def run(args):
if len(args)==0: print Usage; exit()
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
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 = faster_methods_for_pixel_histograms(
hist_d, work_params)
result = xes_from_histograms(
pixel_histograms, output_dirname=output_dirname,
gain_map_path=gain_map_path, estimated_gain=estimated_gain,
roi=roi, run=work_params.run)
def run(args):
if len(args) == 0:
print Usage
exit()
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
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 = faster_methods_for_pixel_histograms(hist_d, work_params)
result = xes_from_histograms(pixel_histograms,
output_dirname=output_dirname,
gain_map_path=gain_map_path,
estimated_gain=estimated_gain,
roi=roi,
run=work_params.run)
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 __init__(self,
address,
pickle_dirname=".",
pickle_basename="hist",
roi=None,
hist_min=None,
hist_max=None,
n_slots=None,
**kwds):
"""
@param address Address string XXX Que?!
@param pickle_dirname Directory portion of output pickle file
XXX mean, mu?
@param pickle_basename Filename prefix of output pickle file
image XXX mean, mu?
@param calib_dir Directory with calibration information
@param dark_path Path to input dark image
@param dark_stddev Path to input dark standard deviation
"""
super(pixel_histograms, self).__init__(address=address, **kwds)
self.pickle_dirname = cspad_tbx.getOptString(pickle_dirname)
self.pickle_basename = cspad_tbx.getOptString(pickle_basename)
self.hist_min = cspad_tbx.getOptFloat(hist_min)
self.hist_max = cspad_tbx.getOptFloat(hist_max)
self.n_slots = cspad_tbx.getOptInteger(n_slots)
self.histograms = {}
self.dimensions = None
self.roi = cspad_tbx.getOptROI(roi)
self.values = flex.long()
self.sigma_scaling = False
if self.hist_min is None: self.hist_min = -50
if self.hist_max is None: self.hist_max = 150
if self.n_slots is None: self.n_slots = 200
def __init__(self,
address,
pickle_dirname=".",
pickle_basename="",
roi=None,
**kwds):
"""The mod_average class constructor stores the parameters passed
from the pyana configuration file in instance variables. All
parameters, except @p address are optional, and hence need not be
defined in pyana.cfg.
@param address Full data source address of the DAQ device
@param pickle_dirname Directory portion of output pickle file
XXX mean, mu?
@param pickle_basename Filename prefix of output pickle file
image XXX mean, mu?
"""
super(mod_xes, self).__init__(address=address, **kwds)
self.pickle_dirname = cspad_tbx.getOptString(pickle_dirname)
self.pickle_basename = cspad_tbx.getOptString(pickle_basename)
self.roi = cspad_tbx.getOptROI(roi)
def __init__(self,
address,
pickle_dirname=".",
pickle_basename="hist",
roi=None,
hist_min=None,
hist_max=None,
n_slots=None,
**kwds):
"""
@param address Address string XXX Que?!
@param pickle_dirname Directory portion of output pickle file
XXX mean, mu?
@param pickle_basename Filename prefix of output pickle file
image XXX mean, mu?
@param calib_dir Directory with calibration information
@param dark_path Path to input dark image
@param dark_stddev Path to input dark standard deviation
"""
super(pixel_histograms, self).__init__(
address=address,
**kwds
)
self.pickle_dirname = cspad_tbx.getOptString(pickle_dirname)
self.pickle_basename = cspad_tbx.getOptString(pickle_basename)
self.hist_min = cspad_tbx.getOptFloat(hist_min)
self.hist_max = cspad_tbx.getOptFloat(hist_max)
self.n_slots = cspad_tbx.getOptInteger(n_slots)
self.histograms = {}
self.dimensions = None
self.roi = cspad_tbx.getOptROI(roi)
self.values = flex.long()
self.sigma_scaling = False
if self.hist_min is None: self.hist_min = -50
if self.hist_max is None: self.hist_max = 150
if self.n_slots is None: self.n_slots = 200
def __init__(self,
address,
pickle_dirname=".",
pickle_basename="",
roi=None,
**kwds):
"""The mod_average class constructor stores the parameters passed
from the pyana configuration file in instance variables. All
parameters, except @p address are optional, and hence need not be
defined in pyana.cfg.
@param address Full data source address of the DAQ device
@param pickle_dirname Directory portion of output pickle file
XXX mean, mu?
