diff --git a/docs/notebooks/path_and_velocity_copy.ipynb b/docs/notebooks/path_and_velocity_copy.ipynb
deleted file mode 100644
index 2baf4e9..0000000
--- a/docs/notebooks/path_and_velocity_copy.ipynb
+++ /dev/null
@@ -1,299 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Show some statistics of the enclosed geometric object with psydat file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "from psychopy.misc import fromFile\n",
-    "from vstt.stats import get_velocity"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Import\n",
-    "A psydat file can be imported using the psychopy `fromFile` function: \n",
-    "If you want to know the detailed content of the data in psydat file, please check the notebook 'raw_data.ipynb'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "psydata = fromFile(\"example.psydat\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plot of results for each trial\n",
-    "For example, a scatter plot of the mouse positions for each trial, labelled by the condition, trial number and repetition number:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "colors = [\"blue\", \"green\", \"red\", \"cyan\", \"magenta\", \"yellow\", \"black\", \"orange\"]\n",
-    "\n",
-    "nTrials, nReps = psydata.sequenceIndices.shape\n",
-    "fig, axs = plt.subplots(nTrials, nReps, figsize=(6, 6 * nTrials * nReps))\n",
-    "axs = np.reshape(\n",
-    "    axs, (nTrials, nReps)\n",
-    ")  # ensure axs is a 2d-array even if nTrials or nReps is 1\n",
-    "for trial in range(nTrials):\n",
-    "    for rep in range(nReps):\n",
-    "        loc = (trial, rep)\n",
-    "        condition = psydata.sequenceIndices[loc]\n",
-    "        target_radius = psydata.trialList[condition][\"target_size\"]\n",
-    "        central_target_radius = psydata.trialList[condition][\"central_target_size\"]\n",
-    "        ax = axs[loc]\n",
-    "        ax.set_title(f\"Trial {trial}, Rep {rep} [Condition {condition}]\")\n",
-    "        for positions, target_pos, color in zip(\n",
-    "            psydata.data[\"to_target_mouse_positions\"][loc],\n",
-    "            psydata.data[\"target_pos\"][loc],\n",
-    "            colors,\n",
-    "        ):\n",
-    "            ax.plot(positions[:, 0], positions[:, 1], color=color)\n",
-    "            ax.add_patch(\n",
-    "                plt.Circle(\n",
-    "                    target_pos,\n",
-    "                    target_radius,\n",
-    "                    edgecolor=\"none\",\n",
-    "                    facecolor=color,\n",
-    "                    alpha=0.1,\n",
-    "                )\n",
-    "            )\n",
-    "        if not psydata.trialList[condition][\"automove_cursor_to_center\"]:\n",
-    "            for positions, color in zip(\n",
-    "                psydata.data[\"to_center_mouse_positions\"][loc],\n",
-    "                colors,\n",
-    "            ):\n",
-    "                ax.plot(positions[:, 0], positions[:, 1], color=color)\n",
-    "            ax.add_patch(\n",
-    "                plt.Circle(\n",
-    "                    [0, 0],\n",
-    "                    central_target_radius,\n",
-    "                    edgecolor=\"none\",\n",
-    "                    facecolor=\"black\",\n",
-    "                    alpha=0.1,\n",
-    "                )\n",
-    "            )\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plot of results for each target\n",
-    "For example, a scatter plot of the mouse positions for each target, labelled by trial number, repetition number, target number and condition:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, axs = plt.subplots(nTrials, nReps * 8, figsize=(6 * 8, 6 * nTrials * nReps))\n",
-    "axs = np.reshape(\n",
-    "    axs, (nTrials, nReps * 8)\n",
-    ")  # ensure axs is a 2d-array even if nTrials or nReps is 1\n",
-    "for trial in range(nTrials):\n",
-    "    for rep in range(nReps):\n",
-    "        loc = (trial, rep)\n",
-    "        condition = psydata.sequenceIndices[loc]\n",
-    "        for positions, color, i in zip(\n",
-    "            psydata.data[\"to_target_mouse_positions\"][loc],\n",
-    "            colors,\n",
-    "            range(8),\n",
-    "        ):\n",
-    "            ax = axs[(trial, rep + i)]\n",
-    "            ax.set_title(\n",
-    "                f\"Trial {trial}, Rep {rep}, Target {i} [Condition {condition}]\"\n",
-    "            )\n",
-    "            ax.set_xlim(-0.5, 0.5)\n",
-    "            ax.set_ylim(-0.5, 0.5)\n",
-    "            if not psydata.trialList[condition][\"automove_cursor_to_center\"]:\n",
-    "                positions = np.concatenate(\n",
-    "                    (positions, psydata.data[\"to_center_mouse_positions\"][loc][i]),\n",
-    "                    axis=0,\n",
-    "                )\n",
-    "            ax.plot(positions[:, 0], positions[:, 1], color=color)\n",
-    "\n",
-    "fig.delaxes(axs[2][6])\n",
-    "fig.delaxes(axs[2][7])\n",
-    "fig.delaxes(axs[3][6])\n",
-    "fig.delaxes(axs[3][7])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plot velocity for each trial\n",
-    "For example, a scatter plot of the velocity for each target, labelled by trial number, repetition number and condition:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, axs = plt.subplots(nTrials, nReps, figsize=(6, 6 * nTrials * nReps))\n",
-    "axs = np.reshape(\n",
-    "    axs, (nTrials, nReps)\n",
-    ")  # ensure axs is a 2d-array even if nTrials or nReps is 1\n",
-    "for trial in range(nTrials):\n",
-    "    for rep in range(nReps):\n",
-    "        loc = (trial, rep)\n",
-    "        condition = psydata.sequenceIndices[loc]\n",
-    "        ax = axs[loc]\n",
-    "        ax.set_title(f\"Trial {trial}, Rep {rep} [Condition {condition}]\")\n",
-    "\n",
-    "        final_position = []\n",
-    "        final_timesteps = []\n",
-    "        for positions, timestamps, i in zip(\n",
-    "            psydata.data[\"to_target_mouse_positions\"][loc],\n",
-    "            psydata.data[\"to_target_timestamps\"][loc],\n",
-    "            range(8),\n",
-    "        ):\n",
-    "            final_position.append(positions)\n",
-    "            # print(f'only to target positions : {final_position}')\n",
-    "            final_timesteps.append(timestamps)\n",
-    "            if not psydata.trialList[condition][\"automove_cursor_to_center\"]:\n",
-    "                final_position.append(psydata.data[\"to_center_mouse_positions\"][loc][i])\n",
-    "                # print(f'add to center positions : {final_position}')\n",
-    "                final_timesteps.append(psydata.data[\"to_center_timestamps\"][loc][i])\n",
-    "        ax.plot(\n",
-    "            np.concatenate(final_timesteps)[:-1],\n",
-    "            get_velocity(\n",
-    "                np.concatenate(final_timesteps), np.concatenate(final_position)\n",
-    "            ),\n",
-    "        )\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, axs = plt.subplots(nTrials, nReps * 8, figsize=(6 * 8, 6 * nTrials * nReps))\n",
-    "axs = np.reshape(\n",
-    "    axs, (nTrials, nReps * 8)\n",
-    ")  # ensure axs is a 2d-array even if nTrials or nReps is 1\n",
-    "for trial in range(nTrials):\n",
-    "    for rep in range(nReps):\n",
-    "        loc = (trial, rep)\n",
-    "        condition = psydata.sequenceIndices[loc]\n",
-    "        for positions, timestamps, color, i in zip(\n",
-    "            psydata.data[\"to_target_mouse_positions\"][loc],\n",
-    "            psydata.data[\"to_target_timestamps\"][loc],\n",
-    "            colors,\n",
-    "            range(8),\n",
-    "        ):\n",
-    "            ax = axs[(trial, rep + i)]\n",
-    "            ax.set_title(\n",
-    "                f\"Trial {trial}, Rep {rep}, Target {i} [Condition {condition}]\"\n",
-    "            )\n",
-    "            if not psydata.trialList[condition][\"automove_cursor_to_center\"]:\n",
-    "                positions = np.concatenate(\n",
-    "                    (positions, psydata.data[\"to_center_mouse_positions\"][loc][i]),\n",
-    "                    axis=0,\n",
-    "                )\n",
-    "                timestamps = np.concatenate(\n",
-    "                    (timestamps, psydata.data[\"to_center_timestamps\"][loc][i])\n",
-    "                )\n",
-    "            ax.plot(timestamps[:-1], get_velocity(timestamps, positions), color=color)\n",
-    "fig.delaxes(axs[2][6])\n",
-    "fig.delaxes(axs[2][7])\n",
-    "fig.delaxes(axs[3][6])\n",
-    "fig.delaxes(axs[3][7])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, axs = plt.subplots(nTrials, nReps, figsize=(6, 6 * nTrials * nReps))\n",
-    "axs = np.reshape(\n",
-    "    axs, (nTrials, nReps)\n",
-    ")  # ensure axs is a 2d-array even if nTrials or nReps is 1\n",
-    "for trial in range(nTrials):\n",
-    "    for rep in range(nReps):\n",
-    "        loc = (trial, rep)\n",
-    "        condition = psydata.sequenceIndices[loc]\n",
-    "        ax = axs[loc]\n",
-    "        ax.set_title(f\"Trial {trial}, Rep {rep} [Condition {condition}]\")\n",
-    "        for positions, timestamps, color, i in zip(\n",
-    "            psydata.data[\"to_target_mouse_positions\"][loc],\n",
-    "            psydata.data[\"to_target_timestamps\"][loc],\n",
-    "            colors,\n",
-    "            range(8),\n",
-    "        ):\n",
-    "            if not psydata.trialList[condition][\"automove_cursor_to_center\"]:\n",
-    "                positions = np.concatenate(\n",
-    "                    (positions, psydata.data[\"to_center_mouse_positions\"][loc][i]),\n",
-    "                    axis=0,\n",
-    "                )\n",
-    "                timestamps = np.concatenate(\n",
-    "                    (timestamps, psydata.data[\"to_center_timestamps\"][loc][i])\n",
-    "                )\n",
-    "            ax.plot(\n",
-    "                timestamps[:-1] - timestamps[0],\n",
-    "                get_velocity(timestamps, positions),\n",
-    "                color=color,\n",
-    "            )\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}