{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1647fec9",
   "metadata": {},
   "source": [
    "# General Information (All Reactors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a020188f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import flowtube"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33bbaa0e",
   "metadata": {},
   "source": [
    "**Workflow For All Reactor Types**\n",
    "---\n",
    "\n",
    "1. Create an flow reactor object with dimensions and gases.\n",
    "2. Create an experiment by definine flow rates, temperature, and pressure.\n",
    "3. Upon initialization (steps 1 + 2), three tables will be outputed describing the experimental conditions:\n",
    "   1. Flow rates and reactant concentrations.\n",
    "   2. Fluid dynamics to check for laminar flow amongst other potentially important quanities.\n",
    "   3. Reactant diffusion parameters including the temperature-dependent diffusion rate.\n",
    "4. Determine the hypothetical loss rate and diffusion coefficient for a given uptake coefficient using the KPS method (see documentation for more information).\n",
    "5. Fit experimental data assuming pseudo-first order kinetics to calculate an effective uptake coefficient.\n",
    "6. Finally, using the diffusion correction factor, calculate the uptake coefficient.\n",
    "\n",
    "**Supported Gases**\n",
    "---\n",
    "\n",
    "Carrier gases: Ar, He, N2, and O2.\n",
    "\n",
    "Reactant gases: Ar, He, Air, Br2, Cl2, HBr, HCl, HI, H2O, I2, NO, N2, O2, and manual.\n",
    "\n",
    "NOTE: the reactant diffusion coefficient must be manually inputted if the gas is manually inputted (see manual reactant gas example).\n",
    "\n",
    "**Reactant Gas Sources**\n",
    "---\n",
    "\n",
    "Supported reactant gas sources include gas cylinders, permeation tubes, or sources that rely on saturation vapor pressure (e.g., temperature controlled cold trap containing a volatile species).\n",
    "\n",
    "**Units**\n",
    "---\n",
    "\n",
    "All dimensions are in centimeters and all flow rates are in standard cubic centimeters per second (sccm).\n",
    "\n",
    "Pressure units can be \"Torr\", \"hPa\", \"mbar\", \"Pa\", or \"bar\".\n",
    "\n",
    "**Flows**\n",
    "---\n",
    "The gas flow through the flow tube is made up of a reactant gas flow through an injector and a carrier gas flow through the main flow tube. The reactant gas flow rate is made up of a main flow (reactant_FR) and a dilution flow (reactant_carrier_FR). If no reactant dilution flow is desired, set reactant_carrier_FR to 0 and input the total desired flow rate as reactant_FR."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67187c1a",
   "metadata": {},
   "source": [
    "# Coated Wall Reactor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98c2b4bf",
   "metadata": {},
   "source": [
    "**Reactor Dimensions and Gases**\n",
    "\n",
    "Start by instantiating a coated wall reactor object by defining its dimensions and input gases\n",
    "\n",
    "The use of an cylindrical insert placed inside of the flow tube is supported (see below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7bd75d04",
   "metadata": {},
   "outputs": [],
   "source": [
    "### 1. Create Coated Wall Reactor (CWR) Object ###\n",
    "# Flow tube inner diameter (cm)\n",
    "FT_ID = 2.60\n",
    "\n",
    "# Flow tube length (cm)\n",
    "FT_length = 100\n",
    "\n",
    "# Injector inner diameter (cm)\n",
    "injector_ID = 1.05\n",
    "\n",
    "# Injector outer diameter (cm)\n",
    "injector_OD = 1.275\n",
    "\n",
    "# Reactant gas (options: Ar, He, Air, Br2, Cl2, HBr, HCl, HI, H2O, I2, NO, N2, and O2)\n",
    "reactant_gas = \"HCl\"\n",
    "\n",
    "# Carrier gas (options: Ar, He, N2, and O2)\n",
    "carrier_gas = \"N2\"\n",
    "\n",
    "# Reactant concentration type (options: \"ppm\" or \"ppb\" for mixing ratio, \"ng/min\" for\n",
    "# permeation rate, \"Pa\" or \"hPa\" or \"Torr\" or \"mbar\" or \"mbar\" for vapor pressure.\n",
    "reactant_conc_type = \"ppm\"\n",
    "\n",
    "# Reactant concentration value\n",
    "reactant_conc = 30\n",
    "\n",
    "# If using a cylindrical insert, input the inner diameter and length of the insert. If\n",
    "# not using an insert, leave these parameters as their default value of None.\n",
    "# Insert inner diameter (cm) - optional\n",
    "insert_ID = 1.89\n",
    "\n",
    "# Insert length (cm) - optional\n",
    "insert_length = 20\n",
    "\n",
    "cwr = flowtube.CoatedWallReactor(\n",
    "    FT_ID=FT_ID,\n",
    "    FT_length=FT_length,\n",
    "    injector_ID=injector_ID,\n",
    "    injector_OD=injector_OD,\n",
    "    reactant_gas=reactant_gas,\n",
    "    carrier_gas=carrier_gas,\n",
    "    reactant_conc_type=reactant_conc_type,\n",
    "    reactant_conc=reactant_conc,\n",
    "    # insert_ID=insert_ID,\n",
    "    # insert_length=insert_length,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ec922f8",
   "metadata": {},
   "source": [
    "**Experimental Conditions and Flow Rates**\n",
    "\n",
    "Define experimental conditions including pressure, temperatures, and flow rates.\n",
    "\n",
    "Can optionally input the axial and radial temperature gradients to check for convection-influenced transport."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d43fa60d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         \u001b[1mFlow Setpoints and Conditions\u001b[0m          \n",
      "╒═════════════════════════════╤══════════╤═════════════╕\n",
      "│ Reactant Flow Rate          │ 0.10     │ sccm        │\n",
      "├─────────────────────────────┼──────────┼─────────────┤\n",
      "│ Reactant Carrier Flow Rate  │ 0.0      │ sccm        │\n",
      "├─────────────────────────────┼──────────┼─────────────┤\n",
      "│ Total Reactant Flow Rate    │ 0.1      │ sccm        │\n",
      "├─────────────────────────────┼──────────┼─────────────┤\n",
      "│ Minimum Carrier Flow Rate   │ 0.6      │ sccm        │\n",
      "├─────────────────────────────┼──────────┼─────────────┤\n",
      "│ Carrier Flow Rate           │ 500.0    │ sccm        │\n",
      "├─────────────────────────────┼──────────┼─────────────┤\n",
      "│ Total Flow Rate             │ 500.1    │ sccm        │\n",
      "├─────────────────────────────┼──────────┼─────────────┤\n",
      "│ Injector HCl Concentration  │ 3e+04    │ ppb         │\n",
      "├─────────────────────────────┼──────────┼─────────────┤\n",
      "│ Flow Tube HCl Concentration │ 6        │ ppb         │\n",
      "├─────────────────────────────┼──────────┼─────────────┤\n",
      "│ Flow Tube HCl Concentration │ 7.85e+09 │ molec. cm-3 │\n",
      "├─────────────────────────────┼──────────┼─────────────┤\n",
      "│ Flow Tube Velocity          │ 32.2     │ cm s-1      │\n",
      "├─────────────────────────────┼──────────┼─────────────┤\n",
      "│ Flow Tube Residence Time    │ 3.1      │ s           │\n",
      "╘═════════════════════════════╧══════════╧═════════════╛\n",
      "             \u001b[1mFluid Dynamics of Carrier Gas\u001b[0m              \n",
      "╒══════════════════════════════════════╤══════════╤════════════╕\n",
      "│ Carrier Gas Dynamic Viscosity        │ 1.77e-05 │ kg m-1 s-1 │\n",
      "├──────────────────────────────────────┼──────────┼────────────┤\n",
      "│ Carrier Gas Density                  │ 0.0609   │ kg m-3     │\n",
      "├──────────────────────────────────────┼──────────┼────────────┤\n",
      "│ Flow Tube Reynolds Number            │ 29       │ unitless   │\n",
      "├──────────────────────────────────────┼──────────┼────────────┤\n",
      "│ Flow Tube Entrance length            │ 3.8      │ cm         │\n",
      "├──────────────────────────────────────┼──────────┼────────────┤\n",
      "│ Flow Tube Pressure Gradient          │ 0.01     │ %          │\n",
      "├──────────────────────────────────────┼──────────┼────────────┤\n",
      "│ Radial Buoyancy Parameter (ΔT=1.0 C) │ 0.00     │ unitless   │\n",
      "├──────────────────────────────────────┼──────────┼────────────┤\n",
      "│ Axial Buoyancy Parameter (ΔT=1.0 C)  │ 4.73     │ unitless   │\n",
      "╘══════════════════════════════════════╧══════════╧════════════╛\n",
