53 lines
1.9 KiB
Python
53 lines
1.9 KiB
Python
from collections import OrderedDict
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from cam_server.pipeline.data_processing import functions
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from logging import getLogger
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_logger = getLogger(__name__)
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def process_image(image, pulse_id, timestamp, x_axis, y_axis, parameters, bsdata):
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ret = OrderedDict()
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prefix = parameters["camera_name"]
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(x_profile, y_profile) = functions.get_x_y_profile(image)
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_, _, _, x_fit_mean, x_fit_standard_deviation, _, _ = functions.gauss_fit(x_profile, x_axis)
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_, _, _, y_fit_mean, y_fit_standard_deviation, _, _ = functions.gauss_fit(y_profile, y_axis)
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x_fwhm = functions.get_fw(x_axis,x_profile)
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y_fwhm = functions.get_fw(y_axis,y_profile)
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intensity = x_profile.sum()
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ret[prefix+":intensity"] = intensity
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ret[prefix+":x_fit_mean"] = x_fit_mean
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ret[prefix+":y_fit_mean"] = y_fit_mean
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ret[prefix+":x_fwhm"] = x_fwhm
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ret[prefix+":y_fwhm"] = y_fwhm
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ret[prefix+":x_fit_standard_deviation"] = x_fit_standard_deviation
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ret[prefix+":y_fit_standard_deviation"] = y_fit_standard_deviation
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return ret
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"""
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from collections import OrderedDict
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from cam_server.pipeline.data_processing import functions, processor
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from logging import getLogger
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import numpy as np
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_logger = getLogger(__name__)
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def process_image(image, pulse_id, timestamp, x_axis, y_axis, parameters, bsdata):
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r = processor.process_image(image, pulse_id, timestamp, x_axis, y_axis, parameters, bsdata)
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ret = OrderedDict()
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channels = ["intensity","x_fit_mean","y_fit_mean"]
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prefix = parameters["camera_name"]
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for c in channels:
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value = r[c]
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if value is None:
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_logger.warning("None:" + str(c))
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return None
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if value == float('nan'):
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_logger.warning("NAN:" + str(c))
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return None
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if isinstance(value,np.floating):
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value=float(value)
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if isinstance(value,np.integer):
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value=int(value)
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ret[prefix+":"+c] = value
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return ret
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""" |