@param pickle_basename Filename prefix of output pickle file
image XXX mean, mu?
"""
super(mod_xes, self).__init__(
address=address,
**kwds
)
self.pickle_dirname = cspad_tbx.getOptString(pickle_dirname)
self.pickle_basename = cspad_tbx.getOptString(pickle_basename)
self.roi = cspad_tbx.getOptROI(roi)
def __init__(self, runs, output_dirname=".", roi=None):
avg_basename = "avg_"
stddev_basename = "stddev"
self.sum_img = None
self.sumsq_img = None
self.nmemb = 0
self.roi = cspad_tbx.getOptROI(roi)
self.unbound_pixel_mask = cspad_unbound_pixel_mask()
for i_run, run in enumerate(runs):
run_scratch_dir = run
result = finalise_one_run(run_scratch_dir)
if result.sum_img is None: continue
if self.sum_img is None:
self.sum_img = result.sum_img
self.sumsq_img = result.sumsq_img
else:
self.sum_img += result.sum_img
self.sumsq_img += result.sumsq_img
self.nmemb += result.nmemb
self.avg_img = self.sum_img.as_double() / self.nmemb
self.stddev_img = flex.sqrt(
(self.sumsq_img.as_double() -
self.sum_img.as_double() * self.avg_img) / (self.nmemb - 1))
self.mask = flex.int(self.sum_img.accessor(), 0)
self.mask.set_selected(self.sum_img == 0, 1)
self.mask.set_selected(self.unbound_pixel_mask > 0, 1)
if (output_dirname is not None and avg_basename is not None):
if (not os.path.isdir(output_dirname)):
os.makedirs(output_dirname)
d = cspad_tbx.dpack(
address='CxiSc1-0|Cspad2x2-0',
data=self.avg_img,
distance=1,
)
cspad_tbx.dwritef(d, output_dirname, avg_basename)
d = cspad_tbx.dpack(
address='CxiSc1-0|Cspad2x2-0',
data=self.sum_img,
distance=1,
)
cspad_tbx.dwritef(d, output_dirname, "sum_")
if 1:
output_image(self.avg_img, "%s/avg.png" % output_dirname)
output_image(self.avg_img,
"%s/avg_inv.png" % output_dirname,
invert=True)
if 1:
output_matlab_form(self.sum_img, "%s/sum.m" % output_dirname)
output_matlab_form(self.avg_img, "%s/avg.m" % output_dirname)
output_matlab_form(self.stddev_img,
"%s/stddev.m" % output_dirname)
if (stddev_basename is not None):
d = cspad_tbx.dpack(
address='CxiSc1-0|Cspad2x2-0',
data=self.stddev_img,
distance=1,
)
cspad_tbx.dwritef(d, output_dirname, stddev_basename)
# XXX we should really figure out automatically the area where the spectrum is
#write an integrated spectrum from lines 186-227
#spectrum_focus = self.sum_img.as_numpy_array()[186:228,:]
img = self.sum_img
if self.roi is None:
spectrum_focus = img
mask_focus = self.mask
else:
slices = (slice(self.roi[2],
self.roi[3]), slice(self.roi[0], self.roi[1]))
spectrum_focus = img[slices]
mask_focus = self.mask[slices]
if False:
from matplotlib import pylab
pylab.imshow(spectrum_focus.as_numpy_array())
pylab.show()
output_spectrum(spectrum_focus,
mask_focus=mask_focus,
output_dirname=output_dirname)
print "Total number of images used from %i runs: %i" % (i_run + 1,
self.nmemb)
def __init__(self,
address,
calib_dir=None,
common_mode_correction="none",
photon_threshold=None,
two_photon_threshold=None,
dark_path=None,
dark_stddev=None,
mask_path=None,
gain_map_path=None,
gain_map_level=None,
cache_image=True,
roi=None,
laser_1_status=None,
laser_4_status=None,
laser_wait_time=None,
override_beam_x=None,
override_beam_y=None,
bin_size=None,
crop_rayonix=False,
**kwds):
"""The common_mode_correction class constructor stores the
parameters passed from the pyana configuration file in instance
variables.
