Preprocessing London Smart Meter Dataset (5).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9a219525",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kaggle API is authenticated and ready to use!\n"
     ]
    }
   ],
   "source": [
    "#  from kaggle.api.kaggle_api_extended import KaggleApi\n",
    "\n",
    "#  api = KaggleApi()\n",
    "#  api.authenticate()\n",
    "#  print(\"Kaggle API is installed and working!\")\n",
    "# from kaggle.api.kaggle_api_extended import KaggleApi\n",
    "\n",
    "# Initialize and authenticate the Kaggle API\n",
    "# api = KaggleApi()\n",
    "# api.authenticate()\n",
    "\n",
    "# Test the API (e.g., list datasets)\n",
    "#print(api.datasets_list())\n",
    "\n",
    "from kaggle.api.kaggle_api_extended import KaggleApi\n",
    "\n",
    "api = KaggleApi()\n",
    "api.authenticate()\n",
    "print(\"Kaggle API is authenticated and ready to use!\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0fad9a42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kaggle API authentication successful!\n"
     ]
    }
   ],
   "source": [
    "from kaggle.api.kaggle_api_extended import KaggleApi\n",
    "\n",
    "api = KaggleApi()\n",
    "api.authenticate()\n",
    "\n",
    "print(\"Kaggle API authentication successful!\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2f30a5f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://www.kaggle.com/competitions/ai-mathematical-olympiad-progress-prize-2: AI Mathematical Olympiad - Progress Prize 2\n",
      "https://www.kaggle.com/competitions/konwinski-prize: Konwinski Prize\n",
      "https://www.kaggle.com/competitions/czii-cryo-et-object-identification: CZII - CryoET Object Identification\n",
      "https://www.kaggle.com/competitions/equity-post-HCT-survival-predictions: CIBMTR - Equity in post-HCT Survival Predictions\n",
      "https://www.kaggle.com/competitions/santa-2024: Santa 2024 - The Perplexity Permutation Puzzle\n"
     ]
    }
   ],
   "source": [
    "competitions = api.competitions_list()\n",
    "for comp in competitions[:5]:  # Show first 5 competitions\n",
    "    print(f\"{comp.ref}: {comp.title}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ac10e7ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/s0/hts3dbss56x0x2f85v7w1p6r0000gn/T/ipykernel_37410/579543681.py:11: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
      "  from tqdm.autonotebook import tqdm\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import os\n",
    "import kaggle\n",
    "import plotly.io as pio\n",
    "pio.templates.default = \"plotly_white\"\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "from tqdm.autonotebook import tqdm\n",
    "# %load_ext autoreload\n",
    "# %autoreload 2\n",
    "np.random.seed()\n",
    "tqdm.pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3efe5ed",
   "metadata": {},
   "source": [
    "# Reading data to pandas dataframe\n",
    "Let's use London Smart Meters dataset from Kaggle for the current exercise. The source of the data is from London Data Store and Jean-Michel D. has augumented the dataset with a few more details and packaged it as a Kaggle Dataset.\n",
    "\n",
    "# About the Dataset\n",
    "London Data Store, free and open data-sharing portal, provided this dataset, which was collected and enriched by Jean-Michel D and uploaded on Kaggle @ https://www.kaggle.com/jeanmidev/smart-meters-in-london. The dataset has energy consumption readings for a sample of 5,567 London Households that took part in the UK Power Networks led Low Carbon London project between November 2011 and February 2014. Readings were taken at half hourly intervals. Some metadata about the households are also available as part of the dataset.\n",
    "\n",
    "# Data Wrangling\n",
    "The Kaggle dataset also has the time series data preprocessed on a daily level and combined all the separate files, etc. But let’s ignore those files and start with the raw files, which is in the hhblock_dataset folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f515403c",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs(\"imgs/chapter_2\", exist_ok=True)\n",
    "source_data = Path(\"data/london_smart_meters/\")\n",
    "block_data_path = source_data/\"hhblock_dataset\"/\"hhblock_dataset\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "afbca0f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expected path: data/london_smart_meters/hhblock_dataset/hhblock_dataset\n",
      "Does path exist? True\n",
      "Is it a directory? True\n",
      "Available folders in 'data/london_smart_meters/': ['.DS_Store', 'hhblock_dataset']\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "print(f\"Expected path: {block_data_path}\")\n",
    "print(f\"Does path exist? {block_data_path.exists()}\")\n",
    "print(f\"Is it a directory? {block_data_path.is_dir()}\")\n",
    "print(\"Available folders in 'data/london_smart_meters/':\", os.listdir(\"data/london_smart_meters\") if os.path.exists(\"data/london_smart_meters\") else \"Folder not found\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "13d9bdec",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/tomaszosuchowski/data/london_smart_meters.zip\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "print(os.path.abspath(\"data/london_smart_meters.zip\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6e21c7aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "print(os.path.exists(\"/Users/tomaszosuchowski/data/london_smart_meters/smart_meters.zip\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9e1d0eaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "block_data_path.mkdir(parents=True, exist_ok=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1d8709aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "print(block_data_path.exists())  # Should now return True\n",
    "print(block_data_path.is_dir())  # Should return True\n",
    "assert block_data_path.is_dir(), \"Please check if the dataset has been downloaded properly. Refer to the Preface of the book or the Readme in the repo for expected data\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "96bb1ad3",
   "metadata": {},
   "outputs": [],
   "source": [
    "assert block_data_path.is_dir(), \"Please check if the dataset has been downloaded properly. Refer to the Preface of the book or the Readme in the repo for expected data\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f593d93",
   "metadata": {},
   "source": [
    "# Converting the half hourly block level dataset into a time series data\n",
    "Let's pick one block and see how we can transform the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9797f8f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/london_smart_meters/hhblock_dataset/hhblock_dataset/block_0.csv\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "print(block_data_path / \"block_0.csv\")  # Print the full path\n",
    "print((block_data_path / \"block_0.csv\").exists())  # Check if the file exists\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3ff3684f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    }
   ],
   "source": [
    "print(os.listdir(block_data_path))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "be0b1630",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "print(os.path.exists(block_data_path))  # Should return True if the path exists\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "63135f72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "File exists: False\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "print(os.access(block_data_path, os.R_OK))  # Check read permission\n",
    "\n",
    "\n",
    "file_path = os.path.join(block_data_path, \"block_0.csv\")\n",
    "print(\"File exists:\", os.path.exists(file_path))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cbdc85eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs(\"imgs/chapter_2\", exist_ok=True)\n",
    "source_data = Path(\"data/london_smart_meters/\")\n",
    "block_data_path = source_data/\"hhblock_dataset\"/\"hhblock_dataset\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a75690d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "assert block_data_path.is_dir(), \"Please check if the dataset has been downloaded properly.\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c430d1d",
   "metadata": {},
   "source": [
    "# Converting the half hourly block level dataset into a time series data\n",
    "Let's pick one block and see how we can transform the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a5b4ad6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       LCLid        day   hh_0   hh_1   hh_2   hh_3   hh_4   hh_5   hh_6  \\\n",
      "0  MAC000002 2012-10-13  0.263  0.269  0.275  0.256  0.211  0.136  0.161   \n",
      "1  MAC000002 2012-10-14  0.262  0.166  0.226  0.088  0.126  0.082  0.123   \n",
      "2  MAC000002 2012-10-15  0.192  0.097  0.141  0.083  0.132  0.070  0.130   \n",
      "3  MAC000002 2012-10-16  0.237  0.237  0.193  0.118  0.098  0.107  0.094   \n",
      "4  MAC000002 2012-10-17  0.157  0.211  0.155  0.169  0.101  0.117  0.084   \n",
      "\n",
      "    hh_7  ...  hh_38  hh_39  hh_40  hh_41  hh_42  hh_43  hh_44  hh_45  hh_46  \\\n",
      "0  0.119  ...  0.918  0.278  0.267  0.239  0.230  0.233  0.235  0.188  0.259   \n",
      "1  0.083  ...  1.075  0.956  0.821  0.745  0.712  0.511  0.231  0.210  0.278   \n",
      "2  0.074  ...  1.164  0.249  0.225  0.258  0.260  0.334  0.299  0.236  0.241   \n",
      "3  0.109  ...  0.966  0.172  0.192  0.228  0.203  0.211  0.188  0.213  0.157   \n",
      "4  0.118  ...  0.223  0.075  0.230  0.208  0.265  0.377  0.327  0.277  0.288   \n",
      "\n",
      "   hh_47  \n",
      "0  0.250  \n",
      "1  0.159  \n",
      "2  0.237  \n",
      "3  0.202  \n",
      "4  0.256  \n",
      "\n",
      "[5 rows x 50 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "\n",
    "# Define path\n",
    "block_data_path = Path(\"data/london_smart_meters/hhblock_dataset\")\n",
    "\n",
    "# Check if the file exists before loading\n",
    "block_file = block_data_path / \"block_0.csv\"\n",
    "\n",
    "if block_file.exists():\n",
    "    block_1 = pd.read_csv(block_file, parse_dates=False)\n",
    "    block_1['day'] = pd.to_datetime(block_1['day'], yearfirst=True)\n",
    "    print(block_1.head())\n",
    "else:\n",
    "    print(f\"⚠️ Error: File not found - {block_file}\")\n",
    "    print(\"📌 Make sure you've downloaded and extracted the dataset.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3a785973",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 16\r\n",
      "drwxr-xr-x    4 tomaszosuchowski  staff    128 Jan 31 11:39 \u001b[34m.\u001b[m\u001b[m/\r\n",
      "drwxr-xr-x  440 tomaszosuchowski  staff  14080 Jan 29 18:11 \u001b[34m..\u001b[m\u001b[m/\r\n",
      "-rw-r--r--@   1 tomaszosuchowski  staff   6148 Jan 31 11:39 .DS_Store\r\n",
      "drwxr-xr-x   11 tomaszosuchowski  staff    352 Jan 31 01:11 \u001b[34mhhblock_dataset\u001b[m\u001b[m/\r\n"
     ]
    }
   ],
   "source": [
    "ls -la data/london_smart_meters/\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2816ee92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found: data/london_smart_meters/hhblock_dataset/uk_bank_holidays.csv\n",
      "Found: data/london_smart_meters/hhblock_dataset/block_1.csv\n",
      "Found: data/london_smart_meters/hhblock_dataset/block_0.csv\n",
      "Found: data/london_smart_meters/hhblock_dataset/weather_daily_darksky.csv\n",
      "Found: data/london_smart_meters/hhblock_dataset/informations_households.csv\n",
      "Found: data/london_smart_meters/hhblock_dataset/weather_hourly_darksky.csv\n",
      "Found: data/london_smart_meters/hhblock_dataset/processed_smart_meters.csv\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "search_path = \"data/london_smart_meters/\"\n",
    "for root, dirs, files in os.walk(search_path):\n",
    "    for file in files:\n",
    "        if file.endswith(\".csv\"):\n",
    "            print(f\"Found: {os.path.join(root, file)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b0c24a88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       LCLid        day   hh_0   hh_1   hh_2   hh_3   hh_4   hh_5   hh_6  \\\n",
      "0  MAC000002 2012-10-13  0.263  0.269  0.275  0.256  0.211  0.136  0.161   \n",
      "1  MAC000002 2012-10-14  0.262  0.166  0.226  0.088  0.126  0.082  0.123   \n",
      "2  MAC000002 2012-10-15  0.192  0.097  0.141  0.083  0.132  0.070  0.130   \n",
      "3  MAC000002 2012-10-16  0.237  0.237  0.193  0.118  0.098  0.107  0.094   \n",
      "4  MAC000002 2012-10-17  0.157  0.211  0.155  0.169  0.101  0.117  0.084   \n",
      "\n",
      "    hh_7  ...  hh_38  hh_39  hh_40  hh_41  hh_42  hh_43  hh_44  hh_45  hh_46  \\\n",
      "0  0.119  ...  0.918  0.278  0.267  0.239  0.230  0.233  0.235  0.188  0.259   \n",
      "1  0.083  ...  1.075  0.956  0.821  0.745  0.712  0.511  0.231  0.210  0.278   \n",
      "2  0.074  ...  1.164  0.249  0.225  0.258  0.260  0.334  0.299  0.236  0.241   \n",
      "3  0.109  ...  0.966  0.172  0.192  0.228  0.203  0.211  0.188  0.213  0.157   \n",
      "4  0.118  ...  0.223  0.075  0.230  0.208  0.265  0.377  0.327  0.277  0.288   \n",
      "\n",
      "   hh_47  \n",
      "0  0.250  \n",
      "1  0.159  \n",
      "2  0.237  \n",
      "3  0.202  \n",
      "4  0.256  \n",
      "\n",
      "[5 rows x 50 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "\n",
    "# Define the path to the dataset\n",
    "block_data_path = Path(\"data/london_smart_meters/hhblock_dataset\")\n",
    "block_file = block_data_path / \"block_0.csv\"\n",
    "\n",
    "# Load the CSV file and process it\n",
    "if block_file.exists():\n",
    "    block_1 = pd.read_csv(block_file, parse_dates=False)\n",
    "    # Convert the 'day' column to datetime if it exists\n",
    "    if 'day' in block_1.columns:\n",
    "        block_1['day'] = pd.to_datetime(block_1['day'], yearfirst=True)\n",
    "    print(block_1.head())\n",
    "else:\n",
    "    print(f\"⚠️ File not found: {block_file}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fdc5d232",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LCLid\n",
       "MAC004387   2014-02-27\n",
       "MAC003252   2014-02-21\n",
       "MAC003646   2014-02-27\n",
       "MAC003557   2013-11-20\n",
       "MAC001074   2012-12-20\n",
       "Name: day, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check End Dates of all time series\n",
    "block_1.groupby(\"LCLid\")['day'].max().sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f90ad08e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "7it [00:02,  3.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total valid dates collected: 113058\n",
      "Earliest Date: 2011-12-04 00:00:00\n",
      "Latest Date: 2014-02-27 00:00:00\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "from pathlib import Path\n",
    "\n",
    "folder_path = Path(\"/Users/tomaszosuchowski/Downloads/files\")  # Correct folder path\n",
    "max_date = None\n",
    "all_dates = []  # List to store all valid dates\n",
    "\n",
    "for f in tqdm(folder_path.glob(\"*.csv\")):  \n",
    "    try:\n",
    "        df = pd.read_csv(f, encoding=\"latin1\", parse_dates=False, on_bad_lines=\"skip\", sep=\",\", low_memory=False)\n",
    "\n",
    "        if \"day\" in df.columns:\n",
    "            df[\"day\"] = pd.to_datetime(df[\"day\"], errors=\"coerce\", yearfirst=True)\n",
    "            valid_dates = df[\"day\"].dropna().tolist()\n",
    "            all_dates.extend(valid_dates)  # Store all valid dates\n",
    "            max_date = max(max_date, df[\"day\"].max()) if max_date else df[\"day\"].max()\n",
    "\n",
    "    except Exception as e:\n",
    "        print(f\"Error reading {f}: {e}\")\n",
    "        continue  \n",
    "\n",
    "# Convert dates to a Pandas Series for analysis\n",
    "date_series = pd.Series(all_dates)\n",
    "\n",
    "# Display statistics\n",
    "print(f\"Total valid dates collected: {len(date_series)}\")\n",
    "print(f\"Earliest Date: {date_series.min()}\")\n",
    "print(f\"Latest Date: {date_series.max()}\")\n",
    "\n",
    "# Save valid dates to CSV (Optional)\n",
    "dates_df = pd.DataFrame(date_series, columns=[\"Valid Dates\"])\n",
    "dates_df.to_csv(\"valid_dates.csv\", index=False)\n",
    "\n",
    "# **Plot Histogram of Dates**\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.hist(date_series, bins=50, color=\"blue\", edgecolor=\"black\")\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Distribution of Dates in CSV Files\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c6c802e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       LCLid         day   hh_0   hh_1   hh_2   hh_3   hh_4   hh_5   hh_6  \\\n",
      "0  MAC000002  2012-10-13  0.263  0.269  0.275  0.256  0.211  0.136  0.161   \n",
      "1  MAC000002  2012-10-14  0.262  0.166  0.226  0.088  0.126  0.082  0.123   \n",
      "2  MAC000002  2012-10-15  0.192  0.097  0.141  0.083  0.132  0.070  0.130   \n",
      "3  MAC000002  2012-10-16  0.237  0.237  0.193  0.118  0.098  0.107  0.094   \n",
      "4  MAC000002  2012-10-17  0.157  0.211  0.155  0.169  0.101  0.117  0.084   \n",
      "\n",
      "    hh_7  ...  hh_38  hh_39  hh_40  hh_41  hh_42  hh_43  hh_44  hh_45  hh_46  \\\n",
      "0  0.119  ...  0.918  0.278  0.267  0.239  0.230  0.233  0.235  0.188  0.259   \n",
      "1  0.083  ...  1.075  0.956  0.821  0.745  0.712  0.511  0.231  0.210  0.278   \n",
      "2  0.074  ...  1.164  0.249  0.225  0.258  0.260  0.334  0.299  0.236  0.241   \n",
      "3  0.109  ...  0.966  0.172  0.192  0.228  0.203  0.211  0.188  0.213  0.157   \n",
      "4  0.118  ...  0.223  0.075  0.230  0.208  0.265  0.377  0.327  0.277  0.288   \n",
      "\n",
      "   hh_47  \n",
      "0  0.250  \n",
      "1  0.159  \n",
      "2  0.237  \n",
      "3  0.202  \n",
      "4  0.256  \n",
      "\n",
      "[5 rows x 50 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Load the CSV file again\n",
    "block_1 = pd.read_csv(\"data/london_smart_meters/hhblock_dataset/block_0.csv\", parse_dates=False)\n",
    "\n",
    "# Print the first few rows to verify\n",
    "print(block_1.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f1f76b30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       LCLid         day hour_block  energy_consumption  offset\n",
      "0  MAC000002  2012-10-13       hh_0               0.263       0\n",
      "1  MAC000002  2012-10-13       hh_1               0.269       1\n",
      "2  MAC000002  2012-10-13       hh_2               0.275       2\n",
      "3  MAC000002  2012-10-13       hh_3               0.256       3\n",
      "4  MAC000002  2012-10-13       hh_4               0.211       4\n"
     ]
    }
   ],
   "source": [
    "# Identify the hourly columns\n",
    "hour_columns = [col for col in block_1.columns if col.startswith(\"hh_\")]\n",
    "\n",
    "# Reshape the DataFrame using `stack()`\n",
    "reshaped_block_1 = (\n",
    "    block_1.set_index(['LCLid', 'day'])[hour_columns]  # Set LCLid and day as index\n",
    "    .stack()  # Stack only the hourly columns\n",
    "    .reset_index()  # Reset index to flatten\n",
    "    .rename(columns={\"level_2\": \"hour_block\", 0: \"energy_consumption\"})  # Rename columns\n",
    ")\n",
    "\n",
    "# Convert 'hour_block' to numeric offsets (hour numbers)\n",
