Dendritic spine analysis functions and examples.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dendritic spine analysis for neuronstudio outputs\n",
    "\n",
    "Some useful functions and templates to perform dendritic spine analysis of data generated by neuronstudio. Neuronstudio spine info output files can be saved as .txt files for each dendritic segment. Before analysis can be performed, .txt files need to be combined (for example by using the combine_files function defined in a seperate notebook). For the analysis performed below, this step has been performed and information for all spines are combined and saved in .csv files for each experimental group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics.pairwise import euclidean_distances #needed for nearest neighbor/min distances analyis\n",
    "from sklearn.cluster import DBSCAN #for clustering analysis\n",
    "from scipy import stats\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import spine data for each group and store as a pandas DataFrame\n",
    "\n",
    "M_HC_DRN = pd.read_csv('head diameter csvs/M_HC_DRN.csv')\n",
    "M_HC_neg = pd.read_csv('head diameter csvs/M_HC_neg.csv')\n",
    "F_HC_DRN = pd.read_csv('head diameter csvs/F_HC_DRN.csv')\n",
    "F_HC_neg = pd.read_csv('head diameter csvs/F_HC_neg.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>head_diameter</th>\n",
       "      <th>neck_diameter</th>\n",
       "      <th>...</th>\n",
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       "      <td>6.600001</td>\n",
       "      <td>20.049063</td>\n",
       "      <td>10_BM10 N3S3.oib.txt</td>\n",
       "      <td>BM10</td>\n",
       "      <td>N3S3</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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      "text/plain": [
       "   Unnamed: 0   ID  SECTION-NUMBER  section_length  BRANCH-ORDER          X  \\\n",
       "0          60   60               0       40.660000           NaN  14.181243   \n",
       "1         138  138               0       14.871739           NaN  20.555826   \n",
       "2          91   91               0       26.252407           NaN  28.317223   \n",
       "3          16   16               0        5.524887           NaN   26.88809   \n",
       "4          46   48               1       40.231255           NaN  19.476236   \n",
       "\n",
       "           Y         Z  head_diameter  neck_diameter   ...    XYPLANE-ANGLE  \\\n",
       "0   4.059513  6.211911         0.0033            NaN   ...        74.917552   \n",
       "1  25.654324  7.463107         0.0033            NaN   ...        80.977398   \n",
       "2   5.242963  6.189742         0.0033            NaN   ...        62.086356   \n",
       "3   0.435232      4.29         0.0033            NaN   ...       -50.293154   \n",
       "4   8.342301  4.928425         0.0033            NaN   ...        81.831267   \n",
       "\n",
       "  SWC-NODE-ID SWC-NODE-OFFSET   ATTACH-X   ATTACH-Y  ATTACH-Z  SOMA-DISTANCE  \\\n",
       "0          51        0.417538  14.235135   4.245377  6.930000      17.896643   \n",
       "1          16        0.125502  20.436758  25.616440  8.250000       7.863744   \n",
       "2          55        0.000000  28.517002   5.157363  6.600001      20.049063   \n",
       "3          10        0.000000  27.656555   1.216903  2.970000       4.216601   \n",
       "4          40        0.741605  19.551682   8.280011  5.610000       9.435739   \n",
       "\n",
       "                 segment  animal  neuron  \n",
       "0  10_BM10 N10S2.oib.txt    BM10   N10S2  \n",
       "1   10_BM10 N3S2.oib.txt    BM10    N3S2  \n",
       "2   10_BM10 N3S3.oib.txt    BM10    N3S3  \n",
       "3   10_BM10 N5S2.oib.txt    BM10    N5S2  \n",
       "4   10_BM10 N5S2.oib.txt    BM10    N5S2  \n",
       "\n",
       "[5 rows x 23 columns]"
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#example of what the data should look like\n",
    "M_HC_DRN.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Head diameter analysis\n",
    "\n",
    "Example will show thin spine head diameter analysis only, but can be performed on mushroom spines and thin+mushroom spines as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
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QIaxYsYIaNWo8kX0eP36ccePG0b9/f7p27Wopejt06JDjOs7OztSvX98yVOBumUWGvb09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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19355fd3e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot cumulative distribution for thin spine head diameters \n",
    "sns.set_style('whitegrid',{'axes.grid' : False}) #changes default styles\n",
    "F_color = (214/255,39/255,40/255) #RGB colors\n",
    "M_color = (23/255,190/255,207/255)\n",
    "\n",
    "M_HC_DRN_thin = M_HC_DRN[M_HC_DRN.TYPE == 'thin'] #use only thin spines\n",
    "F_HC_DRN_thin = F_HC_DRN[F_HC_DRN.TYPE == 'thin']\n",
    "M_HC_neg_thin = M_HC_neg[M_HC_neg.TYPE == 'thin']\n",
    "F_HC_neg_thin = F_HC_neg[F_HC_neg.TYPE == 'thin']\n",
    "\n",
    "x_MDRN = np.sort(M_HC_DRN_thin['head_diameter'])  #sets up values to be plotted \n",
    "y_MDRN = np.arange(1, len(x_MDRN)+1) / len(x_MDRN)\n",
    "x_FDRN = np.sort(F_HC_DRN_thin['head_diameter'])\n",
    "y_FDRN = np.arange(1, len(x_FDRN)+1) / len(x_FDRN)\n",
    "\n",
    "x_Mneg = np.sort(M_HC_neg_thin['head_diameter'])\n",
    "y_Mneg = np.arange(1, len(x_Mneg)+1) / len(x_Mneg)\n",
    "x_Fneg = np.sort(F_HC_DRN_thin['head_diameter'])\n",
    "y_Fneg = np.arange(1, len(x_Fneg)+1) / len(x_Fneg)\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(12,5)) #set up figure instance and define figure size\n",
    "\n",
    "fig.suptitle('HC males vs females thin head diameters',fontsize=16,y=0.99)\n",
    "\n",
    "\n",
    "\n",
    "ax2 = fig.add_subplot(1,2,1)\n",
    "ax2.plot(x_MDRN, y_MDRN, c= M_color, label='M')\n",
    "ax2.plot(x_FDRN, y_FDRN, c=F_color, label='F')\n",
    "ax2.set_xlabel('head diameter', fontsize=12)\n",
    "ax2.set_ylabel('Cumulative frequency',fontsize=12)\n",
    "ax2.set_xlim([0,0.5])\n",
    "ax2.legend(fontsize=15)\n",
    "ax2.set_title('PL-DRN, KS-test p<0.0001') # p value based on KS-test performed in cells below\n",
    "\n",
    "ax3 = fig.add_subplot(1,2,2, sharex=ax2)\n",
    "ax3.plot(x_Mneg, y_Mneg, c=M_color, label='M')\n",
    "ax3.plot(x_Fneg, y_Fneg, c=F_color, label='F')\n",
    "ax3.set_xlim([0,0.5])\n",
    "ax3.set_xlabel('head diameter',fontsize=12)\n",
    "ax3.set_ylabel('Cumulative frequency',fontsize=12)\n",
    "ax3.legend(fontsize=15)\n",
    "ax3.set_title('Unlabeled, KS-test p=0.726')\n",
    "\n",
    "\n",
    "\n",
    "#plt.savefig('HC males vs females thin spine hd.tiff',dpi=300)  <-uncomment to save figure\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ks_2sampResult(statistic=0.044337168046158171, pvalue=3.9230145048310296e-06)\n",
      "Ks_2sampResult(statistic=0.01007024668020251, pvalue=0.72567208800457028)\n"
     ]
    }
   ],
   "source": [
    "print(stats.ks_2samp(M_HC_DRN_thin['head_diameter'], F_HC_DRN_thin['head_diameter']))\n",
