def __init__(self, filename):
self.frame = read_image(filename)
self.nx, self.ny = self.frame.data.shape
self.x_axis = numpy.arange(self.nx)
self.y_axis = numpy.arange(self.ny)
self.cx, self.cy = self.frame.header['beam_center']
self.rot_x, self.rot_y = 0.0, 0.0
self.mask = (self.frame.data > 0.0) & (
self.frame.data < self.frame.header['saturated_value'])
def get_parameters(img_file):
"""
Determine parameters for the data set represented by img_fiile
returns a dictionary of results
"""
data = read_image(os.path.abspath(img_file))
if not data.header['dataset'] or len(
data.header['dataset']['sequence']) == 0:
logger.error("Dataset not found")
raise autoprocess.errors.DatasetError('Dataset not found')
info = data.header
info['energy'] = misc.wavelength_to_energy(info['wavelength'])
info['first_frame'] = info['dataset']['sequence'][0]
info['frame_count'] = info['dataset']['sequence'][-1]
info['name'] = info['dataset']['label']
info['file_template'] = os.path.join(info['dataset']['directory'],
info['dataset']['template'])
# Generate a list of wedges. each wedge is a tuple. The first value is the
# first frame number and the second is the number of frames in the wedge
wedges = summarize_list(info['dataset']['sequence'])
# determine spot ranges from wedges
# up to 4 degrees per wedge starting at 0 and 45 and 90
spot_range = []
spot_span = int(4.0 // info['delta_angle']) # frames in 4 deg
first_wedge = wedges[0]
for _ang in [0.0, 45.0, 90.0]:
_rs = first_wedge[0] + int(_ang // info['delta_angle'])
_re = _rs + spot_span
_exp_set = set(range(_rs, _re))
for wedge in wedges:
_obs_set = set(range(wedge[0], wedge[0] + wedge[1]))
_range = (_exp_set & _obs_set)
if len(_range) > 0:
spot_range.append((min(_range), max(_range)))
biggest_wedge = sorted(wedges, key=lambda x: x[1], reverse=True)[0]
info['spot_range'] = spot_range
info['data_range'] = info['dataset']['sequence'][0], info['dataset'][
'sequence'][-1]
info['background_range'] = (biggest_wedge[0], biggest_wedge[0] +
min(10, biggest_wedge[1]) - 1)
info['skip_range'] = summarize_gaps(info['dataset']['sequence'])
info['max_delphi'] = info['delta_angle'] * biggest_wedge[1]
return info
def load(self, path):
attempts = 0
success = False
while not success and attempts < 10:
path_obj = pathlib.Path(path)
if path_obj.exists(
) and path_obj.stat().st_ctime < time.time() - MAX_SAVE_JITTER:
try:
dataset = read_image(path)
self.show(dataset)
success = True
except (ValueError, KeyError) as err:
success = False
attempts += 1
time.sleep(0.1)
if not success:
logger.info("Unable to load {}".format(path))
return success
def load(self, path):
self.set_state(busy=True)
attempts = 0
success = False
while not success and attempts < 10:
loadable = (os.path.exists(path)
and time.time() - os.path.getmtime(path) >
self.MAX_SAVE_JITTER)
if loadable:
try:
dataset = read_image(path)
if self.master:
self.master.process_frame(dataset)
success = True
except Exception:
success = False
attempts += 1
time.sleep(1 / MAX_FILE_FREQUENCY)
self.set_state(busy=False)
return success
def detect_beam_peak(filename):
img_info = read_image(filename)
img = img_info.image
img_array = numpy.fromstring(img.tostring(), numpy.uint32)
img_array.shape = img.size[1], img.size[0]
# filter the array so that features less than 8 pixels wide are blurred out
# assumes that beam center is at least 8 pixels wide
arr = filters.gaussian_filter(img_array, 8)
beam_y, beam_x = measurements.maximum_position(arr)
# valid beam centers must be within the center 1/5 region of the detector surface
shape = img_array.shape
cmin = [2 * v / 5 for v in shape]
cmax = [3 * v / 5 for v in shape]
good = False
if cmin[0] < beam_y < cmax[0] and cmin[1] < beam_x < cmax[1]:
good = True
return beam_x, beam_y, good