def test_sub_overscan():
datasec = np.zeros((10, 10), dtype=int)
datasec[:5, :-3] = 1
datasec[5:, :-3] = 2
oscan = np.zeros((10, 10), dtype=int)
oscan[:5, -3:] = 1
oscan[5:, -3:] = 2
raw = np.zeros((10, 10), dtype=float)
raw[datasec == 1] = 10.
raw[datasec == 2] = 20.
raw[oscan == 1] = 9.
raw[oscan == 2] = 19.
raw_sub, _ = procimg.subtract_overscan(raw,
datasec,
oscan,
method='median')
assert np.array_equal(raw_sub[datasec > 0], np.ones(np.sum(datasec > 0), dtype=float)), \
'Bad overscan subtraction'
var = np.ones((10, 10), dtype=float)
raw_sub, var_sub = procimg.subtract_overscan(raw,
datasec,
oscan,
method='median',
var=var)
assert np.array_equal(var_sub[datasec > 0],
np.full(np.sum(datasec > 0), np.pi/2/15, dtype=float)), \
'Bad variance calculation'
def bias_subtract(self, msbias, trim=True, force=False, par=None):
"""
Subtract the bias.
Parameters
----------
msbias : ndarray, str (optional)
If ndarray, the input is a Bias image
If str, the input is guides the Bias subtraction method e.g. 'overscan'
trim
Returns
-------
"""
# Check if the bias has already been subtracted
if (inspect.stack()[0][3] in self.steps) & (not force):
msgs.warn("Images already bias subtracted. Use force=True to reset proc_images "
"and do it again. Returning...")
return
# Set the parameters
if par is not None and not isinstance(par, pypeitpar.ProcessImagesPar):
raise TypeError('Provided ParSet for must be type ProcessImagesPar.')
if par is not None:
self.proc_par = par
# If trimming, get the image identifying amplifier used for the
# data section
datasec_img = self.spectrograph.get_datasec_img(self.files[0], det=self.det)
msgs.info("Bias subtracting your image(s)")
# Reset proc_images -- Is there any reason we wouldn't??
numamplifiers = self.spectrograph.detector[self.det-1]['numamplifiers']
for kk,image in enumerate(self.raw_images):
# Bias subtract (move here from procimg)
if isinstance(msbias, np.ndarray):
msgs.info("Subtracting bias image from raw frame")
# Trim?
if trim:
image = procimg.trim_frame(image, datasec_img < 1)
temp = image-msbias
elif isinstance(msbias, str) and msbias == 'overscan':
msgs.info("Using overscan to subtract")
temp = procimg.subtract_overscan(image, numamplifiers, self.datasec[kk],
self.oscansec[kk],
method=self.proc_par['overscan'],
params=self.proc_par['overscan_par'])
# Trim?
if trim:
temp = procimg.trim_frame(temp, datasec_img < 1)
else:
msgs.error('Could not subtract bias level with the input bias approach.')
# Save
if kk==0:
# Instantiate proc_images
self.proc_images = np.zeros((temp.shape[0], temp.shape[1], self.nloaded))
self.proc_images[:,:,kk] = temp.copy()
# Step
self.steps.append(inspect.stack()[0][3])
def subtract_overscan(self, force=False):
"""
Analyze and subtract the overscan from the image
Args:
force (bool, optional):
Force the processing even if the image was already processed
"""
step = inspect.stack()[0][3]
# Check if already trimmed
if self.steps[step] and (not force):
msgs.warn("Image was already trimmed")
temp = procimg.subtract_overscan(self.image, self.rawdatasec_img, self.oscansec_img,
method=self.par['overscan'],
params=self.par['overscan_par'])
# Fill
self.steps[step] = True
self.image = temp