Worksheet_update.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sequential update of worksheet files\n",
    "This worksheet exports other worksheets to .sage files in order to save time when executing every particular worksheet. It can also be used to reproduce all computational results with one click, just click on `Cell` -> `Run All`.\n",
    "This converts all other worksheets to .sage files and them loads them into the current worksheet, which executes all the code in each worksheet. This can take tens of minutes! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def fun_export_load_ipynb(list_wsnames, folder, load1 = True):\n",
    "    '''\n",
    "    Exports worksheet 'name' to .sage file in specified folder and then imports it \n",
    "    into current worksheet, executing all code and displaying its output. \n",
    "    If you only want to export but not load the files, set option load1 = False.\n",
    "    '''\n",
    "    for name in list_wsnames:\n",
    "        fun_export_ipynb(name, folder)\n",
    "        print 'created file ' + folder + name\n",
    "        if load1:\n",
    "            load(folder + name + '.sage')\n",
    "            print 'imported file ' + folder + name\n",
    "import json   # for reading notebook metadata\n",
    "def fun_export_ipynb(worksheet, folder):\n",
    "    '''\n",
    "    Exports worksheet.ipynb to a .py or .sage depending on kernel.\n",
    "    Example:\n",
    "    fun_export_ipynb('Worksheet_setup', 'temp/')\n",
    "    Need to import json before using this function. Unless option output=True is given,\n",
    "    each line of text has a semicolon added in the .py file, preventing any output.\n",
    "    '''\n",
    "    str1 = 'jupyter nbconvert  --to=python \\'' + worksheet+'.ipynb\\''\n",
    "    print str1\n",
    "    print 'Exporting specified worksheet to .py file...'\n",
    "    \n",
    "    try:\n",
    "        retcode = os.system(str1)\n",
    "        if retcode < 0:\n",
    "            print >>sys.stderr, \"nbconvert was terminated by signal\", -retcode\n",
    "        else:\n",
    "            print >>sys.stderr, \"nbconvert returned\", retcode\n",
    "    except OSError as e:\n",
    "        print >>sys.stderr, \"Execution failed:\", e\n",
    "        print >>sys.stderr, \"Trying ipython nbconvert instead...\"\n",
    "        str1 = 'ipython nbconvert  --to script \\'' + worksheet+'.ipynb\\''    # for new version of python\n",
    "        try:      \n",
    "            retcode = os.system(str1)\n",
    "            if retcode < 0:\n",
    "                print >>sys.stderr, \"nbconvert was terminated by signal\", -retcode\n",
    "            else:\n",
    "                print >>sys.stderr, \"nbconvert returned\", retcode\n",
    "        except OSError as e:\n",
    "            print >>sys.stderr, \"Execution failed:\", e              \n",
    "\n",
    "    str1 = worksheet + '.py'\n",
    "    \n",
    "    print 'Checking if specified ipynb file was run with sage kernel...'\n",
    "    with open(worksheet+'.ipynb') as data_file:    \n",
    "        data = json.load(data_file)\n",
    "    \n",
    "    if data['metadata']['kernelspec']['name'][0:4] == 'sage':\n",
    "        print 'Renaming .py file to .sage if notebook kernel was sage (to avoid exponent error)'\n",
    "        str2 = folder + worksheet + '.sage'\n",
    "        os.rename(str1, str2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fun_export_load_ipynb(list_wsnames, folder, load1 = True):\n",
    "    '''\n",
    "    Exports worksheet 'name' to .sage file in specified folder and then imports it \n",
    "    into current worksheet, executing all code and displaying its output. \n",
    "    If you only want to export but not load the files, set option load1 = False.\n",
    "    '''\n",
    "    for name in list_wsnames:\n",
    "        fun_export_ipynb(name, folder)\n",
    "        print 'created file ' + folder + name\n",
    "        if load1:\n",
    "            load(folder + name + '.sage')\n",
    "            print 'imported file ' + folder + name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "jupyter nbconvert  --to=python 'Worksheet_setup.ipynb'\n",
      "Exporting specified worksheet to .py file...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "nbconvert returned 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking if specified ipynb file was run with sage kernel...\n",
      "Renaming .py file to .sage if notebook kernel was sage (to avoid exponent error)\n",
      "created file temp/Worksheet_setup\n",
      "imported file temp/Worksheet_setup\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sschyman/Programs/sage-upgrade/local/lib/python2.7/site-packages/traitlets/traitlets.py:809: DeprecationWarning: A parent of InlineBackend._config_changed has adopted the new (traitlets 4.1) @observe(change) API\n",
      "  clsname, change_or_name), DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "fun_export_load_ipynb(['Worksheet_setup'], 'temp/', load1 = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Executing all worksheets in the correct order to reproduce figures saved in subfolder `figures`\n",
    "Below, list_wsnames contains a list of worksheet names that need to be executed in the specified order to recreate all figures in the directory /figures. This will take a few minutes. If you only want to export the worksheets as .sage files to the temp folder, just change `load1 = True` to `load1 = False`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "jupyter nbconvert  --to=python 'leaf_enbalance_eqs.ipynb'\n",
      "Exporting specified worksheet to .py file...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "nbconvert returned 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking if specified ipynb file was run with sage kernel...\n",
      "Renaming .py file to .sage if notebook kernel was sage (to avoid exponent error)\n",
      "created file temp/leaf_enbalance_eqs\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}R_{s} = E_{l} + H_{l} + {R_{ll}}</script></html>"
      ],
      "text/plain": [
       "R_s == E_l + H_l + R_ll"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{R_{ll}} = {\\left(T_{l}^{4} - T_{w}^{4}\\right)} {a_{sh}} \\epsilon_{l} {\\sigma}</script></html>"
      ],
      "text/plain": [
       "R_ll == (T_l^4 - T_w^4)*a_sh*epsilon_l*sigm"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}H_{l} = -{\\left(T_{a} - T_{l}\\right)} {a_{sh}} h_{c}</script></html>"
      ],
      "text/plain": [
       "H_l == -(T_a - T_l)*a_sh*h_c"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = {E_{l,mol}} M_{w} \\lambda_{E}</script></html>"
      ],
      "text/plain": [
       "E_l == E_lmol*M_w*lambda_E"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}M_{w} \\lambda_{E}</script></html>"
      ],
      "text/plain": [
       "M_w*lambda_E"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44100.0000000000kilogram*meter^2/(mole*second^2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{E_{l,mol}} = -{\\left({C_{wa}} - {C_{wl}}\\right)} {g_{tw}}</script></html>"
      ],
      "text/plain": [
       "E_lmol == -(C_wa - C_wl)*g_tw"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{g_{tw}} = \\frac{{g_{bw}} {g_{sw}}}{{g_{bw}} + {g_{sw}}}</script></html>"
      ],
      "text/plain": [
       "g_tw == g_bw*g_sw/(g_bw + g_sw)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{g_{tw}} = \\frac{1}{\\frac{1}{{g_{bw}}} + \\frac{1}{{g_{sw}}}}</script></html>"
      ],
      "text/plain": [
       "g_tw == (1/(1/g_bw + 1/g_sw))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{g_{bw}} = \\frac{{D_{va}} {N_{Sh_L}}}{L_{l}}</script></html>"
      ],
      "text/plain": [
       "g_bw == D_va*Sh/L_l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{g_{bw}} = \\frac{a_{s} h_{c}}{{N_{Le}}^{\\frac{2}{3}} {c_{pa}} \\rho_{a}}</script></html>"
      ],
      "text/plain": [
       "g_bw == a_s*h_c/(Le^(2/3)*c_pa*rho_a)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "meter/second == meter/second\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{N_{Le}} = \\frac{\\alpha_{a}}{{D_{va}}}</script></html>"
      ],
      "text/plain": [
       "Le == alpha_a/D_va"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{C_{wl}} = \\frac{{P_{wl}}}{{R_{mol}} T_{l}}</script></html>"
      ],
      "text/plain": [
       "C_wl == P_wl/(R_mol*T_l)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mole/meter^3 == mole/meter^3\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{P_{wl}} = 611 \\, e^{\\left(-\\frac{M_{w} \\lambda_{E} {\\left(\\frac{273}{T_{l}} - 1\\right)}}{273 \\, {R_{mol}}}\\right)}</script></html>"
      ],
      "text/plain": [
       "P_wl == 611*e^(-1/273*M_w*lambda_E*(273/T_l - 1)/R_mol)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{E_{l,mol}} = -\\frac{{\\left({P_{wa}} - {P_{wl}}\\right)} {g_{tw,mol}}}{P_{a}}</script></html>"
      ],
      "text/plain": [
       "E_lmol == -(P_wa - P_wl)*g_twmol/P_a"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mole/(meter^2*second) == mole/(meter^2*second)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{g_{tw,mol}} = -\\frac{{\\left(P_{a} {P_{wl}} T_{a} - P_{a} {P_{wa}} T_{l}\\right)} {g_{tw}}}{{\\left({P_{wa}} - {P_{wl}}\\right)} {R_{mol}} T_{a} T_{l}}</script></html>"
      ],
      "text/plain": [
       "g_twmol == -(P_a*P_wl*T_a - P_a*P_wa*T_l)*g_tw/((P_wa - P_wl)*R_mol*T_a*T_l)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mole/(meter^2*second) == mole/(meter^2*second)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{g_{tw,mol}} = \\frac{P_{a} {g_{tw}}}{{R_{mol}} T_{a}}</script></html>"
      ],
      "text/plain": [
       "g_twmol == P_a*g_tw/(R_mol*T_a)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mole/(meter^2*second) == mole/(meter^2*second)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{C_{wa}} = \\frac{{P_{wa}}}{{R_{mol}} T_{a}}</script></html>"
      ],
      "text/plain": [
       "C_wa == P_wa/(R_mol*T_a)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mole/meter^3 == mole/meter^3\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}h_{c} = \\frac{{N_{Nu_L}} k_{a}}{L_{l}}</script></html>"
      ],
      "text/plain": [
       "h_c == Nu*k_a/L_l"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kilogram/(kelvin*second^3) == kilogram/(kelvin*second^3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{N_{Re_L}} = \\frac{L_{l} v_{w}}{\\nu_{a}}</script></html>"
      ],
      "text/plain": [
       "Re == L_l*v_w/nu_a"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 == 1\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{N_{Gr_L}} = \\frac{L_{l}^{3} g {\\left(\\rho_{a} - \\rho_{\\mathit{al}}\\right)}}{\\nu_{a}^{2} \\rho_{\\mathit{al}}}</script></html>"
      ],
      "text/plain": [
       "Gr == L_l^3*g*(rho_a - rho_al)/(nu_a^2*rho_al)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 == 1\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{N_{Nu_L}} = {N_{Pr}}^{\\frac{1}{3}} {\\left(-0.0370000000000000 \\, {\\left({N_{Re_L}} + {N_{Re_c}} - \\frac{1}{2} \\, {\\left| {N_{Re_L}} - {N_{Re_c}} \\right|}\\right)}^{\\frac{4}{5}} + 0.0370000000000000 \\, {N_{Re_L}}^{\\frac{4}{5}} + 0.664000000000000 \\, \\sqrt{{N_{Re_L}} + {N_{Re_c}} - \\frac{1}{2} \\, {\\left| {N_{Re_L}} - {N_{Re_c}} \\right|}}\\right)}</script></html>"
      ],
      "text/plain": [
       "Nu == Pr^(1/3)*(-0.0370000000000000*(Re + Re_c - 1/2*abs(Re - Re_c))^(4/5) + 0.0370000000000000*Re^(4/5) + 0.664000000000000*sqrt(Re + Re_c - 1/2*abs(Re - Re_c)))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\rho_{a} = \\frac{M_{N_{2}} {P_{N2}} + M_{O_{2}} {P_{O2}} + M_{w} {P_{wa}}}{{R_{mol}} T_{a}}</script></html>"
      ],
      "text/plain": [
       "rho_a == (M_N2*P_N2 + M_O2*P_O2 + M_w*P_wa)/(R_mol*T_a)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kilogram/meter^3 == kilogram/meter^3\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}P_{a} = {P_{N2}} + {P_{O2}} + {P_{wa}}</script></html>"
      ],
      "text/plain": [
       "P_a == P_N2 + P_O2 + P_wa"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kilogram/(meter*second^2) == kilogram/(meter*second^2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{P_{O2}} = \\frac{21}{100} \\, P_{a} - \\frac{21}{100} \\, {P_{wa}}</script></html>"
      ],
      "text/plain": [
       "P_O2 == 21/100*P_a - 21/100*P_wa"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kilogram/(meter*second^2) == kilogram/(meter*second^2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{P_{N2}} = \\frac{79}{100} \\, P_{a} - \\frac{79}{100} \\, {P_{wa}}</script></html>"
      ],
      "text/plain": [
       "P_N2 == 79/100*P_a - 79/100*P_wa"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\rho_{a} = \\frac{79 \\, M_{N_{2}} {\\left(P_{a} - {P_{wa}}\\right)} + 21 \\, M_{O_{2}} {\\left(P_{a} - {P_{wa}}\\right)} + 100 \\, M_{w} {P_{wa}}}{100 \\, {R_{mol}} T_{a}}</script></html>"
      ],
      "text/plain": [
       "rho_a == 1/100*(79*M_N2*(P_a - P_wa) + 21*M_O2*(P_a - P_wa) + 100*M_w*P_wa)/(R_mol*T_a)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1.13400000000000e-7)*T_l^4 - 900.306522304087\n",
      "C_wa            1.29441408346663\n",
      "C_wl            1.91570361006325\n",
      "D_va            0.0000248765000000000\n",
      "E_l             185.424519010311\n",
      "E_lmol          0.00420463761928142\n",
      "H_l             325.157459266011\n",
      "L_l             0.0300000000000000\n",
      "Le              0.888469037042992\n",
      "M_N2            0.0280000000000000\n",
      "M_O2            0.0320000000000000\n",
      "M_w             0.0180000000000000\n",
      "Nu              26.1863624980041\n",
      "P_a             101325         \n",
      "P_wa            3212.56734153661\n",
      "P_wl            4868.42309771766\n",
      "Pr              0.710000000000000\n",
      "R_ll            89.4180217236781\n",
      "R_mol           8.31447200000000\n",
      "R_s             600            \n",
      "Re              1927.40122068744\n",
      "Re_c            3000           \n",
      "T_a             298.500000000000\n",
      "T_l             305.650648423  \n",
      "T_w             298.500000000000\n",
      "a_s             1.00000000000000\n",
      "a_sh            2              \n",
      "alpha_a         0.0000221020000000000\n",
      "c_pa            1010           \n",
      "epsilon_l       1              \n",
      "g               9.81000000000000\n",
      "g_bw            0.0209367439791524\n",
      "g_sw            0.0100000000000000\n",
      "g_tw            0.00676759777734245\n",
      "h_c             22.7362219510171\n",
      "k_a             0.0260474000000000\n",
      "lambda_E        2.45000000000000e6\n",
      "nu_a            0.0000155650000000000\n",
      "rho_a           1.16339248053449\n",
      "sigm            5.67000000000000e-8\n",
      "v_w             1              \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -H_{l} - {R_{ll}} + R_{s}</script></html>"
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      "text/plain": [
       "E_l == -H_l - R_ll + R_s"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "jupyter nbconvert  --to=python 'leaf_enbalance_eqs.ipynb'\n",
      "Exporting specified worksheet to .py file...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "nbconvert returned 0\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking if specified ipynb file was run with sage kernel...\n",
      "Renaming .py file to .sage if notebook kernel was sage (to avoid exponent error)\n",
      "imported file temp/leaf_enbalance_eqs\n",
      "jupyter nbconvert  --to=python 'E_PM_eqs.ipynb'\n",
      "Exporting specified worksheet to .py file...\n"
     ]
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "nbconvert returned 0\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking if specified ipynb file was run with sage kernel...\n",
      "Renaming .py file to .sage if notebook kernel was sage (to avoid exponent error)\n",
      "created file temp/E_PM_eqs\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{w} = -{\\left({P_{wa}} - {P_{wl}}\\right)} f_{u}</script></html>"
      ],
      "text/plain": [
       "E_w == -(P_wa - P_wl)*f_u"
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    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{\\beta_B} = \\frac{{\\left(T_{a} - T_{l}\\right)} \\gamma_{v}}{{P_{wa}} - {P_{wl}}}</script></html>"
      ],
      "text/plain": [
       "beta_B == (T_a - T_l)*gamma_v/(P_wa - P_wl)"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{\\Delta_{eTa}} = \\frac{{P_{was}} - {P_{wl}}}{T_{a} - T_{l}}</script></html>"
      ],
      "text/plain": [
       "Delta_eTa == (P_was - P_wl)/(T_a - T_l)"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{P_{was}} = 611 \\, e^{\\left(-\\frac{M_{w} \\lambda_{E} {\\left(\\frac{273}{T_{a}} - 1\\right)}}{273 \\, {R_{mol}}}\\right)}</script></html>"
      ],
      "text/plain": [
       "P_was == 611*e^(-1/273*M_w*lambda_E*(273/T_a - 1)/R_mol)"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{\\Delta_{eTa}} = \\frac{611 \\, M_{w} \\lambda_{E} e^{\\left(-\\frac{M_{w} \\lambda_{E} {\\left(\\frac{273}{T_{a}} - 1\\right)}}{273 \\, {R_{mol}}}\\right)}}{{R_{mol}} T_{a}^{2}}</script></html>"
      ],
      "text/plain": [
       "Delta_eTa == 611*M_w*lambda_E*e^(-1/273*M_w*lambda_E*(273/T_a - 1)/R_mol)/(R_mol*T_a^2)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{\\beta_B} = \\frac{{\\left({P_{was}} - {P_{wl}}\\right)} \\gamma_{v}}{{\\Delta_{eTa}} {\\left({P_{wa}} - {P_{wl}}\\right)}}</script></html>"
      ],
      "text/plain": [
       "beta_B == (P_was - P_wl)*gamma_v/(Delta_eTa*(P_wa - P_wl))"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{w} = -\\frac{{R_{ll}} - R_{s}}{{\\beta_B} + 1}</script></html>"
      ],
      "text/plain": [
       "E_w == -(R_ll - R_s)/(beta_B + 1)"
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "[P_wl == (Delta_eTa*P_wa*f_u + P_was*f_u*gamma_v - Delta_eTa*R_ll + Delta_eTa*R_s)/(Delta_eTa*f_u + f_u*gamma_v), E_w == -((P_wa - P_was)*f_u*gamma_v + Delta_eTa*R_ll - Delta_eTa*R_s)/(Delta_eTa + gamma_v), beta_B == -((P_wa - P_was)*f_u - R_ll + R_s)*gamma_v/((P_wa - P_was)*f_u*gamma_v + Delta_eTa*R_ll - Delta_eTa*R_s)]\n",
      "]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{P_{wl}} = \\frac{{\\Delta_{eTa}} {P_{wa}} f_{u} + {P_{was}} f_{u} \\gamma_{v} - {\\Delta_{eTa}} {R_{ll}} + {\\Delta_{eTa}} R_{s}}{{\\left({\\Delta_{eTa}} + \\gamma_{v}\\right)} f_{u}}</script></html>"
      ],
      "text/plain": [
       "P_wl == (Delta_eTa*P_wa*f_u + P_was*f_u*gamma_v - Delta_eTa*R_ll + Delta_eTa*R_s)/((Delta_eTa + gamma_v)*f_u)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{w} = -\\frac{{\\left({P_{wa}} - {P_{was}}\\right)} f_{u} \\gamma_{v} + {\\Delta_{eTa}} {R_{ll}} - {\\Delta_{eTa}} R_{s}}{{\\Delta_{eTa}} + \\gamma_{v}}</script></html>"
      ],
      "text/plain": [
       "E_w == -((P_wa - P_was)*f_u*gamma_v + Delta_eTa*R_ll - Delta_eTa*R_s)/(Delta_eTa + gamma_v)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}T_{l} = \\frac{T_{a} f_{u} \\gamma_{v} + {\\left({\\Delta_{eTa}} T_{a} + {P_{wa}} - {P_{was}}\\right)} f_{u} - {R_{ll}} + R_{s}}{{\\Delta_{eTa}} f_{u} + f_{u} \\gamma_{v}}</script></html>"
      ],
      "text/plain": [
       "T_l == (T_a*f_u*gamma_v + (Delta_eTa*T_a + P_wa - P_was)*f_u - R_ll + R_s)/(Delta_eTa*f_u + f_u*gamma_v)"
      ]
     },
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    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}T_{l} = \\frac{T_{a} f_{u} \\gamma_{v} + {\\left({\\Delta_{eTa}} T_{a} + {P_{wa}} - 611 \\, e^{\\left(-\\frac{M_{w} \\lambda_{E} {\\left(\\frac{273}{T_{a}} - 1\\right)}}{273 \\, {R_{mol}}}\\right)}\\right)} f_{u} - {R_{ll}} + R_{s}}{{\\Delta_{eTa}} f_{u} + f_{u} \\gamma_{v}}</script></html>"
      ],
      "text/plain": [
       "T_l == (T_a*f_u*gamma_v + (Delta_eTa*T_a + P_wa - 611*e^(-1/273*M_w*lambda_E*(273/T_a - 1)/R_mol))*f_u - R_ll + R_s)/(Delta_eTa*f_u + f_u*gamma_v)"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}T_{l} = \\frac{{\\left({\\Delta_{eTa}} f_{u} + f_{u} \\gamma_{v}\\right)} T_{a} + {P_{wa}} f_{u} - {P_{was}} f_{u} - {R_{ll}} + R_{s}}{{\\Delta_{eTa}} f_{u} + f_{u} \\gamma_{v}}</script></html>"
      ],
      "text/plain": [
       "T_l == ((Delta_eTa*f_u + f_u*gamma_v)*T_a + P_wa*f_u - P_was*f_u - R_ll + R_s)/(Delta_eTa*f_u + f_u*gamma_v)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kelvin == kelvin\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{P_{wl}} = \\frac{{P_{was}} f_{u} \\gamma_{v} + {\\left({P_{wa}} f_{u} - {R_{ll}} + R_{s}\\right)} {\\Delta_{eTa}}}{{\\Delta_{eTa}} f_{u} + f_{u} \\gamma_{v}}</script></html>"
      ],
      "text/plain": [
       "P_wl == (P_was*f_u*gamma_v + (P_wa*f_u - R_ll + R_s)*Delta_eTa)/(Delta_eTa*f_u + f_u*gamma_v)"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}T_{l} = \\frac{273 \\, M_{w} \\lambda_{E}}{M_{w} \\lambda_{E} + 273 \\, {R_{mol}} \\log\\left(\\frac{611 \\, {\\left({\\Delta_{eTa}} f_{u} + f_{u} \\gamma_{v}\\right)}}{{\\Delta_{eTa}} {P_{wa}} f_{u} + {P_{was}} f_{u} \\gamma_{v} - {\\Delta_{eTa}} {R_{ll}} + {\\Delta_{eTa}} R_{s}}\\right)}</script></html>"
      ],
      "text/plain": [
       "T_l == 273*M_w*lambda_E/(M_w*lambda_E + 273*R_mol*log(611*(Delta_eTa*f_u + f_u*gamma_v)/(Delta_eTa*P_wa*f_u + P_was*f_u*gamma_v - Delta_eTa*R_ll + Delta_eTa*R_s)))"
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    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -{\\left({P_{wa}} - {P_{wl}}\\right)} S f_{u}</script></html>"
      ],
      "text/plain": [
       "E_l == -(P_wa - P_wl)*S*f_u"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}H_{l} = -{\\left(T_{a} - T_{l}\\right)} f_{u} \\gamma_{v}</script></html>"
      ],
      "text/plain": [
       "H_l == -(T_a - T_l)*f_u*gamma_v"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}H_{l} = -\\frac{{\\left({P_{was}} - {P_{wl}}\\right)} f_{u} \\gamma_{v}}{{\\Delta_{eTa}}}</script></html>"
      ],
      "text/plain": [
       "H_l == -(P_was - P_wl)*f_u*gamma_v/Delta_eTa"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}T_{l} = \\frac{{\\Delta_{eTa}} T_{a} - {P_{was}} + {P_{wl}}}{{\\Delta_{eTa}}}</script></html>"
      ],
      "text/plain": [
       "T_l == (Delta_eTa*T_a - P_was + P_wl)/Delta_eTa"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}H_{l} = -\\frac{{\\left({P_{was}} - {P_{wl}}\\right)} f_{u} \\gamma_{v}}{{\\Delta_{eTa}}}</script></html>"
      ],
      "text/plain": [
       "H_l == -(P_was - P_wl)*f_u*gamma_v/Delta_eTa"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\Delta_{eTa}} {\\left({R_{ll}} - R_{s}\\right)} S + {\\left({P_{wa}} S - {P_{was}} S\\right)} f_{u} \\gamma_{v}}{{\\Delta_{eTa}} S + \\gamma_{v}}</script></html>"
      ],
      "text/plain": [
       "E_l == -(Delta_eTa*(R_ll - R_s)*S + (P_wa*S - P_was*S)*f_u*gamma_v)/(Delta_eTa*S + gamma_v)"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kilogram/second^3 == kilogram/second^3\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}H_{l} = \\frac{{\\left({\\left({P_{wa}} S - {P_{was}} S\\right)} f_{u} - {R_{ll}} + R_{s}\\right)} \\gamma_{v}}{{\\Delta_{eTa}} S + \\gamma_{v}}</script></html>"
      ],
      "text/plain": [
       "H_l == ((P_wa*S - P_was*S)*f_u - R_ll + R_s)*gamma_v/(Delta_eTa*S + gamma_v)"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kilogram/second^3 == kilogram/second^3\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{P_{wl}} = \\frac{{\\Delta_{eTa}} {P_{wa}} S f_{u} + {P_{was}} f_{u} \\gamma_{v} - {\\Delta_{eTa}} {\\left({R_{ll}} - R_{s}\\right)}}{{\\Delta_{eTa}} S f_{u} + f_{u} \\gamma_{v}}</script></html>"
      ],
      "text/plain": [
       "P_wl == (Delta_eTa*P_wa*S*f_u + P_was*f_u*gamma_v - Delta_eTa*(R_ll - R_s))/(Delta_eTa*S*f_u + f_u*gamma_v)"
      ]
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kilogram/(meter*second^2) == kilogram/(meter*second^2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}T_{l} = \\frac{{P_{wa}} S f_{u} - {P_{was}} S f_{u} + {\\left({\\Delta_{eTa}} S f_{u} + f_{u} \\gamma_{v}\\right)} T_{a} - {R_{ll}} + R_{s}}{{\\Delta_{eTa}} S f_{u} + f_{u} \\gamma_{v}}</script></html>"
      ],
      "text/plain": [
       "T_l == (P_wa*S*f_u - P_was*S*f_u + (Delta_eTa*S*f_u + f_u*gamma_v)*T_a - R_ll + R_s)/(Delta_eTa*S*f_u + f_u*gamma_v)"
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     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}S = \\frac{M_{w} {g_{tw,mol}} \\lambda_{E}}{P_{a} f_{u}}</script></html>"
      ],
      "text/plain": [
       "S == M_w*g_twmol*lambda_E/(P_a*f_u)"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}H_{l} = -{\\left(T_{a} - T_{l}\\right)} f_{u} \\gamma_{v}</script></html>"
      ],
      "text/plain": [
       "H_l == -(T_a - T_l)*f_u*gamma_v"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}H_{l} = -{\\left(T_{a} - T_{l}\\right)} {a_{sh}} h_{c}</script></html>"
      ],
      "text/plain": [
       "H_l == -(T_a - T_l)*a_sh*h_c"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\gamma_{v} = \\frac{{a_{sh}} h_{c}}{f_{u}}</script></html>"
      ],
      "text/plain": [
       "gamma_v == a_sh*h_c/f_u"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -{\\left({P_{wa}} - {P_{wl}}\\right)} S f_{u}</script></html>"
      ],
      "text/plain": [
       "E_l == -(P_wa - P_wl)*S*f_u"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{w} = -\\frac{M_{w} {\\left({P_{wa}} - {P_{wl}}\\right)} {g_{bw}} \\lambda_{E}}{{R_{mol}} T_{a}}</script></html>"
      ],
      "text/plain": [
       "E_w == -M_w*(P_wa - P_wl)*g_bw*lambda_E/(R_mol*T_a)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}f_{u} = \\frac{M_{w} {g_{bw}} \\lambda_{E}}{{R_{mol}} T_{a}}</script></html>"
      ],
      "text/plain": [
       "f_u == M_w*g_bw*lambda_E/(R_mol*T_a)"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\gamma_{v} = \\frac{{a_{sh}} h_{c}}{f_{u}}</script></html>"
      ],
      "text/plain": [
       "gamma_v == a_sh*h_c/f_u"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\gamma_{v} = \\frac{{N_{Le}}^{\\frac{2}{3}} {R_{mol}} T_{a} {a_{sh}} {c_{pa}} \\rho_{a}}{M_{w} a_{s} \\lambda_{E}}</script></html>"
      ],
      "text/plain": [
       "gamma_v == Le^(2/3)*R_mol*T_a*a_sh*c_pa*rho_a/(M_w*a_s*lambda_E)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}S = \\frac{{g_{sw}}}{{g_{bw}} + {g_{sw}}}</script></html>"
      ],
      "text/plain": [
       "S == g_sw/(g_bw + g_sw)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\left(M_{w} {P_{wa}} - M_{w} {P_{was}}\\right)} \\gamma_{v} \\lambda_{E} + {\\left({\\Delta_{eTa}} {R_{ll}} {R_{mol}} - {\\Delta_{eTa}} {R_{mol}} R_{s}\\right)} T_{a} {r_{bw}}}{{R_{mol}} T_{a} \\gamma_{v} {r_{sw}} + {\\left({\\Delta_{eTa}} {R_{mol}} T_{a} + {R_{mol}} T_{a} \\gamma_{v}\\right)} {r_{bw}}}</script></html>"
      ],
      "text/plain": [
       "E_l == -((M_w*P_wa - M_w*P_was)*gamma_v*lambda_E + (Delta_eTa*R_ll*R_mol - Delta_eTa*R_mol*R_s)*T_a*r_bw)/(R_mol*T_a*gamma_v*r_sw + (Delta_eTa*R_mol*T_a + R_mol*T_a*gamma_v)*r_bw)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\left(M_{w} {P_{wa}} - M_{w} {P_{was}}\\right)} {N_{Le}}^{\\frac{2}{3}} {a_{sh}} {c_{pa}} {g_{bw}} {g_{sw}} \\lambda_{E} \\rho_{a} + {\\left({\\Delta_{eTa}} M_{w} {R_{ll}} - {\\Delta_{eTa}} M_{w} R_{s}\\right)} a_{s} {g_{sw}} \\lambda_{E}}{{\\Delta_{eTa}} M_{w} a_{s} {g_{sw}} \\lambda_{E} + {\\left({R_{mol}} T_{a} {a_{sh}} {c_{pa}} {g_{bw}} + {R_{mol}} T_{a} {a_{sh}} {c_{pa}} {g_{sw}}\\right)} {N_{Le}}^{\\frac{2}{3}} \\rho_{a}}</script></html>"
      ],
      "text/plain": [
       "E_l == -((M_w*P_wa - M_w*P_was)*Le^(2/3)*a_sh*c_pa*g_bw*g_sw*lambda_E*rho_a + (Delta_eTa*M_w*R_ll - Delta_eTa*M_w*R_s)*a_s*g_sw*lambda_E)/(Delta_eTa*M_w*a_s*g_sw*lambda_E + (R_mol*T_a*a_sh*c_pa*g_bw + R_mol*T_a*a_sh*c_pa*g_sw)*Le^(2/3)*rho_a)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\left(M_{w} {P_{wa}} - M_{w} {P_{was}}\\right)} \\gamma_{v} \\lambda_{E} + {\\left({\\Delta_{eTa}} {R_{ll}} {R_{mol}} - {\\Delta_{eTa}} {R_{mol}} R_{s}\\right)} T_{a} {r_{bw}}}{{R_{mol}} T_{a} \\gamma_{v} {r_{sw}} + {\\left({\\Delta_{eTa}} {R_{mol}} T_{a} + {R_{mol}} T_{a} \\gamma_{v}\\right)} {r_{bw}}}</script></html>"
      ],
      "text/plain": [
       "E_l == -((M_w*P_wa - M_w*P_was)*gamma_v*lambda_E + (Delta_eTa*R_ll*R_mol - Delta_eTa*R_mol*R_s)*T_a*r_bw)/(R_mol*T_a*gamma_v*r_sw + (Delta_eTa*R_mol*T_a + R_mol*T_a*gamma_v)*r_bw)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\left(M_{w} {P_{wa}} - M_{w} {P_{was}}\\right)} {N_{Le}}^{\\frac{2}{3}} {a_{sh}} {c_{pa}} \\lambda_{E} \\rho_{a} + {\\left({\\Delta_{eTa}} M_{w} {R_{ll}} - {\\Delta_{eTa}} M_{w} R_{s}\\right)} a_{s} \\lambda_{E} {r_{bw}}}{{\\Delta_{eTa}} M_{w} a_{s} \\lambda_{E} {r_{bw}} + {\\left({R_{mol}} T_{a} {a_{sh}} {c_{pa}} {r_{bw}} + {R_{mol}} T_{a} {a_{sh}} {c_{pa}} {r_{sw}}\\right)} {N_{Le}}^{\\frac{2}{3}} \\rho_{a}}</script></html>"
      ],
      "text/plain": [
       "E_l == -((M_w*P_wa - M_w*P_was)*Le^(2/3)*a_sh*c_pa*lambda_E*rho_a + (Delta_eTa*M_w*R_ll - Delta_eTa*M_w*R_s)*a_s*lambda_E*r_bw)/(Delta_eTa*M_w*a_s*lambda_E*r_bw + (R_mol*T_a*a_sh*c_pa*r_bw + R_mol*T_a*a_sh*c_pa*r_sw)*Le^(2/3)*rho_a)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\left(M_{w} {P_{wa}} - M_{w} {P_{was}}\\right)} {N_{Le}}^{\\frac{2}{3}} {a_{sh}} {c_{pa}} {g_{bw}} {g_{sw}} \\lambda_{E} \\rho_{a} + {\\left({\\Delta_{eTa}} M_{w} {R_{ll}} - {\\Delta_{eTa}} M_{w} R_{s}\\right)} a_{s} {g_{sw}} \\lambda_{E}}{{\\Delta_{eTa}} M_{w} a_{s} {g_{sw}} \\lambda_{E} + {\\left({R_{mol}} T_{a} {a_{sh}} {c_{pa}} {g_{bw}} + {R_{mol}} T_{a} {a_{sh}} {c_{pa}} {g_{sw}}\\right)} {N_{Le}}^{\\frac{2}{3}} \\rho_{a}}</script></html>"
      ],
      "text/plain": [
       "E_l == -((M_w*P_wa - M_w*P_was)*Le^(2/3)*a_sh*c_pa*g_bw*g_sw*lambda_E*rho_a + (Delta_eTa*M_w*R_ll - Delta_eTa*M_w*R_s)*a_s*g_sw*lambda_E)/(Delta_eTa*M_w*a_s*g_sw*lambda_E + (R_mol*T_a*a_sh*c_pa*g_bw + R_mol*T_a*a_sh*c_pa*g_sw)*Le^(2/3)*rho_a)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}f_{u} = \\frac{{c_{pa}} \\rho_{a}}{\\gamma_{v} r_{a}}</script></html>"
      ],
      "text/plain": [
       "f_u == c_pa*rho_a/(gamma_v*r_a)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{w} = -\\frac{{\\Delta_{eTa}} {R_{ll}} - {\\Delta_{eTa}} R_{s} + \\frac{{\\left({P_{wa}} - {P_{was}}\\right)} {c_{pa}} \\rho_{a}}{r_{a}}}{{\\Delta_{eTa}} + \\gamma_{v}}</script></html>"
      ],
      "text/plain": [
       "E_w == -(Delta_eTa*R_ll - Delta_eTa*R_s + (P_wa - P_was)*c_pa*rho_a/r_a)/(Delta_eTa + gamma_v)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\gamma_{v} = \\gamma_{v} {\\left(\\frac{r_{s}}{r_{a}} + 1\\right)}</script></html>"
      ],
      "text/plain": [
       "gamma_v == gamma_v*(r_s/r_a + 1)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\Delta_{eTa}} {R_{ll}} - {\\Delta_{eTa}} R_{s} + \\frac{{\\left({P_{wa}} - {P_{was}}\\right)} {c_{pa}} \\rho_{a}}{r_{a}}}{\\gamma_{v} {\\left(\\frac{r_{s}}{r_{a}} + 1\\right)} + {\\Delta_{eTa}}}</script></html>"
      ],
      "text/plain": [
       "E_l == -(Delta_eTa*R_ll - Delta_eTa*R_s + (P_wa - P_was)*c_pa*rho_a/r_a)/(gamma_v*(r_s/r_a + 1) + Delta_eTa)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\gamma_{v} = \\gamma_{v} n_{\\mathit{MU}} {\\left(\\frac{r_{s}}{r_{a}} + 1\\right)}</script></html>"
      ],
      "text/plain": [
       "gamma_v == gamma_v*n_MU*(r_s/r_a + 1)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\Delta_{eTa}} {R_{ll}} - {\\Delta_{eTa}} R_{s} + \\frac{{\\left({P_{wa}} - {P_{was}}\\right)} {c_{pa}} \\rho_{a}}{r_{a}}}{\\gamma_{v} n_{\\mathit{MU}} {\\left(\\frac{r_{s}}{r_{a}} + 1\\right)} + {\\Delta_{eTa}}}</script></html>"
      ],
      "text/plain": [
       "E_l == -(Delta_eTa*R_ll - Delta_eTa*R_s + (P_wa - P_was)*c_pa*rho_a/r_a)/(gamma_v*n_MU*(r_s/r_a + 1) + Delta_eTa)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\gamma_{v} = \\frac{P_{a} {c_{pa}}}{\\epsilon \\lambda_{E}}</script></html>"
      ],
      "text/plain": [
       "gamma_v == P_a*c_pa/(epsilon*lambda_E)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\epsilon = \\frac{M_{w} P_{a}}{{R_{mol}} T_{a} \\rho_{a}}</script></html>"
      ],
      "text/plain": [
       "epsilon == M_w*P_a/(R_mol*T_a*rho_a)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\epsilon = \\frac{100 \\, M_{w} P_{a}}{79 \\, M_{N_{2}} {\\left(P_{a} - {P_{wa}}\\right)} + 21 \\, M_{O_{2}} {\\left(P_{a} - {P_{wa}}\\right)} + 100 \\, M_{w} {P_{wa}}}</script></html>"
      ],
      "text/plain": [
       "epsilon == 100*M_w*P_a/(79*M_N2*(P_a - P_wa) + 21*M_O2*(P_a - P_wa) + 100*M_w*P_wa)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}H_{l} = -{\\left(T_{a} - T_{l}\\right)} {a_{sh}} h_{c}</script></html>"
      ],
      "text/plain": [
       "H_l == -(T_a - T_l)*a_sh*h_c"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\left({P_{wa}} - {P_{wl}}\\right)} a_{s} \\epsilon \\lambda_{E} \\rho_{a}}{P_{a} {\\left(r_{s} + {r_{v}}\\right)}}</script></html>"
      ],
      "text/plain": [
       "E_l == -(P_wa - P_wl)*a_s*epsilon*lambda_E*rho_a/(P_a*(r_s + r_v))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kilogram/second^3 == kilogram/second^3\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}H_{l} = -\\frac{{\\left(T_{a} - T_{l}\\right)} {a_{sh}} {c_{pa}} \\rho_{a}}{r_{a}}</script></html>"
      ],
      "text/plain": [
       "H_l == -(T_a - T_l)*a_sh*c_pa*rho_a/r_a"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -M_{w} {g_{tw}} \\lambda_{E} {\\left(\\frac{{P_{wa}}}{{R_{mol}} T_{a}} - \\frac{{P_{wl}}}{{R_{mol}} T_{l}}\\right)}</script></html>"
      ],
      "text/plain": [
       "E_l == -M_w*g_tw*lambda_E*(P_wa/(R_mol*T_a) - P_wl/(R_mol*T_l))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{r_{v}} = \\frac{a_{s}}{{g_{bw}}}</script></html>"
      ],
      "text/plain": [
       "r_v == a_s/g_bw"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -M_{w} {g_{tw}} \\lambda_{E} {\\left(\\frac{{P_{wa}}}{{R_{mol}} T_{a}} - \\frac{{P_{wl}}}{{R_{mol}} T_{l}}\\right)}</script></html>"
      ],
      "text/plain": [
       "E_l == -M_w*g_tw*lambda_E*(P_wa/(R_mol*T_a) - P_wl/(R_mol*T_l))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{M_{w} {\\left({P_{wa}} - {P_{wl}}\\right)} a_{s} \\lambda_{E}}{{R_{mol}} T_{a} {\\left(r_{s} + {r_{v}}\\right)}}</script></html>"
      ],
      "text/plain": [
       "E_l == -M_w*(P_wa - P_wl)*a_s*lambda_E/(R_mol*T_a*(r_s + r_v))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}r_{s} = \\frac{a_{s}}{{g_{sw}}}</script></html>"
      ],
      "text/plain": [
       "r_s == a_s/g_sw"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}r_{a} = \\frac{{c_{pa}} \\rho_{a}}{h_{c}}</script></html>"
      ],
      "text/plain": [
       "r_a == c_pa*rho_a/h_c"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -{\\left({P_{wa}} - {P_{wl}}\\right)} c_{E}</script></html>"
      ],
      "text/plain": [
       "E_l == -(P_wa - P_wl)*c_E"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kilogram/second^3 == kilogram/second^3\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}H_{l} = -{\\left(T_{a} - T_{l}\\right)} c_{H}</script></html>"
      ],
      "text/plain": [
       "H_l == -(T_a - T_l)*c_H"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}H_{l} = -{\\left(T_{a} - T_{l}\\right)} c_{E} \\gamma_{v}</script></html>"
      ],
      "text/plain": [
       "H_l == -(T_a - T_l)*c_E*gamma_v"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{\\Delta_{eTa}} = \\frac{{P_{was}} - {P_{wl}}}{T_{a} - T_{l}}</script></html>"
      ],
      "text/plain": [
       "Delta_eTa == (P_was - P_wl)/(T_a - T_l)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}H_{l} = -\\frac{{\\left({P_{was}} - {P_{wl}}\\right)} c_{E} \\gamma_{v}}{{\\Delta_{eTa}}}</script></html>"
      ],
      "text/plain": [
       "H_l == -(P_was - P_wl)*c_E*gamma_v/Delta_eTa"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kilogram/second^3 == kilogram/second^3\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\left({P_{wa}} - {P_{was}}\\right)} c_{E} c_{H} + {\\left({\\Delta_{eTa}} {R_{ll}} - {\\Delta_{eTa}} R_{s}\\right)} c_{E}}{{\\Delta_{eTa}} c_{E} + c_{H}}</script></html>"
      ],
      "text/plain": [
       "E_l == -((P_wa - P_was)*c_E*c_H + (Delta_eTa*R_ll - Delta_eTa*R_s)*c_E)/(Delta_eTa*c_E + c_H)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kilogram/second^3 == kilogram/second^3\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}H_{l} = \\frac{{\\left({\\left({P_{wa}} - {P_{was}}\\right)} c_{E} - {R_{ll}} + R_{s}\\right)} c_{H}}{{\\Delta_{eTa}} c_{E} + c_{H}}</script></html>"
      ],
      "text/plain": [
       "H_l == ((P_wa - P_was)*c_E - R_ll + R_s)*c_H/(Delta_eTa*c_E + c_H)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kilogram/second^3 == kilogram/second^3\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{P_{wl}} = \\frac{{\\Delta_{eTa}} {P_{wa}} c_{E} - {\\Delta_{eTa}} {R_{ll}} + {\\Delta_{eTa}} R_{s} + {P_{was}} c_{H}}{{\\Delta_{eTa}} c_{E} + c_{H}}</script></html>"
      ],
      "text/plain": [
       "P_wl == (Delta_eTa*P_wa*c_E - Delta_eTa*R_ll + Delta_eTa*R_s + P_was*c_H)/(Delta_eTa*c_E + c_H)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kilogram/(meter*second^2) == kilogram/(meter*second^2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}T_{l} = \\frac{{\\Delta_{eTa}} T_{a} c_{E} + {P_{wa}} c_{E} - {P_{was}} c_{E} + T_{a} c_{H} - {R_{ll}} + R_{s}}{{\\Delta_{eTa}} c_{E} + c_{H}}</script></html>"
      ],
      "text/plain": [
       "T_l == (Delta_eTa*T_a*c_E + P_wa*c_E - P_was*c_E + T_a*c_H - R_ll + R_s)/(Delta_eTa*c_E + c_H)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kelvin == kelvin\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}T_{l} = \\frac{{\\left({\\Delta_{eTa}} T_{a} + {P_{wa}} - {P_{was}}\\right)} c_{E} + T_{a} c_{H} - {R_{ll}} + R_{s}}{{\\Delta_{eTa}} c_{E} + c_{H}}</script></html>"
      ],
      "text/plain": [
       "T_l == ((Delta_eTa*T_a + P_wa - P_was)*c_E + T_a*c_H - R_ll + R_s)/(Delta_eTa*c_E + c_H)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}T_{l} = T_{a} + \\frac{{\\left({P_{wa}} - {P_{was}}\\right)} c_{E} - {R_{ll}} + R_{s}}{{\\Delta_{eTa}} c_{E} + c_{H}}</script></html>"
      ],
      "text/plain": [
       "T_l == T_a + ((P_wa - P_was)*c_E - R_ll + R_s)/(Delta_eTa*c_E + c_H)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{P_{wl}} = 611 \\, e^{\\left(-\\frac{M_{w} \\lambda_{E} {\\left(\\frac{273}{T_{l}} - 1\\right)}}{273 \\, {R_{mol}}}\\right)}</script></html>"
      ],
      "text/plain": [
       "P_wl == 611*e^(-1/273*M_w*lambda_E*(273/T_l - 1)/R_mol)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}T_{l} = \\frac{273 \\, M_{w} \\lambda_{E}}{M_{w} \\lambda_{E} + 273 \\, {R_{mol}} \\log\\left(\\frac{611 \\, {\\left({\\Delta_{eTa}} c_{E} + c_{H}\\right)}}{{\\Delta_{eTa}} {P_{wa}} c_{E} - {\\Delta_{eTa}} {R_{ll}} + {\\Delta_{eTa}} R_{s} + {P_{was}} c_{H}}\\right)}</script></html>"
      ],
      "text/plain": [
       "T_l == 273*M_w*lambda_E/(M_w*lambda_E + 273*R_mol*log(611*(Delta_eTa*c_E + c_H)/(Delta_eTa*P_wa*c_E - Delta_eTa*R_ll + Delta_eTa*R_s + P_was*c_H)))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}c_{E} = \\frac{M_{w} {g_{tw,mol}} \\lambda_{E}}{P_{a}}</script></html>"
      ],
      "text/plain": [
       "c_E == M_w*g_twmol*lambda_E/P_a"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "meter/second == meter/second\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}c_{H} = {a_{sh}} h_{c}</script></html>"
      ],
      "text/plain": [
       "c_H == a_sh*h_c"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}H_{l} = \\frac{{\\left(M_{w} {P_{wa}} - M_{w} {P_{was}}\\right)} {a_{sh}} {g_{tw,mol}} h_{c} \\lambda_{E} - {\\left(P_{a} {R_{ll}} - P_{a} R_{s}\\right)} {a_{sh}} h_{c}}{{\\Delta_{eTa}} M_{w} {g_{tw,mol}} \\lambda_{E} + P_{a} {a_{sh}} h_{c}}</script></html>"
      ],
      "text/plain": [
       "H_l == ((M_w*P_wa - M_w*P_was)*a_sh*g_twmol*h_c*lambda_E - (P_a*R_ll - P_a*R_s)*a_sh*h_c)/(Delta_eTa*M_w*g_twmol*lambda_E + P_a*a_sh*h_c)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\left({\\left(M_{w} {P_{wa}} - M_{w} {P_{was}}\\right)} {a_{sh}} {g_{tw,mol}} h_{c} + {\\left({\\Delta_{eTa}} M_{w} {R_{ll}} - {\\Delta_{eTa}} M_{w} R_{s}\\right)} {g_{tw,mol}}\\right)} \\lambda_{E}}{{\\Delta_{eTa}} M_{w} {g_{tw,mol}} \\lambda_{E} + P_{a} {a_{sh}} h_{c}}</script></html>"
      ],
      "text/plain": [
       "E_l == -((M_w*P_wa - M_w*P_was)*a_sh*g_twmol*h_c + (Delta_eTa*M_w*R_ll - Delta_eTa*M_w*R_s)*g_twmol)*lambda_E/(Delta_eTa*M_w*g_twmol*lambda_E + P_a*a_sh*h_c)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}T_{l} = \\frac{P_{a} T_{a} {a_{sh}} h_{c} + {\\left({\\Delta_{eTa}} M_{w} T_{a} + M_{w} {P_{wa}} - M_{w} {P_{was}}\\right)} {g_{tw,mol}} \\lambda_{E} - P_{a} {R_{ll}} + P_{a} R_{s}}{{\\Delta_{eTa}} M_{w} {g_{tw,mol}} \\lambda_{E} + P_{a} {a_{sh}} h_{c}}</script></html>"
