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 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 process_ref_data(datalist,
conf,
signal_roi_file,
bkg_roi_file=None,
no_bkg=False,
**kwargs):
"""
This function combines Steps 1 through 6 in section 2.4.5 of the data
reduction process for Reflectometers (without Monitors) 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,
signal and background region-of-interest (ROI) files and an optional flag
about background subtraction and processes the data accordingly.
@param datalist: 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 signal_roi_file: The file containing the list of pixel IDs for the
signal region of interest.
@type signal_roi_file: C{string}
@param bkg_roi_file: The file containing the list of pixel IDs for the
(possible) background region of interest.
@type bkg_roi_file: C{string}
@param no_bkg: (OPTIONAL) Flag which determines if the background will be
calculated and subtracted.
@type no_bkg: C{boolean}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword inst_geom_dst: Object that contains the instrument geometry
information.
@type inst_geom_dst: C{DST.getInstance()}
@keyword dataset_type: The practical name of the dataset being processed.
The default value is I{data}.
@type dataset_type: C{string}
@keyword tof_cuts: Time-of-flight bins to remove (zero) from the data
@type tof_cuts: C{list} of C{string}s
@keyword no_tof_cuts: Flag to stop application of the TOF cuts
@type no_tof_cuts: C{boolean}
@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"
if dataset_type != "data" and dataset_type != "norm":
raise RuntimeError("Please use data or norm to specify the dataset "\
+"type. Do not understand how to handle %s." \
% dataset_type)
try:
t = kwargs["timer"]
except KeyError:
t = None
try:
i_geom_dst = kwargs["inst_geom_dst"]
except KeyError:
i_geom_dst = None
try:
tof_cuts = kwargs["tof_cuts"]
except KeyError:
tof_cuts = None
no_tof_cuts = kwargs.get("no_tof_cuts", False)
so_axis = "time_of_flight"
# Step 0: Open data files and select signal (and possible background) ROIs
if conf.verbose:
print "Reading %s file" % dataset_type
if len(conf.norm_data_paths) and dataset_type == "norm":
data_path = conf.norm_data_paths.toPath()
else:
data_path = conf.data_paths.toPath()
(d_som1, b_som1) = dr_lib.add_files_bg(datalist,
Data_Paths=data_path,
SO_Axis=so_axis,
dataset_type=dataset_type,
Signal_ROI=signal_roi_file,
Bkg_ROI=bkg_roi_file,
Verbose=conf.verbose,
Timer=t)
if t is not None:
t.getTime(msg="After reading %s " % dataset_type)
if i_geom_dst is not None:
i_geom_dst.setGeometry(conf.data_paths.toPath(), d_som1)
# Calculate delta t over t
if conf.verbose:
print "Calculating delta t over t"
dtot = dr_lib.calc_deltat_over_t(d_som1[0].axis[0].val)
# Calculate delta theta over theta
if conf.verbose:
print "Calculating delta theta over theta"
dr_lib.calc_delta_theta_over_theta(d_som1, dataset_type)
# Step 1: Sum all spectra along the low resolution direction
# Set sorting
(y_sort, cent_pixel) = hlr_utils.get_ref_integration_direction(
conf.int_dir, conf.inst, d_som1.attr_list.instrument)
if dataset_type == "data":
d_som1.attr_list["ref_sort"] = y_sort
d_som1A = dr_lib.sum_all_spectra(d_som1,
y_sort=y_sort,
stripe=True,
pixel_fix=cent_pixel)
del d_som1
if b_som1 is not None:
b_som1A = dr_lib.sum_all_spectra(b_som1,
y_sort=y_sort,
stripe=True,
pixel_fix=cent_pixel)
del b_som1
else:
b_som1A = b_som1
# Set the TOF cuts
if no_tof_cuts:
tof_cut_min = None
tof_cut_max = None
else:
tof_cut_min = conf.tof_cut_min
tof_cut_max = conf.tof_cut_max
# Cut the spectra if necessary
d_som2 = dr_lib.cut_spectra(d_som1A, tof_cut_min, tof_cut_max)
del d_som1A
if b_som1A is not None:
b_som2 = dr_lib.cut_spectra(b_som1A, tof_cut_min, tof_cut_max)
del b_som1A
else:
b_som2 = b_som1A
# Fix TOF cuts to make them list of integers
try:
tof_cuts = [int(x) for x in tof_cuts]
# This will trigger if tof_cuts is None
except TypeError:
pass
d_som3 = dr_lib.zero_bins(d_som2, tof_cuts)
del d_som2
if b_som2 is not None:
b_som3 = dr_lib.zero_bins(b_som2, tof_cuts)
del b_som2
else:
b_som3 = b_som2
if conf.dump_specular:
if no_tof_cuts:
d_som3_1 = dr_lib.cut_spectra(d_som3, conf.tof_cut_min,
conf.tof_cut_max)
else:
d_som3_1 = d_som3
hlr_utils.write_file(conf.output,
"text/Spec",
d_som3_1,
output_ext="sdc",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="specular TOF information")
del d_som3_1
# Steps 2-4: Determine background spectrum
if conf.verbose and not no_bkg:
print "Determining %s background" % dataset_type
if dataset_type == "data":
peak_excl = conf.data_peak_excl
elif dataset_type == "norm":
peak_excl = conf.norm_peak_excl
if b_som3 is not None:
B = dr_lib.calculate_ref_background(b_som3,
no_bkg,
conf.inst,
None,
aobj=d_som3)
else:
B = dr_lib.calculate_ref_background(d_som3, no_bkg, conf.inst,
peak_excl)
if t is not None:
t.getTime(msg="After background determination")
if not no_bkg and conf.dump_bkg:
if no_tof_cuts:
B_1 = dr_lib.cut_spectra(B, conf.tof_cut_min, conf.tof_cut_max)
else:
B_1 = B
hlr_utils.write_file(conf.output,
"text/Spec",
B_1,
output_ext="bkg",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="background TOF information")
del B_1
# Step 5: Subtract background spectrum from data spectra
if not no_bkg:
d_som4 = dr_lib.subtract_bkg_from_data(d_som3,
B,
verbose=conf.verbose,
timer=t,
dataset1="data",
dataset2="background")
else:
d_som4 = d_som3
del d_som3
if not no_bkg and conf.dump_sub:
if no_tof_cuts:
d_som4_1 = dr_lib.cut_spectra(d_som4, conf.tof_cut_min,
conf.tof_cut_max)
else:
d_som4_1 = d_som4
hlr_utils.write_file(conf.output,
"text/Spec",
d_som4_1,
output_ext="sub",
extra_tag=dataset_type,
verbose=conf.verbose,
data_ext=conf.ext_replacement,
path_replacement=conf.path_replacement,
message="subtracted TOF information")
del d_som4_1
dtot_int = dr_lib.integrate_axis_py(dtot, avg=True)
param_key = dataset_type + "-dt_over_t"
d_som4.attr_list[param_key] = dtot_int[0]
if conf.store_dtot:
d_som4.attr_list["extra_som"] = dtot
# Step 6: Scale by proton charge
pc = d_som4.attr_list[dataset_type + "-proton_charge"]
pc_new = hlr_utils.scale_proton_charge(pc, "C")
d_som5 = common_lib.div_ncerr(d_som4, (pc_new.getValue(), 0.0))
del d_som4
return d_som5