@param address Full data source address of the DAQ device
@param calib_dir Directory with calibration information
@param common_mode_correction The type of common mode correction to apply
@param dark_path Path to input average dark image
@param dark_stddev Path to input standard deviation dark
image, required if @p dark_path is given
@param mask_path Path to input mask. Pixels to mask out should be set to -2
@param gain_map_path Path to input gain map. Multiplied times the image.
@param gain_map_level If set, all the '1' pixels in the gain_map are set to this multiplier
and all the '0' pixels in the gain_map are set to '1'. If not set,
use the values in the gain_map directly
@param laser_1_status 0 or 1 to indicate that the laser should be off or on respectively
@param laser_4_status 0 or 1 to indicate that the laser should be off or on respectively
@param laser_wait_time Length of time in milliseconds to wait after a laser
change of status to begin accepting images again.
(rejection of images occurs immediately after status
change).
@param override_beam_x override value for x coordinate of beam center in pixels
@param override_beam_y override value for y coordinate of beam center in pixels
@param bin_size bin size for rayonix detector used to determin pixel size
@param crop_rayonix whether to crop rayonix images such that image center is the beam center
"""
# Cannot use the super().__init__() construct here, because
# common_mode_correction refers to the argument, and not the
# class.
mod_event_info.__init__(self, address=address, **kwds)
# The paths will be substituted in beginjob(), where evt and env
# are available.
self._dark_path = cspad_tbx.getOptString(dark_path)
self._dark_stddev_path = cspad_tbx.getOptString(dark_stddev)
self._gain_map_path = cspad_tbx.getOptString(gain_map_path)
self._mask_path = cspad_tbx.getOptString(mask_path)
self.gain_map_level = cspad_tbx.getOptFloat(gain_map_level)
self.common_mode_correction = cspad_tbx.getOptString(common_mode_correction)
self.photon_threshold = cspad_tbx.getOptFloat(photon_threshold)
self.two_photon_threshold = cspad_tbx.getOptFloat(two_photon_threshold)
self.cache_image = cspad_tbx.getOptBool(cache_image)
self.filter_laser_1_status = cspad_tbx.getOptInteger(laser_1_status)
self.filter_laser_4_status = cspad_tbx.getOptInteger(laser_4_status)
if self.filter_laser_1_status is not None:
self.filter_laser_1_status = bool(self.filter_laser_1_status)
if self.filter_laser_4_status is not None:
self.filter_laser_4_status = bool(self.filter_laser_4_status)
self.filter_laser_wait_time = cspad_tbx.getOptInteger(laser_wait_time)
self.override_beam_x = cspad_tbx.getOptFloat(override_beam_x)
self.override_beam_y = cspad_tbx.getOptFloat(override_beam_y)
self.bin_size = cspad_tbx.getOptInteger(bin_size)
self.crop_rayonix = cspad_tbx.getOptBool(crop_rayonix)
self.cspad_img = None # The current image - set by self.event()
self.sum_common_mode = 0
self.sumsq_common_mode = 0
self.roi = cspad_tbx.getOptROI(roi) # used to ignore the signal region in chebyshev fit
assert self.common_mode_correction in \
("gaussian", "mean", "median", "mode", "none", "chebyshev")
# Get and parse metrology.
self.sections = None
device = cspad_tbx.address_split(self.address)[2]
if device == 'Andor':
self.sections = [] # XXX FICTION
elif device == 'Cspad':
if self.address == 'XppGon-0|Cspad-0':
self.sections = [] # Not used for XPP
else:
self.sections = calib2sections(cspad_tbx.getOptString(calib_dir))
elif device == 'Cspad2x2':
# There is no metrology information for the Sc1 detector, so
# make it up. The sections are rotated by 90 degrees with
# respect to the "standing up" convention.
self.sections = [[Section(90, (185 / 2 + 0, (2 * 194 + 3) / 2)),
Section(90, (185 / 2 + 185, (2 * 194 + 3) / 2))]]
elif device == 'marccd':
self.sections = [] # XXX FICTION
elif device == 'pnCCD':
self.sections = [] # XXX FICTION
elif device == 'Rayonix':
self.sections = [] # XXX FICTION
elif device == 'Opal1000':
self.sections = [] # XXX FICTION
if self.sections is None:
raise RuntimeError("Failed to load metrology")
def run(args):
assert len(args) > 0
command_line = (option_parser()
.option("--roi",
type="string",
help="Region of interest for summing up histograms"
"from neighbouring pixels.")
.option("--log_scale",
action="store_true",
default=False,
help="Draw y-axis on a log scale.")
.option("--normalise",
action="store_true",
default=False,
help="Normalise by number of member images.")
.option("--save",
action="store_true",
default=False,
help="Save each plot as a png.")
.option("--start",
type="string",
help="Starting pixel coordinates")
.option("--fit_gaussians",
action="store_true",
default=False,
help="Fit gaussians to the peaks.")
.option("--n_gaussians",
type="int",
default=2,
help="Number of gaussians to fit.")
.option("--estimated_gain",
type="float",
default=30,
help="The approximate position of the one photon peak.")
).process(args=args)
log_scale = command_line.options.log_scale
fit_gaussians = command_line.options.fit_gaussians
roi = cspad_tbx.getOptROI(command_line.options.roi)
normalise = command_line.options.normalise
save_image = command_line.options.save
starting_pixel = command_line.options.start
n_gaussians = command_line.options.n_gaussians
estimated_gain = command_line.options.estimated_gain
if starting_pixel is not None:
starting_pixel = eval(starting_pixel)
assert isinstance(starting_pixel, tuple)
args = command_line.args
path = args[0]
window_title = path
d = easy_pickle.load(path)
args = args[1:]
pixels = None
if len(args) > 0:
pixels = [eval(arg) for arg in args]
for pixel in pixels:
assert isinstance(pixel, tuple)
assert len(pixel) == 2
if roi is not None:
summed_hist = {}
for i in range(roi[2], roi[3]):
for j in range(roi[0], roi[1]):
if (i,j) not in summed_hist:
summed_hist[(0,0)] = d[(i,j)]
else:
summed_hist[(0,0)].update(d[(i,j)])
d = summed_hist
#if roi is not None:
#summed_hist = None
#for i in range(roi[2], roi[3]):
#for j in range(roi[0], roi[1]):
#if summed_hist is None:
#summed_hist = d[(i,j)]
#else:
#summed_hist.update(d[(i,j)])
#title = str(roi)
#plot(hist, window_title=window_title, title=title,log_scale=log_scale,
#normalise=normalise, save_image=save_image, fit_gaussians=fit_gaussians)
#return
histograms = pixel_histograms(d, estimated_gain=estimated_gain)
histograms.plot(
pixels=pixels, starting_pixel=starting_pixel, fit_gaussians=fit_gaussians,
n_gaussians=n_gaussians, window_title=window_title, log_scale=log_scale,
save_image=save_image)
def run(args):
assert len(args) > 0
command_line = (
option_parser().option(
"--roi",
type="string",
help="Region of interest for summing up histograms"
"from neighbouring pixels.").option(
"--log_scale",
action="store_true",
default=False,
help="Draw y-axis on a log scale.").option(
"--normalise",
action="store_true",
default=False,
help="Normalise by number of member images.").option(
"--save",
action="store_true",
default=False,
help="Save each plot as a png.").option(
"--start",
type="string",
help="Starting pixel coordinates").option(
"--fit_gaussians",
action="store_true",
default=False,
help="Fit gaussians to the peaks.").option(
"--n_gaussians",
type="int",
default=2,
help="Number of gaussians to fit.").
