def subtract_time_indep_bkg(obj, B_list):
"""
This function takes spectrum object(s) and time-independent background
value(s) and subtracts the numbers from the appropriate spectrum. The
time-independent background number(s) are assumed to be in the same order
as the spectrum object(s).
@param obj: Object from which to subtract the individual background numbers
@type obj: C{SOM.SOM} or C{SOM.SO}
@param B_list: The time-independent backgrounds to subtract from the
individual spectra.
@type B_list: C{list} of C{tuple}s or C{tuple}
@return: Object with the time-independent backgrounds subtracted
@rtype: C{SOM.SOM} or C{SOM.SO}
@raise IndexError: The B_list object is empty
@raise TypeError: The first argument is not a C{SOM} or C{SO}
@raise RuntimeError: The C{SOM} and list are not the same length
"""
if len(B_list) <= 0:
raise IndexError("List of time-independent background cannot be empty")
# List is correct size, go on
else:
pass
# import the helper functions
import hlr_utils
(o_descr, l_descr) = hlr_utils.get_descr(obj, B_list)
if o_descr == "number" or o_descr == "list":
raise TypeError("First argument must be a SOM or a SO!")
# Have a SOM or SO, go on
else:
pass
(result, res_descr) = hlr_utils.empty_result(obj)
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
import common_lib
# iterate through the values
for i in xrange(hlr_utils.get_length(obj)):
val1 = hlr_utils.get_value(obj, i, o_descr, "all")
val2 = hlr_utils.get_value(B_list, i, l_descr, "all")
value = common_lib.sub_ncerr(val1, val2)
hlr_utils.result_insert(result, res_descr, value, None, "all")
return result
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 subtract_bkg_from_data(data_som, bkg_som, **kwargs):
"""
This function subtracts one data set from another.
@param data_som: Object containing the data to subtract from
@type data_som: C{SOM.SOM} or C{SOM.SO}
@param bkg_som: Object containing the data to be subtracted
@type bkg_som: C{SOM.SOM} or C{SOM.SO}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword dataset1: The type name of the first dataset. Default is
I{dataset1}.
@type dataset1: C{string}
@keyword dataset2: The type name of the second dataset. Default is
I{dataset2}.
@keyword scale: The constant by which to scale the background spectra
@type scale: L{hlr_utils.DrParameter}
@keyword verbose: A flag for turning on information from the function.
@type verbose: C{boolean}
@keyword timer: Timing object so the function can perform timing
@type timer: C{sns_timer.DiffTime}
@return: The data subtracted by the background
@rtype: C{SOM.SOM} or C{SOM.SO}
@raise TypeError: Both objects are not C{SOM}s, a C{SO} and a C{SOM} or
a C{SOM} and a C{SO}
"""
# Kickout if data object is NoneType
if data_som is None:
return None
# Kickout if background object is NoneType
if bkg_som is None:
return data_som
# import the helper functions
import hlr_utils
(data_descr, bkg_descr) = hlr_utils.get_descr(data_som, bkg_som)
if data_descr == "SOM" and bkg_descr == "SOM":
hlr_utils.math_compatible(data_som, data_descr, bkg_som, bkg_descr)
elif data_descr == "SOM" and bkg_descr == "SO" or \
data_descr == "SO" and bkg_descr == "SOM":
# You have SO-SOM or SOM-SO, so assume everything is OK
pass
else:
raise TypeError("The object combinations must be SOM-SOM, SO-SOM "\
+"or SOM-SO. You provided a %s and a %s" % \
(data_descr, bkg_descr))
# Check for keywords
try:
verbose = kwargs["verbose"]
except KeyError:
verbose = False
try:
t = kwargs["timer"]
except KeyError:
t = None
try:
dataset1 = kwargs["dataset1"]
except KeyError:
dataset1 = "dataset1"
try:
dataset2 = kwargs["dataset2"]
except KeyError:
dataset2 = "dataset2"
try:
scale = kwargs["scale"]
except KeyError:
scale = None
import common_lib
if scale is not None:
if verbose:
print "Scaling %s for %s" % (dataset2, dataset1)
bkg_som2 = common_lib.mult_ncerr(bkg_som, scale.toValErrTuple())
if t is not None:
t.getTime(msg="After scaling %s for %s " % (dataset2, dataset1))
else:
bkg_som2 = bkg_som
del bkg_som
if verbose:
print "Subtracting %s from %s" % (dataset2, dataset1)
data_som2 = common_lib.sub_ncerr(data_som, bkg_som2)
if t is not None:
t.getTime(msg="After subtracting %s from %s " % (dataset2, dataset1))
return data_som2
def subtract_bkg_from_data(data_som, bkg_som, **kwargs):
"""
This function subtracts one data set from another.
@param data_som: Object containing the data to subtract from
@type data_som: C{SOM.SOM} or C{SOM.SO}
@param bkg_som: Object containing the data to be subtracted
@type bkg_som: C{SOM.SOM} or C{SOM.SO}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword dataset1: The type name of the first dataset. Default is
I{dataset1}.
@type dataset1: C{string}
@keyword dataset2: The type name of the second dataset. Default is
I{dataset2}.
@keyword scale: The constant by which to scale the background spectra
@type scale: L{hlr_utils.DrParameter}
@keyword verbose: A flag for turning on information from the function.
@type verbose: C{boolean}
@keyword timer: Timing object so the function can perform timing
@type timer: C{sns_timer.DiffTime}
@return: The data subtracted by the background
@rtype: C{SOM.SOM} or C{SOM.SO}
@raise TypeError: Both objects are not C{SOM}s, a C{SO} and a C{SOM} or
a C{SOM} and a C{SO}
"""
# Kickout if data object is NoneType
if data_som is None:
return None
# Kickout if background object is NoneType
if bkg_som is None:
return data_som
# import the helper functions
import hlr_utils
(data_descr, bkg_descr) = hlr_utils.get_descr(data_som, bkg_som)
if data_descr == "SOM" and bkg_descr == "SOM":
hlr_utils.math_compatible(data_som, data_descr, bkg_som, bkg_descr)
elif data_descr == "SOM" and bkg_descr == "SO" or \
data_descr == "SO" and bkg_descr == "SOM":
# You have SO-SOM or SOM-SO, so assume everything is OK
pass
else:
raise TypeError("The object combinations must be SOM-SOM, SO-SOM "\
+"or SOM-SO. You provided a %s and a %s" % \
(data_descr, bkg_descr))
# Check for keywords
try:
verbose = kwargs["verbose"]
except KeyError:
verbose = False
try:
t = kwargs["timer"]
except KeyError:
t = None
try:
dataset1 = kwargs["dataset1"]
except KeyError:
dataset1 = "dataset1"
try:
dataset2 = kwargs["dataset2"]
except KeyError:
dataset2 = "dataset2"
try:
scale = kwargs["scale"]
except KeyError:
scale = None
import common_lib
if scale is not None:
if verbose:
print "Scaling %s for %s" % (dataset2, dataset1)
bkg_som2 = common_lib.mult_ncerr(bkg_som, scale.toValErrTuple())
if t is not None:
t.getTime(msg="After scaling %s for %s " % (dataset2, dataset1))
else:
bkg_som2 = bkg_som
del bkg_som
if verbose:
print "Subtracting %s from %s" % (dataset2, dataset1)
data_som2 = common_lib.sub_ncerr(data_som, bkg_som2)
if t is not None:
t.getTime(msg="After subtracting %s from %s " % (dataset2, dataset1))
return data_som2
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_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 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