diff --git a/cal_tools/cal_tools/agipdlib.py b/cal_tools/cal_tools/agipdlib.py
index 212e5c64eb6231f042baf157281fff6db76e220a..e65d4952f21b66c143006f910b46ac17bf25db56 100644
--- a/cal_tools/cal_tools/agipdlib.py
+++ b/cal_tools/cal_tools/agipdlib.py
@@ -458,7 +458,7 @@ class AgipdCorrections:
for c in cells:
std = np.nanstd(data[cellid == c, ...], axis=0)
- mask_std[:, c, std == 0] |= BadPixels.DATA_STD_IS_ZERO.value
+ mask_std[:, c, std == 0] |= BadPixels.DATA_STD_IS_ZERO
def offset_correction(self, i_proc: int, first: int, last: int):
"""
@@ -659,7 +659,7 @@ class AgipdCorrections:
if self.corr_bools.get("zero_nans"):
bidx = ~np.isfinite(data)
data[bidx] = 0
- msk[bidx] |= BadPixels.VALUE_IS_NAN.value
+ msk[bidx] |= BadPixels.VALUE_IS_NAN
del bidx
# Add pixels with unrealistically high and low values to the mask.
@@ -667,7 +667,7 @@ class AgipdCorrections:
if self.corr_bools.get("zero_orange"):
bidx = (data < -1e7) | (data > 1e7)
data[bidx] = 0
- msk[bidx] |= BadPixels.VALUE_OUT_OF_RANGE.value
+ msk[bidx] |= BadPixels.VALUE_OUT_OF_RANGE
del bidx
# Mask entire ADC if they are noise above a threshold
diff --git a/cal_tools/cal_tools/agipdutils.py b/cal_tools/cal_tools/agipdutils.py
index 347198785c517a8c4e6621466fa374de351ec3b6..d1ff0755f75bcebe94e03e96e2cf3dc2542f0ad0 100644
--- a/cal_tools/cal_tools/agipdutils.py
+++ b/cal_tools/cal_tools/agipdutils.py
@@ -426,8 +426,8 @@ def make_noisy_adc_mask(bmask, noise_threshold):
""" Mask entire ADC if they are noise above a relative threshold
"""
msk = np.count_nonzero(
- ((bmask & BadPixels.NOISE_OUT_OF_THRESHOLD.value != 0)
- | (bmask & BadPixels.OFFSET_NOISE_EVAL_ERROR.value != 0)).astype(
+ ((bmask & BadPixels.NOISE_OUT_OF_THRESHOLD != 0)
+ | (bmask & BadPixels.OFFSET_NOISE_EVAL_ERROR != 0)).astype(
np.int), axis=0)
fmask = np.zeros_like(msk).astype(np.uint32)
@@ -440,7 +440,7 @@ def make_noisy_adc_mask(bmask, noise_threshold):
bmask.shape[0] // 32)
if adc_cnt / (adc_i * adc_j) >= noise_threshold:
fmask[i * adc_i:(i + 1) * adc_i,
- j * adc_j:(j + 1) * adc_j] = BadPixels.NOISY_ADC.value
+ j * adc_j:(j + 1) * adc_j] = BadPixels.NOISY_ADC
return fmask
@@ -630,7 +630,7 @@ def melt_snowy_pixels(raw, im, gain, rgain, resolution=None):
mim[labels == implabel] = mimb[labels == implabel]
mimg[labels == implabel] = 0
# identify these pixels in a bad pixel mask
- mmsk[labels == implabel] = BadPixels.INTERPOLATED.value
+ mmsk[labels == implabel] = BadPixels.INTERPOLATED
# now set back to data
asic[fidx] = mim
diff --git a/cal_tools/cal_tools/agipdutils_ff.py b/cal_tools/cal_tools/agipdutils_ff.py
index 97d38ce87718cc5759ac807fd5a506decdffa02d..4cae853275d5683c9760ca5565472d019ea56763 100644
--- a/cal_tools/cal_tools/agipdutils_ff.py
+++ b/cal_tools/cal_tools/agipdutils_ff.py
@@ -204,7 +204,7 @@ def get_mask(fit_summary: Dict[str, Any],
:return: Bad pixel mask