      "                 \u001b[1mReactant Diffusion Parameters\u001b[0m                 \n",
      "╒════════════════════════════════════════════════╤═════════╤══════════╕\n",
      "│ Reactant Diffusion Rate                        │ 3.25    │ cm2 s-1  │\n",
      "├────────────────────────────────────────────────┼─────────┼──────────┤\n",
      "│ Flow Tube Advection Rate                       │ 83.8    │ cm2 s-1  │\n",
      "├────────────────────────────────────────────────┼─────────┼──────────┤\n",
      "│ Peclet Number                                  │ 25.76   │ unitless │\n",
      "├────────────────────────────────────────────────┼─────────┼──────────┤\n",
      "│ Flow Tube Mixing Time                          │ 0.1     │ s        │\n",
      "├────────────────────────────────────────────────┼─────────┼──────────┤\n",
      "│ Flow Tube Mixing Length                        │ 3.3     │ cm       │\n",
      "├────────────────────────────────────────────────┼─────────┼──────────┤\n",
      "│ Diffusion Limited Rate Constant                │ 7.1     │ s-1      │\n",
      "├────────────────────────────────────────────────┼─────────┼──────────┤\n",
      "│ Diffusion Limited Effective Uptake Coefficient │ 0.00045 │ unitless │\n",
      "├────────────────────────────────────────────────┼─────────┼──────────┤\n",
      "│ Approx. Diffusion Limited Uptake Coefficient   │ 0.0045  │ unitless │\n",
      "╘════════════════════════════════════════════════╧═════════╧══════════╛\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Corey/Desktop/Harvard/flowtube/flowtube/coated_wall_reactor.py:504: UserWarning: Axial buoyancy parameter > 1. Flow may be affected by buoyancy effects\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "### 2. Input Experimental Conditions ###\n",
    "# Pressure (options: \"Torr\", \"hPa\", \"mbar\", \"Pa\", \"bar\")\n",
    "P = 40\n",
    "P_units = \"Torr\"\n",
    "\n",
    "# Temperature (˚C)\n",
    "T = 22\n",
    "\n",
    "\n",
    "### Flow Setpoints ###\n",
    "# Reactant Gas Flow Rate (sccm)\n",
    "reactant_FR = 0.1\n",
    "\n",
    "# Reactant Carrier Gas Flow Rate (sccm)\n",
    "reactant_carrier_FR = 0\n",
    "\n",
    "# Carrier Gas Flow Rate (sccm)\n",
    "carrier_FR = 500\n",
    "\n",
    "# Radial temperature gradient (C) - optional, default value is 1\n",
    "radial_delta_T = 1\n",
    "\n",
    "# Axial temperature gradient along the flow tube (C) - optional, default value is 1\n",
    "axial_delta_T = 1\n",
    "\n",
    "# Initialize the CWR object with the experimental conditions and flow setpoints\n",
    "cwr.initialize(\n",
    "    reactant_FR=reactant_FR,\n",
    "    reactant_carrier_FR=reactant_carrier_FR,\n",
    "    carrier_FR=carrier_FR,\n",
    "    P=P,\n",
    "    P_units=P_units,\n",
    "    T=T,\n",
    "    # axial_delta_T=axial_delta_T,\n",
    "    # radial_delta_T=radial_delta_T\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7a750a8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         \u001b[1mReactant Uptake\u001b[0m                         \n",
      "╒═════════════════════════════════════════════════╤══════════╤══════════╕\n",
      "│ Coated wall surface area (1/4 length)           │ 408.4    │ cm2      │\n",
      "├─────────────────────────────────────────────────┼──────────┼──────────┤\n",
      "│ Flow Tube Diffusion Correction Factor (γ_eff/γ) │ 0.69     │ unitless │\n",
      "├─────────────────────────────────────────────────┼──────────┼──────────┤\n",
      "│ Flow Tube Diffusion Correction                  │ 31.0     │ %        │\n",
      "├─────────────────────────────────────────────────┼──────────┼──────────┤\n",
      "│ Effective Uptake Coefficient                    │ 1.38e-04 │ unitless │\n",
      "├─────────────────────────────────────────────────┼──────────┼──────────┤\n",
      "│ Observed Loss Rate                              │ 2.2      │ s-1      │\n",
      "├─────────────────────────────────────────────────┼──────────┼──────────┤\n",
      "│ Flow Tube Loss - 1/4 Length                     │ 81.8     │ %        │\n",
      "├─────────────────────────────────────────────────┼──────────┼──────────┤\n",
      "│ Estimated Wall Loss                             │ 22       │ %        │\n",
      "╘═════════════════════════════════════════════════╧══════════╧══════════╛\n"
     ]
    }
   ],
   "source": [
    "### 3. Calculate Reactant Gas Uptake and Diffusion Correction ###\n",
    "# Define a hypothetical gamma value (or array of values) for the reactant gas uptake calculations\n",
    "hypothetical_gamma = 2e-4\n",
    "\n",
    "cwr.reactant_uptake(hypothetical_gamma=hypothetical_gamma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9960fa3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "### 4. Fit Experimental Data to Extract Gamma ###\n",
    "# If you have experimental data for the reactant gas uptake, you can fit the data to\n",
    "# extract the effective uptake coefficient. Example experimental data:\n",
    "\n",
    "# Signal (arbitrary units)\n",
    "signal = [0.95, 0.85, 0.7, 0.65, 0.5]\n",
    "\n",
    "# Corresponding exposure values (can be in seconds or centimeters of injector travel)\n",
    "# If using exposure in centimeters, the code will convert to cm of injector travel\n",
    "# using the flow rate and reactor geometry. If using exposure in s, the code will use\n",
    "# the input values directly. NOTE: Exposure and signal should be anti-correlated so as\n",
    "# the injector is pulled upstream (increasing exposure), the signal decreases.\n",
    "exposure = [1, 3, 5, 6, 9]\n",
    "exposure_units = \"cm\"\n",
    "\n",
    "k, intercept, r_value, gamma, gamma_lower, gamma_upper = cwr.calculate_gamma(\n",
    "    concentrations=signal, exposure=exposure, exposure_units=exposure_units\n",
    ")\n",
    "\n",
    "# k is fitted first order rate constant (cm/s)\n",
    "# intercept is the y-intercept of the fit\n",
    "# r_value is the correlation coefficient for the fit\n",
    "# gamma is the fitted uptake coefficient\n",
    "# gamma_lower is the lower bound of the 95% confidence interval for gamma\n",
    "# gamma_upper is the upper bound of the 95% confidence interval for gamma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6c142870",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# It's a good idea to plot the data and the fit to visually inspect the quality of the\n",
    "# fit and the agreement between the data and the fitted curve.\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Convert exposure (cm) to time-like axis using flow velocity\n",
    "x = np.array(exposure) / cwr.FT_flow_velocity\n",
    "y = np.array(signal)\n",
    "\n",
    "# Linear fit from previously fitted parameters\n",
    "y_fit = np.exp(-k * x + intercept)\n",
    "\n",
    "plt.figure(figsize=(6, 4))\n",
    "plt.scatter(x, y, label=\"Signal data\")\n",
    "plt.plot(\n",
    "    x,\n",
    "    y_fit,\n",
    "    color=\"red\",\n",
    "    label=f\"Gamma fit: y = {gamma:.1e} (95% CI: {gamma_lower:.1e} to {gamma_upper:.1e})\",\n",
    ")\n",
    "plt.xlabel(\"Exposure time (s)\")\n",
    "plt.ylabel(\"Signal (arbitrary units)\")\n",
    "plt.title(\"Signal vs Exposure Time\")\n",
    "plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b21b3da1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Diffusion-Corrected Gamma: 2.64e-04\n"
     ]
    }
   ],
   "source": [
    "### 5. Correct for Diffusion ###\n",
    "corrected_gamma = cwr.diffusion_corrected_uptake_coefficient(effective_gamma=gamma)\n",
    "print(f\"Diffusion-Corrected Gamma: {corrected_gamma:.2e}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e35dba6",
   "metadata": {},
   "source": [
    "# Boat Reactor\n",
    "\n",
    "The Boat Reactor is analogous to the Coated Wall Reactor. The differences will be highlighted in this example. Please refer to the Coated Wall Reactor example for full usage.\n",
    "\n",
    "The Boat Reactor lacks an analytical solution to the diffusion equations and thus, the hypothetical diffusion correction for the flow tube itself (i.e., the maximum diffusive distance) must be sufficiently small to ignore diffusion effects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "97a5f2fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "### 1. Create Boat Reactor Object ###\n",
    "\n",
    "# Boat parameters\n",
    "boat_liquid_width = 1.0  # width of the liquid in the boat (cm)\n",
    "boat_length = 10.0  # length of the boat (cm)\n",
    "boat_cross_section = 1.0  # cross-sectional area of the boat (cm^2)\n",
    "boat_perimeter = 4.0  # perimeter of the boat (cm)\n",
    "\n",
    "### Create Boat Reactor Object ###\n",
    "boat = flowtube.BoatReactor(\n",
    "    FT_ID=FT_ID,\n",
    "    FT_length=FT_length,\n",
    "    injector_ID=injector_ID,\n",
    "    injector_OD=injector_OD,\n",
    "    reactant_gas=reactant_gas,\n",
    "    carrier_gas=carrier_gas,\n",
    "    reactant_conc_type=reactant_conc_type,\n",
    "    reactant_conc=reactant_conc,\n",
    "    boat_liquid_width=boat_liquid_width,\n",
    "    boat_length=boat_length,\n",
    "    boat_cross_section=boat_cross_section,\n",
    "    boat_perimeter=boat_perimeter,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "716b80f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         \u001b[1mFlow Setpoints and Conditions\u001b[0m          \n",
      "╒═════════════════════════════╤══════════╤═════════════╕\n",
      "│ Reactant Flow Rate          │ 0.10     │ sccm        │\n",
      "├─────────────────────────────┼──────────┼─────────────┤\n",
      "│ Reactant Carrier Flow Rate  │ 0.0      │ sccm        │\n",
      "├─────────────────────────────┼──────────┼─────────────┤\n",
      "│ Total Reactant Flow Rate    │ 0.1      │ sccm        │\n",
      "├─────────────────────────────┼──────────┼─────────────┤\n",
      "│ Minimum Carrier Flow Rate   │ 0.5      │ sccm        │\n",
      "├─────────────────────────────┼──────────┼─────────────┤\n",
      "│ Carrier Flow Rate           │ 500.0    │ sccm        │\n",
      "├─────────────────────────────┼──────────┼─────────────┤\n",
      "│ Total Flow Rate             │ 500.1    │ sccm        │\n",
      "├─────────────────────────────┼──────────┼─────────────┤\n",
      "│ Injector HCl Concentration  │ 3e+04    │ ppb         │\n",
      "├─────────────────────────────┼──────────┼─────────────┤\n",
      "│ Flow Tube HCl Concentration │ 6        │ ppb         │\n",
      "├─────────────────────────────┼──────────┼─────────────┤\n",
      "│ Flow Tube HCl Concentration │ 7.85e+09 │ molec. cm-3 │\n",
      "├─────────────────────────────┼──────────┼─────────────┤\n",
      "│ Flow Velocity Over Boat     │ 39.7     │ cm s-1      │\n",
      "├─────────────────────────────┼──────────┼─────────────┤\n",
      "│ Residence Time Over Boat    │ 0.252    │ s           │\n",
      "╘═════════════════════════════╧══════════╧═════════════╛\n",
      "               \u001b[1mFluid Dynamics of Carrier Gas\u001b[0m               \n",
      "╒═════════════════════════════════════════╤══════════╤════════════╕\n",
      "│ Carrier Gas Dynamic Viscosity           │ 1.77e-05 │ kg m-1 s-1 │\n",
      "├─────────────────────────────────────────┼──────────┼────────────┤\n",
      "│ Carrier Gas Density                     │ 0.0609   │ kg m-3     │\n",
      "├─────────────────────────────────────────┼──────────┼────────────┤\n",
      "│ Reynolds Number Over Boat (upper limit) │ 59       │ unitless   │\n",
      "├─────────────────────────────────────────┼──────────┼────────────┤\n",
      "│ Entrance length Over Boat (upper limit) │ 7.7      │ cm         │\n",
      "├─────────────────────────────────────────┼──────────┼────────────┤\n",
      "│ Flow Tube Pressure Gradient (approx.)   │ 0.02     │ %          │\n",
      "├─────────────────────────────────────────┼──────────┼────────────┤\n",
      "│ Radial Buoyancy Parameter (ΔT=1.0 C)    │ 0.00     │ unitless   │\n",
      "├─────────────────────────────────────────┼──────────┼────────────┤\n",
      "│ Axial Buoyancy Parameter (ΔT=1.0 C)     │ 1.14     │ unitless   │\n",
      "╘═════════════════════════════════════════╧══════════╧════════════╛\n",
      "                \u001b[1mReactant Diffusion Parameters\u001b[0m                 \n",
      "╒═════════════════════════════════════════════════╤═══════╤══════════╕\n",
      "│ Reactant Diffusion Rate                         │ 3.25  │ cm2 s-1  │\n",
      "├─────────────────────────────────────────────────┼───────┼──────────┤\n",
      "│ Flow Tube Advection Rate (lower limit for boat) │ 103   │ cm2 s-1  │\n",
      "├─────────────────────────────────────────────────┼───────┼──────────┤\n",
      "│ Flow Tube Peclet Number (lower limit for boat)  │ 31.73 │ unitless │\n",
      "├─────────────────────────────────────────────────┼───────┼──────────┤\n",
      "│ Flow Tube Mixing Time (upper limit for boat)    │ 0.1   │ s        │\n",
      "├─────────────────────────────────────────────────┼───────┼──────────┤\n",
      "│ Flow Tube Mixing Length (upper limit for boat)  │ 4.1   │ cm       │\n",
      "╘═════════════════════════════════════════════════╧═══════╧══════════╛\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Corey/Desktop/Harvard/flowtube/flowtube/boat_reactor.py:507: UserWarning: Axial buoyancy parameter > 1. Flow may be affected by buoyancy effects\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "### 2. Input Experimental Conditions ###\n",
    "# Same behavior as CWR, but the outputs are different for some parameters\n",
    "\n",
    "boat.initialize(\n",
    "    reactant_FR=reactant_FR,\n",
    "    reactant_carrier_FR=reactant_carrier_FR,\n",
    "    carrier_FR=carrier_FR,\n",
    "    P=P,\n",
    "    P_units=P_units,\n",
    "    T=T,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "08a06fb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     \u001b[1mReactant Uptake\u001b[0m                     \n",
      "╒═════════════════════════════════════════════╤══════╤══════════╕\n",
      "│ Boat Surface Area                           │ 10.0 │ cm2      │\n",
      "├─────────────────────────────────────────────┼──────┼──────────┤\n",
      "│ Flow Tube Wall Diffusion Correction         │ 31.0 │ %        │\n",
      "│ (must be small to neglect for boat reactor) │      │          │\n",
      "├─────────────────────────────────────────────┼──────┼──────────┤\n",
      "│ Boat geometry correction factor             │ 6.63 │ unitless │\n",
      "├─────────────────────────────────────────────┼──────┼──────────┤\n",
      "│ Loss Rate                                   │ 0.48 │ s-1      │\n",
      "├─────────────────────────────────────────────┼──────┼──────────┤\n",
      "│ Loss to Boat - 1/4 Length                   │ 3.0  │ %        │\n",
      "├─────────────────────────────────────────────┼──────┼──────────┤\n",
      "│ Estimated Wall Loss (upper limit)           │ 2    │ %        │\n",
      "╘═════════════════════════════════════════════╧══════╧══════════╛\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Corey/Desktop/Harvard/flowtube/flowtube/boat_reactor.py:694: UserWarning: Diffusion correction is > 5%. Negligible diffusion may no longer be a valid assumption\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "### 3. Calculate Reactant Gas Uptake and Diffusion Correction ###\n",
    "# Again, same behavior as CWR, but the outputs are different for some parameters\n",
    "# A warning will be raised if the diffusion correction is greater than 5% because there\n",
    "# there is no diffusion correction for the boat reactor, so the uptake coefficient is\n",
    "# only valid if the diffusion correction is small.\n",
    "\n",
    "boat.reactant_uptake(hypothetical_gamma=hypothetical_gamma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "447759bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "### 4. Fit Experimental Data to Extract Gamma ###\n",
    "# Again, same behavior as CWR, but the outputs are different for some parameters\n",
    "\n",
    "k, intercept, r_value, gamma, gamma_lower, gamma_upper = boat.calculate_gamma(\n",
