def __setattr__(self, attr, val):
if attr in ['num', 'den']:
self.__dict__[attr] = val
self.__dict__['zeros'], self.__dict__['poles'], \
self.__dict__['gain'] = \
tf2zpk(self.num, self.den)
self.__dict__['A'], self.__dict__['B'], \
self.__dict__['C'], \
self.__dict__['D'] = \
tf2ss(self.num, self.den)
elif attr in ['zeros', 'poles', 'gain']:
self.__dict__[attr] = val
self.__dict__['num'], self.__dict__['den'] = \
zpk2tf(self.zeros,
self.poles, self.gain)
self.__dict__['A'], self.__dict__['B'], \
self.__dict__['C'], \
self.__dict__['D'] = \
zpk2ss(self.zeros,
self.poles, self.gain)
elif attr in ['A', 'B', 'C', 'D']:
self.__dict__[attr] = val
self.__dict__['zeros'], self.__dict__['poles'], \
self.__dict__['gain'] = \
ss2zpk(self.A, self.B,
self.C, self.D)
self.__dict__['num'], self.__dict__['den'] = \
ss2tf(self.A, self.B,
self.C, self.D)
else:
self.__dict__[attr] = val
def __init__(self, *args, **kwords):
"""Initialize the LTI system using either:
(numerator, denominator)
(zeros, poles, gain)
(A, B, C, D) -- state-space.
"""
N = len(args)
if N == 2: # Numerator denominator transfer function input
self.__dict__["num"], self.__dict__["den"] = normalize(*args)
self.__dict__["zeros"], self.__dict__["poles"], self.__dict__["gain"] = tf2zpk(*args)
self.__dict__["A"], self.__dict__["B"], self.__dict__["C"], self.__dict__["D"] = tf2ss(*args)
self.inputs = 1
if len(self.num.shape) > 1:
self.outputs = self.num.shape[0]
else:
self.outputs = 1
elif N == 3: # Zero-pole-gain form
self.__dict__["zeros"], self.__dict__["poles"], self.__dict__["gain"] = args
self.__dict__["num"], self.__dict__["den"] = zpk2tf(*args)
self.__dict__["A"], self.__dict__["B"], self.__dict__["C"], self.__dict__["D"] = zpk2ss(*args)
self.inputs = 1
if len(self.zeros.shape) > 1:
self.outputs = self.zeros.shape[0]
else:
self.outputs = 1
elif N == 4: # State-space form
self.__dict__["A"], self.__dict__["B"], self.__dict__["C"], self.__dict__["D"] = abcd_normalize(*args)
self.__dict__["zeros"], self.__dict__["poles"], self.__dict__["gain"] = ss2zpk(*args)
self.__dict__["num"], self.__dict__["den"] = ss2tf(*args)
self.inputs = self.B.shape[-1]
self.outputs = self.C.shape[0]
else:
raise ValueError("Needs 2, 3, or 4 arguments.")
def __setattr__(self, attr, val):
if attr in ['num', 'den']:
self.__dict__[attr] = val
self.__dict__['zeros'], self.__dict__['poles'], \
self.__dict__['gain'] = \
tf2zpk(self.num, self.den)
self.__dict__['A'], self.__dict__['B'], \
self.__dict__['C'], \
self.__dict__['D'] = \
tf2ss(self.num, self.den)
elif attr in ['zeros', 'poles', 'gain']:
self.__dict__[attr] = val
self.__dict__['num'], self.__dict__['den'] = \
zpk2tf(self.zeros,
self.poles, self.gain)
self.__dict__['A'], self.__dict__['B'], \
self.__dict__['C'], \
self.__dict__['D'] = \
zpk2ss(self.zeros,
self.poles, self.gain)
elif attr in ['A', 'B', 'C', 'D']:
self.__dict__[attr] = val
self.__dict__['zeros'], self.__dict__['poles'], \
self.__dict__['gain'] = \
ss2zpk(self.A, self.B,
self.C, self.D)
self.__dict__['num'], self.__dict__['den'] = \
ss2tf(self.A, self.B,
self.C, self.D)
else:
self.__dict__[attr] = val
def ss2zpk(A, B, C, D, input=0):
"""State-space representation to zero-pole-gain representation.
Inputs:
A, B, C, D -- state-space matrices.
input -- for multiple-input systems, the input to use.
Outputs:
z, p, k -- zeros and poles in sequences and gain constant.
"""
return tf2zpk(*ss2tf(A, B, C, D, input=input))
def ss2zpk(A,B,C,D,input=0):
"""State-space representation to zero-pole-gain representation.
Inputs:
A, B, C, D -- state-space matrices.
input -- for multiple-input systems, the input to use.
Outputs:
z, p, k -- zeros and poles in sequences and gain constant.
"""
return tf2zpk(*ss2tf(A,B,C,D,input=input))
def __init__(self, *args, **kwords):
"""
Initialize the LTI system using either:
- (numerator, denominator)
- (zeros, poles, gain)
- (A, B, C, D) : state-space.
