def test__to_tuple(self):
self.assertEqual(_to_tuple((1, 2, 3)), (1, 2, 3))
self.assertEqual(_to_tuple('1.Zeile'), ('1.Zeile', ))
self.assertEqual(_to_tuple(['1.Zeile']), ('1.Zeile', ))
self.assertEqual(_to_tuple(['1.Zeile', '2.Zeile']),
('1.Zeile', '2.Zeile'))
self.assertEqual(_to_tuple(None), (None, ))
def normalize(self,
reference='data', ref_dtype='mag', norm_dtypes='all', vval=None,
norm_method='max', norm_factor=None,
normalize_variable=False, dont_normalize=None,
norm_initial_state=True):
"""
normalizes all available data to reference value, using norm_method
Parameter
---------
reference: str
reference state, to which to normalize to e.g. 'NRM'
also possible to normalize to mass
ref_dtype: str
component of the reference, if applicable. standard - 'mag'
norm_dtypes: list
dtype to be normalized, if dtype = 'all' all variables will be normalized
vval: float
variable value, if reference == value then it will search for the point closest to the vval
norm_method: str
how the norm_factor is generated, could be min
normalize_variable: bool
if True, variable is also normalized
default: False
dont_normalize: list
list of dtypes that will not be normalized
default: None
norm_initial_state: bool
if true, initial state values are normalized in the same manner as normal data
default: True
"""
# todo normalize by results
#getting normalization factor
if not norm_factor: # if norm_factor specified
norm_factor = self._get_norm_factor(reference, ref_dtype, vval, norm_method)
norm_dtypes = _to_tuple(norm_dtypes) # make sure its a list/tuple
for dtype, dtype_data in self.data.iteritems(): #cycling through all dtypes in data
if dtype_data: #if dtype_data == None
if 'all' in norm_dtypes: # if all, all non stype data will be normalized
norm_dtypes = [i for i in dtype_data.column_names if not 'stype' in i]
### DO not normalize:
# variable
if not normalize_variable:
variable = dtype_data.column_names[dtype_data.column_dict['variable'][0]]
norm_dtypes = [i for i in norm_dtypes if not i == variable]
if dont_normalize:
dont_normalize = _to_tuple(dont_normalize)
norm_dtypes = [i for i in norm_dtypes if not i in dont_normalize]
for ntype in norm_dtypes: #else use norm_dtypes specified
try:
dtype_data[ntype] = dtype_data[ntype].v / norm_factor
except KeyError:
self.logger.warning('CAN\'T normalize << %s, %s >> to %s' %(self.sample_obj.name, self.mtype, ntype))
if 'mag' in dtype_data.column_names:
try:
self.data[dtype]['mag'] = self.data[dtype].magnitude(('x', 'y', 'z'))
except:
self.logger.debug('no (x,y,z) data found keeping << mag >>')
if self.initial_state and norm_initial_state:
for dtype, dtype_rpd in self.initial_state.data.iteritems():
self.initial_state.data[dtype] = dtype_rpd / norm_factor
if 'mag' in self.initial_state.data[dtype].column_names:
self.initial_state.data[dtype]['mag'] = self.initial_state.data[dtype].magnitude(('x', 'y', 'z'))
return self
def normalize(self,
reference='data',
ref_dtype='mag',
norm_dtypes='all',
vval=None,
norm_method='max',
norm_factor=None,
normalize_variable=False,
dont_normalize=None,
norm_initial_state=True):
"""
normalizes all available data to reference value, using norm_method
Parameter
---------
reference: str
reference state, to which to normalize to e.g. 'NRM'
also possible to normalize to mass
ref_dtype: str
component of the reference, if applicable. standard - 'mag'
norm_dtypes: list
dtype to be normalized, if dtype = 'all' all variables will be normalized
vval: float
variable value, if reference == value then it will search for the point closest to the vval
norm_method: str
how the norm_factor is generated, could be min
normalize_variable: bool
if True, variable is also normalized
default: False
dont_normalize: list
list of dtypes that will not be normalized
default: None
norm_initial_state: bool
if true, initial state values are normalized in the same manner as normal data
default: True
"""
# todo normalize by results
#getting normalization factor
if not norm_factor: # if norm_factor specified
norm_factor = self._get_norm_factor(reference, ref_dtype, vval,
norm_method)
norm_dtypes = _to_tuple(norm_dtypes) # make sure its a list/tuple
for dtype, dtype_data in self.data.iteritems(
): #cycling through all dtypes in data
if dtype_data: #if dtype_data == None
if 'all' in norm_dtypes: # if all, all non stype data will be normalized
norm_dtypes = [
i for i in dtype_data.column_names if not 'stype' in i
]
### DO not normalize:
# variable
if not normalize_variable:
variable = dtype_data.column_names[
dtype_data.column_dict['variable'][0]]
norm_dtypes = [i for i in norm_dtypes if not i == variable]
if dont_normalize:
dont_normalize = _to_tuple(dont_normalize)
norm_dtypes = [
i for i in norm_dtypes if not i in dont_normalize
]
for ntype in norm_dtypes: #else use norm_dtypes specified
try:
dtype_data[ntype] = dtype_data[ntype].v / norm_factor
except KeyError:
self.logger.warning(
'CAN\'T normalize << %s, %s >> to %s' %
(self.sample_obj.name, self.mtype, ntype))
if 'mag' in dtype_data.column_names:
try:
self.data[dtype]['mag'] = self.data[dtype].magnitude(
('x', 'y', 'z'))
except:
self.logger.debug(
'no (x,y,z) data found keeping << mag >>')
if self.initial_state and norm_initial_state:
for dtype, dtype_rpd in self.initial_state.data.iteritems():
self.initial_state.data[dtype] = dtype_rpd / norm_factor
if 'mag' in self.initial_state.data[dtype].column_names:
self.initial_state.data[dtype][
'mag'] = self.initial_state.data[dtype].magnitude(
('x', 'y', 'z'))
return self
def test__to_tuple(self):
self.assertEqual(_to_tuple( (1,2,3)), (1,2,3))
self.assertEqual(_to_tuple( '1.Zeile'), ('1.Zeile',))
self.assertEqual(_to_tuple( ['1.Zeile']), ('1.Zeile',))
self.assertEqual(_to_tuple( ['1.Zeile', '2.Zeile']), ('1.Zeile', '2.Zeile'))
self.assertEqual(_to_tuple( None), (None,))