def __init__(self, x, y, dx=None, dy=None, smearer=None, data=None):
"""
:param smearer: is an object of class QSmearer or SlitSmearer
that will smear the theory data (slit smearing or resolution
smearing) when set.
The proper way to set the smearing object would be to
do the following: ::
from sas.models.qsmearing import smear_selection
smearer = smear_selection(some_data)
fitdata1d = FitData1D( x= [1,3,..,],
y= [3,4,..,8],
dx=None,
dy=[1,2...], smearer= smearer)
:Note: that some_data _HAS_ to be of
class DataLoader.data_info.Data1D
Setting it back to None will turn smearing off.
"""
Data1D.__init__(self, x=x, y=y, dx=dx, dy=dy)
self.num_points = len(x)
self.sas_data = data
self.smearer = smearer
self._first_unsmeared_bin = None
self._last_unsmeared_bin = None
# Check error bar; if no error bar found, set it constant(=1)
# TODO: Should provide an option for users to set it like percent,
# constant, or dy data
if dy is None or dy == [] or dy.all() == 0:
self.dy = numpy.ones(len(y))
else:
self.dy = numpy.asarray(dy).copy()
## Min Q-value
#Skip the Q=0 point, especially when y(q=0)=None at x[0].
if min(self.x) == 0.0 and self.x[0] == 0 and\
not numpy.isfinite(self.y[0]):
self.qmin = min(self.x[self.x != 0])
else:
self.qmin = min(self.x)
## Max Q-value
self.qmax = max(self.x)
# Range used for input to smearing
self._qmin_unsmeared = self.qmin
self._qmax_unsmeared = self.qmax
# Identify the bin range for the unsmeared and smeared spaces
self.idx = (self.x >= self.qmin) & (self.x <= self.qmax)
self.idx_unsmeared = (self.x >= self._qmin_unsmeared) \
& (self.x <= self._qmax_unsmeared)
def __init__(self, x, y, dx=None, dy=None, smearer=None, data=None):
"""
:param smearer: is an object of class QSmearer or SlitSmearer
that will smear the theory data (slit smearing or resolution
smearing) when set.
The proper way to set the smearing object would be to
do the following: ::
from sas.models.qsmearing import smear_selection
smearer = smear_selection(some_data)
fitdata1d = FitData1D( x= [1,3,..,],
y= [3,4,..,8],
dx=None,
dy=[1,2...], smearer= smearer)
:Note: that some_data _HAS_ to be of
class DataLoader.data_info.Data1D
Setting it back to None will turn smearing off.
"""
Data1D.__init__(self, x=x, y=y, dx=dx, dy=dy)
self.num_points = len(x)
self.sas_data = data
self.smearer = smearer
self._first_unsmeared_bin = None
self._last_unsmeared_bin = None
# Check error bar; if no error bar found, set it constant(=1)
# TODO: Should provide an option for users to set it like percent,
# constant, or dy data
if dy is None or dy == [] or dy.all() == 0:
self.dy = numpy.ones(len(y))
else:
self.dy = numpy.asarray(dy).copy()
## Min Q-value
#Skip the Q=0 point, especially when y(q=0)=None at x[0].
