make the filter plot interactive

docs/notebooks/path_and_velocity.ipynb

@@ -18,6 +18,7 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import vstt\n",
+    "from ipywidgets import interact\n",
     "from scipy.signal import savgol_filter\n",
     "from vstt.stats import get_velocity"
    ]
@@ -291,7 +292,7 @@
    "metadata": {},
    "source": [
     "### apply a Savitzky-Golay filter example\n",
-    "Here is an example for illustrating how to apply a Savitzky-Golay filter to the velocity"
+    "Here is an example for illustrating how to apply a [Savitzky-Golay filter](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.savgol_filter.html) to the velocity"
    ]
   },
   {
@@ -300,25 +301,28 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# The length of the filter window (i.e., the number of coefficients). If mode is ‘interp’, window_length must be less than or equal to the size of x.\n",
-    "window_length = 40\n",
+    "def plot_filter(window_length, polyorder):\n",
+    "    \"\"\"\n",
+    "    plot the original function and filtered function\n",
+    "    :param window_length: The length of the filter window (i.e., the number of coefficients). If mode is ‘interp’, window_length must be less than or equal to the size of x.\n",
+    "    :param polyorder: The order of the polynomial used to fit the samples. polyorder must be less than window_length.\n",
+    "    \"\"\"\n",
+    "    for _, group in stats.groupby([\"i_trial\", \"i_rep\", \"condition_index\"]):\n",
+    "        for positions, timestamps in zip(\n",
+    "            group.to_target_mouse_positions,\n",
+    "            group.to_target_timestamps,\n",
+    "        ):\n",
+    "            velocity = get_velocity(timestamps, positions)\n",
+    "            plt.plot(timestamps[:-1], velocity, linestyle=\"dashed\")\n",
+    "            filtered_velocity = savgol_filter(velocity, window_length, polyorder)\n",
+    "            plt.plot(timestamps[:-1], filtered_velocity)\n",
+    "            break\n",
+    "        break\n",
+    "    plt.legend([\"original velocity\", \"filtered velocity\"], loc=\"upper left\")\n",
+    "    plt.show()\n",
     "\n",
-    "# The order of the polynomial used to fit the samples. polyorder must be less than window_length.\n",
-    "polyorder = 8\n",
     "\n",
-    "for _, group in stats.groupby([\"i_trial\", \"i_rep\", \"condition_index\"]):\n",
-    "    for positions, timestamps in zip(\n",
-    "        group.to_target_mouse_positions,\n",
-    "        group.to_target_timestamps,\n",
-    "    ):\n",
-    "        velocity = get_velocity(timestamps, positions)\n",
-    "        plt.plot(timestamps[:-1], velocity, linestyle=\"dashed\")\n",
-    "        filtered_velocity = savgol_filter(velocity, window_length, polyorder)\n",
-    "        plt.plot(timestamps[:-1], filtered_velocity)\n",
-    "        break\n",
-    "    break\n",
-    "plt.legend([\"original velocity\", \"filtered velocity\"], loc=\"upper left\")\n",
-    "plt.show()"
+    "interact(plot_filter, window_length=30, polyorder=8);"
    ]
   },
   {
@@ -378,11 +382,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "jupyter": {
-     "is_executing": true
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "fig, axs = plt.subplots(nTrials, nReps * 8, figsize=(6 * 8, 6 * nTrials * nReps))\n",
@@ -439,11 +439,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "jupyter": {
-     "is_executing": true
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "fig, axs = plt.subplots(nTrials, nReps * 8, figsize=(6 * 8, 6 * nTrials * nReps))\n",
@@ -485,11 +481,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "jupyter": {
-     "is_executing": true
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }