How to use external_step method in grail

Best Python code snippet using grail_python

ExtendedKalmanFilter.py

Source:ExtendedKalmanFilter.py

...36        elif self.x[2,:] > np.pi:37            self.x[2,:] = self.x[2,:] - 2*np.pi38        else:39            pass40    def external_step(self, u):41        self.internal_step(u)42        z = np.matmul(self.C, self.x) + np.reshape(self.v_sig * np.random.randn(2), (2,1))43        return z44class ExtendedKalmanFilter(object):45    def __init__(self, T=0.05):46        self.T = T47        self.X = np.array([0,0,0], dtype=float).reshape([-1,1])48        self.P = np.diag([2,2,2])49        self.Q = self.T * np.diag([0,0,0.04])50        self.R = np.diag([0.01,0.01])/self.T51        self.H = np.array([52                            [1,0,0],53                            [0,1,0],54        ], dtype=float)55    def prediction(self,u):56        dX_pre = np.zeros((3,1), dtype=float)57        dX_pre[0,:] = u[0] * np.cos(self.X[2,:])58        dX_pre[1,:] = u[0] * np.sin(self.X[2,:])59        dX_pre[2,:] = u[1]60        self.X += (self.T * dX_pre)61        if self.X[2,:] < -np.pi:62            self.X[2,:] = self.x[2,:] + 2*np.pi63        elif self.X[2,:] > np.pi:64            self.X[2,:] = self.x[2,:] - 2*np.pi65        else:66            pass67            68        self.calc_F(self.X,u)69        self.P = self.F.dot(self.P).dot(self.F.T) + self.Q70    def correction(self, Z):71        K = self.P.dot(self.H.T).dot(np.linalg.inv(self.H.dot(self.P).dot(self.H.T) + self.R))72        Y = self.H.dot(self.X)73        self.X = self.X + K.dot(Z-Y)74        self.P = self.P - K.dot(self.H).dot(self.P)75    def calc_F(self, x, u):76        self.F = np.zeros_like(self.Q, dtype=float)77        self.F[0,2] = -u[0]*np.sin(x[2,:])78        self.F[1,2] = u[0] *np.cos(x[2,:])79        self.F = np.identity(x.shape[0]) + self.T * self.F80x_hat_hist, P_hist, z_hist, p_actual = [], [], [], []81env = UNICAR()82env.init_state()83ekf_estimator = ExtendedKalmanFilter()84u = 0.5 * np.ones((100,2))85for i in range(100):86    z = env.external_step(u[i,:])87    ekf_estimator.prediction(u[i,:])88    ekf_estimator.correction(z)89    print(ekf_estimator.X)90    x_hat_hist.append(ekf_estimator.X)91    P_hist.append(ekf_estimator.P)92    z_hist.append(z)93    p_actual.append(env.x[:2,:])94pos_hat = np.matmul(ekf_estimator.H, np.hstack(x_hat_hist))95z_np = np.hstack(z_hist)96p_np = np.hstack(p_actual)97plt.figure(figsize=(15,15))98plt.plot(pos_hat[0,:], pos_hat[1,:],"r-")99plt.plot(z_np[0,:], z_np[1,:], "k-")100plt.plot(p_np[0,:], p_np[1,:],"b-")...

KalmanFilter.py

Source:KalmanFilter.py

...15        self.w_sig = w_sig16        self.v_sig = v_sig17    def internal_step(self, u):18        self.x = np.matmul(self.Ad, self.x) + self.Bd * u + self.w_sig * np.random.randn(1)19    def external_step(self,u):20        self.internal_step(u)21        z = np.matmul(self.C, self.x) + self.v_sig * np.random.randn(1)22        return z23class KALMANFilter(object):24    def __init__(self, T=0.1, Q=np.diag([0,0.25]),R=1):25        self.T = T26        self.A = np.array([[0,1],[0,0]])27        self.B = np.array([[0],[1]])28        self.Ad = np.identity(2) + self.T * self.A29        self.Bd = self.T * self.B30        self.C = np.array([[1,0]])31        self.x_hat = np.array([[0],[0]])32        self.P = np.diag([10,10])33        self.Q = Q34        self.R = R/self.T35    def prediction(self, u):36        self.x_hat = np.matmul(self.Ad, self.x_hat) + self.Bd * u37        self.P = np.matmul(np.matmul(self.Ad, self.P), self.Ad.T) + self.Q38    def measurement(self, z):39        S = np.matmul(self.C, np.matmul(self.P, self.C.T))+ self.R40        K = np.matmul(np.matmul(self.P,self.C.T),np.linalg.inv(S))41        self.x_hat = self.x_hat + np.matmul(K, (z - np.matmul(self.C, self.x_hat)))42        self.P = np.matmul(np.identity(2) - np.matmul(K, self.C), self.P)43x_hat_hist = []44p_hist = []45z_hist = []46v_actual =[]47env = one_dim_env()48estimator = KALMANFilter()49u = np.concatenate([np.ones((30,)), np.zeros((40,)), -0.5*np.ones((30,))])50for i in range(100):51    z = env.external_step(u[i])52    estimator.prediction(u[i])53    estimator.measurement(z)54    x_hat_hist.append(estimator.x_hat)55    p_hist.append(estimator.P)56    z_hist.append(z)57    v_actual.append(env.x[1,:])58pos_hat = np.matmul(estimator.C, np.hstack(x_hat_hist)).squeeze(0)59vel_hat = np.hstack(x_hat_hist)[1,:]60sig_p = np.sqrt(np.vstack(p_hist).reshape([-1,2,2])[:,0,0])61sig_v = np.sqrt(np.vstack(p_hist).reshape([-1,2,2])[:,1,1])62z_np = np.hstack(z_hist).squeeze()63v_np = np.hstack(v_actual).squeeze()64con=365plt.figure(figsize=(20, 20))...

7_treat_nested_steps_as_methods.py

Source:7_treat_nested_steps_as_methods.py

1from grail import BaseTest, step2class DoItInVerySpecialCases(BaseTest):3    def test_its_not_recommended_to_do_this(self):4        self.external_step()5    @step(treat_nested_steps_as_methods=True)6    def external_step(self):7        self.this_is_not_a_step_anymore()8    @step9    def this_is_not_a_step_anymore(self):...

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