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February 19, 2013 16:47
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An attempt to make some sense of Tai's data.
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| { | |
| "metadata": { | |
| "name": "kalman-filter-test" | |
| }, | |
| "nbformat": 3, | |
| "nbformat_minor": 0, | |
| "worksheets": [ | |
| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "%pylab inline" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "\n", | |
| "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n", | |
| "For more information, type 'help(pylab)'.\n" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 1 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "import pandas\n", | |
| "import numpy\n", | |
| "from scipy import integrate, interpolate\n", | |
| "from dtk.process import spline_over_nan, derivative, butterworth" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 2 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Load in a sample data file. I skip the first set of rows and also the units row. Skip the last two lines are the end date stamp. The index_col=False is so that the first column isn't used as the row index." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "data = pandas.read_csv('vehicle-data/id084.22/logs/id084.22_p0_g00107.csv', skiprows=range(18) + [19], skipfooter=2, index_col=False)\n", | |
| "# delete the rows that are not used\n", | |
| "del data['b']\n", | |
| "del data['CalcMAF']\n", | |
| "del data['Unnamed: 11']\n", | |
| "data = data[:-50] # get rid of crap data at tail\n", | |
| "data = data.dropna(axis=0) # drop rows with missing data" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 3 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Here are some constants that we will need for the various calculations.\n", | |
| "\n", | |
| "The 2003 Lexus ES weights 3439 lbs, has a Cd of 0.28 all from http://www.edmunds.com/lexus/es-300/2003/features-specs.html?style=&sub=sedan\n", | |
| "\n", | |
| "Cd and Area for a bunch of cars are on wikipedia: http://en.wikipedia.org/wiki/Automobile_drag_coefficient\n", | |
| "\n", | |
| "I'm guessing at the frontal area: 0.714 m^2 from similar shape to the 1994 Chrysler LHS.\n", | |
| "\n", | |
| "The rolling resistance guess is 0.015 from http://en.wikipedia.org/wiki/Rolling_resistance.\n", | |
| "\n", | |
| "The energy density of gasoline came from here: http://people.hofstra.edu/geotrans/eng/ch8en/conc8en/energycontent.html" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "drag_coefficient = 0.28 # drag coefficient\n", | |
| "frontal_area = 0.714 # m**2 frontal area\n", | |
| "air_density = 1.2 # kg * m**3 air density\n", | |
| "mass = 1635 # kg mass (added 75 kg for one passenger)\n", | |
| "air_to_fuel = 14.6 # air to fuel ratio\n", | |
| "rolling_resistance_coefficient = 0.015\n", | |
| "g = 9.81\n", | |
| "gasoline_energy_density = 45.8e6 # J / kg" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 4 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Below I cleanse the data and resample it at a constant sample rate by interpolation." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "# convert to sensible units!\n", | |
| "data['Time'] = data['Time'] / 1000.0 # convert to seconds\n", | |
| "data['Speed'] = data['Speed'] * 0.44704 # convert to m/s\n", | |
| "\n", | |
| "data = data[data['Time'] >= 0.0] # drop all data below t = 0\n", | |
| "data['Alt'][data['Alt'] < 100] = numpy.nan # some weird 0 alt values seemed to exist\n", | |
| "data = data.dropna(axis=0) \n", | |
| "\n", | |
| "# resample at a constant sample rate so we can filter later on\n", | |
| "time = numpy.linspace(data['Time'].values[0], data['Time'].values[-1], num=len(data['Time']))\n", | |
| "time_step = time[500] - time[499]\n", | |
| "sample_rate = 1.0 / time_step / 2.0 / numpy.pi # hertz\n", | |
| "for col in ['Speed', 'Alt', 'RPM']:\n", | |
| " new_col = interpolate.interp1d(data['Time'], data[col])\n", | |
| " data[col] = new_col(time)\n", | |
| "data['Time'] = time" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 5 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "At this point we can see the vehicle speed and altitude measurments over time." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "data.plot(x='Time', y='Speed')" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 6, | |
| "text": [ | |
| "<matplotlib.axes.AxesSubplot at 0x43f9190>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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1UQccPpy2iURo4GjHDuDaa+k5Lyb5OBWpoUOdXdEoS08YoZfON3cuxWYvvNDZ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\nTkSuQmCC\n" | |
| } | |
| ], | |
| "prompt_number": 6 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "data.plot(x='Time', y='Alt')" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 7, | |
| "text": [ | |
| "<matplotlib.axes.AxesSubplot at 0x44007d0>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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RUVGIj49HSEgIACAlJQWLFy+GVqtFYGBghwPKUvPzozK4TBrHjkkzHhMURCt+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\n5pv2i1OpTM/FW28Jcd99Qvz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| |
| } | |
| ], | |
| "prompt_number": 7 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "The distnance traveled over the curve can be computed by integrating the vehicle speed with respect to time." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "data['Distance'] = numpy.hstack((0.0, integrate.cumtrapz(data['Speed'].values, x=data['Time'].values)))\n", | |
| "data.plot(x='Time', y='Distance')" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 8, | |
| "text": [ | |
| "<matplotlib.axes.AxesSubplot at 0x5144290>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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SUmbPnu2bDTEnJ4f8/Hzcbjdut5vS0tIAHFb39M471jiJLVus5zk51uyuIiJ2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\n" | |
| } | |
| ], | |
| "prompt_number": 8 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Now we need to compute the longitudinal distance in the inertial reference. The following equation describes the trigonometric relationship between the vertical and longitudinal distances (alt and x) and the distance travels from one point on the curve to another.\n", | |
| "\n", | |
| "$$[s(n)-s(n-1)]^2=[x(n)-x(n-1)]^2+[y(n)-y(n-1)]^2$$\n", | |
| "\n", | |
| "$$\\Delta x = x(n)-x(n-1)$$" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "data['deltax'] = numpy.hstack((0.0, numpy.sqrt(numpy.diff(data['Distance'].values)**2 - numpy.diff(data['Alt'].values)**2)))\n", | |
| "data['deltax'] = spline_over_nan(data['Time'].values, data['deltax'].values)\n", | |
| "data.plot(x='Time', y='deltax', figsize=(8, 8), style='.')" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 9, | |
| "text": [ | |
| "<matplotlib.axes.AxesSubplot at 0x4d0ba90>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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kuKeNxYkT3u9tbeHX9EaNCn/eIOM3ke7u8OcoMl1d8u2f+Yz6P5Rq1j7V1cVb\nNz77bJb5cMECJtgJNTwxEhGPWMLccRzYuHEjdHR0wJVXXind54ILLoBbb70VAAD27NkD5XK5QsWe\nNMePe78fPOhm2FIhrvklcQl/9Vf2zzGceOop+fY771Qb2bz7LsA//qO9MhWV005j71VVTI1atNnp\n88+zJExvvglwyimUNAkjRtLUWeIkgomlZt+1axd89rOfhTlz5kDpz7qT6667Dl588UUAALj8z+mC\nvvKVr8C9994LjY2NcMstt3hU7ADJq9lFNQ/Hrwg4aYTO/lEhNXs4xPrav59F0uOcfjrrWI8f969L\nqmezFDXJCke2tENtjDF+PMBbb3m3FbFuTMu9WAZwPT09cELUWUv40Y9+FOc0iRBkzS66NX3qU/bK\nwgnSFhCVLFwI8N577vdDh/zXywk7FD3a3pIlLOYBhxL7uJx5JsBtt7nfp01LryzDiUJGgBPVPACV\nFu4iotUznv2ZhK+n19UB/N//a+ccwwkckKOqqjIqn45bUBGNb2zT0sJeRXX9u+MOZgAHwAQ5T/JD\nsHgdmKC+l9CjkPnMZWr2MWPYGpcKrDZsamJhV20IAVy2RYsAfvMb8+cYTuD6mj2bqdkxBw4wA7hZ\ns9janCrEQdFCjtoG35elSwEeeSS9sqRFXx/ztW9oYDEOaNDIKLqnA8e03CNhjvArwurVbITd3Mz8\ny20Z9NCaeTh06+v009mM4J13XB9gEaprcxS9HZ9+OsBzz7ntrIh2Aypk/W/R2gdABl3ThgtB0dZ4\ngpTnn0/OMpciwIVj+3b1b4cOMUEOIBfkKrc2Ij5c3VwkDh1y21lNTTHtBnRpa0u7BMODwglzlarr\nsstYVi2//4XxETdBDuwGU2fFCvbe2enmgpcRtHZexJmBTebPZ+/TpwP88pfpliUNcHs7dgzgF79I\nryxZY9Ik7/ejRynxigkKp2avqormopTU+lfR1ZNhwbYMra0s9aTs3hw4wHyfP/yQdbRPPAEwZ453\nH6prc+hEOxzOHDjA/Msx1L4Yhw+7aYtVFKGuSM0eE7+683MFSyNpxJw5xc42pQN2GTx0SH1vpk5l\nQXhaWgCWLQNA6QIAAGDrVntlLCLXXAPw+usAl1xSzDYsLsXRkpmLOLirr/d+X7AgubIMJwonzMeM\nUf/2pS+pf0sqzzh+6PfvL3a2qSBKJa//7pw5/muTBw4AvPEGwAMPAMyc6f3tz0EKU6GqylUxDpdI\nYTfd5A6Cg0bUAAAgAElEQVR+ixovYdky9j5nDsCaNemWJUv09XkH4e+/z4xSObSGHo3CCfNPf1r9\n2+bNesewmWd8wwaAVavY56IG3IjKm2/6q3TFWTxmzx47ZdIBa4v4vR9OvPBC2iVIFxqUexkY8AZ2\n6upy40V0dQHccks65co7hRPmovEFJmgdh+NnNR2Xvj4Wy7q2FqCx0d55hhulEsCjj8p/6+sDOOkk\n5rPPDZPEJDZZSWozHNeX169PuwTpUPQoeCp4vdTUsFzm48YxjeS6dQAPPjg8n4EkKJwwFzMYcYOz\nUqky9jqGC/Dt2wE+/3k7ZQNgo9bXXmMpWnfupBG9HzwEL89Hr3IZHBhgM/HDh5llcVsbwH33sWxe\nnAkT7JdXh9ratEtgBlyfH36YXjnSpOhR8FRwN99Pfxrg7bfZstfVVyfvLTTcKJwwxwK7sdFVcToO\ni6es4r772BrYv/yLXYMeMQb8hRfaO1de6etjAvzP+XzAcQDOO0+9P67TUaPc8JE4e54qMlzSvP76\n8EiZ+frr7mebNiZZ5tZbXRuN4bh8EhXu5rt7t7tt+3bXbuTUU9MrW54pnGuaKvobR1WMpLJAydw2\n0r9D2UKWwQ6A+Zlv3145uj98mNk57N7N1tUB2D0Ul0vSqmexTU6aBPDyy+mUxRTkYkl1oKKvj8Ue\nEO1WMEWoK3JNs4hf8pSk1r9EQYQTiRCM55+Xb3/gAfmyRLnMcprzdXHZPczKmjkAiyM/nCiXi+me\nhiHXNBe+7KWCbIWiUWhhLibl+K//Uqs4uQvU448D/DlNuzW4n+WMGQB33WX3XHlEla42yDWNr9Xd\nd1/loCmNNKlcrSjCw87mFfGaeACZotHR4X6+++70ypEl+vr8bZMA7GWkHO4UTpj/xV+wzmbJksrA\nIceOsQxbQdhOmHDGGcxwJijHelFRxQo4+WR/Axq/QCa/+5258sUlTTc5GxTVmvvdd9l7UxPA9den\nW5asMDAQvM+ePaTJiULhhPlbb7H1mN275bMFnYQbto3SBgddw5kizmiCEHPLc7B1ugy/KH5ZigA3\nnJZWamrkmpAiwAfjR44wa22i0sBXBfV74SmcMMcqngsvZDnDMevWyf/HH8wJEwB+8hM7ZeMkFW0u\nj1RVyaO1jRkD8IMf+P/Xz+7hG99IdjbALepFRo0aXksrP/hBMQU5gKtBKqpmQkZ/f3DSI6qvaBRO\nmGPWrfOGAwVQR3drb2fvr7+e7KjRZrS5PKIy/nznneDZj9+aedLruioDoOEWl1o3quJwZMIE8jMX\nKZeDlzJlHilEMIUW5j09lY1GZXWaVjSnLKl/s4zOffFbM582Ldn7qpqd7NwJ8IUvJFcO2xTZipuW\ny+QEqdoXLaI18ygUTpjL8izrWJ1ia/avf91e+QC8jf266+yeaziwdq3euqy4Zs79zBcvBti71/5s\nACdU4f7uMu68k+3jl/gny+AkNkW24sbLZS+9lF454tDX57bbqipvoJcoiMmRZBw6BLB8ebzzFJHC\nCXNuKY5zDfMwoLqz7ptvtlK0T+DCvKEBYNcuu+fKOyNHAtxxh54gFrUrn/88U9s/+mgyar2w8SFs\ntzNb8GeL1j5d8uqhMDDgjZKZlHFmlrxL8kLhhDlOg8lVX2HXtmyn6Nu7l53j6afV8cYJxuOP6+/r\nt2aeRSZNyqe6MW/1nARVOe1pxQBNPT3JnJeWF8OT0yYWnb172Xt1NcC3vsU+33efK+BV65XY7cm2\nkdJ3v8vWcK+4Ip+deVL09oYLMMFjQmMB09fHjrN6tf26VoUSnjxZ7jv/yiv5XGuV1XPRyWtsdjHW\nhSrGgy6jR7ufx43z/sY9i7ZuZX0fEY5CCfNSCeBPf2Kfjx9niVMAAF591d1ncFD+X54FaswYgO9/\n31oRAcDfH7rI4NlNqQTwwx/GP2ZSdV0qqdXsR4/Ko76NG0dq6jzD+5fZswF++tN0yxKFUqlyjTxu\nQiJs+IkHt6tXu0sRmzcD3H57vPMUkUIJc5GhocptqrUa7pqm4wIVlz/+kb1T5CgvWBg6DsA558Q/\nZhZyTh85It/+1lv5nN0mqe3IMnfeyfqNMWPkHhR5hGs2o1JXx97HjHFzEHR3A/zbv3n3U8X7INQU\nWpjL3GZUrjRJBoDg6+QUOcofEwlJsrC+q2pzkyYlWgxjkGaJUS4zNfXu3cOnLuK6GuJJUVMT+15b\nywY7Js9TRAolzDs73c9btwJs2MA+84azbZu7DVMue13T/vAHm6X0qrYoApyL6NLy9tvxj5nU+q6f\nceUDD8i3v/IKwL/+q53y2IQiGLrguhg5Mr1ymOKOO+L9H/dt69d7BzsjRri/FdmlMSqFymfOo3zd\neGO4zruqyqviraoC+Phj8+XjUB5kNXmtm3Hjog8+8nKNnLzeIxvkuS5kBptr18YT6OIxV61igrym\nxs1cWF0N8Nxzw9+Th/KZx0A1Cwta4xPr+5FHrBWxAlI3uYhqSh70Je4xk1jf5WuFMvwS9+TdRaej\ng9bOOevXp12CeDQ1Adxyi7nj9fS4y1w4h/nx4wBf+5q58xSFQglzHn1LjK51003uGt+8eZX/W7LE\n/bxjh/e7DbKQBxnXVVYsS2+6yf08fToL+mLimPzen3lm/ONhcB3izkrEcQCuvVb+W3+/2TIlwYoV\n7L2xkcVKGC7rxVFobXU/f/hheuUIS21t5bYjR9RLQiK47f/4x+52MQInD7Es+uHv2xet3EWmUGp2\nlcorSBUWVT0fldWrWQfY3Z2eYVYW1YM2ymTzOlV+5SJDQ+weq/bPQt2HobfXu1acZjtOmyw8y1Hw\na7s67VH1XG3YwOqjs5Np1jo6vK7BYc+TZ0jNbghVdC0xSAKAf4IOG2Qt21J3d9olqKRcNn8vbC5p\nbN3qnaVxggT+4sV2ymMT7u7X1aUfN3+4krVnOS5R2uOWLe5nMQLnBx/I/7NxY7TyFZlCCXMe+AXA\nG10LW5nK1OxJu9pkIdsSFjLNzemUQQR7I5hKWRrkyWDq2FdcAfDMM5XqS8dxr0OWSW3sWLNlSgK+\nDvrgg/px84crWXiWoyAbZNbVAfzsZ3r/nzbN/fzYY+5nHh6Wx9DganccGQ7APxERocDJAEkVY9Uq\nx2Hdp+N0dTnO0BDbvnJl5TZv+dzXtm32y5n0+VTnFl/LlydbFpFLL3WcmhpWlnHjHGfiRMcZO5bd\nP9l9Sxu/usSvb3zDcTZtkv+2aJH+8UePTu7a/Eiz/WYNXBfbt6ddGn1UbXXNGv99N26U///CCyu3\nz5jBntt16xxncND729y5yV1rWpiWe4VaMz98GOCyy9io85Zb3BnDZZcB3HOPu44jziSSXj9Oc706\nSO2bZmvBa7HjxrEIaZx165inQpbQXTMHYKE/8TozRlXnsuOn/zRn094iLfJaF6q2K3NNk12jqm36\n1Ude6yoqtGYeg2uuYQJdtCoNowrjyQCSglzTXHDo1a4ud/vo0fkOe9vR4YbwFQmbcjJrLmDUfl1M\nuFKmSVdXsGuaahAg2z59uvo4eXfJTINCCXPshnTBBe52PCOS+fzytdr29sooZLb5539O9nzjx6t/\nwxGa0gBH4Zs61c269O670cPeph1HfNIkFgELB8jgGfpmzAC46y71f2W2DM3N6bsS8gHIrFkAa9ak\nW5a0wVn9sqY5Csu+fZWuaaef7v2ummjyPpQbGI8fz1x8TzqJPcdnnuldZ3/oITNlLhRGlfYRSaoY\n4jpO0HYOX9dJal02qDw2GRpy16VlrzQRy8JtILq7o9+bZcvc461bZ7S4vuvka9Z42xS+lsFBvfa2\nZEk275PNOs0bJtpoGvi1XUxTk/x31f9wX4rbCYDjtLbms66iYlruFWpmjlHNsGVqzaRd04LKY5Nr\nrpFb9AMAjBqVbFn8qKsD+N//O36SlDSyptXWMvUzjkaIE75MnaoXL/7FF+XbFywwWtzQZCETXVbI\nQiIfk4iuaeKSJdcqifBrx30p9t7o6mJBhmpqmMW7KpMg4YPRoUFEkirG2Wezkd/8+d6R38iRbHtV\nlePs3y8rn/tavNh+ORcs8Fp7mkYcMW/dqv7N77Vjh/my+XHaad7zl0qOM2IE0yTI7psO+Hh+luNR\nmD49XL1t2sRmJ1VVjlNdzSz2Bwf1yo5f555r9jrCgsuSJwtuwqWhQd62cF/hOOH6i298w/u9u5tp\nqKZOZZ4p4v5Rn+m8YFruFcqaHVtDY+vnpiaWkg8AoK0N4KWXxPJ5v9suqu2Ic35W0GEssPH/kgDf\nP75+zxPe1NcDvPde+GPavLdiJDSOqqwnnQRw6JB3m6w9clT3avXqdLNOFc0q2Y++PhanoqGBzdLz\nMjvv6fFmOMPg+xm2v5AdK+xzMlwga/YYcIthHrCAwwN5NDQA7Nrlf4wkrCyTSsuJzxcFP2M5G3D1\nbU0NM8bh8ZyrqryBKaKiUhFGhZcXUyqpy/rRR97vI0YEt0cZsrjaaZF3C+645DW3+5gx8u1x+j8x\nMAz3dJA9JwAA998f/VxFpFDCnFsMHznitX4+80wWBe7Tn5aHXayvdz9fd53dMgIwC9FymYWBPHDA\n/vlwFjjdnMulEovwlSR8/fH115mV8BNPsHvzu995rYbDgGcWZ59tppyc/n7W5rAXQFOTuqPEtgrV\n1WzA4pcGUnacctlsZqsocAG+fbuZZDh5Zu9e9l5dDfCtb6VbljC0tDAr8wkTwluZ4/4Ss3s3wMyZ\n7neuPervZ14PPT3e/X/wg3BlLjxGlfYRSaoYqrWf+nrvb7t2ef83fjzb3tDgv4ZpCmwh2tZm9tiy\ntSy+ViyuSY8Zk611802bmAXsqlXmbAn81gNNHVd8qe7p0BBbQ8T7btkS/hxhrkNc0yTMgp8h08+y\nLcT2NHq0u8YtPnfTpjHbFZ018wULgq37i2RvYVruFWrNXLWWV1PDcuhyqqrctVgANjvu6WEqT7+Z\nkilaWlhs4oYGlkLS5Dn9MnOVy9GsSJNqQXhtraWFWW3HXYe0tb6rqueqKoDPftY/ZrlumeJmtgpz\nrjDkdZ3YBjafZVvI2lVLizxCZtg+Y2jI3x6oSPYWtGZuCByZSlSti8ZD3/0uUzVdcUUyrml79zLD\np6Qefp7MQ0z0sXUrU2v7kWRWL762Nno0i9hneh3SZna4MWPYWvaJEwA7d+qXG2ec0kXlWhhEW1u0\n/4nkdZ3YBkk/yzbgz9sDDwB84Qve32TJgVR0d4dz803aJif3GJ3nRySpYmzbxtQ3YvIHMci/6KKE\nf0vCNc2GOpnjpwbbutX7ffx4dQKQNFSHl17qOC0tzGXLT1UXBlGdaGoZZcIE97jt7aycugFEtmwJ\nVrGLZccvnuxCB/G/JhKj5DVQig3yqDbGZb7wwkpVOUcWMCbM66yzKvs6viwxcmQyS5ppYlruFUrN\n7l8G73dcnKRVP9hFac0agDvvNHfssK4kfglAOGmo2TnNzcGGYn6I9TFpEsDLL0c7FkaW1Me0y6EN\nNXuY/6qw7VqZJ/KoNtZNtlRd7V2ODEtdHcDChe4z3drKguusXp3ckmaakJo9AfzUrUm42mAXpbh+\nnGEQVeY9PWq3EU57u73yiMjKMjQE8JnPmDvHrFlmjiNL6pOUy+HGjfr7Vgk9gAnXy6RdK/NCHt30\n5s71fsfXILadsPz2t95n+tAhgGuvZXEVhrsgt0GhhHmp5L5+/GP1fmJCkRUr2HtnJ8DKlfbKxxka\ncj8fPMiEQlWVW/Z772W/4esplQD+6q/8jxs0MPjNb9zPLS0Av/wlM2DyW7uaONH/mCZRheAVO5w4\nx+TBg+KCk/rgDG+m8BOUutdQKrE1fMw3vxm9TEQlvL+YOzeZvsM0b72ldjU8//xwx8KulGeeydxJ\n+/vZjByATaIaGtJNfJRrjCrtI5JUMcQ1G45sXRiTdOIIsSzr1snLJ1uHCnPcoBdHTIgQ5pwmkZ17\n9myz6+amrkc8pum14xEj5PXR1KS/1mj7ftq0/cgLeUw6o9se8LU1NclDsspeoi2FKvlKXuorKqbl\nXqFm5hisThwY8P4mJjdJOnEEnkGffnrlOVUW5K2t4UazPB2hDJ6yEMBf1d7cnM4IuroaYO1aFvDG\npDrXVv7tefPMzjhEFScP1CEGRApLS0v0/4qQVXv+k86cfLL6N35tzc0Av/89wDnn+B+rq8ubdIan\nH77kEte+Iu/1lSpGhwYRSaoY3FpbDKrBrW8BWBAEcRaBR5UmrH2D2LWLBWJYscItizi63bVLPuqd\nPl193Lq64Bk/f61Z4/4vKCgEtnC1hey8pkbujY3uMU1Z5+NyVlV563DtWv3/qtrbX/yFu8/EiY4z\neXL4mfkpp1TW6fr14a5TBp+R82BLRbZqT7rvMAEu8+rVevvt2OHfR9TVsXaJtTU4hS9/lpNON50m\npuVeoYS5iqEhlrln/HjHWbnSX5gnVdTTTmMd8/jxla5zAP4CVsWkSe4+c+aw66yurvx/V5e3DnRU\nZ7YJKmMcbET4a2nRGyjJ0KlbrI5cs0beMQbR1mbnXuKytbUVo2NWkUbfEZdyWW/gqdMv4Fd7u9ed\nrWj5y0VMy71YavYvfvGLMHHiRJitCIy9c+dOaGpqgq6uLujq6oLvfOc7cU5njXIZ4JRTWKSmBx7w\nVwnaUsOKHDrEVKZvvlkZsxiAxU2W4Rf4A1uet7ez6xaPPXYsi7nup7oW1e5JJJ8RmTTJnHo9KDZ/\nFESXHb500tUVrg2pPCuwOnLbNte4KIx68tixym0mvBNw2Z56iqzaOUn1HXFZsIC9d3Xpx/mX9VEA\n7vPU3c2eWR4trrkZYM8epnbv6GBLZmT0FpM4I4FHHnnEefLJJ51Zs2ZJf3/ooYec888/P/A4MYuh\njZ9BDp+lyNSUS5ey32bNSm4EKc4WxZn4WWcx4y+ZOkuFLJgHDtQwahSb4Yn1I567udlxTj01/Eww\nDjNnesswdqw5wyobRjdcCzJqlOOcdBLLzayrPpwzx53JqPbnAXS4JmnDBu93HZqbvXU6bpyZ+hwa\nYmWXtaWioQpUlWVEVbeq3+TBm3gfNXJkpfZwcNA9Fu9/mpu9fSx+/trbi2M0aVruxT7aCy+84CvM\nzzvvvOBCJCDMN23yqnjEThs3wp4e729pWFgODrIBBm/0uqosXbUYjkbFH178+7Rp8v/xF7dcTUpF\nhu0a8MvE/cDHM9Xp4mOWy+HqqKbG+1+Z6l8cXEVRs+NjjBjBIh/G7UQ3baoc/A13q2Q/hoNFP+7/\nWlvd68DW62KCID758VO5c/Akw68fHm6YlnuGMzh7KZVK8Oijj0JnZydMnjwZtmzZAh0dHdJ9v/3t\nb3/yube3F3p7e42WZWDAq+LxU0WKOaTTsLCcOpUFTwiLbrS4devYIwPgxkvG8NzvKj76iFnPi4kX\nbCGzsrZxPy67DGDDBrPH5BHRfv5zvf2x+vvwYabC9GsLQ0Pu/YpaJx9/zNSeAOHKKjIw4LYrTpGt\nkn/1K4BXX2Wfv/AFlmAn64iJcsTALrx94HYqi2Hxn/+pPscLL7if+/vdiIHNze52sR/OOzt37oSd\nO3faO0Hc0YDfzPydd95x/vSnPzmO4zj33HOPc+qpp0r3M1CMQFQqHrcM6hGhqNJMA1y+qir/2bnO\nMXD8eZkf+ebN8v/ZmBnrIJbRpEYAH9eUZT4+Zn19OMM6UVW5f7//PqNGedWZUc7DX3ENC0UNSpIp\ncrMIXsoIMnzMCqImcmhIbqzGl5LGjJEb6fqlUK6rk2staGYe43hxD+AnzEVOOeUU56233qosRALC\nPMjlwc+iWRQiSYAbum6+YADHufZa9THnzmX7fOpT3nrgHTBX74oWyHzdj7+4BXySVqiqgYYJRo2K\nVp9+rFgRfdDT2Rn83/nzvYIctxFdAdrQUHm9cQXO0JDj9PayAeeuXfGONRxYudLMIClJVLY1Yv+J\n240sr3lHR+U6On8NDnoHDS0t7LziOvxwJlfC/NChQ86JEyccx3Gcxx57zJk6daq8EAkI8yD81hx1\nZ74mCYq65vdSodIw8Ae1vt49huhfKp4jaV9Qm/cAd7hh6tMPfP+am8PVFZ7dqoSA2LlGKTNu8wDh\nfNRVbNrEZnFjx6arycoKefSb1i2z2OZkkw7V7HzdOredjx7tbufCPOwAOI9kSphfdNFFzkknneTU\n1NQ4bW1tzs033+zccMMNzg033OA4juP86Ec/cs444wyns7PTWbx4sfOb3/xGXogMCHO/tI24ESZl\nlaoyCgl6+aVZDDLkwz7nkyZ5f8PnmDzZ6wOfBPj8ptVvuPMS61NT6VSBuKwTxhBqaIjNkNeuVe8r\nHg+XeenScGXEmom4Hag4CB3uHXJRCFKJT5niNdwEYCl8ucZTfA0Nuc8dH0x3d3s/52kAFIVMCXNT\nJCHMcUOaO7eyofiNRtNwTcPlbW/XF+aya5Md88IL3e2yETX+3XHYIIEPFsQ8xkmsi3K18vTpdnO8\ni/YIUQZvYl3u2hXOI0JH8IvCe8EC9nnGDL368WtDce6njlaByDa4LfAlLbyN29vgbd3dzLZDHMjJ\n1tJFGxDc9+ZRkxEVEuaRzxF9xpCGa5pY3jVr/KOK6VybuJ9qu/i7iGy0bRubD3lQfcY9XlWVv+ZH\nROUK5HeOsH7mpq+Zw7UKU6eSn7nj5NM1TdYWdLeJ7VyWxCov9WAbEuaRz+G+GhsrG5TfQxemI7ZR\n3pUrWdm4CsrvVVWlVn3j/fDMW3Ycvxjd4mjbT7WfB/C1dHR4v5uamYcZjIgW4bLBGf69pyf8gNOv\nDemq6f0oUvYrP/JWD6Lwlc3M+TOBt40dy7aJ7Vxm+5OHekgCEuYR4RbCKivJk05yG5sYeCUN1Q9W\na+MHQmYdKgZnUFkk4xk1fqCwAYruA4ejzyXxcNo0rMJW3Q0N3mWHKNbd2JAwykBH5QqEEZcdwg44\na2u995vbS4R1o1ORxgA4i+StHsRobBwcr50/E7j9qJZmxIFpbS1pbDgkzCMS5CuOGxz2wcYdO/en\nTAKsKfCbRZ11VqUBFC4/RtWx8Bk/79B1rJpPPll/XxPYNKzCbomyOlbVpwoxel8UgjQfYnsOGwuB\n3/OaGne9Per1yijS2qcfUVwG00TVR8jaB95WUyM/nsyoNM5AeThhWu4VJp/5gQMAb7wRnEgFwI2E\nBcCaHeedd9QJBUyDc0H7wXMDY3D5MS0t7CUmE9m+nUWE4wkWdHJiv/mmu++Xv+y/rwlwFKquLrNR\nxfbuZQlqnn5a/ruqPlV897sA06YBXHGFmcQR69ZVbhPbc5j2DcCSXrS0ACxbpt9+wlAusyhhRU+y\ngvuPVauyn0hE1UdgZO1DlrQHwP/+y6LGEdEpjDAPE5JVJUBHjkwuxCAurx/r11duW7pUvq+qw+cd\nLw+lqFNH+OHdt89/XxNMmMAyxU2YwEJimhQSWPjKqA4Z9BgPxHQEaxDbt1duE9szD+fa1ARw/fXB\nx8Rt4dlnvb/V1MQrL6HGRHuwic6gUNY/qvocFTwjW18fQG8vZUwzgtF5fkR0iyGqabZsCXOOSqMO\nzDnnsN8WLPCql/B6tBhIxSZYTYmjfW3Z4pYHG6kFuSaJPqBXXeX+Jhq9VFXJQ4hi8P5bt5q5Zj9s\nGhL5LWNEWfs2UTfYZiLoHJs3s0QpeFtQ9DW87+mnR3+uCH927HDr1cS6eVA/Fhd8/Dlz3PLK+kdd\nd0jZ8yQzkFu3Lp/W/1ExLX5zLczD1EXQ/1TCIgsGLDrroUGuSX51J7M4ra9Xl0fmbmIbm2v0OsLc\nZFuzUWYxVkBVVbj/mypzkTpjHTZtYtnFamtZmFuTwtxG2xKPz42BZQbCUSPF4XKL/WverP/jYFqY\n51rN3tkZ7X+bN1duU6nh+/vZmqVsbTopdFRfg4Ph1ky/8Q33M16PBmBrWY89pv7vwID3++LFweeL\ny0cfsfcjRwCuvNL++URkqm4duB1CWMKoHxsaKpcCHnlE/1xiGePcT9NLDHlnYADgtddY+92502yd\n1NfbV007Dnv/4IPKbTzb4iWXhCsHbl9i/5pGhsphg9GhQUR0i4FHduPGhRvlnnYa+5+YRISThcxo\nKvB1q/y/g1TC+Pdzz1X/pqOiFS3skwhxi8+3eLHZY69d6z9LDetexpdmpkyJ3paCZig4Ut3+/exV\nU6Of4IS7ara3V1ocx1Gz87Yxfjy5IDlO5bMSNyaD2DanTDFTTg5OEDR7tnvvZAljdGfRZ53l7jdt\nmn97KJIXhGnxm1thHrYeghpeltU74nXL/K2D6sbvd/G3oOuXuZvYJmn1In9F8Ws30SEFLe9MmFCp\n9gyDOHg1Vb/82v0SFxUJ08+Kn8raBLgfxK5jsjatuwS5aRNrD62twz8TWhhMC/PcqtnDqry5+mb0\naIChoUq1UF7UO62tAIcOAbz9tlqlHqTa9au7k08Ovn7x/1FV0FFJ8nx+9awiqvoRE7S8w10DAQDe\ney/88f2WbrZuDX88TrnMXtzNz7QbYd4Q713UZRcV7e1mj4f7wW3b3O0yV0PdJciBAYDdu1m/1d1N\nluvWMDo0iIhuMcQZU9jZEo5tLs4Wsqze2biRlXnjRnUiC2zNLGY8cxw3M1ZNTeXoGEcsq60NF3TE\nL7GLSbBXgY2ZnhgFD38fPTrcjCIJLU/Q/Q5CNCjcupV9N+GZoJrdFRVsnNjaGu9Y4qzc9LOnWm6M\nY9goS3VaZG0Nx7T4za0wj9Ig/NRCqsaaNetcVXpMngdYFa72oouYoF62zD/FK4DemnTSyxI2vQq4\nPQV/8bSleFuYtUn8PzH7nCnwOaKsw4a931GOW1eXTRuUpMF1MneuuWPNnm2mfKrjd3e728XnHbu6\nlsv+g11ZqlNVm8hbxLw4kDBHr7DrL36zb5VwysJaus6AIiiEqN91RFmHS9plz6aBopjSFYC5+kVd\nmzSxphl0z/Hxg9zQbJVR57g0CzNb17Zn5qqyis+7uF9bW/CxdbSfttplFiFhbqmTUAmntPzMcWdu\nwg41Df4AACAASURBVJjIz09brNeg2NymfWd1sDmokqV0FdPN6nRWHPw/v+xzfvBEKwByVTU+R09P\n+OPj/5v0RhDrMS8JRmxiUgtie6CkaheiIMb7jRypN7HSmZTg444fH/tyMk1hhblsxB+mEwpqSKpR\nY1pr6bJALgDR1zT9OhQebQzAdVUKU7YFC6KVKQy2hI/jVKrUAZg6FH8Po2Zfvz6eIHec4MEVXn+M\nYs0+c6YdgcBdm2bNqlwKKiq8LQW5ZQUhC9ZkOv3wtm3yZ0xUf3PXRgD9yJg6A/IxY8INEDAmBtFJ\nQsJceOmSBXV5GLBGQOeaw6hlxWOEdR2RZXKzje3zqdpXktfoVx4Rmd9vGGxpnPL2nCVBUHRGXVQD\nfJOo+hHxnFHaj85/4mQcTPN5jYJpYZ5b1zSAcC5KeXE942C3Dwx2F8GEibwl1ht2HVm0KNhtpL/f\n+z2OK1MUknBNW7nS+12MkpcksiQWPOvZuHHRjqmTHSsKzz/P3nUTvhSBsNEZVcjaoOnoizr9yNKl\n0dqPjivb1KkAL73E3uMwaVK8/+cSo0ODiOgUA6t1oqiXgv6rUmVnwbpy6VJXdaka0QaNern6s7Oz\n8ndxpr10aXCZZsxIdgYWlHgkLuKoXnRV4y+d5BYmPCDwOWUR2eLOgPHxp03zfq+vjx7cAx/HdKS+\nvKLqW8IyNFSZMMn0EpeqHxGTqoRpf2GehzjPDq6XuF4DSWBa/OZGmMe1Zg5Swah+z4LqRufBCVrb\n9ztGlChVSRsG2nYR1FGz69aNCVVz0Dnb2th2bNAYpo6CrjGMwZ/fcWnN3GwfYnuJS9d2KMzzH+Z5\niPPsZKGvDkNhhTm+ybW14YV6mJk5HtXh7WPH6p/PJCYEZ9AxgupHJGnDQNtrsWJHMGWKXMjpuIGZ\nuF9Bs7mJE93fuQFcmDrCxxdn5tXVZmbmtu5V3sD1Ecd4k9u24OMlpf0QB4phnv8wz0OcZwfXS3Nz\nuP+mQWGFuaxjDdNRBEUs49a9YiIWrm6trU0nrrB4zarOIMxgRXYM7GutYx2Nlx/EHPA2MNUhqqiu\nDp6t8ldQIhMTAx2/iGxieXjinDAdYWMj23fECNaur7rK/R7H3RB7RthIV5tHVBbiYZEZwJmI2IfB\nx964UX7usAM0/jyceiprE+PHq9tFnGsTJ15Z1wqRMP/zK6wVrywfL0bVAftFTksCmTDR2S/s783N\n7m86ITjF49kO26lTB3FYtEh+TbL6jxKkxSSqug8ziBBjF2za5B3QxZlRU5IVO8hU7KafBdWxTWib\ncPtSLePEubak+6S4mBbmubRmb2sDePDBcMlWZPl4MbJEAgAAr77KchE//HA28jOrrNkxQdbesmPM\nn8/eu7r0ziFy/Hj4/0Rl0SLzx2xu9n6/6iqAO++U7/uZz5g/f1RaWtz7pWrDMsaMYe/cu2NggOWK\nB2B1EdXjo6+PkqzYor+f3W9MlGdVl7Y277mxJXpfH0Bvb7ikKTU17L2hAWDXruD943rJlErx/p87\njA4NIqJTDJzyMcpoP6pfbloR4Dg4uIef5WrQMkKQqi+sanjHDrmq1xZi/m3TDA05zvTp7BxXXeVu\nl82EogRpMQk3fosz+1AZNPG49FGhJCt24ZEcAeLlnVeBE/jgdi6umUdRu+v4kEdN+CMG1GlszP4S\nj2nxmxthjm9UlLWnqNbwcc8bFbwmDcDW7v3KHhT+08/SWXwQdK8T/8d2FLi0IvHJhHlQ/di2vFcN\nMHEZ/YwY8X480pap+k178Dsc8GtrtpcwJk1ixx4zxisMcZl6erzf8dp6Wsie06Cw1GlDwjziOlFU\nAw6b61NhzhtU9rFj/WeOftcvM6yJUkabJJG9TnYO1X3ww7blvWpgqltGcT++fmmijrOcSjgvqO7j\npk2ubcuoUXaSDqkGC6rnIOl+UUVWy+UHCfOII66oM4YkZ5+q83LLY78lgqBlBL/rFw1roszMbbvI\nBBkwmgAL4ZYWtcFRUP2YmJ36CVbVYEH3fuD9sBsahWLNBvj+YHWzbNBt+j6pEjLhc4ohpsPkLRAx\nNUiP8pymTSGFuagGjhIFLOqMYdYst8EmOduYPbuycfqtQQZdn9/v+ByTJ+tfZ9x7Eoaw1vZR4EIY\nR38bOTJcByouj0Rd1/QbvOLfcEIJ3YQe3Dagqspx9u+XH9f2/SwSYQSW2H5wW5cJrNGjza0Ni/3s\njBnyc48Z43XljPM8mhpAYtuivAxICynMo6qBTZCW2pCPkPlDEzTLMxUGMcxDkOQ9Ua3lmYTfa67l\n6O5m5xK3hcnHHLVe/I6h+k23rerM7JN8xoY7UYP5AHgNS2VtCyB6tD4RrP0S24C4PYxBsV/fZMrG\ngh+Hu7/lwWbDtDDPhWuamGCgujqcS0QcrrkG4PXXAS65JJnzcaZMYe/HjzMXEb/kBADhEq340d4e\nzZ3IdvKT9nb2/s47AFdfbf74fX0Aa9cCHD0K8M//7LrhTJ3K3L0mT46WmGTLlvhl80umsX69+1nX\nNU0n6ZAsuYsuUdyWhjNxkjw5jv/vpRLAPfdEK5cIdt8FYO6ZMrZtY8/7unV6LsJ+fZNO8hUd+vtZ\nH1Fby16NjdGPlVuMDg0iElQMWezwoFlFe7t4DvcVZi0F/y/JxBGy9Vq/RC9Rr89xXLc1/tJ1C8FL\nAbZVWratpINmT7h+/Gw25s9397v22ujlGTeOHUOW9CSumya+FqxOF5NpRMFk8JnhQhjtHr/v+MXX\npLl2ypY6efJk95iihXqcKHamQrRil1G//fLS9kyL31wIc7aP9+XnlhNGNRn2vEnBOwDd88cpp7hW\npvv/JN2QbC93hIld71c/cRMCcfxckOLWu+paTOTdxoOi5ubsqzqzhhh7Hd8jmU2HyTq25fYW5tkV\nVfJR+7+w8UTSwLQwz4WaXVTNLF4crJapq1P/FjVqUhJ5tDlcZYrZsUPvv7Jy+qk+Bwa833UiL/X1\nMZV3ays7XxwVmQ62lzu4mu7FFwGmTQM480z1efzU3gcOmMldLUZow5jMRc7vdV8fi3gXt+xcpdzc\nDLBvn/12Mdzg910GV0l//LG77bTTzJ87ynKAH2EiE/qp5Ovr9c4XJULosMDo0CAiQcXAo32VilPM\nPz1ypPd3nZzgMmzn0Q7i7LPZ+efP9y93UAQ4PzUyVunrWl/j47W22h8FJ+E2JRpa4vP4qb0xKtee\nsPjNklV1oWsEWV/v/p8bT+FjxsmaRn7m8Rga8kb4A2DtADNmjB11cpwZtClErRO+ztpa9f/w0hN3\nK816GzQtfnMhzPENxa4beA350ku9+3V0eI+RVx9aXf/qoOvzU80ODTEbgyVL9B8CcU3fZp2KKjQ/\n24E44FCZc+aol3H84g3g/eLYWODj+LmmRclsNX4826ehwRXa4v3s6YlWbtXzSeghU7GXy+rfANLx\np7bVn4oDCvFasSslRmZjlGRckCgUXpir1lDwbEO2vpK3MJN85FtT416Pnz9n0PUFjbrDPpxDQ24I\nWdt1Kt5XW1nL8JqhWNd+bSvKfkH4HUf1m24bl8XIDrM+GbXcRSXMLNavn9PpB5Miqf5UvM66Ovl+\np57q7Svz0P5ImKPX0qXuPmI+anG0ym/2uHHhVIhJhBGVIap8gww64qg3cZjIMIYjpoy9ghDve1A+\n8Shs2sTUeABsyebii9WGOH4zIbxfnKUZfBw8MxeNFbHXRlwVt+rZMlHuIhPHzxy3I1U/aGpmHqav\nS2o5RbzWs86S74c9KEzXiy0KL8xPOYW9Y/eZ007z7tPQ4G1k4u8TJwafhz9AaannZWqjOJ2jn5AR\nz6PrmpZUx42jO9lS3YqDp5YW733nv4vqd5EVK9h+nZ3xOjoea19coxfvlanOVBwkhM1axRk1yj1G\n2tnlskIYAXPhhXKBvWsXi+wn+y3qkoiIX1+Hz6eyyzEBPg/O4BbULvMmyB2HhPknDQ03JtmoDDdG\n8XfROE51HsdJTz0f1jUtCL/jyDqIuMc0SRIaADx46uqqjPimO6gzNfjTTXgRBdkMzFSUxaiphocz\nYetV9jxWVXn7IhvPnl9fJ57PVkhl2bXrXGtSfZFJCi/MR45kwnn8eHfGwg16+GvMGG9jFH9XGVHg\nffjsNW3rXFymOAYd+Dh+BlUA+hbt+D9jx0YvWxBJaEeGhlgHtXYt+yzed91BnenwlH7xFMTASLpq\nUll9Rk22I5L285JFwtarTIDt2OFdKsS/mZqZ6+ZvAHCcqVPtLD8GCXKdmXlelncKL8yx6oW71Vx0\nUeVNx6qgwUHmulBXpxbkjhPs3pUGPGNaXEHW2ekKAPHauPtd2PNwd0CeE9sWskGWLVQCUbcMJgSi\n47COmx+npsZtt9xtqbq6UlOBLcl7e9XHlg0UhobcCHBxyp2WjUmW6ejQf7ZkAZz4/2TbsUdCXPza\nOO4jurq8+0a1r5CBXYF37ZLXgwwsF1pa8tH2Ci/MZY1YpiIMK/yyGobS1EwvSFUd5TwXX8wEeW9v\nsgZwNombhMRUWcWlofp6tv3SS71Wu6oylkrqY6tmYCYEcV5dQG0S5tkS/f3x/1SzVVP1HNR2cbux\n9UzK2qBO/ZXLdurEJqaFeS4iwGFmz2YRfp5+miXBAKhMxALAogVdf73+cQcGAI4cYZ+bm81GQIpD\nSwvAyJEAzz3Hoj9FjX4WFJksSsKDV14B+PBDgJ0740U7C0NPj93j87Y0ejTA0JC8vhcs0DtW1EiD\nAAA1Nd7v99/P3g8cADh2jH32a6fLl6uPrYrIZSJZT5ykIsOVCRP0I/bhCHpPPhn8TNrqq2RRJFXt\nxuQzKWuDzz3Hnofnn3f7aBH8THZ1FbTtGR0aRCSoGFjVLFurEWcrUUZnfPTX3GxXZRwW7ssddE1B\nsypTkcnw+bgrl820pI4jD3Jii6GhSkt2x9G30o6TkAIjLh2Ja9uydsqjYDU2hq+nqO6JIlFdQIcz\nYbQVfPa7YUPl8zxrlrdNVFeb1Yr5RbsU+xeeUGj6dLsR4BzHq6VSpXu99FLW5iZMyE+7My1+My/M\nZZbq/MUNHfC2qqrKxqCjPsyi4Y54vY2N6vLJ6kX1u4k1LnFpw6bRia0EECpkHYqulbapNWNVwhJc\n52KnG6ee8PlUlso6XiH4d5tGkXkC14nuIM/PSFFcszZlAOfXdsXwzYODdvpLP1W+ajAv2hlMm2a2\nTLYonDDHRj2yF/u/9yWuDeuOjLNmvKO6Xp19w/4eFpkfvC1MaxWCkA3sdAd7ptaMVTNwvzrXraeo\n65KyZ1EkqTaRJ6LUCc6QxvsylTW7qXrWzd+Q1KBavMbVq+X7yWymstKH+1E4Ye4nyGWRkWSWo7oG\nKLLGnKaAF69XN5+5LCZ4lNmBH+LI2aaV+cSJ7nnSCkSi2w5MGSyqBg9+dY7ryc8PWNbOdQYrYnuU\nJb5Iqk3kiSh1IlvuUWkp4+QAwATlb0gqfDMHX6MYCAyDcyokPeCIQ6GFOV63xGpdvNYjE1q6syrZ\nQ5eWde6mTd6ITwsW+Jd/7ly237Rp8v0aGtxjqdadwpKUKx9fyw0SUjZRDST99os7aJINIHgb7+jw\nd5/zW/bA++FELUEDFlmubfEa084ymEXC1AmuW/5MB1mzz51rppxB/ST+3dYkBx8XR37Er2uv9f5H\nlQM+61HgCi3M+ctvZCjuG64clf9NKwKcTHXkN5gIehBtGJElNdDhg4ZRo+zHgVcha4c6+8VBVb+2\n3OewsaVs0DQ05Bo9qs6TtaWqLBAn0YpfBjFT7SwKtp590XZD5V+PsR0ZzxaFFuZNTeFUgWFnB/i/\nXKWdlmEcb6BctRZ3MHHRRawjXrbM3LUkNdBRWZgniWxAGbRf3BSMqvrViQ6nG9hmyhR3OxbU555b\n+b9Nmyr9ecXZD/mZVxI10cr48epkP/g1b14SV+EdlIjhjk0ha9vi9U6f7v2PymjO1PKDLQotzMXG\nLdtn8+bKzky3kzvnHLcT5sdPKz8zPm9tLVObRp3tyEb7JkhyoJN2Ctvp09VCEMPdh6ZMiV9OfD4e\nxnLTJqZW5BbFGN3lCOzGiW0Q8P9F2wSZEJElnUn7PmWRMHWC3XDxq7e30jWNv0yp2YPA51y0yM6z\njyMfAjDLfVmdqFToQcuNWaLQwlwlkMTfgo6husmyEXTQsW2hc91Rj5X1Ri4jbddBXet9k0lhZOfz\nm+XxTGsyYYyZNIntI8YH8HO/022Pad+nLBKmTvC9wfWcRKKVIJI4p58rss7589T+TAvz3EWAA/CP\nLjVmTHCUNFV0q6DoVePH65fRJPX17N3vuvv6AHp7AVav9r/+bdv0I7xlCVX0qaTo7/d+b2yU7xcU\naS8qW7eyd782Om8ee+/qArjlFvWx2tvZ+zvvAFx9tbt9+3YWcezBB/XqWVaGa64BeP11gEsuiR6t\ncLgRpu3ie4O5+251JLnubjPl1O1DAFiENhuIkQ8BAMaOrdzGnwcAb7kB0u0nUiXOSOALX/iCM2HC\nBGfWrFnKff7mb/7GmT59ujNnzhznySeflO6jKoaYhxyARYBbssQ/MlJLi/d3bBWJA3CIyEZ1fIRs\nO5mICJ5lnXVW8GjTb8ZGFsb6nHZaZVY+Tl0dq8fqanVbwO0wak5wztatlcfxm3nE8dpwHP8lJdXs\nyG8/kwk4igKuPx6bHYDZMOBnnPdLssRJUQla25ctQ5pmcNBbB2LGS1n5cLnb2/NjgBlT/FYeL86f\nH3nkEefJJ59UCvO7777bWbVqleM4jrNnzx5n4cKF8kIoLkpmOavykeWWuDyTF/49TqjWwUHmypV0\niMCw7lh+63JJuJHYfHCStJD2Cx2p0xbSUH+GRVVGv7LrqjvzcP1ZRlXHkyZ5n/GLLza3nMMJWttP\nSmgGqdf9DEKTjhYZh0wJc8dxnBdeeEEpzC+//HLntttu++T7aaed5hw6dKiyEADO3//933/yeuih\nhxzH8Y5MAVzXJPGGYqOg3t7K3/O0jsLRDR/K8bvGJNxIbD44SVpI+7nw6QwqcHu15ecad3CDy4hD\ngfrNqlUd6/jx6v2SNBgdLsjquL6etUX8jNt4JoL6yaSEJr52MeeGLPUvLneWDTAfeughj5zLlTA/\n77zznN27d3/y/XOf+5yzd+/eykIoLqq+vrJhc99DfKNkvolZu5FhGRpio19xSSEKthp4Ug9Okg+o\n3+xbpwM1lWjFj7gduWrAwvOZz5hRWc+yUK6lkptnncNVsfPn5/8ZTAOuPsf1LcvPzfPaJxXi2HGS\nE5q4jZXLrrYV5yxXtfs8TdxyJ8x37dr1yffPfe5zzhNPPFFZCMVFiRadqoaL98ERrcIiWy9NKwiG\nyeQBtho4Lp/N9Xh8ns2b7Z3HcVi9t7YymwVxBqAz606ivcTtSFWzqg0b1Kpb1cxcdIs66ST3t7RC\n7+YZPpjkGkasQcFti2fI4y/UzUYmzPNs89lXaSfEgWTQZ/FVXV05+EyTXAnzyy+/3PnZz372yXc/\nNbsMbnCEX7IRmbhPVGTrpWkFwZBFgMsaSZUvyXoQ6z1shDWT7UU2MPDzM9dFNRjwK7uqgxTrIQuh\nd/MMv+d8mQ2/8P0R7YmqquKfO6h94/Zo85n0a2txX/X1ZssaB9PC3Kpr2gUXXAC33norAADs2bMH\nyuUyTJw4Ufv/nZ3e736uWZwpUyrdKvr6AE46CWDcOIAzz1S7XXC3iIYGgF273M+65zYJPy9n40b/\n/cO4ldhg+/ZkzsNdd2yB672rS33PVddrsr0MDAA8/DBzA+Jubr/8JcDu3QCHDgF87Wve/XXaQF8f\nc3tqbWXXgF14opQduwgBAMyfz967upgbJBEOfs8feMC7fccO7/0ZN877+913my2HeF9x2US3tEWL\nzJ5bxubNZo5z//1mjpNJ4owELrroIuekk05yampqnLa2Nufmm292brjhBueGG274ZJ8vf/nLzrRp\n05w5c+ZIVeyOox6h4BFVTY1apSi6sImzCr/ZFka2XprWGoyoLgqa5WE1lGwt0wZJJVrh64Njx9q/\nD0NDbEa5dm3luXSu12R7wfefq/XxNjFcpY5WQHf2rYqgiF8y17s8rVkmRdTY7Fddxd5loaXF5CIm\ntIZByZjw+XimMpOucRz+nHHjNx5NET9/svY4fbpco4FffgmIkiam+K08ntGjRURHmPtdN+6gZH7k\nWC2kax2eNuK1B5VZtPxPQp2U1BKEyahqcUh6yUXW/v2eCZ21dL99VMdWdYyEHrrtRrST8atjG31a\nUDImsWy2Bm2qJFOy7WIfGbRPltqtaWGemwhw1dXq37j6qbkZYN++yug//f0Aa9YArF2rH90qS3R3\nB5cZR4UqlQAee8xumQCSW4KwFVVNhUpd/cc/svemJoDrr7dfDoxMZS2q+vv7WQS3++5TtxedfWTH\nDvs74aL7nPzqV97vK1eql01s9GkrVwLU1rJyilHmRJYtsxfpjz9nvM/n9YbrUcYll8gjyGEWLzZT\nxkxidGgQEVUxuCtCqeQ/AjSl2ksrqYoMHPxGxyr44ovd8i9alMwMNqkZMx5VJ5GjWCfFqCrRiklw\nhjLeBnC0w9pa7wwKly+KhbEqUqA4s1m2LD9RtrJAlMh8dXXJB0DB55dF7+vokM90TWcnw9fd1sbq\nbdMmf0t1/jr3XHXaVPyK4/VkCtPiN9PCPOn1N5k6Ji3XtLBBY/zsAmxdQ1Jq56TVZDopRlXlMFnX\nMstw0ZIYr23aqifxuH7paCmfeXTEeuZRLVXLJqbrOqj9JJXoRfb86ajP8XOis2/aFEqYm/L71v2P\nbGSalmta2IGM3xqarWtIKnAFvi9JzMxVdY/L0d4u/6/Jup48mR1n9Gi3fi+91J2hiGubuHyqmbnf\ns6D6DR936lT/XNaUzzw6uJ6nTWP3FrdD8f6Yrmt8fplmEj8XOm0tKrLnT+YOJ75wv0cz85RQXRT2\npZw0iW0LasCyDkm30csiYKUZHjDMwMXPCtvWNSSlBsTBg9IMRMKtfWtq1IMXk3Utq1/cllev9u6v\nk1DH71lQ/TZjhrt93DgmzGXtzHGyHU4z6/D7t3ixXlhm03UdlEgF90c80qHNYFH4fGICFvyaMKGy\nPZ59NvtNlt9j/Xp7ZQ5DoYQ5jst77rlsm6wB45su6wB1R2SyEWHQaNUmpkbetpYrGhvd8kVJYqOD\n6Ha4fLn5c+iC21Zrq7w+Tc5YZBoJPy1F2Njx4rOgEg58uyyJEQYHXRoxIlvRttIE92PlcrTnRHwO\ndu0yPzsO6m/SWlbj5/ObaZdKzFZIprUQX93d9soehkIJc35DmpvdGOWi6gnvxztZsUMSb2a4skX/\nbxw2bXJHlVjNGvSfJNcrxXC7XHtiEnFkPXKk+XPoIqr6bEYjVB3L7/i87QOoo6/5/V816OPb/dTr\njuONnQ2QrWhbaSLWucyHOwg8UOJ1a7pvCprpJ6V1kQlfUbXv92ppYf2E3z5Z0BqZFuaZdk27807m\nRtPRwaJe7dgBcPXVlcnnscvCnj3+rjdBkdT86OmJ/t+wDAwAfPQR+/zuu+y6df4jRgyzSW2t9/us\nWebPceKE9/uyZebPoUt/P4ucBqDnjmcyMpbMFUx0s+HtBYC5JwYxZYr3e7lc+WwBAFxzDXNDAmCu\nUKpnqwr1Jkm5R+YBfC+qqgDuuSf8MbDLlaxuTUTba2lhL5Vb2oQJ7u9XXplMxMnu7mA3Skx1NXNj\n/fBD//2S6B8Tx+jQICKyYshGod3dcvcxvK9MFY5nLGHVQ3wd/dRTkx3NibNAHYMNldrNtDqOMzjo\nvR82YnFjrUtjY/J55UWC6rKzk/1mIjKWbA187Fj3mRDrQscDAms6RPsDHQO4adPUdbB/Pzv+yJGk\nYseIKnFVH+SnWbvoIqb5KJXcpCr4mAsWxC9nGDW7n0eDyXLgfn3mzOBZ+Zw57F3UZIivLVvMljkK\npsVvLoQ5AFPhylQtsn1F4qiH0oo+prpWP1T7m1bHYcK60IXl0ku90e3StpAOqkvb7pR+Roc65/a7\nXyo1fVAHSgSj0weFNU40fR/CqNmDllzioFrv1rFo5/UzbVplLvSstdvCCvOpU72dDYDrPoa3jR9f\nefw4HWyarjb4usLOzHHQB7zddGxi28IrKFRv0uC6FINlmLZZkB0v7rql3/3is34A76wdX/Pmzeo6\nIB9zNTrPid+9lf2G74MJl82gMuLfbT73MqG9bZubotdPQPOARmJ4a/ElyyuQNIUU5jU1lYkFRo1y\nGxI3xBKNHrgKULeTsdF5RkVsfDoqbNWSgEnVb9Jk7wFUd6D4NxMWs+JAUozd7RepLUrnjhNt4NzP\n3JKaR+PiiS6mTUsmnkFR8BOQslzz3D0sidgLjuPf9mydh7/a2vQCxwQZvgGw3PBZoJDCHMA7WhPd\nOy66iK3VqXKA63Yysv2SjkLHEa+Du+b5IXvgHcfuNdiejcnuZ5r4lcd0WcWBZFCOe53z+90v0TuB\nW6OLz4WqPZGPuVnwvZo40b0Ha9emowVJ6lkUz8ODI/H2VS67tjrYXbKhwXF6e4OFeRb6EccpqDDn\nM4I1a5i6nbupBYX64ypl3UhlPK0f3i8t1WEUYZ7GzMj2OZOaDUQpz7x56t9sqD1F9WOUmbnf/eJZ\nswC8aXTF50f1TKQ18B2u4HuF13/XrKl0x02izpN6FvF5qqvZtWLVPh7YlMtsNl5Tw/bjrst+gvyq\nq+yVPQyFFOY4sYNoACRaLU6fzt6xSlkc1aoQjx02p7hJxHPrrBenMTOyfU6dqGZJgu9Juez9belS\ntn3WLDt1MTTEBqiq+tBRu/rdr8FBluCjqoqpImVGmIsXkzrdFrie169Xx0Lftk0v5kFYghJNJfUs\nfu5z8nanM+PmtkLz5/vvl4X+pJDCHL/EoDCi0Kurq5wdyBJWyBA7OvHcSc44ZBmCgh7YNGZGts+Z\nNaMq8Z5gTAs5G9cedL/Ea5A9g6ROD0/Y6Hy8v5HFQue/cZddU7kR/Np2kugmVVG9HIfVz7hxDNKU\ngAAAIABJREFUwfulSSGF+ahRbuchRoATb5DMv1XXfUrs6PBxTVuBB4EtiwHY2lARO06dqGZJgu9J\nTY33N9NCTjY4sDm42bTJHfjyZ0U2QyJ1enh0Bnq4nsUocbJZpencCPgc1dWVvyc1sObLOmL/qyPI\nsdGzLC67uF+amBbmmY0AN3Om+/noURb16OabAaZO9UapWrKEvZdKALt2AcyeXXms7dtZVLgHH/SP\nJCRGwBo7lr03NADcdlv8awrDk0+6UZ9GjACYPz/6sfr6konWZIOwUc2SZPly7/egCFph+eMf2XtT\nE8D117PPflH+dO6z3z4DAwBDQ+zzlCnsORg/3v29sZFFL1NFiiPU4CiVqsiBPLpguQzw1FPe31as\nYO+dnQArV7LPY8YEHzMqTz5ZuQ23vXnz7PUpU6d6v48axfrA6urKfUeN8n7/+c/dsuK+A4AdAwBg\n7ly3DocVRocGEZEVQ+ZrKItprDNLCFoLUpFUVjAVQ0NmIi3leY1z0iRW7jFj0o/+5jje9ihGkRJn\nsCbPxd1p/Gb/YWd/OBaB6th8G06kg4+RlFtU3tHpp8QAVbieOzoq761pDcn+/Ux1r4reh9uHzb6R\nn6eri9k4iW7J/CX6nOOoiDL5ge2rstAPmha/mRXm3Hqdq0rE3M3hju996ZKFtUETZcjCdUQl7QGV\niF9bitrOdM+1aROrj9ZW+bOgc5/9yigTDrIIiKavk2Do2Cuk+QzjtmAzAlyQFwd/8YE+fnEDZ9kS\nURbqEFMYYc7XZ5YtYzctzqzMbzbiRxbWBk2UIQvXEZWsDURwWxKD2Jhek8PH27EjeOatc59ls30/\ngsKIZiWd5HDAzwB32zb7z3DQmji2X1m9Ork+hbdrrGHlrpOioOZ2NWLKWAAWTEvMe54mhRDm4k1Y\nvJjdnKYm5gsbVrBzq8ag2X3WLKc5cSLYZaVsUcnaQGTrVrkgdxzzrjvnnMOOt2ABu34TAxvusjN9\nuvwY4v0UBYrjqActWX1+8oJYr3z2O3duMvUZNFiUzXJtGpKJ7UlMWLN4sXf5gbdRMVIinslniUIK\ncwDvegdeOxdvuKxD0VXVZnVtOU4Eu6yUjQiPbuS1MMR1TXMc+TbZfwmXKK5pQfVpevAUNFhUqa1t\ncdJJ7jm4+lw8d9DaOH5dfHG2BpuFFOYdHW4EJHF2LTZ42QOgO6PJmkqXk+Xy2z5nnmZ7psuqG7nQ\nJEGqXsfxbsOJjbL6/GSBsMaJ5XJwfZoePAUN9GQC0qbLriw+iKgV2LChUlbIynnttdkbbBZCmM+a\n5VZ6Y6P7WZbDWWzwQdmF/JJ1iA0lK+jOyFT72bwu23WGj2/CQtxkeURLbtOdBT4XX99OMhb+1q3e\nHNL8mnBiI/w84v/qZPkrEjoeNVVV7j6y9WDxOfNrizbgy0hTprB324mbZPFBxPaJv+NEQeKLB9hJ\nsr6CKIQwF12ysHAWOzNRgMkEmnhj1eXwvvI0K/RD9/qzduwkjm+yPKZnprJzJRkLH0B+TYODTGsg\nDqyzdq+yhE7dLFzovbcqwcT/n1Z9J2XHotOXx3mljWlhnsmgMeUywIIF7HNXF8DatQAdHez9P/7D\nGzRDDGARFNBiyxa9Mmzf7h+gI690d9s79uLF9o4NALBtm93jh2XrVu/3/n4WnOi++8wHVOHBW3SC\nj5hi61Z5IJypUwFWrQLYsEEdNGT9ertlyzMTJsi38yBVQfd20aLKbSaeDd3gUraDBvFyXHIJqwfV\neaZN835vb1cfc+NG7/ft22MVMZsYHRpERFYMcVQmxusNM/uZNk1vNiNaI+dtDVClScBLFaZDovLc\n1p/6lJ06kql50+T005MrDzfkwepsv1mRCU2SaK2v0gTItk+YkK17lSXKZVYvdXVq+wfRpx/njedL\nj1i1LQskE4ekDe6ilIMn0gLwusrxvm3HDrcP58sBfMknae+AIEyL30wK802b2I0aO9Zt2GJUINka\nimrNNqpQzppbVBC4LrBhim5s+ijYrqOsDaiSLM/FFzNB3ttbaYwma+s2VPD4nB0dlYE8ZNHisnKv\nsoR4L2Xgul661PtsyYL32FzWka0p4/bV0mJPqPtdF/5tcLAy8RZGlCPZC0BVAGEuzsL93HJ01kFk\nD4KMvK+Rq+oib4MSTNbKvmGDui2Zbj86bmIYnc49qIx+fua4E5TdF93nrIiEtWYX76/s/6afDd32\nNXq0XaHod13ib37PI3ZtwzP5rAw2CyHMse9g0GxSZ2aeZT9tk6jqIs+DlKyV3a+NmG4/Om5iGJ3O\nPaiMfgMIrOKV3Ze8Pz82CRtqV7R450tl1dVu7HTTz0ZQX8rbl81QrkGI1yy2Ofw7X9oAYJ/FjJtp\nUwhhPjTE1j+4Oh3fIO4WI746O+U3ifunAzjOiBHqJAKsHP6dZRqEeWBVEcjwdWXBvSsMuOwzZqRd\nGnmeed7xmm4/l17qPSaORa0SlkEuUGECg2zbxl6y/fF+fElHZ2BdVHQGWpMnuzNfP08B7tuPt+mE\n5g1CN4KhbW0ZbsPnnOP2f6JP+YIFle0VC/e6usoBUJYwLcxLfz5oqpRKJfArRm8vsypn+7JbJaO2\nllmg4xR6YtrMtjaAl15SlcP7Pf2a8V77unVuir8wZPG6dMla2VVpWB3HfFnxvcd0d8st5vv6AG66\nyb8Mhw+z/VRWwrrXINsva/cqS/T1sb6poYF5PcjqvlwGOHKEfW5rA3j3XZb+uaoK4Ngx777Dub5V\nz1hdHcAHH/j/9+STWf/e1ARw6qkAe/ey7evWsfoNugdJEiT3wpJJ1zQR7I4jy2nL+egjgJ4e7zbc\nMOrqWM5zHbZty0YecNOuSHl2yZg+Pe0SyDsa7lKEMeEqxO89Z+tWf9e3gQHvd9m9DuNWpNtWZNeK\n86ATem6uNTXsvaGB9VNHjwJ8/HGlIN+xo/K/w7W+uYtydzfrvzHYXRKAucZOmcI+HzkCcPCg+98b\nbxyersYejM7zIxJUDKzW2b9fruoEkCdS4cH5J0wIDokpunrgY4fJtmYSEyqtFSv8lyLiYHtNm7uX\njB+fjbUuMdkDVmmbdqMbGvIuKwUdU4xTHZQsQ6ZO1W0rMrcoVWQ4IngJRszytWOHu0RYVeW6DOKl\nk+Fa3zzBEF4aHBqqTHlaW+uNmgfgdUMWl4d0ovAliWnxm0lhHpQhTZXfNm6D9jM4ysawJxo2DZNs\nGz1lzZpdlZEJwI5rFj7mhg3+A6ehoWCL3aA2Ld7PpiZma1JT4113lF3rRRexDnbZsuzcr6wQVO+y\n5CD797MwpPv3ywfNqkh8eUf0ZuL1hV3Lwrw44va022ghhDlu2CNHVlY67rTw6C0uouDAxzdhYJIW\nNv1/i+ZbLOtouObGxsADH1Nn4BRUhqBnRryfeDZTV+fuJ3MJImt2NUEakfHjK2fmGFkGseEKTzAk\n1pc4ieOprfFL1HBs2eIeV9Topt1GCyHMxYYtq/ShIVcNhROwmFT7BuV+zhqqa7c5u7U9c86aa5pM\nK1RXl8z1m3AJwlHFdPx4cQd41lnufjLBffLJ7HuSWd7yQpCl+OAgC24iE+SOI88gljS8Lba1sVmy\nrWcSz8DHjHHPMTTkCvopUyq1p1VVbBtOqoLTZeMlsiy00UIIc3yDZszQUxfyiERilJ84wiBrKt4g\nhuPMKGvXJFt+KZUqXShNtRmVJkC15hdUhqA27Rc0BmccDMpOmJaNSVaJ2zZsRnHURdYWbTyTOrYf\nHLxfczPbxieDMhuqLEWBK5wwB9AT5vwlrhlmTRjYZDiqvLN4TbJ2t26dnbaGr188p4y48bV1o87J\nBgU65SsqcdtGFiYWvC3yZVBbz6SO7QdHHFQ7jr8tQZb6k0IKc7Hxi0ZI3KKxq6syyk+Ym5c1lW5Y\nsvDAm8YvXGNaqGbmNjoKfE/xOaPmIQgSKmGjzmHC7Fs0gu5LlL7HdH+lq9VJIpKabl/mp62SXU+W\n+pNCCPOzz3ZvkEytpFI9rllT6X4QRsDl3cgk74MRGVnUrIjtjq8l2xBm+Jg8QphfXeD9cbIdTpBQ\nEZ8XHgFOvB7ZwAJn58NrlURwPxSlnZt+NoKO5xf90DS6z1Jnp7rMsuvBx12wwE7ZdSmEMMc3QWbs\noXJNi+tOlgUjkzhkUfDFJUtqMY6s3cm22zgXV3OqjHeCymBKeyM7z8iRrqYsi+Ezs0yUdm762QgT\n6td0Ow86l2qSwg3i+DOBM6Vxa3eVXYetsutSCGGuM3vgwUTE0VucUWMWjEzioKq3PM/Ys5iJC7ex\niRPlxmI2ZuY4IIbOzJyvIcZB1XZk19rS4m7Lo2YrTWSDrLjGjCbKgJEJclu5HvA5Tj9d3e5FgzZR\na9vWFtxu06IQwhxXOE8sX/mfyteOHW5y+ijJL/K+5oyvFa+p2pyx23448PGzYiG9cWNl21u0SK2S\njgM+B9cc6WbeuvZa/991kqGo2o4YAU5Uwc6ZE+lyCQSu+/Z2uwNynJBKlZhk9uzKdm9Lg7l0KTv+\nrFn+LpniBCYo4yZ/RvHrqqvsXEMQhRPmquuVCXOA4HzMwxnVtdpUVduu3yzeP97GkiibeI6gwWZQ\nmcKWWdV2RPWm7Fkk4oHr3rZLlXjv6usr9+FxBEaMUAtLU4hLrbr5zYeGvBk3ZYiBZdJqq4UT5mFm\n5j093shwa9Z4fw9r8JA39bRq1mVT44DPaXtmnoV4yo4jF+Tbt9tpL2GvH++Po1/Jfu/uDj6equ2I\nwkWsD+yTTkRrG7jubduOYM1KqSSfmeN7LqqvTbJpk6uFMj1g2LTJHYzwV0ODueOHoRDCnKsxVYJc\nZlXJo7TxKEoAbHQ2axb7zCMG+SFawoft+NKGq6Pmzk1u8GFDtYzhSRcWLMjOgIp3rKNGeevbxnJG\n2OsPGrxii/M4KlJx0MCjJaoGEUUnSttQJZSyMag97zxXkO/aJd8nKWNUcbAs9md+AyMxxr04qFR5\nQq1fb+96VBRCmAfv732NHesaSIlGbGEMqGQ3OW1VTBiGozV7Fq+Jz5jE2amNzg67S06dGjy7C2qz\npow8xfNk0VAxS0RpG0n2RWHi/gcl/ImLzFsJl8mvrOKsW6wrlSdUGv07CXNHfTO4SgqrBcMIA3ws\nboDBX2lbPuqQhhuX7aWILLqmccSy2VjOwO6SOLGEjjW7rM3acE3butX7nLW2Zu9epU2Uelf1c/y+\nmnz2dJ4zfj6s/bSVKRFfb2Ojt0x+ZcWGfHxWj8u/ZAnL7CfW6YUXmr+OIEiYO94cz/yl8r0NIwwW\nLGD71tezGQbPIZwHQe446g7DpsC1PXPO8oxPrG8b9cwDxYwZ4zi9vcFtmd+POXOilUH3GvizwdWY\n3Dgqa1qUPIM9cxoa3LrlAXlMPns6gw1RRW1zgM2Dwcjiq/uVdf9+t3wnn+zdR6ViB3Cc1avtXIcf\nJMwdtfWszHUJ/x7kijM05PWVHS4dEl57W7HC7LFtz5yzqGZXgduaKf9brMr3s+rlxK2v/9/euQdX\nVd17/HdCIs+8EAIhEYI5JDxCcqJBySCVXgkIrUBrddRWuQ4GxhnpdWrV6e2dUcdR6Gj/cEozVqe1\njjOthdbXDEm1tyW3CgIXRZkLMxdvSWp4RLlwcGi9NQTW/WO7c9ZZ2e+z195rnfP9zGROztlr77X2\nev3W+q31+y3+fnNPANHotVQnq5FJk9QbeMWNWz/kNoiyOjzET98WBlYqalnxipofP9hNAMSjVcXJ\nYNQUhDD3MjuwKxS3cG6orNZ1w4uDjzAcifDI9nWsU3n4rWte8Pv+ueYXf7+oASsqyoTzeiALMHDL\nH7dBmNXhIVHnuTkjjiJeMQ4/ddkuL/mBsRfZIZuCEOZeZhdWhWG1y9PvbEklR/x+scs3Pg+mTIkm\nzrDQyZGPjBmL3/d3C+/Hq5g5GzT/+Jm504EsUcwSdcOtH7IahLmVFf/MKB0q8fHKWoIU+3Y/fYvd\ngNZp81sc1koFIcy9zC74zUBEho05Y6MbQCplXK+v92/aM21aftiZmzOssWPtfXqHEWeh2RaLdW3H\njvCFWdjr8H4O0/jlLw2BXlQ02lxJNOM0TUBlDep0x60fsmq7bh7gzD0+DQ3R9E9WzlZklTUfx4QJ\nwTcO8maS6TRjFRXWwtzqUCLZKCfMe3p6WGNjI0smk2zr1q2jru/atYuVlZWxVCrFUqkUe9zCx6T4\nUl5mI7zKpLLSfve635mN3chNhw7KTm3kdL6vrDjDQmXHPWJdk5HWsF16+j1Mww4xnE7LIXHg1g9Z\n5bubB7iotVbiTnEieXGL8Vx/vdGHLVniXv+d6vD69fZ9fNQoJcyHh4dZfX096+vrY0NDQ6ylpYUd\nOXIkK8yuXbvYTTfd5JwIny/V2ZkxLxD9CIudit8Oli9cc+SrSwfFpz0qb2l8nDJUbipvgBPrmoy0\n8nFMm5Z5ftCDTPwIFac6JIabM8fo7C+/XM6gMd+xmiVG6QHOSz8pCnOZqn0+ntJSf5MrPpw4d7Tb\n0R7HuQ9KCfM9e/awlStXjnzfsmUL27JlS1aYXbt2sa9//evOiSBijzzyyMgf0S5HAcG7bDX/TC9B\novrPbwfLm4PotF7LWDze0mR7gFN5xifWNRlptfMC5rT/IxcNgXhQkR0rVxrhrr569Jp52PsyCgHT\nFGv2bO9q+DDx0k+appEyB+8mfDxz5hifpnc3t/bF3yuuhVu1paiWKXbt2iXIOYWE+Y4dO9g999wz\n8v2ll15i9913X1aY3t5eNnnyZNbc3MxWrVrFDh8+PDoRwku5qT94pwXiaE38zU8Hy5+Fm08b4HRG\n5QGVWNdkpNWqntu1C5Mo6oHTGQgh91EFgZU5FT8ok52/XvpJcdAms5yt+vb+fm/tyyqNZt9u1Y7i\n6lvCFuZFlAOJRMI1zFVXXUUDAwP04Ycf0ubNm2ndunW+4ujqGv3b+PGjf6uvJ3ruuezf2tuJfvUr\noltuIXrrLaKKCue4jh4lGhwkOnuW6N//nejuu30lNXaOHTM+y8uJnnoq3rSERUUF0fbt7mUXN+3t\n0aa1p8f+2oQJxueiRaPbxMaNRMuWEa1eTXTu3Oh758410j91KtEdd9iHHRrK/C92A05pK1Tc8v2v\nfyU6fdrodzZuNH47epToP/5jdH4uXuztmX7w0k+Kv+/YkVucXnn6aaNdzZrlv321thqfZt8uMnas\nUc9zzT8VyEmY19TU0MDAwMj3gYEBqq2tzQpTWlpKE77sXVatWkUXLlygs2fPOj63oSHz/65do6/X\n1WV/nzyZ6MABo5AnTcr8XlXlr4M1O0ETxtzvUYmZM43Pzz4jevDBeNMSFmF2WGEj1jUZFBdn/i8p\nMT6vvjrToVvh1DHzAsIUGjyDg0b9+d//JXrllUzYefOy8/+qq4zP1laiF14gWrnSW9oKFbd8txqA\n8b81NRn/z56dEe5uz/SD137SFOA7dhB961u5xenE009nPh94wN+9fJuZMcP4FPt2ky++CCf/lCCX\naf2FCxfYlVdeyfr6+tgXX3xhuQFucHCQXbp0iTHG2L59+9isWbNGPUdMhpvKh1c7TZqUrZoSD5Lw\ns8aTTjNWVZV9v07ko292lZcOTFerpaXyNn3xm468+sQW1/JNOjuz24PV6Vi8pzFxjZSPU1xSULmc\nVMCPFYHZT/F5bLWEk4/tPQz4vOQ3E5p9u9VfHC67cxS/o5+X6wO6u7tZQ0MDq6+vZ08++SRjjLFn\nn32WPfvss4wxxrZt28YWLFjAWlpaWHt7O3v33XdHJ0J4KTd/3Om0sU7HO7UwD3cQK73fNR6V12jd\niCPtsjtxlTfAWZkLhY0pUJubMwNVPxuA+Dov7uQdN270vbwZYzqdWWd0i1PlclKBIKZpuT5TBjoM\n2uzy0sksLY59HsoJ8zAQX8qtwpijQ9FDlVXYuEdfKiBzNC27E1d5cGX6ejYP+ZGRz/z7e/VOyNd5\nfmYuHoayYoW/+J3Q2XOiCsTdT3mtu2YdGjPGGGiqWNZ8XprOxBhzPmglrLMU/KUzz4W56GXIzUWr\nm9rQPMq0qUnNihcFMkfTMoUtr2KuqFDPfllU58metXh9vp2JIq9JKCvLrczEzl+HGZvKyDbxdMPr\nYUyif3MVy/rqq420JZPO7lxN+3Wv3kHDJu+FuXjWLH+4Qya89d/48aPDopPRVwUqlq959KMqiOmT\nkc+80DTX6O2O+zWxq/Nm+iorcx8YiXHoWseiQvW1Zr4eOx3GxAtEVfcV2U0wxGUjr6Zussh7YV5c\n7G9mPmZMRujznuBM0Mmorap2gp8tjBun9sy8vV1OPvNCkz+PwGlgaqpCRaEfZvrEdqVrHYuKIJMK\nfgCwfr3cwQBfl5ctsw9n7ldaty7esg46OOLraWOj0UamTImnb8l7YS6ua1hV/H/6J+Oa6TXJ/LPy\njFRInUwco3+3/M+Fd94xnltVpZ4gZ8xeQ2Q3CA0zDid1bBQb87ymBRi4tROrtsv3hVOnyi3Ta681\nnt3Y6Nx3qKJhCEPjanqUi0vrl/fCPJ021OVO6kSnjQy5okplDUIcSwph5z+P6mXhJMzDyosgz49C\nGyWmRfWyihu3srNqu3w5WlkyhJnnXic9qixbOtVxr/nCm2FiZh4S/Et1dmaO7LSrMLwaMeyZYdin\nVEVJHEsKMmfmqnQcdsQ5M3fK67C0UU6dIp+Wri71yypu+Pzid1ibWLVdNzvzOPJclWVLpzruNV9k\nnibphbwX5nxBlJRYZzSvRpw0KVxVn9uxgyoTx5KCjDO8TVTpOOwQN2uGLcgZy85fmXlthVOnyFud\nYAOcO26zwCBtN8w89zqb1WHZ0i1fzHf1eqSqLPJemIsdo5UQNQuLPxovLGHLV1Z0UO7IVK+q3nGY\nqk9ZywxxYHe4hzhYFm3sVS+ruJExMUinwxtEep3NOtUJVXCri1bLtJWV0dfdghPmdh7gbrlFnXWk\nQqaQ1atiZ0qkz5KMHV73o+imtYobWRODsAaTdhYQsuKLE6tT6IiMXfpRErYwz+mgFdl0dVk7/jcP\nBdixY/SBEnEcPqAKcRxK4nRCV75jVS90P7SBL08e8YSssrJMuOeeU/tAHBXwc3pjUKZMCX5vkEOa\nfvnL4PHFiVkWIsPD0aclVEIdGgSET8a8ee4jfqeNQLmOgO0OqdABPl/MAwYYK2xVuGxMkyHTP0LY\nM68gZZdLefPlyZuAis8xvfMlEhkTQvNv6VJ/cQJ73Pojc7Pw2LG5beTy2m/G7alOJJe63tOTXW/b\n2qQk0Zawxa9ywtxLpXJS9YjCxW9h66xGskt7IavCZWP6JL/+ejmONIKUXVjl7fQcO1W8ju0mCoIK\nHbdlnLB2ZPsZlKtkhsjXUfOwLT/EWW/DFubKqdmnTjX+ysu9he/qyv4uqsZzUbu3t/sLrxK8WlRX\nVbgOqtv+fqLTp406VlISvgr12DHjs7yc6KmnvN1jV95+8/Mvf7GPO5Gwv++667yls5AIa/lPvH/W\nLKKBAeMzF/wsKYa5lOmEl/rKn1M+OJhbenTu74lIjTE0nwwvDv/5kVRFhfOz/ard+R3y/J8OKndz\nQ2Aqlf2uTssSKsOnO45TjbxgVVfCzOMgakC7WZaXGTvf/pxU56Jq3VT3iodbAAM+r/yoqCdOzL5X\nNG0La5bsp/5G1Z94qa+iv3W/eZBKGffGcdhK2OJXOWHOVxQ7h/9+VCN+13RNgdjaqp/q0K7y6/Ye\nJjqkW7aaOcznBlnCcoqbbytxH1qhOkHLUTzmWWzbYS2p+ElfVO3S60Qsl307ce75CVuYK6dm59m+\n3T1MWZmzGsbvjnRzh/yf/jT6d9Xxok4P+z2iUoXrtHO2rU3Oc3PNg1x2VFupzmtqjCWxyy83VPE6\nWX7EiZ82eOBA9veGhuy2LWMJbfFi72F7esKJ0wqv9TUXq6OHHiL69FOiO+5QdynPM6EODQLCJ4NX\nK9k5vxcPWCGydpGYKwsXhjPqjYo5c6yXBmR6DpO5uc6LZUPcmHnL/61bF97z+d3DXlWqQVW6jBmq\nfCLGGhoy7cxOdW5nk97V5S/OQsBuCczEqWzNXddLlzq71c1lh7mpbr7ySveDVkw32lZWDrrB5x9v\nARRN3HmuZvfi/D6ZHH1UqoxhiVdHCqog+qqPYqgm00ueLh740mnjZDdT5SwrrUG8dPmtA27+wHn4\n8tFhSSRO3MrO7rrbAC6sfA9y0IrKg2yvxFlvwxbmyqnZly8nuuwyQ21kt6P99OnRBv4y1D1BHCnE\nSUlJ9neZKjATmc4wonC0EQYPPUQ0Zw7R9OlEr74qL61BVKp+VfO82pHIWX3Jlw+PaGEC3MvO7jq/\nc3zePGdVcC7LMF5V1fzu8dZWvaxj3BAdJWlHqEODgPDJ8DL7GDvWWp1sRS67PXWZGZr09zM2ebJ7\nnoBwkW3Hb9bh5cu92bKb6Wlu9l9vg75LVxdU7E64zXztrouuR8UyCcuJi5+DVtauleNTIQ54De/X\nvhZt3GGLX+WEuZc1ID5MWZlzBfRi6mZHoXs38wJfFjqZvYWJ7EGfXwGby+BCtwFsvpOr6ZVXCtWx\nVD6tmSe+fGisJBIJMpMhOqOwSp2Vw4pbbrHe/c6HTSSILl1yTsvGjYZqa8IEQ42osnrXCjH9Dz0k\n9328lFe+c+6cke/PPSenvqxebahZp0whamw0LDicytIMv2iR/yUK/l3c6g5f16qqDAc6urYblZFd\nv4hyqzM6E2f/xcu9MCgO7UkS8LqGUVpKlE4blV7sgHi6u92fZa5RERkNyIt5nEqI6f/00+jeR3sP\nSgGpqDD+1q2TI8x+9Suj7E6eJNq92/jNqSyrqvx5UeRZvNjwpDVnDlFRkVF/iIj++Z+JXnstOyxf\n16ZONfayuKUN+IMfMMnErGNuA4a4JjtRxKuT+asloc7zA8IngzdNszuSzjQHMteLzD9xV21bm3FO\nrR+TId3VjGL6Zb9PnB6UVCIKNaXXsrRTHfpdwuKXqKzaDh+2uVnvdhM3dstV/O8yzG8GvBu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QQqAAAAANyBMAcAAAA0B8IcAAAA0BwIcwAAAEBzIMwBAAAAzYEwBwAAADQHwhwAAADQHAhzAAAA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\nAAAAQHMgzAEAAADN+X/mKbxFFI8UGAAAAABJRU5ErkJggg==\n" | |
| } | |
| ], | |
| "prompt_number": 9 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "The distance traveled is the cumalitve sum of all the $\\Delta x$'s." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "data['X'] = data['deltax'].cumsum()\n", | |
| "data.plot(x='Time', y='X', figsize=(8, 8))" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 10, | |
| "text": [ | |
| "<matplotlib.axes.AxesSubplot at 0x40216d0>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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TkkzCwIFw//0wYEDUaSQ1JRa+1ESsWwcXXgh33QVf/3rUaSQ1NRa+1AT85S/Q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\n8G//FnUSSVKceA6/Eb30Ejz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| |
| } | |
| ], | |
| "prompt_number": 10 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Now we can plot the location of the vehicle in the inertial reference frame." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "data.plot(x='X', y='Alt', figsize=(20, 10))" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 11, | |
| "text": [ | |
| "<matplotlib.axes.AxesSubplot at 0x585f050>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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l89ATKJXgJYYPL3aBe+qpspMA3aW1NXnooeISts4YMMDlswDQaO65J3nnO8tO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\nJ5o8OTnppLJTQM+hVIJ26tM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yfn3y858X14T/7Gdlp4Ke5/OfL+bg9FSf/GTyy18m3/52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\nn74nB0qXlj74oPBxwn1ZWVb9m5IiHTzo3WOWLrXPGLGxgY0NAI4iqQQ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| |
| } | |
| ], | |
| "prompt_number": 11 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "The slope of this curve is needed to compute the forces due to gravity and rolling resistance. It can be computed by differentiating the above curve, but this leads to lots of noise, so I filter it with a low pass filter to try to give the true slope." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "slope = numpy.hstack((0.0, numpy.diff(data['Alt'].values) / data['deltax'][1:]))\n", | |
| "slope = spline_over_nan(data['Time'].values, slope)\n", | |
| "data['Slope'] = butterworth(slope, 0.25, sample_rate)\n", | |
| "data.plot(x='Time', y='Slope', ylim=(-1, 1), figsize=(10, 8))" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 12, | |
| "text": [ | |
| "<matplotlib.axes.AxesSubplot at 0x5e085d0>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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9EGjfXncmXbvqurRvn47f8zrCdjIVsW3bvIPgKafoa3qNLXJrLohZlq5Xv/yl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\n3iqjRo2SAQMGyPjx46V///5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wHoUWQ5y7JkVEjj9er7yRy9UOEA4mVAS9roWFc103xRHD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\nQOQQfgDEBUEMAAAgIAQxAMgD1TkA+SCIAUCWCF8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| |
| } | |
| ], | |
| "prompt_number": 12 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Now the angle of the curve at each point can be computed." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "data['theta'] = numpy.tan(data['Slope'])\n", | |
| "data.plot(x='Time', y='theta', ylim=(-numpy.pi / 4.0, numpy.pi / 4.0), figsize=(8, 6))" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 13, | |
| "text": [ | |
| "<matplotlib.axes.AxesSubplot at 0x6562390>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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8KXkeOKAfjy++kNqHXbvkPNm5U5bdvl3vROrxyPPjx31H4fzrX/L7zjhDH84F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\neQX9+vVDcXExampqgm5IVVVV2/OysjK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| |
| } | |
| ], | |
| "prompt_number": 13 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "The drag force can be estimated with the instantaneous speed measurements, but this doesn't include a head or tail wind. We could potentially pull the wind data from weather data to resolve this issue." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "data['Fd'] = -0.5 * drag_coefficient * frontal_area * air_density * data['Speed']**2 # this neglects head/tail wind\n", | |
| "data.plot(x='Time', y='Fd', figsize=(8, 6))" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 14, | |
| "text": [ | |
| "<matplotlib.axes.AxesSubplot at 0x6d2e210>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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Bc5FIcbBnOailh9X7LfbsMXeO6+qAX/yi9HEzrvzGRvocrS1/LOzqmHXFNzdL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\n2AHtdJEjI+RSHz3afF58LwP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s7CWXSDsnxM4Fq7S0UF+bNcv6e7Vgi12iWteML7ig+Bxq\nCbuwwMV4dd11xY+n08Bpp5W+XotMpth1z5gj0MKut8Zeia54t4RdWOpqrni1SczwcGmWOaeF0Axu\nCHs2S+fejSx68v5mxdWdyZhbJpEPknaF3Q1CIbbYBdW6ZqwcK7UMH5HJMpmkPismMbGYVFZYvsyl\nlvlSjhvLYkGgCruYc9gV9sZGSrvqJW4K+0c/SrfnnVf6XH098NBDxY/Jt7eJW6/3sAPuCPt997m3\n7CHvb2eeCfzqV+bel82a2wPf2Ejehi99CfjkJ4tdp36SSEiJc4JOtbri5S5zsW1NbfI7YQLw7LPA\nqlXFY+vAAHD33bQFTj6uptPAkSPan7t/f3X+Xn7Dwq4j7FoWj9gK5WSAkRFuCvt//icd/5ZbSp9b\nurS0XGhjI7B6Nd3PZOi9flx8bgj7wADwiU84e0yBvL/t3Ak8/bS595m12EV/fewxuvV6S6YWkyd7\nX0egUqlWYZdb1r29wNix6pb0/Pm0PW73bspiKfiv/5IS1lx8sfT4lCn6W0sbG43LFTOlsLDbsNi1\n6pi7iRdR8WqoucqGh91JlmMVN4Td7J5xOyj7m9m2m7XYBSKToNvBlmYxCpAKEtUs7OIcJpP61388\nTkFyci9ePE6em6Gh4gmB0Rq7mAww1gi0sOsVgTlyRP8C9Dqt7LFj/gzUYlD+zndoYhEKURapShD2\ncNh5d7PZPeN2CIWAZ54Bvvc9+v+ee8y9z6zFLhBJatrbrbXPLYzWUYNEtQp7OCz1K73Kl4CUJla5\nre2BByh5jXzsMCPs1ZbQpxIItLDLqwwpyeX01429tkIKBee3YJlBXHg33VS89FAJAS1nnaWfjtIO\nbg68ixfTVrobb5QeM7OcYzSQqvGP/2h+4uA2fhVNqkSqVdgXL5b2sRvVk7/2WrqVu9DlSzGTJ0v3\njSZ91Va7vlJwTdi/+tWvIhwO48SJE28+tn79esyZMwfz58/Ho48+6tZHm2bUKO2BNRbTjyr20goR\nkbR+rFNqfc9KsNibmpyfYLg58E6dWup1MeOOTySsn/vVq+0lqHEDFnaJat3uNmGCNLk0KjsrPEXy\nnTJyI0npitfrG+yKt4crq4kHDx7E5s2bMWPGjDcf2717NzZu3Ijdu3eju7sbl1xyCfbu3Yuwj3s/\n9MTZqEOl087WrNZjaMibBDVq1NWp772uBIvdDcHI5dzbJhaLSUVa5J+XTAL33w+84x3q+9WTSevC\nXklpS3mNXWJgoDq3/sViwN/+Rh4nI/e4GBvkfVY+lioT1HR0aB/r2DG22O3giqp+4hOfwJe+9KWi\nxzZt2oQ1a9YgFoth5syZmD17Nrb5vB9Hb8AxcgFpCZ4bDA76F1VcVwf8+79L/99yCzB3bmXMot0S\ndreC58Qc9txzpV0FuRytu3/kI8DPfqb+vkTCfFGaa68F3v9+4C1vKb+9TsFr7BLVVtlNIK73X/9a\nf8cQQAlobr9dKvcKSGOpSFgjmDiRKkhqMTDgzxJkteP4ELZp0ya0tbXhLYqR5dChQzhXVimkra0N\n3d3dTn+8Jcqx2CdP9s4KyWT8SdkKkJfgi1+k/ad//zvtRa0UIhEp+YlT7k03g+dEfxFBSA8/TJ8n\n6ldr9Scrrvhf/KK8NroBu+IlcjnnCwx5gRgLIxFzFrvCrnvz/T/4QfHjp56qv6wXjfqT/KrasSXs\nK1aswBGVrAJ33XUX1q9fX7R+XtCJDgpp+JbXrVv35v3ly5dj+fLldpppiFZEZj5vbLl56V6shHWm\nSh2YxeTMKY+Gm2vsyt9QbNcbHKT/lX1RXDrJpPNlZL2EhV3C6g6HSkH0RZFBzup4JN6v/O56lf8y\nGXrO77HPDbZs2YItW7a4dnxbXWzz5s2qj7/00kvo7OzE4sWLAQBdXV1YsmQJtm7ditbWVhw8ePDN\n13Z1daFVIwmwXNjdZNw4YO/e0qIfu3fTrd6atpfuxUqIDH33u4EdO/xtgxqplLNLFZ2d7q1Pt7UV\nBxEJYf/Qh6ivKcXvxhup1v1nPlO9LlzA+62hlYydHQ6VgBgf16yhBE5axbO0kLvl5egVCHruOf9i\ni9xGabDeeeedjh7f0TX2hQsX4ujRo+js7ERnZyfa2trwwgsvYMqUKVi1ahXuv/9+pNNpdHZ2oqOj\nA8uWLXPy4y0zZQr9CVeooL8f+Id/0H9v0Cz2r3wFePxxf9ugRlubs3n7m5vNFVyxQ3u7ZJ0Dklsz\nHKbfVyl+3/8+3WYy9D2rFbbYJapV2OVLgfv3Wy/MMm2a+g4kvSRTAwPApZda+xyGcNUpJHe1t7e3\nY/Xq1Whvb0c0GsWGDRs0XfFeomZ5mxFSry32ahwMvMDp85DPe5fhLxIhoY/H9SeKJ08C06d70yY3\nYGGXyGar0xUvJ5l0bitlPF5qWAkqwaCpVlwdwvbt24fxMh/M2rVr8eqrr2LPnj24tEKmYmqDjhkh\nDbltVegAACAASURBVIWcT46ihUgMwZTS2Qls3erc8bysvnXkCLB5s7RmuXOn+uu+853KyBtgFxZ2\nib/9rfqF/eGHtcXYKmPGAF1d6s+xQWOfQGeeA9QtJTNr2l4mmfArT3y18P73O3csrxOI3HILsHAh\nWeR6FdDGjvWuTU7Da+wSfX3klq5G5EtxZgsYGSEsfzU3fSXEFlUrgZcLu674SZO8s0JSqeI0jIx7\neOmKFyxYQFug5AGAyop51TzAscUukc8Dc+b43Qp7yHcwO7VLIxSScssrYVe8fQIv7IC6sBtZbV4O\nVqmUe9nQmGK8dMUL4nH6THkmw0ymuB0s7LVBNbuX5UsITm6/1IqTSaerf9nCLwIv7IUCZf6Sc+iQ\n8UCkNct0AxZ2fd76VueO5Ucu73nzSMRfeYUq5wG0nUgrJWe1wcIuUc3Bc/L+6GRKaa3A0Z4e7jd2\nCbywv/3tpfskYzHjKGQvBysuXajNXXcB55/v3PG8dMWLwfG226TJRG8v3Y6MkCUvtvJVs7DzGrtE\nNVvs9fXSWriTwq5lsYfD1RuP4DeBF/b6+tJ90GYuvljMO4vdKDdzkHF6guWlK16ecUtEvQtrTgi7\n6IfVauUB1PZqLHziBtls9Qq7HKdd8WrXsJ2qhgzBwl5fOls0I+zRKPDCC+61Sw4HkWgTiwFf/ar2\nlhmreOmKr6+XJhEiH/bzz9NtR0dx1q0KSPlgm0ikujPnOcXwMLBlS3VP0gROlgROpykZDQC8733A\nxz4GbN8OHDjAwm6XwAt7XZ09i336dODoUffaJYdd8dpccQXdfvnLzhzPS1f888/TujpA6Y0B4NZb\n6bariwpkAFTYRVY/qerQyy4WJPbto9szz/S3HeXy/PNSVkQnGDVK2hf/i18AGzYADz3E293KIfDC\nbtcVP2+ee21Swh1cm9NOo9u+PmeO56Ur/tRTgdmz6b7SIk+ngfnz6f6111Z36UovA00rmVyOtozJ\n6wVUI2edRdt9nWLq1NL+MTJC14DV1LUMwcKu4oo3YyF7vcbOwq6PcOWVix9R8WrU0jlni50wqhgZ\nVKLR0jX2kRFKXcuxRfYIvLCrueIPHjQuLOKlFVJLg7xb/Pa3ZPWWWxCmEuIZfv/72jrn0SjlxN++\n3e+W+Es2WxmTxkpDaSQtXUqBc9VerthPAi/saq74aBSYOVP/fWqzTLeopUHeDeTWoNVykkqGhvxz\ne19/Pd0eOFBb51yI2YED/rbDb9hiV0cYSWKXyG23scVeLoEXdrU9tma2WbArvnKQr4mXu196aMjZ\nPbpWEP1JrC/67TlwCiHsQRc1ttjVEUaSiG1qaqJrIJXiqHi7BF7Y1Sx2ZdYvNaJR4LXX3GuXnFoa\n5N2mXGE/csT/4KbnnqNo+VqZzAkxUyv0ESRyORZ2Lf7+dynjZzwuueLZYrdHwOfQ6sI+OGjsjp01\ni1IeikQibsIWu3nKFfaREWlPudfccQe5I7dto61RZ5zhTzucRnhUgi5q7IpXZ/p02t8P0C6XxkbJ\nFc9r7PYIvMWu5oofHja22iZOJPH3wh3P293MU46wZ7MkQn65/xYvpn28Ym9+W5s/7XCDVas4Mp5d\n8eqsXEm/SyJBWzvjcckVz8Juj8ALu5rFPjRkzh3rVQAdW+zmKUfYEwkaVPzO8iaWXWppUONCMGyx\nayG2QwrvpxB2ttjtE3hhb2ggN5AomXnwILBnjzlh9yqAjoXdPN/9rv33Hj5c/nY5Jzhxgm5rKXAo\nEqGlqyBz6FBxfQCGEFHxXV00HsfjtBR15AgLu10CL+xLltCtSA+7cyet+RhVdwO828vOwm7Mrl3A\nb34D/PSn9o/R0wPMmOFcm+yyZw/dvu1t/rbDSaJR/z0hfpPL+bfjopIRFnuhQMZSS4uUUXLsWH/b\nVq0EXtjr66kTCTfhyAjl5TYzCHnpiueoeH0WLqR13OFh+9HXySRwyinOtssOwmvgd3S+k0ydKuUD\nDyrZLP0OTDFC2HM5YMIEGnvf+U56LuiTQbsEXtiB4vW/4WHzUe7siq8sIhE6J8mkvfcnEpXh+rPb\n/kqmqYliV4JMJsNr7GoIYZeXtA361shyYWEHuT4/8xm6f+iQ+U7V309b49yGo+LNk0oBL71k773J\nZGWsa198MbB8ud+tcJZwGHj8cb9b4S+1UovdaeTCLiY+LOzlwfPHN3joIboNh4EpU8y9p6XFvfbI\nYYvdPBdeSBMuO1SKxf61r9Xe1rALLgA2b/a7Ff7CFrs60SgLu9NwN1MwMmI+V3hdnTcDMAu7eZqb\n7W95qxSLPRKpvf3OlTBh8hsz5aCDSCRCoi6f+LCwlwe74hX09Zkf3MVM021Y2M2jlnDILMPDLEBu\nwfvY2RWvhdoaO1MeLOxvcPbZdPv735vfkiJmmm7DUfHmqauzLyBdXbXnAq8UWNjZFa+FPEGNyA1/\n+eXANdf4265qhrsZgC99CTh2jO43N9N6oBlEh3QbttjNU47FHg5Xxj72WoSFnS1SLcQ4OjwsLYNe\nfjn9MfZgYUdxohkr66xeueI5Kt485Qi7mXK9jD28LHNcqbDFro4Q9kSCE/g4BbviQRfb3r10v6fH\n/DqrV674/n4WdrPYFfaBAeDPf2Zhd4tolH7jIMPBc+pEo5T5c/9+Fnan4PkjqKrWxz9OkZgnTwLj\nxpl7n5eueBYcc9gVdpG6slZKpVYa9fVAb6/frfAX+XYuRmLKFApaBijzHFM+bLEDeOtbaSY9PEwX\nnpXgObeFPZOhz+FobXOU44oHgKVLnWsLI9HUxNYYW+zqjBlDt7NmScFzTHmwsL9BQ4O1rW6AN2vs\nopQoY45YrDxhZ9xBrTxy0ODgOX1qLXeDn7Bj6A0GB4FHHrE2+BQKwIED7rUJoHXJoAcdWSGXA559\n1vzrd+zgZQ4vYGGncrzsitfm5Em/W1A7cDeTceONUrlAM4wZQ3WD3aS7Gxg/3t3PqCV+9jPK92+W\ns86S7q9d63x7GEIIWpDXmQsFnqTrIbYcM+XDrngFZ55p/rXnnOO+2zeZtDbZCDoiCMcOd93lXDuY\nUsqNf6gFeJLOeAELuwIra2ANDe6X2EwmOXDOCpxjunIJujueg+cYr2BhV2BV2J96yl0x4aQp1rBy\n/hIJ99rBlJJIBHvLGwfPMV7Bwv4GN95It1YuvPPOAzo63B2sWNit8YMfmH/t3/7mXjuYUqZNA159\n1e9W+AdnnmO8goX9DT70Ibq1IuyzZ1NyBTcDYtgVb41zzgFaW829Nui5y72mvT3YRXbYFa8NJ6Zx\nFhb2NxAXnNUZtTzPvBuwxW4NKznJOULZW9y+VioddsVrw7ExzsLC/gYiF7vVC8/tweroUbbYrSDy\nTpvZExtkkfGDIAt7MklLP+yKV2f0aE5Q4yTczd5g5kzgppuslwp0e7AKh4PtvrTK2LEUff3MM8Bl\nl+m/NpOh3/emm4CLL/amfUEmyML+4otAPk9pU5lSfv5zSunNOAML+xuMGgVs2GD9fW4PVtksMGmS\ne8evNaJR4JJLaBA1Ipulidy3vuV+u5hgC3s2S8G2o0f73ZLK5Lzz/G5BbcGu+DJxe7DiSFrrhMPm\nhZ1/W+8IurBzX2O8goW9TJJJ4PBh947PATfWGRgA9uwxfh1PmrwlEgH6+/1uhT+wsDNewsJeJi0t\nwK5d7h2fxcc6Tz0FfPKTxq/jSZO3BDmdLAs74yUs7GVy/vnu7odm8XEPHmy9ZdKk4AaCcl9jvMQV\nYf/mN7+JBQsWYOHChfikzHRav3495syZg/nz5+PRRx9146M9x+188Wyxuwf/tt5iJcdArcHCzniJ\n413tySefxG9/+1u8+OKLiMVi6OnpAQDs3r0bGzduxO7du9Hd3Y1LLrkEe/fuRThc3U6D+npg7173\nLtyBAbbY3YIHW2+JRoOb7a+nh5OwMN7huKp++9vfxqc//WnE3lCjSW/s1dq0aRPWrFmDWCyGmTNn\nYvbs2di2bZvTH+85S5YADz1E+6bd4ORJcxHejMR732vudbzM4S1BjorfuROIx/1uBRMUHLdXOjo6\n8NRTT2Ht2rVoaGjAV77yFZx99tk4dOgQzj333Ddf19bWhu7ubtVjrFu37s37y5cvx/Lly51upmNc\neintwXSzUpjZ3OcM8c//bK4wD7vivSUWC24AXTYLXHSR361gKoUtW7Zgy5Ytrh3f1rC2YsUKHDly\npOTxu+66C9lsFn19fXj22Wexfft2rF69Gvv27VM9TigUUn1cLuzVgJsDViLBKWWtYtbly654b4lG\ng5tdbGSELXZGQmmw3nnnnY4e39awtnnzZs3nvv3tb+Pqq68GACxduhThcBjHjx9Ha2srDh48+Obr\nurq60FojpmhdnXtrh/39LOxWMRukxa54bwly8FxvL9DU5HcrmKDg+Br7lVdeiSeeeAIAsHfvXqTT\naUycOBGrVq3C/fffj3Q6jc7OTnR0dGDZsmVOf7wv1NW5Y7G//DLlmG5pcf7YtYzZtVy22L0lqMFz\nAwPA738PnHKK3y1hgoLjw9oNN9yAG264AYsWLUJdXR1+/OMfAwDa29uxevVqtLe3IxqNYsOGDZqu\n+GrDLVd8ZyewciUwY4bzx65lzAo718f2lqBa7AMDQFsbcM45freECQqOC3ssFsNPfvIT1efWrl2L\ntWvXOv2RvuOWKz6TkcrJMuaxssbO657eEdSo+GQSaGz0uxVMkKjuTeQVgluu+FyOXcV2sLLGzr+v\ndwTVFZ9IsLAz3sLC7gANDcAnPmH//Tt3qtdpvv9+d7fR1SpNTcDu3UBXl/7reLubtwTVFX/8OJBK\n+d0KJkiwsDvAf/xHeRZ7Zyewf3/p4wMDwAc/aP+4QWXmTODUU4Fjx/Rfx1Hx3hJUiz2f5wBYxltY\n2B2guZlKUtpFy4pJpYDJk+0fN8hMmGAsIuyK95agWuzJJG91Y7yFhd0BIhFaD7ebC1pY+8rUscPD\nPCDYxYyIsCveW4JqsadSnIuC8RYWdgcIhSRxt8OmTXSrfH93N0dt28VMBPbx4yzsXhKNGsc91CLb\nt3M/Y7yFhd0hytnKs3Mn3SrfPzQEjB5dXruCihnrsMoLC1Yd8XgwXfFHjwKnneZ3K5ggwUObQ5Qj\n7CJPj9Jij0Ro/Z6xjhlXfCoFvFF8kPGAsWODOZlKpYDTT/e7FUyQCOBl5g7lCLvY0qZ8fzLJa3N2\nMXM++Pf1llgsmGvsnKCG8Rpe+XGIgQGgowNYutTa+xIJ4PXX6f4f/0gBdBMmkBWfTgP19c63NSho\nVAUGADzzDPDkk8B//Zd37Qk6QRX2zk4WdsZbWNgdYulS4L//G/j1r62972tfo9vrrwd++lPg4YeB\nOXNokgBIbnrGGs3NwN692s9feCHd8tqndwRV2Hfu5AIwjLewK94h7rjDnggLa/273wX+8Ae6/6lP\nAW97m3NtCyJmCwdOmeJuOxiJoAp7NEqTdYbxChZ2h7C7xi72rsu3w4TD9rfOMUQkEswI7EomiMKe\nyVB+C85wyHgJC7tD2E2+IQRcnrmORal8glpJrJIJorAnEpyLgvEeFnaHiEZpffzvf7f2vh/+kG7l\nbvxTTwVWrAAuusix5gWOri5gwwa/W8HIqasDenv9boW37NgBDA763QomaHDwnEMIV1tHB7Bggf3j\niDSn558PfPazzrQtiLz8st8tYJQ0NAQvA1tfH3DZZX63ggkabLE7hBiwyh24xPtDoeANgk5iN28/\n4x6RSGk9hFonleJ6D4z3sLA7hBDhcqq8MUwtI4Q9SJOuVIpzUTDew8LuEHaEPYgFMbxCL8c+7zjw\nh1AoeDs+jhyh2AKG8RIWdoewkwt6yxagtRX4+c8db07gufNO7ecSCRKZBx7wrj0MEY0GS9jz+WDm\nx2f8hbucQzQ3A+94h7UtVqkUvWfNGvfaFVSmT9e2lBIJStt7zTXetokJ3jbERAJoa/O7FUzQYGF3\nEKv7dHn9zT30cgGMjHDubr8IWo4G7muMH7CwO0gsRpWcjCgUaAIwOMjC7hYiUCuXo2I6+TwJSj7P\ng62fhELBcsUPDnKCGsZ7WNgdZMYMYPVq4JZb9F8XDpOb+FOfovcwziMS/qxdS5OnSIQmXpEIJUkR\npXIZbzl5Eti2ze9WeMOf/gR8//tcAIbxHhZ2B/nGN+j20UfNv+fWW91pC0OFdfr6Sh/P5YBZs7xv\nDwNceSV5TIJAby/wj/8IrFrld0uYoMHC7gJBy4ddqdTXqy+NJJOUBY3xnro6WhoJAskkL7Ux/sDC\n7gIs7JVBfT0FKCpJJFjY/ULrnNQiHBzL+AULuwucOKH93M6d3rUj6NTXU64AJe99r7qLnnGfIFns\nL7zAws74A2cjd5glS4C3vEX7+QcflO5zDml3WbgQOHas9PF0Gnj6ae/bwwTLYj90CFi+3O9WMEGE\nLXaHueEGfTev3FoRFeEYd5g6lW7PPNPfdjASQbLYczmaXDKM17CwO4xRZi25tSKvwc44j8jbz7m6\nK4cgWey8xs74BQu7w7CwVw5C2HlwrRyCZLGzsDN+wcLuMKkUcN99wP/3/6k//8tfSvevu86bNgUV\nUXFPWOzyJRIuzOEP2SwFlQWB/fu5nzH+wMFzDjM4SLcHD6o/H48Dr7wCzJ7NFrvbiEE1FqP1znBY\n+s3F+jvjLYsXAx0dfrfCGxobgUmT/G4FE0RY2B1GuNq13PHZLFmOPJN3HyHi0Wjp710oeN8eRppk\nBYFEgvPEM/7A8uIwItOZVpIaXnfzHrHWzviP1QqI1czwMAs74w8s7A4jAoPULPbBQeD4cRZ2r1Fb\n8uDqbv4QlHrs6TQVvGFhZ/yAhd1hbrwRuOQSdavkj38kN/yoUd63K6j8v/9HFfcE//ZvwMyZwA9+\n4FuTAk1QLPbnn6fvylstGT9gYXeYOXNIPNSskkwGuPpqdg17yTe/CVx7rfT/178OdHYCF13kX5uC\nTFAs9mQSOP98v1vBBBUWdheIRtWtkmxW2oLFMEEkKBY7x9IwfsLC7gJagxcLOxN0tCa9tUYqxRUE\nGf9gYXeBWIyqip1yChWEOXyYHs9m2Q3PBJtIBOjp8bsV7tPby5N4xj9Y2F1g8WK6PXgQ2LUL+NOf\n6H+22JmgU1cXjBwC2SwHzjH+wcLuAsqod7HWlsuxsDPBpqEhGIKXSgETJvjdCiaosLC7gHLftBB2\nttiZoBOJBCMqnoPnGD9hYfcAEUTDws4EnUgkGCllWdgZP2Fh9wCR5ezhh4NTspJh1AjKPvaDB/la\nZ/zDcWHftm0bli1bhjPPPBNLly7F9u3b33xu/fr1mDNnDubPn49HH33U6Y+uKDIZ4Pbb6X4sRrfh\nMPDOd/rXJobxm6BY7PX1wLRpfreCCSqOO4bvuOMOfP7zn8ell16KP/7xj7jjjjvw5JNPYvfu3di4\ncSN2796N7u5uXHLJJdi7dy/CNVrmLBoF8nm6LwaybJZzlDPBJhoNhrDncsEIEmQqE8dVtaWlBQMD\nAwCA/v5+tLa2AgA2bdqENWvWIBaLYebMmZg9eza2bdvm9MdXFIkE3Y6M0C2vuzFBJyjBcxxPw/iJ\n413vv//7v3HBBRfgtttuQz6fx1//+lcAwKFDh3Duuee++bq2tjZ0d3erHmPdunVv3l++fDmWL1/u\ndDM9QQj7Sy8BK1YA+/fzLJ4JNpGIdF3UMizsjB5btmzBli1bXDu+ra63YsUKHDlypOTxu+66C3ff\nfTfuvvtuXHXVVXjggQdwww03YPPmzarHCanV00SxsFczV18N3HcfcMcdwIsvAseOAS0tfreKYfwj\nHifPVaGgXk63VuAsk4weSoP1zjvvdPT4toRdS6gB4AMf+AAee+wxAMA111yDD3/4wwCA1tZWHDx4\n8M3XdXV1vemmr1Uuvxx48kngbW+jiHiAhZ0JNrGYFEBXyxZtrX8/prJxfI199uzZ+POf/wwAeOKJ\nJzB37lwAwKpVq3D//fcjnU6js7MTHR0dWLZsmdMfX3GIWbtYZ2eYoBOELW/simf8xPGud++99+Jj\nH/sYUqkUGhsbce+99wIA2tvbsXr1arS3tyMajWLDhg2arvhaQgh7MulvOximUghCAB0LO+MnoUKh\nskoyhEIhVFiTyuKvfwXOP1/6v4a+GsPYYswY4MABuhUcOAA8/TTFpdTCltCzzwY+8xngqqv8bglT\nDTite7W5ibyCYCFnmGLUXPGXXw584APA44/70yan2bEDqPEQIqaCYWF3Gbmw33qrf+1gmEpBTdh3\n7aLbWlmdGzUKmDPH71YwQYWFnWEYT9ELnhPZGquddJpzVjD+wcLuMuyKZ5hi9ILnaiWojrNMMn7C\nwu4hPINnGBI9rexztZBHPpslzwNHxTN+wV3PZc4+G1i0CBg7Fvj4x/1uDcP4T329dknTWhD2N0pl\nMIxvsLC7TEMDpZNlGIaYMoWsdjVqwRWfzQKTJvndCibIsCueYRhPaWjQTthUCxZ7Lsd54hl/YWFn\nGMZT6uu1LXYWdoYpHxZ2hmE8JRoFjh5Vf+4nPyl97L77gMWLaV/4I4+42zYnYGFn/IbX2BmG8ZRQ\nqHgbqNxKf/LJ0tffcIN0f+XKyt9CypXdGL9hi51hGE+ZNKlYzNPp2trzzbXYGb9hYWcYxlNEPXZB\nrWVpY1c84zcs7AzDeIqasNfX147VzsLO+A0LO8MwnhKNFgv74CBFyU+fbu79x4650y6nYGFn/IaF\nnWEYT1Hmih8aAkaPpuh3NZRu+hdecK9tTpBM0l59hvELFnaGYTxF6YpPJIBp04AFC4Bx40pf395e\n/H+lZ6cbGQHicb9bwQQZFnaGYTxFKezCwtVKXKMU8koX9uFhoKnJ71YwQYaFnWEYT1ET9sZGbWFX\nZqOrdGFni53xGxZ2hmE8JRIBenul/3t6gFhMCqrbvFl6rlAA9uwpfv8993jTTrscOcLCzvgLCzvD\nMJ6Sz6tHjYdCwKpVwLe+JT3W10fi/utfAz/9KT22ZYsnzSyLSs+Ox9Q2LOwMw3jKhAml+9gnT6b7\nH/wgCbwglaIyr1dfDbz//Z420zby78MwfsDCzjCMpyi3u8lTymoF1impZIu41jLpMdUHCzvDMJ6i\nFO9UShJCtefUMtJVcgCd/PswjB+wsDMM4ylK8T56lILnxHN79gD79tH/jz+uLuI9Pe630y6HD9dO\nelymOmFhZxjGU5QpZVOpYmHv7ASuuor+/9//Bd75Tum1jz1Gt+vXe9NWO2Szlb1UwNQ+LOwMw3iK\nco29txeYM0d6DpC2uIXDFFAnuPhiuu3vd72ZtikUgJYWv1vBBBkWdoZhPEVtHV0EyEWjxa/VWmMP\nV/DIxfXYGb+p4MuDYZhaRCns8mpo4la4srUizJXZ6CoJru7G+A0LO8MwnhKNAt3d0v+Dg6XCnsmQ\n5btvn7rF3tHhfjvtwsLO+A0LO8MwnjJhAvD889L/mQxZ5kCxiG/fTuI+ZUrpMbZtc7eN5cDCzvgN\nCzvDMJ4ybx4wdqz0fzQqlWuVJ6MZGqJguWrLu57NlsYKMIyXsLAzDOMpkQjlixfILVy5sI+MVGf5\nU7bYGb9hYWcYxlPCYe3gObkrvlrLn7KwM37Dws4wjKfoRcUrhb3SLfZEojQzXjLJws74Cws7wzCe\nEg5ru+LlQj4yAjQ2lr5/3jx322eF8eOBf/7n4se2bKHHGcYvWNgZhvEUPYu9oQE4cQIYM4asYTVh\n//a3gYsu8qatRiSTlAJXTkMDcPrp/rSHYQAWdoZhPEYveA6QcslrlWyNRiurupvS7Z7JSLnvGcYP\nWNgZhvEUveA5QBJuPWGvpMxz8rYXCtQ23u7G+AkLO8MwnmJksQtXvZawK4vI+I287ZkMiXoo5F97\nGIaFnWEYT5Fb7IUCsHNn8Vq6sHbvuw8YNUr9GM89Zy6t7I4dJLIzZhQ/fuIEcMYZwCc+Yb39giee\noNtHH5UeYzc8Uwmww4hhGE+RW+yZDN3KI93DYeC11yjz3OzZpe8XWep27JDKvWrxpz/R7YEDxY8f\nOUITijFjrLdf8NOflj7Gws5UAizsDMN4itxiTyTIKle6rqdP136/2OtuZq+43OUvJ5Gg23LW6lOp\n0sdY2JlKgF3xDMN4itxi19rSpocVYdcS7kSCJhMs7EwtwsLOMIynKC12q8Iu6rOXk90tkQCam1nY\nmdqEhZ1hGE8JhylorlAA+vutv19Y7L/7nbnPUuPFF+k427dL6/xW6emR7h85Qrcs7EwlwMLOMIyn\nhEL0l8+T1au2pU0PYeF/73vGr506VbpfKEj3jxyRIuW7u619vuCCC6Tj33MP3bKwM5WAbWF/4IEH\ncPrppyMSieCFF14oem79+vWYM2cO5s+fj0dle0Gef/55LFq0CHPmzMG//uu/2m81wzBVjVhnTyaB\nKVOsvdfqHvHRo+lWvvc9nQYWLaL7Wla9Gf7936XjASzsTGVgu0svWrQIDz74IN761rcWPb57925s\n3LgRu3fvxiOPPIKbb74ZhTemyjfddBP+93//Fx0dHejo6MAjjzxSXusZhqlKxDq7VhIapygUSrfW\nAeQpEPvltSLnjUinpfV+4Q1gYWcqAdvCPn/+fMydO7fk8U2bNmHNmjWIxWKYOXMmZs+eja1bt+Lw\n4cMYHBzEsmXLAADXX389HnroIfstZximapFb7G4Ku1y05cKeTkvBd3YD6OTCLv8MFnbGbxzfx37o\n0CGce+65b/7f1taG7u5uxGIxtLW1vfl4a2srujUWt9atW/fm/eXLl2P58uVON5NhGB8RdcxPniwV\nRyukUsU13JX09UnCLXfFnzwpLQGUI+zKz04m7R2LCRZbtmzBli1bXDu+rrCvWLECR0S4p4wvfOEL\nuOKKK1xrlFzYGYapPUIhYO9eSu0qD2ozy3veA/z615T9TVkPXc7LLwPnnUfpX+UW+8mTwPnn0327\nwj48LHkbpk2j26GhyipQw1QmSoP1zjvvdPT4usK+efNmywdsbW3FwYMH3/y/q6sLbW1taG1tb3ep\nWwAAEHZJREFURVdXV9Hjra2tlo/PMEz1s2wZWdD5vH6WOS1+9SvgYx+TMshpEQoB110HvPJKsbDX\n1QFz5wLt7faFOJkE4nGaWDQ10WPhMDB5sr3jMYxTOLLdrSCbcq9atQr3338/0uk0Ojs70dHRgWXL\nlmHq1KkYPXo0tm7dikKhgJ/85Ce48sornfh4hmGqDFGa1ciVroeoAqeHOH4sVizsuRyJsJljaCHi\nA2Ixyc2fzXLJVsZ/bAv7gw8+iOnTp+PZZ5/Fu971LqxcuRIA0N7ejtWrV6O9vR0rV67Ehg0bEHpj\nf8qGDRvw4Q9/GHPmzMHs2bPxzne+05lvwTBMVWFUc90M5Qh7Pk/vd0LYo1Hp2CzsTCVguwteddVV\nuOqqq1SfW7t2LdauXVvy+JIlS7Br1y67H8kwTI0gt9jtVlgrV9jLtdhFch222JlKgzPPMQzjOULY\nv/hFYGDA3jHkovyNb9B6+urVxa/ZsoWEfWgI+OY3pceFsA8OUhCeFv39wOWXqz+3Y4e0jLBjB92y\nsDOVAAs7wzCeI4QdAGQxtZaQC/utt9LtY48VvyaRAJYuBT77WarxLhBr7NddBxw9qv0ZXV3AH/6g\n/lw6TfXg581ji52pLFjYGYbxHLmwOxk8p/w/EqGI9VNPLU5WI9bYZ8zQLwKjlb42l6Pn4nFg/HgW\ndqayYGFnGMZz5MLuZPCcPAmNuB+N0uRBXmZVuOKVj5tleJgmDKEQbZ0TueJZ2JlKgIWdYRjPkUeS\nu2WxyyPunRb2oSGq5w6QsItjsLAzlQB3QYZhPCcUkta87QphJALs20dpYwWpFDAyQi5ypbAfPy69\nTqyx19fTGvqmTcDs2cDpp5e2EwAOHaJAupMnJde7yDVfVycde98+6XGG8Qu22BmG8ZymJkr3CgA3\n3WTvGOPG0eTg058ufvw736FbubBHo8VZ6sQaezRK96+8Eli4sPQzxLr87beT6J93HnDFFeSKl3sa\nhKdg/36pTCzD+AULO8MwnrN4MQnvtGnArFn2jrFwIVnLr79e/Liw4OXC3tRUvJYvXPETJuh/hlin\nP3FCeqy7m9bURRGZpiapkE0+DyxZYu/7MIxTsLAzDOM5kQi5zO2urwNSAJ48YE5OIlFssctfJ1zx\nRpXlhCUuL1QTixWnwg2HpedTqfKq1TGME7CwMwzjOU4IeySiL+xyi12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| |
| } | |
| ], | |
| "prompt_number": 14 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "cos_theta = numpy.hstack((0.0, data['deltax'][1:] / numpy.diff(data['Distance'].values)))\n", | |
| "data['Fr'] = -rolling_resistance_coefficient * mass * g * cos_theta\n", | |
| "data.plot(x='Time', y='Fr', figsize=(8, 6))" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 15, | |
| "text": [ | |
| "<matplotlib.axes.AxesSubplot at 0x6d595d0>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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PPRX+NN2cR8/lNLBuuMHdeJ2wC1ivj16NY30k2IPjZN5alj/PBcgfp9fyuMUe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\nuf3/v/RSpi1IKid1sJQPfSj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| |
| } | |
| ], | |
| "prompt_number": 15 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "sin_theta = numpy.hstack((0.0, numpy.diff(data['Alt'].values) / numpy.diff(data['Distance'].values)))\n", | |
| "data['Fg'] = -mass * g * sin_theta\n", | |
| "data.plot(x='Time', y='Fg', figsize=(8, 6))" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 16, | |
| "text": [ | |
| "<matplotlib.axes.AxesSubplot at 0x7298150>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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kVACt1DeM/2+uJq1munZFKi6CgYaGBowfPx5OpxMPyWvqaJSUpH0d+fnqupyV\nOi1yOMSDVN6e/6c/Db6sVMYv/Qe8FQFtNuUe6dQ2ETt3Tn0NaKky1dGjYtMwKbDQo+23mU4oNU/6\n0g1UXpM8Ev4Xm0AtXX73O23fES2R7rNvf1vfdADBR70M9+bjX5nQv3dEvcaHkBcRSQH1b3/rnRbu\n7+vfY6Z0QzRDLo6c1nO9pUX8HyzIkQcDgY4NpeHA/dfJnAGTOHv2LO6++240NDSgpaUFv//97/FB\nJHmaAcgr/QSidgcrXWiCLR9O73PSU4g8DpI/ufq3bJCoDXbk7d7VamgQ/0tZr/La7mfOeD8Ph5lO\nqLIydfPp0Sbf//j58EOxsqZ8jIjkZO3fo0RNbWkloXrmjKVgN7zLLhs4LVBumBTs/vjHvtN//3vl\ndcv7kZAEy6WQ5ybKO6+St5iQhHtOKF3TzFYEF2q7du70tmp58UXfHFXAe94F2y55MPCDHwz8XOkB\n6KGHWGfAlHbt2oWMjAykp6cjOTkZt9xyC+rr63VZd6hgQC2pAlw4/u3ffN9fdx0webJYdPDAA94n\nQaVWD/LKW1//ulgZ0b+sUe32RdKvtn+PaI884n39yivAjTeGv854PKH0GNTJ/ybW1wd89JFvoHHB\nBdq/R8mUKZEvG2hMjkjose+l8vxAAtW5CdRRkZR1/NFHvtMffHDgvO+8I940brll4GdDhohFg4HI\nW5TIO9+R72MpwAj3d/F/ADC6mEB6gvcXaruWLPH+rqWlA1s7ScGalu1SurYeOuT73irBgA4Z5dHl\ndrsxZswYz3uHw4Hm5uYB8z344GrZu7zzf8EFi/YB8Ym7uFhsExyM0lOwUgXFQKQn6+9+11tjv61t\nYBvniorAy//612KdAqm50pAh2roW/uADYMIE8fXnnwMBfvIBpJwLKYu9okK8GEm1c0eMEKf39Ijv\nBw/2PdGikZV56JBYkeqf/xQrSQqCt1MRqdtRKX3SCR3rE9u/3klrqzdbWvpN/XOSKirEp92+PrFz\npM8+E2940sV/8GBx+6TtlWzd6m1OJ/0OwepHfPop8OUve9/754Q89xzgdHp/R0BMg90OjB4t5nB0\ndvr+toECG6XcLSUVFQO7hL79dvHYki7y/vvVn7zYTfKLX3iPe3+//KXv+2nTxP9KAywtXCgG7VJz\nxN5e8XfyHwL7oYfENMpzE+bPB/7zPwPfiD78EBg/fuD03bsH5nZI9ZHefVcM0uXHmnQOSPxr0Ydr\n//6BAdAHlgFkAAAgAElEQVTcud7eWeXne28vUF8vHtdSBWbpWLXZvEPJS8f/Y48BmZnib3nunPcY\nfughse7SoEHeYlNAvJZIvUxK/EuYf/MbMRD2Dwp27PDN7VEa1v6JJ7y/3+nT4vuCAuXjR6umpiY0\nRbPLUsHk6urqhDvvvNPzvqamRrj77rt95gEg/Pzn0qGk75+4/sj+du6MTppi8Tdvnvf3fe+92Hyn\n06n/8VNSYvxvGcnfhAnGpwEQfz+5X/wiOt9z7bXGb2u8/BUXBz7Wta63r0/buWb072KWv1jR+/Zt\n+mICu92ONtnjSFtbGxwOx4D5fvaz8HaZnNJnUl/oSvPJ5/3qVwd+NnNm6O9XSpO/QJ+Hs6wgeMuG\n5U+MSmnZtMk7fdKkwGm56y5136/Gj38c+Sh+wfg/hYVDzXEklZlHeumQ5yr9z/94p0eavRpqe9Ru\nr9Tm3n/f//Sn3nlCjWMhdecbyKhRvt+3Y0dk566UbjXNW+XrkPeXobTe+npxuv+IhvJ5jxxRTmeo\n0sxAvaAGq+Eu/dXVBV8voG4e//XrWS9Fa6duRjh4UCxulTidvp+rud4uXBidtMWC6YOB6dOnw+Vy\n4dChQ+jr68PGjRtRVFQUk+8OJ8vsttuilw69SFl0gbYrkk5n/G8UWkSrglykFaeUimP85eQAaWmR\nfQfgm75Qx9uFF0b+PaH4b4OaoENLsY7e23LhheHVAfra18T/8iaOt97qO8/cueL/0lLl9chKMAeY\nO1espBboBv/cc2JdH39DhyqvLxxq+i6JpmhWeI2WwYN9f7dIWptFu3OwaDJ90pOSkvDUU09h1qxZ\nyMrKwoIFCzAhWoUyGsybp30d3/xm8M+1brZSRZiODvV9GEjlpu+/D9x9t7b0yEXr4hHpk7Tazmyu\nvlpbs8pwggGpDop/5c3Tp4G8vMjTAAysCyD9bsHSlJERfJ3Blg2QuRcR+TDI8twsNQTBtxMf/x4K\npfQH6kTniScCVyr0X37kyMAtGZRu+hdeqE9O28iR2tehhdZOsgL18hhtgwf7Hk+RVDCPdudg0WT6\nYAAAbrzxRnz00Uf4+OOP8cADDxidHB/SDTqSiHDcON/3Usc1d9/tzY7q7/dGq/Ia+/7Wrg39fUrB\nwOWXq784SyPaTZwY2YGv1EnTkCHhr0uNSHMG9GppEko4wYCUposv9p2elKT9STCSpyAtFz6l4W3V\nGD3a+/p//9f7OtgxLF9GyVe+4n1dUBB83nvuUXfOKZFXyoyGUOk3u8JCfdYTzjE6eDCQm+v7Pprf\nZzZxEQyYmZS9GsmBs2SJ73ubTXw6kWrfSuuVLtSBahBLpJrNwUgj50WD/0kQ6GkqN1esvRvot5Kf\nhHqK9CkrUJvvaJCnT+2FpLxc7JHwrbe8LSL0bomh9nebOlX5s2Db4x/QhMO/Yx01aQnUF4C/L33J\n+1opOAp2DgbjX9QQKCfn+98PvOzll4f3Xbfeao6bUrjplopuAHWdUqnJvQsnx3HwYN/jPlYPBGbB\nYCAINSfUz38u/g8327OzM3A2+7p1YlOZQORPLv6+/W1v8zIlKSnasiCDdf7o37wqUGc8UpMyqSxW\n7pprIk9XMJHmDNjt+qZDSTg5A5LBg8V+KnJzvctcfbX+aVOTpvvui873BhNJLpzaeh333y/+VwoG\nIu0AVamXUfkDgTRWiL9wb0qBzi8jZGWFnkdeHPDII95ePdVUC1MzyFs4OV5f/rL2YMAMQVikGAxo\n9PWviwfQpZeGt1xamv6R59ChYjoCDZOr1dixwes0fOUrYl8HkosuGjiP9OQjL5eTRCsK16P8NZoi\nCQYCWbxYe1oiEey4j9aFMZxATSpuU5sWKSj1H15YCibCGRhJTqnTKHmOjlKlynDPDbNUYgtV9Ddo\nELBqlff9xInh5V6qCYAvvnhgaxAl/n1SxLrozGgmOWzMb9kyo1OgnrxXvOXL9Vnnxx+Hzjr//vfF\naH3RIvG9f9a/1OLie98buGy0LmBm64bVX7BignD2nZYKY3v3Kn8W6uL2b/+mnB2stGyooYRDmT5d\n2/LBSEUFxcW+0599Vvwf6XGqdJObONH7Wl4hU37cBmp1EIxZbkj+nf74s9m8xSdSRcv//V/vAG6h\nBHqo8PfMM8All6hbH+B7PkYyeJdZArFIxHHSY8ssJ9idd4aeR+rpD4he9ruS1lbvyGnf+Y765aL1\n+5o9Z0BevOT/G4Rzg1dTQU5JoHJVtb+bzeZb1quGlrTKBaojIG/qp1TcFsz114v//S/qUq5YpBd7\n+b6UZ58HCowB32PhP/8zvO8KN41638Ck3iQDtaLw/96kJDFQkrL8R44cmP2vlBOkVH/jqae8lTvD\nLRqVB2HBRqZUyiEyy30iEgwGgpDvWLNEfOH2hR/JuAl6+fGPvS0i/AcaiRWz5wzMnOnNKva/kEgt\nN6ItUF2UcC6gUr0ZtfS6YAYa1nnNGvH/uXPBhxdXYrOJrXz8K8AOHSr+TnoEMvIgJVBF20BpCke4\nzXTHjg1v/lDUple6ph48GHwI8GHDAk9X2s7/+A+xEzrpOyINBoJth9I6GQzEoXAHPjTLTpYOwlDD\nHpvJ4MH6PQ2GK5JWClp6LYyEVCbsf4xFs5MhOS01+wF1LVnk9AisP/kk8BPjokXieAo2W+Tf8+GH\ngeu8HDyofGMKhzznTiqnDnbDCufaM3nywEHQQtH72ub/uyv1wSIVnYwcGfx3VXoAUqpLIY1TIL0O\nJxjwL7ZTasnOYCCBBGsiFOgiYpadfN114v9QN9cTJyIbRjjRRHJDXbBA/3SoEewYk49SqcS/3wot\n9CheUdqea6/Vvm6ldvo2m7qnbSP5D4+s5PRpsbVBOMHW17+ufJNUqoOi97UtM9P7WuraPNDx9PTT\nwdcj1Q158cXwvl++Pf7NBUORByXBAgmzFz9GwrLBgOS//3vgtECjVBndo5dEupCGqmE8bBgwa1b0\n06OHP/3J6BT4CvdJVy/BLspqmsb94hf6pUWi5UYRaNmJE/XtxjoeBevCWO6CC4DKSv065FLq9VCq\n8KsXtcdMqK6XpRyBSDtoev758CsByisb2mzKxYxKwYBZipMjYfohjKMpOXngYETAwJPv4MHYtTtX\nS6rolAjCbZYZTfIe7WIt2EVUzUXm5pvF+gfnzik3ZVMrWjkDVuvIJRCj+u0PtE8ffVS/Fkd601rf\nRwpyIj2Wx4wJf1mz5CBHIo7jGO36+gI32/Hvl/0rX9F+cdWLlDb/EbXimVlOoJ//XF1zpWgJ9DtI\n5apqb6KDB+t7s1G7b+R9TAQjNdGj6Ai2v268UbzeyTs30qPIRq0ZM3zfhzq2IgkGAuWihLse6Vo/\nZEjoYgL/67BZrmWRsHQwEIoZy4WSkoAXXjA6FdqZpdhF0tvrrYFslEDtoaXxJMJtm//Pf4rLhhr8\n6rPPwluvEqWudP0FyomzilOngOPHjfv+a64Rh032r8AYK2qLRySRnI+Bej0Mt6jgyBGxq28gdCDh\nf49gMJBgzBgESOQddcQz+U0hVuVszz+vPMZ8OB2TRMOBA8Ds2QOnSxejcPpsAMQ23oMGhS6fVyq3\nVTNqYSj+55HU1a9VXXih743YKEZ1oCYdD2o7jZoxw/cYki+n1N9KoN4GA7UoChYgpKZ6lwmVM8Bg\nIMGZORhIFHV13texKoJZtEjs8zzc2smx4HQGvpD8+79rW6+Rx7L/9pit3o1VyYuR9L55BWumqrVZ\npjytUk+R/j07qg221OZMhluBkMFAgmEwEH3y3IBY19737/UtUOsRs/jv/9ZWkSpUMYESngPxSc3N\nKJr7NtjgQU8+KeaARfL9X/2q77Esbaf/uiZPVrc+tWkI1QV7Ip0nDAYCkLqajOcoz+yGDPH2aR/t\n3zlQXwPSkLbbtpm/qZuW36ekRLxgBRvNMloSKQs1Xsh/czWVNfXeJ8HWN3SomAMWyQ30H/8AHn7Y\n+14q1vMPlNVuj9o0FBYGXz6RjnEGAwEY1fTHSgYNGniCR0ug+gBSJz6BxpVPRFLzTbWtJSJ94hky\nxNtrWyJdKOORUrl6sMGxYkHL0/T774tdF0s9sErdT0crDdJ8/rmJDAaIdHTRRcAPfxj971m7Fli9\n2neawyGeyJEMUxrP5L3DqRHOxW3fPrG2vNKF1n8kQDJGNLO2o30znDhRDOCl7qLvu8/7mXw46FDC\n/Q0eeyzw8t/8pu/Ik/EcDFjsUkhWtGSJ0Skwp+pqfdcnjSL4/e8DFRUDP1fTpTJFXzznDAQTTodW\nauvhSGn17wUxIwP417+8wzRLvyODgQQVzzuWKJDly8U6EmPGqOtwJpJzwH9412ee0d4qgtQx+poV\nTgVGPdN65ZXhza82IBk+PHAlwoaGwDkRRv/+WrCYgMhCLrsMWLgwdCCQSLWkydeIEd4eI2NZgTCa\nwm01I1UqDjQ6pdwFFwA7dw6cfvHFA5s1AsDdd4eXDjNhMEBEA+gZDMTz01Iistm8PUaGGiwoknUb\nIZzvXbzYOwiS3kGv2XpWDQeDASJSpMfFncGAcYL1rHnsmDjuip6M6ucgnGMsJyd6OV/xfKwzGCCi\nAfS4WLKoIfb8b0affqo8bzSeYo0IBr7//fCGYb7tNuWmgVrF8zHPCoRBxHOUR0Q0ZIhYy14a8MoM\n9K5AqHbETInNFrvxUOIJfxIiigr/5lhkjHh+Wo0WqelrolSg1AODASJSpOXilghtryk8Ro+NoIbN\nBqSkGJsGM2IwQEQD6HHBZhBgDrG8+XKfxy8GA0Q0AJsWxiejf2uz5gzIh8+OZn0Bo39/LRgMEJEi\nNi2kcISzr2N5XHz0kff1BReI/4uLxWaGJGIwQEQDsJiAImHWfX7xxQOn1dWJPXLqyazbrwaDASJS\nxJyB+Gd0hT1/ZksPidjPABENcM012tdhs4n94H/ta9rXReoYGXg9+iiQnR16PjMFA2ZKi9EYDATB\nJxqyqquvFv9rPQeOH9eeFooPy5ermy8aoxaqMW6cb90BeVqIxQREFAQvlhQtsT62An2fNK2rK7Zp\nMSMGA0RECSIegjczpTHpfN74qFHGpsMMGAwQkSI9eiAkMoPqauCPf/Sdtnatvt8Rz8c86wwQESWI\nQDcjNZX6Yik9HThwIPbfm5s7cNqwYbFPh1kxZyCIeI7yiPTAcyD+7dwp/pmF9DSeiMdWPG9TxMHA\nj370I0yYMAFTpkzBTTfdhBMnTng+27BhA5xOJ8aPH4/GxkbP9D179iA7OxtOpxPLli3zTD99+jQW\nLFgAp9OJ3NxcHD582PNZdXU1MjMzkZmZiRdeeCHS5BJRjMXzhTGRfOlL4p9ZJDE/2pQiDgYKCwux\nf/9+/P3vf0dmZiY2bNgAAGhpacHGjRvR0tKChoYGLF26FML5GiNLlixBVVUVXC4XXC4XGhoaAABV\nVVVISUmBy+XCihUrsHLlSgBAd3c31q5di127dmHXrl1Ys2YNenp6tG4zERGRqSozGi3iYKCgoACD\nzo/4MHPmTLS3twMA6uvrsXDhQiQnJyM9PR0ZGRlobm5GZ2cnent7kXO+M+iysjJs2rQJALB582aU\nl5cDAIqLi7F9+3YAwCuvvILCwkKMGDECI0aMQEFBgSeAIKLoYwVCImvQJcPmueeew8KFCwEAHR0d\nyJXV1HA4HHC73UhOTobD4fBMt9vtcLvdAAC3240xY8aICUpKwvDhw3Hs2DF0dHT4LCOtK5DVq1d7\nXufl5SEvL0+PTSOiCI0caXQKrCceAjA+jUemqakJTU1NUVt/0GCgoKAAR48eHTB9/fr1mDt3LgBg\n3bp1uOCCC3DrrbdGJ4UqyYMBvcTDiUUUTZGeA11d+g8CQ4nFDNdXvQOTaG6T/0PumjVrdF1/0GBg\n27ZtQRf+zW9+gy1btniy9QHxib+trc3zvr29HQ6HA3a73VOUIJ8uLXPkyBGMHj0a/f39OHHiBFJS\nUmC3230ioba2Nlx//fVhbaAWjGDJ6i69NLLl2IkLUXyJuM5AQ0MDHn74YdTX1+PCCy/0TC8qKkJt\nbS36+vrQ2toKl8uFnJwcpKWlYdiwYWhuboYgCKipqcG8efM8y1RXVwMA6urqkJ+fD0CspNjY2Iie\nnh4cP34c27Ztw6xZs7RsLxGpdOQIYHCGHyUgM+QISMyUFqNFXGfgnnvuQV9fHwoKCgAAV199NSor\nK5GVlYWSkhJkZWUhKSkJlZWVsJ3/xSsrK7Fo0SKcOnUKc+bMwezZswEAixcvRmlpKZxOJ1JSUlBb\nWwsAGDlyJB588EHMmDEDALBq1SqMGDFC0wYTkTrnq/FQHOHNLTzxVEwQbTZBiP/McJvNBr03w2YD\nrrsOeP11XVdLRBQVNps4cuCjjxqdkuB27wZmzABeew0wup53WxtwxRXagwIpCPj889j16aD3fY89\nEAYRz1EeEREFF/+PwvphMEBERKSDeH6AZDBARERkcQwGiIgSRDw8mZopa17PtNx/P3DRRfqtL9YY\nDAQRDycWEVE8SrTr6/kW8XGLwQAREZHFMRggIqKYM0NxgZmGdjYagwEiIrKkyy4DTp40OhXmwGCA\niIhixgw5AnIXX6zPeuK9DgSDgSDifecSEZkVr6/mwmCAiIjI4hgMEBERWRyDASKiBMGsd+PE+2/P\nYICIiMjiGAwQEVHMxfuTdKJhMEBERGRxDAaIiIg0ivecDgYDREQJIh5uSGbrdEgv8b5dDAaCiIcT\ni4goHvH6ai4MBoiIiCyOwQAREZFG8Z7TwWCAiChBxPsNiYzDYICIiGIm3ivaJSoGA0EwyiYiig5e\nX82FwQAREZFG8R7cMBggIiKyOAYDREQJIp6eTll3wFwYDBARUcwwCDAnBgNBxFOUTUQUTzdaXl/N\nhcFAEPF0YhERkXHiPbhhMEBElCDi4YYUD2m0IgYDREREFsdggIiIYkYqfk2kHIJHHwVmzjQ6Fdok\nGZ0AM0ukg5WIiKJj+XKjU6AdcwaIiIgsjsEAEVGCYG4mRYrBABERxQybbJsTg4EgGGUTEUUHr6/m\nwmCAiIjI4jQHA4888ggGDRqE7u5uz7QNGzbA6XRi/PjxaGxs9Ezfs2cPsrOz4XQ6sWzZMs/006dP\nY8GCBXA6ncjNzcXhw4c9n1VXVyMzMxOZmZl44YUXtCaXiIiI/GgKBtra2rBt2zZceeWVnmktLS3Y\nuHEjWlpa0NDQgKVLl0I4X0i0ZMkSVFVVweVyweVyoaGhAQBQVVWFlJQUuFwurFixAitXrgQAdHd3\nY+3atdi1axd27dqFNWvWoKenR0uSiYgSFrPeKVKagoH77rsPv/zlL32m1dfXY+HChUhOTkZ6ejoy\nMjLQ3NyMzs5O9Pb2IicnBwBQVlaGTZs2AQA2b96M8vJyAEBxcTG2b98OAHjllVdQWFiIESNGYMSI\nESgoKPAEEEREFH9YgdCcIu50qL6+Hg6HA5MnT/aZ3tHRgdzcXM97h8MBt9uN5ORkOBwOz3S73Q63\n2w0AcLvdGDNmjJigpCQMHz4cx44dQ0dHh88y0roCWb16ted1Xl4e8vLyIt00D0bZRETRwetreJqa\nmtDU1BS19QcNBgoKCnD06NEB09etW4cNGzb41AcQDA735MEAERFRIvF/yF2zZo2u6w8aDGzbti3g\n9Pfffx+tra2YMmUKAKC9vR1XXXUVmpubYbfb0dbW5pm3vb0dDocDdrsd7e3tA6YDYi7BkSNHMHr0\naPT39+PEiRNISUmB3W73iYTa2tpw/fXXR7yxRESJjE/bFKmI6gxMmjQJXV1daG1tRWtrKxwOB/bu\n3YvU1FQUFRWhtrYWfX19aG1thcvlQk5ODtLS0jBs2DA0NzdDEATU1NRg3rx5AICioiJUV1cDAOrq\n6pCfnw8AKCwsRGNjI3p6enD8+HFs27YNs2bN0mnTiYjIKIMHG50CktNloCKbLBzNyspCSUkJsrKy\nkJSUhMrKSs/nlZWVWLRoEU6dOoU5c+Zg9uzZAIDFixejtLQUTqcTKSkpqK2tBQCMHDkSDz74IGbM\nmAEAWLVqFUaMGKFHkkNavx645pqYfBURkWVMmgTY7cD5yzqZhE0wurBfBzabzfA6C0RERrLZgPvv\nBzZsMDolFAt63/c4hDERUQJ44AGgrMzoVFC8Ys4AERFRnNH7vsexCYiIiCyOwQAREZHFMRggIiKy\nOAYDREREFsdggIiIyOIYDBAREVkcgwEiIiKLYzBARERkcQwGiIiILI7BABERkcUxGCAiIrI4BgNE\nREQWx2CAiIjI4hgMEBERWRyDASIiIotjMEBERGRxDAaIiIgsjsEAERGRxTEYICIisjgGA0RERBbH\nYICIiMjiGAwQERFZHIMBIiIii2MwQEREZHEMBoiIiCyOwQAREZHFMRggIiKyOAYDREREFsdggIiI\nyOIYDBAREVkcgwEiIiKLYzBARERkcQwGiIiILI7BABERkcUxGCAiIrI4BgNEREQWpykYePLJJzFh\nwgRMmjQJK1eu9EzfsGEDnE4nxo8fj8bGRs/0PXv2IDs7G06nE8uWLfNMP336NBYsWACn04nc3Fwc\nPnzY81l1dTUyMzORmZmJF154QUtyyYSampqMTgJpwP0Xv7jvSC7iYOC1117D5s2b8e677+L999/H\nD3/4QwBAS0sLNm7ciJaWFjQ0NGDp0qUQBAEAsGTJElRVVcHlcsHlcqGhoQEAUFVVhZSUFLhcLqxY\nscITWHR3d2Pt2rXYtWsXdu3ahTVr1qCnp0frNpOJ8IIU37j/4hf3HclFHAz86le/wgMPPIDk5GQA\nwGWXXQYAqK+vx8KFC5GcnIz09HRkZGSgubkZnZ2d6O3tRU5ODgCgrKwMmzZtAgBs3rwZ5eXlAIDi\n4mJs374dAPDKK6+gsLAQI0aMwIgRI1BQUOAJIIiIiEgfEQcDLpcLf/vb35Cbm4u8vDzs3r0bANDR\n0QGHw+GZz+FwwO12D5hut9vhdrsBAG63G2PGjAEAJCUlYfjw4Th27JjiuoiIiEg/ScE+LCgowNGj\nRwdMX7duHfr7+3H8+HHs3LkTb7/9NkpKSnDw4MGoJTQUm81m2HeTNmvWrDE6CaQB91/84r4jSdBg\nYNu2bYqf/epXv8JNN90EAJgxYwYGDRqETz/9FHa7HW1tbZ752tvb4XA4YLfb0d7ePmA6IOYSHDly\nBKNHj0Z/fz9OnDiBlJQU2O12n3KttrY2XH/99QPSItVJICIiovBFXEwwf/58/PWvfwUAHDhwAH19\nffjyl7+MoqIi1NbWoq+vD62trXC5XMjJyUFaWhqGDRuG5uZmCIKAmpoazJs3DwBQVFSE6upqAEBd\nXR3y8/MBAIWFhWhsbERPTw+OHz+Obdu2YdasWVq3mYiIiGSC5gwEc8cdd+COO+5AdnY2LrjgAk+z\nv6ysLJSUlCArKwtJSUmorKz0ZOFXVlZi0aJFOHXqFObMmYPZs2cDABYvXozS0lI4nU6kpKSgtrYW\nADBy5Eg8+OCDmDFjBgBg1apVGDFihKYNJiIiIj9CnNu6daswbtw4ISMjQ6ioqDA6ORTAlVdeKWRn\nZwtTp04VZsyYIQiCIBw7dky44YYbBKfTKRQUFAjHjx/3zL9+/XohIyNDGDdunPDKK68YlWxLuv32\n24VRo0YJkyZN8kyLZF/t3r1bmDRpkpCRkSHce++9Md0GKwu0/1atWiXY7XZh6tSpwtSpU4UtW7Z4\nPuP+M48jR44IeXl5QlZWljBx4kTh8ccfFwQhdudfXAcD/f39wtixY4XW1lahr69PmDJlitDS0mJ0\nsshPenq6cOzYMZ9pP/rRj4SHHnpIEARBqKioEFauXCkIgiDs379fmDJlitDX1ye0trYKY8eOFc6e\nPRvzNFvV3/72N2Hv3r0+N5Nw9tW5c+cEQRCEGTNmCM3NzYIgCMKNN94obN26NcZbYk2B9t/q1auF\nRx55ZMC83H/m0tnZKezbt08QBEHo7e0VMjMzhZaWlpidf3HdHfGuXbuQkZGB9PR0JCcn45ZbbkF9\nfb3RyaIABL9KnvK+JcrLyz19TgTqp2LXrl0xT69VfeMb38Cll17qMy2cfRWqTxGKrkD7DwhcyZr7\nz1zS0tIwdepUAMAll1yCCRMmwO12x+z8i+tgQN4/AcB+CMzKZrPhhhtuwPTp0/Hss88CALq6upCa\nmgoASE1NRVdXFwDlfirIOOHuq2B9ipAxnnzySUyZMgWLFy/29OLK/Wdehw4dwr59+zBz5syYnX9x\nHQywb4H48MYbb2Dfvn3YunUrnn76aezYscPnc5vNFnRfcj+bR6h9ReazZMkStLa24p133sHll1+O\nH/zgB0YniYI4efIkiouL8fjjj2Po0KE+n0Xz/IvrYMC/T4O2tjafiIjM4fLLLwcgdln9ne98B7t2\n7UJqaqqnQ6vOzk6MGjUKwMB92t7eDrvdHvtEk0c4+0qpTxHuQ+OMGjXKcxO58847PcVu3H/mc+bM\nGRQXF6O0tBTz588HELvzL66DgenTp8PlcuHQoUPo6+vDxo0bUVRUZHSySOaLL75Ab28vAODzzz9H\nY2MjsrOzffqWqK6u9hz4Sv1UkHHC3VeB+hSRlqHY6+zs9Lx++eWXkZ2dDYD7z2wEQcDixYuRlZWF\n5cuXe6bH7PzTtz5k7G3ZskXIzMwUxo4dK6xfv97o5JCfgwcPClOmTBGmTJkiTJw40bOPjh07JuTn\n5wdsLrNu3Tph7Nixwrhx44SGhgajkm5Jt9xyi3D55ZcLycnJgsPhEJ577rmI9pXUtGns2LHCPffc\nY8SmWJL//quqqhJKS0uF7OxsYfLkycK8efOEo0ePeubn/jOPHTt2CDabTZgyZYqnGejWrVtjdv7Z\nBIF9+RIREVlZXBcTEBERkXYMBoiIiCyOwQAREZHFMRggIiKyOAYDRORx7NgxTJs2DdOmTcPll18O\nh8OBadOmYejQobj77ruNTh4RRQlbExBRQGvWrMHQoUNx3333GZ0UIooy5gwQkSLpWaGpqQlz584F\nAMyfFywAAAFwSURBVKxevRrl5eW47rrrkJ6ejj/+8Y/44Q9/iMmTJ+PGG29Ef38/AGDPnj3Iy8vD\n9OnTMXv2bE8vakRkPgwGiChsra2teO2117B582bcdtttKCgowLvvvouLLroIf/nLX3DmzBncc889\n+MMf/oDdu3fj9ttvx09/+lOjk01ECpKMTgARxRebzYYbb7wRgwcPxqRJk3Du3DnMmjULAJCdnY1D\nhw7hwIED2L9/P2644QYAwNmzZzF69Ggjk01EQTAYIKKwXXDBBQCAQYMGITk52TN90KBB6O/vhyAI\nmDhxIt58802jkkhEYWAxARGFRU2d43HjxuGTTz7Bzp07AYijsbW0tEQ7aUQUIQYDRKRIGjtdPo66\n/5jq/uOr22w2JCcno66uDitXrsTUqVMxbdo0vPXWW7FLOBGFhU0LiYiILI45A0RERBbHYICIiMji\nGAwQERFZHIMBIiIii2MwQEREZHEMBoiIiCyOwQAREZHF/X8JBC932jIbYgAAAABJRU5ErkJggg==\n" | |
| } | |
| ], | |
| "prompt_number": 16 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "This is the motive force, i.e. the propulsive/braking force require for the vehicle to traverse this path at the given speed, neglecting head wind and tail wind. I do a simple fill na here, but there may be some issues with that." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "data['Fm'] = numpy.hstack((0.0, mass * numpy.diff(data['Speed'].values)**2 / numpy.diff(data['Distance'].values))) - data['Fg'] - data['Fr'] - data['Fd']\n", | |
| "data['Fm'] = data['Fm'].fillna(0.0)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 17 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "data.plot(x='Time', y='Fm', figsize=(8, 6))" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 18, | |
| "text": [ | |
| "<matplotlib.axes.AxesSubplot at 0x5e19490>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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ZsHy5+N7/fDzvvODLfv/7ytODNVGU51oFIi/jDvQkHyxYDkYq95coFYcEalXR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\nA9rK63/9iVycAAAAAElFTkSuQmCC\n" | |
| } | |
| ], | |
| "prompt_number": 18 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "If you use the engine to brake there may be more fuel consumed (I think), but if you just press the brake pedal then just nominal fuel is used (idling fuel).\n", | |
| "\n", | |
| "The energy used over each time step is\n", | |
| "\n", | |
| "$$F_m(n)[s(n) -s(n-1)]$$\n", | |
| "\n", | |
| "where $F_m = F_m, F_m > 0$ and $F_m = 0,F_m<0$" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "data['Fm'][data['Fm'] < 0.0] = 0.0\n", | |
| "data.plot(x='Time', y='Fm')" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 19, | |
| "text": [ | |
| "<matplotlib.axes.AxesSubplot at 0x7fa7810>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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JW7Qy8eyz6iw/mr6+2J9/9BFQXz92eqx9LeW1r/v3J095MVWwAMwTLADfe4TV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\niIhI1P8H6zr/O+gbSjgAAAAASUVORK5CYII=\n" | |
| } | |
| ], | |
| "prompt_number": 19 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Power can be estimated by multiplying the instantaneous motive force by the instantatneous speed." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "data['Power'] = data['Fm'] * data['Speed'] # watts\n", | |
| "data.plot(x='Time', y='Power', figsize=(8, 6))" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 20, | |
| "text": [ | |
| "<matplotlib.axes.AxesSubplot at 0x8800750>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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++AK47Tbgm98Eli2z1/NPOxYS//u/yuaz43e1O7ds8z/8oe+0yO/++uvKEpZo\n29Cub0CYjUkAae7kSWD9+kCvd8uXA2vXmh2RdFYspJXexf3mN7H//uijypZLJEbuudPYGH85paXK\n45FLEIDOTuPWZxVMAkh3p0+bHYE7LVgQ++8XLhgTB5FSRo5VUVgI9O/fd7oVbwy0xCTAJtzwTE8P\nrCK0hni1Em7h9AtKLPX1ZkcQ26ZNZkdgDiYBFJPHA9TWmh2FcpdfLu/zTiukjX6dauNG8c/Gq5XQ\nOg65nzOKEfFY9YYhsttipXGq2YZir2Za7RgxGpMAiivaszupzDrJjhyRP48VCwQ1MX38sXZxSPHq\nqz0/79lj7LrtxKoXajtQcz7Mn69dHE7BJMChzLyYWaWA++wzsyMwn5n7gm0O3EWrMscq5YcUL70E\nVFWZHYU6CWYHQERE7mOXi32s5Obmm4ErrzS2AaPWWBMQIbzv+H//d2D1avNiCWeXE8burPg4gPpq\nb5f2Oan706jzi8eXvrZvZ6+fcjEJiDBgQM9z1I0bgTVrzI3HCVjwmcep257d60bn5huGadOAXbvM\njsJemASI4Ehy6lilEFIShxUvmlaMSQqrHAdkTWa8HWDE8uyGSYBDuf3AJn049bgyOmFx4na0SsNA\no7et3fclkwAR4TvV7juYyInYT4C563ATp29PJgGkO6efRFam9ba3ShW/3auEo21HN50rVjmW3I5J\nAFEYNxXCemMh35cR20RsHUb2VGj2ORRr/Twm+2ISQJp55BHg7betc6JZJQ7SntR9a/YxEHlBCv5u\n9oVSD8Hv1NVlzHqswmrxyMUkwKHMODBXruRAMXqweyGjB6kXd7PbDlRWiq9nyhR91kfyuf38YhIg\nQouGgZ2dwIcfahOP3bn9JDOT3Z+dh7Pjq7unTvX+vaFB/HPB7Wp2zYUV2O3tALtjEqCTBx4ARo6U\n/vk9e4Ann4z+dw6JS3aj9QVNSXL+8svaxqDW8ePi0//xD2PjIOmcnlQwCdBJZ6e8z69YATz4YPS/\nL12qLh6jWOmEUXIR+uQT7eMgikfLwZbMrk3Qu71GvDLGSmWQHTAJMJggKLur37Yt+t/+8hfggw/6\nrscqzCqUlGyDtjbt4yBthB9HVjq+Y5HbduHTT/WLRW9G7ROr7XurxSMXk4A4Yu1guXf7ALBoEXDN\nNcrjEVNYCFx/vfl3AKQPuxcyJF1+vtkRkNswCVDowgWgf3/gl78U/3u0C3JtrfvuNnkRM4+Z217r\npNTJSa7mCR7tAAAgAElEQVSTzhG1jwPizX/0aOy/x9qWYn9z0rZXgkmACCkHRbBK/6mntFmnUwo4\nQQBuu633tH37zInFKdtUT3oWgG4vXAHrHINf+pLZEUR34IDZEcQW7zi2yj5WikmAQ/3qV8auL9gm\n4ezZvu0Xfv97Y2Oxu6am3r+rKWTizVtXJ2954QVivGXfe6+8Zcdj98I2Fi0TJrHtZMW3i269NfB/\nRgZw7hwwdGjgd7slj3aLNxKTgDii7WClXWQaVZDV1hqznqDrrw/8z3He1TtyxLh1nT+v37LlJhhk\nb2ouhidPxq/mNyION2ISoBO5F3un3OXIPQF5wurLSttXbSHvlHNEjJX2k1pO209O2jdimATEobTx\nip6sflDK2Tavv65fHFasAlXCKc/tg9W9bmLlC+LAgWZHYLyzZ+N/pru79+9//KM+sVgFk4AwTh7c\nwyiCIK/g03OwkRtu0G/ZetLywrFjh3bLshKeo+p1dJgdQY/wY97oxCnyWFq7tvfvJ070/v2jjwLz\nBG9g7H4sKk4C0tLSMGbMGGRlZSE7OxsA0NbWhry8PKSnpyM/Px8dYUfZypUr4fP5MHLkSFRVVYWm\n7927F5mZmfD5fFi4cGFo+vnz5zFr1iz4fD7k5OTg8OHDSkOV7IUX5M+jVZsAK98x6MnuJ5DVBU+p\n//ovc+PQgh07C5LKad/HzmLdmHz4IeDzAR9/DNx4Y2BadzdQU2NMbHpQnAR4PB5UV1ejrq4OtZda\noa1atQp5eXk4ePAgJk6ciFWrVgEA6uvrsWHDBtTX16OyshILFiyAcOmonz9/PsrKytDQ0ICGhgZU\nXhp2q6ysDMnJyWhoaMD999+PxYsXq/2ucYk1aovXMDAauRd1pxQCTvkeTrNrl/h07i/nMfuGwuxj\nSu36ow3/DATeYgCAixd7f+bHP1a3TjOpehwgRGytrVu3ori4GABQXFyMzZs3AwC2bNmC2bNnIzEx\nEWlpaRgxYgRqamrQ2tqKzs7OUE3CnDlzQvOEL6uwsBC7opViJot2wDnlOa4SZhdCdmfl7WeVY8/K\n20gJq2xXcp8EpTN6PB5MmjQJl19+Ob7//e/jrrvuwrFjx5CSkgIASElJwbFjxwAALS0tyMnJCc3r\n9Xrh9/uRmJgIr9cbmp6amgq/3w8A8Pv9GDZsWCDIhAQMGDAAbW1tGDRoUK84li1bFvo5NzcXubm5\nSr+SIlZ9RdBMbviORtLiAtHYqH4ZRHqxUplhtYSsuroa1dXVui1fcRKwe/duDB06FJ999hny8vIw\nMmLcXI/HA48BezY8CTCS1o0IrXQSGMlqJ5xTffih2RGoZ8dzxI4xm03tNtO6TBHrICsyRj3Lscib\n2+XLl2u6fMWPA4Zeet/nmmuuwS233ILa2lqkpKTg6KWXgVtbWzF48GAAgTv8prBu0Jqbm+H1epGa\nmorm5uY+04PzHLnUa0p3dzdOnjzZpxZAL0rGLY+k58l/6pR+y1ZLELQdFpX0xSSMjNTRYb9OpJx+\njihKAs6cOYPOS0PonT59GlVVVcjMzERBQQHKy8sBAOXl5ZgxYwYAoKCgABUVFejq6kJjYyMaGhqQ\nnZ2NIUOGoH///qipqYEgCFi7di2mT58emie4rI0bN2LixImqv6xTPP+82RFE5/QTxghWvnu0cmx2\npne3wUaQ8h0WLQK+9rW+063U74rbyjBFjwOOHTuGW265BUDgLv0//uM/kJ+fj/Hjx2PmzJkoKytD\nWloaXnzxRQBARkYGZs6ciYyMDCQkJKC0tDT0qKC0tBRz587F2bNnMW3aNEyZMgUAUFJSgqKiIvh8\nPiQnJ6OiokKL76s5NzwOsHJsTmRkIcR9qy9u396idVMdfsxL6dAnFj3PHzMeB+hNURJw7bXXYp/I\n0HCDBg3Cyy+/LDrPI488gkceeaTP9BtuuAHvvfden+lXXHFFKImwMjvvfKnWrwdmztRn2W7YflbG\nbp6twS3btbU1/mfmz9c/jlgiH7c6fd+wx8A4tG4ToMWdgdEH5aW3NiVx+gljBCfdZeiF28Seog1s\nFn7Mf/qpunWoPTac0IhWDiYBKpnxOMBJBeCGDWZHQCTdp58GzlWp41KsX69vPFYipVyyw+MRJd/D\nzmUykwARcnaonXe+FSjpqpm0Y6fjd9MmsyPouahH9hgXjdQnmmr2Q2MjcKlfNcuzYxIg9oqgkzAJ\nsJjgwBQbNgBz5pgdjXx2uqhYlRMLGi188EHPzx99JH0+PV5Z1fNddLm2bQPWrNEuFrtjOxd5FHcW\nRAFyxxYQmy4IwP/9X+Dn3Fzg73/v+ZvYyW3lg9bKsZE7nTsHJCaaHUVsP/sZ8K//anYU5rFS4uu2\nMow1AXHEOyAix54OktNN65kzPT+HJwDRYtCiMyOyDzePQ6EFPb6j1sv8xz+0XZ5VWeliH43b+g1g\nEqDS55+LT3/nHfHpe/YE/tdqLO+DB7VZjlacfsIYwcoFpR7798wZac/YnXxsOeG7adEw0AoNKZkE\nkKSGIEoPjOBY1adPK5s/UrSaCLM4/YSxik8/7RnW1O6uugp46CH9lm+HmgA1y4sso6ycREazbVvg\n/9tuMzcOwH1lGJMAk0RLNPQ6gYcOld5SWY36+uh/c9vJpZXwhLFfv0C7kZQUbTpVudT7t+nc9m52\nJDXnhtXOq1jJabTyTctG0Gq3h5S3A/iKoIsYPYCQlPmUvLJy9Cjw2mvKYpIjVrWunE6HqMd99/X8\nfOoU8OqrgZ9bWtQv+957lc1ntRqoWOSew1J6taPoYo1tEiyvJk82JhYp3Fb9H4lJgE7iXZyNrgmI\nXKdW3npLesyXBoUkmU6etN6y9YzJbNHa+ejJCReeYDkgJUGsqtI3FpKOSYBOrFYTEDmfVg4d0n6Z\n1Fu0/eaECwcFaNkmwCytrdY4JoNDFSstm/g4gAwht38BPddJ5vN4el4TM7KAMfpxl1NY8Vw6ejTw\nv5n75t13Y6/fiNh+/vPAaIVKRyPcuVN6t9BO4Mok4LXXxPusDw5maPX38J3ejaVbafGMX8z27dH/\nJrews+L5EE9kzM3NgMggqLZ33XXi0/fuNS6GeI8C2tqMiSO875V4Ih9tHT/e8yp3NMOHy4/JqlyZ\nBMybB9x6a9/pwSQg+BqfGnIuzuGfFStktSp47ViAk/rHAcE7RDXLkKO93dp3UtOnA1lZ6pZhpVcE\ngyO0R2vLYGQbB0Gwxo2JnO1ZWdl3mtzjV07SYTWuTAKiNVALHry5uerXYVSbADn0KPCtcMI7hRlJ\nmh6PAw4dkt9fv5HfPd7dqp32A+DsRppGiLftpdS82rnHR1cmAdHu9M26oMl9O8COd/RadY7kBlLb\nBGhxHNihIx21IuPR4jy3Uk1APHqXa+G9llqpJkBNHPFqZ53ElUlANNEG99GDmoaBdmwTcOgQcOKE\n2VHYkxULITmvwFqNFc8ZLbeX0d+vvd3Y9Ulh5ePPapgEKBTvILNiK3+zT4yf/tTc9TuNGXe0dqyp\nssN3tHNNgBXvmgsL1c1vxe+kFyYBYbQ8WZTeKTm1JoCkk9oXvFUfByhh5HFsxXPGKvtBCStuT7HR\nWKOxYvxGYhIQxqyDwaiLup0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| |
| } | |
| ], | |
| "prompt_number": 20 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "And energy is the integral of the power vs time curve." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "data['Energy'] = numpy.hstack((0.0, integrate.cumtrapz(data['Power'].values, x=data['Time'].values))) # joules\n", | |
| "data.plot(x='Time', y='Energy', figsize=(8, 6))" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 21, | |
| "text": [ | |
| "<matplotlib.axes.AxesSubplot at 0x8832d10>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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hbL/9ok4iSY3PR7eU8P7xD+jXD959F1q0iDqNJO2aj24p5fz+93DttRa1pNTl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\n" | |
| } | |
| ], | |
| "prompt_number": 21 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "The fuel consumed at each time step can be estimated from the mass air flow rate, the standard air to fuel ratio, and lambda (not sure what that is). Then cumlative sum to see fuel loss in mass over time.\n", | |
| "\n", | |
| "MAF is in lb/min" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "data.plot(x='Time', y='MAF', figsize=(8, 6))" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 22, | |
| "text": [ | |
| "<matplotlib.axes.AxesSubplot at 0x728bdd0>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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y33FM02VuRFymGLQZa5wHruMa84J/dmmUZeTIKJK/yBox33FqawMOPbTc31DW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| |
| } | |
| ], | |
| "prompt_number": 22 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "data.plot(x='Time', y='OSB1S1w', figsize=(8, 6)) # lambda, mostly stays around 1" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 23, | |
| "text": [ | |
| "<matplotlib.axes.AxesSubplot at 0x955e210>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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sUrMN7URzvOoazKDIydGPVGckjFlZPP+8JhitIKZht1PIzbV2PrM2PPectrGD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\nI0di69atytcS4SPKo1AeN8yhUS2LdOuYguL0061fY7T8afl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YPXo0AKCtrQ19+/YNMtsE\nQUggIU4QGUanTp0AAFlZ/7+9O7aNEIiCADogbS0EJFAdTVEHUAFN0AISF1g+nS5Clmxr7ffCjX42\n2tV+TZtSyvO8bduc55nrutL3fZZl+a0RgZs8p8M/cucfa9d1OY4j27Yl+Whn2vf9u0cDvkCIwx/1\n2V382mP83mn83m/cNE1KKZnnOdM0ZRiGjOOYdV1/bnDgNitmAFApN3EAqJQQB4BKCXEAqJQQB4BK\nCXEAqJQQB4BKPQAaNkxU7iO6yAAAAABJRU5ErkJggg==\n" | |
| } | |
| ], | |
| "prompt_number": 23 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Now the fuel consumed can be estimated by from the mass air flow rate and the air to fuel ratio. We measure instantenous mass air flow rate and you can compute the fuel loss in kg per time step, then cumulative sum to see fuel use over time." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "pound_per_min_to_kg_per_second = 0.00755987283\n", | |
| "data['FuelLossInstantaneous'] = data['MAF'] / air_to_fuel / data.OSB1S1w * pound_per_min_to_kg_per_second * time_step # kg\n", | |
| "data['FuelLoss'] = data['FuelLossInstantaneous'].cumsum()\n", | |
| "data.plot(x='Time', y='FuelLoss', figsize=(8, 6))" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 24, | |
| "text": [ | |
| "<matplotlib.axes.AxesSubplot at 0xa0e3090>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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fhyefDHcbkyRpZ9myjpMnngjXRrduDWVlBrUkadfZso6D226DsWNtTUuSYsOw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\n" | |
| } | |
| ], | |
| "prompt_number": 24 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Now the joules of energy consumed per kg of fuel can be computed." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "data['FuelEnergyLoss'] = data['FuelLoss'] * gasoline_energy_density # joules\n", | |
| "data.plot(x='Energy', y='FuelEnergyLoss', style='.')" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 25, | |
| "text": [ | |
| "<matplotlib.axes.AxesSubplot at 0xa78c050>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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| |
| } | |
| ], | |
| "prompt_number": 25 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Now we see that there is some kind of linear relationship between the amount of fuel energy depleted and the energy needed to move the car. This ratio is essentially the efficiency from fuel energy to motive energy. A simple linear regression can give us a predictor model." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "model = pandas.ols(x=data['Energy'], y=data['FuelEnergyLoss'])\n", | |
| "print model" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "\n", | |
| "-------------------------Summary of Regression Analysis-------------------------\n", | |
| "\n", | |
| "Formula: Y ~ <x> + <intercept>\n", | |
| "\n", | |
| "Number of Observations: 20349\n", | |
| "Number of Degrees of Freedom: 2\n", | |
| "\n", | |
| "R-squared: 0.9972\n", | |
| "Adj R-squared: 0.9972\n", | |
| "\n", | |
| "Rmse: 1345467.4909\n", | |
| "\n", | |
| "F-stat (1, 20347): 7167379.9609, p-value: 0.0000\n", | |
| "\n", | |
| "Degrees of Freedom: model 1, resid 20347\n", | |
| "\n", | |
| "-----------------------Summary of Estimated Coefficients------------------------\n", | |
| " Variable Coef Std Err t-stat p-value CI 2.5% CI 97.5%\n", | |
| "--------------------------------------------------------------------------------\n", | |
| " x 6.5251 0.0024 2677.20 0.0000 6.5204 6.5299\n", | |
| " intercept 3988333.5317 17081.1768 233.49 0.0000 3954854.4252 4021812.6383\n", | |
| "---------------------------------End of Summary---------------------------------\n", | |
| "\n" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 26 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "It looks like we need 6.5 times the energy needed to move the car in fuel. We can plot our model prediction to see how well it fits the data. This is the **key finding**. We can potentially compute this model for a variety of cars and it shows a simple relationship from fuel energy consumed and energy needed to travel the route. If we have this number for a variety of cars, we may be able to predict fuel economy from just speed and altitude." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "data['PredictedFuelEnergyLoss'] = model.beta[0] * data['Energy'] + model.beta[1]\n", | |
| "data.plot(x='Energy', y=['FuelEnergyLoss', 'PredictedFuelEnergyLoss'], style=['.', '-'], figsize=(10,6))" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 27, | |
| "text": [ | |
| "<matplotlib.axes.AxesSubplot at 0xb4fb490>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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MmU4oECgCOkwAgGJxPv28/rP0P7oz/k71Cu6ljYM2qkWNFjKZlPNTkPh4whLcGx0mAIBd\nDMPQ9ynfa9iiYWpTq412Ru9UtXLVLAaky6pWlY4dc3yNgL0ITACAIvvt1G8amjBUv536TfH/ile7\nuu0kWe4mXVa+PGEJnoMhOQBAoV3KvKSxK8aq+dTmalOrjbY/ub1QYSk8XDpzxsFFAsWIDhMAoFB+\n3PejYhfGKqRqiLYM3qLagbVtPpflAuCpCEwAAJscPntYI5eM1MbDGzWh6wT9363/l+v1gjpLgYHS\nqVNOKBBwIIbkAAAWZWZn6r2176nJh010a+VbtStmV56wZAlhCd6ADhMAoECrf1+tmIUxqnpdVa0e\nsFoNqzTMc4ylOUudOjmwOMCJCEwAgDyOnz+uUUtHafG+xXq307vqE9JHpmuSUWCg5YnbnTpJixc7\nuFDASRiSAwDkyDay9fHmjxUSF6IKpStod+xu3R96vypWNOVagNJksn6XG2EJ3sRkFHXbXtm36y8A\nwL1s+WOLYhbEyM/kp7XPT5GONSnye/HVAHdkT25hSA4AfNyZi2f04k8vambSTP054w1p26OSUfgB\niCpVpOPHi78+wB0QmADARxmGoa93fa1nljyjP1b8n7Q0WbpQudDv06OHNHeuAwoE3AiBCQB80O7j\nuxX871ip9ClpwRzpUEubzmvXTlq+3MHFAW6IwAQAPuTWkPP65abXpGZTpT0vSRtjpOyCvwr69JFm\nznRigYCb4i45APARpqDv9UunECnwgBS3U1r/VIFhaexY88RtwhJgxl1yAODl9p/ar1tin5Iq/yIt\niJP2t8/3OJNJWrlSat3ayQUCTmJPbqHDBABe6lLmJZXu9LpuGddcOthKmrI937AUH2/uJmVnE5aA\ngjCHCQC80NLflqrje7FS5SDp403S6Tq5Xq9fX/rlF9fUBngiAhMAeJGHnjyiGSeelmqulX6cIKXc\nk+cYJnIDhceQHAB4OJNJMpXIlKnlB5pRobF06hZpcnK+YWnWLMISUBR0mADAwwQFSSkpVz1RY630\nf9FSWmXps1XSiaA857AKN2AfAhMAeAiT6Zonyp6QOvxHqp8gLRkv7XpA0rUHSXFxUnS0U0oEvBZD\ncgDg5kyma8KSKVtq+okUEyKllzMPv+3qq6vDUkyM+c43wyAsAcWBDhMAuLE8XaVq26Tu/ySgLxdL\nR8NzjmMNJcBxrHaYTp8+rV69eqlRo0YKDg7WunXrnFEXAPi0oKBrwlKps1KX4dLDnaWtA6XPVqtP\n2/CcLhJrKAGOZbXDNGzYMHXr1k2zZ89WZmamzp8/74y6AMDn5OkmSZIMKXSm1Olp6deuUlySenSq\normbnV0d4Nssbo1y5swZRURE6Lfffsv/ZLZGAQC75R+UJFXZI3WLNU/uXjBFkTe20saNTi0N8CoO\n2xpl//79uuGGG/TYY4+padOmGjRokNLS0or0QQCAvPINSwFpUvvnpQF3SHvvlj7erPixhCXAlSwO\nyWVmZmrLli2aNGmSmjdvruHDh2vcuHF69dVXc44ZM2ZMzuOoqChFRUU5qlYA8Cr5hqVb50ldn5IO\ntZSm7FB4vZu1NcvppQFeITExUYmJicXyXhaH5I4eParbb79d+/fvlyStWrVK48aN0/z5880nMyQH\nAIWWb1AKTJW6DJOq7FGTw5O1bU4HZ5cFeD2HDclVq1ZNNWvW1N69eyVJS5cuVUhISJE+CACQT1gq\nkS7d8YY0OFJNbrhNF9/bQVgC3JDFDpMkbd++XY8//rjS09NVr149TZs2TRUqVDCfTIcJAGyWJyzV\nXW6e1H2qnqb1nqhHe9R1SV2Ar7Ant1gNTI76YADwFXn2fiv3h9TpGanWKpVYMkEZu+6RqcBb5QAU\nF3tyCyt9A4AD5cpBfplS8zjpzlelzYNUfnqyzpy4zmW1AbAdgQkAHCRXWKqxzrylycVAadrPCqsW\nrB0nXFYagEIiMAFAMcsVlMqclDo8J906X1oyXtrZV3FxJjbEBTwMgQkAiklgoHTmzD+/mLKl8Hjp\nruekpPulyckqYwpUGtM+AY9EYAIAO+WZr33jDvPwm1+m9FWC9EdThYdLW7e6pDwAxcDiOkwAgIKZ\nTNeEpVJnpc4jpX4dpO39pU/XSn801axZhCXA0xGYAKAQLoek3F0lQwr5nxQbLJU+LU1OkjYP1sAB\nfjIMqVcvV1ULoLgwJAcANihwmaTKe82LT5Y7Js2aKR1srYYNpT17nFoeAAejwwQAFuTtJv3D/4LU\n7kVpYCvp167SR5s1sFNrGQZhCfBGdJgAoAAFdpUaLJC6DZUON9d107craW111a7t1NIAOBmBCQCu\n4ecn5bt7QoUDUpfhMlXbpdmPfaieTTo5vTYArkFgAoB/FNhRKpEutXxPav227q8zTPGDvlZp/9JO\nrQ2AaxGYAPgsm/a7rZModY9RwPm62j1qvepVqufosgC4IQITAJ9iU0iSpHJHpU7PSLV/1l0ZH+jH\nz3rIZPPJALwNgQmAz7Ap75iypOZTpDtfUa2TA5Q0LlnlSpZzeG0A3BuBCYDXs7kxVH2DSvaMVvPG\n1+ujexIVUjXEoXUB8BwEJgBeq2RJKSPD+nGvvv2XDgeN1vcp3+vtjm/robCHGH4DkAsLVwLwOpcX\nm7QUloKDpazsbE3bGq/JCpa/n792x+7Ww40fJiwByIMOEwCvUJiMs2qVVL7+Tt0ZH6NLmZc0/8H5\nirw50nHFAfB4dJgAeLQCty7Jx8CB0tmLf2vO+ad11xd36aGwh7R24FrCEgCrCEwAPJatQSksTMrO\nNtR5xCwFxwXrZNpJ7YrZpScjn1QJvxKOLRKAV2BIDoBHsiUsxcVJ0dHSLyd/UZevhujI30c0o+cM\ntandxvEFAvAqdJgAeBxLYemOO8z7wBmG9OjjF/Ry4su6/dPb1emWTtoyeAthCUCR0GEC4FEKCksB\nAVJ6+pXfE35J0JCEIWp6U1Nte3KbapSv4ZwCAXglAhMAj2Cpq3R1WDp45qCGLx6u7Ue3a3K3yepS\nv4tzCgTg1RiSA+DWrN0FV6aMOSxlZGXordVvKfyjcDWu2li7YnYRlgAUGwITALczeLBtywXUry+l\npUk/H/hZ4R+F66fUn7T+8fV6OepllfYv7ZxiAfgEhuQAuI3CLD4ZFyf1fOSY+s39txJTE/V+l/d1\nb9C9rNINwCGsdpjq1Kmjxo0bKyIiQrfddpszagLgQy53kmzNOWPHSplZWTIi4xQ6JVTVylVTcmyy\nejbqSVgC4DBWO0wmk0mJiYmqVKmSM+oB4OUGD5amTi38eQkJUpcu0sbDG9Xik2iVDSirn/r/pNCq\nocVfJABcw6YhOcMwHF0HAC/n52deG6kwevSQ5s41Pz514ZRiFjyvuXvm6s0Ob+qRxo/QUQLgNFaH\n5Ewmkzp06KDIyEhNLcpfCwH4tMvDbYUJS7NmmY+fO9f8F7Yvtn+h4LhgSVJyTLL6NelHWALgVFY7\nTKtXr9ZNN92k48ePq2PHjgoKClKbNldWyh0zZkzO46ioKEVFRTmiTgAeqDCZ5vKQ29V2/blLMQti\nlJaRph8e+EHNqzcv3gIBeLXExEQlJiYWy3uZjEKMt73yyisqV66cnn76afPJJhPDdQDysCUode8u\nzZ+f/2vn0s/plRWvKH5bvF6JekVPNHuCTXIB2M2e3GJxSC4tLU1///23JOn8+fNasmSJwsLCivRB\nALzH1Xe25fdjScuW5uG2/MKSYRj6NvlbNZrcSMfOHdOu6F2KaR5DWALgchaH5I4dO6Z7771XkpSZ\nmamHHnpInTp1ckphANxTUacODRwoffJJwa//+tevGpowVL+f+V1f3vul7qxzZ9E+CAAcoFBDcnlO\nZkgO8ClFDUupqVLt2vm/djHzot5c9aYmbpioUa1HaXjL4QooEVDkGgGgIA4bkgOAy4oSlsaONQ+/\nFRSWFv26SKFxodrx5w5teWKL/t3634QlAG6JrVEAWFWyZP7PW5q4bcnBMwc1YvEIbT26VRO7TlS3\nBt3sKxAAHIwOEwCLTCYpIyPv8+HhhQ9LGVkZGr9mvCI+ilBI1RDtit5FWALgEegwAciXpSG4ChWk\nrVsL934rD6xU9IJoVS9fXWsHrlWDyg3sKxAAnIjABCCPoKCCX/Pzk06ftv29/jz/p5798Vkt279M\n73V+T/c1uo9VugF4HIbkAOSRklLwa1lZtr1HVnaWPtz0oULjQlWlbBUlxySrV3AvwhIAj0SHCYBN\nGjaU9uyx7djNRzYrekG0SpYoqWX9linsRha8BeDZCEwAcsmvAWTrsiWnL57WC8tf0Ozk2RrXYZz6\nNeknPxONbACejz/JAOTILyyVK2f9PMMwNH37dDWa3EiZ2ZlKjk3Wo+GPEpYAeA06TAAkFXxX3MGD\nls9LPp6smAUxOnvprL67/zu1qNGi+IsDABfjr38ACgxLdetKgYH5v3Yu/ZxGLR2lO+PvVO/g3to4\naCNhCYDXosME+DhLN6399lve5wzD0Hd7vtOwRcN0Z507tTN6p6qVq+a4AgHADRCYAB9mKSzlN9H7\nt1O/aWjCUO0/tV+f9/hc7eq2c1xxAOBGGJIDfFBQUOHC0sXMixq7Yqxum3qb2tZqq21PbiMsAfAp\ndJgAH2RpYcprw9KSfUs0ZOEQhVQN0ebBm1U7sLZjiwMAN0RgAnyIpa5S+fLSmTNXfj989rBGLhmp\njYc3amLXiep+a3fHFwgAboohOcCLDR5sDkmXfwrSo8eVsJSRlaF3176rJh820a2Vb9WumF2EJQA+\njw4T4GUKu1VbaqpU+59RttW/r1b0gmjdWO5GrRm4RrdWvrXY6wMAT0RgArxAUfazNZmk/fvNYen4\n+eMatXSUluxbonc7v6vewb3ZJBcArsKQHODBrA21FWTWLCk7W6pZK1sfb/5YIXEhCiwdqOTYZPUJ\n6UNYAoBr0GECPFRhMk27dtLy5bmf2/LHFkUviJa/n79+fORHNanWpHgLBAAvQmACvNSqVVLr1nmf\nP33xtF786UX9L+l/euOuN9gkFwBswJ+SgAcqqLsUE2NeR8kw8oYlwzD01Y6vFDw5WJcyLyk5JlkD\nIgYQlgDABnSYAC+R31Yml+0+vlsxC2N0+uJpzbl/jlrWaOm8wgDAC/BXS8DD5NddGj8+/2PPp5/X\nc8ueU9v4tuoZ1FMbB20kLAFAEZgMw9LfS62cbDLJjtMBFEJh9n4zDEM/pPygYYuGqXWt1hrfcbxu\nuv4mxxYIAG7OntzCkBzgASyFpYSE3L/vP7VfTy16Sr+c/EWf/eszta/b3rHFAYAPYEgOcHPWlg/o\n0sX8v5cyL+n1n19X86nN1apGK+2I3kFYAoBiQocJcGNBQZZfv9xZXvrbUsUujFVQlSBtGrxJdQLr\nOLw2APAlNs1hysrKUmRkpGrUqKF58+ZdOZk5TIBD5dddCgiQ0tPNj4/8fUQjF4/U+sPr9UGXD3RP\nw3ucWyAAeBB7cotNQ3IffPCBgoOD2S4BcJKgoPzDUpUq5rCUmZ2p99e9r8ZTGqt+pfpKikkiLAGA\nA1kNTIcOHdLChQv1+OOP000CnMDPT0pJyft8uXLS8ePSmoNr1OzjZpq/d75WDVil19q/prIBZZ1f\nKAD4EKtzmEaMGKG3335bZ8+ezff1MWPG5DyOiopSVFRUcdUG+JyCmrjXXSftP3ZCj//wHyX8mqB3\nOr2j+0Pup+sLABYkJiYqMTGxWN7L4hym+fPnKyEhQZMnT1ZiYqLeeecd5jABDlJQ9il7XbY++Pkz\nPb/8efUN7atXol5RhdIVnFscAHgBe3KLxcA0evRoTZ8+Xf7+/rp48aLOnj2r++67T1988YXdHwzA\nzM+v4G1Nyt+6TcHPRkuSpnSfovBq4U6sDAC8i8MC09VWrFih8ePH02ECitHgwdLUqfm8UOqMbn7o\nJWU2+kb/bf9fPRbxGJvkAoCdnLbSN/MlgOJR8H9KhhT6jcrd94y6Nu2qcR2SVKVsFWeWBgDIB3vJ\nAU5g0981quyRusXqltCTmv5AnFrVbOXwugDAl7CXHOCmbApKAWlSm9elyI/0/r0vKva2WPn78Z8m\nALgT/lQGHMDm0etb50ldn9L1Z1tqzws7dPP1Nzu0LgBA0RCYgGJkc1AKTJW6DJOq7NGPT01Vh1s6\nOLIsAIAeNZWHAAAdgklEQVSdCEyAnQpzL8T3Cy5p1/Xv6N2172pEyxF6ptX/VMq/lOOKAwAUCwIT\nUAQlS0oZGbYfv2qVdPGmZYpdGKv6lepr46CNqluxruMKBAAUKxZ2AQrJZLI9LI0cKR05+4cmH31Q\nA38YqDc7vKl5fecRlgDAwxCYgEKwdfht5EgpIytTtftMUOMPG6tOYB0lxSTpX0H/Yj0zAPBADMkB\nNrKWc+Ljpf79zY/XHVqn5lOjVbF0Rf386M9qdEMjh9cHAHAcAhNghbWgdPUaaCfTTuq5Zc9p/t75\nGt9pvPqG9qWjBABegCE5oAAmk+WwVLbslbCUbWTr0y2fKjguWKX9S2t37G49GPYgYQkAvAQdJuAa\ntmScqlWlY8fMj7cf3a7oBdHKMrKU8FCCmt7U1LEFAgCcjg4TcBVbwlKfPuawdPbSWY1YPEIdp3fU\nY+GPae3AtYQlAPBSBCbgH9bCUvfu5iG4b74xNHPXTAVPDtbZS2eVFJOkQc0Gyc/Ef04A4K0YkoPP\nsxaUBg6UPvnE/Hjvyb2KXRirY+eOaWavmWpdq7XjCwQAuByBCT7LWlC6+Wbp8GHz47SMNL2x6g1N\n2ThFz7d5XkNbDJW/H//5AICvYAwBPslaWBo9+kpYmr93vkLjQrX35F5tf3K7Rtw+grAEAD6GP/Xh\ncyyFpXr1pE2bpMBA6cDpAxq+eLiS/kzSh//3oTrV6+S8IgEAboUOEyDJz09KTZV+/VUqe326xq0a\np2YfN1Ozm5ppZ/ROwhIA+Dg6TPAp+XWXqlWT/vjD/Pin/T8pZmGMbql4izYM2qBbKt7i3AIBAG7J\nZBhXb+xQyJNNJtlxOuB0+QUmw5COnjuqZ5Y8o5W/r9T7nd9Xj6AerNINAF7GntzCkBx8QsmS+Yel\nJuFZmrRhksKmhKlG+RpKjknWvY3uJSwBAHJhSA5er8DsU329/J6M1uzk8lrx6AoF3xDs1LoAAJ6D\nwASvZLFBVOYv6a7R8mv0vUbe/rYeCnuIjhIAwCICE7yKxdxjypaafC51eE5lU3vp8Ku7FVg60Gm1\nAQA8F4EJHm/wYGnqVCsH3bhD6h4jlUjXphEL1OzmZk6pDQDgHQhM8Fg2jaKV/FuKGiM1ma5a+17V\nb7MGqYRfCUeXBgDwMgQmeAQ/P/Pt/7YzpODZUueRevTODnqzwy5Vva6qo8oDAHg5AhPcWpHmYlf6\nReo2RKGtjiiu2wy1qd2m2OsCAPgWi+swXbx4US1atFB4eLiCg4P13HPPOasu+DiTqfBhqVS5C3px\n+UuqPOp2jY/upC2DtxCWAADFwmKHqXTp0vrpp59UtmxZZWZm6o477tCqVat0xx13OKs++KDCBqVZ\ns6SyTRZqaMJQ7T7RVNue3KYa5Ws4pjgAgE+yOiRXtmxZSVJ6erqysrJUqVIlhxcF32UtLF2975sk\n/X7mdw1fNFw7F+1UXLc4da7f2bEFAgB8ktXAlJ2draZNm2rfvn2Kjo5WcHDu1ZDHjBmT8zgqKkpR\nUVHFXSN8hKWwdO2E7/SsdL2/7n29tfotPdXiKc24b4ZK+5d2bIEAAI+SmJioxMTEYnkvmzffPXPm\njDp37qxx48blhCI230VxCAyUzpzJ/7XISGnjxtzPrUhdoZiFMapVoZYmdZ2kepXqOb5IAIDHc8rm\nuxUqVFD37t21adOmIn0QcK3Bg81dpYLCUlxc7rB07Nwx9ZvbT4/MfURj243VwgcXEpYAAE5hMTCd\nOHFCp0+fliRduHBBP/74oyIiIpxSGLxbYKDl1blnzZKio82Ps7KzFLcxTqFTQlWtXDUlxyarZ6Oe\n7P8GAHAai3OY/vjjD/Xv31/Z2dnKzs7WI488orvuustZtcELBQVJKSkFv96wobRnz5XfNx7eqOgF\n0SobUFY/9f9JoVVDHV8kAADXsHkOU74nM4cJhWCtITR+vPT00+bHpy6c0vPLn9fcPXP1Zoc39Ujj\nR+goAQDsYk9uYaVvOFzJklJGRsGvX91VMgxDX2z/QqOWjtJ9wfcpOSZZFctUdE6hAAAUgMAEh7LW\nFIqLuzJXadefuxSzIEZpGWma13eemldv7vgCAQCwAYEJDmEtKHXvLs2fb358Lv2cXlnxiuK3xevV\nqFc1uNlglfAr4fgiAQCwkc3LCgC2sGUPuPh4c1gyDEPfJn+r4MnB+vP8n9oVvUvRzaMJSwAAt0OH\nCcXClvnYV0/q/vWvXzU0Yah+P/O7pt87XXfWudOxBQIAYAcCE+xiS1C6+WYpKcm89tLFzIt6c9Wb\nmrhhoka1HqXhLYcroESA4wsFAMAOBCYUiS1ByWSStm+XwsLMvy/6dZGGLByiJtWaaOsTW1WzQk3H\nFgkAQDEhMKFQbF0K6eq73w6eOagRi0do69GtmtR1kro26Oq4AgEAcAAmfcMmtkzmlqRVqyTDMIel\njKwMjV8zXhEfRSi0aqh2Re8iLAEAPBIdJlhlS1CKj5f697/y+8oDKxW9IFrVy1fX2oFr1aByA4fV\nBwCAoxGYUKDAQOnMmYJf9/OTtm27MkdJkv48/6ee/fFZLdu/TO91fk/3NbqPLU0AAB6PITnkq2RJ\ny2EpIUHKyroSlrKyszRl4xSFxoWqStkqSo5JVq/gXoQlAIBXoMOEPCxlnKvXUrps05FNil4QrVIl\nSmlZv2UKuzEs/5MBAPBQBCbkYiksJSRIXbpc+f30xdN6fvnz+jb5W43rME79mvSTn4mmJQDA+/Dt\nhhwFhaWwMPOdb5fDkmEYmr59uhpNbqRsI1vJscl6NPxRwhIAwGvRYYKkgsNSjx7S3LlXfk/6M0kx\nC2N0Lv2cvn/ge91W/TbnFAgAgAvREkCBYal79yth6Vz6OY1aOkpRn0epT3AfbXh8A2EJAOAz6DD5\nuILCUp8+0syZ5uG3uXvmasTiEWpbu612Ru9UtXLVnFskAAAuRmDyYdbC0r6/9mlowlClnk7V5z0+\nV1SdKKfWBwCAu2BIDrl06iR9/tVFjV0xVi0+aaE7a9+pbU9uIywBAHwaHSbkqFFDejpuicKmxCqs\napi2PLFFtSrUcnVZAAC4nMkwDKPIJ5tMsuN0uFiuIbnrD6v3pyO06cgmTew6Ud1v7e6yugAAcAR7\ncgtDcj4mKMgclHLCkl+GdPu7UnQTBVUJUlJMEmEJAIBr0GHyAQWu3l1rldQ9Rjp3o7RwsowTtzq1\nLgAAnMme3MIcJi9lcc/bsseljqOkekukxe9KSb0lsUkuAAAFYUjOCxUYlkzZUrOPpNgQ6WKgNDlZ\nSuojySQahQAAFIzA5CUGD75mbtK1btoiDbxdavKF9MWP6mS8K+NieRmGCEsAAFjBkJwXsDj8Vvq0\n1O5FKWSWHr7pv/p8BJvkAgBQWBa/OQ8ePKh27dopJCREoaGhmjBhgrPqgg0sdpRkSGFfyX9YsAbH\npOvEK0maPnIAYQkAgCKweJfc0aNHdfToUYWHh+vcuXNq1qyZvvvuOzVq1Mh8MnfJOV3JklJGhpWD\nquyWuseoXshpfdl3ilrWaOmU2gAAcGcOu0uuWrVqqlbNvNFquXLl1KhRIx05ciQnMMG5LA69SVLA\neVW7/zVlNv5EL7V9SdHNo+Xvx6grAAD2svnbNDU1VVu3blWLFi1yPT9mzJicx1FRUYqKiiqu2mAz\nQze3/14Bdw9X61qtNb7jDt10/U2uLgoAAJdKTExUYmJisbyXTQtXnjt3TlFRUXrhhRfUo0ePKycz\nJOc0BXWXwtr+plpPPKVf//pVcd3j1L5ue+cWBgCAh3Do1igZGRm677779PDDD+cKS3CefMNSiUsa\nu+I1Hel+m1rXbK0d0TsISwAAOIjFITnDMDRw4EAFBwdr+PDhzqoJV8k3LN3yo24dMUSbjjTSpsGb\nVCewjrPLAgDAp1gcklu1apXatm2rxo0by/TPN/cbb7yhLl26mE9mSM4h/PwKWEzy+iNS55Gqftt6\nTblngu5ueLfTawMAwFPZk1vYfNfN5NtR8suUbpsktX1N/YKf1JQHR6tsQFmn1wYAgCcjMHmBArtK\nNddI3aOltBvUJXuyEqY3dHptAAB4A4etwwTnGDw4n7BU9oTUYZRUf5G05B09FH6/vpxubSEmAADg\nCHSY3ECuYThTthTxmdT+eWnngzrz/SsqX6q8y2oDAMBb0GHyFtW2St1jJJnU8dhiLVkU7uqKAACA\nCEwuZzJJKnVGaveSFPqNtOy/ytr8GJvkAgDgRvhWdiHDMKTQr6XYYCkgTYpLkrYOJCwBAOBm6DC5\nyJ4TexT8TKx0x0npf7OlQ7dLkmbNcnFhAAAgDyZ9O1laRppeX/m6Ptr0kU7OeVHaGCtlX8mtXE4A\nAByDSd8e4oeUH/SvD5+SDt4uLdkh/X1zrtfDwlxUGAAAsIgOkxOknk7VUwlPad6aFGnhZOm3Dvke\nx6UEAMBx6DC5qUuZl/TO2nf07tp3dXL+SGnNLCmrVL7HhrOCAAAAbovA5CDLflum2IWxSlnTQErY\nKJ2uW+Cx4eHS1q1OLA4AABQKgamY/fH3H3p6ydP6ZvUaGQsnSCn3FHgsQ3AAAHgGFvwpJpnZmZqw\nfoIaf9hYdQLryJiURFgCAMBLMOnbTiaTpBrrpO7R0sWK0oLJ0olGBR5fpYp0/Ljz6gMAAGb25BY6\nTIVUsqQ5JJlMkqnsSenuQdL9PaU1/5Y+X1ZgWIqPN3eVCEsAAHgeApONLoekjAxJpmwp4lPzliaZ\nZaRJu6WdD0oy5Xtuu3ZS//5OLRcAABQjJn1b4ed3zXyjG7dL/xdtDk1fLpKORlg8v107aflyx9YI\nAAAciw6TBSbTVWGp1Fmp8wipX0dp62PSp2tyhaXRo83HXvtDWAIAwPPRYcqHKdfImiGFzpQ6PSP9\n2lmanCSl3aA77pBWrnRVhQAAwJkITMpn2O2yyilS91jpuj+lWTOlg60VGSlt3Oj0EgEAgAv5dGAy\n5T9HWwpIk9r8V4r8UPr5eWnDUJUt7a/zvr2CAgAAPssn5zBdvuMtX7fOl2JCpEq/SlO2S+tGKP4z\nf50/79QSAQCAG/GpDlOBIUmSKhyQug6TbkiW5n2sPpEdNfOs00oDAABuzOtX+i5wftJlJdKl29+V\nWo1X25LDteTlf6uUfymn1QcAAJzDntzitR0mq0FJkur8JHWPUc1ytyhx1AbdUvEWp9QGAAA8i1cG\nJotDb5JU7qjU6RmVvnWlvu7/gf7V8F8yWT0JAAD4Kq8KTFYzjylLaj5Fpbu8omFtBurFtsm6ruR1\nTqkNAAB4LquBacCAAVqwYIGqVq2qnTt3OqOmQrOlOdT2wfX6u220KpSuoMndVij4hmDHFwYAALyC\n1WUFHnvsMS1atMgZtRSJtbBUN/gvDZ73hPY27aGRt4/U8n7LCUsAAKBQrAamNm3aqGLFis6opVAC\nA62EJVO2Bk6cprTHglWyREntjt2thxs/zFwlAABQaB45h8la5ol9dYe2VY/Rjqx0Lei5QM1ubuac\nwgAAgFeyOzCNGTMm53FUVJSioqLsfUuLLIWl4Ii/1WXcGE3fPl1jG4/V400fVwm/Eg6tBwAAuKfE\nxEQlJiYWy3vZtHBlamqq7r777jyTvp29cGXBYcnQyE9ma+aZEepYr6Pe7PCmql5X1Wl1AQAA9+cT\nC1cWGJYq71XH94Zoybk/9PV9X6tN7TZOrQsAAHg/q5O++/btq1atWmnv3r2qWbOmpk2b5oy6csk3\nLPlfUIkOL6nys63UuV5nbRm8hbAEAAAcwu33kss3LDVYKP+7h+rels30bud3VaN8DYfWAAAAPJ/X\nDsnlCUsVfpe6DNd1t+zUtwPi1Ll+Z5fUBQAAfItbB6YcJdKllu9Lrd9S4wtPaf2rM1Tav7SrqwIA\nAD7CbQNTTnep9gqpe4x0ppYarVqv7avrubQuAADge9x2DpOp3DGp0zNSnRXSovel3ffKMFilGwAA\nFI09ucXqXXLOlpWdpdYjJksxodK5m6TJydLunoqLIywBAADXcKsO04bDGxSzIEab114nLYiTjofk\nvObE9TEBAIAX8vgO06kLpxS9IFr/+uZfGtZimBSfmCssderkutoAAABcGpgMw9Dn2z5Xo8mN5Gfy\nU3JMsh5p8oik3MNvixe7pj4AAADJhXfJ7fpzl2IWxOhC5gXNf3C+Im+OlGR5c10AAABXcHqH6Vz6\nOf37x3+r3eft1De0r9YNXEdYAgAAbs1pHSbDMDRn9xwNXzxc7eu2167oXbqx3I1WQ9KOHc6pDwAA\noCBOuUvu179+1ZCFQ3To7CHFdY9T29ptbe4mcXccAAAoDm57l9zFzIsakzhGLT9pqQ63dNDWJ7YS\nlgAAgMdx2JDcol8XacjCIQqvFq6tT2xVzQo1Jdk2Tyk1Vapd21GVAQAAFE6xB6aDZw5qxOIR2np0\nqyZ1naSuDbrmvGYpLJ06JQUGFnc1AAAA9iu2IbmMrAyNXzNeER9FKLRqqHZF78oVlgpSoYJ56I2w\nBAAA3FWxdJh+PvCzYhbEqEb5Glo7cK0aVG6Q55j8ukt+ftLp08VRAQAAgOPYHZj6f9dfy/cv13ud\n39N9je6TqRCLKWVl2fvpAAAAjmd3YGp2UzNN6jpJ15e6vsBj8stQDz9s7ycDAAA4h1PWYcovMLFk\nAAAAcCa3XYdp8OD8w1LDho78VAAAgOLlsA6TpalMdJcAAICzuV2HiU10AQCAN3HokFx+6C4BAABP\nU+wrfRfUXSIoAQAAT+WUDhNhCQAAeLJi6TBZmrNUqlRxfAIAAIDr2H2XnGT5dLpLAADAHbjdXXKX\nxcc78t09X2JioqtL8GhcP/tw/YqOa2cfrp99uH6uYTUwLVq0SEFBQWrQoIHefPNNm984Pl7q39+e\n0rwf/9Lbh+tnH65f0XHt7MP1sw/XzzUszmHKysrSkCFDtHTpUlWvXl3NmzfXPffco0aNGhV4DkNw\nAADA21jsMG3YsEH169dXnTp1FBAQoAceeEDff/99gccTlgAAgDeyOOl79uzZWrx4saZOnSpJ+vLL\nL7V+/XpNnDjRfDJLegMAAA9S1EnfFofkrAUiO26wAwAA8BgWh+SqV6+ugwcP5vx+8OBB1ahRw+FF\nAQAAuBOLgSkyMlK//PKLUlNTlZ6erpkzZ+qee+5xVm0AAABuweKQnL+/vyZNmqTOnTsrKytLAwcO\ntHiHHAAAgDeyug5T165dlZKSokmTJunzzz+3uB7TU089pQYNGqhJkybaunVrsRfryaytZ/XVV1+p\nSZMmaty4sVq3bq0dO3a4oEr3ZOtaYBs3bpS/v7/mzJnjxOrcny3XLzExUREREQoNDVVUVJRzC3Rz\n1q7fiRMn1KVLF4WHhys0NFTxrNibY8CAAbrxxhsVFhZW4DF8b+TP2rXjO8MyW/7dkwr5vWHYIDMz\n06hXr56xf/9+Iz093WjSpImRnJyc65gFCxYYXbt2NQzDMNatW2e0aNHClrf2CbZcvzVr1hinT582\nDMMwEhISuH7/sOXaXT6uXbt2Rvfu3Y3Zs2e7oFL3ZMv1O3XqlBEcHGwcPHjQMAzDOH78uCtKdUu2\nXL+XX37Z+M9//mMYhvnaVapUycjIyHBFuW7n559/NrZs2WKEhobm+zrfGwWzdu34zrDM2vUzjMJ/\nb9i0NYot6zH98MMP6v/P0t4tWrTQ6dOndezYMVve3uvZcv1uv/12VahQQZL5+h06dMgVpbodW9cC\nmzhxonr16qUbbrjBBVW6L1uu34wZM3Tffffl3NBRpUoVV5Tqlmy5fjfddJPOnj0rSTp79qwqV64s\nf/9i2dfc47Vp00YVK1Ys8HW+Nwpm7drxnWGZtesnFf57w6bAdPjwYdWsWTPn9xo1aujw4cNWj+H/\nQDNbrt/VPv30U3Xr1s0Zpbk9W//d+/777xUdHS2J9cGuZsv1++WXX/TXX3+pXbt2ioyM1PTp051d\nptuy5foNGjRISUlJuvnmm9WkSRN98MEHzi7TY/G9UTz4zii8onxv2PTXIFu/gIxr1mXii8usMNfh\np59+0meffabVq1c7sCLPYcu1Gz58uMaNG5ezC/W1/x76MluuX0ZGhrZs2aJly5YpLS1Nt99+u1q2\nbKkGDRo4oUL3Zsv1++9//6vw8HAlJiZq37596tixo7Zv367rr7/eCRV6Pr437MN3RtEU5XvDpsBk\ny3pM1x5z6NAhVa9e3dbavZqt61nt2LFDgwYN0qJFi6y2En2FLddu8+bNeuCBBySZJ+AmJCQoICCA\nJTBk2/WrWbOmqlSpojJlyqhMmTJq27attm/fTmCSbddvzZo1ev755yVJ9erVU926dZWSkqLIyEin\n1uqJ+N6wD98ZRVek7w1bJk9lZGQYt9xyi7F//37j0qVLVid9r127lgloV7Hl+h04cMCoV6+esXbt\nWhdV6Z5suXZXe/TRR41vv/3WiRW6N1uu3+7du4277rrLyMzMNM6fP2+EhoYaSUlJLqrYvdhy/UaM\nGGGMGTPGMAzDOHr0qFG9enXj5MmTrij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| |
| } | |
| ], | |
| "prompt_number": 27 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Meters per kilogram of fuel can be computed by dividing the distance traveled over $\\Delta t$ by the fuel consumed over $\\Delta t$." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "data['FuelEconomy'] = numpy.hstack((0.0, numpy.diff(data['Distance'].values))) / data['FuelLossInstantaneous']\n", | |
| "data.plot(x='Time', y='FuelEconomy', figsize=(8, 6))" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 28, | |
| "text": [ | |
| "<matplotlib.axes.AxesSubplot at 0xb50d890>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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979SUJxVkugiIArEY8Mkn6S5F+ODxVmbvqJU4p5E+9tAt8oDoDqiv9++4TnsD\nYqPlNCbgD38ATjrJ2fkyHbMeR1lZ4t+lpcGXJVVkmwgIY4/7iCPYMpMtTrqI32BHB1uavaNW7y6J\nAHvoFrmA9whFd0BYcOoOoJ5HMmYiYODA1JYjlYRJBISxgXaK25iA/PxwPYsg4K5TXXcAv5dm98Xq\nXpMLwR4SAS7gvf5YDDjsMPfHCeIFdeoOuP12/8sQdeTn4uR+trREsxKPYpndEHaBkQ2je5y6H/n9\ncHNfSATYQyLABTwOIBYDhg1Lb1lknFoC/JiJK9PG73qpOEaNAurq/CtLqgh7wxP2xtsvskEE6MzF\nIf5GIiBYSAS4gIuAlSvDM38AJx1DBD/4ILXnCxonFYfqXu/e7V9ZADaGXw5K9Ivt29mSR2CHGS8V\nelREhBcR8N570bhOp9fHtze7NqtrppgAe+gWuSA/ny23bg3m+F4quw8+AB5/3L+yWHH++ak5T6qJ\nxYDDD9fbVtV4+t37KC8HbrzR32NyiovZ8hvfCOb4bohCQxYUXkRARUU0rFD8+lJhCSARYA/dIhf4\nFQwYVGX3xBPBHFfm739nywcfTM35UoWTRlxVMQVR8axa5f8xieBw+23n5HizyoQlaZkVTu+NnQig\nwEBvkAhwgSwCHngAuPrq9JRFRap9ipnoDtCtPFQV9po1/pYHCM8EVVEn7FYGrzEBYRutpMJpTIDd\n6AAryBJgD90iFwwZkvj3EUeEy6dq5TsLYqKjsFesTvFqCUiVJcZPJk5MdwmSCfK9Cus7m5vrTQSk\nc/Kr11/XE6t+BwZaPctMSugVFCQCXCCrba8fbqpYujRxelYRL5ViFK7dCXLlZHV9YRJ/XvAy1NVv\nwtpAO+HAAXf7ebUEpFMEnHUW8Mgj9tu5DQx0c1/27XO+T7ZBIsAHvPrx/MIuP3pTk7klwEuWskyb\nelcWAUcdZb6t6rm7bQDc8vHHwMiR3o6xerU/ZSEYe/e62y/KIgAA+ve338ZqLgAebOzEEhCGujfK\nkAjwgbBZAlpb1ev5qAYVe/a4P1+Y4iH8QI4JsOolp7rBV7FlC9DWlu5SRINUWRkGDHC3n1sR8Mor\nbJluEaCTN+Wpp8x/UwkDfj/Mhsm+8IL9OQlzSAT4QFgsAZyPP1avt6ogrD5MO8xcDFHDzBpiJQLC\n4EvPtOAnVUPttfGOSpS4WxHAv/l0iwCdd/GUU9hS9UysRIDZ90lzLXgjw6qP9JBuS4BuBWlVQXjp\n0WZK5HpwLjgVAAAgAElEQVRtLVvG40ZldNttwJFHmu/zzjtsuX07MHNmsOUzIyoNnFeidJ1ehghG\n2R2gMzqBP0ddEWB3LwcPtj8nYQ6JAB9wawlIdaVmVUEcfbT742aKCFBdh+4Mixs2AI8+avz98sv+\nlEmHTLMEZDNRFwHbtnnbXyUQ7O7H8OHezpntUPXhA+m2BOhiVUF4mQMhU0SAeH+cCLQvfSl5e+6j\n7+gILrMkJxtFgNuedthHHnh1LaY7T8D3v6+/rZUlQPztRz9iS/nZXXqpej3hjCysPvwnbDEBZlgF\nBnrxq4UhOM4P3Pai6uuBjz5KXMcr47lzgdGjvZTKYMkS4Iwzktf7IQIKC70fwy/sKnU/LGhBNxyp\ndgfojL0PEn7+IPKQrFunXn/EEfb7VlX5W5ZMhESAC+QP3K0lwK+KyI8P34sIeP557+cPA15Mqap3\nAvB3nPLf/gZs2pS83g8REAURmw3s2wfs3Gn8rftdpjs4jr8/Vh0NjpVgsYoXcMO4cf4cJ5MhEeCR\ngoLoWAKsRIeXnON2aXJjMWDzZvfHTxVeAoxuuinxb94w+9kzM2vs/ThHFN7fKOFW4A8dmigc8/P1\nOhjpNonz98drTIKbd5nmDvBGVomAESMSVbZXKiuB229nvb6oV6JB9ySiIALEpEBeKw+/ezSAuQjw\nwxIQxpiWdDds6aCoKHmdTsxNup8frz8WLNDfx6klIBvfh1SQVSLg44+DmewmKoGBVqTbnJhp8ErM\nSxImGbOgr0xzB2TjnAEichl1Ym74lNDpgr8/Tqxp1EsPB1kjApYvZ8sgGrswuQOssvcF5Q6YNs1+\nmyhUvn4ycCBb+hm5byY0M00EqIja++O2vKrZ83QsAekeGuhHvRqPG9fq14gjEhr2ZI0IWLqULYOo\n7MJkCTBT4i+9BPz1r+rfTjvN20esc+27d7s/fjpxU5mfdprRa/dTBJhVaJnqDpBJd4VulonTb+R3\nTufbSffzc1KvqgIDd+4ETj2VjbSRGTpU73gq0v3ORIGsEQE89WumWwLMfHKXXMKEgMzgwcyU6Oa+\njB4N3HyzXgUUxNAhv/Grtyk2yn72YM3cARQYGA50hqzZobIE6HybURIBKv76VyP7pgzPPRA1a1BU\nyBoRwGe3MvugGhqAP/zB3bHDZAk4/HB19r+ODvN98vLcuQMKCth91akA0p3EJJWIIuDZZ4M5rs56\nHaZPB7797XCJAKeVvZPtU9WQ+HmeKIgAN50IUfBYDS20Stvt5ByEmqwRAfxlaGlR//7TnwI33ujs\nmPxDD5MlQDVSoa7Oep/8fPcWEjsBdPvtxnZRwWsFHlQGPzsR4EbI9e8PnH46u+Yo9bRWrQL+8x/2\n/5wc85kzOXJjEMS1+tXgyGVzIgLS9Qyd1H+zZrGlWCekO6Yhm8kaEcDhjZKM20CUWMy9JSAIlaoS\nAQ8/bL1PXp57EWCX4ezJJ41yRRE3lWpOjr+V8VVXsaWdO+DTT90dn0+dnO7epIzV/AsbNgD332/8\nLWdsTAd+fM/Z5A4Q32crS4DdtxQl8RpGsk4EfPKJer2bl5i/fB0dQGOj+/39RCUCrFR2PM4+QLej\nA+ysILxSy6b89vxa/X6+XkZw2BHFXBfi/WhvT185VHh59m4sAeluCL1OoKZjCXBzjeQOsCeLqmaG\n2YvkpQL0MgOf3zgVAYBzd8B117HeaTxubwXhDWJULQEyf/+78f8f/tB6WzEewI/0wWIyI78JkwjQ\nrexFi4vu+xVkY0mWAPfopBt2A4kAe7JGBPCXwexj8TJqYORII/DQD7y8uKrKXK4g5YrQqTvgsceA\nFSuMY1tVQGaWgOHD1cOBws555xn/t3rmF1wA/M//GH/v2uX93GYNnfw8f/ITYPZs58dOd0PiFNEV\nZffNeG38UzldrVxWHQtQup+d1/Nnk6UwbGTdrTd7Wb0oWa9zgPuJjiVAJQKcmJrF4+XksFm+zNws\nZiJg504WmR52dBqPceOM7QYMYMvPPgPWrjW2SaUl5IEHgEcecbZPmIJbdcnJCdcMlqpevJdjvP46\nW2aDJUCHhgb1esoT4I2sEwFmL0UmiwD5muWyOnUHiA0+b9zvvtt6H9V937tX/5xhRh5+uWNH8jZe\n3i9eyek2LG4aoDC6A8Tet3hNtbVsmZMD/PGPyb+nC78bnK1b2ZJEAONf/3K+D4kAezJeBEyZYgwl\nAszHy3vpUYQlspqPVJA/yEGDEv/26g4Q4T1cszm/rYYuHX64u3MGjVhWnUpky5bEv1Wm/1S+H+L7\nrktnp3lPKyzwZ9HWxpZuLAH8PZefWZgQhx4D/oiAWAy49VZv5bLCbxGgimM69VR/z0EwMl4ErF7N\n5mB/+mnzbXbvdhbdLzdofg8J83IsXhbxGPIHJVcYn37qvgGwC/yz6u079VsHjarx9ysi+amn3JXJ\nDW5dD6+84m85vGJ373NyjLgM3efE8wl0drovlxl+uwP4//2yBNx5p7sy6eC3CPAyrTfhjIwXAYB9\ndLwY8e0GLw2G38Riyf5dOfJWLufvfuc+sx0XAWb3+N131efkZY06xx+fvE7M9c6tHWYpUYPguuvc\n7Re1mSRzcpzn9+DvnNNAP5131e9kQX6LgCDxWwQce6z+tnYxAQcOhKNuDiuuRUBPTw/GjRuHSy+9\nFADQ3t6O6upqlJeXY+LEiegQ7O6LFy9GWVkZxowZg7VCtNTGjRsxduxYlJWVYf78+X3rDxw4gOnT\np6OsrAzjx4/HVu4cc8myZda/u41MFV+sMMUFyOZ9UQQMGOD9gzjuOLbkQwQB+96neE5uHQhy3Huq\nsBva9J3vsGUqTZluG6OwxQToWAL4O6SbUMZpfEWq8WoJSHfGQD/Oz+d58YNYjNV5fBZZFcuXAwsX\n+nfOqOFaBNx7772oqKhA7NCbumTJElRXV2Pz5s2oqqrCkiVLAACNjY1YsWIFGhsbUVdXh3nz5iF+\n6E2ZO3cuampq0NTUhKamJtQdym9bU1ODgoICNDU1YcGCBbjllls8XeSjj1r/7qXxFqPfwyIC5LiA\nfv3Y8vvf9yd+gYsAfi7A3BKgEljPPceWfk0Xmk4ee8za1fTOO8AVV/hzrbrD4KIuAnTJydG3XqRC\nBPjhDhD35ceLwhBBP9+dIFI8//vf5r8tXgwsWuT9HFHFlQhoaWnB008/jeuvv76vQV+zZg1mH3Ly\nzp49G6tWrQIArF69GjNmzEB+fj5Gjx6N0tJSNDQ0oK2tDXv37kVlZSUAYNasWX37iMeaOnUqnuOt\nRkD40SMNS3AgwGbsE+dI4CLgjjv8iV+QLSCAuSWA95RV+4TVEiCW1e5enXYacPHF5r+//z5QVOTP\nOGgnSXTcEDYRYHa9M2eypRtLQFi+UTPcWgLS/ezSfX4zdFy1YbUKpQpX0zYsWLAAd911F/bs2dO3\nbseOHSgsLAQAFBYWYsehcVLbt2/H+PHj+7YrKSlBa2sr8vPzUVJS0re+uLgYrYeidlpbWzFq1ChW\nwLw8DB48GO3t7Rg2bFhSWRYKdpwJEyZgwoQJjq/Hj17awYNAczNQWur9WH7w8svACSew/3PzWk6O\nP2JF/Gjs3AGqXinfNqwiwG/690+t1eO3v3W3X9gaSLPK+YILmAk3J8d6fgHVsYI0m/sd4+JkdIDZ\nnCgiJ53krTxWeH13vNw7nWcZtnfbCfX19agPMLOaYxHw5JNPYsSIERg3bpxpwWKxWJ+bIGgWWjhz\ndD90PuTIK9u3h0cEiAFrsgjw0xLAH7OdCFBZAjLBHaDiyCONuIf2dmaJCfu1DhgAfOlL6S4Fw+79\nFLN/PvOM3j5RcQc89RRLpPVf/+XMEnDBBUYQrhmnneauTDqkwhLg5ZlFWQTIndtFPvsuHBsOX3rp\nJaxZswbHHnssZsyYgfXr12PmzJkoLCzER4em8mpra8OIESMAsB5+c3Nz3/4tLS0oKSlBcXExWgSb\nNV/P99m2bRsAoLu7G52dnUorgB26w4Dy84HPfQ6oqnJ8igTC8qKdeWZiSltukucjB+SP6dvf9n5O\nJ0mYfvELtsxUS8D11xv/j4oIqK5OdwmSMXPL8Hfts8+As87SOxb/Nt1+o6kcycKH6/Jz6iTJ0Slf\nkNP1futbwR3bC/y+mGU0JVyIgF/84hdobm7Gli1bUFtbiy9/+ctYvnw5Jk+ejGWHwvCXLVuGKVOm\nAAAmT56M2tpadHV1YcuWLWhqakJlZSWKioowaNAgNDQ0IB6PY/ny5bjsssv69uHHWrlyJapctM6t\nrcDQoXrb9vSw3pvX3kFYREBeXmLjG4sZKXpV7oCLLwbOOcfdufhHZlYJ8fG+4r3duJEtoyAC3LwT\nPHUwwCaXSpcIcFL2MGUMNEN+18rLgbFj2f/DZgnwAreq8ePpRMvrzE0RZOpqN9n8/EInbfB997nb\nPxvwHK7Ezf633nornn32WZSXl2P9+vW49VB6qoqKCkybNg0VFRW4+OKLsXTp0r59li5diuuvvx5l\nZWUoLS3FRRddBACYM2cOdu3ahbKyMtxzzz19Iw2c4GRu9Z4e1nB6fRmGDPG2v1/k5pqbEFXuAKvt\nVYgVKf/IePChjGoIHbdS+OWGCRtz5hj/P+44dm/8yHHv9P10IkrDKALsrtfJzHNeLQE6+OEOKCxk\nz6GjwzjeW2/Z76fTy8+0SXoo2M8fPBmIvvjFL+KLX/wiAGDYsGFYZ5I79rbbbsNtt92WtP6MM87A\nO4osKv3798fjjz/upWiOXng/REB5eXjS4FpV6Cp3gGw5sENlmp08Wb3td78LfO97iftwERC2Ssmv\nikOMx/j971lK5XRYArq79Xt/YRIBXFyaPQ8xzkS3Z5+KwEA/uOQS4KGHWM+aX+ff/ma/n464CfN0\n3n4/D/m9uOIKf4+fSYSsGvYP1Qv/5S+rt+3pYb0Kp70EOUo+LO4Aqwpd5Q5wagkwO6eKgQOT18m5\n0aOKjvm3pCR97gCdRp0/Cy/vQGcnsHSpu33N0DWt6zYeYW/8OVddxZYHDzpzL+g86yBjApxw7rnB\nn0N+zmF/7ukk4tWwOapGadYsFkUr8/LLLMjI6YsiR7yHpScliwC5nCp3gFdLgJN9+AQ3UUhT67Xy\nGDzYuzvAbRIg3fsbizm3BomsWgV885vu9rVCxxJgt638e2+vuxEyTu+92/eGN9Siq00HnYx9YbEE\nqDoGXtC512Gpm8NIxooAVS/T7KN68kngjTe8TRYT5oyBQGJQlVjOeNxbA6CL6t6GaS54vxGj1vfs\nYQ1lqnEisvr3Z0LYDU588zrYNYAq33vQIkAHPwIDVSJAJ8BZ5/sNUgTcfLP+tqnolcvnCEvdHEZC\nYiDyH9WL1q9fcMEkQVUsbrBzB6hiAtwEBgLeTPphHzbnhZ/9jGULBFjynt27gQ8+SEy57BTdho7j\n5Jl6EYJ+iwBA73sSYwLsEAMD3byzOufxIzCwvZ0te3vZyBKADfm1I90iIMi6z82x5X2s7k9Y6u10\nkbGWANWD/egjI2+97j662/k9nbAXnAYGOhUw4rY8K6GTfTiZPF3ohRcakwf98pdsaRaTEhRORIBO\nTMCWLeoRHUGJACeEYYigH/DcJt3dhljR6cWmWwS4dSdydJ63mzqKL/ft098328gqEfDPf1rv43Ui\noXSZnOQPyGlgoBcBk5sLTJnibv+wBgY68TXrcChlBjxOhmmKWRmdVMw61qDjjgPOPz95fRAiALB/\nDm4sAUBwiX/8OK4qVbBdvdLdreduClIEcDeLDkGNBBD5058S/37hhdSVJ2qEtBr2Tm8vC0C56SZj\nnV1ASja4A1QNvtt4BicBa6p7E4XAwCgThDtAlXktiEAvM4uVuOTbikszxMj4oAID/XAHHJqZPeHZ\n2T0XYdZ2JeIIkKDo6dEX9amoJ5uagtk2E8lYERCPA8OHA//4h7Fu3jz1tmefDfzoR+4Do4BwiQCr\nCl1lCXBqxXAzOkAFRewGi9/uAED9vIMYeua3eVh0W6UyBbBTuIts0iRjnd23aWeJ4fuLeUzuvht4\n8UXn5TNj82b97zkVLhmre9bcnPj7KacEV44okNEiQJxqdMAAFhhYXJy87RFHsN905xpQESYRIFfo\ncqOtsgS4jQnQ3Va1D4mAYElVYKDOdK1usBsiKG5jd+4g5qi3O4df2IkA1Xl5UCpgPFdxu+9+V2/m\nQV1M8sSlDat7dswxwCOP6G2bDWSsCOA+Kj4M7dZbrRu7I49kYsAtfooAr5WJ1RBBM3dAOhrksIoA\n3ee4e7f+MW+8Efja19yVR+axx/S2c3J/vVgCOHfcYf7bffc5y9qmawlwO6w3aBHgx/HlLIdOGDMG\n2LSJ/d/suaar0+JUsLk5nt09OzTXHYDw1kOpImNFAB9ny+cQGDHC2uztdJgcwHIL8KlM02kJkH3z\nbjIGbt+u7w7xOmTnpptYfv2wfXy8jK2tehWRnS9WpLLSMNv++MfsHG5ZvFhvOydzM+i+/1b35Y03\nzH+rrQX+8he9svBvV8cSIO5jd0yr/cOMXYNmdu3797NlOkTAvn2JUxvPmWO886moJ+UMkfJIJHF4\nctjqoVST0SIgJ8cYd/v1r1tbAtyIAMCo2NIpAvjwF+76sBIBXV3J5eSNUyqn2wxTrnqZn/3M/2MO\nGGBYpX7+c8DL1Bi6SZacpPLVdQdYNaBW++vMhCefx888AU6P7YagxIXdc7EbHWK2v5/34MILWdwF\nP+YddwAnn2z8/tBDhssgFfWkXJfLcRO8XQCsRdawYcCrr/pXrjCSsSKAuwO+9z32d06OuSWAZ81z\nM7Xtr3/NlukUAVzV6oiAlhbg0CzNfTgd4uVH8o4wiwBdi4gTM23//omNt5dr37NHb7uKCv1j6roD\nVLNz8sbPan+nw0FVPnxVcik3QWZuGutUpQ0GgPvvB047zfibT73t9Lz8/UzFKBw+CRvHr2flFvk9\nlZ+fzrf4ySfM5ff22/6WLWxkrAjgJsVTTzXWWVkC8vOdV8xHH21kgEunCODl5h+7XQO7fXvi32Kq\nUh3cfMzymO9UpCp2i1iZWV2jExEwYECiuPBy7WYiRS5rZaX+MXWfx1FHmf+2bZv5byNG6JeFI17P\nww8DL72U/Lvbby7MloDRo4GRI/W3N7sW/n6azSvg5z2QZ6xUTS1u5ZKxund29Y1qvV1yIPF8Zu/9\njh1syWc9zVQyWgTk5AATJhiNnlVD7cYdYBd1nyr4S2xmCZDLJWeu85LsxW1keJgtAbpD3pxcsxdL\ngFwJ6jY2TuZm0LUEWM3JoZgVvA+nglG+xmOPTT6f2+Ob3b/WVuuMom6P6xQxb4dOsDK/9rFjE9fb\nWQL8rK9kS4AqJwE/38sv2x/Pa9n4NesIB7NvkbuwwprUzC8y9vK4OyAWM3JwWyXFcSsCxAQmTl9c\nvz5CM0vAJ58AN9xglA9gAqCoKHF/LgJ0U2uaJXFxsk+YRYBuUhUz15IKWQR4GZakMyrhqKOciQDd\n9z9VFaL8PZ1yCnD55cZvgP8xAbffrp5l1A1evm2xnsrPN+ovK4YOTf4OU+kOkC0BVhaj7m7r++NF\nTPE6jF/zAw+w5ccfJ8bIiOc3C9Ll7/qQIe7LEwUyVgSoehN2loCdO91nEguDJUAWAf/4B/Dgg4nb\nqj6wWIz5IHft8r9sZvckzCJA1xLg1B2wYQOrjIBgr33IEOYGczKEUdcd4GR2ThE335WO2HQbE6Da\nnkfTuyUIS4BONk+za+cBbakIDNSZmMmpu9ENAweyeAR+zdykDyQG+Fmd49VXgalTjW3EJEuZSFaJ\nADtLAAA8+6yzc3CCEAG33gr88If228nuAF6hOwl0dGIJyfTAwMmTjf9bXauq/GYNAX+/eAZLN9fu\nRHQ89xwwf77x9/r1iSm0ZfxwBwSJyp/s9nuze0ZO9lFtI7ou3CDm7dAVAary/fjHbJkKd4BZGdwe\ny8v2PT3GNYu5Kf7zH73jrV8PPPFEagMZ00lGiwC5wrKzBADOTKhe3QF2H82ddxqjD6xQWQK6u81F\ngKqcOuXv7WX//PgowigCeGVbUuJsex14w/Dkk2zp5tr58xw3zn7b668HvvpV4+/77wfuvdd8+3Xr\ngJoa++O6dQc4jQmwmjtAdVw/LAF+TYTktUOQm5toCdAZIuhm6Kafjdurr5pnKRXX6ZzTa73Q26uO\nCXjtNevyifsDxjEyPaNgAFm/w4FqVisdS4CTSTZSERioY4riHw1Pe8wbWJUIMKssdMp/9dXu88TL\nowP692fm6p6eYCc2ccJvfuNseyeVA3+OXNS5SVHNn6dOJTlyZGKyILsAqaef1iuDWxGgGjJmhU6a\nXy8xASq8zoGgmuDIDWI9lZur1zGxOmeqJuqyexZWz0s1tNFLOdauZf8Xh/eJ766qHO++C6xZYyQB\n4/edLAERxU1MAKBXyalMk+kUAWeeyZZixaHrDhAjzu3Kv2IFy/zmh5nsyCPZ0qsf1k/Ky9ly0SI9\nf7qTykoWOm7uHW9Idcy7Awcmls+qrLEYix3REWNuRYCT7IUcJwGofgTlBjUlslP8igngBG0J4MfR\neX90zulUBMjH7O016r5//9tYL2YNlPcZPJh1Am67zXhX+WgXEgERZe/eZAVt9UG5Ue/iy+F0Eh5d\ndCpdOdqfiwDewNqND+ajKHTK72R8r9U5ASYEwuQSOOMMtty501inKjdPQOVFBLh533jjb3bP/vrX\nxPNZDROVKSnRyyvgNiZgwAD7bTgqAW8WE+CnJaCsjC3dpnT2q0OQk8Ny23O3iNuYAE7QMQGiYLE6\ntu75vIqWvXvV68XnanUs/q1OncqWme4OyFgR0NubnLDC7OOUTdW6yDEBfr0sYhl0h+2J+/EGQDQ5\n83KuW5c8YoD/risC/Ko8RN9nlLjrLrZ0UnY/ZrGzswRwM2Y8nnxv7c6nG6Ph1nUTptEBZttzoeLW\nIuCXO2DgwETXns57ZhUsaTdm3iuqWQpVfPihUQbZ9eJncOmVV6rXf+c7evvzjKrcWkmWgIjywQfJ\n4zt1LAH8wevC99u9O3E4il+ce67zfXiFbiYg/vSn5HVuLQEcHX+gTLpmL/QLL2WX78cPf8gqSSue\nf54tdd5Rp5YAnR4n305GpwJ3Kvbk99FqdIBTYWpmtbMSFHKWTbPjAs6EuwpxRlMn7gCz5yDHDMn7\neUX32S5enDiCKSi2bEle953vJFpMV6+2f2//+7/ZkkRARLnhBqC+PnGdXWVx9tnOXk7xWJs2ATNn\nOiqiFnJAVXc3UFdn/L1+fXJua94A+BExrbOd23HiYRsh4PRj9yL65HMtWaKfrU6eE0DVCDgVAbrP\nwm0Mhx8jZ/g6MdbFzhSue2x+LMC9dUqcQ8FLwyF+Y36MDuCWgDvvdF8mK5x8w3wIthOLkt0oJ/le\n9/QA3/gGcP75xjp5CKwqJ4p8D3kMQTzORvU8+qh+maNExooAFXaqWrc3JBL0GGk5T/zGjcDFFxt/\nV1UBP/0p+7/sDnBqrtYVAU6HUZoRNhGgwqr8Tu6DznF1g+7kfcUYBo58b+3eBd1noZou2O9kQXYx\nAcOGGeu439zJ8XfuBLZuNf5etIiJG68iwC/E63HqDlARdGAgL19Bgf223NXipLP1gx9Y/y73/Lu7\n2T/RreNmrhLxfbj00mA6eWEgq0SAXUPnVAR4/Yjs9i8sBF54IXGKX52Px60lQHc73aQbIirTbthi\nApw+TzfD/KzO5VYE8NEhsiXAaUyA1x6wFX5aAuQJr9wIcTFx0sKFzGpoF1+gOx2y0+GQMmJ8kR+j\nA4IeIqgbEwAYbg4nIuArX7H+ffHixL87Otg1y3MZ2N0HeQQLv64NG/TKGVVIBAiEzRLAP5hnnjHK\nzV9kq3HdubnM3OU0aEp3O36PvJo8oxwTUFFhDCn0C6/vkri/fG/t3msvzyKIYV/ysc3Owa0GTt/F\n8eMT/25vt7cE6J7j4EH/3AH8/1bH6+xkownMSNXoAJ33l88pYCYCRo0CJk1KXDd0KAuWNEN1fQ8/\nnOgmtcuIGouxHAEi/Lp4IHCmkrHJglSIAUWqFzbVlgA7+BjXK69kZtjiYmNCoEsuMR/6d+BA8vSX\ndh8ozwaogygCvAwRjII7wIyNG7012k4sAXZDPDlWMQF2BP0svIgAOepeHiLo9Dmce64xGRFHbLi9\nWqfk2fycIosA/rfZdT71lPXxgnYHqCwBVqINMBcBF1ygft+tnrE4XbyI2LN3835nekAgJ6ssAYB1\nQ++0N+SmAnJLfj5LXmE1XStn6FC2dPISv/gi8K1v2W8Xixn3yGlluX49MGWK8XfYRICT+zVggLd5\nxr24A8xIRWDgF7/ormxOYwIAvUaFb+d0dID87l57rTECwGvlX1Dg7RhyjINd5+Sss6yPl448AXbI\nwzC5C0X1TfG4DxW5ucxfb4ebWWJbWpxtH1WyTgRYVRhu/KJeRICTfZubjSFiZsh+RKcfeWOj/Tai\nv9Kp1eTPf2ZDczhhEwFBc8wxxv9Vz8aLVUXGjQjQeZ4nn2y/jQp+7JNPNgJZrVCNe5dHB7i1BJjl\n9FiyJLGsZmWy4pFH3A3rlc8jlsHOnz1woJHoSkXQ35iTmAD+POVhrlbJpPgMhV6+GTciINPdAJys\nEwF2loAwuAP4cSdOTFz/f/+X+Lc8XOvTT9mSVyJm5RNHFzhFtAQ4vX7ZBOgmBiNIgjb/rV+fOOxI\nxm1gIMcqMNBu+KGuFcyu0r3/fvV6XuZ332UxLjqYWQLE997N6ACz7f2YMOaMM/yJ7RDLZ9eAqeZJ\nEZk2jS3lALsgYgJ0jjlmjDHCgyPndBGxsgTYMXgwUF3N4qvEAGvVOezgZYjF9CbxigpZJwKsXlSn\njdLevcG4A3hj2dNj9B57epJNsWZjtnklp7rOn/yE5UNwSyymriztPqJ4PDmqO9ssAQMHGj2eVLgD\nnPR8dJ+F3fu+cqV6vXi9TsqlsgRcfDFwyinuLQGiQFJ9734IU78CAwFmOre6Z+I9sDqvbmyJU8Rp\nj2sq3pYAACAASURBVO2Obdegq97BTz91V88ef7xRjoIC9VBaJ4i5Dd5809uxwkTWiQBVQ89flLD0\nTPkLv3cvG888aRIr4wUXGNvk55sn0eDXoboWP9IbyxWo7gcq+gFjsWiIAD+tA2JvOwh3gLh/Xp7e\nBFKc3FyWW93ueu3KaNZYie+cnQiwiwnIzU00f3uJCejuTm6U7ILazHjvPVY2rx0DWcTbWQJ4w5qq\n+CQZ3ZgAnkfASrCorESPP65O7sNRHaewkGUJ5ALpsMOSc64QjKwTAWYVBh+yFpYhgkcdxXzo/Bxi\npdC/PzB8eGLmwKuuMoIGZXeA3Jvy2ktxMkRQLIPsDgibCAjaHZCba/RGgo4JsBKJKngF/q9/WW/n\nVgSIPScdS4BVTADHj5iAnp7kOUbciuT+/dkUzl4RyxeLuXcH2Fn8ghwdYHdOs21PP93Zua1SJYs9\ndzcxATJOvqcokdEiQP64AX9iAlIxdKSqis3sJp93yhTmBti+HbjmGuO30lIjaEvl8xSHV3kVATxt\nrdORFCNGJK7r7jbiGLIBsVJy6g741a+M/3NxKCPmLXCaIY2Xza6itOvt6VSUuuUS31Wz99lrTEB3\nN7v2F180fncrAkQrgJ/uAB1LgKox1HHR+YGuRdBOAADmQwdPOEG/PCecwN4xOVmQ3Xtnl5QoU8lY\nETBhQnLyB8DfmAB+PLfofoRPPcV8+fE4i6q1O6fV6ACvUx7HYswfC9gHJMkMGsSWvOHv6UmfCVNF\n0OLOzmdqdS/EkQVmiA2QWxHgxh0g7qOTLe+f/7T+XWyg+cyIqnPrNCqqMooxAfv3s/fx859Xbyti\nd22yWX7fPmNGOifI36ida8dt4FyqLQE65xs+PHnd0UdbWwjk4w4YYIg7jo4lwC6LoRzMmClkrAjI\nzVVPUuFnnoBU8uab+o2u1egArzEB4vmdjqTg2/NZ1ng+gzDjpzAQ30fVe2ZXkf/732xiFEA9hllu\nONyIALtn6tYd4BTVcECRfftYMhjeC3YbE/Dpp8x/LPLjH6snh7K7N3woG6e+PtFap4v8jbp1B6Ta\nEmD3/uoMW1ZNcmQlclTX3dtrWAKcBCGbTWrEzyFmlcwkMlIEvPmmeSCJVYXxxBOJfnYdUtmT1fV/\nWo0OkK/f6Ust7u9UMMlBcV6tElHDizsAAI47DrjvPvb/G29M/l3u7TppkPm5ddILW+Gn3/SooxJd\neuK7X1trWMf4+qVLrdPnisfh13n//ckN/urVwAMPOC+v2BjH4/ZTQ1uVz+noANVzsfu2/OrwOLEE\nWNVhZ5xh5A8Qy+7U4shFgGwJsLpeHqgsc9JJwHnnGX+7nUUzzGSkCBg3znwIh53Jv6bG2bnCKgKs\nLAFm/lUdxP2dTKUbjxsqn3/UfoxU8JMwuwM44vBRGVkE6FTyYk/JrFxOyujnZDW6Qaj8nfzmN5kw\nsEMUn7//vXqb3/428e/iYvvjyu6Ab37Tfh8VfuUJSJUI4M+Iu/uszq0Tv6By+zjpMPT0JIsAHSsv\nF7BW0xyTCMgAvAbGpYMlS4yc2vwDWbgwcRvxw7GLCdi+3duYWbf3j8+F4DS5SJSwGobk1RIgonKl\nWLkDVL5WjtgL8uoO8MMSYGYpMms8YjGjcrby6/JJg4qK7E3Yu3cDb71l/M3vzwUXmDcmTnusZqhi\nAtwEBtrhlwDv6WEdL1UgtoidJcBsfW+vdcMsf0u9vcmzCOrcH14/idkL5f28TB8eVjJaBKgevN+5\nAFJhCTjhBJZoRvyAfvpTlkPg+uvVZbIaIrh8eaKJywlyw82nFXbifxQDusIkAlRl8bN8YoOjegfN\nGiS5cTn7bGDy5OTtrNwBdr0gO3eArsnXrLE6/HDr/WTkIbtWPcucHODuu623A5gIiMeZIOrtBR58\nkOXiUJGfD5x2WmJ5AJZ50SxAUDTLew2+dRIToOsOUDWWfiD3us3gdZITsbt7N3PtOhEO/F45dTdy\nS0ZvL7BokXqbTMw1kNEiQMXOnd7VnDgJDuecc5wfx4mAUKnoY44BvvSl5G1zcoCGhsSPXBwiCHjP\nnsUpKnIuhDLZEmCFWfIb1e8i776bGJl87LHmAVEc2RKgGxT14IPq380C9OTz6k5Wo/Pc5cbQqiEo\nKmL/l4ehquDigs/IqRLSPL2z6txmYpF/n147Bip3gJWFRY5F0MUvESAHRJqVgwcHO7k/fFInJ8Lh\n4EF3SZv45EW9veZpgZubnR0zCmSdCABYT1iEv7DXXgssXmy//5Ytyfv++tfe0vFaIX7g8ottZu3Y\nuRP4//4/9ve8eYapi39MdsNhrMoifuA8Z4AZKmtEWC0BqUTXHfDBB8D77yeb81X7i3EwsiVAd/y/\n3PjJqNwQv/mN8f/2duv9OTr+aCtLwMMPM2sI/ya4SOJZ6ayIxYCHHko8j4zVhDYqnAxXtMNNYKBK\nfKQyMFC0BNxwA/D976u3deq64IGCTp4HFwFOOekkthRdCfxZ8HdEdBFlClkpAlQfFK9InL48fMx7\nKlIOq0Y8iJNacORewb59RjIUfu1+iQAnfP3rbBlWS0C6y6JqjPjzEkWAWSUqNuCyJUAnKdNVVxk5\nIJywdav9NnKZuUBVoRMTMHCgMQSstxf4+GO2Xqdh+89/Ent0qvtpJWbMerk6ufN1kK97wwbr0Qo6\n7gB5MjLA38BA8fxm1iS7mAAVubksT8Bhh5lvI9/rri7z+s1sEqF4nI1GARJFjWhBPflkYP58/bJH\nhawUAWYfqJs0trxyTVUKXFnAqD4o1YQenLY2tnQ7V7bYcDuxfIhDFsXRAelueO3Q7dk6xenoADlR\niZ1Z3k2aVKt32Oo5VVXZH1ve384lZxcTIL47OTnGvBQ616wj9LlpWPWOmt17scHguTDcJPFR5fmw\nqlt0RgdMmKDezw+cxgQ4tZZYba/6ravLfLTLI4/onVN1PZma3TSjRYDZy2MlApx+GFxZBm0J4D0e\nOTe5lW9YZRq9915v5eCV4n33Ac8+q79fFNwBqrL4HQ3Mx44/8UTyb2bP8oQTzMfLi4gNYF5esoDh\nDZsZboWsnUtIhAdf6dxXq5gA0dpVUGBcu44I+MUv7LcRz3vwoP17KveG+f5OAyIBdm38OmIxFrOg\niv3h2A27GzEicfgex09LgNhoyunOxfLs3evsm3JTP4jZAuV7oxsLJVtY43F/h7+GCVcioLm5GV/6\n0pdw0kkn4eSTT8ZvDjkF29vbUV1djfLyckycOBEdQs7PxYsXo6ysDGPGjMHatWv71m/cuBFjx45F\nWVkZ5gu2lgMHDmD69OkoKyvD+PHjsVXH5qiJ2YvlJmMgHz/sRgTovuD8RezpSe5ZqHoafHvxN24e\n4z2m44/XL6eK4mLDX+eUsIoAFU6igXV6OJ/7nPlvqmfZ0WGYuu0Q313+vMU4Abvy9fayuBgVVs9J\np3Lk+19xBVuKEfYffQTcfruRAZAjmsWtgvFEq4fO93vEEcw1xdOKW0WYA4kxQFZlUYkANx0D/uye\nf571Pu3EmZ07QBW457ZsKlT1kgpeHqcuV7ugR3m9WWCgaI3UPafIWWcl/q2TmCoKuBIB+fn5uPvu\nu/Huu+/ilVdewf/+7//ivffew5IlS1BdXY3NmzejqqoKS5YsAQA0NjZixYoVaGxsRF1dHebNm4f4\noacxd+5c1NTUoKmpCU1NTag7lLKvpqYGBQUFaGpqwoIFC3DLLbf4dMnsRXj88WRF6qYndOGF7vd1\nimq8rBz1LyK+8LwX2NTE5mO/8kp3ZXDbcIv7hDUmQIWTXotT068c46F6hrGYWrCp7ptsCQASo8rt\nyifm6XeCmx6SaJZdsYINeR05Evjf/00081tZAsRGxYklAAB+9zsjH71qyJ/4LV90EVs2NTEBoeMO\n4N+qm/c7FmOpjBcsYAG9XpMFmYkAJ8m+rLAbxy+WJx53l4PfyRBBlXvCj4BN+ZiZMqugKxFQVFSE\n0w4Noj3iiCNw4oknorW1FWvWrMHs2bMBALNnz8aqVasAAKtXr8aMGTOQn5+P0aNHo7S0FA0NDWhr\na8PevXtRWVkJAJg1a1bfPuKxpk6diueee87blQrs2QNMn84CbkTcuAM47e1sKJdXZsxg/1SoPmb+\ncosftGpaWG5O/tznmH/Q7XW2tbG0qk4/KjEGgVdO3d2G7zQMyBX2kCGJM/PZ4fSe7N6deF4zd4Bu\nhSY2FPx5ixWuXfmsgkWtGjN+HU54+23j/6LQ+va32ZLHjJjFBLS3s/dQtgQ4ESQ8kHLPnuTfenqA\n665j/+fiKC/P2sUoBwbm5bn/zsRoeLeWAI6ZCPBLgMuWAKseu11Z/cJp4LPZqCvRCuE2mDrseL6s\nDz/8EG+88QbOPvts7NixA4WHZuMoLCzEjkMt0/bt2zGep+sCUFJSgtbWVuTn56NEcCAVFxejtbUV\nANDa2opRo0axQublYfDgwWhvb8cwSUYuFFLnTZgwARNUETAS3I/Px6ByWlqA115jpkkdfvhD4/9u\n0kmKH/brrzO/XW2tett4XD33OT/G5s3GOv7ibtpkrBMbko4Olh75l7801k2a5Kzsci/WrkK57z5j\nPDbf9rnn2L+wWgPa2uz96CJOK7c1a4A33mDD3cwwq8BViA1gLMamlxbfsaBEwIIFeuUTjyN698yM\nfFajA3iQFq+c+ffnxBp3xBFsKYpljtiwdXQkjrxQ3YuurkRBy8Wb296ieH/sRICOJSBIq5sTS4Bf\nWRX58cwwCwz04g6QrzFV9VZ9fT3q6+sDO74nEfDJJ59g6tSpuPfee3Gk5CCOxWKI+fW0LRBFgIzZ\n6cePZ8NYZJX+hz/onfPcc5mfUAww4r52J9xyCyvH5s3J/iYRfh2qj43f9vXrk7cXEV/Yhx9ODhr7\nr/8ChFANW8ShNrqPmQ8dCtN8ASLiPdq0yflYcaev++9/z569OGZdxkkvjluDRDO5k0Zx40b9bYNE\n5Q6Qr3fiRBYwyUUAtyy4cU2oAmi7u9n5r70W+OMfjUbZ7BmLGea4BSMvz5/AUh1LgNU3H3Tv26kl\nwMl3YtfQmokbN8mC5OOKy3jcXe4BP5A7t4vM0hm6xPWrcfDgQUydOhUzZ87ElEMp9AoLC/HRoWiJ\ntrY2jDiUvqu4uBjNwsDclpYWlJSUoLi4GC2CnZiv5/ts27YNANDd3Y3Ozs4kK4Abzj/fqNz5Q/3g\nA2fHGD06ecpLNyIAYH5GXVQBOLwH9Mwzxjr+UYgRwWLDq3qZ778fOOSJ0UK3Ibfzn4aVMWOc7+O0\nouVBh1YVnZULSEYeQWDlU1chB8CJWJXxkNcOo0ebDxd02muyK/+AASyJkvweOhUBO3YATz7J/v+n\nPwGXX87+z781MR6DZyXUuRYu2Ht6Et2E8bjz1LNO3AGqUThuhuU5QeycWN0bt+4Ap0MEAfMG2658\nOufMNFyJgHg8jjlz5qCiogI33XRT3/rJkydj2bJlAIBly5b1iYPJkyejtrYWXV1d2LJlC5qamlBZ\nWYmioiIMGjQIDQ0NiMfjWL58OS677LKkY61cuRJVOoORNYjFjA+KvyjNzcA//6l/DFVjbDd5hhWq\n3oIqBEJlCTj2WNbYi8lA+As8cKCxTvTbqj4QPgeALjqZ2cwIqyVAxE0l4HSfQxrX8n44mSFO3k5u\nPIKq2I4+mi1LSqxHP3BOPBH43vfst7OKCeBumhdfZCmyOatXO4tRGDHC6BRceSXLrgkY3/jNNxvb\nDhhg3vOUr1v8Vk8+2VhfW2ud+IbDA44BfXeAWcbAoEUAv1c650hVB8DMteXUHSA+b/kZhNWN6RRX\nIuDFF1/Eo48+iueffx7jxo3DuHHjUFdXh1tvvRXPPvssysvLsX79etx6660AgIqKCkybNg0VFRW4\n+OKLsXTp0j5XwdKlS3H99dejrKwMpaWluOhQKO6cOXOwa9culJWV4Z577ukbaeCVWCxxykg+bvTL\nX0708VtRW5vcg+cvnZsG7txzk9fJ41l5TIAsPk49FejsTF4HGD5PGVEE8PJ+4xv65QWAmTOdbQ8Y\nGbnC+vF47Qm4rdz8sgTIOBUBVu4Pq6F6HN0htvn5ib5ys0DYzz5jk/jwwDyx/GIGRbFnvWULC4Y0\nywxnxwUXMKsYwK5nwAAmkPlwMLN7OGyY8U1w8aLq8eqIHyBRBMjZH2V03AGpsgSI5xXhnSReloYG\ndSyGG8zcAWa/6aAadcWfwcsve+sEhQ1XMQFf+MIX0GvS2q1bt065/rbbbsNtt92WtP6MM87AO++8\nk7S+f//+ePzxx90UzxJRBOTkAL/6Ffv/0KGsMb74Yr3j1NezYU0c/sHv35/YA1chv5jcF7t7N/s4\n+JAkscyAfgDO0UezJCViohIx5uCCC1gaUvEFnziRjUvWxU2lotOQhAU3PlS3flcr4ejEfCrfV6ci\nYNw4VsG5bTRk8/2yZcBXv5rcK5NFgFnwFhfa//3fwNixiduIx+zXL3mY3513Aj/7mfNrAFisBmDc\nd1FwiGPNr7+exQWNGJHc6Mv38Pe/Z8GFcjCyGeI9sQswtMsTkCpLgHxekQED2DPauZNtu38/cOON\nxiRObjG7rsMOc3/Nh+LRk3jjDbYcP96+jo8SGZ0xUIWYjQtIHB7k5KWprk4+LqAXDaza5qij2JC0\nCy9kQUhi737XLuCpp5jbQtfScPTRiR+j6K7g066KvzkJItNJXvPuu8kjCLwkUEkFqbQEiNPUWk2X\n62V4l1MRYGWp0TmfnOHvmmuA3/42eTtZBIhl5N+VWFbuizcrf0MDG+nC/fmAt0A43idRHaOzk+Uy\nANgIGy6c5bHp/Lnx6/zGN8wtHirEmUrdjg7gpNoSoILXPwcOBGMxkzk0SC1pf52EQ/L94r/JLuMo\ndGZ0yGgRYJZ8RZwWVUzPydfZceKJwKHQhT74S67TwImmyt/9jgVWieP8jzkmMajvH/9gy7/8Bfj5\nz+2PzxHLIqYbln2STkUAbyysOPlkYOrUxHWyJWD8eGdD8FKJm0bEbJ55FWJPIih3wMGDzvKd8/NY\nvcOqOSfMkvsA6ngXWQSI+/AGX+TUU9X3aPdu1qM87TQ2a90TTwA/+hH7TXeYrwqeuNQsxexPfmKM\nFhDvmTjpDG+YVb7pgQPZt1xaav7sjzuOHaOpSX90wKZNRpCj6veg0MkYKIoAu2137Uoe6eK0/LIr\n1Mn+cuInFZkUOJjRIkCFKAJ6etwFTnV1JW974olsqSMCeET/f/7D0pc+/LD1hyGOPNA1n4m9si98\nIXFmL7MxsH5lPBR7RwBw3nnGcC7A+Mjmz0/s8aQbsUJ2IwJ0hxS2tjKrD8cuMFDXEiCve/PNxGh9\nu/fbSgTwY/NesArZEgAYglMsm5UIsOpRyuUfMiT5nv/854brYMMGd7013mBZzR0/ejRb8nsmN4Qt\nLcbfYszCoQFTuOIKlg3QakQGz/XgZHSA+Hxky5b4txis6JX29sTyWcUE7NhhPEez4L01awDRc2z3\n3qrOx+tMN3kCghZNYSMrRYCYZ1weUmT3kqxezT5eVYap4cP1RABP7CPPEW/Gd79rTCCikQupD16W\nU05RTyDy7W8bvVevIkC8b1/+MlvySvS00xJ9utzVkYrpl1OJrggYOTKxh753r/m2XiwBMnajDHRE\ngN3xZdeGPGtbPM4qaDGRlfjeial233qLve9WZlwVv/41W55/vvvZMgG1247Pwsnh1yu6A/r3Z3/z\n+92/v2GhuPDCxGuxmlKZ48QdIL6DVjEBfuYNOOww++Pxe9PZaZRl7lzjd68BfKrzqUZyOBUBQSZZ\nCgtZKQJUlgDVEBsV4nAkGbvo6BdfZMPC7rpLv7wAM79zN4CThBX85b300uTf3ngDWLjQUMw6IqCo\nSJ1QRrxv4vCsU05Rj1vmvrWwigAx54ITnFSsYgIwuwreaYVtJirsTJs67gAVVsl9Xn018W+ABR++\n957xt3j94uxtp5ySOEmVrvjxatHhDbbKlSG7K95/ny1Fd0BREatjxHPzFMRynheeY8FqdI7O6AB+\nLl0RIKZt9ko8bj4SiSM2qnzo5THHqLdxwt696jpJrCd1jis29qr7pTtMN4pktQjo7nY+9nPxYrZU\n9frsGrUvfIGNJ776amc56QFjtkJVj96MeBz461+TRxsArHcu5l7SEQEDBrBRFFa88gpbTp7Mzg2w\ne/7eeywIUxyxEDYREI8DX/taYs4FJ7jtobtNCfvBB8kBTLxRclM+VcIZ8dh2WI3rF5Gtb4cyhQNQ\ni1ynlgCxB88tXffckzxXiBk8RbBZjEd3NxMIY8awwMf332d5Pfj969fPCC7kHHccu4YRI9TzZXz9\n6+blESdIUiG+I2IAcCqHCKoC6WRKStiz4R0uv759PppDxEt2Px3rQSa5CzJaBJgFBvJhOt3diRHs\nOg+Wm+W5T1BEdwKiWAw44wz77US4L1FWz1Y4ScxhJgLEykzneFykLFyYmHWPZ00T/YBhEwGAt487\nCBGgGv7Fz6MabuYlTa1bS4C4vywCSkvZUrw3cgUtWi6sJkvSvb8XXsjEaHm50ZBv2+ZsApjS0uQR\nQGIZ+/VjHYLt29l7zueBAIz7qJr9XLwGMU231URaTtIGp0ME6AxjjceNiZ64CDATC2eeadSvOuJP\nmH6mDy9zB8juAHGZiWS0CFARi7Fc4ECiJUB3dMCAAWy4nuqlsGrUxI943z69rGEihx3GXBGq6GkV\nYoSyDmYVzSmnGFHSuh/75z/P7pPqXor7h00EeDXvOakoxLHIbt0Bqsh/bpZV3Vc/AgOt6OxMFgGq\n70pOsS2LAFXF3dysP9Vxbi5w9tnMF89F0Ze/7GxkTVMT8D//Y72NnMSUx8DojBIZPZrtz90AVsmN\ndAMDjziCWbJUv6fSEmAGz3eQk5M8OZTITTex58exOvaZZwLTpiWv/9e/vA1F1LFskDsgoogPl5v1\nVL+piMeBv/3NvEdh1ajxYX6XXsqitlVzxNtRWensxdZptDn797OARxXi+H678x88yCp5s+14UCD3\nDYZJBADeKksn/mcx+tmNCIjHgRdeMP6+5hqjtwWoLQJeRIAVYmWo4w6QRYDYAPLy87KuWcNiCJYs\nAR57zFm5CgrYMNUVK9h76SW1t4ojjzQEyg03AD/+MVt/7LFsqZp/gl/Xhx+y+/3b37K4CW5hVKEb\nGHjVVYm++VRaAswaTfH/3K1h5w4YOFB/aKvZdZnNPO9XYGAmWQayWgT09CRPAWz1knCTnSoRBWDd\nqPFx4T09rGdt51v3AyeWAKueiNibs2vouAjg23O46JGDtsImArzgpGIQYyPs/L3yPX/0UTbOvL6e\n/dbSApx+OvuN319xCKJu+Xj+e11LwObN7Jh8mJtuTABvJDliA6gq4y9/ySxgctZAO158kfUIb7yR\n9c7dTvJlR0kJy8B57bXs75NPZvdBnDiIo+pAnHWWddl03QFygyU+i3S5A8T4DFEEWFkCAPN8B2bn\n1y2nGR0dyUOYAXIHZCTiwzx40BABOqMDXnqJLXlufhmrRk3sTXd16Zv1veDEEsAbJZUY4PdFR1T8\nv/8HrFuXvJ1qtsSwiQCv5j0nlgAxWZBTS0B3N6skP/6YBbvxoFHOMceokzDZlY/PnaH7THgyqJUr\njXX8Wqx8sVdemfibLIj37zdcBNXVLL5k8mRjch9deAO0Z0+iOE0FvKGT4WXgwYc66I4OMBMBABva\n+Je/6J/TCVaBgaLAzcsz3AFWlgAe/8SPtX+/9cgW1Xcr1686jTh341hZNkTxTu6ACGAWGMjp6XE2\nradd0JWOCODndDpXvVO6uhLHKtshzn0g48QScMwxQFmZsf1NN7EKPRZjwwd/8hP22/PPBy8C3ATJ\neVH8TjIGyu8hoK5UVCJg1iyWbCYvL7EBtauU7K7t8MPNc13wY4vJnbiLSzy+yhIgR8rn5ycO/ZMt\nIZ2dRmV72mnGt+L02Yjf9qZN/rsDvMCzEuqQm2s9M6I4i6AsAvjzsMpF4RUdSwB3VYmjA8ze13Hj\nDBG7a5e1pczsneDJ0ZwEBuoMEZRjXTKBjBYBKry4A/iYXjOsGrUPPjDOWVcXvAjYupUNvdJ9Wfmo\nA1WPQ7QE2ImAwkI2QZF4Xh6IOWSIEbX7+OP6s865YdMm5/fYq7J3Mixp8mTj/06HCL74IkuRK5q4\n/aqUzMy0YgXJ+cEPkreRRcBVV7HgUrlHKF6zfP09Pcb7mJ/P9nXzbPr3ZyKU42R4bZBs26Y/YylH\nnilUhDda8rPjVkeAjZjgLiN5X69YWQjF567rDuATQvH36bjjzM+9fXviKAuAWTMvuURdJitBwd/D\nTz813uNMauzNyGoR0N2dOEWo3QNvb7f+3apRu+QStuSK3GmeALfomqiHDGEmNKuocquPXaWi+TrR\nhMYbykGD9IdUukHO7KaLl4/+xRcNsWeH6Bu2qphUvax772UzXoom7o8/Bv7858Ttli1L/Fvn2uys\nM+JvO3ca2fnk3/mz53EGHHGoGEdOOdvdbdwf3nt069e++27m7/3zn9XDetPBqFHOBOqIEdZWDNEd\nID4fngyNZ/RTWWn8+P74+V97jbmpxOPfcYfxfz46wC4wkI8Q2b2bvQtWbpzmZqCxMXGd1Qx/ViNM\n+HvY3Z04eku8Hp1RA1Ejq0VAT0/yGFOrB3vccWz6UDN0GrXXXmMRvLIfNyicVJxm5bdzB8gfRk4O\nC6Lk84W/9ZbxO9//nnuCdQeYTf4SJMXFyUFvVrz/PvMNO40JGDqUCY7mZqOC/P3vmaAV39+qKjab\nJMeLCPjPf9hSPP7y5YkJp3h5xe1U35OdJUAUAQcOsMBDL3PPDx7M3CdRRXd0gModwNMZm4kAPyxx\n/PwbNrBgVfH44oylubnsPfrkE2t3AKegIPFdUHHzzclC066sZqjmkZGX5A7IAGIxY0a97m6jZz54\nsPWD7elhvbwvfMF8G6tG7fTTjZfVKhLfb5y8rGbldxIYyLcR1bgY6MMbtOLiYEWAm+OmWtmX3SST\nyQAAH39JREFUl7N75VQEiHNOcBHAc9CLPWa5sfUiAvh3It6jsWOBk05i5n6xvOJ2qkl4eKPGt7ES\nAb/8JZt/oL7eWTBdJqGbJ0BuWLklQBYB4vP10xLAkS2mfK6UvDz2r6jIfnQAR3wXursTp34HmPtx\n2zbrsonI30A8zjplcgcokxp5OzJaBJgFBg4bxsw93d3sJXrpJXVSExEeZCZWeDJvvWWkyhWJx5mP\nuqbGWfm9wIPznESsyw2AKuGLTrKgnBzj/OL+ALBqlXGuIEWA2x5Oqj9+u7iIv/0tOapbHAvOp3Xm\nmfnEGBfZ7G5VWYrlUT0TOXixsZG907m5zCrBkUWAakpfOUeElQhYsgQ455zEY2YbUbEEmHHffYYb\n6LPPjDwiVt/+888z0Se+Cz/5SbIQHDLEfN4ClbVBdi3E40bZzARzpr93GS0CVPAXgz/0/v2NXqvV\ni3zgANtu3Djr469bp94XYMLjqqucBwW54cwz2dKLJUA26epYAnbuNI+sBYwJVOSGwG+iYAkAWK9J\nTF0NsF49Tzm7cmWyH/Poo5l/u64u8d7m5ydGkcsiQIetW1nQpowsAn71K7bMzU2shHXyBMhlsxIB\nRx9tHFM1EVY24CVPALckiWJTjhvwCj//smWJGQsnTWJLPm1xbi6rC/Py7C0BlZVMMNx7L6tT77xT\nvV1+vv2oHPEbkesFHnyYn58okuVkQap6L1PEQVaLgO7uZHOrlSXAKuCEYzXk8JxzWNazX/zCWZnd\nIM7G5mQfK1OhjiWgt9eo3HmyFHHMOnfF8PvMJxzym6hYAoBkk/l55wELFhh/q4LItmxhEd8iw4cn\nvr9uRAAAPP108jq5AeEuHjH9sfg7L4ecE4AjToojBwa+/rqRh/+DD4yZO3W+v0zETixb5QnglgDR\n3C1u42RYq935ZbFSXg5cfrmRGjk3V98ScPjhrN546in2N3/PLr88cbt+/dRTPpuV02wYbl4e8Kc/\nGevE+lO1Tya5CzJaBKgm5eAvH7cEiCLA6sF+9pneBCSqCX7icfZCp/LF4dfkhzuAozNEsLvb+GAr\nKljPVDTB8biIlhZvc73bEdTQQ78ZO1bt6xatA7qTTfF3W4wJcCMCdNwBmzYxV4Scp0BuaGSRwNm/\nH9ixI/HYABPb775rBByqJofJNvx2B4jP1w8RwM8vl/PznzesAPw69u83RIBdT7qoCLj4YmYV4KO4\n5PqHjyQwuz+yS9PMEiDX7VbWzEwjo0WAKiiJv3y8J2KV7Upk/nz1rG0ikyYZWdRU500l3JzshzuA\no+MOkNOzyulreZKYjo7knqyfuBEB6TDvmQUG8ucwfrz+xDetrYnpVt2KAFV5ZBHw7LPqBtrOHSCu\nf/315PMtXcqEEe/xzZnDsk3efLN++TMNp+4APgeIWWCg+Ax0e9FWmFkCYjHgW99iYpFbXzs62Lh+\nncBA7gqaMAF4+OHk39vaDFM+H0YqoqqrrNwBnIEDk0cFqCB3QASQZ/kCkmMCRBO31QMvLzfG+psx\naJB1opVU8ve/s6WT6VPN3AFibICTuQPMuO8+FhuRn69Ob+sHbmMNwhIYyMtvNRmTCtE/z5+n1b34\n4AO1T16mp4e9S2JvX84RIJbbbIgg/72y0rAKyeeX9yktNWIQshGnowP4SA4dS4AfIkC0BIjHzslh\nliKel5/XRa+8wra3y9Y6bJhhEZsxg5nr5Qj+ww9nHQ/uMtIpq2qdmOirrMz45jZsSHwfaYhgxFCN\n2VZZAnRiArq72XSkVpj57twmOvEDs8mOVMjll7Mp6iQLWr3aXgR861vM1Oe2p6pDlCwB/B6oKhu+\njQ6XXMKSBon7WTUg8TirkOUZ18wsAfn57B3Yv59VvuLEP9/8JjB9unUKZIDNLbF+vXlMgFh2guHU\nHSCOuuANXCosAbKgzckxMj5u3ZrY0H72mZF7gv/d2pp43P79WQPP34X6+sSRMrGY4UqbMEG/rPLf\n8Xiia3LQIOOc551nnMsMJ6nnw4iDfmJ0yMszT7MpWwJ0YwL27LFPO2oVwJOuSs1s+IwKufx8djiA\nreeTf8iI19bamrifFaI1xu/7E5XAQDNLgFWyHTNiMeD++xMz41kJrTlz2HLu3MT1qoZBtATs2ZNc\nrvvvZ8MGv/Md+/J//HHikCy5fJliZvULHRHApyU+cCAxgVNPj2El4O7RIC0BcpCnfB2ckSONeuLl\nl1nHQHa38vTBHHlSID6KZOpUfcufvJ34Ny//YYcZ9cCxxwJvv60+VjwOvPcei32K8jubsZYAs96o\nGBjoJCZAVwSY9aJUE/OkAnGiFjtkESB+dFdfzZZW+fF5oJeYpc6KWMxIC+s3UQkMFC0BImJyHl1h\nwi1VYkCs1Qx0fE4HPs0zx84d0Nysngr7qKOMREZWIuBnPzO3BFx0EVuSJcBARwTk5rJ3puv/b+/c\ng6Oq7jj+zWMDRFJrGQkx0aYkIYEkbNJB7PhGiZg6opZpRStSQW3tBGV8wFhtB2eqyFDGarTTqdWW\nakd0UAxUEpkyUhmRYABrIVRwSCQJIVpQGoI0r9s/jr+959495+7dzT7uZn+fGSZks4+z99xz7/f8\nnv1WUUfuALnITrRFAFkCDh82o/kBcYOUaWw0/y/XHrn4YtFkinbdRFaWtQnYT34ifpKYGRgQ5+SP\nf+xc5ExGJQJIJNExHjs2eGOoCxSMtDy5lxiVIsBJldlTBN3GBLgRAbqyu62t8VeKFKA4kpgAeaH+\n4x/mc3R8+qkQALQbdAMF9UQbp4smteK1kwg1rxOO+/eL0sqyOTQUlFYom1n7+pzrpb/zjrUCIaAX\nspmZwmx6ySXqIFmdydmOnJ1j/7xwsllSBbeWAEIuSU4iQF5j0XYH0EZKtkABZqfElSvFT/t5aBhm\n7BIg/O8yZAmg85/q+VMGFlkCzjpLnOeh6O0NPi/JUiJvGjMyrGuOgqxlDh4UmU+xqnMST0btktNd\nOO3FH9zGBHR3R+4OoIpn8cTNorDjlB2wZIkIznGitxcoKwtvFxeruABdbMZll4kFf/31Ygdixyvu\nAEDc1D/4YGQ3xsJCvRWqrMxa0pnQpQhmZooa/rr5UokA1Xrq7nauE5DMptVY4FYEUJtnuZ4CiYDS\nUuvziWisPbqeFhcL0zhB46ACa3LNCPsOu75e9L+wv68cJzBhgvXv1FxozJjQbcMPHhSWCfu5TSKA\n3A719cDq1ea49uzRt3FubGQRkJSocmnli6wcWCVj74muQicCpk61VtKKB089JdrNhkOoFEG70rdz\n6lR4MQhAfC0BHR1mjX1A+CIff1ykpV13HfD730d/HKGQ3QF0IauqErue4mKRYulUqtqOXahmZekv\nVKpsj/HjgR/+MPi5FBhIyDcVIpQl4Oabxc+ZM/WWgNEYfT1S3IoAak9NRbrobxkZYu5ormMVGKgT\n9NQxlc4ZaslO+f1XXCFiAu6+2/o6yh6RzwU5Q+v4cfGZOhFABacGBkR7c0DtDpDfv65ONIqjxyiN\nWbaKAmITUV5uBhTG4hoWL0alCHDjDqDnySfBRx+Jzm52qCGGqh+3jE4EHDgQnok8GpSVBVfXCoVT\niqCb1L/29uC6AKGIlQhQzcNXXwkh09Bgth999FER2b55s7ho2Ev4xhrZEvDLX4pjPTgocqkPHRK7\nkHCEVWeneJ38/joRoMr2uOuu4M6AgBij/FzVOgllCVi2TPyem6u3BDQ1xbfBVjIQSgSoTNr2v+lK\n50Y7MFAlAsjaRPFENTXmddjpukJuDbkU+9Kl4udf/yqsBFlZehFAlr6//11cl8aO1ccE2KHjZa+F\nQZ+TnS2OLZWAT0TX0mgxKkWAE3Z3gLwbqq1Vv2bbNnFhDNUDXHXB/eorcaJSLX8v42QJ6O3VxxeQ\nCbelJdhkF4pYuQNUF83Tp8XFYO5cYZ2RC9DQjTNc68lIkS0B+/aJ6oD79kXuAsjJMZsJAc4iQGUJ\n0M2Hqu22HbkULFU51DVlOXnSDKqyz9Wzz7IlQMZtYKBqfZI7gJ5nd7dE2xKgGie5BSiVLiNDZAb8\n5S/i87Oy1O87f774KfdrqawUMSxkWS0pEdccOQ6G8PvN/8sxYDIkktraxCbGDgXZ0rGl+BmaE+pn\nkcwurFErApxiAuQdkKwEx45VR7b/5z+hawQAwn9rL1px4EB4wV2JxKl3wJo1wO7d6tetWyfqg7/8\nsro2gxO6YMqRoroYnTljFXK/+Y15UZRvnPFEtgRkZJgmTNVuPNL3l48vNXjavl1tCdBdyCkmABCx\nIaqSz3IVOCrzqirdDYhA05//XPx/eDjYjeEUzJhq2AP77NA1TCUC+vrMOgGAELu6NR4pdB7Rjbas\nLDjtFDDP6fR04MMPxf/7+/UZR/R9Jk82HyPL4c03C2sAIEpTd3eb1j2CrJLf/KZppRgaAm6/3Tr2\n9HRhIST3A40LMNfCmDHie9XUmGMbHDStdNEov5woRq0I0KFyB4RqFnH6tNn4xomWFqFuZTo71cFX\nXsTJEnD55WYjEBVkwq2rG9lnRgvVe/b363cdgIij0AmdWCGnJlFa4E03mX7UkWI/vtTbvaFBnX6o\nS9mUq6r5/dYIdPtnyaZROTVNJ4RlgUGwCDBJTxfrSz6WMnKam52TJ81o92nTgsvrRmPtyWWDBwfF\nDdXe8dEwzJiijAzh+qqqEoF4OqEIiO/84IPm7yQC+vvNaqP0vanuBUHXdepamJ4ObNxodT989pn6\nuFHPCzo+Y8aIWIuXXjK/g2xlYRHgIQYGxKTqzKmq7AA5UtUuAk6cEAuQ0lOcWLkyuMb58LCo/54M\nOLUS/uwzffVB+n5794Zvxg5l6owU1Xv+73/OImDp0tBxH9FGlR3w+uvRS5XTiaw1a4QJNDvb+rgu\nRkMeo86HS58l78jk3HAdw8Nm3A2lHsaj02ayQFYSXWU6EgF265HPJ15DNyqygsrnQzTWnmwJ6OkR\naacqaBwUvzA8LOb7kUf0752TY7UU+HzCunHkiLXk+DPPmFY0++vJvUVCU74eHz2qrnkBAH/4g2mt\n6uw0hQS9FxWbA1gEeIoTJ8SkhkoRJOx1AuwiYMIEoVpVddLtHDwoTOIyslr0Ok7ugH//W28NIXNd\nVdXIPzNayOVrT50SZtA5c2JXpjhS5JiAiy8Wbolouo5CHV+7lcpJBNBOVBcbQ58lpyTKNy5dTIk8\nvrw84ZuNljtktDBxon4eya9NN8WNG8XNLTfXam7fv19kmsTCEkCBgYAQ26pzmP5O8QtnzogAUzmt\nMBQk4nfvtloQfvYz9Y143z6R5geIz8zIsHZ6feutYCFM3HWXKIClQi47D7AI8BTHjjkXyHFKEXS6\n+LoJoPnTn8yqeUSozoNeQmcJ2LRJ/NS5NZ56ypqWNJLPjBYkAvLyxG6Acqhj2b44EmRLwI4dIg8/\n2u8/PGxWfzxyxJpyaBeoTiKARJ7OfEufZd9dknjUnT/2+Zd9s4xA56oETFcNuVCuukqc95mZ4oZs\ntyo9+aT1tSOFNlKyhUhVqZSuy3TOk2sq3P4mhBxsrbNOXXCBGac0bpz4XDn+5M9/dtfE7KKLrL+T\nBZPO9WQWAaOud0Co3agcGGiPCQD0TVzIXBku995rLZLhZeymabpAZGaKABidSJo4MfK4h1i7A0iU\ntbaKmgCU0uMV5JiAWEA3ZjqXqeDTsmXqteIkAujiqZtrWlt2a0ttLfCjH1lf19RkWteGh4UrJtzM\nklTCqfUuXcNmzBC59hSNTyKAhF5pqdh5y/UwohkYKN/45Yh+QnYHOLnlQvHee+JckgMG3VBQIFKA\nZdfurFn62jCE6rhnZoobP1sCkhB7TICc/5yWJk4IygWVzZo6v5HMCy9YC+pQ1b5ly0Y87LigswS8\n9poo8BKPz4wWqve85BLvZWnYhdfhw9F/f1kEzJol/q1apa4AmZ6ubr4lu7WuuEL9WXRs7alWWVnA\nNddYH5MDEIeHRb2GRx919ZVSEjn90g6JgIkTrVX37CJg0SLr6268MbqWAOKCC9QmdhpHVlb4N3AZ\ncpvZkS0cKshaQGPLyREZXUeOhD+GnBxxfR8NloBRKQIWLND/TTarDQ0JvxSdFLQg6OYdrim/oMB6\nclLuaryDzSLFnq5Hx2nPntg1yohHYGBHh7jpUe66l6CYgAsvBHbtCj/FMhR2EbBnj/NxaGgwI6Bl\nSAT09+vN9bqmKqpdn1yPgHzajJ5Q7gDV8bOLAHkzUlwsLAbRtAQQcotp+3gAYX6nG7Kq6FSk3Huv\nqOmigzKYJk8WboDVq4WlUJXpEoqBAXFdYUuAB+jsFBHIPT2mT0xX9AewLqb//leYueWAFsD0eVK6\nmNzkwgl768uTJ0VuabIHBnZ1BXcEi9VnRgsSAd/5jhBny5aFX9I4HpAloKRECIFos2OHCOqkc/7k\nSVEfQcfdd6vHQSIgVNVIwF38jM8nLG1kUk2WNZIonESATkSRyVo+tpRut3lz9NaebAk4cEC0NFYh\nWwLo+0QrFRYQZn6dlQoQ64DGsXChGXz6gx+E/1m/+x3wxz+yCPAEDzwAfP/7IvCpqUk85jSpsjvg\n44+t5SbJIkAiYPx48d72Fpc67P7Uo0dDVxn0EnbTdDyqYMWqWBAtSq+X8yRLQCx3wsuWWWv/v/ii\n/rkTJqjFUjhZLvZUNlXPic5OUVdj0iT9TpYxcRMTYIci8OW/rV4t3qekBNiwQZ/OFw6yJaCsTJ9O\nTeePm0C8WED9AwgqJjR1avjvtWGDEBHsDvAAcoW/Q4dEFKfTSZaWJuqxf/qpyCSgBhGAKAlZUWHW\nj+/rs3bkCoVdBGzf7j0ftBNjx1ov4NHuNqaipcVdLnm4ULAPFeDxKiS8YnmenDkjLlJ0bt5xh/65\n2dnq6P9wRMBzz1l/V1kGKNqa2rGyCHDGKSZAV3/f5xMmcN1G5KqrolPITFV+WoVsCUgEDQ3W7C1K\nFZRTBt1SXm61pKxbN/LxJYqkzw6Qb/gffBDcG92OvYWlfYHs2ycUY19f+CIgI0Pc1IhQgSpeY9w4\nqwiQdx6xNNf+6leiFkM0ibZvPVZQ+iV1VosFp0+771+Rna1uQz00FLlVi7rbyVCg7dlnB+9WmWCc\n3AE6EZCZKUSw7qY7bZo6lS9cVOWnVVBMQKJEQHa2NWCxtNT92O2ce66wMlLtjA0bojPGRJD0S0+O\n2t+0KXR0NU04ta2srLT+/dZbTZ9ouCKACkt0dZmZBckkBP72N+suMR7ugHPOEVH70SY7GygqcvZ/\newGqlhbrm+DgILBzpxDKTpx9thDCcuc2ILKiV+XlIhdc1XaYBMXkyWwJcEN7u/BDq9CJgL4+UbxM\nF8cRrQ6eqvLTKhItAlREaoHLyBDugHffFYGFbmJlvErSLz37TdperMcOWQ5IPNirVd16q6kWN21S\nd6fS8e1vi/zYnh7hmkhPB5Yvd//6RGNvoxsLX72dX/wiMnNcKIaGgPvuCy7j7DWo3nmsboLU+Mrt\njZaCpexiwa0IkEsG19cL37OKrCxg61azTgKLgNCsWaN+fHBQXSCNKuXpblBbtwIrVox8XG7dAeXl\nwJ13hlccyMucOCHcWWVlsdnIxIukX3pffWUNBLz/fufn0w6H0kLs5rBx48xd/FlnhR+xTRkC//pX\n8lU+++1vrb8bhkhvjKVFoKAgNmJDd2H0GnTxjNVNcOVK8/9udj05OcI6ZhdmbkUABYXV1elTxYhv\nfcssvcrZAc689praogLoLQGE7tyy1w2IFLcm9TFjgOefT1xgYKyoqvJeOfJwSHoRcOaMtQxkKN/M\nP/8pfl56qfhpj4SWRUB2tllu1S0+nxABixaJBi3JxLXXWndukfrLwkHXtW6kJIsIkJu7xIKZM8O/\nwfr9wcLMrQiQ68O7eS5dPJMpgDYRFBXpo+5DiQCdiCfX30hFvltLwGhj507x89JLWQQkFLnYDwD8\n+tfOz3//fVE2k9wB9gAyWQT094fv65FbXSYb9uwAt76+SNj2dVWPaPkl7SSLCJDrqceKcOdQlT9u\nFwHbNFVZ6Pu4OfaUx56KN5BwGTPGms4sMzjofJ2yF+OiuaN6/yO9gcVjs+BFvvtd0ZEwLy85r/eE\np5dfU1MTysrKUFJSglWrVimfc+aMCC6ifNdQJ/T3vgf89KfCAtDTE7zTl0XAiRPhdzOTb2qxKP4S\nS8aOtV5oYqnwZREQCxWdLCKATKOjTQS4tQQMDLAIcIOTCBgYcD7XdSIAEO5L3fu6JVUtAT6f2FRm\nZ4/8GCYSz07d0NAQ6urq0NTUhNbWVrzyyis4oChbR6YwqhQVTuEHVY6sLAK2bg0vOwCwigCn6lVe\nRFUngN0BsYUipb0kAsilJePWHcCWgNgQSgSoLAEU3Okksu1VTiMhVS0BhNPcJAOeXX67du1CcXEx\nCgsL4fP5MH/+fDQ0NAQ9jy5OlEc7ksYUgFB1cuMgVTcsJ7KyzJvaddeNbCzxJp7uACKW7oBkCDYj\nS0Asj3O4N9nx480664Tb4L1wLAEkAFkEhMZupZPRuQOoLoTT+oqWJYBFQKJHETlphhGPbPDwWb9+\nPd5++208//zzAICXX34Zzc3NqK+vDzwnLZXPPIZhGCYlieZt27MGUzc3eI/qF4ZhGIZJCjxriMvP\nz0dHR0fg946ODhQUFCRwRAzDMAwzuvCsCJgxYwYOHTqE9vZ29Pf349VXX8VcVRFyhmEYhmEiwrPu\ngMzMTDz77LOYM2cOhoaGsHjxYkyNpOcjwzAMwzBKPGsJAIDa2lp8/PHH+OSTT/Dwww9b/uamhgCT\nWAoLCzF9+nRUV1dj5syZAIATJ06gpqYGU6ZMwTXXXIMvv/wy8PyVK1eipKQEZWVl2LJlS6KGnZIs\nWrQIubm5qJQ6akUyV7t370ZlZSVKSkpw3333xfU7pDKq+VuxYgUKCgpQXV2N6upqNEo9u3n+vENH\nRwdmzZqF8vJyVFRU4JlnngEQx/VnJCGDg4NGUVGR0dbWZvT39xt+v99obW1N9LAYG4WFhcbx48ct\njz300EPGqlWrDMMwjCeffNJYvny5YRiGsX//fsPv9xv9/f1GW1ubUVRUZAwNDcV9zKnKu+++a+zZ\ns8eoqKgIPBbOXA0PDxuGYRgXXnih0dzcbBiGYdTW1hqNjY1x/iapiWr+VqxYYaxZsybouTx/3qK7\nu9vYu3evYRiG0dvba0yZMsVobW2N2/rztCVAh9saAkziMWwZHBs3bsTChQsBAAsXLsSbb74JAGho\naMAtt9wCn8+HwsJCFBcXY9euXXEfb6py2WWX4Ry5LzfCm6vm5mZ0d3ejt7c3YPW5/fbbA69hYotq\n/gB1BhXPn7eYNGkSqqqqAADjx4/H1KlT0dXVFbf1l5QioKurC+eff37g94KCAnR1dSVwRIyKtLQ0\nzJ49GzNmzAjUe+jp6UHu171Ec3Nz0fN17+ejR49asj94ThNPuHNlfzw/P5/nMMHU19fD7/dj8eLF\nAXMyz593aW9vx969e3HRRRfFbf0lpQjgIkHJwXvvvYe9e/eisbERzz33HLZv3275e1pamuNc8jx7\nh1BzxXiPe+65B21tbfjwww+Rl5eHBx54INFDYhw4deoU5s2bh6effho5th73sVx/SSkCuIZAcpCX\nlwcAOPfcc3HTTTdh165dyM3NxbFjxwAA3d3dmPh1Awf7nHZ2diI/Pz/+g2YChDNXBQUFyM/PR2dn\np+VxnsPEMXHixMDN48477wy413j+vMfAwADmzZuHBQsW4MYbbwQQv/WXlCKAawh4n9OnT6O3txcA\n0NfXhy1btqCyshJz587F2rVrAQBr164NnPBz587FunXr0N/fj7a2Nhw6dCjg22ISQ7hzNWnSJHzj\nG99Ac3MzDMPASy+9FHgNE3+6u7sD/9+wYUMgc4Dnz1sYhoHFixdj2rRpWLp0aeDxuK2/6MY5xo/N\nmzcbU6ZMMYqKiownnngi0cNhbBw+fNjw+/2G3+83ysvLA3N0/Phx4+qrrzZKSkqMmpoa44svvgi8\n5vHHHzeKioqM0tJSo6mpKVFDT0nmz59v5OXlGT6fzygoKDBefPHFiOaqpaXFqKioMIqKiowlS5Yk\n4qukJPb5e+GFF4wFCxYYlZWVxvTp040bbrjBOHbsWOD5PH/eYfv27UZaWprh9/uNqqoqo6qqymhs\nbIzb+vNsAyGGYRiGYWJLUroDGIZhGIYZOSwCGIZhGCZFYRHAMAzDMCkKiwCGYRiGSVFYBDAMg+PH\njwcazeTl5QUaz+Tk5KCuri7Rw2MYJkZwdgDDMBYee+wx5OTk4P7770/0UBiGiTFsCWAYJgjaG2zb\ntg3XX389ANGaduHChbj88stRWFiIN954Aw8++CCmT5+O2tpaDA4OAhDtTK+88krMmDED1157baDq\nGcMw3oNFAMMwrmlra8M777yDjRs34rbbbkNNTQ0++ugjjBs3Dm+99RYGBgawZMkSvP7662hpacEd\nd9yBRx55JNHDZhhGQ2aiB8AwTHKQlpaG2tpaZGRkoKKiAsPDw5gzZw4AoLKyEu3t7Th48CD279+P\n2bNnAwCGhoZw3nnnJXLYDMM4wCKAYRjXZGVlAQDS09Ph8/kCj6enp2NwcBCGYaC8vBw7duxI1BAZ\nhgkDdgcwDOMKNzHEpaWl+Pzzz7Fz504Aojtaa2trrIfGMEyEsAhgGCYI6l0u9zG39zS39zdPS0uD\nz+fD+vXrsXz5clRVVaG6uhrvv/9+/AbOMExYcIogwzAMw6QobAlgGIZhmBSFRQDDMAzDpCgsAhiG\nYRgmRWERwDAMwzApCosAhmEYhklRWAQwDMMwTIryf7lZlUTtSp9sAAAAAElFTkSuQmCC\n" | |
| } | |
| ], | |
| "prompt_number": 28 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "gasoline_density = 719.7 # kg/m**3\n", | |
| "cubic_meter_to_gallon = 264.172\n", | |
| "meter_to_mile = 0.000621371\n", | |
| "plt.figure(figsize=(8, 6))\n", | |
| "plt.plot(data['Time'], data['FuelEconomy'] * gasoline_density / cubic_meter_to_gallon * meter_to_mile)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 29, | |
| "text": [ | |
| "[<matplotlib.lines.Line2D at 0xc0ad2d0>]" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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Bz5eFdrnUBG4+pkyZggEDBmDgwIE4//zzsWPHDmzcuBEjRoxA3759MXLkSGze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\n5HsS7rS8q27s3u2sOvfd7/r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V6yBq4aZzBsWvMe3YsTi6+dVXi8tICQMIaoTjFG5Ta9NN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| |
| } | |
| ], | |
| "prompt_number": 29 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "data['FuelLossGal'] = data['FuelLoss'] * gasoline_density / cubic_meter_to_gallon\n", | |
| "data['DistanceMiles'] = data['Distance'] * meter_to_mile\n", | |
| "data.plot(x='FuelLossGal', y='DistanceMiles')" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 30, | |
| "text": [ | |
| "<matplotlib.axes.AxesSubplot at 0xc08e050>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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iNjbW6ihuVVJSQrt27QgKCqJLly6EhYVZHcltHnnkEV588UUCA71qt2a3CQgI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\nHy/VMTvstWevAAAAAElFTkSuQmCC\n" | |
| } | |
| ], | |
| "prompt_number": 30 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "model = pandas.ols(x=data['FuelLossGal'], y=data['DistanceMiles'])\n", | |
| "print model" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "\n", | |
| "-------------------------Summary of Regression Analysis-------------------------\n", | |
| "\n", | |
| "Formula: Y ~ <x> + <intercept>\n", | |
| "\n", | |
| "Number of Observations: 20349\n", | |
| "Number of Degrees of Freedom: 2\n", | |
| "\n", | |
| "R-squared: 0.9948\n", | |
| "Adj R-squared: 0.9948\n", | |
| "\n", | |
| "Rmse: 0.3671\n", | |
| "\n", | |
| "F-stat (1, 20347): 3889331.2332, p-value: 0.0000\n", | |
| "\n", | |
| "Degrees of Freedom: model 1, resid 20347\n", | |
| "\n", | |
| "-----------------------Summary of Estimated Coefficients------------------------\n", | |
| " Variable Coef Std Err t-stat p-value CI 2.5% CI 97.5%\n", | |
| "--------------------------------------------------------------------------------\n", | |
| " x 3.3744 0.0017 1972.14 0.0000 3.3710 3.3777\n", | |
| " intercept -0.2193 0.0050 -43.86 0.0000 -0.2291 -0.2095\n", | |
| "---------------------------------End of Summary---------------------------------\n", | |
| "\n" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 31 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "This model shows an average of 3.37 mpg for this trip. Seems really low. I probably have an error." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "The rest was some attempts at computing distace from long, lat, and altitude that doesn't quite work yet." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "first = numpy.array([data['LonDeg'][:-1], data['LatDeg'][:-1]]).T\n", | |
| "second = numpy.array([data['LonDeg'][1:], data['LatDeg'][1:]]).T" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 32 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "%load_ext rmagic" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 33 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "%%R -i first,second -o d\n", | |
| "library(geosphere)\n", | |
| "d <- distMeeus(first, second)\n", | |
| "print(dim(first))\n", | |
| "print(dim(second))" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "text": [ | |
| "Loading required package: sp\n", | |
| "[1] 20348 2\n", | |
| "[1] 20348 2\n" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 34 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "data['Sea Level Distance'] = numpy.zeros_like(data['Time'])\n", | |
| "data['Sea Level Distance'][1:] = d" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 35 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "data.plot(x='Time', y='Sea Level Distance')" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 36, | |
| "text": [ | |
| "<matplotlib.axes.AxesSubplot at 0xe9ef810>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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| |
| } | |
| ], | |
| "prompt_number": 36 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "data.plot(x='Time', y='LonDeg')" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 37, | |
| "text": [ | |
| "<matplotlib.axes.AxesSubplot at 0xea02a10>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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| |
| } | |
| ], | |
| "prompt_number": 37 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "data.plot(x='Time', y='LatDeg')" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 38, | |
| "text": [ | |
| "<matplotlib.axes.AxesSubplot at 0xfbbb050>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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hMHaslt4Q91SDEGnjdu+G2FirFnHeeXZHI+eiSWsQlZWVxMbGEh0dTXh4OHPm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\nFBQUVFtWz9i9M31+Pz7fq1c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| |
| } | |
| ], | |
| "prompt_number": 38 | |
| } | |
| ], | |
| "metadata": {} | |
| } | |
| ] | |
| } |
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