def run(config, tim=None):
"""
This method is where the data reduction process gets done.
@param config: Object containing the data reduction configuration
information.
@type config: L{hlr_utils.Configure}
@param tim: (OPTIONAL) Object that will allow the method to perform
timing evaluations.
@type tim: C{sns_time.DiffTime}
"""
import common_lib
import dr_lib
import DST
if tim is not None:
tim.getTime(False)
old_time = tim.getOldTime()
if config.data is None:
raise RuntimeError("Need to pass a data filename to the driver "\
+"script.")
# Read in geometry if one is provided
if config.inst_geom is not None:
if config.verbose:
print "Reading in instrument geometry file"
inst_geom_dst = DST.getInstance("application/x-NxsGeom",
config.inst_geom)
else:
inst_geom_dst = None
# Perform early background subtraction if the hwfix flag is used
if config.hwfix:
if not config.mc:
so_axis = "time_of_flight"
else:
so_axis = "Time_of_Flight"
bkg_som0 = dr_lib.add_files(config.back,
Data_Paths=config.data_paths.toPath(),
SO_Axis=so_axis,
Signal_ROI=config.roi_file,
dataset_type="background",
Verbose=config.verbose, Timer=tim)
bkg_som = dr_lib.fix_bin_contents(bkg_som0)
del bkg_som0
else:
bkg_som = None
# Perform Steps 1-15 on sample data
d_som1 = dr_lib.process_igs_data(config.data, config, timer=tim,
inst_geom_dst=inst_geom_dst,
tib_const=config.tib_data_const,
bkg_som=bkg_som)
# Perform Steps 1-15 on empty can data
if config.ecan is not None:
e_som1 = dr_lib.process_igs_data(config.ecan, config, timer=tim,
inst_geom_dst=inst_geom_dst,
dataset_type="empty_can",
tib_const=config.tib_ecan_const,
bkg_som=bkg_som)
else:
e_som1 = None
# Perform Steps 1-15 on normalization data
if config.norm is not None:
n_som1 = dr_lib.process_igs_data(config.norm, config, timer=tim,
inst_geom_dst=inst_geom_dst,
dataset_type="normalization",
tib_const=config.tib_norm_const,
bkg_som=bkg_som)
else:
n_som1 = None
# Perform Steps 1-15 on background data
if config.back is not None and not config.hwfix:
b_som1 = dr_lib.process_igs_data(config.back, config, timer=tim,
inst_geom_dst=inst_geom_dst,
dataset_type="background",
tib_const=config.tib_back_const)
else:
b_som1 = None
# Perform Step 1-15 on direct scattering background data
if config.dsback is not None:
ds_som1 = dr_lib.process_igs_data(config.dsback, config, timer=tim,
inst_geom_dst=inst_geom_dst,
tib_const=config.tib_dsback_const,
dataset_type="dsbackground",
bkg_som=bkg_som)
# Note: time_zero_slope MUST be a tuple
if config.time_zero_slope is not None:
ds_som1.attr_list["Time_zero_slope"] = \
config.time_zero_slope.toValErrTuple()
# Note: time_zero_offset MUST be a tuple
if config.time_zero_offset is not None:
ds_som1.attr_list["Time_zero_offset"] = \
config.time_zero_offset.toValErrTuple()
# Step 16: Linearly interpolate TOF elastic range in direct scattering
# background data
# First convert TOF elastic range to appropriate pixel initial
# wavelengths
if config.verbose:
print "Determining initial wavelength range for elastic line"
if tim is not None:
tim.getTime(False)
if config.tof_elastic is None:
# Units are in microseconds
tof_elastic_range = (140300, 141300)
else:
tof_elastic_range = config.tof_elastic
ctof_elastic_low = dr_lib.convert_single_to_list(\
"tof_to_initial_wavelength_igs_lin_time_zero",
(tof_elastic_range[0], 0.0),
ds_som1)
ctof_elastic_high = dr_lib.convert_single_to_list(\
"tof_to_initial_wavelength_igs_lin_time_zero",
(tof_elastic_range[1], 0.0),
ds_som1)
ctof_elastic_range = [(ctof_elastic_low[i][0], ctof_elastic_high[i][0])
for i in xrange(len(ctof_elastic_low))]
if tim is not None:
tim.getTime(msg="After calculating initial wavelength range for "\
+"elastic line ")
del ctof_elastic_low, ctof_elastic_high
# Now interpolate spectra between TOF elastic range (converted to
# initial wavelength)
if config.verbose:
print "Linearly interpolating direct scattering spectra"
if tim is not None:
tim.getTime(False)
ds_som2 = dr_lib.lin_interpolate_spectra(ds_som1, ctof_elastic_range)
if tim is not None:
tim.getTime(msg="After linearly interpolating direct scattering "\
+"spectra ")
if config.dump_dslin:
ds_som2_1 = dr_lib.sum_all_spectra(ds_som2,\
rebin_axis=config.lambda_bins.toNessiList())
hlr_utils.write_file(config.output, "text/Spec", ds_som2_1,
output_ext="lin",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="dsbackground linear interpolation")
del ds_som2_1
del ds_som1
else:
ds_som2 = None
if inst_geom_dst is not None:
inst_geom_dst.release_resource()
# Steps 17-18: Subtract background spectrum from sample spectrum
if config.dsback is None:
back_som = b_som1
bkg_type = "background"
else:
back_som = ds_som2
bkg_type = "dsbackground"
d_som2 = dr_lib.subtract_bkg_from_data(d_som1, back_som,
verbose=config.verbose,
timer=tim,
dataset1="data",
dataset2=bkg_type,
scale=config.scale_bs)
if config.dsback is not None:
del ds_som2
# Step 19: Zero region outside TOF elastic for background for empty can
if config.dsback is None:
bcs_som = b_som1
cs_som = e_som1
else:
if config.verbose and b_som1 is not None:
print "Zeroing background spectra"
if tim is not None and b_som1 is not None:
tim.getTime(False)
bcs_som = dr_lib.zero_spectra(b_som1, ctof_elastic_range)
if tim is not None and b_som1 is not None:
tim.getTime(msg="After zeroing background spectra")
if config.verbose and e_som1 is not None:
print "Zeroing empty can spectra"
if tim is not None and e_som1 is not None:
tim.getTime(False)
cs_som = dr_lib.zero_spectra(e_som1, ctof_elastic_range)
if tim is not None and e_som1 is not None:
tim.getTime(msg="After zeroing empty can spectra")
del ctof_elastic_range
# Steps 20-21: Subtract background spectrum from empty can spectrum
e_som2 = dr_lib.subtract_bkg_from_data(cs_som, bcs_som,
verbose=config.verbose,
timer=tim,
dataset1="empty_can",
dataset2="background",
scale=config.scale_bcs)
# Steps 22-23: Subtract background spectrum from empty can spectrum for
# normalization
e_som3 = dr_lib.subtract_bkg_from_data(e_som1, b_som1,
verbose=config.verbose,
timer=tim,
dataset1="empty_can",
dataset2="background",
scale=config.scale_bcn)
# Steps 24-25: Subtract background spectrum from normalization spectrum
n_som2 = dr_lib.subtract_bkg_from_data(n_som1, b_som1,
verbose=config.verbose,
timer=tim,
dataset1="normalization",
dataset2="background",
scale=config.scale_bn)
del b_som1, e_som1, bcs_som, cs_som
# Steps 26-27: Subtract empty can spectrum from sample spectrum
d_som3 = dr_lib.subtract_bkg_from_data(d_som2, e_som2,
verbose=config.verbose,
timer=tim,
dataset1="data",
dataset2="empty_can",
scale=config.scale_cs)
del d_som2, e_som2
# Steps 28-29: Subtract empty can spectrum from normalization spectrum
n_som3 = dr_lib.subtract_bkg_from_data(n_som2, e_som3,
verbose=config.verbose,
timer=tim,
dataset1="normalization",
dataset2="empty_can",
scale=config.scale_cn)
del n_som2, e_som3
# Step 30-32: Integrate normalization spectra
if config.verbose and n_som3 is not None:
print "Integrating normalization spectra"
norm_int = dr_lib.integrate_spectra(n_som3, start=config.norm_start,
end=config.norm_end, norm=True)
del n_som3
# Step 33: Normalize data by integrated values
if config.verbose and norm_int is not None:
print "Normalizing data by normalization data"
if norm_int is not None:
d_som4 = common_lib.div_ncerr(d_som3, norm_int)
else:
d_som4 = d_som3
if norm_int is not None:
if tim is not None:
tim.getTime(msg="After normalizing data ")
del d_som3, norm_int
# Step 35: Convert initial wavelength to E_initial
if config.verbose:
print "Converting initial wavelength to E_initial"
if tim is not None:
tim.getTime(False)
d_som6 = common_lib.wavelength_to_energy(d_som4)
if tim is not None:
tim.getTime(msg="After converting initial wavelength to E_initial ")
if config.dump_initial_energy:
hlr_utils.write_file(config.output, "text/Spec", d_som6,
output_ext="ixl",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="pixel initial energy information")
del d_som4
# Steps 36-37: Calculate energy transfer
if config.verbose:
print "Calculating energy transfer"
if tim is not None:
tim.getTime(False)
d_som7 = dr_lib.igs_energy_transfer(d_som6)
if tim is not None:
tim.getTime(msg="After calculating energy transfer ")
if config.dump_energy:
hlr_utils.write_file(config.output, "text/Spec", d_som7,
output_ext="exl",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="pixel energy transfer information")
# Write 3-column ASCII file for E_t
d_som7_1 = dr_lib.sum_all_spectra(d_som7,
rebin_axis=config.E_bins.toNessiList())
hlr_utils.write_file(config.output, "text/Spec", d_som7_1,
output_ext="etr",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="combined energy transfer information")
del d_som7_1
# Steps 34,36-37: Calculate scaled energy transfer
if config.verbose:
print "Calculating scaled energy transfer"
d_som9 = dr_lib.igs_energy_transfer(d_som6, scale=True)
if tim is not None:
tim.getTime(msg="After calculating scaled energy transfer ")
if config.dump_energy:
hlr_utils.write_file(config.output, "text/Spec", d_som9,
output_ext="sexl",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="pixel scaled energy transfer "\
+"information")
# Write 3-column ASCII file for scaled E_t
d_som9_1 = dr_lib.sum_all_spectra(d_som9,
rebin_axis=config.E_bins.toNessiList())
hlr_utils.write_file(config.output, "text/Spec", d_som9_1,
output_ext="setr",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="combined scaled energy transfer "\
+"information")
del d_som9_1
del d_som6, d_som7
d_som9.attr_list["config"] = config
hlr_utils.write_file(config.output, "text/rmd", d_som9,
output_ext="rmd",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="metadata")
if tim is not None:
tim.setOldTime(old_time)
tim.getTime(msg="Total Running Time")
def run(config, tim=None):
"""
This method is where the data reduction process gets done.
@param config: Object containing the data reduction configuration
information.
@type config: L{hlr_utils.Configure}
@param tim: (OPTIONAL) Object that will allow the method to perform
timing evaluations.
@type tim: C{sns_time.DiffTime}
"""
import common_lib
import dr_lib
import DST
if tim is not None:
tim.getTime(False)
old_time = tim.getOldTime()
if config.data is None:
raise RuntimeError("Need to pass a data filename to the driver "\
+"script.")
