{ "cells": [ { "cell_type": "markdown", "id": "ce39487e", "metadata": {}, "source": [ "# Crude Onshore Inventories Notebook\n", "\n", "This notebook will give a tutorial on how to query the onshore crude inventories data through pythonSDK.\n", "\n", "\n", "Content:\n", "1. [Import Libraries](#Import-Libraries)\n", "2. [Extract storage location ids](#Extract-storage-location-ids)\n", "3. [Crude Inventories Search endpoint](#1.-Crude-Inventories-Search-endpoint)\n", "4. [Crude Onshore Inventories Time Series](#2.-Crude-Onshore-Inventories-Time-Series-endpoint)\n", "5. [Case study](#Case-study:-US-Cushing-Stockpile-vs-Brent-prices)\n", "6. [Benchmarking result](#Benchmarking-result)" ] }, { "cell_type": "markdown", "id": "a6ca5e01", "metadata": {}, "source": [ "## Import Libraries" ] }, { "cell_type": "code", "execution_count": 1, "id": "7cb5f613", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "c:\\Users\\OuiWeinJien\\anaconda3\\lib\\site-packages\\pandas\\core\\arrays\\masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n", " from pandas.core import (\n" ] } ], "source": [ "import vortexasdk as v\n", "from datetime import datetime\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import plotly.express as px\n", "import json\n", "from pprint import pprint\n", "import plotly.graph_objects as go" ] }, { "cell_type": "markdown", "id": "4ba5cde9", "metadata": {}, "source": [ "## 1. Crude Inventories Search endpoint" ] }, { "cell_type": "code", "execution_count": 2, "id": "05083ab3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2024-07-30 10:32:15,377 vortexasdk.client — WARNING — You are using vortexasdk version 0.72.5, however version 0.72.6 is available.\n", "You should consider upgrading via the 'pip install vortexasdk --upgrade' command.\n" ] } ], "source": [ "cushing = 'cde783a902c7837b814dfa5988ed74fb14841c2999d6984952c7cbfc543d5073'\n", "china = [p.id for p in v.Geographies().search('China').to_list() if p.name=='China']" ] }, { "cell_type": "code", "execution_count": 7, "id": "fb4925b5", "metadata": {}, "outputs": [], "source": [ "data_df = v.OnshoreInventoriesSearch().search(\n", " crude_confidence=['confirmed', 'probable'],\n", " location_ids = china,\n", " time_min=datetime(2023, 3, 1),\n", " time_max=datetime(2023, 3, 14)\n", " ).to_df(columns = 'all')" ] }, { "cell_type": "code", "execution_count": 11, "id": "a149cf2f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['measurement_id', 'tank_id', 'tank_details.id', 'tank_details.lat',\n", " 'tank_details.lon', 'tank_details.name', 'tank_details.leaf',\n", " 'tank_details.ref_type', 'tank_details.storage_terminal_name',\n", " 'tank_details.storage_terminal_id', 'tank_details.storage_type',\n", " 'tank_details.radius', 'tank_details.capacity_bbl',\n", " 'tank_details.capacity_cbm', 'tank_details.capacity_ton',\n", " 'tank_details.crude_confidence', 'tank_details.last_updated',\n", " 'tank_details.location_id', 'tank_details.location_details.0.id',\n", " 'tank_details.location_details.0.label',\n", " 'tank_details.location_details.0.layer',\n", " 'tank_details.location_details.1.id',\n", " 'tank_details.location_details.1.label',\n", " 'tank_details.location_details.1.layer',\n", " 'tank_details.location_details.2.id',\n", " 'tank_details.location_details.2.label',\n", " 'tank_details.location_details.2.layer',\n", " 'tank_details.location_details.3.id',\n", " 'tank_details.location_details.3.label',\n", " 'tank_details.location_details.3.layer',\n", " 'tank_details.location_details.4.id',\n", " 'tank_details.location_details.4.label',\n", " 