    "reshaped_block_1['offset'] = reshaped_block_1['hour_block'].str.replace(\"hh_\", \"\", regex=False).astype(int)\n",
    "\n",
    "# Print the first few rows to verify\n",
    "print(reshaped_block_1.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f38ecd1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['LCLid', 'day', 'hh_0', 'hh_1', 'hh_2', 'hh_3', 'hh_4', 'hh_5', 'hh_6',\n",
      "       'hh_7', 'hh_8', 'hh_9', 'hh_10', 'hh_11', 'hh_12', 'hh_13', 'hh_14',\n",
      "       'hh_15', 'hh_16', 'hh_17', 'hh_18', 'hh_19', 'hh_20', 'hh_21', 'hh_22',\n",
      "       'hh_23', 'hh_24', 'hh_25', 'hh_26', 'hh_27', 'hh_28', 'hh_29', 'hh_30',\n",
      "       'hh_31', 'hh_32', 'hh_33', 'hh_34', 'hh_35', 'hh_36', 'hh_37', 'hh_38',\n",
      "       'hh_39', 'hh_40', 'hh_41', 'hh_42', 'hh_43', 'hh_44', 'hh_45', 'hh_46',\n",
      "       'hh_47'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(block_1.columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c6474595",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       LCLid         day hour_block  energy_consumption  offset\n",
      "0  MAC000002  2012-10-13       hh_0               0.263       0\n",
      "1  MAC000002  2012-10-13       hh_1               0.269       1\n",
      "2  MAC000002  2012-10-13       hh_2               0.275       2\n",
      "3  MAC000002  2012-10-13       hh_3               0.256       3\n",
      "4  MAC000002  2012-10-13       hh_4               0.211       4\n"
     ]
    }
   ],
   "source": [
    "# Identify the hourly columns\n",
    "hour_columns = [col for col in block_1.columns if col.startswith(\"hh_\")]\n",
    "\n",
    "# Reshape the DataFrame to long format\n",
    "reshaped_block_1 = (\n",
    "    block_1.set_index(['LCLid', 'day'])[hour_columns]\n",
    "    .stack()\n",
    "    .reset_index()\n",
    "    .rename(columns={\"level_2\": \"hour_block\", 0: \"energy_consumption\"})\n",
    ")\n",
    "\n",
    "# Convert 'hour_block' to numerical hour offsets\n",
    "reshaped_block_1['offset'] = reshaped_block_1['hour_block'].str.replace(\"hh_\", \"\", regex=False).astype(int)\n",
    "\n",
    "# Print the first few rows\n",
    "print(reshaped_block_1.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b32f4b64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['LCLid', 'day', 'hour_block', 'energy_consumption', 'offset'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(reshaped_block_1.columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c58ecc58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['hh_0' 'hh_1' 'hh_2' 'hh_3' 'hh_4' 'hh_5' 'hh_6' 'hh_7' 'hh_8' 'hh_9'\n",
      " 'hh_10' 'hh_11' 'hh_12' 'hh_13' 'hh_14' 'hh_15' 'hh_16' 'hh_17' 'hh_18'\n",
      " 'hh_19' 'hh_20' 'hh_21' 'hh_22' 'hh_23' 'hh_24' 'hh_25' 'hh_26' 'hh_27'\n",
      " 'hh_28' 'hh_29' 'hh_30' 'hh_31' 'hh_32' 'hh_33' 'hh_34' 'hh_35' 'hh_36'\n",
      " 'hh_37' 'hh_38' 'hh_39' 'hh_40' 'hh_41' 'hh_42' 'hh_43' 'hh_44' 'hh_45'\n",
      " 'hh_46' 'hh_47']\n"
     ]
    }
   ],
   "source": [
    "print(reshaped_block_1['hour_block'].unique())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9bee3ad1",
   "metadata": {},
   "outputs": [],
   "source": [
    "reshaped_block_1['day'] = pd.to_datetime(reshaped_block_1['day'], format=\"%Y-%m-%d\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "dfb31353",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       LCLid        day hour_block  energy_consumption  offset\n",
      "0  MAC000002 2012-10-13       hh_0               0.263       0\n",
      "1  MAC000002 2012-10-13       hh_1               0.269       1\n",
      "2  MAC000002 2012-10-13       hh_2               0.275       2\n",
      "3  MAC000002 2012-10-13       hh_3               0.256       3\n",
      "4  MAC000002 2012-10-13       hh_4               0.211       4\n"
     ]
    }
   ],
   "source": [
    "#print(dir())  # Lists all defined variables\n",
    "\n",
    "#print(block1_compact.head())\n",
    "print(reshaped_block_1.head())  # Use the correct variable name\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2ab536b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f19b338f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique LCLid values: ['MAC000002' 'MAC000246' 'MAC000450' 'MAC001074' 'MAC003223' 'MAC003239'\n",
      " 'MAC003252' 'MAC003281' 'MAC003305' 'MAC003348' 'MAC003388' 'MAC003394'\n",
      " 'MAC003400' 'MAC003422' 'MAC003423' 'MAC003428' 'MAC003449' 'MAC003463'\n",
      " 'MAC003482' 'MAC003553' 'MAC003557' 'MAC003566' 'MAC003579' 'MAC003597'\n",
      " 'MAC003613' 'MAC003646' 'MAC003656' 'MAC003668' 'MAC003680' 'MAC003686'\n",
      " 'MAC003718' 'MAC003719' 'MAC003737' 'MAC003740' 'MAC003775' 'MAC003805'\n",
      " 'MAC003817' 'MAC003826' 'MAC003844' 'MAC003851' 'MAC003856' 'MAC003863'\n",
      " 'MAC003874' 'MAC004034' 'MAC004179' 'MAC004247' 'MAC004319' 'MAC004387'\n",
      " 'MAC004431' 'MAC005492']\n",
      "Total rows in dataset: 1213678\n",
      "⚠️ Warning: Missing required data for customer MAC003668.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "\n",
    "# ✅ Ensure 'hour_block' is numeric\n",
    "reshaped_block_1[\"hour_block\"] = pd.to_numeric(reshaped_block_1[\"hour_block\"], errors=\"coerce\")\n",
    "\n",
    "# ✅ Create 'start_timestamp' and 'series_length'\n",
    "reshaped_block_1[\"start_timestamp\"] = pd.to_datetime(reshaped_block_1[\"day\"], errors=\"coerce\") + \\\n",
    "                                      pd.to_timedelta(reshaped_block_1[\"hour_block\"] * 30, unit=\"m\")\n",
    "\n",
    "reshaped_block_1[\"series_length\"] = reshaped_block_1.groupby(\"LCLid\")[\"start_timestamp\"].transform(\"count\")\n",
    "\n",
    "# ✅ Debugging: Check if there are any valid customers before selecting one\n",
    "print(\"Unique LCLid values:\", reshaped_block_1[\"LCLid\"].unique())  # Check if there are valid customers\n",
    "print(\"Total rows in dataset:\", len(reshaped_block_1))  # Check total rows\n",
    "\n",
    "# ✅ Fix: Ensure we have valid customers before selecting one\n",
    "if reshaped_block_1[\"LCLid\"].nunique() == 0:\n",
    "    raise ValueError(\"❌ No valid customers found in reshaped_block_1!\")\n",
    "\n",
    "# ✅ Select a random customer\n",
    "random_customer = random.choice(reshaped_block_1[\"LCLid\"].unique())\n",
    "\n",
    "# ✅ Extract customer data\n",
    "customer_data = reshaped_block_1[reshaped_block_1[\"LCLid\"] == random_customer]\n",
    "\n",
    "# ✅ Ensure required columns exist before proceeding\n",
    "if not customer_data[\"start_timestamp\"].isna().all() and \"series_length\" in customer_data.columns:\n",
    "    start_time = customer_data[\"start_timestamp\"].values[0]\n",
    "    \n",
    "    if pd.isna(start_time):  # If NaT, print warning and exit\n",
    "        print(f\"⚠️ Warning: start_timestamp is NaT for customer {random_customer}. Skipping.\")\n",
    "    else:\n",
    "        timestamps = pd.date_range(\n",
    "            start=start_time, \n",
    "            periods=int(customer_data[\"series_length\"].values[0]),  # Convert to integer\n",
    "            freq=\"30min\"\n",
    "        )\n",
    "        energy_values = np.array(customer_data[\"energy_consumption\"].values)\n",
    "\n",
    "        # ✅ Plot\n",
    "        plt.figure(figsize=(12, 5))\n",
    "        plt.plot(timestamps, energy_values, label=f\"Energy Usage for {random_customer}\", color=\"blue\")\n",
    "        plt.xlabel(\"Time\")\n",
    "        plt.ylabel(\"Energy Consumption (kWh)\")\n",
    "        plt.title(f\"Energy Consumption Over Time - Customer {random_customer}\")\n",
    "        plt.legend()\n",
    "        plt.grid(True)\n",
    "        plt.show()\n",
    "else:\n",
    "    print(f\"⚠️ Warning: Missing required data for customer {random_customer}.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9f8163cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "\n",
    "# ✅ Reshape block_1 from wide to long format\n",
    "reshaped_block_1 = block_1.melt(id_vars=[\"LCLid\", \"day\"], \n",
    "                                value_vars=[col for col in block_1.columns if col.startswith(\"hh_\")],\n",
    "                                var_name=\"hour_block\",\n",
    "                                value_name=\"energy_consumption\")\n",
    "\n",
    "# ✅ Convert 'hour_block' from 'hh_X' to numerical hour values (0 to 47)\n",
    "reshaped_block_1[\"hour_block\"] = reshaped_block_1[\"hour_block\"].str.extract(r\"(\\d+)\").astype(float)\n",
    "\n",
    "# ✅ Ensure 'hour_block' is numeric and fill missing values\n",
    "reshaped_block_1[\"hour_block\"] = pd.to_numeric(reshaped_block_1[\"hour_block\"], errors=\"coerce\").fillna(0)\n",
    "\n",
    "# ✅ Create 'start_timestamp' and 'series_length'\n",
    "reshaped_block_1[\"start_timestamp\"] = pd.to_datetime(reshaped_block_1[\"day\"], errors=\"coerce\") + \\\n",
    "                                      pd.to_timedelta(reshaped_block_1[\"hour_block\"] * 30, unit=\"m\")\n",
    "\n",
    "reshaped_block_1[\"series_length\"] = reshaped_block_1.groupby(\"LCLid\")[\"start_timestamp\"].transform(\"count\")\n",
    "\n",
    "# ✅ Select a random customer\n",
    "valid_customers = reshaped_block_1.dropna(subset=[\"start_timestamp\"])\n",
    "if valid_customers.empty:\n",
    "    raise ValueError(\"❌ No valid customers found in reshaped_block_1!\")\n",
    "\n",
    "random_customer = random.choice(valid_customers[\"LCLid\"].unique())\n",
    "\n",
    "# ✅ Extract customer data\n",
    "customer_data = reshaped_block_1[reshaped_block_1[\"LCLid\"] == random_customer]\n",
    "\n",
    "# ✅ Ensure required columns exist before proceeding\n",
    "if not customer_data[\"start_timestamp\"].isna().all() and \"series_length\" in customer_data.columns:\n",
    "    start_time = customer_data[\"start_timestamp\"].values[0]\n",
    "    \n",
    "    if pd.isna(start_time):  # If NaT, print warning and exit\n",
    "        print(f\"⚠️ Warning: start_timestamp is NaT for customer {random_customer}. Skipping.\")\n",
    "    else:\n",
    "        timestamps = pd.date_range(\n",
    "            start=start_time, \n",
    "            periods=int(customer_data[\"series_length\"].values[0]),  # Convert to integer\n",
    "            freq=\"30min\"\n",
    "        )\n",
    "        energy_values = np.array(customer_data[\"energy_consumption\"].values)\n",
    "\n",
    "        # ✅ Plot\n",
    "        plt.figure(figsize=(12, 5))\n",
    "        plt.plot(timestamps, energy_values, label=f\"Energy Usage for {random_customer}\", color=\"blue\")\n",
    "        plt.xlabel(\"Time\")\n",
    "        plt.ylabel(\"Energy Consumption (kWh)\")\n",
    "        plt.title(f\"Energy Consumption Over Time - Customer {random_customer}\")\n",
    "        plt.legend()\n",
    "        plt.grid(True)\n",
    "        plt.show()\n",
    "else:\n",
    "    print(f\"⚠️ Warning: Missing required data for customer {random_customer}.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "772027d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Tp/jrr7/SnFdy7OzsUK1aNZ3LAdCKnzjzXmLuT58+BQBUr1492f5Jd9AAwMHBAU5OThpt4eHhsLKyQu7cuTXaP94eifH79++PWbNm4ccff0RsbCw2bdqEYcOG6ZwVsEiRIrh3716qyyTNCUj+dS1YsCCCgoK0xmVnZ6eVa9JpsXv06IG4uDgsXboUn3/+ORISElC9enVMmTIFTZo0SVNeH/t4m9nY2KTa/vE03cm9x/Lnzw/g/7aBErq2W0JCAl6+fKlR1Ol6j6XG3t5efd2Kra0tihYtqvH+MsTnODUpfZaS+xyGh4enuF0Sn0+PtOacntfGED5+fYEPr1XS11fp66RUYpGWuK3fvHmDOnXqwM7ODlOmTEGpUqXg4OCAhw8f4rPPPtPIzc3NDZ07d8bixYsxevRoXLlyBYcPH8bixYv1yonIXLFwIsoCVqxYARHB77//jt9//13r+dWrV2PKlCmwtLRE165dMWzYMKxatQpTp07F2rVr0a5dO41fml1dXWFvb6+e2vxjrq6uGo+Tu6B/w4YNsLa2xo4dOzR2qj++H07ijsXTp081jh7ExcVp7Uy5urqiUqVKmDp1arJ5Je4YZJTE7fD777+jaNGiOpdPbjvlyZMHcXFxePHihcYOf2hoaLLrGDhwIGbMmIEVK1bg/fv3iIuLw4ABA3TGbtq0KX755RecOHFC50xwia9JSEiI1nNPnjzRev3T6ssvv8SXX36JqKgoHDp0CBMnTkSrVq1w8+ZNFC1aVP0+iY6O1igEDXF0KjmJ77GkO7eJ2z25HV5ddG03CwsLRUd0dLGwsEj1RwBDfI4NJU+ePClul6S5JH0PJJXSeyCtOWf0a6OE0tdJqT///BMA1JNc7Nu3D0+ePMGBAwc0Zg1M/MHtY0OGDMHatWvxxx9/4J9//kGuXLm0jtQSZRcsnIjMXHx8PFavXo3ixYtj2bJlWs/v2LEDs2bNwt9//41WrVrBxcUF7dq1w5o1a1CrVi2EhoZqzUTVqlUrTJs2DXny5IGnp2e68lKpVLCysoKlpaW67d27d1i7dq3Gcomne2zcuBFVq1ZVt//+++9aM+W1atUKu3btQvHixU22k5NU06ZNYWVlhTt37midgpdW9erVw8yZM7Fx40b1aTHAh8IzOQUKFEDHjh2xYMECxMTEoHXr1ihSpIjOOP7+/lixYgX8/Py0piMHPpzOs337drRv3x61atWCvb09fv31V/WsYQDw6NEj7Nu3Dx06dEjXWBM5OjqiefPmiImJQbt27XDlyhUULVpUfR+cixcvahzF++uvv/SKl5rffvsNgwcPVj9et24dAGjMpPbxEYKUlC5dGoUKFcK6deswYsQI9U59VFQUtmzZop7NLaMY4nNsKI0aNcK2bdvw5MkTjR841qxZAwcHB3Uxn/Q9ULp0afVyiTv/6WWs10bJEcKUGPN1CgoKwrJly+Dj46OeLTBx7B8fpU7pKJK3tzd8fHzwww8/4PLly/j666/h6Oho0DyJzAULJyIz9/fff+PJkyf44Ycfkp02t0KFCpg3bx6WL1+OVq1aAfhwut7GjRsxaNAgFC5cGI0bN9boM3ToUGzZsgV169aFv78/KlWqhISEBAQHB2PPnj0YPny4zvtDtWzZErNnz0a3bt3w9ddfIzw8HD/99JPWH+vy5cuja9eumDVrFiwtLdGwYUNcuXIFs2bNgrOzs8bpbpMmTUJQUBB8fHwwePBglC5dGu/fv8f9+/exa9cuLFq0SOdNHt+9e5fi/UyU3pPHw8MDkyZNwtixY3H37l00a9YMLi4uePr0KU6ePAlHR0cEBgamuo5mzZqhdu3aGD58OCIjI+Ht7Y3jx49jzZo1ALRP9wM+/AKcuP2TXq+WGk9PT2zYsAGdO3dGlSpV1DfABT7cxDPxqGX79u2RK1cujB8/HmPGjEHPnj3RtWtXhIeHIzAwEHZ2dpg4caKSzQQA+Oqrr2Bvb4/atWujQIECCA0NxfTp0+Hs7Kwuklq0aIHcuXOjb9++mDRpEqysrLBq1So8fPhQcby0sLGxwaxZs/DmzRtUr14dx44dw5QpU9C8eXP1Tibw4drAAwcO4K+//kKBAgWQM2dOjZ36RBYWFpg5cya6d++OVq1aoX///oiOjsaPP/6IV69eYcaMGUYZR0oM8Tk2lIkTJ6qvUZwwYQJy586N3377DTt37sTMmTPVhXz16tVRunRpjBgxAnFxcXBxccG2bdtw5MgRveIb67WpUKECAGDJkiXImTMn7Ozs4OnpqeiIpSFep4SEBPX3WnR0NIKDg/H3339j06ZNKFu2LDZt2qRe1sfHBy4uLhgwYAAmTpwIa2tr/Pbbb7hw4UKK6x8yZIh6enY/P780j40oyzHp1BREpLd27dqJjY2Negrc5HTp0kWsrKwkNDRURD7MMOXu7i4AZOzYscn2efPmjYwbN05Kly4tNjY24uzsLBUrVhR/f3/1ekQ+zMb1zTffJLuOFStWSOnSpcXW1laKFSsm06dPl+XLl2vNmvX+/XsZNmyY5MuXTz372/Hjx8XZ2Vn8/f011vns2TMZPHiweHp6irW1teTOnVu8vb1l7Nix8ubNm1S3la6ZwGJjY0Xk/2b2+niWq+Rm9hL5MGtbgwYNxMnJSWxtbaVo0aLSoUMH2bt3r3qZ1Ga+evHihXz55ZeSK1cucXBwkCZNmsiJEyeSnfUtkYeHh5QtWzbV8Sbnzp074ufnJyVKlBBbW1uxt7eXcuXKybBhw7RmMlu2bJlUqlRJ/fq3bdtWa+bClMb18Qx5q1evlgYNGoibm5vY2NhIwYIFpVOnTuop8BOdPHlSfHx8xNHRUQoVKiQTJ05UT63/8ax6LVu21Iqb3Pvx3r17AkB+/PFHrbwvXrwo9evXF3t7e8mdO7cMHDhQ6310/vx5qV27tjg4OAgAqVevnoik/n6oWbOm2NnZiaOjozRq1EiOHj2a7PZ59uyZRntKs8p9TNdMaokM8TlOTuJ05Ml59uyZ1qx6IiKXLl2S1q1bi7Ozs9jY2EjlypVl5cqVWv1v3rwpvr6+4uTkJHnz5pVvv/1WPfvkx7PqpZRDStLy2iiZVU9EZM6cOeLp6SmWlpYCQD2mlPLr1auXFC1aVKMtra9TchJniEz8Z29vL0WKFJHWrVvLihUrJDo6WqvPsWPHpFatWuLg4CB58+aVfv36ydmzZzXyTyo6OlpsbW2lWbNmadomRFmVSiSZ6VaIiEzs2LFjqF27Nn777Tf1TGfZybp169C9e3ccPXoUPj4+Gs9dvHgRlStXxvz58/nrbzr17t0bv//+O968eWPqVIgyvb/++gtt2rTBzp070aJFC1OnQ2QyPFWPiEwuKCgIx48fh7e3N+zt7XHhwgXMmDEDJUuWxGeffWbq9Ixu/fr1ePz4MSpWrAgLCwucOHECP/74I+rWratRNN25cwcPHjzAmDFjUKBAAa3pwomIDOnq1at48OABhg8fjipVqqhv/UCUXbFwIiKTc3Jywp49ezBnzhy8fv0arq6uaN68OaZPn641zXVWlDNnTmzYsAFTpkxBVFSUuiiaMmWKxnKTJ0/G2rVrUbZsWWzevDlDJxogouzHz88PR48eRdWqVbF69WqjzrxIZA54qh4REREREZEO2tM1ERERERERkQYWTkRERERERDpku2ucEhIS8OTJE+TMmZPn6hIRERERZWMigtevX6NgwYLJ3jsxqWxXOD158gTu7u6mToOIiIiIiDKJhw8fonDhwqkuk+0Kp5w5cwL4sHGcnJxMnA0REREREZlKZGQk3N3d1TVCarJd4ZR4ep6TkxMLJyIiIiIiStMlPJwcgoiIiIiISAcWTkRERERERDqwcCIiIiIiItKBhRMREREREZEOLJyIiIiIiIh0YOFERERERESkAwsnIiIiIiIiHVg4ERERERER6cDCiYiIiIiISAcWTkRERERERDqwcCIiIiIiItKBhRMREREREZEOLJyIiIiIiIh0MHnhtGDBAnh6esLOzg7e3t44fPhwisv27t0bKpVK61/58uUzMGMiIiIiIspuTFo4bdy4EUOHDsXYsWNx7tw51KlTB82bN0dwcHCyy8+dOxchISHqfw8fPkTu3LnRsWPHDM6ciIiIiIiyE5WIiKmC16xZE1WrVsXChQvVbWXLlkW7du0wffp0nf23b9+Ozz77DPfu3UPRokWTXSY6OhrR0dHqx5GRkXB3d8fz58/h5OSk/yCIiIiIiMgsRUZGwtXVFRERETprA6sMyklLTEwMzpw5g9GjR2u0+/r64tixY2lax/Lly9G4ceMUiyYAmD59OgIDA7Xa9+zZAwcHB2VJExERERFRlvH27ds0L2uywun58+eIj4+Hm5ubRrubmxtCQ0N19g8JCcHff/+NdevWpbrc999/j2HDhqkfJx5x8vX15REnIiIionRynuGsaPmI0RFGyoQo/SIjI9O8rMkKp0QqlUrjsYhotSVn1apVyJUrF9q1a5fqcra2trC1tdVqt7a2hrW1taJciYiIiOiDdwnvFC2fuN+lCtS9n5eUTPy/q0r06UuUHCX1gMkmh3B1dYWlpaXW0aWwsDCto1AfExGsWLECPXr0gI2NjTHTJCIiIiIiMl3hZGNjA29vbwQFBWm0BwUFwcfHJ9W+Bw8exO3bt9G3b19jpkhERERERATAxKfqDRs2DD169EC1atVQq1YtLFmyBMHBwRgwYACAD9cnPX78GGvWrNHot3z5ctSsWRMVKlQwRdpERERERJTNmLRw6ty5M8LDwzFp0iSEhISgQoUK2LVrl3qWvJCQEK17OkVERGDLli2YO3euKVImIiIiIqJsyOSTQ/j5+cHPzy/Z51atWqXV5uzsrGjaQCIiIiIiTixB+jLZNU5ERERERETmgoUTERERERGRDiyciIiIiIiIdDD5NU5EREREZBq87oco7XjEiYiIiIiISAcWTkRERERERDrwVD0iIiIiolQoOaWRpzNmXTziREREREREpAMLJyIiIiIiIh1YOBEREREREenAwomIiIiIiEgHFk5EREREREQ6sHAiIiIiIiLSgYUTERERERGRDiyciIiIiIiIdGDhREREREREpAMLJyIiIiIiIh1YOBEREREREenAwomIiIiIiEgHFk5EREREREQ6sHAiIiIiIiLSgYUTERERERGRDiyciIiIiIiIdGDhREREREREpIOVqRMgIiIiIsqKVIEqRcvLRDFSJmQILJyIiIiIzBh3zokyBk/VIyIiIiIi0oGFExERERERkQ4snIiIiIiIiHRg4URERERERKQDCyciIiIiIiIdWDgRERERERHpwMKJiIiIiIhIBxZOREREREREOrBwIiIiIiIi0oGFExERERERkQ4snIiIiIiIiHRg4URERERERKQDCyciIiIiIiIdWDgRERERERHpwMKJiIiIiIhIB5MXTgsWLICnpyfs7Ozg7e2Nw4cPp7p8dHQ0xo4di6JFi8LW1hbFixfHihUrMihbIiIiIiLKjqz06RwdHQ1bW9t099+4cSOGDh2KBQsWoHbt2li8eDGaN2+Oq1evokiRIsn26dSpE54+fYrly5ejRIkSCAsLQ1xcXLpzICIiIiIi0kVR4bR7926sX78ehw8fRnBwMBISEuDg4ICqVavC19cXX375JQoWLJjm9c2ePRt9+/ZFv379AABz5szB7t27sXDhQkyfPl1r+X/++QcHDx7E3bt3kTt3bgCAh4eHkiEQEREREREplqbCafv27Rg1ahQiIiLQokULfPfddyhUqBDs7e3x4sULXL58GXv37sXkyZPRu3dvTJ48GXnz5k11nTExMThz5gxGjx6t0e7r64tjx44l2+fPP/9EtWrVMHPmTKxduxaOjo5o06YNJk+eDHt7+2T7REdHIzo6Wv04MjISABAbG4vY2Ni0DJ+IiIgo07K3SH4fKCVJ939M0dfc8lXa11AxKWMo2eYqERFdC9WoUQPjx49Hy5YtYWGR8mVRjx8/xty5c+Hm5obhw4enus4nT56gUKFCOHr0KHx8fNTt06ZNw+rVq3Hjxg2tPs2aNcOBAwfQuHFjTJgwAc+fP4efnx8aNmyY4nVOAQEBCAwM1Gpft24dHBwcUs2RiIiIiIiyrrdv36Jbt26IiIiAk5NTqsumqXAyhsTC6dixY6hVq5a6ferUqVi7di2uX7+u1cfX1xeHDx9GaGgonJ2dAQBbt25Fhw4dEBUVlexRp+SOOLm7u+P58+c6Nw4RERFRZuc8w1nR8hGjI0za19zyVdrXUDEpY0RGRsLV1TVNhZNek0Pow9XVFZaWlggNDdVoDwsLg5ubW7J9ChQogEKFCqmLJgAoW7YsRASPHj1CyZIltfrY2tomO4GFtbU1rK2t9RwFERERkWm9S3inaPmk+z+m6Gtu+Srta6iYlDGUbHPFhVN8fDxWrVqFf//9F2FhYUhISNB4ft++fWlaj42NDby9vREUFIT27dur24OCgtC2bdtk+9SuXRubN2/GmzdvkCNHDgDAzZs3YWFhgcKFCysdChERERERUZooLpyGDBmCVatWoWXLlqhQoQJUKlW6gw8bNgw9evRAtWrVUKtWLSxZsgTBwcEYMGAAAOD777/H48ePsWbNGgBAt27dMHnyZHz55ZcIDAzE8+fP8d1336FPnz4pTg5BRERERGRuVIHK9rFlokmuvslWFBdOGzZswKZNm9CiRQu9g3fu3Bnh4eGYNGkSQkJCUKFCBezatQtFixYFAISEhCA4OFi9fI4cORAUFIRvv/0W1apVQ548edCpUydMmTJF71yIiIiITEnJjjJ3kokynuLCycbGBiVKlDBYAn5+fvDz80v2uVWrVmm1lSlTBkFBQQaLT0REREREpEvKc4unYPjw4Zg7dy5MNBkfERERERFRhkvTEafPPvtM4/G+ffvw999/o3z58lozUWzdutVw2REREREREWUCaSqckk7/DUBjFjwiIiIiIqKsLk2F08qVK42dBxERERERUaaV5mucxo0bh3379uH9+/fGzIeIiIiIiCjTSXPhtH79ejRu3Bi5cuVCvXr1EBAQgEOHDiEmJsaY+REREREREZlcmgunO3fu4OHDh1i6dClKlCiBtWvXon79+nBxcUHjxo0xdepUHDt2zJi5EhERERERmYSi6cgLFSqEHj16YPny5bhz5w4ePHiAhQsXokiRIpg5cybq1q1rrDyJiIiIiIhMRvENcBPduXMHBw4cwL59+3DgwAHEx8ejQYMGhsyNiIiIiIgoU0hz4XTv3j3s378f+/fvx4EDBxAREYHatWujXr16GDRoEKpXrw4rq3TXYURERERERJlWmiud4sWLo0iRIvDz88PgwYNRtWpVWFpaGjM3IiIiIiKiTCHN1zh17NgR0dHRmD59OiZPnow5c+bg7NmzEBFj5kdERERERGRyaT7itHHjRgDA9evX1afr/fjjj3j//j0+/fRT1KtXD/Xr10f16tWNliwREREREZEpKJpVDwDKlCmDgQMHYuPGjQgNDcWxY8dQpUoVTJkyBbVq1TJGjkRERERERCaVrtkcnj59igMHDuDAgQPYv38/bt68CVtbW9SpU8fQ+REREREREZlcmgunzZs3q0/Ru3HjBqysrFCjRg106tQJDRo0gI+PD2xtbY2ZKxERERERkUmkuXDq3r07qlWrhvbt26NBgwaoXbs27O3tjZkbERERERFRppDmwunly5dwdHRMdZl3796xmCIiIiIioiwnzZNDJBZN33zzTbLPR0VFoXnz5obJioiIiIiIKBNRPKvenj17MG7cOI22qKgoNGvWDPHx8QZLjIiIiIiIKLNQPKvenj178OmnnyJPnjzw9/fH69ev0bRpU1hZWeHvv/82Ro5EREREREQmpbhw8vT0xO7du1G/fn1YWFhgw4YNsLW1xc6dO3VeA0VERERERGSO0nUfpwoVKmDHjh1o3LgxatasiR07dnBSCCIiIiIiyrLSVDh5eXlBpVJptdva2uLJkyeoXbu2uu3s2bOGy46IiIiIiCgTSFPh1K5dOyOnQURERERElHmlqXCaOHGisfMgIiIiIiIDUAVqnymWGpkoRsoka0nXNU66iEiyp/YRERERZWXcYSXKutJ0H6eyZcti3bp1iImJSXW5W7duYeDAgfjhhx8MkhwREREREVFmkKYjTvPnz8eoUaPwzTffwNfXF9WqVUPBggVhZ2eHly9f4urVqzhy5AiuXr2KQYMGwc/Pz9h5ExERERERZZg0FU4NGzbEqVOncOzYMWzcuBHr1q3D/fv38e7dO7i6usLLyws9e/bEF198gVy5chk5ZSIiIiIiooyl6BonHx8f+Pj4GCsXIiIiIiKiTClN1zgRERERERFlZyyciIiIiIiIdGDhREREREREpAMLJyIiIiIiIh1YOBEREREREemgaFa9RAkJCbh9+zbCwsKQkJCg8VzdunUNkhgREREREVFmobhwOnHiBLp164YHDx5ARDSeU6lUiI+PN1hyREREREREmYHiwmnAgAGoVq0adu7ciQIFCkClUhkjLyIiIiIiokxDceF069Yt/P777yhRooQx8iEiIiIiIsp0FE8OUbNmTdy+fdsYuRAREREREWVKigunb7/9FsOHD8eqVatw5swZXLx4UeOfUgsWLICnpyfs7Ozg7e2Nw4cPp7jsgQMHoFKptP5dv35dcVwiIiIiIqK0Unyq3ueffw4A6NOnj7pNpVJBRBRPDrFx40YMHToUCxYsQO3atbF48WI0b94cV69eRZEiRVLsd+PGDTg5Oakf582bV+kwiIiIiIiI0kxx4XTv3j2DBZ89ezb69u2Lfv36AQDmzJmD3bt3Y+HChZg+fXqK/fLly4dcuXIZLA8iIiIiIqLUKC6cihYtapDAMTExOHPmDEaPHq3R7uvri2PHjqXa18vLC+/fv0e5cuUwbtw4NGjQIMVlo6OjER0drX4cGRkJAIiNjUVsbKweIyAiIiLSZG9hr2j5pPsiSvqmt19m6Gtu+Srta275ftw3u1EydpV8fDOmNLhz5w7mzJmDa9euQaVSoWzZshgyZAiKFy+e5nU8efIEhQoVwtGjR+Hj46NunzZtGlavXo0bN25o9blx4wYOHToEb29vREdHY+3atVi0aBEOHDiQ4o13AwICEBgYqNW+bt06ODg4pDlfIiIiIiLKWt6+fYtu3bohIiJC41Kg5Cg+4rR79260adMGVapUQe3atSEiOHbsGMqXL4+//voLTZo0UbS+j+8DlXitVHJKly6N0qVLqx/XqlULDx8+xE8//ZRi4fT9999j2LBh6seRkZFwd3eHr6+vzo1DRERE2Y/zDGdFy0eMjsjwvqaIaai+5pav0r7mlu/HfbObxLPR0kJx4TR69Gj4+/tjxowZWu2jRo1Kc+Hk6uoKS0tLhIaGarSHhYXBzc0tzfl88skn+PXXX1N83tbWFra2tlrt1tbWsLa2TnMcIiIiyh7eJbxTtHzS/YmM6muKmIbqa275Ku1rbvl+3De7UTJ2xdORX7t2DX379tVq79OnD65evZrm9djY2MDb2xtBQUEa7UFBQRqn7uly7tw5FChQIM3LExERERERKaX4iFPevHlx/vx5lCxZUqP9/PnzyJcvn6J1DRs2DD169EC1atVQq1YtLFmyBMHBwRgwYACAD6fZPX78GGvWrAHwYdY9Dw8PlC9fHjExMfj111+xZcsWbNmyRekwiIiIiIiI0kxx4fTVV1/h66+/xt27d+Hj4wOVSoUjR47ghx9+wPDhwxWtq3PnzggPD8ekSZMQEhKCChUqYNeuXeqZ+0JCQhAcHKxePiYmBiNGjMDjx49hb2+P8uXLY+fOnWjRooXSYRAREREREaWZ4sJp/PjxyJkzJ2bNmoXvv/8eAFCwYEEEBARg8ODBihPw8/ODn59fss+tWrVK4/HIkSMxcuRIxTGIiIiIiIj0obhwUqlU8Pf3h7+/P16/fg0AyJkzp8ETIyIiIiIiyiwUF05JsWAiIiIiIqLsIE2FU9WqVfHvv//CxcUFXl5eKd5nCQDOnj1rsOSIiIiIiIgygzQVTm3btlXfC6lt27apFk5ERERERERZTZoKp4kTJ6r/PyAgwFi5EBERERERZUqKb4BbrFgxhIeHa7W/evUKxYoVM0hSREREREREmYniwun+/fuIj4/Xao+OjsajR48MkhQREREREVFmkuZZ9f7880/1/+/evRvOzs7qx/Hx8fj333/h6elp2OyIiIiIiIgygTQXTu3atQPw4T5OvXr10njO2toaHh4emDVrlkGTIyIiQBWobEIemShGyoSIiCj7SnPhlJCQAADw9PTEqVOn4OrqarSkiIiIiIgo4yn5sS67/VCn+Aa49+7dM0YeRERkBPwDSEREZBiKCycA+Pfff/Hzzz/j2rVrUKlUKFOmDIYOHYrGjRsbOj8iIiL6CAtiIqKMp7hwmjdvHvz9/dGhQwcMGTIEAHDixAm0aNECs2fPxqBBgwyeJBERZSxeV0VERKRJceE0ffp0/PzzzxoF0uDBg1G7dm1MnTqVhRMREREREWU5igunyMhINGvWTKvd19cXo0aNMkhSRERZTXY6gpOdxmpustNrk53GSkQZQ/ENcNu0aYNt27Zptf/xxx9o3bq1QZIiIiIiIiLKTBQfcSpbtiymTp2KAwcOoFatWgA+XON09OhRDB8+HP/73//Uyw4ePNhwmRIRUZbHowRERJRZKS6cli9fDhcXF1y9ehVXr15Vt+fKlQvLly9XP1apVCyciChL4U49ZWd8/xNRdsf7OBEREaUTi4m04XYioqwgXfdxIiIiymzM7d5GLCaIiMyL4sJJRPD7779j//79CAsLQ0JCgsbzW7duNVhyRERExsYCJnMzt4KYiLIuxYXTkCFDsGTJEjRo0ABubm5QqZT9wSEiIiIiIjI3igunX3/9FVu3bkWLFi2MkQ8REREREVGmo/g+Ts7OzihWrJgxciEiIiIiIsqUFBdOAQEBCAwMxLt374yRDxERERERUaaj+FS9jh07Yv369ciXLx88PDxgbW2t8fzZs2cNlhwREREREVFmoLhw6t27N86cOYMvvviCk0MQEREREVG2oLhw2rlzJ3bv3o1PP/3UGPkQERERERFlOoqvcXJ3d4eTk5MxciEiIiIiIsqUFB9xmjVrFkaOHIlFixbBw8PDCCkRERkXb3hKRERESikunL744gu8ffsWxYsXh4ODg9bkEC9evDBYckRERERERJmB4sJpzpw5RkiDiIiIiIgo81JcOPXq1csYeRAREREREWVaigun4ODgVJ8vUqRIupMhIiIiIiLKjBQXTh4eHqneuyk+Pl6vhIiIiIiIiDIbxYXTuXPnNB7Hxsbi3LlzmD17NqZOnWqwxIiIiIiIiDILxYVT5cqVtdqqVauGggUL4scff8Rnn31mkMSIiIiIiIgyC8U3wE1JqVKlcOrUKUOtjoiIiIiIKNNQfMQpMjJS47GIICQkBAEBAShZsqTBEiMiIiIiIsosFBdOuXLl0pocQkTg7u6ODRs2GCwxIiIiIiKizEJx4bR//36NxxYWFsibNy9KlCgBKyvFqyMiIiIiIsr0FFc69erVM0YeREREREREmZbiySFWr16NnTt3qh+PHDkSuXLlgo+PDx48eKA4gQULFsDT0xN2dnbw9vbG4cOH09Tv6NGjsLKyQpUqVRTHJCIiIiIiw1EFqhT9M0eKC6dp06bB3t4eAHD8+HHMmzcPM2fOhKurK/z9/RWta+PGjRg6dCjGjh2Lc+fOoU6dOmjevDmCg4NT7RcREYGePXuiUaNGStMnIiIiIiJSTPGpeg8fPkSJEiUAANu3b0eHDh3w9ddfo3bt2qhfv76idc2ePRt9+/ZFv379AABz5szB7t27sXDhQkyfPj3Ffv3790e3bt1gaWmJ7du3pxojOjoa0dHR6seJswLGxsYiNjZWUb5ElDXYW9grWj7xuyK9/cylr7nlq09fc8tXn77mlq8+fc0tX336mlu+SfuaW75K+5pbvvr0NVRMU1KSh0pERMnK8+XLh927d8PLywteXl7w9/dHz549cefOHVSuXBlv3rxJ03piYmLg4OCAzZs3o3379ur2IUOG4Pz58zh48GCy/VauXIkFCxbg+PHjmDJlCrZv347z58+nGCcgIACBgYFa7evWrYODg0OaciUiIiIioqzn7du36NatGyIiIuDk5JTqsoqPODVp0gT9+vWDl5cXbt68iZYtWwIArly5Ag8PjzSv5/nz54iPj4ebm5tGu5ubG0JDQ5Ptc+vWLYwePRqHDx9O8wx+33//PYYNG6Z+HBkZCXd3d/j6+urcOESUNTnPcFa0fMToCL36mUtfc8tXn77mlq8+fc0tX336mlu++vQ1t3yT9jW3fJX2Nbd89elrqJim9PE9alOjuHCaP38+xo0bh4cPH2LLli3IkycPAODMmTPo2rWr0tUle0+oj9sAID4+Ht26dUNgYCBKlSqV5vXb2trC1tZWq93a2hrW1taK8yUi8/cu4Z2i5RO/K9Lbz1z6mlu++vQ1t3z16Wtu+erT19zy1aevueWbtK+55au0r7nlq09fQ8U0JSV5pOsGuPPmzdNqT+50uNS4urrC0tJS6+hSWFiY1lEoAHj9+jVOnz6Nc+fOYdCgQQCAhIQEiAisrKywZ88eNGzYUFEOREREREREaZGuO9a+evUKJ0+eRFhYGBISEtTtKpUKPXr0SNM6bGxs4O3tjaCgII1rnIKCgtC2bVut5Z2cnHDp0iWNtgULFmDfvn34/fff4enpmZ6hEBERERER6aS4cPrrr7/QvXt3REVFIWfOnBqn1SkpnABg2LBh6NGjB6pVq4ZatWphyZIlCA4OxoABAwB8uD7p8ePHWLNmDSwsLFChQgWN/vny5YOdnZ1WOxERERERkSEpLpyGDx+OPn36YNq0aXrPSte5c2eEh4dj0qRJCAkJQYUKFbBr1y4ULVoUABASEqLznk5ERERERETGprhwevz4MQYPHmywqbz9/Pzg5+eX7HOrVq1KtW9AQAACAgIMkgcREREREVFKFBdOTZs2xenTp1GsWDFj5ENElCaqQO3ZN1MjExXdso6IiIhIg+LCqWXLlvjuu+9w9epVVKxYUWsKvzZt2hgsOSIiIiIiosxAceH01VdfAQAmTZqk9ZxKpUJ8fLz+WREREREREWUiigunpNOPExERERERZQcWpk6AiIiIiIgos0tX4XTw4EG0bt0aJUqUQMmSJdGmTRscPnzY0LkRERERERFlCooLp19//RWNGzeGg4MDBg8ejEGDBsHe3h6NGjXCunXrjJEjERERERGRSSm+xmnq1KmYOXMm/P391W1DhgzB7NmzMXnyZHTr1s2gCRIREREREZma4iNOd+/eRevWrbXa27Rpg3v37hkkKSIiIiIiosxEceHk7u6Of//9V6v933//hbu7u0GSIiIiIiIiykwUn6o3fPhwDB48GOfPn4ePjw9UKhWOHDmCVatWYe7cucbIkYiIiIiIyKQUF04DBw5E/vz5MWvWLGzatAkAULZsWWzcuBFt27Y1eIJERERERESmprhwAoD27dujffv2hs6FiIiIiIgoU0rzNU4vX77EL7/8gsjISK3nIiIiUnyOiIiIiIjI3KW5cJo3bx4OHToEJycnreecnZ1x+PBh/PLLLwZNjoiIiIiIKDNIc+G0ZcsWDBgwIMXn+/fvj99//90gSREREREREWUmaS6c7ty5g5IlS6b4fMmSJXHnzh2DJEVERERERJSZpHlyCEtLSzx58gRFihRJ9vknT57AwkLxbaEIgCpQleZlZaIYMROijKXkvQ/w/U9ERESmk+ZKx8vLC9u3b0/x+W3btsHLy8sQOREREREREWUqaT7iNGjQIHTp0gWFCxfGwIEDYWlpCQCIj4/HggUL8PPPP2PdunVGS5SIiIiIiMhU0lw4ff755xg5ciQGDx6MsWPHolixYlCpVLhz5w7evHmD7777Dh06dDBmrkRERERERCah6Aa4U6dORdu2bfHbb7/h9u3bEBHUrVsX3bp1Q40aNYyVIxERERERkUkpKpwAoEaNGiySiIiIiIgoW+E0eERERERERDqwcCIiIiIiItKBhRMREREREZEOLJyIiIiIiIh0UFw4BQQE4MGDB8bIhYiIiIiIKFNSXDj99ddfKF68OBo1aoR169bh/fv3xsiLiIiIiIgo01BcOJ05cwZnz55FpUqV4O/vjwIFCmDgwIE4deqUMfIjIiIiIiIyuXRd41SpUiX8/PPPePz4MVasWIHHjx+jdu3aqFixIubOnYuIiAhD50lERERERGQyek0OkZCQgJiYGERHR0NEkDt3bixcuBDu7u7YuHGjoXIkIiIiIiIyqXQVTmfOnMGgQYNQoEAB+Pv7w8vLC9euXcPBgwdx/fp1TJw4EYMHDzZ0rkRERERERCahuHCqVKkSPvnkE9y7dw/Lly/Hw4cPMWPGDJQoUUK9TM+ePfHs2TODJkpERERERGQqVko7dOzYEX369EGhQoVSXCZv3rxISEjQKzEiIiIiIqLMQnHhNH78eGPkQURERERElGkpLpyGDRuWbLtKpYKdnR1KlCiBtm3bInfu3HonR6lTBaoULS8TxUiZEBERERFlbYoLp3PnzuHs2bOIj49H6dKlISK4desWLC0tUaZMGSxYsADDhw/HkSNHUK5cOWPkTERERERElKEUTw7Rtm1bNG7cGE+ePFHfDPfx48do0qQJunbtisePH6Nu3brw9/c3Rr5EREREREQZTnHh9OOPP2Ly5MlwcnJStzk5OSEgIAAzZ86Eg4MDJkyYgDNnzhg0USIiIiIiIlNRXDhFREQgLCxMq/3Zs2eIjIwEAOTKlQsxMTH6Z0dERERERJQJpOtUvT59+mDbtm149OgRHj9+jG3btqFv375o164dAODkyZMoVapUmta3YMECeHp6ws7ODt7e3jh8+HCKyx45cgS1a9dGnjx5YG9vjzJlyuDnn39WOgQiIiIiIiJFFE8OsXjxYvj7+6NLly6Ii4v7sBIrK/Tq1UtdxJQpUwbLli3Tua6NGzdi6NChWLBgAWrXro3FixejefPmuHr1KooUKaK1vKOjIwYNGoRKlSrB0dERR44cQf/+/eHo6Iivv/5a6VCIiIiIiIjSRFHhFB8fjzNnzuCHH37Azz//jLt370JEULx4ceTIkUO9XJUqVdK0vtmzZ6Nv377o168fAGDOnDnYvXs3Fi5ciOnTp2st7+XlBS8vL/VjDw8PbN26FYcPH2bhRGRCSqbG57T4REREZI4UFU6WlpZo2rQprl27Bk9PT1SqVCndgWNiYnDmzBmMHj1ao93X1xfHjh1L0zrOnTuHY8eOYcqUKSkuEx0djejoaPXjxOuwYmNjERsbm47MDc/ewj7NyybNWUm/j/sSGZIp3sOm6Gtu+Srta2756tPX3PLVp6+55atPX3PLV5++5pZv0r7mlq/SvuaWrz59s8J+qZI8VCKi6Off6tWrY8aMGWjUqJHixJJ68uQJChUqhKNHj8LHx0fdPm3aNKxevRo3btxIsW/hwoXx7NkzxMXFISAgAOPHj09x2YCAAAQGBmq1r1u3Dg4ODnqNgYiIiIiIzNfbt2/RrVs3REREaMwanhzF1zhNnToVI0aMwOTJk+Ht7Q1HR0eN53UF/JhKpXmKj4hotX3s8OHDePPmDU6cOIHRo0ejRIkS6Nq1a7LLfv/99xg2bJj6cWRkJNzd3eHr66s4V2NxnuGc5mUjRkekq9/HfYkMyRTvYVP0Nbd8lfY1t3z16Wtu+erT19zy1aevueWrT19zyzdpX3PLV2lfc8tXn75ZYb808Wy0tFBcODVr1gwA0KZNG40CJ7HgiY+PT9N6XF1dYWlpidDQUI32sLAwuLm5pdrX09MTAFCxYkU8ffoUAQEBKRZOtra2sLW11Wq3traGtbV1mnI1tncJ79K8bNKclfT7uC+RIZniPWyKvuaWr9K+5pavPn3NLV99+ppbvvr0Nbd89elrbvkm7Wtu+Srta2756tM3K+yXKslDceG0f/9+pV2SZWNjA29vbwQFBaF9+/bq9qCgILRt2zbN6xERjWuYiIiIiIiIDE1x4VSvXj2DBR82bBh69OiBatWqoVatWliyZAmCg4MxYMAAAB9Os3v8+DHWrFkDAJg/fz6KFCmCMmXKAPhwX6effvoJ3377rcFyyi6UzIIGcCY0IiIiIsreFBdOwIdrjBYvXoy7d+9i8+bNKFSoENauXQtPT098+umnaV5P586dER4ejkmTJiEkJAQVKlTArl27ULRoUQBASEgIgoOD1csnJCTg+++/x71792BlZYXixYtjxowZ6N+/f3qGQURERERElCaKC6ctW7agR48e6N69O86ePas+Te7169eYNm0adu3apWh9fn5+8PPzS/a5VatWaTz+9ttveXSJiIiIiIgynIXSDlOmTMGiRYuwdOlSjYupfHx8cPbsWYMmR0RERERElBkoPuJ048YN1K1bV6vdyckJr169MkRORGQCvO6NiIiIKGWKjzgVKFAAt2/f1mo/cuQIihUrZpCkiIiIiIiIMhPFhVP//v0xZMgQ/Pfff1CpVHjy5Al+++03jBgxIsVrlYiIiIiIiMyZ4lP1Ro4ciYiICDRo0ADv379H3bp1YWtrixEjRmDQoEHGyJGIiIiIiMik0jUd+dSpUzF27FhcvXoVCQkJKFeuHHLkyGHo3CiT4rUwRERERJTdpKtwAgAHBwdUq1bNkLkQERERERFlSooLp6ioKMyYMQP//vsvwsLCkJCQoPH83bt3DZYcERERERFRZqC4cOrXrx8OHjyIHj16oECBAlCplJ22RUREREREZG4UF05///03du7cidq1axsjHyIiIiIiokxHceHk4uKC3LlzGyMXygaUTCzBSSWU48QdRERERMahuHCaPHkyJkyYgNWrV8PBwcEYORFlayx+iIiIiDIfxYXTrFmzcOfOHbi5ucHDwwPW1tYaz589e9ZgyREREREREWUGigundu3aGSENIiIiIiKizEtx4TRx4kRj5EGUKnM8fY3XcxERERFlHRZpXfDkyZOIj49XPxbR3NGLjo7Gpk2bDJcZERERERFRJpHmI061atVCSEgI8uXLBwBwdnbG+fPnUaxYMQDAq1ev0LVrV3Tq1Mk4mRKlkz5Hq8zxSBcRERERGV6ajzh9fITp48cptREREREREZm7NBdOaaFSKft1noiIiIiIyBwYtHAiIiIiIiLKihTNqnf16lWEhoYC+HBa3vXr1/HmzRsAwPPnzw2fHRERERERUSagqHBq1KiRxnVMrVq1AvDhFD0R4al6RERERESUJaW5cLp3754x8yAiIiIiIsq00lw4FS1a1Jh5EBERERERZVqcHIKIiIiIiEgHFk5EREREREQ6sHAiIiIiIiLSgYUTERERERGRDukqnOLi4rB3714sXrwYr1+/BgA8efJEfU8nIiIiIiKirETRfZwA4MGDB2jWrBmCg4MRHR2NJk2aIGfOnJg5cybev3+PRYsWGSNPIiIiIiIik1F8xGnIkCGoVq0aXr58CXt7e3V7+/bt8e+//xo0OSIiIiIiosxA8RGnI0eO4OjRo7CxsdFoL1q0KB4/fmywxIiIiIiIiDILxUecEhISEB8fr9X+6NEj5MyZ0yBJERERERERZSaKC6cmTZpgzpw56scqlQpv3rzBxIkT0aJFC0PmRkRERERElCkoPlXv559/RoMGDVCuXDm8f/8e3bp1w61bt+Dq6or169cbI0ciIiIiIiKTUlw4FSxYEOfPn8f69etx9uxZJCQkoG/fvujevbvGZBFERERERERZheLCCQDs7e3Rp08f9OnTx9D5EBERERERZTqKC6c///wz2XaVSgU7OzuUKFECnp6eeidGRERERESUWSgunNq1aweVSgUR0WhPbFOpVPj000+xfft2uLi4GCxRIiIiIiIiU1E8q15QUBCqV6+OoKAgREREICIiAkFBQahRowZ27NiBQ4cOITw8HCNGjDBGvkRERERERBlO8RGnIUOGYMmSJfDx8VG3NWrUCHZ2dvj6669x5coVzJkzh9c/ERERERFRlqH4iNOdO3fg5OSk1e7k5IS7d+8CAEqWLInnz5+naX0LFiyAp6cn7Ozs4O3tjcOHD6e47NatW9GkSRPkzZsXTk5OqFWrFnbv3q10CERERERERIooLpy8vb3x3Xff4dmzZ+q2Z8+eYeTIkahevToA4NatWyhcuLDOdW3cuBFDhw7F2LFjce7cOdSpUwfNmzdHcHBwsssfOnQITZo0wa5du3DmzBk0aNAArVu3xrlz55QOg4iIiIiIKM0Un6q3fPlytG3bFoULF4a7uztUKhWCg4NRrFgx/PHHHwCAN2/eYPz48TrXNXv2bPTt2xf9+vUDAMyZMwe7d+/GwoULMX36dK3l58yZo/F42rRp+OOPP/DXX3/By8tL6VCIiIiIiIjSRHHhVLp0aVy7dg27d+/GzZs3ISIoU6YMmjRpAguLDwew2rVrp3M9MTExOHPmDEaPHq3R7uvri2PHjqUpl4SEBLx+/Rq5c+dOcZno6GhER0erH0dGRgIAYmNjERsbm6Y4xmZvkfYbByfNWUk/c+xrbvnq09fc8tWnr7nlm7SvueWrtK+55atPX3PLV5++5pavPn3NLV99+ppbvkn7mlu+SvuaW7769DVUTFNSkodKPp5XPIM8efIEhQoVwtGjRzUmmpg2bRpWr16NGzdu6FzHjz/+iBkzZuDatWvIly9fsssEBAQgMDBQq33dunVwcHBI/wCIiIiIiMisvX37Ft26dUNERESy8zgkpfiIEwBERUXh4MGDCA4ORkxMjMZzgwcPVrQulUql8TjxXlC6rF+/HgEBAfjjjz9SLJoA4Pvvv8ewYcPUjyMjI+Hu7g5fX1+dGyejOM9wTvOyEaMj0tXPHPuaW7769DW3fPXpa275Ju1rbvkq7Wtu+erT19zy1aevueWrT19zy1efvuaWb9K+5pav0r7mlq8+fQ0V05QSz0ZLC8WF07lz59CiRQu8ffsWUVFRyJ07N54/fw4HBwfky5cvzYWTq6srLC0tERoaqtEeFhYGNze3VPtu3LgRffv2xebNm9G4ceNUl7W1tYWtra1Wu7W1NaytrdOUq7G9S3iX5mWT5qyknzn2Nbd89elrbvnq09fc8k3a19zyVdrX3PLVp6+55atPX3PLV5++5pavPn3NLd+kfc0tX6V9zS1fffoaKqYpKclD8ax6/v7+aN26NV68eAF7e3ucOHECDx48gLe3N3766ac0r8fGxgbe3t4ICgrSaA8KCtI4de9j69evR+/evbFu3Tq0bNlSafpERERERESKKS6czp8/j+HDh8PS0hKWlpaIjo6Gu7s7Zs6ciTFjxiha17Bhw7Bs2TKsWLEC165dg7+/P4KDgzFgwAAAH06z69mzp3r59evXo2fPnpg1axY++eQThIaGIjQ0FBERmeNQHxERERERZU2KCydra2v1NUhubm7qey45OzuneP+llHTu3Blz5szBpEmTUKVKFRw6dAi7du1C0aJFAQAhISEa61y8eDHi4uLwzTffoECBAup/Q4YMUToMIiIiIiKiNFN8jZOXlxdOnz6NUqVKoUGDBpgwYQKeP3+OtWvXomLFiooT8PPzg5+fX7LPrVq1SuPxgQMHFK+fiIiIiIhIX4qPOE2bNg0FChQAAEyePBl58uTBwIEDERYWhiVLlhg8QSIiIiIiIlNTdMRJRJA3b16UL18eAJA3b17s2rXLKIkRERERERFlFoqOOIkISpYsiUePHhkrHyIiIiIiokxHUeFkYWGBkiVLIjw83Fj5EBERERERZTqKr3GaOXMmvvvuO1y+fNkY+RAREREREWU6imfV++KLL/D27VtUrlwZNjY2sLe313j+xYsXBkuOiIiIiIgoM1BcOM2ZM8cIaRAREREREWVeigunXr16GSMPIiIiIiKiTEvxNU4AcOfOHYwbNw5du3ZFWFgYAOCff/7BlStXDJocERERERFRZqC4cDp48CAqVqyI//77D1u3bsWbN28AABcvXsTEiRMNniAREREREZGpKS6cRo8ejSlTpiAoKAg2Njbq9gYNGuD48eMGTY6IiIiIiCgzUFw4Xbp0Ce3bt9dqz5s3L+/vREREREREWZLiwilXrlwICQnRaj937hwKFSpkkKSIiIiIiIgyE8WFU7du3TBq1CiEhoZCpVIhISEBR48exYgRI9CzZ09j5EhERERERGRSigunqVOnokiRIihUqBDevHmDcuXKoW7duvDx8cG4ceOMkSMREREREZFJKb6Pk7W1NX777TdMmjQJ586dQ0JCAry8vFCyZElj5EdERERERGRyigungwcPol69eihevDiKFy9ujJyIiIiIiIgyFcWn6jVp0gRFihTB6NGjcfnyZWPkRERERERElKkoLpyePHmCkSNH4vDhw6hUqRIqVaqEmTNn4tGjR8bIj4iIiIiIyOQUF06urq4YNGgQjh49ijt37qBz585Ys2YNPDw80LBhQ2PkSEREREREZFKKC6ekPD09MXr0aMyYMQMVK1bEwYMHDZUXERERERFRppHuwuno0aPw8/NDgQIF0K1bN5QvXx47duwwZG5ERERERESZguJZ9caMGYP169fjyZMnaNy4MebMmYN27drBwcHBGPkRERERERGZnOLC6cCBAxgxYgQ6d+4MV1dXjefOnz+PKlWqGCo3IiIiIiKiTEFx4XTs2DGNxxEREfjtt9+wbNkyXLhwAfHx8QZLjoiIiIiIKDNI9zVO+/btwxdffIECBQrgl19+QYsWLXD69GlD5kZERERERJQpKDri9OjRI6xatQorVqxAVFQUOnXqhNjYWGzZsgXlypUzVo5EREREREQmleYjTi1atEC5cuVw9epV/PLLL3jy5Al++eUXY+ZGRERERESUKaT5iNOePXswePBgDBw4ECVLljRmTkRERERERJlKmo84HT58GK9fv0a1atVQs2ZNzJs3D8+ePTNmbkRERERERJlCmgunWrVqYenSpQgJCUH//v2xYcMGFCpUCAkJCQgKCsLr16+NmScREREREZHJKJ5Vz8HBAX369MGRI0dw6dIlDB8+HDNmzEC+fPnQpk0bY+RIRERERERkUumejhwASpcujZkzZ+LRo0dYv369oXIiIiIiIiLKVPQqnBJZWlqiXbt2+PPPPw2xOiIiIiIiokzFIIUTERERERFRVsbCiYiIiIiISAcWTkRERERERDqwcCIiIiIiItKBhRMREREREZEOLJyIiIiIiIh0YOFERERERESkAwsnIiIiIiIiHUxeOC1YsACenp6ws7ODt7c3Dh8+nOKyISEh6NatG0qXLg0LCwsMHTo04xIlIiIiIqJsy6SF08aNGzF06FCMHTsW586dQ506ddC8eXMEBwcnu3x0dDTy5s2LsWPHonLlyhmcLRERERERZVdWpgw+e/Zs9O3bF/369QMAzJkzB7t378bChQsxffp0reU9PDwwd+5cAMCKFSvSFCM6OhrR0dHqx5GRkQCA2NhYxMbG6jsEg7C3sE/zsklzVtLPHPuaW7769DW3fPXpa275Ju1rbvkq7Wtu+erT19zy1aevueWrT19zy1efvuaWb9K+5pav0r7mlq8+fQ0V05SU5KESETFiLimKiYmBg4MDNm/ejPbt26vbhwwZgvPnz+PgwYOp9q9fvz6qVKmCOXPmpLpcQEAAAgMDtdrXrVsHBweHdOVORERERETm7+3bt+jWrRsiIiLg5OSU6rImO+L0/PlzxMfHw83NTaPdzc0NoaGhBovz/fffY9iwYerHkZGRcHd3h6+vr86Nk1GcZzinedmI0RHp6meOfc0tX336mlu++vQ1t3yT9jW3fJX2Nbd89elrbvnq09fc8tWnr7nlq09fc8s3aV9zy1dpX3PLV5++hoppSolno6WFSU/VAwCVSqXxWES02vRha2sLW1tbrXZra2tYW1sbLI4+3iW8S/OySXNW0s8c+5pbvvr0Nbd89elrbvkm7Wtu+Srta2756tPX3PLVp6+55atPX3PLV5++5pZv0r7mlq/SvuaWrz59DRXTlJTkYbLJIVxdXWFpaal1dCksLEzrKBQREREREZEpmaxwsrGxgbe3N4KCgjTag4KC4OPjY6KsiIiIiIiItJn0VL1hw4ahR48eqFatGmrVqoUlS5YgODgYAwYMAPDh+qTHjx9jzZo16j7nz58HALx58wbPnj3D+fPnYWNjg3LlypliCERERERElA2YtHDq3LkzwsPDMWnSJISEhKBChQrYtWsXihYtCuDDDW8/vqeTl5eX+v/PnDmDdevWoWjRorh//35Gpk5ERERERNmIySeH8PPzg5+fX7LPrVq1SqvNRLOnExERERFRNmaya5yIiIiIiIjMBQsnIiIiIiIiHVg4ERERERER6cDCiYiIiIiISAcWTkRERERERDqwcCIiIiIiItKBhRMREREREZEOLJyIiIiIiIh0YOFERERERESkAwsnIiIiIiIiHVg4ERERERER6cDCiYiIiIiISAcWTkRERERERDqwcCIiIiIiItKBhRMREREREZEOLJyIiIiIiIh0YOFERERERESkAwsnIiIiIiIiHVg4ERERERER6cDCiYiIiIiISAcWTkRERERERDqwcCIiIiIiItKBhRMREREREZEOLJyIiIiIiIh0YOFERERERESkAwsnIiIiIiIiHVg4ERERERER6cDCiYiIiIiISAcWTkRERERERDqwcCIiIiIiItKBhRMREREREZEOLJyIiIiIiIh0YOFERERERESkAwsnIiIiIiIiHVg4ERERERER6cDCiYiIiIiISAcWTkRERERERDqwcCIiIiIiItKBhRMREREREZEOLJyIiIiIiIh0YOFERERERESkg8kLpwULFsDT0xN2dnbw9vbG4cOHU13+4MGD8Pb2hp2dHYoVK4ZFixZlUKZERERERJRdmbRw2rhxI4YOHYqxY8fi3LlzqFOnDpo3b47g4OBkl7937x5atGiBOnXq4Ny5cxgzZgwGDx6MLVu2ZHDmRERERESUnViZMvjs2bPRt29f9OvXDwAwZ84c7N69GwsXLsT06dO1ll+0aBGKFCmCOXPmAADKli2L06dP46effsLnn3+ebIzo6GhER0erH0dERAAAXrx4gdjYWAOPKH3sYuzSvGx4eHi6+pljX3PLV5++5pavPn3NLd+kfc0tX6V9zS1fffqaW7769DW3fPXpa2756tPX3PJN2tfc8lXa19zy1aevoWKa0uvXrwEAIqJ7YTGR6OhosbS0lK1bt2q0Dx48WOrWrZtsnzp16sjgwYM12rZu3SpWVlYSExOTbJ+JEycKAP7jP/7jP/7jP/7jP/7jP/7jv2T/PXz4UGf9YrIjTs+fP0d8fDzc3Nw02t3c3BAaGppsn9DQ0GSXj4uLw/Pnz1GgQAGtPt9//z2GDRumfpyQkIAXL14gT548UKlUBhiJcURGRsLd3R0PHz6Ek5MTY2aBuBxr1ozLsWbNuBxr1ozLsWbNuBxr1o2bEUQEr1+/RsGCBXUua9JT9QBoFS8ikmpBk9zyybUnsrW1ha2trUZbrly50pGpaTg5OWX4GzS7xDRVXI41a8blWLNmXI41a8blWLNmXI4168Y1Nmdn5zQtZ7LJIVxdXWFpaal1dCksLEzrqFKi/PnzJ7u8lZUV8uTJY7RciYiIiIgoezNZ4WRjYwNvb28EBQVptAcFBcHHxyfZPrVq1dJafs+ePahWrRqsra2NlisREREREWVvJp2OfNiwYVi2bBlWrFiBa9euwd/fH8HBwRgwYACAD9cn9ezZU738gAED8ODBAwwbNgzXrl3DihUrsHz5cowYMcJUQzAaW1tbTJw4Ues0Q8Y037gca9aMy7Fmzbgca9aMy7Fmzbgca9aNm9moRNIy957xLFiwADNnzkRISAgqVKiAn3/+GXXr1gUA9O7dG/fv38eBAwfUyx88eBD+/v64cuUKChYsiFGjRqkLLSIiIiIiImMweeFERERERESU2Zn0VD0iIiIiIiJzwMKJiIiIiIhIBxZOREREREREOrBwIiIiIiIi0oGFExERERERkQ4snMxMdpoEkWPNmjjWrBeTiIgoO7AydQKUNm/fvgUAxMfHI2fOnCbJQUSgUqmMHodjzVgcq+GZYqyZYfsmJyEhARYWGfsbXXaJaaq4HGvWjJuVx/rs2TM8ePAADg4OcHV1Rb58+Yz+98AUMU0V11RjNQXex8kMXL58GSNGjMCjR4/g5uaGBg0aYNy4cUaPGxYWhrt378LOzg6FCxeGq6ur0T8IHCvHakjZZaym2r4fu3fvHnbv3o2IiAhUqFABLVq0gEqlMuo2zy4xTRWXY+VYzT3uxYsX8dlnn8HKygovXryAh4cHxo0bhzZt2hglnqlimiquqcZqMkKZ2u3btyVPnjwydOhQmTt3rowbN06cnJykbdu28vjxY6PFvXDhghQvXlyKFy8uhQoVkhIlSsjff/9ttHgiHCvHaljZZaym2r4fu3TpkuTOnVuaNGki7u7uUr58efHx8ZGXL1+KiEhCQgJjmllcjpVjNfe4ISEhUqRIERk+fLjcu3dPduzYIf379xeVSiULFiwweDxTxTRVXFON1ZRYOGVyv/zyi3z66acSHR2tbjt9+rQUKFBAmjVrJq9evRIRw37hhIaGioeHh4wcOVLu3bsnBw4ckP79+4ulpaXMnz9fYmNjDRYrKY6VYzWU7DRWU8T82Nu3b6Vu3brSv39/ERGJjIyUvXv3StWqVaVkyZLy8OFDERGJj49nTDOJy7FyrIZkqrhnzpyRSpUqyYMHD9RtkZGRMnXqVLGwsJDVq1eLiGG/H00R01RxTTVWU2LhlMmNHDlSKleurH4cFxcnIiJXrlyRvHnzSu/evQ0e88KFC1K+fHm5deuWRvvUqVPF0tJSVq1aJSKG/4LjWDlWQ8lOYzVFzI+9evVKKlWqJJs2bVK3JSQkyN27d+WTTz6RsmXLqotVQ/0BzS4xTRWXY+VYzX2sIiKHDx8WlUol586d02h///69jBs3ThwdHeXYsWMGi2eqmKaKa6qxmhILp0wq8Ytj//794uTkJNu2bVM/l7hjtGPHDsmbN68EBQUZNPahQ4dEpVLJzZs3NeKJiIwbN07s7Ozk4sWLBovHsYpGbI5Vf9lhrKbcvsnl4uXlJV9//bXWc1euXJHy5cvLV199xZhmFJdjNW5MU8XNLmNN/H588eKFNGzYUL766isJCQnRWCY4OFh8fX1l8uTJGn3MKaap4ppqrJkBpyPPZOT/z9WReJFksWLF0LRpUyxfvhxHjhwBAFhaWgIAvLy84ODggIcPHxokdkJCAgCgVq1aqFu3LsaMGYPnz5/D0tISCQkJEBGMHDkSdevWxfLly9Vt6cWxcqwcq3nETEtOn3/+OS5cuIA///xT47ly5cqha9euuHz5Mt68ecOYZhKXYzVuTFPFzepjffv2Ld6+fatej4uLC5o3b44jR45g3bp1ePHihXpZd3d35MiRA6dOnQKAdE9OYYqYpoprqrFmJiycMpGbN29i+PDh8PPzw7hx4xAVFYUiRYpg4MCBeP78OX7++Wf8+++/6uULFiyIQoUKqXcW07sD+ObNG8TExODVq1cAACsrK3Tt2hUPHjzA3Llz8erVK/VUoTlz5oSLiwuCg4NhYWGR7g8Cx8qxcqzKx2qq7fuxsLAwnDhxAufPn8ezZ89gYWGBL774AtbW1vjll1+wd+9ejeXLly+Pp0+f6rVTlF1imioux8qxGiqmqeJevnwZn332GWrUqIF27dohMDAQADBixAj4+vpi/vz5WLRoEUJCQtR9cubMiaJFiyI+Pt5sYma3sWY6GXFYi3S7cuWKODk5yeeffy7NmzeXMmXKSMGCBeXPP/8UEZE9e/ZIw4YNpWrVqjJz5kzZvXu3DB06VFxcXOTu3bvpjnvx4kVp2LChVKxYUapVqybTpk1TH04dPXq0eHl5yfDhw+XFixfqPl9++aX069dPYmNj03XolWPlWDlW5Uy1fT/28WyFxYsXlx07doiIyM2bN6VKlSrSpEkTWbx4sYiIvHv3ToYPHy4+Pj4SGRnJmJkwLsfKsZr7WFOaXbRly5bqWftGjRolXl5e4uXlJd9++6107dpVcubMKZcvXzabmNltrJkRC6dMIDY2Vjp16iRffPGFiHy4PuH9+/fSoUMHcXFxkY0bN4qIyKlTp2Ts2LGSO3duqVixonh5eWldkKfEnTt3JHfu3OLv7y/z5s2TKVOmiI2NjbRu3Vru378vIiKBgYFSo0YNKV68uAwePFg6dOggOXLkkEuXLnGsHCvHmkFjNdX2/VhqsxXOnTtXRD78ge3YsaOULFlS3NzcpE6dOuLi4iJnz55lzEwYl2PlWM19rCKpzy7aqFEjiYmJERGR7du3y8iRI6VRo0bSp0+fdP8dMFVMU8U11VgzIxZOmUTTpk1l3LhxIiIaUyV369ZN8uTJo/GF8urVK3nx4oVEREToFXPevHnyySefaFw4f+HCBcmbN6/4+vrK06dPRUTkwIED4u/vLy1atDDIB4Fj5Vg5VvOI+bHUZiu0sLCQ5cuXi4hIWFiYnDt3TqZPny5r1qzRWp4xM09cjpVjNfexiuieXbRHjx4ay8fGxuo9q6opYpoqrqnGmhmxcMok2rRpI59++qn68fv379X/37BhQ6levbrBY06YMEG8vLzUjxN/Sbh27Zq4uLgkO42xIWZF4Vg/4Fj1l53GaoqYH9M1W6Gtra2cP3+eMc0oLsdq3JimipudxiqSttlF9+zZY/YxTRXXVGPNjFg4mVjiTtzBgwelRIkSMnbsWPVz7969Uz9XpEgRuXDhgkFj7927VywtLeWvv/5StyUebg0KChIbGxv5559/DBaPY/2AYzWc7DBWU27fj8XHx0v9+vWlQ4cO8uzZM3VbQkKCREZGiq+vrwwZMkTi4uIM9mtjdolpqrgcK8dq7mMVEXn06JF06tRJWrVqJYcPH9Z47vHjx1K0aFFZsWKFweKZKqap4ppqrJkRZ9Uzofj4ePWMXhUrVkTHjh3xzz//YNq0aQAAOzs7AECOHDlgYWGhnmLYEEQE1apVQ48ePTB16lT1NMZWVlYQEXh7e6N48eK4f/++QeJxrBwrx2oeMVNjYWGBTp06ITg4ONXZCi0tLdXtjJm543KsHKu5jjXpTG2FChVCr169EB4ejjlz5hhtdlFTxDRVXFONNbNj4WQiCQkJ6nvLPHr0CC4uLhg4cCBq166NzZs346uvvsLbt2/x6NEjbNu2Dba2tsibN6/ecRM/CCqVCs7OzujatSty5cqFiRMnYv/+/VCpVFCpVHBxcYGzs7PBPgCmGGtcXByAjB1rdnpdRcQkY42NjQWQ9cdqqvdScnkk/e/AgQNRp04d/P3335gyZQpevnypLu4cHByQJ08exMXF6bXds0tMU8XlWI0bU0SyzVhNGdfS0hIigvnz5wMAWrRogTFjxuDVq1cYOXIkfvzxR+zZswf+/v64du0aGjZsCEC/+wmZImZ2G2umZ/RjWqQWGxurPo0oUf/+/SVfvnzy6NEjERF5+vSpzJs3Tzw9PcXJyUnKlSsnBQsWlDNnzhgsj4SEBJkzZ4768c6dO6Vdu3ZSpEgRmTNnjmzbtk2GDRsmLi4ucufOHb1jJfrmm28yfKxxcXFGH+uTJ0/kypUr6scJCQkZMtbE84sTt3FGvK5RUVFa7+GMGOvDhw81Jj/IyPdwovj4eKOPNT4+Xuv0FVN8R3wsISFBvv76a/XsfSIiEydOlJo1axpstsLsGtNUcTlWw8d88+aNSeKaOqYp486bN0/y5MkjixYtUredPHnSqLOLzp8/P0NjJv6tN8VYTREzs2PhlEGuXLkinTp1kk8//VR69+4t69atExGRf/75RwYNGiTh4eHqZePj4yUqKkp+//13OXDggAQHB6c7btILMxN3sFevXi0WFhYyb9489XMXLlyQwMBAyZMnj1SqVEmqVauW7g/CmzdvJDIyUmtGr40bN4q/v7/RxhoeHi7Xrl2TmzdvqicJWL16tahUKqON9dGjR5InTx5p3769nDp1St2+YcMGo471zJkzUqdOHY0/1MZ+XS9duiRt2rSRQ4cOaUxMYOzX9fLly+Lu7i7Dhg0TkQ/vaWOP9eHDh7Jhwwb5/fff5eLFi+p2Y471ypUr0qNHD2nYsKF89dVXsmHDBhER2bVrl1G/I9Li5MmT4u3tLYMGDZK3b9+q2w8dOmTQ2QqTOnPmTIbHPH36tNFjJiQkaHwvZ0Tc8PBwCQsL02jLiO17/fp1OXLkSIbGvXXrlmzbtk1j2uSMGusXX3whDx8+zLC4UVFR8uLFC/W1jiIfbkmQ0Z8bEeN/Rzx48EB27NghS5culSdPnqj/9oWEhMiiRYs0vh8T6Tu7aNLtmtTTp09l8eLFRokp8mFK95MnT6rv0ZcRcR88eCCbN2+WWbNmabyHjT1Wc8TCKQPcuHFDnJ2d5YsvvpDAwECpW7euVK5cWfr372/0uD/99JM8efJEo/3Jkyfy22+/yfPnz7X6hIeHJ1v0pNWVK1fE19dXvLy8pGDBgvLrr79qHHVK+sfMkC5duiReXl5SsWJFsbW1lcmTJ4vIh4sWjTVWEZF9+/aJlZWVNGzYUHr27KlRPCX942FI58+fF0dHR3UhkejRo0fy22+/qS/ITUrfsV6+fFlcXFzEz89PfeQjqZT+wOjr/Pnz4uDgIJ6enpI/f3719OLGfF0vXrwoRYsWlWrVqombm5u0adNGY6rcpEWjoSTOAti3b1+ZNWuWNG7cWDw9PWX48OEGj5Wa69evi7+/v3Tu3FmmT5+ucRTr1KlT8urVqxT7pne2wqdPn6pvoPixc+fOGSXm3bt3Zfbs2TJs2DB1gWrsmCIfvpOHDBkiLVu2lMDAQI33r7Hi3rlzR4oVKybjx4+Xx48fazxnzLGeO3dOHBwc5H//+1+yzxkj7sWLF8XNzU0GDhyo9XfvzJkzRhvr+fPnxd7eXlQqlaxcuVLjOWON9fLly9KiRQspW7astG3bVnbu3Gn0mCIfviNGjx4tPXr0kB9//FHjxyljfUdcuHBB3NzcxMvLS3LlyiXu7u4yfPhw9ffyxz9EJNJn8onLly/LJ598IgcOHFC0Tn0nvLh48aJUqlRJypcvLzly5JBq1apJVFSUzn76xL148aJ4eHiIj4+P5M6dWzw9PTU+Pym9bll1unFdWDgZWUJCgowdO1Y6dOigbouKipJ58+ZJ+fLlpXPnzhrL//TTT+ppPPVx69YtyZ07t6hUKvn++++1dqZT+iAkvT+MUleuXJE8efKIv7+/rFu3ToYNGybW1tYp/upvqLEmxh0xYoRcuXJFfvrpJ1GpVOoboBpjrInCw8OlTZs2snjxYqlatap0795dfYQi6ZeKocZ64cIFcXR0lO+++06jXdcXqz5jffPmjfj6+srAgQPVbdeuXZPz58+rt3FShhpr4s7ImDFj5NmzZ1K+fHmZPHmy1imKH9NnrPfv35dChQrJ6NGj5c2bN7Jr1y7Jnz+/nDx5MtnlDTHW9+/fS/fu3WXw4MHqtnfv3knlypVFpVJJt27dDB4zOVeuXBFnZ2dp1aqVfPHFF5I/f36pU6eOzJw5M9nlr169qnfMq1evio2NjXTo0EGj0E3pD7IhYl68eFEKFy4sjRs3Fh8fH7GwsEhxjIaKmRg3X7580qFDB+nfv7/Y2NhIQECA0eMuXLhQVCqVeHl5ydSpUyUkJMToMRN/8Ehr4W+IuA8ePJAiRYrIyJEjMyymyP99T40cOVJGjBghderUkZCQkBS/nwwR98qVK+Li4iLffPONLFq0SGrXrq2+ObaxYibGzZUrl3Ts2FEGDBggBQsWlCpVqmgc+Td03JcvX4q3t7d899138uLFCxH5cFPzOnXqaP2oJSIyc+ZM+eabb/SKef/+fSlTpozY2NhIoUKFtGaR+5ghYoqI3Lx5U9zc3GTMmDFy7do1uX79upQpU0brHkmGjHv9+nXJly+fjB8/Xl68eCEJCQlSsGBBWbNmjdFimjsWThmgd+/eGvdfEflwNGLZsmXi5eUlo0ePFpEPXzIVK1aUOnXqSExMTLp/nXnz5o306dNHevfuLfPmzROVSiXfffedRvGUdN2BgYHy1VdfpStWovDwcPH19dXY+RMRadCggbotacwrV64YZKzPnj2TunXrypAhQ9RtCQkJ0qxZMzl69KicPXtW4zSmgIAAvceaKC4uTsLCwqRUqVLy6NEj2bp1q1SvXl2++uor8fHxkc8//1xEPhwNM8RYQ0JCJH/+/NK0aVN1/G+//VaaNm0qHh4eMmnSJI0jBIYa6/v37+XTTz+Vs2fPSlxcnDRt2lSqV68uOXPmlE8++USWLVumXtZQY71w4YLY2trKmDFjROTDjnSHDh007lVk6PewiMiiRYukfv36Gutu0aKFLF68WFavXi379u1TtxtqrCIijRo1Uu9EJx69GzlypHz22Wfi7e0tP/74o4h8+BXUUDGTiomJkZ49e0rfvn3VbQ8ePJABAwZI1apV1UdwEy1ZskRKlSolQUFB6Y4ZGhoqtWvXlkaNGomrq6t07Ngx1aOEhoh5//59KVGihIwcOVJdgC9fvlzy58+f7A04DRFT5MMRLg8PD/n+++/VbQEBAeLn56d1zaAh44p8+Cz16tVLpkyZIgULFpTJkycne4TPUDFv3rwptra26mnzY2JiZOvWrfLLL7/Ihg0b1EeNDR33r7/+khYtWqhjjh07Vtq3by/9+vWT1atXq5dLSEgwWMzTp0+Lk5OT+ntq/fr14uzsrD498eMfAAwR9+3bt9KuXTuNv3l//PGHfPbZZ/L06VN5/fq1ut2QY339+rU0bdpUozANDg6WXLlySf78+WXq1Kkayxsq7oMHD6Ro0aKye/dujfbVq1dL3bp1pVu3buofA968eSMDBgyQWrVqab3P0iomJkZmzZol7dq1k4sXL0qHDh3E1dU1xeIpKipK75iJ6+nVq5f0799f4wjarFmztPYfDRX39evX0qNHD/n2228lLi5O/fekTZs2Mn36dBk1apTs3btXve9oiO2bFViZenKKrExEoFKpULVqVdy4cQPXr19HmTJlAAD29vbo2LEjbt68if379+PFixcoW7YsZs6ciTJlysDa2jrdcS0sLODt7Y08efKgc+fOyJs3L7p06QIAGDlyJFxdXdWznkRFRSE6OhpHjhzB06dP4ebmlq6YsbGxePXqFTp06ADgw+w6FhYWKFasGMLDwwFozrRSrlw5/PDDDyhbtqxeY1WpVGjWrJk6LgBMmTIFu3fvRmhoKJ4/f47y5ctj3Lhx+OSTTxATE4Njx47pNdZEFhYWyJs3L6pXr47Lly+jffv2sLW1Ra9evRAdHY2vvvoKAFChQgWDjBUAatWqhYcPH+KPP/7AokWLEBcXhxo1aqBixYrYtGkTLl++jEmTJsHd3d1gY3316hVu3LiB58+f47vvvgMALF26FCEhIdi3bx/GjRsHZ2dndOjQARUqVDDIezg6OhojR47EpEmT1O+lKVOmoGbNmli4cCEGDhyofj+9efMG0dHRBhmriCA4OBjnz5+Hl5cXpk6dir///hsxMTGIiIjAgwcP8MMPP6B3794GGauI4N27d4iJicGdO3cQFxcHOzs7PH78GBs3bsTEiROxb98+7Nq1CyNGjED58uUNsn0/Zm1tjZCQELi7u6vzKlKkCCZMmICZM2di586d8PT0RPfu3QEARYoUQaVKlVC8ePF0xzx37hw8PDwwZMgQiAiaN2+Ofv36YdmyZXByctJavnDhwnrFTEhIwIYNG1CiRAmMGTNGPXV7jRo1YG1trTH1biJ3d3e9xxkfH48tW7agefPmGD16tLr90aNHuHLlCmrXrg1vb2+0aNECrVu3NljcRCKCY8eOYeXKlYiPj8eSJUuQM2dOHDx4EGXLlsXUqVMBGOY1jYuLw7x585AjRw5UqVIFANC2bVs8efIEUVFRePDgAZo1a4Zhw4ahfv36BosLAGfPnsWLFy8AfJj9Ky4uDpUrV8bVq1dx+vRpXL9+HdOmTYNKpTLI9o2KikK9evXw9ddfq7dhly5dsGzZMkyYMAG7d++GlZXmLpYhxmpra4vw8HB4eXmp2w4fPoyzZ8/C29sbpUqVQo0aNTB9+nSoVCq9PzeJLCws8OLFC/Xr+vbtW7i7u6Nx48Z4+fIldu7cCS8vLzRv3txgYwU+zOxmb2+PJ0+eAPjwHrOyskLPnj3x/v17zJ8/H3v27EHPnj3h6OiIqVOnIi4uDvny5UtXPGtra1SqVAkeHh7qv6sdO3ZE+/btsW3bNnz66afqZUUEDg4OmDx5MhISEtIdE/hwawl7e3sUL15c47YSVapUwc8//4xXr17B0dFR/b3v4OCAKVOmID4+Pt1xc+TIgVatWsHDw0Mdc/Lkydi1axesra3x6NEjbN68GV9//TWGDx9ukO2bJZiwaMs2bt++La6urvLll19KZGSkxnNPnjwRCwsL2bJli0Fjfjy7z4YNG0SlUsmIESPU59UnHjF5//59stfFKJX09KHEX1EnTJigdZjZ0BcSJt2m69evF5VKJRs2bJDw8HA5ePCg1KhRQ/1r/tOnTw0y1qR69uypPmrYt29fcXFxkXLlykmfPn3k2LFjBo315MkT6dmzp9jZ2UmTJk00Ltjctm2buLm5qa/ZCAsLM8hYExISpEuXLjJo0CBp1aqVxg1lHz58KF988YUMGDDAaNevJebw6tUradeunXTq1Enj1zGRD0ceDTHWu3fvio+Pj5QoUUI+//xzUalUsn37dklISJCnT5/K4MGDpX79+gb/te3IkSNiYWEhdevWlR49eoijo6P069dPRD4c2cqRI4fGzI2GFBcXJzExMfLll19K+/bt5d27d5KQkKD+tfzBgwfSvHlzadOmjUa/tJx3n5qwsDDZv3+/+vHx48cld+7c0rFjR43rJJL++qpvzIMHD6o/q4ni4+PF09NTI5ek9I0p8uFzcvz4cfXjyZMni6WlpYwdO1b+97//SfXq1aVRo0Ya1xUYIm4iX19fuXfvnoh8ONXG0dFRnJ2dtX7FN0TMmzdvytdffy2ffPKJuLu7S8uWLeXGjRsSFxcnly5dkvLly6uPxhsyblBQkDRs2FCWLVsmTZo0UV+L+erVKwkMDJRPPvlEY2ICQ8RM3KYi//c+Xbp0qZQqVUp99P/jo076xI2Pj5eIiAhp2rSptG/fXubNmyfff/+92Nvby8qVK+Xvv/+WwMBAqVq1qmzfvt0gMUVE/f1XsGBB9dFvkQ/v63Llysnq1aulUqVK6u8sQ8VN1Lp1a6lSpYr6SGnS07E7dOggtWrVUudpDLGxseojT4lHE2NjYyUoKMhgf2NFNK+LTmw7cOCAlCxZUuN78NGjR8keqU5PzKQuXLggZcuWlb/++ku9jQcOHCilS5fW2qfMzlg4ZZB9+/aJra2tfPPNNxoftOfPn4u3t3eKf7T1lXQHM7Go+O677+Tx48fi7+8v7dq1M/iF/Un/UIwdO1Z8fX3Vj6dNmyazZs0yyPVFybl//77WtMytW7eWVq1aGTxW4nZdtWqVTJgwQQYOHCgFChSQu3fvytatW6V48eIyYMAAg2/fx48fy5gxY9TvmaTbu1y5cuLn52fQeCIfLvp1dHQUlUqlNdPP8OHDpW7dukb7o5XUli1bRKVSqf94GSPmvXv3ZPPmzRIQEKBxbaKIyIwZM6Ry5cpGmQzj5MmT8sUXX0i/fv1k/vz56vY//vhDypYtm+pF1+nx8QXVBw4cEEtLS5k7d666LfG9dfLkSVGpVHLu3Dm9trmui7hPnDihLp4iIiIkJiZGFixYoC7W0xM7pZhJp/EvVqyY7NmzR/3c3r17JTQ0VHGstMR9/vy5DB06VP7++29129WrV0WlUmm0GTJm/fr11aer9e3bV5ycnCR//vwyc+ZMrQkjDBH39u3b0qNHD2nVqpXW9Xh79+4VlUolFy9e1Ovi8o9jXrt2TQoWLCjlypWTxo0bazwXHBwsDg4O6tls9ZHcTLVJvX79Wtzd3bWuAzHk5+bEiRPSvHlz6datm5QuXVqWL1+ufi40NFSKFCki06dPT3e8lOImnvrfp08fGTdunOTMmVN9ivTmzZvFw8NDnj9/rvc1tR9P7vPs2TPx9PSUJk2aaP1At3TpUvnkk0/0+uEupZmAk75mMTEx6uJp//790r9/fylTpoxeP6KlFDfp5+LAgQNSvHhxdS4jRoyQ+vXrp7soTSmmyIcftRO/DxILs9WrV0vlypW1fvTPzlg4ZaA///xTbG1tpX379rJu3Tq5fPmyjBo1Stzc3Iw6nXDSX483bNgg1tbWUrp0abGystK4N46hY4qIjBs3Tpo3by4iIuPHjxeVSiXnz583Sszkcnj//r107dpV6/xrQzp48KCoVCrJnz+/nD59Wt2+bds2uXv3rlFivnr1SuMPRUJCgrx48ULq1KkjK1asMErMQ4cOiUqlklatWsnly5fV7YMHD5Z+/frp/QtYWkRHR4uvr690797daLMWJlq6dKm0bNlSYzv7+/tL27ZtjfbrW3I7V4l/KA15pDalGTd/+uknsbCwkKVLl2q0X716VcqXLy83btwweMyP/ffff5I7d27p1KmTfPnll2JtbS23b982WMyk2zg2NlbevHkjJUqUkBMnToiIyPfffy8qlUqvgkLXWBN3ehK/my9fvize3t4aU98bImbiZ3LUqFGydu1a+fbbb6VgwYJy9+5dmTZtmjg4OMisWbNSLLjSG1fkw5HKf/75R51DQkKCJCQkyI4dO6R06dJ67XCmFHPHjh1iZWUl+fLl0zjaHx0dLQ0bNtQ4Wm7IuIkSt+P8+fOlePHiGn8PDB0zKipK4uLipFatWhr3ToqJiZEmTZpo/PhiqLjx8fGyatUqqVGjhjRr1kx++OEH9XO//PKLeHl56VUgJjcrb+K+y/Hjx8Xd3V3q1asn169fV/949dVXX0mTJk3SPdtpcjFFkv8ujo2NlY4dO4pKpZIcOXJozKJrqLgfO3bsmBQsWFBiY2NlzJgxYm9vr/6uMlTMpGP9eNzffPONdOzY0Wgz55ojFk4Z7MyZM1KvXj0pUqSIFCtWTEqXLm204iWpxD9aIiINGzaU3Llz6/VHWpfEL7uJEyfK119/LT/++KPY2toa9SadyRk/frwUKVLEKLOQJYqJiZHly5fLhQsXRMR4pwvoMn78eClRooTG6SOGdvDgQSlYsKDUqFFD+vbtKz169BBnZ2eD3xMkNdOnTxcnJ6dUZwczhMRZ5mbOnClr1qyRkSNHSq5cuYz6uUnq4sWL4ufnJ05OTgb9sSG1GTejoqIkMDBQVCqVjB07Vk6fPi3Pnj2T0aNHS7FixdJ9FEbXLJ8fO3LkiKhUKsmdO3e6vzPSEjM+Pl7evXun3smdNGmSODo6pjiLor5xkx7lSmrs2LFSs2ZNrXstGSKmiMiKFStEpVJJgQIFNHb2fvjhB72+G9Mze+vIkSOlUaNG6T6Cqivm+vXrxcLCQpo2bSrr16+XW7duyejRo6VgwYJ6/UCp5D18+vRpKVSokN7FS2ox4+Li5M2bN1KzZk0ZP368vHz5Ul6/fi3jx49Xn/lgjLgiHyaw+bhQGTRokHTo0EF9qq9SKc3Km3T/KHFCnuLFi0u1atWkdevWkjNnznR/PyqdCTguLk6+/vpryZ07t16nTiuJe+TIEalcubL4+/uLjY1Nur8PlY717du3Mm7cOHF1dTXaaeLmioWTCURERMi9e/fk0qVLBr/eJjVxcXHi7+8vKpVKvZNvbFOmTBGVSiXOzs56/Tqj1ObNm+Wbb76RPHnyZEhhasr7Gaxfv1769+8vLi4uGTLW69evy7hx46Rx48YycODADCuaEv8Yv3jxQry9vY1aICbat2+fFC9eXEqWLCn169fPsM/N+/fvZevWrdKlSxeDxkxpxs2kO+zx8fGyZs0ayZ8/vxQsWFDKlCkjhQoVSvd7Ky2zfCYVHR0tAwYMkJw5c6b7D7bSmF5eXlK9enWxsbHR63tKadwrV67IuHHjxMnJKd2vc1pi3rhxQ8aNG6feSTLE95XS2VsvXbokY8eOFScnp3T/+JDW7bt3716pVauWuLm5SZkyZaRUqVJ6fTcqfV1FRHr16iWlS5dO9+yXaY25ceNGUalUUqpUKalZs6YULVrU6GNNOp5r167J0KFDJWfOnOl+XZXOyjtv3jwZPXq0BAYGyvXr1zMkpsj//QChz5FEpXETT23NkydPuosmpTH/+ecfadmypd7vpayKhVM2EhcXJ8uWLUvxFwZjOHXqlKhUqgz/xeLy5cvSqVOnbPFLyYULF6Rly5Yap89lhPj4eJMUjAkJCRl6oWp4eLiEhoameJNWY3n//r3Bx/n27VuZP3++egKRxJ2uj4snkQ/Xeh08eFD++eefZG96bIiYye14njx5UsqXL6/XUZ+0xoyLi5Pw8HBxdnYWS0tLvY8mKhnrgwcPpH379lK2bFm9jiimNWbSayIMcVRcyVjv3bsnzZo1k2LFiun190dJzOfPn8vNmzfl3Llzev9AqSRu4rY9ceKEXkd9lMQ8cuSITJkyRRYtWqT3D0pK4kZGRsr//vc/qVevnl6va2hoqNSoUUMOHTokIv9X2Pft21e6d++uXk6fU0rTGzOpU6dO6b19lcaNiIgQX19fvX5AUxrz7du3RrtfYFbAwimbMcVpZKaajSUjrrnJLIw5ox1lLanNuJm4YxQbGysPHjzIkJiJs3zGx8erT6VKvNGlsWPGxsbK8+fP5Z9//jHYDw9pndH06dOn8vDhQ3n48KFRYyYWxPHx8Qa/5lLJ7K337t0zyHsqra+roY9Ip/U9fOfOnQyJmfhZjYmJMfiZK0rew7GxsQb5vKZ1Vt6kkxTouz+TnpiGkNa4iaezGmJfxlSzHmdFvI9TNpP0XkoZxdHRMcNjAjDofW4yOxsbG1OnQGYi8fMYHx8PCwsLdO7cGSKCbt26QaVSYejQofjpp5/w4MEDrFmzBg4ODnp/b6Q15r1797Bu3Tq4uLhk2Djv37+PX3/9FQ4ODnrHVBL33r17WL9+Pezs7DIs5oMHD7B27dpsM1ZDvX+Vxl27di3s7e0z7HNz//599euakWM15Oe1ZMmSAD7cby3xb3d8fDyePn2qXmb69OmwtbXF4MGDYWVlpfdY0xPTENIa18bGBkOGDDHIvoypxpolmapiIyIi00ptxk1jndJrqlk+k4tpaWlp1FOXM9NYjfma6oqbncZqis9NVnxdRTJ2Vl5TzQScncaaVahERExdvBERkWkk/glQqVRo1KgRzp8/jwMHDqBixYqMaaZxOVaO1ZzjJiQkwMLCAgEBAQgJCUHJkiUxbtw4HDt2DFWrVs0yMU0V11RjzTJMU68REVFmYYoZN7NLTFPF5VizZtzsNFZTzMprqpmAs9NYzZ2FqQs3IiIyvfLly+Ps2bOoVKkSY2aRuBxr1oybXcbatGlTAMCxY8dQrVq1LBvTVHFNNVZzx1P1iIgIIpLhk8dkl5imisuxZs242WmsUVFRGT7BlClimiquqcZqzlg4ERERERER6cBT9YiIiIiIiHRg4URERERERKQDCyciIiIiIiIdWDgRERERERHpwMKJiIiIiIhIBxZOREREREREOrBwIiLKppYsWQJ3d3dYWFhgzpw5ae7Xu3dvtGvXzmh5mdK+fftQpkwZJCQkGHzdAQEBqFKlilabm5sbVCoVtm/fnqY+mdWBAwegUqnw6tUro8W4dOkSChcujKioKKPFICJKCQsnIiI9pFREZMROpD4iIyMxaNAgjBo1Co8fP8bXX3+ttcz9+/ehUqlw/vz5DMnJw8MDKpUKKpUK9vb28PDwQKdOnbBv374MiQ8AI0eOxNixY2Fh8eHP46pVq5ArV65kl02p2Emra9euITAwEIsXL0ZISAiaN2+e7nWZKw8PD0VFe8WKFVGjRg38/PPPxkuKiCgFLJyIiMxYbGxsuvoFBwcjNjYWLVu2RIECBeDg4GDgzNJn0qRJCAkJwY0bN7BmzRrkypULjRs3xtSpU40e+9ixY7h16xY6duxo9FgAcOfOHQBA27ZtkT9/ftja2mZI3LRI7/sqI3z55ZdYuHAh4uPjTZ0KEWUzLJyIiDLIli1bUL58edja2sLDwwOzZs3SeD65Ixi5cuXCqlWrAPzfEaBNmzahfv36sLOzw6+//ppsrODgYLRt2xY5cuSAk5MTOnXqhKdPnwL4cBSlYsWKAIBixYpBpVLh/v37Wuvw9PQEAHh5eUGlUqF+/foaz//0008oUKAA8uTJg2+++UZjZzsmJgYjR45EoUKF4OjoiJo1a+LAgQM6t1HOnDmRP39+FClSBHXr1sWSJUswfvx4TJgwATdu3AAAxMfHo2/fvvD09IS9vT1Kly6NuXPnqtdx6NAhWFtbIzQ0VGPdw4cPR926dVOMvWHDBvj6+sLOzk5nnskZNWoUSpUqBQcHBxQrVgzjx49PsQAJCAhA69atAQAWFhZQqVSprnvt2rXw8PCAs7MzunTpgtevX6ufi46OxuDBg5EvXz7Y2dnh008/xalTp9TPJ3fUbPv27RoxE08JXLFiBYoVKwZbW1uIiFYeDx48QOvWreHi4gJHR0eUL18eu3btSjHv1N7z9evXx4MHD+Dv768+0piWGE2bNkV4eDgOHjyY6jYjIjI0Fk5ERBngzJkz6NSpE7p06YJLly4hICAA48ePVxdFSowaNQqDBw/GtWvX0LRpU63nRQTt2rXDixcvcPDgQQQFBeHOnTvo3LkzAKBz587Yu3cvAODkyZMICQmBu7u71npOnjwJANi7dy9CQkKwdetW9XP79+/HnTt3sH//fqxevRqrVq3SGMuXX36Jo0ePYsOGDbh48SI6duyIZs2a4datW4rHO2TIEIgI/vjjDwBAQkICChcujE2bNuHq1auYMGECxowZg02bNgEA6tati2LFimHt2rXqdcTFxeHXX3/Fl19+mWKcQ4cOoVq1aorzS5QzZ06sWrUKV69exdy5c7F06dIUTykbMWIEVq5cCQAICQlBSEhIiuu9c+cOtm/fjh07dmDHjh04ePAgZsyYoX5+5MiR2LJlC1avXo2zZ8+iRIkSaNq0KV68eKEo/9u3b2PTpk3YsmVLiqdnfvPNN4iOjsahQ4dw6dIl/PDDD8iRI0eyy+p6z2/duhWFCxdWH2VM3Aa6YtjY2KBy5co4fPiwovEREelNiIgo3Xr16iWWlpbi6Oio8c/Ozk4AyMuXL0VEpFu3btKkSRONvt99952UK1dO/RiAbNu2TWMZZ2dnWblypYiI3Lt3TwDInDlzUs1pz549YmlpKcHBweq2K1euCAA5efKkiIicO3dOAMi9e/dSXE9ivHPnzmmNuWjRohIXF6du69ixo3Tu3FlERG7fvi0qlUoeP36s0a9Ro0by/fffpxivaNGi8vPPPyf7nJubmwwcODDFvn5+fvL555+rH//www9StmxZ9ePt27dLjhw55M2bNymuw9nZWdasWaPRtnLlSgGg9fo6Ojom+3olNXPmTPH29lY/njhxolSuXFn9eNu2baLrz/DEiRPFwcFBIiMj1W3fffed1KxZU0RE3rx5I9bW1vLbb7+pn4+JiZGCBQvKzJkz1WNwdnbWWO/HsSdOnCjW1tYSFhaWaj4VK1aUgICAZJ/bv3+/4vd8cq95ajEStW/fXnr37p3qMkREhsYjTkREemrQoAHOnz+v8W/ZsmUay1y7dg21a9fWaKtduzZu3bql+FoNXUdFrl27Bnd3d42jSOXKlUOuXLlw7do1RbFSUr58eVhaWqofFyhQAGFhYQCAs2fPQkRQqlQp5MiRQ/3v4MGD6ut6lBIRjVPLFi1ahGrVqiFv3rzIkSMHli5diuDgYPXzvXv3xu3bt3HixAkAwIoVK9CpUyc4OjqmGOPdu3fJnqaXM2dOrdc3uSMyv//+Oz799FPkz58fOXLkwPjx4zVy0iXpthowYIC63cPDAzlz5lQ/Trqt79y5g9jYWI33lrW1NWrUqKH4tS5atCjy5s2b6jKDBw/GlClTULt2bUycOBEXL15Mcdn0vufTEsPe3h5v377VMSIiIsOyMnUCRETmztHRESVKlNBoe/Tokcbjj3f8E9uSUqlUWm3JXSOT2s5/SrFSa08Pa2trjccqlUo9hXdCQgIsLS1x5swZjeIKQIqndaUmPDwcz549U19ztWnTJvj7+2PWrFmoVasWcubMiR9//BH//fefuk++fPnQunVrrFy5EsWKFcOuXbt0XmPl6uqKly9farVbWFhovb4fO3HiBLp06YLAwEA0bdoUzs7O2LBhg9Z1bKlJWow5OTmp/z+1bZ34fknuvZXYZmFhYZD3FQD069cPTZs2xc6dO7Fnzx5Mnz4ds2bNwrfffqu1bFre8+mN8eLFCxQvXlznuoiIDIlHnIiIMkC5cuVw5MgRjbZjx46hVKlS6uIib968Gte63Lp1K12/qpcrVw7BwcF4+PChuu3q1auIiIhA2bJl07weGxsbAFB8RMzLywvx8fEICwtDiRIlNP7lz59f0boAYO7cubCwsFBP+3748GH4+PjAz88PXl5eKFGiRLJHsvr164cNGzZg8eLFKF68uNbRj+Tyvnr1quL8AODo0aMoWrQoxo4di2rVqqFkyZJ48OCBonUk3U758uVLcx8bGxuN91ZsbCxOnz6tfq3z5s2L169fa9z7SJ8p5t3d3TFgwABs3boVw4cPx9KlS5NdLi3veRsbm2TfX7piXL58GV5eXukeAxFRevCIExFRBhg+fDiqV6+OyZMno3Pnzjh+/DjmzZuHBQsWqJdp2LAh5s2bh08++QQJCQkYNWqU1tGGtGjcuDEqVaqE7t27Y86cOYiLi4Ofnx/q1aunaPKDfPnywd7eHv/88w8KFy4MOzs7ODs76+xXqlQpdO/eHT179sSsWbPg5eWF58+fY9++fahYsSJatGiRYt/Xr18jNDQUsbGxuHfvHn799VcsW7YM06dPVx/1KVGiBNasWYPdu3fD09MTa9euxalTp9RHpBIlHvmZMmUKJk2apDPvpk2bYvXq1TqXS06JEiUQHByMDRs2oHr16ti5cye2bduWrnUp4ejoiIEDB+K7775D7ty5UaRIEcycORNv375F3759AQA1a9aEg4MDxowZg2+//RYnT55M16QkADB06FA0b94cpUqVwsuXL7Fv374Ui/G0vOc9PDxw6NAhdOnSBba2tnB1ddUZ4/79+3j8+DEaN26crjEQEaUXjzgREWWAqlWrYtOmTdiwYQMqVKiACRMmYNKkSejdu7d6mVmzZsHd3R1169ZFt27dMGLEiHTdXylxWnMXFxfUrVsXjRs3RrFixbBx40ZF67GyssL//vc/LF68GAULFkTbtm3T3HflypXo2bMnhg8fjtKlS6NNmzb477//kp29L6kJEyagQIECKFGiBHr06IGIiAj8+++/GDVqlHqZAQMG4LPPPkPnzp1Rs2ZNhIeHw8/PT2tdFhYW6N27N+Lj49GzZ0+dOX/xxRe4evWqetpzJdq2bQt/f38MGjQIVapUwbFjxzB+/HjF60mPGTNm4PPPP0ePHj1QtWpV3L59G7t374aLiwsAIHfu3Pj111+xa9cuVKxYEevXr0dAQEC6YsXHx+Obb75B2bJl0axZM5QuXVqjEEoqLe/5SZMm4f79+yhevLj6+ipdMdavXw9fX18ULVo0XWMgIkovlaTlhGMiIiIz9NVXX+Hp06f48/+1d+c2EMJAAEWnEyL6oBs6IHElFIDIiamALijBIdk2sGiW1R4Sei924PRrfCzLS+uHYYhaa4zj+OWd8Y7jOKJt25jnOT16CfBpJk4A3E6tNdZ1jWmanj5ccKaUEk3TXL7XxW/s+x6lFNEE/IWJEwC303VdbNsWfd+ffkILAFcIJwAAgISjegAAAAnhBAAAkBBOAAAACeEEAACQEE4AAAAJ4QQAAJAQTgAAAAnhBAAAkHgA+DqIh7AB0I0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the CSV file\n",
    "block_1 = pd.read_csv(\"data/london_smart_meters/hhblock_dataset/block_0.csv\", parse_dates=False)\n",
    "\n",
    "# Extract only half-hourly energy consumption columns\n",
    "energy_columns = [f\"hh_{i}\" for i in range(48)]\n",
    "\n",
    "# ✅ Convert all energy columns to float, replacing non-numeric values with NaN\n",
    "block_1[energy_columns] = block_1[energy_columns].apply(pd.to_numeric, errors=\"coerce\")\n",
    "\n",
    "# ✅ Initialize arrays\n",
    "hourly_consumption = np.zeros(48, dtype=np.float64)  # 48 half-hour slots\n",
    "counts = np.zeros(48, dtype=np.float64)\n",
    "\n",
    "# ✅ Iterate through the DataFrame\n",
    "for _, row in block_1.iterrows():\n",
    "    # ✅ Convert row values to float64\n",
    "    energy_values = row[energy_columns].astype(np.float64).values\n",
    "    energy_values = np.nan_to_num(energy_values)  # Replace NaNs with 0\n",
    "    \n",
    "    hourly_consumption += energy_values  # Sum up energy consumption\n",
    "    counts += ~np.isnan(row[energy_columns].astype(np.float64).values)  # ✅ Convert row to float before checking NaN\n",
    "\n",
    "# ✅ Compute the average energy consumption per half-hour slot\n",
    "hourly_avg = np.divide(hourly_consumption, counts, out=np.zeros_like(hourly_consumption), where=counts != 0)\n",
    "\n",
    "# ✅ Plot\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.bar(range(48), hourly_avg, color=\"green\")\n",
    "plt.xlabel(\"Hour of the Day (Half-hour slots)\")\n",
    "plt.ylabel(\"Average Energy Consumption (kWh)\")\n",
    "plt.title(\"Average Energy Consumption Per Hour of the Day\")\n",
    "plt.xticks(range(0, 48, 2), labels=[f\"{h//2}:30\" if h % 2 else f\"{h//2}:00\" for h in range(0, 48, 2)], rotation=45)\n",
    "plt.grid(axis=\"y\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "17b12296",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['LCLid', 'day', 'hh_0', 'hh_1', 'hh_2', 'hh_3', 'hh_4', 'hh_5', 'hh_6',\n",
      "       'hh_7', 'hh_8', 'hh_9', 'hh_10', 'hh_11', 'hh_12', 'hh_13', 'hh_14',\n",
      "       'hh_15', 'hh_16', 'hh_17', 'hh_18', 'hh_19', 'hh_20', 'hh_21', 'hh_22',\n",
      "       'hh_23', 'hh_24', 'hh_25', 'hh_26', 'hh_27', 'hh_28', 'hh_29', 'hh_30',\n",
      "       'hh_31', 'hh_32', 'hh_33', 'hh_34', 'hh_35', 'hh_36', 'hh_37', 'hh_38',\n",
      "       'hh_39', 'hh_40', 'hh_41', 'hh_42', 'hh_43', 'hh_44', 'hh_45', 'hh_46',\n",
      "       'hh_47'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(block_1.columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "9cacf5cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available columns: Index(['LCLid', 'day', 'hh_0', 'hh_1', 'hh_2', 'hh_3', 'hh_4', 'hh_5', 'hh_6',\n",
      "       'hh_7', 'hh_8', 'hh_9', 'hh_10', 'hh_11', 'hh_12', 'hh_13', 'hh_14',\n",
      "       'hh_15', 'hh_16', 'hh_17', 'hh_18', 'hh_19', 'hh_20', 'hh_21', 'hh_22',\n",
      "       'hh_23', 'hh_24', 'hh_25', 'hh_26', 'hh_27', 'hh_28', 'hh_29', 'hh_30',\n",
      "       'hh_31', 'hh_32', 'hh_33', 'hh_34', 'hh_35', 'hh_36', 'hh_37', 'hh_38',\n",
      "       'hh_39', 'hh_40', 'hh_41', 'hh_42', 'hh_43', 'hh_44', 'hh_45', 'hh_46',\n",
      "       'hh_47'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ✅ Load CSV file\n",
    "block1_compact = pd.read_csv(\"data/london_smart_meters/hhblock_dataset/block_1.csv\", parse_dates=False)\n",
    "\n",
    "# ✅ Check available columns\n",
    "print(\"Available columns:\", block1_compact.columns)\n",
    "\n",
    "# ✅ Extract the correct energy consumption columns (hh_0 to hh_47)\n",
    "energy_columns = [f\"hh_{i}\" for i in range(48)]  \n",
    "\n",
    "# ✅ Convert energy values to numeric (handle non-numeric values as NaN)\n",
    "block1_compact[energy_columns] = block1_compact[energy_columns].apply(pd.to_numeric, errors=\"coerce\")\n",
    "\n",
    "# ✅ Compute the average energy consumption per half-hour slot\n",
    "hourly_avg = block1_compact[energy_columns].mean()\n",
    "\n",
    "# ✅ Plot the results\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.bar(range(48), hourly_avg, color=\"green\")\n",
    "plt.xlabel(\"Hour of the Day (Half-hour slots)\")\n",
    "plt.ylabel(\"Average Energy Consumption (kWh)\")\n",
    "plt.title(\"Average Energy Consumption Per Hour of the Day\")\n",
    "plt.xticks(range(0, 48, 2), labels=[f\"{h//2}:30\" if h % 2 else f\"{h//2}:00\" for h in range(0, 48, 2)], rotation=45)\n",
    "plt.grid(axis=\"y\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e325c848",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available columns: Index(['LCLid', 'day', 'hh_0', 'hh_1', 'hh_2', 'hh_3', 'hh_4', 'hh_5', 'hh_6',\n",
      "       'hh_7', 'hh_8', 'hh_9', 'hh_10', 'hh_11', 'hh_12', 'hh_13', 'hh_14',\n",
      "       'hh_15', 'hh_16', 'hh_17', 'hh_18', 'hh_19', 'hh_20', 'hh_21', 'hh_22',\n",
      "       'hh_23', 'hh_24', 'hh_25', 'hh_26', 'hh_27', 'hh_28', 'hh_29', 'hh_30',\n",
      "       'hh_31', 'hh_32', 'hh_33', 'hh_34', 'hh_35', 'hh_36', 'hh_37', 'hh_38',\n",
      "       'hh_39', 'hh_40', 'hh_41', 'hh_42', 'hh_43', 'hh_44', 'hh_45', 'hh_46',\n",
      "       'hh_47'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "\n",
    "# ✅ Load data\n",
    "block1_compact = pd.read_csv(\"data/london_smart_meters/hhblock_dataset/block_1.csv\", parse_dates=[\"day\"])\n",
    "\n",
    "# ✅ Check available columns\n",
    "print(\"Available columns:\", block1_compact.columns)\n",
    "\n",
    "# ✅ Select 5 random customers\n",
    "random_customers = random.sample(list(block1_compact[\"LCLid\"].unique()), 5)\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "for customer in random_customers:\n",
    "    # ✅ Get customer data\n",
    "    customer_data = block1_compact[block1_compact[\"LCLid\"] == customer]\n",
    "\n",
    "    # ✅ Generate timestamps for each half-hour slot of the day\n",
    "    timestamps = pd.date_range(start=customer_data[\"day\"].values[0], periods=48, freq=\"30min\")\n",
    "\n",
    "    # ✅ Extract energy consumption values\n",
    "    energy_values = customer_data.iloc[0, 2:].values.astype(float)  # Select hh_0 to hh_47 columns\n",
    "\n",
    "    # ✅ Plot each customer's energy usage\n",
    "    plt.plot(timestamps, energy_values, label=f\"Customer {customer}\")\n",
    "\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Energy Consumption (kWh)\")\n",
    "plt.title(\"Comparison of Energy Usage Between Customers\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "6cd213b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "# ✅ Load data\n",
    "block1_compact = pd.read_csv(\"data/london_smart_meters/hhblock_dataset/block_1.csv\", parse_dates=[\"day\"])\n",
    "\n",
    "# ✅ Select a random customer\n",
    "random_customer = random.choice(block1_compact[\"LCLid\"].unique())\n",
    "\n",
    "# ✅ Extract customer data\n",
    "customer_data = block1_compact[block1_compact[\"LCLid\"] == random_customer]\n",
    "\n",
    "# ✅ Generate timestamps assuming each row represents one full day\n",
    "timestamps = pd.date_range(start=customer_data[\"day\"].values[0], periods=48, freq=\"30min\")\n",
    "\n",
    "# ✅ Extract energy consumption values (hh_0 to hh_47)\n",
    "energy_values = customer_data.iloc[0, 2:].values.astype(float)  # Convert to float\n",
    "\n",
    "# ✅ Convert timestamps to numerical values for 3D plotting\n",
    "time_numeric = np.arange(len(timestamps))\n",
    "\n",
    "# ✅ Create 3D plot\n",
    "fig = plt.figure(figsize=(12, 6))\n",
    "ax = fig.add_subplot(111, projection=\"3d\")\n",
    "\n",
    "ax.plot(time_numeric, energy_values, zs=0, zdir=\"z\", label=\"Energy Consumption\", color=\"blue\")\n",
    "\n",
    "ax.set_xlabel(\"Time Step\")\n",
    "ax.set_ylabel(\"Energy Consumption (kWh)\")\n",
    "ax.set_zlabel(\"Index\")\n",
    "ax.set_title(f\"3D Energy Consumption Over Time - Customer {random_customer}\")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "6f875d4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# ✅ Load dataset\n",
    "block1_compact = pd.read_csv(\"data/london_smart_meters/hhblock_dataset/block_1.csv\", parse_dates=[\"day\"])\n",
    "\n",
    "# ✅ Define the correct energy columns\n",
    "energy_columns = [f\"hh_{i}\" for i in range(48)]\n",
    "\n",
    "# ✅ Convert energy values to numeric (if necessary)\n",
    "block1_compact[energy_columns] = block1_compact[energy_columns].apply(pd.to_numeric, errors=\"coerce\")\n",
    "\n",
    "# ✅ Create a matrix of hourly energy consumption averages (7 days × 48 half-hour slots)\n",
    "hourly_avg_matrix = np.zeros((7, 48))\n",
    "\n",
    "# ✅ Iterate over the first 7 rows (customers or days)\n",
    "for i, row in block1_compact.iloc[:7].iterrows():\n",
    "    energy_values = row[energy_columns].values.astype(float)  # Convert to float\n",
    "    hourly_avg_matrix[i, :] = energy_values  # Store in matrix\n",
    "\n",
    "# ✅ Plot the heatmap\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.heatmap(hourly_avg_matrix, cmap=\"coolwarm\", xticklabels=range(48), yticklabels=[\"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\", \"Sun\"])\n",
    "plt.xlabel(\"Hour of the Day (Half-hour slots)\")\n",
    "plt.ylabel(\"Day of the Week\")\n",
    "plt.title(\"Heatmap of Energy Usage Per Hour & Day\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "9387f337",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ✅ Define energy consumption columns\n",
    "energy_columns = [f\"hh_{i}\" for i in range(48)]\n",
    "\n",
    "# ✅ Load dataset (if not already loaded)\n",
    "block1_compact = pd.read_csv(\"data/london_smart_meters/hhblock_dataset/block_1.csv\")\n",
    "\n",
    "# ✅ Convert values to float (in case of strings or errors)\n",
    "block1_compact[energy_columns] = block1_compact[energy_columns].apply(pd.to_numeric, errors=\"coerce\")\n",
    "\n",
    "# ✅ Flatten the energy consumption data into a single array\n",
    "all_energy_values = block1_compact[energy_columns].values.flatten()\n",
    "\n",
    "# ✅ Remove NaN values (if any)\n",
    "all_energy_values = all_energy_values[~np.isnan(all_energy_values)]\n",
    "\n",
    "# ✅ Plot histogram\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.hist(all_energy_values, bins=50, color=\"purple\", alpha=0.7)\n",
    "\n",
    "plt.xlabel(\"Energy Consumption (kWh)\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Histogram of Energy Consumption\")\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "829a8339",
   "metadata": {},
   "source": [
    "# Compact Form"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "d454bdea",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_compact(x):\n",
    "    start_date = x['day'].min()\n",
    "    name = x['LCLid'].unique()[0]\n",
    "    ### Fill missing dates with NaN ###\n",
    "    # Create a date range from  min to max\n",
    "    dr = pd.date_range(start=x['day'].min(), end=max_date, freq=\"1D\")\n",
    "    # Add hh_0 to hh_47 to columns and with some unstack magic recreating date-hh_x combinations\n",
    "    dr = pd.DataFrame(columns=[f\"hh_{i}\" for i in range(48)], index=dr).unstack().reset_index()\n",
    "    # renaming the columns\n",
    "    dr.columns = [\"hour_block\", \"day\", \"_\"]\n",
    "    # left merging the dataframe to the standard dataframe\n",
    "    # now the missing values will be left as NaN\n",
    "    dr = dr.merge(x, on=['hour_block','day'], how='left')\n",
    "    # sorting the rows\n",
    "    dr.sort_values(['day',\"offset\"], inplace=True)\n",
    "    # extracting the timeseries array\n",
    "    ts = dr['energy_consumption'].values\n",
    "    len_ts = len(ts)\n",
    "    return start_date, name, ts, len_ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "7f768cf7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "block_1 columns: Index(['LCLid', 'day', 'hh_0', 'hh_1', 'hh_2', 'hh_3', 'hh_4', 'hh_5', 'hh_6',\n",
      "       'hh_7', 'hh_8', 'hh_9', 'hh_10', 'hh_11', 'hh_12', 'hh_13', 'hh_14',\n",
      "       'hh_15', 'hh_16', 'hh_17', 'hh_18', 'hh_19', 'hh_20', 'hh_21', 'hh_22',\n",
      "       'hh_23', 'hh_24', 'hh_25', 'hh_26', 'hh_27', 'hh_28', 'hh_29', 'hh_30',\n",
      "       'hh_31', 'hh_32', 'hh_33', 'hh_34', 'hh_35', 'hh_36', 'hh_37', 'hh_38',\n",
      "       'hh_39', 'hh_40', 'hh_41', 'hh_42', 'hh_43', 'hh_44', 'hh_45', 'hh_46',\n",
      "       'hh_47'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(\"block_1 columns:\", block_1.columns)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "42d66b66",
   "metadata": {},
   "outputs": [],
   "source": [
    "block_1_melted = block_1.melt(id_vars=[\"LCLid\", \"day\"], var_name=\"hour_block\", value_name=\"energy_consumption\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "34da937e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       LCLid         day hour_block  energy_consumption\n",
      "0  MAC000002  2012-10-13       hh_0               0.263\n",
      "1  MAC000002  2012-10-14       hh_0               0.262\n",
      "2  MAC000002  2012-10-15       hh_0               0.192\n",
      "3  MAC000002  2012-10-16       hh_0               0.237\n",
      "4  MAC000002  2012-10-17       hh_0               0.157\n"
     ]
    }
   ],
   "source": [
    "print(block_1_melted.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "a1937341",
   "metadata": {},
   "outputs": [],
   "source": [
    "block_1_melted[\"hour_block\"] = block_1_melted[\"hour_block\"].astype(str).str.replace(\"hh_\", \"\").astype(int)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "dccb0b17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       LCLid         day  hour_block  energy_consumption\n",
      "0  MAC000002  2012-10-13           0               0.263\n",
      "1  MAC000002  2012-10-14           0               0.262\n",
      "2  MAC000002  2012-10-15           0               0.192\n",
      "3  MAC000002  2012-10-16           0               0.237\n",
      "4  MAC000002  2012-10-17           0               0.157\n"
     ]
    }
   ],
   "source": [
    "print(block_1_melted.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "1c4e16de",
   "metadata": {},
   "outputs": [],
   "source": [
    "block_1_melted[\"hour_block\"] = block_1_melted[\"hour_block\"].astype(str)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "0a562931",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_process_block_compact(block_df, freq=\"30min\", ts_identifier=\"series_name\", value_name=\"series_value\"):\n",
    "    grps = block_df.groupby('LCLid')\n",
    "    all_series = []\n",
    "    all_start_dates = []\n",
    "    all_names = []\n",
    "    all_data = {}\n",
    "    all_len = []\n",
    "\n",
    "    for idx, df in tqdm(grps, leave=False):\n",
    "        start_date, name, ts, len_ts = preprocess_compact(df)\n",
    "        all_series.append(ts)\n",
    "        all_start_dates.append(start_date)\n",
    "        all_names.append(name)\n",
    "        all_len.append(len_ts)\n",
    "\n",
    "    all_data[ts_identifier] = all_names\n",
    "    all_data['start_timestamp'] = all_start_dates\n",
    "    all_data['frequency'] = freq\n",
    "    all_data[value_name] = all_series\n",
    "    all_data['series_length'] = all_len\n",
    "\n",
    "    return pd.DataFrame(all_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "ce0eb79f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_compact(x):\n",
    "    # Creating a copy of the input DataFrame\n",
    "    dr = x.copy()\n",
    "\n",
    "    # Sorting the rows by 'day'\n",
    "    dr.sort_values(['day'], inplace=True)\n",
    "\n",
    "    # Extracting the timeseries array\n",
    "    ts = dr['energy_consumption'].values\n",
    "    len_ts = len(ts)\n",
    "\n",
    "    # Returning extracted information\n",
    "    return dr['day'].min(), x['LCLid'].iloc[0], ts, len_ts\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "39f9320a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "block1_compact = load_process_block_compact(block_1_melted, freq=\"30min\", ts_identifier=\"LCLid\", value_name=\"energy_consumption\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "070fd89c",
   "metadata": {},
   "outputs": [],
   "source": [
    "block1_compact.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "ba8a244e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index                  132\n",
       "LCLid                 2900\n",
       "start_timestamp       2950\n",
       "frequency             2700\n",
       "energy_consumption    6000\n",
       "series_length          400\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total: 0.014383316040039062 MB\n"
     ]
    }
   ],
   "source": [
    "display(block1_compact.memory_usage(deep=True))\n",
    "print(f\"Total: {block1_compact.memory_usage(deep=True).sum()/1024**2} MB\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9eeeed3e",
   "metadata": {},
   "source": [
    "# Expanded Form"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "b30477f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_expanded(x):\n",
    "    start_date = x['day'].min()\n",
    "\n",
    "    ### Fill missing dates with NaN ###\n",
    "    dr = pd.date_range(start=start_date, end=x['day'].max(), freq=\"1D\")\n",
    "    dr = pd.MultiIndex.from_product([dr, range(48)], names=[\"day\", \"hour_block\"]).to_frame(index=False)\n",
    "\n",
    "    # Format 'hour_block' correctly\n",
    "    dr[\"hour_block\"] = dr[\"hour_block\"].apply(lambda x: f\"hh_{x}\")\n",
    "\n",
    "    # Debugging prints\n",
    "    print(\"Columns in x before melting:\", x.columns)\n",
    "\n",
    "    # Reshape x: Convert hh_0, hh_1, ..., hh_47 into a long format with 'hour_block'\n",
    "    x_melted = x.melt(id_vars=['LCLid', 'day'], var_name='hour_block', value_name='energy_consumption')\n",
    "\n",
    "    # Ensure 'hour_block' is formatted correctly\n",
    "    x_melted['hour_block'] = x_melted['hour_block'].astype(str)\n",
    "\n",
    "    # Debugging print after melting\n",
    "    print(\"Columns in x after melting:\", x_melted.columns)\n",
    "\n",
    "    # Merge with the corrected format\n",
    "    dr = dr.merge(x_melted, on=['hour_block', 'day'], how='left')\n",
    "\n",
    "    return dr\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "692452fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of block_1 DataFrame: (25286, 50)\n",
      "       LCLid         day   hh_0   hh_1   hh_2   hh_3   hh_4   hh_5   hh_6  \\\n",
      "0  MAC000002  2012-10-13  0.263  0.269  0.275  0.256  0.211  0.136  0.161   \n",
      "1  MAC000002  2012-10-14  0.262  0.166  0.226  0.088  0.126  0.082  0.123   \n",
      "2  MAC000002  2012-10-15  0.192  0.097  0.141  0.083  0.132  0.070  0.130   \n",
      "3  MAC000002  2012-10-16  0.237  0.237  0.193  0.118  0.098  0.107  0.094   \n",
      "4  MAC000002  2012-10-17  0.157  0.211  0.155  0.169  0.101  0.117  0.084   \n",
      "\n",
      "    hh_7  ...  hh_38  hh_39  hh_40  hh_41  hh_42  hh_43  hh_44  hh_45  hh_46  \\\n",
      "0  0.119  ...  0.918  0.278  0.267  0.239  0.230  0.233  0.235  0.188  0.259   \n",
      "1  0.083  ...  1.075  0.956  0.821  0.745  0.712  0.511  0.231  0.210  0.278   \n",
      "2  0.074  ...  1.164  0.249  0.225  0.258  0.260  0.334  0.299  0.236  0.241   \n",
      "3  0.109  ...  0.966  0.172  0.192  0.228  0.203  0.211  0.188  0.213  0.157   \n",
      "4  0.118  ...  0.223  0.075  0.230  0.208  0.265  0.377  0.327  0.277  0.288   \n",
      "\n",
      "   hh_47  \n",
      "0  0.250  \n",
      "1  0.159  \n",
      "2  0.237  \n",
      "3  0.202  \n",
      "4  0.256  \n",
      "\n",
      "[5 rows x 50 columns]\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of block_1 DataFrame:\", block_1.shape)\n",
    "print(block_1.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "c9a8f439",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial shape of block_df: (25286, 50)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  2%|▉                                           | 1/50 [00:00<00:06,  7.73it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing LCLid: MAC000002 with shape: (498, 50)\n",
      "Initial shape of x: (498, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC000246 with shape: (814, 50)\n",
      "Initial shape of x: (814, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC000450 with shape: (410, 50)\n",
      "Initial shape of x: (410, 50)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  8%|███▌                                        | 4/50 [00:00<00:02, 17.30it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC001074 with shape: (222, 50)\n",
      "Initial shape of x: (222, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003223 with shape: (526, 50)\n",
      "Initial shape of x: (526, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 12%|█████▎                                      | 6/50 [00:00<00:02, 17.70it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing LCLid: MAC003239 with shape: (526, 50)\n",
      "Initial shape of x: (526, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003252 with shape: (513, 50)\n",
      "Initial shape of x: (513, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003281 with shape: (519, 50)\n",
      "Initial shape of x: (519, 50)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 16%|███████                                     | 8/50 [00:00<00:02, 18.41it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003305 with shape: (519, 50)\n",
      "Initial shape of x: (519, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003348 with shape: (440, 50)\n",
      "Initial shape of x: (440, 50)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 22%|█████████▍                                 | 11/50 [00:00<00:01, 20.43it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003388 with shape: (516, 50)\n",
      "Initial shape of x: (516, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003394 with shape: (510, 50)\n",
      "Initial shape of x: (510, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003400 with shape: (511, 50)\n",
      "Initial shape of x: (511, 50)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 28%|████████████                               | 14/50 [00:00<00:01, 22.26it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003422 with shape: (494, 50)\n",
      "Initial shape of x: (494, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003423 with shape: (503, 50)\n",
      "Initial shape of x: (503, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003428 with shape: (508, 50)\n",
      "Initial shape of x: (508, 50)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 34%|██████████████▌                            | 17/50 [00:00<00:01, 23.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003449 with shape: (507, 50)\n",
      "Initial shape of x: (507, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003463 with shape: (311, 50)\n",
      "Initial shape of x: (311, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40%|█████████████████▏                         | 20/50 [00:00<00:01, 23.82it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing LCLid: MAC003482 with shape: (509, 50)\n",
      "Initial shape of x: (509, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003553 with shape: (503, 50)\n",
      "Initial shape of x: (503, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003557 with shape: (405, 50)\n",
      "Initial shape of x: (405, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003566 with shape: (503, 50)\n",
      "Initial shape of x: (503, 50)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 46%|███████████████████▊                       | 23/50 [00:01<00:01, 24.18it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003579 with shape: (505, 50)\n",
      "Initial shape of x: (505, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003597 with shape: (501, 50)\n",
      "Initial shape of x: (501, 50)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 52%|██████████████████████▎                    | 26/50 [00:01<00:01, 22.84it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003613 with shape: (500, 50)\n",
      "Initial shape of x: (500, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003646 with shape: (502, 50)\n",
      "Initial shape of x: (502, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003656 with shape: (500, 50)\n",
      "Initial shape of x: (500, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003668 with shape: (499, 50)\n",
      "Initial shape of x: (499, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003680 with shape: (493, 50)\n",
      "Initial shape of x: (493, 50)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 58%|████████████████████████▉                  | 29/50 [00:01<00:00, 22.19it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003686 with shape: (497, 50)\n",
      "Initial shape of x: (497, 50)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 64%|███████████████████████████▌               | 32/50 [00:01<00:00, 22.11it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003718 with shape: (361, 50)\n",
      "Initial shape of x: (361, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003719 with shape: (497, 50)\n",
      "Initial shape of x: (497, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003737 with shape: (496, 50)\n",
      "Initial shape of x: (496, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003740 with shape: (495, 50)\n",
      "Initial shape of x: (495, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 70%|██████████████████████████████             | 35/50 [00:01<00:00, 22.70it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing LCLid: MAC003775 with shape: (448, 50)\n",
      "Initial shape of x: (448, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003805 with shape: (492, 50)\n",
      "Initial shape of x: (492, 50)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 76%|████████████████████████████████▋          | 38/50 [00:01<00:00, 21.85it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003817 with shape: (493, 50)\n",
      "Initial shape of x: (493, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003826 with shape: (491, 50)\n",
      "Initial shape of x: (491, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003844 with shape: (488, 50)\n",
      "Initial shape of x: (488, 50)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 82%|███████████████████████████████████▎       | 41/50 [00:01<00:00, 22.92it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003851 with shape: (489, 50)\n",
      "Initial shape of x: (489, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003856 with shape: (484, 50)\n",
      "Initial shape of x: (484, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003863 with shape: (487, 50)\n",
      "Initial shape of x: (487, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 90%|██████████████████████████████████████▋    | 45/50 [00:02<00:00, 25.76it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing LCLid: MAC003874 with shape: (487, 50)\n",
      "Initial shape of x: (487, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC004034 with shape: (435, 50)\n",
      "Initial shape of x: (435, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC004179 with shape: (646, 50)\n",
      "Initial shape of x: (646, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC004247 with shape: (637, 50)\n",
      "Initial shape of x: (637, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC004319 with shape: (642, 50)\n",
      "Initial shape of x: (642, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC004387 with shape: (708, 50)\n",
      "Initial shape of x: (708, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing LCLid: MAC004431 with shape: (697, 50)\n",
      "Initial shape of x: (697, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC005492 with shape: (549, 50)\n",
      "Initial shape of x: (549, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Shape of concatenated DataFrame: (2400, 4)\n",
      "Number of null timestamps: 0\n",
      "Final shape of block_df before return: (2400, 4)\n",
      "Resulting DataFrame shape: (2400, 4)\n",
      "       LCLid  energy_usage           timestamp frequency\n",
      "0  MAC000002         0.263 2012-10-13 00:00:00     30min\n",
      "1  MAC000002         0.269 2012-10-13 00:30:00     30min\n",
      "2  MAC000002         0.275 2012-10-13 01:00:00     30min\n",
      "3  MAC000002         0.256 2012-10-13 01:30:00     30min\n",
      "4  MAC000002         0.211 2012-10-13 02:00:00     30min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "\n",
    "def preprocess_expanded(x):\n",
    "    \"\"\"\n",
    "    Preprocesses a block of data by melting it into a long format and merging with a time reference DataFrame.\n",
    "    \"\"\"\n",
    "    print(\"Initial shape of x:\", x.shape)  # Debugging print\n",
    "\n",
    "    # Convert 'day' column to datetime format\n",
    "    x[\"day\"] = pd.to_datetime(x[\"day\"], errors=\"coerce\")\n",
    "\n",
    "    # Ensure 'day' conversion was successful\n",
    "    if x[\"day\"].isnull().any():\n",
    "        print(\"Warning: Some 'day' values could not be converted to datetime.\")\n",
    "\n",
    "    # Melt the DataFrame to long format (Convert hh_0 to hh_47 into a single column)\n",
    "    x_melted = x.melt(id_vars=[\"LCLid\", \"day\"], var_name=\"hour_block\", value_name=\"energy_usage\")\n",
    "\n",
    "    # Ensure 'hour_block' column is properly formatted\n",
    "    x_melted[\"hour_block\"] = x_melted[\"hour_block\"].str.replace(\"hh_\", \"\").astype(int)\n",
    "\n",
    "    print(\"Columns in x after melting:\", x_melted.columns)  # Debugging print\n",
    "\n",
    "    # Create a reference DataFrame for timestamps\n",
    "    dr = pd.DataFrame({\"hour_block\": range(48)})  # 48 half-hour slots in a day\n",
    "    dr[\"day\"] = x[\"day\"].iloc[0]  # Use the first available day value\n",
    "    dr[\"day\"] = pd.to_datetime(dr[\"day\"])  # Ensure datetime type\n",
    "\n",
    "    # ✅ Merge the processed data with the reference time DataFrame\n",
    "    dr = dr.merge(x_melted, on=['hour_block', 'day'], how='left')\n",
    "\n",
    "    return dr\n",
    "\n",
    "def load_process_block_expanded(block_df, freq=\"30min\"):\n",
    "    \"\"\"\n",
    "    Processes a DataFrame by grouping it by 'LCLid' and expanding its time series.\n",
    "    \"\"\"\n",
    "    print(\"Initial shape of block_df:\", block_df.shape)  # Print shape from the start\n",
    "\n",
    "    grps = block_df.groupby('LCLid')\n",
    "    all_series = []\n",
    "\n",
    "    for idx, df in tqdm(grps, leave=False):\n",
    "        print(f\"Processing LCLid: {idx} with shape: {df.shape}\")\n",
    "        ts = preprocess_expanded(df)\n",
    "        all_series.append(ts)\n",
    "\n",
    "    if not all_series:\n",
    "        print(\"No series generated from groups.\")\n",
    "        return pd.DataFrame()  # Return empty DataFrame if no groups processed\n",
    "\n",
    "    block_df = pd.concat(all_series, ignore_index=True)\n",
    "\n",
    "    # Debugging the shape of block_df after concatenation\n",
    "    print(\"Shape of concatenated DataFrame:\", block_df.shape)\n",
    "\n",
    "    if block_df.empty:\n",
    "        print(\"Concatenated DataFrame is empty.\")\n",
    "        return block_df  # Return empty DataFrame\n",
    "\n",
    "    # ✅ Recreate Offset for time alignment\n",
    "    block_df['offset'] = block_df['hour_block']\n",
    "\n",
    "    # ✅ Creating a timestamp column\n",
    "    block_df['timestamp'] = block_df['day'] + pd.to_timedelta(block_df['offset'] * 30, unit='m')\n",
    "\n",
    "    print(\"Number of null timestamps:\", block_df['timestamp'].isnull().sum())\n",
    "\n",
    "    block_df['frequency'] = freq\n",
    "    block_df.sort_values([\"LCLid\", \"timestamp\"], inplace=True)\n",
    "\n",
    "    # ✅ Drop unnecessary columns if they exist\n",
    "    cols_to_drop = [\"_\", \"hour_block\", \"offset\", \"day\"]\n",
    "    existing_cols_to_drop = [col for col in cols_to_drop if col in block_df.columns]\n",
    "    block_df.drop(columns=existing_cols_to_drop, inplace=True)\n",
    "\n",
    "    print(\"Final shape of block_df before return:\", block_df.shape)  # Print final shape\n",
    "    return block_df\n",
    "\n",
    "# ✅ Call the function and check the returned DataFrame\n",
    "block1_expanded = load_process_block_expanded(block_1, freq=\"30min\")\n",
    "print(\"Resulting DataFrame shape:\", block1_expanded.shape)\n",
    "print(block1_expanded.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "b7a43cd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index             132\n",
       "LCLid           19200\n",
       "energy_usage    19200\n",
       "timestamp       19200\n",
       "frequency       19200\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total: 0.07336807250976562 MB\n"
     ]
    }
   ],
   "source": [
    "display(block1_expanded.memory_usage())\n",
    "print(f\"Total: {block1_expanded.memory_usage().sum()/1024**2} MB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "3aea6ad1",
   "metadata": {},
   "outputs": [],
   "source": [
    "del block1_expanded, block_1, block1_compact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "30666e60",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing Blocks..:   0%|                                | 0/7 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Processing file: block_0.csv\n",
      "Available columns: Index(['LCLid', 'day', 'hh_0', 'hh_1', 'hh_2', 'hh_3', 'hh_4', 'hh_5', 'hh_6',\n",
      "       'hh_7', 'hh_8', 'hh_9', 'hh_10', 'hh_11', 'hh_12', 'hh_13', 'hh_14',\n",
      "       'hh_15', 'hh_16', 'hh_17', 'hh_18', 'hh_19', 'hh_20', 'hh_21', 'hh_22',\n",
      "       'hh_23', 'hh_24', 'hh_25', 'hh_26', 'hh_27', 'hh_28', 'hh_29', 'hh_30',\n",
      "       'hh_31', 'hh_32', 'hh_33', 'hh_34', 'hh_35', 'hh_36', 'hh_37', 'hh_38',\n",
      "       'hh_39', 'hh_40', 'hh_41', 'hh_42', 'hh_43', 'hh_44', 'hh_45', 'hh_46',\n",
      "       'hh_47'],\n",
      "      dtype='object')\n",
      "Initial shape of block_df: (1212334, 4)\n",
      "Unique values in hour_block before filtering: ['hh_0' 'hh_1' 'hh_2' 'hh_3' 'hh_4' 'hh_5' 'hh_6' 'hh_7' 'hh_8' 'hh_9'\n",
      " 'hh_10' 'hh_11' 'hh_12' 'hh_13' 'hh_14' 'hh_15' 'hh_16' 'hh_17' 'hh_18'\n",
      " 'hh_19' 'hh_20' 'hh_21' 'hh_22' 'hh_23' 'hh_24' 'hh_25' 'hh_26' 'hh_27'\n",
      " 'hh_28' 'hh_29' 'hh_30' 'hh_31' 'hh_32' 'hh_33' 'hh_34' 'hh_35' 'hh_36'\n",
      " 'hh_37' 'hh_38' 'hh_39' 'hh_40' 'hh_41' 'hh_42' 'hh_43' 'hh_44' 'hh_45'\n",
      " 'hh_46' 'hh_47']\n",
      "Unique values in hour_block after filtering: ['hh_0' 'hh_1' 'hh_2' 'hh_3' 'hh_4' 'hh_5' 'hh_6' 'hh_7' 'hh_8' 'hh_9'\n",
      " 'hh_10' 'hh_11' 'hh_12' 'hh_13' 'hh_14' 'hh_15' 'hh_16' 'hh_17' 'hh_18'\n",
      " 'hh_19' 'hh_20' 'hh_21' 'hh_22' 'hh_23' 'hh_24' 'hh_25' 'hh_26' 'hh_27'\n",
      " 'hh_28' 'hh_29' 'hh_30' 'hh_31' 'hh_32' 'hh_33' 'hh_34' 'hh_35' 'hh_36'\n",
      " 'hh_37' 'hh_38' 'hh_39' 'hh_40' 'hh_41' 'hh_42' 'hh_43' 'hh_44' 'hh_45'\n",
      " 'hh_46' 'hh_47']\n",
      "Number of null timestamps: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing Blocks..:  14%|███▍                    | 1/7 [00:02<00:16,  2.70s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final shape of block_df before return: (1212334, 4)\n",
      "\n",
      "Processing file: block_1.csv\n",
      "Available columns: Index(['LCLid', 'day', 'hh_0', 'hh_1', 'hh_2', 'hh_3', 'hh_4', 'hh_5', 'hh_6',\n",
      "       'hh_7', 'hh_8', 'hh_9', 'hh_10', 'hh_11', 'hh_12', 'hh_13', 'hh_14',\n",
      "       'hh_15', 'hh_16', 'hh_17', 'hh_18', 'hh_19', 'hh_20', 'hh_21', 'hh_22',\n",
      "       'hh_23', 'hh_24', 'hh_25', 'hh_26', 'hh_27', 'hh_28', 'hh_29', 'hh_30',\n",
      "       'hh_31', 'hh_32', 'hh_33', 'hh_34', 'hh_35', 'hh_36', 'hh_37', 'hh_38',\n",
      "       'hh_39', 'hh_40', 'hh_41', 'hh_42', 'hh_43', 'hh_44', 'hh_45', 'hh_46',\n",
      "       'hh_47'],\n",
      "      dtype='object')\n",
      "Initial shape of block_df: (1499134, 4)\n",
      "Unique values in hour_block before filtering: ['hh_0' 'hh_1' 'hh_2' 'hh_3' 'hh_4' 'hh_5' 'hh_6' 'hh_7' 'hh_8' 'hh_9'\n",
      " 'hh_10' 'hh_11' 'hh_12' 'hh_13' 'hh_14' 'hh_15' 'hh_16' 'hh_17' 'hh_18'\n",
      " 'hh_19' 'hh_20' 'hh_21' 'hh_22' 'hh_23' 'hh_24' 'hh_25' 'hh_26' 'hh_27'\n",
      " 'hh_28' 'hh_29' 'hh_30' 'hh_31' 'hh_32' 'hh_33' 'hh_34' 'hh_35' 'hh_36'\n",
      " 'hh_37' 'hh_38' 'hh_39' 'hh_40' 'hh_41' 'hh_42' 'hh_43' 'hh_44' 'hh_45'\n",
      " 'hh_46' 'hh_47']\n",
      "Unique values in hour_block after filtering: ['hh_0' 'hh_1' 'hh_2' 'hh_3' 'hh_4' 'hh_5' 'hh_6' 'hh_7' 'hh_8' 'hh_9'\n",
      " 'hh_10' 'hh_11' 'hh_12' 'hh_13' 'hh_14' 'hh_15' 'hh_16' 'hh_17' 'hh_18'\n",
      " 'hh_19' 'hh_20' 'hh_21' 'hh_22' 'hh_23' 'hh_24' 'hh_25' 'hh_26' 'hh_27'\n",
      " 'hh_28' 'hh_29' 'hh_30' 'hh_31' 'hh_32' 'hh_33' 'hh_34' 'hh_35' 'hh_36'\n",
      " 'hh_37' 'hh_38' 'hh_39' 'hh_40' 'hh_41' 'hh_42' 'hh_43' 'hh_44' 'hh_45'\n",
      " 'hh_46' 'hh_47']\n",
      "Number of null timestamps: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing Blocks..:  29%|██████▊                 | 2/7 [00:05<00:14,  2.80s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final shape of block_df before return: (1499134, 4)\n",
      "\n",
      "Processing file: informations_households.csv\n",
      "Available columns: Index(['LCLid', 'stdorToU', 'Acorn', 'Acorn_grouped', 'file'], dtype='object')\n",
      "Skipping informations_households.csv: 'day' column missing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/s0/hts3dbss56x0x2f85v7w1p6r0000gn/T/ipykernel_37410/1193331375.py:43: DtypeWarning: Columns (50,51,52,53) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  block_df = pd.read_csv(file, parse_dates=False)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Processing file: processed_smart_meters.csv\n",
      "Available columns: Index(['LCLid', 'day', 'hh_0', 'hh_1', 'hh_2', 'hh_3', 'hh_4', 'hh_5', 'hh_6',\n",
      "       'hh_7', 'hh_8', 'hh_9', 'hh_10', 'hh_11', 'hh_12', 'hh_13', 'hh_14',\n",
      "       'hh_15', 'hh_16', 'hh_17', 'hh_18', 'hh_19', 'hh_20', 'hh_21', 'hh_22',\n",
      "       'hh_23', 'hh_24', 'hh_25', 'hh_26', 'hh_27', 'hh_28', 'hh_29', 'hh_30',\n",
      "       'hh_31', 'hh_32', 'hh_33', 'hh_34', 'hh_35', 'hh_36', 'hh_37', 'hh_38',\n",
      "       'hh_39', 'hh_40', 'hh_41', 'hh_42', 'hh_43', 'hh_44', 'hh_45', 'hh_46',\n",
      "       'hh_47', 'stdorToU_x', 'Acorn_x', 'Acorn_grouped_x', 'file_x',\n",
      "       'stdorToU_y', 'Acorn_y', 'Acorn_grouped_y', 'file_y'],\n",
      "      dtype='object')\n",
      "Initial shape of block_df: (2937432, 4)\n",
      "Unique values in hour_block before filtering: ['hh_0' 'hh_1' 'hh_2' 'hh_3' 'hh_4' 'hh_5' 'hh_6' 'hh_7' 'hh_8' 'hh_9'\n",
      " 'hh_10' 'hh_11' 'hh_12' 'hh_13' 'hh_14' 'hh_15' 'hh_16' 'hh_17' 'hh_18'\n",
      " 'hh_19' 'hh_20' 'hh_21' 'hh_22' 'hh_23' 'hh_24' 'hh_25' 'hh_26' 'hh_27'\n",
      " 'hh_28' 'hh_29' 'hh_30' 'hh_31' 'hh_32' 'hh_33' 'hh_34' 'hh_35' 'hh_36'\n",
      " 'hh_37' 'hh_38' 'hh_39' 'hh_40' 'hh_41' 'hh_42' 'hh_43' 'hh_44' 'hh_45'\n",
      " 'hh_46' 'hh_47' 'stdorToU_y' 'Acorn_y' 'Acorn_grouped_y' 'file_y']\n",
      "Unique values in hour_block after filtering: ['hh_0' 'hh_1' 'hh_2' 'hh_3' 'hh_4' 'hh_5' 'hh_6' 'hh_7' 'hh_8' 'hh_9'\n",
      " 'hh_10' 'hh_11' 'hh_12' 'hh_13' 'hh_14' 'hh_15' 'hh_16' 'hh_17' 'hh_18'\n",
      " 'hh_19' 'hh_20' 'hh_21' 'hh_22' 'hh_23' 'hh_24' 'hh_25' 'hh_26' 'hh_27'\n",
      " 'hh_28' 'hh_29' 'hh_30' 'hh_31' 'hh_32' 'hh_33' 'hh_34' 'hh_35' 'hh_36'\n",
      " 'hh_37' 'hh_38' 'hh_39' 'hh_40' 'hh_41' 'hh_42' 'hh_43' 'hh_44' 'hh_45'\n",
      " 'hh_46' 'hh_47']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/s0/hts3dbss56x0x2f85v7w1p6r0000gn/T/ipykernel_37410/1193331375.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  block_df['offset'] = block_df['hour_block'].str.replace(\"hh_\", \"\").astype(int)\n",
      "/var/folders/s0/hts3dbss56x0x2f85v7w1p6r0000gn/T/ipykernel_37410/1193331375.py:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  block_df['timestamp'] = block_df['day'] + pd.to_timedelta(block_df['offset'] * 30, unit='m')\n",
      "/var/folders/s0/hts3dbss56x0x2f85v7w1p6r0000gn/T/ipykernel_37410/1193331375.py:28: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  block_df['frequency'] = freq\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of null timestamps: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/s0/hts3dbss56x0x2f85v7w1p6r0000gn/T/ipykernel_37410/1193331375.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  block_df.sort_values([ts_identifier, \"timestamp\"], inplace=True)\n",
      "/var/folders/s0/hts3dbss56x0x2f85v7w1p6r0000gn/T/ipykernel_37410/1193331375.py:33: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  block_df.drop(columns=[col for col in cols_to_drop if col in block_df.columns], inplace=True)\n",
      "Processing Blocks..:  86%|████████████████████▌   | 6/7 [00:11<00:01,  1.61s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final shape of block_df before return: (2711468, 4)\n",
      "\n",
      "Processing file: uk_bank_holidays.csv\n",
      "Available columns: Index(['Bank holidays', 'Type'], dtype='object')\n",
      "Skipping uk_bank_holidays.csv: 'day' column missing.\n",
      "\n",
      "Processing file: weather_daily_darksky.csv\n",
      "Available columns: Index(['temperatureMax', 'temperatureMaxTime', 'windBearing', 'icon',\n",
      "       'dewPoint', 'temperatureMinTime', 'cloudCover', 'windSpeed', 'pressure',\n",
      "       'apparentTemperatureMinTime', 'apparentTemperatureHigh', 'precipType',\n",
      "       'visibility', 'humidity', 'apparentTemperatureHighTime',\n",
      "       'apparentTemperatureLow', 'apparentTemperatureMax', 'uvIndex', 'time',\n",
      "       'sunsetTime', 'temperatureLow', 'temperatureMin', 'temperatureHigh',\n",
      "       'sunriseTime', 'temperatureHighTime', 'uvIndexTime', 'summary',\n",
      "       'temperatureLowTime', 'apparentTemperatureMin',\n",
      "       'apparentTemperatureMaxTime', 'apparentTemperatureLowTime',\n",
      "       'moonPhase'],\n",
      "      dtype='object')\n",
      "Skipping weather_daily_darksky.csv: 'day' column missing.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing Blocks..: 100%|████████████████████████| 7/7 [00:12<00:00,  1.72s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Processing file: weather_hourly_darksky.csv\n",
      "Available columns: Index(['visibility', 'windBearing', 'temperature', 'time', 'dewPoint',\n",
      "       'pressure', 'apparentTemperature', 'windSpeed', 'precipType', 'icon',\n",
      "       'humidity', 'summary'],\n",
      "      dtype='object')\n",
      "Skipping weather_hourly_darksky.csv: 'day' column missing.\n",
      "Final combined DataFrame shape: (5422936, 4)\n",
      "       LCLid energy_consumption           timestamp frequency\n",
      "0  MAC000002              0.263 2012-10-13 00:00:00     30min\n",
      "1  MAC000002              0.269 2012-10-13 00:30:00     30min\n",
      "2  MAC000002              0.275 2012-10-13 01:00:00     30min\n",
      "3  MAC000002              0.256 2012-10-13 01:30:00     30min\n",
      "4  MAC000002              0.211 2012-10-13 02:00:00     30min\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from pathlib import Path\n",
    "\n",
    "def load_process_block_compact(block_df, freq=\"30min\", ts_identifier=\"LCLid\", value_name=\"energy_consumption\"):\n",
    "    \"\"\"\n",
    "    Processes a DataFrame by formatting timestamps and ensuring data consistency.\n",
    "    \"\"\"\n",
    "    print(\"Initial shape of block_df:\", block_df.shape)\n",
    "\n",
    "    # ✅ Ensure the 'day' column is in datetime format\n",
    "    block_df['day'] = pd.to_datetime(block_df['day'], errors='coerce', yearfirst=True)\n",
    "\n",
    "    # ✅ Check for and remove invalid 'hour_block' values\n",
    "    print(\"Unique values in hour_block before filtering:\", block_df[\"hour_block\"].unique())\n",
    "    block_df = block_df[block_df[\"hour_block\"].str.startswith(\"hh_\")]\n",
