PEP-8 compliance

07_Kalman_Filter_Math.ipynb

@@ -1141,7 +1141,7 @@
        "APATwjUAAADgJ/8LwRmria3cWEkAAAAASUVORK5CYII=\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f80a140a6a0>"
+       "<matplotlib.figure.Figure at 0x4fe66d8>"
       ]
      },
      "metadata": {},
@@ -1332,21 +1332,21 @@
    ],
    "source": [
     "import sympy\n",
-    "from sympy import  init_printing, Matrix,MatMul, integrate, symbols\n",
+    "from sympy import init_printing, Matrix,MatMul, integrate, symbols\n",
     "\n",
     "init_printing(use_latex='mathjax')\n",
     "dt, phi = symbols('\\Delta{t} \\Phi_s')\n",
     "F_k = Matrix([[1, dt, dt**2/2],\n",
     "              [0,  1,      dt],\n",
     "              [0,  0,       1]])\n",
-    "Q_c = Matrix([[0,0,0],\n",
-    "              [0,0,0],\n",
-    "              [0,0,1]])*phi\n",
+    "Q_c = Matrix([[0, 0, 0],\n",
+    "              [0, 0, 0],\n",
+    "              [0, 0, 1]])*phi\n",
     "\n",
-    "Q=sympy.integrate(F_k*Q_c*F_k.T,(dt, 0, dt))\n",
+    "Q=sympy.integrate(F_k * Q_c * F_k.T, (dt, 0, dt))\n",
     "\n",
     "# factor phi out of the matrix to make it more readable\n",
-    "Q = Q/phi\n",
+    "Q = Q / phi\n",
     "sympy.MatMul(Q, phi)"
    ]
   },
@@ -1432,14 +1432,14 @@
    "source": [
     "F_k = sympy.Matrix([[1, dt],\n",
     "                    [0, 1]])\n",
-    "Q_c = sympy.Matrix([[0,0],\n",
-    "                    [0,1]])*phi\n",
+    "Q_c = sympy.Matrix([[0, 0],\n",
+    "                    [0, 1]])*phi\n",
     "\n",
-    "Q = sympy.integrate(F_k*Q_c*F_k.T,(dt, 0, dt))\n",
+    "Q = sympy.integrate(F_k * Q_c * F_k.T, (dt, 0, dt))\n",
     "\n",
     "print('1st order discrete process noise')\n",
     "# factor phi out of the matrix to make it more readable\n",
-    "Q = Q/phi\n",
+    "Q = Q / phi\n",
     "sympy.MatMul(Q, phi)"
    ]
   },
@@ -1504,7 +1504,7 @@
    ],
    "source": [
     "var=symbols('sigma^2_v')\n",
-    "v = Matrix([[dt**2/2], [dt]])\n",
+    "v = Matrix([[dt**2 / 2], [dt]])\n",
     "\n",
     "Q = v * var * v.T\n",
     "\n",
@@ -1571,7 +1571,7 @@
    ],
    "source": [
     "var=symbols('sigma^2_v')\n",
-    "v = Matrix([[dt**2/2], [dt], [1]])\n",
+    "v = Matrix([[dt**2 / 2], [dt], [1]])\n",
     "\n",
     "Q = v * var * v.T\n",
     "\n",
@@ -2249,7 +2249,7 @@
        "foOTcEB19QAAAABJRU5ErkJggg==\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f807e4aa160>"
+       "<matplotlib.figure.Figure at 0x8f220f0>"
       ]
      },
      "metadata": {},
@@ -2283,7 +2283,7 @@
     "    ys.append(y)\n",
     "    ts.append(t)\n",
     "\n",
-    "exact = [(t**2+4)**2 / 16. for t in ts]\n",
+    "exact = [(t**2 + 4)**2 / 16. for t in ts]\n",
     "plt.plot(ts, ys)\n",
     "plt.plot(ts, exact)\n",
     "plt.show()\n",
@@ -2903,7 +2903,7 @@
        "hBDiM/8/3QDBZRpvieYAAAAASUVORK5CYII=\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f807e43e630>"
+       "<matplotlib.figure.Figure at 0x8d10c18>"
       ]
      },
      "metadata": {},
@@ -2912,7 +2912,7 @@
    ],
    "source": [
     "import ukf_internal\n",
-    "with book_format.figsize(10,6):\n",
+    "with book_format.figsize(10, 6):\n",
     "    ukf_internal.show_four_gps()"
    ]
   },
@@ -3196,8 +3196,8 @@
     "    N = len(sat_pos)\n",
     "    H = np.zeros((N, 2))\n",
     "    for j in range(N):\n",
-    "        H[j,0] = (pos[0] - sat_pos[j,0]) / range_est[j]\n",
-    "        H[j,1] = (pos[1] - sat_pos[j,1]) / range_est[j]\n",
+    "        H[j, 0] = (pos[0] - sat_pos[j, 0]) / range_est[j]\n",
+    "        H[j, 1] = (pos[1] - sat_pos[j, 1]) / range_est[j]\n",
     "    return H\n",
     "\n",
     "\n",
@@ -3212,10 +3212,10 @@
     "            r_est = satellite_range(pos, sat_pos)\n",
     "            print('iteration:', i)\n",
     "\n",
-    "            H=hx(pos, sat_pos, r_est)        \n",
+    "            H = hx(pos, sat_pos, r_est)        \n",
     "            Hinv = inv(dot(H.T, H)).dot(H.T)\n",
     "\n",
-    "            #update position estimate\n",
+    "            # update position estimate\n",
     "            y = zs - r_est\n",
     "            print('innovation', y)\n",
     "\n",
@@ -3230,9 +3230,9 @@
     "    return pos, converged\n",
     "\n",
     "# compute measurement of where you are with respect to each sensor\n",
-    "rz= satellite_range(user_pos, sat_pos)\n",
+    "rz = satellite_range(user_pos, sat_pos)\n",
     "\n",
-    "pos, converted = lop_ils(rz, sat_pos, (900,90), hx=hx_ils)\n",
+    "pos, converted = lop_ils(rz, sat_pos, (900, 90), hx=hx_ils)\n",
     "print('Iterated solution: ', pos)"
    ]
   },
@@ -3385,7 +3385,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.2"
+   "version": "3.4.3"
   }
  },
  "nbformat": 4,

12_Designing_Nonlinear_Kalman_Filters.ipynb

@@ -1266,8 +1266,8 @@
     "    the partial derivative of H\n",
     "    \"\"\"\n",
     "\n",
-    "    theta_a = atan2(pos_a[1]-pos[1], pos_a[0] - pos[0])\n",
-    "    theta_b = atan2(pos_b[1]-pos[1], pos_b[0] - pos[0])\n",
+    "    theta_a = atan2(pos_a[1] - pos[1], pos_a[0] - pos[0])\n",
+    "    theta_b = atan2(pos_b[1] - pos[1], pos_b[0] - pos[0])\n",
     "\n",
     "    return np.array([[0, -cos(theta_a), 0, -sin(theta_a)],\n",
     "                     [0, -cos(theta_b), 0, -sin(theta_b)]])"
@@ -1808,7 +1808,7 @@
     "\n",
     "# pos will contain our nominal position since the filter does not\n",
     "# maintain position.\n",
-    "pos = [0,0]\n",
+    "pos = [0, 0]\n",
     "\n",
     "for i in range(count):\n",
     "    # move (1,1) each step, so just use i\n",
@@ -1818,7 +1818,7 @@
     "    # and measured ranges\n",
     "    ra,rb = d.range_of(pos)\n",
     "    rx,ry = d.range_of((pos[0] + f1.x[0, 0], pos[1] + f1.x[2, 0]))\n",
-    "    z = np.array([[ra-rx], [rb-ry]])\n",
+    "    z = np.array([[ra - rx], [rb - ry]])\n",
     "\n",
     "    # compute linearized H for this time step\n",
     "    f1.H = H_of (pos, pos_a, pos_b)\n",
@@ -1833,7 +1833,6 @@
     "    f1.predict()\n",
     "    f1.update(z)\n",
     "\n",
-    "\n",
     "bp.plot_filter(xs, ys)\n",
     "bp.plot_track(pxs, pys)\n",
     "plt.legend(loc=2)\n",
@@ -1887,7 +1886,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.2"
+   "version": "3.4.3"
   }
  },
  "nbformat": 4,

15_HInfinity_Filters.ipynb

@@ -718,7 +718,7 @@
        "IiLqQ/8f7C/6jEqnSzcAAAAASUVORK5CYII=\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f33b92df518>"
+       "<matplotlib.figure.Figure at 0x7219898>"
       ]
      },
      "metadata": {},
@@ -767,7 +767,7 @@
     "plt.subplot(212)\n",
     "plt.plot(vs) \n",
     "plt.title('velocity')\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   }
  ],