Python numpy.poly() Examples

The following are 30 code examples of numpy.poly(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module numpy , or try the search function .
Example #1
Source File: test_polynomial.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 6 votes vote down vote up
def test_poly(self):
        assert_array_almost_equal(np.poly([3, -np.sqrt(2), np.sqrt(2)]),
                                  [1, -3, -2, 6])

        # From matlab docs
        A = [[1, 2, 3], [4, 5, 6], [7, 8, 0]]
        assert_array_almost_equal(np.poly(A), [1, -6, -72, -27])

        # Should produce real output for perfect conjugates
        assert_(np.isrealobj(np.poly([+1.082j, +2.613j, -2.613j, -1.082j])))
        assert_(np.isrealobj(np.poly([0+1j, -0+-1j, 1+2j,
                                      1-2j, 1.+3.5j, 1-3.5j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j, 1+3j, 1-3.j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 2j, -2j])))
        assert_(np.isrealobj(np.poly([1j, -1j])))
        assert_(np.isrealobj(np.poly([1, -1])))

        assert_(np.iscomplexobj(np.poly([1j, -1.0000001j])))

        np.random.seed(42)
        a = np.random.randn(100) + 1j*np.random.randn(100)
        assert_(np.isrealobj(np.poly(np.concatenate((a, np.conjugate(a)))))) 
Example #2
Source File: test_polynomial.py    From vnpy_crypto with MIT License 6 votes vote down vote up
def test_poly(self):
        assert_array_almost_equal(np.poly([3, -np.sqrt(2), np.sqrt(2)]),
                                  [1, -3, -2, 6])

        # From matlab docs
        A = [[1, 2, 3], [4, 5, 6], [7, 8, 0]]
        assert_array_almost_equal(np.poly(A), [1, -6, -72, -27])

        # Should produce real output for perfect conjugates
        assert_(np.isrealobj(np.poly([+1.082j, +2.613j, -2.613j, -1.082j])))
        assert_(np.isrealobj(np.poly([0+1j, -0+-1j, 1+2j,
                                      1-2j, 1.+3.5j, 1-3.5j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j, 1+3j, 1-3.j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 2j, -2j])))
        assert_(np.isrealobj(np.poly([1j, -1j])))
        assert_(np.isrealobj(np.poly([1, -1])))

        assert_(np.iscomplexobj(np.poly([1j, -1.0000001j])))

        np.random.seed(42)
        a = np.random.randn(100) + 1j*np.random.randn(100)
        assert_(np.isrealobj(np.poly(np.concatenate((a, np.conjugate(a)))))) 
Example #3
Source File: _cools_haegemans.py    From quadpy with GNU General Public License v3.0 6 votes vote down vote up
def _gener(delta2, m, mu, symbolic):
    # Computes the lambda_q from the article, eq. (9).
    lmbdas2 = []
    for q in range(1, m + 1):
        if not lmbdas2:
            # https://github.com/numpy/numpy/issues/16152
            coeffs = [1]
        else:
            coeffs = numpy.poly(lmbdas2)

        a0 = [c * mu(2 * (q - k) + 2, symbolic) for k, c in enumerate(coeffs)]
        a1 = [c * mu(2 * (q - k), symbolic) for k, c in enumerate(coeffs)]
        prod_ = prod([1 - lmbda2 / delta2 for lmbda2 in lmbdas2])
        a = sum(a0) - mu(2, symbolic) ** (q + 1) / mu(0, symbolic) ** q * prod_
        b = sum(a1) - mu(2, symbolic) ** (q + 1) / mu(0, symbolic) ** q * prod_ / delta2
        lmbdas2.append(a / b)
    return lmbdas2 
Example #4
Source File: test_polynomial.py    From elasticintel with GNU General Public License v3.0 6 votes vote down vote up
def test_poly(self):
        assert_array_almost_equal(np.poly([3, -np.sqrt(2), np.sqrt(2)]),
                                  [1, -3, -2, 6])

        # From matlab docs
        A = [[1, 2, 3], [4, 5, 6], [7, 8, 0]]
        assert_array_almost_equal(np.poly(A), [1, -6, -72, -27])

        # Should produce real output for perfect conjugates
        assert_(np.isrealobj(np.poly([+1.082j, +2.613j, -2.613j, -1.082j])))
        assert_(np.isrealobj(np.poly([0+1j, -0+-1j, 1+2j,
                                      1-2j, 1.+3.5j, 1-3.5j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j, 1+3j, 1-3.j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 2j, -2j])))
        assert_(np.isrealobj(np.poly([1j, -1j])))
        assert_(np.isrealobj(np.poly([1, -1])))

        assert_(np.iscomplexobj(np.poly([1j, -1.0000001j])))

        np.random.seed(42)
        a = np.random.randn(100) + 1j*np.random.randn(100)
        assert_(np.isrealobj(np.poly(np.concatenate((a, np.conjugate(a)))))) 
Example #5
Source File: get_mfcc.py    From ResGAN with MIT License 6 votes vote down vote up
def lsf2poly(L):

    # always use double precision 
    dtype = L.dtype
    L = L.astype(np.float64)

    order = len(L)
    Q = L[::2]
    P = L[1::2]
    poles_P = np.r_[np.exp(1j*P),np.exp(-1j*P)]
    poles_Q = np.r_[np.exp(1j*Q),np.exp(-1j*Q)]
    
    P = np.poly(poles_P)
    Q = np.poly(poles_Q)
    
    # convolve from scipy.signal
    # only supports even orders
    P = convolve(P, np.array([1.0, -1.0]))
    Q = convolve(Q, np.array([1.0, 1.0]))
    
    a = 0.5*(P+Q)
 
    a = a[:-1]

    return a.astype(dtype) 
Example #6
Source File: test_polynomial.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 6 votes vote down vote up
def test_poly(self):
        assert_array_almost_equal(np.poly([3, -np.sqrt(2), np.sqrt(2)]),
                                  [1, -3, -2, 6])

        # From matlab docs
        A = [[1, 2, 3], [4, 5, 6], [7, 8, 0]]
        assert_array_almost_equal(np.poly(A), [1, -6, -72, -27])

        # Should produce real output for perfect conjugates
        assert_(np.isrealobj(np.poly([+1.082j, +2.613j, -2.613j, -1.082j])))
        assert_(np.isrealobj(np.poly([0+1j, -0+-1j, 1+2j,
                                      1-2j, 1.+3.5j, 1-3.5j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j, 1+3j, 1-3.j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 2j, -2j])))
        assert_(np.isrealobj(np.poly([1j, -1j])))
        assert_(np.isrealobj(np.poly([1, -1])))

        assert_(np.iscomplexobj(np.poly([1j, -1.0000001j])))

        np.random.seed(42)
        a = np.random.randn(100) + 1j*np.random.randn(100)
        assert_(np.isrealobj(np.poly(np.concatenate((a, np.conjugate(a)))))) 
Example #7
Source File: test_polynomial.py    From coffeegrindsize with MIT License 6 votes vote down vote up
def test_poly(self):
        assert_array_almost_equal(np.poly([3, -np.sqrt(2), np.sqrt(2)]),
                                  [1, -3, -2, 6])

        # From matlab docs
        A = [[1, 2, 3], [4, 5, 6], [7, 8, 0]]
        assert_array_almost_equal(np.poly(A), [1, -6, -72, -27])

        # Should produce real output for perfect conjugates
        assert_(np.isrealobj(np.poly([+1.082j, +2.613j, -2.613j, -1.082j])))
        assert_(np.isrealobj(np.poly([0+1j, -0+-1j, 1+2j,
                                      1-2j, 1.+3.5j, 1-3.5j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j, 1+3j, 1-3.j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 2j, -2j])))
        assert_(np.isrealobj(np.poly([1j, -1j])))
        assert_(np.isrealobj(np.poly([1, -1])))

        assert_(np.iscomplexobj(np.poly([1j, -1.0000001j])))

        np.random.seed(42)
        a = np.random.randn(100) + 1j*np.random.randn(100)
        assert_(np.isrealobj(np.poly(np.concatenate((a, np.conjugate(a)))))) 
Example #8
Source File: test_polynomial.py    From GraphicDesignPatternByPython with MIT License 6 votes vote down vote up
def test_poly(self):
        assert_array_almost_equal(np.poly([3, -np.sqrt(2), np.sqrt(2)]),
                                  [1, -3, -2, 6])

        # From matlab docs
        A = [[1, 2, 3], [4, 5, 6], [7, 8, 0]]
        assert_array_almost_equal(np.poly(A), [1, -6, -72, -27])

        # Should produce real output for perfect conjugates
        assert_(np.isrealobj(np.poly([+1.082j, +2.613j, -2.613j, -1.082j])))
        assert_(np.isrealobj(np.poly([0+1j, -0+-1j, 1+2j,
                                      1-2j, 1.+3.5j, 1-3.5j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j, 1+3j, 1-3.j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 2j, -2j])))
        assert_(np.isrealobj(np.poly([1j, -1j])))
        assert_(np.isrealobj(np.poly([1, -1])))

        assert_(np.iscomplexobj(np.poly([1j, -1.0000001j])))

        np.random.seed(42)
        a = np.random.randn(100) + 1j*np.random.randn(100)
        assert_(np.isrealobj(np.poly(np.concatenate((a, np.conjugate(a)))))) 
Example #9
Source File: test_polynomial.py    From lambda-packs with MIT License 6 votes vote down vote up
def test_poly(self):
        assert_array_almost_equal(np.poly([3, -np.sqrt(2), np.sqrt(2)]),
                                  [1, -3, -2, 6])

        # From matlab docs
        A = [[1, 2, 3], [4, 5, 6], [7, 8, 0]]
        assert_array_almost_equal(np.poly(A), [1, -6, -72, -27])

        # Should produce real output for perfect conjugates
        assert_(np.isrealobj(np.poly([+1.082j, +2.613j, -2.613j, -1.082j])))
        assert_(np.isrealobj(np.poly([0+1j, -0+-1j, 1+2j,
                                      1-2j, 1.+3.5j, 1-3.5j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j, 1+3j, 1-3.j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 2j, -2j])))
        assert_(np.isrealobj(np.poly([1j, -1j])))
        assert_(np.isrealobj(np.poly([1, -1])))

        assert_(np.iscomplexobj(np.poly([1j, -1.0000001j])))

        np.random.seed(42)
        a = np.random.randn(100) + 1j*np.random.randn(100)
        assert_(np.isrealobj(np.poly(np.concatenate((a, np.conjugate(a)))))) 
Example #10
Source File: test_polynomial.py    From twitter-stock-recommendation with MIT License 6 votes vote down vote up
def test_poly(self):
        assert_array_almost_equal(np.poly([3, -np.sqrt(2), np.sqrt(2)]),
                                  [1, -3, -2, 6])

        # From matlab docs
        A = [[1, 2, 3], [4, 5, 6], [7, 8, 0]]
        assert_array_almost_equal(np.poly(A), [1, -6, -72, -27])

        # Should produce real output for perfect conjugates
        assert_(np.isrealobj(np.poly([+1.082j, +2.613j, -2.613j, -1.082j])))
        assert_(np.isrealobj(np.poly([0+1j, -0+-1j, 1+2j,
                                      1-2j, 1.+3.5j, 1-3.5j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j, 1+3j, 1-3.j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 2j, -2j])))
        assert_(np.isrealobj(np.poly([1j, -1j])))
        assert_(np.isrealobj(np.poly([1, -1])))

        assert_(np.iscomplexobj(np.poly([1j, -1.0000001j])))

        np.random.seed(42)
        a = np.random.randn(100) + 1j*np.random.randn(100)
        assert_(np.isrealobj(np.poly(np.concatenate((a, np.conjugate(a)))))) 
Example #11
Source File: test_polynomial.py    From mxnet-lambda with Apache License 2.0 6 votes vote down vote up
def test_poly(self):
        assert_array_almost_equal(np.poly([3, -np.sqrt(2), np.sqrt(2)]),
                                  [1, -3, -2, 6])

        # From matlab docs
        A = [[1, 2, 3], [4, 5, 6], [7, 8, 0]]
        assert_array_almost_equal(np.poly(A), [1, -6, -72, -27])

        # Should produce real output for perfect conjugates
        assert_(np.isrealobj(np.poly([+1.082j, +2.613j, -2.613j, -1.082j])))
        assert_(np.isrealobj(np.poly([0+1j, -0+-1j, 1+2j,
                                      1-2j, 1.+3.5j, 1-3.5j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j, 1+3j, 1-3.j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 2j, -2j])))
        assert_(np.isrealobj(np.poly([1j, -1j])))
        assert_(np.isrealobj(np.poly([1, -1])))

        assert_(np.iscomplexobj(np.poly([1j, -1.0000001j])))

        np.random.seed(42)
        a = np.random.randn(100) + 1j*np.random.randn(100)
        assert_(np.isrealobj(np.poly(np.concatenate((a, np.conjugate(a)))))) 
Example #12
Source File: test_polynomial.py    From recruit with Apache License 2.0 6 votes vote down vote up
def test_poly(self):
        assert_array_almost_equal(np.poly([3, -np.sqrt(2), np.sqrt(2)]),
                                  [1, -3, -2, 6])

        # From matlab docs
        A = [[1, 2, 3], [4, 5, 6], [7, 8, 0]]
        assert_array_almost_equal(np.poly(A), [1, -6, -72, -27])

        # Should produce real output for perfect conjugates
        assert_(np.isrealobj(np.poly([+1.082j, +2.613j, -2.613j, -1.082j])))
        assert_(np.isrealobj(np.poly([0+1j, -0+-1j, 1+2j,
                                      1-2j, 1.+3.5j, 1-3.5j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j, 1+3j, 1-3.j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 2j, -2j])))
        assert_(np.isrealobj(np.poly([1j, -1j])))
        assert_(np.isrealobj(np.poly([1, -1])))

        assert_(np.iscomplexobj(np.poly([1j, -1.0000001j])))

        np.random.seed(42)
        a = np.random.randn(100) + 1j*np.random.randn(100)
        assert_(np.isrealobj(np.poly(np.concatenate((a, np.conjugate(a)))))) 
Example #13
Source File: linear_prediction.py    From spectrum with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up
def poly2ac(poly, efinal):
    """ Convert prediction filter polynomial to autocorrelation sequence

    :param array poly: the AR parameters
    :param efinal: an estimate of the final error
    :return: the autocorrelation  sequence in complex format.

    .. doctest::

        >>> from numpy import array
        >>> from spectrum import poly2ac
        >>> poly = [ 1. ,  0.38 , -0.05]
        >>> efinal = 4.1895
        >>> poly2ac(poly, efinal)
        array([ 5.00+0.j, -2.00+0.j,  1.01-0.j])

    """
    results = rlevinson(poly, efinal)
    return results[0] 
Example #14
Source File: test_polynomial.py    From pySINDy with MIT License 6 votes vote down vote up
def test_poly(self):
        assert_array_almost_equal(np.poly([3, -np.sqrt(2), np.sqrt(2)]),
                                  [1, -3, -2, 6])

        # From matlab docs
        A = [[1, 2, 3], [4, 5, 6], [7, 8, 0]]
        assert_array_almost_equal(np.poly(A), [1, -6, -72, -27])

        # Should produce real output for perfect conjugates
        assert_(np.isrealobj(np.poly([+1.082j, +2.613j, -2.613j, -1.082j])))
        assert_(np.isrealobj(np.poly([0+1j, -0+-1j, 1+2j,
                                      1-2j, 1.+3.5j, 1-3.5j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j, 1+3j, 1-3.j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 2j, -2j])))
        assert_(np.isrealobj(np.poly([1j, -1j])))
        assert_(np.isrealobj(np.poly([1, -1])))

        assert_(np.iscomplexobj(np.poly([1j, -1.0000001j])))

        np.random.seed(42)
        a = np.random.randn(100) + 1j*np.random.randn(100)
        assert_(np.isrealobj(np.poly(np.concatenate((a, np.conjugate(a)))))) 
Example #15
Source File: test_polynomial.py    From Mastering-Elasticsearch-7.0 with MIT License 6 votes vote down vote up
def test_poly(self):
        assert_array_almost_equal(np.poly([3, -np.sqrt(2), np.sqrt(2)]),
                                  [1, -3, -2, 6])

        # From matlab docs
        A = [[1, 2, 3], [4, 5, 6], [7, 8, 0]]
        assert_array_almost_equal(np.poly(A), [1, -6, -72, -27])

        # Should produce real output for perfect conjugates
        assert_(np.isrealobj(np.poly([+1.082j, +2.613j, -2.613j, -1.082j])))
        assert_(np.isrealobj(np.poly([0+1j, -0+-1j, 1+2j,
                                      1-2j, 1.+3.5j, 1-3.5j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j, 1+3j, 1-3.j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j])))
        assert_(np.isrealobj(np.poly([1j, -1j, 2j, -2j])))
        assert_(np.isrealobj(np.poly([1j, -1j])))
        assert_(np.isrealobj(np.poly([1, -1])))

        assert_(np.iscomplexobj(np.poly([1j, -1.0000001j])))

        np.random.seed(42)
        a = np.random.randn(100) + 1j*np.random.randn(100)
        assert_(np.isrealobj(np.poly(np.concatenate((a, np.conjugate(a)))))) 
Example #16
Source File: test_polynomial.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 5 votes vote down vote up
def test_zero_dims(self):
        try:
            np.poly(np.zeros((0, 0)))
        except ValueError:
            pass 
Example #17
Source File: test_polynomial.py    From ImageFusion with MIT License 5 votes vote down vote up
def test_zero_dims(self):
        try:
            np.poly(np.zeros((0, 0)))
        except ValueError:
            pass 
Example #18
Source File: test_polynomial.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up
def test_zero_dims(self):
        try:
            np.poly(np.zeros((0, 0)))
        except ValueError:
            pass 
Example #19
Source File: test_polynomial.py    From keras-lambda with MIT License 5 votes vote down vote up
def test_objects(self):
        from decimal import Decimal
        p = np.poly1d([Decimal('4.0'), Decimal('3.0'), Decimal('2.0')])
        p2 = p * Decimal('1.333333333333333')
        assert_(p2[1] == Decimal("3.9999999999999990"))
        p2 = p.deriv()
        assert_(p2[1] == Decimal('8.0'))
        p2 = p.integ()
        assert_(p2[3] == Decimal("1.333333333333333333333333333"))
        assert_(p2[2] == Decimal('1.5'))
        assert_(np.issubdtype(p2.coeffs.dtype, np.object_))
        p = np.poly([Decimal(1), Decimal(2)])
        assert_equal(np.poly([Decimal(1), Decimal(2)]),
                     [1, Decimal(-3), Decimal(2)]) 
Example #20
Source File: test_polynomial.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 5 votes vote down vote up
def test_poly_int_overflow(self):
        """
        Regression test for gh-5096.
        """
        v = np.arange(1, 21)
        assert_almost_equal(np.poly(v), np.poly(np.diag(v))) 
Example #21
Source File: test_polynomial.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up
def test_objects(self):
        from decimal import Decimal
        p = np.poly1d([Decimal('4.0'), Decimal('3.0'), Decimal('2.0')])
        p2 = p * Decimal('1.333333333333333')
        assert_(p2[1] == Decimal("3.9999999999999990"))
        p2 = p.deriv()
        assert_(p2[1] == Decimal('8.0'))
        p2 = p.integ()
        assert_(p2[3] == Decimal("1.333333333333333333333333333"))
        assert_(p2[2] == Decimal('1.5'))
        assert_(np.issubdtype(p2.coeffs.dtype, np.object_))
        p = np.poly([Decimal(1), Decimal(2)])
        assert_equal(np.poly([Decimal(1), Decimal(2)]),
                     [1, Decimal(-3), Decimal(2)]) 
Example #22
Source File: audio_tools.py    From dagbldr with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
def lsf_to_lpc(all_lsf):
    if len(all_lsf.shape) < 2:
        all_lsf = all_lsf[None]
    order = all_lsf.shape[1]
    all_lpc = np.zeros((len(all_lsf), order + 1))
    for i in range(len(all_lsf)):
        lsf = all_lsf[i]
        zeros = np.exp(1j * lsf)
        sum_zeros = zeros[::2]
        diff_zeros = zeros[1::2]
        sum_zeros = np.hstack((sum_zeros, np.conj(sum_zeros)))
        diff_zeros = np.hstack((diff_zeros, np.conj(diff_zeros)))
        sum_filt = np.poly(sum_zeros)
        diff_filt = np.poly(diff_zeros)

        if order % 2 != 0:
            deconv_diff = sg.convolve(diff_filt, [1, 0, -1])
            deconv_sum = sum_filt
        else:
            deconv_diff = sg.convolve(diff_filt, [1, -1])
            deconv_sum = sg.convolve(sum_filt, [1, 1])

        lpc = .5 * (deconv_sum + deconv_diff)
        # Last coefficient is 0 and not returned
        all_lpc[i] = lpc[:-1]
    return np.squeeze(all_lpc) 
Example #23
Source File: filter.py    From OpenNFB with GNU General Public License v3.0 5 votes vote down vote up
def init(self, freq=50.0, mod=0.9):
        theta = 2 * np.pi * 50 / self.input.sample_rate
        zero = np.exp(np.array([1j, -1j]) * theta)
        pole = mod * zero

        a, b = np.poly(pole), np.poly(zero)
        #notch_ab = numpy.poly(zero), numpy.poly(pole)
        #notch_ab = scipy.signal.iirfilter(32, [30.0 / 125], btype='low')

        self.gr_block = filter.iir_filter_ffd(b, a, oldstyle=False) 
Example #24
Source File: gnuradio_protocol.py    From OpenNFB with GNU General Public License v3.0 5 votes vote down vote up
def init(self, freq=50.0, mod=0.9):
        theta = 2 * np.pi * 50 / self.input.sample_rate
        zero = np.exp(np.array([1j, -1j]) * theta)
        pole = mod * zero

        a, b = np.poly(pole), np.poly(zero)
        #notch_ab = numpy.poly(zero), numpy.poly(pole)
        #notch_ab = scipy.signal.iirfilter(32, [30.0 / 125], btype='low')

        self.gr_block = filter.iir_filter_ffd(b, a, oldstyle=False) 
Example #25
Source File: filter.py    From OpenNFB with GNU General Public License v3.0 5 votes vote down vote up
def compute_filter(self):
		theta = 2 * np.pi * self.frequency / self.nyquist * 2
		zero = np.exp(np.array([1j, -1j]) * theta)
		pole = self.module_pole * zero

		self._filter_b = np.poly(zero)
		self._filter_a = np.poly(pole) 
Example #26
Source File: lpc.py    From pyvocoder with GNU General Public License v2.0 5 votes vote down vote up
def lsf2poly(L):
    order = len(L)
    Q = L[::2]
    P = L[1::2]
    poles_P = np.r_[np.exp(1j*P),np.exp(-1j*P)]
    poles_Q = np.r_[np.exp(1j*Q),np.exp(-1j*Q)]
    
    P = np.poly(poles_P)
    Q = np.poly(poles_Q)
    
    P = convolve(P, np.array([1.0, -1.0]))
    Q = convolve(Q, np.array([1.0, 1.0]))
    
    a = 0.5*(P+Q)
    return a[:-1] 
Example #27
Source File: test_polynomial.py    From mxnet-lambda with Apache License 2.0 5 votes vote down vote up
def test_poly_int_overflow(self):
        """
        Regression test for gh-5096.
        """
        v = np.arange(1, 21)
        assert_almost_equal(np.poly(v), np.poly(np.diag(v))) 
Example #28
Source File: test_polynomial.py    From mxnet-lambda with Apache License 2.0 5 votes vote down vote up
def test_zero_dims(self):
        try:
            np.poly(np.zeros((0, 0)))
        except ValueError:
            pass 
Example #29
Source File: test_polynomial.py    From mxnet-lambda with Apache License 2.0 5 votes vote down vote up
def test_objects(self):
        from decimal import Decimal
        p = np.poly1d([Decimal('4.0'), Decimal('3.0'), Decimal('2.0')])
        p2 = p * Decimal('1.333333333333333')
        assert_(p2[1] == Decimal("3.9999999999999990"))
        p2 = p.deriv()
        assert_(p2[1] == Decimal('8.0'))
        p2 = p.integ()
        assert_(p2[3] == Decimal("1.333333333333333333333333333"))
        assert_(p2[2] == Decimal('1.5'))
        assert_(np.issubdtype(p2.coeffs.dtype, np.object_))
        p = np.poly([Decimal(1), Decimal(2)])
        assert_equal(np.poly([Decimal(1), Decimal(2)]),
                     [1, Decimal(-3), Decimal(2)]) 
Example #30
Source File: test_polynomial.py    From pySINDy with MIT License 5 votes vote down vote up
def test_poly_int_overflow(self):
        """
        Regression test for gh-5096.
        """
        v = np.arange(1, 21)
        assert_almost_equal(np.poly(v), np.poly(np.diag(v)))