def load(self, z, subpath):
"""load data from a zipped project file
z -- zipped project file
subpath -- path to its own storage within project file
returns a tree of internal hierachy
"""
# subpath = projName/myName/
from diffpy.pdfgui.utils import unquote_plain
subs = subpath.split('/')
rootDict = z.fileTree[subs[0]][subs[1]]
if rootDict.has_key('structure'):
for strucName in rootDict['structure'].keys():
struc = FitStructure(unquote_plain(strucName))
struc.load(z, subpath + 'structure/' + strucName + '/')
self.add(struc)
if rootDict.has_key('dataset'):
for datasetName in rootDict['dataset'].keys():
dataset = FitDataSet(unquote_plain(datasetName))
dataset.load(z, subpath + 'dataset/' + datasetName + '/')
self.add(dataset)
if rootDict.has_key('calculation'):
for calcName in rootDict['calculation'].keys():
calc = Calculation(unquote_plain(calcName))
calc.load(z, subpath + 'calculation/' + calcName + '/')
self.add(calc)
self.__forward_spdiameter()
return self.organization()
def load(self, z, subpath):
"""load data from a zipped project file
z -- zipped project file
subpath -- path to its own storage within project file
returns a tree of internal hierachy
"""
# subpath = projName/myName/
from diffpy.pdfgui.utils import unquote_plain
subs = subpath.split('/')
rootDict = z.fileTree[subs[0]][subs[1]]
if rootDict.has_key('structure'):
for strucName in rootDict['structure'].keys():
struc = FitStructure(unquote_plain(strucName))
struc.load(z, subpath + 'structure/' + strucName + '/')
self.add(struc)
if rootDict.has_key('dataset'):
for datasetName in rootDict['dataset'].keys():
dataset = FitDataSet(unquote_plain(datasetName))
dataset.load(z, subpath + 'dataset/' + datasetName + '/')
self.add(dataset)
if rootDict.has_key('calculation'):
for calcName in rootDict['calculation'].keys():
calc = Calculation(unquote_plain(calcName))
calc.load(z, subpath + 'calculation/' + calcName + '/')
self.add(calc)
self.__forward_spdiameter()
return self.organization()
def loadDataset(self, targetID, filename, name=None, position=None):
"""load Dataset from a file to a Fitting
targetID -- reference to Fitting
name -- name of the new Dataset, default is file basename
position -- where the dataset is to be inserted, default is last
return: Dataset reference
"""
self.__validateType(targetID)
if name is None:
name = os.path.basename(filename)
#insert to target
dataset = FitDataSet(name)
dataset.readObs(filename)
targetID.add(dataset, position)
return dataset
def test__resampledPDFDataSet(self):
"""check FitDataSet._resampledPDFDataSet()
"""
fNi_data = datafile("Ni_2-8.chi.gr")
fds = FitDataSet('Ni')
fds.read(fNi_data)
npts = len(fds.rcalc)
rds = fds._resampledPDFDataSet()
self.assertEqual(npts, len(rds.robs))
self.assertEqual(npts, len(rds.drobs))
self.assertEqual(npts, len(rds.Gobs))
self.assertEqual(npts, len(rds.dGobs))
# reduce fitrmax to one half
fds.fitrmax = fds.rmax / 2.0
npts1 = len(fds.rcalc)
self.failUnless(npts1 < npts)
rds1 = fds._resampledPDFDataSet()
self.assertEqual(npts1, len(rds1.robs))
self.assertEqual(npts1, len(rds1.drobs))
self.assertEqual(npts1, len(rds1.Gobs))
self.assertEqual(npts1, len(rds1.dGobs))
return
def read_data_string(self, s, stype, qmax, qdamp, name=""):
"""Read pdf data from a string into memory.
s -- string containing the contents of the data file
stype -- 'X' (xray) or 'N' (neutron)
qmax -- maximum q value
qdamp -- instrumental q-resolution factor
name -- tag with which to label data
"""
curfit = self._fits[-1]
fd = FitDataSet(name)
fd.readStr(s)
fd.stype = stype
fd.qmax = qmax
fd.qdamp = qdamp
curfit.add(fd)
self._curdataset = len(curfit.datasets) - 1
return
def read_data(self, filename, stype, qmax, qdamp):
"""Read pdf data from file into memory.
filename -- name of file from which to read data
stype -- 'X' (xray) or 'N' (neutron)
qmax -- maximum q value
qdamp -- instrumental q-resolution factor
Raises:
IOError when the file cannot be read from disk
"""
curfit = self._fits[-1]
name, ext = os.path.splitext(os.path.basename(filename))
fd = FitDataSet(name)
fd.read(filename)
fd.stype = stype
fd.qmax = qmax
fd.qdamp = qdamp
curfit.add(fd)
self._curdataset = len(curfit.datasets) - 1
return
def read_data_string(self, s, stype, qmax, qdamp, name=""):
"""Read pdf data from a string into memory.
s -- string containing the contents of the data file
stype -- 'X' (xray) or 'N' (neutron)
qmax -- maximum q value
qdamp -- instrumental q-resolution factor
name -- tag with which to label data
"""
curfit = self._fits[-1]
fd = FitDataSet(name)
fd.readStr(s)
fd.stype = stype
fd.qmax = qmax
fd.qdamp = qdamp
curfit.add(fd)
self._curdataset = len(curfit.datasets) - 1
return
def read_data(self, filename, stype, qmax, qdamp):
"""Read pdf data from file into memory.
filename -- name of file from which to read data
stype -- 'X' (xray) or 'N' (neutron)
qmax -- maximum q value
qdamp -- instrumental q-resolution factor
Raises:
IOError when the file cannot be read from disk
"""
curfit = self._fits[-1]
name, ext = os.path.splitext(os.path.basename(filename))
fd = FitDataSet(name)
fd.read(filename)
fd.stype = stype
fd.qmax = qmax
fd.qdamp = qdamp
curfit.add(fd)
self._curdataset = len(curfit.datasets) - 1
return
def makeDopingSeries(control, fit, base, dopant, paths, doping):
"""Make a temperature series.
control -- pdguicontrol instance
fit -- The template fit
base -- Name of the base element
dopant -- Name of the dopant element
paths -- list of path names of new datasets
doping -- list of doping values corresponding to the datasets
returns a list of the new fit organization objects
"""
from diffpy.pdffit2 import is_element
# Make sure that base and dopant are elements
base = base.title()
dopant = dopant.title()
if not is_element(base):
raise ControlValueError("'%s' is not an element!"%base)
if not is_element(dopant):
raise ControlValueError("'%s' is not an element!"%dopant)
# Make sure that base and dopant are in the structure file(s)
hasBase = False
hasDopant = False
for S in fit.strucs:
for atom in S:
if atom.element == base:
hasBase = True
if atom.element == dopant:
hasDopant = True
if hasBase and hasDopant: break
if not hasBase:
message = "The template structure does not contain the base atom."
raise ControlValueError(message)
if not hasDopant:
message = "The template structure does not contain the dopant atom."
raise ControlValueError(message)
# Make sure we're only replacing a single dataset
if len(fit.datasets) != 1:
message = "Can't apply macro to fits with multiple datasets."
raise ControlValueError(message)
fits = []
# holds all of the other information about the dataset
fitbasename = fit.name
fitnewname = fit.name
fitlastname = fit.name
dataset = fit.datasets[0]
for i in range(len(paths)):
filename = paths[i]
fitlastname = fitnewname
fitcopy = control.copy(fit)
# Get rid of the old dataset
temp = fitcopy.datasets[0]
fitcopy.remove(temp)
# Configure the new dataset
dsname = os.path.basename(filename)
newdataset = FitDataSet(dsname)
newdataset.readObs(filename)
newdataset.qdamp = dataset.qdamp
newdataset.qbroad = dataset.qbroad
newdataset.dscale = dataset.dscale
newdataset.fitrmin = dataset.fitrmin
newdataset.fitrmax = dataset.fitrmax
rstep = dataset.fitrstep
st = dataset.getFitSamplingType()
newdataset.setFitSamplingType(st, rstep)
temperature = dataset.metadata.get("temperature")
if temperature is None: temperature = 300.0
newdataset.metadata["temperature"] = temperature
newdataset.constraints = copy.deepcopy(dataset.constraints)
# Set the chosen temperature
newdataset.metadata["doping"] = doping[i]
# Add the dataset to the fitcopy
fitcopy.add(newdataset, None)
# Update the doping information in the structures
for S in fitcopy.strucs:
for A in S:
if A.element == dopant:
A.occupancy = doping[i]
if A.element == base:
A.occupancy = 1-doping[i]
# Set the parameters to the previous fit's name, if one exists.
if fitlastname:
parval = "=%s" % fitlastname
for par in fitcopy.parameters.values():
par.setInitial(parval)
# Now paste the copy into the control.
fitnewname = "%s-%1.4f" % (fitbasename, doping[i])
o = control.paste(fitcopy, new_name = fitnewname)
fits.append(o)
return [f.organization() for f in fits]
def makeTemperatureSeries(control, fit, paths, temperatures):
"""Make a temperature series.
control -- pdguicontrol instance
fit -- The template fit
paths -- list of path names of new datasets
temperatures -- list of temperatures corresponding to the datasets
returns a list of the new fit organization objects
"""
if len(fit.datasets) != 1:
message = "Can't apply macro to fits with multiple datasets."
raise ControlValueError(message)
fits = []
# holds all of the other information about the dataset
fitbasename = fit.name
fitnewname = fit.name
fitlastname = fit.name
dataset = fit.datasets[0]
for i in range(len(paths)):
filename = paths[i]
fitlastname = fitnewname
fitcopy = control.copy(fit)
# Get rid of the old dataset
temp = fitcopy.datasets[0]
fitcopy.remove(temp)
# Configure the new dataset
dsname = os.path.basename(filename)
newdataset = FitDataSet(dsname)
newdataset.readObs(filename)
newdataset.qdamp = dataset.qdamp
newdataset.qbroad = dataset.qbroad
newdataset.dscale = dataset.dscale
newdataset.fitrmin = dataset.fitrmin
newdataset.fitrmax = dataset.fitrmax
rstep = dataset.fitrstep
st = dataset.getFitSamplingType()
newdataset.setFitSamplingType(st, rstep)
doping = dataset.metadata.get("doping")
if doping is None: doping = 0.0
newdataset.metadata["doping"] = doping
newdataset.constraints = copy.deepcopy(dataset.constraints)
# Set the chosen temperature
newdataset.metadata["temperature"] = temperatures[i]
# Add the dataset to the fitcopy
fitcopy.add(newdataset, None)
# Set the parameters to the previous fit's name, if one exists.
if fitlastname:
parval = "=%s" % fitlastname
for par in fitcopy.parameters.values():
par.setInitial(parval)
# Now paste the copy into the control.
fitnewname = "%s-T%i=%g" % (fitbasename, i + 1, temperatures[i])
o = control.paste(fitcopy, new_name = fitnewname)
fits.append(o)
return [f.organization() for f in fits]
def makeDopingSeries(control, fit, base, dopant, paths, doping):
"""Make a temperature series.
control -- pdguicontrol instance
fit -- The template fit
base -- Name of the base element
dopant -- Name of the dopant element
paths -- list of path names of new datasets
doping -- list of doping values corresponding to the datasets
returns a list of the new fit organization objects
"""
from diffpy.pdffit2 import is_element
# Make sure that base and dopant are elements
base = base.title()
dopant = dopant.title()
if not is_element(base):
raise ControlValueError("'%s' is not an element!" % base)
if not is_element(dopant):
raise ControlValueError("'%s' is not an element!" % dopant)
# Make sure that base and dopant are in the structure file(s)
hasBase = False
hasDopant = False
for S in fit.strucs:
for atom in S:
if atom.element == base:
hasBase = True
if atom.element == dopant:
hasDopant = True
if hasBase and hasDopant: break
if not hasBase:
message = "The template structure does not contain the base atom."
raise ControlValueError(message)
if not hasDopant:
message = "The template structure does not contain the dopant atom."
