def _info(self):
'''
Method to summarize the results for alpha.
return generator of tuples(title:str, item)
'''
if self.name:
yield ('Name', self.name)
if self.value is not None:
_index = (f'Sensor {m+1}' for m in range(self.value.shape[0]))
_columns = (f'Plane {n+1}' for n in range(self.value.shape[1]))
yield ('Coefficient Values',
pd.DataFrame(tools.convert_cart_math(self.value),
index=_index,
columns=_columns))
if self.A is not None:
_index = (f'Sensor {m+1}' for m in range(self.A.shape[0]))
yield ('Initial Vibration',
pd.DataFrame(tools.convert_cart_math(self.A),
index=_index,
columns=['Vibration']))
if self.B is not None:
_index = (f'Sensor {m+1}' for m in range(self.B.shape[0]))
_columns = (f'Plane {n+1}' for n in range(self.B.shape[1]))
yield ('Trial Runs Vibration',
pd.DataFrame(tools.convert_cart_math(self.B),
index=_index,
columns=_columns))
if self.U is not None:
_index = (f'Plane {n+1}' for n in range(self.U.shape[0]))
yield ('Trial Masses',
pd.DataFrame(tools.convert_cart_math(self.U),
index=_index,
columns=['Mass']))
def error(self, options='error'):
'''
Method to determine different ways of error in the splitting.
Args:
Instance of split model
options: kwarg to define the type of error.
'error': default value returns the relative value of error.
'problem_error': returns the accuracy of the solution.
'equ': returns the equivilant weight of splitting solution
in math form.
'''
# Error equivilant weight after splitting
_error = abs(self._W_equ_split - self.weight)
# Estimate the error generated by using minmax instead of norm2 in the objective function
_problem_error = (self._prob_S - _error) / abs(self.weight)
if options == 'error':
return _error
elif options == 'problem_error':
return _problem_error
elif options == 'equ':
return tools.convert_cart_math(self._W_equ_split)
else:
raise tools.CustomError(
'Invalid options. Choose options="error" for solution error'
'caused by splitting, options="problem_error" for solution'
' accuracy and options="equ" for equivilant weight.')
def _info(self):
'''
Method to summarize the results for Model.
'''
yield ('MODEL TYPE', type(self).__name__)
if self.name:
yield ('MODEL NAME', self.name)
if self.alpha is not None:
yield ('INFLUENCE COEFFICIENT MATRIX', str(self.alpha))
if self.A is not None and self.conditions is None:
_index = (f'Sensor {m+1}' for m in range(self.A.shape[0]))
yield ('INITIAL VIBRATION',
pd.DataFrame(tools.convert_cart_math(self.A),
index=_index,
columns=['Vibration']))
if self.conditions is not None:
yield ('CONDITIONS',
''.join(str(condition) for condition in self.conditions))
if self.W is not None:
_index = (f'Plane {n+1}' for n in range(self.W.shape[0]))
yield ('SOLUTION',
pd.DataFrame(tools.convert_cart_math(self.W),
index=_index,
columns=['Correction Masses']))
yield ('RMSE', self.rmse())
_index = (f'Sensor {m+1}' for m in range(self.A.shape[0]))
yield ('Resiudal Vibration Expected',
pd.DataFrame(tools.convert_cart_math(
self.expected_residual_vibration()),
index=_index,
columns=['Expected Vibration']))
else:
yield ('SOLUTION', 'No solution calculated.')
if self.split_instance is not None:
if self.split_instance:
yield ('SPLITS', '\n\n'.join(
str(split.results()) for split in self.split_instance))
def _info(self):
'''
Method to summarize the results for condition.
'''
if self.name:
yield ('Name', self.name)
if self.alpha is not None:
yield ('Condition IC Matrix', str(self.alpha))
if self.A is not None:
_index = (f'Sensor {m+1}' for m in range(self.A.shape[0]))
yield ('Initial Vibration',
pd.DataFrame(tools.convert_cart_math(self.A),
index=_index,
columns=['Vibration']))
def test_convert_cart_math(param, expected):
output = tools.convert_cart_math(complex(param))
assert expected == str(output)