diff --git a/01-g-h-filter.ipynb b/01-g-h-filter.ipynb
index 9e7140c..b2b4277 100644
--- a/01-g-h-filter.ipynb
+++ b/01-g-h-filter.ipynb
@@ -859,7 +859,7 @@
     "weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6, \n",
     "           169.6, 167.4, 166.4, 171.0, 171.2, 172.6]\n",
     "\n",
-    "time_step = 1.0 # day\n",
+    "time_step = 1.0  # day\n",
     "scale_factor = 4.0/10\n",
     "\n",
     "def predict_using_gain_guess(weight, gain_rate, do_print=True, sim_rate=0): \n",
@@ -885,6 +885,7 @@
     "\n",
     "        # plot results\n",
     "        gh.plot_gh_results(weights, estimates, predictions, sim_rate)\n",
+    "\n",
     "initial_guess = 160.\n",
     "predict_using_gain_guess(weight=initial_guess, gain_rate=1)     "
    ]
@@ -2515,7 +2516,7 @@
     "import time\n",
     "\n",
     "with interactive_plot():\n",
-    "    for x in range(2,6):\n",
+    "    for x in range(2, 6):\n",
     "        plt.plot([0, 1], [1, x])\n",
     "        plt.gcf().canvas.draw()\n",
     "        time.sleep(0.5)"
@@ -3357,7 +3358,7 @@
    ],
    "source": [
     "weight = 160.  # initial guess\n",
-    "gain_rate = -1.0 # initial guess\n",
+    "gain_rate = -1.0  # initial guess\n",
     "\n",
     "time_step = 1.\n",
     "weight_scale = 4./10\n",
@@ -4005,15 +4006,15 @@
     "    x_est = x0\n",
     "    results = []\n",
     "    for z in data:\n",
-    "        #prediction step\n",
+    "        # prediction step\n",
     "        x_pred = x_est + (dx*dt)\n",
     "        dx = dx\n",
     "\n",
     "        # update step\n",
     "        residual = z - x_pred\n",
-    "        dx = dx    + h * (residual) / dt\n",
-    "        x_est  = x_pred + g * residual     \n",
-    "        results.append(x_est)  \n",
+    "        dx = dx + h * (residual) / dt\n",
+    "        x_est = x_pred + g * residual\n",
+    "        results.append(x_est)\n",
     "    return np.array(results)\n",
     "\n",
     "book_plots.plot_track([0, 11], [160, 172], label='Actual weight')\n",
@@ -4402,7 +4403,7 @@
     "    book_plots.plot_filter(data2, label='g=0.4', marker='v')\n",
     "    book_plots.plot_filter(data3, label='g=0.8', lw=2)\n",
     "    plt.legend(loc=4)\n",
-    "    book_plots.set_limits([20,40], [50, 250])"
+    "    book_plots.set_limits([20, 40], [50, 250])"
    ]
   },
   {