experiments/data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nJy_mtKB6VhN"
   },
   "source": [
    "# Data Analysis\n",
    "\n",
    "Analysis of the provided datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CgR4fxrQc-7k"
   },
   "source": [
    "## space-weather files\n",
    "\n",
    "Load and analyze the data from the `parquet` files."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In Google CoLab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3t8wVO9xLhC_"
   },
   "outputs": [],
   "source": [
    "#!pip install pandas pyarrow google-auth-httplib2 google-auth-oauthlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "utIGHPLkL0jq",
    "outputId": "acd4ecd9-a062-4c6e-f95e-46d46b0208ab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "# Use it only when work in the CoLab:\n",
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0AYevAukOEDe",
    "outputId": "ab7f1ce4-e1f2-40f4-fa09-001895b4874d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('/content/drive/MyDrive/Private (1)/Projects/PerceptiveSpace/TakeHome/data/ml-engineer-take-home-data/space-weather-2000.parquet')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "data_dir = '/content/drive/MyDrive/Private (1)/Projects/PerceptiveSpace/TakeHome/data'\n",
    "original_data_dir = 'ml-engineer-take-home-data'\n",
    "file_name = 'space-weather-2000.parquet'\n",
    "file_path = Path(data_dir) / original_data_dir / file_name\n",
    "file_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Local Jupyter Lab\n",
    "\n",
    "My current Google CoLab accound does not allow me to work with big (>10GB) files easily. So, I'll proceed with my local environment (my laptop)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T23:42:49.137728Z",
     "iopub.status.busy": "2024-10-17T23:42:49.137101Z",
     "iopub.status.idle": "2024-10-17T23:42:49.150518Z",
     "shell.execute_reply": "2024-10-17T23:42:49.149821Z",
     "shell.execute_reply.started": "2024-10-17T23:42:49.137678Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('../data/original/space-weather-2000.parquet')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "data_dir = '../data'\n",
    "original_data_dir = 'original'\n",
    "file_name = 'space-weather-2000.parquet'\n",
    "file_path = Path(data_dir) / original_data_dir / file_name\n",
    "file_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 400
    },
    "execution": {
     "iopub.execute_input": "2024-10-17T23:42:52.722094Z",
     "iopub.status.busy": "2024-10-17T23:42:52.721589Z",
     "iopub.status.idle": "2024-10-17T23:42:53.087883Z",
     "shell.execute_reply": "2024-10-17T23:42:53.087529Z",
     "shell.execute_reply.started": "2024-10-17T23:42:52.722076Z"
    },
    "id": "giM9C8WOQMth",
    "outputId": "291c00ff-a476-423f-eef6-1e59719af949"
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>F10</th>\n",
       "      <th>F10_avg</th>\n",
       "      <th>S10</th>\n",
       "      <th>S10_avg</th>\n",
       "      <th>M10</th>\n",
       "      <th>M10_avg</th>\n",
       "      <th>Y10</th>\n",
       "      <th>Y10_avg</th>\n",
       "      <th>DST_DTC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000-01-01 00:00:00</td>\n",
       "      <td>130.1</td>\n",
       "      <td>166.0</td>\n",
       "      <td>143.7</td>\n",
       "      <td>170.4</td>\n",
       "      <td>125.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>146.4</td>\n",
       "      <td>158.7</td>\n",
       "      <td>136.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-01-01 00:30:00</td>\n",
       "      <td>130.1</td>\n",
       "      <td>166.0</td>\n",
       "      <td>143.7</td>\n",
       "      <td>170.4</td>\n",
       "      <td>125.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>146.4</td>\n",
       "      <td>158.7</td>\n",
       "      <td>137.038462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000-01-01 01:00:00</td>\n",
       "      <td>130.1</td>\n",
       "      <td>166.0</td>\n",
       "      <td>143.7</td>\n",
       "      <td>170.4</td>\n",
       "      <td>125.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>146.4</td>\n",
       "      <td>158.7</td>\n",
       "      <td>137.821429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000-01-01 01:30:00</td>\n",
       "      <td>130.1</td>\n",
       "      <td>166.0</td>\n",
       "      <td>143.7</td>\n",
       "      <td>170.4</td>\n",
       "      <td>125.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>146.4</td>\n",
       "      <td>158.7</td>\n",
       "      <td>138.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000-01-01 02:00:00</td>\n",
       "      <td>130.1</td>\n",
       "      <td>166.0</td>\n",
       "      <td>143.7</td>\n",
       "      <td>170.4</td>\n",
       "      <td>125.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>146.4</td>\n",
       "      <td>158.7</td>\n",
       "      <td>139.093750</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "             datetime    F10  F10_avg    S10  S10_avg    M10  M10_avg    Y10  \\\n",
       "0 2000-01-01 00:00:00  130.1    166.0  143.7    170.4  125.0    163.0  146.4   \n",
       "1 2000-01-01 00:30:00  130.1    166.0  143.7    170.4  125.0    163.0  146.4   \n",
       "2 2000-01-01 01:00:00  130.1    166.0  143.7    170.4  125.0    163.0  146.4   \n",
       "3 2000-01-01 01:30:00  130.1    166.0  143.7    170.4  125.0    163.0  146.4   \n",
       "4 2000-01-01 02:00:00  130.1    166.0  143.7    170.4  125.0    163.0  146.4   \n",
       "\n",
       "   Y10_avg     DST_DTC  \n",
       "0    158.7  136.125000  \n",
       "1    158.7  137.038462  \n",
       "2    158.7  137.821429  \n",
       "3    158.7  138.500000  \n",
       "4    158.7  139.093750  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_parquet(file_path)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-10-17T23:43:06.965783Z",
     "iopub.status.busy": "2024-10-17T23:43:06.965619Z",
     "iopub.status.idle": "2024-10-17T23:43:06.968808Z",
     "shell.execute_reply": "2024-10-17T23:43:06.968355Z",
     "shell.execute_reply.started": "2024-10-17T23:43:06.965771Z"
    },
    "id": "TU7tOiG1jwbJ",
    "outputId": "2800019e-caf9-4666-c5f7-86ccafa1e382"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['datetime',\n",
       " 'F10',\n",
       " 'F10_avg',\n",
       " 'S10',\n",
       " 'S10_avg',\n",
       " 'M10',\n",
       " 'M10_avg',\n",
       " 'Y10',\n",
       " 'Y10_avg',\n",
       " 'DST_DTC']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-10-17T23:43:15.421317Z",
     "iopub.status.busy": "2024-10-17T23:43:15.420868Z",
     "iopub.status.idle": "2024-10-17T23:43:15.434967Z",
     "shell.execute_reply": "2024-10-17T23:43:15.434296Z",
     "shell.execute_reply.started": "2024-10-17T23:43:15.421299Z"
    },
    "id": "hBoq-nPQi7WZ",
    "outputId": "7eb5e9b7-de14-49cc-d2ad-50ba617b2b2a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 10)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moment_df = df[df['datetime'] == '2000-01-01 01:00:00']\n",
    "moment_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 135
    },
    "execution": {
     "iopub.execute_input": "2024-10-17T23:43:25.921862Z",
     "iopub.status.busy": "2024-10-17T23:43:25.921697Z",
     "iopub.status.idle": "2024-10-17T23:43:25.929733Z",
     "shell.execute_reply": "2024-10-17T23:43:25.929076Z",
     "shell.execute_reply.started": "2024-10-17T23:43:25.921849Z"
    },
    "id": "UmLjPpgtjP7-",
    "outputId": "675b9d27-d833-429b-e149-bdb727530886"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>F10</th>\n",
       "      <th>F10_avg</th>\n",
       "      <th>S10</th>\n",
       "      <th>S10_avg</th>\n",
       "      <th>M10</th>\n",
       "      <th>M10_avg</th>\n",
       "      <th>Y10</th>\n",
       "      <th>Y10_avg</th>\n",
       "      <th>DST_DTC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000-01-01 01:00:00</td>\n",
       "      <td>130.1</td>\n",
       "      <td>166.0</td>\n",
       "      <td>143.7</td>\n",
       "      <td>170.4</td>\n",
       "      <td>125.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>146.4</td>\n",
       "      <td>158.7</td>\n",
       "      <td>137.821429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "             datetime    F10  F10_avg    S10  S10_avg    M10  M10_avg    Y10  \\\n",
       "2 2000-01-01 01:00:00  130.1    166.0  143.7    170.4  125.0    163.0  146.4   \n",
       "\n",
       "   Y10_avg     DST_DTC  \n",
       "2    158.7  137.821429  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moment_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "execution": {
     "iopub.execute_input": "2024-10-17T23:46:41.112507Z",
     "iopub.status.busy": "2024-10-17T23:46:41.112041Z",
     "iopub.status.idle": "2024-10-17T23:46:41.129429Z",
     "shell.execute_reply": "2024-10-17T23:46:41.128693Z",
     "shell.execute_reply.started": "2024-10-17T23:46:41.112490Z"
    },
    "id": "0RQr6Yl1kojA",
    "outputId": "83c4ded4-cb08-4c6f-84ce-a708f125d56b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          datetime       F10   F10_avg       S10   S10_avg       M10  \\\n",
      "datetime  1.000000 -0.028663 -0.269982  0.135104  0.233035  0.093690   \n",
      "F10      -0.028663  1.000000  0.283308  0.746800  0.268022  0.662736   \n",
      "F10_avg  -0.269982  0.283308  1.000000  0.319446  0.732264  0.208789   \n",
      "S10       0.135104  0.746800  0.319446  1.000000  0.484940  0.878509   \n",
      "S10_avg   0.233035  0.268022  0.732264  0.484940  1.000000  0.315837   \n",
      "M10       0.093690  0.662736  0.208789  0.878509  0.315837  1.000000   \n",
      "M10_avg   0.251360  0.278343  0.742439  0.490897  0.990308  0.330417   \n",
      "Y10       0.171251  0.634332  0.236249  0.759728  0.333793  0.762935   \n",
      "Y10_avg   0.421827  0.238130  0.683828  0.410002  0.875514  0.263309   \n",
      "DST_DTC   0.006877  0.057544 -0.042575  0.115243 -0.008000  0.040553   \n",
      "\n",
      "           M10_avg       Y10   Y10_avg   DST_DTC  \n",
      "datetime  0.251360  0.171251  0.421827  0.006877  \n",
      "F10       0.278343  0.634332  0.238130  0.057544  \n",
      "F10_avg   0.742439  0.236249  0.683828 -0.042575  \n",
      "S10       0.490897  0.759728  0.410002  0.115243  \n",
      "S10_avg   0.990308  0.333793  0.875514 -0.008000  \n",
      "M10       0.330417  0.762935  0.263309  0.040553  \n",
      "M10_avg   1.000000  0.345783  0.900502 -0.015085  \n",
      "Y10       0.345783  1.000000  0.343168  0.101897  \n",
      "Y10_avg   0.900502  0.343168  1.000000 -0.035697  \n",
      "DST_DTC  -0.015085  0.101897 -0.035697  1.000000  \n"
     ]
    }
   ],
   "source": [
    "correlations = df.corr()\n",
    "print(correlations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "execution": {
     "iopub.execute_input": "2024-10-17T23:47:11.749105Z",
     "iopub.status.busy": "2024-10-17T23:47:11.748619Z",
     "iopub.status.idle": "2024-10-17T23:47:11.761893Z",
     "shell.execute_reply": "2024-10-17T23:47:11.761160Z",
     "shell.execute_reply.started": "2024-10-17T23:47:11.749064Z"
    },
    "id": "YUlA60_Ek_CB",
    "outputId": "595c27cb-8708-48f2-f3c2-ab2ea1d41707"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          datetime       F10   F10_avg       S10   S10_avg       M10  \\\n",
      "datetime  1.000000 -0.028663 -0.269982  0.135104  0.233035  0.093690   \n",
      "F10      -0.028663  1.000000  0.283308  0.746800  0.268022  0.662736   \n",
      "F10_avg  -0.269982  0.283308  1.000000  0.319446  0.732264  0.208789   \n",
      "S10       0.135104  0.746800  0.319446  1.000000  0.484940  0.878509   \n",
      "S10_avg   0.233035  0.268022  0.732264  0.484940  1.000000  0.315837   \n",
      "M10       0.093690  0.662736  0.208789  0.878509  0.315837  1.000000   \n",
      "M10_avg   0.251360  0.278343  0.742439  0.490897  0.990308  0.330417   \n",
      "Y10       0.171251  0.634332  0.236249  0.759728  0.333793  0.762935   \n",
      "Y10_avg   0.421827  0.238130  0.683828  0.410002  0.875514  0.263309   \n",
      "DST_DTC   0.006877  0.057544 -0.042575  0.115243 -0.008000  0.040553   \n",
      "\n",
      "           M10_avg       Y10   Y10_avg   DST_DTC  \n",
      "datetime  0.251360  0.171251  0.421827  0.006877  \n",
      "F10       0.278343  0.634332  0.238130  0.057544  \n",
      "F10_avg   0.742439  0.236249  0.683828 -0.042575  \n",
