Python numpy.vectorize() Examples

def test_static_nonlinearity(self):
        # Linear dynamical system
        linsys = self.siso_linsys
        ioslin = ios.LinearIOSystem(linsys)

        # Nonlinear saturation
        sat = lambda u: u if abs(u) < 1 else np.sign(u)
        sat_output = lambda t, x, u, params: sat(u)
        nlsat =  ios.NonlinearIOSystem(None, sat_output, inputs=1, outputs=1)

        # Set up parameters for simulation
        T, U, X0 = self.T, 2 * self.U, self.X0
        Usat = np.vectorize(sat)(U)

        # Make sure saturation works properly by comparing linear system with
        # saturated input to nonlinear system with saturation composition
        lti_t, lti_y, lti_x = ct.forced_response(linsys, T, Usat, X0)
        ios_t, ios_y, ios_x = ios.input_output_response(
            ioslin * nlsat, T, U, X0, return_x=True)
        np.testing.assert_array_almost_equal(lti_t, ios_t)
        np.testing.assert_array_almost_equal(lti_y, ios_y, decimal=2) 

def test_keywords2_ticket_2100(self):
        # Test kwarg support: enhancement ticket 2100

        def foo(a, b=1):
            return a + b

        f = vectorize(foo)
        args = np.array([1, 2, 3])
        r1 = f(a=args)
        r2 = np.array([2, 3, 4])
        assert_array_equal(r1, r2)
        r1 = f(b=1, a=args)
        assert_array_equal(r1, r2)
        r1 = f(args, b=2)
        r2 = np.array([3, 4, 5])
        assert_array_equal(r1, r2) 

def dict_lookup(dict_, keys, default=None):
    """Looks up :attr:`keys` in the dict, returns the corresponding values.

    The :attr:`default` is used for keys not present in the dict.

    Args:
        dict_ (dict): A dictionary for lookup.
        keys: A numpy array or a (possibly nested) list of keys.
        default (optional): Value to be returned when a key is not in
            :attr:`dict_`. Error is raised if :attr:`default` is not given and
            key is not in the dict.

    Returns:
        A numpy array of values with the same structure as :attr:`keys`.

    Raises:
        TypeError: If key is not in :attr:`dict_` and :attr:`default` is `None`.
    """
    return np.vectorize(lambda x: dict_.get(x, default))(keys) 

def xindex(array, row_num, col_num=None, area_num=1):
    is_reference = isinstance(array, Ranges)
    if is_reference:
        arrays = [Ranges((rng,), array.values).value for rng in array.ranges]
    else:
        arrays = [array]

    row_num, col_num, area_num = parse_ranges(row_num, col_num, area_num)[0]

    res = np.vectorize(_index, excluded={0}, otypes=[object])(
        arrays, row_num, col_num, area_num, is_reference,
        isinstance(row_num, np.ndarray)
    )
    if not res.shape:
        res = res.reshape(1, 1)
    return res.view(Array) 

def testRelativePositionalEmbeddingLayer(self):
    with self.session(use_gpu=False):
      radius = 3
      p = layers.RelativePositionalEmbeddingLayer.Params().Set(
          name='rel_position_emb', radius=radius, dim=4)
      layer = p.Instantiate()
      indices = np.array([-5, -2, 0, 1, 4], dtype=np.int32)
      pos_emb = layer.FPropDefaultTheta(tf.convert_to_tensor(indices))

      self.evaluate(tf.global_variables_initializer())
      actual_pos_emb, full_emb = self.evaluate([pos_emb, layer.vars.w])

      clipped_indices = np.vectorize(lambda x: max(-radius, min(radius, x)))(
          indices) + radius
      expected_output = np.take_along_axis(full_emb,
                                           np.expand_dims(clipped_indices, -1),
                                           0)
      print('expected_position_embs:', expected_output)
      print('actual_position_embs:', actual_pos_emb)
      self.assertAllClose(actual_pos_emb, expected_output) 

def plot3d(self, scale=0.32):
		r"""Plot 3d scatter plot of benchmark function.

		Args:
			scale (float): Scale factor for points.
		"""
		fig = plt.figure()
		ax = Axes3D(fig)
		func = self.function()
		Xr, Yr = arange(self.Lower, self.Upper, scale), arange(self.Lower, self.Upper, scale)
		X, Y = meshgrid(Xr, Yr)
		Z = vectorize(self.__2dfun)(X, Y, func)
		ax.plot_surface(X, Y, Z, rstride=8, cstride=8, alpha=0.3)
		ax.contourf(X, Y, Z, zdir='z', offset=-10, cmap=cm.coolwarm)
		ax.set_xlabel('X')
		ax.set_ylabel('Y')
		ax.set_zlabel('Z')
		plt.show()

# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3 

def _munp(self, n, beta, m):
        """
        Returns the n-th non-central moment of the crystalball function.
        """
        N = 1.0 / (m/beta / (m-1) * np.exp(-beta**2 / 2.0) + _norm_pdf_C * _norm_cdf(beta))

        def n_th_moment(n, beta, m):
            """
            Returns n-th moment. Defined only if n+1 < m
            Function cannot broadcast due to the loop over n
            """
            A = (m/beta)**m * np.exp(-beta**2 / 2.0)
            B = m/beta - beta
            rhs = 2**((n-1)/2.0) * sc.gamma((n+1)/2) * (1.0 + (-1)**n * sc.gammainc((n+1)/2, beta**2 / 2))
            lhs = np.zeros(rhs.shape)
            for k in range(n + 1):
                lhs += sc.binom(n, k) * B**(n-k) * (-1)**k / (m - k - 1) * (m/beta)**(-m + k + 1)
            return A * lhs + rhs

        return N * _lazywhere(np.atleast_1d(n + 1 < m),
                              (n, beta, m),
                              np.vectorize(n_th_moment, otypes=[np.float]),
                              np.inf) 

def get_forces(self):
        # Return the force vector for the problem
        topx_to_id = np.vectorize(
            lambda x: xy_to_id(x, 0, self.nelx, self.nely))
        topx = 2 * topx_to_id(np.arange((self.nelx + 1) // 2)) + 1
        f = np.zeros((2 * (self.nelx + 1) * (self.nely + 1), 1))
        f[topx, 0] = -100
        return f 

def get_forces(self):
        # Return the force vector for the problem
        topx_to_id = np.vectorize(
            lambda x: xy_to_id(x, 0, self.nelx, self.nely))
        topx = 2 * topx_to_id(np.arange((self.nelx + 1) // 2)) + 1
        f = np.zeros((2 * (self.nelx + 1) * (self.nely + 1), 1))
        f[topx, 0] = -100
        return f 

def get_fixed_nodes(self):
        """ Return a list of fixed nodes for the problem. """
        x = np.arange(self.passive_min_x)
        topx_to_id = np.vectorize(
            lambda x: xy_to_id(x, 0, self.nelx, self.nely))
        ids = topx_to_id(x)
        fixed = np.union1d(2 * ids, 2 * ids + 1)
        return fixed 

def get_passive_elements(self):
        X, Y = np.mgrid[self.passive_min_x:self.passive_max_x + 1,
            self.passive_min_y:self.passive_max_y]
        pairs = np.vstack([X.ravel(), Y.ravel()]).T
        passive_to_ids = np.vectorize(lambda pair: xy_to_id(*pair,
            nelx=self.nelx - 1, nely=self.nely - 1), signature="(m)->()")
        return passive_to_ids(pairs) 

def __getitem__(self, index):
        raw_data = np.fromfile(self.im_idx[index], dtype=np.float32).reshape((-1, 4))
        if self.imageset == 'test':
            annotated_data = np.expand_dims(np.zeros_like(raw_data[:,0],dtype=int),axis=1)
        else:
            annotated_data = np.fromfile(self.im_idx[index].replace('velodyne','labels')[:-3]+'label', dtype=np.int32).reshape((-1,1))
            annotated_data = annotated_data & 0xFFFF #delete high 16 digits binary
            annotated_data = np.vectorize(self.learning_map.__getitem__)(annotated_data)
        data_tuple = (raw_data[:,:3], annotated_data.astype(np.uint8))
        if self.return_ref:
            data_tuple += (raw_data[:,3],)
        return data_tuple 

def getTransform(img, pc, s, m_p, mean_m):
	z = np.copy(img)
	p, m = getLandmarks(img, pc, m_p)

	#using img, p, m, s, mean_m get the normalized image
	p_1, p_2 = p[0], p[1]
	s_1, s_2 = s[0], s[1]

	#histogram values at locations (pc + landmarks)
	m = [p_1] + list(m) + [p_2]
	#map scale corresponding to these values
	mean_m = [s_1] + list(mean_m) + [s_2]
	new_img = np.zeros_like(img, dtype=np.int64)
	hist_indices = np.zeros_like(img, dtype=np.int64)

	hist_indices = np.copy(new_img)

	for m_ in m:
		hist_indices += (img > m_).astype(int)

	hist_indices = np.clip(hist_indices, 1, len(m) - 1, out=hist_indices)

	indexer_m = lambda v: m[v]
	indexer_mm = lambda v: mean_m[v]
	f_m = np.vectorize(indexer_m)
	f_mm = np.vectorize(indexer_mm)
	
	new_p_1 = f_m(hist_indices - 1)
	new_p_2 = f_m(hist_indices)
	new_s_1 = f_mm(hist_indices - 1)
	new_s_2 = f_mm(hist_indices)
	
	new_img = mapLandmarksVec([new_p_1, new_p_2], [new_s_1, new_s_2], img)
	
	new_img = np.clip(new_img, s_1-1, s_2+1, out=new_img)
	
	return new_img

################################################################## 

def calc_EVPOC(self):
        Comp = self.Completeness

        bins = 1000
        # xedges is array of separation values for interpolant
        xedges = np.linspace(0., Comp.PlanetPopulation.rrange[1].value, bins)*\
                Comp.PlanetPopulation.arange.unit
        xedges = xedges.to('AU').value

        # yedges is array of delta magnitude values for interpolant
        ymin = np.round(-2.5*np.log10(float(Comp.PlanetPopulation.prange[1]*\
                Comp.PlanetPopulation.Rprange[1]/Comp.PlanetPopulation.rrange[0])**2))
        ymax = np.round(-2.5*np.log10(float(Comp.PlanetPopulation.prange[0]*\
                Comp.PlanetPopulation.Rprange[0]/Comp.PlanetPopulation.rrange[1])**2*1e-11))
        yedges = np.linspace(ymin, ymax, bins)

        # number of planets for each Monte Carlo simulation
        nplan = int(np.min([1e6,Comp.Nplanets]))
        # number of simulations to perform (must be integer)
        steps = int(Comp.Nplanets/nplan)
        
        Cpath = os.path.join(Comp.classpath, Comp.filename+'.comp')
        H, xedges, yedges = self.genC(Cpath, nplan, xedges, yedges, steps)
        EVPOCpdf = interpolate.RectBivariateSpline(xedges, yedges, H.T)
        EVPOC = np.vectorize(EVPOCpdf.integral)

        self.EVPOC = EVPOC 

def predict_proba(self, X):
        f = np.vectorize(self._platt_func)
        raw_predictions = self.decision_function(X)
        platt_predictions = f(raw_predictions).reshape(-1, 1)
        prob_positive = platt_predictions / platt_predictions.sum(axis=1)[:, None]
        prob_negative = 1.0 - prob_positive
        probabilities = np.hstack([prob_negative, prob_positive])
        return probabilities 

def dcg(true_order_relevance, validate_order, top_values=10):
    # retrieve relevance score for each value in validation order
    relevance = np.vectorize(lambda x: true_order_relevance.get(x, 0))(
        validate_order[:top_values]
    )
    gain = 2 ** relevance - 1
    discount = np.log2(np.arange(1, len(gain) + 1) + 1)
    sum_dcg = np.sum(gain / discount)
    return sum_dcg


# TODO: remove this and replace with current contrib method once azureml-contrib-explain-model moved to release 

def testExpandCoefficients(self):
    for _ in range(10):
      num_variables = np.random.randint(1, 4)
      degrees = np.random.randint(0, 4, [num_variables])
      coefficients = np.random.randint(-3, 3, degrees + 1)
      entropy = np.random.uniform(0, 10)
      expanded = polynomials.expand_coefficients(coefficients, entropy)
      collapsed = np.vectorize(sum)(expanded)
      self.assertAllEqual(coefficients, collapsed) 

def generate_csv_data(func, output_csv_filename, x_range=(0, 1), N=500):
    x_vec = np.linspace(x_range[0], x_range[1], N)
    y_vec = np.vectorize(func)(x_vec)
    with open(output_csv_filename, "w") as f:
        writer = csv.writer(f)
        field_names = ["x1", "y"]
        writer.writerow(field_names)
        for (x, y) in zip(x_vec, y_vec):
            field_values = [x, y]
            writer.writerow(field_values) 

def test_simple(self):
        def addsubtract(a, b):
            if a > b:
                return a - b
            else:
                return a + b

        f = vectorize(addsubtract)
        r = f([0, 3, 6, 9], [1, 3, 5, 7])
        assert_array_equal(r, [1, 6, 1, 2]) 

def test_scalar(self):
        def addsubtract(a, b):
            if a > b:
                return a - b
            else:
                return a + b

        f = vectorize(addsubtract)
        r = f([0, 3, 6, 9], 5)
        assert_array_equal(r, [5, 8, 1, 4]) 

def test_large(self):
        x = np.linspace(-3, 2, 10000)
        f = vectorize(lambda x: x)
        y = f(x)
        assert_array_equal(y, x) 

def test_ufunc(self):
        import math
        f = vectorize(math.cos)
        args = np.array([0, 0.5 * np.pi, np.pi, 1.5 * np.pi, 2 * np.pi])
        r1 = f(args)
        r2 = np.cos(args)
        assert_array_almost_equal(r1, r2) 

def test_keywords(self):

        def foo(a, b=1):
            return a + b

        f = vectorize(foo)
        args = np.array([1, 2, 3])
        r1 = f(args)
        r2 = np.array([2, 3, 4])
        assert_array_equal(r1, r2)
        r1 = f(args, 2)
        r2 = np.array([3, 4, 5])
        assert_array_equal(r1, r2) 

def test_keywords3_ticket_2100(self):
        # Test excluded with mixed positional and kwargs: ticket 2100
        def mypolyval(x, p):
            _p = list(p)
            res = _p.pop(0)
            while _p:
                res = res * x + _p.pop(0)
            return res

        vpolyval = np.vectorize(mypolyval, excluded=['p', 1])
        ans = [3, 6]
        assert_array_equal(ans, vpolyval(x=[0, 1], p=[1, 2, 3]))
        assert_array_equal(ans, vpolyval([0, 1], p=[1, 2, 3]))
        assert_array_equal(ans, vpolyval([0, 1], [1, 2, 3])) 

def test_keywords4_ticket_2100(self):
        # Test vectorizing function with no positional args.
        @vectorize
        def f(**kw):
            res = 1.0
            for _k in kw:
                res *= kw[_k]
            return res

        assert_array_equal(f(a=[1, 2], b=[3, 4]), [3, 8]) 

def test_keywords5_ticket_2100(self):
        # Test vectorizing function with no kwargs args.
        @vectorize
        def f(*v):
            return np.prod(v)

        assert_array_equal(f([1, 2], [3, 4]), [3, 8]) 

def test_coverage1_ticket_2100(self):
        def foo():
            return 1

        f = vectorize(foo)
        assert_array_equal(f(), 1) 

def test_UnboundMethod_ticket_1156(self):
        # Regression test for issue 1156
        class Foo:
            b = 2

            def bar(self, a):
                return a ** self.b

        assert_array_equal(vectorize(Foo().bar)(np.arange(9)),
                           np.arange(9) ** 2)
        assert_array_equal(vectorize(Foo.bar)(Foo(), np.arange(9)),
                           np.arange(9) ** 2) 

def test_execution_order_ticket_1487(self):
        # Regression test for dependence on execution order: issue 1487
        f1 = vectorize(lambda x: x)
        res1a = f1(np.arange(3))
        res1b = f1(np.arange(0.1, 3))
        f2 = vectorize(lambda x: x)
        res2b = f2(np.arange(0.1, 3))
        res2a = f2(np.arange(3))
        assert_equal(res1a, res2a)
        assert_equal(res1b, res2b) 

def test_string_ticket_1892(self):
        # Test vectorization over strings: issue 1892.
        f = np.vectorize(lambda x: x)
        s = '0123456789' * 10
        assert_equal(s, f(s))