    "print(stats.ks_2samp(M_HC_neg_thin['head_diameter'], F_HC_neg_thin['head_diameter']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tinag\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "image/png": 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zIklzFAqVZlyaHE/ZvP8CkDjlzKUZIV3tFvrGWVlZWol7yFBMqalUfPgRAZer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\nDmmahRBCCCGEaIc0zUIIIYQQQrTj/wPoAopGHrb/cgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19355fc4ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# a different way to look at differences in distribution is a kernel density estimate plot. \n",
    "\n",
    "fig = plt.figure(figsize=(12,5))\n",
    "fig.suptitle('HC males vs females thin head diameters')\n",
    "ax1 = fig.add_subplot(1,2,1)\n",
    "sns.distplot(M_HC_DRN_thin['head_diameter'],hist=False,kde=True,color=M_color,label='M',ax=ax1,)\n",
    "sns.distplot(F_HC_DRN_thin['head_diameter'],hist=False,kde=True,color=F_color,label='F',ax=ax1)\n",
    "ax1.set_xlim([-0.06,0.8])\n",
    "ax1.set_title('PL-DRN')\n",
    "ax1.set_xlabel('head diameter')\n",
    "ax1.set_ylabel('density')\n",
    "plt.legend(fontsize=15)\n",
    "\n",
    "ax2 = fig.add_subplot(1,2,2)\n",
    "sns.distplot(M_HC_neg_thin['head_diameter'],hist=False,color=M_color,label='M',ax=ax2)\n",
    "sns.distplot(F_HC_neg_thin['head_diameter'],hist=False,color=F_color,label='F',ax=ax2)\n",
    "#ax2.set_xlim([0,1.0])\n",
    "ax2.set_xlim([-0.06,0.8])\n",
    "ax2.set_title('Unlabeled')\n",
    "ax2.set_xlabel('head diameter')\n",
    "ax2.set_ylabel('density')\n",
    "plt.legend(fontsize=15)\n",
    "\n",
    "\n",
    "#plt.savefig('KDE females vs males thin hd.tiff',dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To imporve visualization of the differences between groups, we can plot the differences between the graphs. But, the data sets don't share the same x values, so we first neet to interpolate the y values to a common array of x-values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tinag\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x19356445d30>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZxVt+6Z5VRckE1nZ/rGsKgBDi0iSQlpA1XeOePIQ//BAA5435C6TzK91Apk/VdcstYDAQ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\nCCGEEIaSglQIIYQQQhjq/wPhdhIo7UQXVQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19355f40630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(18,5))\n",
    "ax1 = fig.add_subplot(1,3,1)\n",
    "#fig.suptitle(,fontsize=14)\n",
    "sns.kdeplot(M_HC_DRN_thin.head_diameter,color=M_color,label='M',ax=ax1,)\n",
    "sns.kdeplot(F_HC_DRN_thin.head_diameter,color=F_color,label='F',ax=ax1)\n",
    "\n",
    "#get the x and y values from the plots\n",
    "line = ax1.lines[0]\n",
    "line2 = ax1.lines[1]\n",
    "y1 = (line.get_ydata())\n",
    "y2 = (line2.get_ydata())\n",
    "x1 = (line.get_xdata())\n",
    "x2 = (line2.get_xdata())\n",
    "\n",
    "#set up an array of x values that cover the x-axis evenly \n",
    "x = np.linspace(0,0.55, num=128)\n",
    "#get interpolated y values for each data set\n",
    "y1_interp = np.interp(x,x1,y1)\n",
    "y2_interp = np.interp(x,x2,y2)\n",
    "y_diff = y1_interp - y2_interp\n",
    "\n",
    "ax3 = fig.add_subplot(1,3,2)\n",
    "ax3.plot(x, y_diff)\n",
    "\n",
    "ax3.plot(x, [0]*len(x), color='gray', alpha=0.8)\n",
    "ax3.fill_between(x,0,y_diff, where= y_diff<0,facecolor='red', alpha=0.4)\n",
    "ax3.fill_between(x,0,y_diff, where= y_diff>0,facecolor='green', alpha=0.4)\n",
    "\n",
    "#plt.savefig()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Minimum distance to nearest neighbor(s)\n",
    "\n",
    "To look at one measure of clustering of spines, we can look at the distance of each spine to its closest neighboring spine (or closest 2 neighboring spines). Neuronstudio output files include X,Y,Z coordinates of center of mass for each spine. These coordinates will be used to get euclidean distances for each spine. Analysis has to be performed for each segment and min distance values for each spine will be compiled. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def min_distances(data):\n",
    "    \"\"\"Return distance to closest neighboring spine for each spine.\n",
    "    \n",
    "       data -- DataFrame with dendritic spine data from neuronstudio and added segment column that identifies \n",
    "               which segement the spine comes from.\"\"\"\n",
    "    \n",
    "    segments = data.segment.unique()\n",
    "    combined = pd.DataFrame()\n",
    "    for filename in segments:\n",
    "        df = data[data.segment == filename]\n",
    "        length = df.section_length.unique().sum()\n",
    "        spines = len(df)\n",
    "        expected_distance = length/spines\n",
    "        location = np.array(df.loc[:, ['X','Y','Z']])\n",
    "        distances = pd.DataFrame(euclidean_distances(location,location))\n",
    "        min_distances = distances[distances > 0].min(axis=1) / expected_distance\n",
    "        frame = [combined,min_distances]\n",
    "        combined = pd.concat(frame)\n",
    "        \n",
    "    return combined\n",
    "\n",
    "def two_min_distances(data):\n",
    "    \"\"\"Return average distance to the two closest neighboring spines for each spine.\n",
    "    \n",
    "       data -- DataFrame with dendritic spine data from neuronstudio and added segment column that identifies \n",
    "               which segement the spine comes from.\"\"\"\n",
    "    segments = data.segment.unique()\n",
    "    combined = pd.DataFrame()\n",
    "    for filename in segments:\n",
    "        df = data[data.segment == filename]\n",
    "        length = df.section_length.unique().sum()\n",
    "        spines = len(df)\n",
    "        expected_distance = length/spines\n",
    "        location = np.array(df.loc[:, ['X','Y','Z']])\n",
    "        distances = np.array(euclidean_distances(location,location))\n",
    "        sorted_distances = np.sort(distances)\n",
    "        min_distances = pd.DataFrame(sorted_distances[:,1:3]).mean(axis=1) / expected_distance\n",
    "        frame = [combined,min_distances]\n",
    "        combined = pd.concat(frame)\n",
    "        \n",
    "    return combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "F_DRN_min_thin = min_distances(F_HC_DRN_thin)\n",
    "F_neg_min_thin = min_distances(F_HC_neg_thin)\n",
    "M_DRN_min_thin = min_distances(M_HC_DRN_thin)\n",
