Simple_Bar_Chart_Baseline_Vs_Design.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a_DO30YB4kwl"
      },
      "source": [
        "# Import the necessary packages."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "vkzrwIzZ4QbA"
      },
      "outputs": [],
      "source": [
        "%matplotlib inline\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from tabulate import tabulate\n",
        "import matplotlib as mpl\n",
        "import math"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "from matplotlib import font_manager\n",
        "\n",
        "font_path = 'C:\\Windows\\Fonts\\SEGOEUI.TTF'  # Your font path goes here\n",
        "font_manager.fontManager.addfont(font_path)\n",
        "prop = font_manager.FontProperties(fname=font_path)\n",
        "\n",
        "mpl.rcParams['font.family'] = 'sans-serif'\n",
        "mpl.rcParams['font.sans-serif'] = prop.get_name()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "# import matplotlib.font_manager\n",
        "# matplotlib.font_manager.findSystemFonts(fontpaths=None, fontext='ttf')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "# import matplotlib.font_manager\n",
        "# from IPython.core.display import HTML\n",
        "\n",
        "# def make_html(fontname):\n",
        "#     return \"<p>{font}: <span style='font-family:{font}; font-size: 24px;'>{font}</p>\".format(font=fontname)\n",
        "\n",
        "# code = \"\\n\".join([make_html(font) for font in sorted(set([f.name for f in matplotlib.font_manager.fontManager.ttflist]))])\n",
        "\n",
        "# HTML(\"<div style='column-count: 2;'>{}</div>\".format(code))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "mpl.rcParams['figure.dpi'] = 300\n",
        "# mpl.rcParams['font.family'] = 'sans-serif'\n",
        "# mpl.rcParams['font.sans-serif'] = ['Lucida Grande'] # ['Tahoma', 'DejaVu Sans', 'Lucida Grande', 'Verdana']\n",
        "mpl.rcParams['font.stretch'] = 'extra-condensed'\n",
        "### CAN WE IMPORT CUSTOM FONTS\n",
        "### EXPORT SVG\n",
        "### REDUCE FONT SIZE"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-VNWk06SWH56"
      },
      "source": [
        "# Project Information"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "H-HBhIdNWKBz"
      },
      "outputs": [],
      "source": [
        "# Blank"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CbfvwFe7dZVb"
      },
      "source": [
        "# Import and check the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "7cFB56xMJudz"
      },
      "outputs": [],
      "source": [
        "# Energy consumption \n",
        "Baseline = [  131490,                   141255.8,                 137310,             794833,     865554.58,  176627]\n",
        "Design = [    131490,                   106740.3,                 59175,              216054,     169969.9,   132103]\n",
        "end_uses = [  \"Receptacle equipment\",   \"Service water heating\",  \"Pumps and Fans\",   \"Cooling\",  \"Heating\",  \"Lighting\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "jfbGMiJsZ1Ow"
      },
      "outputs": [],
      "source": [
        "# df = pd.read_csv(\"B246EndUses.csv\",index_col=\"End Use\")\n",
        "df = pd.DataFrame(\n",
        "    list(zip(Baseline, Design)),\n",
        "    index= end_uses,\n",
        "    columns = [\"LEED v4 Baseline\", \"Design\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "NH9sKqJALrtN"
      },
      "outputs": [],
      "source": [
        "# Convert kWh to kBtu\n",
        "df = df * 3.412141\n",
        "\n",
        "# Convert to EUI\n",
        "area = 68876.49 # ft2\n",
        "df = df / area\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "25ARIoWy50X-",
        "outputId": "e2b54e2b-9c71-4057-b121-c3374c38689d"
      },
      "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>Receptacle equipment</th>\n",
              "      <th>Service water heating</th>\n",
              "      <th>Pumps and Fans</th>\n",
              "      <th>Cooling</th>\n",
              "      <th>Heating</th>\n",
              "      <th>Lighting</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>LEED v4 Baseline</th>\n",
              "      <td>6.514014</td>\n",
              "      <td>6.997812</td>\n",
              "      <td>6.802337</td>\n",
              "      <td>39.376023</td>\n",
              "      <td>42.879570</td>\n",
              "      <td>8.750101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EYP's Design</th>\n",
              "      <td>6.514014</td>\n",
              "      <td>5.287914</td>\n",
              "      <td>2.931529</td>\n",
              "      <td>10.703314</td>\n",
              "      <td>8.420308</td>\n",
              "      <td>6.544382</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Receptacle equipment  Service water heating  Pumps and Fans  \\\n",
              "LEED v4 Baseline              6.514014               6.997812        6.802337   \n",
              "EYP's Design                  6.514014               5.287914        2.931529   \n",
              "\n",
              "                    Cooling    Heating  Lighting  \n",
              "LEED v4 Baseline  39.376023  42.879570  8.750101  \n",
              "EYP's Design      10.703314   8.420308  6.544382  "
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Format and view dataframe\n",
        "df = df.T\n",
        "# df = df.drop(\"Total Energy Use Intensity\", axis=1)\n",
        "columns = df.columns.tolist()\n",
        "# columns = columns[::-1]\n",
        "df = df[columns]\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Uk2TCQUlzV9"
      },
      "source": [
        "# Graphical Standards"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "IIKDQ8N36hW-"
      },
      "outputs": [],
      "source": [
        "# Create hex colors for each patch and create Legend labels\n",
        "color_lighting = \"#e6c845\"\n",
        "color_heating = \"#f26127\"\n",
        "color_cooling =\"#1e4e71\"\n",
        "color_pumps_and_fans = \"#906ca6\"\n",
        "color_service_water_heating = \"#1a998a\"\n",
        "color_receptacle_equipment = \"#38424d\"\n",
        "color_map = [color_receptacle_equipment,color_service_water_heating,color_pumps_and_fans,color_cooling,color_heating,color_lighting]\n",
        "legendLabels = ['Receptacle Equipment','Service Water Heating','Pumps and Fans','Cooling','Heating','Lighting']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1fRn9wRzRfn-"
      },
      "source": [
        "# Calculate Total by ECM"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0zDLKeVKdgKo"
      },
      "source": [
        "# Filter data for Meter 1 and create plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lLK9d1IsRjXI",
        "outputId": "a8f061f0-c0a3-41a0-f651-5152a2d23340"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "LEED v4 Baseline    111.319856\n",
            "EYP's Design         40.401461\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "totalDF = df.sum(axis=1)\n",
        "print(totalDF)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cg5-U28BeAHY"
      },
      "source": [
        "## Create Energy Conservation Measure Stacked Bar Chart"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 962
        },
        "id": "94LY27RW58c1",
        "outputId": "b477ffa8-1f8c-41ad-da69-88bc16970fa8"
      },
      "outputs": [
        {
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",
            "text/plain": [
              "<Figure size 1800x2100 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "### Create the plot ###\n",
        "ax = df.plot.bar(stacked=True, color=color_map, figsize=(6,7), rot=0, fontsize=16)\n",
        "#### DECREASE TEXT SIZE\n",
        "\n",
        "### Edit the plot characteristics ###\n",
        "#ax.set_xlabel('Energy Conservation Measures',fontsize=14)\n",
        "#ax.set_title('Chancery* Annual Energy Consumption of Energy Conservation Measures (w/o Plug Loads)',fontsize=14)\n",
        "#ax.set_xticklabels(rotation=45)\n",
        "ax.set_ylabel('EUI\\n(kBtu/sf)',fontsize=20, rotation=0, labelpad=55)\n",
        "start, end = ax.get_ylim()\n",
        "ax.yaxis.set_ticks(np.arange(start,end,20))\n",
        "ax.spines['top'].set_visible(False)\n",
        "ax.spines['right'].set_visible(False)\n",
        "ax.spines['bottom'].set_color('grey')\n",
        "ax.spines['left'].set_color('grey')\n",
        "ax.tick_params(axis='y',direction='in',color='gray', length=10)\n",
        "ax.tick_params(axis='x',bottom=False)\n",
        "\n",
        "### Legends Parameters ###\n",
        "#handles, labels = ax.get_legend_handles_labels()\n",
        "#ax.legend(handles[::-1], legendLabels[::-1], loc='center left', bbox_to_anchor=(1,0.19), title='End Uses')\n",
        "ax.get_legend().remove()\n",
        "\n",
        "\n",
        "### Add labels -- .patches are everything inside of the chart ###\n",
        "maxHeight = df.iloc[0:1].sum().sum()\n",
        "for count, rect in enumerate(ax.patches):\n",
        "  #print(count, rect)\n",
        "\n",
        "  ### Find where each patch is located ###\n",
        "  height = rect.get_height()\n",
        "  width = rect.get_width()\n",
        "  x = rect.get_x()\n",
        "  y = rect.get_y()\n",
        "  \n",
        "  ### The height of the bar is the data value and can be used as the label ###\n",
        "  #heightPercentage = height/maxHeight * 100\n",
        "  #label_text = f'{heightPercentage:.1f}%'  #f'{height:.2f}' to format decimal values\n",
        "  EUI_text = totalDF[count%len(totalDF)] #EUI Value\n",
        "  total_text = f'{EUI_text:.1f}' # $kBtu/ft^2$' #EUI display text\n",
        "  \n",
        "  ### Find center of patch to place text ###\n",
        "  label_x = x + width / 2\n",
        "  label_y = y + height / 2\n",
        "  label_y_full = y + height + 6\n",
        "\n",
        "  #  ax.text(label_x, label_y, label_text, ha='center', va='center', color=\"white\",fontsize=10)\n",
        "  \n",
        "  ### Place the EUI above the top patch ###\n",
        "  if label_y_full > totalDF[count % len(totalDF)]:\n",
        "    # print(label_y_full, totalDF[count % len(totalDF)], total_text)\n",
        "    ax.text(label_x, label_y_full+3, total_text, ha=\"center\", va='top', color='black', fontsize=24, fontweight='normal')\n",
        "  \n",
        "  ### Place the names to the right of the EYP's Design bar ###\n",
        "  if x > 0:\n",
        "    ax.text(label_x+0.4, label_y, legendLabels[math.floor(count/2)], ha='left', va='center', fontsize=16, color=color_map[math.floor(count/2)])"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Simple Bar Chart Baseline Vs Design.ipynb",
      "provenance": []
    },
    "interpreter": {
      "hash": "ad5864f56b9468bc1f0b6fefcee62edaf7f96ab9f6e9ae38690d443c98662615"
    },
    "kernelspec": {
      "display_name": "Python 3.9.9 64-bit",
      "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": "3.9.9"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}