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Merge pull request 'Fixed bad-pix files generation' (#1) from amukhin/nuwavdet:master into master

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Reviewed-on: #1
This commit is contained in:
Андрей Мухин 2023-05-24 16:29:46 +03:00
commit a58a1f612e
1 changed files with 625 additions and 626 deletions
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23
nuwavdet/nuwavdet.py
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@ -235,7 +235,7 @@ class Observation:
resized_coeff = (coeff).reshape(2, 2).repeat(180, 0).repeat(180, 1)
return resized_coeff
def get_data(self, file, E_borders=[3, 20]):
def get_data(self, file, E_borders=[3, 20], generate_mask=True):
"""
Returns masked array with DET1 image data for given energy band.
Mask is created from observations badpix tables and to mask the border and gaps.
@ -248,10 +248,12 @@ class Observation:
data_mask = data[np.logical_not(idx_mask)]
build_hist = lambda array: np.histogram2d(array['DET1Y'], array['DET1X'], 360, [[0, 360], [0, 360]])[0]
output = build_hist(data_output)
mask = build_hist(data_mask)
mask = np.logical_or(mask, add_borders(output))
mask = np.logical_or(mask, self.get_bad_pix(file))
return output, mask
if generate_mask:
mask = build_hist(data_mask)
mask = np.logical_or(mask, add_borders(output))
mask = np.logical_or(mask, self.get_bad_pix(file))
return output, mask
return output
def get_bad_pix(self, file, threshold=0.9):
"""
@ -330,7 +332,7 @@ class Observation:
"""
Returns a hdu_list with positions of masked pixels in RAW coordinates.
"""
x_region, y_region = np.where(region)
y_region, x_region = np.where(region)
hdus = []
for i in range(4):
current_dir = os.path.dirname(__file__)
@ -411,12 +413,12 @@ def process(obs_path, thresh):
rem_region = np.logical_and(region, np.logical_not(obs.data.mask))
masked_obs = np.ma.masked_array(obs.data, mask=region)
good_lvl = np.zeros(bin_num, dtype=bool)
good_idx = 0
if obs.exposure > 1000:
wav_obs = obs.wavdecomp('atrous', thresh, occ_coeff=True)
wav_sum = wav_obs[2:-1].sum(0)
occ_coeff = obs.get_coeff()
binary_arr = binary_array(bin_num)
good_idx = len(binary_arr) - 1
for idx, lvl in enumerate(binary_arr):
try:
@ -434,12 +436,9 @@ def process(obs_path, thresh):
rms[idx] = np.sqrt(((masked_obs-masked_obs.mean())**2).mean())
for idx in range(len(poiss_comp)):
if ((poiss_comp[idx] < poiss_comp[good_idx]) and
(poiss_comp[idx] < poiss_comp[-1] + 0.05) and
(rem_area[idx] > rem_area[-1])):
if ((poiss_comp[idx] < poiss_comp[-1] + 0.05) and
(rem_area[idx] > rem_area[good_idx])):
good_idx = idx
if good_idx == 0:
good_idx = len(binary_arr) - 1
good_lvl = binary_arr[good_idx]
try: