00_environment_setup.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "cellView": "form",
    "id": "ur8xi4C7S06n"
   },
   "outputs": [],
   "source": [
    "# Copyright 2023 Google LLC\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#     https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fsv4jGuU89rX"
   },
   "source": [
    "# FraudFinder - Environment Setup\n",
    "\n",
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a href=\"https://console.cloud.google.com/ai-platform/notebooks/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/fraudfinder/raw/main/00_environment_setup.ipynb\">\n",
    "       <img src=\"https://www.gstatic.com/cloud/images/navigation/vertex-ai.svg\" alt=\"Google Cloud Notebooks\">Open in Cloud Notebook\n",
    "    </a>\n",
    "  </td> \n",
    "  <td>\n",
    "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/fraudfinder/blob/main/00_environment_setup.ipynb\">\n",
    "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Open in Colab\n",
    "    </a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a href=\"https://github.com/GoogleCloudPlatform/fraudfinder/blob/main/00_environment_setup.ipynb\">\n",
    "        <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
    "      View on GitHub\n",
    "    </a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "827c41ab1a12"
   },
   "source": [
    "## Overview\n",
    "\n",
    "[FraudFinder](https://github.com/googlecloudplatform/fraudfinder) is a series of labs on how to build a real-time fraud detection system on Google Cloud. Throughout the FraudFinder labs, you will learn how to read historical bank transaction data stored in data warehouse, read from a live stream of new transactions, perform exploratory data analysis (EDA), do feature engineering, ingest features into a feature store, train a model using feature store, register your model in a model registry, evaluate your model, deploy your model to an endpoint, do real-time inference on your model with feature store, and monitor your model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "45f6e923dc75"
   },
   "source": [
    "### Objective\n",
    "\n",
    "Before you run this notebook, make sure that you have completed the steps in [README](README.md).\n",
    "\n",
    "In this notebook, you will setup your environment for Fraudfinder to be used in subsequent labs.\n",
    "\n",
    "This lab uses the following Google Cloud services and resources:\n",
    "\n",
    "- [Vertex AI](https://cloud.google.com/vertex-ai/)\n",
    "- [BigQuery](https://cloud.google.com/bigquery/)\n",
    "- [Google Cloud Storage](https://cloud.google.com/storage)\n",
    "- [Pub/Sub](https://cloud.google.com/pubsub/)\n",
    "\n",
    "Steps performed in this notebook:\n",
    "\n",
    "- Setup your environment.\n",
    "- Load historical bank transactions into BigQuery.\n",
    "- Read data from BigQuery tables.\n",
    "- Read data from Pub/Sub topics, which contain a live stream of new transactions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8b5e2e2a7bdb"
   },
   "source": [
    "### Costs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "04c1dae4ca17"
   },
   "source": [
    "This tutorial uses billable components of Google Cloud:\n",
    "\n",
    "* Vertex AI\n",
    "* Cloud Storage\n",
    "* Pub/Sub\n",
    "* BigQuery\n",
    "\n",
    "Learn about [Vertex AI\n",
    "pricing](https://cloud.google.com/vertex-ai/pricing), [Cloud Storage\n",
    "pricing](https://cloud.google.com/storage/pricing), [Pub/Sub pricing](https://cloud.google.com/pubsub/pricing), [BigQuery pricing](https://cloud.google.com/bigquery/pricing) and use the [Pricing\n",
    "Calculator](https://cloud.google.com/products/calculator/)\n",
    "to generate a cost estimate based on your projected usage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "773901ca47fd"
   },
   "source": [
    "### Install additional packages\n",
    "\n",
    "Install the following packages required to execute this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pip in /opt/conda/lib/python3.10/site-packages (24.3.1)\n"
     ]
    }
   ],
   "source": [
    "!pip3 install --upgrade pip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "b7c7ce6bbf03",
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip install --upgrade -q -r 'requirements.txt'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d07214a67580"
   },
   "source": [
    "After you install the additional packages, you need to restart the notebook kernel so it can find the packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "18c113700b6f",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Automatically restart kernel after installs\n",
    "import os\n",
    "\n",
    "if not os.getenv(\"IS_TESTING\"):\n",
    "    import IPython\n",
    "\n",
    "    app = IPython.Application.instance()\n",
    "    app.kernel.do_shutdown(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>⚠️ The kernel is going to restart. Please wait until it is finished before continuing to the next step. ⚠️</b>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f31ae3fed8ab"
   },
   "source": [
    "### Setup your environment\n",
    "\n",
    "Run the next cells to import libraries used in this notebook and configure some options."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7d61a362443d"
   },
   "source": [
    "Run the next cell to set your project ID and some of the other constants used in the lab.  \n",
    "\n",
    "### Replace REGION Below ***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>NOTE: Please make sure REGION is set correct, or replace accordingly.<br>\n",
    "    You will need to run from this cell and below after Kernel restart\n",
    "   </b>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "id": "wxiE6dEWOFm3",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import string\n",
    "from typing import Union\n",
    "\n",
    "import pandas as pd\n",
    "from google.cloud import bigquery\n",
    "\n",
    "# Generate unique ID to help w/ unique naming of certain pieces\n",
    "ID = \"\".join(random.choices(string.ascii_lowercase + string.digits, k=5))\n",
    "\n",
    "# Replace Region here\n",
    "REGION = \"us-central1\"\n",
    "\n",
    "# static parameters\n",
    "GCP_PROJECTS = !gcloud config get-value project\n",
    "PROJECT_ID = GCP_PROJECTS[0]\n",
    "BUCKET_NAME = f\"{PROJECT_ID}-fraudfinder\"\n",
    "UPLOAD_BUCKET = f\"{PROJECT_ID}-model-upload\"\n",
    "AGENT_BUCKET = f\"{PROJECT_ID}-ai-workshops\"\n",
    "TRAINING_DS_SIZE = 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bd738fc1e201"
   },
   "source": [
    "### Create a Google Cloud Storage bucket and save the config data.\n",
    "\n",
    "Next, we will create a Google Cloud Storage bucket and will save the config data in this bucket. After the cell operation finishes, you can navigate to [Google Cloud Storage](https://console.cloud.google.com/storage/) to see the GCS bucket. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7d3556c598a6",
    "tags": []
   },
   "outputs": [],
   "source": [
    "config = f\"\"\"\n",
    "BUCKET_NAME          = \\\"{BUCKET_NAME}\\\"\n",
    "PROJECT              = \\\"{PROJECT_ID}\\\"\n",
    "REGION               = \\\"{REGION}\\\"\n",
    "ID                   = \\\"{ID}\\\"\n",
    "FEATURESTORE_ID      = \\\"fraudfinder_{ID}\\\"\n",
    "MODEL_NAME           = \\\"ff_model\\\"\n",
    "ENDPOINT_NAME        = \\\"ff_model_endpoint\\\"\n",
    "TRAINING_DS_SIZE     = \\\"{TRAINING_DS_SIZE}\\\"\n",
    "DATA_DIR             = \"data\"\n",
    "TRAIN_DATA_DIR       = \"train\"\n",
    "CUSTOMER_ENTITY      = \"customer\"\n",
    "TERMINAL_ENTITY      = \"terminal\"\n",
    "TARGET               = \"tx_fraud\"\n",
    "\"\"\"\n",
    "\n",
    "!gsutil mb -l {REGION} gs://{BUCKET_NAME}\n",
    "!gsutil mb -l {REGION} gs://{UPLOAD_BUCKET}\n",
    "\n",
    "\n",
    "!echo '{config}' | gsutil cp - gs://{BUCKET_NAME}/config/notebook_env.py\n",
    "#!echo '{config}' | gsutil cp - gs://{BUCKET_NAME}/config/notebook_env_v02.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating gs://fraud123-438914-ai-workshops/...\n",
      "Copying file://vertex-agent/agents/data/wakanda.txt [Content-Type=text/plain]...\n",
      "/ [1 files][  634.0 B/  634.0 B]                                                \n",
      "Operation completed over 1 objects/634.0 B.                                      \n"
     ]
    }
   ],
   "source": [
    "# AGENT lab\n",
    "!gsutil mb -l {REGION} gs://{AGENT_BUCKET}\n",
    "!gsutil cp vertex-agent/agents/data/wakanda.txt  gs://{AGENT_BUCKET}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/jupyter/ai-takeoff\n",
      "-rw-r--r-- 1 jupyter jupyter 634 Nov  5 08:01 vertex-agent/agents/data/wakanda.txt\n"
     ]
    }
   ],
   "source": [
    "!pwd\n",
    "!ls -l vertex-agent/agents/data/wakanda.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dc2dff7ba2e0"
   },
   "source": [
    "### Copy the historical transaction data into BigQuery tables\n",
    "\n",
    "Now we will copy the historical transaction data and ingest it into BigQuery tables. For this, we will need to run `copy_bigquery_data.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "4ac6e0bc33b1",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File copied from gs://cymbal-fraudfinder/datagen/hacked_customers_history.txt \n",
      "\t\t to gs://fraud123-438914-fraudfinder/datagen/hacked_customers_history.txt\n",
      "File copied from gs://cymbal-fraudfinder/datagen/hacked_terminals_history.txt \n",
      "\t\t to gs://fraud123-438914-fraudfinder/datagen/hacked_terminals_history.txt\n",
      "File copied from gs://cymbal-fraudfinder/datagen/demographics/customer_profiles.csv \n",
      "\t\t to gs://fraud123-438914-fraudfinder/datagen/demographics/customer_profiles.csv\n",
      "File copied from gs://cymbal-fraudfinder/datagen/demographics/terminal_profiles.csv \n",
      "\t\t to gs://fraud123-438914-fraudfinder/datagen/demographics/terminal_profiles.csv\n",
      "File copied from gs://cymbal-fraudfinder/datagen/demographics/customer_with_terminal_profiles.csv \n",
      "\t\t to gs://fraud123-438914-fraudfinder/datagen/demographics/customer_with_terminal_profiles.csv\n",
      "BigQuery table created: `fraud123-438914`.tx.tx\n",
      "BigQuery table created: `fraud123-438914`.tx.txlabels\n",
      "BigQuery table created: `fraud123-438914`.demographics.customers\n",
      "BigQuery table created: `fraud123-438914`.demographics.terminals\n",
      "BigQuery table created: `fraud123-438914`.demographics.customersterminals\n"
     ]
    }
   ],
   "source": [
    "!python3 fraudfinder/scripts/copy_bigquery_data.py $BUCKET_NAME"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "29dbf432339c"
   },
   "source": [
    "### Check data in BigQuery\n",
    "\n",
    "After ingesting our data into BigQuery, it's time to run some queries against the tables to inspect the data. You can also go to the [BigQuery console](https://console.cloud.google.com/bigquery) to see the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e12ec3dae852"
   },
   "source": [
    "#### Initialize BigQuery SDK for Python "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ace8667cc99e"
   },
   "source": [
    "Use a helper function for sending queries to BigQuery."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "f7afa36c6090",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Wrapper to use BigQuery client to run query/job, return job ID or result as DF\n",
    "def run_bq_query(sql: str) -> Union[str, pd.DataFrame]:\n",
    "    \"\"\"\n",
    "    Run a BigQuery query and return the job ID or result as a DataFrame\n",
    "    Args:\n",
    "        sql: SQL query, as a string, to execute in BigQuery\n",
    "    Returns:\n",
    "        df: DataFrame of results from query,  or error, if any\n",
    "    \"\"\"\n",
    "\n",
    "    bq_client = bigquery.Client()\n",
    "    \n",
    "\n",
    "    # Try dry run before executing query to catch any errors\n",
    "    job_config = bigquery.QueryJobConfig(dry_run=True, use_query_cache=False)\n",
    "    bq_client.query(sql, job_config=job_config)\n",
    "\n",
    "    # If dry run succeeds without errors, proceed to run query\n",
    "    job_config = bigquery.QueryJobConfig()\n",
    "    client_result = bq_client.query(sql, job_config=job_config)\n",
    "\n",
    "    job_id = client_result.job_id\n",
    "\n",
    "    # Wait for query/job to finish running. then get & return data frame\n",
    "    df = client_result.result().to_arrow().to_pandas()\n",
    "    print(f\"Finished job_id: {job_id}\")\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "20875916c5d4"
   },
   "source": [
    "#### tx.tx\n",
    "The `tx.tx` table contains the basic information about each transaction:\n",
    "- `TX_ID` is a unique ID per transaction\n",
    "- `TX_TS` is the timestamp of the transaction, in UTC\n",
    "- `CUSTOMER_ID` is a unique 16-digit string ID per customer\n",
    "- `TERMINAL_ID` is a unique 16-digit string ID per point-of-sale terminal\n",
    "- `TX_AMOUNT` is the amount of money spent by the customer at a terminal, in dollars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "cc0e50b158d2",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished job_id: c7c19e38-0e88-4793-b457-5ebb79e767ab\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TX_ID</th>\n",
       "      <th>TX_TS</th>\n",
       "      <th>CUSTOMER_ID</th>\n",
       "      <th>TERMINAL_ID</th>\n",
       "      <th>TX_AMOUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>d7c3ea43ac39b3cff323f875c65b5ebfa16ffa3a</td>\n",
