{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Visualization of different data types with python\n",
"==========\n",
"Here, will learn some of the most basic `plotting` functionalities with `Python`, to give you the tools you need to assess basic distributions and relationships within you dataset. We will focus on the [Seaborn library](https://seaborn.pydata.org/index.html), which is designed to make nice looking `plots` quickly and (mostly) intuitively."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import pandas\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's first gather our dataset. We'll use participant related information from the [OpenNeuro dataset ds000228 \"MRI data of 3-12 year old children and adults during viewing of a short animated film\"](https://openneuro.org/datasets/ds000228/versions/1.0.0) ."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" % Total % Received % Xferd Average Speed Time Time Time Current\n",
" Dload Upload Total Spent Left Speed\n",
"\r",
" 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0\r",
"100 16041 0 16041 0 0 66769 0 --:--:-- --:--:-- --:--:-- 66837\n"
]
}
],
"source": [
"%%bash\n",
"curl https://openneuro.org/crn/datasets/ds000228/snapshots/1.0.0/files/participants.tsv -o /data/participants.tsv"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# simple histogram with seaborn\n",
"sns.displot(pheno['Age'],\n",
" #bins=30, # increase \"resolution\"\n",
" #color='red', # change color\n",
" #kde=False, # get rid of KDE (y axis=N)\n",
" #rug=True, # add \"rug\"\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What kind of distribution do we have here? \n",
"\n",
"Let's try log normalization as a solution. Here's one way to do that:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/miniconda-latest/envs/neuro/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
" warnings.warn(msg, FutureWarning)\n",
"/opt/miniconda-latest/envs/neuro/lib/python3.7/site-packages/seaborn/distributions.py:2055: FutureWarning: The `axis` variable is no longer used and will be removed. Instead, assign variables directly to `x` or `y`.\n",
" warnings.warn(msg, FutureWarning)\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"\n",
"log_age = np.log(pheno['Age'])\n",
"sns.distplot(log_age,\n",
" bins=30, \n",
" color='black', \n",
" kde=False, \n",
" rug=True, \n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is another approach for log-transforming that is perhaps better practice, and generalizable to *nearly any* type of transformation. With [sklearn](https://scikit-learn.org/stable/index.html), you can great a custom transformation object, which can be applied to different datasets.\n",
"\n",
"_Advantages_ :\n",
"* Can be easily reversed at any time\n",
"* Perfect for basing transformation off one dataset and applying it to a different dataset\n",
"\n",
"_Distadvantages_ :\n",
"* Expects 2D data (but that's okay)\n",
"* More lines of code :("
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import FunctionTransformer\n",
"\n",
"log_transformer = FunctionTransformer(np.log, validate=True)\n",
"\n",
"age2d = pheno['Age'].values.reshape(-1,1)\n",
"log_transformer.fit(age2d)\n",
"\n",
"sk_log_Age = log_transformer.transform(age2d)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Are two log transformed datasets are equal?"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all(sk_log_Age[:,0] == log_age)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And we can easily reverse this normalization to return to the original values for age."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"reverted_age = log_transformer.inverse_transform(age2d)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The inverse transform should be the same as our original values:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all(reverted_age == age2d)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another strategy would be `categorization`. Two type of `categorization` have already been done for us in this dataset. We can visualize this with `pandas value_counts()` or with `seaborn countplot()`:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5yo 34\n",
"8-12yo 34\n",
"Adult 33\n",
"7yo 23\n",
"3yo 17\n",
"4yo 14\n",
"Name: AgeGroup, dtype: int64"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Value counts of AgeGroup\n",
"pheno['AgeGroup'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/miniconda-latest/envs/neuro/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
" FutureWarning\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Countplot of Child_Adult\n",
"\n",
"sns.countplot(pheno['Child_Adult'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Bivariate visualization: Linear x Linear"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cool! Now let's play around a bit with `bivariate visualization`. \n",
"\n",
"For example, we could look at the association between `age` and a cognitive phenotype like `Theory of Mind` or `\"intelligence\"`. We can start with a `scatterplot`. A quick and easy `scatterplot` can be built with `regplot()`:"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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NLn1+Tzvberu5/vwulrblnJQC2dm9LSnfQD5mus5ilxdLPeWXlIOKmoYqQSO9ESy0L5tRHeaa/1BMxc/xaIJ/fPxFvv/cq5M2+zRLWvy8+aLlbNvYw+qlMztFPenG7U0+Wvzeghu3z3SduXIZ8i0vNt+invJLiqGkNwIRuSzfQS0LuLJkftkWN/sJjUb4+Pef5xNQ1182o/ZMjR1vCbiO5HseGZjxu5Nu9zhaQMXPeNJh96FhdvSHeHzgRNbbgkegPeinyScs72jmXdeszXkMn8fjlnaYQ+/ema7zvscO0dXeVPDyfPelmPMWe5x6Ip9p6G9Tv4PAZuA5XMf+RcBTwLWVFa2xWYhftkanlLIEpbwRHj45weJmf9aymeLcowm35MNYJP/s31Hl+aMj9O0b5OEXhiYT29K0NXnpCPppTZWdUJTB0UjWNuUu5TzTdY7HkqzOEWM/0/JiY/mLub/zhRkVgapeDyAi9wN3qOovU58vBP6sOuI1Lgvxy9bIFPuGN5c3wlWdLYRGI1mO2MwY87Ttv5DZ/0snxunrD7GjP8SxkTMDu0fg0lWLuXFTD//nF8c4FY5lJS6lcweCfq9b2iEwu72/WGa6ztaAa64pdHmxsfyz3d/5SCFPZmNaCQCo6q+ASyomkQHUVzKTMXdKKUtQagmEmWLM//DqcwiNRHh5eILjo9EZlcDxsSjf3nOYO/75Gd719T1846mXJ5XA+u423nvdOv7tjtfy6bddzH+74CzecdXqrNyBSCKJKrz/hvM4e3Ezi5r9ZVcC+a7z9mvXFrW82Fj+esovKReFRA31i8h9wL/g5n/8HtBfUakM7tyyjo9//3kmYoksh9R8/rI1MsW+4c3ljXDrxm4+gatMDg+Pc9aiZn7n8pWsP6udsRlq/k/EEjx64Dh9ewf52eFTWaUszuoIsq23m2293axZOr1m2ZXrlvCnng38257DHDsdZtWS1qoENmRe59RY+pnyKsqRb5HvvPOVWaOGRCQIvBfYklr0CPB3qhqZea/K0YhRQwvly9bI3Hbvk9PMCROxBN3tQb55x2vnvH0mjuM6fsei+U0/iaTD0y+epK9/kMd/fYJohtO3I+jjuvO7uLG3hwvO7sjpzA34PLSk6vgHC6hnY9SWOeURqGpERL4C/FBVXyi7dMaMbN3YbQN/HVCOMN5i3/BKeSOMxJOMROKMR2dO+lJVnn9lhL7+ELteCGU5fQM+D1evW8r23m6uXLskZwKXJXgtTGZVBCJyM/BpIACsFZFLgE+o6s0Vls0wak65wniLNSfMtn1aOb08PM7Zi5r5nc2ruHxN54znf3l4gr7+QXb0h3j19JmXeQEuXb2Ybb09vH79Mtqapg8J6cG/tan8Dl+jPijENPQMcAOwS1UvTS37hapeVAX5ptFIpiGj9szFRFMpHuof5GPffx6vuLP4zAYsmXV6hsdjqZ6+g+wfHMs6xnldbWzf1M3153fT1T490zddwrm1yWcz/wXCXEtMJFT1dKP28qwm9ZhJ/Pm+/dz32CHGY2743e3XruUD2zfUVKapTL1vV69bwhMDw1n3EahoTP5t9zzOE4dOTn72e2DzmqVlfYaxhMNoJM7ndx5EYDIWP206uv/pw1y4soPHDhznwf4QP3v5ZJbTt6ejiW0bu9nW28PaZdOdvn7vmcE/4Jt58C9HC1CjvijkjeCrwA7go8BvAx8A/Kr6nsqLN52F+kZQj2nrn+/bz907D+IRN27cUffngzecVzfKYOp9OzEeJTQao6stwLK2JsLxJKfDcQS3DWKx97aQN4KpSiBNkxd6FrXM6Rk6jjIeSzCSEfN/298/SUfQh6S6EKi62wyPx1HIcvq2B31s3dDFtt5uLlyxCM+UCV168C80yascLUCN2pDvjaCQd773AxcAUeCbwAjwJ2WTzgDqqy1imvseO4RH3HIAHvGkfrvL64Wp920knMAjMBpJTN7HsVTphHLG5Gc6bXMpAYBokpKfYSSeZGg0ysvDEwxNiflf3tFMOJYkHE8yOBpl4Pg4r5yOEkk4RBMOfq+wZcMyPvmWC/j2nVfzoRs3cNHKxZNKwO/1sLglwIrOZlYtaaEzo4XjbJSjBahRfxQSNTQB/FXqx6gQ9ZhJPB5LMtVC4BF3eb0w9b7Fkg6ejPaJ4LYEnPrmW0pMfilhvMU8w6SjjEUSjEZnrvb58vAES1oD/PKV0ySd7Gs6t6uV37p0Ba/f0DXN6ev3erKat5dKId/TevwuG/kpJGpoA25JiTWZ26vqDZUTq/Gox7T1dEp+ZjFIR93l9cLU+xbweoglHQIZDk6vR0CzTSLF3Nu5hPEWcp6JVLmHiRlq/Q+Px3johRB9e0O8MDiatc7nEbrbm3jn1Wu48YKeKes8tDa5JR7KFedfyPe0Hr/LRn4KcRZ/G/gKcB9QP1PBBUY9ZhLffu1a7t55kITjZPkIbr82d0XJWjD1vnU0+wiNxmgP+lB1yx60NfkQqNi9vXpt54w+gpnOE0s4jEXdYm8JZ/rsPxxL8ujB4+zoH+SZl7Kdvt3tTWzr7WZ7DqevR4TWJh9tTT6aK6CwC/me1uN32chPQeGjqnp5leSZlfnqLC4m0qKeMonnU9RQ+r6lo4Yy7yNMN++kl5UjsqWQqKGko+7gP0PGbyLpsOelk+zoD/HTg8eJZJiH2pp8XLehi+293bxmZbbTN13Pv7XJzfKtdIRfOVqAGtUnn7N4RkUgIumA5A8AIeB7uA5jAFR1uMxyFsR8VAQWRVF/VOuZuA7mJGPR3KYfVWXfsVEe3DvIrheGOBWOT67ze4Wr1y1lW28PV61dkhXSKRmDf2sVBn9j/lNqHsEzuEXm0t+wD2esU8De8wrEegvUH5V+JumY/7FoYppTF+DIyQn6+t1kr1dOZWf6XrxqMdt7u9myvou2YPZ/0XRZ57Ymt4m7YZSDfP0I6scQPM+pdhRFPjNUrnUAn/qvfg6dcOVZt6yVj9y0cUErqWKfyWymvV37Qvy/P9w7eQ9XdrZwx+vXZWX6npyI8dC+Ifr6B9l3LNvpu66rle0bu7lhYzfdHcGsdZlZvj89cNwStYyyU0jU0PuAb6jqqdTnTuA2Vf1yhWVbMFQziiJfbRxg2roPP/AckXiScNyZjA46EBrjww88x6dvuXjBDjLFPJN893TLhi5+/Ktj/OW//5KRcHzyHr50Ypy7fryPD23bQCSZpG/vIHumOH272tJO327WdbVlnTNXiQdrX2pUikKihv5IVb+U/qCqJ0XkjwBTBAVSzSiKfCYPYNq6oyfDxJIOfo9nsmm4qDIaWdimq2KeSa57OhaN84WdB1mzrJV7HhlgPJbAI4LHIyk/gHI6HOdvfvB81uDf2uTluvVdbN/Uw0VTnL4+j4e2oGv2yVXiwUyMRqUoRBF4REQ05eUSES9uJVKjQKrZyCKfyUNh2rqE4+AoZPoaRdwIloWcAFTMM0nfU1Ul6SiOuvH7R09N4Kjy6kiYZFJT901JTnEJ+L3CVWvd8s6vXbc0a5D3iNDS5KW9yT9ruKclahmVohBF8BPgW6meBAq8B/hRRaVagFSrt8BsJo+p63weD446aIYyUHWXL/QEoEKeieMoyxcFCY1EsjJy0z15j54KI4g7+E9RAAKsXNzMF99xKe3BMwO4iBup1NrkdXMcCoz4sUQto1IUUmvoz3GLzr0XeF/q7w/n3cOoGflq4+Ra1x50ww+TqiQdJ/WjtAd9DZsApKqMRxOERiK8NDzBb1+6kljyTE/esWiC0+E4J8aj/P5Xd2c1dQe3DIcHWNzi533XnzepBJr8Xpa2NbF6SQtnLQrSHvQXFfa5EHvlGvVBIQllH1TVu2dbVi3mYx5BtcmXzJNrHTRe1FAuwpPx/tNDPh/bf5yv/vQQx0YiWdU9AZa2BbhgeQe/Do0RGnNTbValooauWb9sMtwzX2nnQrFELaNUSkooy9j5WVW9bMqyn6Wb1FQbUwRGOYnEk4xHE4xHk9NKPSQd5dmXT9LXH+LRA0NE4mfWtwa8bEll+l60cnFWTL/Xc6bMg/XyNeqFkhLKROQ24O247Sm/n7GqHThRXhENo3qk6/yMRxPEk9mDv6qyf3CMB/sHeWhfiJMTZzJ9fR7hqnVL2N7bw9VTnL7gRvG0B6tT5sEwykk+Z/HjwKvAMuBvM5aPAr+opFDlolG6JO3aF+KuH+1j4Pg4Scch4PXS0uSlq60JVWUsliz5+uu51lDm821LDb6j0UTOa40lHMZTdX6mDv4AR0+F2ZnK9D18Mpy17jUrFnHjJjfTtyMjamf3wDD/tucwx0YirF7SwjXnLp3sjDabPOW45vnwnZ5v8jYqs5qG6o1CTUONUt9n174QH37gOU5OxN3wxtTjFFynpYiwYnEQn9dT9PXXc4eyzOebSDocTZVpyLzWv/6NTWxes2TG+v6nJmLsemGIvv4Qe18dyVp3ztIWbuzt4Ybebs6akunrEeG5w6f49E9eoMnnmdYZrcnnySnPXL978+07Pd/kXejMqUOZiLxWRJ4WkTERiYlIUkRGZtuv1jRKl6R7HhlgNJLA65HJ6EXBjWR0FLwiHB+LlXT99dyhLPP5Hh+L4RXB6xGGRqM0+bwIyhd2HuTEeDRLCUTiSXbuC/GX3/slb7vnST6/8+CkEljaFuBtl6/k3t+/nK+9czNvv2r1pBKQVHnn7o4g5yxt4f6nD9Pk8+TsjJYpT6n3frZrng/f6fkmbyNTSB7BF4FbcfsSbAb+ADivkkKVg0ZJvjl8coKE4+Dzepj6cqe4uQHpbl3FXn89dyjLfL7RRBJvyiYfSyqJpEPA5+HYiGviSTt9d/SHePTAccIZJaBbAl62rHedvhevWjytkFvQ76Ut6KM1kF3kbbbOaF4RmMO9n+2a09Tzd3q+ydvIFKIIUNWDIuJV1STwDyLyeCH7ichNwN2AF7hPVT81Zf0i4F+A1SlZPqOq/1DMBczEqs4WDh0fYzSSmOxY1R70sXZZ2+w7V4BK2UpXdbZwfDQ6mRCWqQwE93O6W1exyUf13KFs5eJmBkciNPm9+L0eEimbWLouTziWpCPo58u7DrJz3xDD47HJfX0e4aq1S9jW28PV65bQNCWyJ93QvS14ps7PVGbrjBaNJ90uTgoDQ2Nl+e7Nt4Sy+SZvI1NIYPOEiASAn4vI/xaRDwGts+2UKkXxJeCNwCbgNhHZNGWz9wF7VfViYCvwt6lzzZmr1y1haCyWNVMbGotxdUY1yGqRtpWGRiNZxcJ27QvN+dh3bllHe9BH0tHJeuHp2uEegaQqy9oCJSUf3X7tWhxNl6FwJstR1KpDmaoSjrlN3d966QoiKQdwZ4sfRxUnlQh3bCTCqyNR9ofGeOCZo5NK4DUrOviT7ev59nuu5pO/eSFbz++aVAIeEdqDfs5efKah+0xKAKYnd3U0+3CUVNSQh4SmM7TL992bbwll803eRqYQRfD7uDP6PwbGgVXAbxew35XAQVUdUNUYcD/wlinbKNAubqxdGzAMJAqUPS9PDAzT3R4g4PXgpGbF3e0Bnhiofj+dStpKt27s5tO3XMz67jZ8Xg8+D7T4vSxtC7Chp53zulpxFLrbg0U76T6wfQMfvOE8mv1eEo77Wl9tR3F6AAmNRnh5eIJXT4cZjcS5Yu0SPnjDepa2NpFIKp0tfnxeD6HRGCORMwlh5yxp4b9fs4Zv3H4ld996KTdffDaLUuaKtN2/J2X372pvKjjuf+vGbj5x8wV0twc5HY6zZmkbH7zhPNYua2MkkiTgFYI+D4iU7bs39ZylPNNqMt/kbWRmNQ2p6kupP8PA/yzi2CuAwxmfjwBXTdnmi8D3gVdw8xN+V1WnhXeIyB3AHQCrV68u6OSHT06wtLWJZW1nIj5UtSb2yUrbSitZx+gD2zdUPUIo3dVrPJZgIprEyRHZFom765sDXk5MxLMygZe2Brhho1ve+bzutmkx/U1+t8bPXJu75LrvHwCuvWsni5uzy0eU67tXrZpV5WK+yduo5Esoy5sroKoXzXLsXP/Dpv6PfgPwc+AG4FzgQRF5VFWzopJU9V7gXnDDR2c5L1Bf9sl6kqVeSTruzH8iliQcyz34Jx3l54dP0dc/yKMHjjOR4bRu9nt5/fpl3Liph0tyOH19Hg+tTV7ag/6ylHrIhz1vY76R743AwR24/xX4T9w3gmI4gmtGSrMSd+afybuAT6VKXB8UkUPARmB3keeaRjV7AMwnWeqJeNJhIurO7CM5mrmDO5M+GBqjrz/Ezn0hTmQ4fb0e4co1S9je283V5y6dZtZJ9/VtD/po9lcv29eetzHfyNeq8hIR2QjchqsM9qZ+/0RVC7HjPw2sF5G1wFHcENS3T9nmZWAb8KiI9ADnA2UJMq5mD4BayTJT28l0ljHA2qUtfPSNvXXzeh5NJCcH/1xJXmmOnY6wY98gfXtDvDScbVK54OwOtvf2sHVDF4task1u6WzfwVS273uuO7fq115P3z3DKISCM4tF5Hdxo4DuUtVPF7jPm4DP4Tqbv6aq/0tE3gOgql8RkbOBrwPLcU1Jn1LVf8l3TCs655Ira/N0OE486TAePRPy6ahbDvkzNWw7mR78ZyrvkGYkHOfh/W5P318ezc5ZXL2khW293Wzb2M3Zi5un7SsiPPfyKT7z4JlsX8tkNYwzlFx9VERW4M7k3wqcBL4FfE9VxyohaCGUqggWWs2T2+59cpod+kBolGjcTabyiJB0lHjSQYGOoI/P33pp1a45Ek+6Dt9ZBv9oPMkTA8P09Q+y+9AwiQyn75LWANef38WNm3pYn8PpC67jtz3ooy3g4x33PTXtnkzEEnS3B/nmHa8t7wUaxjyj1OqjD+NG8nwL+EPc0E6AgIgsUdXqx2GWyEJs+p0rEinp6GQ2caYSAJiIJSt+zemSzhOxZN7BP+kozx0+RV9/iEcODE1z+l67fhk39nZz6erOnFE9Xo/Q1uSb5vi1TFbDKI18zuJzcJ3Fd5IK3YSsnKV54/laiE2/c0WmeD1CIqloKgksjQBNPs9k7kK5rlnV7do1Hk3mbOYyddtfD43z4N5Bdr4Q4sRYttP3ijWdbO/t4XU5nL5pZivzbNE6hlEa+ZzFa6ooR0VZiDPFXJEpbU0+mnyujyBzTBaBZW1NZblmx1Em4kkmUjP/XGGemUw6fftDvHQi+9yblre7Tt/zu1jckjuhPF3uoT3ow5cn0xcsWscwSqWgWkNpRORvVPVvKiRLxViIM8VckSkfe7NbweOuH+1j37FRAAJe4axFzXQ0+5mIJUq6ZsdRN7kr5tr9ZwswOOP0DfHLo6ez1q3sbJ4s77wih9MXUhm/ATfmv7mIukYWrWMYpVFUP4JcbSurTSnO4kasiz7Xa04neI1Hk27T9lm+J7GEwxMDJ+jrH+SpgWynb2eLP5Xp28OGntxOX3Bn/x1BP23BuWX8GoYxnZKcxTMdqwzyVJ1GnCmWcs2FJHhl4qjr9N3RH+LhA0OMR8/sE/R7eH2qvPNlMzh9IV3vx0tH0G/9fQ2jRsyqCETkGlX9aerj5TmWzQsaseZJ+nrTYbPpIneZ9+HB549NKouzOpq59YpVXDlLlcxfD43Rt3eQnfuGGBqLTi73CFyxxu3p+7rzltKcZ2AP+Dx0NPtpC/jwlGn2v9BChA2jWsxqGsplDqqlicgSygpnJvPQx97cy+VrlrBj7yCf7duPzyME/R4icYeEo3zwhvXTlMHgSISd+0L09Yc4lMpaTtO7vJ1tG3u4fmMXnTM4fcEt9dyacvyWe/bfiOY/wyiGUvMIrgZeB3SJyJ9mrOrAzRSuOdWcAZbzXNWSOx022+z3TjaoiScTfOmhX/PZ323jG0+9jM8jkzP39AB6/9OHuXLdEkYjcR7ef5y+/kF+cWS603dbyu6/ojO30zdNS8CX6vJVuXo/CzFE2DCqRT7TUAC3R4APN7EszQhwSyWFKoRqJomV81zVkltVeWl4nPYmn9suMfXi15TRwvHVkTAdweyvQMAnvHhijI//x/M8degE8eSZN8bFzX6uT5V33nhWe95B3Z/qCNfWNHvYZzlYiCHChlEt8uURPAw8LCJfV9WXRKRVVcdn2r7aVHMGWM5zVVLuqXX8u9uCnBiPZtnqI3GHszrcGfzyjmZOjEcJ+jyE4w4jkTij0QSq8NjB4wAEfR6uXb+Mbb3dbD5nSd5onlo6fhdiiLBhVItCoobOFpH/wn07WC0iFwN3qur/XVnR8lPNGWA5z1VuufMleN16xSru3nmAcDyZ5QO49Qq3OvjWDV38/U8HiMQckhn7eQQ2n9PJtt4erj1v2ayx/AGfh/agf86NXuaCJZMZRukUogg+h9tA5vsAqvqciGyppFCFUM0ZYDnPVY5jZTZxyZfgdeW6JXyQ9dz/9GGOjYQ5q6OZmy7oYeD4GPc+NsDAUPYLXovfy7bebt75ujUsac3fOrqSjt9SaMQQYcMoFwXlEajq4Sn24NmDzCtMNWeA5TxXqceKJRzCscJj/NNcuW4Jm87u4OH9Q+zYN8hdP34hq03cisXNbOt17f6FKKOg30tbqtpnucI+y0UjhggbRjkoRBEcFpHXASoiAdy2rP2VFWt2qjkDLOe5Cj2WqhKJO5Mz/3zVPHMRSzg8dcgt7/zkwHSn79bzu9je20Pv8vxOX5i52qdhGAuDQvIIlgF3A9txM4t/AnxQVU9UXrzpLOQ8gmILuk3bX5VfHj1N394QD+8fYix6ppFc0OfhmvPSTt/OgiJ5gula/02+qrV5nCuWVGYYuZlTiQlVPQ68o+xS1SG1GETiSSdl60/w8AtD3L/7MK+OhFleYJYvwKHj4/T1D7KjP0RoNJq1zivg8whNPuHEWAwPklMJ7B4YnvQlrOxs4T1b1rH9grPKdp3VYCH2nTCMajDjG4GIfAGYcUqqqh+olFD5qNQbQTUzU3P17d09MMzdOw8UlOULMDQaZce+EDv6B/n1FKevzyM46vYlUMADeDyuScjv80475u6BYT7/0AGavB5aAl4iCWdeZuXm6tpmHcoMw6XUN4KFaX+ZgUrnJczWvev+pw/nzfIFGIsmeHT/EH37Qvz85VNZWnr5oiCOo4jA6XCcRBISKSWvgCCMx5IsC/gmj5m2/X/v50dp9nszrt0zL7NyLanMMEojX0LZPwKIyBpVfTFznYhcUWG5qk65B5FiundB7izfoN/Dq6cn+OnB4zzYP8gTv852+i5KOX1vTDl9337fU7QHfRwfi+IRmVQU6faV8aRD0O9hcDRCT0dwstPX0VPhBTGAWlKZYZRGIVFD3xGRm1X1KICIXAd8EXhNRSWrMuUYRDIze8Ox5KyDfybpLF+3LpASjjucnIgRiTt87D+en9yuKeX03Z7D6Zs+ht/rIZFUhPTbgPtHwOsh6ShrlrbS2nTmOhfKAGpJZYZRGoXEAr4H+HcROUtE3oQbQfSmyopVfe7cso540k3Ucgf0REGDiOMoY9EEoZEIL52YYHAkwlhk9jeAqdx6xSrC8SSvjkQ4dGKCI6fCjMeSJFUnM30/etP5fOe9V/M/3tzLa9ctneb0vfWKVSQcpTXgRXHNROAqAhXoaPaTcJh2TaVee72xdWM3n7j5Arrbg5wOx+luD847P4dh1IKCOpSlKpHeA0SAN6vqUKUFm4lKho+mo4ZmyxVIJB3GY0nCscK6d+VjaDTKQy+E6Nsb4uDQWNa6FYubufmSs7nh/C6WtjUVdLzdA8P8257DvHRijHhSJ8M+A15hfU/HjNdU6LUbhjE/yecszhc19J9kRw1tAl4FTgKo6s1llrMgapVHEE0kU5m9SaJFZPbmYjya4NEDbnnnn+Vw+m7r7Wb7xh5WLy3cNJPu89vRbJ2+DMOYTqlRQ5+pkDx1Q768gbSzdyI1888V6ZOOvS8k7j+edNh9aJi+/hBPDJyYDBsF6Aj62Hq+W+bhgrM7ikresj6/hmHMlUJNQz1AOlJot6qGKipVHsr1RpArbyCWcPjLN/Zy2ZpOwrNk9hYS9++o8vzREfr2DfLwC0OMRM5k+gZ8Hq45dynberu5Ys0S/EXW7G9t8tER9M9aGdQwDAPmmFksIr8DfBrYhet3/IKIfFhVHyirlFUms3uXo+DzeIjhcM8jA3x2+cWz7p8v7r9nURN9/SF29Ic4NhKZ3EeAy1YvZvsmt7xzZuROIaTj/jua/UUrDsMwjJkoZCT6K+CK9FuAiHQBfUBNFMHA0DjX3rVzTiUgookkL55Ide/KMNEE/We6d83G1Lj/RNIhHE/yq1dO866vZ7+xnNfdxo293Vy/sZtlBTp9M0k3em+fRzV/DMOYPxSiCDxTTEEnKCzstCIkHKekOjKxhMN4NMFYNEE86dDTnr9712ws72hmaCxCIqmMRhJMTHEgn9URnCzvfM7S1qKuEWrb7cswjMaiEEXwIxH5MfDN1OffBX5YOZHy4xFBRAoqARFJOXvHU4N/JrN175qJRNLh6RdP4qhy9FQka50IXLlmCW+/cjUXrijO6ZvGnL+GYVSbQqqPflhEfgu4FtfMfa+qfq/ikhXA1DIIk2WcC8jszdW9a6aoH1Xl+VdG2NEf4qEXQllOX8HN9l2+qJl3X7uG1523rOjrSId+tpvz1zCMGlCot/KnQBw3r2B35cQpjnA8ydmLmzkdjpeU3HXluiV5yzy/fGKCvn1ueedXT2c7fS9ZvZgbe3t4/frinb5pbPZvGEY9UNGoIRG5CbckhRe4T1U/lWObrbh9kf3AcVW9Lt8xHVWSjlvDP5ZweOslKzgxFs23S1EMj8fYuS9EX/8g+wezM33P62pj+6Zurj+/m6724p2+YLZ/wzDqj4pFDYmIF/gScCNwBHhaRL6vqnsztlkMfBm4SVVfFpFZvb5ejzA8HstryslFvuSviViCxw6eoG/vIM++fJJMi1J3exPbe7vZ1tvD2mXFO33TNKW7fQV8PLJ/yLpoGYZRN1QyauhK4KCqDgCIyP3AW4C9Gdu8Hfiuqr4MUEii2qolLfzrHxXXZCQz+asj6OPEeJTP7djPTcfO4uWTYX568DjRjDDS9qCPrRu62NbbzYUrFuEpMWTT6xFam3y0B300+dzZf6ldtHbtC/Gp/+rn0AnXJ7JuWSsfuWlj1j6V6LBmrR8NY+FTyaihFcDhjM9HgKumbLMB8IvILqAduFtV/6mAYxdFOvkr6HOjg0YiCUYjcb7+xEuT2/i9wtXnLuXG3h6uWLNkTk3am1OO39ZUvf9MSmmAs2tfiD974DlOTcRJuxIOhMb48APP8elbLmbrxu6KtGm01o+G0RhUMmoo1zR6qifXB1wObAOagSdE5ElV3Z91IJE7gDsAVqzMH96Zi8Mnx1GFY6cTxKdEEl2yajE39nbz+g1dtJXo9AV39t8e9NMe9OXN+i2lAc49jwwwFk3gFcGT0gSibv5CWoFUosNapbu2GYZRHxQ08qnqd4HvisgyXNNQIRwBMkftlcArObY5rqrjwLiIPAJcDGQpAlW9F7gX4KJLLysoLGh4POaWd+4PcWI8nrUu4PPQ7PewYlELn/2d2ctJ5KM54Dp+W3LM/nNRShOYwycnSDqKN+P4Im5OQ1qBVKJNo7V+NIzGYEZFICKvBT4FDAOfBP4ZWAZ4ROQPVPVHsxz7aWC9iKwFjgK34voEMvkP4Isi4gMCuKaj/6+UCwEIx5I8dtAt7/zMS9lOX4+4hdo6W/yoQsJR/uDqc0o6T7rmT3vQX7QJqZQuWqs6Wzg+FkUdJpvNaKo+UlqBVKLL2ELpXGYYRn7yvRF8EfhLYBGwE3ijqj4pIhtx/QV5FYGqJkTkj4Ef44aPfk1VnxeR96TWf0VV+0XkR8AvAAc3xPRXxVxAIumw56WT7OgP8dODx4lkOH3bmnxct6GL7Zu6CUeTfGvPkVmTx/LR5PfSEfTRNoeaP1s3dvMJKKoJzJ1b1k36CDSl3RyFzhb/pAKpRJtGa/1oGI1BvsY0P1fVS1J/96tqb8a6n6nqpdURMZuLLr1Mv/fjh9l3bJQH9w6y64UhToXPmH78XuHqdUvZ3tvDlWvn5vQFt6RFa5OPjuYzkT+1oJiooXJ2GbPOZYaxMCi1Q9mzqnrZ1L9zfa4mK9ZfoGve/QWOnsquEnrJqkVs29jDlg3LaA/6Z9i7cNJx/3sODfP3jx7iQGiUWMLB7xU2zNDy0UItDcOoV0pVBElgHDf6pxlIewgFCKrq3EfbEmhavl6Xv/NzgDsr3t7bzQ0bu+nuCM752B4R2oJn4v7T4ZPxZJLjo7HJOKilrQECPm9WY/RcjW7iSbXm6YZh1AUlNaZR1bqsf+DzCLdesYptvd2c29VWlmM2B7y0NU23/afDJ0+MJfB4BI8IjuOGbZ61yJcVRmmhloZhzFdKD5yvEed2tXFHGZyVHhHag/kjf9Lhk7GkM1kUTgRiSWdaGGU1Qy1nMkGZacowjFKYd4ogZ5paERTT7SsdPhnwekg4iogbthnweqaFUVYr1HKmbN9bjpzigWePWhawYRhF0xCNb0XcuP+zFzezsrOFjqC/oPDPO7esI55UOpp9OI6ScBwclPagb1oYZXrbiVgCVfd3JUItM01Q6QY9fq9w32OHci6/55GBsp7fMIyFx/x7IygCv9fjVvxs8uErodl7Zsx/POlGDQW8wtplbdPMLqXkB5TCTCao8ViS1VPKWlsWsGEYhZAvs3iU7NpAkvosgKpqR4VlKwl3NuyGfmaaaUpl68buggfzYrYtlZlMUK0BN1LJsoANwyiWfCPlDuAs4LvA/elS0fXA7oFhPrdjP8dG3IY0TT7h9646h/dvW1/y7H++MFO27+3XruWBZ49aFrBhGEUzYx4BgIgsAn4Lt05QEPg3XKUwXB3xpnPupou0+W3/m7FoMmu5AB/avp4PbN9QG8GqyEzZvpYFbBjGTJSUUDblAB7cPgRfAP4fVf1seUUsnM7VG3XR2/82QzY3kkdwG8r84m/eUCvRDMMw6paSEspSO74OuA14PfAY8FZVfbT8IhZOup+ATP7josB4LJlrF8MwDCMP+ZzFLwKngPtxm8IkUssvA1DVZysv3nQCXk/OVAIBWgN1mQxdESx5zDCMcpHvjeBF3In2G1I/mShwQ4VkyktXexPBZh+nw4lp/c5uv3ZtLUSqOtZC0jCMcpKv1tDWKspRMO1BH5/53Uv52H/8iiMnwyjQ7Pfw3uvObQhHMVhdI8MwysusgfYi4gfeC2xJLdoF3KOq8Rl3qjBbN3bz6MaavJDUBdZC0jCMclJIwP3f4TaY/3Lq5/LUMqNGrOpsIRzPdoxb8phhGKUyoyJI9REGuEJV36mqO1M/7wKuqI54Ri6qVdfIMIzGIN8bwe7U76SInJteKCLrAIvTrCFbN3bziZsvoLs9yOlwnO72oDXAMQyjZPL5CNJRmn8GPCQi6TKWa4B3VVIoY3aqUdfIMIzGIJ8i6BKRP039fQ/gxW1dGQQuBR6qsGyGYRhGFcinCLxAG9mtYNK9IdsrJpFhGIZRVfIpgldV9RNVk6TOsMxdwzAahXzO4jk2hZy/pDN3Q6ORrMzdXftCtRbNMAyj7ORTBNuqJkWdMVM7SGv7aBjGQiRfiYma9RyoBZmmoKHRKGd1NGWtt8xdwzAWKgu3lVcRTDUFicDRUxFGI2eqaFjmrmEYC5UF3bw+F7mcwFOLuPW0Bzl6Ksyx0xHamnzW9tEwjAVNQymCmco3j0fjLF/UPLldR7MfUI6NRDkdjlvbR8MwFjTzThHsOzbKbfc+WdLAPFP55nhSCceTk8sBfF4Pl63u5Jt3vLas8huGYdQb885H4PNIyeGch09O0OzP7mLW7PcS8HmsiJthGA3LvFMEQMnhnDOVb17f3W5F3AzDaFjmnWkoTSnhnHduWcfHv/88E7EEzX5vlhPYirgZhtGoVPSNQERuEpEXROSgiHw0z3ZXiEhSRG4p9NilhHNu3djNLZetYGg0Sv+xUYZGo9xy2QpTAIZhNDQVUwQi4gW+BLwR2ATcJiKbZtjuLuDHhR67VBv+rn0hHnj2KF3tTfSe1U5XexMPPHvUSkcYhtHQVPKN4ErgoKoOqGoMuB94S47t3g98ByhoNE46WrIN30pHGIZhTKeSPoIVwOGMz0eAqzI3EJEVwFuBG8jT/lJE7gDuAFi9enXJIZ3W9N0wDGM6lXwjyFW9VKd8/hzwEVXN2/pSVe9V1c2qurmrq6tkgazpu2EYxnQqqQiOAKsyPq8EXpmyzWbgfhF5EbgF+LKI/GalBLKm74ZhGNOppGnoaWC9iKwFjgK3Am/P3EBV16b/FpGvAz9Q1X+vlEBbN3bzCVxfwZGTE1Y6wjAMgwoqAlVNiMgf40YDeYGvqerzIvKe1PqvVOrc+Zhv+QLWKc0wjEojqlPN9vXN5s2bdc+ePTOuTw+c+wdHiCeVgM/D+u72eTmAZhbJy0yAs6xnwzCKRUSeUdXNudbNyxITM5EeOA8dH2MkkiAcT3J6Is6LJ8bmZatJC3c1DKMaLChFkB44RyMJPAg+jwePRxgJJ+blADpTkTwLdzUMo5zM21pDuWzn6TyBWNLBK270qgjEkg7Nfi8HQm4J6/lib1/V2UJoNJJVHtvCXQ3DKDfz8o1gamvJdFnqtoBrRw94PaRdH6oQ8Ho4MR5lNJKYtk89m4ss3NUwjGowLxXBTLZzESGeVNqDPhyUhOPgOEpHs4/h8TidLf55ZW/furHbymMbhlFx5qVpaKZSEafDcT75lgu555EBEskRYqmooTVL24glRljW1jRtn3q3t8+3cFfDMOYf81IR5LOdzzRw3nbvk2ZvNwzDyMG8NA2VYjs3e7thGEZu5uUbQSmlIhZieQnLOjYMoxwsuMziRsGyjg3DKIaGySxuJCzr2DCMcmGKYJ5iWceGYZQLUwTzFGuyYxhGuWh4RbBrX4jb7n2Sa+/ayW33PlnXmcaZWBSUYRjloqEVwUylKuaDMrCsY8MwysW8DB8tF5kOV4CWgI+JWIJ7HhmYFwOqZR0bhlEOGvqNwByuhmEYDa4IzOFqGIbR4IrAHK6GYRgNrgjM4WoYhtHgzmIwh6thGEZDvxEYhmEYpggMwzAaHlMEhmEYDc68UwQDQ+PzrhyEYRhGPTPvFEHCceZdOQjDMIx6Zt4pAo+I1d83DMMoI/NOEWRi5SAMwzDmzrxWBFYOwjAMY+7Mu4QyRxVVnezRa+UgDMMw5sa8UwQ+j4fT4TgrO1u4c8s6ywo2DMOYI/NOEazrauXRj9xQazEMwzAWDBX1EYjITSLygogcFJGP5lj/DhH5RerncRG5uJLyGIZhGNOpmCIQES/wJeCNwCbgNhHZNGWzQ8B1qnoR8Eng3krJYxiGYeSmkm8EVwIHVXVAVWPA/cBbMjdQ1cdV9WTq45PAygrKYxiGYeSgkopgBXA44/OR1LKZeDfwX7lWiMgdIrJHRPYMDQ2VUUTDMAyjkopAcizTnBuKXI+rCD6Sa72q3quqm1V1c1dXVxlFNAzDMCoZNXQEWJXxeSXwytSNROQi4D7gjap6YraDPvPMM8dF5KUyybgMOF6mY5UTk6s4TK7iMLmKo17lguJkO2emFaKac5I+Z0TEB+wHtgFHgaeBt6vq8xnbrAZ2An+gqo9XRJD8Mu5R1c3VPu9smFzFYXIVh8lVHPUqF5RPtoq9EahqQkT+GPgx4AW+pqrPi8h7Uuu/AnwcWAp8WUQAEvV6ww3DMBYqFU0oU9UfAj+csuwrGX/fDtxeSRkMwzCM/MzronNloF7zFkyu4jC5isPkKo56lQvKJFvFfASGYRjG/KDR3wgMwzAaHlMEhmEYDU7DKgIR8YrIz0TkB7WWJY2IvCgivxSRn4vInlrLk0ZEFovIAyKyT0T6ReTqWssEICLnp+5V+mdERP6k1nIBiMiHROR5EfmViHxTRIK1lglARD6Ykun5Wt4rEfmaiIRE5FcZy5aIyIMiciD1u7NO5Hpb6n45IlKTqMYZ5Pp06v/kL0TkeyKyuNTjN6wiAD4I9NdaiBxcr6qX1FkY7d3Aj1R1I3AxdXLfVPWF1L26BLgcmAC+V1upQERWAB8ANqvqhbjh07fWVioQkQuBP8KtA3Yx8Bsisr5G4nwduGnKso8CO1R1PbAj9bnafJ3pcv0K+C3gkapLc4avM12uB4ELU0U79wN/UerBG1IRiMhK4M24Gc1GHkSkA9gCfBVAVWOqeqqmQuVmG/BrVS1X1vlc8QHNqcTKFnJk1deAXuBJVZ1Q1QTwMPDWWgiiqo8Aw1MWvwX4x9Tf/wj8ZjVlgtxyqWq/qr5QbVmmyJBLrp+kniPMsWhnQyoC4HPAnwNOjeWYigI/EZFnROSOWguTYh0wBPxDypR2n4i01lqoHNwKfLPWQgCo6lHgM8DLwKvAaVX9SW2lAtyZ7RYRWSoiLcCbyC4DU2t6VPVVgNRvaz9YOP+dGYp2FkLDKQIR+Q0gpKrP1FqWHFyjqpfh9nB4n4hsqbVAuDPby4C/U9VLgXFq88o+IyISAG4Gvl1rWQBStu23AGuBs4FWEfm92krlzmyBu3BNCj8CngMSeXcy6h4R+Svc5/iNUo/RcIoAuAa4WURexO2RcIOI/EttRXJR1VdSv0O4tu4raysR4BYPPKKqT6U+P4CrGOqJNwLPqupgrQVJsR04pKpDqhoHvgu8rsYyAaCqX1XVy1R1C66p4UCtZcpgUESWA6R+h2osT90jIu8EfgN4h84hKazhFIGq/oWqrlTVNbjmhJ2qWvPZmoi0ikh7+m/gv+G+ytcUVT0GHBaR81OLtgF7ayhSLm6jTsxCKV4GXisiLeIW0dpGnTjYRaQ79Xs1rgO0nu7b94F3pv5+J/AfNZSl7hGRm3BL99+sqhNzOda8a16/gOkBvpcqvucD/lVVf1RbkSZ5P/CNlAlmAHhXjeWZJGXrvhG4s9aypFHVp0TkAeBZ3Ff2n1E/ZQq+IyJLgTjwvowOgVVFRL4JbAWWicgR4K+BTwHfEpF34yrTt9WJXMPAF4Au4P+IyM9V9Q11INdfAE3Ag6lx40lVfU9Jx7cSE4ZhGI1Nw5mGDMMwjGxMERiGYTQ4pggMwzAaHFMEhmEYDY4pAsMwjAbHFIFhFIGIvFVEVEQ21loWwygXpggMozhuAx6jDqqJGka5MEVgGAUiIm24JUreTUoRiIhHRL6cqlf/AxH5oYjcklp3uYg8nCoi+ON0+QTDqDdMERhG4fwmbl+G/cCwiFyGW6ZhDfAa4HbgagAR8eNmo96iqpcDXwP+Vw1kNoxZsRIThlE4t+GWMAe3YOFtgB/4tqo6wDEReSi1/nzgQs6k/3txS1IbRt1hisAwCiBVn+cG4EIRUdyBXZm5I5oAz6tqXbT1NIx8mGnIMArjFuCfVPUcVV2jqquAQ8Bx4LdTvoIe3MJgAC8AXen+ziLiF5ELaiG4YcyGKQLDKIzbmD77/w5u45kjuCXD7wGewu1IFsNVHneJyHPAz6mTngSGMRWrPmoYc0RE2lR1LGU+2o3bae5YreUyjEIxH4FhzJ0fiMhiIAB80pSAMd+wNwLDMIwGx3wEhmEYDY4pAsMwjAbHFIFhGEaDY4rAMAyjwTFFYBiG0eD8//+xT31Wi8sxAAAAAElFTkSuQmCC\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.regplot(x=pheno['Age'], y=pheno['ToM Booklet-Matched'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`regplot()` will automatically drop missing values (`pairwise`). There are also a number of handy and very quick arguments to change the nature of the plot:"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"## Try uncommenting these lines (one at a time) to see how\n",
"## the plot changes.\n",
"\n",
"sns.regplot(x=pheno['Age'], y=pheno['ToM Booklet-Matched'],\n",
" order=2, # fit a quadratic curve\n",
" #lowess=True, # fit a lowess curve\n",
" #fit_reg = False # no regression line\n",
" #marker = '' # no points\n",
" #marker = 'x', # xs instead of points\n",
" )\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Take a minute to try plotting another set of variables. Don't forget -- you may have to change the data type!"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"#sns.regplot(x=, y=)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This would be as good a time as any to remind you that `seaborn` is built on top of `matplotlib`. Any `seaborn` object could be built from scratch from a `matplotlib` object. For example, `regplot()` is built on top of `plt.scatter`:"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(x=pheno['Age'], y=pheno['ToM Booklet-Matched'])\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you want to get really funky/fancy, you can play around with `jointplot()` and change the `\"kind\"` argument.\n",
"\n",
"However, note that `jointplot` is a different `type` of `object` and therefore follows different rules when it comes to editing. More on this later ..."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"mean, cov = [0, 1], [(1, .5), (.5, 1)]\n",
"x, y = np.random.multivariate_normal(mean, cov, 1000).T\n",
"sns.jointplot(x=x, y=y, kind=\"scatter\")\n",
"sns.jointplot(x=x, y=y, kind=\"hex\")\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"More on dealing with \"overplotting\" here: https://python-graph-gallery.com/134-how-to-avoid-overplotting-with-python/."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"However, note that `jointplot` is a different type of object and therefore follows different rules when it comes to editing. This is perhaps one of the biggest drawbacks of `seaborn`.\n",
"\n",
"For example, look at how the same change requires different syntax between `regplot` and `jointplot`:"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 0, 'Participant Age')"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"g = sns.jointplot(x=pheno['Age'], y=pheno['ToM Booklet-Matched'],\n",
" kind='scatter')\n",
"g.ax_joint.set_xlabel('Participant Age')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, `lmplot()` is another nice `scatterplot` option for observing `multivariate interactions`.\n",
"\n",
"However, `lmplot()` cannot simply take two `arrays` as input. Rather (much like `R`), you must pass `lmplot` some data (in the form of a `pandas DataFrame` for example) and `variable` names. Luckily for us, we already have our data in a `pandas DataFrame`, so this should be easy.\n",
"\n",
"Let's look at how the relationship between `Age` and `Theory of Mind` varies by `Gender`. We can do this using the `\"hue\"`, `\"col\"` or `\"row\"` arguments: "
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.lmplot(x='Age', y = 'ToM Booklet-Matched', \n",
" data = pheno, hue='Gender')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Unfortunately, these plots can be a bit sub-optimal at times. The `regplot` is perhaps more flexible. You can read more about this type of plotting here: https://seaborn.pydata.org/tutorial/distributions.html."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Bivariate visualization: Linear x Categorical"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's take a quick look at how to look at `bivariate relationships` when one `variable` is `categorical` and the other is `scalar`.\n",
"\n",
"For consistency can continue to look at the same relationship, but look at `\"AgeGroup\"` instead of `age`.\n",
"\n",
"There are many ways to visualize such relationships. While there are some advantages and disadvantes of each type of plot, much of the choice will come down to personal preference."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here are several ways of visualizing the same relationship. Note that adults to not have cognitive tests, so we won't\n",
"include adults in any of these plots. Note also that we explicitly pass the order of x:\n"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['3yo', '4yo', '5yo', '7yo', '8-12yo']"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"order = sorted(pheno.AgeGroup.unique())[:-1]\n",
"order"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"order = sorted(pheno.AgeGroup.unique())[:-1]\n",
"\n",
"sns.barplot(x='AgeGroup', \n",
" y = 'ToM Booklet-Matched',\n",
" data = pheno[pheno.AgeGroup!='Adult'])\n",
"plt.show()\n",
"\n",
"sns.boxplot(x='AgeGroup', \n",
" y = 'ToM Booklet-Matched',\n",
" data = pheno[pheno.AgeGroup!='Adult'])\n",
"plt.show()\n",
"\n",
"sns.boxenplot(x='AgeGroup', \n",
" y = 'ToM Booklet-Matched',\n",
" data = pheno[pheno.AgeGroup!='Adult'],\n",
" order = order)\n",
"plt.show()\n",
"\n",
"sns.violinplot(x='AgeGroup', \n",
" y = 'ToM Booklet-Matched',\n",
" data = pheno[pheno.AgeGroup!='Adult'])\n",
"plt.show()\n",
"\n",
"sns.stripplot(x='AgeGroup', jitter=True,\n",
" y = 'ToM Booklet-Matched',\n",
" data = pheno[pheno.AgeGroup!='Adult'])\n",
"plt.show()\n",
"\n",
"sns.pointplot(x='AgeGroup', \n",
" y = 'ToM Booklet-Matched',\n",
" data = pheno[pheno.AgeGroup!='Adult'])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generally, `lineplots` and `barplots` are frowned upon because they do not show the actual data, and therefore can mask troublesome distributions and outliers. \n",
"\n",
"But perhaps you're really into `barplots`? No problem! One nice thing about many `seaborn plots` is that they can be overlaid very easily. Just call two plots at once before doing `plt.show()` (or in this case, before running the cell). Just overlay a `stripplot` on top!"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.barplot(x='AgeGroup', \n",
" y = 'ToM Booklet-Matched',\n",
" data = pheno[pheno.AgeGroup!='Adult'],\n",
" order = order, palette='Blues')\n",
"\n",
"sns.stripplot(x='AgeGroup', \n",
" y = 'ToM Booklet-Matched',\n",
" data = pheno[pheno.AgeGroup!='Adult'],\n",
" jitter=True,\n",
" order = order, color = 'black')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can find more info on these types of plots here: https://seaborn.pydata.org/tutorial/categorical.html.\n",
"\n",
"Having trouble deciding which type of plot you want to use? Checkout the raincloud plot, which combines multiple types of plots to achieve a highly empirical visualization. \n",
"\n",
"Read more about it here:\n",
"https://wellcomeopenresearch.org/articles/4-63/v1?src=rss."
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting ptitprince\n",
" Downloading ptitprince-0.2.5.tar.gz (9.2 kB)\n",
"Requirement already satisfied: seaborn>=0.10 in /opt/miniconda-latest/envs/neuro/lib/python3.7/site-packages (from ptitprince) (0.11.0)\n",
"Requirement already satisfied: matplotlib in /opt/miniconda-latest/envs/neuro/lib/python3.7/site-packages (from ptitprince) (3.3.2)\n",
"Requirement already satisfied: numpy>=1.13 in /opt/miniconda-latest/envs/neuro/lib/python3.7/site-packages (from ptitprince) (1.18.5)\n",
"Requirement already satisfied: scipy in /opt/miniconda-latest/envs/neuro/lib/python3.7/site-packages (from ptitprince) (1.4.1)\n",
"Collecting PyHamcrest>=1.9.0\n",
" Downloading PyHamcrest-2.0.2-py3-none-any.whl (52 kB)\n",
"Requirement already satisfied: cython in /opt/miniconda-latest/envs/neuro/lib/python3.7/site-packages (from ptitprince) (0.29.21)\n",
"Requirement already satisfied: pandas>=0.23 in /opt/miniconda-latest/envs/neuro/lib/python3.7/site-packages (from seaborn>=0.10->ptitprince) (1.1.4)\n",
"Requirement already satisfied: cycler>=0.10 in /opt/miniconda-latest/envs/neuro/lib/python3.7/site-packages (from matplotlib->ptitprince) (0.10.0)\n",
"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /opt/miniconda-latest/envs/neuro/lib/python3.7/site-packages (from matplotlib->ptitprince) (2.4.7)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /opt/miniconda-latest/envs/neuro/lib/python3.7/site-packages (from matplotlib->ptitprince) (1.3.1)\n",
"Requirement already satisfied: certifi>=2020.06.20 in /opt/miniconda-latest/envs/neuro/lib/python3.7/site-packages (from matplotlib->ptitprince) (2020.6.20)\n",
"Requirement already satisfied: python-dateutil>=2.1 in /opt/miniconda-latest/envs/neuro/lib/python3.7/site-packages (from matplotlib->ptitprince) (2.8.1)\n",
"Requirement already satisfied: pillow>=6.2.0 in /opt/miniconda-latest/envs/neuro/lib/python3.7/site-packages (from matplotlib->ptitprince) (8.0.1)\n",
"Requirement already satisfied: pytz>=2017.2 in /opt/miniconda-latest/envs/neuro/lib/python3.7/site-packages (from pandas>=0.23->seaborn>=0.10->ptitprince) (2020.4)\n",
"Requirement already satisfied: six in /opt/miniconda-latest/envs/neuro/lib/python3.7/site-packages (from cycler>=0.10->matplotlib->ptitprince) (1.15.0)\n",
"Building wheels for collected packages: ptitprince\n",
" Building wheel for ptitprince (setup.py): started\n",
" Building wheel for ptitprince (setup.py): finished with status 'done'\n",
" Created wheel for ptitprince: filename=ptitprince-0.2.5-py3-none-any.whl size=8428 sha256=b233423b170924d0614d65d9166e74f70961972a4506ae798d248d5ed0897163\n",
" Stored in directory: /home/neuro/.cache/pip/wheels/58/a5/f2/55920bbc5d0e6fb74b2370e1e52e07c236ba7b621236ea5a81\n",
"Successfully built ptitprince\n",
"Installing collected packages: PyHamcrest, ptitprince\n",
"Successfully installed PyHamcrest-2.0.2 ptitprince-0.2.5\n"
]
}
],
"source": [
"%%bash\n",
"pip install ptitprince"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import ptitprince as pt\n",
"\n",
"dx = \"AgeGroup\"; dy = \"ToM Booklet-Matched\"; ort = \"v\"; pal = \"Set2\"; sigma = .2\n",
"f, ax = plt.subplots(figsize=(7, 5))\n",
"\n",
"pt.RainCloud(x = dx, y = dy, data = pheno[pheno.AgeGroup!='Adult'], palette = pal, bw = sigma,\n",
" width_viol = .6, ax = ax, orient = ort)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Bivariate visualization: Categorical x Categorical"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What if we want to observe the relationship between two `categorical variables`? Since we are usually just looking at `counts` or `percentages`, a simple `barplot` is fine in this case.\n",
"\n",
"Let's look at `AgeGroup` x `Gender`. `Pandas.crosstab` helps sort the data in an intuitive way. "
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
Gender
\n",
"
F
\n",
"
M
\n",
"
\n",
"
\n",
"
AgeGroup
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
"
\n",
"
3yo
\n",
"
10
\n",
"
7
\n",
"
\n",
"
\n",
"
4yo
\n",
"
8
\n",
"
6
\n",
"
\n",
"
\n",
"
5yo
\n",
"
16
\n",
"
18
\n",
"
\n",
"
\n",
"
7yo
\n",
"
11
\n",
"
12
\n",
"
\n",
"
\n",
"
8-12yo
\n",
"
19
\n",
"
15
\n",
"
\n",
"
\n",
"
Adult
\n",
"
20
\n",
"
13
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"Gender F M\n",
"AgeGroup \n",
"3yo 10 7\n",
"4yo 8 6\n",
"5yo 16 18\n",
"7yo 11 12\n",
"8-12yo 19 15\n",
"Adult 20 13"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pandas.crosstab(index=pheno['AgeGroup'],\n",
" columns=pheno['Gender'],)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can actually plot this directly from `pandas`."
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"pandas.crosstab(index=pheno['AgeGroup'],\n",
" columns=pheno['Gender'],).plot.bar()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The above plot gives us absolute `counts`. Perhaps we'd rather visualize differences in `proportion` across `age groups`. Unfortunately we must do this manually."
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"crosstab = pandas.crosstab(index=pheno['AgeGroup'],\n",
" columns=pheno['Gender'],)\n",
"\n",
"crosstab.apply(lambda r: r/r.sum(), axis=1).plot.bar()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Style points"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You will be surprised to find out exactly how customizable your `python plots` are. Its not so important when you're first `exploring` your data, but `aesthetic value` can add a lot to `visualizations` you are communicating in the form of `manuscripts`, `posters` and `talks`.\n",
"\n",
"Once you know the relationships you want to `plot`, spend time adjusting the `colors`, `layout`, and fine details of your `plot` to `maximize interpretability`, `transparency`, and if you can spare it, `beauty`!\n",
"\n",
"You can easily edit `colors` using many `matplotlib` and `python arguments`, often listed as `col`, `color`, or `palette`. "
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"## try uncommenting one of these lines at a time to see how the \n",
"## graph changes\n",
"\n",
"sns.boxplot(x='AgeGroup', \n",
" y = 'ToM Booklet-Matched',\n",
" data = pheno[pheno.AgeGroup!='Adult'],\n",
" #palette = 'Greens_d'\n",
" #palette = 'spectral',\n",
" #color = 'black'\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can find more about your palette choices here: https://matplotlib.org/3.1.0/tutorials/colors/colormaps.html.\n",
"\n",
"More about your color choices here:\n",
"https://matplotlib.org/3.1.0/gallery/color/named_colors.html."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also easily change the style of the plots by setting `\"style\"` or `\"context\"`:"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set_style('whitegrid')\n",
"sns.boxplot(x='AgeGroup', \n",
" y = 'ToM Booklet-Matched',\n",
" data = pheno[pheno.AgeGroup!='Adult'],\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set_context('notebook',font_scale=2)\n",
"sns.boxplot(x='AgeGroup', \n",
" y = 'ToM Booklet-Matched',\n",
" data = pheno[pheno.AgeGroup!='Adult'],\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice these changes do not reset after the `plot` is shown. To learn more about controlling `figure aesthetics`, as well as how to produce temporary style changes, visit here: https://seaborn.pydata.org/tutorial/aesthetics.html."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, remember that these `plots` are `extremely customizable`. Literally every aspect can be changed. Once you know the relationship you want to `plot`, don't be afraid to spend a good chunk of time `tweaking` your `plot` to perfection:"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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8KF+3dPjwYcTGxir99dprrwGouBFh69at5Yu2lW1dUFRUhOvXryu8J1tX8+IbR7Vl0KBBsLGxQW5uLs6cOYPU1FR5nKqSqRd16NABX375pTxeXU33VZebm5t8TdMff/yhVl3ZaN3Dhw+RlpamsExsbKx8+qhLly4ax+nl5QUrKyvcu3cPV65ckSdVmoxKGRsbo2PHjgDKX1ZQRnavJnGrY8iQIbCxsZGvYZOtFdMV2ee+ePGi0jKqtrwgasyYTJFGZKNSnTp1QqdOnWBlZaX0l5eXF4DyV71lC7oNDAzk03O7d+9W+Jr7vn37UFBQoLD9cePGAShfMK1st2iZ5xdYV5dQKJQvYpZN90mlUnTt2lXhHj9VTQ/J1qHUl2kkZSwtLeWH7G7ZsqXCmqQXlZaWIj8/X/7ngQMHwtLSEmKxGD/99FOl8mVlZdi8eTOA8lfpFb01V10mJibyqeXAwEDcunULQqFQ4zVFsv/Xhw4dUjjyEhUVJf/hYcSIERpGrR5jY2N88sknePfddzFr1iz5dg26Ivt3HB8frzBpSkpK0tku/0S6xmSK1CaVSuV7S73++utVlh86dCiEQiFEIhGioqLk199//30IhULcvXsXc+bMkU+xFBcXY+/evVi7di2srKwUPnPw4MHyb/qffPIJvv/++wrfBLOzsxEREYEPPvgAgYGBGn1O2dYKZ86ckY98KBuV2r9/P6ZNm4ajR49WiCMnJwc//vijfETq+R3T66sFCxbI30x86623Kkx1AuUjT7t27cLIkSMrjByam5vLX7ffs2cPtmzZIk+20tLS8NFHHyEuLg4GBgaYP39+jeOULUS/dOkSAOBf//qXfJpSXf7+/nBwcEBRURHee+89XLt2DUB5AhgWFoaPPvoIADBgwAD079+/xrFX15gxY7Bo0aIqTwPQhl69esnXD86dOxenT5+Wr12Mi4vDe++9p3CLEyJ9UG8XoD948AB//vknrl27huvXr+Off/6BVCrFhg0b5D8hVZdYLMbFixdx9uxZXLp0CSkpKcjKyoKtrS169OiBt99+W/7aM1XtwoUL8sSnOq+gW1lZoW/fvoiKisKhQ4fki2hffvllrFixAkuXLsXp06dx+vRpWFtbo6CgAGKxGF5eXjA1NUVISIjCL9LffvutfL+qTZs2YdOmTWjSpAmkUqn8bTMAVZ7lpkyvXr3g5OSEJ0+e4P79+zAwMFC6j45UKkVUVJQ8WTQ3N4eRkVGF18vffPPNer+AGABatWqF7du3Y9asWUhKSsLs2bNhZGQES0tLFBQUVEisXtwOY9q0abh//z5CQkKwfv16/PDDD7C0tEROTg6kUikMDAywbNmyWnmFvlOnTnBzc5MndDVZeG5tbY3Nmzfjvffew507dzBhwgRYWFigtLRU/pKFi4sLVq9eXeO4G7LAwED4+/vj4cOH+Pe//w1TU1MYGBigoKAATZs2xaJFi7Bs2TJdh0mkdfU2mdq/fz92795dK8+KjY2Vb0Do4OAAV1dXmJmZ4f79+wgLC0NYWBhmzpyJefPm1Up7jZ1sWq1t27bytSZV8fT0RFRUFCIjI5GTkyMfcfL19UWbNm3w448/4vLlyygpKcHLL78MX19f+Pv7Y/bs2QDKF3a/yNzcHJs2bcKZM2cQHByMK1euIDMzEwYGBmjTpg26du2K4cOHa5zAyM4YlE1Z9e3bV+lr4aNGjYKFhQXOnz+PO3fuQCQSoaCgAA4ODujatSsmTJhQ628d1qVu3brhxIkT2L9/PyIjI/HgwQPk5ubCwsICzs7O6N+/Pzw9PSvtY2VoaCg/BPe3337D9evXkZ+fDwcHB/Tp0wcBAQFwc3OrtTiHDx+O69evw8HBAYMHD67Rs7p164bQ0FD89NNPOHPmDJ48eQJDQ0O4ublh5MiR8Pf3l0/X6qtmzZrhwIED2Lx5M/744w88ffoU1tbWGDlyJObMmYOHDx/qOkQinRBI6+m7rL///jsSExPh5uYGNzc3LF26FDExMRqNTEVHR2P//v2YMmUKevXqVeHe8ePHsXDhQpSVleGXX35RuWkfaZdUKsVrr72GJ0+eYPfu3Rw9pEoCAgJw/vx5TJ8+vcK5eURE2lRvR6Zk6yFqQ//+/ZWucxg5cqT8sNIjR44wmapHQkND8eTJE1haWup88S3VPw8fPkR0dDQEAkGtfr0gIlJXvU2mtEn2yq+y17mp7vz444+wsLCAh4cHHB0dYWBggOzsbISEhMgPl500aZJaO1pT45efn48vv/xSPnqp6PxBIiJtYTIF4J9//gGAGr2qTZq5d+8ejh49iq+++gpCoRDm5ubyxcpA+dtTsnVTRLt27cLu3bshEolQUlICExMTTu8Rkc7pfTIlEonkeybJXrUn7Zk0aRIsLS0RFxcHkUiE3NxcWFtbw8XFBaNHj8bYsWPlm0gS5ebmIjk5GWZmZujRowcWLlyIDh066DosItJz9XYB+osmT56s8QJ0ZUpLS/Hee+8hOjoa/fv3x65du9SqL9uh28HBocrzxIiIqPErKyuDSCSCm5sbTE1NdR0OaYle/8j/+eefIzo6Gk5OTvjuu+/Urn/9+nW8/fbbdRAZERE1ZHv37q309nhNFBcXIzMzE7m5ufKTJKjuGRoaokmTJmjatKnKrVH0Npn66quvcODAATg4OGDXrl0arZeS1dm7dy+aN29e2yESEVEDk5qairfffrtW1+AWFxfj0aNHsLW1Rdu2bSEUCittmEu1TyqVQiwWIycnB48ePcJLL72kNKHSy2QqMDAQe/bsQdOmTbFr1y6FZ61Vh2xqr3nz5mjVqlUtRkhERA1ZbS79yMzMhK2tLezt7WvtmVQ1gUAAY2Njeb9nZmbCyclJYVm9O5tv1apV2LlzJ2xsbLBz504uXiUionotNzdX6TmlpB1WVlbIzc1Vel+vkqnVq1fj559/hrW1NXbu3IlOnTrpOiQiIiKVysrKIBQKdR2GXhMKhSrXqjWqZGrNmjXw8vLCmjVrKt1bv349tm/fDisrK+zYsUO+UScREVF9xzVSulVV/9fbNVM3btzAihUr5H++d+8eAGDdunXYsWOH/Ppvv/0m/71IJEJiYiJEIlGFZ0VGRmLLli0AgJdeeglBQUEK22zfvj1mzJhRa5+BiIiIGr96m0zl5eXhypUrla7LditXR3Z2tvz3169fx/Xr1xWW69OnD5MpIiIiUku9Tab69u2LO3fuqFUnMDAQgYGBla6PHz8e48ePr63QiIiIiOTqbTJFRI3D4sWLkZycjD179ug6FKJG64PZ85CWnq7rMKrN0d4eWzZuqJVnDR06FMnJyQCAGTNmYMGCBUrLLliwAMeOHQNQPhtVW1+XmEwRERE1cGnp6RBZN6A31NNv18ljQ0JCMH/+fIX7fOXl5SEiIqJO2m1Ub/MRkW5JJBLMnDkTkZGRSstcvHgR06ZNQ15enhYjI6LGzs3NDU+fPsVff/2l8H5oaCiKiorQtWvXWm+byRQR1ZqCggJYW1tj9uzZ+Pe//43Hjx/L72VmZmLJkiXw9/eHsbEx8vPzdRgpETU2srXRhw4dUnj/0KFDMDQ0xJgxY2q9bSZTRFRrLC0tsXLlShw+fBhSqRTe3t6IiYnBgwcP4OXlhcTEROzduxdbtmyBo6OjrsMlokakW7du6NChAyIjI5GTk1Ph3oMHDxAfH49BgwbV6rmJMkymiKjWOTs7Y+vWrZgxYwaSk5ORnp4ODw8P/Prrr3B3d9d1eETUSI0bNw7FxcXyReYystGqunqzn8kUEdW6+/fv49///je2bduGFi1awN7eHhEREfDz80N8fLyuwyOiRmrMmDEwNDSsMNVXVlaGkJAQ2NjYYOjQoXXSLpMpIqo1+fn5+PTTTzFq1CgAwLFjx9C3b1+0b98eJ06cQJs2beDn54c5c+YgLS1Nx9ESUWPj4OCAV199FVevXsX9+/cBAFFRUXj69Cl8fHxgbGxcJ+0ymSKiWmNqaor09HR8//33+PHHH9G6dWv5PTs7OwQGBiIoKAj5+fmwsLDQYaRE1FiNGzcOAHDw4EEA/5vik12vC0ymiKjWGBoaYsuWLfDw8FBaplevXtixYwcsLS21GBkR6YuhQ4fCxsYGhw8fRmZmJiIjI+Hs7Aw3N7c6a5PJFBERETUaxsbGGDVqFEQiET755BOUlJTA19e3TttkMkVEdSowMJBHyRCRVsmm9E6fPg0jIyP5Os66wmSKiIiIGhVXV1e4u7vDxsYGXl5esLOzq9P2eDYfwdvbG8ePH9d1GESk50aOHInQ0FBdh0GNxL59+7TWFkemiIiIiGqAI1PEnwSJiIhqgMkUERFRA+dobw+k39Z1GNXmaG9fa886deqUWuW9vLxw586dWmsfYDJFRETU4G3ZuEHXIeg1rpkiIiIiqgEmU0REREQ1wGSKiIiIqAaYTBERERHVAJMpIiIiohpgMkVERERUA0ymiIiIiGqAyRQRERFRDTCZIiIiIqoBJlNERERENVBvj5N58OAB/vzzT1y7dg3Xr1/HP//8A6lUig0bNsDLy0vj5x49ehT79+/HnTt3IJFI0K5dO/j6+sLPzw8GBswtiYiISD31Npnav38/du/eXavPXLFiBfbt2wcTExP0798fRkZGiI6OxhdffIHo6Ghs2LABhoaGtdomERERNW71NplydnbGtGnT4ObmBjc3NyxduhQxMTEaPy8sLAz79u2Dg4MDgoKC0LZtWwBAeno6pkyZgvDwcAQFBWHq1Km19AmIiIhIH9TbZGrixIm1+rytW7cCABYuXChPpADA3t4ey5cvx+TJk7F9+3ZMnjyZ031ERNSgzPxwLtKepes6jGpztLXH5nXf18qzhg4diuTkZJVlNm3aBA8Pj1ppT5F6m0zVptTUVNy4cQNCoVDheqs+ffrA0dERaWlpuHz5Mnr27KmDKImIiDST9iwduV5tdB1G9Z18WOuPHDRoEBwcHBTec3JyqvX2nqcXydTNmzcBAB07doSpqanCMl27dkVaWhpu3brFZIqIiKiBmTFjBvr27auTtvViPuvx48cAgBYtWigtI8taZWWJiIiIqkMvRqYKCgoAAGZmZkrLWFhYAADy8/O1EhMR1U8nTpzAsWPHFN7LzMwEADRt2lThfR8fH4wYMaJBtKnK+vXrkZCQoPBeRkaGPCZ1NW3aFHZ2dgrvpaSkKP36KxaLIRaLNWpTKBRCKBQqvNelSxesX79eo+cSPU8vkimpVAoAEAgEOo6EiBqyjIwMAMoTm8bSZkJCAuKvXAPMrCrfFBcDpcUaPTevJAOPMvIq3yjMgYnQEMUlxYBQwfY0EikglWjUZnGZGMXS0so3xGVITU3V6JlEL9KLZEo26iQboVJE9hORrCwR6acRI0YoHemZNWsWgPI3gxp6m1Uys4Jhe+2sPyl7cAFCQQnE9qYw9+2ulTYLgi8rHSUjUpdeJFMtW7YEUD6MrIzsJxRZWSIiImo4pkyZovD6uHHjEBgYWKdt60Uy1aVLFwDlQ9dFRUUK3+i7du0aAKBz585ajY2IiIhqTtnWCO7u7nXettJkauPGjbXWyOzZs2vtWZpwcnKCq6srbty4gZMnT2Ls2LEV7sfExCA1NRUODg7o0aOHboIkIiIijelyawSVyVRNF2xLpVIIBAKtJVNr1qxBeHg4Xn/9dSxYsKDCvRkzZmDevHlYvXo1evTogTZtyjc3y8jIwIoVKwAA06dP5+7nREREpBalydTYsWMVJlNSqRSRkZHIzc2FmZkZXF1d4ejoCKlUCpFIhOvXr6OwsBBWVlYYOnSoxgnZjRs35EkOANy7dw8AsG7dOuzYsUN+/bfffpP/XiQSITExESKRqNLzvLy84Ofnh/3792PUqFEYMGCA/KDjvLw8eHh4wN/fX6NYiYiISH8pTaYULdaSSqWYN28eCgoKMH/+fEyZMgXm5uYVyhQWFmL37t34/vvvUVBQgO+/1+zsnby8PFy5cqXS9X/++Uej5wHA8uXL4e7ujr179yImJgYSiQTt27eHr68v/Pz8OCpFREREalNrAfqePXsQHh6ORYsW4Z133lFYxszMDO+//z5MTEzw7bffIigoSKMRn759++LOnTtq1QkMDKxyxf6oUaMwatQoteMhItIXGRkZQGEOyh5c0E6DhTkQCw0BKNhjiqgBUCuZOnjwIAwNDeHn51dlWT8/P6xevRoHDhzg9BkREVVJIspDQfBltepIC0oAAAJzY7XbQjO1qhAppVYy9fDhQ1hYWMDExKTKsiYmJjA3N8fDh7V/MjQRNRw1OSoFqJvjUkg1Ozs7PMrI0+qmnVbmhmjVqpXadWXH3nRs1l69is2Ajh07qt0ekSJqJVPGxsbIyclBcnJylZtbPn78GDk5ObC2tq5RgETUeOniqBSqn1q1aqXRLu862yG+nnG0tQdONpzBC0db+1p71qlTp2rtWZpSK5nq0aMHzp49i+XLl2PTpk0wNlY8rCoWi7FixQoIBALu20Sk5+rlUSlEjczmdZq97EW1Q63X1z744AMYGBggKioKY8eOxe+//47ExETk5+cjPz8fiYmJ+P333zFu3DhERUXB0NAQM2fOrKvYiYiIiHROrZGpV155BatWrcInn3yCBw8e4LPPPlNYTiqVwsTEBCtXrkS3bt1qJVAiIiKi+kjts/m8vb3RrVs3bNmyBREREcjJyalw38rKCsOHD8f777+P1q1b11qgRESkRZpsjSAuLv+vsOqXlF5si6gh0+ig49atW+Obb77BN998g6SkpApv5DCBIiJq2DR9y03+Zl3HNlprk6g+0CiZel7r1q2ZQBERNSLz58/XqB5fKCB9xfNTiIiIiGpA45GpJ0+eICEhATk5OSgtLVVZduzYsZo2Q0REpPekUikEAoGuw9BbUqlU5X21k6krV67g66+/xrVr16pdh8kUUf2gajdyoOodyRvLbuTr16+Xr+9Rh6yObDpLHSkpKcjPz1e7XkFBAQBg+PDhatft0qUL1q9fr3Y9VVT9HaqqfzT9+6OLNusTAwMDSCQSGBry7EJdkUgkMDBQPpmnVjJ1/fp1TJ06FcXFxZBKpWjevDkcHR2Vbt5JRA2LvuxInpCQgPgr1wAzK/Uqist/Oo2/q+ZO04U5MBEaorikGBCq+Q3x/79+55UUqldPXIbU1FT16tSQnZ2dVtvTVZvaZmpqioKCAjRp0kTXoeitgoICmJmZKb2vVjK1ceNGFBUVwdnZGStXroSrq2uNAyQi7VG1GzmgZwuIzay0evacUFACsb0pzH27a6XNguDLdZJoVPV3qC7oos36xNLSEllZWbC0tORUnw5IpVJkZWXBwsJCaRm1FqDHx8dDIBBg9erVTKSIiIi0wNbWFqWlpXjy5Il8ZojqnlQqRXFxMZ48eYLS0lLY2toqLavWyFRxcTHMzc3h7Oxc4yCJiIioagYGBmjdujUyMzPx6NGjKl/6otpjZGQEa2trNGvWrPbWTL300ktITExEaWkpjIxqvEUVERERVYORkRGaNWuGZs2a6ToUUkCtjGj8+PEIDAxEZGQkPD096yomqmdUvUmjL29/ERERKaPWmqkpU6Zg0KBB+PzzzxEfH19XMVEDkpGRIX8DjIhI29LT0zFz5kx+HSKdUmtkavPmzejatSuuXr2KSZMmoVevXnBzc1O5wh0AZs+eXaMgSbdUvUmjV29/UaORkZGh2UG+mirMgVhoCID7BNW2nTt34sqVK9i5cycWLlyo63BIT6m9NYJAIJC/SRAbG4uLFy8qLS/bsZXJFBER1bb09HSEhoZCKpUiNDQUAQEBerHvFNU/aiVTY8eO5R4XRNTg2dnZ4VFGnvb3mdJKa/pj586d8h/uJRIJR6dIZ9RKpgIDA+sqDiIiIrX88ccfEIvLU1SxWIywsDAmU6QTai1AJyIiqi+GDx8OoVAIABAKhXzLnHSGyRQRETVIAQEB8qUnBgYGCAgI0HFEpK+YTBERUYNkb28Pb29vCAQCeHt7c/E56YzSNVOdO3cGALRv3x6hoaEVrqlDIBDg5s2bGoZHRNR4SER5KAi+rFYdaUEJAEBgbqx2W9CDzbIDAgKQmJjIUSnSKaXJlOwNiecPVNTkcEUeyEhEBJiZmaFjx45q10tISAAAdGzWXr2KzaBRew2Nvb09Nm/erOswSM8pTaYiIyPLCzx3Bp/sGhERqadVq1YabW7bkDbGTU9Px2effYYvv/xSa1Nud+/exaxZs7BlyxZ06NBBK20SvUhpMtWyZctqXatrR48exf79+3Hnzh1IJBK0a9cOvr6+8PPzU3mCsyKpqanYvn07oqKi8OTJE0ilUjg5OaFfv36YPn06WrduXUefgqhxW79+vXwERR2yOrKEQV0dO3bE/PnzNapLtU8Xu5GvWLEC+fn5+Pzzz7F3716ttEn0IrX2mdK2FStWYN++fTAxMUH//v1hZGSE6OhofPHFF4iOjsaGDRtgaFi94xlu3ryJqVOnIicnB82bN8egQYMAANevX8d///tfHD16FD///DN69uxZlx+JqFFKSEhA/I2rMHCwVKueVFgGALjy9IHabUpEeWrXobqji93I7969i8TERABAYmIi7t27x9Ep0ol6m0yFhYVh3759cHBwQFBQENq2bQug/B/slClTEB4ejqCgIEydOrVaz/viiy+Qk5ODN954A5999pl8bxKxWIzPP/8cwcHBWL58OY4cOVJXH4m04MSJEzh27JjCe5mZmQCApk2bKrzv4+Oj9AzChkTTUSJA85GihIQEGDhYwty3u0btakLdhdyVaHI2n7i4/L9CE7Xbaux0sRv5ihUrKvyZo1OkKyqTqdjY2FpppHfv3mrX2bp1KwBg4cKF8kQKKF9suHz5ckyePBnbt2/H5MmTq5zuKy4uRnx8PABg7ty58kQKKN/obd68eQgODsadO3dQWFgIMzMzteOl+k92qryyZKqxSEhIQPyVa4CZlfqVxeXfDOPvPlSvXn4eDKyt1W9PRzRdmC1fDN6xjdbabCh0sRu5bFRK2Z+JtEVlMjV58uQan8WnydYIqampuHHjBoRCIby8vCrd79OnDxwdHZGWlobLly9XOTVnYGAAIyMjlJaWKny7UPYZzc3NYWpqqlasVL+MGDFC6ehSQ1rIW2NmVlo7dw4Aym6Ea62t2qDpOiu9+jukpuHDh+Po0aMoLS2FkZGRVnYjb9euXYUEql27dnXeJpEi1VrBLZVKNf4lkUjUDkqWfHXs2FFpctO1a1cAwK1bt6p8nlAoRL9+/QAAP/zwg/ynJ6D8J6j169cDAHx9fXmQMxGRBgICAuRf7yUSiVb2ffr8888r/PnFaT8ibanWmqmWLVti9OjRGDNmjFbeeHv8+DEAoEWLFkrLODk5VShbleXLl+O9997Db7/9hnPnzsHNzQ0AcO3aNeTk5GDKlCn4z3/+U8PIiYhIW5ydneWjU+3atePic9IZlcnU4sWLceTIEdy8eRM//vgjfvzxR/Ts2RNjx47FiBEjYGmp3ps71VVQUAAAKtcuWVhYAADy8/Or9czWrVtj//79WLRoEc6dO4fU1FT5PTc3N/Tu3bvCWqr6igus6x/+PyEqX4BuYGAAiUQCAwMDrW2P8Pnnn2PWrFkclSKdUplMvfPOO3jnnXdw7949HDx4EMeOHUNcXBwuXbqEr776CkOHDsWYMWMwePBgtfd8UkW2rqk2p9wuXbqEOXPmwNLSEps3b0bPnj0hlUpx6dIlfPvtt5gzZw7mzJmD2bNn11qb2qYvC6wbEl38P8nIyNDsTbWakJTKjz0h/fTHH3+gtLQUAFBaWqqVBehA+ehUeHjDWrNHjU+1pvk6dOiA//znP/j4449x/vx5HDp0CJGRkThx4gROnjyJpk2bwsfHB6NHj4arq2uNg5KNOslGqBSRjUjJyqqSk5ODWbNmobCwEL/++muFqUoPDw907NgRo0ePxpYtW+Dj41Ph7cH6hgus6x/+PyEqX4B+7NgxiMViCIVCrSxAJ6ov1NpnSiAQYODAgRg4cCAKCgpw8uRJhISE4OLFi/jll1+we/dudOjQAQsWLMC//vUvjYOS7bSekpKitIxsmq46u7KfOXMGmZmZ6Nevn8I1X23atEG3bt0QExODmJiYep1MEVXFzs4OjzLytP42n7oH8VLjEhAQgNDQUADlb1Dz4GHSJxrPzZmbm2P8+PHYvXs3Tp06hZkzZ8LQ0BD37t1DTExMjYLq0qULgPI9XYqKihSWuXbtGgCgc+fOVT7vyZMnAIAmTZooLWNlVb4nT1ZWljqhEhERyvcA9Pb2hkAggLe3t9bO5iOqD2q80OnixYvYvHkzgoKC5Ps41XT9lJOTE1xdXSEWi3Hy5MlK92NiYpCamgoHBwf06NGjyuc1a9YMAHDjxo0K2yLIiMVi3LhxA0D5YaRERKS+gIAAvPLKKxyVIr2j0XEyjx49wuHDh3HkyBE8fvwYUqkURkZGGDp0KMaOHYvXXnutxoHNmDED8+bNw+rVq9GjRw+0aVO+43BGRob8rY3p06dXSNzWrFmD8PBwvP7661iwYIH8+uDBg2FmZoaUlBSsXLkSixcvhrFx+ZRESUkJvv76azx58gTW1tZ49dVXaxw7EZE6VL0RWtURP/XpjVB7e3ts3rxZ12EQaV21k6mcnBwcP34cISEhuHLlCoDyt+66du2KsWPHwtvbGzY2NrUWmJeXF/z8/LB//36MGjUKAwYMkB90nJeXBw8PD/j7+1eoIxKJkJiYCJFIVOG6nZ0dPv/8cyxduhR79+5FeHi4fKH89evXIRKJYGxsjG+++UblVCARkbZxuoyo/lOZTJWVleHMmTM4fPgwzpw5A7FYDKlUihYtWmDUqFEYM2YM2rdvX2fBLV++HO7u7ti7dy9iYmIgkUjQvn17+Pr6ws/PT63pxHHjxsHZ2Rm//PILLl68iL/++gsA4OjoiAkTJiAgIIAbvhGRTqh6I5SI6j+VydSgQYOQlZUFqVQKCwsLeHt7Y8yYMfKjWbRh1KhRGDVqVLXKBgYGIjAwUOl9V1dXrFq1qrZCIyIiIlKdTD179gwCgQAtWrTAsGHDYGZmhvPnz+P8+fNqNfLRRx/VKEgiIiKi+qpaa6aePHmCoKAgtR8ulUohEAiYTBEREVGjpTKZ6t27t7biICIiImqQVCZTe/bs0VYcRERERA1S7Z1OTERERKSHmEwRERER1QCTKSIiIqIaYDJFREREVANMpoiIiIhqgMkUERERUQ0wmSIiIiKqgWrtgE5EpJJUAokoDwXBl7XWpESUhwzDDK21R0SkjNJkasqUKbCxscH3338vv5aSkgJDQ0M4OjpqJTh9tX79eiQkJKhdT1Zn1qxZatft2LEj5s+fr3Y9IiIifac0mYqJiYG9vX2Fa0OHDoWDgwP+/PPPOg9MnyUkJCD+yjXAzEq9imIpACD+7kP16hXmqFee6EUCAxg4WMDct7vWmiwIvgw7OzuttUdEpIzSZMrIyAhisbjSdalUWqcB0f8zs4Jh+75aaarswQWttENERNQYKU2m7O3tkZaWhqSkJLRu3VqbMZEOPH78WKPpQU4tEhGRvlOaTA0cOBDBwcGYOHEi+vbtC3NzcwBAXl4elixZUu0GBAIBvvnmm5pHSnWqsLAQ8TeuwsDBUq16UmEZAODK0wdq1ZOI8tQqT6QtJ06cwLFjxxTeq+qHBx8fH4wYMaLOYiOi+klpMvXhhx8iPj4eDx48QFhYmPx6UVERDh06VO0GmEw1HAYOllpb86LNt76IagvXaBGRIiqn+Y4ePYqoqCgkJCSgqKgIGzduhLm5Od59911txkhEpDUjRozg6BIRqUXlPlOGhoYYMmQIhgwZAgDyZGr27NlaCY6IiIiovlNr086xY8eiSZMmdRULERERUYOjVjIVGBhYV3EQERERNUg1Ok7m6tWruHHjBjIzMwEATZs2haurK7p161YrwRFRw6HJcTLSghIAgMDcWKP20EztakREtU6jZOro0aNYv349UlJSFN5v1aoV5s+fD29v7xoFR0QNhIEhLI2F6NisvVrVZFsNqFsPANCsfK8yIiJdUzuZWrduHbZt2ybfCd3R0RHNmzcHAKSmpso3+ly4cCHu3r2LDz/8sHYjJqL6x8QCHTu2waZNm9SqJtuvSd16RET1iVrJ1N9//42tW7cCALy9vTF79my0a9euQpl//vkHP/zwA0JDQ7Ft2zYMGDAAfftq51gUIiIiIm0zUKdwUFAQBAIBJk+ejDVr1lRKpACgbdu2WLNmDfz9/SGVSrFnz55aC5aIiIiovlErmbp8+TIEAkG19pmaPXs2DAwMEB8fr3FwRERERPWdWtN8WVlZaNKkCaytrassa2NjgyZNmiAnJ0fj4IDyxe779+/HnTt3IJFI0K5dO/j6+sLPzw8GBmrlggDKj8PZs2cPTp48iYcPH0IsFsPOzg5ubm6YOnUq3N3daxQvERER6Re1kikbGxtkZmYiKysLNjY2KstmZWUhNzcXTZs21Ti4FStWYN++fTAxMUH//v1hZGSE6OhofPHFF4iOjsaGDRtgaGhY7eclJSVh2rRpePjwIezs7NC7d28YGxsjOTkZp06dQqdOnZhMERERkVrUSqa6d++OyMhIbNq0CUuXLlVZduPGjZBIJOjevbtGgYWFhWHfvn1wcHBAUFAQ2rZtCwBIT0/HlClTEB4ejqCgIEydOrVazysoKMC7776LR48eYebMmZg5cyaEQqH8/rNnz5CVlaVRrI2BWCyGRFSotQOIJaI8ZBhmaFR3/fr18lfq1SGrI3uDTB0dO3bE/Pnz1a5HRESNn1rJlL+/PyIiIhAUFIRnz57hgw8+wMsvv1yhzLVr17B161ZERkbKF6trQvbW4MKFC+WJFFB+APPy5csxefJkbN++HZMnT67WdN+WLVvw6NEjjB07FvPmzat039bWFra2thrFStqVkJCA+CvXADMr9SqKy7fziL/7UL16hTl4/PgxEzgiIlJIrWSqX79+eP/997F161aEhoYiNDQUTZs2haOjI0pKSpCSkoLCwkIAgFQqxQcffKDRtgipqam4ceMGhEIhvLy8Kt3v06cPHB0dkZaWhsuXL6Nnz54qn1dSUoLffvsNADBjxgy149EHQqEQYmtTmPt210p7BcGXYWdnp/kDzKxg2F47W26UPbiAwsJCxN+4CgMHS7XqSoVlAIArTx+oVU8iylOrfCWFOSh7cEH9euLi8v8KTdRuj4hIX6m9aeeHH34IZ2dnbNiwAY8ePUJGRgYyMipO17Rp0wbz5s3DyJEjNQrq5s2bAMp/Mjc1NVVYpmvXrkhLS8OtW7eqTKZu3LiBrKwsODk54eWXX8alS5dw5swZZGVlwd7eHq+++ip69OihUaykPwwcLLWabGqqJruCy3ck79hGq+0SETVkGh0n4+3tDW9vb9y6dUvh2XydO3euUVCPHz8GALRo0UJpGScnpwplVbl79y6A8iRv8eLFOHToUIX7mzZtgqenJ1atWqU0eSNqKGoyNcgdyYmI1Fejg447d+5c48RJkYKCAgCAmZmZ0jIWFhYAgPz8/Cqfl52dDQC4ePEiysrK8O6778LPzw82NjaIjY3FihUrEBYWBgsLC6xcubIWPkHNZGRkaD5No4nCHIiFhgCq/2YkERERlVN/oyYtkJ37JxAIauV5EokEAFBaWooJEyZg0aJFeOmll2BlZYVhw4Zh06ZNEAgECAkJQVJSUq20SURERPqhRiNTdUU26iQboVJENiIlK1ud5wHAG2+8Uel+165d4erqiuvXr+PChQto3bq1uiHXKjs7OzzKyNPqAmuhoARirbRGRETUuNTLkamWLVsCAFJSUpSWSU1NrVC2Os8DgFatWiksI7uenp5e7TiJiIiI6mUy1aVLFwDlbxYVFRUpLHPt2jUAqNaaLVdXV/nvnz17prCM7Lq5ublasRIREZF+q5fJlJOTE1xdXSEWi3Hy5MlK92NiYpCamgoHB4dqbWng6OiIV155BQDw999/V7qfnZ0t347Bzc2thtETERGRPqmXyRTwv801V69ejYcP/7djdUZGBlasWAEAmD59eoXdz9esWQMvLy+sWbOm0vP+/e9/Ayh/5fvWrVvy68XFxVi+fDlyc3Ph6urK/aaIiIhILfVyAToAeHl5wc/PD/v378eoUaMwYMAA+UHHeXl58PDwgL+/f4U6IpEIiYmJEIlElZ43dOhQvPvuu9ixYwcmTpyIV155BTY2Nrh69SqePn0KR0dHrF27ttbeICQiIiL9UG+TKQBYvnw53N3dsXfvXsTExEAikaB9+/bw9fWFn59ftc7ke96iRYvQs2dP7NmzB7du3UJhYSFatGiBgIAAzJgxA02bNq2jT0JERESNlVrJ1Lp16zBhwgStbh0watQojBo1qlplAwMDERgYqLLM66+/jtdff702QqtbmmzaWZNz1cy58zsREZEm1Eqmtm7dim3btqFXr17w9fWFl5cXj1+pA5qecVaTc9UeP36MApRo1C4REZE+UyuZ6tWrF+Li4hAbG4uLFy/iq6++wsiRI+Hr6yt/W45qTtOz1WpyrtqsWbOQ8fSBRu0SERHpM7WSqaCgICQlJeHAgQM4cuQInjx5gt9//x2///472rdvjwkTJmDMmDFce0REcidOnMCxY8cU3pONpsp+EFDEx8cHI0aMqJPYiIhqg9pbI7Ru3RoffvghTp06hZ9++gmenp4QCoW4f/8+Vq1ahcGDB2POnDk4ffq0/Ew8IiJF7OzsYGdnp+swiIhqROO3+QQCAQYNGoRBgwYhOzsbR48eRXBwMG7duoXw8HBERETAzs4O48aNw/jx49GuXbvajJuIGogRI0ZwZImIGrVa2bTT2toa/v7+OHToEEJCQtCzZ09IpVJkZGTgp59+wsiRIzF58mRERETURnNERERE9Uat7TOVkZGBw4cP4+DBg7h//z4AQCqVom3btnj06JF80Xrv3r3xww8/wNrauraaploiEeWhIPiyWnWkBeVvAArMjdVuC83UqkJERFQv1SiZKisrw+nTpxEcHIw///wTZWVlkEqlsLGxwZgxYzBx4kR06NABqamp+PXXX7Fnzx7ExsZizZo1+OKLL2rrM1AtqPF2DM3aq1exmeZtEhER1ScaJVN3797FwYMHceTIETx79gxSqRQCgQD9+vXDxIkT4eHhAWPj/41UNG/eHPPnz8fw4cPh6+uLU6dOMZmqZ3SxHYOmMjIyNNvUVFOFORALDQEYaqc9IiJqUNRKpvbu3YuDBw/i5s2bAMqn8Zo1a4Zx48ZVa2f0Ll26wN7eHunp6ZpHTERERFSPqJVMffnllwAAQ0NDDBkyBBMnTsSQIUPUOiOvVatWEAqF6kVJ9Bw7Ozs8ysiDYfu+Wmmv7MEFCAUlEGulNSIiamjUSqZat26NCRMmYPz48XBwcNCowf3792tUj4iIiKg+UiuZ+v777yEQCGBubl5X8RARERE1KGolU+PGjYOBgQGioqJgYWFRVzERERERNRhqJVNNmjSBgYEBz94jIiIi+n9q7YDetm1b5Ofno7i4uK7iISIiImpQ1EqmxowZg9LSUoSEhNRROEREREQNi1rTfG+//Taio6PxzTffwMDAAL6+vmpti0BERETU2KiVTH3yySewsrKCoaEhPvvsM6xduxZubm5o2rSp0qRKIBDgm2++qZVgiXRFLBZDIipU++xCTUlEecgwzNBKW0REVDNqJVOHDh2CQCCAVCoFADx79gx//vmnyjpMpoiIiKgxUyuZmj17dl3FQVSvCYVCiK1NYe7bXSvtFQRfhp2dnVbaIiKimmEyRURERFQDXD1OREREVANqjUyR7p04cQLHjh1TeC8hIQEAMGvWLIX3fXx8MGLEiDqLjYiISB9plEyJxWIcPXoUJ06cwM2bN5GVlQUAsLGxQZcuXTBy5Ej4+PhAKBTWZqxUBa6xISIi0j61k6lHjx5h1qxZuHfvnvytPpmMjAz8+eefiIqKws6dO7Fx40a89NJLtRYsASNGjODoEhERUT2iVjKVl5eHd955BykpKTAyMoKnpyf69euH5s2bAwBSU1Px999/IywsDHfv3kVAQAAOHz4MS0vLOgmeiIiISNfUSqZ27tyJlJQUtGjRAtu2bUOHDh0qlZk4cSL+/e9/4/3330dKSgp27drFtwCJiIio0VLrbb7w8HD5JpyKEimZjh074uuvv4ZUKsUff/xRowCPHj2KSZMmwd3dHT169MD48eOxd+9eSCSSGj0XANauXQsXFxe4uLjg559/rvHziIiISP+oNTKVlJQEU1NT9OvXr8qy/fv3h5mZGZKSkjQObsWKFdi3bx9MTEzQv39/GBkZITo6Gl988QWio6OxYcMGGBoaavTsq1ev4qeffqqwozsRERGRuurt1ghhYWHYt28fHBwcEBQUhLZt2wIA0tPTMWXKFISHhyMoKAhTp05V+9klJSVYsmQJ7Ozs0K1bN0RERNRy9ERERKQv1Jrme+mll1BUVITo6Ogqy0ZHR6OwsBCtW7fWKLCtW7cCABYuXChPpADA3t4ey5cvBwBs375do+m+DRs24N69e1ixYgWaNGmiUXxEREREgJrJlIeHB6RSKZYuXYr79+8rLXf79m0sXboUAoEAw4cPVzuo1NRU3LhxA0KhEF5eXpXu9+nTB46OjhCJRLh8+bJaz75y5Qp27twJHx8fDB06VO3YiIiIiJ6n1jRfQEAADh06hJSUFIwZMwYeHh7o27cvHB0dUVJSgpSUFFy4cAHnzp2DVCpFy5Yt8c4776gd1M2bNwGUL2Q3NTVVWKZr165IS0vDrVu30LNnz2o9t7i4GIsWLYK1tTWWLl2qdlxEREREL1IrmbK0tMTOnTsxZ84c3L17F2FhYQgLC6tQRraY28XFBT/88INGe0w9fvwYANCiRQulZZycnCqUrY5169YhMTER69atQ9OmTdWOi4iIiOhFai9Ab9OmDYKDg3H8+HGEhYXh5s2byMzMBAA0bdoUXbp0gaenJ0aOHKnxcTIFBQUAADMzM6VlLCwsAAD5+fnVeualS5fwyy+/wMPDAyNHjtQoLiIiIqIXafQ2n1AoxJgxYzBmzJjajgfA/0a3BAJBrTyvqKgIS5YsgaWlJT7//PNaeSYRERERoOYCdG2RjTrJRqgUkY1IycqqsnbtWvzzzz9YvHgxmjVrVjtBEhEREaEG+0yVlpbixo0bePLkCYqKijB27NhaC6ply5YAgJSUFKVlUlNTK5RVJSIiAgYGBggJCUFISEiFew8ePAAA7N+/H2fOnMFLL72Er7/+WsPIiYiISN9olExt27YNP//8M3JycuTXnk+mcnJy4Ofnh5KSEvz3v/9Ve7F3ly5dAAAJCQkoKipS+EbftWvXAACdO3eu1jMlEgliYmKU3k9KSkJSUlKFz0T1WGEOyh5cUK+OuLj8v0ITtduCueK3SomIiNROphYsWIDjx48DAFq3bo2UlBSUlZVVKGNlZYU+ffrg119/RUREBN544w212nBycoKrqytu3LiBkydPVhr1iomJQWpqKhwcHNCjR48qn3fq1Cml9xYvXoxDhw7hP//5D6ZNm6ZWnKQbHTt21KheQkLC/9dvo3bdx48fowAlGrVLyqWnp+Ozzz7Dl19+CTs7O12HQ0SkEbWSqdDQUISGhqJZs2bYuHEjunXrhkGDBiEjI6NSWR8fH+zfvx+RkZFqJ1MAMGPGDMybNw+rV69Gjx490KZN+TfAjIwMrFixAgAwffp0GBj8b9nXmjVrEB4ejtdffx0LFixQu01qGObPn69RvVmzZgEANm3apFHdjKcPNGqXlNu5c6d8I92FCxfqOhwiIo2olUwdOHAAAoEAS5cuRbdu3VSW7dq1KwwMDHDnzh2NAvPy8oKfnx/279+PUaNGYcCAAfKDjvPy8uDh4QF/f/8KdUQiERITEyESiTRqk0gViSgPBcGX1aojLSgfzRKYG6vdFhr5uxLp6ekIDQ2FVCpFaGgoAgICODpFRA2SWsnUzZs3YWBggNdee63KssbGxmjSpIl8DypNLF++HO7u7ti7dy9iYmIgkUjQvn17+Pr6ws/Pr8KoFFFdqvHUYrP26lVspnmbDcXOnTvl26BIJBKOThFRg6VWMlVQUAAzMzMYG1fvp+ySkhIYGhpqFJjMqFGjMGrUqGqVDQwMRGBgoFrP16QO6R9dTC02dn/88QfEYjEAQCwWIywsjMkUETVIag3tNG3aFPn5+cjLy6uybEJCAgoLC+Ho6KhxcETUeA0fPlx+SoJQKISnp6eOIyIi0oxayZTsQOHQ0NAqy27atAkCgQB9+/bVLDIiatQCAgLkpxwYGBggICBAxxEREWlGrWk+f39/nDhxAt9//z1cXV3h5uZWqUx2djZWrVqFkydPwsDAoNIicaLG5sSJEzh27JjCe7I1U7Lpvhf5+PhgxIgRdRZbfWZvbw9vb2+EhITA29ubi8+JqMFSK5lyd3fHtGnT8PPPP+Ott96Cu7u7fMrv22+/xb179xAbG4vi4vLNEefOndvoF9ESqcIEQbWAgAAkJiZyVIqIGjS1N+38+OOP0axZM2zYsAEXLvxvB+pdu3bJ38wxMzPDggULOCpFemHEiBF6O7pUU/b29ti8ebOuwyAiqhGNjpOZOnUqxo8fj7CwMMTHx0MkEkEikcDe3h7du3eHl5cXbGxsajlUIiIiovpH44OOmzRpggkTJmDChAm1GQ8RERFRg8JdL4mIiIhqgMkUERERUQ1oNM137tw5hIWFISEhAdnZ2SgtLVVaViAQICIiQuMAiaj2qNrGAeBWDkREmlArmRKLxfjwww8RGRkJAPK391SRbcpHRPUft3IgIlKfWsnU9u3bERERAYFAgCFDhsDDwwOOjo4wMTGpq/iIqBZxGwciotqnVjJ19OhRCAQCfPTRR5g+fXpdxURERETUYKi1AD05ORkGBgaYPHlyXcVDRERE1KCoNTJlZWWFkpISmJqa1lU8RERERA2KWslU7969cfLkSTx58gROTk51FRPVMzzIl4iISDm1pvk++OADmJiYYPXq1XUVDzUwdnZ2fAOMiIj0mlojU87Ozti0aRM+/PBDvPfee5g+fTq6du0Kc3PzuoqP6gG+AUZERKSc0mSqc+fOKiv+9ddf+Ouvv6psQCAQ4ObNm+pHRkREAIDFixfj0KFDmD17NubMmaPrcIjoBUqn+aRSaa38kkgk2vw8REQVLF68GC4uLlX+2rVrl65DpVowduxYuLi4IDMzU35ty5YtcHFxwW+//abDyKgxUzoyJdvlnIioMRAKhbC2tlZ6n8sVGr68vDzcvXsX7dq1Q9OmTeXX4+LiAADu7u66Co0aOaXJVMuWLbUZBxFRnerRowf27Nmj6zCoDsXHx6OsrAy9evWSX5NIJIiPj4eNjQ3at2+vw+ioMVP5Nt+UKVMwd+5cbcVCRESkMdkI1PPJ1O3bt5GXl4eePXvyrFiqMyrf5ouJiYG9vb22YiEiIjUlJSUhPDwcZ8+eRVJSEkQiEUxMTNC+fXt4eXlh0qRJCjdaPnjwIJYsWYI+ffpgz549OHLkCH777TckJCQgKysLmzZtgoeHBwCgrKwMe/bsQXBwMB4+fAhzc3O88sormDFjBtzd3eHi4gKgfHlIq1atKrWVmZmJnTt34syZM3j8+DGkUilatWqFYcOGISAgADY2NrXSF4qm8y5evFjpGlFtU2trBCIiql/mzZuHGzduACh/e7pJkybIzc3FlStXcOXKFYSGhuKXX36BpaWl0md89dVX2LNnDwwMDNCkSRMYGPxv0kIsFmPmzJk4d+4cAMDIyAhlZWU4c+YMoqKisHbtWpXxXbx4EbNmzUJWVhaA8rVrhoaGSEhIQEJCAg4fPowdO3aoPQWXmpqKSZMmVboGAFOnTpVfk7X7888/Y9++ffLrp06dUqs9IlWYTBERNWBdunTBmDFj8Nprr6F58+YwNjZGSUkJoqKi8O233+L69etYs2YNPv/8c4X1r1+/jtjYWMyZMwdTpkyBlZUV8vLyUFxcDKD8Tbhz587B0NAQixYtwptvvglTU1MkJyfjyy+/xLJly5TGlpycjA8++AA5OTmYOHEi3n33XbRt2xYCgQD37t3Dt99+iz///BNz5szBkSNHYGhoWO3PXVpaiuTkZKXtvuj5t/uIahuTKWpUePQNKRMfH4+BAwcqvDd48GCsXLlSrefJprY0cefOHY3rvuirr76qdM3Y2BhDhw5Fx44d4eXlhUOHDuE///kPzMzMKpUtKCjA+++/j9mzZ8uvWVpawtLSEvn5+di5cycAYO7cuRVGfFq2bIkffvgBEyZMQE5OjsLY1q1bh5ycHEyePLlS0tWxY0ds3rwZEydOxO3btxEeHg4vL69qf+5WrVpV6Mdt27ZhzZo1+PTTT+Hv7w8AePDgAUaMGIF+/frhl19+qfazidTFZIr0Bo+90W9isRjp6ekK72VnZ6v9vIawnrR169bo0KEDbt++jVu3bqFnz56VyhgaGuKdd95RWD8qKgoFBQUwMTHB5MmTK90XCoUICAjAokWLKt0rKirCyZMnAQABAQEKn29sbAxPT0/cvn0b58+fVyuZepFsbdTzi88VLUgnqgtVJlN5eXlYsmSJxg0IBAJ88803GtcnUgePviFlZAuta0t1ToDQlr/++gvBwcG4evUqRCIRioqKKpV5+vSpwrovvfRShT2Znic7vaJz586wsLBQWEbZwu7r169DLBYDAN544w2lsctiffLkidIyVZFtf2BlZQVnZ2f5de4vRdpSZTJVXFyMkJAQjR4ulUprnEwdPXoU+/fvx507dyCRSNCuXTv4+vrCz8+vwiJJVcRiMS5evIizZ8/i0qVLSElJQVZWFmxtbdGjRw+8/fbb6Nu3r8YxEhHpimzxuIxQKISNjQ2MjMq/vGdnZ0MsFqOwsFBhfWWJFAA8e/YMAODg4KC0jKOjo8LrzydvykYEn6coAayuO3fuICcnB//6178qfF+Ii4uDkZERXnnlFY2fTVQdVSZTRkZG6N69uxZCqWzFihXYt28fTExM0L9/fxgZGSE6OhpffPEFoqOjsWHDhmotWIyNjZUPMzs4OMDV1RVmZma4f/8+wsLCEBYWhpkzZ2LevHl1/ZGIiGrN2bNnsWfPHhgaGmLmzJkYPXo0WrduXWE/pUmTJiEuLg5SqVThM9RZ9K0OWXvW1taIiYmp1Wf//PPP2LFjh/zPJSUlAIALFy5UWBeXnp4OAwMDDB8+XH5txIgRKhfNE2miymTK2tpaJ7sGh4WFYd++fXBwcEBQUBDatm0LoPwfx5QpUxAeHo6goKAKCyKVEQgE8PT0xJQpUyrNnR8/fhwLFy7E5s2b0bdvX/Tr168uPg4RNTLKFrNXR21NEcrWJE2YMKHCAvLnZWRkaPx8W1tbAIBIJFJaRtn0oWyNYnZ2NkQikcrRLXUVFBQoHO0qLCysNAInkUgqlM3Ly6u1OIhk6u0C9K1btwIAFi5cKE+kgPJFn8uXL8fkyZOxfft2TJ48ucrpvv79+6N///4K740cORJ//fUXDhw4gCNHjjCZIqJqqc7UVV1LS0sDUL49giLJycl4+PChxs+XPffWrVvIz89XuG5KtvD7RW5ubjAyMkJpaSn++OMPvP322xrH8aI5c+Zgzpw58j8PGTIEWVlZiI2NhbGxMQDgyy+/RFBQUIXNR4nqSr1MplJTU3Hjxg0IhUKFb3f06dMHjo6OSEtLw+XLlxW+oaIO2RcM2RcmIqKq1Ob2BpqSbcR59+5dhffXrl2rdHqvOgYOHAhzc3MUFBRg7969mDFjRoX7paWlSrccsLS0xPDhw3H8+HFs2bIFnp6eSt+ALC0tRXFxsdJF7qokJSUhNTUVffr0kSdSQPnyDoFAwMXnpBXVW8GtZbI3SDp27KjwGAQA6Nq1K4Dyn5hq6p9//gGgepElEVF9I5tq/O9//4sDBw7I1w6lpKRg0aJFCA0NhbW1tcbPt7S0lC+l2LBhA/bs2SNfKJ6SkoK5c+fi8ePHSusvWLAANjY2EIlEeOuttxAeHi6PEQAePnyIXbt2YeTIkbh+/bpGMcbGxgIAevfuLb+Wk5ODhIQEdOzYUT5VSVSX6uXIlOwfZ4sWLZSWcXJyqlBWUyKRCIcOHQKACosUiYjqu3HjxuHgwYO4fPkyli5dis8++wwWFhbyTTTnzp2Lv//+u0YLwGfOnIlr164hKioKX331Fb799luYm5sjOzsbQqEQ69atk6/Xen5kCCjfWHP79u2YNWsWkpKSMHv2bBgZGcHS0hIFBQUVEitNDyGWJVMv7i8lkUi4vxRpTb0cmSooKAAAhbv1ysiGg/Pz8zVup7S0FB9//DFyc3PRv39/DB06VONnERFpm7GxMXbu3IkZM2agdevWMDAwgKGhIQYOHIgff/xR6W7/6raxdetWLF68GM7OzhAIBDAwMMBrr72GoKCgCtvKWFlZVarfrVs3nDhxAgsXLkSPHj1gYWGB3NxcmJqaws3NDdOnT8eBAwfQp08fjeKLjY2t9Na5bB3X86NVRHVJIK3JhHod2bJlC9avX4/Ro0fju+++U1hm3bp1+PHHH/Hmm2/iiy++0KidpUuX4sCBA3BycsLvv/+u9jTf48ePMWzYMKUnpRMRNXbR0dF455130LJlSx4eDH5f0Ff1cmRKNuokG6FSRDYipcmCRaB8o7sDBw7AwcEBu3bt4nopIiIN/PTTTwCAAQMG6DgSIt2pl8lUy5YtAZQvcFQmNTW1Qll1BAYGYs+ePWjatCl27dpVYesFIiL6n7KyMsydOxfnzp1Dbm6u/HpCQgLmzp2LqKgoCIVChWf3EemLerkAXbZVQUJCAoqKihS+0Xft2jUA5WdGqWPVqlXYuXMnbGxssHPnTnTo0KHmARMRNVJSqVR+UgRQ/oZfWVmZfHNMAwMDfPrpp3BxcdFlmEQ6VS9HppycnODq6gqxWCzf4fd5MTExSE1NhYODA3r06FHt565evRo///wzrK2tsXPnTnTq1Kk2wyYianQMDQ3x+eefY9iwYWjdujUkEgnKysrQsmVLjBkzBgcOHMCbb76p6zCJdKpejkwBwIwZMzBv3jysXr0aPXr0QJs2bQCUH42wYsUKAMD06dMr7H6+Zs0ahIeH4/XXX8eCBQsqPG/9+vXYvn07rKyssGPHDqU7BhMR0f8IBAJMmjQJkyZN0nUoRPVWvU2mvLy84Ofnh/3792PUqFEYMGCA/KDjvLw8eHh4wN/fv0IdkUiExMTESudIRUZGYsuWLQCAl156CUFBQQrbbN++faUdfomIiIhUqbfJFAAsX74c7u7u2Lt3L2JiYiCRSNC+fXv4+vrCz8+vyjP5ZLKzs+W/v379utKddvv06cNkioiIiNRSL/eZaii4nwgRET2P3xf0U71cgE5ERETUUDCZIiIiIqoBJlNERERENcBkioiIiKgGmEwRERER1QCTKSIiIqIaYDJFRERaNXnyZLi4uODgwYMVrj9+/BguLi48548aHCZTRETUaH3zzTdwcXHBiRMn5Nfi4uLg4uJS6dgxIk0xmSIiokYrLi4OANCrV69K19zd3XUSEzU+TKaIiKhRKigowO3bt9GmTRs4ODjIrzOZotrGZIqIiBqlK1euoLS0tELSJJFIcOnSJVhZWcHZ2VmH0VFjUq8POiYiqqmhQ4ciOTkZu3fvRuvWrbFp0yZERUUhMzMTDg4O8PT0xMyZM9GkSRNdh1ql27dvY8eOHbh48SKePn0KoVCIpk2bom3btnj11Vfx5ptvwszMTF7++c/erl07bNy4EWfPnsWzZ8/QsmVLvPnmm5gyZYr80PgTJ05gz549uHv3LiQSCXr16oWFCxcqTDpKSkpw/vx5RERE4OrVq0hLS0NBQQHs7e3Rs2dPBAQEwM3NTWt9o4iiKb67d+8iJycHQ4YMgUAg0FVo1MgwmSIivfDo0SPMnz8fmZmZMDc3h0AgQHJyMnbs2IHIyEgEBQWhWbNmug5TqbNnz2LWrFkQi8UAAGNjYxgYGODx48d4/PgxoqKi8Oqrr+Lll1+uVPfx48dYsGABRCIRLC0tUVpaigcPHmDlypVISkrCp59+itWrV2P79u0wNDSEqakp8vPzcfbsWcTHx+P3339H27ZtKzzzr7/+wr///W/5n83MzCAQCJCSkoKUlBScPHkSX3/9NcaOHVuX3VKBn58f0tLS5H9+9uwZAGDdunXYtGkTAKCwsBAAcPHiRQwdOlRedu3atejevbvWYqXGhckUEemFb7/9Fk2bNsUPP/yAXr16QSKR4NSpU1i2bBkePnyIxYsXY8eOHboOU6kvv/wSYrEYr732GhYtWoR27doBAPLy8nD79m0cPnwYJiYmCuuuXLkSHTp0wE8//YROnTqhsLAQO3fuxIYNG7B37144ODhg165d+OSTTzBx4kSYm5vj7t27mDt3LhITE7Fu3Tps2LChwjPNzc0xfvx4jB49Gp06dYKtrS0AICUlBbt27cIvv/yCzz77DH369EGLFi3qtnP+X1paGpKTkytdF4lEla7l5+cjPz9f/ufi4uI6jY0aNyZTRKQXSkpKsH37drRp0wYAYGBgAA8PD1haWmLq1Kn466+/cPHixQpTQlVZvHgxDh06pFE8K1euxPjx46tVNiMjA0lJSQCAr776Cvb29vJ7lpaW6NWrl8q4DQwMsG3bNlhZWQEoH0WaOXMmLly4gL///hvr1q3DnDlzMHXqVHkdZ2dnfPXVV3j77bdx6tQplJSUwNjYWH6/b9++6Nu3b6W2WrRogU8++QR5eXkIDg7GwYMHMXv27Gp9zpo6deqU/PdXrlzBG2+8AU9PT3z//ffy64MGDUJOTg4uXrxY4fMQ1QQXoBORXhgxYoQ8kXpev3790KNHDwBAWFiYWs+0tLSEvb29Rr9MTU2r3Y6FhYV8XZOiUZaqvPXWW/JE6nkDBgwAAAiFQgQEBFS637NnT5iYmKCkpASPHj1Sq03ZFNqlS5fUjrc2xMbGAqj4xt6jR48gEonwyiuvMJGiWsWRKSLSC3369FF5Lz4+Hjdv3lTrmcuWLcOyZctqGlqVTE1N0bt3b1y4cAHTpk2Dv78/XnvtNTg7O8PQ0LDK+sreWmvatCkAoGXLlrCwsKh038DAALa2tkhNTUV2dnal+1lZWdi7dy/+/PNPJCYmIjc3F2VlZRXKPH36tDofsdZdvHgRANC7d+9K13r27KmTmKjxYjJFRHrB0dGxynuZmZnaCkdtX3/9Nd5//33cv38fGzZswIYNG2Bubo7evXvD29sb3t7eMDJS/CX9+T2WnidLxFQtvJeVKS0trXD93r17mDp1KtLT0+XXLCwsYGpqCoFAALFYjOzsbBQUFKj1OWuDVCpFfHw8LC0tKxxNo+jtPqLawGSKiPSeVCrVdQhVat26NY4cOYIzZ87g3LlzuHjxIu7fv4+zZ8/i7Nmz+OWXX7Bnzx6FI0x1YcmSJUhPT4erqys+/PBD9OzZs0Lb0dHReOedd7QSy6VLlzBnzhz5n6VSKbKysmBkZITBgwfLr8tG1z7++GN5kti8eXMEBwdrJU5qvJhMEZFeUDXdJFuHJJv2qq6vvvqqwplv6li6dClGjhypVh0jIyN4eHjAw8MDQHncR44cwYYNG3Djxg1s3LgRixYt0igedaSkpODq1aswNDTEli1bFI76PT9iVdfEYrHC9kpLSxVel22ZAEDpG5BE6mAyRUR6ITY2VunbczExMQCALl26qPXMvLw8jZOGoqIijeo9z8HBAdOmTUNWVha2bdsmX3Rd11JTUwGUJ5/Kpk/Pnz+vlViA8jcL79y5I//zwoULcfToUezdu1c+pRcWFoa5c+diypQpWLp0qdZiI/3AZIqI9MLx48cxc+ZMtG7dusL12NhY+RtnXl5eaj0zMDAQgYGBtRajMmKxGEZGRkp37JaNrpSUlNR5LADku8Wnp6cjIyMDdnZ2Fe7fuXMHx44d00osisTFxcHExATdunWTX5Mlms8vSCeqLdwagYj0glAoxHvvvSdPnGSbds6dOxcAMHDgwHp78O29e/fg4+ODXbt2ITExUb7GSywWIywsDLt27QJQvoeSNrz88sto3rw5pFIp5s+fj4cPH8rj+eOPP/Duu+/C3NxcK7G86PHjx0hJSUG3bt0qbH8QFxcHgUDAxedUJzgyRUR6YdGiRVi7di38/Pxgbm4OiUQin2pr06aNVkaYauLevXtYuXIlVq5cCWNjY5ibmyMnJwcSiQQA4ObmhpkzZ2olFgMDAyxbtgxz585FTEwMhg8fDgsLC5SUlEAsFqNFixb4z3/+g//85z9aied5ivaXku0S//LLL6u9Lo6oOjgyRUR64aWXXkJwcDB8fX3RpEkTlJWVoWXLlnj33XcRHBxcr8/le/nll/H999/jrbfeQpcuXWBlZYW8vDxYWlrC3d0dn376Kfbv3w9LS0utxfT666/jl19+wcCBA2FhYYHS0lJ5fx46dAjNmzfXWizPUzSdFxcXJz+4maguCKQN4Z3geurx48cYNmwYIiMj0apVK12HQ0QKDB06FMnJydi9e7fC40+IahO/L+gnjkwRERER1QCTKSIiIqIaqPcL0I8ePYr9+/fjzp07kEgkaNeuHXx9feHn5yc/+FOXzyMiIiL9Vq+TqRUrVmDfvn0wMTFB//79YWRkhOjoaHzxxReIjo7Ghg0bqnXIZ109j4iIiKjeJlNhYWHYt28fHBwcEBQUhLZt2wIo3yRuypQpCA8PR1BQEKZOnaqT5xFRw3Dq1Cldh0BEjVy9ndfaunUrgPJjAWSJDwDY29tj+fLlAIDt27fL91jR9vOIiIiIgHqaTKWmpuLGjRsQCoUKj3fo06cPHB0dIRKJcPnyZa0/j4iIiEimXiZTN2/eBAB07NgRpqamCst07doVAHDr1i2tP4+IiIhIpl4mU48fPwYAtGjRQmkZJyenCmW1+TwiIiIimXq5AL2goAAAYGZmprSMhYUFACA/P1/rz5MpKysDUD6NSEREJPt+IPv+QPqhXiZTshNuBAJBvXyejEgkAgC8/fbbtfpcIiJq2EQiEdq0aaPrMEhL6mUyJRslko0oKSIbQZKV1ebzZNzc3LB37144ODhwfyoiIkJZWRlEIhHc3Nx0HQppUb1Mplq2bAkASElJUVpGNpQqK6vN58mYmpryFHIiIqqAI1L6p14uQO/SpQsAICEhAUVFRQrLXLt2DQDQuXNnrT+PiIiISKZeJlNOTk5wdXWFWCzGyZMnK92PiYlBamoqHBwc0KNHD60/j4iIiEimXiZTADBjxgwAwOrVq/Hw4UP59YyMDKxYsQIAMH369AqHE69ZswZeXl5Ys2ZNrTyPiIiIqCr1cs0UAHh5ecHPzw/79+/HqFGjMGDAAPnBxHl5efDw8IC/v3+FOiKRCImJifK37Gr6PCIiIqKq1NtkCgCWL18Od3d37N27FzExMZBIJGjfvj18fX3h5+en9ihSbT+PiIiISCCVbcJERERERGrjUAwRERFRDTCZIiIiIqoBJlOES5cuITIyUtdhEBERNUhcM0WYPHkyLl68iFu3buk6FJ16+PAhTExM0Lx58wrXb9++jYiICGRkZKBNmzbw9vaGg4ODjqKsX0QiEc6fP4+0tDRYWFiga9eu6Natm67D0pqlS5eiX79+8PT0hLGxsa7DqffS0tLke/q1aNFCZdnExESkp6ejd+/eWoqOSHNMpkjvk6mLFy9i8eLFSE5OBlC+C/6aNWvQrl077NmzB4GBgZBIJJBKpRAIBLCwsMC6devw6quv6jjyunf8+HG0atVKYYK0YcMG/PTTTygtLa1wvUePHli3bh0cHR21FabOdOrUCQKBAE2aNIGPjw98fX3h6uqq67DqneTkZCxevBgXL16UX3N1dcXixYuVHsm1ZMkShISE6O3XJWpYmEw1Yj179qxWueLiYkgkEpiZmcmvCQQCxMXF1VVo9UZycjJ8fHxQWFgIExMTGBgYoLCwEO3atcP69esxfvx4ODo6YujQobC1tcXff/+N2NhYWFpa4sSJE41+hKpTp04YN24cVq5cWeH6unXrsG3bNkilUnTp0gXt2rVDVlYW4uLiUFRUhA4dOuDgwYONfrSmU6dO8t8LBAIAgIuLCyZOnAgfHx9YW1vrKrR6Iy8vD2PGjJH/sGJra4ucnByUlZXB0NAQ8+bNk2+q/DwmU9SQcM1UI1ZQUIDCwkIUFBSo/FVWVgapVFrhWn5+vq7D14odO3agsLAQAQEBiIuLQ1xcHGbNmoXExEQsWbIEHTp0QEhICJYtW4ZZs2Zhz549ePfdd5GXl4f9+/frOnydSEtLw88//wyhUIgtW7bg4MGDWLNmDX7++WeEh4fDzc0N9+/fx2+//abrULXCx8cH69atw4ABAyAQCHD79m189dVXGDx4MBYsWIDz58/rOkSd+uWXX5CcnIy+ffvi7NmziI6ORlRUFN577z0A5Yn5d999p+MoiWqmXm/aSTVjbW2N3NxcvP3225g6darSTUk/+ugjXL16FREREVqOUPfOnz8PBwcHLFy4EIaGhgCA2bNn49ChQ7h16xY2bdoEKyurCnVmzpyJ3377DVFRUZg7d64uwtapyMhIlJaWYsaMGXjttdcq3HNwcEBgYCDGjh2LsLAwvThVQCgUYsSIERgxYgRSU1MRHByMkJAQJCUlITQ0FMePH4eTkxN8fX0xfvx4ODk56TpkrYqMjESTJk2wfv162NraAigfnVq4cCEGDBiAefPmYceOHSguLsayZct0HC2RZjgy1YiFhoZiyJAhCAoKwscff4yioiK0bNmy0i/ZVMyL1/XBkydP4OrqKk+kgPLpGtn0jaK1QpaWlujcuXOFMx71yYMHDyAQCDB69GiF9zt06IBOnTohISFBy5HpXvPmzTFr1iyEh4fjl19+gY+PD0xMTJCSkoKNGzdi2LBheO+993Dy5EmIxWJdh6sV//zzD7p16yZPpJ43YMAABAUFwdbWFnv37sXnn3+ugwiJao7JVCNmb2+PLVu2IDAwEImJiRg3bhy2bNmCsrIyXYdWb0ilUlhYWFS6LlvrYm9vr7Bes2bN9GYq9EUlJSUAgNatWyst07p1a+Tl5WkrpHqpb9++WL16NaKiovD555/D1dUVEokEUVFR+PDDDzF48GBdh6gVEokENjY2Su+7uLhgz549sLOzw2+//cbRKWqQmEzpgbFjx+Lo0aPo378/NmzYgAkTJnBR5/+ztbVVeDC2VCqFqnczxGIxLC0t6zK0eqtVq1YAgJycHKVlcnNzFSap+sjS0hJ+fn44cOAAjh49iilTpsDa2hpZWVm6Dk0rnJyckJSUpLLMyy+/jD179sDe3h7BwcFYsmQJJBKJliIkqjkmU3qiWbNm2Lp1K77++ms8fvwYEydOxLp16+SjDPqqZcuW+Oeffypd/+STT3D27Fml9R48eNDo3+STCQsLw7Bhw+S/du3aBQC4d++e0jpPnjxB06ZNtRRhw9GxY0d88skniIqKwoYNG3Qdjla8/PLLuHnzJrKzs1WWk21F4uDggJCQEJw8eVJLERLVHJMpPePr64tjx46hX79+2Lp1K8aOHYsnT57oOiyd6dKlC0QiUaWEysrKSuk+SQ8ePMC9e/fg5uamhQh1r6CgAMnJyfJfmZmZkEqlSr/ZpaWl4cGDB3B2dtZypA2HkZERhg8fruswtGLgwIEoLS3FkSNHqizbtm1bBAUFwdHREcXFxVqIjqh28G0+PeTo6IiffvoJv//+O7799lvk5eXJ98jRN2+99RacnZ3V2g8pIiICLVq0wKBBg+owsvpB1TFDyvrs3LlzcHFx0Ys1Qb1790b79u11HUa99q9//QtHjhxBfHw8Jk+eXGX5l156CXv27MHcuXOrHM0iqi+4aaeeS01NRXR0NABg3LhxOo6GiIio4WEyRURERFQDXDNFREREVANMpoiIqN45cOAANm7cqOswiKqF03xERFTvvPnmm7h69Sr3xKMGgSNTRERERDXAZIqIiIioBrjPFBER1ZlRo0ZpVK+qI2iI6hMmU0REVGcSEhIgEAhUnnWpjL5uJkwND5MpPZWbm4tr164hMzMTLVq0QM+ePXUdUr3DPlKN/aMa+6ecpaUl8vPzsXXrVpiZmVW73ooVK/DgwYM6jIyo9jCZ0jO5ubn45ptvcPToUZSVlQEAxo4dK/9Cv3fvXmzZsgUbN25E9+7ddRip7rCPVGP/qMb+qcjV1RUxMTFo0qSJWgmlpaVlHUZFVLu4AF2PFBQUYPLkyTh06BCsra0xePDgSkPvgwcPRnp6OiIiInQUpW6xj1Rj/6jG/qmsa9euAIDr16/rOBKiusORKT2yY8cO3L59G6NHj8aKFStgZmaGTp06VSjTunVrtG3bFn///beOotQt9pFq7B/V2D+Vubm5QSqV4saNG2rV69y5s1oHkBPpEpMpPXLy5Ek0a9YMX331lcovUi1atEBCQoIWI6s/2EeqsX9UY/9UNnz4cMTGxsLISL1vN8uXL6+bgIjqAKf59EhSUhK6du1a5U97tra2yMrK0k5Q9Qz7SDX2j2rsn8oMDAzQpEkTtRafEzU0TKb0iJGREYqLi6ssl5qaCnNzcy1EVP+wj1Rj/6jG/iHST0ym9Ei7du1w69YtlV/ss7Ozcfv2bTg7O2sxsvqDfaQa+0c19o9mLl26hJCQEF2HQaQxJlN6xNPTExkZGVi9erXSMmvXrkVBQQFGjBihxcjqD/aRauwf1dg/mvn999+xZMkSXYdBpDEuQNcj/v7+CAkJQVBQEK5fv47hw4cDAJKTk7Fv3z6cPHkSsbGxcHZ2xoQJE3QcrW6wj1Rj/6jG/iHSTwKpJnv8U4OVlpaGefPm4fLly/IjHmRHNkilUri6umLz5s1wdHTUcaS6wz5Sjf2jGvtHfUuWLEFISAhu3bql61CINMJkSk+dO3cO586dQ1JSEsrKyuDk5ITBgwfDw8OD52H9P/aRauwf1dg/1cdkiho6JlNERKRTTKaooeOaKSIi0qkJEyagT58+ug6DSGMcmSIiIiKqAY5M6ZHOnTtXq5yRkRFsbW3h5uaG8ePHw8PDo44jqz/YR6qxf1Rj/xDpJ45M6ZEXD1ytDoFAgLFjx2LlypV1EFH9wz5Sjf2jGvtHuYiICFy+fBm2trbw8fGRv82YlpaG9evX4/z588jOzkarVq3g7e2NadOm8aBjajCYTOmZVatW4ddff8WkSZPg4+ODli1bQiAQIDk5GceOHcO+ffvwxhtvYOrUqfj777/x3XffITMzE6tWrcKoUaN0Hb5WsI9UY/+oxv6pSCqVYt68eQgPD5dfMzc3x86dO9GyZUu88cYbSE5OrlBHIBCgT58+2LlzJwwMuLc0NQBS0hsHDhyQurq6SuPj45WWuXz5stTV1VX622+/SaVSqTQ+Pl7q4uIiDQgI0FKUusU+Uo39oxr7p7Lg4GCpi4uL1N3dXfrpp59KP/30U6m7u7t09OjR0pUrV0pdXFykn376qTQmJkZ669Yt6b59+6T9+vWTdurUSfrrr7/qOnyiauHIlB4ZP348mjRpgl9++UVlualTpyI3NxcHDx6U10tJScHff/+tjTB1in2kGvtHNfZPZZMnT8alS5cQHBwsnwa9efMmJkyYAGNjY0yYMAHLli2rUOf69et444034O7ujj179ugibCK1cPxUjyQmJsLe3r7Kcvb29khMTJT/uVWrVsjLy6vL0OoN9pFq7B/V2D+V3b17Fz179qywnqxLly7o2bMniouL4e/vX6mOm5sbunXrhrt372ozVCKNMZnSI8bGxrh9+3aV5W7fvl1h4adYLIaFhUVdhlZvsI9UY/+oxv6pLD8/H82aNat0XbYA3cnJSWE9Jycn5Ofn12lsRLWFyZQe6dmzJx48eICNGzcqLbN582bcv38f7u7u8muPHz9W+MWwMWIfqcb+UY39U5mlpSWePn1a6brs2pMnTxTWe/LkCczNzes0NqLawn2m9Mi8efNw/vx5bNq0CaGhoRg5ciRatGgBgUCAlJQUnDhxAg8ePICJiQnmzJkDAEhJSUFCQgImTZqk4+i1g32kGvtHNfZPZZ06dUJsbCzu3r0LZ2dnAOUjc3FxcTA3N8f+/fuxZMmSCnVu3bqFq1evonv37jqImEh9XICuZ6Kjo/Hxxx8jPT290mGrUqkU9vb2WLVqFQYMGAAAyMzMxJ07d9C+fXu9OeWefaQa+0c19k9FoaGhWLBgAaysrDBy5EgAwPHjx5GXl4fvvvsOCxcuxFtvvQUfHx80adIEV65cwfr165GZmYnFixdj6tSpOv4ERFVjMqWHioqKcPLkScTGxiItLQ0A0KxZM/Tu3RteXl4wMzPTcYS6xz5Sjf2jGvunosWLFyMkJKTCtXnz5uGDDz7AJ598goMHD1ZIPKVSKdzc3LB//34IhUItR0ukPiZTRERU5/766y9cuHABxsbGGDRokHwKr7S0FNu3b0dISAhSUlLQrFkzeHp6YubMmbC0tNRt0ETVxGSKKpBKpTh37hyCg4Px/fff6zqceol9pBr7RzX2D1HjwwXoBAB4+PAhgoODERISApFIpOtw6iX2kWrsH9XYP0SNF5MpPVZYWIgTJ04gODgYly5dAlD+U7OtrS28vb11HF39wD5Sjf2jGvuHSD8wmdJDsqMdTp48iYKCAkilUggEAnh6emLMmDF49dVXYWSk33812EeqsX9UY/8Q6ReumdITIpEIISEhOHjwIP755x/I/rd37twZ6enpSE9Px61bt3QcpW6xj1Rj/6jG/iHSX/zRqBErKyvD6dOnceDAAURFRaGsrAxSqRQ2Njbw8fGBr68vOnfujEmTJiE9PV3X4eoE+0g19o9q7B8iAphMNWqDBw9GZmYmpFIpDA0N8eqrr2L8+PEYNmwY9275f+wj1dg/qrF/iAhgMtWoZWRkQCAQoHnz5li7di169uyp65DqHfaRauwf1dg/RATwoONGrXnz5pBKpUhNTYW/vz8CAgJw5MgRFBcX6zq0eoN9pBr7RzX2DxEBXIDeqEmlUkRFReHAgQM4ffo0SkpKIBAIYGFhgZEjR2L8+PHo3r07Jk2ahPj4eL1cHMs+Uo39oxr7p2Zyc3Nx7do1ZGZmokWLFhzZowaLyZSeyM7OxuHDh3Hw4EHcvn0bACAQCNCmTRvk5uYiMzNT77/Qs49UY/+oxv6pvtzcXHzzzTc4evQoysrKAABjx47FypUrAQB79+7Fli1bsHHjRvmxM0T1GZMpPXTr1i38/vvvCA0NRXZ2NoDyL/oDBgzA6NGj8frrr8Pc3FzHUeoW+0g19o9q7B/lCgoKMGnSJNy+fRt2dnZwc3PD2bNnMW7cOHkylZSUhNdffx3vvfceFi5cqOOIiarGZEqPlZSUIDw8HAcPHkR0dDQkEgkEAgFMTU3h4eGB7777Ttch6hz7SDX2j2rsn8o2btyIjRs3YvTo0VixYgXMzMzQqVOnCskUAHh5ecHS0hIHDhzQYbRE1cNkigAAqampOHDgAA4fPoykpCQIBAJOSbyAfaQa+0c19k85Hx8f5OTkICIiAsbGxgCgMJl69913kZCQgD///FNXoRJVG9/mIwDlbyXNnj0b4eHh2LlzJ0aNGqXrkOod9pFq7B/V2D/lkpKS0LVrV3kipYytrS2ysrK0ExRRDXGfKaqkf//+6N+/v67DqNfYR6qxf1TT5/4xMjKq1tYRqamperuujBoejkwREZHWtGvXDrdu3VKZUGVnZ+P27dtwdnbWYmREmmMyRUREWuPp6YmMjAysXr1aaZm1a9eioKAAI0aM0GJkRJrjNB8REWmNv78/QkJCEBQUhOvXr2P48OEAgOTkZOzbtw8nT55EbGwsnJ2dMWHCBB1HS1Q9fJuPiIi0Ki0tDfPmzcPly5chEAgglUohEAgAlO8q7+rqis2bN8PR0VHHkRJVD5MpIiLSiXPnzuHcuXNISkpCWVkZnJycMHjwYHh4eMiTK6KGgMkUERERUQ1wAToRERFRDTCZIiIiIqoBvs1HRERa07lz52qVMzIygq2tLdzc3DB+/Hh4eHjUcWREmuOaKSIi0ppOnTqpXUcgEGDs2LEVzu4jqk84zUdERFpz+/ZtvPvuuzA3N8d7772HkJAQxMbG4uLFizh8+DCmT58OCwsLBAQE4MyZMwgMDETTpk0REhKCo0eP6jp8IoU4zUdERFoTHByM3bt3IygoCN27d69wz8XFBS4uLvDw8MDbb7+N9u3bY+LEiWjbti3eeustHDp0SG8PiKb6jSNTRESkNXv37oW7u3ulROp5r7zyCtzd3bF//34AQPfu3dGlSxfcvHlTS1ESqYfJFBERaU1iYiLs7e2rLGdvb4/ExET5n1u1aoW8vLy6DI1IY0ymiIhIa4yNjXH79u0qy92+fRvGxsbyP4vFYlhYWNRlaEQaYzJFRERa07NnTzx48AAbN25UWmbz5s24f/8+3N3d5dceP36MZs2aaSNEIrVxawQiItKa27dv480330RJSQnatm2LkSNHokWLFhAIBEhJScGJEyfw4MEDGBsb49dff0Xnzp2RkpKCoUOHYtKkSfjss890/RGIKmEyRUREWhUdHY2PP/4Y6enplQ40lkqlsLe3x6pVqzBgwAAAQGZmJu7cuYP27dvD0dFRFyETqcRkioiItK6oqAgnT55EbGws0tLSAADNmjVD79694eXlBTMzMx1HSFR9TKaIiIiIaoAL0ImIqF6RSqU4e/Ys5s6dq+tQiKqFO6ATEVG98PDhQwQHByMkJAQikUjX4RBVG5MpIiLSmcLCQpw4cQLBwcG4dOkSgPKRKVtbW3h7e+s4OqLqYTJFRERad+nSJQQHB+PkyZMoKCiAVCqFQCCAp6cnxowZg1dffRVGRvwWRQ0DF6ATEZFWiEQihISE4ODBg/jnn38g+/bTuXNnpKenIz09Hbdu3dJxlETqY9pPRER1pqysDKdPn8aBAwcQFRWFsrIySKVS2NjYwMfHB76+vujcuTMmTZqE9PR0XYdLpBEmU0REVGcGDx6MzMxMSKVSGBoa4tVXX8X48eMxbNgwCIVCXYdHVCuYTBERUZ3JyMiAQCBA8+bNsXbtWvTs2VPXIRHVOu4zRUREdaZ58+aQSqVITU2Fv78/AgICcOTIERQXF+s6NKJawwXoRERUZ6RSKaKionDgwAGcPn0aJSUlEAgEsLCwwMiRIzF+/Hh0794dkyZNQnx8PBegU4PEZIqIiLQiOzsbhw8fxsGDB3H79m0AgEAgQJs2bZCbm4vMzEwmU9QgMZkiIiKtu3XrFn7//XeEhoYiOzsbQHliNWDAAIwePRqvv/46zM3NdRwlUfUwmSIiIp0pKSlBeHg4Dh48iOjoaEgkEggEApiamsLDwwPfffedrkMkqhKTKSIiqhdSU1Nx4MABHD58GElJSRAIBJz2owaByRQREdU70dHROHToEFatWqXrUIiqxGSKiIiIqAa4zxQRERFRDTCZIiIiIqoBJlNERERENcBkiogQEREBFxcXuLi44N1339V1OEREDQqTKSLCoUOH5L+Pjo5GamqqDqMhImpYmEwR6blnz57h7NmzMDMzg4+PDyQSCY4cOaLrsIiIGgwmU0R67tixYxCLxRg2bBjeeustABVHqoiISDUmU0R6TpY4jRo1Cr169UKLFi3w4MEDXL16VWW9uLg4zJgxA3369EH37t0xevRo7Nq1CxKJBIsXL4aLiwt++OEHhXUlEglCQkIQEBCAfv36wc3NDYMGDcL8+fNx5cqVWv+MRER1ickUkR5LSEjAjRs3YGNjg4EDB0IgEMDb2xuA6tGpkJAQ+Pv74+zZs8jOzoZQKMT9+/excuVKzJ07V2WbeXl5mDZtGhYtWoTz588jKysLJiYmEIlEOHHiBN566y0EBQXV6uckIqpLTKaI9JgsYRoxYgSEQiGA8hEqADh+/DhKSkoq1bl//z6WLVsGiUSCIUOGIDIyErGxsYiLi8OyZctw+vRpREZGKm1TlkS5uLhg27ZtuHz5MuLi4hAbG4uPPvoIhoaG+PrrrxEXF1cHn5iIqPYxmSLSU2VlZfKF5j4+PvLrLi4ucHZ2RlZWFk6fPl2p3rZt2yAWi+Hs7IyNGzeiVatWAABTU1NMnjwZ8+fPR05OjsI2z58/j4iICLRs2RK7d+/GkCFDYGpqCgCwsrLC+++/j3nz5kEikWDbtm21/ZGJiOoEkykiPRUVFQWRSISWLVvC3d29wj3Z6NSLU30SiQQREREAgClTpsDY2LjSc/39/WFubq6wTdnzxo8fDxsbG4VlZG1fuHABZWVl1f9AREQ6YqTrAIhIN0JCQgAA3t7eEAgEFe75+Phg7dq1+PPPP5GZmYmmTZsCAJKSkpCXlwcAlRIwGTMzM7i6uiI2NrbSvfj4eADArl27sH//fpXxFRYWIisrC3Z2dmp9LiIibePIFJEeys3Nla9ren6KT6ZFixbo1asXSktLcfToUfn1Z8+eyX/frFkzpc9Xdk8kEsnbT09PV/pLprCwUL0PRkSkAxyZItJDx48fR3FxMQBg9OjRKsuGhIRg6tSpAACpVFqjdiUSCQBg8+bNGDZsWI2eRURUX3BkikgPqbMp582bN3Hnzh0AkE/3AcDTp0+V1pGNQL3I3t4eQPkbgUREjQWTKSI98/DhQ/napcOHDyM2Nlbpr9deew3A/9ZXtW7dGpaWlgCgdOuCoqIiXL9+XeG97t27AwDCwsJq8RMREekWkykiPSMblerUqRM6deoEKysrpb+8vLwAAEePHkVZWRkMDAzk03O7d++GWCyu9Px9+/ahoKBAYdvjxo0DAFy/fl2eoCmTnZ2t6UckItIqJlNEekQqlcr3lnr99derLD906FAIhUKIRCJERUUBAN5//30IhULcvXsXc+bMQXJyMgCguLgYe/fuxdq1a2FlZaXweYMHD8bw4cMBAJ988gm+//77CtOF2dnZiIiIwAcffIDAwMAafVYiIm3hAnQiPXLhwgV58uPp6VlleSsrK/Tt2xdRUVE4dOgQhgwZgpdffhkrVqzA0qVLcfr0aZw+fRrW1tYoKCiAWCyGl5cXTE1NERISonAfqm+//Va+X9WmTZuwadMmNGnSBFKpVL7tAlC+FxURUUPAZIpIj8im1tq2bYuOHTtWq46npyeioqIQGRmJnJwcWFlZwdfXF23atMGPP/6Iy5cvo6SkBC+//DJ8fX3h7++P2bNnAwCaNGlS6Xnm5ubYtGkTzpw5g+DgYFy5cgWZmZkwMDBAmzZt0LVrVwwfPhxDhgyptc9NRFSXBNKavutMRPQcqVSK1157DU+ePMHu3bvRt29fXYdERFSnuGaKiGpVaGgonjx5AktLS3Tr1k3X4RAR1TlO8xGR2n788UdYWFjAw8MDjo6OMDAwQHZ2NkJCQrB27VoAwKRJk2BmZqbjSImI6h6n+YhIbQsXLpQfMyMUCmFubo6cnBz5DukDBgzAjz/+CBMTE12GSUSkFRyZIiK1TZo0CZaWloiLi4NIJEJubi6sra3h4uKC0aNHY+zYsTAy4pcXItIPHJkiIiIiqgEuQCciIiKqASZTRERERDXAZIqIiIioBphMEREREdUAkykiIiKiGmAyRURERFQD/wcFyGJCVO2TJgAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# set style\n",
"sns.set_style('white')\n",
"sns.set_context('notebook',font_scale=2)\n",
"\n",
"# set figure size\n",
"plt.subplots(figsize=(7,5))\n",
"\n",
"g = sns.boxplot(x='AgeGroup', \n",
" y = 'ToM Booklet-Matched',\n",
" hue = 'Gender',\n",
" data = pheno[pheno.AgeGroup!='Adult'],\n",
" palette = 'viridis')\n",
"\n",
"# Change X axis\n",
"new_xtics = ['Age 4','Age 3','Age 5', 'Age 7', 'Age 8-12']\n",
"g.set_xticklabels(new_xtics, rotation=90)\n",
"g.set_xlabel('Age')\n",
"\n",
"# Change Y axis\n",
"g.set_ylabel('Theory of Mind')\n",
"g.set_yticks([0,.2,.4,.6,.8,1,1.2])\n",
"g.set_ylim(0,1.2)\n",
"\n",
"# Title\n",
"g.set_title('Age vs Theory of Mind')\n",
"\n",
"# Add some text\n",
"g.text(2.5,0.2,'F = large #')\n",
"g.text(2.5,0.05,'p = small #')\n",
"\n",
"# Add significance bars and asterisks\n",
"plt.plot([0,0, 4, 4], \n",
" [1.1, 1.1, 1.1, 1.1], \n",
" linewidth=2, color='k')\n",
"plt.text(2,1.08,'*')\n",
"\n",
"# Move figure legend outside of plot\n",
"\n",
"plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That's all for now. There's so much more to visualization, but this should at least get you started."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Recommended reading:\n",
"\n",
"multidimensional plotting with seaborn: https://jovianlin.io/data-visualization-seaborn-part-3/\n",
"\n",
"Great resource for complicated plots, creative ideas, and data!: https://python-graph-gallery.com/\n",
"\n",
"A few don'ts of plotting: https://www.data-to-viz.com/caveats.html"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.8"
}
},
"nbformat": 4,
"nbformat_minor": 4
}