option(
"--estimated_gain",
type="float",
default=30,
help="The approximate position of the one photon peak.")).process(
args=args)
log_scale = command_line.options.log_scale
fit_gaussians = command_line.options.fit_gaussians
roi = cspad_tbx.getOptROI(command_line.options.roi)
normalise = command_line.options.normalise
save_image = command_line.options.save
starting_pixel = command_line.options.start
n_gaussians = command_line.options.n_gaussians
estimated_gain = command_line.options.estimated_gain
if starting_pixel is not None:
starting_pixel = eval(starting_pixel)
assert isinstance(starting_pixel, tuple)
args = command_line.args
path = args[0]
window_title = path
d = easy_pickle.load(path)
args = args[1:]
pixels = None
if len(args) > 0:
pixels = [eval(arg) for arg in args]
for pixel in pixels:
assert isinstance(pixel, tuple)
assert len(pixel) == 2
if roi is not None:
summed_hist = {}
for i in range(roi[2], roi[3]):
for j in range(roi[0], roi[1]):
if (i, j) not in summed_hist:
summed_hist[(0, 0)] = d[(i, j)]
else:
summed_hist[(0, 0)].update(d[(i, j)])
d = summed_hist
#if roi is not None:
#summed_hist = None
#for i in range(roi[2], roi[3]):
#for j in range(roi[0], roi[1]):
#if summed_hist is None:
#summed_hist = d[(i,j)]
#else:
#summed_hist.update(d[(i,j)])
#title = str(roi)
#plot(hist, window_title=window_title, title=title,log_scale=log_scale,
#normalise=normalise, save_image=save_image, fit_gaussians=fit_gaussians)
#return
histograms = pixel_histograms(d, estimated_gain=estimated_gain)
histograms.plot(pixels=pixels,
starting_pixel=starting_pixel,
fit_gaussians=fit_gaussians,
n_gaussians=n_gaussians,
window_title=window_title,
log_scale=log_scale,
save_image=save_image)
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 __init__(self,
address,
dispatch=None,
integration_dirname=None,
integration_basename=None,
out_dirname=None,
out_basename=None,
roi=None,
distl_min_peaks=None,
distl_flags=None,
threshold=None,
xtal_target=None,
negate_hits=False,
trial_id=None,
db_logging=False,
progress_logging=False,
sql_buffer_size=1,
db_host=None,
db_name=None,
db_table_name=None,
db_experiment_tag=None,
db_user=None,
db_password=None,
db_tags=None,
trial=None,
rungroup_id=None,
db_version='v1',
**kwds):
"""The mod_hitfind class constructor stores the parameters passed
from the pyana configuration file in instance variables. All
parameters, except @p address are optional, and hence need not be
defined in pyana.cfg.
@param address Full data source address of the DAQ device
@param dispatch Function to call
@param integration_dirname
Directory portion of output integration file
pathname
@param integration_basename
Filename prefix of output integration file
pathname
@param out_dirname Directory portion of output image pathname
@param out_basename Filename prefix of output image pathname
@param roi Region of interest for thresholding, on the
form fast_low:fast_high,slow_low:slow_high
@param threshold Minimum value in region of interest to pass
"""
super(mod_hitfind, self).__init__(address=address, **kwds)
self.m_dispatch = cspad_tbx.getOptString(dispatch)
self.m_integration_basename = cspad_tbx.getOptString(
integration_basename)
self.m_integration_dirname = cspad_tbx.getOptString(
integration_dirname)
self.m_out_basename = cspad_tbx.getOptString(out_basename)
self.m_out_dirname = cspad_tbx.getOptString(out_dirname)
self.m_distl_min_peaks = cspad_tbx.getOptInteger(distl_min_peaks)
self.m_distl_flags = cspad_tbx.getOptStrings(distl_flags)
self.m_threshold = cspad_tbx.getOptInteger(threshold)
self.m_xtal_target = cspad_tbx.getOptString(xtal_target)
self.m_negate_hits = cspad_tbx.getOptBool(negate_hits)
self.m_trial_id = cspad_tbx.getOptInteger(trial_id)
self.m_db_logging = cspad_tbx.getOptBool(db_logging)
self.m_progress_logging = cspad_tbx.getOptBool(progress_logging)
self.m_sql_buffer_size = cspad_tbx.getOptInteger(sql_buffer_size)
self.m_db_host = cspad_tbx.getOptString(db_host)
self.m_db_name = cspad_tbx.getOptString(db_name)
self.m_db_table_name = cspad_tbx.getOptString(db_table_name)
self.m_db_experiment_tag = cspad_tbx.getOptString(db_experiment_tag)
self.m_db_user = cspad_tbx.getOptString(db_user)
self.m_db_password = cspad_tbx.getOptString(db_password)
self.m_db_tags = cspad_tbx.getOptString(db_tags)
self.m_trial = cspad_tbx.getOptInteger(trial)
self.m_rungroup_id = cspad_tbx.getOptInteger(rungroup_id)
self.m_db_version = cspad_tbx.getOptString(db_version)
# A ROI should not contain any ASIC boundaries, as these are
# noisy. Hence circular ROI:s around the beam centre are probably
# not such a grand idea.
self.m_roi = cspad_tbx.getOptROI(roi)
# Verify that dist_min_peaks is either "restrictive" or
# "permissive", but not both. ^ is the logical xor operator
if self.m_distl_min_peaks is not None:
if (not (('permissive' in self.m_distl_flags) ^
('restrictive' in self.m_distl_flags))):
raise RuntimeError("""Sorry, with the distl_min_peaks option,
distl_flags must be set to 'permissive' or 'restrictive'.""")
