def test_csv_gz_factory():
data = b'\x1f\x8b\x08\x08z\x1e}R\x00\x03test.csv\x00\xab\xe02\xe42\xe22\xe6\x02\x00y\xffzx\x08\x00\x00\x00'
with make_file(data, '.csv.gz') as fname:
d = df.load_data(fname)
assert df.find_factory(fname) is df.tabular_data
assert_array_equal(d['x'], [1, 2, 3])
def test_ambiguous_format(tmpdir):
@data_factory('b', identifier=df.has_extension('spam'), priority=34)
def reader1(filename):
return Data()
@data_factory('a', identifier=df.has_extension('spam'), priority=34)
def reader2(filename):
return Data()
@data_factory('c', identifier=df.has_extension('spam'), priority=22)
def reader3(filename):
return Data()
filename = tmpdir.join('test.spam').strpath
with open(filename, 'w') as f:
f.write('Camelot!')
# Should raise a warning and pick the highest priority one in alphabetical
# order
with warnings.catch_warnings(record=True) as w:
factory = df.find_factory(filename)
assert len(w) == 1
assert str(w[0].message) == "Multiple data factories matched the input: 'a', 'b'. Choosing 'a'."
assert factory is reader2
def test_grey_png_loader():
# Greyscale PNG
data = b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x02\x00\x00\x00\x02\x08\x00\x00\x00\x00W\xddR\xf8\x00\x00\x00\x0eIDATx\x9ccdddab\x04\x00\x00&\x00\x0b\x8e`\xe7A\x00\x00\x00\x00IEND\xaeB`\x82'
with make_file(data, '.png') as fname:
d = df.load_data(fname)
assert df.find_factory(fname) is df.img_data
assert_array_equal(d['PRIMARY'], [[3, 4], [1, 2]])
def test_csv_gz_factory():
data = b'\x1f\x8b\x08\x08z\x1e}R\x00\x03test.csv\x00\xab\xe02\xe42\xe22\xe6\x02\x00y\xffzx\x08\x00\x00\x00'
with make_file(data, '.csv.gz') as fname:
d = df.load_data(fname)
assert df.find_factory(fname) is df.tabular_data
assert_array_equal(d['x'], [1, 2, 3])
def test_ambiguous_format(tmpdir):
@data_factory('b', identifier=df.has_extension('spam'), priority=34)
def reader1(filename):
return Data()
@data_factory('a', identifier=df.has_extension('spam'), priority=34)
def reader2(filename):
return Data()
@data_factory('c', identifier=df.has_extension('spam'), priority=22)
def reader3(filename):
return Data()
filename = tmpdir.join('test.spam').strpath
with open(filename, 'w') as f:
f.write('Camelot!')
# Should raise a warning and pick the highest priority one in alphabetical
# order
with warnings.catch_warnings(record=True) as w:
factory = df.find_factory(filename)
assert len(w) == 1
assert str(w[0].message) == "Multiple data factories matched the input: 'a', 'b'. Choosing 'a'."
