packages/pymotif/examples/introduction.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we start, you should install motif and the jupyter widget\n",
    "```\n",
    "!pip install pymotif\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A brief introduction to Pymotif's functionality. Start by importing it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymotif import Motif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Data Import\n",
    "\n",
    "Pymotif offers a variety of data import options:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### JSON Data\n",
    "\n",
    "If you download your graph data from the Motif UI (floppy disk button right above 'Data Sources'), it will be saved as a JSON file. This makes it easy to share your graph with someone else - you just have to send the file to them and they can import it like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Reading JSON file, all other params will be ignored...\n"
     ]
    },
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       "Motif(value=None, state={'data': [{'nodes': [{'create_date': '2019-01-03T00:00:00', 'icon': 'account_balance',…"
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     "metadata": {},
     "output_type": "display_data"
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   "source": [
    "# path to local dataset\n",
    "json_path = 'data/sample.json'\n",
    "\n",
    "motif_json = Motif(json_path=json_path)\n",
    "motif_json.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the message that appears: `Reading JSON file, all other params will be ignored...`, which means the `json_path` parameter overwrites any other parameter passed to the `Motif()` object."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### NetworkX Graphs\n",
    "\n",
    "If you have NetworkX or NetworkX-compatible graph data, it can be imported into Motif. In this example, we convert a `.gml` file into a NetworkX graph, then plot it in Motif:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "\n",
    "# path to local dataset\n",
    "gml = 'data/karate.gml'\n",
    "\n",
    "# read using networkx\n",
    "nx_graph = nx.read_gml(gml)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "Motif(value=None, state={'data': [{'nodes': [{'id': '1', 'value': '1', 'name': '1'}, {'id': '2', 'value': '2',…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "motif_nx = Motif(nx_graph=nx_graph, title='NetworkX')\n",
    "motif_nx.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Neo4j Data\n",
    "\n",
    "If you have data stored in a Neo4j database, it can also be plotted in Motif (change the placeholders to your own connection data):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import neo4j\n",
    "\n",
    "neo4j_uri = 'bolt://3.238.89.105:7687'\n",
    "neo4j_auth = ('neo4j', 'surveyors-beat-weed')\n",
    "neo4j_driver = neo4j.GraphDatabase.driver(uri=neo4j_uri, auth=neo4j_auth)\n",
    "neo4j_query = 'MATCH p=()-[r:ACTED_IN]->() RETURN p LIMIT 25'\n",
    "\n",
    "# get query results and create a Motif object from it\n",
    "neo4j_results = neo4j_driver.session().run(neo4j_query)\n",
    "motif_neo4j = Motif(neo4j_graph=neo4j_results.graph(), title='Neo4j')\n",
    "\n",
    "motif_neo4j.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See `neo4j_demo.ipynb` for more examples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### CSV Edgelists\n",
    "\n",
    "Plot graphs stored in CSV edgelists:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fcd096b48571406e87e5124b5bfbae7d",
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      },
      "text/plain": [
       "Motif(value=None, state={'data': [{'nodes': [{'id': 'credit_card_13', 'value': 'credit_card_13', 'name': 'cred…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# path to local dataset\n",
    "csv_path = 'data/edgelist.csv'\n",
    "\n",
    "motif_csv = Motif(csv_path=csv_path, title='CSV')\n",
    "motif_csv.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 2. Save Data And Style\n",
    "\n",
    "You might have noticed a button at the bottom of each Motif widget. It will be useful when interacting with the graph via the UI. We'll import a graph, make a small change via the UI, then save it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading JSON file, all other params will be ignored...\n"
     ]
    },
    {
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      },
      "text/plain": [
       "Motif(value=None, state={'data': [{'nodes': [{'create_date': '2019-01-03T00:00:00', 'icon': 'account_balance',…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "motif_saved_style = Motif(json_path=json_path)\n",
    "motif_saved_style"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the graph above, change the layout with these steps:\n",
    "- Click on the 'Styles' icon on the left panel\n",
    "- Under `Layout`, change the selection from `Concentric` to `Radial`\n",
    "\n",
    "The nodes and edges should now be arranged differently.\n",
    "\n",
    "Save the current style by clicking on the `SAVE DATA AND STYLE` button, then plot the graph again in the following cell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "7cb8118520924d6db7a8d2937f638cc4",
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       "version_minor": 0
      },
      "text/plain": [
       "Motif(value=None, state={'data': [{'nodes': [{'create_date': '2019-01-03T00:00:00', 'icon': 'account_balance',…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "motif_saved_style"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note how the style persists and the graph is still in a radial layout!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 3. Set Style With Code\n",
    "\n",
    "We can also set styles via code. Let's change the graph above back to a `Concentric` layout:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "style = {'layout': {'type': 'concentric'}}\n",
    "motif_saved_style.set_style(style)\n",
    "\n",
    "motif_saved_style"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also apply the current styling of one graph to another. An example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nice_graph = Motif()\n",
    "\n",
    "# do styling here via UI / code\n",
    "# ...\n",
    "\n",
    "# extract the styles from graph\n",
    "nice_style = nice_graph.state['style']\n",
    "\n",
    "# apply style to another graph\n",
    "# ugly_graph = Motif(...)\n",
    "ugly_graph.set_style(nice_style)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 4. Add Graphs\n",
    "\n",
    "You can add new graphs to existing Motif objects. We'll create a graph with the same JSON data as above, then add the CSV data into it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading JSON file, all other params will be ignored...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "515282aa729c499192672589fc6c2cc5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Motif(value=None, state={'data': [{'nodes': [{'create_date': '2019-01-03T00:00:00', 'icon': 'account_balance',…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create initial graph with just json data\n",
    "motif_json_and_csv = Motif(json_path=json_path)\n",
