{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Adjust AP-ML Plane" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import anndata as ad" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "adata = ad.read_h5ad(\"D:/wochong_2023/3D Registration/h5ad_back/10dpa2_align_merged.h5ad\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import scipy.linalg as sl\n", "import matplotlib.pyplot as plt\n", "from skimage import io as skio\n", "\n", "def polyfix(x, y, xfix, yfix, n=3):\n", " \"\"\"\n", " This code is copy from https://github.com/jbae11/polyfix.py with minor changes \n", " Fits polynomial p with degree n to data with constrains\n", " \n", " param (x,y): 2D coordinates of all training data.\n", " param n: degree of polynomial.\n", " param (xfix,yfix): 2D coordinates of all constrain points.\n", " \"\"\"\n", " nfit = len(x)\n", " if len(y) != nfit:\n", " raise ValueError('x and y must have the same size')\n", " nfix = len(xfix)\n", " if len(yfix) != nfix:\n", " raise ValueError('xfit adn yfit must have the same size')\n", " x = np.vstack(x)\n", " y = np.vstack(y)\n", " if nfix > 1 :\n", " xfix = np.vstack(xfix)\n", " yfix = np.vstack(yfix)\n", "\n", " nspec = nfix\n", " specval = yfix\n", " # first find A and pc such that A*pc = specval\n", " A = np.zeros((nspec, n+1))\n", " # specified y values\n", " for i in range(n+1):\n", " A[:nfix, i] = np.hstack(np.ones((nfix, 1)) * xfix**(n+1-(i+1)))\n", "\n", " if nfix > 0:\n", " lastcol = n+1\n", " nmin = nspec - 1\n", " else:\n", " lastcol = n\n", " nmin = nspec\n", "\n", " if n < nmin:\n", " raise ValueError('Polynomial degree too low, cannot match all constraints')\n", " # find unique polynomial of degree nmin that fits the constraints\n", " firstcol = n-nmin\n", " pc0 = np.linalg.solve(A[:, firstcol:lastcol], specval)\n", " pc = np.zeros((n+1, 1))\n", " pc[firstcol:lastcol] = pc0\n", " \n", " X = np.zeros((nfit, n+1))\n", " for i in range(n+1):\n", " X[:, i] = (np.ones((nfit, 1)) * x**(n+1-(i+1))).flatten()\n", "\n", " yfit = y - np.polyval(pc, x)\n", "\n", " B = sl.null_space(A)\n", " #z = np.linalg.lstsq(X @ B, yfit,rcond=None)[0]\n", " z = np.linalg.lstsq(X @ B, yfit)[0]\n", " if len(z) == 0:\n", " z = z[0]\n", " p0 = B*z\n", " else:\n", " p0 = B@z\n", " p = np.transpose(p0) + np.transpose(pc)\n", " return p\n", "\n", "def getMidline(f1, x_max, x_min, y_max, y_min,w):\n", " xvals = np.array(range(0,w))\n", " yvals = np.polyval(f1[0], xvals)\n", " line_df = pd.DataFrame(columns=['x','y'])\n", " line_df['x'] = xvals\n", " # half adjust\n", " line_df['x'] = (line_df['x']+0.5).astype('int')\n", " line_df['y'] = yvals\n", " # half adjust \n", " line_df['y'] = (line_df['y']+0.5).astype('int')\n", " line_df = line_df[line_df['y']y_min]\n", " line_df = line_df[line_df['x']>x_min]\n", " return line_df\n", "\n", "def drawImageWithLine(x,y,line_df,w,h,prefix='draw01'):\n", " line = np.zeros((h,w))\n", " line[y,x]=200\n", " line[line_df['y'],line_df['x']]=255\n", " skio.imsave(f'{prefix}.png',line)\n", "\n", "def scatterWithLine(x,y,line_df):\n", " plt.figure()\n", " plt.scatter(x,y,s=2)\n", " plt.plot(line_df['x'],line_df['y'],c='r')\n", " plt.gca().set_aspect('equal')\n", " #plt.show()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AnnData object with n_obs × n_vars = 225385 × 19574\n", " obs: 'x', 'y', 'nSpots', 'nGenes', 'nUMI', 'sample', 'slice', 'cell_id', 'seurat_cluster_res0.8', 'new_x', 'new_y', 'z', 'seurat_cluster_res0.8_nofilter', 'seurat_cluster_res1.6', 'seurat_cluster_res1.6_nofilter'\n", " obsm: 'spatial', 'spatial_3d', 'spatial_3d_all17'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "adata" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[4504.98014798, 1537.38002575, 780. ],\n", " [4658.09057716, 2183.85150184, 780. ],\n", " [4934.05057174, 1503.53607418, 780. ],\n", " ...,\n", " [4152.13058727, 1290.76371267, 400. ],\n", " [2867.15496118, 1427.44542456, 400. ],\n", " [3873.35221 , 1663.83725283, 400. ]])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "adata.obs['new_z'] = adata.obs['z'] \n", "adata.obsm['spatial'] = adata.obs[ ['new_x','new_y','new_z'] ].to_numpy()\n", "adata.obsm['spatial']" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
genename
SMESG000035834.1
SMESG000068721.1
SMESG000005930.1
\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: []\n", "Index: [SMESG000035834.1, SMESG000068721.1, SMESG000005930.1]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fz583 = 'SMESG000035834.1'\n", "slit1 = 'SMESG000068721.1'\n", "wnt11_1 = 'SMESG000005930.1'\n", "\n", "used_adata = adata[:,[fz583,slit1,wnt11_1]].copy()\n", "used_adata.var" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "#used_adata.obs['fz583'] \n", "import numpy as np\n", "xxx = used_adata[:,fz583].X.todense().reshape(-1).tolist()\n", "used_adata.obs['fz583'] = xxx[0] \n", "xxx = used_adata[:,slit1].X.todense().reshape(-1).tolist()\n", "used_adata.obs['slit1'] = xxx[0] \n", "xxx = used_adata[:,wnt11_1].X.todense().reshape(-1).tolist()\n", "used_adata.obs['wnt11_1'] = xxx[0] " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "View of AnnData object with n_obs × n_vars = 697 × 3\n", " obs: 'x', 'y', 'nSpots', 'nGenes', 'nUMI', 'sample', 'slice', 'cell_id', 'seurat_cluster_res0.8', 'new_x', 'new_y', 'z', 'seurat_cluster_res0.8_nofilter', 'seurat_cluster_res1.6', 'seurat_cluster_res1.6_nofilter', 'new_z', 'fz583', 'slit1', 'wnt11_1'\n", " obsm: 'spatial', 'spatial_3d', 'spatial_3d_all17'" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "used_adata = used_adata[ ( (used_adata.obs['fz583'] >0)|(used_adata.obs['slit1'] >0)|(used_adata.obs['wnt11_1'] >0)) ]\n", "used_adata" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "fz583_data = used_adata[used_adata.obs['fz583'] >0].copy()\n", "slit1_data = used_adata[used_adata.obs['slit1'] >0].copy()\n", "wnt11_1_data = used_adata[used_adata.obs['wnt11_1'] >0].copy()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "plt.figure()\n", "plt.scatter(fz583_data.obs['new_x'],fz583_data.obs['new_y'],c='g',s=3)\n", "plt.scatter(wnt11_1_data.obs['new_x'],wnt11_1_data.obs['new_y'],c='y',s=3)\n", "plt.scatter(slit1_data.obs['new_x'],slit1_data.obs['new_y'],c='black',s=3)\n", "plt.gca().set_aspect('equal')\n", "#plt.plot(line_df['x'],line_df['y'])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\guolidong\\AppData\\Local\\Temp\\ipykernel_13104\\1304777353.py:10: UserWarning: 10dpa2_body.png is a low contrast image\n", " skio.imsave(f'{prefix}.png',line)\n", "Lossy conversion from float64 to uint8. Range [0.0, 255.0]. Convert image to uint8 prior to saving to suppress this warning.\n" ] } ], "source": [ "def drawImage(x0,y0,x,y,w,h,prefix='draw01'):\n", " x0 = x0.to_numpy().astype(int)\n", " y0 = y0.to_numpy().astype(int)\n", " x = x.to_numpy().astype(int)\n", " y = y.to_numpy().astype(int)\n", "\n", " line = np.zeros((h,w))\n", " line[y0,x0]=150\n", " line[y,x]=255\n", " skio.imsave(f'{prefix}.png',line)\n", "\n", "# draw image to find the fixed points:\n", "drawImage(adata.obs['new_x'],adata.obs['new_y'],used_adata.obs['new_x'],used_adata.obs['new_y'],8000,4000,'10dpa2_body')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\guolidong\\AppData\\Local\\Temp\\ipykernel_13104\\727444766.py:59: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n", "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n", " z = np.linalg.lstsq(X @ B, yfit)[0]\n" ] } ], "source": [ "#fixx = [2300,3350,8000,8700]\n", "#fixy = [2500,2850,2800,2600]\n", "fixx = [1525 ,7200]\n", "fixy = [2225,1850]\n", "f1 = polyfix(used_adata.obs['new_x'].to_list(),used_adata.obs['new_y'].to_list(),fixx,fixy,n=4)\n", "#f1 = polyfix(slit1_data.obs['new_x'].to_list(),slit1_data.obs['new_y'].to_list(),fixx,fixy,n=4)\n", "midline = getMidline(f1, 7500, 1500, 3500, 500,8000)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "scatterWithLine(slit1_data.obs['new_x'].to_list(),slit1_data.obs['new_y'].to_list(),midline)\n", "plt.scatter(adata.obs['new_x'],adata.obs['new_y'],c='gray',s=1)\n", "plt.scatter(fz583_data.obs['new_x'],fz583_data.obs['new_y'],c='g',s=3)\n", "plt.scatter(wnt11_1_data.obs['new_x'],wnt11_1_data.obs['new_y'],c='y',s=3)\n", "plt.scatter(slit1_data.obs['new_x'],slit1_data.obs['new_y'],c='r',s=3)\n", "plt.scatter(fixx,fixy,s=25,c='cyan',marker='^')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[-1.40596268e-12, 9.81619394e-09, 4.52222132e-05,\n", " -4.61221827e-01, 2.83054135e+03]])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f1" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "def update_np(the_raw_pos, the_midline, f1):\n", " def Yinline(f1,xvals):\n", " return np.polyval(f1[0], xvals)\n", " the_result = pd.DataFrame()\n", " the_result['raw_x'] = the_raw_pos[:,0]\n", " the_result['raw_y'] = the_raw_pos[:,1]\n", " the_result['raw_yline'] = Yinline(f1,the_raw_pos[:,0])\n", "\n", " def Ytag(item):\n", " if item['raw_y']>item['raw_yline']:\n", " return 1;\n", " elif item['raw_y']" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.scatter(adata.obs['new_x'],adata.obs['new_y'],c='gray',s=1)\n", "plt.scatter(fz583_data.obs['new_x'],fz583_data.obs['new_y'],c='green',s=1)\n", "plt.scatter(wnt11_1_data.obs['new_x'],wnt11_1_data.obs['new_y'],c='y',s=1)\n", "plt.scatter(slit1_data.obs['new_x'],slit1_data.obs['new_y'],c='r',s=2)\n", "plt.gca().set_aspect('equal')\n", "plt.scatter(fixx,fixy,s=25,c='cyan' ,marker='^')\n", "plt.plot(midline['x'],midline['y'],c='cyan')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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rxbZt22C32/nvvPzyy1i8eDHmzJkDs9mMyspK/PKXv+S3OxwO7NixA4sWLcK0adMwYsQIPPLIIxSSniFIHY49Ho9oYud+l4ZXx5sPJ9m1qzKVZIV3l5aWigSaZJlJtAhs8US7ySEVqPQSpIDY2bCleDyehBNGjhw5csBLg9/vF9X0omzORCaTVIHnS1/6UtSH1mQyYdWqVVi1apVim8LCQvz+97+PepxrrrkGu3fvjrufROpQiqgSTqpq0booZCtC7YYUteOZrPDuWGYQvaqnaxHY2p1OfOJ24PKWID5xO3QzbeolSAHaBfrm5ua4jiNETkMqvT6cwNrQ0IDi4mLKPUNkFIMySotIPX6/n89uDOhTvoCEnX6i+ZKkMgxcjlimFr1KdWgR2JjFgvIqhuHtwGknw/06mTbVpg1QEy6fk5MjW8NNiXiKxApREkyjXR8uOrK6uho1NTWk9SEMDwk8RErw+XwinxutC7HWBWAwEU27obewI/VRiaZdAmKbWmbPnq2LD49Wyi2z4Buhrwkvmo+YcJzi9ZdKpkZTSTCNdn2EfeFqeAmzqZP2hzAaJPAQSYUzYQ0dOlS08PX19SEvLw89PT2i4pxKkLCjTKqyDcv5qPgs0SOVhEELRiJZY6bkI1Yf2Y1R7SHUO3fDa5kV0/wmd79HE3YSdVpW0hBJfbBiEQwGRe1ra2tJ4CEMAwk8hK4Ia8oAF9Tecm/5nZ2dKc03QsSP3++X9VHxjojuOxNLmNXLh8fImCIR1G804fIW4BO3CZurpmK6Rbuw5Xa7FSMREzVpJQth1BfVmyLSDQk8hG4Isx7X1dWJcuQokU7/kmwkWblSduzYgV4ZH5XpGBFVUxLLBKNGu5fpONvbcXlLv8B/eUswbmfmjo4OvbvGs23btqTsNxKJYOXKlXC73ejo6KDwdiKtkMBDJAz35vbFF1/wC5waYccoxJs7xYi1mmJpTOIt5RAOh4EYeYzk9h1LoNG7mKgRaXc6ceYyCwo+jeDMZZa4o8Ki+UMlWoVe6Tq98MILCe2Xo6WlBXa7HSaTicr4EGnDxCjUBaFQCA6HA8FgkMpMxEAaVu71elVrc4xIIrlT1mEdggjCAQeWYmmSe6oOi8WCdyLviMxMQqEsXsdX7vmIBqUJUMY9ag8mWnfjYHgWWk6UxbUPpcSA8ZJOgT0vLw+zZ88mTQ+RMFrWb3KgIDRRV1eHYDCIhoYGBINBVcKO1WpNUe+0I/VL+d929eUVvPDCAYchinJyRCIRURSQNGTd5XLFtd9QKBSzTUlJyYDPyEern5YTZXjj2A9VCzvC5Ksc0YQdufaxEN4bDodD8/cTobOzM2GtFEFohWYjQhNc9IjJZILJZEJ3d3fU9nuxF2vDa5NSYFEPhPW19rkteN95TnVtpHQW5YyGUBCTCmXtcRZMlRNmpBQXFw/4TC8frWQW6jQi0rIpercHxPeJUHuXqrEOh8NYs2YN1q5dS3W6iJRAPjyEavx+P/+WyZkuYpkwklWjSS+EuVN2Og9jmOVtQ2lsoqFkkpCGXAt/T9QkEs3fKZ7QaLVmMKPfR5mI8D4RXgfhWM91zNUlSagSnKBWXV2No0eP4vDhw3wNvYMHDyIcDiMnJwcVFRX9faMoLyIByIcH5MMTDaHPjlwdpFgLVir8BIzoPJwK5HyIYl2PRPxAYvk7zZ8/X3NotNr+DNZrrBY9M2obday5c7RarZg3bx4JPwQAbes3aXgIRfx+P7+AKb3lxZKXU5EUb7C+/ctlWM7JyREJEHo5ElutVlx06pRutaI41PYtVckV04nLtRdFRT40NXkRCGg7Vz0FHqOONXd+4XCYn5coxJ3QAvnwELJwOXVioVctpEQwovNwKpDzIcrJEb/D6KXAHTlypMjfSa5WlJxJK5bDOmXQvkBRkQ92exBFRfKmwWgO4EZ4DtNBKBTCmjVryAeIUAWZtEAmLSnCBIIcDocDoVAI+fn5qm36RlWNZzOTJk2SNT1yWCyWhJL9RfPhsdvtA5xn9Q6lzmZiaXiiaXGk2wbbs5eTk4NIJEIFTAchZNIi4kJa0RzoT2ff3t7OCzlaHBiNamrK5sWgubk56vZEMxsr1YoC5COFMkHYMcr9EAhMj2rKimaykm7zwYcbXEF8p6gGZ5qg2USWaXCaQk7YJ6GHkIMEHoJHWtEcgGLtHjVEq+KdTowqiOmBtEirFDUJBFNJXl5e2pNWqrkfYmlfjCI0cXjhxXeKajDKzpBf5Mt6gUdIQ0MDGhoaYLVaMXLkSLS0tPCOzuTvM7ghHx6CR++U75mQpybbCAQCUbcbSdgBAGecZRb0RM39EMu/RprgMd1Mx3ScaboZXV0ONDXFvs9TnXgwFYTDYf6FjXN0Jn+fwQ1peAieQ4cO8b9zpqx0v30nA6NGoeiBzWaLKwldupBqENOhKVFzPzQ1eXkNjxxG1GbGMpEJMZognCy6urqwY8cO0vQMUshpGeS0zPH444/zPhdWqxU5OTlZKfAYhVQt7kYztwgxm83Y07eH7x+nKTFSfTIiO3G73Whra4PJZKK6XhkM1dIi4kLo0BoOh0nYSTKpMoMYzdwihDEm6l82mxsJY9HS0oKuri50dnaiurqaTF2DADJpDVK4DMpe7/mK2j6fbonLCHWkygxiRHMLB2NM1L9sNjcmE6M5o2ci1dXVqKmpgd1uJ41PlkImLQw+k5Ywg3JeXh56e3tTFj4cLY9LohjZdKOFbDkPgshkJk2ahEOHDpHJy+CQSYuISl1dHf97OBxOqbBTtXEjfvDMM6jauBGmBHLCyFV0NrLpRgvZch4Ekck0NDTwJq+amhoyeWUBJPAMIvx+P9asWSPyzent7ZUVHpKBs719QC2mePHBh3ORIAKn3uIFp1T7f5giERSeOpWQ4CZHqs4jVdedSD4WnbWlhBjGGLZt24ZVq1Zhy5Yt6e4OESfkw5PlxKp2DqQuER9Xi4mrti2txaSFGyIzsWLjDkxtOYdj7o3YWFWF6RZ9/T+imZZiVQ5PBK1+LPGawLI5AeNgg/zvkg8X1NHQ0IDi4mIAoIrtGUbaNTwrVqyAyWQS/VxxxRX89q6uLixatAjDhw/HsGHDUFlZiePHj4v20dTUhPnz52PIkCEYNWoUfvSjHw3qooR+vx/r16/nS0UEg8Go9ZVSpVFgFgs2VlXh6cWLExYQvtJ+Kaa29E9AnLZITmORiBYjmmlJT21VosRrAkulRixRbVKytGnZArlippbq6mps27YNwWBQ5CJAGJu0CzwAUFJSgkAgwP8Iqy4vXboUf/3rX/GnP/0Jb731FlpaWvAv//Iv/PZIJIL58+ejp6cHb7/9Nn7zm99g06ZNeOSRR9JxKoaAE3Kqq6tVJaHTMyNyrIWJq8WUqDZErnK33MKfiD9MNIEgVuXwVBKv4JLKTNjxXAdOSHovskc33y+C0AtO49PZ2Un+PRlC2qO0VqxYgddeew0HDhwYsC0YDGLkyJH4/e9/j2984xsAgI8//hhXXnkl6uvrcd1112Hr1q34p3/6J7S0tGD06NEAgA0bNmD58uU4efIkcnNzY/Yhm6K0hBFYqUYvM49aE4004kvue8mMeEpmxFkq9h+NRMdN+v149rcO6xBEEFNPDcN7z5zjP3968WLFAqZEdFIdAThYIg5zcnIwdOhQMm+lgYyL0vr000/hdrtxySWX4I477kBTUxMA4L333kM4HMbcuXP5tldccQWKiopQX18PAKivr8fVV1/NCzsAUFFRgVAohMbGRtnjdXd3IxQKiX4yGakJS45UOKjqZeZRqw2QaovkNBbJ1GKo0VbFO+56RrSpOZZUK6f2Grhce1FWtg4ul/j8pN+P5zpwmquxzlmG0aZlOqmOABwsEYe9vb0IBoPYunUr1q5dSzW7DEranZbLysqwadMmTJw4EYFAACtXrsSsWbPQ0NCA1tZW5ObmoqCgQPSd0aNHo7W1FQDQ2toqEna47dw2OVavXo2VK1fqfzIphnNI7unpQWdnJ3w+nyhbspBUOKi2O53Y57ZgaksE+9yWuBcmIyfK00q84y4nPOql1RC+dc+ITJXVyqm9BsKimq2tM3hfEj2uIe+8bQE2VpXy2q4xF++LWrk82aRT85YoqX62sulZVkNfXx8fBbt9+3bS9hiMtAs8N910E//7Nddcg7KyMhQXF+OVV15BXl5eUo758MMPY9myZfzfoVBI90rhyUIYddXY2AjGGHJycmAymTB06FDFbKupmHiYxYIVVfNwrH03xjpnYVqci4FRsu3qoY6PZ9xNkQhMfX045nJhbCCgu1aDE8JqUYtA+1t4pKXfXCQUrNReA2FRTaF1XO9ryGnTALGQlWqBJ5nReakQpFL9bBnlWU4Hvb29WLlyJex2O2w2G5m7DEDaBR4pBQUFuPzyy3Ho0CF85StfQU9PD86cOSPS8hw/fhxjxowBAIwZMwbvvvuuaB9cFBfXRorNZoPNZkvOCSQRv9+PmpoaMMZEgo3JZAJjDIFAQPG7sSYeNZOtGgFgmqUM00aUqTwjY6OHVkzrhC9aUF0uPPP976Nt+HBdF0BOCOtBD953nuO1cvEIVloqcutFrMrlyUQPzZvcs5ZMQYpIL11dXejq6uJ9K0noSR+G8OERcu7cORw+fBgulwvTpk2D1WoV+aUcPHgQTU1NKC8vBwCUl5fj73//O06cOMG32blzJ/Lz83HVVVelvP/JpLa2ln+LNplM/OecGSte/3O1/iKptsenOxRZr7BtJT8XOUQLaiAAZjbrvvBx/jSzMRvDLA6sqJqnS6qAVBEITMeePUvTYs5KNDpP6VlT8n+j5JDZBRUpTS9p1/D88Ic/xNe+9jUUFxejpaUFjz76KCwWC26//XY4HA5UVVVh2bJlKCwsRH5+Pn7wgx+gvLwc1113HQBg3rx5uOqqq/Dd734Xa9euRWtrK37yk59g0aJFGanFkYMzY3V3d/OfCYWbRJOOqX1rVWue0UM1r+WNN1mRIHqp47WYYPRMzhgLoY9MmwGDnowY4cPlkor3/lZ61pSuu5GTQxrx+mQCNTU1/O+UuDC1pF3g+cc//oHbb78dp0+fxsiRI+H1evHOO+9g5MiRAIB169bBbDajsrIS3d3dqKiowK9+9Sv++xaLBa+//jruu+8+lJeXY+jQoViwYAFWrVqVrlPSFaEZS0hOTo5uyRXVLrJqBAC9VPNaTAdGXhQAbSaYRBfUbELNdXW59qbcgVnoT6QVpWdN6bob2ek33c9dpgpcjDFR6pDq6mocPXoUlZWVaezV4CDteXiMgJHz8Kxfvx7BYBAmkwn5+fkIBoMwm8246qqromZP1opeDpOFp07hB888w/8db84UI2h4jAidq5iysnWw24MIduVhwZ7cmONihPHL5CgvIekeSy5PkwMOLMXSlB8/GVitVsybN480PhrQsn6nXcNDRMfj8SAYDCInJwc9PT0AgNzcXF2FHSCxt1YheplktGg6BlMkSLrfqlOJmuvKac9+39StalzSNX4i4cAyPSsSJ6b7uTOy9itewuEwfD4fCTxJggQeg9Pc3Ayg/0HgTFhdXV0pe7vSepxETTLSt1+lhSHaW3K8b9DpfmNVQ6om+UwYC+BClFgH9sKhYlzkxi8VGhclQStTxtmIpFvgShZDhw7FmjVrYDKZMHv2bBJ+dIQEHoPj9XpRW1uLSCSCcDjMf56qN9V4jsMJKlyUlZqFZC/2oj6yG/UbTbi8JRjVjBXN3JWID5GeYxqr0rqzvR07nYexy/K2psUuVZO8lgXaCIu22nGRtktVOLiSoJppGjsjXOtsp+W87yIA0vbojOHC0gkxpaWlmDNnzgAHZaWQab3DWOMNzY4V6i7tpw8+DG8P4fKW/vxC0UpTRCthkUh5Cz2rhyuF8AvHZcXGHTgXSTzMP5FrrvRdpbHQu0BruklV1Xul0hqprFivB5l8rTMJ7rn8/4L/H1auXImf/exnFM6uAyTwGJwtW7agurp6QJSW0gSq94QUby2qWAuJtJ9eeHHamY9P3A4AwCduB1Y4fyu7iAtzoUjbJZInRc+6W0oL2T/ad/HjMrUlgivbc9GDnoQE1ESuudJ3tSzQsRZtI+eSSXfV+1RWrNcDvQW0dOfaMirS57Kvrw/bt2/nayYS8UEmLQPj9/s1OycbxZEvlvOyXD8jFhN+WDUTX2m/FCucv0WbJSSr6hf6CUnbGSWsW8nEssW5H99zAzNa+hfYFuc5dEL+PNUS65pHC93Wer/InVcsc5IeZptk+dkY5X7JFPQ0qVJ2aWXknkuuQCmZueKHwtJh3LD0n/3sZwknFUwnwkUKQNRFRRpiqtZXINN8Cjhfpcr2KbjYeQPetexLev+50O2uLgf27El9+G6814j73g2RmXhu4/tZuzDGI8xl2n0vh14pLAYTZrMZubm5mDNnDgk956Gw9CzA7/drFnaMNgkKnZdjvclJ32jidUJNBOEC+5X2S5Pyxj8d0zHdMh0YATCkxgk5nbWngPjPkdMMHWvfjbEyBU6FCLVY/xeA4nNgtGckXi2HnNbMaOcWi1RmFc8W+vr60NXVhZqaGsrSHAck8BiMF154QeSlrwWjhr6qyZpshBBTH3w4FwlixcYdfDFNI2sT1F7XZBb4TOa9xQnBY50zccz9ftSFUVi+wxeAognNaFFR8RYjlTN5GO3cYkHmxPjhCkhXV1ejrq6OwtdVQk7LBsHv92PNmjVxCztA/ySYhzx0o1vkIJruyIp0O4aqdYz0wovJ7cMwtaW/nZ5RO8lw3OWuay1q0+YUnMx7i3PonWYpw8aqqqgFTpuavOjqcqCpyRvVsZbbdkNkpiGcZaM9G9HuGTln50yL+AIuaIFTIexoKeKbSXR2dmLHjh3p7kZGQD48MIYPz5o1a9DV1ZXwfuTSrUv9RtLxJpWudPpaTQamSATf2vhLXN4SxCduBzZX3S9qH69GQ+m6JKId4b7fgx50ojMtKfYT9c9Jh9YxHjNSMu9fpX3rVTpBy1gn8zzTUfdMSLp92aKh19gMxtIU5MOTgQiTCiaCnKp7RmQq/nvjfoxteQvH3J8qTvDJXISUsiYnexLUajJgFgvKqxiGtwOnnQz3S8YpmtkgWkLBZJggODPge5E9/X4uzplAimXZRP1zUm1+cbn24grzWzF9goToHU0kFSqUng0vvKhDHa+xjTVOSs+v2rHWep5a5wuh2TEdAk+6fdmiodfYhMNhbN26FTt27EA4HB6UAlA0SOBJI36/H1u3btU1EktuAVK76KdjEYr2oOvxthmPY2S5ZRZ8I+RDtZXCuIWLxfVuC2ZUReCzXBhHuesSTwoB6SJjikTORzCdwzH3+9hYVarrm7maaxCP0Krm3JOhbSgq8oFZz+HMZRYUfBpRdU/E62cjhxahYjqma3omldqqvc+0nOde7EUNasDAVM8X6RY4kunLpoZoAqKeY9PX18evKeFwGNXV1dixYwcsFsugj+4igSfF+P1+3rt++/btSQs7Fy5CrZGpqhb9VOfwMUUiCO69Fqap+9F0TFmAEC4MWhfBeBwjo2ktlLYJF4upLRFMbh8G14jo4xiPdkS6qOm5GEtRuzjH83YqPHe5hSAeU2S0a8wdY1GTB7OKgD8tnYmuD9VF4ukZTSS9Xv9o34VXRhxQ1JJoeSaV2qq9z7Scpw8+MDCYYFI9X6Rb4Eg30YTXZI9NOBxGOBzGtm3bUFtbO2jrdJHAk2J8Ph+fPEpaLkJPpIuQmkU/2sSot7lLuqA9W9WHXZZ1/P7lFvJ2pzMu04JeleCjIV0svu6sAkuCfUm6qCUztFetMBXr7TSWMCK3EGgR5EyRCKp+/WuMDQRwzOXCxu99b8BxuGM8GwByA+f9N1TeEnpGE0mv1xbnfgSjJJ7UIhQnGumo5TyF92EmRIQZASMkhY1EIoicd9Svrq5GdXX1oDJ7kcCTQvx+P7744gsAQDDYXzNK6e020clVugjFW3mcQ4tqXY1wJF3QjrWHEBxxjt+/3EIejzYjVY6SqQqxlS5qyTyu3DWQu1eivZ3Gk4NJ6dhKFJ4+jbGBAABgbCCAwtOncXrUqJjHiIZaPxutSK9XuSDxpBFQe55GSCORaRh1zDizV3V1NSZNmoTKysp0dylpkMCTQjhHMiFSQUK6QLx4110oCAZjLmZSIUOtilSt6UDLgqFGOGp3OrHPbcHUlgj2uS0Y65yFw3ib37/cQh6PNiMZjpLcYnjG4RBdm1RokuRI1nGl1wCAZg1bvDmY9Bbk1C42XERj/UYTLm8JJiUXk/B6GXURzDTUaqDTHSlmdBoaGtDc3Jy1CQ1J4EkRfr9fNhJLKkhIF4h7XngBo0+cwD63BSuq5mGapWzAPuJxIORQqzVRMzFzk44HHv7clGCW/vPpjy6ahWmWMkxD2YA2wr7EswhKNV1Kwoqw/1548c8uyE6M5nAY333hFxh34hy6rBbYw5Go1yYVcJP47iYPng0062p2FI514alTmjVsiZjcmMUC29VHMKPofxQXqL3Yi/rhuzHPlY/LAyEcc7nQNny4pvMUXncffBjVHsLl59NhqXHeJdOOvsQjlKjVQKc7UiwT4BIaAsg6oYcEnhRRV1cn+7lUkBAuEMdHjcLoEycA9DvCHmvfjWkjBi6qWhwIpRO01gUpmvmLm3QAqMobMs1SJns+0dCqzRBquoTarB6rFbnhsOgNXjhpLi7CgInRFImcF0D7Q5rt4X5beLRrEws9zJfcJD65KIRgQLvQq9QvqTZHqpVTI7wkqqmJtUD54EPQEkL59/Kxon2xpmNwY1/v3I3g+QK0XnhR79yNT9wXNDyxnHf1imxUey9ku5AVj1CiVgOd7kixdMMJkx+3VuAvlyzC9W+/DXt3t2zbHTt2oLa2FpFIBBaLBRMmTMChQ4dgMplw6aWXorm5GR6PJ6M0QiTwJBkuKkvqoCw3ae3FXvgsPvirZmJM+2V4zbEPvpccuLwlyJt95NDiQCidoLUsSLHMX3o45Unf7oSLQKKFNoXarNzz2jbhG/wNkZl8PpumJvOAidHZ3s4LoADQnWOGrbcP+1xmTO+bClMkomlB1yu/CzeJv9/kgQPNCYW5cyhp/sRaOZWLsmV6XCa3vdiLD5u68e2iPBxv8sr2lbvnyi1eTccQjn2F24HyqnyUW/r3O90yHZur1AkfejmiarkXMq2EhFbiEUrUmgYHe6QYJ0zumVWK3ZfeABOA2W+8IduWi+zifm9oaOC3cb9zvqh1dXUk8BAXorKk1KEOnehEHer4B9UHH25wBfGdom3Y0mTDyUAXyqsuvLkqLTBa/AC4CfqGyEwUtp/iJ3Q1i0Us85eWfigttMK3u9Z/TBUtAr+oOse/iccz0Qu1WUIND+eMK85nUzVgYpRq316oqkJBMIhbX30VG361C8fchzQJLUqRaIWnTwMA2oYPV7UvbhLPBTTn471wz9XgTBP4c1bS/KnVyumxKPvgQzDQhe0BB5ZiOnznMw/XonbAvcOVtlB7LOHYX94SxIr2xdg+4gjWYZ1ISHsvsgfHTl0wu0qRu+e1mGS45+Bf269VbS40QrRPMhnsQkkyaWryouDSRvyl6AcAgPrrrsN19fUYIpPl3+12o62tDZFIJGZi3Ewp2EACT5IZOnSorMDDwET/A4AHHny7KIhRdoZvF5mwPeBAucWL7SOOwIf/SUiFLRQwlkXuPy9IbNWkWdAzBFppQRS+3UkFgsr2G/HKiAPwwHNhYdIwHkJtltSH59ipN2IuOHLaMGY2X4gQUvieknAnHc8zDgcfXg1AMcQ6UYRaM6/Fi+8U1WCUnSFfYEJI1BSlx6Is3Qf3dw96RPdOPMKV3L3sw/8MCCDgCsnuc+/A6yqTOgqFdu5vJeGH6/sW5348qvLZIkdnIl4Cgen43RUPImzJBQD05uTgnfJyWS1PIBBASUkJDh8+jEgkEjVn3IQJE5LWZz0hgSfJKBUDnYM5AxaEZjTj903Ad4pMONM0G0vPT2pcTZ2E35bP76Oifbzi4h7Nj0DPyBmlBVHkc+OMiBali503YCm+nNB4CLVZQsFki3M/vucGZrQoFziVG5t2pxPHXC4+B4zc95QWZOl4OtvbeWEH6A+xVuMYrLVWklBrhqoqnGkC8mVMCEqaP0VzrOCzRBdloQO8UHszHdNFxwKU76Vomha5e1kugIArJDu1JYK/qXxOhEJ7LH8UoUluY9VUqhyeZAZ7lNYXeXl457rrwMz9dcOZ2ayo5WGMicxYHA6HAwDg8Xj47Q0NDSguLja8WYsEniRjMplk1X3TMV0QCdS/0Hvhxa6AD30B8cKl99uykqZGjR+BXiHQahZE6aLE+fDEigKLx6mz3DILc6pqMa69D+OdkweYDxPxt5FeP+liyY2nUHgCgE9cDqxw/hblmBX1PLRoOOTMaAGLNhOC3PG4z4a6alHGZfj+R/wLOLe/EEJ89CH3uRdekVO80r0US9MivZdlAwhkhNlY94LUJBPNH0V4TGZBWtIaDCYGe5RW/XXXoTdHvOxH0/LIEQwGYbfbcfjwYeTk5PD+qdu3byeBZ7ATzbY5uqgOdnsnRhfVIRBQfivWIwOydGKV09Qks0xBvAgXJbVRYHK5jdRkmfZZfGgYEUQz3h4QIq80NkKtjJJGRjj2Sosl77D+vZmYd/pWAMCjw3+HNhU+S9EEYum5S/2Qzpx/W9OC3PG4z75d1AO7PYjisbsxf+V+jG1pwYliB/Y/y3D02CzNUTceeNB83hFbTtBSSjPAlS0xT96H4PvXonjqbtjsIdFCF2+0k/ReGP/ZZzhyySUw9fVh3Oef4/Nx49BntQIgfxSjaVQGc5SWVLvDEU3Lo0TX+XbW8/c5gKRWDtCLrBJ4nn32WTz55JNobW3F5MmT8fTTT2PGjBnp7pYiv29iuLUIeLWJ4SrB50J1PjfZK03IcouAmomcWSwDfIOSWaZAD7zwoha16EFP1OrRwgVZi2YmmuAg529TeOoUah2f4vrzodqJFKLkruMuy9uYNqpf2CrHLFWaPSWBWOncX7zrLj6/090vvaQ5OuyfXcDiIpzXTIr7cLxpL2xFPgT3XYuxLW8BAEYdDaKgDWDnHdGFAphcMVRneztmOKdiukV8P0s1e0ppBl686y7c/dJLgs/fwoliBz5+eCia+2by5xFLMJYKs+M/+wyfjxsHU18fr/npsVrx3ZdfRovLhREnTyK3txc9VivWPPggL/QkE61pDVItgBhNo6KnAJpp6QH2lpYinJsLMNb/w2EyIZybi73Tp+PG3bs17VPqzOz3+w2t5ckageePf/wjli1bhg0bNqCsrAzr169HRUUFDh48iFGSNPOp5s/4MxrRiBKUoBjFFxaxwBzcGxi4oHETMafNkHvDl1sEuM+60Y0udMXUDCQSop4O1DqoCgUAZ7v6ZHnRNGlSh2duQb3ebUH5XRFMCg7rr58VY8zOOBx8fiWhgCQnbCXqB6MkXBUEg3x4/diWFky07sLHfV9Wvd9oixi3oJgiERxzf9qv4SnKR09nBP84dN0AAcxnuXA9Z0SmygpoSpo9pTQD4z7/fMDno44GMepe4FJBRflYgrE0qu+7L7+McE4OrL29OOZy4Xe3347v/OEPAAC3wPcqNxzGuM8/x2eXXab+YsVBLGFeThjSWwCJJkC5XHthsXQjHM7LSo1KpqUHcLe04KrGxqjb1ZKXlwfGGK/p4fD5fCTwpIKnnnoKCxcuxF133QUA2LBhA6qrq/Hiiy/ioYceSkufGIAv7HY04BiAAjTYzuKTGRXoebcBu7obcB/uQwn6c+t8AWA/9uMdvIOxKEEQH5/fgwkz8BX8DR/hHbyD63AdpmAKdqEBZ2FGH87iPjwMAHgOz+EszLCjEBfBihm4Dl/Arti/Gfgq3sE7A9p1DB2atDFJFKU+K9HpdqPhqqvgDgTQ4nL1O+oCKDhzBsH8fDhCIZwpKBig5lWiY+hQONvakNfZiTanE+M6gSvCwzD84pnogPy4cdd1Zt8M/PyVRljDYXx8+eX4zb/9G5jZjIIzZzCp4HqUmC/cC8Lvcdfc1NeHgjNnBvRX2i7auTOzWfR56DLAUvY5vtivPJbS4350/Ku4+OJ38I/j1+ELu/L3nl68GM62NsyvrkbJQ60YOfIIP255nZ3I7e7GjMIL1zO3rXvA9vbCQsVrLjwPTsPT4nLhwyuvvPB5Tg5yBap24X5LMIt//nIF11TY5unFi1H8+ee47U9/Ep1bXlcXjo0de2F8x4zBiFOneA3Ph1deib4c/adX4bWe3VYs22eg/5p953e/46/9777znQHX7m/2j7C37x18vW0SrsSVaC8sVP0ccDgvbUCPzQznpQ043D5rwLZOWx66u/P7t8V+XDMKrXNRuhl77BhOHHtddq7giPY8i9oxBrPZjD5Je++NN6ItxncL1XY4CZhYpgTQR6GnpwdDhgzBn//8Z9xyyy385wsWLMCZM2fwv//7v6L23d3d6BZklwyFQvB4PAgGg8jPz9evXwBsuu2NIAiCIDIXGwB1XkLqCYVCcDgcqtZvbeK8QTl16hQikQhGjx4t+nz06NFobW0d0H716tVwOBz8j8fjSVVXCYIgCIJIA1lj0tLCww8/jGXLlvF/cxoevbEC+OmqVQCANV8agh7vUsBsAfoiyPWtw5w3d6EUYnvnPtcq3F4E/KEJmBp4BEC/evruF1/E2JYW7HNb4L0zAt+mfkfZT9wOeO9muM7sRSlK8Rf8hfcXKkIR/oa/4Xpczx/H5fKjqOhvaGq6HoGANlurH/4B+8s0hGMpcnS9+27N6vyW029hw7Nv8X8/s2hRzMKVpr4+UfVxri/H3G68dOeduHnTmv5Edy4z6m/5d+wcfgS7zfWY1VeOZ1/8gG/L9Ze7ns1HynHT4xe2T7n7HIIIYXL7MHzNuUTzuQn7K+yjmnEqK/sF7PYguroc2LPnP0TnzMzmqPfRM33r8fqLIT4fktzxpH166c47sfDXvxaV/Xhm0SK0O50D+i4dcy3X3dzbi3s3bMCItgtKe+6aF54+jcXPPst//ux994GZzfw5J4J032ruM+DCvXbG4cBdmzYNOGdTXx/u3rjxQqLLMWPw4ve+JzvewuunN3rMK4nMa4Q2zGbzgCSEkyZNQnNzM66//nry4Uk2I0aMgMViwfHjx0WfHz9+HGPGjBnQ3mazwWZLvrHJBMDCGL7Iy0PkusX9wg4AmC1g192Pq+sjsEicvr7riWC0DfiuB/i4pd/aWHj6NIqbmwEAM5ojmP/ZEMxo7vf0uKq5DSNOA2+P2IUyTMOH+AAAw4f4AP/A5wghiLfRvw0Axnt2wW4LYsK4nRjv2aUYrSHn8Nhfz3za+RYZagk1mfCbO+8cEMpsNpnEkQsqeNXhxz2jI/ziHHI4YFHYh3A8g4WFvGqV60u704nh7e2Y0dwDAJjxjwhmPPMMvuF2Y2PVIjCLBb+580KEE9df7npelfMWipv7i5oWNzfjX0/egHte+xumtgRxzP1C3HW6hOOldpyOHS1HUZEPx5rKYWEMe01++ArPR7Ow6VHvo2+cvgYzm9/iz2PE6dMiJ/O92IvA6bewUnCul372mShh4/FRoxByOGCW6XvhqVP8s1Tc3IwJhw7hyCWXqBsbiwUv3HMPH+G2z2XGx70H4O69ESGHA8dHj+4XKlwufGPLlv78PTEiA9VEWYn2HeM+E+/chGBhIYZLzpkfU5MJv12wAPc8/zxGnzyJnL4+WPr6wEwmfhfuUXvwzXU7UPBpJKF6b9F4G7sGzFNa4Z6D8Z5dONES3z4IlUQi4O6AvLw8zJ4929BCjpCsMGnl5uZi2rRpqK2t5T/r6+tDbW0tysvL09izfuqvuw59Obmiz7hkT1I+aJqEE10mfNA0if+MixYB+hfWqeOW8n9/4nbgtDOfj+4pQQkAIAc58MADBxyiyJ+mJi+6uhwATKKkbEK46I8fPPMMqjZuhCkSSWwA0sRe7MU6rMNe7BV9zuX26bNa0TZiBB8eLddW6XOgP1nhTVX5uGfxjTEXNeF4msNhFJ46xRcb5fogvM4cXHSVsN/C43DX82B4lugemYRJfJZg4T7igeubs71d1b0QCEzHnj1LeUFaGM0Si4udN4jOQxrm74MPDY5z6Dgf8d1jteJoURH/neOjRuH5hQv5a/qU5ZfYPuKIODP2+bZc5JWWe7zPasV///u/Y8b3h6IXfdjwq12o2rgRQH9uq6cXL8art94qKjfC1UaTovY54yIEn168OC6BQzp/CMe0IBjE6JMn+/t6Po+UsH/Xnn4LBZ/qcx8p4YV3wDylFe45yMZosHRhEgi+clitVjz44IMZI+wAWaLhAYBly5ZhwYIFKC0txYwZM7B+/Xp0dHTwUVvpIpyfrzrZU4/rz5hc1Ii6Jhc2B5rhPZ9rRi5cXPj3/YIJ8Bv4BprRjCCCaEbzgAR9XNiwMJxUihETEMaDlrBRpbbR9sFV1saI6Pou6Xh+54VfYPyJc/jE7cDmqvv5BYy7roWnT+PWV1/lNQTRcvsI84psrCoVmcv0yqmUaFV3tZnC+eSLVTPxlfZ/kdV6eOFFIPgWhob7NTy54TAcZ89iY1UV/tG+C1uc+1FuOaCYwoAb457PtuG/Xu4XYrXe48xiwbXmUswIvDXg+20jRvSH5AsyZt/66quyNdG0PGeJZDiPlm4idtb1L9BrA3K6Y5dc2ek8jF2WtzXnpeHSL/Tv51RUbZcSgz3BYzKIFc+Ucz4K0e/3w+fzwePx4NChQzCZTIbV+mSNwHPbbbfh5MmTeOSRR9Da2oprr70W27ZtG+DInGpO/+u/qk72NLmoEaPsDF8qasF/BzBgohZOeNEmQDULTLQJwugJCNUSq6RDtLaxPteSdEya3Xj8eV+Ty1uCAxY5ZrHg9KhRePHuu/msvWonf+k9IVeWI54kaUoLc7TxFG6bblGXT0iUfFGhIvt0TIfJORXH3BtF9yezWPDKiAMI4kJmaqVrxywWPHfJQfxzjNppSpgiEVzTV4Jjrk9khVJmseDVW2/F4l/9qn/MFDJwp/I5U5ovlIQh4TXP6Qb+5447ZE1/QmH4ercFM6oi8FnkXzCiPTOJCtVE6sjJyYHVasXs2bMB9OfeCQaDCIVCvJBk1Hw8WSPwAMDixYuxePHidHdDxNfHjsUumWRPbWjHKZzCuZZ9wHmL6PtNJZhc1Ig3m1xwoCNuFW+iCetSkYBQa4bYeFBT0kGurdI+hGjRHkmTFt760lOY1NKJBnee4hszl9jwE7cD5VUM5Zbo9bSUjistyxFPkrR2pxOfuB24vCWIT9wOtDudUccz3sVLrSZI6f6Ufj/ac9BvjtyNyvYpuNh5g+p7UHRuLhee+f730TZ8+IDvtw0fHlOYifc50zvDr5wwJBXGlPychILR1JYIJrcPg2uE/PWLdg9mi1Y5mzCbzbjooov42lnd3d0oKSlBZWWlqJ3X6x2g4fF6jWlazCqBx4h8FUDHxx8PqDq7DuvQiyA+gQPzz5udcgPfwEeBb8AFRKkUdQHpxKc0EcYzQepVJFSOdLzN6T2hai3oKhzPG6usGN7eidNOq8gcKdfXy1uCmPYZUH/Jbr7UQiL9vSEyE4UCs4EawZNZLCivYhjeDpx2MtxvsaDwlHIGa6VaU7GusRZBXe7+VPP9AaUrYpgjpYjOLRAAM5tlz0utMKP2ORNeJ2F26mRl+FXbf6lg9HVnFRjk22op3ZKpWuVsoq+vDz09/UEUNpsNy5cvl21XWlpqSG2OHCTwpIDKysoBAo8eFdClb0yx/FBqUIOjOBqzPpcS70X24Fj7box1zsI0i7zJQQ3JeJuLJdTpPaEmokUrt8yCb4TytRf2tctqwY6XI/jEbcLmqohqIUWuvxdKN2wdUHMqluAp7XO08ZQryWAEM0UigjZ3f93gnIl/UXkf6fXSIO23v2om7ysT7TuJao7U9F+Llkpt6RYjlrUZrDDG4HA4DKux0QoJPCnCbreL6o4kanYCxELTXuxFN7qRhzx44ME6rOMnLi+8qEENGBga0QgGxkfMqNX8mCIRrNi4oz9HjHsHXj9fjygekvE2F8tkk84JVbqQxLr2XF/Hf/YZvvvyywAu+Pu0O51xLdqmSATjP/tMJGgKa05prTMWbTzl+m8EM0UigrbQv2hj1f0pvY+k/f5K+78o+jgB8Qt28Zo99RLskqlVJtTjdrvR1tZmaOfjeMmKsPRMQC7vT7SQZzVMx3QsxVJeu9OFLuQil4/S4oSa6ZiOm3EzHHCgBCV8CKiWcGFnezsf5jy1JZJwmHMiYbZy46YmtFUY1u1y7UVZ2Tq4XPGNvRa0jDMHs1hw5JJLBoQTyy3aQPR7iVsAv/vyy+g5X8H7mNuNz8eNE+2fqwCvNkRbLkxe2v997v5t+9yWtJspooVnx0KP0GkppkhE1Xir6bdwX0r3SCyScY5E5hEIBNDV1YXc3NysEnYA0vCkDK/Xi+rqatHbvp7VdqUmMrWVt9Wa1fTWyjCLBdtHHIEP/xPTB0muz9Jx06ox07tqdDTiNV/KaVGUroN0TIQmDWlV8d99+9v47NJLFSvA62V+YhYLVlTNE5hB02umSETLJwyd1sP/TMt+YvVbuq8X77orrmdVD60zkZmYTCa4XC50dHTA4/Ggubk5a8xYQkjgSRGcpLyueh2/MMW7EMoJBtLJSs3EpWaC449l8QI6m4TU+iBJ0cP/qanJq5iHSG8SWUjk0hHILX4eeBBCCB545BdAQV6Yq97YjFcurcA0lPH7j+aErBY5v5FpljLe/CLM/SQUMvWOOopGomYTOYfsP13Spjn/jFbzWrR+S/dVEAySPwyhCcYYOjo6sGTJknR3JamQSSuFlJaWYhZm8WpjoUlKC/GYSOSIZVLbi72oQQ2CCKIWtQOy1mrdnxSpCl2tSl3tuEUzGUizAWslUXOkVoQmODlTUjOawcDQjGbZBfDVW2/l204N9OFY+27R/hMx9wDKWYOF10CoVROi1/2cCuQyNa/YuAPnItr6L9zPJ24HVjh/q3gvxTJ9yV27aOZGghBiNpuzyjE5GqThSTGz8mahtDO2XTTaW68eGg4gtpOiDz4wMJjO/4ulfdFqopPmyaloH48ZzqlxRyIJUTIZCMeV63M8mgU9zZFqiGWCE94TSmYv7rN9bgvGOmeJvh/LbBJLC1N4+vQAjYXUwbrGNROe8W8P0KophcwbETmH7Fj5Z6Ltx9nejhXO36LNEpK9l9SYvijCiUiE3NzcrNfscJDAk2Jmz56N6urqmO1iljQ47/MijMbSipzgJFzYuO0eeHAIh5CHvKhClhde1KEO3ejG3vNlMeQEF+niKWeCSdSfRMlkINUmyI2xGhOLXkInR6z0/LFMcELhkVkguwAKP5Pzp4lmNol2P5oiEdz66qv83/tcZlkH664P/wV7TgyMLpILmd9YVQUAhlzEhQ7lavLPRNtP24gRKMcs/jmTPs9qTV8U4ZQeUmmO1YJcRfOcnBz09vYC6K9uXlxcDJ/PNyg0Oxwk8KSY0tJSHD16dEBeHileeFGLWvSghxcepCSqZZDzLRHukzMbrcM6dKELDjgAgJ+UufbCcGvh9y8sYmLBRdrvAX4RR47EnOTjzbsTy7lb7bhq8cuJ1Vc16fnlSoFE04LJLYDCz5S+q+RnIyfgcfsw9fWJqpU/f6sXrigO1nJI74HC06dx62uvGbbUgFSrAgCFp7Rrp4Sazacsvxxw3xktIV8qMqRnEqnW9KpFKuyYzWZYrVZe4Dl06BAqKyuzLgorFiTwpIHKykq0tbWh5fwEr+SEHOth0lvLoLRP4WfRNCR7sRc96IEddnjhVXw7lR6j3ekUOdV+ua6O/1sYLi2cZOPNu6PGuTva4i6d6NUsALH6qiU9v/C48UYLRfuukulMOm7SEgvC6+UefiMYtJlapAs7AMOXGuAEyHivhZqEgkYyV5kiEXxr4y/5EiPCwreZTryammTMwcmAMSayLsSqhJ6tkMCTJhYuXIjHHnsMvb29igtirIdJuAjppVqV01xIP1PSkPjgQyc64YAD0zEd7c6I7NupXBI7UbHF1lY88/3vg5nNiuHSaiaaeNX8URd3QR/ULnSx+iqXnv9d7MM6rIMHHtnM2Ikk0ZP7LmeCaj4i72cTdR+BAH+95JIQKvVLdM9apg/QmBhJsxGNeK+F2oSCRjFXOdvbcXlLEIB84dtMJl5NTapD+ZU0sEo4HA6EQiGUlJTw2pzBZsYSQgJPGqmoqOi/+YLyCyL3IAkTCCqRKtVqNA2JdGHX8nYqLbbIFWQ8duoN2bpMaitw64HSgqZ2oYs1KcqNE3c9QwjxmbGF+0jE1CH97hmHQyK4xX5zl+5DroBmLKT3rDQ3k95pEJJFvNciUXOV1sUvUeSKyALG9WPRQqZoauLJH3bzzTfD5/PB7/dnVN2rZGBiXD33QUwoFILD4UAwGER+fn7Kj7927Vp0dnbCZDJBejnWoT9vjwMOLI1SUjQbJh0589AvI09h68YQZrT0hwHnhsOK2hQ1YxDPOCWq4YkHrp9KGh6uX3ICgZpzlCYm/MEzz/Dbnl68WHNBy3jOW66fau93LcTqZ7zPjlDgaP3H1LjGIt4xdLn24rLLamAyMXR1ObBnjz5jFQu5/ibjmhkJI82tWoXcSZMmobm5GcFgEA6HIyujsbSs36ThSRF+v59XJUol7NmzZ4vUjMIoLrVvHqlWrSYDOdV9uWUWbqrajfs+m4j/erk/T4mSNkWNliseTZiSpioe/wq1C5ya66lk6lBzjsLvxqtpSNTUIneON0Rmns/MPBOuixPXYKgRSqXjpXaBk75txzMWcmMYbVHj+vaboh6YTAyMmVKSPDNafzNFOxIvRnJMlgteiEZDQwPcbjdCoRA8Hk8Se5YZkMCTInw+H4LBIGprawcIPlI1Y21tLV9oNBsEmXgQLjr3W5bBdEkEx9zHoi7KaibeRMo8JBoOnEyNkBCt55gMx1gtb6J8JfLITDy38X2MbTmHY+730fD8OdjsoYTKf6gxO8pF7kkXODlBNVnZuqOZLbi+/b7JjruKHEkzZ2