# Read in geometry if one is provided
if config.inst_geom is not None:
if config.verbose:
print "Reading in instrument geometry file"
inst_geom_dst = DST.getInstance("application/x-NxsGeom",
config.inst_geom)
else:
inst_geom_dst = None
# Perform early background subtraction if the hwfix flag is used
if config.hwfix:
if not config.mc:
so_axis = "time_of_flight"
else:
so_axis = "Time_of_Flight"
bkg_som0 = dr_lib.add_files(config.back,
Data_Paths=config.data_paths.toPath(),
SO_Axis=so_axis,
Signal_ROI=config.roi_file,
dataset_type="background",
Verbose=config.verbose, Timer=tim)
bkg_som = dr_lib.fix_bin_contents(bkg_som0)
del bkg_som0
else:
bkg_som = None
# Perform Steps 1-15 on sample data
d_som1 = dr_lib.process_igs_data(config.data, config, timer=tim,
inst_geom_dst=inst_geom_dst,
tib_const=config.tib_data_const,
bkg_som=bkg_som)
# Perform Steps 1-15 on empty can data
if config.ecan is not None:
e_som1 = dr_lib.process_igs_data(config.ecan, config, timer=tim,
inst_geom_dst=inst_geom_dst,
dataset_type="empty_can",
tib_const=config.tib_ecan_const,
bkg_som=bkg_som)
else:
e_som1 = None
# Perform Steps 1-15 on normalization data
if config.norm is not None:
n_som1 = dr_lib.process_igs_data(config.norm, config, timer=tim,
inst_geom_dst=inst_geom_dst,
dataset_type="normalization",
tib_const=config.tib_norm_const,
bkg_som=bkg_som)
else:
n_som1 = None
# Perform Steps 1-15 on background data
if config.back is not None:
b_som1 = dr_lib.process_igs_data(config.back, config, timer=tim,
inst_geom_dst=inst_geom_dst,
dataset_type="background",
tib_const=config.tib_back_const,
bkg_som=bkg_som)
else:
b_som1 = None
# Perform Step 1-15 on direct scattering background data
if config.dsback is not None:
ds_som1 = dr_lib.process_igs_data(config.dsback, config, timer=tim,
inst_geom_dst=inst_geom_dst,
tib_const=config.tib_dsback_const,
dataset_type="dsbackground",
bkg_som=bkg_som)
# Note: time_zero_slope MUST be a tuple
if config.time_zero_slope is not None:
ds_som1.attr_list["Time_zero_slope"] = \
config.time_zero_slope.toValErrTuple()
# Note: time_zero_offset MUST be a tuple
if config.time_zero_offset is not None:
ds_som1.attr_list["Time_zero_offset"] = \
config.time_zero_offset.toValErrTuple()
# Step 16: Linearly interpolate TOF elastic range in direct scattering
# background data
# First convert TOF elastic range to appropriate pixel initial
# wavelengths
if config.verbose:
print "Determining initial wavelength range for elastic line"
if tim is not None:
tim.getTime(False)
if config.tof_elastic is None:
# Units are in microseconds
tof_elastic_range = (140300, 141300)
else:
tof_elastic_range = config.tof_elastic
ctof_elastic_low = dr_lib.convert_single_to_list(\
"tof_to_initial_wavelength_igs_lin_time_zero",
(tof_elastic_range[0], 0.0),
ds_som1)
ctof_elastic_high = dr_lib.convert_single_to_list(\
"tof_to_initial_wavelength_igs_lin_time_zero",
(tof_elastic_range[1], 0.0),
ds_som1)
ctof_elastic_range = [(ctof_elastic_low[i][0], ctof_elastic_high[i][0])
for i in xrange(len(ctof_elastic_low))]
if tim is not None:
tim.getTime(msg="After calculating initial wavelength range for "\
+"elastic line ")
del ctof_elastic_low, ctof_elastic_high
if config.split:
lambda_filter = [(d_som1[i].axis[0].val[0],
d_som1[i].axis[0].val[-1])
for i in xrange(len(d_som1))]
else:
lambda_filter = None
# Now interpolate spectra between TOF elastic range (converted to
# initial wavelength)
if config.verbose:
print "Linearly interpolating direct scattering spectra"
if tim is not None:
tim.getTime(False)
ds_som2 = dr_lib.lin_interpolate_spectra(ds_som1, ctof_elastic_range,
filter_axis=lambda_filter)
if tim is not None:
tim.getTime(msg="After linearly interpolating direct scattering "\
+"spectra ")
if config.dump_dslin:
ds_som2_1 = dr_lib.sum_all_spectra(ds_som2,\
rebin_axis=config.lambda_bins.toNessiList())
hlr_utils.write_file(config.output, "text/Spec", ds_som2_1,
output_ext="lin",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="dsbackground linear interpolation")
del ds_som2_1
del ds_som1
else:
ds_som2 = None
if inst_geom_dst is not None:
inst_geom_dst.release_resource()
# Steps 17-18: Subtract background spectrum from sample spectrum
if config.dsback is None:
back_som = b_som1
bkg_type = "background"
else:
back_som = ds_som2
bkg_type = "dsbackground"
d_som2 = dr_lib.subtract_bkg_from_data(d_som1, back_som,
verbose=config.verbose,
timer=tim,
dataset1="data",
dataset2=bkg_type,
scale=config.scale_bs)
if config.dsback is not None:
del ds_som2
# Step 19: Zero region outside TOF elastic for background for empty can
if config.dsback is None:
bcs_som = b_som1
cs_som = e_som1
else:
if config.verbose and b_som1 is not None:
print "Zeroing background spectra"
if tim is not None and b_som1 is not None:
tim.getTime(False)
bcs_som = dr_lib.zero_spectra(b_som1, ctof_elastic_range)
if tim is not None and b_som1 is not None:
tim.getTime(msg="After zeroing background spectra")
if config.verbose and e_som1 is not None:
print "Zeroing empty can spectra"
if tim is not None and e_som1 is not None:
tim.getTime(False)
cs_som = dr_lib.zero_spectra(e_som1, ctof_elastic_range)
if tim is not None and e_som1 is not None:
tim.getTime(msg="After zeroing empty can spectra")
del ctof_elastic_range
# Steps 20-21: Subtract background spectrum from empty can spectrum
e_som2 = dr_lib.subtract_bkg_from_data(cs_som, bcs_som,
verbose=config.verbose,
timer=tim,
dataset1="data-empty_can",
dataset2="background",
scale=config.scale_bcs)
# Steps 22-23: Subtract background spectrum from empty can spectrum for
# normalization
try:
config.pre_norm
except AttributeError:
config.pre_norm = False
if not config.pre_norm:
e_som3 = dr_lib.subtract_bkg_from_data(e_som1, b_som1,
verbose=config.verbose,
timer=tim,
dataset1="norm-empty_can",
dataset2="background",
scale=config.scale_bcn)
else:
e_som3 = None
# Steps 24-25: Subtract background spectrum from normalization spectrum
if not config.pre_norm:
n_som2 = dr_lib.subtract_bkg_from_data(n_som1, b_som1,
verbose=config.verbose,
timer=tim,
dataset1="normalization",
dataset2="background",
scale=config.scale_bn)
else:
n_som2 = n_som1
del b_som1, e_som1, bcs_som, cs_som
# Steps 26-27: Subtract empty can spectrum from sample spectrum
d_som3 = dr_lib.subtract_bkg_from_data(d_som2, e_som2,
verbose=config.verbose,
timer=tim,
dataset1="data",
dataset2="empty_can",
scale=config.scale_cs)
del d_som2, e_som2
# Steps 28-29: Subtract empty can spectrum from normalization spectrum
if not config.pre_norm:
n_som3 = dr_lib.subtract_bkg_from_data(n_som2, e_som3,
verbose=config.verbose,
timer=tim,
dataset1="normalization",
dataset2="empty_can",
scale=config.scale_cn)
else:
n_som3 = n_som2
del n_som2, e_som3
# Step 30-31: Integrate normalization spectra
if config.verbose and n_som3 is not None and not config.pre_norm:
print "Integrating normalization spectra"
if not config.pre_norm:
norm_int = dr_lib.integrate_spectra(n_som3, start=config.norm_start,
end=config.norm_end, norm=True)
else:
norm_int = n_som3
del n_som3
# Step 32: Normalize data by integrated values
if config.verbose and norm_int is not None:
print "Normalizing data by normalization data"
if norm_int is not None:
d_som4 = common_lib.div_ncerr(d_som3, norm_int)
else:
d_som4 = d_som3
if norm_int is not None:
if tim is not None:
tim.getTime(msg="After normalizing data ")
del d_som3, norm_int
if config.dump_norm:
if tim is not None:
tim.getTime(False)
hlr_utils.write_file(config.output, "text/Spec", d_som4,
output_ext="wvn",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="wavelength (vanadium norm) information")
if tim is not None:
tim.getTime(msg="After writing wavelength (vanadium norm) info ")
# Steps 33 to end: Creating S(Q,E)
if config.Q_bins is not None:
if config.verbose:
print "Creating 2D spectrum"
if tim is not None:
tim.getTime(False)
d_som5 = dr_lib.create_E_vs_Q_igs(d_som4,
config.E_bins.toNessiList(),
config.Q_bins.toNessiList(),
so_id="Full Detector",
y_label="counts",
y_units="counts / (ueV * A^-1)",
x_labels=["Q transfer",
"energy transfer"],
x_units=["1/Angstroms","ueV"],
split=config.split,
Q_filter=False,
configure=config)
if tim is not None:
tim.getTime(msg="After creation of final spectrum ")
del d_som4
# Steps 33 to 36: Create S(-cos(polar), E)
elif config.ncospol_bins is not None:
if config.verbose:
print "Convert wavelength to energy transfer"
if tim is not None:
tim.getTime(False)
d_som4a = dr_lib.energy_transfer(d_som4, "IGS", "Wavelength_final",
sa_norm=True, scale=True,
change_units=True)
if tim is not None:
tim.getTime(msg="After wavelength to energy transfer conversion ")
del d_som4
if config.verbose:
print "Creating 2D spectrum"
if tim is not None:
tim.getTime(False)
d_som5 = dr_lib.create_param_vs_Y(d_som4a, "polar",
"negcos_param_array",
config.ncospol_bins.toNessiList(),
rebin_axis=config.E_bins.toNessiList(),
y_label="counts",
y_units="counts / ueV",
x_labels=["-cos(polar)",
"Energy Transfer"],
x_units=["", "ueV"])
if tim is not None:
tim.getTime(msg="After creation of final spectrum ")
# If rescaling factor present, rescale the data
if config.rescale_final is not None and not config.split:
d_som6 = common_lib.mult_ncerr(d_som5, (config.rescale_final, 0.0))
else:
d_som6 = d_som5
if tim is None:
old_time = None
if not __name__ == "amorphous_reduction_sqe":
del d_som5
__write_output(d_som6, config, tim, old_time)
else:
if config.create_output:
del d_som5
__write_output(d_som6, config, tim, old_time)
else:
return d_som6
def process_sas_data(datalist, conf, **kwargs):
"""
This function combines Steps 1 through 9 of the data reduction process for
Small-Angle Scattering section 2.5.1 as specified by the documents at
U{http://neutrons.ornl.gov/asg/projects/SCL/reqspec/DR_Lib_RS.doc}. The
function takes a list of file names, a L{hlr_utils.Configure} object and
processes the data accordingly. This function should really only be used in
the context of I{sas_reduction}.
@param datalist: A list containing the filenames of the data to be
processed.
@type datalist: C{list} of C{string}s
@param conf: Object that contains the current setup of the driver.
@type conf: L{hlr_utils.Configure}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword inst_geom_dst: File object that contains instrument geometry
information.
@type inst_geom_dst: C{DST.GeomDST}
@keyword dataset_type: The practical name of the dataset being processed.
The default value is I{data}.
@type dataset_type: C{string}
@keyword trans_data: Alternate data for the transmission spectrum. This is
used in the absence of transmission monitors.
@type trans_data: C{string}
@keyword transmission: A flag that signals the function to stop after
doing the conversion from TOF to wavelength. The
default is I{False}.
@type transmission: C{boolean}
@keyword bkg_subtract: A list of coefficients that help determine the
wavelength dependent background subtraction.
@type bkg_subtract: C{list}
@keyword get_background: A flag that signals the function to convert the
main data to wavelength and exit before
normalizing to the beam monitor.
@type get_background: C{boolean}
@keyword acc_down_time: The information for the accelerator downtime.
@type acc_down_time: C{tuple}
@keyword bkg_scale: The scaling used for the axis dependent background
parameters.
@type bkg_scale: C{float}
@keyword timer: Timing object so the function can perform timing estimates.
@type timer: C{sns_timer.DiffTime}
@return: Object that has undergone all requested processing steps
@rtype: C{SOM.SOM}
"""
import common_lib
import dr_lib
import hlr_utils
# Check keywords
try:
dataset_type = kwargs["dataset_type"]
except KeyError:
dataset_type = "data"
try:
i_geom_dst = kwargs["inst_geom_dst"]
except KeyError:
i_geom_dst = None
try:
t = kwargs["timer"]
except KeyError:
t = None
try:
transmission = kwargs["transmission"]
except KeyError:
transmission = False
try:
bkg_subtract = kwargs["bkg_subtract"]
except KeyError:
bkg_subtract = None
try:
trans_data = kwargs["trans_data"]
except KeyError:
trans_data = None
try:
get_background = kwargs["get_background"]
except KeyError:
get_background = False
acc_down_time = kwargs.get("acc_down_time")
bkg_scale = kwargs.get("bkg_scale")
# Add so_axis to Configure object
conf.so_axis = "time_of_flight"
# Step 0: Open appropriate data files
# Data
if conf.verbose:
print "Reading %s file" % dataset_type
# The [0] is to get the data SOM and ignore the None background SOM
dp_som = dr_lib.add_files(datalist, Data_Paths=conf.data_paths.toPath(),
SO_Axis=conf.so_axis, Signal_ROI=conf.roi_file,
dataset_type=dataset_type,
Verbose=conf.verbose, Timer=t)
if t is not None:
t.getTime(msg="After reading %s " % dataset_type)
dp_som1 = dr_lib.fix_bin_contents(dp_som)
del dp_som
if conf.inst_geom is not None:
i_geom_dst.setGeometry(conf.data_paths.toPath(), dp_som1)
if conf.dump_tof_r:
dp_som1_1 = dr_lib.create_param_vs_Y(dp_som1, "radius", "param_array",
conf.r_bins.toNessiList(),
y_label="counts",
y_units="counts / (usec * m)",
x_labels=["Radius", "TOF"],
x_units=["m", "usec"])
hlr_utils.write_file(conf.output, "text/Dave2d", dp_som1_1,
output_ext="tvr",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="TOF vs radius information")
del dp_som1_1
if conf.dump_tof_theta:
dp_som1_1 = dr_lib.create_param_vs_Y(dp_som1, "polar", "param_array",
conf.theta_bins.toNessiList(),
y_label="counts",
y_units="counts / (usec * rads)",
x_labels=["Polar Angle", "TOF"],
x_units=["rads", "usec"])
hlr_utils.write_file(conf.output, "text/Dave2d", dp_som1_1,
output_ext="tvt",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="TOF vs polar angle information")
del dp_som1_1
# Beam monitor
if not get_background:
if conf.beammon_over is None:
if conf.verbose:
print "Reading in beam monitor data from %s file" \
% dataset_type
# The [0] is to get the data SOM and ignore the None
# background SOM
dbm_som0 = dr_lib.add_files(datalist,
Data_Paths=conf.bmon_path.toPath(),
SO_Axis=conf.so_axis,
dataset_type=dataset_type,
Verbose=conf.verbose,
Timer=t)
if t is not None:
t.getTime(msg="After reading beam monitor data ")
if conf.inst_geom is not None:
i_geom_dst.setGeometry(conf.bmon_path.toPath(), dbm_som0)
else:
if conf.verbose:
print "Reading in vanadium data"
dbm_som0 = dr_lib.add_files(datalist,
Data_Paths=conf.data_paths.toPath(),
Signal_ROI=conf.roi_file,
SO_Axis=conf.so_axis,
dataset_type=dataset_type,
Verbose=conf.verbose,
Timer=t)
if t is not None:
t.getTime(msg="After reading vanadium data ")
if conf.inst_geom is not None:
i_geom_dst.setGeometry(conf.data_paths.toPath(), dbm_som0)
dbm_som1 = dr_lib.fix_bin_contents(dbm_som0)
del dbm_som0
else:
dbm_som1 = None
# Transmission monitor
if trans_data is None:
if conf.verbose:
print "Reading in transmission monitor data from %s file" \
% dataset_type
try:
dtm_som0 = dr_lib.add_files(datalist,
Data_Paths=conf.tmon_path.toPath(),
SO_Axis=conf.so_axis,
dataset_type=dataset_type,
Verbose=conf.verbose,
Timer=t)
if t is not None:
t.getTime(msg="After reading transmission monitor data ")
if conf.inst_geom is not None:
i_geom_dst.setGeometry(conf.tmon_path.toPath(), dtm_som0)
dtm_som1 = dr_lib.fix_bin_contents(dtm_som0)
del dtm_som0
# Transmission monitor cannot be found
except KeyError:
if conf.verbose:
print "Transmission monitor not found"
dtm_som1 = None
else:
dtm_som1 = None
