def test_fits_vector():
# Regression test for bug that caused tables with vector columns to not load
with warnings.catch_warnings(record=True) as w:
df.load_data(os.path.join(DATA, 'events.fits'), factory=df.fits_reader)
assert len(w) == 1
assert str(w[0].message
) == "Dropping column 'status' since it is not 1-dimensional"
def test_fits_vector():
# Regression test for bug that caused tables with vector columns to not load
with warnings.catch_warnings(record=True) as w:
df.load_data(os.path.join(DATA, 'events.fits'), factory=df.fits_reader)
for warning in w:
if str(warning.message) == "Dropping column 'status' since it is not 1-dimensional":
break
else:
raise ValueError("Missing warning about dropping column")
def test_fits_vector():
# Regression test for bug that caused tables with vector columns to not load
with warnings.catch_warnings(record=True) as w:
df.load_data(os.path.join(DATA, 'events.fits'), factory=df.fits_reader)
for warning in w:
if str(warning.message) == "Dropping column 'status' since it is not 1-dimensional":
break
else:
raise ValueError("Missing warning about dropping column")
def test_pandas_parse_delimiters(delim, suffix):
data = ("a%sb\n1%s2" % (delim, delim)).encode('ascii')
with make_file(data, suffix) as fname:
d = df.load_data(fname, factory=df.pandas_read_table)
assert_array_equal(d['a'], [1])
assert_array_equal(d['b'], [2])
def test_csv_pandas_factory():
data = """a,b,c,d
1,2.1,some,True
2,2.4,categorical,False
3,1.4,data,True
4,4.0,here,True
5,6.3,,False
6,8.7,,False
8,9.2,,True"""
with make_file(data, '.csv') as fname:
d = df.load_data(fname, factory=df.pandas_read_csv)
assert d['a'].dtype == np.int
assert d['b'].dtype == np.float
assert d['c'].dtype == np.float
cat_comp = d.find_component_id('c')
assert isinstance(d.get_component(cat_comp), CategoricalComponent)
correct_cats = np.unique(
np.asarray(
['some', 'categorical', 'data', 'here', np.nan, np.nan, np.nan],
dtype=np.object))
np.testing.assert_equal(
d.get_component(cat_comp)._categories, correct_cats)
cat_comp = d.find_component_id('d')
assert isinstance(d.get_component(cat_comp), CategoricalComponent)
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 jglue(*args, settings=None, show=False, links=None, **kwargs):
"""
Create a new Jupyter-based glue application.
It is typically easiest to call this function without arguments and load
data and add links separately in subsequent calls. However, this function
can also take the same inputs as the `~glue.qglue` function.
Once this function is called, it will return a
`~glue_jupyter.JupyterApplication` object, which can then be used to
load data, set up links, and create visualizations. See the documentation
for that class for more details.
"""
from glue.qglue import parse_data, parse_links
from glue.core.data_factories import load_data
japp = JupyterApplication(settings=settings)
dc = japp.data_collection
for label, data in kwargs.items():
if isinstance(data, str):
data = load_data(data)
dc.extend(parse_data(data, label))
for data in args:
dc.append(data)
if links is not None:
dc.add_link(parse_links(dc, links))
if show:
display(japp)
return japp
def test_casalike():
d = df.load_data(os.path.join(DATA, 'casalike.fits'), factory=df.casalike_cube)
assert d.shape == (1, 2, 2, 2)
d['STOKES 0']
d['STOKES 1']
d['STOKES 2']
d['STOKES 3']
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, df.tabular_data)
np.testing.assert_array_equal(d['a'], [1])
np.testing.assert_array_equal(d['b'], [2])
def test_csv_pandas_factory():
data = """a,b,c,d
1,2.1,some,True
2,2.4,categorical,False
3,1.4,data,True
4,4.0,here,True
5,6.3,,False
6,8.7,,False
8,9.2,,True"""
with make_file(data, '.csv') as fname:
d = df.load_data(fname, factory=df.pandas_read_csv)
assert d['a'].dtype == np.int
assert d['b'].dtype == np.float
assert d['c'].dtype == np.float
cat_comp = d.find_component_id('c')
assert isinstance(d.get_component(cat_comp), CategoricalComponent)
correct_cats = np.unique(np.asarray(['some', 'categorical',
'data', 'here',
np.nan, np.nan, np.nan],
dtype=np.object))
np.testing.assert_equal(d.get_component(cat_comp)._categories,
correct_cats)
cat_comp = d.find_component_id('d')
assert isinstance(d.get_component(cat_comp), CategoricalComponent)
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 load_data(self, paths, skip_merge=False, auto_merge=False):
"""
Given a path to a file, load the file as a Data object and add it to
the current session.
This returns the added `Data` object.
"""
if isinstance(paths, string_types):
paths = [paths]
datasets = []
for path in paths:
result = load_data(path)
if isinstance(result, BaseData):
datasets.append(result)
else:
datasets.extend(result)
self.add_datasets(self.data_collection, datasets,
skip_merge=skip_merge, auto_merge=auto_merge)
if len(datasets) == 1:
return datasets[0]
else:
return datasets
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_casalike():
d = df.load_data(os.path.join(DATA, 'casalike.fits'), factory=df.casalike_cube)
assert d.shape == (1, 2, 2, 2)
d['STOKES 0']
d['STOKES 1']
d['STOKES 2']
d['STOKES 3']
def _parse_data_path(path, label):
from glue.core.data_factories import load_data, as_list
data = load_data(path)
for d in as_list(data):
d.label = label
return as_list(data)
def load_data(self):
"""Highest level method to interactively load a data set.