    "    concentrations=signal, exposure=exposure, exposure_units=exposure_units\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "49b7536a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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JEiRQUU8REYnwH+u3b9826TWULl26MG9Lc8/5EBPBOYru2rVr0aLkALvkDh8+bAKmkEYZioiIeBsbQRg45cqV64muOk+/j1VyQJ4ZRw4TW5hERESeJcd3T3jyaRU0yTOnefZERCQyfvcoaIqK7txhJ66v90JERCRKUdAUFb37LlCtGvD3377ek2j1C2b+/PnP/HGzZs1q5kQMjTfffBOvvPJKhO2TiEhUpaApqvnnH+CXX4AVK4CCBYEBAzjpna/3KlJj4uC7775rylCwOCmHrNaoUQMbN24MXIejK0Oa/DkyO3HihAkKWY5DRCQ6U9AU1WTMCOzZw5mF7WCJEwgXLgysWYOo4tFjCxuPXsKvu/41f3k9IjVs2BB//fUXpk+fbkb/LViwAJUqVcLly5cD12EgpWrvIiJRm4KmqChHDmDxYuDnnznDMHDoEFCpEtC6NWciRmS2eO9ZlB+8Eq9P2oROM3aZv7zO5RGBNaX+/PNPDB482NT0Yo2uUqVKmXkEa9euHWz33IYNG1CkSBHEixfPFEflbc6tNatXrzbXV6xYYW7nqI5y5crhEJ+r/3f06FFTfZ7T9iRKlAglS5bE8uXLQ13mgfXHWNSNZR66d+9uapY4W7x4McqXLx+4Tp06dcxjO2TLls385aTU3GcGjLR161ZUq1bNTDnEoboVK1bEjh07Qn2ORUQiCwVNURVHCTRtChw8CDgqmE+bBuTJY/+NhIniDIza/7ADZ6/ddVl+7tpdszwiAicGK7ww6LnnYTfnjRs3ULduXRQsWNAEEQMGDMDHH3/sdt1evXph2LBhptBqQEAA2rRp41Kx/uWXXzaB0s6dO02XILfLqvSe4ra//fZbTJkyxQR/bB2bN2+eyzq3bt0ygRWDIAZxMWPGNFMHPX782Ny+ZcsW85f7wW7IuXPnBh4nC8GuW7cOmzZtQs6cOc3+crmISJRkic9cu3aNkYv5G+E2bLCsggUZKtmXihUt68AB61m6c+eOtX//fvM3tB4+emyVGbTcyvLxb24vWT/+zdzO9bztl19+sZIlS2bFixfPKleunNWzZ0/rr7/+clmHz+O8efPM/8eNG2elSJHC5TgnTZpk1tm5c6e5vmrVKnN9+fLlgessWrTILAvp/OTLl88aNWpU4PUsWbJYI0aMCHb9dOnSWV9++WXg9QcPHlgZM2a06tevH+x9Lly4YPZjz5495vrx48dd9j04Dx8+tBInTmwtXLgwxPVERHwhpO8gT7+P1dIUxQSb71O2LLB9OzB4MBA/vp3jxFynPn2Au64tN/5oy/HLT7QwOeNR8nauFxE5TWfOnDG5TGztYddasWLFzFQ77rCLrVChQqZrzoFdeu5wPQdHaX9HqX+2ALE7LV++fKbrjC1eBw8e9LiliZVt2TLEeRMd2JrF7kBn7Ipr1qwZsmfPbirhOrrjnvY43E/Ow8jquuye44WtY6FpCRMRiUyi9dxzUQ27p/ot3O8SXKRLEg996uZDzQLpOM0z0L070Lgx8P77wO+/A/3727lP48YBVavCX124cder64UWAyDm7/DSu3dvtG3bFn369DHD94Niw1PQImpB84icZ952cNzH0S320UcfYcmSJfjqq6/w/PPPI378+GjUqBHu37/v1WNjl1+mTJkwadIkpE+f3jw+51l82uPw2P/77z9T8oC5XkyEZ4Dm7f0TEfEXammKIkKV78OWhN9+A2bPZvOGXc/ppZeAFi3YfAB/lDpxPK+uF15s/WFLkDt58uTB7t27XXKgmLMUWswVYmDC/CLmR3GEHof/e4otP2y9Yr6Rw8OHD7GdLY5OczEdOHAAn376KapWrYq8efPiypUrLtuJEydOYFJ50P3r2LGjyWPKnz+/CZouRvKBBiIiIVHQFAWwC44tTO7aMhzLeLvL0Hy2ajRqBBw4YLc68foPP9iJ4lOmsLkD/qRUtuSm1Sy4Ivhcztu5njcxqKhSpQp++OEHEwgdP34cs2fPxpAhQ8zINnfY1cXWmnfeeccEJI7WotCW8WfrEpOuOeKOJQ8c2w2NTp064csvvzTJ3+zae++998yIQIdkyZKZEXMTJ07EkSNHsHLlSpMU7owTLLOVi6Pszp8/b7r9HPv3/fffm2PcvHkz3njjDbOeiEhUpaApCghXvk+SJMCoUQBbI5jjxFaGtm2BihWB/fvhL2LFjGG6GSlo2OG4ztu5njcxj6h06dIYMWIEXnzxRdNt9dlnn+Htt9/G6NGj3d6HeUELFy40wQ7LDnCEHLv0yDnP6Wn4mAxqWIqAXWjMp2IuVWh069YNLVu2NC1W7DpLnDixably4Ei5GTNmmNYnHluXLl0wdOhQl20wD2rkyJGYMGGC6b5zBIsclcdWKZYiaNGihWl1YoAlIhJVxWA2uK93Irq6fv266ULhL3d+0YYVk75Zs+hpvmlaBPWLZAh+hYcPgZEjgc8+A27ftnOgPvoI+PRTO3k8nO7evWtaaphoHJrgIVR5W37qxx9/ROvWrc1zrdYYEZFnL6TvIE+/j5UIHgV4Ld8nIABg1wy77dhlt3AhMGgQMGOGnSjOKuM+xsCoWr60ptWMSd88JnbJebuFKby+++47MxotQ4YMpmuNdZqaNGmigElEJBJT91wU4PV8n8yZgV9/BVjEMEMG4NgxoEYNJusA587B1xgglc2RwrSa8a+/BUx07tw5NG/e3CRWs8urcePGJm9IREQiLwVNUUCE5PswYZm5L0wU79SJyS92aYK8eYEJE/wuUdzfsL4SR7o5moOZn8SpUkREJPJS0BRFsNtqXPNiSJvEtQuO17k8zPk+iRMDX3/NuTQAJiFz5BWnZSlf3p4YWEREJJpQTlMUEqH5PsWLA5s3A2PG2InhGzfaQRRzoDgyLGFCbxyCiIiI31JLUxQTofk+TBRnVx277Nh1x9F2Q4YABQrY1cVFRESiMAVNEnoZM9pJ4kwWz5QJYJXq2rWBJk2AM2d0RkVEJEpS0CRhV6+eXQCTXXSxYtnTsjBRnF14QabcEBERiewUNEn4JEoEDBvGydWAUqVYIcyu8VSuHLDr6QU3RUREIgsFTeIdRYoAGzYAnFqE1VQ52q5ECeDDD4GbN3WWI5H58+ebeeVixYqFzp07Y9q0aUiaNKmvd0ue4v79++Z5W79+vc6VRDslS5Y0c3VGNAVN4j3souvQwU4Ub9zY7qJjK1S+fHZ18UiMxSo5+S2/lFh+P02aNChfvjzGjx+P25xyJgp599130ahRI5w+fRoDBgzAa6+9hsOHDwfe3rdvXzOnXmTB56148eKIGzduqPebs0zVqlXLTLTMYNIbxo4dGziNA/dr3bp1Lrfz/ObJkwcJEyY0cw++9NJLZkLkp2Hx1CxZsuCFF14IXLZjxw5Uq1bNBL2cmJmTSN8M8iOGxxb0wte1A+uNcd5FzsNYsWJFnDx50uX+tWvXxpw5czw6dk4KzemEMmbMaJ4PnofXX38d29hS7bQ/oT3XAwcONHM0shaaJwH+gwcPTJX+ggULmvPMORU5R+MZL+Vk8nzky5fPHCP/csJsZ+PGjUOhQoXMdB28cF7IP/74I8RtevN9F9rzRZy/MkaQ10mZMmX85nxxTtAePXqEelLz0FLQJN6XPj0waxawaBGQJQtw+rSd/9Sxoz3iLpI5duyYmZR26dKlGDRoEHbu3Inly5ebSt+cmJf/jyr4hXrhwgUzOTC/SDjBL+fKi8wT8TLwadOmjQn+Quvrr782Xw7eMnPmTNN6x0mc+TqqUKGCCcpOnToVuE6uXLnMZNB79uzBn3/+iaxZs6J69er477//Qtz2qFGj0JaTbf8/BgAMuBjoM+havHgx9u3bZ778gpo6dSrOnj0beGnVqpXLpM+cDoj7mzZtWnzI1uP/x8me2SLZsGHDpx47AyMGiQzAOfnz/v37zZcjA0Q+Rnhb2Vh1v3379h6tzx86DCj5Rcu/bKHgftXj51Q4bdy40bzWOIk1p1DiX06h5Bz4Mmj88ssvzTnhpUqVKmYibD4/z0Joz5dDzZo1XV4nv3th1LS3zheDd84bt2TJEkQoTtgrvnHt2jVOlmz+Rlk3b1pW9+6WFSuWdSdLFmv/4sXWndOnLevxYyuyqFGjhpUxY0brJo/FjcdOxzJs2DCrQIECVoIECcx92rdvb924cSPw9qlTp1pJkiSxFi5caOXKlcuKHz++1bBhQ7PtadOmWVmyZLGSJk1qvf/++9bDhw8D78flAwYMsFq0aGElTJjQypw5szV//nzrwoULVr169cwyPu7WrVsD73Px4kWradOmVoYMGczj8Paffvop2ONctWqVeT06X7jMsc+O/Q+6Dpd5onXr1lbt2rVdlj148MBKkyaNNWXKFCui9enTxypcuLDH6+/atcs8h2fPnjXHOW/ePJfb//nnH6tJkybm+UqePLl5Ho4fPx7iNkuVKmW1a9fOZVmePHmsHj16PPVzYvny5cGus337ditmzJgunyUTJkywUqdObT169Chw2c6dO822/v7778Bl7o7NWd68ea0//vjD/P/333+38uXLZ/5/5coVK0eOHNbJkyetp+F7JH/+/Fbx4sVd9seB2/J0f0Li/FoNrS1btpjHdj6eq1evWm+//baVKlUqK3HixFblypXN6yIkfE3UrFnzic8QvhdDkixZMmvy5Mlubwvpfcf9dXwGcB8bN25snTt3zuvnq1WrVlb9+vVDXMfX5+vNN980n5HBuXPnjrV//37zN6zfx2ppkojFopeDB7OfwM574mci569jFx67CW7d8s3FfDY/3aVLl0wLU4cOHUwzvjvOLRExY8bEyJEjsXfvXkyfPh0rV640U6oE/ZXLdfgrnb/+V69ejVdffdX8auPl+++/N10tv/zyi8v9OBULu174i5+/qviLjF0KnOOOv5bZosDr9veOPaM3f9n/9ttvZn/YNcP7BNfVw+b6Q4cOBTaX85cklznjL0K2CuTPnz/w16ajBYctGJUqVQr2XLIVhMfL+zjweNm6xV+W7rAFht1CIV3asUK9l/E5YrcRW3zYuuLu9sqVK5vHX7t2rWkR4v/5S5y/4t3h8u3bt5tWI2e8voH5gMHch68Fzr5euHDhYPeX+8AWKufZ2e/du4c4ceKY16QDWw2J++vs/fffR8qUKU1eCLvmnLs4+LhsTeUyvhfYTUJsceL9MnOuyqfYtWuXaRXga8d5fxxC6iLia8pd65i3sZWC72XHvvB9xPcZu+b5OuVzV6xYMVStWhWXL18OseUk6HPMltvgnuNHjx6Zz4Jbt26Zbid3gnvfcR9feeUVsz9r1qzBsmXLcPTo0TC1qnpi9erVptWZr7W3337btEo7+MP5KlWq1BPd3V4XYkglESpatDQ5uXPrlrV/82brzvbtlsUWkbVr+fXum0swrUZBbdq0yTxHc+fOdVmeIkUK88uOl+5sSQvGrFmzzLpBfzEeOXIkcNm7775rWqacW6T4S4vLnVuamjdvHnjd0frx2WefBS7buHGjWcbbgvPyyy9b3bp1C/Z2/uJ3tDAF92s0uBYbtpaE9CuP2EoxePDgwOuvvPKK+XUYHLZEsVUkpMv58+dDfMyn7bc777zzjvXWW28F2/rBlrHcuXO7tDLeu3fPtOgtWbLE7Tb//fdfs53169e7LB84cKBpdXTGlki+tmLEiGGlT5/etIKEpFOnTlaVKlVclu3du9cKCAiwhgwZYvbt8uXL1quvvmr2YdCgQYHrsQVzw4YNphXqq6++Mq9FLnNuUWMLYaZMmcxfXl+zZo1VokQJ69KlS6ZlI1u2bOb1ysdxZ+bMmeZxd+zYYT1N0HPN11RILXHeaGliywNbwd54443AZStWrLCee+456+7duy7rsnWNrXjBiR07tvXjjz+6LOP1OHHiuCzbvXu3eY5jxYpl9nnRokWhfv0uXbrU3P/UqVOBy/bt22fO4dNeM6E9XzNmzLB+++03a8+ePdaCBQvMvrD10HF+/OF8/frrr6bF1V1rprdamjSNijw7/IXJuexYHJP5Gf/+G2nOftC8li1btphf3m+88Yb5Re+watUqk/fEfI3r16/j4cOHpsWHv4ocLVVMvsyRI0fgfZhUzrwVtlQ4L3P+FUeOX/iO24mJrEGX8X5sHeEvMuYBMI/m33//NfvJS3AtZuH1xRdfPHUdtjax5YStb9zPRYsWYcWKFcGuHxAQYFrQnqUFCxaYFkK26AWHv6KZ1MycL2d8rvlLn792mavkwBwetky5ey0xTgi6jOuydebixYuYNGlSYI5HcLlld+7cMYnlztgqwdbOrl27omfPnib3qGPHjuZ1wv87fMppkf6fI9G4f//+gcuZz8TWSge+htgS8N133+Hzzz8354AtlGxl43F+8MEHT+yfo/UzLPlhfJyIxKTwpk2bmvczk/Sdn2O2gjKBPui55nPMVlAmLTt88skn5uLpc5w7d27zHF+9etW07DKPjK1Fztt8mgMHDiBTpkzm4sD7s7WMt7Hl0FucW68KFCiAEiVKmIEHfA+zpdwfzhdbUvk88jXqaFX1NgVNXtSgQQPTfMnmyKBdK+Ikdmwge3YgeXJg0yb2QdjLkySxA6q4cSP+dCVI4NFq/MLmm/fgwYMuy7Nz/526O4ijil5++WXTXcRRZ8mTJzfdIG+99Zb5YHaIzeN3wu27WxZ0FIjzOo4PFHfLHPcbNmyY6dJjMrNjlBCTkIPrPnoW2H3IES5skueFwSKToYMT9IPWHXZPOo/2Ci8GTPyQD9plxGRn7ivf4zzH7Pr88ccfn7h/qlSpTLcYP+AdGKhwZBCDFXZfOGPw6Ah4Hfhc8bXHC0co5cyZE1OmTDHBjzvsWmPieFDNmjUzl/Pnz5tt8jUyfPhwM2otOHw8Bvy8T9D9coy8YncKu14YBDNw4uuQX5w8d+6CJnbnEL/I/WnkJd+XDEiPHz9u9t25e5PPcbp06czzHRRfG7w4P8d8vxN/sHjyHPM14vhBwABk69at+Oabb0zg6Sl3wUVIy70pXbp0Jmj6+++//eZ8sRuQP0ojKmAiBU1exF9xHKXDX3fiAX4p8ZcQc1z4puGXOadk4eg7vmEi+E3vCf5q4pBt5rbwyyCkVhqO6mDLEoMVR97GLI4i9BG2dnCECYMKx4caP+Dysmp7OPDDi61YYT2fzMHgaC0GTRx+HhKO4HP+oHXH+YvOGxjUOY9CIwadDEDr1q1rrjNgYAseW36Ce3x3LWQMtJh3wh9YDrzO5ykk/BJ0btEMiqM7OSw7uC9LxxfQt99+a1qk+JoODlvYuI67PCMGPT///HNgKxxfB44fBPwb3OuCgRKDX7432GIRNK+JrQfPuhaYI2Die4ItxEFbSPgc88ucrZ0M7j19jplnw+eUo2sdmAsWND8wtM+xu/cdzyl/WLA8iKO1ia3czM8K7/vck3zP06dPm0DJX84Xcze5HxEqxM47CTXmgnA0lCeiXU5TCP3J1u3blnXggJ3rxMu+fR7nHUU05h9xhBdHObFfn8dw8OBB6/vvvzfLu3bt6jIy6euvv7aOHj1qfffdd2bkGpc5Rge5yyFwl6sQdKQKc5pGjBgRYu4HR25xGfeDOnfubPJQmEPDfW7btq3JOQhpBIwnOU3MN2BuAR/nv//+C8xh8CSnyZGHwXwF5iYwzyeiMe+J+8qcG+YO8f+8OPJvmKPD/KTNmzcHu42g5/rWrVtWzpw5rUqVKllr1661jh07Zq1evdrq2LGjdZqjQ4PB1w9zOJgTxeeEzxHP5YkTJ8ztHEXZs2dPk5/GZRwVx9yquHHjmhyl4HCkJM8p802cjRo1ymzj0KFD1ujRo03O1TfffBN4O3NTJk6caO7H1/mkSZPMa4THERTzt8qXL2/yrRw4OpR5TjyWokWLmvyp4PD8ckTVCy+8YPJR+B7566+/rM8//9x68cUXw5XTxBFkfE779etnJUqUKPA5ds4T5HPsyE1krhxHnHF0JEd3MQ/QcXG8LhzHy/fm4sWLzfuL76VevXq5jFINiuvwtf3ll19aBw4cMH+ZW8b8SAc+x3zdcJvM1fnkk09MLg7fG8Fx977jPvK8V6hQwTzPPMfMzapYsaJXzxeXMxdyw4YNZp/5+VC2bFnz+Xb9+nW/OV887v79+wf7WN7IafJ50MQTziRGDqGOFy+eeSI8SWALDSYt1qlTx0qXLl2Iw1nHjBljZc2a1XxAFStWzDxJoaWgKYxBEzGp9sIFy2KyqCN44vDfBw8sXztz5owpA8CEV37p8YOGw8eHDh1qvkAdhg8fbl5n/HJiMjcDJ18FTUzS5Ta4rxx6/umnn1otW7YMd9DED2v+MOBQe+ehz9znp31YOz5ceTxMSn8WuE9Bh2vz4igP4DhvzscclLvPDX7B8nymTJnSfGZkz57dDLd+2ocuP2d4/Axy+DnDzycHvjcaNGhgkr95O19L/HL35DORQ7SDBhcMOFgOgdsqVKiQeT06YymBIkWKmNcIE8BZloJBP4OKoMaPH//ED0Im4VetWjVwqLvze8EdBm88Z47j43l4/fXXXRLEg55rPn98bYWEt7t7jp2fU+fXquM5f9p9+P30wQcfmP3l+54/Qpgs7px47c7s2bNN0MH78MfWnDlzXG5v06ZN4GuAw/N5DkMKmEJ634Wl5EBoz9ft27et6tWrm33lMfH7mtsIeh58eb7444f3D+lHizeCphj/f3J8hk21bFJj0zKb4n/44QfTDM4mRiYgBsUpAjisMGgOCHNO2LzrbngwK4fyfmy2Y14CC6qxi8AZm9o5HJuJgBzWzX7SyZMnm/1wDKll07q75lM2JXLfif257MrxJKeJeQMcSsymVG93MfgjJskyd8BRDTlYbO5nQUzHMFU+12x6TpbML7rsJHw4XJ/vF3YVMQ9GvIM5TSxm6S5BXSSq++ijj8x3KQeahOU7yOPvY8uHGL2yWY7DGJ3xVzeb9ILiMELe1qhRI5fCf/z1kjZtWpehzMEJrqUpLEXn3FFLUzhamoJixL979/9anQ4f5s+tUD0f4j/4/mV3HLsz+UvVXWuGhA8LpLL7QiS6GTJkyFNb2CJ9cUsmzTKxLWjEx8z3oMXXiMmDLJrFJESOwmFiK0e5sKQ6y98HLSLoqbAUnQuPMWPGmAQ+bw4HjZIY7efPz2EadgvTtWsAy+YzaTyC5xcS72PCKluPmRzPViYmjIp3cRi2cxkKkejU0pTGzWhPb/PppxabkJk5z+HZzPTnAXNkBuuRcIitO2zW59BQTiDJobQcgcMh/uEZcsxaKAzegp5wXg86FDIkrF3CysysycO5ctgN6C4wYnVpXhzNgRICjrJhNy2Hp3KiUFYR/+cfDt2w57Vzqm0k/o0janycDSAiEi4+/6nHKSM4TJ+/QFnDhHlHDIYYfASHOUYseMYZt1kvh/VLvFGTwpMCWyGJ8IkCozPW3cid2w6WGDTducNENhbFsYMqtVqIiEgE8/ncc6yMzKqerCTKmg+stMz6GSEVX2PRNc6jxZopTCp1ru8QFiwM52nROfEhBrApU9pddo6aKqwszi47Jo2rFUNERKJy0OTAooEsknXlyhXTYhNcsTd2pbE7jt15c+fONV11zJHg5JFhxaJhjqJzznj9aQW2JPSCVroONY6mY1DNlifmw3G03bFjACvT3r2rp0RERLz/3eMP3XMMkNgNxnllOFSWyVz8v7tKwTxgzm/E0u0sEcBEUgZPnIGbczWxi89dqxNbsbhtBw45ZJVhlnF3lBPg/EwsOcDy7Myz4rBFJq5GxAzq0ZVjxvUzZ84ETjURrm5Vx3QsFy/aLU7Xr9utTuyyY4uUm9nURUQkerEsywz4+u+//8x3EL97Im3QxJoInE/pn3/+MUEM6yhxbqOgdZiIB8tJQTn/k/NBc7QIA6egZfCdp7dwTJbpCJAcI02mTZsWWC+KZeE5UeXZs2fNhIQcqccATbyDzx+7XXl+GTh5FV8v7KJjSxMDKF5n8nhI9aBERCTaSJAggWkoCTqNT2j4vLhldBbdils68CXnKDfh5Q1zinrgyy+BK1fsZY0bA9262fPciYhItBQrVizTOxVc74an38cKmnwougZNEY4tTh9/DEyebF9nd93w4cAbb6iiuIiIhPn7WEkfEvWwW27SJGDtWk4DbnfXtWgBcGZ3JouLiIiEgYImiboqVAB27gQGDrRzm1asYAIcMGAA4GYOQRERkZAoaJKojQMGPvkE2LuX8+LYwVLv3kCRIsCaNb7eOxERiUQUNEn0kCMHsHgx8NNPnB/HriZeqRLQpo1dskBEROQpFDRJ9MFRE6+/Dhw4ALz7rr1s6lQgTx5g+nRVFBcRkRApaJLoJ1kygBM8r18PFChgz2f35ptAlSrAoUO+3jsREfFTCpok+uIUOZwYmnWdOCHw6tVAoUJA376ajkVERJ6goEmiN1YOZ00nTr9SqxZw/z7Qr58dPK1c6eu9ExERP6KgSYQ4AfCiRcCsWUDatHY9p6pVgZYt7TpPIiIS7SloEnFOFOe0KxxZ16GDff377+1E8SlTOGO0zpWISDSmoEkkqCRJgNGjgU2bgMKF7WlZ2ra1SxTs3x8h5+vRYwsbj17Cr7v+NX95XURE/IvmnvMhzT0XCTx8CHzzjV0Q8/ZtOweqe3egVy87edwLFu89i34L9+PstbuBy9IliYc+dfOhZoF0XnkMEREJnuaeE/GGgACgWze7haluXeDBA3taFk7HsnSpVwKm9j/scAmY6Ny1u2Y5bxcREf+g7jkRT2TJAvz6KzB3LpAhA3D0KFCjBvDGG8D582E6h+yCYwuTu444xzLerq46ERH/oKBJxFNMDG/QwK4o3qkTEDOmPS0LE8UnTgx1oviW45efaGEKGjjxdq4nIiK+p6BJJLQSJwa+/hrYvBkoVgy4etWelqVCBWDPHo83c+HGXa+uJyIiEUtBk0hYlShhB04MoBIlAjZssIOoHj3spPGnSJ04nkcP4+l6IiISsRQ0iYQ3UZxddUwUZ9cdR9sNHgzkzw/88UeIdy2VLbkZJRcjmNu5nLdzPRER8T0FTSLekCmTnSTOZHH+/8QJ4OWXgddeA866HwEXK2YMU1aAggZOjuu8neuJiIjvKWgS8aZ69exWp65d7URxTsvCRPGxY4FHj55YnXWYxjUvhrRJXLvgeJ3LVadJRCSSF7d88OABzp07h9u3byNVqlRInlzdB2Gh4pZR3M6ddoL41q329VKlgAkTgCJFnliVZQU4So5J38xhYpecWphERCJpccubN29iwoQJqFSpktlw1qxZkS9fPhM0ZcmSBW+//Ta2Or4cRAQoWhTYuBEYNcoecbdli508/uGHfEO5nCEGSGVzpED9IhnMXwVMIiL+x6OgacSIESZImjRpEqpUqYK5c+di165dOHToEDZu3Ig+ffrg4cOHqFatGmrWrIm/OUO8iACxYgHvv29PAszJgNlFN2yYnSj+2286QyIiUa17rnHjxujduzcKcuqIENy7dw9TpkxBnDhx0JYTnEqI1D0XDS1aBHToAJw8aV9/9VVg5Ei7yriIiPj197Em7PUhBU3R1K1bQP/+dosTW57Ydff553YwxZYpERGJmhP28oHmz5+PA5xaQkSeLmFCu5bTjh1AmTLAjRt2rSf+n8tERMQvhTpoatKkCUaPHm3+f+fOHZQoUcIsK1SoEObMmRMR+ygSNRUqBKxfD4wbByRJAmzbBpQsCXTpYgdSIiISuYOmtWvXogLn2AIwb948MCXq6tWrGDlyJD5nF4OIhOIdGBNo185OFG/a1J70l9Oy5MsHzJ+vMykiEpmDJvb3OeoyLV68GA0bNkSCBAlQu3ZtjZoTCau0aYGff+abCsiWDfjnH3talldeAU6f1nkVEYmMQVOmTJlMmYFbt26ZoKl69epm+ZUrVxAvniYWFQmXGjWAvXuBnj3tee04LUvevMDw4fa8diIiEnmCps6dO+ONN95AxowZkT59elPs0tFt97SSBCLigQQJgEGDgF27gBdesEfbdetm5zupgKyIiM+EqeTA9u3bcerUKVPMMlGiRGbZokWLkCxZMpQrVy4i9jNKUskBeSrmOH37LdC9O5tzgRgx7NIEAwcCIQyLFRERPyg50L9/f+TNmxcNGjQIDJiIlcKXL18e2s2JyNMSxVkolonizZsD/I3D0avssvvlF/u6iIj4Z0tTrFixcPbsWaROndpl+aVLl8yyR25mchf31NIkocYfJu3bA0eO2Ndr17aDqKxZdTJFRPytpYkxVgx2EQTx119/BY6qi67Y+sYuykaNGvl6VySqeuklYM8e4LPPgNix7WlZOI/d0KHAgwe+3jsRkSjN46CJwQCDIgZMuXLlMv93XBidMb+JRS6js44dO+K7777z9W5IVMdRqpyG5a+/gBdfBG7ftnOeSpQANm3y9d6JiERZAZ6u+PXXX5tWpjZt2qBfv34mUHLgBL1Zs2ZF2bJlEZ1VrlwZq1ev9vVuSHTBvCa+3qZPt0fX7d4NcCAGi2Vy9F3SpL7eQxGR6NnS1KpVK7z55ptYtWoV2rdvb647Lq+//nqYA6aHDx/i008/RbZs2RA/fnxkz57dJJs/5qghL2E5hLp165oSCWwp41x57owdO9bsB+tNFS9eHOvWrfPaPohECHaVv/mmnSjeqpWdGM5pWRhQzZypRHERkWcdNDFByqFo0aJmzjkuc3cJrcGDB2P8+PFmPjtO+jtkyBAMHToUo0aNcrv++vXr8cBN7sbBgwdx7tw5t/dhIc7ChQsHzpnnzsyZM00Nql69emHnzp1mqphatWqZ0goODKQKFCjwxOXMmTOhPm4Rr0qVCpg2DVi5EsiVC+B7gdOy1KoFHDumky0i4g2WB2LGjGmdP3/e/D9GjBjmetCLY3lo1a5d22rTpo3LsldffdVq3rz5E+s+evTIKly4sNWoUSPr4cOHgcsPHTpkpU2b1ho8ePBTH4+HPG/evCeWlypVymrXrp3Lsjx58lg9evQI1fGsWrXKatiwoUfrXrt2zewP/4p4zd27ltWvn2XFicMXvGXFi2dZX3xhWffv6ySLiITj+9ijlqaVK1cGjoxj9xyvB704lodW+fLlsWLFChw+fDhwFN6ff/6Jl19++Yl1Y8aMid9//920BLVs2dJ04R09etTUiKpXrx66Mxk2DO7fv28KdjqmhHHg9Q0bNsDbxowZg3z58qEkKzyLeFvcuEDv3vYouypVgLt37WlZihZlU63Ot4hIWFk+9vjxY9Oaw5aqgIAA83fQoEEh3ufkyZNWlixZrNdee83KnDmz1bJlS7MdT7hrafr333/N8vXr17ssHzhwoJUrVy6Pj6V69epWypQprfjx41sZMmSwtmzZEuL6ammSCMf3xXffWVbKlHarEy9vv21Zly7p5IuIhPL72OPRc86uXr2KLVu24MKFC08kbLMFKDSYS/TDDz/gp59+Qv78+bFr1y6TW8SkbSaZu5M5c2YztL9ixYomcXzKlClua0eFVtBtBFeTKjhLliwJ9z6IeBVfvy1aAGy5/fhjYMoUYNIkgIMhRowAmjWz1xERkacKddC0cOFCM2Evk6sTJ07sElTw/6ENmj766CP06NEDTZm0CphJf0+ePIkvvvgi2KDp/PnzeOedd8yIuK1bt6JLly7BJo57ImXKlKbSedBEcgaFadKkCfN2RfxGihTA5Mn2CLt33wUOHLCnZWHyOEfbPf+8r/dQRMTvhboieLdu3Uytphs3bpgWpytXrgReLl++HOoduH37tslVcsYAJriSAxcvXkTVqlXN/Hdz5841eVSzZs3Chx9+iLBinSmOjFu2bJnLcl7XBMQSpVSoAOzaZU/4yyKZnJalQAHg88+Be/d8vXciIlEraPr3339N5esECRJ4ZQfYWjRw4EAsWrQIJ06cwLx58zB8+HAzJUlQDKRq1qyJLFmymG69gIAAEzxxouBp06ZhBLsb3Lh586bp9uOFjh8/bv7vXE6ga9eumDx5Mr799ltT+oCtV7y9HQsFikQlceIAn3xiJ4pXq2YHS5yWpUgRFjXz9d6JiPgvK5QaNGhgzZw50/KW69evW506dTIJ3fHixbOyZ89u9erVy7p3757b9ZcuXWrduXPnieU7d+60Tp06FWwZAB5q0EurVq1c1hszZoxJMI8TJ45VrFgxa82aNVZEUiK4+EWi+E8/WVbq1P9LFG/d2rIuXvT1nomIPDOefh/H4D+hCbKYdM2K3a1btzb5R7E5aagTDv0X786qLBLhrlyxyxJMmGBfT5kS+OorjuxQoriIRHnXPfw+DnXQFDT/yGVjMWLg0aNHodvTaExBk/gd1iVjovjevfb1SpWA8eOB3Ll9vWciIj7/Pg51ThPzioK7KGASieQ44e+OHcCXXwLx49sTAhcqBPTtaxfJFBGJxkIdNIlIFMcud9Z02rcPqFmTJfOBfv2AwoU5JYCv905ExGdC3T3HfKaQ9Ob0DeIRdc+J3+PHw+zZQKdO9iTAxDwn5jtxkmARkSggwnKainL+KicPHjwwQ/g5/D9HjhzYwaZ98eqTJOJz167ZZQpYCJMfGZyLcuhQoHVrJYqLSKQXYUFTcA/25ptvmtpKLThlg3j1SRLxG5s324nif/1lX3/xRTtRPG9eX++ZiIj/JYK7wwdgt91nLJAnIlFX6dLAtm129xwL3LIYJnOdPv0UuHPH13snIhI5EsE5pQojNBGJ4gICOJ8SsH8/UKcO++jtaVkKFuTcQ77eOxER/5mwd+TIkS7X2bt39uxZfP/992aKExGJJrJkARYsAObNAzp2BI4eBapXB5o1A4YPBzTZtYhEMaHOacqWLdsTxS5TpUqFKlWqoGfPnkicOLG39zHKUk6TRBnXr9vz140ezWJuQNKkwODBQNu2/JDw9d6JiPhPIriEjYImiXKY78REcccoWhbL5NQsBQr4es9ERPwjEVxExChRwh5hN2IEkCiRPS0Ly5RwXrvbt3WSRCRSU9AkIt5PFO/c2U4Uf+UV4OFDe1oWtjYtXqyzLSKRloImEYkYmTLZSeLz59v/P34cqFULaNoUOHtWZ11EIh0FTSISserXt1uduna1k8JnzgTy5LGrizNpXEQkqgZNt27dipg9EZGoi/lNw4bZieIlS9qj7d57z04Ud1QXFxGJakFTmjRp0KZNG/z5558Rs0ciEnUxKXzjRmDUKIDlSZg0Xrw48NFH/EXm670TEfFu0PTzzz+bIXlVq1ZFrly58OWXX+LMmTOh3YyIRFexYgHvvw8cOAA0agQ8emRPy5IvH/Dbb77eOxER7wVNdevWxZw5c0yg1L59exNEZcmSBXXq1MHcuXPxkCNlRESeJkMGYPZsO1BidfFTp/gBYwdS//6r8yciUScRPEWKFOjSpQv++usvDB8+HMuXL0ejRo2QPn169O7dG7dVk0VEPFG7NrBvn91Fx1aoOXOAvHntLjy2QomIRPag6dy5cxgyZAjy5s2LHj16mIBpxYoVGDFiBObNm4dXWJ9FRMQTCRMCQ4YA27cDpUsDN27Y89mVKfO/6uIiIj4W6mlU2AU3depULFmyBPny5UPbtm3RvHlzJOVcU/9v3759KFq0KO7fvx8R+xxlaBoVETdYhmDiRKBHD+DaNbtMQadOQP/+9ig8EZHIMo1K69atkSFDBqxfvx67du3C+++/7xIwUfbs2dGrV6+w7bmIRG8Mktq1Aw4etAthMojitCzssvv1V1/vnYhEY6FqaWKS98SJE/Hqq68ibdq0Ebtn0YBamkQ8sGQJ0L69XVHcUSyT+U6sMi4i4q8tTQEBAfjwww9x7949b+yjiMjT1agB7N1rT/rLee3Y2sRWJ7Y+abSuiDxDoe6eK126NHbu3BkxeyMi4k6CBMCgQQA/e154wS6EyWlZSpWyq4yLiDwDAaG9w3vvvYdu3brhn3/+QfHixZGQo16cFCpUyJv7JyLyPwUKAGvXAlOmAN2720EUR9t16AB8/jkQQrO6iMgzHz0Xk0maQTcSIwa4Gf59pLoqHlNOk0g4nD8PdOsG/PijfT19emDkSODVV/mhpFMrIl7/Pg510HTy5MkQb2d1cPHukyQiIVi+3E4UP3Lkf8UyR48GsmbVaRMR3wZN4j0KmkS85M4dO+dp8GDgwQM7B6pvX6BzZyB2bJ1mEfFt0LR//36cOnXqiQKW9erVC8vmoiUFTSJexkmAWeOJeU/EHMsJE+zK4iIizzpoOnbsGBo0aIA9e/YE5jKZDf1/DoFymjynoEkkAvAzado04MMPgcuX7fwmBlJsiQpSiFdEJEIrgnfq1AnZsmXD+fPnkSBBAjNlytq1a1GiRAmsXr06tJsTEfEuBkmtW9sVxVu1soOocePs2k4zZ9rXRUTCINRB08aNG9G/f3+kSpXKjKTjpXz58vjiiy/QkRNsioj4g1Sp7BanlSuBXLk4y7g9LcvLL7PJ3Nd7JyLRIWhi91ui/580M2XKlDhz5kzgqLlDhw55fw9FRMKjcmVg9247MTxOHGDxYiB/fuDLL+2kcRGRiAqaChQogN38APr/6uBDhgwxk/ey9YkT9YqI+J24cYE+fezgiUHU3bv2tCzFigEbNvh670QkqgZNn376KR5z1nGwAO/npm5ThQoV8Pvvv2MkC8uJiPir3LmBFSuA775jU7k9px2nZXn3XeDKFV/vnYj4Oa/Uabp8+TKSJUsWOIJOPKPRcyI+dOkS8PHH9pQslDq1PQnw66+rorhINHM9IkbPPXz4EAEBAdjLX2dOkidProBJRCKXFCmAyZOBNWvskXUXLgBvvAFUr/6/6uIiImENmhgwMeFbtZhEJMp48UVg1y57wl/mPnFaFk4MPHAgEKR4r4hEb2HKaerZs6fpkhMRiRI4qq5XLzvHqVo14N49ftgBRYr8r7q4iER7oc5pKlq0KI4cOYIHDx6YVqeECRO63L5jx45of1I9pZwmET/Ej8QZM+x569hlR23aAEOG2F16IhJtv48DQrvh+vXrK38pGJxehlXRq1atil9++SW0p1ZE/AEHtDAZvGZNoEcPYOJE4NtvgQULgGHDgBYtlCguEk15ZfSc2FatWoWbN29i+vTpHgVNamkSiQTWr7fnrnMMgKlSxZ6WJVcuPHpsYcvxy7hw4y5SJ46HUtmSI1ZMjSIWiWwirKWJBSy3bt2KFEGaqa9evYpixYqZCX2jq8qVK2v+PZGohnWcmHYwfDjQr589LUvBgvi77Qd4K21VnLpl162jdEnioU/dfKhZIJ1Pd1lE/CQR/MSJE25Hz927dw///PNPqHcga9asprsv6KVDhw7wFk4oXLduXaRPn95se/78+W7XGzt2rJmMOF68eChevDjWrVvntX0QkUgsdmy7ptO+fXa33f37yDl2GKZ+/TbKnrRnSKBz1+6i/Q87sHjvWZ/urohEDI9bmhawP///LVmyxDRjOTCIWrFihQk4QoutVs5BGGtAVatWDY0bN3a7PqdsKVWqFGLzQ8zJwYMHkTRpUqRNm/aJ+9y6dQuFCxdG69at0bBhQ7fbnTlzJjp37mwCpxdeeAETJkxArVq1sH//fmTOnNmsw0CKwWFQS5cuNQGZiERx2bLh0W+L0Kd5H3RcOAY5Lv+Ln2d8gjkFqmBg5bdwOUESsHOu38L9qJYvrbrqRKIay0MxYsQwl5gxYwb+33GJEyeOlStXLmvhwoVWeHXq1MnKkSOH9fjx4ydue/TokVW4cGGrUaNG1sOHDwOXHzp0yEqbNq01ePDgp26fhzxv3rwnlpcqVcpq166dy7I8efJYPXr0CNX+r1q1ymrYsKFH6167ds3sD/+KSOSw4chFK8vHv1kFO82wphetbT1iaihgXY6X2PqwVicrS/eF5nauJyKRg6ffxx53z3G+OV7Y6nLhwoXA67yw9eXQoUOoU6dOuAK4+/fv44cffkCbNm3cjtCLGTOmmeNu586daNmypXnso0ePokqVKqhXrx66d+8e5sfdvn07qrMSsBNe3xABk3mOGTMG+fLlQ8mSJb2+bRGJWEz6puvxEqF39fZ4tcVXOJAqK5LdvYGhf3yDmT/3RI6LpwPXE5FonNN0/PhxpORElxGAuUZMKH/zzTeDXYfdYCtXrjTddM2aNTMBE4f4jx8/PsyPe/HiRdNFmCZNGpflvH7u3DmPt1OjRg3TrcjALmPGjKbr0R3ma7HbL7jbRcR/cZScs13pc6Nuq68xsFIb3I4dF6VP78UfUz9A8ckjgDt3fLafIuKjnKaRI0finXfeMQnS/H9IOnbsGOadmTJliskjelp+EFu7vvvuO1SsWNGM5uP9vDFZcNBtsDcvNNtlrpeIRG0sK8BRckz6dtRreRgrAJNKv4rf85RH/2XjUPXoVmQcMwxY8qtdnuCll3y81yLyzIKmESNG4I033jBBE/8fHAYYYQ2aTp48ieXLl2Pu3LlPXff8+fMmiOOIOLbWdOnSBaNGjUJYseUsVqxYT7QqsRsyaOuTiERvrMPEsgIcJcefVM6F7s4kSY22DXtjbrrzKDq0tz3xL6dl4UTALFmQOrUP91xEnkn3HLvkHHWZ+P/gLuGp0TR16lSkTp0atWvXfmpXGrvj8ubNawIsdtXNmjULH374YZgfO06cOGZk3LJly1yW83q5cuXCvF0RiZpYh2lc82JIm8S1q47Xx7UojqKd3wIOHAA++MCuHv7jj0Du3MCkSUwQ9dl+i0j4hLq4pTNHMfHwdo0xoZtBU6tWrRAQEBDiejVr1jRz3rFEANdl8MQWKhaWzJAhg2l1CopVujlfngMDvF27diF58uSB5QS6du2KFi1aoESJEihbtiwmTpyIU6dOoR0rAYuIuAmcWFYg2IrgrCrMdIaWLYF337ULZL7zDjBtGjBhAlCggM6pSGQTlqF5kydPtvLnz29KDfDC/0+aNCmsI/2sJUuWmKF+LB3wNEuXLrXu3LnzxPKdO3dap06dCrYMwP+3ortcWrVq5bLemDFjrCxZsphjKlasmLVmzRorIqnkgEg08eCBZY0YYVmJEpnyBFZAgGWxnMmtW77eMxGxPP8+DvXcc5999pnJa/rggw9Miwxt3LgRo0ePRqdOnfD5559HVHwX5WjuOZFo5vRpjpbhUGH7OgsCjx1rVxkXEb//Pg510MSkaSZdv85ZwJ38/PPPJpBizpF490kSkSjm11+B998HHFNPvfYaR9wA6TRnnYg/fx+Huk4T6xkx7ycoJlI/fPgw9HsqIhLd1K8P7N8PMAczZkzO4wTkzWuXJ1CiuIjfCnXQ1Lx5c4zjGzsIJk6zLIGIiHggcWK7DAGL3PKH6LVrwHvvAS+8AOz+3yTAIhLJRs9xZJkDR8pNnjzZTFJbpkwZs2zTpk04ffq0mdpERERCoVgxfojarUyffGL/n8vYCtW3L5AwoU6niJ/wKKeJw/k92liMGKZuknhGOU0i4uLff4HOnYFffrGvsyQKE8WfUr9ORPw0EVy8R0GTiLj122+cpBI4dcq+3qgR8M03nHxTJ0wksiSCM9GbBSX37t3rjX0UERF36tSxE8U/+giIFctuecqTB+B0UY8e6ZyJ+EiogiYGTKzGzRF0IiISgZjLNGQIsH07ULo0cOOGXeOJuaSsLi4i/j967tNPP0XPnj1x+fLliNkjERH5n8KFgQ0b7ETxJEmAbduAkiU5QodzROlMiTxDoc5pKlq0qJnH7cGDB6bVKWGQkR079AvIY8ppEpFQOXvWHlXHuk6UKZPdZce6TyIS4d/HoZ6w95VXXgn7XomISNixYviMGcCbb9o1nY4f54eyHTQxeGIQJSIRRqPnfEgtTSISZrdvA5zrc+hQjtKxc6AGDAA++IAJqDqxIv4wjYqIiPiBBAmAQYOAnTvtKuK3btl5TkwaZ96TiHhdmOae++qrr1CqVCmkTZsWyZMnd7mIiMgzVKAAsHYt57ICkia1R9YxcOJIu+vX9VSI+DJo6tevH4YPH44mTZqYZixOsfLqq68iZsyY6MuS/yIi8mxx0t+33wYOHgQ4Bygn/WWOEycBnjMHUA1jEd/kNOXIkQMjR45E7dq1kThxYuzatStwGeeg++mnn7yzZ9GAcppEJEIsW2Ynih85Yl/nNCyjRwNZs+qEizzLnKZz586hYMGC5v+JEiUyD0B16tTBokWLQrs5ERHxtmrVgN27WVgPiB0b4Gdz/vzAV18BDx7ofIuEUaiDpowZM+Isa4UAeP7557F06VLz/61btyJu3Lhh3Q8REfGm+PHt0XR//QW8+KI92o7TspQoAWzapHMt8iyCpgYNGmDFihXm/506dcJnn32GnDlzomXLlmjTpk1Y9kFERCIK85pWrwa+/RbgYB22QJUrZ3ffXb0a7N0ePbaw8egl/LrrX/OX10Wiu3DXadq8eTPWr19vWp3q1avnvT2LBpTTJCLP1H//2a1N06fb19OmBb75BmjcGIgRI3C1xXvPot/C/Th77W7gsnRJ4qFP3XyoWSCdnjSJtt/HKm7pQwqaRMQnVq0C2rUDDh+2r9esCYwdC2TLZgKm9j/sQNBf046QalzzYgqcJMrxaiL4xo0bPX7gW7duYd++fR6vLyIiz1jlynY3HcvExIkDLF5sEsUff/EFPp+/+4mAiRzL2AKlrjqJrjwKmpivVK1aNcyaNQs3g5lVe//+/fjkk09MN50m7RUR8XMcuNOnjx08MYi6cwcxP/kEk795F8X+OeD2Lgyc2GW35fjlZ767IpEmaGJAVL9+ffTu3RvJkiVD/vz5TRBVt25dlC9fHilTpkTx4sVx8uRJLFu2DC1atIj4PRcRkfDLnRvg4J7p03EvaXLkuXgSc3/8CIMWj8Zzd93/SL5w43+5TiLRSahzmtiKtG7dOpw4cQJ37twxAVPRokVRuXJlTaMSSsppEhF/snXb3zjWpgNe27PMXP8vQVIMqNoWC/JWdEkU//ntMiibI4UP91TEu5QIHgkoaBIRf8JcpfKDVyLz3m34fMkY5Lx02ixfm7UoPqveHqeSpUfaJPHw58dVECvm/4IokcguwiqCi4hI1MRAiGUFtmQqgNqtR2JohRa4Fys2XjyxE0undECHDTPRt+bzCpgk2lLJAR9SS5OI+CPnOk1ZrpzBgKXjTOAUWCxzwgSgQgVf76aI16h7LhJQ0CQi/txVx1FyTPpOnSguSm9agpjdugIXLtgrvPUWMHgwkEK5TRL5KWiKBBQ0iUikcuUK0KMHMHGifT1lSmDYMIAjpp0SxUUiG+U0iYiIdyVLZnfN/fknUKAAcPEi0KoV8NJL/6suLhLdc5pGjhzp8QY7duwY3n2KNtTSJCKR1oMHwPDhQL9+pjCmqSz+ySd2SxQLZ4pE1+65bNmyefSgMWLEwLFjx0K3p9GYgiYRifT4md+hgz0VC+XKZbdGVark6z0T8ZhymiIBBU0iEiXwt/fs2UCnTsC5c/Yydtt99ZWd9yTi55TTJCIizwaTwJs0AQ4cAN57z74+fbo9RcvUqXZQJRJd6zT9888/WLBgAU6dOoX79++73DacfdziEbU0iUiUtHkz8M479mTA9OKLwPjxdo0nkUj8fRwQ2g2vWLEC9erVM3lOhw4dQoECBcw8dIy9ihUrFt79FhGRyK50aWDbNuCbb4A+fYC1a4HChe0kcSaLx4vn6z0UCZNQT6PSs2dPdOvWDXv37kW8ePEwZ84cnD59GhUrVkTjxo3DthciIhK1xI4NfPghsH8/ULu2PdpuwACgYEFg+XJf753IswmaDhw4gFZM8GMzVUAA7ty5g0SJEqF///4YzOqwIiIiDlmyAAsXAr/8AqRPDxw5AlSrBjRv/r/q4iJRNWhKmDAh7t27Z/6fPn16HD16NPC2iyx0JiIi4oyJ4Q0b2oniH3xgX//xRztRfNIk4PFjnS+JmkFTmTJlsH79evP/2rVrm666gQMHok2bNuY2ERERt5hgy2LJTBQvWhS4etVOGGei+N69OmkS9YImjo4rzSQ/AH379kW1atUwc+ZMZMmSBVOmTImIfRQRkaikZElgyxa7onjChAB/iDOIYpL47du+3jsR75YcEO9QyQERifZOn+b8W8D8+fap4AwUY8cCNWtG+1MjUai4JeszsV4TazU5X0RERDyWKRMwb54dNGXMCBw/DtSqBTRtCpw9qxMpfiXUQdPhw4dRoUIFxI8f33TJsV4TL1mzZvV4jjoREREX9evb5Qm6dAFixgRmzrSLYY4bp0Rxibzdcy+88IIpNdCjRw+kS5fOTNLrrDALmIlH1D0nIuLGjh3Au+/aBTKJg4w4CXChQjpdErkm7GXJge3btyNPnjze2M9oTUGTiEgwHj2yc5t69QJu3ABixQK6drUrjDN5XCQy5DTly5dP9ZhERCRiMUhiTSfWdmKNJwZRQ4cC+fMDixbp7ItPhDpoYtXv7t27Y/Xq1bh06ZKJzpwvIiIiXpMhg11NnFXFM2cGTp4E6tQBOG3XmTM60fJMhbp7LiYT9EyBV9dcJm6Gyx7x14B4RN1zIiKhcOsWCwQCI0bYLU+JEwMDBwLvvWe3TIn4W07TmjVrQrydE/dGVw0aNDAtcFWrVsUv/GX0FAqaRETC4K+/7ERxVhanEiWAiRPtApki/hQ0SfBWrVqFmzdvYvr06QqaREQiEluaGCj17Alcu2aXKejUCejfH0iUSOdeIiRoCgjdZoHdu3e7Xc6uuXjx4iFz5syIGzcuoqPKlSubliYREYlg7I5r3x545RW7thPrOrHbjq38o0bZdZ9EfJ0IXqRIERQtWvSJC5ezDAEjtVatWuHu3bseb/Pff/9F8+bNkSJFCiRIkMBsi2UNvGXt2rWoW7cu0qdPb4K7+Y5y/UGMHTvWFOhk8Fe8eHGsW7fOa/sgIiIRIF06YMYM4I8/7ClYOC0LA6kGDez/i/gyaJo3bx5y5syJiRMnYteuXdi5c6f5f+7cufHTTz+ZSXtXrlyJTz/91KPtXblyxRTMjB07Nv744w/s378fw4YNQ9KkSd2uv379ejx48OCJ5QcPHsS5c+fc3ufWrVum6Obo0aOD3Q9OOty5c2f06tXLHBOrnteqVctlahgGUgUKFHjickYjOEREfItz1e3dC/ToAQQE2NOy5MsHfP018PChnh3xDiuUSpYsaS1evPiJ5VzG22jevHlW9uzZPdrexx9/bJUvX96jdR89emQVLlzYatSokfXw4cPA5YcOHbLSpk1rDR48+Knb4CFz/4IqVaqU1a5dO5dlefLksXr06GGFxqpVq6yGDRt6tO61a9fM/vCviIh4yZ49llWuHD/w7UuxYpa1datOr4T7+zjULU179uwxc84FxWW8jdi9dtbDiRYXLFiAEiVKoHHjxkidOrXp6ps0aVKw5Q5+//130xLUsmVLPH78GEePHkWVKlVQr149Uz8qrJMPszuwevXqLst5fcOGDfC2MWPGmCKhJUuW9Pq2RUSivQIFAKZXMFGcvRaclqV0aTtRXPUEJRxCHTQxb+nLL780gYYDu8u4zDG1CnOU0qRJ49H2jh07hnHjxpkuvyVLlqBdu3bo2LEjvvvuO7frMy+J3X/spmvWrJkJmDjEf/z48QirixcvmvpSQfeZ14Pr8nOnRo0aJvhjYJcxY0Zs3brV7XodOnQw3ZDB3S4iIuHE0XRvv83cDeCNN+xJf0eOtCcBnjvXboMSCaWAsLSSsFWHQUGhQoVMYjVH1DHo+O233wIDofdYbMwDbC1iS9OgQYPMdbY07du3zwRSbE1yhyP0GFSxJlT27NlNHlXQYpthEVzBTk8x6BMRET/CH8M//AC0amWPtjt61J6WhVXFmefqpudExGstTeXKlcOJEyfQv39/EzQxEZr/P378OMpwJmoALVq0wEcffeTR9tKlS2e6qpzlzZvXJQE7qPPnz+Odd94xI+Ju376NLhxuGg4pU6ZErFixnmhVunDhgsctZiIi4seqVWN+CcBBSrFjA/yRz++eYcOUKC4R19JEiRIlMt1o3sCRc4cOHXJZdvjwYbd5U46uNHbHMbCaPXs2/v77b1SqVMnUhvrqq6/CtA9x4sQxI+OWLVtmqno78Hp91foQEYka4scHBgwAmjWzK4oz7+nDD4HvvwcmTLDznkTCGzQxWZvD71kWgP8PCbvuQoOtRGy9YvdckyZNsGXLFlPCgBd3XXk1a9Y0ARVLBAQEBJjgafny5aawZIYMGdy2OrFK95EjRwKvs1WM5RKSJ09uuvqoa9eupoWMXYVly5Y1j8/WLm8FhyIi4ieY18RCxNOmAewV4bQsZcva3XdMFUmSxNd7KP7K8kCMGDGs8+fPB/4/uEvMmDGtsFi4cKFVoEABK27cuGaY/8SJE4Ndd+nSpdadO3eeWL5z507r1KlTwZYB4KEGvbRq1cplvTFjxlhZsmSx4sSJYxUrVsxas2aNFZFUckBExMcuXLCsli3/V54gbVrLmjnTsh4/9vWeyTPk6fex5p7zIU3YKyLiJ1autFuaDh+2r9eqxZFPdpVxifKuezj3XKgTwUVERKKcKlXsbrq+fZnoak/Lkj8/MHgw6+r4eu/ET3gcNG3evNlMc+KMw/45VxuLUnI027179yJiH0VERCJevHhAnz6cmZ4zsAN37tjTshQrBkRAoWOJwkFT3759TT0mB1b/fuutt/DSSy+hR48eWLhwIb744ouI2k8REZFnI3duYMUKYPp0IEUKe067F16wR9xduaJnIRrzOGjiaDMO9XeYMWMGSpcubaY84cizkSNHYtasWRG1nyIiIs8OCxuzwDJL4rRpYy/jqG7OfPHTT6ooHk15HDRduXLFpdDjmjVrzPB/B86jdvr0ae/voYiIiK+wpWnKFH7p2QHThQv2tCw1atjVxSVa8ThoYsDE+kbEeed27Nhh6hk53Lhxw9RxEhERiXJefJFdLnZxzLhxWf0YZmLggQP5pejrvRN/C5rYqsTcpXXr1qFnz55IkCABKlSoEHg7851y5MgRUfspIiLiWwyWOA0Lc5xeegm4e9e+XqSIXV1cojyPg6bPP//czM/GSXKZx8QLpx9x+Pbbb1G9evWI2k8