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 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 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 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 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 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