"""
N = len(args)
if N == 2: # Numerator denominator transfer function input
self.__dict__['num'], self.__dict__['den'] = normalize(*args)
self.__dict__['zeros'], self.__dict__['poles'], \
self.__dict__['gain'] = tf2zpk(*args)
self.__dict__['A'], self.__dict__['B'], \
self.__dict__['C'], \
self.__dict__['D'] = tf2ss(*args)
self.inputs = 1
if len(self.num.shape) > 1:
self.outputs = self.num.shape[0]
else:
self.outputs = 1
elif N == 3: # Zero-pole-gain form
self.__dict__['zeros'], self.__dict__['poles'], \
self.__dict__['gain'] = args
self.__dict__['num'], self.__dict__['den'] = zpk2tf(*args)
self.__dict__['A'], self.__dict__['B'], \
self.__dict__['C'], \
self.__dict__['D'] = zpk2ss(*args)
# make sure we have numpy arrays
self.zeros = numpy.asarray(self.zeros)
self.poles = numpy.asarray(self.poles)
self.inputs = 1
if len(self.zeros.shape) > 1:
self.outputs = self.zeros.shape[0]
else:
self.outputs = 1
elif N == 4: # State-space form
self.__dict__['A'], self.__dict__['B'], \
self.__dict__['C'], \
self.__dict__['D'] = abcd_normalize(*args)
self.__dict__['zeros'], self.__dict__['poles'], \
self.__dict__['gain'] = ss2zpk(*args)
self.__dict__['num'], self.__dict__['den'] = ss2tf(*args)
self.inputs = self.B.shape[-1]
self.outputs = self.C.shape[0]
else:
raise ValueError("Needs 2, 3, or 4 arguments.")
def __init__(self, *args, **kwords):
"""
Initialize the LTI system using either:
- (numerator, denominator)
- (zeros, poles, gain)
- (A, B, C, D) : state-space.
"""
N = len(args)
if N == 2: # Numerator denominator transfer function input
self.__dict__['num'], self.__dict__['den'] = normalize(*args)
self.__dict__['zeros'], self.__dict__['poles'], \
self.__dict__['gain'] = tf2zpk(*args)
self.__dict__['A'], self.__dict__['B'], \
self.__dict__['C'], \
self.__dict__['D'] = tf2ss(*args)
self.inputs = 1
if len(self.num.shape) > 1:
self.outputs = self.num.shape[0]
else:
self.outputs = 1
elif N == 3: # Zero-pole-gain form
self.__dict__['zeros'], self.__dict__['poles'], \
self.__dict__['gain'] = args
self.__dict__['num'], self.__dict__['den'] = zpk2tf(*args)
self.__dict__['A'], self.__dict__['B'], \
self.__dict__['C'], \
self.__dict__['D'] = zpk2ss(*args)
# make sure we have numpy arrays
self.zeros = numpy.asarray(self.zeros)
self.poles = numpy.asarray(self.poles)
self.inputs = 1
if len(self.zeros.shape) > 1:
self.outputs = self.zeros.shape[0]
else:
self.outputs = 1
elif N == 4: # State-space form
self.__dict__['A'], self.__dict__['B'], \
self.__dict__['C'], \
self.__dict__['D'] = abcd_normalize(*args)
self.__dict__['zeros'], self.__dict__['poles'], \
self.__dict__['gain'] = ss2zpk(*args)
self.__dict__['num'], self.__dict__['den'] = ss2tf(*args)
self.inputs = self.B.shape[-1]
self.outputs = self.C.shape[0]
else:
raise ValueError("Needs 2, 3, or 4 arguments.")
def ss2zpk(A, B, C, D, input=0):
"""State-space representation to zero-pole-gain representation.
Parameters
----------
A, B, C, D : ndarray
State-space representation of linear system.
input : int, optional
For multiple-input systems, the input to use.
Returns
-------
z, p : sequence
Zeros and poles.
k : float
System gain.
"""
return tf2zpk(*ss2tf(A, B, C, D, input=input))
def ss2zpk(A, B, C, D, input=0):
"""State-space representation to zero-pole-gain representation.
Parameters
----------
A, B, C, D : ndarray
State-space representation of linear system.
input : int, optional
For multiple-input systems, the input to use.
Returns
-------
z, p : sequence
Zeros and poles.
k : float
System gain.
"""
return tf2zpk(*ss2tf(A, B, C, D, input=input))
def __setattr__(self, attr, val):
if attr in ["num", "den"]:
self.__dict__[attr] = val
self.__dict__["zeros"], self.__dict__["poles"], self.__dict__["gain"] = tf2zpk(self.num, self.den)
self.__dict__["A"], self.__dict__["B"], self.__dict__["C"], self.__dict__["D"] = tf2ss(self.num, self.den)
elif attr in ["zeros", "poles", "gain"]:
self.__dict__[attr] = val
self.__dict__["num"], self.__dict__["den"] = zpk2tf(self.zeros, self.poles, self.gain)
self.__dict__["A"], self.__dict__["B"], self.__dict__["C"], self.__dict__["D"] = zpk2ss(
self.zeros, self.poles, self.gain
)
elif attr in ["A", "B", "C", "D"]:
self.__dict__[attr] = val
self.__dict__["zeros"], self.__dict__["poles"], self.__dict__["gain"] = ss2zpk(
self.A, self.B, self.C, self.D
)
self.__dict__["num"], self.__dict__["den"] = ss2tf(self.A, self.B, self.C, self.D)
else:
self.__dict__[attr] = val