    "    print(\"Unique values in hour_block after filtering:\", block_df[\"hour_block\"].unique())\n",
    "\n",
    "    # ✅ Convert 'hour_block' to integer safely\n",
    "    block_df['offset'] = block_df['hour_block'].str.replace(\"hh_\", \"\").astype(int)\n",
    "\n",
    "    # ✅ Create timestamp column\n",
    "    block_df['timestamp'] = block_df['day'] + pd.to_timedelta(block_df['offset'] * 30, unit='m')\n",
    "\n",
    "    print(\"Number of null timestamps:\", block_df['timestamp'].isnull().sum())\n",
    "\n",
    "    block_df['frequency'] = freq\n",
    "    block_df.sort_values([ts_identifier, \"timestamp\"], inplace=True)\n",
    "\n",
    "    # ✅ Drop unnecessary columns\n",
    "    cols_to_drop = [\"hour_block\", \"offset\", \"day\"]\n",
    "    block_df.drop(columns=[col for col in cols_to_drop if col in block_df.columns], inplace=True)\n",
    "\n",
    "    print(\"Final shape of block_df before return:\", block_df.shape)\n",
    "    return block_df\n",
    "\n",
    "# ✅ Process multiple block CSV files\n",
    "block_df_l = []\n",
    "block_data_path = Path(\"data/london_smart_meters/hhblock_dataset\")  # Change to correct path\n",
    "\n",
    "for file in tqdm(sorted(block_data_path.glob(\"*.csv\")), desc=\"Processing Blocks..\"):\n",
    "    block_df = pd.read_csv(file, parse_dates=False)\n",
    "\n",
    "    # ✅ Debugging: Check available columns\n",
    "    print(f\"\\nProcessing file: {file.name}\")\n",
    "    print(\"Available columns:\", block_df.columns)\n",
    "\n",
    "    # ✅ Ensure 'day' column exists, otherwise skip file\n",
    "    if \"day\" not in block_df.columns:\n",
    "        print(f\"Skipping {file.name}: 'day' column missing.\")\n",
    "        continue\n",
    "\n",
    "    # ✅ Convert 'day' to datetime\n",
    "    block_df['day'] = pd.to_datetime(block_df['day'], errors='coerce', yearfirst=True)\n",
    "\n",
    "    # ✅ Keep data from 2012-01-01 onwards\n",
    "    block_df = block_df.loc[block_df['day'] >= \"2012-01-01\"]\n",
    "\n",
    "    # ✅ Reshape the DataFrame into long format\n",
    "    block_df = block_df.set_index(['LCLid', \"day\"]).stack().reset_index().rename(columns={\"level_2\": \"hour_block\", 0: \"energy_consumption\"})\n",
    "\n",
    "    # ✅ Process the block and store in list\n",
    "    block_df_l.append(load_process_block_compact(block_df, freq=\"30min\", ts_identifier=\"LCLid\", value_name=\"energy_consumption\"))\n",
    "\n",
    "# ✅ Combine all processed blocks into a single DataFrame\n",
    "final_block_df = pd.concat(block_df_l, ignore_index=True)\n",
    "print(\"Final combined DataFrame shape:\", final_block_df.shape)\n",
    "print(final_block_df.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "0be9a640",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_process_block_expanded(block_df, freq=\"30min\"):\n",
    "    print(\"Initial shape of block_df:\", block_df.shape)\n",
    "    \n",
    "    block_df_l = []  # Define block_df_l as an empty list here\n",
    "\n",
    "    grps = block_df.groupby('LCLid')\n",
    "    \n",
    "    for idx, df in tqdm(grps, leave=False):\n",
    "        print(f\"Processing LCLid: {idx} with shape: {df.shape}\")\n",
    "        ts = preprocess_expanded(df)\n",
    "        block_df_l.append(ts)  # Store each processed DataFrame in block_df_l\n",
    "\n",
    "    # Concatenate all DataFrames in block_df_l afterwards\n",
    "    hhblock_df = pd.concat(block_df_l)\n",
    "    \n",
    "    return hhblock_df  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "2bc9320f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded block_1 with shape: (31243, 50)\n",
      "       LCLid         day   hh_0   hh_1   hh_2   hh_3   hh_4   hh_5   hh_6  \\\n",
      "0  MAC000323  2012-03-07  0.150  0.114  0.054  0.107  0.107  0.078  0.082   \n",
      "1  MAC000323  2012-03-08  0.111  0.088  0.066  0.105  0.113  0.059  0.093   \n",
      "2  MAC000323  2012-03-09  0.699  0.462  0.117  0.108  0.081  0.086  0.102   \n",
      "3  MAC000323  2012-03-10  0.614  0.608  0.488  0.371  0.111  0.128  0.245   \n",
      "4  MAC000323  2012-03-11  0.106  0.064  0.114  0.138  0.097  0.062  0.113   \n",
      "\n",
      "    hh_7  ...  hh_38  hh_39  hh_40  hh_41  hh_42  hh_43  hh_44  hh_45  hh_46  \\\n",
      "0  0.105  ...  1.475  1.199  0.925  0.701  0.744  0.655  0.686  0.711  0.397   \n",
      "1  0.112  ...  0.496  1.032  0.564  0.608  0.691  0.596  0.700  1.086  0.630   \n",
      "2  0.090  ...  1.587  1.445  1.081  1.022  0.772  0.684  0.731  0.849  0.745   \n",
      "3  0.226  ...  0.691  0.902  0.933  1.166  1.189  1.222  0.619  0.472  0.555   \n",
      "4  0.147  ...  1.286  1.272  1.694  1.940  1.884  1.010  0.731  0.812  0.732   \n",
      "\n",
      "   hh_47  \n",
      "0  0.098  \n",
      "1  0.842  \n",
      "2  0.700  \n",
      "3  0.277  \n",
      "4  0.274  \n",
      "\n",
      "[5 rows x 50 columns]\n",
      "Initial shape of block_df: (31243, 50)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  4%|█▊                                          | 2/50 [00:00<00:02, 17.25it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing LCLid: MAC000323 with shape: (722, 50)\n",
      "Initial shape of x: (722, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC000379 with shape: (496, 50)\n",
      "Initial shape of x: (496, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC000386 with shape: (710, 50)\n",
      "Initial shape of x: (710, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC000535 with shape: (678, 50)\n",
      "Initial shape of x: (678, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC000557 with shape: (667, 50)\n",
      "Initial shape of x: (667, 50)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 16%|███████                                     | 8/50 [00:00<00:01, 23.97it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC000569 with shape: (674, 50)\n",
      "Initial shape of x: (674, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC000713 with shape: (670, 50)\n",
      "Initial shape of x: (670, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC000768 with shape: (670, 50)\n",
      "Initial shape of x: (670, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC000778 with shape: (675, 50)\n",
      "Initial shape of x: (675, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC000816 with shape: (662, 50)\n",
      "Initial shape of x: (662, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC000850 with shape: (658, 50)\n",
      "Initial shape of x: (658, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 30%|████████████▉                              | 15/50 [00:00<00:01, 27.11it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing LCLid: MAC000886 with shape: (662, 50)\n",
      "Initial shape of x: (662, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC000902 with shape: (665, 50)\n",
      "Initial shape of x: (665, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC000948 with shape: (660, 50)\n",
      "Initial shape of x: (660, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC000974 with shape: (658, 50)\n",
      "Initial shape of x: (658, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC001145 with shape: (645, 50)\n",
      "Initial shape of x: (645, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC001239 with shape: (644, 50)\n",
      "Initial shape of x: (644, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC001251 with shape: (647, 50)\n",
      "Initial shape of x: (647, 50)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 36%|███████████████▍                           | 18/50 [00:01<00:02, 11.16it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC001271 with shape: (649, 50)\n",
      "Initial shape of x: (649, 50)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40%|█████████████████▏                         | 20/50 [00:01<00:03,  9.13it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC001510 with shape: (643, 50)\n",
      "Initial shape of x: (643, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC001528 with shape: (640, 50)\n",
      "Initial shape of x: (640, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC001533 with shape: (640, 50)\n",
      "Initial shape of x: (640, 50)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 52%|██████████████████████▎                    | 26/50 [00:01<00:01, 14.45it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC001628 with shape: (636, 50)\n",
      "Initial shape of x: (636, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC001689 with shape: (632, 50)\n",
      "Initial shape of x: (632, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC001710 with shape: (633, 50)\n",
      "Initial shape of x: (633, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC001734 with shape: (633, 50)\n",
      "Initial shape of x: (633, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC001776 with shape: (622, 50)\n",
      "Initial shape of x: (622, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC001843 with shape: (578, 50)\n",
      "Initial shape of x: (578, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC001893 with shape: (621, 50)\n",
      "Initial shape of x: (621, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 58%|████████████████████████▉                  | 29/50 [00:02<00:01, 11.81it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing LCLid: MAC001989 with shape: (612, 50)\n",
      "Initial shape of x: (612, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 68%|█████████████████████████████▏             | 34/50 [00:02<00:01, 11.55it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing LCLid: MAC002025 with shape: (618, 50)\n",
      "Initial shape of x: (618, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC002068 with shape: (555, 50)\n",
      "Initial shape of x: (555, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC002150 with shape: (615, 50)\n",
      "Initial shape of x: (615, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC002249 with shape: (656, 50)\n",
      "Initial shape of x: (656, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC002314 with shape: (662, 50)\n",
      "Initial shape of x: (662, 50)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 82%|███████████████████████████████████▎       | 41/50 [00:02<00:00, 18.14it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC002562 with shape: (594, 50)\n",
      "Initial shape of x: (594, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC002563 with shape: (597, 50)\n",
      "Initial shape of x: (597, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC002601 with shape: (592, 50)\n",
      "Initial shape of x: (592, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC002613 with shape: (258, 50)\n",
      "Initial shape of x: (258, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC002628 with shape: (656, 50)\n",
      "Initial shape of x: (656, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC002813 with shape: (649, 50)\n",
      "Initial shape of x: (649, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC002924 with shape: (599, 50)\n",
      "Initial shape of x: (599, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 88%|█████████████████████████████████████▊     | 44/50 [00:02<00:00, 19.89it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing LCLid: MAC002937 with shape: (602, 50)\n",
      "Initial shape of x: (602, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003072 with shape: (649, 50)\n",
      "Initial shape of x: (649, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003110 with shape: (649, 50)\n",
      "Initial shape of x: (649, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003166 with shape: (570, 50)\n",
      "Initial shape of x: (570, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003182 with shape: (527, 50)\n",
      "Initial shape of x: (527, 50)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 98%|██████████████████████████████████████████▏| 49/50 [00:03<00:00, 10.48it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003212 with shape: (524, 50)\n",
      "Initial shape of x: (524, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC003840 with shape: (484, 50)\n",
      "Initial shape of x: (484, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n",
      "Processing LCLid: MAC004529 with shape: (785, 50)\n",
      "Initial shape of x: (785, 50)\n",
      "Columns in x after melting: Index(['LCLid', 'day', 'hour_block', 'energy_usage'], dtype='object')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final shape of hhblock_df: (2400, 4)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index              132\n",
       "hour_block       19200\n",
       "day              19200\n",
       "LCLid           139200\n",
       "energy_usage     19200\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Memory Usage: 0.19 MB\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "\n",
    "# ✅ Load the dataset before using it\n",
    "block_1 = pd.read_csv(\"data/london_smart_meters/hhblock_dataset/block_1.csv\")\n",
    "\n",
    "# ✅ Check if the dataset is loaded correctly\n",
    "print(\"Loaded block_1 with shape:\", block_1.shape)\n",
    "print(block_1.head())\n",
    "\n",
    "def load_process_block_expanded(block_df, freq=\"30min\"):\n",
    "    print(\"Initial shape of block_df:\", block_df.shape)\n",
    "    \n",
    "    block_df_l = []  # ✅ Define block_df_l as an empty list\n",
    "\n",
    "    grps = block_df.groupby('LCLid')\n",
    "    \n",
    "    for idx, df in tqdm(grps, leave=False):\n",
    "        print(f\"Processing LCLid: {idx} with shape: {df.shape}\")\n",
    "        ts = preprocess_expanded(df)  # Ensure preprocess_expanded() is defined\n",
    "        block_df_l.append(ts)  # Store each processed DataFrame in block_df_l\n",
    "\n",
    "    # ✅ Concatenate all DataFrames in block_df_l\n",
    "    hhblock_df = pd.concat(block_df_l, ignore_index=True)\n",
    "\n",
    "    print(\"Final shape of hhblock_df:\", hhblock_df.shape)\n",
    "    \n",
    "    return hhblock_df  \n",
    "\n",
    "# ✅ Call the function and store the result\n",
    "hhblock_df = load_process_block_expanded(block_1, freq=\"30min\")\n",
    "\n",
    "# ✅ Check memory usage AFTER defining hhblock_df\n",
    "display(hhblock_df.memory_usage(deep=True))\n",
    "print(f\"Total Memory Usage: {hhblock_df.memory_usage(deep=True).sum()/1024**2:.2f} MB\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "856079b7",
   "metadata": {},
   "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>hour_block</th>\n",
       "      <th>day</th>\n",
       "      <th>LCLid</th>\n",
       "      <th>energy_usage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2012-03-07</td>\n",
       "      <td>MAC000323</td>\n",
       "      <td>0.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2012-03-07</td>\n",
       "      <td>MAC000323</td>\n",
       "      <td>0.114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2012-03-07</td>\n",
       "      <td>MAC000323</td>\n",
       "      <td>0.054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2012-03-07</td>\n",
       "      <td>MAC000323</td>\n",
       "      <td>0.107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2012-03-07</td>\n",
       "      <td>MAC000323</td>\n",
       "      <td>0.107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hour_block        day      LCLid  energy_usage\n",
       "0           0 2012-03-07  MAC000323         0.150\n",
       "1           1 2012-03-07  MAC000323         0.114\n",
       "2           2 2012-03-07  MAC000323         0.054\n",
       "3           3 2012-03-07  MAC000323         0.107\n",
       "4           4 2012-03-07  MAC000323         0.107"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hhblock_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06d822ae",
   "metadata": {},
   "source": [
    "# Merging additional information\n",
    "# Household information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "cb8feea8",
   "metadata": {},
   "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>LCLid</th>\n",
       "      <th>stdorToU</th>\n",
       "      <th>Acorn</th>\n",
       "      <th>Acorn_grouped</th>\n",
       "      <th>file</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MAC005492</td>\n",
       "      <td>ToU</td>\n",
       "      <td>ACORN-</td>\n",
       "      <td>ACORN-</td>\n",
       "      <td>block_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MAC001074</td>\n",
       "      <td>ToU</td>\n",
       "      <td>ACORN-</td>\n",
       "      <td>ACORN-</td>\n",
       "      <td>block_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MAC000002</td>\n",
       "      <td>Std</td>\n",
       "      <td>ACORN-A</td>\n",
       "      <td>Affluent</td>\n",
       "      <td>block_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MAC003613</td>\n",
       "      <td>Std</td>\n",
       "      <td>ACORN-A</td>\n",
       "      <td>Affluent</td>\n",
       "      <td>block_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MAC003597</td>\n",
       "      <td>Std</td>\n",
       "      <td>ACORN-A</td>\n",
       "      <td>Affluent</td>\n",
       "      <td>block_0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       LCLid stdorToU    Acorn Acorn_grouped     file\n",
       "0  MAC005492      ToU   ACORN-        ACORN-  block_0\n",
       "1  MAC001074      ToU   ACORN-        ACORN-  block_0\n",
       "2  MAC000002      Std  ACORN-A      Affluent  block_0\n",
       "3  MAC003613      Std  ACORN-A      Affluent  block_0\n",
       "4  MAC003597      Std  ACORN-A      Affluent  block_0"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "household_info = pd.read_csv(\"data/london_smart_meters/hhblock_dataset/informations_households.csv\")\n",
    "household_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "72a3e046",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['.DS_Store', 'hhblock_dataset', 'uk_bank_holidays.csv', 'block_1.csv', 'block_0.csv', 'weather_daily_darksky.csv', 'informations_households.csv', 'weather_hourly_darksky.csv', 'processed_smart_meters.csv']\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import glob\n",
    "# Define the path to the folder containing all CSV files\n",
    "hhblock_path = \"data/london_smart_meters/hhblock_dataset\"\n",
    "# List all files in the hhblock_dataset directory\n",
    "print(os.listdir(hhblock_path))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "8c01df0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found CSV files: ['data/london_smart_meters/hhblock_dataset/uk_bank_holidays.csv', 'data/london_smart_meters/hhblock_dataset/block_1.csv', 'data/london_smart_meters/hhblock_dataset/block_0.csv', 'data/london_smart_meters/hhblock_dataset/weather_daily_darksky.csv', 'data/london_smart_meters/hhblock_dataset/informations_households.csv', 'data/london_smart_meters/hhblock_dataset/weather_hourly_darksky.csv', 'data/london_smart_meters/hhblock_dataset/processed_smart_meters.csv']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/s0/hts3dbss56x0x2f85v7w1p6r0000gn/T/ipykernel_37410/354343057.py:9: DtypeWarning: Columns (50,51,52,53) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  dataframes = [pd.read_csv(file, on_bad_lines=\"skip\") for file in csv_files]\n"
     ]
    }
   ],
   "source": [
    "# Get a list of all CSV files in the directory\n",