if min(self.x) == 0.0 and self.x[0] == 0 and\
not numpy.isfinite(self.y[0]):
self.qmin = min(self.x[self.x != 0])
else:
self.qmin = min(self.x)
## Max Q-value
self.qmax = max(self.x)
# Range used for input to smearing
self._qmin_unsmeared = self.qmin
self._qmax_unsmeared = self.qmax
# Identify the bin range for the unsmeared and smeared spaces
self.idx = (self.x >= self.qmin) & (self.x <= self.qmax)
self.idx_unsmeared = (self.x >= self._qmin_unsmeared) \
& (self.x <= self._qmax_unsmeared)
def test_allowed_bins(self):
x = numpy.asarray(numpy.asarray([0, 1, 2, 3]))
y = numpy.asarray(numpy.asarray([1, 1, 1, 1]))
dy = numpy.asarray(numpy.asarray([1, 1, 1, 1]))
g = invariant.Guinier()
data = Data1D(x=x, y=y, dy=dy)
self.assertEqual(g.get_allowed_bins(data), [False, True, True, True])
data = Data1D(x=y, y=x, dy=dy)
self.assertEqual(g.get_allowed_bins(data), [False, True, True, True])
data = Data1D(x=dy, y=y, dy=x)
self.assertEqual(g.get_allowed_bins(data), [False, True, True, True])
def __str__(self):
"""
print data
"""
_str = "%s\n" % LoadData1D.__str__(self)
return _str
def __str__(self):
"""
print data
"""
_str = "%s\n" % LoadData1D.__str__(self)
return _str
def read(self, path):
"""
Load data file
@param path: file path
@return: Data1D object, or None
@raise RuntimeError: when the file can't be opened
@raise ValueError: when the length of the data vectors are inconsistent
"""
if os.path.isfile(path):
basename = os.path.basename(path)
root, extension = os.path.splitext(basename)
if extension.lower() in self.ext:
try:
input_f = open(path, 'r')
except:
raise RuntimeError, "ascii_reader: cannot open %s" % path
buff = input_f.read()
lines = buff.split('\n')
x = numpy.zeros(0)
y = numpy.zeros(0)
dy = numpy.zeros(0)
output = Data1D(x, y, dy=dy)
self.filename = output.filename = basename
for line in lines:
x = numpy.append(x, float(line))
output.x = x
return output
else:
raise RuntimeError, "%s is not a file" % path
return None
def __init__(self, x=None, y=None, dx=None, dy=None):
"""
"""
if x is None:
x = []
if y is None:
y = []
PlotData1D.__init__(self, x, y, dx, dy)
LoadData1D.__init__(self, x, y, dx, dy)
self.id = None
self.list_group_id = []
self.group_id = None
self.is_data = True
self.path = None
self.xtransform = None
self.ytransform = None
self.title = ""
self.scale = None
def __init__(self, x=None, y=None, dx=None, dy=None):
"""
"""
if x is None:
x = []
if y is None:
y = []
PlotData1D.__init__(self, x, y, dx, dy)
LoadData1D.__init__(self, x, y, dx, dy)
self.id = None
self.list_group_id = []
self.group_id = None
self.is_data = True
self.path = None
self.xtransform = None
self.ytransform = None
self.title = ""
self.scale = None
def setUp(self):
"""
Generate a power law distribution. After extrapolating, we will
verify that we obtain the scale and m parameters
"""
self.scale = 1.5
self.m = 3.0
x = numpy.arange(0.0001, 0.1, 0.0001)
y = numpy.asarray([self.scale * math.pow(q, -1.0 * self.m) for q in x])
dy = y * .1
self.data = Data1D(x=x, y=y, dy=dy)
def setUp(self):
"""
Generate a Guinier distribution. After extrapolating, we will
verify that we obtain the scale and rg parameters
"""
self.scale = 1.5
self.rg = 30.0
x = numpy.arange(0.0001, 0.1, 0.0001)
y = numpy.asarray(
[self.scale * math.exp(-(q * self.rg)**2 / 3.0) for q in x])
dy = y * .1
self.data = Data1D(x=x, y=y, dy=dy)
def test_linearization(self):
"""
Check that the linearization process filters out points
that can't be transformed
"""
x = numpy.asarray(numpy.asarray([0, 1, 2, 3]))
y = numpy.asarray(numpy.asarray([1, 1, 1, 1]))
g = invariant.Guinier()
data_in = Data1D(x=x, y=y)
data_out = g.linearize_data(data_in)
x_out, y_out, dy_out = data_out.x, data_out.y, data_out.dy
self.assertEqual(len(x_out), 3)
self.assertEqual(len(y_out), 3)
self.assertEqual(len(dy_out), 3)
def test_error_treatment(self):
x = numpy.asarray(numpy.asarray([0, 1, 2, 3]))
y = numpy.asarray(numpy.asarray([1, 1, 1, 1]))
# These are all the values of the dy array that would cause
# us to set all dy values to 1.0 at __init__ time.
dy_list = [[], None, [0, 0, 0, 0]]
for dy in dy_list:
data = Data1D(x=x, y=y, dy=dy)
inv = invariant.InvariantCalculator(data)
self.assertEqual(len(inv._data.x), len(inv._data.dy))
self.assertEqual(len(inv._data.dy), 4)
for i in range(4):
self.assertEqual(inv._data.dy[i], 1)
def _check_for_empty_data(self, data1d):
"""
Creates an empty data set if no data is passed to the reader
:param data1d: presumably a Data1D object
"""
if data1d == None:
self.errors = set()
x_vals = numpy.empty(0)
y_vals = numpy.empty(0)
dx_vals = numpy.empty(0)
dy_vals = numpy.empty(0)
dxl = numpy.empty(0)
dxw = numpy.empty(0)
data1d = Data1D(x_vals, y_vals, dx_vals, dy_vals)
data1d.dxl = dxl
data1d.dxw = dxw
return data1d
def test_cyl_times_square(self):
""" Simple cylinder model fit """
out = Loader().load("cyl_400_20.txt")
data = Data1D(x=out.x, y=out.y, dx=out.dx, dy=out.dy)
# Receives the type of model for the fitting
model1 = MultiplicationModel(CylinderModel(), SquareWellStructure())
model1.setParam('background', 0.0)
model1.setParam('sldCyl', 3e-006)
model1.setParam('sldSolv', 0.0)
model1.setParam('length', 420)
model1.setParam('radius', 40)
model1.setParam('scale_factor', 2)
model1.setParam('volfraction', 0.04)
model1.setParam('welldepth', 1.5)
model1.setParam('wellwidth', 1.2)
model = Model(model1)
pars1 = ['length', 'radius', 'scale_factor']
fitter = Fit('bumps')
fitter.set_data(data, 1)
fitter.set_model(model, 1, pars1)
fitter.select_problem_for_fit(id=1, value=1)
result1, = fitter.fit()
self.assert_(result1)
self.assertTrue(len(result1.pvec) >= 0)
self.assertTrue(len(result1.stderr) >= 0)
#print "results",list(zip(result1.pvec, result1.stderr))
self.assertTrue(
math.fabs(result1.pvec[0] - 612) / 3.0 <= result1.stderr[0])
self.assertTrue(
math.fabs(result1.pvec[1] - 20.3) / 3.0 <= result1.stderr[1])
self.assertTrue(
math.fabs(result1.pvec[2] - 25) / 3.0 <= result1.stderr[2])
self.assertTrue(result1.fitness / len(data.x) < 1.0)
def read(self, xml_file):
"""
Validate and read in an xml_file file in the canSAS format.
:param xml_file: A canSAS file path in proper XML format
"""
# output - Final list of Data1D objects
output = []
# ns - Namespace hierarchy for current xml_file object
ns_list = []
# Check that the file exists
if os.path.isfile(xml_file):
basename = os.path.basename(xml_file)
_, extension = os.path.splitext(basename)
# If the file type is not allowed, return nothing
if extension in self.ext or self.allow_all:
# Get the file location of
cansas_defaults = self.load_file_and_schema(xml_file)
# Try to load the file, but raise an error if unable to.
# Check the file matches the XML schema
try:
if self.is_cansas(extension):
# Get each SASentry from XML file and add it to a list.
entry_list = self.xmlroot.xpath(
'/ns:SASroot/ns:SASentry',
namespaces={'ns': cansas_defaults.get("ns")})
ns_list.append("SASentry")
# If multiple files, modify the name for each is unique
increment = 0
# Parse each SASentry item
for entry in entry_list:
# Define a new Data1D object with zeroes for
# x_vals and y_vals
data1d = Data1D(numpy.empty(0), numpy.empty(0),
numpy.empty(0), numpy.empty(0))
data1d.dxl = numpy.empty(0)
data1d.dxw = numpy.empty(0)
# If more than one SASentry, increment each in order
name = basename
if len(entry_list) - 1 > 0:
name += "_{0}".format(increment)
increment += 1
# Set the Data1D name and then parse the entry.
# The entry is appended to a list of entry values
data1d.filename = name
data1d.meta_data["loader"] = "CanSAS 1D"
# Get all preprocessing events and encoding
self.set_processing_instructions()
data1d.meta_data[PREPROCESS] = \
self.processing_instructions
# Parse the XML file
return_value, extras = \
self._parse_entry(entry, ns_list, data1d)
del extras[:]
return_value = self._final_cleanup(return_value)
output.append(return_value)
else:
output.append("Invalid XML at: {0}".format(\
self.find_invalid_xml()))
except:
# If the file does not match the schema, raise this error
raise RuntimeError, "%s cannot be read" % xml_file
return output
# Return a list of parsed entries that dataloader can manage
return None
def read(self, path):
"""
Load data file
:param path: file path
:return: Data1D object, or None
:raise RuntimeError: when the file can't be opened
:raise ValueError: when the length of the data vectors are inconsistent
"""
if os.path.isfile(path):
basename = os.path.basename(path)
root, extension = os.path.splitext(basename)
if extension.lower() in self.ext:
try:
input_f = open(path, 'r')
except:
raise RuntimeError, "hfir1d_reader: cannot open %s" % path
buff = input_f.read()
lines = buff.split('\n')
x = numpy.zeros(0)
y = numpy.zeros(0)
dx = numpy.zeros(0)
dy = numpy.zeros(0)
output = Data1D(x, y, dx=dx, dy=dy)
self.filename = output.filename = basename
data_conv_q = None
data_conv_i = None
if has_converter == True and output.x_unit != '1/A':
data_conv_q = Converter('1/A')
# Test it
data_conv_q(1.0, output.x_unit)
if has_converter == True and output.y_unit != '1/cm':
data_conv_i = Converter('1/cm')
# Test it
data_conv_i(1.0, output.y_unit)
for line in lines:
toks = line.split()
try:
_x = float(toks[0])
_y = float(toks[1])
_dx = float(toks[3])
_dy = float(toks[2])
if data_conv_q is not None:
_x = data_conv_q(_x, units=output.x_unit)
_dx = data_conv_q(_dx, units=output.x_unit)
if data_conv_i is not None:
_y = data_conv_i(_y, units=output.y_unit)
_dy = data_conv_i(_dy, units=output.y_unit)
x = numpy.append(x, _x)
y = numpy.append(y, _y)
dx = numpy.append(dx, _dx)
dy = numpy.append(dy, _dy)
except:
# Couldn't parse this line, skip it
pass
# Sanity check
if not len(y) == len(dy):
msg = "hfir1d_reader: y and dy have different length"
raise RuntimeError, msg
if not len(x) == len(dx):
msg = "hfir1d_reader: x and dx have different length"
raise RuntimeError, msg
# If the data length is zero, consider this as
# though we were not able to read the file.
if len(x) == 0:
raise RuntimeError, "hfir1d_reader: could not load file"
output.x = x
output.y = y
output.dy = dy
output.dx = dx
if data_conv_q is not None:
output.xaxis("\\rm{Q}", output.x_unit)
else:
output.xaxis("\\rm{Q}", 'A^{-1}')
if data_conv_i is not None:
output.yaxis("\\rm{Intensity}", output.y_unit)
else:
output.yaxis("\\rm{Intensity}", "cm^{-1}")
# Store loading process information
output.meta_data['loader'] = self.type_name
return output
else:
raise RuntimeError, "%s is not a file" % path
return None
def read(self, path):
"""
Load data file
:param path: file path
:return: Data1D object, or None
:raise RuntimeError: when the file can't be opened
:raise ValueError: when the length of the data vectors are inconsistent
"""
if os.path.isfile(path):
basename = os.path.basename(path)
_, extension = os.path.splitext(basename)
if self.allow_all or extension.lower() in self.ext:
try:
# Read in binary mode since GRASP frequently has no-ascii
# characters that brakes the open operation
input_f = open(path, 'rb')
except:
raise RuntimeError, "ascii_reader: cannot open %s" % path
buff = input_f.read()
lines = buff.splitlines()
x = numpy.zeros(0)
y = numpy.zeros(0)
dy = numpy.zeros(0)
dx = numpy.zeros(0)
#temp. space to sort data
tx = numpy.zeros(0)
ty = numpy.zeros(0)
tdy = numpy.zeros(0)
tdx = numpy.zeros(0)
output = Data1D(x, y, dy=dy, dx=dx)
self.filename = output.filename = basename
data_conv_q = None
data_conv_i = None
if has_converter == True and output.x_unit != '1/A':
data_conv_q = Converter('1/A')
# Test it
data_conv_q(1.0, output.x_unit)
if has_converter == True and output.y_unit != '1/cm':
data_conv_i = Converter('1/cm')
# Test it
data_conv_i(1.0, output.y_unit)
# The first good line of data will define whether
# we have 2-column or 3-column ascii
has_error_dx = None
has_error_dy = None
#Initialize counters for data lines and header lines.
is_data = False # Has more than 5 lines
# More than "5" lines of data is considered as actual
# data unless that is the only data
mum_data_lines = 5
# To count # of current data candidate lines
i = -1
# To count total # of previous data candidate lines
i1 = -1
# To count # of header lines
j = -1
# Helps to count # of header lines
j1 = -1
#minimum required number of columns of data; ( <= 4).
lentoks = 2
for line in lines:
# Initial try for CSV (split on ,)
toks = line.split(',')
# Now try SCSV (split on ;)
if len(toks) < 2:
toks = line.split(';')
# Now go for whitespace
if len(toks) < 2:
toks = line.split()
try:
#Make sure that all columns are numbers.
for colnum in range(len(toks)):
float(toks[colnum])
_x = float(toks[0])
_y = float(toks[1])
#Reset the header line counters
if j == j1:
j = 0
j1 = 0
if i > 1:
is_data = True
if data_conv_q is not None:
_x = data_conv_q(_x, units=output.x_unit)
if data_conv_i is not None:
_y = data_conv_i(_y, units=output.y_unit)
# If we have an extra token, check
# whether it can be interpreted as a
# third column.
_dy = None
if len(toks) > 2:
try:
_dy = float(toks[2])
if data_conv_i is not None:
_dy = data_conv_i(_dy, units=output.y_unit)
except:
# The third column is not a float, skip it.
pass
# If we haven't set the 3rd column
# flag, set it now.
if has_error_dy == None:
has_error_dy = False if _dy == None else True
#Check for dx
_dx = None
if len(toks) > 3:
try:
_dx = float(toks[3])
if data_conv_i is not None:
_dx = data_conv_i(_dx, units=output.x_unit)
except:
# The 4th column is not a float, skip it.
pass
# If we haven't set the 3rd column
# flag, set it now.
if has_error_dx == None:
has_error_dx = False if _dx == None else True
#After talked with PB, we decided to take care of only
# 4 columns of data for now.
#number of columns in the current line
#To remember the # of columns in the current
#line of data
new_lentoks = len(toks)
#If the previous columns not equal to the current,
#mark the previous as non-data and reset the dependents.
if lentoks != new_lentoks:
if is_data == True:
break
else:
i = -1
i1 = 0
j = -1
j1 = -1
#Delete the previously stored lines of data candidates
# if is not data.
if i < 0 and -1 < i1 < mum_data_lines and \
is_data == False:
try:
x = numpy.zeros(0)
y = numpy.zeros(0)
except:
pass
x = numpy.append(x, _x)
y = numpy.append(y, _y)
if has_error_dy == True:
#Delete the previously stored lines of
# data candidates if is not data.
if i < 0 and -1 < i1 < mum_data_lines and \
is_data == False:
try:
dy = numpy.zeros(0)
except:
pass
dy = numpy.append(dy, _dy)
if has_error_dx == True:
#Delete the previously stored lines of
# data candidates if is not data.
if i < 0 and -1 < i1 < mum_data_lines and \
is_data == False:
try:
dx = numpy.zeros(0)
except:
pass
dx = numpy.append(dx, _dx)
#Same for temp.
#Delete the previously stored lines of data candidates
# if is not data.
if i < 0 and -1 < i1 < mum_data_lines and\
is_data == False:
try:
tx = numpy.zeros(0)
ty = numpy.zeros(0)
except:
pass
tx = numpy.append(tx, _x)
ty = numpy.append(ty, _y)
if has_error_dy == True:
#Delete the previously stored lines of
# data candidates if is not data.
if i < 0 and -1 < i1 < mum_data_lines and \
is_data == False:
try:
tdy = numpy.zeros(0)
except:
pass
tdy = numpy.append(tdy, _dy)
if has_error_dx == True:
#Delete the previously stored lines of
# data candidates if is not data.
if i < 0 and -1 < i1 < mum_data_lines and \
is_data == False:
try:
tdx = numpy.zeros(0)
except:
pass
tdx = numpy.append(tdx, _dx)
#reset i1 and flag lentoks for the next
if lentoks < new_lentoks:
if is_data == False:
i1 = -1
#To remember the # of columns on the current line
# for the next line of data
lentoks = len(toks)
#Reset # of header lines and counts #
# of data candidate lines
if j == 0 and j1 == 0:
i1 = i + 1
i += 1
except:
# It is data and meet non - number, then stop reading
if is_data == True:
break
lentoks = 2
#Counting # of header lines
j += 1
if j == j1 + 1:
j1 = j
else:
j = -1
#Reset # of lines of data candidates
i = -1
# Couldn't parse this line, skip it
pass
input_f.close()
# Sanity check
if has_error_dy == True and not len(y) == len(dy):
msg = "ascii_reader: y and dy have different length"
raise RuntimeError, msg
if has_error_dx == True and not len(x) == len(dx):
msg = "ascii_reader: y and dy have different length"
raise RuntimeError, msg
# If the data length is zero, consider this as
# though we were not able to read the file.
if len(x) == 0:
raise RuntimeError, "ascii_reader: could not load file"
#Let's re-order the data to make cal.
# curve look better some cases
ind = numpy.lexsort((ty, tx))
for i in ind:
x[i] = tx[ind[i]]
y[i] = ty[ind[i]]
if has_error_dy == True:
dy[i] = tdy[ind[i]]
if has_error_dx == True:
dx[i] = tdx[ind[i]]
# Zeros in dx, dy
if has_error_dx:
dx[dx == 0] = _ZERO
if has_error_dy:
dy[dy == 0] = _ZERO
#Data
output.x = x[x != 0]
output.y = y[x != 0]
output.dy = dy[x != 0] if has_error_dy == True\
else numpy.zeros(len(output.y))
output.dx = dx[x != 0] if has_error_dx == True\
else numpy.zeros(len(output.x))
if data_conv_q is not None:
output.xaxis("\\rm{Q}", output.x_unit)
else:
output.xaxis("\\rm{Q}", 'A^{-1}')
if data_conv_i is not None:
output.yaxis("\\rm{Intensity}", output.y_unit)
else:
output.yaxis("\\rm{Intensity}", "cm^{-1}")
# Store loading process information
output.meta_data['loader'] = self.type_name
if len(output.x) < 1:
raise RuntimeError, "%s is empty" % path
return output
else:
raise RuntimeError, "%s is not a file" % path
return None
def test_guinier_incompatible_length(self):
g = invariant.Guinier()
data_in = Data1D(x=[1], y=[1, 2], dy=None)
self.assertRaises(AssertionError, g.linearize_data, data_in)
data_in = Data1D(x=[1, 1], y=[1, 2], dy=[1])
self.assertRaises(AssertionError, g.linearize_data, data_in)
def read(self, path):
"""
Load data file.
:param path: file path
:return: Data1D object, or None
:raise RuntimeError: when the file can't be opened
:raise ValueError: when the length of the data vectors are inconsistent
"""
if os.path.isfile(path):
basename = os.path.basename(path)
root, extension = os.path.splitext(basename)
if extension.lower() in self.ext:
try:
input_f = open(path, 'r')
except:
raise RuntimeError, "abs_reader: cannot open %s" % path
buff = input_f.read()
lines = buff.split('\n')
x = numpy.zeros(0)
y = numpy.zeros(0)
dy = numpy.zeros(0)
dx = numpy.zeros(0)
output = Data1D(x, y, dy=dy, dx=dx)
detector = Detector()
output.detector.append(detector)
output.filename = basename
is_info = False
is_center = False
is_data_started = False
data_conv_q = None
data_conv_i = None
if has_converter == True and output.x_unit != '1/A':
data_conv_q = Converter('1/A')
# Test it
data_conv_q(1.0, output.x_unit)
if has_converter == True and output.y_unit != '1/cm':
data_conv_i = Converter('1/cm')
# Test it
data_conv_i(1.0, output.y_unit)
for line in lines:
# Information line 1
if is_info == True:
is_info = False
line_toks = line.split()
# Wavelength in Angstrom
try:
value = float(line_toks[1])
if has_converter == True and \
output.source.wavelength_unit != 'A':
conv = Converter('A')
output.source.wavelength = conv(
