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test_fits.py
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test_fits.py
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import re
import pytest
from astropy.io import fits
import numpy as np
from numpy.testing import assert_array_almost_equal, assert_array_equal
import asdf.schema
from jsonschema import ValidationError
from stdatamodels import DataModel
from stdatamodels import fits_support
from models import FitsModel, PureFitsModel
def records_equal(a, b):
a = a.item()
b = b.item()
a_size = len(a)
b_size = len(b)
equal = a_size == b_size
for i in range(a_size):
if not equal: break
equal = a[i] == b[i]
return equal
def test_from_new_hdulist():
with pytest.raises(AttributeError):
from astropy.io import fits
hdulist = fits.HDUList()
with FitsModel(hdulist) as dm:
dm.foo
def test_from_new_hdulist2():
from astropy.io import fits
hdulist = fits.HDUList()
data = np.empty((50, 50), dtype=np.float32)
primary = fits.PrimaryHDU()
hdulist.append(primary)
science = fits.ImageHDU(data=data, name='SCI')
hdulist.append(science)
with FitsModel(hdulist) as dm:
dq = dm.dq
assert dq is not None
def test_setting_arrays_on_fits():
from astropy.io import fits
hdulist = fits.HDUList()
data = np.empty((50, 50), dtype=np.float32)
primary = fits.PrimaryHDU()
hdulist.append(primary)
science = fits.ImageHDU(data=data, name='SCI')
hdulist.append(science)
with FitsModel(hdulist) as dm:
dm.data = np.empty((50, 50), dtype=np.float32)
dm.dq = np.empty((10,), dtype=np.uint32)
def test_from_scratch(tmp_path):
file_path = tmp_path/"test.fits"
with FitsModel((50, 50)) as dm:
data = np.asarray(np.random.rand(50, 50), np.float32)
dm.data[...] = data
dm.meta.telescope = "EYEGLASSES"
dm.to_fits(file_path)
with FitsModel.from_fits(file_path) as dm2:
assert dm2.shape == (50, 50)
assert dm2.meta.telescope == "EYEGLASSES"
assert dm2.dq.dtype.name == 'uint32'
assert np.all(dm2.data == data)
def test_extra_fits(tmp_path):
file_path = tmp_path/"test.fits"
with FitsModel() as dm:
dm.save(file_path)
with fits.open(file_path) as hdul:
hdul[0].header["FOO"] = "BAR"
hdul.writeto(file_path, overwrite=True)
with DataModel(file_path) as dm:
assert any(h for h in dm.extra_fits.PRIMARY.header if h == ["FOO", "BAR", ""])
def test_hdu_order(tmp_path):
file_path = tmp_path/"test.fits"
with FitsModel(data=np.array([[0.0]]),
dq=np.array([[0.0]]),
err=np.array([[0.0]])) as dm:
dm.save(file_path)
with fits.open(file_path, memmap=False) as hdulist:
assert hdulist[1].header['EXTNAME'] == 'SCI'
assert hdulist[2].header['EXTNAME'] == 'DQ'
assert hdulist[3].header['EXTNAME'] == 'ERR'
def test_fits_comments(tmp_path):
file_path = tmp_path/"test.fits"
with FitsModel() as dm:
dm.meta.origin = "STScI"
dm.save(file_path)
from astropy.io import fits
with fits.open(file_path, memmap=False) as hdulist:
assert any(c for c in hdulist[0].header.cards if c[-1] == "Organization responsible for creating file")
def test_metadata_doesnt_override(tmp_path):
file_path = tmp_path/"test.fits"
with FitsModel() as dm:
dm.save(file_path)
from astropy.io import fits
with fits.open(file_path, mode='update', memmap=False) as hdulist:
hdulist[0].header['ORIGIN'] = 'UNDER THE COUCH'
with FitsModel(file_path) as dm:
assert dm.meta.origin == 'UNDER THE COUCH'
def test_non_contiguous_array(tmp_path):
file_path = tmp_path/"test.fits"
data = np.arange(60, dtype=np.float32).reshape(5, 12)
err = data[::-1, ::2]
with FitsModel() as dm:
dm.data = data
dm.err = err
dm.save(file_path)
with FitsModel(file_path) as dm:
assert_array_equal(dm.data, data)
assert_array_equal(dm.err, err)
def test_table_with_metadata(tmp_path):
file_path = tmp_path/"test.fits"
schema = {
"allOf": [
asdf.schema.load_schema("http://example.com/schemas/core_metadata", resolve_references=True),
