{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Motivation & Project Description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The Colorado Avalanche Information Center (CAIC) uses SNOWPACK, a land and snow surface model, to evaluate snow layers and predict avalanches. They currenty use High Resolution Rapid Refresh (HRRR) data from the National Oceanic and Atmospheric Administration (NOAA) for model inputs, but the 3km resolution is relatively imprecise; namely, snow depth data at that resolution are not as useful for finer forecasting as lower resolution data.\n",
    "\n",
    "The Natural Resources Conservation Service (NRCS) has hundreds of Snow Telemetry (SnoTel) weather reporting sites in place across the country, and they record various weather values on an hourly basis; these include precipitation, temperature, wind speed, and--the focus of our project--snow depth. These data are not perfect, however, as the ultrasonic depth sensors are sensitive to factors such as wind, obstruction, and surfaces that are poor reflectors of sound (such as low density snow). \n",
    "\n",
    "Our goal is to provide the CAIC with a more accurate estimate of the true snow depth at any given hour by incorporating and modifying the methods laid out by previous researchers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-13T05:12:12.242600Z",
     "start_time": "2021-01-13T05:12:12.233228Z"
    },
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "outputs": [],
   "source": [
    "# Libraries - Pandas for data wrangling; Numpy for math; Matplotlib for visualization\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-13T05:12:23.480945Z",
     "start_time": "2021-01-13T05:12:23.445786Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
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       "      <th>Date</th>\n",
       "      <th>Wolf Creek Summit (874) Snow Depth (in)</th>\n",
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       "<p>4368 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "                 Date  Wolf Creek Summit (874) Snow Depth (in)  \\\n",
       "0      10/1/2018 0:00                                      0.0   \n",
       "1      10/1/2018 1:00                                      0.0   \n",
       "2      10/1/2018 2:00                                      0.0   \n",
       "3      10/1/2018 3:00                                      0.0   \n",
       "4      10/1/2018 4:00                                      0.0   \n",
       "...               ...                                      ...   \n",
       "4363  3/31/2019 19:00                                    131.0   \n",
       "4364  3/31/2019 20:00                                    131.0   \n",
       "4365  3/31/2019 21:00                                    130.0   \n",
       "4366  3/31/2019 22:00                                    130.0   \n",
       "4367  3/31/2019 23:00                                    130.0   \n",
       "\n",
       "      Wolf Creek Summit (874) Change In Snow Depth (in)  \\\n",
       "0                                                   0.0   \n",
       "1                                                   0.0   \n",
       "2                                                   0.0   \n",
       "3                                                   0.0   \n",
       "4                                                   0.0   \n",
       "...                                                 ...   \n",
       "4363                                                0.0   \n",
       "4364                                                0.0   \n",
       "4365                                               -1.0   \n",
       "4366                                                0.0   \n",
       "4367                                                0.0   \n",
       "\n",
       "      Wolf Creek Summit (874) Snow Water Equivalent (in)  \\\n",
       "0                                                   0.0    \n",
       "1                                                   0.1    \n",
       "2                                                   0.1    \n",
       "3                                                   0.1    \n",
       "4                                                   0.1    \n",
       "...                                                 ...    \n",
       "4363                                               46.4    \n",
       "4364                                               46.4    \n",
       "4365                                               46.4    \n",
       "4366                                               46.4    \n",
       "4367                                               46.4    \n",
       "\n",
       "      Wolf Creek Summit (874) Change In Snow Water Equivalent (in)  \n",
       "0                                                  -0.1             \n",
       "1                                                   0.1             \n",
       "2                                                   0.0             \n",
       "3                                                   0.0             \n",
       "4                                                   0.0             \n",
       "...                                                 ...             \n",
       "4363                                                0.0             \n",
       "4364                                                0.0             \n",
       "4365                                                0.0             \n",
       "4366                                                0.0             \n",
       "4367                                                0.0             \n",
       "\n",
       "[4368 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load CSV into DataFrame\n",
    "WolfCreekSummit2018 = pd.read_csv('WolfCreekSummit2018.csv', ',', header=0)\n",
    "WolfCreekSummit2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-13T05:12:29.858960Z",
     "start_time": "2021-01-13T05:12:29.842441Z"
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   },
   "outputs": [
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      ],
      "text/plain": [
       "                 Date  Wolf Creek Summit (874) Snow Depth (in)  \\\n",
       "0      10/1/2018 0:00                                      0.0   \n",
       "1      10/1/2018 1:00                                      0.0   \n",
       "2      10/1/2018 2:00                                      0.0   \n",
       "3      10/1/2018 3:00                                      0.0   \n",
       "4      10/1/2018 4:00                                      0.0   \n",
       "...               ...                                      ...   \n",
       "4363  3/31/2019 19:00                                    131.0   \n",
       "4364  3/31/2019 20:00                                    131.0   \n",
       "4365  3/31/2019 21:00                                    130.0   \n",
       "4366  3/31/2019 22:00                                    130.0   \n",
       "4367  3/31/2019 23:00                                    130.0   \n",
       "\n",
       "      Wolf Creek Summit (874) Change In Snow Depth (in)  \\\n",
       "0                                                   0.0   \n",
       "1                                                   0.0   \n",
       "2                                                   0.0   \n",
       "3                                                   0.0   \n",
       "4                                                   0.0   \n",
       "...                                                 ...   \n",
       "4363                                                0.0   \n",
       "4364                                                0.0   \n",
       "4365                                               -1.0   \n",
       "4366                                                0.0   \n",
       "4367                                                0.0   \n",
       "\n",
       "      Wolf Creek Summit (874) Snow Water Equivalent (in)  \\\n",
       "0                                                   0.0    \n",
       "1                                                   0.1    \n",
       "2                                                   0.1    \n",
       "3                                                   0.1    \n",
       "4                                                   0.1    \n",
       "...                                                 ...    \n",
       "4363                                               46.4    \n",
       "4364                                               46.4    \n",
       "4365                                               46.4    \n",
       "4366                                               46.4    \n",
       "4367                                               46.4    \n",
       "\n",
       "      Wolf Creek Summit (874) Change In Snow Water Equivalent (in)  \n",
       "0                                                  -0.1             \n",
       "1                                                   0.1             \n",
       "2                                                   0.0             \n",
       "3                                                   0.0             \n",
       "4                                                   0.0             \n",
       "...                                                 ...             \n",
       "4363                                                0.0             \n",
       "4364                                                0.0             \n",
       "4365                                                0.0             \n",
       "4366                                                0.0             \n",
       "4367                                                0.0             \n",
       "\n",
       "[4368 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WolfCreekSummit2018"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Here we have taken all snow depth values for the snow year to date and loaded them into our DataFrame. In practice, this will be performed on a daily/weekly basis starting in October when the snow season begins. \n",
    "\n",
    "Now we can apply Avanzi et al.'s methods for data cleanup. They are:\n",
    "\n",
    "1) Missing data filling - forward-fill zero and NaN entries surrounded by non-zero entries \n",
    "\n",
    "2) Out of range peaks removal - replace entries greater than determined maximum depth with maximum depth\n",
    "\n",
    "3) Summer filter - only dates between October and June are read\n",
    "\n",
    "4) Variations check - replace entries that have increased beyond maximum hourly increase with previous value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-13T05:12:34.655655Z",
     "start_time": "2021-01-13T05:12:34.635292Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Code to apply Avanzi et al.'s methods\n",
    "def dataClean(csv,stationID):\n",
    "    stationData = pd.read_csv(csv, ',', header=0)\n",
    "    \n",
    "    maxDepth = 240\n",
    "    maxHourly = 10\n",
    "    \n",
    "    # Check station ID to assign proper column to depthColumn variable. The way the report is generated makes this step\n",
    "    # a hassle. For now, this seems like the easiest way. Additional stationIDs can be added as desired.\n",
    "    if stationID == 629:\n",
    "        depthColumn = stationData['Mineral Creek (629) Snow Depth (in)'] \n",
    "    elif stationID == 842:\n",
    "        depthColumn = stationData['Vail Mountain (842) Snow Depth (in)']\n",
    "    elif stationID == 335:\n",
    "        depthColumn = stationData['Berthoud Summit (335) Snow Depth (in)']\n",
    "    elif stationID == 874:\n",
    "        depthColumn = stationData['Wolf Creek Summit (874) Snow Depth (in)']\n",
    "    \n",
    "    replaceMaxDepth = depthColumn.where(depthColumn < maxDepth, other=0)\n",
    "    stationData.insert(loc = 2, column = 'Fixed Depth', value = replaceMaxDepth)\n",
    "    \n",
    "    replaceVariations = stationData['Fixed Depth'].where((stationData['Fixed Depth'] - stationData['Fixed Depth'].shift(1)) \n",
    "                                                          <= maxHourly, other=0)\n",
    "    stationData['Fixed Depth'] = replaceVariations\n",
    "    \n",
    "    fillDepthZeros = stationData['Fixed Depth'].replace(to_replace=[0, -1, np.nan], method='ffill')\n",
    "    stationData['Fixed Depth'] = fillDepthZeros\n",
    "    \n",
    "    return stationData"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Run it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-13T05:12:39.090004Z",
     "start_time": "2021-01-13T05:12:39.055590Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Save output in a variable for later use   \n",
    "cleanData = dataClean(\"WolfCreekSummit2018.csv\",874)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Example of Data Post Cleaning:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src=\"avanziExample.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Now that our data are cleaned up, we can do math."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### The Kalman Filter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The Kalman Filter is a recursive solution to the linear filtering of discrete data; it uses the linear stochastic difference equation to estimate the state, $x$, of a discrete-time controlled system. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Before applying the filter to our data, we will perform a test to make sure our implementation works correctly in an environment we can control. We pick a point, $x$, and create 50 random observations. Then we run 50 iterations of the filter using those observations. If the filter is working as desired, two things will happen: the Kalman estimations approach the $x$ value we chose, and the error covariance will approach zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-13T05:15:26.146507Z",
     "start_time": "2021-01-13T05:15:25.758933Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+HLjDzP6TKPG9JteLm9kFRHsxTJs2bXBrUIBUXaPOcYhIrBRzj8NylGV3I9tXnXxtPwhc7O5TgYuBH+V6cXe/2t0XuPuCpqamAkMeuFRto66qEpFYKWbiaAamZkxP4eWHlfqqk6/tecCvw/gviQ5rlUxSJ8dFJGaKmTiWArPMbIaZJYGzgMVZdRYD54arq44Ftrn7un7avgj8Qxg/EXi2iOvQLx2qEpG4Kdo5DnfvNrOLgDuABHCNuz9pZheG+VcBtwOnASuBduC9+dqGRb8f+I6ZVQK7COcxSiVV30hHeyueTmMVui1GRMpfMU+O4+63EyWHzLKrMsYd+HChbUP5n4FXDW+kg5eqawR3Onfu2N0FiYhIOdNP5CHa07X6thJHIiIyMpQ4hkjP5BCRuFHiGKJUrRKHiMSLEscQaY9DROJGiWOIUvV6mJOIxIsSxxBpj0NE4kaJY4iSvYmjTYlDROJBiWOIKpPVVFRW0dGuxCEi8aDEMUR6mJOIxI0SxzBQ4hCROFHiGAZKHCISJ0ocwyDqIbe11GGIiIwIJY5hkKprZFeb+qoSkXhQ4hgGST2TQ0RiRIljGKTqGnU5rojEhhLHMEjVNdDZ3ka6p6fUoYiIFJ0SxzBI1Y0BoHNnW4kjEREpPiWOYaD+qkQkTpQ4hsHuxKErq0QkBgpKHGZWY2azix3MaKU9DhGJk34Th5m9GXgM+F2YPsLMFhc7sNEkWdcA6JkcIhIPhexxXAYsBLYCuPtjwPTihTT6aI9DROKkkMTR7e46eJ9H71VVHe3qdkREyl9lAXWeMLN3AQkzmwV8FLi/uGGNLtrjEJE4KWSP4yPAfKADuAHYBny8mEGNNpXJFImqlK6qEpFY6HePw93bgc+GQfqgrtVFJC4KuapqiZmNzZgeZ2Z3FDes0SdV16DEISKxUMihqonuvrV3wt23APsVL6TRKVnXSIeeySEiMVBI4kib2bTeCTM7CPDihTQ6VdeNoVM95IpIDBRyVdVngT+b2T1h+nXABcULaXRK1jXStnl9qcMQESm6Qk6O/87MjgKOBQy42N03FT2yUSZV16irqkQkFgrZ4wBIAZtD/XlmhrvfW7ywRp/o5LjOcYhI+es3cZjZ14EzgSeBdCh2QIkjQ6quka5dO0j3dFORKDQfi4iMPoV8w70FmO3uHcUOZjTbc/d4KzWN40ocjYhI8RRyVdUqoGowCzezU8zsaTNbaWaX5phvZnZFmL8snEvpt62ZfSTMe9LMvjGY2IZbqj70V6V7OUSkzBWyx9EOPGZmdxF1OwKAu380XyMzSwDfA04GmoGlZrbY3ZdnVDsVmBWGY4ArgWPytTWz1wOLgMPcvcPM9ol7SlK10R6HLskVkXJXSOJYHIaBWgisdPdVAGb2c6Iv/MzEsQi4zt0deMDMxprZJKJu2/tq+0Hg8t5DZ+6+cRCxDbtUeCbHLl1ZJSJlrpDLcX8yyGVPBtZmTDcT7VX0V2dyP20PAY43s68Cu4BPuvvS7Bc3swsI95tMmzYte/awS4ZzHHqYk4iUu0L6qpplZr8ys+Vmtqp3KGDZlqMs+47zvurka1sJjCO6r+RTwE1m9rL67n61uy9w9wVNTU0FhDs0mSfHRUTKWSEnx39MdO6hG3g9cB3w0wLaNQNTM6anAC8WWCdf22bg1x55iOgS4YkFxFNUeiaHiMRFIYmjxt3vAszdn3P3y4ATC2i3FJhlZjPMLAmcxcvPlSwGzg1XVx0LbHP3df20vbX39c3sECAJlPxOdiUOEYmLQk6O7zKzCuBZM7sIeIECesd19+5Q/w4gAVzj7k+a2YVh/lXA7cBpwEqiq7fem69tWPQ1wDVm9gTQCZwXTq6XVKIqSWWqRolDRMpeIYnj40At0SNjv0z0a/+8Qhbu7rcTJYfMsqsyxh34cKFtQ3kn8O5CXn+kRd2O6KoqESlvhVxV1XvFUhthj0ByS9XqmRwiUv76TBxm9m13/7iZ/YYcz99w99OLGtkolKxr1OW4IlL28u1x9F459Z8jEUg50HPHRSQO+kwc7v5I6Prj/e6+T55T2Nek6sfQ2vJCqcMQESmqvJfjunsP0BQuiZV+RCfHtcchIuWtkKuq1gD3mdliYEdvobv/V7GCGq10qEpE4qCQxPFiGCqAhuKGM7qlahvp7thJT3cXicpB9UQvIrLPK+Ry3C+ORCDlIPPu8doxE0ocjYhIcRTy6Ngm4BJgPlDdW+7uhXQ7EiuZPeQqcYhIuSqkr6rrgaeAGcAXic55vKwbc4HqevVXJSLlr5DEMcHdfwR0ufs97n4+UZfmkqV3j6NDD3MSkTJWyMnxrvB3nZm9iehE+ZTihTR66ZkcIhIHhSSOr5jZGOATwH8DjcDFRY1qlNqdOPTccREpY4UkjgfdfRuwjehBTtIHPZNDROKgkHMc95vZnWb2PjMbV/SIRrFkbXSbixKHiJSzfhOHu88CPkd0Oe4jZnabmanvqhwSlVVUVdcpcYhIWStkjwN3f8jd/xVYCGwGflLUqEaxmjHjad+8sdRhiIgUTb+Jw8wazew8M/stcD+wjiiBSA4Tps1m03NPlToMEZGiKeTk+OPArcCX3P0vRY5n1Js4Yy7P/fUe9VclImWrkMQx093dzBrMrN7d24oe1SjWNGMe6e4utjT/nYnT55Q6HBGRYVfIOY75ZvZX4AlguZk9YmaHFjmuUatpxjwAWlY/WeJIRESKo5DEcTXwr+5+kLtPI7oR8OrihjV6jZ86i4pEJZtWryh1KCIiRVFI4qhz9z/2Trj73UBd0SIa5RJVScZNOZiW1ctLHYqISFEUkjhWmdnnzWx6GD4HrC52YKNZ08x5bFqjPQ4RKU+FJI7zgSbg12GYCLy3mEGNdk3T57F9w1rdCCgiZanPq6rMrBpocPcW4KMZ5fsDO0cgtlFr4oy5AGxas4LJ848pcTQiIsMr3x7HFcDxOcpPAr5VnHDKw8TdV1bpPIeIlJ98ieM4d/91dqG7Xw+8rnghjX6N+00hWdugK6tEpCzlSxw2yHaxZ2Y0zZjHJu1xiEgZypcANprZy/qkMrOjgZbihVQeJs6YS8vq5bh7qUMRERlW+boc+RRwk5ldCzwSyhYA5wJnFTmuUW/i9Hl07LiW1pYXadxvcqnDEREZNn3ucbj7Q0S94Brwz2Ew4Bh3f3AkghvNmmZGJ8g3rdHhKhEpL3k7OXT3jcC/j1AsZWXi9OiS3JbVK5i58OQSRyMiMnx0krtIquvH0NA0mU2r1NmhiJQXJY4imjhjLi3qekREykzexGFmCTP75mAXbmanmNnTZrbSzC7NMd/M7Iowf5mZHTWAtp80MzeziYONr9iaZsxn89pn6enuKnUoIiLDJm/icPce4FVmlu+ejpzMLAF8DzgVmAecbWbzsqqdCswKwwXAlYW0NbOpwMnA8wONayQ1zZhLuruLzWtXljoUEZFhU8ihqr8C/2tm7zGzt/UOBbRbCKx091Xu3gn8HFiUVWcRcJ1HHgDGmtmkAtp+C7gE2KdvkujtemSTHuokImWkkEfHjgdeAk7MKHOinnLzmQyszZhuBrJ7/MtVZ3K+tmZ2OvCCuz+eb0fIzC4g2oth2rRp/YRaHOOnHExFopKW1SuYW5IIRESGX7+Jw90H24V6rm/17D2EvurkLDezWuCzwBv6e3F3v5rwpMIFCxaUZM8kUZVk/LRDdC+HiJSVfg9VmdkUM7vFzDaa2QYzu9nMphSw7GZgasb0FODFAuv0Vf4KYAbwuJmtCeWPmtkBBcRTEk3T59Kizg5FpIwUco7jx8Bi4ECiQ0i/CWX9WQrMMrMZZpYk6qZkcVadxcC54eqqY4Ft7r6ur7bu/jd338/dp7v7dKIEc5S7ry8gnpKYOGMerRub2dW2rdShiIgMi0ISR5O7/9jdu8NwLdETAfNy927gIuAOYAVwk7s/aWYXmtmFodrtwCpgJfAD4EP52g5s1fYNmQ91EhEpB4WcHN9kZu8GbgzTZxOdLO+Xu99OlBwyy67KGHfgw4W2zVFneiFxlFLTjPkAbFq9nCmHHlviaEREhq7QZ46/E1gPrAPeHsqkAA1NB5Kqa9R5DhEpG3n3OMKNeGe4++kjFE/ZMTMmzpinQ1UiUjYKuXM8+6Y9GaCJ0+eySQ91EpEyUcihqvvM7LtmdryZHdU7FD2yMtI0cx4dO7bT2vJCqUMRERmyQk6Ovyb8/VJGmbP3neSSR1PoeqRl9XIa9yvkFhgRkX1Xf+c4KoAr3f2mEYqnLPU+1GnT6hW84ph+b3oXEdmn9XeOI010P4UMQaqukYb9ptCizg5FpAwUco5jSXj2xVQzG987FD2yMtM0Yx6bdEmuiJSBQs5x9N6zkXmjngMzhz+c8nXA7CNZvfT3tL20nvoJ+2zXWiIi/ep3j8PdZ+QYlDQGaPbr3oKn0zx9z62lDkVEZEj6TBxmdknG+Duy5n2tmEGVownTZrH/wYex4o83lzoUEZEhybfHcVbG+Kez5p1ShFjK3pwTz2D9039lywt/L3UoIiKDli9xWB/juaalAHNOeBuYseIP2usQkdErX+LwPsZzTUsBGiZOYuphr2XFH36l7kdEZNTKlzgON7PtZtYKHBbGe6dfOULxlZ25J57BlhdWseHZx0odiojIoPSZONw94e6N7t7g7pVhvHe6aiSDLCeHHPdmElVJHa4SkVGrkBsAZRhVN4xlxtEn8dTdt5Du6Sl1OCIiA6bEUQJzT3w7OzZvYO2y+0odiojIgClxlMDMY04mWVvPij/8qtShiIgMmBJHCVSlaph13Jt55k+/obtzV6nDEREZECWOEpn7+jPobG9l1YNLSh2KiMiAKHGUyLQjjqN23H7qgkRERh0ljhKpSFQy54S3surBO9nVtq3U4YiIFEyJo4Tmvv4Mero6efbPt5U6FBGRgilxlNABs49k7IEzdHWViIwqShwlZGbMPfEMnn/8z7RuWlfqcERECqLEUWLzTzqTiooEf/nZN0sdiohIQZQ4SmzsgTM4ctG/sOy3P2X9M+r4UET2fUoc+4DXvOcSasc28YfvfxpPp0sdjohIXkoc+4BUXSOve9/neXH5Upbf9ctShyMikpcSxz5i/klnMmnuAu754Rfp2NFa6nBERPqkxLGPsIoKTvzQ12jf2sJfrteJchHZdylx7EMmzT6KV77xXTx6y9W89PwzpQ5HRCQnJY59zHHv/RxV1bX84fuf0XPJRWSfVNTEYWanmNnTZrbSzC7NMd/M7Iowf5mZHdVfWzP7ppk9FerfYmZji7kOI61uXBOvPfdSnnv0blbed3upwxEReZmiJQ4zSwDfA04F5gFnm9m8rGqnArPCcAFwZQFtlwCHuvthwDPAp4u1DqVyxOnnM3H6XP74P5+nq2NnqcMREdlLMfc4FgIr3X2Vu3cCPwcWZdVZBFznkQeAsWY2KV9bd7/T3btD+weAKUVch5KoSFRy4of/g+0bnuf+n36j1OGIiOylmIljMrA2Y7o5lBVSp5C2AOcDv8314mZ2gZk9bGYPt7S0DDD00pt2+HEcdtq5LL3pv1n225+WOhwRkd2KmTgsR1n22d6+6vTb1sw+C3QD1+d6cXe/2t0XuPuCpqamAsLd9/zjRV9n+oJ/ZMl3Psmqh/SkQBHZNxQzcTQDUzOmpwAvFlgnb1szOw/4J+AcL+NLjxKVVZz++R+x38z5LP7y+1j39KOlDklEpKiJYykwy8xmmFkSOAtYnFVnMXBuuLrqWGCbu6/L19bMTgH+DTjd3duLGP8+IVlTz9u+ciO1Yyfy68+9iy0vrCp1SCISc0VLHOEE9kXAHcAK4CZ3f9LMLjSzC0O124FVwErgB8CH8rUNbb4LNABLzOwxM7uqWOuwr6gbvz9v/9ovwNPc/Nkzad+6qdQhiUiMWRkf6dltwYIF/vDDD5c6jCF7cflSbrrkrUycMY93fuMWkjV1pQ5JRMqYmT3i7guyy3Xn+Chy4LyjedOnr2bDs4/xm6+8j86dO0odkojEkBLHKDPrtadx0ke+yeqH7+LGi09j67o1pQ5JRGJGiWMUOvxN53HGV3/B9o3N/Oyik3nu0XtKHZKIxIgSxyg1Y8GJvPu/l1A3fn9+9Zl3sPSX31WniCIyIpQ4RrFxky6fCewAAA2jSURBVGdyzhW/4+DXvIl7fnAZ/3f5B+jaVfZXKItIiSlxjHLJmnpO//w1HPfez/LU3bdw48VvYtOap0odloiUMSWOMmBmHHv2xbztyzewfWMz133wBO6++gt0tusRtCIy/JQ4ysjMhSdz/jUPcOgbzubhm6/kR+cfy4o/3qxzHyIyrJQ4ykztmAm84eJvcc537qB+4iT+7z8+wC8+tUiHr0Rk2ChxlKlJc47inO/cwckf+39sWr2C6z54Akuu+KTu+xCRIVOXIzGwc/tm/nztf/DEHdeT7unmkONPZ+E7P8L+sw4vdWgisg/rq8sRJY4YaXtpHY/ccjWP33Ytne2tHHTkP7DwzI8y7cjXYZbrESgiEmdKHEocu3Xs2M5jt13Lo7dcxY7NG2maeSivfOO7mPP6t1E7dmKpwxORfYQShxLHy3R3drD8rpt4/Dc/ZsPKZVQkKpmx8CTmn3QmM495A5XJVKlDFJESUuJQ4sirZfUKlv/+Fyy/65fs2LyB6oaxzP6Ht3DIcW9myitfTaIqWeoQRWSEKXEocRQk3dPDc3+9h+VLfsGz999Od8dOkrX1TH/Vibzi2DcyY+FJ1I6ZUOowRWQEKHEocQxY584dPP/Yvfz9gTtY9eCd7Ni8Eauo4MC5RzN9wYlMPew1HDD7KB3SEilTShxKHEPi6TTrn32MVQ/eyd8fuIONK/8GQGWymklzFzD18Ncy9bDXMGnOq6hMVpc4WhEZDkocShzDauf2zTT/7QHWLruP5mX3s3HVE+BOoipJ08z5HHDIkRww+0gOmH0U46ccTEUiUeqQRWSAlDiUOIpqV+tWmp/4Cy888SDrn/krG559nM72NgCqaurYf9bh7PeKV9I0fS4TZ85j4kFzqKquLXHUIpKPEocSx4jydJrNzStZ99SjUSJ55jFaVq+guyM8L8SMsZOm0zRzPhOmzWb8lFcwburBjJ8yi1RdQ2mDFxFAiUOJYx/g6TRb161h0+oVtKxZTsuq5Wxa/SRb163B0+nd9erG78e4KQczfvIrGDNpOmMmHcTYSQcx5oDpVDeM1V3uIiOkr8RRWYpgJJ6sooJxk2cybvJMZh33pt3l3Z0dbF23hi1rV7K5ORq2NK/k2ftvZ+e2l/ZaRrK2gbGTptOw32Qa95tCQ9OBNDT1jk+mfsIBVCS0WYsUkz5hUnKVyRQTD5rNxINmv2xeZ3sr29Y/z9Z1z7FtfTRsXbeGbeufp3nZ/XTs2L53AzNqxzZRP2F/6iccQP34A6ibcAD14/endtx+1I1ronZcE7Vjm0jW1I3MCoqUGSUO2aclaxtomjmfppnzc87v2NFKa8sLtLa8wPaNzbS9tI62l9bvHtY/8zjtW1sgxyHZylQtdeOaqBkzgdqxE6hpnEDNmGiobRxPzZjxVDeMo7phHDWN0V/dQS+ixCGjXKqugVTdHCZOn9NnnZ7uLtq3tLBjy0bat7awY0sL7Vs30b5lI+1bWti5fTNtL62nZdVydm57ie7OXX0uq6q6juqGsVQ3jCVV10iqfgypujF7pusaSdU2kHzZeD3JmnqqquuwCj0GR0Y3JQ4pe4nKqnAu5MCC6nfu3MHObS+xq3ULu1q3snP75j3jrVvYtX0LHTu2s6ttK9s3NNOx40k62ra9/LBZLmYka+pI1oREUlsfxuuoqqkjWR39raqpo6q6jqpUDVXVtVlDHZWpGqqqa6hK1VCZqqUyVU2ismqI75RIYZQ4RLJEX+x1jDlg2oDapXt66NzZRmd7Kx07ttOxY/vu8c4drXTubKOjvZWu9jY62ttC3Ta6du2gteVFOndG4507d9C1c8eA465IVFKZqqYyWRP9TdVQlYz+ViarSSRTVCaro3lVKRLJaiqTqd3liarknr9VUXmisopEVYpEVXKvv5XJJBWVyYz50d+KRKWueosBJQ6RYVKRSFBdP4bq+jFDXpa7092xk65d7VnDDro6dtLdsSua39FO966ddIW63Z1RefR3F92dUd2ujp3sat1CV+cuejo76O7cRU9XR1SnqyPnOaDBqqisIlGZJFFVtTu5RGW5/1YkqkhUVobxyozyymiorKIikaAiEcoqQ3lmWSIRJa1EJYnQzhJ76llF4uXjFQkskQhlvcuI5ltFgoqKrOlEAquo2D0vKq+IZaJU4hDZB5nZ7kNTxebupLu7okTSGQ09Xbvo7uykp6uDnq6sv91dYbyTnu7OPeNdnaR7uujp6qKnu5N0dzfp7k66uzpJd3ftnpdZp7tjF+meVtLd3fSEOlG7LtLp7mi8p5ue7u4wr6vo78dAvTyZ2O5prIKKRAVmFXsSTUWCiopQlkhkzIvamVVEScpCUgp1K0J7rCKqaxUhcWX+NcD2Kj/67R/u8+KSwVLiEIk5MwuHoJIka/f9u/Y9nQ7JpAtP95DuCYmmJ4z3dO8e93RvvfSeuj09eE8P6XR39Lenh3S6B+/pJp2Opj0dDXtN99brXVa6tzy9Z9zToV46Gt+rfnr3+J55vrtOend5GHzP33RPNz1dHbj77jLS6Wja06R7eoCMee67677ylHOG/X+gxCEio4pVVJCoSOrS6BLSdYEiIjIgShwiIjIgShwiIjIgRU0cZnaKmT1tZivN7NIc883Mrgjzl5nZUf21NbPxZrbEzJ4Nf8cVcx1ERGRvRUscZpYAvgecCswDzjazeVnVTgVmheEC4MoC2l4K3OXus4C7wrSIiIyQYu5xLARWuvsqd+8Efg4syqqzCLjOIw8AY81sUj9tFwE/CeM/Ad5SxHUQEZEsxbwcdzKwNmO6GTimgDqT+2m7v7uvA3D3dWa2X64XN7MLiPZiANrM7Ol+4p0IbOqnTrmK87pDvNdf6x5fhaz/QbkKi5k4ct2Hn92vQV91Cmmbl7tfDVxdaH0zezjXk67iIM7rDvFef617PNcdhrb+xTxU1QxMzZieArxYYJ18bTeEw1mEvxuHMWYREelHMRPHUmCWmc0wsyRwFrA4q85i4NxwddWxwLZwGCpf28XAeWH8POB/i7gOIiKSpWiHqty928wuAu4AEsA17v6kmV0Y5l8F3A6cBqwE2oH35msbFn05cJOZvQ94HnjHMIVc8GGtMhTndYd4r7/WPb4Gvf7mw9idsoiIlD/dOS4iIgOixCEiIgMS+8TRX7co5cbMrjGzjWb2REZZLLpxMbOpZvZHM1thZk+a2cdCedmvv5lVm9lDZvZ4WPcvhvKyX/dMZpYws7+a2W1hOhbrb2ZrzOxvZvaYmT0cyga97rFOHAV2i1JurgVOySqLSzcu3cAn3H0ucCzw4fD/jsP6dwAnuvvhwBHAKeFKxjise6aPASsypuO0/q939yMy7t0Y9LrHOnFQWLcoZcXd7wU2ZxXHohsXd1/n7o+G8VaiL5DJxGD9Q7c+bWGyKgxODNa9l5lNAd4E/DCjODbrn8Og1z3uiaOvLk/iZq9uXICc3biUEzObDhwJPEhM1j8cpnmM6KbZJe4em3UPvg1cAqQzyuKy/g7caWaPhO6YYAjrHvdHxw65axMZfcysHrgZ+Li7bzfLtRmUH3fvAY4ws7HALWZ2aKljGilm9k/ARnd/xMxOKHU8JfBad38x9O23xMyeGsrC4r7HUUi3KHEQm25czKyKKGlc7+6/DsWxWX8Ad98K3E10risu6/5a4HQzW0N0SPpEM/sZMVl/d38x/N0I3EJ0mH7Q6x73xFFItyhxEItuXCzatfgRsMLd/ytjVtmvv5k1hT0NzKwGOAl4ihisO4C7f9rdp7j7dKLP+R/c/d3EYP3NrM7MGnrHgTcATzCEdY/9neNmdhrRsc/erk2+WuKQisrMbgROIOpSeQPw78CtwE3ANEI3Lu6efQJ91DOz44A/AX9jz3HuzxCd5yjr9Tezw4hOgCaIfjDe5O5fMrMJlPm6ZwuHqj7p7v8Uh/U3s5lEexkQnZ64wd2/OpR1j33iEBGRgYn7oSoRERkgJQ4RERkQJQ4RERkQJQ4RERkQJQ4RERkQJQ6RATCztvB3upm9a5iX/Zms6fuHc/kiw0WJQ2RwpgMDShyhN+Z89koc7v6aAcYkMiKUOEQG53Lg+PB8g4tDB4LfNLOlZrbMzD4A0c1m4RkgNxDdeIiZ3Ro6m3uyt8M5M7scqAnLuz6U9e7dWFj2E+GZCmdmLPtuM/uVmT1lZtdbXDrekpKKeyeHIoN1KeHuY4CQALa5+9FmlgLuM7M7Q92FwKHuvjpMn+/um0PXH0vN7GZ3v9TMLnL3I3K81tuInqFxONEd/0vN7N4w70hgPlEfa/cR9cn05+FfXZE9tMchMjzeAJwbui1/EJgAzArzHspIGgAfNbPHgQeIOtmcRX7HATe6e4+7bwDuAY7OWHazu6eBx4gOoYkUlfY4RIaHAR9x9zv2Koz6RdqRNX0S8Gp3bzezu4HqApbdl46M8R70mZYRoD0OkcFpBRoypu8APhi6bcfMDgk9kWYbA2wJSWMO0SNse3X1ts9yL3BmOI/SBLwOeGhY1kJkEPTrRGRwlgHd4ZDTtcB3iA4TPRpOULeQ+1GcvwMuNLNlwNNEh6t6XQ0sM7NH3f2cjPJbgFcDjxM9aOwSd18fEo/IiFPvuCIiMiA6VCUiIgOixCEiIgOixCEiIgOixCEiIgOixCEiIgOixCEiIgOixCEiIgPy/wE0auLHS1FA5QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nIter = 50 # Iterations\n",
    "\n",
    "sz = [nIter] # Size\n",
    "\n",
    "x = -0.37727 # Truth value (picked randomly)\n",
    "\n",
    "z = np.random.normal(x,0.1,size=sz) # Observations normal around x, sigma 0.1\n",
    "\n",
    "Q = 1e-5 # Example process covariance\n",
    "\n",
    "R = 0.1**2 # Measurement covariance estimate. Changes here dramatically change output.\n",
    "\n",
    "# Make storage space for arrays:\n",
    "xHat=np.zeros(sz) # A Posteriori estimate of x\n",
    "\n",
    "P=np.zeros(sz) # A Posteriori error estimate\n",
    "\n",
    "xHatMinus=np.zeros(sz) # A Priori estimate of x\n",
    "\n",
    "PMinus=np.zeros(sz) # A Priori error estimate\n",
    "\n",
    "K=np.zeros(sz) # Kalman Gain\n",
    "\n",
    "# Initial guess\n",
    "xHat[0] = 0.0\n",
    "P[0] = 1.0\n",
    "\n",
    "for k in range(1,nIter):\n",
    "    # time update\n",
    "    xHatMinus[k] = xHat[k-1]\n",
    "    PMinus[k] = P[k-1]+Q\n",
    "\n",
    "    # measurement update\n",
    "    K[k] = PMinus[k]/(PMinus[k]+R)\n",
    "    xHat[k] = xHatMinus[k]+K[k]*(z[k]-xHatMinus[k])\n",
    "    P[k] = (1-K[k])*PMinus[k]\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(z,'x',color='darkolivegreen',label='Noisy Test Measurements')\n",
    "plt.plot(xHat,'b-',label='A Posteriori Estimate')\n",
    "plt.axhline(x,color='r',label='Truth Value')\n",
    "plt.legend()\n",
    "plt.title('Estimate vs. Iteration')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Value')\n",
    "\n",
    "plt.figure()\n",
    "startingIter = range(1,nIter) # Doesn't work at 0th step\n",
    "plt.plot(startingIter,PMinus[startingIter],label='A Priori Error Estimate', color='saddlebrown')\n",
    "plt.title('Estimated A Priori Error Covariance vs. Iteration')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Error Covariance')\n",
    "plt.setp(plt.gca(),'ylim',[0,.01])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "This is what we wanted, so we can proceed with a more refined implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-13T05:15:29.206984Z",
     "start_time": "2021-01-13T05:15:29.190358Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "def kalmanFilter(data, measurementVar, processVar, depthColumnIndex=2):\n",
    "    nIter = len(data)\n",
    "    sz = [nIter]\n",
    "\n",
    "    xHat=np.zeros(sz)\n",
    "    P=np.zeros(sz)\n",
    "    xHatMinus=np.zeros(sz)\n",
    "    PMinus=np.zeros(sz)\n",
    "    K=np.zeros(sz)\n",
    "    Q = processVar\n",
    "    R = measurementVar**2\n",
    "    xHat[0] = data.iloc[0,2]\n",
    "    P[0] = 1\n",
    "\n",
    "    for k in range(1,nIter):\n",
    "        xHatMinus[k] = xHat[k-1]\n",
    "        PMinus[k] = P[k-1] + Q\n",
    "        \n",
    "        K[k] = PMinus[k]/(PMinus[k] + R)\n",
    "        xHat[k] = xHatMinus[k]+K[k]*(data.iloc[k,depthColumnIndex]-xHatMinus[k])\n",
    "        P[k] = (1-K[k])*PMinus[k]\n",
    "    \n",
    "    return [xHat,P,K]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Now to run the filter on our clean data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-13T05:15:31.449645Z",
     "start_time": "2021-01-13T05:15:31.328709Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "kEstimate = kalmanFilter(cleanData,0.5,1E-2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Visualization of the snow year:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-13T05:15:34.881034Z",
     "start_time": "2021-01-13T05:15:34.598040Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9419eb7198>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cleanArray = cleanData.to_numpy(copy=True)\n",
    "plt.figure(figsize=(16,6))\n",
    "plt.plot(cleanArray[2300:2500,2],'dodgerblue',label='Cleaned Data')\n",
    "plt.plot(kEstimate[0][2300:2500],'crimson',label='Kalman Estimate')\n",
    "plt.xlabel('Hourly Iteration')\n",
    "plt.ylabel('Depth Value (in)')\n",
    "plt.title('Data & Kalman Estimates vs. Time')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Here we demonstrate how different process covariances affect the filter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-13T05:15:40.357189Z",
     "start_time": "2021-01-13T05:15:39.829370Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9419f3fcf8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cleanArray = cleanData.to_numpy(copy=True)\n",
    "plt.figure(figsize=(16,6))\n",
    "plt.plot(cleanArray[1475:1625,2], label='Data')\n",
    "plt.xlabel('Hourly Iteration')\n",
    "plt.ylabel('Depth Value (in)')\n",
    "plt.title('Data & Kalman Estimates (Changing Q) vs. Time')\n",
    "loop = [1E-1, 1E-2, 1E-3]\n",
    "\n",
    "for i in range(len(loop)):\n",
    "    kEstimate = kalmanFilter(cleanData,0.5,loop[i])\n",
    "    plt.plot(kEstimate[0][1475:1625],label=loop[i])\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Smaller process covariances result in a smaller Kalman gain, which weigh down the change between observations. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Results\n",
    "The main result of our project is the hourly filtered depth data and the ability to run SNOWPACK on a significantly more local scale. We are working with Coop and John at the CAIC to see how our data compare to HRRR, as well as how SNOWPACK accuracy is effected by the choice of process covariance. With this feedback, we will be able to more concretely state our results.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Wrapping Up\n",
    "Now we can append our results to the existing results for a complete look at the snow year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-13T05:15:45.935471Z",
     "start_time": "2021-01-13T05:15:45.877501Z"
    }
   },
   "outputs": [],
   "source": [
    "cleanData.insert(loc = 3, column = 'Kalman Filtered Depth', value = kEstimate[0])\n",
    "cleanData.to_csv('WolfCreekSummitKalmanDepth2018.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-13T05:15:49.660356Z",
     "start_time": "2021-01-13T05:15:48.341920Z"
    }
   },
   "outputs": [],
   "source": [
    "%pycat WolfCreekSummitKalmanDepth2018.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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