    "M_neg_min_thin = min_distances(M_HC_neg_thin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tinag\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "image/png": 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DgwNatmwJKysrjVk6r8pPIoWHhyM8PLzIdsnJyXBwcMCSJUvg7u6OPXv2ICQkBCEhIVi6\ndCnc3d2xcOHCErej7t27N/bv348jR47gww8/xNGjR6FSqdCrVy91G1dXV+zcuRM//fQTzp49i507\nd2Lnzp0wNjbG+++/j2nTppV5Nkv+jK3Dhw8X2SYlJUXj55drybyqNO/Lb7/9hh9++EFdxNrc3Bxe\nXl5wdnbGlStXiizGnF9YOC4urtDZZ6/GO3jwYNjY2GDz5s0IDQ3FnTt38PPPP8PBwQEzZ85E9+7d\nS4y1PBT2muS/T/nPmv8+/Pbbb0Xe5+X34fjx41i2bBmioqIA5CXf3N3d4erqir/++qvAa1jYe5bf\nZ3E77+X3qY3PXknyE4evozSvORERUVXF5A8REVVZLVq0gLGxMUJDQ5GdnV1kgkChUGDAgAHIzc3F\n6dOnYWFhobWYFi9ejG3btuHdd9/FsGHD4OLiop6h1K1bt2KTP/mzGSZMmFBgiVJhJBIJRo8ejdGj\nR+Pp06f4888/ERwcjAsXLuDDDz/EqVOnIJFIiry+bdu2sLOzw9GjR9XJH0tLS3To0EGjnaurK1as\nWAGFQoGwsDCcO3cOe/bswU8//QQHBwcMHTq0NC9NgefctGkT2rZtW6ZrX8fRo0cxZ84cuLi4YM6c\nOXBzc1PP0JgzZw6uXLlSYqwtWrTAr7/+Wqr+OnfujM6dOyM1NRWXL1/G6dOncfDgQcyYMQMNGzYs\n8w5e2pL/bCdPnlQnUoty5coVTJ06FY6Ojli+fDk8PDxQu3ZtiEQirF27Vj37pzR96unp4cqVK8V+\nNvOV92ePiIiICseaP0REVGVJpVJ0794dWVlZ6q3YC7Nv3z6kpKTAzc1Nq4kfIG+bbxsbG6xcuRKt\nW7dWJ36ys7PVNWiKmgWQv6wtfznOq7777jusXbsWCoUC0dHRWL58Oc6cOQMAqFmzJgYMGID169ej\nTZs2iI2NLbFOiZ6eHnr06IFbt27h33//RXh4OLp27arxn/J9+/ZhwYIFEAQBUqkUrVu3xieffKJe\nrhUaGlqGV6fk50xOTsaiRYuwf//+Mt+3KIcOHQIALFu2DJ06ddJYmvPgwQMARb8nZmZmqFmzJu7f\nv4/s7OwC5/ft24dVq1bh8ePHUCgUWL16tXoLcHNzc3Tu3BlBQUH46KOPoFKpEBYWVm7P9aby34dX\na+oAQFRUFJYsWYLTp08DyJulpVKpMGfOHPTo0QN16tRRz2op6TV8tc/c3Fz10q6XhYeHY+nSpfjn\nn38AaOezR0RERIVj8oeIiKq06dOnw8zMDN9//z127dpV4D+g586dw6JFi6Cvr68uJqxNBgYGkMvl\nGnWAcnNzsWjRInXyIL/myqtq166Nli1b4ty5cwgODtY4t2/fPvzwww84f/48pFIpDA0N8fPPP2Pl\nypUa9UQUCgXi4uIglUphZ2dXYry9e/cGAMybNw+CIKh/zhceHo6tW7eqi/Dmy08svbxNfGl17twZ\npqamWLduHSIjIzXOffPNN9i8eXOplvKVVv5Snvz6PPn27duHkJAQAIBSqSzy+sDAQCQnJ2Pp0qUa\ndW3u37+P+fPnY+PGjbC0tIRUKsWhQ4ewcuVKREdHa9wjv+Dv67xe2tK7d2/o6elhxYoVGjPSlEol\nFixYgA0bNiA5ORlA0a/hxYsX1cm14l7DfIGBgQCAr776Sr2kDshbXjd37lz8/PPP6rpYZfnsSSSS\nUvVf1rZERERvCy77IiKiKs3W1hYbNmzA2LFj8cUXX2DTpk1o2bIlJBIJbt26hb///hsSiQRBQUHw\n8vLSejy9evXChg0b0L9/f3Tq1AlKpRIXLlxAZGQkrK2tkZiYiOTkZNjb2xd6/fz58zFs2DBMnToV\nvr6+aNSoESIjI/HHH3/A0tISc+bMAQDY2dlh5MiR2LhxI3r27Knelej8+fOIiIjAhAkT1MVmi+Pm\n5gZnZ2fcvn0btWrVUu9slu///u//cPToUXz88ccIDg5G3bp18eTJExw/fhx2dnYYPnx4mV8jc3Nz\nLFy4EB9//DECAwPRqVMn2Nvb4++//8bVq1fh4eGB0aNHl/m+RenduzcOHz6MSZMmoUePHjA1NcW1\na9cQEhICGxsbJCQkqJMchRk3bhwuXLiALVu2IDQ0FK1atUJqaiqCg4ORlZWFpUuXql/r//znP5g4\ncSICAwPRtWtXWFhY4Pr167h06RJatWqF9u3bl9tzval69erhk08+weLFi9GzZ08EBATAwsIC586d\nQ0REBPz9/dXJwO7du2Pjxo2YN28e/v77b9jZ2eHOnTu4cOECrKysSnwN87Vp0wYjRozAli1b0KNH\nD/j5+UEqleLkyZN49uwZBg8ejNatWwMo22fP3t4eUVFR+Pjjj+Hj41Ngt72XlaUtERHR24Izf4iI\nqMrz9PTEkSNHMHXqVBgYGODYsWPYvn07nj59ivfeew/79+8vMKNFW6ZPn47JkydDLBZj27ZtOHny\nJGrVqoX169dj/PjxAICzZ88WeX2DBg2wZ88eDBw4EHfu3MHmzZtx584d9OnTB7t379bYyeuTTz7B\n3LlzYWpqir1792Lnzp0wMTEpdFvz4uS/Nr169SpQQNfJyQnbt29H9+7dcf36dWzcuBF///03evfu\njZ07d8LBwaEsL49at27dsHXrVrRp0wbnz5/H1q1bkZ6ejgkTJmDTpk0a222/qY4dO+Lbb79FnTp1\ncPDgQezduxdyuRyzZ8/GunXrABT/nhgaGmLz5s2YPHky5HI5tm3bhrNnz6J58+bYvHkzevbsqW77\nzjvvYP369XB3d8eZM2ewefNmxMTEYOLEiVi7dm2F7vhVGqNGjcLatWvh6uqK48ePY8eOHdDX18fM\nmTPx3XffqQukN27cGGvXroWbmxtOnjyJnTt3Ij4+HlOmTMH+/fshFouLfQ1f9sUXX+Drr79GjRo1\ncODAAezduxe2trb46quv1MlNoGyfvU8++QSNGjVCcHBwiUsGy9KWiIjobSESuD0BEREREREREZHO\nqlpfTxERERERERERUbli8oeIiIiIiIiISIcx+UNEREREREREpMOY/CEiIiIiIiIi0mFM/hARERER\nERER6TAmf4iIiIiIiIiIdBiTP0REREREREREOozJHyIiIiIiIiIiHcbkDxERERERERGRDmPyh4iI\niIiIiIhIhzH5Q0RERERERESkw5j8ISIiIiIiIiLSYUz+EBERERERERHpMCZ/iIiIiIiIiIh0GJM/\nREREREREREQ6jMkfIiIiIiIiIiIdxuQPEREREREREZEOY/KH6C0zYsQIuLi4aPzy8vJC7969sXXr\nVgDA48eP4eLiguDg4DLfPyAgQOPebm5uaN++PSZPnowbN25otL18+XKBWFxcXNC0aVP06NED69at\nKxC7p6cnoqKiCvR769YtuLi44PLly2WOmYiIiKiqyR8nXbt2rdTXzJw5Ez179nzjvgMCAjB//vw3\nusebjCeJqPzpV3YARFTxmjdvjs8++0z9c2ZmJvbs2YMFCxYAADp27PhG93/33XcxevRoAIBCocCz\nZ8+wceNGDBo0CBs3bkTLli012gcFBaFBgwbqnxMTE7F792588803MDIywrBhw9Tn5HI5vvzyS2zZ\nsuWNYiQiIiIiInpbMPlD9BYyNzdH06ZNNY61adMG169fx9atW984+WNra1vg/p07d0b//v0xa9Ys\nBAcHQ1//f3/9NGrUCB4eHhrt/fz80KlTJ+zdu1cj+WNmZoaQkBDs2rULAwYMeKM4iYiIiIiI3gZc\n9kVEAACxWAxXV1c8ffpUK/c3NjbGmDFjEB0djUuXLpXYXk9PD4aGhgWOe3t7w9/fH19//TXi4uK0\nESoRERHRG3FxccH69es1jk2YMAEjRowA8L8lUadPn8aYMWPg5eWFDh06YPXq1cXe98CBA+jfvz+8\nvLzg5eWFwYMH4++//y7Qbt26dWjXrh2aN2+OGTNmICEhQeP8n3/+iQEDBsDT0xO+vr5YuXIlcnNz\ni+w3MzMTCxYsQLt27eDp6YkRI0bg5s2bGm2uXLmCIUOGwMvLC7169SpwnogqF5M/RKT28OFDODk5\nae3+bdu2BQCEhYVpHFepVFAqlVAqlVAoFIiJicGyZcvw4MED9O7du8B9Zs+eDaVSqV6mRkRERFQd\nzZo1C15eXvjpp5/g7++PFStW4OzZs4W2DQ4OxqeffoqOHTti7dq1CAoKQmpqKqZPnw6FQqFu9+DB\nA+zcuROzZ8/G7NmzcenSJXz44YdQqVQAgIsXL2Ls2LFwcnLC999/jzFjxmDjxo1YuHBhof0KgoCP\nPvoIhw8fxrRp07By5UpIpVKMGDECjx49ApCXzPrggw9gYGCA7777Dv3798fMmTPL+dUiojfBZV9E\nbyFBEKBUKtW/j4uLw/bt23Hz5k3MmjVLa/3a2NgAAOLj4zWODxw4sEBbJycnfP755+pvyF5Ws2ZN\nTJ8+HYsWLcKpU6fwzjvvaCdgIiIiIi3q1q0bpkyZAgBo3bo1jh07hnPnzsHPz69A20ePHmHYsGGY\nPHmy+phEIsGkSZMQFRUFmUymPr5mzRrUr18fAGBlZYVx48bh0qVLaNeuHVasWAEvLy98++23AABf\nX19YWFhg1qxZGDNmTIEvAi9cuIBLly5h48aNaNeuHQCgQ4cO6NGjB1avXo2goCBs2bIFUqkUq1ev\nhpGREfz8/CAIAhYvXly+LxgRvTYmf4jeQmfPnoWbm5vGMUNDQ3zwwQcYPnw4YmJiSrxHfvIon56e\nHkQi0WvFs2TJEjg7O0Mul2Pz5s24fPmyempxUYYPH46DBw9i/vz5aN269Wv1S0RERFSZXq6RKBaL\nYW9vj8zMzELbjhs3DgCQmpqKBw8eIDIyEqdPnwYAjZk/jRo1Uid+gLzkjkQiQWhoKJo1a4arV69i\n+vTpGmM5X19fqFQqXL58uUDy5/LlyzAyMkLLli01rvHx8VH3/++//6Jly5YwMjJSn+/SpQuTP0RV\nCJM/RG8hb29v9QwfkUgEY2Nj1K5dGxKJpNT3eDV5FBQUhH79+hV7TWxsLADAwcFB47izs7O64HPz\n5s3xwQcfYOLEifjtt9/g4uJS6L3EYjEWLlyIfv36YdmyZYXOHiIiIiKqyl6tbygWiyEIQqFt4+Li\n8Pnnn+PcuXOQSCRo1KgRatWqBQAa19ja2mpcJxKJYGlpibS0NKSmpkKlUmHZsmVYtmxZoX28Kjk5\nGVlZWXB3dy9wLn/smJqaCldXV41zdnZ2hT4HEVUOJn+I3kJmZmYFdtcqq927d2v8XJpaQZcvXwaQ\nl3wqSn5Sp0ePHvjvf/+LXbt2QSwuvDyZi4sLxowZg7Vr16Jhw4ZliJ6IiIhIu/Jr7OQrakZPac2Y\nMQOxsbHYsWMH3NzcoK+vj7Nnz+L48eMa7VJSUgrEkZSUBGtra5iYmAAAPvroo0KXzdvb2xc4ZmZm\nBhsbG6xZs6bI2CwtLQsUlU5KSir1sxGR9rHgMxG9Fg8PD41fVlZWxbaXy+XYtGkT6tWrh5YtWxbb\ntk6dOhg9ejSuX7+OPXv2FNt24sSJqFOnDpYvX17mZyAiIiLSBlNTUzx//lz9c2ZmJm7duvVG9wwP\nD0f37t3h5eUFff287/DPnz8PQHPmz+3btzXqK546dQpKpRKtWrWCqakpXF1dER0drTGOk0gkWL58\neaFL/729vZGYmAhjY2ONaw4ePIgDBw4AyKtXdPnyZaSmpqqvO3fu3Bs9LxGVL878IaIinT17tsAg\nQF9fH8OHDy/2uvj4eISHhwMAcnJy8PjxY2zZsgVPnjzB+vXri5zJ87KxY8di165dWLFiBbp166b+\npupVBgYGmD9/PkaOHFnKpyIiIiLSLl9fX+zZswdubm6wtrbGunXr3vieHh4e2Lt3L1xcXGBhYYET\nJ05g+/btAIDs7Gx1O7FYjPHjx2Py5MmIi4vDN998A19fXzRv3hwAMGXKFEycOBGmpqbo3LkzkpKS\nsGLFCojFYo2i0fn8/f3h4eGBcePGYdKkSahRowaOHTuGbdu2Yd68eQCAkSNHYseOHRg7dizGjx+P\nmJgYfP/992/8zERUfpj8IaIiFTbrRiqVlpj8OXbsGI4dOwYgrxC0tbU1WrZsiaCgoCJr+LzK1NQU\nU6ZMwZwMmDiiAAAgAElEQVQ5c7BmzRr85z//KbJtmzZt0L9/f/z++++lujcRERGRNs2aNQtyuRxz\n5syBqakphg4diiZNmuDGjRuvfc+goCDMmzcPs2bNgoGBAVxcXLB582aMGzcO4eHhaNWqFQCgZcuW\naNasGT799FPk5uaie/fuGtuuv/POO/jxxx/xww8/YM+ePTA1NUW7du3w8ccfaxRszqenp4f169dj\n6dKl+Oabb5Ceno66detq1Hu0sbHB1q1bsWjRIkybNg2Ojo6YN28eJk6c+NrPS0TlSyQUVVGMiIiI\niIiIiIiqPdb8ISIiIiIiIiLSYUz+EBERERERERHpMCZ/iIiIiIiIiIh0GJM/REREREREREQ6rMJ3\n+woNDa3oLomIiKiCeXt7V3YI9AqOwYiIiHRfUWOwStnqnQNCIiIi3cUkQ9XFMRgREZHuKm4MxmVf\nREREREREREQ6jMkfIiIiIiIiIiIdxuQPEREREREREZEOY/KHiIiIiIiIiEiHMflDRERERERERKTD\nmPwhIiIiIiIiItJhTP4QEREREREREekwJn+IiIiIiIiIiHQYkz9ERERERERERDqMyR8iIiIiIiIi\nIh3G5A8RERERERERkQ5j8oeIiIiIiIiISIcx+UNEREREREREpMOY/CEiIiIiIiIi0mFM/hBy09KQ\nfetWZYdBRERERERERFrA5M9bTBAEpBw8hIiu3RAZ2A8J69dXdkhEREREREREVM70KzsAqhyKx48R\nM3sOMv76CyIDA+jZ2OD5N0sh5KpgO25sZYdHREREREREROWEM3/eQoIg4PGEicj46y+Y+HZAg0MH\nUe+37dCvWQNxy5cjfvXqyg6RiIhe06pVq+Di4gIXFxf8+OOPxbZduHChuu3jx48rKEIiIiIi3VPV\nx2AlJn9UKhVmz56NQYMGYcSIEXj48KHG+atXr2Lo0KEYMmQIpkyZArlcrrVgqXxk37wJ+d27MOvc\nCbXXrIG0dm1Ia9dG3c1bIKlZE3Erv0Py73sqO0wiInpDJ06cKPKcIAg4fvx4BUZDZcHxFxERUfVV\nFcdgJSZ/Tp48CYVCgR07dmDGjBlYvHix+pwgCPjyyy8RFBSE7du3o0OHDnjy5IlWA6Y3l3rwEADA\nIjAQIpFIfVzqVAt1Nv8CsYkJYhcvRk5sbGWFSEREb8jOzg43b94s8tuksLAwxMbGwtjYuIIjo9Lg\n+IuIiKh6qqpjsBKTP6GhoejQoQMAoGnTprh+/br6XGRkJCwtLbFp0yYMHz4cycnJaNCggfaipTcm\n5OYi5fAh6FlYwNTHp8B5qZMT7D/9FKq0NMTMmQtBECohSiIielPvvPMOgLwkQmGOHTsGMzMztGjR\noiLDolLi+IuIiKh6qqpjsBKTP+np6TA1NVX/rKenB6VSCQBISkpCWFgYhg8fjo0bN+LSpUu4ePGi\n9qKlN5Zx6RJy4+Jh1q0rRFJpoW0sBw6AcZs2SP/jD6QeOlzBERIRUXlo06YNzM3Ni5xWfPz4cQQE\nBEAikVRwZFQaHH8RERFVT1V1DFZi8sfU1BQZGRnqn1UqFfT18zYJs7S0RN26deHs7AyJRIIOHTpo\nfDNFVY96yVevXkW2EYlEqLFgPkRGRohduBDK+PiKCo+IiMqJRCJBQEAAwsLCEP/K3+NXr17F06dP\n0bVr10qKjkrC8RcREVH1VFXHYCVu9d68eXOcOXMG3bt3R3h4OGQymfpc7dq1kZGRgYcPH6Ju3br4\n559/8N5772k1YHp9qqwspB0/DkmtWjBq1qzYttLatWE/fTpiv/oKz+bOhdOqVRr1gYiIqpt595/g\nYFxyZYdRrF52lpjTsFa53a9Lly7Yt28fTp06hUGDBqmPBwcHw9TUFD4+Pti9e3e59Uflh+MvIiLS\nFRyDVY0xWIkzfzp37gypVIrBgwcjKCgIs2bNwsGDB7Fjxw5IpVIsWrQIM2bMQP/+/eHo6IiOHTtW\nQNj0OtJOn4YqMxPmPXtCJC7xrYfV8GEwbtUK6SdPIeX33ysgQiIiKk8+Pj4wNjYuMO04f7qxtIjl\nv1T5OP4iIiKqvqriGKzEmT9isRjz58/XOObs7Kz+fdu2bfmtYTXxvyVfPUvVXiQWo+biIDzo0xcx\nXwXBuGVLSOvW1WaIRERaM6dhrXL9Rqc6MDAwQMeOHXHixAmkpaXBzMwMN27cQHR0NGbNmlXZ4VEx\nOP4iIiJdwTFY1RiDlTz9g3SCKiMD6RcuwKBxYxg0bFjq6yQ1a8JxzhwImZl48umnEHJytBglERGV\nty5duiAnJwdnzpwBkLfDhImJiXonKSIiIiIqf1VtDMbkz1si6/oNQKmESZs2Zb7WomcPmPfqhewr\nVxH/0xotREdERNri5+cHQ0NDnDhxAkDewMPf359LvoiIiIi0qKqNwZj8eUtkXb0CADDy8nyt6x2/\n/AL6NWogfs0aZN+9W56hERGRFhkbG8PHxwfnz5/H1atXERUVhW7dulV2WEREREQ6raqNwZj8eUtk\nX70KADDyfL3kj565ORznzAaUSsTMmQtBpSrP8IiISIu6dOmCrKwsLFq0CMbGxlzyRURERFQBqtIY\njMmft0TWlavQs7OFfo0ar30Ps44dYda1K7LCwpC8c2c5RkdERNoUEBAAiUSC8PBwBAQEwMDAoLJD\nIiIiItJ5VWkMxuTPWyAnJgbK589h5OkFkUj0RvdymDULYlNTPF+2HDnPn5dThEREpE1mZmZo27Yt\nAODdd9+t5GiIiIiI3g5VaQwmEgRBqMgOQ0ND4e3tXZFdvvVSjx3Hk6lTYTd9Omw/HPfG90vavh0x\n8+bDrGtXOK34thwiJCIiXcJ/66smvi9ERES6rbh/6znz5y3wpsWeX2U5aBAMvTyRFhwM+f375XJP\nIiIiIiIiItIOJn/eAtlXrgIiEQzd3cvlfiKxGDajRgMAknftKpd7EhEREREREZF2MPmj4wSlElk3\nbsCgoTP0TE3L7b5mAf7Qs7FB8r79UGVnl9t9iYiIiIiIiKh8Mfmj4+T370PIyoLha27xXhSRVArL\nfv2gSklB2vHj5XpvIiIiIiIiIio/TP7ouKwrVwEARp5e5X5vywHvAQCSuO07ERERERERUZXF5I+O\nK+9izy+T1qkDk3btkPVPKOQREeV+fyIiIiIiIiJ6c0z+6Ljsq1chMjKCQcOGWrm/5cCBAIBkzv4h\nIiIiIiIiqpKY/NFhuenpkN+PgJGbG0T6+lrpQ6Pws1yulT6IiIiIiIiI6PUx+aPDsm/eBASh3Is9\nv0wklcIysC9UKSnI+PNPrfVDRERERERERK+HyR8dJr9zFwBg2NhVq/2Y+vkBADL+uqjVfoiIiIiI\niIio7Jj80WHyu3nJHwOZTKv9GHl5QWRkhIyLTP4QERERERERVTVM/ugw+d27gL4+DOrX12o/IqkU\nxi1bQBERgZzY51rti4iIiIiIiIjKhskfHSWoVJDfuweD+vUgkkq13p9J23YAgMxLnP1DRERERERE\nVJVoZwsoqnQ5T59ClZkJg0baXfKVz6RtGwB5dX8s+vSpkD6JiKigPXv2YNasWSW2++GHH9CpU6cK\niIiIiIhI91X1MRiTPzqqour95DOQyaBnbY2MixchCAJEIlGF9EtERIVr1aoVWrVqVeT5+lpeEkxE\nRET0NqqqYzAmf3RURSd/RGIxTNq0QeqRI1A8eAADZ+cK6ZeIiArXqlUrTJ48ubLDICIiInqrVNUx\nGGv+6Cj53XsAAANZowrr06RdWwDc8p2IiIiIiIioKmHyR0fJ792F2NgYkpo1K6xPk7Yvkj/c8p2I\niIiIiIioymDyRwcJCgXkkVEwaNQIInHFvcWSWrUgqVsHmSEhEJTKCuuXiIiIiIiIiIrGmj86SB4Z\nCSiVFVbv52Umbdsi+bcdyL5+HUZNm1Z4/0RERYn9+hukBQdXdhjFMuvaFQ6fflIu9woJCcGqVasK\nPRcYGAgnJ6dy6YeIiIioOByD/U9ljsGY/NFBFV3s+WUmbfKSPxmXQ5j8ISKqRCEhIQgJCSn0XKtW\nrZj8ISIiItKCqjoGY/JHB1Vm8seoWTMAQNbVqxXeNxFRcRw+/aTcvtGpDiZNmlQld5ogIiKitwvH\nYFUDa/7ooGx18qfidvrKJ3Gwh76DA7KuXoEgCBXePxERERERERFpYvJHB8nv3oO+nR30rawqpX8j\nTw/kxsVDGRtbKf0TERERERER0f8w+aNjclNToXz2rFKWfOUz9PAEwKVfRERERERERFUBkz86Rn7v\nHoDKqfeTz8jTAwCQfe1apcVARERERERERHmY/NExVSH5Y+jmBohEyLrK5A8RERERERFRZeNuXzpG\nfu8+AMCgYcNyve/jbAW2PE1Aco4S7axM0c7SFHZSSaFt9czMIG3QANnXr0PIzYVIT69cYyEiIiIi\nIiKi0mPyR8fIH0QAAAwa1C+X+91Mz8K3UbE4Ep+M3Bebd/3yNAEA0MXGHD82qQtT/YLJHSMPD6RE\nREARGVnuiSgiIipav3790K9fv8oOg4hekyIqCoroaJj4+EAkElV2OEREVEpVfQzGZV86RnE/ApKa\nNSE2MXnje93LyEbfsHs4GJeMxiZG+K5xHRxu3gj/bVADTc2McTwhFe+FRyAxR1ngWsMXdX+49IuI\niIioZNk3b+LxtOmI6NYd0WPH4fFHE6CMj6/ssIiISEcw+aNDclNToYyLg9TZ+Y3vlZSjxPvXHiBV\nqcJyl9o40UKGgY7W8LYwwZS6DjjUvBEGOVojPC0Tff69h6fZCo3rjfJ3/LrGHb+IiIiIiqJSKPB4\n8hRE9uuPtOBgGDZuDOOWLZH+xx940LsP0k6fruwQiYhIBzD5o0PkES+WfL1h8idHJeD/rkchMkuB\nKXXsMbSmTYFpx/piEb51rY0PnexwL1OOoVcfQKFSqc8busggkkiQzZk/REREREVK3LARaSdOwNDL\nE7XXrUO933ejzi+b4DBrJlTp6Xg8YSISNm2q7DCJiKiaY/JHhyheJH+kzg3e6D4LIp7iz+R0dLO1\nwMwGNYpsJxaJMLdhTQytYY3bGdlYEx2nPieSSmHQpDGy79yBSi5/o3iIiIiIdJHi8WPEr14NPVtb\n1Pn5Z5j6tIdIJIJILIb1yJGot3sX9GxtEbf8W8gfRFZ2uEREVI0x+aND5BEPAAAGzq9fYDkqS471\nT+LQwMgA3zeuA3EJhQZFIhFmO9eEjUQfy6Ni8Cjrf4keIw9PQKmE/Nat146HiIiISFfFLvoKglwO\nh88+hZ65eYHzhjIZHGd/CUGhwLMvv4Tw0ixrIiKismDyR4fII15s8/4GM3+WRcUgVwA+re8Ik0J2\n8SqMpUQfcxvWRJZKwBf3nqiPG7HoMxEREVGh0k6fRvqZMzBu1QrmPXsW2c68SxeYdemCrNBQJG3f\nXoEREhGRLmHyR4coIh5Az84WehYWr3X9/cxs/B6TBFcTQ/S2tyzTte85WKGdpSmOJ6QiOC4FAGDo\nkZ/8YdFnIiIionyqzEzELFwISCRwnDO7xC3dHb/8AmILC8QtW46cJ0+KbUtERFQYJn90hCozEzlP\nnrzRkq9lkTFQAfikvmOJy71eJRKJsETmBIlIhAURT6ESBEjr1oXY1BTZN2++dkxEREREuibxl1+g\nfPoMNqNGlWqjDn07OzjMnAlVZiaezZ0HQRAqIEoiItIlTP7oiPwigAYNXm/J1+2MLOx7ngx3UyN0\ns329mUONTAzRz8EKEVlynE5Mg0gshmGTJlBERiI3PeO17klERESkSwSVCsm7dkNkbAybceNKfZ1F\n3z4wad8eGefPI+PcOS1GSEREuojJHx2hePBip6+Gr7fN+8qoWAjIq/VT1lk/LxvrZAsAWPdi5y/D\nJk0AQYD8Nos+ExEREWWGhCDn6VOYd+0KPVOTUl8nEolg/9mngFiM50uXQcjN1WKURESka5j80RHy\n+3nJn9dZ9pWYo8ThuBTIjA3R2abgThNl4W5mjLaWJvgjKQ13MrJh6OYGAFz6RURERAQgZe9eAIBl\nv8AyX2sok8Gib1/I791Dyv4D5R0aERHpMCZ/dIT8QX7yp+zLvvbEJkEhCBhaw7rEgoOlMc7JDgCw\n/nEcDN2aAACyb9x44/sSERERVWe56elIPXYckjp1YOTt/Vr3sJsyGSIDA8R99x1U2dnlHCEREekq\nJn90hOJ+BPQsLKBnY1Om6wRBwLanCdAXAf0drcolli62FqhjKMWumERk1HKC2NiYM3+IiIjorZd6\n9CiE7GxYBvZ97S/cJI6OsH5/BJQxMUjaurWcIyQiIl2lX1IDlUqFuXPn4s6dO5BKpVi4cCHq1q2r\nPr9p0ybs2rUL1tbWAIB58+ahwWsWHabXo1IooIiOhpGXV5kHElfTs3AzIxs97CxgJ5WUSzx6IhHG\nONlizv2n+DUmCb2aNEbWv2FQZWZCbGxcLn0QERHpMo6/dFPK3n2ASASLPn1e+x4ZubkICxwE+992\nIPLHnzCkvieMraxgK9VHQ2NDfFjbDlaSEof4RET0linxX4aTJ09CoVBgx44dCA8Px+LFi7F69Wr1\n+evXr2PJkiVwd3fXaqBUNEVUFJCbW6qtQl+1/VkiAGCwo3W5xjSkhg2WRMbg12cJGNC4CbL+CUX2\n7Tswbt6sXPshIiLSRRx/6R55ZCSy/v0XJu3aQlKzZpmvf5Qlx9eRMTgUl4xslYABXfpgwu9b8c7+\n3VgROBR5m7+nYPPTeMx2rolBjuWznJ+IiHRDicu+QkND0aFDBwBA06ZNcf36dY3zN27cwNq1azFk\nyBCsWbNGO1FSsRQRL+r9lHGnr6xcFfbEJsJBqg9/6zcr9Pwqc309dLe1QFSWAjEN8opQc+kXERFR\n6XD8pXtS9u0HAFgE9ivbdTlKzL//FD6Xb2N3bBKcDKWYUsceI6dNgH7NmuhzJhiRjWxxrb0bvnSu\niaxcAdNuR6Nv2H3cz2RNICIiylNi8ic9PR2mpqbqn/X09KBUKtU/9+jRA3PnzsUvv/yC0NBQnDlz\nRjuRUpHkEQ8AANIGZUv+HI1PQapShUGO1tAXl/83Q++9qCF0xDbv2y0WfSYiIiodjr90i6BSIWX/\nfohNTWHW6Z3SXSMI2P4sAW0u3cKP0c9hJ9XHj03q4lwrV/zXuSZa2FnBftpUCDk5SFy1CnZSCSbW\nsceF1q7oYWeByykZGBAegThFjpafjoiIqoMSkz+mpqbIyMhQ/6xSqaCvn7daTBAEjBw5EtbW1pBK\npfDz88NNzu6ocPKI+wDKvtPXrpgXS75qlK1IdGn5WJrBXqqPX6VmEBkZceYPERFRKXH8pVvkt29D\nGRMDs3fegdjIqMT2T7IVGHr1AabfjkaOIOCLBjXwZ+vG6OdgBfFLS7nMe/aEQePGSDlwENm3bwMA\nahlKsd69Pv7boAaeyXMw/sZDKFWC1p6NiIiqhxKTP82bN8e5c+cAAOHh4ZDJZOpz6enp6NmzJzIy\nMiAIAi5fvsy155VAEfUQIiMj6Ds6lvqa5BwlzielwdPMCA2MDbQSl75YhEB7KySoBGQ5N4T8/n1u\nSUpERFQKHH/plvTzFwAAJi+W8hVn+7ME+IXcxpnENHS0MsMfrVwxqa4DDPUKDttFYjHsZ8wABAHP\nly7TODe5jj2621rgz+R0BEU+K58HISKiaqvEgs+dO3fGn3/+icGDB0MQBHz11Vc4ePAgMjMzMWjQ\nIEyfPh3vv/8+pFIp2rZtCz8/v4qIm14QVCoooqIgrVcPInGJuTy1Y/GpUApATztLLUaXt/RrzeM4\n3KpdD82uX4P8zh0YeXlptU8iIqLqjuMv3ZJx4QIgEsGkfbsi2+QKAmbfe4L1T+JhpifGctfaGFKK\nos2mPu1h0q4tMi5cQMZff8GkXV4fIpEIKxrXwZ1/7uKHR8/RzMwYPe21O+4jIqKqq8Tkj1gsxvz5\n8zWOOb+0q1Tfvn3Rt2/f8o+MSkX5/DmErCxI69UtufFLDsclAwB62FloIyw1d1MjyIwNcdq+Fpoh\nr+gzkz9ERETF4/hLd+SmpyMzLAyG7u7Qt7IqtE26Mhfjbz7EyYRUuJgYYotHfdQxKv3MbLsZM5Dx\n13uIXboU9XftgkhPD0DeBhzrPeqh2z/3MPX2I3iYGaFuGe5LRES6o/RTRahKUkRGAgAM6tcv9TXp\nylycTUqDq4khnI0NtRUagLxvnQY4WuFG7bz4slj0mYiIiN4imZcvA0olTDv4FHr+mVyBPmH3cDIh\nFR2tzHCweaMyJX4AwMjNDRZ9ekN+8xaSd/+ucc7VxAhBslrIyFVheVTsaz8HERFVb0z+VHPyF8kf\naRmSPycTUiFXCVqf9ZMv0MEKDx1rIUciRfYNFqQkIiKit0f6hRf1fnwKJn9yBQFjr0fhRno23q9p\ng62eDWCur/da/djNmAGxsTHili+HMilJ49xAR2vIjA2xOzYRD7Pkr3V/IiKq3pj8qeYUUVEAAGm9\neqW+5nBcCgDt1/vJ52QohbeVOe451YX83j2o5Bx0EBERke4TBAEZ5y9AbGYGI0/PAud/jo7DP6mZ\n6GNviSUyJ+iLi6/vUxyJvT1sJ05EbkoK4r77TuOcWCTCf+o5IFcAVj7k7B8iorcRkz/VnCIyCkDp\nkz9ZuSqcSkxFAyMDuJpod8nXy3rZW+B23QaAUgn5rVsV1i8RERFRZcl5+BA5jx/DpG1biPQ1S20+\nyJRjceQz2Ej0saiRU4mFnUvDesRwSBs0QPJvO5B9U3O2dS97SzQyNsDOmEQ84uwfIqK3DpM/1Zwi\nKgp6trbQMzMrVfs/ElORmatCDzuLchlklFZPO0vcrpdXqDLr2vUK65eIiIiosqi3ePdpr3FcJQj4\nz+1HyFYJ+EpWC7bSEvdgKRWRVAqHz/8LCAJiFiyEIAjqc3oiEabVdYBSAFY9el4u/RERUfXB5E81\nplIokPPkCQzKsOTrSHzekq8eFbTkK19NQyn03dwAAMlXrlRo30RERESVIeNFvR/TV+r9bHgSj0sp\nGehhZ4He5TwmM23fHmZduiArLAypBw5onOvrYAVnIwP89iwRj7MV5dovERFVbUz+VGM5jx4BKhWk\n9euVqr1KEHA6IQ0OUn14mRlpNbbCtPZognRDIyRfuVrhfRMRERFVJJVCgYyQEEidnSGpWVN9PFae\ng0URz2Clr4fFsvJZ7vUqh88+hcjQELHfLEVuerr6uJ5IhGn1HJAjCPiOtX+IiN4qTP5UY+qdvuqV\nbqevq2lZSMhRwt/avEKXfOXr6WiNu3Xqwyj6EXLT0iq8fyIiIqKKkhUaCiErq8Csn92xSchSqTCj\nviPspBKt9C2pVQs248YiNz4e8d//oHEu0N4KdQ2l2BmTiJQcpVb6JyKiqofJn2pMvdNX/Xqlan86\nMRUAEGBjrp2ASuBkKEWyiysAIDacs3+IiIhId2VcugwAMGnfTuP47phESEQi9HOw0mr/NmPGQFK7\nNhK3bIH83j31cX2xCMNq2iBbJeBAXLJWYyAioqqDyZ9q7H87fZVu5s/phFToiQA/K1MtRlU8+6ZN\nAQA3Qv6ptBiIiIiItC0rPBwQiWDUrJn62I30LNzKyEYnG3NYS8qnyHNRxAYGcJg1C8jNRcyirzSK\nPw9wtIIYwG/PErUaAxERVR1M/lRjiqgoQE8PUqdaJbZNylHi39RMtDA3gYWWBxvFadWuFQAglUWf\niYiISEcJSiWyrl2DQUNnjR1Zd8fkJVvec9TurJ98pv4dYeLni8xLl5B27Lj6eA0DKfyszRCamom7\nGdkVEgsREVUuJn+qMUVkJKROThBJpSW2PZuYBhWAAOvKWfKVr069Okgzt4DtvbtIUHCdOREREeke\n+b17EDIzYfRixjMA5AoC9sYmw0JfD50qaAm+SCSC46xZEEkkiF2yBKrMTPW5QY7WAIAdMZz9Q0T0\nNmDyp5rKTU5GblISpPVLt+TrlLrej1kJLbVLJBJB3rgJ7JMScOJuZKXGQkRERKQNmWFhAKCR/LmQ\nlI4YRQ5621vCQFxxQ3BpvXqwHjUKymfPEL92rfp4V1sLWOjrYXdMIpQqoZg7EBGRLmDyp5pSF3uu\nV6/EtipBwJmENNhJ9eFmWvFbvL+qRrMXdX8us+4PERER6Z6s8HAA0Kj3szv2xZIvLRd6Lozt+A+h\n7+iIxPUboHj4EABgqCdGoIMVYhVK/JHEXViJiHQdkz/VlDy/2HMpZv5cS89CfI4S/tZmEFfCFu+v\ncmyel/zJvXGdS7+IiIhI52SFX4HYwkL9JV1Gbi4Ox6WgjqEUrSxMKjwesbExHD77FEJODmK/ClIf\nz1/69duzhAqPiYiIKhaTP9VUWWb+nE54seSrkuv95DP08AAAyKIicCw+pZKjISIiIio/yoQE5Dx6\nBCMvT4heLO86Fp+KzFwV+jtYQVRJX8SZde0K49atkX72LNLOnAEANDUzgouJIY7HpyIxh1/IERHp\nMiZ/qilFZF69HGn9eiW2vZCUDhEAX+vKrfeTT9/KCqKaNeH6MAIHnydVdjhERERE5Ua95Oulej+/\nx+SNdypql6/CiEQiOH7xOaCnh9ivgqCSyyESiTDY0RoKQcC+WI7JiIh0GZM/1ZQiKgpiY2Po29kV\n2y47V4XQ1Ay4mRrBuhK3eH+VqZcnLDLScf9eBJL4TRMRERHpiPzkj/GLej9KlYCLKeloZGwAZ2PD\nygwNBo0awXr4MORERyN5924AQD8HK4gAHHieXKmxERGRdjH5Uw0JggBFdDQkdeuWOHU4LC0T2SoB\n7SxNKyi60jFu1hwA0OTebRzl0i8iIiLSEVlh4YBYDEMPTwDA9fQsZOaq0KaKjMVsxo2DyMAAies3\nQMjJgYOBBK0tTHA5JQMx8pzKDo+IiLSEyZ9qKDchAUJWFqROTiW2/SspHQCqXPLHyDsv+eMecQeH\nnzP5Q0RERNWfkJODrOvXYdCoEfRM8wo7h6TkjcUqo9BzYfRtbGD53nvIefoUqUeOAAB621tCAHAo\njsDTOH8AACAASURBVLN/iIh0FZM/1VDO48cAAElpkj/JefV+WltWjQFHPkMXF4j/n707DbLqvs99\n/117HnuiB+ZJswQSAhmJ0QJN0RzbkiXbkXPPSU7uTeKT5JZf3PKtil64Ktc+dZM6Tpw4dZKbnDg+\nsYUcjxpsyYrQhIQGEJIQSICApruhJ+hpj2utvdZ9sQdo6KYbQe+pn88rs9feqn9JruJfz/oNkQg3\nHTnAq0NjjNm5Sh9JRERE5KJkPj6Am8mMm/fz5kgSgJurJPwBaPlP/wm8Xgb/8R9xHYf72powgKfU\n+iUiUrcU/tQgs6sQ/iw6f/hTnPdzbSxEcxXN+wEwfD7Cq1Yx93gP4dERXjw1WukjiYiIiFyU9Lvv\nAhC+MR/+uK7Lm8NJ5gX9LAoFKnm0cQILF9B4372Yhz4hsX077UE/tzTlW79OZM1KH09ERGaAwp8a\nZPXkw5+p2r6qdd5PUfimNQCsOHyAXw2o9UtERERqW2nYc6Hy50jaZNCyubkxWrEV75OZ8/u/D8Dg\nP/wDruvyQHt+E9nTascXEalLCn9qkDnNtq9qnfdTFFmdD382HDnICydHyTpOhU8kIiIi8uml33sP\nb1MT/iVLANhZZfN+zhS84gpit91G5r33Sb35Fve2NuIBntLcHxGRuqTwpwZZ3T0A+BcsOO/3ivN+\nqmW7xNnC168En4/PHD1AIuewoxBWiYiIiNSa3PAwVnc3oZUrS1U+bw3n5/1U612s9Q/+CwAn/+c/\n0x70s64pxlsjSY5n1PolIlJvFP7UIKurC197O55gcNLvZJ3qnfdT5IlECF17LXMOHSRoZrXyXURE\nRGpW5qOPAAhdfXXpszdHEjT4PFwVDVXqWOcVvuEGQitXknz1Naz+fu5vbwK09UtEpB4p/Kkxrm1j\n9fZO2fL17mh1z/spiqxZg5HLcXPXUX49OILjupU+koiIiMgFy+wvhD/XXgNAf9biSNrkpoYo3iqb\n93Omxt9+EByH0aef4d62fOvXL7X1S0Sk7ij8qTFWby/kcvgXTt3yBdU776cosmY1APcfP8KAabNr\nNFXhE4mIiIhcuMz+fQCErsmHP8UV79Xa8lXUcM894Pcz8vOf0+r3sb4pxjujKXrU+iUiUlcU/tQY\nq7u46WvReb/3duHC8ZnG6r5whFfnw58Vhz4G0NYvERERqUnZ/fvxRKP4Fy8G4K0qHvZ8Jl9zM/Fb\nP0v2wAGyH32k1i8RkTql8KfGmF1dwPk3fbmuy57RFEtCAVoD1Tnvp8jX0kJg2TLCH35AFJffnFT4\nIyIiIrXFyWTIHj5C8OqrMTz56/Wbw0kChsGqeKTCp5ta44MPAjDy859zj1q/RETqksKfGlPa9HWe\ntq8jaZMhO8fqhuq/bABEblqDm0zyuZEBDqaydKazlT6SiIiIyLRlDxyAXK7U8pWwc+xNpFnVECHk\nrf7rdmzzZrxNTYw8/QytBmxojrFrNEW3Wr9EROpG9f9tJOOcbvuavPJn92i+5WtNlZcZF0VuugmA\nO44eAOCFk6OVPI6IiIjIBcns2w+cnvezazSFQ/W3fBUZgQAN991H7uRJEq+9xgPF1i9V/4iI1A2F\nPzXG6u4Gvx9fR8ek39ldGJq8ugbKjAEi69YBsOy93YDCHxEREaktmf3F8Ce/5n1P4S62pkaqsOGM\n1q9f/JK7W5vwGvBLzf0REakbCn9qjNndjX/+PAyvd9Lv7BrN95hfFw+X8WSfnr+9neCVV+Ls3s31\nfoPXhxMkc7lKH0tERERkWjIf7Qe/n+DllwOwL5kG4LpYbdzFAEIrriNw+WUk/uM/aE4n2dAUY/do\nii61fomI1AWFPzXESaXInTxJYMHkLV+ZnMO+RIbrYmGCntr5zxvduBE3m+Xh3k6yjsuOoUSljyQi\nIiIyJTeXI/vxAYKXX44RCACwL5Em7vWwKBSo8OmmzzAMGu+7D9eySLzyKg+0NwNq/RIRqRe1kw4I\nVk9x2PPk4c/eRBrLdWtm2HNRdMN6AD6z/wNArV8iIiJSG8wjR3AzmdK8n3TO4ZNUlmtjYQzDqPDp\nLkxsy1YAEtu3c3drY771S+GPiEhdUPhTQ8zCsGf/oqmHPdda+BNZswYjGCT+9ls0+by8cHIU13Ur\nfSwRERGR8zo97ycf/nyczOAA19ZQy1dR8Mor8M+fT+LVV2kxXDY2xXl3LMUxbWIVEal5Cn9qiNU1\nnU1fhWHPDbWxXaLIEwoRuekmzAMHuNewOJ612J/MVPpYIiIiIudV2vR1bT782ZeovXk/RYZhELv1\nVpyxMVK73z299WtgpMInExGRi6Xwp4ZYPYXKnynCnxa/l6Xh2ukxL4pu2ADA3Z/kL1Fq/RIREZFq\nl9m/HwyD4FX5TV8fFsKfa6OhSh7rU4tt2QIUWr/a1PolIlIvFP7UELP7/DN/BkyLYxmTG+PRmusx\nh9Phz2Xvv4sBvKjwR0RERKqY67pk9+8nsHgx3li+6npfMo0BXBWrzfAncvNajEiExPbttPh9bGqK\ns2csRadav0REaprCnxpidXfjiUbxNjVN+PzdUstXbc37KQpeeQW+tjasnTtZFQ3xzmiSMVsr30VE\nRKQ62SdOkBsZIViY9+O6LvsSGZaHg0S93gqf7tPxBALENqzH7Owke+RIqfXrKVX/iIjUNIU/NcJ1\nXayuLvwLF05a1VPr4Y9hGEQ3bCB38iQPjPRju2jlu4iIiFSts4c9H89ajNi5mhz2fKbYrcXWr5f4\nrbZGfAY8NaDwR0Sklin8qRG54WGcVGrKNe8AK+O1Gf7A6davdfvfB2D7KbV+iYiISHXKHjgAQPCq\nK4Ez5v3UaMtXUeyzm8EwSLz0Ur71qznOe2NptX6JiNQwhT81wj5xAgD//PmTfufDRJq5AT+tAV+5\njnXJRTduAI+Hxtd30ODzsP3UmFa+i4iISFXKHjwEQOiKK4Da3vR1Jl9rK6HrV5LatYvcyAj3F1q/\nNPhZRKR2KfypEVZfHwD+jvYJn580bY5nrdq/bDQ3E1mzhsyePfwW+QHWR9JmpY8lIiIico7swYN4\nIhF8hZdz+5IZgJpv+wKIb9kCuRyJV1/j7la1fomI1DqFPzXCLoQ/vo65Ez4vvmlaEa+Dy8YdtwNw\nz4fvAmr9EhERkerjWhbZo0cJXnFFaR7jvkSaRp+XBUF/hU938WK33gpA4uWXafb72Nwc5/2xNEfV\n+iUiUpMU/tQIq7cXAP/cjgmf762TMmOA+G23AbD8rTcAeOnUWCWPIyIiInIOs7MTLIvAFZcDkMo5\nHE5luSYamnQ5Ry0JXnUV3tZWkjvfwHXdUuuXtn6JiNQmhT81wu4tVP7MnbjypzhgcEUdhD/+BQsI\nXnsNubff5npsXhtKkHWcSh9LREREpCR78CBwet7Px8kMDvXxIg4KW1hvuYXcwCDmoUPc3dqI3zA0\n90dEpEYp/KkRVl++8sfXPvHMnw8TaSJeD0vDgXIea8bEb78dLIsvHtpH2nF4eyRZ6SOJiIiIlBTD\nn2CdDXs+U3TdOgCSb+ykqdD69UEizZGUWr9ERGrNlOGP4zg8/vjjPPLIIzz22GN0dnZO+L0///M/\n5y//8i8v+QElz+7tw9vSgicYPOdZJudwMJXh2mgITx2UGUMh/AFW734TgO1q/RIRkVlE96/qV9z0\nFbg83/ZVrMK+pq7Cn1sASL6Rb8V/oNj6pcHPIiI1Z8rw54UXXsA0TbZt28bXv/51vv3tb5/znSee\neIIDBw7MyAEFXNfF6uvDN8m8nwOpDLZbX2+agldcgX/xYiI73yCWs3hJQ59FRGQW0f2r+mUPHsTb\n2IivrQ3IV/54gKujocoe7BLyz59PYMkSUm+9hWvb/FZrg1q/RERq1JThz65du9i0aRMAq1atYu/e\nveOe7969m/fee49HHnlkZk4oOGNjuKkU/kk2fe2to01fRYZhEL/9dtx0mi91HuLDRIa+rFXpY4mI\niJSF7l/VzclkMI8dK236cl2X/ckMyyNBwt76mqoQWb8OJ5kk/cEHNPp9fLYlzt5EmsNq/RIRqSlT\n/u2USCSIxWKlP3u9XmzbBqC/v5+/+7u/4/HHH5+5E8oZa94nmfczVn895nC69WvL++8A2volIiKz\nh+5f1c08fBgcp7Tpq9+0GbFzXFVHVT9F0VuKc3/Oav1S9Y+ISE3xTfWFWCxGMnl62K7jOPh8+Z/9\n+te/ZmhoiD/4gz9gYGCATCbD8uXL+fznPz9zJ56FrMKmL/95Nn3ly4zrK/wJr7oBb2sr8958Hc/9\nX+alU6M8Mq+l0scSERGZcbp/Vbfsofy8n+Kw54OpDABXROow/Ll5LRgGqTd2wh/9EXfNKbR+DQzx\np0snHkkgIiLVZ8rwZ/Xq1Wzfvp177rmHPXv2cOWVV5aeffWrX+WrX/0qAD/96U85fPiwLh4zwC5u\n+pqg7ct1XT5MpLksEiRSZ2XGhsdDfOtWhp98ks92HeblUADHdetmqLWIiMhkdP+qbmeveT9UaIG6\nPHLuYo5a521qInTttaT27MFJpWiMRLi1Jc5vTo7ySSrDZXUYeImI1KMp04I77riDQCDAo48+yre+\n9S2+8Y1v8NRTT7Ft27ZynE84s/Ln3LcrxzImYzmn7lq+iuJ35Fu/PrfvXU5ZOd4vtLiJiIjUM92/\nqlv2QD78KW76OlSo/Lm8ToOQ6Pp1YFmkdu0C1PolIlKLpqz88Xg8fPOb3xz32WWXXXbO9/TGaeac\nr/KnuFa0XsOf6M0344nFuPrtN+Duh3np1CirGiKVPpaIiMiM0v2rumUPHcLb1oqvuRmAQ8n6rfwB\niNxyCyf/8f8j+cZOYps2cVdrI4HC1q8/WzrxWAIREaku9dUnVKdKlT8TDHyu9/DHCASIbd6Mv7eX\nK7o72a6hzyIiIlJBuUQSq6en1PIF+Zk/84J+Yj5vBU82cyJr1mAEAqWhzw0+L7e2xNmXzHAwmanw\n6UREZDoU/tQAu7cXT0MDnmj0nGcfF/7CvboOt0sUFVu/Htq/h3dGk4zauQqfSERERGYr85P8sOdi\ny1cyl6Mna9Vt1Q+AJxQifOONZPfvxx4aAs5o/RpQ65eISC1Q+FMDrL6+Cat+AA4ks8S8HuYF/WU+\nVflEN23GCAS45d23ybnw2pCqf0RERKQyzt70dbg07Ll+X8QBRNflV76ndu4E4K7WRoIeQ3N/RERq\nhMKfKuekUjijoxPO+zEdh8PpDFdGQxh1vAHLG4sSXbeOhqOHmT/Qx29Ojlb6SCIiIjJLFYc9B0vD\nnut73k9RdN0tACTfyIc/cZ+XjU1x9iczdGXMSh5NRESmQeFPlbP68vN+fBNs+jqSNrFduKqOW76K\nYrffBsBde3fz/OAoOdet8IlERERkNiqueS9W/hwsbPq6os4rf0LXXYcnHi/N/QG4vbUBgBf0Yk5E\npOop/Klydl9x2PO5lT/FeT9X1vllAyC+dSt4PNz5wS5OWjbvjqYqfSQRERGZhbKHD+ObOxdvLAbM\nnsofw+cjsnYtVlcXZnc3ALfPKYQ/gwp/RESqncKfKmf1Fta8T1D5c6AQ/syGyh/fnDmEb7yRjo/2\n0ZAY5deDI5U+koiIiMwyTjKJ3dtLcPmy0meHkhkidT5/sag496dY/bMoFOCaaIjXhsdI5rSQQ0Sk\nmin8qXJ2cc373HMrfw4UyoyvnAXhD0Bs82YM12X9R3t5TuGPiIiIlFn26FEAAsuWA5BzXQ6ns1we\nCdb1/MWi6PrC0OczW7/mNJB1XHYMJSp1LBERmQaFP1XO6itU/nScW/nzcTJD1OthwSx40wQQ27QR\ngHsO7eNgKlvariEiIiJSDubhIwAEluUrf7ozJhnHrft5P0WBZcvwtbeT3PkmruMAcMcczf0REakF\nCn+q3GSVP5bjcjiV5cpIfW/6OlPwmmvwtrVy9fu7MRxH1T8iIiJSVuaRwwCltq/ZMu+nyDAMouvW\nkTt1iuyBAwCsaYzS7PPym5OjuFrIISJStRT+VDm7rw8jHMYTj4/7/Eg6i+W6s2LeT5FhGMQ2bMQ/\nNMQV3Z0Kf0RERKSsskcKlT/L821fhwot+JfPksofgEhx5fvr+dYvr2Fw25wGTmQtPkykK3k0ERE5\nD4U/Vc7q68Pf0XFOdU9x2PNsmfdTFC20fv32J/t4ayTJKcuu8IlERERktjAPH8GIRErt+LOt8gfO\nGPq8c/zcH1Drl4hINVP4U8Uc0yR38iS+CYY9fzyLNn2dKbp+PXg83LLvfRzgeVX/iIiISBm4joN5\n9CjBpUtLL+UOJjN4gGXh2RP++Ds6CCxfTurtd3BNE4BbW+J4DfiNwh8Rkaql8KeK2f39QP4v2bPN\ntk1fRb7mZsIrV9K8/0OiqSS/6B+u9JFERERkFrCOn8DNZkvDniFf+bM4HCDknV1X6ui6dbjpNOn3\n3wegye9jbWOU3aMpBk1VZYuIVKPZ9TdVjbF7C5u+Jqn8icyiTV9nim7aBLkcX+g8yCtDYwyYVqWP\nJCIiInXOLM37yYc/Q5bNoGXPqnk/RdHS3J/XS5/dPqcRF3jxlKp/RESqkcKfKmb15Td9+Trax31u\nOy6fFDZ9eWbJpq8zxTZvAuDugx+Sc+GXqv4RERGRGVba9FWo/PlkFs77KYrcfDN4vSRe21H6bGtL\nfjnJy6fGKnUsERE5D4U/VcweGADA19Y27vOjmfymryujs++yARC67jq8TU3M3/02HtflZ31DlT6S\niIiI1Lns4Xz4U9z0dbDQgn/FLKz88cbjhG9cReaDD7CH8vewq6Mh2gI+Xh0a08p3EZEqpPCnihXD\nH/9Z4c/pYc/hsp+pGhheL9GNG3H6+vhcaoh3RlN0prOVPpaIiIjUMfPIUTAMAkuWAKc3fV02Cyt/\nAGKbNoPrktyRb/0yDIPNzXH6TZuPC8GYiIhUD4U/VawY/nhbx4c/B5PFN02z87IBELl5LQAP9uT7\n73/ep9YvERERmTnm4cP4583DE86/fDsy68OfjQAkX3219Nmm5hgAr55KVORMIiIyOYU/VSw3OAiA\nr6113OfFN02zscy4KLJmDQBXHvyIoMfgJ31DKjEWERGRGZFLJLAHBkotXwBH0lmiXg+tfl8FT1Y5\nwauvxtvaSmLHDlzHAWBTc37uzytDmvsjIlJtFP5UMXtgAE9jI57g+DdKh9NZ/IbBolCgQiervMCy\nZXibmrDffZfb5zRwIJVhX1IlxiIiInLplTZ9FYY9u67L0XSW5eEgxixcvgFgeDzENmwgNzhI9qOP\nAFgQCnB5JMjrwwksRy/lRESqicKfKmb3D5xT9eO6+U1fS8MBfJ7ZedmAfF95ePVqrJ4eHjbyq963\nnThV4VOJiIhIPSqGP8HCmvc+0ybtuCwNz86Wr6LopvwG1sSrr5U+29QcJ5lzeHc0WaljiYjIBBT+\nVCnHNMmNjOA7a97PoGUzYudYPkv7y88UWbMagLVHD9EW8PFk7ykyOafCpxIREZF6U9r0tSzf9nW4\n0IK/LDx7q7ABohvWg2GMm/uzuTD355Uhzf0REakmCn+q1Ol5P+PDn+Jl47Lw7J33UxRenQ9/zN27\neXRuC8N2jmcGNPhZRERELi3z8Pi2r6OFLaNLZ/nLOF9zM6GVK0nt2UNuLD/nZ31TDA/wqub+iIhU\nFYU/Vaq46evs8OeTQvhz+Sy/bACErrsOIxgktXs3X5k/B4AfHD9Z4VOJiIhIvTGPHMETjeJrz9/L\njhTCn+WzvO0LILZpE9g2yZ07AWj0+1jVEGHXaJKEnavw6UREpEjhT5WaNPwpXjYU/uAJBAivXEn2\n449ZZJtsao6xcyTJQQ1+FhERkUvEzeUwOzsJLFtWGu5cDH+WKfw5Y+X76bk/m5vj2C68MazWLxGR\naqHwp0rZxbav1vEDnz9J5YONyxT+ABBeswYch/Se9/idQvXPv51Q9Y+IiIhcGtbx47imSaAw7Bny\n4U/Y46E9MDvXvJ8ptHIlnsZGEq++iuvmN3xtKsz9eVVzf0REqobCnypl90/e9tXg89Dq12UDTg99\nTu3exd2tjczx5wc/Zx0NfhYREZGLV9r0dcaa9yNpk2XhwKxd834mw+sltmED9okTZA8cAOCmxihh\nj8ErmvsjIlI1FP5UqVLbV/vp8Md2XI6mTS4Lh3TZKAivWgWGQXrXbgIeD1+c28wpK8ezAyOVPpqI\niIjUAbPzGACBJUsAGDBtUjmHZarCLolt2QJAYvt2AIIeD7c0xfgomaE/a1XyaCIiUqDwp0qVwp8z\n2r66MiaW66rl6wzehgaCV1xB+v33cU2Tx+bn/319v2ewwicTERGRemAey4c//sX58Efzfs4V27wJ\nvF7GCuEPwIamfOvX65r7IyJSFRT+VCl7cBAjGMQTj5c+Kw57VvgzXnjNatxMhsz+/SyPBNlcGPz8\nsQY/i4iIyEUyj3UCEFi8CIDDCn/O4W1sJLJ6NZn3Pyi9wNzYnL/Dvqa5PyIiVUHhT5WyBwbwtbWN\na+86Pew5VKljVaXI6sLcn3ffBeCrheqff1X1j4iIiFwkq/MY3uZmvA0NABxNmwAsDQcqeayqE9u6\nFVyXxMsvA7AyHqbB5+G1Yc39ERGpBgp/qpDrONgnT0447BlU+XO28MqVAGT2fgjAXa2NdAR8/Ljv\nFMlcrpJHExERkRrm2jZmTw+BxYtLn6nta2LxLbcCMLb9JQC8hsG6phhH0yZdGbNyBxMREUDhT1XK\nDQ2BbU+w5l2XjYn4lyzBE4+T+eCD/J89Bl+eN4dR2+EX/cMVPp2IiIjUKqu3FywL/xnhz9FUlrDH\nYG7QX8GTVZ/A0qUEli0j+frrONn8nXVjU771a4e2fomIVJzCnypkD+bblSaq/FkQ9BPx6j/bmQzD\nILTiOszOTnKjowD8zvw5eNDgZxEREfn0zM7ivJ98+JNf855lSTiIR5tXzxHbsgU3nSa1cycAG5vz\nQ58190dEpPKUIlQhu//cNe8JO0evaanlaxLhFYXWrw/zrV8LQgHuaG3gvbE0e0ZTlTyaiIiI1Cjr\nWHHNez78GbRsxnKOqrAnEd+aX/le3Pp1VTTEHL+P14cTuK5byaOJiMx6Cn+qUGnN+xmVP4dLm740\n7HkioRUrAEjv3Vv6rDT4+biqf0REROTCmZ2F8KdQ+aNhz+cXXrUKb2Mjie0v4bouHsNgQ3OM41mL\nI2nN/RERqSSFP1Wo1PZ1xswfDXs+v/DKfPiT+eB0+HNrS5xFoQA/6xtmxLIrdTQRERGpUWZXF5Cf\nLwga9jwVw+cj+tnN2H19ZPbtA2BDU7H1S3N/REQqSeFPFZqo8keXjfPzzZuHt6WFzBmVP17D4Kvz\n55B2HP69b6iCpxMREZFaZB3rxBOP421qAuBI4WXccr2Mm1R8S771K1HY+lWa+zOsuT8iIpWk8KcK\nnS/8Wa7wZ0LFoc/W8ePYp06VPn90Xgt+w+D7PSfVay4iIiLT5joO5rEuAosXYxSGOxfvY0t1H5tU\ndONG8PlIFOb+LA8HmRf0s2MogaO7mIhIxSj8qUL2wAB4PHhbWkqfdaZNvAYsDKnHfDKloc9nVP+0\nBfzc29bIgVSGN0eSlTqaiIiI1Bi7vx83my0Ne4Z8+BP0GMzXmvdJeeNxoms/Q+bDD7H6+jAMgw1N\nMU5aNh8nM5U+nojIrKXwpwrZgwN457RgeL2lz46ksywMBvB7tFZ0MhMNfYYzBz+fLPuZREREpDYV\nhz37z1rzvjgU0Jr3KcRunaT1SyvfRUQqRuFPFbIHBse1fCXsHAOmrXk/UwituA4YP/QZYF1TlCsi\nQZ7uH2bQ1OBnERERmZp5rBOAwOL8sOchO8eo7WjezzTEthbDn3zr18bmOACvaOiziEjFKPypMrlE\nEjeVGhf+dGbyqzGXaK3oefnb2/F1dIxr+4L8PKDH5s/BdF1+psHPIiIiMg3WscKa90Lb19HCsOel\nIYU/UwksXEjwistJ7tyJk06zMBTgsnCQ14cTmI5T6eOJiMxKCn+qjD3QD4xf817cLKHKn6mFVq7A\nHhjA6usf9/nnOprxGfDj3lOT/FJERETktGLbV6DQ9nVUL+MuSGzLVtxsluQbbwBwa0ucZM7hbc1g\nFBGpCIU/VSY3OAhMsuZdZcZTChfm/mT2fjDu87aAn60tDbyfSLM/ka7E0URERKSGmMeOYUQieAsv\n5DoL97Elehk3LbEttwIw9uKLQD78AXjplFq/REQqQeFPlZlozXtnWm+apit03cRDnwEenpvfnvbj\nXrV+iYiIyORc18U8dmzcmvejhfvYUt3HpiV8/fV4W1pIvPQyruOwvjlGwDAU/oiIVIjCnyozUfhT\nrPxZoh7zKU029BngjjkNNPq8/KTvFDnXLffRREREpEbkBgdxU6lSyxfkK38MYFFI4c90GF4vsc9+\nltzgIJm9e4l6vaxtjPJBIs2AaVX6eCIis86U4Y/jODz++OM88sgjPPbYY3R2do57/txzz/GFL3yB\nhx56iO9///szdtDZohT+tJ4Of46ms8wP+gl7ldVNxdfcjH/hQjJ79+KeFfCEvB4ebG+iz7R5RW+d\nRESkiun+VVnmWcOeIb+AY37QT8Cj+9h0Fbd+jRW2fhVbv17WPUxEpOym/NvrhRdewDRNtm3bxte/\n/nW+/e1vl57lcjn+6q/+in/5l39h27Zt/PCHP+TUKQ3UvRj2QGHmT3s+/MnkHI5nLbV8XYDQyhXk\nhoexenrOefbFYuuXtn6JiEgV0/2rsorDnv2Fyp90zuFE1mKp5v1ckNj69Rh+P4kX8+HPljkNgOb+\niIhUwpThz65du9i0aRMAq1atYu8Zs1S8Xi/PPvss8Xic4eFhHMchEFBIcTHObvs6ljFx0aavC3F6\n6PO5rV9rGiIsCwf41cAwY3au3EcTERGZFt2/Kss8lq+0CixeAkBXRvN+Pg1PNErkM58h+/HH2AMD\nXBsN0Rbw8fLQGI5a8EVEymrK8CeRSBCLxUp/9nq92LZd+rPP5+P555/nwQcfZO3atYTD4Zk5UxMU\nqAAAIABJREFU6SxhDwzgaWjAE8yHPUfTWvN+oUIrVgIThz+GYfBQRwtpx+XXgyPlPpqIiMi06P5V\nWVax7WvxIuD0fUybvi5cdMN6AJJvvIFhGHy2Oc6AabNP21dFRMpqyvAnFouRTCZLf3YcB5/PN+47\nd955J6+88gqWZfHzn//80p9yFrEHB/EVVorC6cuGyoynL3TdtQCkJxj6DPBgRxMAv+gfLtuZRERE\nLoTuX5VldnVj+P34OjoAbV69GNH1hfBnx+sAbCnM/dmu1i8RkbKaMvxZvXo1r7zyCgB79uzhyiuv\nLD1LJBL8zu/8DqZp4vF4CIfDeDQE71NzTZPc0NBZm75UZnyhvLEYgWXLyHz4Ia7jnPP88kiI62Ih\nXj41xrBlT/BPEBERqSzdvyrL6u7Gv2ABRuHfa2dGm1c/reBVV+FtaSH5+uu4rsvmQvijuT8iIuU1\n5U3hjjvuIBAI8Oijj/Ktb32Lb3zjGzz11FNs27aNWCzG/fffz1e+8hW+9KUvYRgGDzzwQDnOXZfs\nkyeB8WveVfnz6YRWrsBJJDCPdk74/MH2ZizX5Vdq/RIRkSqk+1flOMkkuaEh/AsWlD47qpdxn5rh\n8RBdtw57YADz0CHaAn6uj4V5ayRJUvMXRUTKxjfVFzweD9/85jfHfXbZZZeV/vcjjzzCI488culP\nNgudPewZ8uFPq99H3Oet1LFqUnjFCkZ/+RSZD/cSXL7snOcPtjfx/xw+wS/7h/nSvDkVOKGIiMjk\ndP+qHLOwLdS/cGHps850liaflyb/lFdnmUB0/XpGn3mG5OuvE7ziCrbMaeD9RJqXh8a4p62p0scT\nEZkVVCNcRezBwpr3wswfy3Hpyph6y/QpnG/oM+QHNt4QD/PK0BgnTbV+iYiISJ7VXQh/CpU/juty\nLGOyWPexT6049Dnxen7uz92tjQA8M6AKbBGRclH4U0Xs/kLlT3u+8qcna2K7avn6NELXXA1e76RD\nnwEeaG8m58Kzgxr8LCIiInlWofInsDAf/vRmLbKOq/vYRfDPnUtg+XJSb7+Da5rcEA+zMOTn+cER\nshPMZxQRkUtP4U8VObvtS2vePz1POEzw8svJ7N+Pa09c2fNAe2HrV5/CHxEREcmzuruB021fnZnC\npq+QKn8uRnT9etxUitSePRiGwb1tTYzlHF7R4GcRkbJQ+FNFSuFPoe3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6LkfSWZaGg3gM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s2uy7yv6egC4CAVf6SPCq++CsPtpvHue0gEg9wwrhS7AbduqiHSyxVdRUTS5WPHD5umyc0337zb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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19356547438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot distributions of min distances\n",
    "\n",
    "fig = plt.figure(figsize=(20,7))\n",
    "fig.suptitle('HC males vs females 1 nearest thin',fontsize=20)\n",
    "ax1 = fig.add_subplot(1,2,1)\n",
    "sns.distplot(M_DRN_min_thin, hist=False, label='M', color=M_color)\n",
    "sns.distplot(F_DRN_min_thin, label='F',hist=False, color=F_color)\n",
    "#plt.axis([0,5.5,0,0.8])\n",
    "plt.legend(fontsize=20)\n",
    "plt.title('PL-DRN',fontsize=16)\n",
    "\n",
    "\n",
    "ax2 = fig.add_subplot(1,2,2,sharex=ax1, sharey=ax1)\n",
    "sns.distplot(M_neg_min_thin, hist=False, label='M',color=M_color)\n",
    "sns.distplot(F_neg_min_thin, hist=False, label='F', color=F_color)\n",
    "plt.legend(fontsize=20)\n",
    "plt.title('unlabeled',fontsize=16)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional clustering analysis\n",
    "The above analysis only tells us how close the nearest neighbor is, but we don't know if that means spines are arranged in pairs or in larger clusters. Another way of looking at clustering is to use DBSCAN to see how many spines are in a cluster on average. Two values need to be defined: minimum number of spines in a cluster to be considered a cluster (in this example I chose 2, but 3 would work as well), and maximum distance(eps) between a spine and another spine to be considered part of the cluster. This value is difficult to choose. For this example I chose 1.79 micro meters, because that is the average (non-normalized) minimum distance for two of the groups.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "def cluster_size(data):\n",
    "    \"\"\"Return average number of spines in a cluster on each segment.\n",
    "       \n",
    "       data -- DataFrame with dendritic spine data from neuronstudio and added segment column that identifies \n",
    "               which segement the spine comes from.\"\"\"\n",
    "    \n",
    "    segments = data.segment.unique()\n",
    "    all_cluster_size = []\n",
    "    for filename in segments:\n",
    "        df = data[data.segment == filename]\n",
    "        total_spines = len(df)    \n",
    "        location = np.array(df.loc[:, ['X','Y','Z']])\n",
    "        db = DBSCAN(eps=1.79, min_samples=2).fit(location)\n",
    "        labels = db.labels_\n",
    "        counted = Counter(labels)\n",
    "        del counted[-1]\n",
    "        if len(counted) > 0:\n",
    "            cluster_size = np.mean(list(counted.values()))\n",
    "            all_cluster_size.append(cluster_size)\n",
    "        \n",
    "    return all_cluster_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "F_DRN_cs = cluster_size(F_HC_DRN_thin)\n",
    "F_neg_cs = cluster_size(F_HC_neg_thin)\n",
    "M_DRN_cs = cluster_size(M_HC_DRN_thin)\n",
    "M_neg_cs = cluster_size(M_HC_neg_thin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.32151870982\n",
      "3.55096560803\n",
      "3.6454527217\n",
      "3.6533510403\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(F_DRN_cs))\n",
    "print(np.mean(F_neg_cs))\n",
    "print(np.mean(M_DRN_cs))\n",
    "print(np.mean(M_neg_cs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Analysis based on additional variables, for example estrous cycle\n",
    "Ways to look for effects of additional variables through visualization. Also showing a way to easily add variables to spine data so grouping by variable is possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal</th>\n",
       "      <th>stress</th>\n",
       "      <th>thin_DRN</th>\n",
       "      <th>thin_neg</th>\n",
       "      <th>mush_DRN</th>\n",
       "      <th>mush_neg</th>\n",
       "      <th>estrous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BF54</td>\n",
       "      <td>IS</td>\n",
       "      <td>0.775380</td>\n",
       "      <td>0.987385</td>\n",
       "      <td>0.210669</td>\n",
       "      <td>0.263222</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BF52</td>\n",
       "      <td>ES</td>\n",
       "      <td>0.735815</td>\n",
       "      <td>0.959195</td>\n",
       "      <td>0.258580</td>\n",
       "      <td>0.293858</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BF33</td>\n",
       "      <td>ES</td>\n",
       "      <td>1.046190</td>\n",
       "      <td>1.163167</td>\n",
       "      <td>0.245352</td>\n",
       "      <td>0.297376</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BF50</td>\n",
       "      <td>HC</td>\n",
       "      <td>0.905936</td>\n",
       "      <td>0.947902</td>\n",
       "      <td>0.279380</td>\n",
       "      <td>0.228465</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BF53</td>\n",
       "      <td>IS</td>\n",
       "      <td>0.734008</td>\n",
       "      <td>0.965500</td>\n",
       "      <td>0.235745</td>\n",
       "      <td>0.262671</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  animal stress  thin_DRN  thin_neg  mush_DRN  mush_neg estrous\n",
       "0   BF54     IS  0.775380  0.987385  0.210669  0.263222       P\n",
       "1   BF52     ES  0.735815  0.959195  0.258580  0.293858       P\n",
       "2   BF33     ES  1.046190  1.163167  0.245352  0.297376       P\n",
       "3   BF50     HC  0.905936  0.947902  0.279380  0.228465       P\n",
       "4   BF53     IS  0.734008  0.965500  0.235745  0.262671       E"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load in new data set that includes animal name and estrous cycle info for each animal.\n",
    "#This example inlcudes spine density data but if only estrous stage info is needed, animal name and estrous stage is enough\n",
    "df = pd.read_csv('Female estrous spines.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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Oxpo1a7BmzZpCY5o/fz4WLVqEX375BT/99BOUSiV69uyJqVOnwtnZudh95B6f6tWrmy13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\nAQC4BmPHjlVSUpLGjBmjG2+8Ub6+vlXaO3bsWGv/mJgY5ef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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19356968dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,5))\n",
    "\n",
    "ax1 = fig.add_subplot(1,2,1)\n",
    "fig.suptitle('Spine densities for different stages of the estrous cycle',fontsize=16)\n",
    "sns.swarmplot(x='stress', y='thin_DRN', data=df, hue='estrous',ax=ax1, size=10,marker='o', alpha=0.65,palette='bright')\n",
    "ax1.set_ylim([0,1.3])\n",
    "ax1.set_xticklabels(labels=['IS','ES','HC'],fontsize=14)\n",
    "sns.swarmplot(x='stress', y='thin_neg', data=df, hue='estrous', size=10, marker='s',alpha=0.65,ax=ax1, palette='bright')\n",
