Python numpy.polymul() Examples
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code examples of numpy.polymul().
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Example #1
Source File: test_regression.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
def test_mem_polymul(self): # Ticket #448 np.polymul([], [1.])
Example #2
Source File: test_regression.py From keras-lambda with MIT License | 5 votes |
def test_mem_polymul(self, level=rlevel): # Ticket #448 np.polymul([], [1.])
Example #3
Source File: test_regression.py From twitter-stock-recommendation with MIT License | 5 votes |
def test_mem_polymul(self): # Ticket #448 np.polymul([], [1.])
Example #4
Source File: test_regression.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
def test_mem_polymul(self): # Ticket #448 np.polymul([], [1.])
Example #5
Source File: spl_lib.py From spl-meter-with-RPi with MIT License | 5 votes |
def A_weighting(fs): """Design of an A-weighting filter. b, a = A_weighting(fs) designs a digital A-weighting filter for sampling frequency `fs`. Usage: y = scipy.signal.lfilter(b, a, x). Warning: `fs` should normally be higher than 20 kHz. For example, fs = 48000 yields a class 1-compliant filter. References: [1] IEC/CD 1672: Electroacoustics-Sound Level Meters, Nov. 1996. """ # Definition of analog A-weighting filter according to IEC/CD 1672. f1 = 20.598997 f2 = 107.65265 f3 = 737.86223 f4 = 12194.217 A1000 = 1.9997 NUMs = [(2*numpy.pi * f4)**2 * (10**(A1000/20)), 0, 0, 0, 0] DENs = numpy.polymul([1, 4*numpy.pi * f4, (2*numpy.pi * f4)**2], [1, 4*numpy.pi * f1, (2*numpy.pi * f1)**2]) DENs = numpy.polymul(numpy.polymul(DENs, [1, 2*numpy.pi * f3]), [1, 2*numpy.pi * f2]) # Use the bilinear transformation to get the digital filter. # (Octave, MATLAB, and PyLab disagree about Fs vs 1/Fs) return bilinear(NUMs, DENs, fs)
Example #6
Source File: test_regression.py From coffeegrindsize with MIT License | 5 votes |
def test_mem_polymul(self): # Ticket #448 np.polymul([], [1.])
Example #7
Source File: test_regression.py From elasticintel with GNU General Public License v3.0 | 5 votes |
def test_mem_polymul(self, level=rlevel): # Ticket #448 np.polymul([], [1.])
Example #8
Source File: filter_design.py From Splunking-Crime with GNU Affero General Public License v3.0 | 5 votes |
def sos2tf(sos): """ Return a single transfer function from a series of second-order sections Parameters ---------- sos : array_like Array of second-order filter coefficients, must have shape ``(n_sections, 6)``. See `sosfilt` for the SOS filter format specification. Returns ------- b : ndarray Numerator polynomial coefficients. a : ndarray Denominator polynomial coefficients. Notes ----- .. versionadded:: 0.16.0 """ sos = np.asarray(sos) b = [1.] a = [1.] n_sections = sos.shape[0] for section in range(n_sections): b = np.polymul(b, sos[section, :3]) a = np.polymul(a, sos[section, 3:]) return b, a
Example #9
Source File: test_regression.py From ImageFusion with MIT License | 5 votes |
def test_mem_polymul(self, level=rlevel): # Ticket #448 np.polymul([], [1.])
Example #10
Source File: test_regression.py From mxnet-lambda with Apache License 2.0 | 5 votes |
def test_mem_polymul(self, level=rlevel): # Ticket #448 np.polymul([], [1.])
Example #11
Source File: test_regression.py From pySINDy with MIT License | 5 votes |
def test_mem_polymul(self): # Ticket #448 np.polymul([], [1.])
Example #12
Source File: margins.py From python-control with BSD 3-Clause "New" or "Revised" License | 5 votes |
def _polysqr(pol): """return a polynomial squared""" return np.polymul(pol, pol) # Took the framework for the old function by # Sawyer B. Fuller <minster@caltech.edu>, removed a lot of the innards # and replaced with analytical polynomial functions for LTI systems. # # idea for the frequency data solution copied/adapted from # https://github.com/alchemyst/Skogestad-Python/blob/master/BODE.py # Rene van Paassen <rene.vanpaassen@gmail.com> # # RvP, July 8, 2014, corrected to exclude phase=0 crossing for the gain # margin polynomial # RvP, July 8, 2015, augmented to calculate all phase/gain crossings with # frd data. Correct to return smallest phase # margin, smallest gain margin and their frequencies # RvP, Jun 10, 2017, modified the inclusion of roots found for phase # crossing to include all >= 0, made subsequent calc # insensitive to div by 0 # also changed the selection of which crossings to # return on basis of "A note on the Gain and Phase # Margin Concepts" Journal of Control and Systems # Engineering, Yazdan Bavafi-Toosi, Dec 2015, vol 3 # issue 1, pp 51-59, closer to Matlab behavior, but # not completely identical in edge cases, which don't # cross but touch gain=1
Example #13
Source File: filter_design.py From GraphicDesignPatternByPython with MIT License | 5 votes |
def sos2tf(sos): """ Return a single transfer function from a series of second-order sections Parameters ---------- sos : array_like Array of second-order filter coefficients, must have shape ``(n_sections, 6)``. See `sosfilt` for the SOS filter format specification. Returns ------- b : ndarray Numerator polynomial coefficients. a : ndarray Denominator polynomial coefficients. Notes ----- .. versionadded:: 0.16.0 """ sos = np.asarray(sos) b = [1.] a = [1.] n_sections = sos.shape[0] for section in range(n_sections): b = np.polymul(b, sos[section, :3]) a = np.polymul(a, sos[section, 3:]) return b, a
Example #14
Source File: test_regression.py From GraphicDesignPatternByPython with MIT License | 5 votes |
def test_mem_polymul(self): # Ticket #448 np.polymul([], [1.])
Example #15
Source File: test_regression.py From Mastering-Elasticsearch-7.0 with MIT License | 5 votes |
def test_mem_polymul(self): # Ticket #448 np.polymul([], [1.])
Example #16
Source File: test_regression.py From Computable with MIT License | 5 votes |
def test_mem_polymul(self, level=rlevel): """Ticket #448""" np.polymul([], [1.])
Example #17
Source File: test_regression.py From vnpy_crypto with MIT License | 5 votes |
def test_mem_polymul(self): # Ticket #448 np.polymul([], [1.])
Example #18
Source File: test_regression.py From auto-alt-text-lambda-api with MIT License | 5 votes |
def test_mem_polymul(self, level=rlevel): # Ticket #448 np.polymul([], [1.])
Example #19
Source File: test_regression.py From lambda-packs with MIT License | 5 votes |
def test_mem_polymul(self, level=rlevel): # Ticket #448 np.polymul([], [1.])
Example #20
Source File: filter_design.py From lambda-packs with MIT License | 5 votes |
def sos2tf(sos): """ Return a single transfer function from a series of second-order sections Parameters ---------- sos : array_like Array of second-order filter coefficients, must have shape ``(n_sections, 6)``. See `sosfilt` for the SOS filter format specification. Returns ------- b : ndarray Numerator polynomial coefficients. a : ndarray Denominator polynomial coefficients. Notes ----- .. versionadded:: 0.16.0 """ sos = np.asarray(sos) b = [1.] a = [1.] n_sections = sos.shape[0] for section in range(n_sections): b = np.polymul(b, sos[section, :3]) a = np.polymul(a, sos[section, 3:]) return b, a
Example #21
Source File: test_regression.py From recruit with Apache License 2.0 | 5 votes |
def test_mem_polymul(self): # Ticket #448 np.polymul([], [1.])
Example #22
Source File: margins.py From python-control with BSD 3-Clause "New" or "Revised" License | 5 votes |
def phase_crossover_frequencies(sys): """Compute frequencies and gains at intersections with real axis in Nyquist plot. Call as: omega, gain = phase_crossover_frequencies() Returns ------- omega: 1d array of (non-negative) frequencies where Nyquist plot intersects the real axis gain: 1d array of corresponding gains Examples -------- >>> tf = TransferFunction([1], [1, 2, 3, 4]) >>> PhaseCrossoverFrequenies(tf) (array([ 1.73205081, 0. ]), array([-0.5 , 0.25])) """ # Convert to a transfer function tf = xferfcn._convert_to_transfer_function(sys) # if not siso, fall back to (0,0) element #! TODO: should add a check and warning here num = tf.num[0][0] den = tf.den[0][0] # Compute frequencies that we cross over the real axis numj = (1.j)**np.arange(len(num)-1,-1,-1)*num denj = (-1.j)**np.arange(len(den)-1,-1,-1)*den allfreq = np.roots(np.imag(np.polymul(numj,denj))) realfreq = np.real(allfreq[np.isreal(allfreq)]) realposfreq = realfreq[realfreq >= 0.] # using real() to avoid rounding errors and results like 1+0j # it would be nice to have a vectorized version of self.evalfr here gain = np.real(np.asarray([tf._evalfr(f)[0][0] for f in realposfreq])) return realposfreq, gain