{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Obliczanie optymalnego kąta nachylenia kolektora słonecznego\n",
    "\n",
    "Przy projektowaniu i analizie funkcjonowania słonecznych instalacji służących do podgrzewania wody użytkowej oraz instalacji grzewczych zachodzi konieczność obliczenia natężenia promieniowania na powierzchni czołowej kolektora słonecznego zainstalowanego pod określonym kątem $\\beta$ oraz w odniesieniu względem osi północ-południe $\\gamma$.\n",
    "\n",
    "Do obliczeń są niezbędne dane dotyczące natężenia promieniowania słonecznego dla każdego dnia roku. Mogą to być np.  wygenerowane losowo dane z programu SolarSym (http://fluid.itcmp.pwr.wroc.pl/~kasper/solarsym/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "# wyciszenie komunikatów o błędach\n",
    "import warnings;warnings.simplefilter('ignore')\n",
    "\n",
    "# załadowanie modułów\n",
    "%pylab inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "rcParams['savefig.dpi'] = 120\n",
    "\n",
    "import math\n",
    "import operator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Szerokość geograficzna oraz orientacja\n",
    "\n",
    "* Szerokość geograficzna $\\phi = 51.10$ stopni została przyjęta dla Wrocławia (podobnie jak zestaw danych)\n",
    "* Kolektora zorientowany jest idealnie w osi północ-południe $\\gamma = 0$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ϕ = math.radians(51.10) # [°]\n",
    "γ = 0 # [°]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Dzień roku (do obliczenia deklinacji)\n",
    "* Różnica w minutach pomiędzy godziną 12:00, a godziną w której zarejestrowana maksymalne natężenie. Wartość ujemna oznacza, że wskazówka wychylona jest w lewo, przykładowo: godzina **11:50 = -10 minut**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n = 180 # numer dnia\n",
    "minuty = -10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deklinacja słońca dla wskazanego dnia\n",
    "\n",
    "Deklinacja jest to długość łuku chwilowego koła godzinnego Słońca zawartego pomiędzy płaszczyzną równika niebieskiego a Słońcem. Deklinacja dowolnego punktu sfery niebieskiej przyjmuje wartości od 0° (na równiku niebieskim), do +/-90° (\"+\" na N od równika i \"-\" na S od równika). Maksymalna wartość deklinacji Słońca (w momentach przesileń) wynosi obecnie +/-23°26,4'. Deklinację oblicza się wg nastepującego równania:\n",
    "\n",
    "$$\\delta = 23.45 \\cdot \\rm{sin}\\left( 360 \\cdot \\left( \\frac{284 + n}{365} \\right)\\right)$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deklinacja słońca: 23.24°\n"