# Note: time_zero_offset_det MUST be a tuple
if conf.time_zero_offset_det is not None:
dp_som1.attr_list["Time_zero_offset_det"] = \
conf.time_zero_offset_det.toValErrTuple()
# Note: time_zero_offset_mon MUST be a tuple
if conf.time_zero_offset_mon is not None and not get_background and \
conf.beammon_over is None:
dbm_som1.attr_list["Time_zero_offset_mon"] = \
conf.time_zero_offset_mon.toValErrTuple()
if conf.beammon_over is not None:
dbm_som1.attr_list["Time_zero_offset_det"] = \
conf.time_zero_offset_det.toValErrTuple()
if trans_data is None and dtm_som1 is not None:
dtm_som1.attr_list["Time_zero_offset_mon"] = \
conf.time_zero_offset_mon.toValErrTuple()
# Step 1: Convert TOF to wavelength for data and monitor
if conf.verbose:
print "Converting TOF to wavelength"
if t is not None:
t.getTime(False)
if not get_background:
# Convert beam monitor
if conf.beammon_over is None:
dbm_som2 = common_lib.tof_to_wavelength_lin_time_zero(
dbm_som1,
units="microsecond",
time_zero_offset=conf.time_zero_offset_mon.toValErrTuple())
else:
dbm_som2 = common_lib.tof_to_wavelength_lin_time_zero(
dbm_som1,
units="microsecond",
time_zero_offset=conf.time_zero_offset_det.toValErrTuple(),
inst_param="total")
else:
dbm_som2 = None
# Convert detector pixels
dp_som2 = common_lib.tof_to_wavelength_lin_time_zero(
dp_som1,
units="microsecond",
time_zero_offset=conf.time_zero_offset_det.toValErrTuple(),
inst_param="total")
if get_background:
return dp_som2
if dtm_som1 is not None:
# Convert transmission monitor
dtm_som2 = common_lib.tof_to_wavelength_lin_time_zero(
dtm_som1,
units="microsecond",
time_zero_offset=conf.time_zero_offset_mon.toValErrTuple())
else:
dtm_som2 = dtm_som1
if t is not None:
t.getTime(msg="After converting TOF to wavelength ")
del dp_som1, dbm_som1, dtm_som1
if conf.verbose and (conf.lambda_low_cut is not None or \
conf.lambda_high_cut is not None):
print "Cutting data spectra"
if t is not None:
t.getTime(False)
dp_som3 = dr_lib.cut_spectra(dp_som2, conf.lambda_low_cut,
conf.lambda_high_cut)
if t is not None:
t.getTime(msg="After cutting data spectra ")
del dp_som2
if conf.beammon_over is not None:
dbm_som2 = dr_lib.cut_spectra(dbm_som2, conf.lambda_low_cut,
conf.lambda_high_cut)
if conf.dump_wave:
hlr_utils.write_file(conf.output, "text/Spec", dp_som3,
output_ext="pxl",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="pixel wavelength information")
if conf.dump_bmon_wave:
if conf.beammon_over is None:
hlr_utils.write_file(conf.output, "text/Spec", dbm_som2,
output_ext="bmxl",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="beam monitor wavelength information")
else:
dbm_som2_1 = dr_lib.sum_by_rebin_frac(dbm_som2,
conf.lambda_bins.toNessiList())
hlr_utils.write_file(conf.output, "text/Spec", dbm_som2_1,
output_ext="bmxl",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="beam monitor override wavelength "\
+"information")
del dbm_som2_1
# Step 2: Subtract wavelength dependent background if necessary
if conf.verbose and bkg_subtract is not None:
print "Subtracting wavelength dependent background"
if bkg_subtract is not None:
if t is not None:
t.getTime(False)
duration = dp_som3.attr_list["%s-duration" % dataset_type]
scale = duration.getValue() - acc_down_time[0]
dp_som4 = dr_lib.subtract_axis_dep_bkg(dp_som3, bkg_subtract,
old_scale=bkg_scale,
new_scale=scale)
if t is not None:
t.getTime(msg="After subtracting wavelength dependent background ")
else:
dp_som4 = dp_som3
del dp_som3
# Step 3: Efficiency correct beam monitor
if conf.verbose and conf.mon_effc:
print "Efficiency correct beam monitor data"
if t is not None:
t.getTime(False)
if conf.mon_effc:
dbm_som3 = dr_lib.feff_correct_mon(dbm_som2, inst_name=conf.inst,
eff_const=conf.mon_eff_const)
else:
dbm_som3 = dbm_som2
if t is not None and conf.mon_effc:
t.getTime(msg="After efficiency correcting beam monitor ")
if conf.dump_bmon_effc and conf.mon_effc:
hlr_utils.write_file(conf.output, "text/Spec", dbm_som3,
output_ext="bmel",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="beam monitor wavelength information "\
+"(efficiency)")
del dbm_som2
# Step 4: Efficiency correct transmission monitor
if dtm_som2 is not None:
if conf.verbose and conf.mon_effc:
print "Efficiency correct transmission monitor data"
if t is not None:
t.getTime(False)
if conf.mon_effc:
dtm_som3 = dr_lib.feff_correct_mon(dtm_som2)
else:
dtm_som3 = dtm_som2
else:
dtm_som3 = dtm_som2
if t is not None and conf.mon_effc and dtm_som2 is not None:
t.getTime(msg="After efficiency correcting beam monitor ")
# Step 5: Efficiency correct detector pixels
if conf.det_effc:
if conf.verbose:
print "Calculating detector efficiency"
if t is not None:
t.getTime(False)
det_eff = dr_lib.create_det_eff(dp_som4, inst_name=conf.inst,
eff_scale_const=conf.det_eff_scale_const,
eff_atten_const=conf.det_eff_atten_const)
if t is not None:
t.getTime(msg="After calculating detector efficiency")
if conf.verbose:
print "Applying detector efficiency"
if t is not None:
t.getTime(False)
dp_som5 = common_lib.div_ncerr(dp_som4, det_eff)
if t is not None:
t.getTime(msg="After spplying detector efficiency")
else:
dp_som5 = dp_som4
del dp_som4
# Step 6: Rebin beam monitor axis onto detector pixel axis
if conf.beammon_over is None:
if not conf.no_bmon_norm:
if conf.verbose:
print "Rebin beam monitor axis to detector pixel axis"
if t is not None:
t.getTime(False)
dbm_som4 = dr_lib.rebin_monitor(dbm_som3, dp_som5, rtype="frac")
if t is not None:
t.getTime(msg="After rebinning beam monitor ")
else:
dbm_som4 = dbm_som3
else:
dbm_som4 = dbm_som3
del dbm_som3
if conf.dump_bmon_rebin:
hlr_utils.write_file(conf.output, "text/Spec", dbm_som4,
output_ext="bmrl",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="beam monitor wavelength information "\
+"(rebinned)")
# Step 7: Normalize data by beam monitor
if not conf.no_bmon_norm:
if conf.verbose:
print "Normalizing data by beam monitor"
if t is not None:
t.getTime(False)
dp_som6 = common_lib.div_ncerr(dp_som5, dbm_som4)
if t is not None:
t.getTime(msg="After normalizing data by beam monitor ")
else:
dp_som6 = dp_som5
del dp_som5
if transmission:
return dp_som6
if conf.dump_wave_bmnorm:
dp_som6_1 = dr_lib.sum_by_rebin_frac(dp_som6,
conf.lambda_bins.toNessiList())
write_message = "combined pixel wavelength information"
write_message += " (beam monitor normalized)"
hlr_utils.write_file(conf.output, "text/Spec", dp_som6_1,
output_ext="pbml",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message=write_message)
del dp_som6_1
if conf.dump_wave_r:
dp_som6_1 = dr_lib.create_param_vs_Y(dp_som6, "radius", "param_array",
conf.r_bins.toNessiList(),
rebin_axis=conf.lambda_bins.toNessiList(),
y_label="counts",
y_units="counts / (Angstrom * m)",
x_labels=["Radius", "Wavelength"],
x_units=["m", "Angstrom"])
hlr_utils.write_file(conf.output, "text/Dave2d", dp_som6_1,
output_ext="lvr",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="wavelength vs radius information")
del dp_som6_1
if conf.dump_wave_theta:
dp_som6_1 = dr_lib.create_param_vs_Y(dp_som6, "polar", "param_array",
conf.theta_bins.toNessiList(),
rebin_axis=conf.lambda_bins.toNessiList(),
y_label="counts",
y_units="counts / (Angstrom * rads)",
x_labels=["Polar Angle", "Wavelength"],
x_units=["rads", "Angstrom"])
hlr_utils.write_file(conf.output, "text/Dave2d", dp_som6_1,
output_ext="lvt",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="wavelength vs polar angle information")
del dp_som6_1
# Step 8: Rebin transmission monitor axis onto detector pixel axis
if trans_data is not None:
print "Reading in transmission monitor data from file"
dtm_som3 = dr_lib.add_files([trans_data],
dataset_type=dataset_type,
dst_type="text/Spec",
Verbose=conf.verbose,
Timer=t)
if conf.verbose and dtm_som3 is not None:
print "Rebin transmission monitor axis to detector pixel axis"
if t is not None:
t.getTime(False)
dtm_som4 = dr_lib.rebin_monitor(dtm_som3, dp_som6, rtype="frac")
if t is not None and dtm_som3 is not None:
t.getTime(msg="After rebinning transmission monitor ")
del dtm_som3
# Step 9: Normalize data by transmission monitor
if conf.verbose and dtm_som4 is not None:
print "Normalizing data by transmission monitor"
if t is not None:
t.getTime(False)
if dtm_som4 is not None:
# The transmission spectra derived from sas_tranmission does not have
# the same y information by convention as sample data or a
# tranmission monitor. Therefore, we'll fake it by setting the
# y information from the sample data into the transmission
if trans_data is not None:
dtm_som4.setYLabel(dp_som6.getYLabel())
dtm_som4.setYUnits(dp_som6.getYUnits())
dp_som7 = common_lib.div_ncerr(dp_som6, dtm_som4)
else:
dp_som7 = dp_som6
if t is not None and dtm_som4 is not None:
t.getTime(msg="After normalizing data by transmission monitor ")
del dp_som6
# Step 10: Convert wavelength to Q for data
if conf.verbose:
print "Converting data from wavelength to scalar Q"
if t is not None:
t.getTime(False)
dp_som8 = common_lib.wavelength_to_scalar_Q(dp_som7)
if t is not None:
t.getTime(msg="After converting wavelength to scalar Q ")
del dp_som7
if conf.facility == "LENS":
# Step 11: Apply SAS correction factor to data
if conf.verbose:
print "Applying geometrical correction"
if t is not None:
t.getTime(False)
dp_som9 = dr_lib.apply_sas_correct(dp_som8)
if t is not None:
t.getTime(msg="After applying geometrical correction ")
return dp_som9
else:
return dp_som8
def run(config, tim=None):
"""
This method is where the data reduction process gets done.
@param config: Object containing the data reduction configuration
information.
@type config: L{hlr_utils.Configure}
@param tim: (OPTIONAL) Object that will allow the method to perform
timing evaluations.
@type tim: C{sns_time.DiffTime}
"""
import dr_lib
import DST
if tim is not None:
tim.getTime(False)
old_time = tim.getOldTime()
if config.data is None:
raise RuntimeError("Need to pass a data filename to the driver "\
+"script.")
# Read in geometry if one is provided
if config.inst_geom is not None:
if config.verbose:
print "Reading in instrument geometry file"
inst_geom_dst = DST.getInstance("application/x-NxsGeom",
config.inst_geom)
else:
inst_geom_dst = None
# Perform Steps 1-11 on sample data
d_som1 = dr_lib.process_sas_data(config.data, config, timer=tim,
inst_geom_dst=inst_geom_dst,
bkg_subtract=config.bkg_coeff,
acc_down_time=config.data_acc_down_time.toValErrTuple(),
bkg_scale=config.bkg_scale,
trans_data=config.data_trans)
# Perform Steps 1-11 on buffer/solvent only data
if config.solv is not None:
s_som1 = dr_lib.process_sas_data(config.solv, config, timer=tim,
inst_geom_dst=inst_geom_dst,
dataset_type="solvent",
bkg_subtract=config.bkg_coeff,
acc_down_time=config.solv_acc_down_time.toValErrTuple(),
bkg_scale=config.bkg_scale,
trans_data=config.solv_trans)
else:
s_som1 = None
# Step 12: Subtract buffer/solvent only spectrum from sample spectrum
d_som2 = dr_lib.subtract_bkg_from_data(d_som1, s_som1,
verbose=config.verbose,
timer=tim,
dataset1="data",
dataset2="solvent")
del s_som1, d_som1
# Perform Steps 1-11 on empty-can data
if config.ecan is not None:
e_som1 = dr_lib.process_sas_data(config.ecan, config, timer=tim,
inst_geom_dst=inst_geom_dst,
dataset_type="empty_can",
bkg_subtract=config.bkg_coeff,
acc_down_time=config.ecan_acc_down_time.toValErrTuple(),
bkg_scale=config.bkg_scale,
trans_data=config.ecan_trans)
else:
e_som1 = None
# Step 13: Subtract empty-can spectrum from sample spectrum
d_som3 = dr_lib.subtract_bkg_from_data(d_som2, e_som1,
verbose=config.verbose,
timer=tim,
dataset1="data",
dataset2="empty_can")
del e_som1, d_som2
# Perform Steps 1-11 on open beam data
if config.open is not None:
o_som1 = dr_lib.process_sas_data(config.open, config, timer=tim,
inst_geom_dst=inst_geom_dst,
dataset_type="open_beam",
bkg_subtract=config.bkg_coeff,
acc_down_time=config.open_acc_down_time.toValErrTuple(),
bkg_scale=config.bkg_scale)
else:
o_som1 = None
# Step 14: Subtract open beam spectrum from sample spectrum
d_som4 = dr_lib.subtract_bkg_from_data(d_som3, o_som1,
verbose=config.verbose,
timer=tim,
dataset1="data",
dataset2="open_beam")
del o_som1, d_som3
# Perform Steps 1-11 on dark current data
if config.dkcur is not None:
dc_som1 = dr_lib.process_sas_data(config.open, config, timer=tim,
inst_geom_dst=inst_geom_dst,
dataset_type="dark_current",
bkg_subtract=config.bkg_coeff)
else:
dc_som1 = None
# Step 15: Subtract dark current spectrum from sample spectrum
d_som5 = dr_lib.subtract_bkg_from_data(d_som4, dc_som1,
verbose=config.verbose,
timer=tim,
dataset1="data",
dataset2="dark_current")
del dc_som1, d_som4
# Create 2D distributions is necessary
if config.dump_Q_r:
d_som5_1 = dr_lib.create_param_vs_Y(d_som5, "radius", "param_array",
config.r_bins.toNessiList(),
rebin_axis=config.Q_bins.toNessiList(),
binnorm=True,
y_label="S",
y_units="Counts / A^-1 m",
x_labels=["Radius", "Q"],
x_units=["m", "1/Angstroms"])
hlr_utils.write_file(config.output, "text/Dave2d", d_som5_1,
output_ext="qvr", verbose=config.verbose,
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
message="S(r, Q) information")
del d_som5_1
if config.dump_Q_theta:
d_som5_1 = dr_lib.create_param_vs_Y(d_som5, "polar", "param_array",
config.theta_bins.toNessiList(),
rebin_axis=config.Q_bins.toNessiList(),
binnorm=True,
y_label="S",
y_units="Counts / A^-1 rads",
x_labels=["Polar Angle", "Q"],
x_units=["rads", "1/Angstroms"])
hlr_utils.write_file(config.output, "text/Dave2d", d_som5_1,
output_ext="qvt", verbose=config.verbose,
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
message="S(theta, Q) information")
del d_som5_1
# Steps 16 and 17: Rebin and sum all spectra
if config.verbose:
print "Rebinning and summing for final spectrum"
if tim is not None:
tim.getTime(False)
if config.dump_frac_rebin:
set_conf = config
else:
set_conf = None
d_som6 = dr_lib.sum_by_rebin_frac(d_som5, config.Q_bins.toNessiList(),
configure=set_conf)
if tim is not None:
tim.getTime(msg="After rebinning and summing for spectrum")
del d_som5
if config.facility == "LENS":
# Step 18: Scale final spectrum by Q bin centers
if config.verbose:
print "Scaling final spectrum by Q centers"
if tim is not None:
tim.getTime(False)
d_som7 = dr_lib.fix_bin_contents(d_som6, scale=True, width=True,
units="1/Angstroms")
if tim is not None:
tim.getTime(msg="After scaling final spectrum")
else:
d_som7 = d_som6
del d_som6
# If rescaling factor present, rescale the data
if config.rescale_final is not None:
import common_lib
d_som8 = common_lib.mult_ncerr(d_som7, (config.rescale_final, 0.0))
else:
d_som8 = d_som7
del d_som7
hlr_utils.write_file(config.output, "text/Spec", d_som8,
verbose=config.verbose,
replace_path=False,
replace_ext=False,
message="combined S(Q) information")
# Create 1D canSAS file
hlr_utils.write_file(config.output, "text/canSAS", d_som8,
verbose=config.verbose,
output_ext="xml",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
message="combined S(Q) information")
d_som8.attr_list["config"] = config
hlr_utils.write_file(config.output, "text/rmd", d_som8,
output_ext="rmd",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="metadata")
if tim is not None:
tim.setOldTime(old_time)
tim.getTime(msg="Total Running Time")
def run(config, tim=None):
"""
This method is where the data reduction process gets done.
@param config: Object containing the data reduction configuration
information.
@type config: L{hlr_utils.Configure}
@param tim: (OPTIONAL) Object that will allow the method to perform
timing evaluations.
@type tim: C{sns_time.DiffTime}
"""
import common_lib
import dr_lib
import DST
if tim is not None:
tim.getTime(False)
old_time = tim.getOldTime()
if config.data is None:
raise RuntimeError("Need to pass a data filename to the driver "\
+"script.")
# Read in geometry if one is provided
if config.inst_geom is not None:
if config.verbose:
print "Reading in instrument geometry file"
inst_geom_dst = DST.getInstance("application/x-NxsGeom",
config.inst_geom)
else:
inst_geom_dst = None
# Add so_axis to Configure object
config.so_axis = "time_of_flight"
dataset_type = "background"
# Step 0: Open appropriate data files
# Data
if config.verbose:
print "Reading %s file" % dataset_type
# The [0] is to get the data SOM and ignore the None background SOM
dp_som = dr_lib.add_files(config.data,
Data_Paths=config.data_paths.toPath(),
SO_Axis=config.so_axis,
Signal_ROI=config.roi_file,
dataset_type=dataset_type,
Verbose=config.verbose,
Timer=tim)
if tim is not None:
tim.getTime(msg="After reading %s " % dataset_type)
dp_som0 = dr_lib.fix_bin_contents(dp_som)
del dp_som
if inst_geom_dst is not None:
inst_geom_dst.setGeometry(config.data_paths.toPath(), dp_som0)
# Note: time_zero_offset_det MUST be a tuple
if config.time_zero_offset_det is not None:
dp_som0.attr_list["Time_zero_offset_det"] = \
config.time_zero_offset_det.toValErrTuple()
# Step 2: Convert TOF to wavelength for data
if config.verbose:
print "Converting TOF to wavelength"
if tim is not None:
tim.getTime(False)
# Convert detector pixels
dp_som1 = common_lib.tof_to_wavelength_lin_time_zero(
dp_som0,
units="microsecond",
time_zero_offset=config.time_zero_offset_det.toValErrTuple(),
inst_param="total")
if tim is not None:
tim.getTime(msg="After converting TOF to wavelength ")
del dp_som0
if config.verbose:
print "Cutting spectra"
if tim is not None:
tim.getTime(False)
dp_som2 = dr_lib.cut_spectra(dp_som1, config.lambda_low_cut,
config.lambda_high_cut)
if tim is not None:
tim.getTime(msg="After cutting spectra ")
del dp_som1
rebin_axis = config.lambda_bins.toNessiList()
# Put the data on the same axis
if config.verbose:
print "Rebinning data onto specified wavelength axis"
if tim is not None:
tim.getTime(False)
dp_som3 = dr_lib.sum_by_rebin_frac(dp_som2, rebin_axis)
if tim is not None:
tim.getTime(msg="After rebinning data onto specified wavelength axis ")
del dp_som2
data_run_time = dp_som3.attr_list["background-duration"]
# Calculate the accelerator on time
if config.verbose:
print "Calculating accelerator on time"
acc_on_time = hlr_utils.DrParameter(
data_run_time.getValue() - config.acc_down_time.getValue(), 0.0,
"seconds")
# Get the number of data bins
num_wave_bins = len(rebin_axis) - 1
# Calculate the scaled accelerator uptime
if config.verbose:
print "Calculating the scaled accelerator uptime"
if tim is not None:
tim.getTime(False)
final_scale = acc_on_time.toValErrTuple()[0] / num_wave_bins
if tim is not None:
tim.getTime(msg="After calculating the scaled accelerator uptime ")
# Create the final background spectrum
if config.verbose:
print "Creating the background spectrum"
if tim is not None:
tim.getTime(False)
dp_som4 = common_lib.div_ncerr(dp_som3, (final_scale, 0))
dp_som4.attr_list["%s-Scaling" % dataset_type] = final_scale
if tim is not None:
tim.getTime(msg="After creating background spectrum ")
del dp_som3
# Write out the background spectrum
hlr_utils.write_file(config.output,
"text/Spec",
dp_som4,
verbose=config.verbose,
output_ext="bkg",
data_ext=config.ext_replacement,
replace_path=False,
replace_ext=True,
message="background spectrum")
dp_som4.attr_list["config"] = config
hlr_utils.write_file(config.output,
"text/rmd",
dp_som4,
output_ext="rmd",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="metadata")
if tim is not None:
tim.setOldTime(old_time)
tim.getTime(msg="Total Running Time")
def calibrate_dgs_data(datalist, conf, dkcur, **kwargs):
"""
This function combines Steps 3 through 6 in Section 2.1.1 of the data
reduction process for Direct Geometry Spectrometers as specified by the
document at
U{http://neutrons.ornl.gov/asg/projects/SCL/reqspec/DR_Lib_RS.doc}. The
function takes a list of file names, a L{hlr_utils.Configure} object and
processes the data accordingly.
@param datalist: A list containing the filenames of the data to be
processed.
@type datalist: C{list} of C{string}s
@param conf: Object that contains the current setup of the driver.
@type conf: L{hlr_utils.Configure}
@param dkcur: The object containing the TOF dark current data.
@type dkcur: C{SOM.SOM}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword inst_geom_dst: File object that contains instrument geometry
information.
@type inst_geom_dst: C{DST.GeomDST}
@keyword tib_const: A time-independent background constant to subtract
from every pixel.
@type tib_const: L{hlr_utils.DrParameter}
@keyword dataset_type: The practical name of the dataset being processed.
The default value is I{data}.
@type dataset_type: C{string}
@keyword cwp: A list of chopper phase corrections in units of microseconds.
@type cwp: C{list} of C{float}s
@keyword timer: Timing object so the function can perform timing estimates.
@type timer: C{sns_timer.DiffTime}
@return: Object that has undergone all requested processing steps
@rtype: C{SOM.SOM}
"""
import common_lib
import dr_lib
import hlr_utils
# Check keywords
try:
tib_const = kwargs["tib_const"]
except KeyError:
tib_const = None
try:
dataset_type = kwargs["dataset_type"]
except KeyError:
dataset_type = "data"
try:
t = kwargs["timer"]
except KeyError:
t = None
try:
i_geom_dst = kwargs["inst_geom_dst"]
except KeyError:
i_geom_dst = None
dataset_cwp = kwargs.get("cwp")
# Open the appropriate datafiles
if conf.verbose:
print "Reading %s file" % dataset_type
data_paths = conf.data_paths.toPath()
if conf.no_mon_norm:
mon_paths = None
else:
mon_paths = conf.usmon_path.toPath()
# Check for mask file since normalization drive doesn't understand option
try:
mask_file = conf.mask_file
except AttributeError:
mask_file = None
if t is not None:
oldtime = t.getOldTime()
(dp_som0, dm_som0) = dr_lib.add_files_dm(datalist, Data_Paths=data_paths,
Mon_Paths=mon_paths,
SO_Axis=conf.so_axis,
Signal_ROI=conf.roi_file,
Signal_MASK=mask_file,
dataset_type=dataset_type,
dataset_cwp=dataset_cwp,
Verbose=conf.verbose, Timer=t)
if t is not None:
t.setOldTime(oldtime)
t.getTime(msg="After reading %s file" % dataset_type)
# Cut the spectra if necessary
dp_somA = dr_lib.cut_spectra(dp_som0, conf.tof_cut_min, conf.tof_cut_max)
del dp_som0
dp_somB = dr_lib.fix_bin_contents(dp_somA)
del dp_somA
if dp_somB.attr_list.instrument.get_name() != "CNCS":
if conf.verbose:
print "Cutting spectrum at minimum TOF"
if t is not None:
t.getTime(False)
# Calculate minimum TOF for physical neutrons
if conf.initial_energy is not None:
initial_wavelength = common_lib.energy_to_wavelength(\
conf.initial_energy.toValErrTuple())
initial_velocity = common_lib.wavelength_to_velocity(\
initial_wavelength)
else:
# This should actually calculate it, but don't have a way right now
pass
if conf.time_zero_offset is not None:
time_zero_offset = conf.time_zero_offset.toValErrTuple()
else:
# This should actually calculate it, but don't have a way right now
time_zero_offset = (0.0, 0.0)
ss_length = dp_somB.attr_list.instrument.get_primary()
tof_min = (ss_length[0] / initial_velocity[0]) + time_zero_offset[0]
# Cut all spectra a the minimum TOF
dp_som1 = dr_lib.cut_spectra(dp_somB, tof_min, None)
if t is not None:
t.getTime(msg="After cutting spectrum at minimum TOF ")
else:
dp_som1 = dp_somB
del dp_somB
if dm_som0 is not None:
dm_som1 = dr_lib.fix_bin_contents(dm_som0)
else:
dm_som1 = dm_som0
del dm_som0
# Override geometry if necessary
if conf.inst_geom is not None:
i_geom_dst.setGeometry(data_paths, dp_som1)
if conf.inst_geom is not None and dm_som1 is not None:
i_geom_dst.setGeometry(mon_paths, dm_som1)
# Step 3: Integrate the upstream monitor
if dm_som1 is not None:
if conf.verbose:
print "Integrating upstream monitor spectrum"
if t is not None:
t.getTime(False)
if conf.mon_int_range is None:
start_val = float("inf")
end_val = float("inf")
else:
start_val = conf.mon_int_range[0]
end_val = conf.mon_int_range[1]
dm_som2 = dr_lib.integrate_spectra(dm_som1, start=start_val,
end=end_val,
width=True)
if t is not None:
t.getTime(msg="After integrating upstream monitor spectrum ")
else:
dm_som2 = dm_som1
del dm_som1
tib_norm_const = None
# Step 4: Divide data set by summed monitor spectrum
if dm_som2 is not None:
if conf.verbose:
print "Normalizing %s by monitor sum" % dataset_type
if t is not None:
t.getTime(False)
dp_som2 = common_lib.div_ncerr(dp_som1, dm_som2, length_one_som=True)
tib_norm_const = dm_som2[0].y
if t is not None:
t.getTime(msg="After normalizing %s by monitor sum" % dataset_type)
elif conf.pc_norm:
if conf.verbose:
print "Normalizing %s by proton charge" % dataset_type
pc_tag = dataset_type+"-proton_charge"
pc = dp_som1.attr_list[pc_tag]
# Scale the proton charge and then set the scale PC back to attributes
if conf.scale_pc is not None:
if conf.verbose:
print "Scaling %s proton charge" % dataset_type
pc = hlr_utils.scale_proton_charge(pc, conf.scale_pc)
dp_som1.attr_list[pc_tag] = pc
tib_norm_const = pc.getValue()
if t is not None:
t.getTime(False)
dp_som2 = common_lib.div_ncerr(dp_som1, (pc.getValue(), 0.0))
if t is not None:
t.getTime(msg="After normalizing %s by proton charge" \
% dataset_type)
else:
dp_som2 = dp_som1
del dp_som1, dm_som2
# Step 5: Scale dark current by data set measurement time
if dkcur is not None:
if conf.verbose:
print "Scaling dark current by %s acquisition time" % dataset_type
if t is not None:
t.getTime(False)
dstime_tag = dataset_type+"-duration"
dstime = dp_som2.attr_list[dstime_tag]
dkcur1 = common_lib.div_ncerr(dkcur, (dstime.getValue(), 0.0))
if t is not None:
t.getTime(msg="After scaling dark current by %s acquisition time" \
% dataset_type)
else:
dkcur1 = dkcur
del dkcur
# Step 6: Subtract scaled dark current from data set
if dkcur1 is not None:
if conf.verbose:
print "Subtracting %s by scaled dark current" % dataset_type
if t is not None:
t.getTime(False)
dp_som3 = common_lib.sub_ncerr(dp_som2, dkcur1)
if t is not None:
t.getTime(msg="After subtracting %s by scaled dark current" \
% dataset_type)
elif tib_const is not None and dkcur1 is None:
if conf.verbose:
print "Subtracting TIB constant from %s" % dataset_type
# Normalize the TIB constant by dividing by the current normalization
# the duration (if necessary) and the conversion from seconds to
# microseconds
tib_c = tib_const.toValErrTuple()
conv_sec_to_usec = 1.0e-6
if tib_norm_const is None:
tib_norm_const = 1
duration = 1
else:
duration_tag = dataset_type+"-duration"
duration = dp_som2.attr_list[duration_tag].getValue()
norm_const = (duration * conv_sec_to_usec) / tib_norm_const
tib_val = tib_c[0] * norm_const
tib_err2 = tib_c[1] * (norm_const * norm_const)
if t is not None:
t.getTime(False)
dp_som3 = common_lib.sub_ncerr(dp_som2, (tib_val, tib_err2))
if t is not None:
t.getTime(msg="After subtracting TIB constant from %s" \
% dataset_type)
elif conf.tib_range is not None and dkcur1 is None:
if conf.verbose:
print "Determining TIB constant from %s" % dataset_type
if t is not None:
t.getTime(False)
TIB = dr_lib.determine_time_indep_bkg(dp_som2, conf.tib_range,
is_range=True)
if t is not None:
t.getTime(msg="After determining TIB constant from %s" \
% dataset_type)
if conf.dump_tib:
file_comment = "TIB TOF Range: [%d, %d]" % (conf.tib_range[0],
conf.tib_range[1])
hlr_utils.write_file(conf.output, "text/num-info", TIB,
output_ext="tib",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="time-independent background "\
+"information",
tag="Average TIB",
units="counts/usec",
comments=[file_comment])
if conf.verbose:
print "Subtracting TIB constant from %s" % dataset_type
if t is not None:
t.getTime(False)
dp_som3 = common_lib.sub_ncerr(dp_som2, TIB)
if t is not None:
t.getTime(msg="After subtracting TIB constant from %s" \
% dataset_type)
del TIB
else:
dp_som3 = dp_som2
del dp_som2, dkcur1
if conf.dump_ctof_comb:
dp_som3_1 = dr_lib.sum_all_spectra(dp_som3)
hlr_utils.write_file(conf.output, "text/Spec", dp_som3_1,
output_ext="ctof",
extra_tag=dataset_type,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
verbose=conf.verbose,
message="combined calibrated TOF information")
del dp_som3_1
return dp_som3
def run(config):
"""
This method is where the data reduction process gets done.
@param config: Object containing the data reduction configuration
information.
@type config: L{hlr_utils.Configure}
"""
import sys
import dr_lib
import DST
try:
data_dst = DST.getInstance("application/x-NeXus", config.data[0])
except SystemError:
print "ERROR: Failed to data read file %s" % config.data[0]
sys.exit(-1)
so_axis = "time_of_flight"
if config.verbose:
print "Reading data file"
if config.roi_file is None:
d_som0 = data_dst.getSOM(config.data_paths.toPath(), so_axis,
start_id=config.starting_ids,
end_id=config.ending_ids)
else:
d_som0 = data_dst.getSOM(config.data_paths.toPath(), so_axis,
roi_file=config.roi_file)
if config.width:
d_som1 = dr_lib.fix_bin_contents(d_som0)
else:
d_som1 = d_som0
del d_som0
if config.dump_pxl:
hlr_utils.write_file(config.data[0], "text/Spec", d_som1,
output_ext="tfp", verbose=config.verbose,
path_replacement=config.path_replacement,
message="pixel TOF information")
else:
pass
if config.tib_const is not None:
import common_lib
d_som2 = common_lib.sub_ncerr(d_som1, config.tib_const.toValErrTuple())
if config.dump_sxl:
hlr_utils.write_file(config.data[0], "text/Spec", d_som2,
output_ext="tsp", verbose=config.verbose,
path_replacement=config.path_replacement,
message="TIB const sub pixel TOF information")
else:
d_som2 = d_som1
del d_som1
if len(d_som2) == 1:
if config.verbose:
print "Summing 1 spectrum."
d_som3 = d_som2
else:
if config.verbose:
print "Summing %d spectra." % len(d_som2)
d_som3 = dr_lib.sum_all_spectra(d_som2)
d_som3[0].id = d_som2[0].id
del d_som2
hlr_utils.write_file(config.output, "text/Spec", d_som3, replace_ext=False,
verbose=config.verbose,
path_replacement=config.path_replacement,
message="combined TOF information")
def process_igs_data(datalist, conf, **kwargs):
"""
This function combines Steps 1 through 8 of the data reduction process for
Inverse Geometry Spectrometers as specified by the documents at
U{http://neutrons.ornl.gov/asg/projects/SCL/reqspec/DR_Lib_RS.doc}. The
function takes a list of file names, a L{hlr_utils.Configure} object and
processes the data accordingly. This function should really only be used in
the context of I{amorphous_reduction} and I{calc_norm_eff}.
@param datalist: A list containing the filenames of the data to be
processed.
@type datalist: C{list} of C{string}s
@param conf: Object that contains the current setup of the driver.
@type conf: L{hlr_utils.Configure}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword inst_geom_dst: File object that contains instrument geometry
information.
@type inst_geom_dst: C{DST.GeomDST}
@keyword dataset_type: The practical name of the dataset being processed.
The default value is I{data}.
@type dataset_type: C{string}
@keyword tib_const: Object providing the time-independent background
constant to subtract.
@type tib_const: L{hlr_utils.DrParameter}
@keyword bkg_som: Object that will be used for early background subtraction
@type bkg_som: C{SOM.SOM}
@keyword timer: Timing object so the function can perform timing estimates.
@type timer: C{sns_timer.DiffTime}
@return: Object that has undergone all requested processing steps
@rtype: C{SOM.SOM}
"""
import hlr_utils
# Check keywords
try:
dataset_type = kwargs["dataset_type"]
except KeyError:
dataset_type = "data"
try:
t = kwargs["timer"]
except KeyError:
t = None
try:
if kwargs["tib_const"] is not None:
tib_const = kwargs["tib_const"].toValErrTuple()
else:
tib_const = None
except KeyError:
tib_const = None
try:
i_geom_dst = kwargs["inst_geom_dst"]
except KeyError:
i_geom_dst = None
try:
bkg_som = kwargs["bkg_som"]
except KeyError:
bkg_som = None
# Step 1: Open appropriate data files
if not conf.mc:
so_axis = "time_of_flight"
else:
so_axis = "Time_of_Flight"
# Add so_axis to Configure object
conf.so_axis = so_axis
if conf.verbose:
print "Reading %s file" % dataset_type
# Special case handling for normalization data. Dynamically trying to
# determine if incoming file is a previously calculated one.
if dataset_type == "normalization":
try:
# Check the first incoming file
dst_type = hlr_utils.file_peeker(datalist[0])
# If file_peeker succeeds, the DST is different than the function
# returns
dst_type = "text/num-info"
# Let ROI file handle filtering
data_paths = None
except RuntimeError:
# It's a NeXus file
dst_type = "application/x-NeXus"
data_paths = conf.data_paths.toPath()
else:
dst_type = "application/x-NeXus"
data_paths = conf.data_paths.toPath()
# The [0] is to get the data SOM and ignore the None background SOM
dp_som0 = dr_lib.add_files(datalist,
Data_Paths=data_paths,
SO_Axis=so_axis,
Signal_ROI=conf.roi_file,
dataset_type=dataset_type,
dst_type=dst_type,
Verbose=conf.verbose,
Timer=t)
if t is not None:
t.getTime(msg="After reading %s " % dataset_type)
if dst_type == "text/num-info":
# Since we have a pre-calculated normalization dataset, set the flag
# and return the SOM now
conf.pre_norm = True
# Make the labels and units compatible with a NeXus file based SOM
dp_som0.setAxisLabel(0, "wavelength")
dp_som0.setAxisUnits(0, "Angstroms")
dp_som0.setYUnits("Counts/A")
return dp_som0
else:
if dataset_type == "normalization":
# Since we have a NeXus file, we need to continue
conf.pre_norm = False
# Cut the spectra if necessary
dp_somA = dr_lib.cut_spectra(dp_som0, conf.tof_cut_min, conf.tof_cut_max)
del dp_som0
dp_som1 = dr_lib.fix_bin_contents(dp_somA)
del dp_somA
if conf.inst_geom is not None:
i_geom_dst.setGeometry(conf.data_paths.toPath(), dp_som1)
if conf.no_mon_norm:
dm_som1 = None
else:
if conf.verbose:
print "Reading in monitor data from %s file" % dataset_type
# The [0] is to get the data SOM and ignore the None background SOM
dm_som0 = dr_lib.add_files(datalist,
Data_Paths=conf.mon_path.toPath(),
SO_Axis=so_axis,
dataset_type=dataset_type,
Verbose=conf.verbose,
Timer=t)
if t is not None:
t.getTime(msg="After reading monitor data ")
dm_som1 = dr_lib.fix_bin_contents(dm_som0)
del dm_som0
if conf.inst_geom is not None:
i_geom_dst.setGeometry(conf.mon_path.toPath(), dm_som1)
if bkg_som is not None:
bkg_pcharge = bkg_som.attr_list["background-proton_charge"].getValue()
data_pcharge = dp_som1.attr_list[dataset_type +
"-proton_charge"].getValue()
ratio = data_pcharge / bkg_pcharge
bkg_som1 = common_lib.mult_ncerr(bkg_som, (ratio, 0.0))
del bkg_som
dp_som2 = dr_lib.subtract_bkg_from_data(dp_som1,
bkg_som1,
verbose=conf.verbose,
timer=t,
dataset1=dataset_type,
dataset2="background")
else:
dp_som2 = dp_som1
del dp_som1
# Step 2: Dead Time Correction
# No dead time correction is being applied to the data yet
# Step 3: Time-independent background determination
if conf.verbose and conf.tib_tofs is not None:
print "Determining time-independent background from data"
if t is not None and conf.tib_tofs is not None:
t.getTime(False)
B = dr_lib.determine_time_indep_bkg(dp_som2, conf.tib_tofs)
if t is not None and B is not None:
t.getTime(msg="After determining time-independent background ")
if conf.dump_tib and B is not None:
file_comment = "TOFs: %s" % conf.tib_tofs
hlr_utils.write_file(conf.output, "text/num-info", B,
output_ext="tib",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="time-independent background "\
+"information",
tag="Average",
units="counts",
comments=[file_comment])
# Step 4: Subtract time-independent background
if conf.verbose and B is not None:
print "Subtracting time-independent background from data"
if t is not None:
t.getTime(False)
if B is not None:
dp_som3 = common_lib.sub_ncerr(dp_som2, B)
else:
dp_som3 = dp_som2
if B is not None and t is not None:
t.getTime(msg="After subtracting time-independent background ")
del dp_som2, B
# Step 5: Subtract time-independent background constant
if conf.verbose and tib_const is not None:
print "Subtracting time-independent background constant from data"
if t is not None and tib_const is not None:
t.getTime(False)
if tib_const is not None:
dp_som4 = common_lib.sub_ncerr(dp_som3, tib_const)
else:
dp_som4 = dp_som3
if t is not None and tib_const is not None:
t.getTime(msg="After subtracting time-independent background "\
+"constant ")
del dp_som3
# Provide override capability for final wavelength, time-zero slope and
# time-zero offset
if conf.wavelength_final is not None:
dp_som4.attr_list["Wavelength_final"] = \
conf.wavelength_final.toValErrTuple()
# Note: time_zero_slope MUST be a tuple
if conf.time_zero_slope is not None:
dp_som4.attr_list["Time_zero_slope"] = \
conf.time_zero_slope.toValErrTuple()
if dm_som1 is not None:
dm_som1.attr_list["Time_zero_slope"] = \
conf.time_zero_slope.toValErrTuple()
# Note: time_zero_offset MUST be a tuple
if conf.time_zero_offset is not None:
dp_som4.attr_list["Time_zero_offset"] = \
conf.time_zero_offset.toValErrTuple()
if dm_som1 is not None:
dm_som1.attr_list["Time_zero_offset"] = \
conf.time_zero_offset.toValErrTuple()
# Step 6: Convert TOF to wavelength for data and monitor
if conf.verbose:
print "Converting TOF to wavelength"
if t is not None:
t.getTime(False)
# Convert monitor
if dm_som1 is not None:
dm_som2 = common_lib.tof_to_wavelength_lin_time_zero(
dm_som1, units="microsecond")
else:
dm_som2 = None
# Convert detector pixels
dp_som5 = common_lib.tof_to_initial_wavelength_igs_lin_time_zero(
dp_som4, units="microsecond", run_filter=conf.filter)
if t is not None:
t.getTime(msg="After converting TOF to wavelength ")
if conf.dump_wave:
hlr_utils.write_file(conf.output,
"text/Spec",
dp_som5,
output_ext="pxl",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="pixel wavelength information")
if conf.dump_mon_wave and dm_som2 is not None:
hlr_utils.write_file(conf.output,
"text/Spec",
dm_som2,
output_ext="mxl",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="monitor wavelength information")
del dp_som4, dm_som1
# Step 7: Efficiency correct monitor
if conf.verbose and dm_som2 is not None and not conf.no_mon_effc:
print "Efficiency correct monitor data"
if t is not None:
t.getTime(False)
if not conf.no_mon_effc:
dm_som3 = dr_lib.feff_correct_mon(dm_som2)
else:
dm_som3 = dm_som2
if t is not None and dm_som2 is not None and not conf.no_mon_effc:
t.getTime(msg="After efficiency correcting monitor ")
if conf.dump_mon_effc and not conf.no_mon_effc and dm_som3 is not None:
hlr_utils.write_file(conf.output, "text/Spec", dm_som3,
output_ext="mel",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="monitor wavelength information "\
+"(efficiency)")
del dm_som2
# Step 8: Rebin monitor axis onto detector pixel axis
if conf.verbose and dm_som3 is not None:
print "Rebin monitor axis to detector pixel axis"
if t is not None:
t.getTime(False)
dm_som4 = dr_lib.rebin_monitor(dm_som3, dp_som5)
if t is not None and dm_som4 is not None:
t.getTime(msg="After rebinning monitor ")
del dm_som3
if conf.dump_mon_rebin and dm_som4 is not None:
hlr_utils.write_file(conf.output, "text/Spec", dm_som4,
output_ext="mrl",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="monitor wavelength information "\
+"(rebinned)")
# The lambda-dependent background is only done on sample data (aka data)
# for the BSS instrument at the SNS
if conf.inst == "BSS" and conf.ldb_const is not None and \
dataset_type == "data":
# Step 9: Convert chopper center wavelength to TOF center
if conf.verbose:
print "Converting chopper center wavelength to TOF"
if t is not None:
t.getTime(False)
tof_center = dr_lib.convert_single_to_list(\
"initial_wavelength_igs_lin_time_zero_to_tof",
conf.chopper_lambda_cent.toValErrTuple(), dp_som5)
# Step 10: Calculate beginning and end of detector TOF spectrum
if conf.verbose:
print "Calculating beginning and ending TOF ranges"
half_inv_chop_freq = 0.5 / conf.chopper_freq.toValErrTuple()[0]
# Above is in seconds, need microseconds
half_inv_chop_freq *= 1.0e6
tof_begin = common_lib.sub_ncerr(tof_center, (half_inv_chop_freq, 0.0))
tof_end = common_lib.add_ncerr(tof_center, (half_inv_chop_freq, 0.0))
# Step 11: Convert TOF_begin and TOF_end to wavelength
if conf.verbose:
print "Converting TOF_begin and TOF_end to wavelength"
# Check for time-zero slope information
try:
tz_slope = conf.time_zero_slope.toValErrTuple()
except AttributeError:
tz_slope = (0.0, 0.0)
# Check for time-zero offset information
try:
tz_offset = conf.time_zero_offset.toValErrTuple()
except AttributeError:
tz_offset = (0.0, 0.0)
l_begin = common_lib.tof_to_initial_wavelength_igs_lin_time_zero(\
tof_begin, time_zero_slope=tz_slope, time_zero_offset=tz_offset,
iobj=dp_som5, run_filter=False)
l_end = common_lib.tof_to_initial_wavelength_igs_lin_time_zero(\
tof_end, time_zero_slope=tz_slope, time_zero_offset=tz_offset,
iobj=dp_som5, run_filter=False)
# Step 12: tof-least-bkg to lambda-least-bkg
if conf.verbose:
print "Converting TOF least background to wavelength"
lambda_least_bkg = dr_lib.convert_single_to_list(\
"tof_to_initial_wavelength_igs_lin_time_zero",
conf.tof_least_bkg.toValErrTuple(), dp_som5)
if t is not None:
t.getTime(msg="After converting boundary positions ")
# Step 13: Create lambda-dependent background spectrum
if conf.verbose:
print "Creating lambda-dependent background spectra"
if t is not None:
t.getTime(False)
ldb_som = dr_lib.shift_spectrum(dm_som4, lambda_least_bkg, l_begin,
l_end, conf.ldb_const.getValue())
if t is not None:
t.getTime(msg="After creating lambda-dependent background "\
+"spectra ")
# Step 14: Subtract lambda-dependent background from sample data
if conf.verbose:
print "Subtracting lambda-dependent background from data"
if t is not None:
t.getTime(False)
dp_som6 = common_lib.sub_ncerr(dp_som5, ldb_som)
if t is not None:
t.getTime(msg="After subtracting lambda-dependent background "\
+"from data ")
else:
dp_som6 = dp_som5
del dp_som5
# Step 15: Normalize data by monitor
if conf.verbose and dm_som4 is not None:
print "Normalizing data by monitor"
if t is not None:
t.getTime(False)
if dm_som4 is not None:
dp_som7 = common_lib.div_ncerr(dp_som6, dm_som4)
if t is not None:
t.getTime(msg="After normalizing data by monitor ")
else:
dp_som7 = dp_som6
if conf.dump_wave_mnorm:
dp_som7_1 = dr_lib.sum_all_spectra(dp_som7,\
rebin_axis=conf.lambda_bins.toNessiList())
write_message = "combined pixel wavelength information"
if dm_som4 is not None:
write_message += " (monitor normalized)"
hlr_utils.write_file(conf.output,
"text/Spec",
dp_som7_1,
output_ext="pml",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message=write_message)
del dp_som7_1
del dm_som4, dp_som6
return dp_som7
def calibrate_dgs_data(datalist, conf, dkcur, **kwargs):
"""
This function combines Steps 3 through 6 in Section 2.1.1 of the data
reduction process for Direct Geometry Spectrometers as specified by the
document at
U{http://neutrons.ornl.gov/asg/projects/SCL/reqspec/DR_Lib_RS.doc}. The
function takes a list of file names, a L{hlr_utils.Configure} object and
processes the data accordingly.
@param datalist: A list containing the filenames of the data to be
processed.
@type datalist: C{list} of C{string}s
@param conf: Object that contains the current setup of the driver.
@type conf: L{hlr_utils.Configure}
@param dkcur: The object containing the TOF dark current data.
@type dkcur: C{SOM.SOM}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword inst_geom_dst: File object that contains instrument geometry
information.
@type inst_geom_dst: C{DST.GeomDST}
@keyword tib_const: A time-independent background constant to subtract
from every pixel.
@type tib_const: L{hlr_utils.DrParameter}
@keyword dataset_type: The practical name of the dataset being processed.
The default value is I{data}.
@type dataset_type: C{string}
@keyword cwp: A list of chopper phase corrections in units of microseconds.
@type cwp: C{list} of C{float}s
@keyword timer: Timing object so the function can perform timing estimates.
@type timer: C{sns_timer.DiffTime}
@return: Object that has undergone all requested processing steps
@rtype: C{SOM.SOM}
"""
import common_lib
import dr_lib
import hlr_utils
# Check keywords
try:
tib_const = kwargs["tib_const"]
except KeyError:
tib_const = None
try:
dataset_type = kwargs["dataset_type"]
except KeyError:
dataset_type = "data"
try:
t = kwargs["timer"]
except KeyError:
t = None
try:
i_geom_dst = kwargs["inst_geom_dst"]
except KeyError:
i_geom_dst = None
dataset_cwp = kwargs.get("cwp")
# Open the appropriate datafiles
if conf.verbose:
print "Reading %s file" % dataset_type
data_paths = conf.data_paths.toPath()
if conf.no_mon_norm:
mon_paths = None
else:
mon_paths = conf.usmon_path.toPath()
# Check for mask file since normalization drive doesn't understand option
try:
mask_file = conf.mask_file
except AttributeError:
mask_file = None
if t is not None:
oldtime = t.getOldTime()
(dp_som0, dm_som0) = dr_lib.add_files_dm(datalist,
Data_Paths=data_paths,
Mon_Paths=mon_paths,
SO_Axis=conf.so_axis,
Signal_ROI=conf.roi_file,
Signal_MASK=mask_file,
dataset_type=dataset_type,
dataset_cwp=dataset_cwp,
Verbose=conf.verbose,
Timer=t)
if t is not None:
t.setOldTime(oldtime)
t.getTime(msg="After reading %s file" % dataset_type)
# Cut the spectra if necessary
dp_somA = dr_lib.cut_spectra(dp_som0, conf.tof_cut_min, conf.tof_cut_max)
del dp_som0
dp_somB = dr_lib.fix_bin_contents(dp_somA)
del dp_somA
if dp_somB.attr_list.instrument.get_name() != "CNCS":
if conf.verbose:
print "Cutting spectrum at minimum TOF"
if t is not None:
t.getTime(False)
# Calculate minimum TOF for physical neutrons
if conf.initial_energy is not None:
initial_wavelength = common_lib.energy_to_wavelength(\
conf.initial_energy.toValErrTuple())
initial_velocity = common_lib.wavelength_to_velocity(\
initial_wavelength)
else:
# This should actually calculate it, but don't have a way right now
pass
if conf.time_zero_offset is not None:
time_zero_offset = conf.time_zero_offset.toValErrTuple()
else:
# This should actually calculate it, but don't have a way right now
time_zero_offset = (0.0, 0.0)
ss_length = dp_somB.attr_list.instrument.get_primary()
tof_min = (ss_length[0] / initial_velocity[0]) + time_zero_offset[0]
# Cut all spectra a the minimum TOF
dp_som1 = dr_lib.cut_spectra(dp_somB, tof_min, None)
if t is not None:
t.getTime(msg="After cutting spectrum at minimum TOF ")
else:
dp_som1 = dp_somB
del dp_somB
if dm_som0 is not None:
dm_som1 = dr_lib.fix_bin_contents(dm_som0)
else:
dm_som1 = dm_som0
del dm_som0
# Override geometry if necessary
if conf.inst_geom is not None:
i_geom_dst.setGeometry(data_paths, dp_som1)
if conf.inst_geom is not None and dm_som1 is not None:
i_geom_dst.setGeometry(mon_paths, dm_som1)
# Step 3: Integrate the upstream monitor
if dm_som1 is not None:
if conf.verbose:
print "Integrating upstream monitor spectrum"
if t is not None:
t.getTime(False)
if conf.mon_int_range is None:
start_val = float("inf")
end_val = float("inf")
else:
start_val = conf.mon_int_range[0]
end_val = conf.mon_int_range[1]
dm_som2 = dr_lib.integrate_spectra(dm_som1,
start=start_val,
end=end_val,
width=True)
if t is not None:
t.getTime(msg="After integrating upstream monitor spectrum ")
else:
dm_som2 = dm_som1
del dm_som1
tib_norm_const = None
# Step 4: Divide data set by summed monitor spectrum
if dm_som2 is not None:
if conf.verbose:
print "Normalizing %s by monitor sum" % dataset_type
if t is not None:
t.getTime(False)
dp_som2 = common_lib.div_ncerr(dp_som1, dm_som2, length_one_som=True)
tib_norm_const = dm_som2[0].y
if t is not None:
t.getTime(msg="After normalizing %s by monitor sum" % dataset_type)
elif conf.pc_norm:
if conf.verbose:
print "Normalizing %s by proton charge" % dataset_type
pc_tag = dataset_type + "-proton_charge"
pc = dp_som1.attr_list[pc_tag]
# Scale the proton charge and then set the scale PC back to attributes
if conf.scale_pc is not None:
if conf.verbose:
print "Scaling %s proton charge" % dataset_type
pc = hlr_utils.scale_proton_charge(pc, conf.scale_pc)
dp_som1.attr_list[pc_tag] = pc
tib_norm_const = pc.getValue()
if t is not None:
t.getTime(False)
dp_som2 = common_lib.div_ncerr(dp_som1, (pc.getValue(), 0.0))
if t is not None:
t.getTime(msg="After normalizing %s by proton charge" \
% dataset_type)
else:
dp_som2 = dp_som1
del dp_som1, dm_som2
# Step 5: Scale dark current by data set measurement time
if dkcur is not None:
if conf.verbose:
print "Scaling dark current by %s acquisition time" % dataset_type
if t is not None:
t.getTime(False)
dstime_tag = dataset_type + "-duration"
dstime = dp_som2.attr_list[dstime_tag]
dkcur1 = common_lib.div_ncerr(dkcur, (dstime.getValue(), 0.0))
if t is not None:
t.getTime(msg="After scaling dark current by %s acquisition time" \
% dataset_type)
else:
dkcur1 = dkcur
del dkcur
# Step 6: Subtract scaled dark current from data set
if dkcur1 is not None:
if conf.verbose:
print "Subtracting %s by scaled dark current" % dataset_type
if t is not None:
t.getTime(False)
dp_som3 = common_lib.sub_ncerr(dp_som2, dkcur1)
if t is not None:
t.getTime(msg="After subtracting %s by scaled dark current" \
% dataset_type)
elif tib_const is not None and dkcur1 is None:
if conf.verbose:
print "Subtracting TIB constant from %s" % dataset_type
# Normalize the TIB constant by dividing by the current normalization
# the duration (if necessary) and the conversion from seconds to
# microseconds
tib_c = tib_const.toValErrTuple()
conv_sec_to_usec = 1.0e-6
if tib_norm_const is None:
tib_norm_const = 1
duration = 1
else:
duration_tag = dataset_type + "-duration"
duration = dp_som2.attr_list[duration_tag].getValue()
norm_const = (duration * conv_sec_to_usec) / tib_norm_const
tib_val = tib_c[0] * norm_const
tib_err2 = tib_c[1] * (norm_const * norm_const)
if t is not None:
t.getTime(False)
dp_som3 = common_lib.sub_ncerr(dp_som2, (tib_val, tib_err2))
if t is not None:
t.getTime(msg="After subtracting TIB constant from %s" \
% dataset_type)
elif conf.tib_range is not None and dkcur1 is None:
if conf.verbose:
print "Determining TIB constant from %s" % dataset_type
if t is not None:
t.getTime(False)
TIB = dr_lib.determine_time_indep_bkg(dp_som2,
conf.tib_range,
is_range=True)
if t is not None:
t.getTime(msg="After determining TIB constant from %s" \
% dataset_type)
if conf.dump_tib:
file_comment = "TIB TOF Range: [%d, %d]" % (conf.tib_range[0],
conf.tib_range[1])
hlr_utils.write_file(conf.output, "text/num-info", TIB,
output_ext="tib",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="time-independent background "\
+"information",
tag="Average TIB",
units="counts/usec",
comments=[file_comment])
if conf.verbose:
print "Subtracting TIB constant from %s" % dataset_type
if t is not None:
t.getTime(False)
dp_som3 = common_lib.sub_ncerr(dp_som2, TIB)
if t is not None:
t.getTime(msg="After subtracting TIB constant from %s" \
% dataset_type)
del TIB
else:
dp_som3 = dp_som2
del dp_som2, dkcur1
if conf.dump_ctof_comb:
dp_som3_1 = dr_lib.sum_all_spectra(dp_som3)
hlr_utils.write_file(conf.output,
"text/Spec",
dp_som3_1,
output_ext="ctof",
extra_tag=dataset_type,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
verbose=conf.verbose,
message="combined calibrated TOF information")
del dp_som3_1
return dp_som3
def run(config, tim=None):
"""
This method is where the data reduction process gets done.
@param config: Object containing the data reduction configuration
information.
@type config: L{hlr_utils.Configure}
@param tim: (OPTIONAL) Object that will allow the method to perform
timing evaluations.
@type tim: C{sns_time.DiffTime}
"""
import common_lib
import dr_lib
import DST
if tim is not None:
tim.getTime(False)
old_time = tim.getOldTime()
if config.data is None:
raise RuntimeError("Need to pass a data filename to the driver "\
+"script.")
# Read in geometry if one is provided
if config.inst_geom is not None:
if config.verbose:
print "Reading in instrument geometry file"
inst_geom_dst = DST.getInstance("application/x-NxsGeom",
config.inst_geom)
else:
inst_geom_dst = None
# Perform early background subtraction if the hwfix flag is used
if config.hwfix:
if not config.mc:
so_axis = "time_of_flight"
else:
so_axis = "Time_of_Flight"
bkg_som0 = dr_lib.add_files(config.back,
Data_Paths=config.data_paths.toPath(),
SO_Axis=so_axis,
Signal_ROI=config.roi_file,
dataset_type="background",
Verbose=config.verbose,
Timer=tim)
bkg_som = dr_lib.fix_bin_contents(bkg_som0)
del bkg_som0
else:
bkg_som = None
# Perform Steps 1-15 on sample data
d_som1 = dr_lib.process_igs_data(config.data,
config,
timer=tim,
inst_geom_dst=inst_geom_dst,
tib_const=config.tib_data_const,
bkg_som=bkg_som)
# Perform Steps 1-15 on empty can data
if config.ecan is not None:
e_som1 = dr_lib.process_igs_data(config.ecan,
config,
timer=tim,
inst_geom_dst=inst_geom_dst,
dataset_type="empty_can",
tib_const=config.tib_ecan_const,
bkg_som=bkg_som)
else:
e_som1 = None
# Perform Steps 1-15 on normalization data
if config.norm is not None:
n_som1 = dr_lib.process_igs_data(config.norm,
config,
timer=tim,
inst_geom_dst=inst_geom_dst,
dataset_type="normalization",
tib_const=config.tib_norm_const,
bkg_som=bkg_som)
else:
n_som1 = None
# Perform Steps 1-15 on background data
if config.back is not None:
b_som1 = dr_lib.process_igs_data(config.back,
config,
timer=tim,
inst_geom_dst=inst_geom_dst,
dataset_type="background",
tib_const=config.tib_back_const,
bkg_som=bkg_som)
else:
b_som1 = None
# Perform Step 1-15 on direct scattering background data
if config.dsback is not None:
ds_som1 = dr_lib.process_igs_data(config.dsback,
config,
timer=tim,
inst_geom_dst=inst_geom_dst,
tib_const=config.tib_dsback_const,
dataset_type="dsbackground",
bkg_som=bkg_som)
# Note: time_zero_slope MUST be a tuple
if config.time_zero_slope is not None:
ds_som1.attr_list["Time_zero_slope"] = \
config.time_zero_slope.toValErrTuple()
# Note: time_zero_offset MUST be a tuple
if config.time_zero_offset is not None:
ds_som1.attr_list["Time_zero_offset"] = \
config.time_zero_offset.toValErrTuple()
# Step 16: Linearly interpolate TOF elastic range in direct scattering
# background data
# First convert TOF elastic range to appropriate pixel initial
# wavelengths
if config.verbose:
print "Determining initial wavelength range for elastic line"
if tim is not None:
tim.getTime(False)
if config.tof_elastic is None:
# Units are in microseconds
tof_elastic_range = (140300, 141300)
else:
tof_elastic_range = config.tof_elastic
ctof_elastic_low = dr_lib.convert_single_to_list(\
"tof_to_initial_wavelength_igs_lin_time_zero",
(tof_elastic_range[0], 0.0),
ds_som1)
ctof_elastic_high = dr_lib.convert_single_to_list(\
"tof_to_initial_wavelength_igs_lin_time_zero",
(tof_elastic_range[1], 0.0),
ds_som1)
ctof_elastic_range = [(ctof_elastic_low[i][0], ctof_elastic_high[i][0])
for i in xrange(len(ctof_elastic_low))]
if tim is not None:
tim.getTime(msg="After calculating initial wavelength range for "\
+"elastic line ")
del ctof_elastic_low, ctof_elastic_high
if config.split:
lambda_filter = [(d_som1[i].axis[0].val[0],
d_som1[i].axis[0].val[-1])
for i in xrange(len(d_som1))]
else:
lambda_filter = None
# Now interpolate spectra between TOF elastic range (converted to
# initial wavelength)
if config.verbose:
print "Linearly interpolating direct scattering spectra"
if tim is not None:
tim.getTime(False)
ds_som2 = dr_lib.lin_interpolate_spectra(ds_som1,
ctof_elastic_range,
filter_axis=lambda_filter)
if tim is not None:
tim.getTime(msg="After linearly interpolating direct scattering "\
+"spectra ")
if config.dump_dslin:
ds_som2_1 = dr_lib.sum_all_spectra(ds_som2,\
rebin_axis=config.lambda_bins.toNessiList())
hlr_utils.write_file(config.output,
"text/Spec",
ds_som2_1,
output_ext="lin",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="dsbackground linear interpolation")
del ds_som2_1
del ds_som1
else:
ds_som2 = None
if inst_geom_dst is not None:
inst_geom_dst.release_resource()
# Steps 17-18: Subtract background spectrum from sample spectrum
if config.dsback is None:
back_som = b_som1
bkg_type = "background"
else:
back_som = ds_som2
bkg_type = "dsbackground"
d_som2 = dr_lib.subtract_bkg_from_data(d_som1,
back_som,
verbose=config.verbose,
timer=tim,
dataset1="data",
dataset2=bkg_type,
scale=config.scale_bs)
if config.dsback is not None:
del ds_som2
# Step 19: Zero region outside TOF elastic for background for empty can
if config.dsback is None:
bcs_som = b_som1
cs_som = e_som1
else:
if config.verbose and b_som1 is not None:
print "Zeroing background spectra"
if tim is not None and b_som1 is not None:
tim.getTime(False)
bcs_som = dr_lib.zero_spectra(b_som1, ctof_elastic_range)
if tim is not None and b_som1 is not None:
tim.getTime(msg="After zeroing background spectra")
if config.verbose and e_som1 is not None:
print "Zeroing empty can spectra"
if tim is not None and e_som1 is not None:
tim.getTime(False)
cs_som = dr_lib.zero_spectra(e_som1, ctof_elastic_range)
if tim is not None and e_som1 is not None:
tim.getTime(msg="After zeroing empty can spectra")
del ctof_elastic_range
# Steps 20-21: Subtract background spectrum from empty can spectrum
e_som2 = dr_lib.subtract_bkg_from_data(cs_som,
bcs_som,
verbose=config.verbose,
timer=tim,
dataset1="data-empty_can",
dataset2="background",
scale=config.scale_bcs)
# Steps 22-23: Subtract background spectrum from empty can spectrum for
# normalization
try:
config.pre_norm
except AttributeError:
config.pre_norm = False
if not config.pre_norm:
e_som3 = dr_lib.subtract_bkg_from_data(e_som1,
b_som1,
verbose=config.verbose,
timer=tim,
dataset1="norm-empty_can",
dataset2="background",
scale=config.scale_bcn)
else:
e_som3 = None
# Steps 24-25: Subtract background spectrum from normalization spectrum
if not config.pre_norm:
n_som2 = dr_lib.subtract_bkg_from_data(n_som1,
b_som1,
verbose=config.verbose,
timer=tim,
dataset1="normalization",
dataset2="background",
scale=config.scale_bn)
else:
n_som2 = n_som1
del b_som1, e_som1, bcs_som, cs_som
# Steps 26-27: Subtract empty can spectrum from sample spectrum
d_som3 = dr_lib.subtract_bkg_from_data(d_som2,
e_som2,
verbose=config.verbose,
timer=tim,
dataset1="data",
dataset2="empty_can",
scale=config.scale_cs)
del d_som2, e_som2
# Steps 28-29: Subtract empty can spectrum from normalization spectrum
if not config.pre_norm:
n_som3 = dr_lib.subtract_bkg_from_data(n_som2,
e_som3,
verbose=config.verbose,
timer=tim,
dataset1="normalization",
dataset2="empty_can",
scale=config.scale_cn)
else:
n_som3 = n_som2
del n_som2, e_som3
# Step 30-31: Integrate normalization spectra
if config.verbose and n_som3 is not None and not config.pre_norm:
print "Integrating normalization spectra"
if not config.pre_norm:
norm_int = dr_lib.integrate_spectra(n_som3,
start=config.norm_start,
end=config.norm_end,
norm=True)
else:
norm_int = n_som3
del n_som3
# Step 32: Normalize data by integrated values
if config.verbose and norm_int is not None:
print "Normalizing data by normalization data"
if norm_int is not None:
d_som4 = common_lib.div_ncerr(d_som3, norm_int)
else:
d_som4 = d_som3
if norm_int is not None:
if tim is not None:
tim.getTime(msg="After normalizing data ")
del d_som3, norm_int
if config.dump_norm:
if tim is not None:
tim.getTime(False)
hlr_utils.write_file(config.output,
"text/Spec",
d_som4,
output_ext="wvn",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="wavelength (vanadium norm) information")
if tim is not None:
tim.getTime(msg="After writing wavelength (vanadium norm) info ")
# Steps 33 to end: Creating S(Q,E)
if config.Q_bins is not None:
if config.verbose:
print "Creating 2D spectrum"
if tim is not None:
tim.getTime(False)
d_som5 = dr_lib.create_E_vs_Q_igs(
d_som4,
config.E_bins.toNessiList(),
config.Q_bins.toNessiList(),
so_id="Full Detector",
y_label="counts",
y_units="counts / (ueV * A^-1)",
x_labels=["Q transfer", "energy transfer"],
x_units=["1/Angstroms", "ueV"],
split=config.split,
Q_filter=False,
configure=config)
if tim is not None:
tim.getTime(msg="After creation of final spectrum ")
del d_som4
# Steps 33 to 36: Create S(-cos(polar), E)
elif config.ncospol_bins is not None:
if config.verbose:
print "Convert wavelength to energy transfer"
if tim is not None:
tim.getTime(False)
d_som4a = dr_lib.energy_transfer(d_som4,
"IGS",
"Wavelength_final",
sa_norm=True,
scale=True,
change_units=True)
if tim is not None:
tim.getTime(msg="After wavelength to energy transfer conversion ")
del d_som4
if config.verbose:
print "Creating 2D spectrum"
if tim is not None:
tim.getTime(False)
d_som5 = dr_lib.create_param_vs_Y(
d_som4a,
"polar",
"negcos_param_array",
config.ncospol_bins.toNessiList(),
rebin_axis=config.E_bins.toNessiList(),
y_label="counts",
y_units="counts / ueV",
x_labels=["-cos(polar)", "Energy Transfer"],
x_units=["", "ueV"])
if tim is not None:
tim.getTime(msg="After creation of final spectrum ")
# If rescaling factor present, rescale the data
if config.rescale_final is not None and not config.split:
d_som6 = common_lib.mult_ncerr(d_som5, (config.rescale_final, 0.0))
else:
d_som6 = d_som5
if tim is None:
old_time = None
if not __name__ == "amorphous_reduction_sqe":
del d_som5
__write_output(d_som6, config, tim, old_time)
else:
if config.create_output:
del d_som5
__write_output(d_som6, config, tim, old_time)
else:
return d_som6
def scaled_summed_data(datalist, conf, **kwargs):
"""
This function takes a list of TOF datafiles, creates the dataset
representation, integrates each pixel spectrum and then scales those
integrations with the acquisition time.
@param datalist: A list containing the filenames of the data to be
processed.
@type datalist: C{list} of C{string}s
@param conf: Object that contains the current setup of the driver.
@type conf: L{hlr_utils.Configure}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword dataset_type: The practical name of the dataset being processed.
The default value is I{data}.
@type dataset_type: C{string}
@keyword timer: Timing object so the function can perform timing estimates.
@type timer: C{sns_timer.DiffTime}
@return: Object that has undergone all requested processing steps
@rtype: C{SOM.SOM}
"""
# Kick-out is no data list is present
if datalist is None:
return None
import common_lib
import dr_lib
# Check keywords
try:
dataset_type = kwargs["dataset_type"]
except KeyError:
dataset_type = "data"
try:
t = kwargs["timer"]
except KeyError:
t = None
# Open the appropriate datafiles
if conf.verbose:
print "Reading %s file" % dataset_type
dst_type = "application/x-NeXus"
data_paths = conf.data_paths.toPath()
dp_som0 = dr_lib.add_files(datalist, Data_Paths=data_paths,
SO_Axis=conf.so_axis, Signal_ROI=conf.roi_file,
Signal_MASK=conf.mask_file,
dataset_type=dataset_type,
dst_type=dst_type,
Verbose=conf.verbose, Timer=t)
if t is not None:
t.getTime(msg="After reading %s " % dataset_type)
dp_som1 = dr_lib.fix_bin_contents(dp_som0)
del dp_som0
if conf.verbose:
print "Integrating %s data" % dataset_type
if t is not None:
t.getTime(False)
dp_som2 = dr_lib.integrate_spectra(dp_som1, width=True)
if t is not None:
t.getTime(msg="After integrating %s data " % dataset_type)
del dp_som1
# Make dataset duration tag
duration_tag = dataset_type+"-duration"
duration = dp_som2.attr_list[duration_tag]
if conf.verbose:
print "Scaling %s integration by acquisition duration " % dataset_type
if t is not None:
t.getTime(False)
dp_som3 = common_lib.div_ncerr(dp_som2, (duration.getValue(), 0.0))
if t is not None:
t.getTime("After scaling %s integration by acquisition duration " \
% dataset_type)
del dp_som2
return dp_som3
def process_igs_data(datalist, conf, **kwargs):
"""
This function combines Steps 1 through 8 of the data reduction process for
Inverse Geometry Spectrometers as specified by the documents at
U{http://neutrons.ornl.gov/asg/projects/SCL/reqspec/DR_Lib_RS.doc}. The
function takes a list of file names, a L{hlr_utils.Configure} object and
processes the data accordingly. This function should really only be used in
the context of I{amorphous_reduction} and I{calc_norm_eff}.
@param datalist: A list containing the filenames of the data to be
processed.
@type datalist: C{list} of C{string}s
@param conf: Object that contains the current setup of the driver.
@type conf: L{hlr_utils.Configure}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword inst_geom_dst: File object that contains instrument geometry
information.
@type inst_geom_dst: C{DST.GeomDST}
@keyword dataset_type: The practical name of the dataset being processed.
The default value is I{data}.
@type dataset_type: C{string}
@keyword tib_const: Object providing the time-independent background
constant to subtract.
@type tib_const: L{hlr_utils.DrParameter}
@keyword bkg_som: Object that will be used for early background subtraction
@type bkg_som: C{SOM.SOM}
@keyword timer: Timing object so the function can perform timing estimates.
@type timer: C{sns_timer.DiffTime}
@return: Object that has undergone all requested processing steps
@rtype: C{SOM.SOM}
"""
import hlr_utils
# Check keywords
try:
dataset_type = kwargs["dataset_type"]
except KeyError:
dataset_type = "data"
try:
t = kwargs["timer"]
except KeyError:
t = None
try:
if kwargs["tib_const"] is not None:
tib_const = kwargs["tib_const"].toValErrTuple()
else:
tib_const = None
except KeyError:
tib_const = None
try:
i_geom_dst = kwargs["inst_geom_dst"]
except KeyError:
i_geom_dst = None
try:
bkg_som = kwargs["bkg_som"]
except KeyError:
bkg_som = None
# Step 1: Open appropriate data files
if not conf.mc:
so_axis = "time_of_flight"
else:
so_axis = "Time_of_Flight"
# Add so_axis to Configure object
conf.so_axis = so_axis
if conf.verbose:
print "Reading %s file" % dataset_type
# Special case handling for normalization data. Dynamically trying to
# determine if incoming file is a previously calculated one.
if dataset_type == "normalization":
try:
# Check the first incoming file
dst_type = hlr_utils.file_peeker(datalist[0])
# If file_peeker succeeds, the DST is different than the function
# returns
dst_type = "text/num-info"
# Let ROI file handle filtering
data_paths = None
except RuntimeError:
# It's a NeXus file
dst_type = "application/x-NeXus"
data_paths = conf.data_paths.toPath()
else:
dst_type = "application/x-NeXus"
data_paths = conf.data_paths.toPath()
# The [0] is to get the data SOM and ignore the None background SOM
dp_som0 = dr_lib.add_files(
datalist,
Data_Paths=data_paths,
SO_Axis=so_axis,
Signal_ROI=conf.roi_file,
dataset_type=dataset_type,
dst_type=dst_type,
Verbose=conf.verbose,
Timer=t,
)
if t is not None:
t.getTime(msg="After reading %s " % dataset_type)
if dst_type == "text/num-info":
# Since we have a pre-calculated normalization dataset, set the flag
# and return the SOM now
conf.pre_norm = True
# Make the labels and units compatible with a NeXus file based SOM
dp_som0.setAxisLabel(0, "wavelength")
dp_som0.setAxisUnits(0, "Angstroms")
dp_som0.setYUnits("Counts/A")
return dp_som0
else:
if dataset_type == "normalization":
# Since we have a NeXus file, we need to continue
conf.pre_norm = False
# Cut the spectra if necessary
dp_somA = dr_lib.cut_spectra(dp_som0, conf.tof_cut_min, conf.tof_cut_max)
del dp_som0
dp_som1 = dr_lib.fix_bin_contents(dp_somA)
del dp_somA
if conf.inst_geom is not None:
i_geom_dst.setGeometry(conf.data_paths.toPath(), dp_som1)
if conf.no_mon_norm:
dm_som1 = None
else:
if conf.verbose:
print "Reading in monitor data from %s file" % dataset_type
# The [0] is to get the data SOM and ignore the None background SOM
dm_som0 = dr_lib.add_files(
datalist,
Data_Paths=conf.mon_path.toPath(),
SO_Axis=so_axis,
dataset_type=dataset_type,
Verbose=conf.verbose,
Timer=t,
)
if t is not None:
t.getTime(msg="After reading monitor data ")
dm_som1 = dr_lib.fix_bin_contents(dm_som0)
del dm_som0
if conf.inst_geom is not None:
i_geom_dst.setGeometry(conf.mon_path.toPath(), dm_som1)
if bkg_som is not None:
bkg_pcharge = bkg_som.attr_list["background-proton_charge"].getValue()
data_pcharge = dp_som1.attr_list[dataset_type + "-proton_charge"].getValue()
ratio = data_pcharge / bkg_pcharge
bkg_som1 = common_lib.mult_ncerr(bkg_som, (ratio, 0.0))
del bkg_som
dp_som2 = dr_lib.subtract_bkg_from_data(
dp_som1, bkg_som1, verbose=conf.verbose, timer=t, dataset1=dataset_type, dataset2="background"
)
else:
dp_som2 = dp_som1
del dp_som1
# Step 2: Dead Time Correction
# No dead time correction is being applied to the data yet
# Step 3: Time-independent background determination
if conf.verbose and conf.tib_tofs is not None:
print "Determining time-independent background from data"
if t is not None and conf.tib_tofs is not None:
t.getTime(False)
B = dr_lib.determine_time_indep_bkg(dp_som2, conf.tib_tofs)
if t is not None and B is not None:
t.getTime(msg="After determining time-independent background ")
if conf.dump_tib and B is not None:
file_comment = "TOFs: %s" % conf.tib_tofs
hlr_utils.write_file(
conf.output,
"text/num-info",
B,
output_ext="tib",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="time-independent background " + "information",
tag="Average",
units="counts",
comments=[file_comment],
)
# Step 4: Subtract time-independent background
if conf.verbose and B is not None:
print "Subtracting time-independent background from data"
if t is not None:
t.getTime(False)
if B is not None:
dp_som3 = common_lib.sub_ncerr(dp_som2, B)
else:
dp_som3 = dp_som2
if B is not None and t is not None:
t.getTime(msg="After subtracting time-independent background ")
del dp_som2, B
# Step 5: Subtract time-independent background constant
if conf.verbose and tib_const is not None:
print "Subtracting time-independent background constant from data"
if t is not None and tib_const is not None:
t.getTime(False)
if tib_const is not None:
dp_som4 = common_lib.sub_ncerr(dp_som3, tib_const)
else:
dp_som4 = dp_som3
if t is not None and tib_const is not None:
t.getTime(msg="After subtracting time-independent background " + "constant ")
del dp_som3
# Provide override capability for final wavelength, time-zero slope and
# time-zero offset
if conf.wavelength_final is not None:
dp_som4.attr_list["Wavelength_final"] = conf.wavelength_final.toValErrTuple()
# Note: time_zero_slope MUST be a tuple
if conf.time_zero_slope is not None:
dp_som4.attr_list["Time_zero_slope"] = conf.time_zero_slope.toValErrTuple()
if dm_som1 is not None:
dm_som1.attr_list["Time_zero_slope"] = conf.time_zero_slope.toValErrTuple()
# Note: time_zero_offset MUST be a tuple
if conf.time_zero_offset is not None:
dp_som4.attr_list["Time_zero_offset"] = conf.time_zero_offset.toValErrTuple()
if dm_som1 is not None:
dm_som1.attr_list["Time_zero_offset"] = conf.time_zero_offset.toValErrTuple()
# Step 6: Convert TOF to wavelength for data and monitor
if conf.verbose:
print "Converting TOF to wavelength"
if t is not None:
t.getTime(False)
# Convert monitor
if dm_som1 is not None:
dm_som2 = common_lib.tof_to_wavelength_lin_time_zero(dm_som1, units="microsecond")
else:
dm_som2 = None
# Convert detector pixels
dp_som5 = common_lib.tof_to_initial_wavelength_igs_lin_time_zero(
dp_som4, units="microsecond", run_filter=conf.filter
)
if t is not None:
t.getTime(msg="After converting TOF to wavelength ")
if conf.dump_wave:
hlr_utils.write_file(
conf.output,
"text/Spec",
dp_som5,
output_ext="pxl",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="pixel wavelength information",
)
if conf.dump_mon_wave and dm_som2 is not None:
hlr_utils.write_file(
conf.output,
"text/Spec",
dm_som2,
output_ext="mxl",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="monitor wavelength information",
)
del dp_som4, dm_som1
# Step 7: Efficiency correct monitor
if conf.verbose and dm_som2 is not None and not conf.no_mon_effc:
print "Efficiency correct monitor data"
if t is not None:
t.getTime(False)
if not conf.no_mon_effc:
dm_som3 = dr_lib.feff_correct_mon(dm_som2)
else:
dm_som3 = dm_som2
if t is not None and dm_som2 is not None and not conf.no_mon_effc:
t.getTime(msg="After efficiency correcting monitor ")
if conf.dump_mon_effc and not conf.no_mon_effc and dm_som3 is not None:
hlr_utils.write_file(
conf.output,
"text/Spec",
dm_som3,
output_ext="mel",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="monitor wavelength information " + "(efficiency)",
)
del dm_som2
# Step 8: Rebin monitor axis onto detector pixel axis
if conf.verbose and dm_som3 is not None:
print "Rebin monitor axis to detector pixel axis"
if t is not None:
t.getTime(False)
dm_som4 = dr_lib.rebin_monitor(dm_som3, dp_som5)
if t is not None and dm_som4 is not None:
t.getTime(msg="After rebinning monitor ")
del dm_som3
if conf.dump_mon_rebin and dm_som4 is not None:
hlr_utils.write_file(
conf.output,
"text/Spec",
dm_som4,
output_ext="mrl",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="monitor wavelength information " + "(rebinned)",
)
# The lambda-dependent background is only done on sample data (aka data)
# for the BSS instrument at the SNS
if conf.inst == "BSS" and conf.ldb_const is not None and dataset_type == "data":
# Step 9: Convert chopper center wavelength to TOF center
if conf.verbose:
print "Converting chopper center wavelength to TOF"
if t is not None:
t.getTime(False)
tof_center = dr_lib.convert_single_to_list(
"initial_wavelength_igs_lin_time_zero_to_tof", conf.chopper_lambda_cent.toValErrTuple(), dp_som5
)
# Step 10: Calculate beginning and end of detector TOF spectrum
if conf.verbose:
print "Calculating beginning and ending TOF ranges"
half_inv_chop_freq = 0.5 / conf.chopper_freq.toValErrTuple()[0]
# Above is in seconds, need microseconds
half_inv_chop_freq *= 1.0e6
tof_begin = common_lib.sub_ncerr(tof_center, (half_inv_chop_freq, 0.0))
tof_end = common_lib.add_ncerr(tof_center, (half_inv_chop_freq, 0.0))
# Step 11: Convert TOF_begin and TOF_end to wavelength
if conf.verbose:
print "Converting TOF_begin and TOF_end to wavelength"
# Check for time-zero slope information
try:
tz_slope = conf.time_zero_slope.toValErrTuple()
except AttributeError:
tz_slope = (0.0, 0.0)
# Check for time-zero offset information
try:
tz_offset = conf.time_zero_offset.toValErrTuple()
except AttributeError:
tz_offset = (0.0, 0.0)
l_begin = common_lib.tof_to_initial_wavelength_igs_lin_time_zero(
tof_begin, time_zero_slope=tz_slope, time_zero_offset=tz_offset, iobj=dp_som5, run_filter=False
)
l_end = common_lib.tof_to_initial_wavelength_igs_lin_time_zero(
tof_end, time_zero_slope=tz_slope, time_zero_offset=tz_offset, iobj=dp_som5, run_filter=False
)
# Step 12: tof-least-bkg to lambda-least-bkg
if conf.verbose:
print "Converting TOF least background to wavelength"
lambda_least_bkg = dr_lib.convert_single_to_list(
"tof_to_initial_wavelength_igs_lin_time_zero", conf.tof_least_bkg.toValErrTuple(), dp_som5
)
if t is not None:
t.getTime(msg="After converting boundary positions ")
# Step 13: Create lambda-dependent background spectrum
if conf.verbose:
print "Creating lambda-dependent background spectra"
if t is not None:
t.getTime(False)
ldb_som = dr_lib.shift_spectrum(dm_som4, lambda_least_bkg, l_begin, l_end, conf.ldb_const.getValue())
if t is not None:
t.getTime(msg="After creating lambda-dependent background " + "spectra ")
# Step 14: Subtract lambda-dependent background from sample data
if conf.verbose:
print "Subtracting lambda-dependent background from data"
if t is not None:
t.getTime(False)
dp_som6 = common_lib.sub_ncerr(dp_som5, ldb_som)
if t is not None:
t.getTime(msg="After subtracting lambda-dependent background " + "from data ")
else:
dp_som6 = dp_som5
del dp_som5
# Step 15: Normalize data by monitor
if conf.verbose and dm_som4 is not None:
print "Normalizing data by monitor"
if t is not None:
t.getTime(False)
if dm_som4 is not None:
dp_som7 = common_lib.div_ncerr(dp_som6, dm_som4)
if t is not None:
t.getTime(msg="After normalizing data by monitor ")
else:
dp_som7 = dp_som6
if conf.dump_wave_mnorm:
dp_som7_1 = dr_lib.sum_all_spectra(dp_som7, rebin_axis=conf.lambda_bins.toNessiList())
write_message = "combined pixel wavelength information"
if dm_som4 is not None:
write_message += " (monitor normalized)"
hlr_utils.write_file(
conf.output,
"text/Spec",
dp_som7_1,
output_ext="pml",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message=write_message,
)
del dp_som7_1
del dm_som4, dp_som6
return dp_som7
def scaled_summed_data(datalist, conf, **kwargs):
"""
This function takes a list of TOF datafiles, creates the dataset
representation, integrates each pixel spectrum and then scales those
integrations with the acquisition time.
@param datalist: A list containing the filenames of the data to be
processed.
@type datalist: C{list} of C{string}s
@param conf: Object that contains the current setup of the driver.
@type conf: L{hlr_utils.Configure}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword dataset_type: The practical name of the dataset being processed.
The default value is I{data}.
@type dataset_type: C{string}
@keyword timer: Timing object so the function can perform timing estimates.
@type timer: C{sns_timer.DiffTime}
@return: Object that has undergone all requested processing steps
@rtype: C{SOM.SOM}
"""
# Kick-out is no data list is present
if datalist is None:
return None
import common_lib
import dr_lib
# Check keywords
try:
dataset_type = kwargs["dataset_type"]
except KeyError:
dataset_type = "data"
try:
t = kwargs["timer"]
except KeyError:
t = None
# Open the appropriate datafiles
if conf.verbose:
print "Reading %s file" % dataset_type
dst_type = "application/x-NeXus"
data_paths = conf.data_paths.toPath()
dp_som0 = dr_lib.add_files(datalist,
Data_Paths=data_paths,
SO_Axis=conf.so_axis,
Signal_ROI=conf.roi_file,
Signal_MASK=conf.mask_file,
dataset_type=dataset_type,
dst_type=dst_type,
Verbose=conf.verbose,
Timer=t)
if t is not None:
t.getTime(msg="After reading %s " % dataset_type)
dp_som1 = dr_lib.fix_bin_contents(dp_som0)
del dp_som0
if conf.verbose:
print "Integrating %s data" % dataset_type
if t is not None:
t.getTime(False)
dp_som2 = dr_lib.integrate_spectra(dp_som1, width=True)
if t is not None:
t.getTime(msg="After integrating %s data " % dataset_type)
del dp_som1
# Make dataset duration tag
duration_tag = dataset_type + "-duration"
duration = dp_som2.attr_list[duration_tag]
if conf.verbose:
print "Scaling %s integration by acquisition duration " % dataset_type
if t is not None:
t.getTime(False)
dp_som3 = common_lib.div_ncerr(dp_som2, (duration.getValue(), 0.0))
if t is not None:
t.getTime("After scaling %s integration by acquisition duration " \
% dataset_type)
del dp_som2
return dp_som3
def run(config, tim=None):
"""
This method is where the data reduction process gets done.
@param config: Object containing the data reduction configuration
information.
@type config: L{hlr_utils.Configure}
@param tim: (OPTIONAL) Object that will allow the method to perform
timing evaluations.
@type tim: C{sns_time.DiffTime}
"""
import common_lib
import dr_lib
import DST
if tim is not None:
tim.getTime(False)
old_time = tim.getOldTime()
if config.data is None:
raise RuntimeError("Need to pass a data filename to the driver " + "script.")
# Read in geometry if one is provided
if config.inst_geom is not None:
if config.verbose:
print "Reading in instrument geometry file"
inst_geom_dst = DST.getInstance("application/x-NxsGeom", config.inst_geom)
else:
inst_geom_dst = None
# Add so_axis to Configure object
config.so_axis = "time_of_flight"
dataset_type = "background"
# Step 0: Open appropriate data files
# Data
if config.verbose:
print "Reading %s file" % dataset_type
# The [0] is to get the data SOM and ignore the None background SOM
dp_som = dr_lib.add_files(
config.data,
Data_Paths=config.data_paths.toPath(),
SO_Axis=config.so_axis,
Signal_ROI=config.roi_file,
dataset_type=dataset_type,
Verbose=config.verbose,
Timer=tim,
)
if tim is not None:
tim.getTime(msg="After reading %s " % dataset_type)
dp_som0 = dr_lib.fix_bin_contents(dp_som)
del dp_som
if inst_geom_dst is not None:
inst_geom_dst.setGeometry(config.data_paths.toPath(), dp_som0)
# Note: time_zero_offset_det MUST be a tuple
if config.time_zero_offset_det is not None:
dp_som0.attr_list["Time_zero_offset_det"] = config.time_zero_offset_det.toValErrTuple()
# Step 2: Convert TOF to wavelength for data
if config.verbose:
print "Converting TOF to wavelength"
if tim is not None:
tim.getTime(False)
# Convert detector pixels
dp_som1 = common_lib.tof_to_wavelength_lin_time_zero(
dp_som0, units="microsecond", time_zero_offset=config.time_zero_offset_det.toValErrTuple(), inst_param="total"
)
if tim is not None:
tim.getTime(msg="After converting TOF to wavelength ")
del dp_som0
if config.verbose:
print "Cutting spectra"
if tim is not None:
tim.getTime(False)
dp_som2 = dr_lib.cut_spectra(dp_som1, config.lambda_low_cut, config.lambda_high_cut)
if tim is not None:
tim.getTime(msg="After cutting spectra ")
del dp_som1
rebin_axis = config.lambda_bins.toNessiList()
# Put the data on the same axis
if config.verbose:
print "Rebinning data onto specified wavelength axis"
if tim is not None:
tim.getTime(False)
dp_som3 = dr_lib.sum_by_rebin_frac(dp_som2, rebin_axis)
if tim is not None:
tim.getTime(msg="After rebinning data onto specified wavelength axis ")
del dp_som2
data_run_time = dp_som3.attr_list["background-duration"]
# Calculate the accelerator on time
if config.verbose:
print "Calculating accelerator on time"
acc_on_time = hlr_utils.DrParameter(data_run_time.getValue() - config.acc_down_time.getValue(), 0.0, "seconds")
# Get the number of data bins
num_wave_bins = len(rebin_axis) - 1
# Calculate the scaled accelerator uptime
if config.verbose:
print "Calculating the scaled accelerator uptime"
if tim is not None:
tim.getTime(False)
final_scale = acc_on_time.toValErrTuple()[0] / num_wave_bins
if tim is not None:
tim.getTime(msg="After calculating the scaled accelerator uptime ")
# Create the final background spectrum
if config.verbose:
print "Creating the background spectrum"
if tim is not None:
tim.getTime(False)
dp_som4 = common_lib.div_ncerr(dp_som3, (final_scale, 0))
dp_som4.attr_list["%s-Scaling" % dataset_type] = final_scale
if tim is not None:
tim.getTime(msg="After creating background spectrum ")
del dp_som3
# Write out the background spectrum
hlr_utils.write_file(
config.output,
"text/Spec",
dp_som4,
verbose=config.verbose,
output_ext="bkg",
data_ext=config.ext_replacement,
replace_path=False,
replace_ext=True,
message="background spectrum",
)
dp_som4.attr_list["config"] = config
hlr_utils.write_file(
config.output,
"text/rmd",
dp_som4,
output_ext="rmd",
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
verbose=config.verbose,
message="metadata",
)
if tim is not None:
tim.setOldTime(old_time)
tim.getTime(msg="Total Running Time")
