diff --git a/docs/cookbook/floweaver-path/README.md b/docs/cookbook/floweaver-path/README.md
new file mode 100644
index 0000000..f0dd4e5
--- /dev/null
+++ b/docs/cookbook/floweaver-path/README.md
@@ -0,0 +1,51 @@
+# floweaver-path example
+
+![Demo](https://github.com/fullflu/floweaver-path/blob/master/demo/floweaver_path_demo.gif)
+
+I implement [floweaver-path](https://github.com/fullflu/floweaver-path), an extension of the [floweaver](https://sankeyview.readthedocs.io/en/latest/) to handle the visualization of paths that pass through a selected node.
+
+It would be reasonable to maintain floweaver-path apart from the original floweaver at the moment.
+Therefore, I describe only the summary of the extension and put the minimum resources (`data/template.csv` and `template_before_separation.ipynb`) here.
+
+Please check my repository above if you want to know the details of how floweaver-path works.
+
+If you have any questions, please feel free to ask me ([@fullflu](https://github.com/fullflu)).
+
+## Summary
+We focus on the visualization of longitudinal data.
+
+The idea of our visualization is based on [pathSankey](https://bl.ocks.org/jeinarsson/e37aa55c3b0e11ae6fa1), that is an extension of [d3-sankey](https://github.com/d3/d3-sankey).
+
+The color of paths that pass through a selected node is yellow-green (highlighted), and that of other paths is gray.
+
+You can interactively select a node by using dropdowns in jupyter notebook.
+
+We have two technical contributions to the field of visualization using Sankey diagrams.
+
+One is to extend the layer number:
+- Ordinary Sankey diagrams can only visualize paths between 2 layers.
+- pathSankey can only visualize paths between 3 layers.
+- We can visualize the comparison of paths between two layers before and after (up to 5 layers).
+
+The other is to create a notebook that can interact with users. We integrate several functions of ipywidgets into floweaver.
+
+## Requirement
+
+### in this repository (using pip or conda)
+- floweaver (==2.0.0a5)
+- ipysankeywidget (==0.2.5)
+
+### in floweaver-path repository (using docker)
+- docker (installs two libraries: floweaver, ipysankeywidget)
+
+### in both repositories
+- input file (`*.csv, *.pickle or *.xlsx` should be put in `data` directory)
+
+
+## Structure
+```
+├── README.md
+├── data
+│   └── template.csv
+└── template_before_separation.ipynb
+```
\ No newline at end of file
diff --git a/docs/cookbook/floweaver-path/data/template.csv b/docs/cookbook/floweaver-path/data/template.csv
new file mode 100644
index 0000000..2f1e23e
--- /dev/null
+++ b/docs/cookbook/floweaver-path/data/template.csv
@@ -0,0 +1,401 @@
+index,date,x1,x3
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diff --git a/docs/cookbook/floweaver-path/template_before_separation.ipynb b/docs/cookbook/floweaver-path/template_before_separation.ipynb
new file mode 100644
index 0000000..a03e7b1
--- /dev/null
+++ b/docs/cookbook/floweaver-path/template_before_separation.ipynb
@@ -0,0 +1,485 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import pickle\n",
+    "from IPython.display import display\n",
+    "from ipywidgets import interact, Dropdown\n",
+    "from floweaver import ProcessGroup, Waypoint, Partition, Bundle, SankeyDefinition, weave\n",
+    "from ipysankeywidget import SankeyWidget\n",
+    "from IPython.display import clear_output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# modules"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dir = \"data\"\n",
+    "base_extensions = [\"csv\", \"xlsx\", \"pickle\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def extract_files(extensions=base_extensions):\n",
+    "    file_candidate = os.listdir(data_dir)\n",
+    "    file_list = [os.path.join(data_dir, file_path) for file_path in file_candidate\n",
+    "                 if sum([extension == file_path.split(\".\")[-1] for extension in extensions])]\n",
+    "    return file_list\n",
+    "\n",
+    "\n",
+    "def load_file(file_path, extensions=base_extensions):\n",
+    "    assert sum([extension == file_path.split(\".\")[-1] for extension in extensions]), \"the extension of your file ({}) is not supported\".format(file_path)\n",
+    "    extension = file_path.split(\".\")[-1]\n",
+    "    if \"csv\" == extension:\n",
+    "        data = pd.read_csv(file_path)\n",
+    "    elif \"xlsx\" == extension:\n",
+    "        data = pd.read_excel(file_path)\n",
+    "    elif \"pickle\" == extension:\n",
+    "        with open(file_path, \"rb\") as f:\n",
+    "            data = pickle.load(f)\n",
+    "    return data\n",
+    "\n",
+    "\n",
+    "def get_palette(y_values, yl):\n",
+    "    nan_color = 'gray'\n",
+    "    base_color = 'yellowgreen'\n",
+    "    palette = {value: nan_color for value in y_values}\n",
+    "    palette[yl] = base_color\n",
+    "    return palette\n",
+    "\n",
+    "\n",
+    "def concat_in_out_df(df, i, col, val):\n",
+    "    n = df[i].unique().shape[0]\n",
+    "    df = df[[i, col, val]].copy()\n",
+    "    add_df = pd.DataFrame()\n",
+    "    add_df[i] = np.arange(n)\n",
+    "    add_df[col] = \"Start\"\n",
+    "    add_df[val] = 'in'\n",
+    "    df = pd.concat([df, add_df])\n",
+    "    add_df = pd.DataFrame()\n",
+    "    add_df[i] = np.arange(n)\n",
+    "    add_df[col] = \"End\"\n",
+    "    add_df[val] = 'out'\n",
+    "    df = pd.concat([df, add_df]).reset_index(drop=True)\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "def get_node_order_bundle(df, i, x, y):\n",
+    "    df = df.copy()\n",
+    "    df = concat_in_out_df(df, i, x, y)\n",
+    "    df_piv = df.pivot_table(values=y, index=i, columns=x, aggfunc='sum').reset_index()\n",
+    "    x_values = [\"Start\"] + df_piv.columns.drop([i, \"Start\", \"End\"]).sort_values().tolist() + [\"End\"]\n",
+    "    assert len(x_values) > 3, \"the cardinality of x need to be larger than 1\"\n",
+    "    df_piv_count = df_piv.groupby(x_values)[i].count().reset_index().rename(columns={i: \"value\"})\n",
+    "    nodes = {}\n",
+    "    nodes[x_values[0]] = ProcessGroup(np.sort(df_piv_count[x_values[0]].unique()).tolist(), title=x_values[0])\n",
+    "    nodes[x_values[0]].partition = Partition.Simple(\"process\", np.sort(df_piv_count[x_values[0]].unique()).tolist())\n",
+    "    nodes[x_values[-1]] = ProcessGroup(np.sort(df_piv_count[x_values[-1]].unique()).tolist(), title=x_values[-1])\n",
+    "    nodes[x_values[-1]].partition = Partition.Simple(\"process\", np.sort(df_piv_count[x_values[-1]].unique()).tolist())\n",
+    "    for x_value in x_values[1:-1]:\n",
+    "        part = Partition.Simple(x_value, np.sort(df_piv_count[x_value].unique()).tolist())\n",
+    "        nodes[x_value] = Waypoint(part, title=x_value)\n",
+    "\n",
+    "    ordering = [[x_value] for x_value in x_values]\n",
+    "\n",
+    "    bundles = [Bundle(x_values[0], x_values[-1], waypoints=x_values[1:-1])]\n",
+    "    tmp_flows = df_piv_count.copy().rename(columns={x_values[0]: \"source\", x_values[-1]: \"target\"})\n",
+    "    return tmp_flows, nodes, bundles, ordering, df_piv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## class"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class TsSankey(object):\n",
+    "    def __init__(self, multiple_display_widget, path_widget, index_widget, column_widget, value_widget, column_level_widget, value_level_widget):\n",
+    "        self.df = None\n",
+    "        self.flows = None\n",
+    "        self.column_values = None\n",
+    "        self.multiple_display_widget = multiple_display_widget\n",
+    "        self.path_widget = path_widget\n",
+    "        self.index_widget = index_widget\n",
+    "        self.column_widget = column_widget\n",
+    "        self.value_widget = value_widget\n",
+    "        self.column_level_widget = column_level_widget\n",
+    "        self.value_level_widget = value_level_widget\n",
+    "        self.sdd = None\n",
+    "        # display flag\n",
+    "        self.display_flag = False\n",
+    "\n",
+    "    def on_path_update(self, change):\n",
+    "        # extract changed value\n",
+    "        file_path = change[\"new\"]\n",
+    "        self.file_path = file_path\n",
+    "\n",
+    "        # load dataframe\n",
+    "        self.df = load_file(file_path)\n",
+    "        self.index_widget.options = self.df.columns.tolist()\n",
+    "        self.index_widget.value = None\n",
+    "        # reset column level observation\n",
+    "        self.column_widget.unobserve(self.on_column_update, names=\"value\")\n",
+    "        self.column_level_widget.unobserve(self.on_column_to_value_update, names=\"value\")\n",
+    "        # set column list\n",
+    "        self.column_widget.options = self.df.columns.tolist()\n",
+    "        # reset default values\n",
+    "        self.column_widget.value = None\n",
+    "        self.column_level_widget.options = []\n",
+    "        self.column_level_widget.value = None\n",
+    "\n",
+    "        # reset value level observation\n",
+    "        self.value_widget.unobserve(self.on_value_update, names=\"value\")\n",
+    "        self.value_level_widget.unobserve(self.on_value_level_update, names=\"value\")\n",
+    "        # set column list\n",
+    "        self.value_widget.options = self.df.columns.tolist()\n",
+    "        # reset default values\n",
+    "        self.value_widget.value = None\n",
+    "        self.value_level_widget.options = []\n",
+    "        self.value_level_widget.value = None\n",
+    "\n",
+    "        # observe level observations\n",
+    "        self.column_widget.observe(self.on_column_update, names=\"value\")\n",
+    "        self.value_widget.observe(self.on_value_update, names=\"value\")\n",
+    "        self.column_level_widget.observe(self.on_column_to_value_update, names=\"value\")\n",
+    "        self.value_level_widget.observe(self.on_value_level_update, names=\"value\")\n",
+    "\n",
+    "        # reset floweaver resources\n",
+    "        self.sdd = None\n",
+    "        self.flows = None\n",
+    "        self.palette = None\n",
+    "\n",
+    "    def on_column_update(self, change):\n",
+    "        # extract changed value\n",
+    "        column_name = change[\"new\"]\n",
+    "        # reset column level observation\n",
+    "        self.column_level_widget.unobserve(self.on_column_to_value_update, names=\"value\")\n",
+    "        # reset value level observation\n",
+    "        self.value_level_widget.unobserve(self.on_value_level_update, names=\"value\")\n",
+    "        # set column level options\n",
+    "        self.column_level_widget.options = np.sort(self.df[column_name].unique()).tolist()\n",
+    "        # reset column level value and value level variables\n",
+    "        self.column_level_widget.value = None\n",
+    "        self.value_level_widget.options = []\n",
+    "        self.value_level_widget.value = None\n",
+    "        # reset floweaver resources\n",
+    "        self.sdd = None\n",
+    "        # observe level observations\n",
+    "        self.column_level_widget.observe(self.on_column_to_value_update, names=\"value\")\n",
+    "        self.value_level_widget.observe(self.on_value_level_update, names=\"value\")\n",
+    "\n",
+    "    def on_value_update(self, change):\n",
+    "        value_name = change[\"new\"]\n",
+    "        if self.column_widget.value is not None and self.column_level_widget.value is not None:\n",
+    "            # reset value level observation\n",
+    "            self.value_level_widget.unobserve(self.on_value_level_update, names=\"value\")\n",
+    "            # update　options\n",
+    "            options = np.sort(\n",
+    "                self.df[self.df[self.column_widget.value] == self.column_level_widget.value][value_name].unique()).tolist()\n",
+    "            if self.value_level_widget.value in options:\n",
+    "                self.sdd = self.get_sdd()\n",
+    "            else:\n",
+    "                self.value_level_widget.value = None\n",
+    "                self.sdd = None\n",
+    "            self.value_level_widget.options = options\n",
+    "            self.value_level_widget.observe(self.on_value_level_update, names=\"value\")\n",
+    "\n",
+    "    def on_column_to_value_update(self, change):\n",
+    "        column_level = change[\"new\"]\n",
+    "        if self.column_widget.value is not None and self.value_widget.value is not None:\n",
+    "            # reset value level observation\n",
+    "            self.value_level_widget.unobserve(self.on_value_level_update, names=\"value\")\n",
+    "            # update　options\n",
+    "            options = np.sort(\n",
+    "                self.df[self.df[self.column_widget.value] == column_level][self.value_widget.value].unique()).tolist()\n",
+    "            if self.value_level_widget.value in self.value_level_widget.options:\n",
+    "                self.sdd = self.get_sdd()\n",
+    "            else:\n",
+    "                self.sdd = None\n",
+    "            self.value_level_widget.options = options\n",
+    "            self.value_level_widget.value = None\n",
+    "            self.value_level_widget.observe(self.on_value_level_update, names=\"value\")\n",
+    "\n",
+    "    def on_value_level_update(self, change):\n",
+    "        value = change[\"new\"]\n",
+    "        if self.df is not None:\n",
+    "            self.sdd = self.get_sdd()\n",
+    "\n",
+    "    def on_clear_output(self, change):\n",
+    "        flag = change[\"new\"]\n",
+    "        if self.sdd is not None:\n",
+    "            if flag == \"Yes\":\n",
+    "                display(self.multiple_display_widget)\n",
+    "                display(self.path_widget)\n",
+    "                display(self.index_widget)\n",
+    "                display(self.column_widget)\n",
+    "                display(self.value_widget)\n",
+    "                display(self.column_level_widget)\n",
+    "                display(self.value_level_widget)\n",
+    "                print(\"file: {}, index: {}, date: {} is {}, target: {} is {}\".format(\n",
+    "                    self.path_widget.value,\n",
+    "                    self.index_widget.value,\n",
+    "                    self.column_widget.value,\n",
+    "                    self.column_level_widget.value,\n",
+    "                    self.value_widget.value,\n",
+    "                    self.value_level_widget.value\n",
+    "                ))\n",
+    "                display(weave(self.sdd, self.flows, palette=self.palette).to_widget(**self.size))\n",
+    "            else:\n",
+    "                clear_output(wait=False)\n",
+    "                display(self.multiple_display_widget)\n",
+    "                display(self.path_widget)\n",
+    "                display(self.index_widget)\n",
+    "                display(self.column_widget)\n",
+    "                display(self.value_widget)\n",
+    "                display(self.column_level_widget)\n",
+    "                display(self.value_level_widget)\n",
+    "                print(\"file: {}, index: {}, date: {} is {}, target: {} is {}\".format(\n",
+    "                    self.path_widget.value,\n",
+    "                    self.index_widget.value,\n",
+    "                    self.column_widget.value,\n",
+    "                    self.column_level_widget.value,\n",
+    "                    self.value_widget.value,\n",
+    "                    self.value_level_widget.value\n",
+    "                ))\n",
+    "                display(weave(self.sdd, self.flows, palette=self.palette).to_widget(**self.size))\n",
+    "                clear_output(wait=True)\n",
+    "\n",
+    "    def get_sdd(self):\n",
+    "        tmp_flows, nodes, bundles, orderings, df_piv = get_node_order_bundle(\n",
+    "            self.df,\n",
+    "            self.index_widget.value,\n",
+    "            self.column_widget.value,\n",
+    "            self.value_widget.value\n",
+    "        )\n",
+    "        flow_partition = nodes[self.column_level_widget.value].partition\n",
+    "        y_list = np.sort(df_piv[self.column_level_widget.value].unique()).tolist()\n",
+    "        palette = get_palette(y_list, self.value_level_widget.value)\n",
+    "        sdd = SankeyDefinition(nodes, bundles, orderings,\n",
+    "                               flow_partition=flow_partition)\n",
+    "        self.flows = tmp_flows\n",
+    "        self.palette = palette\n",
+    "        self.size = dict(width=1070, height=500)\n",
+    "        if self.multiple_display_widget.value == \"No\" and self.display_flag is True:\n",
+    "            display(self.multiple_display_widget)\n",
+    "            display(self.path_widget)\n",
+    "            display(self.index_widget)\n",
+    "            display(self.column_widget)\n",
+    "            display(self.value_widget)\n",
+    "            display(self.column_level_widget)\n",
+    "            display(self.value_level_widget)\n",
+    "        print(\"file: {}, index: {}, date: {} is {}, target: {} is {}\".format(\n",
+    "            self.path_widget.value,\n",
+    "            self.index_widget.value,\n",
+    "            self.column_widget.value,\n",
+    "            self.column_level_widget.value,\n",
+    "            self.value_widget.value,\n",
+    "            self.value_level_widget.value\n",
+    "        ))\n",
+    "        display(weave(sdd, self.flows, palette=self.palette).to_widget(**self.size))\n",
+    "        if self.multiple_display_widget.value == \"No\":\n",
+    "            clear_output(wait=True)\n",
+    "        self.display_flag = True\n",
+    "        return sdd\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## main function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def main():\n",
+    "    # define widgets\n",
+    "    style = {'description_width': 'initial'}\n",
+    "    multiple_display_widget = Dropdown(options=[\"Yes\", \"No\"], value=None, description=\"multiple display?\", style=style)\n",
+    "    path_widget = Dropdown(options=extract_files(), value=None, description=\"file path\", style=style)\n",
+    "    i_widget = Dropdown(options=[], value=None, description=\"index column\", style=style)\n",
+    "    x_widget = Dropdown(options=[], value=None, description=\"date column\", style=style)\n",
+    "    y_widget = Dropdown(options=[], value=None, description=\"target variable\", style=style)\n",
+    "    x_level_widget = Dropdown(options=[], value=None, description=\"target date\", style=style)\n",
+    "    y_level_widget = Dropdown(options=[], value=None, description=\"target value\", style=style)\n",
+    "\n",
+    "    # define an instance\n",
+    "    ts = TsSankey(multiple_display_widget, path_widget, i_widget, x_widget, y_widget, x_level_widget, y_level_widget)\n",
+    "\n",
+    "    # update widgets\n",
+    "    multiple_display_widget.observe(ts.on_clear_output, names='value')\n",
+    "    path_widget.observe(ts.on_path_update, names='value')\n",
+    "    x_widget.observe(ts.on_column_update, names='value')\n",
+    "    y_widget.observe(ts.on_value_update, names='value')\n",
+    "    x_level_widget.observe(ts.on_column_to_value_update, names='value')\n",
+    "    y_level_widget.observe(ts.on_value_level_update, names='value')\n",
+    "\n",
+    "    # temp function to for interaction\n",
+    "    def f(multiple_display_widget, data_path, i_widget, x_widget, y_widget, x_level_widget, y_level_widget):\n",
+    "        pass\n",
+    "\n",
+    "    interact(f,\n",
+    "             multiple_display_widget=multiple_display_widget,\n",
+    "             data_path=path_widget,\n",
+    "             i_widget=i_widget,\n",
+    "             x_widget=x_widget,\n",
+    "             y_widget=y_widget,\n",
+    "             x_level_widget=x_level_widget,\n",
+    "             y_level_widget=y_level_widget)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "type `main()` to run the interaction between floweaver-path and data/templace.csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# main()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.6.1"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}