{ "cells": [ { "cell_type": "code", "execution_count": 35, "id": "912ed572-1658-406b-976c-cd6de2d4e89e", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " const force = true;\n", " const py_version = '3.6.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", " const reloading = false;\n", " const Bokeh = root.Bokeh;\n", "\n", " // Set a timeout for this load but only if we are not already initializing\n", " if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n", " root._bokeh_timeout = Date.now() + 5000;\n", " root._bokeh_failed_load = false;\n", " }\n", "\n", " function run_callbacks() {\n", " try {\n", " root._bokeh_onload_callbacks.forEach(function(callback) {\n", " if (callback != null)\n", " callback();\n", " });\n", " } finally {\n", " 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load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 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root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n if (root.Bokeh) {\n root.Bokeh = undefined;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n }\n // Give older versions of the 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*/\nfunction handle_add_output(event, handle) {\n var output_area = handle.output_area;\n var output = handle.output;\n if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n return\n }\n var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n if (id !== undefined) {\n var nchildren = toinsert.length;\n var html_node = toinsert[nchildren-1].children[0];\n html_node.innerHTML = output.data[HTML_MIME_TYPE];\n var scripts = [];\n var nodelist = html_node.querySelectorAll(\"script\");\n for (var i in nodelist) {\n if (nodelist.hasOwnProperty(i)) {\n scripts.push(nodelist[i])\n }\n }\n\n scripts.forEach( function (oldScript) {\n var newScript = document.createElement(\"script\");\n var attrs = [];\n var nodemap = oldScript.attributes;\n for (var j in nodemap) {\n if (nodemap.hasOwnProperty(j)) {\n attrs.push(nodemap[j])\n }\n }\n attrs.forEach(function(attr) { 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py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n", " if (!reloading && !bokeh_loaded) {\n", " if (root.Bokeh) {\n", " root.Bokeh = undefined;\n", " }\n", " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " }\n", " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", " }\n", " // Give older versions of the autoload script a head-start to ensure\n", " // they initialize before we start loading newer version.\n", " setTimeout(load_or_wait, 100)\n", "}(window));" ], "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = false;\n const py_version = '3.6.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = true;\n const Bokeh = root.Bokeh;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 && js_modules.length 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document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (let i = 0; i < js_modules.length; i++) {\n const url = js_modules[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n const url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n 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" ], "text/plain": [ "" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "# Cấu hình Daskgateway\n", "cluster, client = notebook_utils.initialize_dask(use_gateway=True, workers=(1, 10))\n", "# Khai báo 1 Datacube là dc\n", "dc = datacube.Datacube()\n", "\n", "# Cấu hình truy cập dịch vụ S3\n", "configure_s3_access(aws_unsigned=False, requester_pays=True, client=client)\n", "\n", "client" ] }, { "cell_type": "code", "execution_count": 37, "id": "fbed4c80-bbf8-4ea8-aa45-2460b2ba04c7", "metadata": { "tags": [] }, "outputs": [], "source": [ "## cấu hình thời gian lấy ảnh và tọa độ\n", "date_range = (\"2022-09-01\", \"2023-10-01\")\n", "longtitude_range = (105.5, 106.4)\n", "latitude_range = (9.2, 10.0)\n", "\n", "coordinates = (longtitude_range, latitude_range)" ] }, { "cell_type": "code", "execution_count": 38, "id": "6b90c49b-0665-4478-a23b-d111ef88eb79", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Most common native CRS: EPSG:32648\n", "No datasets require offset correction\n", "The valid_data_mask and scale (no offset) have been applied to the reflectance bands\n" ] }, { "data": { "text/html": [ "

Dataset size: 111.21 GB

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<xarray.Dataset> Size: 119GB\n",
       "Dimensions:      (time: 151, y: 8874, x: 9902)\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 1kB 2022-09-02T03:35:23.960000 ... 202...\n",
       "  * y            (y) float64 71kB 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
       "  * x            (x) float64 79kB 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n",
       "    spatial_ref  int32 4B 32648\n",
       "Data variables:\n",
       "    red          (time, y, x) float32 53GB dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
       "    nir          (time, y, x) float32 53GB dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
       "    scl          (time, y, x) uint8 13GB dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
       "Attributes:\n",
       "    crs:           EPSG:32648\n",
       "    grid_mapping:  spatial_ref
" ], "text/plain": [ " Size: 119GB\n", "Dimensions: (time: 151, y: 8874, x: 9902)\n", "Coordinates:\n", " * time (time) datetime64[ns] 1kB 2022-09-02T03:35:23.960000 ... 202...\n", " * y (y) float64 71kB 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n", " * x (x) float64 79kB 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n", " spatial_ref int32 4B 32648\n", "Data variables:\n", " red (time, y, x) float32 53GB dask.array\n", " nir (time, y, x) float32 53GB dask.array\n", " scl (time, y, x) uint8 13GB dask.array\n", "Attributes:\n", " crs: EPSG:32648\n", " grid_mapping: spatial_ref" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## truy vấn ảnh vệ tinh sen2\n", "data = load_data(dc, date_range, longtitude_range, latitude_range)\n", "notebook_utils.heading(notebook_utils.xarray_object_size(data))\n", "display(data)" ] }, { "cell_type": "code", "execution_count": 39, "id": "2f6938b6-82e2-4916-bc1d-719c169e25e4", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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bitsvaluesdescription
qa[0, 1, 2, 3, 4, 5, 6, 7]{'0': 'no data', '1': 'saturated or defective'...Sen2Cor Scene Classification
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" ], "text/plain": [ " bits \\\n", "qa [0, 1, 2, 3, 4, 5, 6, 7] \n", "\n", " values \\\n", "qa {'0': 'no data', '1': 'saturated or defective'... \n", "\n", " description \n", "qa Sen2Cor Scene Classification " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "{'0': 'no data',\n", " '1': 'saturated or defective',\n", " '2': 'dark area pixels',\n", " '3': 'cloud shadows',\n", " '4': 'vegetation',\n", " '5': 'bare soils',\n", " '6': 'water',\n", " '7': 'unclassified',\n", " '8': 'cloud medium probability',\n", " '9': 'cloud high probability',\n", " '10': 'thin cirrus',\n", " '11': 'snow or ice'}" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.52 s, sys: 13.9 ms, total: 1.54 s\n", "Wall time: 1.53 s\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cab521703e1b4bb79d0a94193b8017bb", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%time\n", "# Tiến hành loại bỏ các vị trí bị mây ảnh hưởng\n", "result = mask_clean(data)\n", "progress(result)" ] }, { "cell_type": "code", "execution_count": 40, "id": "435f9f78-a9a4-4226-86ca-d4bec42d454e", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'NDVI' (time: 151, y: 8874, x: 9902)> Size: 53GB\n",
       "dask.array<truediv, shape=(151, 8874, 9902), dtype=float32, chunksize=(1, 2048, 2048), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 1kB 2022-09-02T03:35:23.960000 ... 202...\n",
       "  * y            (y) float64 71kB 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
       "  * x            (x) float64 79kB 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n",
       "    spatial_ref  int32 4B 32648
" ], "text/plain": [ " Size: 53GB\n", "dask.array\n", "Coordinates:\n", " * time (time) datetime64[ns] 1kB 2022-09-02T03:35:23.960000 ... 202...\n", " * y (y) float64 71kB 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n", " * x (x) float64 79kB 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n", " spatial_ref int32 4B 32648" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Tiến hành tính toán NDVI\n", "ds1 = calculate_indices(result, index=\"NDVI\", satellite_mission=\"s2\")\n", "ndvi = ds1[\"NDVI\"]\n", "display(ndvi)" ] }, { "cell_type": "code", "execution_count": null, "id": "84992d28-8e3f-468e-be08-ded511f2c662", "metadata": { "tags": [] }, "outputs": [], "source": [ "## Hiển thị ảnh NDVI chưa điền các giá trị mây (chưa fill nan)\n", "plt.imshow(ndvi.isel(time=6))" ] }, { "cell_type": "code", "execution_count": null, "id": "72318b60-532a-4f08-a5f7-94762d08a42c", "metadata": { "tags": [] }, "outputs": [], "source": [ "# Thiết lập giá trị trung bình mùa vụ để xử lý các điểm ảnh bị mây dựa vào sự thay đổi theo mùa\n", "time_split = [\n", " slice(\"2022-09-01\", \"2023-01-01\"),\n", " slice(\"2023-01-01\", \"2023-05-01\"),\n", " slice(\"2023-05-01\", \"2023-07-01\"),\n", " slice(\"2023-07-01\", \"2023-10-01\"),\n", "]\n", "\n", "# Điền mây ở các vị trí mang giá trị nan (fill nan)\n", "fill_nan_ndvi = fill_nan(ndvi, time_split)\n", "\n", "# In kết quả ảnh NDVI đã điền mây (đã fill nan)\n", "plt.imshow(fill_nan_ndvi.isel(time=6))" ] }, { "cell_type": "code", "execution_count": null, "id": "375b1cfb-37f5-49fe-8061-ea32eb47f9f6", "metadata": { "tags": [] }, "outputs": [], "source": [ "%%time\n", "## tính ndvi theo tháng\n", "average_ndvi = fill_nan_ndvi.resample(time=\"1M\").mean().persist()\n", "progress(average_ndvi)\n", "\n", "# compute average_ndvi\n", "average_ndvi = average_ndvi.compute()" ] }, { "cell_type": "code", "execution_count": null, "id": "187cc640-aef9-476b-91fc-b63f4d3ff2e3", "metadata": { "tags": [] }, "outputs": [], "source": [ "#Load dữ liệu ảnh Sentinel 1\n", "dsvh, dsvv = load_data_sen1(dc, date_range, coordinates)\n", "average_vv = calculate_average(dsvv, time_pattern='1M')\n", "average_vh = calculate_average(dsvh, time_pattern='1M')" ] }, { "cell_type": "code", "execution_count": null, "id": "d2585562-88aa-4c7d-bf70-1f6affcf65d4", "metadata": { "tags": [] }, "outputs": [], "source": [ "## cấu hình bộ dữ liệu điểm huấn luyện mô hình (train file)\n", "train_path = \"train/ST_training data_updated_1130points_new.shp\" # đường dẫn shp file train\n", "\n", "## load dữ liệu điểm huấn luyện mô hình (train file)\n", "train = load_train_data(train_path)\n", "train.head()\n", "\n", "# cấu hình nhãn dữ liệu \n", "label_mapping = {\n", " \"Lua tom\": \"0\",\n", " \"Lua\": \"1\",\n", " \"CHN\": \"2\",\n", " \"CLN\": \"3\",\n", " \"TS\": \"4\",\n", " \"Song\": \"5\",\n", " \"Dat xay dung\": \"6\",\n", " \"Rung\": \"7\",\n", "}\n", "\n", "# xây dựng tập dữ liệu (dataset) chứa dữ liệu VH, VV, NDVI\n", "datasets = get_data_sen1_and_sen2(train, average_ndvi, average_vh, average_vv)\n", "\n", "# chia tập dữ liệu thành các phần theo tỉ lệ 80(80-20)-20 tương ứng với tập train, validate, test\n", "X_train, X_val, X_test, y_train, y_val, y_test = split_train_data(\n", " train, label_mapping, datasets\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "2e955884-d4af-422d-a8e6-d436199540e0", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [], "source": [ "# Import CNN model\n", "from cnn_model import CNNTrainer, reshape_for_cnn\n", "import torch\n", "import numpy as np\n", "\n", "# --- SỬA LỖI LOGIC CHUYỂN ĐỔI ---\n", "# Kiểm tra sự tồn tại của '.values' (dành cho Pandas) trước\n", "# Nếu không, np.array() sẽ xử lý tốt cả list và numpy.ndarray\n", "print(\"🔄 Chuyển đổi dữ liệu sang NumPy...\")\n", "X_train_np = X_train.values if hasattr(X_train, 'values') else np.array(X_train)\n", "X_val_np = X_val.values if hasattr(X_val, 'values') else np.array(X_val)\n", "X_test_np = X_test.values if hasattr(X_test, 'values') else np.array(X_test)\n", "y_train_np = y_train.values if hasattr(y_train, 'values') else np.array(y_train)\n", "y_val_np = y_val.values if hasattr(y_val, 'values') else np.array(y_val)\n", "y_test_np = y_test.values if hasattr(y_test, 'values') else np.array(y_test)\n", "print(\"✅ Chuyển đổi hoàn tất.\\n\")\n", "# -------------------------------\n", "\n", "# Reshape data for CNN (n_samples, 39) -> (n_samples, 3, 13)\n", "print(\"🔄 Reshaping data for CNN...\")\n", "X_train_cnn = reshape_for_cnn(X_train_np)\n", "X_val_cnn = reshape_for_cnn(X_val_np)\n", "X_test_cnn = reshape_for_cnn(X_test_np)\n", "\n", "print(f\" Train shape: {X_train_cnn.shape}\")\n", "print(f\" Val shape: {X_val_cnn.shape}\")\n", "print(f\" Test shape: {X_test_cnn.shape}\\n\")\n", "\n", "# Normalize data (important for neural networks)\n", "from sklearn.preprocessing import StandardScaler\n", "scaler = StandardScaler()\n", "X_train_flat = X_train_cnn.reshape(X_train_cnn.shape[0], -1)\n", "X_val_flat = X_val_cnn.reshape(X_val_cnn.shape[0], -1)\n", "X_test_flat = X_test_cnn.reshape(X_test_cnn.shape[0], -1)\n", "\n", "scaler.fit(X_train_flat)\n", "X_train_flat = scaler.transform(X_train_flat)\n", "X_val_flat = scaler.transform(X_val_flat)\n", "X_test_flat = scaler.transform(X_test_flat)\n", "\n", "X_train_cnn = X_train_flat.reshape(X_train_cnn.shape)\n", "X_val_cnn = X_val_flat.reshape(X_val_cnn.shape)\n", "X_test_cnn = X_test_flat.reshape(X_test_cnn.shape)\n", "\n", "print(\"✅ Data normalized\\n\")\n", "\n", "# Initialize trainer\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "print(f\"🚀 Using device: {device}\\n\")\n", "\n", "trainer = CNNTrainer(num_classes=8, learning_rate=0.001, device=device)\n", "\n", "# Train model\n", "trainer.fit(\n", " X_train_cnn, y_train_np,\n", " X_val_cnn, y_val_np,\n", " epochs=50,\n", " batch_size=32,\n", " verbose=True\n", ")\n", "\n", "# Plot training history\n", "trainer.plot_history()" ] }, { "cell_type": "code", "execution_count": null, "id": "b2a1e42c-cf1b-4d82-a6af-b06e3496918f", "metadata": { "tags": [] }, "outputs": [], "source": [ "%%time\n", "# Evaluate on test data\n", "print(\"📊 Evaluating CNN model on test set...\\n\")\n", "\n", "# --- SỬA LỖI ---\n", "# Sử dụng y_test_np (đã được tạo ở cell trước) thay vì y_test.values\n", "results = trainer.evaluate(X_test_cnn, y_test_np)\n", "# ---------------\n", "\n", "# Plot confusion matrix\n", "from sklearn.metrics import ConfusionMatrixDisplay\n", "import matplotlib.pyplot as plt\n", "\n", "# (Đảm bảo 'label_mapping' đã được định nghĩa ở các cell trên)\n", "class_names = list(label_mapping.keys()) \n", "disp = ConfusionMatrixDisplay(confusion_matrix=results['confusion_matrix'], \n", " display_labels=class_names)\n", "\n", "fig, ax = plt.subplots(figsize=(10, 8)) # Tạo fig và ax\n", "disp.plot(ax=ax, cmap='Blues') # Vẽ vào ax đã tạo\n", "\n", "plt.xticks(rotation=45, ha='right')\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "f1a14379-ed6e-4897-9ca4-2669743fab40", "metadata": { "tags": [] }, "outputs": [], "source": [ "# Lưu mô hình huấn luyện\n", "import json\n", "\n", "# Save CNN model\n", "model_path = \"model_cnn_pytorch.pth\"\n", "trainer.save(model_path)\n", "\n", "# Save training info\n", "info = {\n", " \"model_type\": \"PyTorch 1D CNN\",\n", " \"num_classes\": 8,\n", " \"classes\": list(label_mapping.keys()),\n", " \"input_shape\": [3, 13], # 3 channels, 13 timesteps\n", " \"accuracy\": float(results['accuracy']),\n", " \"precision\": float(results['precision']),\n", " \"recall\": float(results['recall']),\n", " \"f1_score\": float(results['f1']),\n", "}\n", "\n", "with open(\"model_info.json\", \"w\") as f:\n", " json.dump(info, f, indent=2)\n", "\n", "print(f\"✅ Model saved to {model_path}\")\n", "print(f\"✅ Model info saved to model_info.json\")" ] }, { "cell_type": "code", "execution_count": null, "id": "33dd516d-9824-499e-96b9-5cd9224c194c", "metadata": { "tags": [] }, "outputs": [], "source": [ "# đóng client, cluster\n", "client.close()\n", "cluster.close()" ] } ], "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": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 }