'tank_details.location_details.4.layer',\n", " 'tank_details.location_details.5.id',\n", " 'tank_details.location_details.5.label',\n", " 'tank_details.location_details.5.layer',\n", " 'tank_details.location_details.6.id',\n", " 'tank_details.location_details.6.label',\n", " 'tank_details.location_details.6.layer',\n", " 'tank_details.location_details.7.id',\n", " 'tank_details.location_details.7.label',\n", " 'tank_details.location_details.7.layer',\n", " 'tank_details.location_details.8.id',\n", " 'tank_details.location_details.8.label',\n", " 'tank_details.location_details.8.layer',\n", " 'tank_details.location_details.9.id',\n", " 'tank_details.location_details.9.label',\n", " 'tank_details.location_details.9.layer',\n", " 'tank_details.location_details.10.id',\n", " 'tank_details.location_details.10.label',\n", " 'tank_details.location_details.10.layer',\n", " 'tank_details.location_details.11.id',\n", " 'tank_details.location_details.11.label',\n", " 'tank_details.location_details.11.layer',\n", " 'tank_details.location_details.12.id',\n", " 'tank_details.location_details.12.label',\n", " 'tank_details.location_details.12.layer',\n", " 'tank_details.location_details.13.id',\n", " 'tank_details.location_details.13.label',\n", " 'tank_details.location_details.13.layer',\n", " 'tank_details.location_details.14.id',\n", " 'tank_details.location_details.14.label',\n", " 'tank_details.location_details.14.layer',\n", " 'tank_details.location_details.15.id',\n", " 'tank_details.location_details.15.label',\n", " 'tank_details.location_details.15.layer',\n", " 'tank_details.location_details.16.id',\n", " 'tank_details.location_details.16.label',\n", " 'tank_details.location_details.16.layer',\n", " 'tank_details.location_details.17.id',\n", " 'tank_details.location_details.17.label',\n", " 'tank_details.location_details.17.layer',\n", " 'tank_details.location_details.18.id',\n", " 'tank_details.location_details.18.label',\n", " 'tank_details.location_details.18.layer',\n", " 'tank_details.location_details.19.id',\n", " 'tank_details.location_details.19.label',\n", " 'tank_details.location_details.19.layer',\n", " 'tank_details.corporate_entity_id',\n", " 'tank_details.corporate_entity_details.id',\n", " 'tank_details.corporate_entity_details.label',\n", " 'tank_details.corporate_entity_details.layer', 'measurement_timestamp',\n", " 'publish_timestamp', 'fill_bbl', 'fill_tons', 'fill_cbm',\n", " 'reference_data_version', 'tank_details.location_details.20.id',\n", " 'tank_details.location_details.20.label',\n", " 'tank_details.location_details.20.layer'],\n", " dtype='object')" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_df.columns" ] }, { "cell_type": "code", "execution_count": 5, "id": "04d21ca6", "metadata": {}, "outputs": [], "source": [ "data_list = v.OnshoreInventoriesSearch().search(\n", " crude_confidence=['confirmed', 'probable'],\n", " location_ids = china,\n", " time_min=datetime(2023, 3, 1),\n", " time_max=datetime(2023, 3, 14)).to_list()" ] }, { "cell_type": "code", "execution_count": 6, "id": "f0a56295", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'fill_bbl': 53429,\n", " 'fill_cbm': 8494.569851999999,\n", " 'fill_tons': 7305.2937409999995,\n", " 'measurement_id': '000e025d8be0f76d3fe6a23bbd6867436ff751ad8fe9d85f30846afcfc077d19',\n", " 'measurement_timestamp': '2023-03-02T10:13:39',\n", " 'publish_timestamp': '2023-03-03T18:00:01',\n", " 'reference_data_version': 'current-1718644569619',\n", " 'tank_details': {'capacity_bbl': 318884,\n", " 'capacity_cbm': 50698,\n", " 