TMHAltimeters.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import *\n",
    "from matplotlib.pyplot import *\n",
    "%matplotlib inline\n",
    "poly = np.polynomial.Polynomial\n",
    "\n",
    "from astropy.io import ascii\n",
    "\n",
    "from scipy.integrate import quad\n",
    "from scipy.interpolate import interp1d\n",
    "from scipy.ndimage.filters import gaussian_filter1d\n",
    "from scipy.misc import derivative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Object `poly.polyfit` not found.\n"
     ]
    }
   ],
   "source": [
    "poly.polyfit?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ascii.read(\"TMHEasyMiniData.txt\", data_start = 14)\n",
    "#ascii.read(\"TMHStratoData.txt\", data_start = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "easymini = ascii.read(\"TMHEasyMiniData.txt\", data_start = 14)\n",
    "stratologger = ascii.read(\"TMHStratoData.txt\", data_start = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "emAlt = easymini[\"altitude\"]\n",
    "emPress = easymini[\"pressure\"]\n",
    "emAccel = easymini[\"acceleration\"]\n",
    "emTime = easymini[\"time\"]\n",
    "\n",
    "slAlt = stratologger[\"col2\"]\n",
    "slVel = stratologger[\"col3\"]\n",
    "slTime = stratologger[\"col1\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(emTime, emAlt, c = \"forestgreen\", label = \"EasyMini\")\n",
    "plot(slTime, slAlt, c = \"tomato\", label = \"Stratologger\")\n",
    "legend()\n",
    "title(\"Altitude vs Time\")\n",
    "xlabel(\"Time (s)\")\n",
    "ylabel(\"Altitude (m and ft)\")\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "mToFt = 3.2808\n",
    "slAltM = [x/mToFt for x in slAlt]\n",
    "slVel = [x/mToFt for x in slVel]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(emTime, emAlt, c = \"forestgreen\", label = \"EasyMini\")\n",
    "plot(slTime, slAltM, c = \"tomato\", label = \"Stratologger\")\n",
    "legend()\n",
    "title(\"Altitude vs Time\")\n",
    "xlabel(\"Time (s)\")\n",
    "ylabel(\"Altitude (m)\")\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "399.58\n",
      "399.29285540112164\n",
      "-0.8099999999999996\n"
     ]
    }
   ],
   "source": [
    "print emAlt[500]\n",
    "print slAltM[116]\n",
    "\n",
    "timeConversion = emTime[500] - slTime[116]\n",
    "print timeConversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.240000000000002\n"
     ]
    }
   ],
   "source": [
    "slTimeC = []\n",
    "for i in range(0, len(slTime)):\n",
    "    slTimeC.append(slTime[i] - .8099999999999996)\n",
    "t_ap = slTimeC[argmax(slAlt)]\n",
    "print t_ap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize = (8, 5))\n",
    "plot(emTime, emAlt, c = \"forestgreen\", label = \"EasyMini\")\n",
    "plot(slTimeC, slAltM, c = \"tomato\", label = \"Stratologger\")\n",
    "legend()\n",
    "title(\"Altitude vs Time\")\n",
    "xlabel(\"Time (s)\")\n",
    "ylabel(\"Altitude (m)\")\n",
    "# xlim(0, .5)\n",
    "# ylim(-50, 0)\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "535.16 547.1226530114606\n",
      "2.2106337892738845\n",
      "2092\n",
      "2093\n"
     ]
    }
   ],
   "source": [
    "emMax =  max(emAlt)\n",
    "slMax =  max(slAltM)\n",
    "\n",
    "print emMax, slMax\n",
    "\n",
    "# percent difference from the average\n",
    "percentDiff = abs(slMax - emMax)/((slMax + emMax) / 2.) * 100\n",
    "print percentDiff\n",
    "print len(emTime[0:-1])\n",
    "print len(emVel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.5099999999999993\n",
      "1.2900000000000005\n",
      "2.8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python27\\lib\\site-packages\\ipykernel_launcher.py:4: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "# emVel = diff(emAlt) / (emTime[1]-emTime[0]) # divide differences between altitudes by time step for velocity\n",
    "emVel = [0.0]\n",
    "for i in range(1, len(emAlt)) :\n",
    "    emVel.append((emAlt[i] - emAlt[i-1]) / (emTime[i] - emTime[i-1]))\n",
    "smoothVel = gaussian_filter1d(emVel, 10)\n",
    "\n",
    "time_diff = slTimeC[argmax(slVel)] - emTime[argmax(smoothVel)]\n",
    "print time_diff\n",
    "print slTimeC[argmax(slVel)]\n",
    "print emTime[argmax(smoothVel)]\n",
    "# emTime = [t + time_]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1166d0d0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot(emTime[1:], emVel)\n",
    "plot(emTime, smoothVel, label = \"EM\")\n",
    "# plot(emTime[1:], emVel, label = \"EM\")\n",
    "plot([x-.32 for x in slTimeC],slVel, label = \"SL\")# this in not long-term acceptable\n",
    "legend()\n",
    "# xlim(0, 3)\n",
    "# ylim(-20, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gaussian_filter1d?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "smoothEM = gaussian_filter1d(emAlt, 2)\n",
    "smoothSL = gaussian_filter1d(slAltM, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11994ef0>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(emTime, smoothEM)\n",
    "plot(slTime, smoothSL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1191e9f0>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "emAccel[970:] = emAccel[970:] * -1\n",
    "plot(emTime, [x+9.8 for x in emAccel])\n",
    "# xlim(2.5,3)\n",
    "# ylim(-5, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x118cb0b0>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(emTime, emPress)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "535.16"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(emAlt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "535.16"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emAlt[970]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.82"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emTime[970]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " time\n",
      "-----\n",
      "  0.0\n",
      " 0.01\n",
      " 0.02\n",
      " 0.03\n",
      " 0.04\n",
      " 0.05\n",
      " 0.06\n",
      " 0.07\n",
      " 0.08\n",
      " 0.09\n",
      "  ...\n",
      "95.36\n",
      "95.46\n",
      "95.56\n",
      "95.66\n",
      "95.76\n",
      "95.86\n",
      "95.96\n",
      "96.06\n",
      "96.16\n",
      "96.26\n",
      "96.36\n",
      "Length = 2093 rows\n"
     ]
    }
   ],
   "source": [
    "print emTime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1]\n"
     ]
    }
   ],
   "source": [