{"type": "object",
"properties": {
"flux_table": {
"title": "Photometric flux conversion table",
"fits_hdu": "FLUX",
"datatype":
[
{"name": "parameter", "datatype": ['ascii', 7]},
{"name": "factor", "datatype": "float64"},
{"name": "uncertainty", "datatype": "float64"}
]
},
"meta": {
"type": "object",
"properties": {
"fluxinfo": {
"title": "Information about the flux conversion",
"type": "object",
"properties": {
"exposure": {
"title": "Description of exposure analyzed",
"type": "string",
"fits_hdu": "FLUX",
"fits_keyword": "FLUXEXP"
}
}
}
}
}
}
}
]
}
class FluxModel(DataModel):
def __init__(self, init=None, flux_table=None, **kwargs):
super().__init__(init=init, schema=schema, **kwargs)
if flux_table is not None:
self.flux_table = flux_table
flux_im = [
('F560W', 1.0e-5, 1.0e-7),
('F770W', 1.1e-5, 1.6e-7),
]
with FluxModel(flux_table=flux_im) as datamodel:
datamodel.meta.fluxinfo.exposure = 'Exposure info'
datamodel.save(file_path, overwrite=True)
del datamodel
from astropy.io import fits
with fits.open(file_path, memmap=False) as hdulist:
assert len(hdulist) == 3
assert isinstance(hdulist[1], fits.BinTableHDU)
assert hdulist[1].name == 'FLUX'
assert hdulist[2].name == 'ASDF'
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'
def test_table_with_unsigned_int(tmp_path):
file_path = tmp_path/"test.fits"
schema = {
'title': 'Test data model',
'$schema': 'http://stsci.edu/schemas/fits-schema/fits-schema',
'type': 'object',
'properties': {
'meta': {
'type': 'object',
'properties': {}
},
'test_table': {
'title': 'Test table',
'fits_hdu': 'TESTTABL',
'datatype': [
{'name': 'FLOAT64_COL', 'datatype': 'float64'},
{'name': 'UINT32_COL', 'datatype': 'uint32'}
]
}
}
}
with DataModel(schema=schema) as dm:
float64_info = np.finfo(np.float64)
float64_arr = np.random.uniform(size=(10,))
float64_arr[0] = float64_info.min
float64_arr[-1] = float64_info.max
uint32_info = np.iinfo(np.uint32)
uint32_arr = np.random.randint(uint32_info.min, uint32_info.max + 1, size=(10,), dtype=np.uint32)
uint32_arr[0] = uint32_info.min
uint32_arr[-1] = uint32_info.max
test_table = np.array(list(zip(float64_arr, uint32_arr)), dtype=dm.test_table.dtype)
def assert_table_correct(model):
for idx, (col_name, col_data) in enumerate([('float64_col', float64_arr), ('uint32_col', uint32_arr)]):
# The table dtype and field dtype are stored separately, and may not
# necessarily agree.
assert np.can_cast(model.test_table.dtype[idx], col_data.dtype, 'equiv')
assert np.can_cast(model.test_table.field(col_name).dtype, col_data.dtype, 'equiv')
assert np.array_equal(model.test_table.field(col_name), col_data)
# The datamodel casts our array to FITS_rec on assignment, so here we're
# checking that the data survived the casting.
dm.test_table = test_table
assert_table_correct(dm)
# Confirm that saving the table (and converting the uint32 values to signed int w/TZEROn)
# doesn't mangle the data.
dm.save(file_path)
assert_table_correct(dm)
# Confirm that the data loads from the file intact (converting the signed ints back to
# the appropriate uint32 values).
with DataModel(file_path, schema=schema) as dm2:
assert_table_correct(dm2)
def test_metadata_from_fits(tmp_path):
file_path = tmp_path/"test.fits"
file_path2 = tmp_path/"test2.fits"
mask = np.array([[0, 1], [2, 3]])
fits.ImageHDU(data=mask, name='DQ').writeto(file_path)
with FitsModel(file_path) as dm:
dm.save(file_path2)
with fits.open(file_path2, memmap=False) as hdulist:
assert hdulist[2].name == 'ASDF'
def test_get_short_doc():
assert fits_support.get_short_doc({}) == ""
assert fits_support.get_short_doc({"title": "Some schema title."}) == "Some schema title."
assert fits_support.get_short_doc({
"title": "Some schema title.\nWhoops, another line."