       "      <td>2024-07-17 21:35:04+00:00</td>\n",
       "      <td>4377145325468797</td>\n",
       "      <td>00064542</td>\n",
       "      <td>41.790000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f890314b0ddb69ba596a4191f216e8cface75b7f</td>\n",
       "      <td>2024-07-17 04:34:52+00:00</td>\n",
       "      <td>3203165883677802</td>\n",
       "      <td>00064542</td>\n",
       "      <td>74.910000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2d6352558595617ed4f08e02364a62f4b083cd80</td>\n",
       "      <td>2024-07-17 19:53:22+00:00</td>\n",
       "      <td>5584066316288213</td>\n",
       "      <td>00064542</td>\n",
       "      <td>71.310000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cc73796e59265f86728b2e0c7907ba99cb9e4e5a</td>\n",
       "      <td>2024-07-17 18:28:00+00:00</td>\n",
       "      <td>9749780341102749</td>\n",
       "      <td>00064542</td>\n",
       "      <td>86.040000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4f82c3d19591c1354039cb8211681f0f5c091c00</td>\n",
       "      <td>2024-07-17 22:54:17+00:00</td>\n",
       "      <td>5868556736765899</td>\n",
       "      <td>00064542</td>\n",
       "      <td>11.380000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      TX_ID                     TX_TS  \\\n",
       "0  d7c3ea43ac39b3cff323f875c65b5ebfa16ffa3a 2024-07-17 21:35:04+00:00   \n",
       "1  f890314b0ddb69ba596a4191f216e8cface75b7f 2024-07-17 04:34:52+00:00   \n",
       "2  2d6352558595617ed4f08e02364a62f4b083cd80 2024-07-17 19:53:22+00:00   \n",
       "3  cc73796e59265f86728b2e0c7907ba99cb9e4e5a 2024-07-17 18:28:00+00:00   \n",
       "4  4f82c3d19591c1354039cb8211681f0f5c091c00 2024-07-17 22:54:17+00:00   \n",
       "\n",
       "        CUSTOMER_ID TERMINAL_ID     TX_AMOUNT  \n",
       "0  4377145325468797    00064542  41.790000000  \n",
       "1  3203165883677802    00064542  74.910000000  \n",
       "2  5584066316288213    00064542  71.310000000  \n",
       "3  9749780341102749    00064542  86.040000000  \n",
       "4  5868556736765899    00064542  11.380000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_bq_query(\n",
    "    \"\"\"\n",
    "SELECT\n",
    "  *\n",
    "FROM\n",
    "  tx.tx\n",
    "LIMIT 5\n",
    "\"\"\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5e0ab0d56773"
   },
   "source": [
    "#### tx.txlabels\n",
    "The `tx.txlabels` table contains information on whether each transation was fraud or not:\n",
    "- `TX_ID` is a unique ID per transaction\n",
    "- `TX_FRAUD` is 1 if the transaction was fraud, and 0 if the transaction was not fraudulent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "c128a6c78e82",
    "tags": []
   },
   "outputs": [],
   "source": [
    "run_bq_query(\n",
    "    \"\"\"\n",
    "SELECT\n",
    "  *\n",
    "FROM\n",
    "  tx.txlabels\n",
    "LIMIT 5\n",
    "\"\"\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ffdfcfed70bd"
   },
   "source": [
    "### Check live streaming transactions via public Pub/Sub topics\n",
    "\n",
    "As part of the [README](README.md), you've created [subscriptions](https://console.cloud.google.com/cloudpubsub/subscription/) to public Pub/Sub topics, where there is a constant flow of new transactions. This means you have, in your own Google Cloud project, subscriptions to the public Pub/Sub topics. You will receive a Pub/Sub message in your subscription every time a new transaction is streamed into the Pub/Sub topic.\n",
    "\n",
    "There are two public Pub/Sub topics where there is a constant stream of live transactions occurring.\n",
    "\n",
    "The following Pub/Sub topics are used for transactions:\n",
    "```\n",
    "projects/cymbal-fraudfinder/topics/ff-tx\n",
    "projects/cymbal-fraudfinder/topics/ff-txlabels\n",
    "```\n",
    "\n",
    "Note: If you haven't completed the steps in the README, please make sure that you complete them first before continuing this notebook, otherwise you may not have Pub/Sub subscriptions."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "00_environment_setup.ipynb",
   "toc_visible": true
  },
  "environment": {
   "kernel": "conda-base-py",
   "name": "workbench-notebooks.m125",
   "type": "gcloud",
   "uri": "us-docker.pkg.dev/deeplearning-platform-release/gcr.io/workbench-notebooks:m125"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel) (Local)",
   "language": "python",
   "name": "conda-base-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}