class FieldContainer(DataContainer):
u"""FieldContainer(data, unit=1, error=None,dimensions=None, longname=u"Sampled Field",
\t\t\t shortname=u"\\Psi",rescale=False)
\t Class describing sampled fields:
\t .data\t\t- Numpy.array representing the sampled field.
\t .unit\t\t- Quantity object denoting the unit of the sampled field.
\t .dimensions\t- List of FieldContainer instances
\t\t\t describing the dimensions of the sampled field.
\t .data \t- Sampled field stored as numpy.array, which is rescaled to reasonable basic units if option rescale is chosen.
\t .error\t- Absolut error of the sampled field stored as numpy.array
\t .longname \t- Notation of the data, e.g. 'electric field',
\t\t\t which is used for the automatic annotation of charts.
\t .shortname \t- Symbol of the physical variable in LaTeX notation, e.g. 'E_\\alpha',
\t\t\t which is also used for the automatic annotation of charts.
\t .id \t\t- Identifier of Enhanced MD5 (emd5) format
\t\t\t\temd5://NODE/USER/DATETIME/MD5-HASH.TYPESTRING
\t\t\t which is set by calling method .seal() and
\t\t\t indicates that the stored information are unchangable.
\t .label\t- Typical axis description composed from the meta information of the DataContainer.
Concerning the ordering of data matrices and the dimension list consult http://wiki.pyphant.org/xwiki/bin/view/Main/Dimension+Handling+in+Pyphant.
"""
typeString = u"field"
def __init__(self, data, unit=1, error=None, mask=None,
dimensions=None, longname=u"Sampled Field",
shortname=u"\\Psi", attributes=None, rescale=False):
DataContainer.__init__(self, longname, shortname, attributes)
self.data = data
self.mask = mask
try:
if isinstance(unit, (str, unicode)):
unit = unit.replace('^', '**')
if isinstance(unit, unicode):
unit = unit.encode('utf-8')
self.unit = Quantity(unit)
except:
try:
self.unit = Quantity("1"+unit)
except:
self.unit = unit
self.error = error
if dimensions != None:
self.dimensions = dimensions
else:
N = len(data.shape)-1
self.dimensions = [generateIndex(N-i,n) for i,n in enumerate(data.shape)]
if rescale:
self.rescale()
for dim in self._dimensions:
dim.rescale()
assert self.isValid()
def _set_dimensions(self,dimensions):
self._dimensions = DimensionList(dimensions)
def _get_dimensions(self):
return self._dimensions
dimensions = property(_get_dimensions,_set_dimensions)
def _getLabel(self):
if len(self._dimensions)>0:
shortnames = [dim.shortname for dim in self._dimensions]
shortnames.reverse()
dependency = '(%s)' % ','.join(shortnames)
else:
dependency = ''
label = u"%s $%s%s$ / %s" % (self.longname.title(), self.shortname, dependency, self.unit)
try:
if not isQuantity(self.unit) and self.unit == 1:
label = u"%s $%s%s$ / a.u." % (self.longname.title(),self.shortname,dependency)
except:
pass #just a ScientificPython bug
return label.replace('1.0 ',r'')#.replace('mu',u'\\textmu{}')
label=property(_getLabel)
def _getShortLabel(self):
if not isQuantity(self.unit) and self.unit == 1:
if self.longname == 'index':
label = u"%s $%s$" % (self.longname.title(),self.shortname)
else:
label = u"%s $%s$ / a.u." % (self.longname.title(),self.shortname)
else:
label = u"%s $%s$ / %s" % (self.longname.title(), self.shortname, self.unit)
return label.replace('1.0 ',r'')#.replace('mu',u'\\textmu{}')
shortlabel=property(_getShortLabel)
def _getRawDataBytes(self):
return self.data.nbytes \
+ sum([dim.rawDataBytes for dim in self.dimensions])
rawDataBytes = property(_getRawDataBytes)
def __deepcopy__(self, memo):
self.lock.acquire()
data=copy.deepcopy(self.data, memo)
data.setflags(write=True)
mask=copy.deepcopy(self.mask, memo)
if mask!=None:
mask.setflags(write=True)
error=copy.deepcopy(self.error, memo)
if error!=None:
error.setflags(write=True)
dimensions=copy.deepcopy(self._dimensions, memo)
res = FieldContainer(data, self.unit, error, mask, dimensions,
self.longname, self.shortname)
self.lock.release()
return res
def generateHash(self, m=None):
if m == None:
m = hashlib.md5()
super(FieldContainer, self).generateHash(m)
#m.update(str(self.data.tolist()))
m.update(self.data.dumps())
m.update(str(self.unit))
if self.error!=None:
#m.update(str(self.error.tolist()))
m.update(self.error.dumps())
if self.mask!=None:
#m.update(str(self.mask.tolist()))
m.update(self.mask.dumps())
[m.update(dim.hash) for dim in self._dimensions]
return enc(m.hexdigest())
def seal(self, id=None):
with self.lock:
assert self.isValid()
self.data.setflags(write=False)
if self.mask!=None:
self.mask.setflags(write=False)
if self.error!=None:
self.error.setflags(write=False)
if not id:
self._dimensions.write = False
for dim in self._dimensions:
dim.seal()
super(FieldContainer, self).seal(id)
def inUnitsOf(self, other):
if not isQuantity(self.unit):
if isQuantity(other.unit):
raise ValueError("Incompatible Units: self.unit = <%s>, other.unit = <%s>"%(self.unit, other.unit))
factor = float(self.unit)/float(other.unit)
elif not isQuantity(other.unit):
raise ValueError("Incompatible Units: self.unit = <%s>, other.unit = <%s>"%(self.unit, other.unit))
else:
if not self.unit.isCompatible(other.unit.unit):
raise ValueError("Incompatible Units: self.unit = <%s>, other.unit = <%s>"%(self.unit, other.unit))
factor = self.unit.inUnitsOf(other.unit.unit).value/other.unit.value
newSelf = copy.deepcopy(self)
newSelf.data *= factor
if newSelf.error != None:
newSelf.error *= factor
newSelf.unit = copy.deepcopy(other.unit)
return newSelf
def rescale(self):
if isQuantity(self.unit):
oldUnit = self.unit.inBaseUnits()
else:
return
#Compute decade of field and multiply it to oldUnit
oldFieldAmplitude = max(abs(numpy.amax(self.data)),abs(numpy.amin(self.data)))
oldUnit *= oldFieldAmplitude
#Compute next lower decade
decade = scipy.log10(oldUnit.value)
newDecade = 10**(scipy.floor(decade))
#Find appropriate prefix
baseUnit=oldUnit.unit.name()
if baseUnit == 'm':
prefixes = PREFIXES_METER
else:
prefixes = PREFIXES
prefixCandidates = map(lambda i: (i[0],abs(i[1]-newDecade)),prefixes)
optPrefix = min([prefix[1] for prefix in prefixCandidates])
newPrefix = filter(lambda prefix: prefix[1]==optPrefix,prefixCandidates)[0][0]
newUnitName = newPrefix+baseUnit
#Convert to new unit
newUnit = oldUnit.inUnitsOf(newUnitName)
unitAmplitude = newUnit.value
if self.data.dtype.name.startswith('int'):
self.unit = newUnit/oldFieldAmplitude
return
self.data *= unitAmplitude/oldFieldAmplitude
self.unit = newUnit/unitAmplitude
def __eq__(self, other, rtol=1e-5, atol=1e-8):
if type(self) != type(other):
if type(other) != IndexMarker and type(other) != NoneType:
_logger.debug('Cannot compare objects with different type (%s and %s).' % (type(self),type(other)))
return False
if not (self.typeString == other.typeString):
_logger.debug('The typeString is not identical.')
return False
if (self.mask==None) and (other.mask!=None):
_logger.debug('The mask of the first field container has not been set, while the mask of the second field container is set to %s.' % other.mask)
return False
elif self.mask!=None and (other.mask==None):
_logger.debug('The mask of the second field container has not been set, while the mask of the first field container is set to %s.' % self.mask)
return False
if not (numpy.alltrue(self.mask==other.mask)):
_logger.debug('The masks are not identical: %s\n%s' % (self.mask,other.mask))
return False
if self.mask!=None:
data = self.data[numpy.logical_not(self.mask)]
otherData = other.data[numpy.logical_not(other.mask)]
if self.error!=None:
error = self.error[numpy.logical_not(self.mask)]
else:
error = self.error
if other.error!=None:
otherError = other.error[numpy.logical_not(other.mask)]
else:
otherError = other.error
else:
data = self.data
error = self.error
otherData = other.data
otherError = other.error
if (isQuantity(self.unit) or isQuantity(other.unit)):
try:
if not (self.unit.inBaseUnits().unit == other.unit.inBaseUnits().unit):
_logger.debug('The units are different.')
return False
except AttributeError:
_logger.debug('Cannot compare unit with normed quantity: %s, %s' % (self.unit,other.unit))
return False
try:
scaledData = data*self.unit.value
scaledOtherData = otherData*other.unit.inUnitsOf(self.unit.unit).value
if not numpy.allclose(scaledData,scaledOtherData,rtol,atol):
if numpy.sometrue(numpy.isnan(scaledData)):
_logger.debug('The fields cannot be compared, because some elements of the first field are NaN and the mask has not been set.')
if numpy.sometrue(numpy.isnan(scaledOtherData)):
_logger.debug('The fields cannot be compared, because some elements of the second field are NaN and the mask has not been set.')
else:
difference = numpy.abs(scaledData-scaledOtherData)
_logger.debug('The scaled fields differ, data-otherData: %s\n%s\n%s' % (difference.max(),
scaledData,
scaledOtherData))
return False
except ValueError:
_logger.debug('Shape mismatch: %s != %s' % (self.data.shape,other.data.shape))
return False
if error!=None:
scaledError = error*self.unit.value
if otherError!=None:
otherScaledError = otherError*other.unit.inUnitsOf(self.unit.unit).value
else:
_logger.debug('The errors differ: The error of the second argument is none, while the error of the first argument is %s.' % error)
return False
if not numpy.allclose(scaledError,otherScaledError,rtol,atol):
_logger.debug('The normed errors differ: %s\n%s' % (scaledError,otherScaledError))
return False
else:
if not data.dtype.char in ['S','U']:
try:
scaledData = data*self.unit
scaledOtherData = otherData*other.unit
if not numpy.allclose(scaledData,scaledOtherData,rtol,atol):
_logger.debug('The scaled fields differ: %s\n%s'%(scaledData,scaledOtherData))
return False
except ValueError:
_logger.debug('Shape mismatch: %s != %s' % (self.data.shape,other.data.shape))
return False
if error==None:
if not (otherError==None):
_logger.debug('The errors differ: Error of first argument is None, but the error of the second argument is not None.')
return False
else:
scaledError = error*self.unit
otherScaledError = otherError*other.unit
if not numpy.allclose(scaledError,otherScaledError,rtol,atol):
_logger.debug('The errors differ: %s\n%s' % (scaledError,otherScaledError))
return False
if not self.attributes == other.attributes:
_logger.debug('The attribute dictionary differs.')
return False
for dimSelf,dimOther in zip(self._dimensions,other.dimensions):
if dimSelf != dimOther:
_logger.debug('Different dimensions: %s, %s' % (dimSelf,dimOther))
return False
return True
def __ne__(self, other):
return not self.__eq__(other)
def __add__(self, other):
if isinstance(other, FieldContainer):
if self.error!=None or other.error!=None:
return NotImplemented
else:
error = None
if len(self._dimensions) != len(other.dimensions):
return NotImplemented
for i in xrange(len(self._dimensions)):
if not self._dimensions[i] == other.dimensions[i]:
return NotImplemented
if isQuantity(self.unit):
if not isQuantity(other.unit):
return NotImplemented
if not self.unit.isCompatible(other.unit.unit):
return NotImplemented
if self.unit >= other.unit:
data = self.data+(other.data*other.unit.value*other.unit.unit.conversionFactorTo(self.unit.unit))/self.unit.value
unit = self.unit
else:
data = other.data+(self.data*self.unit.value*self.unit.unit.conversionFactorTo(other.unit.unit))/other.unit.value
unit = other.unit
elif isQuantity(other.unit):
return NotImplemented
else:
data = (self.data*self.unit) + (other.data*other.unit)
unit = 1.0
if self.mask==None:
mask=other.mask
elif other.mask==None:
mask=self.mask
else:
mask=self.mask+other.mask
longname = u"Sum of %s and %s." % (self.longname, other.longname)
shortname = u"%s + %s" % (self.shortname, other.shortname)
return FieldContainer(data, unit, error, mask,
copy.deepcopy(self._dimensions),
longname, shortname)
return NotImplemented
def __sub__(self, other):
if isinstance(other, FieldContainer):
if self.error!=None or other.error!=None:
error = other.error + self.error
else:
error = None
if len(self._dimensions) != len(other.dimensions):
return NotImplemented
for i in xrange(len(self._dimensions)):
if not self._dimensions[i] == other.dimensions[i]:
return NotImplemented
if isQuantity(self.unit):
if not (isQuantity(other.unit) and self.unit.isCompatible(other.unit.unit)):
return NotImplemented
if self.unit >= other.unit:
data = self.data - (other.data*other.unit.value*other.unit.unit.conversionFactorTo(self.unit.unit))/self.unit.value
unit = self.unit
else:
data = ((self.data*self.unit.value*self.unit.unit.conversionFactorTo(other.unit.unit))/other.unit.value) - other.data
unit = other.unit
else:
if isQuantity(other.unit):
return NotImplemented
data = (self.data*self.unit) - (other.data*other.unit)
unit = 1.0
if self.mask==None:
mask=other.mask
elif other.mask==None:
mask=self.mask
else:
mask=self.mask+other.mask
longname = u"Difference of %s and %s." % (self.longname, other.longname)
shortname = u"%s - %s" % (self.shortname, other.shortname)
return FieldContainer(data, unit, error, mask,
copy.deepcopy(self._dimensions),
longname, shortname)
return NotImplemented
def __str__(self):
deps = [ dim for dim in self._dimensions if type(dim)!=type(IndexMarker()) ]
report = "\nFieldContainer %s of shape %s with field\n%s\n"% (self.label,self.data.shape,self.data)
if self.error != None:
report += ", error\n%s\n" % self.error
if self.mask !=None:
report += ", mask\n%s\n" % self.mask
if len(deps)>0:
report += 'depending on dimensions %s\n' % deps
if len(self.attributes)>0:
report += 'and attributes\n%s\n' % self.attributes
return report
def __repr__(self):
return self.__str__()
def __getitem__(self, args):
if isinstance(args, type("")):
args=[args]
if isinstance(args, type(1)):
if args>=len(self.data):
raise IndexError, 'index out of bound'
try:
len(args)
except:
args = [args]
args = [ slice2ind(arg, self._dimensions[dim]) for dim, arg in enumerate(args) ]
data = self.data[args]
attributes = copy.deepcopy(self.attributes)
mask = None
error = None
dimensions = []
for i,l in enumerate(data.shape[:len(args)]):
dim = self._dimensions[i]
if l==1:
attributes[dim.longname] = (dim.shortname, dim.data[args[i]].squeeze()*dim.unit)
else:
if isinstance(dim, IndexMarker):
dimensions.append(dim)
else:
dimensions.append(dim[args[i],])
for i in xrange(len(args),len(data.shape)):
dimensions.append(copy.deepcopy(self._dimensions[i]))
if data.shape != (1,):
data = data.squeeze()
if self.mask!=None:
mask = self.mask[args].squeeze()
if self.error!=None:
error = self.error[args].squeeze()
else:
if self.mask!=None:
mask = self.mask[args]
if self.error!=None:
error = self.error[args]
field = FieldContainer(data, dimensions=dimensions,
longname=self.longname,
shortname=self.shortname,
mask=mask,
error=error,
unit=self.unit,
attributes=attributes)
return field
def isValid(self):
# Valid dimensions?
if (not (len(self._dimensions)==1
and isinstance(self._dimensions[0], IndexMarker)) #IndexMarkers are valid and...
and not (self.data.shape == (1,) and len(self._dimensions)==0)): #...so are zero dim fields.
dimshape = []
for d in self._dimensions:
if len(d.data.shape)>1:
_logger.debug("Dimension %s is not 1-d." % d.longname)
return False
dimshape.append(d.data.shape[0])
if self.data.shape!=tuple(dimshape):
_logger.debug("Shape of data %s and of dimensions %s do not match for field\n:%s" %
(self.data.shape, dimshape, self))
return False
for d in self._dimensions:
if not d.isValid():
_logger.debug("Invalid dimension %s."%d.longname)
return False
# Valid mask?
if (self.mask!=None) and (self.data.shape!=self.mask.shape):
_logger.debug("Shape of data %s and of mask %s do not match."%(self.data.shape, self.mask.shape))
return False
# Valid error?
if (self.error!=None) and (self.data.shape!=self.error.shape):
_logger.debug("Shape of data %s and of error %s do not match."%(self.data.shape, self.error.shape))
return False
return True
def isIndex(self):
return self.dimensions==INDEX
def isIndependent(self):
for d in self.dimensions:
if not d.isIndex():
return False
return True
def isLinearlyDiscretised(self):
for dim in self.dimensions:
d = numpy.diff(dim.data)
if not numpy.alltrue(numpy.logical_not(d-d[0])):
return False
return True
def getMaskedFC(self, numpymask):
"""
Return an unsealed FieldContainer instance that emerges from
this instance by clipping along the primary axis according to the
entries in the parameter numpymask, i.e. all entries where numpymask
is set to False are discarded.
numpymask -- Numpy array with Boolean values
"""
mdims = []
for dim in self.dimensions:
if mdims == []:
# only primary axis has to be masked:
mdims.append(dim.getMaskedFC(numpymask))
else:
mdims.append(copy.deepcopy(dim))
to_mask = [self.data, self.error, self.mask]
masked = []
for item in to_mask:
if item != None:
masked.append(item[numpymask])
else:
masked.append(None)
return FieldContainer(masked[0],
copy.deepcopy(self.unit),
masked[1],
masked[2],
mdims,
longname=self.longname,
shortname=self.shortname,
attributes=copy.deepcopy(self.attributes),
rescale=False)
maskedData = property( lambda self: numpy.ma.array(self.data, mask=self.mask) )
maskedError = property( lambda self: numpy.ma.array(self.error, mask=self.mask) )
def test_degC(self):
temperature = Quantity('0 degC')
self.assertEqual(temperature.inBaseUnits(), Quantity('273.15 K'))
class FieldContainer(DataContainer):
u"""FieldContainer(data, unit=1, error=None,dimensions=None, longname=u"Sampled Field",
\t\t\t shortname=u"\\Psi",rescale=False)
\t Class describing sampled fields:
\t .data\t\t- Numpy.array representing the sampled field.
\t .unit\t\t- Quantity object denoting the unit of the sampled field.
\t .dimensions\t- List of FieldContainer instances
\t\t\t describing the dimensions of the sampled field.
\t .data \t- Sampled field stored as numpy.array, which is rescaled to reasonable basic units if option rescale is chosen.
\t .error\t- Absolut error of the sampled field stored as numpy.array
\t .longname \t- Notation of the data, e.g. 'electric field',
\t\t\t which is used for the automatic annotation of charts.
\t .shortname \t- Symbol of the physical variable in LaTeX notation, e.g. 'E_\\alpha',
\t\t\t which is also used for the automatic annotation of charts.
\t .id \t\t- Identifier of Enhanced MD5 (emd5) format
\t\t\t\temd5://NODE/USER/DATETIME/MD5-HASH.TYPESTRING
\t\t\t which is set by calling method .seal() and
\t\t\t indicates that the stored information are unchangable.
\t .label\t- Typical axis description composed from the meta information of the DataContainer.
Concerning the ordering of data matrices and the dimension list consult http://wiki.pyphant.org/xwiki/bin/view/Main/Dimension+Handling+in+Pyphant.
"""
typeString = u"field"
def __init__(self,
data,
unit=1,
error=None,
mask=None,
dimensions=None,
longname=u"Sampled Field",
shortname=u"\\Psi",
attributes=None,
rescale=False):
DataContainer.__init__(self, longname, shortname, attributes)
self.data = data
self.mask = mask
try:
if isinstance(unit, (str, unicode)):
unit = unit.replace('^', '**')
if isinstance(unit, unicode):
unit = unit.encode('utf-8')
self.unit = Quantity(unit)
except:
try:
self.unit = Quantity("1" + unit)
except:
self.unit = unit
self.error = error
if dimensions != None:
self.dimensions = dimensions
else:
N = len(data.shape) - 1
self.dimensions = [
generateIndex(N - i, n) for i, n in enumerate(data.shape)
]
if rescale:
self.rescale()
for dim in self._dimensions:
dim.rescale()
assert self.isValid()
def _set_dimensions(self, dimensions):
self._dimensions = DimensionList(dimensions)
def _get_dimensions(self):
return self._dimensions
dimensions = property(_get_dimensions, _set_dimensions)
def _getLabel(self):
if len(self._dimensions) > 0:
shortnames = [dim.shortname for dim in self._dimensions]
shortnames.reverse()
dependency = '(%s)' % ','.join(shortnames)
else:
dependency = ''
label = u"%s $%s%s$ / %s" % (self.longname.title(), self.shortname,
dependency, self.unit)
try:
if not isQuantity(self.unit) and self.unit == 1:
label = u"%s $%s%s$ / a.u." % (self.longname.title(),
self.shortname, dependency)
except:
pass #just a ScientificPython bug
return label.replace('1.0 ', r'') #.replace('mu',u'\\textmu{}')
label = property(_getLabel)
def _getShortLabel(self):
if not isQuantity(self.unit) and self.unit == 1:
if self.longname == 'index':
label = u"%s $%s$" % (self.longname.title(), self.shortname)
else:
label = u"%s $%s$ / a.u." % (self.longname.title(),
self.shortname)
else:
label = u"%s $%s$ / %s" % (self.longname.title(), self.shortname,
self.unit)
return label.replace('1.0 ', r'') #.replace('mu',u'\\textmu{}')
shortlabel = property(_getShortLabel)
def _getRawDataBytes(self):
return self.data.nbytes \
+ sum([dim.rawDataBytes for dim in self.dimensions])
rawDataBytes = property(_getRawDataBytes)
def __deepcopy__(self, memo):
self.lock.acquire()
data = copy.deepcopy(self.data, memo)
data.setflags(write=True)
mask = copy.deepcopy(self.mask, memo)
if mask != None:
mask.setflags(write=True)
error = copy.deepcopy(self.error, memo)
if error != None:
error.setflags(write=True)
dimensions = copy.deepcopy(self._dimensions, memo)
res = FieldContainer(data, self.unit, error, mask, dimensions,
self.longname, self.shortname)
self.lock.release()
return res
def generateHash(self, m=None):
if m == None:
m = hashlib.md5()
super(FieldContainer, self).generateHash(m)
#m.update(str(self.data.tolist()))
m.update(self.data.dumps())
m.update(str(self.unit))
if self.error != None:
#m.update(str(self.error.tolist()))
m.update(self.error.dumps())
if self.mask != None:
#m.update(str(self.mask.tolist()))
m.update(self.mask.dumps())
[m.update(dim.hash) for dim in self._dimensions]
return enc(m.hexdigest())
def seal(self, id=None):
with self.lock:
assert self.isValid()
self.data.setflags(write=False)
if self.mask != None:
self.mask.setflags(write=False)
if self.error != None:
self.error.setflags(write=False)
if not id:
self._dimensions.write = False
for dim in self._dimensions:
dim.seal()
super(FieldContainer, self).seal(id)
def inUnitsOf(self, other):
if not isQuantity(self.unit):
if isQuantity(other.unit):
raise ValueError(
"Incompatible Units: self.unit = <%s>, other.unit = <%s>" %
(self.unit, other.unit))
factor = float(self.unit) / float(other.unit)
elif not isQuantity(other.unit):
raise ValueError(
"Incompatible Units: self.unit = <%s>, other.unit = <%s>" %
(self.unit, other.unit))
else:
if not self.unit.isCompatible(other.unit.unit):
raise ValueError(
"Incompatible Units: self.unit = <%s>, other.unit = <%s>" %
(self.unit, other.unit))
factor = self.unit.inUnitsOf(
other.unit.unit).value / other.unit.value
newSelf = copy.deepcopy(self)
newSelf.data *= factor
if newSelf.error != None:
newSelf.error *= factor
newSelf.unit = copy.deepcopy(other.unit)
return newSelf
def rescale(self):
if isQuantity(self.unit):
oldUnit = self.unit.inBaseUnits()
else:
return
#Compute decade of field and multiply it to oldUnit
oldFieldAmplitude = max(abs(numpy.amax(self.data)),
abs(numpy.amin(self.data)))
oldUnit *= oldFieldAmplitude
#Compute next lower decade
decade = scipy.log10(oldUnit.value)
newDecade = 10**(scipy.floor(decade))
#Find appropriate prefix
baseUnit = oldUnit.unit.name()
if baseUnit == 'm':
prefixes = PREFIXES_METER
else:
prefixes = PREFIXES
prefixCandidates = map(lambda i: (i[0], abs(i[1] - newDecade)),
prefixes)
optPrefix = min([prefix[1] for prefix in prefixCandidates])
newPrefix = filter(lambda prefix: prefix[1] == optPrefix,
prefixCandidates)[0][0]
newUnitName = newPrefix + baseUnit
#Convert to new unit
newUnit = oldUnit.inUnitsOf(newUnitName)
unitAmplitude = newUnit.value
if self.data.dtype.name.startswith('int'):
self.unit = newUnit / oldFieldAmplitude
return
self.data *= unitAmplitude / oldFieldAmplitude
self.unit = newUnit / unitAmplitude
def __eq__(self, other, rtol=1e-5, atol=1e-8):
if type(self) != type(other):
if type(other) != IndexMarker and type(other) != NoneType:
_logger.debug(
'Cannot compare objects with different type (%s and %s).' %
(type(self), type(other)))
return False
if not (self.typeString == other.typeString):
_logger.debug('The typeString is not identical.')
return False
if (self.mask == None) and (other.mask != None):
_logger.debug(
'The mask of the first field container has not been set, while the mask of the second field container is set to %s.'
% other.mask)
return False
elif self.mask != None and (other.mask == None):
_logger.debug(
'The mask of the second field container has not been set, while the mask of the first field container is set to %s.'
% self.mask)
return False
if not (numpy.alltrue(self.mask == other.mask)):
_logger.debug('The masks are not identical: %s\n%s' %
(self.mask, other.mask))
return False
if self.mask != None:
data = self.data[numpy.logical_not(self.mask)]
otherData = other.data[numpy.logical_not(other.mask)]
if self.error != None:
error = self.error[numpy.logical_not(self.mask)]
else:
error = self.error
if other.error != None:
otherError = other.error[numpy.logical_not(other.mask)]
else:
otherError = other.error
else:
data = self.data
error = self.error
otherData = other.data
otherError = other.error
if (isQuantity(self.unit) or isQuantity(other.unit)):
try:
if not (self.unit.inBaseUnits().unit
== other.unit.inBaseUnits().unit):
_logger.debug('The units are different.')
return False
except AttributeError:
_logger.debug(
'Cannot compare unit with normed quantity: %s, %s' %
(self.unit, other.unit))
return False
try:
scaledData = data * self.unit.value
scaledOtherData = otherData * other.unit.inUnitsOf(
self.unit.unit).value
if not numpy.allclose(scaledData, scaledOtherData, rtol, atol):
if numpy.sometrue(numpy.isnan(scaledData)):
_logger.debug(
'The fields cannot be compared, because some elements of the first field are NaN and the mask has not been set.'
)
if numpy.sometrue(numpy.isnan(scaledOtherData)):
_logger.debug(
'The fields cannot be compared, because some elements of the second field are NaN and the mask has not been set.'
)
else:
difference = numpy.abs(scaledData - scaledOtherData)
_logger.debug(
'The scaled fields differ, data-otherData: %s\n%s\n%s'
% (difference.max(), scaledData, scaledOtherData))
return False
except ValueError:
_logger.debug('Shape mismatch: %s != %s' %
(self.data.shape, other.data.shape))
return False
if error != None:
scaledError = error * self.unit.value
if otherError != None:
otherScaledError = otherError * other.unit.inUnitsOf(
self.unit.unit).value
else:
_logger.debug(
'The errors differ: The error of the second argument is none, while the error of the first argument is %s.'
% error)
return False
if not numpy.allclose(scaledError, otherScaledError, rtol,
atol):
_logger.debug('The normed errors differ: %s\n%s' %
(scaledError, otherScaledError))
return False
else:
if not data.dtype.char in ['S', 'U']:
try:
scaledData = data * self.unit
scaledOtherData = otherData * other.unit
if not numpy.allclose(scaledData, scaledOtherData, rtol,
atol):
_logger.debug('The scaled fields differ: %s\n%s' %
(scaledData, scaledOtherData))
return False
except ValueError:
_logger.debug('Shape mismatch: %s != %s' %
(self.data.shape, other.data.shape))
return False
if error == None:
if not (otherError == None):
_logger.debug(
'The errors differ: Error of first argument is None, but the error of the second argument is not None.'
)
return False
else:
scaledError = error * self.unit
otherScaledError = otherError * other.unit
if not numpy.allclose(scaledError, otherScaledError, rtol,
atol):
_logger.debug('The errors differ: %s\n%s' %
(scaledError, otherScaledError))
return False
if not self.attributes == other.attributes:
_logger.debug('The attribute dictionary differs.')
return False
for dimSelf, dimOther in zip(self._dimensions, other.dimensions):
if dimSelf != dimOther:
_logger.debug('Different dimensions: %s, %s' %
(dimSelf, dimOther))
return False
return True
def __ne__(self, other):
return not self.__eq__(other)
def __add__(self, other):
if isinstance(other, FieldContainer):
if self.error != None or other.error != None:
return NotImplemented
else:
error = None
if len(self._dimensions) != len(other.dimensions):
return NotImplemented
for i in xrange(len(self._dimensions)):
if not self._dimensions[i] == other.dimensions[i]:
return NotImplemented
if isQuantity(self.unit):
if not isQuantity(other.unit):
return NotImplemented
if not self.unit.isCompatible(other.unit.unit):
return NotImplemented
if self.unit >= other.unit:
data = self.data + (other.data * other.unit.value *
other.unit.unit.conversionFactorTo(
self.unit.unit)) / self.unit.value
unit = self.unit
else:
data = other.data + (
self.data * self.unit.value * self.unit.unit.
conversionFactorTo(other.unit.unit)) / other.unit.value
unit = other.unit
elif isQuantity(other.unit):
return NotImplemented
else:
data = (self.data * self.unit) + (other.data * other.unit)
unit = 1.0
if self.mask == None:
mask = other.mask
elif other.mask == None:
mask = self.mask
else:
mask = self.mask + other.mask
longname = u"Sum of %s and %s." % (self.longname, other.longname)
shortname = u"%s + %s" % (self.shortname, other.shortname)
return FieldContainer(data, unit, error, mask,
copy.deepcopy(self._dimensions), longname,
shortname)
return NotImplemented
def __sub__(self, other):
if isinstance(other, FieldContainer):
if self.error != None or other.error != None:
error = other.error + self.error
else:
error = None
if len(self._dimensions) != len(other.dimensions):
return NotImplemented
for i in xrange(len(self._dimensions)):
if not self._dimensions[i] == other.dimensions[i]:
return NotImplemented
if isQuantity(self.unit):
if not (isQuantity(other.unit)
and self.unit.isCompatible(other.unit.unit)):
return NotImplemented
if self.unit >= other.unit:
data = self.data - (other.data * other.unit.value *
other.unit.unit.conversionFactorTo(
self.unit.unit)) / self.unit.value
unit = self.unit
else:
data = (
(self.data * self.unit.value *
self.unit.unit.conversionFactorTo(other.unit.unit)) /
other.unit.value) - other.data
unit = other.unit
else:
if isQuantity(other.unit):
return NotImplemented
data = (self.data * self.unit) - (other.data * other.unit)
unit = 1.0
if self.mask == None:
mask = other.mask
elif other.mask == None:
mask = self.mask
else:
mask = self.mask + other.mask
longname = u"Difference of %s and %s." % (self.longname,
other.longname)
shortname = u"%s - %s" % (self.shortname, other.shortname)
return FieldContainer(data, unit, error, mask,
copy.deepcopy(self._dimensions), longname,
shortname)
return NotImplemented
def __str__(self):
deps = [
dim for dim in self._dimensions if type(dim) != type(IndexMarker())
]
report = "\nFieldContainer %s of shape %s with field\n%s\n" % (
self.label, self.data.shape, self.data)
if self.error != None:
report += ", error\n%s\n" % self.error
if self.mask != None:
report += ", mask\n%s\n" % self.mask
if len(deps) > 0:
report += 'depending on dimensions %s\n' % deps
if len(self.attributes) > 0:
report += 'and attributes\n%s\n' % self.attributes
return report
def __repr__(self):
return self.__str__()
def __getitem__(self, args):
if isinstance(args, type("")):
args = [args]
if isinstance(args, type(1)):
if args >= len(self.data):
raise IndexError, 'index out of bound'
try:
len(args)
except:
args = [args]
args = [
slice2ind(arg, self._dimensions[dim])
for dim, arg in enumerate(args)
]
data = self.data[args]
attributes = copy.deepcopy(self.attributes)
mask = None
error = None
dimensions = []
for i, l in enumerate(data.shape[:len(args)]):
dim = self._dimensions[i]
if l == 1:
attributes[dim.longname] = (dim.shortname,
dim.data[args[i]].squeeze() *
dim.unit)
else:
if isinstance(dim, IndexMarker):
dimensions.append(dim)
else:
dimensions.append(dim[args[i], ])
for i in xrange(len(args), len(data.shape)):
dimensions.append(copy.deepcopy(self._dimensions[i]))
if data.shape != (1, ):
data = data.squeeze()
if self.mask != None:
mask = self.mask[args].squeeze()
if self.error != None:
error = self.error[args].squeeze()
else:
if self.mask != None:
mask = self.mask[args]
if self.error != None:
error = self.error[args]
field = FieldContainer(data,
dimensions=dimensions,
longname=self.longname,
shortname=self.shortname,
mask=mask,
error=error,
unit=self.unit,
attributes=attributes)
return field
def isValid(self):
# Valid dimensions?
if (not (len(self._dimensions) == 1
and isinstance(self._dimensions[0], IndexMarker)
) #IndexMarkers are valid and...
and not (self.data.shape == (1, ) and len(self._dimensions)
== 0)): #...so are zero dim fields.
dimshape = []
for d in self._dimensions:
if len(d.data.shape) > 1:
_logger.debug("Dimension %s is not 1-d." % d.longname)
return False
dimshape.append(d.data.shape[0])
if self.data.shape != tuple(dimshape):
_logger.debug(
"Shape of data %s and of dimensions %s do not match for field\n:%s"
% (self.data.shape, dimshape, self))
return False
for d in self._dimensions:
if not d.isValid():
_logger.debug("Invalid dimension %s." % d.longname)
return False
# Valid mask?
if (self.mask != None) and (self.data.shape != self.mask.shape):
_logger.debug("Shape of data %s and of mask %s do not match." %
(self.data.shape, self.mask.shape))
return False
# Valid error?
if (self.error != None) and (self.data.shape != self.error.shape):
_logger.debug("Shape of data %s and of error %s do not match." %
(self.data.shape, self.error.shape))
return False
return True
def isIndex(self):
return self.dimensions == INDEX
def isIndependent(self):
for d in self.dimensions:
if not d.isIndex():
return False
return True
def isLinearlyDiscretised(self):
for dim in self.dimensions:
d = numpy.diff(dim.data)
if not numpy.alltrue(numpy.logical_not(d - d[0])):
return False
return True
def getMaskedFC(self, numpymask):
"""
Return an unsealed FieldContainer instance that emerges from
this instance by clipping along the primary axis according to the
entries in the parameter numpymask, i.e. all entries where numpymask
is set to False are discarded.
numpymask -- Numpy array with Boolean values
"""
mdims = []
for dim in self.dimensions:
if mdims == []:
# only primary axis has to be masked:
mdims.append(dim.getMaskedFC(numpymask))
else:
mdims.append(copy.deepcopy(dim))
to_mask = [self.data, self.error, self.mask]
masked = []
for item in to_mask:
if item != None:
masked.append(item[numpymask])
else:
masked.append(None)
return FieldContainer(masked[0],
copy.deepcopy(self.unit),
masked[1],
masked[2],
mdims,
longname=self.longname,
shortname=self.shortname,
attributes=copy.deepcopy(self.attributes),
rescale=False)
maskedData = property(
lambda self: numpy.ma.array(self.data, mask=self.mask))
maskedError = property(
lambda self: numpy.ma.array(self.error, mask=self.mask))
def test_degC(self):
temperature = Quantity('0 degC')
self.assertEqual(temperature.inBaseUnits(),
Quantity('273.15 K'))