    "\n",
    "# add csv data to it\n",
    "motif_json_and_csv.add_graph(csv_path=csv_path)\n",
    "\n",
    "motif_json_and_csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hopefully the graphs you combine turn out more meaningful than this!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 5. Plotting\n",
    "\n",
    "You've already done a lot of this - just know that for now, a graph can be plotted with or without using the `.plot()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = Motif()\n",
    "\n",
    "# this\n",
    "m\n",
    "\n",
    "# is the same as this\n",
    "m.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 6. State\n",
    "\n",
    "To find out the underlying state of a Motif object at a point in time, access the `.state` instance attribute. It will show its graph data (i.e. the nodes and edges) and style:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# should be empty since nothing was defined above\n",
    "m.state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Export"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save widget state to share the notebook or export it to html.\n",
    "\n",
    "For Jupyter Lab: Settings -> Save Widget State Automatically\n",
    "For Jupyter Notebook: Widgets -> Save Notebook Widget State"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To convert to html:\n",
    "\n",
    "```bash\n",
    "jupyter nbconvert --to html [file].ipynb \n",
    "```"
   ]
  }
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            "target_owner": "other_banks",
            "time": "02:27"
           },
           {
            "amount": "$13.00",
            "category": "credit_card_txn",
            "date": "2020-01-01",
            "id": "credit_card_txn_53",
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            "source_owner": "customer_38",
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            "target_owner": "other_banks",
            "time": "02:46"
           },
           {
            "amount": "$164.00",
            "category": "credit_card_txn",
            "date": "2020-01-01",
            "id": "credit_card_txn_5",
            "key": 0,
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            "source_owner": "customer_94",
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            "target_owner": "other_banks",
            "time": "00:08"
           },
           {
            "amount": "$143.00",
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            "date": "2020-01-01",
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            "target_owner": "other_banks",
            "time": "00:20"
           },
           {
            "amount": "$60.00",
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            "id": "credit_card_txn_58",
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            "target_owner": "other_banks",
            "time": "12:14"
           },
           {
            "amount": "$150.00",
            "category": "credit_card_txn",
            "date": "2020-01-01",
            "id": "credit_card_txn_6",
            "key": 0,
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            "time": "00:11"
           },
           {
            "amount": "$144.00",
            "category": "credit_card_txn",
            "date": "2020-01-01",
            "id": "credit_card_txn_28",
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            "target_owner": "other_banks",
            "time": "01:15"
           },
           {
            "amount": "$79.00",
            "category": "credit_card_txn",
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            "id": "credit_card_txn_7",
            "key": 0,
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           },
           {
            "amount": "$27.00",
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            "time": "01:56"
           },
           {
            "amount": "$193.00",
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            "time": "00:18"
           },
           {
            "amount": "$190.00",
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            "time": "00:21"
           },
           {
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           },
           {
            "amount": "$74.00",
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           },
           {
            "amount": "$99.00",
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            "time": "02:24"
           },
           {
            "amount": "$83.00",
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           },
           {
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            "time": "01:51"
           },
           {
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           },
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           },
           {
            "amount": "$184.00",
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           },
           {
            "amount": "$138.00",
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            "time": "00:32"
           },
           {
            "amount": "$24.00",
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            "time": "00:34"
           },
           {
            "amount": "$128.00",
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            "time": "00:40"
           },
           {
            "amount": "$23.00",
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           },
           {
            "amount": "$29.00",
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           },
           {
            "amount": "$188.00",
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           },
           {
            "amount": "$56.00",
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            "time": "15:03"
           },
           {
            "amount": "$20.00",
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            "time": "00:50"
           },
           {
            "amount": "$182.00",
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            "time": "00:54"
           },
           {
            "amount": "$197.00",
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            "time": "00:58"
           },
           {
            "amount": "$45.00",
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            "time": "01:00"
           },
           {
            "amount": "$77.00",
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            "time": "01:35"
           },
           {
            "amount": "$32.00",
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           },
           {
            "amount": "$138.00",
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            "time": "02:03"
           },
           {
            "amount": "$108.00",
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            "time": "01:05"
           },
           {
            "amount": "$25.00",
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            "time": "01:06"
           },
           {
            "amount": "$16.00",
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            "time": "12:00"
           },
           {
            "amount": "$133.00",
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            "time": "01:07"
           },
           {
            "amount": "$27.00",
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            "time": "01:11"
           },
           {
            "amount": "$106.00",
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            "time": "01:20"
           },
           {
            "amount": "$132.00",
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           },
           {
            "amount": "$43.00",
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           },
           {
            "amount": "$187.00",
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           },
           {
            "amount": "$154.00",
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           },
           {
            "amount": "$128.00",
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            "time": "01:46"
           },
           {
            "amount": "$56.00",
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            "time": "02:13"
           },
           {
            "amount": "$11.00",
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           },
           {
            "amount": "$151.00",
            "category": "credit_card_txn",
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            "time": "02:30"
           },
           {
            "amount": "$93.00",
            "category": "credit_card_txn",
            "date": "2020-01-01",
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           },
           {
            "amount": "$183.00",
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            "time": "02:33"
           },
           {
            "amount": "$170.00",
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            "time": "02:43"
           },
           {
            "amount": "$18.00",
            "category": "credit_card_txn",
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            "key": 0,
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            "time": "12:00"
           },
           {
            "amount": "$31.00",
            "category": "credit_card_txn",
            "date": "2020-01-01",
            "id": "credit_card_txn_56",
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            "time": "12:04"
           },
           {
            "amount": "$124.00",
            "category": "credit_card_txn",
            "date": "2020-01-01",
            "id": "credit_card_txn_75",
            "key": 1,
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            "time": "15:26"
           },
           {
            "amount": "$94.00",
            "category": "credit_card_txn",
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            "id": "credit_card_txn_57",
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           },
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            "time": "00:50"
           },
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            "time": "00:54"
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           },
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           },
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           },
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          ],
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           },
           {
            "id": "credit_card_1",
            "name": "credit_card_1",
            "value": "credit_card_1"
           },
           {
            "id": "credit_card_25",
            "name": "credit_card_25",
            "value": "credit_card_25"
           },
           {
            "id": "credit_card_93",
            "name": "credit_card_93",
            "value": "credit_card_93"
           },
           {
            "id": "credit_card_2",
            "name": "credit_card_2",
            "value": "credit_card_2"
           },
           {
            "id": "credit_card_43",
            "name": "credit_card_43",
            "value": "credit_card_43"
           },
           {
            "id": "credit_card_12",
            "name": "credit_card_12",
            "value": "credit_card_12"
           },
           {
            "id": "credit_card_100",
            "name": "credit_card_100",
            "value": "credit_card_100"
           },
           {
            "id": "credit_card_48",
            "name": "credit_card_48",
            "value": "credit_card_48"
           },
           {
            "id": "credit_card_86",
            "name": "credit_card_86",
            "value": "credit_card_86"
           },
           {
            "id": "credit_card_22",
            "name": "credit_card_22",
            "value": "credit_card_22"
           },
           {
            "id": "credit_card_57",
            "name": "credit_card_57",
            "value": "credit_card_57"
           },
           {
            "id": "credit_card_50",
            "name": "credit_card_50",
            "value": "credit_card_50"
           },
           {
            "id": "credit_card_31",
            "name": "credit_card_31",
            "value": "credit_card_31"
           },
           {
            "id": "credit_card_61",
            "name": "credit_card_61",
            "value": "credit_card_61"
           },
           {
            "id": "credit_card_58",
            "name": "credit_card_58",
            "value": "credit_card_58"
           },
           {
            "id": "credit_card_78",
            "name": "credit_card_78",
            "value": "credit_card_78"
           },
           {
            "id": "credit_card_102",
            "name": "credit_card_102",
            "value": "credit_card_102"
           },
           {
            "id": "account_101",
            "name": "account_101",
            "value": "account_101"
           },
           {
            "id": "credit_card_88",
            "name": "credit_card_88",
            "value": "credit_card_88"
           },
           {
            "id": "credit_card_63",
            "name": "credit_card_63",
            "value": "credit_card_63"
           },
           {
            "id": "credit_card_70",
            "name": "credit_card_70",
            "value": "credit_card_70"
           },
           {
            "id": "credit_card_30",
            "name": "credit_card_30",
            "value": "credit_card_30"
           },
           {
            "id": "credit_card_27",
            "name": "credit_card_27",
            "value": "credit_card_27"
           },
           {
            "id": "credit_card_87",
            "name": "credit_card_87",
            "value": "credit_card_87"
           },
           {
            "id": "credit_card_84",
            "name": "credit_card_84",
            "value": "credit_card_84"
           },
           {
            "id": "credit_card_103",
            "name": "credit_card_103",
            "value": "credit_card_103"
           }
          ]
         }
        ],
        "style": {}
       }
      }
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}