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 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: 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 array_manip
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_frac(axis_in, val, err2, axis_out)
frac_err = nessi_list.NessiList(len(value[2]))
value1 = array_manip.div_ncerr(value[0], value[1], value[2], frac_err)
xvals = []
xvals.append(axis_out)
map_so = hlr_utils.get_map_so(obj, None, i)
hlr_utils.result_insert(result, res_descr, value1, map_so, "all",
0, xvals)
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 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: 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 array_manip
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_frac(axis_in, val, err2, axis_out)
frac_err = nessi_list.NessiList(len(value[2]))
value1 = array_manip.div_ncerr(value[0], value[1], value[2], frac_err)
xvals = []
xvals.append(axis_out)
map_so = hlr_utils.get_map_so(obj, None, i)
hlr_utils.result_insert(result, res_descr, value1, map_so, "all", 0,
xvals)
return result