{ "cells": [ { "cell_type": "markdown", "id": "d5a466a9-0be2-48ea-8648-49ad2c97a359", "metadata": {}, "source": [ "# Calculating pairwise distances between a contour and every grid cell\n", "In this notebook, we will demonstrate how to calculate the distance from every grid cell to the nearest grid cell along a contour. We will demonstrate this by computing the distance from each grid cell in a data array to the sea ice edge. We will use monthly sea ice concentration from ACCESS-OM2-01 over the Southern Ocean to perform these calculations. \n", " \n", "*Useful definitions* \n", "**Nearest neighbour**: Refers to the search of the point within a predetermined set of points that is located closest (spatially) to a given point. In order words, what grid cell along the sea ice edge is closest to a grid cell with coordinates `i`, `j`. \n", "**Sea ice edge**: Refers to the northernmost grid cell where sea ice concentration is $10\\%$ or above." ] }, { "cell_type": "markdown", "id": "3df39598-3b8d-4fae-956d-f50399f3aaaf", "metadata": {}, "source": [ "## Loading modules" ] }, { "cell_type": "code", "execution_count": 1, "id": "5d591a48-9911-4c59-a8ee-481d883777cc", "metadata": { "tags": [] }, "outputs": [], "source": [ "import cosima_cookbook as cc\n", "import xarray as xr\n", "import numpy as np\n", "import datetime as dt\n", "from sklearn.neighbors import BallTree\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "id": "f95ceb03-d722-49c3-b4cb-71bd9e0b1304", "metadata": {}, "source": [ "## Creating a session in the COSIMA cookbook" ] }, { "cell_type": "code", "execution_count": 2, "id": "5076a238-0d66-4c10-8f95-b6de4824a47a", "metadata": { "tags": [] }, "outputs": [], "source": [ "session = cc.database.create_session()" ] }, { "cell_type": "markdown", "id": "c45e1335-cd0b-4773-9e8f-4df654d38aa8", "metadata": {}, "source": [ "## Accessing ACCESS-OM2-01 data\n", "We will use monthly sea ice outputs from cycle four. For this example, we will load only two months of sea ice data. Note the use of `decode_coords = False`. This avoids lengthly delays in accessing sea ice data. See more information [here](https://forum.access-hive.org.au/t/issues-loading-access-om2-01-data-from-cycle-4/418/3)." ] }, { "cell_type": "code", "execution_count": 3, "id": "b1de38e7-862a-4974-9a64-0b1046391571", "metadata": { "tags": [] }, "outputs": [], "source": [ "var_ice = cc.querying.getvar('01deg_jra55v140_iaf_cycle4', 'aice_m', session, \n", " start_time='1978-01', end_time='1978-03',\n", " decode_coords=False)" ] }, { "cell_type": "markdown", "id": "ce17902e-7eae-4a71-aa8a-94703a1bb4ad", "metadata": {}, "source": [ "The sea ice outputs need some processing before we can start our calculations. You can check this [example](IcePlottingExample.ipynb) for a guide on how to load and plot sea ice data. \n", " \n", "We will follow these processing steps:\n", "1. Correct time dimension values by subtracting 12 hours,\n", "2. Attach the ocean model grid so we can calculate distances." ] }, { "cell_type": "code", "execution_count": 4, "id": "5f3c2530-9225-4b59-ba62-22f374698b00", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'aice_m' (time: 3, yt_ocean: 713, xt_ocean: 3600)>\n",
       "dask.array<getitem, shape=(3, 713, 3600), dtype=float32, chunksize=(1, 270, 360), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time      (time) datetime64[ns] 1977-12-31T12:00:00 ... 1978-02-28T12:00:00\n",
       "  * xt_ocean  (xt_ocean) float64 -279.9 -279.8 -279.7 ... 79.75 79.85 79.95\n",
       "  * yt_ocean  (yt_ocean) float64 -79.97 -79.93 -79.88 ... -45.18 -45.11 -45.04\n",
       "Attributes:\n",
       "    units:          1\n",
       "    long_name:      ice area  (aggregate)\n",
       "    coordinates:    TLON TLAT time\n",
       "    cell_measures:  area: tarea\n",
       "    cell_methods:   time: mean\n",
       "    time_rep:       averaged\n",
       "    ncfiles:        ['/g/data/cj50/access-om2/raw-output/access-om2-01/01deg_...\n",
       "    contact:        Andrew Kiss\n",
       "    email:          andrew.kiss@anu.edu.au\n",
       "    created:        2022-04-27\n",
       "    description:    0.1 degree ACCESS-OM2 global model configuration under in...\n",
       "    notes:          Run configuration and history: https://github.com/COSIMA/...
" ], "text/plain": [ "\n", "dask.array\n", "Coordinates:\n", " * time (time) datetime64[ns] 1977-12-31T12:00:00 ... 1978-02-28T12:00:00\n", " * xt_ocean (xt_ocean) float64 -279.9 -279.8 -279.7 ... 79.75 79.85 79.95\n", " * yt_ocean (yt_ocean) float64 -79.97 -79.93 -79.88 ... -45.18 -45.11 -45.04\n", "Attributes:\n", " units: 1\n", " long_name: ice area (aggregate)\n", " coordinates: TLON TLAT time\n", " cell_measures: area: tarea\n", " cell_methods: time: mean\n", " time_rep: averaged\n", " ncfiles: ['/g/data/cj50/access-om2/raw-output/access-om2-01/01deg_...\n", " contact: Andrew Kiss\n", " email: andrew.kiss@anu.edu.au\n", " created: 2022-04-27\n", " description: 0.1 degree ACCESS-OM2 global model configuration under in...\n", " notes: Run configuration and history: https://github.com/COSIMA/..." ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Loading area_t to get ocean grid\n", "area_t = cc.querying.getvar('01deg_jra55v140_iaf_cycle4', 'area_t', session, n=1)\n", "\n", "# Apply time correction so data appears in the middle (12:00) of the day rather than at the beginning of the day (00:00)\n", "var_ice['time'] = var_ice.time.to_pandas() - dt.timedelta(hours = 12)\n", "\n", "# Change coordinates so they match ocean dimensions \n", "var_ice.coords['ni'] = area_t['xt_ocean'].values\n", "var_ice.coords['nj'] = area_t['yt_ocean'].values\n", "\n", "# Rename coordinate variables so they match ocean data\n", "var_ice = var_ice.rename(({'ni': 'xt_ocean', 'nj': 'yt_ocean'}))\n", "\n", "# Subsetting data for the Southern Ocean\n", "var_ice = var_ice.sel(yt_ocean = slice(-80, -45))\n", "var_ice" ] }, { "cell_type": "markdown", "id": "f1c80d3d-fcea-4ced-b637-ea9e4bcbe701", "metadata": {}, "source": [ "## Finding sea ice edge\n", "The sea ice edge is the defined as the northernmost areas where sea ice concentration (SIC) is over $10\\%$. This is a multistep process:\n", "\n", "1. Identify cells where $\\text{SIC} >= 0.1$, classifying them with a value of 1;\n", "2. Calculate the cumulative sum along latitude;\n", "3. Apply mask to constrain to only cells with $\\text{SIC} >= 0.1$;\n", "4. Choose the maximum for each longitude.\n", "\n", "We'll demonstrate this process over the first timestep of data." ] }, { "cell_type": "code", "execution_count": 5, "id": "06538e50-5575-4e5a-906e-1604f0ab7275", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "is_ice = var_ice.isel(time=0) >= 0.1\n", "cumulative_ice = is_ice.cumsum(dim='yt_ocean')\n", "ice_edge = (cumulative_ice == cumulative_ice.max('yt_ocean')) * is_ice\n", "\n", "ice_edge.plot()" ] }, { "cell_type": "markdown", "id": "ae74ff21-3795-4e80-ae7c-5d1fb9819bb2", "metadata": {}, "source": [ "### Checking results in relation to SIC data" ] }, { "cell_type": "code", "execution_count": 6, "id": "1ff776bb-82ac-41cb-8b32-8f6cd40ce82b", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "var_ice.where(var_ice >= 0.1).isel(time=0).plot()\n", "ice_edge.plot.contour(colors=[\"red\"])" ] }, { "cell_type": "markdown", "id": "508d16c8-31e8-45a8-9572-7266d84a7caf", "metadata": {}, "source": [ "Above we plotted only cells with SIC greater or equal to 0.1. The red line represents the sea ice edge we identified in the previous step. We can be satisfied that we identified the sea ice edge correctly." ] }, { "cell_type": "markdown", "id": "298b40ce-44e2-491a-9a20-20c5075686ed", "metadata": {}, "source": [ "## Getting coordinate pairs for our entire grid\n", "We will use the latitude and longitude values in our data to create coordinate pairs. We only need to get this information once if we are calculating distances from the same