if (self.m_roi is not None):
raise RuntimeError("""Sorry, either specify region of interest
(roi) or distl_min_peaks, but not both.""")
if self.m_db_logging:
self.buffered_sql_entries = []
assert self.m_sql_buffer_size >= 1
if self.m_progress_logging:
if self.m_db_version == 'v1':
self.buffered_progress_entries = []
assert self.m_sql_buffer_size >= 1
self.isoforms = {}
elif self.m_db_version == 'v2':
from xfel.ui import db_phil_str
from libtbx.phil import parse
extra_phil = """
input {
trial = None
.type = int
trial_id = None
.type = int
rungroup = None
.type = int
}
"""
self.db_params = parse(db_phil_str + extra_phil).extract()
self.db_params.experiment_tag = self.m_db_experiment_tag
self.db_params.db.host = self.m_db_host
self.db_params.db.name = self.m_db_name
self.db_params.db.user = self.m_db_user
self.db_params.db.password = self.m_db_password
self.db_params.input.trial = self.m_trial
self.db_params.input.rungroup = self.m_rungroup_id
if self.m_db_tags is None:
self.m_db_tags = ""
def __init__(self,
address,
dispatch = None,
integration_dirname = None,
integration_basename = None,
out_dirname = None,
out_basename = None,
roi = None,
distl_min_peaks = None,
distl_flags = None,
threshold = None,
xtal_target = None,
negate_hits = False,
trial_id = None,
db_logging = False,
progress_logging = False,
sql_buffer_size = 1,
db_host = None,
db_name = None,
db_table_name = None,
db_experiment_tag = None,
db_user = None,
db_password = None,
db_tags = None,
rungroup_id = None,
**kwds):
"""The mod_hitfind class constructor stores the parameters passed
from the pyana configuration file in instance variables. All
parameters, except @p address are optional, and hence need not be
defined in pyana.cfg.
@param address Full data source address of the DAQ device
@param dispatch Function to call
@param integration_dirname
Directory portion of output integration file
pathname
@param integration_basename
Filename prefix of output integration file
pathname
@param out_dirname Directory portion of output image pathname
@param out_basename Filename prefix of output image pathname
@param roi Region of interest for thresholding, on the
form fast_low:fast_high,slow_low:slow_high
@param threshold Minimum value in region of interest to pass
"""
super(mod_hitfind, self).__init__(address=address, **kwds)
self.m_dispatch = cspad_tbx.getOptString(dispatch)
self.m_integration_basename = cspad_tbx.getOptString(integration_basename)
self.m_integration_dirname = cspad_tbx.getOptString(integration_dirname)
self.m_out_basename = cspad_tbx.getOptString(out_basename)
self.m_out_dirname = cspad_tbx.getOptString(out_dirname)
self.m_distl_min_peaks = cspad_tbx.getOptInteger(distl_min_peaks)
self.m_distl_flags = cspad_tbx.getOptStrings(distl_flags)
self.m_threshold = cspad_tbx.getOptInteger(threshold)
self.m_xtal_target = cspad_tbx.getOptString(xtal_target)
self.m_negate_hits = cspad_tbx.getOptBool(negate_hits)
self.m_trial_id = cspad_tbx.getOptInteger(trial_id)
self.m_db_logging = cspad_tbx.getOptBool(db_logging)
self.m_progress_logging = cspad_tbx.getOptBool(progress_logging)
self.m_sql_buffer_size = cspad_tbx.getOptInteger(sql_buffer_size)
self.m_db_host = cspad_tbx.getOptString(db_host)
self.m_db_name = cspad_tbx.getOptString(db_name)
self.m_db_table_name = cspad_tbx.getOptString(db_table_name)
self.m_db_experiment_tag = cspad_tbx.getOptString(db_experiment_tag)
self.m_db_user = cspad_tbx.getOptString(db_user)
self.m_db_password = cspad_tbx.getOptString(db_password)
self.m_db_tags = cspad_tbx.getOptString(db_tags)
self.m_rungroup_id = cspad_tbx.getOptInteger(rungroup_id)