RERH/8PzzwNKl9kTAqVMDBw7YLVFt2wKXLvl67yQChbq4JQs/ce45BlDOLl++bJY7B1ISMhW3FBGJ5C5ftssSTJpkX0+ZEhg+HGje3E4ml+hd3JIbDRowEedxU8AkIiLRSvLk9qi6P/+0i2FevGiPumP3naO6uEQZqgguIiISXqzjtGMHwHqF8ePb07IULAj06weo8HOUoaBJRETEG5iewq46JoqzJAFH1XFalsKFgdWrdY6jAAVNIiIi3pQ9uz133YwZQNq0doFMTsvy5pt2951EWgqaREREvI1J4K+9Zo+sa9/evs5pWThFy9SpqigeSSloEhERiShJkwJjx9oT/hYqZI+247QsbHk6eFDnPZJR0CQiIhLRypQBtm0Dhg4FEiSwp2VhENW7t10kUyIFBU0iIiLPAqca+/BDYN8+oHZt4MEDe1oWjrJbvlzPQSSgoElERORZypoVWLgQ+OUXIH164MgRoFo1uyAmJwQWv6WgSURE5FljYnjDhnai+Acf2Nd//BHIkweYPBl4/FjPiR9S0CQiIuIrnLJj5Ehg82agaFHgyhXg7bftuezYjSd+RUGTiIiIr5UsCWzZYs9blzAhsH49UKQI8MknwO3bvt47+X8KmkRERPxBQADQpQuwfz9Qvz7w8KE9LUuBAsDixb7eO1HQJCIi4mcyZwbmzwfmzQMyZgSOHwdq1QJefx04d87XexetqaVJRETEH73yit3qxNanmDHtaVmYKD5+vBLFfURBk4iIiL9KnNjOc9q6FSheHLh2zZ6W5YUXgN27fb130Y6CJhEREX9XrJg9wo4j7RhIbdpkL+veHbh1y9d7F20oaBIREYkMYsWyazqxthNrPD16ZE/Lkj8/8Pvvvt67aEFBk4iISGSSIYNdTZxVxZk0fvKkPS1L48bAmTO+3rsoTUGTiIhIZFSnjp0ozvns2ArFQIqJ4qNH261Q4nUKmkRERCIrFsJkF9327UDp0sCNG3YXXtmywM6dvt67KEdBk4iISGRXuLBdRXzMGHtqFo62K1EC6NoVuHnT13sXZShoEhERiQrYRffee8DBg0CTJnYtpxEjgHz5gAULfL13UYKCJhERkagkXTpg5kx7RF3WrMDp0/a0LA0a2P+XMFPQJCIiEhVx6pV9+4CPP7bntePULGx1+vpre147CTUFTSIiIlFVggTAl18CO3YA5crZ+U2cloVJ49u2+XrvIh0FTSIiIlFdwYLAunXAhAlA0qR2EMXAqVMn4Pp1X+9dpKGgSUREJDrgpL/vvGMnijdrZieKc1oWdtnNnQtYlq/30O8paBIREYlO0qQBfvwRWLoUyJED+Pdfe1oWJouzurgES0GTiIhIdFStGrBnD9CrFxA7tj0tC1udhg1TongwFDSJiIhEV/HjA59/DuzaBVSoANy+bU/LwsKYmzf7eu/8joImERGR6I4tTKtXA1OmAMmTA3/9ZU/F0qEDcO2ar/fObyhoEhERETtRvE0bO1G8ZUs7MXzsWCBvXmD2bCWKK2gSERERF6lSAdOnAytWADlzAmfP2tOy1K4NHD8erU+WWppERETkSVWqALt3A336AHHiAH/8AeTPDwweDDx4EC3PmIImERERcS9ePKBvXzt4qlQJuHMH6NEDKFYM2Lgx2p01BU0iIiISsty5gZUr7W67FCmAvXvtaVnatQOuXIk2Z09Bk4iIiDxdjBh2gjgTxVu3tpdxWpY8eYCff44WieIKmkRERMRzKVMC335rlyhgwHThgj0tS40awNGjUfpMKmgSERGR0KtY0S6KOWAAEDcusGwZUKAAMHAgcP9+lDyjCppEREQkbOLGBT791J6O5aWXgLt37etFigDr1kW5s6qgSURERMInZ057AuAffrDrPB04ALz4ItC2LXD5cpQ5uwqaRERExDuJ4m+8YSeKv/22vYzTsjDv6fvvo0SiuIImERER8Z7kyYGJE+3uORbD/O8/e9Qdu+8OH47UZ1pBk4iIiHhf+fLAjh3AoEF2kUzWeSpYEOjfH7h3L1KecQVNIiIiEjHixAF69gT27bNLEnBUHadlKVzYLlkQyShoEhERkYiVPbs9dx2LYKZJAxw6BFSuDLz5JnDxYqQ5+wqaRERE5NkkijdtaieKc/oVXue0LEwUnzo1UiSKK2gSERGRZydpUmDcOGDDBqBQIeDSJaBNG7vliQGVH1PQJCIiIs9emTLAtm3AkCFA/PjAmjV2ENW7t10k0w8paBIRERHfiB0b+OgjYP9+4OWXgQcP7GlZGDytWOF3z4qCJhEREfGtrFmB334DZs8G0qUD/v7bruvUooU9IbCfUNAkIiIivhcjBtCokT0Fy/vv29c5LQsTxSdPBh4/9vUeKmgSERERP5IkCTBqFLBpkz3x75Ur9rQsnMuO9Z58SC1NXtSgQQMkS5YMjRgpi4iISNiVKgVs3QoMHw4kTAisX28HUb16AXfuwBcUNHlRx44d8d1333lzkyIiItFXQADQpYtJFLfq1gMePsTjwYPx14otePT42dd1CnjmjxiFVa5cGasjYVl4ERERf7b4emz0K9sRBQMKIvvlfzH+z5tIt2cl+tTNh5oF0kWflqa+ffsiRowYLpe0adN69THWrl2LunXrIn369Gb78+fPd7ve2LFjkS1bNsSLFw/FixfHOs7QLCIiIj6zeO9ZtP9hB85eu4ulucpifBk7BebctbtmOW+PNkET5c+fH2fPng287NmzJ9h1169fjwes4xDEwYMHce7cObf3uXXrFgoXLozRo0cHu92ZM2eic+fO6NWrF3bu3IkKFSqgVq1aOHXqVOA6DKQKFCjwxOXMmTOhPmYREREJGbvg+i3cD3cdcY5lvP1ZddX5RfdcQECAR61Ljx8/RocOHZAzZ07MmDEDsWLFMssPHz5susa6dOmC7t27P3E/Bj+8hGT48OF466230LZtW3P966+/xpIlSzBu3Dh88cUXZtn27dvDeIQiIiISWluOXzYtTMFhqMTbuV7ZHCkQLVqa/v77b9N1xq6xpk2b4tixY27XixkzJn7//XfTEtSyZUsTRB09ehRVqlRBvXr13AZMnrh//74JiKpXr+6ynNc3cG4cLxszZgzy5cuHkiVLen3bIiIiUcWFG3e9ul6kD5pKly5tRpyxVWfSpEmmi61cuXK4xAn83GBwtXLlStNN16xZMxMwVa1aFePHjw/zPly8eBGPHj1CmjRpXJbzenBdfu7UqFEDjRs3NoFdxowZsZVDJd1ga9n+/fuDvV1ERESA1InjeXW9SN8959xtVrBgQZQtWxY5cuTA9OnT0bVrV7f3yZw5swm0KlasiOzZs2PKlCkmwTu8gm7DsqxQbZeBn4iIiHhHqWzJkS5JPJP07S5rid/QaZPEM+tFi5amoBImTGiCJ3bZBef8+fN45513zIi427dvm1ym8EiZMqXJjwraqnThwoUnWp9ERETk2YgVM4YpK0BBmzAc13k714uWQdO9e/dw4MABpOOEfcF0pbE7Lm/evJg7d67pqps1axY+/PDDMD9mnDhxzMi4ZcuWuSzndXYVioiIiG+wDtO45sVMi5IzXufyZ1mnyefdcwx22GLELje27Hz++ee4fv06WrVq9cS6TPyuWbMmsmTJYkoEcNQdg6fly5eb0XMZMmRw2+p08+ZNHDlyJPD68ePHsWvXLiRPntw8LrErsEWLFihRooTpIpw4caIpN9CuXbsIPgMiIiISEgZG1fKlNaPkmPTNHCZ2yT2rFia/CZr++ecfvP7666YFKVWqVChTpgw2bdpkAiN3o+c4/J81lNg65MDuPAZOKVK4H264bds2E1Q5OHKlGJhNmzbN/P+1114zyef9+/c3taJYf4kJ3e72Q0RERJ4tBkjPoqxASGJYzHYWn2CLWpIkSXDt2jU899xzehZERET8+PvY73KaRERERPyRgiYRERERDyhoEhEREfGAgiYRERERDyhoEhEREYkMJQeiM8fARWbti4iIiG84voefVlBAQZMP3bhxw/zNlCmTL3dDREREYH8vs/RAcFSnyYdY4fzMmTNInDixVyYcdo6YGYidPn06StV/iqrHFZWPLaoeF+nYIh89Z5HP9Wf0GcIWJgZM6dOnN4W0g6OWJh/iE5MxY8YI2z5fYFHtiyoqH1dUPraoelykY4t89JxFPs89g8+QkFqYHJQILiIiIuIBBU0iIiIiHlDQFAXFjRsXffr0MX+jkqh6XFH52KLqcZGOLfLRcxb5xPWzzxAlgouIiIh4QC1NIiIiIh5Q0CQiIiLiAQVNIiIiIh5Q0OSHxo4di2zZsiFevHgoXrw41q1bF+L6a9asMetx/ezZs2P8+PEut0+aNAkVKlRAsmTJzOWll17Cli1bXNbp27evKbDpfEmbNq1fH9e0adOe2Gde7t69G67H9Ydjq1Spkttjq127tt8+Z2fPnkWzZs2QO3duU4Osc+fObtebM2cO8uXLZxI7+XfevHnhelx/OTZ/eZ9FxLH5y3vN28flL++z0B7b3LlzUa1aNaRKlcrULipbtiyWLFnil++1sV4+Lp+/zyzxKzNmzLBix45tTZo0ydq/f7/VqVMnK2HChNbJkyfdrn/s2DErQYIEZj2uz/vx/r/88kvgOs2aNbPGjBlj7dy50zpw4IDVunVrK0mSJNY///wTuE6fPn2s/PnzW2fPng28XLhwwa+Pa+rUqdZzzz3nss+8hOdx/eXYLl265HJMe/futWLFimWO2V+fs+PHj1sdO3a0pk+fbhUpUsSsH9SGDRvMcQwaNMi8Fvk3ICDA2rRpU5gf11+OzR/eZxF1bP7wXouI4/KH91lYjo23Dx482NqyZYt1+PBhq2fPnub+O3bs8Kv32owIOC5fv88UNPmZUqVKWe3atXNZlidPHqtHjx5u1+/evbu53dm7775rlSlTJtjHePjwoZU4cWLzYeL8IitcuLAVmY6LH2x8s3jzcf31ORsxYoR5zm7evOm3z5mzihUruv2SatKkiVWzZk2XZTVq1LCaNm3qlcf15bH5w/ssoo7NH95rz+I588X7zFvnLl++fFa/fv386r1WKgKOy9fvM3XP+ZH79+9j+/btqF69ustyXt+wYYPb+2zcuPGJ9WvUqIFt27bhwYMHbu9z+/Ztc1vy5Mldlv/9999m3h02pTZt2hTHjh2Dvx/XzZs3kSVLFjMdTZ06dbBz585wPa4/HZuzKVOmmOckYcKEfvuceSK4Y3ds01+fs7B41u+ziD42X77XntVz9qzfZ946Ns5jynnTnF9rvn6v3Y+g4/L1+0xBkx+5ePEiHj16hDRp0rgs5/Vz5865vQ+Xu1v/4cOHZnvu9OjRAxkyZDB9wQ6lS5fGd999Z/qP2WfM7ZYrVw6XLl3y2+PKkyePybVYsGABfv75Z9Nn/sILL5g3S1gf11+OzRn76/fu3Yu2bdu6LPe358wTwR27Y5v++pyFxbN+n0Xksfn6vfYsnjNfvM+8dWzDhg3DrVu30KRJE795r12MoOPy9ftME/b6ISatOWM3atBlT1vf3XIaMmSI+dBbvXq1+eBzqFWrVuD/CxYsaBLwcuTIgenTp6Nr167hOp6IOq4yZcqYiwM/xIsVK4ZRo0Zh5MiRYX5cf3vO+Ou3QIECKFWqlMtyf3zOvLVNf3zOQsOX77OIODZ/ea9F5PZ9+T4Lz7HxdcbE519//RWpU6cO9Tb99Tn7OYTj8uX7TC1NfiRlypSIFSvWE1H4hQsXnojWHTgiwN36AQEBSJEihcvyr776CoMGDcLSpUtRqFChEPeFzdN8sTl+SfrzcTlwhEzJkiUD9zksj+tvx8am5xkzZjzx69cfnzNPBHfsjm3663MWGr56nz2r8+eL91pEb99X77PwHtvMmTPx1ltvYdasWS4tLf7wXksZQcfl6/eZgiY/EidOHDMkc9myZS7LeZ1Ni+4wgg66Pl9EJUqUQOzYsQOXDR06FAMGDMDixYvNbU9z7949HDhwAOnSpYM/H1fQXzC7du0K3OewPK6/HRs/NPhcNG/e3O+fM08Ed+yObfrrc+YpX77PntX588V7LaK376v3WXiOjS0sb775Jn766SeXEgn+8l6LE0HH5fP3WYSkl0u4h2hOmTLFDNHs3LmzGaJ54sQJcztHHbRo0eKJ4etdunQx6/N+QYevcwhnnDhxzDLnIZg3btwIXKdbt27W6tWrzfY4JLVOnTpmRILjcf3xuPr27WstXrzYOnr0qBl+yqGnHFK7efNmjx/XX4/NoXz58tZrr73m9nH97TkjPg+8FC9e3AwN5v/37dsXePv69evNMOgvv/zSDBfm3+CGQfvTc+bJsfnD+yyijs0f3msRcVz+8D4Ly7H99NNP5vxz6L3za+3q1at+9V6bEQHH5ev3mYImP8QXTJYsWcwLo1ixYtaaNWsCb2vVqpUZPuuML46iRYua9bNmzWqNGzfO5XZui/Fx0AuHZTrwAyNdunTmBZ4+fXrr1Vdfdfvh4k/HxTdg5syZze2pUqWyqlevbmqThOZx/fXY6NChQ+Z5Wrp0qdvH9MfnzN3rjPd3Nnv2bCt37txmvzn8eM6cOaF6XH89Nn95n0XEsfnLey0iXo/+8D4L7bHx/+6Ojev523ttjJePy9fvsxj8J/ztVSIiIiJRm3KaRERERDygoElERETEAwqaRERERDygoElERETEAwqaRERERDygoElERETEAwqaRERERDygoElERETEAwqaRER8hHNsvfLKKz47/y1atDCTnnqiUaNGGD58eITvk4g/U0VwEYmQYGD69OlPLK9Ro4aZZDO6OXHiBLJly4adO3eiSJEigcuvXbtmJr9NmjTpM9+n3bt3o1KlSjh58iQSJ07s0fqVK1fG8ePH8dxzzz2TfRTxN2ppEpEIUbNmTZw9e9blwhnMI7MHDx54dXtJkiTxScBEo0ePRuPGjT0KmKhQoULImjUrfvzxxwjfNxF/paBJRCJE3LhxkTZtWpdLsmTJzG2rV69GnDhxsG7dusD1hw0bhpQpU5rgitgK8v7775sLA4sUKVLg008/NS0zDleuXEHLli3NdhMkSIBatWrh77//DrydrSh169Y1tydMmBD58+fH77//bm6bNm3aEwHL/PnzESNGjMDrffv2NS1D3377LbJnz26OiY/PFqJ33nkHqVOnNq0uVapUwV9//RXsuWArExUtWtRsn8fmrnuOyz/44AN07tzZ7HOaNGkwceJE3Lp1C61btzYBTo4cOfDHH3+4bH///v14+eWXkShRInMfdrtdvHgx2P15/PgxZs+ejXr16rksHzt2LHLmzIl48eKZ7bBLzhnXj+yBr0h4KGgSkWeOwQEDA365MwBhwNGrVy9MmjQJ6dKlC1yPXXwBAQHYvHkzRo4ciREjRmDy5MmBtzPo2LZtGxYsWICNGzeagIbBg6NFqEOHDrh37x7Wrl2LPXv2YPDgwSawCI0jR45g1qxZmDNnDnbt2mWW1a5dG+fOnTMB2Pbt21GsWDFUrVoVly9fdruNLVu2mL/Lly83QeHcuXODfTweM4NH3ocBVPv27U2LULly5bBjxw7Txcnzdvv2bbM+t1exYkUT3PFcsPvz/PnzaNKkSYhdbVevXkWJEiUCl/G+HTt2RP/+/XHo0CGznRdffNHlfqVKlTL7xXMqEi1ZIiJe1qpVKytWrFhWwoQJXS79+/cPXOfevXtW0aJFrSZNmlj58+e32rZt67KNihUrWnnz5rUeP34cuOzjjz82y+jw4cNscrLWr18fePvFixet+PHjW7NmzTLXCxYsaPXt29ftPk6dOtVKkiSJy7J58+aZbTr06dPHih07tnXhwoXAZStWrLCee+456+7duy73zZEjhzVhwgS3j3X8+HGz3Z07dz5xnurXr+9yzOXLlw+8/vDhQ3PeWrRoEbjs7NmzZlsbN2401z/77DOrevXqLts9ffq0WefQoUNu94fHyefH+dzOmTPHHNf169et4Pz1119muydOnAh2HZGoLMDXQZuIRE1MGh43bpzLsuTJkwf+n91zP/zwg8mVyZIlC77++usntlGmTBmX7rKyZcuabrxHjx7hwIEDphWqdOnSgbezCy937tzmNmLLCVtqli5dipdeegkNGzY0jxca3LdUqVIFXmfL0s2bN81jObtz5w6OHj2K8HLev1ixYpnHKViwYOAydpvRhQsXAvdn1apVblvQuD+5cuV6Yjn3lV2Nzue2WrVq5ljZDcl8NF4aNGhguj0d4sePb/46WrlEohsFTSISIZhD9Pzzz4e4zoYNG8xfdmvxwvt4yjm3KehyRzDQtm1b0521aNEiEzh98cUXJuhit1fMmDGf2Ia7RO+g+8R8IHYhMi8rKG8kdceOHdvlOo/FeZnj2Lgfjr/M22LXY1DOXZ3O2P3HwOf+/fsmeCXmS7H7j8fFc9W7d2+T07V169bA43J0PzoHkSLRiXKaRMQn2ArSpUsXk8fEFiUmdDsCAYdNmzY9cZ2JymyByZcvHx4+fGjynRwuXbqEw4cPI2/evIHLMmXKhHbt2pk8om7dupnHc3zx37hxwyRZOzhylkLC/CXmM7GVi0Gh84XBiDuOwIQtZN7G/dm3b58Z2RZ0f4ILQh1lD5hA7ozHxBa5IUOGmLwnlkpYuXJl4O179+5FxowZgz1OkahOQZOIRAgmCzO4cL44RnQxeGAyc/Xq1c2osKlTp5ovZLYCOTt9+jS6du1qEpM5amvUqFHo1KmTuY3BU/369fH222/jzz//NMnkzZs3R4YMGcxyYrL5kiVLTG0htqIwAHAEVOzWY9fTJ598YpK9f/rpJzOi7mkYVLCbkKPeuG0GFmwx48g+JlO7w1F27NpyJGkz+d1bmOzOFqDXX3/dJGkfO3bMtBS1adMm2CCNASODLZ43h99++80k2zNw5KjD7777zgSx7O504GhHPmci0ZWCJhGJEAwQ2D3kfClfvry5beDAgSbY4HB6YjkCjopj4OHc2sPWJ+bfcNQWgwN2q3GovwODreLFi6NOnTomkGF3G0e0ObqzGDTwfgyUmKPDAIDD6h35Vcyp4vrMGWJQxu6op2H3GO/DkWUMTJgz1LRpU3M8jnyjoNiCw4BkwoQJSJ8+fWBQ5w3c3vr1682xsiuyQIECJrBkDSh2QQaH59G55hK74Ngax/IJPF/jx48354RlGuju3buYN2+eCVJFoitVBBcRvy1LwG4kdwniEn4MghhEzpgxwwScTzNmzBj8+uuvphVLJLpSS5OISDTEApbsggupCKYztt6xe1QkOtPoORGRaIpFMT3l3C0qEl2pe05ERETEA+qeExEREfGAgiYRERERDyhoEhEREfGAgiYRERERDyhoEhEREfGAgiYRERERDyhoEhEREfGAgiYRERERDyhoEhEREcHT/R8R2BIArqWOQQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# It's a good idea to plot the data and the fit to visually inspect the quality of the\n",
    "# fit and the agreement between the data and the fitted curve.\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Convert exposure (cm) to time-like axis using flow velocity\n",
    "x = np.array(exposure) / boat.flow_velocity\n",
    "y = np.array(signal)\n",
    "\n",
    "# Linear fit from previously fitted parameters\n",
    "y_fit = np.exp(-k * x + intercept)\n",
    "\n",
    "plt.figure(figsize=(6, 4))\n",
    "plt.scatter(x, y, label=\"Signal data\")\n",
    "plt.plot(\n",
    "    x,\n",
    "    y_fit,\n",
    "    color=\"red\",\n",
    "    label=f\"Gamma fit: y = {gamma:.1e} (95% CI: {gamma_lower:.1e} to {gamma_upper:.1e})\",\n",
    ")\n",
    "plt.xlabel(\"Exposure time (s)\")\n",
    "plt.ylabel(\"Signal (arbitrary units)\")\n",
    "plt.title(\"Signal vs Exposure Time\")\n",
    "plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41cfa3b8",
   "metadata": {},
   "source": [
    "# Manual Reactant Gas\n",
    "\n",
    "If a non-natively supported gas is inputted as reactant_gas, the diffusion coefficient (cm^2/s) must be inputted. If a natively supported gas is inputted, leave this parameter as its default value of None and the diffusion coefficient will be calculated based on the defined pressure and temperature values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5602b090",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Create Coated Wall Reactor (CWR) Object with a Manual Reactant Gas ###\n",
    "# Manual reactant gas\n",
    "reactant_gas = \"ClONO2\"\n",
    "\n",
    "cwr = flowtube.CoatedWallReactor(\n",
    "    FT_ID=2.60,\n",
    "    FT_length=100,\n",
    "    injector_ID=1.05,\n",
    "    injector_OD=1.275,\n",
    "    reactant_gas=reactant_gas,\n",
    "    carrier_gas=\"N2\",\n",
    "    reactant_conc_type=\"ppm\",\n",
    "    reactant_conc=30,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3dcc97a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           \u001b[1mFlow Setpoints and Conditions\u001b[0m           \n",
      "╒════════════════════════════════╤══════════╤═════════════╕\n",
      "│ Reactant Flow Rate             │ 0.10     │ sccm        │\n",
      "├────────────────────────────────┼──────────┼─────────────┤\n",
      "│ Reactant Carrier Flow Rate     │ 0.0      │ sccm        │\n",
      "├────────────────────────────────┼──────────┼─────────────┤\n",
      "│ Total Reactant Flow Rate       │ 0.1      │ sccm        │\n",
      "├────────────────────────────────┼──────────┼─────────────┤\n",
      "│ Minimum Carrier Flow Rate      │ 0.6      │ sccm        │\n",
      "├────────────────────────────────┼──────────┼─────────────┤\n",
      "│ Carrier Flow Rate              │ 500.0    │ sccm        │\n",
      "├────────────────────────────────┼──────────┼─────────────┤\n",
      "│ Total Flow Rate                │ 500.1    │ sccm        │\n",
      "├────────────────────────────────┼──────────┼─────────────┤\n",
      "│ Injector ClONO2 Concentration  │ 3e+04    │ ppb         │\n",
      "├────────────────────────────────┼──────────┼─────────────┤\n",
      "│ Flow Tube ClONO2 Concentration │ 6        │ ppb         │\n",
      "├────────────────────────────────┼──────────┼─────────────┤\n",
      "│ Flow Tube ClONO2 Concentration │ 7.85e+09 │ molec. cm-3 │\n",
      "├────────────────────────────────┼──────────┼─────────────┤\n",
      "│ Flow Tube Velocity             │ 32.2     │ cm s-1      │\n",
      "├────────────────────────────────┼──────────┼─────────────┤\n",
      "│ Flow Tube Residence Time       │ 3.1      │ s           │\n",
      "╘════════════════════════════════╧══════════╧═════════════╛\n",
      "             \u001b[1mFluid Dynamics of Carrier Gas\u001b[0m              \n",
      "╒══════════════════════════════════════╤══════════╤════════════╕\n",
      "│ Carrier Gas Dynamic Viscosity        │ 1.77e-05 │ kg m-1 s-1 │\n",
      "├──────────────────────────────────────┼──────────┼────────────┤\n",
      "│ Carrier Gas Density                  │ 0.0609   │ kg m-3     │\n",
      "├──────────────────────────────────────┼──────────┼────────────┤\n",
      "│ Flow Tube Reynolds Number            │ 29       │ unitless   │\n",
      "├──────────────────────────────────────┼──────────┼────────────┤\n",
      "│ Flow Tube Entrance length            │ 3.8      │ cm         │\n",
      "├──────────────────────────────────────┼──────────┼────────────┤\n",
      "│ Flow Tube Pressure Gradient          │ 0.01     │ %          │\n",
      "├──────────────────────────────────────┼──────────┼────────────┤\n",
      "│ Radial Buoyancy Parameter (ΔT=1.0 C) │ 0.00     │ unitless   │\n",
      "├──────────────────────────────────────┼──────────┼────────────┤\n",
      "│ Axial Buoyancy Parameter (ΔT=1.0 C)  │ 4.73     │ unitless   │\n",
      "╘══════════════════════════════════════╧══════════╧════════════╛\n",
      "                 \u001b[1mReactant Diffusion Parameters\u001b[0m                 \n",
      "╒════════════════════════════════════════════════╤═════════╤══════════╕\n",
      "│ Manually Inputted Reactant Diffusion Rate      │ 2       │ cm2 s-1  │\n",
      "├────────────────────────────────────────────────┼─────────┼──────────┤\n",
      "│ Flow Tube Advection Rate                       │ 83.8    │ cm2 s-1  │\n",
      "├────────────────────────────────────────────────┼─────────┼──────────┤\n",
      "│ Peclet Number                                  │ 41.9    │ unitless │\n",
      "├────────────────────────────────────────────────┼─────────┼──────────┤\n",
      "│ Flow Tube Mixing Time                          │ 0.17    │ s        │\n",
      "├────────────────────────────────────────────────┼─────────┼──────────┤\n",
      "│ Flow Tube Mixing Length                        │ 5.4     │ cm       │\n",
      "├────────────────────────────────────────────────┼─────────┼──────────┤\n",
      "│ Diffusion Limited Rate Constant                │ 4.39    │ s-1      │\n",
      "├────────────────────────────────────────────────┼─────────┼──────────┤\n",
      "│ Diffusion Limited Effective Uptake Coefficient │ 0.00045 │ unitless │\n",
      "├────────────────────────────────────────────────┼─────────┼──────────┤\n",
      "│ Approx. Diffusion Limited Uptake Coefficient   │ 0.0045  │ unitless │\n",
      "╘════════════════════════════════════════════════╧═════════╧══════════╛\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Corey/Desktop/Harvard/flowtube/flowtube/coated_wall_reactor.py:504: UserWarning: Axial buoyancy parameter > 1. Flow may be affected by buoyancy effects\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "### Input Reactant Diffusion Coefficient ###\n",
    "reactant_diffusion_rate = 2\n",
    "\n",
    "cwr.initialize(\n",
    "    reactant_FR=0.1,\n",
    "    reactant_carrier_FR=0,\n",
    "    carrier_FR=500,\n",
    "    P=40,\n",
    "    P_units=\"Torr\",\n",
    "    T=22,\n",
    "    reactant_diffusion_rate=reactant_diffusion_rate,\n",
    ")"
   ]
  }
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