Python sympy.diff方法代碼示例

# 需要導入模塊: import sympy [as 別名]
# 或者: from sympy import diff [as 別名]
def marginal_product_capital(self):
        r"""
        Symbolic expression for the marginal product of capital (per
        unit effective labor).

        :getter: Return the current marginal product of capital (per
        unit effective labor).
        :type: sym.Basic

        Notes
        -----
        The marginal product of capital is defined as follows:

        .. math::

            \frac{\partial F(K, AL)}{\partial K} \equiv f'(k)

        where :math:`k=K/AL` is capital stock (per unit effective labor)

        """
        return sym.diff(self.intensive_output, k) 

# 需要導入模塊: import sympy [as 別名]
# 或者: from sympy import diff [as 別名]
def testCalculusIntegrate(self):
    dataset_objects = algorithmic_math.math_dataset_init(
        8, digits=5, functions={"log": "L"})
    counter = 0
    for d in algorithmic_math.calculus_integrate(8, 0, 3, 10):
      counter += 1
      decoded_input = dataset_objects.int_decoder(d["inputs"])
      var, expression = decoded_input.split(":")
      target = dataset_objects.int_decoder(d["targets"])

      for fn_name, fn_char in six.iteritems(dataset_objects.functions):
        target = target.replace(fn_char, fn_name)

      # Take the derivative of the target.
      derivative = str(sympy.diff(target, var))

      # Check that the derivative of the integral equals the input.
      self.assertEqual(0, sympy.simplify("%s-(%s)" % (expression, derivative)))
    self.assertEqual(counter, 10) 

# 需要導入模塊: import sympy [as 別名]
# 或者: from sympy import diff [as 別名]
def marginal_product_capital(self):
        r"""
        Symbolic expression for the marginal product of capital (per unit
        effective labor).

        :getter: Return the current marginal product of capital.
        :type: sympy.Basic

        Notes
        -----
        The marginal product of capital is defined as follows:

        .. math::

            \frac{\partial F(K, AL)}{\partial K} \equiv f'(k)

        where :math:`k=K/AL` is capital stock (per unit effective labor).

        """
        return sym.diff(self.intensive_output, k) 

# 需要導入模塊: import sympy [as 別名]
# 或者: from sympy import diff [as 別名]
def newton_raphson(func: str, a: int, precision: int = 10 ** -10) -> float:
    """ Finds root from the point 'a' onwards by Newton-Raphson method
    >>> newton_raphson("sin(x)", 2)
    3.1415926536808043
    >>> newton_raphson("x**2 - 5*x +2", 0.4)
    0.4384471871911695
    >>> newton_raphson("x**2 - 5", 0.1)
    2.23606797749979
    >>> newton_raphson("log(x)- 1", 2)
    2.718281828458938
    """
    x = a
    while True:
        x = Decimal(x) - (Decimal(eval(func)) / Decimal(eval(str(diff(func)))))
        # This number dictates the accuracy of the answer
        if abs(eval(func)) < precision:
            return float(x)


# Let's Execute 

# 需要導入模塊: import sympy [as 別名]
# 或者: from sympy import diff [as 別名]
def Simple_manifold_with_scalar_function_derivative():
    coords = (x,y,z) = symbols('x y z')
    basis = (e1, e2, e3, grad) = MV.setup('e_1 e_2 e_3',metric='[1,1,1]',coords=coords)
    # Define surface
    mfvar = (u,v) = symbols('u v')
    X = u*e1+v*e2+(u**2+v**2)*e3
    print X
    MF = Manifold(X,mfvar)

    # Define field on the surface.
    g = (v+1)*log(u)

    # Method 1: Using old Manifold routines.
    VectorDerivative = (MF.rbasis[0]/MF.E_sq)*diff(g,u) + (MF.rbasis[1]/MF.E_sq)*diff(g,v)
    print 'Vector derivative =', VectorDerivative.subs({u:1,v:0})

    # Method 2: Using new Manifold routines.
    dg = MF.Grad(g)
    print 'Vector derivative =', dg.subs({u:1,v:0})
    return 

# 需要導入模塊: import sympy [as 別名]
# 或者: from sympy import diff [as 別名]
def Simple_manifold_with_scalar_function_derivative():
    Print_Function()
    coords = (x,y,z) = symbols('x y z')
    basis = (e1, e2, e3, grad) = MV.setup('e_1 e_2 e_3',metric='[1,1,1]',coords=coords)
    # Define surface
    mfvar = (u,v) = symbols('u v')
    X = u*e1+v*e2+(u**2+v**2)*e3
    print '\\f{X}{u,v} =',X
    MF = Manifold(X,mfvar)
    (eu,ev) = MF.Basis()
    # Define field on the surface.
    g = (v+1)*log(u)

    print '\\f{g}{u,v} =',g

    # Method 1: Using old Manifold routines.
    VectorDerivative = (MF.rbasis[0]/MF.E_sq)*diff(g,u) + (MF.rbasis[1]/MF.E_sq)*diff(g,v)
    print '\\eval{\\nabla g}{u=1,v=0} =', VectorDerivative.subs({u:1,v:0})

    # Method 2: Using new Manifold routines.
    dg = MF.Grad(g)
    print '\\eval{\\f{Grad}{g}}{u=1,v=0} =', dg.subs({u:1,v:0})
    dg = MF.grad*g
    print '\\eval{\\nabla g}{u=1,v=0} =', dg.subs({u:1,v:0})
    return 

# 需要導入模塊: import sympy [as 別名]
# 或者: from sympy import diff [as 別名]
def _is_non_decreasing(fn, q, bounds):
  """Verifies whether the function is non-decreasing within a range.

  Args:
    fn: Symbolic function of a single variable.
    q: The name of f's variable.
    bounds: Pair of (lower_bound, upper_bound) reals.

  Returns:
    True iff the function is non-decreasing in the range.
  """
  diff_fn = sp.diff(fn, q)  # Symbolically compute the derivative.
  diff_fn_lambdified = sp.lambdify(
      q,
      diff_fn,
      modules=[
          "numpy", {
              "erfc": scipy.special.erfc,
              "erfcinv": scipy.special.erfcinv
          }
      ])
  r = scipy.optimize.minimize_scalar(
      diff_fn_lambdified, bounds=bounds, method="bounded")
  assert r.success, "Minimizer failed to converge."
  return r.fun >= 0  # Check whether the derivative is non-negative. 

# 需要導入模塊: import sympy [as 別名]
# 或者: from sympy import diff [as 別名]
def _computeTransitionJacobian(self):
        if self._GMat is None:
            self._computeDependencyMatrix()

        F = sympy.zeros(self.num_transitions, self.num_transitions)
        for i in range(self.num_transitions):
            for j, eqn in enumerate(self._transitionVector):
                for k, state in enumerate(self._iterStateList()):
                    diffEqn = sympy.diff(eqn, state, 1)
                    tempEqn, isDifficult = simplifyEquation(diffEqn)
                    F[i,j] += tempEqn*self._vMat[k,i]
                    self._isDifficult = self._isDifficult or isDifficult

        self._transitionJacobian = F

        self._hasNewTransition.reset('transitionJacobian')
        return F 

# 需要導入模塊: import sympy [as 別名]
# 或者: from sympy import diff [as 別名]
def _integrate_exact(f, c2):
    xi = sympy.DeferredVector("xi")
    pxi = (
        c2[0] * 0.25 * (1.0 + xi[0]) * (1.0 + xi[1])
        + c2[1] * 0.25 * (1.0 - xi[0]) * (1.0 + xi[1])
        + c2[2] * 0.25 * (1.0 - xi[0]) * (1.0 - xi[1])
        + c2[3] * 0.25 * (1.0 + xi[0]) * (1.0 - xi[1])
    )
    pxi = [sympy.expand(pxi[0]), sympy.expand(pxi[1])]
    # determinant of the transformation matrix
    det_J = +sympy.diff(pxi[0], xi[0]) * sympy.diff(pxi[1], xi[1]) - sympy.diff(
        pxi[1], xi[0]
    ) * sympy.diff(pxi[0], xi[1])
    # we cannot use abs(), see <https://github.com/sympy/sympy/issues/4212>.
    abs_det_J = sympy.Piecewise((det_J, det_J >= 0), (-det_J, det_J < 0))

    g_xi = f(pxi)

    exact = sympy.integrate(
        sympy.integrate(abs_det_J * g_xi, (xi[1], -1, 1)), (xi[0], -1, 1)
    )
    return float(exact) 

# 需要導入模塊: import sympy [as 別名]
# 或者: from sympy import diff [as 別名]
def test_stencil_derivative(self, SymbolType, dim):
        """Test symbolic behaviour when expanding stencil derivatives"""
        i = dim(self.grid)
        u = SymbolType(name='u', grid=self.grid)
        u.data[:] = 66.6
        di = u.diff(i)
        dii = u.diff(i, i)
        # Check for sympy Derivative objects
        assert(isinstance(di, Derivative) and isinstance(dii, Derivative))
        s_di = di.as_finite_difference([i - i.spacing, i])
        s_dii = dii.as_finite_difference([i - i.spacing, i, i + i.spacing])
        # Check stencil length of first and second derivatives
        assert(len(s_di.args) == 2 and len(s_dii.args) == 3)
        u_di = s_di.args[0].args[1]
        u_dii = s_di.args[0].args[1]
        # Ensure that devito meta-data survived symbolic transformation
        assert(u_di.grid.shape == self.shape and u_dii.grid.shape == self.shape)
        assert(u_di.shape == u.shape and u_dii.shape == u.shape)
        assert(np.allclose(u_di.data, 66.6))
        assert(np.allclose(u_dii.data, 66.6)) 

# 需要導入模塊: import sympy [as 別名]
# 或者: from sympy import diff [as 別名]
def _njacobian(self):
        """
        :return: The `numerical_jacobian` of the components of the ODEModel with
            regards to the dependent variables. This is not to be confused with
            the jacobian of the model as a whole, which is 2D and computed with
            regards to the dependent vars and the fit parameters, and the
            ODEModel still needs to integrated to compute that.

            Instead, this function is used by the ODE integrator, and is not
            meant for human consumption.
        """
        return [
            [sympy_to_py(
                    sympy.diff(expr, var), self.independent_vars + self.dependent_vars + self.model_params
                ) for var in self.dependent_vars
            ] for _, expr in self.items()
        ] 

# 需要導入模塊: import sympy [as 別名]
# 或者: from sympy import diff [as 別名]
def spherical_bessel_formulas(n):
    x = sym.symbols('x')

    f = [sym.sin(x) / x]
    a = sym.sin(x) / x
    for i in range(1, n):
        b = sym.diff(a, x) / x
        f += [sym.simplify(b * (-x)**i)]
        a = sym.simplify(b)
    return f 

# 需要導入模塊: import sympy [as 別名]
# 或者: from sympy import diff [as 別名]
def uniform_bspline_basis(d, p=0):
    """Generate a "Numpy friendly" function to facilitate fast evaluation of
    uniform B-spline basis functions.

    Parameters
    ----------
    d : int
        The degree of the uniform B-spline.

    p : optional, int
        The order of the derivative with respect to the interpolation
        parameter.

    Returns
    -------
    uniform_bspline_basis_d : function
        "Numpy friendly" function to evaluate the uniform B-spline
        interpolation components.
    """
    t = sp.Symbol('t')
    b = basis_functions(d, t)
    for i in range(p):
        b = [sp.diff(e, t) for e in b]

    func_name = 'uniform_bspline_basis_{}_{}'.format(d, p)
    W = ['    W[:, {}] = {}'.format(ie[0], ie[1].evalf())
         for ie in enumerate(b)]
    code = UNIFORM_BSPLINE_TEMPLATE.format(func_name=func_name,
                                           num_control_points=len(W),
                                           W='\n'.join(W))
    globals_ = {'np' : np}
    exec(code, globals_)
    return globals_[func_name]


# UniformBSpline 

# 需要導入模塊: import sympy [as 別名]
# 或者: from sympy import diff [as 別名]
def __init__(self, **traits):
        TaylorPoly.__init__(self, **traits)
        #Declare the analytical function
  
        Z=sympy.Function("Z")
        Ax,Ay,Kx,Ky=sympy.symbols(("Ax","Ay","Kx","Ky"))
        x, y =sympy.symbols('xy')
        Z=(Ax*x**2+Ay*y**2)/(1+sympy.sqrt(1-(1+Kx)*Ax**2*x**2-(1+Ky)*Ay**2*y**2));
        
        #Calculate taylor polynomial coheficients
        cohef=[[Z, ],]
        order=self.n
        for i in range(0, order+1, 2):
            if i!=0:
                cohef.append([sympy.diff(cohef[i/2-1][0], y, 2), ])
            for j in range(2, order-i+1, 2):
                cohef[i/2].append(sympy.diff(cohef[i/2][j/2 -1], x, 2))
        

        A_x=self.Ax
        A_y=self.Ay
        K_x=self.Kx
        K_y=self.Ky
        
        c=zeros((self.n+1, self.n+1))
        for i in range(0, order/2+1):
            for j in range(0,order/2- i+1):
                cohef[j][i]=cohef[j][i].subs(x, 0).subs(y, 0).subs(Ax, A_x).subs(Ay, A_y).subs(Kx, K_x).subs(Ky, K_y)/(sympy.factorial(2*i)*sympy.factorial(2*j))
                c[2*j, 2*i]=cohef[j][i].evalf()
        
        # Add the high order corrections
        if len(self.ho_cohef.shape)==2:
            cx, cy = c.shape 
            dx, dy =self.ho_cohef.shape
            mx=array((cx, dx)).max()
            my=array((cy, dy)).max()
            self.cohef=zeros((mx, my))
            self.cohef[0:cx, 0:cy]=c
            self.cohef[0:dy, 0:dy]=self.cohef[0:dy, 0:dy]+self.ho_cohef
        else:
            self.cohef=c 

# 需要導入模塊: import sympy [as 別名]
# 或者: from sympy import diff [as 別名]
def grad(f, basis, for_numerical=True):
    """
    Compute the symbolic gradient of a vector-valued function with respect to a
    basis.

    Parameters
    ----------
    f : 1D array_like of sympy Expressions
        The vector-valued function to compute the gradient of.
    basis : 1D array_like of sympy symbols
        The basis symbols to compute the gradient with respect to.
    for_numerical : bool, optional
        A placeholder for the option of numerically computing the gradient.

    Returns
    -------
    grad : 2D array_like of sympy Expressions
        The symbolic gradient.
    """
    if hasattr(f, '__len__'):  # as of version 1.1.1, Array isn't supported
        f = sp.Matrix(f)

    return f.__class__([
        [
            sp.diff(f[x], basis[y])
            if not for_numerical or not f[x].has(sp.sign(basis[y])) else 0
            for y in range(len(basis))
        ] for x in range(len(f))
    ])