raise ControlValueError(message)
# Make sure we're only replacing a single dataset
if len(fit.datasets) != 1:
message = "Can't apply macro to fits with multiple datasets."
raise ControlValueError(message)
fits = []
# holds all of the other information about the dataset
fitbasename = fit.name
fitnewname = fit.name
fitlastname = fit.name
dataset = fit.datasets[0]
for i in range(len(paths)):
filename = paths[i]
fitlastname = fitnewname
fitcopy = control.copy(fit)
# Get rid of the old dataset
temp = fitcopy.datasets[0]
fitcopy.remove(temp)
# Configure the new dataset
dsname = os.path.basename(filename)
newdataset = FitDataSet(dsname)
newdataset.readObs(filename)
newdataset.qdamp = dataset.qdamp
newdataset.qbroad = dataset.qbroad
newdataset.dscale = dataset.dscale
newdataset.fitrmin = dataset.fitrmin
newdataset.fitrmax = dataset.fitrmax
rstep = dataset.fitrstep
st = dataset.getFitSamplingType()
newdataset.setFitSamplingType(st, rstep)
temperature = dataset.metadata.get("temperature")
if temperature is None: temperature = 300.0
newdataset.metadata["temperature"] = temperature
newdataset.constraints = copy.deepcopy(dataset.constraints)
# Set the chosen temperature
newdataset.metadata["doping"] = doping[i]
# Add the dataset to the fitcopy
fitcopy.add(newdataset, None)
# Update the doping information in the structures
for S in fitcopy.strucs:
for A in S:
if A.element == dopant:
A.occupancy = doping[i]
if A.element == base:
A.occupancy = 1 - doping[i]
# Set the parameters to the previous fit's name, if one exists.
if fitlastname:
parval = "=%s" % fitlastname
for par in fitcopy.parameters.values():
par.setInitial(parval)
# Now paste the copy into the control.
fitnewname = "%s-%1.4f" % (fitbasename, doping[i])
o = control.paste(fitcopy, new_name=fitnewname)
fits.append(o)
return [f.organization() for f in fits]
def makeTemperatureSeries(control, fit, paths, temperatures):
"""Make a temperature series.
control -- pdguicontrol instance
fit -- The template fit
paths -- list of path names of new datasets
temperatures -- list of temperatures corresponding to the datasets
returns a list of the new fit organization objects
"""
if len(fit.datasets) != 1:
message = "Can't apply macro to fits with multiple datasets."
raise ControlValueError(message)
fits = []
# holds all of the other information about the dataset
fitbasename = fit.name
fitnewname = fit.name
fitlastname = fit.name
dataset = fit.datasets[0]
for i in range(len(paths)):
filename = paths[i]
fitlastname = fitnewname
fitcopy = control.copy(fit)
# Get rid of the old dataset
temp = fitcopy.datasets[0]
fitcopy.remove(temp)
# Configure the new dataset
dsname = os.path.basename(filename)
newdataset = FitDataSet(dsname)
newdataset.readObs(filename)
newdataset.qdamp = dataset.qdamp
newdataset.qbroad = dataset.qbroad
newdataset.dscale = dataset.dscale
newdataset.fitrmin = dataset.fitrmin
newdataset.fitrmax = dataset.fitrmax
rstep = dataset.fitrstep
st = dataset.getFitSamplingType()
newdataset.setFitSamplingType(st, rstep)
doping = dataset.metadata.get("doping")
if doping is None: doping = 0.0
newdataset.metadata["doping"] = doping
newdataset.constraints = copy.deepcopy(dataset.constraints)
# Set the chosen temperature
newdataset.metadata["temperature"] = temperatures[i]
# Add the dataset to the fitcopy
fitcopy.add(newdataset, None)
# Set the parameters to the previous fit's name, if one exists.
if fitlastname:
parval = "=%s" % fitlastname
for par in fitcopy.parameters.values():
par.setInitial(parval)
# Now paste the copy into the control.
fitnewname = "%s-T%i=%g" % (fitbasename, i + 1, temperatures[i])
o = control.paste(fitcopy, new_name=fitnewname)
fits.append(o)
return [f.organization() for f in fits]
