def rebin_axis_1D(obj, axis_out):
"""
This function rebins the primary axis for a C{SOM} or a C{SO} based on the
given C{NessiList} axis.
@param obj: Object to be rebinned
@type obj: C{SOM.SOM} or C{SOM.SO}
@param axis_out: The axis to rebin the C{SOM} or C{SO} to
@type axis_out: C{NessiList}
@return: Object that has been rebinned according to the provided axis
@rtype: C{SOM.SOM} or C{SOM.SO}
@raise TypeError: The rebinning axis given is not a C{NessiList}
@raise TypeError: The object being rebinned is not a C{SOM} or a C{SO}
"""
# import the helper functions
import hlr_utils
# set up for working through data
try:
axis_out.__type__
except AttributeError:
raise TypeError("Rebinning axis must be a NessiList!")
o_descr = hlr_utils.get_descr(obj)
if o_descr == "number" or o_descr == "list":
raise TypeError("Do not know how to handle given type: %s" % \
o_descr)
else:
pass
(result, res_descr) = hlr_utils.empty_result(obj)
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
# iterate through the values
import axis_manip
for i in xrange(hlr_utils.get_length(obj)):
axis_in = hlr_utils.get_value(obj, i, o_descr, "x", 0)
val = hlr_utils.get_value(obj, i, o_descr)
err2 = hlr_utils.get_err2(obj, i, o_descr)
value = axis_manip.rebin_axis_1D(axis_in, val, err2, axis_out)
xvals = []
xvals.append(axis_out)
map_so = hlr_utils.get_map_so(obj, None, i)
hlr_utils.result_insert(result, res_descr, value, map_so, "all",
0, xvals)
return result
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 filter_normalization(obj, lothreshold, hithreshold, config=None):
"""
This function takes an object with normalization integration information
and a threshold and creates a mask file containing the pixel IDs that do
not make it above the threshold.
@param obj: The object containing the normalization information
@type obj: C{SOM.SOM}
@param lothreshold: The value below which a pixel will be masked.
@type lothreshold: C{float}
@param hithreshold: The value above which a pixel will be masked.
@type hithreshold: C{float}
@param config: The object holding the DR configuration
@type config: L{hlr_utils.Configure}
@raise TypeError: The incoming object is not a C{SOM}.
"""
import hlr_utils
o_descr = hlr_utils.get_descr(obj)
if o_descr != "SOM":
raise TypeError("Only SOMs are allowed in this function!")
if config is None:
# Make mask file name from object information
instname = obj.attr_list.inst.get_name()
runnum = obj.attr_list["run_number"]
outfile = "%s_%s_mask.dat" % (instname, str(runnum))
else:
# Make mask file name from configuration information
outfile = hlr_utils.ext_replace(config.output, config.ext_replacement,
"dat")
outfile = hlr_utils.add_tag(outfile, "mask")
ofile = open(outfile, "w")
import SOM
import utils
len_obj = hlr_utils.get_length(obj)
for i in xrange(len_obj):
norm = hlr_utils.get_value(obj, i, o_descr)
if utils.compare(norm, lothreshold) <= 0 or \
utils.compare(norm, hithreshold) >= 0:
map_so = hlr_utils.get_map_so(obj, None, i)
pix_id = SOM.NeXusId.fromString(str(map_so.id)).toJoinedStr()
print >> ofile, pix_id
ofile.close()
def filter_normalization(obj, lothreshold, hithreshold, config=None):
"""
This function takes an object with normalization integration information
and a threshold and creates a mask file containing the pixel IDs that do
not make it above the threshold.
@param obj: The object containing the normalization information
@type obj: C{SOM.SOM}
@param lothreshold: The value below which a pixel will be masked.
@type lothreshold: C{float}
@param hithreshold: The value above which a pixel will be masked.
@type hithreshold: C{float}
@param config: The object holding the DR configuration
@type config: L{hlr_utils.Configure}
@raise TypeError: The incoming object is not a C{SOM}.
"""
import hlr_utils
o_descr = hlr_utils.get_descr(obj)
if o_descr != "SOM":
raise TypeError("Only SOMs are allowed in this function!")
if config is None:
# Make mask file name from object information
instname = obj.attr_list.inst.get_name()
runnum = obj.attr_list["run_number"]
outfile = "%s_%s_mask.dat" % (instname, str(runnum))
else:
# Make mask file name from configuration information
outfile = hlr_utils.ext_replace(config.output, config.ext_replacement, "dat")
outfile = hlr_utils.add_tag(outfile, "mask")
ofile = open(outfile, "w")
import SOM
import utils
len_obj = hlr_utils.get_length(obj)
for i in xrange(len_obj):
norm = hlr_utils.get_value(obj, i, o_descr)
if utils.compare(norm, lothreshold) <= 0 or utils.compare(norm, hithreshold) >= 0:
map_so = hlr_utils.get_map_so(obj, None, i)
pix_id = SOM.NeXusId.fromString(str(map_so.id)).toJoinedStr()
print >> ofile, pix_id
ofile.close()
def reverse_array_cp(obj):
"""
This function reverses the y and var_y values of all the C{SO}s in a
C{SOM} or an individual C{SO}. This is assuming that there was a previous
transformation on the x-axis of the C{SO} or C{SOM}.
@param obj: Object that needs to have its y and var_y values reversed
@type obj: C{SOM.SOM} or C{SOM.SO}
@return: Object containing the results of the reversal process
@rtype: C{SOM.SOM} or C{SOM.SO}
@raise TypeError: A C{tuple} or C{list} of C{tuple}s is presented to the
function
"""
# 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)
if o_descr == "number" or o_descr == "list":
raise TypeError("Do not know how to handle given type: %s" % \
o_descr)
else:
pass
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
# iterate through the values
import axis_manip
for i in xrange(hlr_utils.get_length(obj)):
val = hlr_utils.get_value(obj, i, o_descr)
err2 = hlr_utils.get_err2(obj, i, o_descr)
value1 = axis_manip.reverse_array_cp(val)
value2 = axis_manip.reverse_array_cp(err2)
map_so = hlr_utils.get_map_so(obj, None, i)
hlr_utils.result_insert(result, res_descr, (value1, value2), map_so)
return result
def reverse_array_cp(obj):
"""
This function reverses the y and var_y values of all the C{SO}s in a
C{SOM} or an individual C{SO}. This is assuming that there was a previous
transformation on the x-axis of the C{SO} or C{SOM}.
@param obj: Object that needs to have its y and var_y values reversed
@type obj: C{SOM.SOM} or C{SOM.SO}
@return: Object containing the results of the reversal process
@rtype: C{SOM.SOM} or C{SOM.SO}
@raise TypeError: A C{tuple} or C{list} of C{tuple}s is presented to the
function
"""
# 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)
if o_descr == "number" or o_descr == "list":
raise TypeError("Do not know how to handle given type: %s" % \
o_descr)
else:
pass
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
# iterate through the values
import axis_manip
for i in xrange(hlr_utils.get_length(obj)):
val = hlr_utils.get_value(obj, i, o_descr)
err2 = hlr_utils.get_err2(obj, i, o_descr)
value1 = axis_manip.reverse_array_cp(val)
value2 = axis_manip.reverse_array_cp(err2)
map_so = hlr_utils.get_map_so(obj, None, i)
hlr_utils.result_insert(result, res_descr, (value1, value2), map_so)
return result
def calculate_ref_background(obj, no_bkg, inst, peak_excl, **kwargs):
"""
This function takes a set of reflectometer data spectra in TOF, slices the
data along TOF channels, fits a linear function to the slice to determine
the background and then reassembles the slice back into TOF spectra.
@param obj: Object containing data spectra
@type obj: C{SOM.SOM} or C{SOM.SO}
@param no_bkg: Flag for actually requesting a background calculation
@type no_bkg: C{boolean}
@param inst: String containing the reflectometer short name
@type inst: C{string} (I{REF_L} or I{REF_M})
@param peak_excl: The bounding pixel IDs for peak exclusion from fit
@type peak_excl: C{tuple} containging the minimum and maximum pixel ID
@param kwargs: A list of keyword arguments that the function accepts:
@keyword aobj: An alternate data object containing the sizing information
for the constructed background spectra.
@type aobj: C{SOM.SOM} or C{SOM.SO}
@return: Background spectra
@rtype: C{SOM.SOM}
"""
if obj is None:
return None
if no_bkg:
return None
# import the helper functions
import hlr_utils
# Setup instrument specific stuff
if inst == "REF_L":
inst_pix_id = 1
elif inst == "REF_M":
inst_pix_id = 0
else:
raise RuntimeError("Do not know how to deal with instrument %s" % inst)
# Check keywords
try:
aobj = kwargs["aobj"]
except KeyError:
aobj = None
# 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)
# Set spectrum object to obtain background SOs from
if aobj is None:
bobj = obj
else:
bobj = aobj
# Get the number of spectra for background calculation
len_som = len(obj)
# Get the number of spectra for final background object
if aobj is None:
len_bsom = len(obj)
else:
len_bsom = len(aobj)
# Get the number of TOF channels
len_tof = len(obj[0])
# Create blank SOs for background spectra
so_list = []
import nessi_list
import SOM
import utils
# Setup pixel axes
pix_axis = nessi_list.NessiList()
if peak_excl is not None:
pix_axis_no_peak = nessi_list.NessiList()
# Fill pixel axes and background SOs
for k in xrange(len_bsom):
map_so = hlr_utils.get_map_so(bobj, None, k)
cur_pix_id = map_so.id[1][inst_pix_id]
pix_axis.append(cur_pix_id)
if peak_excl is not None:
if cur_pix_id < peak_excl[0] or cur_pix_id > peak_excl[1]:
pix_axis_no_peak.append(cur_pix_id)
so = SOM.SO()
hlr_utils.result_insert(so, "SO", map_so, None, "all")
so_list.append(so)
# Slice data, calculate weighted average and repackage spectra
for i in xrange(len_tof):
sliced_data = nessi_list.NessiList()
sliced_data_err2 = nessi_list.NessiList()
for j in xrange(len_som):
obj1 = hlr_utils.get_value(obj, j, o_descr, "all")
cur_pix_id = obj1.id[1][inst_pix_id]
if peak_excl is None:
filter_pixel = False
else:
if cur_pix_id < peak_excl[0] or cur_pix_id > peak_excl[1]:
filter_pixel = False
else:
filter_pixel = True
if not filter_pixel:
if not (utils.compare(obj1.var_y[i], 0.0) == 0 and \
utils.compare(obj1.y[i], 0.0) == 0):
sliced_data.append(obj1.y[i])
sliced_data_err2.append(obj1.var_y[i])
len_fit = len(sliced_data)
if not len_fit:
value = (0.0, 0.0)
else:
value = utils.weighted_average(sliced_data, sliced_data_err2,
0, len_fit-1)
for j in xrange(len_bsom):
so_list[j].y[i] = value[0]
so_list[j].var_y[i] = value[1]
for m in xrange(len_bsom):
hlr_utils.result_insert(result, res_descr, so_list[m], None, "all")
return result
def wavelength_to_velocity(obj, **kwargs):
"""
This function converts a primary axis of a C{SOM} or C{SO} from wavelength
to velocity. The wavelength axis for a C{SOM} must be in units of
I{Angstroms}. The primary axis of a C{SO} is assumed to be in
units of I{Angstroms}. A C{tuple} of C{(wavelength, wavelength_err2)}
(assumed to be in units of I{Angstroms}) can be converted to
C{(velocity, velocity_err)}.
@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 lojac: A flag that allows one to turn off the calculation of the
linear-order Jacobian. The default action is True for
histogram data.
@type lojac: C{boolean}
@keyword units: The expected units for this function. The default for this
function is I{Angstroms}
@type units: C{string}
@return: Object with a primary axis in wavelength converted to velocity
@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{Angstroms}
"""
# 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)
if o_descr == "list":
raise TypeError("Do not know how to handle given type: %s" % \
o_descr)
else:
pass
# Setup keyword arguments
try:
units = kwargs["units"]
except KeyError:
units = "Angstroms"
try:
lojac = kwargs["lojac"]
except KeyError:
lojac = hlr_utils.check_lojac(obj)
# 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, "meters/microseconds", axis)
result.setAxisLabel(axis, "velocity")
result.setYUnits("Counts/meters/microseconds")
result.setYLabel("Intensity")
else:
pass
# iterate through the values
import axis_manip
if lojac:
import utils
len_obj = hlr_utils.get_length(obj)
for i in xrange(len_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)
value = axis_manip.wavelength_to_velocity(val, err2)
if lojac:
y_val = hlr_utils.get_value(obj, i, o_descr, "y")
y_err2 = hlr_utils.get_err2(obj, i, o_descr, "y")
counts = utils.linear_order_jacobian(val, value[0],
y_val, y_err2)
else:
pass
if o_descr != "number":
value1 = axis_manip.reverse_array_cp(value[0])
value2 = axis_manip.reverse_array_cp(value[1])
rev_value = (value1, value2)
else:
rev_value = value
if map_so is not None:
if not lojac:
map_so.y = axis_manip.reverse_array_cp(map_so.y)
map_so.var_y = axis_manip.reverse_array_cp(map_so.var_y)
else:
map_so.y = axis_manip.reverse_array_cp(counts[0])
map_so.var_y = axis_manip.reverse_array_cp(counts[1])
else:
pass
hlr_utils.result_insert(result, res_descr, rev_value, map_so, "x",
axis)
return result
def d_spacing_to_tof_focused_det(obj, **kwargs):
"""
This function converts a primary axis of a C{SOM} or C{SO} from d-spacing
to a focused time-of-flight. The focusing is done using the geometry
information from a single detector pixel. The d-spacing axis for a C{SOM}
must be in units of I{Angstroms}. The primary axis of a C{SO} is assumed
to be in units of I{Angstroms}.
@param obj: Object to be converted
@type obj: C{SOM.SOM} or C{SOM.SO}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword polar: The polar angle and its associated error^2
@type polar: C{tuple} or C{list} of C{tuple}s
@keyword pathlength: The total pathlength and its associated error^2
@type pathlength: C{tuple} or C{list} of C{tuple}s
@keyword pixel_id: The pixel ID from which the geometry information will
be retrieved from the instrument
@type pixel_id: C{tuple}=(\"bankN\", (x, y))
@keyword verbose: This determines if the pixel geometry information is
printed. The default is False
@type verbose: C{boolean}
@keyword units: The expected units for this function. The default for
this function is I{microseconds}.
@type units: C{string}
@return: Object with a primary axis in d-spacing converted to
time-of-flight
@rtype: C{SOM.SOM} or C{SOM.SO}
@raise RuntimeError: A C{SOM} or C{SO} is not provided to the function
@raise RuntimeError: No C{SOM.Instrument} is provided in a C{SOM}
@raise RuntimeError: No C{SOM} is given and both the pathlength and polar
angle are not provided
@raise RuntimeError: No C{SOM} is given and the pathlength is not provided
@raise RuntimeError: No C{SOM} is given and the polar angle is not provided
"""
# 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)
if o_descr == "number" or o_descr == "list":
raise RuntimeError("Must provide a SOM of a SO to the function.")
# Go on
else:
pass
# Setup keyword arguments
try:
polar = kwargs["polar"]
except KeyError:
polar = None
try:
pathlength = kwargs["pathlength"]
except KeyError:
pathlength = None
try:
pixel_id = kwargs["pixel_id"]
except KeyError:
pixel_id = None
try:
verbose = kwargs["verbose"]
except KeyError:
verbose = False
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)
if res_descr == "SOM":
result = hlr_utils.force_units(result, "microseconds", axis)
result.setAxisLabel(axis, "time-of-flight")
result.setYUnits("Counts/usec")
result.setYLabel("Intensity")
else:
pass
if pathlength is None or polar is None:
if o_descr == "SOM":
try:
obj.attr_list.instrument.get_primary()
inst = obj.attr_list.instrument
except RuntimeError:
raise RuntimeError("An instrument was not provided")
else:
if pathlength is None and polar is None:
raise RuntimeError("If no SOM is provided, then pathlength "\
+"and polar angle information must be provided")
elif pathlength is None:
raise RuntimeError("If no SOM is provided, then pathlength "\
+"information must be provided")
elif polar is None:
raise RuntimeError("If no SOM is provided, then polar angle "\
+"information must be provided")
else:
raise RuntimeError("If you get here, see Steve Miller for "\
+"your mug.")
else:
pass
if pathlength is not None:
p_descr = hlr_utils.get_descr(pathlength)
if polar is not None:
a_descr = hlr_utils.get_descr(polar)
# iterate through the values
if pixel_id is not None:
tmp_so = SOM.SO()
tmp_so.id = pixel_id
(pl, pl_err2) = hlr_utils.get_parameter("total", tmp_so, inst)
(angle, angle_err2) = hlr_utils.get_parameter("polar", tmp_so, inst)
if verbose:
format_str = "Pixel ID %s has polar angle: (%f,%f) and "
format_str += "pathlength: (%f,%f)"
print format_str % (str(pixel_id), angle, angle_err2, pl, pl_err2)
else:
pass
else:
pl = hlr_utils.get_value(pathlength, 0, p_descr)
pl_err2 = hlr_utils.get_err2(pathlength, 0, p_descr)
angle = hlr_utils.get_value(polar, 0, a_descr)
angle_err2 = hlr_utils.get_err2(polar, 0, a_descr)
# iterate through the values
import axis_manip
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)
value = axis_manip.d_spacing_to_tof_focused_det(val, err2, pl, pl_err2,
angle, angle_err2)
hlr_utils.result_insert(result, res_descr, value, map_so, "x", axis)
return result
def shift_spectrum(obj, shift_point, min_ext, max_ext, scale_const=None):
"""
This function takes a given spectrum and a central value and creates
a spectrum that is shifted about that point. Values greater than the point
are moved to the beginning of the new spectrum and values less than the
point are move towards the end of the new spectrum.
@param obj: Monitor object that will be shifted
@type obj: C{SOM.SOM} or C{SOM.SO}
@param shift_point: The point in the spectrum about which to shift the
data.
@type shift_point: C{list} of C{floats}
@param min_ext: The minimum extent of the axis to shift.
@type min_ext: C{list} of C{floats}
@param max_ext: The maximum extent of the axis to shift.
@type max_ext: C{list} of C{floats}
@param scale_const: A scaling constant to apply (multiply) to the newly
shifted spectrum. The default is I{None}.
@type scale_const: C{float}
@return: Monitor spectrum that have been shifted
@rtype: C{SOM.SOM} or C{SOM.SO}
"""
# 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)
s_descr = hlr_utils.get_descr(shift_point)
ie_descr = hlr_utils.get_descr(min_ext)
ae_descr = hlr_utils.get_descr(max_ext)
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
import array_manip
import utils
len_obj = hlr_utils.get_length(obj)
for i in xrange(len_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", 0)
x_err2 = hlr_utils.get_err2(obj, i, o_descr, "x", 0)
map_so = hlr_utils.get_map_so(obj, None, i)
bin_center = utils.calc_bin_centers(x_axis, x_err2)
# Get shift point and extents
sp = hlr_utils.get_value(shift_point, i, s_descr, "y")
ie = hlr_utils.get_value(min_ext, i, ie_descr, "y")
ae = hlr_utils.get_value(max_ext, i, ae_descr, "y")
# Make shifted spectrum
value0 = utils.shift_spectrum(val, err2, x_axis, bin_center[0], sp, ie,
ae)
# Scale spectrum if necessary
if scale_const is not None:
value1 = array_manip.mult_ncerr(value0[0], value0[1], scale_const,
0.0)
else:
value1 = value0
hlr_utils.result_insert(result, res_descr, value1, map_so, "y")
return result
def create_X_vs_pixpos(som, *args, **kwargs):
"""
This function takes a group of single spectrum with any given axes
(wavelength, energy etc.) and rebins those axes to the given axis and
converts the spectra into a single I{I(X, pixel)} spectrum.
@param som: The input object with arbitrary (but same) axis spectra
@type som: C{SOM.SOM}
@param args: A mandatory list of axes for rebinning. There is a particular
order to them. They should be present in the following order:
Without errors
1. Axis
With errors
1. Axis
2. Axis error^2
@type args: C{nessi_list.NessiList}s
@param kwargs: A list of keyword arguments that the function accepts:
@keyword withXVar: A flag for whether the function should be expecting the
associated axes to have errors. The default value will
be I{False}.
@type withXVar: C{boolean}
@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 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_label: The second primary axis label
@type x_label: C{string}
@keyword x_units: The second primary axis units
@type x_units: C{string}
@keyword rebin: A flag for turning rebin on or off. The default is I{True}.
@type rebin: C{boolean}
@return: Object with a single 2D C{SO} with the given axis and global pixel
position
@rtype: C{SOM.SOM}
@raise RuntimeError: The parameter given to the keyword argument withXVar
is not I{True} or I{False}
@raise RuntimeError: The parameter given to the keyword argument data_type
is not I{histogram} or I{density} or I{coordinate}.
@raise RuntimeError: The number of given arguments (x-axes) is not either 2
(no errors) or 4 (with errors)
"""
import common_lib
import nessi_list
# Setup some variables
dim = 2
N_y = []
N_tot = 1
N_args = len(args)
# Check withXVar keyword argument and also check number of given args.
# Set xvar to the appropriate value
try:
value = kwargs["withXVar"]
if value.lower() == "true":
if N_args != 2:
raise RuntimeError("Since you have requested x errors, 2 x "\
+"axes must be provided.")
else:
xvar = True
elif value.lower() == "false":
if N_args != 2:
raise RuntimeError("Since you did not requested x errors, 2 "\
+"x axes must be provided.")
else:
xvar = False
else:
raise RuntimeError("Do not understand given parameter %s" % \
value)
except KeyError:
if N_args != 1:
raise RuntimeError("Since you did not requested x errors, 1 "\
+"x axes must be provided.")
else:
xvar = 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
so_dim = SOM.SO(dim)
arb_axis = 1
pixel_axis = 0
# Set the x-axis arguments from the *args list into the new SO
if not xvar:
so_dim.axis[arb_axis].val = args[0]
else:
so_dim.axis[arb_axis].val = args[0]
so_dim.axis[arb_axis].var = args[1]
# Set individual value axis sizes (not x-axis size)
N_y.append(len(args[0]) - offset)
# Calculate total 2D array size
N_som = len(som)
N_tot = N_som * N_y[-1]
# Make second axis on total number of pixels
so_dim.axis[pixel_axis].val = hlr_utils.make_axis(0, N_som, 1)
if xvar:
so_dim.axis[pixel_axis].var = nessi_list.NessiList(N_som+1)
# Create y and var_y lists from total 2D size
so_dim.y = nessi_list.NessiList(N_tot)
so_dim.var_y = nessi_list.NessiList(N_tot)
# Check for rebinning
try:
rebin = kwargs["rebin"]
except KeyError:
rebin = True
# Rebin data to X axis
if rebin:
som_1 = common_lib.rebin_axis_1D(som, args[0])
else:
som_1 = som
del som
import array_manip
for i in xrange(hlr_utils.get_length(som_1)):
val = hlr_utils.get_value(som_1, i, "SOM", "y")
err2 = hlr_utils.get_err2(som_1, i, "SOM", "y")
start = i * N_y[0]
(so_dim.y, so_dim.var_y) = array_manip.add_ncerr(so_dim.y,
so_dim.var_y,
val,
err2,
a_start=start)
# Check for so_id keyword argument
try:
so_dim.id = kwargs["so_id"]
except KeyError:
so_dim.id = 0
comb_som = SOM.SOM()
comb_som.copyAttributes(som_1)
del som_1
# 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(["Pixel Number", kwargs["x_label"]])
except KeyError:
comb_som.setAllAxisLabels(["Pixel Number", "Arbitrary"])
# Check for x_units keyword argument
try:
comb_som.setAllAxisUnits(["Pixel#", kwargs["x_units"]])
except KeyError:
comb_som.setAllAxisUnits(["Pixel#", "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 filter_ref_data(som, **kwargs):
"""
This function takes in an object containing reflectometer data, scans it
for bad data and removes that data from the arrays. The following criteria
are what is considered bad data:
- R or dR^2 is nan, inf, -inf
- R < 0
- dR^2 >= R^2
@param som: Object containing a single spectrum to be cleaned
@type som: C{SOM.SOM}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword zero_mode: A flag that tells the function to zero the bad values
instead of removing them
@type zero_mode: C{boolean}
@return: Object containing a spectrum that has been cleaned of all bad data
@rtype: C{SOM.SOM}
@raise RuntimeError: The incoming object is not a C{SOM}.
"""
import hlr_utils
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(som)
o_descr = hlr_utils.get_descr(som)
if o_descr != "SOM":
raise RuntimeError("Must provide a SOM to the function.")
# Go on
else:
pass
result = hlr_utils.copy_som_attr(result, res_descr, som, o_descr)
try:
dtot = result.attr_list["extra_som"]
except KeyError:
dtot = None
# Check keyword arguments
try:
zero_mode = kwargs["zero_mode"]
except KeyError:
zero_mode = False
import copy
import itertools
import utils
index_map = {}
len_som = hlr_utils.get_length(som)
if dtot is not None:
# Deal with delta t / t information
len_dtot = len(dtot)
if len_dtot == 1 and len_som > 1:
# If the delta t / t information has only one spectrum and the som
# data has more than one, we'll need to clone that delta t / t
# spectrum
multiple_dtot = True
(res_dtot, resd_descr) = hlr_utils.empty_result(dtot)
res_dtot = hlr_utils.copy_som_attr(res_dtot, resd_descr, dtot,
"SOM")
else:
# Everything should be on equal footing with respect to the number
# of spectra, so we don't have to do anything.
res_dtot = dtot
multiple_dtot = False
else:
len_dtot = 0
res_dtot = None
multiple_dtot = False
# Parse through the data to find the bad data locations.
for i in xrange(len_som):
counter = 0
indicies = []
so = hlr_utils.get_value(som, i, o_descr, "all")
if multiple_dtot:
if i > 0:
dso = hlr_utils.get_value(dtot, 0, resd_descr, "all")
dtot.append(copy.deepcopy(dso))
for (yval, yerr2) in itertools.izip(so.y, so.var_y):
tofilter = False
yval2 = yval * yval
syval = str(yval)
syerr2 = str(yerr2)
if syval == "nan" or syval == "inf" or syval == "-inf":
tofilter = True
if syerr2 == "nan" or syerr2 == "inf" or syerr2 == "-inf":
tofilter = True
if yval < 0:
tofilter = True
if yerr2 > yval2 or not utils.compare(yerr2, yval2):
tofilter = True
if tofilter:
indicies.append(counter)
counter += 1
index_map[so.id] = indicies
# Parse through data to remove bad data at requested indicies
for j in xrange(len_som):
map_so = hlr_utils.get_map_so(som, None, j)
y_val = hlr_utils.get_value(som, j, o_descr, "y")
y_err2 = hlr_utils.get_err2(som, j, o_descr, "y")
x_val = hlr_utils.get_value(som, j, o_descr, "x", 0)
x_err2 = hlr_utils.get_err2(som, j, o_descr, "x", 0)
y_val_new = copy.deepcopy(y_val)
y_err2_new = copy.deepcopy(y_err2)
x_val_new = copy.deepcopy(x_val)
x_err2_new = copy.deepcopy(x_err2)
if dtot is not None:
dso = hlr_utils.get_value(dtot, j, "SOM", "all")
dso_new = copy.deepcopy(dso)
offset = 0
for index in index_map[map_so.id]:
if not zero_mode:
# Index arithmetic since list length get shorter with every
# element deleted
dindex = index - offset
del y_val_new[dindex]
del y_err2_new[dindex]
del x_val_new[dindex]
del x_err2_new[dindex]
if dtot is not None:
del dso_new.y[dindex]
offset += 1
else:
y_val_new[index] = 0.0
y_err2_new[index] = 0.0
if dtot is not None and multiple_dtot:
hlr_utils.result_insert(res_dtot, resd_descr, dso_new, None, "all")
hlr_utils.result_insert(result, res_descr, (y_val_new, y_err2_new),
map_so, "all", 0, [x_val_new])
if dtot is not None:
result.attr_list["extra_som"] = res_dtot
return result
def data_filter(obj, **kwargs):
"""
This function takes in an object containing data, scans it for bad data and
removes that data from the arrays. The criteria is instrument dependent
and subfunctions will be written that handle the instrument dependent
criteria. This function will operate in a zeroing mode. This means that
bins with bad data will have their value and error^2 set to zero.
@param obj: Object containing a single spectrum to be cleaned
@type obj: C{SOM.SOM}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword clean_axis: A flag that tells the function to check for I{inf}
of I{-inf} values (normally at the ends of the axis) and remove those
bins. The default behavior is I{False}.
@type clean_axis: C{boolean}
@keyword axis_index: The index on the axis to check for bad_values. This
is assumed to be a single value and the default is -1 (last bin)
@type axis_index: C{int}
@keyword axis_pos: The position of the axis within the data object. This is
necessary for greater than 1D spectra. The default value is 0.
@type axis_pos: C{int}
@return: Object containing a spectrum that has been cleaned of all bad data
@rtype: C{SOM.SOM}
@raise RuntimeError: The incoming object is not a C{SOM}.
@raise AttributeError: 1D or 2D data passed is clean_axis is I{True}.
@raise AttributeError: Axis position not 0 or 1 for 2D data.
"""
import hlr_utils
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(obj)
o_descr = hlr_utils.get_descr(obj)
if o_descr != "SOM":
raise RuntimeError("Must provide a SOM to the function.")
# Go on
else:
pass
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
# See if there is extra information present
try:
extra_som = result.attr_list["extra_som"]
except KeyError:
extra_som = None
# Get instrument name
inst_name = obj.attr_list.instrument.get_name()
# Get dimension of incoming data
data_dims = obj.getDimension()
# Check keyword arguments
try:
clean_axis = kwargs["clean_axis"]
except KeyError:
clean_axis = False
try:
axis_index = kwargs["axis_index"]
except KeyError:
axis_index = -1
try:
axis_pos = kwargs["axis_pos"]
except KeyError:
axis_pos = 0
if clean_axis:
# Can only support 1D and 2D data axis cleaning for now
if data_dims not in [1, 2]:
raise AttributeError("Do not know how to clean %d-dimensional " +
"data" % data_dims)
if data_dims == 2:
if axis_pos not in [0, 1]:
raise AttributeError("2D data cannot have axis position %d!" \
% axis_pos)
if axis_pos:
other_axis = 0
else:
other_axis = 1
axis_lengths = (len(obj[0].axis[0].val) - 1,
len(obj[0].axis[1].val) - 1)
else:
axis_lengths = None
if obj[0].axis[0].var is None:
with_x_var = False
else:
with_x_var = True
else:
axis_lengths = None
import copy
import itertools
len_som = hlr_utils.get_length(obj)
if extra_som is not None:
# Deal with extra information
len_extra_som = len(extra_som)
if len_extra_som == 1 and len_som > 1:
# If the extra information has only one spectrum and the som
# data has more than one, we'll need to clone that spectrum
multiple_extra_som = True
(res_extra_som, resd_descr) = hlr_utils.empty_result(extra_som)
res_extra_som = hlr_utils.copy_som_attr(res_extra_som, resd_descr,
extra_som, "SOM")
else:
# Everything should be on equal footing with respect to the number
# of spectra, so we don't have to do anything.
res_extra_som = extra_som
multiple_extra_som = False
else:
len_extra_som = 0
res_extra_som = None
multiple_extra_som = False
# Parse through the data to find the bad data locations.
for i in xrange(len_som):
so = hlr_utils.get_value(obj, i, o_descr, "all")
map_so = hlr_utils.get_map_so(obj, None, i)
y_val = hlr_utils.get_value(obj, i, o_descr, "y")
y_err2 = hlr_utils.get_err2(obj, i, o_descr, "y")
y_val_new = copy.deepcopy(y_val)
y_err2_new = copy.deepcopy(y_err2)
if extra_som is not None:
eso = hlr_utils.get_value(extra_som, i, "SOM", "all")
eso_new = copy.deepcopy(eso)
else:
eso_new = None
if multiple_extra_som:
if i > 0:
eso = hlr_utils.get_value(extra_som, 0, resd_descr, "all")
extra_som.append(copy.deepcopy(eso))
counter = 0
for (yval, yerr2) in itertools.izip(so.y, so.var_y):
to_filter = False
if inst_name == "BSS":
to_filter = __filter_sns_bss(yval, yerr2)
elif inst_name == "REF_L" or inst_name == "REF_M":
to_filter = __filter_sns_ref(yval, yerr2)
elif inst_name == "SANS":
to_filter = __filter_ieee(str(yval), str(yerr2))
if to_filter:
y_val_new[counter] = 0.0
y_err2_new[counter] = 0.0
counter += 1
if clean_axis:
x_val = hlr_utils.get_value(obj, i, o_descr, "x", axis_pos)
x_err2 = hlr_utils.get_err2(obj, i, o_descr, "x", axis_pos)
x_val_new = copy.deepcopy(x_val)
x_err2_new = copy.deepcopy(x_err2)
(y_val_new, y_err2_new, x_val_new, x_err2_new,
eso_new) = __clean_axis(data_dims,
axis_pos,
axis_index,
x_val_new,
x_err2_new,
y_val_new,
y_err2_new,
ext_so=eso_new,
axis_len=axis_lengths)
if extra_som is not None and multiple_extra_som:
hlr_utils.result_insert(res_extra_som, resd_descr, eso_new, None,
"all")
if not clean_axis:
hlr_utils.result_insert(result, res_descr, (y_val_new, y_err2_new),
map_so, "y")
else:
if data_dims == 1:
xvals = [x_val_new, x_err2_new]
if not with_x_var:
del xvals[-1]
hlr_utils.result_insert(result, res_descr,
(y_val_new, y_err2_new), map_so, "all",
axis_pos, xvals)
elif data_dims == 2:
ox_val = hlr_utils.get_value(obj, i, o_descr, "x", other_axis)
ox_err2 = hlr_utils.get_err2(obj, i, o_descr, "x", other_axis)
if axis_pos == 0:
xvals = [
x_val_new, x_err2_new,
copy.deepcopy(ox_val),
copy.deepcopy(ox_err2)
]
else:
xvals = [
copy.deepcopy(ox_val),
copy.deepcopy(ox_err2), x_val_new, x_err2_new
]
if not with_x_var:
del xvals[1::2]
hlr_utils.result_insert(result, res_descr,
(y_val_new, y_err2_new), map_so, "all",
axis_pos, xvals)
if extra_som is not None:
result.attr_list["extra_som"] = res_extra_som
return result
def tof_to_initial_wavelength_igs(obj, **kwargs):
"""
This function converts a primary axis of a C{SOM} or C{SO} from
time-of-flight to initial_wavelength_igs. 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{(initial_wavelength_igs, initial_wavelength_igs_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 lambda_f:The final wavelength and its associated error^2
@type lambda_f: C{tuple}
@keyword time_zero: The time zero offset and its associated error^2
@type time_zero: C{tuple}
@keyword dist_source_sample: The source to sample distance information and
its associated error^2
@type dist_source_sample: C{tuple} or C{list} of C{tuple}s
@keyword dist_sample_detector: The sample to detector distance information
and its associated error^2
@type dist_sample_detector: C{tuple} or C{list} of C{tuple}s
@keyword run_filter: This determines if the filter on the negative
wavelengths is run. The default setting is True.
@type run_filter: C{boolean}
@keyword units: The expected units for this function. The default for this
function is I{microseconds}
@type units: C{string}
@return: Object with a primary axis in time-of-flight converted to
initial_wavelength_igs
@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}
"""
# 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:
lambda_f = kwargs["lambda_f"]
except KeyError:
lambda_f = None
try:
time_zero = kwargs["time_zero"]
except KeyError:
time_zero = None
try:
dist_source_sample = kwargs["dist_source_sample"]
except KeyError:
dist_source_sample = None
try:
dist_sample_detector = kwargs["dist_sample_detector"]
except KeyError:
dist_sample_detector = None
try:
units = kwargs["units"]
except KeyError:
units = "microseconds"
try:
run_filter = kwargs["run_filter"]
except KeyError:
run_filter = 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
# Where to get instrument information
if dist_source_sample is None or dist_sample_detector is None:
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:
if dist_source_sample is None and dist_sample_detector is None:
raise RuntimeError("If a SOM is not passed, the "\
+"source-sample and sample-detector "\
+"distances must be provided.")
elif dist_source_sample is None:
raise RuntimeError("If a SOM is not passed, the "\
+"source-sample distance must be provided.")
elif dist_sample_detector is None:
raise RuntimeError("If a SOM is not passed, the "\
+"sample-detector distance must be "\
+"provided.")
else:
raise RuntimeError("If you get here, see Steve Miller for "\
+"your mug.")
else:
pass
if lambda_f is not None:
l_descr = hlr_utils.get_descr(lambda_f)
else:
if o_descr == "SOM":
try:
som_l_f = obj.attr_list["Wavelength_final"]
except KeyError:
raise RuntimeError("Please provide a final wavelength "\
+"parameter either via the function call "\
+"or the SOM")
else:
raise RuntimeError("You need to provide a final wavelength")
if time_zero is not None:
t_descr = hlr_utils.get_descr(time_zero)
else:
if o_descr == "SOM":
try:
t_0 = obj.attr_list["Time_zero"][0]
t_0_err2 = obj.attr_list["Time_zero"][1]
except KeyError:
raise RuntimeError("Please provide a time-zero "\
+"parameter either via the function call "\
+"or the SOM")
else:
t_0 = 0.0
t_0_err2 = 0.0
if dist_source_sample is not None:
ls_descr = hlr_utils.get_descr(dist_source_sample)
# Do nothing, go on
else:
pass
if dist_sample_detector is not None:
ld_descr = hlr_utils.get_descr(dist_sample_detector)
# Do nothing, go on
else:
pass
# iterate through the values
import axis_manip
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 dist_source_sample is None:
(L_s, L_s_err2) = hlr_utils.get_parameter("primary", map_so, inst)
else:
L_s = hlr_utils.get_value(dist_source_sample, i, ls_descr)
L_s_err2 = hlr_utils.get_err2(dist_source_sample, i, ls_descr)
if dist_sample_detector is None:
(L_d, L_d_err2) = hlr_utils.get_parameter("secondary", map_so,
inst)
else:
L_d = hlr_utils.get_value(dist_sample_detector, i, ld_descr)
L_d_err2 = hlr_utils.get_err2(dist_sample_detector, i, ld_descr)
if lambda_f is not None:
l_f = hlr_utils.get_value(lambda_f, i, l_descr)
l_f_err2 = hlr_utils.get_err2(lambda_f, i, l_descr)
else:
l_f_tuple = hlr_utils.get_special(som_l_f, map_so)
l_f = l_f_tuple[0]
l_f_err2 = l_f_tuple[1]
if time_zero is not None:
t_0 = hlr_utils.get_value(time_zero, i, t_descr)
t_0_err2 = hlr_utils.get_err2(time_zero, i, t_descr)
else:
pass
value = axis_manip.tof_to_initial_wavelength_igs(
val, err2, l_f, l_f_err2, t_0, t_0_err2, L_s, L_s_err2, L_d,
L_d_err2)
# Remove all wavelengths < 0
if run_filter:
index = 0
for val in value[0]:
if val >= 0:
break
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)
else:
pass
hlr_utils.result_insert(result, res_descr, value, map_so, "x", axis)
return result
def tof_to_ref_scalar_Q(obj, **kwargs):
"""
This function converts a primary axis of a C{SOM} or C{SO} from
time-of-flight to reflectometer scalar Q. This means that a single angle
and a single flightpath is used. 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{(time-of-flight,
time-of-flight_err2)} (assumed to be in units of I{microseconds}) can be
converted to C{(scalar_Q, scalar_Q_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 polar: The polar angle and its associated error^2
@type polar: C{tuple}
@keyword pathlength: The pathlength and its associated error^2
@type pathlength: C{tuple}
@keyword angle_offset: A constant offset for the polar angle and its
associated error^2. The units of the offset should
be in radians.
@type angle_offset: C{tuple}
@keyword lojac: A flag that allows one to turn off the calculation of the
linear-order Jacobian. The default action is 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 configure: This is the object containing the driver configuration.
This will signal the function to write out the counts
and fractional area to files.
@type configure: C{Configure}
@return: Object with a primary axis in time-of-flight converted to
reflectometer scalar Q
@rtype: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@raise TypeError: The incoming object is not a type the function recognizes
@raise RuntimeError: A C{SOM} is not passed and no polar angle is provided
@raise RuntimeError: The C{SOM} x-axis units are not I{microseconds}
"""
# 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)
if o_descr == "list":
raise TypeError("Do not know how to handle given type: %s" % \
o_descr)
else:
pass
# Setup keyword arguments
polar = kwargs.get("polar")
pathlength = kwargs.get("pathlength")
units = kwargs.get("units", "microseconds")
lojac = kwargs.get("lojac", hlr_utils.check_lojac(obj))
angle_offset = kwargs.get("angle_offset")
config = kwargs.get("configure")
if config is None:
beamdiv_corr = False
else:
beamdiv_corr = config.beamdiv_corr
# 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, "1/Angstroms", axis)
result.setAxisLabel(axis, "scalar wavevector transfer")
result.setYUnits("Counts/A-1")
result.setYLabel("Intensity")
else:
pass
if pathlength is None or polar is None:
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:
if pathlength is None and polar is None:
raise RuntimeError("If no SOM is provided, then pathlength "\
+"and polar angle information must be "\
+"provided")
elif pathlength is None:
raise RuntimeError("If no SOM is provided, then pathlength "\
+"information must be provided")
elif polar is None:
raise RuntimeError("If no SOM is provided, then polar angle "\
+"information must be provided")
else:
raise RuntimeError("If you get here, see Steve Miller for "\
+"your mug.")
else:
pass
if pathlength is None:
(pl, pl_err2) = obj.attr_list.instrument.get_total_path(obj[0].id,
det_secondary=True)
else:
(pl, pl_err2) = pathlength
if polar is None:
angle = hlr_utils.get_special(obj.attr_list["data-theta"], obj[0])[0]
angle_err2 = 0.0
else:
(angle, angle_err2) = polar
if angle_offset is not None:
angle += angle_offset[0]
angle_err2 += angle_offset[1]
# Need to multiply angle by 2.0 in order to make it be Theta to
# underlying conversion function
angle *= 2.0
angle_err2 *= 4.0
# iterate through the values
import axis_manip
if lojac:
import utils
if beamdiv_corr:
import dr_lib
for i in xrange(hlr_utils.get_length(obj)):
skip_pixel = False
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 beamdiv_corr:
dangle = dr_lib.ref_beamdiv_correct(obj.attr_list, map_so.id,
config.det_spat_res,
config.center_pix)
# We subtract due to the inversion of the z coordinates from the
# mirror reflection of the beam at the sample.
if dangle is not None:
pangle = angle - (2.0 * dangle)
else:
pangle = angle
skip_pixel = True
else:
pangle = angle
value = axis_manip.tof_to_scalar_Q(val, err2, pl, pl_err2, pangle,
angle_err2)
if lojac:
y_val = hlr_utils.get_value(obj, i, o_descr, "y")
y_err2 = hlr_utils.get_err2(obj, i, o_descr, "y")
counts = utils.linear_order_jacobian(val, value[0],
y_val, y_err2)
else:
pass
if o_descr != "number":
value1 = axis_manip.reverse_array_cp(value[0])
value2 = axis_manip.reverse_array_cp(value[1])
rev_value = (value1, value2)
else:
rev_value = value
if map_so is not None:
if not lojac:
map_so.y = axis_manip.reverse_array_cp(map_so.y)
map_so.var_y = axis_manip.reverse_array_cp(map_so.var_y)
else:
map_so.y = axis_manip.reverse_array_cp(counts[0])
map_so.var_y = axis_manip.reverse_array_cp(counts[1])
else:
pass
if not skip_pixel:
hlr_utils.result_insert(result, res_descr, rev_value, map_so, "x",
axis)
return result
def integrate_axis(obj, **kwargs):
"""
This function takes a spectrum and integrates the given axis. The function
assumes that the incoming data is in the histogram form.
@param obj: Spectrum to be integrated
@type obj: C{SOM.SOM} or C{SOM.SO}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword start: Value for the start range of the integration.
@type start: C{float}
@keyword end: Value for the end range of the integration.
@type end: C{float}
@keyword width: This is a flag to turn on the multiplication of the
individual bin contents with the bins corresponding width.
@type width: C{boolean}
@keyword width_pos: This is the position of the x-axis in the axis array
from which to calculate the bin widths in support of
the width flag. If no argument is given, the default
value is I{0}.
@type width_pos: C{int}
@return: The integration value and its associated error
@rtype: C{tuple}
@raise RuntimError: A C{SOM} or C{SO} is not given to the function.
"""
# import the helper functions
import hlr_utils
# set up for working through data
o_descr = hlr_utils.get_descr(obj)
if o_descr == "number" or o_descr == "list":
raise RuntimeError("Must provide a SOM of a SO to the function.")
# Go on
else:
pass
# Check for starting bin
try:
start = kwargs["start"]
except KeyError:
start = float("inf")
# Check for ending bin
try:
end = kwargs["end"]
except KeyError:
end = float("inf")
# Check for width keyword argument
try:
width = kwargs["width"]
except KeyError:
width = False
if width:
# Check for width_pos keyword argument
try:
width_pos = kwargs["width_pos"]
except KeyError:
width_pos = 0
else:
width_pos = 0
import array_manip
import utils
integration = float(0)
integration_error2 = float(0)
len_obj = hlr_utils.get_length(obj)
for i in xrange(len_obj):
value = hlr_utils.get_value(obj, i, o_descr, "y")
error = hlr_utils.get_err2(obj, i, o_descr, "y")
x_axis = hlr_utils.get_value(obj, i, o_descr, "x", width_pos)
(int_val) = utils.integrate_1D_hist(value, error, x_axis,
width=width,
min_int=start,
max_int=end)
(integration,
integration_error2) = array_manip.add_ncerr(int_val[0],
int_val[1],
integration,
integration_error2)
return (integration, integration_error2)
def energy_transfer(left, right, **kwargs):
"""
This function takes a C{tuple} and a C{SOM}, a C{tuple} and a C{SO} or two
C{tuple}s and calculates the energy transfer in units of I{THz}. The C{SOM}
principle axis must be in units of I{meV}. The C{SO} and C{tuple}s are
assumed to be in units of I{meV}.
@param left: Object on the left side of the subtraction
@type left: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@param right: Object on the right side of the subtraction
@type right: C{SOM.SOM}, C{SOM.SO} or 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{meV}
@type units: C{string}
@return: Object based on left - right in units of I{THz}
@rtype: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@raise RuntimeError: The x-axis units are not I{meV}
@raise TypeError: C{SOM}-C{SOM} operation not supported
@raise TypeError: C{SOM}-C{SO} operation not supported
@raise TypeError: C{SO}-C{SOM} operation not supported
@raise TypeError: C{SO}-C{SO} operation not supported
"""
# import the helper functions
import hlr_utils
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(left, right)
(l_descr, r_descr) = hlr_utils.get_descr(left, right)
# error checking for types
if l_descr == "SOM" and r_descr == "SOM":
raise TypeError("SOM-SOM operation not supported")
elif l_descr == "SOM" and r_descr == "SO":
raise TypeError("SOM-SO operation not supported")
elif l_descr == "SO" and r_descr == "SOM":
raise TypeError("SO-SOM operation not supported")
elif l_descr == "SO" and r_descr == "SO":
raise TypeError("SO-SO operation not supported")
else:
pass
# Setup keyword arguments
try:
units = kwargs["units"]
except KeyError:
units = "meV"
result = hlr_utils.copy_som_attr(result, res_descr, left, l_descr, right,
r_descr)
if res_descr == "SOM":
index = hlr_utils.one_d_units(result, units)
result = hlr_utils.force_units(result, "THz", index)
result.setAxisLabel(index, "energy transfer")
result.setYUnits("Counts/THz")
result.setYLabel("Intensity")
else:
pass
# iterate through the values
import axis_manip
for i in xrange(hlr_utils.get_length(left, right)):
val1 = hlr_utils.get_value(left, i, l_descr, "x")
err2_1 = hlr_utils.get_err2(left, i, l_descr, "x")
val2 = hlr_utils.get_value(right, i, r_descr, "x")
err2_2 = hlr_utils.get_err2(right, i, r_descr, "x")
value = axis_manip.energy_transfer(val1, err2_1, val2, err2_2)
map_so = hlr_utils.get_map_so(left, right, i)
hlr_utils.result_insert(result, res_descr, value, map_so, "x")
return result
def wavelength_to_scalar_k(obj, **kwargs):
"""
This function converts a primary axis of a C{SOM} or C{SO} from wavelength
to scalar k. The wavelength axis for a C{SOM} must be in units of
I{Angstroms}. The primary axis of a C{SO} is assumed to be in units of
I{Angstroms}. A C{tuple} of C{(wavelength, wavelength_err2)} (assumed to
be in units of I{Angstroms}) can be converted to C{(scalar_k,
scalar_k_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 units: The expected units for this function. The default for this
function is I{Angstroms}.
@type units: C{string}
@return: Object with a primary axis in wavelength converted to scalar k
@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{Angstroms}
"""
# 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)
if o_descr == "list":
raise TypeError("Do not know how to handle given type: %s" % \
o_descr)
else:
pass
# 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)
if res_descr == "SOM":
result = hlr_utils.force_units(result, "1/Angstroms", axis)
result.setAxisLabel(axis, "scalar wavevector")
result.setYUnits("Counts/A-1")
result.setYLabel("Intensity")
else:
pass
# iterate through the values
import axis_manip
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)
value = axis_manip.wavelength_to_scalar_k(val, err2)
if o_descr != "number":
value1 = axis_manip.reverse_array_cp(value[0])
value2 = axis_manip.reverse_array_cp(value[1])
rev_value = (value1, value2)
else:
rev_value = value
map_so = hlr_utils.get_map_so(obj, None, i)
if map_so is not None:
map_so.y = axis_manip.reverse_array_cp(map_so.y)
map_so.var_y = axis_manip.reverse_array_cp(map_so.var_y)
else:
pass
hlr_utils.result_insert(result, res_descr, rev_value, map_so, "x",
axis)
return result
def create_Qvec_vs_E_dgs(som, E_i, conf, **kwargs):
"""
This function starts with the energy transfer axis from DGS reduction and
turns this into a 4D spectra with Qx, Qy, Qz and Et axes.
@param som: The input object with initial IGS wavelength axis
@type som: C{SOM.SOM}
@param E_i: The initial energy for the given data.
@type E_i: C{tuple}
@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 timer: Timing object so the function can perform timing estimates.
@type timer: C{sns_timer.DiffTime}
@keyword corner_angles: The object that contains the corner geometry
information.
@type corner_angles: C{dict}
@keyword make_fixed: A flag that turns on writing the fixed grid mesh
information to a file.
@type make_fixed: C{boolean}
@keyword output: The output filename and or directory.
@type output: C{string}
"""
import array_manip
import axis_manip
import common_lib
import hlr_utils
import os
# Check keywords
try:
t = kwargs["timer"]
except KeyError:
t = None
corner_angles = kwargs["corner_angles"]
try:
make_fixed = kwargs["make_fixed"]
except KeyError:
make_fixed = False
try:
output = kwargs["output"]
except KeyError:
output = None
# Convert initial energy to initial wavevector
l_i = common_lib.energy_to_wavelength(E_i)
k_i = common_lib.wavelength_to_scalar_k(l_i)
# Since all the data is rebinned to the same energy transfer axis, we can
# calculate the final energy axis once
E_t = som[0].axis[0].val
if som[0].axis[0].var is not None:
E_t_err2 = som[0].axis[0].var
else:
import nessi_list
E_t_err2 = nessi_list.NessiList(len(E_t))
E_f = array_manip.sub_ncerr(E_i[0], E_i[1], E_t, E_t_err2)
# Check for negative final energies which will cause problems with
# wavelength conversion due to square root
if E_f[0][-1] < 0:
E_f[0].reverse()
E_f[1].reverse()
index = 0
for E in E_f[0]:
if E >= 0:
break
index += 1
E_f[0].__delslice__(0, index)
E_f[1].__delslice__(0, index)
E_f[0].reverse()
E_f[1].reverse()
len_E = len(E_f[0]) - 1
# Now we can get the final wavevector
l_f = axis_manip.energy_to_wavelength(E_f[0], E_f[1])
k_f = axis_manip.wavelength_to_scalar_k(l_f[0], l_f[1])
# Grab the instrument from the som
inst = som.attr_list.instrument
if make_fixed:
import SOM
fixed_grid = {}
for key in corner_angles:
so_id = SOM.NeXusId.fromString(key).toTuple()
try:
pathlength = inst.get_secondary(so_id)[0]
points = []
for j in range(4):
points.extend(
__calc_xyz(pathlength, corner_angles[key].getPolar(j),
corner_angles[key].getAzimuthal(j)))
fixed_grid[key] = points
except KeyError:
# Pixel ID is not in instrument geometry
pass
CNT = {}
ERR2 = {}
V1 = {}
V2 = {}
V3 = {}
V4 = {}
# Output positions for Qx, Qy, Qz coordinates
X = 0
Y = 2
Z = 4
if t is not None:
t.getTime(False)
# Iterate though the data
len_som = hlr_utils.get_length(som)
for i in xrange(len_som):
map_so = hlr_utils.get_map_so(som, None, i)
yval = hlr_utils.get_value(som, i, "SOM", "y")
yerr2 = hlr_utils.get_err2(som, i, "SOM", "y")
CNT[str(map_so.id)] = yval
ERR2[str(map_so.id)] = yerr2
cangles = corner_angles[str(map_so.id)]
Q1 = axis_manip.init_scatt_wavevector_to_Q(k_i[0], k_i[1], k_f[0],
k_f[1],
cangles.getAzimuthal(0),
0.0, cangles.getPolar(0),
0.0)
V1[str(map_so.id)] = {}
V1[str(map_so.id)]["x"] = Q1[X]
V1[str(map_so.id)]["y"] = Q1[Y]
V1[str(map_so.id)]["z"] = Q1[Z]
Q2 = axis_manip.init_scatt_wavevector_to_Q(k_i[0], k_i[1], k_f[0],
k_f[1],
cangles.getAzimuthal(1),
0.0, cangles.getPolar(1),
0.0)
V2[str(map_so.id)] = {}
V2[str(map_so.id)]["x"] = Q2[X]
V2[str(map_so.id)]["y"] = Q2[Y]
V2[str(map_so.id)]["z"] = Q2[Z]
Q3 = axis_manip.init_scatt_wavevector_to_Q(k_i[0], k_i[1], k_f[0],
k_f[1],
cangles.getAzimuthal(2),
0.0, cangles.getPolar(2),
0.0)
V3[str(map_so.id)] = {}
V3[str(map_so.id)]["x"] = Q3[X]
V3[str(map_so.id)]["y"] = Q3[Y]
V3[str(map_so.id)]["z"] = Q3[Z]
Q4 = axis_manip.init_scatt_wavevector_to_Q(k_i[0], k_i[1], k_f[0],
k_f[1],
cangles.getAzimuthal(3),
0.0, cangles.getPolar(3),
0.0)
V4[str(map_so.id)] = {}
V4[str(map_so.id)]["x"] = Q4[X]
V4[str(map_so.id)]["y"] = Q4[Y]
V4[str(map_so.id)]["z"] = Q4[Z]
if t is not None:
t.getTime(msg="After calculating verticies ")
# Form the messages
if t is not None:
t.getTime(False)
jobstr = 'MR' + hlr_utils.create_binner_string(conf) + 'JH'
num_lines = len(CNT) * len_E
linestr = str(num_lines)
if output is not None:
outdir = os.path.dirname(output)
if outdir != '':
if outdir.rfind('.') != -1:
outdir = ""
else:
outdir = ""
value = str(som.attr_list["data-run_number"].getValue()).split('/')
topdir = os.path.join(outdir, value[0].strip() + "-mesh")
try:
os.mkdir(topdir)
except OSError:
pass
outtag = os.path.basename(output)
if outtag.rfind('.') == -1:
outtag = ""
else:
outtag = outtag.split('.')[0]
if outtag != "":
filehead = outtag + "_bmesh"
if make_fixed:
filehead1 = outtag + "_fgrid"
filehead2 = outtag + "_conf"
else:
filehead = "bmesh"
if make_fixed:
filehead1 = "fgrid"
filehead2 = "conf"
hfile = open(os.path.join(topdir, "%s.in" % filehead2), "w")
print >> hfile, jobstr
print >> hfile, linestr
hfile.close()
import utils
use_zero_supp = not conf.no_zero_supp
for k in xrange(len_E):
ofile = open(os.path.join(topdir, "%s%04d.in" % (filehead, k)), "w")
if make_fixed:
ofile1 = open(os.path.join(topdir, "%s%04d.in" % (filehead1, k)),
"w")
for pid in CNT:
if use_zero_supp:
write_value = not utils.compare(CNT[pid][k], 0.0) == 0
else:
write_value = True
if write_value:
result = []
result.append(str(k))
result.append(str(E_t[k]))
result.append(str(E_t[k + 1]))
result.append(str(CNT[pid][k]))
result.append(str(ERR2[pid][k]))
__get_coords(V1, pid, k, result)
__get_coords(V2, pid, k, result)
__get_coords(V3, pid, k, result)
__get_coords(V4, pid, k, result)
__get_coords(V1, pid, k + 1, result)
__get_coords(V2, pid, k + 1, result)
__get_coords(V3, pid, k + 1, result)
__get_coords(V4, pid, k + 1, result)
print >> ofile, " ".join(result)
if make_fixed:
result1 = []
result1.append(str(k))
result1.append(str(E_t[k]))
result1.append(str(E_t[k + 1]))
result1.append(str(CNT[pid][k]))
result1.append(str(ERR2[pid][k]))
result1.extend([str(x) for x in fixed_grid[pid]])
print >> ofile1, " ".join(result1)
ofile.close()
if make_fixed:
ofile1.close()
if t is not None:
t.getTime(msg="After creating messages ")
def rebin_efficiency(obj1, obj2, **kwargs):
"""
This function takes two objects and rebins the data for obj1 onto the axis
provided by obj2. The units on the x-axes needs to be I{Angstroms}, since
this is what the efficiencies will be present as.
@param obj1: Object that will be rebinned
@type obj1: C{SOM.SOM} or C{SOM.SO}
@param obj2: Object that will provide the axis for rebinning
@type obj2: C{SOM.SOM} or C{SOM.SO}
@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: Object that has been rebinned
@rtype: C{SOM.SOM} or C{SOM.SO}
@raise TypeError: The C{SOM}-C{SO} operation is attempted
@raise TypeError: The C{SO}-C{SOM} operation is attempted
@raise TypeError: obj1 not a C{SOM} or C{SO}
@raise TypeError: obj2 not a C{SOM} or C{SO}
@raise IndexError: The C{SOM}s do not have the same number of C{SO}s
@raise RuntimeError: The C{SOM} x-axis units are not I{Angstroms}
@raise RuntimeError: The x-axis units of the C{SOM}s do not match
"""
# import the helper functions
import hlr_utils
# Kickout if monitor object is None
if obj1 is None:
return obj1
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(obj1, obj2)
(o1_descr, o2_descr) = hlr_utils.get_descr(obj1, obj2)
# error checking for types
if o1_descr == "SOM" and o2_descr == "SO":
raise TypeError, "SOM-SO operation not supported"
elif o1_descr == "SO" and o2_descr == "SOM":
raise TypeError("SO-SOM operation not supported")
# Have the right object combination, go on
else:
pass
# Setup keyword arguments
try:
units = kwargs["units"]
except KeyError:
units = "Angstroms"
if o1_descr == "SOM" and o2_descr == "SOM":
hlr_utils.math_compatible(obj1, o1_descr, obj2, o2_descr)
# If both objects are not SOMs, do nothing
else:
pass
result = hlr_utils.copy_som_attr( result, res_descr, obj2, o2_descr)
if res_descr == "SOM":
result = hlr_utils.force_units(result, units)
# Can't force units on anything other than a SOM
else:
pass
# iterate through the values
import common_lib
for i in xrange(hlr_utils.get_length(obj1, obj2)):
val1 = hlr_utils.get_value(obj1, i, o1_descr, "all")
val2 = hlr_utils.get_value(obj2, i, o2_descr, "x")
value = common_lib.rebin_axis_1D(val1, val2)
hlr_utils.result_insert(result, res_descr, value, None, "all")
return result
def sumw_ncerr(obj1, obj2, **kwargs):
"""
This function sums by weighting errors of two objects (C{SOM} or C{SO}) and
returns the result of that action in an C{SOM}. The function does not
handle the cases of C{SOM}+C{tuple}, C{SO}+C{tuple} or C{tuple}+C{tuple}.
@param obj1: First object in the weighted sum
@type obj1: C{SOM.SOM} or C{SOM.SO} or C{tuple}
@param obj2: Second object in the the weighted sum
@type obj2: C{SOM.SOM} or C{SOM.SO} or C{tuple}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword axis: This is the axis one wishes to manipulate. If no argument
is given the default value is y
@type axis: C{string}=<y or x>
@keyword axis_pos: This is position of the axis in the axis array. If no
argument is given, the default value is 0
@type axis_pos: C{int}
@keyword length_one_som: This is a flag that lets the function know it is
dealing with a length 1 C{SOM} so that attributes
may be passed along. The length 1 C{SOM} will be
turned into a C{SO}. The default value is False.
@type length_one_som: C{boolean}
@keyword length_one_som_pos: This is the argument position of the
length 1 C{SOM} since the check is done
before the arguments are swapped. The default
value is 2.
@type length_one_som_pos: C{int}=<1 or 2>
@return: Object containing the results of the addition
@rtype: C{SOM.SOM} or C{SOM.SO}
@raise TypeError: The C{SOM}+C{tuple}, C{SO}+C{tuple} or C{tuple}+C{tuple}
cases are presented to the function
@raise IndexError: The two C{SOM}s do not contain the same number of
spectra
@raise RunTimeError: The x-axis units of the C{SOM}s do not match
@raise RunTimeError: The y-axis units of the C{SOM}s do not match
@raise RunTimeError: The x-axes of the two C{SO}s are not equal
"""
# import the helper functions
import hlr_utils
# Check to see if we are working with a length 1 SOM
try:
length_one_som = kwargs["length_one_som"]
except KeyError:
length_one_som = False
try:
length_one_som_pos = kwargs["length_one_som_pos"]
if length_one_som_pos != 1 or length_one_som_pos != 2:
raise RuntimeError("length_one_som_pos must be either 1 or 2 and "\
+"%d" % length_one_som_pos)
except KeyError:
length_one_som_pos = 2
if length_one_som:
if length_one_som_pos == 1:
som_copy = obj1
obj1 = obj1[0]
else:
som_copy = obj2
obj2 = obj2[0]
else:
# Not working with a length 1 SOM, do nothing
pass
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(obj1, obj2)
(o1_descr, o2_descr) = hlr_utils.get_descr(obj1, obj2)
# error check information
if o1_descr == "number" or o2_descr == "number":
raise RuntimeError("Operations with tuples are not supported!")
elif o2_descr == "SOM" and o1_descr == "SO":
(obj1, obj2) = hlr_utils.swap_args(obj1, obj2)
(o1_descr, o2_descr) = hlr_utils.swap_args(o1_descr, o2_descr)
elif o2_descr == "SOM" and o1_descr == "SOM":
hlr_utils.math_compatible(obj1, o1_descr, obj2, o2_descr)
else:
pass
# Check for axis keyword argument
try:
axis = kwargs["axis"]
except KeyError:
axis = "y"
# Check for axis_pos keyword argument
try:
axis_pos = kwargs["axis_pos"]
except KeyError:
axis_pos = 0
if length_one_som:
if length_one_som_pos == 1:
result = hlr_utils.copy_som_attr(result, res_descr, som_copy,
"SOM", obj2, o2_descr)
else:
result = hlr_utils.copy_som_attr(result, res_descr, obj1, o1_descr,
som_copy, "SOM")
else:
result = hlr_utils.copy_som_attr(result, res_descr, obj1, o1_descr,
obj2, o2_descr)
# iterate through the values
import array_manip
for i in xrange(hlr_utils.get_length(obj1, obj2)):
val1 = hlr_utils.get_value(obj1, i, o1_descr, axis, axis_pos)
err2_1 = hlr_utils.get_err2(obj1, i, o1_descr, axis, axis_pos)
val2 = hlr_utils.get_value(obj2, i, o2_descr, axis, axis_pos)
err2_2 = hlr_utils.get_err2(obj2, i, o2_descr, axis, axis_pos)
(descr_1, descr_2) = hlr_utils.get_descr(val1, val2)
hlr_utils.math_compatible(val1, descr_1, val2, descr_2)
value = array_manip.sumw_ncerr(val1, err2_1, val2, err2_2)
map_so = hlr_utils.get_map_so(obj1, None, i)
hlr_utils.result_insert(result, res_descr, value, map_so, axis,
axis_pos)
return result
def sum_by_rebin_frac(obj, axis_out, **kwargs):
"""
This function uses the C{axis_manip.rebin_axis_1D_frac} function from the
SCL to perform the rebinning. The function tracks the counts and fractional
area from all spectra separately. The counts and fractional area are
divided after all spectra have been parsed.
@param obj: Object to be rebinned and summed
@type obj: C{SOM.SOM} or C{SOM.SO}
@param axis_out: The axis to rebin the C{SOM} or C{SO} to
@type axis_out: C{NessiList}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword configure: This is the object containing the driver configuration.
This will signal the function to write out the counts
and fractional area to files.
@type configure: C{Configure}
@return: Object that has been rebinned and summed according to the
provided axis
@rtype: C{SOM.SOM} or C{SOM.SO}
@raise TypeError: The rebinning axis given is not a C{NessiList}
@raise TypeError: The object being rebinned is not a C{SOM} or a C{SO}
@raise TypeError: The dimension of the input object is not 1D
"""
# import the helper functions
import hlr_utils
# set up for working through data
try:
axis_out.__type__
except AttributeError:
raise TypeError("Rebinning axis must be a NessiList!")
o_descr = hlr_utils.get_descr(obj)
if o_descr == "number" or o_descr == "list":
raise TypeError("Do not know how to handle given type: %s" % \
o_descr)
else:
pass
try:
if obj.getDimension() != 1:
raise TypeError("The input object must be 1D!. This one is "\
+"%dD." % obj.getDimension())
except AttributeError:
# obj is a SO
if obj.dim() != 1:
raise TypeError("The input object must be 1D!. This one is "\
+"%dD." % obj.dim())
# Check for keywords
try:
config = kwargs["configure"]
except KeyError:
config = None
(result, res_descr) = hlr_utils.empty_result(obj)
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
import array_manip
import axis_manip
len_data = len(axis_out) - 1
counts = nessi_list.NessiList(len_data)
counts_err2 = nessi_list.NessiList(len_data)
frac_area = nessi_list.NessiList(len_data)
frac_area_err2 = nessi_list.NessiList(len_data)
for i in xrange(hlr_utils.get_length(obj)):
axis_in = hlr_utils.get_value(obj, i, o_descr, "x", 0)
val = hlr_utils.get_value(obj, i, o_descr)
err2 = hlr_utils.get_err2(obj, i, o_descr)
value = axis_manip.rebin_axis_1D_frac(axis_in, val, err2, axis_out)
(counts, counts_err2) = array_manip.add_ncerr(counts, counts_err2,
value[0], value[1])
(frac_area,
frac_area_err2) = array_manip.add_ncerr(frac_area, frac_area_err2,
value[2], frac_area_err2)
# Divide the total counts by the total fractional area
value1 = array_manip.div_ncerr(counts, counts_err2, frac_area,
frac_area_err2)
xvals = []
xvals.append(axis_out)
map_so = hlr_utils.get_map_so(obj, None, 0)
hlr_utils.result_insert(result, res_descr, value1, map_so, "all", 0, xvals)
if config is not None:
if o_descr == "SOM":
import SOM
o_som = SOM.SOM()
o_som.copyAttributes(obj)
so = hlr_utils.get_map_so(obj, None, 0)
so.axis[0].val = axis_out
so.y = counts
so.var_y = counts_err2
o_som.append(so)
# Write out summed counts into file
hlr_utils.write_file(config.output,
"text/Spec",
o_som,
output_ext="cnt",
verbose=config.verbose,
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
message="summed counts")
# Replace counts data with fractional area. The axes remain the
# same
o_som[0].y = frac_area
o_som[0].var_y = frac_area_err2
# Write out summed fractional area into file
hlr_utils.write_file(config.output,
"text/Spec",
o_som,
output_ext="fra",
verbose=config.verbose,
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
message="fractional area")
return result
def create_E_vs_Q_dgs(som, E_i, Q_final, **kwargs):
"""
This function starts with the rebinned energy transfer and turns this
into a 2D spectra with E and Q axes for DGS instruments.
@param som: The input object with initial IGS wavelength axis
@type som: C{SOM.SOM}
@param E_i: The initial energy for the given data.
@type E_i: C{tuple}
@param Q_final: The momentum transfer axis to rebin the data to
@type Q_final: C{nessi_list.NessiList}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword corner_angles: The object that contains the corner geometry
information.
@type corner_angles: C{dict}
@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 y axis label
@type y_label: C{string}
@keyword y_units: The y axis units
@type y_units: C{string}
@keyword x_labels: This is a list of names that sets the individual x axis
labels
@type x_labels: C{list} of C{string}s
@keyword x_units: This is a list of names that sets the individual x axis
units
@type x_units: C{list} of C{string}s
@keyword split: This flag causes the counts and the fractional area to
be written out into separate files.
@type split: C{boolean}
@keyword configure: This is the object containing the driver configuration.
@type configure: C{Configure}
@return: Object containing a 2D C{SO} with E and Q axes
@rtype: C{SOM.SOM}
"""
import array_manip
import axis_manip
import common_lib
import hlr_utils
import nessi_list
import SOM
import utils
# Check for keywords
corner_angles = kwargs["corner_angles"]
configure = kwargs.get("configure")
split = kwargs.get("split", False)
# Setup output object
so_dim = SOM.SO(2)
so_dim.axis[0].val = Q_final
so_dim.axis[1].val = som[0].axis[0].val # E_t
# Calculate total 2D array size
N_tot = (len(so_dim.axis[0].val) - 1) * (len(so_dim.axis[1].val) - 1)
# Create y and var_y lists from total 2D size
so_dim.y = nessi_list.NessiList(N_tot)
so_dim.var_y = nessi_list.NessiList(N_tot)
# Create area sum and errors for the area sum lists from total 2D size
area_sum = nessi_list.NessiList(N_tot)
area_sum_err2 = nessi_list.NessiList(N_tot)
# Convert initial energy to initial wavevector
l_i = common_lib.energy_to_wavelength(E_i)
k_i = common_lib.wavelength_to_scalar_k(l_i)
# Since all the data is rebinned to the same energy transfer axis, we can
# calculate the final energy axis once
E_t = som[0].axis[0].val
if som[0].axis[0].var is not None:
E_t_err2 = som[0].axis[0].var
else:
E_t_err2 = nessi_list.NessiList(len(E_t))
# Get the bin width arrays from E_t
(E_t_bw, E_t_bw_err2) = utils.calc_bin_widths(E_t)
E_f = array_manip.sub_ncerr(E_i[0], E_i[1], E_t, E_t_err2)
# Now we can get the final wavevector
l_f = axis_manip.energy_to_wavelength(E_f[0], E_f[1])
k_f = axis_manip.wavelength_to_scalar_k(l_f[0], l_f[1])
# Output position for Q
X = 0
# Iterate though the data
len_som = hlr_utils.get_length(som)
for i in xrange(len_som):
map_so = hlr_utils.get_map_so(som, None, i)
yval = hlr_utils.get_value(som, i, "SOM", "y")
yerr2 = hlr_utils.get_err2(som, i, "SOM", "y")
cangles = corner_angles[str(map_so.id)]
avg_theta1 = (cangles.getPolar(0) + cangles.getPolar(1)) / 2.0
avg_theta2 = (cangles.getPolar(2) + cangles.getPolar(3)) / 2.0
Q1 = axis_manip.init_scatt_wavevector_to_scalar_Q(k_i[0],
k_i[1],
k_f[0][:-1],
k_f[1][:-1],
avg_theta2,
0.0)
Q2 = axis_manip.init_scatt_wavevector_to_scalar_Q(k_i[0],
k_i[1],
k_f[0][:-1],
k_f[1][:-1],
avg_theta1,
0.0)
Q3 = axis_manip.init_scatt_wavevector_to_scalar_Q(k_i[0],
k_i[1],
k_f[0][1:],
k_f[1][1:],
avg_theta1,
0.0)
Q4 = axis_manip.init_scatt_wavevector_to_scalar_Q(k_i[0],
k_i[1],
k_f[0][1:],
k_f[1][1:],
avg_theta2,
0.0)
# Calculate the area of the E,Q polygons
(A, A_err2) = utils.calc_eq_jacobian_dgs(E_t[:-1], E_t[:-1],
E_t[1:], E_t[1:],
Q1[X], Q2[X], Q3[X], Q4[X])
# Apply the Jacobian: C/dE_t * dE_t / A(EQ) = C/A(EQ)
(jac_ratio, jac_ratio_err2) = array_manip.div_ncerr(E_t_bw,
E_t_bw_err2,
A, A_err2)
(counts, counts_err2) = array_manip.mult_ncerr(yval, yerr2,
jac_ratio,
jac_ratio_err2)
try:
(y_2d, y_2d_err2,
area_new,
bin_count) = axis_manip.rebin_2D_quad_to_rectlin(Q1[X], E_t[:-1],
Q2[X], E_t[:-1],
Q3[X], E_t[1:],
Q4[X], E_t[1:],
counts,
counts_err2,
so_dim.axis[0].val,
so_dim.axis[1].val)
del bin_count
except IndexError, e:
# Get the offending index from the error message
index = int(str(e).split()[1].split('index')[-1].strip('[]'))
print "Id:", map_so.id
print "Index:", index
print "Verticies: %f, %f, %f, %f, %f, %f, %f, %f" % (Q1[X][index],
E_t[:-1][index],
Q2[X][index],
E_t[:-1][index],
Q3[X][index],
E_t[1:][index],
Q4[X][index],
E_t[1:][index])
raise IndexError(str(e))
# Add in together with previous results
(so_dim.y, so_dim.var_y) = array_manip.add_ncerr(so_dim.y,
so_dim.var_y,
y_2d, y_2d_err2)
(area_sum, area_sum_err2) = array_manip.add_ncerr(area_sum,
area_sum_err2,
area_new,
area_sum_err2)
def tof_to_scalar_Q(obj, **kwargs):
"""
This function converts a primary axis of a C{SOM} or C{SO} from
time-of-flight to scalarQ. 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{(time-of-flight,
time-of-flight_err2)} (assumed to be in units of I{microseconds}) can be
converted to C{(scalar_Q, scalar_Q_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 polar: The polar angle and its associated error^2
@type polar: C{tuple} or C{list} of C{tuple}s
@keyword pathlength: The pathlength and its associated error^2
@type pathlength: C{tuple} or C{list} of C{tuple}s
@keyword angle_offset: A constant offset for the polar angle and its
associated error^2. The units of the offset should
be in radians.
@type angle_offset: C{tuple}
@keyword lojac: A flag that allows one to turn off the calculation of the
linear-order Jacobian. The default action is 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}
@return: Object with a primary axis in time-of-flight converted to scalar Q
@rtype: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@raise TypeError: The incoming object is not a type the function recognizes
@raise RuntimeError: A C{SOM} is not passed and no polar angle is provided
@raise RuntimeError: The C{SOM} x-axis units are not I{microseconds}
"""
# 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)
if o_descr == "list":
raise TypeError("Do not know how to handle given type: %s" % \
o_descr)
else:
pass
# Setup keyword arguments
try:
polar = kwargs["polar"]
except KeyError:
polar = None
try:
pathlength = kwargs["pathlength"]
except KeyError:
pathlength = None
try:
units = kwargs["units"]
except KeyError:
units = "microseconds"
try:
lojac = kwargs["lojac"]
except KeyError:
lojac = hlr_utils.check_lojac(obj)
try:
angle_offset = kwargs["angle_offset"]
except KeyError:
angle_offset = None
# 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, "1/Angstroms", axis)
result.setAxisLabel(axis, "scalar wavevector transfer")
result.setYUnits("Counts/A-1")
result.setYLabel("Intensity")
else:
pass
if pathlength is None or polar is None:
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:
if pathlength is None and polar is None:
raise RuntimeError("If no SOM is provided, then pathlength "\
+"and polar angle information must be "\
+"provided")
elif pathlength is None:
raise RuntimeError("If no SOM is provided, then pathlength "\
+"information must be provided")
elif polar is None:
raise RuntimeError("If no SOM is provided, then polar angle "\
+"information must be provided")
else:
raise RuntimeError("If you get here, see Steve Miller for "\
+"your mug.")
else:
pass
if pathlength is not None:
p_descr = hlr_utils.get_descr(pathlength)
else:
pass
if polar is not None:
a_descr = hlr_utils.get_descr(polar)
else:
pass
# iterate through the values
import axis_manip
if lojac:
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("total", map_so, inst)
else:
pl = hlr_utils.get_value(pathlength, i, p_descr)
pl_err2 = hlr_utils.get_err2(pathlength, i, p_descr)
if polar is None:
(angle,
angle_err2) = hlr_utils.get_parameter("polar", map_so, inst)
else:
angle = hlr_utils.get_value(polar, i, a_descr)
angle_err2 = hlr_utils.get_err2(polar, i, a_descr)
if angle_offset is not None:
angle += angle_offset[0]
angle_err2 += angle_offset[1]
value = axis_manip.tof_to_scalar_Q(val, err2, pl, pl_err2, angle,
angle_err2)
if lojac:
y_val = hlr_utils.get_value(obj, i, o_descr, "y")
y_err2 = hlr_utils.get_err2(obj, i, o_descr, "y")
counts = utils.linear_order_jacobian(val, value[0], y_val, y_err2)
else:
pass
if o_descr != "number":
value1 = axis_manip.reverse_array_cp(value[0])
value2 = axis_manip.reverse_array_cp(value[1])
rev_value = (value1, value2)
else:
rev_value = value
if map_so is not None:
if not lojac:
map_so.y = axis_manip.reverse_array_cp(map_so.y)
map_so.var_y = axis_manip.reverse_array_cp(map_so.var_y)
else:
map_so.y = axis_manip.reverse_array_cp(counts[0])
map_so.var_y = axis_manip.reverse_array_cp(counts[1])
else:
pass
hlr_utils.result_insert(result, res_descr, rev_value, map_so, "x",
axis)
return result
def sum_all_spectra(obj, **kwargs):
"""
This function takes all the spectra in the given object and sums them
together. All of the sprectra are assumed to have the same axis scale.
@param obj: Object in which all of the spectra are to be summed together
@type obj: C{SOM.SOM}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword rebin_axis: The axis(es) to rebin the spectra onto.
@type rebin_axis: C{nessi_list.NessiList} or C{list} of
C{nessi_list.NessiList}s
@keyword rebin_axis_dim: Tthe dimension on the spectra being rebinned.
The default value is I{1}.
@type rebin_axis_dim: C{int}
@keyword y_sort: A flag that will sort the spectrum IDs by y. The default
behavior is I{False} and this maintains the original x
ordering.
@type y_sort: C{boolean}
@keyword stripe: A flag that will combine spectra in either the x or y
direction at a given y or x. The integration direction is
based on the setting of y_sort. The default behavior is
I{False} and corresponds to summing all spectra into one.
@type stripe: C{boolean}
@keyword pix_fix: A single or list of pixel IDs with which to override the
summed spectrum pixel ID. The setting of y_sort
determines is the x component (y_sort=False) or the y
component (y_sort=True) of the pixel ID is overridden.
@type pix_fix: C{int} or C{list} of C{int}s
@return: Object containing a single spectrum
@rtype: C{SOM.SOM}
@raise TypeError: Anything other than a C{SOM} is given
@raise RuntimeError: An unknown rebinning dimension is given
"""
o_descr = hlr_utils.get_descr(obj)
if o_descr != "SOM":
raise TypeError("Function argument must be a SOM")
# Have a SOM, go on
else:
pass
# If there is only one SO, why run
if len(obj) == 1:
return obj
# OK, we need to sum
else:
pass
try:
rebin_axis = kwargs["rebin_axis"]
except KeyError:
rebin_axis = None
try:
rebin_axis_dim = kwargs["rebin_axis_dim"]
except KeyError:
rebin_axis_dim = 1
try:
y_sort = kwargs["y_sort"]
except KeyError:
y_sort = False
try:
stripe = kwargs["stripe"]
except KeyError:
stripe = False
try:
pix_fix = kwargs["pixel_fix"]
except KeyError:
pix_fix = None
import common_lib
if rebin_axis is not None:
if rebin_axis_dim == 1:
obj1 = common_lib.rebin_axis_1D(obj, rebin_axis)
elif rebin_axis_dim == 2:
obj1 = common_lib.rebin_axis_2D(obj, rebin_axis[0], rebin_axis[1])
else:
raise RuntimeError("Do not have rebinning method for %dD." % \
rebin_axis_dim)
else:
obj1 = obj
del obj
# Sort SO IDs by y value
if y_sort:
obj1.sort(lambda x, y: cmp(x.id[1][1], y.id[1][1]))
# SO IDs are already sorted by x value
else:
pass
(result, res_descr) = hlr_utils.empty_result(obj1)
result = hlr_utils.copy_som_attr(result, res_descr, obj1, o_descr)
if not stripe:
# iterate through the values
so_id_list = []
val1 = hlr_utils.get_value(obj1, 0, o_descr, "all")
val2 = hlr_utils.get_value(obj1, 1, o_descr, "all")
value = common_lib.add_ncerr(val1, val2)
so_id_list.append(val1.id)
so_id_list.append(val2.id)
for i in xrange(2, hlr_utils.get_length(obj1)):
val = hlr_utils.get_value(obj1, i, o_descr, "all")
value = common_lib.add_ncerr(val, value)
so_id_list.append(val.id)
hlr_utils.result_insert(result, res_descr, value, None, "all")
result.attr_list["Summed IDs"] = so_id_list
if pix_fix is not None:
if y_sort:
fixed_pixel = (so_id_list[0][0], (pix_fix,
so_id_list[0][1][1]))
else:
fixed_pixel = (so_id_list[0][0], (so_id_list[0][1][0],
pix_fix))
else:
fixed_pixel = so_id_list[0]
result[0].id = fixed_pixel
else:
# iterate through the values
so_id_list = []
i_start = 0
stripe_count = 0
total_size = hlr_utils.get_length(obj1)
while i_start < total_size:
stripe_list = []
counted = 2
val1 = hlr_utils.get_value(obj1, i_start, o_descr, "all")
val2 = hlr_utils.get_value(obj1, i_start+1, o_descr, "all")
value = common_lib.add_ncerr(val1, val2)
stripe_list.append(val1.id)
stripe_list.append(val2.id)
if y_sort:
comp_id = val2.id[1][1]
else:
comp_id = val2.id[1][0]
for i in xrange(i_start+2, total_size):
val = hlr_utils.get_value(obj1, i, o_descr, "all")
if y_sort:
new_id = val.id[1][1]
else:
new_id = val.id[1][0]
if new_id > comp_id:
break
value = common_lib.add_ncerr(val, value)
stripe_list.append(val.id)
counted += 1
i_start += counted
so_id_list.append(stripe_list)
hlr_utils.result_insert(result, res_descr, value, None, "all")
if pix_fix is not None:
try:
if y_sort:
fixed_pixel = (stripe_list[0][0],
(pix_fix[stripe_count],
stripe_list[0][1][1]))
else:
fixed_pixel = (stripe_list[0][0],
(stripe_list[0][1][0],
pix_fix[stripe_count]))
except TypeError:
if y_sort:
fixed_pixel = (stripe_list[0][0],
(pix_fix,
stripe_list[0][1][1]))
else:
fixed_pixel = (stripe_list[0][0],
(stripe_list[0][1][0],
pix_fix))
else:
fixed_pixel = stripe_list[0]
result[stripe_count].id = fixed_pixel
stripe_count += 1
result.attr_list["summed_ids"] = so_id_list
return result
def sum_spectra_weighted_ave(obj, **kwargs):
"""
This function takes a set of data and sums the individual bins by weighted
average. That information is then assembled back into a single spectrum.
The individual spectra should already have been rebinned.
@param obj: Object containing data spectra
@type obj: C{SOM.SOM} or C{SOM.SO}
@param kwargs: A list of keyword arguments that the function accepts:
@return: The summed spectra (one)
@rtype: C{SOM.SOM}
"""
if obj is None:
return None
# 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)
# Get the number of axis channels
len_axis = len(obj[0])
import nessi_list
import SOM
import utils
# Empty SO for final spctrum
so = SOM.SO()
len_som = hlr_utils.get_length(obj)
# Slice data, calculate weighted average and repackage spectra
for i in xrange(len_axis):
sliced_data = nessi_list.NessiList()
sliced_data_err2 = nessi_list.NessiList()
for j in xrange(len_som):
obj1 = hlr_utils.get_value(obj, j, o_descr, "all")
if i == 0 and j == 0:
map_so = hlr_utils.get_map_so(obj, None, j)
hlr_utils.result_insert(so, "SO", map_so, None, "all")
sliced_data.append(obj1.y[i])
sliced_data_err2.append(obj1.var_y[i])
len_fit = len(sliced_data)
value = utils.weighted_average(sliced_data, sliced_data_err2,
0, len_fit-1)
so.y[i] = value[0]
so.var_y[i] = value[1]
hlr_utils.result_insert(result, res_descr, so, None, "all")
return result
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 rebin_monitor(obj1, obj2, **kwargs):
"""
This function takes objects (1st is the monitor, 2nd is the detector data)
and rebins the data for obj1 onto the axis provided by obj2. The pixel ID
can be transferred as the pixel ID of the rebinned monitor histogram. A
prefix is placed on the bank ID to dilineate that this is a rebinned
monitor spectrum for that pixel.
@param obj1: Monitor object that will be rebinned
@type obj1: C{SOM.SOM} or C{SOM.SO}
@param obj2: Detector data object that will provide the axis for rebinning
@type obj2: C{SOM.SOM} or C{SOM.SO}
@param kwargs: A list of keyword arguments that the function accepts
@keyword use_pix_id: A flag that specifies if the pixel ID is to be used
in the rebinned monitor spectrum. The default is I{False}.
@type use_pix_id: C{boolean}
@keyword prefix: Allows one to specify a prefix for the bank ID. The
default is I{m}.
@type prefix: C{string}
@keyword rtype: A short string that defines the rebinning function to use.
The default is I{None} which uses
L{common_lib.rebin_axis_1D()}. The other possibilities are
I{linint} which uses L{common_lib.rebin_axis_1D_linint()}
and I{frac} which uses L{common_lib.rebin_axis_1D_frac()}.
@type rtype: C{boolean}
@return: Object that has been rebinned
@rtype: C{SOM.SOM} or C{SOM.SO}
@raise TypeError: The C{SOM}-C{SO} operation is attempted
@raise TypeError: The C{SO}-C{SOM} operation is attempted
@raise TypeError: obj1 not a C{SOM} or C{SO}
@raise TypeError: obj2 not a C{SOM} or C{SO}
"""
# import the helper functions
import hlr_utils
# Kickout if monitor object is None
if obj1 is None:
return obj1
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(obj1, obj2)
(o1_descr, o2_descr) = hlr_utils.get_descr(obj1, obj2)
# error checking for types
if o1_descr == "SOM" and o2_descr == "SO":
raise TypeError, "SOM-SO operation not supported"
elif o1_descr == "SO" and o2_descr == "SOM":
raise TypeError("SO-SOM operation not supported")
# Have the right object combination, go on
else:
pass
# Check for keywords
try:
prefix = kwargs["prefix"]
except KeyError:
prefix = "m"
try:
use_pix_id = kwargs["use_pix_id"]
except KeyError:
use_pix_id = False
try:
rtype = kwargs["rtype"]
except KeyError:
rtype = None
# Set the name of the rebinning function
rebin_function_name = "rebin_axis_1D"
if rtype is not None:
rebin_function_name += "_" + str(rtype)
# Get function pointer
import common_lib
rebin_function = common_lib.__getattribute__(rebin_function_name)
result = hlr_utils.copy_som_attr(result, res_descr, obj1, o1_descr)
# iterate through the values
val1 = hlr_utils.get_value(obj1, 0, o1_descr, "all")
# Cache length
len_obj2 = hlr_utils.get_length(obj2)
for i in xrange(len_obj2):
val2 = hlr_utils.get_value(obj2, i, o2_descr, "x")
value = rebin_function(val1, val2)
if use_pix_id:
# Set the pixel ID to the spectrum with modified bank ID
try:
value.id = (prefix + obj2[i].id[0], (obj2[i].id[1][0],
obj2[i].id[1][1]))
except TypeError:
value.id = prefix + str(obj2[i].id)
hlr_utils.result_insert(result, res_descr, value, None, "all")
return result
def subtract_axis_dep_bkg(obj, coeffs, **kwargs):
"""
This function takes spectrum object(s) and a set of coefficients and
subtracts an axis dependent background based on a polynomial. The order
of the polynomial is based on the number of coefficients provided.
@param obj: Object from which to subtract the individual background numbers
@type obj: C{SOM.SOM} or C{SOM.SO}
@param coeffs: The set of coefficients for the polynomial representation
of the background to be subtracted.
@type coeffs: C{list} of C{floats}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword old_scale: The scale factor used to obtain the coefficients used
in this function.
@type old_scale: C{float}
@keyword new_scale: The scale factor for the current data set from which
the axis dependent background will be subtracted from.
@type new_scale: C{float}
@return: Object with the axis dependent background subtracted
@rtype: C{SOM.SOM} or C{SOM.SO}
@raise TypeError: The first argument is not a C{SOM} or C{SO}
"""
# Kickout is coeffs is None, or length is zero
if coeffs is None:
return obj
poly_len = len(coeffs)
if poly_len == 0:
return obj
# Check for keywords
old_scale = kwargs.get("old_scale", 1.0)
new_scale = kwargs.get("new_scale", 1.0)
# Reverse coefficients for __eval_poly function
coeffs.reverse()
# import the helper functions
import hlr_utils
o_descr = hlr_utils.get_descr(obj)
if o_descr != "SOM" and o_descr != "SO":
raise TypeError("Incoming object must be a SOM or a SO")
# Have a SOM or SO
else:
pass
(result, res_descr) = hlr_utils.empty_result(obj)
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
obj_len = hlr_utils.get_length(obj)
import utils
# iterate through the values
for i in xrange(obj_len):
axis = hlr_utils.get_value(obj, i, o_descr, "x", 0)
val = hlr_utils.get_value(obj, i, o_descr, "y")
err2 = hlr_utils.get_err2 (obj, i, o_descr, "y")
map_so = hlr_utils.get_map_so(obj, None, i)
len_val = len(val)
new_scale_p = new_scale / len_val
ratio = old_scale / new_scale_p
axis_centers = utils.calc_bin_centers(axis)
for j in xrange(len(val)):
val[j] -= (ratio * __eval_poly(axis_centers[0][j], coeffs,
poly_len))
value = (val, err2)
hlr_utils.result_insert(result, res_descr, value, map_so, "y")
return result
def initial_wavelength_igs_lin_time_zero_to_tof(obj, **kwargs):
"""
This function converts a primary axis of a C{SOM} or C{SO} from
initial_wavelength_igs_lin_time_zero to time-of-flight. The
initial_wavelength_igs_lin_time_zero axis for a C{SOM} must be in units of
I{Angstroms}. The primary axis of a C{SO} is assumed to be in units of
I{Angstroms}. A C{tuple} of C{(initial_wavelength_igs_lin_time_zero,
initial_wavelength_igs_lin_time_zero_err2)} (assumed to be in units of
I{Angstroms}) can be converted to C{(tof, tof_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 lambda_f:The final wavelength and its associated error^2
@type lambda_f: C{tuple}
@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 dist_source_sample: The source to sample distance information and
its associated error^2
@type dist_source_sample: C{tuple} or C{list} of C{tuple}s
@keyword dist_sample_detector: The sample to detector distance information
and its associated error^2
@type dist_sample_detector: C{tuple} or C{list} of C{tuple}s
@keyword lojac: A flag that allows one to turn off the calculation of the
linear-order Jacobian. The default action is True for
histogram data.
@type lojac: C{boolean}
@keyword units: The expected units for this function. The default for this
function is I{Angstroms}
@type units: C{string}
@return: Object with a primary axis in initial_wavelength_igs converted to
time-of-flight
@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{Angstroms}
"""
# 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:
lambda_f = kwargs["lambda_f"]
except KeyError:
lambda_f = 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:
dist_source_sample = kwargs["dist_source_sample"]
except KeyError:
dist_source_sample = None
try:
dist_sample_detector = kwargs["dist_sample_detector"]
except KeyError:
dist_sample_detector = None
try:
lojac = kwargs["lojac"]
except KeyError:
lojac = hlr_utils.check_lojac(obj)
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)
if res_descr == "SOM":
result = hlr_utils.force_units(result, "Microseconds", axis)
result.setAxisLabel(axis, "time-of-flight")
result.setYUnits("Counts/uS")
result.setYLabel("Intensity")
else:
pass
# Where to get instrument information
if dist_source_sample is None or dist_sample_detector is None:
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:
if dist_source_sample is None and dist_sample_detector is None:
raise RuntimeError("If a SOM is not passed, the "\
+"source-sample and sample-detector "\
+"distances must be provided.")
elif dist_source_sample is None:
raise RuntimeError("If a SOM is not passed, the "\
+"source-sample distance must be provided.")
elif dist_sample_detector is None:
raise RuntimeError("If a SOM is not passed, the "\
+"sample-detector distance must be "\
+"provided.")
else:
raise RuntimeError("If you get here, see Steve Miller for "\
+"your mug.")
else:
pass
if lambda_f is not None:
l_descr = hlr_utils.get_descr(lambda_f)
else:
if o_descr == "SOM":
try:
som_l_f = obj.attr_list["Wavelength_final"]
except KeyError:
raise RuntimeError("Please provide a final wavelength "\
+"parameter either via the function call "\
+"or the SOM")
else:
raise RuntimeError("You need to provide a final wavelength")
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]
if dist_source_sample is not None:
ls_descr = hlr_utils.get_descr(dist_source_sample)
# Do nothing, go on
else:
pass
if dist_sample_detector is not None:
ld_descr = hlr_utils.get_descr(dist_sample_detector)
# Do nothing, go on
else:
pass
# iterate through the values
len_obj = hlr_utils.get_length(obj)
MNEUT_OVER_H = 1.0 / 0.003956034
MNEUT_OVER_H2 = MNEUT_OVER_H * MNEUT_OVER_H
for i in xrange(len_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 dist_source_sample is None:
(L_s, L_s_err2) = hlr_utils.get_parameter("primary", map_so, inst)
else:
L_s = hlr_utils.get_value(dist_source_sample, i, ls_descr)
L_s_err2 = hlr_utils.get_err2(dist_source_sample, i, ls_descr)
if dist_sample_detector is None:
(L_d, L_d_err2) = hlr_utils.get_parameter("secondary", map_so,
inst)
else:
L_d = hlr_utils.get_value(dist_sample_detector, i, ld_descr)
L_d_err2 = hlr_utils.get_err2(dist_sample_detector, i, ld_descr)
if lambda_f is not None:
l_f = hlr_utils.get_value(lambda_f, i, l_descr)
l_f_err2 = hlr_utils.get_err2(lambda_f, i, l_descr)
else:
l_f_tuple = hlr_utils.get_special(som_l_f, map_so)
l_f = l_f_tuple[0]
l_f_err2 = l_f_tuple[1]
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
# Going to violate rules since the current usage is with a single
# number. When an SCL equivalent function arises, this code can be
# fixed.
front_const = MNEUT_OVER_H * L_s + t_0_slope
term2 = MNEUT_OVER_H * l_f * L_d
tof = (front_const * val) + term2 + t_0_offset
front_const2 = front_const * front_const
eterm1 = l_f * l_f * L_d_err2
eterm2 = L_d * L_d * l_f_err2
eterm3 = MNEUT_OVER_H2 * L_s_err2
tof_err2 = (front_const2 * err2) + (val * val) * \
(eterm3 + t_0_slope_err2) + (MNEUT_OVER_H2 * \
(eterm1 + eterm2)) + \
t_0_offset_err2
hlr_utils.result_insert(result, res_descr, (tof, tof_err2), None,
"all")
return result
def zero_bins(obj, z_bins):
"""
This function takes spectra and a set of bins and zeros the values in each
spectrum at the bin location.
@param obj: The object containing the spectra to be zeroed
@type obj: C{SOM.SOM}
@param z_bins: The set of bins from a given spectrum that will be zeroed
@type z_bins: C{list} of C{int}s
@return: Object containing the spectra with zeroed bins
@rtype: C{SOM.SOM}
@raise TypeError: The first argument is not a C{SOM} or C{SO}
"""
# Kickout if there are no bins to zero
if z_bins 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)
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 = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
# iterate through the values
import nessi_list
for i in xrange(hlr_utils.get_length(obj)):
map_so = hlr_utils.get_map_so(obj, None, i)
# Get information from SOM
y_val = hlr_utils.get_value(obj, i, o_descr, "y")
y_err2 = hlr_utils.get_err2(obj, i, o_descr, "y")
y_new = nessi_list.NessiList()
var_y_new = nessi_list.NessiList()
for j in xrange(len(y_val)):
if j in z_bins:
y_new.append(0.0)
var_y_new.append(0.0)
else:
y_new.append(y_val[j])
var_y_new.append(y_err2[j])
hlr_utils.result_insert(result, res_descr, (y_new, var_y_new),
map_so, "y")
return result
def sumw_ncerr(obj1, obj2, **kwargs):
"""
This function sums by weighting errors of two objects (C{SOM} or C{SO}) and
returns the result of that action in an C{SOM}. The function does not
handle the cases of C{SOM}+C{tuple}, C{SO}+C{tuple} or C{tuple}+C{tuple}.
@param obj1: First object in the weighted sum
@type obj1: C{SOM.SOM} or C{SOM.SO} or C{tuple}
@param obj2: Second object in the the weighted sum
@type obj2: C{SOM.SOM} or C{SOM.SO} or C{tuple}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword axis: This is the axis one wishes to manipulate. If no argument
is given the default value is y
@type axis: C{string}=<y or x>
@keyword axis_pos: This is position of the axis in the axis array. If no
argument is given, the default value is 0
@type axis_pos: C{int}
@keyword length_one_som: This is a flag that lets the function know it is
dealing with a length 1 C{SOM} so that attributes
may be passed along. The length 1 C{SOM} will be
turned into a C{SO}. The default value is False.
@type length_one_som: C{boolean}
@keyword length_one_som_pos: This is the argument position of the
length 1 C{SOM} since the check is done
before the arguments are swapped. The default
value is 2.
@type length_one_som_pos: C{int}=<1 or 2>
@return: Object containing the results of the addition
@rtype: C{SOM.SOM} or C{SOM.SO}
@raise TypeError: The C{SOM}+C{tuple}, C{SO}+C{tuple} or C{tuple}+C{tuple}
cases are presented to the function
@raise IndexError: The two C{SOM}s do not contain the same number of
spectra
@raise RunTimeError: The x-axis units of the C{SOM}s do not match
@raise RunTimeError: The y-axis units of the C{SOM}s do not match
@raise RunTimeError: The x-axes of the two C{SO}s are not equal
"""
# import the helper functions
import hlr_utils
# Check to see if we are working with a length 1 SOM
try:
length_one_som = kwargs["length_one_som"]
except KeyError:
length_one_som = False
try:
length_one_som_pos = kwargs["length_one_som_pos"]
if length_one_som_pos != 1 or length_one_som_pos != 2:
raise RuntimeError("length_one_som_pos must be either 1 or 2 and "\
+"%d" % length_one_som_pos)
except KeyError:
length_one_som_pos = 2
if length_one_som:
if length_one_som_pos == 1:
som_copy = obj1
obj1 = obj1[0]
else:
som_copy = obj2
obj2 = obj2[0]
else:
# Not working with a length 1 SOM, do nothing
pass
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(obj1, obj2)
(o1_descr, o2_descr) = hlr_utils.get_descr(obj1, obj2)
# error check information
if o1_descr == "number" or o2_descr == "number":
raise RuntimeError("Operations with tuples are not supported!")
elif o2_descr == "SOM" and o1_descr == "SO":
(obj1, obj2) = hlr_utils.swap_args(obj1, obj2)
(o1_descr, o2_descr) = hlr_utils.swap_args(o1_descr, o2_descr)
elif o2_descr == "SOM" and o1_descr == "SOM":
hlr_utils.math_compatible(obj1, o1_descr, obj2, o2_descr)
else:
pass
# Check for axis keyword argument
try:
axis = kwargs["axis"]
except KeyError:
axis = "y"
# Check for axis_pos keyword argument
try:
axis_pos = kwargs["axis_pos"]
except KeyError:
axis_pos = 0
if length_one_som:
if length_one_som_pos == 1:
result = hlr_utils.copy_som_attr(result, res_descr,
som_copy, "SOM",
obj2, o2_descr)
else:
result = hlr_utils.copy_som_attr(result, res_descr, obj1, o1_descr,
som_copy, "SOM")
else:
result = hlr_utils.copy_som_attr(result, res_descr, obj1, o1_descr,
obj2, o2_descr)
# iterate through the values
import array_manip
for i in xrange(hlr_utils.get_length(obj1, obj2)):
val1 = hlr_utils.get_value(obj1, i, o1_descr, axis, axis_pos)
err2_1 = hlr_utils.get_err2(obj1, i, o1_descr, axis, axis_pos)
val2 = hlr_utils.get_value(obj2, i, o2_descr, axis, axis_pos)
err2_2 = hlr_utils.get_err2(obj2, i, o2_descr, axis, axis_pos)
(descr_1, descr_2) = hlr_utils.get_descr(val1, val2)
hlr_utils.math_compatible(val1, descr_1, val2, descr_2)
value = array_manip.sumw_ncerr(val1, err2_1, val2, err2_2)
map_so = hlr_utils.get_map_so(obj1, None, i)
hlr_utils.result_insert(result, res_descr, value, map_so, axis,
axis_pos)
return result
def rebin_axis_1D_frac(obj, axis_out):
"""
This function rebins the primary axis for a C{SOM} or a C{SO} based on the
given C{NessiList} axis.
@param obj: Object to be rebinned
@type obj: C{SOM.SOM} or C{SOM.SO}
@param axis_out: The axis to rebin the C{SOM} or C{SO} to
@type axis_out: C{NessiList}
@return: Two objects that have been rebinned according to the provided
axis. The first object contains the rebinned counts and the second
contains the fractional area.
@rtype: C{tuple} of two C{SOM.SOM}s or C{SOM.SO}s
@raise TypeError: The rebinning axis given is not a C{NessiList}
@raise TypeError: The object being rebinned is not a C{SOM} or a C{SO}
"""
# import the helper functions
import hlr_utils
# set up for working through data
try:
axis_out.__type__
except AttributeError:
raise TypeError("Rebinning axis must be a NessiList!")
o_descr = hlr_utils.get_descr(obj)
if o_descr == "number" or o_descr == "list":
raise TypeError("Do not know how to handle given type: %s" % \
o_descr)
else:
pass
(result1, res1_descr) = hlr_utils.empty_result(obj)
result1 = hlr_utils.copy_som_attr(result1, res1_descr, obj, o_descr)
(result2, res2_descr) = hlr_utils.empty_result(obj)
result2 = hlr_utils.copy_som_attr(result2, res2_descr, obj, o_descr)
# iterate through the values
import axis_manip
len_obj = hlr_utils.get_length(obj)
for i in xrange(len_obj):
axis_in = hlr_utils.get_value(obj, i, o_descr, "x", 0)
val = hlr_utils.get_value(obj, i, o_descr)
err2 = hlr_utils.get_err2(obj, i, o_descr)
value = axis_manip.rebin_axis_1D_frac(axis_in, val, err2, axis_out)
frac_err = nessi_list.NessiList(len(value[2]))
xvals = []
xvals.append(axis_out)
map_so = hlr_utils.get_map_so(obj, None, i)
hlr_utils.result_insert(result1, res1_descr, (value[0], value[1]),
map_so, "all", 0, xvals)
hlr_utils.result_insert(result2, res2_descr, (value[2], frac_err),
map_so, "all", 0, xvals)
return (result1, result2)
def init_scatt_wavevector_to_scalar_Q(initk, scattk, **kwargs):
"""
This function takes an initial wavevector and a scattered wavevector as a
C{tuple} and a C{SOM}, a C{tuple} and a C{SO} or two C{tuple}s and
calculates the quantity scalar Q units of I{1/Angstroms}. The C{SOM}
principle axis must be in units of I{1/Angstroms}. The C{SO}s and
C{tuple}(s) is(are) assumed to be in units of I{1/Angstroms}. The polar
angle must be provided if one of the initial arguments is not a C{SOM}. If
a C{SOM} is passed, by providing the polar angle at the function call time,
the polar angle carried in the C{SOM} instrument will be overridden.
@param initk: Object holding the initial wavevector
@type initk: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@param scattk: Object holding the scattered wavevector
@type scattk: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword polar: The polar angle and its associated error^2
@type polar: C{tuple} or C{list} of C{tuple}s
@keyword units: The expected units for this function. The default for this
function is I{1/Angstroms}.
@type units: C{string}
@return: Object converted to scalar Q
@rtype: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@raise TypeError: The C{SOM}-C{SOM} operation is attempted
@raise TypeError: The C{SOM}-C{SO} operation is attempted
@raise TypeError: The C{SO}-C{SOM} operation is attempted
@raise TypeError: The C{SO}-C{SO} operation is attempted
@raise RuntimeError: The C{SOM} x-axis units are not I{1/Angstroms}
@raise RuntimeError: A C{SOM} is not passed and no polar angle is provided
@raise RuntimeError: No C{SOM.Instrument} is provided in a C{SOM}
"""
# import the helper functions
import hlr_utils
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(initk, scattk)
(i_descr, s_descr) = hlr_utils.get_descr(initk, scattk)
# error checking for types
if i_descr == "SOM" and s_descr == "SOM":
raise TypeError("SOM-SOM operation not supported")
elif i_descr == "SOM" and s_descr == "SO":
raise TypeError("SOM-SO operation not supported")
elif i_descr == "SO" and s_descr == "SOM":
raise TypeError("SO-SOM operation not supported")
elif i_descr == "SO" and s_descr == "SO":
raise TypeError("SO-SO operation not supported")
else:
pass
# Setup keyword arguments
try:
polar = kwargs["polar"]
except KeyError:
polar = None
try:
units = kwargs["units"]
except KeyError:
units = "1/Angstroms"
result = hlr_utils.copy_som_attr(result, res_descr, initk, i_descr,
scattk, s_descr)
if res_descr == "SOM":
index = hlr_utils.one_d_units(result, units)
result = hlr_utils.force_units(result, units, index)
result.setAxisLabel(index, "scalar wavevector transfer")
result.setYUnits("Counts/A-1")
result.setYLabel("Intensity")
else:
pass
if polar is None:
if i_descr == "SOM":
try:
initk.attr_list.instrument.get_primary()
inst = initk.attr_list.instrument
except RuntimeError:
raise RuntimeError("A detector was not provided!")
elif s_descr == "SOM":
try:
scattk.attr_list.instrument.get_primary()
inst = scattk.attr_list.instrument
except RuntimeError:
raise RuntimeError("A detector was not provided!")
else:
raise RuntimeError("If no SOM is provided, then polar "\
+"information must be given.")
else:
p_descr = hlr_utils.get_descr(polar)
# iterate through the values
import axis_manip
for i in xrange(hlr_utils.get_length(initk, scattk)):
val1 = hlr_utils.get_value(initk, i, i_descr, "x")
err2_1 = hlr_utils.get_err2(initk, i, i_descr, "x")
val2 = hlr_utils.get_value(scattk, i, s_descr, "x")
err2_2 = hlr_utils.get_err2(scattk, i, s_descr, "x")
map_so = hlr_utils.get_map_so(initk, scattk, i)
if polar is None:
(angle, angle_err2) = hlr_utils.get_parameter("polar", map_so,
inst)
else:
angle = hlr_utils.get_value(polar, i, p_descr)
angle_err2 = hlr_utils.get_err2(polar, i, p_descr)
value = axis_manip.init_scatt_wavevector_to_scalar_Q(val1, err2_1,
val2, err2_2,
angle, angle_err2)
hlr_utils.result_insert(result, res_descr, value, map_so, "x")
return result
def rebin_efficiency(obj1, obj2, **kwargs):
"""
This function takes two objects and rebins the data for obj1 onto the axis
provided by obj2. The units on the x-axes needs to be I{Angstroms}, since
this is what the efficiencies will be present as.
@param obj1: Object that will be rebinned
@type obj1: C{SOM.SOM} or C{SOM.SO}
@param obj2: Object that will provide the axis for rebinning
@type obj2: C{SOM.SOM} or C{SOM.SO}
@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: Object that has been rebinned
@rtype: C{SOM.SOM} or C{SOM.SO}
@raise TypeError: The C{SOM}-C{SO} operation is attempted
@raise TypeError: The C{SO}-C{SOM} operation is attempted
@raise TypeError: obj1 not a C{SOM} or C{SO}
@raise TypeError: obj2 not a C{SOM} or C{SO}
@raise IndexError: The C{SOM}s do not have the same number of C{SO}s
@raise RuntimeError: The C{SOM} x-axis units are not I{Angstroms}
@raise RuntimeError: The x-axis units of the C{SOM}s do not match
"""
# import the helper functions
import hlr_utils
# Kickout if monitor object is None
if obj1 is None:
return obj1
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(obj1, obj2)
(o1_descr, o2_descr) = hlr_utils.get_descr(obj1, obj2)
# error checking for types
if o1_descr == "SOM" and o2_descr == "SO":
raise TypeError, "SOM-SO operation not supported"
elif o1_descr == "SO" and o2_descr == "SOM":
raise TypeError("SO-SOM operation not supported")
# Have the right object combination, go on
else:
pass
# Setup keyword arguments
try:
units = kwargs["units"]
except KeyError:
units = "Angstroms"
if o1_descr == "SOM" and o2_descr == "SOM":
hlr_utils.math_compatible(obj1, o1_descr, obj2, o2_descr)
# If both objects are not SOMs, do nothing
else:
pass
result = hlr_utils.copy_som_attr(result, res_descr, obj2, o2_descr)
if res_descr == "SOM":
result = hlr_utils.force_units(result, units)
# Can't force units on anything other than a SOM
else:
pass
# iterate through the values
import common_lib
for i in xrange(hlr_utils.get_length(obj1, obj2)):
val1 = hlr_utils.get_value(obj1, i, o1_descr, "all")
val2 = hlr_utils.get_value(obj2, i, o2_descr, "x")
value = common_lib.rebin_axis_1D(val1, val2)
hlr_utils.result_insert(result, res_descr, value, None, "all")
return result
def wavelength_to_scalar_k(obj, **kwargs):
"""
This function converts a primary axis of a C{SOM} or C{SO} from wavelength
to scalar k. The wavelength axis for a C{SOM} must be in units of
I{Angstroms}. The primary axis of a C{SO} is assumed to be in units of
I{Angstroms}. A C{tuple} of C{(wavelength, wavelength_err2)} (assumed to
be in units of I{Angstroms}) can be converted to C{(scalar_k,
scalar_k_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 units: The expected units for this function. The default for this
function is I{Angstroms}.
@type units: C{string}
@return: Object with a primary axis in wavelength converted to scalar k
@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{Angstroms}
"""
# 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)
if o_descr == "list":
raise TypeError("Do not know how to handle given type: %s" % \
o_descr)
else:
pass
# 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)
if res_descr == "SOM":
result = hlr_utils.force_units(result, "1/Angstroms", axis)
result.setAxisLabel(axis, "scalar wavevector")
result.setYUnits("Counts/A-1")
result.setYLabel("Intensity")
else:
pass
# iterate through the values
import axis_manip
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)
value = axis_manip.wavelength_to_scalar_k(val, err2)
if o_descr != "number":
value1 = axis_manip.reverse_array_cp(value[0])
value2 = axis_manip.reverse_array_cp(value[1])
rev_value = (value1, value2)
else:
rev_value = value
map_so = hlr_utils.get_map_so(obj, None, i)
if map_so is not None:
map_so.y = axis_manip.reverse_array_cp(map_so.y)
map_so.var_y = axis_manip.reverse_array_cp(map_so.var_y)
else:
pass
hlr_utils.result_insert(result, res_descr, rev_value, map_so, "x",
axis)
return result
def create_axis_from_data(obj, **kwargs):
"""
This function inspects the independent axis of the data searching for the
minimum and maximum axis values. It also determines the bin width from
the first bin in the axis and searches for the smallest value. From these
values, an Axis object is created. This function assusmes that the axis is
sorted in ascending order.
@param obj: The object containing the data with the indpendent axis to be
searched.
@type obj: C{SOM.SOM} or C{SOM.SO}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword axis_pos: This is position of the axis in the axis array. If no
argument is given, the default value is 0
@type axis_pos: C{int}
@keyword width: A override parameter for specifying the axis bin width.
@type width: C{float}
@return: The axis based on the found values from the data
@rtype: L{hlr_utils.Axis}
@raise TypeError: The incoming object is not a C{SOM} or a C{SO}
"""
import hlr_utils
# Check for keyword arguments
try:
axis_pos = kwargs["axis_pos"]
except KeyError:
axis_pos = 0
try:
axis_width = kwargs["width"]
width_given = True
except KeyError:
axis_width = 999999999999999999999999999999999999.0
width_given = False
o_descr = hlr_utils.get_descr(obj)
if o_descr == "number" or o_descr == "list":
raise TypeError("Function argument must be a SOM or a SO")
# Have a SOM or a SO, go on
else:
pass
# Set extreme values for axis minimum and maximum
axis_min = 999999999999999999999999999999999999
axis_max = -99999999999999999999999999999999999
# iterate through the values
for i in xrange(hlr_utils.get_length(obj)):
axis = hlr_utils.get_value(obj, i, o_descr, "x", axis_pos)
min_index = 0
max_index = -1
index_ok = False
while not index_ok:
value = axis[min_index]
if value == float('-inf'):
min_index += 1
else:
index_ok = True
axis_min = min(axis_min, axis[min_index])
index_ok = False
while not index_ok:
value = axis[max_index]
if value == float('inf'):
max_index -= 1
else:
index_ok = True
axis_max = max(axis_max, axis[max_index])
if not width_given:
test_width = axis[min_index + 1] - axis[min_index]
axis_width = min(axis_width, test_width)
return hlr_utils.Axis(axis_min, axis_max, axis_width)
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 zero_bins(obj, z_bins):
"""
This function takes spectra and a set of bins and zeros the values in each
spectrum at the bin location.
@param obj: The object containing the spectra to be zeroed
@type obj: C{SOM.SOM}
@param z_bins: The set of bins from a given spectrum that will be zeroed
@type z_bins: C{list} of C{int}s
@return: Object containing the spectra with zeroed bins
@rtype: C{SOM.SOM}
@raise TypeError: The first argument is not a C{SOM} or C{SO}
"""
# Kickout if there are no bins to zero
if z_bins 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)
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 = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
# iterate through the values
import nessi_list
for i in xrange(hlr_utils.get_length(obj)):
map_so = hlr_utils.get_map_so(obj, None, i)
# Get information from SOM
y_val = hlr_utils.get_value(obj, i, o_descr, "y")
y_err2 = hlr_utils.get_err2(obj, i, o_descr, "y")
y_new = nessi_list.NessiList()
var_y_new = nessi_list.NessiList()
for j in xrange(len(y_val)):
if j in z_bins:
y_new.append(0.0)
var_y_new.append(0.0)
else:
y_new.append(y_val[j])
var_y_new.append(y_err2[j])
hlr_utils.result_insert(result, res_descr, (y_new, var_y_new), map_so,
"y")
return result
def rebin_monitor(obj1, obj2, **kwargs):
"""
This function takes objects (1st is the monitor, 2nd is the detector data)
and rebins the data for obj1 onto the axis provided by obj2. The pixel ID
can be transferred as the pixel ID of the rebinned monitor histogram. A
prefix is placed on the bank ID to dilineate that this is a rebinned
monitor spectrum for that pixel.
@param obj1: Monitor object that will be rebinned
@type obj1: C{SOM.SOM} or C{SOM.SO}
@param obj2: Detector data object that will provide the axis for rebinning
@type obj2: C{SOM.SOM} or C{SOM.SO}
@param kwargs: A list of keyword arguments that the function accepts
@keyword use_pix_id: A flag that specifies if the pixel ID is to be used
in the rebinned monitor spectrum. The default is I{False}.
@type use_pix_id: C{boolean}
@keyword prefix: Allows one to specify a prefix for the bank ID. The
default is I{m}.
@type prefix: C{string}
@keyword rtype: A short string that defines the rebinning function to use.
The default is I{None} which uses
L{common_lib.rebin_axis_1D()}. The other possibilities are
I{linint} which uses L{common_lib.rebin_axis_1D_linint()}
and I{frac} which uses L{common_lib.rebin_axis_1D_frac()}.
@type rtype: C{boolean}
@return: Object that has been rebinned
@rtype: C{SOM.SOM} or C{SOM.SO}
@raise TypeError: The C{SOM}-C{SO} operation is attempted
@raise TypeError: The C{SO}-C{SOM} operation is attempted
@raise TypeError: obj1 not a C{SOM} or C{SO}
@raise TypeError: obj2 not a C{SOM} or C{SO}
"""
# import the helper functions
import hlr_utils
# Kickout if monitor object is None
if obj1 is None:
return obj1
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(obj1, obj2)
(o1_descr, o2_descr) = hlr_utils.get_descr(obj1, obj2)
# error checking for types
if o1_descr == "SOM" and o2_descr == "SO":
raise TypeError, "SOM-SO operation not supported"
elif o1_descr == "SO" and o2_descr == "SOM":
raise TypeError("SO-SOM operation not supported")
# Have the right object combination, go on
else:
pass
# Check for keywords
try:
prefix = kwargs["prefix"]
except KeyError:
prefix = "m"
try:
use_pix_id = kwargs["use_pix_id"]
except KeyError:
use_pix_id = False
try:
rtype = kwargs["rtype"]
except KeyError:
rtype = None
# Set the name of the rebinning function
rebin_function_name = "rebin_axis_1D"
if rtype is not None:
rebin_function_name += "_" + str(rtype)
# Get function pointer
import common_lib
rebin_function = common_lib.__getattribute__(rebin_function_name)
result = hlr_utils.copy_som_attr(result, res_descr, obj1, o1_descr)
# iterate through the values
val1 = hlr_utils.get_value(obj1, 0, o1_descr, "all")
# Cache length
len_obj2 = hlr_utils.get_length(obj2)
for i in xrange(len_obj2):
val2 = hlr_utils.get_value(obj2, i, o2_descr, "x")
value = rebin_function(val1, val2)
if use_pix_id:
# Set the pixel ID to the spectrum with modified bank ID
try:
value.id = (prefix+obj2[i].id[0],
(obj2[i].id[1][0], obj2[i].id[1][1]))
except TypeError:
value.id = prefix+str(obj2[i].id)
hlr_utils.result_insert(result, res_descr, value, None, "all")
return result
def create_E_vs_Q_igs(som, *args, **kwargs):
"""
This function starts with the initial IGS wavelength axis and turns this
into a 2D spectra with E and Q axes.
@param som: The input object with initial IGS wavelength axis
@type som: C{SOM.SOM}
@param args: A mandatory list of axes for rebinning. There is a particular
order to them. They should be present in the following order:
Without errors
1. Energy transfer
2. Momentum transfer
With errors
1. Energy transfer
2. Energy transfer error^2
3. Momentum transfer
4. Momentum transfer error ^2
@type args: C{nessi_list.NessiList}s
@param kwargs: A list of keyword arguments that the function accepts:
@keyword withXVar: Flag for whether the function should be expecting the
associated axes to have errors. The default value will
be I{False}.
@type withXVar: C{boolean}
@keyword data_type: 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 Q_filter: Flag to turn on or off Q filtering. The default behavior
is I{True}.
@type Q_filter: 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 y axis label
@type y_label: C{string}
@keyword y_units: The y axis units
@type y_units: C{string}
@keyword x_labels: This is a list of names that sets the individual x axis
labels
@type x_labels: C{list} of C{string}s
@keyword x_units: This is a list of names that sets the individual x axis
units
@type x_units: C{list} of C{string}s
@keyword split: This flag causes the counts and the fractional area to
be written out into separate files.
@type split: C{boolean}
@keyword configure: This is the object containing the driver configuration.
@type configure: C{Configure}
@return: Object containing a 2D C{SO} with E and Q axes
@rtype: C{SOM.SOM}
@raise RuntimeError: Anything other than a C{SOM} is passed to the function
@raise RuntimeError: An instrument is not contained in the C{SOM}
"""
import nessi_list
# Setup some variables
dim = 2
N_y = []
N_tot = 1
N_args = len(args)
# Get T0 slope in order to calculate dT = dT_i + dT_0
try:
t_0_slope = som.attr_list["Time_zero_slope"][0]
t_0_slope_err2 = som.attr_list["Time_zero_slope"][1]
except KeyError:
t_0_slope = float(0.0)
t_0_slope_err2 = float(0.0)
# Check withXVar keyword argument and also check number of given args.
# Set xvar to the appropriate value
try:
value = kwargs["withXVar"]
if value.lower() == "true":
if N_args != 4:
raise RuntimeError("Since you have requested x errors, 4 x "\
+"axes must be provided.")
else:
xvar = True
elif value.lower() == "false":
if N_args != 2:
raise RuntimeError("Since you did not requested x errors, 2 "\
+"x axes must be provided.")
else:
xvar = False
else:
raise RuntimeError("Do not understand given parameter %s" % \
value)
except KeyError:
if N_args != 2:
raise RuntimeError("Since you did not requested x errors, 2 "\
+"x axes must be provided.")
else:
xvar = 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
try:
Q_filter = kwargs["Q_filter"]
except KeyError:
Q_filter = True
# Check for split keyword
try:
split = kwargs["split"]
except KeyError:
split = False
# Check for configure keyword
try:
configure = kwargs["configure"]
except KeyError:
configure = None
so_dim = SOM.SO(dim)
for i in range(dim):
# Set the x-axis arguments from the *args list into the new SO
if not xvar:
# Axis positions are 1 (Q) and 0 (E)
position = dim - i - 1
so_dim.axis[i].val = args[position]
else:
# Axis positions are 2 (Q), 3 (eQ), 0 (E), 1 (eE)
position = dim - 2 * i
so_dim.axis[i].val = args[position]
so_dim.axis[i].var = args[position + 1]
# Set individual value axis sizes (not x-axis size)
N_y.append(len(args[position]) - offset)
# Calculate total 2D array size
N_tot = N_tot * N_y[-1]
# Create y and var_y lists from total 2D size
so_dim.y = nessi_list.NessiList(N_tot)
so_dim.var_y = nessi_list.NessiList(N_tot)
# Create area sum and errors for the area sum lists from total 2D size
area_sum = nessi_list.NessiList(N_tot)
area_sum_err2 = nessi_list.NessiList(N_tot)
# Create area sum and errors for the area sum lists from total 2D size
bin_count = nessi_list.NessiList(N_tot)
bin_count_err2 = nessi_list.NessiList(N_tot)
inst = som.attr_list.instrument
lambda_final = som.attr_list["Wavelength_final"]
inst_name = inst.get_name()
import bisect
import math
import dr_lib
import utils
arr_len = 0
#: Vector of zeros for function calculations
zero_vec = None
for j in xrange(hlr_utils.get_length(som)):
# Get counts
counts = hlr_utils.get_value(som, j, "SOM", "y")
counts_err2 = hlr_utils.get_err2(som, j, "SOM", "y")
arr_len = len(counts)
zero_vec = nessi_list.NessiList(arr_len)
# Get mapping SO
map_so = hlr_utils.get_map_so(som, None, j)
# Get lambda_i
l_i = hlr_utils.get_value(som, j, "SOM", "x")
l_i_err2 = hlr_utils.get_err2(som, j, "SOM", "x")
# Get lambda_f from instrument information
l_f_tuple = hlr_utils.get_special(lambda_final, map_so)
l_f = l_f_tuple[0]
l_f_err2 = l_f_tuple[1]
# Get source to sample distance
(L_s, L_s_err2) = hlr_utils.get_parameter("primary", map_so, inst)
# Get sample to detector distance
L_d_tuple = hlr_utils.get_parameter("secondary", map_so, inst)
L_d = L_d_tuple[0]
# Get polar angle from instrument information
(angle, angle_err2) = hlr_utils.get_parameter("polar", map_so, inst)
# Get the detector pixel height
dh_tuple = hlr_utils.get_parameter("dh", map_so, inst)
dh = dh_tuple[0]
# Need dh in units of Angstrom
dh *= 1e10
# Calculate T_i
(T_i, T_i_err2) = axis_manip.wavelength_to_tof(l_i, l_i_err2,
L_s, L_s_err2)
# Scale counts by lambda_f / lambda_i
(l_i_bc, l_i_bc_err2) = utils.calc_bin_centers(l_i, l_i_err2)
(ratio, ratio_err2) = array_manip.div_ncerr(l_f, l_f_err2,
l_i_bc, l_i_bc_err2)
(counts, counts_err2) = array_manip.mult_ncerr(counts, counts_err2,
ratio, ratio_err2)
# Calculate E_i
(E_i, E_i_err2) = axis_manip.wavelength_to_energy(l_i, l_i_err2)
# Calculate E_f
(E_f, E_f_err2) = axis_manip.wavelength_to_energy(l_f, l_f_err2)
# Calculate E_t
(E_t, E_t_err2) = array_manip.sub_ncerr(E_i, E_i_err2, E_f, E_f_err2)
if inst_name == "BSS":
# Convert E_t from meV to ueV
(E_t, E_t_err2) = array_manip.mult_ncerr(E_t, E_t_err2,
1000.0, 0.0)
(counts, counts_err2) = array_manip.mult_ncerr(counts, counts_err2,
1.0/1000.0, 0.0)
# Convert lambda_i to k_i
(k_i, k_i_err2) = axis_manip.wavelength_to_scalar_k(l_i, l_i_err2)
# Convert lambda_f to k_f
(k_f, k_f_err2) = axis_manip.wavelength_to_scalar_k(l_f, l_f_err2)
# Convert k_i and k_f to Q
(Q, Q_err2) = axis_manip.init_scatt_wavevector_to_scalar_Q(k_i,
k_i_err2,
k_f,
k_f_err2,
angle,
angle_err2)
# Calculate dT = dT_0 + dT_i
dT_i = utils.calc_bin_widths(T_i, T_i_err2)
(l_i_bw, l_i_bw_err2) = utils.calc_bin_widths(l_i, l_i_err2)
dT_0 = array_manip.mult_ncerr(l_i_bw, l_i_bw_err2,
t_0_slope, t_0_slope_err2)
dT_tuple = array_manip.add_ncerr(dT_i[0], dT_i[1], dT_0[0], dT_0[1])
dT = dT_tuple[0]
# Calculate Jacobian
if inst_name == "BSS":
(x_1, x_2,
x_3, x_4) = dr_lib.calc_BSS_coeffs(map_so, inst, (E_i, E_i_err2),
(Q, Q_err2), (k_i, k_i_err2),
(T_i, T_i_err2), dh, angle,
E_f, k_f, l_f, L_s, L_d,
t_0_slope, zero_vec)
else:
raise RuntimeError("Do not know how to calculate x_i "\
+"coefficients for instrument %s" % inst_name)
(A, A_err2) = dr_lib.calc_EQ_Jacobian(x_1, x_2, x_3, x_4, dT, dh,
zero_vec)
# Apply Jacobian: C/dlam * dlam / A(EQ) = C/EQ
(jac_ratio, jac_ratio_err2) = array_manip.div_ncerr(l_i_bw,
l_i_bw_err2,
A, A_err2)
(counts, counts_err2) = array_manip.mult_ncerr(counts, counts_err2,
jac_ratio,
jac_ratio_err2)
# Reverse counts, E_t, k_i and Q
E_t = axis_manip.reverse_array_cp(E_t)
E_t_err2 = axis_manip.reverse_array_cp(E_t_err2)
Q = axis_manip.reverse_array_cp(Q)
Q_err2 = axis_manip.reverse_array_cp(Q_err2)
counts = axis_manip.reverse_array_cp(counts)
counts_err2 = axis_manip.reverse_array_cp(counts_err2)
k_i = axis_manip.reverse_array_cp(k_i)
x_1 = axis_manip.reverse_array_cp(x_1)
x_2 = axis_manip.reverse_array_cp(x_2)
x_3 = axis_manip.reverse_array_cp(x_3)
x_4 = axis_manip.reverse_array_cp(x_4)
dT = axis_manip.reverse_array_cp(dT)
# Filter for duplicate Q values
if Q_filter:
k_i_cutoff = k_f * math.cos(angle)
k_i_cutbin = bisect.bisect(k_i, k_i_cutoff)
counts.__delslice__(0, k_i_cutbin)
counts_err2.__delslice__(0, k_i_cutbin)
Q.__delslice__(0, k_i_cutbin)
Q_err2.__delslice__(0, k_i_cutbin)
E_t.__delslice__(0, k_i_cutbin)
E_t_err2.__delslice__(0, k_i_cutbin)
x_1.__delslice__(0, k_i_cutbin)
x_2.__delslice__(0, k_i_cutbin)
x_3.__delslice__(0, k_i_cutbin)
x_4.__delslice__(0, k_i_cutbin)
dT.__delslice__(0, k_i_cutbin)
zero_vec.__delslice__(0, k_i_cutbin)
try:
if inst_name == "BSS":
((Q_1, E_t_1),
(Q_2, E_t_2),
(Q_3, E_t_3),
(Q_4, E_t_4)) = dr_lib.calc_BSS_EQ_verticies((E_t, E_t_err2),
(Q, Q_err2), x_1,
x_2, x_3, x_4,
dT, dh, zero_vec)
else:
raise RuntimeError("Do not know how to calculate (Q_i, "\
+"E_t_i) verticies for instrument %s" \
% inst_name)
except IndexError:
# All the data got Q filtered, move on
continue
try:
(y_2d, y_2d_err2,
area_new,
bin_count_new) = axis_manip.rebin_2D_quad_to_rectlin(Q_1, E_t_1,
Q_2, E_t_2,
Q_3, E_t_3,
Q_4, E_t_4,
counts,
counts_err2,
so_dim.axis[0].val,
so_dim.axis[1].val)
except IndexError, e:
# Get the offending index from the error message
index = int(str(e).split()[1].split('index')[-1].strip('[]'))
print "Id:", map_so.id
print "Index:", index
print "Verticies: %f, %f, %f, %f, %f, %f, %f, %f" % (Q_1[index],
E_t_1[index],
Q_2[index],
E_t_2[index],
Q_3[index],
E_t_3[index],
Q_4[index],
E_t_4[index])
raise IndexError(str(e))
# Add in together with previous results
(so_dim.y, so_dim.var_y) = array_manip.add_ncerr(so_dim.y,
so_dim.var_y,
y_2d, y_2d_err2)
(area_sum, area_sum_err2) = array_manip.add_ncerr(area_sum,
area_sum_err2,
area_new,
area_sum_err2)
if configure.dump_pix_contrib or configure.scale_sqe:
if inst_name == "BSS":
dOmega = dr_lib.calc_BSS_solid_angle(map_so, inst)
(bin_count_new,
bin_count_err2) = array_manip.mult_ncerr(bin_count_new,
bin_count_err2,
dOmega, 0.0)
(bin_count,
bin_count_err2) = array_manip.add_ncerr(bin_count,
bin_count_err2,
bin_count_new,
bin_count_err2)
else:
del bin_count_new
def sum_spectra_weighted_ave(obj, **kwargs):
"""
This function takes a set of data and sums the individual bins by weighted
average. That information is then assembled back into a single spectrum.
The individual spectra should already have been rebinned.
@param obj: Object containing data spectra
@type obj: C{SOM.SOM} or C{SOM.SO}
@param kwargs: A list of keyword arguments that the function accepts:
@return: The summed spectra (one)
@rtype: C{SOM.SOM}
"""
if obj is None:
return None
# 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)
# Get the number of axis channels
len_axis = len(obj[0])
import nessi_list
import SOM
import utils
# Empty SO for final spctrum
so = SOM.SO()
len_som = hlr_utils.get_length(obj)
# Slice data, calculate weighted average and repackage spectra
for i in xrange(len_axis):
sliced_data = nessi_list.NessiList()
sliced_data_err2 = nessi_list.NessiList()
for j in xrange(len_som):
obj1 = hlr_utils.get_value(obj, j, o_descr, "all")
if i == 0 and j == 0:
map_so = hlr_utils.get_map_so(obj, None, j)
hlr_utils.result_insert(so, "SO", map_so, None, "all")
sliced_data.append(obj1.y[i])
sliced_data_err2.append(obj1.var_y[i])
len_fit = len(sliced_data)
value = utils.weighted_average(sliced_data, sliced_data_err2, 0,
len_fit - 1)
so.y[i] = value[0]
so.var_y[i] = value[1]
hlr_utils.result_insert(result, res_descr, so, None, "all")
return result
def determine_time_indep_bkg(obj, tof_vals, **kwargs):
"""
This functions calculates the average counts at four given TOF channels
for determining the time-independent background.
@param obj: Object used for determining the time-independent background
@type obj: C{SOM.SOM} or C{SOM.SO}
@param tof_vals: The four TOF channels from which the average counts will
be determined
@type tof_vals: C{list}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword is_range: A flag that tells the function that tof_vals is a range
from which to determine the time-independent
background. The default is I{False}.
@type is_range: C{bool}
@return: Object containing the time-independent background and the
associated error
@rtype: C{list} of C{tuple}s
@raise TypeError: The incoming object is not a C{SOM} or C{SO}.
"""
# Kickout if tof_vals is NoneType
if tof_vals is None:
return None
# import the helper functions
import hlr_utils
# Get keyword arguments
is_range = kwargs.get("is_range", False)
o_descr = hlr_utils.get_descr(obj)
if o_descr != "SOM" and o_descr != "SO":
raise TypeError("Incoming object must be a SOM or a SO")
# Have a SOM or SO
else:
pass
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(obj)
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
if not is_range:
num_tof_vals = float(len(tof_vals))
import bisect
else:
num_tof_vals = tof_vals[1] - tof_vals[0]
import dr_lib
# iterate through the values
len_obj = hlr_utils.get_length(obj)
for i in xrange(len_obj):
obj1 = hlr_utils.get_value(obj, i, o_descr, "all")
if not is_range:
average = 0.0
ave_err2 = 0.0
for tof in tof_vals:
index = bisect.bisect(obj1.axis[0].val, float(tof)) - 1
average += obj1.y[index]
ave_err2 += obj1.var_y[index]
else:
(average, ave_err2) = dr_lib.integrate_axis(obj1,
width=True,
start=tof_vals[0],
end=tof_vals[1])
average /= num_tof_vals
ave_err2 /= num_tof_vals
hlr_utils.result_insert(result, res_descr, (average, ave_err2), obj1,
"yonly")
return result
def shift_spectrum(obj, shift_point, min_ext, max_ext, scale_const=None):
"""
This function takes a given spectrum and a central value and creates
a spectrum that is shifted about that point. Values greater than the point
are moved to the beginning of the new spectrum and values less than the
point are move towards the end of the new spectrum.
@param obj: Monitor object that will be shifted
@type obj: C{SOM.SOM} or C{SOM.SO}
@param shift_point: The point in the spectrum about which to shift the
data.
@type shift_point: C{list} of C{floats}
@param min_ext: The minimum extent of the axis to shift.
@type min_ext: C{list} of C{floats}
@param max_ext: The maximum extent of the axis to shift.
@type max_ext: C{list} of C{floats}
@param scale_const: A scaling constant to apply (multiply) to the newly
shifted spectrum. The default is I{None}.
@type scale_const: C{float}
@return: Monitor spectrum that have been shifted
@rtype: C{SOM.SOM} or C{SOM.SO}
"""
# 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)
s_descr = hlr_utils.get_descr(shift_point)
ie_descr = hlr_utils.get_descr(min_ext)
ae_descr = hlr_utils.get_descr(max_ext)
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
import array_manip
import utils
len_obj = hlr_utils.get_length(obj)
for i in xrange(len_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", 0)
x_err2 = hlr_utils.get_err2(obj, i, o_descr, "x", 0)
map_so = hlr_utils.get_map_so(obj, None, i)
bin_center = utils.calc_bin_centers(x_axis, x_err2)
# Get shift point and extents
sp = hlr_utils.get_value(shift_point, i, s_descr, "y")
ie = hlr_utils.get_value(min_ext, i, ie_descr, "y")
ae = hlr_utils.get_value(max_ext, i, ae_descr, "y")
# Make shifted spectrum
value0 = utils.shift_spectrum(val, err2, x_axis, bin_center[0],
sp, ie, ae)
# Scale spectrum if necessary
if scale_const is not None:
value1 = array_manip.mult_ncerr(value0[0], value0[1],
scale_const, 0.0)
else:
value1 = value0
hlr_utils.result_insert(result, res_descr, value1, map_so, "y")
return result
def integrate_spectra(obj, **kwargs):
"""
This function takes a set of spectra and calculates the integration for the
primary axis. If the integration range for a spectrum cannot be found, an
error report will be generated with the following information:
Range not found: pixel ID, start bin, end bin, length of data array
A failing pixel will have the integration tuple set to C{(nan, nan)}.
@param obj: Object containing spectra that will have the integration
calculated from them.
@type obj: C{SOM.SOM} or C{SOM.SO}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword start: The start range for the integration.
@type start: C{int}
@keyword end: The end range for the integration.
function.
@type end: C{int}
@keyword axis_pos: This is position of the axis in the axis array. If no
argument is given, the default value is I{0}.
@type axis_pos: C{int}
@keyword norm: This is a flag to turn on the division of the individual
spectrum integrations by the solid angle of the
corresponding pixel. This also activates the multiplication
of the individual spectrum bin values by their
corresponding bin width via the I{width} flag in
L{integrate_axis}. The default value of the flag is
I{False}.
@type norm: C{boolean}
@keyword total: This is a flag to turn on the summation of all individual
spectrum integrations. The default value of the flag is
I{False}.
@type total: C{boolean}
@keyword width: This is a flag to turn on the removal of the individual bin
width in the L{integrate_axis} function while doing the
integrations. The default value of the flag is I{False}.
@type width: C{boolean}
@return: Object containing the integration and the uncertainty squared
associated with the integration
@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)
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
# Check for axis_pos keyword argument
try:
axis_pos = kwargs["axis_pos"]
except KeyError:
axis_pos = 0
# Check for norm keyword argument
try:
norm = kwargs["norm"]
if norm:
if o_descr == "SO":
raise RuntimeError("Cannot use norm keyword with SO!")
width = True
inst = obj.attr_list.instrument
else:
width = False
except KeyError:
norm = False
width = False
# Check for total keyword argument
try:
total = kwargs["total"]
except KeyError:
total = False
# Check for width keyword argument only if norm isn't present
if not norm:
try:
width = kwargs["width"]
except KeyError:
width = False
# If the integration start bound is not given, set to infinity
try:
i_start = kwargs["start"]
except KeyError:
i_start = float("inf")
# If the integration end bound is not given, set to infinity
try:
i_end = kwargs["end"]
except KeyError:
i_end = float("inf")
# iterate through the values
import dr_lib
len_obj = hlr_utils.get_length(obj)
for i in xrange(len_obj):
obj1 = hlr_utils.get_value(obj, i, o_descr, "all")
# If there's a NaN at the front and back, there are NaN's everywhere
if str(obj1.axis[axis_pos].val[0]) == "nan" and \
str(obj1.axis[axis_pos].val[-1]) == "nan":
print "Range not found:", obj1.id, i_start, i_end, len(obj1)
value = (float('nan'), float('nan'))
else:
value = dr_lib.integrate_axis(obj1,
start=i_start,
end=i_end,
width=width)
if norm:
if inst.get_name() == "BSS":
map_so = hlr_utils.get_map_so(obj, None, i)
dOmega = dr_lib.calc_BSS_solid_angle(map_so, inst)
value1 = (value[0] / dOmega, value[1] / (dOmega * dOmega))
else:
raise RuntimeError("Do not know how to get solid angle from "\
+"%s" % inst.get_name())
else:
value1 = value
hlr_utils.result_insert(result, res_descr, value1, obj1, "yonly")
if not total:
return result
else:
# Sum all integration counts
total_counts = 0
total_err2 = 0
for j in xrange(hlr_utils.get_length(result)):
total_counts += hlr_utils.get_value(result, j, res_descr, "y")
total_err2 += hlr_utils.get_err2(result, j, res_descr, "y")
# Create new result object
(result2, res2_descr) = hlr_utils.empty_result(result)
result2 = hlr_utils.copy_som_attr(result2, res2_descr, result,
res_descr)
res1 = hlr_utils.get_value(result, 0, res_descr, "all")
hlr_utils.result_insert(result2, res2_descr,
(total_counts, total_err2), res1, "yonly")
return result2
def sum_all_spectra(obj, **kwargs):
"""
This function takes all the spectra in the given object and sums them
together. All of the sprectra are assumed to have the same axis scale.
@param obj: Object in which all of the spectra are to be summed together
@type obj: C{SOM.SOM}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword rebin_axis: The axis(es) to rebin the spectra onto.
@type rebin_axis: C{nessi_list.NessiList} or C{list} of
C{nessi_list.NessiList}s
@keyword rebin_axis_dim: Tthe dimension on the spectra being rebinned.
The default value is I{1}.
@type rebin_axis_dim: C{int}
@keyword y_sort: A flag that will sort the spectrum IDs by y. The default
behavior is I{False} and this maintains the original x
ordering.
@type y_sort: C{boolean}
@keyword stripe: A flag that will combine spectra in either the x or y
direction at a given y or x. The integration direction is
based on the setting of y_sort. The default behavior is
I{False} and corresponds to summing all spectra into one.
@type stripe: C{boolean}
@keyword pix_fix: A single or list of pixel IDs with which to override the
summed spectrum pixel ID. The setting of y_sort
determines is the x component (y_sort=False) or the y
component (y_sort=True) of the pixel ID is overridden.
@type pix_fix: C{int} or C{list} of C{int}s
@return: Object containing a single spectrum
@rtype: C{SOM.SOM}
@raise TypeError: Anything other than a C{SOM} is given
@raise RuntimeError: An unknown rebinning dimension is given
"""
o_descr = hlr_utils.get_descr(obj)
if o_descr != "SOM":
raise TypeError("Function argument must be a SOM")
# Have a SOM, go on
else:
pass
# If there is only one SO, why run
if len(obj) == 1:
return obj
# OK, we need to sum
else:
pass
try:
rebin_axis = kwargs["rebin_axis"]
except KeyError:
rebin_axis = None
try:
rebin_axis_dim = kwargs["rebin_axis_dim"]
except KeyError:
rebin_axis_dim = 1
try:
y_sort = kwargs["y_sort"]
except KeyError:
y_sort = False
try:
stripe = kwargs["stripe"]
except KeyError:
stripe = False
try:
pix_fix = kwargs["pixel_fix"]
except KeyError:
pix_fix = None
import common_lib
if rebin_axis is not None:
if rebin_axis_dim == 1:
obj1 = common_lib.rebin_axis_1D(obj, rebin_axis)
elif rebin_axis_dim == 2:
obj1 = common_lib.rebin_axis_2D(obj, rebin_axis[0], rebin_axis[1])
else:
raise RuntimeError("Do not have rebinning method for %dD." % \
rebin_axis_dim)
else:
obj1 = obj
del obj
# Sort SO IDs by y value
if y_sort:
obj1.sort(lambda x, y: cmp(x.id[1][1], y.id[1][1]))
# SO IDs are already sorted by x value
else:
pass
(result, res_descr) = hlr_utils.empty_result(obj1)
result = hlr_utils.copy_som_attr(result, res_descr, obj1, o_descr)
if not stripe:
# iterate through the values
so_id_list = []
val1 = hlr_utils.get_value(obj1, 0, o_descr, "all")
val2 = hlr_utils.get_value(obj1, 1, o_descr, "all")
value = common_lib.add_ncerr(val1, val2)
so_id_list.append(val1.id)
so_id_list.append(val2.id)
for i in xrange(2, hlr_utils.get_length(obj1)):
val = hlr_utils.get_value(obj1, i, o_descr, "all")
value = common_lib.add_ncerr(val, value)
so_id_list.append(val.id)
hlr_utils.result_insert(result, res_descr, value, None, "all")
result.attr_list["Summed IDs"] = so_id_list
if pix_fix is not None:
if y_sort:
fixed_pixel = (so_id_list[0][0], (pix_fix,
so_id_list[0][1][1]))
else:
fixed_pixel = (so_id_list[0][0], (so_id_list[0][1][0],
pix_fix))
else:
fixed_pixel = so_id_list[0]
result[0].id = fixed_pixel
else:
# iterate through the values
so_id_list = []
i_start = 0
stripe_count = 0
total_size = hlr_utils.get_length(obj1)
while i_start < total_size:
stripe_list = []
counted = 2
val1 = hlr_utils.get_value(obj1, i_start, o_descr, "all")
val2 = hlr_utils.get_value(obj1, i_start + 1, o_descr, "all")
value = common_lib.add_ncerr(val1, val2)
stripe_list.append(val1.id)
stripe_list.append(val2.id)
if y_sort:
comp_id = val2.id[1][1]
else:
comp_id = val2.id[1][0]
for i in xrange(i_start + 2, total_size):
val = hlr_utils.get_value(obj1, i, o_descr, "all")
if y_sort:
new_id = val.id[1][1]
else:
new_id = val.id[1][0]
if new_id > comp_id:
break
value = common_lib.add_ncerr(val, value)
stripe_list.append(val.id)
counted += 1
i_start += counted
so_id_list.append(stripe_list)
hlr_utils.result_insert(result, res_descr, value, None, "all")
if pix_fix is not None:
try:
if y_sort:
fixed_pixel = (stripe_list[0][0],
(pix_fix[stripe_count],
stripe_list[0][1][1]))
else:
fixed_pixel = (stripe_list[0][0],
(stripe_list[0][1][0],
pix_fix[stripe_count]))
except TypeError:
if y_sort:
fixed_pixel = (stripe_list[0][0],
(pix_fix, stripe_list[0][1][1]))
else:
fixed_pixel = (stripe_list[0][0],
(stripe_list[0][1][0], pix_fix))
else:
fixed_pixel = stripe_list[0]
result[stripe_count].id = fixed_pixel
stripe_count += 1
result.attr_list["summed_ids"] = so_id_list
return result
def tof_to_ref_scalar_Q(obj, **kwargs):
"""
This function converts a primary axis of a C{SOM} or C{SO} from
time-of-flight to reflectometer scalar Q. This means that a single angle
and a single flightpath is used. 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{(time-of-flight,
time-of-flight_err2)} (assumed to be in units of I{microseconds}) can be
converted to C{(scalar_Q, scalar_Q_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 polar: The polar angle and its associated error^2
@type polar: C{tuple}
@keyword pathlength: The pathlength and its associated error^2
@type pathlength: C{tuple}
@keyword angle_offset: A constant offset for the polar angle and its
associated error^2. The units of the offset should
be in radians.
@type angle_offset: C{tuple}
@keyword lojac: A flag that allows one to turn off the calculation of the
linear-order Jacobian. The default action is 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 configure: This is the object containing the driver configuration.
This will signal the function to write out the counts
and fractional area to files.
@type configure: C{Configure}
@return: Object with a primary axis in time-of-flight converted to
reflectometer scalar Q
@rtype: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@raise TypeError: The incoming object is not a type the function recognizes
@raise RuntimeError: A C{SOM} is not passed and no polar angle is provided
@raise RuntimeError: The C{SOM} x-axis units are not I{microseconds}
"""
# 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)
if o_descr == "list":
raise TypeError("Do not know how to handle given type: %s" % \
o_descr)
else:
pass
# Setup keyword arguments
polar = kwargs.get("polar")
pathlength = kwargs.get("pathlength")
units = kwargs.get("units", "microseconds")
lojac = kwargs.get("lojac", hlr_utils.check_lojac(obj))
angle_offset = kwargs.get("angle_offset")
config = kwargs.get("configure")
if config is None:
beamdiv_corr = False
else:
beamdiv_corr = config.beamdiv_corr
# 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, "1/Angstroms", axis)
result.setAxisLabel(axis, "scalar wavevector transfer")
result.setYUnits("Counts/A-1")
result.setYLabel("Intensity")
else:
pass
if pathlength is None or polar is None:
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:
if pathlength is None and polar is None:
raise RuntimeError("If no SOM is provided, then pathlength "\
+"and polar angle information must be "\
+"provided")
elif pathlength is None:
raise RuntimeError("If no SOM is provided, then pathlength "\
+"information must be provided")
elif polar is None:
raise RuntimeError("If no SOM is provided, then polar angle "\
+"information must be provided")
else:
raise RuntimeError("If you get here, see Steve Miller for "\
+"your mug.")
else:
pass
if pathlength is None:
(pl,
pl_err2) = obj.attr_list.instrument.get_total_path(obj[0].id,
det_secondary=True)
else:
(pl, pl_err2) = pathlength
if polar is None:
angle = hlr_utils.get_special(obj.attr_list["data-theta"], obj[0])[0]
angle_err2 = 0.0
else:
(angle, angle_err2) = polar
if angle_offset is not None:
angle += angle_offset[0]
angle_err2 += angle_offset[1]
# Need to multiply angle by 2.0 in order to make it be Theta to
# underlying conversion function
angle *= 2.0
angle_err2 *= 4.0
# iterate through the values
import axis_manip
if lojac:
import utils
if beamdiv_corr:
import dr_lib
for i in xrange(hlr_utils.get_length(obj)):
skip_pixel = False
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 beamdiv_corr:
dangle = dr_lib.ref_beamdiv_correct(obj.attr_list, map_so.id,
config.det_spat_res,
config.center_pix)
# We subtract due to the inversion of the z coordinates from the
# mirror reflection of the beam at the sample.
if dangle is not None:
pangle = angle - (2.0 * dangle)
else:
pangle = angle
skip_pixel = True
else:
pangle = angle
value = axis_manip.tof_to_scalar_Q(val, err2, pl, pl_err2, pangle,
angle_err2)
if lojac:
y_val = hlr_utils.get_value(obj, i, o_descr, "y")
y_err2 = hlr_utils.get_err2(obj, i, o_descr, "y")
counts = utils.linear_order_jacobian(val, value[0], y_val, y_err2)
else:
pass
if o_descr != "number":
value1 = axis_manip.reverse_array_cp(value[0])
value2 = axis_manip.reverse_array_cp(value[1])
rev_value = (value1, value2)
else:
rev_value = value
if map_so is not None:
if not lojac:
map_so.y = axis_manip.reverse_array_cp(map_so.y)
map_so.var_y = axis_manip.reverse_array_cp(map_so.var_y)
else:
map_so.y = axis_manip.reverse_array_cp(counts[0])
map_so.var_y = axis_manip.reverse_array_cp(counts[1])
else:
pass
if not skip_pixel:
hlr_utils.result_insert(result, res_descr, rev_value, map_so, "x",
axis)
return result
def div_ncerr(left, right, **kwargs):
"""
This function divides two objects (C{SOM}, C{SO} or {tuple(val,val_err2)})
and returns the result of the division in an C{SOM}, C{SO} or C{tuple}.
@param left: Object on the left of the division sign
@type left: C{SOM.SOM} or C{SOM.SO} or C{tuple}
@param right: Object on the right of the division sign
@type right: C{SOM.SOM} or C{SOM.SO} or C{tuple}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword axis: This is the axis one wishes to manipulate. If no argument
is given the default value is y
@type axis: C{string}=<y or x>
@keyword axis_pos: This is position of the axis in the axis array. If no
argument is given, the default value is 0
@type axis_pos: C{int}
@keyword length_one_som: This is a flag that lets the function know it is
dealing with a length 1 C{SOM} so that attributes
may be passed along. The length 1 C{SOM} will be
turned into a C{SO}. The default value is False.
@type length_one_som: C{boolean}
@keyword length_one_som_pos: This is the argument position of the length 1
C{SOM} since division order is not
commutative. The default value is 2.
@type length_one_som_pos: C{int}=<1 or 2>
@return: Object containing the results of the division
@rtype: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@raise IndexError: The two C{SOM}s do not contain the same number of
spectra
@raise RunTimeError: The x-axis units of the C{SOM}s do not match
@raise RunTimeError: The y-axis units of the C{SOM}s do not match
@raise RunTimeError: The x-axes of the two C{SO}s are not equal
"""
# import the helper functions
import hlr_utils
# Check to see if we are working with a length 1 SOM
try:
length_one_som = kwargs["length_one_som"]
except KeyError:
length_one_som = False
try:
length_one_som_pos = kwargs["length_one_som_pos"]
if length_one_som_pos != 1 or length_one_som_pos != 2:
raise RuntimeError("length_one_som_pos must be either 1 or 2 and "\
+"%d" % length_one_som_pos)
except KeyError:
length_one_som_pos = 2
if length_one_som:
if length_one_som_pos == 1:
som_copy = left
left = left[0]
else:
som_copy = right
right = right[0]
else:
# Not working with a length 1 SOM, do nothing
pass
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(left, right)
(l_descr, r_descr) = hlr_utils.get_descr(left, right)
is_number = False
# error check information
if r_descr == "SOM" and l_descr == "SOM":
hlr_utils.math_compatible(left, l_descr, right, r_descr)
elif l_descr == "number" and r_descr == "number":
is_number = True
else:
pass
# Check for axis keyword argument
try:
axis = kwargs["axis"]
except KeyError:
axis = "y"
# Check for axis_pos keyword argument
try:
axis_pos = kwargs["axis_pos"]
except KeyError:
axis_pos = 0
if length_one_som:
if length_one_som_pos == 1:
result = hlr_utils.copy_som_attr(result, res_descr,
som_copy, "SOM",
right, r_descr)
else:
result = hlr_utils.copy_som_attr(result, res_descr, left, l_descr,
som_copy, "SOM")
else:
result = hlr_utils.copy_som_attr(result, res_descr, left, l_descr,
right, r_descr)
# iterate through the values
import array_manip
for i in xrange(hlr_utils.get_length(left, right)):
val1 = hlr_utils.get_value(left, i, l_descr, axis, axis_pos)
err2_1 = hlr_utils.get_err2(left, i, l_descr, axis, axis_pos)
val2 = hlr_utils.get_value(right, i, r_descr, axis, axis_pos)
err2_2 = hlr_utils.get_err2(right, i, r_descr, axis, axis_pos)
(descr_1, descr_2)=hlr_utils.get_descr(val1, val2)
hlr_utils.math_compatible(val1, descr_1, val2, descr_2)
value = array_manip.div_ncerr(val1, err2_1, val2, err2_2)
map_so = hlr_utils.get_map_so(left, right, i)
hlr_utils.result_insert(result, res_descr, value, map_so, axis,
axis_pos)
if is_number:
return tuple(result)
else:
return result
def tof_to_wavelength(obj, **kwargs):
"""
This function converts a primary axis of a C{SOM} or C{SO} from
time-of-flight to wavelength. The wavelength 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 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}
@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:
units = kwargs["units"]
except KeyError:
units = "microseconds"
try:
lojac = kwargs["lojac"]
except KeyError:
lojac = hlr_utils.check_lojac(obj)
# 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")
# iterate through the values
import axis_manip
if lojac:
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)
value = axis_manip.tof_to_wavelength(val, err2, pl, pl_err2)
if lojac:
y_val = hlr_utils.get_value(obj, i, o_descr, "y")
y_err2 = hlr_utils.get_err2(obj, i, o_descr, "y")
counts = utils.linear_order_jacobian(val, value[0],
y_val, y_err2)
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 wavelength_to_velocity(obj, **kwargs):
"""
This function converts a primary axis of a C{SOM} or C{SO} from wavelength
to velocity. The wavelength axis for a C{SOM} must be in units of
I{Angstroms}. The primary axis of a C{SO} is assumed to be in
units of I{Angstroms}. A C{tuple} of C{(wavelength, wavelength_err2)}
(assumed to be in units of I{Angstroms}) can be converted to
C{(velocity, velocity_err)}.
@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 lojac: A flag that allows one to turn off the calculation of the
linear-order Jacobian. The default action is True for
histogram data.
@type lojac: C{boolean}
@keyword units: The expected units for this function. The default for this
function is I{Angstroms}
@type units: C{string}
@return: Object with a primary axis in wavelength converted to velocity
@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{Angstroms}
"""
# 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)
if o_descr == "list":
raise TypeError("Do not know how to handle given type: %s" % \
o_descr)
else:
pass
# Setup keyword arguments
try:
units = kwargs["units"]
except KeyError:
units = "Angstroms"
try:
lojac = kwargs["lojac"]
except KeyError:
lojac = hlr_utils.check_lojac(obj)
# 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, "meters/microseconds", axis)
result.setAxisLabel(axis, "velocity")
result.setYUnits("Counts/meters/microseconds")
result.setYLabel("Intensity")
else:
pass
# iterate through the values
import axis_manip
if lojac:
import utils
len_obj = hlr_utils.get_length(obj)
for i in xrange(len_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)
value = axis_manip.wavelength_to_velocity(val, err2)
if lojac:
y_val = hlr_utils.get_value(obj, i, o_descr, "y")
y_err2 = hlr_utils.get_err2(obj, i, o_descr, "y")
counts = utils.linear_order_jacobian(val, value[0], y_val, y_err2)
else:
pass
if o_descr != "number":
value1 = axis_manip.reverse_array_cp(value[0])
value2 = axis_manip.reverse_array_cp(value[1])
rev_value = (value1, value2)
else:
rev_value = value
if map_so is not None:
if not lojac:
map_so.y = axis_manip.reverse_array_cp(map_so.y)
map_so.var_y = axis_manip.reverse_array_cp(map_so.var_y)
else:
map_so.y = axis_manip.reverse_array_cp(counts[0])
map_so.var_y = axis_manip.reverse_array_cp(counts[1])
else:
pass
hlr_utils.result_insert(result, res_descr, rev_value, map_so, "x",
axis)
return result
def add_ncerr(left, right, **kwargs):
"""
This function adds two objects (C{SOM}, C{SO} or C{tuple(val,val_err2)})
and returns the result of the addition in an C{SOM}, C{SO} or C{tuple}.
@param left: Object on the left of the addition sign
@type left: C{SOM.SOM} or C{SOM.SO} or C{tuple}
@param right: Object on the right of the addition sign
@type right: C{SOM.SOM} or C{SOM.SO} or C{tuple}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword axis: This is the axis one wishes to manipulate. If no argument
is given the default value is y
@type axis: C{string}=<y or x>
@keyword axis_pos: This is position of the axis in the axis array. If no
argument is given, the default value is 0
@type axis_pos: C{int}
@keyword length_one_som: This is a flag that lets the function know it is
dealing with a length 1 C{SOM} so that attributes
may be passed along. The length 1 C{SOM} will be
turned into a C{SO}. The default value is False.
@type length_one_som: C{boolean}
@keyword length_one_som_pos: This is the argument position of the
length 1 C{SOM} since the check is done
before the arguments are swapped. The default
value is 2.
@type length_one_som_pos: C{int}=<1 or 2>
@keyword add_nxpars: This is a flag that will turn on code to add
C{SOM.NxParameters} in the two C{SOM}'s attribute
lists.
@type add_nxpars: C{boolean}
@return: Object containing the results of the addition
@rtype: C{SOM.SOM}, C{SOM.SO} or C{tuple}
@raise IndexError: The two C{SOM}s do not contain the same number of
spectra
@raise RunTimeError: The x-axis units of the C{SOM}s do not match
@raise RunTimeError: The y-axis units of the C{SOM}s do not match
@raise RunTimeError: The x-axes of the two C{SO}s are not equal
"""
# import the helper functions
import hlr_utils
# Check to see if we are working with a length 1 SOM
try:
length_one_som = kwargs["length_one_som"]
except KeyError:
length_one_som = False
try:
length_one_som_pos = kwargs["length_one_som_pos"]
if length_one_som_pos != 1 or length_one_som_pos != 2:
raise RuntimeError("length_one_som_pos must be either 1 or 2 and "\
+"%d" % length_one_som_pos)
except KeyError:
length_one_som_pos = 2
if length_one_som:
if length_one_som_pos == 1:
som_copy = left
left = left[0]
else:
som_copy = right
right = right[0]
else:
# Not working with a length 1 SOM, do nothing
pass
# set up for working through data
(result, res_descr) = hlr_utils.empty_result(left, right)
(l_descr, r_descr) = hlr_utils.get_descr(left, right)
is_number = False
# error check information
if (r_descr == "SOM" and l_descr != "SOM") \
or (r_descr == "SO" and l_descr == "number"):
left, right = hlr_utils.swap_args(left, right)
(l_descr, r_descr) = hlr_utils.swap_args(l_descr, r_descr)
elif r_descr == "SOM" and l_descr == "SOM":
hlr_utils.math_compatible(left, l_descr, right, r_descr)
elif l_descr == "number" and r_descr == "number":
is_number = True
else:
pass
# Check for axis keyword argument
try:
axis = kwargs["axis"]
except KeyError:
axis = "y"
# Check for axis_pos keyword argument
try:
axis_pos = kwargs["axis_pos"]
except KeyError:
axis_pos = 0
# Check for add_nxpars keyword argument
try:
add_nxpars_val = kwargs["add_nxpars"]
except KeyError:
add_nxpars_val = False
if length_one_som:
if length_one_som_pos == 1:
result = hlr_utils.copy_som_attr(result, res_descr,
som_copy, "SOM",
right, r_descr)
else:
result = hlr_utils.copy_som_attr(result, res_descr, left, l_descr,
som_copy, "SOM")
else:
result = hlr_utils.copy_som_attr(result, res_descr, left, l_descr,
right, r_descr,
add_nxpars=add_nxpars_val)
# iterate through the values
import array_manip
for i in xrange(hlr_utils.get_length(left, right)):
val1 = hlr_utils.get_value(left, i, l_descr, axis, axis_pos)
err2_1 = hlr_utils.get_err2(left, i, l_descr, axis, axis_pos)
val2 = hlr_utils.get_value(right, i, r_descr, axis, axis_pos)
err2_2 = hlr_utils.get_err2(right, i, r_descr, axis, axis_pos)
(descr_1, descr_2) = hlr_utils.get_descr(val1, val2)
hlr_utils.math_compatible(val1, descr_1, val2, descr_2)
value = array_manip.add_ncerr(val1, err2_1, val2, err2_2)
map_so = hlr_utils.get_map_so(left, None, i)
hlr_utils.result_insert(result, res_descr, value, map_so, axis,
axis_pos)
if is_number:
return tuple(result)
else:
return result
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 sum_by_rebin_frac(obj, axis_out, **kwargs):
"""
This function uses the C{axis_manip.rebin_axis_1D_frac} function from the
SCL to perform the rebinning. The function tracks the counts and fractional
area from all spectra separately. The counts and fractional area are
divided after all spectra have been parsed.
@param obj: Object to be rebinned and summed
@type obj: C{SOM.SOM} or C{SOM.SO}
@param axis_out: The axis to rebin the C{SOM} or C{SO} to
@type axis_out: C{NessiList}
@param kwargs: A list of keyword arguments that the function accepts:
@keyword configure: This is the object containing the driver configuration.
This will signal the function to write out the counts
and fractional area to files.
@type configure: C{Configure}
@return: Object that has been rebinned and summed according to the
provided axis
@rtype: C{SOM.SOM} or C{SOM.SO}
@raise TypeError: The rebinning axis given is not a C{NessiList}
@raise TypeError: The object being rebinned is not a C{SOM} or a C{SO}
@raise TypeError: The dimension of the input object is not 1D
"""
# import the helper functions
import hlr_utils
# set up for working through data
try:
axis_out.__type__
except AttributeError:
raise TypeError("Rebinning axis must be a NessiList!")
o_descr = hlr_utils.get_descr(obj)
if o_descr == "number" or o_descr == "list":
raise TypeError("Do not know how to handle given type: %s" % \
o_descr)
else:
pass
try:
if obj.getDimension() != 1:
raise TypeError("The input object must be 1D!. This one is "\
+"%dD." % obj.getDimension())
except AttributeError:
# obj is a SO
if obj.dim() != 1:
raise TypeError("The input object must be 1D!. This one is "\
+"%dD." % obj.dim())
# Check for keywords
try:
config = kwargs["configure"]
except KeyError:
config = None
(result, res_descr) = hlr_utils.empty_result(obj)
result = hlr_utils.copy_som_attr(result, res_descr, obj, o_descr)
import array_manip
import axis_manip
len_data = len(axis_out) - 1
counts = nessi_list.NessiList(len_data)
counts_err2 = nessi_list.NessiList(len_data)
frac_area = nessi_list.NessiList(len_data)
frac_area_err2 = nessi_list.NessiList(len_data)
for i in xrange(hlr_utils.get_length(obj)):
axis_in = hlr_utils.get_value(obj, i, o_descr, "x", 0)
val = hlr_utils.get_value(obj, i, o_descr)
err2 = hlr_utils.get_err2(obj, i, o_descr)
value = axis_manip.rebin_axis_1D_frac(axis_in, val, err2, axis_out)
(counts, counts_err2) = array_manip.add_ncerr(counts, counts_err2,
value[0], value[1])
(frac_area, frac_area_err2) = array_manip.add_ncerr(frac_area,
frac_area_err2,
value[2],
frac_area_err2)
# Divide the total counts by the total fractional area
value1 = array_manip.div_ncerr(counts, counts_err2, frac_area,
frac_area_err2)
xvals = []
xvals.append(axis_out)
map_so = hlr_utils.get_map_so(obj, None, 0)
hlr_utils.result_insert(result, res_descr, value1, map_so, "all",
0, xvals)
if config is not None:
if o_descr == "SOM":
import SOM
o_som = SOM.SOM()
o_som.copyAttributes(obj)
so = hlr_utils.get_map_so(obj, None, 0)
so.axis[0].val = axis_out
so.y = counts
so.var_y = counts_err2
o_som.append(so)
# Write out summed counts into file
hlr_utils.write_file(config.output, "text/Spec", o_som,
output_ext="cnt",
verbose=config.verbose,
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
message="summed counts")
# Replace counts data with fractional area. The axes remain the
# same
o_som[0].y = frac_area
o_som[0].var_y = frac_area_err2
# Write out summed fractional area into file
hlr_utils.write_file(config.output, "text/Spec", o_som,
output_ext="fra",
verbose=config.verbose,
data_ext=config.ext_replacement,
path_replacement=config.path_replacement,
message="fractional area")
return result