Conversation
Contributor
Author
|
Fixes error [ 58s] ==================================== ERRORS ====================================
[ 58s] ___ ERROR at setup of TestTimeresp.test_step_response_siso[siso_ss1-kwargs0] ___
[ 58s] [gw0] linux -- Python 3.10.14 /usr/bin/python3.10
[ 58s]
[ 58s] self = <control.tests.timeresp_test.TestTimeresp object at 0x7fed247aa830>
[ 58s] request = <SubRequest 'tsystem' for <Function test_step_response_siso[siso_ss1-kwargs0]>>
[ 58s]
[ 58s] @pytest.fixture
[ 58s] def tsystem(self, request):
[ 58s] """Define some test systems"""
[ 58s]
[ 58s] """continuous"""
[ 58s] A = np.array([[1., -2.], [3., -4.]])
[ 58s] B = np.array([[5.], [7.]])
[ 58s] C = np.array([[6., 8.]])
[ 58s] D = np.array([[9.]])
[ 58s] siso_ss1 = TSys(StateSpace(A, B, C, D, 0))
[ 58s] siso_ss1.t = np.linspace(0, 1, 10)
[ 58s] siso_ss1.ystep = np.array([9., 17.6457, 24.7072, 30.4855, 35.2234,
[ 58s] 39.1165, 42.3227, 44.9694, 47.1599,
[ 58s] 48.9776])
[ 58s] siso_ss1.X0 = np.array([[.5], [1.]])
[ 58s] siso_ss1.yinitial = np.array([11., 8.1494, 5.9361, 4.2258, 2.9118,
[ 58s] 1.9092, 1.1508, 0.5833, 0.1645, -0.1391])
[ 58s] ss1 = siso_ss1.sys
[ 58s]
[ 58s] """D=0, continuous"""
[ 58s] siso_ss2 = TSys(StateSpace(ss1.A, ss1.B, ss1.C, 0, 0))
[ 58s] siso_ss2.t = siso_ss1.t
[ 58s] siso_ss2.ystep = siso_ss1.ystep - 9
[ 58s] siso_ss2.initial = siso_ss1.yinitial - 9
[ 58s] siso_ss2.yimpulse = np.array([86., 70.1808, 57.3753, 46.9975, 38.5766,
[ 58s] 31.7344, 26.1668, 21.6292, 17.9245,
[ 58s] 14.8945])
[ 58s]
[ 58s] """System with unspecified timebase"""
[ 58s] siso_ss2_dtnone = TSys(StateSpace(ss1.A, ss1.B, ss1.C, 0, None))
[ 58s] siso_ss2_dtnone.t = np.arange(0, 10, 1.)
[ 58s] siso_ss2_dtnone.ystep = np.array([0., 86., -72., 230., -360., 806.,
[ 58s] -1512., 3110., -6120., 12326.])
[ 58s]
[ 58s] siso_tf1 = TSys(TransferFunction([1], [1, 2, 1], 0))
[ 58s]
[ 58s] siso_tf2 = copy(siso_ss1)
[ 58s] siso_tf2.sys = ss2tf(siso_ss1.sys)
[ 58s]
[ 58s] """MIMO system, contains ``siso_ss1`` twice"""
[ 58s] mimo_ss1 = copy(siso_ss1)
[ 58s] A = np.zeros((4, 4))
[ 58s] A[:2, :2] = siso_ss1.sys.A
[ 58s] A[2:, 2:] = siso_ss1.sys.A
[ 58s] B = np.zeros((4, 2))
[ 58s] B[:2, :1] = siso_ss1.sys.B
[ 58s] B[2:, 1:] = siso_ss1.sys.B
[ 58s] C = np.zeros((2, 4))
[ 58s] C[:1, :2] = siso_ss1.sys.C
[ 58s] C[1:, 2:] = siso_ss1.sys.C
[ 58s] D = np.zeros((2, 2))
[ 58s] D[:1, :1] = siso_ss1.sys.D
[ 58s] D[1:, 1:] = siso_ss1.sys.D
[ 58s] mimo_ss1.sys = StateSpace(A, B, C, D)
[ 58s]
[ 58s] """MIMO system, contains ``siso_ss2`` twice"""
[ 58s] mimo_ss2 = copy(siso_ss2)
[ 58s] A = np.zeros((4, 4))
[ 58s] A[:2, :2] = siso_ss2.sys.A
[ 58s] A[2:, 2:] = siso_ss2.sys.A
[ 58s] B = np.zeros((4, 2))
[ 58s] B[:2, :1] = siso_ss2.sys.B
[ 58s] B[2:, 1:] = siso_ss2.sys.B
[ 58s] C = np.zeros((2, 4))
[ 58s] C[:1, :2] = siso_ss2.sys.C
[ 58s] C[1:, 2:] = siso_ss2.sys.C
[ 58s] D = np.zeros((2, 2))
[ 58s] mimo_ss2.sys = StateSpace(A, B, C, D, 0)
[ 58s]
[ 58s] """discrete"""
[ 58s] siso_dtf0 = TSys(TransferFunction([1.], [1., 0.], 1.))