    "print range(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# argmax?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fd = ma = .5 * cd * v^2 * r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x116d6250>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot([x**2 for x in smoothVel], emAccel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "T = 283 # [K]\n",
    "R = 287 # [J/(kg*K)]\n",
    "rho = [x / (T*R) for x in emPress]  # [kg/m^2]\n",
    "#print rho"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "inToM = 0.0254\n",
    "A = (1.55**2 * pi + 1.24*4) * inToM**2   #[m^2]\n",
    "m = 5.127    # [kg] dry mass\n",
    "C = 2 * m / A   # [kg/m^2]\n",
    "cdEm = []\n",
    "for i in range(len(emPress)) :\n",
    "    v = smoothVel[i]\n",
    "    a = emAccel[i]\n",
    "    #stupid time issue, will work on later\n",
    "    cdEm.append(C * a / (v**2 * rho[i]))\n",
    "#print cdEm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x224fb070>]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(smoothVel[0:281], cdEm[0:281])\n",
    "#xlim(0,)\n",
    "#ylim(-20,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "expected an indented block (<ipython-input-67-8af01bde9980>, line 8)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-67-8af01bde9980>\"\u001b[1;36m, line \u001b[1;32m8\u001b[0m\n\u001b[1;33m    #although, all the other numbers dont make sense which is odd\u001b[0m\n\u001b[1;37m                                                                 ^\u001b[0m\n\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m expected an indented block\n"
     ]
    }
   ],
   "source": [
    "for i in range(0, len(smoothVel)):\n",
    "    #plot(i, smoothVel[i], \".\")\n",
    "    #plot(i, emAlt[i], \".\")\n",
    "    #plot(i, cdEm[i], \".\")\n",
    "    #print(i, cdEm[i])\n",
    "    \n",
    "#there are a bunch of nans around value 280, and 1200 that may be messing up the data plot\n",
    "#although, all the other numbers dont make sense which is odd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "slAccel = []\n",
    "for i in range(0, len(slVel)):\n",
    "    slAccel.append((slVel[i] - slVel[i-1]) / (slTime[i] - slTime[i-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2191c570>]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot(slTime, slAccel)\n",
    "plot(slTime, slVel)\n",
    "slAccelSmooth = gaussian_filter1d(slAccel, 10)\n",
    "plot(slTime, slAccelSmooth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nan, nan, nan, nan, 6088.682849933102, 1519.2632126196404, 671.8953531238973, 362.69031387058345, 226.57353460720543, 119.61444001438569, 67.81174861002711, 66.47420091684201, 63.342725777837046, 64.41426746727358, 65.88817121280036, 56.39193461817459, 48.48948256590072, 41.89988971268143, 32.83453177944367, 26.276752166250986, 21.363498241411193, 14.919402604477087, 7.120319698003961, 1.411154856892409, -7.391065671455484, -12.768593055993422, -11.978476131308541, -7.1296618657902435, 3.492658046389671, 12.896892385930398, 15.966348700168963, 12.035921513769324, 1.325555638414397, -16.11942901168122, -29.396862741121865, -32.77600037670441, -4.189479760381001, 32.53008584754695, 47.40545647682165, 40.46718356405386, 28.548544140700123, 16.272750330069776, 5.551257003715892, -1.8898747557850162, -6.049629418886061, -5.895717218331853, -4.488410612280893, -1.1354923590221773, 1.1224923068374921, 1.1096491905463561, -0.5580879484919636, -1.7038301520941623, -3.5302066134029477, 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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python27\\lib\\site-packages\\ipykernel_launcher.py:5: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  \"\"\"\n",
      "c:\\python27\\lib\\site-packages\\ipykernel_launcher.py:5: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "cdSl = []\n",
    "for i in range(len(slVel)) :\n",
    "    v = slVel[i]\n",
    "    a = slAccel[i]\n",
    "    cdSl.append(C * a / (v**2 * rho[i]))\n",
    "#print cdSl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x217b87f0>]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oipKgKEqCn5+fucKUrNAr61Moq9Xy/JWjUKnk6XeSbVs8PhiADScGVoWUWZKFEMIeY6L4QlGU7003FzZPL5neFpluzwXOvnwMBvLNEYdke47lVvLZ7ixumhrOqKEelg5HknptmK8L0QGurEsssHQoZmWOaigBfAAkKYry77M+tRpYZnp/GfDTWbffZKqKmgJUNk9XSYOL3qDw+I/H8HFx4C/zoi0djiSZzfy4QPZmllE+gFqCmGNkMR24EZgthDhs+rcAeBGYK4RIBeaaPgZYC2QAacB7wN1miEGyQav2ZXMkt5LHF47E3dHe0uFIktnMiw3EoMDG5KLO72wjer3ArSjK77S9DgEwp437K8A9vX1eybaV1jTyr/+lMCXCm0Vjh1g6HEkyq1FD3Rni4ci6xAKunhBs6XDMQm6RlSzixV+TqW3U8dyiURhnMiVp4BBCMC8ukO2pxdRrB0Y7c5kspH6371QZ3xzI5baZEUQFuFk6HEnqE/NiA2hoMrAttdjSoZiFTBZSv9LpDTzx43GGeDhy/5xIS4cjSX1m4jBvPJzsWZ84MEpoZbKQ+tXHO0+RXFDNk5fH4awx655QSbIq9moVc0b4szG5EJ3e9tt/yGQh9ZuCygb+s+EkF8X4MT8uwNLhSFKfmxcXSEVdE3tPlVk6lF6TyULqN8+vOUGTQeGZK+LkorY0KFwQ7YuDnWpATEXJZCH1i+2pxfxy9DT3XBRJmI+LpcORpH7hrLFjZpQfG04U2nzrcpkspD7XqNPz5E+JhPk4c8eFEZYOR5L61fy4APIq6knM76hlnvWTyULqc+9tyyCzpJZnrojD0V5t6XAkqV/NGRmASsB6G+8VJZOF1Kdyyup4Y1MaC+IDuSjGv/MvkKQBxttFw8Rwb9bZ+LqFTBZSn3rm50TUKsETl8VaOhRJspj5cYGkFFZzqqTW0qH0mEwWUp/ZcKKQ35KKeODiKII8nCwdjiRZzNxYY6m4LZ9xIZOF1CfqtXqeXp1IdIArN08fZulwJMmiQrydiQ1yt+kzLmSykPrEm5tTyauo57lFo7BXy18zSZoXF8CB7HKKqxstHUqPyL9iyezSimpYsS2DxeOHMjnCx9LhSJJVmB8XiKLAxiTbnIqSyUIyK0VReGr1cRzt1Txy6UhLhyNJVmNEoBsh3k6st9F1C5ksJLP6+ehpdqSV8v/mx+Dn5mDpcCTJagghmBcbyO+pJdQ06iwdTrfJZCGZTXVDE8//coL4oR5cPznM0uFIktWZHxeIVm9ga4rtnXEhk4VkNv/ZkEpxTSPPXTkKtUo2CpSkc00I88LbRcP6E7ZXFSWThWQWJ/Kr+HhnJtdPCmVsiKelw5Ekq6RWCS4e6c+m5CK0Ots640ImC6nXDAaFJ346jqezhofmx1g6HEmyavPjAqlu0LE7o9TSoXSLTBZSr317IJcDWeU8cukIPJ01lg5Hkqza9EhfnDVqm5uKkslC6pXyWi0v/JpEQpgXV40PtnQ4kmT1HO3VXBjtx/rEQgwG2znjQiYLqVf+tS6FqgYdz105CpVc1JakLpkXF0BRdSNHcissHUqXyWQh9dih7HJW7cvm5mnhjAxyt3Q4kmQzZscEYKcSNrVBTyYLqUf0BoXHfzyOv5sDD8yNtnQ4kmRTPJztmRLhY1MHIslkIfXI57uzSMyv4onLYnF1sLN0OJJkc+bFBZBeXEtaUY2lQ+kSmSykbiuqbuDldSnMiPRlYXyQpcORJJvUfMaFrVRFyWQhddsLa5Np1Bl4dlEcQshFbUnqiSAPJ8YEe7DeRo5blclC6pZd6aX8cCiPOy6MIMLP1dLhSJJNmxcXyOGcCgoqGywdSqdkspC6TKsz8ORPxwn2cuLuiyItHY4k2bx5zcet2sAZFzJZSF324Y5MUotqeOaKOJw0akuHI0k2L9LflQhfF5uoipLJQuqSvIp6XvstlYtHBjBnZIClw5GkAUEIwdy4AHall1JZ32TpcDokk4XUJf9Yk4SCwlOXx1o6FEkaUObFBqIzKGxJKbJ0KB2SyULqVJ1Wx7rEAm6YHEaIt7Olw5GkAWVciCd+bg5WXxUlk4XUqQNZ5egMCjOjfC0diiQNOCqVYG5sAFtSimho0ls6nHbJZCF1andGKWqVICHc29KhSNKANC82gFqtnp3pJZYOpV0yWUid2pNRRvxQD9nWQ5L6yLThvrg52Fn1VJRZkoUQ4kMhRJEQ4vhZt3kLITYIIVJNb71MtwshxOtCiDQhxFEhxHhzxCD1jTqtjiO5FUyJ8LF0KJI0YGnsVFw0wp/fkgrRW+kZF+YaWXwMXHLObQ8DGxVFiQI2mj4GuBSIMv1bDrxtphikPnAwq4ImvcKUCDkFJUl9aV5sACU1Wg5ml1s6lDaZJVkoirINKDvn5kXAJ6b3PwGuPOv2TxWj3YCnEEJ2o7NScr1CkvrHRTF+aNQqq92g15drFgGKopwGML31N90+FMg56365pttaEUIsF0LsF0LsLy4u7sMwbY+iKNQ06vrluXZnlMr1CknqB26O9kyL9GH9iUIUxfqmoizxCtBWm9LzfjKKoqwAVgAkJCRY30+un1XUafk9rYStKcVsSy2msKqRmAA3ZkT5MjPKl8nDfMzegqN5veLWGRFmfVxJkto2LzaQR384RkphNSMCrev0yb5MFoVCiCBFUU6bppmatyfmAiFn3S8YyO/DOGyS3qBwLK+SrSnFbD1ZxOGcCgwKuDvaMTPaj0g/V/adKuOzXVl88HsmGrWKCWFeLckjbogH6l6eiS3XKySpf10c689jP8L6xMJBlSxWA8uAF01vfzrr9nuFEKuAyUBl83TVYFdWq2VzchFbTxazPbWY8romhIDRwZ7cOzuKC6P9GBPsgZ36zOxhvVbP3lNl/J5azPbUEl5al8JL61LwdLZn+nBfZkT5MiPSt0c7r+V6hST1L383R8aHerH+RAH3z4mydDitmCVZCCFWAhcBvkKIXOApjEniayHErUA2cI3p7muBBUAaUAfcbI4YbFVpTSPrEgtZe+w0uzJK0RsUfF0dmDXCnwuj/ZgZ5Ye3i6bdr3fSqLkw2o8Lo/0AKK5uZEdaCdtTS/g9rZg1x4x5ONzH2ZQ4/Jg63AcPJ/tOY5PrFZLU/+bFBvDCr8nkVdQz1NPJ0uG0ENa4kHKuhIQEZf/+/ZYOw2zaShDDfF1YEB/IJXFBxA1xR9XLKSQwLoSnFdWYEkcJuzNKqdPqUQkYE+LJzEhfZkT5MS7UE3t161qHOq2OMc+s55YZw3jk0pG9jkWSpK7JLKll1stbeOryWG6ePqxXjyWEOKAoSoI54pKXjP2kqqGJNUdPs+Zo6wRx54URLIgPIjbI3exHlAohiApwIyrAjVtmDEOrM3Aou5zfTSOPNzen8fqmNFw0aqZE+LSsdwz3cz1rvUJuxpOk/jTM14XoAFfWJxb2OlmYk0wWfUhRFI7kVrJyTzarj+RT36Tv8wTREY2diskRPkyO8OHBeTFU1jWxK6OkZeSxMdlYgxDk4chp0zGPEb4u/RafJElG82IDeXtrOuW1Wrw6mIbuTzJZ9IGqhiZ+OpzPyj3ZnDhdhbNGzaKxQ1gyKZQxwR79miA64uFszyWjgrhklHFPZE5ZXctax+ljxo1BF760hdggd2ZGGRfLJ4Z742gvT8mTpL40Ly6ANzensTG5iKsnBFs6HGAQrVlU1jfx5Z5s6rWtN7OpVIJrE0IYYoaFpPJaLf/8XzI/HTaOImKD3Ll+ciiLxg7BzbHzBWVrUa/VM+rpdUwK92ZGlC/bU4s5kFVOk15BY6diUrg3C0cHsWRiiNUkPkkaSBRFYdqLm4gf6sGKm3q+5CDXLHrguV9O8O2BXM59bVMUqGnQ8fhlvTsBTlEUHvr2CFtPFrN4XDDXTw5ltBWNIrrjYHY5eoPC8gsjmBXjzz2zIqnT6tiTWcbvqSVsO1nMI98fI6O4hkcXjLTJ71GSrJkQgnmxAXy1P4d6rd4qzrwfFMnieF4l3x3M5Y4LInhkQevKnqvf3smhnIpeP8c3B3L5LamIxxeO5LaZtr3juWV/RZhXy23OGjtmxfgzK8YfRVF45ucTvLc9E4MCjy+UCUOSzG1eXCCf7MpiW2ox8+MCLR3O4DjP4h9rk/By1nD3rMjzPjcu1JNjeZVodYYeP35OWR3P/nyCycO8ucWKqhd6andGKaOGerQ7dSaEMJX1hfPB75k8+8sJq+xlI0m2bNIwbzyc7K3mjIsBnyxyyurYmV7K7TMj2tyINjbEC63OQHJBVY8e32BQePCbIwC8cu0Ys+yPsKR6rZ7DORWdtvgQQvDkZbHcOmMYH+04xdOrE2XCkCQzslerCPdxprCqwdKhAIMgWWxMMmblS0a1PYwbF+oJwKHsnk1Ffbgjk72ZZTx1eSzBXt1vqWFtDmaXd3l/hRCCxxeOZPkFEXyyK4snf0rEYKUHt0iSLcouqyPUxzpeVwZ8stiUUkyEnwvD2tkvEOThSIC7A4d6cODIycJq/rUuhbmxAVZT3tZbba1XdEQIwSOXjuCOCyP4bHcWj/90vFXCyCiu4fZP97POSnv0S5K1qqxvoryuiXArSRYDfoE7v6KemAC3dj8vhGBciFe3F7m1OgP/99Vh3BzseGFx/IBZ4O1svaItQggevmQEaiH475Z0FEXh71fGczi3gls/3kd5XRMbThRy9YRgnro81qbKiCXJUrJL6wAI9baOjbEDPlkYFKXTdYSxoZ78L7GA0ppGfFwduvS4b2xKJTG/inf+OAHfLn6NtWter7hlRvcX6YUQPDQ/BpUQvLk5jfWJhVQ36gjycOSbO6fy0+F83tqcxq70Ul65doxsIyJJncgqqwUgzEpGFgN+GiqjuJY1RzvugD4uxLhucSS3a6OLg9nlvLU5javGB7e7FmKLWtYrhvXshVwIwYPzovF1daC0VotWZ+C7u6YR6e/Gg/Ni+ObOadirBUvf280/1ibR0KQ383cgSQNHlmlkIZOFFYkPNh4U1JVF7ppGHQ+sOkyQhxNPXdG7jXzWZk9GKSoBCeFdW684l6Io/HvDSUpqGgFwc7TD2/lMX5sJYV6s/fNMrp8UyoptGSx6cweJ+ZVmiV2SBpqs0lr83Bxw1ljHBJBMFhg3nI0IdOtSsnh6dSK55XW8umQs7gNs7n13Rhnx3VyvaNakN/DQt0d5Y1Ma1yWE8K+rR1PdoONoXutk4Kyx4+9/iOejmydSVqflyrd28NbmNPSyikqSWjlVWmc1i9swiJJF+MNr+GpfdrufHxfqyeGcig5ftH45ms+3B3K5Z1YkEwfY6XFn9ld0fwqqtlHHrZ/s59sDufx5ThQvXhXPvNgA1CrRbhXUrBh/1j9wAfNiA3lpXQqvb0zt7bcgSQNKdmmd1SxuwyBKFgDfHchr93NjQ7yoadSRXlzT5ufzK+p59PtjjAnxtLrjDs3hUHY5Wr2h28miuLqRJSt2syOthBcXx/N/c6MRQuDprGFKhDfrOyiZ9XLR8Ob14wj3cW735y5Jg1FDk56CqgY5srCk9nYZN2/OO9zGVJTeoPCXrw+jMyi8dt3Y806VGwh292C9IqO4hsVv7yC1qJoVN05gyaTQVp+fFxtIenEtaUXtJwIhBC4OdtRr5WK3JDXLLjOVzcpkYRl7T5WxP6vtzXfDfFzwcLLnUE7rzxsMCk/8dJzdGWU8fXkc4QP0MKDurlcoisLtn+6ntlHPytunMGdkwHn3mRtrvG39iY435DnZq6mXlVGS1OJUibFsNtzHel5vBlWyAGOvqLaoVIKxIZ6tFrn1BoW/fXeUL/dkc+eFw7kmYWDs0j5XT9Yr9mSWkV5cy2MLRjIutO3RyBBPJ0YHe3TaCM1Jo6ZOjiwkqUXzyMJaymZhECaLslptu58bF+pJSmE1NY06dHoDf/n6MN+YFm3/dknMgNmlfa6erFd8tS8HN0c7FsQHdXi/ebEBHM6p6LAZmpO9Wk5DSdJZTpXW4uFkj6ezdRypCjJZtDI2xBNFgaM5FTy/JomfDufz0PyYlkXbgaq76xWVdU2sPXaaK8cO7fRQluY+/OtPtD+6cNbIaShJOltWaZ1VjSpAJotWxpp2cr/wazIf7zzFrTOGcU8bZ2AMNN1dr/jpSB6NOgOZ3tvtAAAgAElEQVTXTQzp9L6R/q4M83XpsCrKSWMnp6Ek6SzGZGE96xUgk0Urns4aIvxcOJZXyQXRfjxy6Yh+jMwyurteoSgKK/fmEDfEnVFDPTq9f/PxkLvSS6msb2rzPk72atn6Q5JMmvQG8irqCfOWIwuLcXOw6zBZAMwZ4c+IQDfeWDoOuwFYInuu7q5XHM+rIul0FUu6MKpoNi8uEJ1BYUtKUZufd9aoqdPq5OFJkgTkldejNyhWNw1lHU1H+kmAhyNldR0ni8cWxmIwdN6pdqDYnVnWrfWKVfuycbBTccXYoV1+jnEhnvi5ObA+sZBFbXydk0aNQQGt3oCDneUPppckS8pqqYSS01AWE+ju2OnIAhg0iQK6d35FnVbH6sP5LIwPavOI2vaoVIK5sQFsSSlqc7rJyd6YIAZ6RdSPh/I42INDtqTBJau0eY+FdY0sBlWyCHB3pLK+CZ3eYOlQrEJDk57D2V1fr1h7rIDqRl2XFrbPNW24D7VaPZmmzUZna66oGsgVUTvSSnjgq8Ms/3Q/lXVtr91IEhgXt53s1fi5Wdc5OYMqWQR5OKIoUNHOQutgc7BlvaJrTRG/2pfNMF8XJg3rfhPF5gOiytsY2TmbksVArYiqbmji/317lCEexpHtS+uTLR2SZMWySmsJ83G2unL9QZUsAjwcgbZfsAaj3RnN6xWdv/inFdWw71Q5100M6dEvsbeLcXNRaRs/e8cBPg319zVJnK6s580bxrNsWjhf7MnmcDeP8ZUGj6zSOkKtrBIKBluyMA3r2nrBGoya1yu6ci7H1/tzsFMJFo/v+sL22bxMO1HL2ygwcB7A01CbU4pYtS+H5RcMZ3yoF3+ZG42/mwOP/XBMTodK5zEYFLLK6qyyB92AThbnlmIGeTgBcmQB3Vuv0OoMfHcglzkj/fF3c+zR83k5GxNSac35P/uBusBdWdfEw98dJTrAlf+ba2xr7+Zoz1OXx5GYX8Wnu7IsHKFkbQqrG9DqDHJk0d/OPceoecHori8O8sj3xywQkfXoznrFxqRCSmu1LJkY2ul922OnVuHhZN/myMJpgK5ZPPNzIiU1Wl65ZmyrkuBLRwVyYbQf/95wst2NitLgdKrEWDZrTd1mmw3wZNE6W3i5nJluWbk3e1BvAuvOesWqfTkEujtyQbRfr57T20XTZuly88hiIO3iXp9YwPeH8rjnouHEB7fe6S6E4KoJwdQ06iiubr/BojT4ZJcZqwWtbUMeDLJkce6Gr6oGXX+GY1W6ul6RV1HPttRirk0IRt3L/SftJYvmA+kHysiivFbLoz8cZ2SQO/fObvtUReuqc5GsxanSOuzVgiCPnk339qUBvYP73IHD7Z/ub/Xx+sQCrkno/p4BW9e8XvGn6eGd3nf14XwUBbP8nLycNeSWn3+eSMuaRR+PLAwGhXmvbms5ue/eWZH8dX6MWZ9DURQe/v4olfVaPr1lEhq7AX09ZjVqG3XMeWUrBaZW+I72Kr65Y9p5ozprl11aR7CXs1W2GrK+iMzo3JHF3syyVh+vPpLfn+FYjUPZFV1er8gsqcHfzYEQMyy4+bQ3DdVcDaXt25FeTnkdaUU1XBIXyNgQTz7bnUWjzrwJ6r9b0lmXWMjfLhlB7BB3sz621L69p8ooqGpgycQQLokLpKHJgFZveyPVU6Y9FtZogCeL1h+fu5i4PbWkH6OxHmfOr+g8WRRXN+Lvbp6dpF4uGsrrtOetFdmrBWqV6PORRdLpKgDuumg4/zc3msr6JjYltd3csCc2pxTx8voUFo0dwq0zhpntcaXO7U4vxV4teOryOEaHGEcTUQFuFo6qexRFIbu0zuq6zTazWLIQQlwihEgRQqQJIR7ui+eo6KRpIBinJgab3RmlxA3p2v6K4ppG/FzNkyycNWqa9AoNTa33FxgUUKsEWl3f7js4cboalYCYQDdmRPri6WzPhg4OZeoORVF46JujjAh058XFo61u9+1AtzujlLEhnjhp1KQW1hDk4dil329rUlarpbpRZ3UNBJtZJFkIIdTAW8ClQCywVAgRa+7nKaxq7PQ+VQ2Dq3SxoUnPoZyKLrf4KK5uNFuPmiM5FYT5OJ93ut7hnHK0OgNjTIdP9ZWk01UM83XB0V6NQVGoa9Tja6bvraxWS0lNI9cmBHd6eqBkXlUNTRzLq2Sqac9QSkE10TY2qoCzu83KkcXZJgFpiqJkKIqiBVYBi8z9JE1d2CG7K7200/tU1Gm71K22mcGgkFlSy75TZdQ0WlfF1aHsCrS6zs+vyKuop06ro6RG2+ONeGfT6Q3sySxjeqTveZ/bmFSEWiWYGdX10lxFUcgorulWDEmnqxgRZFxHSC+uQas3EBtknnWFbNMfujVuphro9mWWYVBgSoQPeoNCWnEN0QGulg6r25q7zcqRRWtDgZyzPs413dZCCLFcCLFfCLG/uLi4R0/S2IVpjbu+ONjpfcY+u4Hxz23o8vM+t+YEs17ewjXv7GLWy1u6/HX9oSvrFbWNOub9eyv/2XASvUExy8jiSG4lNY06pg8/P1lsSi4iIcyrW23P//NbKrNf2UpaUXWX7l/V0ERueX1LcmhevxhpoWRRXG0c9crzO3pvd0YpGrWK8WFeZJXWotUZbHJkcaqkDiEgxNvJ0qG0yVLJoq0J3VaLB4qirFAUJUFRlAQ/v55tBhvqaZla5S/2ZLe83/yiYC2a1ys6emHenVFKrVZPRrHxSsccyWJHmrGYYOrw1iOa/Ip6kguqmTPSv1uP9/rGVKBrFwRgnJoAGBlkfBFJOl2Nxk5FhJ95ruJyTMki2KvzZKEoCl/vz2HUUHeCvazzhcGW7MooZVyoJ472ak4WGkebtpgsssvqGOLhZLUXEJZKFrnA2YX7wYDZ61i7+kM/d5H7QFZZy+L42c3edqWX8vX+HHamt19FVduo6/VC7Yn8Kr7Yk8XOtI6rtWobdXy9L4cv9mTxxZ4sDmSVdXj/3Rml7MksI9TbmWO5le3eb+tJ40iuyJTo/HuQLI7lVra8gIIxWcQNcW/pPttsU7KxGmn2iM6ThVZn4NsDuXy661TLbaKL29vOHUmcyK8iOsAVezPVs2eX1eHv5tCl9YoDWeUkF1TjrLHjmwO5A2rnen+rrGsiMb+qZVo1tdB4URBlg9NQp0prrXoa01Kb8vYBUUKIYUAesAS43txPcu4+i/YcyqlgQpjxWNHK+iauensXM6N8+ezWyZyuPNOOYel7u1veT/37pW2+0Hy889R5t6UUVBMT2LUrnSa9gVs+3kdBVQP2asHRp+a3+wL05uY03t6S3vKxm6Mdh5+c1+ZO69OV9SxZYYx/zbHTHMouZ+cjc9p83G2mZNE8KuruyCK3vI5r393F+DBPvrhtCnVaHYfa2QS4ObmIUG9nhvt1/sf9+e4snv3lRLdiaZZ0ugpPZ3sC3R0pqGxgf1YZV08I7tFjtSW7rOttpT8xNRDcm1nG3swySmu03HXRcLPFMpjsPVWGohhHrJX1TXxzIJfoANeWrgC2JLu0jnlxAZYOo10WGVkoiqID7gXWAUnA14qiJJr7ebpaFVtYdSYhHDIde9k8bXH21fHZcsvr27z95yP5xAS4ceiJufx0z3QATpxu/yr+XOsSCyioauDahGCa9ArH89v+2sr6Jj7blcUlcYHsfXQOf//DKKobdC1X0Od6fWNaq4/zKxvavKI9VVLLqVLj91xc07Nk8fTqE9Q36dmdUUZlXRP7ThmbFk47ZwqqoUnPjvQSZo/w77TUVKc38MHvmSSEebH30Tm8dPXobsV04nQ1IwPdEULwyvoUDAa44wLzvUDnlNV3KVmU1jTys2kz6KMLRjB5mDef785CPwhLuM1hV3opGjsVY0M8eeibI+RX1PPC4u79bliD6oYmSmu1hHpb5+I2WLDdh6Ioa4G1ffkcXR1ZbEouwtXBjvFhXjz2w3HAeIX/xsZUPJ3bnts/VVLLsHN6zqcV1ZBcUM2Tl8Xi5aLB1dEOjVpF8ulqGNd5HIqi8PGOU4R6O/PX+TF8vT+X1zemEjvEHQ8ne5bPjEAlBF/syWJLSjE1jTrumxOJv7sjc0YE8BjHW3o+lddq+XRXFnVNOgwG4xx5M7VKoDco5JQZF9S+O5iHAJZOCmVb6pliAr1BwdXBrltXaf87XsBvSYUsjA9izbHTPPrDMdYcO93yc96VUUq0vxtXTQhmV3opDU0GZnVhCmrt8QLyKup5+oo4/N0dW84MV+j8/1hvUEgpqOL6SWEkna7i24O53DZjmFl2pYNxeiy/sr5Lj3f9e3sAmBDmxe0zIwj2cubuLw6yObmIi2Ot96rSWu3OKGVCqBef785i/YlCHl84smWWwJZklTZ3m5XTUBYxxKPzxUONWsW3B3L59kAuz1wRR16FccRQq9XzyoaTBLq3XiRfceME7vz8AIdyKs57kft4Zyb2asHC0UEA2KtVjBrqzrbUEh7pQrzrEgvYn1XO05fH4u/myPRIH/ZmlrEnswytzsBQTydKarQ898sJNHYqrhw7hLghxt2qgR6OxAS48e2BXP40LZz7Vh7i97QSHEy9iQLcHMg3Tak9MCeKVzacZN+pcl797SSltcZd1TvSSvBxdSDMx5maBh2ltdpujSqO5lbw4NeHiQ1y5z/XjSWnvI7fks5sevtqXw6KAlq9AXcne7adLMZZo2ZyJ8e0KorCim3pRPi5MMf0Mx/qafy//eFgXsvPoD1NegMGBXQGAy/8moy7oz33zmq7wV9P6A0K9ipVm+3XmxkMCq9tTCXFNKf+5e2TEUIwNzYAbxcNvxzNl8miBwqqGmjSG3jx12TmxwXY7M755mQRKpOFZXS22Pjl7ZP569dHWl5EP9l1ikh/V377y4U0NOkZ88x6Cqoa+OOUUJ6/Mr7l68aFerE5uYi/zI1uuS2/op6v9uVwTUIIAWclmCvGDOHpn0+QdLqqwzLNqoYmnlqdyMggd26YEgbAF7dNAYwvNBf/x1jKml/ZwMUj/XnvpoTzpm7uumg4D3x1mGUf7WVHWikvLI5n6aQzZ1Bc9NJmxoR4snRyKK9sOMnTqxOxUwvW3D+DY7mVPPTtUQBumhpGRV0Tq4/k4+vaekG6PRnFNfzpo314Omv46OaJaOxUrL53BuW1WsY/v4EH5kTz54uj0OoMXPHm7zz+4zHqtXqmR/q2HKvanl0ZpRzPq+KFxfGoTOsx8cEe/HFKKO//nsmsEf5t7t9o5mivZm5sQMthQ48vHIlHOyPGnnDSqLk0PpAfD+XxyKUjz/u9q9PqePDrI/x6vACAl68Z01J8Ya9WMTPKl+2pJRgMSsv3J3WNl7M9qUU1hHo786+rx9jszvmsMuveYwEDvDdUZ8aGeNJwVuVSRnEtl44KBIwvMONDjcPZ6xJaH/oze4Q/x/IqKaxqoLqhiSd+PM5tnxg72t59zkLl5WOGYKcSfHcgt8NY/vlrMkXVjbywOP68hXOVSnD7zAhOldbh6mDHC+20k7hsdBDDfF3YkVbKlWOHsGRi606xjvZq6rV6fFw0OGvUaPUGXrlmDCMC3Vk8PrglmV0Q5cdFMcZy5aIulP5mldZy4wd7EcDnt01ulSx3ZZSiKDAjyrheobFT8c+rRlNY1UhVg+68Kqji6kaeXp3YqlrrvW0Z+Lpq+MO41ke6PrYglgg/F/686hB3fX6Auz4/wF9N89bnuuisszhunBrW6ffUXUsnhVLVoGOtacqtWXJBFVe9vYt1icZE4euq4YoxQ1rd54IoP0prtZxoZ72pLzXpDXy+O4u3NqfZ3PkuTXoD6aby7v/eML5b+3SsTVZJHb6uGlwdrPf63Xoj6wfOGjveWDqOT3edoqpeR02jrlWFzI1Twwj1dmbU0NYjgktHBfLSuhRW7s2moq6Jz/dkEeXvyp/nRJ1XZ+/j6sAlowL5cm82yy+IwN/9/L0f3+zP4Ys92dw+cxhj22l58YdxQ9mRVsJ1E0PanRqyU6t4dlEcq/bm8Pc/xJ+XUJw0auqb9AghWDIxlCGejlwab5wyU6sEz185ijc3pTI90rdl53nz8Lg9uzNKufPzAygKfH7r5PPWcXakleCiUTM6+Mz3NSbEEzuVQGdQ2JdZxpKJIQgh2JNRyn0rD1FU3ch3B3L59NZJuDjYsTmlmAfnRp83AnHSqHlj6Tge//E46abd3Lnl9exMK+Gz2ya3VFitTyzg+TVJADjYqfqkjn3yMG8ifF1YuTebqyYEU6fV8drGVD7Ynom7kz2Lxwfz7YFcbp4+7Ly25TOjjaOirSeLGTW0f1pqK4rCb0lFvPBrUst+miGejvxhnPkqxPpSk97AvV+e2VBrrpY0lpJVVmvVowoYhMli1FB3judVMTPK+Ac6PdK33SmMBfFBLDC9mJ4tws+VOSP8+WB7JjVaHTdNCeOZRaPafc6H5sewLrGAV9af5J/nVPEcyCrnsR+OMz3Sh79dMqLdx3C0V/Pm9eM7/f5mRvm12zbD0U5No6mJ35OXn9+Ka0KYFx/dPKnT52i2cm82T/x4nDAfZz5YNrHNQ+Z3ppcyOcLnvNHS2BBP9meV8/2hPIZ6OeGkUfPyuhTCfVz459WjeXp1Ijd+sJe4Ie442av545S2RwNxQzz44e7pLR8n5ldy0wd7ufadXbx5/Xi+O2hcj4ob4s6YEE92pZdQXqvFy6Vr02tdJYRg6aRQ/r42iQ9/z+SD3zPJq6jn2oRglk0LZ+mK3YwN8eSOCyLO+1p/N0dig9zZdrKYe2ZFmjWuthzLreTva0+wO6OMCD8XVtw4gRXbMnjyp0SmRPi0nFVvrZoTxbrEQubGBrDhRCEnC6tbjWhtTVZpXUtvK2s16JLF8TzjUL+3J1EtvyCCjclFBLg78GAnB+iE+biwbGo4H+zI5IYpoS1X2Vmltdzx2QGCPB15c+n4Pj/wxEmj7vKO8pKaM/db+Pr28z6v0yukFFZzYbQfb1w/rs0On3kV9WSW1J73Ql9eq+Vgdjn3zoqkpKaRNzYZy3ovHzOEFxbH4+pgx4jlbixdsZs9mWUsmxrW5Rf3uCEefHPnVG78YC9L39uNSsB9syO5b3YUaUU1LHh9O78czefGqeFderzuuGpCMC+tS+HZX04Q6e/K13dMJSHMixve34POoPDqdWPb/T++INqP97dnUN3Q1FLp1RuVdU0880tiSwl4M71BIbmgGm8XDc8timPJpFDs1SqiA9y49LXt/L9vj/LpLZOsdu6/SW/gvi8PsS6xkKcuj+XyMUNMyaKmW73FrElZrZaCqgY5srAWTvZqnDTqloaAgb28epo0zJu/zotm6nCfLrVCvm92FGuOneaeLw/yy70zqaxvYumK3egMBt6/abLZr3Tb4mSv7vKZEWfH015iXRAfxD2zhrf7Atjc4mN6ZOsrpm2pxRgUmDPSnzHBnvi7OzLU05FrE0JaXqSCPJxYtXwqb29J6/bVdoSfK9/cOZXXN6Zy7cSQlrWn2CHujAh049uDeX2SLLxdNDx5eSz1Wj3LpoWjsVPx7tZ0dmWU8q+rRrc58mp2QbQv72xNZ1d6KfPiAnsVx9HcCu7+4iCFVQ3MiPQ9b5PmvNgAbrsgotXvbbivC48tHMnjPx7n8z3Z3NjOSM6SmhPF/xILePKyWG6ebqx88nbRtOzctjU6vYH7Vh7EXqXi4tjutbzpb4MmWbg52jEjypfvD+YBvR9ZCCHaPV+5LR7O9rx1w3iue3cX9648SEZxLXVNer64bXK/HdLiYK/qcmsJVwc7Fo8fyobEQt5YOr5Hbbd3ppXg66oh5pzvb1NyET4uGsYEe6JSiVZVZWcL9HDscHqvI0M8nXjxqvM3Z109IZjn1ySRVlRDpL/5W0KcPYpKzK/k5fUpXBIXyDUJHa8FJIR546xRsy21uMfJQlEUPt+dxXO/JOHrquHrO6YyLrTrew5umBzK+hOF/GNNEjMjfTtMbv2tSW/g/pVnEsUtZ5XIRvm7tpQk25q/r01iR1op/7p6dKcl4JY2aKqhHO3VzBlxpo490AIHoo8P9eKxBSPZnlpCTaOOz2+d3K+/IE726m71Ibo2IYTqRh3/Szzd+Z3PUVGnZVtqCVOH+7aa0tDpDWxJKeaiGH+LlIleMXYIapXgh0MdV6f1VkOTnj+vOoyXs4YXFp9fbHAujZ2KacN9WLk3h/in1p33b9bLW/h4RyZ17Rw9W9Oo4/5Vh3nip0SmR/qw5v6Z3UoUYLwA+tdVo7FXC276cC9f7smmXmv5vlXNieLX4wU8cU6iAGPTwLTCGpur5vp6fw4f7TjFzdPDudYMZ9z3tUEzsrBTiVbDPEstJhmnJ9RMCPPqcr8oc3G0V593Sl1HJg/zJszHma/35XarSkanN3Dvl4eoadCdt0nqUE4FlfVNXWoc2Bf83Ry5IMqXHw7m8eDcmD5LWC+sNY5ePr1lUpenGP9vbjRhPi609Zp3JLeCp38+wWsbU1k2LZxlU8NbHjeloJq7vjjAqZJaHpofw10XDu/x9xXo4cg7N07g72uSePSHY/xrXTJLJoZy09Qwhnj2/8J3k97An1edSRRtbbqLDnClulHH6coGi8TYEweyynn8h+PMiPTlsQUjLR1OlwyaZAHGLrSuDnZcmxDS6UawviKE4PrJoZ3fsQ80r1koitKlBUwhBNdMCObl9SfJLq3r8u7Sf6xN5ve0Ev519ejzSoE3JhVhpxIt5aKWsHh8MPetPMTujFKmdbCZr6c2pxTxya4sbp4ezgXRXV90jRvi0eFIc9+pMt7Zks6rv6Xy7tYMrpsYQoSfC/9Ym4Srgz1f3DblvBbwPTFtuC+/3DeDfafK+WhHJiu2pfPe9gwuiQtk2bTwDqfvVAI8nOzNskDenCjWHms/UcCZs7ZPFlbbRLIoqGzgzs8PEOjhyJvXj+vzwhZzGTTJwtXRDp3eQE2jDnenQfNtt+KkUaM3KDTpFTR2XftjvmpCMK9sOMm3B3L4y7yOq77AuGfkwx2Z/Gla20PrzclFTAz3tuj5yHNjA3BztOPbg7lmTxalNY089M1RYgLcOiyF7omJ4d5M/JM3KQXVvLstnc93Z6EzKEyJ8Ob1pePMcqJhMyEEk4Z5M2mYN7nldXy2K4uVe7Nb+nx1ZEakLy9dM7rHJbiKorAxqYh/bzjJidNVPL5wZIdtPJrPrkgtrOGiGOteJG5o0rP8s/3UNer44rbJeDr3fWGLuQz4V81Ad0cKqhoI9XZu2Whmawe5m0tzn6gGnf68jWHtCfJw4oIoP749kMufL45us/15s4PZZ/aMPL7w/KF1bnkdKYXVFh92O9qruWx0ED8dzue5RTpczLRrVlEUHv7+GFX1TXx266Q+G73GBLrx72vH8uC8GA5mlXPpqMA+vToN9nLmkQUj+fPFUaxPLKSyvv1z68vrtKzYlsH8/2zj+T/En7dbvSOKorAttYR/bzjZcl77m9eP47LRHT+Gt4sGX1cNJ618kVtRFB75/hhHcytZceMEmzugacAnCxcH4x+sokBVvTFZuDkO+G+7Tc0VTQ1afbcS5rUJIdzz5UF2pJW0O61SUNnAHZ+Zhtbt7BnZ3HzQUTdPxesLi8cHs3JvDv87XsBVZjrXYtW+HDaYOp+a67jWjgz1dGppqNgfnDV2XHlOy5W2XDl2KP/39WHuX3mIjUmFPHvFqE57ce1ML+Hf60+yP6ucoZ5O/POqeBaPD+7y4VRR/m6cLOremez97f3tmfxwKI+/zI3udXm0JdjGZFkvNP+yNer0VDUYr4jcbbiHTG84ma50u7rXotnFsf74uGi458uD/Ht9CuW1rburNjTpucM0tH5/WUK7C7qbkosI83EmwgpKMhPCvAj1duZ7M1RFldQ08ugPx3jsh2NMj/Thlum22fnUXMJ9Xfjmjqk8ODeaNUdPc8lr21r23Jxr/6kylq7YzfXv7SG3vJ7nrhzF5r9exHUTQ7t1imFMoBtphdVWWxG19WQxL/yaxIL4QO6b3fe79PvCgE8Wvq7GnjF5FQ1nksUgnYZqnhbpTkUUGAsDvrx9CtOH+/L6pjSm/3MTL6xNoqi6oWVofSS3kv9cN7bdoXW9Vs/O9NIuHXTUH4QQLB4/lJ3ppW02HuyKRp2ed7emM+ulLXy1L4ebpobz3xsmyM6xGPuU3Tcniu/vnoaTRs0N7+/h2Z9PtJRuH86p4KYP93L1O7tILarhycti2fLQRdw4JazLU6RniwpwpVarbzliwJpkFNdw75cHiQ5w4yUb7ow74Odjmg9Ayimra5mGGrQL3D0cWYDxyu2dGyeQUlDNf7ek8d72DD7eeYpJw7zZnlrS6dB6Z3oJjTqDxUpm27J4XDCv/pbKF3uyulVGqygK/ztewAu/JpNdVsfsEf48umBkn2zys3Wjgz1Zc99MXvw1iQ93ZLI9tZhQb2c2Jhfh7aLh0QUjuHFKeI82fZ4t+qyKqHObeVpSVUMTt3+6H3u1ivduSjDb+pgl2G7kXdR8JVPTqBv0IwsHe+MVW282WsUEuvHaknE8cHE0b29J4/uDeSyMD+p0aL0puQhnjZpJnRx01J9CfZyZGeXLW5vT+e5AHpeMCmTh6CAmhHq1mziO51Xy7C8n2JtZRkyAG5/dOslmexL1FyeNmmcWjWL2yAAe+uYI+06V8dD8GJZNCzdbS+6YQDec7NU8+VMins6alhYvlqQ3KDyw6jBZpXV8fttks53MaCmDIFmcmXJpbqI32NcsGnS935U7zNeFf109hscWxOLqaNfh0FpRFDYlFzEzyrdP2oP3xrs3TmB9YiFrjp3my73ZfLzzFP5uxrbyC+KDmBjujVolKKxq4KV1KXx3MBdvZw3PXzmKJRNDbKZG3hpcGO3Hzodno1cUs/8euDva88Xtk7l/5SGufWcXD86L4Y4LIiw6JfjK+hQ2JRfx3KI4plh5R9muGPjJ4qwXxkPZFQgBbjY8FOyNs6uhzKUrJ84lF1RzurKBB3E11AkAAA1wSURBVC4231Gm5tJc4XPluKHUNOrYlFzE2qOn+WpfDp/uysLXVcOUCB82JReh0yssnxnBPbMjB+3otLfs1Ko+e9EZH+rFmvtn8uj3x/jn/5LZmV7CK9eOMev+k65afSSf/25JZ+mk0Hbb69uaAf+q2XjWyGJ7ajGuDnaDdgHS0c58I4vu2GQqmZ1l5RumXB3suGLMEK4YM4TaRh1bUopZe+w0204Wc0GUH48sGGH1baQHOw8ne968fhzT9/ryzM+JLHhtO/++dmy3dtL31vG8Sv7ft0eYGO7FM1fE2eyC9rkGfLJoXrOYEObFgazyluqowah5ZFGv7V41VG9tSi4ifqhHm6cEWisXBzsWjg5i4ejzD7+SrFtzS52EcC/u/fIgN324lzsuiODBeTE9qrTqjuLqRpZ/uh9vZw3/vWFCnz9ffxo430k7mpPFQ/NjUInBu14BZ0pne1IN1VNlpoOOZllRFZQ0OEQHuLH63hncMDmUd7dlcM27u8ju5Jjg3tDqDNz9xQHK6rSsuCnB5o96PdfATxY641X0yCB37rpoOBdbwe5hS3E0VUN1p015b209WYSiwByZLCQLcLRX8/c/xPP2DePJLK5h4evb+flIvtmfR1EUnlqdyL5T5bx09Zh+O0u9Pw34aSi9wbjPQqNW8dB88zZ2szUatQqV6N9ksSm5GF9XB+IH4B+PZDsujQ8iPtiD+1ce4r6Vh9iRVsJTl8f1en9Hs8/3ZLNybzZ3XzScy7vRD8uWDPhk0cxePTAWmXpDCIGjvbrfDrSpbmhia0oR8+MCB21RgWQ9gr2c+eqOqbz620n+u8V43O2F0X5EB7gRE+hGdIAbHj2Ypt6dUcozqxOZPcKfB7vQmdlWDZpk0VG31MHEyV7dL9VQzQcg1Wr1LJlk/aeASYODvWmGYdpwX/6z4STfH8xr6UYNxuOWm5NHjOltpL9rux2Ec8rquPuLg4T5OPPqkrED+nVm0CSLgVK+1lvGkUXfVkMpisIzP58wNk9bHM+EMOvZtS1JANMjfZke6YuiKORXNnCyoJrkgmpOFlaTUlDNroxStKb1TpWAMB8XYgLciG5JIq74uzty+6f7adIbeO+mhAG/92bQJAvJyNFe1edrFh/vPMVnu7NYfkEESydZ5lRASeoKIURLq/ezK/Z0egNZZXWkFBiTx8nCalIKq1l/ogDTMihCgAA+unkSEX4Dvy+YTBaDjJNG3afJYmNSIc/9coJ5sQE8bOaT4iSpv9ipVQz3c2W4nysL4s/stWlo0pNWVNOSPCaEenFhP274sySZLAaZ5nO4+0JifiX3rTxE3BAPXl0yVi5qSwOOo72aUUM9BmRpbGcG/D4LqTVH+74ZWRRWNXDrx/vxcLLn/WUJOGvkdYgkDSQyWQwyjvZq6rt5+FFn6rQ6bv1kH9UNTXywbCIBNtTWQ5KkrpGXf4OMuUcWeoPCn1cd5kR+Fe8vSyB2SN+fPS1JUv+TI4tBxsnM1VD//F8yG04U8uRlscweEWC2x5UkybrIZDHImHOB+8s92azYlsGyqWH8afowszymJEnWSSaLQcZc7T62pxbzxE/HuSjGjycuizVDZJIkWTOZLAYZR3s1jToDhuadRT2QWljN3Z8fJMrflTeWjpNHi0rSINCrv3IhxDVCiEQhhEEIkXDO5x4RQqQJIVKEEPPPuv0S021pQoiHe/P8Uvc197hp1PWsIqqkppGbP96Ho0bNB3+aiNsAb3EgSZJRby8JjwOLgW1n3yiEiAWWAHHAJcB/hRBqIYQaeAu4FIgFlpruK/UTJ9OZFkdzK9Dpu5cwGpr03P7pfkpqGnn/pgSGejr1RYiSJFmhXpXOKoqSBG026VsErFIUpRHIFEKkAZNMn0tTFCXD9HWrTPc90Zs4pK4L9XEG4LoVu3HWqBkb4smEMC/Gh3kxPsQLD+e2RwoGg8JfvznC/2/vXGPsKOsw/nvapaUXbLc3WHqRbkusLQlQ1mDVkErAXiQgX0xRExQNBtB4iZE2TYzG8AEw2uCtNAYDpmBrpWIqpBGiiX6gtS20tNhtt/et1bZhwQQ/QPX1w/y3PXs4Zw/szplzun1+yeS883/fmfOcZ87M/8z7zpx56ejrrP7cfK6ePr5I2caYBlOv+yymAi+WzHdHDOBYWfz6SiuQdDdwN8CMGf4zury4cc6l/PX+j7P9SA87jvSw/WgPP/vzgbMPiZo9ZSzXzWg9m0DaJ41h2DDxo+f3sWnXCZYvmcPiq/xcamMuNGomC0nPA5dVqFqZUnqm2mIVYonK3V4VR1pTSmuANQAdHR0DH40172Ba62imtY7mtmuy/P2ft86w89gb7Djaw/YjPWx+9Z+s25bl9HGjLuKDbZfw4sHXWPah6Xz5hvZGSjfGNIiaySKldNMA1tsNlD7xZhrQ++DbanHTIEaPaGHBrIksmDURyLqcDp5+kx1H4+zjSA83z72U73/qKj8XxJgLlHp1Q/0eeFLSD4HLgSuBrWRnHFdKmgkcJxsE/0ydNJgBMmyYmD1lLLOnjOXTHX7KnTFmkMlC0u3Aj4HJwB8kvZxSWpRS2iNpPdnA9RngvpTSf2OZrwCbgeHAYymlPYP6BMYYY+rOYK+G2ghsrFL3APBAhfizwLODeV9jjDHFMqRvvU3J4+LGGJMHQzpZvBU3nY0b5buMjTFmMAzp51mMbBnO/YvnsGie/zrbGGMGw5BOFgD3LJzVaAnGGHPeM6S7oYwxxuSDk4UxxpiaOFkYY4ypiZOFMcaYmjhZGGOMqYmThTHGmJo4WRhjjKmJk4Uxxpia6Hz4/yRJp4Ajg1jFJOB0TnKKwpqLwZqLwZqLo1T3+1NKk/NY6XmRLAaLpG0ppY5G63gvWHMxWHMxWHNx1Eu3u6GMMcbUxMnCGGNMTS6UZLGm0QIGgDUXgzUXgzUXR110XxBjFsYYYwbHhXJmYYwxZhAM6WQhabGkTkldkpY3WMt0SX+S9HdJeyR9LeITJP1R0v54bY24JD0S2ndJml+yrjuj/X5JdxagfbiklyRtivmZkrbE+6+TNCLiI2O+K+qvKFnHioh3SlpUZ73jJW2QtDf8XtDsPkv6Rnwvdkt6StLFzeizpMcknZS0uySWm7eSrpP0SizziCTVSfPD8f3YJWmjpPEldRU9rHY8qbad8tZcUvctSUnSpJgvxueU0pCcgOHAAaAdGAHsBOY2UE8bMD/KlwD7gLnAQ8DyiC8HHozyUuA5QMCHgS0RnwAcjNfWKLfWWfs3gSeBTTG/HlgW5dXAPVG+F1gd5WXAuijPDf9HAjNjuwyvo97HgS9FeQQwvpl9BqYCh4BRJf5+vhl9Bm4A5gO7S2K5eQtsBRbEMs8BS+qk+RNAS5QfLNFc0UP6OZ5U2055a474dGAz2X1nk4r0uW4HmEZPYcTmkvkVwIpG6yrR8wxwM9AJtEWsDeiM8qPAHSXtO6P+DuDRknifdnXQOQ14AbgR2BRfrtMlO9pZn+NLvCDKLdFO5d6XtquD3veRHXhVFm9an8mSxbHYqVvC50XN6jNwBX0PvLl4G3V7S+J92uWpuazudmBtlCt6SJXjSX/7Qz00AxuAq4HDnEsWhfg8lLuhenfAXroj1nCi2+BaYAtwaUrpBEC8Tolm1fQX/blWAd8G/hfzE4HXU0pnKrz/WW1R/0a0L1JzO3AK+KWyrrNfSBpDE/ucUjoO/AA4Cpwg8207ze1zKXl5OzXK5fF6cxfZr2tqaKsU729/yBVJtwLHU0o7y6oK8XkoJ4tKfXANv/RL0ljgt8DXU0r/7q9phVjqJ547km4BTqaUtr8LXf3VFbktWshO33+eUroWeJOsa6QaDdccffy3kXV7XA6MAZb08/4N1/wuea86C9cvaSVwBljbG6qioaGaJY0GVgLfqVRdRUOumodysugm69/rZRrwjwZpAUDSRWSJYm1K6ekI/0tSW9S3AScjXk1/kZ/ro8Ctkg4DvybriloFjJfUUuH9z2qL+nHAawVr7ga6U0pbYn4DWfJoZp9vAg6llE6llN4GngY+QnP7XEpe3nZHuTxeF2LA9xbgsyn6Ywag+TTVt1OezCL7MbEz9sdpwA5Jlw1A88B8zrs/s1kmsl+YB8Pg3gGpeQ3UI+AJYFVZ/GH6Dg4+FOVP0nfQamvEJ5D1ybfGdAiYUID+hZwb4P4NfQf07o3yffQdeF0f5Xn0HTQ8SH0HuP8CfCDK3w2Pm9Zn4HpgDzA6dDwOfLVZfeadYxa5eQv8Ldr2DrwurZPmxcCrwOSydhU9pJ/jSbXtlLfmsrrDnBuzKMTnuuyszTKRXSWwj+wqhpUN1vIxslO9XcDLMS0l6/N8Adgfr70bU8BPQ/srQEfJuu4CumL6QkH6F3IuWbSTXU3RFTvKyIhfHPNdUd9esvzK+Cyd5HCFSw2t1wDbwuvfxY7S1D4D3wP2AruBX8XBqul8Bp4iG1d5m+wX6hfz9BboCA8OAD+h7EKFHDV3kfXn9+6Lq2t5SJXjSbXtlLfmsvrDnEsWhfjsO7iNMcbUZCiPWRhjjMkJJwtjjDE1cbIwxhhTEycLY4wxNXGyMMYYUxMnC2OMMTVxsjDGGFMTJwtjjDE1+T+7aTdDP1ojGAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot([x**2 for x in slVel], slAccel)\n",
    "\n",
    "#oooo pretty"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# In the following cells Im trying to get a best fit line for the vel vs cd plot\n",
    "\n",
    "Im just googling it and I dont know what Im doing, just fyi\n",
    "\n",
    "However, this plot does look promising if you look at the final plot. With a best fit, I feel like we could get a rough determination of the cd of TMH at higher velocites (cd is irregular and not clear at lower velocites). Also, notice how the plot for v**(2) vs accel is very similar to the plot for vel vs cd, interesting right?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 65.53373515  65.20513568 189.95480468         -inf          inf]\n",
      "[39.31967813 39.35055341 39.3814287  39.41230399 39.44317928 39.47405456\n",
      " 39.50492985 39.53580514 39.56668042 39.59755571 39.628431   39.65930629\n",
      " 39.69018157 39.72105686 39.75193215 39.78280743 39.81368272 39.84455801\n",
      " 39.8754333  39.90630858 39.93718387 39.96805916 39.99893444 40.02980973\n",
      " 40.06068502 40.0915603  40.12243559 40.15331088 40.18418617 40.21506145\n",
      " 40.24593674 40.27681203 40.30768731 40.3385626  40.36943789 40.40031318\n",
      " 40.43118846 40.46206375 40.49293904 40.52381432 40.55468961 40.5855649\n",
      " 40.61644019 40.64731547 40.67819076 40.70906605 40.73994133 40.77081662\n",
      " 40.80169191 40.8325672  40.86344248 40.89431777 40.92519306 40.95606834\n",
      " 40.98694363 41.01781892 41.04869421 41.07956949 41.11044478 41.14132007\n",
      " 41.17219535 41.20307064 41.23394593 41.26482121 41.2956965  41.32657179\n",
      " 41.35744708 41.38832236 41.41919765 41.45007294 41.48094822 41.51182351\n",
      " 41.5426988  41.57357409 41.60444937 41.63532466 41.66619995 41.69707523\n",
      " 41.72795052 41.75882581 41.7897011  41.82057638 41.85145167 41.88232696\n",
      " 41.91320224 41.94407753 41.97495282 42.00582811 42.03670339 42.06757868\n",
      " 42.09845397 42.12932925 42.16020454 42.19107983 42.22195512 42.2528304\n",
      " 42.28370569 42.31458098 42.34545626 42.37633155 42.40720684 42.43808212\n",
      " 42.46895741 42.4998327  42.53070799 42.56158327 42.59245856 42.62333385\n",
      " 42.65420913 42.68508442 42.71595971 42.746835   42.77771028 42.80858557\n",
      " 42.83946086 42.87033614 42.90121143 42.93208672 42.96296201 42.99383729\n",
      " 43.02471258 43.05558787 43.08646315 43.11733844 43.14821373 43.17908902\n",
      " 43.2099643  43.24083959 43.27171488 43.30259016 43.33346545 43.36434074\n",
      " 43.39521603 43.42609131 43.4569666  43.48784189 43.51871717 43.54959246\n",
      " 43.58046775 43.61134303 43.64221832 43.67309361 43.7039689  43.73484418\n",
      " 43.76571947 43.79659476 43.82747004 43.85834533 43.88922062 43.92009591\n",
      " 43.95097119 43.98184648 44.01272177 44.04359705 44.07447234 44.10534763\n",
      " 44.13622292 44.1670982  44.19797349 44.22884878 44.25972406 44.29059935\n",
      " 44.32147464 44.35234993 44.38322521 44.4141005  44.44497579 44.47585107\n",
      " 44.50672636 44.53760165 44.56847694 44.59935222 44.63022751 44.6611028\n",
      " 44.69197808 44.72285337 44.75372866 44.78460394 44.81547923 44.84635452\n",
      " 44.87722981 44.90810509 44.93898038 44.96985567 45.00073095 45.03160624\n",
      " 45.06248153 45.09335682 45.1242321  45.15510739 45.18598268 45.21685796\n",
      " 45.24773325 45.27860854 45.30948383 45.34035911 45.3712344  45.40210969\n",
      " 45.43298497 45.46386026 45.49473555 45.52561084 45.55648612 45.58736141\n",
      " 45.6182367  45.64911198 45.67998727 45.71086256 45.74173784 45.77261313\n",
      " 45.80348842 45.83436371 45.86523899 45.89611428 45.92698957 45.95786485\n",
      " 45.98874014 46.01961543 46.05049072 46.081366   46.11224129 46.14311658\n",
      " 46.17399186 46.20486715 46.23574244 46.26661773 46.29749301 46.3283683\n",
      " 46.35924359 46.39011887 46.42099416 46.45186945 46.48274474 46.51362002\n",
      " 46.54449531 46.5753706  46.60624588 46.63712117 46.66799646 46.69887175\n",
      " 46.72974703 46.76062232 46.79149761 46.82237289 46.85324818 46.88412347\n",
      " 46.91499875 46.94587404 46.97674933 47.00762462 47.0384999  47.06937519\n",
      " 47.10025048 47.13112576 47.16200105 47.19287634 47.22375163 47.25462691\n",
      " 47.2855022  47.31637749 47.34725277 47.37812806 47.40900335 47.43987864\n",
      " 47.47075392 47.50162921 47.5325045  47.56337978 47.59425507 47.62513036\n",
      " 47.65600565 47.68688093 47.71775622 47.74863151 47.77950679 47.81038208\n",
      " 47.84125737 47.87213266 47.90300794 47.93388323 47.96475852 47.9956338\n",
      " 48.02650909 48.05738438 48.08825966 48.11913495 48.15001024 48.18088553\n",
      " 48.21176081 48.2426361  48.27351139 48.30438667 48.33526196 48.36613725\n",
      " 48.39701254 48.42788782 48.45876311 48.4896384  48.52051368 48.55138897\n",
      " 48.58226426 48.61313955 48.64401483 48.67489012 48.70576541 48.73664069\n",
      " 48.76751598 48.79839127 48.82926656 48.86014184 48.89101713 48.92189242\n",
      " 48.9527677  48.98364299 49.01451828 49.04539357 49.07626885 49.10714414\n",
      " 49.13801943 49.16889471 49.19977    49.23064529 49.26152057 49.29239586\n",
      " 49.32327115 49.35414644 49.38502172 49.41589701 49.4467723  49.47764758\n",
      " 49.50852287 49.53939816 49.57027345 49.60114873 49.63202402 49.66289931\n",
      " 49.69377459 49.72464988 49.75552517 49.78640046 49.81727574 49.84815103\n",
      " 49.87902632 49.9099016  49.94077689 49.97165218 50.00252747 50.03340275\n",
      " 50.06427804 50.09515333 50.12602861 50.1569039  50.18777919 50.21865448\n",
      " 50.24952976 50.28040505 50.31128034 50.34215562 50.37303091 50.4039062\n",
      " 50.43478148 50.46565677 50.49653206 50.52740735 50.55828263 50.58915792\n",
      " 50.62003321 50.65090849 50.68178378 50.71265907 50.74353436 50.77440964\n",
      " 50.80528493 50.83616022 50.8670355  50.89791079 50.92878608 50.95966137\n",
      " 50.99053665 51.02141194 51.05228723 51.08316251 51.1140378  51.14491309\n",
      " 51.17578838 51.20666366 51.23753895 51.26841424 51.29928952 51.33016481\n",
      " 51.3610401  51.39191539 51.42279067 51.45366596 51.48454125 51.51541653\n",