      "S10       0.490897  0.759728  0.410002  0.115243  \n",
      "S10_avg   0.990308  0.333793  0.875514 -0.008000  \n",
      "M10       0.330417  0.762935  0.263309  0.040553  \n",
      "M10_avg   1.000000  0.345783  0.900502 -0.015085  \n",
      "Y10       0.345783  1.000000  0.343168  0.101897  \n",
      "Y10_avg   0.900502  0.343168  1.000000 -0.035697  \n",
      "DST_DTC  -0.015085  0.101897 -0.035697  1.000000  \n"
     ]
    }
   ],
   "source": [
    "correlations = df.corr()\n",
    "print(correlations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "execution": {
     "iopub.execute_input": "2024-10-17T23:47:32.611964Z",
     "iopub.status.busy": "2024-10-17T23:47:32.611474Z",
     "iopub.status.idle": "2024-10-17T23:47:33.105680Z",
     "shell.execute_reply": "2024-10-17T23:47:33.105076Z",
     "shell.execute_reply.started": "2024-10-17T23:47:32.611927Z"
    },
    "id": "010KDI5elJ-u",
    "outputId": "1f801e06-10cc-4813-e45e-3227c0e7881f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(correlations, annot=True, cmap='viridis', vmin=-1, vmax=1)\n",
    "plt.title('Cross-Correlation Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "execution": {
     "iopub.execute_input": "2024-10-17T23:48:45.028558Z",
     "iopub.status.busy": "2024-10-17T23:48:45.027874Z",
     "iopub.status.idle": "2024-10-17T23:48:49.500470Z",
     "shell.execute_reply": "2024-10-17T23:48:49.499894Z",
     "shell.execute_reply.started": "2024-10-17T23:48:45.028499Z"
    },
    "id": "QdgiEVG_lKH1",
    "outputId": "aae2b4a0-63ec-4a3f-b9eb-e9f8fc93e408"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 81 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import scatter_matrix\n",
    "\n",
    "scatter_matrix(df, alpha=0.5, figsize=(8, 8), diagonal='kde')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 528
    },
    "id": "n_71CGk6Tsl3",
    "outputId": "c96f569c-1292-4301-bf7a-00d57897c15b"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"desc\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"datetime\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00.000017568\",\n        \"max\": \"2000-12-31 23:30:00\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"17568\",\n          \"2000-07-01 23:45:00\",\n          \"2000-12-31 23:30:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F10\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6154.444739997137,\n        \"min\": 29.079429739579272,\n        \"max\": 17568.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          179.5363387978142,\n          199.2,\n          17568.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F10_avg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6156.665967869441,\n        \"min\": 8.158396156063322,\n        \"max\": 17568.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          179.86393442622952,\n          186.0,\n          17568.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"S10\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6154.2820685746765,\n        \"min\": 17.489512367455976,\n        \"max\": 17568.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          185.4642076502732,\n          196.6,\n          17568.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"S10_avg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6155.227395850369,\n        \"min\": 6.623434730859226,\n        \"max\": 17568.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          185.2311475409836,\n          189.6,\n          17568.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"M10\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6156.425102578825,\n        \"min\": 22.874267597458353,\n        \"max\": 17568.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          176.34890710382516,\n          191.3,\n          17568.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"M10_avg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6157.926969480396,\n        \"min\": 5.7492968058729135,\n        \"max\": 17568.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          176.03852459016395,\n          180.6,\n          17568.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Y10\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6160.784279103162,\n        \"min\": 11.343523250258183,\n        \"max\": 17568.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          164.91174863387977,\n          172.8,\n          17568.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Y10_avg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6161.3949660550115,\n        \"min\": 2.897370680568767,\n        \"max\": 17568.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          164.98579234972678,\n          166.9,\n          17568.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DST_DTC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6178.05230764668,\n        \"min\": 0.3541666666666667,\n        \"max\": 17568.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          69.26248002513266,\n          86.64583333333333,\n          17568.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe",
       "variable_name": "desc"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-64e69c06-53c3-4252-a179-8a0e75daf441\" class=\"colab-df-container\">\n",
       "    <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>datetime</th>\n",
       "      <th>F10</th>\n",
       "      <th>F10_avg</th>\n",
       "      <th>S10</th>\n",
       "      <th>S10_avg</th>\n",
       "      <th>M10</th>\n",
       "      <th>M10_avg</th>\n",
       "      <th>Y10</th>\n",
       "      <th>Y10_avg</th>\n",
       "      <th>DST_DTC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17568</td>\n",
       "      <td>17568.000000</td>\n",
       "      <td>17568.000000</td>\n",
       "      <td>17568.000000</td>\n",
       "      <td>17568.000000</td>\n",
       "      <td>17568.000000</td>\n",
       "      <td>17568.000000</td>\n",
       "      <td>17568.000000</td>\n",
       "      <td>17568.000000</td>\n",
       "      <td>17568.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2000-07-01 23:45:00</td>\n",
       "      <td>179.536339</td>\n",
       "      <td>179.863934</td>\n",
       "      <td>185.464208</td>\n",
       "      <td>185.231148</td>\n",
       "      <td>176.348907</td>\n",
       "      <td>176.038525</td>\n",
       "      <td>164.911749</td>\n",
       "      <td>164.985792</td>\n",
       "      <td>69.262480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2000-01-01 00:00:00</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>163.200000</td>\n",
       "      <td>142.100000</td>\n",
       "      <td>170.200000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>162.900000</td>\n",
       "      <td>138.300000</td>\n",
       "      <td>157.400000</td>\n",
       "      <td>0.354167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2000-04-01 11:52:30</td>\n",
       "      <td>157.800000</td>\n",
       "      <td>172.500000</td>\n",
       "      <td>173.600000</td>\n",
       "      <td>182.000000</td>\n",
       "      <td>161.600000</td>\n",
       "      <td>173.500000</td>\n",
       "      <td>156.900000</td>\n",
       "      <td>163.400000</td>\n",
       "      <td>40.057292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2000-07-01 23:45:00</td>\n",
       "      <td>177.700000</td>\n",
       "      <td>180.050000</td>\n",
       "      <td>185.300000</td>\n",
       "      <td>184.200000</td>\n",
       "      <td>177.850000</td>\n",
       "      <td>175.250000</td>\n",
       "      <td>165.100000</td>\n",
       "      <td>165.700000</td>\n",
       "      <td>59.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2000-10-01 11:37:30</td>\n",
       "      <td>199.200000</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>196.600000</td>\n",
       "      <td>189.600000</td>\n",
       "      <td>191.300000</td>\n",
       "      <td>180.600000</td>\n",
       "      <td>172.800000</td>\n",
       "      <td>166.900000</td>\n",
       "      <td>86.645833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2000-12-31 23:30:00</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>196.400000</td>\n",
       "      <td>233.500000</td>\n",
       "      <td>197.200000</td>\n",
       "      <td>236.300000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>194.400000</td>\n",
       "      <td>170.500000</td>\n",
       "      <td>378.270833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>29.079430</td>\n",
       "      <td>8.158396</td>\n",
       "      <td>17.489512</td>\n",
       "      <td>6.623435</td>\n",
       "      <td>22.874268</td>\n",
       "      <td>5.749297</td>\n",
       "      <td>11.343523</td>\n",
       "      <td>2.897371</td>\n",
       "      <td>45.224106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-64e69c06-53c3-4252-a179-8a0e75daf441')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-64e69c06-53c3-4252-a179-8a0e75daf441 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-64e69c06-53c3-4252-a179-8a0e75daf441');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "<div id=\"df-e6e54066-73ee-4b26-9e18-d7839b61fb9a\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e6e54066-73ee-4b26-9e18-d7839b61fb9a')\"\n",
       "            title=\"Suggest charts\"\n",
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       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-e6e54066-73ee-4b26-9e18-d7839b61fb9a button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "  <div id=\"id_500f7a19-f0a0-43d4-a8e9-37096863cabf\">\n",
       "    <style>\n",
       "      .colab-df-generate {\n",
       "        background-color: #E8F0FE;\n",
       "        border: none;\n",
       "        border-radius: 50%;\n",
       "        cursor: pointer;\n",
       "        display: none;\n",
       "        fill: #1967D2;\n",
       "        height: 32px;\n",
       "        padding: 0 0 0 0;\n",
       "        width: 32px;\n",
       "      }\n",
       "\n",
       "      .colab-df-generate:hover {\n",
       "        background-color: #E2EBFA;\n",
       "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "        fill: #174EA6;\n",
       "      }\n",
       "\n",
       "      [theme=dark] .colab-df-generate {\n",
       "        background-color: #3B4455;\n",
       "        fill: #D2E3FC;\n",
       "      }\n",
       "\n",
       "      [theme=dark] .colab-df-generate:hover {\n",
       "        background-color: #434B5C;\n",
       "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "        fill: #FFFFFF;\n",
       "      }\n",
       "    </style>\n",
       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('desc')\"\n",
       "            title=\"Generate code using this dataframe.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "    <script>\n",
       "      (() => {\n",
       "      const buttonEl =\n",
       "        document.querySelector('#id_500f7a19-f0a0-43d4-a8e9-37096863cabf button.colab-df-generate');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      buttonEl.onclick = () => {\n",
       "        google.colab.notebook.generateWithVariable('desc');\n",
       "      }\n",
       "      })();\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "                  datetime           F10       F10_avg           S10  \\\n",
       "count                17568  17568.000000  17568.000000  17568.000000   \n",
       "mean   2000-07-01 23:45:00    179.536339    179.863934    185.464208   \n",
       "min    2000-01-01 00:00:00    126.000000    163.200000    142.100000   \n",
       "25%    2000-04-01 11:52:30    157.800000    172.500000    173.600000   \n",
       "50%    2000-07-01 23:45:00    177.700000    180.050000    185.300000   \n",
       "75%    2000-10-01 11:37:30    199.200000    186.000000    196.600000   \n",
       "max    2000-12-31 23:30:00    262.000000    196.400000    233.500000   \n",
       "std                    NaN     29.079430      8.158396     17.489512   \n",
       "\n",
       "            S10_avg           M10       M10_avg           Y10       Y10_avg  \\\n",
       "count  17568.000000  17568.000000  17568.000000  17568.000000  17568.000000   \n",
       "mean     185.231148    176.348907    176.038525    164.911749    164.985792   \n",
       "min      170.200000    125.000000    162.900000    138.300000    157.400000   \n",
       "25%      182.000000    161.600000    173.500000    156.900000    163.400000   \n",
       "50%      184.200000    177.850000    175.250000    165.100000    165.700000   \n",
       "75%      189.600000    191.300000    180.600000    172.800000    166.900000   \n",
       "max      197.200000    236.300000    187.000000    194.400000    170.500000   \n",
       "std        6.623435     22.874268      5.749297     11.343523      2.897371   \n",
       "\n",
       "            DST_DTC  \n",
       "count  17568.000000  \n",
       "mean      69.262480  \n",
       "min        0.354167  \n",
       "25%       40.057292  \n",
       "50%       59.125000  \n",
       "75%       86.645833  \n",
       "max      378.270833  \n",
       "std       45.224106  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desc = df.describe(include='all')\n",
    "desc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5OeC50unToMr",
    "outputId": "f1a96a51-fe84-4dc1-e865-e287d8cdc437"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17568"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# validate if all samples are presented:\n",
    "\n",
    "year_samples_num = 2 * 24 * 366 # 2 samples per hour; a leap year\n",
    "year_samples_num\n",
    "\n",
    "# 17568 equal the number of samples in the file hence all samples are presented and No NaNs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install sweetviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T23:55:08.793195Z",
     "iopub.status.busy": "2024-10-17T23:55:08.792501Z",
     "iopub.status.idle": "2024-10-17T23:55:11.848093Z",
     "shell.execute_reply": "2024-10-17T23:55:11.847762Z",
     "shell.execute_reply.started": "2024-10-17T23:55:08.793178Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9f388eb8e34a46d78097777f61f8f645",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "                                             |      | [  0%]   00:00 -> (? left)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<sweetviz.dataframe_report.DataframeReport at 0x719f33f8a260>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It produces the SWEETVIZ_REPORT.html\n",
    "\n",
    "import sweetviz as sv\n",
    "\n",
    "report = sv.analyze(df) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2YWQct4xsi-q"
   },
   "source": [
    "## Raw density files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-26T00:14:47.879839Z",
     "iopub.status.busy": "2024-10-26T00:14:47.879284Z",
     "iopub.status.idle": "2024-10-26T00:14:48.119066Z",
     "shell.execute_reply": "2024-10-26T00:14:48.118524Z",
     "shell.execute_reply.started": "2024-10-26T00:14:47.879796Z"
    },
    "id": "udtBFEYSspfk"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import tarfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-10-26T00:14:50.237427Z",
     "iopub.status.busy": "2024-10-26T00:14:50.236974Z",
     "iopub.status.idle": "2024-10-26T00:14:50.243838Z",
     "shell.execute_reply": "2024-10-26T00:14:50.243516Z",
     "shell.execute_reply.started": "2024-10-26T00:14:50.237395Z"
    },
    "id": "XWp7yR0vswSq",
    "outputId": "b26503ea-6d4a-4176-a288-d7317b5a179b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('../data/original/density-2000.tar')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "data_dir = '../data'\n",
    "original_data_dir = 'original'\n",
    "year = '2000'\n",
    "file_name = f'density-{year}.tar'\n",
    "file_path = Path(data_dir) / original_data_dir / file_name\n",
    "file_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-20T17:24:15.100530Z",
     "iopub.status.busy": "2024-10-20T17:24:15.100291Z",
     "iopub.status.idle": "2024-10-20T17:24:15.180224Z",
     "shell.execute_reply": "2024-10-20T17:24:15.179625Z",
     "shell.execute_reply.started": "2024-10-20T17:24:15.100514Z"
    },
    "id": "7_-yIpJ9tSCK"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../data/original/2000/2000-01-01.parquet\n",
      "../data/original/2000/2000-01-02.parquet\n",
      "Extracted files: 2\n"
     ]
    }
   ],
   "source": [
    "# Extract all files from a TAR file:\n",
    "\n",
    "# Get a list of all members (files and directories) in the archive\n",
    "\n",
    "with tarfile.open(file_path, 'r') as tar:\n",
    "  members = tar.getmembers()\n",
    "  for i, member in enumerate(members):\n",
    "    if '/' in member.name:  # a file\n",
    "        date, file_name = member.name.split('=')[1].split('/')\n",
    "        where = Path(data_dir) / original_data_dir / year / f\"{date}.parquet\"\n",
    "        tar.extract(member.name, path=where)\n",
    "        print(where)\n",
    "        if i > 12: break\n",
    "  print(\"Extracted files:\", i-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-19T17:53:20.198831Z",
     "iopub.status.busy": "2024-10-19T17:53:20.198251Z",
     "iopub.status.idle": "2024-10-19T17:53:20.215726Z",
     "shell.execute_reply": "2024-10-19T17:53:20.215379Z",
     "shell.execute_reply.started": "2024-10-19T17:53:20.198774Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "366 files have been renamed.\n"
     ]
    }
   ],
   "source": [
    "# Rename files: '2000-01-01.e744a8ed50e64f5b8f56d3bab9f2cd1d-0.parquet' -> '2000-01-01.parquet'\n",
    "import os\n",
    "\n",
    "data_dir = '../data'\n",
    "original_data_dir = 'original'\n",
    "year = '2000'\n",
    "directory = Path(data_dir) / original_data_dir / year\n",
    "\n",
    "# Iterate over all the files in the directory\n",
    "n = 0\n",
    "for filename in os.listdir(directory):\n",
    "    old_file_path = os.path.join(directory, filename)    \n",
    "    new_filename = filename.split('.')[0] + '.parquet'\n",
    "    new_file_path = os.path.join(directory, new_filename)\n",
    "    os.rename(old_file_path, new_file_path)\n",
    "    \n",
    "\n",
    "print(f\"{n} files have been renamed.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### File anomalies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-18T21:46:26.475391Z",
     "iopub.status.busy": "2024-10-18T21:46:26.474332Z",
     "iopub.status.idle": "2024-10-18T21:47:13.384565Z",
     "shell.execute_reply": "2024-10-18T21:47:13.383494Z",
     "shell.execute_reply.started": "2024-10-18T21:46:26.475337Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".............................................................................................................................................................................................................................................................................................................................................................................."
     ]
    },
    {
     "data": {
      "text/plain": [
       "Counter({9120912: 354,\n",
       "         4593744: 2,\n",
       "         9120606: 1,\n",
       "         9120898: 1,\n",
       "         9120902: 1,\n",
       "         9120720: 1,\n",
       "         9120672: 1,\n",
       "         9120895: 1,\n",
       "         9120722: 1,\n",
       "         9120480: 1,\n",
       "         9120732: 1,\n",
       "         9120822: 1})"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Are all files with the same # of samples?\n",
    "\n",
    "import os\n",
    "from pathlib import Path\n",
    "\n",
    "from collections import Counter\n",
    "\n",
    "lengths = [] # Counter()\n",
    "\n",
    "# Specify the directory path\n",
    "directory = Path(data_dir) / original_data_dir  / str(year)\n",
    "file_names = os.listdir(directory)\n",
    "# print(file_names)\n",
    "\n",
    "for ff in file_names:\n",
    "    print('.', end='')\n",
    "    lengths.append(len(pd.read_parquet(directory / ff)))\n",
    "\n",
    "res = Counter(lengths)\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-18T21:54:52.020661Z",
     "iopub.status.busy": "2024-10-18T21:54:52.019055Z",
     "iopub.status.idle": "2024-10-18T21:55:33.121976Z",
     "shell.execute_reply": "2024-10-18T21:55:33.121151Z",
     "shell.execute_reply.started": "2024-10-18T21:54:52.020570Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000-12-30.73390a90a7be41cb8426f3921613b49b-0.parquet len: 4593744\n",
      "2000-12-31.88a3d27ea28349ccbbd8ae3376512c9c-0.parquet len: 4593744\n",
      "2000-10-15.e175ed82c7cc4f99984eb70ca25a3cf1-0.parquet len: 9120606\n",
      "2000-02-09.c20d8fff3c6440a6a33b6ebbe94a0460-0.parquet len: 9120898\n",
      "2000-12-10.419508b94f0349f182e78a8e02e2a2eb-0.parquet len: 9120902\n",
      "2000-01-21.ad3ba62fecc746ff875714f87bab3531-0.parquet len: 9120720\n",
      "2000-12-14.3f86e3eaf2fa41c8b1768769ffde5dc1-0.parquet len: 9120672\n",
      "2000-10-13.b8e8bd278afb41daa5caee68baa7e8ee-0.parquet len: 9120895\n",
      "2000-05-28.efbe09804e86473c8f73b01c06217900-0.parquet len: 9120722\n",
      "2000-04-25.b885ebc152fb42bd8a9a873a94288434-0.parquet len: 9120480\n",
      "2000-05-06.9dc276b3a3534b5dae4cacbdb826a562-0.parquet len: 9120732\n",
      "2000-04-26.ec0dd1474bed45b8943fe3ee56ada25b-0.parquet len: 9120822\n"
     ]
    }
   ],
   "source": [
    "# What are files that have \"broken\" lengths?\n",
    "\n",
    "for ff in os.listdir(directory):\n",
    "    l = len(pd.read_parquet(directory / ff))\n",
    "    if l != 9120912:\n",
    "        print(f\"{ff} len: {l}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusions\n",
    "\n",
    "1. 12 files have anomalies in the number of samples. \n",
    "    For this project I safely assume, these files are broken. I'll remove these files from training and evaluation.\n",
    "    For now, I assume all files have the same number of samples.\n",
    "\n",
    "2. These dimensions have a constant number of unique values:\n",
    "    - latitude (19,)\n",
    "    - longitude (73,)\n",
    "    - altitude (137,)\n",
    "    - datetime (48,)\n",
    "    \n",
    "    Hence, the number of rows in the most of files equals 9120912."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-18T23:07:33.894457Z",
     "iopub.status.busy": "2024-10-18T23:07:33.892980Z",
     "iopub.status.idle": "2024-10-18T23:07:33.900355Z",
     "shell.execute_reply": "2024-10-18T23:07:33.899764Z",
     "shell.execute_reply.started": "2024-10-18T23:07:33.894382Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9120912"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "19*73*137*48"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 380
    },
    "execution": {
     "iopub.execute_input": "2024-10-18T23:01:10.035366Z",
     "iopub.status.busy": "2024-10-18T23:01:10.035042Z",
     "iopub.status.idle": "2024-10-18T23:01:10.161751Z",
     "shell.execute_reply": "2024-10-18T23:01:10.161056Z",
     "shell.execute_reply.started": "2024-10-18T23:01:10.035342Z"
    },
    "id": "04DVQQPRy9Fb",
    "outputId": "cdc45647-0df5-46d4-8a00-7f8b98c50d1e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>datetime</th>\n",
       "      <th>date</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>altitude</th>\n",
       "      <th>local_solar_time</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>-90.0</td>\n",
       "      <td>-180.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.682600e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>-90.0</td>\n",
       "      <td>-180.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.893600e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>-90.0</td>\n",
       "      <td>-180.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.349600e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>-90.0</td>\n",
       "      <td>-180.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.369200e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>-90.0</td>\n",
       "      <td>-180.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.173900e-09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    datetime        date  latitude  longitude  altitude  local_solar_time  \\\n",
       "0 2000-01-01  2000-01-01     -90.0     -180.0     120.0              12.0   \n",
       "1 2000-01-01  2000-01-01     -90.0     -180.0     125.0              12.0   \n",
       "2 2000-01-01  2000-01-01     -90.0     -180.0     130.0              12.0   \n",
       "3 2000-01-01  2000-01-01     -90.0     -180.0     135.0              12.0   \n",
       "4 2000-01-01  2000-01-01     -90.0     -180.0     140.0              12.0   \n",
       "\n",
       "        density  \n",
       "0  1.682600e-08  \n",
       "1  9.893600e-09  \n",
       "2  6.349600e-09  \n",
       "3  4.369200e-09  \n",
       "4  3.173900e-09  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_name = '2000-01-01.parquet'\n",
    "file_path = Path('../data/original/2000') / file_name\n",
    "# reorder columns\n",
    "df_raw = pd.read_parquet(file_path, columns=['datetime', 'date','latitude',\t'longitude', 'altitude', 'local_solar_time','density'])\n",
    "df_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-19T04:18:15.047522Z",
     "iopub.status.busy": "2024-10-19T04:18:15.046274Z",
     "iopub.status.idle": "2024-10-19T04:18:15.053951Z",
     "shell.execute_reply": "2024-10-19T04:18:15.053252Z",
     "shell.execute_reply.started": "2024-10-19T04:18:15.047475Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9120912"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 771
    },
    "execution": {
     "iopub.execute_input": "2024-10-18T23:01:13.024581Z",
     "iopub.status.busy": "2024-10-18T23:01:13.024219Z",
     "iopub.status.idle": "2024-10-18T23:01:13.032867Z",
     "shell.execute_reply": "2024-10-18T23:01:13.032442Z",
     "shell.execute_reply.started": "2024-10-18T23:01:13.024565Z"
    },
    "id": "e7y-pXU7X2F6",
    "outputId": "4fe0d0bf-1788-490d-80ad-dcfbed62e2c9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>datetime</th>\n",
       "      <th>date</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>altitude</th>\n",
       "      <th>local_solar_time</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9120907</th>\n",
       "      <td>2000-01-01 23:30:00</td>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>90.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1.329200e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9120908</th>\n",
       "      <td>2000-01-01 23:30:00</td>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>90.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>785.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1.268300e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9120909</th>\n",
       "      <td>2000-01-01 23:30:00</td>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>90.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>790.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1.211100e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9120910</th>\n",
       "      <td>2000-01-01 23:30:00</td>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>90.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>795.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1.157500e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9120911</th>\n",
       "      <td>2000-01-01 23:30:00</td>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>90.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1.107200e-14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   datetime        date  latitude  longitude  altitude  \\\n",
       "9120907 2000-01-01 23:30:00  2000-01-01      90.0      180.0     780.0   \n",
       "9120908 2000-01-01 23:30:00  2000-01-01      90.0      180.0     785.0   \n",
       "9120909 2000-01-01 23:30:00  2000-01-01      90.0      180.0     790.0   \n",
       "9120910 2000-01-01 23:30:00  2000-01-01      90.0      180.0     795.0   \n",
       "9120911 2000-01-01 23:30:00  2000-01-01      90.0      180.0     800.0   \n",
       "\n",
       "         local_solar_time       density  \n",
       "9120907              11.5  1.329200e-14  \n",
       "9120908              11.5  1.268300e-14  \n",
       "9120909              11.5  1.211100e-14  \n",
       "9120910              11.5  1.157500e-14  \n",
       "9120911              11.5  1.107200e-14  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-18T23:01:15.600054Z",
     "iopub.status.busy": "2024-10-18T23:01:15.599823Z",
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    {
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     "text": [
      "4560456\n"
     ]
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>date</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>altitude</th>\n",
       "      <th>local_solar_time</th>\n",
       "      <th>density</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4560451</th>\n",
       "      <td>2000-01-01 11:30:00</td>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>90.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>23.5</td>\n",
       "      <td>1.801100e-14</td>\n",
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       "      <th>4560452</th>\n",
       "      <td>2000-01-01 11:30:00</td>\n",
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       "      <td>90.0</td>\n",
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       "    <tr>\n",
       "      <th>4560453</th>\n",
       "      <td>2000-01-01 11:30:00</td>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>90.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>790.0</td>\n",
       "      <td>23.5</td>\n",
       "      <td>1.633000e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4560454</th>\n",
       "      <td>2000-01-01 11:30:00</td>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>90.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>795.0</td>\n",
       "      <td>23.5</td>\n",
       "      <td>1.556400e-14</td>\n",
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       "    <tr>\n",
       "      <th>4560455</th>\n",
       "      <td>2000-01-01 11:30:00</td>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>90.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>23.5</td>\n",
       "      <td>1.484300e-14</td>\n",
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       "      <th>4560456</th>\n",
       "      <td>2000-01-01 12:00:00</td>\n",
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       "      <td>120.0</td>\n",
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       "    <tr>\n",
       "      <th>4560457</th>\n",
       "      <td>2000-01-01 12:00:00</td>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>-90.0</td>\n",
       "      <td>-180.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.932600e-09</td>\n",
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       "    <tr>\n",
       "      <th>4560458</th>\n",
       "      <td>2000-01-01 12:00:00</td>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>-90.0</td>\n",
       "      <td>-180.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.384000e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4560459</th>\n",
       "      <td>2000-01-01 12:00:00</td>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>-90.0</td>\n",
       "      <td>-180.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.398500e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4560460</th>\n",
       "      <td>2000-01-01 12:00:00</td>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>-90.0</td>\n",
       "      <td>-180.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.198500e-09</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "                   datetime        date  latitude  longitude  altitude  \\\n",
       "4560451 2000-01-01 11:30:00  2000-01-01      90.0      180.0     780.0   \n",
       "4560452 2000-01-01 11:30:00  2000-01-01      90.0      180.0     785.0   \n",
       "4560453 2000-01-01 11:30:00  2000-01-01      90.0      180.0     790.0   \n",
       "4560454 2000-01-01 11:30:00  2000-01-01      90.0      180.0     795.0   \n",
       "4560455 2000-01-01 11:30:00  2000-01-01      90.0      180.0     800.0   \n",
       "4560456 2000-01-01 12:00:00  2000-01-01     -90.0     -180.0     120.0   \n",
       "4560457 2000-01-01 12:00:00  2000-01-01     -90.0     -180.0     125.0   \n",
       "4560458 2000-01-01 12:00:00  2000-01-01     -90.0     -180.0     130.0   \n",
       "4560459 2000-01-01 12:00:00  2000-01-01     -90.0     -180.0     135.0   \n",
       "4560460 2000-01-01 12:00:00  2000-01-01     -90.0     -180.0     140.0   \n",
       "\n",
       "         local_solar_time       density  \n",
       "4560451              23.5  1.801100e-14  \n",
       "4560452              23.5  1.714500e-14  \n",
       "4560453              23.5  1.633000e-14  \n",
       "4560454              23.5  1.556400e-14  \n",
       "4560455              23.5  1.484300e-14  \n",
       "4560456               0.0  1.686800e-08  \n",
       "4560457               0.0  9.932600e-09  \n",
       "4560458               0.0  6.384000e-09  \n",
       "4560459               0.0  4.398500e-09  \n",
       "4560460               0.0  3.198500e-09  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It is interesting, how local_solar_time is switched in the noon, from 23.5 to 0\n",
    "\n",
    "middle = df_raw.shape[0]//2\n",
    "print(middle)\n",
    "df_raw[middle-5:middle+5]"
   ]
  },
  {
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   "outputs": [
    {
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>date</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>altitude</th>\n",
       "      <th>local_solar_time</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9120912</td>\n",
       "      <td>9120912</td>\n",
       "      <td>9.120912e+06</td>\n",
       "      <td>9.120912e+06</td>\n",
       "      <td>9.120912e+06</td>\n",
       "      <td>9.120912e+06</td>\n",
       "      <td>9.120912e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9120912</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2000-01-01 11:45:00.000000640</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.600000e+02</td>\n",
       "      <td>1.191438e+01</td>\n",
       "      <td>5.303945e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2000-01-01 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9.000000e+01</td>\n",
       "      <td>-1.800000e+02</td>\n",
       "      <td>1.200000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>7.932200e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2000-01-01 05:52:30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.000000e+01</td>\n",
       "      <td>-9.000000e+01</td>\n",
       "      <td>2.900000e+02</td>\n",
       "      <td>5.958333e+00</td>\n",
       "      <td>1.340000e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2000-01-01 11:45:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.600000e+02</td>\n",
       "      <td>1.191667e+01</td>\n",
       "      <td>1.669400e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2000-01-01 17:37:30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.000000e+01</td>\n",
       "      <td>9.000000e+01</td>\n",
       "      <td>6.300000e+02</td>\n",
       "      <td>1.787500e+01</td>\n",
       "      <td>3.565200e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2000-01-01 23:30:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000000e+01</td>\n",
       "      <td>1.800000e+02</td>\n",
       "      <td>8.000000e+02</td>\n",
       "      <td>2.383333e+01</td>\n",
       "      <td>3.401000e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.477226e+01</td>\n",
       "      <td>1.053565e+02</td>\n",
       "      <td>1.977372e+02</td>\n",
       "      <td>6.928045e+00</td>\n",
       "      <td>2.627367e-09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             datetime        date      latitude     longitude  \\\n",
       "count                         9120912     9120912  9.120912e+06  9.120912e+06   \n",
       "unique                            NaN           1           NaN           NaN   \n",
       "top                               NaN  2000-01-01           NaN           NaN   \n",
       "freq                              NaN     9120912           NaN           NaN   \n",
       "mean    2000-01-01 11:45:00.000000640         NaN  0.000000e+00  0.000000e+00   \n",
       "min               2000-01-01 00:00:00         NaN -9.000000e+01 -1.800000e+02   \n",
       "25%               2000-01-01 05:52:30         NaN -5.000000e+01 -9.000000e+01   \n",
       "50%               2000-01-01 11:45:00         NaN  0.000000e+00  0.000000e+00   \n",
       "75%               2000-01-01 17:37:30         NaN  5.000000e+01  9.000000e+01   \n",
       "max               2000-01-01 23:30:00         NaN  9.000000e+01  1.800000e+02   \n",
       "std                               NaN         NaN  5.477226e+01  1.053565e+02   \n",
       "\n",
       "            altitude  local_solar_time       density  \n",
       "count   9.120912e+06      9.120912e+06  9.120912e+06  \n",
       "unique           NaN               NaN           NaN  \n",
       "top              NaN               NaN           NaN  \n",
       "freq             NaN               NaN           NaN  \n",
       "mean    4.600000e+02      1.191438e+01  5.303945e-10  \n",
       "min     1.200000e+02      0.000000e+00  7.932200e-15  \n",
       "25%     2.900000e+02      5.958333e+00  1.340000e-13  \n",
       "50%     4.600000e+02      1.191667e+01  1.669400e-12  \n",
       "75%     6.300000e+02      1.787500e+01  3.565200e-11  \n",
       "max     8.000000e+02      2.383333e+01  3.401000e-08  \n",
       "std     1.977372e+02      6.928045e+00  2.627367e-09  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desc = df_raw.describe(include='all')\n",
    "desc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-18T23:13:58.996212Z",
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     "shell.execute_reply.started": "2024-10-18T23:13:58.996122Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime            datetime64[ns]\n",
       "date                      category\n",
       "latitude                   float32\n",
       "longitude                  float32\n",
       "altitude                   float32\n",
       "local_solar_time           float32\n",
       "density                    float32\n",
       "dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- No NaNs !\n",
    "- `latitude`, `longitude`, `altitude` can be int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 465
    },
    "execution": {
     "iopub.execute_input": "2024-10-18T00:19:07.429370Z",
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    },
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    "outputId": "cb9a9bdc-4e2e-4479-af34-76c14b50bf33"
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "df_raw.plot(kind='scatter', x='latitude', y='density', s=32, alpha=.8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 465
    },
    "execution": {
     "iopub.execute_input": "2024-10-18T00:19:18.221051Z",
     "iopub.status.busy": "2024-10-18T00:19:18.220939Z",
     "iopub.status.idle": "2024-10-18T00:19:29.042092Z",
     "shell.execute_reply": "2024-10-18T00:19:29.041637Z",
     "shell.execute_reply.started": "2024-10-18T00:19:18.221041Z"
    },
    "id": "9UgEq60iY7Hw",
    "outputId": "b9678010-43f4-4954-cea3-2e182592262b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_raw.plot(kind='scatter', x='longitude', y='density', s=32, alpha=.8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 465
    },
    "execution": {
     "iopub.execute_input": "2024-10-18T00:19:29.042765Z",
     "iopub.status.busy": "2024-10-18T00:19:29.042652Z",
     "iopub.status.idle": "2024-10-18T00:19:39.574029Z",
     "shell.execute_reply": "2024-10-18T00:19:39.573638Z",
     "shell.execute_reply.started": "2024-10-18T00:19:29.042755Z"
    },
    "id": "LFpJV3ZbY7Zm",
    "outputId": "f5cfb4c3-6c44-4575-bc1f-19fe864ea8f9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_raw.plot(kind='scatter', x='altitude', y='density', s=32, alpha=.8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 465
    },
    "execution": {
     "iopub.execute_input": "2024-10-18T00:19:49.318328Z",
     "iopub.status.busy": "2024-10-18T00:19:49.317596Z",
     "iopub.status.idle": "2024-10-18T00:20:00.143556Z",
     "shell.execute_reply": "2024-10-18T00:20:00.143027Z",
     "shell.execute_reply.started": "2024-10-18T00:19:49.318266Z"
    },
    "id": "VlVfHo7FZD2j",
    "outputId": "79c7fbc4-cde7-4c6a-ea9c-2ba90cdce5d0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_raw.plot(kind='scatter', x='local_solar_time', y='density', s=32, alpha=.8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 771
    },
    "execution": {
     "iopub.execute_input": "2024-10-18T00:19:39.574726Z",
     "iopub.status.busy": "2024-10-18T00:19:39.574608Z",
     "iopub.status.idle": "2024-10-18T00:19:39.601767Z",
     "shell.execute_reply": "2024-10-18T00:19:39.601351Z",
     "shell.execute_reply.started": "2024-10-18T00:19:39.574716Z"
    },
    "id": "eANi4-LZWaVM",
    "outputId": "222cdd28-da14-4167-e883-dd33b640b732"
   },
   "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>datetime</th>\n",
       "      <th>local_solar_time</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>altitude</th>\n",
       "      <th>density</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8930893</th>\n",
       "      <td>2000-01-01 23:30:00</td>\n",
       "      <td>11.5</td>\n",
       "      <td>-90.0</td>\n",
       "      <td>-180.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.681700e-08</td>\n",
       "      <td>2000-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8930894</th>\n",
       "      <td>2000-01-01 23:30:00</td>\n",
       "      <td>11.5</td>\n",
       "      <td>-90.0</td>\n",
       "      <td>-180.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>9.877600e-09</td>\n",
       "      <td>2000-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8930895</th>\n",
       "      <td>2000-01-01 23:30:00</td>\n",
       "      <td>11.5</td>\n",
       "      <td>-90.0</td>\n",
       "      <td>-180.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>6.332000e-09</td>\n",
       "      <td>2000-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8930896</th>\n",
       "      <td>2000-01-01 23:30:00</td>\n",
       "      <td>11.5</td>\n",
       "      <td>-90.0</td>\n",
       "      <td>-180.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>4.352700e-09</td>\n",
       "      <td>2000-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8930897</th>\n",
       "      <td>2000-01-01 23:30:00</td>\n",
       "      <td>11.5</td>\n",
       "      <td>-90.0</td>\n",
       "      <td>-180.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3.159100e-09</td>\n",
       "      <td>2000-01-01</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9120907</th>\n",
       "      <td>2000-01-01 23:30:00</td>\n",
       "      <td>11.5</td>\n",
       "      <td>90.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>1.329200e-14</td>\n",
       "      <td>2000-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9120908</th>\n",
       "      <td>2000-01-01 23:30:00</td>\n",
       "      <td>11.5</td>\n",
       "      <td>90.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>785.0</td>\n",
       "      <td>1.268300e-14</td>\n",
       "      <td>2000-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9120909</th>\n",
       "      <td>2000-01-01 23:30:00</td>\n",
       "      <td>11.5</td>\n",
       "      <td>90.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>790.0</td>\n",
       "      <td>1.211100e-14</td>\n",
       "      <td>2000-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9120910</th>\n",
       "      <td>2000-01-01 23:30:00</td>\n",
       "      <td>11.5</td>\n",
       "      <td>90.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>795.0</td>\n",
       "      <td>1.157500e-14</td>\n",
       "      <td>2000-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9120911</th>\n",
       "      <td>2000-01-01 23:30:00</td>\n",
       "      <td>11.5</td>\n",
       "      <td>90.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>1.107200e-14</td>\n",
       "      <td>2000-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>190019 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   datetime  local_solar_time  latitude  longitude  altitude  \\\n",
       "8930893 2000-01-01 23:30:00              11.5     -90.0     -180.0     120.0   \n",
       "8930894 2000-01-01 23:30:00              11.5     -90.0     -180.0     125.0   \n",
       "8930895 2000-01-01 23:30:00              11.5     -90.0     -180.0     130.0   \n",
       "8930896 2000-01-01 23:30:00              11.5     -90.0     -180.0     135.0   \n",
       "8930897 2000-01-01 23:30:00              11.5     -90.0     -180.0     140.0   \n",
       "...                     ...               ...       ...        ...       ...   \n",
       "9120907 2000-01-01 23:30:00              11.5      90.0      180.0     780.0   \n",
       "9120908 2000-01-01 23:30:00              11.5      90.0      180.0     785.0   \n",
       "9120909 2000-01-01 23:30:00              11.5      90.0      180.0     790.0   \n",
       "9120910 2000-01-01 23:30:00              11.5      90.0      180.0     795.0   \n",
       "9120911 2000-01-01 23:30:00              11.5      90.0      180.0     800.0   \n",
       "\n",
       "              density        date  \n",
       "8930893  1.681700e-08  2000-01-01  \n",
       "8930894  9.877600e-09  2000-01-01  \n",
       "8930895  6.332000e-09  2000-01-01  \n",
       "8930896  4.352700e-09  2000-01-01  \n",
       "8930897  3.159100e-09  2000-01-01  \n",
       "...               ...         ...  \n",
       "9120907  1.329200e-14  2000-01-01  \n",
       "9120908  1.268300e-14  2000-01-01  \n",
       "9120909  1.211100e-14  2000-01-01  \n",
       "9120910  1.157500e-14  2000-01-01  \n",
       "9120911  1.107200e-14  2000-01-01  \n",
       "\n",
       "[190019 rows x 7 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw[df_raw['datetime'] == '2000-01-01 23:30:00']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-10-18T18:44:30.201811Z",
     "iopub.status.busy": "2024-10-18T18:44:30.201198Z",
     "iopub.status.idle": "2024-10-18T18:44:30.248021Z",
     "shell.execute_reply": "2024-10-18T18:44:30.247387Z",
     "shell.execute_reply.started": "2024-10-18T18:44:30.201762Z"
    },
    "id": "CAH0Vyv9W_yu",
    "outputId": "d07c3478-d2db-4ece-b434-1a498f7ebcfa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(190019, 7)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moment_df_raw = df_raw[df_raw['datetime'] == '2000-01-01 01:00:00']\n",
    "moment_df_raw.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_raw."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-10-18T00:20:11.102666Z",
     "iopub.status.busy": "2024-10-18T00:20:11.102260Z",
     "iopub.status.idle": "2024-10-18T00:20:11.275949Z",
     "shell.execute_reply": "2024-10-18T00:20:11.275492Z",
     "shell.execute_reply.started": "2024-10-18T00:20:11.102635Z"
    },
    "id": "y36hQHFvjwAO",
    "outputId": "335219ad-723c-41d6-b73f-6a33446509a3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datetime (48,)\n",
      "local_solar_time (144,)\n",
      "latitude (19,)\n",
      "longitude (73,)\n",
      "altitude (137,)\n",
      "date (1,)\n"
     ]
    }
   ],
   "source": [
    "_ = [print(name, df_raw[name].unique().shape) for name in df_raw.columns if name != 'density']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-10-18T00:20:13.678112Z",
     "iopub.status.busy": "2024-10-18T00:20:13.677730Z",
     "iopub.status.idle": "2024-10-18T00:20:13.682092Z",
     "shell.execute_reply": "2024-10-18T00:20:13.681545Z",
     "shell.execute_reply.started": "2024-10-18T00:20:13.678083Z"
    },
    "id": "PKEsECSekKwA",
    "outputId": "6fa4e9c4-2ebc-4043-af74-16626742ee12"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1313411328"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numb = 48 * 144 * 19 * 73 * 137\n",
    "numb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-10-18T03:43:21.902294Z",
     "iopub.status.busy": "2024-10-18T03:43:21.901825Z",
     "iopub.status.idle": "2024-10-18T03:43:21.908036Z",
     "shell.execute_reply": "2024-10-18T03:43:21.907345Z",
     "shell.execute_reply.started": "2024-10-18T03:43:21.902257Z"
    },
    "id": "umilQ6dVkqr0",
    "outputId": "2af1fe3f-3a08-47b0-b521-10cb63bbb76b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48.0, 190019)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "9120912 / (19*73*137), 19*73*137"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-10-18T00:20:32.322717Z",
     "iopub.status.busy": "2024-10-18T00:20:32.322050Z",
     "iopub.status.idle": "2024-10-18T00:20:32.364892Z",
     "shell.execute_reply": "2024-10-18T00:20:32.364005Z",
     "shell.execute_reply.started": "2024-10-18T00:20:32.322678Z"
    },
    "id": "vdBuvZWAk0vq",
    "outputId": "87dc2ba5-c99c-49ef-8967-b9baa6339dd9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0,\n",
       " 0.16666999459266663,\n",
       " 0.3333300054073334,\n",
       " 0.5,\n",
       " 0.666670024394989,\n",
       " 0.833329975605011,\n",
       " 1.0,\n",
       " 1.1666699647903442,\n",
       " 1.3333300352096558,\n",
       " 1.5]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# local_solar_time is not in multiplicators!\n",
    "\n",
    "sorted(df_raw['local_solar_time'].unique().tolist())[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "YFykWYvAlVgc"
   },
   "outputs": [],
   "source": [
    "# for each datetime we have 72 unique local_solar_time\n",
    "# local_solar_time intervals are 10 minutes.\n",
    "# datetime intervals are 30 minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true,
    "execution": {
     "iopub.execute_input": "2024-10-18T03:01:02.290028Z",
     "iopub.status.busy": "2024-10-18T03:01:02.289764Z",
     "iopub.status.idle": "2024-10-18T03:01:02.317899Z",
     "shell.execute_reply": "2024-10-18T03:01:02.317530Z",
     "shell.execute_reply.started": "2024-10-18T03:01:02.290013Z"
    },
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((72,),\n",
       " [0.0,\n",
       "  0.33333,\n",
       "  0.66667,\n",
       "  1.0,\n",
       "  1.33333,\n",
       "  1.66667,\n",
       "  2.0,\n",
       "  2.33333,\n",
       "  2.66667,\n",
       "  3.0,\n",
       "  3.33333,\n",
       "  3.66667,\n",
       "  4.0,\n",
       "  4.33333,\n",
       "  4.66667,\n",
       "  5.0,\n",
       "  5.33333,\n",
       "  5.66667,\n",
       "  6.0,\n",
       "  6.33333,\n",
       "  6.66667,\n",
       "  7.0,\n",
       "  7.33333,\n",
       "  7.66667,\n",
       "  8.0,\n",
       "  8.33333,\n",
       "  8.66667,\n",
       "  9.0,\n",
       "  9.33333,\n",
       "  9.66667,\n",
       "  10.0,\n",
       "  10.33333,\n",
       "  10.66667,\n",
       "  11.0,\n",
       "  11.33333,\n",
       "  11.66667,\n",
       "  12.0,\n",
       "  12.33333,\n",
       "  12.66667,\n",
       "  13.0,\n",
       "  13.33333,\n",
       "  13.66667,\n",
       "  14.0,\n",
       "  14.33333,\n",
       "  14.66667,\n",
       "  15.0,\n",
       "  15.33333,\n",
       "  15.66667,\n",
       "  16.0,\n",
       "  16.33333,\n",
       "  16.66667,\n",
       "  17.0,\n",
       "  17.33333,\n",
       "  17.66667,\n",
       "  18.0,\n",
       "  18.33333,\n",
       "  18.66667,\n",
       "  19.0,\n",
       "  19.33333,\n",
       "  19.66667,\n",
       "  20.0,\n",
       "  20.33333,\n",
       "  20.66667,\n",
       "  21.0,\n",
       "  21.33333,\n",
       "  21.66667,\n",
       "  22.0,\n",
       "  22.33333,\n",
       "  22.66667,\n",
       "  23.0,\n",
       "  23.33333,\n",
       "  23.66667])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_moment = df_raw[df_raw['datetime'] == '2000-01-01 00:00:00']['local_solar_time'].unique()\n",
    "one_moment.shape, sorted(one_moment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-18T22:52:12.615780Z",
     "iopub.status.busy": "2024-10-18T22:52:12.613323Z",
     "iopub.status.idle": "2024-10-18T22:52:12.650601Z",
     "shell.execute_reply": "2024-10-18T22:52:12.650047Z",
     "shell.execute_reply.started": "2024-10-18T22:52:12.615711Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(190019, 7)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_moment = df_raw[df_raw['datetime'] == '2000-01-01 00:00:00']\n",
    "one_moment.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sweetviz as sv\n",
    "\n",
    "report = sv.analyze(df_raw) \n",
    "\n",
    "# Display the report in Colab (or save as HTML)\n",
    "# report.show_notebook()\n",
    "report.show_html(\"SQEETVIZ_REPORT.html\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preprocessing\n",
    "\n",
    "Here I evaluated different options of the input preprocessing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-19T00:02:05.725530Z",
     "iopub.status.busy": "2024-10-19T00:02:05.725021Z",
     "iopub.status.idle": "2024-10-19T00:02:05.741014Z",
     "shell.execute_reply": "2024-10-19T00:02:05.740636Z",
     "shell.execute_reply.started": "2024-10-19T00:02:05.725500Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[[1.2 nan]\n",
      "   [1.3 nan]]\n",
      "\n",
      "  [[nan nan]\n",
      "   [nan nan]]]\n",
      "\n",
      "\n",
      " [[[nan nan]\n",
      "   [nan nan]]\n",
      "\n",
      "  [[nan 1.1]\n",
      "   [nan nan]]]]\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "This code creates a multidimensional array to hold value of 'density'.\n",
    "\n",
    "\"\"\"\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Example DataFrame\n",
    "data = {\n",
    "    'datetime': ['2024-10-18 12:00:00', '2024-10-18 12:00:00', '2024-10-18 13:00:00'],\n",
    "    'latitude': [45.0, 45.0, 46.0],\n",
    "    'longitude': [-75.0, -76.0, -75.0],\n",
    "    'altitude': [300, 300, 350],\n",
    "    'local_solar_time': ['12:30', '12:30', '13:30'],\n",
    "    'density': [1.2, 1.3, 1.1]\n",
    "}\n",
    "\n",
    "# Convert datetime strings to pandas datetime objects\n",
    "df = pd.DataFrame(data)\n",
    "df['datetime'] = pd.to_datetime(df['datetime'])\n",
    "\n",
    "# Set the multi-index\n",
    "df.set_index(['datetime', 'latitude', 'longitude', 'altitude'], inplace=True)\n",
    "\n",
    "# Get unique values for each dimension\n",
    "datetimes = df.index.get_level_values('datetime').unique()\n",
    "latitudes = df.index.get_level_values('latitude').unique()\n",
    "longitudes = df.index.get_level_values('longitude').unique()\n",
    "altitudes = df.index.get_level_values('altitude').unique()\n",
    "\n",
    "# Create an empty multidimensional array to hold the 'density' values\n",
    "tensor_shape = (len(datetimes), len(latitudes), len(longitudes), len(altitudes))\n",
    "density_tensor = np.full(tensor_shape, [np.nan, np.nan])  # Initialize with NaNs\n",
    "\n",
    "# Populate the tensor with density values\n",
    "for idx, row in df.iterrows():\n",
    "    datetime_idx = np.where(datetimes == idx[0])[0][0]\n",
    "    latitude_idx = np.where(latitudes == idx[1])[0][0]\n",
    "    longitude_idx = np.where(longitudes == idx[2])[0][0]\n",
    "    altitude_idx = np.where(altitudes == idx[3])[0][0]\n",
    "    \n",
    "    # Fill in the 'density' value into the correct location in the tensor\n",
    "    density_tensor[datetime_idx, latitude_idx, longitude_idx, altitude_idx] = row['density'] # , row['local_solar_time']]\n",
    "\n",
    "# Display the multidimensional tensor with 'density' values\n",
    "print(density_tensor)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-19T00:08:16.511035Z",
     "iopub.status.busy": "2024-10-19T00:08:16.510572Z",
     "iopub.status.idle": "2024-10-19T00:08:16.525118Z",
     "shell.execute_reply": "2024-10-19T00:08:16.524798Z",
     "shell.execute_reply.started": "2024-10-19T00:08:16.510997Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[[array([ 1.2, 11. ]) None]\n",
      "   [array([ 1.3, 22. ]) None]]\n",
      "\n",
      "  [[None None]\n",
      "   [None None]]]\n",
      "\n",
      "\n",
      " [[[None None]\n",
      "   [None None]]\n",
      "\n",
      "  [[None array([ 1.1, 33. ])]\n",
      "   [None None]]]]\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "This code creates a multidimensional array to hold vectors of ['density', 'local_solar_time'].\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Example DataFrame\n",
    "data = {\n",
    "    'datetime': ['2024-10-18 12:00:00', '2024-10-18 12:00:00', '2024-10-18 13:00:00'],\n",
    "    'latitude': [45.0, 45.0, 46.0],\n",
    "    'longitude': [-75.0, -76.0, -75.0],\n",
    "    'altitude': [300, 300, 350],\n",
    "    'local_solar_time': [11, 22, 33],\n",
    "    'density': [1.2, 1.3, 1.1]\n",
    "}\n",
    "\n",
    "# Convert datetime strings to pandas datetime objects\n",
    "df = pd.DataFrame(data)\n",
    "df['datetime'] = pd.to_datetime(df['datetime'])\n",
    "\n",
    "# Set the multi-index\n",
    "df.set_index(['datetime', 'latitude', 'longitude', 'altitude'], inplace=True)\n",
    "\n",
    "# Get unique values for each dimension\n",
    "datetimes = df.index.get_level_values('datetime').unique()\n",
    "latitudes = df.index.get_level_values('latitude').unique()\n",
    "longitudes = df.index.get_level_values('longitude').unique()\n",
    "altitudes = df.index.get_level_values('altitude').unique()\n",
    "\n",
    "# Create an empty multidimensional array to hold vectors of ['density', 'local_solar_time']\n",
    "tensor_shape = (len(datetimes), len(latitudes), len(longitudes), len(altitudes))\n",
    "tensor = np.full(tensor_shape, None, dtype=object)  # Initialize with None (or you can use NaNs)\n",
    "\n",
    "# Populate the tensor with vectors [density, local_solar_time]\n",
    "for idx, row in df.iterrows():\n",
    "    datetime_idx = np.where(datetimes == idx[0])[0][0]\n",
    "    latitude_idx = np.where(latitudes == idx[1])[0][0]\n",
    "    longitude_idx = np.where(longitudes == idx[2])[0][0]\n",
    "    altitude_idx = np.where(altitudes == idx[3])[0][0]\n",
    "    \n",
    "    # Fill in the vector [density, local_solar_time] into the correct location in the tensor\n",
    "    tensor[datetime_idx, latitude_idx, longitude_idx, altitude_idx] = np.array([row['density'], row['local_solar_time']])\n",
    "\n",
    "# Display the multidimensional tensor with vectors [density, local_solar_time]\n",
    "print(tensor)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare Input\n",
    "\n",
    "#### Hypothesis: stable structure of files\n",
    "\n",
    "I have a hypothesis: \n",
    "- all files with a stable number of rows (9120912) have a stable structure.\n",
    "  The elements in these \"index\" fields, ['datetime' (time part of it), 'latitude',\t'longitude', 'altitude', 'local_solar_time'] ,\n",
    "  always have the same combinations in groups of 30 min (this group is defined as a `model sample`). Also, these    combinations are placed in the same order in rows of \"stable\" files. If this hypothesis is true, I can format the model input as a sequence of vectors without explicitly providing the \"index\" field values but only the \"density\" values.\n",
    "\n",
    "Let's verify this hypothesis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-26T02:08:14.412906Z",
     "iopub.status.busy": "2024-10-26T02:08:14.412351Z",
     "iopub.status.idle": "2024-10-26T02:08:14.587503Z",
     "shell.execute_reply": "2024-10-26T02:08:14.587134Z",
     "shell.execute_reply.started": "2024-10-26T02:08:14.412865Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9120912, 6)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from pathlib import Path\n",
    "\n",
    "# reorder columns\n",
    "def read_df(file_name, dir_path=Path('../data/original/2000'), stable_size=9120912, remove_date=True,\n",
    "           verbose=False):\n",
    "    file_path = dir_path / file_name    \n",
    "    if remove_date:\n",
    "        columns = ['datetime', 'latitude',\t'longitude', 'altitude', 'local_solar_time','density']\n",
    "    else:\n",
    "        columns = ['datetime', 'date','latitude',\t'longitude', 'altitude', 'local_solar_time','density']\n",
    "\n",
    "    df = pd.read_parquet(file_path, columns=columns)\n",
    "    if verbose:\n",
    "        print(f\"Loaded {file_name} {df.shape}\")\n",
    "    if len(df) != stable_size:\n",
    "        print(f\"  {file_name} rows: {len(df)} != {stable_size}\")\n",
    "        return None\n",
    "    date_from_file_name = file_name.split('.')[0]\n",
    "    if not remove_date:\n",
    "        if date_from_file_name != df.loc[0, 'date']:\n",
    "            print(f\"  {file_name}[0] has wrong 'date' value: {df.loc[0, 'date']}\")\n",
    "            print(df[0])\n",
    "            return None\n",
    "    dt = df['datetime'][0].strftime('%Y-%m-%d')\n",
    "    if date_from_file_name != dt:\n",
    "        print(f\"  {file_name}[0] has wrong 'datetime' value: {dt}\")\n",
    "        print(df[0])\n",
    "        return None\n",
    "    return df\n",
    "\n",
    "\n",
    "file_name = '2000-01-01.parquet'\n",
    "df = read_df(file_name)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-26T02:08:41.268816Z",
     "iopub.status.busy": "2024-10-26T02:08:41.268500Z",
     "iopub.status.idle": "2024-10-26T02:08:44.635795Z",
     "shell.execute_reply": "2024-10-26T02:08:44.635295Z",
     "shell.execute_reply.started": "2024-10-26T02:08:41.268792Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 2000-01-01.parquet (9120912, 6)\n",