assert factory is reader2
def test_fits_gz_factory():
data = b'\x1f\x8b\x08\x08\xdd\x1a}R\x00\x03test.fits\x00\xed\xd1\xb1\n\xc20\x10\xc6q\x1f\xe5\xde@ZA]\x1cZ\x8d\x10\xd0ZL\x87\xe2\x16m\x0b\x1d\x9aHR\x87n>\xba\xa5".\tRq\x11\xbe_\xe6\xfb\x93\xe3\x04\xdf\xa7;F\xb4"\x87\x8c\xa6t\xd1\xaa\xd2\xa6\xb1\xd4j\xda\xf2L\x90m\xa5*\xa4)\\\x03D1\xcfR\x9e\xbb{\xc1\xbc\xefIcdG\x85l%\xb5\xdd\xb5tW\xde\x92(\xe7\x82<\xff\x0b\xfb\x9e\xba5\xe7\xd2\x90\xae^\xe5\xba)\x95\xad\xb5\xb2\xfe^\xe0\xed\x8d6\xf4\xc2\xdf\xf5X\x9e\xb1d\xe3\xbd\xc7h\xb1XG\xde\xfb\x06_\xf4N\xecx Go\x16.\xe6\xcb\xf1\xbdaY\x00\x00\x00\x80?r\x9f<\x1f\x00\x00\x00\x00\x00|\xf6\x00\x03v\xd8\xf6\x80\x16\x00\x00'
with make_file(data, '.fits.gz') as fname:
d = df.load_data(fname)
assert df.find_factory(fname) is df.fits_reader
assert_array_equal(d['PRIMARY'], [[0, 0], [0, 0]])
def test_fits_gz_factory():
data = '\x1f\x8b\x08\x08\xdd\x1a}R\x00\x03test.fits\x00\xed\xd1\xb1\n\xc20\x10\xc6q\x1f\xe5\xde@ZA]\x1cZ\x8d\x10\xd0ZL\x87\xe2\x16m\x0b\x1d\x9aHR\x87n>\xba\xa5".\tRq\x11\xbe_\xe6\xfb\x93\xe3\x04\xdf\xa7;F\xb4"\x87\x8c\xa6t\xd1\xaa\xd2\xa6\xb1\xd4j\xda\xf2L\x90m\xa5*\xa4)\\\x03D1\xcfR\x9e\xbb{\xc1\xbc\xefIcdG\x85l%\xb5\xdd\xb5tW\xde\x92(\xe7\x82<\xff\x0b\xfb\x9e\xba5\xe7\xd2\x90\xae^\xe5\xba)\x95\xad\xb5\xb2\xfe^\xe0\xed\x8d6\xf4\xc2\xdf\xf5X\x9e\xb1d\xe3\xbd\xc7h\xb1XG\xde\xfb\x06_\xf4N\xecx Go\x16.\xe6\xcb\xf1\xbdaY\x00\x00\x00\x80?r\x9f<\x1f\x00\x00\x00\x00\x00|\xf6\x00\x03v\xd8\xf6\x80\x16\x00\x00'
with make_file(data, '.fits.gz') as fname:
d = df.load_data(fname)
assert df.find_factory(fname) is df.gridded_data
np.testing.assert_array_equal(d['PRIMARY'], [[0, 0], [0, 0]])
def test_fits_catalog_factory():
data = '\x1f\x8b\x08\x08\x19\r\x9cQ\x02\x03test.fits\x00\xed\xd7AO\x830\x18\xc6\xf1\xe9\'yo\x1c\'\x1c\x8c\x97\x1d\x86c\xa6\x911"5\xc1c\x91n\x92\x8cBJ\x97\xb8o\xef\x06\xd3\x98H\xdd\x16\x97]|~\x17\x12H\xfeyI{h\x136\x8b\xc3\x80hD=8\r\xe9\xb5R\x8bJ\x97\r\x99\x8a\xa6\x8c\'\xd4\x18\xa1r\xa1s\xea\xe53\x1e\xb3\xd4\xd2\xbb\xdb\xf6\x84\xd6bC\xb90\x82\xcc\xa6\x96t@4NYB\x96\xde\xcd\xb6\xa7\xd6e&5U\x8b\xcfrQJ\xd5\x14\x95jz{A\xca\x83hb\xfd\xdf\x93\xb51\x00\x00\x00\x00\xf87v\xc7\xc9\x84\xcd\xa3\x119>\x8b\xf8\xd8\x0f\x03\xe7\xdb\xe7!e\x85\x12zCFd+I\xf2\xddt\x87Sk\xef\xa2\xe7g\xef\xf4\xf3s\xdbs\xfb{\xee\xed\xb6\xb7\x92ji\xdev\xbd\xaf\x12\xb9\x07\xe6\xf3,\xf3\xb9\x96\x9eg\xef\xc5\xf7\xf3\xe7\x88\x1fu_X\xeaj]S-\xb4(\xa5\x91\xba\xff\x7f\x1f~\xeb\xb9?{\xcd\x81\xf5\xe0S\x16\x84\x93\xe4\x98\xf5\xe8\xb6\xcc\xa2\x90\xab\xdc^\xe5\xfc%\x0e\xda\xf5p\xc4\xfe\x95\xf3\x97\xfd\xcc\xa7\xf3\xa7Y\xd7{<Ko7_\xbb\xbeNv\xb6\xf9\xbc\xf3\xcd\x87\xfb\x1b\x00\x00\xc0\xe5\r:W\xfb\xe7\xf5\x00\x00\x00\x00\x00\x00\xac>\x00\x04\x01*\xc7\xc0!\x00\x00'
with make_file(data, '.fits') as fname:
d = df.load_data(fname)
assert df.find_factory(fname) is df.tabular_data
np.testing.assert_array_equal(d['a'], [1])
np.testing.assert_array_equal(d['b'], [2])
def test_ascii_catalog_factory(delim, suffix):
data = ("#a%sb\n1%s2" % (delim, delim)).encode('ascii')
with make_file(data, suffix) as fname:
d = df.load_data(fname)
assert df.find_factory(fname) is df.tabular_data
assert_array_equal(d['a'], [1])
assert_array_equal(d['b'], [2])
def test_ascii_catalog_factory(delim, suffix):
data = ("#a%sb\n1%s2" % (delim, delim)).encode('ascii')
with make_file(data, suffix) as fname:
d = df.load_data(fname)
assert df.find_factory(fname) is df.tabular_data
assert_array_equal(d['a'], [1])
assert_array_equal(d['b'], [2])
def test_fits_catalog_factory():
data = b'\x1f\x8b\x08\x08\x19\r\x9cQ\x02\x03test.fits\x00\xed\xd7AO\x830\x18\xc6\xf1\xe9\'yo\x1c\'\x1c\x8c\x97\x1d\x86c\xa6\x911"5\xc1c\x91n\x92\x8cBJ\x97\xb8o\xef\x06\xd3\x98H\xdd\x16\x97]|~\x17\x12H\xfeyI{h\x136\x8b\xc3\x80hD=8\r\xe9\xb5R\x8bJ\x97\r\x99\x8a\xa6\x8c\'\xd4\x18\xa1r\xa1s\xea\xe53\x1e\xb3\xd4\xd2\xbb\xdb\xf6\x84\xd6bC\xb90\x82\xcc\xa6\x96t@4NYB\x96\xde\xcd\xb6\xa7\xd6e&5U\x8b\xcfrQJ\xd5\x14\x95jz{A\xca\x83hb\xfd\xdf\x93\xb51\x00\x00\x00\x00\xf87v\xc7\xc9\x84\xcd\xa3\x119>\x8b\xf8\xd8\x0f\x03\xe7\xdb\xe7!e\x85\x12zCFd+I\xf2\xddt\x87Sk\xef\xa2\xe7g\xef\xf4\xf3s\xdbs\xfb{\xee\xed\xb6\xb7\x92ji\xdev\xbd\xaf\x12\xb9\x07\xe6\xf3,\xf3\xb9\x96\x9eg\xef\xc5\xf7\xf3\xe7\x88\x1fu_X\xeaj]S-\xb4(\xa5\x91\xba\xff\x7f\x1f~\xeb\xb9?{\xcd\x81\xf5\xe0S\x16\x84\x93\xe4\x98\xf5\xe8\xb6\xcc\xa2\x90\xab\xdc^\xe5\xfc%\x0e\xda\xf5p\xc4\xfe\x95\xf3\x97\xfd\xcc\xa7\xf3\xa7Y\xd7{<Ko7_\xbb\xbeNv\xb6\xf9\xbc\xf3\xcd\x87\xfb\x1b\x00\x00\xc0\xe5\r:W\xfb\xe7\xf5\x00\x00\x00\x00\x00\x00\xac>\x00\x04\x01*\xc7\xc0!\x00\x00'
with make_file(data, '.fits') as fname:
d = df.load_data(fname)
assert df.find_factory(fname) is df.fits_reader
assert_array_equal(d['a'], [1])
assert_array_equal(d['b'], [2])
def test_color_png_loader():
# Colorscale PNG
data = b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x02\x00\x00\x00\x02\x08\x02\x00\x00\x00\xfd\xd4\x9as\x00\x00\x00\x15IDAT\x08\xd7\x05\xc1\x01\x01\x00\x00\x00\x80\x10\xffO\x17B\x14\x1a!\xec\x04\xfc\xf2!Q\\\x00\x00\x00\x00IEND\xaeB`\x82'
with make_file(data, '.png') as fname:
d = df.load_data(fname)
assert df.find_factory(fname) is df.img_data
assert_array_equal(d['red'], [[255, 0], [255, 0]])
assert_array_equal(d['green'], [[255, 0], [0, 255]])
assert_array_equal(d['blue'], [[0, 255], [0, 0]])
def test_basic(filename, factory_name, shape):
# This is a basic test to excercise the data factory infrastructure. Above
# is a list containing (filename, factory, shape) where filename is
# the name of a file inside the data/ directory, factory is the factory that
# should be identified to be used for the file, and shape is the expected
# shape of the data.
filename = os.path.join(DATA, filename)
factory = find_factory(filename)
assert factory_label(factory) == factory_name
data = load_data(filename)
assert data.meta[CUBEVIZ_LAYOUT] == factory_name
assert data.shape == shape
def test_export_roundtrip(tmpdir):
# TODO: generalize this to all example data files once we
# have more than one format in the data directory.
DataFactoryConfiguration(check_ifu_valid=False)
# Make sure the right factory was identified
factory = find_factory(TEST_DATA_PATH)
assert factory.__self__.name == 'kmos'
# Load in the data
data = factory(TEST_DATA_PATH)
# Generate temporary output filename
filename = tmpdir.join('test.fits').strpath
# Export the data
cubeviz_fits_exporter(filename, data)
# And check that we identify the file correctly again
factory = find_factory(filename)
assert factory.__self__.name == 'kmos'
def test_basic(filename, factory_name, shape):
# This is a basic test to excercise the data factory infrastructure. Above
# is a list containing (filename, factory, shape) where filename is
# the name of a file inside the data/ directory, factory is the factory that
# should be identified to be used for the file, and shape is the expected
# shape of the data.
filename = os.path.join(DATA, filename)
factory = find_factory(filename)
assert factory_label(factory) == factory_name
data = load_data(filename)
assert data.meta[CUBEVIZ_LAYOUT] == factory_name
assert data.shape == shape
def test_export_roundtrip(tmpdir):
# TODO: generalize this to all example data files once we
# have more than one format in the data directory.
DataFactoryConfiguration(check_ifu_valid=False)
# Make sure the right factory was identified
factory = find_factory(TEST_DATA_PATH)
assert factory.__self__.name == 'kmos'
# Load in the data
data = factory(TEST_DATA_PATH)
# Generate temporary output filename
filename = tmpdir.join('test.fits').strpath
# Export the data
cubeviz_fits_exporter(filename, data)
# And check that we identify the file correctly again
factory = find_factory(filename)
assert factory.__self__.name == 'kmos'
def test_fits_image_loader():
with make_file(TEST_FITS_DATA, '.fits', decompress=True) as fname:
d = df.load_data(fname)
assert df.find_factory(fname) is df.fits_reader
assert_array_equal(d['PRIMARY'], [1, 2, 3])
def test_fits_image_loader():
data = '\x1f\x8b\x08\x08\xac\xf6\x9bQ\x00\x03test.fits\x00\xed\xd0\xb1\n\xc20\x14\x85\xe1\xaa/r\xde@\x8a\xe2\xe6\xa0X!\xa0\xa5\xd0\x0c]\xa3m\xa1C\x13I\xe2\xd0\xb7\xb7b\xc5\xa1)\xe2\xe6p\xbe\xe5N\xf7\xe7rsq\xceN\t\xb0E\x80\xc4\x12W\xa3kc[\x07op\x142\x87\xf3J\x97\xca\x96\xa1\x05`/d&\x8apo\xb3\xee{\xcaZ\xd5\xa1T^\xc1w\xb7*\\\xf9Hw\x85\xc81q_\xdc\xf7\xf4\xbd\xbdT\x16\xa6~\x97\x9b\xb6\xd2\xae1\xdaM\xf7\xe2\x89\xde\xea\xdb5cI!\x93\xf40\xf9\xbf\xdf{\xcf\x18\x11\x11\x11\x11\xfd\xad\xe8e6\xcc\xf90\x17\x11\x11\x11\x11\x11\x11\x8d<\x00\x8d,\xdc\xe8\x80\x16\x00\x00'