grid. " ] }, { "cell_type": "code", "execution_count": 7, "id": "304d6a4b-11bd-4fd9-ab78-f113014a0fd1", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "array([[ -79.96816911, -279.95 ],\n", " [ -79.96816911, -279.85 ],\n", " [ -79.96816911, -279.75 ],\n", " ...,\n", " [ -45.03607147, 79.75 ],\n", " [ -45.03607147, 79.85 ],\n", " [ -45.03607147, 79.95 ]])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid_x, grid_y = np.meshgrid(\n", " var_ice.xt_ocean.values,\n", " var_ice.yt_ocean.values,\n", ")\n", "\n", "# Changing shape so there are two values per row\n", "grid_coords = np.vstack([grid_y.flat, grid_x.flat]).T\n", "grid_coords" ] }, { "cell_type": "markdown", "id": "83cb7c8c-d921-4cc5-a1b8-b472a420457f", "metadata": {}, "source": [ "## Getting coordinate pairs for sea ice edge\n", "We will find the index for the sea ice edge so we can identify the latitude at which the sea ice edge occurs. This will be combined with all longitude values to create the coordinate pairs for the sea ice edge. \n", " \n", "Note that this step has to be done once for every time step as the sea ice edge changes over time." ] }, { "cell_type": "code", "execution_count": 8, "id": "86cf7d0e-cc59-459e-9ecb-2a1e4f5a0fd7", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "array([[ -61.84325357, -279.95 ],\n", " [ -61.84325357, -279.85 ],\n", " [ -61.84325357, -279.75 ],\n", " ...,\n", " [ -61.60640174, 79.75 ],\n", " [ -61.65391774, 79.85 ],\n", " [ -61.84325357, 79.95 ]])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Getting the indices for cell with maximum value along yt_ocean dimension\n", "ice_yt = ice_edge.yt_ocean[ice_edge.argmax(dim='yt_ocean')]\n", "ice_coords = np.vstack([ice_yt, ice_edge.xt_ocean]).T\n", "ice_coords" ] }, { "cell_type": "markdown", "id": "dace170b-eefc-4377-9bbf-4c6d78fdd500", "metadata": {}, "source": [ "## Using Nearest Neighbours algorithm to calculate distance to closest sea ice edge cell\n", "\n", "We will build a data structure called [BallTree](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.BallTree.html#sklearn.neighbors.BallTree) for our calculations, from the points comprising the sea ice edge. This structure allows us to efficiently query the nearest point within the sea ice edge set to any arbitrary point, according to some kind of metric. Because we're on a sphere, we use the [haversine (great circle) distance](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.haversine_distances.html), which also requires that we transform coordinates from degrees to radians. `scikit-learn` also offers a [KDTree](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KDTree.html) for a similar purpose, but this doesn't support the haversine distance as a metric, so we opt for the ball tree instead.\n", "\n", "The advantage of using a data structure like a ball tree is that we trade off slightly more time and memory to construct the tree for more efficient querying. This makes it feasible to query the closest sea ice edge cell to every point on the grid, which may otherwise have excessive time and/or memory requirements for a brute force approach." ] }, { "cell_type": "code", "execution_count": 9, "id": "09edbc2f-c5a0-48fc-bbdd-2b13da68ce69", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.65 ms, sys: 1.38 ms, total: 3.03 ms\n", "Wall time: 6.81 ms\n" ] } ], "source": [ "%%time\n", "\n", "# First we set up our Ball Tree. Coordinates must be given in radians.\n", "ball_tree = BallTree(np.deg2rad(ice_coords), metric='haversine')" ] }, { "cell_type": "code", "execution_count": 10, "id": "b39e0332-8172-493a-8dbc-c4e716432980", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 23 s, sys: 0 ns, total: 23 s\n", "Wall time: 23 s\n" ] } ], "source": [ "%%time\n", "\n", "# The nearest neighbour calculation will give two outputs: distances and indices\n", "# We only need the distances for now, so ignore the second output\n", "distances_radians, _ = ball_tree.query(np.deg2rad(grid_coords), return_distance=True)" ] }, { "cell_type": "markdown", "id": "7b60acd8-9860-4cf0-a614-1c0724700bd0", "metadata": {}, "source": [ "## Transforming distance from radians to kilometers\n", "\n", "Because the haversine distance operates in terms of radians only, we will need to multiply by the resulting distances by Earth's radius to get distances in radians. We will also reshape the results so it matches our original grid." ] }, { "cell_type": "code", "execution_count": 11, "id": "ee5eb05a-8e86-4423-859f-366a4a4e067e", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray (yt_ocean: 713, xt_ocean: 3600)>\n",
       "array([[ 805.00144028,  803.19226827,  801.38249999, ...,  810.42536522,\n",
       "         808.61799012,  806.81001469],\n",
       "       [ 806.69292563,  804.88000362,  803.06648432, ...,  812.12809432,\n",
       "         810.31697234,  808.50524899],\n",
       "       [ 808.40803346,  806.59143086,  804.77423025, ...,  813.85423949,\n",
       "         812.03943914,  810.22403667],\n",
       "       ...,\n",
       "       [1607.76336769, 1606.55053756, 1605.36467069, ..., 1611.56299844,\n",
       "        1610.26966326, 1609.00309777],\n",
       "       [1615.51041181, 1614.30178279, 1613.12003095, ..., 1619.29692977,\n",
       "        1618.0080497 , 1616.74585538],\n",
       "       [1623.26793377, 1622.06347241, 1620.88580274, ..., 1627.04144284,\n",
       "        1625.75698268, 1624.4991249 ]])\n",
       "Coordinates:\n",
       "  * yt_ocean  (yt_ocean) float64 -79.97 -79.93 -79.88 ... -45.18 -45.11 -45.04\n",
       "  * xt_ocean  (xt_ocean) float64 -279.9 -279.8 -279.7 ... 79.75 79.85 79.95
" ], "text/plain": [ "\n", "array([[ 805.00144028, 803.19226827, 801.38249999, ..., 810.42536522,\n", " 808.61799012, 806.81001469],\n", " [ 806.69292563, 804.88000362, 803.06648432, ..., 812.12809432,\n", " 810.31697234, 808.50524899],\n", " [ 808.40803346, 806.59143086, 804.77423025, ..., 813.85423949,\n", " 812.03943914, 810.22403667],\n", " ...,\n", " [1607.76336769, 1606.55053756, 1605.36467069, ..., 1611.56299844,\n", " 1610.26966326, 1609.00309777],\n", " [1615.51041181, 1614.30178279, 1613.12003095, ..., 1619.29692977,\n", " 1618.0080497 , 1616.74585538],\n", " [1623.26793377, 1622.06347241, 1620.88580274, ..., 1627.04144284,\n", " 1625.75698268, 1624.4991249 ]])\n", "Coordinates:\n", " * yt_ocean (yt_ocean) float64 -79.97 -79.93 -79.88 ... -45.18 -45.11 -45.04\n", " * xt_ocean (xt_ocean) float64 -279.9 -279.8 -279.7 ... 79.75 79.85 79.95" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "distances_km = distances_radians * 6371\n", "\n", "distances_t0 = xr.DataArray(\n", " data=distances_km.reshape(var_ice.yt_ocean.size, -1),\n", " dims=['yt_ocean', 'xt_ocean'],\n", " coords={'yt_ocean': var_ice.yt_ocean, 'xt_ocean': var_ice.xt_ocean}\n", ")\n", "\n", "distances_t0" ] }, { "cell_type": "markdown", "id": "a84eef60-4d30-4510-9a77-d4cd22a972b0", "metadata": {}, "source": [ "## Turning results into data array\n", "We will also apply a mask to our results, so we will only keep values for cells where SIC was greater or equal to 0.1." ] }, { "cell_type": "code", "execution_count": 12, "id": "a1a74720-552f-4dec-85cb-8bb4eea1b895", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "distances_t0.where(is_ice).plot()" ] }, { "cell_type": "markdown", "id": "7f1dad1e-e835-4685-be38-9f0eb36f662f", "metadata": {}, "source": [ "## Calculating results for other time steps\n", "\n", "We can wrap this up into a function to calculate distances for a given timestep. This function returns the ice mask as a second value, so that we can make the plots in the same format as demonstrated so far." ] }, { "cell_type": "code", "execution_count": 13, "id": "f2de3cdc-351c-4b73-8786-57b71c436915", "metadata": { "tags": [] }, "outputs": [], "source": [ "def ice_edge_distances(ice_ds):\n", " x, y = np.meshgrid(np.deg2rad(var_ice.xt_ocean.values),\n", " np.deg2rad(var_ice.yt_ocean.values))\n", " grid_coords = np.vstack([y.flat, x.flat]).T\n", " \n", " is_ice = ice_ds >= 0.1\n", " cumulative_ice = is_ice.cumsum(dim='yt_ocean')\n", " ice_edge = (cumulative_ice == cumulative_ice.max('yt_ocean')) * is_ice\n", " \n", " ice_yt = ice_edge.yt_ocean[ice_edge.argmax(dim='yt_ocean')]\n", " ice_coords = np.vstack([ice_yt, ice_edge.xt_ocean]).T\n", " \n", " ball_tree = BallTree(np.deg2rad(ice_coords), metric='haversine')\n", " distances_radians, _ = ball_tree.query(grid_coords, return_distance=True)\n", " \n", " distances_km = distances_radians * 6371\n", " distances = xr.DataArray(\n", " data=distances_km.reshape(ice_ds.yt_ocean.size, -1),\n", " dims=['yt_ocean', 'xt_ocean'],\n", " coords={'yt_ocean': ice_ds.yt_ocean, 'xt_ocean': ice_ds.xt_ocean},\n", " name='ice_edge_distance'\n", " )\n", " \n", " return distances, is_ice" ] }, { "cell_type": "code", "execution_count": 14, "id": "d14e67c2-d1c8-4ba3-9d75-2aae21d2ebf0", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "distances_t1, mask = ice_edge_distances(var_ice.isel(time=1))\n", "\n", "distances_t1.where(mask).plot()" ] }, { "cell_type": "markdown", "id": "c6442376-de87-4bb3-bc07-4f6e448f8763", "metadata": {}, "source": [ "## Stacking results into one data array" ] }, { "cell_type": "code", "execution_count": 15, "id": "916f236d-6607-4812-a658-64ca364296ac", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray (time: 2, yt_ocean: 713, xt_ocean: 3600)>\n",
       "dask.array<concatenate, shape=(2, 713, 3600), dtype=float64, chunksize=(1, 270, 360), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * yt_ocean  (yt_ocean) float64 -79.97 -79.93 -79.88 ... -45.18 -45.11 -45.04\n",
       "  * xt_ocean  (xt_ocean) float64 -279.9 -279.8 -279.7 ... 79.75 79.85 79.95\n",
       "  * time      (time) datetime64[ns] 1977-12-31T12:00:00 1978-01-31T12:00:00
" ], "text/plain": [ "\n", "dask.array\n", "Coordinates:\n", " * yt_ocean (yt_ocean) float64 -79.97 -79.93 -79.88 ... -45.18 -45.11 -45.04\n", " * xt_ocean (xt_ocean) float64 -279.9 -279.8 -279.7 ... 79.75 79.85 79.95\n", " * time (time) datetime64[ns] 1977-12-31T12:00:00 1978-01-31T12:00:00" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dist_ice = xr.concat([distances_t0.where(is_ice), distances_t1.where(mask)], dim='time')\n", "dist_ice" ] }, { "cell_type": "code", "execution_count": 16, "id": "6b9c254a-dc1c-4d7e-9c78-18bc2099d936", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dist_ice.plot(col='time')" ] }, { "cell_type": "markdown", "id": "d0057e34-7fc6-469c-a850-1b07f2da8239", "metadata": {}, "source": [ "You are done! You should be able to follow the same workflow to calculate distances to any line/point of your interest." ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:analysis3-22.10]", "language": "python", "name": "conda-env-analysis3-22.10-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": "3.9.15" } }, "nbformat": 4, "nbformat_minor": 5 }