Python numpy.polynomial.polynomial.polyval2d() Examples

The following are 30 code examples of numpy.polynomial.polynomial.polyval2d(). 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.polynomial.polynomial , or try the search function .
Example #1
Source File: test_polynomial.py    From GraphicDesignPatternByPython with MIT License 7 votes vote down vote up 
def test_polyval2d(self):
        x1, x2, x3 = self.x
        y1, y2, y3 = self.y

        #test exceptions
        assert_raises(ValueError, poly.polyval2d, x1, x2[:2], self.c2d)

        #test values
        tgt = y1*y2
        res = poly.polyval2d(x1, x2, self.c2d)
        assert_almost_equal(res, tgt)

        #test shape
        z = np.ones((2, 3))
        res = poly.polyval2d(z, z, self.c2d)
        assert_(res.shape == (2, 3))
Example #2
Source File: test_polynomial.py    From recruit with Apache License 2.0 6 votes vote down vote up 
def test_polyval2d(self):
        x1, x2, x3 = self.x
        y1, y2, y3 = self.y

        #test exceptions
        assert_raises(ValueError, poly.polyval2d, x1, x2[:2], self.c2d)

        #test values
        tgt = y1*y2
        res = poly.polyval2d(x1, x2, self.c2d)
        assert_almost_equal(res, tgt)

        #test shape
        z = np.ones((2, 3))
        res = poly.polyval2d(z, z, self.c2d)
        assert_(res.shape == (2, 3))
Example #3
Source File: test_polynomial.py    From elasticintel with GNU General Public License v3.0 6 votes vote down vote up 
def test_polyval2d(self):
        x1, x2, x3 = self.x
        y1, y2, y3 = self.y

        #test exceptions
        assert_raises(ValueError, poly.polyval2d, x1, x2[:2], self.c2d)

        #test values
        tgt = y1*y2
        res = poly.polyval2d(x1, x2, self.c2d)
        assert_almost_equal(res, tgt)

        #test shape
        z = np.ones((2, 3))
        res = poly.polyval2d(z, z, self.c2d)
        assert_(res.shape == (2, 3))
Example #4
Source File: test_polynomial.py    From ImageFusion with MIT License 6 votes vote down vote up 
def test_polyval2d(self):
        x1, x2, x3 = self.x
        y1, y2, y3 = self.y

        #test exceptions
        assert_raises(ValueError, poly.polyval2d, x1, x2[:2], self.c2d)

        #test values
        tgt = y1*y2
        res = poly.polyval2d(x1, x2, self.c2d)
        assert_almost_equal(res, tgt)

        #test shape
        z = np.ones((2, 3))
        res = poly.polyval2d(z, z, self.c2d)
        assert_(res.shape == (2, 3))
Example #5
Source File: test_polynomial.py    From mxnet-lambda with Apache License 2.0 6 votes vote down vote up 
def test_polyval2d(self):
        x1, x2, x3 = self.x
        y1, y2, y3 = self.y

        #test exceptions
        assert_raises(ValueError, poly.polyval2d, x1, x2[:2], self.c2d)

        #test values
        tgt = y1*y2
        res = poly.polyval2d(x1, x2, self.c2d)
        assert_almost_equal(res, tgt)

        #test shape
        z = np.ones((2, 3))
        res = poly.polyval2d(z, z, self.c2d)
        assert_(res.shape == (2, 3))
Example #6
Source File: test_polynomial.py    From coffeegrindsize with MIT License 6 votes vote down vote up 
def test_polyval2d(self):
        x1, x2, x3 = self.x
        y1, y2, y3 = self.y

        #test exceptions
        assert_raises(ValueError, poly.polyval2d, x1, x2[:2], self.c2d)

        #test values
        tgt = y1*y2
        res = poly.polyval2d(x1, x2, self.c2d)
        assert_almost_equal(res, tgt)

        #test shape
        z = np.ones((2, 3))
        res = poly.polyval2d(z, z, self.c2d)
        assert_(res.shape == (2, 3))
Example #7
Source File: test_polynomial.py    From pySINDy with MIT License 6 votes vote down vote up 
def test_polyval2d(self):
        x1, x2, x3 = self.x
        y1, y2, y3 = self.y

        #test exceptions
        assert_raises(ValueError, poly.polyval2d, x1, x2[:2], self.c2d)

        #test values
        tgt = y1*y2
        res = poly.polyval2d(x1, x2, self.c2d)
        assert_almost_equal(res, tgt)

        #test shape
        z = np.ones((2, 3))
        res = poly.polyval2d(z, z, self.c2d)
        assert_(res.shape == (2, 3))
Example #8
Source File: test_polynomial.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 6 votes vote down vote up 
def test_polyval2d(self):
        x1, x2, x3 = self.x
        y1, y2, y3 = self.y

        #test exceptions
        assert_raises(ValueError, poly.polyval2d, x1, x2[:2], self.c2d)

        #test values
        tgt = y1*y2
        res = poly.polyval2d(x1, x2, self.c2d)
        assert_almost_equal(res, tgt)

        #test shape
        z = np.ones((2, 3))
        res = poly.polyval2d(z, z, self.c2d)
        assert_(res.shape == (2, 3))
Example #9
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_polyval2d(self):
        x1, x2, x3 = self.x
        y1, y2, y3 = self.y

        #test exceptions
        assert_raises(ValueError, poly.polyval2d, x1, x2[:2], self.c2d)

        #test values
        tgt = y1*y2
        res = poly.polyval2d(x1, x2, self.c2d)
        assert_almost_equal(res, tgt)

        #test shape
        z = np.ones((2, 3))
        res = poly.polyval2d(z, z, self.c2d)
        assert_(res.shape == (2, 3))
Example #10
Source File: test_polynomial.py    From Mastering-Elasticsearch-7.0 with MIT License 6 votes vote down vote up 
def test_polyval2d(self):
        x1, x2, x3 = self.x
        y1, y2, y3 = self.y

        #test exceptions
        assert_raises(ValueError, poly.polyval2d, x1, x2[:2], self.c2d)

        #test values
        tgt = y1*y2
        res = poly.polyval2d(x1, x2, self.c2d)
        assert_almost_equal(res, tgt)

        #test shape
        z = np.ones((2, 3))
        res = poly.polyval2d(z, z, self.c2d)
        assert_(res.shape == (2, 3))
Example #11
Source File: test_polynomial.py    From twitter-stock-recommendation with MIT License 6 votes vote down vote up 
def test_polyval2d(self):
        x1, x2, x3 = self.x
        y1, y2, y3 = self.y

        #test exceptions
        assert_raises(ValueError, poly.polyval2d, x1, x2[:2], self.c2d)

        #test values
        tgt = y1*y2
        res = poly.polyval2d(x1, x2, self.c2d)
        assert_almost_equal(res, tgt)

        #test shape
        z = np.ones((2, 3))
        res = poly.polyval2d(z, z, self.c2d)
        assert_(res.shape == (2, 3))
Example #12
Source File: test_polynomial.py    From Computable with MIT License 6 votes vote down vote up 
def test_polyval2d(self):
        x1, x2, x3 = self.x
        y1, y2, y3 = self.y

        #test exceptions
        assert_raises(ValueError, poly.polyval2d, x1, x2[:2], self.c2d)

        #test values
        tgt = y1*y2
        res = poly.polyval2d(x1, x2, self.c2d)
        assert_almost_equal(res, tgt)

        #test shape
        z = np.ones((2, 3))
        res = poly.polyval2d(z, z, self.c2d)
        assert_(res.shape == (2, 3))
Example #13
Source File: test_polynomial.py    From vnpy_crypto with MIT License 6 votes vote down vote up 
def test_polyval2d(self):
        x1, x2, x3 = self.x
        y1, y2, y3 = self.y

        #test exceptions
        assert_raises(ValueError, poly.polyval2d, x1, x2[:2], self.c2d)

        #test values
        tgt = y1*y2
        res = poly.polyval2d(x1, x2, self.c2d)
        assert_almost_equal(res, tgt)

        #test shape
        z = np.ones((2, 3))
        res = poly.polyval2d(z, z, self.c2d)
        assert_(res.shape == (2, 3))
Example #14
Source File: test_polynomial.py    From keras-lambda with MIT License 6 votes vote down vote up 
def test_polyval2d(self):
        x1, x2, x3 = self.x
        y1, y2, y3 = self.y

        #test exceptions
        assert_raises(ValueError, poly.polyval2d, x1, x2[:2], self.c2d)

        #test values
        tgt = y1*y2
        res = poly.polyval2d(x1, x2, self.c2d)
        assert_almost_equal(res, tgt)

        #test shape
        z = np.ones((2, 3))
        res = poly.polyval2d(z, z, self.c2d)
        assert_(res.shape == (2, 3))
Example #15
Source File: test_polynomial.py    From auto-alt-text-lambda-api with MIT License 6 votes vote down vote up 
def test_polyval2d(self):
        x1, x2, x3 = self.x
        y1, y2, y3 = self.y

        #test exceptions
        assert_raises(ValueError, poly.polyval2d, x1, x2[:2], self.c2d)

        #test values
        tgt = y1*y2
        res = poly.polyval2d(x1, x2, self.c2d)
        assert_almost_equal(res, tgt)

        #test shape
        z = np.ones((2, 3))
        res = poly.polyval2d(z, z, self.c2d)
        assert_(res.shape == (2, 3))
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_polyvander2d(self):
        # also tests polyval2d for non-square coefficient array
        x1, x2, x3 = self.x
        c = np.random.random((2, 3))
        van = poly.polyvander2d(x1, x2, [1, 2])
        tgt = poly.polyval2d(x1, x2, c)
        res = np.dot(van, c.flat)
        assert_almost_equal(res, tgt)

        # check shape
        van = poly.polyvander2d([x1], [x2], [1, 2])
        assert_(van.shape == (1, 5, 6))
Example #17
Source File: test_polynomial.py    From coffeegrindsize with MIT License 5 votes vote down vote up 
def test_polyvander2d(self):
        # also tests polyval2d for non-square coefficient array
        x1, x2, x3 = self.x
        c = np.random.random((2, 3))
        van = poly.polyvander2d(x1, x2, [1, 2])
        tgt = poly.polyval2d(x1, x2, c)
        res = np.dot(van, c.flat)
        assert_almost_equal(res, tgt)

        # check shape
        van = poly.polyvander2d([x1], [x2], [1, 2])
        assert_(van.shape == (1, 5, 6))
Example #18
Source File: test_polynomial.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def test_polyvander2d(self):
        # also tests polyval2d for non-square coefficient array
        x1, x2, x3 = self.x
        c = np.random.random((2, 3))
        van = poly.polyvander2d(x1, x2, [1, 2])
        tgt = poly.polyval2d(x1, x2, c)
        res = np.dot(van, c.flat)
        assert_almost_equal(res, tgt)

        # check shape
        van = poly.polyvander2d([x1], [x2], [1, 2])
        assert_(van.shape == (1, 5, 6))
Example #19
Source File: test_polynomial.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def test_polyvander2d(self):
        # also tests polyval2d for non-square coefficient array
        x1, x2, x3 = self.x
        c = np.random.random((2, 3))
        van = poly.polyvander2d(x1, x2, [1, 2])
        tgt = poly.polyval2d(x1, x2, c)
        res = np.dot(van, c.flat)
        assert_almost_equal(res, tgt)

        # check shape
        van = poly.polyvander2d([x1], [x2], [1, 2])
        assert_(van.shape == (1, 5, 6))
Example #20
Source File: test_polynomial.py    From keras-lambda with MIT License 5 votes vote down vote up 
def test_polyvander2d(self):
        # also tests polyval2d for non-square coefficient array
        x1, x2, x3 = self.x
        c = np.random.random((2, 3))
        van = poly.polyvander2d(x1, x2, [1, 2])
        tgt = poly.polyval2d(x1, x2, c)
        res = np.dot(van, c.flat)
        assert_almost_equal(res, tgt)

        # check shape
        van = poly.polyvander2d([x1], [x2], [1, 2])
        assert_(van.shape == (1, 5, 6))
Example #21
Source File: normalize_sicd.py    From sarpy with MIT License 5 votes vote down vote up 
def deskewmem(input_data, DeltaKCOAPoly, dim0_coords_m, dim1_coords_m, dim, fft_sgn=-1):
    """Performs deskew (centering of the spectrum on zero frequency) on a complex dataset.

    INPUTS:
       input_data:  Complex FFT Data
       DeltaKCOAPoly:  Polynomial that describes center of frequency support of data.
       dim0_coords_m:  Coordinate of each "row" in dimension 0
       dim1_coords_m:  Coordinate of each "column" in dimension 1
       dim:  Dimension over which to perform deskew
       fft_sgn:  FFT sign required to transform data to spatial frequency domain
    OUTPUTS:
       output_data:  Deskewed data
       new_DeltaKCOAPoly:  Frequency support shift in the non-deskew dimension
          caused by the deskew.
    """

    # Integrate DeltaKCOA polynomial (in meters) to form new polynomial DeltaKCOAPoly_int
    DeltaKCOAPoly_int = polynomial.polyint(DeltaKCOAPoly, axis=dim)
    # New DeltaKCOAPoly in other dimension will be negative of the derivative of
    # DeltaKCOAPoly_int in other dimension (assuming it was zero before).
    new_DeltaKCOAPoly = - polynomial.polyder(DeltaKCOAPoly_int, axis=dim-1)
    # Apply phase adjustment from polynomial
    [dim1_coords_m_2d, dim0_coords_m_2d] = np.meshgrid(dim1_coords_m, dim0_coords_m)
    output_data = np.multiply(input_data, np.exp(1j * fft_sgn * 2 * np.pi *
                                                 polynomial.polyval2d(
                                                     dim0_coords_m_2d,
                                                     dim1_coords_m_2d,
                                                     DeltaKCOAPoly_int)))
    return output_data, new_DeltaKCOAPoly
Example #22
Source File: normalize_sicd.py    From sarpy with MIT License 5 votes vote down vote up 
def deskewmem(input_data, DeltaKCOAPoly, dim0_coords_m, dim1_coords_m, dim, fft_sgn=-1):
    """
    Performs deskew (centering of the spectrum on zero frequency) on a complex dataset.

    Parameters
    ----------
    input_data : numpy.ndarray
        Complex FFT Data
    DeltaKCOAPoly : numpy.ndarray
        Polynomial that describes center of frequency support of data.
    dim0_coords_m : numpy.ndarray
    dim1_coords_m : numpy.ndarray
    dim : int
    fft_sgn : int|float

    Returns
    -------
    Tuple[numpy.ndarray, numpy.ndarray]
        * `output_data` - Deskewed data
        * `new_DeltaKCOAPoly` - Frequency support shift in the non-deskew dimension caused by the deskew.
    """

    # Integrate DeltaKCOA polynomial (in meters) to form new polynomial DeltaKCOAPoly_int
    DeltaKCOAPoly_int = polynomial.polyint(DeltaKCOAPoly, axis=dim)
    # New DeltaKCOAPoly in other dimension will be negative of the derivative of
    # DeltaKCOAPoly_int in other dimension (assuming it was zero before).
    new_DeltaKCOAPoly = - polynomial.polyder(DeltaKCOAPoly_int, axis=dim-1)
    # Apply phase adjustment from polynomial
    dim1_coords_m_2d, dim0_coords_m_2d = np.meshgrid(dim1_coords_m, dim0_coords_m)
    output_data = np.multiply(input_data, np.exp(1j * fft_sgn * 2 * np.pi *
                                                 polynomial.polyval2d(
                                                     dim0_coords_m_2d,
                                                     dim1_coords_m_2d,
                                                     DeltaKCOAPoly_int)))
    return output_data, new_DeltaKCOAPoly
Example #23
Source File: test_utils.py    From sarpy with MIT License 5 votes vote down vote up 
def test_two_dim_poly_fit(self):
        coeffs = numpy.arange(9).reshape((3, 3))
        y, x = numpy.meshgrid(numpy.arange(2, 6), numpy.arange(-2, 2))
        z = polynomial.polyval2d(x, y, coeffs)
        t_coeffs, residuals, rank, sing_vals = two_dim_poly_fit(x, y, z, x_order=2, y_order=2)
        diff = (numpy.abs(coeffs - t_coeffs) < 1e-10)
        self.assertTrue(numpy.all(diff))
Example #24
Source File: test_polynomial.py    From ImageFusion with MIT License 5 votes vote down vote up 
def test_polyvander2d(self):
        # also tests polyval2d for non-square coefficient array
        x1, x2, x3 = self.x
        c = np.random.random((2, 3))
        van = poly.polyvander2d(x1, x2, [1, 2])
        tgt = poly.polyval2d(x1, x2, c)
        res = np.dot(van, c.flat)
        assert_almost_equal(res, tgt)

        # check shape
        van = poly.polyvander2d([x1], [x2], [1, 2])
        assert_(van.shape == (1, 5, 6))
Example #25
Source File: test_polynomial.py    From mxnet-lambda with Apache License 2.0 5 votes vote down vote up 
def test_polyvander2d(self):
        # also tests polyval2d for non-square coefficient array
        x1, x2, x3 = self.x
        c = np.random.random((2, 3))
        van = poly.polyvander2d(x1, x2, [1, 2])
        tgt = poly.polyval2d(x1, x2, c)
        res = np.dot(van, c.flat)
        assert_almost_equal(res, tgt)

        # check shape
        van = poly.polyvander2d([x1], [x2], [1, 2])
        assert_(van.shape == (1, 5, 6))
Example #26
Source File: test_polynomial.py    From pySINDy with MIT License 5 votes vote down vote up 
def test_polyvander2d(self):
        # also tests polyval2d for non-square coefficient array
        x1, x2, x3 = self.x
        c = np.random.random((2, 3))
        van = poly.polyvander2d(x1, x2, [1, 2])
        tgt = poly.polyval2d(x1, x2, c)
        res = np.dot(van, c.flat)
        assert_almost_equal(res, tgt)

        # check shape
        van = poly.polyvander2d([x1], [x2], [1, 2])
        assert_(van.shape == (1, 5, 6))
Example #27
Source File: test_polynomial.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def test_polyvander2d(self):
        # also tests polyval2d for non-square coefficient array
        x1, x2, x3 = self.x
        c = np.random.random((2, 3))
        van = poly.polyvander2d(x1, x2, [1, 2])
        tgt = poly.polyval2d(x1, x2, c)
        res = np.dot(van, c.flat)
        assert_almost_equal(res, tgt)

        # check shape
        van = poly.polyvander2d([x1], [x2], [1, 2])
        assert_(van.shape == (1, 5, 6))
Example #28
Source File: test_polynomial.py    From GraphicDesignPatternByPython with MIT License 5 votes vote down vote up 
def test_polyvander2d(self):
        # also tests polyval2d for non-square coefficient array
        x1, x2, x3 = self.x
        c = np.random.random((2, 3))
        van = poly.polyvander2d(x1, x2, [1, 2])
        tgt = poly.polyval2d(x1, x2, c)
        res = np.dot(van, c.flat)
        assert_almost_equal(res, tgt)

        # check shape
        van = poly.polyvander2d([x1], [x2], [1, 2])
        assert_(van.shape == (1, 5, 6))
Example #29
Source File: test_polynomial.py    From Mastering-Elasticsearch-7.0 with MIT License 5 votes vote down vote up 
def test_polyvander2d(self):
        # also tests polyval2d for non-square coefficient array
        x1, x2, x3 = self.x
        c = np.random.random((2, 3))
        van = poly.polyvander2d(x1, x2, [1, 2])
        tgt = poly.polyval2d(x1, x2, c)
        res = np.dot(van, c.flat)
        assert_almost_equal(res, tgt)

        # check shape
        van = poly.polyvander2d([x1], [x2], [1, 2])
        assert_(van.shape == (1, 5, 6))
Example #30
Source File: test_polynomial.py    From Computable with MIT License 5 votes vote down vote up 
def test_polyvander2d(self) :
        # also tests polyval2d for non-square coefficient array
        x1, x2, x3 = self.x
        c = np.random.random((2, 3))
        van = poly.polyvander2d(x1, x2, [1, 2])
        tgt = poly.polyval2d(x1, x2, c)
        res = np.dot(van, c.flat)
        assert_almost_equal(res, tgt)

        # check shape
        van = poly.polyvander2d([x1], [x2], [1, 2])
        assert_(van.shape == (1, 5, 6))