Metcalfes-Law-and-Cryptocurrencies.ipynb

{
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   "source": [
    "<div style=\"text-align: center;\">\n",
    "    \n",
    "# Energy Consumption Of Ethereum Pre-Merge\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "id": "5299c09a",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>99.661780</td>\n",
       "      <td>2022-08-09T00:00:00.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>83.583900</td>\n",
       "      <td>2022-08-10T00:00:00.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>78.110896</td>\n",
       "      <td>2022-08-11T00:00:00.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>73.361224</td>\n",
       "      <td>2022-08-12T00:00:00.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>74.158757</td>\n",
       "      <td>2022-08-13T00:00:00.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>1.941332</td>\n",
       "      <td>2023-06-03T00:00:00.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>1.930251</td>\n",
       "      <td>2023-06-04T00:00:00.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>2.061882</td>\n",
       "      <td>2023-06-05T00:00:00.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>1.723397</td>\n",
       "      <td>2023-06-06T00:00:00.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>1.770803</td>\n",
       "      <td>2023-06-07T00:00:00.000Z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          open                 timestamp\n",
       "0    99.661780  2022-08-09T00:00:00.000Z\n",
       "1    83.583900  2022-08-10T00:00:00.000Z\n",
       "2    78.110896  2022-08-11T00:00:00.000Z\n",
       "3    73.361224  2022-08-12T00:00:00.000Z\n",
       "4    74.158757  2022-08-13T00:00:00.000Z\n",
       "..         ...                       ...\n",
       "298   1.941332  2023-06-03T00:00:00.000Z\n",
       "299   1.930251  2023-06-04T00:00:00.000Z\n",
       "300   2.061882  2023-06-05T00:00:00.000Z\n",
       "301   1.723397  2023-06-06T00:00:00.000Z\n",
       "302   1.770803  2023-06-07T00:00:00.000Z\n",
       "\n",
       "[303 rows x 2 columns]"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df2 = pd.read_excel('ETHW_ALL_graph_coinmarketcap.xlsx')\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "id": "d33b7e3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "index = pd.date_range(start='2022-08-09', end='2023-06-07', freq='D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "id": "7b721291",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.index = index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "id": "ff331b3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df2[['open']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "id": "ca5f1ed6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-08-09</th>\n",
       "      <td>99.661780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-10</th>\n",
       "      <td>83.583900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-11</th>\n",
       "      <td>78.110896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-12</th>\n",
       "      <td>73.361224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-13</th>\n",
       "      <td>74.158757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-03</th>\n",
       "      <td>1.941332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-04</th>\n",
       "      <td>1.930251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-05</th>\n",
       "      <td>2.061882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-06</th>\n",
       "      <td>1.723397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-07</th>\n",
       "      <td>1.770803</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>303 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 open\n",
       "2022-08-09  99.661780\n",
       "2022-08-10  83.583900\n",
       "2022-08-11  78.110896\n",
       "2022-08-12  73.361224\n",
       "2022-08-13  74.158757\n",
       "...               ...\n",
       "2023-06-03   1.941332\n",
       "2023-06-04   1.930251\n",
       "2023-06-05   2.061882\n",
       "2023-06-06   1.723397\n",
       "2023-06-07   1.770803\n",
       "\n",
       "[303 rows x 1 columns]"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "id": "04b2bb18",
   "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>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-09-15</th>\n",
       "      <td>69.943455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-16</th>\n",
       "      <td>69.318655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-17</th>\n",
       "      <td>67.800092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-18</th>\n",
       "      <td>66.362962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-19</th>\n",
       "      <td>64.828495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-28</th>\n",
       "      <td>16.230195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-29</th>\n",
       "      <td>19.718534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-30</th>\n",
       "      <td>19.313183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>17.855489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01</th>\n",
       "      <td>14.670383</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>109 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Value\n",
       "2022-09-15  69.943455\n",
       "2022-09-16  69.318655\n",
       "2022-09-17  67.800092\n",
       "2022-09-18  66.362962\n",
       "2022-09-19  64.828495\n",
       "...               ...\n",
       "2022-12-28  16.230195\n",
       "2022-12-29  19.718534\n",
       "2022-12-30  19.313183\n",
       "2022-12-31  17.855489\n",
       "2023-01-01  14.670383\n",
       "\n",
       "[109 rows x 1 columns]"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Dates range from 15th September 2022 to 1st January 2023\n",
    "dates = pd.date_range(start=\"2022-09-15\", end=\"2023-01-01\")\n",
    "\n",
    "# Initialize the series with linear decrease from 68 to 30\n",
    "values = np.linspace(68, 30, len(dates))\n",
    "\n",
    "# Apply the specific changes you mentioned\n",
    "values[(dates >= \"2022-09-21\") & (dates < \"2022-09-29\")] = 30\n",
    "values[dates == \"2022-09-29\"] = 50\n",
    "\n",
    "# For dates after 29th September, apply logarithmic decay from 50 to 15\n",
    "decay_start_date = np.where(dates == \"2022-09-29\")[0][0]\n",
    "decay_end_value = 15\n",
    "decay_values = np.logspace(np.log10(50), np.log10(decay_end_value), num=len(dates) - decay_start_date)\n",
    "values[dates >= \"2022-09-29\"] = decay_values\n",
    "\n",
    "# Add some random noise to the time series\n",
    "values += np.random.normal(0, 2, len(dates))  # adjust the second argument to change the noise level\n",
    "\n",
    "# Create a dataframe\n",
    "df1 = pd.DataFrame(values, index=dates, columns=[\"Value\"])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "id": "b86548c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
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     "execution_count": 346,
     "metadata": {},
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    {
     "data": {
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jxo3DiRMnHB47efIkkpKSAADJyckwGAzYuHGj/LzJZEJGRgbGjh3rguV2XkMmhkEMERGRJ3PqdNLvfvc7jB07FkuWLMGdd96J3bt3480338Sbb74JwLaNtGDBAixZsgRpaWlIS0vDkiVL4Ofnh9mzZ3fJN+As6Yi1mbOTiIiIPJpTQcyoUaOwfv16LFy4EM899xySk5OxYsUKzJkzR77mqaeeQm1tLebPn4+ysjKMHj0aGzZsQGBgoMsX3xHSdhJPJxEREXk2QRRFRaUkKisrERwcjIqKii6pj/n5v37CgZxyvHXfSEzr33ydDhERETmnqz+/m+NVs5OAhiGQ7NhLRETk2bwviNHYTycxiCEiIvJoXhfEaORMjKJ20YiIiMhJXhfEaDkAkoiIqEfwwiDGPnaAQQwREZFH88IgRhoAye0kIiIiT+Z1QYyGmRgiIqIeweuCGB1rYoiIiHoErwti5AGQ3E4iIiLyaF4XxPB0EhERUc/gtUEMB0ASERF5Ni8MYuwDIM3MxBAREXkyrwtipI69ZiuDGCIiIk/mdUGMTmOviTFzO4mIiMiTeV0Qo1HZTycxE0NEROTRvC6IaTidxEwMERGRJ/O+IMa+ncSOvURERJ7N+4IYaTuJQQwREZFH874ghttJREREPYLXBTENYweYiSEiIvJkXhfESAMgzczEEBEReTSvC2I09iDGxEwMERGRR/O6IEbL7SQiIqIewQuDGG4nERER9QReG8QwE0NEROTZvC6IkU8ncewAERGRR/O6IEY6ncQBkERERJ7N64IYKRNjZiaGiIjIo3ldECPVxJjMDGKIiIg8mfcFMSr76SQrt5OIiIg8mfcFMRr2iSEiIuoJvC6I0agaBkCKIrMxREREnsrrghjpdBLALSUiIiJP5nVBjHQ6CWDXXiIiIk/mdUGMtlEmhkMgiYiIPJcXBjGNMzEMYoiIiDyV1wUxgiBAo5JOKHE7iYiIyFN5XRADcAgkERFRT+CVQYw8BJJBDBERkcfyyiBGOmbNI9ZERESeyyuDGCkTw/lJREREnssrgxjWxBAREXk+rw5iuJ1ERETkubw0iLEX9nI7iYiIyGN5ZRAjD4FUaCamoqYeFoWujYiISCm8MojRauxBjAIzMbmlNRjx/Ebc/dZOFh4TERG1wjuDGHvHXrNVeUHCicIqmK0idmeX4sVvj7t7OURERIrlnUGMvbDXpMCxA3Vmi/z/39mWje+yCt24GiIiIuXyyiBG6hOjxAGQdfW2NQn2OZW///gQcktr3LgiIiIiZfLKIEan4D4xdfW2TMzkPlEYlhiCyjozHl6zn/UxREREV3AqiFm8eDEEQXD4MhgM8vOiKGLx4sWIjY2Fr68vJk2ahKysLJcvurMaZicpcDvJHsQE6DVYOXs4Qvy0OHShAkv+d8zNKyMiIlIWpzMxAwYMQEFBgfyVmZkpP7ds2TIsX74cK1euxJ49e2AwGDBt2jRUVVW5dNGdpeSOvUZ7xsVHq0JciC9eumMIAOD97efwyg+nYOXRayIiIgAdCGI0Gg0MBoP8FRkZCcCWhVmxYgUWLVqEmTNnYuDAgVi1ahVqamqwZs0aly+8M+SOvQrOxPho1QCAa/tF49EpqQCA5RtPYt77e1BabWr2taKovO+HiIioqzgdxJw6dQqxsbFITk7GXXfdhbNnzwIAsrOzUVhYiOnTp8vX6vV6TJw4Edu3b2/xfkajEZWVlQ5fXU3q2GtSYCbmyiAGAB6f3gd/v30wfLQqbDl5CTe+shX7zpcBALKLq/Gvzadxy8ptSF30Df6774Jb1k1ERNTdNM5cPHr0aPz73/9Geno6Ll68iOeffx5jx45FVlYWCgttR4Gjo6MdXhMdHY3z58+3eM+lS5fiL3/5SweW3nHKzsTYt5M0jvHlHSMTMCg+GPM/2I+zxdWY9cYOJEf441TRZYfrPtqbi9tHxHfbeomIiNzFqUzMDTfcgNtuuw2DBg3C1KlT8fXXXwMAVq1aJV8jSGeD7URRbPJYYwsXLkRFRYX8lZub68ySOkTJNTFSJkbfKBMj6WsIwucPj8ONg2Ngtoo4VXQZapWAa9Ii8OT0dADAgZwy1JosTV5LRETU0ziVibmSv78/Bg0ahFOnTmHGjBkAgMLCQsTExMjXFBUVNcnONKbX66HX6zuzDKfJAyAV2LG3Ti7sbRrEAECgjxYr7x6GGwYaYKy34tp+UQjx00EURazZlYP8ijrsO1+G8WkR3blsIiKibtepPjFGoxHHjh1DTEwMkpOTYTAYsHHjRvl5k8mEjIwMjB07ttMLdSWNlIkxK3E7SaqJafmPRhAE3DQ4FreNiEeIn05+7OqUcADA9jPFXb9QIiIiN3MqiHnyySeRkZGB7Oxs7Nq1C7fffjsqKysxd+5cCIKABQsWYMmSJVi/fj2OHDmCefPmwc/PD7Nnz+6q9XeIXBOjxEyMFMRoms/EtGZsii37suNsiUvXREREpERObSdduHABd999N4qLixEZGYmrr74aO3fuRFJSEgDgqaeeQm1tLebPn4+ysjKMHj0aGzZsQGBgYJcsvqOkAZBKrIkx1re+ndSaMfZMzOELFbhsNCNA36ndQiIiIkVz6lNu3bp1rT4vCAIWL16MxYsXd2ZNXU5rP/ljUuJ2krnt7aSWxIX4IincD+dLarAnuxST+0a5enlERESK4ZWzkzT2TIyit5M6kIkBgDG9WRdDRETewSuDGJ1GyUesG8YOdIS0peTKupjc0hos/eYY8sprXXZPIiKizvLKogmNSgpiFLidJPWJ6UBhL9CQicnKr0R5jUk+vdRRFbX1mPvubpwtrka9WcSfb+7fqfsRERG5ildmYuQ+MYrMxHRuOykqyAcpkf4QRWBXdmmn1mKxiliw7gDOFlcDAM5cutzGK4iIiLqPlwYxCh47YO7cdhLQ6Kj1mc5tKS3feAI/nrgk//p8SXWn7kdERORKXh3EKG0ApNUqwtRGx972kOtiOhHEfH24AP/88QwAYOENfQEAuWW1isxeERGRd/LKIEZj304yK+wD2WhuWE9ngpir7XUxJy5Wofiy0enXHyuoxJMfHwIA/OqaZPx6Qm/4aFWwWEXklbG4l4iIlMErgxidWpmFvVI9DNB0irUzwvx16GuwNRjc6eQpJaPZggf/sw+19RZckxaBp6/vC0EQ0CvcHwCQzS0lIiJSCK8MYjQKLeyVGt1pVII836mjpLqY7U5uKe08W4qc0hpEBOjw6t3D5HVIQcz5YgYxRESkDF4ZxGjVyuwTU9eJkQNXkupidjoZxGw9aSvkvbZvtMPx7KQIPwDAuZKaTq+NiIjIFbw0iJE69ipzO6kzJ5MkVyWHQSUAZ4urUVhR1+7XbTllC2KuSY9weDzZnok5x+0kIiJSCC8NYuyZGLPSMjGda3TXWLCvFgNigwEAe861r19MQUUtTl68DJUAjE91DGKSpCCG20lERKQQ3h3EKC4T0/keMY2NSAoFAOxtZxCz9aRt3tLg+JAmnX572beTLvCYNRERKYSXBjHKLux1RU0MAIzqFQYA2HOurF3XS1tJE9IjmzwXHegDH60KZh6zJiIihfDSIEaZHXuNnRw5cKWRvWyZmOOFlaiqq2/1WotVxLbTtkzMhLSIJs+rVAKSwlgXQ0REyuGVQYxGoR17Xb2dFB3kg8QwP1hF4EBOeavXZuZVoLymHoE+GgxNCGn2GmlLiXUxRESkBF4ZxGgV2rFXPp3kgsJeych21sVssR+tHpcS0WKPml7yCSUesyYiIvfzziBGZfu2raJtG0UpOjvBujkj21kXIwUxzdXDSHpFcDuJiIiUwzuDmEYt/ZVU3CtNsNa7aDsJAEbZ62IO5pa3+L1W1tXjQG45AOCaZuphJEnhtu2k88zEEBGRAnhlEKNRCfL/V1QQ0wWZmJTIAIT4aVFbb8HR/Mpmr9l+ugQWq4jeEf5ICPNr8V7J9kxMbmmN4rbiiIjI+3hlEKNVN87EKGk7yV7Y68KaGJVKwIhEWzampaZ3rR2tbiw60Ad6jf2YdTmPWRMRkXt5ZRCjVgmQkjFKyii4cuxAY1JdzN5m6mJEUZTrYVrbSgJsAZE8zZonlIiIyM28MogBGrIxSjpmbXRxszuJVBez93wZRNEx83SupAYXymqhVQu4und4m/diXQwRESmF1wcxSmp45+o+MZJB8cHQaVQovmxsEnxIWZiRSWHw12vavJdUF8NMDBERuZsXBzHKGz3QFYW9gG2g5OC4psMg6+otWLcnF0Db9TASaRDkeR6zJiIiN/PaIEZq6Kaswl7XN7uTNFcXs/iLLBwrqESonxYzh8e16z697NtJbHhHRETu5rVBjE4OYpSUiXF9nxhJQ12MLROzbncO1u3JhSAAr9w9DNFBPu26Ty8esyYiIoXw2iBGI40esCrng9jVU6wbG2EfP3DmUjU2nyjCn7/IAgA8Ob0Prklr31YSABiCmj9mLYoidpwpQUEFj14TEVH38NogRj6dZFbSdpJU2Ov6ICbET4f06AAAwK//vQ8msxXT+kfjtxNTnLqPSiXIJ5Qabyn9a/MZ3P3WTlzz4o9YsO4AjuRVuG7xREREzfD6IEZJmRijXBPTNX8sI5JsdTEmixW9wv3w0p1DoGrUvbi9pOJeaZr1T6eL8dKGEwAAs1XEZwfzcdOr2zD7rZ3IsJ9+IiIicjUvDmK853SS5Kpk25aSr1aN1+8dgSAfbYfuk9xoEGRBRS0eXXsAVhG4c2Q8vnpkPG4dGgu1SsD2MyWY++5u/GvzaZd9D0RERJK2G4P0UFolnk4yd912EgDcOCgWJy9exsT0SPQ1BHX4PtJ20umiy3ho9X6UVJvQPyYIz906ED5aNV6+axieur4vXtt8Gh/szMGyb08gyEeLe65OctW30qx950thCPZFXIhvl74PEREpg9cGMdIQSGVmYromQabTqPD09X07fZ9k+3bS1lPFAIBAHw1ev2eEQ/AVF+KL52cMQoivDit/PI0/fX4EgT4a3Dq0fUe5nZVdXI3bX9+BvoYgfPPYNV3yHkREpCxeu52k0yirY68oil2+neQqSfbtJMnyO4ciMbz56ddPTE/HfWOSIIrAEx8dwg/HLnbJmg5fKIcoAqcuVvHoNxGRl/DaIEbKxChldlK9RYTVHk/pu6iw11Vignzgaw+0fjspBdP6R7d4rSAIWHzzAPx8WBzMVhHzV+/HjjMlLl/T6aLLAGyFxRerjC6/PxERKY+yPy27kNJmJ0k9YgDlZ2JUKgFLZw7CY9em4Ylp6e26ftntgzG1XzSMZivmr96H0mqTS9ckBTEAkFfGXjVERN7A64MYpdTESFtJgPIzMQAwY1gcfjctXR7f0BatWoWVs4ehryEQZTX1ePGb4y5dT+Mg5kIZRyIQEXkD5X9adhGlHbE2SiMHNCoIgvO9WzyBj1aN52cMBAB8uDcX+86XtvGK9qm3WB2mal9gJoaIyCt4bRCjtAGQnlLU21kje4XhzpHxAIBF64+4pAj3fEkNzNaGP0duJxEReQevDWKUt50k9Yjp+X8kf7ihH0L8tDheWIX3t5/r9P0abyUBwIVybicREXmDnv+J2QJpO0kpx3G7cvij0oT56/AHe7+a/9t4EoUVdZ2635lLtiBGanLH7SQiIu/gxUGMfQCk0raTND0/iAGAO0cmYHhiCKpNFvz166OdupeUiZnYxzaNO7+8FlarMv5ciYio63htEKNRWibGi7aTANux6+dnDIJKAL4+XICtpzo+KPJUURUAYHxqBDQqAfUWEUXsFUNE1ON5xydmM3SKq4mxZWL0XrCdJOkfG4T7xvQCAPxjw0mIovPZE6tVxJki28mkPoZAGIJ9APCYNRGRN/DaIEajsgcxCtl28JbTSVd6eEoqfLQqHMotxxb7LCZn5FfUorbeAq1aQFKYH+JDWRdDROQtvDaI0WrsfWLMCsnESBOsPaDRnStFBOgxZ7RtuvUrP5xyOhsj1cP0CveHRq1CfKhthlNeOYMYIqKezrs+MRvR2jMxZoVkYoxemokBgAcn9IZOo8K+82VOz1WSgpjUqAAAaJSJ4XYSEVFP571BjFpZAyAbtpO8748kKsgHd49KAAC8sumUU6+Vgpg0exDDY9ZERN7D+z4x7bQaaQCkUoIY6XSS92ViAOA3k1KgU6uw82wpdp1tfzZGCmJS5EyMfTuJQQwRUY/XqSBm6dKlEAQBCxYskB8TRRGLFy9GbGwsfH19MWnSJGRlZXV2nS4nbSdx7IAyxAT74nb7OIJXN51u12tEUcTpSy1sJ7FXDBFRj9fhIGbPnj148803MXjwYIfHly1bhuXLl2PlypXYs2cPDAYDpk2bhqqqqk4v1pXkwl6lZGKkjr1eVtjb2G8npkCjErDtdDH2nS9r8/qSahPKa+ohCEBKpC2IMQT7QCUAJrMVxZfZK4aIqCfr0Cfm5cuXMWfOHLz11lsIDQ2VHxdFEStWrMCiRYswc+ZMDBw4EKtWrUJNTQ3WrFnjskW7gnzEWilBjDTF2kszMQCQEOaHmcPjAACvtqM25tRFWxYmPtRXzmBp1SrEBNuyMbncUiIi6tE6FMQ89NBDuPHGGzF16lSHx7Ozs1FYWIjp06fLj+n1ekycOBHbt29v9l5GoxGVlZUOX91BGjtg5naSojw0ORUqAdh84lKbJ4ykraS0qECHx+PsW0o8Zk1E1LM5HcSsW7cO+/fvx9KlS5s8V1hYCACIjo52eDw6Olp+7kpLly5FcHCw/JWQkODskjpEOp2ktEyMN55Oaiwp3B+D4oIBoM0tpTNXHK+W8Jg1EZF3cOoTMzc3F4899hg++OAD+Pj4tHidIAgOvxZFscljkoULF6KiokL+ys3NdWZJHaZVK6uw12j2rgGQrRmeZNuibCuIkXvERF4RxPCYNRGRV3AqiNm3bx+KioowYsQIaDQaaDQaZGRk4JVXXoFGo5EzMFdmXYqKippkZyR6vR5BQUEOX91Bo7hMDLeTJCOcDGJSmmRieMyaiMgbOBXEXHvttcjMzMTBgwflr5EjR2LOnDk4ePAgevfuDYPBgI0bN8qvMZlMyMjIwNixY12++M5Q3gBIbidJpCDmWEElqo3mZq+prKtHYWUdAG4nERF5K40zFwcGBmLgwIEOj/n7+yM8PFx+fMGCBViyZAnS0tKQlpaGJUuWwM/PD7Nnz3bdql1Ao7DtJGZiGsQE+yI22Af5FXU4lFuOsakRTa6R6mGiAvUI9tU6PNe4sLe1rUwiIvJsTgUx7fHUU0+htrYW8+fPR1lZGUaPHo0NGzYgMDCw7Rd3I8UV9pq9d+xAc4YnhSL/cAH2nS9rNoi5cmZSYzHBvhAEW3arpNqEiAB9l6+XiIi6X6eDmM2bNzv8WhAELF68GIsXL+7srbuUfMRaIV1d5T4xLOwFAIxMCsVXhwuwL6f5upgrO/U2ptOoYAjyQUFFHS6U1TKIISLqobz2n/3y6SSzQjIx3E5yMCIpDACw/3xZs+MDTl9sOYgBWBdDROQNvDaI0ajs20lWZQQxRjMLexvrGxMIX60alXVmOesiqTaasdM+JHJAbHCzr5emWfOEEhFRz+W1n5g6jXIKe61WESazd0+xvpJWrcKQhOab3n15KB/VJguSI/wxPDGk2ddLx6yv7BVTWm1yyUylGpMZu7NLYVHIdiQRkTfy2iBGysRYrKLbpx0bG21pMYhp0FK/mLW7cwAAd41KaPHkUXPbSbmlNZi6PANT/rG5UyMJckpqcMvKn3DnGzuw/kBeh+9DRESd47VBjLbRtGh3bylJ9TCAd0+xvpIUxOxvFMRk5Vfg0IUKaNUCbhsR3+Jr40Idu/bW1Vswf/V+lFabUFlnxqL1mRBF54PXfedLMeNfP8mno7aduuT0PYiIyDW89hNTq2r41t09BFI6Xq1RCXL/GgKGJ9qCmLPF1SitNgEA1u22jaWYPsDQ6qkjuWuvvVfMc18dRWZeBUL8tNBpVNh84hI+P5jv1Ho+P5iHu9/ahdJqEyIDbe+9P6fc2W+LiIhcxGs/MaU+MYD7e8U0dOvlVlJjIX46+fTR/vNlqDVZ8Jl9++buUYmtvjY2xDbbq8Zkwdtbs7FmVw4EAXj5rmF47No0AMBfvsxCSTvqY0RRxIrvT+KxdQdhMlsxvX80vnpkPAQByCmtwaWqztfYEBGR87w2iFGrBEjlFO4u7m04Xu21fxwtGmHPxuzLKcNXh/NRZTQjMcwPY1PCW32dXqNGdJAtW/K3/x0DADw6JQ0T0yPx6wm90S8mCGU19fjLl0fbXMMHO89jxfenAAAPTuyN1+8ZgeggH6RJAVYLvWyIiKhree2npiAI8paS+zMxtiCGje6akot7z5U1FPRelQCVqu1RAtIxawCYkB6JR+0ZGK1ahRdvGwSVAHxxKB+bjl9s8R5Z+RX461e2IOip6/tg4Q395PeWa3YYxBARuYXXBjFAw5aS22tiOPyxRcOTGjIx+3PKoVEJuL2Vgt7GpLqY2GAfrJg1FOpGgc/g+BD86preAIBF64+gqq6+yesvG814eM0BmCxWTO0Xhd9OTHF4fpg9S3TgfLnT3xcREXWeV39qSkW0JndnYszs1tuS3hH+CPHTyv1YpvaLRlSgT7teO3dsEqb0jcJbc0cizF/X5PkFU9ORFO6Hgoo6/OK9PfKJI8BWB/PMp5nILq5GbLAP/nHHkCbHuaXC40MXyuU+P0RE1H28OohpmJ/k3g8gI0cOtEilEuS6GAC4e3TrBb2NjUgKw7vzRrXY1ddXp8Y/7hgCX60ae8+X4YaXt2D5hhOoq7fgo725+OJQPtQqAa/OHoYQv6ZBUO8IfwT7amE0W3GsoLLJ87UmC977KRuFFXXtXjMREbWflwcx9tEDZm4nKZm0pRQX4otrmplo3RmjeoVh4+MTMKVvFOotIl7ZdBrXrdiCZ7/IAgA8Ob2PPMfpSiqVIHcMbq4u5tVNp/CXL49izts7cdlodum6iYjI64MYe2GvQprd+bCwt1mzRiVgWv9oPP/zge0q6HVWfKgf3pk7Eq/NGY7oID3Ol9Sgrt6KCemReHBC71ZfK20pXdlVuN5ixcf7LgAAzlyqxpMfHepQcz1X++++C7jz9R2d6lhMRKQUXh3EaORMjEKCGG4nNSsiQI+37huJyX2iuuw9BEHADYNi8P3jE/Gra5IxvX80lt85pM2gScoSHbii6d2Px4twqcqIIB8NdGoVvs0qxOsZZ7tq+e1yrrgaz6zPxO5zpXgj44xb10JE5ApeHcTo1MoYAllnD6L03E5yu0AfLRbd2B9v3jey1Y7AkiEJIVAJts7AFysbal/W7bF1Fr57dCIW3zIAAPD3745jq5vGFIiiiD99fkQuQP50fx63uIjI43n1p6aciVHKdhIzMR4nQK9BH0MQgIYZTwUVtdh8oggAMGtkAu6+KgGzRibAKgKPrj2A3NKaFu/XVb46XICtp4qh06gQG+yDy0az3P2YiMhTeXUQI9fEuH07yV7Yy5oYjyQV90p1Mf/dewFWERidHIbekQEQBAF/uXUABscHo6ymHr9dva9bj2RX1Nbjua9snYkfmpSKB+z9cT7YeV4RdTpERB3l3UGMSjpi7e7TSRw74Mmk4t79OWWwWkV8uNe2lXTXVQnyNT5aNV67ZwRC/bQ4kleJb7MKu219L204gUtVRvSO8MdvJvXG7cPj4aNV4XhhVZOCZCIiT+LVn5pajX07yc3N7oxsdufRpOLeI3mV+PFEES6U1SLQR4MbBsY4XBcX4ot7r04CYDsl1B0O5pbjPzvPAwCenzEQeo0awX5a3DokDgDk54iIPJFXBzEalUIKe9knxqP1CvdDmL8OJosVf7Vv2/x8WFyzQelt9pEJ205d6vImeGaLFYvWZ0IUbesZ26jHzj32YOp/mQUobsckbyIiJfLqT025JsbdYwdY2OvRBKGh6d25ElvR7l2jmu8snBTuj6t6hcEqAuu7uLB249GLyMqvRJCPBotu7Ofw3KD4YAxJCEG9RcRH9u0vIiJP4+VBjDQAUiFBDAt7PdawRqMRBscHo39sUIvX3jbCtpXz3325XVpY+80RW93NXVclNntcXNraWr0zR55NRUTkSbw8iJEGQCpjO4l9YjzX8EZBzKxRCa1cCfxsUAx8tCqcuVSNg7nlXbIek9mKH4/bjnlfNyC62WtuGhyDYF8t8sobjoQTEXkSr/7U1CglE8PCXo83JCEYEQE6hPvrcMuQ2FavDfTRykW/n+zvmgLfHWdLUGU0IzJQj2EJoc1e46NV486Rthqdv393Amt25eDkxSpYmZUhIg+hcfcC3EmnmJoYqbCXQYyn8tNp8NUj10AQbEFKW24bHo/1B/LwxcF8/PHG/i7/s//OfoR7Wv/oVkcnzBmdhPd+OofjhVV4Zn0mACDYV4tRvUKx6Mb+SI7wd+m6iIhcyaszMVqFjB0wyjUxXv3H4fEMwT6IDvJp17VjUsIRG+yDyjozfjjm2q0cq1XExqMXAQDT+ze/lSTpFeGP//52LB6dkooxvcPhq1WjorYe3x8rwrvbsl26LiIiV/PqTIw8dsDtmRhuJ3kbtUrAzOHxWPnjafx3Xy5uHBzT9ova6UBuOS5VGRGo12BsSkSb1w9NCMHQhBAAtv8W3v/pHP72v2M4ebHKZWsiIuoKXv1Pf2k7ye0de83cTvJGM4fbTillnLyEIvvwyNzSGny8NxfLN55ExslLcoDrjA1HbVtJk/tGQedkdk+rVmFMSjgA4FTRZaffm4ioOzETA3TrHJvmcOyAd+odGYARSaHYd74Mv/7PPlyqMiKvvNbhGh+tCmNTIjC5TyRuGBTT5mRtURSxIcu+ldTCqaS2pEQGQBCA0moTSi4bEd6Oad5ERO7g1Z+aWjkT474gRhRFbid5sdvtHXwP5pYjr7wWGpWtcd6MobGIDtKjrt6KTceL8KfPs3D9ii24WNl6l99TRZeRXVwNnUaFSX2iOrQmX50aiWF+AICTF5mNISLl8upMTMMUa/dtJ9VbREi7WWx2531mDo9DdnE11CoBY3qHY0RSKPz1tv8sRVHE8cIq/HiiCB/uycX5kho8/tFB/Of+0S2eONpgP5U0PjUCAfqO/+edFhWA8yU1OFVUJW8vEREpjZcHMfbCXjdmYqQeMQCb3XkjvUaNZ37Wr9nnBEFAv5gg9IsJwnUDDLjplW346XQJ3thyFr+dlNLsa77Lat+ppLakRQfi+2NFOMVMDBEpmFd/aiphAKS0lSQIgJ5HrKkFKZEBWHxLfwDASxtONNvpN6+8Fpl5FRAEYGpng5ioAADgCSUiUjSv/tTUaqTtpIZMTEVNPZ79/AgyL1R0yxqM0sgBjQqC0HJTMqI7RybgxkExMFtFPLbuAC4bzQ7Pb7RvJY1MCm2zALgt6dGBAIDTPKFERArm3UGMva6gcWHvqh3nsGrHeTywag/Kqk1dvgYW9VJ7CYKAJTMHIS7EF+dLavDnz46goKIW32QWYOn/juHNLWcBANcNMHT6vaQTSiX2E0pERErk3UFMMwMgt50uBgAUVRnxp8+PdPka5JEDLOqldgj21eLlu4ZCJQCfHsjDmKWb8NvV+/HGlrPIr6iDTq3CDYM63zjPV6dGQqjthBL7xRCRUnl1EHPlAMgakxkHcsoAACoB+OpwAb44lN+la2gY/ujVfxTkhJG9wvD4tHQAts6//WOCMHt0IpbdNhg/PDERcSG+LnkfqS7mFOtiiEihvPp00pUDIHdnl6LeIiIuxBe3j4jHyz+cwp8+O4LRyWHtnonjLG4nUUc8PCUNNw2ORXSQD3x1XfOzkxYdiB+OF7FXDBEpllf/819zxQDI7WdKAADjUsPx8JRUDIoLRkVtPZ7672GIYtecYJK2k/QMYshJvSL8uyyAAYD0aHsmpoiZGCJSJq8OYrRXDIDcdspWDzMuNQJatQr/N2sIdBoVMk5ewprdOV2yhjpOsCaFSouynVDqaK8Yq1XE8g0nsP7ABVcui4hI5tWfnPLYAYuI0moTjhZUAoA8+Tc1KhBPX98XAPC3r4+12fK9I7idREqVGtW5E0pbTxfjlU2n8fR/M1FjMrf9AiIiJzGIgS0Ts8O+ldQnOhCRgQ09Nn4xthcGxQWjxmTBxqMXXb6GhgnWXv1HQQrkq1MjPtRWJNyRE0pfH7YVxZsa/fdFRORKXv3JqWk0duCnM7atpLGpjnNiVCoB19mnAUvbTa5kZCaGFCxd3lJyri6m3mKVRyAAwOYTl1y6LiIiwMuDGF2jAZA/2fvDjE+NaHLdOPtj288Uw2J1bYFvQ00MgxhSnlS5uNe5TMxPp4tRUVsv/3rzyaIuK44nIu/l1UGMtJ1UfNmI8yU1UKsEXJUc1uS6wfEhCPTRoLLOjMw8144jkJvdcTuJFEjKxDg7Q+nrwwUAbFO6dWoVcktrcba42qVry8qv6JI6NSLyHF79ySk3u7NnV4bEByPQR9vkOrVKwNgU2zbTtlOuTYuzsJeULM2eiXFmhpLJbMUGe/3YHSMSMCo5FIBrt5T2nivFza9uwx2v75BPFxKR9/HqIEbaTpI0t5UkP5cWCaBhLIGrSB172SeGlCjV3rW3+LIJpe2cJfbTGdtWUkSAHlclh2FSehQAIOOka4IYURSx7NsTsIpATmkNvjrctV21iUi5vDqIkTIxkrGtBTH25/adL3PpcVFuJ5GS+ek0SAiznVBq75bS/+xbSTcMNECtEjCpj+0fADvPlqDWZGly/fdHL+KzA3ntXlPGyUvYfa5U/vUbGWdZb0Pkpbz6k1PbKBPjo1VhWGJIi9f2CvdDXIgv6i0idmeXtnids1jYS0onN71rx5aSyWzFd1mFAIAbB9sGUaZGBSAuxBcmsxU7zzoetT5WUIlf/2cvFnx4EPnltW3e32oV8ffvTgAAZo1MgJ9OjeOFVS7L8hCRZ3EqiHnttdcwePBgBAUFISgoCGPGjME333wjPy+KIhYvXozY2Fj4+vpi0qRJyMrKcvmiXUWravj2R/UKg76VQEIQBDkb48qj1g2ZGAYxpExSXUx7jln/dKYYlXVmRAToMaqXrUheEARMtGdjNp8okq8VRRHPfXkU0oG/9mR6vjlSiKz8SgToNXj6hr64+6pEALZsDBF5H6eCmPj4eLzwwgvYu3cv9u7diylTpuDWW2+VA5Vly5Zh+fLlWLlyJfbs2QODwYBp06ahqkqZs1e0mobtpNbqYeRr0uxBjAvrYoycYk0K58z4AelU0s8G2baSJJPS7UFMo4zJd1kXsaNRZubMpdZPL5ktVry00ZaF+eU1yQjz1+H+8cnQqATsOFuCwxfK2/cNEVGP4dQn580334yf/exnSE9PR3p6Ov72t78hICAAO3fuhCiKWLFiBRYtWoSZM2di4MCBWLVqFWpqarBmzZquWn+nNN5OGteOIEY6oXS8sAqXqpxvw94cnk4ipWvvIEiT2YoN9q2knw2KcXhubGoEtGoB50tqkF1cDaPZgiX/OwYACPGznQg8c6n1IOnT/Xk4e6kaoX5aPDA+GQAQF+KLW4bEAgDe2MJsDJG36fA//y0WC9atW4fq6mqMGTMG2dnZKCwsxPTp0+Vr9Ho9Jk6ciO3bt7d4H6PRiMrKSoev7qJVq3DXqATcOjQW/WOC2rw+PECPAbG267afcU02hoW9pHQpkQ0nlAoqWq5b+em0bSspMrBhK0kSoNdgZJLtsc0nivDutnPIKa1BVKAeT07vAwA400rNjdFswYrvTwIAHpqc6tAK4dcTewMAvskswPkS1/aiISJlc/qTMzMzEwEBAdDr9fjNb36D9evXo3///igstP0LLDo62uH66Oho+bnmLF26FMHBwfJXQkKCs0vqlBduG4yX7xoGlUpo+2I0bCltdVFdDAt7Sen89RoMtxe9/9/Gky1et95+wkg6lXQl6ZTSZwfysHLTKQDA09f3xeD4YACtZ2JW78xBfkUdDEE+uOfqJIfn+hqCMKlPJKwi8PbW7PZ/Y0Tk8ZwOYvr06YODBw9i586d+O1vf4u5c+fi6NGj8vOC4PiXlyiKTR5rbOHChaioqJC/cnNznV1St2pc3NvZY53bTxfjfGkNADTbZI9IKf54U38AwEd7L+BATlmT5zNOXsIXh2z9WmYOj2/2HpP62PrFHLpQgWqTBUMSQvDzYXHo3SjTU17TfC+aD/fY/l54eEpqs1uvD05Isa8vt0MTt4nIMzkdxOh0OqSmpmLkyJFYunQphgwZgpdffhkGgwEAmmRdioqKmmRnGtPr9fJpJ+lLyUb1CoNOo0JhZV2bhYit+fF4Eea9vwcmsxUT0yMxME7Z3zd5t+GJobh9hC04efaLLFgbzRArrzHhqf8eAgDMG9sLQxNCmr1HenQAYoJ95F8/e3N/qFQCAvQa+fHm/puqNVnkepxp/Zv/u+Tq3mEYEh8Mo9mKj/ddcP4bbMRktuKJjw7hoz3K/gcVEbmgT4woijAajUhOTobBYMDGjRvl50wmEzIyMjB27NjOvo1i+GjVGNXL1ka9oyMIvj1SiF//Zy9MZium9ovGm/eNaDVbRaQET1/fF4F6DQ5fqMBHexs+4P/8eRYuVhrRO9IfT1/ft8XXC4KAqf1sQciMobEYnhgqPyfV3TS3pXS0oBJWEYgI0CMqUN/ivaUM0JZO9ozJOHkJn+y/gMVfZsnbvUSkTBpnLn7mmWdwww03ICEhAVVVVVi3bh02b96Mb7/9FoIgYMGCBViyZAnS0tKQlpaGJUuWwM/PD7Nnz+6q9bvF+NRI/HS6BP/cfAYZJy8h1E+HED8dYoJt+/W+upbrWz4/mIfHPzoEi1XEjYNisOKuoQ6npIiUKjJQj8empuH5r49h2XcncMPAGGw5ZdtGUqsELL9zaKs/+wDw5PQ+GBAbhFuGxjo8nhLpj22ni5sNYrLybUNXB8UFtRrsS/Vqe8+VodZkaXMtLTliH/JaY7Jg84lLuH6goUP3IaKu51QQc/HiRdx7770oKChAcHAwBg8ejG+//RbTpk0DADz11FOora3F/PnzUVZWhtGjR2PDhg0IDAzsksW7y7T+Ufj7d8dxqcqIH68YaldQUYc/39y/2df9eLwICz48CFG0TfdddttgaBjAkAeZO7YXPtyTi1NFl/HHz49gqz0b+dDk1Ba3kRoL9tPiLnuDusZS7DOazhQ13U7KvGALKgbGBbd6794R/ogN9kF+RR12ZZfINTjOkoImAPjmSAGDGCIFcyqIeeedd1p9XhAELF68GIsXL+7MmhQvNSoQGx+fiNNFl1FeY0JZTT0ulNXgg505+M/Oc5g7NglJ4f4Or6mrt+BPnx+BKAJ3jIjHi7cNbveJKCKl0KpV+MstAzD77V340l7IOyguGI9MSe3UfaXtpLPNZGKO5NvaLrQVxAiCgGvSIvHh3lxsO1Xc4SAmM68hiPnhWBHq6i3s40SkUEwDdFBKZACuG2DArFGJ+M3EFDw/YxAmpEei3mKbsHulN7ecxYWyWhiCfPCXWwcwgCGPNTY1Ajfam9npNCr836whnd4SlaZlny+tgclslR+vq7fI4w7aCmKAznfVLqqqw8VKIwQBiArU47LR3OkaGyLqOgxiXOiZn/WFSgC+zizAvvMNx1AvlNXgX5tP2665sR/8dE4lwIgU59mb+2N6/2gsv3MIUqM6v10cFahHgF4Di1V0aFh3orAKZquIMH8dYhudbGrJuNQICIKtq3ZRVZ3T68iyZ316R/jjZnsn4G+OtNzniojci0GMC/U1BMnHUP/29VG5j8zS/x1HXb0Vo5PDcPPgmNZuQeQRooJ88OZ9I3HT4Ni2L24HQRCQEmnbgm1c3Ctt7QyIbb2oVxLmr5O7av/UgWxMVl5D/c3PBtlqYb4/elGecUZEysIgxsUen9YHvlo19ueU45sjhdh+uhhfZxZAJQCLbxnAo9RELWg4Zt2QiWk4mdT2VpJkfKqtM/DWk84HMUfy7PU3scEYlhAKQ5APqozmDt2LiLoegxgXMwT74FcTbLNcXvjmOBZ/aZvwfc/VSejXjvlMRN6q4YRS00xMe+phJNc0qotxtqv2EXvQNCAuCCqVgBvs2Zj/HSlw6j5E1D0YxHSBByf0RkSAHjmlNTh58TJC/bR4fFq6u5dFpGhXbieZzFacKLQV9TqTiRmRFAq9RoWiKiNOXmx9MnZj5TUmXCizDbgcEGt7P2ka90ZuKREpEoOYLuCv1+CJ6Q1By++v64sQP50bV0SkfNIJpTOXqiGKIk5erEK9RUSwrxbxob7tvo+PVo3RvcMBQO5j0x5SUW9SuB+CfW2zzEYkhiIqUI+qOjO2ny5p972IqHswiOkid4yIxy1DYjFjaCxmjereydxEnigxzB9qlYDLRjMuVhrlzrkD2+jU25xrUp0/ai2/X2xD1kelEnCDvdnd15ncUiJSGgYxXUSjVuGVu4dhxV3DoGZPGKI26TQqJIX5AbBtKUn1KY2DivaS+sXsPFvS7m0gqanegCuGsUpbShuyCh162BCR+zGIISLF6N1oEGRmXvs69TanryEQEQF61NVbHXo2taa5TAwAjOwVhshAPSrrzB06tk1EXYdBDBEpRkqUrbj3eGEVjhV0PIgRBAHjU211MdtOtR14VNXVI7vYdrRb6jMjUasEuUPx+9vPOb0WIuo6DGKISDFS7ZmY749ehMlsRaBeI28xOWt8mq1fTHvqYo7at5Jig30QHqBv8vz945KhEoCMk5fka4nI/RjEEJFiSL1iiqqMAID+sUEdnjMm9YvJzKtAabWp1Wsb6mGaz/okhvvJtTFvbjnTofUQkesxiCEixUiJCHD4tTP9Ya4UHeSDvoZAiCKQcbKo1WuzWqiHaew3E1MAAF8eLkBuaU2z1+SV18Jida7BHhF1HIMYIlKMYD8tIhpt53SkHqaxKX2jAAA/Hm+9X4x8Eiqu5a7aA+OCMT41AhariHe2ZTd5/qUNJzDuhU14ddOpTqyYiJzBIIaIFEXq3At0PoiZbA9iMk5egtnS/PHoWpMFp+2jDtrK/EjZmHV7chy2qD7YeR6vbrJNqt/ajkJiInINBjFEpChS514/nRrJEf5tXN26YQkhCPbVoqK2Hgdyy5u95lhhJawiEBmoR1SQT6v3G5cajoFxQairt2KV/aTS90cv4s+fH2m4X0ElrF2wpeTsHCgib8AghogURQpiBsQGdbpRpEatwsR02ymlH483XxfTUA/T9oBWQRDkbMyqHeew40wJHll7AFbR1qVbr1GhxmTB+RZqZjpq49GL6Punb/Hp/gsuvS+Rp2MQQ0SKMnN4PH4+LA5PTO/jkvtN7msLYja1EMQ4Oyn7hoExSAr3Q3lNPea8vRO19RZMTI/EkpmD0NcQCAAuPYYtiiKWfXscRrMVnzCIIXLAIIaIFCXYV4v/mzUUV9uHOHbWxPQoCIKtgV5+ea3Dc2aLFbuySwE0bXLXErVKwK+u6Q0AsIq2YuB/zRkOrVqF/vbTTVn2QmFX2HzyEk7Za3YO5pS3WNtD5I0YxBBRjxbmr8OwhBAAwOYTjqeUPj2Qh/MlNQjx02KcfWhke9w+Ih5pUQFIifTHu/NGwV+vAWDrawMARwuaz8TU1Vvwh08OY+Gnh/HJvgvIKalps9blna0NJ6GqTRYcL6xq9zqJejqNuxdARNTVpvSNwv6ccmw6XoTZoxMBAEazBS9/bzsOPX9SCgJ9tO2+n49WjQ2/mwCrCIe6nf4x9iCmhe2kb44UYN2eXADA2t22/40O0mNcagSe+Vk/h+Pl0n22nS6GSgDSogJx4mIV9ueUdfrUFlFPwUwMEfV4k/rYjlr/dLoYdfW2qdard+Ygr7wWhiAf3Deml9P3FAShSeFxX0MgBMHWcfiSvetwY9tPlwAAhiSEYHhiCLRqARcrjfh0fx5++8E+1F+xVfT2trMAgBsGxcgdg9s70JLIGzCIIaIeb0BsEKKD9Kitt2B3dikuG81Y+aOtr8tjU9Pgo1W75H389Rr5WPiVW0qiKGL7GVsQ8/i0dHw6fxwyF1+Hf99/FQL1Guw5V4a/fX1Mvv5iZR2+PJQPAPjVNb0xIikUALD3nOuCmOOFlXhmfSam/GMztp5quSGgxSpi7e4cnLMPySRSCgYxRNTjCYKAyfZszKbjRXhnazZKq03oHeGPO0bEu/S9WtpSyi2tRV55LTQqAaN62QISH60aE9IjsXzWUAC2KdmfH8wDAKzafg71FhEjk0IxNCEEQxNDoBJsow0KK+o6vL56ixX/yyzArDd24PoVW7FmVw7OFlfjb18fa7E+572fsrHw00z8/r+HOvy+RF2BQQwReQVpS+m7rEK8tdW2TfP49HRo1K79a7Cl4t4dZ22dfIclhsBP51iOOK1/NB6enAoAePqTw9h3vgyrd+UAAH5pPwkVoNegr8F2745uKV02mnHzq9swf/V+7MouhVol4GeDDPDVqnG8sAo77JmixuotVrxrH7Ow93xZm8M0iboTgxgi8grj0yKgVQsoqKjDZaMZA+OC8LOBMS5/nwEtHLOWtpLGtHB0/HfT0jEhPRJ19Vbc/dZOVNTWIyncD9P6R8vXSFtKzQUxVquI/+67gOxWtnz+s+M8jhdWIdhXi0empGLb05PxrzkjcLs9G/XuT01nQn11OB/59syPKAJbTrY+h4qoOzGIISKvEKDXYHRyQwDx1HV9oepkR+DmSNtJ2cXVqDGZATjWw4xJaf4ot1ol4OVZQxEf6guT2Vbge/+4ZIfi4ZH2bah9OU2DmP/uv4AnPz6Ee97eJRcvN1ZrsuBtewbqTzf1xxPT+yAm2BcA8ItxvQAAPxwvcgiCRFHEGxm210gnp1pqGkjkDgxiiMhrXDfAltUY0zsc16S1vy+MMyID9YgM1EMUIfd0OXPpMi5VGaHXqDAsMaTF14b66/D6PSPgo1UhMlAvZ0gkwxNtQUxWXgVqTQ2BiiiK8pZPXnkt3tpytsm91+zOQUm1CQlhvrh1aKzDc70jAzClbxRE0Vb/ItlyqhjHC6vgp1PjhZmDANiGaVq6YDYUUUcwiCEirzF7dBJevmsoXrtnOATB9VkYiZSNybIX90q1JiN7hbZ5EmpgXDA2PTEJXz86Xm6iJ4kP9UV0kB5mq4jDF8rlx3ecLcHxwipI39K/Np9BQUVDd+K6egveyDgDAJg/KRXaZuqAHhifDAD4eO8FVNTUAwDe3GJ7zaxRCZjUJ7JhmGYzmSAid2AQQ0ReQ60ScOvQOIT46br0faQRBtIJpbbqYa4UG+KLqMCmE7UFQWg4at2oLubdbecAAHNGJ2JkUihq6y144Zvj8vMf781FUZURMcE+mDk8rtn3HJsSjr6GQNTWW7BuTw6O5FXgp9MlUKsEPDA+GRq1ChOkYZonmt9SKqyoQ2Vdfbu+RyJXYBBDRORijU8oWa0idpxtvR7GGSOSwgAA++1BzPmSavxw/CIA4BfjkrH4lgEQBODzg/nYe64UJrMVr9vrWn4zMQV6TfOZIEEQcP84WzZm1fZzeG2zLQtz0+AYxIf6AQAm95GGaTYt7j1dVIXJ/9iMOW/t6vT3SNReDGKIiFxM2k46XlCJrPxKlNfUw1+nxuD4zo8LkE8o5ZRBFEWs2n4eoghM6hOJlMgADIwLxqyRCQCAv3x5FJ/sv4C88lpEBuoxa1RCq/e+ZWgswv11yK+ow9eZBQCAX0/oLT8/MT0SggAcK6hs0qvmxW9PoLbegsy8Cnk7iqirMYghInKxXuH+8NOpYTRbsXrXeQDAVclhzdaiOKt/TBD0GhXKa+px+EIFPtprm8H0C3sWBQCemN4HgXoNMvMqsPiLLADAgxN6t1mP46NVY87VSfKvx6dGyEfGASA8QI8h8SEAHLeU9p4rxcajF+Vfn7jY9UMqj+RV4LF1B7DrbNPeNuQ9GMQQEbmYSiWgnz0b8+kBWwfeMSntq4dpi06jwhD7VO4/fnYEl41mpET6Y0Kj01aRgXo8em0aAMBotiLMXycPvmzLvVcnQWcPthpnYSRT+tqaBv5oP2otiiKWNqq/AYAThc0PwHSlN7ecxecH8zHrzZ1YtD4TVazF8UoMYoiIuoC0pST1fBnrgnoYibSllJlna6j3i3HJTU5bzR3bC73tc5weGJ/cpEtwSyID9Xjj3hFY8vNBzR5Dl8Y3bDtdDKPZgo1HL2Lf+TL4aFW4bbjtSPixwq7PxJwquiz//9W7cjBt+RZ83ygbRN6BQQwRUReQTigBQLCvVs7MuMJIexADAEE+mmZPHOk0Krz3i1H4660Dms2otGZy3yjMHp3Y7DH0AbFBiAzUo8ZkwfYzJXjxW1sW5oHxyZiQbgt6TnRxEGOxijhzyRbEvHjbICSF+6Gwsg6//PdePPv5kS59b1IWBjFERF2gf6Mg5ureYQ6ddztLanoHAHdfldhiliUp3B/3junlklociUolyKeU/vTZEZy5VI1QPy0enJgiz3Y6WVjV4jBJV7hQVgOT2QqdRoXbRyTg28cm4EF7oLZqx3kUVXZ8QGZPczC3XA74eiIGMUREXSA9OlAOXNrbH6a9Qv11uCYtAmH+Oswd28ul924PaUvpQpmtod7DU9IQ5KNF70h/aNUCqoxm5JXXtnaLTjlt30rqHeEPtUqAr06NhT/rh9hgW2+drnxvT1JUVYc7Xt+O2W/t7NKg0p0YxBARdQEfrRrjUiPgp1Pj2n7Rbb/ASe/NG4VtT09GbIivy+/dlvFpEdDYA7T4UF/cc7WtaFirViElMgBA124pSUFMalSAw+Mx9t+LggpmYgAg80IF6i0iLlYacanK6O7ldAkGMUREXeSNe0Zgy1OTkRDm5/J7a9SqdhfrulqgjxaT7FtKT13f16GBXh9DIICGuVFd4VQLQYwU0OUzEwPA8c/gdA/dUnLPfwFERF7AV6eGr6713iye6qU7huJ8aTUG2/vGSKQgpjsyMWlRgQ6PS9tJ+eXMxACOQcyZS9UuPSGnFAxiiIjIacF+Wgz2C2nyeF85E9M1vWJEUcSZlraT7EFM4+GX3ux4QcOfwZminpmJ4XYSERG5TB/7CaWzl6rlHjmuVFRlRJXRDJUA9Ipw3KbjdlKDunoLzhZXy7/uqSeUGMQQEZHLxAb7INBHA3OjXi6udOqi7Z5J4f5NhlnKQQwLe3G66DIs1oYTSWcvVbdytediEENERC4jCAL6RHddXczpIts9r9xKAhqCmEtVRhjNFpe/tyeR6mGk7b288lrUmMzuXFKXYBBDREQu1ZUnlKRTNs0FMaF+Wug1to+1ixU980hxe0n1MFf3DkeYvw5Az8zGsLCXiIhcqq99xEJzgyBPF13GvvOluGy0oNpoRrXRjHqLiLuuSkB6dGCT65t7PQCkRjYNYgRBQGyIL7KLq5FfUYvEcNcfbfcUUgDZLyYQWfn+KK024cylyxgYF9zGKz0LgxgiInKpvi0csy6vMWHmv35CZV3TbY2c0hq8PXdkm/duqdGdJDbExxbEeHlxr3Q6rK8hCCmRAdhzrgxnmIkhIiJqnZRRya+oQ0VNPYL9tACAt7dmo7LODEOQD0YlhyFAr0ZlnRlfHy5AdnHbRcDlNSYUXzYBAFJaCGJigtm191KVEcWXTRAE25+F1EW5J55QcqomZunSpRg1ahQCAwMRFRWFGTNm4MSJEw7XiKKIxYsXIzY2Fr6+vpg0aRKysrJcumgiIlKuYF+t3HjuxEVbNqas2oT3t58DACy+ZQBevXsYls4cjKeu6wPANoeprfk+UhYmNtgHAfrm/w3O+UkNWZjkcH/46tRIifIH0DN7xTgVxGRkZOChhx7Czp07sXHjRpjNZkyfPh3V1Q0pqmXLlmH58uVYuXIl9uzZA4PBgGnTpqGqqmtHsxMRkXI0dO61faC+sy0bl41m9I8JwnUDGmZJxQT7QiUARrNVzrK0RApiWsrCAA0nlAq8OYgpsJ9MirH9GUiZmOziaodj1z2BU0HMt99+i3nz5mHAgAEYMmQI3nvvPeTk5GDfvn0AbFmYFStWYNGiRZg5cyYGDhyIVatWoaamBmvWrOmSb4CIiJRHanp3vLAKZdUmvPdTNgDgsalpEARBvk6nUcEQZMueXCirafWeLc1MaoxDIIFjjephACA+1A86tQpGs7XH1Qp16oh1RUUFACAsLAwAkJ2djcLCQkyfPl2+Rq/XY+LEidi+fXuz9zAajaisrHT4IiIiz9a4uPftbWdRbbKgf0wQpvdvOtE7PtR2iii3rPUP2JZmJjUWF8LtJDkTY/8zUKsEJEfYtpR62iDIDgcxoiji8ccfx/jx4zFw4EAAQGFhIQAgOtrxhzQ6Olp+7kpLly5FcHCw/JWQkNDRJRERkUJI20nHCirx/k/nAAALrsjCSOJDbdmTtjIxbZ1MAhoKe6vqzLhsVH5zN1EUsW53Dr490vxnpLPqLVb596mf/ag7AKfqYkRRxIasQpy6qPwykA4HMQ8//DAOHz6MtWvXNnnuyh9SURSb/cEFgIULF6KiokL+ys3N7eiSiIhIIVIiA6BRCag2WVBtsmBAbBCmNZOFAYD4MFsm5kIrmZgak1nOrrQWxPjrNQjysRX9ekJdzAe7cvCHTzPxyNr9qKqr7/T9sourYbJYEaDXIM6+tQY01MU0nqfUkg1HL+LX/9mH6Su24ImPDrUZXLpTh4KYRx55BF988QV+/PFHxMfHy48bDAYAaJJ1KSoqapKdkej1egQFBTl8ERGRZ9NpVPIHJwAsmJre4j9mpUxMbmnLH5ZnimwfvmH+OrkDbUuk4t7mtpTKqk04klfR+uKbYbGKMFtcO9DyQE4ZnvvSdnq33iJif055p+95zN6pt48hECpVw++3fMy6HZmYHWdKAACiCHyy/wKm/CMDz391FGXVrRdeu4NTQYwoinj44Yfx6aefYtOmTUhOTnZ4Pjk5GQaDARs3bpQfM5lMyMjIwNixY12zYiIi8gjSltKA2CBM7RfV4nVSEJPXSibm9KWWZyZdKbaV4t4H/7MPN726DQs/PYy6+vbNV6o2mjF1eQamr9iC8hrXfJCXXDZi/ur9qLeI0NiDjT3ZpZ2+75UzkyQNvWLazsQcyCkDAPx2UgrG9A6HyWLF29uyMekfm+XZVUrhVBDz0EMP4YMPPsCaNWsQGBiIwsJCFBYWorbW9oMnCAIWLFiAJUuWYP369Thy5AjmzZsHPz8/zJ49u0u+ASIiUqZ7xyRhWGIInp8xsMUsDAAk2At7L5TXwtrCEeD21MNIYuy9Yq48iVNVV4+9522Bwtrdufj5v7Yjux3bK29vzUZ2cTXOXqrGHz7JbLGfjcUqtqsOx2IV8di6gyioqEPvSH/84Ya+AIDdrghi7JmYvjGOuxq9I201McWXjaioaXnbqq7egqP2e8y+KhFrfjUaq+6/CmlRAaiorccL35xo8bXu4FQQ89prr6GiogKTJk1CTEyM/PXhhx/K1zz11FNYsGAB5s+fj5EjRyIvLw8bNmxAYGDbMzGIiKjnGNUrDOvnj8OwxNBWrzME+0AlACazFcWXmx/c2NrMpCtJmZj8csdMzIGcclhF25ZUuL8OxwoqcfOr2/C/zIIW71V82Yg3t5yRf/1tViHW7m5au1lUVYebXt2Gq/72PQoqWq/F+b+NJ7HtdDF8tWq8fs8ITOlry1IdvFDe7uxQS1rKxPjrNfJR9jOtdEfOyq9EvUVEuL8O8aG+EAQBE9Mj8do9I6ASgO+PXcTB3PJOrdGVnN5Oau5r3rx58jWCIGDx4sUoKChAXV0dMjIy5NNLREREV9KqVfKpotwWikjb0yNGEms/Zn1lMLH3nC3TMalPJL5+9Bpc1SsMl41mzF+9Hy9+e7zZDMurP5xCtcmCwfHBcsbkua+yHE7u5JfXYtYbO3GsoBI1JgsyTlxqcW0/Hi/Cyh9PAwBeuG0Q0qMDkRzhj4gAPUxmKw5fcL5eR1JeY5K30PoYmiYO2nNCSQpQhiaEOGTPUqMCMHO4rQb2pQ3KycZ0qk8MERGRKzQcs26axTCZrThfYgtu0qLbEcQES5kYx3vtOWer9RjVKwyGYB+s+dVo/HZSCgDgtc1n8M62bIfrz5dUY/WuHADAH27oi19f0xvXpEWgrt6KR9YeQF29BedLqnHH6zsctqVa2xZ6LcOW1blvTBJuHRoHwPaP/6uSQ+1r7PiWkpSFiQvxRZCPtsnz7amLkephhiWGNHnusWvToFUL2HqqGDvPlnR4na7EIIaIiNxOanjXXBBzrsTWLj+g0ZZIaxoX9krZlXqLFQdypSDGFjBo1Co8fX1fLPpZPwDA818fw9eHG7aW/v7dCZitIiamR2JsSgRUKgEv3TkE4f46HC+swhMfH8Idr+9AXnktekf4Y9ltgwEAu1sIRGpMZjlIuH+c48GYq3rZmsbu6kRdjFQP0y+m+fKN9gyCbMjENN0CTAjzw6xRtl5uL2040easq+7AIIaIiNyutYZ3mfYtlr6GwFYLhCXRQT4Q7POYSu3Hgo/kVaCu3opQP63D0W8A+OU1yZg7JgkA8LuPDmLPuVIcvlCOrw4XQBCAp6/vK18bFeiDf9w5BADw9eECFFUZ0Sc6EOsevBo/GxwDtUrAhbLaZo937zlXhnqLiLgQXySF+zk8NyrZFsTsP1/W4aPcDfUwzbcqaSuIuVRlxIWyWggCMDghuNlrHpmSBr1GhT3nypBxsuVts+7CIIaIiNwuwd7wLre06Yf/4QvlAIDB8SHtupdOo0JkgB5AQ3HvXvtW0oiksCaBkCAI+PPNAzCtfzRMZit+9e+9+ONnRwAAPx8ah/6xjkHB5D5R+OV4WyZlUFww1v36akQF2iZrD7Bf29xx6e2niwEAY1PCm6yhryEIgT4aXDaacaygY8eYj+Tbgr1+MS0EMfaamJySGtQ3EyhJWZjUyIBmt6MAW4B479W2gO+lDSfdno1hEENERG7XWibmkD0TM6SF7EBzpEGQ+fbiXqnWRNpKupJaJeCVu4ZhSEIIymvqcfhCBXRqFR6fnt7s9Ytu7If188fi49+MQWij5nvStlBzW0o/nbEHManhzb7/yKTQFl/blrp6izwzqaXfJ0OQD/x0apitolxj1Fhr9TCN/XZSCvx1amTmVeC7rItOr9WVGMQQEZHbyQ3vrugVYzJb5b4lQ9qZiQGA2Ea9YkRRxN7ztg/okfYgozm+OjXemTsSifas0H1jkuRanSsJgoBhiaHw0aodHr/Kvi10ZXFveY0JWfm272NsSkSz97wqOdz+WueLZo/kVcBsFRERoHcYN3DlmlvbUmqtHqax8AA97rdnol754ZTTa3UlBjFEROR2hiAfqFUC6i0iiqoaesWcKKyCyWxFsK+2SR1JaxoX954trkZptQl6jQoD41ofbRMRoMdHD47B8zMG4snr+jj9fYyyB0mniy6jpFHPm51nSyCKtqPK0S0UJzecUCpzepumpaPRV0qxN727crijxSrKx7vbysQAwC/GJUMQgKMFlSiqatoZubswiCEiIrfTqFVyp93GvWIOyfUwwe0q6pU07tor9YcZkhACvUbd2ssA2Jrv3XN1UpMsS3uE+uuQbj8GLh3pBoCfTtuyK+NSmm4lSQbFhUCvUaG02tTqCaLmHLAHMW0FIMPtW1Zrd+fCaG5orHe66DIuG83w06mRHt12c9owfx362K/b2+j77G4MYoiISBHk8QONgpjDjYIYZ8SFNPSKaegP0/o2ias0t6Uk1cOMaWErCbAVJA+3dzfene1cYHDQPjxyaEJIq9fdMSIB0UF65JXXYq29Bw4AHLQfPx8UFwy1qn3BopR16kxvm85iEENERIogF/c2OqEkbXE4Uw8DNBT2FlTUyZmYUa3Uw7jSlR/uhRV1OHupGioBGNO75UwM0HDU2pm6mEtVRuSV249GtxHs+erUePTaNADAyh9Po9o+6+mAPQhqa0REc2tlEENERF7vyoZ3NSYzTl6UTtyEOHUvqbC3sLIO50pqIAgNWyldTcrEZOVXoKquHtvtWZiBccEI9mv+6LJktBwYtD8T0/hodGALR6Mbu3NkAnqF+6H4sgnv/ZTtcI+2MjmNSZmto/mVqKpreahkV2IQQ0REiiBlYqSamCN5lbCKQHSQvsVi2JZEBOihVQuQ6mP7GoJa7H3iajHBvkgM84NVBPadL5PrYVo6ldTYsMQQaFQC8sprmz1u3hxpK6i9AYhWrcLvptmOjr+RcRYXympwwh4stqeoVxIT7Iv4UF9YxYZMTndjEENERIogNbyTMjHONrlrTKUSYAhuCHy6qx6m4f0a6mKkTMzYVop6JX46DQbG2baE2rtNcyjXtuU21IkA5ObBsegXE4QqoxmPrj0AUbRlr5wNFq9yc10MgxgiIlIEKROTX14Li1WUm9w5s8XRmDQZG2i9P0xXkLaFPjuQh4KKOujUqnbX5Eiv/fJQQRtXAlariEMd2ApSqQT8/jpbNmZ/B+phJKNa6IvTXRjEEBGRIkQH+UCjEmC2irhYWdfhk0mSxk3fujsTI9XF5FfYeqgMSwyBr659R7ZnjUqAWiVg0/GiNoODs8WXUWU0w0erko88t9fkPlFyl2CgY8Gi9Pt6MLccJnPHZj51BoMYIiJSBLVKkJvUZeZVyK3xB8eFdOh+Uq+YuBBfh6xMd0gK90NkoF7+9bjUtuthJL0jA3CXfVr00m+Otdr4TqpFGRQXDI3auY90QRDwVKPhls7Uw0hSIgMQ5q+D0WxFln12U3diEENERIqREGYLNv6XadtK6RXu1+aJnpZItTTX9otyydqcIQiCnI0BgHHNzEtqzWNT0+CnU+NATjm+PVLY4nUdOVXU2FXJYXhkSipuGx7foXsIQsPMp/053d/0jkEMEREpRnyIrbj3h2NFAJw/Wt3YdQOi8en8sVh4Qz9XLM1pUtGrv07tdHFyVKAPfnlNbwDAsu9ONDt1Gmj/vKPWPDG9D166c4jTmRyJFKztP88ghoiIvJhU3HvZ3oStIyeTJIIgYHhiaLtrUVzthoEG9I7wx9yxvaDtQIDw6wm9ERGgQ3ZxNdbtyW3yfK3JguOFtqPRzpxMcjWpaNodx6wZxBARkWLEhznWrgzpYFGvEkQF+WDTk5Mc6k6cEaDXyN11X/7+lNxdV3IkvwIWq4jIQL3c3M8dBsQGwVerRmWdue2LXYxBDBERKYbUtRewFfoOiPXcIMYV7hqViKRwPxRfNuKtrWcdnms8L8mZ4ZiuplWrMDwpxC3vzSCGiIgUI6FREJMeHei2rSCl0GlU+P11fQDYuuuu3nUeFqvttFJni3pdqbvmUl2JQQwRESlGVKBtXADg2VtJrnTjoBiMSw1Hbb0Fi9YfwY2vbMX208VyEDOMQQwREZH7qVSC3KSuM0W9PYkgCHj/F1dh8c39EeyrxfHCKsx+e5c8uXqQAoI9aeZTd2MQQ0REijJrVCLSowMwtX/393dRKq1ahXnjkrH5yUmYN7YX1PaAIS2qfZOru5qfToO+MUHd/r6C2ForQDeorKxEcHAwKioqEBTU/b8hRERESne6qArvbDuH6wcaMDE90t3LAQD88aPd+Nus0d36+c0ghoiIiDpt94kLGN03oVs/v7mdRERERJ3mju0kBjFERETkkRjEEBERkUdiEENEREQeiUEMEREReSQGMUREROSRGMQQERGRR2IQQ0RERB6JQQwRERF5JAYxRERE5JEYxBAREZFHYhBDREREHolBDBEREXkkBjFERETkkTTuXsCVRFEEAFRWVrp5JURERNRe0ue29DneHRQXxJSUlAAAEhIS3LwSIiIiclZJSQmCg4O75b0UF8SEhYUBAHJyclzymzBq1Cjs2bOn0/dR2vtXVlYiISEBubm5CAoK6vb3by9vf38lrKHx+7f356ar3t8d+P7Ov78rf07c/f0rYQ3e8v4VFRVITEyUP8e7g+KCGJXKVqYTHBzskr9k1Wp1t/1l7Y73DwoKavX+Pf37V/r7K2ENzb1/Wz83Xf3+3Ynv3/H3d8XPibu/fyWswdveX/oc75b36rZ3cpOHHnqI78/39+o18P35/t78/kpYg7e/f1cSxO6swGmHyspKBAcHo6Kiwu3Ru5Lx94k6gj831B78OaGOcMfPjeIyMXq9Hs8++yz0er27l6Jo/H2ijuDPDbUHf06oI9zxc6O4TAwRERFReyguE0NERETUHgxiiIiIyCMxiCEiIiKPxCCGiIiIPBKDGIXKzc3FAw88gNjYWOh0OiQlJeGxxx6TxzK0ZfPmzRAEAeXl5V27UHK7efPmQRAEvPDCCw6Pf/bZZxAEwU2rIqWRfk4EQYBWq0V0dDSmTZuGd999F1ar1d3LIw8wb948zJgxw93LcMAgRoHOnj2LkSNH4uTJk1i7di1Onz6N119/HT/88APGjBmD0tJSdy+RFMbHxwcvvvgiysrK3L0UUrDrr78eBQUFOHfuHL755htMnjwZjz32GG666SaYzWZ3L4/IaQxiFOihhx6CTqfDhg0bMHHiRCQmJuKGG27A999/j7y8PCxatAgAYDQa8dRTTyEhIQF6vR5paWl45513cO7cOUyePBkAEBoaCkEQMG/ePDd+R9TVpk6dCoPBgKVLl7Z4zSeffIIBAwZAr9ejV69eeOmll+TnFi5ciKuvvrrJawYPHoxnn322S9ZM3U+v18NgMCAuLg7Dhw/HM888g88//xzffPMN3n//fQC2+Te//vWvERUVhaCgIEyZMgWHDh1yuM8XX3yBkSNHwsfHBxEREZg5c6Ybvhtyp2+//Rbjx49HSEgIwsPDcdNNN+HMmTPy8+fOnYMgCPj0008xefJk+Pn5YciQIdixY4dL18EgRmFKS0vx3XffYf78+fD19XV4zmAwYM6cOfjwww8hiiLuu+8+rFu3Dq+88gqOHTuG119/HQEBAUhISMAnn3wCADhx4gQKCgrw8ssvu+PboW6iVquxZMkSvPrqq7hw4UKT5/ft24c777wTd911FzIzM7F48WL86U9/kj+45syZg127djn8JZSVlYXMzEzMmTOnu74NcoMpU6ZgyJAh+PTTTyGKIm688UYUFhbif//7H/bt24fhw4fj2muvlTPAX3/9NWbOnIkbb7wRBw4cwA8//ICRI0e6+bug7lZdXY3HH38ce/bswQ8//ACVSoWf//znTbYmFy1ahCeffBIHDx5Eeno67r77btdm/URSlJ07d4oAxPXr1zf7/PLly0UA4q5du0QA4saNG5u97scffxQBiGVlZV23WFKEuXPnirfeeqsoiqJ49dVXi/fff78oiqK4fv16UfpPfPbs2eK0adMcXvf73/9e7N+/v/zrwYMHi88995z864ULF4qjRo3q4tVTd2n8c3KlWbNmif369RN/+OEHMSgoSKyrq3N4PiUlRXzjjTdEURTFMWPGiHPmzOnq5ZICtfYzVFRUJAIQMzMzRVEUxezsbBGA+Pbbb8vXZGVliQDEY8eOuWxNzMR4GNHeYDk7OxtqtRoTJ05084pISV588UWsWrUKR48edXj82LFjGDdunMNj48aNw6lTp2CxWADYsjGrV68GYPs5W7t2LbMwXkIURQiCgH379uHy5csIDw9HQECA/JWdnS1n6Q4ePIhrr73WzSsmdztz5gxmz56N3r17IygoCMnJyQCAnJwch+sGDx4s//+YmBgAQFFRkcvWoXHZncglUlNTIQgCjh492mwV+PHjxxEaGgo/P7/uXxwp3oQJE3DdddfhmWeecaiDkj6kGhOvmDgye/Zs/OEPf8D+/ftRW1uL3Nxc3HXXXd2xbHKzY8eOITk5GVarFTExMdi8eXOTa0JCQgCgyTY3eaebb74ZCQkJeOuttxAbGwur1YqBAwfCZDI5XKfVauX/L/0d5MrTcMzEKEx4eDimTZuGf/3rX6itrXV4rrCwEKtXr8asWbMwaNAgWK1WZGRkNHsfnU4HAPK/ssl7vPDCC/jyyy+xfft2+bH+/ftj27ZtDtdt374d6enpUKvVAID4+HhMmDABq1evxurVqzF16lRER0d369qp+23atAmZmZm47bbbMHz4cBQWFkKj0SA1NdXhKyIiAoDtX9Y//PCDm1dN7lRSUoJjx47hj3/8I6699lr069fPbScjGcQo0MqVK2E0GnHddddhy5YtyM3Nxbfffotp06YhLi4Of/vb39CrVy/MnTsX999/Pz777DNkZ2dj8+bN+OijjwAASUlJEAQBX331FS5duoTLly+7+bui7jJo0CDMmTMHr776qvzYE088gR9++AF//etfcfLkSaxatQorV67Ek08+6fDaOXPmYN26dfj4449xzz33dPfSqYsZjUYUFhYiLy8P+/fvx5IlS3Drrbfipptuwn333YepU6dizJgxmDFjBr777jucO3cO27dvxx//+Efs3bsXAPDss89i7dq1ePbZZ3Hs2DFkZmZi2bJlbv7OqDuFhoYiPDwcb775Jk6fPo1Nmzbh8ccfd89iXFZdQy517tw5cd68eaLBYBC1Wq2YkJAgPvLII2JxcbF8TW1trfi73/1OjImJEXU6nZiamiq+++678vPPPfecaDAYREEQxLlz57rhu6Du0Fyx3blz50S9Xi82/k/8v//9r9i/f39Rq9WKiYmJ4t///vcm9yorKxP1er3o5+cnVlVVdfXSqRvNnTtXBCACEDUajRgZGSlOnTpVfPfdd0WLxSJfV1lZKT7yyCNibGys/HfPnDlzxJycHPmaTz75RBw6dKio0+nEiIgIcebMme74lqib3XvvveJtt90miqIobty4UezXr5+o1+vFwYMHi5s3b3Y4lCIV9h44cEB+fVlZmQhA/PHHH122JkEUr9gYJyIiIrrC9ddfj9TUVKxcudLdS5FxO4mIiIhaVFZWhq+//hqbN2/G1KlT3b0cBzydRERERC26//77sWfPHjzxxBO49dZb3b0cB9xOIiIiIo/E7SQiIiLySAxiiIiIyCMxiCEiIiKPxCCGiIiIAABLly7FqFGjEBgYiKioKMyYMQMnTpxwuEYURSxevBixsbHw9fXFpEmTkJWVJT9fWlqKRx55BH369IGfnx8SExPx6KOPoqKiwuE+t9xyCxITE+Hj44OYmBjce++9yM/Pd2q9DGKIiIgIAJCRkYGHHnoIO3fuxMaNG2E2mzF9+nRUV1fL1yxbtgzLly/HypUrsWfPHhgMBkybNg1VVVUAgPz8fOTn5+Mf//gHMjMz8f777+Pbb7/FAw884PBekydPxkcffYQTJ07gk08+wZkzZ3D77bc7tV6eTiIiIqJmXbp0CVFRUcjIyMCECRMgiiJiY2OxYMECPP300wBs4yyio6Px4osv4sEHH2z2PtIok+rqamg0zXd3+eKLLzBjxgwYjUaHwZGtYSaGiIiImiVtAYWFhQEAsrOzUVhYiOnTp8vX6PV6TJw40WHobHP3CQoKajGAKS0txerVqzF27Nh2BzAAgxgiIiJqhiiKePzxxzF+/HgMHDgQAFBYWAgATSbcR0dHy89dqaSkBH/961+bzdI8/fTT8Pf3R3h4OHJycvD55587tUYGMURERNTEww8/jMOHD2Pt2rVNnhMEweHXoig2eQwAKisrceONN6J///549tlnmzz/+9//HgcOHMCGDRugVqtx3333wZkqF44dICIiIgePPPIIvvjiC2zZsgXx8fHy4waDAYAtIxMTEyM/XlRU1CQ7U1VVheuvvx4BAQFYv359s9tEERERiIiIQHp6Ovr164eEhATs3LkTY8aMadc6mYkhIiIiALaMysMPP4xPP/0UmzZtQnJyssPzycnJMBgM2Lhxo/yYyWRCRkYGxo4dKz9WWVmJ6dOnQ6fT4YsvvoCPj0+73huwFQq3FzMxREREBAB46KGHsGbNGnz++ecIDAyU61yCg4Ph6+sLQRCwYMECLFmyBGlpaUhLS8OSJUvg5+eH2bNnA7BlYKZPn46amhp88MEHqKysRGVlJQAgMjISarUau3fvxu7duzF+/HiEhobi7Nmz+POf/4yUlJR2Z2EAHrEmIiIiu+bqWgDgvffew7x58wDYMiZ/+ctf8MYbb6CsrAyjR4/GP//5T7n4d/PmzZg8eXKz98nOzkavXr2QmZmJxx57DIcOHUJ1dTViYmJw/fXX449//CPi4uLav14GMUREROSJWBNDREREHolBDBEREXkkBjFERETkkRjEEBERkUdiEENEREQeiUEMEREReSQGMUREROSRGMQQERGRR2IQQ0RERB6JQQwRERF5JAYxRERE5JH+Hw159ZW8MObvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "id": "b3f69a7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import necessary packages\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "id": "c20d418e",
   "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>ETH / Tx per Second Cnt</th>\n",
       "      <th>ETH / Mean Tx Fee (native units)</th>\n",
       "      <th>ETH / Mean Hash Rate</th>\n",
       "      <th>ETH / USD Denominated Closing Price</th>\n",
       "      <th>ETC / Tx per Second Cnt</th>\n",
       "      <th>ETC / Mean Tx Fee (native units)</th>\n",
       "      <th>ETC / Mean Hash Rate</th>\n",
       "      <th>ETC / USD Denominated Closing Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-07-25</th>\n",
       "      <td>0.634907</td>\n",
       "      <td>0.000719</td>\n",
       "      <td>4.051341</td>\n",
       "      <td>13.840295</td>\n",
       "      <td>0.294352</td>\n",
       "      <td>0.000907</td>\n",
       "      <td>0.213432</td>\n",
       "      <td>0.609005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-26</th>\n",
       "      <td>0.716782</td>\n",
       "      <td>0.000735</td>\n",
       "      <td>3.864700</td>\n",
       "      <td>11.963285</td>\n",
       "      <td>0.359028</td>\n",
       "      <td>0.000833</td>\n",
       "      <td>0.387970</td>\n",
       "      <td>2.550458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-27</th>\n",
       "      <td>0.613831</td>\n",
       "      <td>0.000721</td>\n",
       "      <td>3.672282</td>\n",
       "      <td>13.061564</td>\n",
       "      <td>0.394005</td>\n",
       "      <td>0.000788</td>\n",
       "      <td>0.566014</td>\n",
       "      <td>1.619800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28</th>\n",
       "      <td>0.561979</td>\n",
       "      <td>0.001317</td>\n",
       "      <td>3.919557</td>\n",
       "      <td>12.888203</td>\n",
       "      <td>0.271377</td>\n",
       "      <td>0.000926</td>\n",
       "      <td>0.437650</td>\n",
       "      <td>1.727024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-29</th>\n",
       "      <td>0.546794</td>\n",
       "      <td>0.000967</td>\n",
       "      <td>3.816590</td>\n",
       "      <td>12.878013</td>\n",
       "      <td>0.212523</td>\n",
       "      <td>0.001070</td>\n",
       "      <td>0.502883</td>\n",
       "      <td>1.649872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-19</th>\n",
       "      <td>11.601840</td>\n",
       "      <td>0.003101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1683.207350</td>\n",
       "      <td>0.397234</td>\n",
       "      <td>0.000082</td>\n",
       "      <td>123.292721</td>\n",
       "      <td>22.564623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-20</th>\n",
       "      <td>12.264352</td>\n",
       "      <td>0.003729</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1700.884873</td>\n",
       "      <td>0.452488</td>\n",
       "      <td>0.000096</td>\n",
       "      <td>123.727006</td>\n",
       "      <td>23.168527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-21</th>\n",
       "      <td>12.549653</td>\n",
       "      <td>0.003606</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1655.094683</td>\n",
       "      <td>0.478322</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>122.470624</td>\n",
       "      <td>22.742119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-22</th>\n",
       "      <td>12.509711</td>\n",
       "      <td>0.003953</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1641.941195</td>\n",
       "      <td>0.447593</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>125.802607</td>\n",
       "      <td>22.460283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-23</th>\n",
       "      <td>12.656678</td>\n",
       "      <td>0.004049</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1649.392884</td>\n",
       "      <td>0.490671</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>122.578418</td>\n",
       "      <td>22.084054</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2405 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ETH / Tx per Second Cnt  ETH / Mean Tx Fee (native units)  \\\n",
       "Time                                                                    \n",
       "2016-07-25                 0.634907                          0.000719   \n",
       "2016-07-26                 0.716782                          0.000735   \n",
       "2016-07-27                 0.613831                          0.000721   \n",
       "2016-07-28                 0.561979                          0.001317   \n",
       "2016-07-29                 0.546794                          0.000967   \n",
       "...                             ...                               ...   \n",
       "2023-02-19                11.601840                          0.003101   \n",
       "2023-02-20                12.264352                          0.003729   \n",
       "2023-02-21                12.549653                          0.003606   \n",
       "2023-02-22                12.509711                          0.003953   \n",
       "2023-02-23                12.656678                          0.004049   \n",
       "\n",
       "            ETH / Mean Hash Rate  ETH / USD Denominated Closing Price  \\\n",
       "Time                                                                    \n",
       "2016-07-25              4.051341                            13.840295   \n",
       "2016-07-26              3.864700                            11.963285   \n",
       "2016-07-27              3.672282                            13.061564   \n",
       "2016-07-28              3.919557                            12.888203   \n",
       "2016-07-29              3.816590                            12.878013   \n",
       "...                          ...                                  ...   \n",
       "2023-02-19                   NaN                          1683.207350   \n",
       "2023-02-20                   NaN                          1700.884873   \n",
       "2023-02-21                   NaN                          1655.094683   \n",
       "2023-02-22                   NaN                          1641.941195   \n",
       "2023-02-23                   NaN                          1649.392884   \n",
       "\n",
       "            ETC / Tx per Second Cnt  ETC / Mean Tx Fee (native units)  \\\n",
       "Time                                                                    \n",
       "2016-07-25                 0.294352                          0.000907   \n",
       "2016-07-26                 0.359028                          0.000833   \n",
       "2016-07-27                 0.394005                          0.000788   \n",
       "2016-07-28                 0.271377                          0.000926   \n",
       "2016-07-29                 0.212523                          0.001070   \n",
       "...                             ...                               ...   \n",
       "2023-02-19                 0.397234                          0.000082   \n",
       "2023-02-20                 0.452488                          0.000096   \n",
       "2023-02-21                 0.478322                          0.000101   \n",
       "2023-02-22                 0.447593                          0.000090   \n",
       "2023-02-23                 0.490671                          0.000106   \n",
       "\n",
       "            ETC / Mean Hash Rate  ETC / USD Denominated Closing Price  \n",
       "Time                                                                   \n",
       "2016-07-25              0.213432                             0.609005  \n",
       "2016-07-26              0.387970                             2.550458  \n",
       "2016-07-27              0.566014                             1.619800  \n",
       "2016-07-28              0.437650                             1.727024  \n",
       "2016-07-29              0.502883                             1.649872  \n",
       "...                          ...                                  ...  \n",
       "2023-02-19            123.292721                            22.564623  \n",
       "2023-02-20            123.727006                            23.168527  \n",
       "2023-02-21            122.470624                            22.742119  \n",
       "2023-02-22            125.802607                            22.460283  \n",
       "2023-02-23            122.578418                            22.084054  \n",
       "\n",
       "[2405 rows x 8 columns]"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loading the data from an excel file\n",
    "df = pd.read_excel('Ethereum+.xlsx', index_col=0) \n",
    "df.index = pd.to_datetime(df.index)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "id": "0055c038",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.join(df1, how='outer') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "40aa5f38",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.join(df2, how='outer') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "id": "bde2166c",
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ETH / Tx per Second Cnt</th>\n",
       "      <th>ETH / Mean Tx Fee (native units)</th>\n",
       "      <th>ETH / Mean Hash Rate</th>\n",
       "      <th>ETH / USD Denominated Closing Price</th>\n",
       "      <th>ETC / Tx per Second Cnt</th>\n",
       "      <th>ETC / Mean Tx Fee (native units)</th>\n",
       "      <th>ETC / Mean Hash Rate</th>\n",
       "      <th>ETC / USD Denominated Closing Price</th>\n",
       "      <th>Value</th>\n",
       "      <th>open</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-07-25</th>\n",
       "      <td>0.634907</td>\n",
       "      <td>0.000719</td>\n",
       "      <td>4.051341</td>\n",
       "      <td>13.840295</td>\n",
       "      <td>0.294352</td>\n",
       "      <td>0.000907</td>\n",
       "      <td>0.213432</td>\n",
       "      <td>0.609005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-26</th>\n",
       "      <td>0.716782</td>\n",
       "      <td>0.000735</td>\n",
       "      <td>3.864700</td>\n",
       "      <td>11.963285</td>\n",
       "      <td>0.359028</td>\n",
       "      <td>0.000833</td>\n",
       "      <td>0.387970</td>\n",
       "      <td>2.550458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-27</th>\n",
       "      <td>0.613831</td>\n",
       "      <td>0.000721</td>\n",
       "      <td>3.672282</td>\n",
       "      <td>13.061564</td>\n",
       "      <td>0.394005</td>\n",
       "      <td>0.000788</td>\n",
       "      <td>0.566014</td>\n",
       "      <td>1.619800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28</th>\n",
       "      <td>0.561979</td>\n",
       "      <td>0.001317</td>\n",
       "      <td>3.919557</td>\n",
       "      <td>12.888203</td>\n",
       "      <td>0.271377</td>\n",
       "      <td>0.000926</td>\n",
       "      <td>0.437650</td>\n",
       "      <td>1.727024</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-29</th>\n",
       "      <td>0.546794</td>\n",
       "      <td>0.000967</td>\n",
       "      <td>3.816590</td>\n",
       "      <td>12.878013</td>\n",
       "      <td>0.212523</td>\n",
       "      <td>0.001070</td>\n",
       "      <td>0.502883</td>\n",
       "      <td>1.649872</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-03</th>\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>1.941332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-04</th>\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>1.930251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-05</th>\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>2.061882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-06</th>\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>1.723397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-07</th>\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>1.770803</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2509 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ETH / Tx per Second Cnt  ETH / Mean Tx Fee (native units)  \\\n",
       "2016-07-25                 0.634907                          0.000719   \n",
       "2016-07-26                 0.716782                          0.000735   \n",
       "2016-07-27                 0.613831                          0.000721   \n",
       "2016-07-28                 0.561979                          0.001317   \n",
       "2016-07-29                 0.546794                          0.000967   \n",
       "...                             ...                               ...   \n",
       "2023-06-03                      NaN                               NaN   \n",
       "2023-06-04                      NaN                               NaN   \n",
       "2023-06-05                      NaN                               NaN   \n",
       "2023-06-06                      NaN                               NaN   \n",
       "2023-06-07                      NaN                               NaN   \n",
       "\n",
       "            ETH / Mean Hash Rate  ETH / USD Denominated Closing Price  \\\n",
       "2016-07-25              4.051341                            13.840295   \n",
       "2016-07-26              3.864700                            11.963285   \n",
       "2016-07-27              3.672282                            13.061564   \n",
       "2016-07-28              3.919557                            12.888203   \n",
       "2016-07-29              3.816590                            12.878013   \n",
       "...                          ...                                  ...   \n",
       "2023-06-03                   NaN                                  NaN   \n",
       "2023-06-04                   NaN                                  NaN   \n",
       "2023-06-05                   NaN                                  NaN   \n",
       "2023-06-06                   NaN                                  NaN   \n",
       "2023-06-07                   NaN                                  NaN   \n",
       "\n",
       "            ETC / Tx per Second Cnt  ETC / Mean Tx Fee (native units)  \\\n",
       "2016-07-25                 0.294352                          0.000907   \n",
       "2016-07-26                 0.359028                          0.000833   \n",
       "2016-07-27                 0.394005                          0.000788   \n",
       "2016-07-28                 0.271377                          0.000926   \n",
       "2016-07-29                 0.212523                          0.001070   \n",
       "...                             ...                               ...   \n",
       "2023-06-03                      NaN                               NaN   \n",
       "2023-06-04                      NaN                               NaN   \n",
       "2023-06-05                      NaN                               NaN   \n",
       "2023-06-06                      NaN                               NaN   \n",
       "2023-06-07                      NaN                               NaN   \n",
       "\n",
       "            ETC / Mean Hash Rate  ETC / USD Denominated Closing Price  Value  \\\n",
       "2016-07-25              0.213432                             0.609005    NaN   \n",
       "2016-07-26              0.387970                             2.550458    NaN   \n",
       "2016-07-27              0.566014                             1.619800    NaN   \n",
       "2016-07-28              0.437650                             1.727024    NaN   \n",
       "2016-07-29              0.502883                             1.649872    NaN   \n",
       "...                          ...                                  ...    ...   \n",
       "2023-06-03                   NaN                                  NaN    NaN   \n",
       "2023-06-04                   NaN                                  NaN    NaN   \n",
       "2023-06-05                   NaN                                  NaN    NaN   \n",
       "2023-06-06                   NaN                                  NaN    NaN   \n",
       "2023-06-07                   NaN                                  NaN    NaN   \n",
       "\n",
       "                open  \n",
       "2016-07-25       NaN  \n",
       "2016-07-26       NaN  \n",
       "2016-07-27       NaN  \n",
       "2016-07-28       NaN  \n",
       "2016-07-29       NaN  \n",
       "...              ...  \n",
       "2023-06-03  1.941332  \n",
       "2023-06-04  1.930251  \n",
       "2023-06-05  2.061882  \n",
       "2023-06-06  1.723397  \n",
       "2023-06-07  1.770803  \n",
       "\n",
       "[2509 rows x 10 columns]"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "id": "d96d8637",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ETH / Tx Fee per Second (native units)']= df['ETH / Mean Tx Fee (native units)'] * df['ETH / Tx per Second Cnt']\n",
    "df['ETH / Tx Fee per Second (USD)']= df['ETH / Tx Fee per Second (native units)']*df['ETH / USD Denominated Closing Price']\n",
    "#df['ETH / Tx Fee per Day (native units)'] = df['ETH / Tx Fee per Second (native units)']*24*60*60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "id": "e9a37687",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2022-05-01 00:00:00')"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_index = df['ETH / Tx Fee per Second (USD)'].idxmax()\n",
    "max_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "id": "5d984434",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "# Assuming df is your DataFrame and 'column_name' is the name of your column\n",
    "index_value = datetime.strptime(\"2021-05-11 00:00:00\", \"%Y-%m-%d %H:%M:%S\")\n",
    "\n",
    "# Get the value before and after the specific index\n",
    "value_before = df.loc[max_index - pd.Timedelta(days=3), 'ETH / Tx Fee per Second (USD)']\n",
    "value_after = df.loc[max_index + pd.Timedelta(days=3), 'ETH / Tx Fee per Second (USD)']\n",
    "\n",
    "# Calculate the average\n",
    "average_value = (value_before + value_after) / 2\n",
    "\n",
    "# Replace the value at the specific index with the average value\n",
    "df.loc[max_index, 'ETH / Tx Fee per Second (USD)'] = average_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "id": "68c0e66e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Block Reward\n",
    "df['ETH / Block Reward (native units)'] = 5\n",
    "cutoff_date = pd.to_datetime('2017-10-16')\n",
    "df.loc[df.index > cutoff_date, 'ETH / Block Reward (native units)'] = 3\n",
    "cutoff_date = pd.to_datetime('2019-02-28')\n",
    "df.loc[df.index > cutoff_date, 'ETH / Block Reward (native units)'] = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "id": "04762aa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ETH Block Reward per Second (USD)'] = (df['ETH / Block Reward (native units)'] * df['ETH / USD Denominated Closing Price'])/15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "id": "abfa63db",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ElectricityPrice'] = 0.05/3600000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "id": "9caea14b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['MaxEnergy'] = (df['ETH Block Reward per Second (USD)']+df['ETH / Tx Fee per Second (USD)'])/df['ElectricityPrice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "id": "e7e3a0c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Digiconomist'] = ((df['ETH Block Reward per Second (USD)']+df['ETH / Tx Fee per Second (USD)'])*0.6)/df['ElectricityPrice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "id": "b5b2a302",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['BestGuessTopDown'] = ((df['ETH Block Reward per Second (USD)']+df['ETH / Tx Fee per Second (USD)'])*0.55)/(df['ElectricityPrice']*2*0.65)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "id": "d8aa7eb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['BestGuessTopDown (GW)'] = df['BestGuessTopDown']/1000000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "71b0e500",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Digiconomist (GW)'] = df['Digiconomist']/1000000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "id": "445c605f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['MaxEnergy (GW)'] = df['MaxEnergy']/1000000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "id": "ca64195f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/_b/28jfj19121qf32rdzd8twc000000gn/T/ipykernel_17817/2946070092.py:22: MatplotlibDeprecationWarning: savefig() got unexpected keyword argument \"transparant\" which is no longer supported as of 3.3 and will become an error in 3.6\n",
      "  plt.savefig(\"TopDown.png\", dpi=300, transparant = True)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "start_date = pd.Timestamp('2017-01-01')\n",
    "end_date = pd.Timestamp('2022-09-30')\n",
    "#sns.set(style=\"whitegrid\")\n",
    "ax = df.loc[start_date:end_date, ['MaxEnergy (GW)','Digiconomist (GW)','BestGuessTopDown (GW)']].plot(figsize=(10,6), alpha =0.5, legend = False)\n",
    "ax.patch.set_facecolor((0, 0, 0, 0))\n",
    "# add vertical line at Merge\n",
    "#ax.axvline(x='2022-09-15', color='green', linestyle='-', linewidth=2)\n",
    "#ax.set_yscale('log')\n",
    "\n",
    "legend = ax.legend()\n",
    "legend.remove()\n",
    "\n",
    "# set the labels and title\n",
    "ax.set_title('', fontsize=20, fontweight='bold')\n",
    "ax.set_xlabel('', fontsize=16)\n",
    "ax.set_ylabel('', fontsize=16)\n",
    "ax.tick_params(axis='both', labelsize=12)\n",
    "plt.savefig(\"TopDown.png\", dpi=300, transparant = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "id": "d801752f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Best Guess'] = (df['ETH / Mean Hash Rate'] / 0.2262046179789677)/1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "id": "758591f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2022-09-14    10.671122\n",
       "Name: Digiconomist (GW), dtype: float64"
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value = df.loc[df.index == '2022-09-14', 'Digiconomist (GW)']\n",
    "value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "id": "5f09ab9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2022-09-14    7.524509\n",
       "Name: BestGuessTopDown (GW), dtype: float64"
      ]
     },
     "execution_count": 367,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value = df.loc[df.index == '2022-09-14', 'BestGuessTopDown (GW)']\n",
    "value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "id": "1600c0b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2022-09-14    17.785203\n",
       "Name: MaxEnergy (GW), dtype: float64"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value = df.loc[df.index == '2022-09-14', 'MaxEnergy (GW)']\n",
    "value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "id": "70a0225f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ETH / Mean Hash Rate (TH/s)']= df['ETH / Mean Hash Rate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "id": "a8e52b83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "start_date = pd.Timestamp('2017-01-01')\n",
    "end_date = pd.Timestamp('2022-12-31')\n",
    "#sns.set(style=\"whitegrid\")\n",
    "\n",
    "ax = df.loc[start_date:end_date, ['ETH / Mean Hash Rate','ETC / Mean Hash Rate']].plot(figsize=(10,6), alpha =0.5)\n",
    "ax.patch.set_facecolor((0, 0, 0, 0))\n",
    "\n",
    "legend = ax.legend()\n",
    "legend.remove()\n",
    "\n",
    "\n",
    "# set the labels and title\n",
    "ax.set_title('', fontsize=20, fontweight='bold')\n",
    "ax.set_xlabel('', fontsize=16)\n",
    "ax.set_ylabel('', fontsize=16)\n",
    "ax.tick_params(axis='both', labelsize=12)\n",
    "plt.savefig(\"HashRateETH.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "id": "8ffccd55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54.78771655"
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2022-12-31', 'ETC / Mean Hash Rate'] - df.loc['2022-09-14', 'ETC / Mean Hash Rate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "id": "10cc69ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Jasminer'] = 2.166666667\n",
    "df['Nvidia'] = 0.7285714286\n",
    "df['McDonald'] = 0.38\n",
    "df['Best'] = 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "id": "3fda4d20",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['JasminerEnergy (GW)'] = df['ETH / Mean Hash Rate (TH/s)']/(df['Jasminer'])*1000000/1000000000\n",
    "df['Nvidia (GW)'] = df['ETH / Mean Hash Rate (TH/s)']/(df['Nvidia'])*1000000/1000000000\n",
    "df['McDonald (GW)'] = df['ETH / Mean Hash Rate (TH/s)']/(df['McDonald'])*1000000/1000000000\n",
    "df['Best (GW)'] = df['ETH / Mean Hash Rate (TH/s)']/(df['Best'])*1000000/1000000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "id": "573c1a8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "start_date = pd.Timestamp('2022-07-01')\n",
    "end_date = pd.Timestamp('2022-09-14')\n",
    "#sns.set(style=\"whitegrid\")\n",
    "\n",
    "ax = df.loc[start_date:end_date, ['Best (GW)','McDonald (GW)','Digiconomist (GW)']].plot(figsize=(10,6), alpha =0.5)\n",
    "ax.patch.set_facecolor((0, 0, 0, 0))\n",
    "\n",
    "legend = ax.legend()\n",
    "legend.remove()\n",
    "\n",
    "\n",
    "# set the labels and title\n",
    "ax.set_title('', fontsize=20, fontweight='bold')\n",
    "ax.set_xlabel('', fontsize=16)\n",
    "ax.set_ylabel('', fontsize=16)\n",
    "ax.tick_params(axis='both', labelsize=12)\n",
    "plt.savefig(\"AllEstimates.png\", transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "id": "65d0e4c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "start_date = pd.Timestamp('2017-01-01')\n",
    "end_date = pd.Timestamp('2023-01-01')\n",
    "#sns.set(style=\"whitegrid\")\n",
    "\n",
    "ax = df.loc[start_date:end_date, ['ETH / Mean Hash Rate','ETC / Mean Hash Rate','Value' ]].plot(figsize=(10,6), alpha =0.5, )\n",
    "ax.patch.set_facecolor((0, 0, 0, 0))\n",
    "ax.set_yscale('log')\n",
    "legend = ax.legend()\n",
    "legend.remove()\n",
    "\n",
    "\n",
    "# set the labels and title\n",
    "ax.set_title('', fontsize=20, fontweight='bold')\n",
    "ax.set_xlabel('', fontsize=16)\n",
    "ax.set_ylabel('', fontsize=16)\n",
    "ax.tick_params(axis='both', labelsize=12)\n",
    "plt.savefig(\"HashRate.png\", transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "id": "ce7feb21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "start_date = pd.Timestamp('2022-01-01')\n",
    "end_date = pd.Timestamp('2023-02-03')\n",
    "#sns.set(style=\"whitegrid\")\n",
    "\n",
    "ax = df.loc[start_date:end_date, ['ETCMaxEnergy','ETCMinEnergy','ETCBestEnergy' ]].plot(figsize=(10,6), alpha =0.5 )\n",
    "ax.patch.set_facecolor((0, 0, 0, 0))\n",
    "legend = ax.legend()\n",
    "legend.remove()\n",
    "\n",
    "\n",
    "# set the labels and title\n",
    "ax.set_title('', fontsize=20, fontweight='bold')\n",
    "ax.set_xlabel('', fontsize=16)\n",
    "ax.set_ylabel('', fontsize=16)\n",
    "ax.tick_params(axis='both', labelsize=12)\n",
    "plt.savefig(\"ETCEnergy.png\", transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "id": "cd04ac97",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Profitability Threshold ETC\n",
    "df['ETH Profatibilty Threshold [J/Hash]'] = ((df['ETH Block Reward per Second (USD)']+df['ETH / Tx Fee per Second (USD)'])) /(df['ETH / Mean Hash Rate']*1000000000000*df['ElectricityPrice'])\n",
    "cutoff_date = pd.to_datetime('2022-09-14')\n",
    "df.loc[df.index > cutoff_date, 'ETH Profatibilty Threshold [J/Hash]'] = 0\n",
    "df['ETH Profatibilty Threshold [Hash/J]'] = 1/df['ETH Profatibilty Threshold [J/Hash]']\n",
    "df['ETH Profatibilty Threshold [MHash/J]'] = df['ETH Profatibilty Threshold [Hash/J]']/1000000\n",
    "df['ETH Profatibilty Threshold [MHash/J]'] = df['ETH Profatibilty Threshold [MHash/J]'] /0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "id": "fac9adec",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ETHPOW Profatibilty Threshold [J/Hash]'] = ((df['ETH / Block Reward (native units)']*df['open']/15)) /(df['Value']*1000000000000*df['ElectricityPrice'])\n",
    "df['ETHPOW Profatibilty Threshold [Hash/J]'] = 1/df['ETHPOW Profatibilty Threshold [J/Hash]']\n",
    "df['ETHPOW Profatibilty Threshold [MHash/J]'] = df['ETHPOW Profatibilty Threshold [Hash/J]']/1000000\n",
    "df['ETHPOW Profatibilty Threshold [MHash/J]'] = df['ETHPOW Profatibilty Threshold [MHash/J]']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "id": "c7b0cf86",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Block Reward\n",
    "df['ETC / Block Reward (native units)'] = 5\n",
    "cutoff_date = pd.to_datetime('2017-10-16')\n",
    "df.loc[df.index > cutoff_date, 'ETC / Block Reward (native units)'] = 3\n",
    "cutoff_date = pd.to_datetime('2019-02-28')\n",
    "df.loc[df.index > cutoff_date, 'ETC / Block Reward (native units)'] = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "id": "2f85a9f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Profitability Threshold ETC\n",
    "df['ETC Profatibilty Threshold [J/Hash]'] = ((df['ETC / Block Reward (native units)']*df['ETC / USD Denominated Closing Price']/15)) /(df['ETC / Mean Hash Rate']*1000000000000*df['ElectricityPrice'])\n",
    "df['ETC Profatibilty Threshold [Hash/J]'] = 1/df['ETC Profatibilty Threshold [J/Hash]']\n",
    "df['ETC Profatibilty Threshold [MHash/J]'] = df['ETC Profatibilty Threshold [Hash/J]']/1000000\n",
    "df['ETC Profatibilty Threshold [MHash/J]'] = df['ETC Profatibilty Threshold [MHash/J]'] /0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "id": "e50b9a1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/_b/28jfj19121qf32rdzd8twc000000gn/T/ipykernel_17817/710424429.py:21: MatplotlibDeprecationWarning: savefig() got unexpected keyword argument \"transparant\" which is no longer supported as of 3.3 and will become an error in 3.6\n",
      "  plt.savefig(\"Thresholds.png\", transparant = True)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "start_date = pd.Timestamp('2022-01-01')\n",
    "end_date = pd.Timestamp('2022-12-31')\n",
    "#sns.set(style=\"whitegrid\")\n",
    "\n",
    "ax = df.loc[start_date:end_date, ['ETHPOW Profatibilty Threshold [MHash/J]','ETH Profatibilty Threshold [MHash/J]','ETC Profatibilty Threshold [MHash/J]']].plot(figsize=(10,6), alpha =0.5)\n",
    "ax.patch.set_facecolor((0, 0, 0, 0))\n",
    "\n",
    "legend = ax.legend()\n",
    "legend.remove()\n",
    "\n",
    "\n",
    "# set the labels and title\n",
    "ax.set_title('', fontsize=20, fontweight='bold')\n",
    "ax.set_xlabel('', fontsize=16)\n",
    "ax.set_ylabel('', fontsize=16)\n",
    "ax.tick_params(axis='both', labelsize=12)\n",
    "plt.savefig(\"Thresholds.png\", transparant = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "id": "95f479f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/_b/28jfj19121qf32rdzd8twc000000gn/T/ipykernel_17817/1737669494.py:21: MatplotlibDeprecationWarning: savefig() got unexpected keyword argument \"transparant\" which is no longer supported as of 3.3 and will become an error in 3.6\n",
      "  plt.savefig(\"ETC Energy Consumption.png\", transparant = True)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "start_date = pd.Timestamp('2022-01-01')\n",
    "end_date = pd.Timestamp('2023-02-23')\n",
    "#sns.set(style=\"whitegrid\")\n",
    "\n",
    "ax = df.loc[start_date:end_date, ['ETCMaxEnergy','ETCMinEnergy','ETCBestEnergy']].plot(figsize=(10,6), alpha =0.5)\n",
    "ax.patch.set_facecolor((0, 0, 0, 0))\n",
    "\n",
    "legend = ax.legend()\n",
    "legend.remove()\n",
    "\n",
    "\n",
    "# set the labels and title\n",
    "ax.set_title('', fontsize=20, fontweight='bold')\n",
    "ax.set_xlabel('', fontsize=16)\n",
    "ax.set_ylabel('', fontsize=16)\n",
    "ax.tick_params(axis='both', labelsize=12)\n",
    "plt.savefig(\"ETC Energy Consumption.png\", transparant = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "id": "261788ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ETCMaxEnergy']= df['ETC / Mean Hash Rate']*1000000/df['ETC Profatibilty Threshold [MHash/J]']/1000000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "id": "8da4e822",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ETCMinEnergy']= df['ETC / Mean Hash Rate']*1000000/2.166/1000000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "id": "17211fda",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ETCBestEnergy'] = df['ETC / Mean Hash Rate']*1000000/1.62/1000000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "4881c762",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9925380940175279"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value = df.loc['2022-12-31', 'ETC Profatibilty Threshold [MHash/J]']\n",
    "value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "557341a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Group 1:\n",
      "Estimated quantities: [134192.54553556  16516.67573032  16507.00897799  12544.96318716\n",
      "   7051.0837864   12554.63000995  12544.96325806  23590.69580935\n",
      "  16507.00897844  15373.68224038  19042.69578858  19052.36268184\n",
      "   8885.56140647  33712.46349623   8885.61632082  10710.42895525\n",
      "  11843.86552111  13539.0929532   19052.36268184  15383.29407837\n",
      "  13548.81461943  15373.62725601  22875.14065641  15373.68216991\n",
      "   6465.13487277 134192.54553556  19052.36268184  52197.11972457\n",
      "  54039.67791956   8875.89465459  23590.69580935  12554.68499476\n",
      "  26509.65722331  63360.1852742   13539.09295276  17217.8832229\n",
      "  18341.54327866  10710.42895525  12544.96325762  12544.96325762\n",
      "  16367.68639091   9879.69110205   9879.74601595  17208.16155624\n",
      "  10009.27637657  16507.00897799  16377.47861737   8875.89465415\n",
      "  12545.0181015   11704.61363475  11843.81060677  17208.16155668\n",
      "  11853.60290414  16377.47861737  10710.42895481  11714.28031661\n",
      "  10019.06860348  10710.42888479  18351.2648753   12554.68492474\n",
      "   5346.1486382   12554.63000995   5346.20355583]\n",
      "Group 2:\n",
      "Estimated quantities: [56935.81179387 57011.88216249 56950.15202829 77644.23133063\n",
      " 56921.87794026 56964.4922528  57883.47592675 56926.44109488\n",
      " 55537.78018271 56935.81179387 56935.81179387 55363.36787315\n",
      " 57002.5114635  57883.47592675 56945.46668261 57096.88773676\n",
      " 76897.46436524 57011.88216249 57026.22238446 57026.22238192\n",
      " 56997.94830888 56926.44109488 57006.47714617 57002.5096237\n",
      " 58624.66518942 57402.80466769 57455.2881338  57011.88216249\n",
      " 57082.5474998  56185.48552916 57082.54566    57082.54566\n",
      " 57020.93956175 57087.35504163 57087.76324982 56959.92909564\n",
      " 56213.56849864]\n",
      "Group 3:\n",
      "Estimated quantities: [7083.37197837 6064.73080074 6330.08746699 5954.63354252 6552.81189446\n",
      " 7121.90815364 6358.00408257 6196.47434878 6158.74505842 6434.67392933\n",
      " 6394.26299472 6404.78158876 6851.3441079 ]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.optimize import minimize\n",
    "\n",
    "# Define file names for the Excel files for each group\n",
    "file_names = ['group1.xlsx', 'group2.xlsx', 'group3.xlsx']\n",
    "\n",
    "# Define total hash rates for each group\n",
    "total_hash_rates = [200, 540, 85]  # Replace with the correct values in TH/s\n",
    "\n",
    "# Define the prior means\n",
    "prior_means = np.array([ 6350.,   78475.,   78475.,  103465.,  561939.,  100444.,  103465.,   71271.,\n",
    "                        81924.,   84767.,   58484.,   57868.,  230378.,   30350.,  228603.,  148796.,\n",
    "                        122844.,  114462.,   56690.,   85708.,  107494.,   86684.,   53614.,   85092.,\n",
    "                        132276.,    6350.,   56893.,   17637.,   16709.,  253381.,   71264.,   97658.,\n",
    "                        42690.,   14431.,  111609.,   66800.,   64466.,  148575.,  101959.,  101380.,\n",
    "                        96965.,  309055.,  265280.,   69503.,  198612.,   79366.,   90703.,  241843.,\n",
    "                        100565.,  158958.,  126985.,   70788.,  122520.,   90296.,  144301.,  158731.,\n",
    "                        176367.,  147656.,   64758.,   98346., 1947610.,  101588., 1688619.,   17709.,\n",
    "                        35417.,    2952.,    4570.,    6811.,   25298.,   23612.,   54488.,    1968.,\n",
    "                        108975.,  13622.,    2834.,   15741.,  500802.,   90342.,  446216.,  451502.,\n",
    "                        163251.,  269672.,    9730.,  178379.,  129408.,  634052.,  238413.,  138864.,\n",
    "                        143085.,  310362.,  495977.,  356924.,  294009.,    5614.,   19460.,   20271.,\n",
    "                        242395.,  227756.,  155926.,  231001.,  236121.,  175839.,  236214.,  149111.,\n",
    "                        120456.,   85851.,   29190.,  360771.,  360771.,  475658., 2583400.,  461767.,\n",
    "                        475658.,  327652.])\n",
    "\n",
    "# Initialize an empty list to store approximations for each group\n",
    "results = []\n",
    "\n",
    "for file_name, total_hash_rate, prior_mean in zip(file_names, total_hash_rates, prior_means):\n",
    "    # Load data from the Excel file\n",
    "    df = pd.read_excel(file_name)\n",
    "\n",
    "    # Extract the hash rates for each type of hardware\n",
    "    hash_rates = df['Hashing power (Mh/s)'].values\n",
    "\n",
    "    # Objective function: negative log-likelihood\n",
    "    def nll(n):\n",
    "        pred_hash_rate = np.sum(n * hash_rates)  # n_i * h_i for each hardware\n",
    "        mse = (total_hash_rate * 1e6 - pred_hash_rate)**2  # mean squared error\n",
    "        return mse\n",
    "\n",
    "    # Initial guess: use the prior mean for the quantity\n",
    "    init_n = np.full(len(hash_rates), prior_mean)\n",
    "\n",
    "    # Bounds: quantity must be >= 0\n",
    "    bounds = [(0, None) for _ in range(len(hash_rates))]\n",
    "\n",
    "    # Run the optimizer\n",
    "    result = minimize(nll, init_n, bounds=bounds)\n",
    "\n",
    "    # Append the result to the list of results\n",
    "    results.append(result)\n",
    "\n",
    "# Now, results[i] contains the MLE result for group i\n",
    "for i, result in enumerate(results):\n",
    "    print(f\"Group {i + 1}:\")\n",
    "    print(\"Estimated quantities:\", result.x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f9956482",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7083.37197837, 6064.73080074, 6330.08746699, 5954.63354252,\n",
       "       6552.81189446, 7121.90815364, 6358.00408257, 6196.47434878,\n",
       "       6158.74505842, 6434.67392933, 6394.26299472, 6404.78158876,\n",
       "       6851.3441079 ])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af9babc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_energy_consumption_and_efficiency(df, n):\n",
    "    # Calculate total energy consumption (sum of quantity times energy consumption for each type of hardware)\n",
    "    # Energy consumption = Hash rate / Efficiency\n",
    "    total_energy_consumption = np.sum(n * df['Hashing power (Mh/s)'].values / df['Efficiency (Mh/J)'].values)\n",
    "\n",
    "    # Calculate weighted average efficiency (sum of quantity times efficiency for each type of hardware, divided by total quantity)\n",
    "    weighted_average_efficiency = np.sum(n * df['Efficiency (Mh/J)'].values) / np.sum(n)\n",
    "\n",
    "    return total_energy_consumption, weighted_average_efficiency\n",
    "\n",
    "# Now, use this function to calculate the energy consumption and efficiency for each group\n",
    "results = results\n",
    "\n",
    "for file_name, n in zip(file_names, results):\n",
    "    # Load data from the Excel file\n",
    "    df = pd.read_excel(file_name)\n",
    "\n",
    "    # Calculate energy consumption and efficiency\n",
    "    total_energy_consumption, weighted_average_efficiency = calculate_energy_consumption_and_efficiency(df, n.x)\n",
    "\n",
    "    print(f\"File {file_name}:\")\n",
    "    print(\"Total energy consumption (Joules):\", total_energy_consumption)\n",
    "    print(\"Weighted average efficiency (Mh/J):\", weighted_average_efficiency)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afb73eeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "[134192.54553556  16516.67573032  16507.00897799  12544.96318716\n",
    "   7051.0837864   12554.63000995  12544.96325806  23590.69580935\n",
    "  16507.00897844  15373.68224038  19042.69578858  19052.36268184\n",
    "   8885.56140647  33712.46349623   8885.61632082  10710.42895525\n",
    "  11843.86552111  13539.0929532   19052.36268184  15383.29407837\n",
    "  13548.81461943  15373.62725601  22875.14065641  15373.68216991\n",
    "   6465.13487277 134192.54553556  19052.36268184  52197.11972457\n",
    "  54039.67791956   8875.89465459  23590.69580935  12554.68499476\n",
    "  26509.65722331  63360.1852742   13539.09295276  17217.8832229\n",
    "  18341.54327866  10710.42895525  12544.96325762  12544.96325762\n",
    "  16367.68639091   9879.69110205   9879.74601595  17208.16155624\n",
    "  10009.27637657  16507.00897799  16377.47861737   8875.89465415\n",
    "  12545.0181015   11704.61363475  11843.81060677  17208.16155668\n",
    "  11853.60290414  16377.47861737  10710.42895481  11714.28031661\n",
    "  10019.06860348  10710.42888479  18351.2648753   12554.68492474\n",
    "   5346.1486382   12554.63000995   5346.20355583]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2786354b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_energy_consumption_and_efficiency(df, n):\n",
    "    # Calculate total energy consumption (sum of quantity times energy consumption for each type of hardware)\n",
    "    total_energy_consumption = np.sum(n * df['Energy consumption (J/TH)'].values)\n",
    "    \n",
    "    # Calculate weighted average efficiency (sum of quantity times efficiency for each type of hardware, divided by total quantity)\n",
    "    weighted_average_efficiency = np.sum(n * df['Efficiency (J/TH)'].values) / np.sum(n)\n",
    "    \n",
    "    return total_energy_consumption, weighted_average_efficiency\n",
    "\n",
    "# Now, use this function to calculate the energy consumption and efficiency for each group\n",
    "for file_name, n in zip(file_names, results):\n",
    "    # Load data from the Excel file\n",
    "    df = pd.read_excel(file_name)\n",
    "    \n",
    "    # Calculate energy consumption and efficiency\n",
    "    total_energy_consumption, weighted_average_efficiency = calculate_energy_consumption_and_efficiency(df, n.x)\n",
    "    \n",
    "    print(f\"File {file_name}:\")\n",
    "    print(\"Total energy consumption:\", total_energy_consumption)\n",
    "    print(\"Weighted average efficiency:\", weighted_average_efficiency)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc40f3b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.optimize import minimize\n",
    "\n",
    "# your observed data\n",
    "observed_hash_rates = np.array([112, 33, 50])  # example data\n",
    "\n",
    "# your prior means\n",
    "prior_means = np.array([ 6350., 78475., 78475.])  # example data\n",
    "\n",
    "# Objective function: negative log-likelihood\n",
    "def nll(params):\n",
    "    pred_hash_rates = np.sum(params.reshape(-1, 2), axis=1)  # n_i * h_i for each hardware\n",
    "    mse = np.sum((observed_hash_rates - pred_hash_rates)**2)  # mean squared error\n",
    "    return mse\n",
    "\n",
    "# Initial guess: use the prior means for the hash rates, and guess 1 for the quantities\n",
    "init_params = np.hstack((np.ones(len(prior_means)), prior_means))\n",
    "\n",
    "# Bounds: quantities must be >= 0, hash rates must be >= 0\n",
    "bounds = [(0, None) for _ in range(len(init_params))]\n",
    "\n",
    "# Run the optimizer\n",
    "result = minimize(nll, init_params, bounds=bounds)\n",
    "\n",
    "# Extract the estimated quantities and hash rates\n",
    "estimated_quantities = result.x[:len(prior_means)]\n",
    "estimated_hash_rates = result.x[len(prior_means):]\n",
    "\n",
    "print(\"Estimated quantities:\", estimated_quantities)\n",
    "print(\"Estimated hash rates:\", estimated_hash_rates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f37e4372",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pymc3 as pm\n",
    "import theano.tensor as tt\n",
    "\n",
    "# Define file names for the Excel files for each group\n",
    "file_names = ['group1.xlsx', 'group2.xlsx', 'group3.xlsx']\n",
    "\n",
    "# Define total hash rates for each group\n",
    "total_hash_rates = [200, 540, 85]  # Replace with the correct values in TH/s\n",
    "\n",
    "# Define the prior means\n",
    "# Define your best guess for the number of each type of hardware\n",
    "prior_means = np.array([ 6350.,   78475.,   78475.,  103465.,  561939.,  100444.,  103465.,   71271.,\n",
    "                        81924.,   84767.,   58484.,   57868.,  230378.,   30350.,  228603.,  148796.,\n",
    "                        122844.,  114462.,   56690.,   85708.,  107494.,   86684.,   53614.,   85092.,\n",
    "                        132276.,    6350.,   56893.,   17637.,   16709.,  253381.,   71264.,   97658.,\n",
    "                        42690.,   14431.,  111609.,   66800.,   64466.,  148575.,  101959.,  101380.,\n",
    "                        96965.,  309055.,  265280.,   69503.,  198612.,   79366.,   90703.,  241843.,\n",
    "                        100565.,  158958.,  126985.,   70788.,  122520.,   90296.,  144301.,  158731.,\n",
    "                        176367.,  147656.,   64758.,   98346., 1947610.,  101588., 1688619.,   17709.,\n",
    "                        35417.,    2952.,    4570.,    6811.,   25298.,   23612.,   54488.,    1968.,\n",
    "                        108975.,  13622.,    2834.,   15741.,  500802.,   90342.,  446216.,  451502.,\n",
    "                        163251.,  269672.,    9730.,  178379.,  129408.,  634052.,  238413.,  138864.,\n",
    "                        143085.,  310362.,  495977.,  356924.,  294009.,    5614.,   19460.,   20271.,\n",
    "                        242395.,  227756.,  155926.,  231001.,  236121.,  175839.,  236214.,  149111.,\n",
    "                        120456.,   85851.,   29190.,  360771.,  360771.,  475658., 2583400.,  461767.,\n",
    "                        475658.,  327652.])  # Replace with your array of prior means\n",
    "\n",
    "# Initialize an empty list to store approximations for each group\n",
    "approximations = []\n",
    "\n",
    "for file_name, total_hash_rate, prior_mean in zip(file_names, total_hash_rates, prior_means):\n",
    "    # Load data from the Excel file\n",
    "    df = pd.read_excel(file_name)\n",
    "\n",
    "    # Extract the hash rates for each type of hardware\n",
    "    hash_rates = df['Hashing power (Mh/s)'].values\n",
    "\n",
    "    # Define the model\n",
    "    model = pm.Model()\n",
    "\n",
    "    with model:\n",
    "        # Prior distributions for the n parameters, using the provided prior means\n",
    "        n = pm.Normal('n', mu=prior_mean, sd=1000, shape=len(hash_rates))  # Adjust the standard deviation as needed\n",
    "\n",
    "        # Expected value of the hash rate\n",
    "        mu = pm.math.dot(n, hash_rates)\n",
    "\n",
    "        # Likelihood (sampling distribution) of observations\n",
    "        Y_obs = pm.Poisson('Y_obs', mu=mu, observed=np.array([total_hash_rate * 1e6]))  # Convert TH/s to MH/s\n",
    "\n",
    "        # Run the ADVI algorithm to approximate the posterior\n",
    "        approx = pm.fit(n=50000, method=pm.ADVI())\n",
    "\n",
    "    # Append the approximation to the list of approximations\n",
    "    approximations.append(approx)\n",
    "\n",
    "# Now, approximations[i] contains the variational approximation for group i\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62c3e5f5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce916932",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sample from the approximations\n",
    "samples = [approx.sample(5000) for approx in approximations]\n",
    "\n",
    "# Now, samples[i] is a 5000-sample approximation to the posterior distribution for group i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0547369a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the means of the samples\n",
    "means = [np.mean(sample['n'], axis=0) for sample in samples]\n",
    "\n",
    "# Now, means[i] is the mean of the posterior distribution for group i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0177376e",
   "metadata": {},
   "outputs": [],
   "source": [
    "means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96c4fd19",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pymc3 as pm\n",
    "import theano.tensor as tt\n",
    "\n",
    "# Define file names for the Excel files for each group\n",
    "file_names = ['group1.xlsx', 'group2.xlsx', 'group3.xlsx']\n",
    "\n",
    "# Define total hash rates for each group\n",
    "total_hash_rates = [200, 540, 85]  # Replace with the correct values in TH/s\n",
    "\n",
    "# Initialize an empty list to store traces for each group\n",
    "traces = []\n",
    "\n",
    "for file_name, total_hash_rate in zip(file_names, total_hash_rates):\n",
    "    # Load data from the Excel file\n",
    "    df = pd.read_excel(file_name)\n",
    "\n",
    "    # Extract the hash rates for each type of hardware\n",
    "    hash_rates = df['Hashing power (Mh/s)'].values\n",
    "\n",
    "    # Define the model\n",
    "    model = pm.Model()\n",
    "\n",
    "    with model:\n",
    "        # Prior distributions for the n parameters, assuming uniform distribution\n",
    "        n = pm.DiscreteUniform('n', lower=0, upper=100000, shape=len(hash_rates))  # Adjust the upper limit as needed\n",
    "\n",
    "        # Expected value of the hash rate\n",
    "        mu = pm.math.dot(n, hash_rates)\n",
    "\n",
    "        # Likelihood (sampling distribution) of observations\n",
    "        Y_obs = pm.Poisson('Y_obs', mu=mu, observed=np.array([total_hash_rate * 1e6]))  # Convert TH/s to MH/s\n",
    "\n",
    "        # Run the MCMC algorithm to sample from the posterior\n",
    "        trace = pm.sample(2000, tune=1000, cores=1)\n",
    "\n",
    "    # Append the trace to the list of traces\n",
    "    traces.append(trace)\n",
    "\n",
    "# Now, traces[i] contains the MCMC samples for group i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d66a0802",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pymc3 as pm\n",
    "import theano.tensor as tt\n",
    "\n",
    "# Define file names for the Excel files for each group\n",
    "file_names = ['group1.xlsx', 'group2.xlsx', 'group3.xlsx']\n",
    "\n",
    "# Define total hash rates for each group\n",
    "total_hash_rates = [200, 540, 85]  # Replace with the correct values in TH/s\n",
    "\n",
    "# Initialize an empty list to store traces for each group\n",
    "traces = []\n",
    "\n",
    "# Define your best guess for the number of each type of hardware\n",
    "prior_means = np.array([ 6350.,   78475.,   78475.,  103465.,  561939.,  100444.,  103465.,   71271.,\n",
    "                        81924.,   84767.,   58484.,   57868.,  230378.,   30350.,  228603.,  148796.,\n",
    "                        122844.,  114462.,   56690.,   85708.,  107494.,   86684.,   53614.,   85092.,\n",
    "                        132276.,    6350.,   56893.,   17637.,   16709.,  253381.,   71264.,   97658.,\n",
    "                        42690.,   14431.,  111609.,   66800.,   64466.,  148575.,  101959.,  101380.,\n",
    "                        96965.,  309055.,  265280.,   69503.,  198612.,   79366.,   90703.,  241843.,\n",
    "                        100565.,  158958.,  126985.,   70788.,  122520.,   90296.,  144301.,  158731.,\n",
    "                        176367.,  147656.,   64758.,   98346., 1947610.,  101588., 1688619.,   17709.,\n",
    "                        35417.,    2952.,    4570.,    6811.,   25298.,   23612.,   54488.,    1968.,\n",
    "                        108975.,  13622.,    2834.,   15741.,  500802.,   90342.,  446216.,  451502.,\n",
    "                        163251.,  269672.,    9730.,  178379.,  129408.,  634052.,  238413.,  138864.,\n",
    "                        143085.,  310362.,  495977.,  356924.,  294009.,    5614.,   19460.,   20271.,\n",
    "                        242395.,  227756.,  155926.,  231001.,  236121.,  175839.,  236214.,  149111.,\n",
    "                        120456.,   85851.,   29190.,  360771.,  360771.,  475658., 2583400.,  461767.,\n",
    "                        475658.,  327652.])\n",
    "\n",
    "for file_name, total_hash_rate, prior_mean in zip(file_names, total_hash_rates, prior_means):\n",
    "    # Load data from the Excel file\n",
    "    df = pd.read_excel(file_name)\n",
    "    \n",
    "    # Convert hash rates from GH/s to TH/s\n",
    "    hash_rates = df['Hashing power (Mh/s)'] / 1000\n",
    "    \n",
    "    # Convert energy efficiencies from J/GH to MJ/TH\n",
    "    energy_efficiencies = df['Efficiency_Mh/J'] * 1000\n",
    "    \n",
    "    # Build the model\n",
    "    with pm.Model() as model:\n",
    "        # Prior distributions for the n parameters\n",
    "        n = pm.Poisson('n', mu=prior_mean, shape=len(hash_rates))\n",
    "        \n",
    "        # Likelihood function\n",
    "        likelihood = pm.Normal('likelihood', mu=n*hash_rates, sd=total_hash_rate, observed=total_hash_rate)\n",
    "        \n",
    "        # Run the MCMC sampler\n",
    "        trace = pm.sample(2000, tune=2000)\n",
    "        \n",
    "    # Append the trace to the list of traces\n",
    "    traces.append(trace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5fdd55a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Iterate over each group\n",
    "for i, trace in enumerate(traces):\n",
    "    # Access the samples of n for this group\n",
    "    n_samples = trace['n']\n",
    "\n",
    "    # Compute the mean of the samples to get an estimate of n\n",
    "    n_estimates = n_samples.mean(axis=0)\n",
    "\n",
    "    print(f\"Estimates for group {i+1}:\")\n",
    "    for j, n_estimate in enumerate(n_estimates):\n",
    "        print(f\"n_{j+1} = {n_estimate}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d7608c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "874847cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import poisson\n",
    "from scipy.optimize import minimize\n",
    "\n",
    "# Step 1: Define the initial Poisson distributions as priors\n",
    "num_hardware_types = 112\n",
    "prior_means = np.array([ 6350.,   78475.,   78475.,  103465.,  561939.,  100444.,  103465.,   71271.,\n",
    "                              81924.,   84767.,   58484.,   57868.,  230378.,   30350.,  228603.,  148796.,\n",
    "                              122844.,  114462.,   56690.,   85708.,  107494.,   86684.,   53614.,   85092.,\n",
    "                              132276.,    6350.,   56893.,   17637.,   16709.,  253381.,   71264.,   97658.,\n",
    "                              42690.,   14431.,  111609.,   66800.,   64466.,  148575.,  101959.,  101380.,\n",
    "                              96965.,  309055.,  265280.,   69503.,  198612.,   79366.,   90703.,  241843.,\n",
    "                              100565.,  158958.,  126985.,   70788.,  122520.,   90296.,  144301.,  158731.,\n",
    "                              176367.,  147656.,   64758.,   98346., 1947610.,  101588., 1688619.,   17709.,\n",
    "                              35417.,    2952.,    4570.,    6811.,   25298.,   23612.,   54488.,    1968.,\n",
    "                              108975.,  13622.,    2834.,   15741.,  500802.,   90342.,  446216.,  451502.,\n",
    "                              163251.,  269672.,    9730.,  178379.,  129408.,  634052.,  238413.,  138864.,\n",
    "                              143085.,  310362.,  495977.,  356924.,  294009.,    5614.,   19460.,   20271.,\n",
    "                              242395.,  227756.,  155926.,  231001.,  236121.,  175839.,  236214.,  149111.,\n",
    "                              120456.,   85851.,   29190.,  360771.,  360771.,  475658., 2583400.,  461767.,\n",
    "                              475658.,  327652.])\n",
    " # Array of means for the Poisson distributions\n",
    "priors = np.array([poisson.pmf(np.arange(num_hardware_types), mean) for mean in prior_means])\n",
    "\n",
    "# Step 2: Define the likelihood function\n",
    "def likelihood(total_hash_rate, hardware_counts):\n",
    "    expected_hash_rate = np.dot(hardware_counts, hash_rates)\n",
    "    return np.sum(poisson.logpmf(total_hash_rate, expected_hash_rate))\n",
    "\n",
    "# Step 3: Define the objective function to be minimized\n",
    "def objective(hardware_counts, total_hash_rate):\n",
    "    return -likelihood(total_hash_rate, hardware_counts)\n",
    "\n",
    "# Step 4: Use optimization to update the distribution parameters\n",
    "result = minimize(objective, priors, args=(total_hash_rate,))\n",
    "estimated_counts = result.x\n",
    "\n",
    "# Print the estimated counts\n",
    "print(\"Estimated counts:\", estimated_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9319d83f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymc3 as pm\n",
    "import numpy as np\n",
    "\n",
    "# Define the observed total hash rate\n",
    "total_hash_rate_observed = 825000000\n",
    "\n",
    "# Define the number of hardware types for each contribution\n",
    "num_types_1 = 63\n",
    "num_types_2 = 12\n",
    "num_types_3 = 37\n",
    "\n",
    "# Define the hash rate contributions and corresponding hardware types\n",
    "contribution_1 = 200000000  # TH/s\n",
    "contribution_2 = 85000000  # TH/s\n",
    "contribution_3 = 540000000\n",
    "\n",
    "# Define the hash rate of each hardware type\n",
    "hash_rates_1 = np.array([500, 40.454, 40.454, 30.683, 5.649376, 31.606, 30.683, 44.543, 38.75065, 37.451, 54.282, 54.86, 13.78, 104.6, 13.887, 21.33533, 25.84274, 27.735, 56, 37.04, 29.533, 36.623, 59.21295, 37.30829, 24, 500, 55.8, 180, 190, 12.529, 44.54744, 32.50744, 74.365, 220, 28.444, 47.52453, 49.24512, 21.36714, 31.1363, 31.314, 32.74, 10.272, 11.967, 45.67582, 15.984, 40, 35, 13.12675, 31.5678, 19.9714, 25, 44.84675, 25.911, 35.158, 22, 20, 18, 21.5, 49.023, 32.28, 1.63, 31.25, 1.88])\n",
    "hash_rates_2 = np.array([400, 200, 2400, 1550, 1040, 280, 300, 130, 3600, 65, 520, 2500, 450])\n",
    "hash_rates_3 = np.array([29.14245, 161.55, 32.70748, 32.32455, 89.4, 54.12, 1500, 81.818, 112.78, 23.018, 61.21572, 105.1, 102, 47.02455, 29.426, 40.89, 49.64, 2600, 750, 720, 60.21, 64.08, 93.6, 63.18, 61.81, 83, 61.7857, 97.878, 121.162, 170, 500, 40.454, 40.454, 30.683, 5.649376, 31.606, 30.683, 44.543])\n",
    "\n",
    "\n",
    "# Calculate the lambda parameter for each hardware type\n",
    "lambdas_1 = np.ceil(contribution_1 / (num_types_1 * hash_rates_1))\n",
    "lambdas_2 = np.ceil(contribution_2 / (num_types_2 * hash_rates_2))\n",
    "lambdas_3 = np.ceil(contribution_3 / (num_types_3 * hash_rates_3))\n",
    "\n",
    "print(f\"Lambda 1: {lambdas_1}\")\n",
    "print(f\"Lambda 2: {lambdas_2}\")\n",
    "print(f\"Lambda 3: {lambdas_3}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b760debf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def likelihood_function(total_hash_rate, lambda_1, lambda_2, lambda_3):\n",
    "    # Calculate the expected hash rate for each contribution\n",
    "    expected_hash_rate_1 = np.sum(lambda_1)\n",
    "    expected_hash_rate_2 = np.sum(lambda_2)\n",
    "    expected_hash_rate_3 = np.sum(lambda_3)\n",
    "\n",
    "    # Calculate the logarithmic probabilities of the observed total hash rate given the expected hash rates\n",
    "    log_prob_1 = (-expected_hash_rate_1) + (total_hash_rate * np.log(expected_hash_rate_1)) - np.sum(np.log(np.arange(1, total_hash_rate+1)))\n",
    "    log_prob_2 = (-expected_hash_rate_2) + (total_hash_rate * np.log(expected_hash_rate_2)) - np.sum(np.log(np.arange(1, total_hash_rate+1)))\n",
    "    log_prob_3 = (-expected_hash_rate_3) + (total_hash_rate * np.log(expected_hash_rate_3)) - np.sum(np.log(np.arange(1, total_hash_rate+1)))\n",
    "\n",
    "    # Calculate the joint probability of the observed total hash rate using logarithms\n",
    "    log_joint_probability = log_prob_1 + log_prob_2 + log_prob_3\n",
    "    joint_probability = np.exp(log_joint_probability)\n",
    "\n",
    "    return joint_probability\n",
    "\n",
    "# Define the hash rate contributions and corresponding hardware types\n",
    "contribution_1 = 200000000  # TH/s\n",
    "contribution_2 = 85000000  # TH/s\n",
    "contribution_3 = 540000000\n",
    "total_hash_rate = 825000000  # TH/s\n",
    "\n",
    "# Define the estimated lambda values for each hardware specification\n",
    "lambda_1 = np.array([6350, 78475, 78475, 103465, 561939, 100444, 103465, 71271, 81924, 84767, 58484, 57868, 230378, 30350, 228603, 148796, 122844, 114462, 56690, 85708, 107494, 86684, 53614, 85092, 132276, 6350, 56893, 17637, 16709, 253381, 71264, 97658, 42690, 14431, 111609, 66800, 64466, 148575, 101959, 101380, 96965, 309055, 265280, 69503, 198612, 79366, 90703, 241843, 100565, 158958, 126985, 70788, 122520, 90296, 144301, 158731, 176367, 147656, 64758, 98346, 1947610, 101588, 1688619])\n",
    "lambda_2 = np.array([17709, 35417, 2952, 4570, 6811, 25298, 23612, 54488, 1968, 108975, 13622, 2834, 15741])\n",
    "lambda_3 = np.array([500802, 90342, 446216, 451502, 163251, 269672, 9730, 178379, 129408, 634052, 238413, 138864, 143085, 310362, 495977, 356924, 294009, 5614, 19460, 20271, 242395, 227756, 155926, 231001, 236121, 175839, 236214, 149111, 120456, 85851, 29190, 360771, 360771, 475658, 2583400, 461767, 475658, 327652])\n",
    "\n",
    "# Compute the likelihood of the observed total hash rate\n",
    "likelihood = likelihood_function(total_hash_rate, lambda_1, lambda_2, lambda_3)\n",
    "print(\"Likelihood:\", likelihood)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "542460ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the hash rate contributions and corresponding hardware types\n",
    "contribution_1 = 200  # TH/s\n",
    "contribution_2 = 85  # TH/s\n",
    "contribution_3 = 540\n",
    "total_hash_rate = 825  # TH/s\n",
    "\n",
    "# Define the number of hardware types for each contribution\n",
    "num_types_1 = 63\n",
    "num_types_2 = 12\n",
    "num_types_3 = 37\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "hash_rates_1 = np.random.uniform(low=0.05, high=0.6, size=63)\n",
    "hash_rates_2 = np.random.uniform(low=1.00, high=2.16, size=12)\n",
    "hash_rates_3 = np.random.uniform(low=0.6, high=1.0, size=37)\n",
    "\n",
    "# Define the hash rate of each hardware type\n",
    "#hash_rates_1 = [0.2, 0.5, 0.3, 0.2, 0.5, 0.3,0.2, 0.5, 0.3,0.2, 0.5, 0.3,0.2, 0.5, 0.3,0.2, 0.5, 0.3,0.2, 0.5, 0.3,0.2, 0.5, 0.3,0.2, 0.5, 0.3,0.2, 0.5, 0.3,0.2, 0.5, 0.3,0.2, 0.5, 0.3,0.2, 0.5, 0.3,0.2, 0.5, 0.3,0.2, 0.5, 0.3,0.2, 0.5, 0.3,0.2, 0.5, 0.3,0.2, 0.5, 0.3,0.2, 0.5, 0.3,0.2, 0.5, 0.3,0.2, 0.5, 0.3]  # List of hash rates for contribution 1\n",
    "#hash_rates_2 = [1.0, 0.8, 0.6,1.0, 0.8, 0.6,1.0, 0.8, 0.6,1.0, 0.8, 0.6]  # List of hash rates for contribution 2\n",
    "#hash_rates_3 = [1.0, 0.8, 0.6,1.0, 0.8, 0.6,1.0, 0.8, 0.6,1.0, 0.8, 0.6,1.0, 0.8, 0.6,1.0, 0.8, 0.6,1.0, 0.8, 0.6,1.0, 0.8, 0.6,1.0, 0.8, 0.6,1.0, 0.8, 0.6,1.0, 0.8, 0.6,1.0, 0.8, 0.6,0.8]\n",
    "\n",
    "# Calculate the lambda parameter for each hardware type\n",
    "lambda_1 = [contribution_1 / (num_types_1 * hr) for hr in hash_rates_1]\n",
    "lambda_2 = [contribution_2 / (num_types_2 * hr) for hr in hash_rates_2]\n",
    "lambda_3 = [contribution_3 / (num_types_3 * hr) for hr in hash_rates_3]\n",
    "\n",
    "print(f\"Lambda 1: {lambda_1}\")\n",
    "print(f\"Lambda 2: {lambda_2}\")\n",
    "print(f\"Lambda 3: {lambda_3}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f497e83",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Combine the lambda parameters for each contribution\n",
    "lambda_combined = np.sum([lambda_1, lambda_2], axis=0)\n",
    "\n",
    "# Compute the hyperparameters for the overall distribution\n",
    "alpha = np.sum(lambda_combined)\n",
    "beta = total_hash_rate - alpha\n",
    "\n",
    "print(f\"Combined Lambda: {lambda_combined}\")\n",
    "print(f\"Alpha: {alpha}\")\n",
    "print(f\"Beta: {beta}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8baef855",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "\n",
    "# Estimated values of alpha and beta\n",
    "alpha = 799.8611111111111\n",
    "beta = 25.138888888888914\n",
    "\n",
    "# Generate a range of x values\n",
    "x = np.linspace(0, 1, 100)\n",
    "\n",
    "# Calculate the probability density function (PDF) for the beta distribution\n",
    "pdf = stats.beta.pdf(x, alpha, beta)\n",
    "\n",
    "# Plot the PDF\n",
    "plt.plot(x, pdf)\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('PDF')\n",
    "plt.title('Beta Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "373642f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import poisson\n",
    "\n",
    "# Define the hash rate contributions and corresponding hardware types\n",
    "contribution_1 = 200  # TH/s\n",
    "contribution_2 = 85  # TH/s\n",
    "contribution_3 = 540\n",
    "total_hash_rate = 825  # TH/s\n",
    "\n",
    "# Define the number of hardware types for each contribution\n",
    "num_types_1 = 63\n",
    "num_types_2 = 12\n",
    "num_types_3 = 37\n",
    "\n",
    "# Calculate the average hash rate per device for each group\n",
    "average_rate_1 = contribution_1 / num_types_1\n",
    "average_rate_2 = contribution_2 / num_types_2\n",
    "average_rate_3 = contribution_3 / num_types_3\n",
    "\n",
    "# Assign weights to each group\n",
    "weight_1 = 1 / num_types_1\n",
    "weight_2 = 1 / num_types_2\n",
    "weight_3 = 1 / num_types_3\n",
    "\n",
    "# Create the individual Poisson distributions\n",
    "dist_1 = poisson(average_rate_1)\n",
    "dist_2 = poisson(average_rate_2)\n",
    "dist_3 = poisson(average_rate_3)\n",
    "\n",
    "# Calculate the combined distribution\n",
    "combined_pmf = weight_1 * dist_1.pmf(np.arange(0, total_hash_rate + 1)) + \\\n",
    "               weight_2 * dist_2.pmf(np.arange(0, total_hash_rate + 1)) + \\\n",
    "               weight_3 * dist_3.pmf(np.arange(0, total_hash_rate + 1))\n",
    "\n",
    "# Normalize the combined distribution\n",
    "combined_pmf /= np.sum(combined_pmf)\n",
    "\n",
    "# Print the combined distribution\n",
    "for i, pmf_value in enumerate(combined_pmf):\n",
    "    print(f\"Hash Rate {i} TH/s: {pmf_value}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3d90008",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.optimize import minimize\n",
    "import scipy.stats as stats\n",
    "\n",
    "# Define the desired quantiles\n",
    "lower_quantile = 0.01\n",
    "upper_quantile = 0.99\n",
    "\n",
    "# Define the desired quantile values\n",
    "lower_quantile_value = 0.05\n",
    "upper_quantile_value = 2.16\n",
    "\n",
    "# Define the moment equations\n",
    "def moments_equations(params):\n",
    "    alpha, beta = params\n",
    "    moment_eq1 = stats.beta.ppf(lower_quantile, alpha, beta) - lower_quantile_value\n",
    "    moment_eq2 = stats.beta.ppf(upper_quantile, alpha, beta) - upper_quantile_value\n",
    "    return moment_eq1**2 + moment_eq2**2\n",
    "\n",
    "# Initial guess for alpha and beta\n",
    "initial_params = [2, 2]\n",
    "\n",
    "# Solve for alpha and beta using optimization\n",
    "result = minimize(moments_equations, initial_params, method='SLSQP')\n",
    "alpha, beta = result.x\n",
    "\n",
    "print(f\"α = {alpha}, β = {beta}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94ae13a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "\n",
    "# Estimated values of alpha and beta\n",
    "alpha = 1.1358785037699581\n",
    "beta = 0.31675654784979695\n",
    "\n",
    "# Generate a range of x values\n",
    "x = np.linspace(0, 1, 100)\n",
    "\n",
    "# Calculate the probability density function (PDF) for the beta distribution\n",
    "pdf = stats.beta.pdf(x, alpha, beta)\n",
    "\n",
    "# Plot the PDF\n",
    "plt.plot(x, pdf)\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('PDF')\n",
    "plt.title('Beta Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82314775",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.optimize import minimize\n",
    "import scipy.stats as stats\n",
    "\n",
    "# Define the desired quantiles\n",
    "lower_quantile = 0.01\n",
    "upper_quantile = 0.99\n",
    "\n",
    "# Define the desired quantile values\n",
    "lower_quantile_value = 0.6\n",
    "upper_quantile_value = 0.99\n",
    "\n",
    "# Define the moment equations\n",
    "def moments_equations(params):\n",
    "    alpha, beta = params\n",
    "    moment_eq1 = stats.beta.ppf(lower_quantile, alpha, beta) - lower_quantile_value\n",
    "    moment_eq2 = stats.beta.ppf(upper_quantile, alpha, beta) - upper_quantile_value\n",
    "    return moment_eq1**2 + moment_eq2**2\n",
    "\n",
    "# Initial guess for alpha and beta\n",
    "initial_params = [2, 2]\n",
    "\n",
    "# Solve for alpha and beta using optimization\n",
    "result = minimize(moments_equations, initial_params, method='SLSQP')\n",
    "alpha, beta = result.x\n",
    "\n",
    "print(f\"α = {alpha}, β = {beta}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ded3311a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "\n",
    "# Estimated values of alpha and beta\n",
    "alpha = 5.802237048028528\n",
    "beta = 0.3448722300972294\n",
    "\n",
    "# Generate a range of x values\n",
    "x = np.linspace(0, 1, 100)\n",
    "\n",
    "# Calculate the probability density function (PDF) for the beta distribution\n",
    "pdf = stats.beta.pdf(x, alpha, beta)\n",
    "\n",
    "# Plot the PDF\n",
    "plt.plot(x, pdf)\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('PDF')\n",
    "plt.title('Beta Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2b6c272",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymc3 as pm\n",
    "import numpy as np\n",
    "alpha = 1.1358785037699581\n",
    "beta = 0.31675654784979695\n",
    "import numpy as np\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "\n",
    "total_hash_rate = df['ETH / Mean Hash Rate'].values\n",
    "\n",
    "def likelihood_function(total_hash_rate, individual_hash_rates, counts):\n",
    "    # Compute the expected total hash rate based on the individual hash rates and counts\n",
    "    expected_total_hash_rate = np.sum(individual_hash_rates * counts)\n",
    "\n",
    "    # Fit an ARIMA model to the observed total hash rate\n",
    "    arima_model = ARIMA(total_hash_rate, order=(p, d, q))  # Replace p, d, q with your ARIMA order\n",
    "    arima_result = arima_model.fit()\n",
    "\n",
    "    # Compute the likelihood of the observed total hash rate given the ARIMA model\n",
    "    likelihood = arima_result.loglikeobs()\n",
    "\n",
    "    # Compute the joint probability of the observed total hash rate and counts\n",
    "    joint_probability = likelihood + np.log(prior_function(counts))  # Incorporate the prior function\n",
    "\n",
    "    return joint_probability\n",
    "\n",
    "\n",
    "with pm.Model() as model:\n",
    "    # Prior distribution\n",
    "    n_i = pm.Beta('n_i', alpha=alpha, beta=beta, shape=len(individual_hash_rates))\n",
    "\n",
    "    # Likelihood function\n",
    "    likelihood = likelihood_function(total_hash_rate, individual_hash_rates, n_i)\n",
    "\n",
    "    # Define the observed data\n",
    "    total_hash_rate_observed = pm.Data('total_hash_rate_observed', total_hash_rate)\n",
    "    likelihood_observed = pm.Poisson('likelihood_observed', mu=likelihood, observed=total_hash_rate_observed)\n",
    "    \n",
    "    \n",
    "with model:\n",
    "    trace = pm.sample(2000, tune=1000)  # Perform MCMC sampling\n",
    "    \n",
    "    \n",
    "posterior_means = np.mean(trace['n_i'], axis=0)  # Posterior means of the counts\n",
    "posterior_intervals = pm.hpd(trace['n_i'], alpha=0.05)  # Highest Posterior Density (HPD) intervals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "561bb31a",
   "metadata": {},
   "source": [
    "## Information on Data\n",
    "- Currencies:\n",
    "    - **ETH**: Ethereum\n",
    "    - **ETC**: Ethereum Classic\n",
    " \n",
    "- Features:\n",
    "    - **Tx per Second Cnt**: The mean fee per transaction in native units that day.\n",
    "    - **Mean Tx Fee (native units)**: The sum count of transactions that day devided by the number of seconds per day.\n",
    "    - **Mean Hash Rate**: The mean rate at which miners are solving hashes that interval. Hash rate is the speed at which computations are being completed across all miners in the network. The unit of measurement is [Terahashes/s].\n",
    "    - **USD Denominated Closing Price**: The fixed closing price of the asset as of 00:00 UTC the following day (i.e., midnight UTC of the current day) denominated in USD. Real-time PriceUSD is the fixed closing price of the asset as of the timestamp set by the block's miner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3de4365b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import necessary packages\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8cf9cb5d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#loading the data from an excel file\n",
    "df = pd.read_excel('Ethereum+.xlsx', index_col=0) \n",
    "df.index = pd.to_datetime(df.index)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1d278d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2283b598",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "\n",
    "df['log_hash_rate'] = np.log(df['ETH / Mean Hash Rate'])\n",
    "\n",
    "# Perform the ADF test\n",
    "result = sm.tsa.stattools.adfuller(df['log_hash_rate'])\n",
    "print('ADF Statistic:', result[0])\n",
    "print('p-value:', result[1])\n",
    "\n",
    "import pandas as pd\n",
    "from pmdarima import auto_arima\n",
    "\n",
    "# Assume your DataFrame is called 'data' with a column 'hash_rate'\n",
    "model = auto_arima(df['log_hash_rate'], trace=True, error_action='ignore', suppress_warnings=True)\n",
    "best_model_order = model.order\n",
    "\n",
    "print('Best ARIMA order:', best_model_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fedafbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# Assume your DataFrame is called 'data' with a column 'hash_rate'\n",
    "order = (3, 2, 1)  # Best ARIMA order\n",
    "\n",
    "model = sm.tsa.ARIMA(df['log_hash_rate'], order=order)\n",
    "results = model.fit()\n",
    "\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "624e310e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# Assume your DataFrame is called 'data' with a column 'hash_rate'\n",
    "order = (3, 3, 1)  # Best ARIMA order\n",
    "\n",
    "model = sm.tsa.ARIMA(df['ETH / Mean Hash Rate'], order=order)\n",
    "results = model.fit()\n",
    "\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fd27e6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assume your DataFrame is called 'data' with a column 'hash_rate'\n",
    "model = auto_arima(df['ETH / Mean Hash Rate'], trace=True, error_action='ignore', suppress_warnings=True)\n",
    "best_model_order = model.order\n",
    "\n",
    "print('Best ARIMA order:', best_model_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4acb444d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ETH / Tx Fee per Second (native units)']= df['ETH / Mean Tx Fee (native units)'] * df['ETH / Tx per Second Cnt']\n",
    "df['ETH / Tx Fee per Second (USD)']= df['ETH / Tx Fee per Second (native units)']*df['ETH / USD Denominated Closing Price']\n",
    "#df['ETH / Tx Fee per Day (native units)'] = df['ETH / Tx Fee per Second (native units)']*24*60*60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fcce286a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Block Reward\n",
    "df['ETH Block Reward'] = 5\n",
    "cutoff_date = pd.to_datetime('2017-10-16')\n",
    "df.loc[df.index > cutoff_date, 'ETH Block Reward'] = 3\n",
    "cutoff_date = pd.to_datetime('2019-02-28')\n",
    "df.loc[df.index > cutoff_date, 'ETH Block Reward'] = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9901d168",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ETH Block Reward per Second (native units)'] = df['ETH Block Reward']/15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "441b5c76",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ETH Block Reward per Second (USD)'] = df['ETH Block Reward per Second (native units)'] * df['ETH / USD Denominated Closing Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d1d6b85",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ElectricityPrice'] = 0.05/3600000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "119be0fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Profitability Threshold ETC\n",
    "df['ETH Profatibilty Threshold [J/Hash]'] = ((df['ETH Block Reward per Second (USD)']+df['ETH / Tx Fee per Second (USD)'])) /(df['ETH / Mean Hash Rate']*1000000000000*df['ElectricityPrice'])\n",
    "cutoff_date = pd.to_datetime('2022-09-14')\n",
    "df.loc[df.index > cutoff_date, 'ETH Profatibilty Threshold [J/Hash]'] = 0\n",
    "df['ETH Profatibilty Threshold [Hash/J]'] = 1/df['ETH Profatibilty Threshold [J/Hash]']\n",
    "df['ETH Profatibilty Threshold [MHash/J]'] = df['ETH Profatibilty Threshold [Hash/J]']/1000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "078caa35",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Block Reward\n",
    "df['ETC Block Reward'] = 5\n",
    "cutoff_date = pd.to_datetime('2017-12-11')\n",
    "df.loc[df.index > cutoff_date, 'ETC Block Reward'] = 4\n",
    "cutoff_date = pd.to_datetime('2020-03-17')\n",
    "df.loc[df.index > cutoff_date, 'ETC Block Reward'] = 3.2\n",
    "cutoff_date = pd.to_datetime('2021-04-25')\n",
    "df.loc[df.index > cutoff_date, 'ETC Block Reward'] = 2.56"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "0f4219f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transaction Fees\n",
    "df['ETC / Mean Tx Fee per Second'] = df['ETC / Mean Tx Fee (native units)']/df['ETC / Tx per Second Cnt']\n",
    "df['ETC / Mean Tx Fee per Block'] = df['ETC / Mean Tx Fee per Second']*13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "9660aa48",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Cumulative Miner Revenue\n",
    "df['ETC Block Reward per Second (USD)'] = (df['ETC Block Reward']+df['ETC / Mean Tx Fee per Block'])/13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "b459cceb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Profitability Threshold ETC\n",
    "df['ETC Profatibilty Threshold [J/Hash]'] = (df['ETC / USD Denominated Closing Price'] * (df['ETC Block Reward per Second (USD)']+df['ETC / Mean Tx Fee per Second'])*0.6) /(df['ETC / Mean Hash Rate']*1000000000000*df['ElectricityPrice'])\n",
    "df['ETC Profatibilty Threshold [Hash/J]'] = 1/df['ETC Profatibilty Threshold [J/Hash]']\n",
    "df['ETC Profatibilty Threshold [MHash/J]'] = df['ETC Profatibilty Threshold [Hash/J]']/1000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "677322a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "max_index = df['ETH / Tx Fee per Second (USD)'].idxmax()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "547d0c1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "# Assuming df is your DataFrame and 'column_name' is the name of your column\n",
    "index_value = datetime.strptime(\"2021-05-11 00:00:00\", \"%Y-%m-%d %H:%M:%S\")\n",
    "\n",
    "# Get the value before and after the specific index\n",
    "value_before = df.loc[max_index - pd.Timedelta(days=1), 'ETH / Tx Fee per Second (USD)']\n",
    "value_after = df.loc[max_index + pd.Timedelta(days=1), 'ETH / Tx Fee per Second (USD)']\n",
    "\n",
    "# Calculate the average\n",
    "average_value = (value_before + value_after) / 2\n",
    "\n",
    "# Replace the value at the specific index with the average value\n",
    "df.loc[max_index, 'ETH / Tx Fee per Second (USD)'] = average_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7a888f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "start_date = pd.Timestamp('2017-01-01')\n",
    "end_date = pd.Timestamp('2022-12-31')\n",
    "sns.set(style=\"whitegrid\")\n",
    "ax = df.loc[start_date:end_date, ['ETH Block Reward per Second (USD)','ETH / Tx Fee per Second (USD)']].plot(figsize=(10,6), alpha =0.5)\n",
    "# add vertical line at Merge\n",
    "#ax.axvline(x='2022-09-15', color='green', linestyle='-', linewidth=2)\n",
    "#ax.set_yscale('log')\n",
    "\n",
    "# set the labels and title\n",
    "ax.set_title('', fontsize=20, fontweight='bold')\n",
    "ax.set_xlabel('', fontsize=16)\n",
    "ax.set_ylabel('', fontsize=16)\n",
    "ax.legend(fontsize=14)\n",
    "ax.tick_params(axis='both', labelsize=12)\n",
    "plt.savefig(\"MiningReward.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5579532",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Difference'] = df['ETC Profatibilty Threshold [MHash/J]']-df['ETH Profatibilty Threshold [MHash/J]']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b03b79e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "start_date = pd.Timestamp('2017-01-01')\n",
    "end_date = pd.Timestamp('2022-12-17')\n",
    "sns.set(style=\"whitegrid\")\n",
    "ax = df.loc[start_date:end_date, ['ETH Profatibilty Threshold [MHash/J]']].plot(figsize=(10,6), alpha =0.5)\n",
    "# add vertical line at Merge\n",
    "#ax.axvline(x='2022-09-15', color='green', linestyle='-', linewidth=2)\n",
    "#ax.set_yscale('log')\n",
    "\n",
    "# set the labels and title\n",
    "ax.set_title('', fontsize=20, fontweight='bold')\n",
    "ax.set_xlabel('', fontsize=16)\n",
    "ax.set_ylabel('', fontsize=16)\n",
    "ax.legend(fontsize=14)\n",
    "ax.tick_params(axis='both', labelsize=12)\n",
    "plt.savefig(\"MiningReward.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "847694aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "start_date = pd.Timestamp('2017-01-01')\n",
    "end_date = pd.Timestamp('2022-12-31')\n",
    "sns.set(style=\"whitegrid\")\n",
    "ax = df.loc[start_date:end_date, ['ETH / Mean Hash Rate']].plot(figsize=(10,6), alpha =0.5)\n",
    "# add vertical line at Merge\n",
    "#ax.axvline(x='2022-09-15', color='green', linestyle='-', linewidth=2)\n",
    "#ax.set_yscale('log')\n",
    "\n",
    "# set the labels and title\n",
    "ax.set_title('', fontsize=20, fontweight='bold')\n",
    "ax.set_xlabel('', fontsize=16)\n",
    "ax.set_ylabel('', fontsize=16)\n",
    "ax.legend(fontsize=14)\n",
    "ax.tick_params(axis='both', labelsize=12)\n",
    "plt.savefig(\"MiningReward.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c15d3d5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "value = df.loc['2020-08-31', 'ETH / Mean Hash Rate']\n",
    "value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08aeaf3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "start_date = pd.Timestamp('2017-01-01')\n",
    "end_date = pd.Timestamp('2022-09-14')\n",
    "sns.set(style=\"whitegrid\")\n",
    "ax = df.loc[start_date:end_date, ['ETH Block Reward per Day (Mio. USD)','ETH / Tx Fee per Day (Mio. USD)']].plot(figsize=(10,6), alpha =0.5)\n",
    "# add vertical line at Merge\n",
    "#ax.axvline(x='2022-09-15', color='green', linestyle='-', linewidth=2)\n",
    "#ax.set_yscale('log')\n",
    "\n",
    "# set the labels and title\n",
    "ax.set_title('', fontsize=20, fontweight='bold')\n",
    "ax.set_xlabel('', fontsize=16)\n",
    "ax.set_ylabel('', fontsize=16)\n",
    "ax.legend(fontsize=14)\n",
    "ax.tick_params(axis='both', labelsize=12)\n",
    "plt.savefig(\"MiningReward.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d68d74a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#value = df.loc['2022-12-31', 'ETC Profatibilty Threshold [MHash/J]']\n",
    "#value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7304fc95",
   "metadata": {},
   "source": [
    "# Rest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce9170eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ETH / Tx Fee per Day (USD)'] = (df['ETH / Tx Fee per Day (native units)']*\n",
    "df['ETH / USD Denominated Closing Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c5cfa4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['AMD RX 5700 XT / Mean Hash Rate'] = 0.00002"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8791f9e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['AMD RX 5700 XT / Percentage Hash Rate']= df['AMD RX 5700 XT / Mean Hash Rate'] / df['ETH / Mean Hash Rate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5bf83feb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ETH Block Reward per Day (USD)'] = df['ETH Block Reward per Day (native units)'] * df['ETH / USD Denominated Closing Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba7cea14",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ETH / Tx Fee per Day (Mio. USD)'] = df['ETH / Tx Fee per Day (USD)'] /1000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38478fa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ETH Block Reward per Day (Mio. USD)'] = df['ETH Block Reward per Day (USD)'] /1000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18d3e58d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "# Assuming df is your DataFrame and 'column_name' is the name of your column\n",
    "index_value = datetime.strptime(\"2021-05-12 00:00:00\", \"%Y-%m-%d %H:%M:%S\")\n",
    "\n",
    "# Get the value before and after the specific index\n",
    "value_before = df.loc[index_value - pd.Timedelta(days=3), 'ETH / Tx Fee per Day (Mio. USD)']\n",
    "value_after = df.loc[index_value + pd.Timedelta(days=3), 'ETH / Tx Fee per Day (Mio. USD)']\n",
    "\n",
    "# Calculate the average\n",
    "average_value = (value_before + value_after) / 2\n",
    "\n",
    "# Replace the value at the specific index with the average value\n",
    "df.loc[index_value, 'ETH / Tx Fee per Day (Mio. USD)'] = average_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72d744a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "# Assuming df is your DataFrame and 'column_name' is the name of your column\n",
    "index_value = datetime.strptime(\"2022-05-01 00:00:00\", \"%Y-%m-%d %H:%M:%S\")\n",
    "\n",
    "# Get the value before and after the specific index\n",
    "value_before = df.loc[index_value - pd.Timedelta(days=1), 'ETH / Tx Fee per Day (Mio. USD)']\n",
    "value_after = df.loc[index_value + pd.Timedelta(days=1), 'ETH / Tx Fee per Day (Mio. USD)']\n",
    "\n",
    "# Calculate the average\n",
    "average_value = (value_before + value_after) / 2\n",
    "\n",
    "# Replace the value at the specific index with the average value\n",
    "df.loc[index_value, 'ETH / Tx Fee per Day (Mio. USD)'] = average_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fd50432",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Total Reward per Day (Mio. USD)'] = df['ETH Block Reward per Day (Mio. USD)']+ df['ETH / Tx Fee per Day (Mio. USD)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d93811d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df['Total Reward per Day (100k USD)'] = df['Total Reward per Day (USD)']/100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0db49296",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['AMD RX 5700 XT / Total Reward per Day'] = df['Total Reward per Day (Mio. USD)'] * df['AMD RX 5700 XT / Percentage Hash Rate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43edbe72",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['AMD RX 5700 XT / Total Reward per Day'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2dda92b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Electricity Costs'] = 2.4 * 0.065"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11583947",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['AMD RX 5700 XT / Total Profit per Day'] = df['AMD RX 5700 XT / Total Reward per Day']-df['Electricity Costs']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb090e59",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['AMD RX 5700 XT / Total Profit per Day'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40bd9bd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['PercentageElectricity'] = df['Electricity Costs']/df['AMD RX 5700 XT / Total Reward per Day']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ea8acd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['PercentageElectricity'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c07a0a80",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify the start and end dates for filtering\n",
    "start_date = pd.to_datetime('2019-09-14')\n",
    "end_date = pd.to_datetime('2022-09-14')\n",
    "\n",
    "# Filter the DataFrame based on the date range\n",
    "filtered_df = df.loc[(df.index >= start_date)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5756a7fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_df['PercentageElectricity'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5efae8f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resample the DataFrame to weekly frequency and calculate the average\n",
    "weekly_avg = df.resample('W').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9fe0372",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assuming df is your DataFrame and 'column_name' is the name of your column\n",
    "max_value_index = df['ETH / Tx Fee per Day (Mio. USD)'].idxmax()\n",
    "\n",
    "print(max_value_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fcc2ddc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ElectricityPrice'] = 0.065/3600000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "870d37e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Profitability Threshold ETC\n",
    "df['ETH Profatibilty Threshold [J/Hash]'] = ((df['ETH Block Reward per Day (Mio. USD)']+df['ETH Block Reward per Day (Mio. USD)'])*1000000/(24*60*60)) /(df['ETH / Mean Hash Rate']*1000000000000*df['ElectricityPrice'])\n",
    "cutoff_date = pd.to_datetime('2022-09-14')\n",
    "df.loc[df.index > cutoff_date, 'ETH Profatibilty Threshold [J/Hash]'] = 0\n",
    "df['ETH Profatibilty Threshold [Hash/J]'] = 1/df['ETH Profatibilty Threshold [J/Hash]']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6793c989",
   "metadata": {},
   "source": [
    "# Miner Revenue ETH\n",
    "\n",
    "The miner revenue consists of the Block-Reward and the Transaction-Fee. The Block-Reward is a fixed number set by the network itself. For Ethereum the timeline of the Block-Reward was as follows:\n",
    "\n",
    "- **July 30, 2015**: The Ethereum network is launched with a block reward of 5 ETH per block.\n",
    "\n",
    "- **October 12, 2017**: The Byzantium hard fork is implemented, which reduces the block reward from 5 ETH to 3 ETH per block.\n",
    "\n",
    "- **February 28, 2019**: The Constantinople hard fork is implemented, which reduces the block reward from 3 ETH to 2 ETH per block.\n",
    "\n",
    "The Transaction-Fee is a variable number which changes with every transaction. We will calculate the amount of Transaction-Fees per Block via the Amount of Transactions per Seconds and the Mean Transaction Fee that Day."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8887b48",
   "metadata": {},
   "source": [
    "The miner revenue consists of the Block-Reward and the Transaction-Fee. The Block-Reward is a fixed number set by the network itself. For Ethereum the timeline of the Block-Reward was as follows:\n",
    "\n",
    "- Ethereum Classic launched in July 2016. ETC was distributed at a 1:1 ratio for all holders of the Ethereum blockchain’s original currency, ETH, after the fork: The Ethereum Classic network is launched with a block reward of 5 ETC per block.\n",
    "\n",
    "- **December 11, 2017**: The block reward was reduced from 5 ETC to 4 ETC per block.\n",
    "\n",
    "- **March 17, 2020**: The block reward was reduced from 4 ETC to 3.2 ETC per block.\n",
    "\n",
    "- **April 25, 2022**: The block reward was reduced from 3.2 ETC to 2.56 ETC per block.\n",
    "\n",
    "The Transaction-Fee is a variable number which changes with every transaction. We will calculate the amount of Transaction-Fees per Block via the Amount of Transactions per Seconds and the Mean Transaction Fee that Day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ecea47c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Block Reward\n",
    "df['ETC Block Reward'] = 5\n",
    "cutoff_date = pd.to_datetime('2017-12-11')\n",
    "df.loc[df.index > cutoff_date, 'ETC Block Reward'] = 4\n",
    "cutoff_date = pd.to_datetime('2020-03-17')\n",
    "df.loc[df.index > cutoff_date, 'ETC Block Reward'] = 3.2\n",
    "cutoff_date = pd.to_datetime('2021-04-25')\n",
    "df.loc[df.index > cutoff_date, 'ETC Block Reward'] = 2.56"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ce097db",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transaction Fees\n",
    "df['ETC / Mean Tx Fee per Second'] = df['ETC / Mean Tx Fee (native units)']/df['ETC / Tx per Second Cnt']\n",
    "df['ETC / Mean Tx Fee per Block'] = df['ETC / Mean Tx Fee per Second']*13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8e3f045",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Cumulative Miner Revenue\n",
    "df['ETC/SEC'] = (df['ETH Block Reward']+df['ETC / Mean Tx Fee per Block'])/13"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d715379b",
   "metadata": {},
   "source": [
    "## Mining Hardware Efficiencies\n",
    "\n",
    "The single most important running cost for miners is the amount they have to pay for electricty. Therefore, the efficiencies of mining hardware depends on two factors: the amount of hashes the hardware can perform per second and the amount of energy the hardware consumes per second. Together this gives a number of hashes that can be performed per Joule. Follwoing is a timeline of the most efficient Ethereum mining hardware with their respective release dates and mining efficiencies:\n",
    "\n",
    "\n",
    "\n",
    "- **November 19, 2014**: Nvidia GTX 970 (0.208 [Mh/J])\n",
    "\n",
    "- **May 27, 2016**: Nvidia GTX 1080 (0.219 [Mh/J]) \n",
    "- **June 20, 2016**: Nvidia Tesla P100-PCIE-16GB: (0.325 [Mh/J]) \n",
    "- **June 29, 2016**: AMD RX 480 (0.385 [Mh/J])\n",
    "- **August 4, 2016**: AMD RX 470 (0.451 [Mh/J]) \n",
    "- **December 07, 2017**: Nvidia Titan V (0.465 [Mh/J]) \n",
    "- **September 1, 2018**: Innosilicon A10 (0.667 [Mh/J]) \n",
    "- **May 14, 2020**: NVIDIA Tesla A100 (0.85 [Mh/J])\n",
    "- **December 1, 2020**: Linzhi Phoenix: (0.867 [Mh/J]) \n",
    "- **November 1, 2021**: Jasminer X4 Server (2.083 [Mh/J]) \n",
    "- **December 1, 2021**: Jasminer X4 Brick: (2.167 [Mh/J])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1524d1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Efficiency [Hash/J]'] = 0.3846153846*1000000\n",
    "cutoff_date = pd.to_datetime('2016-08-04')\n",
    "df.loc[df.index > cutoff_date, 'Efficiency [Hash/J]'] = 0.4509685714*1000000\n",
    "cutoff_date = pd.to_datetime('2017-12-07')\n",
    "df.loc[df.index > cutoff_date, 'Efficiency [Hash/J]'] = 0.46478125*1000000\n",
    "cutoff_date = pd.to_datetime('2018-09-01')\n",
    "df.loc[df.index > cutoff_date, 'Efficiency [Hash/J]'] = 0.6666666667*1000000\n",
    "cutoff_date = pd.to_datetime('2020-05-14')\n",
    "df.loc[df.index > cutoff_date, 'Efficiency [Hash/J]'] = 0.85*1000000\n",
    "cutoff_date = pd.to_datetime('2020-12-01')\n",
    "df.loc[df.index > cutoff_date, 'Efficiency [Hash/J]'] = 0.8666666667*1000000\n",
    "cutoff_date = pd.to_datetime('2021-11-01')\n",
    "df.loc[df.index > cutoff_date, 'Efficiency [Hash/J]'] = 2.083333333*1000000\n",
    "cutoff_date = pd.to_datetime('2021-12-01')\n",
    "df.loc[df.index > cutoff_date, 'Efficiency [Hash/J]'] = 2.166666667*1000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "994f7234",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Efficiency [J/Hash]'] = 1/(df['Efficiency [Hash/J]'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cea3de6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ETH Lower Bound'] = df['Efficiency [J/Hash]'] *df['ETH / Mean Hash Rate']*1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fcaf0215",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ETH GPU Lower Bound'] = 1/(0.8666666667*1000000)* df['ETH / Mean Hash Rate']*1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8076a334",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ETH ASIC Lower Bound'] = 1/(2.166666667*1000000)* df['ETH / Mean Hash Rate']*1000"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd4ca459",
   "metadata": {},
   "source": [
    "## Electricity Prices\n",
    "\n",
    "Electricity prices available to miners vary significantly from one region to another for a variety of reasons. We assume that on average, miners face a constant electricity price of 5 USD cents per kilowatt-hour (0.05 USD/kWh). This default value is based on in-depth conversations with miners worldwide and is consistent with estimates used in previous research."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "448797f3",
   "metadata": {},
   "source": [
    "# Profitability Thresholds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9881d971",
   "metadata": {},
   "source": [
    "One key idea is that miners will run the equipment as long as it remains economically profitable in electricity terms. To determine the time periods during which a given hardware type is profitable, we model the economic lifetime of each machine by taking into account total miner revenues, total network hashrate, hardware energy efficiency, and the average electricity price per kWh that miners have to pay."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd6b39b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Profitability Threshold ETC\n",
    "df['ETC Profatibilty Threshold [J/Hash]'] = (df['ETC / USD Denominated Closing Price'] * df['ETC/SEC']) /(df['ETC / Mean Hash Rate']*1000000000000*df['ElectricityPrice'])\n",
    "df['ETC Profatibilty Threshold [Hash/J]'] = 1/df['ETC Profatibilty Threshold [J/Hash]']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dbc4051",
   "metadata": {},
   "source": [
    "# The Merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b65ead6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df['Max Energy']= df['ETH Profatibilty Threshold [J/Hash]'] * df['ETH / Mean Hash Rate'] *1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10ce4e68",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ETC Profatibilty Threshold [J/MHash]']=df['ETC Profatibilty Threshold [J/Hash]']*1000000\n",
    "df['ETH Profatibilty Threshold [J/MHash]']=df['ETH Profatibilty Threshold [J/Hash]']*1000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "383be068",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ETC Profatibilty Threshold [MHash/J]']=1/df['ETC Profatibilty Threshold [J/MHash]']\n",
    "df['ETH Profatibilty Threshold [MHash/J]']=1/df['ETH Profatibilty Threshold [J/MHash]']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfc3479d",
   "metadata": {},
   "source": [
    "With The Merge Ethereum switched from Proof-of-Work to Proof-of-Stake and reduced its HashRate to effectively 0 from a value of approximately 850TH/s. Part of the Hashrate has switched over to Ethereum Classic which remains on the Proof-of-Work algorithm. The Profitability Threshold of Ethereum Classic is lower than that of Ethereum, so only the most efficient miners could afford to switch. This means we know the percentage of miners that were at least as efficient as the profitability threshold of ethereum Classic post-Merge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "627238fa",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "start_date = pd.Timestamp('2021-01-01')\n",
    "end_date = pd.Timestamp('2022-12-31')\n",
    "sns.set(style=\"darkgrid\")\n",
    "ax = df.loc[start_date:end_date, ['ETH Profatibilty Threshold [MHash/J]','ETC Profatibilty Threshold [MHash/J]']].plot(figsize=(10,6))\n",
    "# add vertical line at Merge\n",
    "ax.axvline(x='2022-09-15', color='green', linestyle='-', linewidth=2)\n",
    "\n",
    "\n",
    "plt.savefig('myplot.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d729cd31",
   "metadata": {},
   "outputs": [],
   "source": [
    "value = df.loc['2022-09-14', 'ETH Profatibilty Threshold [MHash/J]']\n",
    "value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07a565c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "2.67/100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ddac512",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['MaxEnergy']= df['ETH Profatibilty Threshold [MHash/J]']*df['ETH / Mean Hash Rate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb89d7e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "start_date = pd.Timestamp('2021-01-01')\n",
    "end_date = pd.Timestamp('2022-12-31')\n",
    "sns.set(style=\"darkgrid\")\n",
    "ax = df.loc[start_date:end_date, ['ETH / Mean Hash Rate', 'ETC / Mean Hash Rate']].plot(figsize=(10,6))\n",
    "# add vertical line at Merge\n",
    "ax.axvline(x='2022-09-15', color='green', linestyle='-', linewidth=2)\n",
    "\n",
    "# set the labels and title\n",
    "ax.set_title('Hash Rate of Ethereum and Ethereum Classic', fontsize=20, fontweight='bold')\n",
    "ax.set_xlabel('Date', fontsize=16)\n",
    "ax.set_ylabel('Hashrate in TH/s', fontsize=16)\n",
    "ax.tick_params(axis='both', labelsize=12)\n",
    "legend = ax.legend()\n",
    "legend.remove()\n",
    "bbox_args = dict(boxstyle=\"round\", fc=\"0.75\")\n",
    "ax.annotate('65TH/s', xy=(0.725, 0.225), xycoords='figure fraction',\n",
    "             xytext=(0, 0), textcoords='offset points',\n",
    "             ha=\"right\", va=\"top\",\n",
    "             bbox=bbox_args)\n",
    "bbox_args = dict(boxstyle=\"round\", fc=\"0.75\")\n",
    "ax.annotate('825TH/s', xy=(0.825, 0.7), xycoords='figure fraction',\n",
    "             xytext=(0, 0), textcoords='offset points',\n",
    "             ha=\"right\", va=\"top\",\n",
    "             bbox=bbox_args)\n",
    "bbox_args = dict(boxstyle=\"round\", fc=\"0.75\")\n",
    "ax.annotate('200TH/s', xy=(0.725, 0.35), xycoords='figure fraction',\n",
    "             xytext=(0, 0), textcoords='offset points',\n",
    "             ha=\"right\", va=\"top\",\n",
    "             bbox=bbox_args)\n",
    "plt.savefig('myplot1.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c63c6c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "start_date = pd.Timestamp('2016-01-01')\n",
    "end_date = pd.Timestamp('2022-09-15')\n",
    "sns.set(style=\"whitegrid\")\n",
    "ax = df.loc[start_date:end_date, ['ETH Lower Bound']].plot(figsize=(10,6))\n",
    "# add vertical line at Merge\n",
    "#ax.axvline(x='2022-09-15', color='green', linestyle='-', linewidth=2)\n",
    "\n",
    "# set the labels and title\n",
    "ax.set_title('Power Consumption of Ethereum in GW', fontsize=20, fontweight='bold')\n",
    "ax.set_xlabel('', fontsize=16)\n",
    "ax.set_ylabel('', fontsize=16)\n",
    "ax.tick_params(axis='both', labelsize=12)\n",
    "legend = ax.legend()\n",
    "legend.remove()\n",
    "#bbox_args = dict(boxstyle=\"round\", fc=\"0.75\")\n",
    "#ax.annotate('65TH/s', xy=(0.725, 0.225), xycoords='figure fraction',\n",
    "            # xytext=(0, 0), textcoords='offset points',\n",
    "            # ha=\"right\", va=\"top\",\n",
    "             #bbox=bbox_args)\n",
    "#bbox_args = dict(boxstyle=\"round\", fc=\"0.75\")\n",
    "#ax.annotate('2GW', xy=(0.825, 0.7), xycoords='figure fraction',\n",
    "             #xytext=(0, 0), textcoords='offset points',\n",
    "             #ha=\"right\", va=\"top\",\n",
    "             #bbox=bbox_args)\n",
    "#bbox_args = dict(boxstyle=\"round\", fc=\"0.75\")\n",
    "#ax.annotate('200TH/s', xy=(0.725, 0.35), xycoords='figure fraction',\n",
    "            # xytext=(0, 0), textcoords='offset points',\n",
    "            # ha=\"right\", va=\"top\",\n",
    "            # bbox=bbox_args)\n",
    "plt.savefig('LowerBound.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "481a9e24",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "start_date = pd.Timestamp('2017-01-01')\n",
    "end_date = pd.Timestamp('2022-12-31')\n",
    "sns.set(style=\"darkgrid\")\n",
    "ax = df.loc[start_date:end_date, 'MaxEnergy'].plot(figsize=(10,6))\n",
    "# add vertical line at Merge\n",
    "#ax.axvline(x='2022-09-15', color='green', linestyle='-', linewidth=2)\n",
    "\n",
    "# set the labels and title\n",
    "ax.set_title('Energy Consumption of Ethereum', fontsize=20, fontweight='bold')\n",
    "#ax.set_xlabel('Date', fontsize=16)\n",
    "ax.set_ylabel('Energy in GW', fontsize=16)\n",
    "ax.tick_params(axis='both', labelsize=12)\n",
    "legend = ax.legend()\n",
    "legend.remove()\n",
    "#bbox_args = dict(boxstyle=\"round\", fc=\"0.75\")\n",
    "#ax.annotate('65TH/s', xy=(0.725, 0.225), xycoords='figure fraction',\n",
    "            # xytext=(0, 0), textcoords='offset points',\n",
    "            # ha=\"right\", va=\"top\",\n",
    "             #bbox=bbox_args)\n",
    "bbox_args = dict(boxstyle=\"round\", fc=\"0.75\")\n",
    "ax.annotate('17.44GW', xy=(0.825, 0.3), xycoords='figure fraction',\n",
    "             xytext=(0, 0), textcoords='offset points',\n",
    "             ha=\"right\", va=\"top\",\n",
    "             bbox=bbox_args)\n",
    "#bbox_args = dict(boxstyle=\"round\", fc=\"0.75\")\n",
    "#ax.annotate('200TH/s', xy=(0.725, 0.35), xycoords='figure fraction',\n",
    "            # xytext=(0, 0), textcoords='offset points',\n",
    "            # ha=\"right\", va=\"top\",\n",
    "            # bbox=bbox_args)\n",
    "plt.savefig('myplot.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55efb52d",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(df.loc['2022-09-14', 'Max Energy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "393cae37",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Best Guess'] = df['Max Energy'] * df['PercentageElectricity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e4a2c6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "start_date = pd.Timestamp('2020-01-01')\n",
    "end_date = pd.Timestamp('2022-12-31')\n",
    "sns.set(style=\"darkgrid\")\n",
    "ax = df.loc[start_date:end_date, [ 'Best Guess']].plot(figsize=(10,6))\n",
    "# add vertical line at Merge\n",
    "#ax.axvline(x='2022-09-15', color='green', linestyle='-', linewidth=2)\n",
    "\n",
    "# set the labels and title\n",
    "ax.set_title('Energy Consumption of Ethereum', fontsize=20, fontweight='bold')\n",
    "#ax.set_xlabel('Date', fontsize=16)\n",
    "ax.set_ylabel('Energy in GW', fontsize=16)\n",
    "ax.tick_params(axis='both', labelsize=12)\n",
    "legend = ax.legend()\n",
    "legend.remove()\n",
    "bbox_args = dict(boxstyle=\"round\", fc=\"0.75\")\n",
    "ax.annotate('65TH/s', xy=(0.725, 0.225), xycoords='figure fraction',\n",
    "             xytext=(0, 0), textcoords='offset points',\n",
    "            ha=\"right\", va=\"top\",\n",
    "             bbox=bbox_args)\n",
    "bbox_args = dict(boxstyle=\"round\", fc=\"0.75\")\n",
    "ax.annotate('17.44GW', xy=(0.825, 0.4), xycoords='figure fraction',\n",
    "             xytext=(0, 0), textcoords='offset points',\n",
    "             ha=\"right\", va=\"top\",\n",
    "             bbox=bbox_args)\n",
    "bbox_args = dict(boxstyle=\"round\", fc=\"0.75\")\n",
    "ax.annotate('8.72GW', xy=(0.825, 0.3), xycoords='figure fraction',\n",
    "             xytext=(0, 0), textcoords='offset points',\n",
    "             ha=\"right\", va=\"top\",\n",
    "             bbox=bbox_args)\n",
    "bbox_args = dict(boxstyle=\"round\", fc=\"0.75\")\n",
    "ax.annotate('200TH/s', xy=(0.725, 0.35), xycoords='figure fraction',\n",
    "            xytext=(0, 0), textcoords='offset points',\n",
    "            ha=\"right\", va=\"top\",\n",
    "            bbox=bbox_args)\n",
    "plt.savefig('myplot.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5728bec0",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(df.loc['2022-09-14', 'Best Guess'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbd9c1d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Anteil']= df['ETC/SEC']*df['ETC / USD Denominated Closing Price']*df['ETH / Mean Hash Rate']/(df['ETH/SEC']*df['ETC / Mean Hash Rate']*df['ETH / USD Denominated Closing Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f8ad3e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate your plot here\n",
    "ax = df.loc[start_date:end_date, ['Anteil']].plot(figsize=(10,6))\n",
    "\n",
    "# Remove the title (column name) from the plot\n",
    "ax.set_title('')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16c170dc",
   "metadata": {},
   "source": [
    "One can see that on the Day of the Merge around 135TH/s were added onto the Ethereum Classic network. However at the end of September the addition only is 80-85TH/s. Assuming that non-profitable miners tried to switch but stopped after mining at a loss, only around 10 percent of Ethereum miners were at least as efficent as the Ethereum Classic profitability threshold post-Merge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96907c3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "start_date = pd.Timestamp('2022-07-01')\n",
    "end_date = pd.Timestamp('2022-12-31')\n",
    "sns.set(style=\"darkgrid\")\n",
    "ax = df.loc[start_date:end_date, ['ETH Profatibilty Threshold [J/Hash]','Efficiency [J/Hash]']].plot(figsize=(10,6))\n",
    "# add vertical line at Merge\n",
    "#ax.axvline(x='2022-09-14', color='green', linestyle='-', linewidth=2)\n",
    "\n",
    "# set the labels and title\n",
    "ax.set_title('Profitability Thresholds of ETH & ETC', fontsize=20, fontweight='bold')\n",
    "ax.set_xlabel('Date', fontsize=16)\n",
    "ax.set_ylabel('Efficiency in J/MHash', fontsize=16)\n",
    "ax.legend(fontsize=14)\n",
    "ax.tick_params(axis='both', labelsize=12)\n",
    "bbox_args = dict(boxstyle=\"round\", fc=\"0.75\")\n",
    "ax.annotate('285J/MHash', xy=(0.5, 0.7), xycoords='figure fraction',\n",
    "             xytext=(0, 0), textcoords='offset points',\n",
    "             ha=\"right\", va=\"top\",\n",
    "             bbox=bbox_args)\n",
    "bbox_args = dict(boxstyle=\"round\", fc=\"0.75\")\n",
    "ax.annotate('0.46J/MHash', xy=(0.5, 0.21), xycoords='figure fraction',\n",
    "             xytext=(0, 0), textcoords='offset points',\n",
    "             ha=\"right\", va=\"top\",\n",
    "             bbox=bbox_args)\n",
    "plt.savefig('myplot.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9285c641",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "start_date = pd.Timestamp('2022-07-01')\n",
    "end_date = pd.Timestamp('2022-12-31')\n",
    "sns.set(style=\"darkgrid\")\n",
    "ax = df.loc[start_date:end_date, ['ETH Profatibilty Threshold [J/Hash]','ETC Profatibilty Threshold [J/Hash]']].plot(figsize=(10,6))\n",
    "# add vertical line at Merge\n",
    "#ax.axvline(x='2022-09-14', color='green', linestyle='-', linewidth=2)\n",
    "\n",
    "# set the labels and title\n",
    "ax.set_title('Profitability Thresholds of Ethereum & Ethereum Classic', fontsize=20, fontweight='bold')\n",
    "ax.set_xlabel('Date', fontsize=16)\n",
    "ax.set_ylabel('Efficiency in J/MHash', fontsize=16)\n",
    "legend = ax.legend()\n",
    "legend.remove()\n",
    "ax.tick_params(axis='both', labelsize=12)\n",
    "plt.savefig('myplot2.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61594bcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "start_date = pd.Timestamp('2021-01-01')\n",
    "end_date = pd.Timestamp('2022-12-31')\n",
    "sns.set(style=\"darkgrid\")\n",
    "ax = df.loc[start_date:end_date, ['ETH GPU Lower Bound','ETH ASIC Lower Bound']].plot(figsize=(10,6))\n",
    "# add vertical line at Merge\n",
    "#ax.axvline(x='2022-09-14', color='green', linestyle='-', linewidth=2)\n",
    "\n",
    "# set the labels and title\n",
    "ax.set_title('Energy Consumption of Ethereum', fontsize=20, fontweight='bold')\n",
    "ax.set_xlabel('Date', fontsize=16)\n",
    "ax.set_ylabel('Energy in GW', fontsize=16)\n",
    "bbox_args = dict(boxstyle=\"round\", fc=\"0.75\")\n",
    "ax.annotate('1.9GW', xy=(0.8, 0.7), xycoords='figure fraction',\n",
    "             xytext=(0, 0), textcoords='offset points',\n",
    "             ha=\"right\", va=\"top\",\n",
    "             bbox=bbox_args)\n",
    "bbox_args = dict(boxstyle=\"round\", fc=\"0.75\")\n",
    "ax.annotate('0.76GW', xy=(0.8, 0.3), xycoords='figure fraction',\n",
    "             xytext=(0, 0), textcoords='offset points',\n",
    "             ha=\"right\", va=\"top\",\n",
    "             bbox=bbox_args)\n",
    "legend = ax.legend()\n",
    "legend.remove()\n",
    "ax.tick_params(axis='both', labelsize=12)\n",
    "plt.savefig('myplot3.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "821692e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(df.loc['2022-09-14', 'ETH GPU Lower Bound'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80e7b778",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(df.loc['2022-09-14', 'ETH ASIC Lower Bound'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e96769d2",
   "metadata": {},
   "source": [
    "# Weighted Average Approach to Estimate Energy Consumption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbf6a1d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set(style=\"darkgrid\")\n",
    "\n",
    "# read the list of mining hardware and their efficiencies from a Excel file\n",
    "miners_df = pd.read_excel('/Users/patrickwoitschig/OneDrive/EconometricProject/EfficienciesETCPost.xlsx')\n",
    "\n",
    "# total number of miners\n",
    "num_miners = len(miners_df)\n",
    "\n",
    "# weights for each miner based on the known distribution of hardware efficiencies\n",
    "weights = []\n",
    "for efficiency in miners_df['Efficiency_J/h']:\n",
    "        weights.append(1/ num_miners)\n",
    "\n",
    "# estimate of the total hashrate of the network (in gigahashes per second)\n",
    "total_hashrate = 825506247530000*0.107\n",
    "\n",
    "# number of simulation iterations\n",
    "num_iterations = 1000\n",
    "\n",
    "# list to store the energy consumption estimates\n",
    "energy_consumption = []\n",
    "\n",
    "for i in range(num_iterations):\n",
    "    # generate a sample of mining hardware based on the weighted distribution of hardware efficiencies\n",
    "    miner = miners_df.sample(weights=weights).iloc[0]\n",
    "    # calculate the energy consumption estimate for this miner\n",
    "    energy = total_hashrate * miner['Efficiency_J/h']\n",
    "    energy_consumption.append(energy)\n",
    "\n",
    "# calculate summary statistics of energy consumption estimates\n",
    "mean_energy_low = sum(energy_consumption) / num_iterations\n",
    "median_energy_low = sorted(energy_consumption)[num_iterations // 2]\n",
    "lower_bound_low = sorted(energy_consumption)[int(num_iterations * 0.025)]\n",
    "upper_bound_low = sorted(energy_consumption)[int(num_iterations * 0.975)]\n",
    "\n",
    "# print results\n",
    "print(f\"Mean energy consumption: {mean_energy_low:.2f} J\")\n",
    "print(f\"Median energy consumption: {median_energy_low:.2f} J\")\n",
    "print(f\"95% confidence interval: ({lower_bound_low:.2f} J, {upper_bound_low:.2f} J)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f37fc6fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set(style=\"darkgrid\")\n",
    "\n",
    "# read the list of mining hardware and their efficiencies from a Excel file\n",
    "miners_df = pd.read_excel('/Users/patrickwoitschig/OneDrive/EconometricProject/Efficiencies_ETH_Pre.xlsx')\n",
    "\n",
    "# total number of miners\n",
    "num_miners = len(miners_df)\n",
    "\n",
    "# weights for each miner based on the known distribution of hardware efficiencies\n",
    "weights = []\n",
    "for efficiency in miners_df['Efficiency_J/h']:\n",
    "        weights.append(1/ num_miners)\n",
    "\n",
    "# estimate of the total hashrate of the network (in gigahashes per second)\n",
    "total_hashrate = 825506247530000*(1-0.107)\n",
    "\n",
    "# number of simulation iterations\n",
    "num_iterations = 10000\n",
    "\n",
    "# list to store the energy consumption estimates\n",
    "energy_consumption = []\n",
    "\n",
    "for i in range(num_iterations):\n",
    "    # generate a sample of mining hardware based on the weighted distribution of hardware efficiencies\n",
    "    miner = miners_df.sample(weights=weights).iloc[0]\n",
    "    # calculate the energy consumption estimate for this miner\n",
    "    energy = total_hashrate * miner['Efficiency_J/h']\n",
    "    energy_consumption.append(energy)\n",
    "\n",
    "# calculate summary statistics of energy consumption estimates\n",
    "mean_energy_high = sum(energy_consumption) / num_iterations\n",
    "median_energy_high = sorted(energy_consumption)[num_iterations // 2]\n",
    "lower_bound_high = sorted(energy_consumption)[int(num_iterations * 0.025)]\n",
    "upper_bound_high = sorted(energy_consumption)[int(num_iterations * 0.975)]\n",
    "\n",
    "# print results\n",
    "print(f\"Mean energy consumption: {mean_energy_high:.2f} J\")\n",
    "print(f\"Median energy consumption: {median_energy_high:.2f} J\")\n",
    "print(f\"95% confidence interval: ({lower_bound_high:.2f} J, {upper_bound_high:.2f} J)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cefabf08",
   "metadata": {},
   "outputs": [],
   "source": [
    "(mean_energy_high+ mean_energy_low)/1000000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47229485",
   "metadata": {},
   "outputs": [],
   "source": [
    "(lower_bound_low+lower_bound_high)/1000000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90744eb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "(upper_bound_low+upper_bound_high)/1000000000"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a0a3985",
   "metadata": {},
   "source": [
    "Previous estimates using a bottom-up approach (McDonald) have been 2.5GW and using a top-down approach (Digiconomist) around 9GW."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7dd83f84",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2121e17",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3568a411",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4aac4703",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2fa716f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7265c452",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a90744c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c83edcae",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9c85042",
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00e432da",
   "metadata": {},
   "source": [
    "# Maximum Possible Hash Rate\n",
    "\n",
    "Corresponding to the \n",
    "\n",
    "\n",
    "Assuming that everyone uses the most efficient hardware on the market one can calculate a maximum hash rate, which would let the network work at mining net zero profitability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6d1cb40",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ETH / Max Hash Rate'] = (df['ETH/SEC']*df['ETH / USD Denominated Closing Price'])/(df['ElectricityPrice']*df['Efficiency [J/Hash]'])*(1/1000000000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "722ae2fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "start_date = pd.Timestamp('2017-01-01')\n",
    "end_date = pd.Timestamp('2022-09-30')\n",
    "sns.set(style=\"darkgrid\")\n",
    "ax = df.loc[start_date:end_date, ['ETH / Max Hash Rate','ETH / Mean Hash Rate']].plot(figsize=(10,6))\n",
    "ax.axvline(pd.Timestamp('2016-08-04'), color='b', linestyle='--')\n",
    "ax.axvline(pd.Timestamp('2017-12-07'), color='b', linestyle='--')\n",
    "ax.axvline(pd.Timestamp('2018-09-01'), color='b', linestyle='--')\n",
    "ax.axvline(pd.Timestamp('2020-05-14'), color='b', linestyle='--')\n",
    "ax.axvline(pd.Timestamp('2020-12-01'), color='b', linestyle='--')\n",
    "ax.axvline(pd.Timestamp('2021-11-01'), color='b', linestyle='--')\n",
    "\n",
    "# set the labels and title\n",
    "ax.set_title('Lower Bounds of Ethereum Energy Consumption', fontsize=20, fontweight='bold')\n",
    "ax.set_ylabel('Energy in GW', fontsize=16)\n",
    "ax.legend(fontsize=14)\n",
    "ax.tick_params(axis='both', labelsize=12)\n",
    "plt.savefig('myplot.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4241781",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "start_date = pd.Timestamp('2017-01-01')\n",
    "end_date = pd.Timestamp('2022-09-30')\n",
    "sns.set(style=\"darkgrid\")\n",
    "ax = df.loc[start_date:end_date, ['ETH / Max Hash Rate','ETH / Mean Hash Rate']].plot(figsize=(10,6),logy=True)\n",
    "ax.axvline(pd.Timestamp('2016-08-04'), color='b', linestyle='--')\n",
    "ax.axvline(pd.Timestamp('2017-12-07'), color='b', linestyle='--')\n",
    "ax.axvline(pd.Timestamp('2018-09-01'), color='b', linestyle='--')\n",
    "ax.axvline(pd.Timestamp('2020-05-14'), color='b', linestyle='--')\n",
    "ax.axvline(pd.Timestamp('2020-12-01'), color='b', linestyle='--')\n",
    "ax.axvline(pd.Timestamp('2021-11-01'), color='b', linestyle='--')\n",
    "\n",
    "# set the labels and title\n",
    "ax.set_title('Lower Bounds of Ethereum Energy Consumption', fontsize=20, fontweight='bold')\n",
    "ax.set_ylabel('Energy in GW', fontsize=16)\n",
    "ax.legend(fontsize=14)\n",
    "ax.tick_params(axis='both', labelsize=12)\n",
    "plt.savefig('myplot.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c1a85de",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_log = np.log(df[['ETH / Max Hash Rate','ETH / Mean Hash Rate']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74146f61",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_log.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e51717a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_log['ETH Difference'] = df_log['ETH / Max Hash Rate']-df_log['ETH / Mean Hash Rate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e707c8c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_log = df_log.loc[df_log.index < '2022-09-15']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28149403",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# create a plot\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.plot(df_log.index, df_log['ETH Difference'])\n",
    "ax.axvline(pd.Timestamp('2016-08-04'), color='b', linestyle='--')\n",
    "ax.axvline(pd.Timestamp('2017-12-07'), color='b', linestyle='--')\n",
    "ax.axvline(pd.Timestamp('2018-09-01'), color='b', linestyle='--')\n",
    "ax.axvline(pd.Timestamp('2020-05-14'), color='b', linestyle='--')\n",
    "ax.axvline(pd.Timestamp('2020-12-01'), color='b', linestyle='--')\n",
    "ax.axvline(pd.Timestamp('2021-11-01'), color='b', linestyle='--')\n",
    "# set x-axis label and title\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_title('Difference of Log Hash Rate')\n",
    "\n",
    "# show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65bf283e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d87c8db1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_log['ETH / Active Addr Cnt'] = df['ETH / Active Addr Cnt']\n",
    "df_log['ETH / Tx per Second Cnt'] = df['ETH / Tx per Second Cnt']\n",
    "df_log['ETH / Mean Tx Size (native units)'] = df['ETH / Mean Tx Size (native units)']\n",
    "df_log['ETH / Mean Block Size (in bytes)'] = df['ETH / Mean Block Size (in bytes)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6de42d05",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_log = df_log.loc[df_log.index > '2016-06-30']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbb40370",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_log['ETH / Mean Hash Rate'] = df_log['ETH / Mean Hash Rate'].diff().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e28dfb49",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_log['DiffInDiff'] = df_log['ETH Difference'].diff().dropna()\n",
    "df_log['ETH / Mean Tx Size (native units)'] = np.log(df_log['ETH / Mean Tx Size (native units)'])\n",
    "df_log['ETH / Mean Tx Size (native units)'] = df_log['ETH / Mean Tx Size (native units)'].diff().dropna()\n",
    "df_log['ETH / Active Addr Cnt'] = np.log(df_log['ETH / Active Addr Cnt'])\n",
    "df_log['ETH / Active Addr Cnt'] = df_log['ETH / Active Addr Cnt'].diff().dropna()\n",
    "df_log['ETH / Tx per Second Cnt'] = np.log(df_log['ETH / Tx per Second Cnt'])\n",
    "df_log['ETH / Tx per Second Cnt'] = df_log['ETH / Tx per Second Cnt'].diff().dropna()\n",
    "df_log['ETH / Mean Block Size (in bytes)'] = np.log(df_log['ETH / Mean Block Size (in bytes)'])\n",
    "df_log['ETH / Mean Block Size (in bytes)'] = df_log['ETH / Mean Block Size (in bytes)'].diff().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccff6cc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "ax.axvline(pd.Timestamp('2016-08-04'), color='b', linestyle='--')\n",
    "ax.axvline(pd.Timestamp('2017-12-07'), color='b', linestyle='--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1266330",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_log = df_log.loc[df_log.index > '2016-08-04']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6256f963",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_log = df_log.loc[df_log.index < '2017-12-07']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2dca32c8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_log = df_log.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c47d3d21",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b83c53b9",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# select a subset of the columns\n",
    "cols = ['ETH / Mean Hash Rate', 'ETH / Mean Tx Size (native units)', 'ETH / Active Addr Cnt', 'ETH / Tx per Second Cnt']\n",
    "data_subset = df_log[cols]\n",
    "\n",
    "# create a VAR model with two lags\n",
    "model = sm.tsa.VAR(data_subset)\n",
    "results = model.fit(2)\n",
    "\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "879de12f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, LSTM\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# read the time series data from a CSV file\n",
    "cols = ['DiffInDiff', 'ETH / Active Addr Cnt', 'ETH / Tx per Second Cnt', 'ETH / Mean Tx Size (USD)', 'ETH / Mean Block Size (in bytes)']\n",
    "data = weekly_data[cols]\n",
    "\n",
    "# normalize the data to the range [0, 1]\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "data_normalized = scaler.fit_transform(data)\n",
    "\n",
    "# split the data into training and testing sets\n",
    "train_size = int(len(data_normalized) * 0.95)\n",
    "train_data = data_normalized[:train_size, :]\n",
    "test_data = data_normalized[train_size:, :]\n",
    "\n",
    "# create a sliding window sequence of inputs and outputs\n",
    "window_size = 3\n",
    "X_train, y_train = [], []\n",
    "for i in range(window_size, len(train_data)):\n",
    "    X_train.append(train_data[i-window_size:i, :])\n",
    "    y_train.append(train_data[i, :])\n",
    "X_train, y_train = np.array(X_train), np.array(y_train)\n",
    "\n",
    "# create a neural network model\n",
    "model = Sequential()\n",
    "model.add(LSTM(50, input_shape=(X_train.shape[1], X_train.shape[2])))\n",
    "model.add(Dense(5))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "\n",
    "# train the model on the training data\n",
    "model.fit(X_train, y_train, epochs=100, batch_size=32, verbose=2)\n",
    "\n",
    "# make predictions on the testing data\n",
    "inputs = data_normalized[len(data_normalized) - len(test_data) - window_size:]\n",
    "X_test = []\n",
    "for i in range(window_size, len(inputs)):\n",
    "    X_test.append(inputs[i-window_size:i, :])\n",
    "X_test = np.array(X_test)\n",
    "predictions = model.predict(X_test)\n",
    "predictions = scaler.inverse_transform(predictions)\n",
    "\n",
    "# plot the predicted and actual values\n",
    "plt.plot(data.index[-len(test_data):], test_data[:, 0])\n",
    "plt.plot(data.index[-len(test_data):], predictions[:, 0])\n",
    "plt.legend(['Actual', 'Predicted'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1020175",
   "metadata": {},
   "outputs": [],
   "source": [
    "weekly_data = df_log.resample(rule='W').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77b51f49",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a custom design matrix with the restriction\n",
    "exog = np.zeros_like(dataframe)\n",
    "exog[:, 1, 1] = 1\n",
    "exog[:, 2, 1] = 1\n",
    "\n",
    "# Estimate the VAR model with the restriction\n",
    "var_model = VAR(dataframe)\n",
    "results = var_model.fit(2, exog=exog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c2948f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the VAR model and matplotlib\n",
    "from statsmodels.tsa.api import VAR\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Estimate the VAR model\n",
    "var_model = VAR(data_subset)\n",
    "results = var_model.fit(2)\n",
    "\n",
    "# Generate in-sample predictions for the second variable\n",
    "pred = results.fittedvalues.iloc[:, 1]\n",
    "\n",
    "# Plot the original and estimated series for the second variable\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ax.plot(data_subset.iloc[:, 1], label='Original')\n",
    "ax.plot(pred, label='Estimated')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0ddf49d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_log = df_log.loc[df_log.index > '2016-12-31']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efde4313",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef7d4c66",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08df21e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ETC / Mean Tx Fee per Second'] = df['ETC / Mean Tx Fee (native units)']/df['ETC / Tx per Second Cnt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b0562f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ETC / Mean Tx Fee per Block'] = df['ETC / Mean Tx Fee per Second']*13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db2b92c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ETC Block Reward'] = 5\n",
    "cutoff_date = pd.to_datetime('2017-12-07')\n",
    "df.loc[df.index > cutoff_date, 'ETC Block Reward'] = 4\n",
    "cutoff_date = pd.to_datetime('2020-03-05')\n",
    "df.loc[df.index > cutoff_date, 'ETC Block Reward'] = 3.2\n",
    "cutoff_date = pd.to_datetime('2021-07-15')\n",
    "df.loc[df.index > cutoff_date, 'ETC Block Reward'] = 2.56"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58a411a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "cutoff_date = pd.to_datetime('2017-12-07')\n",
    "df.loc[df.index > cutoff_date, 'ETC Block Reward'] = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48b67cba",
   "metadata": {},
   "outputs": [],
   "source": [
    "cutoff_date = pd.to_datetime('2020-03-05')\n",
    "df.loc[df.index > cutoff_date, 'ETC Block Reward'] = 3.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7fe37ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "cutoff_date = pd.to_datetime('2021-07-15')\n",
    "df.loc[df.index > cutoff_date, 'ETC Block Reward'] = 2.56"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dfafc976",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ETC/SEC'] = (df['ETH Block Reward']+df['ETC / Mean Tx Fee per Block'])/13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "528e98f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ETC / Max Hash Rate'] = (df['ETC/SEC']*df['ETC / USD Denominated Closing Price'])/(df['ElectricityPrice']*df['Efficiency [J/MH]'])*(1/1000000000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4652acc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['ETC / Max Hash Rate','ETC / Mean Hash Rate']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c68d60bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# create plot\n",
    "ax = df[['ETC / Max Hash Rate','ETC / Mean Hash Rate']].plot(logy=True)\n",
    "\n",
    "# set y-axis label\n",
    "ax.set_ylabel('Value (log scale)')\n",
    "\n",
    "# show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2937feba",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_log = np.log(df[['ETC / Max Hash Rate','ETC / Mean Hash Rate']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f053b44",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_log['ETC Difference'] = df_log['ETC / Max Hash Rate']-df_log['ETC / Mean Hash Rate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b33597b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a plot\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.plot(df_log.index, df_log['ETC Difference'])\n",
    "ax.axvline(pd.Timestamp('2016-08-04'), color='r', linestyle='--')\n",
    "ax.axvline(pd.Timestamp('2017-12-07'), color='r', linestyle='--')\n",
    "ax.axvline(pd.Timestamp('2018-09-01'), color='r', linestyle='--')\n",
    "ax.axvline(pd.Timestamp('2020-05-14'), color='r', linestyle='--')\n",
    "ax.axvline(pd.Timestamp('2020-12-01'), color='r', linestyle='--')\n",
    "ax.axvline(pd.Timestamp('2021-11-01'), color='r', linestyle='--')\n",
    "ax.axhline(0, color='b', linestyle='--')\n",
    "# set x-axis label and title\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_title('Difference of Log Hash Rate')\n",
    "\n",
    "# show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "151af5a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ETC Profatibilty Threshold'] = (df['ETC / USD Denominated Closing Price'] * df['ETC/SEC']) /(df['ETC / Mean Hash Rate']*df['ElectricityPrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "623f26a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ETH Profatibilty Threshold'] = (df['ETH / USD Denominated Closing Price'] * df['ETH/SEC']) /(df['ETH / Mean Hash Rate']*df['ElectricityPrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc545c12",
   "metadata": {},
   "outputs": [],
   "source": [
    "cutoff_date = pd.to_datetime('2022-09-14')\n",
    "df.loc[df.index > cutoff_date, 'ETH Profatibilty Threshold'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e09c5913",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df.loc[df.index > '2020-12-31']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81416ce4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1[['ETH Profatibilty Threshold','ETC Profatibilty Threshold']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3475254",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['ETC / Mean Hash Rate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddf64b36",
   "metadata": {},
   "outputs": [],
   "source": [
    "specific_day = pd.to_datetime('2022-09-14')\n",
    "value_for_specific_day1 = df1.loc[specific_day, 'ETC / Mean Hash Rate']\n",
    "\n",
    "print(value_for_specific_day1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b37d414",
   "metadata": {},
   "outputs": [],
   "source": [
    "specific_day = pd.to_datetime('2022-09-15')\n",
    "value_for_specific_day2 = df1.loc[specific_day, 'ETC / Mean Hash Rate']\n",
    "\n",
    "print(value_for_specific_day2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71a5809c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['ETC / Mean Hash Rate'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "912cc2fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "addition =value_for_specific_day2 - value_for_specific_day1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7ac8352",
   "metadata": {},
   "outputs": [],
   "source": [
    "specific_day = pd.to_datetime('2022-09-14')\n",
    "value_for_specific_day = df1.loc[specific_day, 'ETH / Mean Hash Rate']\n",
    "\n",
    "print(value_for_specific_day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a770002f",
   "metadata": {},
   "outputs": [],
   "source": [
    "addition/value_for_specific_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad0f469c",
   "metadata": {},
   "outputs": [],
   "source": [
    "specific_day = pd.to_datetime('2022-09-15')\n",
    "value_for_specific_day = df1.loc[specific_day, 'ETC Profatibilty Threshold']\n",
    "\n",
    "print(value_for_specific_day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1448b027",
   "metadata": {},
   "outputs": [],
   "source": [
    "specific_day = pd.to_datetime('2022-09-15')\n",
    "value_for_specific_day = df1.loc[specific_day, 'Efficiency [J/MH]']\n",
    "\n",
    "print(value_for_specific_day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a1fbff9",
   "metadata": {},
   "outputs": [],
   "source": [
    "specific_day = pd.to_datetime('2022-09-15')\n",
    "value_for_specific_day = df1.loc[specific_day, 'ETC Profatibilty Threshold']\n",
    "\n",
    "print(value_for_specific_day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59162c68",
   "metadata": {},
   "outputs": [],
   "source": [
    "1/1.963570180464531e-06"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d514a87",
   "metadata": {},
   "outputs": [],
   "source": [
    "509276.4241120352/1000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f941d51d",
   "metadata": {},
   "outputs": [],
   "source": [
    "specific_day = pd.to_datetime('2022-09-14')\n",
    "value_for_specific_day = df1.loc[specific_day, 'ETH Profatibilty Threshold']\n",
    "\n",
    "print(value_for_specific_day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41baa74a",
   "metadata": {},
   "outputs": [],
   "source": [
    "1/2.1110398948871554e-05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95c19d47",
   "metadata": {},
   "outputs": [],
   "source": [
    "47370.01903289253/1000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3eceb91",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df.loc[df.index > '2022-07-31']\n",
    "df1 = df1.loc[df1.index < '2022-12-31']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6eb9c337",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1[['ETH Profatibilty Threshold','ETC Profatibilty Threshold', 'Efficiency [J/MH]']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "0215ea47",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/Users/patrickwoitschig/OneDrive/EconometricProject/EfficienciesETCPost.xlsx'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/_b/28jfj19121qf32rdzd8twc000000gn/T/ipykernel_40579/1218628370.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# read the list of mining hardware and their efficiencies from a Excel file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mminers_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_excel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/Users/patrickwoitschig/OneDrive/EconometricProject/EfficienciesETCPost.xlsx'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# total number of miners\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m                 )\n\u001b[0;32m--> 311\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/pandas/io/excel/_base.py\u001b[0m in \u001b[0;36mread_excel\u001b[0;34m(io, sheet_name, header, names, index_col, usecols, squeeze, dtype, engine, converters, true_values, false_values, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, parse_dates, date_parser, thousands, decimal, comment, skipfooter, convert_float, mangle_dupe_cols, storage_options)\u001b[0m\n\u001b[1;32m    455\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mExcelFile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m         \u001b[0mshould_close\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 457\u001b[0;31m         \u001b[0mio\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mExcelFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstorage_options\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstorage_options\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    458\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    459\u001b[0m         raise ValueError(\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/pandas/io/excel/_base.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, path_or_buffer, engine, storage_options)\u001b[0m\n\u001b[1;32m   1374\u001b[0m                 \u001b[0mext\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"xls\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1375\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1376\u001b[0;31m                 ext = inspect_excel_format(\n\u001b[0m\u001b[1;32m   1377\u001b[0m                     \u001b[0mcontent_or_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstorage_options\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstorage_options\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1378\u001b[0m                 )\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/pandas/io/excel/_base.py\u001b[0m in \u001b[0;36minspect_excel_format\u001b[0;34m(content_or_path, storage_options)\u001b[0m\n\u001b[1;32m   1248\u001b[0m         \u001b[0mcontent_or_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontent_or_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1249\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1250\u001b[0;31m     with get_handle(\n\u001b[0m\u001b[1;32m   1251\u001b[0m         \u001b[0mcontent_or_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstorage_options\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstorage_options\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_text\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1252\u001b[0m     ) as handle:\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    793\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    794\u001b[0m             \u001b[0;31m# Binary mode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 795\u001b[0;31m             \u001b[0mhandle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    796\u001b[0m         \u001b[0mhandles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    797\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/Users/patrickwoitschig/OneDrive/EconometricProject/EfficienciesETCPost.xlsx'"
     ]
    }
   ],
   "source": [
    "import random\n",
    "import pandas as pd\n",
    "\n",
    "# read the list of mining hardware and their efficiencies from a Excel file\n",
    "miners_df = pd.read_excel('/Users/patrickwoitschig/OneDrive/EconometricProject/EfficienciesETCPost.xlsx')\n",
    "\n",
    "# total number of miners\n",
    "num_miners = len(miners_df)\n",
    "\n",
    "# weights for each miner based on the known distribution of hardware efficiencies\n",
    "weights = []\n",
    "for efficiency in miners_df['Efficiency_J/h']:\n",
    "        weights.append(1/ num_miners)\n",
    "\n",
    "# estimate of the total hashrate of the network (in gigahashes per second)\n",
    "total_hashrate = 825506247530000*0.165\n",
    "\n",
    "# number of simulation iterations\n",
    "num_iterations = 1000\n",
    "\n",
    "# list to store the energy consumption estimates\n",
    "energy_consumption = []\n",
    "\n",
    "for i in range(num_iterations):\n",
    "    # generate a sample of mining hardware based on the weighted distribution of hardware efficiencies\n",
    "    miner = miners_df.sample(weights=weights).iloc[0]\n",
    "    # calculate the energy consumption estimate for this miner\n",
    "    energy = total_hashrate * miner['Efficiency_J/h']\n",
    "    energy_consumption.append(energy)\n",
    "\n",
    "# calculate summary statistics of energy consumption estimates\n",
    "mean_energy_low = sum(energy_consumption) / num_iterations\n",
    "median_energy_low = sorted(energy_consumption)[num_iterations // 2]\n",
    "lower_bound_low = sorted(energy_consumption)[int(num_iterations * 0.025)]\n",
    "upper_bound_low = sorted(energy_consumption)[int(num_iterations * 0.975)]\n",
    "\n",
    "# print results\n",
    "print(f\"Mean energy consumption: {mean_energy_low:.2f} J\")\n",
    "print(f\"Median energy consumption: {median_energy_low:.2f} J\")\n",
    "print(f\"95% confidence interval: ({lower_bound_low:.2f} J, {upper_bound_low:.2f} J)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a7e63e8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean energy consumption: 1420302584.11 J\n",
      "Median energy consumption: 878092926.03 J\n",
      "95% confidence interval: (336134787.32 J, 12300996780.98 J)\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "import pandas as pd\n",
    "\n",
    "# read the list of mining hardware and their efficiencies from a Excel file\n",
    "miners_df = pd.read_excel('Group1.xlsx')\n",
    "\n",
    "# total number of miners\n",
    "num_miners = len(miners_df)\n",
    "\n",
    "# weights for each miner based on the known distribution of hardware efficiencies\n",
    "weights = []\n",
    "for efficiency in miners_df['Efficiency_Mh/J']:\n",
    "        weights.append(1/ num_miners)\n",
    "\n",
    "# estimate of the total hashrate of the network (in gigahashes per second)\n",
    "total_hashrate = 200506247530000/1000000\n",
    "\n",
    "# number of simulation iterations\n",
    "num_iterations = 10000\n",
    "\n",
    "# list to store the energy consumption estimates\n",
    "energy_consumption = []\n",
    "\n",
    "for i in range(num_iterations):\n",
    "    # generate a sample of mining hardware based on the weighted distribution of hardware efficiencies\n",
    "    miner = miners_df.sample(weights=weights).iloc[0]\n",
    "    # calculate the energy consumption estimate for this miner\n",
    "    energy = total_hashrate / miner['Efficiency_Mh/J']\n",
    "    energy_consumption.append(energy)\n",
    "\n",
    "# calculate summary statistics of energy consumption estimates\n",
    "mean_energy_high = sum(energy_consumption) / num_iterations\n",
    "median_energy_high = sorted(energy_consumption)[num_iterations // 2]\n",
    "lower_bound_high = sorted(energy_consumption)[int(num_iterations * 0.025)]\n",
    "upper_bound_high = sorted(energy_consumption)[int(num_iterations * 0.975)]\n",
    "\n",
    "# print results\n",
    "print(f\"Mean energy consumption: {mean_energy_high:.2f} J\")\n",
    "print(f\"Median energy consumption: {median_energy_high:.2f} J\")\n",
    "print(f\"95% confidence interval: ({lower_bound_high:.2f} J, {upper_bound_high:.2f} J)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "fe16a6a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.420302584"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1420302584/1000000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "63357031",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean energy consumption: 1261518388.01 J\n",
      "Median energy consumption: 1151189343.54 J\n",
      "95% confidence interval: (628846153.82 J, 3744274808.02 J)\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "import pandas as pd\n",
    "\n",
    "# read the list of mining hardware and their efficiencies from a Excel file\n",
    "miners_df = pd.read_excel('Group2.xlsx')\n",
    "\n",
    "# total number of miners\n",
    "num_miners = len(miners_df)\n",
    "\n",
    "# weights for each miner based on the known distribution of hardware efficiencies\n",
    "weights = []\n",
    "for efficiency in miners_df['Efficiency_Mh/J']:\n",
    "        weights.append(1/ num_miners)\n",
    "\n",
    "# estimate of the total hashrate of the network (in gigahashes per second)\n",
    "total_hashrate = (625506247530000-80506247530000)/1000000\n",
    "\n",
    "# number of simulation iterations\n",
    "num_iterations = 10000\n",
    "\n",
    "# list to store the energy consumption estimates\n",
    "energy_consumption = []\n",
    "\n",
    "for i in range(num_iterations):\n",
    "    # generate a sample of mining hardware based on the weighted distribution of hardware efficiencies\n",
    "    miner = miners_df.sample(weights=weights).iloc[0]\n",
    "    # calculate the energy consumption estimate for this miner\n",
    "    energy = total_hashrate / miner['Efficiency_Mh/J']\n",
    "    energy_consumption.append(energy)\n",
    "\n",
    "# calculate summary statistics of energy consumption estimates\n",
    "mean_energy_high = sum(energy_consumption) / num_iterations\n",
    "median_energy_high = sorted(energy_consumption)[num_iterations // 2]\n",
    "lower_bound_high = sorted(energy_consumption)[int(num_iterations * 0.025)]\n",
    "upper_bound_high = sorted(energy_consumption)[int(num_iterations * 0.975)]\n",
    "\n",
    "# print results\n",
    "print(f\"Mean energy consumption: {mean_energy_high:.2f} J\")\n",
    "print(f\"Median energy consumption: {median_energy_high:.2f} J\")\n",
    "print(f\"95% confidence interval: ({lower_bound_high:.2f} J, {upper_bound_high:.2f} J)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "9a674476",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.261518388"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1261518388/1000000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c1769da2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean energy consumption: 52979410.22 J\n",
      "Median energy consumption: 46693623.57 J\n",
      "95% confidence interval: (37156729.62 J, 77631024.41 J)\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "import pandas as pd\n",
    "\n",
    "# read the list of mining hardware and their efficiencies from a Excel file\n",
    "miners_df = pd.read_excel('Group3.xlsx')\n",
    "\n",
    "# total number of miners\n",
    "num_miners = len(miners_df)\n",
    "\n",
    "# weights for each miner based on the known distribution of hardware efficiencies\n",
    "weights = []\n",
    "for efficiency in miners_df['Efficiency_Mh/J']:\n",
    "        weights.append(1/ num_miners)\n",
    "\n",
    "# estimate of the total hashrate of the network (in gigahashes per second)\n",
    "total_hashrate = 80506247530000/1000000\n",
    "\n",
    "# number of simulation iterations\n",
    "num_iterations = 10000\n",
    "\n",
    "# list to store the energy consumption estimates\n",
    "energy_consumption = []\n",
    "\n",
    "for i in range(num_iterations):\n",
    "    # generate a sample of mining hardware based on the weighted distribution of hardware efficiencies\n",
    "    miner = miners_df.sample(weights=weights).iloc[0]\n",
    "    # calculate the energy consumption estimate for this miner\n",
    "    energy = total_hashrate / miner['Efficiency_Mh/J']\n",
    "    energy_consumption.append(energy)\n",
    "\n",
    "# calculate summary statistics of energy consumption estimates\n",
    "mean_energy_high = sum(energy_consumption) / num_iterations\n",
    "median_energy_high = sorted(energy_consumption)[num_iterations // 2]\n",
    "lower_bound_high = sorted(energy_consumption)[int(num_iterations * 0.025)]\n",
    "upper_bound_high = sorted(energy_consumption)[int(num_iterations * 0.975)]\n",
    "\n",
    "# print results\n",
    "print(f\"Mean energy consumption: {mean_energy_high:.2f} J\")\n",
    "print(f\"Median energy consumption: {median_energy_high:.2f} J\")\n",
    "print(f\"95% confidence interval: ({lower_bound_high:.2f} J, {upper_bound_high:.2f} J)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "cbc99d81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05297941"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "52979410/1000000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "77d0f3e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.6493784030826233"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(1.420302584+1.261518388+0.05297941)/(0.9/(1.03*1.1*1.06))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "413bec5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.420776237614119e-15"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3.6493784030826233/825506247530000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "c3eb35a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "226204.6179789677"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1/4.420776237614119e-06"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "e762c6dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2262046179789677"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "226204.6179789677/1000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e5211d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "(mean_energy_high+ mean_energy_low)/1000000000\n",
    "(lower_bound_low+lower_bound_high)/1000000000\n",
    "(upper_bound_low+upper_bound_high)/1000000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbfaca94",
   "metadata": {},
   "outputs": [],
   "source": [
    "(lower_bound_low+lower_bound_high)/1000000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f62d6440",
   "metadata": {},
   "outputs": [],
   "source": [
    "(upper_bound_low+upper_bound_high)/1000000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abd03d17",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_excel('Ethereum_More_Data.xlsx', index_col=0) \n",
    "df.index = pd.to_datetime(df.index)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7c80a05",
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading the data from an excel file\n",
    "df = pd.read_excel('/Users/patrickwoitschig/Documents/Coin_Metrics_Network_Data_2023-03-12T16-04.xlsx', index_col=0) \n",
    "df.index = pd.to_datetime(df.index)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5756b8bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68117d3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df = df.resample('W').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edb52cda",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "df['ETH / USD Denominated Closing Price'] = np.log(df['ETH / USD Denominated Closing Price']).diff()\n",
    "df['ETH / Mean Hash Rate'] = np.log(df['ETH / Mean Hash Rate']).diff()\n",
    "df['ETH / Mean Tx Size (native units)'] = np.log(df['ETH / Mean Tx Size (native units)']).diff()\n",
    "df['ETH / Active Addr Cnt'] = np.log(df['ETH / Active Addr Cnt']).diff()\n",
    "df['ETH / Tx per Second Cnt'] = np.log(df['ETH / Tx per Second Cnt']).diff()\n",
    "df['ETH / Mean Block Size (in bytes)'] = np.log(df['ETH / Mean Block Size (in bytes)']).diff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4996738d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[df.index > '2017-12-31']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed9c7a47",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[df.index < '2021-11-01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f3395f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# select a subset of the columns\n",
    "cols = ['ETH / Mean Hash Rate','ETH / USD Denominated Closing Price']\n",
    "data_subset = df[cols]\n",
    "\n",
    "# create a VAR model with two lags\n",
    "model = sm.tsa.VAR(data_subset)\n",
    "results = model.fit(14)\n",
    "\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89aba6be",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.optimize import minimize\n",
    "\n",
    "# Define the hash rates for each type of hardware\n",
    "hash_rates = np.array([30, 50, 70, 90, 110]) # TH/s\n",
    "\n",
    "# Define the total hash rate\n",
    "total_hash_rate = 100000000 # TH/s\n",
    "\n",
    "# Define the negative log-likelihood function\n",
    "def neg_log_likelihood(n, hash_rates, total_hash_rate):\n",
    "    # Calculate the total hash rate based on the numbers of each type of hardware\n",
    "    calculated_hash_rate = np.sum(n * hash_rates)\n",
    "    \n",
    "    # Calculate the negative log-likelihood\n",
    "    neg_ll = -np.log(calculated_hash_rate)\n",
    "    \n",
    "    return neg_ll\n",
    "\n",
    "# Initial guess for the numbers of each type of hardware\n",
    "n0 = np.ones_like(hash_rates) * (total_hash_rate / np.sum(hash_rates))\n",
    "\n",
    "# Constraints: the numbers of hardware must be non-negative and sum up to the total hash rate\n",
    "constraints = (\n",
    "    {'type': 'eq', 'fun': lambda n: np.sum(n * hash_rates) - total_hash_rate},\n",
    "    {'type': 'ineq', 'fun': lambda n: n}\n",
    ")\n",
    "\n",
    "# Minimize the negative log-likelihood function\n",
    "res = minimize(neg_log_likelihood, n0, args=(hash_rates, total_hash_rate), constraints=constraints)\n",
    "\n",
    "# Print the estimated numbers of each type of hardware\n",
    "print(res.x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "5198d1a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[53838.41382963 53838.41382963 53838.41382963 53838.41382963\n",
      " 53838.41382963 53838.41382963 53838.41382963 53838.41382963\n",
      " 53838.41382963 53838.41382963 53838.41382963 53838.41382963\n",
      " 53838.41382963 53838.41382963 53838.41382963 53838.41382963\n",
      " 53838.41382963 53838.41382963 53838.41382963 53838.41382963\n",
      " 53838.41382963 53838.41382963 53838.41382963 53838.41382963\n",
      " 53838.41382963 53838.41382963 53838.41382963 53838.41382963\n",
      " 53838.41382963 53838.41382963 53838.41382963 53838.41382963\n",
      " 53838.41382963 53838.41382963 53838.41382963 53838.41382963\n",
      " 53838.41382963 53838.41382963 53838.41382963 53838.41382963\n",
      " 53838.41382963 53838.41382963 53838.41382963 53838.41382963\n",
      " 53838.41382963 53838.41382963 53838.41382963 53838.41382963\n",
      " 53838.41382963 53838.41382963 53838.41382963 53838.41382963\n",
      " 53838.41382963 53838.41382963 53838.41382963 53838.41382963\n",
      " 53838.41382963 53838.41382963 53838.41382963 53838.41382963\n",
      " 53838.41382963 53838.41382963 53838.41382963 53838.41382963\n",
      " 53838.41382963 53838.41382963 53838.41382963 53838.41382963\n",
      " 53838.41382963 53838.41382963 53838.41382963 53838.41382963\n",
      " 53838.41382963 53838.41382963 53838.41382963 53838.41382963\n",
      " 53838.41382963 53838.41382963 53838.41382963 53838.41382963\n",
      " 53838.41382963 53838.41382963 53838.41382963 53838.41382963\n",
      " 53838.41382963 53838.41382963 53838.41382963 53838.41382963\n",
      " 53838.41382963 53838.41382963]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.optimize import minimize\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "# Load the efficiencies from an Excel file\n",
    "df = pd.read_excel('EthereumHardwareGPU.xlsx')\n",
    "\n",
    "# Assuming the efficiencies are in a column named 'efficiency',\n",
    "# convert the efficiencies to a numpy array\n",
    "efficiencies = df['Efficiency_Mh/J'].values\n",
    "\n",
    "hash_rates = df['Hashing power (Mh/s)'].values\n",
    "\n",
    "# Define the total hash rate\n",
    "total_hash_rate = 225000000\n",
    "\n",
    "# Define the efficiency thresholds\n",
    "efficiency_threshold_etc = 1.0 # Efficiency threshold for Ethereum Classic\n",
    "efficiency_threshold_profitability = 0.30 # Profitability threshold\n",
    "\n",
    "# Define the percentage of the hash rate that comes from hardware with an efficiency equal to or better than the Ethereum Classic threshold\n",
    "percentage_etc = 0 # Example value\n",
    "\n",
    "# Define the negative log-likelihood function\n",
    "def neg_log_likelihood(n, hash_rates, total_hash_rate):\n",
    "    # Calculate the total hash rate based on the numbers of each type of hardware\n",
    "    calculated_hash_rate = np.sum(n * hash_rates)\n",
    "    \n",
    "    # Calculate the negative log-likelihood\n",
    "    neg_ll = -np.log(calculated_hash_rate)\n",
    "    \n",
    "    return neg_ll\n",
    "\n",
    "# Initial guess for the numbers of each type of hardware\n",
    "n0 = np.ones_like(hash_rates) * (total_hash_rate / np.sum(hash_rates))\n",
    "\n",
    "# Constraints: \n",
    "# - the numbers of hardware must be non-negative \n",
    "# - the total hash rate must be equal to the observed hash rate\n",
    "# - the hash rate from hardware with an efficiency equal to or better than the Ethereum Classic threshold must be equal to the specified percentage\n",
    "# - the rest of the hash rate must come from hardware with an efficiency at least as good as the profitability threshold\n",
    "constraints = (\n",
    "    {'type': 'eq', 'fun': lambda n: np.sum(n * hash_rates) - total_hash_rate},\n",
    "    {'type': 'eq', 'fun': lambda n: np.sum(n[efficiencies >= efficiency_threshold_etc] * hash_rates[efficiencies >= efficiency_threshold_etc]) - percentage_etc * total_hash_rate},\n",
    "    {'type': 'ineq', 'fun': lambda n: np.sum(n[efficiencies < efficiency_threshold_etc] * hash_rates[efficiencies < efficiency_threshold_etc]) - (1 - percentage_etc) * total_hash_rate},\n",
    "    {'type': 'ineq', 'fun': lambda n: n}\n",
    ")\n",
    "\n",
    "# Minimize the negative log-likelihood function\n",
    "res = minimize(neg_log_likelihood, n0, args=(hash_rates, total_hash_rate), constraints=constraints)\n",
    "\n",
    "# Print the estimated numbers of each type of hardware\n",
    "print(res.x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "8a29b330",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "692846547.5671997\n"
     ]
    }
   ],
   "source": [
    "# Calculate the energy consumption of each type of hardware (in joules per second, or watts)\n",
    "energy_consumptions = res.x * hash_rates / efficiencies\n",
    "\n",
    "# Calculate the total energy consumption of the network (in watts)\n",
    "total_energy_consumption = np.sum(energy_consumptions)\n",
    "\n",
    "# Print the total energy consumption\n",
    "print(total_energy_consumption)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "6ffe541e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "224999999.99999997"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = res.x * hash_rates\n",
    "total = np.sum(total)\n",
    "total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fabc0f64",
   "metadata": {},
   "outputs": [],
   "source": [
    "692846547/1000000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "288df9e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "26666666666/1000000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62dd2d9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "800000000/0.07"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5bde20d",
   "metadata": {},
   "outputs": [],
   "source": [
    "11428571428/1000000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c03b60fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "825000000/0.06"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d94baf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "13750000000/1000000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c36de6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "520000000/0.45+225000000/0.27+800000000/1.35"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "692d8937",
   "metadata": {},
   "outputs": [],
   "source": [
    "2581481481/1000000000*(1.1*1.06*1.03/0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adeb5d19",
   "metadata": {},
   "outputs": [],
   "source": [
    "225000000/0.27"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9e77388",
   "metadata": {},
   "outputs": [],
   "source": [
    "833333333/1000000000"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee05624e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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