test/guide/Dataset_nlfff_and_pynlfff_base_use_en.ipynb

{
  "cells": [
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "yeEPX5kNASY2"
      },
      "source": [
        "# Summary\n",
        "\n",
        "This note introduces the NLFFF dataset and the basic use of the pynlfff toolkit. \n",
        "\n",
        "\n",
        "## Description \n",
        "\n",
        "The toolkit is divided into two parts: dataset construction and product usage. \n",
        "\n",
        "\n",
        "### Dataset construction \n",
        "\n",
        "+ There is a cpu version of dataset builder, you can find the dataset builder related program \n",
        " at [pynlfff github](https://github.com/deepsolar/pynlfff/tree/master/pynlfff)\n",
        "\n",
        "\n",
        "\n",
        "### 3D magnetic field data products using \n",
        "\n",
        "+ Product usage is divided into two parts: basic usage and toolkit-based usage. \n",
        "\n",
        " + We have distilled the basic usage in the toolkit to help researchers who are not comfortable downloading the full toolkit and who wish to borrow ideas to implement it through other languages. \n",
        "\n",
        " + We have provided some slightly more advanced features based on the use of the toolkit, such as visualization, etc. More features will be added later. \n",
        "\n",
        "\n",
        "> notebook can be [click here to download](https://github.com/deepsolar/pynlfff/blob/main/test/guide/Dataset_nlfff_and_pynlfff_base_use_en.ipynb), or [click here to open it in colab](https://colab.research.google.com/drive/1KNS2lf-kUAYd3QDhd4Y3lOnaE8UBKUHf?usp=sharing), and the html version can be viewed [here](https://nlfff.dataset.deepsolar.space/en/guide/Dataset_nlfff_and_pynlfff_base_use_en.html)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lvg2Icp3Ex1F"
      },
      "source": [
        "# Data sets and labels\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DjiI4Yk5B0Qt"
      },
      "source": [
        "## Download dataset summary information"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "R63KoCB8Ab2F"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2023-02-27 17:40:58--  https://figshare.com/ndownloader/files/39406012\n",
            "正在解析主机 figshare.com (figshare.com)... 54.217.34.18, 34.252.222.205, 2a05:d018:1f4:d000:647c:7301:fad1:b2b9, ...\n",
            "正在连接 figshare.com (figshare.com)|54.217.34.18|:443... 已连接。\n",
            "已发出 HTTP 请求，正在等待回应... 302 Found\n",
            "位置：https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/39406012/nlfffdatasetinfo.zip?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIYCQYOYV5JSSROOA/20230227/eu-west-1/s3/aws4_request&X-Amz-Date=20230227T094059Z&X-Amz-Expires=10&X-Amz-SignedHeaders=host&X-Amz-Signature=a3b7af9f8a0727b36a17a2762fd496a814e6f918790fadbe56457592f125c600 [跟随至新的 URL]\n",
            "--2023-02-27 17:40:59--  https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/39406012/nlfffdatasetinfo.zip?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIYCQYOYV5JSSROOA/20230227/eu-west-1/s3/aws4_request&X-Amz-Date=20230227T094059Z&X-Amz-Expires=10&X-Amz-SignedHeaders=host&X-Amz-Signature=a3b7af9f8a0727b36a17a2762fd496a814e6f918790fadbe56457592f125c600\n",
            "正在解析主机 s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)... 52.218.88.91, 52.218.90.131, 52.218.49.36, ...\n",
            "正在连接 s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)|52.218.88.91|:443... 已连接。\n",
            "已发出 HTTP 请求，正在等待回应... 200 OK\n",
            "长度：166327650 (159M) [application/zip]\n",
            "正在保存至: “dataset_info.zip”\n",
            "\n",
            "dataset_info.zip    100%[===================>] 158.62M  2.77MB/s  用时 55s       \n",
            "\n",
            "2023-02-27 17:41:56 (2.86 MB/s) - 已保存 “dataset_info.zip” [166327650/166327650])\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Download dataset summary information\n",
        "!wget https://figshare.com/ndownloader/files/39406012 -O dataset_info.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "pz6G_nMLAxBJ"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Archive:  dataset_info.zip\n",
            "   creating: nlfff-dataset-info/\n",
            "  inflating: nlfff-dataset-info/README.txt  \n",
            "   creating: nlfff-dataset-info/archive-202203/\n",
            "  inflating: nlfff-dataset-info/archive-202203/archive-202203-info.csv  \n",
            "  inflating: nlfff-dataset-info/changelog.txt  \n",
            "  inflating: nlfff-dataset-info/config.json  \n",
            "   creating: nlfff-dataset-info/demo/\n",
            "   creating: nlfff-dataset-info/demo/hmi.sharp_cea_720s.7327.20181220_093600_TAI/\n",
            "  inflating: nlfff-dataset-info/demo/hmi.sharp_cea_720s.7327.20181220_093600_TAI/Bout.bin  \n",
            "  inflating: nlfff-dataset-info/demo/hmi.sharp_cea_720s.7327.20181220_093600_TAI/Energy.log  \n",
            "  inflating: nlfff-dataset-info/demo/hmi.sharp_cea_720s.7327.20181220_093600_TAI/NLFFFquality1.log  \n",
            "  inflating: nlfff-dataset-info/demo/hmi.sharp_cea_720s.7327.20181220_093600_TAI/NLFFFquality2.log  \n",
            "  inflating: nlfff-dataset-info/demo/hmi.sharp_cea_720s.7327.20181220_093600_TAI/NLFFFquality3.log  \n",
            "  inflating: nlfff-dataset-info/demo/hmi.sharp_cea_720s.7327.20181220_093600_TAI/allboundaries1.dat  \n",
            "  inflating: nlfff-dataset-info/demo/hmi.sharp_cea_720s.7327.20181220_093600_TAI/allboundaries2.dat  \n",
            "  inflating: nlfff-dataset-info/demo/hmi.sharp_cea_720s.7327.20181220_093600_TAI/allboundaries3.dat  \n",
            " extracting: nlfff-dataset-info/demo/hmi.sharp_cea_720s.7327.20181220_093600_TAI/boundary.ini  \n",
            " extracting: nlfff-dataset-info/demo/hmi.sharp_cea_720s.7327.20181220_093600_TAI/grid1.ini  \n",
            " extracting: nlfff-dataset-info/demo/hmi.sharp_cea_720s.7327.20181220_093600_TAI/grid2.ini  \n",
            "  inflating: nlfff-dataset-info/demo/hmi.sharp_cea_720s.7327.20181220_093600_TAI/grid2n2.ini  \n",
            " extracting: nlfff-dataset-info/demo/hmi.sharp_cea_720s.7327.20181220_093600_TAI/grid3.ini  \n",
            "  inflating: nlfff-dataset-info/demo/hmi.sharp_cea_720s.7327.20181220_093600_TAI/mask1.dat  \n",
            "  inflating: nlfff-dataset-info/demo/hmi.sharp_cea_720s.7327.20181220_093600_TAI/mask2.dat  \n",
            "  inflating: nlfff-dataset-info/demo/hmi.sharp_cea_720s.7327.20181220_093600_TAI/mask3.dat  \n",
            "  inflating: nlfff-dataset-info/demo/hmi.sharp_cea_720s.7327.20181220_093600_TAI/prot1.log  \n",
            "  inflating: nlfff-dataset-info/demo/hmi.sharp_cea_720s.7327.20181220_093600_TAI/prot2.log  \n",
            "  inflating: nlfff-dataset-info/demo/hmi.sharp_cea_720s.7327.20181220_093600_TAI/prot3.log  \n",
            "   creating: nlfff-dataset-info/demo/hmi.sharp_cea_720s.7327.20181220_093600_TAI/raw/\n",
            "  inflating: nlfff-dataset-info/demo/hmi.sharp_cea_720s.7327.20181220_093600_TAI/raw/hmi.sharp_cea_720s.7327.20181220_093600_TAI.Bp.fits  \n",
            "  inflating: nlfff-dataset-info/demo/hmi.sharp_cea_720s.7327.20181220_093600_TAI/raw/hmi.sharp_cea_720s.7327.20181220_093600_TAI.Br.fits  \n",
            "  inflating: nlfff-dataset-info/demo/hmi.sharp_cea_720s.7327.20181220_093600_TAI/raw/hmi.sharp_cea_720s.7327.20181220_093600_TAI.Bt.fits  \n",
            "  inflating: nlfff-dataset-info/demo/hmi.sharp_cea_720s.7327.20181220_093600_TAI/run.log  \n",
            " extracting: nlfff-dataset-info/demo/hmi.sharp_cea_720s.7327.20181220_093600_TAI/step1.log  \n",
            " extracting: nlfff-dataset-info/demo/hmi.sharp_cea_720s.7327.20181220_093600_TAI/step2.log  \n",
            " extracting: nlfff-dataset-info/demo/hmi.sharp_cea_720s.7327.20181220_093600_TAI/step3.log  \n",
            "  inflating: nlfff-dataset-info/demo/hmi.sharp_cea_720s.7327.20181220_093600_TAI/zboundaries2n2.dat  \n",
            "  inflating: nlfff-dataset-info/python_guide_en.ipynb  \n",
            "  inflating: nlfff-dataset-info/python_guide_zh.ipynb  \n",
            "   creating: nlfff-dataset-info/raw-202203/\n",
            "  inflating: nlfff-dataset-info/raw-202203/raw-202203-info.csv  \n"
          ]
        }
      ],
      "source": [
        "# Unpacking Dataset Summary Information\n",
        "!unzip dataset_info.zip"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "ERn5qyWfBTMn"
      },
      "source": [
        "## Download label Information"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "Q7V-haYVBQ8l"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2023-02-27 17:41:59--  https://figshare.com/ndownloader/files/39406015\n",
            "正在解析主机 figshare.com (figshare.com)... 34.252.222.205, 54.217.34.18, 2a05:d018:1f4:d000:647c:7301:fad1:b2b9, ...\n",
            "正在连接 figshare.com (figshare.com)|34.252.222.205|:443... 已连接。\n",
            "已发出 HTTP 请求，正在等待回应... 302 Found\n",
            "位置：https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/39406015/nlfffdatasetflarelabel.zip?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIYCQYOYV5JSSROOA/20230227/eu-west-1/s3/aws4_request&X-Amz-Date=20230227T094200Z&X-Amz-Expires=10&X-Amz-SignedHeaders=host&X-Amz-Signature=2c5d5e692918327678bb12355682195f18fbaaa46291490820674841e3f8f242 [跟随至新的 URL]\n",
            "--2023-02-27 17:42:00--  https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/39406015/nlfffdatasetflarelabel.zip?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIYCQYOYV5JSSROOA/20230227/eu-west-1/s3/aws4_request&X-Amz-Date=20230227T094200Z&X-Amz-Expires=10&X-Amz-SignedHeaders=host&X-Amz-Signature=2c5d5e692918327678bb12355682195f18fbaaa46291490820674841e3f8f242\n",
            "正在解析主机 s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)... 52.218.53.234, 52.92.34.48, 52.92.0.56, ...\n",
            "正在连接 s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)|52.218.53.234|:443... 已连接。\n",
            "已发出 HTTP 请求，正在等待回应... 200 OK\n",
            "长度：3249636 (3.1M) [application/zip]\n",
            "正在保存至: “dataset_label.zip”\n",
            "\n",
            "dataset_label.zip   100%[===================>]   3.10M  1.19MB/s  用时 2.6s      \n",
            "\n",
            "2023-02-27 17:42:04 (1.19 MB/s) - 已保存 “dataset_label.zip” [3249636/3249636])\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget https://figshare.com/ndownloader/files/39406015 -O dataset_label.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "uFbfiqoTBksw"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Archive:  dataset_label.zip\n",
            "   creating: nlfff-dataset-flare-label/\n",
            "  inflating: nlfff-dataset-flare-label/README.txt  \n",
            "  inflating: nlfff-dataset-flare-label/changelog.txt  \n",
            "  inflating: nlfff-dataset-flare-label/config.json  \n",
            "   creating: nlfff-dataset-flare-label/label-202203/\n",
            "   creating: nlfff-dataset-flare-label/label-202203/prepare/\n",
            "  inflating: nlfff-dataset-flare-label/label-202203/prepare/knoaa_vflaretimelist.pickle  \n",
            "  inflating: nlfff-dataset-flare-label/label-202203/prepare/ksharp_vnoaa.pickle  \n",
            "   creating: nlfff-dataset-flare-label/label-202203/process/\n",
            "  inflating: nlfff-dataset-flare-label/label-202203/process/label.csv  \n",
            "  inflating: nlfff-dataset-flare-label/label-202203/process/label_col_description.jpg  \n",
            "   creating: nlfff-dataset-flare-label/label-202203/process/label_failed/\n",
            "  inflating: nlfff-dataset-flare-label/label-202203/process/label_failed/no_flare_info_from_noaa_ars.txt  \n",
            "  inflating: nlfff-dataset-flare-label/label-202203/process/label_failed/no_noaalist_from_sharpnum.txt  \n",
            "   creating: nlfff-dataset-flare-label/label-202203/raw_file/\n",
            "  inflating: nlfff-dataset-flare-label/label-202203/raw_file/all_harps_with_noaa_ars.txt  \n",
            "  inflating: nlfff-dataset-flare-label/label-202203/raw_file/flare_info_processed.csv  \n",
            "  inflating: nlfff-dataset-flare-label/label-202203/raw_file/flare_info_processed_col_description.jpg  \n",
            "  inflating: nlfff-dataset-flare-label/python_guide_en.ipynb  \n",
            "  inflating: nlfff-dataset-flare-label/python_guide_zh.ipynb  \n"
          ]
        }
      ],
      "source": [
        "!unzip dataset_label.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e7hJOdl8Brhc"
      },
      "source": [
        "## Download database archive information"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "ZjJQW8aHB6Gb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2023-02-27 17:42:05--  https://figshare.com/ndownloader/files/39406063\n",
            "正在解析主机 figshare.com (figshare.com)... 34.252.222.205, 54.217.34.18, 2a05:d018:1f4:d000:647c:7301:fad1:b2b9, ...\n",
            "正在连接 figshare.com (figshare.com)|34.252.222.205|:443... 已连接。\n",
            "已发出 HTTP 请求，正在等待回应... 302 Found\n",
            "位置：https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/39406063/nlfffdatasetandflarelabeldbarchive.zip?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIYCQYOYV5JSSROOA/20230227/eu-west-1/s3/aws4_request&X-Amz-Date=20230227T094206Z&X-Amz-Expires=10&X-Amz-SignedHeaders=host&X-Amz-Signature=d5f26997359e1d43f48ddb1487f125deba286eb72fba3b7d6d1c70e5c4ad0740 [跟随至新的 URL]\n",
            "--2023-02-27 17:42:06--  https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/39406063/nlfffdatasetandflarelabeldbarchive.zip?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIYCQYOYV5JSSROOA/20230227/eu-west-1/s3/aws4_request&X-Amz-Date=20230227T094206Z&X-Amz-Expires=10&X-Amz-SignedHeaders=host&X-Amz-Signature=d5f26997359e1d43f48ddb1487f125deba286eb72fba3b7d6d1c70e5c4ad0740\n",
            "正在解析主机 s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)... 52.218.53.234, 52.218.57.243, 52.92.34.216, ...\n",
            "正在连接 s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)|52.218.53.234|:443... 已连接。\n",
            "已发出 HTTP 请求，正在等待回应... 200 OK\n",
            "长度：85650805 (82M) [application/zip]\n",
            "正在保存至: “dataset_archive.zip”\n",
            "\n",
            "dataset_archive.zip 100%[===================>]  81.68M  2.13MB/s  用时 40s       \n",
            "\n",
            "2023-02-27 17:42:47 (2.06 MB/s) - 已保存 “dataset_archive.zip” [85650805/85650805])\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget https://figshare.com/ndownloader/files/39406063 -O dataset_archive.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "0N8WIdnxCHQm"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Archive:  dataset_archive.zip\n",
            "   creating: nlfff-dataset-and-flare-label-db-archive/\n",
            "  inflating: nlfff-dataset-and-flare-label-db-archive/README.txt  \n",
            "  inflating: nlfff-dataset-and-flare-label-db-archive/changelog.txt  \n",
            "  inflating: nlfff-dataset-and-flare-label-db-archive/config.json  \n",
            "  inflating: nlfff-dataset-and-flare-label-db-archive/python_guide_en.ipynb  \n",
            "  inflating: nlfff-dataset-and-flare-label-db-archive/python_guide_zh.ipynb  \n",
            "   creating: nlfff-dataset-and-flare-label-db-archive/sqlite-202203/\n",
            "  inflating: nlfff-dataset-and-flare-label-db-archive/sqlite-202203/nlfff-202203  \n"
          ]
        }
      ],
      "source": [
        "!unzip dataset_archive.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "Nz347BL-CiT_"
      },
      "outputs": [],
      "source": [
        "nlfff_path_dataset_summary=\"nlfff-dataset-info\"\n",
        "nlfff_path_dataset_label=\"nlfff-dataset-flare-label\"\n",
        "nlfff_path_dataset_archive=\"nlfff-dataset-and-flare-label-db-archive\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R1SDySEEDCO-"
      },
      "source": [
        "# Using dataset summary information"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s7b129O8D3nC"
      },
      "source": [
        "## Read configuration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "InblwoBjDLmg"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import json\n",
        "\n",
        "# Read configuration file\n",
        "nlfff_path_dataset_summary=\"nlfff-dataset-info\"\n",
        "config_path=os.path.join(nlfff_path_dataset_summary,\"config.json\")\n",
        "with open(config_path,\"r\") as f:\n",
        "    config_ds=json.loads(f.read())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "w2vAdW7fDwY_"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'config_version': 1.3,\n",
              " 'raw_summary_info_path': 'raw-202203/raw-202203-info.csv',\n",
              " 'product_summary_info_path': 'archive-202203/archive-202203-info.csv',\n",
              " 'demo_sample': [{'harpnum_trec': '7327.20181220_093600_TAI',\n",
              "   'path': 'demo/hmi.sharp_cea_720s.7327.20181220_093600_TAI'}]}"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "config_ds"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1DF31p-nD7aM"
      },
      "source": [
        "## Read raw header file information"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "XA0Q9MFBD1mg"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/home/zander/anaconda3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3444: DtypeWarning: Columns (145,254) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
          ]
        }
      ],
      "source": [
        "import pandas\n",
        "import os\n",
        "\n",
        "raw_info_path=os.path.join(\n",
        "  nlfff_path_dataset_summary,\n",
        "  config_ds[\"raw_summary_info_path\"]\n",
        ")\n",
        "raw_info=pandas.read_csv(raw_info_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "9IT1qbzvEUF_"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>harpnum_trec</th>\n",
              "      <th>HARP_NUM</th>\n",
              "      <th>T_REC_DATETIME</th>\n",
              "      <th>T_REC_STR</th>\n",
              "      <th>FILE_NAME</th>\n",
              "      <th>query</th>\n",
              "      <th>DATE</th>\n",
              "      <th>DATE_S</th>\n",
              "      <th>DATE_B</th>\n",
              "      <th>DATE__OBS</th>\n",
              "      <th>...</th>\n",
              "      <th>continuum</th>\n",
              "      <th>Bp</th>\n",
              "      <th>Bt</th>\n",
              "      <th>Br</th>\n",
              "      <th>Bp_err</th>\n",
              "      <th>Bt_err</th>\n",
              "      <th>Br_err</th>\n",
              "      <th>conf_disambig</th>\n",
              "      <th>BHARP</th>\n",
              "      <th>MHARP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1644.20120516_031200_TAI</td>\n",
              "      <td>1644</td>\n",
              "      <td>2012-05-16 03:12:00.000</td>\n",
              "      <td>20120516_031200_TAI</td>\n",
              "      <td>hmi.sharp_cea_720s.1644.20120516_031200_TAI</td>\n",
              "      <td>hmi.sharp_cea_720s[1644][2012.05.16_03:12:00_TAI]</td>\n",
              "      <td>2018-05-09T03:05:47Z</td>\n",
              "      <td>2012-05-20T19:22:46Z</td>\n",
              "      <td>2015-09-30T20:47:04Z</td>\n",
              "      <td>2012-05-16T03:10:24.10Z</td>\n",
              "      <td>...</td>\n",
              "      <td>/SUM90/D1042885471/S00000/continuum.fits</td>\n",
              "      <td>/SUM90/D1042885471/S00000/Bp.fits</td>\n",
              "      <td>/SUM90/D1042885471/S00000/Bt.fits</td>\n",
              "      <td>/SUM90/D1042885471/S00000/Br.fits</td>\n",
              "      <td>/SUM90/D1042885471/S00000/Bp_err.fits</td>\n",
              "      <td>/SUM90/D1042885471/S00000/Bt_err.fits</td>\n",
              "      <td>/SUM90/D1042885471/S00000/Br_err.fits</td>\n",
              "      <td>/SUM90/D1042885471/S00000/conf_disambig.fits</td>\n",
              "      <td>InvalidLink</td>\n",
              "      <td>hmi.Mharp_720s[1644][2012.05.16_03:12:00_TAI]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4315.20140708_013600_TAI</td>\n",
              "      <td>4315</td>\n",
              "      <td>2014-07-08 01:36:00.000</td>\n",
              "      <td>20140708_013600_TAI</td>\n",
              "      <td>hmi.sharp_cea_720s.4315.20140708_013600_TAI</td>\n",
              "      <td>hmi.sharp_cea_720s[4315][2014.07.08_01:36:00_TAI]</td>\n",
              "      <td>2014-08-13T03:40:53Z</td>\n",
              "      <td>2014-07-12T23:18:52Z</td>\n",
              "      <td>2014-07-16T08:03:00Z</td>\n",
              "      <td>2014-07-08T01:34:25.80Z</td>\n",
              "      <td>...</td>\n",
              "      <td>/SUM22/D597857266/S00000/continuum.fits</td>\n",
              "      <td>/SUM22/D597857266/S00000/Bp.fits</td>\n",
              "      <td>/SUM22/D597857266/S00000/Bt.fits</td>\n",
              "      <td>/SUM22/D597857266/S00000/Br.fits</td>\n",
              "      <td>/SUM22/D597857266/S00000/Bp_err.fits</td>\n",
              "      <td>/SUM22/D597857266/S00000/Bt_err.fits</td>\n",
              "      <td>/SUM22/D597857266/S00000/Br_err.fits</td>\n",
              "      <td>/SUM22/D597857266/S00000/conf_disambig.fits</td>\n",
              "      <td>InvalidLink</td>\n",
              "      <td>hmi.Mharp_720s[4315][2014.07.08_01:36:00_TAI]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4351.20140714_142400_TAI</td>\n",
              "      <td>4351</td>\n",
              "      <td>2014-07-14 14:24:00.000</td>\n",
              "      <td>20140714_142400_TAI</td>\n",
              "      <td>hmi.sharp_cea_720s.4351.20140714_142400_TAI</td>\n",
              "      <td>hmi.sharp_cea_720s[4351][2014.07.14_14:24:00_TAI]</td>\n",
              "      <td>2014-08-19T03:03:14Z</td>\n",
              "      <td>2014-07-18T23:26:26Z</td>\n",
              "      <td>2014-07-22T00:47:31Z</td>\n",
              "      <td>2014-07-14T14:22:25.70Z</td>\n",
              "      <td>...</td>\n",
              "      <td>/SUM1/D599938236/S00000/continuum.fits</td>\n",
              "      <td>/SUM1/D599938236/S00000/Bp.fits</td>\n",
              "      <td>/SUM1/D599938236/S00000/Bt.fits</td>\n",
              "      <td>/SUM1/D599938236/S00000/Br.fits</td>\n",
              "      <td>/SUM1/D599938236/S00000/Bp_err.fits</td>\n",
              "      <td>/SUM1/D599938236/S00000/Bt_err.fits</td>\n",
              "      <td>/SUM1/D599938236/S00000/Br_err.fits</td>\n",
              "      <td>/SUM1/D599938236/S00000/conf_disambig.fits</td>\n",
              "      <td>InvalidLink</td>\n",
              "      <td>hmi.Mharp_720s[4351][2014.07.14_14:24:00_TAI]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>536.20110426_031200_TAI</td>\n",
              "      <td>536</td>\n",
              "      <td>2011-04-26 03:12:00.000</td>\n",
              "      <td>20110426_031200_TAI</td>\n",
              "      <td>hmi.sharp_cea_720s.536.20110426_031200_TAI</td>\n",
              "      <td>hmi.sharp_cea_720s[536][2011.04.26_03:12:00_TAI]</td>\n",
              "      <td>2018-04-29T14:50:56Z</td>\n",
              "      <td>2012-09-05T09:08:03Z</td>\n",
              "      <td>2015-05-12T15:15:34Z</td>\n",
              "      <td>2011-04-26T03:10:21.60Z</td>\n",
              "      <td>...</td>\n",
              "      <td>/SUM90/D1038869818/S00000/continuum.fits</td>\n",
              "      <td>/SUM90/D1038869818/S00000/Bp.fits</td>\n",
              "      <td>/SUM90/D1038869818/S00000/Bt.fits</td>\n",
              "      <td>/SUM90/D1038869818/S00000/Br.fits</td>\n",
              "      <td>/SUM90/D1038869818/S00000/Bp_err.fits</td>\n",
              "      <td>/SUM90/D1038869818/S00000/Bt_err.fits</td>\n",
              "      <td>/SUM90/D1038869818/S00000/Br_err.fits</td>\n",
              "      <td>/SUM90/D1038869818/S00000/conf_disambig.fits</td>\n",
              "      <td>InvalidLink</td>\n",
              "      <td>hmi.Mharp_720s[536][2011.04.26_03:12:00_TAI]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5415.20150409_080000_TAI</td>\n",
              "      <td>5415</td>\n",
              "      <td>2015-04-09 08:00:00.000</td>\n",
              "      <td>20150409_080000_TAI</td>\n",
              "      <td>hmi.sharp_cea_720s.5415.20150409_080000_TAI</td>\n",
              "      <td>hmi.sharp_cea_720s[5415][2015.04.09_08:00:00_TAI]</td>\n",
              "      <td>2015-05-15T03:15:44Z</td>\n",
              "      <td>2015-04-13T14:07:11Z</td>\n",
              "      <td>2015-04-14T18:23:45Z</td>\n",
              "      <td>2015-04-09T07:58:18.30Z</td>\n",
              "      <td>...</td>\n",
              "      <td>/SUM35/D698134494/S00000/continuum.fits</td>\n",
              "      <td>/SUM35/D698134494/S00000/Bp.fits</td>\n",
              "      <td>/SUM35/D698134494/S00000/Bt.fits</td>\n",
              "      <td>/SUM35/D698134494/S00000/Br.fits</td>\n",
              "      <td>/SUM35/D698134494/S00000/Bp_err.fits</td>\n",
              "      <td>/SUM35/D698134494/S00000/Bt_err.fits</td>\n",
              "      <td>/SUM35/D698134494/S00000/Br_err.fits</td>\n",
              "      <td>/SUM35/D698134494/S00000/conf_disambig.fits</td>\n",
              "      <td>InvalidLink</td>\n",
              "      <td>hmi.Mharp_720s[5415][2015.04.09_08:00:00_TAI]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73742</th>\n",
              "      <td>6258.20160115_222400_TAI</td>\n",
              "      <td>6258</td>\n",
              "      <td>2016-01-15 22:24:00.000</td>\n",
              "      <td>20160115_222400_TAI</td>\n",
              "      <td>hmi.sharp_cea_720s.6258.20160115_222400_TAI</td>\n",
              "      <td>hmi.sharp_cea_720s[6258][2016.01.15_22:24:00_TAI]</td>\n",
              "      <td>2016-02-20T04:08:10Z</td>\n",
              "      <td>2016-01-19T17:42:19Z</td>\n",
              "      <td>2016-01-20T19:17:24Z</td>\n",
              "      <td>2016-01-15T22:22:08.30Z</td>\n",
              "      <td>...</td>\n",
              "      <td>/SUM80/D789029067/S00000/continuum.fits</td>\n",
              "      <td>/SUM80/D789029067/S00000/Bp.fits</td>\n",
              "      <td>/SUM80/D789029067/S00000/Bt.fits</td>\n",
              "      <td>/SUM80/D789029067/S00000/Br.fits</td>\n",
              "      <td>/SUM80/D789029067/S00000/Bp_err.fits</td>\n",
              "      <td>/SUM80/D789029067/S00000/Bt_err.fits</td>\n",
              "      <td>/SUM80/D789029067/S00000/Br_err.fits</td>\n",
              "      <td>/SUM80/D789029067/S00000/conf_disambig.fits</td>\n",
              "      <td>InvalidLink</td>\n",
              "      <td>hmi.Mharp_720s[6258][2016.01.15_22:24:00_TAI]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73743</th>\n",
              "      <td>6599.20160607_080000_TAI</td>\n",
              "      <td>6599</td>\n",
              "      <td>2016-06-07 08:00:00.000</td>\n",
              "      <td>20160607_080000_TAI</td>\n",
              "      <td>hmi.sharp_cea_720s.6599.20160607_080000_TAI</td>\n",
              "      <td>hmi.sharp_cea_720s[6599][2016.06.07_08:00:00_TAI]</td>\n",
              "      <td>2017-12-05T18:45:13Z</td>\n",
              "      <td>2017-11-04T07:17:18Z</td>\n",
              "      <td>2017-11-26T22:59:24Z</td>\n",
              "      <td>2016-06-07T07:58:46.60Z</td>\n",
              "      <td>...</td>\n",
              "      <td>/SUM95/D996809863/S00000/continuum.fits</td>\n",
              "      <td>/SUM95/D996809863/S00000/Bp.fits</td>\n",
              "      <td>/SUM95/D996809863/S00000/Bt.fits</td>\n",
              "      <td>/SUM95/D996809863/S00000/Br.fits</td>\n",
              "      <td>/SUM95/D996809863/S00000/Bp_err.fits</td>\n",
              "      <td>/SUM95/D996809863/S00000/Bt_err.fits</td>\n",
              "      <td>/SUM95/D996809863/S00000/Br_err.fits</td>\n",
              "      <td>/SUM95/D996809863/S00000/conf_disambig.fits</td>\n",
              "      <td>InvalidLink</td>\n",
              "      <td>hmi.Mharp_720s[6599][2016.06.07_08:00:00_TAI]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73744</th>\n",
              "      <td>7255.20180512_080000_TAI</td>\n",
              "      <td>7255</td>\n",
              "      <td>2018-05-12 08:00:00.000</td>\n",
              "      <td>20180512_080000_TAI</td>\n",
              "      <td>hmi.sharp_cea_720s.7255.20180512_080000_TAI</td>\n",
              "      <td>hmi.sharp_cea_720s[7255][2018.05.12_08:00:00_TAI]</td>\n",
              "      <td>2018-06-17T03:05:37Z</td>\n",
              "      <td>2018-05-16T20:22:23Z</td>\n",
              "      <td>2018-05-18T07:52:28Z</td>\n",
              "      <td>2018-05-12T07:58:43.20Z</td>\n",
              "      <td>...</td>\n",
              "      <td>/SUM80/D1055843035/S00000/continuum.fits</td>\n",
              "      <td>/SUM80/D1055843035/S00000/Bp.fits</td>\n",
              "      <td>/SUM80/D1055843035/S00000/Bt.fits</td>\n",
              "      <td>/SUM80/D1055843035/S00000/Br.fits</td>\n",
              "      <td>/SUM80/D1055843035/S00000/Bp_err.fits</td>\n",
              "      <td>/SUM80/D1055843035/S00000/Bt_err.fits</td>\n",
              "      <td>/SUM80/D1055843035/S00000/Br_err.fits</td>\n",
              "      <td>/SUM80/D1055843035/S00000/conf_disambig.fits</td>\n",
              "      <td>InvalidLink</td>\n",
              "      <td>hmi.Mharp_720s[7255][2018.05.12_08:00:00_TAI]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73745</th>\n",
              "      <td>892.20110926_080000_TAI</td>\n",
              "      <td>892</td>\n",
              "      <td>2011-09-26 08:00:00.000</td>\n",
              "      <td>20110926_080000_TAI</td>\n",
              "      <td>hmi.sharp_cea_720s.892.20110926_080000_TAI</td>\n",
              "      <td>hmi.sharp_cea_720s[892][2011.09.26_08:00:00_TAI]</td>\n",
              "      <td>2018-04-28T01:47:53Z</td>\n",
              "      <td>2011-09-30T18:34:03Z</td>\n",
              "      <td>2015-07-02T20:56:15Z</td>\n",
              "      <td>2011-09-26T07:58:20.00Z</td>\n",
              "      <td>...</td>\n",
              "      <td>/SUM87/D1038232342/S00000/continuum.fits</td>\n",
              "      <td>/SUM87/D1038232342/S00000/Bp.fits</td>\n",
              "      <td>/SUM87/D1038232342/S00000/Bt.fits</td>\n",
              "      <td>/SUM87/D1038232342/S00000/Br.fits</td>\n",
              "      <td>/SUM87/D1038232342/S00000/Bp_err.fits</td>\n",
              "      <td>/SUM87/D1038232342/S00000/Bt_err.fits</td>\n",
              "      <td>/SUM87/D1038232342/S00000/Br_err.fits</td>\n",
              "      <td>/SUM87/D1038232342/S00000/conf_disambig.fits</td>\n",
              "      <td>InvalidLink</td>\n",
              "      <td>hmi.Mharp_720s[892][2011.09.26_08:00:00_TAI]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73746</th>\n",
              "      <td>1422.20120301_160000_TAI</td>\n",
              "      <td>1422</td>\n",
              "      <td>2012-03-01 16:00:00.000</td>\n",
              "      <td>20120301_160000_TAI</td>\n",
              "      <td>hmi.sharp_cea_720s.1422.20120301_160000_TAI</td>\n",
              "      <td>hmi.sharp_cea_720s[1422][2012.03.01_16:00:00_TAI]</td>\n",
              "      <td>2018-04-29T18:00:12Z</td>\n",
              "      <td>2012-09-05T09:13:59Z</td>\n",
              "      <td>2015-09-01T05:38:35Z</td>\n",
              "      <td>2012-03-01T15:58:13.90Z</td>\n",
              "      <td>...</td>\n",
              "      <td>/SUM94/D1038926095/S00000/continuum.fits</td>\n",
              "      <td>/SUM94/D1038926095/S00000/Bp.fits</td>\n",
              "      <td>/SUM94/D1038926095/S00000/Bt.fits</td>\n",
              "      <td>/SUM94/D1038926095/S00000/Br.fits</td>\n",
              "      <td>/SUM94/D1038926095/S00000/Bp_err.fits</td>\n",
              "      <td>/SUM94/D1038926095/S00000/Bt_err.fits</td>\n",
              "      <td>/SUM94/D1038926095/S00000/Br_err.fits</td>\n",
              "      <td>/SUM94/D1038926095/S00000/conf_disambig.fits</td>\n",
              "      <td>InvalidLink</td>\n",
              "      <td>hmi.Mharp_720s[1422][2012.03.01_16:00:00_TAI]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>73747 rows × 276 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                   harpnum_trec  HARP_NUM           T_REC_DATETIME  \\\n",
              "0      1644.20120516_031200_TAI      1644  2012-05-16 03:12:00.000   \n",
              "1      4315.20140708_013600_TAI      4315  2014-07-08 01:36:00.000   \n",
              "2      4351.20140714_142400_TAI      4351  2014-07-14 14:24:00.000   \n",
              "3       536.20110426_031200_TAI       536  2011-04-26 03:12:00.000   \n",
              "4      5415.20150409_080000_TAI      5415  2015-04-09 08:00:00.000   \n",
              "...                         ...       ...                      ...   \n",
              "73742  6258.20160115_222400_TAI      6258  2016-01-15 22:24:00.000   \n",
              "73743  6599.20160607_080000_TAI      6599  2016-06-07 08:00:00.000   \n",
              "73744  7255.20180512_080000_TAI      7255  2018-05-12 08:00:00.000   \n",
              "73745   892.20110926_080000_TAI       892  2011-09-26 08:00:00.000   \n",
              "73746  1422.20120301_160000_TAI      1422  2012-03-01 16:00:00.000   \n",
              "\n",
              "                 T_REC_STR                                    FILE_NAME  \\\n",
              "0      20120516_031200_TAI  hmi.sharp_cea_720s.1644.20120516_031200_TAI   \n",
              "1      20140708_013600_TAI  hmi.sharp_cea_720s.4315.20140708_013600_TAI   \n",
              "2      20140714_142400_TAI  hmi.sharp_cea_720s.4351.20140714_142400_TAI   \n",
              "3      20110426_031200_TAI   hmi.sharp_cea_720s.536.20110426_031200_TAI   \n",
              "4      20150409_080000_TAI  hmi.sharp_cea_720s.5415.20150409_080000_TAI   \n",
              "...                    ...                                          ...   \n",
              "73742  20160115_222400_TAI  hmi.sharp_cea_720s.6258.20160115_222400_TAI   \n",
              "73743  20160607_080000_TAI  hmi.sharp_cea_720s.6599.20160607_080000_TAI   \n",
              "73744  20180512_080000_TAI  hmi.sharp_cea_720s.7255.20180512_080000_TAI   \n",
              "73745  20110926_080000_TAI   hmi.sharp_cea_720s.892.20110926_080000_TAI   \n",
              "73746  20120301_160000_TAI  hmi.sharp_cea_720s.1422.20120301_160000_TAI   \n",
              "\n",
              "                                                   query  \\\n",
              "0      hmi.sharp_cea_720s[1644][2012.05.16_03:12:00_TAI]   \n",
              "1      hmi.sharp_cea_720s[4315][2014.07.08_01:36:00_TAI]   \n",
              "2      hmi.sharp_cea_720s[4351][2014.07.14_14:24:00_TAI]   \n",
              "3       hmi.sharp_cea_720s[536][2011.04.26_03:12:00_TAI]   \n",
              "4      hmi.sharp_cea_720s[5415][2015.04.09_08:00:00_TAI]   \n",
              "...                                                  ...   \n",
              "73742  hmi.sharp_cea_720s[6258][2016.01.15_22:24:00_TAI]   \n",
              "73743  hmi.sharp_cea_720s[6599][2016.06.07_08:00:00_TAI]   \n",
              "73744  hmi.sharp_cea_720s[7255][2018.05.12_08:00:00_TAI]   \n",
              "73745   hmi.sharp_cea_720s[892][2011.09.26_08:00:00_TAI]   \n",
              "73746  hmi.sharp_cea_720s[1422][2012.03.01_16:00:00_TAI]   \n",
              "\n",
              "                       DATE                DATE_S                DATE_B  \\\n",
              "0      2018-05-09T03:05:47Z  2012-05-20T19:22:46Z  2015-09-30T20:47:04Z   \n",
              "1      2014-08-13T03:40:53Z  2014-07-12T23:18:52Z  2014-07-16T08:03:00Z   \n",
              "2      2014-08-19T03:03:14Z  2014-07-18T23:26:26Z  2014-07-22T00:47:31Z   \n",
              "3      2018-04-29T14:50:56Z  2012-09-05T09:08:03Z  2015-05-12T15:15:34Z   \n",
              "4      2015-05-15T03:15:44Z  2015-04-13T14:07:11Z  2015-04-14T18:23:45Z   \n",
              "...                     ...                   ...                   ...   \n",
              "73742  2016-02-20T04:08:10Z  2016-01-19T17:42:19Z  2016-01-20T19:17:24Z   \n",
              "73743  2017-12-05T18:45:13Z  2017-11-04T07:17:18Z  2017-11-26T22:59:24Z   \n",
              "73744  2018-06-17T03:05:37Z  2018-05-16T20:22:23Z  2018-05-18T07:52:28Z   \n",
              "73745  2018-04-28T01:47:53Z  2011-09-30T18:34:03Z  2015-07-02T20:56:15Z   \n",
              "73746  2018-04-29T18:00:12Z  2012-09-05T09:13:59Z  2015-09-01T05:38:35Z   \n",
              "\n",
              "                     DATE__OBS  ...                                 continuum  \\\n",
              "0      2012-05-16T03:10:24.10Z  ...  /SUM90/D1042885471/S00000/continuum.fits   \n",
              "1      2014-07-08T01:34:25.80Z  ...   /SUM22/D597857266/S00000/continuum.fits   \n",
              "2      2014-07-14T14:22:25.70Z  ...    /SUM1/D599938236/S00000/continuum.fits   \n",
              "3      2011-04-26T03:10:21.60Z  ...  /SUM90/D1038869818/S00000/continuum.fits   \n",
              "4      2015-04-09T07:58:18.30Z  ...   /SUM35/D698134494/S00000/continuum.fits   \n",
              "...                        ...  ...                                       ...   \n",
              "73742  2016-01-15T22:22:08.30Z  ...   /SUM80/D789029067/S00000/continuum.fits   \n",
              "73743  2016-06-07T07:58:46.60Z  ...   /SUM95/D996809863/S00000/continuum.fits   \n",
              "73744  2018-05-12T07:58:43.20Z  ...  /SUM80/D1055843035/S00000/continuum.fits   \n",
              "73745  2011-09-26T07:58:20.00Z  ...  /SUM87/D1038232342/S00000/continuum.fits   \n",
              "73746  2012-03-01T15:58:13.90Z  ...  /SUM94/D1038926095/S00000/continuum.fits   \n",
              "\n",
              "                                      Bp                                 Bt  \\\n",
              "0      /SUM90/D1042885471/S00000/Bp.fits  /SUM90/D1042885471/S00000/Bt.fits   \n",
              "1       /SUM22/D597857266/S00000/Bp.fits   /SUM22/D597857266/S00000/Bt.fits   \n",
              "2        /SUM1/D599938236/S00000/Bp.fits    /SUM1/D599938236/S00000/Bt.fits   \n",
              "3      /SUM90/D1038869818/S00000/Bp.fits  /SUM90/D1038869818/S00000/Bt.fits   \n",
              "4       /SUM35/D698134494/S00000/Bp.fits   /SUM35/D698134494/S00000/Bt.fits   \n",
              "...                                  ...                                ...   \n",
              "73742   /SUM80/D789029067/S00000/Bp.fits   /SUM80/D789029067/S00000/Bt.fits   \n",
              "73743   /SUM95/D996809863/S00000/Bp.fits   /SUM95/D996809863/S00000/Bt.fits   \n",
              "73744  /SUM80/D1055843035/S00000/Bp.fits  /SUM80/D1055843035/S00000/Bt.fits   \n",
              "73745  /SUM87/D1038232342/S00000/Bp.fits  /SUM87/D1038232342/S00000/Bt.fits   \n",
              "73746  /SUM94/D1038926095/S00000/Bp.fits  /SUM94/D1038926095/S00000/Bt.fits   \n",
              "\n",
              "                                      Br  \\\n",
              "0      /SUM90/D1042885471/S00000/Br.fits   \n",
              "1       /SUM22/D597857266/S00000/Br.fits   \n",
              "2        /SUM1/D599938236/S00000/Br.fits   \n",
              "3      /SUM90/D1038869818/S00000/Br.fits   \n",
              "4       /SUM35/D698134494/S00000/Br.fits   \n",
              "...                                  ...   \n",
              "73742   /SUM80/D789029067/S00000/Br.fits   \n",
              "73743   /SUM95/D996809863/S00000/Br.fits   \n",
              "73744  /SUM80/D1055843035/S00000/Br.fits   \n",
              "73745  /SUM87/D1038232342/S00000/Br.fits   \n",
              "73746  /SUM94/D1038926095/S00000/Br.fits   \n",
              "\n",
              "                                      Bp_err  \\\n",
              "0      /SUM90/D1042885471/S00000/Bp_err.fits   \n",
              "1       /SUM22/D597857266/S00000/Bp_err.fits   \n",
              "2        /SUM1/D599938236/S00000/Bp_err.fits   \n",
              "3      /SUM90/D1038869818/S00000/Bp_err.fits   \n",
              "4       /SUM35/D698134494/S00000/Bp_err.fits   \n",
              "...                                      ...   \n",
              "73742   /SUM80/D789029067/S00000/Bp_err.fits   \n",
              "73743   /SUM95/D996809863/S00000/Bp_err.fits   \n",
              "73744  /SUM80/D1055843035/S00000/Bp_err.fits   \n",
              "73745  /SUM87/D1038232342/S00000/Bp_err.fits   \n",
              "73746  /SUM94/D1038926095/S00000/Bp_err.fits   \n",
              "\n",
              "                                      Bt_err  \\\n",
              "0      /SUM90/D1042885471/S00000/Bt_err.fits   \n",
              "1       /SUM22/D597857266/S00000/Bt_err.fits   \n",
              "2        /SUM1/D599938236/S00000/Bt_err.fits   \n",
              "3      /SUM90/D1038869818/S00000/Bt_err.fits   \n",
              "4       /SUM35/D698134494/S00000/Bt_err.fits   \n",
              "...                                      ...   \n",
              "73742   /SUM80/D789029067/S00000/Bt_err.fits   \n",
              "73743   /SUM95/D996809863/S00000/Bt_err.fits   \n",
              "73744  /SUM80/D1055843035/S00000/Bt_err.fits   \n",
              "73745  /SUM87/D1038232342/S00000/Bt_err.fits   \n",
              "73746  /SUM94/D1038926095/S00000/Bt_err.fits   \n",
              "\n",
              "                                      Br_err  \\\n",
              "0      /SUM90/D1042885471/S00000/Br_err.fits   \n",
              "1       /SUM22/D597857266/S00000/Br_err.fits   \n",
              "2        /SUM1/D599938236/S00000/Br_err.fits   \n",
              "3      /SUM90/D1038869818/S00000/Br_err.fits   \n",
              "4       /SUM35/D698134494/S00000/Br_err.fits   \n",
              "...                                      ...   \n",
              "73742   /SUM80/D789029067/S00000/Br_err.fits   \n",
              "73743   /SUM95/D996809863/S00000/Br_err.fits   \n",
              "73744  /SUM80/D1055843035/S00000/Br_err.fits   \n",
              "73745  /SUM87/D1038232342/S00000/Br_err.fits   \n",
              "73746  /SUM94/D1038926095/S00000/Br_err.fits   \n",
              "\n",
              "                                      conf_disambig        BHARP  \\\n",
              "0      /SUM90/D1042885471/S00000/conf_disambig.fits  InvalidLink   \n",
              "1       /SUM22/D597857266/S00000/conf_disambig.fits  InvalidLink   \n",
              "2        /SUM1/D599938236/S00000/conf_disambig.fits  InvalidLink   \n",
              "3      /SUM90/D1038869818/S00000/conf_disambig.fits  InvalidLink   \n",
              "4       /SUM35/D698134494/S00000/conf_disambig.fits  InvalidLink   \n",
              "...                                             ...          ...   \n",
              "73742   /SUM80/D789029067/S00000/conf_disambig.fits  InvalidLink   \n",
              "73743   /SUM95/D996809863/S00000/conf_disambig.fits  InvalidLink   \n",
              "73744  /SUM80/D1055843035/S00000/conf_disambig.fits  InvalidLink   \n",
              "73745  /SUM87/D1038232342/S00000/conf_disambig.fits  InvalidLink   \n",
              "73746  /SUM94/D1038926095/S00000/conf_disambig.fits  InvalidLink   \n",
              "\n",
              "                                               MHARP  \n",
              "0      hmi.Mharp_720s[1644][2012.05.16_03:12:00_TAI]  \n",
              "1      hmi.Mharp_720s[4315][2014.07.08_01:36:00_TAI]  \n",
              "2      hmi.Mharp_720s[4351][2014.07.14_14:24:00_TAI]  \n",
              "3       hmi.Mharp_720s[536][2011.04.26_03:12:00_TAI]  \n",
              "4      hmi.Mharp_720s[5415][2015.04.09_08:00:00_TAI]  \n",
              "...                                              ...  \n",
              "73742  hmi.Mharp_720s[6258][2016.01.15_22:24:00_TAI]  \n",
              "73743  hmi.Mharp_720s[6599][2016.06.07_08:00:00_TAI]  \n",
              "73744  hmi.Mharp_720s[7255][2018.05.12_08:00:00_TAI]  \n",
              "73745   hmi.Mharp_720s[892][2011.09.26_08:00:00_TAI]  \n",
              "73746  hmi.Mharp_720s[1422][2012.03.01_16:00:00_TAI]  \n",
              "\n",
              "[73747 rows x 276 columns]"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "raw_info"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UNkwPihFEgB-"
      },
      "source": [
        "## Read product file information\n",
        "\n",
        "Record some basic information about the 3D magnetic field database products"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "qYDztTT0EXhm"
      },
      "outputs": [],
      "source": [
        "product_info_path=os.path.join(\n",
        "  nlfff_path_dataset_summary,\n",
        "  config_ds[\"product_summary_info_path\"]\n",
        ")\n",
        "product_info=pandas.read_csv(product_info_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "NwfTj5QoEtaz"
      },
      "outputs": [
        {
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>harpnum_trec</th>\n",
              "      <th>sharp_num</th>\n",
              "      <th>t_rec</th>\n",
              "      <th>bout_path</th>\n",
              "      <th>bout_md5</th>\n",
              "      <th>bout_size</th>\n",
              "      <th>grid_x</th>\n",
              "      <th>grid_y</th>\n",
              "      <th>grid_z</th>\n",
              "      <th>grid_xyz</th>\n",
              "      <th>bout_maxlevel</th>\n",
              "      <th>bout_quality</th>\n",
              "      <th>batch</th>\n",
              "      <th>notes</th>\n",
              "      <th>bout_quality_value</th>\n",
              "      <th>identifiers</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6558.20160520_044800_TAI</td>\n",
              "      <td>6558</td>\n",
              "      <td>2016-05-20 04:48:00.000</td>\n",
              "      <td>grid3.done/num_6500_6599/hmi.sharp_cea_720s.65...</td>\n",
              "      <td>ea4c960822a1284fe43637b1717bc30b</td>\n",
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              "      <td>3</td>\n",
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              "      <td>20220301</td>\n",
              "      <td>new</td>\n",
              "      <td>15.568000</td>\n",
              "      <td>6558201605200448003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6558.20160520_044800_TAI</td>\n",
              "      <td>6558</td>\n",
              "      <td>2016-05-20 04:48:00.000</td>\n",
              "      <td>grid2.done/hmi.sharp_cea_720s.6558.20160520_04...</td>\n",
              "      <td>bf41326b2b90597a539a796c7eb72e10</td>\n",
              "      <td>447545088</td>\n",
              "      <td>216</td>\n",
              "      <td>382</td>\n",
              "      <td>226</td>\n",
              "      <td>18647712</td>\n",
              "      <td>2</td>\n",
              "      <td>True</td>\n",
              "      <td>20220301</td>\n",
              "      <td>old</td>\n",
              "      <td>16.364700</td>\n",
              "      <td>6558201605200448002</td>\n",
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              "      <th>2</th>\n",
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              "      <td>2887</td>\n",
              "      <td>2013-06-29 11:12:00.000</td>\n",
              "      <td>grid2.done/hmi.sharp_cea_720s.2887.20130629_11...</td>\n",
              "      <td>c3661aaef993bed04a10fb74483e7613</td>\n",
              "      <td>456693120</td>\n",
              "      <td>214</td>\n",
              "      <td>390</td>\n",
              "      <td>228</td>\n",
              "      <td>19028880</td>\n",
              "      <td>2</td>\n",
              "      <td>True</td>\n",
              "      <td>20220301</td>\n",
              "      <td>old</td>\n",
              "      <td>17.953400</td>\n",
              "      <td>2887201306291112002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2887.20130629_111200_TAI</td>\n",
              "      <td>2887</td>\n",
              "      <td>2013-06-29 11:12:00.000</td>\n",
              "      <td>grid3.done/num_2800_2899/hmi.sharp_cea_720s.28...</td>\n",
              "      <td>38f40bf4cd8e7fa66d2563570ccbbd3f</td>\n",
              "      <td>3653544960</td>\n",
              "      <td>428</td>\n",
              "      <td>780</td>\n",
              "      <td>456</td>\n",
              "      <td>152231040</td>\n",
              "      <td>3</td>\n",
              "      <td>True</td>\n",
              "      <td>20220301</td>\n",
              "      <td>new</td>\n",
              "      <td>18.468100</td>\n",
              "      <td>2887201306291112003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5183.20150215_080000_TAI</td>\n",
              "      <td>5183</td>\n",
              "      <td>2015-02-15 08:00:00.000</td>\n",
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              "      <td>7de9b081558f9069f4a4732f8bdcb42f</td>\n",
              "      <td>198342144</td>\n",
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              "      <td>8264256</td>\n",
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              "      <td>True</td>\n",
              "      <td>20220301</td>\n",
              "      <td>old</td>\n",
              "      <td>17.937401</td>\n",
              "      <td>5183201502150800002</td>\n",
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              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>73742</th>\n",
              "      <td>4616.20141003_142400_TAI</td>\n",
              "      <td>4616</td>\n",
              "      <td>2014-10-03 14:24:00.000</td>\n",
              "      <td>grid3.done/num_4600_4699/hmi.sharp_cea_720s.46...</td>\n",
              "      <td>7c3da7a4dbd08da4ccee60fecd7e0118</td>\n",
              "      <td>1635068160</td>\n",
              "      <td>792</td>\n",
              "      <td>1012</td>\n",
              "      <td>680</td>\n",
              "      <td>545022720</td>\n",
              "      <td>3</td>\n",
              "      <td>True</td>\n",
              "      <td>20220301</td>\n",
              "      <td>need check size</td>\n",
              "      <td>17.964899</td>\n",
              "      <td>4616201410031424003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73743</th>\n",
              "      <td>4698.20141024_160000_TAI</td>\n",
              "      <td>4698</td>\n",
              "      <td>2014-10-24 16:00:00.000</td>\n",
              "      <td>grid3.done/num_4600_4699/hmi.sharp_cea_720s.46...</td>\n",
              "      <td>851980aacc23901e5ff3a654debe013f</td>\n",
              "      <td>3024076800</td>\n",
              "      <td>800</td>\n",
              "      <td>1472</td>\n",
              "      <td>856</td>\n",
              "      <td>1008025600</td>\n",
              "      <td>3</td>\n",
              "      <td>True</td>\n",
              "      <td>20220301</td>\n",
              "      <td>need check size</td>\n",
              "      <td>16.359800</td>\n",
              "      <td>4698201410241600003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73744</th>\n",
              "      <td>4640.20141006_173600_TAI</td>\n",
              "      <td>4640</td>\n",
              "      <td>2014-10-06 17:36:00.000</td>\n",
              "      <td>grid3.done/num_4600_4699/hmi.sharp_cea_720s.46...</td>\n",
              "      <td>e99415002ee27c14dcf7d08490a15840</td>\n",
              "      <td>1137168000</td>\n",
              "      <td>500</td>\n",
              "      <td>1192</td>\n",
              "      <td>636</td>\n",
              "      <td>379056000</td>\n",
              "      <td>3</td>\n",
              "      <td>True</td>\n",
              "      <td>20220301</td>\n",
              "      <td>need check size</td>\n",
              "      <td>23.332500</td>\n",
              "      <td>4640201410061736003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73745</th>\n",
              "      <td>5039.20150112_013600_TAI</td>\n",
              "      <td>5039</td>\n",
              "      <td>2015-01-12 01:36:00.000</td>\n",
              "      <td>grid3.done/num_5000_5099/hmi.sharp_cea_720s.50...</td>\n",
              "      <td>d64654bd4ed69f201c34faf0b036b9c7</td>\n",
              "      <td>5301338112</td>\n",
              "      <td>1008</td>\n",
              "      <td>1712</td>\n",
              "      <td>1024</td>\n",
              "      <td>1767112704</td>\n",
              "      <td>3</td>\n",
              "      <td>True</td>\n",
              "      <td>20220301</td>\n",
              "      <td>need check size</td>\n",
              "      <td>17.475500</td>\n",
              "      <td>5039201501120136003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73746</th>\n",
              "      <td>5028.20150109_044800_TAI</td>\n",
              "      <td>5028</td>\n",
              "      <td>2015-01-09 04:48:00.000</td>\n",
              "      <td>grid3.done/num_5000_5099/hmi.sharp_cea_720s.50...</td>\n",
              "      <td>21f9ab2c5e376aec3f45cfbe487e3e23</td>\n",
              "      <td>1806686976</td>\n",
              "      <td>656</td>\n",
              "      <td>1268</td>\n",
              "      <td>724</td>\n",
              "      <td>602228992</td>\n",
              "      <td>3</td>\n",
              "      <td>True</td>\n",
              "      <td>20220301</td>\n",
              "      <td>need check size</td>\n",
              "      <td>22.137501</td>\n",
              "      <td>5028201501090448003</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>73747 rows × 16 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                   harpnum_trec  sharp_num                    t_rec  \\\n",
              "0      6558.20160520_044800_TAI       6558  2016-05-20 04:48:00.000   \n",
              "1      6558.20160520_044800_TAI       6558  2016-05-20 04:48:00.000   \n",
              "2      2887.20130629_111200_TAI       2887  2013-06-29 11:12:00.000   \n",
              "3      2887.20130629_111200_TAI       2887  2013-06-29 11:12:00.000   \n",
              "4      5183.20150215_080000_TAI       5183  2015-02-15 08:00:00.000   \n",
              "...                         ...        ...                      ...   \n",
              "73742  4616.20141003_142400_TAI       4616  2014-10-03 14:24:00.000   \n",
              "73743  4698.20141024_160000_TAI       4698  2014-10-24 16:00:00.000   \n",
              "73744  4640.20141006_173600_TAI       4640  2014-10-06 17:36:00.000   \n",
              "73745  5039.20150112_013600_TAI       5039  2015-01-12 01:36:00.000   \n",
              "73746  5028.20150109_044800_TAI       5028  2015-01-09 04:48:00.000   \n",
              "\n",
              "                                               bout_path  \\\n",
              "0      grid3.done/num_6500_6599/hmi.sharp_cea_720s.65...   \n",
              "1      grid2.done/hmi.sharp_cea_720s.6558.20160520_04...   \n",
              "2      grid2.done/hmi.sharp_cea_720s.2887.20130629_11...   \n",
              "3      grid3.done/num_2800_2899/hmi.sharp_cea_720s.28...   \n",
              "4      grid2.done/hmi.sharp_cea_720s.5183.20150215_08...   \n",
              "...                                                  ...   \n",
              "73742  grid3.done/num_4600_4699/hmi.sharp_cea_720s.46...   \n",
              "73743  grid3.done/num_4600_4699/hmi.sharp_cea_720s.46...   \n",
              "73744  grid3.done/num_4600_4699/hmi.sharp_cea_720s.46...   \n",
              "73745  grid3.done/num_5000_5099/hmi.sharp_cea_720s.50...   \n",
              "73746  grid3.done/num_5000_5099/hmi.sharp_cea_720s.50...   \n",
              "\n",
              "                               bout_md5   bout_size  grid_x  grid_y  grid_z  \\\n",
              "0      ea4c960822a1284fe43637b1717bc30b  3580360704     432     764     452   \n",
              "1      bf41326b2b90597a539a796c7eb72e10   447545088     216     382     226   \n",
              "2      c3661aaef993bed04a10fb74483e7613   456693120     214     390     228   \n",
              "3      38f40bf4cd8e7fa66d2563570ccbbd3f  3653544960     428     780     456   \n",
              "4      7de9b081558f9069f4a4732f8bdcb42f   198342144     168     286     172   \n",
              "...                                 ...         ...     ...     ...     ...   \n",
              "73742  7c3da7a4dbd08da4ccee60fecd7e0118  1635068160     792    1012     680   \n",
              "73743  851980aacc23901e5ff3a654debe013f  3024076800     800    1472     856   \n",
              "73744  e99415002ee27c14dcf7d08490a15840  1137168000     500    1192     636   \n",
              "73745  d64654bd4ed69f201c34faf0b036b9c7  5301338112    1008    1712    1024   \n",
              "73746  21f9ab2c5e376aec3f45cfbe487e3e23  1806686976     656    1268     724   \n",
              "\n",
              "         grid_xyz  bout_maxlevel  bout_quality     batch            notes  \\\n",
              "0       149181696              3          True  20220301              new   \n",
              "1        18647712              2          True  20220301              old   \n",
              "2        19028880              2          True  20220301              old   \n",
              "3       152231040              3          True  20220301              new   \n",
              "4         8264256              2          True  20220301              old   \n",
              "...           ...            ...           ...       ...              ...   \n",
              "73742   545022720              3          True  20220301  need check size   \n",
              "73743  1008025600              3          True  20220301  need check size   \n",
              "73744   379056000              3          True  20220301  need check size   \n",
              "73745  1767112704              3          True  20220301  need check size   \n",
              "73746   602228992              3          True  20220301  need check size   \n",
              "\n",
              "       bout_quality_value          identifiers  \n",
              "0               15.568000  6558201605200448003  \n",
              "1               16.364700  6558201605200448002  \n",
              "2               17.953400  2887201306291112002  \n",
              "3               18.468100  2887201306291112003  \n",
              "4               17.937401  5183201502150800002  \n",
              "...                   ...                  ...  \n",
              "73742           17.964899  4616201410031424003  \n",
              "73743           16.359800  4698201410241600003  \n",
              "73744           23.332500  4640201410061736003  \n",
              "73745           17.475500  5039201501120136003  \n",
              "73746           22.137501  5028201501090448003  \n",
              "\n",
              "[73747 rows x 16 columns]"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "product_info"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "98CYX2dxFTJd"
      },
      "source": [
        "# Use of label information"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "joeBnL5eFYpP"
      },
      "source": [
        "## Read configuration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "2G56E-BKFz0A"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import json\n",
        "\n",
        "# Read configuration file\n",
        "nlfff_path_dataset_label=\"nlfff-dataset-flare-label\"\n",
        "config_path=os.path.join(nlfff_path_dataset_label,\"config.json\")\n",
        "with open(config_path,\"r\") as f:\n",
        "    config_label=json.loads(f.read())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "8-bidRQdF8rt"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'config_version': 1.3,\n",
              " 'flare_info_path': 'label-202203/raw_file/flare_info_processed.csv',\n",
              " 'prepare_file': {'knoaa_vflaretimelist_path': 'label-202203/prepare/knoaa_vflaretimelist.pickle',\n",
              "  'ksharp_vnoaa_path': 'label-202203/prepare/ksharp_vnoaa.pickle'},\n",
              " 'label_info_path': 'label-202203/process/label.csv'}"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "config_label"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DQRj2sK4GgF8"
      },
      "source": [
        "## Label Information"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "KLMaPiaKGi30"
      },
      "outputs": [],
      "source": [
        "label_info_path=os.path.join(\n",
        "    nlfff_path_dataset_label,\n",
        "    config_label[\"label_info_path\"]\n",
        ")\n",
        "label_info=pandas.read_csv(label_info_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "LJYt1WpnG6ZY"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>harpnum_trec</th>\n",
              "      <th>harp_num</th>\n",
              "      <th>t_rec_str</th>\n",
              "      <th>now_flare_level</th>\n",
              "      <th>now_flare_id</th>\n",
              "      <th>h6_flare_level</th>\n",
              "      <th>h6_flare_id</th>\n",
              "      <th>h12_flare_level</th>\n",
              "      <th>h12_flare_id</th>\n",
              "      <th>h24_flare_level</th>\n",
              "      <th>h24_flare_id</th>\n",
              "      <th>h48_flare_level</th>\n",
              "      <th>h48_flare_id</th>\n",
              "      <th>t_rec_datetime</th>\n",
              "      <th>h24_posmx</th>\n",
              "      <th>h24_poscmx</th>\n",
              "      <th>h48_posmx</th>\n",
              "      <th>h48_poscmx</th>\n",
              "      <th>h24_delta05</th>\n",
              "      <th>h48_delta05</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <td>1</td>\n",
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              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>2010-05-04 19:12:00.000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.20100504_204800_TAI</td>\n",
              "      <td>1</td>\n",
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              "      <td>2010-05-04 20:48:00.000</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.20100504_222400_TAI</td>\n",
              "      <td>1</td>\n",
              "      <td>20100504_222400_TAI</td>\n",
              "      <td>0</td>\n",
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              "      <td>-1</td>\n",
              "      <td>2010-05-04 22:24:00.000</td>\n",
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              "    <tr>\n",
              "      <th>46109</th>\n",
              "      <td>997.20111030_080000_TAI</td>\n",
              "      <td>997</td>\n",
              "      <td>20111030_080000_TAI</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
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              "      <td>324</td>\n",
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              "      <td>324</td>\n",
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              "      <td>324</td>\n",
              "      <td>67189</td>\n",
              "      <td>2011-10-30 08:00:00.000</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>46110</th>\n",
              "      <td>997.20111030_093600_TAI</td>\n",
              "      <td>997</td>\n",
              "      <td>20111030_093600_TAI</td>\n",
              "      <td>324</td>\n",
              "      <td>67189</td>\n",
              "      <td>324</td>\n",
              "      <td>67189</td>\n",
              "      <td>324</td>\n",
              "      <td>67189</td>\n",
              "      <td>324</td>\n",
              "      <td>67189</td>\n",
              "      <td>324</td>\n",
              "      <td>67189</td>\n",
              "      <td>2011-10-30 09:36:00.000</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>46111</th>\n",
              "      <td>997.20111030_111200_TAI</td>\n",
              "      <td>997</td>\n",
              "      <td>20111030_111200_TAI</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>319</td>\n",
              "      <td>67192</td>\n",
              "      <td>319</td>\n",
              "      <td>67192</td>\n",
              "      <td>323</td>\n",
              "      <td>67197</td>\n",
              "      <td>323</td>\n",
              "      <td>67197</td>\n",
              "      <td>2011-10-30 11:12:00.000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46112</th>\n",
              "      <td>997.20111030_124800_TAI</td>\n",
              "      <td>997</td>\n",
              "      <td>20111030_124800_TAI</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>319</td>\n",
              "      <td>67192</td>\n",
              "      <td>319</td>\n",
              "      <td>67192</td>\n",
              "      <td>323</td>\n",
              "      <td>67197</td>\n",
              "      <td>323</td>\n",
              "      <td>67197</td>\n",
              "      <td>2011-10-30 12:48:00.000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46113</th>\n",
              "      <td>997.20111030_142400_TAI</td>\n",
              "      <td>997</td>\n",
              "      <td>20111030_142400_TAI</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>319</td>\n",
              "      <td>67192</td>\n",
              "      <td>323</td>\n",
              "      <td>67197</td>\n",
              "      <td>323</td>\n",
              "      <td>67197</td>\n",
              "      <td>323</td>\n",
              "      <td>67197</td>\n",
              "      <td>2011-10-30 14:24:00.000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>46114 rows × 20 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                  harpnum_trec  harp_num            t_rec_str  \\\n",
              "0        1.20100504_160000_TAI         1  20100504_160000_TAI   \n",
              "1        1.20100504_173600_TAI         1  20100504_173600_TAI   \n",
              "2        1.20100504_191200_TAI         1  20100504_191200_TAI   \n",
              "3        1.20100504_204800_TAI         1  20100504_204800_TAI   \n",
              "4        1.20100504_222400_TAI         1  20100504_222400_TAI   \n",
              "...                        ...       ...                  ...   \n",
              "46109  997.20111030_080000_TAI       997  20111030_080000_TAI   \n",
              "46110  997.20111030_093600_TAI       997  20111030_093600_TAI   \n",
              "46111  997.20111030_111200_TAI       997  20111030_111200_TAI   \n",
              "46112  997.20111030_124800_TAI       997  20111030_124800_TAI   \n",
              "46113  997.20111030_142400_TAI       997  20111030_142400_TAI   \n",
              "\n",
              "       now_flare_level  now_flare_id  h6_flare_level  h6_flare_id  \\\n",
              "0                    0            -1               0           -1   \n",
              "1                    0            -1               0           -1   \n",
              "2                    0            -1               0           -1   \n",
              "3                    0            -1               0           -1   \n",
              "4                    0            -1               0           -1   \n",
              "...                ...           ...             ...          ...   \n",
              "46109                0            -1             324        67189   \n",
              "46110              324         67189             324        67189   \n",
              "46111                0            -1             319        67192   \n",
              "46112                0            -1             319        67192   \n",
              "46113                0            -1             319        67192   \n",
              "\n",
              "       h12_flare_level  h12_flare_id  h24_flare_level  h24_flare_id  \\\n",
              "0                    0            -1                0            -1   \n",
              "1                    0            -1                0            -1   \n",
              "2                    0            -1                0            -1   \n",
              "3                    0            -1                0            -1   \n",
              "4                    0            -1                0            -1   \n",
              "...                ...           ...              ...           ...   \n",
              "46109              324         67189              324         67189   \n",
              "46110              324         67189              324         67189   \n",
              "46111              319         67192              323         67197   \n",
              "46112              319         67192              323         67197   \n",
              "46113              323         67197              323         67197   \n",
              "\n",
              "       h48_flare_level  h48_flare_id           t_rec_datetime  h24_posmx  \\\n",
              "0                    0            -1  2010-05-04 16:00:00.000          0   \n",
              "1                    0            -1  2010-05-04 17:36:00.000          0   \n",
              "2                    0            -1  2010-05-04 19:12:00.000          0   \n",
              "3                    0            -1  2010-05-04 20:48:00.000          0   \n",
              "4                    0            -1  2010-05-04 22:24:00.000          0   \n",
              "...                ...           ...                      ...        ...   \n",
              "46109              324         67189  2011-10-30 08:00:00.000          0   \n",
              "46110              324         67189  2011-10-30 09:36:00.000          0   \n",
              "46111              323         67197  2011-10-30 11:12:00.000          0   \n",
              "46112              323         67197  2011-10-30 12:48:00.000          0   \n",
              "46113              323         67197  2011-10-30 14:24:00.000          0   \n",
              "\n",
              "       h24_poscmx  h48_posmx  h48_poscmx  h24_delta05  h48_delta05  \n",
              "0               0          0           0            0            0  \n",
              "1               0          0           0            0            0  \n",
              "2               0          0           0            0            0  \n",
              "3               0          0           0            0            0  \n",
              "4               0          0           0            0            0  \n",
              "...           ...        ...         ...          ...          ...  \n",
              "46109           1          0           1            3            3  \n",
              "46110           2          0           2            0            0  \n",
              "46111           1          0           1            3            3  \n",
              "46112           1          0           1            3            3  \n",
              "46113           1          0           1            3            3  \n",
              "\n",
              "[46114 rows x 20 columns]"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "label_info"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "84kcrXRSHFQI"
      },
      "source": [
        "## Flare Information\n",
        "\n",
        "Record the flare details, you can perform this step if you need the flare details."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "boiARjh3HPeo"
      },
      "outputs": [],
      "source": [
        "flare_info_path=os.path.join(\n",
        "    nlfff_path_dataset_label,\n",
        "    config_label[\"flare_info_path\"]\n",
        ")\n",
        "\n",
        "flare_info=pandas.read_csv(flare_info_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "UJbRmf5cHbYS"
      },
      "outputs": [
        {
          "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>deeps_flare_id</th>\n",
              "      <th>start_datetime</th>\n",
              "      <th>end_datetime</th>\n",
              "      <th>peak_datetime</th>\n",
              "      <th>xray_class</th>\n",
              "      <th>xray_intensity</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longtitude</th>\n",
              "      <th>noaa_ar</th>\n",
              "      <th>source</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>78524</td>\n",
              "      <td>2016-01-01 23:10:00.000</td>\n",
              "      <td>2016-01-02 01:01:00.000</td>\n",
              "      <td>2016-01-02 00:11:00.000</td>\n",
              "      <td>M</td>\n",
              "      <td>23.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12473.0</td>\n",
              "      <td>swpc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>32340</td>\n",
              "      <td>1991-12-18 06:06:00.000</td>\n",
              "      <td>1991-12-18 06:39:00.000</td>\n",
              "      <td>1991-12-18 06:38:00.000</td>\n",
              "      <td>C</td>\n",
              "      <td>19.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>goes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19726</td>\n",
              "      <td>1986-05-02 16:36:00.000</td>\n",
              "      <td>1986-05-02 16:44:00.000</td>\n",
              "      <td>1986-05-02 16:41:00.000</td>\n",
              "      <td>C</td>\n",
              "      <td>12.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>goes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>36328</td>\n",
              "      <td>1993-05-26 01:54:00.000</td>\n",
              "      <td>1993-05-26 02:03:00.000</td>\n",
              "      <td>1993-05-26 01:59:00.000</td>\n",
              "      <td>C</td>\n",
              "      <td>32.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>goes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>26657</td>\n",
              "      <td>1990-01-23 05:03:00.000</td>\n",
              "      <td>1990-01-23 05:35:00.000</td>\n",
              "      <td>1990-01-23 05:04:00.000</td>\n",
              "      <td>M</td>\n",
              "      <td>23.0</td>\n",
              "      <td>-10.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>5900.0</td>\n",
              "      <td>goes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85509</th>\n",
              "      <td>72167</td>\n",
              "      <td>2014-03-12 22:28:00.000</td>\n",
              "      <td>2014-03-12 22:39:00.000</td>\n",
              "      <td>2014-03-12 22:34:00.000</td>\n",
              "      <td>M</td>\n",
              "      <td>93.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>-78.0</td>\n",
              "      <td>11996.0</td>\n",
              "      <td>goes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85510</th>\n",
              "      <td>44281</td>\n",
              "      <td>1998-12-18 15:59:00.000</td>\n",
              "      <td>1998-12-18 16:13:00.000</td>\n",
              "      <td>1998-12-18 16:05:00.000</td>\n",
              "      <td>C</td>\n",
              "      <td>20.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>8415.0</td>\n",
              "      <td>goes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85511</th>\n",
              "      <td>30992</td>\n",
              "      <td>1991-07-20 06:51:00.000</td>\n",
              "      <td>1991-07-20 07:07:00.000</td>\n",
              "      <td>1991-07-20 06:54:00.000</td>\n",
              "      <td>C</td>\n",
              "      <td>34.0</td>\n",
              "      <td>-14.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>6737.0</td>\n",
              "      <td>goes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85512</th>\n",
              "      <td>76690</td>\n",
              "      <td>2016-08-09 01:55:00.000</td>\n",
              "      <td>2016-08-09 02:09:00.000</td>\n",
              "      <td>2016-08-09 01:58:00.000</td>\n",
              "      <td>B</td>\n",
              "      <td>76.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>goes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85513</th>\n",
              "      <td>11311</td>\n",
              "      <td>1982-06-05 12:39:00.000</td>\n",
              "      <td>1982-06-05 12:46:00.000</td>\n",
              "      <td>1982-06-05 12:42:00.000</td>\n",
              "      <td>C</td>\n",
              "      <td>38.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>goes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>85514 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       deeps_flare_id           start_datetime             end_datetime  \\\n",
              "0               78524  2016-01-01 23:10:00.000  2016-01-02 01:01:00.000   \n",
              "1               32340  1991-12-18 06:06:00.000  1991-12-18 06:39:00.000   \n",
              "2               19726  1986-05-02 16:36:00.000  1986-05-02 16:44:00.000   \n",
              "3               36328  1993-05-26 01:54:00.000  1993-05-26 02:03:00.000   \n",
              "4               26657  1990-01-23 05:03:00.000  1990-01-23 05:35:00.000   \n",
              "...               ...                      ...                      ...   \n",
              "85509           72167  2014-03-12 22:28:00.000  2014-03-12 22:39:00.000   \n",
              "85510           44281  1998-12-18 15:59:00.000  1998-12-18 16:13:00.000   \n",
              "85511           30992  1991-07-20 06:51:00.000  1991-07-20 07:07:00.000   \n",
              "85512           76690  2016-08-09 01:55:00.000  2016-08-09 02:09:00.000   \n",
              "85513           11311  1982-06-05 12:39:00.000  1982-06-05 12:46:00.000   \n",
              "\n",
              "                 peak_datetime xray_class  xray_intensity  latitude  \\\n",
              "0      2016-01-02 00:11:00.000          M            23.0       NaN   \n",
              "1      1991-12-18 06:38:00.000          C            19.0       NaN   \n",
              "2      1986-05-02 16:41:00.000          C            12.0       NaN   \n",
              "3      1993-05-26 01:59:00.000          C            32.0       NaN   \n",
              "4      1990-01-23 05:04:00.000          M            23.0     -10.0   \n",
              "...                        ...        ...             ...       ...   \n",
              "85509  2014-03-12 22:34:00.000          M            93.0      15.0   \n",
              "85510  1998-12-18 16:05:00.000          C            20.0      19.0   \n",
              "85511  1991-07-20 06:54:00.000          C            34.0     -14.0   \n",
              "85512  2016-08-09 01:58:00.000          B            76.0       NaN   \n",
              "85513  1982-06-05 12:42:00.000          C            38.0       NaN   \n",
              "\n",
              "       longtitude  noaa_ar source  \n",
              "0             NaN  12473.0   swpc  \n",
              "1             NaN      NaN   goes  \n",
              "2             NaN      NaN   goes  \n",
              "3             NaN      NaN   goes  \n",
              "4            56.0   5900.0   goes  \n",
              "...           ...      ...    ...  \n",
              "85509       -78.0  11996.0   goes  \n",
              "85510        65.0   8415.0   goes  \n",
              "85511        72.0   6737.0   goes  \n",
              "85512         NaN      NaN   goes  \n",
              "85513         NaN      NaN   goes  \n",
              "\n",
              "[85514 rows x 10 columns]"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "flare_info"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PAJ4yLiJA37P"
      },
      "source": [
        "# Using database archived information"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "EYxT3zyfA4yJ"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "import sqlite3\n",
        "import os"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TyZ6CrLkA_0y"
      },
      "source": [
        "## Read configuration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "KHptYVc7BALI"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import json\n",
        "\n",
        "# Read configuration file\n",
        "nlfff_path_dataset_archive=\"nlfff-dataset-and-flare-label-db-archive\"\n",
        "config_path=os.path.join(nlfff_path_dataset_archive,\"config.json\")\n",
        "with open(config_path,\"r\") as f:\n",
        "    config_db=json.loads(f.read())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "TeW3Oie4BLxq"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'config_version': 1.3, 'archive_db_path': 'sqlite-202203/nlfff-202203'}"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "config_db"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "nc3LIMziBmIG"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'nlfff-dataset-and-flare-label-db-archive/sqlite-202203/nlfff-202203'"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "archive_db_path=os.path.join(\n",
        "    nlfff_path_dataset_archive,\n",
        "    config_db[\"archive_db_path\"]\n",
        ")\n",
        "archive_db_path"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9LMyy3VoBXzU"
      },
      "source": [
        "## Basic introduction to archived files \n",
        "\n",
        "The archive file storage format is a sqlite database file, including the following information: \n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "+ Product information of the sample (nx,ny,nz)\n",
        "+ Sample label (CMX flare label)\n",
        "+ flare information (start time)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MIR3V8jYBe34"
      },
      "source": [
        "#### Basic use \n",
        "\n",
        "Query table and table information"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "8No3OlCSBjmH"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "('flare_info',)\n",
            "('nlfff_archive',)\n",
            "('nlfff_flare_label',)\n",
            "('nlfff_raw',)\n",
            "['flare_info', 'nlfff_archive', 'nlfff_flare_label', 'nlfff_raw']\n"
          ]
        }
      ],
      "source": [
        "# Query which tables exist\n",
        "query_sql=\"select name from sqlite_master where type='table' order by name; \"\n",
        "table_list=[]\n",
        "with sqlite3.connect(archive_db_path) as conn:\n",
        "   c = conn.cursor()\n",
        "   cursor = c.execute(query_sql)\n",
        "   for table in cursor:\n",
        "      print(table)\n",
        "      table_list.append(table[0])\n",
        "print(table_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "TDuNLUXFCAFO"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(0, 'harpnum_trec', 'VARCHAR(50)', 0, None, 0)\n",
            "(1, 'sharp_num', 'INTEGER', 0, None, 0)\n",
            "(2, 't_rec', 'VARCHAR(50)', 0, None, 0)\n",
            "(3, 'bout_path', 'VARCHAR(128)', 0, None, 0)\n",
            "(4, 'bout_md5', 'VARCHAR(50)', 0, None, 0)\n",
            "(5, 'bout_size', 'INTEGER', 0, None, 0)\n",
            "(6, 'grid_x', 'INTEGER', 0, None, 0)\n",
            "(7, 'grid_y', 'INTEGER', 0, None, 0)\n",
            "(8, 'grid_z', 'INTEGER', 0, None, 0)\n",
            "(9, 'grid_xyz', 'INTEGER', 0, None, 0)\n",
            "(10, 'bout_maxlevel', 'INTEGER', 0, None, 0)\n",
            "(11, 'bout_quality', 'BOOLEAN', 0, None, 0)\n",
            "(12, 'batch', 'INTEGER', 0, None, 0)\n",
            "(13, 'notes', 'VARCHAR(50)', 0, None, 0)\n",
            "(14, 'bout_quality_value', 'REAL', 0, None, 0)\n",
            "(15, 'identifiers', 'VARCHAR(50)', 0, None, 0)\n",
            "['harpnum_trec', 'sharp_num', 't_rec', 'bout_path', 'bout_md5', 'bout_size', 'grid_x', 'grid_y', 'grid_z', 'grid_xyz', 'bout_maxlevel', 'bout_quality', 'batch', 'notes', 'bout_quality_value', 'identifiers']\n"
          ]
        }
      ],
      "source": [
        "# Query what columns are in a table\n",
        "table_name=\"nlfff_archive\"\n",
        "query_sql=\"PRAGMA table_info({});\".format(table_name)\n",
        "column_list=[]\n",
        "with sqlite3.connect(archive_db_path) as conn:\n",
        "   c = conn.cursor()\n",
        "   cursor = c.execute(query_sql)\n",
        "   for column in cursor:\n",
        "      print(column)\n",
        "      column_list.append(column[1])\n",
        "print(column_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "kPA03DdbCApB"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "flare_info\n",
            "nlfff_archive\n",
            "nlfff_flare_label\n",
            "nlfff_raw\n"
          ]
        }
      ],
      "source": [
        "# Create a mapping dictionary of table names and column names\n",
        "\n",
        "ktablename_vcolumnlist=dict()\n",
        "\n",
        "for table_name in table_list:\n",
        "   print(table_name)\n",
        "   # table_name=\"nlfff_archive\"\n",
        "   query_sql=\"PRAGMA table_info({});\".format(table_name)\n",
        "   column_list=[]\n",
        "   with sqlite3.connect(archive_db_path) as conn:\n",
        "      c = conn.cursor()\n",
        "      cursor = c.execute(query_sql)\n",
        "      for column in cursor:\n",
        "         # print(column)\n",
        "         column_list.append(column[1])\n",
        "   # print(column_list)\n",
        "   ktablename_vcolumnlist[table_name]=column_list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "lH0Am8WHCEvT"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'flare_info': ['deeps_flare_id',\n",
              "  'start_datetime',\n",
              "  'end_datetime',\n",
              "  'peak_datetime',\n",
              "  'xray_class',\n",
              "  'xray_intensity',\n",
              "  'latitude',\n",
              "  'longtitude',\n",
              "  'noaa_ar',\n",
              "  'source'],\n",
              " 'nlfff_archive': ['harpnum_trec',\n",
              "  'sharp_num',\n",
              "  't_rec',\n",
              "  'bout_path',\n",
              "  'bout_md5',\n",
              "  'bout_size',\n",
              "  'grid_x',\n",
              "  'grid_y',\n",
              "  'grid_z',\n",
              "  'grid_xyz',\n",
              "  'bout_maxlevel',\n",
              "  'bout_quality',\n",
              "  'batch',\n",
              "  'notes',\n",
              "  'bout_quality_value',\n",
              "  'identifiers'],\n",
              " 'nlfff_flare_label': ['harpnum_trec',\n",
              "  'harp_num',\n",
              "  't_rec_str',\n",
              "  'now_flare_level',\n",
              "  'now_flare_id',\n",
              "  'h6_flare_level',\n",
              "  'h6_flare_id',\n",
              "  'h12_flare_level',\n",
              "  'h12_flare_id',\n",
              "  'h24_flare_level',\n",
              "  'h24_flare_id',\n",
              "  'h48_flare_level',\n",
              "  'h48_flare_id',\n",
              "  't_rec_datetime',\n",
              "  'h24_posmx',\n",
              "  'h24_poscmx',\n",
              "  'h48_posmx',\n",
              "  'h48_poscmx',\n",
              "  'h24_delta05',\n",
              "  'h48_delta05'],\n",
              " 'nlfff_raw': ['harpnum_trec',\n",
              "  'HARP_NUM',\n",
              "  'T_REC_DATETIME',\n",
              "  'T_REC_STR',\n",
              "  'FILE_NAME',\n",
              "  'query',\n",
              "  'DATE',\n",
              "  'DATE_S',\n",
              "  'DATE_B',\n",
              "  'DATE__OBS',\n",
              "  'T_OBS',\n",
              "  'T_REC',\n",
              "  'T_REC_epoch',\n",
              "  'T_REC_step',\n",
              "  'T_REC_unit',\n",
              "  'CADENCE',\n",
              "  'USFLUX',\n",
              "  'MEANGAM',\n",
              "  'MEANGBT',\n",
              "  'MEANGBZ',\n",
              "  'MEANGBH',\n",
              "  'MEANJZD',\n",
              "  'TOTUSJZ',\n",
              "  'MEANALP',\n",
              "  'MEANJZH',\n",
              "  'TOTUSJH',\n",
              "  'ABSNJZH',\n",
              "  'SAVNCPP',\n",
              "  'MEANPOT',\n",
              "  'TOTPOT',\n",
              "  'MEANSHR',\n",
              "  'SHRGT45',\n",
              "  'R_VALUE',\n",
              "  'GWILL',\n",
              "  'CTYPE1',\n",
              "  'CTYPE2',\n",
              "  'CRPIX1',\n",
              "  'CRPIX2',\n",
              "  'CRVAL1',\n",
              "  'CRVAL2',\n",
              "  'CDELT1',\n",
              "  'CDELT2',\n",
              "  'CUNIT1',\n",
              "  'CUNIT2',\n",
              "  'IMCRPIX1',\n",
              "  'IMCRPIX2',\n",
              "  'IMCRVAL1',\n",
              "  'IMCRVAL2',\n",
              "  'CROTA2',\n",
              "  'CRDER1',\n",
              "  'CRDER2',\n",
              "  'CSYSER1',\n",
              "  'CSYSER2',\n",
              "  'WCSNAME',\n",
              "  'DSUN_OBS',\n",
              "  'DSUN_REF',\n",
              "  'RSUN_REF',\n",
              "  'CRLN_OBS',\n",
              "  'CRLT_OBS',\n",
              "  'CAR_ROT',\n",
              "  'OBS_VR',\n",
              "  'OBS_VW',\n",
              "  'OBS_VN',\n",
              "  'RSUN_OBS',\n",
              "  'TELESCOP',\n",
              "  'INSTRUME',\n",
              "  'WAVELNTH',\n",
              "  'CAMERA',\n",
              "  'QUALITY',\n",
              "  'QUAL_S',\n",
              "  'QUALLEV1',\n",
              "  'BUNIT_000',\n",
              "  'BUNIT_001',\n",
              "  'BUNIT_002',\n",
              "  'BUNIT_003',\n",
              "  'BUNIT_004',\n",
              "  'BUNIT_005',\n",
              "  'BUNIT_006',\n",
              "  'BUNIT_007',\n",
              "  'BUNIT_008',\n",
              "  'BUNIT_009',\n",
              "  'BUNIT_010',\n",
              "  'ORIGIN',\n",
              "  'CONTENT',\n",
              "  'HISTORY',\n",
              "  'COMMENT',\n",
              "  'BLD_VERS',\n",
              "  'CALVER64',\n",
              "  'CODEVER7',\n",
              "  'HFLID',\n",
              "  'HCFTID',\n",
              "  'QLOOK',\n",
              "  'HARPNUM',\n",
              "  'MASK',\n",
              "  'ARM_QUAL',\n",
              "  'ARM_NCLN',\n",
              "  'H_MERGE',\n",
              "  'H_FAINT',\n",
              "  'ARM_MODL',\n",
              "  'ARM_EDGE',\n",
              "  'ARM_BETA',\n",
              "  'LATDTMIN',\n",
              "  'LONDTMIN',\n",
              "  'LATDTMAX',\n",
              "  'LONDTMAX',\n",
              "  'OMEGA_DT',\n",
              "  'NPIX',\n",
              "  'SIZE',\n",
              "  'AREA',\n",
              "  'NACR',\n",
              "  'SIZE_ACR',\n",
              "  'AREA_ACR',\n",
              "  'MTOT',\n",
              "  'MNET',\n",
              "  'MPOS_TOT',\n",
              "  'MNEG_TOT',\n",
              "  'MMEAN',\n",
              "  'MSTDEV',\n",
              "  'MSKEW',\n",
              "  'MKURT',\n",
              "  'LAT_MIN',\n",
              "  'LON_MIN',\n",
              "  'LAT_MAX',\n",
              "  'LON_MAX',\n",
              "  'LAT_FWT',\n",
              "  'LON_FWT',\n",
              "  'LAT_FWTPOS',\n",
              "  'LON_FWTPOS',\n",
              "  'LAT_FWTNEG',\n",
              "  'LON_FWTNEG',\n",
              "  'T_FRST',\n",
              "  'T_FRST1',\n",
              "  'T_LAST1',\n",
              "  'T_LAST',\n",
              "  'N_PATCH',\n",
              "  'N_PATCH1',\n",
              "  'N_PATCHM',\n",
              "  'NOAA_AR',\n",
              "  'NOAA_NUM',\n",
              "  'NOAA_ARS',\n",
              "  'INVCODEV',\n",
              "  'INVDOCU',\n",
              "  'INVITERA',\n",
              "  'INVSTLGT',\n",
              "  'INVFLPRF',\n",
              "  'INVPHMAP',\n",
              "  'INVVLAVE',\n",
              "  'INVBLAVE',\n",
              "  'INVBBAVE',\n",
              "  'INVNPRCS',\n",
              "  'INVNCNVG',\n",
              "  'AMBCODEV',\n",
              "  'AMBDOCU',\n",
              "  'AMBGMTRY',\n",
              "  'AMBPATCH',\n",
              "  'AMBWEAK',\n",
              "  'AMBNEROD',\n",
              "  'AMBNGROW',\n",
              "  'AMBNPAD',\n",
              "  'AMBNAP',\n",
              "  'AMBNTX',\n",
              "  'AMBNTY',\n",
              "  'AMBBTHR0',\n",
              "  'AMBBTHR1',\n",
              "  'AMBSEED',\n",
              "  'AMBNEQ',\n",
              "  'AMBLMBDA',\n",
              "  'AMBTFCT0',\n",
              "  'AMBTFCTR',\n",
              "  'DATAVALS_000',\n",
              "  'DATAVALS_001',\n",
              "  'DATAVALS_002',\n",
              "  'DATAVALS_003',\n",
              "  'DATAVALS_004',\n",
              "  'DATAVALS_005',\n",
              "  'DATAVALS_006',\n",
              "  'DATAVALS_007',\n",
              "  'DATAVALS_008',\n",
              "  'DATAVALS_009',\n",
              "  'DATAVALS_010',\n",
              "  'MISSVALS_000',\n",
              "  'MISSVALS_001',\n",
              "  'MISSVALS_002',\n",
              "  'MISSVALS_003',\n",
              "  'MISSVALS_004',\n",
              "  'MISSVALS_005',\n",
              "  'MISSVALS_006',\n",
              "  'MISSVALS_007',\n",
              "  'MISSVALS_008',\n",
              "  'MISSVALS_009',\n",
              "  'MISSVALS_010',\n",
              "  'DATAMIN_001',\n",
              "  'DATAMIN_002',\n",
              "  'DATAMIN_003',\n",
              "  'DATAMIN_004',\n",
              "  'DATAMIN_005',\n",
              "  'DATAMIN_006',\n",
              "  'DATAMIN_007',\n",
              "  'DATAMIN_008',\n",
              "  'DATAMIN_009',\n",
              "  'DATAMIN_010',\n",
              "  'DATAMIN_000',\n",
              "  'DATAMAX_000',\n",
              "  'DATAMAX_001',\n",
              "  'DATAMAX_002',\n",
              "  'DATAMAX_003',\n",
              "  'DATAMAX_004',\n",
              "  'DATAMAX_005',\n",
              "  'DATAMAX_006',\n",
              "  'DATAMAX_007',\n",
              "  'DATAMAX_008',\n",
              "  'DATAMAX_009',\n",
              "  'DATAMAX_010',\n",
              "  'DATAMEDN_003',\n",
              "  'DATAMEDN_004',\n",
              "  'DATAMEDN_005',\n",
              "  'DATAMEDN_006',\n",
              "  'DATAMEDN_007',\n",
              "  'DATAMEDN_008',\n",
              "  'DATAMEDN_009',\n",
              "  'DATAMEDN_010',\n",
              "  'DATAMEDN_000',\n",
              "  'DATAMEDN_001',\n",
              "  'DATAMEDN_002',\n",
              "  'DATAMEAN_010',\n",
              "  'DATAMEAN_000',\n",
              "  'DATAMEAN_001',\n",
              "  'DATAMEAN_002',\n",
              "  'DATAMEAN_003',\n",
              "  'DATAMEAN_004',\n",
              "  'DATAMEAN_005',\n",
              "  'DATAMEAN_006',\n",
              "  'DATAMEAN_007',\n",
              "  'DATAMEAN_008',\n",
              "  'DATAMEAN_009',\n",
              "  'DATARMS_000',\n",
              "  'DATARMS_001',\n",
              "  'DATARMS_002',\n",
              "  'DATARMS_003',\n",
              "  'DATARMS_004',\n",
              "  'DATARMS_005',\n",
              "  'DATARMS_006',\n",
              "  'DATARMS_007',\n",
              "  'DATARMS_008',\n",
              "  'DATARMS_009',\n",
              "  'DATARMS_010',\n",
              "  'ERRGAM',\n",
              "  'ERRTAI',\n",
              "  'ERRBH',\n",
              "  'ERRMPOT',\n",
              "  'ERRBT',\n",
              "  'ERRTUI',\n",
              "  'ERRBZ',\n",
              "  'CMASK',\n",
              "  'ERRJZ',\n",
              "  'ERRVF',\n",
              "  'ERRALP',\n",
              "  'ERRMIH',\n",
              "  'ERRMSHA',\n",
              "  'ERRUSI',\n",
              "  'DOFFSET',\n",
              "  'ERRTPOT',\n",
              "  'ERRJHT',\n",
              "  'magnetogram',\n",
              "  'bitmap',\n",
              "  'Dopplergram',\n",
              "  'continuum',\n",
              "  'Bp',\n",
              "  'Bt',\n",
              "  'Br',\n",
              "  'Bp_err',\n",
              "  'Bt_err',\n",
              "  'Br_err',\n",
              "  'conf_disambig',\n",
              "  'BHARP',\n",
              "  'MHARP']}"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ktablename_vcolumnlist"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xw8oFLS6CINY"
      },
      "source": [
        "### Query Table Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "fMy6Z3gnCJg1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'harpnum_trec': '1644.20120516_031200_TAI', 'HARP_NUM': 1644, 'T_REC_DATETIME': '2012-05-16 03:12:00.000', 'T_REC_STR': '20120516_031200_TAI', 'FILE_NAME': 'hmi.sharp_cea_720s.1644.20120516_031200_TAI', 'query': 'hmi.sharp_cea_720s[1644][2012.05.16_03:12:00_TAI]', 'DATE': '2018-05-09T03:05:47Z', 'DATE_S': '2012-05-20T19:22:46Z', 'DATE_B': '2015-09-30T20:47:04Z', 'DATE__OBS': '2012-05-16T03:10:24.10Z', 'T_OBS': '2012.05.16_03:12:06_TAI', 'T_REC': '2012.05.16_03:12:00_TAI', 'T_REC_epoch': '1993.01.01_00:00:00_TAI', 'T_REC_step': 720.0, 'T_REC_unit': 'secs', 'CADENCE': 720.0, 'USFLUX': 7.195475e+21, 'MEANGAM': 47.02, 'MEANGBT': 113.15, 'MEANGBZ': 110.28299999999999, 'MEANGBH': 61.652, 'MEANJZD': 0.12072397, 'TOTUSJZ': 9593281000000, 'MEANALP': 0.01153706, 'MEANJZH': 0.0052498, 'TOTUSJH': 474.784, 'ABSNJZH': 57.433, 'SAVNCPP': 710574700000, 'MEANPOT': 9327.876, 'TOTPOT': 1.355353e+23, 'MEANSHR': 36.965, 'SHRGT45': 33.726, 'R_VALUE': 2.103, 'GWILL': '', 'CTYPE1': 'CRLN-CEA', 'CTYPE2': 'CRLT-CEA', 'CRPIX1': 477.5, 'CRPIX2': 279.0, 'CRVAL1': 134.58140600000002, 'CRVAL2': -22.961349, 'CDELT1': 0.03, 'CDELT2': 0.03, 'CUNIT1': 'degree', 'CUNIT2': 'degree', 'IMCRPIX1': 2035.023315, 'IMCRPIX2': 2052.667725, 'IMCRVAL1': 0.0, 'IMCRVAL2': 0.0, 'CROTA2': 0.0, 'CRDER1': '', 'CRDER2': '', 'CSYSER1': '', 'CSYSER2': '', 'WCSNAME': 'Carrington Heliographic', 'DSUN_OBS': 151288617876.01, 'DSUN_REF': 149597870691, 'RSUN_REF': 696000000, 'CRLN_OBS': 122.84070600000001, 'CRLT_OBS': -2.505391, 'CAR_ROT': 2123, 'OBS_VR': 1939.0652760000003, 'OBS_VW': 30084.039826, 'OBS_VN': 6001.302302, 'RSUN_OBS': 948.920105, 'TELESCOP': 'SDO/HMI', 'INSTRUME': 'HMI_SIDE1', 'WAVELNTH': 6173.0, 'CAMERA': 1, 'QUALITY': '0x00000000', 'QUAL_S': '0x00000000', 'QUALLEV1': '0x00000000', 'BUNIT_000': 'Mx/cm^2', 'BUNIT_001': ' ', 'BUNIT_002': 'cm/s', 'BUNIT_003': 'DN/s', 'BUNIT_004': 'Mx/cm^2', 'BUNIT_005': 'Mx/cm^2', 'BUNIT_006': 'Mx/cm^2', 'BUNIT_007': 'Mx/cm^2', 'BUNIT_008': 'Mx/cm^2', 'BUNIT_009': 'Mx/cm^2', 'BUNIT_010': ' ', 'ORIGIN': 'SDO/JSOC-SDP', 'CONTENT': 'HMI observable', 'HISTORY': ' ', 'COMMENT': ' ', 'BLD_VERS': 'V8R9X', 'CALVER64': '0x0000000000000010', 'CODEVER7': '$Id: sharp.c,v 1.38 2015/03/18 00:28:26 xudong Exp $', 'HFLID': 1021, 'HCFTID': 11, 'QLOOK': 0, 'HARPNUM': 1644, 'MASK': 32, 'ARM_QUAL': '0x00000000', 'ARM_NCLN': 1, 'H_MERGE': 0, 'H_FAINT': 0, 'ARM_MODL': '/builtin/hmi.M_Ic_noLimbDark_720s.production', 'ARM_EDGE': 2.5, 'ARM_BETA': 0.3, 'LATDTMIN': -31.313299, 'LONDTMIN': -2.5699, 'LATDTMAX': -14.6094, 'LONDTMAX': 26.0513, 'OMEGA_DT': 13.2649, 'NPIX': 133203, 'SIZE': 11976.625, 'AREA': 6635.683594, 'NACR': 9287, 'SIZE_ACR': 835.018066, 'AREA_ACR': 462.282288, 'MTOT': 6930884.5, 'MNET': 260587.09375, 'MPOS_TOT': 3595736.0, 'MNEG_TOT': 3335148.75, 'MMEAN': 1.956316, 'MSTDEV': 144.275909, 'MSKEW': 4.339589, 'MKURT': 41.763123, 'LAT_MIN': -30.760214, 'LON_MIN': -1.510393, 'LAT_MAX': -15.120847, 'LON_MAX': 23.849808, 'LAT_FWT': -23.853493, 'LON_FWT': 12.266521000000001, 'LAT_FWTPOS': -23.781185, 'LON_FWTPOS': 14.561793, 'LAT_FWTNEG': -23.969935999999997, 'LON_FWTNEG': 8.570277, 'T_FRST': '2012.05.06_21:12:00_TAI', 'T_FRST1': '2012.05.07_21:12:00_TAI', 'T_LAST1': '2012.05.22_00:48:00_TAI', 'T_LAST': '2012.05.23_00:48:00_TAI', 'N_PATCH': 1939, 'N_PATCH1': 1699, 'N_PATCHM': 0, 'NOAA_AR': 11477, 'NOAA_NUM': 2, 'NOAA_ARS': '11477,11478', 'INVCODEV': 'vfisv FD10 2012 Apr. 13', 'INVDOCU': '', 'INVITERA': 200, 'INVSTLGT': 'No', 'INVFLPRF': ' ', 'INVPHMAP': ' ', 'INVVLAVE': 1.033407e+22, 'INVBLAVE': 20.044, 'INVBBAVE': 127.6648, 'INVNPRCS': 150736, 'INVNCNVG': 11121934, 'AMBCODEV': 'disambig_v3 2013 Dec 06', 'AMBDOCU': '', 'AMBGMTRY': 2, 'AMBPATCH': 0, 'AMBWEAK': 2, 'AMBNEROD': 1, 'AMBNGROW': 5, 'AMBNPAD': 200, 'AMBNAP': 10, 'AMBNTX': 30, 'AMBNTY': 30, 'AMBBTHR0': 200.0, 'AMBBTHR1': 400.0, 'AMBSEED': 4, 'AMBNEQ': 100, 'AMBLMBDA': 1.0, 'AMBTFCT0': 2.0, 'AMBTFCTR': 0.98, 'DATAVALS_000': 531378, 'DATAVALS_001': 531378, 'DATAVALS_002': 531378, 'DATAVALS_003': 531378, 'DATAVALS_004': 531378, 'DATAVALS_005': 531378, 'DATAVALS_006': 531378, 'DATAVALS_007': 531378, 'DATAVALS_008': 531378, 'DATAVALS_009': 531378, 'DATAVALS_010': 531378, 'MISSVALS_000': 0, 'MISSVALS_001': 0, 'MISSVALS_002': 0, 'MISSVALS_003': 0, 'MISSVALS_004': 0, 'MISSVALS_005': 0, 'MISSVALS_006': 0, 'MISSVALS_007': 0, 'MISSVALS_008': 0, 'MISSVALS_009': 0, 'MISSVALS_010': 0, 'DATAMIN_001': 782.0, 'DATAMIN_002': 7730.0, 'DATAMIN_003': -1080.859985, 'DATAMIN_004': -1407.140015, 'DATAMIN_005': -1220.349976, 'DATAMIN_006': 5.495, 'DATAMIN_007': 5.89, 'DATAMIN_008': 3.056, 'DATAMIN_009': 50.0, 'DATAMIN_010': -883.099976, 'DATAMIN_000': 0.0, 'DATAMAX_000': 1839.199951, 'DATAMAX_001': 34.0, 'DATAMAX_002': 3945.0, 'DATAMAX_003': 64898.0, 'DATAMAX_004': 1343.079956, 'DATAMAX_005': 1352.119995, 'DATAMAX_006': 2752.580078, 'DATAMAX_007': 1515.130005, 'DATAMAX_008': 639.731018, 'DATAMAX_009': 361.239014, 'DATAMAX_010': 90.0, 'DATAMEDN_003': -4.422134, 'DATAMEDN_004': 41.280186, 'DATAMEDN_005': 40.248523999999996, 'DATAMEDN_006': 18.449409, 'DATAMEDN_007': 50.000305, 'DATAMEDN_008': -0.7078, 'DATAMEDN_009': 0.999985, 'DATAMEDN_010': 2272.499023, 'DATAMEDN_000': 56360.22265599999, 'DATAMEDN_001': 4.672016999999999, 'DATAMEDN_002': -9.005756, 'DATAMEAN_010': 20.131287, 'DATAMEAN_000': 52.015175, 'DATAMEAN_001': 0.475767, 'DATAMEAN_002': 10.057181, 'DATAMEAN_003': 2277.6877440000003, 'DATAMEAN_004': 56307.226562, 'DATAMEAN_005': 5.432481, 'DATAMEAN_006': -6.025362, 'DATAMEAN_007': 1.694795, 'DATAMEAN_008': 44.34304, 'DATAMEAN_009': 43.243214, 'DATARMS_000': 80.526901, 'DATARMS_001': 14.483751000000002, 'DATARMS_002': 386.348206, 'DATARMS_003': 2445.601807, 'DATARMS_004': 77.775124, 'DATARMS_005': 82.911629, 'DATARMS_006': 115.430428, 'DATARMS_007': 16.401438, 'DATARMS_008': 15.701928, 'DATARMS_009': 9.27594, 'DATARMS_010': 6.639278999999999, 'ERRGAM': 0.033, 'ERRTAI': 7.831, 'ERRBH': 0.237, 'ERRMPOT': 0.1888691, 'ERRBT': 10.901, 'ERRTUI': 0.16, 'ERRBZ': 7.831, 'CMASK': 10941.0, 'ERRJZ': 3261369466163822100, 'ERRVF': 0.07241935, 'ERRALP': 0.00157312, 'ERRMIH': 0.00071585, 'ERRMSHA': 0.003, 'ERRUSI': 105226300000, 'DOFFSET': 1054507008, 'ERRTPOT': 1.58388e+20, 'ERRJHT': 50.0, 'magnetogram': '/SUM90/D1042885471/S00000/magnetogram.fits', 'bitmap': '/SUM90/D1042885471/S00000/bitmap.fits', 'Dopplergram': '/SUM90/D1042885471/S00000/Dopplergram.fits', 'continuum': '/SUM90/D1042885471/S00000/continuum.fits', 'Bp': '/SUM90/D1042885471/S00000/Bp.fits', 'Bt': '/SUM90/D1042885471/S00000/Bt.fits', 'Br': '/SUM90/D1042885471/S00000/Br.fits', 'Bp_err': '/SUM90/D1042885471/S00000/Bp_err.fits', 'Bt_err': '/SUM90/D1042885471/S00000/Bt_err.fits', 'Br_err': '/SUM90/D1042885471/S00000/Br_err.fits', 'conf_disambig': '/SUM90/D1042885471/S00000/conf_disambig.fits', 'BHARP': 'InvalidLink', 'MHARP': 'hmi.Mharp_720s[1644][2012.05.16_03:12:00_TAI]'}\n"
          ]
        }
      ],
      "source": [
        "# Sample Raw Fits File Header File Information(time,noaa number)\n",
        "\n",
        "table_name=\"nlfff_raw\"\n",
        "with sqlite3.connect(archive_db_path) as conn:\n",
        "   c = conn.cursor()\n",
        "\n",
        "   query_sql=\"SELECT *  from {}\".format(table_name)\n",
        "   cursor = c.execute(query_sql)\n",
        "   for row in cursor:\n",
        "      row_data_list=list(row)\n",
        "      row_column_name_list=ktablename_vcolumnlist[table_name]\n",
        "      row_dict=dict(zip(row_column_name_list,row_data_list))\n",
        "      print(row_dict)\n",
        "      break\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "fjepziilCLMf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'harpnum_trec': '6558.20160520_044800_TAI', 'sharp_num': 6558, 't_rec': '2016-05-20 04:48:00.000', 'bout_path': 'grid3.done/num_6500_6599/hmi.sharp_cea_720s.6558.20160520_044800_TAI/Bout.bin', 'bout_md5': 'ea4c960822a1284fe43637b1717bc30b', 'bout_size': 3580360704, 'grid_x': 432, 'grid_y': 764, 'grid_z': 452, 'grid_xyz': 149181696, 'bout_maxlevel': 3, 'bout_quality': 'true', 'batch': 20220301, 'notes': 'new', 'bout_quality_value': 15.568, 'identifiers': '6558201605200448003'}\n"
          ]
        }
      ],
      "source": [
        "# Product information for samples (nx,ny,nz）\n",
        "\n",
        "table_name=\"nlfff_archive\"\n",
        "with sqlite3.connect(archive_db_path) as conn:\n",
        "   c = conn.cursor()\n",
        "\n",
        "   query_sql=\"SELECT *  from {}\".format(table_name)\n",
        "   cursor = c.execute(query_sql)\n",
        "   for row in cursor:\n",
        "      row_data_list=list(row)\n",
        "      row_column_name_list=ktablename_vcolumnlist[table_name]\n",
        "      row_dict=dict(zip(row_column_name_list,row_data_list))\n",
        "      print(row_dict)\n",
        "      break\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "GVB-awjbCN5t"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'harpnum_trec': '1021.20111031_093600_TAI', 'harp_num': 1021, 't_rec_str': '20111031_093600_TAI', 'now_flare_level': 0, 'now_flare_id': -1, 'h6_flare_level': 0, 'h6_flare_id': -1, 'h12_flare_level': 0, 'h12_flare_id': -1, 'h24_flare_level': 0, 'h24_flare_id': -1, 'h48_flare_level': 0, 'h48_flare_id': -1, 't_rec_datetime': '2011-10-31 09:36:00.000', 'h24_posmx': 0, 'h24_poscmx': 0, 'h48_posmx': 0, 'h48_poscmx': 0, 'h24_delta05': 0, 'h48_delta05': 0}\n"
          ]
        }
      ],
      "source": [
        "# Sample labels (CMX flare label)\n",
        "\n",
        "table_name=\"nlfff_flare_label\"\n",
        "with sqlite3.connect(archive_db_path) as conn:\n",
        "   c = conn.cursor()\n",
        "\n",
        "   query_sql=\"SELECT *  from {}\".format(table_name)\n",
        "   cursor = c.execute(query_sql)\n",
        "   for row in cursor:\n",
        "      row_data_list=list(row)\n",
        "      row_column_name_list=ktablename_vcolumnlist[table_name]\n",
        "      row_dict=dict(zip(row_column_name_list,row_data_list))\n",
        "      print(row_dict)\n",
        "      break\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "Zhxa9rSRCPjZ"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'deeps_flare_id': 78524, 'start_datetime': '2016-01-01 23:10:00.000', 'end_datetime': '2016-01-02 01:01:00.000', 'peak_datetime': '2016-01-02 00:11:00.000', 'xray_class': 'M', 'xray_intensity': 23, 'latitude': '', 'longtitude': '', 'noaa_ar': 12473, 'source': 'swpc'}\n"
          ]
        }
      ],
      "source": [
        "# Flare information (start time)\n",
        "\n",
        "table_name=\"flare_info\"\n",
        "with sqlite3.connect(archive_db_path) as conn:\n",
        "   c = conn.cursor()\n",
        "\n",
        "   query_sql=\"SELECT *  from {}\".format(table_name)\n",
        "   cursor = c.execute(query_sql)\n",
        "   for row in cursor:\n",
        "      row_data_list=list(row)\n",
        "      row_column_name_list=ktablename_vcolumnlist[table_name]\n",
        "      row_dict=dict(zip(row_column_name_list,row_data_list))\n",
        "      print(row_dict)\n",
        "      break\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k2uVLh4LCSi4"
      },
      "source": [
        "#### Advanced queries \n",
        "\n",
        "+ python scripts can use pandas + an engine such as pandas+[sqlalchemy](https://www.sqlalchemy.org)\n",
        "+ visualization programs such as [dbeaver](https://dbeaver.io)\n",
        "+ other language toolkit links sqlite\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "txHXyer3HnL2"
      },
      "source": [
        "# Using a single sample (basic use)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Auv1lkO7LR_w"
      },
      "source": [
        "## Basic reading"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TDcr2YjuJfvy"
      },
      "source": [
        "#### Get the storage path and time"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w4P8D6fNHrNg"
      },
      "source": [
        "The following information is required at a minimum to use a sample: \n",
        "\n",
        "+ nonlinear force-free field data Bout.bin\n",
        "\n",
        "+ Bout.bin corresponding to nx, ny, nz\n",
        "\n",
        "+ the harp number and time of the sample"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "_oNi3DtBIrTL"
      },
      "outputs": [],
      "source": [
        "# Inside the dataset summary information there is greater than or equal to one sample, \n",
        "# We use this sample as an example for illustration \n",
        "\n",
        "# Take sample 0 as an example and read the `harpnum_trec` and path  of the already downloaded sample\n",
        "nlfff_path_dataset_summary=\"nlfff-dataset-info\"\n",
        "sample_num=0\n",
        "sample_harpnum_trec=config_ds[\"demo_sample\"][sample_num][\"harpnum_trec\"]\n",
        "sample_path=os.path.join(\n",
        "    nlfff_path_dataset_summary,\n",
        "    config_ds[\"demo_sample\"][sample_num][\"path\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "u2LMp7s5JP0R"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'7327.20181220_093600_TAI'"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Harp number and time of the sample \n",
        "sample_harpnum_trec"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "vjsUJ5_7J5cV"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'nlfff-dataset-info/demo/hmi.sharp_cea_720s.7327.20181220_093600_TAI/Bout.bin'"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Bout path\n",
        "sample_bout_path=os.path.join(sample_path,\"Bout.bin\")\n",
        "sample_bout_path"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DDu_QtFKKnsa"
      },
      "source": [
        "#### Get sample product file information (nx ny nz)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "rtFmPbxqKpRF"
      },
      "outputs": [],
      "source": [
        "# Get sample product file information \n",
        "\n",
        "sample_product_info=product_info[ product_info[\"harpnum_trec\"]==sample_harpnum_trec ]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "sociS1h1KuTJ"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "    }\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>harpnum_trec</th>\n",
              "      <th>sharp_num</th>\n",
              "      <th>t_rec</th>\n",
              "      <th>bout_path</th>\n",
              "      <th>bout_md5</th>\n",
              "      <th>bout_size</th>\n",
              "      <th>grid_x</th>\n",
              "      <th>grid_y</th>\n",
              "      <th>grid_z</th>\n",
              "      <th>grid_xyz</th>\n",
              "      <th>bout_maxlevel</th>\n",
              "      <th>bout_quality</th>\n",
              "      <th>batch</th>\n",
              "      <th>notes</th>\n",
              "      <th>bout_quality_value</th>\n",
              "      <th>identifiers</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>49935</th>\n",
              "      <td>7327.20181220_093600_TAI</td>\n",
              "      <td>7327</td>\n",
              "      <td>2018-12-20 09:36:00.000</td>\n",
              "      <td>grid3.done/num_7300_7399/hmi.sharp_cea_720s.73...</td>\n",
              "      <td>f7487ece2a486ba1aba7943398022fbf</td>\n",
              "      <td>107980800</td>\n",
              "      <td>100</td>\n",
              "      <td>296</td>\n",
              "      <td>152</td>\n",
              "      <td>4499200</td>\n",
              "      <td>3</td>\n",
              "      <td>True</td>\n",
              "      <td>20220301</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18.8298</td>\n",
              "      <td>7327201812200936003</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   harpnum_trec  sharp_num                    t_rec  \\\n",
              "49935  7327.20181220_093600_TAI       7327  2018-12-20 09:36:00.000   \n",
              "\n",
              "                                               bout_path  \\\n",
              "49935  grid3.done/num_7300_7399/hmi.sharp_cea_720s.73...   \n",
              "\n",
              "                               bout_md5  bout_size  grid_x  grid_y  grid_z  \\\n",
              "49935  f7487ece2a486ba1aba7943398022fbf  107980800     100     296     152   \n",
              "\n",
              "       grid_xyz  bout_maxlevel  bout_quality     batch notes  \\\n",
              "49935   4499200              3          True  20220301   NaN   \n",
              "\n",
              "       bout_quality_value          identifiers  \n",
              "49935             18.8298  7327201812200936003  "
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sample_product_info"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "eGKjhKQGKxyr"
      },
      "outputs": [],
      "source": [
        "\n",
        "# Maximum calculation level of the sample \n",
        "sample_bout_maxlevel=int(sample_product_info[\"bout_maxlevel\"])\n",
        "# The nx, ny, nz of the sample are \n",
        "sample_nx=int(sample_product_info[\"grid_x\"])\n",
        "sample_ny=int(sample_product_info[\"grid_y\"])\n",
        "sample_nz=int(sample_product_info[\"grid_z\"])\n",
        "# The identifiers of the sample are\n",
        "sample_identifiers=int(sample_product_info[\"identifiers\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u3bxM1w8K3rZ"
      },
      "source": [
        "#### Read data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "LZ5qK9SPK2Dp"
      },
      "outputs": [],
      "source": [
        "# Read data according to nx ny nz \n",
        "import numpy\n",
        "\n",
        "nx=sample_nx\n",
        "ny=sample_ny\n",
        "nz=sample_nz\n",
        "\n",
        "np_dtype_str=r\"<d\"\n",
        "bin_path=os.path.join(sample_path,\"Bout.bin\")\n",
        "\n",
        "# https://numpy.org/doc/stable/reference/generated/numpy.memmap.html\n",
        "nlfff_data = numpy.memmap(sample_bout_path,\n",
        "                dtype=numpy.dtype(np_dtype_str),\n",
        "                offset=0,\n",
        "                shape=(3, nx, ny, nz),\n",
        "                order='C')  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "17sOOrY_L37z"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(3, 100, 296, 152)"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "nlfff_data.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZRCR-9JqLdzx"
      },
      "source": [
        "## Get association information"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "beWykHkVLjfS"
      },
      "source": [
        "#### Get sample raw header file information"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "vjCQWCWVLuJD"
      },
      "outputs": [],
      "source": [
        "# Get sample raw header file information \n",
        "\n",
        "sample_raw_info=raw_info[ raw_info[\"harpnum_trec\"]==sample_harpnum_trec ]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "5BuETb37Lz_d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
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              "    .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>harpnum_trec</th>\n",
              "      <th>HARP_NUM</th>\n",
              "      <th>T_REC_DATETIME</th>\n",
              "      <th>T_REC_STR</th>\n",
              "      <th>FILE_NAME</th>\n",
              "      <th>query</th>\n",
              "      <th>DATE</th>\n",
              "      <th>DATE_S</th>\n",
              "      <th>DATE_B</th>\n",
              "      <th>DATE__OBS</th>\n",
              "      <th>...</th>\n",
              "      <th>continuum</th>\n",
              "      <th>Bp</th>\n",
              "      <th>Bt</th>\n",
              "      <th>Br</th>\n",
              "      <th>Bp_err</th>\n",
              "      <th>Bt_err</th>\n",
              "      <th>Br_err</th>\n",
              "      <th>conf_disambig</th>\n",
              "      <th>BHARP</th>\n",
              "      <th>MHARP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>33038</th>\n",
              "      <td>7327.20181220_093600_TAI</td>\n",
              "      <td>7327</td>\n",
              "      <td>2018-12-20 09:36:00.000</td>\n",
              "      <td>20181220_093600_TAI</td>\n",
              "      <td>hmi.sharp_cea_720s.7327.20181220_093600_TAI</td>\n",
              "      <td>hmi.sharp_cea_720s[7327][2018.12.20_09:36:00_TAI]</td>\n",
              "      <td>2019-01-25T04:03:11Z</td>\n",
              "      <td>2018-12-24T16:25:13Z</td>\n",
              "      <td>2018-12-26T00:57:23Z</td>\n",
              "      <td>2018-12-20T09:34:30.00Z</td>\n",
              "      <td>...</td>\n",
              "      <td>/SUM34/D1129547914/S00000/continuum.fits</td>\n",
              "      <td>/SUM34/D1129547914/S00000/Bp.fits</td>\n",
              "      <td>/SUM34/D1129547914/S00000/Bt.fits</td>\n",
              "      <td>/SUM34/D1129547914/S00000/Br.fits</td>\n",
              "      <td>/SUM34/D1129547914/S00000/Bp_err.fits</td>\n",
              "      <td>/SUM34/D1129547914/S00000/Bt_err.fits</td>\n",
              "      <td>/SUM34/D1129547914/S00000/Br_err.fits</td>\n",
              "      <td>/SUM34/D1129547914/S00000/conf_disambig.fits</td>\n",
              "      <td>InvalidLink</td>\n",
              "      <td>hmi.Mharp_720s[7327][2018.12.20_09:36:00_TAI]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 276 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                   harpnum_trec  HARP_NUM           T_REC_DATETIME  \\\n",
              "33038  7327.20181220_093600_TAI      7327  2018-12-20 09:36:00.000   \n",
              "\n",
              "                 T_REC_STR                                    FILE_NAME  \\\n",
              "33038  20181220_093600_TAI  hmi.sharp_cea_720s.7327.20181220_093600_TAI   \n",
              "\n",
              "                                                   query  \\\n",
              "33038  hmi.sharp_cea_720s[7327][2018.12.20_09:36:00_TAI]   \n",
              "\n",
              "                       DATE                DATE_S                DATE_B  \\\n",
              "33038  2019-01-25T04:03:11Z  2018-12-24T16:25:13Z  2018-12-26T00:57:23Z   \n",
              "\n",
              "                     DATE__OBS  ...                                 continuum  \\\n",
              "33038  2018-12-20T09:34:30.00Z  ...  /SUM34/D1129547914/S00000/continuum.fits   \n",
              "\n",
              "                                      Bp                                 Bt  \\\n",
              "33038  /SUM34/D1129547914/S00000/Bp.fits  /SUM34/D1129547914/S00000/Bt.fits   \n",
              "\n",
              "                                      Br  \\\n",
              "33038  /SUM34/D1129547914/S00000/Br.fits   \n",
              "\n",
              "                                      Bp_err  \\\n",
              "33038  /SUM34/D1129547914/S00000/Bp_err.fits   \n",
              "\n",
              "                                      Bt_err  \\\n",
              "33038  /SUM34/D1129547914/S00000/Bt_err.fits   \n",
              "\n",
              "                                      Br_err  \\\n",
              "33038  /SUM34/D1129547914/S00000/Br_err.fits   \n",
              "\n",
              "                                      conf_disambig        BHARP  \\\n",
              "33038  /SUM34/D1129547914/S00000/conf_disambig.fits  InvalidLink   \n",
              "\n",
              "                                               MHARP  \n",
              "33038  hmi.Mharp_720s[7327][2018.12.20_09:36:00_TAI]  \n",
              "\n",
              "[1 rows x 276 columns]"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sample_raw_info"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "vyPGi7jVL9Cg"
      },
      "source": [
        "#### Get label information"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "6RTE2MrKMKEn"
      },
      "outputs": [],
      "source": [
        "# Get the label of a sample \n",
        "\n",
        "# sample_harpnum_trec=\"997.20111030_142400_TAI\"\n",
        "sample_label=label_info[label_info[\"harpnum_trec\"]==sample_harpnum_trec]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "K4BXkChJMOeW"
      },
      "outputs": [
        {
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>harpnum_trec</th>\n",
              "      <th>harp_num</th>\n",
              "      <th>t_rec_str</th>\n",
              "      <th>now_flare_level</th>\n",
              "      <th>now_flare_id</th>\n",
              "      <th>h6_flare_level</th>\n",
              "      <th>h6_flare_id</th>\n",
              "      <th>h12_flare_level</th>\n",
              "      <th>h12_flare_id</th>\n",
              "      <th>h24_flare_level</th>\n",
              "      <th>h24_flare_id</th>\n",
              "      <th>h48_flare_level</th>\n",
              "      <th>h48_flare_id</th>\n",
              "      <th>t_rec_datetime</th>\n",
              "      <th>h24_posmx</th>\n",
              "      <th>h24_poscmx</th>\n",
              "      <th>h48_posmx</th>\n",
              "      <th>h48_poscmx</th>\n",
              "      <th>h24_delta05</th>\n",
              "      <th>h48_delta05</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>43896</th>\n",
              "      <td>7327.20181220_093600_TAI</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>2018-12-20 09:36:00.000</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   harpnum_trec  harp_num            t_rec_str  \\\n",
              "43896  7327.20181220_093600_TAI      7327  20181220_093600_TAI   \n",
              "\n",
              "       now_flare_level  now_flare_id  h6_flare_level  h6_flare_id  \\\n",
              "43896                0            -1               0           -1   \n",
              "\n",
              "       h12_flare_level  h12_flare_id  h24_flare_level  h24_flare_id  \\\n",
              "43896                0            -1                0            -1   \n",
              "\n",
              "       h48_flare_level  h48_flare_id           t_rec_datetime  h24_posmx  \\\n",
              "43896                0            -1  2018-12-20 09:36:00.000          0   \n",
              "\n",
              "       h24_poscmx  h48_posmx  h48_poscmx  h24_delta05  h48_delta05  \n",
              "43896           0          0           0            0            0  "
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# All label information for this sample \n",
        "sample_label"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AA3oDT2LMihF"
      },
      "source": [
        "## Get secondary association information"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lKvaY0-xMo4F"
      },
      "source": [
        "#### Label corresponding to flare information"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "5AaZPloZM2pD"
      },
      "outputs": [],
      "source": [
        "# Get a sample of positive samples with flares \n",
        "\n",
        "sample_harpnum_trec_pos=\"997.20111030_142400_TAI\"\n",
        "sample_label_pos=label_info[label_info[\"harpnum_trec\"]==sample_harpnum_trec_pos]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "H0jYraqkNEh_"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>harpnum_trec</th>\n",
              "      <th>harp_num</th>\n",
              "      <th>t_rec_str</th>\n",
              "      <th>now_flare_level</th>\n",
              "      <th>now_flare_id</th>\n",
              "      <th>h6_flare_level</th>\n",
              "      <th>h6_flare_id</th>\n",
              "      <th>h12_flare_level</th>\n",
              "      <th>h12_flare_id</th>\n",
              "      <th>h24_flare_level</th>\n",
              "      <th>h24_flare_id</th>\n",
              "      <th>h48_flare_level</th>\n",
              "      <th>h48_flare_id</th>\n",
              "      <th>t_rec_datetime</th>\n",
              "      <th>h24_posmx</th>\n",
              "      <th>h24_poscmx</th>\n",
              "      <th>h48_posmx</th>\n",
              "      <th>h48_poscmx</th>\n",
              "      <th>h24_delta05</th>\n",
              "      <th>h48_delta05</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>46113</th>\n",
              "      <td>997.20111030_142400_TAI</td>\n",
              "      <td>997</td>\n",
              "      <td>20111030_142400_TAI</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>319</td>\n",
              "      <td>67192</td>\n",
              "      <td>323</td>\n",
              "      <td>67197</td>\n",
              "      <td>323</td>\n",
              "      <td>67197</td>\n",
              "      <td>323</td>\n",
              "      <td>67197</td>\n",
              "      <td>2011-10-30 14:24:00.000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  harpnum_trec  harp_num            t_rec_str  \\\n",
              "46113  997.20111030_142400_TAI       997  20111030_142400_TAI   \n",
              "\n",
              "       now_flare_level  now_flare_id  h6_flare_level  h6_flare_id  \\\n",
              "46113                0            -1             319        67192   \n",
              "\n",
              "       h12_flare_level  h12_flare_id  h24_flare_level  h24_flare_id  \\\n",
              "46113              323         67197              323         67197   \n",
              "\n",
              "       h48_flare_level  h48_flare_id           t_rec_datetime  h24_posmx  \\\n",
              "46113              323         67197  2011-10-30 14:24:00.000          0   \n",
              "\n",
              "       h24_poscmx  h48_posmx  h48_poscmx  h24_delta05  h48_delta05  \n",
              "46113           1          0           1            3            3  "
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# All label information for this sample \n",
        "sample_label_pos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "bUoDHnSoMt1t"
      },
      "outputs": [],
      "source": [
        "# Further information can also be obtained on the flare information \n",
        "\n",
        "# Take the above sample with the information of flare occurrence within 12 hours as an example \n",
        "# Get the id of the flares that occurred in the above sample within 12 hours \n",
        "sample_h12_flare_id=int(sample_label_pos[\"h12_flare_id\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "y8D5lTjMNKDx"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "67197"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sample_h12_flare_id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "GWeB7_aTNMtc"
      },
      "outputs": [],
      "source": [
        "# Corresponding flare details \n",
        "sample_h12_flare_detail_info=flare_info[flare_info[\"deeps_flare_id\"]==sample_h12_flare_id]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "OgaJyNAoNOmq"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>deeps_flare_id</th>\n",
              "      <th>start_datetime</th>\n",
              "      <th>end_datetime</th>\n",
              "      <th>peak_datetime</th>\n",
              "      <th>xray_class</th>\n",
              "      <th>xray_intensity</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longtitude</th>\n",
              "      <th>noaa_ar</th>\n",
              "      <th>source</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>71616</th>\n",
              "      <td>67197</td>\n",
              "      <td>2011-10-31 00:50:00.000</td>\n",
              "      <td>2011-10-31 01:29:00.000</td>\n",
              "      <td>2011-10-31 01:10:00.000</td>\n",
              "      <td>C</td>\n",
              "      <td>23.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11330.0</td>\n",
              "      <td>goes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       deeps_flare_id           start_datetime             end_datetime  \\\n",
              "71616           67197  2011-10-31 00:50:00.000  2011-10-31 01:29:00.000   \n",
              "\n",
              "                 peak_datetime xray_class  xray_intensity  latitude  \\\n",
              "71616  2011-10-31 01:10:00.000          C            23.0       NaN   \n",
              "\n",
              "       longtitude  noaa_ar source  \n",
              "71616         NaN  11330.0   goes  "
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sample_h12_flare_detail_info"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IlMVEv1PN6bb"
      },
      "source": [
        "# Using a single sample (pynlfff toolkit)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9_3LUxiWOftf"
      },
      "source": [
        "## Toolkit initialization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XPSK3LNkOIAE"
      },
      "source": [
        "#### Install pynlfff toolkit \n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "mva4cAn8ON3_"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found existing installation: pynlfff 0.3.3.5\n",
            "Uninstalling pynlfff-0.3.3.5:\n",
            "  Successfully uninstalled pynlfff-0.3.3.5\n",
            "Collecting pynlfff\n",
            "  Using cached pynlfff-0.3.3.5-py3-none-any.whl (44 kB)\n",
            "Requirement already satisfied: numpy in /home/zander/anaconda3/lib/python3.9/site-packages (from pynlfff) (1.20.3)\n",
            "Requirement already satisfied: h5py in /home/zander/anaconda3/lib/python3.9/site-packages (from pynlfff) (3.3.0)\n",
            "Requirement already satisfied: matplotlib in /home/zander/anaconda3/lib/python3.9/site-packages (from pynlfff) (3.4.3)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /home/zander/anaconda3/lib/python3.9/site-packages (from matplotlib->pynlfff) (2.8.2)\n",
            "Requirement already satisfied: pyparsing>=2.2.1 in /home/zander/anaconda3/lib/python3.9/site-packages (from matplotlib->pynlfff) (3.0.4)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /home/zander/anaconda3/lib/python3.9/site-packages (from matplotlib->pynlfff) (1.3.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /home/zander/anaconda3/lib/python3.9/site-packages (from matplotlib->pynlfff) (8.4.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /home/zander/anaconda3/lib/python3.9/site-packages (from matplotlib->pynlfff) (0.10.0)\n",
            "Requirement already satisfied: six in /home/zander/anaconda3/lib/python3.9/site-packages (from cycler>=0.10->matplotlib->pynlfff) (1.16.0)\n",
            "Installing collected packages: pynlfff\n",
            "Successfully installed pynlfff-0.3.3.5\n"
          ]
        }
      ],
      "source": [
        "# Install the latest toolkit \n",
        "!pip uninstall pynlfff -y\n",
        "!pip install pynlfff\n",
        "\n",
        "# Import Toolkit \n",
        "import pynlfff\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cSlJZzBRN9vW"
      },
      "source": [
        "#### Install toolkit dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "XSkU3NeYOoaQ"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "This module is to test some module, for pynlfff which function you need, is install or not.\n",
            "    Can use RCheck().check() or RCheck().check(0) for Full module test\n",
            "    RCheck().check(1) for Base function module test \n",
            "    RCheck().check(2) for Prepare function module test \n",
            "    RCheck().check(3) for Computer function module test \n",
            "    RCheck().check(4) for Product function module test \n",
            "    RCheck().check(5) for Plot function module test \n",
            "    RCheck().check(6) for Download fits raw data function module test \n",
            "    RCheck().check(7) for Label function module test \n",
            "    \n"
          ]
        }
      ],
      "source": [
        "# Because the whole toolkit has more dependencies, \n",
        "# there is no setting to install all when downloading the toolkit\n",
        "\n",
        "# You can use the following command to follow the toolkit as needed \n",
        "# Create object \n",
        "checker=pynlfff.RCheck()\n",
        "# See which modules have dependencies to install \n",
        "checker.h()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "j0tYV4ykOsmd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Start Test\n",
            "<module 'h5py' from '/home/zander/anaconda3/lib/python3.9/site-packages/h5py/__init__.py'>\n",
            "True\n",
            "<module 'numpy' from '/home/zander/anaconda3/lib/python3.9/site-packages/numpy/__init__.py'>\n",
            "True\n",
            "Test Finish, Result: [['h5py', 'Exists'], ['numpy', 'Exists']]\n",
            "['h5py', 'Exists']\n",
            "['numpy', 'Exists']\n",
            "Start Test\n",
            "<module 'pandas' from '/home/zander/anaconda3/lib/python3.9/site-packages/pandas/__init__.py'>\n",
            "True\n",
            "<module 'astropy' from '/home/zander/anaconda3/lib/python3.9/site-packages/astropy/__init__.py'>\n",
            "True\n",
            "<module 'numpy' from '/home/zander/anaconda3/lib/python3.9/site-packages/numpy/__init__.py'>\n",
            "True\n",
            "<module 'sunpy' from '/home/zander/anaconda3/lib/python3.9/site-packages/sunpy/__init__.py'>\n",
            "True\n",
            "<module 'drms' from '/home/zander/anaconda3/lib/python3.9/site-packages/drms/__init__.py'>\n",
            "True\n",
            "<module 'h5py' from '/home/zander/anaconda3/lib/python3.9/site-packages/h5py/__init__.py'>\n",
            "True\n",
            "<module 'matplotlib' from '/home/zander/anaconda3/lib/python3.9/site-packages/matplotlib/__init__.py'>\n",
            "True\n",
            "<module 'psutil' from '/home/zander/anaconda3/lib/python3.9/site-packages/psutil/__init__.py'>\n",
            "True\n",
            "<module 'requests' from '/home/zander/anaconda3/lib/python3.9/site-packages/requests/__init__.py'>\n",
            "True\n",
            "<module 'bs4' from '/home/zander/anaconda3/lib/python3.9/site-packages/bs4/__init__.py'>\n",
            "True\n",
            "<module 'lxml' from '/home/zander/anaconda3/lib/python3.9/site-packages/lxml/__init__.py'>\n",
            "True\n",
            "<module 'zeep' from '/home/zander/anaconda3/lib/python3.9/site-packages/zeep/__init__.py'>\n",
            "True\n",
            "<module 'wget' from '/home/zander/anaconda3/lib/python3.9/site-packages/wget.py'>\n",
            "True\n",
            "<module 'peewee' from '/home/zander/anaconda3/lib/python3.9/site-packages/peewee.py'>\n",
            "True\n",
            "<module 'vtk' from '/home/zander/anaconda3/lib/python3.9/site-packages/vtk.py'>\n",
            "True\n",
            "Test Finish, Result: [['pandas', 'Exists'], ['astropy', 'Exists'], ['numpy', 'Exists'], ['sunpy', 'Exists'], ['drms', 'Exists'], ['h5py', 'Exists'], ['matplotlib', 'Exists'], ['psutil', 'Exists'], ['requests', 'Exists'], ['bs4', 'Exists'], ['lxml', 'Exists'], ['zeep', 'Exists'], ['wget', 'Exists'], ['peewee', 'Exists'], ['vtk', 'Exists']]\n",
            "['pandas', 'Exists']\n",
            "['astropy', 'Exists']\n",
            "['numpy', 'Exists']\n",
            "['sunpy', 'Exists']\n",
            "['drms', 'Exists']\n",
            "['h5py', 'Exists']\n",
            "['matplotlib', 'Exists']\n",
            "['psutil', 'Exists']\n",
            "['requests', 'Exists']\n",
            "['bs4', 'Exists']\n",
            "['lxml', 'Exists']\n",
            "['zeep', 'Exists']\n",
            "['wget', 'Exists']\n",
            "['peewee', 'Exists']\n",
            "['vtk', 'Exists']\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[['pandas', 'Exists'],\n",
              " ['astropy', 'Exists'],\n",
              " ['numpy', 'Exists'],\n",
              " ['sunpy', 'Exists'],\n",
              " ['drms', 'Exists'],\n",
              " ['h5py', 'Exists'],\n",
              " ['matplotlib', 'Exists'],\n",
              " ['psutil', 'Exists'],\n",
              " ['requests', 'Exists'],\n",
              " ['bs4', 'Exists'],\n",
              " ['lxml', 'Exists'],\n",
              " ['zeep', 'Exists'],\n",
              " ['wget', 'Exists'],\n",
              " ['peewee', 'Exists'],\n",
              " ['vtk', 'Exists']]"
            ]
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Use 4 to install Product-related dependencies \n",
        "checker.check(4)\n",
        "\n",
        "# Use 0 i.e. follow all dependencies \n",
        "checker.check(0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rEs0UwSpPNvh"
      },
      "source": [
        "## Data Reading\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YkgWtoeBPTOF"
      },
      "source": [
        "#### Get the storage path and time \n",
        "\n",
        "The following information is required at a minimum to use a sample: \n",
        "\n",
        "+ nonlinear force-free field data Bout.bin\n",
        "\n",
        "+ Bout.bin corresponding to nx, ny, nz\n",
        "\n",
        "+ the harp number and time of the sample"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "fhH8ne40PR-g"
      },
      "outputs": [],
      "source": [
        "# Inside the dataset summary information there is greater than or equal to one sample, \n",
        "# We use this sample as an example for illustration \n",
        "\n",
        "# Take sample 0 as an example and read the `harpnum_trec` and path  of the already downloaded sample\n",
        "nlfff_path_dataset_summary=\"nlfff-dataset-info\"\n",
        "sample_num=0\n",
        "sample_harpnum_trec=config_ds[\"demo_sample\"][sample_num][\"harpnum_trec\"]\n",
        "sample_path=os.path.join(\n",
        "    nlfff_path_dataset_summary,\n",
        "    config_ds[\"demo_sample\"][sample_num][\"path\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "MOyqXbgKPjyu"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'7327.20181220_093600_TAI'"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Harp number and time of the sample \n",
        "sample_harpnum_trec"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "DjgnLBiWPnA8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'nlfff-dataset-info/demo/hmi.sharp_cea_720s.7327.20181220_093600_TAI/Bout.bin'"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Bout path\n",
        "sample_bout_path=os.path.join(sample_path,\"Bout.bin\")\n",
        "sample_bout_path"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PQRgKyS4Pum9"
      },
      "source": [
        "#### Get sample product file information (nx ny nz)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "WskuJfmGPtsR"
      },
      "outputs": [
        {
          "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>harpnum_trec</th>\n",
              "      <th>sharp_num</th>\n",
              "      <th>t_rec</th>\n",
              "      <th>bout_path</th>\n",
              "      <th>bout_md5</th>\n",
              "      <th>bout_size</th>\n",
              "      <th>grid_x</th>\n",
              "      <th>grid_y</th>\n",
              "      <th>grid_z</th>\n",
              "      <th>grid_xyz</th>\n",
              "      <th>bout_maxlevel</th>\n",
              "      <th>bout_quality</th>\n",
              "      <th>batch</th>\n",
              "      <th>notes</th>\n",
              "      <th>bout_quality_value</th>\n",
              "      <th>identifiers</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>49935</th>\n",
              "      <td>7327.20181220_093600_TAI</td>\n",
              "      <td>7327</td>\n",
              "      <td>2018-12-20 09:36:00.000</td>\n",
              "      <td>grid3.done/num_7300_7399/hmi.sharp_cea_720s.73...</td>\n",
              "      <td>f7487ece2a486ba1aba7943398022fbf</td>\n",
              "      <td>107980800</td>\n",
              "      <td>100</td>\n",
              "      <td>296</td>\n",
              "      <td>152</td>\n",
              "      <td>4499200</td>\n",
              "      <td>3</td>\n",
              "      <td>True</td>\n",
              "      <td>20220301</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18.8298</td>\n",
              "      <td>7327201812200936003</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   harpnum_trec  sharp_num                    t_rec  \\\n",
              "49935  7327.20181220_093600_TAI       7327  2018-12-20 09:36:00.000   \n",
              "\n",
              "                                               bout_path  \\\n",
              "49935  grid3.done/num_7300_7399/hmi.sharp_cea_720s.73...   \n",
              "\n",
              "                               bout_md5  bout_size  grid_x  grid_y  grid_z  \\\n",
              "49935  f7487ece2a486ba1aba7943398022fbf  107980800     100     296     152   \n",
              "\n",
              "       grid_xyz  bout_maxlevel  bout_quality     batch notes  \\\n",
              "49935   4499200              3          True  20220301   NaN   \n",
              "\n",
              "       bout_quality_value          identifiers  \n",
              "49935             18.8298  7327201812200936003  "
            ]
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Get sample product file information \n",
        "\n",
        "sample_product_info=product_info[ product_info[\"harpnum_trec\"]==sample_harpnum_trec ]\n",
        "sample_product_info"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "RTkbIr-fP1Z6"
      },
      "outputs": [],
      "source": [
        "\n",
        "# Maximum calculation level of the sample \n",
        "sample_bout_maxlevel=int(sample_product_info[\"bout_maxlevel\"])\n",
        "# The nx, ny, nz of the sample are\n",
        "sample_nx=int(sample_product_info[\"grid_x\"])\n",
        "sample_ny=int(sample_product_info[\"grid_y\"])\n",
        "sample_nz=int(sample_product_info[\"grid_z\"])\n",
        "# The identifiers of the sample are\n",
        "sample_identifiers=int(sample_product_info[\"identifiers\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kIzacbGYQDu5"
      },
      "source": [
        "### Read data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "Gi5DE-7nQGoO"
      },
      "outputs": [],
      "source": [
        "from pynlfff.pyproduct import file\n",
        "\n",
        "# Creating read objects\n",
        "r=file.NlfffFile()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "GOqpOozJQazY"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(3, 100, 296, 152)\n"
          ]
        }
      ],
      "source": [
        "r = file.NlfffFile()\n",
        "\n",
        "\n",
        "bout_data = r.read_bin(sample_bout_path, nx=sample_nx,ny=sample_ny,nz=sample_nz)\n",
        "print(bout_data.shape)\n",
        "# print(s)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "AcUnrhA9FOdc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "memmap([[[[-1.31044950e+01, -1.07726199e+01, -9.31814325e+00, ...,\n",
              "           -6.59102380e-01, -6.59102380e-01, -6.59102380e-01],\n",
              "          [-1.51931409e+01, -1.22127697e+01, -9.59159267e+00, ...,\n",
              "           -6.62805868e-01, -6.62805868e-01, -6.62805868e-01],\n",
              "          [-2.27258283e+01, -1.36529194e+01, -9.86504208e+00, ...,\n",
              "           -6.66509356e-01, -6.66509356e-01, -6.66509356e-01],\n",
              "          ...,\n",
              "          [ 1.58692752e+01,  3.32809154e+00,  2.23416430e+00, ...,\n",
              "            2.44938261e-01,  2.44938261e-01,  2.44938261e-01],\n",
              "          [ 9.62912943e+00,  2.83513561e+00,  2.23416430e+00, ...,\n",
              "            2.44938261e-01,  2.44938261e-01,  2.44938261e-01],\n",
              "          [ 5.19271162e+00,  2.83513561e+00,  2.23416430e+00, ...,\n",
              "            2.44938261e-01,  2.44938261e-01,  2.44938261e-01]],\n",
              "\n",
              "         [[-1.51724241e+01, -1.02950914e+01, -8.20474653e+00, ...,\n",
              "           -6.52507450e-01, -6.52507450e-01, -6.52507450e-01],\n",
              "          [-1.44053712e+01, -1.14289234e+01, -8.58725953e+00, ...,\n",
              "           -6.60360707e-01, -6.57826568e-01, -6.56187112e-01],\n",
              "          [-1.68870379e+01, -1.42346166e+01, -9.05483154e+00, ...,\n",
              "           -6.64460732e-01, -6.61686425e-01, -6.59866773e-01],\n",
              "          ...,\n",
              "          [-3.54836018e+00,  1.46486296e+00,  5.72287187e-01, ...,\n",
              "            2.37319136e-01,  2.36572991e-01,  2.35982653e-01],\n",
              "          [-8.85968063e+00,  1.46227195e+00,  6.43827933e-01, ...,\n",
              "            2.37350424e-01,  2.36506795e-01,  2.35982653e-01],\n",
              "          [-6.50229845e+00, -1.67490716e+00, -4.54792534e-02, ...,\n",
              "            2.35982653e-01,  2.35982653e-01,  2.35982653e-01]],\n",
              "\n",
              "         [[-1.62226139e+01, -9.81756298e+00, -7.09134980e+00, ...,\n",
              "           -6.45912520e-01, -6.45912520e-01, -6.45912520e-01],\n",
              "          [-1.38035766e+01, -9.71475911e+00, -7.22248697e+00, ...,\n",
              "           -6.54930419e-01, -6.51774346e-01, -6.49568355e-01],\n",
              "          [-1.48065590e+01, -1.24087737e+01, -8.23603428e+00, ...,\n",
              "           -6.59895328e-01, -6.56056616e-01, -6.53224190e-01],\n",
              "          ...,\n",
              "          [-1.36240982e+01, -7.44816370e+00, -3.85904540e+00, ...,\n",
              "            2.31412517e-01,  2.29372410e-01,  2.27027045e-01],\n",
              "          [-1.27016182e+01, -8.27758909e+00, -3.86570880e+00, ...,\n",
              "            2.30730356e-01,  2.28920863e-01,  2.27027045e-01],\n",
              "          [-9.61784709e+00, -6.18494992e+00, -2.32512281e+00, ...,\n",
              "            2.27027045e-01,  2.27027045e-01,  2.27027045e-01]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[ 2.65698202e+01,  2.61640129e+01,  2.47086401e+01, ...,\n",
              "            3.48661199e-01,  3.48661199e-01,  3.48661199e-01],\n",
              "          [ 2.31269413e+01,  2.50436836e+01,  2.44801886e+01, ...,\n",
              "            3.43999870e-01,  3.45554837e-01,  3.50740827e-01],\n",
              "          [ 1.77614886e+01,  2.12224817e+01,  2.32910109e+01, ...,\n",
              "            3.46384715e-01,  3.48258325e-01,  3.52820455e-01],\n",
              "          ...,\n",
              "          [-2.73636923e-01, -2.95356138e+00, -7.21513480e+00, ...,\n",
              "           -5.75435624e-01, -5.74366052e-01, -5.73294373e-01],\n",
              "          [ 6.68685970e-01, -4.97443986e+00, -8.15128005e+00, ...,\n",
              "           -5.73772512e-01, -5.73215432e-01, -5.73294373e-01],\n",
              "          [-1.36614229e+00, -6.29618506e+00, -7.54300828e+00, ...,\n",
              "           -5.73294373e-01, -5.73294373e-01, -5.73294373e-01]],\n",
              "\n",
              "         [[ 2.23678771e+01,  2.39879184e+01,  2.47086401e+01, ...,\n",
              "            3.48661199e-01,  3.48661199e-01,  3.48661199e-01],\n",
              "          [ 1.80660345e+01,  2.25698427e+01,  2.34568643e+01, ...,\n",
              "            3.49268518e-01,  3.49212804e-01,  3.50740827e-01],\n",
              "          [ 1.42964605e+01,  1.92277744e+01,  2.21292769e+01, ...,\n",
              "            3.51401893e-01,  3.51455124e-01,  3.52820455e-01],\n",
              "          ...,\n",
              "          [-3.70921716e+00, -4.50725090e+00, -7.41031154e+00, ...,\n",
              "           -5.74330316e-01, -5.73708403e-01, -5.73294373e-01],\n",
              "          [ 5.50775292e+00, -2.39147457e+00, -6.51989304e+00, ...,\n",
              "           -5.73538680e-01, -5.73158879e-01, -5.73294373e-01],\n",
              "          [ 2.25973450e+00, -4.69063304e+00, -7.54300828e+00, ...,\n",
              "           -5.73294373e-01, -5.73294373e-01, -5.73294373e-01]],\n",
              "\n",
              "         [[ 1.76555365e+01,  2.39879184e+01,  2.47086401e+01, ...,\n",
              "            3.48661199e-01,  3.48661199e-01,  3.48661199e-01],\n",
              "          [ 1.69437098e+01,  2.13044874e+01,  2.34641682e+01, ...,\n",
              "            3.50740827e-01,  3.50740827e-01,  3.50740827e-01],\n",
              "          [ 1.66954722e+01,  1.86210564e+01,  2.22196962e+01, ...,\n",
              "            3.52820455e-01,  3.52820455e-01,  3.52820455e-01],\n",
              "          ...,\n",
              "          [-4.91528467e+00, -5.82091821e+00, -7.54300828e+00, ...,\n",
              "           -5.73294373e-01, -5.73294373e-01, -5.73294373e-01],\n",
              "          [ 4.42433594e+00, -4.69063304e+00, -7.54300828e+00, ...,\n",
              "           -5.73294373e-01, -5.73294373e-01, -5.73294373e-01],\n",
              "          [ 2.68535675e+00, -4.69063304e+00, -7.54300828e+00, ...,\n",
              "           -5.73294373e-01, -5.73294373e-01, -5.73294373e-01]]],\n",
              "\n",
              "\n",
              "        [[[-2.67670497e+01, -2.25152001e+01, -2.04868811e+01, ...,\n",
              "           -2.08801030e-01, -2.08801030e-01, -2.08801030e-01],\n",
              "          [-2.51385478e+01, -2.18916655e+01, -1.83235556e+01, ...,\n",
              "           -1.97911847e-01, -1.97911847e-01, -1.97911847e-01],\n",
              "          [-2.85658013e+01, -2.12681308e+01, -1.61602301e+01, ...,\n",
              "           -1.87022664e-01, -1.87022664e-01, -1.87022664e-01],\n",
              "          ...,\n",
              "          [-2.95981882e+01, -1.72691696e+01, -1.21571192e+01, ...,\n",
              "           -1.02677648e-01, -1.02677648e-01, -1.02677648e-01],\n",
              "          [-1.71524835e+01, -1.84816307e+01, -1.21571192e+01, ...,\n",
              "           -1.02677648e-01, -1.02677648e-01, -1.02677648e-01],\n",
              "          [-2.04185727e+01, -1.84816307e+01, -1.21571192e+01, ...,\n",
              "           -1.02677648e-01, -1.02677648e-01, -1.02677648e-01]],\n",
              "\n",
              "         [[-3.11160823e+01, -2.48073797e+01, -2.07034690e+01, ...,\n",
              "           -2.12516097e-01, -2.12516097e-01, -2.12516097e-01],\n",
              "          [-2.84270985e+01, -2.40571643e+01, -2.00794420e+01, ...,\n",
              "           -2.05379378e-01, -2.03295443e-01, -2.01597298e-01],\n",
              "          [-3.00767080e+01, -2.28881370e+01, -1.93103421e+01, ...,\n",
              "           -1.95925588e-01, -1.93365675e-01, -1.90678499e-01],\n",
              "          ...,\n",
              "          [-2.30762165e+01, -2.04364817e+01, -1.41811789e+01, ...,\n",
              "           -1.00283153e-01, -1.00380659e-01, -1.03781461e-01],\n",
              "          [-2.26682784e+01, -2.19042827e+01, -1.37359878e+01, ...,\n",
              "           -1.03683230e-01, -1.02989762e-01, -1.03781461e-01],\n",
              "          [-2.94124834e+01, -2.19047512e+01, -1.19250553e+01, ...,\n",
              "           -1.03781461e-01, -1.03781461e-01, -1.03781461e-01]],\n",
              "\n",
              "         [[-2.88794802e+01, -2.70995592e+01, -2.09200568e+01, ...,\n",
              "           -2.16231163e-01, -2.16231163e-01, -2.16231163e-01],\n",
              "          [-3.01440877e+01, -2.60383186e+01, -2.09649163e+01, ...,\n",
              "           -2.09334155e-01, -2.06914247e-01, -2.05282749e-01],\n",
              "          [-3.53707639e+01, -2.63834378e+01, -2.13554601e+01, ...,\n",
              "           -2.01248779e-01, -1.97478598e-01, -1.94334334e-01],\n",
              "          ...,\n",
              "          [-2.79820620e+01, -2.04184069e+01, -1.48595173e+01, ...,\n",
              "           -1.01777506e-01, -1.01965952e-01, -1.04885274e-01],\n",
              "          [-4.10431063e+01, -2.49123885e+01, -1.57314997e+01, ...,\n",
              "           -1.04902272e-01, -1.04234869e-01, -1.04885274e-01],\n",
              "          [-4.04297586e+01, -2.53278717e+01, -1.16929913e+01, ...,\n",
              "           -1.04885274e-01, -1.04885274e-01, -1.04885274e-01]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[-1.62683325e+01, -1.62658801e+01, -1.48409055e+01, ...,\n",
              "           -3.18822642e-01, -3.18822642e-01, -3.18822642e-01],\n",
              "          [-1.62570995e+01, -1.55577437e+01, -1.29127068e+01, ...,\n",
              "           -3.11377319e-01, -3.09338787e-01, -3.07499417e-01],\n",
              "          [-2.17673428e+01, -1.77323418e+01, -1.28986179e+01, ...,\n",
              "           -3.05632632e-01, -3.00944019e-01, -2.96176192e-01],\n",
              "          ...,\n",
              "          [-2.76910141e+01, -1.74524268e+01, -1.37194241e+01, ...,\n",
              "            4.04452301e-02,  3.90627822e-02,  3.52440062e-02],\n",
              "          [-1.93527245e+01, -1.72753294e+01, -1.27122101e+01, ...,\n",
              "            3.74224606e-02,  3.67205232e-02,  3.52440062e-02],\n",
              "          [-5.81451216e+00, -1.52678896e+01, -1.02138230e+01, ...,\n",
              "            3.52440062e-02,  3.52440062e-02,  3.52440062e-02]],\n",
              "\n",
              "         [[-2.10050470e+01, -1.77386267e+01, -1.48409055e+01, ...,\n",
              "           -3.18822642e-01, -3.18822642e-01, -3.18822642e-01],\n",
              "          [-2.18759322e+01, -1.66845986e+01, -1.36050946e+01, ...,\n",
              "           -3.10826041e-01, -3.08995063e-01, -3.07499417e-01],\n",
              "          [-1.87930035e+01, -1.60993191e+01, -1.29446996e+01, ...,\n",
              "           -3.04191016e-01, -3.00402557e-01, -2.96176192e-01],\n",
              "          ...,\n",
              "          [-2.65976314e+01, -1.78504883e+01, -1.33124233e+01, ...,\n",
              "            3.97576202e-02,  3.94595585e-02,  3.52440062e-02],\n",
              "          [-1.82518194e+01, -1.52351135e+01, -1.10655539e+01, ...,\n",
              "            3.72067978e-02,  3.70316129e-02,  3.52440062e-02],\n",
              "          [-6.83084563e+00, -1.57585155e+01, -1.02138230e+01, ...,\n",
              "            3.52440062e-02,  3.52440062e-02,  3.52440062e-02]],\n",
              "\n",
              "         [[-1.91202026e+01, -1.77386267e+01, -1.48409055e+01, ...,\n",
              "           -3.18822642e-01, -3.18822642e-01, -3.18822642e-01],\n",
              "          [-2.15633270e+01, -1.72611422e+01, -1.48310588e+01, ...,\n",
              "           -3.07499417e-01, -3.07499417e-01, -3.07499417e-01],\n",
              "          [-2.15627099e+01, -1.67836577e+01, -1.48212122e+01, ...,\n",
              "           -2.96176192e-01, -2.96176192e-01, -2.96176192e-01],\n",
              "          ...,\n",
              "          [-1.92399853e+01, -1.32307423e+01, -1.02138230e+01, ...,\n",
              "            3.52440062e-02,  3.52440062e-02,  3.52440062e-02],\n",
              "          [-1.76259994e+01, -1.57585155e+01, -1.02138230e+01, ...,\n",
              "            3.52440062e-02,  3.52440062e-02,  3.52440062e-02],\n",
              "          [-1.26887218e+01, -1.57585155e+01, -1.02138230e+01, ...,\n",
              "            3.52440062e-02,  3.52440062e-02,  3.52440062e-02]]],\n",
              "\n",
              "\n",
              "        [[[-1.25568691e+01, -3.63734765e+00,  4.26934688e-01, ...,\n",
              "            1.56327637e+00,  1.56327637e+00,  1.56327637e+00],\n",
              "          [-7.08903640e+00,  5.58251621e-01,  3.97459783e+00, ...,\n",
              "            1.57197462e+00,  1.57197462e+00,  1.57197462e+00],\n",
              "          [-1.14758675e+01,  4.75385089e+00,  7.52226097e+00, ...,\n",
              "            1.58067286e+00,  1.58067286e+00,  1.58067286e+00],\n",
              "          ...,\n",
              "          [ 1.47498835e+01, -1.11370350e+00, -2.55596764e+00, ...,\n",
              "           -1.52850141e+00, -1.52850141e+00, -1.52850141e+00],\n",
              "          [ 1.78436871e+01,  3.12553805e+00, -2.55596764e+00, ...,\n",
              "           -1.52850141e+00, -1.52850141e+00, -1.52850141e+00],\n",
              "          [ 2.31120637e+01,  3.12553805e+00, -2.55596764e+00, ...,\n",
              "           -1.52850141e+00, -1.52850141e+00, -1.52850141e+00]],\n",
              "\n",
              "         [[-1.38043562e+01, -3.85059525e+00,  9.03193560e-01, ...,\n",
              "            1.57321548e+00,  1.57321548e+00,  1.57321548e+00],\n",
              "          [-3.23470714e+00, -4.16018212e-01,  1.94594457e+00, ...,\n",
              "            1.58479641e+00,  1.58286120e+00,  1.58201088e+00],\n",
              "          [-5.65077822e-01,  3.30084924e+00,  5.71149574e+00, ...,\n",
              "            1.59546865e+00,  1.59253304e+00,  1.59080628e+00],\n",
              "          ...,\n",
              "          [-1.17262496e+01, -3.64092337e+00, -4.63374478e+00, ...,\n",
              "           -1.54373267e+00, -1.53868381e+00, -1.53385555e+00],\n",
              "          [ 1.50436700e+00,  7.88626084e-01, -1.37803351e+00, ...,\n",
              "           -1.53964635e+00, -1.53621682e+00, -1.53385555e+00],\n",
              "          [ 1.58384235e+01,  2.11202417e+00, -1.99169425e+00, ...,\n",
              "           -1.53385555e+00, -1.53385555e+00, -1.53385555e+00]],\n",
              "\n",
              "         [[-1.36876646e+01, -4.06384286e+00,  1.37945243e+00, ...,\n",
              "            1.58315458e+00,  1.58315458e+00,  1.58315458e+00],\n",
              "          [-6.94444318e+00, -1.34377169e+00,  1.59705613e+00, ...,\n",
              "            1.59651792e+00,  1.59373550e+00,  1.59204714e+00],\n",
              "          [-7.15333851e-01,  2.01921434e+00,  3.37710102e+00, ...,\n",
              "            1.60824144e+00,  1.60404185e+00,  1.60093970e+00],\n",
              "          ...,\n",
              "          [-4.20760393e+01, -2.02449911e+01, -1.11003431e+01, ...,\n",
              "           -1.55366119e+00, -1.54640858e+00, -1.53920968e+00],\n",
              "          [-1.47993216e+01, -8.91304327e+00, -5.04027288e+00, ...,\n",
              "           -1.54751576e+00, -1.54280594e+00, -1.53920968e+00],\n",
              "          [ 1.23213648e+01,  1.09851030e+00, -1.42742086e+00, ...,\n",
              "           -1.53920968e+00, -1.53920968e+00, -1.53920968e+00]],\n",
              "\n",
              "         ...,\n",
              "\n",
              "         [[-9.81503980e+00, -8.90668518e+00, -4.63403331e+00, ...,\n",
              "            1.81371842e+00,  1.81371842e+00,  1.81371842e+00],\n",
              "          [-2.57478506e+01, -1.04551020e+01, -3.78519063e+00, ...,\n",
              "            1.83476476e+00,  1.82946347e+00,  1.82460337e+00],\n",
              "          [-2.78454464e+01, -1.48304583e+01, -7.98367816e+00, ...,\n",
              "            1.84959822e+00,  1.84228627e+00,  1.83548832e+00],\n",
              "          ...,\n",
              "          [ 3.26097491e+01,  2.20917464e+01,  1.32505260e+01, ...,\n",
              "           -1.28113336e+00, -1.27370096e+00, -1.26538114e+00],\n",
              "          [ 4.05441763e+01,  1.96914544e+01,  1.05568383e+01, ...,\n",
              "           -1.27532480e+00, -1.27040054e+00, -1.26538114e+00],\n",
              "          [ 2.83989139e+01,  1.61975399e+01,  7.40820167e+00, ...,\n",
              "           -1.26538114e+00, -1.26538114e+00, -1.26538114e+00]],\n",
              "\n",
              "         [[-1.76107734e+01, -1.10968855e+01, -4.63403331e+00, ...,\n",
              "            1.81371842e+00,  1.81371842e+00,  1.81371842e+00],\n",
              "          [-2.27036649e+01, -1.24252386e+01, -6.11022404e+00, ...,\n",
              "            1.83050046e+00,  1.82696463e+00,  1.82460337e+00],\n",
              "          [-2.32000887e+01, -1.48860816e+01, -7.72424970e+00, ...,\n",
              "            1.84347711e+00,  1.83878906e+00,  1.83548832e+00],\n",
              "          ...,\n",
              "          [ 2.31377254e+01,  1.52988707e+01,  9.21385926e+00, ...,\n",
              "           -1.27513300e+00, -1.27020665e+00, -1.26538114e+00],\n",
              "          [ 3.99142344e+01,  1.99417678e+01,  1.14976231e+01, ...,\n",
              "           -1.27148261e+00, -1.26809700e+00, -1.26538114e+00],\n",
              "          [ 2.90517073e+01,  1.52812838e+01,  7.40820167e+00, ...,\n",
              "           -1.26538114e+00, -1.26538114e+00, -1.26538114e+00]],\n",
              "\n",
              "         [[-2.06190927e+01, -1.10968855e+01, -4.63403331e+00, ...,\n",
              "            1.81371842e+00,  1.81371842e+00,  1.81371842e+00],\n",
              "          [-2.19604929e+01, -1.11007737e+01, -4.50158876e+00, ...,\n",
              "            1.82460337e+00,  1.82460337e+00,  1.82460337e+00],\n",
              "          [-1.82218546e+01, -1.11046619e+01, -4.36914422e+00, ...,\n",
              "            1.83548832e+00,  1.83548832e+00,  1.83548832e+00],\n",
              "          ...,\n",
              "          [ 2.14579532e+01,  1.22652075e+01,  7.40820167e+00, ...,\n",
              "           -1.26538114e+00, -1.26538114e+00, -1.26538114e+00],\n",
              "          [ 2.39156764e+01,  1.52812838e+01,  7.40820167e+00, ...,\n",
              "           -1.26538114e+00, -1.26538114e+00, -1.26538114e+00],\n",
              "          [ 1.80771953e+01,  1.52812838e+01,  7.40820167e+00, ...,\n",
              "           -1.26538114e+00, -1.26538114e+00, -1.26538114e+00]]]])"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "bout_data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wPIqQXtJQ9ln"
      },
      "source": [
        "## Format Conversion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FgTkbniIRNsc"
      },
      "source": [
        "### to HDF"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "-4ujz4dfREec"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "r = file.NlfffFile()\n",
        "\n",
        "h5_path = sample_bout_path.replace(\".bin\",\".h5\")\n",
        "\n",
        "r.tran_bin2hdf5(sample_bout_path, h5_path, nx=sample_nx,ny=sample_ny,nz=sample_nz, overwrite=True)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "JA2_3TNPSB7-"
      },
      "source": [
        "## Visualization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O_j9YjdsSE9G"
      },
      "source": [
        "### Three-dimensional cutting and drawing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mPymDEmDSc3r"
      },
      "source": [
        "#### Creating drawing objects\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "Rdwc0AHjSIPd"
      },
      "outputs": [],
      "source": [
        "from pynlfff.pyplot import plot3d_cut\n",
        "d3_drawer = plot3d_cut.NlfffPlotD3CutCake()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZD-QQORuTZBE"
      },
      "source": [
        "#### Set drawing object properties (optional)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "W93mgGLVTJw-"
      },
      "outputs": [],
      "source": [
        "\n",
        "# Set the hue range manually \n",
        "# Set the maximum display value \n",
        "d3_drawer.colormap_set_max=40\n",
        "# Set the minimum display value \n",
        "d3_drawer.colormap_set_max=-30\n",
        "\n",
        "\n",
        "# Automatically set the hue range \n",
        "# The maximum and minimum values in the automatically-sought drawing cut \n",
        "d3_drawer.colormap_auto_value=True\n",
        "# Only effective if colormap_auto_value is True, means compress again on auto_max value, compress ratio, take value range (0,1], 1 means no compress \n",
        "d3_drawer.colormap_auto_zip=0.8\n",
        "# indicates that the value with the largest absolute value is the maximum value, and its opposite is the minimum value, when the palette obtained is symmetric about 0. Note that if the maximum value and the minimum value are both positive or negative, it does not take effect \n",
        "d3_drawer.colormap_auto_mirror=True\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "-ds8XttFT8VX"
      },
      "outputs": [],
      "source": [
        "# The out-of-bounds value is displayed \n",
        "# Default set to True, the out-of-range value is taken according to the closest one; to False, the out-of-range value is not displayed \n",
        "d3_drawer.colormap_out_range_display=True\n",
        "# "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "mTGywdxnUDnT"
      },
      "outputs": [],
      "source": [
        "# Transparency \n",
        "# Set None to be opaque\n",
        "d3_drawer.colormap_alpha=None\n",
        "# Set the overall transparency in the range (0,1)\n",
        "d3_drawer.colormap_alpha=0.8\n",
        "# Default, auto auto-transparency, i.e. the smaller the absolute value the higher the degree of transparency \n",
        "d3_drawer.colormap_alpha=\"auto\"\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "HNffTk10UR7j"
      },
      "outputs": [],
      "source": [
        "# Set the tangent boundary \n",
        "# Do not draw boundary lines \n",
        "d3_drawer.cut_line_edges = None\n",
        "# Default, set boundary line style \n",
        "d3_drawer.cut_line_edges = dict(color='0.6', linewidth=0.6, zorder=1e3) \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "Srr2KHQAUiUm"
      },
      "outputs": [],
      "source": [
        "# Set tangent intersection line \n",
        "# Don't draw cut_line_cross \n",
        "d3_drawer.cut_line_cross = None\n",
        "# Default, set cut line cross style \n",
        "d3_drawer.cut_line_cross = dict(color='0.4', linewidth=1, zorder=1e3)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9kGNgH-UUw6G"
      },
      "source": [
        "#### Load Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "ZSPGEk4GUzag"
      },
      "outputs": [],
      "source": [
        "# Configuration Data\n",
        "use_facker_data=False\n",
        "if use_facker_data:\n",
        "    import numpy as np\n",
        "    array_data = np.random.uniform(-5, 5, size=(3, 100,50,40))\n",
        "else:\n",
        "    array_data=bout_data.copy()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 69,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Loading data\n",
        "d3_drawer.load_data_array(array_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nqxuYZJIVMbb"
      },
      "source": [
        "#### Adding a drawing cut"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "id": "tKafNHYDVaJW"
      },
      "outputs": [],
      "source": [
        "d3_drawer.add_cut(B=\"Bx\", N=\"Nx\", cut_num=None, cut_percent=0.5)\n",
        "d3_drawer.add_cut(\"Bz\", \"Nz\")\n",
        "d3_drawer.add_cut(\"Bz\", \"Nz\", cut_num=0)\n",
        "d3_drawer.add_cut(\"Bz\", \"Nx\",cut_percent=0.3)\n",
        "d3_drawer.add_cut(\"Bz\", \"Nz\", cut_percent=0.2)\n",
        "# add cut surface for cut draw, which can be use many times for different surfance in one picture\n",
        "# :param B: 'Bx' 'By' or 'Bz'; choose one Magnetic field component to draw\n",
        "# :param N: 'Nx' 'Ny' or 'Nz'; choose cut in which angle; when choose 'Nz', The slice will be perpendicular to z axis\n",
        "# :param cut_num: int type , set for cut where, can be [0, max(axis)], if not set this,will use cut_percent default\n",
        "# :param cut_percent: default 0.5, percent of axis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZCbG2SvrVear"
      },
      "source": [
        "#### Performing drawing operations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "15a4fO2CVhSU"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Configuration Information\n",
        "\n",
        "# Canvas Settings https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.figure.html\n",
        "fig_dict=dict(figsize=[6, 6])\n",
        "\n",
        "# Save image settings https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.savefig.html\n",
        "is_save=False\n",
        "save_dict=dict(fname=\"./cut.png\", dpi='figure')\n",
        "\n",
        "# Display settings https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html\n",
        "is_show=True\n",
        "show_dict=dict(block=True)\n",
        "\n",
        "# view setting\n",
        "view_dict=dict(\n",
        "    elev=45,\n",
        "    azim=45,\n",
        "    dist=11\n",
        ")\n",
        "\n",
        "d3_drawer.run_cut(\n",
        "    fig_dict=fig_dict,\n",
        "    is_show=is_show,\n",
        "    view_dict=view_dict,\n",
        "    show_dict=show_dict,\n",
        "    is_save=is_save,\n",
        "    save_dict=save_dict\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wsU5YjkeSTcz"
      },
      "source": [
        "### Three-dimensional body drawing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "id": "pxdkjP1F6Hdj"
      },
      "outputs": [],
      "source": [
        " # Save directory \n",
        "out_dir=\". /\" \n",
        "\n",
        "# Does the system come with gui\n",
        "is_with_gui=True # System with gui\n",
        "is_with_gui=False # System without gui like colab\n",
        "\n",
        "\n",
        "# Configuration data \n",
        "use_facker_data=True\n",
        "if use_facker_data:\n",
        "    import numpy as np\n",
        "    array_data = np.random.uniform(-5, 5, size=(3, 100,50,40))\n",
        "else:\n",
        "    array_data=bout_data.copy()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "id": "wFQ2JjxGXjnt"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "VTK Drawing dependency gui\n"
          ]
        }
      ],
      "source": [
        "\n",
        "if is_with_gui:\n",
        "  from pynlfff.pyplot import plot3d_body\n",
        "  import numpy as np\n",
        "  \n",
        "  try:\n",
        "    dataBout=array_data\n",
        "    result=plot3d_body.main_draw_3d_body(\n",
        "        dataBout,\n",
        "        out_dir,\n",
        "        axis_name=['bx','by','bz']\n",
        "    )\n",
        "  except BaseException as e:\n",
        "    print(e)\n",
        "else:\n",
        "  print(\"VTK Drawing dependency gui\")"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "yO-Hi5Mc-INN"
      },
      "source": [
        "## More sample acquisition methods"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "gpwLVu4d-fya"
      },
      "source": [
        "#### Method 1 Download by checking the official website\n",
        "\n",
        "Visit the download page of <https://nlfff.dataset.deepsolar.space/> and find the **online query download** related website"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "PACgN-Qb-mPU"
      },
      "source": [
        "#### Method 2 Jump to view samples online via identifiers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "id": "YE6jHqOT-XEL"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Visit https://identifiers.org/nlfff:7327201812200936003 for sample details and downloads\n"
          ]
        },
        {
          "ename": "",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
          ]
        }
      ],
      "source": [
        "# sample identifiers can be obtained from inside the product information table \n",
        "\n",
        "sample_identifiers=7327201812200936003\n",
        "# where 7327(harp number)20181220(date)093600(time)3(Max grid level). \n",
        "sample_url=\"https://identifiers.org/nlfff:{}\".format(sample_identifiers)\n",
        "print(\"Visit {} for sample details and downloads\".format(sample_url))"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "K7zLjhp3_Z33"
      },
      "source": [
        "### Method 3 Online request for hard copy by mail\n",
        "\n",
        "For researchers who need to use a large number of samples, we recommend using the hard disk mailing method.\n",
        "\n",
        "You can visit visit the **contact** page at <https://nlfff.dataset.deepsolar.space/> to communicate specific details such as mailing address recipient via email."
      ]
    }
  ],
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      "nbconvert_exporter": "python",
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