"""
if not fit_summary['is_valid']:
- return BadPixelsFF.FIT_FAILED.value
+ return BadPixelsFF.FIT_FAILED
m0 = fit_summary['g0mean']
s0 = fit_summary['g0sigma']
@@ -215,19 +215,19 @@ def get_mask(fit_summary: Dict[str, Any],
mask = 0
if not fit_summary['is_valid']:
- mask |= BadPixelsFF.FIT_FAILED.value
+ mask |= BadPixelsFF.FIT_FAILED
if not fit_summary['has_accurate_covar']:
- mask |= BadPixelsFF.ACCURATE_COVAR.value
+ mask |= BadPixelsFF.ACCURATE_COVAR
if not peak_lim[0] <= m0 <= peak_lim[1]:
- mask |= BadPixelsFF.NOISE_PEAK_THRESHOLD.value
+ mask |= BadPixelsFF.NOISE_PEAK_THRESHOLD
if not d0_lim[0] <= d01 <= d0_lim[1]:
- mask |= BadPixelsFF.GAIN_THRESHOLD.value
+ mask |= BadPixelsFF.GAIN_THRESHOLD
if not chi2_lim[0] <= chi2_ndof <= chi2_lim[1]:
- mask |= BadPixelsFF.CHI2_THRESHOLD.value
+ mask |= BadPixelsFF.CHI2_THRESHOLD
width_lim = peak_width_lim[0] * s0
inside_s1 = width_lim[0] <= s1 <= width_lim[1]
@@ -236,6 +236,6 @@ def get_mask(fit_summary: Dict[str, Any],
inside_s2 = width_lim[0] <= s2 <= width_lim[1]
if not inside_s1 and inside_s2:
- mask |= BadPixelsFF.PEAK_WIDTH_THRESHOLD.value
+ mask |= BadPixelsFF.PEAK_WIDTH_THRESHOLD
return mask
diff --git a/cal_tools/cal_tools/enums.py b/cal_tools/cal_tools/enums.py
index 19420caeacf38580e808ee0da4a76c508e29e464..4abafdc10b646d7d81c92de9912f8b29f27f1e13 100644
--- a/cal_tools/cal_tools/enums.py
+++ b/cal_tools/cal_tools/enums.py
@@ -1,7 +1,7 @@
-from enum import Enum
+from enum import Enum, IntFlag
-class BadPixels(Enum):
+class BadPixels(IntFlag):
""" The European XFEL Bad Pixel Encoding
"""
@@ -28,7 +28,7 @@ class BadPixels(Enum):
NON_LIN_RESPONSE_REGION = 0b100000000000000000000 # bit 21
-class BadPixelsFF(Enum):
+class BadPixelsFF(IntFlag):
""" The SLopesFF Bad Pixel Encoding
"""
diff --git a/cal_tools/cal_tools/lpdlib.py b/cal_tools/cal_tools/lpdlib.py
index 716637a5261cbfddeb752758290644c0c1707c0c..34193b6ef96787f7d971d722ab26fd2f8289da03 100644
--- a/cal_tools/cal_tools/lpdlib.py
+++ b/cal_tools/cal_tools/lpdlib.py
@@ -267,7 +267,7 @@ class LpdCorrections:
for gl, lr in enumerate(self.linear_between):
midx = (gain == gl) & ((im < lr[0]) | (im > lr[1]))
- msk[midx] = BadPixels.NON_LIN_RESPONSE_REGION.value
+ msk[midx] = BadPixels.NON_LIN_RESPONSE_REGION
numnonlin = np.count_nonzero(midx, axis=(1,2))
nlf += numnonlin
nlf = nlf/float(im.shape[0] * im.shape[1])
@@ -314,12 +314,12 @@ class LpdCorrections:
# create bad pixels masks, here non-finite values
bidx = ~np.isfinite(im)
im[bidx] = 0
- msk[bidx] = BadPixels.VALUE_IS_NAN.value
+ msk[bidx] = BadPixels.VALUE_IS_NAN
# values which are unphysically large or small
bidx = (im < -1e7) | (im > 1e7)
im[bidx] = 0
- msk[bidx] = BadPixels.VALUE_OUT_OF_RANGE.value
+ msk[bidx] = BadPixels.VALUE_OUT_OF_RANGE
# on first iteration we create histograms for the report
if cidx == 0: