def create_param_vs_Y(som, param, param_func, param_axis, **kwargs):
"""
This function takes a group of single spectrum with any given axes
(wavelength, energy etc.). The function can optionally rebin those axes to
a given axis. It then creates a 2D spectrum by using a parameter,
parameter functiona and a given axis for the lookup locations and places
each original spectrum in the found location.
@param som: The input object with arbitrary (but same) axis spectra
@type som: C{SOM.SOM}
@param param: The parameter that will be used for creating the lookups.
@type param: C{string}
@param param_func: The function that will convert the parameter into the
values for lookups.
@type param_func: C{string}
@param param_axis: The axis that will be searched for the lookup values.
@type param_axis: C{nessi_list.NessiList}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword rebin_axis: An axis to rebin the given spectra to.
@type rebin_axis: C{nessi_list.NessiList}
@keyword data_type: The name of the data type which can be either
I{histogram}, I{density} or I{coordinate}. The default
value will be I{histogram}.
@type data_type: C{string}
@keyword pixnorm: A flag to track the number of pixels that contribute to
a bin and then normalize the bin by that number.
@type pixnorm: C{boolean}
@keyword prnorm: A parameter to track and determine a range (max - min)
for each bin the requested parameter axis. The range will
then be divided into the final summed spectrum for the
given bin.
@type prnorm: C{string}
@keyword binnorm: A flag that turns on the scaling of each stripe of the
y-axis by the individual bins widths from the y-axis.
@type binnorm: C{boolean}
@keyword so_id: The identifier represents a number, string, tuple or other
object that describes the resulting C{SO}.
@type so_id: C{int}, C{string}, C{tuple}, C{pixel ID}
@keyword y_label: The dependent axis label
@type y_label: C{string}
@keyword y_units: The dependent axis units
@type y_units: C{string}
@keyword x_labels: The two independent axis labels
@type x_labels: C{list} of C{string}s
@keyword x_units: The two independent axis units
@type x_units: C{list} of C{string}s
@return: A two dimensional spectrum with the parameter as the x-axis and
the given spectra axes as the y-axis.
@rtype: C{SOM.SOM}
"""
import array_manip
import dr_lib
import hlr_utils
import nessi_list
import SOM
import utils
# Check for rebinning axis
try:
rebin_axis = kwargs["rebin_axis"]
except KeyError:
rebin_axis = None
# Check for pixnorm flag
try:
pixnorm = kwargs["pixnorm"]
except KeyError:
pixnorm = False
try:
binnorm = kwargs["binnorm"]
except KeyError:
binnorm = False
# Check for prnorm flag
try:
prpar = kwargs["prnorm"]
prnorm = True
except KeyError:
prnorm = False
# Check dataType keyword argument. An offset will be set to 1 for the
# histogram type and 0 for either density or coordinate
try:
data_type = kwargs["data_type"]
if data_type.lower() == "histogram":
offset = 1
elif data_type.lower() == "density" or \
data_type.lower() == "coordinate":
offset = 0
else:
raise RuntimeError("Do not understand data type given: %s" % \
data_type)
# Default is offset for histogram
except KeyError:
offset = 1
# Setup some variables
dim = 2
N_tot = 1
# Create 2D spectrum object
so_dim = SOM.SO(dim)
# Set the axis locations
param_axis_loc = 0
arb_axis_loc = 1
# Rebin original data to rebin_axis if necessary
if rebin_axis is not None:
(som1, som2) = dr_lib.rebin_axis_1D_frac(som, rebin_axis)
len_arb_axis = len(rebin_axis) - offset
so_dim.axis[arb_axis_loc].val = rebin_axis
else:
som1 = som
len_arb_axis = len(som[0].axis[0].val) - offset
so_dim.axis[arb_axis_loc].val = som[0].axis[0].val
del som
# Get parameter axis information
len_param_axis = len(param_axis) - offset
so_dim.axis[param_axis_loc].val = param_axis
if pixnorm:
pixarr = nessi_list.NessiList(len_param_axis)
if prnorm:
prarr = []
for i in xrange(len_param_axis):
prarr.append(nessi_list.NessiList())
# Get the parameters for all the spectra
ppfunc = hlr_utils.__getattribute__("param_array")
prarr_lookup = ppfunc(som1, prpar)
# Get the parameter lookup array
pfunc = hlr_utils.__getattribute__(param_func)
lookup_array = pfunc(som1, param)
# Create y and var_y lists from total 2D size
N_tot = len_param_axis * len_arb_axis
so_dim.y = nessi_list.NessiList(N_tot)
so_dim.var_y = nessi_list.NessiList(N_tot)
if rebin_axis is not None:
frac_area = nessi_list.NessiList(N_tot)
frac_area_err2 = nessi_list.NessiList(N_tot)
# Loop through data and create 2D spectrum
len_som = hlr_utils.get_length(som1)
for i in xrange(len_som):
val = hlr_utils.get_value(som1, i, "SOM", "y")
err2 = hlr_utils.get_err2(som1, i, "SOM", "y")
bin_index = utils.bisect_helper(param_axis, lookup_array[i])
start = bin_index * len_arb_axis
if pixnorm:
pixarr[bin_index] += 1
if prnorm:
prarr[bin_index].append(prarr_lookup[i])
(so_dim.y, so_dim.var_y) = array_manip.add_ncerr(so_dim.y,
so_dim.var_y,
val,
err2,
a_start=start)
if rebin_axis is not None:
val1 = hlr_utils.get_value(som2, i, "SOM", "y")
err1_2 = hlr_utils.get_err2(som2, i, "SOM", "y")
(frac_area, frac_area_err2) = array_manip.add_ncerr(frac_area,
frac_area_err2,
val1,
err1_2,
a_start=start)
if rebin_axis is not None:
(so_dim.y, so_dim.var_y) = array_manip.div_ncerr(so_dim.y,
so_dim.var_y,
frac_area,
frac_area_err2)
# If parameter range normalization enabled, find the range for the
# parameter
if prnorm:
import math
prrange = nessi_list.NessiList(len_param_axis)
for i in xrange(len(prrange)):
try:
max_val = max(prarr[i])
except ValueError:
max_val = 0.0
try:
min_val = min(prarr[i])
except ValueError:
min_val = 0.0
prrange[i] = math.fabs(max_val - min_val)
# If pixel normalization tracking enabled, divided slices by pixel counts
if pixnorm or prnorm:
tmp_y = nessi_list.NessiList(N_tot)
tmp_var_y = nessi_list.NessiList(N_tot)
for i in range(len_param_axis):
start = i * len_arb_axis
end = (i + 1) * len_arb_axis
slice_y = so_dim.y[start:end]
slice_var_y = so_dim.var_y[start:end]
divconst = 1.0
if pixnorm:
divconst *= pixarr[i]
# Scale division constant if parameter range normalization enabled
if prnorm:
divconst *= prrange[i]
(dslice_y, dslice_var_y) = array_manip.div_ncerr(slice_y,
slice_var_y,
divconst,
0.0)
(tmp_y, tmp_var_y) = array_manip.add_ncerr(tmp_y,
tmp_var_y,
dslice_y,
dslice_var_y,
a_start=start)
so_dim.y = tmp_y
so_dim.var_y = tmp_var_y
if binnorm:
tmp_y = nessi_list.NessiList(N_tot)
tmp_var_y = nessi_list.NessiList(N_tot)
if rebin_axis is not None:
bin_const = utils.calc_bin_widths(rebin_axis)
else:
bin_const = utils.calc_bin_widths(som1[0].axis[1].val)
for i in range(len_param_axis):
start = i * len_arb_axis
end = (i + 1) * len_arb_axis
slice_y = so_dim.y[start:end]
slice_var_y = so_dim.var_y[start:end]
(dslice_y, dslice_var_y) = array_manip.mult_ncerr(slice_y,
slice_var_y,
bin_const[0],
bin_const[1])
(tmp_y, tmp_var_y) = array_manip.add_ncerr(tmp_y,
tmp_var_y,
dslice_y,
dslice_var_y,
a_start=start)
so_dim.y = tmp_y
so_dim.var_y = tmp_var_y
# Create final 2D spectrum object container
comb_som = SOM.SOM()
comb_som.copyAttributes(som1)
del som1
# Check for so_id keyword argument
try:
so_dim.id = kwargs["so_id"]
except KeyError:
so_dim.id = 0
# Check for y_label keyword argument
try:
comb_som.setYLabel(kwargs["y_label"])
except KeyError:
comb_som.setYLabel("Counts")
# Check for y_units keyword argument
try:
comb_som.setYUnits(kwargs["y_units"])
except KeyError:
comb_som.setYUnits("Counts / Arb")
# Check for x_label keyword argument
try:
comb_som.setAllAxisLabels(kwargs["x_labels"])
except KeyError:
comb_som.setAllAxisLabels(["Parameter", "Arbitrary"])
# Check for x_units keyword argument
try:
comb_som.setAllAxisUnits(kwargs["x_units"])
except KeyError:
comb_som.setAllAxisUnits(["Arb", "Arb"])
comb_som.append(so_dim)
del so_dim
return comb_som
def tof_to_wavelength_lin_time_zero(obj, **kwargs):
"""
This function converts a primary axis of a C{SOM} or C{SO} from
time-of-flight to wavelength incorporating a linear time zero which is a
described as a linear function of the wavelength. The time-of-flight axis
for a C{SOM} must be in units of I{microseconds}. The primary axis of a
C{SO} is assumed to be in units of I{microseconds}. A C{tuple} of C{(tof,
tof_err2)} (assumed to be in units of I{microseconds}) can be converted to
C{(wavelength, wavelength_err2)}.
@param obj: Object to be converted
@type obj: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword pathlength: The pathlength and its associated error^2
@type pathlength: C{tuple} or C{list} of C{tuple}s
@keyword time_zero_slope: The time zero slope and its associated error^2
@type time_zero_slope: C{tuple}
@keyword time_zero_offset: The time zero offset and its associated error^2
@type time_zero_offset: C{tuple}
@keyword inst_param: The type of parameter requested from an associated
instrument. For this function the acceptable
parameters are I{primary}, I{secondary} and I{total}.
Default is I{primary}.
@type inst_param: C{string}
@keyword lojac: A flag that allows one to turn off the calculation of the
linear-order Jacobian. The default action is I{True} for
histogram data.
@type lojac: C{boolean}
@keyword units: The expected units for this function. The default for this
function is I{microseconds}.
@type units: C{string}
@keyword cut_val: Specify a wavelength to cut the spectra at.
@type cut_val: C{float}
@keyword cut_less: A flag that specifies cutting the spectra less than
C{cut_val}. The default is C{True}.
@type cut_less: C{boolean}
@return: Object with a primary axis in time-of-flight converted to
wavelength
@rtype: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@raise TypeError: The incoming object is not a type the function recognizes
@raise RuntimeError: The C{SOM} x-axis units are not I{microseconds}
@raise RuntimeError: A C{SOM} does not contain an instrument and no
pathlength was provided
@raise RuntimeError: No C{SOM} is provided and no pathlength given
"""
# import the helper functions
import hlr_utils
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(obj)
o_descr = hlr_utils.get_descr(obj)
# Setup keyword arguments
try:
inst_param = kwargs["inst_param"]
except KeyError:
inst_param = "primary"
try:
pathlength = kwargs["pathlength"]
except KeyError:
pathlength = None
try:
time_zero_slope = kwargs["time_zero_slope"]
except KeyError:
time_zero_slope = None
# Current constants for Time Zero Slope
TIME_ZERO_SLOPE = (float(0.0), float(0.0))
try:
time_zero_offset = kwargs["time_zero_offset"]
except KeyError:
time_zero_offset = None
# Current constants for Time Zero Offset
TIME_ZERO_OFFSET = (float(0.0), float(0.0))
try:
lojac = kwargs["lojac"]
except KeyError:
lojac = hlr_utils.check_lojac(obj)
try:
units = kwargs["units"]
except KeyError:
units = "microseconds"
try:
cut_val = kwargs["cut_val"]
except KeyError:
cut_val = None
try:
cut_less = kwargs["cut_less"]
except KeyError:
cut_less = True
# Primary axis for transformation. If a SO is passed, the function, will
# assume the axis for transformation is at the 0 position
if o_descr == "SOM":
axis = hlr_utils.one_d_units(obj, units)
else:
axis = 0
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
if res_descr == "SOM":
result = hlr_utils.force_units(result, "Angstroms", axis)
result.setAxisLabel(axis, "wavelength")
result.setYUnits("Counts/A")
result.setYLabel("Intensity")
else:
pass
if pathlength is not None:
p_descr = hlr_utils.get_descr(pathlength)
else:
if o_descr == "SOM":
try:
obj.attr_list.instrument.get_primary()
inst = obj.attr_list.instrument
except RuntimeError:
raise RuntimeError("A detector was not provided")
else:
raise RuntimeError("If no SOM is provided, then pathlength "\
+"information must be provided")
if time_zero_slope is not None:
t_0_slope_descr = hlr_utils.get_descr(time_zero_slope)
else:
if o_descr == "SOM":
try:
t_0_slope = obj.attr_list["Time_zero_slope"][0]
t_0_slope_err2 = obj.attr_list["Time_zero_slope"][1]
except KeyError:
t_0_slope = TIME_ZERO_SLOPE[0]
t_0_slope_err2 = TIME_ZERO_SLOPE[1]
else:
t_0_slope = TIME_ZERO_SLOPE[0]
t_0_slope_err2 = TIME_ZERO_SLOPE[1]
if time_zero_offset is not None:
t_0_offset_descr = hlr_utils.get_descr(time_zero_offset)
else:
if o_descr == "SOM":
try:
t_0_offset = obj.attr_list["Time_zero_offset"][0]
t_0_offset_err2 = obj.attr_list["Time_zero_offset"][1]
except KeyError:
t_0_offset = TIME_ZERO_OFFSET[0]
t_0_offset_err2 = TIME_ZERO_OFFSET[1]
else:
t_0_offset = TIME_ZERO_OFFSET[0]
t_0_offset_err2 = TIME_ZERO_OFFSET[1]
# iterate through the values
import axis_manip
if lojac or cut_val is not None:
import utils
for i in xrange(hlr_utils.get_length(obj)):
val = hlr_utils.get_value(obj, i, o_descr, "x", axis)
err2 = hlr_utils.get_err2(obj, i, o_descr, "x", axis)
map_so = hlr_utils.get_map_so(obj, None, i)
if pathlength is None:
(pl, pl_err2) = hlr_utils.get_parameter(inst_param, map_so, inst)
else:
pl = hlr_utils.get_value(pathlength, i, p_descr)
pl_err2 = hlr_utils.get_err2(pathlength, i, p_descr)
if time_zero_slope is not None:
t_0_slope = hlr_utils.get_value(time_zero_slope, i,
t_0_slope_descr)
t_0_slope_err2 = hlr_utils.get_err2(time_zero_slope, i,
t_0_slope_descr)
else:
pass
if time_zero_offset is not None:
t_0_offset = hlr_utils.get_value(time_zero_offset, i,
t_0_offset_descr)
t_0_offset_err2 = hlr_utils.get_err2(time_zero_offset, i,
t_0_offset_descr)
else:
pass
value = axis_manip.tof_to_wavelength_lin_time_zero(
val, err2, pl, pl_err2, t_0_slope, t_0_slope_err2, t_0_offset,
t_0_offset_err2)
if cut_val is not None:
index = utils.bisect_helper(value[0], cut_val)
if cut_less:
# Need to cut at this index, so increment by one
index += 1
value[0].__delslice__(0, index)
value[1].__delslice__(0, index)
map_so.y.__delslice__(0, index)
map_so.var_y.__delslice__(0, index)
if lojac:
val.__delslice__(0, index)
err2.__delslice__(0, index)
else:
len_data = len(value[0])
# All axis arrays need starting index adjusted by one since
# they always carry one more bin than the data
value[0].__delslice__(index + 1, len_data)
value[1].__delslice__(index + 1, len_data)
map_so.y.__delslice__(index, len_data)
map_so.var_y.__delslice__(index, len_data)
if lojac:
val.__delslice__(index + 1, len_data)
err2.__delslice__(index + 1, len_data)
if lojac:
counts = utils.linear_order_jacobian(val, value[0], map_so.y,
map_so.var_y)
hlr_utils.result_insert(result, res_descr, counts, map_so, "all",
axis, [value[0]])
else:
hlr_utils.result_insert(result, res_descr, value, map_so, "x",
axis)
return result
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 tof_to_wavelength_lin_time_zero(obj, **kwargs):
"""
This function converts a primary axis of a C{SOM} or C{SO} from
time-of-flight to wavelength incorporating a linear time zero which is a
described as a linear function of the wavelength. The time-of-flight axis
for a C{SOM} must be in units of I{microseconds}. The primary axis of a
C{SO} is assumed to be in units of I{microseconds}. A C{tuple} of C{(tof,
tof_err2)} (assumed to be in units of I{microseconds}) can be converted to
C{(wavelength, wavelength_err2)}.
@param obj: Object to be converted
@type obj: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword pathlength: The pathlength and its associated error^2
@type pathlength: C{tuple} or C{list} of C{tuple}s
@keyword time_zero_slope: The time zero slope and its associated error^2
@type time_zero_slope: C{tuple}
@keyword time_zero_offset: The time zero offset and its associated error^2
@type time_zero_offset: C{tuple}
@keyword inst_param: The type of parameter requested from an associated
instrument. For this function the acceptable
parameters are I{primary}, I{secondary} and I{total}.
Default is I{primary}.
@type inst_param: C{string}
@keyword lojac: A flag that allows one to turn off the calculation of the
linear-order Jacobian. The default action is I{True} for
histogram data.
@type lojac: C{boolean}
@keyword units: The expected units for this function. The default for this
function is I{microseconds}.
@type units: C{string}
@keyword cut_val: Specify a wavelength to cut the spectra at.
@type cut_val: C{float}
@keyword cut_less: A flag that specifies cutting the spectra less than
C{cut_val}. The default is C{True}.
@type cut_less: C{boolean}
@return: Object with a primary axis in time-of-flight converted to
wavelength
@rtype: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@raise TypeError: The incoming object is not a type the function recognizes
@raise RuntimeError: The C{SOM} x-axis units are not I{microseconds}
@raise RuntimeError: A C{SOM} does not contain an instrument and no
pathlength was provided
@raise RuntimeError: No C{SOM} is provided and no pathlength given
"""
# import the helper functions
import hlr_utils
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(obj)
o_descr = hlr_utils.get_descr(obj)
# Setup keyword arguments
try:
inst_param = kwargs["inst_param"]
except KeyError:
inst_param = "primary"
try:
pathlength = kwargs["pathlength"]
except KeyError:
pathlength = None
try:
time_zero_slope = kwargs["time_zero_slope"]
except KeyError:
time_zero_slope = None
# Current constants for Time Zero Slope
TIME_ZERO_SLOPE = (float(0.0), float(0.0))
try:
time_zero_offset = kwargs["time_zero_offset"]
except KeyError:
time_zero_offset = None
# Current constants for Time Zero Offset
TIME_ZERO_OFFSET = (float(0.0), float(0.0))
try:
lojac = kwargs["lojac"]
except KeyError:
lojac = hlr_utils.check_lojac(obj)
try:
units = kwargs["units"]
except KeyError:
units = "microseconds"
try:
cut_val = kwargs["cut_val"]
except KeyError:
cut_val = None
try:
cut_less = kwargs["cut_less"]
except KeyError:
cut_less = True
# Primary axis for transformation. If a SO is passed, the function, will
# assume the axis for transformation is at the 0 position
if o_descr == "SOM":
axis = hlr_utils.one_d_units(obj, units)
else:
axis = 0
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
if res_descr == "SOM":
result = hlr_utils.force_units(result, "Angstroms", axis)
result.setAxisLabel(axis, "wavelength")
result.setYUnits("Counts/A")
result.setYLabel("Intensity")
else:
pass
if pathlength is not None:
p_descr = hlr_utils.get_descr(pathlength)
else:
if o_descr == "SOM":
try:
obj.attr_list.instrument.get_primary()
inst = obj.attr_list.instrument
except RuntimeError:
raise RuntimeError("A detector was not provided")
else:
raise RuntimeError("If no SOM is provided, then pathlength "\
+"information must be provided")
if time_zero_slope is not None:
t_0_slope_descr = hlr_utils.get_descr(time_zero_slope)
else:
if o_descr == "SOM":
try:
t_0_slope = obj.attr_list["Time_zero_slope"][0]
t_0_slope_err2 = obj.attr_list["Time_zero_slope"][1]
except KeyError:
t_0_slope = TIME_ZERO_SLOPE[0]
t_0_slope_err2 = TIME_ZERO_SLOPE[1]
else:
t_0_slope = TIME_ZERO_SLOPE[0]
t_0_slope_err2 = TIME_ZERO_SLOPE[1]
if time_zero_offset is not None:
t_0_offset_descr = hlr_utils.get_descr(time_zero_offset)
else:
if o_descr == "SOM":
try:
t_0_offset = obj.attr_list["Time_zero_offset"][0]
t_0_offset_err2 = obj.attr_list["Time_zero_offset"][1]
except KeyError:
t_0_offset = TIME_ZERO_OFFSET[0]
t_0_offset_err2 = TIME_ZERO_OFFSET[1]
else:
t_0_offset = TIME_ZERO_OFFSET[0]
t_0_offset_err2 = TIME_ZERO_OFFSET[1]
# iterate through the values
import axis_manip
if lojac or cut_val is not None:
import utils
for i in xrange(hlr_utils.get_length(obj)):
val = hlr_utils.get_value(obj, i, o_descr, "x", axis)
err2 = hlr_utils.get_err2(obj, i, o_descr, "x", axis)
map_so = hlr_utils.get_map_so(obj, None, i)
if pathlength is None:
(pl, pl_err2) = hlr_utils.get_parameter(inst_param, map_so, inst)
else:
pl = hlr_utils.get_value(pathlength, i, p_descr)
pl_err2 = hlr_utils.get_err2(pathlength, i, p_descr)
if time_zero_slope is not None:
t_0_slope = hlr_utils.get_value(time_zero_slope, i,
t_0_slope_descr)
t_0_slope_err2 = hlr_utils.get_err2(time_zero_slope, i,
t_0_slope_descr)
else:
pass
if time_zero_offset is not None:
t_0_offset = hlr_utils.get_value(time_zero_offset, i,
t_0_offset_descr)
t_0_offset_err2 = hlr_utils.get_err2(time_zero_offset, i,
t_0_offset_descr)
else:
pass
value = axis_manip.tof_to_wavelength_lin_time_zero(val, err2,
pl, pl_err2,
t_0_slope,
t_0_slope_err2,
t_0_offset,
t_0_offset_err2)
if cut_val is not None:
index = utils.bisect_helper(value[0], cut_val)
if cut_less:
# Need to cut at this index, so increment by one
index += 1
value[0].__delslice__(0, index)
value[1].__delslice__(0, index)
map_so.y.__delslice__(0, index)
map_so.var_y.__delslice__(0, index)
if lojac:
val.__delslice__(0, index)
err2.__delslice__(0, index)
else:
len_data = len(value[0])
# All axis arrays need starting index adjusted by one since
# they always carry one more bin than the data
value[0].__delslice__(index + 1, len_data)
value[1].__delslice__(index + 1, len_data)
map_so.y.__delslice__(index, len_data)
map_so.var_y.__delslice__(index, len_data)
if lojac:
val.__delslice__(index + 1, len_data)
err2.__delslice__(index + 1, len_data)
if lojac:
counts = utils.linear_order_jacobian(val, value[0],
map_so.y, map_so.var_y)
hlr_utils.result_insert(result, res_descr, counts, map_so,
"all", axis, [value[0]])
else:
hlr_utils.result_insert(result, res_descr, value, map_so,
"x", axis)
return result
def dimensionless_mon(obj, min_ext, max_ext, **kwargs):
"""
This function takes monitor spectra and converts them to dimensionless
spectra by dividing each spectrum by the total number of counts within the
range [min_ext, max_ext]. Then, each spectrum is multiplied by the quantity
max_ext - min_ext. The units of min_ext and max_ext are assumed to be the
same as the monitor spectra axis.
@param obj: Object containing monitor spectra
@type obj: C{SOM.SOM} or C{SOM.SO}
@param min_ext: Minimium range and associated error^2 for integrating total
counts.
@type min_ext: C{tuple}
@param max_ext: Maximium range and associated error^2 for integrating total
counts.
@type max_ext: C{tuple}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword units: The expected units for this function. The default for this
function is I{Angstroms}.
@type units: C{string}
@return: Dimensionless monitor spectra
@rtype: C{SOM.SOM} or C{SOM.SO}
"""
# import the helper functions
import hlr_utils
if obj is None:
return obj
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(obj)
o_descr = hlr_utils.get_descr(obj)
# Setup keyword arguments
try:
units = kwargs["units"]
except KeyError:
units = "Angstroms"
# Primary axis for transformation. If a SO is passed, the function, will
# assume the axis for transformation is at the 0 position
if o_descr == "SOM":
axis = hlr_utils.one_d_units(obj, units)
else:
axis = 0
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
import array_manip
import dr_lib
import utils
for i in xrange(hlr_utils.get_length(obj)):
val = hlr_utils.get_value(obj, i, o_descr, "y")
err2 = hlr_utils.get_err2(obj, i, o_descr, "y")
x_axis = hlr_utils.get_value(obj, i, o_descr, "x", axis)
x_err2 = hlr_utils.get_err2(obj, i, o_descr, "x", axis)
map_so = hlr_utils.get_map_so(obj, None, i)
bin_widths = utils.calc_bin_widths(x_axis, x_err2)
# Scale bin contents by bin width
value0 = array_manip.mult_ncerr(val, err2,
bin_widths[0], bin_widths[1])
# Find bin range for extents
min_index = utils.bisect_helper(x_axis, min_ext[0])
max_index = utils.bisect_helper(x_axis, max_ext[0])
# Integrate axis using bin width multiplication
(asum, asum_err2) = dr_lib.integrate_axis_py(map_so, start=min_index,
end=max_index, width=True)
# Get the number of bins in the integration range
num_bins = max_index - min_index + 1
asum /= num_bins
asum_err2 /= (num_bins * num_bins)
# Divide by sum
value1 = array_manip.div_ncerr(value0[0], value0[1], asum, asum_err2)
hlr_utils.result_insert(result, res_descr, value1, map_so, "y")
return result
def dimensionless_mon(obj, min_ext, max_ext, **kwargs):
"""
This function takes monitor spectra and converts them to dimensionless
spectra by dividing each spectrum by the total number of counts within the
range [min_ext, max_ext]. Then, each spectrum is multiplied by the quantity
max_ext - min_ext. The units of min_ext and max_ext are assumed to be the
same as the monitor spectra axis.
@param obj: Object containing monitor spectra
@type obj: C{SOM.SOM} or C{SOM.SO}
@param min_ext: Minimium range and associated error^2 for integrating total
counts.
@type min_ext: C{tuple}
@param max_ext: Maximium range and associated error^2 for integrating total
counts.
@type max_ext: C{tuple}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword units: The expected units for this function. The default for this
function is I{Angstroms}.
@type units: C{string}
@return: Dimensionless monitor spectra
@rtype: C{SOM.SOM} or C{SOM.SO}
"""
# import the helper functions
import hlr_utils
if obj is None:
return obj
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(obj)
o_descr = hlr_utils.get_descr(obj)
# Setup keyword arguments
try:
units = kwargs["units"]
except KeyError:
units = "Angstroms"
# Primary axis for transformation. If a SO is passed, the function, will
# assume the axis for transformation is at the 0 position
if o_descr == "SOM":
axis = hlr_utils.one_d_units(obj, units)
else:
axis = 0
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
import array_manip
import dr_lib
import utils
for i in xrange(hlr_utils.get_length(obj)):
val = hlr_utils.get_value(obj, i, o_descr, "y")
err2 = hlr_utils.get_err2(obj, i, o_descr, "y")
x_axis = hlr_utils.get_value(obj, i, o_descr, "x", axis)
x_err2 = hlr_utils.get_err2(obj, i, o_descr, "x", axis)
map_so = hlr_utils.get_map_so(obj, None, i)
bin_widths = utils.calc_bin_widths(x_axis, x_err2)
# Scale bin contents by bin width
value0 = array_manip.mult_ncerr(val, err2, bin_widths[0],
bin_widths[1])
# Find bin range for extents
min_index = utils.bisect_helper(x_axis, min_ext[0])
max_index = utils.bisect_helper(x_axis, max_ext[0])
# Integrate axis using bin width multiplication
(asum, asum_err2) = dr_lib.integrate_axis_py(map_so,
start=min_index,
end=max_index,
width=True)
# Get the number of bins in the integration range
num_bins = max_index - min_index + 1
asum /= num_bins
asum_err2 /= (num_bins * num_bins)
# Divide by sum
value1 = array_manip.div_ncerr(value0[0], value0[1], asum, asum_err2)
hlr_utils.result_insert(result, res_descr, value1, map_so, "y")
return result
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 create_param_vs_Y(som, param, param_func, param_axis, **kwargs):
"""
This function takes a group of single spectrum with any given axes
(wavelength, energy etc.). The function can optionally rebin those axes to
a given axis. It then creates a 2D spectrum by using a parameter,
parameter functiona and a given axis for the lookup locations and places
each original spectrum in the found location.
@param som: The input object with arbitrary (but same) axis spectra
@type som: C{SOM.SOM}
@param param: The parameter that will be used for creating the lookups.
@type param: C{string}
@param param_func: The function that will convert the parameter into the
values for lookups.
@type param_func: C{string}
@param param_axis: The axis that will be searched for the lookup values.
@type param_axis: C{nessi_list.NessiList}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword rebin_axis: An axis to rebin the given spectra to.
@type rebin_axis: C{nessi_list.NessiList}
@keyword data_type: The name of the data type which can be either
I{histogram}, I{density} or I{coordinate}. The default
value will be I{histogram}.
@type data_type: C{string}
@keyword pixnorm: A flag to track the number of pixels that contribute to
a bin and then normalize the bin by that number.
@type pixnorm: C{boolean}
@keyword prnorm: A parameter to track and determine a range (max - min)
for each bin the requested parameter axis. The range will
then be divided into the final summed spectrum for the
given bin.
@type prnorm: C{string}
@keyword binnorm: A flag that turns on the scaling of each stripe of the
y-axis by the individual bins widths from the y-axis.
@type binnorm: C{boolean}
@keyword so_id: The identifier represents a number, string, tuple or other
object that describes the resulting C{SO}.
@type so_id: C{int}, C{string}, C{tuple}, C{pixel ID}
@keyword y_label: The dependent axis label
@type y_label: C{string}
@keyword y_units: The dependent axis units
@type y_units: C{string}
@keyword x_labels: The two independent axis labels
@type x_labels: C{list} of C{string}s
@keyword x_units: The two independent axis units
@type x_units: C{list} of C{string}s
@return: A two dimensional spectrum with the parameter as the x-axis and
the given spectra axes as the y-axis.
@rtype: C{SOM.SOM}
"""
import array_manip
import dr_lib
import hlr_utils
import nessi_list
import SOM
import utils
# Check for rebinning axis
try:
rebin_axis = kwargs["rebin_axis"]
except KeyError:
rebin_axis = None
# Check for pixnorm flag
try:
pixnorm = kwargs["pixnorm"]
except KeyError:
pixnorm = False
try:
binnorm = kwargs["binnorm"]
except KeyError:
binnorm = False
# Check for prnorm flag
try:
prpar = kwargs["prnorm"]
prnorm = True
except KeyError:
prnorm = False
# Check dataType keyword argument. An offset will be set to 1 for the
# histogram type and 0 for either density or coordinate
try:
data_type = kwargs["data_type"]
if data_type.lower() == "histogram":
offset = 1
elif data_type.lower() == "density" or \
data_type.lower() == "coordinate":
offset = 0
else:
raise RuntimeError("Do not understand data type given: %s" % \
data_type)
# Default is offset for histogram
except KeyError:
offset = 1
# Setup some variables
dim = 2
N_tot = 1
# Create 2D spectrum object
so_dim = SOM.SO(dim)
# Set the axis locations
param_axis_loc = 0
arb_axis_loc = 1
# Rebin original data to rebin_axis if necessary
if rebin_axis is not None:
(som1, som2) = dr_lib.rebin_axis_1D_frac(som, rebin_axis)
len_arb_axis = len(rebin_axis) - offset
so_dim.axis[arb_axis_loc].val = rebin_axis
else:
som1 = som
len_arb_axis = len(som[0].axis[0].val) - offset
so_dim.axis[arb_axis_loc].val = som[0].axis[0].val
del som
# Get parameter axis information
len_param_axis = len(param_axis) - offset
so_dim.axis[param_axis_loc].val = param_axis
if pixnorm:
pixarr = nessi_list.NessiList(len_param_axis)
if prnorm:
prarr = []
for i in xrange(len_param_axis):
prarr.append(nessi_list.NessiList())
# Get the parameters for all the spectra
ppfunc = hlr_utils.__getattribute__("param_array")
prarr_lookup = ppfunc(som1, prpar)
# Get the parameter lookup array
pfunc = hlr_utils.__getattribute__(param_func)
lookup_array = pfunc(som1, param)
# Create y and var_y lists from total 2D size
N_tot = len_param_axis * len_arb_axis
so_dim.y = nessi_list.NessiList(N_tot)
so_dim.var_y = nessi_list.NessiList(N_tot)
if rebin_axis is not None:
frac_area = nessi_list.NessiList(N_tot)
frac_area_err2 = nessi_list.NessiList(N_tot)
# Loop through data and create 2D spectrum
len_som = hlr_utils.get_length(som1)
for i in xrange(len_som):
val = hlr_utils.get_value(som1, i, "SOM", "y")
err2 = hlr_utils.get_err2(som1, i, "SOM", "y")
bin_index = utils.bisect_helper(param_axis, lookup_array[i])
start = bin_index * len_arb_axis
if pixnorm:
pixarr[bin_index] += 1
if prnorm:
prarr[bin_index].append(prarr_lookup[i])
(so_dim.y, so_dim.var_y) = array_manip.add_ncerr(so_dim.y,
so_dim.var_y,
val,
err2,
a_start=start)
if rebin_axis is not None:
val1 = hlr_utils.get_value(som2, i, "SOM", "y")
err1_2 = hlr_utils.get_err2(som2, i, "SOM", "y")
(frac_area, frac_area_err2) = array_manip.add_ncerr(frac_area,
frac_area_err2,
val1,
err1_2,
a_start=start)
if rebin_axis is not None:
(so_dim.y,
so_dim.var_y) = array_manip.div_ncerr(so_dim.y, so_dim.var_y,
frac_area, frac_area_err2)
# If parameter range normalization enabled, find the range for the
# parameter
if prnorm:
import math
prrange = nessi_list.NessiList(len_param_axis)
for i in xrange(len(prrange)):
try:
max_val = max(prarr[i])
except ValueError:
max_val = 0.0
try:
min_val = min(prarr[i])
except ValueError:
min_val = 0.0
prrange[i] = math.fabs(max_val - min_val)
# If pixel normalization tracking enabled, divided slices by pixel counts
if pixnorm or prnorm:
tmp_y = nessi_list.NessiList(N_tot)
tmp_var_y = nessi_list.NessiList(N_tot)
for i in range(len_param_axis):
start = i * len_arb_axis
end = (i + 1) * len_arb_axis
slice_y = so_dim.y[start:end]
slice_var_y = so_dim.var_y[start:end]
divconst = 1.0
if pixnorm:
divconst *= pixarr[i]
# Scale division constant if parameter range normalization enabled
if prnorm:
divconst *= prrange[i]
(dslice_y,
dslice_var_y) = array_manip.div_ncerr(slice_y, slice_var_y,
divconst, 0.0)
(tmp_y, tmp_var_y) = array_manip.add_ncerr(tmp_y,
tmp_var_y,
dslice_y,
dslice_var_y,
a_start=start)
so_dim.y = tmp_y
so_dim.var_y = tmp_var_y
if binnorm:
tmp_y = nessi_list.NessiList(N_tot)
tmp_var_y = nessi_list.NessiList(N_tot)
if rebin_axis is not None:
bin_const = utils.calc_bin_widths(rebin_axis)
else:
bin_const = utils.calc_bin_widths(som1[0].axis[1].val)
for i in range(len_param_axis):
start = i * len_arb_axis
end = (i + 1) * len_arb_axis
slice_y = so_dim.y[start:end]
slice_var_y = so_dim.var_y[start:end]
(dslice_y,
dslice_var_y) = array_manip.mult_ncerr(slice_y, slice_var_y,
bin_const[0], bin_const[1])
(tmp_y, tmp_var_y) = array_manip.add_ncerr(tmp_y,
tmp_var_y,
dslice_y,
dslice_var_y,
a_start=start)
so_dim.y = tmp_y
so_dim.var_y = tmp_var_y
# Create final 2D spectrum object container
comb_som = SOM.SOM()
comb_som.copyAttributes(som1)
del som1
# Check for so_id keyword argument
try:
so_dim.id = kwargs["so_id"]
except KeyError:
so_dim.id = 0
# Check for y_label keyword argument
try:
comb_som.setYLabel(kwargs["y_label"])
except KeyError:
comb_som.setYLabel("Counts")
# Check for y_units keyword argument
try:
comb_som.setYUnits(kwargs["y_units"])
except KeyError:
comb_som.setYUnits("Counts / Arb")
# Check for x_label keyword argument
try:
comb_som.setAllAxisLabels(kwargs["x_labels"])
except KeyError:
comb_som.setAllAxisLabels(["Parameter", "Arbitrary"])
# Check for x_units keyword argument
try:
comb_som.setAllAxisUnits(kwargs["x_units"])
except KeyError:
comb_som.setAllAxisUnits(["Arb", "Arb"])
comb_som.append(so_dim)
del so_dim
return comb_som
def cut_spectra(obj, low_cut, high_cut, **kwargs):
"""
This function takes 1D histogram spectra and a given set of axis cutoff
values and produces spectra that is smaller than the original by removing
information outside the cut range.
@param obj: The object containing the 1D histogram spectra to be zeroed
@type obj: C{SOM.SOM}
@param low_cut: The low-side axis cutoff. All values less than this will
be discarded.
@type low_cut: C{float}
@param high_cut: The high-side axis cutoff. All values greater than this
will be discarded.
@type high_cut: C{float}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword num_bins_clean: The number of extra bins to cut from the spectra.
The operation will be performed symmetrically if
both cuts are requested.
@type num_bins_clean: C{int}
@return: Object containing the zeroed spectra
@rtype: C{SOM.SOM}
"""
# Kickout if both cuts are None
if low_cut is None and high_cut is None:
return obj
# import the helper functions
import hlr_utils
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(obj)
o_descr = hlr_utils.get_descr(obj)
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
# Check keywords
offset = kwargs.get("num_bins_clean", 0)
# iterate through the values
import utils
# Get object length
len_obj = hlr_utils.get_length(obj)
for i in xrange(len_obj):
map_so = hlr_utils.get_map_so(obj, None, i)
axis = hlr_utils.get_value(obj, i, o_descr, "x", 0)
if low_cut is None:
low_bin = 0
else:
low_bin = utils.bisect_helper(axis, low_cut)
# Need to cut directly at the low bin
low_bin += 1
low_bin += offset
if high_cut is None:
high_bin = len(axis)
else:
high_bin = utils.bisect_helper(axis, high_cut)
high_bin -= offset
if high_bin != 0:
# Slice out the requested range
y_new = map_so.y[low_bin:high_bin]
var_y_new = map_so.var_y[low_bin:high_bin]
# Need to increment the high bin for the axis since it carries one
# more bin than the data
axis_new = axis[low_bin:high_bin+1]
else:
y_new = map_so.y
var_y_new = map_so.var_y
axis_new = axis
hlr_utils.result_insert(result, res_descr, (y_new, var_y_new),
map_so, "all", 0, [axis_new])
return result
def cut_spectra(obj, low_cut, high_cut, **kwargs):
"""
This function takes 1D histogram spectra and a given set of axis cutoff
values and produces spectra that is smaller than the original by removing
information outside the cut range.
@param obj: The object containing the 1D histogram spectra to be zeroed
@type obj: C{SOM.SOM}
@param low_cut: The low-side axis cutoff. All values less than this will
be discarded.
@type low_cut: C{float}
@param high_cut: The high-side axis cutoff. All values greater than this
will be discarded.
@type high_cut: C{float}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword num_bins_clean: The number of extra bins to cut from the spectra.
The operation will be performed symmetrically if
both cuts are requested.
@type num_bins_clean: C{int}
@return: Object containing the zeroed spectra
@rtype: C{SOM.SOM}
"""
# Kickout if both cuts are None
if low_cut is None and high_cut is None:
return obj
# import the helper functions
import hlr_utils
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(obj)
o_descr = hlr_utils.get_descr(obj)
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
# Check keywords
offset = kwargs.get("num_bins_clean", 0)
# iterate through the values
import utils
# Get object length
len_obj = hlr_utils.get_length(obj)
for i in xrange(len_obj):
map_so = hlr_utils.get_map_so(obj, None, i)
axis = hlr_utils.get_value(obj, i, o_descr, "x", 0)
if low_cut is None:
low_bin = 0
else:
low_bin = utils.bisect_helper(axis, low_cut)
# Need to cut directly at the low bin
low_bin += 1
low_bin += offset
if high_cut is None:
high_bin = len(axis)
else:
high_bin = utils.bisect_helper(axis, high_cut)
high_bin -= offset
if high_bin != 0:
# Slice out the requested range
y_new = map_so.y[low_bin:high_bin]
var_y_new = map_so.var_y[low_bin:high_bin]
# Need to increment the high bin for the axis since it carries one
# more bin than the data
axis_new = axis[low_bin:high_bin + 1]
else:
y_new = map_so.y
var_y_new = map_so.var_y
axis_new = axis
hlr_utils.result_insert(result, res_descr, (y_new, var_y_new), map_so,
"all", 0, [axis_new])
return result