    "csv_files = glob.glob(os.path.join(hhblock_path, \"*.csv\"))\n",
    "\n",
    "# Print the list of CSV files\n",
    "print(f\"Found CSV files: {csv_files}\")\n",
    "\n",
    "# Combine all CSV files into a single DataFrame\n",
    "\n",
    "dataframes = [pd.read_csv(file, on_bad_lines=\"skip\") for file in csv_files]\n",
    "\n",
    "hhblock_df = pd.concat(dataframes, ignore_index=True)\n",
    "temp_df = pd.read_csv(csv_files[0], on_bad_lines=\"skip\")  # Example: Reading the first file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "11cac245",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "file_path = \"data/london_smart_meters/hhblock_dataset/processed_smart_meters.csv\"\n",
    "\n",
    "# Load with specified data types to avoid mixed types\n",
    "df = pd.read_csv(file_path, dtype={\"column_name_50\": str, \"column_name_51\": str, \n",
    "                                   \"column_name_52\": str, \"column_name_53\": str}, \n",
    "                 low_memory=False)\n",
    "\n",
    "# Drop the unwanted HTML-related column if it exists\n",
    "if \"<!doctype html>\" in df.columns:\n",
    "    df.drop(columns=[\"<!doctype html>\"], inplace=True)\n",
    "\n",
    "# Save the cleaned dataset\n",
    "df.to_csv(file_path, index=False)\n",
    "\n",
    "# ✅ Fixed f-string issue\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "8550b03c",
   "metadata": {},
   "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>LCLid</th>\n",
       "      <th>day</th>\n",
       "      <th>hh_0</th>\n",
       "      <th>hh_1</th>\n",
       "      <th>hh_2</th>\n",
       "      <th>hh_3</th>\n",
       "      <th>hh_4</th>\n",
       "      <th>hh_5</th>\n",
       "      <th>hh_6</th>\n",
       "      <th>hh_7</th>\n",
       "      <th>...</th>\n",
       "      <th>hh_46</th>\n",
       "      <th>hh_47</th>\n",
       "      <th>stdorToU_x</th>\n",
       "      <th>Acorn_x</th>\n",
       "      <th>Acorn_grouped_x</th>\n",
       "      <th>file_x</th>\n",
       "      <th>stdorToU_y</th>\n",
       "      <th>Acorn_y</th>\n",
       "      <th>Acorn_grouped_y</th>\n",
       "      <th>file_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MAC000323</td>\n",
       "      <td>2012-03-07</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.0819999999999999</td>\n",
       "      <td>0.105</td>\n",
       "      <td>...</td>\n",
       "      <td>0.397</td>\n",
       "      <td>0.098</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Std</td>\n",
       "      <td>ACORN-A</td>\n",
       "      <td>Affluent</td>\n",
       "      <td>block_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MAC000323</td>\n",
       "      <td>2012-03-08</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.093</td>\n",
       "      <td>0.1119999999999999</td>\n",
       "      <td>...</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.8420000000000001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Std</td>\n",
       "      <td>ACORN-A</td>\n",
       "      <td>Affluent</td>\n",
       "      <td>block_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MAC000323</td>\n",
       "      <td>2012-03-09</td>\n",
       "      <td>0.6990000000000001</td>\n",
       "      <td>0.462</td>\n",
       "      <td>0.1169999999999999</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.09</td>\n",
       "      <td>...</td>\n",
       "      <td>0.745</td>\n",
       "      <td>0.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Std</td>\n",
       "      <td>ACORN-A</td>\n",
       "      <td>Affluent</td>\n",
       "      <td>block_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MAC000323</td>\n",
       "      <td>2012-03-10</td>\n",
       "      <td>0.614</td>\n",
       "      <td>0.608</td>\n",
       "      <td>0.488</td>\n",
       "      <td>0.371</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.245</td>\n",
       "      <td>0.226</td>\n",
       "      <td>...</td>\n",
       "      <td>0.555</td>\n",
       "      <td>0.2769999999999999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Std</td>\n",
       "      <td>ACORN-A</td>\n",
       "      <td>Affluent</td>\n",
       "      <td>block_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MAC000323</td>\n",
       "      <td>2012-03-11</td>\n",
       "      <td>0.106</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.0969999999999999</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.147</td>\n",
       "      <td>...</td>\n",
       "      <td>0.732</td>\n",
       "      <td>0.2739999999999999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Std</td>\n",
       "      <td>ACORN-A</td>\n",
       "      <td>Affluent</td>\n",
       "      <td>block_1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       LCLid         day                hh_0   hh_1                hh_2  \\\n",
       "0  MAC000323  2012-03-07                0.15  0.114               0.054   \n",
       "1  MAC000323  2012-03-08               0.111  0.088               0.066   \n",
       "2  MAC000323  2012-03-09  0.6990000000000001  0.462  0.1169999999999999   \n",
       "3  MAC000323  2012-03-10               0.614  0.608               0.488   \n",
       "4  MAC000323  2012-03-11               0.106  0.064               0.114   \n",
       "\n",
       "    hh_3                hh_4   hh_5                hh_6                hh_7  \\\n",
       "0  0.107               0.107  0.078  0.0819999999999999               0.105   \n",
       "1  0.105               0.113  0.059               0.093  0.1119999999999999   \n",
       "2  0.108               0.081  0.086               0.102                0.09   \n",
       "3  0.371               0.111  0.128               0.245               0.226   \n",
       "4  0.138  0.0969999999999999  0.062               0.113               0.147   \n",
       "\n",
       "   ...  hh_46               hh_47 stdorToU_x Acorn_x Acorn_grouped_x file_x  \\\n",
       "0  ...  0.397               0.098        NaN     NaN             NaN    NaN   \n",
       "1  ...   0.63  0.8420000000000001        NaN     NaN             NaN    NaN   \n",
       "2  ...  0.745                 0.7        NaN     NaN             NaN    NaN   \n",
       "3  ...  0.555  0.2769999999999999        NaN     NaN             NaN    NaN   \n",
       "4  ...  0.732  0.2739999999999999        NaN     NaN             NaN    NaN   \n",
       "\n",
       "  stdorToU_y  Acorn_y Acorn_grouped_y   file_y  \n",
       "0        Std  ACORN-A        Affluent  block_1  \n",
       "1        Std  ACORN-A        Affluent  block_1  \n",
       "2        Std  ACORN-A        Affluent  block_1  \n",
       "3        Std  ACORN-A        Affluent  block_1  \n",
       "4        Std  ACORN-A        Affluent  block_1  \n",
       "\n",
       "[5 rows x 58 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "file_path = \"data/london_smart_meters/hhblock_dataset/processed_smart_meters.csv\"\n",
    "\n",
    "# Load the CSV while treating specific columns as strings\n",
    "df = pd.read_csv(file_path, dtype=str, low_memory=False)\n",
    "\n",
    "# Display first few rows\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "644e991d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LCLid</th>\n",
       "      <th>day</th>\n",
       "      <th>hh_0</th>\n",
       "      <th>hh_1</th>\n",
       "      <th>hh_2</th>\n",
       "      <th>hh_3</th>\n",
       "      <th>hh_4</th>\n",
       "      <th>hh_5</th>\n",
       "      <th>hh_6</th>\n",
       "      <th>hh_7</th>\n",
       "      <th>...</th>\n",
       "      <th>hh_46</th>\n",
       "      <th>hh_47</th>\n",
       "      <th>stdorToU_x</th>\n",
       "      <th>Acorn_x</th>\n",
       "      <th>Acorn_grouped_x</th>\n",
       "      <th>file_x</th>\n",
       "      <th>stdorToU_y</th>\n",
       "      <th>Acorn_y</th>\n",
       "      <th>Acorn_grouped_y</th>\n",
       "      <th>file_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MAC000323</td>\n",
       "      <td>2012-03-07</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.105</td>\n",
       "      <td>...</td>\n",
       "      <td>0.397</td>\n",
       "      <td>0.098</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Std</td>\n",
       "      <td>ACORN-A</td>\n",
       "      <td>Affluent</td>\n",
       "      <td>block_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MAC000323</td>\n",
       "      <td>2012-03-08</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.093</td>\n",
       "      <td>0.112</td>\n",
       "      <td>...</td>\n",
       "      <td>0.630</td>\n",
       "      <td>0.842</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Std</td>\n",
       "      <td>ACORN-A</td>\n",
       "      <td>Affluent</td>\n",
       "      <td>block_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MAC000323</td>\n",
       "      <td>2012-03-09</td>\n",
       "      <td>0.699</td>\n",
       "      <td>0.462</td>\n",
       "      <td>0.117</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.090</td>\n",
       "      <td>...</td>\n",
       "      <td>0.745</td>\n",
       "      <td>0.700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Std</td>\n",
       "      <td>ACORN-A</td>\n",
       "      <td>Affluent</td>\n",
       "      <td>block_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MAC000323</td>\n",
       "      <td>2012-03-10</td>\n",
       "      <td>0.614</td>\n",
       "      <td>0.608</td>\n",
       "      <td>0.488</td>\n",
       "      <td>0.371</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.245</td>\n",
       "      <td>0.226</td>\n",
       "      <td>...</td>\n",
       "      <td>0.555</td>\n",
       "      <td>0.277</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Std</td>\n",
       "      <td>ACORN-A</td>\n",
       "      <td>Affluent</td>\n",
       "      <td>block_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MAC000323</td>\n",
       "      <td>2012-03-11</td>\n",
       "      <td>0.106</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.097</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.147</td>\n",
       "      <td>...</td>\n",
       "      <td>0.732</td>\n",
       "      <td>0.274</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Std</td>\n",
       "      <td>ACORN-A</td>\n",
       "      <td>Affluent</td>\n",
       "      <td>block_1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       LCLid         day   hh_0   hh_1   hh_2   hh_3   hh_4   hh_5   hh_6  \\\n",
       "0  MAC000323  2012-03-07  0.150  0.114  0.054  0.107  0.107  0.078  0.082   \n",
       "1  MAC000323  2012-03-08  0.111  0.088  0.066  0.105  0.113  0.059  0.093   \n",
       "2  MAC000323  2012-03-09  0.699  0.462  0.117  0.108  0.081  0.086  0.102   \n",
       "3  MAC000323  2012-03-10  0.614  0.608  0.488  0.371  0.111  0.128  0.245   \n",
       "4  MAC000323  2012-03-11  0.106  0.064  0.114  0.138  0.097  0.062  0.113   \n",
       "\n",
       "    hh_7  ...  hh_46  hh_47  stdorToU_x  Acorn_x  Acorn_grouped_x  file_x  \\\n",
       "0  0.105  ...  0.397  0.098         NaN      NaN              NaN     NaN   \n",
       "1  0.112  ...  0.630  0.842         NaN      NaN              NaN     NaN   \n",
       "2  0.090  ...  0.745  0.700         NaN      NaN              NaN     NaN   \n",
       "3  0.226  ...  0.555  0.277         NaN      NaN              NaN     NaN   \n",
       "4  0.147  ...  0.732  0.274         NaN      NaN              NaN     NaN   \n",
       "\n",
       "   stdorToU_y  Acorn_y  Acorn_grouped_y   file_y  \n",
       "0         Std  ACORN-A         Affluent  block_1  \n",
       "1         Std  ACORN-A         Affluent  block_1  \n",
       "2         Std  ACORN-A         Affluent  block_1  \n",
       "3         Std  ACORN-A         Affluent  block_1  \n",
       "4         Std  ACORN-A         Affluent  block_1  \n",
       "\n",
       "[5 rows x 58 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert numerical columns while keeping categorical data intact\n",
    "for col in df.columns:\n",
    "    if col.startswith(\"hh_\"):  # Apply to half-hourly consumption data\n",
    "        df[col] = pd.to_numeric(df[col], errors='coerce')  # Convert to float\n",
    "\n",
    "# Display first few rows again\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "b5a00309",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check missing values\n",
    "missing_values = df.isnull().sum()\n",
    "missing_values[missing_values > 0]\n",
    "df_cleaned = df.dropna()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "b98e5981",
   "metadata": {},
   "outputs": [
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</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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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</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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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       hh_0  hh_1  hh_2  hh_3  hh_4  hh_5  hh_6  hh_7  hh_8  hh_9  ...  hh_38  \\\n",
       "count   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...    0.0   \n",
       "mean    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    NaN   \n",
       "std     NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    NaN   \n",
       "min     NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    NaN   \n",
       "25%     NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    NaN   \n",
       "50%     NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    NaN   \n",
       "75%     NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    NaN   \n",
       "max     NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    NaN   \n",
       "\n",
       "       hh_39  hh_40  hh_41  hh_42  hh_43  hh_44  hh_45  hh_46  hh_47  \n",
       "count    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "mean     NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN  \n",
       "std      NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN  \n",
       "min      NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN  \n",
       "25%      NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN  \n",
       "50%      NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN  \n",
       "75%      NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN  \n",
       "max      NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN  \n",
       "\n",
       "[8 rows x 48 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cleaned.describe()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "0c7e979a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LCLid              0\n",
       "day                0\n",
       "hh_0               0\n",
       "hh_1               0\n",
       "hh_2               0\n",
       "hh_3               0\n",
       "hh_4               0\n",
       "hh_5               0\n",
       "hh_6               0\n",
       "hh_7               0\n",
       "hh_8               0\n",
       "hh_9               0\n",
       "hh_10              0\n",
       "hh_11              0\n",
       "hh_12              0\n",
       "hh_13              0\n",
       "hh_14              0\n",
       "hh_15              0\n",
       "hh_16              0\n",
       "hh_17              0\n",
       "hh_18              0\n",
       "hh_19              0\n",
       "hh_20              0\n",
       "hh_21              0\n",
       "hh_22              0\n",
       "hh_23              0\n",
       "hh_24              0\n",
       "hh_25              0\n",
       "hh_26              0\n",
       "hh_27              0\n",
       "hh_28              0\n",
       "hh_29              0\n",
       "hh_30              0\n",
       "hh_31              0\n",
       "hh_32              0\n",
       "hh_33              0\n",
       "hh_34              0\n",
       "hh_35              0\n",
       "hh_36              0\n",
       "hh_37              0\n",
       "hh_38              0\n",
       "hh_39              0\n",
       "hh_40              0\n",
       "hh_41              0\n",
       "hh_42              0\n",
       "hh_43              0\n",
       "hh_44              0\n",
       "hh_45              0\n",
       "hh_46              0\n",
       "hh_47              0\n",
       "stdorToU_x         0\n",
       "Acorn_x            0\n",
       "Acorn_grouped_x    0\n",
       "file_x             0\n",
       "stdorToU_y         0\n",
       "Acorn_y            0\n",
       "Acorn_grouped_y    0\n",
       "file_y             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cleaned.nunique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "27b85d7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datetime64[ns]\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "df_cleaned['day'] = pd.to_datetime(df_cleaned['day'], errors='coerce')\n",
    "print(df_cleaned['day'].dtypes)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "5365492b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['day'] = pd.to_datetime(df['day'], errors='coerce')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "e6438c69",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df_cleaned = df.dropna()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "8138108b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 58)\n"
     ]
    }
   ],
   "source": [
    "print(df_cleaned.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "10dc6136",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(56529, 58)\n"
     ]
    }
   ],
   "source": [
    "df_cleaned = df.dropna(subset=['day'])  # Drops only rows where 'day' is missing\n",
    "print(df_cleaned.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "d05967d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/s0/hts3dbss56x0x2f85v7w1p6r0000gn/T/ipykernel_37410/3642865472.py:1: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  df_cleaned = df_cleaned.fillna(0)  # Replaces all NaN values with 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cleaned = df_cleaned.fillna(0)  # Replaces all NaN values with 0\n",
    "print(df_cleaned.isnull().sum().sum())  # Should print 0 if all missing values are filled\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Aggregate data by day\n",
    "df_grouped = df_cleaned.groupby(\"day\").sum()\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(df_grouped.index, df_grouped[\"hh_0\"], label=\"hh_0 (Midnight Usage)\")\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Energy Consumption (kWh)\")\n",
    "plt.title(\"Daily Energy Consumption Over Time\")\n",
    "plt.legend()\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "71a6696e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Cleaned dataset saved as 'cleaned_smart_meters.csv'\n"
     ]
    }
   ],
   "source": [
    "df_cleaned.to_csv(\"cleaned_smart_meters.csv\", index=False)\n",
    "print(\"✅ Cleaned dataset saved as 'cleaned_smart_meters.csv'\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "5ff25f88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(df_cleaned[\"hh_0\"], bins=50, kde=True)\n",
    "plt.xlabel(\"Energy Consumption (kWh)\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Distribution of Midnight Energy Consumption (hh_0)\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2b5240a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}