{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Exercicio 04_exemplo.ipynb","provenance":[{"file_id":"1NGkX4FVjjTMwEfI62UfaO756PP1tIvpE","timestamp":1606852793330},{"file_id":"1cjigfQJdncY6sxt_Tzn5WPCD153C-6ef","timestamp":1606326642718}],"collapsed_sections":[],"authorship_tag":"ABX9TyMGTSmbOgBFMS42yvwXOUIS"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"7GFn9LbahnsB"},"source":["Exercício 4 - EC tipo placa"]},{"cell_type":"markdown","metadata":{"id":"bf75AQpI9cnQ"},"source":["Importando as bibliotecas"]},{"cell_type":"code","metadata":{"id":"DzETdxb7hgTM","executionInfo":{"status":"ok","timestamp":1607007770974,"user_tz":180,"elapsed":1110,"user":{"displayName":"Marcelo da Silva Rocha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi3FYsZ2XWU0p23BET3ZB76LIsm48VMwDvmyrK8=s64","userId":"17363196624282943921"}}},"source":["from sympy import *\n","import numpy as np\n","from scipy.optimize import fsolve\n","import matplotlib.pyplot as plt\n","init_printing(pretty_print=true)"],"execution_count":20,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"7nmuwGXY9nco"},"source":["Inserindo os dados do problema"]},{"cell_type":"code","metadata":{"id":"D-4SEgWW9sMY","executionInfo":{"status":"ok","timestamp":1607008409039,"user_tz":180,"elapsed":704,"user":{"displayName":"Marcelo da Silva Rocha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi3FYsZ2XWU0p23BET3ZB76LIsm48VMwDvmyrK8=s64","userId":"17363196624282943921"}}},"source":["Ly = 600/1000\n","df = 0.76/2000\n","dcl = 0.38/1000\n","dch = 2.89/1000\n","Ny = 5\n","dy = Ly/Ny\n","Lz = 63/1000\n","kf = 120\n","kcl = 180\n","h = 8000\n","M = 0.4\n","Cp = 4186\n","Q = 11100/Ny\n","T0 = Tcha = 30"],"execution_count":57,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Y6SgsIphAboR"},"source":["Definição das distâncias"]},{"cell_type":"code","metadata":{"id":"oGTUI5ZwAezB","executionInfo":{"status":"ok","timestamp":1607008292592,"user_tz":180,"elapsed":751,"user":{"displayName":"Marcelo da Silva Rocha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi3FYsZ2XWU0p23BET3ZB76LIsm48VMwDvmyrK8=s64","userId":"17363196624282943921"}}},"source":["Lfcl = df/2 + dcl/2\n","Lfi = df/2\n","Lcli = dcl/2\n","Lchi = dch/2"],"execution_count":51,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Dm5r3XLiAjBU"},"source":["Definição das áreas"]},{"cell_type":"code","metadata":{"id":"tYyLzNKiAlYa","executionInfo":{"status":"ok","timestamp":1607008295561,"user_tz":180,"elapsed":779,"user":{"displayName":"Marcelo da Silva Rocha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi3FYsZ2XWU0p23BET3ZB76LIsm48VMwDvmyrK8=s64","userId":"17363196624282943921"}}},"source":["Ax = dy*Lz\n","Ayf = df*Lz\n","Aycl = dcl*Lz\n","Aych = dch*Lz"],"execution_count":52,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"A9N30uMrAocw"},"source":["Definição das condutâncias"]},{"cell_type":"code","metadata":{"id":"q7gRzDnlAsmP","executionInfo":{"status":"ok","timestamp":1607008297935,"user_tz":180,"elapsed":865,"user":{"displayName":"Marcelo da Silva Rocha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi3FYsZ2XWU0p23BET3ZB76LIsm48VMwDvmyrK8=s64","userId":"17363196624282943921"}}},"source":["Gfi = kf*Ax/Lfi\n","Gcli = kcl*Ax/Lcli\n","Gfcl = (Gfi*Gcli)/(Gfi+Gcli)\n","Gcls = kcl*Ax/Lfi\n","Gchs = h*Ax\n","Gch = M*Cp\n","Gf = kf*Ayf/dy\n","Gcl = kcl*Aycl/dy"],"execution_count":53,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"naOFwBA842Gl"},"source":["Cálculo das equações de energia e exibição dos resultados"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":851},"id":"z6-7RjoESiSg","executionInfo":{"status":"ok","timestamp":1607008414360,"user_tz":180,"elapsed":1541,"user":{"displayName":"Marcelo da Silva Rocha","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gi3FYsZ2XWU0p23BET3ZB76LIsm48VMwDvmyrK8=s64","userId":"17363196624282943921"}},"outputId":"f34a3f38-163d-4ec2-f1b8-46d683c4cafe"},"source":[" Tcam = []\n"," linha = []\n"," Temp = [] \n"," Tlabel = [[],4]\n"," Xlabel = np.array(['T f','T cl','T s','T ch'])\n","\n"," def Tcam(z):\n","  Tf = z[0]\n","  Tcl = z[1]\n","  Ts = z[2]\n","  Tch = z[3]\n","  Tcha = T0\n","\n","  F = np.empty((4))\n","  F[0] = Q + Gfcl*(Tcl-Tf)\n","  F[1] = Gfcl*(Tf-Tcl) + Gcls*(Ts-Tcl)\n","  F[2] = Gcls*(Tcl-Ts) + Gchs*(Tch-Ts)\n","  F[3] = Gchs*(Ts-Tch) + 2*Gch*(T0-Tch)\n","\n","  return F\n","\n"," z0 = np.array([1,1,1,1])\n"," z = fsolve(Tcam,z0)\n"," linha.append(z)\n"," print('cam 1  --->','Tf =', round (z[0],2),'[oC]', ' Tcl=', round(z[1],2),'[oC]',' Ts=', round(z[2],2),'[oC]',' Tch=', round(z[3],2),'[oC]')\n"," plt.figure()\n"," plt.subplot(211)\n"," plt.plot(Xlabel,z,'bo',Xlabel,z,'r--')\n"," plt.show()\n"," print('-----------------------------------------------------------------------')\n","\n","j = 0\n","for j in range(1,Ny):\n","  \n","  j+=1\n","  Tfa = z[0]\n","  Tcla = z[1]\n","  Tsa = z[2]\n","  Tcha = z[3]\n","  \n"," \n","  def Tcam(z):\n","   \n","   Tf = z[0]\n","   Tcl = z[1]\n","   Ts = z[2] \n","   Tch = z[3]\n","  \n","   F = np.empty((4))\n","   F[0] = Q + Gfcl*(Tcl-Tf) + Gf*(Tfa-Tf)\n","   F[1] = Gfcl*(Tf-Tcl) + Gcl*(Tcla-Tcl) + Gcls*(Ts-Tcl)\n","   F[2] = Gcls*(Tcl-Ts) + Gchs*(Tch-Ts)\n","   F[3] = Gchs*(Ts-Tch) + Gch*(Tcha-Tch)\n","\n","   return F\n","\n","  z0 = np.array([1,1,1,1])\n","  z = fsolve(Tcam,z0)\n","  print('cam', j,' --->','Tf =', round (z[0],2),'[oC]', ' Tcl =', round(z[1],2),'[oC]',' Ts =', round(z[2],2),'[oC]',' Tch =', round(z[3],2),'[oC]')\n","  \n","  linha.append(z)\n","  Temp.append(linha)\n","  plt.subplot(211)\n","  plt.plot(Xlabel,z,'bo',Xlabel,z,'r--')\n","  plt.show()\n","  print('------------------------------------------------------------------------')\n","  \n","  Xlabel = np.array(['T f','T cl','T s','T ch'])\n","  \n"," j+=1\n","\n"],"execution_count":58,"outputs":[{"output_type":"stream","text":["cam 1  ---> Tf = 68.45 [oC]  Tcl= 67.68 [oC]  Ts= 67.37 [oC]  Tch= 30.66 [oC]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["-----------------------------------------------------------------------\n","cam 2  ---> Tf = 69.78 [oC]  Tcl = 69.0 [oC]  Ts = 68.69 [oC]  Tch = 31.99 [oC]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["------------------------------------------------------------------------\n","cam 3  ---> Tf = 71.1 [oC]  Tcl = 70.33 [oC]  Ts = 70.02 [oC]  Tch = 33.31 [oC]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["------------------------------------------------------------------------\n","cam 4  ---> Tf = 72.43 [oC]  Tcl = 71.66 [oC]  Ts = 71.35 [oC]  Tch = 34.64 [oC]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["------------------------------------------------------------------------\n","cam 5  ---> Tf = 73.76 [oC]  Tcl = 72.98 [oC]  Ts = 72.67 [oC]  Tch = 35.97 [oC]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXAAAACCCAYAAABfNJOZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAQ3ElEQVR4nO3dfXBU9b3H8fc3T1CEGHm44AhJcKS01EHFIAnI80NRqTJWRY0WrihIBa2CYqWjnVbsvUrFhyoUseD1hjJTrojVCiIXKg+iwBWucsGBogGsQAQtVZAE8rt//LImS4IksLvnbPbzmtnZnP1ld7+bk/2cc37nnN8x5xwiIpJ80oIuQERETo0CXEQkSSnARUSSlAJcRCRJKcBFRJKUAlxEJEllJPLNWrdu7fLz8xP5liIiSW/Dhg2fOefaHP94QgM8Pz+f9evXN+g5JSUwZQrs3Am5uTB1KhQXx6lAEZEQMrPSuh5PaIA3VEkJjBkDhw756dJSPw0KcRGRUPeBT5lSHd6DeYNBLKX7oRUsnLgK3nnHJ3rE9u3w8cfwySewbx8cOABff+3bnPM3iYmSEsjPh7Q0f19SEnRFIqkp1GvgO3dW//wyw2nGYT+xFygExo6FmTPh2DHo1Kn2C0yaBI89BgcPQk4OZGRAZqa/ZWTAAw/AxImwZw/06VPdHrn/2c/g+uth1y64/fbotowMGD0a+vXzhT7+eO32a66BLl1g92545ZXar9+3L5x9NuzdC5s2RbdlZkLnztC8Ofzzn/DZZ9G1Z2bCGWdAenoC5kQ1bRWFl7obU0+oAzw3t3olux8ryKKcDI7S/l8q+M85FdChg280gxdfhIoKfzt61N936+bbs7LgwQej244e9eEKPhAvvji6raLCPw+gvNyH7PHtw4b59r17Yc6c6Dbwr9+lC2zZAnfcUfsDvv66D/A1a+Dqq2u3r1wJl14KL70Eo0bVbt+4ES64AJ59Fu68s3bAv/su5OXBc8/B9Om1F2B//jOcdZb/2y1YUHsBMnOm/xssXAirV0NmJvtnZDDxUCZHaMKjTAag16E32D5hCxxI86vlaWnwne9U17x8uU8Vs+r25s3hyit9++rVUFYW3Z6dDb17+/Z16/xCLC0tur1rV9/+wQdw5Ih/PPIaLVpAx46+/aOP/EK+ZvsZZ0Dr1r69rMzfR17bDJo08Z8B4PDh6PeO/E6IaMGamiyRg1kVFBS4huzEPP6fEqBZM5g1K+T/lM5BZWV1WJSXwz/+Ub2AiQR9+/Y+yPbvh61bq8M/0t6rF7Rs6buHVq2KXkBUVMBPfgJt2sDatT6Mj1+ATZ0KrVrByy/DvHm133/hQh+Ev/sdzJ5d+/23b/cBPmkSzJhR3QYcIYumHAFgDqMYxQvRf4OWLf3nAvjxj/1CqKa8PN/lBTBkCCxdGt1+/vnw/vv+56Ii/xlrKiryC77I727eHN0+ZAgsWVL9XjU35yI1LVjgf27Vyne51TRyJMyd63/OyqpeKEfccYf/u5WX+4VFzYVDWprfsnvoIf+6nTvXXgBMngzjx/uts/79o5+blua3Dm+8EbZtgxtuqN0+ZQpcfrn/3BMmsOrtNA59nUYlaSxmKE9yF2BRf2ZJXma2wTlXcPzjoV4Dj4R00m0WmkV3bWRl+aA9kVatfFifyHnn+duJFBb624kMH+5vJzJ+vL+dyLRp/obv895VeoxMqgPtDp5hevvH2bSxasFVWRm9z2HGDP/8yOOVldF/n5kzfTdXzfYmTaKfH2mP3LKzq9ufeSa63bnov/eTT8KXX0a35+ZWtz/6qN9fUvP1I1tnAA8/7Nfga7Z37+7b0tLgnnui25zzW3Tg5/2111Z/rsgtsnWQlQWXXBL92Ssr/ZYR+C2idu1qt2dUfXUrK/3C9utKmlNJNgd5grtZR3fW0KvWcksal1CvgUv4JO1WUSOXnx/pbnT05a/8lX7+8dxKPioN9bEKUg8nWgPXnJUGKS72YZ2X5zc08vIU3mEwdapfkIJ9E94Dmqxmg10MO3YEWZrEkQJcGqy42PerVlb6e4V38OpasE6+t5KWB0t999rx+xCkUVCAizQSxy9Yh/y6N7z9tt9f0L9/9U5baTQU4CKNWefOfu27Wze/M3X58qArkhgK9VEoIhIDrVvDsmX+aJ8+fYKuRmJIa+AiqaBpU39mcXq6P7P45pv9uQmS1BTgIqlm3TqYP9+fe1Ba5yB3kiQU4CKp5uqrYfFifxZoYSHo3IykpQAXSUUDB/qhCJo29f3iK1cGXZGcAgW4SKrq0sUfoTJihB8UTZKOAlwklbVt60fSzM724yNMm+bHVpGkoAAXEe+ll+Dee+Gqq/zwvRJ6CnAR8W66yY/8uGSJH4t99+6gK5KTUICLSLXbb4fXXvMDYPXo4a8UJaGlABeRaD/8ob+ASNu2/spFEloKcBGprWtX2LDBX0jEOXjjjaArkjoowEWkbpHrfv7xj36t/O67/ZWJJDQ0mJWIfLsRI/wFsp94wveNz5unrpWQ0Bq4iHy79HQf3k89Ba++6s/c/PTToKsSFOAiUl8TJsCiRX4tfOvWoKsR1IUiIg0xbJi/3M+ZZ/rpnTshNzfQklKZ1sBFpGEi4b14sT9KZdasYOtJYQpwETk1vXrBoEEwdizcd5+/GKcklAJcRE5Nixbwyiswbhw89hhcdx0cPhx0VSmlXgFuZjlmtsDMtprZFjMrMrOWZrbUzLZV3Z8V72JFJGQyMuCZZ+C3v/WDYS1cGHRFKaW+a+BPAoudc98DLgC2APcDy5xznYBlVdMikmrM4J574L334MYb/WNHjgRbU4o4aYCb2ZlAH+B5AOdcuXPuC+Aq4IWqX3sBGB6vIkUkCUQuCrFpk9+5uWxZsPWkgPqsgXcEyoA5Zvaemc02szOAts65yNH8e4C28SpSRJJITo4/UmXoUH+xCImb+gR4BtANmOGcuwj4iuO6S5xzDnB1PdnMxpjZejNbX1ZWdrr1ikjY5eXB6tXQvz/ccgv84hc6QiVO6hPgu4Hdzrl3qqYX4AN9r5mdDVB1v6+uJzvnZjnnCpxzBW3atIlFzSISdmee6ccVv/VWmDoVnn8+6IoapZMGuHNuD7DLzDpXPTQQ+D/gFWBk1WMjgUVxqVBEklNmpj/Jp6QERo0KuppGqb5HoUwASszsf4ELgUeAfwMGm9k2YFDVtIhINTN/ZEpmJpSV+WFpP/ww6KoajXqNheKc2wgU1NE0MLbliEijtXu3P9SwqAheftmPaiinRWdiikhiXHQRrF3rL9U2eLDvWpHTogAXkcQ591xYs8aPo3LTTTB3btAVJTUFuIgk1lln+ZEM778frrgi6GqSmgJcRBIvKwt+8xto0wbKy32YHzgQdFVJRwEuIsHasAGmT4eePeFvfwu6mqSiABeRYBUVwZtv+sMMCwt9H7nUiwJcRILXu7c/QiUnBwYM8IcZykkpwEUkHDp18iE+cKA/WkVOSgEuIuHRqpUfQ6VrVz89fz5UVARbU4gpwEUknNauhRtugMsugy++CLqaUFKAi0g4FRb6E33eesuf+PPxx0FXFDoKcBEJr5EjYckS+PvfoUcPePfdoCsKFQW4iIRb//7+0MIWLfyAWPKNeo1GKCISqO9/HzZvhiZN/PT778P55/vhalOY1sBFJDlEwnvjRujWDcaNg6NHg60pYApwEUkuXbvCpEnw+9/Dj34EBw8GXVFgFOAiklzS0vxAWM89B0uXwqWXwq5dQVcVCAW4iCSnW2+F11+H0lJ/wk8K0k5MEUlegwf7HZodOvjpgwchOzvYmhJIa+Aiktxyc/3RKDt2+PFUnnoq6IoSRgEuIo1Du3b+jM277oI774Rjx4KuKO4U4CLSODRrBn/6E0ycCE8/DcOHw5dfBl1VXCnARaTxSE+HadPg2WfhL3+Bhx8OuqK40k5MEWl8xo2DLl2ge3c/7VyjPGtTa+Ai0jj17eu7VQ4e9MeKL14cdEUxpwAXkcbtq6/g0CEYNgxmzAi6mphSgItI43b22bByJQwdCj/9qd/J2UiOUFGAi0jj17w5LFoEEybA44/DAw8EXVFMaCemiKSG9HR/ks8PfgCXXx50NTGhNXARSS1jx/pT748d82vkmzcHXdEpU4CLSGravRsWLICePeHNN4Ou5pQowEUkNeXlwTvv+PvLLoPZs4OuqMEU4CKSunJzYdUqGDAAbrsNHnkk6IoaRAEuIqktOxtefRXGj4d+/YKupkEU4CIimZl+AKyePf30H/4A+/YFW1M9KMBFRGratcuvjRcVwdatQVfzrRTgIiI1degAy5f7oWiLimDFiqArOiEFuIjI8Xr0gLVr/Wn4Q4bAiy8GXVGdFOAiInXp2BHWrIHevaGiIuhq6lTvU+nNLB1YD3zinBtmZh2B+UArYANws3OuPD5liogEICcHli6FtKp13bVr4aKLoEmTYOuq0pA18LuALTWm/x2Y7pw7D/gcGB3LwkREQiES3nv2+OPFBw2C/fuDralKvQLczNoDVwCzq6YNGAAsqPqVF4Dh8ShQRCQU2rWDOXNg3Tq/c3PbtqArqvca+BPAfUBl1XQr4Avn3NGq6d3AOTGuTUQkXEaMgGXL4MABKCz0Z3EG6KQBbmbDgH3OuQ2n8gZmNsbM1pvZ+rKyslN5CRGR8OjVy/eFt24NCxcGWkp9dmL2Aq40s8uBpkA28CSQY2YZVWvh7YFP6nqyc24WMAugoKDAxaRqEZEgnXeeHwirRQs//emnvoslwRdOPukauHPu58659s65fOB64L+dc8XAcuCaql8bCSyKW5UiImGTk+MvErF/P3TvDrfcAuWJPRDvdI4DnwzcY2bb8X3iz8emJBGRJNKypR/JcO5cf93Nzz9P2Fs36JJqzrkVwIqqn3cAl8S+JBGRJGIGDz0E554Lo0f7AbFee81Px5nOxBQRiYWbb/Yn/ezdm7CLJivARURipW9ff4TKzJkAzPuPo+Tn+3OB8vOhpCS2b6cAFxGJpe9+F3JymD/nMB3/tS/XlT6Kc47SUhgzJrYhrgAXEYmDh35plFZ24FEmM41JABw6BFOmxO49GrQTU0RE6mfbrqbcyDy20YkV9Pvm8Z07Y/ceCnARkTjIzYXS0jQe5Ne1Ho8VdaGIiMTB1KnQrFn0Y82a+cdjRQEuIhIHxcUwaxbk5flDxfPy/HRxcezeQ10oIiJxUlwc28A+njmXuPGlzKwMKD3Fp7cGPothOXL6NE/CSfMlfE53nuQ559oc/2BCA/x0mNl651xB0HVINc2TcNJ8CZ94zRP1gYuIJCkFuIhIkkqmAJ8VdAFSi+ZJOGm+hE9c5knS9IGLiEi0ZFoDFxGRGkJ3HLiZtQKWVU22A44BkashX+KcK6/6vWuBXwF7nHP9E15oCqjvvDjBc0cBBc658XEtMkWdzryR2DnN78gvgS+dc9NO9f1DF+DOuf3AhXDSDzgauM05tyqB5aWUBswLSTDNm3AIej4kZReKmT0IXAo8b2aPBV1PqjOzoWb2P2a2ycyWnfwZkghmlm5mc83sAzN738zuDrqmVPUt35EuZrbCzHaY2Z0Nfd3QrYHXh3PuV2Y2AJjknFsfdD2pzMzaAM8BfZxzH5lZy6Brkm9cCJzjnDsfwMxyAq4nJZ3kO/I9oD/QAvjQzGY45yrq+9pJuQYuoVIIvOWc+wjAOXcg4Hqk2g7gXDN72syGAgeDLihFfdt35DXn3BHn3GfAPqBtQ15YAS7SSDnnPgcuAFYAtwOzAy1I6nKkxs/HaGCviAJcTtdaoI+ZdQRQF0p4mFlrIM0591/AL4BuAZeUquL2HUnKPnAJD+dcmZmNAV4yszT8ZuDggMsS7xxgTtV8Afh5kMWkqnh+R3QmpohIklIXiohIklKAi4gkKQW4iEiSUoCLiCQpBbiISJJSgIuIJCkFuIhIklKAi4gkqf8HRX90TVQM44EAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["------------------------------------------------------------------------\n"],"name":"stdout"}]}]}