:rtype: A list of constructed data objects
"""
from glue.core.data_factories import data_label, load_data
paths, fac = self._get_paths_and_factory()
result = []
# Check that the user didn't select a .glu file by mistake
for path in paths:
if path.endswith('.glu'):
mb = QtWidgets.QMessageBox(
QtWidgets.QMessageBox.Critical, "Error loading data",
"It looks like you have selected "
"a .glu session file. You should open "
"this using 'Open Session' under the "
"'File' menu instead")
mb.exec_()
return []
with set_cursor_cm(Qt.WaitCursor):
for path in paths:
self._curfile = path
d = load_data(path, factory=fac.function)
if not isinstance(d, list):
if not d.label:
d.label = data_label(path)
d = [d]
result.extend(d)
return result
def add_data(self, *args, **kwargs):
"""
Add data to the session.
Positional arguments are interpreted using the data factories, while
keyword arguments are interpreted using the same infrastructure as the
`qglue` command.
"""
datasets = []
for path in args:
datasets.append(load_data(path))
links = kwargs.pop('links', None)
from glue.qglue import parse_data, parse_links
for label, data in kwargs.items():
datasets.extend(parse_data(data, label))
self.add_datasets(self.data_collection, datasets)
if links is not None:
self.data_collection.add_link(parse_links(self.data_collection, links))
def test_load_data_auto_assigns_label():
factory = MagicMock()
result = Data(x=[1, 2, 3], label='')
factory.return_value = result
d = df.load_data('test.fits', factory)
factory.assert_called_once_with('test.fits')
assert d.label == 'test'
def test_load_data_auto_assigns_label():
factory = MagicMock()
result = Data(x=[1, 2, 3], label='')
factory.return_value = result
d = df.load_data('test.fits', factory)
factory.assert_called_once_with('test.fits')
assert d.label == 'test'
def _parse_data_path(path, label):
from glue.core.data_factories import load_data, as_list
data = load_data(path)
for d in as_list(data):
d.label = label
return as_list(data)
def load_data(self):
"""Highest level method to interactively load a data set.
:rtype: A list of constructed data objects
"""
from glue.core.data_factories import data_label, load_data
paths, fac = self._get_paths_and_factory()
result = []
# Check that the user didn't select a .glu file by mistake
for path in paths:
if path.endswith('.glu'):
mb = QtWidgets.QMessageBox(QtWidgets.QMessageBox.Critical,
"Error loading data",
"It looks like you have selected "
"a .glu session file. You should open "
"this using 'Open Session' under the "
"'File' menu instead")
mb.exec_()
return []
with set_cursor_cm(Qt.WaitCursor):
for path in paths:
self._curfile = path
d = load_data(path, factory=fac.function)
if not isinstance(d, list):
if not d.label:
d.label = data_label(path)
d = [d]
result.extend(d)
return result
def load_data(self, paths, skip_merge=False, auto_merge=False):
"""
Given a path to a file, load the file as a Data object and add it to
the current session.
This returns the added `Data` object.
"""
if isinstance(paths, string_types):
paths = [paths]
datasets = []
for path in paths:
result = load_data(path)
if isinstance(result, Data):
datasets.append(result)
else:
datasets.extend(result)
self.add_datasets(self.data_collection,
datasets,
skip_merge=skip_merge,
auto_merge=auto_merge)
if len(datasets) == 1:
return datasets[0]
else:
return datasets
def add_data(self, *args, **kwargs):
"""
Add data to the session.
Positional arguments are interpreted using the data factories, while
keyword arguments are interpreted using the same infrastructure as the
`qglue` command.
This returns a list of added `Data` objects.
"""
datasets = []
for path in args:
datasets.append(load_data(path))
links = kwargs.pop('links', None)
from glue.qglue import parse_data, parse_links
for label, data in kwargs.items():
datasets.extend(parse_data(data, label))
self.add_datasets(self.data_collection, datasets)
