def process_reflp_data(datalist, conf, roi_file, bkg_roi_file=None,
no_bkg=False, **kwargs):
"""
This function combines Steps 1 through 3 in section 2.4.6.1 of the data
reduction process for Reduction from TOF to lambda_T 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,
region-of-interest (ROI) file for the normalization dataset, a background
region-of-interest (ROI) file and an optional flag about background
subtractionand 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 roi_file: The file containing the list of pixel IDs for the region
of interest. This only applies to normalization data.
@type 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: File object that contains instrument geometry
information.
@type inst_geom_dst: C{DST.GeomDST}
@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
import common_lib
import dr_lib
# Check keywords
try:
i_geom_dst = kwargs["inst_geom_dst"]
except KeyError:
i_geom_dst = None
try:
t = kwargs["timer"]
except KeyError:
t = None
if roi_file is not None:
# Normalization
dataset_type = "norm"
else:
# Sample data
dataset_type = "data"
so_axis = "time_of_flight"
# Step 0: Open data files and select ROI (if necessary)
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=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)
# Override geometry if necessary
if i_geom_dst is not None:
i_geom_dst.setGeometry(conf.data_paths.toPath(), d_som1)
if dataset_type == "data":
# Get TOF bin width
conf.delta_TOF = d_som1[0].axis[0].val[1] - d_som1[0].axis[0].val[0]
if conf.mon_norm:
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_som1 = 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 ")
else:
dm_som1 = None
# Step 1: Sum all spectra along the low resolution direction
# Set sorting for REF_L
if conf.verbose:
print "Summing over 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 t is not None:
t.getTime(False)
d_som2 = dr_lib.sum_all_spectra(d_som1, y_sort=y_sort, stripe=True,
pixel_fix=cent_pixel)
if b_som1 is not None:
b_som2 = dr_lib.sum_all_spectra(b_som1, y_sort=y_sort, stripe=True,
pixel_fix=cent_pixel)
del b_som1
else:
b_som2 = b_som1
if t is not None:
t.getTime(msg="After summing low resolution direction ")
del d_som1
# Determine background spectrum
if conf.verbose and not no_bkg:
print "Determining %s background" % dataset_type
if b_som2 is not None:
B = dr_lib.calculate_ref_background(b_som2, no_bkg, conf.inst, None,
aobj=d_som2)
if t is not None:
t.getTime(msg="After background determination")
# Subtract background spectrum from data spectra
if not no_bkg:
d_som3 = dr_lib.subtract_bkg_from_data(d_som2, B,
verbose=conf.verbose,
timer=t,
dataset1="data",
dataset2="background")
else:
d_som3 = d_som2
del d_som2
# Zero the spectra if necessary
if roi_file is None and (conf.tof_cut_min is not None or \
conf.tof_cut_max is not None):
import utils
# Find the indicies for the non zero range
if conf.tof_cut_min is None:
conf.TOF_min = d_som3[0].axis[0].val[0]
start_index = 0
else:
start_index = utils.bisect_helper(d_som3[0].axis[0].val,
conf.tof_cut_min)
if conf.tof_cut_max is None:
conf.TOF_max = d_som3[0].axis[0].val[-1]
end_index = len(d_som3[0].axis[0].val) - 1
else:
end_index = utils.bisect_helper(d_som3[0].axis[0].val,
conf.tof_cut_max)
nz_list = []
for i in xrange(hlr_utils.get_length(d_som3)):
nz_list.append((start_index, end_index))
d_som4 = dr_lib.zero_spectra(d_som3, nz_list, use_bin_index=True)
else:
conf.TOF_min = d_som3[0].axis[0].val[0]
conf.TOF_max = d_som3[0].axis[0].val[-1]
d_som4 = d_som3
del d_som3
# Step N: Convert TOF to wavelength
if conf.verbose:
print "Converting TOF to wavelength"
if t is not None:
t.getTime(False)
d_som5 = common_lib.tof_to_wavelength(d_som4, inst_param="total",
units="microsecond")
if dm_som1 is not None:
dm_som2 = common_lib.tof_to_wavelength(dm_som1, units="microsecond")
else:
dm_som2 = None
del dm_som1
if t is not None:
t.getTime(msg="After converting TOF to wavelength ")
del d_som4
if conf.mon_norm:
dm_som3 = dr_lib.rebin_monitor(dm_som2, d_som5, rtype="frac")
else:
dm_som3 = None
del dm_som2
if not conf.mon_norm:
# Step 2: Multiply the spectra by the proton charge
if conf.verbose:
print "Multiply spectra by proton charge"
pc_tag = dataset_type + "-proton_charge"
proton_charge = d_som5.attr_list[pc_tag]
if t is not None:
t.getTime(False)
d_som6 = common_lib.div_ncerr(d_som5, (proton_charge.getValue(), 0.0))
if t is not None:
t.getTime(msg="After scaling by proton charge ")
else:
if conf.verbose:
print "Normalize by monitor spectrum"
if t is not None:
t.getTime(False)
d_som6 = common_lib.div_ncerr(d_som5, dm_som3)
if t is not None:
t.getTime(msg="After monitor normalization ")
del d_som5, dm_som3
if roi_file is None:
return d_som6
else:
# Step 3: Make one spectrum for normalization dataset
# Need to create a final rebinning axis
pathlength = d_som6.attr_list.instrument.get_total_path(
det_secondary=True)
delta_lambda = common_lib.tof_to_wavelength((conf.delta_TOF, 0.0),
pathlength=pathlength)
lambda_bins = dr_lib.create_axis_from_data(d_som6,
width=delta_lambda[0])
return dr_lib.sum_by_rebin_frac(d_som6, lambda_bins.toNessiList())
def process_reflp_data(datalist,
conf,
roi_file,
bkg_roi_file=None,
no_bkg=False,
**kwargs):
"""
This function combines Steps 1 through 3 in section 2.4.6.1 of the data
reduction process for Reduction from TOF to lambda_T 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,
region-of-interest (ROI) file for the normalization dataset, a background
region-of-interest (ROI) file and an optional flag about background
subtractionand 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 roi_file: The file containing the list of pixel IDs for the region
of interest. This only applies to normalization data.
@type 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: File object that contains instrument geometry
information.
@type inst_geom_dst: C{DST.GeomDST}
@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
import common_lib
import dr_lib
# Check keywords
try:
i_geom_dst = kwargs["inst_geom_dst"]
except KeyError:
i_geom_dst = None
try:
t = kwargs["timer"]
except KeyError:
t = None
if roi_file is not None:
# Normalization
dataset_type = "norm"
else:
# Sample data
dataset_type = "data"
so_axis = "time_of_flight"
# Step 0: Open data files and select ROI (if necessary)
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=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)
# Override geometry if necessary
if i_geom_dst is not None:
i_geom_dst.setGeometry(conf.data_paths.toPath(), d_som1)
if dataset_type == "data":
# Get TOF bin width
conf.delta_TOF = d_som1[0].axis[0].val[1] - d_som1[0].axis[0].val[0]
if conf.mon_norm:
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_som1 = 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 ")
else:
dm_som1 = None
# Step 1: Sum all spectra along the low resolution direction
# Set sorting for REF_L
if conf.verbose:
print "Summing over 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 t is not None:
t.getTime(False)
d_som2 = dr_lib.sum_all_spectra(d_som1,
y_sort=y_sort,
stripe=True,
pixel_fix=cent_pixel)
if b_som1 is not None:
b_som2 = dr_lib.sum_all_spectra(b_som1,
y_sort=y_sort,
stripe=True,
pixel_fix=cent_pixel)
del b_som1
else:
b_som2 = b_som1
if t is not None:
t.getTime(msg="After summing low resolution direction ")
del d_som1
# Determine background spectrum
if conf.verbose and not no_bkg:
print "Determining %s background" % dataset_type
if b_som2 is not None:
B = dr_lib.calculate_ref_background(b_som2,
no_bkg,
conf.inst,
None,
aobj=d_som2)
if t is not None:
t.getTime(msg="After background determination")
# Subtract background spectrum from data spectra
if not no_bkg:
d_som3 = dr_lib.subtract_bkg_from_data(d_som2,
B,
verbose=conf.verbose,
timer=t,
dataset1="data",
dataset2="background")
else:
d_som3 = d_som2
del d_som2
# Zero the spectra if necessary
if roi_file is None and (conf.tof_cut_min is not None or \
conf.tof_cut_max is not None):
import utils
# Find the indicies for the non zero range
if conf.tof_cut_min is None:
conf.TOF_min = d_som3[0].axis[0].val[0]
start_index = 0
else:
start_index = utils.bisect_helper(d_som3[0].axis[0].val,
conf.tof_cut_min)
if conf.tof_cut_max is None:
conf.TOF_max = d_som3[0].axis[0].val[-1]
end_index = len(d_som3[0].axis[0].val) - 1
else:
end_index = utils.bisect_helper(d_som3[0].axis[0].val,
conf.tof_cut_max)
nz_list = []
for i in xrange(hlr_utils.get_length(d_som3)):
nz_list.append((start_index, end_index))
d_som4 = dr_lib.zero_spectra(d_som3, nz_list, use_bin_index=True)
else:
conf.TOF_min = d_som3[0].axis[0].val[0]
conf.TOF_max = d_som3[0].axis[0].val[-1]
d_som4 = d_som3
del d_som3
# Step N: Convert TOF to wavelength
if conf.verbose:
print "Converting TOF to wavelength"
if t is not None:
t.getTime(False)
d_som5 = common_lib.tof_to_wavelength(d_som4,
inst_param="total",
units="microsecond")
if dm_som1 is not None:
dm_som2 = common_lib.tof_to_wavelength(dm_som1, units="microsecond")
else:
dm_som2 = None
del dm_som1
if t is not None:
t.getTime(msg="After converting TOF to wavelength ")
del d_som4
if conf.mon_norm:
dm_som3 = dr_lib.rebin_monitor(dm_som2, d_som5, rtype="frac")
else:
dm_som3 = None
del dm_som2
if not conf.mon_norm:
# Step 2: Multiply the spectra by the proton charge
if conf.verbose:
print "Multiply spectra by proton charge"
pc_tag = dataset_type + "-proton_charge"
proton_charge = d_som5.attr_list[pc_tag]
if t is not None:
t.getTime(False)
d_som6 = common_lib.div_ncerr(d_som5, (proton_charge.getValue(), 0.0))
if t is not None:
t.getTime(msg="After scaling by proton charge ")
else:
if conf.verbose:
print "Normalize by monitor spectrum"
if t is not None:
t.getTime(False)
d_som6 = common_lib.div_ncerr(d_som5, dm_som3)
if t is not None:
t.getTime(msg="After monitor normalization ")
del d_som5, dm_som3
if roi_file is None:
return d_som6
else:
# Step 3: Make one spectrum for normalization dataset
# Need to create a final rebinning axis
pathlength = d_som6.attr_list.instrument.get_total_path(
det_secondary=True)
delta_lambda = common_lib.tof_to_wavelength((conf.delta_TOF, 0.0),
pathlength=pathlength)
lambda_bins = dr_lib.create_axis_from_data(d_som6,
width=delta_lambda[0])
return dr_lib.sum_by_rebin_frac(d_som6, lambda_bins.toNessiList())
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