with make_file(data, '.fits') as fname:
d = df.load_data(fname)
assert df.find_factory(fname) is df.gridded_data
np.testing.assert_array_equal(d['PRIMARY'], [1, 2, 3])
def test_hdf5_loader(suffix):
data = b'x\xda\xeb\xf4pq\xe3\xe5\x92\xe2b\x00\x01\x0e\x0e\x06\x16\x06\x01\x06d\xf0\x1f\n*8P\xf90\xf9\x04(\xcd\x08\xa5;\xa0\xf4\n&\x988#XN\x02*.\x085\x1f]]H\x90\xab+H\xf5\x7f4\x00\xb3\xc7\x80\x05Bs0\x8c\x82\x91\x08<\\\x1d\x03@t\x04\x94\x0fK\xa5\'\x98P\xd5U\xa0\xa5G\x0f\n\xeded`\x83\x98\xc5\x08\xe3CR2##D\x80\x19\xaa\x0eA\x0b\x80\x95\np\xc0\xd2\xaa\x03\x98d\x05\xf2@\xe2LLL\x8c\x90t,\x01\xe633&@\x93\xb4\x04\x8a\xbdBP\xdd 5\xc9\xd5]A\x0c\x0c\r\x83"\x1e\x82\xfd\xfc]@9\x1a\x96\x0f\x15\x98G\xd3\xe6(\x18\x05\xa3\x00W\xf9\t\x01Lh\xe5$\x00\xc2A.\xaf'
with make_file(data, suffix, decompress=True) as fname:
d = df.load_data(fname)
assert df.find_factory(fname) is df.hdf5_reader
assert_array_equal(d['x'], [1, 2, 3])
def test_fits_uses_mmapping():
with make_file(TEST_FITS_DATA, '.fits', decompress=True) as fname:
d = df.load_data(fname)
assert df.find_factory(fname) is df.fits_reader
assert not d['PRIMARY'].flags['OWNDATA']
def test_fits_image_loader():
with make_file(TEST_FITS_DATA, '.fits') as fname:
d = df.load_data(fname)
assert df.find_factory(fname) is df.gridded_data
np.testing.assert_array_equal(d['PRIMARY'], [1, 2, 3])
def test_hdf5_loader(suffix):
data = 'x\xda\xeb\xf4pq\xe3\xe5\x92\xe2b\x00\x01\x0e\x0e\x06\x16\x06\x01\x06d\xf0\x1f\n*8P\xf90\xf9\x04(\xcd\x08\xa5;\xa0\xf4\n&\x988#XN\x02*.\x085\x1f]]H\x90\xab+H\xf5\x7f4\x00\xb3\xc7\x80\x05Bs0\x8c\x82\x91\x08<\\\x1d\x03@t\x04\x94\x0fK\xa5\'\x98P\xd5U\xa0\xa5G\x0f\n\xeded`\x83\x98\xc5\x08\xe3CR2##D\x80\x19\xaa\x0eA\x0b\x80\x95\np\xc0\xd2\xaa\x03\x98d\x05\xf2@\xe2LLL\x8c\x90t,\x01\xe633&@\x93\xb4\x04\x8a\xbdBP\xdd 5\xc9\xd5]A\x0c\x0c\r\x83"\x1e\x82\xfd\xfc]@9\x1a\x96\x0f\x15\x98G\xd3\xe6(\x18\x05\xa3\x00W\xf9\t\x01Lh\xe5$\x00\xc2A.\xaf'
with make_file(data, suffix, decompress=True) as fname:
d = df.load_data(fname)
assert df.find_factory(fname) is df.gridded_data
np.testing.assert_array_equal(d['/x'], [1, 2, 3])
def test_fits_image_loader():
with make_file(TEST_FITS_DATA, '.fits') as fname:
d = df.load_data(fname)
assert df.find_factory(fname) is df.gridded_data
np.testing.assert_array_equal(d['PRIMARY'], [1, 2, 3])
def test_fits_image_loader():
with make_file(TEST_FITS_DATA, '.fits', decompress=True) as fname:
d = df.load_data(fname)
assert df.find_factory(fname) is df.fits_reader
assert_array_equal(d['PRIMARY'], [1, 2, 3])
def test_fits_uses_mmapping():
with make_file(TEST_FITS_DATA, '.fits', decompress=True) as fname:
d = df.load_data(fname)
assert df.find_factory(fname) is df.fits_reader
assert not d['PRIMARY'].flags['OWNDATA']
