[英]How should I modify the JSON metadata so that I can open a ipynb file in a jupyter notebook?
我無法再在 VS-Code jupyter 筆記本中打開 ipynb 文件。 我對這個特定文件有問題，因為我的其他文件打開沒有問題。 我認為問題來自元數據中的錯誤，因為此文件中的元數據與成功打開的其他文件的元數據結構看起來非常不同。
這個文件過去打開時沒有任何故障，但現在當我嘗試打開文件時，我看到以下錯誤：
命令失敗：C:/Users/Tony/anaconda3/Scripts/activate && conda activate base && echo 'e8b39361-0157-4923-80e1-22d70d46dee6' && python c:\\Users\\Tony.vscode\\extensions\\ms-python.python -2020.9.112786\\pythonFiles\\pyvsc-run-isolated.py c:/Users/Tony/.vscode/extensions/ms-python.python-2020.9.112786/pythonFiles/printEnvVariables.py
來源：Python（擴展）
我的理解是VS Code找不到指定的python解釋器，這反過來又會阻止內核被激活。（我不是很有經驗，如果我錯了，請糾正我！）。
我曾嘗試將 python 解釋器從文件中復制粘貼到損壞的文件中，但沒有奏效。
請參閱下面損壞的 JSON 文件的副本以及成功打開的 ipynb 文件的元數據結構示例。
非常感謝這方面的所有幫助！
非常感謝，托尼
損壞的 JSON 文件
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "numpy_random.ipynb",
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "lhmyI7sB_bKJ",
"colab_type": "text"
},
"source": [
"# Generate Random Numbers"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MulyzIdD_kxf",
"colab_type": "text"
},
"source": [
"`numpy.random` is frequently used for generating random numbers.\n",
"\n",
"For more details, please refer to [Random sampling (numpy.random)](https://docs.scipy.org/doc/numpy/reference/routines.random.html)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "vtrQ-n7PAoYJ",
"colab_type": "code",
"colab": {}
},
"source": [
"import numpy as np"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "nLB8lH051pDg",
"colab_type": "text"
},
"source": [
"`np.random.seed` sets the seed for the generator."
]
},
{
"cell_type": "code",
"metadata": {
"id": "PzxXe5pR3QI0",
"colab_type": "code",
"colab": {}
},
"source": [
"np.random.seed(1)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "cSSX-VCBAnuJ",
"colab_type": "text"
},
"source": [
"`np.random.rand` generates numbers uniformly distribution over $[0, 1)$"
]
},
{
"cell_type": "code",
"metadata": {
"id": "ran81EdeAKl6",
"colab_type": "code",
"outputId": "9adb1089-0417-490b-f31e-6730df35d688",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 53
}
},
"source": [
"np.random.rand(2, 3) # Generate 2 * 3 random numbers"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[4.17022005e-01, 7.20324493e-01, 1.14374817e-04],\n",
" [3.02332573e-01, 1.46755891e-01, 9.23385948e-02]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 18
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "g_jgVxcfAi00",
"colab_type": "text"
},
"source": [
"`np.radnom.randn` generates numbers following standard normal distribtion."
]
},
{
"cell_type": "code",
"metadata": {
"id": "OG-suj-PA72b",
"colab_type": "code",
"outputId": "6fda962e-bc37-4f93-9bdf-aa82b5502117",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 53
}
},
"source": [
"np.random.randn(2, 3) # Generate 2 * 3 random numbers"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[-0.52817175, -1.07296862, 0.86540763],\n",
" [-2.3015387 , 1.74481176, -0.7612069 ]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 19
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SLw958olA9Uu",
"colab_type": "text"
},
"source": [
"`np.random.randint(low, high)` generates integers ranging from `low` (inclusive) to `high` (exclusive)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "SkRVwD_yCd7r",
"colab_type": "code",
"outputId": "72c58ee8-4ed7-433e-8a95-60e58293f827",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 71
}
},
"source": [
"np.random.randint(low = 0, high = 4, size = (3, 3))"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[3, 0, 2],\n",
" [0, 1, 2],\n",
" [2, 0, 3]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 20
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MLSvOE4UEA6n",
"colab_type": "text"
},
"source": [
"`np.random.choice` geneates random numbers following a given pmf."
]
},
{
"cell_type": "code",
"metadata": {
"id": "NidDNwzK4FRY",
"colab_type": "code",
"outputId": "13962d5d-09fb-41cb-fe32-e90186c6bfcd",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 71
}
},
"source": [
"a = np.arange(4)\n",
"p = [0.1, 0.2, 0.3, 0.4]\n",
"np.random.choice(a = a, size=(3, 4), p = p)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[2, 2, 3, 3],\n",
" [3, 3, 0, 2],\n",
" [3, 3, 3, 1]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 21
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9Arc1wQq5a2V",
"colab_type": "text"
},
"source": [
"The sample space is not necessary to be number sets."
]
},
{
"cell_type": "code",
"metadata": {
"id": "7JqVclm44hgH",
"colab_type": "code",
"outputId": "fc86099d-f55f-4d9c-f75f-930e91160710",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 71
}
},
"source": [
"a = ['Spade', 'Heart', 'Clud', 'Diamond']\n",
"p = [0.25, 0.25, 0.25, 0.25]\n",
"np.random.choice(a = a, size=(3, 4), p = p)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([['Spade', 'Clud', 'Clud', 'Clud'],\n",
" ['Heart', 'Spade', 'Heart', 'Spade'],\n",
" ['Diamond', 'Heart', 'Spade', 'Clud']], dtype='<U7')"
]
},
"metadata": {
"tags": []
},
"execution_count": 22
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "O7pLtNHP5XbL",
"colab_type": "text"
},
"source": [
"It is also possible to draw random samples from other distributions."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Wa7KH_rW6WaU",
"colab_type": "text"
},
"source": [
"Exponential: $f_X(x; \\beta) = \\frac{1}{\\beta}e^{-\\frac{x}{\\beta}}$, $\\beta$ is the scale parameter."
]
},
{
"cell_type": "code",
"metadata": {
"id": "fstao_qt6Yn7",
"colab_type": "code",
"outputId": "e44f81bf-1d3f-4e52-c19b-969cd1da70e0",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 53
}
},
"source": [
"np.random.exponential(scale = 2, size = (2, 3))"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[2.16136241, 0.70905534, 1.18166682],\n",
" [0.50237771, 0.15238928, 1.26688512]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 23
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cS-2YNh162s8",
"colab_type": "text"
},
"source": [
"Binomial: $P_X(k; n,p) = \\binom{n}{k} p^k (1 - p)^{n - k}$."
]
},
{
"cell_type": "code",
"metadata": {
"id": "QCNVWxTx7Utf",
"colab_type": "code",
"outputId": "31fbcc4b-4710-4c67-9ffc-2921a5ddf7e7",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 71
}
},
"source": [
"n = 10\n",
"p = 0.8\n",
"np.random.binomial(n = n, p = p, size = (3, 3))"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[10, 6, 9],\n",
" [ 8, 10, 6],\n",
" [ 6, 9, 8]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 24
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "e16J3-el71gV",
"colab_type": "text"
},
"source": [
"More distribution types are shown in the docs. "
]
},
{
"cell_type": "code",
"metadata": {
"id": "jkt_2jWu8DBe",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}
成功打開的 JSON 文件的元數據
"metadata": {
"kernelspec": {
"display_name": "Python 3.8.3 64-bit ('base': conda)",
"language": "python",
"name": "python_defaultSpec_1597947257101"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3-final"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
},
"varInspector": {
"cols": {
"lenName": 16,
"lenType": 16,
"lenVar": 40
},
"kernels_config": {
"python": {
"delete_cmd_postfix": "",
"delete_cmd_prefix": "del ",
"library": "var_list.py",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
"delete_cmd_postfix": ") ",
"delete_cmd_prefix": "rm(",
"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"types_to_exclude": [
"module",
"function",
"builtin_function_or_method",
"instance",
"_Feature"
],
"window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
我通過從損壞的文件中刪除元數據解決了這個問題。 這是我刪除的元數據。
"metadata": {
"colab": {
"name": "numpy_random.ipynb",
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
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