      "Loaded 2000-01-02.parquet (9120912, 6)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def compare_dfs(df1, df2):\n",
    "    assert df1.shape == df2.shape, f\"DataFrames should be the same shape: {df1.shape} != {df2.shape}\"\n",
    "    i = 0\n",
    "    # # with itertuples():\n",
    "    # for row1, row2 in zip(df1.itertuples(), df2.itertuples()):\n",
    "    #     if pd.to_datetime(row1[0]).time() != pd.to_datetime(row2[0]).time():\n",
    "    #         print(f\"{i}: {pd.to_datetime(row1[0]).time()} != {pd.to_datetime(row2[0]).time()}\")\n",
    "    #     if row1[2:-1] != row2[2:-1]:\n",
    "    #         print(f\"{i}: {row1[2:-1]} != {row2[2:-1]} :: {row1=} {row2=}\")\n",
    "    #     i += 1\n",
    "    \n",
    "    # with iterrows (the same speed as itertuples):\n",
    "    # for (index1, row1), (index2, row2) in zip(df1.iterrows(), df2.iterrows()):\n",
    "    #     if row1['datetime'].time() != row2['datetime'].time():\n",
    "    #         raise ValueError(f\"{index1}: {row1['datetime'].time()} != {row2['datetime'].time()}\")\n",
    "    #     for field in ['latitude',\t'longitude', 'altitude', 'local_solar_time']:\n",
    "    #         if row1[field] != row2[field]:\n",
    "    #             raise ValueError(f\"{index1}: {row1} != {row2}\")\n",
    "    \n",
    "    # the fastest but fails:\n",
    "    if not df1['datetime'].dt.time.equals(df2['datetime'].dt.time):\n",
    "        raise ValueError(f\"datetime is not equal in df1 and df2\")\n",
    "    for field in ['latitude', 'longitude', 'altitude', 'local_solar_time']:\n",
    "        if not df1[field].equals(df2[field]):\n",
    "            raise ValueError(f\"{field} is not equal in df1 and df2\")\n",
    "            \n",
    "    # # the most generic:\n",
    "    # if df1[['latitude', 'longitude', 'altitude', 'local_solar_time']].equals(\n",
    "    #     df2[['latitude', 'longitude', 'altitude', 'local_solar_time']]\n",
    "    # ):\n",
    "    #     raise ValueError(f\"df1  is not equal df2. {list(df1.columns)} {list(df2.columns)}\")\n",
    "\n",
    "    return True\n",
    "    \n",
    "\n",
    "file_name1 = '2000-01-01.parquet'\n",
    "df1 = read_df(file_name1, verbose=True)\n",
    "file_name2 = '2000-01-02.parquet'\n",
    "df2 = read_df(file_name2, verbose=True)\n",
    "compare_dfs(df1, df2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "data_dir = '../data'\n",
    "original_data_dir = 'original'\n",
    "year = '2000'\n",
    "directory = Path(data_dir) / original_data_dir / year\n",
    "\n",
    "# Iterate over all the files in the directory. \n",
    "# Exclude files with rows != 9120912\n",
    "\n",
    "df1, df2 = None, None\n",
    "for filename in sorted(os.listdir(directory)):\n",
    "    if df1 is None:\n",
    "        df1 = read_df(filename, verbose=True)\n",
    "        continue\n",
    "    else:\n",
    "        df2 = read_df(filename, verbose=True)\n",
    "        if df2 is not None:\n",
    "            compare_dfs(df1, df2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion\n",
    "\n",
    "The hypothesis is true. \n",
    "All files with a stable number of rows (9120912) have a stable structure.\n",
    "  The elements in these \"index\" fields, ['datetime' (time part of it), 'latitude', 'longitude', 'altitude', 'local_solar_time'] ,\n",
    "  always have the same combinations in groups of 30 min (this group is defined as a model sample). \n",
    "  These    combinations are placed in the same rows in \"stable\" files."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reducing Samples\n",
    "\n",
    "I need \"just enough\" data to show that the model converges to create a baseline solution.\n",
    "\n",
    "After the baseline is created, the next step would be to train the model on all available data.\n",
    "\n",
    "\n",
    "I can preprocess the original .parquet files by removing all but `datetime` and `density` fields and group rows by `datetime`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-26T02:10:07.148793Z",
     "iopub.status.busy": "2024-10-26T02:10:07.148395Z",
     "iopub.status.idle": "2024-10-26T02:10:07.820517Z",
     "shell.execute_reply": "2024-10-26T02:10:07.819915Z",
     "shell.execute_reply.started": "2024-10-26T02:10:07.148760Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved ../data/reduced/2000/2000-01-01.parquet (9120912, 6)\n",
      "Loaded 2000-01-01.parquet (9120912, 6)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(9120912, 6)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from pathlib import Path\n",
    "\n",
    "def save_file(df, file_name, dir_path=Path('../data/reduced/2000'), verbose=False):\n",
    "    os.makedirs(dir_path, exist_ok=True)\n",
    "    file_path = dir_path / file_name\n",
    "    df.to_parquet(file_path)\n",
    "    if verbose:\n",
    "        print(f\"Saved {file_path} {df.shape}\")\n",
    "    \n",
    "\n",
    "file_name = '2000-01-01.parquet'\n",
    "save_file(df1, file_name, verbose=True)\n",
    "               \n",
    "df_new = read_df(file_name, dir_path=Path('../data/reduced/2000'), verbose=True)\n",
    "df_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-26T02:10:19.662190Z",
     "iopub.status.busy": "2024-10-26T02:10:19.661610Z",
     "iopub.status.idle": "2024-10-26T02:10:20.898358Z",
     "shell.execute_reply": "2024-10-26T02:10:20.897643Z",
     "shell.execute_reply.started": "2024-10-26T02:10:19.662143Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(48, 2) Index(['datetime', 'density'], dtype='object') 2000-01-01 00:00:00 190019\n"
     ]
    }
   ],
   "source": [
    "def reduce_df(df):\n",
    "    df_new = df[['datetime', 'density']]\n",
    "    # val, df_new  = df[['datetime', 'density']].groupby(by='datetime', sort=False)\n",
    "    df_new = df_new[['datetime', 'density']].groupby('datetime', sort=False)['density'].agg(list).reset_index()\n",
    "    df_new.to_parquet('tmp.parquet')\n",
    "    print(df_new.shape, df_new.columns, df_new.loc[0, 'datetime'], len(df_new.loc[0, 'density']))\n",
    "    return df_new\n",
    "\n",
    "df_new2 = reduce_df(df_new)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-20T05:52:38.076669Z",
     "iopub.status.busy": "2024-10-20T05:52:38.075495Z",
     "iopub.status.idle": "2024-10-20T05:52:38.083565Z",
     "shell.execute_reply": "2024-10-20T05:52:38.083113Z",
     "shell.execute_reply.started": "2024-10-20T05:52:38.076580Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48, 2)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-26T02:10:30.906598Z",
     "iopub.status.busy": "2024-10-26T02:10:30.904535Z",
     "iopub.status.idle": "2024-10-26T02:10:30.911291Z",
     "shell.execute_reply": "2024-10-26T02:10:30.910651Z",
     "shell.execute_reply.started": "2024-10-26T02:10:30.906508Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(list,\n",
       " 190019,\n",
       " [1.6826000148739695e-08,\n",
       "  9.893599717258894e-09,\n",
       "  6.349599956934071e-09,\n",
       "  4.3692001128192715e-09,\n",
       "  3.1739000316832744e-09,\n",
       "  2.402299914550099e-09,\n",
       "  1.8760999420663893e-09,\n",
       "  1.5004999465162427e-09,\n",
       "  1.2222000078665474e-09,\n",
       "  1.0095999591896998e-09])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v = df_new2.loc[0, 'density']\n",
    "type(v), len(v), v[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": true,
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     "outputs_hidden": true
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      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = df_new2[df_new2['datetime'] == '2000-01-01 00:00:00']['density'][0]\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-20T02:31:04.995663Z",
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     "shell.execute_reply": "2024-10-20T02:31:05.255680Z",
     "shell.execute_reply.started": "2024-10-20T02:31:04.995623Z"
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   },
   "outputs": [],
   "source": [
    "df_new2[:2].to_csv(\"temp.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-20T16:41:41.280451Z",
     "iopub.status.busy": "2024-10-20T16:41:41.278747Z",
     "iopub.status.idle": "2024-10-20T16:41:42.573453Z",
     "shell.execute_reply": "2024-10-20T16:41:42.572746Z",
     "shell.execute_reply.started": "2024-10-20T16:41:41.280363Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_64393/3993260746.py:8: FutureWarning: 'all' with datetime64 dtypes is deprecated and will raise in a future version. Use (obj != pd.Timestamp(0)).all() instead.\n",
      "  if dt1_unique.all() != dt2_unique.all():\n"
     ]
    }
   ],
   "source": [
    "# validate:\n",
    "\n",
    "def validate(df1, df2):\n",
    "    dt1_unique = df1['datetime'].unique()\n",
    "    dt2_unique = df2['datetime'].unique()\n",
    "    # print(type(dt1_unique), len(dt1_unique))\n",
    "    # print(type(dt2_unique), len(dt2_unique))\n",
    "    if dt1_unique.all() != dt2_unique.all():\n",
    "        print(f\"{dt1_unique} != {dt2_unique}\")\n",
    "    for dt in dt1_unique:\n",
    "        # print(dt)\n",
    "        densities1 = df1[df1['datetime'] == dt]['density'].to_numpy()\n",
    "        densities2 = np.array(np.array(df2[df2['datetime'] == dt]['density'])[0])\n",
    "        # print(type(densities1), len(densities1))\n",
    "        # print(type(densities2), len(densities2))\n",
    "        if densities1.all() != densities2.all():            \n",
    "            print(f\"densities2 != densities2 {set(densities1).difference(set(densities2))}\")\n",
    "\n",
    "    \n",
    "validate(df_new, df_new2)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": true,
    "execution": {
     "iopub.execute_input": "2024-10-20T01:32:25.172422Z",
     "iopub.status.busy": "2024-10-20T01:32:25.171962Z",
     "iopub.status.idle": "2024-10-20T01:32:25.190360Z",
     "shell.execute_reply": "2024-10-20T01:32:25.189522Z",
     "shell.execute_reply.started": "2024-10-20T01:32:25.172383Z"
    },
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    datetime                                            density\n",
      "0 2000-01-01  [1.6826000148739695e-08, 9.893599717258894e-09...\n"
     ]
    },
    {
     "data": {
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       "(0   2000-01-01 00:00:00\n",
       " 1   2000-01-01 00:30:00\n",
       " 2   2000-01-01 01:00:00\n",
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       "  8.053600104515612e-13,\n",
       "  7.489200232191351e-13,\n",
       "  6.966400113023186e-13,\n",
       "  6.481899977699157e-13,\n",
       "  6.032899874108821e-13,\n",
       "  5.616500103541866e-13,\n",
       "  5.230399988988277e-13,\n",
       "  4.872200191040343e-13,\n",
       "  4.539900085639287e-13,\n",
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       "  3.9449999798997493e-13,\n",
       "  3.678999921548354e-13,\n",
       "  3.4318999465182687e-13,\n",
       "  3.202400096404684e-13,\n",
       "  2.9891001241018345e-13,\n",
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       "  1.9891999560126689e-13,\n",
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       "  1.7411999576541493e-13,\n",
       "  1.6298999639102058e-13,\n",
       "  1.5261999563448425e-13,\n",
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       "  1.3395999533174358e-13,\n",
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       "  1.1775999554826388e-13,\n",
       "  1.1047000309958796e-13,\n",
       "  1.036700022702397e-13,\n",
       "  9.73239975480926e-14,\n",
       "  9.140699807307084e-14,\n",
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       "  5.7786001678161725e-11,\n",
       "  5.186499840714731e-11,\n",
       "  4.6631001143770945e-11,\n",
       "  4.1992000154866815e-11,\n",
       "  3.787200067439933e-11,\n",
       "  3.42030015421102e-11,\n",
       "  3.0930001615470104e-11,\n",
       "  2.8004999361175287e-11,\n",
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       "  2.3042000599216017e-11,\n",
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       "  1.9047000088256993e-11,\n",
       "  1.7343999500485374e-11,\n",
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       "  1.0087999879893061e-11,\n",
       "  9.244599877278326e-12,\n",
       "  8.477800059192298e-12,\n",
       "  7.78020009528646e-12,\n",
       "  7.144999869534985e-12,\n",
       "  6.5661000941963366e-12,\n",
       "  6.0379002145283245e-12,\n",
       "  5.555699997272789e-12,\n",
       "  5.115100183694654e-12,\n",
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       "  3.695699996031365e-12,\n",
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       "  2.9114000706792487e-12,\n",
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       "  1.827999990960949e-12,\n",
       "  1.6939000115762104e-12,\n",
       "  1.5702000246906045e-12,\n",
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       "  1.3505999704038762e-12,\n",
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       "  1.0800000217053851e-12,\n",
       "  1.0030999468557678e-12,\n",
       "  9.319900537083092e-13,\n",
       "  8.661499794303251e-13,\n",
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       "  4.538899909135169e-13,\n",
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       "  3.9441000920965863e-13,\n",
       "  3.6782000513956026e-13,\n",
       "  3.4312000940159293e-13,\n",
       "  3.2016999728518014e-13,\n",
       "  2.988400000548952e-13,\n",
       "  2.790199982952851e-13,\n",
       "  2.605899979309101e-13,\n",
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       "  2.275099867613678e-13,\n",
       "  2.1267999839556795e-13,\n",
       "  1.9888000209362933e-13,\n",
       "  1.8603999905279084e-13,\n",
       "  1.7409000402281855e-13,\n",
       "  1.6295000288338302e-13,\n",
       "  1.5259000389188787e-13,\n",
       "  1.4293000648053084e-13,\n",
       "  1.3394000535418837e-13,\n",
       "  1.255500017100994e-13,\n",
       "  1.177300038056675e-13,\n",
       "  1.104499995695056e-13,\n",
       "  1.0364999874015735e-13,\n",
       "  9.730799878978486e-14,\n",
       "  9.139200220177265e-14,\n",
       "  8.587100079398766e-14,\n",
       "  8.0717001821589e-14,\n",
       "  7.590500087197374e-14,\n",
       "  7.141100319581206e-14,\n",
       "  6.72129994890025e-14,\n",
       "  6.328999688698031e-14,\n",
       "  5.962300152293626e-14,\n",
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       "  4.456399916512137e-14,\n",
       "  4.210700053062545e-14,\n",
       "  3.9807001146969015e-14,\n",
       "  3.765200025135536e-14,\n",
       "  3.563299896575285e-14,\n",
       "  3.3742001299139415e-14,\n",
       "  3.196799921133253e-14,\n",
       "  3.030599859607519e-14,\n",
       "  2.8746001076458e-14,\n",
       "  2.7281999494819034e-14,\n",
       "  2.590899974490128e-14,\n",
       "  2.4619000753484978e-14,\n",
       "  2.34079994428614e-14,\n",
       "  1.6826000148739695e-08,\n",
       "  9.893599717258894e-09,\n",
       "  6.349599956934071e-09,\n",
       "  4.3692001128192715e-09,\n",
       "  3.1739000316832744e-09,\n",
       "  2.402299914550099e-09,\n",
       "  1.8760999420663893e-09,\n",
       "  1.5004999465162427e-09,\n",
       "  1.2222000078665474e-09,\n",
       "  1.0095999591896998e-09,\n",
       "  8.434000164925237e-10,\n",
       "  7.108499899821652e-10,\n",
       "  6.035000277293534e-10,\n",
       "  5.154700000176149e-10,\n",
       "  4.4253001263427905e-10,\n",
       "  3.815699978204634e-10,\n",
       "  3.3024000201109516e-10,\n",
       "  2.8669000506909015e-10,\n",
       "  2.4967999889824455e-10,\n",
       "  2.1809999639632593e-10,\n",
       "  1.910400032612003e-10,\n",
       "  1.6777000622081317e-10,\n",
       "  1.4770000500430314e-10,\n",
       "  1.3032000478752082e-10,\n",
       "  1.152399980997032e-10,\n",
       "  1.0214999679458714e-10,\n",
       "  9.076499968285745e-11,\n",
       "  8.082400027031866e-11,\n",
       "  7.212500041653414e-11,\n",
       "  6.449200201652516e-11,\n",
       "  5.777800113349052e-11,\n",
       "  5.185700133192306e-11,\n",
       "  4.662300059909974e-11,\n",
       "  4.198499881091777e-11,\n",
       "  3.786599853117245e-11,\n",
       "  3.4197998599605484e-11,\n",
       "  3.092499867296539e-11,\n",
       "  2.799999988811752e-11,\n",
       "  2.5382999521705862e-11,\n",
       "  2.3038000326880415e-11,\n",
       "  2.093399932567852e-11,\n",
       "  ...])"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df_new2.head(1))\n",
    "df_new2['datetime'][:3], df_new2.loc[0, 'density']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-20T16:48:53.594631Z",
     "iopub.status.busy": "2024-10-20T16:48:53.594221Z",
     "iopub.status.idle": "2024-10-20T16:48:54.020133Z",
     "shell.execute_reply": "2024-10-20T16:48:54.019698Z",
     "shell.execute_reply.started": "2024-10-20T16:48:53.594603Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved ../data/reduced/2000/tmp.parquet (48, 2)\n"
     ]
    }
   ],
   "source": [
    "file_name = \"tmp.parquet\"\n",
    "save_file(df_new2, file_name, verbose=True)\n",
    "\n",
    "# Saved ../data/reduced/2000/tmp.parquet (48, 2)\n",
    "# It has size of 54.7 MB. It is not decreased!!! \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-20T16:50:58.474586Z",
     "iopub.status.busy": "2024-10-20T16:50:58.474192Z",
     "iopub.status.idle": "2024-10-20T16:50:59.035821Z",
     "shell.execute_reply": "2024-10-20T16:50:59.035358Z",
     "shell.execute_reply.started": "2024-10-20T16:50:58.474552Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved ../data/reduced/2000/tmp.parquet (9120912, 6)\n"
     ]
    }
   ],
   "source": [
    "file_name = \"tmp.parquet\"\n",
    "save_file(df_new, file_name, verbose=True)\n",
    "\n",
    "# Saved ../data/reduced/2000/tmp.parquet (9120912, 6)\n",
    "# It has size of 38.7 MB. Reduction actually increases the size! \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-20T16:55:59.149046Z",
     "iopub.status.busy": "2024-10-20T16:55:59.147624Z",
     "iopub.status.idle": "2024-10-20T16:55:59.490262Z",
     "shell.execute_reply": "2024-10-20T16:55:59.489884Z",
     "shell.execute_reply.started": "2024-10-20T16:55:59.148993Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved ../data/reduced/2000/tmp.parquet (9120912, 2)\n"
     ]
    }
   ],
   "source": [
    "file_name = \"tmp.parquet\"\n",
    "save_file(df_new[['datetime','density']], file_name, verbose=True)\n",
    "# It has a size of 38.1 MB\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-20T16:57:19.066717Z",
     "iopub.status.busy": "2024-10-20T16:57:19.066176Z",
     "iopub.status.idle": "2024-10-20T16:57:19.348235Z",
     "shell.execute_reply": "2024-10-20T16:57:19.347778Z",
     "shell.execute_reply.started": "2024-10-20T16:57:19.066680Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved ../data/reduced/2000/tmp.parquet (9120912, 1)\n"
     ]
    }
   ],
   "source": [
    "file_name = \"tmp.parquet\"\n",
    "save_file(df_new[['density']], file_name, verbose=True)\n",
    "# It has a size of 38.1 MB. No changes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-20T17:01:17.717271Z",
     "iopub.status.busy": "2024-10-20T17:01:17.716503Z",
     "iopub.status.idle": "2024-10-20T17:01:18.157960Z",
     "shell.execute_reply": "2024-10-20T17:01:18.157331Z",
     "shell.execute_reply.started": "2024-10-20T17:01:17.717221Z"
    }
   },
   "outputs": [],
   "source": [
    "file_name = \"tmp.parquet\"\n",
    "df_new.to_parquet(file_name, index=False)\n",
    "# It has a size of 38.1 MB. No changes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reducing Samples 2\n",
    "\n",
    "Reducing the size of files by removing all but `density` values didn't work because the `Parquet` is doing a good job by compressing repeated values. All but `density` values are highly repeated and hence highly compressed.\n",
    "\n",
    "Let's do the next reduction step:\n",
    "- decrease the number of the `density` snapshots to a small number. It helps to make the baseline with small hardware resources in a limited time. If the model is still converged, I will be successful.\n",
    "- Since I will reduce the snapshot significantly, the model training should be reduced significantly.\n",
    "  Replacing the input files with reduced files will speed up the training.\n",
    "- In the first iteration I'll reduce the file size to ~1MB (from ~49MB). It is about a 50-time size reduction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-26T02:11:30.719965Z",
     "iopub.status.busy": "2024-10-26T02:11:30.719392Z",
     "iopub.status.idle": "2024-10-26T02:11:30.998870Z",
     "shell.execute_reply": "2024-10-26T02:11:30.998445Z",
     "shell.execute_reply.started": "2024-10-26T02:11:30.719916Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(480000, 6)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "snapshot_size = 10000\n",
    "SAMPLE_SIZE = snapshot_size\n",
    "\n",
    "def reduce_df(df, snapshot_size):\n",
    "    return df.groupby('datetime').head(snapshot_size).reset_index(drop=True)\n",
    "\n",
    "df_reduced = reduce_df(df, snapshot_size)\n",
    "df_reduced.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-26T02:11:35.043509Z",
     "iopub.status.busy": "2024-10-26T02:11:35.043344Z",
     "iopub.status.idle": "2024-10-26T02:11:35.046466Z",
     "shell.execute_reply": "2024-10-26T02:11:35.046102Z",
     "shell.execute_reply.started": "2024-10-26T02:11:35.043498Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['datetime', 'latitude', 'longitude', 'altitude', 'local_solar_time',\n",
       "       'density'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_reduced.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-20T18:09:18.081827Z",
     "iopub.status.busy": "2024-10-20T18:09:18.081458Z",
     "iopub.status.idle": "2024-10-20T18:09:44.327539Z",
     "shell.execute_reply": "2024-10-20T18:09:44.326910Z",
     "shell.execute_reply.started": "2024-10-20T18:09:18.081811Z"
    }
   },
   "outputs": [],
   "source": [
    "# sanity check:\n",
    "\n",
    "datetime_uniques = df_reduced['datetime'].unique()\n",
    "stable_dims = ['latitude', 'longitude', 'altitude', 'date']\n",
    "first_cut = None\n",
    "for i, dt in enumerate(datetime_uniques):\n",
    "    if first_cut is None:\n",
    "        first_cut = df_reduced[df_reduced['datetime']==dt][stable_dims]\n",
    "        continue\n",
    "    second_cut = df_reduced[df_reduced['datetime']==dt][stable_dims]\n",
    "    if not second_cut.equals(first_cut):\n",
    "        if first_cut.shape != second_cut.shape:\n",
    "            print(f\"{first_cut.shape} != {second_cut.shape}\"); break\n",
    "        for i in range(first_cut.shape[0]):\n",
    "            r1 = first_cut.iloc[i]\n",
    "            r2 = second_cut.iloc[i]\n",
    "            if not r1.equals(r2):\n",
    "                print(f\"{r1} != {r2}\"); break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-26T02:12:23.577617Z",
     "iopub.status.busy": "2024-10-26T02:12:23.577378Z",
     "iopub.status.idle": "2024-10-26T02:12:28.414806Z",
     "shell.execute_reply": "2024-10-26T02:12:28.414447Z",
     "shell.execute_reply.started": "2024-10-26T02:12:23.577601Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "............"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "snapshot_size = 5000 # for output file ~1MB size\n",
    "\n",
    "def reduce_files(snapshot_size, input_dir='../data/original/2000', output_dir='../data/reduced/2000', files=None,\n",
    "                verbose=False):\n",
    "    for filename in sorted(os.listdir(input_dir))[:files]:\n",
    "        in_file_path = os.path.join(input_dir, filename)\n",
    "        df = pd.read_parquet(in_file_path)\n",
    "        if df.shape[0] != 9120912:\n",
    "            print(f\"Skip broken file {filename} {df.shape[0]} != 9120912\")\n",
    "            continue\n",
    "        df = reduce_df(df, snapshot_size)\n",
    "        if verbose:\n",
    "            print(f\"Loaded {df.shape} {filename}\")\n",
    "        out_file_path = os.path.join(output_dir, filename)\n",
    "        df.to_parquet(out_file_path, index=False)\n",
    "        if verbose:\n",
    "            print(f\"  Saved {df.shape} {out_file_path}\")\n",
    "        else:\n",
    "            print('.',end='')\n",
    "\n",
    "\n",
    "        \n",
    "\n",
    "reduce_files(snapshot_size=snapshot_size, files=12, verbose=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TODO\n",
    "\n",
    "- Fix broken files (files with size != 9120912) by extrapolation(?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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