def _update_handler(self, metadata):
# as a first step, try to set the detector_name, skip if detector_name is empty
detector_name = metadata.get("detector_name")
if detector_name and detector_name.startswith("JF"):
# check if jungfrau data handler is already set for this detector
if self.handler is None or self.handler.detector_name != detector_name:
try:
self.handler = JFDataHandler(detector_name)
except KeyError:
logging.exception(
f"Error creating data handler for detector {detector_name}"
)
self.handler = None
return
else:
self.handler = None
return
# gain file
gain_file = metadata.get("gain_file", "")
try:
self.handler.gain_file = gain_file
except Exception:
logging.exception(f"Error loading gain file {gain_file}")
self.handler.gain_file = ""
# pedestal file
pedestal_file = metadata.get("pedestal_file", "")
try:
self.handler.pedestal_file = pedestal_file
except Exception:
logging.exception(f"Error loading pedestal file {pedestal_file}")
self.handler.pedestal_file = ""
# module map
module_map = metadata.get("module_map")
self.handler.module_map = None if (
module_map is None) else np.array(module_map)
# highgain
daq_rec = metadata.get("daq_rec")
self.handler.highgain = False if (daq_rec is None) else bool(daq_rec
& 0b1)
def test_handler_init_fail(detector_name):
with pytest.raises(KeyError):
JFDataHandler(detector_name)
def _empty_handler():
empty_handler = JFDataHandler(DETECTOR_NAME)
yield empty_handler
class StreamAdapter:
def __init__(self):
# a placeholder for jf data handler to be initiated with detector name
self.handler = None
def process(self,
image,
metadata,
mask=True,
gap_pixels=True,
double_pixels="keep",
geometry=True):
"""Perform jungfrau detector data processing on an image received via stream.
Args:
image (ndarray): An image to be processed.
metadata (dict): A corresponding image metadata.
Returns:
ndarray: Resulting image.
"""
# Eiger workaround
detector_name = metadata.get("detector_name")
if detector_name and re.match("(^[A-Za-z]*).EG([0-9A-Za-z]*)",
detector_name):
return np.copy(np.flipud(image))
# parse metadata
self._update_handler(metadata)
if self.handler is None or image.dtype != np.uint16:
return image.astype(np.float32, copy=True)
# skip conversion step if jungfrau data handler cannot do it, thus avoiding Exception raise
conversion = self.handler.can_convert()
proc_image = self.handler.process(
image,
conversion=conversion,
mask=False,
gap_pixels=gap_pixels,
double_pixels=double_pixels,
geometry=geometry,
)
if mask:
proc_image = self._apply_mask(proc_image, gap_pixels,
double_pixels, geometry)
return proc_image
def get_gains(self,
image,
metadata,
mask=True,
gap_pixels=True,
double_pixels="keep",
geometry=True):
# parse metadata
self._update_handler(metadata)
if self.handler is None or image.dtype != np.uint16:
return np.zeros((2, 2), dtype="float32")
gains = self.handler.get_gains(image,
mask=mask,
gap_pixels=gap_pixels,
double_pixels=double_pixels,
geometry=geometry)
if mask:
if double_pixels == "interp":
double_pixels = "keep"
gains = self._apply_mask(gains, gap_pixels, double_pixels,
geometry)
return gains
def _update_handler(self, metadata):
# as a first step, try to set the detector_name, skip if detector_name is empty
detector_name = metadata.get("detector_name")
if detector_name and detector_name.startswith("JF"):
# check if jungfrau data handler is already set for this detector
if self.handler is None or self.handler.detector_name != detector_name:
try:
self.handler = JFDataHandler(detector_name)
except KeyError:
logging.exception(
f"Error creating data handler for detector {detector_name}"
)
self.handler = None
return
else:
self.handler = None
return
# gain file
gain_file = metadata.get("gain_file", "")
try:
self.handler.gain_file = gain_file
except Exception:
logging.exception(f"Error loading gain file {gain_file}")
self.handler.gain_file = ""
# pedestal file
pedestal_file = metadata.get("pedestal_file", "")
try:
self.handler.pedestal_file = pedestal_file
except Exception:
logging.exception(f"Error loading pedestal file {pedestal_file}")
self.handler.pedestal_file = ""
# module map
module_map = metadata.get("module_map")
self.handler.module_map = None if (
module_map is None) else np.array(module_map)
# highgain
daq_rec = metadata.get("daq_rec")
self.handler.highgain = False if (daq_rec is None) else bool(daq_rec
& 0b1)
def _apply_mask(self, image, gap_pixels, double_pixels, geometry):
# assign masked values to np.nan
if self.handler.pixel_mask is not None:
mask = self.handler.get_pixel_mask(gap_pixels=gap_pixels,
double_pixels=double_pixels,
geometry=geometry)
# cast to np.float32 in case there was no conversion, but mask should still be applied
if image.dtype != np.float32:
image = image.astype(np.float32)
if image.shape == mask.shape:
_apply_mask_njit(image, mask)
else:
raise ValueError("Image and mask shapes are not the same")
return image