    "ax1.set_ylabel('thin spines/μm',fontsize=14)\n",
    "handles, labels = ax1.get_legend_handles_labels()\n",
    "\n",
    "\n",
    "ax2 = fig.add_subplot(1,2,2)\n",
    "sns.swarmplot(x='stress', y='mush_DRN', data=df, hue='estrous',ax=ax2, size=10,marker='o',alpha=0.65,palette='bright')\n",
    "sns.swarmplot(x='stress', y='mush_neg', data=df, hue='estrous',ax=ax2, size=10,marker='s',alpha=0.65,palette='bright')\n",
    "ax2.set_ylim([0,0.38])\n",
    "ax2.set_ylabel('mushroom spines/μm',fontsize=14)\n",
    "ax2.set_xticklabels(labels=['IS','ES','HC'],fontsize=14)\n",
    "handles, labels = ax2.get_legend_handles_labels()\n",
    "\n",
    "ax1.legend_.remove()\n",
    "\n",
    "plt.legend(handles[:4], labels[:4],bbox_to_anchor=(1.05, 1), loc=2, fontsize= 12,borderaxespad=0.)\n",
    "#plt.savefig('Estrous cicle spines.tiff',dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot helps to visualize potential differences based on estrous cycle, especially when there isn't enough data to perform statistical analyses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Next, add estrous info to data for individual spines to look at head diameters based on estrous cycle**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "F_ES_DRN = pd.read_csv('head diameter csvs/F_ES_DRN.csv')\n",
    "F_IS_DRN = pd.read_csv('head diameter csvs/F_IS_DRN.csv')\n",
    "\n",
    "F_ES_neg = pd.read_csv('head diameter csvs/F_ES_neg.csv')\n",
    "F_IS_neg = pd.read_csv('head diameter csvs/F_IS_neg.csv')\n",
    "\n",
    "\n",
    "#keep only data from animals where I have estrous cycle information\n",
    "animals = list(df.animal)\n",
    "F_ES_DRN = F_ES_DRN[F_ES_DRN.animal.isin(animals)]\n",
    "F_IS_DRN = F_IS_DRN[F_IS_DRN.animal.isin(animals)]\n",
    "F_HC_DRN = F_HC_DRN[F_HC_DRN.animal.isin(animals)]\n",
    "F_ES_neg = F_ES_neg[F_ES_neg.animal.isin(animals)]\n",
    "F_IS_neg = F_IS_neg[F_IS_neg.animal.isin(animals)]\n",
    "F_HC_neg = F_HC_neg[F_HC_neg.animal.isin(animals)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_estrous = df[['animal','estrous']]\n",
    "\n",
    "#merge estrous data with spine data \n",
    "F_ES_DRN = pd.merge(F_ES_DRN,df_estrous,on='animal',how='inner')\n",
    "F_IS_DRN = pd.merge(F_IS_DRN,df_estrous,on='animal',how='inner')\n",
    "F_HC_DRN = pd.merge(F_HC_DRN,df_estrous,on='animal',how='inner')\n",
    "F_ES_neg = pd.merge(F_ES_neg,df_estrous,on='animal',how='inner')\n",
    "F_IS_neg = pd.merge(F_IS_neg,df_estrous,on='animal',how='inner')\n",
    "F_HC_neg = pd.merge(F_HC_neg,df_estrous,on='animal',how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Unnamed: 0</th>\n",
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       "      <th>SECTION-NUMBER</th>\n",
       "      <th>section_length</th>\n",
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       "      <td>6.269167</td>\n",
       "      <td>0.452769</td>\n",
       "      <td>0.003300</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.616839</td>\n",
       "      <td>12.179447</td>\n",
       "      <td>4.950000</td>\n",
       "      <td>9.710042</td>\n",
       "      <td>10_BF33 N11S1.oib.txt</td>\n",
       "      <td>BF33</td>\n",
       "      <td>N11S1</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>37.221363</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.757883</td>\n",
       "      <td>32.078697</td>\n",
       "      <td>7.736818</td>\n",
       "      <td>0.442847</td>\n",
       "      <td>0.003300</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>0.000999</td>\n",
       "      <td>4.244884</td>\n",
       "      <td>32.731667</td>\n",
       "      <td>6.600001</td>\n",
       "      <td>36.814125</td>\n",
       "      <td>10_BF33 N11S1.oib.txt</td>\n",
       "      <td>BF33</td>\n",
       "      <td>N11S1</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>37.221363</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.001896</td>\n",
       "      <td>31.553747</td>\n",
       "      <td>4.186062</td>\n",
       "      <td>0.423342</td>\n",
       "      <td>0.115173</td>\n",
       "      <td>...</td>\n",
       "      <td>77</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.335110</td>\n",
       "      <td>31.357513</td>\n",
       "      <td>5.610000</td>\n",
       "      <td>35.105751</td>\n",
       "      <td>10_BF33 N11S1.oib.txt</td>\n",
       "      <td>BF33</td>\n",
       "      <td>N11S1</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>37.221363</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.130477</td>\n",
       "      <td>25.757494</td>\n",
       "      <td>3.179185</td>\n",
       "      <td>0.373489</td>\n",
       "      <td>0.003300</td>\n",
       "      <td>...</td>\n",
       "      <td>63</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.005873</td>\n",
       "      <td>25.671747</td>\n",
       "      <td>4.950000</td>\n",
       "      <td>28.082220</td>\n",
       "      <td>10_BF33 N11S1.oib.txt</td>\n",
       "      <td>BF33</td>\n",
       "      <td>N11S1</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  ID  SECTION-NUMBER  section_length  BRANCH-ORDER         X  \\\n",
       "0           1   1               0       37.221363           NaN   5.99113   \n",
       "1           2   2               0       37.221363           NaN  3.545391   \n",
       "2           3   3               0       37.221363           NaN  2.757883   \n",
       "3           4   4               0       37.221363           NaN  3.001896   \n",
       "4           5   5               0       37.221363           NaN  6.130477   \n",
       "\n",
       "           Y         Z  head_diameter  neck_diameter   ...     SWC-NODE-ID  \\\n",
       "0  26.808701  7.419472       0.421575       0.003300   ...              65   \n",
       "1  11.603571  6.269167       0.452769       0.003300   ...              20   \n",
       "2  32.078697  7.736818       0.442847       0.003300   ...              80   \n",
       "3  31.553747  4.186062       0.423342       0.115173   ...              77   \n",
       "4  25.757494  3.179185       0.373489       0.003300   ...              63   \n",
       "\n",
       "  SWC-NODE-OFFSET  ATTACH-X   ATTACH-Y  ATTACH-Z  SOMA-DISTANCE  \\\n",
       "0        0.000000  5.071104  26.856352  4.950000      28.818710   \n",
       "1        0.000000  5.616839  12.179447  4.950000       9.710042   \n",
       "2        0.000999  4.244884  32.731667  6.600001      36.814125   \n",
       "3        0.000000  4.335110  31.357513  5.610000      35.105751   \n",
       "4        0.000000  5.005873  25.671747  4.950000      28.082220   \n",
       "\n",
       "                 segment  animal  neuron  estrous  \n",
       "0  10_BF33 N11S1.oib.txt    BF33   N11S1        P  \n",
       "1  10_BF33 N11S1.oib.txt    BF33   N11S1        P  \n",
       "2  10_BF33 N11S1.oib.txt    BF33   N11S1        P  \n",
       "3  10_BF33 N11S1.oib.txt    BF33   N11S1        P  \n",
       "4  10_BF33 N11S1.oib.txt    BF33   N11S1        P  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F_ES_DRN.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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