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@gschivley
Created January 8, 2015 21:27
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{
"worksheets": [
{
"cells": [
{
"metadata": {
"internals": {
"slide_type": "subslide",
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"cell_type": "markdown",
"source": "##Contents\n\n- [Setting up libraries, etc](#Setting-up-libraries,-etc)\n- [Importing data](#Importing-data)\n- [DataFrames and statistical tools](#DataFrames-and-statistical-tools)\n- [Plotting data & regression](#Plotting-data-&-regression)"
},
{
"metadata": {
"internals": {
"slide_type": "subslide"
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"slide_type": "slide"
}
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"cell_type": "heading",
"source": "Setting up libraries, etc",
"level": 2
},
{
"metadata": {
"internals": {
"frag_number": 2
},
"slideshow": {
"slide_type": "fragment"
}
},
"cell_type": "markdown",
"source": "Descriptions of everything in the following cell:\n\n- matplotlib is the plotting tool used by python. I'm saying to use in inline, where plots are inserted directly after the code that creates them.\n- `__future__` is python 3.x (I'm using 2.7 due to better compatability with some libraries). In python 3.x there is no distinction between integers and floats, so dividing one integer by another will not round the answer.\n- Pandas provides DataFrames\n- Numpy provides arrays and lots of math functions\n- Scipy is built on top of numpy and gives lots of additional science/math tools\n- Statsmodel is a library I haven't used before, but it appears to be good for regression and uses R-style formulas. [Documentation](http://statsmodels.sourceforge.net/stable) and [examples](https://github.com/statsmodels/statsmodels/wiki/Examples)\n- I'm directly importing the statistical library (stats) of scipy so I can write *stats.skew()* rather than *sp.stats.skew()*. Scipy stats functions are described [here](http://docs.scipy.org/doc/scipy/reference/stats.html)\n- matplotlib.pyplot is needed when creating plots\n- Seaborn is a plotting tool that helps to make default plots look better and can be used to visualize data more easily. I haven't used may of the features, but you can find more [information here](http://stanford.edu/~mwaskom/software/seaborn/)\n\nIt seems like a lot here but is really easy to learn and copy in from another notebook."
},
{
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"slide_helper": "subslide_end",
"frag_number": 3
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"slideshow": {
"slide_type": "fragment"
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"cell_type": "code",
"input": "%matplotlib inline\nfrom __future__ import division\nimport pandas as pd\nimport numpy as np\nimport scipy as sp\nimport statsmodels.api as sm\nfrom scipy import stats\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nimport seaborn as sns",
"prompt_number": 1,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {
"internals": {
"frag_helper": "fragment_end",
"slide_type": "subslide",
"frag_number": 3
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"slide_type": "slide"
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},
"cell_type": "heading",
"source": "Importing data",
"level": 2
},
{
"metadata": {
"internals": {
"frag_helper": "fragment_end",
"frag_number": 5
},
"slideshow": {
"slide_type": "fragment"
}
},
"cell_type": "markdown",
"source": "It isn't necessary, but I like to assign file and sheet names to variables so that the import code lines are shorter."
},
{
"metadata": {
"internals": {
"frag_helper": "fragment_end",
"frag_number": 6
},
"slideshow": {
"slide_type": "fragment"
}
},
"cell_type": "code",
"input": "fn = 'Sample data.xlsx'\nsn1 = 'Emissions'\nsn2 = 'Oil'",
"prompt_number": 1,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {
"internals": {
"frag_helper": "fragment_end",
"frag_number": 7
},
"slideshow": {
"slide_type": "fragment"
}
},
"cell_type": "markdown",
"source": "I'm importing data from both sheets below and assigning them to variables. The *oil* data is being imported with values of 999999 and 0 as *nan* and the first column (which has the Nering ID) as the index. You can also import from csv files using *pd.read_csv*."
},
{
"metadata": {
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"frag_helper": "fragment_end",
"slide_helper": "subslide_end",
"frag_number": 8
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"slideshow": {
"slide_type": "fragment"
}
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"cell_type": "code",
"input": "emissions = pd.read_excel(fn, sn1)\noil = pd.read_excel(fn, sn2, na_values=['999999', '0'], index_col=0)",
"prompt_number": 3,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {
"internals": {
"frag_helper": "fragment_end",
"slide_type": "subslide",
"frag_number": 8
},
"slideshow": {
"slide_type": "slide"
}
},
"cell_type": "heading",
"source": "DataFrames and statistical tools",
"level": 2
},
{
"metadata": {
"internals": {
"frag_helper": "fragment_end",
"frag_number": 10
},
"slideshow": {
"slide_type": "fragment"
}
},
"cell_type": "markdown",
"source": "Start by defining a variable with a list of the cluster names. *Set* produces a list of unique values."
},
{
"metadata": {
"internals": {
"frag_helper": "fragment_end",
"frag_number": 11,
"slide_helper": "subslide_end"
},
"slideshow": {
"slide_type": "fragment"
},
"slide_helper": "slide_end"
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"cell_type": "code",
"input": "clusters = set(oil['Cluster Desc'])\nclusters",
"prompt_number": 4,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 4,
"metadata": {},
"text": "{u'Alaska',\n u'California',\n u'Gulf Offshore',\n u'Gulf of Mexico',\n u'Rocky Mountain',\n u'Upper Texas'}"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {
"slide_helper": "subslide_end",
"internals": {
"frag_helper": "fragment_end",
"slide_helper": "subslide_end",
"frag_number": 11,
"slide_type": "subslide"
},
"slideshow": {
"slide_type": "slide"
}
},
"cell_type": "markdown",
"source": "It's easy to take a look at the first few items in the DataFrame using df.head() - or df.tail() to get the last 5. You can also use df.describe() to get summary statistics."
},
{
"metadata": {
"slide_helper": "subslide_end",
"internals": {
"frag_helper": "fragment_end",
"slide_type": "subslide",
"slide_helper": "subslide_end",
"frag_number": 11
},
"slideshow": {
"slide_type": "subslide"
}
},
"cell_type": "code",
"input": "oil.head()",
"prompt_number": 6,
"outputs": [
{
"output_type": "pyout",
"html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>DPTHTOP</th>\n <th>APIGRAV</th>\n <th>GORINIT</th>\n <th>GAS2007ANN</th>\n <th>Cluster Desc</th>\n <th>RESTYPE</th>\n </tr>\n <tr>\n <th>NRGID</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2101970230</th>\n <td> 11432</td>\n <td> 40.0</td>\n <td> 500</td>\n <td> 74</td>\n <td> Gulf of Mexico</td>\n <td> OIL</td>\n </tr>\n <tr>\n <th>4301936110</th>\n <td> 4780</td>\n <td> 33.0</td>\n <td> 1064</td>\n <td> 20997</td>\n <td> Upper Texas</td>\n <td> OIL</td>\n </tr>\n <tr>\n <th>2101944030</th>\n <td> 2575</td>\n <td> 20.0</td>\n <td> NaN</td>\n <td> NaN</td>\n <td> Gulf of Mexico</td>\n <td> OIL</td>\n </tr>\n <tr>\n <th>7451901010</th>\n <td> 500</td>\n <td> 20.0</td>\n <td> 24</td>\n <td> 890</td>\n <td> California</td>\n <td> OIL</td>\n </tr>\n <tr>\n <th>4301936090</th>\n <td> 4658</td>\n <td> 32.1</td>\n <td> NaN</td>\n <td> 4483</td>\n <td> Upper Texas</td>\n <td> OIL</td>\n </tr>\n </tbody>\n</table>\n</div>",
"metadata": {},
"prompt_number": 6,
"text": " DPTHTOP APIGRAV GORINIT GAS2007ANN Cluster Desc RESTYPE\nNRGID \n2101970230 11432 40.0 500 74 Gulf of Mexico OIL\n4301936110 4780 33.0 1064 20997 Upper Texas OIL\n2101944030 2575 20.0 NaN NaN Gulf of Mexico OIL\n7451901010 500 20.0 24 890 California OIL\n4301936090 4658 32.1 NaN 4483 Upper Texas OIL"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {
"slide_helper": "subslide_end",
"internals": {
"frag_helper": "fragment_end",
"slide_type": "subslide",
"slide_helper": "subslide_end",
"frag_number": 11
},
"slideshow": {
"slide_type": "subslide"
}
},
"cell_type": "code",
"input": "oil.describe()",
"prompt_number": 7,
"outputs": [
{
"output_type": "pyout",
"html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>DPTHTOP</th>\n <th>APIGRAV</th>\n <th>GORINIT</th>\n <th>GAS2007ANN</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td> 498.000000</td>\n <td> 491.000000</td>\n <td> 328.000000</td>\n <td> 345.000000</td>\n </tr>\n <tr>\n <th>mean</th>\n <td> 5516.546185</td>\n <td> 31.456212</td>\n <td> 2116.350610</td>\n <td> 1953.173913</td>\n </tr>\n <tr>\n <th>std</th>\n <td> 3529.312554</td>\n <td> 10.130226</td>\n <td> 13754.340231</td>\n <td> 12435.042108</td>\n </tr>\n <tr>\n <th>min</th>\n <td> 200.000000</td>\n <td> 7.000000</td>\n <td> 5.000000</td>\n <td> -16331.000000</td>\n </tr>\n <tr>\n <th>25%</th>\n <td> 2863.750000</td>\n <td> 23.000000</td>\n <td> 240.000000</td>\n <td> 13.000000</td>\n </tr>\n <tr>\n <th>50%</th>\n <td> 4807.000000</td>\n <td> 32.000000</td>\n <td> 500.000000</td>\n <td> 109.000000</td>\n </tr>\n <tr>\n <th>75%</th>\n <td> 7400.000000</td>\n <td> 38.700000</td>\n <td> 909.000000</td>\n <td> 576.000000</td>\n </tr>\n <tr>\n <th>max</th>\n <td> 21577.000000</td>\n <td> 59.000000</td>\n <td> 200617.000000</td>\n <td> 202473.000000</td>\n </tr>\n </tbody>\n</table>\n</div>",
"metadata": {},
"prompt_number": 7,
"text": " DPTHTOP APIGRAV GORINIT GAS2007ANN\ncount 498.000000 491.000000 328.000000 345.000000\nmean 5516.546185 31.456212 2116.350610 1953.173913\nstd 3529.312554 10.130226 13754.340231 12435.042108\nmin 200.000000 7.000000 5.000000 -16331.000000\n25% 2863.750000 23.000000 240.000000 13.000000\n50% 4807.000000 32.000000 500.000000 109.000000\n75% 7400.000000 38.700000 909.000000 576.000000\nmax 21577.000000 59.000000 200617.000000 202473.000000"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {
"slide_helper": "subslide_end",
"internals": {
"frag_helper": "fragment_end",
"slide_type": "subslide",
"slide_helper": "subslide_end",
"frag_number": 11
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"slideshow": {
"slide_type": "subslide"
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},
"cell_type": "markdown",
"source": "Here I am filtering the DataFrame so that only items with *RESTYPE* of *OIL* remain. This is a really powerful tool. Looks like 57 items are removed.\n\nThere were also some large values in *GORINIT* and *GAS2007ANN* that I decided to filter out."
},
{
"metadata": {
"internals": {
"frag_helper": "fragment_end",
"slide_type": "subslide",
"frag_number": 11
},
"slideshow": {
"slide_type": "subslide"
}
},
"cell_type": "code",
"input": "oil_filtered = oil[oil['RESTYPE'] == 'OIL']\noil_filtered = oil_filtered[(oil_filtered['GORINIT'] < 5000) & (oil_filtered['GAS2007ANN'] < 50000)]\noil_filtered.describe()",
"prompt_number": 5,
"outputs": [
{
"output_type": "pyout",
"html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>DPTHTOP</th>\n <th>APIGRAV</th>\n <th>GORINIT</th>\n <th>GAS2007ANN</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td> 223.000000</td>\n <td> 223.000000</td>\n <td> 223.000000</td>\n <td> 223.000000</td>\n </tr>\n <tr>\n <th>mean</th>\n <td> 5874.914798</td>\n <td> 30.921076</td>\n <td> 612.726457</td>\n <td> 1083.089686</td>\n </tr>\n <tr>\n <th>std</th>\n <td> 3553.410749</td>\n <td> 10.275794</td>\n <td> 609.458282</td>\n <td> 3238.926416</td>\n </tr>\n <tr>\n <th>min</th>\n <td> 200.000000</td>\n <td> 11.000000</td>\n <td> 5.000000</td>\n <td> -1990.000000</td>\n </tr>\n <tr>\n <th>25%</th>\n <td> 3100.000000</td>\n <td> 22.700000</td>\n <td> 227.000000</td>\n <td> 16.000000</td>\n </tr>\n <tr>\n <th>50%</th>\n <td> 5200.000000</td>\n <td> 30.000000</td>\n <td> 420.000000</td>\n <td> 152.000000</td>\n </tr>\n <tr>\n <th>75%</th>\n <td> 8160.000000</td>\n <td> 37.000000</td>\n <td> 801.500000</td>\n <td> 765.500000</td>\n </tr>\n <tr>\n <th>max</th>\n <td> 15470.000000</td>\n <td> 59.000000</td>\n <td> 3500.000000</td>\n <td> 27454.000000</td>\n </tr>\n </tbody>\n</table>\n</div>",
"metadata": {},
"prompt_number": 5,
"text": " DPTHTOP APIGRAV GORINIT GAS2007ANN\ncount 223.000000 223.000000 223.000000 223.000000\nmean 5874.914798 30.921076 612.726457 1083.089686\nstd 3553.410749 10.275794 609.458282 3238.926416\nmin 200.000000 11.000000 5.000000 -1990.000000\n25% 3100.000000 22.700000 227.000000 16.000000\n50% 5200.000000 30.000000 420.000000 152.000000\n75% 8160.000000 37.000000 801.500000 765.500000\nmax 15470.000000 59.000000 3500.000000 27454.000000"
}
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"language": "python",
"trusted": true,
"collapsed": false
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"internals": {
"frag_helper": "fragment_end",
"frag_number": 17
},
"slideshow": {
"slide_type": "fragment"
}
},
"cell_type": "markdown",
"source": "Determining the correlation between depth and API can be done using the stats function pearsonr. Notice that the answers are the same if I do it for columns of a DataFrame or arrays of the values. Keep in mind that both columns have values for all rows - this doesn't work if one of them has nan values."
},
{
"metadata": {
"internals": {
"frag_helper": "fragment_end",
"frag_number": 17
},
"slideshow": {
"slide_type": "skip"
}
},
"cell_type": "code",
"input": "stats.pearsonr(oil_filtered['DPTHTOP'], oil_filtered['APIGRAV'])",
"prompt_number": 14,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 14,
"metadata": {},
"text": "(0.66956228072613155, 2.2808121983887139e-30)"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {
"slide_helper": "slide_end",
"internals": {
"frag_helper": "fragment_end",
"slide_helper": "subslide_end",
"frag_number": 17
},
"slideshow": {
"slide_type": "skip"
}
},
"cell_type": "code",
"input": "stats.pearsonr(oil_filtered['DPTHTOP'].values, oil_filtered['APIGRAV'].values)",
"prompt_number": 15,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 15,
"metadata": {},
"text": "(0.66956228072613155, 2.2808121983887139e-30)"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {
"internals": {
"frag_helper": "fragment_end",
"slide_type": "subslide",
"frag_number": 17,
"slide_helper": "subslide_end"
},
"slideshow": {
"slide_type": "slide"
},
"slide_helper": "slide_end"
},
"cell_type": "heading",
"source": "Plotting data & regression",
"level": 3
},
{
"metadata": {
"slide_helper": "subslide_end",
"internals": {
"frag_helper": "fragment_end",
"slide_helper": "subslide_end",
"frag_number": 17,
"slide_type": "subslide"
},
"slideshow": {
"slide_type": "slide"
}
},
"cell_type": "markdown",
"source": "The figure below uses a *for* statement to create a series of small multiples plotting depth against the other variables.\n\n- *enumerate* returns the index and values of a list, set, or array one at a time\n- The small multiples are created using the *subplot* function\n- I generally use ppl rather than plt for plotting because it looks cleaner\n- Using %s in the title allows a variable to be referenced. %.2f would insert a float with 2 decimal points.\n- I just discovered the Statsmodels library from http://nbviewer.ipython.org/github/weecology/progbio/blob/master/ipynbs/statistics.ipynb\n- There are lots of other things I could have done here, this is just a short example."
},
{
"metadata": {
"slide_helper": "slide_end",
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"frag_helper": "fragment_end",
"slide_type": "subslide",
"slide_helper": "subslide_end",
"frag_number": 17
},
"slideshow": {
"slide_type": "subslide"
}
},
"cell_type": "code",
"input": "fig = plt.figure(figsize=(15,9))\n\nfor idx, name in enumerate(['APIGRAV', 'GORINIT', 'GAS2007ANN']):\n ax = plt.subplot(2,2,idx+1)\n x = oil_filtered['DPTHTOP'].values\n y = oil_filtered[name].values\n plt.plot(x, y, 'o')\n plt.title('Plot of DEPTH verses %s' %name)\n \n X = sm.add_constant(x, prepend=True) #Add a column of ones to allow the calculation of the intercept\n results = sm.OLS(y, X).fit()\n intercept, slope = results.params\n X = np.array([min(x), max(x)])\n Y = intercept + slope * X\n plt.plot(X, Y, 'k-')\n plt.ylabel(name)\n plt.xlabel('Depth (ft)')",
"prompt_number": 22,
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"png": 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lMjLK92iIdvxNiX3UdRM2PvBNVohEeVE+9xDt+KMcOwTx//y2lyP53SpX5WOb\nXGxUpBilGpeWkBif1hqkG58ndY0ePojystKib7lcsWIF48bdz0knHcteew1g6ov3snrFYnYeOJQ/\n/XUs7777PldccVWjK5fJEg8n4gSpTBPJgz5eEN0vBkXsAOB0YKiZTQ7/HQNcb2bvmdlU4BDgEgB3\nnwHUADOAp4ALwgyyABcAdwKzgNnKICsikl36brU5dZEVEUmjmJM3bdiwgUmT/k11dRWPP/4oq1YF\nXWD33Xd/YrGRHH/8iZSVbZW1/UU16VEUufu/Sf0Q+Kk061wLXJvi/XcAZW6SohPVYQgikp4qmNJq\npCrIElpTgVZfgV7euZRR103YuEwUuj62Vh9++F9qaqoYP76aTz/9BICddupNZeVFVFaeSu/eOxc4\nQhGRhmkOaWkp9LBkc6pgSouVaqxYckGW0NoKtFQFeo+unSI532M6yZ9/pw7tWLV6HRDdyvNXXy3j\n0Ucfprq6ijffDGaj2HLLzpx66umMGHEaQ4bsR5s2mY16aOo4ShWgIpJtmkNaWgI9LNmcxmBKi1Tf\nWLFTDt2F8rJSyjq1p6xT+1b75bj2uMKWNnag9ue/cvW6SI4bXL9+PRMmPMd55/2QAQP6cumlo3nr\nrTc4+OCh3H7735g+fRa33PJn9tvvgEZVLps6jnLMiMGUl5VufJ0oQKP6EEJECi8xDEH3Eom6qORs\nyAe1YEqLVF+FafzE/7bqJ0oJxTyuMBvSJXSC4h83+J//fEB1dRUPPFDNggXzAdhllz7EYiM55ZQR\n7LBDzwa2UL/mjqNUa4OIiEhdLf27VWOogiki6vpYBL78cjEPP/wA1dVVTJkyGYAuXbbmzDNHEYud\nyl577U1JSeFn21ABKiIiIumogiktUpQrTIWYZ7ApYwfSxVnouRLTJXSC4rkW1q5dywsvPMe4cffz\n3HNPs3btWtq2bcsRR3yXESNO47vfPYYOHTpkdZ9R/tsQERGR4lf4x+EFEOVJpCHaE9LmM/ZcDLbO\ndfyJ8XHJsjVRb0Oxf7xg+WZdH8dPnJ22AllfnOMnzs7JMTT23Cd//iUlEI9viqUQLXCJ+OPxONOn\nv0d1dRUPPTSeL774AoB+/XYjFhvJ8OGVdOvWLaexNPZvI8r3HMjdRNItVZTLyKhfq1GOP8qxg+Iv\npCjHDtGOP1flo5L8SIsVxcHWuU62c+O4yYy6bgKjrpvAjeMmb/a75EQLiUpifYlg0sVZLAmDkj//\nHx3bv+BC6fH4AAAgAElEQVTXwoIFC/jzn2/j0EP35/DDD+Jvf7uDeDzOueeezwsvvMLEiZO44IKL\ncl65hGj+bYiIiEg0qIustFgaK7a5K/8yKeOpSJqbCKYY1P78992te95jWL16Nc8++xTV1VVMmPA8\n69evp3379nzve8cRi43k8MOPZIsttsh7XPrbEBERkVxRBVOkiORyfNzU2YvqvNfUSmO6ONN1kW0N\n4vE477zzFtXVY3nkkQdZtmwpAHvttRcnnzyCE088ma5duxY4ShEREZHcUAVTpIg0Z6LebCbWaaii\nmy7O1jrZ8GefzWP8+HFUV1cxe/YsALp1687pp19MLDaSgw7aJ7JjNEREREQypTGYIkWmKePjEkl3\n6hszCbB7n4o669W3jzEjBlNeVrrZcrUnwU4XZ2sZ47dq1SrGjx/HySefwODB/fnd737Dp59+wrBh\nwxk37kEmT57Br399Dd/+dr9ChyoiIiKSF2rBFCkyTRkfl8mYyWt+vD8/uOrpjFsWRw8ftFlW2cbE\n2ZLH+G3YsIE33niN6uoqHnvsEVasCCrxe+89hFhsJCecMIwuXbYucJQiIiIihaEKpkgr0lClMVlL\nriQ2xZw5H1FTM5aamnF88skcAHr23JFzz/0xlZWn8q1v9SlsgCIiIiJFQBVMKWpNHVfYnPGI2RzL\nmC+ZJgdSpbFxli//iscff5Tq6ipee+1VADp12pLKylOJxUZywAEH0aZN00caJF9rnTq0Y9XqdUB0\nrjsRERGR2jQGU4pWJuMKs7lec9ctpEzGTEpm1q9fz8SJEzj//HMYMKAvP/3phbz22qsceODB3Hrr\nHUyfPos//emvHHTQIc2uXCZfaytXr4vcdSciIiJSm1owpWg1dS7G5szhGOX5HxvT/TVTUWzNTSWT\n45g9exbV1VWMHz+Ozz6bB0Dv3jsTi43klFNGsNNOvbIaU6prLVlUrjsRERGRZKpgirQQ2e7+mmhh\nS0i0qo0ePihSLaPpjqNLh3U8/PCD1NRU8c47bwNQVrYVZ5xxFpWVI9lnnyGUlJQUKnQRERGRyIlk\nBdPM2gJvA3Pd/Tgz2waoBnoBc4BKd19awBAlCzIdV5it9Zq7bksT5dbcZLWPY8OG9Xww9VWGj/8d\nC2a/yTfffEObNm047LAjiMVGcvTR36djx445jyvVtZastV53IiLptJSeNSItWSQrmMDFwAwg0Yzy\nC+A5d7/BzH4evv5FoYJryfJ5Yx8zYjCX3v7qxmk1AJYuX8P4ibPT7rf2eg1Nx9GUdXNxHlRo5tZX\ni+Ywd8YE5s18mTWrgudPu+76bSorR3LKKTG6d++Rdv1sfz61r7WSEojHg9815poVEWktWkrPGpGW\nLnJJfsysJ/A94E4g0XfteOCe8Od7gBMLEFqLV4gEOKOHD6Jd201dFDPd7+jhgygvK21SK1BD6+bi\nPBRjcqF+vcvrvBe1VrVFixax8qPnefm+S3j5vp/y4TuPsWHDenbd+zjuGfskL7/8Bhdd9NOMKpe5\n+HySr7UfHdu/ydesiEhrkK5njYgUjyi2YP4BuAzYKum9bu6+MPx5IdAt71G1AoXoMtmrexnr18cb\nvd/mjEdsaN1cnIfGbDNfLZ3NaQkupG+++YZnn32ampoqnn/+WdatW0dJm7Z022UfevYfyq6D9ueP\nFw9t1DZzde3Xvtb23a17k7clIiIiUgwiVcE0s2OBz919spkdmmoZd4+bWd0aSS0VFdHuSlGQ+EsI\nmm9qadOmpFHxNDr2LO03a3IRT4bbvPIvk+p0D7rsjklccfYQ+vTcusHdNDa+X52zL7+96w0Arjh7\nSMH/burbfzwe5+233+aee+5h7NixfPnllwDssccenHXWWex7yPe447EPgSYeR6Gu/SIS5dhFpGVQ\nnoTWRUOHoiuvFUwz6+juXzdjE/sDx5vZ94AOwFZmdh+w0My6u/sCM+sBfN7QhhYtiu78chUVZQWJ\nv1+v1Df2nwwbmHE8TYk9G/vNloqKsoziaexNMdNjnDprUZ11Fy9bzdV3vt5gS1pTzn2X0rb8/vz9\nN74u5N9NqvgXLJjP+PHV1NRU8Z//fBAutx0//vFPiMVGsttuAzYu+/vzN7UOFuIaLNTfbTZEOfZC\nMbO93f2tQsch0pJEtWeNNJ7G20Zbvsdgzjezv5vZfk1Z2d1/6e47uvvOwAhggrufATwGnBkudibw\nSHbClWRjRgymvKx04+vEjT3Xf+iF2m9T42nKeL1iO8Zi9vXXX/PQQ+OJxYaxxx79uOaaX/HRRx9y\n/PHDuP/+GqZO/YCrr752s8plc+nzkSb4W3NWNrMdzexFM3vfzKab2ejw/W3M7DkzczN71sy2Tlrn\ncjObZWYfmNl3k97fy8ymhb+7pTlxiRRac3IsSHRovG205buCORj4DKgys5lmdpmZNWe8ZKLT2nXA\nkWbmwGHha8mBQt3Yi61ASRdPU2+KmRxjS0i80xTxeJxXX32VSy8dzYABffnxj0fx4osvMHjwnlx/\n/c1Mm+bceec9HHnk0bRrl5uOGcV2DUqLtxa4xN13A/YFLjSzfmzKmm7AC+FrzKw/EAP6A0cDfzaz\nRIa0O4BR7t4X6GtmR+f3UESyJzF2XQ/5RIpXXrvIuvtHwK/N7CqCiuAPATezicBd7v5oI7b1EvBS\n+POXwBHZjlfqak7ynCjutz65iCeTbba27kGffvoJNTVjqakZy0cfBWMot99+B84++0dUVp5K376W\nt1iK7RqUoretmV3ApmznyeLu/ud0K7v7AmBB+PMKM5sJ7ECQNf2QcLF7gIkElcwTgLHuvhaYY2az\ngSFm9jFQ5u5vhuvcS5Bp/enmHJyISC5pvG20FSTJj7vHCZ68vmBmvYHxwENA20LEI5JNub4pjh4+\naGNraEu80a5YsYInnniUmpqx/PvfLwPQsWNHTjvtNE444RQOOugQ2rbVrUKKXidg72xsKCwnBwNv\nUH/W9O2B15NWm0tQIV0b/pwwL3xfRKRotbYH6i1NQSqYZtYGOIqgBfMo4BngykLEIs1T6AxfTdl/\nrmPO9U2xV/cyenTtxMw5S7j67rcadQyF/rzqs2HDBl599RWqq6t44onHWLVqJQD77XcAsdhIjjvu\nBL71rR2KJtFMsZ5HKSqfuPsPm7sRM+sMPAhc7O7LzTa12meaNb0popw1OMqxQ7Tjj3LsoPgLKVXs\nxZbJPp1ijq0Q8p1FdlfgLOAM4AvgLuB8d1+czzgkOwqd4asp+89XzLlsZWzqMaSa4qTQGdk+/HB2\n2AV2HHPnfgrATjv1prLyIiorT6V3750LElc6hb7upfUws/YElcv73D2RvK6+rOnzgB2TVu9J0HI5\nL/w5+f15De27WB7mNFbUMx5HOf4oxw6Kv5Dqi72YMtmnE+Vznyv5TvIzCSgDjnf3Pdz91kTl0swO\nynMs0kyFzvDVlP03N+Yr/zKJUddNYNR1E7hx3OR6l8tlEoKmHsPU2XWnOClERrZly5Zy773/x/e/\nfyT77rsnN9/8e5YuXcrIkWfw6KNP8eabU/jZz35ZlJVLKPx1L5HRrGytYYKefwAz3P2PSb+qL2v6\nY8AIM9vCzHYG+gJvhmM5vzK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bbEmkwG4CpgHHuPs6ADNrD9wA3AycXcDYRKSRinGMavL3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\nVMcXuVrPm8u+z8ca0yDrSF2DaXEc8AVgqTFmsb3tBuBmYKExZg52mZJ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efNdYyyZBlh1U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\nnY/tHhb32lXLh2rGAebOmTGm+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"text": "<matplotlib.figure.Figure at 0x1647b2b0>"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {
"internals": {
"frag_helper": "fragment_end",
"slide_type": "subslide",
"frag_number": 17
},
"slideshow": {
"slide_type": "slide"
}
},
"cell_type": "markdown",
"source": "A similar type of analysis can be done using Seaborn. This shows how Seaborn uses a DataFrame and makes it easier to do the same type of thing."
},
{
"metadata": {
"slide_helper": "subslide_end",
"internals": {
"frag_helper": "fragment_end",
"slide_helper": "subslide_end",
"frag_number": 24
},
"slideshow": {
"slide_type": "fragment"
}
},
"cell_type": "code",
"input": "sns.jointplot('DPTHTOP', 'APIGRAV', data=oil_filtered, kind='reg')",
"prompt_number": 29,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 29,
"metadata": {},
"text": "<seaborn.axisgrid.JointGrid at 0x17196710>"
},
{
"output_type": "display_data",
"metadata": {},
"png": 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vntB4ZU87//b4wVSgyrEYed9NDRe1n8spWEnPSgghxtF0nf7hMPGEhmEWbk51\neEd4bGsj3QPB1LJltfncdX0d+U7rRe0rlrg8nrECCVZCCJESjsYY9EdRlPQXSIzFNf6wt51t+zsZ\n6xA5ckzceV0dK+sLpnW8SFSClRBCXFaGRiIEw7OTgLal28/jr5/COxROLVvVUMgd19bitJmnvd9o\nLJGO5s0LEqyEEJe18SmT0h2oorEEL+1q441D3amM3Ll2M3dfX8fS2oIZ7z8swUoIIbJfMBxjaCSC\nwWBI+7Dfqc5hntjayID/dGb0KxYXc/s1Ndis6bn0RqISrIQQImvNZsqkcDTO82+18vbR0wUj3E4L\n99xYz6JKd1qPFZGelRBCZKdoPMHgLKVMOt46yJPbmhgORFPLrl5Wym1XVWO1GNN6LEhm1bhcSLAS\nQlw2kimToigGJa0pk4LhOM/ubGavpy+1rMBl5d4bG6gvd6XvQGcYkWAlhBDZYyxlUjSmoaQ5E8WR\n5gGe2taEfzRwKApct3IBm9ZXYjGlvzc1nj8YvfBKWUKClRAiq0VHM6WDktZANRKK8fSOJg42DqSW\nFbtt3LexnurS2a9cbDUbUwHyciDBSgiRtcZnSk8XXdc5cKqfp3c0E4zEATAosHFNBTevq8BkvDRZ\n7HLtZvxBCVZCCDFvpWb7RRNpnUThC0R5ansTR1tOl/JYUGjnvo0NlBc50nacqci1m2nrHUHT9VlJ\nCzXXSLASQmSVeCJBvy+CpqXvIq7rOnuOe3l2Zwvh0WebjAaFW9ZVcuOaBRgzUIix0JVDU5cffyBK\n3kXmFJyPJFgJIbLGbDzkO+iP8MTrjZzsGE4tqyx2cN/GBkoL7Gk5xnQU5dkA6BsOS7ASQoj5Ymgk\nTCicvod8NV3n7SM9PP92K9FYMmGsyaiw+coqrluxYFbqW12MwrwcIBmsGiryMtqWS0GClRBiXkto\nOt7BIHFNT9tEiv7hMI+9formLn9qWW1ZLvdurE/1aDKtKBWsQhluyaUhwUoIMW+Fo3G6vCMkdNIy\n7KdpOjsOdfHyrvZUrSiLycBtV1dz1bLSOTWRYSxY9Q+HL7BmdpBgJYSYl/zBKP5QjKLC6ZfYGK9n\nIMjjrzfS1juSWrawIo97bqwjPzcnLcdIp/HDgJcDCVZCiHkl3ZV8E5rG1n2dvLq3g8RoVcQci5H3\nbKhhnVqc9mzsZ6PrOgaDQq596oE3x2LCaTNLsBJCiLkmGk8wMBwBJT3Dfp19AR7beoqu/tMl5pdU\n53P3DXW83iemAAAgAElEQVS4HJYZ7/9CdF1HQcHlsODIufgeYlFeDh19geR+5tAQ5WzIaLBSVdUN\n/AewHNCBPwNOAL8GaoBm4P0ej2coU20UQswNgVCU4UAsLbPw4gmNP+zt4PV9HakS83ariTuvq2VV\nQ+Gl6U1pOg6bmVy7edrHK8rLobnbj+8yeNbq0j/JNtG/As96PJ6lwCrgGPAg8JLH41GBV0ZfCyEu\nU7qu0zccwpemQNXW6+f7jx/ktXdOB6qV9QV85v2rWb2waNYDlaZpWM1GSgvtuByWGR1v/LNW2S5j\nPStVVfOAGzwez/0AHo8nDgyrqnoXsHF0tUeA15CAJcRlacKw3wwDVTSe4OXd7ew42IU+GqScNjPv\nvb6O5XUzLzF/IZqmYbUYyXPYMBnTk419bJKFdziU9c9aZXIYsA7wqqr6X8BqYA/wGaDU4/H0jK7T\nA5RmqH1CiAzyB6P4g+npTTV1+Xh8ayP9vtM9kLWLinjPhlrsObN7GdR0HbNRocBlS3vJkEJXMlgN\n+iJp3e9clMlgZQLWAX/h8Xh2qar6Hc7oQXk8Hl1VVT0jrRNCZMSE2X4zDFSRaILn327lrSM9qWV5\nDgt331DH4ur8mTb1vHRdx6gouHMt5FjSM73+TPm5yftUA34JVrOpHWj3eDy7Rl//Fvgi0K2qapnH\n4+lWVXUB0HuhHRUXz37tmEspm84nm84Fsut85uK5hKNx+odD5LkvLudeQcHkjOdHmvr5+XPHRmtZ\nJd2wpoJ7b16IzTp7lz59dIzR7bTitM/ejML8fDsWW3L/gUh8Tv480yljwWo0GLWpqqp6PB4PsAk4\nPPrvfuCh0f+fvNC+vF7/hVaZN4qLc7PmfLLpXGBq5xNPaJzqGMZoMFBf4Trrc0AJTeNUhw+Dopxz\nndk2F382Yw/5Xuz7UVDgYGAgkHodisR5dmcLe457T6+Ta+WejfU0lOcRCkQIBWanJ6JpGg6bBZfd\nPKvHARgcDKLrOiajge6+wJz7eaZbpp+z+kvgF6qqWoBTJKeuG4H/VlX1Y4xOXc9c84SYuoSm8YuX\nPDR3Jy8ay2rzed9NCyeso2k6v3zpBI1dPgCWVufzvpsbsv4ZmfPRdZ1+X5hYbObDfkebB3hqexO+\n0aKECnDtijI2X1mFxTx7JeY1TcdmNZLntF3SDx+KolCQa2VQhgFnl8fj2Q9ceZZvbbrUbRFipjq8\ngVSgAjjSPMigP5K6rwDQ2R9IBSqAo62D9PvCcyY56qWWrtl+I6EYv3+jmQOn+lPLit053LexYVZL\nzGuahsVsJM9lxZzmyRMX4vMlS5bk2o14O0MMDA5iMhrIzXVl5YefTPeshJj3wtE4v9vezKmuYQb9\nEdzO5LMzBgXMpomPMlrP+HRvUJjVT/xzWSAcwzcys5Lzuq6z+2gPj75wjED4dIn5G1aXc8u6yknv\nf7romo7RpJDvtGG1ZObn99JbJ7HZHYSjcXTgtX2d6PEwm69eiMuVfdPYJVgJcR7t3hGef6uVaFzj\nPdfXU1M0+cb/K3vaOdqaLHNuMir4gzHcTivvuqoKp23iLLBit42Na8p5fV8nigKb11ex51gvR5oH\nyXNauPO6OvIuQZqfTEpXyXlfMMrvtjdxpPl0ifmyAjv3baynotiZjqZOoo3O8HM5LdinkR4pnWx2\nB3ZHLvacABDGaLZhzlDgvBQkWAlxDvGExqMvnyAYSX5i/+ULx/jY7UsoyZ8YsIZHoqmvc+0W1Mo8\n/uimhef8VH/TmgquXV6GoigcbRnkhV1tAPT5wjy5rZH7b1syS2eUefFEggFfhMQMSs7rus5ej5dn\n3pxYYv6mtRVsXFOOyThLvSldx2Uzz+oMv+mwmJPnG41rmDMbP2eVBCshziEcTaQCFSQvVoP+yKRg\ntay2gBPjSp6vrC+84PDT2NDfgH9impzxD61mm3A0xqAvimJQpn1PZWgkWWL+RPvp97tmgYv3XldL\n2SyVmNcSozP8HNPP4Tebxh40jsYSOCRYCXH5ceSYqCx20O5NTot22i1nHV5as6gIh81EhzdAZYmT\nhReR9mZRRR7bD3SlSlMsrnKnp/FzzHAgSiAUnfawn6br7Dray/NvtRKJJXtTJqPCpiuquPOmBoaH\n0l8tV9N1csxG3G5bxkvYn8/YB59ITCM5/zE7SbAS4hwUReHDmxfz9tEeonGNzRtq0aLxs667qNLN\nosqLDzQVxU4+8q7FHG8bwu20coVaPNNmzymalpyWnsxGMb1A1e8L88TrjTR2np5FWVOWy3031lPk\ntmFMUyn7MakZfo5LP8NvOk4PAybI5kt69p6ZEGlgtRi5YXU5AIV5tll58LK6NHdWp1dnSjgaY9Af\nRVGmN+ynaTpvHu7mxV1txOKnS8xvuaqaa5anv8S8rukYjQr5rszN8JuO08OAWoZbMrskWAkh0m5o\nJEIwHJt2b6p3MMTjr5+ited0ifmGChf33FBPgSu9JeZTBRCd0yuAmGnWsZ7V6PBotpJgJYRImwmz\n/aYRqBKaxrb9Xbyypz11H89qNnL7NdWsX1KS9gkOuq7jtJnJnWMz/C7G2D2raFx6VkIIcUHBcIzh\nQARFMUwrqHT1B3hsayOdfafz/C2udnP39XVpr4KraRo2q5k8pyUjuRnTySI9KyGEuLCxKf3hWAKD\ncvG9qXhC47V3OnjtnU600YzlNquJO66tYU2aK/fORgHETBvrWY09c5atJFgJIaYtGk8w6Iugw7R6\nKO29Izy29RQ9g6enni+vK+Cu62rTOjQ3XydPTIVBUbCajUQkWAkhxGQjwSi+aVbyjcU1Xt7dxvZx\nJeYdNjN3XVfLyvrCtLVxvk+emCqrxSg9KyHE/KLr+qxmWphpSY/mbh+PbW2kf/h0to41C4u449qa\ntObbG19bai5mnkinHIsRfyCaKvyYjSRYCZEl2r0j/Pa1U4yEYqxuKOSOa2vTfpGeSUmPSCzBi2+3\nsfNwN2OXVJfDwt3X17GkJn0l5hOajj0DtaUutaGBfsKh5PCpoiczr/t8I+ffaB6TYCVElnji9UaG\nA8mkuntP9FG3wMWKNA6pBUIxhgPRafWmTnYM88TrjROKBK5fUsLt11STY0nPZWgs80RhBmpLZYKm\nxdG05NCfdfQtXFSVT26uK4Otmj0SrITIEmP1nMaMhGNp2e+E2X4XGajC0TjP7mxl97He1LL8XCv3\n3FDPwsr01FxK3pfSKXBZybFk732pMxUUlWJ3JDOflAQGaO3zEoqbsnbIU4KVEFniCrWYNw53A8kk\nvEurZz60Fo0nGPCFAeWih9SOtQ7y5LYmfKO9PQW4ZkUZW66smlSEcrp0XcfttGLNzuvzlOWO1kDr\n82VveXsJVkJkic1XVlFd6sQfjLGoyj3jIo7BcIzhaVTyDYZj/P6NFvad7EstK8rL4d6N9dSWpWeI\nStM07Dlm8hwWnHYLoUD2XqSnItee7FF6h7L3fZBgJUQWWZyG3tRMKvkeauznqR3NBELJIUhFgRtW\nlXPrFekpMZ+ND/WmQ67djKJAZ38w002ZNRKshBApsdFhP02/uId8/cEoT+9o5lDTQGpZab6N+zY2\nUFky8xLzuq5jMCgUunKwpmlCRjYxGgy4HWY6+kLEE9qsVUvOJPmpCyGA5Gw/XyA57DfVOKXrOvtO\n9vH7N1oIjVZVNigKN60t56a1FWm5aOq6jstuwWG7fCZPTEd+rpnBkRhtvSPULci+GYESrIS4zCVn\n+4UJxy6uQOLwSIQntzdxvHUotay8yMF9G+tZUOiYcbs0TcdmNeF2WrJ2hls6FeRaaOwK0tjpk2Al\nhMgu05ntp+s6u4/18uzOiSXmb72ikutXlWOcYQn4seel3HlWuS91EYpcyQk1R1sGufWKygy3Jv0k\nWAmR5UKROAO+MAWuHGzW03/y/mAU/0Xm9hvwhXliWyOnOk6XmK8udXLvxgZK3LYZtVPXdQwKl93z\nUunitJkoclk52jJAQtMwTrPw5VwlwUqILNY9EORnLxwnGIljt5r4yG2LKXbb6BkIMhKaeqDSdJ2d\nh3t44e3WVIl5s8nAliur2LC8bFpZLcbTdZ1cmxnnPC6COBcsrnKx47CXxk4fiyrdmW5OWmU0WKmq\n2gz4gAQQ83g8V6mqWgD8GqgBmoH3ezyeoXPuRIjLSFd/gMZOHwW5VpbWFlxw/W37OwmOTnwIRuK8\nureDjWvKKSx0pu4D9fvCtHT5yHVYznqB8w6FeHxrIy09/tSy+nIX99448xLzY/elsqEI4lywpDoZ\nrA6c6pdglWY6cJPH4xkYt+xB4CWPx/OwqqpfGH39YEZaJ8Qc0uEd4f8+f4x4IpkG9ua1YW5cXT7l\n7eMJjUg0PmGyQv9wiN++dorY6D57B0Nct3IBkEwIu/1AJ6/saU8d02o2ctvV1Vy5tGRGwUXTdSwm\nA3mXSR6/S0WtzMViNrD7uJd7b6zPqokpc2FQ88x38y7gkdGvHwHuvrTNEWJuOtIymAoaAAdP9V9w\nmxvXlGO3GonFNXIsRtYvKZnw/aYufypQARxvSw5idA8E+fcnD/HC222pY6pVbv7qfau4elnptANV\nMo9fcuZaUZ5NAlWaWc1GVjcU0TMQpK03uzKwz4We1cuqqiaAH3o8nh8DpR6Pp2f0+z1AacZaJ8Qc\ncmb6pNwppFNyO6y8/+aF+AJR8pzWVAn0MWc+u2S3mnhlTzuvvdNBQhsrMW/kPRtqWbtoZiXm5b5U\neo0vEQIQDgXx+x2sqHGy61gvO/a34b6mgtxcV1b0sDIdrK7zeDxdqqoWAy+pqnps/Dc9Ho+uqmr2\nVhMTYgp6B4MMjURZVlNAV3+Q462DFLpyuPPa2vNu5w9G8YdiWC0mis+R9WFJtZvewSAn2ocxGhQ6\n+wIcGNdjW1abz3uvr5tRiXm5LzU7xpcIAbBYrexrGiGh6RgNCjsOezETZMs1i3C50pPhPpPmzG+O\nqqpfAUaAj5O8j9WtquoC4FWPx7PkXNvp2VwaU1z2dh3p5tEXjyezODitfOYDa8m/wKQGTdPpHQwS\nT2hT+kQdiyf4/fYmXnqrFW30zynXbuYDmxdzxZKSaX8qTyR07DnJh3pNMtw3ZcoU3/D/fn6fnnuO\nIPTK7laONQ+yZX0ZH7l9CXl58yNYne/cM9azUlXVDhg9Ho9fVVUHsAX4GvA74H7godH/n7zQvrxe\n/4VWmTeKi3Oz5nyy6VwgM+fzzLZGoqMP3vYPhXhhRyMWs5FwNMHqhYWU5NsnrB+JJhjwhy8YYAoK\nHAwMBGjp9vPY1lP0jSsxv3phssqwI8fM4ODFJ0Yde6jX5bCgx2BwMH7hjWYg237PpioYioIhfNbv\n1ZXmcqx5kCPNQ/T1+YlG58L0hJnJ5DBgKfCEqqpj7fiFx+N5UVXV3cB/q6r6MUanrmeuiUJk1pmZ\nyvee6EtNRd/r8fKJu5aTn2sFktPan36jmeGRKLVluVy3asE5h90i0QTPvNHMG4fGlZi3m3nvDfUs\nnWaJeV3TMRoV8iXZbMYVuXNwOy109IcZCcVwZUH2pYz9Rnk8niZgzVmWDwCbLn2LRCbE4gmee6uV\nzr4A1aW5bLmyKiszRk/XbVdX86tXThCKJqgodNDeN5LqNYVjCVp7/OQ5LfQPh3nmzWZae5IzwPaf\n6ifPaWFVQ9GkfZ7qHOap7fvpGzp9c3794mLefU3NhAwXU5Wc4afgclpw5EjmiblAURQWVbrZdayX\nt4/1c3fp5N+D+UY+/oiMemVPB++cSBbp6xkMYbOauHltRYZbNTckNI2ivBw++/41hKJxnDYz3/vt\nAYbGVd7NtZnpHQiBAv7gxDL2wyPRCa/D0TjP7Wxl17gS826nhXturJ/2A6S6pmHPseBymLNixlk2\nqS93sed4L28e7eO9N6rz/ucjwUpklHc4NOH1+E/7l7N27wiPvnyCYCROdYmTD21WMSgKf7xpEc/u\nbCEUSbCyvgBbjik1Tap+gYt+nxcAgwK14zJvHx8tMT8cOB3ArllWyruuqsZqufjJD5qWfG7L7XTM\nONWSmB1Wi5HKIhut3hBHWgZZPoWMJ3OZBCuRUYsq8mjsPJ0UdWHl/Ji1NNue29mSujfV2jvC7mO9\nXLdyASX5dj5y2xIGfGFiseRsP13XURSFq5aVkuuwMOSPUFOWS0Wxk2A4zjNvNqd6rwCFrhzuv2MZ\nRc6Ln44+dl+qwG3DIjP85ryFFQ5avSFe2tUmwUqImbhmeRk5VtPoPSsnK+oKM92kOSEa0854nZwR\nGI0nGBiOgAKKQWHv8V7ePtqL0aBw07oKlo27IB1uGuCp7U2MjCsxf/3KBdy6vpKyEhcDA4Ept0fu\nS81PBbkW6socHDjVT1d/IC11xjJFgpXIuDULi1izsChVJ6lrIEhtWS4r6y/fwHXtyjKe3tGMDjhz\nTKxZVMxIMIpvXEmPvqEQbxxOJnuJazov726jutRJLK7zux1NHGo8nXKzJN/GfRvrqSrJvei2yH2p\n+W3j6lKauht5eXc7f/quxZluzrRJsBJzxvaDXfxhbweQnJadSOisWTT/ZzFNx9pFxSwodDDoj1BR\n7CASTRCOaBPuD4WiiQnbxBM67xzv4+U97akhRIOisHFNOTevu/gS83JfKjusrHNT6Mphx6Eu7rmx\nHqdtfvaMZY6wmDPG37uC5BTry1lZgZ3aMieBUIyEpqOcETAWFNgodCWfsUokNMLROL97ozkVqMoL\n7Xz6nhVsvsjHATRdx2CAIreNApdNAtU8ZzQobFpfSTSmsXVfR6abM20SrMSccWal2ZlWns20UCQ+\nYfbdxRoaCTPoj6SG3kKRGIHQ6enpJpORe2+sp7rEyYA/wqA/eSyjQWHLlVV86p4VlBdN/R6FrusY\ngHynhRK3XSZQZJEbVpVjsxp5aXc7sXjiwhvMQTIMKOaMTesrSWg6Xf0BaspcqbpK89E7Hi/P7Gwh\noemsqCvgnhvrp5zENZHQ6PeFSWg6htHS5HuP9/Lm4R50YGV9ARvXVDDoD/PE602c7DjdA60qcXLv\nxnpKz0jDdD5j6TVddsukLOwiO9hzTNy0toLndray/UAXN6+rzHSTLpoEKzFnmE1G7rhAJvH5IKFp\nPDsaqAAONQ2wsr4QterCD96GozGG/FFQlFSPKhiOpQIVwIFT/USiCd441E10rMS80cDmK6u4dsXF\nlZjXNA2HTJ6Yl84sEXI2Y2VDAK5d4ublXW08u7OZNfVOjAZlXpUPkWAlRJppGqlANSaR0M6x9mnD\nI1EC4WiqN5XaViMVqOIJjSF/hNf6O1Pfr1uQy703NlCYN/US85qmYTUbyHPIPan56swSIWczVjZE\nUZKPKVSX2DjVFeTXrzZS7NTZfPXCeVM+RIKVuCR2Hetl2/5OTEaFW6+oYnnd7D6gODQSYfuBLhKa\nzoblpZOyk88ms8nA9asWsO1AFwCVxQ4Wniedkabp9A+HiI8b9hsv125maY2bXUe9+IKn74FZzAZu\nu7qaq5ZOvXLvWEb0skIHQxKk5rWColLsjot7FGH1ohwauxvxdASpWjO/Hg2RYCVm3TsnvPzyJQ+h\ncRkZPn33SurLZycVdCyu8dPnjzM4EgHA0zbEp+9ZcUkfZr1lXSVLqvOJxBJUlTjPORsvHI0x6I+i\njBv2O1PPQJDjrUMTAtXCijzuubE+lXF9zIAvzMHGfoxGA+sWFWEfPWdd1zEoUOCykmMxSzn5y5TT\nbqZugYvGTh9dA5FMN+eiSLASs66l25/KwAAQiWm0dPtmLVgNByKpQAUQjMTpHQxRt+DSTh640Ey8\ncw37jUloGlv3dfLq3tMl5nMsRm6/poYrFhdPCm7BcIzHX28kPPr8VVuPnw/csghFQcrJi5QVdQU0\ndvo41uZnPtWulWAlZtVIKIY9x4zJZCAxehE1mwyzmvbFZbfgzDExEo6j6zqxuMbRlkFyLMa0Hjee\n0BjwhYknNHIsJgouUMH39HYJBvwREomzD/sBdPYFeGzrKbr6Txc/XFqTLDHvclgIhGJEYwnycq2p\nIUDvUDgVqAD6hsPENY3KYqeUkxcp7lwrlSVO2ntHONHhZ33e9DLuX2oSrMSs2X20h589e4R4QqPE\nbSOW0DAZDWy6opIl0yzwNxUWs5EPb1nMK3vbaez0EYtr7DrWy74Tffz5e5ZSVjDz+1ehSJyfvXCc\n421DBMNx8nOtXLui7IKzGQOhGL5gBEUxnHXYLxbXeHVvO6/v72RsjobdauLO62pZ1VCIoigcbuxn\n6+j3a0qc3H5tDUaDAbfTgtGQzGShKApup4XyQocEKjHJqoYC2ntHeH5XF1csrZwXMwIlWIlZ88Rr\nJ5OZFxSFuKbz3uvqWKsWX5JjlxbY+dAmlX9+9J3UEFosoXGibSgtwWqvx0ubdyT1kK4vEGWPx8sV\ni4vP2nvTdZ0Bf5hIVDtnb6q1x89jWxvxjiuTsqohWWJ+LEWOpum8fuB0IGvpHaGx08eiSjcuh4V3\nX1PDwZP9WK0mNq+vlEKW4qyK8myUFVhp7BqZN+VDJFiJWaOdMR5+5utLoSDXmko/BJDvsp5n7anT\nzpiarp9jOUzMlH62aeLReIKXd7Wz42BXaj+5NjPvvaFuQhb1M481YZmm47SbuWZZGdcsK7vIsxGX\no2XVuXQPRHhqWxPLavLnfO9KgpWYNXdeX8+vXjyGpsOCQjsrZphF3ReM8treDsLRBOuXlExpgsY9\nN9bz1PYmBnxhSgvsuJ1WguE4r77Tjj8YY3VDId7hMJ19AapKnFy7omxKf7TrFhdz4FQ/gVCMYCSO\ny25mRV3BpEkVgVCM4UD0nM8yNXb6eOL1Rvp94dP7Vot5z4azl5g3GBQ2LCvjjUPd6EBZvo3FVW6K\n3DYZ7hMXpSDXwvLaPA43D3OkeXDWHyeZKQlWYtZcu6qcQoeZYDhOWaF9xkNSv3zJQ89gcojsRMcQ\nn7xzOUXnyB/Y4R0hEI5TU5rL3TfU85/PHOFUp49TnT7sVlOqt7XH48ViMmAxGzneNoRBUdiw4sI9\nE0eOmY/fuYzugSCRaAKHzcyCQnsq0Om6zmBq2G9yEIlEEzz/ditvHelJLctzJEvMXyjTxVq1mOpS\nJwldR63MI8ciKZLE9Nx2ZTmHm4d5cnsjy2rndu9KgpWYVQWuHArSMEM9EkukAhUkJxF09gfPGqy2\nHehMlRopystBrXQTCJ8eCmzp8VM8ul00lkAhOSkDoK13hA1TbJPFbKS6dPJDmdF4ggFfGFAmZUqH\n5HNfT25rZGjk9HNTVy0t4barq8mxnP9PcqwIYt0CV+oZKiGmq6rYztpFRbxzoo/9J/vndEkeCVZi\nXrCajZTm21IBy2RQWFB4eqLESCjGkD9CsduWyhwByenbBbkT86c5x13kLSYjZtPpHl9F8cymtgdC\nMXyBCMpZJlGEInGefbOFPR5vallBrpV7NtbTUH7hlDe6pmG3WXDZJY+fSJ97b6xn38k+fvPaSVY2\nFGA8xwSgTJNgJeaND21S+cPedsLRBFcuLUn1jlq6/Tz6ygkisQQuu4UzL+OLq/PRNDjZOYwzx8SH\nNi3iWOsQgVCMlfWFeIdDdHoDVJU6pzQEeDbJYb8I4VjirLP9jjQnS8z7g6Ml5klWA968virVqzsX\nKYIoZlNFsZMbVi3g9f1dbDvQxU1rKjLdpLOSYCXmDZfDwt031E9a/tq+DiKjGTJ8wSg1pU46+4PE\n4hqLq9ysWVjEOrWYSDSB2WzAoCgsOk+uvosVjScY9IXRUSZNchgJxfj9G80cONWfWlbszuG+jQ1n\nHUIcT9d0jCaFApdNakuJWXX3DfXsPNLDU9uauGZZ6QWHozNh7rVIiIt05pBYab6dP9m8mEgsMaGE\nt9WS/gt+IBTFF4hNujel6zoHG/t5ekdz6n6ZQYEbV5dz87rKCUOPZxq7L5XntMh9KXFJuJ1Wbruq\nmt/taOaFt9t47/V1mW7SJBKsxIy19Y6wbX8nBoPCTWsrUg/dbtvXwa5DXRS4rGy6oioVLEZCMV7e\n3YY/lJw6vqphZjd1b15bQXd/gFA0gdthYcOKMswmw3kDwkxpus6AL0wspk0KVL5glKe2NXG0ZTC1\nrKzAzn03NVBxxtT2hKbx9pFejrcOEo0nqFvgYvP6KkoLbHJfSpzXVOpZnc/4WlcA1y518+peE8/t\nbOHG1QvIz516yZlL4ZzBSlVVm8fjmf47MUWqqhqB3UC7x+O5U1XVAuDXQA3QDLzf4/EMzXY7xPSM\nhGL84iVPahiuwzvCX9y3ipPtwzy1o4lYXKOxC0KRBH90UwMAj209RXO3H4CmTh8uh4XasulPGawq\ncfKX961iOBCl0GU9Z0bx1h4/vmCU2jLXhB7XxYpEEwz6kw/5nuwcxtM2RK7dzFVLSzjcNMgzb7ak\ncvQZRwP4xjXlZ526v/uYl51Huhkcfc5qOJAsvPinWxZPu33i8jCVelbnc2atK4BFFQ72nhzm5y8c\n5S//aG06mpk25+tZdamq+hvgJx6P581ZbMNfAUeAsQH8B4GXPB7Pw6qqfmH09YOzeHwxA/3D4VSg\nAhgJxxkOROnqD0xYb/zrzr7TX+tAV19wRsEKwGY1nfUh2jE7Dnbx8p52IJkd4mN3LCPPcfFZyIcD\nUQLhGAZFoa13hBfebgOSSW13Hu6ZMB29stjBvRsbzpveqXcgQHy02q/BYCCe0OnqC5xzfSHGTKee\n1YUsa3DS3BPknZOD7D/Zx+qFc2cq+/nGSdYCncAvVVU9qqrq51VVLU3nwVVVrQRuB/4DUpO47gIe\nGf36EeDudB5TXFgklqCpy0ff0IU71sVuG3ariYSmE4kmsFtN5DstkyYPjH9dM+5rg6JQVeKkeyBI\nc7eP+Hkq6o6EYjR2+vCPq+s0VTsPd6e+9odiHG7qP8/ak2laMogERwMVJOtM6bpOIBTDOxRKBSqT\nUeG2q6v55HtXYDEZaO8dIR6f+AlY13XQoaHSTY7VhGG013WuZ7eEuBQMisIVi9wYDPDzF48TjsYv\nvFWzxcUAACAASURBVNElcs6Poh6Ppwn4iqqqXwVuAf4M8Kiq+hrJ3tZTaTj+vwCfB8Z/rC71eDxj\nj/X3AGkNkOL8guE4//XcUfqGwxgURmsnlZxzfXuOiTs21PBfzx0jntAIRY20ewOoVW7+9PZlvH2w\nk4JcK9evKk9tc9/GBrbu68AfirGqoZCTHcNs3Z8s015V4uRPtyyedL+pwzvCz1/0EI4lsJqNfHiL\nSmWxc8rnZbUkS4akXpunfrs2FIkx5I9SWOSccB8px2KkfzhMNH46wNaU5XLfjfUUuW0cONXHtv3J\nfH+FLiv3bqzHajYln5fKseBymCktqMBuNXHgVD/hWIIlVW42ztGpw+LykOcwc+vaMl7a080Trzfx\nx5sWZbpJwBQmWHg8Hh14BXhFVdVa4DfA48CMplapqnoH0OvxeN5RVfWmcx1bVdX5Ux0sCxxo7KNv\nOHn/RNPh1Xc6zhusAE50DOOwjVWkhe0Huqhb4GLd4hKqCiZnmLBajGy5qhpIlsR49OUTqe+19Y5w\nsn2IpWckcH3jUDfh0eHGSCzBGwe7uW5lGcfbhshzWFmrFp03N96d19by36+e5P+x997BcZzpue+v\nuycPZgY5k0hkM+cgSiRFUVmrtNIGr+31xuO1fXx97BN869jle6vuqVPlcMu31r7ldG0f73rX9gZL\nK2lXqyyKEikGMUexARBEzpjB5NDh/tGDAQYZJECQ3P5VscgZzHR/MxzM29/3Pe/zxFOqKWdfPbdP\noWEYhKIpEik1r3dK1w0+vtTHO590ksnOBCVR4LFdK9i3uSo3jhOX+3Oms8PhFNc6QuxYU0bRpH6p\nPRsq2bPBMp+1uHN4fEcVF66P8u7pTvZsqKChammCUhfCnMVKlmUReAJzZvUE8BbwfyzCuR8AnpNl\n+TOAC/DLsvw9oF+W5UpFUfpkWa4CBuY6UFnZvbVsspyvp7gokjer8bgdc47H73PlPaegwJl7zlzP\n1TQdp8OGpo/PTkpKCqY8b/I5DAG+/24zWrZYxDMaLx6c+QqwrMzH9g1VZDQd5xxNuAAZVWMwmMBT\n4MLrGy8sSd3gez+/SltPOHff2roivvzUuinWT06nDc3ImFJ0QWBlVYC1TbMX/tvJvfR7cy+9lvni\ncTvwFSy+Yk8kTVVlgN/90nb+8G+O8v13FP6f3zuw7HEzs6kB1wBfA34NGAL+F/BbiqIsbLF/BhRF\n+UPgD7PnOgD8N0VRfk2W5T8Dvgr8afbvV+Y61uBgZDGGdEdQVuZb1tfTUOalusRLe79ZtB7ZVj3n\neLY1lXCxeZCRSIoCt50H1pUzOBiZ92t5dEctPz/ejm4YrFtZyEVlgJ8ebqG8yM3ju1fitEvskku5\n2jbMaCxNwOvA67KRnBD9cepKP/tv0n1iMtNZJmm6zunmYX760fVcPpbTLvH0/dmIeV1nZCRfGLF3\nQwVvn+rEAJqqA6yqWt7/24ks9+dsMbmXXstCiCfSICbnfuBCjxtLMTQUoTIQYP/mKj660Mu//PwK\nn9lTt+jnWgizzaw+Bv4NeE5RlDMTfyDL8n5FUT5a5LGMrZj8CfAjWZa/SVa6vsjnsZgFu03kK0+u\nYTSaxu2U5tXJHvA6+K3PbiQcS+PzOBbc37RjTRnr6opIqxqftgd56xNTYdc1FEMQBJ55oJ5iv4vf\nfmETkbh5jqvtI5y+Nu6xV+RbuLJvMvm9U+OvoWcoxsuHW+mZEDG/dmUhz+9rIFAwfT6WoetsaCxh\nm1xGKqMTKHBYER4Wdx1fOLiK8y1DvHqkjR1ryqgouvXg0ptltm+iGkVRcmU7uyT3dczZlgisWqxB\nKIpyGDic/fcI8OhiHdti4YiCQJFvYSGFNkmk2H/zSxIelw0PtjxndTAVd2PYbePn2NRYwkAwwZUb\nQQIFDp7be2sd9+mMxkjY7J0aa/JVNZ1DZ7s5fLYnFxzpdtp49oF6tqwqmbZpV9d1nA6JgNeNTZKy\nr+2WhmZhsWwUuO38ymMyf/vqZf75zWv8ty9tXbZm9dnUgElZlm2Y0vFvALsBO/CEoijHb9P4LJaR\naCJD92CUIr+L8hlyo6ZjJJxkMJTA5lx4421TtZ9zLUO52zMFLAqCwIGt1ays8OFySFOKa3tfhLSq\nUV/pn3OmF4mniSQyeTOfzgEzYn5gQvHcvracJ3bW4vNMncUZhoEoCpT43VNsncbej8oS7031dllY\nLCe71pbz8aU+LrQO8/GlPvZuqlqWccy2Z/Vt4JeA08B3gBeBq1ah+sVgaDTBP/38U+IpFVEQeOHB\nBjY2zK2gu94T5gfvNZPRdAqOd/Clg03UlBXwziednL42gMdl57P7G2bsJdrYWIKB6WxRXuxm97rp\nOxcyqsY/vfEpvdmlufs3VPL4rhUAvHG8nZOfmrqc2lIvX3ly7bQFS9cNhsNJVE3PFaqMqvPuqU6O\nXOwlO5miwG3nub31PLhz5ZR9KTALld/jyCkiJ9LSPcoP329G1QycdomvPLFmSpqwhcWdjJB1VPmj\nfzjBD95rZlNTCf5pLtiWmtkuOX8DOAH8saIoP5q4JGhx73Pm2mAuTVc3DI5e7JvjGSbHLvfl5Nyp\ntMrxK/0onSE+vtxHStUJRlO8/OH13ON7h2P87auX+PMfnOXQWTMwcVNjCc/ta2DP+soZ93mau0Zz\nhQrMpt+MqpNIqblCBea+V1tveMrzk+kM/cE4mm7kljXaesP85UsX+OjCeKHatrqU3/vCFjY2Ti3U\num4WoMpiz7SFauz9UDXzYKmMxsmr/dM+zsLiTqYk4OLFBxuJJVV+8F7z3E9YAmbbs6oGfgX4C1mW\nA8D353i8xT2EfZK82zFP0cTkxznsEuFYGlU1491FUSCaGJdz//sHrYxEUgB8eL6HmlLvnLHu5nny\nxydJAqIINgQkUcgp9swxTJC8Z3unkik1J6JIZTTeOtHB8QkR836vgxf2N7BmZdGUc+u6jsMuEZjF\nh3AMuzT1/bCwuBt5ZEctx6/0cfxyPw9sqJz2Am4pmfEbSFGUoKIof6Uoyk7gc0Ax4JJl+UNZln/j\nto3QIkc8qfLjQy389SuXePdUZ27T/2bQdYM3T3Tw169c4qXDrVNsVfasr8g5hBe4bDyRbeKdi0d2\n1JJRNboHo3QNRJEEOK0MMBxO0h+Mk0ipbG4aFyeMxvKtk8ITbhuGwaEzXfzNK5f4wXvNRBOZ3M+a\navxsaTJ/WWyiwMFtNfzru8384+tXWVXtR8qKJHbIZTnfwbSqMRBKkExruULV0jXKX/z4fF6h2rW2\nnN/7wuYphcqM7jAo9jspDbjnLFQAj+6sze1TlRW62b95edb7LSxuFVEU+OqTaxEFgX9+6xrpzM2b\n6N4M85opKYpyAfhdWZZ/H3ge+E3g75ZyYBZTef3YDa5kYycGQwkCBU52rb25JtNjl/s4kV2SGgwl\nsEsiz03IsHE7bXzz6XXEkipupzTvqOuh0SQj4RQCZkH82cftFPmdlAZcJNOmVdIzD9TnHr+xoZjz\n2WBCt0OiqWY83v1cyxAfZiPqB0IJ9KNt/MqjMmCuo392fyOP71qJ3Sbk7V/1BxN84aEm6iv9eFzm\nRzwSTxOJZxBFAUEQSKRU3jjezqk8+buTFx5sZFXN1Ih5wzDwex04FyiEKg24+U+f20w8peJx2Sz5\nusWicasRITMxOTpkIoVueGhLOe+f6+cnhxWe2l097ePmg8/nX5CycD4OFmXAkKIohqIoaVmWXUDT\nTY/Q4qYZs0Eav33zH9TJxxqc5liCICw4SqN/JI6e3QcSBHM5TtUMHHYJr1vE7ZDyvrCf29tAXaWP\naCLDhvriPFXf5DEOj07dNh0rRpMfG46l8bhsqJoZ56FqRs7i6Gp7kFc/uk54QsT8no2VPLFrasS8\nrhu4nTYCBQ78XieDN2GiK4oLfx8tLObiViNCZmK66JCJ+NwCbofIO6d7EdAocC98dygRj/HYfavw\n+6deGM7EbGrARzGbgkuALlmWvwT8GaZ8/dcWPDqLW2Z1bYCBrBO6AKyuublo9kRKZSScJBhO4nba\ncDltyAuIeVc1nebOUG6WklY1VtcU4nRIrKoN4HKOu0sUeOysKPcyHE4hCvDIjhV5xxJFgW2rywBT\n4n2hdZiKIjdlhW58bjvJtIrdJiGJ41H0iZRKa88oXpc951km1xZy+cYIYO4TNVT7iSUzjEZTiKKI\nIAjEkhle/7g9TxpfGjAj5usq89WJumHgsInz2peysFgOliIiZL7sWifw4fleLt6I8fCOmtvSezVb\nSfwzzKJ0GHgB08z2jxRF+fMlH5XFtDy8o5bCAidDo0lW1QRYVTv/q5KJvPzhdToGorhdNtIZnYe2\nlbN/y/ym85qu8723rtExECUcTaPqOkU+J+WFbr759HqqSrz87uc28cbJDjxuB4/vqKG8yEP/SByP\nyz5js3HXYJR/fusaqbRGKGoWtoxmUOC2o6o6D+9awYNbq4knVf7x9Ss5UcbejZU8unMFn93fQFWJ\nh0g8w4aGIuySwGgsjSiKGIbBpbYRXjvSlouYB1OSvn9zZV6hMgwDURAo9jlwOazZkIXFdNRV+qjs\nGqV7KEbvcPy2tGPMVqxERVHezP77X2VZ/p9WoVpeREFg503uUU2kayAKgMthw+WAAvf8eya6B2N0\nDETNHKekuYym6QaDo0mau0NsbCihoTrAf/zspjzPtpo54jzOXBsko+pEExlSaQ1NN5BEgWRKpchv\nmtiKgsC1jmCuUAEcv9LPwztqsUkiezdVkUyrhCJp0qpZdCLxNK8duZGbdYGZN1VY4MRhl7jUFmTX\nOtNT0NANCjz2aZt+LSwsxhEEgR1rysyViuYhqko8Sz67mq1YCbIsjxlBCUBkwm0URYlP/zSLO53a\n8gJaukcB8z+2pmz+V0WubBqvgLkEaDDepzQfH8GJxJMqJ670kdGMnLJx7O+xz/2YAt2VdYVwTXKH\ncNrH98BC0ZQZjpidTZ1rHuJnx26QSJnr+qIgsKGhiKHRZG7MDruIbhi4HRKBAqclgLCwmCclfhcr\nKwro6I/SMxSb84L0Vpnt22UTEJ1039htg1vMs7K4vaTSGq8dbaNnKEZ1qZftq0uJJVU2NZbkRcq/\nd7qLy20jOb+9yct25YVuDm6r4fC5bor9TgzDzHLauaZsWhXdZGLJDK8eaWNgJM5IJIUgmMt1um7g\nc9tQVR1NN3DaRTTdwOOysXZlIVtXm/Haa+uK2NJUwvnWYVx2iRf2NzIUivPS4euEomlW1QZYX1/E\na0ducK0zlDtvTamXFw80Ul7o5s2THTR3jRJLZnDaRK7eGCHgdXLsch9Oh8TTe+qW/BfvdnDx+jAf\nnutBkgSe2L3yjsgksri32LKqhI7+KOdahqku9S7p7Oquv4w0DMO4l+IBliru4PVjN/Jk2mN7PRO5\ndH2Ylya4S6woK+AbT6+b9niqpiMI5gxL041p7Yymey0vHW7lUtsIqqYzGExgk8RclL3LISFJIp7s\nXtrGhmK+9MiqaQUOGVVHkgTiiQzfe+sa3cNmxHw8pRJPqDkXDZsk8MiOWvZtrs71XhmGwQ/eayYY\nTSGJIhlVQ9OM3KyxwG3nv3xxy5RfvLspimJ4NMlfv3IpN1N12iX+8xe35LK87qbXMhf30msBKC/3\nz+t7+Ts/OWksl8BiIh+c7aajP8rju1ZQWTI/V/Z4LMK+TVVT1ICzvfblTdOyuG1M3OcBCEZTN/WY\nMWySiCSKiKKwoEiQkbB5TCnrZqGOhS4KppNENJEhlsjgdkrohjGjEk8SBYZHk0TiGcLxDKqmMxxO\nMhpN5wqV3SbywKYqDmytyRUqXdfxuOykM3qud0zTDNQJjhfRRCYvqv5uZDSWzmsaT2U04kl1lmdY\nWNwc6+rM5vmJKxlLwYzfMrIsf1WW5T+acLtXlmU9++c/LOmoLBad9XX5bgzrprERWl0bwCaNX9is\nXTkuZ0+lNc61DHGpbRhdv3nnjLEPtiAIFPucVBV7sdtEXA4JTTPM2VFSJRxLs3aaMQIkUqavn6rp\ntHaHiCczDAYTpDPjBcbvdVAacNHSNcpIOGlaJNkEKotN5/N1E94PSRKwS0Iudbix2j+vNOGZiCdV\nzjYPcvXGyC25jNwK1aWePIf3asvx3WKJKC9yEyhw0NkfXVJXi9n2rH4L03V9jH7MDKsC4AfAPyzZ\nqCwWnR1ryvG47PQOx1hZ7ptW9l5V4uXrT63jWkcQv9fBNtnsf8qoOt9581P6stlSF2tH+NIjq25q\nfXrf5ioKCxwMhBLUV/qor/JzrSPEmyc6GAjFERBIZzSqS73ctz7fcX3M1y+RUhFFkZ8ebeP0tcG8\nWVBViQdNN3IR3IZhoOn6lOiOZx6op7rUy7mWIW70RZAkgYyqs29zFQe31S74dY2RSOVL67c0lfDZ\n/Y03fbybxeWw8Y2n13FGGcQmiuxaW55rirawWEwEQaChys+55iE6B6J5LjSLyWzrN5KiKO0Tbjcr\nihJTFKUfWFgyn8Udwbq6Ih7eXjtrf1Z1qZeD22vZsaY8p4zrHIjmChWA0hXK8/BbKBsbSyjyOfnB\n+y388fdPE4qmeHSnKT9PpFU03ZgyxrSqMRBMkMroGAgcOtPFscv9uUIlCPDAhkr+txc3saG+GAAD\ng3X1RWxsKJmSMSWKZhtALJHBbhOxSSIupw2HXVpw0vFErveE85ZTz7cO33YPtTH8HgcPba1h3+aq\nKa/fwmIxqaswBUldg9O7XiwGs82siifeUBTlCxNuTh8yZHFXkVF1Xv6wldbuMGWFLr5wcBWF08S0\nj1kajWEThVtyD48mMrx+rD3njP7OJ51867n1OGwiAuZe07WOEPEdpp/eaCxNLGmGI/aNxHnpg1a6\nh8Z/KZx2icICB1tWlyIIAo/sqGGbXEqB205dhW/WGaDHZSM0ofB6nLcWLDD5vXLapdwsz8LiXsXv\ndeBx2ejLCp2WQhU4229mtyzLuxVFOTnxTlmWdwG9iz4Si9tCKq3xk4+u05ltDI4mTHPXtr4If/PK\nJR7eXsuuteVousFrR9u43hOmvMjNvk1VHL/SRyyRYSSh8l//6iguh8SK8gKe2L2S9fXFnL42wOFz\nPdhtIk/tqaOsbFypNBJO8vKH1wlGUtRX+rJqwqw6D+gfSaAbEMgWy+HRJH/+wzO4HHb2bapkRYWP\n9892cfhcT67IOe0Sfq8dh11ky6oyasu8CBiU+N1Ul85Pev7s3gZ+9H4Lo7E0a1cWsmNN2bSPy6g6\n//zzK1xqGaKiyM2LB5qm9ftrqPKzd2Mlx6/047RLPL+vwVp+s7jnEQSBiiI3bb0RIvEM/iXYH52t\nWP1P4CVZlv8HZggjmNH2/yfwrUUficVt4fC57pxqZzSr9nM7bYyEk0Riad482UEwksLjsnGpzXR9\nuNEXweO08evPrudPvn+GZEZD1XRSaQ3DiPCjQy185QmZ14+1MyYn+PdDLWzfMB6H8cqRttxs6Ep7\nkIDXkTOSrS31sqo2QIHLRjSpomo6oWgSQXSRyhi89vENDIO8iPkN9cU8t68en8eBbhgIhoHHZV+w\niKCy2MN/+vxm9KzN0kwcvdTL2WsDZFSdtr4Ib5xo5wsPrZr2sY/uXMHDO2qtBmOLXyiK/S7aeiME\nI6nbW6wURXlbluVvYhanb2fvPgv8uqIoby36SCxuCxPzo9xOG7GkSiqtgQHu7BJWc1eI+knGruFY\nmmhcNVNvDSD7VySeIZHS+NnHHWbRyH5Bp1SdeDKT9/yJbG4qpaLIjW4YrKsrwm6T+PLja3j/TBf9\nwTiptIooCOZ5J+RYeV02ntvXwKZs8NuY4WxhgQObdPNLk3MVlsnjn2vPzipUFkvNUkWELASXy53r\n1nXZzL3Z4VCEsjnavxLxhe9tzbpAryjK28DbCz6qxZKj6wYXrg+TSmtsaCiedwRFeaGbTz4dwG4T\ncdglHtu5gv5gnPMtw7lN+NKAmw0NJZxrGc5Jrzc2llBT6qWs0E33YNT8gBqmsMFuExkOJ/A47SSz\nYoL6Sp9pupstWBsbijl6qQ8wH7++voiqknybpwK3nQNbq1E1g++88Smdg1E0bVz6vWVVCc88UI/X\nZc8GIQoUeh14XHaudQQJRlKsqglQWui+pfc2kVK5eH0YSRTZ3FSC3SayoaGYK+1BMqpGKqNTfovn\nsLC4VZYqImS+JBMx7ltXis9nOqP0DMU5enmEgNfFvk1zh4yOPW++zBYR8jQwY5OIoig/X9CZLBaV\nlw635oIYj13u41vPrsfjmr1gXb0xwocXzD2ljKrz8LYaHtpWg2EYlBe6udYZotjn4jP311HgtvO1\np9Zyo9fcsxpLzf0vv7SFt092MhhKcLV9BLtNwu20IYoCzzxQTyiawm4T2ba6LG+T9dGdZnd7MJxC\nXlFIRfF4p7uqaYSiaTIZnYxm8PYnndzoG3ck8HnsfHZ/Y643ytB1PC4Hfq8dQRD44Fw3h8/1AHDo\nbDffeHodFUXz66SfTDqj8U8/v8pgNh/rctswX35iDU3VAZ57sInv/vQydknkTPMQVSXeRTEWtrC4\nGZYzIgRMFwqfz59zodAEFwAZXVhQTtV8mW1m9fvMUqwAq1gtE6mMlitUYC7ttfVG2NBQPMuzTBm1\nboDDLuGwS7kvZEEQOLi9loPb8/uLVpQXsKI8X6jgddl54UGzb+jQma5cku+aFYWsqy+adflrY0PJ\nlPtiiXQuyuN6bzgnwhhj55oyntpTh9tpG1/yC7jzlvzON4/nU6VVnSs3gjddrLqHYrn3BaCtL8Jo\nNE2Rz0nfcAzPhBns+ZYhq1hZWGQZM5lOpZdmtjfbntVDS3JGi1vGLpmJu4kJHwqfZ+5lwMmPmc9z\nZuPg9lo2NZaQ0XQqij0L2qeZmOCbVnXeOH6DTz4dyP28sMDBCw82srq2EMMwN8jGlvwm4/M48uTn\nvltI5fW67WMrnED2vXaav4QBb76s34oSsbAYZ0ylu1Tq19mWAYuAPwLWAGeAP1YUZXl38ywA88Pw\n+YdW8drRNlJpjfs3VrKyYu7lgB1rynNqnfoq34KdGnTdoGMggl0Sc67kc+0PGYbBD99vNverDNiz\noZJnHqgjnsggiCJKZ4hXPmrLE37s2VDBE7tXYpME3jzRQUdfhLIisw9sumL13N56/v1wKx39UTKq\nxqGz3bgcEhsbp87k5qK80M2T963kg7Pd2CSRR3bU8uMPWunoj1BfFaCx2kfXQIzyIjdP3Ldywce3\nsLhXSaZN78nJMT6LxWzLgH+PeYH5c+A5zOTg31msE8uy7MJMIXYCDuBVRVH+QJblYuCHQB1wA/ii\noihL65B4F9JY7ef3vrBl3o9v74vwr+8qpFUdt0Pi0R21UxpYZ0PTdf7lnWbaesMA3Leugifn8WV9\nuW2EIxf7UFUdXdc5eqGHqmI39VUBXj92nbMTlvBK/C5ePNBIQ5UfQ9e5fCNIW+8ooigyEErys49v\n8LWnprrAlxa6+cJDq/jrVy7idEjEUyqvHmmjsdo/5z7edOxeV8HudWbf+5snOrjeY77m9r4w6+uK\n+IMvr13wMS0s7nXGlv8Wmms3X2ZrrV+HWSj+Gnge2LeYJ1YUJQkcVBRlK7AZOCjL8j7gvwPvKIoi\nA+9lb1vcIkcv9easiRJpjWOX+xb0/Bt9kVyhAjhxtT9Pmj4TkUQGXTfQs+7qmgHNXaN8+8fnc4VK\nEGD/5ip+5/ObqK/0IQBFfnPJTRTHP6LRxMyu4bFkhon+uqpuxoXcKpFEvkQ9NssYLCx+kUnkitXS\nzKxmK1ZpRVEMAEVRZs6KuAUmpA07MMMcg5izuO9m7/8u8NmlOPcvGpMtf2zi/CyAugaiHD7XTWdf\nBAyIJTIMjyYJx9Icu9zPsUt9uen/ZIZHE4yMJrHZBMbNxw1OXB3I9U6VF7n5zec38tSeOmySiNtp\no7zIjcthZ2NjSc6nL6PqeJw2zrcMmXtYk6gq8VI1QWFYX+mj2Oea8/WNhJN8dL6H09cGp3WT37qq\ndHwvThDYsnrhS4szkUprHLvUx9GLvVZ8h8Vdz2DI3CUqCcz9e3czzDZfa5Bl+UeMBzTWy7L84+y/\nDUVRvnirJ5dlWcTcD2sC/kZRlMuyLFdkzXLBdHq3fAgXgYPbaugejBGOpyn2Odm/pXrO59zoC/P9\nt5XcxmkirTIcTpqLwwL85KPrlAXcXLw+zDeeXpdXEEdjaf7+h+cIhpN4HDacNolwLEMmO7sTBYED\nW6s5uN3MmhIEKPW7cEzIr6os9vCtZ9ZzpnmIIxd66ByM0jkYpXc4PmUJ0m4T+eqTa7nUNowoCGxs\nLJlzozcUTfEPP7uSuyK80Rfmcwea8h6zuraQb3xmLV2DMTasLqPAvjg+f6qm8923PqV32LxeO98y\nxK8/u+GWTHQtLJaT/qzZ9cS2lMVktmL1e+S+lgB4nXGRVN1inFxRFB3YKstyAHhLluWDk35uyLK8\nPIFA9xhlhW5+53ObiMQz+Dz2Gc1VI/G0mbw7FMMmCRgIueDCSDyNJIAOGIbZk6TpOr0jcYZHk7kP\naVrVOKP0E4ml0HWDYDSdK1IA1SUeXjzQRHWpF0PXKXDbZ1TWlRa6cWcThMe43DY87X6Z0yGxY838\npeSt3aN5isorN4K88OBU26WasgJqygoWNZF2JJzMFSqAwdEk/cE4tWXz8zS0sLjTGEtmqLzJtpG5\nmE26/p2Jt2VZrga+lv0jAv9jsQahKMqoLMuvAzuAflmWKxVF6ZNluQoYmOPpeYap9wJL+Xrm6iv/\n6384xsXrpiegphs47SJVpabThCgKZPMJMbK3HQ4bDptI3YoifB4HwXCSpG5QXe4nluzIJQODedXz\nxJ46nt3fiCgI2O0SJX4nthnSgMdYWVOI/WJv7sqpvMS7KO9RfSyD3daZu13kd1FRPntX/WL937i9\nTjwuOxnVLJaSKFJfW0SRf2mWUKbjXvq9uZdey3zxuB34Cm7f52UyImlKS30EAuZ73x9M4LBLn2kr\nNQAAIABJREFUyI2lSyJfn1W2IcuyHVNc8Q1ME1s78ISiKMdv9cSyLJcCqqIoIVmW3cBjwP8FvAZ8\nFfjT7N+vzHWsxbravRNYzKv3m6GjN5zbExIFs2HYaZdwOSTTSFYwZ1WSKFBbWoDfbeexXSsYDcZp\n7xxBN8zltZ982JZXqMD0IixwSYSCcfwFDpwiBIPx6YaRh88hkkprDIYSBLwODm6tXpT3qMRrZ9+m\nKk59OoDHZeO+teUcOd1BfaV/2vynxf6/efb+Ot7+pBPDMHhkRy1qKsPg4NyilcVguT9ni8m99FoW\nQjyRBjE59wOX6vyxFENDEdJpkXgyQ0dfBHlFIcPD0SU532x9Vt/GTAo+DXwHeBG4uhiFKksV8N3s\nvpUIfE9RlPdkWT4L/ChronsDuOW9MYv5U1/p51zrUG4BeMuqEr759HqOXerlYusw9uxynCDAf3xh\nI0U+J6FoiuFwAgSBE5f7eetkR156ryiYf9IZlXAsRUXJ9A3E3YNR0qrOyooCpAkCkHdOdSKKQm6Z\n8Wp7cNGWyx7cUs2DW6r54Gw3r318A4DSgItvfGYd7lvMtpqLtXVFrM1aSFlY3M20dI9iAKtXLE1K\nMMw+s/oN4C3g/1YU5SiALMuLdmJFUS4C26e5fwR4dNFOZLEgvv6Zdbx0uJX2/ggNVf6ctVKRz5Xz\nFASzl0LXDfpG4giCwEg4xcsfXs/z9Fu9opBwLEUwnEQ3DNxOG3WVgWkL1dsnOzh2xdTV1FUU8OXH\n1+T21SYr5RKLIEmfiGEYHL04HtE2NJrk044g21ZPn211MwyGEqQyGtUlXivfyuKeo7lrFAC5tnDJ\nzjFbsaoGfgX4i6wA4vtzPN7iNpBKa5y82o+q6WyXy3JhhXNxqW2YvuE49ZX+WWPtnQ6Jg9truHIj\niM9jz82kGqv9rKsroiMb2rhuZSHheIqL10do6Q5zrSNoxocADrvIU/fV8cTeBt78qJXTyhCSJODz\nOFhfP9W/MJFSc4UKoL0/SltvGLtNpKVrlCKfk67BKLph2h9tXVUKmAGN51uHcNoldq8rxz7H3tdE\ndN3g1LUBIvEM6+uLsNtE1AliC8c8j9U9GOVqe5BAgZMdctm0hejD8z0cOtsNmOGMv/rY6ryZo4XF\nzbDcESHJRBzDqARA6QwhCNBUswwzK0VRgsBfAX8ly/Jm4JuAS5blD4F/URTl75ZsVBbTohsG33/7\nGl3ZEMOzLUP85nMb53SiOH65j7c+MYUERy/18YWHmqYtGgADwTj/9PNPyWSVFL3DcZ7f14DDLvHN\np9dzrmWQTEZnZaWP7799jfb+aJ7Sb3VtgBcebCTgdSAKAk/uqcPpsDEcTvLApqppwxFFUUAUhFwc\nCZgy2ENnu3ONvpsbi6mv9LOivIDSQjfhWJr/9fOrucbf671hfu3xNfN8J+G1o22cbx0G4MSVfg5u\nr+bQ2R4yqs76+qKcw/ts9A7H+M6bn+aKdP9InGceqM97TEbV+SBbqADaesO0dI3mXOwtLG6W5Y4I\n0XXzdy+jarT1hllRXrCkS+fzOrKiKBeA35Vl+fcxBRdfB6xidZuJxjO5QgVm8GH3UJTVM0y9k2mV\nN090cPyKORMrcJuRGp92hGYsVs1do7lCBfBpe5Dn9zWQVjVG4ylWVvjQDYM3jnXQ0j3uaCEI8OiO\n2lzkiMshUV3q5bs/vcSpa4OA2WD8H55ZP8VP0GmXePK+lbx5wgxw3NJUQiSe70jRO5LghQfHe6A6\nBiJ5DhXXe8Kk0tq0wojpuNYx7uCV0XR0Hf73X95GOqPP24aqpWs0V6jA3EubXKzAFKlMbGQWrGBG\ni0XgTogIEQSBtt4IqmYs6RIgLHBZT1GUNPDj7B+LJSKd0ZAkYcpSkdtpy3NbFwWBwgnLgLpukFH1\n3Bf2G8c7uHB9mHRGI55UEUUBt8NGsX/mpcPiSdLpIp+DYCRJIq0hCgI9w3FePtya1yPkckgU+5zs\nXleBgECR34nLYUMQBC5dH8EwDAzDTA9u7hqd1vx219pyNtQXo2o6fq+Dk1f7835e7Msfc1GBM88d\n3ee245ilYVfTdTTNwGGXSKU1ivzOvNdQ4ndhk8QZ+8+mY/J7VTKN7NxuE3lsVy3vfNKJbphRKquW\ncKnEwuJ209xlXvitXnEHFSuLpeflQ828f6oTuyTy7N76XHw7mF98X3pkNW+e7CCj6uzfXEVZ9ou/\nrTfMjw+1kEhrrFtZxOcfamIga3/i9zjQNINILIOq6ly9EWTnmvJp04XX1RXx0NZqLrQO43LY2L+5\nklTG/KJ/50wnH53vyc14XA6JskIXfq+D+9dXUuRzUuhz5gkoJEmgP5jA0M3ZVoF75o/cxBnNzjXl\njIRTNHeFKPG7psxYasoKePr+Oo5f7sfpkPjMnroZZywXrw/z2tE2MqqOwyaS0cxcrFK/C90w2NxU\nclOqvA0NxQyEEly6Pozf6+C5vQ3TPm7P+krW1xWTVjVK/C5rZmVxTzEurljaizCrWN1BtPWG+eic\nub+R0XReO9rGurqivKv9lRU+vvXshinP/enRG7kZ19WOIOdbh2iq9ptqPVFAFAUKCxy4nDYGQgk+\nONs97ZIVwAMbK1lfX4SqGgiiQEd/hJcOtzIYGu/p2NRYwrN76ylwmxHzhQUO3M6pxc8wwCYKqIZp\ndPkv7zSzvT3EiwcaZ53FiKLAk/etnNXZfcea8jkdKzKq+T6qmkE8qdIXS1Oc9S6TJIHffn7TrM+f\ni4Pbaji4rWbOx/mn2auzsLjb0XWD5q5Ryovc8xZ73SxWsbqDSGfyN0tVzUDTDOYjTJtsJpvKaDy8\no5YCt53B0SQ3esOMTEjgTWWmbswahsFoLJ1bMszoOu+c6OTji315y23P729gfX0xhm5gtwkU+dwz\nBi8ahkFpoZuhUIKMaqAbBlc7gpxRBnMxHEuJqum5faWxfaOxv9MZfcbnWVhYzE3vSIJESmW7XLrk\n57L0s3cQDVV+aibYxmyXy+YtGLh/Q2Xu3wGvg40NJYiCwJ4NlTz7QD1N1X4i8TTJlIrdJrJrUhx7\nPJmhP/vBE0WB1p5R/vLHFzg6oVBtl8v4vS9uMQuVYe4tlfhnLlQA9280x6UbBpIk4Mqm7i5GfMd8\ncDttbF9dmvu322nDaTfHsGeD5ZFsYXErXO81W1mWWlwB1szqjsJhl/idX9rKiXNdOOzSgnoW9m+p\npq7SRySeoaHKlxc6eORCL59cG8TlsKFqOg9trc4lC6uaRiiaJq3qiIJAOq3zxol2Tl4dt2QMeM2I\neXmFGTEvilDsc2OT5i6k+zdXU1fh4/jlPi7fCGZFHhKbGhYvamMunt1rzgTTGY2KEg89QzGKCpy5\ntGMLC4ubo73fVCfP1ru5WFjF6g7DaZdYN4OsfC5mirZv6TbVOnabiN0m0jeSwDAMwrEM8WQaQRQR\nBQGlM8RPPryeFzF/3/oKnti9IudY4fPM7JA+27hWVvho6w0TiqZorPIv+fr2ZCYW/vnkXFlYWMxN\n11Acp0NasliQiVjF6h4io2ok01qun0rVdOJJlWKfk+s9YURRQBAECj12+kcSGBgIokgsmeGVj9q4\n3DaSO1ax38mLDzbRWO3HMAwGgnFOXRsAQ+DBLdVzXkml0hrhWH7KbkPV7I7mE4kmMthtYm7J7lZJ\npTUy2V6zhaLrBtFEhuJia4/LwmIMVdPpDyZZVTO9hdpiYxWre4TmrhD//kEraVWnqdrPvs1V/PhQ\nK6OxNLFEBgOQBNi5pgy5LgACCAicax7kpcPXcwGLAHs3VvLY7hU4bBK6YeCQRN462ZnbZ/rhoWZ+\n+4VNeT1eEznbPMjrx9oRRYH1dUU8v69h3nJtXTf498OtXG0PYpdEnt/fwIabnGmOcUYZ5OfH29F0\ng22rS2eUmE/HaMzM9xoOJykv9vDFA01LloRqYXE3MRpTMQyoq7w9jcmWwOIe4fVj7Tmn89aeMD96\nv4V4SiUST5NIqWQy5r9PfjrAtfZRookM//ZuMz861JpXqJx2iY2NJaY3ngElPheiKOQJIlTNIBhJ\nTRmD+TOd14+15455vnWY6z3haR87HVc7glxtDwKmfP9nR28s9K3II6PquUIFcLZ5aEHjOXyu20xH\nBoLhJO+f6bql8VhY3CsEo2acTd0M2w+LjTWzukeY6M838bam6Wi6TkYFmySg6QZvnOggmdbyCtBY\njIcgmMuJNknINbDabAJlAReDo+aXdoHbTsUMaaCabuQVv+nGtqDXoenoxtT03vmiTzue+fupzfS+\nWlj8ohMaK1bWzMpiIezfPJ4BXOxz8vjuFeiagcspIYoikiRgCJBWdUYiqVyhKi90EyiwI4kCgihQ\nVeJhY0MxpQF3bulOEkW+8uRa7t9Qye515XztybV4XDaGRhP87OMb/Px4e06U4bRL3L9+XBJeW+qd\nUdWoGwbHr/TxykfXOdc8BMC6lUVUFI3bMe3fXHVL6+FOh8R9E/q5astmHs907Flfkds3s9uknBTf\nwuIXnWA0jd1mfmfcDu563xfDMIx7KSX0VlJPe4djRBMZSvxOkmmN45f7GQgmqCnzcuxyn2l7lJ1k\nSKLAwe01HNhaTSqtcbVtBJfLxv0bKvNk7zMRT6r8zauXiCbMq6tin5PffH4jdpt5/dM5EMXjdeJ3\nSrn7JnP4XDcfnOvJ3X5ubz3bVpeRzmh09EdxO6VFk5d39EdIqzr1lb4F+f+BuW/VPxJnbVMpevr2\n9IctNfdSuu699FoAysv98/pe/vY/vmG43LenUExG0w3ePBOittTFf/3iuKOOz+e/JTux2V67tQx4\nD1EacGGTBFJpnY8v9XGuZRhV0znfOpznWFFT5uXzB5pyclOXU2DflhoCBfOXpA+GErlCBTASSRGK\npigrdGMYBldvjNDSG8HjkHh+XwNFvqlijLbeyKTbYbatLsNhlxa9b2MmWf98CHgdBLwOSgLue+pL\n0eLuZjkjQkZjGgZgt4scyQaXJuIxHrtvFX7/0vRcWcXqHmCyTZIgCtzoCxNLZAjH07nZlE0SeGzn\nCh7YVIU0FhJoQKnfhWOCRFzXDULRFC6Hbca4jGK/E4dNzKX2+jwO/Nn+q3MtQxy70o/dJjKY9eb7\n6pNrpxyjothNe//4l/9M+2BLQTypkkyrU4x3x0hnNMLxNAGvc8aZoYXFcrKcESEjHSEgRkWJ/7aN\nwSpWdzmxRIahcBJN03PLd8OjSboH43nNvWWFLn7t8TW5eA7dMHDZJYp8zrxpe0bV+P7bCh0DUeyS\nyAsPNk4bROjzOGiq8XPkQh+CYBrbjllDhaL5/VUzKQcf3bECDDPgsa7Sl2cZtZRcahvm1Y/aUHWD\nhkofv/yonFeQ+kbi/Mvb14gmVYoKnHzlyTUzyvQtLH4RGVPIlswSN7TYWMXqLiWV1gjFUiidId47\n1YWqGzRW+vB5Hbx7qisvQLGwwMFvPLcej8uc+RiGTsDrxDvN3tT5luFcdH1G03nzRMe0xWoknORq\neyjXc3SlPchAKEF5oRt5RSEfZ5cGANbPEL9ht4k8tacud7t3OEb3YIzKEg+1S2iF9MbxDtSsQrCt\nL8KltmG2rS7L/fz9011Ek+aMMRhNceRC74wO9RYWv4iMhJNT8vSWGqtY3WWYS3RJUmkNQRT54Ew3\najZ08cSnA3nSaqddJFDgxCaJXGoLsmttOaIoUBqY2ddvssxb06eXak9+3NjYAGpKvXztM+voDSaw\nAVtWze0D2NI9yg/ea0bTDUQBPn+g6aZtp+ZCn/watUm3jcnvwdTXamHxi4qmGwQjaYp8TkTx9mn0\nrMX4u4hIPE1LV4g3T3Ty9qku+oZjZDSNSDzNYCiRK1Quh0RNqZdivwtJFIgl0lxoGULpDFJR5JnV\ngHZzUwll2dlSOqPhcth443g78WS+Cq6s0M2WpvEitLGhGI/Txk+PtvHS4VY0TeepBxrYurp0Xuqg\ns82DuaKgG3AmK2UHON8yxI8PtfDe6a5F6XM6uL0GQzcIx9KkM9oUS6cHN1fjyC4Lepw27rfc2S0s\ncoQiKXTDmDVxfCmwZlZ3Aam0RiiaJKMZvHr0Rm4vSukMEUtkiMTHVXnyikJefLCR/mCcd051Eo6Z\nsSDJtMoH53rxe515S16TcTtt/Pqz6znfOszPjt5gOJxkOJykPxjna0+ty3vsZ/c3smttOQZQXeLh\nb1+9nGscVjpD1K+Yf/puwaQlSW9W2HG1PcgrR9py98eSmQXZJU3H7nUVXLo+TGtPGLtN5JUj1wkU\nOHKKwbpKH7/94iZGwknKC93zkvJbWPyiMBA0E8jLJ/RD3g6sYnUHM7bkl0xriKKpvBuNpTEMg0g8\nkycddzkknrm/jm1yGYIgZLOmnLx69AaJpIokjfc/zVaswGx+xQBhwhS/cyCKYRhTZkljfVCxZCZX\nqMBsPu4ejFI1Tx+9A1trGAgl6OiPUl3i4dGdK8zz9udLxTuz+2m3ymAomVNA6oZ53Inydv8EdaOF\nhcU4A8E4YBUriyzhWJpoIo0oiohidknKJWETBXqG47n0W4BNjcU8u7chz1Fc1w1WlPvY2lTKyU/H\ns6mqS7yAKXcfCCVw2KRcD5Sm6wwEEzjtEqIA6bRZ5CRJpLrES0bVGQ4nCXidUyTtbqeNYp8zl0Zs\nk8zN197hGMU+15whkh6Xja8+uXZKQawu8+Y9rqrEO/mpszISTqJqOuWTZPHVpV6u95oegUL2toWF\nxewYhkF/MIHbabupBINbwSpWdxiJZIb+kbjphyeObylmVJ13T3XSORjN9U25nTZeeLCRjQ2ThQgG\npYUuHDaJR3euQJJE+oNx6ioK6ByIcPxKP6FoClXTEQSBh7fVcN/6Cr731jU6B6MMjyZzcfCSKLCh\nvpgn71vJ37xyiVAsjcsu8aVHVud5gomCwJcfX8N7p7tIZzSaagL83U8uMhpN4fPY+coTaygNzH0l\nNnnmtrGhhHhS5VpniFK/i4d31M77vXz/TBcfXejNHqeYFx9szB3/cwcaefdUF+F4ms1NJQuKL7Gw\n+EUlFE2RTGvUV/luyaniZli2YiXL8grgn4FywAD+P0VR/lKW5WLgh0AdcAP4oqIooeUa5+1iLLE3\nqRsY5H9pt/WGefnw9VxvA8C21aU8fX993gxH13XcThuFBeO9U3abyOO7zCW1t092cOH6CKmMxsho\nEq/bjt/r4NDZbuw2ka6hGImkSiqjoWoGAmY/ltIV4rQySCi7V5bMaBw6283Xnspv9C3yOfn8Q00A\n/Ou7CvGkuUwZiWf4+GIfz+27ub2m3esq2L1uYSKHaCKTK1QAl9pG2LW2PLfU53HZb3o8Fha/qNzo\nM5fhV5bf/pTt5VQDZoD/rCjKBmAP8NuyLK8D/jvwjqIoMvBe9vY9i2GYcRsDwSSqlj+bSqU1XjvS\nxt//9EquUAW8Dr765Bq+cHBVXqEyDIMin5Min2vGK57hcLY5NzszUyf0Yk0UZ08Waqu6QWhKY+/C\n5Ny3W/xtGJbc3MJiMTEMg/a+CJIoLJpn50JYtpmVoih9QF/231FZlq8CNcBzwIHsw74LfMA9WrCi\n8TSRRAZBEKb0K4yFKU5U+u1eV86T963E5ZhQpHQDm02kJOCeYhuUUXUOneliMJSkodqHvKIQpSuE\n0y7hcki5faQDW6vZLpdxuW2ErsEorpRKXDel6pIoEPA42NRUzJX2IImUit/joMjn5J/f/JRQLE1R\ngZOSgIvRaBqHXeTh7bUc2FLNQOi6uQzotrN30+K6UyidIU5dG8DjtPHw9lr83nwxhM/jYO/GSo5e\n6gNgQ30xK5bhatDC4l4hFE0TjqVZWVGwLBZkd8SelSzL9cA24ARQoShKf/ZH/cA91+SSVjWCkRS6\nPlVdF09meOlwK6evDebuk0SBymIPz9xfh82W7+Hn99gpmEG19ubJDk5e7ScYTvHRhR5W1xby/N76\nnBN7sd/c1xpzofjaU2sZCCZwOSSau0b54Fw3oiBwcFsNn3w6iMshYZNE0qrGJ58OEktkSKRU3E4b\nybRGoMCB22mjZyjGb7+wiT/82m6a24YoCbjyCuyt0jcS50eHWnJ9WQPBBN96bsOUxz26cwVbV5ei\naQblRe7bvsZuYXEv0dI1CkD9Mu3vLnuxkmW5AHgJ+F1FUSKyLOd+piiKIcvynOs5ZWXLY+a4UHTd\nIBhJoutQVDRVfXaheZB/+dezjEbHl9x8HjuBAgeiICA67BQXjosUSgPuWVV2w5EU0UQGLVsU+0bi\nJDX41ac38M6Jdr7/TjNOu8QvPSazsakUgKpK0zF5TVMZzxxYBZgBjm+f6sRhl3DYYXhURZKMnEBj\nzNpJ1w3sNpFIIoO7wIXXbWfr+qrpB3cLtA3EEMXx2ehQOElRsXfa6I/F/mzcLZ+1+WC9lrubVCKM\nJC7dcrfL7UTIpkhlVJ2W7hBup0RduQOR9JTHi4JKaamPQGBp/i+WtVjJsmzHLFTfUxTllezd/bIs\nVyqK0ifLchUwMPMRTO6G2IZIPE00nsnrXRojlszws49vcL5lOHdfsd+Jyy6CIKLr4HXb0NMqQ8PR\nnAFteDQ+6znLAy5Uzcjt30iSwOBIjHNXenn1w1bAnMn9wysXefHBRnRMq6Tp/L4qCt10DUbJqDqi\nKCAJAjZJJKOq2CUJTdOQJIGMqlMacJGIJfF7HUvyf+O1Cxi6kfP3qy7xEhyJLfp5JnMv5SZZr+Xu\nJxZLoOlLsxyXTMQ4uLMRn8+cRb19qpeMavDY9gp2zGifVkwqJSzZ/8VyqgEF4B+BK4qifHvCj14D\nvgr8afbvV6Z5+l1DPGk6TOiGMaVQGYbBpbYRXjvSRixrZyQKAvu3VPHw9lpCkSRnmoeQRIGda8qR\nJFNgMV9Hhcd3rWAknOT0tUEcdhG/18G21aV5zcSaptM/kuD/ffkihmFQEnDzq4/JU8xrP3+wib/4\n8QWiSRWP08aq2gAOm8hoNE2hz0lpwEUwmsJpkziwreaW0n3norzIwy8/KnP62gBup42HttUs2bks\nLO5UljIiJB6L4PP58fsDxJIZDp0foMBt5+m9q3A7l6dsLOfMai/wZeCCLMtns/f9AfAnwI9kWf4m\nWen68gzv1sioGqFoioxmIArClP2SSDzNq0fauHIjmLuvstjD15/dgM9pLu2VFXl4YvdKDMPsdyr2\nO2f19ZvMp+1BassL8HscjMZSbGoqobasgFRao9TvYiicND3/DFOiDjAaTfHB2e4pxapnKI4BuQbi\n9r4If/DlHcuW9dRY7aex2uqNsrBYat480UEipfLFg8tXqGB51YBHmFk6/+jtHMtiohsGo9EU8ZSG\nJApTZhiGYXC2eYjXj90gkTJTPiVR4KFtZsR8eZmPkQlLWoau43bZKCyYn23RGB+c6+bwuZ5cAGOJ\n38WNvggFLgeragN8/TPruNo+wpUbQc63DOXNtsb2gvpG4rR2j1IacDF59VIUBSy9goXFvU0wkuKd\nU50EChw8vH15VzCWXWBxLzFxX0qaZm8qFE3xykdtKJ3jPc41ZV4+d6CJyuKpKbmGYVDoc+K023j3\nVCcdA1GSaRWnTaK61MujO2tNH79puNI2AmAm+RqQTGs47BJX2kdYVRvA47KxXS4jGE1xvtXsgxKA\nkoCLx3auoGswynff/DRn6/Tw9hrWrCjkWmcIUYAndq+cVtBgYWFxb2AYBt97+xrpjM6vPNqYlya+\nHFjFahFIpjOMRqfflwJztvXJ1QHePNFBKmPOpmySwKM7VrB3c9WUwmYYBjZRoKTQgygKfHi+h6OX\n+sxZUiyN122nayiGbhg8fX/9tGMqLHAyOJrMiiD03DkmiidOXxvk6MU+fG47boeNNSsL+dyBJtxO\nG2+f7MjzH7x4fYTfen4DwUgKh1267b5gFhYWt5dzrUHOtQyxdmUh+zYvvqp3oVjF6hYYs0hKZ0xX\n9On6eEbCSV7+8DrXe8K5+1ZWFPC5A02UFU71ytN1A4/LTmBCk2vfSBzDMEikVHTDIKNquftn4ukH\n6nn1o+v0O23ZXCqJppoAD2wcb87tz7onIwjYbALxbM8UmE21E/F57AiCQLF/YcuRFhYWdx+pjM6b\npzqx20S++tTaJRVMzRerWN0EhmEQjmeIJ9IIE1zRJ6LrBscu9/H2J525wEC7TeSJ3SvYs75y+oRN\nA8qK3EQmHa6s0MXwaJJURkPTDDJZOfpk89Xe4Rj//kEr4ViaDQ3FfPnxNYSiKUbCKSpLPFNmQ3WV\nPk5eHSCt6thEgfrK8ePtWldO30gcpTNEacDFMzPM4O5UDMOgoz+KbhjUVfhua6KphcXdjGEYnGsd\nJZpQ+cLBJiqKpm5RLAdWsVog8WSGcCyDgYEwTZECGAglePlwKx3949lLjdV+XnywcdqZiaEb2O0i\nxX7T6WFil0JHf4SPzveSymgYhhmlYZNEdq4p46Gt+Ruerx5py0V0nG8dRhJFzrcOoekGXpeNrz21\nNs/5fEVZAaIokEypOOwiFRPyaWySyAsPNt7MW3RH8OqRNs63mn1rq2sCfOnR1XfE1aGFxZ1OS9co\nnYMJ6iu8ORPsOwGrWM2TjKoxGkuTVnVTis7ULz5NNzhyoYf3Tnfl9nucdomn9qxk19ryaZcJdd0g\n4LXjdU9vmXT8cj+qbjbiYoAkiRT6nOxeVzFlthCboOgDuHh9KGdJFEuqnPp0kCfvW5n7+Vh0fGFW\njn7kYi9bVpXO9y25YxkJJ3OFCqC5e5SewRi1ljeghcWsBCMpTl4dwG4T+MrjDUgzXJAvB1axmgNT\nip4mkVJNi58Zrs57h2O8fPg63UPjsvM1Kwr57P4GAtM4QpiM507NhM0mIIkifo+DcDyNgGk8OzlM\nEGC7XMaH2VgMt0PC73WO70thijryjj3ptv0eUffZJBGBfKd32zL1g1lY3C1kVJ0Pz/Wg6Qb3rS2i\n2DfT99byYBWrGRiLjo8lTCn6THseqqbzwVmzp2lsFuN2Sjxzfz1bV5fOMJuamjs1HS284GLFAAAW\nMElEQVTdo/SNxInE0zjtEmtXFvHLj6ymYpLMPRhJ8cbxdqLJDNtWlVJV6mVVTYB4SuXf3lWIJVUq\niz08sLESVdN551Qnnf1RyovcVBd76BmJ43ZIPLF75QwjmT/NXSHePNFB73CMlVV+HtlWw+rawls+\n7kLwex0c3F7DoTPdGMD9GyqnbQ2wsLAwMQyD45f7GI2lWVdXRHXJ7Y2snw9WsZqGWDJDJLcvNXMx\n6RqM8vLh63mqvA31xTy3r36Kmm4MXdcpLHDOaZk0Gkvzo/dbyGg6Prcdu03iW8+un/Z5P/6ghd5h\ncwy9w3E2NZVks62c/O7ntxBPqfjcdkRR4NDZbk5eNe0We0fi3Le+gi8+shqP03bLbhShaIofvtdC\nz3AMXTcYjQ0xOBLndz63eVq/waVk/+Zqdsjl6IZhyewtLObgyo0gbb0RSgMutq8pJZVYeq/NhWIV\nqwmk0hqjsRSaZhap6falwJwuv3e6k48u9OYi5r1uO8/trWdT4/Qmj2OWSaVF7nlZJgUjyZybOVln\n83hKnbZYDQYT+bdDiZxS0G4TCdgceT+byFAokSeTvxWCkRRpVUPPph2nMzoDwQRnrg0uKI5+sZgY\nTmlhYTE9PUMxzlwbxO2UeGhbzR21TzUR67cZ0HSdUDRFKp3tl5plNnWjz4yYHxodj5jfuqqUZx6o\nm3G2pOs6XpeDQMH8i0JFkQefx54LXywNuGacnTTVBLiWdcWwSQL1lVPNLXXd4OilXjr7IwQjKfwe\nO5IksqomMO8xzWfMgQIHI5EUyZRpzKtqOh9d6GHNysJlSRe1sLCYmUg8zYfnexAE0/LtTr7Au3NH\ndhswDINwLEM8OXO/1BipjMbbJzs5frkvt3Hv99j57P5G1k4yfZ18jjFJ+kJwO218/al1nLjSjyQK\n3L+xckZ7o88daOLjS73EkiqbGkumFV98eKGHtz/pJBhOomkGuqbz9AP17NmweAm+Hpc55o8u9PDO\nJ504HTbzwy8I9AzHrWJlYbGItLTewOmafW/JMKCyrAiHc+qFsqrpHDo3RDqjs2N1IV67SjxmNs4k\n4tYy4B3DfPqlxmjpHuUnH14nGBkPRdy5tpyn7ls5owuxYRjYpOnj5udLkc+ZJzWfCbtN5MDW2U0m\nOweixBIZDMM0oRUlM95joRjG/9/evQfHeZV3HP/u6i5LsiVbviu+xSeO7YQ4iXMhFzuQBJswDiYh\nkOklKYEpDRSml+lApkNDO23JH6QMQ4HpJIFAW0K4xAFqSEIIwZAbxiY4tvET323ZlmTLki3rrn37\nx/tKXsu6rGRJ+77v/j4zGu+efXf3nD27+/icPe9zPLZYA8caW5k3s5zlC86d9qyqKObOGxdy8nQn\nR06c6Uv1NLf6/M0mRWT05lSXUVIx9Kkm7W2tLJ9XxvTqcz+nnufxzef30tzazY3Lq7n75vO/Z3r3\nsgqLnAtWfSmShjhfqld7ZzcbXzvI5j+e3f+xsryI9Tct5OK5g0+fpVIpJpUUjtlvQWNhzrRJbEkL\nmoX5SQoLRj43/ettR/nFlloANu9qIJXyuHzR+R+Ye265mN++fZz64y2sWFzNrKkKViJjqXRSGSXD\n7GeVTOZRXl5ORcW531cbXzvAm3uacDVT+PO1yyKRlDpnglWm50v1+uPBk2zYtI9TZ86OPq5fNpPb\nr6mhaIjsw/60XxHFheFagbbqitm0d3Tz8ptHSaX8FES3XDnyRQ97apvPub679tSAwaq0OJ+737U4\nJ3dwFQmz7fsa+cHLe6gsL+LB9y+PRKCCHAlWLa2dnG7rIpEY/HypXq3tXfzklQP8fvfxvrKpFcV8\nYNXC83Lxpetb7RdkSg+bvGSS914/n7XXzaOto4eSorwhz/EaTPWUEg6kpZGqnqLEtiJRcbypja8/\n+xZ5yQQPrl9ORYhmf4YT62B1zpbyGXwxv7X3BM/+Zn9f2qJEAm68bBa3Xl0z5DlI/gaJBRN+LtFo\nJBKJC1rxc9vVNfSkPI41tjJ/Rjk3LM/+1gEiMrzOrh6+8sw2zrR3c9+aS1g0e+xWAk+EWAarjs4e\nmls76O72gh1thw5Up1s7+fFv9vNWsGEhwPTKEu5atYiaYfLJeSl/g8SSorGd9mtp7eSlrbUkkwnm\nVpeRn5dkybwpJBIJdh1soqs7xSUXTRlySnI8FBbkse6GBRP6nCJyYTzP41vP7eJgXQs3v2P2sAuy\nwihWwaqzu4dTZzrp6OohL5kcdjrO8zx+v/s4P3nlgL+jLpBMJFi9YjarV8wZci63b9qvauyn/Vrb\nu/nnJzfT3NJBT8pfVThraikL50ymqCDJzgP+OVWzqkr5i/cuGXS3YBERgJe21vLKW8dYMKuCP7nN\nZbs6oxKLYNXd4wep9i5/hV8mZ2A3t3Sw4df72HXw7Bbzs6dN4q5VC4dduTaak3xHYuvbDX0LOzzP\nz5jR1ZNi9+FmelJe35Tk0cZWDtS1jOmJvSISL0cb23jqxT2UlRTwifXLLzitWrZEPlg1tbRTf7I9\noxV+4I+INu9qYOOrB87ZYv7dV83lxstnn7fF/ED3H81JviNRXtpvSjHhj/iSiQTJfoOokkKNqkRk\nYN09KZ765SG6ezw+cselkd7pO/LByk+RlNk0XOOpdp7ZtJc9teduMf+BmxcxvXK4M8Ev/CTfTF2+\naBpXL5nO73Y1kE+KstICigrzeM/KiygqzOMnr+ynu8fjpstnnZcV4uTpDjZs2svJlg6WzqviPdfU\njGrVn4hE3x/2NlPf1MG7rpzDFRHfqy7ywSoTKc/jte11PP/GQTp7t5jPS3LbyhreuXyQLeb73b+8\npGDQTOrj4aPvW8r9a1KQhCSJvtEVwPIFVX2ZKPrbsGkvB+v9peWv76xjZlUpVyyO9ptUREbucEML\nu4+0MGNKEffccnG2q3PBYh+sjje18YNf7eXAsbMnpy6YVcEHVi1kagZDYs/zmFpeTFEWptsG2zAw\nkUgw2GCpqV8KpaYzHQMfKCKx1dWd4vXtdSQS8KFVNRRO8Krh8RDbYNWT8vjNtqP8fPOhc7aYX3Pt\nRay8dPqwU3kTOe03lpbNr+TVHXWAP1/d0dlDQ1Mb1VPCt5maiIyPN3cf50x7N5deVM6sEG6kOBpZ\nDVbOuSeAO4B6M7ssKKsCvgvMA/YD95hZ06APMoBjja388OU9HG44mzl48dzJrL95YUYn7qY8j0nF\nBaHK7Zep21bWMKOqlC3WwJ4jp3htRx2/swbuX7OE2dOUn08k7hpPtbPzwEnKSgq49KJwJaO9ENle\nw/gNYE2/ss8AL5iZA14MrmekJ5XiF1sO858/3NYXqIoL87h79SLuX7sko0DleR5V5UWRDFTgTxG+\n4+JptHX29C1R7epO8dbeE1mumYiMpVTnGdpPNZzz19Zcz6vbavE8uHJBMXQ0x+Y8zKyOrMxsk3Nu\nfr/idcCq4PKTwC/JIGDVHj/DD1/e07e9O8DS+ZWsu3EBFRksjAh7br+RKivOpyHt+iRt7S4SK3fc\nesN5ZVvfbuDEq9u46pJq7l93WRZqNX7C+JvVDDOrCy7XATOGOriru4fn3jjIpjePkAp2RSwtzmfd\nDQu4bGFVRsu2o5TbL1N3XD+f7/1yNyea21lcM4Vrlw75MopIxKU8j2d+tY9EAtbftDDb1RlzYQxW\nfczMc855Qx3z+cde51jaaOryRVN53zvnU5bhSGK8cvtl29TJxXz8zuXZroaITJDf7qzncEML1y+b\nGcvfp8MYrOqcczPN7JhzbhZQP9TBvYFqclkh996+hCtcdUZP4k/7JameUkx+yOZ0q6uH3lAtSuLU\nFohXe9SWaKusLO377vI8j42vv0FeMsFH7lxOdQw3Ow1jsPoRcB/wSPDvhqEOLinKZ9n8StZeN4+S\nonwaG88MdTjg5/YrKcqnsryYkydbhz1+IlVXl8dmw8I4tQXi1R61JfrSv7t2HjjJoboWrls2g7xU\nKpavR7aXrn8HfzHFNOfcIeBzwBeAp51zDxAsXR/qMb78d6toOD58gOqVSqWYUlZEaXG8pv1EJHe9\ntLUWgFtWRG/rj0xlezXgvYPcdGumj5Fp3jvP8/e2ml5ZQn5euKb9RERGq6mlg63WwNzqSbHegSGM\n04BjLn3aT0QkTl7bXkdPymP1ijmxTlod+2ClaT8RibOtbzeQAK5eMj3bVRlXsQ1WmvYTkbg71drJ\n7tpmFs2dnFHygyiLZbDyUimKNe0nIjG3bc8JPA9WRHyvqkzELljF9SRfEZH+tu9rBOByBavo8DyP\n/GSCqVXxyO0nIjKcvUdOMak4n9lTS7NdlXGX7azrYyLleZQWF1BdqUAlIrnhdGsn9U1tLJhVEetV\ngL0iP7LKz08yraI4Fjthiohkat9RP0vFwtnx2bNqKJEfWU2tKFGgEpGcc6jeD1bzZuZGXsTIBysR\nkVxUf7INgJlV8f+9ChSsREQiqf5kGwlg2uSSbFdlQihYiYhEUH1TG1UVRRTk58bXeG60UkQkZppO\ndzC1IncSHyhYiYhEkAdUTIp3iqV0ClYiIhFVHvN8gOkUrEREIqq8NHfSyilYiYhEVC5tfaRgJSIS\nUUUFufMVnjstFRGJmaIcyt6jYCUiElG5lGpOwUpEJKI0shIRkdAr1G9WIiISdnnJ3PkKz52WiojE\nTA7FKgUrEZGoSubADsG9FKxERCIqF7az7xXKbe2dc2uALwF5wGNm9kiWqyQiEjrJ3IlV4RtZOefy\ngK8Aa4ClwL3OuUuzWysRkfDJpZFV6IIVcA2w28z2m1kX8BRwZ5brJCISOskcGlqFMVjNAQ6lXT8c\nlImISJocilWhDFZetisgIhIFuTQNGMYFFrVATdr1GvzR1aCqq8vHtUITLU7tiVNbIF7tUVuirbgw\nj/k1lTmzTUjowrJzLh/YBbwbOAK8AdxrZjsHOt7zPK+h4fQE1nB8VVeXE5f2xKktEK/2qC3hNX16\nRUbfywcONXolRWEcb4zeUG0P3TSgmXUDnwSeA3YA3x0sUImI5Kq4BarhhLK1ZvZT4KfZroeIiIRD\n6EZWIiIi/SlYiYhI6ClYiYhI6ClYiYhI6ClYiYhI6ClYiYhI6ClYiYhI6ClYiYhI6ClYiYhI6ClY\niYhI6ClYiYhI6ClYiYhI6ClYiYhI6ClYiYhI6ClYiYhI6ClYiYhI6ClYiYhI6ClYiYhI6ClYiYhI\n6ClYiYhI6ClYiYhI6ClYiYhI6ClYiYhI6ClYiYhI6ClYiYhI6ClYiYhI6OVn40mdcx8EHgaWACvN\nbEvabZ8FPgL0AJ8ys+ezUUcREQmPbI2stgHrgV+lFzrnlgIfApYCa4CvOuc0+hMRyXFZCQRm9kcz\nswFuuhP4jpl1mdl+YDdwzYRWTkREQidso5bZwOG064eBOVmqi4iIhMS4/WblnHsBmDnATQ+Z2Y9H\n8FDeGFVJREQiatyClZndNoq71QI1adfnBmWDSiQSiVE8j4hIpE2fXpFT331ZWQ3YT/oL/iPgf51z\nj+JP/y0G3shKrUREJDSyEpmdc+uBLwPTgGZgq5mtDW57CH/pejfwaTN7Lht1FBERERERERERERER\nEREREekV6qWPzrmHgY8CDUHRQ2b20+C2AXMIOueuAr4JFAMbzezTQXkR8C3gSuAE8CEzOzBhjRmG\nc24N8CUgD3jMzB7JcpUG5JzbD5zCf927zOwa51wV8F1gHrAfuMfMmoLjR9RPE1D/J4A7gHozuywo\nG7P6T+T7bJC2PEwEPzPOuZrguabjn1v5X2b25Sj2zRBteZgI9k1YhC2DRX8e8KiZrQj+ejt2oByC\nvYH3a8ADZrYYWBwEAYAHgBNB+X8AoQkGzrk84Cv4bVkK3OucuzS7tRqUB6wO+qM3FdZngBfMzAEv\nBtdH20/j7RtBXdKNZf0n8n02UFui+pnpAv7GzJYB1wGfCD4DUeybwdoS1b4JhbAHKxh49DdQDsFr\nnXOzgHIz6z0361vA+4PL64Ang8s/AN49flUesWuA3Wa238y6gKfw2xhW/fsk/bV9krOv+Wj6aVyZ\n2SbgZL/isaz/hL3PBmkLRPAzY2bHzOz3weUWYCf+uZaR65sh2gIR7JuwiEKw+mvn3JvOucedc1OC\nssFyCPYvr+Xsm2QOcAjAzLqB5mCKIQz66hYIc05ED/i5c26zc+5jQdkMM6sLLtcBM4LLo+mnbBjL\n+ofhfRbpz4xzbj6wAnidiPdNWlteC4oi3TfZlPVg5Zx7wTm3bYC/dfhD4AXAFcBR4ItZrez4iVL+\nwxvMbAWwFn9646b0G83MI1rtOUfU60/EPzPOuTL8kcKnzex0+m1R65ugLd/Hb0sLEe+bbMt6uqVM\ncwg65x4DehPgDpRD8HBQPneA8t77XAQccc7lA5PNrPECqj6W+renhnP/RxUaZnY0+LfBOfcM/hRm\nnXNuppkdC6Yu6oPDR9JPQ+aAHGdjUf9QvM/MrLfukfvMOOcK8APVt81sQ1Acyb5Ja8t/97Ylyn0T\nBlkfWQ0leHP2Wo+/aSP4OQQ/7JwrdM4tIMghaGbHgFPOuWuDHyj/DHg27T73BZfvxv+xNiw24/94\nOt85V4j/Y+uPslyn8zjnSp1z5cHlScDt+H2S/treB/R+0YyknzaQPWNR/1C8z6L6mQme+3Fgh5l9\nKe2myPXNYG2Jat+ERdZHVsN4xDl3Bf7Qfx/wlwBmtsM59zSwAz+H4IPBFAHAg/hLPUvwl3r+LCh/\nHPi2c+5t/KWeH56wVgzDzLqdc58EnsNfuv64me3McrUGMgN4xjkH/nvnf8zseefcZuBp59wDBMuL\nYdT9NK6cc98BVgHTnHOHgM8BXxjD+k/Y+2yAtvwTsDqin5kbgD8F/uCc2xqUfZZo9s1AbXkIf5Vv\nFPtGRERERERERERERERERERERERERERERCQXhHqLEJFMOH/bkjagA5gEbAceMbNXnXP342+9sg8o\nxE8q+jHgCWB+8BDvwD9BMwUcM7O1zrkUUGZmrWnPcxy4CvgH4J1B8TJgD9COf/7MVcHz/Bt+gtKu\noG6fN7Nng8dZDWwEduGfr3YU+Fjct3gQuRBhPylYJBMecJeZ7QBwzq0HNjrn3hPc9ryZ3RNkAXga\n+EczW9975yAwXZ8emIZ4Hs/MPpF2333pzx2UfRUoBZaaWadzbhnwM+dcY5ApHWC7ma0Mjv8i8Chw\n14W8CCJxFup0SyKjYWbPAF8H/j4oSgTlHvAScEmGDzXimQfn3Dz8LAt/ZWadwfNuB/4VP8PEQF4c\nQZ1EcpJGVhJXb+Dv+fN/vQXO3111XXBbJl4JRl29pgx65FmX4e9N1tSv/HXgX/of7JxL4o+otmRY\nJ5GcpGAlcZU+Kro1LUfbr4F/z/Axru/3m1XDUAcP8LxDWRrUKQG8CfxthvcTyUkKVhJXKzmb1frn\nZvbBCXrebcDFzrlKM0vfxfc6/KDUa0fvb1YiMjz9ZiVx0Teicc7dCXwcf3O7CV3xGmxL/j3ga8G0\nI8655fhZtz8/kXURiRONrCQuvu+cS1+6vtbMfuucW8rwu8sOdHumZQN5EH/p+g7nXCf+svZPpa0E\njNSOtyIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiLD+n/AzemN1Bk+AQAAAABJRU5ErkJg\ngg==\n",
"text": "<matplotlib.figure.Figure at 0x18953f28>"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {
"internals": {
"frag_helper": "fragment_end",
"slide_type": "subslide",
"frag_number": 24
},
"slideshow": {
"slide_type": "subslide"
}
},
"cell_type": "markdown",
"source": "By changing the *kind*, it is possible to get other cool looking figures :)"
},
{
"metadata": {
"slide_helper": "slide_end",
"internals": {
"frag_helper": "fragment_end",
"slide_helper": "subslide_end",
"frag_number": 26
},
"slideshow": {
"slide_type": "fragment"
}
},
"cell_type": "code",
"input": "sns.jointplot('DPTHTOP', 'APIGRAV', data=oil_filtered, kind='kde')",
"prompt_number": 30,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 30,
"metadata": {},
"text": "<seaborn.axisgrid.JointGrid at 0x170a06d8>"
},
{
"output_type": "display_data",
"metadata": {},
"png": 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p7BkJ7K8V5ySMhBDCprLTkxmb8NE3FP/TuyWMhBDCprIzkoHEGDeSMBJCCJua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\n",
"text": "<matplotlib.figure.Figure at 0x19635f98>"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {
"slide_helper": "subslide_end",
"internals": {
"frag_helper": "fragment_end",
"slide_type": "subslide",
"slide_helper": "subslide_end",
"frag_number": 26
},
"slideshow": {
"slide_type": "slide"
}
},
"cell_type": "code",
"input": "oil.boxplot(column='APIGRAV', by='RESTYPE')\nfor idx, i in enumerate(['GAS', 'OIL', 'OIL AND GAS']):\n y = oil.APIGRAV[oil.RESTYPE==i].dropna()\n # Add some random \"jitter\" to the x-axis\n x = np.random.normal(idx+1, 0.03, size=len(y))\n plt.plot(x, y.values, 'r.', alpha=0.2)",
"prompt_number": 12,
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"png": 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5DqcoKGmLFLHyLZuJ9B7E69pP+X//ECoqgCBR+1VVMDpI9MUX4dgx4g078IB4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\nAAAASUVORK5CYII=\n",
"text": "<matplotlib.figure.Figure at 0x16e3a630>"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {
"slide_helper": "slide_end",
"internals": {
"frag_helper": "fragment_end",
"slide_type": "subslide",
"slide_helper": "subslide_end",
"frag_number": 26
},
"slideshow": {
"slide_type": "subslide"
}
},
"cell_type": "code",
"input": "sns.set(style=\"ticks\")\ng = sns.factorplot('Cluster Desc', 'APIGRAV', 'RESTYPE', oil, kind='box', aspect=1.5)\ng.despine(offset=10, trim=True)",
"prompt_number": 17,
"outputs": [
{
"output_type": "pyout",
"prompt_number": 17,
"metadata": {},
"text": "<seaborn.axisgrid.FacetGrid at 0x16d9f5f8>"
},
{
"output_type": "display_data",
"metadata": {},
"png": 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KAxFxGPBo4G7giL2s70xgTjn5AmBuRBybmYN7Bg8An6NI/H5co66nlfENdRtw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\nkiRJkiar/w8i+QTCRuai1AAAAABJRU5ErkJggg==\n",
"text": "<matplotlib.figure.Figure at 0x1836e080>"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "import IPython",
"prompt_number": 3,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "%%javascript\nIPython.load_extensions('gist')",
"prompt_number": 4,
"outputs": [
{
"javascript": "IPython.load_extensions('gist')",
"text": "<IPython.core.display.Javascript at 0x4a4b278>",
"output_type": "display_data",
"metadata": {}
}
],
"language": "python",
"trusted": true,
"collapsed": false
}
],
"metadata": {}
}
],
"metadata": {
"name": "",
"signature": "sha256:d9b30ed9bd98696c83209da845569332f1aed49be39551cdfaac99035126f7a5"
},
"nbformat": 3
}
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