# A ROI should not contain any ASIC boundaries, as these are
# noisy. Hence circular ROI:s around the beam centre are probably
# not such a grand idea.
self.m_roi = cspad_tbx.getOptROI(roi)
# Verify that dist_min_peaks is either "restrictive" or
# "permissive", but not both. ^ is the logical xor operator
if self.m_distl_min_peaks is not None:
if (not (('permissive' in self.m_distl_flags) ^
('restrictive' in self.m_distl_flags))):
raise RuntimeError("""Sorry, with the distl_min_peaks option,
distl_flags must be set to 'permissive' or 'restrictive'.""")
if (self.m_roi is not None):
raise RuntimeError("""Sorry, either specify region of interest
(roi) or distl_min_peaks, but not both.""")
if self.m_db_logging:
self.buffered_sql_entries = []
assert self.m_sql_buffer_size >= 1
if self.m_progress_logging:
self.buffered_progress_entries = []
assert self.m_sql_buffer_size >= 1
self.isoforms = {}
if self.m_db_tags is None:
self.m_db_tags = ""
def __init__(self,
runs,
output_dirname=".",
roi=None):
avg_basename="avg_"
stddev_basename="stddev"
self.sum_img = None
self.sumsq_img = None
self.nmemb = 0
self.roi = cspad_tbx.getOptROI(roi)
self.unbound_pixel_mask = cspad_unbound_pixel_mask()
for i_run, run in enumerate(runs):
run_scratch_dir = run
result = finalise_one_run(run_scratch_dir)
if result.sum_img is None: continue
if self.sum_img is None:
self.sum_img = result.sum_img
self.sumsq_img = result.sumsq_img
else:
self.sum_img += result.sum_img
self.sumsq_img += result.sumsq_img
self.nmemb += result.nmemb
self.avg_img = self.sum_img.as_double() / self.nmemb
self.stddev_img = flex.sqrt((self.sumsq_img.as_double() - self.sum_img.as_double() * self.avg_img) / (self.nmemb - 1))
self.mask = flex.int(self.sum_img.accessor(), 0)
self.mask.set_selected(self.sum_img == 0, 1)
self.mask.set_selected(self.unbound_pixel_mask > 0, 1)
if (output_dirname is not None and
avg_basename is not None):
if (not os.path.isdir(output_dirname)):
os.makedirs(output_dirname)
d = cspad_tbx.dpack(
address='CxiSc1-0|Cspad2x2-0',
data=self.avg_img,
distance=1,
)
cspad_tbx.dwritef(d, output_dirname, avg_basename)
d = cspad_tbx.dpack(
address='CxiSc1-0|Cspad2x2-0',
data=self.sum_img,
distance=1,
)
cspad_tbx.dwritef(d, output_dirname, "sum_")
if 1:
output_image(self.avg_img, "%s/avg.png" %output_dirname)
output_image(self.avg_img, "%s/avg_inv.png" %output_dirname, invert=True)
if 1:
output_matlab_form(self.sum_img, "%s/sum.m" %output_dirname)
output_matlab_form(self.avg_img, "%s/avg.m" %output_dirname)
output_matlab_form(self.stddev_img, "%s/stddev.m" %output_dirname)
if (stddev_basename is not None):
d = cspad_tbx.dpack(
address='CxiSc1-0|Cspad2x2-0',
data=self.stddev_img,
distance=1,
)
cspad_tbx.dwritef(d, output_dirname, stddev_basename)
# XXX we should really figure out automatically the area where the spectrum is
#write an integrated spectrum from lines 186-227
#spectrum_focus = self.sum_img.as_numpy_array()[186:228,:]
img = self.sum_img
if self.roi is None:
spectrum_focus = img
mask_focus = self.mask
else:
slices = (slice(self.roi[2],self.roi[3]), slice(self.roi[0],self.roi[1]))
spectrum_focus = img[slices]
mask_focus = self.mask[slices]
if False:
from matplotlib import pylab
pylab.imshow(spectrum_focus.as_numpy_array())
pylab.show()
output_spectrum(spectrum_focus, mask_focus=mask_focus,
output_dirname=output_dirname)
print "Total number of images used from %i runs: %i" %(i_run+1, self.nmemb)
