def test_replace_table():
from astropy.io import fits
schema_narrow = {
"allOf": [
mschema.load_schema(os.path.join(os.path.dirname(__file__),
"../schemas/core.schema.yaml"),
resolve_references=True), {
"type": "object",
"properties": {
"data": {
"title":
"relative sensitivity table",
"fits_hdu":
"RELSENS",
"datatype": [{
"name":
"TYPE",
"datatype": ["ascii", 16]
}, {
"name": "T_OFFSET",
"datatype": "float32"
}, {
"name": "DECAY_PEAK",
"datatype": "float32"
}, {
"name": "DECAY_FREQ",
"datatype": "float32"
}, {
"name": "TAU",
"datatype": "float32"
}]
}
}
}
]
}
schema_wide = {
"allOf": [
mschema.load_schema(os.path.join(os.path.dirname(__file__),
"../schemas/core.schema.yaml"),
resolve_references=True), {
"type": "object",
"properties": {
"data": {
"title":
"relative sensitivity table",
"fits_hdu":
"RELSENS",
"datatype": [{
"name":
"TYPE",
"datatype": ["ascii", 16]
}, {
"name": "T_OFFSET",
"datatype": "float64"
}, {
"name": "DECAY_PEAK",
"datatype": "float64"
}, {
"name": "DECAY_FREQ",
"datatype": "float64"
}, {
"name": "TAU",
"datatype": "float64"
}]
}
}
}
]
}
x = np.array([("string", 1., 2., 3., 4.)],
dtype=[('TYPE', 'S16'), ('T_OFFSET', np.float32),
('DECAY_PEAK', np.float32), ('DECAY_FREQ', np.float32),
('TAU', np.float32)])
m = DataModel(schema=schema_narrow)
m.data = x
m.to_fits(TMP_FITS, overwrite=True)
with fits.open(TMP_FITS, memmap=False) as hdulist:
assert records_equal(x, np.asarray(hdulist[1].data))
assert hdulist[1].data.dtype[1].str == '>f4'
assert hdulist[1].header['TFORM2'] == 'E'
with DataModel(TMP_FITS, schema=schema_wide) as m:
m.to_fits(TMP_FITS2, overwrite=True)
with fits.open(TMP_FITS2, memmap=False) as hdulist:
assert records_equal(x, np.asarray(hdulist[1].data))
assert hdulist[1].data.dtype[1].str == '>f8'
assert hdulist[1].header['TFORM2'] == 'D'
def test_replace_table(tmp_path):
file_path = tmp_path / "test.fits"
file_path2 = tmp_path / "test2.fits"
schema_narrow = {
"allOf": [
asdf.schema.load_schema("http://example.com/schemas/core_metadata",
resolve_references=True), {
"type": "object",
"properties": {
"data": {
"title":
"relative sensitivity table",
"fits_hdu":
"RELSENS",
"datatype": [{
"name":
"TYPE",
"datatype": ["ascii", 16]
}, {
"name":
"T_OFFSET",
"datatype":
"float32"
}, {
"name":
"DECAY_PEAK",
"datatype":
"float32"
}, {
"name":
"DECAY_FREQ",
"datatype":
"float32"
}, {
"name":
"TAU",
"datatype":
"float32"
}]
}
}
}
]
}
schema_wide = {
"allOf": [
asdf.schema.load_schema("http://example.com/schemas/core_metadata",
resolve_references=True), {
"type": "object",
"properties": {
"data": {
"title":
"relative sensitivity table",
"fits_hdu":
"RELSENS",
"datatype": [{
"name":
"TYPE",
"datatype": ["ascii", 16]
}, {
"name":
"T_OFFSET",
"datatype":
"float64"
}, {
"name":
"DECAY_PEAK",
"datatype":
"float64"
}, {
"name":
"DECAY_FREQ",
"datatype":
"float64"
}, {
"name":
"TAU",
"datatype":
"float64"
}]
}
}
}
]
}
x = np.array([("string", 1., 2., 3., 4.)],
dtype=[('TYPE', 'S16'), ('T_OFFSET', np.float32),
('DECAY_PEAK', np.float32), ('DECAY_FREQ', np.float32),
('TAU', np.float32)])
m = DataModel(schema=schema_narrow)
m.data = x
m.to_fits(file_path, overwrite=True)
with fits.open(file_path, memmap=False) as hdulist:
assert records_equal(x, np.asarray(hdulist[1].data))
assert hdulist[1].data.dtype[1].str == '>f4'
assert hdulist[1].header['TFORM2'] == 'E'
with DataModel(file_path, schema=schema_wide) as m:
m.to_fits(file_path2, overwrite=True)
with fits.open(file_path2, memmap=False) as hdulist:
assert records_equal(x, np.asarray(hdulist[1].data))
assert hdulist[1].data.dtype[1].str == '>f8'
assert hdulist[1].header['TFORM2'] == 'D'