}) == "Some schema title."
assert fits_support.get_short_doc({
"title": "Some schema title.",
"description": "Some schema description.",
}) == "Some schema title."
assert fits_support.get_short_doc({
"description": "Some schema description.",
}) == "Some schema description."
assert fits_support.get_short_doc({
"description": "Some schema description.\nWhoops, another line.",
}) == "Some schema description."
def test_ensure_ascii():
for inp in [b"ABCDEFG", "ABCDEFG"]:
fits_support.ensure_ascii(inp) == "ABCDEFG"
@pytest.mark.parametrize(
'which_file, skip_fits_update, expected_exp_type',
[
('just_fits', None, 'FGS_DARK'),
('just_fits', False, 'FGS_DARK'),
('just_fits', True, 'FGS_DARK'),
('model', None, 'FGS_DARK'),
('model', False, 'FGS_DARK'),
('model', True, 'NRC_IMAGE')
]
)
@pytest.mark.parametrize(
'use_env',
[False, True]
)
def test_skip_fits_update(tmp_path,
monkeypatch,
use_env,
which_file,
skip_fits_update,
expected_exp_type):
"""Test skip_fits_update setting"""
file_path = tmp_path/"test.fits"
# Setup the FITS file, modifying a header value
if which_file == "just_fits":
primary_hdu = fits.PrimaryHDU()
primary_hdu.header['EXP_TYPE'] = 'NRC_IMAGE'
primary_hdu.header['DATAMODL'] = "FitsModel"
hduls = fits.HDUList([primary_hdu])
hduls.writeto(file_path)
else:
model = FitsModel()
model.meta.exposure.type = 'NRC_IMAGE'
model.save(file_path)
with fits.open(file_path) as hduls:
hduls[0].header['EXP_TYPE'] = 'FGS_DARK'
if use_env:
if skip_fits_update is not None:
monkeypatch.setenv("SKIP_FITS_UPDATE", str(skip_fits_update))
skip_fits_update = None
model = FitsModel(hduls, skip_fits_update=skip_fits_update)
assert model.meta.exposure.type == expected_exp_type
def test_from_hdulist(tmp_path):
file_path = tmp_path/"test.fits"
with FitsModel() as dm:
dm.save(file_path)
with fits.open(file_path, memmap=False) as hdulist:
with FitsModel(hdulist) as dm:
dm.data
assert not hdulist.fileinfo(0)['file'].closed
def test_data_array(tmp_path):
file_path = tmp_path/"test.fits"
file_path2 = tmp_path/"test2.fits"
data_array_schema = {
"allOf": [
asdf.schema.load_schema("http://example.com/schemas/core_metadata", resolve_references=True),
{
"type": "object",
"properties": {
"arr": {
'title': 'An array of data',
'type': 'array',
"fits_hdu": ["FOO", "DQ"],
"items": {
"title": "entry",
"type": "object",
"properties": {
"data": {
"fits_hdu": "FOO",
"default": 0.0,
"max_ndim": 2,
"datatype": "float64"
},
"dq": {
"fits_hdu": "DQ",
"default": 1,
"datatype": "uint8"
},
}
}
}
}
}
]
}
array1 = np.random.rand(5, 5)
array2 = np.random.rand(5, 5)
array3 = np.random.rand(5, 5)
with DataModel(schema=data_array_schema) as x:
x.arr.append(x.arr.item())
x.arr[0].data = array1
assert len(x.arr) == 1
x.arr.append(x.arr.item(data=array2))
assert len(x.arr) == 2
x.arr.append({})
assert len(x.arr) == 3
x.arr[2].data = array3
del x.arr[1]
assert len(x.arr) == 2
x.to_fits(file_path)
with DataModel(file_path, schema=data_array_schema) as x:
assert len(x.arr) == 2
assert_array_almost_equal(x.arr[0].data, array1)
assert_array_almost_equal(x.arr[1].data, array3)
del x.arr[0]
assert len(x.arr) == 1
x.arr = []
assert len(x.arr) == 0
x.arr.append({'data': np.empty((5, 5))})
assert len(x.arr) == 1
x.arr.extend([
x.arr.item(data=np.empty((5, 5))),
x.arr.item(data=np.empty((5, 5)),
dq=np.empty((5, 5), dtype=np.uint8))])
assert len(x.arr) == 3
del x.arr[1]
assert len(x.arr) == 2
x.to_fits(file_path2, overwrite=True)
with fits.open(file_path2) as hdulist:
x = set()
for hdu in hdulist:
x.add((hdu.header.get('EXTNAME'),
hdu.header.get('EXTVER')))
assert x == set(
[('FOO', 2), ('FOO', 1), ('ASDF', None), ('DQ', 2),
(None, None)])
@pytest.mark.parametrize("keyword,result", [
("BZERO", True),
("TFORM53", True),
("SIMPLE", True),
("EXTEND", True),
("INSTRUME", False),
])
def test_is_builtin_fits_keyword(keyword, result):
assert fits_support.is_builtin_fits_keyword(keyword) is result