'capacity_ton': 43600,\n", " 'corporate_entity_details': {'id': 'f78558ef7c9b9f162545a56b330111c274d578a2933f46ec82ee004ca4f449bd',\n", " 'label': 'PETROCHINA',\n", " 'layer': 'commercial_owner'},\n", " 'corporate_entity_id': 'f78558ef7c9b9f162545a56b330111c274d578a2933f46ec82ee004ca4f449bd',\n", " 'crude_confidence': 'confirmed',\n", " 'id': '1432c91cc383424b9fa048a7cc3fbe87747aa5e575a3e2c65479da8a7fd630d0',\n", " 'last_updated': '2022-05-16T16:16:01.000Z',\n", " 'lat': 38.7419159,\n", " 'leaf': True,\n", " 'location_details': [{'id': 'b310103de98ae56631562a9fe1d13d826babe3fafd5e639f81370d944afb7f8f',\n", " 'label': 'Tianjin',\n", " 'layer': 'state_or_province'},\n", " {'id': 'a63890260e29d859390fd1a23c690181afd4bd152943a04c00cd6a5ecf3f7d1e',\n", " 'label': 'North China',\n", " 'layer': 'alternative_region'},\n", " {'id': 'b5fafce6e20de2dc307fb7e0b89978ee91a49a7b6ec6f5461daf2633f3c56674',\n", " 'label': 'China (excl. HK & Macau)',\n", " 'layer': 'alternative_region'},\n", " {'id': 'c7d632f2816340f9daf323ca62d1396f1bc9c7f7bce5cd358e84715c32cc763f',\n", " 'label': 'Alternative Regions',\n", " 'layer': 'root'},\n", " {'id': 'f2d80ac4c87a2006e3ae3af0b345b7129fc8efefc780ccbfeaae8313c28caf76',\n", " 'label': 'State or Province',\n", " 'layer': 'root'},\n", " {'id': '934c47f36c16a58d68ef5e007e62a23f5f036ee3f3d1f5f85a48c572b90ad8b2',\n", " 'label': 'China',\n", " 'layer': 'country'},\n", " {'id': '07ac020aa082d5d116f829ef28a40e9876c0644aeb4f0480202c1a86bb8adf7f',\n", " 'label': 'Far East',\n", " 'layer': 'alternative_region'},\n", " {'id': 'e6955a2c59dc90833986fe0894cf6718dddaa7816bb51bc955cdd3eb4470e554',\n", " 'label': 'Asia',\n", " 'layer': 'region'},\n", " {'id': '693f4f6d59b709e8114e339f2d6c0c3083a453278226e58ea7473c09b2de6d40',\n", " 'label': 'Geographic Regions',\n", " 'layer': 'root'},\n", " {'id': '5600764fc92f8cce630f3994e7af4d900945d54b29f6e4a07fde0c5e17f7d738',\n", " 'label': 'Northeast Asia (NEA)',\n", " 'layer': 'shipping_region_v2'},\n", " {'id': '9247125b0b7d8c78d68fb91a5e1ec0cd1b1c4f7276e3385dbe197004b6081229',\n", " 'label': 'East of Suez',\n", " 'layer': 'alternative_region'},\n", " {'id': 'f5cfd8a913cf02382c275541bec8506be42365e21ea7f82a2655362a210bc73e',\n", " 'label': 'Shipping Regions',\n", " 'layer': 'root'},\n", " {'id': '86bf8a497ce81e6d4768e3297a22a029b3ebf95de037586789232bea76ab4116',\n", " 'label': 'Wider Northeast Asia (WNEA)',\n", " 'layer': 'wider_shipping_region'},\n", " {'id': 'a3d6e07472c8d1db2562af0af9d2c49e4d2df6d2cd65a99f55074828df731ba2',\n", " 'label': 'Wider Shipping Region',\n", " 'layer': 'root'},\n", " {'id': '715b6aab56723cb55c3ddbdc12a2851b379919010f119cf98765d4debbdb793d',\n", " 'label': 'Pacific Basin',\n", " 'layer': 'basin'},\n", " {'id': 'a37c3441872e24e9d0b95c61d98d7a32307abe48e968cf7d3ed352094008e872',\n", " 'label': 'Basins',\n", " 'layer': 'root'},\n", " {'id': '956b03529510459d298c4e537d23c6fbb22ffd8414af933b3475310c661f337a',\n", " 'label': 'Non-OPEC',\n", " 'layer': 'alternative_region'},\n", " {'id': 'e72bcaa8ab1867a6db9e844007653b494d8f325adfe430b8e29e14ed4dc27029',\n", " 'label': 'Tianjin [CN]',\n", " 'layer': 'port'},\n", " {'id': 'e72bcaa8ab1867a6db9e844007653b494d8f325adfe430b8e29e14ed4dc27029',\n", " 'label': 'Tianjin [CN]',\n", " 'layer': 'storage'},\n", " {'id': '42fbc2520c8c4ac8a6700e097c2ad844d039d1b247e41d46475d772748c6472c',\n", " 'label': 'PetroChina Huabei '\n", " 'Petrochemical Company',\n", " 'layer': 'storage_terminal'}],\n", " 'location_id': 'e72bcaa8ab1867a6db9e844007653b494d8f325adfe430b8e29e14ed4dc27029',\n", " 'lon': 