if links is not None:
self.data_collection.add_link(
parse_links(self.data_collection, links))
return datasets
def test_pandas_parse_delimiters(delim, suffix):
data = ("a%sb\n1%s2" % (delim, delim)).encode('ascii')
with make_file(data, suffix) as fname:
d = df.load_data(fname, factory=df.pandas_read_table)
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_pandas_parse_delimiters(delim, suffix):
data = "a%sb\n1%s2" % (delim, delim)
with make_file(data, suffix) as fname:
d = df.load_data(fname, factory=df.pandas_read_table)
print d
np.testing.assert_array_equal(d['a'], [1])
np.testing.assert_array_equal(d['b'], [2])
def load_data_files(datafiles):
"""Load data files and return a list of datasets"""
from glue.core.data_factories import auto_data, load_data
datasets = []
for df in datafiles:
datasets.append(load_data(df, auto_data))
return datasets
def test_pandas_parse_delimiters(delim, suffix):
data = "a%sb\n1%s2" % (delim, delim)
with make_file(data, suffix) as fname:
d = df.load_data(fname, factory=df.pandas_read_table)
print d
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)
print data
with make_file(data, suffix) as fname:
d = df.load_data(fname)
np.testing.assert_array_equal(d['a'], [1])
np.testing.assert_array_equal(d['b'], [2])
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 load_data_files(datafiles):
"""Load data files and return a list of datasets"""
from glue.core.data_factories import auto_data, load_data
datasets = []
for df in datafiles:
datasets.append(load_data(df, auto_data))
return datasets
def test_data_reload_no_file():
data = b'#a, b\n0, 1\n2, 3\n3, 4\n5, 6\n7, 8'
with make_file(data, '.csv') as fname:
d = df.load_data(fname)
# file no longer exists
d._load_log.reload()
assert_array_equal(d['a'], [0, 2, 3, 5, 7])
def test_data_reload_no_file():
data = b'#a, b\n0, 1\n2, 3\n3, 4\n5, 6\n7, 8'
with make_file(data, '.csv') as fname:
d = df.load_data(fname)
# file no longer exists
d._load_log.reload()
assert_array_equal(d['a'], [0, 2, 3, 5, 7])
def load_data_files(datafiles):
"""Load data files and return a DataCollection"""
import glue
from glue.core.data_factories import auto_data, load_data
dc = glue.core.DataCollection()
for df in datafiles:
dc.append(load_data(df, auto_data))
return dc
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 load_data_files(datafiles):
"""Load data files and return a DataCollection"""
import glue
from glue.core.data_factories import auto_data, load_data
dc = glue.core.DataCollection()
for df in datafiles:
dc.append(load_data(df, auto_data))
return dc
def test_casalike():
data = 'x\xda\xed\x98Qo\xa3F\x10\xc7\xfbQ\xe6\xcd\x89\xce`v\xd9] R\x1f0\xde$$\xd8\xf8\x80Xq_*.!\xa9%\x1b\xa7@zM?}w\xc1v.\x97\xf8\xae\xc6\xfb\xd0\x07\xfe\xf2\x83e\xc9?\r\xf3\x1fvf6\xf6\xc7\xd3\x80\x03\xfc\n\x1f(\x81\x01\xdc\xad\xf3\x87u\xb1*\xa1Z\xc3\xb9\x9f\xc4PVi~\x9f\x16\xf7\xf0\xa1\x86~2\xf5o?\xe4i\x8c\x08^Z\x14\xe9\x0b\xdc\xa7U\n\xd5\xcbS\x06?\xd1\xc4\xbd\xf5c\xd8\x13\x9f\xe4\xe5\xcf\xab/Y\x01\xeb\x87-y\xb1\xca\xf2r\xb1\xce\xcb\xfd<\xb4\x87\x87\xe1`\xd5<\xac\x98g*\xe6\x91\xbd\xf9;X\xc3\xb1{\x05\x1b?\x98\xce,\xc2lF\x0cj:\x84kF+\x9e?y\xe5\x99\x14[\x84\xda\x98\x98\x8e\xd1\x927ua\xc3\xd3\x88Nm\xdbf\xb6i"\xc7p\xf8\'\x03\xb5\xe0%\xf3)\xafy=?\xafdeU/=h\xafpx\xc5\xbd\xa4\xe1y\x00&\xc1p\x0cN\xb9\x867\x13?i\x9e\xf7j>\x18rw\xfc&\xbe\xc1\xb0X<\xfeQ\xe5YY\xc2\xc9\xd3\xe2\xefly\n\xcf\xf9\xa2\xda\xcb\xe3\x9fo\xfcIx[\xfb\x8bu\xe3\x1b\t?\xcc\xc3\xe3\x8b\xdc\x11\x8f\xe7\xb1\x8c\xef\xfc\x9a\xd6?\x1d\x95\xbf \x9cLCq\x00\xca\xf8\x90n\xbf\x8d\xaf\xc5\xfb\x16\xb8\xc9\x8e\xc7\x04\xcfa\xc8\xa0\x94:\x94\xb5\xab\xbf\xa9g\x18H|\x9a\xf8\xbe\xcb\x9f\xd1\x8e\x877<C\x11\xcfT\xcc#\x8ay"\x7fX%\x0foxH]\xfe\xb0\xe2\xfca\xc5\xf93\x15\xe7\xcfT\\\x7f\xa6J?\x88\xe2\xf8D\xfe\x88\xe2\xfc\x11\xc5\xf9#\x8a\xeb\x8f\xa8\xf2\xc3\x93\xfd\xb7\x9e\xd7z\x91\xabiZ\xecO\x8e:\xef\xbdh\xe6\x06\x9b\xf9\x8f\x8a\xf9\xc5\xc0\x88 f!j\xb4;\x9f\xbd\x11\x0f\x92\x86\xa71\xdd!\xaf\x12\xf3\x0bm\x13\x9f\x18\x9fw\xf1!\xf3\xd8~\xe4\xc9~\xde\xe4\xef>{<\xbe_\xd6~\xd4\xf3no\xc4=E~`P\xd6/k?v<E~`\xc5~`\xc5~\xd4\xfbBo\x16N\x93\xe3y\xd2\x8ff\xff\xd0\xf0n\x1e\xc2\x04!\xf1\xbc\xb4\xa5\x1f\x9b}\xa69\x0f\x10c\x16\xc3\xd4\x12<\xd2\xd2\x8f7\xbc#\xcf\x17\xe9G\x93\xbf\xd5\xa0T\xe4G\xbdo\xf5\xe2$\xbc\xe6\xb1\n?\x88\xc2\xe7\x95~\xa8\xe4I?\x88b?\x9a\xfcm\x7f:*\x7f\xd3\x19\xfe\x1dm\xf6K%\xfdM\xf0\xb0B^\xc4\xe3\xe4<\xfa\\\xf3L\x9dP\x87\x99\xf6\x96\x87\x0c\x18DYY\xc1y\x91\xfd\xf9\x9c\xe5w/pr\xf9\xcf\xe9\x0fy\xf1\x94{\x9b\xfdh\xe8Fs\x8fO\x927\xfb[\xfc\x94\xddUE\xba\x84"{\xc8\n\xc1\xcc\xe0\xa1HW{\xefa\xdc \x91\x15\xb8\x8d\x8fa\xdb\xc2\x98"j\xa3&>wYeE\x9eV\x12\xb3\r\xf2\x15\xfdW\xba|\xce\xde\xf1\xe4\x05\xd1\x9ez\xf9\t\xaf\xde8\xdf\xf0f<\x88\xf8\xf9\xde\xfb\x92\x01\x82 \x8e\xfa\xe2\xdb%\x0f\xfa`\x8a\xfd;\xee\xc3\'L\x19D\xe9\xfdb\xfd\xbe\xfe\xc2\xf1X\xe4\x0c\xee\xd22\xbd[\x17\x19\xe4\xeb\\\xdb]y=\x97\xe9cv\x06D@G}\xa0p\xc1\xc3>0\x88\xc3\x9b>Xp!\xf2\xf4\xee\n\x8d\x07<\xf6\xc2\xa9\xf0\x83\xcf\x02\xf7\xe8z\x16\xf1\xf3h\xc6#\xd9\x7f\x0b\x1d\xdc\x95\x8c\x0c\xdc\xdd\xb7\xeb\xeci\x99\x1dpC1r\x13\xae\t\xa8\xe0a\x03!M|\xb0\x95\x18\xe8\x8c\x18g\x86\xa5\xd3\xda\x9a\x03"N\xfc\xf1v?\xbfI<P\xf1\xbc\x91\x0b\xea\xe65\xc1\x13s\x8b\xbayC\xf0D\x11h\xb7\xb2_"\x9d\x89\x14R\xd1\xdfL\xf1\x9a\x08\x1ek\xcd\x9bK\x1e\xd5\r\x82\x1c\xdbvlf0v\x14\xef\xb7\xe6\xfd\xa5\x94\xd8\x96\x83Mfc\xd4\x8e\'\xeb\xa5\xb9_\x93\xf5\x82\xc5L%J&A\xd6\x191\xcf(\xd6\x19\xa5u\xbd\x0cF\xf2\x15\xae\xef\x8b\x1f\x16\xcb\x0c\xbe\xa6%|-\x16U\x95\xe5\xdf\xc5\x17\xf9\x17\xf2\x06P\xf0<7v\xe5\x19\xa3\x1bp"\xde\xb8/\xe2o\x05r\x18\xc3\xa7\x07T\xd0\xa5/\xdan4\x87\x1a\x16\'n\x94@\x10^$\xee0\xe0\xad\xea\x8fOF\xd0\xa9S\xa7N\x9d:u\xea\xd4\xa9\xd3\xffE\xbft\xea\xd4\xa9S\xa7N\x9d:u\xea\xd4\xe9?\xeb_\xdc?$\x07'
with make_file(data, '.fits', decompress=True) as fname:
d = df.load_data(fname, factory=df.casalike_cube)
assert d.shape == (1, 2, 2, 2)
d['STOKES 0']
d['STOKES 1']
d['STOKES 2']
d['STOKES 3']
def test_unstruc_npy_load(tmpdir):
data = np.array([[152.2352, -21.513], [21.412, 35.1341]], dtype='f8')
with open(tmpdir.join('test.npy').strpath, 'wb') as f:
np.save(f, data)
f.seek(0)
data2 = df.load_data(f.name)
assert_array_equal(data, data2['array'])
assert data2.label == 'test'
def load_data(self, path):
"""
Given a path to a file, load the file as a Data object and add it to
the current session.
This returns the added `Data` object.
"""
d = load_data(path)
self.add_datasets(self.data_collection, d)
return d
def load_ISO(dc, ga) -> '[ dc, isochrones ]':
globals()['gapp'] = ga # set ga for use later
dc.append(load_data('isochrones.csv'))
isochrones = dc[0]
# dc.append(load_data('profilesDF.csv'))
# profiles = dc[1]
# dc.append(load_data('descDF.csv'))
# desc = dc[2]
globals()['dclist'] = [dc, isochrones]
return dclist
def test_casalike():
data = 'x\xda\xed\x98Qo\xa3F\x10\xc7\xfbQ\xe6\xcd\x89\xce`v\xd9] R\x1f0\xde$$\xd8\xf8\x80Xq_*.!\xa9%\x1b\xa7@zM?}w\xc1v.\x97\xf8\xae\xc6\xfb\xd0\x07\xfe\xf2\x83e\xc9?\r\xf3\x1fvf6\xf6\xc7\xd3\x80\x03\xfc\n\x1f(\x81\x01\xdc\xad\xf3\x87u\xb1*\xa1Z\xc3\xb9\x9f\xc4PVi~\x9f\x16\xf7\xf0\xa1\x86~2\xf5o?\xe4i\x8c\x08^Z\x14\xe9\x0b\xdc\xa7U\n\xd5\xcbS\x06?\xd1\xc4\xbd\xf5c\xd8\x13\x9f\xe4\xe5\xcf\xab/Y\x01\xeb\x87-y\xb1\xca\xf2r\xb1\xce\xcb\xfd<\xb4\x87\x87\xe1`\xd5<\xac\x98g*\xe6\x91\xbd\xf9;X\xc3\xb1{\x05\x1b?\x98\xce,\xc2lF\x0cj:\x84kF+\x9e?y\xe5\x99\x14[\x84\xda\x98\x98\x8e\xd1\x927ua\xc3\xd3\x88Nm\xdbf\xb6i"\xc7p\xf8\'\x03\xb5\xe0%\xf3)\xafy=?\xafdeU/=h\xafpx\xc5\xbd\xa4\xe1y\x00&\xc1p\x0cN\xb9\x867\x13?i\x9e\xf7j>\x18rw\xfc&\xbe\xc1\xb0X<\xfeQ\xe5YY\xc2\xc9\xd3\xe2\xefly\n\xcf\xf9\xa2\xda\xcb\xe3\x9fo\xfcIx[\xfb\x8bu\xe3\x1b\t?\xcc\xc3\xe3\x8b\xdc\x11\x8f\xe7\xb1\x8c\xef\xfc\x9a\xd6?\x1d\x95\xbf \x9cLCq\x00\xca\xf8\x90n\xbf\x8d\xaf\xc5\xfb\x16\xb8\xc9\x8e\xc7\x04\xcfa\xc8\xa0\x94:\x94\xb5\xab\xbf\xa9g\x18H|\x9a\xf8\xbe\xcb\x9f\xd1\x8e\x877<C\x11\xcfT\xcc#\x8ay"\x7fX%\x0foxH]\xfe\xb0\xe2\xfca\xc5\xf93\x15\xe7\xcfT\\\x7f\xa6J?\x88\xe2\xf8D\xfe\x88\xe2\xfc\x11\xc5\xf9#\x8a\xeb\x8f\xa8\xf2\xc3\x93\xfd\xb7\x9e\xd7z\x91\xabiZ\xecO\x8e:\xef\xbdh\xe6\x06\x9b\xf9\x8f\x8a\xf9\xc5\xc0\x88 f!j\xb4;\x9f\xbd\x11\x0f\x92\x86\xa71\xdd!\xaf\x12\xf3\x0bm\x13\x9f\x18\x9fw\xf1!\xf3\xd8~\xe4\xc9~\xde\xe4\xef>{<\xbe_\xd6~\xd4\xf3no\xc4=E~`P\xd6/k?v<E~`\xc5~`\xc5~\xd4\xfbBo\x16N\x93\xe3y\xd2\x8ff\xff\xd0\xf0n\x1e\xc2\x04!\xf1\xbc\xb4\xa5\x1f\x9b}\xa69\x0f\x10c\x16\xc3\xd4\x12<\xd2\xd2\x8f7\xbc#\xcf\x17\xe9G\x93\xbf\xd5\xa0T\xe4G\xbdo\xf5\xe2$\xbc\xe6\xb1\n?\x88\xc2\xe7\x95~\xa8\xe4I?\x88b?\x9a\xfcm\x7f:*\x7f\xd3\x19\xfe\x1dm\xf6K%\xfdM\xf0\xb0B^\xc4\xe3\xe4<\xfa\\\xf3L\x9dP\x87\x99\xf6\x96\x87\x0c\x18DYY\xc1y\x91\xfd\xf9\x9c\xe5w/pr\xf9\xcf\xe9\x0fy\xf1\x94{\x9b\xfdh\xe8Fs\x8fO\x927\xfb[\xfc\x94\xddUE\xba\x84"{\xc8\n\xc1\xcc\xe0\xa1HW{\xefa\xdc \x91\x15\xb8\x8d\x8fa\xdb\xc2\x98"j\xa3&>wYeE\x9eV\x12\xb3\r\xf2\x15\xfdW\xba|\xce\xde\xf1\xe4\x05\xd1\x9ez\xf9\t\xaf\xde8\xdf\xf0f<\x88\xf8\xf9\xde\xfb\x92\x01\x82 \x8e\xfa\xe2\xdb%\x0f\xfa`\x8a\xfd;\xee\xc3\'L\x19D\xe9\xfdb\xfd\xbe\xfe\xc2\xf1X\xe4\x0c\xee\xd22\xbd[\x17\x19\xe4\xeb\\\xdb]y=\x97\xe9cv\x06D@G}\xa0p\xc1\xc3>0\x88\xc3\x9b>Xp!\xf2\xf4\xee\n\x8d\x07<\xf6\xc2\xa9\xf0\x83\xcf\x02\xf7\xe8z\x16\xf1\xf3h\xc6#\xd9\x7f\x0b\x1d\xdc\x95\x8c\x0c\xdc\xdd\xb7\xeb\xeci\x99\x1dpC1r\x13\xae\t\xa8\xe0a\x03!M|\xb0\x95\x18\xe8\x8c\x18g\x86\xa5\xd3\xda\x9a\x03"N\xfc\xf1v?\xbfI<P\xf1\xbc\x91\x0b\xea\xe65\xc1\x13s\x8b\xbayC\xf0D\x11h\xb7\xb2_"\x9d\x89\x14R\xd1\xdfL\xf1\x9a\x08\x1ek\xcd\x9bK\x1e\xd5\r\x82\x1c\xdbvlf0v\x14\xef\xb7\xe6\xfd\xa5\x94\xd8\x96\x83Mfc\xd4\x8e\'\xeb\xa5\xb9_\x93\xf5\x82\xc5L%J&A\xd6\x191\xcf(\xd6\x19\xa5u\xbd\x0cF\xf2\x15\xae\xef\x8b\x1f\x16\xcb\x0c\xbe\xa6%|-\x16U\x95\xe5\xdf\xc5\x17\xf9\x17\xf2\x06P\xf0<7v\xe5\x19\xa3\x1bp"\xde\xb8/\xe2o\x05r\x18\xc3\xa7\x07T\xd0\xa5/\xdan4\x87\x1a\x16\'n\x94@\x10^$\xee0\xe0\xad\xea\x8fOF\xd0\xa9S\xa7N\x9d:u\xea\xd4\xa9\xd3\xffE\xbft\xea\xd4\xa9S\xa7N\x9d:u\xea\xd4\xe9?\xeb_\xdc?$\x07'
with make_file(data, '.fits', decompress=True) as fname:
d = df.load_data(fname, factory=df.casalike_cube)
assert d.shape == (1, 2, 2, 2)
d['STOKES 0']
d['STOKES 1']
d['STOKES 2']
d['STOKES 3']
def test_dendrogram_load():
from ..data_factory import load_dendro
data = b"""x\xda\xed\xda]K\xc2`\x18\xc6\xf1^\xbe\xc8}fA\xe4[X\x14\x1eX\x99<\x90S\xd8\x02O\x9f\xf2Q<\xd8&\xcf&\xe4\xb7\xcft\x82\xc9\xe6\x1be\x91\xff\xdf\xc9\xc5\xd8v\xc1vt\xeff\xaej\xb6\x9f\xeb"UI\xe1I^\xde\xc2\xa0\x17Z?\x928\x94\'\xe5\xb9\x12\xc5:\xe8j\xdb\x95T\xf7\xcak\xabNF\xdf\xcd\xa4O[\xab\xc7\xd2\xd5\xb1\x96x<4\xb2\x86S\xeb(W2\xfa\n\x93\xbe`\xe4\xbf\x1a+ao\xde<\xf0M\x10\r\xc2 J\xed\xabw\xbc\xba\xf3\x98\xf9\xbc[\x9b\x96\x01\x00\x00\xe0`|\x8e\x93\xaej9U\xc9\xa9f\xad1\x99\xa4%\xb7p:/\xca\xd7}#\xe6=\x9eM\xa5\xeb\xfaV\xcd\xcf\x95\xabo\x9e\x9f\x8b\xdb\xcf\xcf\xd3\xbebF_e\xfb\xf7\xd7~h\xbd8\xdeF\xf3\xfdP[\xed\x9b\xd8\xd8hE_cU\xdf\xd7\xe7\xed\xdbp4\x8c\x98\xef\x01\x00\x00\xf6\xeah\xe68\xc9\x93$O3\x8e\xe7\xd7\x01\x00\x00\x00\x07i\x9f\xfb\xe7r\x89\xfd3\xfbg\x00\x00\x80\x7f\xb1\x7fN\xdbA\x03\x00\x00\x00\xf8\xc5\xfd\xf3_\xff\xff\xb9t\xcd\xfe\x19\x00\x00\x00\x1b\xed\x9f\xcf\x96\xb2\x98\xe4m\x92\xe5$/\x93,d\xe4E\x92\xa5\x1d\xef?_:\xde\xf5\xfe;\xbe\x8c\x00\x00\x00\xf0\x13>\x00\x8e\xbe x"""
with make_file(data, 'fits', decompress=True) as fname:
dg, im = df.load_data(fname, factory=load_dendro)
assert_array_equal(im['intensity'], [1, 2, 3, 2, 3, 1])
assert_array_equal(im['structure'], [0, 0, 1, 0, 2, 0])
assert_array_equal(dg['parent'], [-1, 0, 0])
assert_array_equal(dg['height'], [3, 3, 3])
assert_array_equal(dg['peak'], [3, 3, 3])
def load_data(self, path):
"""
Given a path to a file, load the file as a Data object and add it to
the current session.
This returns the added `Data` object.
"""
d = load_data(path)
self.add_datasets(self.data_collection, d)
return d
def test_data_reload_no_file():
data = b'#a, b\n0, 1\n2, 3\n3, 4\n5, 6\n7, 8'
with make_file(data, '.csv') as fname:
d = df.load_data(fname)
# file no longer exists
with pytest.warns(UserWarning, match='Could not reload'):
d._load_log.reload()
assert_array_equal(d['a'], [0, 2, 3, 5, 7])
def test_dendrogram_load():
from ..data_factory import load_dendro
data = b"""x\xda\xed\xda]K\xc2`\x18\xc6\xf1^\xbe\xc8}fA\xe4[X\x14\x1eX\x99<\x90S\xd8\x02O\x9f\xf2Q<\xd8&\xcf&\xe4\xb7\xcft\x82\xc9\xe6\x1be\x91\xff\xdf\xc9\xc5\xd8v\xc1vt\xeff\xaej\xb6\x9f\xeb"UI\xe1I^\xde\xc2\xa0\x17Z?\x928\x94\'\xe5\xb9\x12\xc5:\xe8j\xdb\x95T\xf7\xcak\xabNF\xdf\xcd\xa4O[\xab\xc7\xd2\xd5\xb1\x96x<4\xb2\x86S\xeb(W2\xfa\n\x93\xbe`\xe4\xbf\x1a+ao\xde<\xf0M\x10\r\xc2 J\xed\xabw\xbc\xba\xf3\x98\xf9\xbc[\x9b\x96\x01\x00\x00\xe0`|\x8e\x93\xaej9U\xc9\xa9f\xad1\x99\xa4%\xb7p:/\xca\xd7}#\xe6=\x9eM\xa5\xeb\xfaV\xcd\xcf\x95\xabo\x9e\x9f\x8b\xdb\xcf\xcf\xd3\xbebF_e\xfb\xf7\xd7~h\xbd8\xdeF\xf3\xfdP[\xed\x9b\xd8\xd8hE_cU\xdf\xd7\xe7\xed\xdbp4\x8c\x98\xef\x01\x00\x00\xf6\xeah\xe68\xc9\x93$O3\x8e\xe7\xd7\x01\x00\x00\x00\x07i\x9f\xfb\xe7r\x89\xfd3\xfbg\x00\x00\x80\x7f\xb1\x7fN\xdbA\x03\x00\x00\x00\xf8\xc5\xfd\xf3_\xff\xff\xb9t\xcd\xfe\x19\x00\x00\x00\x1b\xed\x9f\xcf\x96\xb2\x98\xe4m\x92\xe5$/\x93,d\xe4E\x92\xa5\x1d\xef?_:\xde\xf5\xfe;\xbe\x8c\x00\x00\x00\xf0\x13>\x00\x8e\xbe x"""
with make_file(data, 'fits', decompress=True) as fname:
dg, im = df.load_data(fname, factory=load_dendro)
assert_array_equal(im['intensity'], [1, 2, 3, 2, 3, 1])
assert_array_equal(im['structure'], [0, 0, 1, 0, 2, 0])
assert_array_equal(dg['parent'], [-1, 0, 0])
assert_array_equal(dg['height'], [3, 3, 3])
assert_array_equal(dg['peak'], [3, 3, 3])
def test_unstruc_npy_load(tmpdir):
data = np.array([[152.2352, -21.513], [21.412, 35.1341]], dtype='f8')
with open(tmpdir.join('test.npy').strpath, 'wb') as f:
np.save(f, data)
f.seek(0)
data2 = df.load_data(f.name)
assert_array_equal(data, data2['array'])
assert data2.label == 'test'
def test_data_reload():
data = b'#a, b\n0, 1\n2, 3\n3, 4\n5, 6\n7, 8'
with make_file(data, '.csv') as fname:
d = df.load_data(fname)
coords_old = d.coords
with open(fname, 'w') as f2:
f2.write('#a, b\n0, 0\n0, 0\n0, 0\n0, 0\n0, 0')
d._load_log.reload()
assert_array_equal(d['a'], [0, 0, 0, 0, 0])
assert_array_equal(d['b'], [0, 0, 0, 0, 0])
def test_load_data():
data = b'x\xda\xedX=h\x14A\x14\xfef\xf7\xfeI.\xbb\xe7EL\x84\xb0\x04\x8c\x1a\xd3\x04\x1b\x9bdOAS\x19\xa2\x16\x8a\x08z1\x0bJ\xc2E\x8e\x14\xc6\xc6h\xbcR\x10\xac\x14\x9b@\x1a\x9b\xa8\x8d?\x18A;\x0b!\xa2\x85 \x08w\n6V\x82B\x8a\xe4\xd67og\xcd%^q\x07\x1aT\xe6[\xe6\xcd\xdb7\xdf\xdby\xc7\xbcy{\xb3\xaf\x97\xec\xf2\xdc\x83\xce\n6`\x10&\xaa~\x12\xb1\x1a\x9b\xa0\x96\x0co,\xd0\xb8\xefK5\xec\x13\xd4|\x8d\x7f\n\xc9\x04-d,\x8a\xa7\xad\xaf\xe2r\r\xe5zW`\xe0~\xe4\x05I\xe0#\xb5S\xb8\x80\xe1\xc9\x82\xe7l"\x0ep\x0cy!c\x18 )p\x87,itpT\x19\x96gYnay\x8f\x99\x8b,sl\xb9\xcer\x80\xb8eq\x12K\xeep\xef>\x95\xc5\'\x8cn\x1eKC>\xf7\x11\xfb\xbcgK?\xda\xf1Rf\xf1\xe5\x1b"\xe0F\xb1\xbfx>?\xf1\x17\x0c\x08\x1aX4\x1a\xf3\xe8\x8a\xb4`\x1e\xb4\xa0C^\xc1+\xe6\'\xca\xc8\xd2\xca\xce\xe3\xbb\xef\x00\xdf\xc2-\xfc\xdc\xd1\xf6\xcd\xb5\x0b\x90}y\xbd=\xcevZ\xd8e\xdb\xce99\'\xb4\'\xea\xf0o\x1a\x11`\x06\xfe\x19\xde\x11%\xca\xe0\xcff\xb0k\x8f\x9d\xf3\xbc\xa9\xfe\x15*\xcb2E\x8c\xa0Z_\xa46\x9d\x92U\x9bw\xb9\xb5n\x97\xb7r\xf6\xb7\x90\x1cC\x1b\xeb6\xfbY4\xf3\xca\xdd\xafo\x0e\x8f\x8e\xb8\xa7\xd92\xc3\x95=\xa8\xff;d\x04\xf0qEz\x90s\x9aG",e(\xbd\xec\xb1\x87\xe5U~\xeav\xd6;Yf)A\xa9\xef\x19iW\xca\xa1Y\xe6\\\xe3\xd1\x1e\x9ag/\xe3\xad\xbb\xb3F\xdfEz\xe9\xcb\x91\xc7]\xa5O\xeen\xd2\x17\x86*\x97\xb2\x0b\xef\xdc9t\xd3\xfbh\x8c\xfc\xe55\x8b>\xd1\'n\xdf\x92x\xe2\x86\xbdP\xb5\xe2\x03\xcb\x8e_\xeaF\xc2\xb0T\xec\xbez\xc9\xb5a\x15)\xfe\xb56\x1b\x82;\x83\xee\x84\xe2\x8b\r\xfcg\xd8\xc66[\xd5\xa6\x18\xcb\xc0K\xf2\x8d:|\x83\xf9k\xcc\x0c\xd2?\xf9f\x1d\xbe\xc9\xfc5f\x86.\xc9\x1f42x\xc8U!W\xf3\xa6NACCCCCCCCC\xa3\x1e\x84:-\x98\xea_|T\x9d\x0e\xe2\xea\xbb\xce*\xb5\xaa\xfeL\xf2\xdf\xe2(&\xe9\x9a\xa2s\xe6A\x14\xa8/b\xba\xa9\xfc\xd9\x8a\xa8\x08\x9f%\x1a\xf4\t\xbf\x17J\x1c\xa7\xd9\x8b\x18\xc7(\xc71\xdet\xfe\xd2\xe9N\xd4\xfe\x9e\x86\x1d\xad\xdf\xb7\x85\x9a\x9d\xbf\xdaL\x9c\x7fx\xfe\x1f\xf5\x81\xcaV'
with make_file(data, '.xlsx', decompress=True) as fname:
d = df.load_data(fname)
assert_array_equal(d['x'], [1, 2, 3])
assert_array_equal(d['y'], [2, 3, 4])
assert d.label.endswith(':Sheet1')
def test_load_data():
data = b'x\xda\xedX=h\x14A\x14\xfef\xf7\xfeI.\xbb\xe7EL\x84\xb0\x04\x8c\x1a\xd3\x04\x1b\x9bdOAS\x19\xa2\x16\x8a\x08z1\x0bJ\xc2E\x8e\x14\xc6\xc6h\xbcR\x10\xac\x14\x9b@\x1a\x9b\xa8\x8d?\x18A;\x0b!\xa2\x85 \x08w\n6V\x82B\x8a\xe4\xd67og\xcd%^q\x07\x1aT\xe6[\xe6\xcd\xdb7\xdf\xdby\xc7\xbcy{\xb3\xaf\x97\xec\xf2\xdc\x83\xce\n6`\x10&\xaa~\x12\xb1\x1a\x9b\xa0\x96\x0co,\xd0\xb8\xefK5\xec\x13\xd4|\x8d\x7f\n\xc9\x04-d,\x8a\xa7\xad\xaf\xe2r\r\xe5zW`\xe0~\xe4\x05I\xe0#\xb5S\xb8\x80\xe1\xc9\x82\xe7l"\x0ep\x0cy!c\x18 )p\x87,itpT\x19\x96gYnay\x8f\x99\x8b,sl\xb9\xcer\x80\xb8eq\x12K\xeep\xef>\x95\xc5\'\x8cn\x1eKC>\xf7\x11\xfb\xbcgK?\xda\xf1Rf\xf1\xe5\x1b"\xe0F\xb1\xbfx>?\xf1\x17\x0c\x08\x1aX4\x1a\xf3\xe8\x8a\xb4`\x1e\xb4\xa0C^\xc1+\xe6\'\xca\xc8\xd2\xca\xce\xe3\xbb\xef\x00\xdf\xc2-\xfc\xdc\xd1\xf6\xcd\xb5\x0b\x90}y\xbd=\xcevZ\xd8e\xdb\xce99\'\xb4\'\xea\xf0o\x1a\x11`\x06\xfe\x19\xde\x11%\xca\xe0\xcff\xb0k\x8f\x9d\xf3\xbc\xa9\xfe\x15*\xcb2E\x8c\xa0Z_\xa46\x9d\x92U\x9bw\xb9\xb5n\x97\xb7r\xf6\xb7\x90\x1cC\x1b\xeb6\xfbY4\xf3\xca\xdd\xafo\x0e\x8f\x8e\xb8\xa7\xd92\xc3\x95=\xa8\xff;d\x04\xf0qEz\x90s\x9aG",e(\xbd\xec\xb1\x87\xe5U~\xeav\xd6;Yf)A\xa9\xef\x19iW\xca\xa1Y\xe6\\\xe3\xd1\x1e\x9ag/\xe3\xad\xbb\xb3F\xdfEz\xe9\xcb\x91\xc7]\xa5O\xeen\xd2\x17\x86*\x97\xb2\x0b\xef\xdc9t\xd3\xfbh\x8c\xfc\xe55\x8b>\xd1\'n\xdf\x92x\xe2\x86\xbdP\xb5\xe2\x03\xcb\x8e_\xeaF\xc2\xb0T\xec\xbez\xc9\xb5a\x15)\xfe\xb56\x1b\x82;\x83\xee\x84\xe2\x8b\r\xfcg\xd8\xc66[\xd5\xa6\x18\xcb\xc0K\xf2\x8d:|\x83\xf9k\xcc\x0c\xd2?\xf9f\x1d\xbe\xc9\xfc5f\x86.\xc9\x1f42x\xc8U!W\xf3\xa6NACCCCCCCCC\xa3\x1e\x84:-\x98\xea_|T\x9d\x0e\xe2\xea\xbb\xce*\xb5\xaa\xfeL\xf2\xdf\xe2(&\xe9\x9a\xa2s\xe6A\x14\xa8/b\xba\xa9\xfc\xd9\x8a\xa8\x08\x9f%\x1a\xf4\t\xbf\x17J\x1c\xa7\xd9\x8b\x18\xc7(\xc71\xdet\xfe\xd2\xe9N\xd4\xfe\x9e\x86\x1d\xad\xdf\xb7\x85\x9a\x9d\xbf\xdaL\x9c\x7fx\xfe\x1f\xf5\x81\xcaV'
