def subtract_background(signal, background, plot=False):
x, y = smooth_spectrum.interpolate(background[0], background[1])
y_fitted = smoothing.savitzky_golay_filter(x, y, half_window=32, degree=3)[1]
signal_x, signal_y = signal
signal_x, signal_y = smooth_spectrum.interpolate(signal[0], signal[1])
x_interp_size = x.size()
for i, x_i in enumerate(reversed(x)):
if x_i not in signal[0]:
assert x[x_interp_size - i - 1] == x_i
del signal_x[x_interp_size - i - 1]
del signal_y[x_interp_size - i - 1]
del y_fitted[x_interp_size - i - 1]
background_subtracted = signal_y - y_fitted
if plot:
from matplotlib import pyplot
pyplot.plot(signal[0], signal[1], linewidth=2, label="signal+background")
pyplot.plot(background[0], background[1], linewidth=2, label="background")
pyplot.plot(signal_x, y_fitted, linewidth=2, label="background_fit")
pyplot.plot(signal_x, background_subtracted, linewidth=2, label="signal")
pyplot.legend(loc=2)
pyplot.ylabel("Intensity", fontsize=15)
pyplot.xlabel("Pixel column", fontsize=15)
pyplot.show()
return signal_x, background_subtracted
def subtract_background(signal, background, plot=False):
x, y = smooth_spectrum.interpolate(background[0], background[1])
y_fitted = smoothing.savitzky_golay_filter(x, y, half_window=32, degree=3)[1]
signal_x, signal_y = signal
signal_x, signal_y = smooth_spectrum.interpolate(signal[0], signal[1])
x_interp_size = x.size()
for i, x_i in enumerate(reversed(x)):
if x_i not in signal[0]:
assert x[x_interp_size - i - 1] == x_i
del signal_x[x_interp_size - i - 1]
del signal_y[x_interp_size - i - 1]
del y_fitted[x_interp_size - i - 1]
background_subtracted = signal_y - y_fitted
if plot:
from matplotlib import pyplot
pyplot.plot(signal[0], signal[1], linewidth=2, label="signal+background")
pyplot.plot(background[0], background[1], linewidth=2, label="background")
pyplot.plot(signal_x, y_fitted, linewidth=2, label="background_fit")
pyplot.plot(signal_x, background_subtracted, linewidth=2, label="signal")
pyplot.legend(loc=2)
pyplot.ylabel("Intensity", fontsize=15)
pyplot.xlabel("Pixel column", fontsize=15)
pyplot.show()
return signal_x, background_subtracted
def run(args):
processed = iotbx.phil.process_command_line(
args=args, master_string=master_phil_str)
args = processed.remaining_args
work_params = processed.work.extract().xes
processed.work.show()
assert len(args) == 1
output_dirname = work_params.output_dirname
roi = cspad_tbx.getOptROI(work_params.roi)
bg_roi = cspad_tbx.getOptROI(work_params.bg_roi)
gain_map_path = work_params.gain_map
estimated_gain = work_params.estimated_gain
nproc = work_params.nproc
photon_threshold = work_params.photon_threshold
method = work_params.method
print output_dirname
if output_dirname is None:
output_dirname = os.path.join(os.path.dirname(args[0]), "finalise")
print output_dirname
hist_d = easy_pickle.load(args[0])
if len(hist_d.keys())==2:
hist_d = hist_d['histogram']
pixel_histograms = view_pixel_histograms.pixel_histograms(
hist_d, estimated_gain=estimated_gain)
result = xes_from_histograms(
pixel_histograms, output_dirname=output_dirname,
gain_map_path=gain_map_path, estimated_gain=estimated_gain,
method=method, nproc=nproc,
photon_threshold=photon_threshold, roi=roi, run=work_params.run)
if bg_roi is not None:
bg_outdir = os.path.normpath(output_dirname)+"_bg"
bg_result = xes_from_histograms(
pixel_histograms, output_dirname=bg_outdir,
gain_map_path=gain_map_path, estimated_gain=estimated_gain,
method=method, nproc=nproc,
photon_threshold=photon_threshold, roi=bg_roi)
from xfel.command_line.subtract_background import subtract_background
signal = result.spectrum
background = bg_result.spectrum
signal = (signal[0].as_double(), signal[1])
background = (background[0].as_double(), background[1])
signal_x, background_subtracted = subtract_background(signal, background, plot=True)
f = open(os.path.join(output_dirname, "background_subtracted.txt"), "wb")
print >> f, "\n".join(["%i %f" %(x, y)
for x, y in zip(signal_x, background_subtracted)])
f.close()
else:
from xfel.command_line import smooth_spectrum
from scitbx.smoothing import savitzky_golay_filter
x, y = result.spectrum[0].as_double(), result.spectrum[1]
x, y = smooth_spectrum.interpolate(x, y)
x, y_smoothed = savitzky_golay_filter(
x, y, 20, 4)
smooth_spectrum.estimate_signal_to_noise(x, y, y_smoothed)
def run(args):
processed = iotbx.phil.process_command_line(args=args,
master_string=master_phil_str)
args = processed.remaining_args
work_params = processed.work.extract().xes
processed.work.show()
assert len(args) == 1
output_dirname = work_params.output_dirname
roi = cspad_tbx.getOptROI(work_params.roi)
bg_roi = cspad_tbx.getOptROI(work_params.bg_roi)
gain_map_path = work_params.gain_map
estimated_gain = work_params.estimated_gain
nproc = work_params.nproc
photon_threshold = work_params.photon_threshold
method = work_params.method
print output_dirname
if output_dirname is None:
output_dirname = os.path.join(os.path.dirname(args[0]), "finalise")
print output_dirname
hist_d = easy_pickle.load(args[0])
if len(hist_d.keys()) == 2:
hist_d = hist_d['histogram']
pixel_histograms = view_pixel_histograms.pixel_histograms(
hist_d, estimated_gain=estimated_gain)
result = xes_from_histograms(pixel_histograms,
output_dirname=output_dirname,
gain_map_path=gain_map_path,
estimated_gain=estimated_gain,
method=method,
nproc=nproc,
photon_threshold=photon_threshold,
roi=roi,
run=work_params.run)
if bg_roi is not None:
bg_outdir = os.path.normpath(output_dirname) + "_bg"
bg_result = xes_from_histograms(pixel_histograms,
output_dirname=bg_outdir,
gain_map_path=gain_map_path,
estimated_gain=estimated_gain,
method=method,
nproc=nproc,
photon_threshold=photon_threshold,
roi=bg_roi)
from xfel.command_line.subtract_background import subtract_background
signal = result.spectrum
background = bg_result.spectrum
signal = (signal[0].as_double(), signal[1])
background = (background[0].as_double(), background[1])
signal_x, background_subtracted = subtract_background(signal,
background,
plot=True)
f = open(os.path.join(output_dirname, "background_subtracted.txt"),
"wb")
print >> f, "\n".join([
"%i %f" % (x, y) for x, y in zip(signal_x, background_subtracted)
])
f.close()
else:
from xfel.command_line import smooth_spectrum
from scitbx.smoothing import savitzky_golay_filter
x, y = result.spectrum[0].as_double(), result.spectrum[1]
x, y = smooth_spectrum.interpolate(x, y)
x, y_smoothed = savitzky_golay_filter(x, y, 20, 4)
smooth_spectrum.estimate_signal_to_noise(x, y, y_smoothed)