116.1261422,\n", " 'name': 'RNQ017',\n", " 'radius': 28,\n", " 'ref_type': 'asset_tank',\n", " 'storage_terminal_id': '42fbc2520c8c4ac8a6700e097c2ad844d039d1b247e41d46475d772748c6472c',\n", " 'storage_terminal_name': 'PetroChina Huabei Petrochemical '\n", " 'Company',\n", " 'storage_type': 'refinery'},\n", " 'tank_id': '1432c91cc383424b9fa048a7cc3fbe87747aa5e575a3e2c65479da8a7fd630d0'}\n" ] } ], "source": [ "pprint(data_list[0].dict())" ] }, { "cell_type": "markdown", "id": "a95f10b9", "metadata": {}, "source": [ "## 2. Crude Onshore Inventories Time Series endpoint" ] }, { "cell_type": "code", "execution_count": 7, "id": "7933b57c", "metadata": {}, "outputs": [], "source": [ "shandong = [p.id for p in v.StorageTerminals().search(term ='Shandong').to_list() if p.name=='Shandong Province']" ] }, { "cell_type": "code", "execution_count": 8, "id": "26d5ac6b", "metadata": {}, "outputs": [], "source": [ "df = v.OnshoreInventoriesTimeseries().search(\n", " crude_confidence=['confirmed', 'probable'],\n", " location_ids = shandong,\n", " time_min=datetime(2022, 1, 1),\n", " time_max=datetime.today(),\n", " timeseries_frequency = 'day',\n", " timeseries_unit = 'b'\n", ").to_df(columns = 'all').set_index('key')\n", "\n", "df2 = v.OnshoreInventoriesTimeseries().search(\n", " crude_confidence=['confirmed', 'probable'],\n", " location_ids = china,\n", " time_min=datetime(2022, 1, 1),\n", " time_max=datetime.today(),\n", " timeseries_frequency = 'day',\n", " timeseries_unit = 'b'\n", ").to_df(columns = 'all').set_index('key')" ] }, { "cell_type": "code", "execution_count": 9, "id": "63bfdbcf", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "line": { "color": "rgb(0,100,80)" }, "name": "Shandong", "type": "scatter", "x": [ "2022-01-01T00:00:00+00:00", "2022-01-02T00:00:00+00:00", "2022-01-03T00:00:00+00:00", "2022-01-04T00:00:00+00:00", "2022-01-05T00:00:00+00:00", "2022-01-06T00:00:00+00:00", "2022-01-07T00:00:00+00:00", "2022-01-08T00:00:00+00:00", "2022-01-09T00:00:00+00:00", "2022-01-10T00:00:00+00:00", "2022-01-11T00:00:00+00:00", "2022-01-12T00:00:00+00:00", "2022-01-13T00:00:00+00:00", "2022-01-14T00:00:00+00:00", "2022-01-15T00:00:00+00:00", "2022-01-16T00:00:00+00:00", "2022-01-17T00:00:00+00:00", "2022-01-18T00:00:00+00:00", "2022-01-19T00:00:00+00:00", "2022-01-20T00:00:00+00:00", "2022-01-21T00:00:00+00:00", "2022-01-22T00:00:00+00:00", "2022-01-23T00:00:00+00:00", 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"linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Shandong vs China Crude Inventories (bbls)" }, "xaxis": { "anchor": "y", "domain": [ 0, 0.94 ] }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "Shandong volume" } }, "yaxis2": { "anchor": "x", "overlaying": "y", "side": "right", "title": { "text": "China volume" } } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create traces\n", "from plotly.subplots import make_subplots\n", "fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n", "fig.add_trace(go.Scatter(\n", " x=df.index, y=df['value'],\n", " line_color='rgb(0,100,80)',\n", " name='Shandong'),\n", " secondary_y=False\n", ")\n", "\n", "fig.add_trace(go.Scatter(\n", " x=df2.index, y=df2['value'],\n", " line_color='rgb(255,100,80)',\n", " name='china'),\n", " secondary_y=True\n", ")\n", "\n", "fig.update_layout(\n", " title_text=\"Shandong vs China Crude Inventories (bbls)\"\n", ")\n", "\n", "# Set y-axes titles\n", "fig.update_yaxes(title_text=\"Shandong volume\", secondary_y=False)\n", "fig.update_yaxes(title_text=\"China volume\", secondary_y=True)\n", "\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "23539775", "metadata": {}, "source": [ "***Shandong Inventories decline but remain a crucial outlier***\n", "\n", "Shandong crude stocks have been falling consistently sisnce mid last year, but are still well above year-earlier levels.\n", "\n", "This contrasts with total China inventories now falling close to historical norms and global inventories currently at lows.\n", "\n", "Shandong's independent refiners can continue to draw at current rates for the next 6 months and still remain above historical avearage." ] }, { "cell_type": "markdown", "id": "78d47543", "metadata": {}, "source": [ "## Case study: US Cushing Stockpile vs Brent prices" ] }, { "cell_type": "code", "execution_count": 6, "id": "a6addf52", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "BZ=FDSDSDS: Invalid input - interval=2d is not supported. Valid intervals: [1m, 2m, 5m, 15m, 30m, 60m, 90m, 1h, 1d, 5d, 1wk, 1mo, 3mo]\n" ] } ], "source": [ "import yfinance as yf\n", "price_history = yf.Ticker('BZ=Fdsdsds').history(period='2y', # valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max\n", " interval='2d', # valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo\n", " actions=False)" ] }, { "cell_type": "code", "execution_count": 7, "id": "1c6c577c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(price_history)" ] }, { "cell_type": "code", "execution_count": 12, "id": "0c2cc869", "metadata": {}, "outputs": [], "source": [ "df3 = v.OnshoreInventoriesTimeseries().search(\n", " crude_confidence=['confirmed', 'probable'],\n", " location_ids = cushing,\n", " time_min=datetime(2022, 1, 1),\n", " time_max=datetime.today(),\n", " timeseries_frequency = 'day',\n", " timeseries_unit = 'b'\n", ").to_df(columns = 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"gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "US Cushing stockpiles vs Brent Price" }, "xaxis": { "anchor": "y", "domain": [ 0, 0.94 ] }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "Cushing volume (b)" } }, "yaxis2": { "anchor": "x", "overlaying": "y", "side": "right", "title": { "text": "Brent Price ($)" } } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create traces\n", "from plotly.subplots import make_subplots\n", "fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n", "fig.add_trace(go.Scatter(\n", " x=df3.index, y=df3['value'],\n", " line_color='rgb(0,100,80)',\n", " name='US Crude stockpiles'),\n", " secondary_y=False\n", ")\n", "\n", "fig.add_trace(go.Scatter(\n", " x=price_history.index, y=price_history['Open'],\n", " line_color='rgb(255,100,80)',\n", " name='Brent Price'),\n", " secondary_y=True\n", ")\n", "\n", "fig.update_layout(\n", " title_text=\"US Cushing stockpiles vs Brent Price\"\n", ")\n", "\n", "# Set y-axes titles\n", "fig.update_yaxes(title_text=\"Cushing volume (b)\", secondary_y=False)\n", "fig.update_yaxes(title_text=\"Brent Price ($)\", secondary_y=True)\n", "\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "cdd323f1", "metadata": {}, "source": [ "***Vortexa data as a leading indicator of inventories data as it publishes more than a weekly basis, which can be useful for price analysis.***\n", "\n", "Inverse correlation is found for cushing volume vs Brent prices. As Vortexa publishes measurement readings for each tank whenever we receive it, the frequency in data is more competitive than publicly available data such as Energy Information Administration (EIA)." ] } ], "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": "3.10.9" } }, "nbformat": 4, "nbformat_minor": 5 }