@@ -219,7 +219,7 @@ def forced_response(sys, T=None, U=0., X0=0., transpose=False,
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transpose: bool, optional (default=False)
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If True, transpose all input and output arrays (for backward
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- compatibility with MATLAB and scipy.signal.lsim)
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+ compatibility with MATLAB and :func:` scipy.signal.lsim` )
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interpolate: bool, optional (default=False)
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If True and system is a discrete time system, the input will
@@ -249,7 +249,7 @@ def forced_response(sys, T=None, U=0., X0=0., transpose=False,
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Notes
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-----
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For discrete time systems, the input/output response is computed using the
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- :scipy-signal:ref :`scipy.signal.dlsim` function.
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+ :func :`scipy.signal.dlsim` function.
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For continuous time systems, the output is computed using the matrix
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exponential `exp(A t)` and assuming linear interpolation of the inputs
@@ -490,7 +490,7 @@ def step_response(sys, T=None, X0=0., input=None, output=None, T_num=None,
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transpose: bool
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If True, transpose all input and output arrays (for backward
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- compatibility with MATLAB and scipy.signal.lsim)
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+ compatibility with MATLAB and :func:` scipy.signal.lsim` )
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return_x: bool
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If True, return the state vector (default = False).
@@ -662,7 +662,7 @@ def initial_response(sys, T=None, X0=0., input=0, output=None, T_num=None,
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transpose: bool
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If True, transpose all input and output arrays (for backward
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- compatibility with MATLAB and scipy.signal.lsim)
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+ compatibility with MATLAB and :func:` scipy.signal.lsim` )
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return_x: bool
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If True, return the state vector (default = False).
@@ -751,7 +751,7 @@ def impulse_response(sys, T=None, X0=0., input=0, output=None, T_num=None,
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<
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code class="diff-text syntax-highlighted-line"> transpose: bool
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If True, transpose all input and output arrays (for backward
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- compatibility with MATLAB and scipy.signal.lsim)
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+ compatibility with MATLAB and :func:` scipy.signal.lsim` )
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return_x: bool
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If True, return the state vector (default = False).
@@ -882,4 +882,4 @@ def _default_time_vector(sys, N=None, tfinal=None):
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if N is None :
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N = int (np .clip (tfinal / ideal_dt , N_min_ct , N_max )) # N<-[N_min, N_max]
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- return np .linspace (0 , tfinal , N , endpoint = False )
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+ return np .linspace (0 , tfinal , N , endpoint = False )
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