Fix for #377

Somehow this didn't get checked in for commit
91f8010bee. Remove
dt=dt from plot_track calls.

06-Multivariate-Kalman-Filters.ipynb

@@ -830,7 +830,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -1325,7 +1325,7 @@
        "likelihood = 2.2250738585072014e-308\n",
        "mahalanobis = 0.0\n",
        "alpha = 1.0\n",
-       "inv = <function inv at 0x0000022DDDAFAEA0>"
+       "inv = <function inv at 0x0000027CB60C50D8>"
       ]
      },
      "execution_count": 29,
@@ -1377,8 +1377,7 @@
     "\n",
     "    xs, cov = np.array(xs), np.array(cov)\n",
     "    if do_plot:\n",
-    "        plot_track(xs[:, 0], track, zs, cov, \n",
-    "                   dt=dt, **kwargs)\n",
+    "        plot_track(xs[:, 0], track, zs, cov, **kwargs)\n",
     "    return xs, cov"
    ]
   },
@@ -1418,12 +1417,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1466,7 +1465,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -1826,14 +1825,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df9f68b8fa9e483a8faa25afa63896b7",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "<Figure size 900x400 with 1 Axes>"
+       "interactive(children=(IntSlider(value=1, continuous_update=False, description='F00', max=2), FloatSlider(value…"
       ]
      },
      "metadata": {},
@@ -2059,7 +2062,7 @@
     "    cov.append(P)\n",
     "\n",
     "xs, cov = np.array(xs), np.array(cov)\n",
-    "plot_track(xs[:, 0], track, zs, cov, plot_P=False, dt=dt)"
+    "plot_track(xs[:, 0], track, zs, cov, plot_P=False)"
    ]
   },
   {
@@ -2369,7 +2372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -2421,7 +2424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -2675,7 +2678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -3144,7 +3147,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.4"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {