Lab2.ipynb

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    "# Lab 2: Landscape Energy Balance\n",
    "\n",
    "<img src=\"./assets/mpala1.jpeg\" alt=\"mpala\" width=\"800\"/>\n",
    "\n",
    "In the last lab, we used mass balance concepts to calculate the water balance within a watershed. In this lab, will will use energy balance concepts to explore the fluxes of energy across a landscape (more specifically, $\\uparrow$ this landscape!).\n",
    "\n",
    "\n",
    "<div class=\"boxhead3\">\n",
    "    Lab 2 Contents\n",
    "</div>\n",
    "\n",
    "<div class=\"boxtext3\">\n",
    "<ul class=\"a\">\n",
    "    <li> 1. Surface Energy Balance </li>\n",
    "    <ul class=\"b\">\n",
    "        <li> Background </li>\n",
    "    </ul>\n",
    "    <li> 2. The Radiation Budget </li>\n",
    "    <ul class=\"b\">\n",
    "        <li> Shortwave radiation </li>\n",
    "        <li> Longwave radiation </li>\n",
    "        <li> Problems </li>\n",
    "    </ul>\n",
    "    <li> 3. Landscape Energy Budget </li>\n",
    "    <ul class=\"b\">\n",
    "        <li> Ground heat flux </li>\n",
    "        <li> Sensible heat flux </li>\n",
    "        <li> Latent heat flux </li>\n",
    "        <li> Problems </li>\n",
    "    </ul>\n",
    "    <li> 4. Putting it all together </li>\n",
    "</ul>\n",
    "\n",
    "</div>\n",
    "\n",
    "\n",
    "<hr style=\"border-top: 0.2px solid gray; margin-top: 12pt; margin-bottom: 0pt\"></hr>\n",
    "\n",
    "### Instructions\n",
    "Work through the exercise, writing code where indicated. For some of the questions, you may want to add a Markdown cell to discuss your answer. To run a cell, click on the cell and press \"Shift\" + \"Enter\" or click the \"Run\" button in the toolbar at the top. Note: Do not restart the kernel and clear all outputs. If this happens, run the last cell in the notebook before proceeding.\n",
    "\n",
    "<p style=\"color:#408000; font-weight: bold\"> 🐍 &nbsp; &nbsp; This symbol designates an important note about Python structure, syntax, or another quirk.  </p>\n",
    "\n",
    "<p style=\"color:#443823; font-weight: bold\">📊 &nbsp; &nbsp; This symbol designates an important note about environmental data methods, sources, and access. </p>\n",
    "\n",
    "<p style=\"color:#008C96; font-weight: bold\"> ▶️ &nbsp; &nbsp; This symbol designates a cell with code to be run.  </p>\n",
    "\n",
    "<p style=\"color:#008C96; font-weight: bold\"> ✏️ &nbsp; &nbsp; This symbol designates a partially coded cell with an example.  </p>\n",
    "\n",
    "<p style=\"color:#008C96; font-weight: bold\"> 📚 &nbsp; &nbsp; This symbol designates a practice question.  </p>\n",
    "\n",
    "\n",
    "<hr style=\"border-top: 1px solid gray; margin-top: 24px; margin-bottom: 1px\"></hr>"
   ]
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    "# 1. Surface Energy Balance\n",
    "\n",
    "<hr style=\"border-top: 0.5px solid gray; margin-top: 10pt; margin-bottom: 12pt\"></hr>\n",
    "\n",
    "\n",
    "Much like mass balance, the energy balance of a system consists of the sum of inputs and outputs. While the mass balance terms can be discretized into obvious components (e.g. precipitation), the components of the Earth's energy budget are **fluxes**, meaning that they consist of both inputs *and* outputs and can be either positive or negative, depending on environmental conditions. These fluxes are driven by four important processes: **radiation**, **conduction**, **convection**, and **evaporation**.\n",
    "\n",
    "The largest and perhaps most obvious component of the Earth's energy budget is the **net radiation**, $R_n$, consisting of electromagnetic radiation that is *absorbed*, *reflected*, and *emitted* by the Earth's surface. The surplus of incoming solar radiation provides excess energy for transport-driven processes at the Earth's surface. Thermal conduction of heat energy within the ground leads to **ground heat flux**, $G$. **Sensible heat flux**, $H$, refers to the turbulent transfer of energy between the surface and the atmosphere via convective processes. **Latent heat flux**, $\\lambda E$, represents the loss of energy associated with phase changes between liquid water and water vapor through evaporation, transpiration, and condensation.\n",
    "\n",
    "\n",
    "\\begin{equation}\n",
    "\\tag{1.1}\n",
    "    \\frac{dQ}{dt} \\ = \\ R_n \\ - \\ H \\ - \\ \\lambda E \\ - \\ G\n",
    "\\end{equation}\n",
    "\n",
    "These energy fluxes (also referred to as *energy flux densities*) quantify the rate of energy transfer per unit area and are measured in units of J m<sup>-2</sup> s<sup>-1</sup> or W m<sup>-2</sup>.\n",
    "\n",
    "Assuming the system is in *steady state*, a valid assumption over sufficiently long time scales, $ \\frac{dQ}{dt} \\ = \\ 0$, so we can rearrange Equation 1.1 as:\n",
    "\n",
    "\\begin{equation}\n",
    "\\tag{1.2}\n",
    "    R_n \\ = \\ H \\ + \\ \\lambda E \\ + \\ G\n",
    "\\end{equation}\n",
    "\n",
    "### Measuring environmental fluxes\n",
    "\n",
    "In this lab, we will be using data collected from a micrometerological tower that uses [eddy covariance](https://en.wikipedia.org/wiki/Eddy_covariance) techniques to measure turbulent fluxes in the atmospheric boundary layer. The physics of this method are too complex to get into here, but eddy covariance is one of the main ways of determining landscape energy balance. These \"flux towers\" are equipped with sensors to measure wind speed and direction and gas (usually H<sub>2</sub>O and CO<sub>2</sub>) concentrations, and radiation (and usually numerous other sensors!) at very high frequencies (commonly 10 Hz or 10 times per second). These data are then used to calculate fluxes of gas, energy, and momentum across the landscape. \n",
    "\n",
    "This short video provides a basic overview of eddy covariance, focusing on CO<sub>2</sub> fluxes. While we won't be working with carbon fluxes in this lab, you can replace \"CO<sub>2</sub>\" with \"H<sub>2</sub>O\" to think about water vapor fluxes due to sensible and latent heat.\n",
    "\n",
    "[<img src=\"./assets/ec_video.png\" alt=\"ec_video\" width=\"600\"/>](https://youtu.be/CR4Anc8Mkas)\n",
    "\n",
    "### Background\n",
    "\n",
    "The data we'll work with in this lab come from a flux tower in central Kenya just a few kilometers north of the Equator. Like much of East Africa, the flux tower site is situated in a dryland savanna environment. As we saw in Exercise 3, there are two \"rainy\" seasons in this part of Kenya, one beginning in March and another in October. \n",
    "\n",
    "<img src=\"./assets/mpalatower.jpeg\" alt=\"tower\" width=\"400\"/>\n",
    "\n",
    "In this lab, we'll look at the last five months of the year, August through December, encompassing the second wet season and compare this season between two years, 2016 and 2019. \n",
    "\n",
    "As always, we'll start by importing the necessary libraries.\n",
    "\n",
    "<div class=\"run\">\n",
    "    ▶️ <b> Run the cell below. </b>\n",
    "</div>"
   ]
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   "source": [
    "# Imports\n",
    "import os\n",
    "import datetime\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates"
   ]
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    "In this lab, you will use data collected from the flux tower in Kenya to calculate the overall energy balance for this landscape, comparing eddy flux calculations to radiation data. The data are located in the [data](../data) folder of the GitHub repository. The raw eddy covariance data have already been significantly pruned and pre-processed to allow for efficient calculations of turbulent fluxes in half-hourly averages. The data you will need for these calculations are located in a file called [CR3000_SN4709_flux_ed.csv](../data/lab_2/CR3000_SN4709_flux_ed.csv). These data include:\n",
    "\n",
    "| Column name    | Units        | Symbol | Description |\n",
    "| :------------- | :---- | :----- | :---------- |\n",
    "| **P_a**        | $\\text{kPa}$ | $P_a$ | Air pressure |\n",
    "| **e**          | $\\text{kPa}$       | $e$   | Vapor pressure |\n",
    "| **T_a**        | $^{\\circ}\\text{C}$ | $T_a$ | Air temperature |\n",
    "| **cov_Ts_Uz**  | $\\text{K m s}^{-1}$ | $\\overline{w'T'}$ | Eddy covariance term for SHF |\n",
    "| **cov_h2o_Uz** | $\\text{g m}^{-2} \\text{ s}^{-1}$ | $\\overline{w'c'_{_{\\text{H}_2\\text{O}}}}$ | Eddy covariance term for LHF |\n",
    "\n",
    "<br></br>\n",
    "\n",
    "The other file, [CR5000_SN2446_upper_ed.csv](../data/lab_2/CR5000_SN2446_upper_ed.csv), contains data from a net radiometer that you will use to assess your eddy covariance calculations. This file is organized as follows:\n",
    "\n",
    "| Column name | Units | Symbol | Description |\n",
    "| :---------- | :---- | :----- | :---------- |\n",
    "| **DOY** | | | Day of year |\n",
    "| **SW_in**  | $\\text{W m}^{-2}$ | $R^{\\downarrow}_{_{SW}}$ | Incoming shortwave radiation |\n",
    "| **SW_out** | $\\text{W m}^{-2}$ | $R^{\\uparrow}_{_{SW}}$ | Outgoing shortwave radiation |\n",
    "| **LW_in**  | $\\text{W m}^{-2}$ | $R^{\\downarrow}_{_{LW}}$ | Incoming longwave radiation  |\n",
    "| **LW_out** | $\\text{W m}^{-2}$ | $R^{\\uparrow}_{_{LW}}$ | Outgoing longwave radiation  |\n",
    "| **P_mm**   | $\\text{mm}$       | $P$ | Precipitation                |\n",
    "\n",
    "\n"
   ]
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    "<div class=\"run\">\n",
    "    ▶️ <b> Run the cell below. </b>\n",
    "</div>"
   ]
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   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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   "source": [
    "# Data import\n",
    "flux = pd.read_csv('../data/lab_2/CR3000_SN4709_flux_ed.csv', index_col=0, parse_dates=True)\n",
    "rad = pd.read_csv('../data/lab_2/CR5000_SN2446_upper_ed.csv', index_col=0, parse_dates=True)"
   ]
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    "Finally, the following table contains values for several potentially useful constants, which have already been coded in the cell below.\n",
    "\n",
    "<table>\n",
    "<thead>\n",
    "  <tr>\n",
    "    <th style=\"text-align:left;\"> Constant </th>\n",
    "    <th style=\"text-align:center;\"> Symbol </th>\n",
    "    <th style=\"text-align:left; width:158px\"> Value </th>\n",
    "  </tr>\n",
    "</thead>\n",
    "<tbody>\n",
    "  <tr>\n",
    "    <td style=\"text-align:left;\"> Stefan-Boltzmann constant </td>\n",
    "    <td style=\"text-align:center;\"> $\\sigma$ </td>\n",
    "    <td> $5.67 \\times 10^{-8} \\text{ W m}^{-2} \\text{ K}^{-4}$ </td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td style=\"text-align:left;\"> Specific heat capacity of air </td>\n",
    "    <td style=\"text-align:center;\"> $c_p$ </td>\n",
    "    <td> $1004.67 \\text{ J kg}^{-1} \\text{ K}^{-1}$ </td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td style=\"text-align:left;\"> Ideal gas constant </td>\n",
    "    <td style=\"text-align:center;\"> $R$ </td>\n",
    "    <td> $8.314 \\text{ J mol}^{-1} \\text{ K}^{-1}$ </td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td style=\"text-align:left;\"> Latent heat of vaporization </td>\n",
    "    <td style=\"text-align:center;\"> $\\lambda_v$ </td>\n",
    "    <td> $2440 \\text{ J g}^{-1}$ </td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td style=\"text-align:left;\"> Molar mass of water </td>\n",
    "    <td style=\"text-align:center;\"> $M_{_{\\text{H}_2\\text{O}}}$ </td>\n",
    "    <td> $18.01528 \\text{ g mol}^{-1}$ </td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td style=\"text-align:left;\"> Molar mass of dry air </td>\n",
    "    <td style=\"text-align:center;\"> $M_{_{\\text{air}}}$ </td>\n",
    "    <td> $28.9647 \\text{ g mol}^{-1}$ </td>\n",
    "  </tr>\n",
    "</tbody>\n",
    "</table>\n",
    "\n",
    "\n",
    "<div class=\"run\">\n",
    "    ▶️ <b> Run the cell below. </b>\n",
    "</div>"
   ]
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    "# Constants\n",
    "sigma = 5.67e-8  # Stefan-Boltzmann constant, W m-2 K-4\n",
    "c_p = 1004.67    # heat capacity, J kg-1 K-1\n",
    "R = 8.314        # Ideal gas constant, J mol-1 K-1\n",
    "lambda_v = 2440  # Latent heat of vaporization, J g-1\n",
    "M_h2o = 18.01528 # Molar mass of water, g mol-1\n",
    "M_air = 28.9647  # Molar mass of dry air, g mol-1\n",
    "emissivityE = 0.95 #Assume the surface has the same emissivity as the atmosphere, 𝜀𝑠 = 0.95."
   ]
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   "cell_type": "markdown",
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   "source": [
    "# 2. The Radiation Budget ☀️\n",
    "\n",
    "<hr style=\"border-top: 0.5px solid gray; margin-top: 10pt; margin-bottom: 12pt\"></hr>\n",
    "\n",
    "The Earth's radiation budget can be broken into *shortwave* and *longwave* components, referring to the wavelength of transmitted light. Each of these components consists of a flux of incoming (or \"downwelling\") and outgoing (or \"upwelling\") energy. Thus, we can write the \n",
    "\n",
    "\\begin{equation}\n",
    "\\tag{2.1}\n",
    "    R_n \\ = \\ R^{\\downarrow}_{_{SW}} \\, - \\, R^{\\uparrow}_{_{SW}} \\, + \\, R^{\\downarrow}_{_{LW}} \\, - \\, R^{\\uparrow}_{_{LW}}\n",
    "\\end{equation}\n",
    "\n",
    "where the arrows denote the direction of the radiation. We'll explore each of these components in more detail below.\n",
    "\n",
    "<img src=\"./assets/em_spectrum.jpg\" alt=\"spectrum\" width=\"500\"/>"
   ]
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    "## 2.1. Shortwave radiation\n",
    "The vast majority of energy entering the Earth's atmosphere is **shortwave radiation** from the sun, $R^{\\ \\downarrow}_{_{SW}}$, and falls in the ultraviolet, visible, and infrared portions of the electromagnetic spectrum. As we saw in Exercise 1.1, the flux density of the sun's energy at the Earth's atmosphere, known as the *solar constant*, is about 1370 W m<sup>-2</sup>. This is the *maximum* value of $R^{\\downarrow}_{_{SW}}$, but this quantity can fluctuate based on seasonal and diurnal geometry, attenuation by the atmosphere, and the amount of clouds and aerosols in the atmosphere. Of the solar radiation that *does* pass through the atmosphere, about 30% is reflected by the Earth's surface, leading to **outgoing shortwave radiation, $R^{\\uparrow}_{_{SW}}$**. The ratio of reflected to incoming shortwave radiation is known as **albedo**, $\\alpha$.\n",
    "\n",
    "\\begin{equation}\n",
    "\\tag{2.2}\n",
    "    \\alpha \\ = \\ \\frac{R^{\\uparrow}_{_{SW}}}{R^{\\downarrow}_{_{SW}}}\n",
    "\\end{equation}\n",
    "\n",
    "Because ~30% of incoming solar radiation is reflected on average, the Earth's average global albedo is 0.3. This value varies between 0 and 1, however, based on the surface. Dark objects like asphalt absorb most of the incident radiation causing them to have a very low albedo. In contrast, light objects like freshly fallen snow have an albedo close to 1 as they reflect nearly all incident energy.\n",
    "\n",
    "Using Equation 2.2, we can write the net shortwave radiation (i.e. the difference between incoming and outgoing shortwave radiation) as\n",
    "\n",
    "\\begin{equation}\n",
    "\\tag{2.3}\n",
    "    R^{\\downarrow}_{_{SW}} \\ - \\ R^{\\uparrow}_{_{SW}} \\ = \\ (1 \\ - \\ \\alpha) R^{\\downarrow}_{_{SW}}\n",
    "\\end{equation}"
   ]
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   "source": [
    "## 2.2. Longwave radiation\n",
    "\n",
    "Some incoming solar radiation is absorbed by larger particles in the Earth's atmosphere, causing the atmosphere to warm. The heat generated by this absorption is emitted in the far infrared portion of the elecromagnetic spectrum, some of which radiates to the surface to produce **incoming longwave radiation, $R^{\\downarrow}_{_{LW}}$**. The value of $R^{\\downarrow}_{_{LW}}$ varies depending on the temperature profile of the atmosphere and the amount of water vapor and other particles present. As the surface of the Earth heats up, thermal energy is re-emitted as **outgoing longwave radiation, $R^{\\uparrow}_{_{LW}}$**. This quantity depends on the temperature and **emissivity** of the surface, $\\varepsilon$. Both incoming and outgoing longwave radiation can be calculated from the Stefan-Boltzmann law, which describes the energy emitted from an object in terms of its temperature:\n",
    "\n",
    "\\begin{equation}\n",
    "\\tag{2.4}\n",
    "    j^* \\ = \\ \\varepsilon \\sigma T^{4}\n",
    "\\end{equation}\n",
    "\n",
    "where $\\sigma$ is the **Stefan-Boltzman constant** with a value of $5.67 \\times 10^{-8} \\text{ W m}^{-2} \\text{ K}^{-4}$. \n",
    "\n",
    "Thus, we can rewrite Equation 2.1 to express net radiation in terms of these relationships in Equations 2.3 and 2.4:\n",
    "\n",
    "\\begin{equation}\n",
    "\\tag{2.5}\n",
    "    R^{}_{n} \\ = \\ (1-\\alpha) \\ R^{\\downarrow}_{_{SW}} \\ + \\ \\varepsilon_a \\sigma {T^{}_{a}}^{4} \\ - \\ \\varepsilon_s \\sigma {T^{}_{s}}^{4}\n",
    "\\end{equation}\n",
    "\n",
    "where $\\varepsilon_a$ and $\\varepsilon_s$ are the emissivities and $T_a$ and $T_s$ are the temperatures (in Kelvin) of the air and the surface, respectively.\n",
    "\n",
    "The sum of the incoming shortwave radiation (corrected for albedo) and incoming longwave radiation represents the **total energy available to the system, $Q_{av}$**. We can rewrite Equation 2.5 in terms of available energy:\n",
    "\n",
    "\\begin{equation}\n",
    "\\tag{2.6}\n",
    "    Q_{av} \\ = \\ (1-\\alpha) \\ R^{\\downarrow}_{_{SW}} \\ + \\ \\varepsilon_a \\sigma {T^{}_{a}}^{4} \\ = \\ R^{\\downarrow}_{_{SW}} \\, - \\, R^{\\uparrow}_{_{SW}} \\, + \\, R^{\\downarrow}_{_{LW}}  \\ = \\varepsilon_s \\sigma {T^{}_{s}}^{4}\n",
    "\\end{equation}\n"
   ]
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   "source": [
    "<hr style=\"border-top: 0.5px solid gray; margin-top: 6pt; margin-bottom: 12pt\"></hr>\n",
    "\n",
    "## Problems\n",
    "\n",
    "### 2.1. Determine the albedo of the landscape.\n",
    "\n",
    "<div class=\"practice\">\n",
    "    📚  <b> Question 2.1. </b> \n",
    "    Calculate daily albedo values using the average daily $R^{\\downarrow}_{_{SW}}$ and $R^{\\uparrow}_{_{SW}}$ values. What is the mean daily albedo of the landscape over the entire dataset?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-08-01    0.124554\n",
       "2016-08-02    0.129314\n",
       "2016-08-03    0.134172\n",
       "2016-08-04    0.142489\n",
       "2016-08-05    0.128827\n",
       "                ...   \n",
       "2019-12-27    0.148546\n",
       "2019-12-28    0.148212\n",
       "2019-12-29    0.152447\n",
       "2019-12-30    0.152557\n",
       "2019-12-31    0.153873\n",
       "Length: 306, dtype: float64"
      ]
     },
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lab #2:\n",
    "# calculate daily albedo level\n",
    "dayrad = rad.groupby(rad.index.date).mean()      # it is okay to put datetime index in argunmets\n",
    "\n",
    "albedo = dayrad['SW_out'] / dayrad['SW_in']\n",
    "#type(albedo)\n",
    "albedo # is a series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2. Estimate the surface temperature of the earth from $Q_{av}$\n",
    "\n",
    "<div class=\"practice\">\n",
    "    📚  <b> Question 2.2. </b> \n",
    "    Calculate $Q_{av}$ from $R^{\\downarrow}_{_{SW}}$, $R^{\\uparrow}_{_{SW}}$, and $R^{\\downarrow}_{_{LW}}$. Using the mean value of $Q_{av}$ over the entire dataset, use Equation 2.6 to calculate the apparent surface temperature of the earth, $T_s$, in both $^{\\circ}$C and $^{\\circ}$F assuming all of the available energy is used to heat the surface. Assume the surface has the same emissivity as the atmosphere, $\\varepsilon_s \\ = \\ 0.95$.\n",
    "</div>"
   ]
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  {
   "cell_type": "code",
   "execution_count": 14,
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    {
     "data": {
      "text/plain": [
       "563.3803105441089"
      ]
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     "execution_count": 14,
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   "source": [
    "# calculate Qav from equation: 𝑄𝑎𝑣 = (1−𝛼) 𝑅↓𝑆𝑊 + 𝜀𝑎𝜎𝑇𝑎4 = 𝑅↓𝑆𝑊−𝑅↑𝑆𝑊+𝑅↓𝐿𝑊 =𝜀𝑠𝜎𝑇𝑠4\n",
    "rad['Qav'] = rad.SW_in - rad.SW_out + rad.LW_in\n",
    "\n",
    "# take mean of Qav\n",
    "rad['Qav'].mean()\n",
    "# Qav = (1-a) - (shortwave incoming)  (𝜀) (𝑎(𝜎)(𝑇)(𝑎)^4\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3. Net radiation\n",
    "\n",
    "Now, calculate the actual net radiation from the dataset.\n",
    "\n",
    "<div class=\"practice\">\n",
    "    📚  <b> Question 2.3. </b> \n",
    "    Use Equation 2.1 and the actual values of outgoing longwave radiation to calculate net radiation. Plot net radiation from August to December for 2016 and 2019 on the same plot (Hint: use <code>rad.index.dayofyear</code> on the x-axis to display the time series on top of one another). Label your plot appropriately.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
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       "      <th></th>\n",
       "      <th>DOY</th>\n",
       "      <th>SW_in</th>\n",
       "      <th>SW_out</th>\n",
       "      <th>LW_in</th>\n",
       "      <th>LW_out</th>\n",
       "      <th>P_mm</th>\n",
       "      <th>Qav</th>\n",
       "      <th>r_net</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMESTAMP</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>2016-08-01 00:00:00</th>\n",
       "      <td>214</td>\n",
       "      <td>-0.743</td>\n",
       "      <td>1.310</td>\n",
       "      <td>350.4</td>\n",
       "      <td>385.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>348.347</td>\n",
       "      <td>-37.453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-01 00:10:00</th>\n",
       "      <td>214</td>\n",
       "      <td>-0.810</td>\n",
       "      <td>1.422</td>\n",
       "      <td>349.9</td>\n",
       "      <td>386.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>347.668</td>\n",
       "      <td>-38.532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-01 00:20:00</th>\n",
       "      <td>214</td>\n",
       "      <td>-0.583</td>\n",
       "      <td>1.533</td>\n",
       "      <td>347.4</td>\n",
       "      <td>386.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>345.284</td>\n",
       "      <td>-40.716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-01 00:30:00</th>\n",
       "      <td>214</td>\n",
       "      <td>-0.875</td>\n",
       "      <td>1.549</td>\n",
       "      <td>340.4</td>\n",
       "      <td>385.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>337.976</td>\n",
       "      <td>-47.724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-01 00:40:00</th>\n",
       "      <td>214</td>\n",
       "      <td>-1.701</td>\n",
       "      <td>1.548</td>\n",
       "      <td>333.3</td>\n",
       "      <td>385.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>330.051</td>\n",
       "      <td>-54.949</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>2019-12-31 23:10:00</th>\n",
       "      <td>365</td>\n",
       "      <td>-0.850</td>\n",
       "      <td>1.484</td>\n",
       "      <td>344.6</td>\n",
       "      <td>385.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>342.266</td>\n",
       "      <td>-43.034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 23:20:00</th>\n",
       "      <td>365</td>\n",
       "      <td>-0.921</td>\n",
       "      <td>1.203</td>\n",
       "      <td>344.6</td>\n",
       "      <td>385.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>342.476</td>\n",
       "      <td>-42.724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 23:30:00</th>\n",
       "      <td>365</td>\n",
       "      <td>-1.273</td>\n",
       "      <td>1.305</td>\n",
       "      <td>341.5</td>\n",
       "      <td>384.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>338.922</td>\n",
       "      <td>-45.978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 23:40:00</th>\n",
       "      <td>365</td>\n",
       "      <td>-1.753</td>\n",
       "      <td>1.238</td>\n",
       "      <td>328.8</td>\n",
       "      <td>383.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>325.809</td>\n",
       "      <td>-57.791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 23:50:00</th>\n",
       "      <td>365</td>\n",
       "      <td>-1.884</td>\n",
       "      <td>1.517</td>\n",
       "      <td>321.4</td>\n",
       "      <td>381.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>317.999</td>\n",
       "      <td>-63.801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43884 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     DOY  SW_in  SW_out  LW_in  LW_out  P_mm      Qav   r_net\n",
       "TIMESTAMP                                                                    \n",
       "2016-08-01 00:00:00  214 -0.743   1.310  350.4   385.8   0.0  348.347 -37.453\n",
       "2016-08-01 00:10:00  214 -0.810   1.422  349.9   386.2   0.0  347.668 -38.532\n",
       "2016-08-01 00:20:00  214 -0.583   1.533  347.4   386.0   0.0  345.284 -40.716\n",
       "2016-08-01 00:30:00  214 -0.875   1.549  340.4   385.7   0.0  337.976 -47.724\n",
       "2016-08-01 00:40:00  214 -1.701   1.548  333.3   385.0   0.0  330.051 -54.949\n",
       "...                  ...    ...     ...    ...     ...   ...      ...     ...\n",
       "2019-12-31 23:10:00  365 -0.850   1.484  344.6   385.3   0.0  342.266 -43.034\n",
       "2019-12-31 23:20:00  365 -0.921   1.203  344.6   385.2   0.0  342.476 -42.724\n",
       "2019-12-31 23:30:00  365 -1.273   1.305  341.5   384.9   0.0  338.922 -45.978\n",
       "2019-12-31 23:40:00  365 -1.753   1.238  328.8   383.6   0.0  325.809 -57.791\n",
       "2019-12-31 23:50:00  365 -1.884   1.517  321.4   381.8   0.0  317.999 -63.801\n",
       "\n",
       "[43884 rows x 8 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this one calculates net radiation. \n",
    "\n",
    "#LWR_out = pd.Series(rad['LW_out']).mean()\n",
    "#R_net = (SWR_in - SWR_out) + (LWR_in - LWR_out)\n",
    "#R_net # I think we need a continous value for this one. \n",
    "\n",
    "df_rad = rad['r_net'] = rad.SW_in - rad.SW_out + rad.LW_in - rad.LW_out\n",
    "rad\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7effff07c210>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use matplot lib to plot radiation\n",
    "fig = plt.figure()\n",
    "\n",
    "ax1 = fig.add_subplot(1,1,1)\n",
    "ax1.plot(rad.index.dayofyear,rad.r_net)\n",
    "\n",
    "plt.xlabel(r'Day Number')\n",
    "plt.ylabel(r'Radiation W/m^2')\n",
    "\n",
    "plt.title('Net Radiation over Observation Period')\n",
    "plt.legend(labels=['Net Radiation / Day'])\n",
    "# make sure that the vlaues are plotted fo rthe time series that is specified = filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4. Diurnal radiation\n",
    "\n",
    "Next, let's look at the diurnal variation in radiation values. Pick a single day of your choosing and plot all four components of radiation, as well as net radiation. You can use a boolean mask on your `DataFrame` to select a single date. For instance, to select the 11th of October 2019:\n",
    "\n",
    "```python\n",
    "rad[rad.index.date == datetime.date(2019,10,11)]\n",
    "```\n",
    "\n",
    "<div class=\"practice\">\n",
    "    📚  <b> Question 2.4. </b> \n",
    "    Plot net radiation, as well as its four components, over the course of your chosen day. Which component is the largest? When is $R_n$ at its highest during the day? Its lowest? As always, be sure to label your plot appropriately.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'W/m2')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotted 12th of october, 2016\n",
    "date = rad[rad.index.date == datetime.date(2016,10,12)]\n",
    "\n",
    "# select only the values that need to be plotted\n",
    "x = date[['r_net', 'SW_in', 'SW_out', 'LW_in', 'LW_out']]\n",
    "\n",
    "# plot the date that was selected. \n",
    "x.plot()\n",
    "\n",
    "#Add labels and a title\n",
    "plt.title(\"Daily Radiation Profile of a Savanna Environment\")\n",
    "plt.xlabel('Time of Day')\n",
    "plt.ylabel('W/m2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer:\n",
    "       incoming shortwave radiation is the largest component in the system. net radtino is at its highest during mid morning just after 9:00 and its lowest in the afternoon around 3:00 pm. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Landscape energy budget 🏞️\n",
    "\n",
    "<hr style=\"border-top: 0.5px solid gray; margin-top: 10pt; margin-bottom: 12pt\"></hr>\n",
    "\n",
    "As we saw above, the amount of energy available to the Earth's surface from radiation far exceeds the outgoing longwave radiation emitted by the surface, so there must be other processes cooling off the surface.  \n",
    "\n",
    "\n",
    "Energy flows follow a gradient a high to low concentration. We can describe the flow of energy, $F_Q$, in some direction, $x$, in terms of this gradient ($\\frac{dQ}{dx}$) and the **conductivity $(k_c)$** of the surface.\n",
    "\n",
    "\\begin{equation}\n",
    "\\tag{3.1}\n",
    "    F_Q \\ = \\ - k_c \\frac{dQ}{dx}\n",
    "\\end{equation}\n",
    "\n",
    "This equation is known as **Fick's Law**, and the negative sign reflects the convention that energy flows from high to low. Thus, when the gradient is increasing (positive), flow is directed away (negative). Conductance refers to the ability of the substance or surface to transport energy.\n",
    "\n",
    "At the surface of the Earth, there are three important transport-driven processes – **conduction**, **convection**, and **evaporation** – each of which is associated with an energy flux.\n",
    "\n",
    "\n",
    "## 3.1. Ground Heat Flux\n",
    "\n",
    "The transfer of energy between two materials in contact with one another is known as **conduction**. Conduction is driven by temperature gradients between the two surfaces. In the Earth system context, thermal conduction of heat into the soil is quantified by the **ground heat flux**, $G$. In most landscapes, very little energy is stored in the ground, so $G$ is often ignored in the landscape energy balance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2. Sensible Heat Flux 🌡️\n",
    "\n",
    "**Sensible heat flux**, $H$, refers to the loss of energy from the surface to the atmosphere via **convection**. Like conduction, convection is driven by differences in temperature, but this time between the *surface* and the *air*. That is,\n",
    "\n",
    "\\begin{equation}\n",
    "\\tag{3.2}\n",
    "    H \\ \\propto \\ T_s - T_a\n",
    "\\end{equation}\n",
    "\n",
    "This proportionality between sensible heat flux and the temperature difference is determined by the properties of the surface and the turbulent flow across the surface:\n",
    "\n",
    "\\begin{equation}\n",
    "\\tag{3.2}\n",
    "    H \\ = \\ k_H \\rho_a c_p (T_s - T_a)\n",
    "\\end{equation}\n",
    "\n",
    "where $k_H$ is the atmospheric conductivity, $\\rho_a$ is the density of air, and $c_p$ is the specific heat capacity of air (at constant pressure). Heat capacity refers to the amount of energy required to change the temperature of a substance. Though $c_p$ varies slightly with air temperature, to simplify our calculations we will assume a constant value of $1004.67 \\text{ J kg}^{-1} \\text{ K}^{-1}$ for $c_p$.\n",
    "\n",
    "The atmospheric conductivity depends on the roughness of the surface and the wind speed. While we cannot measure this quantity directly, we can use **eddy covariance** to calculate $H$. In the air, heat is transported by the turbulent flow of air in multiple layers. By averaging the product of the fluctuations of temperature and vertical wind, we can determine the amount of energy transported to or from the surface via the air – i.e. the sensible heat flux. Thus, our equation for $H$ becomes\n",
    "\n",
    "\\begin{equation}\n",
    "\\tag{3.3}\n",
    "    H \\ = \\ \\overline{\\rho_{a}} \\ c_p \\ \\overline{w'T'}\n",
    "\\end{equation}\n",
    "\n",
    "The overbars in this equation represent averages taken over a period of time when the mean vertical flow is negligible (conventionally 30 minutes). This method relies on sensors to measure the fluctuations of temperature, wind, and gas concentrations at very high frequencies in order to adequately sample the eddies that are responsible for transport. The two sensors - an anemometer and a gas analyzer – must be a close together as possible to effectively sample the same pocket of air at the same time.\n",
    "\n",
    "<img src=\"./assets/towersensors.jpeg\" alt=\"sensors\" width=\"600\"/>\n",
    "\n",
    "The gas analyzer provides a measurement of the concentration of water vapor in the air, which allows for calculation of air density over time. The average air density over any period of time can be expressed as the sum of the average density of dry air, $\\overline{\\rho_d}$, and the average vapor density, $\\overline{\\rho_v}$:\n",
    "\n",
    "\\begin{equation}\n",
    "\\tag{3.4}\n",
    "    \\overline{\\rho_{a}} \\ = \\ \\overline{\\rho_{v}} + \\overline{\\rho_d}\n",
    "\\end{equation}\n",
    "\n",
    "Because we know the concentration of water vapor in the air, we can determine the **atmospheric vapor pressure** (or partial pressure of water vapor in the air), $\\overline{e_a}$. Using the ideal gas law, which relates the amount, pressure, and temperature of a gas, we can calculate vapor density:\n",
    "\n",
    "\\begin{equation}\n",
    "\\tag{3.5}\n",
    "    \\overline{\\rho_v} \\ = \\ \\frac{M_{_{\\text{H}_2\\text{O}}} \\cdot \\overline{e_a}}{R \\ \\overline{T_a}}\n",
    "\\end{equation}\n",
    "\n",
    "where $M_{_{\\text{H}_2\\text{O}}}$ is the molar mass of water, $R$ is the ideal gas constant, and $\\overline{T_a}$ is the average air temperature.\n",
    "\n",
    "Similarly, we can compute the dry air density over the same time period using the partial pressure of dry air, which is simply the difference between total air pressure, $\\overline{P_a}$, and the vapor pressure:\n",
    "\n",
    "\\begin{equation}\n",
    "\\tag{3.6}\n",
    "    \\overline{\\rho_d} \\ = \\ \\frac{M_{_{\\text{air}}} \\left( \\overline{P_a} \\ - \\ \\overline{e_a}\\right)}{R \\ \\overline{T_a}}\n",
    "\\end{equation}\n",
    "\n",
    "where $M_{_{\\text{air}}}$ is the molar mass of dry air."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          P_a    e_a       T_a  cov_Ts_Uz  cov_h2o_Uz\n",
      "TIMESTAMP                                                            \n",
      "2016-08-01 00:00:00  83.66614  1.278  15.16205  -0.024011    0.013023\n",
      "2016-08-01 00:30:00  83.66602  1.268  14.96254  -0.006669    0.008141\n",
      "2016-08-01 01:00:00  83.66491  1.285  14.89313  -0.008048    0.008641\n",
      "2016-08-01 01:30:00  83.65948  1.307  14.44728  -0.003730    0.007857\n",
      "2016-08-01 02:00:00  83.65968  1.237  14.54416   0.002495    0.003422\n",
      "...                       ...    ...       ...        ...         ...\n",
      "2019-12-31 21:30:00  83.79510  1.463  16.00043   0.000245   -0.000018\n",
      "2019-12-31 22:00:00  83.78394  1.488  15.82906   0.004016    0.001352\n",
      "2019-12-31 22:30:00  83.79444  1.451  14.92903   0.000014    0.000619\n",
      "2019-12-31 23:00:00  83.78236  1.471  14.86763  -0.005701   -0.000977\n",
      "2019-12-31 23:30:00  83.74983  1.463  14.76210   0.005533    0.002608\n",
      "\n",
      "[14585 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "# not sure if I was suppose to write anything herer. \n",
    "#flux\n",
    "\n",
    "#actually we'll go ahead and use this cell to convert t_Celsius to t_k\n",
    "def convertK(T_a):\n",
    "    T_K = T_a + 273.15\n",
    "\n",
    "    return T_K\n",
    "              # apply this equation to the T_a column in 'flux'\n",
    "print(flux)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3. Latent Heat Flux 💧\n",
    "\n",
    "**Latent heat flux**, $\\lambda E$, is the energy flux associated with phase changes (**evaporation** and **condensation**) between water at the Earth's surface and water vapor in the air. $\\lambda E$ is driven by gradients of *vapor pressure* between the surface ($e_s$) and the air ($e_a$):\n",
    "\n",
    "\\begin{equation}\n",
    "\\tag{3.7}\n",
    "    \\lambda E \\ \\propto \\ e_s - \\ e_a\n",
    "\\end{equation}\n",
    "\n",
    "This relationship also depends on the density and total pressure of the air, the atmospheric conductivity to vapor transport, $k_E$, and the latent heat of vaporization of water, $\\lambda_v$ (the energy required to convert one mole of H<sub>2</sub>O from liquid to gas at 100$^{\\circ}$C):\n",
    "\n",
    "\n",
    "\\begin{equation}\n",
    "\\tag{3.8}\n",
    "    \\lambda E \\ = \\ k_E \\lambda_v \\rho_a \\frac{M_{_{\\text{air}}}}{M_{_{\\text{H}_2\\text{O}}}} \\frac{e_s - e_a}{P_a}\n",
    "\\end{equation}\n",
    "\n",
    "Like the sensible heat flux, it is difficult to measure $\\lambda E$ directly, but we can calculate it using eddy covariance:\n",
    "\n",
    "\\begin{equation}\n",
    "\\tag{3.9}\n",
    "    \\lambda E \\ = \\ \\overline{\\rho_a} \\lambda_v \\overline{w' \\chi_{v}'}\n",
    "\\end{equation}\n",
    "\n",
    "where $\\chi_v$ is the mole fraction of water vapor in the air.\n",
    "\n",
    "While Equation 3.9 provides a rough approximation of latent heat flux, in practice, a number of corrections must be made to this equation to account for the separation of the sensors and the varying density of air due to both sensible and latent heat fluxes. These corrections have been implemented in a function (`lhf.py`) for ease of our computations. Thus, we can import the function and calculate $\\lambda E$ as follows:\n",
    "\n",
    "```python \n",
    "from lhf import lhf_wpl\n",
    "\n",
    "lhf_wpl(df)\n",
    "```\n",
    "\n",
    "For now, we'll just import the function.\n",
    "\n",
    "<div class=\"run\">\n",
    "    ▶️ <b> Run the cell below. </b>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import lhf\n",
    "from lhf import lhf_wpl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"border-top: 0.5px solid gray; margin-top: 6pt; margin-bottom: 12pt\"></hr>\n",
    "\n",
    "## Problems\n",
    "\n",
    "### 3.1. Density of air\n",
    "\n",
    "Before calculating $H$, it's useful to calculate the vapor density and densities of dry and wet air.\n",
    "\n",
    "<div class=\"practice\">\n",
    "    📚  <b> Question 3.1. </b> \n",
    "    Use equations 3.4, 3.5, and 3.6 to calculate $\\rho_v$, $\\rho_d$, and $\\rho_a$. Hint: be sure to convert temperature to K and check your units!. To make your life easier later on, add $T$ in K and each of the densities to the original <span class=\"code\">DataFrame</span> with the following names: <code>'T_K'</code>, <code>'rho_v'</code>, <code>'rho_d'</code>, and <code>'rho_a'</code>.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>2016-08-01 00:30:00</th>\n",
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       "      <td>1.005890</td>\n",
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       "    <tr>\n",
       "      <th>2016-08-01 01:00:00</th>\n",
       "      <td>83.66491</td>\n",
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       "      <td>14.89313</td>\n",
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       "      <td>288.04313</td>\n",
       "      <td>0.009667</td>\n",
       "      <td>0.996375</td>\n",
       "      <td>1.006042</td>\n",
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       "    <tr>\n",
       "      <th>2016-08-01 01:30:00</th>\n",
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       "      <td>0.002495</td>\n",
       "      <td>0.003422</td>\n",
       "      <td>287.69416</td>\n",
       "      <td>0.009317</td>\n",
       "      <td>0.998101</td>\n",
       "      <td>1.007418</td>\n",
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       "    <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 21:30:00</th>\n",
       "      <td>83.79510</td>\n",
       "      <td>1.463</td>\n",
       "      <td>16.00043</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>-0.000018</td>\n",
       "      <td>289.15043</td>\n",
       "      <td>0.010964</td>\n",
       "      <td>0.991983</td>\n",
       "      <td>1.002947</td>\n",
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       "    <tr>\n",
       "      <th>2019-12-31 22:00:00</th>\n",
       "      <td>83.78394</td>\n",
       "      <td>1.488</td>\n",
       "      <td>15.82906</td>\n",
       "      <td>0.004016</td>\n",
       "      <td>0.001352</td>\n",
       "      <td>288.97906</td>\n",
       "      <td>0.011158</td>\n",
       "      <td>0.992136</td>\n",
       "      <td>1.003293</td>\n",
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       "    <tr>\n",
       "      <th>2019-12-31 22:30:00</th>\n",
       "      <td>83.79444</td>\n",
       "      <td>1.451</td>\n",
       "      <td>14.92903</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000619</td>\n",
       "      <td>288.07903</td>\n",
       "      <td>0.010914</td>\n",
       "      <td>0.995810</td>\n",
       "      <td>1.006724</td>\n",
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       "    <tr>\n",
       "      <th>2019-12-31 23:00:00</th>\n",
       "      <td>83.78236</td>\n",
       "      <td>1.471</td>\n",
       "      <td>14.86763</td>\n",
       "      <td>-0.005701</td>\n",
       "      <td>-0.000977</td>\n",
       "      <td>288.01763</td>\n",
       "      <td>0.011067</td>\n",
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       "    <tr>\n",
       "      <th>2019-12-31 23:30:00</th>\n",
       "      <td>83.74983</td>\n",
       "      <td>1.463</td>\n",
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       "      <td>287.91210</td>\n",
       "      <td>0.011011</td>\n",
       "      <td>0.995702</td>\n",
       "      <td>1.006713</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>14585 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          P_a    e_a       T_a  cov_Ts_Uz  cov_h2o_Uz  \\\n",
       "TIMESTAMP                                                               \n",
       "2016-08-01 00:00:00  83.66614  1.278  15.16205  -0.024011    0.013023   \n",
       "2016-08-01 00:30:00  83.66602  1.268  14.96254  -0.006669    0.008141   \n",
       "2016-08-01 01:00:00  83.66491  1.285  14.89313  -0.008048    0.008641   \n",
       "2016-08-01 01:30:00  83.65948  1.307  14.44728  -0.003730    0.007857   \n",
       "2016-08-01 02:00:00  83.65968  1.237  14.54416   0.002495    0.003422   \n",
       "...                       ...    ...       ...        ...         ...   \n",
       "2019-12-31 21:30:00  83.79510  1.463  16.00043   0.000245   -0.000018   \n",
       "2019-12-31 22:00:00  83.78394  1.488  15.82906   0.004016    0.001352   \n",
       "2019-12-31 22:30:00  83.79444  1.451  14.92903   0.000014    0.000619   \n",
       "2019-12-31 23:00:00  83.78236  1.471  14.86763  -0.005701   -0.000977   \n",
       "2019-12-31 23:30:00  83.74983  1.463  14.76210   0.005533    0.002608   \n",
       "\n",
       "                           T_K     rho_v     rho_d     rho_a  \n",
       "TIMESTAMP                                                     \n",
       "2016-08-01 00:00:00  288.31205  0.009605  0.995545  1.005150  \n",
       "2016-08-01 00:30:00  288.11254  0.009536  0.996354  1.005890  \n",
       "2016-08-01 01:00:00  288.04313  0.009667  0.996375  1.006042  \n",
       "2016-08-01 01:30:00  287.59728  0.009847  0.997587  1.007435  \n",
       "2016-08-01 02:00:00  287.69416  0.009317  0.998101  1.007418  \n",
       "...                        ...       ...       ...       ...  \n",
       "2019-12-31 21:30:00  289.15043  0.010964  0.991983  1.002947  \n",
       "2019-12-31 22:00:00  288.97906  0.011158  0.992136  1.003293  \n",
       "2019-12-31 22:30:00  288.07903  0.010914  0.995810  1.006724  \n",
       "2019-12-31 23:00:00  288.01763  0.011067  0.995634  1.006701  \n",
       "2019-12-31 23:30:00  287.91210  0.011011  0.995702  1.006713  \n",
       "\n",
       "[14585 rows x 9 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate pv, pd, pa (convert to K and check units for conversions)\n",
    "def convertK(T_a):\n",
    "    T_K = T_a + 273.15\n",
    "\n",
    "    return T_K\n",
    "\n",
    "#flux.T_a.apply(convertK)\n",
    "\n",
    "flux['T_K'] = flux.T_a + 273.15  #succsesfully created column that has temperature in K\n",
    "\n",
    "flux['rho_v'] =  (18.01528 * flux['e_a']) / ( 8.314 * flux['T_K'])\n",
    "flux['rho_d'] = (28.9647 * (flux['P_a'] - flux['e_a']) / (8.314 * flux['T_K']))\n",
    "flux['rho_a'] = flux['rho_v'] + flux['rho_d']\n",
    "\n",
    "\n",
    "\n",
    "   # vakues of rho_a, rho_v, rho_\n",
    "flux"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2. Calculate $H$\n",
    "\n",
    "Now that you've determined $\\rho_a$, you can use this value to calculate sensible heat flux.\n",
    "\n",
    "<div class=\"practice\">\n",
    "    📚  <b> Question 3.2. </b> \n",
    "    Use Equation 3.3 to determine sensible heat flux, $H$.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>2016-08-01 00:30:00</th>\n",
       "      <td>83.66602</td>\n",
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       "      <th>2016-08-01 01:00:00</th>\n",
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       "      <th>2016-08-01 01:30:00</th>\n",
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       "      <td>-3.775342</td>\n",
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       "    <tr>\n",
       "      <th>2016-08-01 02:00:00</th>\n",
       "      <td>83.65968</td>\n",
       "      <td>1.237</td>\n",
       "      <td>14.54416</td>\n",
       "      <td>0.002495</td>\n",
       "      <td>0.003422</td>\n",
       "      <td>287.69416</td>\n",
       "      <td>0.009317</td>\n",
       "      <td>0.998101</td>\n",
       "      <td>1.007418</td>\n",
       "      <td>2.525041</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2019-12-31 21:30:00</th>\n",
       "      <td>83.79510</td>\n",
       "      <td>1.463</td>\n",
       "      <td>16.00043</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>-0.000018</td>\n",
       "      <td>289.15043</td>\n",
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       "      <td>0.991983</td>\n",
       "      <td>1.002947</td>\n",
       "      <td>0.247042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 22:00:00</th>\n",
       "      <td>83.78394</td>\n",
       "      <td>1.488</td>\n",
       "      <td>15.82906</td>\n",
       "      <td>0.004016</td>\n",
       "      <td>0.001352</td>\n",
       "      <td>288.97906</td>\n",
       "      <td>0.011158</td>\n",
       "      <td>0.992136</td>\n",
       "      <td>1.003293</td>\n",
       "      <td>4.047757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 22:30:00</th>\n",
       "      <td>83.79444</td>\n",
       "      <td>1.451</td>\n",
       "      <td>14.92903</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000619</td>\n",
       "      <td>288.07903</td>\n",
       "      <td>0.010914</td>\n",
       "      <td>0.995810</td>\n",
       "      <td>1.006724</td>\n",
       "      <td>0.014109</td>\n",
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       "    <tr>\n",
       "      <th>2019-12-31 23:00:00</th>\n",
       "      <td>83.78236</td>\n",
       "      <td>1.471</td>\n",
       "      <td>14.86763</td>\n",
       "      <td>-0.005701</td>\n",
       "      <td>-0.000977</td>\n",
       "      <td>288.01763</td>\n",
       "      <td>0.011067</td>\n",
       "      <td>0.995634</td>\n",
       "      <td>1.006701</td>\n",
       "      <td>-5.765723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 23:30:00</th>\n",
       "      <td>83.74983</td>\n",
       "      <td>1.463</td>\n",
       "      <td>14.76210</td>\n",
       "      <td>0.005533</td>\n",
       "      <td>0.002608</td>\n",
       "      <td>287.91210</td>\n",
       "      <td>0.011011</td>\n",
       "      <td>0.995702</td>\n",
       "      <td>1.006713</td>\n",
       "      <td>5.595704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14585 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          P_a    e_a       T_a  cov_Ts_Uz  cov_h2o_Uz  \\\n",
       "TIMESTAMP                                                               \n",
       "2016-08-01 00:00:00  83.66614  1.278  15.16205  -0.024011    0.013023   \n",
       "2016-08-01 00:30:00  83.66602  1.268  14.96254  -0.006669    0.008141   \n",
       "2016-08-01 01:00:00  83.66491  1.285  14.89313  -0.008048    0.008641   \n",
       "2016-08-01 01:30:00  83.65948  1.307  14.44728  -0.003730    0.007857   \n",
       "2016-08-01 02:00:00  83.65968  1.237  14.54416   0.002495    0.003422   \n",
       "...                       ...    ...       ...        ...         ...   \n",
       "2019-12-31 21:30:00  83.79510  1.463  16.00043   0.000245   -0.000018   \n",
       "2019-12-31 22:00:00  83.78394  1.488  15.82906   0.004016    0.001352   \n",
       "2019-12-31 22:30:00  83.79444  1.451  14.92903   0.000014    0.000619   \n",
       "2019-12-31 23:00:00  83.78236  1.471  14.86763  -0.005701   -0.000977   \n",
       "2019-12-31 23:30:00  83.74983  1.463  14.76210   0.005533    0.002608   \n",
       "\n",
       "                           T_K     rho_v     rho_d     rho_a          H  \n",
       "TIMESTAMP                                                                \n",
       "2016-08-01 00:00:00  288.31205  0.009605  0.995545  1.005150 -24.246873  \n",
       "2016-08-01 00:30:00  288.11254  0.009536  0.996354  1.005890  -6.739836  \n",
       "2016-08-01 01:00:00  288.04313  0.009667  0.996375  1.006042  -8.134547  \n",
       "2016-08-01 01:30:00  287.59728  0.009847  0.997587  1.007435  -3.775342  \n",
       "2016-08-01 02:00:00  287.69416  0.009317  0.998101  1.007418   2.525041  \n",
       "...                        ...       ...       ...       ...        ...  \n",
       "2019-12-31 21:30:00  289.15043  0.010964  0.991983  1.002947   0.247042  \n",
       "2019-12-31 22:00:00  288.97906  0.011158  0.992136  1.003293   4.047757  \n",
       "2019-12-31 22:30:00  288.07903  0.010914  0.995810  1.006724   0.014109  \n",
       "2019-12-31 23:00:00  288.01763  0.011067  0.995634  1.006701  -5.765723  \n",
       "2019-12-31 23:30:00  287.91210  0.011011  0.995702  1.006713   5.595704  \n",
       "\n",
       "[14585 rows x 10 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 𝐻 = 𝜌𝑎 * 𝑐𝑝 * 𝑤′𝑇′     is the equation\n",
    "\n",
    "flux['H'] = flux['rho_a'] * c_p * flux['cov_Ts_Uz']\n",
    "flux"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3. Latent heat flux\n",
    "\n",
    "<div class=\"practice\">\n",
    "    📚  <b> Question 3.3. </b> \n",
    "    Use the <code>lhf_wpl</code> function to determine latent heat flux, $\\lambda E$.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>P_a</th>\n",
       "      <th>e_a</th>\n",
       "      <th>T_a</th>\n",
       "      <th>cov_Ts_Uz</th>\n",
       "      <th>cov_h2o_Uz</th>\n",
       "      <th>T_K</th>\n",
       "      <th>rho_v</th>\n",
       "      <th>rho_d</th>\n",
       "      <th>rho_a</th>\n",
       "      <th>H</th>\n",
       "      <th>lmbE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMESTAMP</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-08-01 00:00:00</th>\n",
       "      <td>83.66614</td>\n",
       "      <td>1.278</td>\n",
       "      <td>15.16205</td>\n",
       "      <td>-0.024011</td>\n",
       "      <td>0.013023</td>\n",
       "      <td>288.31205</td>\n",
       "      <td>0.009605</td>\n",
       "      <td>0.995545</td>\n",
       "      <td>1.005150</td>\n",
       "      <td>-24.246873</td>\n",
       "      <td>32.267734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-01 00:30:00</th>\n",
       "      <td>83.66602</td>\n",
       "      <td>1.268</td>\n",
       "      <td>14.96254</td>\n",
       "      <td>-0.006669</td>\n",
       "      <td>0.008141</td>\n",
       "      <td>288.11254</td>\n",
       "      <td>0.009536</td>\n",
       "      <td>0.996354</td>\n",
       "      <td>1.005890</td>\n",
       "      <td>-6.739836</td>\n",
       "      <td>20.170297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-01 01:00:00</th>\n",
       "      <td>83.66491</td>\n",
       "      <td>1.285</td>\n",
       "      <td>14.89313</td>\n",
       "      <td>-0.008048</td>\n",
       "      <td>0.008641</td>\n",
       "      <td>288.04313</td>\n",
       "      <td>0.009667</td>\n",
       "      <td>0.996375</td>\n",
       "      <td>1.006042</td>\n",
       "      <td>-8.134547</td>\n",
       "      <td>21.412381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-01 01:30:00</th>\n",
       "      <td>83.65948</td>\n",
       "      <td>1.307</td>\n",
       "      <td>14.44728</td>\n",
       "      <td>-0.003730</td>\n",
       "      <td>0.007857</td>\n",
       "      <td>287.59728</td>\n",
       "      <td>0.009847</td>\n",
       "      <td>0.997587</td>\n",
       "      <td>1.007435</td>\n",
       "      <td>-3.775342</td>\n",
       "      <td>19.475918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-01 02:00:00</th>\n",
       "      <td>83.65968</td>\n",
       "      <td>1.237</td>\n",
       "      <td>14.54416</td>\n",
       "      <td>0.002495</td>\n",
       "      <td>0.003422</td>\n",
       "      <td>287.69416</td>\n",
       "      <td>0.009317</td>\n",
       "      <td>0.998101</td>\n",
       "      <td>1.007418</td>\n",
       "      <td>2.525041</td>\n",
       "      <td>8.475598</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 21:30:00</th>\n",
       "      <td>83.79510</td>\n",
       "      <td>1.463</td>\n",
       "      <td>16.00043</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>-0.000018</td>\n",
       "      <td>289.15043</td>\n",
       "      <td>0.010964</td>\n",
       "      <td>0.991983</td>\n",
       "      <td>1.002947</td>\n",
       "      <td>0.247042</td>\n",
       "      <td>-0.044388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 22:00:00</th>\n",
       "      <td>83.78394</td>\n",
       "      <td>1.488</td>\n",
       "      <td>15.82906</td>\n",
       "      <td>0.004016</td>\n",
       "      <td>0.001352</td>\n",
       "      <td>288.97906</td>\n",
       "      <td>0.011158</td>\n",
       "      <td>0.992136</td>\n",
       "      <td>1.003293</td>\n",
       "      <td>4.047757</td>\n",
       "      <td>3.358266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 22:30:00</th>\n",
       "      <td>83.79444</td>\n",
       "      <td>1.451</td>\n",
       "      <td>14.92903</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000619</td>\n",
       "      <td>288.07903</td>\n",
       "      <td>0.010914</td>\n",
       "      <td>0.995810</td>\n",
       "      <td>1.006724</td>\n",
       "      <td>0.014109</td>\n",
       "      <td>1.536737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 23:00:00</th>\n",
       "      <td>83.78236</td>\n",
       "      <td>1.471</td>\n",
       "      <td>14.86763</td>\n",
       "      <td>-0.005701</td>\n",
       "      <td>-0.000977</td>\n",
       "      <td>288.01763</td>\n",
       "      <td>0.011067</td>\n",
       "      <td>0.995634</td>\n",
       "      <td>1.006701</td>\n",
       "      <td>-5.765723</td>\n",
       "      <td>-2.425356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 23:30:00</th>\n",
       "      <td>83.74983</td>\n",
       "      <td>1.463</td>\n",
       "      <td>14.76210</td>\n",
       "      <td>0.005533</td>\n",
       "      <td>0.002608</td>\n",
       "      <td>287.91210</td>\n",
       "      <td>0.011011</td>\n",
       "      <td>0.995702</td>\n",
       "      <td>1.006713</td>\n",
       "      <td>5.595704</td>\n",
       "      <td>6.475795</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14585 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          P_a    e_a       T_a  cov_Ts_Uz  cov_h2o_Uz  \\\n",
       "TIMESTAMP                                                               \n",
       "2016-08-01 00:00:00  83.66614  1.278  15.16205  -0.024011    0.013023   \n",
       "2016-08-01 00:30:00  83.66602  1.268  14.96254  -0.006669    0.008141   \n",
       "2016-08-01 01:00:00  83.66491  1.285  14.89313  -0.008048    0.008641   \n",
       "2016-08-01 01:30:00  83.65948  1.307  14.44728  -0.003730    0.007857   \n",
       "2016-08-01 02:00:00  83.65968  1.237  14.54416   0.002495    0.003422   \n",
       "...                       ...    ...       ...        ...         ...   \n",
       "2019-12-31 21:30:00  83.79510  1.463  16.00043   0.000245   -0.000018   \n",
       "2019-12-31 22:00:00  83.78394  1.488  15.82906   0.004016    0.001352   \n",
       "2019-12-31 22:30:00  83.79444  1.451  14.92903   0.000014    0.000619   \n",
       "2019-12-31 23:00:00  83.78236  1.471  14.86763  -0.005701   -0.000977   \n",
       "2019-12-31 23:30:00  83.74983  1.463  14.76210   0.005533    0.002608   \n",
       "\n",
       "                           T_K     rho_v     rho_d     rho_a          H  \\\n",
       "TIMESTAMP                                                                 \n",
       "2016-08-01 00:00:00  288.31205  0.009605  0.995545  1.005150 -24.246873   \n",
       "2016-08-01 00:30:00  288.11254  0.009536  0.996354  1.005890  -6.739836   \n",
       "2016-08-01 01:00:00  288.04313  0.009667  0.996375  1.006042  -8.134547   \n",
       "2016-08-01 01:30:00  287.59728  0.009847  0.997587  1.007435  -3.775342   \n",
       "2016-08-01 02:00:00  287.69416  0.009317  0.998101  1.007418   2.525041   \n",
       "...                        ...       ...       ...       ...        ...   \n",
       "2019-12-31 21:30:00  289.15043  0.010964  0.991983  1.002947   0.247042   \n",
       "2019-12-31 22:00:00  288.97906  0.011158  0.992136  1.003293   4.047757   \n",
       "2019-12-31 22:30:00  288.07903  0.010914  0.995810  1.006724   0.014109   \n",
       "2019-12-31 23:00:00  288.01763  0.011067  0.995634  1.006701  -5.765723   \n",
       "2019-12-31 23:30:00  287.91210  0.011011  0.995702  1.006713   5.595704   \n",
       "\n",
       "                          lmbE  \n",
       "TIMESTAMP                       \n",
       "2016-08-01 00:00:00  32.267734  \n",
       "2016-08-01 00:30:00  20.170297  \n",
       "2016-08-01 01:00:00  21.412381  \n",
       "2016-08-01 01:30:00  19.475918  \n",
       "2016-08-01 02:00:00   8.475598  \n",
       "...                        ...  \n",
       "2019-12-31 21:30:00  -0.044388  \n",
       "2019-12-31 22:00:00   3.358266  \n",
       "2019-12-31 22:30:00   1.536737  \n",
       "2019-12-31 23:00:00  -2.425356  \n",
       "2019-12-31 23:30:00   6.475795  \n",
       "\n",
       "[14585 rows x 11 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lmbE = lhf_wpl(flux, rho_v='rho_v',rho_d='rho_d',T_K='T_K',\n",
    "           cov_h2o_Uz='cov_h2o_Uz',cov_Ts_Uz='cov_Ts_Uz')\n",
    "\n",
    "flux['lmbE'] = lmbE\n",
    "\n",
    "\n",
    "#flux.plot()\n",
    "#plt.show\n",
    "flux"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 Comparing turbulent fluxes\n",
    "\n",
    "Now that you've calculated both sensible and latent heat, compare how the two vary. Consider the conditions under which $H$ and $\\lambda E$ are positive or negative. What physical processes drive these fluctuations?\n",
    "\n",
    "<div class=\"practice\">\n",
    "    📚  <b> Question 3.4. </b> \n",
    "    Plot sensible and latent heat for the last week of each year (beginning on December 25th). Be sure your plot(s) is/are appropriately scaled and labeled. How do they compare? When does $H$ exceed $\\lambda E$? When does $\\lambda E$ exceed $H$? Why do are the fluxes so different for the same week of 2016 and 2019 (Hint: there is one column of data you've been provided that you have not yet used.)?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'K*m*S^-1')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot H and lambdaE\n",
    "# is the dataframe thats being manipulated\n",
    "# Initialize empty figure\n",
    "fig = plt.figure()\n",
    "# Add two axes\n",
    "\n",
    "# have one large subplot\n",
    "ax = fig.add_subplot(111) \n",
    "\n",
    "# add the data to each of the two boxes. (last week of each year)\n",
    "ax1 = fig.add_subplot(2,1,1)\n",
    "flux.lmbE['2016-12-25':'2016-12-31'].plot(title = '2016')\n",
    "flux.H['2016-12-25':'2016-12-31'].plot(color = 'red')\n",
    "\n",
    "ax2 = fig.add_subplot(2,1,2)\n",
    "flux.lmbE['2019-12-25':'2019-12-31'].plot(title = '2019')\n",
    "flux.H['2019-12-25':'2019-12-31'].plot(color = 'red')\n",
    "\n",
    "# set labels for both graphs\n",
    "ax.set_xlabel('Date of the week')\n",
    "ax.set_ylabel('K*m*S^-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TIMESTAMP\n",
       "2019-12-25 00:00:00   -0.981773\n",
       "2019-12-25 00:30:00    9.280123\n",
       "2019-12-25 01:00:00    1.905255\n",
       "2019-12-25 01:30:00    2.981449\n",
       "2019-12-25 02:00:00    1.127778\n",
       "                         ...   \n",
       "2019-12-31 21:30:00   -0.044388\n",
       "2019-12-31 22:00:00    3.358266\n",
       "2019-12-31 22:30:00    1.536737\n",
       "2019-12-31 23:00:00   -2.425356\n",
       "2019-12-31 23:30:00    6.475795\n",
       "Name: lmbE, Length: 336, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# H exceeds lambda E during the evenings and night when energy lost due to convection \n",
    "#is greater than the energy changes through phases changes. The values appear different\n",
    "# across the two seperate weeks becuase the eddy covarienace of the latenet heat flux values \n",
    "# are different for the two weeks sampled."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Putting it all together\n",
    "\n",
    "<hr style=\"border-top: 0.5px solid gray; margin-top: 10pt; margin-bottom: 12pt\"></hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that you have all the terms in Equation 1.2 (with the exception of $G$, which we will assume is negligible), you can calculate the overall energy balance of the landscape:\n",
    "\n",
    "\\begin{equation}\n",
    "\\tag{4.1}\n",
    "    \\frac{dQ}{dt} \\ = \\ R_n \\ - \\ H \\ - \\ \\lambda E\n",
    "\\end{equation}\n",
    "\n",
    "\n",
    "In order to compare the radiation data to the flux data, you must first ensure that the two datasets have the same temporal resolution. You can do this in <span class=\"code\">pandas</span> using `df.resample()`, which resamples the dataset over a defined interval. For our dataset, this looks like:\n",
    "\n",
    "```python\n",
    "rad30 = rad.resample(pd.tseries.offsets.Minute(30)).mean()\n",
    "```\n",
    "\n",
    "<div class=\"example\">\n",
    "    ✏️ <b> Try it. </b> \n",
    "    Copy and paste the code above to generate a new <span class=\"codeb\">DataFrame</span> with half-hourly averages of the radiation data. (Note that this will also resample the precipitation data, which is not useful. You can choose to either ignore this column or remove it from the new <span class=\"codeb\">DataFrame</span>.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "59904\n",
      "14585\n",
      "<bound method NDFrame.head of                        DOY     SW_in    SW_out       LW_in      LW_out  P_mm  \\\n",
      "TIMESTAMP                                                                      \n",
      "2016-08-01 00:00:00  214.0 -0.712000  1.421667  349.233333  386.000000   0.0   \n",
      "2016-08-01 00:30:00  214.0 -1.419000  1.627000  334.933333  384.900000   0.0   \n",
      "2016-08-01 01:00:00  214.0 -1.841000  1.836333  332.366667  382.933333   0.0   \n",
      "2016-08-01 01:30:00  214.0 -1.021000  2.132667  328.633333  382.133333   0.0   \n",
      "2016-08-01 02:00:00  214.0 -2.068667  1.617000  318.600000  379.833333   0.0   \n",
      "...                    ...       ...       ...         ...         ...   ...   \n",
      "2019-12-31 21:30:00  365.0 -1.931667  2.422667  323.833333  380.966667   0.0   \n",
      "2019-12-31 22:00:00  365.0 -1.645667  1.962667  324.966667  382.166667   0.0   \n",
      "2019-12-31 22:30:00  365.0 -1.009000  1.317000  342.500000  383.533333   0.0   \n",
      "2019-12-31 23:00:00  365.0 -0.835333  1.351667  345.066667  385.033333   0.0   \n",
      "2019-12-31 23:30:00  365.0 -1.636667  1.353333  330.566667  383.433333   0.0   \n",
      "\n",
      "                            Qav      r_net  \n",
      "TIMESTAMP                                   \n",
      "2016-08-01 00:00:00  347.099667 -38.900333  \n",
      "2016-08-01 00:30:00  331.887333 -53.012667  \n",
      "2016-08-01 01:00:00  328.689333 -54.244000  \n",
      "2016-08-01 01:30:00  325.479667 -56.653667  \n",
      "2016-08-01 02:00:00  314.914333 -64.919000  \n",
      "...                         ...        ...  \n",
      "2019-12-31 21:30:00  319.479000 -61.487667  \n",
      "2019-12-31 22:00:00  321.358333 -60.808333  \n",
      "2019-12-31 22:30:00  340.174000 -43.359333  \n",
      "2019-12-31 23:00:00  342.879667 -42.153667  \n",
      "2019-12-31 23:30:00  327.576667 -55.856667  \n",
      "\n",
      "[59904 rows x 8 columns]>\n",
      "<bound method NDFrame.head of                           P_a    e_a       T_a  cov_Ts_Uz  cov_h2o_Uz  \\\n",
      "TIMESTAMP                                                               \n",
      "2016-08-01 00:00:00  83.66614  1.278  15.16205  -0.024011    0.013023   \n",
      "2016-08-01 00:30:00  83.66602  1.268  14.96254  -0.006669    0.008141   \n",
      "2016-08-01 01:00:00  83.66491  1.285  14.89313  -0.008048    0.008641   \n",
      "2016-08-01 01:30:00  83.65948  1.307  14.44728  -0.003730    0.007857   \n",
      "2016-08-01 02:00:00  83.65968  1.237  14.54416   0.002495    0.003422   \n",
      "...                       ...    ...       ...        ...         ...   \n",
      "2019-12-31 21:30:00  83.79510  1.463  16.00043   0.000245   -0.000018   \n",
      "2019-12-31 22:00:00  83.78394  1.488  15.82906   0.004016    0.001352   \n",
      "2019-12-31 22:30:00  83.79444  1.451  14.92903   0.000014    0.000619   \n",
      "2019-12-31 23:00:00  83.78236  1.471  14.86763  -0.005701   -0.000977   \n",
      "2019-12-31 23:30:00  83.74983  1.463  14.76210   0.005533    0.002608   \n",
      "\n",
      "                           T_K     rho_v     rho_d     rho_a          H  \\\n",
      "TIMESTAMP                                                                 \n",
      "2016-08-01 00:00:00  288.31205  0.009605  0.995545  1.005150 -24.246873   \n",
      "2016-08-01 00:30:00  288.11254  0.009536  0.996354  1.005890  -6.739836   \n",
      "2016-08-01 01:00:00  288.04313  0.009667  0.996375  1.006042  -8.134547   \n",
      "2016-08-01 01:30:00  287.59728  0.009847  0.997587  1.007435  -3.775342   \n",
      "2016-08-01 02:00:00  287.69416  0.009317  0.998101  1.007418   2.525041   \n",
      "...                        ...       ...       ...       ...        ...   \n",
      "2019-12-31 21:30:00  289.15043  0.010964  0.991983  1.002947   0.247042   \n",
      "2019-12-31 22:00:00  288.97906  0.011158  0.992136  1.003293   4.047757   \n",
      "2019-12-31 22:30:00  288.07903  0.010914  0.995810  1.006724   0.014109   \n",
      "2019-12-31 23:00:00  288.01763  0.011067  0.995634  1.006701  -5.765723   \n",
      "2019-12-31 23:30:00  287.91210  0.011011  0.995702  1.006713   5.595704   \n",
      "\n",
      "                          lmbE  \n",
      "TIMESTAMP                       \n",
      "2016-08-01 00:00:00  32.267734  \n",
      "2016-08-01 00:30:00  20.170297  \n",
      "2016-08-01 01:00:00  21.412381  \n",
      "2016-08-01 01:30:00  19.475918  \n",
      "2016-08-01 02:00:00   8.475598  \n",
      "...                        ...  \n",
      "2019-12-31 21:30:00  -0.044388  \n",
      "2019-12-31 22:00:00   3.358266  \n",
      "2019-12-31 22:30:00   1.536737  \n",
      "2019-12-31 23:00:00  -2.425356  \n",
      "2019-12-31 23:30:00   6.475795  \n",
      "\n",
      "[14585 rows x 11 columns]>\n"
     ]
    }
   ],
   "source": [
    "rad30 = rad.resample(pd.tseries.offsets.Minute(30)).mean()\n",
    "\n",
    "print(len(rad30))\n",
    "print(len(flux))\n",
    "\n",
    "print(rad30.head)\n",
    "print(flux.head)    # checks out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1. Residual energy\n",
    "\n",
    "Now that all the data are at the same frequency, calculate $\\frac{dQ}{dt}$ using Equation 4.1.\n",
    "\n",
    "<div class=\"practice\">\n",
    "    📚  <b> Question 4.1. </b> \n",
    "    Calculate the residual energy flux, $\\frac{dQ}{dt}$, using Equation 4.1.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TIMESTAMP\n",
       "2016-08-01 00:00:00   -46.921194\n",
       "2016-08-01 00:30:00   -66.443127\n",
       "2016-08-01 01:00:00   -67.521834\n",
       "2016-08-01 01:30:00   -72.354243\n",
       "2016-08-01 02:00:00   -75.919639\n",
       "                         ...    \n",
       "2019-12-31 21:30:00   -61.690321\n",
       "2019-12-31 22:00:00   -68.214356\n",
       "2019-12-31 22:30:00   -44.910179\n",
       "2019-12-31 23:00:00   -33.962588\n",
       "2019-12-31 23:30:00   -67.928165\n",
       "Length: 59907, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculated residual energy flux\n",
    "\n",
    "# calculation for residual energy\n",
    "res_E = rad30.r_net - flux.H - flux.lmbE\n",
    "res_E"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2. Rainfall\n",
    "\n",
    "<div class=\"practice\">\n",
    "    📚  <b> Question 4.2. </b> \n",
    "    Calculate daily rainfall amounts and use a bar graph to plot daily rainfall totals on two separate subplots. Which year was wetter? \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8666666666666667\n",
      "128.96666666666667\n",
      "(array([ 267,  703, 1085, 1086, 1088, 1090, 1091, 1094, 1099, 1856, 1865,\n",
      "       1886, 2674, 4156, 4636, 4637, 4640, 4660, 5094, 5197, 5362, 5455,\n",
      "       5493, 5545, 5584, 5631]),)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gpqbGr7/+eh8xYoS7u48YMcJ/9KMfubv7hg0b/PXXX/d7773Xr776ak/2s5/9zG+44QZ3d9++fbs3NDR4OieeeKK/8cYb3tzc7EOGDPExY8a4u/t3vvMdv+eee9zdva6uzg8//PC0w+dr6dKlPnnyZHd3X7dunffp08fr6uqKEvMFF1zgDz30kLu7jx071r/2ta91iXjffPNNX7p0qe+5556tPr/77rv9yiuvdHf3UaNG+UUXXVS4QFOUUtzz58/36dOn+2WXXeZPPPFEQeN0L61YX331Vd+4caO7u99zzz1lM4+TDRgwwMePH5/2u7bWZYIHgPUDHgEu8I5shztSuDP+gH/06dPH+/bt60uXLm35sfv27dvqx/jjH/+40w9ZXV3tGzZsaPPHTYzrqKOOann/2GOP+bBhw9zdfdiwYX7rrbe6u/sbb7zhAwcOzDiuQjn33HP9pZdeKkrMNTU1vnjxYnd3b25u9r333jvveNoTdbzJUjcSZ511lr/xxhvu7r5t2zY/8MADvbm5OZcwOqyYcSd84xvfiCQRpCqFWN3dp0yZ4qeeemoHpjw/+cSd8MEHH3h1dXXa5TLTugxMCv7xUEcTQdFPDSUzs17A8XvuuSfLly+nR48eAPTo0YMVK1ZkHHbNmjUA3HjjjQwYMIALL7yQ5cuX71RuyZIlVFdXt7yvrq5myZIlANx00008+uijVFdXc/bZZ/O73/2uIHFlsmDBAqZOncrJJ59clJj79+/fcuj9t7/9jfXr17Nq1aqCxJZOZ8SbyZIlS+jZM+gHsbKykn333TfSeBOKHXdnKqVYH3jgAYYOHZrz8B2RT9zJRo0axcUXX4yZ7fRdpnU5HyWTCMxsL+Ap4LqKio73yd3U1ER9fT2nnXYaU6ZMYeDAgfzwhz/cqZynuVw28YOPGjWKyy+/nPr6esaMGcNll11Gc5bPN8jFhg0bOP/887njjjvYZ5+O90lfiJhvu+02xo8fz/HHH8/48eOpqqqisjKaJyR1VryZZPotolIKcXeWUor10UcfZdKkSVx//fU5Dd8R+cad7PHHH+fSSy9N+11Uy29JJAIz24UgCfzZ3Z8GOOSQQ1i2bBkAy5Yt4+CDD844jgMPPJA99tiDL33pSwBceOGFTJkyhe3bt7c0wPzsZz+jurq6VYNofX09hx56KBDsPVx00UUADBw4kC1btrBy5cpChwvAtm3bOP/88/nqV7/Kl7/8ZaA4MR966KE8/fTTTJ06lVtuuQWAfffdt6zjzaS6uprFi4NnJTU1NbF27VoOOOCAfMNrU6nE3RlKKdZXXnmFW265hdGjR7PbbrvlGVlmhYg7Yfr06TQ1NXHCCScAdGhdzkfRE4EF6ewBYKa73574/Nxzz+Xhhx8G4OGHH+a8885rbzx84QtfYNy4cQCMHTuWmpoaKioqmDZtGtOmTePmm2+mR48e7L333rz11lu4O4888kjLuA877DDGjh0LwMyZM9myZUskvR+6O1dccQXHHHMMP/jBD1o+L0bMK1eubDnqGTFiBN/61rfKPt5Mkut88sknGTRoUGRHBKUUd9RKKdapU6dy5ZVXMnr06Kw3wLkqVNwJo0aNanU00JF1Oe9AivkHfApItHhPA6b17t3bV65c6YMGDfLevXv7oEGDfNWqVS0NJIcffrjvv//+vueee3pVVZXX1dW5u/uCBQv805/+tB933HE+aNAgX7hwYdrGmIkTJ/qxxx7rRx55pF999dUtjTJ1dXV+6qmner9+/bx///7+4osvph0+X6+//roDftxxx3n//v29f//+/txzzxUl5ieeeMJ79+7tffr08SuuuMK3bNnSJeK9/vrrvaqqys3Mq6qq/L//+7/d3X3z5s1+wQUX+Mc//nE/8cQTfe7cuQWPtxTjfvvtt72qqsr32GMPP+CAA7ympqbLxjp48GA/+OCDW6bjC1/4QkFjjSpud/cjjjjCZ86cmbHOttZl4D2Ch4RtBFYBdZ7ldlhdTIiIdAHqYkJERHKmRCAiEnNKBCIiMRfNBeN5WrBgQc7PLBYRian+uQ5YkomgV69esXtWqYhIPsysMddhdWpIRCTmlAhEpCSs3LCVlRu2FnsyYqkkTw2JSPzU/uIVABbcek6RpyR+dEQgIhJzSgQiIjGnRCAiEnNKBCIiMZdXIjCzIWY2y8zmmNnwDOVONLPtHX6gsoiIRC7nRGBmFcDdwFCgBrjUzGraKPcr4MVc6xIRkejkc0RwEjDH3ee5eyPwOJDuCQnfI3j6WPYP7RQRkU6TTyKoAhYnva8PP2thZlXAl4D72huZmQ0zs0lmpr4lREQ6UT6JIN3z/VKfcnMH8GN3397eyNx9pLvX5vpgBRERyU0+dxbXAz2T3lcDS1PK1AKPh8+EPQg428ya3P3vedQrIiIFlE8imAj0MbMjgCXAJcBXkgu4+xGJ12b2EPCskoCISGnJORG4e5OZXUNwNVAF8KC715nZVeH37bYLiIhI8eXV6Zy7jwHGpHyWNgG4++X51CUiItHQncUiIjGnRCAiEnOdlgjMrMLMpprZs51Vp4iItK8zjwiuBWZ2Yn0iIpKFTkkEZlYNnAP8oTPqExGR7HXWEcEdwI+A5rYKJHcx0dDQ0EmTJSIikScCM/tPYIW7T85ULrmLie7du0c9WSIiEuqMI4LTgHPNbAFBD6WDzOzRTqhXRESyEHkicPefuHu1u/ci6IbiVXf/WtT1iohIdnQfgYhIzOXVxURHufs4YFxn1ikiIpnpiEBEJOaUCEREYk6JQEQk5pQIRERiTolARCTmlAhERGJOiUBEJOaUCEREYk6JQEQk5pQIRERiTolARCTmlAhERGJOiUBEJOaUCEREYk6JQEQk5pQIRERiTolARCTmlAhEpCxs2bad5mYv9mR0SUoEIlLympudo298gZueqSv2pHRJSgQiUvK2e3Ak8NiERUWekq5JiUBEJOaUCESkbKiFIBpKBCJS8qzYE9DF5ZUIzGyImc0yszlmNjzN9181sxnh3xtm1j+f+kQknnQkEK2cE4GZVQB3A0OBGuBSM6tJKTYf+Ky79wN+DozMtT4REYlGPkcEJwFz3H2euzcCjwPnJRdw9zfc/aPw7VtAdR71SQa9hj/H7S/NKvZkiERCp4ailU8iqAIWJ72vDz9ryxXA8219aWbDzGySmU3KY5pi7c5X5xR7EkQi5a6TRFHIJxGkS9I7zSUz62lmU4FbgE+Z2bXpRubuI9291t1r85gmEemCtPmPVj6JoB7omfS+Gliaplwf4CDgOOBE4Oo0bQlSIL2GP8cTkxa3X1CkDJnpJFEU8kkEE4E+ZnaEme0KXAKMTi5gZocRNBBf7O4fuPt6YCaZTyFJB6UeLj89ZUmRpkSkbWs3b+P4m19i4oLVOY9Dp4aikXMicPcm4BrgRYKN+1/dvc7MrjKzq8JiPwMOBO4xs2lmNgM4HpiQOr7kNoKGhoZcJ0tEStS0xWv4aNM27hw7u9iTIikq8xnY3ccAY1I+uy/p9beBbwOY2V7AeOA6d1+XZlwjCS8vra2tVdoX6WISJ3W0U196OuXOYjPbBXgK+LO7P90ZdUr5mLNiA72GP8ecFeuLPSkSocTpfc+y6ffi37/JT55+JxhGySNSkScCC1p3HgBmuvvtUdcXR+W+kjw7I7jGYPS0dNcaSFdhHbwbYML81Yx6W72NdobOOCI4DbgMGBS2E0wzs7M7od7YynaPq1R0C3cVy2uqJVflvuPSFeXVRpANd/8XujFQMkgsHM3tbCFuf2kW942fxwe3DI1+oqTguoUzur35nGr1xkYG/PxlQDsLUYk8EUjn66p7XLpzuszluDv4/oc7XVsiBaZuqLuAct/udwt3FbtqApPWOjqfO9q2IB2nRCAlo9yfS163dC1zGzYUezI65C8TF9Fr+HOs3bwt8roSG/Qyn81dkhKBpDV/5cZOq6ujlxWWqnPu/BeDfzO+2JPRIQ/+awEAy9Zujryult4hCjCbO3P5jAMlAtnJM9OXcsZt43j1/eWdUl/LoX9554Gy1hmn5Qpxgscd/jFtCWfcNo5/zlpRgDEKKBF0Can9r+S7Tr+7dC0Asz7snNMc3cowD5z8y1cY/JtxxZ6MvHVmH27Wcplwx+Z06jS+Ux8sn7OX6wbEQlEikKJrOTVURq3Fy9dtZW5D9qcnbntxFlf9aXKEU1S61m3ZxqJVm1rmcz5tQclJQY3IhaPLRyPS2NTMLhVWnG5zy2d72koZ5YEOu+ufpX3pa5S//bm/+xcLVm3iyasGhnXlXpl72S7eJU1HBBFo2t5M358+z8+fnVnsSSkLurM4Om/PX820xWuKOg0LVm0Ccj8NlTpYIo/o0QSFo0QQgabw2PfRCQs7pb6usgHtykcExXLR79/ki3f/u9PrXbpmM/UfbUr7XUdnc+pRdblfXVaKumwi+POEhfx1YnGe1NWZ57x//ux7eT3ooxTk2ohYCtZt2UZzud8AQeF/+1NvfZVP/eqfKZ8W5sbBHUcEOiQolC6bCG7427v86KkZRanbCrTAZ+OBf83nK/fv9JyfslLO/dT3u+klfvPyrGJPRs46c2NqWV4dtmZTY8bvE73VKg0UTpdNBMWU2LvqaOda7Y7XnXGzVrR7pFFue9a5dkZWKv7RBbrP7tT7CNqp7KNNre9yTs1VKzc0pv1ccqdEEKFCr1t/mbiYy/84kScn1xd4zMWV6GuoXBNBVzg11BkSFwUU6udSHigcJYIIJLZnhd6uLVkTdAOwbO2WrOovF1bgDYRkL4qNaVtHrBVhwm9qZ0aX6w5BOVMiKENdbT3pVoY3lCVTo2Vr7y5J3210RUsvs4WZz4kjScmfEkEXVG6b05ZTBs1FnpAY2nGFW+HGub2NkSXm8/Z2jgh2q2y9WWprc59NGliwcmO7iWfNpkY+2pi5gbqrUyIoQ1HtgDZtb2bEmJms3RR9l8TJKqy82wgyWbJmM7e/VPpXFRXyAoM2N9zhF20lioT2EkW2Zi5bx+m3jeP+1+dlLPfJm1/m+PAJaHGlRBCBqLdnO9og0leUa554sW45v39tHo+H91901tVHheiDpi3PzljKhq1NhR9xlq7+85SyeLJaVO0zS9fs6N46sYFvr3G9vTaEFu3sES35KKh7wrzs7rN5+I0F2dXbBSkRlJG2brVPles63d6eWlRaupgocP3vf7iOax6byo+fLM79JABbtm0vWt3ZsAgu3U3ePs9esaMH20QiKNQRQXs7PBUV2TVOJzz85oKsynVFSgQRiGpPOurNdGrbW2f17tgtXAoLfWpoU2OwEa5fE/1DV8pdZzTUJ+Zve21BTdtbT0uup0ITpxwbm9T41B4lgjLW1qqb60pdrG59WxoRC7wtyvYGprzrKYOLV96ev5oZ9W13PlfIU0N3t9HTaqKO9vb4m3bKFOl/4F+98H7G8SwO+zp6c96qjOUy1xIP6oY6AlFtd6JeUIu1QbOIGotL4bLO9z8sjYenXPT7NwFYcOs5AHy4dgunjBjb8n0hb4p7sW7Hk+0a1m9teZ3tqaGVG7Zy5E+ea7ee9Vsyt/3c8Ld3W167e7vLQyksL8WiI4Iy1taef66rdOqpoc5qLC7/+wiKPQUd99oHDa3eR9VY3LR9x959to3Fkxd+1Gp6CvH7bi7xtppiUyKIQHluzqCiW3EWh4qI7iNIbD+ivmO5LO9/SNm4vj0/mh5sp4ePlQRaesktxkUJ6za3f+VYGebzglEiKGNtrU65LtCVKYcEndVmEMWVKwCVHbxqJFdLyrAx+kcpV1L9v1c+iKSeUW8vann96xeD+ynWtHOfSrcIDrHWb2n/3phyPLIrlE5JBGY2xMxmmdkcMxveGXW2Z+3mbWxujOZwsbNOceRz+WjD+q2tDtFXbdiaoXS0oupraNeKYPHetr38dtnvGz+31Ua0XBTictkoEkFqj6bpxPkZyJEnAjOrAO4GhgI1wKVmVhN1ve3p/z8vMeS3rxV7MjrEWq6uyW+LuXzdFk685RXuGDsbCM7ZnvCLV4r2/IaKDt5HkE25F+s+ZNLCj4DSTQRrN2/jjlc+aHUePeHW59/nJ0+/06nT8/epS/Iex1tZXqGTyfyVG1u9L8TmeXobj+tM3hmK8xFBZ9nUuVUAAAvhSURBVFw1dBIwx93nAZjZ48B5wHuFruihf8/f6TTAHzLcXr5w1aaM3+dqa9J1y/e/Nq9gC1jiMrgx7yxjn90r27wMb+qiNRnjSqxod46dzT67V7b8ZslXeAC88O4ydqmIfu2YvTy46WjigtVZzY97xs1l5rJ1PDtjGRfVVtP3kL13KvOL53Y8Lzqb+Zwo/9Nzjmnzu5N6HcBZxx6S9lTTNx58u+V1prqSv3tswiLmrdzI+8vWU9tr/7Tlew1/jm8MPJyeB+yRcfqzlWnarvvLNFbmeWRYtzR9h3MdmY7R01s/3yHT8x7aGs8jb7Z+TOwtY2Zyy5iZ/HjI0fxj2pKWq7k+07d7S5kPlq+PZHvQER/btYKvnnx4p9drUZ/GMLMLgCHu/u3w/WXAye5+TUq5YcAwgMMOO+yEhQs7/rzfY258QVcHiEjZOmiv3Zj008/lNKyZbXL3PXMZtjOOCNLtUu6Ufdx9JDASoLa2NqfsNOGGwbkMFoluZp3SiZr7jt4aGztwCsQ986Fwe98XWra/l7Fj4ck0jTuea5td/ZnKuweVWsqJVCPoWrm9xujENHd0mUiONR/p6k3+7fbYpYKtTc0FvZqnspvRzYzG7c10M6OyWzANic/asltlN7Y2Ne94fGmacW7b3swuFWG5dpbhj+1Swa6V3VizqREza9XbajcLxl9hRkW3zNPVWYp1diqvRGBmQ4DfAhXAH9z91pTvDbgQ+E8zOwm4HKgGInm23z677xLFaMvG7rtUFHsSpExVVkTTXJhumWxvOd2tMvP3ieE7srzvt8eu7ZaJ8/qTcyJIagQ+E6gHJprZaHdPPvc/FNgH+BD4b+A+YDfgKzlPsYiIFFQ+RwTZNAKfBzwMrAXuAHoBv3H3ujzqFRGRAsonEVQBi5Pe1wMnpyvj7v8CxpjZWOCpdCNLbiwO32/KcboqgeJ1QF942cTT1WJOJznGOMSbTiLuuMQfx2U/n3g+lk+lucqmETirhmJo3VhsZpPcvTanicpj2FKUTTxdLeZ0kmOMQ7zpJOKOS/xxXPbz3fblWm8+LUT1QM+k9+kagbMpIyIiRZRPIpgI9DGzI8xsV+ASYHRKmdHA1y1wCrDW3ZflUaeIiBRYzqeG3L3JzK4BXiS4fPRBd68zs6vC7+8DxgBnA3OATcA3sxz9yFynK89hS1E28XS1mNMZ2cbrOBmZ8r+ri+OyX5RtX+R3FouISGlTN9QiIjGnRCAiEnNFSwRJzyhYYGZzzWymmdWZ2bXh9weY2SIzazSz9Wb22fDzA83sn2a2wczuShnnxWY2IxzP/2ao+wQzeyd8PsKdYVcYmNlh4binhuM5O6LYe4b1JGK+N/wt5pnZ7PDvZTPbPyx/ZRjvdjNbZmb9Cxjz4WY2NhzHODOr7oR4k+fxy8nxhsvFbDPbZGZbM8zjuWbWHHZqmK7OW8xssZltSPl8NzP7S/g7TDCzXoWON5e4w89bLdvW+jkej6Wbz2Z2uplNCz8fnyHuz5jZFDNraus3K2asKePaaZm2NM80SY7VzPY1s2fMbLqZfWhmS8JxjDWzyLrzzCHuM81scrguTjazQUnjSruOptT3oJmtNrMtadblxDxutmA7McPMBmQViLt3+h9B4/Jc4EjgMOADgmcV7J30+klgFsG9CHcDS8Nh9wQ+BVwF3JU0zgOBRUD38P3DwOA26n8bGBiO+3lgaPj5SOD/hK9rgAURxd8DGBC+3hdoBM4CbgOWhXUPB34VlhlE0F3HVQRXYk0oYMxPAN9IqudPEcebPI//Fxgefj48fD8XOBY4neDy4z+nmceHAK+G3/9PG3WeEta7IeXz7wL3ha8vAf4S4XKebdyJ+Zy8bN+dtI78R7iMnJY8n4H9CO7kPyz8/OAMcfcC+gGPABeUWKztrcdnJv0WuwLTw3G3xAr8V9K4vwisDsv+nxKbx8cDh4avPwEsaW8dTanvM8C74e+Rui73CpfvJcAF4e8zIZs4inVE0NI9hbsvAv4InOfu64GZBHckfw64w4MIfwEcZGY93H2jB3cqb0kZ55HAB+6eeCr3K8D5qRWbWQ9gH3d/Mxz3IwQLDgQ3u+0Tvt6XiO55cPdl7j4lfFsDrAnr/gLhb0GwAnwxLP+quz9PEPNygvsxoDAx1wBjw9f/DOsuqOR4U+ZxIk7C/xcTLBd17j4OeJMgxoQjCVa0SwnuUJ/HznezJ+p8y9Nfqpxc55PA4HR7XoXQgbgT8zl52T6EHV249CTYOH4mHCYxn78CPB2uQ7j7irbidvcF7j4DiKSLzTxjTZZumb6KHduLRuBxgu1FcqwO7B3Oy+kEiaAJeIsd60vB5RD3VHdPbFfqgN3Do9RM62iy2QR3EG9OLefuC4D+BFdp4u5vAfuF486oWIkgXfcUVeFh+vHABII9hncg+LEJsl9VhnHOAY42s15mVknw4/RMU64qrK9V3eHrm4CvmVk9waWv3+tIUDn6JLAHQcyHEC5IYcwHpyl/DMFeABQm5unsSB5fIliZDsw9nMxS5vEhiRU5/H8grZeLjwiWg4Q5BInrEuB+gqPJgzo4CS3Lnrs3EfSDFVm8Ce3EnW4+78mO32IOcADBvE6ez32B/S04pTfZzL4eaRBZyiHWZOmW6cNJs71IGe4ugnVjKcF241p3bwauYMf6Eqkc4j4fmOruW8m8jiarIjhr0Fa5KmBjhu/TKlYiSLcHVkmwl3edu7f1mKM2r3V1948IDwOB14EFpO+zI1O3F5cCD7l7NcH9D38yS+2FvnDMbC/gh8BbKTG3FefRBBvCH0PBYv4h8Fkzmwp8luCwMpK+W8J425vHbQrjnUdwCD6e4JRAR59ElHW3J4VSoLgfAj5P6/lcCZwAnBN+d6OZ9S3MVOemQLGmLtPp5nHqPPs8MA04lGDn6i4z+zZQC/y6o9PRUR2N28yOBX4FXJn4KE2xdMtle+VyWr6LlQhSu544jOD89J/d/enws43AcdByasNp51SNuz/j7ie7+0CC9oXZZlYRNqZNM7Obw7qTDxWTu724AvhrOK43gd3p+B5nVsxsF4IF51l2LOjLCfdqwphXJJXvB3wLGOPuLQ+GzTdmd1/q7l929+OBG8LP1kYYb/I8Xp44bA3/r6L1crE/rfduINi72ZPg3Gxv4BNm9uWUeDNpWfbCPc59CU4jRCLLuFekGXQjrX+LBuC3yfOZIGn3Av4NfB94jeDUQFHkEWsr6ZZpoGdiHhM81yR1W/BNgtNk7u5zCE633gCcG+5xR6ajcVtwQcbfgK+7+9zw47TraBvrco/Ucknv62l9FJ1dtz7ZNCQU+o9gT2YecARBg85q4JGUMk/RurF4Wcr3l5PUyBR+dnD4f3+CvYO+bdQ/kaAhJdHYcnb4+fPA5eHrxGGmRRC/EZzbuyPlt/gNwWHfsYSNp2H5wwgOmX8RQcwHAd3C17cAN0cZb8rnv6Z1g9qvU5aLRcBjGeJdDXyvnbpTG02vpnVj8V8jXM6zjft/0yzbd6f8FnXhctEyn8NldGy4DO1B0Ij4iXRxJ437IaJpLM4n1vaW6WNSfovpwLHJ8xi4F7gpfD+Y4IjppKjmba5xEzTwTwfOTzOutOtomnLTad1YfHbSd+cQJINEY/HbWcUR9Q+V4Qc8m6DhbwnB3v4MgvOAi8PvEueLG8MZPShp2AXhRmBDGHRN+Pkogqso3gMuyVB3LTta3u9ixx3WNQR7V9PDBfCsiGL/VFLM08LpqAfmh69nA+8DPwjL/4HgqKEp/N9YwJgvCOv7IKxnt06Id1rSPB4b1j+W4Dx4YrnYRtAtyQaCtoKb0sQ7njY2agRXbdQTNI7WJw2/O8GVUnMIrtI4MsJlPOu421i2V4bv54bL5HsEOwp/SCp/ffj5u8B1GeI+MXy/keDIq67EYs24HictF3OBG8LPXgyXjWaCnba5BO0DGwjafhLTMbpU5jHw03AeTEv6SyS+tOtoSn2jwuXCCbYHYwka069KmsfbCLYTW4DabOJQFxMiIjGnO4tFRGJOiUBEJOaUCEREYk6JQEQk5pQIRERiTolARCTmlAhERGLu/wNcnt60NCZn3QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calcualte daily rainfall amounts \n",
    "y2016 =  rad30.index.year == 2016\n",
    "y2019 =  rad30.index.year == 2019 # create boolean masks. \n",
    "\n",
    "# check that there are values in each year.\n",
    "print(rad30[y2016].P_mm.sum())\n",
    "print(rad30[y2019].P_mm.sum())\n",
    "\n",
    "# create objects used to be plotted\n",
    "p16 = rad30[y2016].P_mm\n",
    "p19 = rad30[y2019].P_mm\n",
    "\n",
    "#groupby date\n",
    "dp19 = p19.groupby(p19.index.date)\n",
    "dp16 = p16.groupby(p16.index.date)\n",
    "\n",
    "#find indexes of when it rained!\n",
    "rain = p16 > 0.0001\n",
    "b = np.where(rain)\n",
    "print(b)\n",
    "\n",
    "#initialize figure\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "# Add two axes\n",
    "ax1 = fig.add_subplot(2,1,1)\n",
    "ax1.plot(p16)             \n",
    "ax1.plot(kind='bar')\n",
    "ax2 = fig.add_subplot(2,1,2)\n",
    "ax2.plot(p19)\n",
    "ax2.plot(kind='bar')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3. Diurnal radiation, sensible heat flux, and latent heat flux\n",
    "\n",
    "Examine the figure you generated in 4.2 (and any of the raw data you choose – temperature, pressure, etc.). Pick an interesting day and compare the diurnal variation in net radiation, sensible and latent heat flux, and the residual energy.\n",
    "\n",
    "<div class=\"practice\">\n",
    "    📚  <b> Question 4.3. </b> \n",
    "    Plot $H$, $\\lambda E$, $R_n$, and the net total energy flux (i.e. the quantity you calculated in Question 4.1) across a single day in 2016 and 2019. In which year is the system closer to steady state? Discuss the differences between the two and speculate what is driving these variations.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'fluxes ')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0LjNq0zT4a9R24i2EEAlCiJHatghPaEF+Ra0EP4raZmRUbcmic3kipeSHwz9w/7L7Scitcp85HR2dOqTW5kyklPdX4S+B0bWVv87lTVxOHG//8zabk9T+ckuPLeXJDk/WsVQ6OpcGNpuNhIQEioqKyvi5u7sTFRWF2WwuJ2btUZcT8Do6ZbA77Szav4jZO2djMph4s+eb/Hj0R9bGrdWViY6ORkJCAj4+PjRs2BC176NCSkl6ejoJCQk0atToosqkb6eic8lwKOMQD/76IB9s+4AekT346fafuLv53fSr348DGQdIyk+qOpHzxOqwsiVpC3anverAOjp1TFFREUFBQWcpEgAhBEFBQeX2WGobXZno1Dkn804yZfMUhiwbQmJ+Iu9f9z6z+s4izCsMgL7RfQFYG7+2VvI/lXeKh357iBHLR3DXkrtYGbsSfQNUnUud0oqkKvfaRlcmOnWClJKdKTt5ft3zDFw8kK8Pfs2gJoP4+fafubnhzWe9EI39GtPQtyFr42pemaw/uZ57l91LXE4cT3d6GoHg+XXPM+SXIWw4uUFXKjo61USfM9G5qNicNlaeWMmX+79kb/pefNx8GN5mOENbDiXcq7wPXyr61u/Llwe+JLc4Fx83nwuWwymdfLz7Yz7a+RHNApoxvc90on2jGdF2BMuOLWPOzjk8vupxuoZ15ZnOz9AxtOMF56mjcyWj90x0LhpJ+UkMWjyIl/56iTxbHq92f5VVd6/i+S7PV6pIQA112Z12/j759wXLkW3NZvTq0czZOYdBjQexaOAion3Vx/aMBiO3N72dpXcuZfxV4zmefZxhvw1j7LqxpBWmXXDeOjo1RUW95rrqTes9E52Lxuyds0krTOPDfh9ybdS1GET12zLtg9sT6B7I2ri1DGg04Lxl2J++n+fXPU9yQTKvdX+Ne1vcW+4Ys5vRjaGthnJH0ztYuH8h83bPY2PiRl7o+gJ3NL2jzsaldXRAmf+mp6eXmYQvseZyd3e/6DLpykTnohCTFcOSmCUMazWMPvX7nHN8o8FIn/p9WHFiBTaHDbPx3GzopZQsPrKYdze9S4B7AF/c/AXtQ9pXGc/T7MkTHZ6gf8P+vLXhLd7Y8Aa/HP+FN3u+SX2f+lXG19GpDaKiokhISCA1NbWMX8k6k4uNrkx0Lgqzts/C0+TJo+3O/8uyfev3ZfGRxWxJ2kKver2qHa/QXsjEjRP5OeZnekb0ZPK1kwl0DzynvBv5NWL+zfP5/vD3TNs2jbt+vounOj3Fg60exGgwnuul6OhcEGaz+aKvI6kKfc5Ep9bZlbqLNfFreLjNw/i7+593Oj0ieuBh8mBN/Jpqx4nLiePBXx/k55ifeaLDE3x0w0fnrEhKMAgD97a4l59u/4keET2YunUqD/76oL7Vi44OujI5fzJPQFF2XUtxySOlZMa2GQS6BzKs9bALSsvd5E7PiJ6si19XrUnG1XGruW/ZfSQXJDPn+jmM7ji6RnoR4V7hzOo3i/evfZ/Y3FjuW3Yf60+uv+B0dXQuZ3Rlcj5ICZ/1h5Vv1rUklzwbTm1ga/JWHm//OJ5mzwtOr290X5ILktmfsb/CMA6ng2nbpvHs2mdp4NuAbwd9yzVR11xw3q4IIbi50c18c8s3hHmF8eSqJ5m3ex5O6azRfHR0LhdqXJkIIU4IIfYIIXYKIbZqboFCiJVCiCPab4BL+PFCiKNCiENCiP41LU+tkJcCeUmQoH+kqzKc0snM7TOp512Pe5rfUyNpXhd1HQZhqHABo5SSiZsmMn/vfO5rcR8LBywk0juyRvIuj/q+9Vk0YBE3N7qZWTtm8dza58grzqu1/HR0LlVqq2fSV0rZUUrZVTt/GVgtpWwGrNbOEUK0BoYAbYCbgTlCiEt/NjP1wJlfu7VuZbmEWXFiBQcyDjC64+hztr6qiAD3ADqGdKxwa5XP9n7Gd4e/Y0TbEbzW4zXcjG41km9leJo9mXLNFF7s9iJ/JPzB/b/cz7GsY7Wer47OpcTFGua6HfhC+/8FcIeL+3+llFYp5XHUt02uukgynT8pB9Wv0w4pFQ+3/C9jc9r4cMeHNAtoxsBGA2s07X7R/TicebjMxPeyY8uYuX0mAxoN4JnOz9RonlUhhGBY62HMu2keOcU53P/L/ayOXX1RZdDRqUtqQ5lIYIUQYpsQ4jHNLazkK4rab6jmXg+Id4mboLmdhRDiMSHEViHE1vLsqi86qQehpAOVuLtuZakDsoqymLplKj2+6sHw34bz/eHvySnOOSvMj0d+JC43jmc6PVPjprN966uNH9fFrzvttjlxM6+vf51u4d2Y0HvCOS2IrEm6hXfjm0Hf0NS/Kc+ue5a5u+bq+3vp/E9QG29cbyllZ2AAMFoIcW0lYctbRlzmzZNSfiKl7Cql7BoSElJTcp4/qQchqhtYfCFxV11Lc9EosBXwye5PGLB4AF8e+JJekb3ItGby9j9v0/ebvoxdN5Z18evILc5l7q65dArtxLVRlT3+8yPaN5omfk1OmwgfyTyiJtt9GjCj74yLMrRVGeFe4Xx+8+fc2vhWZu+czbg/x1FoL6xTmXR0apsaX7QopTyl/aYIIX5EDVslCyEipJSJQogIIEULngC4LiOOAk7VtEw1ipSQcgDa3AkGIyRd+T0Tm9PG4sOLmbt7LmmFafSt35enOz1N04CmSCnZl76PpTFL+e34b6yIXYHFaMHqsDL1uqm1tu1Iv+h+fL73c45kHmHU6lG4m9z56IaP8HXzrZX8zhWL0cLEqyeqTSS3TScuJ45Z/WZVuQeZjs7liqjJLrgQwgswSClztf8rgXeA64F0KeVkIcTLQKCU8kUhRBvgK5TCiURNzjeTUjoqyqNr165y69Y6tKLKTYYPmsPNUyArFrbOh1dOKsVyBbI1aStvbniTuNw4Ood25rkuz1W4g67NaWPDyQ0sPbaUEI8QXrrqpVqTa0/qHob+OhQvsxdSSr4Y8AUtA1vWWn4Xwp8Jf/Liny/ibnRnRt8Z+g7EOnWCEKJASulVW+nXdM8kDPhRa42agK+klL8LIbYA3wohRgJxwD0AUsp9Qohvgf2AHRhdmSK5JCix5AptCe5+YC+EtCPq/AojoyiDsX+MxcvsxezrZ3NNvWsq7WmYDWauq38d19W/rtZlaxPchhCPEDKKMph9/exLVpEAXBt1Lf8Z+B/GrBnDiOUjeLPnm9ze9Pa6FktHp0apUWUipTwGdCjHPR3VOykvzkRgYk3KUauUWHKFtAIvbf4mafcVqUwmbpxITnEO826aR/OA5nUtzlkYhIHJ10zGiZMeET3qWpwqaeLfhK8GfsXYP8by2vrXyLJmMbzN8LoWS0enxtBXwJ8rqQfAIwC8QyG4BZjcr8hJ+OUnlrMidgVPdnjyoigSR3Y29szMc4pzVcRVl4UiKcHf3Z+5N87lpgY3MXXrVD7e9XFdi6SjU2PouwafK6mHIKQlCAFGE4S2vuKUSXphOhM3TqR1UGtGtB1R6/kVbN9BwlNPIdwtNF6yBKO3d63nWVeYDWamXDsFy3oL/975b6wOK2M6jdG/j6Jz2aP3TM6FEkuuEJchrYgOapjrClpLMHHTRPJseUzoPQGToXbbG9lLlxH38MMY3N2xJyWTPHlyreZ3KWAymJhw9QQGNxvMvD3zmLp1qr4WReeyR1cm50JeMhRlQWirM24R7dXuwVmxdSdXDfL7id9ZGbuSUR1H0SygWa3lI51OUmfN4tS4cXi0b0/DH74naORIsr//gdx162ot30sFgzDwRs83uL/l/Szcv5CJmybqm0TqXNboyuRcSC2ZfG9xxi1Csze4Aoa60gvTeXfju7QNasvDbR6utXycRUWcHDuWtDkf4Tf4LqI//wxTQADBY57C0qwZSa+/gSMrq9I0jqfl8/w3O5m1+kiNyeXIza2xtKqDQRgYf9V4HmnzCN8c+oa3NryFw3lpGzPq6FSErkzOBVdLrhJC26itVS7zbVVKdtvNs+Ux4eraG96ypaQQ+9Bwcn9fTui4F4iYMAHhplasG9zciJg8CXtmJkkT3y03fmquldd/2suN0/5g8Y6TTFt5mM3HMy5YrvQFCzjcqzcF27dfcFrnghCC57o8xxMdnuDHoz/yxoY39B6KzmWJrkzOBVdLrhLM7moO5TLvmSw/sfz08FYT/yY1nr50Osn57TdO3Hsf1iNHiPr3hwSNHFlm4tmjTRuCH3+cnKVLyVm58rR7ntXOtJWHue79tXy9OY77r4rmz3F9qefvwfjFu7Haz79FX3TwICkfTAObjeTJU5DOsyvzbbEZLNt9ij8Op7I9LpOjKbkkZReRb7XXyFyHEILRHUczuuNolsQs4Z1/3tEVis5lh27NdS6kHFS9ktKWNxHtIab6n5K91DiSeYSJmybSLrhdjQ9vSSnJ//NPUmbMxHrgAJZmTak/ZzburVtXGCf4icfJXbuGpDffwr1TZ/57JJeZq46Qnl/MLe0ieKF/CxoFq4W8E+9sy8PztzBnbQzP3XjGhNlZXIzBreo9upxFRZwaNw6Tvz+BI0aQMmUKOb/8gt+tt3I4OZd3fz3AukMVby7q52HmhZua80D3BhgMF2aR9USHJyh2FDNvzzzMBjOvdH9Ft/LSuWzQlUl1kVLNmbS5s6xfRAfY9TXkJoHP5bX30o6UHYxePRp3ozvvXv1umeGt1DlzKNiyhYi33sKtQYNzSrtgyxZSps+gcPt2zPXrE/n+e/gOHIgwVr71jDCbiZw0mRN3380fo8bxRqO76NEkiM8GtKJj/bO/Id+nRSi3d4xkzrqjDGofQbMwHwq2bSP24UcIe/FFAoc9WGleKdOmYT1ylPrz5uHVuxc5S5eS9P4HTC2I4D87k/GymHhlYEv6tAglt8hGTpGd3CI7uUU2covs/HEoldd/3sfiHSeZdFc7WoZf2N5gYzqNwea0sWDfAtyMbrzQ9QVdoehcHkgpL6ujS5cusk7ISZTyTV8pN84t63f8b+V3aPnFl+sC+CP+D9n1y67ylsW3yITchDL+uX/8Ife3aCn3t2otD3bqLLN++qla6Rbs3i1jRz4q97doKQ9ffY3M+Ppr6bRaz1m+Fa9OkftbtJSfvjVXOp3OCsOl5hbJDm8vl3fNWS8dDqdMnDBRyd2ipUz9+JMK4+X+/bfc36KlTPy/CVJKKQuL7XLR3MVyf4uWcvydz8k3f94r0/Mql9vpdMoftsXLTu+skE3G/yIn/3ZAFhbbz/laS6c5adMk2XZBWzlj24xKr11Hp7oA+bIW62Z9zqS6pGh7coWUs21KeDv1m3T5zJssiVnC02ueprF/Y764+QvqeZ/9GRl7aiqnXh6PpXlzGv+yDEvrVpx66WVOvvgijrzyP0tbuHMncY89xol77qVozx5Cx42jycoVBAwZgnBzw+GUrNiXRJ7VXqV8aw4m86StJacim3D1r/NxVLI6PtjbwqsDW7EtNpOvNseR/9dfePXqhe+gQaROm0bKjBll5jbsmZkkjn8FtyZNCHr+eb7bGk+/qet49bgbR5p3ZdjxdbzaI4RAr8qHyoQQ3NU5ilXPX8cdnerx0boYbpr+J38dOf/v7ggheKnbS9zT/B4+3fMpc3fNPe+0dHQuFvowV3VJPaR+y1Mm7r4Q2PiymYT/Yt8XTN06le4R3ZnZdyZe5rM3EpVOJ6deHo+zoIB60z7A0qgRDRYsIG3ux6TNmUPhjp3U+2AqHu3bA1CwfTtp/55N/oYNGP39CXnuOQIeGFpmJfukXw/w6d/HaRDkyfT7OtI5OqBc+faezOapr3bQKiqQLsOmkHj33eT88mulQ1Z3d4nixx0nmf/d33Q5cYKABx4gYOj9GDzcSZ/7MbKwkNCXX0bs/gZp8SVpznLsmRkkjZvAE3M3cyQlj3b1/Pjg3o50MbUlZtCtpM6aReSECdW6p4Febky9pwN3da7Hqz/uZdhnmwn0csMglHIQgEEIDAICvd144rom3NIuosIhLCEEr/V4DZvTxpxdczAbzTza7tFqyaKjUxfoyqS6lGfJ5Up4ezi14+LKdI5IKZm+fTrz987npgY3MemaSeV+SCpj/gLy168n/O23sTRtCoAwmQh5ajRePXtwctw4Ttz/AP9060+XgkTcdm/HGMy0dvIAACAASURBVBhI6LgXCBgyBINX2V2uv90az6d/H2dgu3B2xWdzz9x/eKpvU8b0a4rJeKaDfCqrkBELthDg6cZnw7vib7GTHhlI7orfKlUmQgjevbMd08csA8D7mqsRRiPh77yD8PAg44uFONPiCff+muxYT3I3+vJ778HMXJ9Do2AvZg/tzMB24VrlHkTgAw+Q8cUXBD74IO4tq7+JZ68mwfz2zDV8+U8scRkFOKXEKUuGk8EpJbsSsnjqqx3MizrGywNa0bNJULlpGYSBt3q+hc1pY+b2mfiYfbiv5X3VlkVH52KiK5PqUpElVwkRHWD/T1CYqZTOJYaUkkmbJ/H1wa+5r8V9jL9qfLmf0y3cs5eU6dPxuekm/O+9p4y/Z5cuRHz7PcuGj6Hnxl/IcPdBPDyKHs88isHDo9y8t57I4NUf93B102BmDelEgc3Bmz/vY+bqI/x5JJUZ93WkQZAXuUU2RizYQmGxgy+f7E6omxUW3YOPfyzpW9Oxr56Bqc9Tak+0cmgY7MVgezyJnkHE5Vnoj1IyYePHYzdbyPrsU4qjQylINOAWYmdjw8ZM6t+Ou7tEYTaePeIb/OQTZP/0E8mTpxA9//NzmgR3Nxv517WNK/R3OCWLtycwbeVh7p+3kb4tQnhpQMtyJ++NBiNv9nibHGseEzdNxNfiy4BGA6oti47OxaJGP45VJnEhbgZmAkbgUynl5FL+fsAiIBql2KZKKedXlmadfBxLSpjSANoOhkHTyw9zdBUsGgzDl0Kjmv9U7YXy6Z5Pmbl9Jg+1fqhCCyFHXj7HB9+FLLbR+KcfMfr5lQkjpeTZb3ayZOdJZnd25/NEIzuTC5kxpCOD2keWCZ+QWcAds9fjbTHx0+je+Hue6Qkt2XWKV3/cg9MpeX1Qa37Zk8g/MekseOQqrq5ngEV3QdIeCps9zYm3/0tE90z8ezSFW6ZCdNndgp3FxRzu3oO/Gl/F3I6DaRHuQ1JOEcnZRQx2/MrTR34gdbcvhWYLTQdl4x3uh+HR1eBVfs8gY9F/SJ4wgajX/oXPjTdD2BlzZltyCnl/rKNozx5AgMmIMJoQRgMYTQiTCZ8brj89FFgeRTYHCzacYPbao+RZ7QzuHEXLcB8Ss4tIzC5Uv1lFpOQWYTTaCWm6kHwRw/vXzuCmRn0qTFdHpzxq++NYtaZMhBBG4DBwI+rzvFuA+6WU+13CvAL4SSlfEkKEAIeAcCllcUXp1okyyU2CD1rAgPeg++Plh8lLhalN4aYJ0GvMxZWvCpbELOHVv19lYKOBTLpmEgZRvt3FqZdeJnvpUhos/ALPrl3LDfPxHzFM+u0gL9zUnKf6NTvdm9gWm8n7d3dgcJeo02HzrXbunvsPCZkF/DiqN01Dy+4GfDKrkLHf7mTjMbWK/b3B7bm3tQd8eYeap7p3IbL5zRzt2w/36EDqdzwAOSeh4wNww9vgHXImv40biXv4ERz/9z7Pxvvg424i3Nedph65jNk/lKzADsT5jKRBs/qERAlYMAjqdYGHfgKTpYxsMjuFY7f0B2sejW/No6jLO+Qds5K3bh1F+/YBYAwIQJhMSIcDHA5kyVFcDEIQNu4FAh56qNKeTWZ+MbPXHmXhP7EUO5x4mI1E+LsT4edOhJ8HkX7uFBQ7+HXfcbL8Z2FwS6WNYRz3t7+W61uF4uNurjBtHZ0SaluZ1JqZGNATWO5yPh4YXyrMeGAOIIBGwFHUZ38vLdPgo2uU6W/MusrDTW0p5fePXhyZqsn6hPWy4xcd5cjfR8pie3GF4bJ+/lnub9FSpnz47wrDrD2YLBu9vEyOWrTtLHPVfKtNDp33j2z48jL5n42xUkopHQ6nfHzhVtno5WVy7cHkSmW0O5xywfrj8st/TkiZkyTlv7tL+X+hUh5ZeTpM4jv/Jw906CgdWWlSrnxTyreDpHy3vpTbFp4Ok/Tee3J/23bSkZd3dgbfPizlOyFSph092333d+q5Ln5cytLmt4dXSPl+M5nzWITc36KlPNiulTI3btlKHr9/qEz9+BNZdPhwhWa79uxsGTdqtNzfoqWMf+ZZac/NKzecK1n5xTIz33p2mvnpUv7wLylndpTOrHi57kiM7LHwRtn2826y0Rufymav/iqHf75Jfv73MRmTkqubEetUCLVsGlybcyb1gHiX8wSge6kw/waWAKcAH+A+KcvuIyGEeAx4DKBLly61ImyllGzw6LpbcHlEdLikLLr2p+/nuXXP0di/MdP7TsdYbCfnr7XYk5KxJSdhT07BnpSELTkZ26lTeHTtQvAT5fe8jqXmMebrHTQP8+H9e9qf1dL2dDPx2fBuPLFoG6/8uIdiu4OMAhu/70vitVta0adFBUYLGkaDYHivhpBzChYMVL8PfHfWcKHPDdeT+Z//kL9lBz43vKV6JsuegyVPQcp+uGkC+X/9jWeXLmcbABxdBfsWQ59XIKjUNjHt7ob0GFj3LgQ3g2vGgjUPVrwG2+ZDaGu83/yOgE+XYE9Lwcf9AF7GbZhufAD6Plrx/Blg9PUl6t8fkvHZZ6RMm4710CGiZs3E0qzinZj9PEv1MA79DkufhoJ0MJgR3z3MdQ//yg/hCxj22zCsLRfS0+NNth4t4O2lqsMfHejJdc1DuK55CD2bBOFl0adFdS4OtTnMdQ/QX0r5qHY+DLhKSjnGJczdQG/geaAJsBLoIKXMqSjdOhnmWvoM7P8ZXjxeaQXC2nfhz/dh/Elw87x48pVDQm4CD/76IG5GNxYNXESIezBxj4ygYNMmQK0yN4WHYw4LU7+RkQQ+NAxTcHCZtHKLbNw5ZwPpeVaWPHU19QPLvzar3cFTX+1g5f5kAO7tGsWUwe2rN3mdHqPmSPLTlSJp0PMsb2mzcfjqa/Dp04fIKdrUm9OhKv6Nc7CF9ePozIOEjnuBoJEjlb+tEOb0BIMRntxQ7lAWUsLix2DPt9D3Vdj5FWSegF5PQd/X1N5rJThsSoHt+BI6DIXbZoGx6iGm/E2bOTl2LM78fCL+7//wG3RL5REKs2D5K7DzP2oj0TvmKJm+Gw7d/gW3TCUmK4bhvw/Hx+zDwgELKSj05M/Dqaw7lMqGmHQKbQ6CvNz4cmR3Wkde2Kp8nSuDK32Y6xfgGpfzNSiFc2kNc316k5Sf3Vx1uAPL1LBJ3Obal6kSMgoz5KDFg2Svr3rJmMwYKaWU6Qu/lPtbtJTpCxZIW0ZGtYdDHA6nHLlgs2w8/he5/mhqleGL7Q754ne75MgFW2SRrZKV4E6nlIl7pFz3npSf9FX3bVJ9KeO3Vhjl5IsvyoNXdZdOm+1sj82fysxhajiqcIvLUOTqCdrw5NoqhC6U8tMbVdjpbdWOBpXJvXaSCvvF7VIW5VSedkkWScny+NAH1Ir7t98uOxRXwpGVUn7QSsq3AqRc9Y6UtqIzfstfVfnu/FpKKeXulN2y26Ju8o6f7pBZRVmngxXZ7HL737/L6e88I7u89Yvck5BVOhed/0Go5WGu2uyZmFAT8NcDJ1ET8EOllPtcwnwEJEsp3xJChAHbUT2TtIrSveg9k+pYcpWQFQ8z2sLAqXDVvy6OfKXItmYzavUoDqYfZN5N8+gc1pniEyc4dsedeHa/ivpz557VU3A4JYs2xjJt5WHyrXbMRgNmo8DNZDhtLpuYXcRbt7bm4d6Nzl8wKSE3EZL3w5HlcOg3yNZGQet1gRYDoN09ENCwwiRyVq7k5JiniV4wH68eZ1tzJTz6AIXbttL0fgfi/q/B3Q8+6qX2Uhs8r2r5CjJgz/fQYYhahFoV279UPdbAxtCkLwQ2UcNoQU3AL7pc82Vps5HywTQyFizAFBZG2Msv4XPtVYjUA5C8D2I3KPPy4BZw50fqvrjisMPC2+HkNnh0JYS3Y1PiJkatGkXLwJZ8ctMneBk9YP0MWDMBpIO9ojlj5TNMGTmozL5mOv9bXLbWXABCiIHADJRp8OdSyolCiCcApJRzhRCRwAIgAjUJP1lKuaiyNC+6MslJhGktYcD70P2xysNKCe81hlaD4LYPL458LsTnxjN69Wjic+KY2ul5rm/3ENLhIPbBYViPHaPxkiWYw87MX+w9mc2rP+5hV0I2vZsG0bG+PzaHpNjupNjhxKb9tqvnx8irG1U9XGW3qvmOnFPqy5PpR9XwVXoMZMSArUCFM3moCrj5zdC8f7U3x3QWFnK4Zy/8Bw8m/PXXTrtLu53DvXrjc81VREb/pfIPaKis8MZsrXih6YVydJWqtNOOQrHLh7UMZghoAF4h4OYFZk9w81b/3TwpiEkh6euNWJOteIZZCe+cjcXPrtYndR4OfcafPbzmSl4KfHwtmNzhsXXg4c+auDU8v+55ugZ3YHZ6LpaY1UqJNrsJ56/jyCuGV+QoHhkxii4NLr01UDoXh8tamdQGF12ZxKxVZqoPLYHG11UdfuHtauHi43/WSPb2jAwMFku5q8pd2Zmyk2fWPoPdmseMU/F0KyqG1reTfqo5KXMXEfn+e/jdeiugfRtkxWEWbDhOoJeF1we14rYOkWcrC2vuGSWQn6YUhcOq5g3sVnAUqzmJvBTISVAVeH6p/aiEUVXqQU0gqOmZ36irzntOKX70UxTt20fTtWtOy1uwfQexQ4dSb8Z0fK+9Cr55EGLXwy3ToNvI88rnnJBS3YcMF8WZHqPKQXG+OmwFZ/4DMqgZmScCSV0Vh9NqJ2jovQQ/MxaDd1nz6TLEbVKGCk1vhCFfgcHA0s0zeOXAZ/QtKOKDLi9g7vaYmt9Lj8H2zXDMKXtYIG+hzbBpdGt6ee1srVMz1LYy0U09qqK6llwlRHSAf+bAlk+h9R3gVXZCu7oUHTrEiSH3I4uKcGvQAPfWrbC0bIV7q1a4t26FKUgttvv9xO+8+terhBndmR1/gkZthoBPGNbln5K6bBM+Lf3x7RSBlJLl+5J5e+k+knKKGNqtPi/19sM3ey/8/Y3qSWQcUxVhfkrFghktYHRTE9reYeAbCZGdwLee+u8bqYZ6AhpUa4L6XPC54QbyVq+maO8+PNq1BSD/77/AYMCrZ0/w9INhP8HJrRDds4rUagghwCdMHQ16VR1eSoQQBAK+6emkTJtG+pf/JXvFWupNn45n506Vx4/uDv0nwW/j4K+pYHTj1tXvkBcaxbue7rxhPcFEJAYEBDXB/K9VFCwbz8O7PmfXl4fYdtd8unSoeDGljs75UOc9k6pWyZfmovdMljwNB5bCi8cqt+QqIe0I/PcBSDukWuaN+ygT1Ja3qHH8auLIzeX43XcjCwrxH3If1oMHKdp/ANvJk6fDuDVsyL4BzXnDZzXtfaKYeWATAS1vI+3mjziWkod5zEjckuKIHpCBj1sO/4iOLCvuTFevNG4ITMEn+5BqPZfgHa56D4GNzswBBDYBnwilOEwWMJiqdx9qCXtmJkeuvoagfz1K6LPPAnD8nnsRJhMNv/6qzuS6UAp27ODUC+MQbm40XvIzwlyFEpYSFv8L9nynzlvfAbd9yLzD3zBrxyyGtBhS5uNa2du+x7R0DHYp2NJgJD49HqFj8wZYTJV/X+a8cNjBlq+GNE1Vf6TsolGYCYeXw6md0KSfOirYnudK44oe5qrOKvnSXHRl8ll/EAYY8Vv140ipJlT3fg97f4CsOJxGC6dCrqbAvwUBfv74B/hjtpSMp3up4aCQFlp0ScKYMeSt+0OtRu/c+XTSjuxsig4cJH/fXg5/9xlBJzLJDXSjUbNUctq14Vnjy+xJLmLIoVUMP/A7E68aRlLL9jxiWcNtBYvxcWQiTR6IsNYQ1lZtnx/WVm0Vcg7Kri6JHf4w9vQ0mixbhj0jgyO9ryZ4zFOEjBpV16JdELlr1pAwajThb75BwP33Vx2hOB+WjIEGvaHrCBBCbea5bTrz983n0XaP8nSnp89SKJkJB0n68jFaWXdRIC0skdewO/JeGrfpxrXNQ2gW6n32cKeUaseBxF1quNOaC9Yc9VuUo/13Pdf8S+bHQPVkLT7qcPcFi69mpl1Oo8RghIBG6l0IbaV26fa4QMOBnEQ4uEwdJ/4Gp101ipx21bNudw90HAphbcrGlVIZiiTuVr9BzdQ74xN2YTLVAVe6MukJvCWl7K+djweQUk6qKM75KpPsQhsnMwtxOooRtkKwFSJthQh7IdiLsHtHglcoJqPAbDRgMgjMBkG9T1pR3PJO7AM/wE2zdKpqItpqd7AnIZutsZlsPZ5Ocexm+tr+ZIBxM+Gi/O9yODGwq9M7pDa9h4Cl3+K94CMcTz6D25AHcEqJzS6x2h0cyT7AhuQVbE1dQ54tk7sONWP4uj1YM82keAawsfcd1Ovans7vj8N4XT8azpx2puVpK1ST0v7R6qW9TMn4chHJEyfS+LdfKdq7j1PjxtHwu2/xaNeurkW7IKSUxA17COvx4zRZvhyj9/m991JK3tn4Dt8f/p7hrYcztuvYMmW2IHY7WetmE3JiCWZZzAZHa75w9OeQpR3X+ydxleU4LexHiMjbh6WonG+zuJUoBh+lHE7/V+dONx+KjR4UFBRQlJtBcUE29oIcKMpBFOdiksVYNItBi8lw2npQOGxnG2uA6jGHtlQ9ZINR9fgNRq2XrP2XEpAgJVI6cTgldocDkbgTS9I2APJ9GpEQ1o+jQX056d6U1nkbaZ60jODEPzA4bTjD2mPoNBSnRxCF8TtwntyFJX0vbsXZZS6/yBJEUVAbCG+HZ/2OuHl4KYvAwkwozND+Z6h3ziMAPIO0I1D9egSqxkDOyTNGKyX/bQUQ2loNHUd2hIiO4BeFBJxSLfI9H650ZXI3cLM8e2FjdynlU6XCnV4BHx0d3SU2Nvac89r26+d02DQWkyizwP40B5zR/O1sy3pnWzY5W+JDIZvdR/OGbTgLHf01WVAvgNFw1je/Xe9joc2BzaHOGwd70aVBAN0aBtK5gT9Wm53jiWnEJadzMjmdxPQMMrOyeNbwLdcZd/PvU7fS58/trI9sx6Ruw0AIhDkds98OzL47MVjSkE4j9rxW1M+O4mf7QnIsERwLG0fYLz9RvG8vCIExOIjGS5ZgCrjyrHdsp05xtN/1hL4wFuuRI+T9+RfN1v+NMFz+33or3LOHE/fcS9CTTxD6zDPnnY5TOpm8eTJfH/yawc0G83qP18vdJZqCDNj+BfZN8zDlnjzLK8YZwS7ZhJ3OJhwUTcm3hGAzeeMwe2Exm7GYlSIAyLc6yLfaybPaybfaKbA5KK9qcTMaCPFRi0cTswtxuoQxGwVhvu6YhCTInkoDZywNnPE0csbRWCYQSDZG4cSIEwPqt+S/UiUCKbVf7TxOhrLc0Y3lzq4clfUorzcUQA63Gv9hsPEvOhiOAWCVZg7K+uxzNmC/bMhxcxPyPerhl3+Cxo5jtBaxtDbE0kwk4CYcZ6XnEEZsbv5Ij0CMbp44C7MwFGbgZs8tkzeAHSPpIpA0YwiZxmAcBjMNimOo74jDiKqvMqQPe2Qjclrcw60PPF1uOlVxpSuTKlfJl+Z8eyZpMdvJ3vINTqMFafJAmtxxmtzVf6MbHtlH8U9cj3/aNtVKESZyPBvgnX6cVb0/Js6vizKZtTuxOpxYbRUrJQ83Ix3r+9OlQQDB3uWsui6FzeEkOSMbn29GkjxvL07fAPa89z7b8rexO3MD8QXKCKCZb0d6hN5Aj7A+RGTH0HjlCIwefjBiOfiEI6Uk/88/yfjqK4KGD8erVzUmgy9Tjg++G0xGbAkn8erZk3pT369rkWqMk8+PJXfNGpos/x1z2PkPp0gp+XDHh8zbM48BDQcw8ZqJmA0VzMU47HD4N0g7rFrC9TqT4fTiaEoeMal5HE/LJ89qx2pzUmR3YLU5sdodWO1OkOBlMeJlMeHlZsLLYsJbOw/2thDm606or4VQHwt+HubTvaRiu5PE7ELiMwqJzywgPqOAU1mFAJi00QGjQWi/Smk5pcTuLOl5SBxOiUNK3E1G3M0G3N2MuJuMeLgZcTcZ8LSY8HU34etuxsfdjI+7CR93E+5mI5kFxaTlFZOeZyUtz0paXjHO1MN4GO24h7ciItCXegEeRPp74OuymWae1U5SdhHJOUUkZ+ZQnHSQ1Ox8DueYOZBl5GiOoDyl5W5w0NLPTkvfYhp7WcHsSZohiDTpR5EDimxOimwO7E6Jh9mIr9FOE3mcxrYjRBcdJrLwEHkt7qbx7ePPqzxc6crkog1zVRdpzado9Tdk//wjOVticBZJmvy+FHN001rLE6DYWsChoffB4RimPiDZHqEmBVsHtaZ/w/4MbDSQcK9w9V2VtROUUYBPJDzyq5ow/x8jbe5cUmfMBCByymT8br+9jiWqOYoTEjg2YCC+t95K5LsTLzi9z/d+zvRt0+kT1YepfaZiMVbdwDlfpJQkFyRzLPsYCbkJ+Lj5EOIRQqhnKMEewXiaa36bIalZx10qFNkcxGcUcCK9gIx8K5G+FqJNdoIdBYicbOwZGTgys7A0bYJHp04XrUd9pZsGbwGaCSEaoVbJDwGG1kZG+ceOkr9mLW7BoZiCgzEFB2EKCjq9hXhxbCzZS5eRs3QpxbGxYDZjaN0GuWsv27asINV2gCxrljqKssguzsbT5EmEVwThXuFEekee/l/e1wtLcH3ZjmcfP320+2ob/fdZmX2HBe9oP95IPMJ1Le8jdOA0NbaWGQsrnoTd/wWzl1rY1mNU9VZrX4H4XH/9aWXi1bt3HUtTs7hFRRHw4INkLFhA4PCHcG/R4oLSG9F2BF4mLyZumsjoVaOZ1W9WjVTqVoeVbcnb2JO653R5PpFzgkJ7YYVxvM3ehHiGEGAJwMvshafZU/2aPPE0e572D/cMJ8wrjDDPsLPeJ2tuFvHb/iRlx0as+w9gOZqAb3Iehd5m8oM8KQ72wxEagCE8FHNEJJZ2bQmIakygeyAB7gGVKlLpcJD3xx9gMODdu3fVFnWlsKenYz1yBOuRo3gfOUKTI4eJOnEcmZ1DgVMSV06cgkBPHP160vjuhwjq0K1CpejIy6f4WAxGPz/cGjQ4J7kuFpeCaXCZVfKVhT/fnsnf8ycRNGVhGXenAKuHEY8CB04BhxuYWNdasrEFCAnzZzj4sq+BpT1U68EgDPi6+eJn8aPAVkBqYdnJSX+LPyaDCYMwYBRGdRiMCATJBclnXjYpaZfqyYADFrpuSCP/9utoPWEaniYP+H08bPoIOj6oFvhtna8mGq/6F/R+rsIPOv2vIKXk2ICBGLy8aPTD93UtTo3jyM7m6E398WjXjuhPy98ORjqd5P/zDx5t25b7IbPSLI1ZyuvrX6dNUBue7vw0DXwbEOoZWuH3bcojLieOv07+xfr4v0nas4nmx6xEpksCnR4EOD3wdZjxLDbgXiwx2ZzI4ACsEYHkhnmTHmQmMQBO+FpJJQ+ZnYMhOxdjdgHm3ELccovwLgR3m8TNBhY7WGzg7XTD2+mGf4aVoNRiSqTN8IbEeh4URgVhzivCM70An0wrgdkO3OwqjN0Af7cRLO1uID5E4GX2IsASgJ/FD5vThtVhpdhWRLudOQz4M596aWr4OsdLsKuDL/u7h5LXMAQfN1+8zF64Gd1wM7phxkRAQg6h+04RuPckXseTcMspOn2f8t0FsSGSU0GCbE/I8RRYvS24B4fiE1oP/+B6OHftJ2TDIdrG2DE5IT3EQvY17QjqeS2m5AwMJ05ijD2F8cQpjKnKcMfwwF20eP38eqtX9DDX+XC+yiQmM4Z1h37FmZ6BzMiCjGyMWbmYs/Ix5xSQG+LFqZ6NMYeF42fxO300fuhd6Noe73deIcA9AB83n7NevmJHMcn5ySTmJ54+0grTsDvtOKUTh3SoX6cDh3QQ6hlK8yJ/Gm2Mx3PNFhwn4hBubvgOHEjEO28j3LRWmJRqq46/piqrlU7D4LoX1YJAHQCsx9TaH0ujK3OYL33+AlKmTKH+Z5/i7dL7klKS//d6UqZPw7r/AF5XX039eZ9Ua6hnddxqXvzjRYqd6vtzFqOF+j71ifaJJtq7PhHFHliNkmKTxCqc2HFgc9rIL84jdt8/BO1PpG2spH2cwKtAVbyGwACM3j4YvLwweHmqX09PDG4WbMnJFMfGYk9MrP6Fm0w4LWYcFhN2k8BqFhSZnOT7WXA0b4BX2/ZEdOpF4yZd8DCV/VS0w+kgOzmerOOHyP/lNwzL1mCw2kjv3IgDN7fgSAMTubY83KSR1tvS6fjbUXyT88iJCuDEnV2xmQVhfx4gYudJjHYnKREebO3kze5GgnrxRbSIKaTlcRu+msFZXDAcrWcgM9KHovohOBtH4R0eRajWs6rvU5/6PvUJdA8s84xsTht7Yv4hdsl/cV+7heijeaeVZbEJEoLgZLAgIViQEAzdrhvCkze9Xv176YKuTEpxsdeZxI0YiSM7+7xav87iYuwpqdhTUrCnpGA7dYrc1asp3KZMFT27dsX39tvw7d8fo28Fw1WHV5zZQFDnfwpncTHHBt6CwdubRj98jzAaKdyzh5QPplGwcSPmevXw7NaN7J9+ImLSJPzvvKNa6aYXpnM06yixObHE58YTm30Cjy0HuGHZKaLSzq4PbEawmQAh8CxSfiIsBN+evfHs0R2v7t0xR0RUfS1FRdji4ymOjaU4NhZnQSHGwABMgYEYAwIwBgRiDPDH5O9/pkFVQ9gzM8n86isyF/0HR2Ym7h3a49PverJ++AFbXByWli0JHvUkPjfccNb8hSMri+xffyX7p58p2r37tLspJASvXj3x6tUL9+5X4Qz2x83gVr613DmSefIYcTv+REaFQ1gowmTEgAEh1JKEYPdgwrzOzyhDVyaluNjKJHnSZDK/+YYW27dVa6KscNcuEt98C3tSEo6srDL+bo0b43fbbfjdOghzvXq1IbLOFUT2L79wauwLBD89BuvhI+T+/jvGgACCn3wS/yH3IUwmtZFnTAxNli3FFBJSdaIuFB04QPJ771Hwz0bM0dF43XsXRqMJYXNAsQ1p1WoZ7gAAIABJREFUtSKLrUibHUuLFnj16I45OvqSmvCuLs7CQrJ+/JGM+Quwxcfj3ro1waNH4d2vX9Vrx44do3DHTjzat8OtadPL8vp1ZVKKi61Msr7/nsTXXqfJiuW4RUdXGT550mQyv/4a/7sHYwoNxRQSqn5DQzGFhmD0978sC6JO3SCl5MR9QyjavRvh6UnQww8TOOIRjC4bQlqP/T975x0eVbH+8c9sTycVSCGNEAJIF8ECitLEAoqC2BBULCjy81K8KuhVRC827EpT8IoICiqKCCgISC/SezoB0ttms21+f5wkJKSHhAQ4n+fZZ3fPmZnz3d2z5z0z7zvvxBI7ZAjuN95I8AezatSu7fRpUt+fRfaPP6L19MTv6afxHjG83nsFTRHpcGBNSMAQFnZF/Rcv92iuJk/xMquFx4/XyJhYDhzAFBNDi6lTG1qayhWAEILAGW+Qs/I3vEcMr3AlTGNEOH5PP03qe++Rs+p3PAf0r7Q9abOR9ulnpM+bBw4HPqMfwW/s2MqHWS9DhFZ72frZGpNLf8pwA2OIVOaXFB49Vm1Z6XRiOXQIU/t2DS1L5QrCGBmJ/7inKzQkxfiOfgRjuxhOv/ZahcOrALazZ4kf9Qhpn3yCR9+biFj5K80nTryiDIlKw6Eak2rQuruhDwyk8Pjxasta4+Nx5udjal9BwjgVlQZE6PUETp+OIyuLM2++VW6/edcuYu++G8vBgwS+/TZB776LITi4EZSqXK6oxqQGGKJaU3is+p6J5aCS7Fg1JiqNgSkmBt9Hx5C9fDl5GzYAis8lY8FC4h96GI2rK2HffovXbYMbWanK5YhqTGqAKSoK68mTSLu9ynKWAwcRBgPGSDWMV6Vx8HvqKQwREaRMm4Y9LY1TEydx5o03cL/hBsKXLMEU3aaxJapcpqjGpAYYWrdG2mxYEypKiHAOy4EDGKOja52GQUWlvtAYDLSc/jr2lNMc7z+AnF9+wf+58QR//JHqG1FpUFRjUgNKIrqOVe43kVJiOXgQUzvV+a7SuLh26YLvo4+iMRoJ+eIL/J544rJIz6/StFHPsBpgjIgAIar0m9gSE3Hm5qqRXCpNgoDn/4+ojRtwv+H6xpaicoWgGpMaoHFxQR8SUmVEl+p8V2lqCO2lu5qmyqWHakxqiDEqqsqeieXAAdDrS4bEVFRUVK4kVGNSQ4ytWyvzSKzWCvdbDhzAFBWF5gpIR6GioqJyPqoxqSHGqCiw27HGxpXbJ6XEcuCg6i9RUVG5YlGNSQ0xRhWlVTlefqjLlnwKR3a26i9RUVG5YlGNSQ0xhIeDVluh38Ry4ACgOt9VVFSuXFRjUkM0BgOG0NAKI7osBw+CToexjTq7WEVF5cpENSa1oLKILsuBAxhbt0ZjNDaCKhUVFZXGRzUmtcDYujW2hEScFkvJNnXmu4qKiopqTGqFMSoKpKTwxImSbfbTp3FkZKiRXCoqKlc0qjGpBcURXdZSfpNi57uL6nxXUVG5glGNSS0wtGoFen0Zv4nl4EHQaDBGRzeiMhUVFZXGRTUmtUDo9RjDw8tkDy44cABjZCQaF5dGVKaioqLSuDSoMRFCDBRCHBFCHBdCTKmkzI1CiD1CiANCiPUNqac+MLZuXa5nojrfVVRUrnTqzZgIIV4RQiQXGYY9QojbgI+BQcCXwKtCiFghxIBSdfoAvwHNgLXAPfWlp6EwtonCduoUzvx8bGfO4khNUycrqqioXPHUd8/kPSllZyllZyAdOA6YUIzEa8AS4BMhRHFu7K+ARUA4EAV0q2c99Y6xdVFalRMnzs1876AaExUVlSubhhzmCgISgTuBb4F4wBXFwPQQQrQEPAEz8CcQDUysqCEhxONCiB1CiB0NqLdGnFt18ZjifBcCk+p8V1FRucKpb2MyTgixVwgxD3Av2lZsVAAkkFS0LQjIR+mNDAaeBa4RQlSZkyQ1NbWeJdcOfXAwwmik8NhxLAcOYIiIQOPm1qiaVFRUVBqbWhkTIcQaIcT+Ch53Ap8CkUBnIAW4GwgBRFH1YOBU0WtZtN0C/CalzAeyUYbGOp1/XCnlF1LK7lLK7v7+/rX/lPWI0GoxRkaW9EzUyYoqKioqoKtNYSnlLTUpJ4SYDaxAGdb6BwhD8ZuMBN5FMSpJgBa4QQihAyJQejOHaqOpMTBGtSZ3zVqc+flqJJeKiooK9RvN1bLU26HAfmAccC/wMvA9in+kK9BZSpmC0hM5COwFPgKWSCn315emhsIYFYUzPx9QZ76rqKioQC17JtXwXyFEZ5QhrDhgbJHBCBNCvAiMBu4HHpJSriyq8yRK2LARJbLrmXrU02AYiiK6AIwxMY2oREVFRaVpUG/GREr5YBX7pgPTK9i+A+hQXxouFqaiiC5DWBhad/dqSquoqKhc/qjpVOqALjAQjbs7pg6XnB1UUVFRaRDqc5jrikEIQchnn6Jv2bL6wioqKipXAKoxqSOu3bs3tgQVFRWVJoM6zKWioqKicsFUa0yEENcJIdyKXj8ghHhXCBHa8NJUVFRUVC4VhJSy6gJC7EWZld4RWAjMBe6SUvZpeHnl0el0snPnznWqm5qaSmPPoK8MVVvdULXVDVVb7WmquqBm2nbu3GmXUuobTISUssoHsKvoeSowpvS2xnh069ZN1pULqdvQqNrqhqqtbqjaak9T1SVlzbQBO2QDXptr4oDPFUK8ADyIkvpECzScdVNRUVFRueSoiQN+OFAIjJZSnkbJ9juzQVWpqKioqFxSVGtMigzI9ygpTwDSgGUNKaqhePzxxxtbQqWo2uqGqq1uqNpqT1PVBU1DW00c8I8BjwM+UspIIUQU8JmU8uaLIfB8unfvLnfsaPQ1slRUVFQuKYQQO6WUDTZBribDXE8D1wE5AFLKY0BAQwlSUVFRUbn0qIkxKZRSWovfFK09UnV3RkVFRUXliqImxmS9EOLfgIsQoh+wBPi5YWWBEGKeEOKsEKLM+iaZmZm0b98ejUZDVcNdEydOpG3btnTs2JGhQ4eSlZVVZn9CQgLu7u68/fbbFdaPjY3lmmuuISoqiuHDh2O1KvZUSkm7du3QarWYTCZ27dpVUmfJkiWNqu3HH3/Ex8cHvV6Pi4sLGzdubDLaRo8ejbe3Ny4uLrRv354+ffo0CV2ZmZmEhoai0+lwdXVl//5zp9vF0vbRRx/RunVrhBCkpaWVbP/f//6Hj49PibZ//vmnyWhbt24dBoOh5Fz7z3/+02S0ZWdn06pVK3Q6HSaTifnz5190bffffz/R0dF06NCB0aNHY7PZADh8+DABAQEIIWjRokWZOheqLS4uDhcXFzp37kznzp154oknKqwvhAgXQmwVQhwTQiwWQhiKtgshxAdCiONFy693rVREBdTEmEwBUoF9wFjgV+Cl2hykjnwJDDx/o8lk4ocffqB3795VVu7Xrx/79+9n7969tGnThhkzZpTZP2HCBAYNGlRp/cmTJzNhwgSOHTuGt7c3c+fOBWDlypV4eHiwbds2goKCePLJJ0vqdOjQoVG13XzzzSxbtoytW7cSFBTEo48+2mS03X333TRr1oxWrVpx4MABlixZ0iR0vfHGG/Tt27fk9xw/fnxJnYul7brrrmPNmjWEhpZNLBEeHs7ChQvZtm0bAQEBZZysja0NoFu3bmzdupXIyEimTp3aZLR9/PHHXH/99Wzbto3w8HCef/75kpuHi6Xt/vvv5/Dhw+zbt4+CggLmzJkDgI+PD6+99hqjR48uV6c+tEVGRrJnzx727NnDZ599VlkTbwHvSSmjgExgTNH2QUBU0eNxlKXYa0xNjImflHK2lPIeKeUwKeVsoE1tDlIXpJR/ARnnb3dxcSE6Orra+v3790enU6bR9OzZk6SkpJJ9y5cvJyIigvaVrJIopeSPP/5g2LBhADz88MMsX74cUO7+n3vuOXx9fXFxcSErK4uUlBQAYmJiGlWbu7s7ffr0wcfHB6fTiRCipF5ja4uPj2fw4MHo9coUpYCAgCah6+DBg4wZMwYfHx+MRiNxcXGcOXPmomkD6NKlC2FhYeW2X3vttQwePBgfHx9cXFzKtNvY2gB8fX3x8fEpt72xtQkhaNasGd7e3jidzpLe3cXUduuttyKEQAhBjx49SuoHBAQwduxYPDw8ytWpD23VIZSLQl9gadGmr4AhRa/vBBYUzXHcAjQ7bwXdKqmJMdkghLi3lJjnucRCg+fNm1dyF5Gfn89bb73FtGnTKi2fnp5Os2bNSn6w4OBgkpOTAUhOTiYkJKSkbOl9ja0NYNmyZdx8883Ex8czb968Ouuqb21Hjx4lOzub2NhYunXrxoIFC5qErk6dOvHDDz8AUFBQQHx8fK3+nBeqraZkZmZWeSfcGNo2b97MoEGDiI+P58CBA01G27hx4zh06BA9evTg2LFjzJo1C42m7jltL0SbzWZj4cKFDBxYbpClXiitDZTh3C5dutCnTx82bNhQURVfIEtKaS96n4Qyd5Ci58RSZUvvq5aafMM3Ag8KIZYIIf5C6ZX0qOkBGpvp06ej0+m4//77AZg2bRoTJkzAvYoVEisKly6+y69qX2NrAxg6dChr164lNDSUl19+uU66GkKb3W5n//79hIaGsmrVKl577TWOHj3a6LqmTJlCZmYmt956K+np6XTp0qXE6FwMbTVh8+bNZGZm8tZbb9W5jfrW1rVrV+Lj41m5ciW+vr4MGTKk+koXSduqVavo3Lkz27Zto3Xr1owbN46cnJxG0fbUU0/Ru3dvbrjhhjodvzbaWrZsSUJCArt37+bdd99l5MiRFX3uii5Wsgb7qqXaf42UMkUI8RvwAuAEXpBS5tX0ABeLRx55hN27dxMYGMivv/4KwFdffcWKFStYu3ZtycVj69atLF26lEmTJpGVlYVGo8FkMjFu3LiStvz8/MjKysJut6PT6UhKSiIwMBBQ7moTExMJDg4GKLOvsbWVxtXVlRMnTpCWloafn1+jawsODqZPnz6sWbMGPz8/evfuzT///EObNhWPmF4sXZ6ensyfP5+4uDhuu+02UlNTCQ8Pr+rnrFdt1bF3714mT55MaGgovr6+1Za/WNo8PT0BSEtLw93dnby8vIt6rlXF/PnzmTJlCkIIDAYDwcHBHD58mB49Kr8Hbghtr776KqmpqXz++ec10n2h2oxGI0ajMre8W7duREZGcvToUbqXXXspDWX4SlfUOwkGThXtSwJCSpUtva96qkveBawGFgDNUNZr3wa83ZAJw0odOwzYX3pbcUKzPn36yO3bt1ea1GzlypUyJiZGnj17ttIy06ZNkzNnzqxw37Bhw+SiRYuklFKOHTtWfvzxx1JKKVesWCEHDhwoT548KSMiIuTVV19drm5jaTt27Jh0Op0yNjZWRkZGysDAQOl0OpuEtoMHD8pevXrJ9u3by/z8fNm+fXu5b9++RteVmZkpCwsLZWxsrAwMDJQPPvhguboNra2Y0NBQmZqaWvI+Pj5eRkZGyqVLl8r27dtXWKextKWkpJScaxERETIkJOSin2uVaXviiSfktGnTZGxsrIyOjpaBgYFl9l8MbbNnz5a9evWSZrO5wv3jx4+XzZs3r3BfXbWdPXtW2u12KaWUJ06ckIGBgTI9Pb1kP0WJHlEickcUvf4MeKro9WBgJUoPpSewTdbmel1tARhy3nsd8HJtDlKXB7AISAFsRRZzjJSSiIgIGRQUJA0GgwwICJD9+/ev8AuPjIyUwcHBslOnTrJTp05y7Nix5cpUdTKcOHFCXn311TIyMlIOGzZMWiwWKaWUTqdTtm7dWmq1WgnIgIAAOWfOHCmllD/88EOjanvzzTelp6en1Ol0Uggh/f39m4y2ESNGSA8PDwlInU4nhw8f3iR0/f3339Ld3V1qtVophJAtW7a86N/ZrFmzZFBQkNRqtbJly5ZyzJgxUkopx4wZI/V6vdTpdBKQer2+yWj78MMPG/1cq0xbcnKybN68ecn35u3tfdG1abVaGRERUVL/1VdflVIqRtjFxUUKISQgNRqN/PDDD+tF29KlS2W7du1kx44dZZcuXeRPP/1Upl4pYxJR1Ck4XmRYjEXbBfAxcAIlere7rMU1u9p0Kk0NNZ2KioqKSu1p6HQq1fpMhBC5nHPCGFDSz+dJKb0aSpSKioqKyqVFTRzwZQKihRBDuISiuVRUVFRUGp5aB19LKZejTHpRUVFRUVEBajbMdVeptxqgO42Y6DEuLu78UDcVFRUVlerp1JCN12R21u2lXtuBOJRp941CWFhYlUnQVFRUVFTKI4SwVl+q7tTEZ/JIQwpQUVFRUbn0qdSYCCE+pIrhLCnlsw2iSEWliZGcl4y73h0voxrAqKJSGVX1TOo0liSEiANyAQdgl1J2F0L4AItRZrTHAfdKKTOLyr+AkgLZATwrpVxVl+OqqDQEWZYs7v35Xq5ucTXv3/R+Y8tRUWmyVGVMbpFSPiiEGC+lnFXLdm+SUqaVej8FWCulfFMIMaXo/WQhRDtgBNAeCATWCCHaSCkdtTyeikqD8Nnez8ix5rAhaQN51jzcDReWtFFF5XKlqtDgbkKIUGC0EMJbCOFT+lHL49yJkjcfyufP/1ZKWSiljEWZ3q/OYVFpEsRmx7L48GI6+nXE6rSyLmldY0tSUWmyVGVMPgN+A9oCO897VDUEJoHfhRA7hRDFS8M1l1KmgJKFGAgo2l6j/PlCiMeFEDuEEDtSU1Or/1QqKvXAuzvexaQzMavvLAJcA1gVp47AqqhURqXGREr5gZQyBpgnpYyQUoaXekRU0eZ1UsquKEtAPi2EqGoNyhrlz5dSfiGl7C6l7O7v719Fcyoq9cOWlC2sS1rHYx0fw8/Fj/6h/fk7+W/yrE1u9QUVlSZBtTPgpZRPVlfmvPKnip7PomSeXAq4CiGmAxQtA3m2qHgSECKEuFoI4QC6UZv8+SoqDYDD6WDm9pkEuQdxf4yy8NCAsAHqUJeKShXUfS3LChBCuAkhPIpeewDDgEnAhyi+l3bAw8CPRVV+QnHA/xf4C2iJkhpZRaXRWH58OUczjzKh2wSMWmWxoY7+HdWhLhWVKqjb+qSV0xxYVrTylweQKKX8Ugjhi+Js/wv4B7gHQEp5QAiRhOJ0NwAfqZFcKo1Jvi2fD3d/SJeALvQP7V+yXSM09A/tz3dHvlOjulRUKqBeeyZSypNSyk5Syk4oPZL1RdvTgVdRIrdullJmAAghggB3oAWwDNhdUbulHfD1qfdKw56aSsG+/Y0to0kzZ98c0i3pTLp6UslyqMWoQ10qKpVTr8bkPGriXH8fmFxdb6S0A77e1F2BpH70MQmjRiEdTbvz55ROjmYeZfHhxUz+azIjVowgNju2wY97Ku8UCw4s4LaI2+jg16HcfnWoS0Wlcup7mKs0NVmcvjvwbdEdoB9wqxDCXpTmXqWeKTx2DGd+PtaEBIzh4Y0tpwQpJQfSD7AlZQu7zuxiz9k95NpyAfB38SfPlsdb297i01s+LddbqE/e3/k+GqFhfNfxFe5Xh7pUVCqnIY3JdiBKCBEOJKM42keWLiClLLmiCSG+BFaohqThsJ48CUDhkSONbkwcTgd7UvewJn4NaxLWcDr/NAARXhH0D+tP1+Zd6RLQhWD3YBYeXMjMHTPZkLyB3sFVRZrXXcviI4tZGbeSsR3H0sKtRaVlB4QN4OtDX7MuaR23RdxW71pUVC5VGsyYSCntQohxwCpAizJf5YAQ4omi/Z811LFVymPPzMSRlQWA5cgRPAcObBQdu87s4peTv/BH4h+kFaRh0Bi4NvBaxnUeR+/g3nibvMvVua/tfSw5uoSZ22fSK7AXeo2+3vTsTd3L61te51DGIXq27MnoDqOrLF881PV73O+qMVFRKUVD9kyQUv4K/HretgqNiJRyVENqudKxxp7zORQePtIoGv5J/YeHf3sYF50L1wddT7/QfvQO7o2b3q3Kenqtnn91/xfj/hjH4sOLeaDdAxesJcOSwaxds/jh2A8EuAQws/dMBoQNqHYYrSZDXekF6fx747+5NvBaHm7/8AVrVVG5FGhQY6LSdCg2JqaOHbEcOdwoGlacWIFRa2T1sNW1TufeO7g31wZeyyf/fMLgiMEV9mBqgsPpYOnRpXyw+wPMNjOj2o/iiU5PVGvQSlPVUFdKXgqPr36cuJw4Np/aTJR3FNcGXlsnrSoqlxINGc2l0oQoPHkSodfj0bcv9lMpOLKzL+rxHU4Hq+NX0zu4d53WBRFCMLH7RMw2Mx/v+bhOGtIK0nhk1SO8vvV1on2iWXL7Ep7v/nytDAmUHeoqTWx2LA/99hDpBel83u9zIptF8sKGFzhrPltJSyoqlw+qMblCsJ6MxRAWiql9O0Dxm1xMdpzZQbolnYFhdffVtPZuzT1t7mHJ0SUcyzxWq7pHMo4w8peRHEo/xPTrpzO3/1xae7euk47ioa5NyZtKcnUdTD/IwysfxuqwMm/gPK4NvJZ3+rxDgb2AyX9Nxu601+lYKiqXCqoxuUKwxsZiCI/A2CYagMIjRy/q8VfGrsRF58INwTdcUDtPd34aN70bM7fPRMpKFwItw58Jf/LgygdxSAdfDfqKOyLvuOAQ49ITGHec3sGYVWMw6Ux8NfAr2vq0BSCiWQQv9XyJHWd28Ok/n17Q8VRUmjqqMbkCkFYr1sREDBHh6AL80Xp7X1S/ic1pY03CGm4MuREXncsFtdXM1IynOj3F5pTN/JX0V5VlpZTM3z+f8X+OJ8IrgkWDF9HOt90FHb+Y4qGuOXvn8MSaJ/B39WfBoAWEeYWVKXdH5B0MbT2U2Xtn83fy3/VybBWVpohqTK4ArImJ4HBgDA9HCIGxbfRFjejamrKV7MLsCxriKs3wtsMJ9wpn5o6Z2By2CsvYHDam/j2Vd3e+S7/QfswfOJ8A14AKy9aF4qGuE9kniPCK4MuBX1Y6P+WFa15Q/CcbVf+JyuWLGs11BVAcyWWIUJahMUW3JXPRIqTdjtA1/CnwW+xvuOvduT7o+nppT6/RM7H7RJ5a+xQT/5pIiEcIAgECNGgQQrDj9A72pO7hiU5P8GSnJ9GI+r9vGtV+FK56V0a1H4WHwaPSci46F97p8w4jfhnB5L8mM7v/bHQa9a+ncnmhntFXAIUni4xJ0ax3Y3Q0srBQSasSUdU6ZxeO1WHlj4Q/6NuqLwatofKCNgtoDaCp2UX/huAbuCvqLlbGrkRKiUTilE4kEiS46F1464a3uDXi1nr6JOVp7tacZ7o8U6Oyxf6TFze+yKf/fFrjeioqlwqqMbkCsJ48ic7fH627MsHO1LbICX/4cIMbk79P/U2uLbf8EJeUkHoYjq+F42sg/m9oFgIP/QhewTVq+9VrX+XVa1+tF52O7Gyyly/H+/77G6y3dkfkHWw/vZ3Ze2dzR+QdhHqGNshxVFQaA9VncgVgjY0tGeICMERGgk6HpSZ+k/w0OLVHufjXBocNCjL57egPeOnd6enUw4k/Ye8S+HEcvNcePukJv78IOaeg60OQdxbm3wpZCbX8hBdO2udfcGbGm+StW1c/DR78CdJPlNv8bJdn0Wq0fHv42/o5jopKE0HtmVzmSCkpjI3Fc/C54R6NwYAxIqJmEV0rJ8P+pRB8NVw3HqJvBY224rKWHNj3HeyYD2f2YxGCP1sFMSjfjH7egHPljF4Q0Qf6TILIm5UeCUDn+2DhUJg/GEb9DN5hdf/gtcCZn0/WkiUAZK/4BY9bbrmwBvcugR8ehZBrYEzZiY3+rv70D+3P8uPLeabLM7jqXS/sWCoqTQTVmFzmONLTcebklMsSbIyOxrx9e9WVpYS4jdC8A+SnwuIHwLc1XPsMdBwBepNS7vQ+2D4X9i0Bax606Ah9prDBloY55XcGdH0S/DqDwQ1MXuDfFrQVnHpB3ZRhrgVD4Mvb4OGfwKdhh+EAspYtx5mbi0uXLuT9+SeOvLySIUEA/v4IXH2g88jKGynm1B74aZzyORO3QvIuCOpapsjImJH8GvsrP534iRFtR9Tzp7n8kFKSvfxHtF6eePTt29hyVCpBHea6zCmJ5Aove1E2tY3Gfvp0SSbhCsmMg7zT0P0RGLcThs0Hgzv8PB7evwpWT4U5t8Bn18M/i6DdnfDoHzD2L7jpBX4zgI/Jh6t7/h+06Q9h10GLDhUbkmICuyhGxJqnGJQKhorqE+l0krFwAaZOHWk+eRKysJDc31efK1CQBWumwfInYeP7VTeWn6YYXFc/eOxP5bva+nm5Yh39OtLBtwPfHP6mxhMvr1TsmZkkPfU0KS+8wJk332psOSpVoBqTy5ziSC5jxPk9E2WWdpV+k8StynOrXooB6HAXPL4OHvoJWlwFm2YpF9sBM+D5wzDkEwjuBkJgtpn5K+kv+oX2q30YbMtO8PDPYLfAl4MhrXapU2pD3vr12OIT8HnoIUydOqEPCSFnxc/nCpz4A5x2ZZhvzTRY92bF/iOHDZaMUnpwI74G30jofD/s/x5yz5QpKoRgZMxIYrNj2ZyyucE+26VO/pYtxN5xJ/kbN+LSuTO2hAQcubmNLUulElRjcpljPXkSYTKha9myzPaSiK6q/CYJmxX/hn/MuW1CKP6OB3+ASbEwbjv0egpcymbxXZ+0HovDUveJii2ugodXKBfpLwfDmYN1a6caMr5agK5FCzz790cIgedtg8nfshXb2aLJhUdXgYsPjPoVOj8A62bAmlfKG5TfX4K4DXD7B0rvCqDH4+C0wc4vyx13QNgAfEw+LDq0qEE+16WMtNk4++57JDwyGo2HB2HfLcb3ibGAsrCbStNENSaXOYVxsRjCwhDnzd/Q+fmh9fXFUlWOroQt0Oqayud+uPooxqUCfov9jQCXALo271rh/hrRvB2M+gWEBuYPhPj6vYu3HDmCecsWvO8fidArC2553X47OJ3krlwJTgcc+x2i+oPOAHd8CN3HwKb34bcp5wzK7v/B1s+g1zjoNPzcAfxFlMIzAAAgAElEQVRaQ+t+sGMu2K1ljm3QGhjWZhjrk9aTmJtYr5/rUsaamEjcAw+Q/sUXNBs2jPClSzDFxGCKKUpQevBQIytUqQzVmFzmWE/GlhviKsYUHU3h4Up6JuYMZR5Iq561PmauNZeNyRvpH9b/wmeeB7SF0avAzR8WDoEjKy+svVJkLFiAcHHB+557SrYZIyIwtWtH9s8rIGkHFGRAm6JINI0GBr8DPZ9WjMeK5yBxO6yYAOF94JYK5rz0fALyzsDB8qtR39vmXrRCDRMuxnb6NLF33Y01No6g99+n5Wv/QeOqRLvpAvyVm59DqjFpqqjG5DLGWViILSkJQ1jFxsTYti2Fx48j7RWkRy/2l4TU3pisjl+N1WllQNiA6gvXBO9QxaAExMC398Pury+4SXt6Ojk/r8BryJ1omzUrs8/z9tux7N9P4YbvQKODyFIRRELAgOlww/PK8NX8QeDRHO75suLAgoi+4BulGJ/zaO7WnFtCb2HZ8WWYbeYL/kyXOnnr1uHMzSV0wVd4Dix77gghMMXEqMakCaMak8sYa3w8SFlmwmJpTG2jlYzCpZb0LSFhC2j05cJaz0dKycmskyw9upR/b/g3A78fyLS/pxHiEUIn/0718TEU3PwUH0p4b/jxadj4Xu0nUpYi89tvkVYrPg8+VG6f5623ghDkrPpDCT5wKWtsEAJungp9XwZXXxjxjTLkVxEaDVwzFpJ3Kj2d8xgZM5Jcay4rTq6o82e5XMj/ezO6wJYYo6Mr3G+KaUvhiRNIq7XC/SqNi2pMLmOslURyFVP8p63Qb5KwRXEk6ytOGZ9jzWHSX5Pos7gPd/54J69ufpVNpzbRzrcdk66exLwB8y54zZDygt1h5HfQYZjiBF/1IjidtW7GabWSuehb3Pr0rvC70TcPwLVbJ7IP5lEY0Y8H5mxlxsoK7oh7/0uJYmtxVdUH7DQCjJ6wpfyaJp39OxPjE8Oiw4uu6DBh6XBg3roVt569Kj1vTDExYLNRePz4RVanUhPUSYuXMdbYkwAYwsIq3G8MDwe9Xonoum3wuR02C5zaBdc8UWE9h9PBlL+msPnUZgZHDKZb8250CehCqGdo/RuQ89EZ4K7ZSk9ly8fKHX+HuyB6EDRrVXEdKZWJlYdXwJGV5KS3xpGWhs9D5XslxXh1CiBlh44vNmjZmJvGxuNpdAlpxsAOZaPiKgtAKIPRA7o8CNs+h5zXwfNcG8Vhwi9vepltp7dxTctravItXHZYDh3GkZ2NW69elZYxxsQUlT2EqV39rEujUn+oPZPLmMLYWHQtW5Y4Mc9HGAwYIyPLzzU5tRscVmWIpwI+3vMxG5I3MKXHFF6//nWGRg0lzCus4Q1JMRoNDHwTBs1UHOQrJymTKD+9Hv6Yrsw6d9ghbhP89gLM6gif3wDr/4vMSyNjxQaMEaG4XXttpYfw8E4ALeRt+od/9W9Dx2AvJn+/j1NZBXXT3ONRJTpsx7xyuwaFD8Lb6M03h76pW9uXAeYtSqSeW8/KjakhNBTh6orl0MVb2K1GZCWCJbuxVTQ6qjG5jLGejC2XRuV8KozoSigKwQ0p/8deHb+a2ftmc1fUXdwbfW99Sa09QsA1jyvzXMbthP6vg8kTNrwNs2+C6S3gy1uVNC/+MUpY77+OYe7+LoWZery7uFZu/Kz5cGojaS286H9mL0/dEMasEV2wOZw8t3gPDmcdhqN8IqDNQMWY2AvL7DJqjdzd5m7WJa3jVN6pOnwZlz75m7dgjGqNzt+/0jJCo8EUHd20nPCWHOVGZU6/K96gqMbkMkVKifXkyZI1TCrDGB2NPTUVe0bGuY2JW8GvDbj5lil7LPMYL258kY7+HXnxmhcvWk/EWZ3D1a8oX9gjv8LEEzD0c7h6jBJhNekE3P8dssuD5B+M5ewHc9C6GfDSrofM+AqbS965Eq3TyoF21+Kan4N5y1bC/dz4z50d2BabwSd/1nHM/pqxYE4jY2v5iYrDo4cjEIxeNZov9n7BmfwzFTRweeK0WjHv3IlrFUNcxZhiYig8dAhZB19Zg7B9NhRkQsYJWPKI0iO+QlGNyWWK/WwqTrMZQyXO92LOzYQvGupyOosmK5YNCc4uzGb8n+Nx07vx3o3vVb3QVT2Ss3o1R7t1J33+lzWr4OqjOLwHvQXth+LEQNb33xM79C4SHnoYW3IyzSc/j0avgY3vlquemW9l15pvycOVO/49BY2HR0l6lbu7BnFHp0DeX3uMnfGZtf8wETdy2hjGqVXvsfFo2eV7W7i14IO+HxDkHsSHuz+k//f9eWbtM/yZ8Cf2zZ/AFzcpqV0uQwp270FaLLj1rIExaReD02zGlnDxlykoR2GekgQ0qj8MfhdOrFUyIVyhqMbkMqXY+V7d4lfGtufl6Eo7ApasMv4Sh9PB5L8mk5Kfwns3vleva6lXhS0lhZSXXgadjrNvvUXap5/WOOLJduYMZ997n+M33kTKiy+B00nL11+j9fp1eN37EHR9WJmvUmrtFLvDyTPf7KCHfQe2sJsIbBmAx4D+5K5eg7OgACEErw/tQEsvE+O/3U2OpeL15ytj2Z5kZuYOpIMmjrxFj3A2s2yeqd7BvZk7YC6/DP2F0R1Gsz99P8/++Sz9D37Ip5YEbAuHwtIx5XJ91ZomFjWWv2UzaLW49ri62rLGtkVO+Mom25Yi5eWpJI59AntmHQx/TdgxV/HZ9Z4E3R6Gnk/B1k8rTJ9zJaAak8uUwpNFkVzVDHPpfHzQ+fuf85sU+0tK9Uw+3P0hm05t4sVrXqRzQOc6a5JSkr9lK87CwurLOhycmjgJbDbCv/8erzvvJHXWB6S++16VBsVZUMDZd97h+M23kD57Ni7dutLqq68I/3E5zYYNQ2MqSpt//QQlTcuGd0rqvv7LIbJO7qS5yMS78+0AeN12O06zmdw/lF6Bp0nPB/d1ISXbwovL9tfYuMWm5fPSsv0kthrC2WumMFBuJPGzu7Bb8suVbeXZivFdx/N74J18cCaVGIMPn3i58ljb7mQc+Rk+uhq2z1Ec+rXl0Ap4JxrW/qfJGBXz35txueqqsmn/K8HYJkpZ2K2atCqO7Gyyli0jb/164obdg6W+c3pZzfD3h8qE1pAiI9jvNWh9C/zyPMRuqN/jXQKoxuQyxRobh3B1Rde8ebVljdHRWI4WzTVJ2ApuAeAdjpSSbw9/y9z9c7m3zb0MazPsgjTl//UXCaNGkTj2CZzmczO+pZTM2XCSJxbu5O1VR1i+O5mDMz/AvGMHLaZNxRgRTssZb9BsxHDSZ8/m0IuvsHp/Cl/9HceKvafIL1TGqfM2beLkHXeSPnsOXnfeQeTvqwj56CPcrulR3r/jFQRdH0Lu/h+LVm1k6Ceb+PLvOP4VFgcIiOoHgGuPq9EHBXH6lVfJWroUKSVdW3kz4ZYofv7nFN/vSq72cxfaHTyzaBd6nYb3R3QmYNAL7LpqKl0s20n5ZLDixD2fTR+gXzONm8IH8vHI9cy4YQb77dmMiOrA4cAY5YI1tx+k7K3Zl2+3KpFti+9XjMiGd5QouEb2PThycynYvx/XXjXLtKApjkAsdsKbM2D5U5CfXqZc3vr1YLfT/KWXkDYbcfeNJOf33ytosY7s/FLJEN170rltWh0Mmwc+kfDdgw2+fEJTQzUmlynWkycxhofXyEluahuN9fhxpM2m9Exa9eRkTixjfh/D9K3T6dmyJ1N6TLlgTVnLlyNcXTFv20bCo4/hyMtDSsnbvx/h9V8O8U9SFp+uP8HnnyxHfjWHP0K6cvsRT+6fs4VBH2ykv+zJ95G9ET98x/7np/Dqj/sY981ubnxpGT/c8xiJYx5FarS0+uorAqdPxxBc8Vry8en5fLb+BKOPX4/N4cS54V1sDif/vrUtfdippJt38wOUCKJW8+ZiatuWlJdeJmH0aKxJSTx5Y2t6Rvjw8vL9LN+dXKaHYo2LI/Xjj8n89lsc2dm8ufIw+5NzmDmsE4HNlEmgXe9+nkUhL9Miey+5nw9U1kIpZtMsWP0ytB8Kd80BrY7bIm7jq4Ff4RCCh3QZrLppgjJE90Uf+OHxCmfXl5AZB/MGwJZPoMdYeG6fkpRy2xfw0zN16+HUE+bt28HhqJG/pBhT27ZYDhcZk2OrYc//YPfCMmVyV69GFxCA98j7CFuyBGNUa5KfHU/qhx9duPPeZlF+o7AbIPQ83SYvGFmUa23RfVdUhJc6afEyxRobi0vXmmXsNUa3RdpsFO7bCtkJzInsytyf7sZF58LUXlO5O+ruC07Y6MjNJW/tHzQbNgzXHleT/K+JJDwymp9HTuLjrWe4r0cI04dcRWFWNnF3vY3Nvzny2Ul0zHWQlFlAsLcL14T7ENB3Ipm/hzLw+4Xc0daX7PZd0Xz2AVqLmW+ib2FZu35cf1TLTZoErHYnqbmFpOZZScsrVF7nFpJcNFekU7A/x4LuYuTp5dz/4PugNcAfu5U0KaUwhIbS6qsvyfpuCWdnzuTk7XcQMOE5Zt17N+O+/YfnFu/hz72JTHE7jfWnZZi3bSupm/L6G7QMiOHfA27llqiyKVfueug5/vM+vJj5Jva5A9E9/KOyRPLqqdD+LmVyZql8X+392rP4tsVM+HMC/4r7nqN9xvJ0ejqa3V/D3sVKxoIejyt1i1fBPLQCfnwKJHDvQmh3h7K9/+vKZMp1M8Bmhru+AK3+gn7jupC/eQvCZMKlS82HT03tYsj+8UfsqanoThf1zPYvheufA5ShzrwNG2l2110IjQZ98wBCFyzg9LRXSPv4YwqPHiXwzRlo3NzqJnr3QmXRuLvnVLzfJ0L5rhcOgaWjYeSSyjNv1xQpYe2rEHNHtSmOGgvVmFyGOAsKsJ06hdewu2tUvjiia//aBUwNakFixk5ui7iN57s/j5+LX71oyl21Cmm14nXnHbh06oQwGEl49lkiZkzmoadf5ZUhVyEEZLz+H0g9S9T/vqZj50pye/X5N2mh/qS++y4uq1bi0qULAa+8wm16XzT7Uli5/zS/HTgNKNNRfFwN+HsY8XM3EubryqjAMAZ2aEGIjytkhcIHy2HDu+f+pG3Kr8EiNBq8RwzHvU9vUl55hTNvzMDl15XMGTuWbbv/pNnbf5BlK8DRMogWEybgNWQIp+OSWfLG59yQsAv3+W9y7KfZeN02GI8BA9C3aIHRx4eHR43lsY8En2fORPvZdYiCzAoNSTF+Ln7MHTCX6Vun88WhBRwNuZFnxqwgLHYzhh3zlRUhf38Juj4EtgIlwWTLzkqYtE8p/5kQcOMU0LsqvSBbgVKm2AhdJMxbNuParRsaQ82jA0vPhHdP+UfZeHofpB4B/2jyN21CWix49LulpI7GaKTljDcwto3m7H9nEnffSFrNm4vOr5bnt71QyQvXqheEXV95ufAblIm1v/5LyRjd4a4yu807d6Jr3gJDcFD1x3Q6YeVExU+m0anGROXiYY2LA6qP5CrmuFcBNlc9W7dvRNNfw+ybP6Nn8HX1qin7x58whIZi6tgRgLnOINb3GM1/tn9JzOIZOAbNJ3/DRnJ+XYn/c8/h0rnqO1W/xx9D37KFYqCGDkVoNFwDXBPhy9Tb2xOXno+HSYePqwGdtoq7wmYh0PVB2LVQuSB5BkPz9pUW17dsSchnn5Hz88+cmf4Gp558khCDAccNNzLLpR2rdIGMDorkeW8fnl92ggOdhjDk87fwP7iT7GXLyfhmERlfLShpT7i6MtHVg432NkS5peDfvx/ulRiSYgxaA6/0eoXWXm14e8d/WZe4Dq3QEhIUTERYFJFZpwnfM5vOFgshPcZC/9dAZ6y4seueBYOr4oNZNFxJWmmo4x17LbGdPUvhseN43XlnreqZiiMQDx7EPWsfRN8KR3+DfUuh74vkrl6NxssL1+7dy9QTQuA7ahTG1lEkPfMMiWOfIHTBV7Xroez5BnKS4c6Pqk+l0300bJsN699SlrTWaAGwJiUR//AotB4ehMyZjUv7ys83nE745f9g53y49lm46cWaa73IqMbkMuTcuu+VR3IV2Av4LfY3vjvyHfvT9zOxFVwbC2N0YZjq2ZDYkpMxb9+O37PPIITgw7XHeH/NMYYN6kv42OtIfvJJ4u9/AHt6Oq49euD72KM1atfr9tsr3K7VCCL9q48MKuH6/1OMSfIOZfGrai4SQgi87rgDt+uuw7x9B249r0HbrBnvWR3MWHmIuRtj+X5XEllmG+8P70xECy9o0RePvn2xZ2ZSsGsX9owMHBmZODIzcWRmcPZQAgmJVvK/Poam0yZiBvSu1N+VkG7mm20JLN0ZQE7h82hdEtEZz3KmMIM001nWOdNx+vugE1qmRvViaGWGpJirH1V6KD8+DR92U/xFWoOSNVqrV17rjOAZpAzh+EYqTmbv0AsaGjNvVZY5qMlkxdJoPT3RBwdj2bcL/LOUCCprHuxfirx+Irl/rsOjb9+SBc/Ox/366wh6712Snh5H0nMTCPnk40rLlsFhU+YmBXWHiJuqL6/RKr2/pY/AgWVwlRLAkvbZZwiNBuFiImHUI4R8/jmuXbuUr+90worxsGuBco7ePLVmueAaiUY3JkKIgcAsQAvMkVK+2ciSLnkKT8aCEBhCQ8vtO5l9kiVHlvDjiR/JteYS6RXJCz1e4CaZS9ab7yNMHetdT/bPRenVbxnIrDXHeG/NUe7qGsRbd3dEqxGEzJ1D4uNj0ej1BM78L0KrrXcNVdIsBLo8oNz9VTDEVRk6X98y6264GLT8584O3NQ2gCnf72XkNa0Y0qXsMIbO2xuPm28u15aPzcH4z9dx95ev4v2v57h37QRa9+pC37YBXNfaF4NWw5pDZ/nf1ng2HEtDqxHc3DaAET06IiX8k5jFnqRs/jmZRXZBARpDOqYWK5j691TeXbeJ9qbhtPBypYWXiZZeJoK9XQn1dcXXzaAYrc4jleWJ//lGuWg6rEUPmzIEZrdA3EYoLBV5JrTKd9eslRIB6OavGCI3f+Xh6gNOuxJGayt6WPOV9gK7kL95C1ovLyUbcC0xxcRg2b8b+gAtOirDPz8/i/m3b3Hm5JQZ4jqfo2dyWUsIvcdPIv/dGaRMnUbLN6ZXH6yyd7ES9HDrOzW/qLcbAgEzFd9UuyFYT6WQvWw53vfdh+/oR0h4ZDQJjz5KyCcf49azVESb06EER+z5H/SeqPRImrAhARANmfa6OkMhhHgAmAvEApmADzBUSlnpgt/du3eXO3ZUEblSUxw2JSTTkqU8pFTuwLQGJTNt8WuD24V3+x02JYww76zyGkqdGAIEyl2gq6/yqOm4tb0QspMgO1F5zkqE7ESSFuwkL11H2jdvEZ8TT3xOPAk5CcTlxJGcl4xOo6Nfq37cG30v3Zp3QwiBdeNiTjz6Cs2fvBef8a9idzjJKrCRZbZitjqQEpxSIimeniCREuxOicMpsTsldoez5H2m2UpCupn4tHxGfDKRDJ0r/3f9UwAM7RLE2/d0Qqs59+ewJScj7fYKDeBFIT8ddi+AXs9UOcRUU5xOiRDUOuVMypGTpI56mEKbncl9nuGk3guDToOHUUd6vpWWXiZGXN2K4VeH0MKr/HkipSQ+3cw/SVkcSslkXfpskh1/YCzsREHyveQXljXUbgYtob5uhPq60srXlUg/d9oFetKmuQcGneb8xsGcroS8ZpyAjJPK6+xEJRotPw2sZSdiVoZ0DeD4r8G4dOxI8AezavUdAaR9+impsz6gzbAzaKcmgaMQZkaREt+D7F2nabP573Nzioo4lJLDh38c49d9p0u2PZe0ngE7fsb9sccJeX6C8nlS9ig9Nb1L0XPR66/vVqK1Hl9Xuwv7wR/hu4dg6OecWryPnF9+IfL339E3D8CemkrC6DFY4+MJ+mAWHjfeqBiS5U/B3m/hxheU3k09IIQwSykbbAyzwYyJEEILHAX6AUnAduC+0oZCCPE4MFxKebMQYhDwOfCplHJGZe3W1Zgc/3s5LutexU3m4eLIw+is+cp2ZmMA2R6tyXaPJNcjkhzP1uR5tMbucCLyz6DNP43enIrRchYXy1lcrel42DNwt2fiYc/A1VHBPIIqKNS4UKDzwqxrhkXnhcCJ3mlB7zBjcFoQjgKStTZOae2c0ek4rdNyRqvltE7HaZ2BCQscZLkJZgxXLhw6TBhpjtYRgM4egqe9J0bhhUYINBplWOjurAVctWg1u3078dq1j5FrufAcQwadhuvsZ5m0/E223zUW+613EhngzvWt/coYEpWyFB47RtwDD6L18uLsGx/xx2kbp3MsDOkcxI3R/lX7gM5DSsnXh75m5vaZxPjGMOO693BYPUjIMBOfbi56zic+3UxiphmbQ7ke6LWCNs09aB/oSYcgL9q19MTLRY9Bp0GvVR4GnQaDVoNeK9BqhGI4bQVFhiUVzBlIrQ4zJrLtejKtOjLtelxPrKTD39M58UtzCp/5F+73DMfbVY+Xix6dVoOUkkK7k7xCO3kWO3mFdnItdjLyraRkF3Amx4Jxx2YGL3obwy2CoX7v0T7Qi9cKXsf55XFM1/Wj1UcflnwH+5Oz+WDtMX4/eAYPo45R14UxrFsw64+m8t32BPr+Mo9B8VvZMXAY9/nNRWcvP5G0GOuwr7G3GYTDKXE6wSGVmyebw4nV7qTQXvzswGp3YnU4EUi6/XYnzvR8kpY6sd5+N4Vjx+OUKO1kZ+E17Xn0cSdIe/YFInW/EpS4giMxz3I85qkyx4/wdyOmpWctzyiFS9mY9AJekVIOKHr/AkBpQyGEGAYMlFI+KoTwBk4A30gpx53X1uPA4wCtWrXqFh9fcYK+qvhh7Tz+PPQ5BVKPBS1mqaUQLVa0WIUGCWiQaFB+fI1wosGJHgf+1gLCMs0EZxXikyXxyBK4ZwucOonF24m1mRNbMweOZg5sJi1W3HA63bA7PSh0emFxepPj9CVDNsMm9AghQUik1oYUdpwaB1pRiJs04+HMx13m4ylz8ZBmPMjjtA5iDToS9JJshwNhtuGXLWmWDwY76O0Cvc2AxmpEZzMw4NBZYtsamdDxUTQOfzwNPvi4GmjmqsfFoEMW/QEcTomz6PUrmS9g2pyBOdHA8mnz8fJwwdtVj7ebATeDDiFAI0RJR0oIgQB0GoFOq0GrESUXFb1Wg7tRRwtPE2dnzCDr22+J2rgBrZdXnc6lKxHz7t0kPDIaY0QErRZ8VaPZ4VWxPnE9E/+aiKfBk49u/oi2Pm3LlXE4JfHp+Rw4lVP0yObAqRwy8mu2sqFOo/z+xeeERkCuxY79vCzLzcngt6RJnN7RjDG3TOaU+7lMwe5GHRabo1ydIoUIXT5GYy4RmlTeWfg12/q4812fXqTl59Eu/gSTlp7liwE+/N25OW5GMNvs5JgdaDVaWnq6EeztjlGnQ6/R423yxsfkg81sou1/lxNxLA5bbzNTAx7D7nDDTTpwEVZMFOKCFQsGfnNejfIPqB39NDt4ddc80hPceaj/S2SayhoEV1sB/9kyl3bpcXj1zOaTsJtZJHohtPkIrRmhLUBozQyI7MWHQ+6r9fGh4Y1JQ/pMgoDEUu+TgPNzmpf+VcYAe1Ei4ivFv4oU1VXWy7MwYFk+CIFAFPVSRYkEjQSNEzROicYJ2qJnY6ETj/ziSV06nEC6lyDeG0xWSfAxcC31X8szQbIvnPI1c8qngFO+ZznlIzjjLXBx8cApnVjsFhzy3EQxISWmQvA0g5cZvMwSz/yi1/kSvxy4LsvC0GyByVrJ16O1gl6C3onWVU+fMCd7pzyBq0Fb/VCLww5vHifn2r4kL9jJpHAnrl3a1Ol7Lo202cj55Rfcb7pJNSS1xLVLF4JnvU/iU0+TNO4ZQr74vFbhs+fTJ6QPCwct5Om1T/PQyoe4I/IOOvl3orN/Z4I9ghFCMQQR/u5E+Ltze6dAQOnZnM6xcDgll3yrHavdWXIXbnXIkvfK8KazaLjz3M2Kp4sOb1eD8nDTl7zOfegttB6CGc/cWjScaiPTbCWnwI5eZ8emPYNZJpHjSCLdmsBZSzzphWeQKBMOE6Uk2xXyM/Mx6/bh42OixzYrDo3kdLQdhxNSMgU6jZFW3kaae+kBJ1ZnAQUWB4XOQvan7SfTkold2jHeLpn2DYRscqVtn0Xkm8Cp0yF0JoTOBYfeFaebO1cFJSs9eqFFIzRo0BS9B4RECCcgQTgAWaRXok81k7nMlT1dJZF9fkUCNllIocNCocOMxWHm7ba5TPhe0HGLF3a3Xbj12FPmN9QILUEtqk6P1Jg0pDGp6Ap2/pUwCQgRQtyEYkyWAuXGn6SUXwBfgDLMVRcx3QKvJi2slIOrTI9MgkarOH51OoRWi9BpQatDYzKiD2mFISwUQ1gYhtBQNEYlOsbhdGB1WCk8fYqC48coPHECXWwc7ifjaBOfhNibVXIEp0Zg9rPj1GvR2YxobQ60hXY0Vjsae+UzkB1uJkSAHy7twnANDccQHIw+OBh9UDC6AH80JhPCYCjrtP7jdWXehNYBogY/cdpRsOXj2q8fLNxF/ubNuHapILqkluRt2oQjIwOvIbUL/VRRcO/Th8A3pnNq8hSS/+//CHzjDbSedRviAIj2iWbR4EW8tuU1fj7xM4uPLAbAx+RDR/+OdPLvRIxPDL4uvviYfPA2eqPX6mnp5UJLr4qXbwbF4ORYc8i0ZJJZmEmGJYMMSwbZhdnYnXZsSM5IyWmLxFngRJ510CdFkhxmY33KfzE7Csm352O2mcm2ZnMq8xROqRgNnUZHmGcY3Vt2opVnK5q7NsffxZ+AjASMnq8yMC+Sp+5djZSSE18MxBAJi3JT4PEN5NgFBq0Gk77ygA6ndJIbv5H0b+4h44FeaL7NZNTa4ozO1qLHuWHq2U+GERtmwimd2KUTp3TikA6EFOg0uiLjokEndGiEBq3QIhD0+TMRh06wqZsNN3squDfHqG2Gm94NV50LbvGbcTUnkfv4TWT+nMOotYcZHjAQ439lBrIAAB1DSURBVFOP4GnyoplRKXuhk4cbkoY0JklASKn3wcD5K/9sB/6/vTOPr6o69/73yck8kBnCFMMQhiAWxQmUVqWFggMqeGsdcKKKFsFW7+sMal9bUWwrKiIiThW04rVixeKtYPW9yAWriMQgEIKQMGQiARIyr/ePtU5ycnJOcpKTk4RkfT+f/cnJ3mvv/dtr772evdZ61rNGAa8CU4GVwDWBEBN51lmkntVyVNLW4AhyEBEUQcTAIcQNHAJu3oK1x45RtXcvVTk5VObk6PEfNbVIeDhB4WFIWDhBEeHmbwSOxASCExNxJDT8bdPXaNIwULVQnAO9mzZnNKFAh6YIHjKW8IwMyjZuJPmOO1rYqWWOrlmDIy6O6PObGdxlaZbYadOoLS3l8BMLyZ4yld7/eQ+x06Y1W9usLS3lyKpV1BQU0ueB+xt9aCRHJrP4osXU1tWyu2Q33xR8U798uv/TJseKCY0hMTyRuLA4FEp/PNVWUllbWf+7vLqcGtW0j81Rq6gNolFntSAMPqT42Yk6NgwOYvvhL4mKSCIqJIr48HhSY1K5ZMglDI0bytC4oaT2SiUkyIPb7v7F5MdVUbT7EKqqisqcvVT/sI/E26+CI89A9np6DW/ZMy+o+gSxH/yG2PBEBl/zCmpmNDXFR1DVVaiqalR1NaqqClVVSe6dc7lrW39S53kZ+e6Fyj057PnqEhJuuIGl0f+A3Dy4c4129Kmp0q7Du7+Cyb+Hcb9GTavl0GO/g1VvE1cTScqCBUhwpzvetkggFW4B0kVkEJAHXE1TQ9EPHR+sDlgDrFBKZQZQU4fiiIkhYvRoIkaP7tgTJ6Xrv4U7fTMm+VnazTMpnajx4yh65VXqysraHm4CbUiPfbKeuOnTET+aZyyQMHMmkWeeyaFHH+PgffdT8s5qUh5+qH7wnpPqw/kUv/YaJW+9VR9IM2TAABJvurHJMR1BDoYnDGd4wvD6GTOPVBwhpzSHIxVHKKooqq9hFFcUU1JRQpAEER8WT6gjlDBHGKGOUEIdoUQ6IkgpCyE5r4y4/SVE5hwiKHs/dfvzkMhIQvqmENwnhZC+KYSk9KXqcA5HWcvvQ4sIiZ+uQ7u0lkPbCOsXAztqqMzO1lGdRYj5xWx49TUdXsUHY8LHD0PRbpi5BiLiESCkj+cpFhJuupGCp//IiW3biDjNdxf6whdeQMLCSJw1C4rHwpvTYetfYMx18M6N8P2HerT8ubcDIA4HKY8swJEQT9ELS6ktKaHfokX1LSJdlYAZE6VUjYjMAdahXYNXKKUyRWS22b4UmA9EAM4e9SuAxwOlqceQ6GJMfCE/Sw9ECw4javx4il5aTtmWLdpNsY0c+/hjVGUlsdMua/MxLA2EZ2RwyqqVlL73HvmLnibnyunEX3styXPvpLaoiKKXV1D6t7+hamvpNXUqibNuoWDxsxT8+c9E/+THPkVDiA+PJz48vtk0Simq9++nIjOTisxMTmRmUvldFrWlDQENQ1JTCR85irBLL6P2+HFqDh6i+tAhynbtoqawEJQiPCODkOGVkP1p2zLk4DYdBmj991R8l8Wxf35CxOmnE5zST482/3a1Ht8SGun9GDvX6TlJxs2BwT9p8ZTxv7yGouUvU7j0RQYued4nmZV79nD0ww9JuOlGghMTIWGiDiT62SLY+THs/AimPKWnoHZBROg9bx7B8Qkc/v3v2T/rVwxY8jyOmBifztsZBLTupJRaC6x1W7fU5fcswLfhzhbfCYvWo5ULd/mWPj+rPoRIxBlnIGFhlG3c6JcxcQ+fYvEfCQoibvp0YiZOpGDxYo785S+Uvv8+dceOISEhxM6YTuLNNxM6ULcu9330EfZccikH7r+ftJUrWzUYtK6qiuq8PKpz86jO3U/Vvv1UZGVR8d131B3VfQgSEkLYsGHETJpE2MgRhI8YQdiw4TiivddoVVUV1fkFOGJ7wdZlel6V4/kQ3YoJ16rKoWgXoedfhkTs49g//0llVha9/48JBz/6KvjqNV1Qn+olPt3xAj3iv/coPbLcBxzRUSTMvJ7CZ5+jYseOJjVDTxQ+vwQJDyfxllv0ChG48AF44wodlmXqIjj7V173T5h5PY74eA7cfz8/zLyB1JeWtT6eWAfR9RviLG0jKV3PmtgS1SfgSI5+AdEB8SLHjqX8iy+a3a22pIS8e+/VI5hHjCQ8YyThI0bgiIuj+sAByjdvrg+fYmlfHHFxpMyfT+yV0ylatozQU04h4YaZTQqZ4ORk+jz8MAfuuYfiV17RzSzNUPrBBxx5+22q9+dSk5/fyElFQkMJGzaMXlOmED4qg/BRowhPT291E6aEhjYENxxykTYmez6F0/7D94PkfweqDuk3hvDhmRzfsAGgYdT7KeMhpq+unXgyJkrp0eUVpTDzfe9xyzyQcN11FK94hcKlLzLgz39qNm3Fzp0cXbuWxFm3EJzgEjF68IVw3jzonaGnmG6B2EsvwREXS+7ceRQuWULKfN+MX0djjUl3JWm4DsWgVPOjdQt3gqpr1LcSNX4c+Yuepvpwvtf24/xnnqHs8/9HcHIyR9d8UL8+uG/f+qp47GW2iSuQRJw6qsXR470unsqxdf+gYPGzRF9wAWFDhzZJo5Si8NlnKVzyAmHp6USNG6c9Bgf0J3TgQEIGDCA4ORnxN4y6Oyk/0iFcste3zpg4IwWnjCY8YyQntm4lbMSI+hoZQQ4deXnzMjhxBCLidbSInM/h+7Xw/Udw7IDu8G4mqKcnHLGxxF93HUXLllGZnU3YkCEe09WWlpI3dx6O2FgSbr658UYR+NljrTpv9IQJpK18k9C0tFbt15FYY9JdSUrXwe+OHYRe/bynyzfT9fbOqF8VNX488DTlm77wGNG1IiuLkrf/Svw115Dy0IPUFBVRsWMHlTt2UJG1g4qsLHpdeqnXyaksHYeIkLJggWnueoC0VSsbeQapqioOPvwwpe+vIXb6lfR95BHfgh62B0FBMPgCyN7Q8kePK4e2QXgcxKUSZpqaYn7qFotr9AzY9Dz89wJdA9n9iQ71EhKpa0QZj3lvAmuBhBtmUvz66xS++CL9n3yyyXZVXU3uvLuozssj9dVXCI5vvh/KV9oSw6wjscaku5JkBh0W7mzemBRk6bhgCQ0dtGEjRuCIj6ds48YmxkQpxaHHH8cRG0vynTpQQXBiItHnnUf0ee0bbdjSPgQnJZGyYD55v/ktRS+vIOk23dlbW1pK7p1zKd+8meR5c0mcPbvjmyWHXASZ/2X67TJaTg96quKU0SBC9HnnET5qVFNHj36nQ+JQ3XcS3UfPJzLiYhj0E7/nbAlOSCD+F7+g+PXXSZ4zh9DU1PptSikO/e7/Ur5pE32f+AORY8f6da6Tia47AsbiH/XGpIVO+PwsXYtxCSUuQUFEjTuXso1f4B5u5+iHaznx5b9J/s1ddlT7SUSvKVOImTyZgueeo2LnTqpy89h7zbWUf/01/Z5cSNLtt3dO/9YQMzgre71v6WtrdJ9JinbsCOnfn0Hvrm5o4nIiAteuhl+th9/ugMsWw7DJ7Tb5V8LNNyHBwRS99FKj9cWvvUbJX/9K4m23EXf55e1yrpMFa0y6KzEpEBrTsntwfhb0blp9jhw3jpqCAqp2765fV1dWRv5TTxGekUHc9LY1EVg6j5QF83HExHDg7rvZe/XV1BQUkPry8s7t24odoD989mzwLX3RLh0Ov68PXoIJg6D/WP+nzPVASO/exM2YQcnf3qf6gB6LfWz9BvIXPknMpEkkz5vb7ufs6lhj0l0RMR5dzRiTyuNQ8gMkNzUm0ePHA1C2cWP9usJlL1Fz+DB9Hnqo4+ccsfhNcEICKQsWULlrN0FhYaStWknU2Wd3tizd1LX3f6C6ouW0B82c7ymd73KeOOsWUIqi5S9TsWMHeffcQ3hGBv0WPtH+zgonAT3vinsSScOgoBlj4nQd9lAzCenfn5BTUinbqF2Eq/bto3jFCmKnXeZ5VjjLSUGvyZMY+NJLpL3zV6+eSB3O4Auh5gTs39Ry2kPbwBHW0IzbiYT060fs5dMoWb2a/bNvxxETw4AlSwiK8B7HrDtjjUl3Jildu0BWepmwKF/H5PJkTEB7dZVt2YKqquLwH55AQkJIvvvuAIm1dBTRE85vPO6hs0k7XzuBZPvQ1HVom+6ob4cJzNqDpFtvRdXUUFtaysAXlnh1pe8JWGPSnUkerv9664TPz4LgcIhP87g5avx4VHk5Bc89z/ENG0j69R2E9O65L4slQIRFw8BzWu6EV8p4cnV+E5eT0NRU+i96itSXlxOe4aM3WjfFGpPuTEseXQU7dJogz/0fUeecA0FBepR1WhoJ118fIKGWHs+QC3Sto6zQe5rS/XqKbV863zuQXlOnEnnGGZ0to9OxxqQ7Ez9IRwP21gmfn9VosKI7jl69CB99KgB9HnzARv+1BI4hF+m/ez71nqa+8/1HAZdjaT1do+HREhiCQ7V7pCdjUlGqA821EKI+6dZbqcjMJHrChACJtFiAvmN02JPs9Xr0uicOfQuI74MbLR2KNSbdnaRhnpu5nGFUPLgFuxIzcSIxEycGQJjF4kKQQ49Oby60yqFt2qkkNGDTmFv8wDZzdXeS0vXkP7VuM+EVNO/JZbF0OEMu0t6H2Z943t7FOt8tjbHGpLuTNAzqqvXgRFfysyAkCmIHet7PYuloRlysn8e/TNch4suLG7aVF8PRXB2Ty9Ilscaku+Ma8NGV/CzdX9IDR+pauihRSXDHJhg/F7auhGfHwlevQ12dbuKCLufJZWnAliTdnSQvU/gW7Gixv8Ri6XDComHS7+C2zyF5hK6hrJisJ7oC68nVhbHGpLsTEQ9RvRsbk/JiOH7Y9pdYui59MuCmtXD5UijeA1+/oaeijkrsbGUWL1hvrp6Au0dXfRiVluewtlg6DREY80sY/nP4bBHEpba8j6XTsMakJ5CUDpnvNbhc5n+n19tmLsvJQEQ8TH68s1VYWsA2c/UEkobpMBTlRfr/gh0QFtv8DIwWi8XSCqwx6Qk4PboKTMh5pydXZ8ysZ7FYuiXWmPQEXD26lPI6u6LFYrG0FWtMegKxAyE4QnfClxXAiWLbX2KxWNoVa0x6AkFBkDRU10ycne+2ZmKxWNoRa0x6CknDjDExAR6tMbFYLO2INSY9haRhULIPDnwNEQkQldzZiiwWSzfCGpOeQlI6oGDnR3pCLOvJZbFY2hFrTHoKTvfgilI78t1isbQ71pj0FBKGAKY2YvtLLBZLO2ONSU8hNBLizNwl1i3YYrG0M9aY9CScTV22ZmKxWNoZG+ixJzHox3rQYmRCZyuxWCzdDFsz6UmcNw9u+6yzVVgslm5IQI2JiPxcRL4Xkd0icp+H7SIii832bSJyRiD1WCwWiyUwBMyYiIgDeB6YAmQAvxSRDLdkU4B0s9wKvBAoPRaLxWIJHIGsmZwN7FZK7VFKVQFvAdPc0kwDXleaTUCciPQNoCaLxWKxBIBAdsD3B/a7/J8LnONDmv7AQddEInIruubi/L+8jZqCgZo27hto2qKto67nZMi3rqixszT5ct6umF9O2lNbVz1We+OLtohACwgUnuJ1qDakQSm1DFgGICJfKqXObJMgP/YNNG3R1lHXczLkW1fU2FmafDlvV8wvJ+2praseq73x9Z4HUkMgm7lygYEu/w8ADrQhjcVisVi6OIE0JluAdBEZJCKhwNXAGrc0a4CZxqvrXKBUKXXQ/UAWi8Vi6doErJlLKVUjInOAdYADWKGUyhSR2Wb7UmAtMBXYDZQDN/lw6GV+yPJn30DTFm0ddT0nQ751RY2dpcmX83bF/HLSntq66rHam06/56JUky4Ki8VisVhahR0Bb7FYLBa/scbEYrFYLP6jlOqSC7qvpQaoBO5zWX8VkAnUoQdC7ga2AWe47f8UsMNsew+Ic9ueChwH7vFy/kHA/wK7gLeBULNezPlrgArX87ppO7OZa2uVNuDnwPfmWu/zoG2mOdZeoNpoOwGcH2htrci3FcARoysT+Fcbde0Bjpm/9zWTZ2cBtcCMFnTFm3yrQffbndoJeTbH3FsFJLmsvxYodtH2IzdtP5h99rvmhdux3zLPaQVQ2I7aLgCqzPN2Apjfyfn2HzS8IwuAfUZbBbovNhb4wNzrig7Q9qbRsx397IeY9SOAfKP5oNs+fuUbkGaeFec9WephXwFeNXlQiS5nXcu2xXgpU1taumTNxIRiORW4An1hrqFYtgNXmr8D8R6K5b/RBcNpwE7gfrftfwI+akbGQuBPSql0dCF4i1k/BV2YnQ3kuZ3Xqa2laIo+a/MUlsac01XbaOAn6Dx7AMhCu10vD6Q2L3jLt9Xm/31KqVHoF6e1uk5Dv4RvAu/S+Llwz7OF6BelJV0PAOvR9zMXeMZln47Ks/8Bfoo2Dq7kANcZbfk07kD9Dv3y/y86L5uEKxKROGAcMFIpFQ6sakdtAF+iByJnK6Uec1nfGfm2kIZ3ZDa6MDwHnYdPA3PReTYFbQhrad4ByV9tb6INx2j0YMFZZn0x8BDawLjTHvm2j4Z7MtvDvlOAycCN6DJjDI3LtjaHt+qSxgT98mwHvkUXHvWhWJRSWUqp74Ek4EOlaRKKRSn1sVLKOSJ0E3oMCwAicjn6yzbT08lFRICL0AUgwGvA5eb3NHSBU4y2/vXnddHWLK3U5ikszQQ3bacppY4opY6jDUgy+t7We1cESFsjWsi3NOBD9FcTSqn81uoCxqI/LtYB/TDPhQddd6KNTb4PujLQL3Yx+ks7TUT6tFZbW/PM7P+1Umqvh/UblVJrjbZy1+MCvdAfDRXor1FP4YquAd5QSuWY/9e3lzZDsVnc9+nofAsDclzekW/Q5UMx+j0oRhuPGPQX/WF0ntUGUNtaUzYpYLNzf6VUvtKDsI952MfvfEM/J03uiQvTgChgtSk3K9C1Oue2Noe36qrGxFuYFVfC0A9Fc2mc3EzDV2sUcC/waDPnTwRKXG6Y67F90dYaWtLmfr4SoNqLNtBfTJHAKebY/tCe+TYM3dQwSET+LSIz26DHmRdOXbno63TV1Qtdo13qo65v0F+DoL8gT6Hxy9laWptnvhJP4y9hX57DYUC8iHwqIv8G5reztnFo9/5TRGSUH8cB//LNgW4lcPIu+h5uBoYC84BngZHoQdHfops7fXVlbbM2EQkBrgf+4eO5Wku9NsMg9EfbIBGZ4CF9GnDU5V3YT8PAcb/Ktq5qTHwKs+JLGhF5EP0V8qZZ9Si6ueN4G8/fVm1NT+KbNvfzeT2/iFyIruL+GF3d/V1bdLVCW5PdvGlDNymMRrdZTwYeFpFhrZWFrpa76jrbTdc04F6lVK3bft50PYEuqNeijc7XtDH+UhvzzBfONRrvdT2dh3Tuz2EwujZ3MbowS0cXsO2h7Su04Z0KFAF/a+NxApFvo4EC9LORDTyHroluRddox6DzIqoDtC0BPlNKfd6aC/AFD9oOovtyLja/V4pIL7fdPJX57VK2ddWZFn0Js1IJ9HH5/0zgNRHZp5SaCiAiNwCXABNNdRN0YTtDRJ4E4oA6EalQSj3ncqxCdBUv2Fhw1/M7teU2o60RIvIKcDpwoLXa0E0wrnkRC4S4axOR09BNXFPQzTXlwBARSVJKFQZCWxvy7V/AT5VShSLyGfAjdJuvT7rQhcRQ4MdKKSUiA9CF7JMuumKAD0XkGLqpYyq6/dejLqXUUeAmEUkD/o5uInQ2C3VEnjWLua8L0X1NRS6bPL4jrtrQbe+FwAwamvmcee6XNpNviEgSuiM6uoOfNVdqafwFPQn9rIF+F3KB24D7zTl2i0gF+iv9X4HSJiIL0M/Tbd7yxBd81aaUqgQqRSQG3XyVi66dusbk2gOc4/IuDDTrwN/wVqoVvfUdtaCN3B7gfHS75DfAKLc029AdcIL+ctvstv3n6A635GbO8wjevTHeAa42v5cCd5jfF6OrlWnor57NHvb9lOa9MXzW5pIXg4BQkxfr3LQ9iO5LuMrkR5r5Pw8zMDUQ2lqZbyPN/dqObobbTmPPKV917XXLi1HedKG9Vma0oCvOHCvN5NfrgbyfLTz3e2nsMZVq7uOVwHYv78gm9PPv6R0Zia5pfWeO1SjP/dSW4vKsZaNrwh36rLlpc30uCtFOK2nofqU84BXgEZO+D/pjdGIA34NZwEYgwsv2P+PmzeVvvqENl8Nc9/fmuhPc0jhrLVeb5yafpmWbxzK1pcXnhB29mAytQVezStEeB7PRBUGueRjKzfKte+abl3A/umq7Fc9ucs09DIPRTQK70QVRmFkv6C87p7bDwC1m2xUu2g4D67wcu1Xa0F/XO9Ev7YNGW455EN6hwe32gIuuOrM9oNpakW+rgKNGWzWwqo26stFfXaXAg2bbbLM00kVjY+JN1zh0Z2iNybO8TsizueY8NeYeLjfrl9PgfqvMb1dthUZzDbDLNS9cjl1Ig6toXjtqm2PuQXUnPmuu2oqMnmx00+VBdI1FoTukfwt8jPb8qjZLILXVGC3O/eeb9SnoMqvOaKsBft0e+QZMd7knytx7Z7k526X8eoMG1+B/0rhse97oblKmtrTYcCoWi8Vi8Zuu2gFvsVgslpMIa0wsFovF4jfWmFgsFovFb6wxsVgsFovfWGNisVgsFr+xxsTSJRGRWhHZKiKZIvKNiPxWRJp9XkUkTUSu6SiNrUFE7hKRSJf/2zS6W0R6i0iOiKS4rFsiIve1h06Lpa1YY2LpqpxQSo1ROsLwz9BjbRa0sE8aOrhhpyIa93frLvRgTb9QOkDmQmCROdcZ6MG9T/tzXC+aLRafsQ+PpctjCtBbgTmm0EsTkc9F5CuzjDdJnwAmmBrNb0TEISJPicgWEdkmIh7DWphaz3az3GXWLRSRO1zSPCIid5vf/+lyzEfNujQRyRKRJei4VQNd9p2Ljgm1QUQ2uKx/3NS6NomJVCwiySLyrjn+FhE5z4PkZehQORei407NUUpVi0iwiPxRRDYbbbPMMXuJyHqTV9tE5BKzfqi55qVGs88RYi2WJrRmhKNd7NJRC3Dcw7oj6FAYkUC4WZcOfGl+XwD83SX9rcBD5ncYOkbRILdjjkWP9o0CotHhe043y79c0jlDkkxCF+aC/hj7OzqwZhp6VPO5Xq5nL43DkSjgUvP7SRedKzGTmpnzZXk53hj0yO5XXdbdgZkky1zv1+YYIUCMWd+bhtHyQ43mszr7ftvl5F+6aqBHi8UTzqimIcBzIjIGHTLDW/ThScBpIjLD/B+LNj45LmnOB95TSpUBiMh/AROUUotN/0Q/dMyjI0qpfaaWMQldUIM2QOno2FQ/KD0PhC9UoQ0RwL/RTXmgJ3vKEKkP4NpLRGKUUo3mv1BKbRWR7eiotK7XO1JErna73kPAQhE5H208BpoAjaAnUdrio2aLxSvWmFhOCkRkMNpw5KP7Tg6jI+AGoeMMedwNuFMptc7Ldmcab6xGR9xNQU8+5Uz/B6XUi2760oCyZi+iMdVKKWcsI9dZ/4KAcUqpEz4co84s9TLQQfs+cdM2C21YzlBK1YhILhBuNrdGs8XiFdtnYunyiEgyOsDnc6YAjkVHXK1DTzzkMEmPoUPQO1kH3C56giJEZJjoyY1c+Qy4XEQizbYrAOfcE2+ho6vOoGGWxnXAzSISbY7ZX0R6+3AZ7tq88TE6iCLm+GN82MfJOuAOEQk2+w4XkQh0fuUbQ/Iz/JvMzWLxiK2ZWLoqESKyFd2kVYOOdPpHs20J8K6IXAVsoOHrehtQIyLfoCMGP4Puy/hKdLtRAQ3T9QKglPpKRF6lYdKo5Uqpr822TDM3RJ5S6qBZ97GIjAS+ME1Rx9HztHudAtawDPhIRA4qpS5sJt1c4HkR2YZ+Pz9DR331hRfRfSRbjbZ89GRhbwAfiMiX6I72XT4ez2LxGRs12GKxWCx+Y5u5LBaLxeI31phYLBaLxW+sMbFYLBaL31hjYrFYLBa/scbEYrFYLH5jjYnFYrFY/MYaE4vFYrH4zf8H65u3Ict0nzwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#select only the things to be plotted from both flux and rad30\n",
    "rad_rnet = rad30[['r_net']]\n",
    "fluxybois = flux[['H', 'lmbE']]\n",
    "\n",
    "# select the dates to be used. \n",
    "daterad1 = rad_rnet[rad_rnet.index.date == datetime.date(2016,11,24)]\n",
    "dateflux1 = fluxybois[fluxybois.index.date == datetime.date(2016, 11, 24)]\n",
    "\n",
    "daterad = rad_rnet[rad_rnet.index.date == datetime.date(2019,11,24)]\n",
    "dateflux = fluxybois[fluxybois.index.date == datetime.date(2019, 11, 24)]\n",
    "\n",
    "# res_E selection to be plotted\n",
    "dateE1 = res_E['2016-11-24']\n",
    "dateE2 = res_E['2019-11-24']\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "# Add two axes\n",
    "ax1 = fig.add_subplot(2,1,1)\n",
    "ax1.plot(dateflux1,)             \n",
    "ax1.plot(daterad1)\n",
    "ax1.plot(dateE1)\n",
    "plt.legend()\n",
    "ax2 = fig.add_subplot(2,1,2)\n",
    "ax2.plot(dateflux)\n",
    "ax2.plot(daterad)\n",
    "ax2.plot(dateE2)\n",
    "\n",
    "#vis metadata\n",
    "ax.set_title(\"Measurements of Energy fluxes: Top = 2016, bottom = 2019\")\n",
    "ax.set_xlabel('Date over the Year')\n",
    "ax.set_ylabel('fluxes ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer:\n",
    "           The sytem more closely approximates steady state during the same day in 2019 than it does in 2016. This could be becuase there is greater rcorded precipitaion on this day then there was on the same day in 2016. The precipitation seems to have set the system into a steady state. The increased cloud cover reduces the energy fluxes compared to the day where there was greater rain. Ina ddtiotni, the data from 2016 seems to be from a drought year which could have unbalanced energy fluxes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4. Average residual energy\n",
    "\n",
    "Calculate the average residual energy, $\\frac{dQ}{dt}$, for each year.\n",
    "\n",
    "<div class=\"practice\">\n",
    "    📚  <b> Question 4.4. </b> \n",
    "    Calculate the average residual energy, $\\frac{dQ}{dt}$, for each year.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17.884696611674062\n",
      "16.599656220153545\n"
     ]
    }
   ],
   "source": [
    "#averaging residual enegy by year. \n",
    "res_E16 = res_E['2016'].mean()\n",
    "res_E19 = res_E['2019'].mean()\n",
    "\n",
    "print(res_E16)\n",
    "print(res_E19)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.5. Energy balance closure.\n",
    "\n",
    "In addition to the sensors on the flux tower, the field site in Kenya is also equipped with soil heat flux plates that measure (you guessed it) the soil (or ground) heat flux, $G$. Import the soil heat flux data in the cell below (the file has a single column of values of $G$ in W m<sup>-2</sup>).\n",
    "\n",
    "<div class=\"run\">\n",
    "    ▶️ <b> Run the cell below. </b>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "shf = pd.read_csv('../data/lab_2/CR3000_SN9945_Table1_ed.csv',header=0,names=['G'],index_col=0,parse_dates=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"practice\">\n",
    "    📚  <b> Question 4.5. </b> \n",
    "    Re-calculate the average annual residual energy (i.e. repeat Questions 4.1 and 4.4) using the soil heat flux data and Equation 1.1. Was our previous assumption of negligible soil heat flux valid? If so, suggest a possible explanation for the discrepancy between $R_n$ and $H + \\lambda E$. Otherwise, state why you think including $G$ made a difference.\n",
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     "text": [
      "14.28338002757307\n",
      "19.126995380704194\n"
     ]
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   "source": [
    "res_EG = rad30.r_net - flux.H - flux.lmbE - shf.G\n",
    "res_EG16 = res_EG['2016'].mean()\n",
    "res_EG19 = res_EG['2019'].mean()\n",
    "\n",
    "#Without ground\n",
    "print(res_E16)\n",
    "print(res_E19)\n",
    "\n",
    "#with gournd\n",
    "print(res_EG16)\n",
    "print(res_EG19)\n",
    "\n",
    "# I Do not believe that the initial assumption of negligible soil heat flux was valid becuase \n",
    "# factoring in the soil heat flux to each year increased the spread between the two values. \n",
    "# if the values were to change together, I would have accepted the null hypotheis that the extra \n",
    "# parameter did mnot ake an important difference but since the two means increase in range from one \n",
    "# another this means that the soil is a vlaue that is shifting with the temperaature fluxes/\n",
    "# my explanatino for why the soil energy value changes the overally means if becuase the precific heat \n",
    "# capacity of the soils is different form the air which mean the land realeases heat energy after the air \n",
    "# has lost its own during cooling cycles. "
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