      ],
      "text/plain": [
       "T_l == (P_a*T_a*a_sh*h_c + (Delta_eTa*M_w*T_a + M_w*P_wa - M_w*P_was)*g_twmol*lambda_E - P_a*R_ll + P_a*R_s)/(Delta_eTa*M_w*g_twmol*lambda_E + P_a*a_sh*h_c)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$ g $ $ v_{w} $ $ {P_{wa}} $ $ {a_{sh}} $ $ M_{O_{2}} $ $ T_{a} $ $ {R_{mol}} $ $ T_{w} $ $ R_{s} $ $ {c_{pa}} $ $ M_{N_{2}} $ $ \\epsilon_{l} $ $ {\\sigma} $ $ a_{s} $ $ P_{a} $ $ {N_{Pr}} $ $ {N_{Re_c}} $ $ M_{w} $ $ \\lambda_{E} $ $ \\epsilon $ $ {g_{sw}} $ $ L_{l} $ \n",
      "$ g $ $ v_{w} $ $ {P_{wa}} $ $ {a_{sh}} $ $ M_{O_{2}} $ $ T_{a} $ $ {R_{mol}} $ $ T_{w} $ $ R_{s} $ $ {c_{pa}} $ $ M_{N_{2}} $ $ \\epsilon_{l} $ $ {\\sigma} $ $ a_{s} $ $ P_{a} $ $ {N_{Pr}} $ $ {N_{Re_c}} $ $ M_{w} $ $ \\lambda_{E} $ $ \\epsilon $ $ {g_{sw}} $ $ L_{l} $ \n",
      "T_l = 305.650648423\n",
      "E_l = 185.424519010311\n",
      "H_l = 325.157459266011\n",
      "R_ll = 89.4180217236781\n",
      "Direct estimates: \n",
      "E_l == 201.520517691209\n",
      "H_l == 398.479482308791\n",
      "T_l == 307.263098002106\n",
      "P_wl == 4888.37878666472\n",
      "Using estimated T_l: \n",
      "5332.58270455183\n",
      "E_l == 254.937149826832\n",
      "H_l == 398.479482308791\n",
      "0 == 110.468903558398\n",
      "Using estimated T_l only to calculate R_ll: \n",
      "E_l == 164.417599968095\n",
      "H_l == 325.113496473507\n",
      "307.263098002106 == 305.649681621994\n",
      "Using 1 iteration to get T_l: \n",
      "T_l(R_ll=0): 307.263098002106\n",
      "R_ll(T_l) = 110.468903558398\n",
      "T_l = 305.649681621994\n",
      "E_l == 199.088662380171\n",
      "H_l == 325.113496473508\n",
      "185.424519010311 == 199.120796183645\n",
      "185.424519010311 == 185.424519010311\n",
      "T_l = 305.650648423\n",
      "E_l = 185.424519010311\n",
      "H_l = 325.157459266011\n",
      "R_ll = 89.4180217236781\n",
      "Direct estimates: \n",
      "E_l == 201.520517691209\n",
      "H_l == 398.479482308791\n",
      "T_l == 307.263098002106\n",
      "Using estimated T_l: \n",
      "E_l == 236.648612148471\n",
      "H_l == 398.479482308791\n",
      "0 == 110.468903558398\n",
      "Using estimated T_l only to calculate R_ll: \n",
      "E_l == 156.390826464557\n",
      "H_l == 333.140269977045\n",
      "307.263098002106 == 305.826201131718\n",
      "Using 1 iteration to get T_l: \n",
      "T_l(R_ll=0): 307.479451379050\n",
      "R_ll(T_l) = 113.318783813752\n",
      "T_l = 305.783550529736\n",
      "E_l == 189.502633426895\n",
      "H_l == 331.200842871040\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -{\\left({P_{wa}} - {P_{wl}}\\right)} c_{E}</script></html>"
      ],
      "text/plain": [
       "E_l == -(P_wa - P_wl)*c_E"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}H_{l} = -{\\left(T_{a} - T_{l}\\right)} c_{H}</script></html>"
      ],
      "text/plain": [
       "H_l == -(T_a - T_l)*c_H"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\gamma_{v} = \\frac{c_{H}}{c_{E}}</script></html>"
      ],
      "text/plain": [
       "gamma_v == c_H/c_E"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{\\Delta_{eTa}} = \\frac{{P_{was}} - {P_{wl}}}{T_{a} - T_{l}}</script></html>"
      ],
      "text/plain": [
       "Delta_eTa == (P_was - P_wl)/(T_a - T_l)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{P_{wa}} c_{H} - {P_{wl}} c_{H}}{\\gamma_{v}}</script></html>"
      ],
      "text/plain": [
       "E_l == -(P_wa*c_H - P_wl*c_H)/gamma_v"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kilogram/second^3 == kilogram/second^3\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\left({\\Delta_{eTa}} T_{a} - {\\Delta_{eTa}} T_{l} + {P_{wa}} - {P_{was}}\\right)} c_{H}}{\\gamma_{v}}</script></html>"
      ],
      "text/plain": [
       "E_l == -(Delta_eTa*T_a - Delta_eTa*T_l + P_wa - P_was)*c_H/gamma_v"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kilogram/second^3 == kilogram/second^3\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\left({P_{wa}} - {P_{was}}\\right)} c_{E} c_{H} + {\\left({\\Delta_{eTa}} {R_{ll}} - {\\Delta_{eTa}} R_{s}\\right)} c_{E}}{{\\Delta_{eTa}} c_{E} + c_{H}}</script></html>"
      ],
      "text/plain": [
       "E_l == -((P_wa - P_was)*c_E*c_H + (Delta_eTa*R_ll - Delta_eTa*R_s)*c_E)/(Delta_eTa*c_E + c_H)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kilogram/second^3 == kilogram/second^3\n",
      "E_{l} = -\\frac{{\\left({P_{wa}} - {P_{was}}\\right)} c_{E} c_{H} + {\\left({\\Delta_{eTa}} {R_{ll}} - {\\Delta_{eTa}} R_{s}\\right)} c_{E}}{{\\Delta_{eTa}} c_{E} + c_{H}}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}H_{l} = \\frac{{\\left({\\left({P_{wa}} - {P_{was}}\\right)} c_{E} - {R_{ll}} + R_{s}\\right)} c_{H}}{{\\Delta_{eTa}} c_{E} + c_{H}}</script></html>"
      ],
      "text/plain": [
       "H_l == ((P_wa - P_was)*c_E - R_ll + R_s)*c_H/(Delta_eTa*c_E + c_H)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kilogram/second^3 == kilogram/second^3\n",
      "H_{l} = \\frac{{\\left({\\left({P_{wa}} - {P_{was}}\\right)} c_{E} - {R_{ll}} + R_{s}\\right)} c_{H}}{{\\Delta_{eTa}} c_{E} + c_{H}}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}T_{l} = \\frac{{\\left({\\Delta_{eTa}} T_{a} + {P_{wa}} - {P_{was}}\\right)} c_{E} + T_{a} c_{H} - {R_{ll}} + R_{s}}{{\\Delta_{eTa}} c_{E} + c_{H}}</script></html>"
      ],
      "text/plain": [
       "T_l == ((Delta_eTa*T_a + P_wa - P_was)*c_E + T_a*c_H - R_ll + R_s)/(Delta_eTa*c_E + c_H)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kelvin == kelvin\n",
      "T_{l} = \\frac{{\\left({\\Delta_{eTa}} T_{a} + {P_{wa}} - {P_{was}}\\right)} c_{E} + T_{a} c_{H} - {R_{ll}} + R_{s}}{{\\Delta_{eTa}} c_{E} + c_{H}}\n",
      "0 == 0\n",
      "0 == 0\n",
      "0 == 0\n",
      "[\n",
      "[E_l == -(Delta_eTa*(R_ll - R_s)*c_E + (P_wa*c_E - P_was*c_E)*c_H)/(Delta_eTa*c_E + c_H), H_l == (P_wa*c_E - P_was*c_E - R_ll + R_s)*c_H/(Delta_eTa*c_E + c_H), P_wl == ((P_wa*c_E - R_ll + R_s)*Delta_eTa + P_was*c_H)/(Delta_eTa*c_E + c_H), T_l == (Delta_eTa*T_a*c_E + P_wa*c_E - P_was*c_E + T_a*c_H - R_ll + R_s)/(Delta_eTa*c_E + c_H)]\n",
      "]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\Delta_{eTa}} {\\left({R_{ll}} - R_{s}\\right)} c_{E} + {\\left({P_{wa}} c_{E} - {P_{was}} c_{E}\\right)} c_{H}}{{\\Delta_{eTa}} c_{E} + c_{H}}</script></html>"
      ],
      "text/plain": [
       "E_l == -(Delta_eTa*(R_ll - R_s)*c_E + (P_wa*c_E - P_was*c_E)*c_H)/(Delta_eTa*c_E + c_H)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kilogram/second^3 == kilogram/second^3\n",
      "E_{l} = -\\frac{{\\Delta_{eTa}} {\\left({R_{ll}} - R_{s}\\right)} c_{E} + {\\left({P_{wa}} c_{E} - {P_{was}} c_{E}\\right)} c_{H}}{{\\Delta_{eTa}} c_{E} + c_{H}}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}H_{l} = \\frac{{\\left({P_{wa}} c_{E} - {P_{was}} c_{E} - {R_{ll}} + R_{s}\\right)} c_{H}}{{\\Delta_{eTa}} c_{E} + c_{H}}</script></html>"
      ],
      "text/plain": [
       "H_l == (P_wa*c_E - P_was*c_E - R_ll + R_s)*c_H/(Delta_eTa*c_E + c_H)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kilogram/second^3 == kilogram/second^3\n",
      "H_{l} = \\frac{{\\left({P_{wa}} c_{E} - {P_{was}} c_{E} - {R_{ll}} + R_{s}\\right)} c_{H}}{{\\Delta_{eTa}} c_{E} + c_{H}}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{P_{wl}} = \\frac{{\\left({P_{wa}} c_{E} - {R_{ll}} + R_{s}\\right)} {\\Delta_{eTa}} + {P_{was}} c_{H}}{{\\Delta_{eTa}} c_{E} + c_{H}}</script></html>"
      ],
      "text/plain": [
       "P_wl == ((P_wa*c_E - R_ll + R_s)*Delta_eTa + P_was*c_H)/(Delta_eTa*c_E + c_H)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kilogram/(meter*second^2) == kilogram/(meter*second^2)\n",
      "{P_{wl}} = \\frac{{\\left({P_{wa}} c_{E} - {R_{ll}} + R_{s}\\right)} {\\Delta_{eTa}} + {P_{was}} c_{H}}{{\\Delta_{eTa}} c_{E} + c_{H}}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}T_{l} = \\frac{{\\Delta_{eTa}} T_{a} c_{E} + {P_{wa}} c_{E} - {P_{was}} c_{E} + T_{a} c_{H} - {R_{ll}} + R_{s}}{{\\Delta_{eTa}} c_{E} + c_{H}}</script></html>"
      ],
      "text/plain": [
       "T_l == (Delta_eTa*T_a*c_E + P_wa*c_E - P_was*c_E + T_a*c_H - R_ll + R_s)/(Delta_eTa*c_E + c_H)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kelvin == kelvin\n",
      "T_{l} = \\frac{{\\Delta_{eTa}} T_{a} c_{E} + {P_{wa}} c_{E} - {P_{was}} c_{E} + T_{a} c_{H} - {R_{ll}} + R_{s}}{{\\Delta_{eTa}} c_{E} + c_{H}}\n",
      "0 == 0\n",
      "0 == 0\n",
      "0 == 0\n",
      "[\n",
      "[E_l == -(Delta_eTa*(R_ll - R_s)*c_E + (P_wa*c_E - P_was*c_E)*c_H)/(Delta_eTa*c_E + c_H), H_l == (P_wa*c_E - P_was*c_E - R_ll + R_s)*c_H/(Delta_eTa*c_E + c_H), P_wl == ((P_wa*c_E - R_ll + R_s)*Delta_eTa + P_was*c_H)/(Delta_eTa*c_E + c_H), T_l == (Delta_eTa*T_a*c_E + P_wa*c_E - P_was*c_E + T_a*c_H - R_ll + R_s)/(Delta_eTa*c_E + c_H)]\n",
      "]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\Delta_{eTa}} {\\left({R_{ll}} - R_{s}\\right)} c_{E} + {\\left({P_{wa}} c_{E} - {P_{was}} c_{E}\\right)} c_{H}}{{\\Delta_{eTa}} c_{E} + c_{H}}</script></html>"
      ],
      "text/plain": [
       "E_l == -(Delta_eTa*(R_ll - R_s)*c_E + (P_wa*c_E - P_was*c_E)*c_H)/(Delta_eTa*c_E + c_H)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}H_{l} = \\frac{{\\left({P_{wa}} c_{E} - {P_{was}} c_{E} - {R_{ll}} + R_{s}\\right)} c_{H}}{{\\Delta_{eTa}} c_{E} + c_{H}}</script></html>"
      ],
      "text/plain": [
       "H_l == (P_wa*c_E - P_was*c_E - R_ll + R_s)*c_H/(Delta_eTa*c_E + c_H)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{P_{wl}} = \\frac{{\\left({P_{wa}} c_{E} - {R_{ll}} + R_{s}\\right)} {\\Delta_{eTa}} + {P_{was}} c_{H}}{{\\Delta_{eTa}} c_{E} + c_{H}}</script></html>"
      ],
      "text/plain": [
       "P_wl == ((P_wa*c_E - R_ll + R_s)*Delta_eTa + P_was*c_H)/(Delta_eTa*c_E + c_H)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}T_{l} = \\frac{{\\Delta_{eTa}} T_{a} c_{E} + {P_{wa}} c_{E} - {P_{was}} c_{E} + T_{a} c_{H} - {R_{ll}} + R_{s}}{{\\Delta_{eTa}} c_{E} + c_{H}}</script></html>"
      ],
      "text/plain": [
       "T_l == (Delta_eTa*T_a*c_E + P_wa*c_E - P_was*c_E + T_a*c_H - R_ll + R_s)/(Delta_eTa*c_E + c_H)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\Delta_{eTa}} {R_{ll}} - {\\Delta_{eTa}} R_{s} + \\frac{{\\left({P_{wa}} - {P_{was}}\\right)} {c_{pa}} \\rho_{a}}{r_{a}}}{\\gamma_{v} {\\left(\\frac{r_{s}}{r_{a}} + 1\\right)} + {\\Delta_{eTa}}}</script></html>"
      ],
      "text/plain": [
       "E_l == -(Delta_eTa*R_ll - Delta_eTa*R_s + (P_wa - P_was)*c_pa*rho_a/r_a)/(gamma_v*(r_s/r_a + 1) + Delta_eTa)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}r_{a} = \\frac{{c_{pa}} \\rho_{a}}{h_{c}}</script></html>"
      ],
      "text/plain": [
       "r_a == c_pa*rho_a/h_c"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\gamma_{v} = \\frac{P_{a} {c_{pa}}}{\\epsilon \\lambda_{E}}</script></html>"
      ],
      "text/plain": [
       "gamma_v == P_a*c_pa/(epsilon*lambda_E)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\epsilon = \\frac{M_{w} P_{a}}{{R_{mol}} T_{a} \\rho_{a}}</script></html>"
      ],
      "text/plain": [
       "epsilon == M_w*P_a/(R_mol*T_a*rho_a)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\left({P_{wa}} - {P_{wl}}\\right)} a_{s} \\epsilon \\lambda_{E} \\rho_{a}}{P_{a} {\\left(r_{s} + {r_{v}}\\right)}}</script></html>"
      ],
      "text/plain": [
       "E_l == -(P_wa - P_wl)*a_s*epsilon*lambda_E*rho_a/(P_a*(r_s + r_v))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\Delta_{eTa}} {R_{ll}} - {\\Delta_{eTa}} R_{s} + \\frac{{\\left({P_{wa}} - {P_{was}}\\right)} {c_{pa}} \\rho_{a}}{r_{a}}}{\\gamma_{v} n_{\\mathit{MU}} {\\left(\\frac{r_{s}}{r_{a}} + 1\\right)} + {\\Delta_{eTa}}}</script></html>"
      ],
      "text/plain": [
       "E_l == -(Delta_eTa*R_ll - Delta_eTa*R_s + (P_wa - P_was)*c_pa*rho_a/r_a)/(gamma_v*n_MU*(r_s/r_a + 1) + Delta_eTa)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numerical solution: \n",
      "T_l = 308.321395271\n",
      "E_l = 180.542235053941\n",
      "H_l = 150.521099595469\n",
      "R_ll = 68.9366653505872\n",
      "g_bw = 0.0131620455576424\n",
      "g_tw = 0.00291849206962754\n",
      "Direct estimates: \n",
      "E_l = 198.222104889662\n",
      "H_l = 201.777895110338\n",
      "T_l == 310.133484539870\n",
      "T_l == 310.133484539870\n",
      "T_l == 309.093414355984\n",
      "400 == 400.000000000000\n",
      "Using T_l from eq_Tl_Delta: \n",
      "T_l = 310.133484539870\n",
      "P_wl = 6256.38161942359\n",
      "E_l = 216.096641092879\n",
      "H_l = 201.777895110338\n",
      "R_ll = 93.2413750164809\n",
      "400 == 511.115911219698\n",
      "Using T_l from eq_Tl_Delta only to calculate R_ll: \n",
      "T_l = 310.133484539870\n",
      "R_ll = 93.2413750164809\n",
      "E_l = 169.892070077829\n",
      "H_l = 136.866554905691\n",
      "400 == 400.000000000000\n",
      "Using T_l from eq_Tl_Delta2: \n",
      "T_l = 309.093414355984\n",
      "P_wl = 5906.50495424572\n",
      "E_l = 198.222104889661\n",
      "H_l = 172.358447812369\n",
      "R_ll = 79.2391719599513\n",
      "400 == 449.819724661981\n",
      "Using T_l from eq_Tl_Delta2 only to calculate R_ll: \n",
      "T_l = 309.093414355984\n",
      "R_ll = 79.2391719599513\n",
      "E_l = 174.146435695707\n",
      "H_l = 146.614392344342\n",
      "400 == 400.000000000000\n",
      "T_l = 305.650648423\n",
      "E_l = 185.424519010311\n",
      "H_l = 325.157459266011\n",
      "R_ll = 89.4180217236781\n",
      "Direct estimates: \n",
      "E_l == 201.520517691209\n",
      "H_l == 398.479482308791\n",
      "T_l == 307.263098002106\n",
      "Using estimated T_l: \n",
      "E_l == 254.937149826832\n",
      "H_l == 398.479482308791\n",
      "0 == 110.468903558398\n",
      "Using estimated T_l only to calculate R_ll: \n",
      "E_l == 164.417599968095\n",
      "H_l == 325.113496473507\n",
      "307.263098002106 == 305.649681621994\n",
      "Using 1 iteration to get T_l: \n",
      "T_l(R_ll=0): 307.263098002106\n",
      "R_ll(T_l) = 110.468903558398\n",
      "T_l = 305.649681621994\n",
      "E_l == 199.088662380171\n",
      "H_l == 325.113496473508\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\left({P_{wa}} - {P_{was}}\\right)} f_{u} \\gamma_{v} + {\\Delta_{eTa}} {R_{ll}} - {\\Delta_{eTa}} R_{s}}{{\\Delta_{eTa}} + \\gamma_{v}}</script></html>"
      ],
      "text/plain": [
       "E_l == -((P_wa - P_was)*f_u*gamma_v + Delta_eTa*R_ll - Delta_eTa*R_s)/(Delta_eTa + gamma_v)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{w} = -\\frac{{\\left({P_{wa}} - {P_{was}}\\right)} f_{u} \\gamma_{v} + {\\Delta_{eTa}} {R_{ll}} - {\\Delta_{eTa}} R_{s}}{{\\Delta_{eTa}} + \\gamma_{v}}</script></html>"
      ],
      "text/plain": [
       "E_w == -((P_wa - P_was)*f_u*gamma_v + Delta_eTa*R_ll - Delta_eTa*R_s)/(Delta_eTa + gamma_v)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\left({P_{wa}} - {P_{was}}\\right)} {c_{pa}} \\rho_{a} + {\\left({\\Delta_{eTa}} {R_{ll}} - {\\Delta_{eTa}} R_{s}\\right)} r_{a}}{{\\left({\\Delta_{eTa}} + \\gamma_{v}\\right)} r_{a}}</script></html>"
      ],
      "text/plain": [
       "E_l == -((P_wa - P_was)*c_pa*rho_a + (Delta_eTa*R_ll - Delta_eTa*R_s)*r_a)/((Delta_eTa + gamma_v)*r_a)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{w} = -\\frac{{\\Delta_{eTa}} {R_{ll}} - {\\Delta_{eTa}} R_{s} + \\frac{{\\left({P_{wa}} - {P_{was}}\\right)} {c_{pa}} \\rho_{a}}{r_{a}}}{{\\Delta_{eTa}} + \\gamma_{v}}</script></html>"
      ],
      "text/plain": [
       "E_w == -(Delta_eTa*R_ll - Delta_eTa*R_s + (P_wa - P_was)*c_pa*rho_a/r_a)/(Delta_eTa + gamma_v)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\left({\\left({P_{wa}} - {P_{was}}\\right)} f_{u} \\gamma_{v} + {\\Delta_{eTa}} {R_{ll}} - {\\Delta_{eTa}} R_{s}\\right)} S}{{\\Delta_{eTa}} S + \\gamma_{v}}</script></html>"
      ],
      "text/plain": [
       "E_l == -((P_wa - P_was)*f_u*gamma_v + Delta_eTa*R_ll - Delta_eTa*R_s)*S/(Delta_eTa*S + gamma_v)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\Delta_{eTa}} {\\left({R_{ll}} - R_{s}\\right)} S + {\\left({P_{wa}} S - {P_{was}} S\\right)} f_{u} \\gamma_{v}}{{\\Delta_{eTa}} S + \\gamma_{v}}</script></html>"
      ],
      "text/plain": [
       "E_l == -(Delta_eTa*(R_ll - R_s)*S + (P_wa*S - P_was*S)*f_u*gamma_v)/(Delta_eTa*S + gamma_v)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\Delta_{eTa}} {\\left({R_{ll}} - R_{s}\\right)} S + {\\left({P_{wa}} S - {P_{was}} S\\right)} f_{u} \\gamma_{v}}{{\\Delta_{eTa}} S + \\gamma_{v}}</script></html>"
      ],
      "text/plain": [
       "E_l == -(Delta_eTa*(R_ll - R_s)*S + (P_wa*S - P_was*S)*f_u*gamma_v)/(Delta_eTa*S + gamma_v)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\Delta_{eTa}} {R_{ll}} - {\\Delta_{eTa}} R_{s} + \\frac{{\\left({P_{wa}} - {P_{was}}\\right)} {c_{pa}} \\rho_{a}}{r_{a}}}{\\gamma_{v} {\\left(\\frac{r_{s}}{r_{a}} + 1\\right)} + {\\Delta_{eTa}}}</script></html>"
      ],
      "text/plain": [
       "E_l == -(Delta_eTa*R_ll - Delta_eTa*R_s + (P_wa - P_was)*c_pa*rho_a/r_a)/(gamma_v*(r_s/r_a + 1) + Delta_eTa)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\left({P_{wa}} - {P_{wl}}\\right)} a_{s} \\epsilon \\lambda_{E} \\rho_{a}}{P_{a} {\\left(r_{s} + {r_{v}}\\right)}}</script></html>"
      ],
      "text/plain": [
       "E_l == -(P_wa - P_wl)*a_s*epsilon*lambda_E*rho_a/(P_a*(r_s + r_v))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}H_{l} = -\\frac{{\\left(T_{a} - T_{l}\\right)} {a_{sh}} {c_{pa}} \\rho_{a}}{r_{a}}</script></html>"
      ],
      "text/plain": [
       "H_l == -(T_a - T_l)*a_sh*c_pa*rho_a/r_a"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\gamma_{v} = \\frac{P_{a} {c_{pa}}}{\\epsilon \\lambda_{E}}</script></html>"
      ],
      "text/plain": [
       "gamma_v == P_a*c_pa/(epsilon*lambda_E)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\left({P_{wa}} - {P_{was}}\\right)} {c_{pa}} \\epsilon \\lambda_{E} \\rho_{a} + {\\left({\\Delta_{eTa}} {R_{ll}} - {\\Delta_{eTa}} R_{s}\\right)} \\epsilon \\lambda_{E} r_{a}}{P_{a} {c_{pa}} r_{s} + {\\left({\\Delta_{eTa}} \\epsilon \\lambda_{E} + P_{a} {c_{pa}}\\right)} r_{a}}</script></html>"
      ],
      "text/plain": [
       "E_l == -((P_wa - P_was)*c_pa*epsilon*lambda_E*rho_a + (Delta_eTa*R_ll - Delta_eTa*R_s)*epsilon*lambda_E*r_a)/(P_a*c_pa*r_s + (Delta_eTa*epsilon*lambda_E + P_a*c_pa)*r_a)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\left({P_{wa}} - {P_{was}}\\right)} {c_{pa}} \\epsilon \\lambda_{E} \\rho_{a} + {\\left({\\Delta_{eTa}} {R_{ll}} - {\\Delta_{eTa}} R_{s}\\right)} \\epsilon \\lambda_{E} r_{a}}{P_{a} {c_{pa}} r_{s} + {\\left({\\Delta_{eTa}} \\epsilon \\lambda_{E} + P_{a} {c_{pa}}\\right)} r_{a}}</script></html>"
      ],
      "text/plain": [
       "E_l == -((P_wa - P_was)*c_pa*epsilon*lambda_E*rho_a + (Delta_eTa*R_ll - Delta_eTa*R_s)*epsilon*lambda_E*r_a)/(P_a*c_pa*r_s + (Delta_eTa*epsilon*lambda_E + P_a*c_pa)*r_a)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\left({P_{wa}} - {P_{was}}\\right)} {c_{pa}} \\rho_{a} + {\\left({\\Delta_{eTa}} {R_{ll}} - {\\Delta_{eTa}} R_{s}\\right)} r_{a}}{{\\left({\\Delta_{eTa}} + \\gamma_{v}\\right)} r_{a} + \\gamma_{v} r_{s}}</script></html>"
      ],
      "text/plain": [
       "E_l == -((P_wa - P_was)*c_pa*rho_a + (Delta_eTa*R_ll - Delta_eTa*R_s)*r_a)/((Delta_eTa + gamma_v)*r_a + gamma_v*r_s)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\left({P_{wa}} - {P_{was}}\\right)} {c_{pa}} \\rho_{a} + {\\left({\\Delta_{eTa}} {R_{ll}} - {\\Delta_{eTa}} R_{s}\\right)} r_{a}}{{\\left({\\Delta_{eTa}} + \\gamma_{v}\\right)} r_{a} + \\gamma_{v} r_{s}}</script></html>"
      ],
      "text/plain": [
       "E_l == -((P_wa - P_was)*c_pa*rho_a + (Delta_eTa*R_ll - Delta_eTa*R_s)*r_a)/((Delta_eTa + gamma_v)*r_a + gamma_v*r_s)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\left({P_{wa}} - {P_{was}}\\right)} a_{s} {a_{sh}} {c_{pa}} \\epsilon \\lambda_{E} \\rho_{a} + {\\left({\\Delta_{eTa}} {R_{ll}} - {\\Delta_{eTa}} R_{s}\\right)} a_{s} \\epsilon \\lambda_{E} r_{a}}{P_{a} {a_{sh}} {c_{pa}} r_{s} + {\\left({\\Delta_{eTa}} a_{s} \\epsilon \\lambda_{E} + P_{a} {a_{sh}} {c_{pa}}\\right)} r_{a}}</script></html>"
      ],
      "text/plain": [
       "E_l == -((P_wa - P_was)*a_s*a_sh*c_pa*epsilon*lambda_E*rho_a + (Delta_eTa*R_ll - Delta_eTa*R_s)*a_s*epsilon*lambda_E*r_a)/(P_a*a_sh*c_pa*r_s + (Delta_eTa*a_s*epsilon*lambda_E + P_a*a_sh*c_pa)*r_a)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kilogram/second^3 == kilogram/second^3\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\left({P_{wa}} - {P_{was}}\\right)} a_{s} {c_{pa}} \\epsilon \\lambda_{E} \\rho_{a} + {\\left({\\Delta_{eTa}} {R_{ll}} - {\\Delta_{eTa}} R_{s}\\right)} a_{s} \\epsilon \\lambda_{E} r_{a}}{P_{a} {a_{sh}} {c_{pa}} r_{s} + {\\left({\\Delta_{eTa}} a_{s} \\epsilon \\lambda_{E} + P_{a} {a_{sh}} {c_{pa}}\\right)} r_{a}}</script></html>"
      ],
      "text/plain": [
       "E_l == -((P_wa - P_was)*a_s*c_pa*epsilon*lambda_E*rho_a + (Delta_eTa*R_ll - Delta_eTa*R_s)*a_s*epsilon*lambda_E*r_a)/(P_a*a_sh*c_pa*r_s + (Delta_eTa*a_s*epsilon*lambda_E + P_a*a_sh*c_pa)*r_a)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\Delta_{eTa}} {R_{ll}} - {\\Delta_{eTa}} R_{s} + \\frac{{\\left({P_{wa}} - {P_{was}}\\right)} {c_{pa}} \\rho_{a}}{r_{a}}}{{\\Delta_{eTa}} + \\frac{P_{a} {a_{sh}} {c_{pa}} {\\left(\\frac{r_{s}}{r_{a}} + 1\\right)}}{a_{s} \\epsilon \\lambda_{E}}}</script></html>"
      ],
      "text/plain": [
       "E_l == -(Delta_eTa*R_ll - Delta_eTa*R_s + (P_wa - P_was)*c_pa*rho_a/r_a)/(Delta_eTa + P_a*a_sh*c_pa*(r_s/r_a + 1)/(a_s*epsilon*lambda_E))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{\\frac{{\\left({P_{wa}} - {P_{was}}\\right)} a_{s} {a_{sh}} {c_{pa}} \\epsilon \\lambda_{E} \\rho_{a}^{2}}{P_{a} {\\left(r_{a} + r_{s}\\right)} r_{a}} + \\frac{{\\left({\\Delta_{eTa}} {R_{ll}} - {\\Delta_{eTa}} R_{s}\\right)} a_{s} \\epsilon \\lambda_{E} \\rho_{a}}{P_{a} {\\left(r_{a} + r_{s}\\right)}}}{\\frac{{\\Delta_{eTa}} a_{s} \\epsilon \\lambda_{E} \\rho_{a}}{P_{a} {\\left(r_{a} + r_{s}\\right)}} + \\frac{{a_{sh}} {c_{pa}} \\rho_{a}}{r_{a}}}</script></html>"
      ],
      "text/plain": [
       "E_l == -((P_wa - P_was)*a_s*a_sh*c_pa*epsilon*lambda_E*rho_a^2/(P_a*(r_a + r_s)*r_a) + (Delta_eTa*R_ll - Delta_eTa*R_s)*a_s*epsilon*lambda_E*rho_a/(P_a*(r_a + r_s)))/(Delta_eTa*a_s*epsilon*lambda_E*rho_a/(P_a*(r_a + r_s)) + a_sh*c_pa*rho_a/r_a)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{w} = -\\frac{{\\Delta_{eTa}} {R_{ll}} - {\\Delta_{eTa}} R_{s} + \\frac{{\\left({P_{wa}} - {P_{was}}\\right)} {c_{pa}} \\rho_{a}}{r_{a}}}{{\\Delta_{eTa}} + \\gamma_{v}}</script></html>"
      ],
      "text/plain": [
       "E_w == -(Delta_eTa*R_ll - Delta_eTa*R_s + (P_wa - P_was)*c_pa*rho_a/r_a)/(Delta_eTa + gamma_v)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\gamma_{v} = \\frac{c_{H}}{c_{E}}</script></html>"
      ],
      "text/plain": [
       "gamma_v == c_H/c_E"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\Delta_{eTa}} {\\left({R_{ll}} - R_{s}\\right)} c_{E} + {\\left({P_{wa}} c_{E} - {P_{was}} c_{E}\\right)} c_{H}}{{\\Delta_{eTa}} c_{E} + c_{H}}</script></html>"
      ],
      "text/plain": [
       "E_l == -(Delta_eTa*(R_ll - R_s)*c_E + (P_wa*c_E - P_was*c_E)*c_H)/(Delta_eTa*c_E + c_H)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\Delta_{eTa}} {\\left({R_{ll}} - R_{s}\\right)} c_{E} + {\\left({P_{wa}} c_{E} - {P_{was}} c_{E}\\right)} c_{H}}{{\\Delta_{eTa}} c_{E} + c_{H}}</script></html>"
      ],
      "text/plain": [
       "E_l == -(Delta_eTa*(R_ll - R_s)*c_E + (P_wa*c_E - P_was*c_E)*c_H)/(Delta_eTa*c_E + c_H)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\gamma_{v} = \\frac{P_{a} {c_{pa}}}{\\epsilon \\lambda_{E}}</script></html>"
      ],
      "text/plain": [
       "gamma_v == P_a*c_pa/(epsilon*lambda_E)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\left({P_{wa}} - {P_{was}}\\right)} a_{s} {a_{sh}} {c_{pa}} \\epsilon \\lambda_{E} \\rho_{a} + {\\left({\\Delta_{eTa}} {R_{ll}} - {\\Delta_{eTa}} R_{s}\\right)} a_{s} \\epsilon \\lambda_{E} r_{a}}{P_{a} {a_{sh}} {c_{pa}} r_{s} + {\\left({\\Delta_{eTa}} a_{s} \\epsilon \\lambda_{E} + P_{a} {a_{sh}} {c_{pa}}\\right)} r_{a}}</script></html>"
      ],
      "text/plain": [
       "E_l == -((P_wa - P_was)*a_s*a_sh*c_pa*epsilon*lambda_E*rho_a + (Delta_eTa*R_ll - Delta_eTa*R_s)*a_s*epsilon*lambda_E*r_a)/(P_a*a_sh*c_pa*r_s + (Delta_eTa*a_s*epsilon*lambda_E + P_a*a_sh*c_pa)*r_a)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kilogram/second^3 == kilogram/second^3\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\left({P_{wa}} - {P_{was}}\\right)} a_{s} {a_{sh}} {c_{pa}} \\epsilon \\lambda_{E} \\rho_{a} + {\\left({\\Delta_{eTa}} {R_{ll}} - {\\Delta_{eTa}} R_{s}\\right)} a_{s} \\epsilon \\lambda_{E} r_{a}}{P_{a} {a_{sh}} {c_{pa}} r_{s} + {\\left({\\Delta_{eTa}} a_{s} \\epsilon \\lambda_{E} + P_{a} {a_{sh}} {c_{pa}}\\right)} r_{a}}</script></html>"
      ],
      "text/plain": [
       "E_l == -((P_wa - P_was)*a_s*a_sh*c_pa*epsilon*lambda_E*rho_a + (Delta_eTa*R_ll - Delta_eTa*R_s)*a_s*epsilon*lambda_E*r_a)/(P_a*a_sh*c_pa*r_s + (Delta_eTa*a_s*epsilon*lambda_E + P_a*a_sh*c_pa)*r_a)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kilogram/second^3 == kilogram/second^3\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\left({P_{wa}} - {P_{was}}\\right)} a_{s} {a_{sh}} {c_{pa}} \\epsilon \\lambda_{E} \\rho_{a} + {\\left({\\Delta_{eTa}} {R_{ll}} - {\\Delta_{eTa}} R_{s}\\right)} a_{s} \\epsilon \\lambda_{E} r_{a}}{P_{a} {a_{sh}} {c_{pa}} r_{s} + {\\left({\\Delta_{eTa}} a_{s} \\epsilon \\lambda_{E} + P_{a} {a_{sh}} {c_{pa}}\\right)} r_{a}}</script></html>"
      ],
      "text/plain": [
       "E_l == -((P_wa - P_was)*a_s*a_sh*c_pa*epsilon*lambda_E*rho_a + (Delta_eTa*R_ll - Delta_eTa*R_s)*a_s*epsilon*lambda_E*r_a)/(P_a*a_sh*c_pa*r_s + (Delta_eTa*a_s*epsilon*lambda_E + P_a*a_sh*c_pa)*r_a)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\left({P_{wa}} - {P_{was}}\\right)} a_{s} {a_{sh}} {c_{pa}} \\rho_{a} + {\\left({\\Delta_{eTa}} {R_{ll}} - {\\Delta_{eTa}} R_{s}\\right)} a_{s} r_{a}}{{a_{sh}} \\gamma_{v} r_{s} + {\\left({\\Delta_{eTa}} a_{s} + {a_{sh}} \\gamma_{v}\\right)} r_{a}}</script></html>"
      ],
      "text/plain": [
       "E_l == -((P_wa - P_was)*a_s*a_sh*c_pa*rho_a + (Delta_eTa*R_ll - Delta_eTa*R_s)*a_s*r_a)/(a_sh*gamma_v*r_s + (Delta_eTa*a_s + a_sh*gamma_v)*r_a)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}r_{a} = \\frac{{c_{pa}} \\rho_{a}}{h_{c}}</script></html>"
      ],
      "text/plain": [
       "r_a == c_pa*rho_a/h_c"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{r_{v}} = \\frac{a_{s}}{{g_{bw}}}</script></html>"
      ],
      "text/plain": [
       "r_v == a_s/g_bw"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\frac{r_{a}}{{r_{v}}} = \\frac{1}{{N_{Le}}^{\\frac{2}{3}}}</script></html>"
      ],
      "text/plain": [
       "r_a/r_v == Le^(-2/3)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.08204645272521\n"
     ]
    },
    {
     "data": {
      "image/png": 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gs9koKSlh4sSJlz3vuXPnCAgIoGfPnoA5hRcTE0OPHj0oKSmhpKSE0NBQYmNj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\njDHGGNMBJ02MMcYYYzr4f6pggqORC5TpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "Graphics object consisting of 1 graphics primitive"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T_l = 308.321395271\n",
      "E_l = 180.542235053941\n",
      "H_l = 150.521099595469\n",
      "R_ll = 68.9366653505872\n",
      "g_bw = 0.0131620455576424\n",
      "g_tw = 0.00291849206962754\n",
      "Direct estimates: \n",
      "E_l = 198.222104889662\n",
      "H_l = 201.777895110338\n",
      "T_l == 310.133484539870\n",
      "T_l == 310.133484539870\n",
      "T_l == 309.093414355984\n",
      "400 == 400.000000000000\n",
      "Penman-stomata: \n",
      "E_l = 198.222104889662\n",
      "H_l = 201.777895110338\n",
      "T_l = 310.133484539870\n",
      "400 == 400.000000000000\n",
      "PM-equation: \n",
      "E_l = 241.448619283973\n",
      "H_l = 158.551380716027\n",
      "400 == 400.000000000000\n",
      "MU-equation: \n",
      "E_l = 156.668183937778\n",
      "H_l = 243.331816062222\n",
      "400 == 400.000000000000\n",
      "Corrected MU-equation: \n",
      "E_l = 195.741933442269\n",
      "H_l = 204.258066557731\n",
      "400 == 400.000000000000\n",
      "4*T_l^3*a_sh*epsilon_l*sigm\n",
      "[\n",
      "Rll1 == -3*T1^4*a_sh*epsilon_l*sigm - T_w^4*a_sh*epsilon_l*sigm\n",
      "]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{R_{ll}} = -{\\left(3 \\, T_{a}^{4} - 4 \\, T_{a}^{3} T_{l} + T_{w}^{4}\\right)} {a_{sh}} \\epsilon_{l} {\\sigma}</script></html>"
      ],
      "text/plain": [
       "R_ll == -(3*T_a^4 - 4*T_a^3*T_l + T_w^4)*a_sh*epsilon_l*sigm"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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t2ixcuJAVK1awZ88eHGqMKiJ5QGio6WD111/mGfSdd9odkeRnmUrSDoeDlStX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\nLVu20LVrV44dO0aBAgXSPZZcTj+Tvn4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      "text/plain": [
       "Graphics object consisting of 2 graphics primitives"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -{\\left({P_{wa}} - {P_{wl}}\\right)} c_{E}</script></html>"
      ],
      "text/plain": [
       "E_l == -(P_wa - P_wl)*c_E"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}H_{l} = -{\\left(T_{a} - T_{l}\\right)} c_{H}</script></html>"
      ],
      "text/plain": [
       "H_l == -(T_a - T_l)*c_H"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{R_{ll}} = -{\\left(3 \\, T_{a}^{4} - 4 \\, T_{a}^{3} T_{l} + T_{w}^{4}\\right)} {a_{sh}} \\epsilon_{l} {\\sigma}</script></html>"
      ],
      "text/plain": [
       "R_ll == -(3*T_a^4 - 4*T_a^3*T_l + T_w^4)*a_sh*epsilon_l*sigm"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\gamma_{v} = \\frac{c_{H}}{c_{E}}</script></html>"
      ],
      "text/plain": [
       "gamma_v == c_H/c_E"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{\\Delta_{eTa}} = \\frac{{P_{was}} - {P_{wl}}}{T_{a} - T_{l}}</script></html>"
      ],
      "text/plain": [
       "Delta_eTa == (P_was - P_wl)/(T_a - T_l)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{P_{wa}} c_{H} - {P_{wl}} c_{H}}{\\gamma_{v}}</script></html>"
      ],
      "text/plain": [
       "E_l == -(P_wa*c_H - P_wl*c_H)/gamma_v"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kilogram/second^3 == kilogram/second^3\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\left({\\Delta_{eTa}} T_{a} - {\\Delta_{eTa}} T_{l} + {P_{wa}} - {P_{was}}\\right)} c_{H}}{\\gamma_{v}}</script></html>"
      ],
      "text/plain": [
       "E_l == -(Delta_eTa*T_a - Delta_eTa*T_l + P_wa - P_was)*c_H/gamma_v"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kilogram/second^3 == kilogram/second^3\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\left({\\Delta_{eTa}} T_{a}^{4} - {\\Delta_{eTa}} T_{w}^{4} + 4 \\, {\\left({P_{wa}} - {P_{was}}\\right)} T_{a}^{3}\\right)} {a_{sh}} c_{E} \\epsilon_{l} {\\sigma} - {\\Delta_{eTa}} R_{s} c_{E} + {\\left({P_{wa}} - {P_{was}}\\right)} c_{E} c_{H}}{4 \\, T_{a}^{3} {a_{sh}} \\epsilon_{l} {\\sigma} + {\\Delta_{eTa}} c_{E} + c_{H}}</script></html>"
      ],
      "text/plain": [
       "E_l == -((Delta_eTa*T_a^4 - Delta_eTa*T_w^4 + 4*(P_wa - P_was)*T_a^3)*a_sh*c_E*epsilon_l*sigm - Delta_eTa*R_s*c_E + (P_wa - P_was)*c_E*c_H)/(4*T_a^3*a_sh*epsilon_l*sigm + Delta_eTa*c_E + c_H)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kilogram/second^3 == kilogram/second^3\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}H_{l} = -\\frac{{\\left(T_{a}^{4} - T_{w}^{4}\\right)} {a_{sh}} c_{H} \\epsilon_{l} {\\sigma} - {\\left({\\left({P_{wa}} - {P_{was}}\\right)} c_{E} + R_{s}\\right)} c_{H}}{4 \\, T_{a}^{3} {a_{sh}} \\epsilon_{l} {\\sigma} + {\\Delta_{eTa}} c_{E} + c_{H}}</script></html>"
      ],
      "text/plain": [
       "H_l == -((T_a^4 - T_w^4)*a_sh*c_H*epsilon_l*sigm - ((P_wa - P_was)*c_E + R_s)*c_H)/(4*T_a^3*a_sh*epsilon_l*sigm + Delta_eTa*c_E + c_H)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kilogram/second^3 == kilogram/second^3\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}T_{l} = \\frac{{\\left(3 \\, T_{a}^{4} + T_{w}^{4}\\right)} {a_{sh}} \\epsilon_{l} {\\sigma} + {\\left({\\Delta_{eTa}} T_{a} + {P_{wa}} - {P_{was}}\\right)} c_{E} + T_{a} c_{H} + R_{s}}{4 \\, T_{a}^{3} {a_{sh}} \\epsilon_{l} {\\sigma} + {\\Delta_{eTa}} c_{E} + c_{H}}</script></html>"
      ],
      "text/plain": [
       "T_l == ((3*T_a^4 + T_w^4)*a_sh*epsilon_l*sigm + (Delta_eTa*T_a + P_wa - P_was)*c_E + T_a*c_H + R_s)/(4*T_a^3*a_sh*epsilon_l*sigm + Delta_eTa*c_E + c_H)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kelvin == kelvin\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}T_{l} = \\frac{{\\left(3 \\, T_{a}^{4} + T_{w}^{4}\\right)} {a_{sh}} \\epsilon_{l} {\\sigma} + {\\left({\\Delta_{eTa}} T_{a} + {P_{wa}} - {P_{was}}\\right)} c_{E} + T_{a} c_{H} + R_{s}}{4 \\, T_{a}^{3} {a_{sh}} \\epsilon_{l} {\\sigma} + {\\Delta_{eTa}} c_{E} + c_{H}}</script></html>"
      ],
      "text/plain": [
       "T_l == ((3*T_a^4 + T_w^4)*a_sh*epsilon_l*sigm + (Delta_eTa*T_a + P_wa - P_was)*c_E + T_a*c_H + R_s)/(4*T_a^3*a_sh*epsilon_l*sigm + Delta_eTa*c_E + c_H)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\left({\\Delta_{eTa}} T_{a}^{4} - {\\Delta_{eTa}} T_{w}^{4} + 4 \\, {\\left({P_{wa}} - {P_{was}}\\right)} T_{a}^{3}\\right)} {a_{sh}} c_{E} \\epsilon_{l} {\\sigma} - {\\Delta_{eTa}} R_{s} c_{E} + {\\left({P_{wa}} - {P_{was}}\\right)} c_{E} c_{H}}{4 \\, T_{a}^{3} {a_{sh}} \\epsilon_{l} {\\sigma} + {\\Delta_{eTa}} c_{E} + c_{H}}</script></html>"
      ],
      "text/plain": [
       "E_l == -((Delta_eTa*T_a^4 - Delta_eTa*T_w^4 + 4*(P_wa - P_was)*T_a^3)*a_sh*c_E*epsilon_l*sigm - Delta_eTa*R_s*c_E + (P_wa - P_was)*c_E*c_H)/(4*T_a^3*a_sh*epsilon_l*sigm + Delta_eTa*c_E + c_H)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{{\\left(M_{w} {P_{wa}} T_{w}^{4} + {\\left(3 \\, M_{w} {P_{wa}} - 4 \\, M_{w} {P_{wl}}\\right)} T_{a}^{4}\\right)} {a_{sh}} \\epsilon_{l} {g_{tw}} \\lambda_{E} {\\sigma} + {\\left(M_{w} {P_{wa}} R_{s} + {\\left(M_{w} {P_{wa}} - M_{w} {P_{wl}}\\right)} T_{a} c_{H} + {\\left(M_{w} {P_{wa}}^{2} - M_{w} {P_{wa}} {P_{was}} + {\\left({\\Delta_{eTa}} M_{w} {P_{wa}} - {\\Delta_{eTa}} M_{w} {P_{wl}}\\right)} T_{a}\\right)} c_{E}\\right)} {g_{tw}} \\lambda_{E}}{{R_{mol}} T_{a}^{2} c_{H} + {\\left(3 \\, {R_{mol}} T_{a}^{5} + {R_{mol}} T_{a} T_{w}^{4}\\right)} {a_{sh}} \\epsilon_{l} {\\sigma} + {R_{mol}} R_{s} T_{a} + {\\left({\\Delta_{eTa}} {R_{mol}} T_{a}^{2} + {\\left({P_{wa}} - {P_{was}}\\right)} {R_{mol}} T_{a}\\right)} c_{E}}</script></html>"
      ],
      "text/plain": [
       "E_l == -((M_w*P_wa*T_w^4 + (3*M_w*P_wa - 4*M_w*P_wl)*T_a^4)*a_sh*epsilon_l*g_tw*lambda_E*sigm + (M_w*P_wa*R_s + (M_w*P_wa - M_w*P_wl)*T_a*c_H + (M_w*P_wa^2 - M_w*P_wa*P_was + (Delta_eTa*M_w*P_wa - Delta_eTa*M_w*P_wl)*T_a)*c_E)*g_tw*lambda_E)/(R_mol*T_a^2*c_H + (3*R_mol*T_a^5 + R_mol*T_a*T_w^4)*a_sh*epsilon_l*sigm + R_mol*R_s*T_a + (Delta_eTa*R_mol*T_a^2 + (P_wa - P_was)*R_mol*T_a)*c_E)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}H_{l} = -\\frac{{\\left(T_{a}^{4} - T_{w}^{4}\\right)} {a_{sh}} c_{H} \\epsilon_{l} {\\sigma} - {\\left({\\left({P_{wa}} - {P_{was}}\\right)} c_{E} + R_{s}\\right)} c_{H}}{4 \\, T_{a}^{3} {a_{sh}} \\epsilon_{l} {\\sigma} + {\\Delta_{eTa}} c_{E} + c_{H}}</script></html>"
      ],
      "text/plain": [
       "H_l == -((T_a^4 - T_w^4)*a_sh*c_H*epsilon_l*sigm - ((P_wa - P_was)*c_E + R_s)*c_H)/(4*T_a^3*a_sh*epsilon_l*sigm + Delta_eTa*c_E + c_H)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{R_{ll}} = \\frac{{\\left(4 \\, R_{s} T_{a}^{3} {a_{sh}} + {\\left({\\Delta_{eTa}} T_{a}^{4} - {\\Delta_{eTa}} T_{w}^{4} + 4 \\, {\\left({P_{wa}} - {P_{was}}\\right)} T_{a}^{3}\\right)} {a_{sh}} c_{E} + {\\left(T_{a}^{4} - T_{w}^{4}\\right)} {a_{sh}} c_{H}\\right)} \\epsilon_{l} {\\sigma}}{4 \\, T_{a}^{3} {a_{sh}} \\epsilon_{l} {\\sigma} + {\\Delta_{eTa}} c_{E} + c_{H}}</script></html>"
      ],
      "text/plain": [
       "R_ll == (4*R_s*T_a^3*a_sh + (Delta_eTa*T_a^4 - Delta_eTa*T_w^4 + 4*(P_wa - P_was)*T_a^3)*a_sh*c_E + (T_a^4 - T_w^4)*a_sh*c_H)*epsilon_l*sigm/(4*T_a^3*a_sh*epsilon_l*sigm + Delta_eTa*c_E + c_H)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T_l = 308.321395271\n",
      "E_l = 180.542235053941\n",
      "H_l = 150.521099595469\n",
      "R_ll = 68.9366653505872\n",
      "g_bw = 0.0131620455576424\n",
      "g_tw = 0.00291849206962754\n",
      "Direct estimates: \n",
      "E_l = 177.353891811830\n",
      "H_l = 153.963492920038\n",
      "T_l = 308.443094724766\n",
      "R_ll = 68.6826152681320\n",
      "308.443094724766 == 308.443094724766\n",
      "308.443094724766 == 308.443094724766\n",
      "308.443094724766 == 307.807922524752\n",
      "400 == 400.000000000000\n",
      "Using T_l from eq_Tl_Delta_Rlllin.rhs() only to calculate R_ll: \n",
      "T_l = 308.443094724766\n",
      "R_ll = 70.5556017512511\n",
      "E_l = 176.784812127415\n",
      "H_l = 152.659586121334\n",
      "400 == 400.000000000000\n",
      "Penman-stomata: \n",
      "E_l = 198.222104889662\n",
      "H_l = 201.777895110338\n",
      "T_l = 310.133484539870\n",
      "400 == 400.000000000000\n",
      "PM-equation: \n",
      "E_l = 241.448619283973\n",
      "H_l = 158.551380716027\n",
      "400 == 400.000000000000\n",
      "MU-equation: \n",
      "E_l = 156.668183937778\n",
      "H_l = 243.331816062222\n",
      "400 == 400.000000000000\n",
      "Corrected MU-equation: \n",
      "E_l = 195.741933442269\n",
      "H_l = 204.258066557731\n",
      "400 == 400.000000000000\n",
      "imported file temp/E_PM_eqs\n",
      "jupyter nbconvert  --to=python 'stomatal_cond_eqs.ipynb'\n",
      "Exporting specified worksheet to .py file...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "nbconvert returned 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking if specified ipynb file was run with sage kernel...\n",
      "Renaming .py file to .sage if notebook kernel was sage (to avoid exponent error)\n",
      "created file temp/stomatal_cond_eqs\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{k_{dv}} = \\frac{{D_{va}}}{V_{m}}</script></html>"
      ],
      "text/plain": [
       "k_dv == D_va/V_m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mole/(meter*second) == mole/(meter*second)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}g_{\\mathit{sp}} = \\frac{A_{p} {k_{dv}} n_{p}}{d_{p}}</script></html>"
      ],
      "text/plain": [
       "g_sp == A_p*k_dv*n_p/d_p"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mole/(meter^2*second) == mole/(meter^2*second)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}A_{p} = \\pi r_{p}^{2}</script></html>"
      ],
      "text/plain": [
       "A_p == pi*r_p^2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "meter^2 == meter^2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}r_{\\mathit{sp}} = \\frac{d_{p}}{A_{p} {k_{dv}} n_{p}}</script></html>"
      ],
      "text/plain": [
       "r_sp == d_p/(A_p*k_dv*n_p)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "meter^2*second/mole == meter^2*second/mole\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}r_{\\mathit{sp}} = \\frac{d_{p}}{\\pi {k_{dv}} n_{p} r_{p}^{2}}</script></html>"
      ],
      "text/plain": [
       "r_sp == d_p/(pi*k_dv*n_p*r_p^2)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "meter^2*second/mole == meter^2*second/mole\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}r_{\\mathit{vs}} = \\frac{\\frac{1}{r_{p}} - \\frac{4}{\\pi s_{p}}}{4 \\, {k_{dv}} n_{p}}</script></html>"
      ],
      "text/plain": [
       "r_vs == 1/4*(1/r_p - 4/(pi*s_p))/(k_dv*n_p)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}r_{\\mathit{vs}} = \\frac{\\frac{1}{r_{p}} - \\frac{4}{\\pi s_{p}}}{4 \\, {k_{dv}} n_{p}}</script></html>"
      ],
      "text/plain": [
       "r_vs == 1/4*(1/r_p - 4/(pi*s_p))/(k_dv*n_p)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}s_{p} = \\frac{1}{\\sqrt{n_{p}}}</script></html>"
      ],
      "text/plain": [
       "s_p == 1/sqrt(n_p)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}r_{\\mathit{end}} = \\frac{1}{4 \\, {k_{dv}} n_{p} r_{p}}</script></html>"
      ],
      "text/plain": [
       "r_end == 1/4/(k_dv*n_p*r_p)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}r_{\\mathit{end}} = \\frac{\\log\\left(\\frac{2 \\, l_{p}}{r_{p}}\\right)}{\\pi {k_{dv}} l_{p} n_{p}}</script></html>"
      ],
      "text/plain": [
       "r_end == log(2*l_p/r_p)/(pi*k_dv*l_p*n_p)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{g_{sw,mol}} = \\frac{1}{r_{\\mathit{end}} + r_{\\mathit{sp}} + r_{\\mathit{vs}}}</script></html>"
      ],
      "text/plain": [
       "g_swmol == (1/(r_end + r_sp + r_vs))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}n_{p} = \\frac{1}{s_{p}^{2}}</script></html>"
      ],
      "text/plain": [
       "n_p == s_p^(-2)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{r_{bw,mol}} = \\frac{B_{l}}{{k_{dv}}}</script></html>"
      ],
      "text/plain": [
       "r_bwmol == B_l/k_dv"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\frac{2 \\, \\pi B_{l} r_{p}^{2}}{2 \\, \\pi B_{l} r_{p}^{2} + {\\left(\\pi r_{p} + 2 \\, d_{p}\\right)} s_{p}^{2}}</script></html>"
      ],
      "text/plain": [
       "2*pi*B_l*r_p^2/(2*pi*B_l*r_p^2 + (pi*r_p + 2*d_p)*s_p^2)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\frac{2}{\\frac{s_{p}^{2} {\\left(\\frac{1}{r_{p}} + \\frac{2 \\, d_{p}}{\\pi r_{p}^{2}}\\right)}}{B_{l}} + 2}</script></html>"
      ],
      "text/plain": [
       "2/(s_p^2*(1/r_p + 2*d_p/(pi*r_p^2))/B_l + 2)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}A_{p} = \\pi r_{p}^{2}</script></html>"
      ],
      "text/plain": [
       "A_p == pi*r_p^2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}r_{p} = \\sqrt{\\frac{A_{p}}{\\pi}}</script></html>"
      ],
      "text/plain": [
       "r_p == sqrt(A_p/pi)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{g_{sw}} = -\\frac{{\\left({P_{wa}} - {P_{wl}}\\right)} {R_{mol}} T_{a} T_{l} {g_{sw,mol}}}{P_{a} {P_{wl}} T_{a} - P_{a} {P_{wa}} T_{l}}</script></html>"
      ],
      "text/plain": [
       "g_sw == -(P_wa - P_wl)*R_mol*T_a*T_l*g_swmol/(P_a*P_wl*T_a - P_a*P_wa*T_l)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{g_{sw}} = \\frac{{R_{mol}} T_{a} {g_{sw,mol}}}{P_{a}}</script></html>"
      ],
      "text/plain": [
       "g_sw == R_mol*T_a*g_swmol/P_a"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "imported file temp/stomatal_cond_eqs\n",
      "jupyter nbconvert  --to=python 'leaf_chamber_eqs.ipynb'\n",
      "Exporting specified worksheet to .py file...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "nbconvert returned 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking if specified ipynb file was run with sage kernel...\n",
      "Renaming .py file to .sage if notebook kernel was sage (to avoid exponent error)\n",
      "created file temp/leaf_chamber_eqs\n",
      "C_wa            0.647207041733317\n",
      "C_wl            1.12479267904924\n",
      "D_va            0.0000248765000000000\n",
      "E_l             142.258640360008\n",
      "E_lmol          0.00322581950929723\n",
      "H_l             -112.722448184652\n",
      "L_l             0.0300000000000000\n",
      "Le              0.888469037042992\n",
      "M_N2            0.0280000000000000\n",
      "M_O2            0.0320000000000000\n",
      "M_w             0.0180000000000000\n",
      "Nu              26.1863624980041\n",
      "P_a             101325         \n",
      "P_wa            1606.28367076831\n",
      "P_wl            2768.40610422238\n",
      "Pr              0.710000000000000\n",
      "R_ll            -29.5361921753575\n",
      "R_mol           8.31447200000000\n",
      "R_s             0.000000000000000\n",
      "Re              1927.40122068744\n",
      "Re_c            3000           \n",
      "T_a             298.500000000000\n",
      "T_l             296.021082253  \n",
      "T_w             298.500000000000\n",
      "a_s             1.00000000000000\n",
      "a_sh            2              \n",
      "alpha_a         0.0000221020000000000\n",
      "c_pa            1010           \n",
      "epsilon_l       1              \n",
      "g               9.81000000000000\n",
      "g_bw            0.0208112438130012\n",
      "g_sw            0.0100000000000000\n",
      "g_tw            0.00675443157676732\n",
      "h_c             22.7362219510171\n",
      "k_a             0.0260474000000000\n",
      "lambda_E        2.45000000000000e6\n",
      "nu_a            0.0000155650000000000\n",
      "rho_a           1.17040820486688\n",
      "sigm            5.67000000000000e-8\n",
      "v_w             1              \n",
      "Volume = 0.310000000000000 l\n",
      "min flow rate for flushing = 1.55000000000000e-11 m3/s\n",
      "min flow rate for flushing = 0.930000000000000 l/min\n",
      "flow rate for 1 m/s direct wind = 0.00150000000000000 m3/s\n",
      "flow rate for 1 m/s direct wind = 90.0000000000000 l/m\n",
      "flow rate for 5 m/s direct wind = 0.00750000000000000 m3/s\n",
      "flow rate for 5 m/s direct wind = 450.000000000000 l/m\n",
      "Volume = 0.400000000000000 l\n",
      "min flow rate for flushing = 2.00000000000000e-11 m3/s\n",
      "min flow rate for flushing = 1.20000000000000 l/min\n",
      "flow rate for 1 m/s direct wind = 0.00150000000000000 m3/s\n",
      "flow rate for 1 m/s direct wind = 90.0000000000000 l/m\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}Q_{i} = \\frac{A_{i} \\mathit{dT}_{i} \\lambda_{i}}{L_{i}}</script></html>"
      ],
      "text/plain": [
       "Q_i == A_i*dT_i*lambda_i/L_i"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}L_{i} = \\frac{A_{i} \\mathit{dT}_{i} \\lambda_{i}}{Q_{i}}</script></html>"
      ],
      "text/plain": [
       "L_i == A_i*dT_i*lambda_i/Q_i"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}L_{i} {c_{pi}} \\mathit{dT}_{i} \\rho_{i}</script></html>"
      ],
      "text/plain": [
       "L_i*c_pi*dT_i*rho_i"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}A_{i} L_{i} \\mathit{dT}_{i} \\lambda_{i}</script></html>"
      ],
      "text/plain": [
       "A_i*L_i*dT_i*lambda_i"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "[R_d == S_s, R_lu == -S_a + S_s, R_ld == S_b, R_u == 0]\n",
      "]\n",
      "B_l == 0.0106559443973775\n",
      "B_l == 0.00119137080184970\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}V_{c} = H_{c} L_{c} W_{c}</script></html>"
      ],
      "text/plain": [
       "V_c == H_c*L_c*W_c"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "meter^3 == meter^3\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{in,mol,a}} = \\frac{{F_{in,v}} {\\left(P_{a} - {P_{w,in}}\\right)}}{{R_{mol}} {T_{in}}}</script></html>"
      ],
      "text/plain": [
       "F_in_mola == F_in_v*(P_a - P_w_in)/(R_mol*T_in)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mole/second == mole/second\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{in,mol,w}} = \\frac{{F_{in,v}} {P_{w,in}}}{{R_{mol}} {T_{in}}}</script></html>"
      ],
      "text/plain": [
       "F_in_molw == F_in_v*P_w_in/(R_mol*T_in)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mole/second == mole/second\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{out,mol,a}} = \\frac{{F_{out,v}} {\\left(P_{a} - {P_{w,out}}\\right)}}{{R_{mol}} {T_{out}}}</script></html>"
      ],
      "text/plain": [
       "F_out_mola == F_out_v*(P_a - P_w_out)/(R_mol*T_out)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mole/second == mole/second\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{out,mol,w}} = \\frac{{F_{out,v}} {P_{w,out}}}{{R_{mol}} {T_{out}}}</script></html>"
      ],
      "text/plain": [
       "F_out_molw == F_out_v*P_w_out/(R_mol*T_out)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mole/second == mole/second\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{out,v}} = \\frac{{\\left({F_{out,mol,a}} + {F_{out,mol,w}}\\right)} {R_{mol}} {T_{out}}}{P_{a}}</script></html>"
      ],
      "text/plain": [
       "F_out_v == (F_out_mola + F_out_molw)*R_mol*T_out/P_a"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "meter^3/second == meter^3/second\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{out,v}} = \\frac{{E_{l,mol}} L_{A} {R_{mol}} {T_{in}} {T_{out}} + {F_{in,v}} P_{a} {T_{out}}}{P_{a} {T_{in}}}</script></html>"
      ],
      "text/plain": [
       "F_out_v == (E_lmol*L_A*R_mol*T_in*T_out + F_in_v*P_a*T_out)/(P_a*T_in)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{out,mol,w}} = {E_{l,mol}} L_{A} + {F_{in,mol,w}}</script></html>"
      ],
      "text/plain": [
       "F_out_molw == E_lmol*L_A + F_in_molw"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{out,v}} = \\frac{{\\left({F_{out,mol,a}} + {F_{out,mol,w}}\\right)} {R_{mol}} {T_{out}}}{P_{a}}</script></html>"
      ],
      "text/plain": [
       "F_out_v == (F_out_mola + F_out_molw)*R_mol*T_out/P_a"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}0 = H_{l} L_{A} + {\\left({F_{in,mol,a}} {M_{air}} {c_{pa}} + {F_{in,mol,w}} M_{w} {c_{pv}}\\right)} {T_{in}} - {\\left({F_{out,mol,a}} {M_{air}} {c_{pa}} + {F_{out,mol,w}} M_{w} {c_{pv}}\\right)} {T_{out}} + {Q_{in}}</script></html>"
      ],
      "text/plain": [
       "0 == H_l*L_A + (F_in_mola*M_air*c_pa + F_in_molw*M_w*c_pv)*T_in - (F_out_mola*M_air*c_pa + F_out_molw*M_w*c_pv)*T_out + Q_in"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}H_{l} = {E_{l,mol}} M_{w} {T_{out}} {c_{pv}} - \\frac{{F_{in,mol,a}} {M_{air}} {T_{in}} {c_{pa}}}{L_{A}} + \\frac{{F_{in,mol,a}} {M_{air}} {T_{out}} {c_{pa}}}{L_{A}} - \\frac{{F_{in,mol,w}} M_{w} {T_{in}} {c_{pv}}}{L_{A}} + \\frac{{F_{in,mol,w}} M_{w} {T_{out}} {c_{pv}}}{L_{A}} - \\frac{{Q_{in}}}{L_{A}}</script></html>"
      ],
      "text/plain": [
       "H_l == E_lmol*M_w*T_out*c_pv - F_in_mola*M_air*T_in*c_pa/L_A + F_in_mola*M_air*T_out*c_pa/L_A - F_in_molw*M_w*T_in*c_pv/L_A + F_in_molw*M_w*T_out*c_pv/L_A - Q_in/L_A"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kilogram/second^3 == kilogram/second^3\n",
      "[\n",
      "T_out == (F_in_mola*M_air*T_in*c_pa + F_in_molw*M_w*T_in*c_pv + H_l*L_A + Q_in)/(E_lmol*L_A*M_w*c_pv + F_in_mola*M_air*c_pa + F_in_molw*M_w*c_pv)\n",
      "]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{T_{out}} = \\frac{{F_{in,mol,a}} {M_{air}} {T_{in}} {c_{pa}} + {F_{in,mol,w}} M_{w} {T_{in}} {c_{pv}} + H_{l} L_{A} + {Q_{in}}}{{E_{l,mol}} L_{A} M_{w} {c_{pv}} + {F_{in,mol,a}} {M_{air}} {c_{pa}} + {F_{in,mol,w}} M_{w} {c_{pv}}}</script></html>"
      ],
      "text/plain": [
       "T_out == (F_in_mola*M_air*T_in*c_pa + F_in_molw*M_w*T_in*c_pv + H_l*L_A + Q_in)/(E_lmol*L_A*M_w*c_pv + F_in_mola*M_air*c_pa + F_in_molw*M_w*c_pv)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T_out == (F_in_v*M_w*P_w_in*T_in*c_pv + H_l*L_A*R_mol*T_in + Q_in*R_mol*T_in + (F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*T_in*c_pa)/(E_lmol*L_A*M_w*R_mol*T_in*c_pv + F_in_v*M_w*P_w_in*c_pv + (F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*c_pa)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{T_{out}} = \\frac{{F_{in,v}} M_{w} {P_{w,in}} {T_{in}} {c_{pv}} + H_{l} L_{A} {R_{mol}} {T_{in}} + {Q_{in}} {R_{mol}} {T_{in}} + {\\left({F_{in,v}} {M_{air}} P_{a} - {F_{in,v}} {M_{air}} {P_{w,in}}\\right)} {T_{in}} {c_{pa}}}{{E_{l,mol}} L_{A} M_{w} {R_{mol}} {T_{in}} {c_{pv}} + {F_{in,v}} M_{w} {P_{w,in}} {c_{pv}} + {\\left({F_{in,v}} {M_{air}} P_{a} - {F_{in,v}} {M_{air}} {P_{w,in}}\\right)} {c_{pa}}}</script></html>"
      ],
      "text/plain": [
       "T_out == (F_in_v*M_w*P_w_in*T_in*c_pv + H_l*L_A*R_mol*T_in + Q_in*R_mol*T_in + (F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*T_in*c_pa)/(E_lmol*L_A*M_w*R_mol*T_in*c_pv + F_in_v*M_w*P_w_in*c_pv + (F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*c_pa)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{T_{out}} = \\frac{{F_{in,v}} M_{w} {P_{w,in}} {T_{in}} {c_{pv}} + H_{l} L_{A} {R_{mol}} {T_{in}} + {Q_{in}} {R_{mol}} {T_{in}} + {\\left({F_{in,v}} {M_{air}} P_{a} - {F_{in,v}} {M_{air}} {P_{w,in}}\\right)} {T_{in}} {c_{pa}}}{{E_{l,mol}} L_{A} M_{w} {R_{mol}} {T_{in}} {c_{pv}} + {F_{in,v}} M_{w} {P_{w,in}} {c_{pv}} + {\\left({F_{in,v}} {M_{air}} P_{a} - {F_{in,v}} {M_{air}} {P_{w,in}}\\right)} {c_{pa}}}</script></html>"
      ],
      "text/plain": [
       "T_out == (F_in_v*M_w*P_w_in*T_in*c_pv + H_l*L_A*R_mol*T_in + Q_in*R_mol*T_in + (F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*T_in*c_pa)/(E_lmol*L_A*M_w*R_mol*T_in*c_pv + F_in_v*M_w*P_w_in*c_pv + (F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*c_pa)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{out,mol,a}} + {F_{out,mol,w}} = \\frac{{F_{out,v}} P_{a}}{{R_{mol}} {T_{out}}}</script></html>"
      ],
      "text/plain": [
       "F_out_mola + F_out_molw == F_out_v*P_a/(R_mol*T_out)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{E_{l,mol}} L_{A} + \\frac{{F_{in,v}} {\\left(P_{a} - {P_{w,in}}\\right)}}{{R_{mol}} {T_{in}}} + \\frac{{F_{in,v}} {P_{w,in}}}{{R_{mol}} {T_{in}}} = \\frac{{F_{out,v}} P_{a}}{{R_{mol}} {T_{out}}}</script></html>"
      ],
      "text/plain": [
       "E_lmol*L_A + F_in_v*(P_a - P_w_in)/(R_mol*T_in) + F_in_v*P_w_in/(R_mol*T_in) == F_out_v*P_a/(R_mol*T_out)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "F_out_v == (E_lmol*L_A*R_mol*T_in*T_out + F_in_v*P_a*T_out)/(P_a*T_in)\n",
      "]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{out,v}} = \\frac{{E_{l,mol}} L_{A} {R_{mol}} {T_{in}} {T_{out}} + {F_{in,v}} P_{a} {T_{out}}}{P_{a} {T_{in}}}</script></html>"
      ],
      "text/plain": [
       "F_out_v == (E_lmol*L_A*R_mol*T_in*T_out + F_in_v*P_a*T_out)/(P_a*T_in)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{out,v}} = \\frac{{E_{l,mol}} L_{A} {R_{mol}} {T_{in}} {T_{out}} + {F_{in,v}} P_{a} {T_{out}}}{P_{a} {T_{in}}}</script></html>"
      ],
      "text/plain": [
       "F_out_v == (E_lmol*L_A*R_mol*T_in*T_out + F_in_v*P_a*T_out)/(P_a*T_in)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}0 = H_{l} L_{A} + {\\left({F_{in,mol,a}} {M_{air}} {c_{pa}} + {F_{in,mol,w}} M_{w} {c_{pv}}\\right)} {T_{in}} - {\\left({F_{out,mol,a}} {M_{air}} {c_{pa}} + {F_{out,mol,w}} M_{w} {c_{pv}}\\right)} {T_{out}} + {Q_{in}}</script></html>"
      ],
      "text/plain": [
       "0 == H_l*L_A + (F_in_mola*M_air*c_pa + F_in_molw*M_w*c_pv)*T_in - (F_out_mola*M_air*c_pa + F_out_molw*M_w*c_pv)*T_out + Q_in"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "T_in == (F_in_v*M_w*P_w_in*T_out*c_pv + (F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*T_out*c_pa)/(F_in_v*M_w*P_w_in*c_pv - (E_lmol*M_w*R_mol*T_out*c_pv - H_l*R_mol)*L_A + Q_in*R_mol + (F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*c_pa)\n",
      "]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{T_{in}} = \\frac{{F_{in,v}} M_{w} {P_{w,in}} {T_{out}} {c_{pv}} + {\\left({F_{in,v}} {M_{air}} P_{a} - {F_{in,v}} {M_{air}} {P_{w,in}}\\right)} {T_{out}} {c_{pa}}}{{F_{in,v}} M_{w} {P_{w,in}} {c_{pv}} - {\\left({E_{l,mol}} M_{w} {R_{mol}} {T_{out}} {c_{pv}} - H_{l} {R_{mol}}\\right)} L_{A} + {Q_{in}} {R_{mol}} + {\\left({F_{in,v}} {M_{air}} P_{a} - {F_{in,v}} {M_{air}} {P_{w,in}}\\right)} {c_{pa}}}</script></html>"
      ],
      "text/plain": [
       "T_in == (F_in_v*M_w*P_w_in*T_out*c_pv + (F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*T_out*c_pa)/(F_in_v*M_w*P_w_in*c_pv - (E_lmol*M_w*R_mol*T_out*c_pv - H_l*R_mol)*L_A + Q_in*R_mol + (F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*c_pa)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "Q_in == ((E_lmol*M_w*R_mol*T_in*T_out*c_pv - H_l*R_mol*T_in)*L_A - ((F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*T_in - (F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*T_out)*c_pa - (F_in_v*M_w*P_w_in*T_in - F_in_v*M_w*P_w_in*T_out)*c_pv)/(R_mol*T_in)\n",
      "]\n",
      "[\n",
      "H_l == (E_lmol*L_A*M_w*R_mol*T_in*T_out*c_pv - Q_in*R_mol*T_in - ((F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*T_in - (F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*T_out)*c_pa - (F_in_v*M_w*P_w_in*T_in - F_in_v*M_w*P_w_in*T_out)*c_pv)/(L_A*R_mol*T_in)\n",
      "]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}H_{l} = \\frac{{E_{l,mol}} L_{A} M_{w} {R_{mol}} {T_{in}} {T_{out}} {c_{pv}} - {Q_{in}} {R_{mol}} {T_{in}} - {\\left({\\left({F_{in,v}} {M_{air}} P_{a} - {F_{in,v}} {M_{air}} {P_{w,in}}\\right)} {T_{in}} - {\\left({F_{in,v}} {M_{air}} P_{a} - {F_{in,v}} {M_{air}} {P_{w,in}}\\right)} {T_{out}}\\right)} {c_{pa}} - {\\left({F_{in,v}} M_{w} {P_{w,in}} {T_{in}} - {F_{in,v}} M_{w} {P_{w,in}} {T_{out}}\\right)} {c_{pv}}}{L_{A} {R_{mol}} {T_{in}}}</script></html>"
      ],
      "text/plain": [
       "H_l == (E_lmol*L_A*M_w*R_mol*T_in*T_out*c_pv - Q_in*R_mol*T_in - ((F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*T_in - (F_in_v*M_air*P_a - F_in_v*M_air*P_w_in)*T_out)*c_pa - (F_in_v*M_w*P_w_in*T_in - F_in_v*M_w*P_w_in*T_out)*c_pv)/(L_A*R_mol*T_in)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "H_l == -((F_in_mola*M_air*T_in - F_in_mola*M_air*T_out)*c_pa + (F_in_molw*M_w*T_in - F_out_molw*M_w*T_out)*c_pv + Q_in)/L_A\n",
      "]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}H_{l} = -\\frac{{\\left({F_{in,mol,a}} {M_{air}} {T_{in}} - {F_{in,mol,a}} {M_{air}} {T_{out}}\\right)} {c_{pa}} + {\\left({F_{in,mol,w}} M_{w} {T_{in}} - {F_{out,mol,w}} M_{w} {T_{out}}\\right)} {c_{pv}} + {Q_{in}}}{L_{A}}</script></html>"
      ],
      "text/plain": [
       "H_l == -((F_in_mola*M_air*T_in - F_in_mola*M_air*T_out)*c_pa + (F_in_molw*M_w*T_in - F_out_molw*M_w*T_out)*c_pv + Q_in)/L_A"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[19.1756620432331, 35.0631107274011, 62.0367042507648, 106.473009443655, 177.667645607840, 288.831780737493, 458.305321164456, 710.999051614674, 1080.07052944457, 1608.82992645281, 2352.86266113812, 3382.34604242497, 4784.52773726516, 6666.32514525647, 9156.99713166349]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{in,mol,w}} = \\frac{{F_{in,v}} {P_{w,in}}}{{R_{mol}} {T_{in}}}</script></html>"
      ],
      "text/plain": [
       "F_in_molw == F_in_v*P_w_in/(R_mol*T_in)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{in,v}} = \\frac{{\\left({F_{in,mol,a}} + {F_{in,mol,w}}\\right)} {R_{mol}} {T_{in}}}{P_{a}}</script></html>"
      ],
      "text/plain": [
       "F_in_v == (F_in_mola + F_in_molw)*R_mol*T_in/P_a"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{in,mol,a}} = \\frac{{F_{in,v,a,n}} P_{r}}{{R_{mol}} T_{r}}</script></html>"
      ],
      "text/plain": [
       "F_in_mola == F_in_va_n*P_r/(R_mol*T_r)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mole/second == mole/second\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{in,mol,w}} = \\frac{{F_{in,v}} {P_{w,in}}}{{R_{mol}} {T_{in}}}</script></html>"
      ],
      "text/plain": [
       "F_in_molw == F_in_v*P_w_in/(R_mol*T_in)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mole/second == mole/second\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{in,v}} = \\frac{{F_{in,mol,a}} {R_{mol}} {T_{in}}}{P_{a} - {P_{w,in}}}</script></html>"
      ],
      "text/plain": [
       "F_in_v == F_in_mola*R_mol*T_in/(P_a - P_w_in)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "meter^3/second == meter^3/second\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{in,mol,w}} = \\frac{{F_{in,mol,a}} {P_{w,in}}}{P_{a} - {P_{w,in}}}</script></html>"
      ],
      "text/plain": [
       "F_in_molw == F_in_mola*P_w_in/(P_a - P_w_in)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mole/second == mole/second\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{in,v}} = \\frac{{F_{in,v,a,n}} P_{r} {T_{in}}}{{\\left(P_{a} - {P_{w,in}}\\right)} T_{r}}</script></html>"
      ],
      "text/plain": [
       "F_in_v == F_in_va_n*P_r*T_in/((P_a - P_w_in)*T_r)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "meter^3/second == meter^3/second\n",
      "1227.86016957787\n",
      "0.000166666666666667\n",
      "Volumentric flow at 0 oC: 0.000168711114309656 m3/s\n",
      "Volumentric flow at 25 oC: 0.000184152365848157 m3/s\n",
      "25oC/0oC: 1.09152480322167\n",
      "Volumentric flow at 25 oC without added vapour: 0.000181920800536946 m3/s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{out,v}} = \\frac{{E_{l,mol}} L_{A} {R_{mol}} {T_{in}} {T_{out}} + {F_{in,v}} P_{a} {T_{out}}}{P_{a} {T_{in}}}</script></html>"
      ],
      "text/plain": [
       "F_out_v == (E_lmol*L_A*R_mol*T_in*T_out + F_in_v*P_a*T_out)/(P_a*T_in)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{out,v}} = \\frac{{\\left({F_{out,mol,a}} + {F_{out,mol,w}}\\right)} {R_{mol}} {T_{out}}}{P_{a}}</script></html>"
      ],
      "text/plain": [
       "F_out_v == (F_out_mola + F_out_molw)*R_mol*T_out/P_a"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{in,mol,a}} = \\frac{{F_{in,v,a,n}} P_{r}}{{R_{mol}} T_{r}}</script></html>"
      ],
      "text/plain": [
       "F_in_mola == F_in_va_n*P_r/(R_mol*T_r)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{in,v}} = \\frac{{F_{in,v,a,n}} P_{r} {T_{in}}}{{\\left(P_{a} - {P_{w,in}}\\right)} T_{r}}</script></html>"
      ],
      "text/plain": [
       "F_in_v == F_in_va_n*P_r*T_in/((P_a - P_w_in)*T_r)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{in,mol,w}} = \\frac{{F_{in,v}} {P_{w,in}}}{{R_{mol}} {T_{in}}}</script></html>"
      ],
      "text/plain": [
       "F_in_molw == F_in_v*P_w_in/(R_mol*T_in)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{out,mol,w}} = \\frac{{F_{out,v}} {P_{w,out}}}{{R_{mol}} {T_{out}}}</script></html>"
      ],
      "text/plain": [
       "F_out_molw == F_out_v*P_w_out/(R_mol*T_out)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{out,mol,w}} = {E_{l,mol}} L_{A} + {F_{in,mol,w}}</script></html>"
      ],
      "text/plain": [
       "F_out_molw == E_lmol*L_A + F_in_molw"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}\\frac{{F_{out,v}} {P_{w,out}}}{{R_{mol}} {T_{out}}} = {E_{l,mol}} L_{A} + \\frac{{F_{in,v}} {P_{w,in}}}{{R_{mol}} {T_{in}}}</script></html>"
      ],
      "text/plain": [
       "F_out_v*P_w_out/(R_mol*T_out) == E_lmol*L_A + F_in_v*P_w_in/(R_mol*T_in)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{P_{w,out}} = \\frac{{E_{l,mol}} L_{A} P_{a} {R_{mol}} {T_{in}} + {F_{in,v}} P_{a} {P_{w,in}}}{{E_{l,mol}} L_{A} {R_{mol}} {T_{in}} + {F_{in,v}} P_{a}}</script></html>"
      ],
      "text/plain": [
       "P_w_out == (E_lmol*L_A*P_a*R_mol*T_in + F_in_v*P_a*P_w_in)/(E_lmol*L_A*R_mol*T_in + F_in_v*P_a)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kilogram/(meter*second^2) == kilogram/(meter*second^2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{P_{w,out}} = \\frac{{E_{l,mol}} L_{A} P_{a} {R_{mol}} {T_{in}} + {F_{in,v}} P_{a} {P_{w,in}}}{{E_{l,mol}} L_{A} {R_{mol}} {T_{in}} + {F_{in,v}} P_{a}}</script></html>"
      ],
      "text/plain": [
       "P_w_out == (E_lmol*L_A*P_a*R_mol*T_in + F_in_v*P_a*P_w_in)/(E_lmol*L_A*R_mol*T_in + F_in_v*P_a)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{P_{w,out}} = \\frac{{E_{l,mol}} L_{A} P_{a} {R_{mol}} {T_{in}} + {F_{in,v}} P_{a} {P_{w,in}}}{{E_{l,mol}} L_{A} {R_{mol}} {T_{in}} + {F_{in,v}} P_{a}}</script></html>"
      ],
      "text/plain": [
       "P_w_out == (E_lmol*L_A*P_a*R_mol*T_in + F_in_v*P_a*P_w_in)/(E_lmol*L_A*R_mol*T_in + F_in_v*P_a)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{P_{w,out}} = \\frac{{E_{l,mol}} L_{A} P_{a} {R_{mol}} {T_{in}} + {F_{in,v}} P_{a} {P_{w,in}}}{{E_{l,mol}} L_{A} {R_{mol}} {T_{in}} + {\\left({F_{in,mol,a}} + {F_{in,mol,w}}\\right)} {R_{mol}} {T_{in}}}</script></html>"
      ],
      "text/plain": [
       "P_w_out == (E_lmol*L_A*P_a*R_mol*T_in + F_in_v*P_a*P_w_in)/(E_lmol*L_A*R_mol*T_in + (F_in_mola + F_in_molw)*R_mol*T_in)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{P_{w,out}} = \\frac{{F_{in,v,a,n}} P_{a} P_{r} {P_{w,in}} + {\\left({E_{l,mol}} P_{a}^{2} - {E_{l,mol}} P_{a} {P_{w,in}}\\right)} L_{A} {R_{mol}} T_{r}}{{\\left({E_{l,mol}} P_{a} - {E_{l,mol}} {P_{w,in}}\\right)} L_{A} {R_{mol}} T_{r} + {F_{in,v,a,n}} P_{a} P_{r}}</script></html>"
      ],
      "text/plain": [
       "P_w_out == (F_in_va_n*P_a*P_r*P_w_in + (E_lmol*P_a^2 - E_lmol*P_a*P_w_in)*L_A*R_mol*T_r)/((E_lmol*P_a - E_lmol*P_w_in)*L_A*R_mol*T_r + F_in_va_n*P_a*P_r)"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{P_{w,out}} = \\frac{{E_{l,mol}} L_{A} {R_{mol}} {T_{in}} {T_{out}} + {F_{in,v}} {P_{w,in}} {T_{out}}}{{F_{out,v}} {T_{in}}}</script></html>"
      ],
      "text/plain": [
       "P_w_out == (E_lmol*L_A*R_mol*T_in*T_out + F_in_v*P_w_in*T_out)/(F_out_v*T_in)"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{out,v}} = \\frac{{E_{l,mol}} L_{A} {R_{mol}} {T_{in}} {T_{out}} + {F_{in,v}} P_{a} {T_{out}}}{P_{a} {T_{in}}}</script></html>"
      ],
      "text/plain": [
       "F_out_v == (E_lmol*L_A*R_mol*T_in*T_out + F_in_v*P_a*T_out)/(P_a*T_in)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{F_{in,mol,w}} = \\frac{{F_{in,v}} {P_{w,in}}}{{R_{mol}} {T_{in}}}</script></html>"
      ],
      "text/plain": [
       "F_in_molw == F_in_v*P_w_in/(R_mol*T_in)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{P_{w,out}} = \\frac{{\\left({E_{l,mol}} L_{A} {R_{mol}} {T_{in}} {T_{out}} + {F_{in,v}} {P_{w,in}} {T_{out}}\\right)} P_{a}}{{E_{l,mol}} L_{A} {R_{mol}} {T_{in}} {T_{out}} + {F_{in,v}} P_{a} {T_{out}}}</script></html>"
      ],
      "text/plain": [
       "P_w_out == (E_lmol*L_A*R_mol*T_in*T_out + F_in_v*P_w_in*T_out)*P_a/(E_lmol*L_A*R_mol*T_in*T_out + F_in_v*P_a*T_out)"
      ]
     },
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    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{P_{w,out}} = \\frac{{E_{l,mol}} L_{A} P_{a} {R_{mol}} {T_{in}} {T_{out}}}{{E_{l,mol}} L_{A} {R_{mol}} {T_{in}} {T_{out}} + {F_{in,v}} P_{a} {T_{out}}} + \\frac{{F_{in,v}} P_{a} {P_{w,in}} {T_{out}}}{{E_{l,mol}} L_{A} {R_{mol}} {T_{in}} {T_{out}} + {F_{in,v}} P_{a} {T_{out}}}</script></html>"
      ],
      "text/plain": [
       "P_w_out == E_lmol*L_A*P_a*R_mol*T_in*T_out/(E_lmol*L_A*R_mol*T_in*T_out + F_in_v*P_a*T_out) + F_in_v*P_a*P_w_in*T_out/(E_lmol*L_A*R_mol*T_in*T_out + F_in_v*P_a*T_out)"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{E_{l,mol}} = \\frac{E_{l}}{M_{w} \\lambda_{E}}</script></html>"
      ],
      "text/plain": [
       "E_lmol == E_l/(M_w*lambda_E)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mole/(meter^2*second) == mole/(meter^2*second)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{P_{w,in}} = -\\frac{{\\left({E_{l,mol}} P_{a} - {E_{l,mol}} {P_{w,out}}\\right)} L_{A} {R_{mol}} {T_{in}} - {F_{in,v}} P_{a} {P_{w,out}}}{{F_{in,v}} P_{a}}</script></html>"
      ],
      "text/plain": [
       "P_w_in == -((E_lmol*P_a - E_lmol*P_w_out)*L_A*R_mol*T_in - F_in_v*P_a*P_w_out)/(F_in_v*P_a)"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1227.86016957787\n",
      "0.000166666666666667\n",
      "Volumentric flow at 0 oC: 0.000168711114309656 m3/s\n",
      "Volumentric flow at 25 oC: 0.000184152365848157 m3/s\n",
      "25oC/0oC: 1.09152480322167\n",
      "Volumentric flow at 25 oC without added vapour: 0.000181920800536946 m3/s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}F_{s} = \\frac{L_{\\mathit{ls}} {\\left(\\sqrt{{\\left(\\frac{L_{l}}{L_{\\mathit{ls}}} + \\frac{L_{s}}{L_{\\mathit{ls}}}\\right)}^{2} + 4} - \\sqrt{{\\left(\\frac{L_{l}}{L_{\\mathit{ls}}} - \\frac{L_{s}}{L_{\\mathit{ls}}}\\right)}^{2} + 4}\\right)}}{2 \\, L_{s}}</script></html>"
      ],
      "text/plain": [
       "F_s == 1/2*L_ls*(sqrt((L_l/L_ls + L_s/L_ls)^2 + 4) - sqrt((L_l/L_ls - L_s/L_ls)^2 + 4))/L_s"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.821854415126695\n",
      "jupyter nbconvert  --to=python 'leaf_chamber_eqs.ipynb'\n",
      "Exporting specified worksheet to .py file...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "nbconvert returned 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking if specified ipynb file was run with sage kernel...\n",
      "Renaming .py file to .sage if notebook kernel was sage (to avoid exponent error)\n",
      "imported file temp/leaf_chamber_eqs\n",
      "jupyter nbconvert  --to=python 'leaf_chamber_data.ipynb'\n",
      "Exporting specified worksheet to .py file...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "nbconvert returned 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking if specified ipynb file was run with sage kernel...\n",
      "Renaming .py file to .sage if notebook kernel was sage (to avoid exponent error)\n",
      "created file temp/leaf_chamber_data\n",
      "E_l             180.542235053941\n",
      "H_l             150.521099595469\n",
      "T_l             308.321395271  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}H_{l} = -\\frac{{\\left(T_{a} - T_{l}\\right)} c_{\\mathit{pmol}}}{r_{\\mathit{bstar}}}</script></html>"
      ],
      "text/plain": [
       "H_l == -(T_a - T_l)*c_pmol/r_bstar"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kilogram/second^3 == kilogram/second^3\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}E_{l} = -\\frac{L_{E} {\\left({P_{wa}} - {P_{wl}}\\right)}}{P_{a} {\\left(r_{b} + r_{s}\\right)}}</script></html>"
      ],
      "text/plain": [
       "E_l == -L_E*(P_wa - P_wl)/(P_a*(r_b + r_s))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kilogram/second^3 == kilogram/second^3\n",
      "h_c(Ball): 21.8153792127944\n",
      "g_vmol(Ball): 0.110506932339455\n",
      "g_vmol(SS): g_twmol == 0.117381005752392\n",
      "T_l(Ball): 309.125596874492\n",
      "T_l(SS): 308.321395271\n",
      "T_a: 303\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{g_{sw}} = \\frac{{g_{bw}} {g_{tw}}}{{g_{bw}} - {g_{tw}}}</script></html>"
      ],
      "text/plain": [
       "g_sw == g_bw*g_tw/(g_bw - g_tw)"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}{g_{tw}} = \\frac{{E_{l,mol}} {R_{mol}} T_{a} T_{l}}{{P_{wl}} T_{a} - {P_{wa}} T_{l}}</script></html>"
      ],
      "text/plain": [
       "g_tw == E_lmol*R_mol*T_a*T_l/(P_wl*T_a - P_wa*T_l)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<html><script type=\"math/tex; mode=display\">\\newcommand{\\Bold}[1]{\\mathbf{#1}}h_{c} = -\\frac{H_{l}}{{\\left(T_{a} - T_{l}\\right)} {a_{sh}}}</script></html>"
      ],
      "text/plain": [
       "h_c == -H_l/((T_a - T_l)*a_sh)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div class=\"notruncate\">\n",
       "<table  class=\"table_form\">\n",
       "<tbody>\n",
       "<tr>\n",
       "<th>lc_time</th>\n",
       "<th>T_in1</th>\n",
       "<th>T_in2</th>\n",
       "<th>T_wall_in</th>\n",
       "<th>T_wall_out</th>\n",
       "<th>T_outside</th>\n",
       "<th>T_outside_PT</th>\n",
       "<th>T_chamber</th>\n",
       "<th>T_chamber1</th>\n",
       "<th>T_leaf1</th>\n",
       "<th>T_leaf2</th>\n",
       "<th>T_leaf_in</th>\n",
       "<th>Wind</th>\n",
       "<th>T_dew</th>\n",
       "<th>Air_inflow</th>\n",
       "<th>Air_outflow</th>\n",
       "<th>nr_time</th>\n",
       "<th>T_leaf_IR</th>\n",
       "<th>V_leaf_IR</th>\n",
       "<th>Rn_above_leaf</th>\n",
       "<th>Rn_below_leaf</th>\n",
       "<th>Rn_beside_leaf</th>\n",
       "<th>RnV_above_leaf</th>\n",
       "<th>RnV_below_leaf</th>\n",
       "<th>RnV_beside_leaf</th>\n",
       "<th>sens_timefirst</th>\n",
       "<th>sens_timelast</th>\n",
       "<th>water_flow_avg</th>\n",
       "<th>water_flow_std</th>\n",
       "<th>water_flow_n</th>\n",
       "<th>pow_timefirst</th>\n",
       "<th>pow_timelast</th>\n",
       "<th>fan_power_avg</th>\n",
       "<th>fan_power_std</th>\n",
       "<th>fan_power_n</th>\n",
       "<th>Comment...................................................</th>\n",
       "</tr>\n",
       "<tr class =\"row-a\">\n",
       "<td>TS</td>\n",
       "<td>K</td>\n",
       "<td>K</td>\n",
       "<td>K</td>\n",
       "<td>K</td>\n",
       "<td>K</td>\n",
       "<td>Deg C</td>\n",
       "<td>K</td>\n",
       "<td>K</td>\n",
       "<td>K</td>\n",
       "<td>K</td>\n",
       "<td>K</td>\n",
       "<td>m/s</td>\n",
       "<td>degC</td>\n",
       "<td>SLPM</td>\n",
       "<td>SLPM</td>\n",
       "<td>TS</td>\n",
       "<td>Deg C</td>\n",
       "<td>mV</td>\n",
       "<td>W/m^2</td>\n",
       "<td>W/m^2</td>\n",
       "<td>W/m^2</td>\n",
       "<td>mV</td>\n",
       "<td>mV</td>\n",
       "<td>mV</td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td>ul/min</td>\n",
       "<td>ul/min</td>\n",
       "<td>ul/min</td>\n",
       "<td>TS</td>\n",
       "<td>TS</td>\n",
       "<td>W</td>\n",
       "<td>W</td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "</tr>\n",
       "<tr class =\"row-b\">\n",
       "<td>2014-03-25 12:39:34</td>\n",
       "<td>23.98</td>\n",
       "<td>23.97</td>\n",
       "<td>22.56</td>\n",
       "<td>22.52</td>\n",
       "<td>22.50</td>\n",
       "<td>25.93</td>\n",
       "<td>22.58</td>\n",
       "<td>21.99</td>\n",
       "<td>19.92</td>\n",
       "<td>21.18</td>\n",
       "<td>16.53</td>\n",
       "<td>0.9999</td>\n",
       "<td>-19.32</td>\n",
       "<td>9.052</td>\n",
       "<td>4.451</td>\n",
       "<td>2014-03-25 12:39:35</td>\n",
       "<td>19.36</td>\n",
       "<td>-0.1045</td>\n",
       "<td>16.74</td>\n",
       "<td>-6.927</td>\n",
       "<td>-26.00</td>\n",
       "<td>-0.02004</td>\n",
       "<td>0.007208</td>\n",
       "<td>0.03366</td>\n",
       "<td>2014-03-25 12:39:21</td>\n",
       "<td>2014-03-25 12:39:28</td>\n",
       "<td>-8.233</td>\n",
       "<td>0.1577</td>\n",
       "<td>100</td>\n",
       "<td>2014-03-25 12:38:37</td>\n",
       "<td>2014-03-25 12:39:35</td>\n",
       "<td>0.06427</td>\n",
       "<td>0.00001266</td>\n",
       "<td>54</td>\n",
       "<td>Black leaf, silver perforated alu (35.4-1), varying Tdew</td>\n",
       "</tr>\n",
       "<tr class =\"row-a\">\n",
       "<td>2014-03-25 13:01:40</td>\n",
       "<td>23.97</td>\n",
       "<td>23.97</td>\n",
       "<td>22.57</td>\n",
       "<td>22.58</td>\n",
       "<td>22.54</td>\n",
       "<td>26.05</td>\n",
       "<td>22.58</td>\n",
       "<td>22.00</td>\n",
       "<td>20.00</td>\n",
       "<td>21.22</td>\n",
       "<td>16.68</td>\n",
       "<td>1.020</td>\n",
       "<td>-14.97</td>\n",
       "<td>8.490</td>\n",
       "<td>4.180</td>\n",
       "<td>2014-03-25 13:01:41</td>\n",
       "<td>19.36</td>\n",
       "<td>-0.1045</td>\n",
       "<td>16.25</td>\n",
       "<td>-6.859</td>\n",
       "<td>-25.52</td>\n",
       "<td>-0.01945</td>\n",
       "<td>0.007137</td>\n",
       "<td>0.03303</td>\n",
       "<td>2014-03-25 13:01:22</td>\n",
       "<td>2014-03-25 13:01:29</td>\n",
       "<td>-7.859</td>\n",
       "<td>0.3323</td>\n",
       "<td>100</td>\n",
       "<td>2014-03-25 13:00:39</td>\n",
       "<td>2014-03-25 13:01:38</td>\n",
       "<td>0.06428</td>\n",
       "<td>0.00002267</td>\n",
       "<td>55</td>\n",
       "<td>varying Tdew</td>\n",
       "</tr>\n",
       "<tr class =\"row-b\">\n",
       "<td>2014-03-25 14:07:31</td>\n",
       "<td>23.91</td>\n",
       "<td>23.91</td>\n",
       "<td>22.58</td>\n",
       "<td>22.59</td>\n",
       "<td>22.59</td>\n",
       "<td>26.10</td>\n",
       "<td>22.57</td>\n",
       "<td>22.04</td>\n",
       "<td>20.10</td>\n",
       "<td>21.26</td>\n",
       "<td>16.88</td>\n",
       "<td>1.021</td>\n",
       "<td>-10.08</td>\n",
       "<td>8.556</td>\n",
       "<td>4.293</td>\n",
       "<td>2014-03-25 14:07:31</td>\n",
       "<td>19.53</td>\n",
       "<td>-0.09854</td>\n",
       "<td>15.18</td>\n",
       "<td>-6.506</td>\n",
       "<td>-24.44</td>\n",
       "<td>-0.01817</td>\n",
       "<td>0.006771</td>\n",
       "<td>0.03163</td>\n",
       "<td>2014-03-25 14:07:11</td>\n",
       "<td>2014-03-25 14:07:18</td>\n",
       "<td>-7.807</td>\n",
       "<td>0.1403</td>\n",
       "<td>100</td>\n",
       "<td>2014-03-25 14:06:32</td>\n",
       "<td>2014-03-25 14:07:31</td>\n",
       "<td>0.06429</td>\n",
       "<td>0.00001291</td>\n",
       "<td>54</td>\n",
       "<td>varying Tdew</td>\n",
       "</tr>\n",
       "<tr class =\"row-a\">\n",
       "<td>2014-03-25 16:30:31</td>\n",
       "<td>24.25</td>\n",
       "<td>24.24</td>\n",
       "<td>22.76</td>\n",
       "<td>22.68</td>\n",
       "<td>22.67</td>\n",
       "<td>26.14</td>\n",
       "<td>22.77</td>\n",
       "<td>22.26</td>\n",
       "<td>20.43</td>\n",
       "<td>21.52</td>\n",
       "<td>17.38</td>\n",
       "<td>1.028</td>\n",
       "<td>-5.017</td>\n",
       "<td>7.524</td>\n",
       "<td>3.821</td>\n",
       "<td>2014-03-25 16:30:34</td>\n",
       "<td>19.81</td>\n",
       "<td>-0.08857</td>\n",
       "<td>14.46</td>\n",
       "<td>-6.187</td>\n",
       "<td>-22.65</td>\n",
       "<td>-0.01730</td>\n",
       "<td>0.006439</td>\n",
       "<td>0.02931</td>\n",
       "<td>2014-03-25 16:30:18</td>\n",
       "<td>2014-03-25 16:30:25</td>\n",
       "<td>-7.440</td>\n",
       "<td>0.07817</td>\n",
       "<td>100</td>\n",
       "<td>2014-03-25 16:29:33</td>\n",
       "<td>2014-03-25 16:30:32</td>\n",
       "<td>0.06417</td>\n",
       "<td>0.00001307</td>\n",
       "<td>54</td>\n",
       "<td>varying Tdew</td>\n",
       "</tr>\n",
       "<tr class =\"row-b\">\n",
       "<td>2014-03-25 16:48:46</td>\n",
       "<td>24.28</td>\n",
       "<td>24.23</td>\n",
       "<td>22.73</td>\n",
       "<td>22.73</td>\n",
       "<td>22.72</td>\n",
       "<td>26.02</td>\n",
       "<td>22.75</td>\n",
       "<td>22.25</td>\n",
       "<td>20.57</td>\n",
       "<td>21.61</td>\n",
       "<td>17.81</td>\n",
       "<td>1.019</td>\n",
       "<td>0.08945</td>\n",
       "<td>6.557</td>\n",
       "<td>3.387</td>\n",
       "<td>2014-03-25 16:48:48</td>\n",
       "<td>19.92</td>\n",
       "<td>-0.08459</td>\n",
       "<td>13.34</td>\n",
       "<td>-5.753</td>\n",
       "<td>-20.79</td>\n",
       "<td>-0.01596</td>\n",
       "<td>0.005988</td>\n",
       "<td>0.02690</td>\n",
       "<td>2014-03-25 16:48:26</td>\n",
       "<td>2014-03-25 16:48:33</td>\n",
       "<td>-6.759</td>\n",
       "<td>0.2183</td>\n",
       "<td>100</td>\n",
       "<td>2014-03-25 16:47:46</td>\n",
       "<td>2014-03-25 16:48:45</td>\n",
       "<td>0.06423</td>\n",
       "<td>0.00002734</td>\n",
       "<td>55</td>\n",
       "<td>varying Tdew</td>\n",
       "</tr>\n",
       "<tr class =\"row-a\">\n",
       "<td>2014-03-25 17:08:19</td>\n",
       "<td>24.24</td>\n",
       "<td>24.21</td>\n",
       "<td>22.75</td>\n",
       "<td>22.67</td>\n",
       "<td>22.68</td>\n",
       "<td>26.02</td>\n",
       "<td>22.74</td>\n",
       "<td>22.34</td>\n",
       "<td>20.86</td>\n",
       "<td>21.74</td>\n",
       "<td>18.41</td>\n",
       "<td>1.026</td>\n",
       "<td>5.120</td>\n",
       "<td>5.555</td>\n",
       "<td>2.939</td>\n",
       "<td>2014-03-25 17:08:22</td>\n",
       "<td>20.11</td>\n",
       "<td>-0.07734</td>\n",
       "<td>11.71</td>\n",
       "<td>-5.082</td>\n",
       "<td>-18.50</td>\n",
       "<td>-0.01401</td>\n",
       "<td>0.005290</td>\n",
       "<td>0.02393</td>\n",
       "<td>2014-03-25 17:08:08</td>\n",
       "<td>2014-03-25 17:08:15</td>\n",
       "<td>-5.926</td>\n",
       "<td>0.1492</td>\n",
       "<td>100</td>\n",
       "<td>2014-03-25 17:07:21</td>\n",
       "<td>2014-03-25 17:08:20</td>\n",
       "<td>0.06411</td>\n",
       "<td>0.00001511</td>\n",
       "<td>55</td>\n",
       "<td>varying Tdew</td>\n",
       "</tr>\n",
       "<tr class =\"row-b\">\n",
       "<td>2014-03-25 17:33:07</td>\n",
       "<td>24.16</td>\n",
       "<td>24.14</td>\n",
       "<td>22.79</td>\n",
       "<td>22.74</td>\n",
       "<td>22.69</td>\n",
       "<td>26.06</td>\n",
       "<td>22.80</td>\n",
       "<td>22.46</td>\n",
       "<td>21.25</td>\n",
       "<td>21.97</td>\n",
       "<td>19.27</td>\n",
       "<td>1.016</td>\n",
       "<td>10.21</td>\n",
       "<td>4.554</td>\n",
       "<td>2.509</td>\n",
       "<td>2014-03-25 17:33:08</td>\n",
       "<td>20.48</td>\n",
       "<td>-0.06401</td>\n",
       "<td>9.708</td>\n",
       "<td>-4.205</td>\n",
       "<td>-15.42</td>\n",
       "<td>-0.01162</td>\n",
       "<td>0.004379</td>\n",
       "<td>0.01994</td>\n",
       "<td>2014-03-25 17:32:52</td>\n",
       "<td>2014-03-25 17:32:59</td>\n",
       "<td>-5.070</td>\n",
       "<td>0.1435</td>\n",
       "<td>100</td>\n",
       "<td>2014-03-25 17:32:06</td>\n",
       "<td>2014-03-25 17:33:05</td>\n",
       "<td>0.06434</td>\n",
       "<td>0.00002586</td>\n",
       "<td>55</td>\n",
       "<td>varying Tdew</td>\n",
       "</tr>\n",
       "<tr class =\"row-a\">\n",
       "<td>2014-03-25 18:20:04</td>\n",
       "<td>23.97</td>\n",
       "<td>23.93</td>\n",
       "<td>22.78</td>\n",
       "<td>22.77</td>\n",
       "<td>22.75</td>\n",
       "<td>26.06</td>\n",
       "<td>22.79</td>\n",
       "<td>22.53</td>\n",
       "<td>21.71</td>\n",
       "<td>22.21</td>\n",
       "<td>20.36</td>\n",
       "<td>1.040</td>\n",
       "<td>15.16</td>\n",
       "<td>3.047</td>\n",
       "<td>1.719</td>\n",
       "<td>2014-03-25 18:20:06</td>\n",
       "<td>20.90</td>\n",
       "<td>-0.04869</td>\n",
       "<td>6.990</td>\n",
       "<td>-2.973</td>\n",
       "<td>-10.52</td>\n",
       "<td>-0.008364</td>\n",
       "<td>0.003099</td>\n",
       "<td>0.01360</td>\n",
       "<td>2014-03-25 18:19:53</td>\n",
       "<td>2014-03-25 18:20:00</td>\n",
       "<td>-3.535</td>\n",
       "<td>0.2791</td>\n",
       "<td>100</td>\n",
       "<td>2014-03-25 18:19:05</td>\n",
       "<td>2014-03-25 18:20:04</td>\n",
       "<td>0.06408</td>\n",
       "<td>0.00002733</td>\n",
       "<td>55</td>\n",
       "<td>varying Tdew</td>\n",
       "</tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        lc_time         T_in1   T_in2   T_wall_in   T_wall_out   T_outside   T_outside_PT   T_chamber   T_chamber1   T_leaf1   T_leaf2   T_leaf_in    Wind     T_dew    Air_inflow   Air_outflow         nr_time         T_leaf_IR   V_leaf_IR   Rn_above_leaf   Rn_below_leaf   Rn_beside_leaf   RnV_above_leaf   RnV_below_leaf   RnV_beside_leaf     sens_timefirst         sens_timelast      water_flow_avg   water_flow_std   water_flow_n      pow_timefirst         pow_timelast       fan_power_avg   fan_power_std   fan_power_n   Comment...................................................\n",
       "+---------------------+-------+-------+-----------+------------+-----------+--------------+-----------+------------+---------+---------+-----------+--------+---------+------------+-------------+---------------------+-----------+-----------+---------------+---------------+----------------+----------------+----------------+-----------------+---------------------+---------------------+----------------+----------------+--------------+---------------------+---------------------+---------------+---------------+-------------+------------------------------------------------------------+\n",
       "          TS              K       K         K           K            K          Deg C           K           K           K         K          K        m/s      degC        SLPM         SLPM               TS              Deg C        mV           W/m^2           W/m^2           W/m^2              mV               mV               mV                                                          ul/min           ul/min          ul/min              TS                    TS                  W               W\n",
       "  2014-03-25 12:39:34   23.98   23.97     22.56       22.52        22.50        25.93         22.58       21.99       19.92     21.18      16.53     0.9999   -19.32      9.052         4.451      2014-03-25 12:39:35     19.36      -0.1045        16.74          -6.927           -26.00          -0.02004         0.007208          0.03366       2014-03-25 12:39:21   2014-03-25 12:39:28       -8.233           0.1577           100        2014-03-25 12:38:37   2014-03-25 12:39:35      0.06427       0.00001266         54         Black leaf, silver perforated alu (35.4-1), varying Tdew\n",
       "  2014-03-25 13:01:40   23.97   23.97     22.57       22.58        22.54        26.05         22.58       22.00       20.00     21.22      16.68     1.020    -14.97      8.490         4.180      2014-03-25 13:01:41     19.36      -0.1045        16.25          -6.859           -25.52          -0.01945         0.007137          0.03303       2014-03-25 13:01:22   2014-03-25 13:01:29       -7.859           0.3323           100        2014-03-25 13:00:39   2014-03-25 13:01:38      0.06428       0.00002267         55                               varying Tdew\n",
       "  2014-03-25 14:07:31   23.91   23.91     22.58       22.59        22.59        26.10         22.57       22.04       20.10     21.26      16.88     1.021    -10.08      8.556         4.293      2014-03-25 14:07:31     19.53     -0.09854        15.18          -6.506           -24.44          -0.01817         0.006771          0.03163       2014-03-25 14:07:11   2014-03-25 14:07:18       -7.807           0.1403           100        2014-03-25 14:06:32   2014-03-25 14:07:31      0.06429       0.00001291         54                               varying Tdew\n",
       "  2014-03-25 16:30:31   24.25   24.24     22.76       22.68        22.67        26.14         22.77       22.26       20.43     21.52      17.38     1.028    -5.017      7.524         3.821      2014-03-25 16:30:34     19.81     -0.08857        14.46          -6.187           -22.65          -0.01730         0.006439          0.02931       2014-03-25 16:30:18   2014-03-25 16:30:25       -7.440          0.07817           100        2014-03-25 16:29:33   2014-03-25 16:30:32      0.06417       0.00001307         54                               varying Tdew\n",
       "  2014-03-25 16:48:46   24.28   24.23     22.73       22.73        22.72        26.02         22.75       22.25       20.57     21.61      17.81     1.019    0.08945     6.557         3.387      2014-03-25 16:48:48     19.92     -0.08459        13.34          -5.753           -20.79          -0.01596         0.005988          0.02690       2014-03-25 16:48:26   2014-03-25 16:48:33       -6.759           0.2183           100        2014-03-25 16:47:46   2014-03-25 16:48:45      0.06423       0.00002734         55                               varying Tdew\n",
       "  2014-03-25 17:08:19   24.24   24.21     22.75       22.67        22.68        26.02         22.74       22.34       20.86     21.74      18.41     1.026     5.120      5.555         2.939      2014-03-25 17:08:22     20.11     -0.07734        11.71          -5.082           -18.50          -0.01401         0.005290          0.02393       2014-03-25 17:08:08   2014-03-25 17:08:15       -5.926           0.1492           100        2014-03-25 17:07:21   2014-03-25 17:08:20      0.06411       0.00001511         55                               varying Tdew\n",
       "  2014-03-25 17:33:07   24.16   24.14     22.79       22.74        22.69        26.06         22.80       22.46       21.25     21.97      19.27     1.016     10.21      4.554         2.509      2014-03-25 17:33:08     20.48     -0.06401        9.708          -4.205           -15.42          -0.01162         0.004379          0.01994       2014-03-25 17:32:52   2014-03-25 17:32:59       -5.070           0.1435           100        2014-03-25 17:32:06   2014-03-25 17:33:05      0.06434       0.00002586         55                               varying Tdew\n",
       "  2014-03-25 18:20:04   23.97   23.93     22.78       22.77        22.75        26.06         22.79       22.53       21.71     22.21      20.36     1.040     15.16      3.047         1.719      2014-03-25 18:20:06     20.90     -0.04869        6.990          -2.973           -10.52         -0.008364         0.003099          0.01360       2014-03-25 18:19:53   2014-03-25 18:20:00       -3.535           0.2791           100        2014-03-25 18:19:05   2014-03-25 18:20:04      0.06408       0.00002733         55                               varying Tdew"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_s = (0.0, 0.0) joule/(R_s*meter^2*second)\n",
      "P_wa = (218.598991722, 1694.78883295) pascal/P_wa\n",
      "T_a = (295.72, 295.95) kelvin/T_a\n",
      "v_w = (0.9999, 1.04) meter/(second*v_w)\n",
      "g_sw = (0.035, 0.035) meter/(g_sw*second)\n"
     ]
    },
    {
     "data": {
      "image/png": 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ksrWFefNg2jQZWREVi1TtCFMyTV2wEJWEnZ3aq2XhQjh8GJo00T1+/766InONGjBgACxb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      "text/plain": [
       "Graphics object consisting of 7 graphics primitives"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0UVu+nDKpBX79FVi7luqnmzUDOnQAJkzQ6BQZjGKMGkWLMhkZdDMWKCigssHW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\nkJKSEnXv3p3Wrl3LtSFm7969Z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      "text/plain": [
       "Graphics object consisting of 10 graphics primitives"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div class=\"notruncate\">\n",
       "<table  class=\"table_form\">\n",
       "<tbody>\n",
       "<tr>\n",
       "<th>lc_time</th>\n",
       "<th>T_in1</th>\n",
       "<th>T_in2</th>\n",
       "<th>T_wall_in</th>\n",
       "<th>T_wall_out</th>\n",
       "<th>T_outside</th>\n",
       "<th>T_outside_PT</th>\n",
       "<th>T_chamber</th>\n",
       "<th>T_chamber1</th>\n",
       "<th>T_leaf1</th>\n",
       "<th>T_leaf2</th>\n",
       "<th>T_leaf_in</th>\n",
       "<th>Wind</th>\n",
       "<th>T_dew</th>\n",
       "<th>Air_inflow</th>\n",
       "<th>Air_outflow</th>\n",
       "<th>nr_time</th>\n",
       "<th>T_leaf_IR</th>\n",
       "<th>V_leaf_IR</th>\n",
       "<th>Rn_above_leaf</th>\n",
       "<th>Rn_below_leaf</th>\n",
       "<th>Rn_beside_leaf</th>\n",
       "<th>RnV_above_leaf</th>\n",
       "<th>RnV_below_leaf</th>\n",
       "<th>RnV_beside_leaf</th>\n",
       "<th>sens_timefirst</th>\n",
       "<th>sens_timelast</th>\n",
       "<th>water_flow_avg</th>\n",
       "<th>water_flow_std</th>\n",
       "<th>water_flow_n</th>\n",
       "<th>pow_timefirst</th>\n",
       "<th>pow_timelast</th>\n",
       "<th>fan_power_avg</th>\n",
       "<th>fan_power_std</th>\n",
       "<th>fan_power_n</th>\n",
       "<th>Comment...................................................</th>\n",
       "</tr>\n",
       "<tr class =\"row-a\">\n",
       "<td>TS</td>\n",
       "<td>K</td>\n",
       "<td>K</td>\n",
       "<td>K</td>\n",
       "<td>K</td>\n",
       "<td>K</td>\n",
       "<td>Deg C</td>\n",
       "<td>K</td>\n",
       "<td>K</td>\n",
       "<td>K</td>\n",
       "<td>K</td>\n",
       "<td>K</td>\n",
       "<td>m/s</td>\n",
       "<td>degC</td>\n",
       "<td>SLPM</td>\n",
       "<td>SLPM</td>\n",
       "<td>TS</td>\n",
       "<td>Deg C</td>\n",
       "<td>mV</td>\n",
       "<td>W/m^2</td>\n",
       "<td>W/m^2</td>\n",
       "<td>W/m^2</td>\n",
       "<td>mV</td>\n",
       "<td>mV</td>\n",
       "<td>mV</td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "<td>ul/min</td>\n",
       "<td>ul/min</td>\n",
       "<td>ul/min</td>\n",
       "<td>TS</td>\n",
       "<td>TS</td>\n",
       "<td>W</td>\n",
       "<td>W</td>\n",
       "<td></td>\n",
       "<td></td>\n",
       "</tr>\n",
       "<tr class =\"row-b\">\n",
       "<td>2014-03-27 11:22:28</td>\n",
       "<td>15.30</td>\n",
       "<td>15.41</td>\n",
       "<td>21.98</td>\n",
       "<td>21.97</td>\n",
       "<td>21.88</td>\n",
       "<td>25.24</td>\n",
       "<td>22.00</td>\n",
       "<td>21.67</td>\n",
       "<td>20.29</td>\n",
       "<td>21.07</td>\n",
       "<td>18.41</td>\n",
       "<td>0.8333</td>\n",
       "<td>10.21</td>\n",
       "<td>6.827</td>\n",
       "<td>3.491</td>\n",
       "<td>2014-03-27 11:22:29</td>\n",
       "<td>20.30</td>\n",
       "<td>-0.07058</td>\n",
       "<td>9.835</td>\n",
       "<td>-3.912</td>\n",
       "<td>-13.66</td>\n",
       "<td>-0.01177</td>\n",
       "<td>0.004074</td>\n",
       "<td>0.01766</td>\n",
       "<td>2014-03-27 11:22:19</td>\n",
       "<td>2014-03-27 11:22:26</td>\n",
       "<td>-4.825</td>\n",
       "<td>0.1412</td>\n",
       "<td>100</td>\n",
       "<td>2014-03-27 11:21:28</td>\n",
       "<td>2014-03-27 11:22:27</td>\n",
       "<td>1.195</td>\n",
       "<td>0.0003885</td>\n",
       "<td>54</td>\n",
       "<td>Black leaf, silver perforated alu (35.4-1), varying wind speed</td>\n",
       "</tr>\n",
       "<tr class =\"row-a\">\n",
       "<td>2014-03-27 13:01:58</td>\n",
       "<td>15.54</td>\n",
       "<td>15.65</td>\n",
       "<td>22.22</td>\n",
       "<td>22.26</td>\n",
       "<td>22.22</td>\n",
       "<td>25.53</td>\n",
       "<td>22.22</td>\n",
       "<td>21.95</td>\n",
       "<td>20.72</td>\n",
       "<td>21.46</td>\n",
       "<td>18.72</td>\n",
       "<td>1.313</td>\n",
       "<td>10.21</td>\n",
       "<td>6.427</td>\n",
       "<td>3.324</td>\n",
       "<td>2014-03-27 13:02:01</td>\n",
       "<td>20.41</td>\n",
       "<td>-0.06664</td>\n",
       "<td>10.02</td>\n",
       "<td>-3.833</td>\n",
       "<td>-16.85</td>\n",
       "<td>-0.01199</td>\n",
       "<td>0.003993</td>\n",
       "<td>0.02180</td>\n",
       "<td>2014-03-27 13:01:48</td>\n",
       "<td>2014-03-27 13:01:55</td>\n",
       "<td>-5.543</td>\n",
       "<td>0.1600</td>\n",
       "<td>100</td>\n",
       "<td>2014-03-27 13:00:59</td>\n",
       "<td>2014-03-27 13:01:58</td>\n",
       "<td>1.181</td>\n",
       "<td>0.0004875</td>\n",
       "<td>54</td>\n",
       "<td>varying wind speed</td>\n",
       "</tr>\n",
       "<tr class =\"row-b\">\n",
       "<td>2014-03-27 14:04:25</td>\n",
       "<td>15.68</td>\n",
       "<td>15.80</td>\n",
       "<td>22.41</td>\n",
       "<td>22.40</td>\n",
       "<td>22.37</td>\n",
       "<td>25.69</td>\n",
       "<td>22.43</td>\n",
       "<td>22.17</td>\n",
       "<td>21.05</td>\n",
       "<td>21.76</td>\n",
       "<td>18.95</td>\n",
       "<td>1.796</td>\n",
       "<td>10.21</td>\n",
       "<td>6.251</td>\n",
       "<td>3.274</td>\n",
       "<td>2014-03-27 14:04:23</td>\n",
       "<td>20.36</td>\n",
       "<td>-0.06849</td>\n",
       "<td>10.02</td>\n",
       "<td>-3.781</td>\n",
       "<td>-17.83</td>\n",
       "<td>-0.01198</td>\n",
       "<td>0.003939</td>\n",
       "<td>0.02307</td>\n",
       "<td>2014-03-27 14:04:10</td>\n",
       "<td>2014-03-27 14:04:17</td>\n",
       "<td>-6.124</td>\n",
       "<td>0.1336</td>\n",
       "<td>100</td>\n",
       "<td>2014-03-27 14:03:24</td>\n",
       "<td>2014-03-27 14:04:23</td>\n",
       "<td>1.185</td>\n",
       "<td>0.0003885</td>\n",
       "<td>54</td>\n",
       "<td>varying wind speed</td>\n",
       "</tr>\n",
       "<tr class =\"row-a\">\n",
       "<td>2014-03-27 15:55:10</td>\n",
       "<td>15.74</td>\n",
       "<td>15.85</td>\n",
       "<td>22.40</td>\n",
       "<td>22.42</td>\n",
       "<td>22.36</td>\n",
       "<td>25.61</td>\n",
       "<td>22.42</td>\n",
       "<td>22.18</td>\n",
       "<td>21.22</td>\n",
       "<td>21.85</td>\n",
       "<td>19.06</td>\n",
       "<td>2.299</td>\n",
       "<td>10.21</td>\n",
       "<td>6.140</td>\n",
       "<td>3.202</td>\n",
       "<td>2014-03-27 15:55:11</td>\n",
       "<td>19.58</td>\n",
       "<td>-0.09654</td>\n",
       "<td>9.917</td>\n",
       "<td>-3.843</td>\n",
       "<td>-17.14</td>\n",
       "<td>-0.01187</td>\n",
       "<td>0.004003</td>\n",
       "<td>0.02218</td>\n",
       "<td>2014-03-27 15:55:01</td>\n",
       "<td>2014-03-27 15:55:08</td>\n",
       "<td>-6.583</td>\n",
       "<td>0.1710</td>\n",
       "<td>100</td>\n",
       "<td>2014-03-27 15:54:09</td>\n",
       "<td>2014-03-27 15:55:08</td>\n",
       "<td>1.169</td>\n",
       "<td>0.0004755</td>\n",
       "<td>55</td>\n",
       "<td>varying wind speed</td>\n",
       "</tr>\n",
       "<tr class =\"row-b\">\n",
       "<td>2014-03-27 17:01:01</td>\n",
       "<td>15.90</td>\n",
       "<td>16.02</td>\n",
       "<td>22.43</td>\n",
       "<td>22.48</td>\n",
       "<td>22.40</td>\n",
       "<td>25.81</td>\n",
       "<td>22.45</td>\n",
       "<td>22.22</td>\n",
       "<td>21.42</td>\n",
       "<td>21.96</td>\n",
       "<td>19.24</td>\n",
       "<td>2.995</td>\n",
       "<td>10.21</td>\n",
       "<td>5.851</td>\n",
       "<td>3.136</td>\n",
       "<td>2014-03-27 17:01:03</td>\n",
       "<td>20.03</td>\n",
       "<td>-0.08036</td>\n",
       "<td>8.408</td>\n",
       "<td>-3.625</td>\n",
       "<td>-16.34</td>\n",
       "<td>-0.01006</td>\n",
       "<td>0.003776</td>\n",
       "<td>0.02114</td>\n",
       "<td>2014-03-27 17:00:38</td>\n",
       "<td>2014-03-27 17:00:48</td>\n",
       "<td>-7.255</td>\n",
       "<td>0.1174</td>\n",
       "<td>100</td>\n",
       "<td>2014-03-27 17:00:04</td>\n",
       "<td>2014-03-27 17:01:03</td>\n",
       "<td>1.144</td>\n",
       "<td>0.0000</td>\n",
       "<td>54</td>\n",
       "<td>varying wind speed</td>\n",
       "</tr>\n",
       "<tr class =\"row-a\">\n",
       "<td>2014-03-27 19:00:22</td>\n",
       "<td>15.88</td>\n",
       "<td>15.99</td>\n",
       "<td>22.40</td>\n",
       "<td>22.38</td>\n",
       "<td>22.21</td>\n",
       "<td>25.57</td>\n",
       "<td>22.46</td>\n",
       "<td>22.24</td>\n",
       "<td>21.53</td>\n",
       "<td>22.03</td>\n",
       "<td>19.38</td>\n",
       "<td>3.831</td>\n",
       "<td>10.20</td>\n",
       "<td>5.750</td>\n",
       "<td>3.057</td>\n",
       "<td>2014-03-27 19:00:26</td>\n",
       "<td>19.20</td>\n",
       "<td>-0.1106</td>\n",
       "<td>8.123</td>\n",
       "<td>-3.345</td>\n",
       "<td>-14.00</td>\n",
       "<td>-0.009719</td>\n",
       "<td>0.003485</td>\n",
       "<td>0.01811</td>\n",
       "<td>2014-03-27 19:00:07</td>\n",
       "<td>2014-03-27 19:00:17</td>\n",
       "<td>-8.006</td>\n",
       "<td>0.1148</td>\n",
       "<td>100</td>\n",
       "<td>2014-03-27 18:59:24</td>\n",
       "<td>2014-03-27 19:00:23</td>\n",
       "<td>1.155</td>\n",
       "<td>0.0004130</td>\n",
       "<td>55</td>\n",
       "<td>varying wind speed</td>\n",
       "</tr>\n",
       "<tr class =\"row-b\">\n",
       "<td>2014-03-28 10:08:04</td>\n",
       "<td>15.46</td>\n",
       "<td>15.57</td>\n",
       "<td>21.81</td>\n",
       "<td>21.91</td>\n",
       "<td>21.85</td>\n",
       "<td>25.20</td>\n",
       "<td>21.85</td>\n",
       "<td>21.67</td>\n",
       "<td>20.98</td>\n",
       "<td>21.44</td>\n",
       "<td>18.93</td>\n",
       "<td>3.804</td>\n",
       "<td>10.20</td>\n",
       "<td>6.147</td>\n",
       "<td>3.245</td>\n",
       "<td>2014-03-28 10:08:10</td>\n",
       "<td>19.58</td>\n",
       "<td>-0.09655</td>\n",
       "<td>8.239</td>\n",
       "<td>-2.960</td>\n",
       "<td>-12.37</td>\n",
       "<td>-0.009857</td>\n",
       "<td>0.003085</td>\n",
       "<td>0.01600</td>\n",
       "<td>2014-03-28 10:07:52</td>\n",
       "<td>2014-03-28 10:08:02</td>\n",
       "<td>-7.736</td>\n",
       "<td>0.1666</td>\n",
       "<td>100</td>\n",
       "<td>2014-03-28 10:07:07</td>\n",
       "<td>2014-03-28 10:08:06</td>\n",
       "<td>1.159</td>\n",
       "<td>0.0001872</td>\n",
       "<td>55</td>\n",
       "<td>New day</td>\n",
       "</tr>\n",
       "<tr class =\"row-a\">\n",
       "<td>2014-03-28 11:29:49</td>\n",
       "<td>15.71</td>\n",
       "<td>15.83</td>\n",
       "<td>22.23</td>\n",
       "<td>22.21</td>\n",
       "<td>22.15</td>\n",
       "<td>25.57</td>\n",
       "<td>22.24</td>\n",
       "<td>22.08</td>\n",
       "<td>21.38</td>\n",
       "<td>21.84</td>\n",
       "<td>19.29</td>\n",
       "<td>4.335</td>\n",
       "<td>10.20</td>\n",
       "<td>5.858</td>\n",
       "<td>3.204</td>\n",
       "<td>2014-03-28 11:29:51</td>\n",
       "<td>19.47</td>\n",
       "<td>-0.1005</td>\n",
       "<td>8.058</td>\n",
       "<td>-3.227</td>\n",
       "<td>-12.36</td>\n",
       "<td>-0.009641</td>\n",
       "<td>0.003363</td>\n",
       "<td>0.01599</td>\n",
       "<td>2014-03-28 11:29:27</td>\n",
       "<td>2014-03-28 11:29:37</td>\n",
       "<td>-7.984</td>\n",
       "<td>0.08737</td>\n",
       "<td>100</td>\n",
       "<td>2014-03-28 11:28:49</td>\n",
       "<td>2014-03-28 11:29:48</td>\n",
       "<td>1.174</td>\n",
       "<td>0.0004692</td>\n",
       "<td>55</td>\n",
       "<td>New day</td>\n",
       "</tr>\n",
       "<tr class =\"row-b\">\n",
       "<td>2014-03-28 13:47:28</td>\n",
       "<td>15.80</td>\n",
       "<td>15.90</td>\n",
       "<td>22.35</td>\n",
       "<td>22.28</td>\n",
       "<td>22.21</td>\n",
       "<td>25.57</td>\n",
       "<td>22.31</td>\n",
       "<td>22.16</td>\n",
       "<td>21.54</td>\n",
       "<td>21.99</td>\n",
       "<td>19.46</td>\n",
       "<td>5.102</td>\n",
       "<td>10.20</td>\n",
       "<td>5.858</td>\n",
       "<td>3.131</td>\n",
       "<td>2014-03-28 13:47:29</td>\n",
       "<td>19.50</td>\n",
       "<td>-0.09967</td>\n",
       "<td>6.985</td>\n",
       "<td>-3.328</td>\n",
       "<td>-12.71</td>\n",
       "<td>-0.008357</td>\n",
       "<td>0.003468</td>\n",
       "<td>0.01643</td>\n",
       "<td>2014-03-28 13:47:18</td>\n",
       "<td>2014-03-28 13:47:27</td>\n",
       "<td>-8.505</td>\n",
       "<td>0.1932</td>\n",
       "<td>100</td>\n",
       "<td>2014-03-28 13:46:29</td>\n",
       "<td>2014-03-28 13:47:28</td>\n",
       "<td>1.186</td>\n",
       "<td>0.0004914</td>\n",
       "<td>54</td>\n",
       "<td>Reduced head difference (transpiration seemed low)</td>\n",
       "</tr>\n",
       "<tr class =\"row-a\">\n",
       "<td>2014-03-28 15:04:55</td>\n",
       "<td>15.76</td>\n",
       "<td>15.86</td>\n",
       "<td>22.38</td>\n",
       "<td>22.34</td>\n",
       "<td>22.20</td>\n",
       "<td>25.53</td>\n",
       "<td>22.37</td>\n",
       "<td>22.21</td>\n",
       "<td>21.55</td>\n",
       "<td>22.01</td>\n",
       "<td>19.44</td>\n",
       "<td>4.611</td>\n",
       "<td>10.20</td>\n",
       "<td>5.855</td>\n",
       "<td>3.163</td>\n",
       "<td>2014-03-28 15:04:56</td>\n",
       "<td>19.39</td>\n",
       "<td>-0.1037</td>\n",
       "<td>7.228</td>\n",
       "<td>-3.377</td>\n",
       "<td>-12.89</td>\n",
       "<td>-0.008648</td>\n",
       "<td>0.003519</td>\n",
       "<td>0.01667</td>\n",
       "<td>2014-03-28 15:04:45</td>\n",
       "<td>2014-03-28 15:04:55</td>\n",
       "<td>-8.219</td>\n",
       "<td>0.1147</td>\n",
       "<td>100</td>\n",
       "<td>2014-03-28 15:03:56</td>\n",
       "<td>2014-03-28 15:04:55</td>\n",
       "<td>1.182</td>\n",
       "<td>0.0003857</td>\n",
       "<td>55</td>\n",
       "<td>Reduced head difference (transpiration seemed low)</td>\n",
       "</tr>\n",
       "<tr class =\"row-b\">\n",
       "<td>2014-03-28 15:38:46</td>\n",
       "<td>15.77</td>\n",
       "<td>15.86</td>\n",
       "<td>22.30</td>\n",
       "<td>22.37</td>\n",
       "<td>22.30</td>\n",
       "<td>25.57</td>\n",
       "<td>22.33</td>\n",
       "<td>22.08</td>\n",
       "<td>21.44</td>\n",
       "<td>21.94</td>\n",
       "<td>19.34</td>\n",
       "<td>4.168</td>\n",
       "<td>10.21</td>\n",
       "<td>5.948</td>\n",
       "<td>3.144</td>\n",
       "<td>2014-03-28 15:38:49</td>\n",
       "<td>19.42</td>\n",
       "<td>-0.1023</td>\n",
       "<td>7.755</td>\n",
       "<td>-3.358</td>\n",
       "<td>-12.98</td>\n",
       "<td>-0.009278</td>\n",
       "<td>0.003499</td>\n",
       "<td>0.01679</td>\n",
       "<td>2014-03-28 15:38:37</td>\n",
       "<td>2014-03-28 15:38:47</td>\n",
       "<td>-7.979</td>\n",
       "<td>0.1037</td>\n",
       "<td>100</td>\n",
       "<td>2014-03-28 15:37:49</td>\n",
       "<td>2014-03-28 15:38:47</td>\n",
       "<td>1.168</td>\n",
       "<td>0.001412</td>\n",
       "<td>54</td>\n",
       "<td>Varying Tdew</td>\n",
       "</tr>\n",
       "<tr class =\"row-a\">\n",
       "<td>2014-03-31 12:42:52</td>\n",
       "<td>16.15</td>\n",
       "<td>16.28</td>\n",
       "<td>23.06</td>\n",
       "<td>23.06</td>\n",
       "<td>22.93</td>\n",
       "<td>26.30</td>\n",
       "<td>23.10</td>\n",
       "<td>22.89</td>\n",
       "<td>22.13</td>\n",
       "<td>22.64</td>\n",
       "<td>19.90</td>\n",
       "<td>3.325</td>\n",
       "<td>10.20</td>\n",
       "<td>5.259</td>\n",
       "<td>2.863</td>\n",
       "<td>2014-03-31 12:42:54</td>\n",
       "<td>19.57</td>\n",
       "<td>-0.09719</td>\n",
       "<td>8.972</td>\n",
       "<td>-3.413</td>\n",
       "<td>-14.55</td>\n",
       "<td>-0.01074</td>\n",
       "<td>0.003556</td>\n",
       "<td>0.01882</td>\n",
       "<td>2014-03-31 13:42:34</td>\n",
       "<td>2014-03-31 13:42:41</td>\n",
       "<td>-7.484</td>\n",
       "<td>0.09733</td>\n",
       "<td>100</td>\n",
       "<td>2014-03-31 13:41:52</td>\n",
       "<td>2014-03-31 13:42:51</td>\n",
       "<td>1.147</td>\n",
       "<td>0.0001906</td>\n",
       "<td>53</td>\n",
       "<td>New daz</td>\n",
       "</tr>\n",
       "<tr class =\"row-b\">\n",
       "<td>2014-03-31 14:49:10</td>\n",
       "<td>16.20</td>\n",
       "<td>16.33</td>\n",
       "<td>23.25</td>\n",
       "<td>23.34</td>\n",
       "<td>23.21</td>\n",
       "<td>26.63</td>\n",
       "<td>23.28</td>\n",
       "<td>23.04</td>\n",
       "<td>22.17</td>\n",
       "<td>22.75</td>\n",
       "<td>19.91</td>\n",
       "<td>2.637</td>\n",
       "<td>10.18</td>\n",
       "<td>5.346</td>\n",
       "<td>2.915</td>\n",
       "<td>2014-03-31 14:49:14</td>\n",
       "<td>19.81</td>\n",
       "<td>-0.08855</td>\n",
       "<td>9.860</td>\n",
       "<td>-3.661</td>\n",
       "<td>-16.83</td>\n",
       "<td>-0.01180</td>\n",
       "<td>0.003814</td>\n",
       "<td>0.02177</td>\n",
       "<td>2014-03-31 15:48:56</td>\n",
       "<td>2014-03-31 15:49:03</td>\n",
       "<td>-7.032</td>\n",
       "<td>0.1764</td>\n",
       "<td>100</td>\n",
       "<td>2014-03-31 15:48:12</td>\n",
       "<td>2014-03-31 15:49:11</td>\n",
       "<td>1.147</td>\n",
       "<td>0.0000</td>\n",
       "<td>53</td>\n",
       "<td>Varying wind</td>\n",
       "</tr>\n",
       "<tr class =\"row-a\">\n",
       "<td>2014-03-31 16:28:13</td>\n",
       "<td>16.03</td>\n",
       "<td>16.16</td>\n",
       "<td>23.37</td>\n",
       "<td>23.41</td>\n",
       "<td>23.29</td>\n",
       "<td>26.63</td>\n",
       "<td>23.38</td>\n",
       "<td>23.08</td>\n",
       "<td>21.63</td>\n",
       "<td>22.51</td>\n",
       "<td>19.59</td>\n",
       "<td>1.042</td>\n",
       "<td>10.19</td>\n",
       "<td>5.857</td>\n",
       "<td>3.016</td>\n",
       "<td>2014-03-31 16:28:15</td>\n",
       "<td>19.31</td>\n",
       "<td>-0.1065</td>\n",
       "<td>10.95</td>\n",
       "<td>-4.276</td>\n",
       "<td>-14.55</td>\n",
       "<td>-0.01310</td>\n",
       "<td>0.004454</td>\n",
       "<td>0.01881</td>\n",
       "<td>2014-03-31 17:28:06</td>\n",
       "<td>2014-03-31 17:28:13</td>\n",
       "<td>-5.120</td>\n",
       "<td>0.1585</td>\n",
       "<td>100</td>\n",
       "<td>2014-03-31 17:27:14</td>\n",
       "<td>2014-03-31 17:28:13</td>\n",
       "<td>1.147</td>\n",
       "<td>0.0000</td>\n",
       "<td>53</td>\n",
       "<td>Varying wind</td>\n",
       "</tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        lc_time         T_in1   T_in2   T_wall_in   T_wall_out   T_outside   T_outside_PT   T_chamber   T_chamber1   T_leaf1   T_leaf2   T_leaf_in    Wind    T_dew   Air_inflow   Air_outflow         nr_time         T_leaf_IR   V_leaf_IR   Rn_above_leaf   Rn_below_leaf   Rn_beside_leaf   RnV_above_leaf   RnV_below_leaf   RnV_beside_leaf     sens_timefirst         sens_timelast      water_flow_avg   water_flow_std   water_flow_n      pow_timefirst         pow_timelast       fan_power_avg   fan_power_std   fan_power_n     Comment...................................................\n",
       "+---------------------+-------+-------+-----------+------------+-----------+--------------+-----------+------------+---------+---------+-----------+--------+-------+------------+-------------+---------------------+-----------+-----------+---------------+---------------+----------------+----------------+----------------+-----------------+---------------------+---------------------+----------------+----------------+--------------+---------------------+---------------------+---------------+---------------+-------------+----------------------------------------------------------------+\n",
       "          TS              K       K         K           K            K          Deg C           K           K           K         K          K        m/s     degC       SLPM         SLPM               TS              Deg C        mV           W/m^2           W/m^2           W/m^2              mV               mV               mV                                                          ul/min           ul/min          ul/min              TS                    TS                  W               W\n",
       "  2014-03-27 11:22:28   15.30   15.41     21.98       21.97        21.88        25.24         22.00       21.67       20.29     21.07      18.41     0.8333   10.21     6.827         3.491      2014-03-27 11:22:29     20.30     -0.07058        9.835          -3.912           -13.66          -0.01177         0.004074          0.01766       2014-03-27 11:22:19   2014-03-27 11:22:26       -4.825           0.1412           100        2014-03-27 11:21:28   2014-03-27 11:22:27       1.195         0.0003885         54        Black leaf, silver perforated alu (35.4-1), varying wind speed\n",
       "  2014-03-27 13:01:58   15.54   15.65     22.22       22.26        22.22        25.53         22.22       21.95       20.72     21.46      18.72     1.313    10.21     6.427         3.324      2014-03-27 13:02:01     20.41     -0.06664        10.02          -3.833           -16.85          -0.01199         0.003993          0.02180       2014-03-27 13:01:48   2014-03-27 13:01:55       -5.543           0.1600           100        2014-03-27 13:00:59   2014-03-27 13:01:58       1.181         0.0004875         54                              varying wind speed\n",
       "  2014-03-27 14:04:25   15.68   15.80     22.41       22.40        22.37        25.69         22.43       22.17       21.05     21.76      18.95     1.796    10.21     6.251         3.274      2014-03-27 14:04:23     20.36     -0.06849        10.02          -3.781           -17.83          -0.01198         0.003939          0.02307       2014-03-27 14:04:10   2014-03-27 14:04:17       -6.124           0.1336           100        2014-03-27 14:03:24   2014-03-27 14:04:23       1.185         0.0003885         54                              varying wind speed\n",
       "  2014-03-27 15:55:10   15.74   15.85     22.40       22.42        22.36        25.61         22.42       22.18       21.22     21.85      19.06     2.299    10.21     6.140         3.202      2014-03-27 15:55:11     19.58     -0.09654        9.917          -3.843           -17.14          -0.01187         0.004003          0.02218       2014-03-27 15:55:01   2014-03-27 15:55:08       -6.583           0.1710           100        2014-03-27 15:54:09   2014-03-27 15:55:08       1.169         0.0004755         55                              varying wind speed\n",
       "  2014-03-27 17:01:01   15.90   16.02     22.43       22.48        22.40        25.81         22.45       22.22       21.42     21.96      19.24     2.995    10.21     5.851         3.136      2014-03-27 17:01:03     20.03     -0.08036        8.408          -3.625           -16.34          -0.01006         0.003776          0.02114       2014-03-27 17:00:38   2014-03-27 17:00:48       -7.255           0.1174           100        2014-03-27 17:00:04   2014-03-27 17:01:03       1.144          0.0000           54                              varying wind speed\n",
       "  2014-03-27 19:00:22   15.88   15.99     22.40       22.38        22.21        25.57         22.46       22.24       21.53     22.03      19.38     3.831    10.20     5.750         3.057      2014-03-27 19:00:26     19.20      -0.1106        8.123          -3.345           -14.00         -0.009719         0.003485          0.01811       2014-03-27 19:00:07   2014-03-27 19:00:17       -8.006           0.1148           100        2014-03-27 18:59:24   2014-03-27 19:00:23       1.155         0.0004130         55                              varying wind speed\n",
       "  2014-03-28 10:08:04   15.46   15.57     21.81       21.91        21.85        25.20         21.85       21.67       20.98     21.44      18.93     3.804    10.20     6.147         3.245      2014-03-28 10:08:10     19.58     -0.09655        8.239          -2.960           -12.37         -0.009857         0.003085          0.01600       2014-03-28 10:07:52   2014-03-28 10:08:02       -7.736           0.1666           100        2014-03-28 10:07:07   2014-03-28 10:08:06       1.159         0.0001872         55                                   New day\n",
       "  2014-03-28 11:29:49   15.71   15.83     22.23       22.21        22.15        25.57         22.24       22.08       21.38     21.84      19.29     4.335    10.20     5.858         3.204      2014-03-28 11:29:51     19.47      -0.1005        8.058          -3.227           -12.36         -0.009641         0.003363          0.01599       2014-03-28 11:29:27   2014-03-28 11:29:37       -7.984          0.08737           100        2014-03-28 11:28:49   2014-03-28 11:29:48       1.174         0.0004692         55                                   New day\n",
       "  2014-03-28 13:47:28   15.80   15.90     22.35       22.28        22.21        25.57         22.31       22.16       21.54     21.99      19.46     5.102    10.20     5.858         3.131      2014-03-28 13:47:29     19.50     -0.09967        6.985          -3.328           -12.71         -0.008357         0.003468          0.01643       2014-03-28 13:47:18   2014-03-28 13:47:27       -8.505           0.1932           100        2014-03-28 13:46:29   2014-03-28 13:47:28       1.186         0.0004914         54              Reduced head difference (transpiration seemed low)\n",
       "  2014-03-28 15:04:55   15.76   15.86     22.38       22.34        22.20        25.53         22.37       22.21       21.55     22.01      19.44     4.611    10.20     5.855         3.163      2014-03-28 15:04:56     19.39      -0.1037        7.228          -3.377           -12.89         -0.008648         0.003519          0.01667       2014-03-28 15:04:45   2014-03-28 15:04:55       -8.219           0.1147           100        2014-03-28 15:03:56   2014-03-28 15:04:55       1.182         0.0003857         55              Reduced head difference (transpiration seemed low)\n",
       "  2014-03-28 15:38:46   15.77   15.86     22.30       22.37        22.30        25.57         22.33       22.08       21.44     21.94      19.34     4.168    10.21     5.948         3.144      2014-03-28 15:38:49     19.42      -0.1023        7.755          -3.358           -12.98         -0.009278         0.003499          0.01679       2014-03-28 15:38:37   2014-03-28 15:38:47       -7.979           0.1037           100        2014-03-28 15:37:49   2014-03-28 15:38:47       1.168         0.001412          54                                 Varying Tdew\n",
       "  2014-03-31 12:42:52   16.15   16.28     23.06       23.06        22.93        26.30         23.10       22.89       22.13     22.64      19.90     3.325    10.20     5.259         2.863      2014-03-31 12:42:54     19.57     -0.09719        8.972          -3.413           -14.55          -0.01074         0.003556          0.01882       2014-03-31 13:42:34   2014-03-31 13:42:41       -7.484          0.09733           100        2014-03-31 13:41:52   2014-03-31 13:42:51       1.147         0.0001906         53                                   New daz\n",
       "  2014-03-31 14:49:10   16.20   16.33     23.25       23.34        23.21        26.63         23.28       23.04       22.17     22.75      19.91     2.637    10.18     5.346         2.915      2014-03-31 14:49:14     19.81     -0.08855        9.860          -3.661           -16.83          -0.01180         0.003814          0.02177       2014-03-31 15:48:56   2014-03-31 15:49:03       -7.032           0.1764           100        2014-03-31 15:48:12   2014-03-31 15:49:11       1.147          0.0000           53                                 Varying wind\n",
       "  2014-03-31 16:28:13   16.03   16.16     23.37       23.41        23.29        26.63         23.38       23.08       21.63     22.51      19.59     1.042    10.19     5.857         3.016      2014-03-31 16:28:15     19.31      -0.1065        10.95          -4.276           -14.55          -0.01310         0.004454          0.01881       2014-03-31 17:28:06   2014-03-31 17:28:13       -5.120           0.1585           100        2014-03-31 17:27:14   2014-03-31 17:28:13       1.147          0.0000           53                                 Varying wind"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0420000000000000\n",
      "R_s = (0.0, 0.0) joule/(R_s*meter^2*second)\n",
      "P_wa = (1187.38813505, 1278.34796382) pascal/P_wa\n",
      "T_a = (295.0, 296.53) kelvin/T_a\n",
      "v_w = (0.8333, 5.102) meter/(second*v_w)\n",
      "g_sw = (0.042, 0.042) meter/(g_sw*second)\n"
     ]
    },
    {
     "data": {
      "image/png": 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CK1asQLt27eDu7t6ofU/s7e2xZs0aODo6AgC2b9+ON954A1evXsWrr76K0NBQnDx5EgcP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      "text/plain": [
       "Graphics object consisting of 7 graphics primitives"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bCtCXVzHrILGxqiPYwkIa+b75Zvnnys3NKad9aSQni6P7M2doHv/FCxrFu7iQ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\n",
      "text/plain": [
       "Graphics object consisting of 10 graphics primitives"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18\n",
      "['Date', 'Time', 'Inflow rate', 'Tdew humidifier', 'Incoming2 Temp_C(5)', 'Incoming3 Temp_C(6)', 'wall inside Temp_C(3) ', 'wall outside Temp_C(4)', 'chamber air Temp_C(1) ', 'Tl1', 'Tl2', 'TlIR', 'Tlin', 'Fan power', 'FlowMeter out', 'Wind speed', 'Sensirion', 'Comment']\n",
      "['', '', 'l/min', 'oC', 'oC', 'oC', 'oC', 'oC', 'oC', 'oC', 'oC', 'oC', 'oC', 'W', 'l/min', 'm/s', 'ul/min', '']\n",
      "R_s = (0.0, 0.0) joule/(R_s*meter^2*second)\n",
      "P_wa = (168.502255462, 1143.55502295) pascal/P_wa\n",
      "T_a = (296.05, 296.71) kelvin/T_a\n",
      "v_w = (0.7, 0.7) meter/(second*v_w)\n",
      "g_sw = (0.0065, 0.0065) meter/(g_sw*second)\n"
     ]
    },
    {
     "data": {
      "image/png": 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EqFaLTU6CgoLg6+uLLl264O+//8aaNWugoaGB8ePHw9DQENOnT8fixYthZGQE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\npqDOCYVCoVAolDYFdU4oFAqFQqG0Kf4XaakHhB3vFvMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "Graphics object consisting of 6 graphics primitives"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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yREotGxho3Jns2jUpoQxTN3Fzozfv6Gjd9jfe0A35+/FH4NVXyUFWpLjYMJe/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\nHatWrQLHcfj8888hl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      "text/plain": [
       "Graphics object consisting of 10 graphics primitives"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JiPiKC8rTn5OTQ0JCQvFG3oSEBLKzs82Bf53SadiwIf369SMqKoqBAwfSr18/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\nNMyOEbw/xQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "Graphics object consisting of 8 graphics primitives"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_s = (0.0, 700.0) joule/(R_s*meter^2*second)\n",
      "P_wa = (1300.96492908905, 1300.96492908905) pascal/P_wa\n",
      "T_a = (295, 295) kelvin/T_a\n",
      "v_w = (1.0, 1.0) meter/(second*v_w)\n",
      "g_sw = (0.045, 0.045) meter/(g_sw*second)\n"
     ]
    },
    {
     "data": {
      "image/png": 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CgoJS/XMJDg4mODg4U8eLiIiIZFdMDMydC2+9BS+8AEOHerqivM/hcOBwOFI8FxcX56Fq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VbNy4UU536NBBM88Wq4KCAnz88ce6+f/44w9s2bIFQgjceeedmsfSABWD+TvuuANEhC1btuDk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\nJUW21cfHh86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7y8vLERMTA0VRHL4GDx4sv5AuX74sj347c/HiRUN1UZ/2pvfcVaDiGreX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\nY4wxxhhjjHnR/wG+6yFOLN0M+QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "Graphics object consisting of 6 graphics primitives"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_s = (350.0, 350.0) joule/(R_s*meter^2*second)\n",
      "P_wa = (567.937364287074, 567.937364287074) pascal/P_wa\n",
      "T_a = (282.0, 298.0) kelvin/T_a\n",
      "v_w = (1.0, 1.0) meter/(second*v_w)\n",
      "g_sw = (0.045, 0.045) meter/(g_sw*second)\n"
     ]
    },
    {
     "data": {
      "image/png": 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kaMPRTtWqVZkzZw5jxowp9D3oj6+IiMjly2vI+6hRZluu+vU9XZ2AmX6QfWj7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\niAhgDzsRWSH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      "text/plain": [
       "Graphics object consisting of 6 graphics primitives"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "imported file temp/leaf_chamber_data\n",
      "jupyter nbconvert  --to=python 'Tables_of_variables.ipynb'\n",
      "Exporting specified worksheet to .py file...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "nbconvert returned 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking if specified ipynb file was run with sage kernel...\n",
      "Renaming .py file to .sage if notebook kernel was sage (to avoid exponent error)\n",
      "created file temp/Tables_of_variables\n",
      "Tables_of_variables.ipynb\n",
      "Worksheet_update.ipynb\n",
      "Worksheet_setup.ipynb\n",
      "stomatal_cond_eqs.ipynb\n",
      "leaf_chamber_eqs.ipynb\n",
      "leaf_chamber_data.ipynb\n",
      "leaf_enbalance_eqs.ipynb\n",
      "E_PM_eqs.ipynb\n",
      "imported file temp/Tables_of_variables\n"
     ]
    }
   ],
   "source": [
    "list_wsnames = ['leaf_enbalance_eqs', 'E_PM_eqs', 'stomatal_cond_eqs', 'leaf_chamber_eqs', 'leaf_chamber_data', 'Tables_of_variables' ]\n",
    "folder = 'temp/'\n",
    "fun_export_load_ipynb(list_wsnames, folder, load)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "SageMath 7.3",
   "language": "",
   "name": "sagemath"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  },
  "nav_menu": {},
  "toc": {
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 6,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}