def test_no_asdf_extension(tmp_path):
"""Verify an ASDF extension is not written out"""
path = tmp_path / "no_asdf.fits"
with PureFitsModel((5, 5)) as m:
m.save(path)
with fits.open(path, memmap=False) as hdulist:
assert "ASDF" not in hdulist
def test_no_asdf_extension_extra_fits(tmp_path):
path = tmp_path / "no_asdf.fits"
extra_fits = {
'ASDF': {
'header': [
['CHECKSUM', '9kbGAkbF9kbFAkbF', 'HDU checksum updated 2018-03-07T08:48:27'],
['DATASUM', '136453353', 'data unit checksum updated 2018-03-07T08:48:27']
]
}
}
with PureFitsModel((5, 5)) as m:
m.extra_fits = {}
m.extra_fits.instance.update(extra_fits)
assert "ASDF" in m.extra_fits.instance
assert "CHECKSUM" in m.extra_fits.ASDF.header[0]
assert "DATASUM" in m.extra_fits.ASDF.header[1]
m.save(path)
with fits.open(path, memmap=False) as hdulist:
assert "ASDF" not in hdulist
with PureFitsModel(path) as m:
with pytest.raises(AttributeError):
m.extra_fits
def test_ndarray_validation(tmp_path):
file_path = tmp_path / "test.fits"
# Wrong dtype
hdu = fits.ImageHDU(data=np.ones((4, 4), dtype=np.float64), name="SCI")
hdul = fits.HDUList([fits.PrimaryHDU(), hdu])
hdul.writeto(file_path)
# Should be able to cast
with FitsModel(file_path, strict_validation=True, validate_arrays=True) as model:
model.validate()
# But raise an error when casting is disabled
with pytest.raises(ValidationError, match="Array datatype 'float64' is not compatible with 'float32'"):
with FitsModel(file_path, strict_validation=True, cast_fits_arrays=False, validate_arrays=True) as model:
model.validate()
# Wrong dimensions
hdu = fits.ImageHDU(data=np.ones((4,), dtype=np.float64), name="SCI")
hdul = fits.HDUList([fits.PrimaryHDU(), hdu])
hdul.writeto(file_path, overwrite=True)
# Can't cast this problem away
with pytest.raises(ValueError, match="Array has wrong number of dimensions"):
with FitsModel(file_path, strict_validation=True, validate_arrays=True) as model:
model.validate()
# Should also be caught by validation
with pytest.raises(ValidationError, match="Wrong number of dimensions: Expected 2, got 1"):
with FitsModel(file_path, strict_validation=True, cast_fits_arrays=False, validate_arrays=True) as model:
model.validate()
def test_resave_duplication_bug(tmp_path):
"""
An issue in asdf (https://github.com/asdf-format/asdf/issues/1232)
resulted in duplication of data when a model was read from and then
written to a fits file.
"""
fn1 = tmp_path / "test1.fits"
fn2 = tmp_path / "test2.fits"
arr = np.zeros((1000, 100), dtype='f4')
m = FitsModel(arr)
m.save(fn1)
m2 = FitsModel.from_fits(fn1)
m2.save(fn2)
with fits.open(fn1) as ff1, fits.open(fn2) as ff2:
assert ff1['ASDF'].size == ff2['ASDF'].size
def test_table_linking(tmp_path):
file_path = tmp_path / "test.fits"
schema = {
'title': 'Test data model',
'$schema': 'http://stsci.edu/schemas/fits-schema/fits-schema',
'type': 'object',
'properties': {
'meta': {
'type': 'object',
'properties': {}
},
'test_table': {
'title': 'Test table',
'fits_hdu': 'TESTTABL',
'datatype': [
{'name': 'A_COL', 'datatype': 'int8'},
{'name': 'B_COL', 'datatype': 'int8'}
]
}
}
}
with DataModel(schema=schema) as dm:
test_array = np.array([(1, 2), (3, 4)], dtype=[('A_COL', 'i1'), ('B_COL', 'i1')])
# assigning to the model will convert the array to a FITS_rec
dm.test_table = test_array
assert isinstance(dm.test_table, fits.FITS_rec)
# save the model (with the table)
dm.save(file_path)
# open the model and confirm that the table was linked to an hdu
with fits.open(file_path) as ff:
# read the bytes for the embedded ASDF content
asdf_bytes = ff['ASDF'].data.tobytes()
# get only the bytes for the tree (not blocks) by splitting
# on the yaml end document marker '...'
# on the first block magic sequence
tree_string = asdf_bytes.split(b'...')[0].decode('ascii')
unlinked_arrays = re.findall(r'source:\s+[^f]', tree_string)
assert not len(unlinked_arrays), unlinked_arrays