value, units=output.source.wavelength_unit)
else:
output.source.wavelength = value
except:
#goes to ASC reader
msg = "abs_reader: cannot open %s" % path
raise RuntimeError, msg
# Distance in meters
try:
value = float(line_toks[3])
if has_converter == True and \
detector.distance_unit != 'm':
conv = Converter('m')
detector.distance = conv(
value, units=detector.distance_unit)
else:
detector.distance = value
except:
#goes to ASC reader
msg = "abs_reader: cannot open %s" % path
raise RuntimeError, msg
# Transmission
try:
output.sample.transmission = float(line_toks[4])
except:
# Transmission is not a mandatory entry
pass
# Thickness in mm
try:
value = float(line_toks[5])
if has_converter == True and \
output.sample.thickness_unit != 'cm':
conv = Converter('cm')
output.sample.thickness = conv(
value, units=output.sample.thickness_unit)
else:
output.sample.thickness = value
except:
# Thickness is not a mandatory entry
pass
#MON CNT LAMBDA DET ANG DET DIST TRANS THICK
# AVE STEP
if line.count("LAMBDA") > 0:
is_info = True
# Find center info line
if is_center == True:
is_center = False
line_toks = line.split()
# Center in bin number
center_x = float(line_toks[0])
center_y = float(line_toks[1])
# Bin size
if has_converter == True and \
detector.pixel_size_unit != 'mm':
conv = Converter('mm')
detector.pixel_size.x = conv(
5.0, units=detector.pixel_size_unit)
detector.pixel_size.y = conv(
5.0, units=detector.pixel_size_unit)
else:
detector.pixel_size.x = 5.0
detector.pixel_size.y = 5.0
# Store beam center in distance units
# Det 640 x 640 mm
if has_converter == True and \
detector.beam_center_unit != 'mm':
conv = Converter('mm')
detector.beam_center.x = conv(
center_x * 5.0,
units=detector.beam_center_unit)
detector.beam_center.y = conv(
center_y * 5.0,
units=detector.beam_center_unit)
else:
detector.beam_center.x = center_x * 5.0
detector.beam_center.y = center_y * 5.0
# Detector type
try:
detector.name = line_toks[7]
except:
# Detector name is not a mandatory entry
pass
#BCENT(X,Y) A1(mm) A2(mm) A1A2DIST(m) DL/L
# BSTOP(mm) DET_TYP
if line.count("BCENT") > 0:
is_center = True
# Parse the data
if is_data_started == True:
toks = line.split()
try:
_x = float(toks[0])
_y = float(toks[1])
_dy = float(toks[2])
_dx = float(toks[3])
if data_conv_q is not None:
_x = data_conv_q(_x, units=output.x_unit)
_dx = data_conv_i(_dx, units=output.x_unit)
if data_conv_i is not None:
_y = data_conv_i(_y, units=output.y_unit)
_dy = data_conv_i(_dy, units=output.y_unit)
x = numpy.append(x, _x)
y = numpy.append(y, _y)
dy = numpy.append(dy, _dy)
dx = numpy.append(dx, _dx)
except:
# Could not read this data line. If we are here
# it is because we are in the data section. Just
# skip it.
pass
#The 6 columns are | Q (1/A) | I(Q) (1/cm) | std. dev.
# I(Q) (1/cm) | sigmaQ | meanQ | ShadowFactor|
if line.count("The 6 columns") > 0:
is_data_started = True
# Sanity check
if not len(y) == len(dy):
msg = "abs_reader: y and dy have different length"
raise ValueError, msg
# If the data length is zero, consider this as
# though we were not able to read the file.
if len(x) == 0:
raise ValueError, "ascii_reader: could not load file"
output.x = x[x != 0]
output.y = y[x != 0]
output.dy = dy[x != 0]
output.dx = dx[x != 0]
if data_conv_q is not None:
output.xaxis("\\rm{Q}", output.x_unit)
else:
output.xaxis("\\rm{Q}", 'A^{-1}')
if data_conv_i is not None:
output.yaxis("\\rm{Intensity}", output.y_unit)
else:
output.yaxis("\\rm{Intensity}", "cm^{-1}")
# Store loading process information
output.meta_data['loader'] = self.type_name
return output
else:
raise RuntimeError, "%s is not a file" % path
return None
def setUp(self):
x = numpy.asarray([1., 2., 3., 4., 5., 6., 7., 8., 9.])
y = numpy.asarray([1., 2., 3., 4., 5., 6., 7., 8., 9.])
dy = y / 10.0
self.data = Data1D(x=x, y=y, dy=dy)
