # 2d collection of spectra
((6, 4), DataAndMetadata.DataDescriptor(True, 0,
1)), # sequence of spectra
((6, 4), DataAndMetadata.DataDescriptor(False, 0, 2)), # image
((6, 4, 2),
DataAndMetadata.DataDescriptor(False, 1,
2)), # 1d collection of images
((6, 5, 4, 2), DataAndMetadata.DataDescriptor(
False, 2, 2)), # 2d collection of images. not possible?
((6, 8, 10),
DataAndMetadata.DataDescriptor(True, 0, 2)), # sequence of images
)
for version in (3, 4):
for array_type in array_types:
for dtype in dtypes:
for shape, data_descriptor_in in shape_data_descriptors:
for signal_type in (
(None, "eels")
if data_descriptor_in.datum_dimension_count
== 1 else (None, )):
# print(f"--- {array_type} {dtype} {shape} {data_descriptor_in} {signal_type}")
s = io.BytesIO()
with array_type(shape, dtype) as a:
data_in = a.data
dimensional_calibrations_in = list()
for index, dimension in enumerate(shape):
dimensional_calibrations_in.append(
Calibration.Calibration(
1.0 + 0.1 * index,
2.0 + 0.2 * index,
"µ" + "n" * index))
intensity_calibration_in = Calibration.Calibration(
4, 5, "six")
metadata_in = dict()
if signal_type:
metadata_in.setdefault(
"hardware_source",
dict())["signal_type"] = signal_type
xdata_in = DataAndMetadata.new_data_and_metadata(
data_in,
data_descriptor=data_descriptor_in,
dimensional_calibrations=
dimensional_calibrations_in,
intensity_calibration=
intensity_calibration_in,
metadata=metadata_in)
dm3_image_utils.save_image(
xdata_in, s, version)
s.seek(0)
xdata = dm3_image_utils.load_image(s)
self.assertTrue(
numpy.array_equal(data_in, xdata.data))
self.assertEqual(data_descriptor_in,
xdata.data_descriptor)
self.assertEqual(
dimensional_calibrations_in,
xdata.dimensional_calibrations)
self.assertEqual(intensity_calibration_in,
xdata.intensity_calibration)
