def setSuffix(selforcls, newSuffix):
if newSuffix is None:
return
testfor(
isString(newSuffix) and len(newSuffix) > 0, SuffixError,
"Parameter suffix has to be some text!")
selforcls._suffix = newSuffix
def setGenerator(selforcls, newGenerator):
if isinstance(newGenerator, type):
testfor(issubclass(newGenerator, NumberGenerator),
ParameterGeneratorError, "NumberGenerator type expected!")
else:
newGenerator = RandomUniform
selforcls._generator = newGenerator
def setValue(selforcls, newValue):
testfor(newValue is not None, DefaultValueError,
"Default value is mandatory!")
if selforcls._value == newValue:
return # no update necessary
selforcls._value = newValue
if isCallable(selforcls.onValueUpdate()):
selforcls.onValueUpdate()()
def setValue(selforcls, newValue, clip=True):
if newValue is None:
return # ignore
testfor(isNumber(newValue), DefaultValueError,
u"A value has to be numerical! ({})".format(newValue))
if clip:
# clip to min/max values:
newValue = selforcls.clip(newValue)
super(ParameterNumerical, selforcls).setValue(newValue)
def setValueRange(self, newRange):
testfor(isList(newRange), ValueRangeError,
"A value range for a string type parameter has to be a list!")
testfor(all([isString(v) for v in newRange]), ValueRangeError,
"A value range for a string has to be a list of strings!")
self._valueRange = newRange
if not (self.value() in self.valueRange()):
# where are the default values?
self.setValue(self.valueRange()[0])
def __call__(self, dataset):
if self.model is None:
logging.warning("No model set!")
return
# start log file writing
testfor(isinstance(dataset, DataSet), Exception,
"{cls} requires a DataSet!".format(cls=type(self)))
self._outFn = OutputFilename(dataset)
fn = self._outFn.filenameVerbose("log", "this log")
logFile = logging.FileHandler(fn, encoding="utf8")
widgetHandler = log.getWidgetHandlers()[0]
log.replaceHandler(widgetHandler) # remove everything else
log.addHandler(logFile)
try:
# show last lines about pdf output from separate thread
widgetHandler.widget.addWatchDir(self._outFn.outDir)
except AttributeError:
pass
self._writeSettings(dict(), dataset)
if self.nolog: # refers to the widgethandler
log.removeHandler(widgetHandler)
#set data in the algorithm
self._algo.data = dataset
# write HDF5, show exceptions traceback, if any
try:
self.hdfStore(self._outFn.filenameVerbose(
"hdf5archive",
"Complete state of the calculation",
extension='.hdf5'),
rootLocation="mcsasentry")
except Exception as e:
import traceback
print(traceback.format_exc())
self._algo.calc()
if self.nolog:
log.addHandler(widgetHandler)
if isList(self._algo.result) and len(self._algo.result):
res = self._algo.result[0]
# quick hack for now, will get fixed with Parameter design
for p in self.model.activeParams():
self._writeDistrib(p)
self._writeStatistics(p)
self._updateSeries(dataset, self.model)
# plotting last so stats were already calculated
if res is not None:
self._writeFit(res)
self._writeContribs(res)
self._algo.plot(outputFilename=self._outFn,
autoClose=self._algo.autoClose())
else:
logging.info("No results available!")
log.removeHandler(logFile)
def setDecimals(selforcls, newDecimals):
if newDecimals is not None:
testfor(
isNumber(newDecimals) and newDecimals >= 0, DecimalsError,
"Parameter decimals has to be a positive number!")
else:
start, end = selforcls._valueRange
newDecimals = round(math_log10(math_fabs(end - start)))
newDecimals = max(newDecimals, 0)
newDecimals = min(newDecimals, sys.float_info.max_10_exp)
selforcls._decimals = int(newDecimals)
def setParams(cls, *parameters):
"""Expects a list of ParameterBase classes/types and sets them as
class attributes to this class. They will become instances later,
please see __init__()"""
cls._parameters = []
for i, p in enumerate(parameters):
testfor(
isinstance(p, type) and issubclass(p, ParameterBase),
AlgorithmParameterError, "{name}: Expected a "
"ParameterBase type for parameter {index}, got {type}!".format(
name=cls.__name__, index=i, type=p))
cls.setParam(p)
def __init__(self):
"""Creates instances from defined parameters and replaces the class
attributes accordingly."""
testfor(
self._name is not None, AlgorithmNameError, "No name provided! "
"Set up {name} by calling factory() first.".format(
name=type(self).__name__))
testfor(
self._parameters is not None, AlgorithmParameterError,
"Parameters not configured! "
"Set up {name} by calling factory() first.".format(
name=type(self).__name__))
paramTypes = self.params()
self._parameters = None
for ptype in paramTypes:
self.setParam(ptype())
def test(cls, filename):
"""Regression test of a scattering model. File names are expected
to contain the parameter values which produce the provided intensity.
Otherwise implement fixTestParams() for the particular model.
- *filename*: Name of the file in cls.testDataDir to test against.
- *cls.testRelErr*: Acceptable mean of relative error against reference
intensity. Default: 1e-5
- *cls.testVolExp*: Volume compensation exponent, sets the amount of
volume contribution the intensity is scaled by.
- *cls.testDataDir*: Directory of test data relative to program dir.
Default: "testdata"
"""
return # this does not work anymore
relerr = getattr(cls, "testRelErr", 1e-5)
datadir = getattr(cls, "testDataDir", "testdata")
volumeExponent = getattr(cls, "testVolExp", 1.0)
filename = os.path.abspath(os.path.join(datadir, filename))
#dataset = SASData.load(filename)
if dataset is None:
return
model = cls()
testParams = model.getParametersFromFilename(dataset.filename)
model.update(testParams)
# intensity how it's calculated in SASfit
intensity = (model.vol(None,
compensationExponent = volumeExponent)
* model._formfactor(dataset, None))**2.
# computing the relative error to reference data
delta = abs((dataset.f.binnedData - intensity) / dataset.f.binnedData)
dmax = argmax(delta)
testfor(delta.mean() < relerr, AssertionError,
"Could not verify {model} intensity against\n'{fn}',"
"\nmean(delta): {mean} >= relErr: {relerr}"
"\nQ, Int_ref, Int_calc, Delta around dmax({dmax}):"
"\n{data}"
.format(model = cls.name(), fn = filename,
mean = delta.mean(), relerr = relerr,
dmax = dmax, data = hstack((
dataset.x0.binnedData.reshape(-1, 1),
dataset.f.binnedData.reshape(-1, 1),
intensity.reshape(-1, 1),
delta.reshape(-1, 1)))[max(0, dmax-4):dmax+5]
)
)
def getParametersFromFilename(cls, filename):
"""Derives model parameters for testing from reference data file."""
pnames = os.path.splitext(os.path.basename(filename))[0]
pnames = pnames.split("-")
errorMsg = ("Could not infer {model} parameters from '{fn}'!"
.format(model = cls.name(), fn = filename))
testfor(len(pnames) > cls.paramCount(), NameError, errorMsg)
# exclude the base name at front
pnames = tuple(enumerate(pnames[1:]))
# store all values by index for reference in fixTestParams()
result = dict(pnames)
# map values to parameter names beginning at front
for i, valueStr in pnames:
try:
p = cls.param(i) # raises IndexError eventually
result[p.name()] = p.dtype(valueStr)
except IndexError: continue
return result
def _makeEntry(self,
name,
dtype,
value,
minmax=None,
widgetType=None,
parent=None,
decimals=None,
**kwargs):
testfor(
name not in self.keys, KeyError,
"Input widget '{w}' exists already in '{s}'".format(
w=name, s=self.objectName()))
if widgetType is None:
if dtype is float:
widgetType = SciEntryBox
else:
widgetType = self.getInputWidget(dtype)
if parent is None:
parent = self
widget = widgetType(parent, **kwargs)
widget.setObjectName(name)
if decimals is not None: # set precision before the value is set
widget.setDecimals(decimals)
if dtype is bool:
widget.setCheckable(True)
widget.setChecked(value)
else:
widget.setAlignment(Qt.AlignRight | Qt.AlignVCenter)
if isList(minmax) and len(minmax):
widget.setMinimum(min(minmax))
widget.setMaximum(max(minmax))
widget.setValue(value)
# update tooltip with help text about valid input values
decimals = getattr(widget, "decimals", None)
if decimals is not None:
decimals = decimals()
try:
SciEntryBox.updateToolTip(widget, decimals)
except:
pass # for non-float input widgets
self.connectInputWidgets(widget)
return widget
def setDisplayValues(selforcls, newDisplayValues):
if newDisplayValues is None:
return
testfor(isMap(newDisplayValues), DisplayValuesError,
"Expected a display value mapping of numbers to text!")
testfor(all([isNumber(v) for v in newDisplayValues.keys()]),
DisplayValuesError, "Display value keys have to be numbers!")
testfor(all([isString(s) for s in newDisplayValues.values()]),
DisplayValuesError, "Display values have to be text!")
# TODO: also add reverse lookup
selforcls._displayValues = newDisplayValues
def setValueRange(selforcls, newRange):
testfor(isList(newRange), ValueRangeError,
"A value range is mandatory for a numerical parameter!")
testfor(
len(newRange) == 2, ValueRangeError,
"A value range has to consist of two values!")
testfor(all([isNumber(v) for v in newRange]), ValueRangeError,
"A value range has to consist of numbers only!")
minVal, maxVal = min(newRange), max(newRange)
# minVal = max(minVal, -sys.float_info.max)
# maxVal = min(maxVal, sys.float_info.max)
# avoid inf/nan showing up somewhere
# otherwise, inf might be ok if UI elements support it (going in&out)
minVal = max(minVal, -1e200) # as good as -inf?...
maxVal = min(maxVal, 1e200) # as good as inf?...
selforcls._valueRange = minVal, maxVal
# apply limits to value:
selforcls.setValue(selforcls.clip())
def setStepping(selforcls, newStepping):
if newStepping is None:
return
testfor(isNumber(newStepping), SteppingError,
"Parameter has to be a number!")
selforcls._stepping = newStepping
def __init__(self):
self._path = self._getPath()
testfor(
self._path is not None and os.path.isfile(self._path), OSError,
"{name}: '{path}' not found!".format(name=self._name,
path=self._path))