[ 58s] siso_dtf0.t = np.arange(4)
[ 58s] siso_dtf0.yimpulse = [0., 1., 0., 0.]
[ 58s]
[ 58s] siso_dtf1 = TSys(TransferFunction([1], [1, 1, 0.25], True))
[ 58s] siso_dtf1.t = np.arange(0, 5, 1)
[ 58s] siso_dtf1.ystep = np.array([0. , 0. , 1. , 0. , 0.75])
[ 58s]
[ 58s] siso_dtf2 = TSys(TransferFunction([1], [1, 1, 0.25], 0.2))
[ 58s] siso_dtf2.t = np.arange(0, 5, 0.2)
[ 58s] siso_dtf2.ystep = np.array(
[ 58s] [0. , 0. , 1. , 0. , 0.75 , 0.25 ,
[ 58s] 0.5625, 0.375 , 0.4844, 0.4219, 0.457 , 0.4375,
[ 58s] 0.4482, 0.4424, 0.4456, 0.4438, 0.4448, 0.4443,
[ 58s] 0.4445, 0.4444, 0.4445, 0.4444, 0.4445, 0.4444,
[ 58s] 0.4444])
[ 58s]
[ 58s] """Time step which leads to rounding errors for time vector length"""
[ 58s] num = [-0.10966442, 0.12431949]
[ 58s] den = [1., -1.86789511, 0.88255018]
[ 58s] dt = 0.12493963338370018
[ 58s] siso_dtf3 = TSys(TransferFunction(num, den, dt))
[ 58s] siso_dtf3.t = np.linspace(0, 9*dt, 10)
[ 58s] siso_dtf3.ystep = np.array(
[ 58s] [ 0. , -0.1097, -0.1902, -0.2438, -0.2729,
[ 58s] -0.2799, -0.2674, -0.2377, -0.1934, -0.1368])
[ 58s]
[ 58s] """dtf1 converted statically, because Slycot and Scipy produce
[ 58s] different realizations, wich means different initial condtions,"""
[ 58s] siso_dss1 = copy(siso_dtf1)
[ 58s] siso_dss1.sys = StateSpace([[-1., -0.25],
[ 58s] [ 1., 0.]],
[ 58s] [[1.],
[ 58s] [0.]],
[ 58s] [[0., 1.]],
[ 58s] [[0.]],
[ 58s] True)
[ 58s] siso_dss1.X0 = [0.5, 1.]
[ 58s] siso_dss1.yinitial = np.array([1., 0.5, -0.75, 0.625, -0.4375])
[ 58s]
[ 58s] siso_dss2 = copy(siso_dtf2)
[ 58s] siso_dss2.sys = tf2ss(siso_dtf2.sys)
[ 58s]
[ 58s] mimo_dss1 = TSys(StateSpace(ss1.A, ss1.B, ss1.C, ss1.D, True))
[ 58s] mimo_dss1.t = np.arange(0, 5, 0.2)
[ 58s]
[ 58s] mimo_dss2 = copy(mimo_ss1)
[ 58s] mimo_dss2.sys = c2d(mimo_ss1.sys, mimo_ss1.t[1]-mimo_ss1.t[0])
[ 58s]
[ 58s] mimo_tf2 = copy(mimo_ss2)
[ 58s] tf_ = ss2tf(siso_ss2.sys)
[ 58s] mimo_tf2.sys = TransferFunction(
[ 58s] [[tf_.num[0][0], [0]], [[0], tf_.num[0][0]]],
[ 58s] [[tf_.den[0][0], [1]], [[1], tf_.den[0][0]]],
[ 58s] 0)
[ 58s]
[ 58s] mimo_dtf1 = copy(siso_dtf1)
[ 58s] tf_ = siso_dtf1.sys
[ 58s] mimo_dtf1.sys = TransferFunction(
[ 58s] [[tf_.num[0][0], [0]], [[0], tf_.num[0][0]]],
[ 58s] [[tf_.den[0][0], [1]], [[1], tf_.den[0][0]]],
[ 58s] True)
[ 58s]
[ 58s] # for pole cancellation tests
[ 58s] pole_cancellation = TSys(TransferFunction(
[ 58s] [1.067e+05, 5.791e+04],
[ 58s] [10.67, 1.067e+05, 5.791e+04]))
[ 58s]
[ 58s] no_pole_cancellation = TSys(TransferFunction(
[ 58s] [1.881e+06],
[ 58s] [188.1, 1.881e+06]))
[ 58s]
[ 58s] # System Type 1 - Step response not stationary: G(s)=1/s(s+1)
[ 58s] siso_tf_type1 = TSys(TransferFunction(1, [1, 1, 0]))
[ 58s] siso_tf_type1.step_info = {
[ 58s] > 'RiseTime': np.NaN,
[ 58s] 'SettlingTime': np.NaN,
[ 58s] 'SettlingMin': np.NaN,
[ 58s] 'SettlingMax': np.NaN,
[ 58s] 'Overshoot': np.NaN,
[ 58s] 'Undershoot': np.NaN,
[ 58s] 'Peak': np.Inf,
[ 58s] 'PeakTime': np.Inf,
[ 58s] 'SteadyStateValue': np.NaN}
[ 58s]
[ 58s] control/tests/timeresp_test.py:176:
[ 58s] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
[ 58s]
[ 58s] attr = 'NaN'
[ 58s]
[ 58s] def __getattr__(attr):
[ 58s] # Warn for expired attributes
[ 58s] import warnings
[ 58s]
[ 58s] if attr == "linalg":
[ 58s] import numpy.linalg as linalg
[ 58s] return linalg
[ 58s] elif attr == "fft":
[ 58s] import numpy.fft as fft
[ 58s] return fft
[ 58s] elif attr == "dtypes":
[ 58s] import numpy.dtypes as dtypes
[ 58s] return dtypes
[ 58s] elif attr == "random":
[ 58s] import numpy.random as random
[ 58s] return random
[ 58s] elif attr == "polynomial":
[ 58s] import numpy.polynomial as polynomial
[ 58s] return polynomial
[ 58s] elif attr == "ma":
[ 58s] import numpy.ma as ma
[ 58s] return ma
[ 58s] elif attr == "ctypeslib":
[ 58s] import numpy.ctypeslib as ctypeslib
[ 58s] return ctypeslib
[ 58s] elif attr == "exceptions":
[ 58s] import numpy.exceptions as exceptions
[ 58s] return exceptions
[ 58s] elif attr == "testing":
[ 58s] import numpy.testing as testing
[ 58s] return testing
[ 58s] elif attr == "matlib":
[ 58s] import numpy.matlib as matlib
[ 58s] return matlib
[ 58s] elif attr == "f2py":
[ 58s] import numpy.f2py as f2py
[ 58s] return f2py
[ 58s] elif attr == "typing":
[ 58s] import numpy.typing as typing
[ 58s] return typing
[ 58s] elif attr == "rec":
[ 58s] import numpy.rec as rec
[ 58s] return rec
[ 58s] elif attr == "char":
[ 58s] import numpy.char as char
[ 58s] return char
[ 58s] elif attr == "array_api":
[ 58s] raise AttributeError("`numpy.array_api` is not available from "
[ 58s] "numpy 2.0 onwards")
[ 58s] elif attr == "core":
[ 58s] import numpy.core as core
[ 58s] return core
[ 58s] elif attr == "strings":
[ 58s] import numpy.strings as strings
[ 58s] return strings
[ 58s] elif attr == "distutils":
[ 58s] if 'distutils' in __numpy_submodules__:
[ 58s] import numpy.distutils as distutils
[ 58s] return distutils
[ 58s] else:
[ 58s] raise AttributeError("`numpy.distutils` is not available from "
[ 58s] "Python 3.12 onwards")
[ 58s]
[ 58s] if attr in __future_scalars__:
[ 58s] # And future warnings for those that will change, but also give
[ 58s] # the AttributeError
[ 58s] warnings.warn(
[ 58s] f"In the future `np.{attr}` will be defined as the "
[ 58s] "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
[ 58s]
[ 58s] if attr in __former_attrs__:
[ 58s] raise AttributeError(__former_attrs__[attr])
[ 58s]
[ 58s] if attr in __expired_attributes__:
[ 58s] > raise AttributeError(
[ 58s] f"`np.{attr}` was removed in the NumPy 2.0 release. "
[ 58s] f"{__expired_attributes__[attr]}"
[ 58s] )
[ 58s] E AttributeError: `np.NaN` was removed in the NumPy 2.0 release. Use `np.nan` instead.. Did you mean: 'nan'?
[ 58s]
[ 58s] /usr/lib64/python3.10/site-packages/numpy/__init__.py:397: AttributeError |
Contributor
Author
|
We don't have a CI job with numpy 2.0.0rc1 set up and I don't think we need to, but tests are executed here: https://build.opensuse.org/package/show/home:bnavigator:numpy/python-control |
Member
|
These changes look good to me. @bnavigator Any reason to wait before merging? If not, I'll merge in a day or so. |
Contributor
Author
|
No reason to wait. Thanks for the review. I suggest releasing a 0.10.1 soon, so that our users don't get caught by surprise after the numpy 2 release. |
slivingston
approved these changes
Apr 21, 2024
Member
slivingston
left a comment
slivingston
left a comment
There was a problem hiding this comment.
For posterity, I verified these changes with the migration guide at https://numpy.org/devdocs/numpy_2_0_migration_guide.html and https://numpy.org/devdocs/release/2.0.0-notes.html#numpy-linalg-linalg-made-private.
bmwiedemann
pushed a commit
to bmwiedemann/openSUSE
that referenced
this pull request
Apr 21, 2024
https://build.opensuse.org/request/show/1169382 by user bnavigator + anag+factory - Add control-pr994-numpy2.patch gh#python-control/python-control#994 (forwarded request 1169381 from bnavigator)
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
4 participants
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Numpy 2.0.0rc1 is out and a release is coming soon.