nRaug9RxktEizTBDppSHogEABjDM3NzWnslTEggSdFeL1e+Hw+9PT0IBgMwufzKdpS58yZwwtIsYi1sKTazq8Xcv4dsRZlNRNvKgRIpcUiEUdjLcRzjno7xkZbtKULGnetj7XvxtiWcwD6x8e370Y4ph9ISKBQo72Sjpd0gVMSVLW+baslmiDF9a0j4MWeJD7P6dJq6PVSoKcZKtNeOqUh6SUlJVlbG0srJPCkCE6LIzRtxWq7ZcsWNDQ0yPr2cMRyYktlkUw9kXurMkq0ihC5t1GlxUIYfn/M5Upa5JGek328+1JatOUWNK+l/1qPdc7EMff7/LaD4RvA9nw5of7Ho72SLnCpElQ5oglSqVp81Wg1kuHbIjfW/Y7s2o5jJDOU3sglFQQgu07Mnz9/UDspSyGBJ8Vo8ZKvrKxEZWUlAODxxx9HOBwe0CaWWj0etXs6nfSEx06VA2S8WjClt1HhYiE8nxmYmsSzuECsyV7p+sp9rmXhEH1fYdGWW9Cmjzi/iFuAjVWlcZkzot2ziQrKRkv+lwrUCFbJECrkxtqH/9F8nEwzQ2nBbDaDMQaXy8Wbq6TCjslkEoWiE/2QwJMhzJs3Dz7f+WR/AlNXLLV6PGr3dL4dpePY8WrBlN78hYsFF8Higw8V7eP55IpjA4GENQVKi3ysyV5pjLnP6yO7UdE+Hu1OJ699UbNwqLl2ZxwOPtqux2rFGYdDtD1e4SSZ942Rkv8ZiWQIFXIZrP/11LXY4tyPcov642SaGUoNXGmISCQCxhhaW1tht9sRDodhtVrR3d2Nvr4+WK1W/Od//qfq/UYLqMk2SODJEISaIc7UlSzS+XaUjmPH63yq5s1fTVFPIVq0a0qLfKzJXmmMvfCiPrIb9RtNuLzlmf6Mx1VVmG7R7626IBhE7nlNZW44jIJgUBfzULLvGyOaU9NNsoQKuQzWj7rd2Fg1FUwgaxopXDzZWCwWviwEp8np6+tDZ2cnAKC3txdWqxWMMUycOFHTvuvq6tDZ2Ym6urqsF3goDw/Sn4cnXrhMzUYm2yclrRElsdpryWmSjLEtPHUqrpw8aklVpBqR+cS6F7lnxQ47bLBl7RwjRcmn02q1IhwOa8q34/f7+TQodrsdy5cv17OrKUHL+m1OUZ+IJFBRUZHuLsREqIXQG65a/F7s1X3fauHeRtUu2rHae+GFAw5VmorpmM4XeBWSyLhwWigASfFX4UwWTy9eTMJODIxwf6eTWPci96yYYEraHJNurFYrcnLEhhiLwjNjsVjgcDg0RWNxbhImkwlz5syJv6MZApm0MhhhbZSzZ8/Keu5rRW+tQTJNDenyNUpmqL8eZoJExiUZ/irSeyqd5qFo97fRtJHZHGmkhlj3IvesCK8bh9GuZbzIBaqYTCb+Rzjnz5kzR7NJyuPxIBgMDhCqspW0anjGjRsnungmkwlPPPGEqM0HH3yAWbNmwW63w+PxYO3atQP286c//QlXXHEF7HY7rr76atTU1KTqFNJOaWkplixZohi2rhW9NTJKWgg90KIN0RPOyXl0UZ0h38ATHRetWqtYJFPLp2dfjNRPIH33t5FQcy/KzTFGu5ZaMJvNMJlMipoczmlZKOzEG37OJSMMh8O8tiebSbtJa9WqVQgEAvzPD37wA35bKBTCvHnzUFxcjPfeew9PPvkkVqxYgeeff55v8/bbb+P2229HVVUV9u/fj1tuuQW33HJLUp16jUhJSUnU7WrV40adZOX6n0xhKhpNTV50dTnw+yZmyEk1leOi5r6S3lMu116Ula2Dy5V6QTHa/W20ez9d97eepOtap/NaJmKKtNvtfNh5JBKB1Wod0IYTdKxWKxwOR0K5drxeL+x2O/Ly8gZFYsK0Oi2PGzcOS5YsUXSweu655/DjH/8Yra2tyM3NBQA89NBDeO211/Dxxx8DAG677TZ0dHTg9ddf57933XXX4dprr8WGDRtU9SNTnZalRIve0rvAX6pVxkYsUJjMMdC673Sl44/nupSVrYPdHkxp0UsiPai51kYrJZEo8c5VbrcbU6ZMEdVS5PLpHDp0iC83ZLFYMGzYsEERRq6GjHJafuKJJzB8+HBMmTIFTz75pCjqqL6+HjfccAMv7AD9jroHDx5Ee3s732bu3LmifVZUVKC+vj41J2AgKisrMWnSJNltXniRhzx0oxs9rj8n/NaVapWx0d6+geS+gWsZXy7y6QfPPIOqjRthikRk2xSeOiW7LRHiuS6chixZRS/TqUEixMS61mru3Uwj3rmqpaUFO3bsEH3GGOOz7XNYrVYsWbKEhJ04SKun0v3334+pU6eisLAQb7/9Nh5++GEEAgE89dRTAIDW1laMHz9e9J3Ro0fz25xOJ1pbW/nPhG1aW1sVj9vd3Y3u7m7+71AopNcppZ3Kyko0NzcPqMM1HdP5RXRyUSPsdpZQuYlUp56P5cxr1LfEeB2ctTh7xyp9kMxQ8HicrJNVg4qD87EqHrsb3X8fb7h7YjAR61qnumxHKlD7THCJBIWEw2Hk5eUhHA6LtjHGkJeXB8bYoIimSha6CzwPPfQQ1qxZE7XNRx99hCuuuALLli3jP7vmmmuQm5uLf//3f8fq1aths9n07hrP6tWrsXLlyqTtP914vV6RWpT//Pwi+n6TB7OKmhN6w44n9XyyTEBGzu0SbxZnLYJErISGSouK0QvLJlLHq3jsbkxZZELF0WcMd08QFxiMZTs4rrjiChQXFw+IsmWMDRCE4onAIgaiu8DzwAMP4M4774za5pJLLpH9vKysDL29vfj8888xceJEjBkzBsePHxe14f4eM2YM/79cG267HA8//LBI2AqFQvB4PFH7nElwD4ZU6OEX0QCwJ5D8fki1FMkKszXyW2KsLM56CIGxwneVFhWhMMb9nWzhR49M0rEIBKaj++/jUXG0P2md0e4J4gKDuWxHc3MzXyuxtraW99ExmUyw2+2IRCLIycnB7NmzSdjRCd0FnpEjR2LkyJFxfffAgQMwm80YNWoUAKC8vBw//vGP+VohALBz505MnDgRzvOTdnl5OWpra0WOzzt37kR5ebnicWw2W1I1SEagtLQUR48eTWu0mlRLkaycPEZ+S4yl0tdLCIyW20ZpUREKY/FqorSi5XzjvV9MkQhMfX0XKtOrvCeyJXdLpjFYy3Z4PB74/X7U1NTwaUXy8vJIwEkiaYvSqq+vx549e/DlL38ZF110Eerr67F06VLcdNNN+M1vfgOgv0jmxIkTMW/ePCxfvhwNDQ24++67sW7dOtxzzz0A+sPSb7zxRjzxxBOYP38+Nm/ejMcffxz79u1TdOCVki1RWlLWr18/wJcnWzGqD08spItsuhZdLeYti8WCiAbnUmliuGSen8i86XLh1VtvRdvw4VHvCe7eWeH8LdosoZSWKiAha3BhNptFYeVDhgwRzdFaykIQ/WRElJbNZsPmzZtx4403oqSkBI899hiWLl0qyrHjcDiwY8cOHDlyBNOmTcMDDzyARx55hBd2AGDmzJn4/e9/j+effx6TJ0/Gn//8Z7z22muqhZ1sRphjwW63p7s7caE2ukjvZHmpQhrpla6EaYHAdDyzx4sHA76Y+UOEESNqkGp1kplbRmTeDATAzOaYwg4XJVS/0YTCSH5KSxXoeb2TFYlHJI5cMsFwOAyv1wuHw4FJkyZpLgtBaCdtUVpTp07FO++8E7PdNddcg927d0dt881vfhPf/OY39epa1iCssL5+/XreRpwpGNkZOVloMePordVSa26SK1ir1Je92ItudCMPeSlJKaDVvCkUkC5vCWJF+2JsH3EkKaZXOfQy8w7GZ8UouN1utJy/hzisVitGjhzJf85pdSwWC18uwmq1iuZoIvkMjgIaBLxer8gxLhNIpjOyUU0JUr8npX6aIhH8y8afY1JLJxrcefhL1Q8TXuAS8ZlRWmx98KELXXDAkdA4q71ecv5Kwu/OiEwVbZMTkKZjRMpSIOhROw0wtuN+NpOTk4MpU6bg5MmTorpXOTk56OjoAHAheWBzczOvwfH5fKTNSQMk8AwSuDcJv98vG7JuRPR2RhYufJlSmFGpn872dkxq6QQATGrpxBs6LHBqF1+TySSq3RZtsfXCi1rUogc92Iu9cY+1luvFmTe5692NbnShC/WR3Xhqow+XtwTxiduBzVX3a44SMqomxciO+9lITk4Ohg4dimAwCJ/PJ9LcAP2h5V6vlxdspFoc0uqkBxJ4BhnCCutGd2jWO2RVuGgms4q7nij1s93pRIM7j9fwpHKBk8Y5RFtshQkv1QgrStqTeK4Xd9w85MEBByrbr8XlLW8B6DdfcYKZligho2pSMj2826gaVznMZjMqKipw9OhRhEIhDB06VJTI1mQy8XlzSLAxFmmtpWUUsjVKKxaPP/646K0kU4h3csykSVUN6YpMs1qtA+6baH1RO+56a0+kxzVFIvjWxl8O0PBoQdjHT9wOlFcxlFtmyZ5Xtt1vycSItfKU4CKp1qxZI3IRyMvLQ25uLtW4SjFa1m/S8Axi5s2bJ8oBkSnEa47Sy1/CKKQrf8nEiRMH5HeS9kW62KsZd6n2pPD0aTCzOW6BTnpcZrFgc9X9CQmJQk0KF8audB9mitnUCBhN48qlXhCmYLBYLOjr6+Pz53DCDhdeToKO8SGBZxCjlJHZ6BhtchSi11u9kbUDhw4ditkmnsX+jMOBHqsVueEweqxW3PqXv2Bsa2vC2h7RWFqmJywkcsJdOWZFvQ+NfJ8aDaO9jHBCjtlsRm5uLl/ugTGGQ4cOobm5GUC/+WrevHkk6GQIaa+WTqSX0tJSPot1piDN5bIXe7EO62Lmj4kHrZW39cqrkqx8PEpjpWUM1SQdjKdidEEwiNzzprLccBhjzxcA5nxl4kXtWGq9j2LlFEp2ziFCP9xuN+x2O6xWK+x2O8zm/qUxEomAMQaTycTnnzKZTHz+nJtvvpmEnQyCBB4C8+bNy9jEhEByk/VJ603FIp6FPpn7kaI0VlrGUE7gkSa9i2ex55yfAeCYy4VjLlf/7wlGHakdS7kxoGR+2YXcy53JZMKUKVNgs9kQDodFZYf6+vrQ1dWFzs5O5OTkwOFw8KUflixZQsJOhkFOyxi8TstSMilkXQhnsvDAg2Y062oGMnpFca0omcoSMaHp6WwsdH429fVh3Oef4/Nx49CXAi2knJOzEUPQifhwu90oLCxEY2MjLBaLKIEml+W4rq4OjDGRM7LdbofJZKIaVwaFnJaJuMhUnx7O/s9FeujpJBqr+GemoeQrkYgPhZ6h2px/jCkSwd0vvZRSYUM6BkYNQSf6keaDEiIXSdjR0YGOjg7eHwfoF4I6Ojrg8XhQV1eHzs5O/vu9vb0oKSnhK5oTmQ+ZtAgRpaWlmD9/fsb59QDJMwNlAtFML+7zZqJkITJFnTc/aa23JUVO2Eg1cudlJBId40wnlnEiJ6f/fd5sNiMvLw9erxder1c0bh0dHViyZAmam5t5YYdzRH7kkUdI2MkySMNDDIBLmJVp1dZTHelhlEiqWKaXhQsXYuXKlUk7Pheq/Y/2Xdji3I9yyz6suHlFQprCdqcTJ4odGHU0iBPFDt2EDS3XzOjJ/MgboZ+cnByMGjUKgUAA+fn5CIVCAPprvplMJvT19SE3N1dkjuJMV1x5B6E5i0saSGQfJPAQini9XuzYsQPhcBhms5kvgEf0Y5Q8K7FML36/P+l9YBYLXhlxAEGEdHEeZxYL9j/LUNAGnClkYH59hA2t10yvXEfZ5guWSrhaVNLcTxxDhw7FwoULAfQXSWaM8ZFVLpcLHR0dorpVchmQKSvy4IAEHkIR4STg9/vh8/nQ0dEhWy07W9CiAUh3nhXOwfeMwxG1jpLPp3/0mhzC8di+fXvC+zt6bBbYeSFBL9J1zYTRfiTwxIYzqVssFpHGhRN6rFYrLBYLHyLOwdWv6unpQW9vL2+yIgiAorQAUJSWFtauXcvburORTElxLzVjvXjXXSgIBmVNL263Gy3nNUBEeuhx/RmTixrxflMJcgPfSHd3DA9XvkEK9+IVK6ux2nZE5kNRWkTSmD17NmpraxGJRBCJRLLOzJVurY1apGasgmBQ0fQSCAQSPp5Q8/V199djClDcBET082ygGcEAgwPNBhaj08ukSZNw+PBhkW+NFLWmJzJREXKQwENoQjiRZJpTsxqMluJeiWgVyqVE839Qi9D3ZW7H3JjtOcdRop9MEaRTSU5ODqxWKzkKEymDBB4ibrxerygShxybU4eWCKLi4uKEBR7hgu3xeGIKui6XK2lmtHRViU+ETBGkk4nFYoHVauWLcpKQQ6QaEniIuOEmK85WLkzcpQWjhHdnGmojiGI5LasxPwkXbK5wYjQ6OjpittECd4/cEJmJ5za+T9mPU4QepknKVEwYBRJ4iISQ2spramo05wcxSnh3tjJ06NCoi1Z3d7em/Uk1e/G20QJ3jxxr342xLecAUPbjVPDFF19o/o7b7UYgEOBDw5cvX56EnhGEdijTMqEbpaWluPnmmzVngB3MGZJTQSynZWkKfq3IVZTX+02eu0fGOmcZOvtxsuCyBuuF2mdU7b1hMpkwadIkPProo1i4cCFuvvlmvpo4QRgF0vAQusItdLW1tTCZTLj00ktx8ODBqMkLM9W/IR2muHiOGctpWa76eTSkJrJU5Jjh7xELsLGqNK0+PKm47lJTktZrFItodajUYrfb0dXVBbvdPkCLQ1FShBEhgYfQHaXJbsuWLQk7zxqJdJji4jlmZWWlruMuNVc1NXn5LMKpQK/sx/GSiusujXLLz8/XNSJSbXCBNIcT97fb7eazGxNEpkAmLSJlcM6uJpNJdxW9FvZiL9ZhHfZib+zGUUiHKU7NMaMVEtUDqTAbCEzHnj1LB00G4VRcd5fLJfpba5i/3W6Pul3JpOV2u/lis5xQI/ybc0bX2ymdIFIBaXiIlMGlfeeSigkdnFMZ0q7XG3o6THGxjilXSFSKtK5TTk6OLuVChKaebCYV1/3kyZOiv7WG+RcWFkZtr2TOmjJlCm+y5IQaoSZHmMGYIDKNpGl4HnvsMcycORNDhgxBQUGBbJumpibMnz8fQ4YMwahRo/CjH/1owMT75ptvYurUqbDZbJgwYQI2bdo0YD/PPvssxo0bB7vdjrKyMrz77rtJOCMiUUpLS7FkyRLe5CV0cO7r60NeXh7sdjtfRydZZLOTtFwhUSlCnxsAutVGEwqSRGJInYXlNCrRHI9jOapPmjRJ9nNOmHE4HLJCjfAZJohMI2kanp6eHnzzm99EeXk5Nm7cOGB7JBLB/PnzMWbMGLz99tsIBAL4t3/7N1itVjz++OMAgCNHjmD+/Pm499578fLLL6O2thbf+9734HK5UFFRAQD44x//iGXLlmHDhg0oKyvD+vXrUVFRgYMHD2LUqFHJOj1CB6QOzlyeDr/fz1dpT0aJgkx1klaDmgzMUp+beGptyWnk4skmnIzrm415neRSC+Tk5MhGUVmtVkycOHGA35bdbh+Q7K+xsRElJSUoLi4W1Z4igYbIRpJePHTTpk1YsmQJzpw5I/p869at+Kd/+ie0tLRg9OjRAIANGzZg+fLlOHnyJHJzc7F8+XJUV1eLHtxvfetbOHPmDLZt2wYAKCsrw/Tp0/HMM88A6NcUeDwe/OAHP8BDDz2kqo9UPNRYcCUrHA4HAPATPZeKvqurK+EIk2xGmon40UcfxcqVKxXbz58/X1POnFj7U9sG6F+cEw2Ll5IpBWCjITUzykVV5eXlobOzExaLBZFIBGazGTabTfTiUFdXR6UbiKwmI4qH1tfX4+qrr+aFHQCoqKjAfffdh8bGRkyZMgX19fWYO1dct6eiooKvotvT04P33nsPDz/8ML/dbDZj7ty5qK+vT8l5EPoj9fWR/t7d3U0CD5RLLGiNYoqViTmZ6C3sANlRt6qiokIkhJaUlKCxsRE2mw1dXV1wu928v41SRXDS1BCEmLQJPK2trSJhBwD/d2tra9Q2oVAInZ2daG9vRyQSkW3z8ccfKx67u7tblF2WCh0aC+lErVSs1Gw289lcB1sNLznn5Hhz0uidFTkaUlNYMjQ8mWqyFOa1kZZtKS0tRWVl5YDvkECT3QidxOlaJ44mp+WHHnoIJpMp6k80QcMorF69Gg6Hg//xeDzp7hKhAs6ZctKkSXA4HLDZbGCMwWazIS8vb0B7SxbXWFLjnKyWdE6kEydOTNuxkw2XeiEnJ0cU2i1E+Pny5cvx6KOP8kn8yEGY8Pl8CAaDadXCZhOaNDwPPPAA7rzzzqhtLrnkElX7GjNmzIBoquPHj/PbuP+5z4Rt8vPzkZeXB4vFAovFItuG24ccDz/8MJYtW8b/HQqFSOjJAKSaH+nbj9/vFzlAAxhQ0FSvEOx0o8Y52YhINXFqCpFmKpyj8dChQ0Wh3S+88AIl7yNUITXvE4mhSeAZOXIkRo4cqcuBy8vL8dhjj+HEiRN8NNXOnTuRn5+Pq666im9TU1Mj+t7OnTtRXl4OAMjNzcW0adNQW1uLW265BUD/hFpbW4vFixcrHttms8Fms+lyHkT6kDN9Sd+GOUFIqhbO9KzPzGLBxqoqXUos+P1+HXumDY/Ho3uUVrKROhDn5eXhwQcfBNB/X8lFPgkhIYdQC/lh6UvSfHiamprQ1taGpqYmRCIRHDhwAAAwYcIEDBs2DPPmzcNVV12F7373u1i7di1aW1vxk5/8BIsWLeKFkXvvvRfPPPMMHnzwQdx9992oq6vDK6+8IvI3WLZsGRYsWIDS0lLMmDED69evR0dHB+66665knRqRYchNGpWVlaisrITf78f27dsHaH0sFguuvPJKfPjhh0n1D0okhFqNc7KaIpHpVJcfPnw4bcfmUEp6yYV3NzY2Ij8/H6FQSCTIeDweNDc3iwQa7r7ioMWKGEwY3ecoaQLPI488gt/85jf831OmTAEAvPHGG/jSl74Ei8WC119/Hffddx/Ky8sxdOhQLFiwAKtWreK/M378eFRXV2Pp0qX4xS9+gYsvvhi//vWv+Rw8AHDbbbfh5MmTeOSRR9Da2oprr70W27ZtG+DITBByCIUhv9/PZ3+ORCI4ePAgLrroInzxxRe8Y63eGaGTXZcplsBjMpniclqOt/gkF0rNkcpoO6U+22w2UZ+ESAUYDiNO5gSRToTzp8/nM+QzkrRMy5s2bQJjbMDPl770Jb5NcXExampq8MUXX+DkyZP4+c9/PqDG0pe+9CXs378f3d3dOHz4sKwP0eLFi3H06FF0d3djz549KCsrS9ZpEVkMl/2ZIxwOIxgM8pFgbrdbd21PsrM+x+ovYyyuiUmNoCJ10AWASy+9VPT3nDlzNB9bislkwqRJk/j/ud+F2x0OB26++WbMnz9f5Pg+f/58zJ49W/QZNwdls9M7QeiNz+fj54WhQ4di/fr1aTWXy0G1tAhCgDAcmDNZ9PT0oLe3N2q6fjntARc2H004SHYIdawirWpMXnKoCSdfuHDhgOSDhw4dEv1dWlrKR6JEy7osrNJdWFiIxsZGuFwudHR0yIZtc+ZKOfW6Us4aDqoXRRDa8Xq9vIYnEAgYUtNDAg9BSFCKBvN4PDh8+DDC4bDI5ycnJwcRmcrkZrMZV1xxhcg5OpVFUgGIzL9yxGtSmjdvHl/+IxpSQVBunIRMmjRJsdyBEDkzk5R4HT7JUZQgtCP3smi0lwYSeAgiBnILoDBtv8lkkg11N5lMA8Ku4xV27HY7bDYbhg4dikAggJKSErS1tfFaj7a2Nj5pnc1m4zUmcgu33W5HOBxGJBJRLCKpBqXwfrP5gqWcywwM9NfN6unp4YUk7tjC0FuppoYED4LIHIz+skACD0HESW5uLjweD59s0+FwIBQKwWw2IxKJICcnB16vF3V1dejt7YXFYsGECRNw+PBhMMYwYcIEXvtjtVoxZMgQXqDhskfb7XY+EV0izJ8/P2r0BKdZcblcaG9vRzgchslkQiQSEQlpnIlMaK+XIsyBNWHCBF5jo2RmMvokSRBEdkACD0HEAed3EgqF+IWf8z/htD6XXnppzMVcyWyjtx9JrH4oRSMp9YfTyng8Hhw6dAiRSITX3Ah9nZqbm8EYy+oEgwRBZAZJr5aeCVC1dEIrQr+eQ4cOwWQyDfDtcTgcfKHbwYAw6R4nPEk1Olw9tME2NgRBJAct6zcJPCCBh9AHoRDEOeyRqUaM0ROTEQSRWZDAoxESeAiCIAgi89Cyfict8SBBEARBEIRRIIGHIAiCIIishwQegiAIgiCyHhJ4CIIgCILIekjgIQiCIAgi6yGBhyAIgiCIrIcEHoIgCIIgsh4SeAiCIAiCyHpI4CEIgiAIIushgYcgCIIgiKyHBB6CIAiCILIeEngIgiAIgsh6SOAhCIIgiAzF7/dj/fr18Pv96e6K4SGBhyAIgiAylLq6OgSDQdTV1aW7K4aHBB6CIAiCyFAYY6L/CWVI4CEIgiCIDGXOnDlwOByYM2dOurtieHLS3QGCIAiCIOKjtLQUpaWl6e5GRpA0Dc9jjz2GmTNnYsiQISgoKJBtYzKZBvxs3rxZ1ObNN9/E1KlTYbPZMGHCBGzatGnAfp599lmMGzcOdrsdZWVlePfdd5NwRgRBZAJbtmzBqlWrsGXLlnR3hSAIA5E0gaenpwff/OY3cd9990Vt99JLLyEQCPA/t9xyC7/tyJEjmD9/Pr785S/jwIEDWLJkCb73ve9h+/btfJs//vGPWLZsGR599FHs27cPkydPRkVFBU6cOJGsUyMIwsA0NjaCMYbGxsZ0d4UgCAORNIFn5cqVWLp0Ka6++uqo7QoKCjBmzBj+x26389s2bNiA8ePH4//9v/+HK6+8EosXL8Y3vvENrFu3jm/z1FNPYeHChbjrrrtw1VVXYcOGDRgyZAhefPHFZJ0aQRAGpqSkBCaTCSUlJenuCkEQBiLtTsuLFi3CiBEjMGPGDLz44osiT/P6+nrMnTtX1L6iogL19fUA+rVI7733nqiN2WzG3Llz+TZydHd3IxQKiX4IgsgOKisr8cgjj6CysjLdXSEIwkCk1Wl51apVmD17NoYMGYIdO3bg+9//Ps6dO4f7778fANDa2orRo0eLvjN69GiEQiF0dnaivb0dkUhEts3HH3+seNzVq1dj5cqV+p8QQRAEQRCGRJOG56GHHpJ1NBb+RBM0pPz0pz/F9ddfjylTpmD58uV48MEH8eSTT2o+Ca08/PDDCAaD/E9zc3PSj0kQBEEQRPrQpOF54IEHcOedd0Ztc8kll8TdmbKyMvzsZz9Dd3c3bDYbxowZg+PHj4vaHD9+HPn5+cjLy4PFYoHFYpFtM2bMGMXj2Gw22Gy2uPtJEARBEERmoUngGTlyJEaOHJmsvuDAgQNwOp28MFJeXo6amhpRm507d6K8vBwAkJubi2nTpqG2tpaP7urr60NtbS0WL16ctH4SBEEQBJFZJM2Hp6mpCW1tbWhqakIkEsGBAwcAABMmTMCwYcPw17/+FcePH8d1110Hu92OnTt34vHHH8cPf/hDfh/33nsvnnnmGTz44IO4++67UVdXh1deeQXV1dV8m2XLlmHBggUoLS3FjBkzsH79enR0dOCuu+5K1qkRBEEQBJFpsCSxYMECBmDAzxtvvMEYY2zr1q3s2muvZcOGDWNDhw5lkydPZhs2bGCRSES0nzfeeINde+21LDc3l11yySXspZdeGnCsp59+mhUVFbHc3Fw2Y8YM9s4772jqazAYZABYMBiM93QJgiAIgkgxWtZvE2NUcSwUCsHhcCAYDCI/Pz/d3SEIgiBi4Pf74fP54PV6qbSCDmTqeGpZv9Oeh4cgCIIgtOLz+RAMBuHz+dLdlaxgMIwnCTwEQRBExuH1euFwOOD1etPdFUPg9/uxfv16+P1+0e9qGQzjSSYtkEmLIAiCMDacycnj8aC5uXmA6Wn9+vUIBoNwOBwAwP++ZMmSNPU4NWhZv9OaaZkgCIIgCGDLli1obGxESUmJbFkUzuQUCoXAGIPP5xMJPF6vl/fB4dpns7YmHkjgIQiCIIg009jYCMYYGhsbZQUeTqARaniElJaWigSgTHI8ThUk8BAEQRBEmikpKeE1PHJIBRpCO+TDA/LhIQiCIIhMhMLSCYIgCIIgBJDAQxAEQRBE1kMCD0EQBEEQWQ8JPARBEARBZD0k8BAEQRAEkfWQwEMQBEEQRNZDeXgAcJH5oVAozT0hCIIgCEIt3LqtJsMOCTwAzp49CwDweDxp7glBEARBEFo5e/YsX0dMCUo8CKCvrw8tLS246KKLYDKZdN13KBTiU4FTUsOB0PgoQ2MTHRofZWhsokPjE51MGh/GGM6ePQu32w2zObqXDml4AJjNZlx88cVJPUZ+fr7hb5x0QuOjDI1NdGh8lKGxiQ6NT3QyZXxiaXY4yGmZIAiCIIishwQegiAIgiCyHhJ4kozNZsOjjz4Km82W7q4YEhofZWhsokPjowyNTXRofKKTreNDTssEQRAEQWQ9pOEhCIIgCCLrIYGHIAiCIIishwQegiAIgiCyHhJ4CIIgCILIekjgSTLPPvssxo0bB7vdjrKyMrz77rvp7pLu7Nq1C1/72tfgdrthMpnw2muvibYzxvDII4/A5XIhLy8Pc+fOxaeffipq09bWhjvuuAP5+fkoKChAVVUVzp07J2rzwQcfYNasWbDb7fB4PFi7dm2yTy1hVq9ejenTp+Oiiy7CqFGjcMstt+DgwYOiNl1dXVi0aBGGDx+OYcOGobKyEsePHxe1aWpqwvz58zFkyBCMGjUKP/rRj9Db2ytq8+abb2Lq1Kmw2WyYMGECNm3alOzTS4jnnnsO11xzDZ/crLy8HFu3buW3D9ZxUeKJJ56AyWTCkiVL+M8G8xitWLECJpNJ9HPFFVfw2wfz2ADAsWPH8J3vfAfDhw9HXl4err76avj9fn77oJyXGZE0Nm/ezHJzc9mLL77IGhsb2cKFC1lBQQE7fvx4urumKzU1NezHP/4x+8tf/sIAsFdffVW0/YknnmAOh4O99tpr7P3332f//M//zMaPH886Ozv5Nl/96lfZ5MmT2TvvvMN2797NJkyYwG6//XZ+ezAYZKNHj2Z33HEHa2hoYH/4wx9YXl4e++///u9UnWZcVFRUsJdeeok1NDSwAwcOsJtvvpkVFRWxc+fO8W3uvfde5vF4WG1tLfP7/ey6665jM2fO5Lf39vaySZMmsblz57L9+/ezmpoaNmLECPbwww/zbT777DM2ZMgQtmzZMvbhhx+yp59+mlksFrZt27aUnq8W/u///o9VV1ezTz75hB08eJD953/+J7NarayhoYExNnjHRY53332XjRs3jl1zzTXsP/7jP/jPB/MYPfroo6ykpIQFAgH+5+TJk/z2wTw2bW1trLi4mN15551sz5497LPPPmPbt29nhw4d4tsMxnmZBJ4kMmPGDLZo0SL+70gkwtxuN1u9enUae5VcpAJPX18fGzNmDHvyySf5z86cOcNsNhv7wx/+wBhj7MMPP2QA2N69e/k2W7duZSaTiR07dowxxtivfvUr5nQ6WXd3N99m+fLlbOLEiUk+I305ceIEA8Deeustxlj/WFitVvanP/2Jb/PRRx8xAKy+vp4x1i9Qms1m1trayrd57rnnWH5+Pj8eDz74ICspKREd67bbbmMVFRXJPiVdcTqd7Ne//jWNi4CzZ8+yyy67jO3cuZPdeOONvMAz2Mfo0UcfZZMnT5bdNtjHZvny5czr9SpuH6zzMpm0kkRPTw/ee+89zJ07l//MbDZj7ty5qK+vT2PPUsuRI0fQ2toqGgeHw4GysjJ+HOrr61FQUIDS0lK+zdy5c2E2m7Fnzx6+zQ033IDc3Fy+TUVFBQ4ePIj29vYUnU3iBINBAEBhYSEA4L333kM4HBaNzxVXXIGioiLR+Fx99dUYPXo036aiogKhUAiNjY18G+E+uDaZcq9FIhFs3rwZHR0dKC8vp3ERsGjRIsyfP3/AedAYAZ9++incbjcuueQS3HHHHWhqagJAY/N///d/KC0txTe/+U2MGjUKU6ZMwQsvvMBvH6zzMgk8SeLUqVOIRCKihwkARo8ejdbW1jT1KvVw5xptHFpbWzFq1CjR9pycHBQWForayO1DeAyj09fXhyVLluD666/HpEmTAPT3PTc3FwUFBaK20vGJde5KbUKhEDo7O5NxOrrw97//HcOGDYPNZsO9996LV199FVddddWgHxeOzZs3Y9++fVi9evWAbYN9jMrKyrBp0yZs27YNzz33HI4cOYJZs2bh7Nmzg35sPvvsMzz33HO47LLLsH37dtx33324//778Zvf/AbA4J2XqVo6QaSIRYsWoaGhAT6fL91dMQwTJ07EgQMHEAwG8ec//xkLFizAW2+9le5uGYLm5mb8x3/8B3bu3Am73Z7u7hiOm266if/9mmuuQVlZGYqLi/HKK68gLy8vjT1LP319fSgtLcXjjz8OAJgyZQoaGhqwYcMGLFiwIM29Sx+k4UkSI0aMgMViGRAVcPz4cYwZMyZNvUo93LlGG4cxY8bgxIkTou29vb1oa2sTtZHbh/AYRmbx4sV4/fXX8cYbb+Diiy/mPx8zZgx6enpw5swZUXvp+MQ6d6U2+fn5hp78c3NzMWHCBEybNg2rV6/G5MmT8Ytf/GLQjwvQb5Y5ceIEpk6dipycHOTk5OCtt97CL3/5S+Tk5GD06NGDfoyEFBQU4PLLL8ehQ4cG/f3jcrlw1VVXiT678soreZPfYJ2XSeBJErm5uZg2bRpqa2v5z/r6+lBbW4vy8vI09iy1jB8/HmPGjBGNQygUwp49e/hxKC8vx5kzZ/Dee+/xberq6tDX14eysjK+za5duxAOh/k2O3fuxMSJE+F0OlN0NtphjGHx4sV49dVXUVdXh/Hjx4u2T5s2DVarVTQ+Bw8eRFNTk2h8/v73v4smn507dyI/P5+f1MrLy0X74Npk2r3W19eH7u5uGhcAc+bMwd///nccOHCA/yktLcUdd9zB/z7Yx0jIuXPncPjwYbhcrkF//1x//fUD0l988sknKC4uBjCI5+V0e01nM5s3b2Y2m41t2rSJffjhh+yee+5hBQUFoqiAbODs2bNs//79bP/+/QwAe+qpp9j+/fvZ0aNHGWP94Y8FBQXsf//3f9kHH3zAvv71r8uGP06ZMoXt2bOH+Xw+dtlll4nCH8+cOcNGjx7Nvvvd77KGhga2efNmNmTIEMOGP3Lcd999zOFwsDfffFMUPvvFF1/wbe69915WVFTE6urqmN/vZ+Xl5ay8vJzfzoXPzps3jx04cIBt27aNjRw5UjZ89kc/+hH76KOP2LPPPmv48NmHHnqIvfXWW+zIkSPsgw8+YA899BAzmUxsx44djLHBOy7REEZpMTa4x+iBBx5gb775Jjty5Aj729/+xubOnctGjBjBTpw4wRgb3GPz7rvvspycHPbYY4+xTz/9lL388stsyJAh7He/+x3fZjDOyyTwJJmnn36aFRUVsdzcXDZjxgz2zjvvpLtLuvPGG28wAAN+FixYwBjrD4H86U9/ykaPHs1sNhubM2cOO3jwoGgfp0+fZrfffjsbNmwYy8/PZ3fddRc7e/asqM3777/PvF4vs9lsbOzYseyJJ55I1SnGjdy4AGAvvfQS36azs5N9//vfZ06nkw0ZMoTdeuutLBAIiPbz+eefs5tuuonl5eWxESNGsAceeICFw2FRmzfeeINde+21LDc3l11yySWiYxiRu+++mxUXF7Pc3Fw2cuRINmfOHF7YYWzwjks0pALPYB6j2267jblcLpabm8vGjh3LbrvtNlGemcE8Nowx9te//pVNmjSJ2Ww2dsUVV7Dnn39etH0wzssmxhhLj26JIAiCIAgiNZAPD0EQBEEQWQ8JPARBEARBZD0k8BAEQRAEkfWQwEMQBEEQRNZDAg9BEARBEFkPCTwEQRAEQWQ9JPAQBEEQBJH1kMBDEARBEETWQwIPQRAEQRBZDwk8BEEQBEFkPSTwEARBEASR9ZDAQxAEQRBE1vP/AyhravGE/1MqAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fixed_ret = update_np(np.array([[1525,2225],[7200, 1850]]), midline, f1)\n", "mid_ret = update_np(midline.to_numpy(),midline, f1)\n", "plt.figure()\n", "plt.scatter(adata.obs['AP_adj_x'],adata.obs['AP_adj_y'],c='gray',s=1)\n", "plt.scatter(fz583_data.obs['AP_adj_x'],fz583_data.obs['AP_adj_y'],c='green',s=1)\n", "plt.scatter(wnt11_1_data.obs['AP_adj_x'],wnt11_1_data.obs['AP_adj_y'],c='y',s=1)\n", "plt.scatter(slit1_data.obs['AP_adj_x'],slit1_data.obs['AP_adj_y'],c='r',s=2)\n", "plt.gca().set_aspect('equal')\n", "plt.scatter(fixed_ret['nx'],fixed_ret['ny'],s=25,c='cyan' ,marker='^')\n", "plt.plot(mid_ret['nx'],mid_ret['ny'],c='cyan')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "adata.obsm['spatial_APadj'] = adata.obs[['AP_adj_x','AP_adj_y','new_z']].to_numpy()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "adata.write('10dpa2.apadj.h5ad',compression='gzip')" ] } ], "metadata": { "kernelspec": { "display_name": "stereopy1", "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": "3.8.16" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }