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    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt         # Used for plotting spectrum\n",
    "import numpy as np"
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q3j+ftQEACvKRpb4xIuZs/7WkFyX9n6RXJS027BO243qPbXuXpF2SNDAwsNQSAQBNtDUw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\nCSMEACBh/w8KsBRO0TtLBQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116334e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot([Run1_x,Run3_x,Run5_x,Run7_x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-17T11:53:30.055537Z",
     "start_time": "2018-05-17T11:53:29.898944Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x116646d30>,\n",
       "  <matplotlib.lines.Line2D at 0x116668160>,\n",
       "  <matplotlib.lines.Line2D at 0x116681a90>,\n",
       "  <matplotlib.lines.Line2D at 0x1166a0400>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x11665b748>,\n",
       "  <matplotlib.lines.Line2D at 0x11665bf60>,\n",
       "  <matplotlib.lines.Line2D at 0x116671a20>,\n",
       "  <matplotlib.lines.Line2D at 0x1166798d0>,\n",
       "  <matplotlib.lines.Line2D at 0x1166939e8>,\n",
       "  <matplotlib.lines.Line2D at 0x116693ba8>,\n",
       "  <matplotlib.lines.Line2D at 0x1165324a8>,\n",
       "  <matplotlib.lines.Line2D at 0x11652bc50>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x116662fd0>,\n",
       "  <matplotlib.lines.Line2D at 0x116681940>,\n",
       "  <matplotlib.lines.Line2D at 0x116699c18>,\n",
       "  <matplotlib.lines.Line2D at 0x1165d2a20>],\n",
       " 'means': [],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x116662160>,\n",
       "  <matplotlib.lines.Line2D at 0x116679a90>,\n",
       "  <matplotlib.lines.Line2D at 0x116699400>,\n",
       "  <matplotlib.lines.Line2D at 0x1165df198>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x116646ef0>,\n",
       "  <matplotlib.lines.Line2D at 0x116651ef0>,\n",
       "  <matplotlib.lines.Line2D at 0x116668f60>,\n",
       "  <matplotlib.lines.Line2D at 0x116671860>,\n",
       "  <matplotlib.lines.Line2D at 0x116689978>,\n",
       "  <matplotlib.lines.Line2D at 0x116689b38>,\n",
       "  <matplotlib.lines.Line2D at 0x1166a0c50>,\n",
       "  <matplotlib.lines.Line2D at 0x1166aab00>]}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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83VmqRKGj5WwPSrpH0qv1JuluxRDB65IWJJ2ICPbnxn1V0pck/abuIFWi0NFStn9H0n9I\n+mJEfFh3nm4WEZciYpek7ZLusz1cd6ZuZPvzkhYi4lTdWapGoaNlbG/RSpl/PSK+VXeeLCLiA0kz\n4vuejXpA0iO235b0DUkP2v5avZGqQaGjJWxb0hFJ8xHxD3Xn6Xa2+23fXrz+tKSHJL1Rb6ruFBFf\niYjtETEo6QlJ0xHxhZpjVYJCX8V2Q9L3JN1l+13bY3Vn6mIPSPpLrRz9vF48/rzuUF1sm6QZ2z+W\n9AOtjKGnmW6HanCmKAAkwRE6ACRBoQNAEhQ6ACRBoQNAEhQ6ACRBoQNAEhQ6ACRBoQNAEv8HHvkx\n8c1b6PwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116517b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot([Run1_z,Run3_z,Run5_z,Run7_z])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-17T11:58:10.809326Z",
     "start_time": "2018-05-17T11:58:10.651490Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x116d60978>,\n",
       "  <matplotlib.lines.Line2D at 0x116d81390>,\n",
       "  <matplotlib.lines.Line2D at 0x116d9a6d8>,\n",
       "  <matplotlib.lines.Line2D at 0x116db9048>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x116d6acf8>,\n",
       "  <matplotlib.lines.Line2D at 0x116d72ba8>,\n",
       "  <matplotlib.lines.Line2D at 0x116d88cc0>,\n",
       "  <matplotlib.lines.Line2D at 0x116d88e80>,\n",
       "  <matplotlib.lines.Line2D at 0x116da3f98>,\n",
       "  <matplotlib.lines.Line2D at 0x116da9e48>,\n",
       "  <matplotlib.lines.Line2D at 0x116c4d828>,\n",
       "  <matplotlib.lines.Line2D at 0x116c4a908>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x116d7bc18>,\n",
       "  <matplotlib.lines.Line2D at 0x116d91ef0>,\n",
       "  <matplotlib.lines.Line2D at 0x116db0eb8>,\n",
       "  <matplotlib.lines.Line2D at 0x116cef198>],\n",
       " 'means': [],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x116d72d68>,\n",
       "  <matplotlib.lines.Line2D at 0x116d916d8>,\n",
       "  <matplotlib.lines.Line2D at 0x116da9fd0>,\n",
       "  <matplotlib.lines.Line2D at 0x116c3c048>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x116d60b38>,\n",
       "  <matplotlib.lines.Line2D at 0x116d6ab38>,\n",
       "  <matplotlib.lines.Line2D at 0x116d81c50>,\n",
       "  <matplotlib.lines.Line2D at 0x116d81e10>,\n",
       "  <matplotlib.lines.Line2D at 0x116d9af28>,\n",
       "  <matplotlib.lines.Line2D at 0x116da3dd8>,\n",
       "  <matplotlib.lines.Line2D at 0x116db9ef0>,\n",
       "  <matplotlib.lines.Line2D at 0x116dc2748>]}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AzwJ7Vy3e27XR/XslcLIbv7gM+NIwv1uSNJwN6W5K8rozA9VJvgP4buBzVfUc8EKSa7vx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116c36ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot([Run2_x,Run4_x,Run6_x,Run8_x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-17T11:55:08.039714Z",
     "start_time": "2018-05-17T11:55:07.877856Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x116bc19e8>,\n",
       "  <matplotlib.lines.Line2D at 0x116be4400>,\n",
       "  <matplotlib.lines.Line2D at 0x116bfa748>,\n",
       "  <matplotlib.lines.Line2D at 0x116c1c0b8>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x116bcad68>,\n",
       "  <matplotlib.lines.Line2D at 0x116bd3c18>,\n",
       "  <matplotlib.lines.Line2D at 0x116bebd30>,\n",
       "  <matplotlib.lines.Line2D at 0x116bebef0>,\n",
       "  <matplotlib.lines.Line2D at 0x116c02fd0>,\n",
       "  <matplotlib.lines.Line2D at 0x116c0beb8>,\n",
       "  <matplotlib.lines.Line2D at 0x116aeb860>,\n",
       "  <matplotlib.lines.Line2D at 0x116ae5940>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x116bd9c88>,\n",
       "  <matplotlib.lines.Line2D at 0x116bf2f60>,\n",
       "  <matplotlib.lines.Line2D at 0x116c12f28>,\n",
       "  <matplotlib.lines.Line2D at 0x116acf3c8>],\n",
       " 'means': [],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x116bd3dd8>,\n",
       "  <matplotlib.lines.Line2D at 0x116bf2748>,\n",
       "  <matplotlib.lines.Line2D at 0x116c120b8>,\n",
       "  <matplotlib.lines.Line2D at 0x116ad7f28>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x116bc1ba8>,\n",
       "  <matplotlib.lines.Line2D at 0x116bcaba8>,\n",
       "  <matplotlib.lines.Line2D at 0x116be4cc0>,\n",
       "  <matplotlib.lines.Line2D at 0x116be4e80>,\n",
       "  <matplotlib.lines.Line2D at 0x116bfaf98>,\n",
       "  <matplotlib.lines.Line2D at 0x116c02e48>,\n",
       "  <matplotlib.lines.Line2D at 0x116c1cf60>,\n",
       "  <matplotlib.lines.Line2D at 0x116c227b8>]}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2JHk4yee7zjIoFvrP+1vg3V2HaMTzwJ9U1RuBtwIfSPLGjjONsx8D76yq64EbgHcneWvH\nmcbd3cCJrkMMkoV+lqr6J+CZrnO0oKpOVtVXe+9/wOZ/OK/tNtX4qk3/21vd23t5AWyHklwDvAf4\nWNdZBslC19AlOQC8CXio2yTjrTdE8AhwCniwqjyeO/dR4EPAT7sOMkgWuoYqySuBzwAfrKpnu84z\nzqrqTFXdAFwDvCXJTNeZxlGS9wKnqup411kGzULX0CTZy2aZf6KqPtt1nlZU1feAFbzes1NvA96X\n5HHgU8A7k3y820iDYaFrKJIEWAJOVNVfdJ1n3CWZSvKq3vuXAzcD3+g21Xiqqg9X1TVVdQC4HfhS\nVb2/41gDYaGfJcky8M/AG5J8O8l815nG2NuA32Pz7OeR3ut3ug41xq4GVpL8G/AVNsfQm7ndToPh\nTFFJaoRn6JLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNcJCl6RG/B/GgfW26xDs1AAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1168beac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot([Run2_z,Run4_z,Run6_z,Run8_z])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-17T12:16:11.074605Z",
     "start_time": "2018-05-17T12:16:10.938903Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1172e2278>]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AjLPnmT22J50a1/C6JCkhhYCIXJED6WcYPCWRzHO5zBkbR4dG1b0uSa6CQkBEim3f8SyG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\nIiFMISAiEsIUAiIiIez/A5AkCXx20hiKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1171d9400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([np.mean(Run1_x),np.mean(Run3_x),np.mean(Run5_x),np.mean(Run7_x)],'*-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-17T12:16:28.864534Z",
     "start_time": "2018-05-17T12:16:28.717519Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1173d4438>]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bFJYb3XXUWW70gQ7RdoekgJZRNfngqc5M+cWtHDlziftnrOPPS3b57fhmmiRU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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11745ce10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([np.mean(Run2_x),np.mean(Run4_x),np.mean(Run6_x),np.mean(Run8_x)],'*-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-17T12:18:11.791135Z",
     "start_time": "2018-05-17T12:18:11.649674Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11767fd68>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T2Lovx+toEgAqdJFyoFvLmnz+p9/w6NUtmbdpLz1e+pbhX6zjyPF8r6NJKVKh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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1176c1cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([np.mean(Run2_z),np.mean(Run4_z),np.mean(Run6_z),np.mean(Run8_z)],'*-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-17T12:18:20.987950Z",
     "start_time": "2018-05-17T12:18:20.850169Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1177bb7f0>]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RkfBrnpTAtPxc2rVsxoSCQlaXVDb+Jo8FcobeFnjPzBYD86gfQ38VuN3Mlvhf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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11778edd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([np.mean(Run1_z),np.mean(Run3_z),np.mean(Run5_z),np.mean(Run7_z)],'*-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-17T12:42:51.313129Z",
     "start_time": "2018-05-17T12:42:51.171721Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11803fef0>]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UVvqT50dKjrDyyEqKKooIDQql/xX9GZo4lGtbXlvnv5C01ny862NeyHyB9rHt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\n6HjJiVf/p1kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11800eb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Run1_z)\n",
    "plt.plot(Run3_z)\n",
    "plt.plot(Run5_z)\n",
    "plt.plot(Run7_z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-17T12:43:31.946014Z",
     "start_time": "2018-05-17T12:43:31.806706Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1181454e0>]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Gix1fZEm/JbSPaM/UXVO55/t72JO2x8HViqrAKQJ9ZPc45j3SAc+LzIctjKe1\nZtGhRTyw/AE83T2Z02sOfWL6GF1WhWhXL4ThHery2cYT7DxV/kFFMYExTL5pMrNum0WJpYT7fryP\nabumYbKaHFitcHXlSkilVJBSarFS6qBS6oBSqqNSKkQptVIpdaTsNtiRhUqYV04llhJe3vQyEzZP\noH1kexb2WUjDkKr1l9SY2xsSEeDD2K9+p9RsvaL3to9sz5K+S+gd05sPd3/I8GXDSchJcFClwtWV\nNyUnA8u11o2AlsABYCywWmsdD6wueyzspMRSQoGpwOgyLik5P5n7f7yfJUeWMKLFCKbdNI1A70Cj\ny6pw1X08eeXOZhxKzWPGmmNX/n6v6rx6w6u82+1dkvKTGPj9QOYdmHd+/nchyuuy/dCVUoHALiBG\nX7CxUuoQ0E1rnayUigR+1Vpf8tSsohaJdmZJ+UnMPzCfJUeXUGAqoHloczrW6kjHyI40D2teadqk\ntyZv5Zk1z1BqLeW1G17jpuibjC7JcI/P28FP+1JZ9lQX4mpe3cyfaYVpvLTxJdYlraNjZEcmdJ5A\nuJ90BKjq7DYfulKqFTAT2I/t7Hw78BSQpLUOKttGAVnnHv8dCfSL01qzLXUbc/bP4dfTv6JQ3FL3\nFqKrR7MleQt7M/Zi1Vb8PP24LuI6OkZ2pFOtTtQNqFvh/bq11ny+/3Pe3f4u9QLqMan7JOoH1q/Q\nGiqrtLwSbnl3DQ3C/Vk4oiNublf3u9Fa8+XhL3l729t4uHkwvsN4etbvaedqhTOxZ6C3AzYDnbXW\nW5RSk4Fc4IkLA1wplaW1/ks7ulJqBDACIDo6uu3Jkyev7JO4sGJzMcuOL2PugbkczjpMkHcQAxoM\nYFDDQUT4/W+l+ZySHLambGXTmU1sPLORpPwkACL9Is+fvbePbE+wj0MvY1BoKuTFjS+y4sQKbq17\nKxM6T8DPU+bSudCX2xJ5dvEeJtzZjOEdrm064JO5J3lh/QvsSdtDz3o9GddhXJVs0hL2DfQIYLPW\nul7Z4y7Y2svjkCaXq5JSkMKCgwtYfGQxOSU5NAhuwL2N76Vn/Z7lWrMyMTeRTcmb2HRmE1uSt5Bn\nykOhaFyj8fmz91Y1W+Hl7mW3mk/mnuTpX54mISeBp9o8xT+a/sMlRn3am9aaez/ewu7EHFaO6kpk\n4LUtlWe2mvlk7ydM3zWdEN8QJnSeQKdasmB1VWPXJeiUUuuAh7XWh5RSLwPnTssytNavK6XGAiFa\n6zGX2k+IvdcXAAAgAElEQVRVDnStNbvSdjFn/xxWn1qNRtO9TneGNR5Gu/B2Vx2OZquZfRn72HTG\nFvB70vZg1mZ8PXxpE96GTpGd6FirI3FBcVd9jDWJa3h+3fO4u7nzZtc36Vir41Xtp6o4mVFAj0lr\nuSEulI/uu/rf7YX2ZezjhXUv2KYQaDSUp9s+XWHrqgrj2TvQWwGzAC8gAfgHth4yi4Bo4CQwUGt9\nybHMVTHQSy2lLD+xnLkH5rI/Yz/VvarTP74/gxsNJso/yu7HKzAV8FvKb2w8s5FNZzZxIvcEAGG+\nYXSs1ZEOkR3oWKtjuUZvWrWVD3d/yPTd02kc0pj3ur/nkJpd0cy1x3ht2UGmDW1D7xb2mVmy2FzM\n5B2TmXNgDvUC6vF6l9dl+bsqososEl1ZpRWmsejwIhYdWkRmcSYxgTEMazyMPjF9KnR1nuT8ZDYn\nb2bjmY1sTt5Mdkk2APHB8efP3tuEt/nL2V5uaS7Pr3veNvdIbF/GdxhfruYgYWO2WLnzgw2k5BSz\natSNBFWzX/PX5uTN/Hv9v8koymBEyxE80vyRSrGYtnAcCXSD/J72O3MPzmXFiRVYrBa61u7K0MZD\n6RjZ0fA2Z6u2cjDzoC3cz2xmx9kdmKwmvNy8aB3emo6RHelYqyPuyp1//fovkvOTee765xjUcJDh\ntTujfWdy6Dt1A/3bRPHmAPtOTpZbmstrW15jacJSmoc257UbXqNeYD27HkNUHhLoFchkNbHyxErm\nHpzLnrQ9+Hn6cVfcXQxpNITogGijy/tbReYitqdut7W/J2/iSNaR86+F+Ybxbrd3aVWzlYEVOr83\nlh9k+q/HmPtwezrH2X+SsuUnljNh0wRKLaU80+4ZBjYcKF++LkgCvQJkFmfy5aEvWXRoEWeLzlI3\noC5DGg3hzrg7nbI7X1phGpuTN3Mi9wSDGw4mrFqY0SU5vWKThdsnrUUDy5/qiq+X/de/PVt4lhc3\nvMiGMxvoHNWZ/3b6LzWr1bT7cYRxJNAd6EDGAeYemMuPx3+k1FpKp1qdGNZ4GDdE3YCbkjlnxB9t\nPJbO0I+28M8bY3i+Z2OHHENrzcJDC3ln2zt4e3gzvsN4etTr4ZBjiYpX3kCXKynlZLaa+fnUz8w9\nMJcdZ3fg6+HLXfF3MbTRUGKCYowuT1RinWJDGXxdHWatO84dLWrRLMr+g4OUUgxuNJgOkR14Yf0L\nPLPmGX5J/IUX2r9AgFeA3Y8nKic5Q7+MnJIcFh9ezIJDC0gpSCHKP4ohjYZwV/xd8j+KKLecQhO3\nvLeGMH9vvn28s0NnDzVbzXz0+0fM2D2DUN9QXrnhFTpEdnDY8SqzYnMxx3OOcyT7CEezjhLuF86Q\nRkOc7i9paXKxg33p+3jop4coMBVwfcT1DGs8jBtr34i7m/3bQYXr+/H3ZB6du4OxPRvxfzfGOvx4\ne9P38vy65zmRe4J7G9/LU22ectmup2armVN5pziadZSj2Uc5knWEo9lHOZV36vyslR5uHpitZm6q\ncxMTu0ys0O7D10oC/RqlF6Uz+IfBuCk3ptw0pcrN8S3sT2vNP7/YzprDaax4uiv1Qh1/4bzIXMSk\n7ZOYd3AeMYExTOwykSY1mjj8uI6itSalIMV2xn1BcCdkJ1BqLQXATbkRXT2auKA44oPjiQuKIy44\njujq0Sw8tJA3f3uTuKA4ptw0hVr+tQz+ROUjgX4NTBYTD/30EAcyDvBFry9oFNLI6JKEi0jJKebW\nd9fQLCqQeY+0r7AuhhvPbGT8+vFkFmfyaKtHebDZg5V+MFJWcRZHs49yOOswR7OPnj/7zjfln98m\nvFo48cHxxAfFExccR1xQHDGBMZf8S2RD0gaeXfMsnu6eTOo+idY1W1fEx7kmEujX4L+b/suXh7/k\nra5vcXv9240uR7iYuVtOMu7rvbzZvwUDr6tTYcfNKcnh1c2v8uOJH2kZ1pJx7ccRVi0MTzdP24+7\nJx7Ko8L7sReaCjmWfYwj2UfOn3EfyTpCRnHG+W0CvQNtoV121h0fHE9sUOxVX8dKyEngidVPkFyQ\nzIsdX+TOuDvt9XEcQgL9Ki06tIgJmyfwULOHeLrt00aXI1yQ1aoZ/NFmDibnsmr0jdSsXrHt2j8e\n/5EJmyeQV5r3l9cU6ny4e7l5nb//5+e83L3+8EXw5+fO3z/33guec1fuJOYlciTrCEeyj5yfDhrA\n18OX2MBY4oLjzp91xwfFE+obavcvmpySHEavGc2W5C080PQBnm7zdKW9PiaBfhV2pO7goRUP0aFW\nB6beNLXS/nKF8zuWlk/Pyeu4tXE404a1qfDjny08y4akDZisJkxWE6WW0j/cnrtvtpr/8twfXrOW\nYrKUvVZ2v9Rqe81kMWHW5ose30N5UC+w3h+aSuKD44nyj6rQHigmq4k3t77JgkML6Fq7K290eQN/\nr6tbbcqRJNCvUEpBCoN+GESAVwBze8+VLonC4ab9cpS3Vhxi5vC23NY04vJvcEIWqwWzvuBLoSzk\na/rWxNO9ciynCLDw4EImbp1IvYB6TLl5CnWqV1xTWHmUN9CdqzOmgxSbi3ny5ycpsZQwuftkCXNR\nIUZ0jaF5VCCJWUVGl+Iw7m7ueLt7U92rOiE+IYT7hRPlH1WpwhxgUKNBzLh1BmlFaQxdOpTfUn4z\nuqSrUuUDXWvNy5te5mDmQV7v8rqM+hQVxtPdja8f68RDN8iarJVB+8j2zO89n2CfYEb8NILFhxcb\nXdIVq/KB/vn+z1masJSRrUbSrU43o8sRVYyHA0eMiisXHRDNnF5zaB/Znv9s+g+vb30ds/Xi1wEq\noyr9r2lj0kbe3f4ut9a9lREtRhhdjhCiEgjwCmDqzVMZ3mQ4cw/MZeTqkeSW5hpdVrlU2UBPzE3k\n2bXPEhsUyyudX5E5pIUQ53m4eTDmujH8p9N/2JqylWFLh3Ei54TRZV1WlQz0AlMBT/7yJEopJnef\n7FRzOgghKs7d8Xfz0a0fkVOSw9BlQ9l0ZpPRJV2SUwR60Z495K5cibZar3lfVm1l3PpxHM85zts3\nvl3puicJISqXdhHtmNd7HuHVwnl01aPMPzjf6JL+llMEeta8+SQ98SQJvfuQ/dUSdGnpVe9rxp4Z\nrD61mtHtRlfZKUWFEFemdvXafNHzC26IuoHXtrzGK5tfwWQ1GV3WXzhFoEe+MoGod99BeXuTPG4c\nR2/rQebs2VgLCq5oP6tPreaDXR/QN7Yv9za+10HVXjtraSm5y5eTt3o1FTnwSwjx9/y9/JncfTL/\naPYPFh5ayP+t/D+yi7ONLusPnGqkqNaagvXryZj5EYW//YZ7YCDBw4cTPGwoHsHBl3zv0ayjDFs2\njJjAGD7r+Rne7t5XXYejlCYmkr1oEdlfLcGSmQmAb7u2RIwfj09Dmb5XiMriu2Pf8fLGl4nwi2Dq\nTVMdPn7F5Yf+F+7cScZHs8j/+WeUry/BA+8h5IEH8IyM/Mu2OSU5DF06lAJTAQv6LCDCr/IMs9Zm\nM/lr1pC1YCEF69eDmxv+3bsRPGgwpuQzpL37Hpa8PIKHDiXsicdxD5BRrEJUBrvO7uKpX56i1FLK\nm13fpEvtLg47lssH+jklR46QMWsWOT8sBTc3Au+4gxoPP4R3jO0b02K18Njqx9iaspVPe3xKq5qt\n7Hr8q2VKTSV78WKyv1yMOSUFj/Bwgu65h6AB/fGM+N8Xjjkri7TJk8leuAj3kBBqjh5N4J39UG5O\n0VomhEtLzk/miZ+f4Ej2EUa3Hc3wJsMd0gW6ygT6OaakJDI+/YzsxYvRJSVUv+VmaowYwfSSlXy6\n71Ne6vgSAxoMcMixy0tbrRRs2kT2goXk/fwzWCz4de5M8JDB+HfrhvL4+wUHivbtI/W/EyjavRvf\nVq2IeHE8Pk2cd+UZIVxFoamQF9a/wOpTq7kr7i7Gdxhv97lqqlygn2POzCTziy/ImjsPa24uv9dV\nZN/TnUcemmrY4CFzVhY5S74ma9FCTCdP4R4cTFD/uwkaOBCv6Ohy70dbreR8/Q1n33kHS3Y2wYMH\nEfbkk7gHBTmweiHE5Vi1lWm7pjFzz0za1GzDe93fI8QnxG77r7KBfs6+U9tY8OZD3PGbpnqOCZ+m\nTanxyCNUv/UWlLvj5znXWlO0cydZCxaQt3wFurQU37ZtCR48mOo9bsPNy+uq923JzSXt/SlkzZuH\ne2AgYaP+RVD//tIMI4TBliUs48WNL1LDpwZTbp5Cg+AGdtmvXQNdKXUCyAMsgFlr3U4pFQIsBOoB\nJ4CBWuusS+2nogI9oyiDwUsHAzD/1s9x/2k9mbM+pvTkSbzq1iXk4YcI7NfvmkL171jy88n57juy\nFyyk5PBh3Pz9CezXj6BBA/FpYJ9f7jnFBw+SMuEVirZvx6dFCyLGj8e3eTO7HkMIcWV+T/udp355\nigJTAa93eZ3u0d2veZ+OCPR2Wuv0C557E8jUWr+ulBoLBGutn7vUfioi0E0WEw//9DD7M/Yzu+fs\n8yuca4uFvJWryJg5k+L9+/GoWZOQBx4gaOBA3P2vffX14v37yVqwkJwffkAXFuLTpAlBQwYT2KsX\nbn6OW91da03u99+T+tZbWNIzCBowgLBR/7psN04hhOOkFqTy5C9PciDjAE+1eYoHmz14TU2+FRHo\nh4BuWutkpVQk8KvW+pKdpSsi0F/Z/AoLDy3kjS5v0Cum119e11pTsHEjGR/NonDzZtwCAggeNpSQ\n4cPxCLmyNi9rURG5Py4na+ECinfvQfn4ENC7F8GDB+PbvLm9PlK5WPLzSZ8ylcw5c3D39yfsX08T\ndM89FdK8JIT4qyJzES9ueJHlJ5ZzR8wdvNTppase/2LvQD8O5GBrcpmhtZ6plMrWWgeVva6ArHOP\n/46jA33x4cX8Z9N/+EfTfzCq3ajLbl+0Zw8ZH80ib9UqlLc3QQMGUOMfD+AZFXXJ95UkJJC9cCHZ\nX3+DNTcXr9hYggcNIvDOfob3Ey8+fJjUCa9Q+Ntv+DRpQsSL4/FtVTm6agpR1WitmbFnBtN2TePd\nbrapuq+GvQM9SmudpJSqCawEngC+uzDAlVJZWuu//J2vlBoBjACIjo5ue/LkySv4GOW38+xOHlzx\nIO0j2jPt5mlXtMBzybFjZHz8CTnffQdAYO/etr7s8fHnt9GlpeStXk3W/AUUbt0Knp4E3HorQYMH\nUe266yrV9Ltaa3KXLePsG29iPnuWwLvvpuboUXjUqGF0aUJUSQczD9IopNFVv99hvVyUUi8D+cAj\nVJIml5SCFAb/MBg/Tz/m9Z5HoHfgVe3HlJxM5mezyVq0CF1UhP9NNxE8eBCF23eQ/dVXWNLT8YyK\nImjQIILuvguP0FA7fxL7suQXkD79AzJnf45btWqEPfkkwYMHXbK/uxCi8rFboCul/AA3rXVe2f2V\nwH+Bm4GMCy6Khmitx1xqXw4ZKWop4YEfHyAhJ4F5vecRGxR7zfs0Z2WRNXceWV98gSUnxzYcv1s3\nggcPwu+GG5yue2BJQgKpr7xCwcZNeDdsSMSL46nWtq3RZf2BJT+f0uMn8KobbXizlRCVjT0DPQb4\nuuyhBzBPa/2qUqoGsAiIBk5i67aYeal92TvQtdaMWz+O7xO+Z3L3ydwUfZPd9g1gLSggf8MGfJs1\nw7NWLbvuu6Jprclb8ROpb7yBOTmZgL53UPOZZ/CsWbNC6zBnZlJ67Bglx45RciyB0mNHKTmWgDk1\nFQCP8HDqfDTT7l08hXBmVWJg0ef7PuetbW/xWKvHeLTlo3bbryuzFhaS/uEMMj79FDcvL0KfeJyQ\nYcNQnvYbqqy1xpyaSsnRY5QmHKPk6DFKEo5ReiwBS9b/hiqoatXwjonBOzYGr5hYPMJrkvbOu1iL\ni6k9bSp+119vt5qEcGYuH+ibzmzi/1b9H93rdOfdbu/ippyrGcRoJcePk/raRArWrcM7Po7wf4/H\nr/2VBai2WDCdPk3JsQRKjh2l9FgCJceOUZqQ8Ie56t0CA/GOjcU7Nhav2Jjz9z0iIv7SfGVKSuLU\nIyMwJSZS6623CLi9h10+rxDOzKUDPTEvkSFLhxDmG8acXnPw83TcwB1XprUm/+efSX1tIqakJAJ6\n9aLmc2PwDA//w3bW0lJKT5ygNKEssM81lxw//ofVozzCwvCKi8U75lxwx+EdG4N7jRpX1AvInJXF\n6cdGUrRrF+EvvEDI8Mq7GIkQFcFlA73QVMiwZcM4W3iWBb0XUCdA1gS9VtbiYjJmfkTGrFkoDw+C\n778PLFZbM8nRY5QmJoLFcn57z9q1bYEdE4t3XCxeMbazbntezLQWF5M0+hnyV6+mxiMPEzZqVKXq\nGipERXLJQNdaM3rNaFafWs30W6bTqVYnO1YnSk+dInXi6+T/8gt4eOAVHf2XZhKv+vVx8/WtkHq0\n2UzKhFfIXriQwH59iZwwAeWA+XeEqOzKG+hO1SF55p6ZrDy5kmfaPSNh7gBe0dHUmf4BptSzeAQH\nGR6eysODiJdfwjMinLTJ72NOzyBq8mS7zL0jhCtymiuJvyb+ytRdU+kT04f7mtxndDkuzTO8puFh\nfo5SitBHHyXy1Vco2LyZU/fdhzk9/fJvFKIKcopAT8hOYOy6sTSp0YSXOr4kbalVUFD//tSeNpWS\n48c5MXgIpSdOGF2SEJWOUwT6x3s/xtvdm8ndJ+Pj4WN0OcIg1bt1o+7sz7AWFHBiyFCK9uwxuiQh\nKhWnuChqsphIzE8kJjDGAVUJZ1Ny/DiJj4zAnJFB7Unv4X/jjUaXJIRDlfeiqFOcoXu6e0qYi/O8\n69en3oL5eNevT+JjI8n+aonRJQlRKThFoAvxZx6hoUR//jl+7duTPG4c6dOnU5F/bTq7vF9/JXXi\nRAo2bkSbzUaXI+zEqbotCnEhd38/6nw4nTP//jdpk9/HlJpKxPjxskrTJejSUs6+8y6Zs2eDUmTO\n/hz3GjUI6NGDgN698G3d2ulmExX/I4EunJry8qLWG2/gGR5OxkezMKenE/X227j5yMXzPzMlJXF6\n1CiKd+8heNgwwp5+ioJNm8hduozsr74ia948PCIiCOjZk4BevfBp1lR6lDkZp7goKkR5ZH4xh9TX\nXsO3VSvqTP8A96BLrohYpeT98gtnxj4PFguRr7zyl0nPLPkF5P/yM7lLl5G/YQOYTHhGRxPQqyzc\nZTpjQ7nk0H8hLid3+XLOPDsGzzp1iP5o5mXXh3V12mTi7HuTyPzkE7ybNKb2e+/hVbfuJd9jyc4m\nb9Uqcpcto2DzFrBa8Y6PI6BXLwJ69brs+4X9SaCLKqtg61ZOj3wcN19f22IZDS+5MqLLMiUnkzRq\nNEU7dxI0ZDDhY8fi5n1lq86b09PJXbGC3GU/UrR9OwA+TZsS0Ls3AT1vxzMy0hGliz+RQBdVWvHh\nwyQ+MgJrQQG1p07Fr0N7o0uqUPlr1nDmubFok4nICf8loFeva96nKTmZ3B+Xk7tsGcV79wLg27at\nrVmmR49Kv8auM5NAF1WeKTmZU488gunkKWq98bpdQq2y02YzaZMnk/HRLLwbNaL2pPfwqlfP7scp\nPXmS3B9/JHfpMkqOHAE3N/w6tCegVy+q33KLXL+wMwl0IQBLTg6JI0dStG074c+PJeT++40uyWFM\nqam2Jpbt2wkaOJDwF56vkN4+xYcP28J92TJMJ0+Bpyf+nTsT0LsX/t1vktkx7UACXYgy1pISzjzz\nLHkrVxLy4IPUfGa0y/W1zl+3njNjxmAtKSHyP/8h8I4+FV6D1priffvJXbaM3B9/xJycjPL2xr9b\nNwJ69cL/xq7SnfQqSaALcQFtsZD66mtkzZtHwB13UOvVVyrNFMHXQpvNpE2ZSsaMGXg3aEDUpEl4\nx9Q3uiy01UrRrl3kLl1G7vLlWDIycKtWDf9bbraFe5cuMgDsCkigC/EnWmsyZn5E2nvv4depI1Hv\nv4+7v7/RZV01U+pZzjzzDIW//UbggP5EjBtXYatJXQltNlP422+2M/efVmLNycG7QQPCnx+LX8eO\nRpfnFCTQhfgb2V9/Q/L48XjHxxM9cwYeYWFGl3TF8jds4MyzY7AWFRH58ksE9utndEnloktLyV25\nkrR338OUlIT/TTcRPuZZh1y4dSUuNduiEPYUdNed1Jn+AaUnT3Ji8BBKEo4bXVK5aYuFtPffJ/Hh\nR/CoEUL9xV86TZiDbaqGwN69iVm2lLBRoyjcvJljd/Ql9fU3sOTmGl2e05NAF1WSf5cu1J09G2tR\nESeHDqVo1y6jS7os09mznPrHg6R/MJ3AO++k3sKFeMfGGl3WVXHz9iZ0xCPErlhOYL++ZM6ezbEe\nt5M1f77M/ngNpMlFVGmlp05x6uFHMCUl4duiBX6dO+PXuRO+zZujPCrP3HUFmzaR9OwYrPn5RLz4\nIkF332V0SXZVvH8/qa9NpHDbNrzj46k59jn8O3c2uqxKQ9rQhSgnc2YmmV98QcGGjRT//jtojVv1\n6vh16GAL+Bs641W7tiG1aYuF9Okfkj5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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1181d92e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Run2_z)\n",
    "plt.plot(Run4_z)\n",
    "plt.plot(Run6_z)\n",
    "plt.plot(Run8_z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}