     ]
    }
   ],
   "source": [
    "δ = math.radians(23.45 * sin(math.radians(360 * (284 + n)/365)))\n",
    "\n",
    "print ('Deklinacja słońca: %.2f°' % math.degrees(δ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kąt godzinowy $\\omega$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kąt godzinowy: -2.50°\n"
     ]
    }
   ],
   "source": [
    "ω = math.radians(15 * (minuty / 60.))\n",
    "print ('Kąt godzinowy: %.2f°' % math.degrees(ω))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Obliczanie kąta nachylenia kolektora $\\beta$\n",
    "Celem jest ustalenie takiego kąta nachylenia kolektora $\\beta$ aby promienie słońca padały na niego w sposób optymalny, tj. mozliwie zbliżony do kierunku normalnego do jego powierzchni."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Definicje funkcji do obliczeń $\\rm{cos}\\left(\\theta_{\\beta}\\right)$ oraz kąta $\\theta_{\\beta}$\n",
    "\n",
    "Zależność opisująca $\\rm{cos}\\left(\\theta_{\\beta}\\right)$ w funkcji kątów $\\beta$ i $\\gamma$ oraz szerokości geograficznej $\\phi$, deklinacji słońca $\\delta$ i kąta godzinnego $\\omega$.\n",
    "\n",
    "$$\\rm{cos}\\theta_{\\beta} = \\rm{sin}\\left(\\delta\\right) \\rm{sin}\\left(\\phi\\right) \\rm{cos}\\left(\\beta\\right) - \\rm{sin}\\left(\\delta\\right) \\rm{cos}\\left(\\phi\\right) \\rm{sin}\\left(\\beta\\right) \\rm{cos}\\left(\\gamma\\right) + \\rm{cos}\\left(\\delta\\right) \\rm{cos}\\left(\\phi\\right) \\rm{cos}\\left(\\beta\\right) \\rm{cos}\\left(\\omega\\right) + \\\\ + \\rm{cos}\\left(\\delta\\right) \\rm{sin}\\left(\\phi\\right) \\rm{sin}\\left(\\beta\\right) \\rm{cos}\\left(\\gamma\\right) + \\rm{cos}\\left(\\delta\\right) \\rm{sin}\\left(\\beta\\right) \\rm{sin}\\left(\\gamma\\right) \\rm{sin}\\left(\\omega\\right)$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def CTB(aphi, adelta, agamma, abeta, aomega):\n",
    "    a1 = math.sin(adelta)*math.sin(aphi)*math.cos(abeta) \n",
    "    a2 = - math.sin(adelta)*math.cos(aphi)*math.sin(abeta)*math.cos(agamma) \n",
    "    a3 = math.cos(adelta)*math.cos(aphi)*math.cos(abeta)*math.cos(aomega) \n",
    "    a4 = math.cos(adelta)*math.sin(aphi)*math.sin(abeta)*math.cos(agamma)*math.cos(aomega)\n",
    "    a5 = math.cos(adelta)*math.sin(abeta)*math.sin(agamma)*math.sin(aomega)\n",
    "    return a1+a2+a3+a4+a5\n",
    "\n",
    "def TB(ctb):\n",
    "    return math.degrees(math.acos(ctb))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wyznaczenie optymalnego kąta nachylenia kolektora $\\beta$ w zakresie 0 do 90 stopni."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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tWjUHEwIAABRsTCwCQD5JS0vTDz/8oHvvvVdNmzbVV199lVEqtmjRQnPnztX+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\nxv/b2LklSZIkqTTk5eXx97//neOPPx6Al156iXbt2vH5559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enEceeYSaNWuycuVKunTp\nwksvvZR1LEmqUDIvFoG/AfXJPVPx8O9/mFJamVI6D+gG3BURO5RzPkmSJElSNXT44Ydzzz33APDF\nF1/Qvn175s+fn3EqSao4Mi0WI2Ifcpu1/BYYA6xtxSIAKaV5wCWFL0mSJEmSytxxxx3HzTffDMDi\nxYtp164dH330UcapJKliyHrF4p7ACyml+1NKnVNKM9cz/gmgVdnHkiRJkiQp59xzz+UPf/gDAPPm\nzaNTp04sW7Ys41SSlL2si8WGwGfFHZxSSuRum5YkSZIkqdxcc801nHDCCQDMmjWLHj16sHr16oxT\nSVK2si4W3wd2K+7giPh/wFdlF0eSJEmSpP8VEQwZMoTDD89tDfDYY49x2mmnkVv/IknVU9bF4gzg\n5MLC8AdFRG3g5sJrJEmSJEkqV7Vq1WL48OH88pe/BODOO++kf//+GaeSpOxkWiwWbsgyCZgREadF\nRL21jYuIlsBkcrtG31x+CSVJkiRJ+k6DBg0YO3YsP/3pTwEYMGAAgwYNyjiVJGUj6xWLAKcBXwJ/\nAz6KiJkR8VBE3BsRj0XEB+RKxRbA9cXY4EWSJEmSpDKz7bbb8vjjj7PVVlsBcOaZZ/Loo49mnEqS\nyl/mxWJKaRG5nZ5nAZsA+wDdgJ5AO2BbIAFXpZT+mFFMSZIkSZK+tcsuuzB27Fjq1q1LQUEBxx13\nHFOnTs06liSVq8yLRYCU0nzgQHJl4uPAh8AK4E3gDmCflNIl2SWUJEmSJOm/NW/enIceeogaNWrw\n9ddfc+SRR/L6669nHUuSyk2FKBYBUkprUkoPppQ6ppS2TynVTSn9v5TSaSmlf2WdT5IkSZKk7+vY\nsSN33HEHAEuXLqV9+/YsXLgw41SSVD4qTLEoSZIkSVJldPLJJzNgwAAAFixYQIcOHfjss88yTiVJ\nZc9iUZIkSZKkEurbty+nn346AP/+97/p2rUrK1asyDiVJJUti0VJkiRJkkooIhg4cCBHHXUUAJMm\nTaJ3794UFBRknEySyo7FoiRJkiRJpaBGjRrcf//9HHDAAQDcf//99O3bN+NUklR2LBYlSZIkSSol\nderUYeTIkeyyyy4AXHvttdx+++0Zp5KksmGxKEmSJElSKdpqq60YN24cW2+9NQBnnXUWY8aMyTiV\nJJU+i0VJkiRJkkrZTjvtxJgxY6hTpw4FBQUce+yxzJo1K+tYklSqLBYlSZIkSSoDzZs358EHHyQv\nL4+vvvqKI444grfeeivrWJJUaiwWJUmSJEkqI507d+bWW28FYPHixXTo0IGPP/4441SSVDosFiVJ\nkiRJKkNnnnkmF154IQBz5syhS5cufP311xmnkqSSs1iUJEmSJKmMXXPNNfzqV78CYOrUqfTq1YuC\ngoKMU0lSyVgsSpIkSZJUxvLy8rjrrrs4+OCDAXjooYe+XcUoSZWVxaIkSZIkSeUgPz+fRx99lN12\n2w2AG2644dvnL0pSZWSxKEmSJElSOdliiy147LHH2G677QD43e9+x4gRIzJOJUkbx2JRkiRJkqRy\ntOOOOzJ27Fjq1atHSomePXsyY8aMrGNJ0gazWJQkSZIkqZztvffePPTQQ9SoUYOvv/6azp07M3/+\n/KxjSdIGsViUJEmSJCkDHTp04LbbbgPgo48+omPHjixdujTjVJJUfBaLkiRJkiRl5NRTT/12d+g5\nc+bQrVs3Vq5cmXEqSSoei0VJkiRJkjJ0zTXX0L17dwAmT57MKaecQkop41SStH4Wi5IkSZIkZSgv\nL4+7776bFi1aAHDvvfcyYMCAjFNJ0vpZLEqSJEmSlLE6deowcuRImjRpAkD//v257777Mk4lST/M\nYlGSJEmSpApgm2224bHHHmOLLbYAoHfv3jz99NMZp5KkdbNYlCRJkiSpgth111155JFHqFWrFqtW\nraJr167MmTMn61iStFYWi5IkSZIkVSCtWrVi8ODBACxdupSOHTvy0UcfZZxKkv6XxaIkSZIkSRVM\nr1696N+/PwDz58+nS5cufP311xmnkqT/ZrEoSZIkSVIF1L9/f379618DMH36dE488UQKCgoyTiVJ\n37FYlCRJkiSpAooIBg8ezMEHHwzAsGHD6Nu3b8apJOk7FouSJEmSJFVQ+fn5jBgxgp/97GcAXHvt\ntd8+f1GSsmaxKEmSJElSBbblllsyduxYttpqKwDOOOMMJk6cmHEqSbJYlCRJkiSpwttpp50YOXIk\n+fn5rF69mmOOOYbZs2dnHUtSNWexKEmSJElSJXDAAQfwj3/8A4BPP/2UI444giVLlmScSlJ1ZrEo\nSZIkSVIlcdxxx9G/f38A3nzzTbp168aKFSsyTiWpurJYlCRJkiSpEunfvz/HHXccAM888wynnXYa\nKaWMU0mqjiwWJUmSJEmqRCKCO++8k/333x+Au+++m2uvvTbjVJKqI4tFSZIkSZIqmU022YRHH32U\nHXfcEYCLL76Y4cOHZxtKUrVjsShJkiRJUiW0zTbbMHr0aBo0aADACSecwKxZszJOJak6sViUJEmS\nJKmS2n333Rk2bBh5eXl8/fXXdO7cmQULFmQdS1I1YbEoSZIkSVIl1r59e2655RYAPvzwQ4488ki+\n+OKLjFNJqg4sFiVJkiRJquTOOusszjnnHAD+/e9/07NnT9asWZNxKklVncWiJEmSJElVwF/+8hc6\ndOgAwJgxY7jgggsyTiSpqrNYlCRJkiSpCqhZsyYPPvggu+++OwA33ngjgwYNyjiVpKrMYlGSJEmS\npCpi0003ZcyYMWyzzTZA7hbpiRMnZpxKUlVlsShJkiRJUhXSuHFjRo4cSX5+PmvWrKF79+7MnTs3\n61iSqiCLRUmSJEmSqpgWLVpw5513ArB06VKOPPJIli5dmnEqSVWNxaIkSZIkSVVQz5496du3LwBz\n586lR48erF69OuNUkqoSi0VJkiRJkqqoK664gq5duwIwYcIEzjvvvIwTSapKLBYlSZIkSaqi8vLy\nuPfee9lzzz0BGDhwILfffnvGqSRVFRaLkiRJkiRVYfXq1WPUqFFsu+22AJx99tk89dRTGaeSVBVY\nLEqSJEmSVMU1atSIRx999Nudoo855hjeeOONrGNJquQsFiVJkiRJqgZatGjBkCFDgO92iv70008z\nTiWpMrNYlCRJkiSpmjj++OO5+OKLAZgzZw7HHnusO0VL2mgWi5IkSZIkVSMDBgz4dqfoJ598kvPP\nPz/jRJIqK4tFSZIkSZKqkby8PO655x5+8YtfAHDrrbcyaNCgjFNJqowsFiVJkiRJqmbq16/vTtGS\nSsxiUZKkDDVs2LBCfIckSap+dthhB0aMGEHt2rVZvXo1xxxzDPPmzcs6lqRKpGbWASRJqs6aNGnC\nxRdfzN13382KFSs26NqIoGXLlnTv3r2M0kmSpKpu//33Z8iQIZxwwgksXbqUzp07M2PGDDbddNOs\no0mqBCKllHUGVSIR0Qx45ZufX3nlFZo1a5ZhIkmSJElSSf3xj3/k2muvBaBTp06MHDmSGjVqZJxK\nqj5effVVdt9996Kndk8pvZpVnuLyVmhJkiRJkqq5K6+8kk6dOgEwduxY+vXrl3EiSZWBxaIkSZKk\n/8/efUdZVR1uH//uGXpHRIMNW2ygSZQYSww/TWJXFKVXQQU02MCCKBLEhWBHug4gRVBEo6JYorEr\nGhUVLOS1oXRBKdKZ/f4xwzjIANPPlO9nrbvuuefsc/YzzF0ueThFUjmXmprKww8/zOGHHw7A7bff\nzpQpUxJOJamks1iUJEmSJEnUqlWLp556irp16wLQpUsX/vvf/yacSlJJZrEoSZIkSZIAOPjgg3n0\n0UdJTU1l/fr1nHfeeSxatCjpWJJKKItFSZIkSZKU5W9/+xt33303AAsWLKB58+Zs2LAh4VSSSiKL\nRUmSJEmStI2ePXvSpUsXAN555x26d+9OjDHhVJJKGotFSZIkSZK0jRACI0aM4IQTTgBg/Pjx3Hvv\nvQmnklTSWCwWghBCxRBChxDCMyGEb0II60IIC0MIb4YQeoUQ6hXiXE1DCOkFeHUsrCySJEmSpLKr\ncuXKPP744+yzzz4A9O7dmxdeeCHhVJJKEovFAgohHAa8CzwEnAHsC1QC9gSOB+4A5oYQzijkqWM+\nX4sLOYckSZIkqYzac889efLJJ6latSrp6em0atWKefPmJR1LUglRIekApVkIYW/gJeA3/FLcvQZ8\nCdQH/gZUBfYAngghnB5jfKWA0y4AhuVh/GnAbzOXFwP/LuD8kiRJkqRy5Oijj2bcuHG0bt2an376\niXPPPZdZs2ZRu3btpKNJSpjFYsFMARpkLn8DnBtjnLN1YwhhN2AqGQVjRWBaCOGgGOOq/E4YY/x/\nwBW5GRtCSAG+37orMCnGmJ7fuSVJkiRJ5VOrVq345JNPuO222/jiiy9o27YtTz31FKmpqUlHk5Qg\nL4XOpxDCmcCfMz9uAM7OXioCxBhXAOcBXwEB2A24rhhjnk7G2ZRbTSjGuSVJkiRJZciAAQNo1qwZ\nAM8++yw33nhjwokkJc1iMf8uy3yPwPgY46c5DYoxrgX6ZX4MQLfMMwmLw9YHtUTgw18Xn5IkSZIk\n5VZKSgoTJ06kUaNGAAwZMoSpU6cmnEpSkiwW8yGEUB34a7ZV43exy3RgTebybsBfiiDWNkIItYFz\nySgVYdcZJUmSJEnaqZo1a/Lkk09St25dALp06cKHH36YcCpJSbFYzJ8TgMqZyz8D7+1scIxxA/B2\ntlWnFFGu7FoBVcg4S3ITGfeDlCRJkiSpQA466CCmTp1KSkoK69at47zzzmPZsmVJx5KUAIvF/Dk8\n8z0Cn+TygSgf5LB/Ucp+GfSzMcblxTCnJEmSJKkcOPXUUxk8eDAA8+fPp1WrVmz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ouSJEmSJEmF5JBDDmHUqFEA/PDDD7Rr144tW7YknEoqGhaLkiRJkiRJhahdu3Z06dIFgFdffZVb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\nUF1FtD8TnwXvra42qjPvhsvMO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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c723828>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 451,
       "width": 651
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Definicja tablic\n",
    "abetaD = np.linspace(0,90,100) # ... beta[] jest tylko po to aby wyświetlić na osi x wartości w stopniach\n",
    "cos_TB = np.linspace(0,90,100) # ... tablica wyników\n",
    "\n",
    "abetaR = np.linspace(0,90,100) # ... do obliczeń należy użyć tablicy wartości w radianach\n",
    "\n",
    "# Obliczenie cos(θ_β)\n",
    "x = 0\n",
    "for (i) in abetaR:\n",
    "    abetaR[x] = math.radians(i) # ... przeliczanie stopni na radiany\n",
    "    cos_TB[x] = CTB(ϕ,δ,γ,abetaR[x],ω) # obliczanie wartości cos_theta_beta\n",
    "    x += 1\n",
    "\n",
    "# Poszukiwanie wartości maksymalnej cos(θ_β)\n",
    "max_index, max_value = max(enumerate(cos_TB), key=operator.itemgetter(1))\n",
    "\n",
    "# Optymalna wartość kąta β\n",
    "β = math.radians(abetaD[max_index])\n",
    "θ_β = math.acos(cos_TB[max_index])\n",
    "\n",
    "# Wizualizacja graficzna wyniku obliczeń\n",
    "plt.figure()\n",
    "plt.plot(abetaD, cos_TB, 'k-', linewidth=0.75, ms=4, fillstyle='none',label=\"Prim. chiller\")\n",
    "plt.ylabel(r'$\\rm{cos} \\left( \\theta_{\\beta} \\right)$',fontsize=9)\n",
    "plt.xlabel(r'$\\beta$',fontsize=9)\n",
    "plt.annotate(r'$\\beta = $' + str(round(abetaD[max_index],2)) + \n",
    "             r'$\\degree$ $\\theta_{\\beta} = $' +str(round(math.degrees(math.acos(cos_TB[max_index])),2)), \n",
    "             xy=(abetaD[max_index],cos_TB[max_index]), xytext=(abetaD[max_index], cos_TB[max_index]-0.3),\n",
    "             arrowprops=dict(facecolor='black', shrink=0.05), horizontalalignment='center', \n",
    "             verticalalignment='top', fontsize=8)\n",
    "plt.ylim(0.5,1.1);"
   ]
  }
 ],
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