with make_file(data, '.xlsx', decompress=True) as fname:
d = df.load_data(fname)
assert_array_equal(d['x'], [1, 2, 3])
assert_array_equal(d['y'], [2, 3, 4])
assert d.label.endswith(':Sheet1')
def test_npy_load(tmpdir):
data = np.array(
[("a", 152.2352, -21.513), ("b", 21.412, 35.1341)], dtype=[("name", "|S1"), ("ra", "f8"), ("dec", "f8")]
)
with open(tmpdir.join("test.npy").strpath, "wb") as f:
np.save(f, data)
f.seek(0)
data2 = df.load_data(f.name)
for name in data.dtype.names:
assert_array_equal(data[name], data2[name])
def test_sextractor_factory():
data = b"""# 1 NUMBER Running object number
# 2 X_IMAGE Object position along x [pixel]
# 3 Y_IMAGE Object position along y [pixel]
1 2988.249 2.297
2 2373.747 3.776
3 3747.026 4.388"""
with make_file(data, '.cat') as fname:
d = df.load_data(fname, factory=df.sextractor_factory)
assert_allclose(d['NUMBER'], [1, 2, 3])
assert_allclose(d['X_IMAGE'], [2988.249, 2373.747, 3747.026])
assert_allclose(d['Y_IMAGE'], [2.297, 3.776, 4.388])
def test_data_reload_no_file():
data = b'#a, b\n0, 1\n2, 3\n3, 4\n5, 6\n7, 8'
with make_file(data, '.csv') as fname:
d = df.load_data(fname)
# file no longer exists
with warnings.catch_warnings(record=True) as w:
d._load_log.reload()
assert len(w) == 1
assert str(w[0].message).startswith('Could not reload')
assert_array_equal(d['a'], [0, 2, 3, 5, 7])
def test_data_reload():
data = b'#a, b\n0, 1\n2, 3\n3, 4\n5, 6\n7, 8'
with make_file(data, '.csv') as fname:
d = df.load_data(fname)
coords_old = d.coords
with open(fname, 'w') as f2:
f2.write('#a, b\n0, 0\n0, 0\n0, 0\n0, 0\n0, 0')
d._load_log.reload()
assert_array_equal(d['a'], [0, 0, 0, 0, 0])
assert_array_equal(d['b'], [0, 0, 0, 0, 0])
assert d.coords is not coords_old
def test_container_fits():
from astropy.io import fits
expected = {
'generic[ATAB]': Expected(
shape=(20,),
ndim=1
),
'generic[TWOD]': Expected(
shape=(4, 5),
ndim=2
),
'generic[ONED]': Expected(
shape=(20,),
ndim=1
),
'generic[THREED]': Expected(
shape=(2, 2, 5),
ndim=3
)
}
# Make sure the factory gets used
d_set = df.load_data(os.path.join(DATA, 'generic.fits'),
factory=df.fits_reader)
_assert_equal_expected(d_set, expected)
# Check that fits_reader takes HDUList objects
with fits.open(os.path.join(DATA, 'generic.fits')) as hdulist:
d_set = fits_reader(hdulist)
_assert_equal_expected(d_set, expected)
# Sometimes the primary HDU is empty but with an empty array rather than
# None
hdulist[0].data = np.array([])
d_set = fits_reader(hdulist)
_assert_equal_expected(d_set, expected)
# Check that exclude_exts works
d_set = fits_reader(hdulist, exclude_exts=['TWOD'])
expected_reduced = deepcopy(expected)
expected_reduced.pop('generic[TWOD]')
_assert_equal_expected(d_set, expected_reduced)
