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fixed StateSpace for complex inputs#468
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -70,7 +70,7 @@ | ||
| # Define module default parameter values | ||
| _statesp_defaults = { | ||
| 'statesp.use_numpy_matrix':False, | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others.Learn more. Please don't change the default setting here. We have a road map in mind when this should happen and it should be independent of the support for complex types. There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others.Learn more. That said, actually#438 takescomplex inputs into account already! There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others.Learn more. Ok, I will change this back There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others.Learn more.
Oh, I see, should I close my PR? | ||
| 'statesp.default_dt': None, | ||
| 'statesp.remove_useless_states': True, | ||
| } | ||
| @@ -98,11 +98,11 @@ def _ssmatrix(data, axis=1): | ||
| # Convert the data into an array or matrix, as configured | ||
| # If data is passed as a string, use (deprecated?) matrix constructor | ||
| if config.defaults['statesp.use_numpy_matrix']: | ||
| arr = np.matrix(data, dtype=complex) | ||
| elif isinstance(data, str): | ||
| arr = np.array(np.matrix(data, dtype=complex)) | ||
| else: | ||
| arr = np.array(data, dtype=complex) | ||
| ndim = arr.ndim | ||
| shape = arr.shape | ||
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,42 @@ | ||
| from matplotlib import pyplot as plt | ||
| import numpy as np | ||
| from scipy.linalg import eig | ||
| from control import step_response, StateSpace | ||
| # Example of diagonalization of StateSpace representation | ||
| # In this example the diagonalized system has complex values on the matrices | ||
| def diagonalize(A, B, C, D): | ||
| eigenvectors_matrix = eig(A)[1] | ||
| A = np.matmul(np.matmul(np.linalg.inv(eigenvectors_matrix), A), eigenvectors_matrix) | ||
| B = np.matmul(np.linalg.inv(eigenvectors_matrix), B) | ||
| C = np.matmul(C, eigenvectors_matrix) | ||
| print(A) | ||
| print(B) | ||
| print(C) | ||
| print(D) | ||
| return A, B, C, D | ||
| def main(): | ||
| A = np.array([[0, 0, 1000], [0.1, 0, 0], [0, 0.5, 0.5]]) | ||
| B = np.array([[0], [0], [1]]) | ||
| C = np.array([[0, 0, 1]]) | ||
| D = np.array([[0]]) | ||
| Aw, Bw, Cw, Dw = diagonalize(A, B, C, D) | ||
| system = StateSpace(A, B, C, D, 1) | ||
| system_w = StateSpace(Aw, Bw, Cw, Dw, 1) | ||
| length = 20 | ||
| plt.figure(figsize=(14, 9), dpi=100) | ||
| response_w = step_response(system_w, T=np.arange(length)) | ||
| response = step_response(system, T=np.arange(length)) | ||
| plt.plot(response[0], response[1], color="red", label="System") | ||
| plt.plot(response_w[0], response_w[1], color="blue", label="System W") | ||
| plt.xticks(np.arange(length)) | ||
| plt.legend() | ||
| plt.savefig("diagonalization.png") | ||
| main() |