def test_orders(instr, instrument, mode, files, settings, mask):
if len(files["orders"]) == 0:
pytest.skip(f"No order definition files found for instrument {instrument}")
files = files["orders"][0]
order_img, _ = instr.load_fits(files, mode, mask=mask)
settings = settings["orders"]
orders, column_range = mark_orders(
order_img,
min_cluster=settings["min_cluster"],
filter_size=settings["filter_size"],
noise=settings["noise"],
opower=settings["degree"],
border_width=settings["border_width"],
manual=False,
plot=False,
)
assert isinstance(orders, np.ndarray)
assert np.issubdtype(orders.dtype, np.floating)
assert orders.shape[1] == settings["degree"] + 1
assert isinstance(column_range, np.ndarray)
assert np.issubdtype(column_range.dtype, np.integer)
assert column_range.shape[1] == 2
assert np.all(column_range >= 0)
assert np.all(column_range <= order_img.shape[1])
assert orders.shape[0] == column_range.shape[0]
def orders(instrument, mode, extension, files, settings, mask, output_dir):
"""Load or if necessary calculate the order traces
Parameters
----------
instrument : str
instrument name
mode : str
observing mode
extension : int
fits data extension
files : dict(str:str)
calibration data files
settings : dict(str:obj)
settings for this run
mask : array(bool)
Bad pixel map
output_dir : str
output file directory
Returns
-------
orders : array(float) of size (norders, ndegree+1)
polynomial coefficients of the order tracing
column_range : array(int) of size (norders, 2)
valid columns that include traces/data
"""
settings = settings["orders"]
orderfile = os.path.join(output_dir, "test_orders.pkl")
try:
with open(orderfile, "rb") as file:
orders, column_range = pickle.load(file)
except FileNotFoundError:
files = files["orders"][0]
order_img, _ = util.load_fits(files,
instrument,
mode,
extension,
mask=mask)
orders, column_range = mark_orders(
order_img,
min_cluster=settings["min_cluster"],
filter_size=settings["filter_size"],
noise=settings["noise"],
opower=settings["degree"],
border_width=settings["border_width"],
manual=False,
plot=False,
)
with open(orderfile, "wb") as file:
pickle.dump((orders, column_range), file)
return orders, column_range
def test_orders(instrument, mode, extension, files, settings, mask):
"""
Test the order tracing for each test dataset
Parameters
----------
instrument : str
Instrument Name
mode : str
Current Mode
extension : int
fits extension for that mode
files : dict(str:str)
dict of files, sorted by type
settings : dict(str:obj)
configuration settings of this run
mask : array(bool) of size (n,)
Bad pixel mask
"""
files = files["orders"][0]
order_img, _ = util.load_fits(files,
instrument,
mode,
extension,
mask=mask)
settings = settings["orders"]
orders, column_range = mark_orders(
order_img,
min_cluster=settings["min_cluster"],
filter_size=settings["filter_size"],
noise=settings["noise"],
opower=settings["degree"],
border_width=settings["border_width"],
manual=False,
plot=False,
)
assert isinstance(orders, np.ndarray)
assert np.issubdtype(orders.dtype, np.floating)
assert orders.shape[1] == settings["degree"] + 1
assert isinstance(column_range, np.ndarray)
assert np.issubdtype(column_range.dtype, np.integer)
assert column_range.shape[1] == 2
assert np.all(column_range >= 0)
assert np.all(column_range <= order_img.shape[1])
assert orders.shape[0] == column_range.shape[0]
def test_simple():
img = np.full((100, 100), 1)
img[45:56, :] = 100
orders, column_range = mark_orders(
img, manual=False, opower=1, plot=False, border_width=0
)
assert orders.shape[0] == 1
assert np.allclose(orders[0], [0, 50])
assert column_range.shape[0] == 1
assert column_range[0, 0] == 0
assert column_range[0, 1] == 100
def test_orders(instr, instrument, mode, files, settings, mask):
if len(files["orders"]) == 0:
pytest.skip(
f"No order definition files found for instrument {instrument}")
order_img, _ = combine_frames(files["orders"], instrument, mode, mask=mask)
settings = settings["orders"]
orders, column_range = mark_orders(
order_img,
min_cluster=settings["min_cluster"],
min_width=settings["min_width"],
filter_size=settings["filter_size"],
noise=settings["noise"],
opower=settings["degree"],
degree_before_merge=settings["degree_before_merge"],
regularization=settings["regularization"],
closing_shape=settings["closing_shape"],
border_width=settings["border_width"],
manual=False,
auto_merge_threshold=settings["auto_merge_threshold"],
merge_min_threshold=settings["merge_min_threshold"],
sigma=settings["split_sigma"],
plot=False,
)
assert isinstance(orders, np.ndarray)
assert np.issubdtype(orders.dtype, np.floating)
assert orders.shape[1] == settings["degree"] + 1
assert isinstance(column_range, np.ndarray)
assert np.issubdtype(column_range.dtype, np.integer)
assert column_range.shape[1] == 2
assert np.all(column_range >= 0)
assert np.all(column_range <= order_img.shape[1])
assert orders.shape[0] == column_range.shape[0]
def test_parameters():
img = np.full((100, 100), 1)
img[45:56, :] = 100
with pytest.raises(TypeError):
mark_orders(None)
with pytest.raises(TypeError):
mark_orders(img, min_cluster="bla")
with pytest.raises(TypeError):
mark_orders(img, filter_size="bla")
with pytest.raises(ValueError):
mark_orders(img, filter_size=0)
with pytest.raises(TypeError):
mark_orders(img, noise="bla")
with pytest.raises(TypeError):
mark_orders(img, border_width="bla")
with pytest.raises(ValueError):
mark_orders(img, border_width=-1)
with pytest.raises(TypeError):
mark_orders(img, opower="bla")
with pytest.raises(ValueError):
mark_orders(img, opower=-1)