      " 51.54629182 51.57716711 51.60804239 51.63891768 51.66979297 51.70066826\n",
      " 51.73154354 51.76241883 51.79329412 51.8241694  51.85504469 51.88591998\n",
      " 51.91679527 51.94767055 51.97854584 52.00942113 52.04029641 52.0711717\n",
      " 52.10204699 52.13292228 52.16379756 52.19467285 52.22554814 52.25642342\n",
      " 52.28729871 52.318174   52.34904929 52.37992457 52.41079986 52.44167515\n",
      " 52.47255043 52.50342572 52.53430101 52.5651763  52.59605158 52.62692687\n",
      " 52.65780216 52.68867744 52.71955273 52.75042802 52.7813033  52.81217859\n",
      " 52.84305388 52.87392917 52.90480445 52.93567974 52.96655503 52.99743031\n",
      " 53.0283056  53.05918089 53.09005618 53.12093146 53.15180675 53.18268204\n",
      " 53.21355732 53.24443261 53.2753079  53.30618319 53.33705847 53.36793376\n",
      " 53.39880905 53.42968433 53.46055962 53.49143491 53.5223102  53.55318548\n",
      " 53.58406077 53.61493606 53.64581134 53.67668663 53.70756192 53.7384372\n",
      " 53.76931249 53.80018778 53.83106307 53.86193835 53.89281364 53.92368893\n",
      " 53.95456421 53.9854395  54.01631479 54.04719008 54.07806536 54.10894065\n",
      " 54.13981594 54.17069122 54.20156651 54.2324418  54.26331709 54.29419237\n",
      " 54.32506766 54.35594295 54.38681823 54.41769352 54.44856881 54.4794441\n",
      " 54.51031938 54.54119467 54.57206996 54.60294524 54.63382053 54.66469582\n",
      " 54.69557111 54.72644639 54.75732168 54.78819697 54.81907225 54.84994754\n",
      " 54.88082283 54.91169811 54.9425734  54.97344869 55.00432398 55.03519926\n",
      " 55.06607455 55.09694984 55.12782512 55.15870041 55.1895757  55.22045099\n",
      " 55.25132627 55.28220156 55.31307685 55.34395213 55.37482742 55.40570271\n",
      " 55.436578   55.46745328 55.49832857 55.52920386 55.56007914 55.59095443\n",
      " 55.62182972 55.65270501 55.68358029 55.71445558 55.74533087 55.77620615\n",
      " 55.80708144 55.83795673 55.86883202 55.8997073  55.93058259 55.96145788\n",
      " 55.99233316 56.02320845 56.05408374 56.08495902 56.11583431 56.1467096\n",
      " 56.17758489 56.20846017 56.23933546 56.27021075 56.30108603 56.33196132\n",
      " 56.36283661 56.3937119  56.42458718 56.45546247 56.48633776 56.51721304\n",
      " 56.54808833 56.57896362 56.60983891 56.64071419 56.67158948 56.70246477\n",
      " 56.73334005 56.76421534 56.79509063 56.82596592 56.8568412  56.88771649\n",
      " 56.91859178 56.94946706 56.98034235 57.01121764 57.04209293 57.07296821\n",
      " 57.1038435  57.13471879 57.16559407 57.19646936 57.22734465 57.25821993\n",
      " 57.28909522 57.31997051 57.3508458  57.38172108 57.41259637 57.44347166\n",
      " 57.47434694 57.50522223 57.53609752 57.56697281 57.59784809 57.62872338\n",
      " 57.65959867 57.69047395 57.72134924 57.75222453 57.78309982 57.8139751\n",
      " 57.84485039 57.87572568 57.90660096 57.93747625 57.96835154 57.99922683\n",
      " 58.03010211 58.0609774  58.09185269 58.12272797 58.15360326 58.18447855\n",
      " 58.21535384 58.24622912 58.27710441 58.3079797  58.33885498 58.36973027\n",
      " 58.40060556 58.43148084 58.46235613 58.49323142 58.52410671 58.55498199\n",
      " 58.58585728 58.61673257 58.64760785 58.67848314 58.70935843 58.74023372\n",
      " 58.771109   58.80198429 58.83285958 58.86373486 58.89461015 58.92548544\n",
      " 58.95636073 58.98723601 59.0181113  59.04898659 59.07986187 59.11073716\n",
      " 59.14161245 59.17248774 59.20336302 59.23423831 59.2651136  59.29598888\n",
      " 59.32686417 59.35773946 59.38861475 59.41949003 59.45036532 59.48124061\n",
      " 59.51211589 59.54299118 59.57386647 59.60474175 59.63561704 59.66649233\n",
      " 59.69736762 59.7282429  59.75911819 59.78999348 59.82086876 59.85174405\n",
      " 59.88261934 59.91349463 59.94436991 59.9752452  60.00612049 60.03699577\n",
      " 60.06787106 60.09874635 60.12962164 60.16049692 60.19137221 60.2222475\n",
      " 60.25312278 60.28399807 60.31487336 60.34574865 60.37662393 60.40749922\n",
      " 60.43837451 60.46924979 60.50012508 60.53100037 60.56187566 60.59275094\n",
      " 60.62362623 60.65450152 60.6853768  60.71625209 60.74712738 60.77800266\n",
      " 60.80887795 60.83975324 60.87062853 60.90150381 60.9323791  60.96325439\n",
      " 60.99412967 61.02500496 61.05588025 61.08675554 61.11763082 61.14850611\n",
      " 61.1793814  61.21025668 61.24113197 61.27200726 61.30288255 61.33375783\n",
      " 61.36463312 61.39550841 61.42638369 61.45725898 61.48813427 61.51900956\n",
      " 61.54988484 61.58076013 61.61163542 61.6425107  61.67338599 61.70426128\n",
      " 61.73513656 61.76601185 61.79688714 61.82776243 61.85863771 61.889513\n",
      " 61.92038829 61.95126357 61.98213886 62.01301415 62.04388944 62.07476472\n",
      " 62.10564001 62.1365153  62.16739058 62.19826587 62.22914116 62.26001645\n",
      " 62.29089173 62.32176702 62.35264231 62.38351759 62.41439288 62.44526817\n",
      " 62.47614346 62.50701874 62.53789403 62.56876932 62.5996446  62.63051989\n",
      " 62.66139518 62.69227047 62.72314575 62.75402104 62.78489633 62.81577161\n",
      " 62.8466469  62.87752219 62.90839747 62.93927276 62.97014805 63.00102334\n",
      " 63.03189862 63.06277391 63.0936492  63.12452448 63.15539977 63.18627506\n",
      " 63.21715035 63.24802563 63.27890092 63.30977621 63.34065149 63.37152678\n",
      " 63.40240207 63.43327736 63.46415264 63.49502793 63.52590322 63.5567785\n",
      " 63.58765379 63.61852908 63.64940437 63.68027965 63.71115494 63.74203023\n",
      " 63.77290551 63.8037808  63.83465609 63.86553138 63.89640666 63.92728195\n",
      " 63.95815724 63.98903252 64.01990781 64.0507831  64.08165838 64.11253367\n",
      " 64.14340896 64.17428425 64.20515953 64.23603482 64.26691011 64.29778539\n",
      " 64.32866068 64.35953597 64.39041126 64.42128654 64.45216183 64.48303712\n",
      " 64.5139124  64.54478769 64.57566298 64.60653827 64.63741355 64.66828884\n",
      " 64.69916413 64.73003941 64.7609147  64.79178999 64.82266528 64.85354056\n",
      " 64.88441585 64.91529114 64.94616642 64.97704171 65.007917   65.03879229\n",
      " 65.06966757 65.10054286 65.13141815 65.16229343 65.19316872 65.22404401\n",
      " 65.25491929 65.28579458 65.31666987 65.34754516 65.37842044 65.40929573\n",
      " 65.44017102 65.4710463  65.50192159 65.53279688]\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "x and y must have same first dimension, but have shapes (850,) and (5,)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-156-2f895765a5e5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mffit\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mx_new\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_new\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mffit\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\python27\\lib\\site-packages\\matplotlib\\pyplot.pyc\u001b[0m in \u001b[0;36mplot\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   3361\u001b[0m                       mplDeprecation)\n\u001b[0;32m   3362\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3363\u001b[1;33m         \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3364\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3365\u001b[0m         \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python27\\lib\\site-packages\\matplotlib\\__init__.pyc\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1865\u001b[0m                         \u001b[1;34m\"the Matplotlib list!)\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1866\u001b[0m                         RuntimeWarning, stacklevel=2)\n\u001b[1;32m-> 1867\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1868\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1869\u001b[0m         inner.__doc__ = _add_data_doc(inner.__doc__,\n",
      "\u001b[1;32mc:\\python27\\lib\\site-packages\\matplotlib\\axes\\_axes.pyc\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1526\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_alias_map\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1527\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1528\u001b[1;33m         \u001b[1;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1529\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1530\u001b[0m             \u001b[0mlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python27\\lib\\site-packages\\matplotlib\\axes\\_base.pyc\u001b[0m in \u001b[0;36m_grab_next_args\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    404\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    405\u001b[0m                 \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 406\u001b[1;33m             \u001b[1;32mfor\u001b[0m \u001b[0mseg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    407\u001b[0m                 \u001b[1;32myield\u001b[0m \u001b[0mseg\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    408\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python27\\lib\\site-packages\\matplotlib\\axes\\_base.pyc\u001b[0m in \u001b[0;36m_plot_args\u001b[1;34m(self, tup, kwargs)\u001b[0m\n\u001b[0;32m    381\u001b[0m             \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindex_of\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    382\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 383\u001b[1;33m         \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_xy_from_xy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    384\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    385\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcommand\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'plot'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python27\\lib\\site-packages\\matplotlib\\axes\\_base.pyc\u001b[0m in \u001b[0;36m_xy_from_xy\u001b[1;34m(self, x, y)\u001b[0m\n\u001b[0;32m    240\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    241\u001b[0m             raise ValueError(\"x and y must have same first dimension, but \"\n\u001b[1;32m--> 242\u001b[1;33m                              \"have shapes {} and {}\".format(x.shape, y.shape))\n\u001b[0m\u001b[0;32m    243\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m2\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    244\u001b[0m             raise ValueError(\"x and y can be no greater than 2-D, but have \"\n",
      "\u001b[1;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (850,) and (5,)"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_new = np.linspace(slVel[15], slVel[100], num=85*10)\n",
    "coefs = polyfit(slVel[15:100], cdSl[15:100], 4)\n",
    "ffit = polyval(x_new, coefs)\n",
    "print ffit\n",
    "print x_new\n",
    "plot(x_new, ffit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.61003972e-02 -2.86594192e+00  1.24623484e+02]\n"
     ]
    }
   ],
   "source": [
    "bestfit = polyfit(slVel[15:100], cdSl[15:100], 2)\n",
    "print bestfit\n",
    "deg1 = bestfit[0]\n",
    "deg2 = bestfit[1]\n",
    "deg3 = bestfit[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x232f0570>]"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(slVel[15:100], cdSl[15:100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-5, 5)"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uGP3/74DfGv193eixNwK/19I63wpc2/RxHKvzycCdwIUMhyh/BPihNp2fa9TYinMTeBpwBXDn2GMTjx/wbOCDQAAzwKdaWufTgX9qybF8EvBE4OPAYOzxy4E7gPOBy4AvADvX20edXTpXAV/IzAXgGuBto8ffBvxyjXWsZ7zONtoFXBARuxiGwAPAM4C/H/28LcdzZZ1fabieSZ4EzGbmw5m5BHwCeB7tOj9Xq7EVMvOTwH+teHi143cN8PYcmgW+LyIe38I6GzGpxsy8OzPvmfD0a4B3Z+YjmXkvcAK4cr191Bn41wHvGv37+zPzAYDR34+rsY71jNcJ8OLR5edbmr60z8z7gT8FvsQw6L8JzAPfGIUBwH0MW9iNmVRnZn549OPXjI7nGyLi/MaKHLoTeFpE7I2ICxm2QC+lXefnajVCi87NFVY7fpcAXx57XtPn6lrv809HxB0R8cGI+NFmylvTlo5lLYE/6lN+LvDeOva3VRPq/BvgB4CnMAyu1zdUGgCjD/U1DC/hngA8Crh6wlMbHXo1qc6IeCFwA/AjwE8CjwVe0ViRDFtPwGuBW4F/ZniJvLTmi2q2Ro2tOjc3KCY81sZhgrcDBzPzJ4C/Av6x4Xom2dKxrKuFfzVwe2Y+OPr/g2cv5UZ/f62mOtbzXXVm5oOZ+Z3MXAbexAYumbbZM4F7M/NUZp4B3gf8DMNL47PLZOyn+e6TiXVm5gOjy/lHgL+l+eNJZr45M6/IzKcxvJz+PC07PyfV2MJzc9xqx+8+/v/qBJo/VyfWmZnfysz/Hv37FuC8iLiouTIn2tKxrCvwn893d5N8AHjR6N8vAt5fUx3r+a46V/QvPo/h5XWTvgTMRMSFEREM7zf8B3AbcO3oOW04npPqvHvswxUM+0ubPp5ExONGfx8AfoXh+9+q83NSjS08N8etdvw+APzmaLTODMOuvgeaKHCsnnPqjIiLR+coEXElw5xs21rOHwCui4jzI+Iyhjfy/23dV9Vw5/lChgfre8ce2wt8lGFr6qPAY+u6E77JOt8BfBb4zOgAP74Fdf4x8J8MP+DvYHiX/vDozT7BsDvq/JbW+bHR8bwTeCfw6BbU+S8Mf2neAVw1eqxV5+cqNbbi3GT4C/IB4AzDVufvrnb8GHZD3MRwRMlnGRt10rI6XwzcNTreswyvTpuq8Xmjfz8CPAh8aOz5N46O5T3A1RvZhzNtJakQzrSVpEIY+JJUCANfkgph4EtSIQx8SSqEgS9JhTDwJakQBr4kFeL/ALpNyWsK6D48AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(slVel[15:100], cdSl[15:100], \".\")\n",
    "#plot(slVel[15:80], ([x**deg1 + x**deg2 + x**deg3 for x in slVel[15:80]]))\n",
    "#plot(slVel[15:100], bf(slVel))\n",
    "xlim(70, 110)\n",
    "ylim(-5, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}