From 38ce1fa67c60bd6763899b0c873a88b7717b6f80 Mon Sep 17 00:00:00 2001 From: jpvermeer Date: Mon, 22 Sep 2025 13:22:47 +0200 Subject: [PATCH] submit 1 --- icecream.csv | 2 +- lab7_3.ipynb | 36 +++++++++++++++++++++++++++++------- 2 files changed, 30 insertions(+), 8 deletions(-) diff --git a/icecream.csv b/icecream.csv index 380e6da..9eab787 100644 --- a/icecream.csv +++ b/icecream.csv @@ -41,7 +41,7 @@ jessevanderlaan jialeidng jipposteenstra jordstub -jpvermeer +jpvermeer,strawberry,white,195 julianvanheijningen Julievdl kawater31 diff --git a/lab7_3.ipynb b/lab7_3.ipynb index 56fc1bb..af266d6 100644 --- a/lab7_3.ipynb +++ b/lab7_3.ipynb @@ -22,13 +22,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 5, "id": "3742e4e6", "metadata": {}, "outputs": [ { "data": { - "image/png": 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8MGtra8u6uLiwS5YsYf/3v/812pu3R48ejR6rsZyHDx9me/XqxYrFYhaAVo/KtLQ0dt68eayXlxcrFApZFxcXdtCgQeybb77Z6P7v92BvXpZl2ZqaGnbt2rVsYGAgKxKJWCcnJ3b48OHsmTNntLbbtm0b279/f9ba2pqVSqVsly5d2FmzZrEXL15s8bgsW99Ldvbs2Wznzp1ZkUjEWltbs7169WLXrl3L5ufnN5vxnoKCAnbp0qWsn58fKxQKWUdHR7Z3797s6tWr2crKSq2s3bp1Y8ViMevv78++88477DfffNOg93VTxwLAPv/881q3paWlsQDY999/v9mf815v3p9//rnZ7Rr7veua+57p06ezANjBgwc3eoyamhp25cqVrI+PDysUClkPDw/2ueeeY0tKSrS2q62tZVesWMF6e3uzUqmUjYyMZOPi4prszfvg36JcLmefffZZNiwsjLW1tWWlUinbrVs3dt26dVo9ool5Ylj2gRHahBBCCGkVumZKCCGE6ImKKSGEEKInKqaEEEKInqiYEkIIIXqiYkoIIYToiYopIYQQoicqpoQQQoieqJgSQggheqJiSgghhOiJiikhhBCiJyqmhBBCiJ6omBJCCCF6omJKCCGE6ImKKSGEEKInKqaEEEKInqiYEkIIIXqiYkoIIYToiYopIYQQoicqpoQQQoieqJgSQggheqJiSgghhOiJiikhhBCiJyqmhBBCiJ6omBJCCCF6omJKCCGE6ImKKSGEEKInKqaEEEKInqiYEkIIIXqiYkoMZv369QgPD+c0Q1RUFJYtW9bsNgzDYN++fe2ShxDSMVAxJa0yZ84cMAwDhmEgFArh7++Pl19+GVVVVXj55ZcRExOj0364LLw5OTkYM2YMJ8cmhFgmAdcBiPkZPXo0tm/fDoVCgZMnT2L+/PmoqqrCli1bIJPJuI7XInd3d64jEEIsDJ2ZklYTi8Vwd3eHt7c3pk+fjhkzZmDfvn0NzjaPHTuGfv36wdraGvb29hg8eDAyMjKwY8cOvP7667h69armLHfHjh0AgA8//BChoaGwtraGt7c3Fi1ahMrKSq3jnz59GpGRkbCysoKDgwOio6NRUlKiuV+tVmPFihVwdHSEu7s71q9fr/X4B5t5ExISMHz4cEilUjg5OWHBggUNjkkIIc2hYkr0JpVKoVAotG5TKpWYPHkyIiMjER8fj7Nnz2LBggVgGAbTpk3DSy+9hB49eiAnJwc5OTmYNm0aAIDH4+HTTz/FtWvXsHPnThw5cgQrVqzQ7DcuLg4jRoxAjx49cPbsWZw6dQoTJkyASqXSbLNz505YW1vj/PnzeO+997BhwwYcOnSo0ezV1dUYPXo0HBwcEBsbi59//hmHDx/G4sWLjfBMEUIsFTXzEr1cuHABe/bswYgRI7RuLy8vR1lZGcaPH48uXboAAIKCgjT3y2QyCASCBk2u93ce8vPzwxtvvIHnnnsOmzdvBgC899576NOnj+Z7AOjRo4fWPsLCwrBu3ToAQGBgIDZt2oSYmBiMGjWqQf7du3ejpqYG3377LaytrQEAmzZtwoQJE/Duu+/Czc2ttU8JIaQDojNT0mp//vknZDIZJBIJBg4ciKFDh+Kzzz7T2sbR0RFz5sxBdHQ0JkyYgE8++QQ5OTkt7vvo0aMYNWoUvLy8YGNjg1mzZqGoqAhVVVUA/j0zbU5YWJjW9x4eHsjPz29026SkJPTs2VNTSAFg8ODBUKvVuHnzZot5CSEEoGJK2mDYsGGIi4vDzZs3IZfL8csvv8DV1bXBdtu3b8fZs2cxaNAg/Pjjj+jatSvOnTvX5H4zMjIwduxYhISEYO/evbh06RI+//xzANA0I0ul0hbzCYVCre8ZhoFarW50W5ZlwTBMo/c1dTshhDyIiilpNWtrawQEBMDHx6dB4XpQr169sGrVKpw5cwYhISHYs2cPAEAkEmld5wSAixcvQqlUYuPGjRgwYAC6du2K7OxsrW3CwsJ0Hn6ji+DgYMTFxWnOfIH6Dk48Hg9du3Y12HEIIZaNiikxirS0NKxatQpnz55FRkYGDh48iFu3bmmum/r6+iItLQ1xcXEoLCxEbW0tunTpAqVSic8++wy3b9/Grl278MUXX2jtd9WqVYiNjcWiRYsQHx+PGzduYMuWLSgsLGxTzhkzZkAikWD27Nm4du0ajh49iiVLlmDmzJl0vZQQojMqpsQorKyscOPGDUydOhVdu3bFggULsHjxYixcuBAAMHXqVIwePRrDhg2Di4sLvv/+e4SHh+PDDz/Eu+++i5CQEOzevRvvvPOO1n67du2KgwcP4urVq+jXrx8GDhyI3377DQJB2/rSWVlZ4cCBAyguLkbfvn3x6KOPYsSIEdi0aZPezwEhpONgWJZluQ5BCCGEmDM6MyWEEEL0RMWUEEII0RMVU0IIIURPVEwJIYQQPVExJYQQQvRExZQQQgjRExVTQgghRE9UTAkhhBA90RJshLSnumpAXtbIV+k/X2VAbSXANj4xvxaGAYRWgMTuvi/7B763A8Q29dsSQoyGiikhhqJWAxXZQGkmUJJR/29pJlD6z/8rcgBVXfvnYviAtQtg37n+y8Hn3//b+wB23oBA1P65CLEgNJ0gIa1VWwnkXQfyrgH5iUDhrfpiWZYFqBVcp2s9hgfI3OuLrFMXwC0U8AgD3EPrz2oJIS2iYkpIc6qLgewr9V85cUBuQv1ZJzrCnw0DOPoB7v8UVo+e9f+3odV0CHkQFVNC7ld8G0g7AaSdBO5eqD/jJNpkboBnL8D3IcBvaH2BpWuypIOjYko6trKsf4rnCSD9JFB2h+tE5kfqAPgMBvwiAb8hgGsQ14kIaXdUTEnHUl0M3D7679lncSrXiSyPtWt9UfUdAnQZBjj4cp2IEKOjYkosX0UecOMPIPF3IOM0oFZynahjce0BdB8HBI2vv+5KiAWiYkosU9ldIOmfAnrnnG7jNonx2XXGvtDP0blrKCI6O3CdhhCDoWJKLEdxGpD0O5D4G5B1GR2jx615UUudEFT2CWrVPHjZSzE+zAPjwzwR2smO62iE6IWKKTFvddXA9V+ByzuBO+e5TkNacNt7CoYnP9rgdh8nK0zq6Ykn+nWGp72Ug2SE6IeKKTFPOfHApR1Awn+B2jKu0xAdbXR9C59l+jV5P5/HYHh3V8wc4IMhgc5gaMgNMRNUTIn5qK0EEn6uPwvNvsJ1GtJKrNgWIZWbUaXSbX0NP2drzOjfGY/19oadldDI6QjRDxVTYvqyrwAXtwHXfgHqKrlOQ9oos9MEDE15stWPkwh5mNjTE7MG+iLEi66tEtNExZSYrpQY4NRH9ZMpELP3udvreD8jUK999PS2x8wBPhgf5gGJkG+gZIToj4opMS1qNZD0G3Dq4/q5cIlFYEXWCKveggqlYRaqcrASYnr/znhmiD/srWjFG8I9KqbENCjrgKvfA2c+BYpSuE5DDCzbazQGpc4y+H5lYgHmDfbF/KH+sJXQdVXCHSqmhFu1lcCl7cDZzfVrgRKL9I3HWryR1t1o+7eVCDB/iD/mPeQHmZiWaSbtj4op4YayFjj/Rf010ZoSrtMQI2IFEvSr+xIFdcY/c3SwEuKZof6YM8gXViIqqqT9UDEl7Ytl64e3xLwBlNHyZh1BnucI9L/9dLse01kmwrORXfDUAB/qqETaBRVT0n5uHwcOrQFyrnKdhLSj7zxX47XbPTg5tquNGIuiuuDJ/p0hFlBRJcZDxZQYX34ScGgtkHyQ6ySknbF8EQapvkKOnNsetx52Evzf2CBM6OnJaQ5iuaiYEuMpzwGOvgXE7QFYFddpCAeKPCLRO20h1zE0hgQ6441JIfB1tuY6CrEwus3rRUhrqJT140Q/iwCu7KJC2oEdZQZwHUHLyeRCPPzxCXx06BZqlfS6JIZDZ6bEsLIuA38sBXITuE5COMYyfIxgtuJ2tYTrKI3ydbLChkkhGNrVhesoxAJQMSWGUVcFHHkTOP8lnYkSAECp+0CEpy/hOkaLxoV5YO34YLjZmmbRJ+aBBmIR/d06CPzvJRrqQrScEAziOoJO/hefgxM3C/DiqK6YPcgXfB4t+0Zaj85MSdtV5gN/rwSu/8J1EmJiWIaHMfyvcKPSiusordLD0xZvTg5Br84OXEchZoaKKWmbK7uBA/8HyEu5TkJMUIVrH4RmLuc6RpvwGGBRVACWjQyEgE99NIlu6JVCWqemFPhpNvDbIiqkpElnRIO5jtBmahbYdDQF0746h7sl1VzHIWaCzkyJ7jLPA3vn07VR0iwWDB4RfYm4chnXUfRmKxHgnSlhGBfmwXUUYuKomJKWqdXAyQ+AY/+hnrqkRVUu4ehxZwXXMQxqWh9vrJ/YA1IRTUlIGkfNvKR55dnAtxPrZzKiQkp0cF5ivk28Tfnx4h2M/+wkErPLuY5CTBQVU9K0G/8DtgwG0k9ynYSYka2FIVxHMIrUgipM3nwaO06ncR2FmCBq5iUNKevqe+rGbuU6CTEzcqdgdM96jesYRjcyyBXvPdoTjtbcTuBPTAedmRJtFbnAjrFUSEmbXLIawnWEdnE4KR9jPjmBi+nFXEchJoKKKfnX3YvAV1HA3ViukxAz9U1xKNcR2k1eeS2mf30e+65kcR2FmAAqpqTeld3A9rFARQ7XSYiZqnMIxJEiR65jtKs6pRrLfozDR4ducR2FcIyKaQenZtXYeHEjtqX9AahquY5DzFicbCjXETjzSUwylv1whZZ168BoovsOrEZZg1UnVyEmMwY8hgf/gIcQlXKK61jETH1b2pPrCJzaF5eNrNIafDmzD3VM6oCoN28HVVBdgCVHluB60XXNbdYCK+wqZxGYd5PDZMQcKWx9EJj/DtcxTIKPkxW2zemLLi7mPwMU0R0183ZAN4tvYvpf07UKKQBUKauxxNkWJdZOHCUj5uq6XSTXEUxGRlE1pmw+gzOphVxHIe2IimkHc/LuSczePxu5VbmN3p9VnYflgeFQ8ITtnIyYs+8qenEdwaSU1Sgwe9sF/HzxDtdRSDuhYtqB7E/fjyVHlqBKUdXsdhfLkvFW+MPtlIqYO6WNF/bmuXIdw+QoVCxe+W883tt/A3Q1zfJRMe0g9qfvx6snXoVKx/l195YkYHdItJFTEUtw0z4SLMtwHcNkbT6WirW/XaeCauGomHYArS2k97xffQtn/PobKRWxFD9WRXAdweTtOpdBBdXCUTG1cG0tpACgYlV4WViBdJcuRkhGLIHaygW7c9y5jmEWqKBaNiqmFkyfQnpPhaISS9xcUS61M2AyYimSnaKgYultRFdUUC0X/RVYKEMU0nvSq7LwSvd+UDG0MDLRtremN9cRzM6ucxlY89s1KqgWhoqpBTJkIb3nTOlNfNBrjMH2R8yfWuqIndmduI5hlr47l0kF1cJQMbUwxiik93xXEo+9PUYafL/EPKU7RaJWTW8hbUUF1bLQX4IFMWYhvedN+W1c9KGmPQL8VkevA31RQW0oKioKy5Yt4zpGq1ExtRDtUUgBQKlWYrmkDlmOnY16HGLaWLEtvs7y5TqGRfjuXCZe29e+BTU/Px8LFy5E586dIRaL4e7ujujoaJw9exYAwDAM9u3b1255LAGtGmMB2quQ3lNSV4bFXt74rqoE1rUV7XJMYlruuESiqow+ixvK7vOZcLAS4eXobu1yvKlTp0KhUGDnzp3w9/dHXl4eYmJiUFxcrPM+FAoFhELzmHZUpVKBYRjweNqv2bq6OohEhlnhh/4azFxsbixWnVzVboX0npTKO3g1eDDUDL2EOqI/FX24jmBxNh1NwX8v3TX6cUpLS3Hq1Cm8++67GDZsGHx8fNCvXz+sWrUK48aNg6+vLwDgkUceAcMwmu/Xr1+P8PBwbNu2Df7+/hCLxWBZFvv378dDDz0Ee3t7ODk5Yfz48UhNTdUcb+rUqViyZInm+2XLloFhGFy/Xr/QhlKphI2NDQ4cOKDZRqlUYvHixZp9vvbaa1pn7nV1dVixYgW8vLxgbW2N/v3749ixY5r7d+zYAXt7e/z5558IDg6GWCxGRkYGfH198eabb2LOnDmws7PDM888g+HDh2Px4sVaz1FRURHEYjGOHDmi8/NK74Rm7G7FXSw/thxKtZKT4x8rTcSn4dTDt6Nhhdb4MtuP6xgW6f9+ScC520VGPYZMJoNMJsO+fftQW1vb4P7Y2FgAwPbt25GTk6P5HgBSUlLw008/Ye/evYiLiwMAVFVVYfny5YiNjUVMTAx4PB4eeeQRqNVqAPXXQO8vdMePH4ezszOOHz+uOZ5cLsfgwYM12+zcuRMCgQDnz5/Hp59+io8++ghff/215v65c+fi9OnT+OGHHxAfH4/HHnsMo0ePRnJysmab6upqvPPOO/j6669x/fp1uLrWzx/9/vvvIyQkBJcuXcKaNWswf/587NmzR+u52L17Nzw9PTFs2DCdn1daz9RMVSmq8NRfTyGlNIXrKHhHHIDxN3T/BEfMW7bXaAxKncV1DItlbyXEL88Ngr8R10Pdu3cvnnnmGdTU1CAiIgKRkZF44oknEBYWBqD+mumvv/6KyZMnax6zfv16vP3228jKyoKLi0uT+y4oKICrqysSEhIQEhKChIQE9OzZE/n5+eDz+XBzc8O6detw9epV/PTTT3jnnXfw22+/4dy5cwDqi29+fj6uX78Ohqmf8/nVV1/F77//jsTERKSmpiIwMBB3796Fp6en5rgjR45Ev3798Pbbb2PHjh2YO3cu4uLi0LPnv4vW+/r6olevXvj11181t9XW1sLT0xNbtmzB448/DgDo1asXJk+ejHXr1un8nNKZqRlSs2qsPLHSJAopAKxX3kF8p54tb0gswt/qvlxHsGil1QrM2xGLkqo6ox1j6tSpyM7Oxu+//47o6GgcO3YMERER2LFjR7OP8/HxaVBIU1NTMX36dPj7+8PW1hZ+fvWtFpmZmQCAkJAQODk54fjx4zh58iR69uyJiRMnas5Mjx07hshI7fVwBwwYoCmkADBw4EAkJydDpVLh8uXLYFkWXbt21Zxly2QyHD9+XKt5WSQSaT4c3K9PH+1LFGKxGE899RS2bdsGAIiLi8PVq1cxZ86cZp+LB1EHJDP08eWPcfzuca5jaNSqavGCzBo/2HnCrSyb6zjEiFiBBF9kBXAdw+KlF1Vjwa6L+G5+f4gFxpl5TCKRYNSoURg1ahTWrl2L+fPnY926dc0WEWtr6wa3TZgwAd7e3ti6dSs8PT2hVqsREhKCurr6DwMMw2Do0KE4duwYRCIRoqKiEBISApVKhYSEBJw5c6ZVQ2HUajX4fD4uXboEPl/7uZHJ/j2bl0qlWgW5uZ9h/vz5CA8Px927d7Ft2zaMGDECPj4+OmcC6MzU7PyR+ge2X9vOdYwGCmuLsdQnAHKhlOsoxIgKXAejoM48enCau9j0Eqz8b3y7HS84OBhVVfVrHQuFQqhULXdqLCoqQlJSEl577TWMGDECQUFBKCkpabDdveumx44dQ1RUFBiGwZAhQ/DBBx+gpqZG63opAE2T7/3fBwYGgs/no1evXlCpVMjPz0dAQIDWl7t72xZdCA0NRZ8+fbB161bs2bMH8+bNa/U+qJiakasFV7H+zHquYzQpsSIdr4VGcR2DGNEh0JJ87WlfXDY+PnzLoPssKirC8OHD8d133yE+Ph5paWn4+eef8d5772HSpEkA6q8txsTEIDc3t9HieI+DgwOcnJzw1VdfISUlBUeOHMHy5csbbBcVFYXr168jISEBQ4YM0dy2e/duREREwNbWVmv7O3fuYPny5bh58ya+//57fPbZZ3jhhRcAAF27dsWMGTMwa9Ys/PLLL0hLS0NsbCzeffdd/PXXX21+XubPn4///Oc/UKlUeOSRR1r9eCqmZiK3KhcvHHkBdWrjXUcxhAMl17Gl51iuYxAjYHlCbM4O5DpGh/Px4WTsu5JlsP3JZDL0798fH330EYYOHYqQkBCsWbMGzzzzDDZt2gQA2LhxIw4dOgRvb2/06tWryX3xeDz88MMPuHTpEkJCQvDiiy/i/fffb7BdSEgInJ2d0bNnT03hjIyMhEqlanC9FABmzZqFmpoa9OvXD88//zyWLFmCBQsWaO7fvn07Zs2ahZdeegndunXDxIkTcf78eXh7e7f5eXnyySchEAgwffp0SCSSVj+eevOagRplDWb/PRtJxUlcR9EJAwYbhT4YdesE11GIARV5DEXvtGe5jtEhiQQ87J7fH319HbmOYrHu3LkDX19fxMbGIiKi9Qve05mpiWNZFqtPrTabQgoALFisVufihkcw11GIAR1lBnAdocOqU6rx/O7LKKxsOC6U6EehUCAzMxMrV67EgAED2lRIASqmJm/L1S04lHGI6xitVqOSY6mDFIUyV66jEANgGT4253bnOkaHll9Rixd/jKNJ8Q3s9OnT8PHxwaVLl/DFF1+0eT/UzGvCDqYfxMvHXwYL8/0V9bTtgm3XzkCkok/U5qzUfSDC05e0vCExupcf7orFw+natamhM1MTlVOZg3Vn1pl1IQWAq+WpeL0nrYFq7k4KBnEdgfzjo8PJOG/kKQdJ61ExNUEsy2LNmTWoVFRyHcUgfi9JwI4wmsPXXLFgsDk3iOsY5B8qNYsXfohDEV0/NSlUTE3QDzd/wPmc81zHMKiPKpNwogud3ZijStfeSKq04joGuU9uuRwr2nFCB9IyKqYmJrM8Ex9d+ojrGAanZtVYyS9BqmtXrqOQVjojGtzyRqTdxdzIx3fnMriOQf5BxdSEqFk1Xjv9GmqUNVxHMYpKRRWWuNij1IrGypmTLwt6cB2BNOGt/yUhtcAyLgeZO06L6Zw5c7SW+Onodl7fiSv5V7iOYVR3qnPxUtcIKHm0xoI5qHbuictlxlsKjOinRqHCCz9cgUKl5jpKh0dnpiYipSQFm65s4jpGu7hQdgvvhEdzHYPo4Lz0Ia4jkBZcyyrHh4cMO38vaT2TLaaJiYkYO3YsZDIZ3NzcMHPmTBQWFmruj4qKwpIlS7Bs2TI4ODjAzc0NX331FaqqqjB37lzY2NigS5cu+Pvvv7X2e/z4cfTr1w9isRgeHh549dVXoVQqtfa7dOlSrFixAo6OjnB3d8f69euN+rMq1Ur836n/M/l5dw3pp5IEfB/yMNcxSAu+LgzhOgLRwZfHU3E5s+kJ6YnxmWQxzcnJQWRkJMLDw3Hx4kXs378feXl5mlXQ79m5cyecnZ1x4cIFLFmyBM899xwee+wxDBo0CJcvX0Z0dDRmzpyJ6upqAEBWVhbGjh2Lvn374urVq9iyZQu++eYbvPnmmw32a21tjfPnz+O9997Dhg0bcOiQ8WYh+ir+K7OaLtBQ3qtOwTm/flzHIE2QOwbhdIkd1zGIDtQs8Nqv16BSm/e4dHPG6QxIc+bMQWlpKfbt26d1+9q1a3H+/HkcOHBAc9vdu3fh7e2NmzdvomvXroiKioJKpcLJkycBACqVCnZ2dpgyZQq+/fZbAEBubi48PDxw9uxZDBgwAKtXr8bevXuRlJSkWTR28+bNWLlyJcrKysDj8RrsFwD69euH4cOH4z//+Y/Bn4PEokTM+N8MKFllyxtbIFuRDb4vqkbnwjSuo5AHnPZeiBnJDVf0IKZr7fhgzHvIj+sYHZJJnpleunQJR48ehUwm03x1714/L2hqaqpmu7CwMM3/+Xw+nJycEBoaqrnNzc0NAJCfnw8ASEpKwsCBA7VWXx88eDAqKytx9+7dRvcLAB4eHpp9GFKdqg6rT63usIUUAMrrKrDY3R0VEjoDMjXbikNb3oiYlA8P3UJeuZzrGB2SSRZTtVqNCRMmIC4uTusrOTkZQ4cO1WwnFAq1HscwjNZt94qmWl3f041lWa1Ceu+2+7dtar/39mFIm65sQkppisH3a27SqrLwSlB/qBg+11HIP+rsAxBTREOYzE1lrRIb/kzkOkaHZJLFNCIiAtevX4evry8CAgK0vqytrdu83+DgYJw5c0Zr1YUzZ87AxsYGXl5ehoius6sFV7EzcWe7HtOUnS69gY3ho7mOQf5x1WZoyxsRk/S/+BycuFXAdYwOh/NiWlZW1uAMdOHChSguLsaTTz6JCxcu4Pbt2zh48CDmzZsHlUrV5mMtWrQId+7cwZIlS3Djxg389ttvWLduHZYvXw4er/2eCpZl8fb5t6FmaWzY/XaVJuDXYJoU3xTsLO3JdQSih7W/XUOtsu3vlaT1OB85f+zYMfTq1UvrttmzZ+P06dNYuXIloqOjUVtbCx8fH4wePVqvoufl5YW//voLr7zyCnr27AlHR0c8/fTTeO211/T9MVrl99TfkVhETTGNeaM2Db7evdDrjmVPXmHKFLY++DPfhesYRA/pRdXYciwVy0bS9J3thdYzbWfVimpM+HUC8msM36HJUjiKHfB9Xgk8SzK5jtIhxXnPwuRkanI3dyIBDweXDYWvc9svjRHdcd7M29F8c+0bKqQtKK4twZJOnVEtojcBLuyuCOc6AjGAOqUaa367xnWMDoOKaTvKqczBt9e/5TqGWbhVmYlVIUPAgml5Y2IwKpkn/pvnxnUMYiAnkwvxZ3w21zE6BCqm7eijSx9BrqIxYLo6UpKIz8LHch2jQ7npEAmWpQ8wluTNP5MgV1BnJGOjYtpO4gvi8Xf63y1vSLRsLUvAX92HcR2jw/ixKoLrCMTAcsvl2HOe+h8YGxXTdvLx5Y+5jmC21irv4poXzcZjbGorF3yX48F1DGIEW46n0tmpkVExbQdnss4gNjeW6xhmq1ZVixdsBci3ozd6Y0pxioKKpbcES1RQUYtvz6ZzHcOi0V+OkbEsi0+ufMJ1DLOXLy/CC75dIRdKuY5isfbW9OY6AjGiL4/fRnVdx50H3NiomBrZwYyDNEGDgVwrT8PaULp+agxqiQN2ZHfiOgYxoqKqOuw4k851DItFxdSIlGolNl3ZxHUMi/J3yTV81ZN6+BpahnMkatX0dmDptp64jcpaOjs1BvrrMaLfUn5Denk61zEszqby64gJHMJ1DIuyr64P1xFIOyipVmD7KVo72BiomBqJSq3CV/FfcR3DIrFgsQr5uOkezHUUi8CKbfF1li/XMUg72XryNsrlCq5jWBwqpkZy5M4RZFfRzCPGUqOswRJHKYpkNCG7vu66DEWVit4KOopyuRJfn6SzU0OjvyAj+S7xO64jWLycmgK8GBAKBV/EdRSz9qeiL9cRSDvbfioNZdV0dmpIVEyNIKkoCZfzL3Mdo0O4UpaCN3qO4jqG2WKF1vgi24/rGKSdVdQqsfXkba5jWBQqpkbwXRKdlbanX0sSsDOUlgxri1zXh1Cm4HxZY8KB785n0KxIBkTF1MCKaoqwP20/1zE6nI+qbuJkl4FcxzA7f6v7cR2BcKS0WoE/43O4jmExqJga2M+3fkaduo7rGB2OilVhJb8Mt10DuI5iNliBBFuy6PnqyHady+A6gsWgYmpACrUCP938iesYHVaFohJLXJxQZuXAdRSzUOA6GAV1Qq5jEA5dvVOKhLtlXMewCFRMDehA+gEU1BRwHaNDy6zOwUvdekPJo+uALTkMauIlwK5z6VxHsAhUTA1oT9IeriMQAOdLb+E/4dFcxzBpLE+Iz7O7ch2DmIDfr2ajrIaGyeiLiqmBXC24ioTCBK5jkH/8WJKAH0Me5jqGySp2G4gsuZjrGMQEyBVq/HzxDtcxzB4VUwPZnbib6wjkAf+pTsEFX5qQoDHHeAO4jkBMyJ7zmWBZlusYZo2KqQHkVeXhUMYhrmOQByhZJZaLa3DHyZfrKCaFZfj4PKc71zGICbldWIVTKYVcxzBrVEwN4MebP0LJ0rJGpqisrhxLPD1RKbHlOorJKHfti9vVEq5jEBOz6ywNk9EHFVM9KdVK7E3ey3UM0ozUyrtYETQQaoZe7gBwQjCI6wjEBMXcyEdOWQ3XMcwWvbvo6WLeRRTLi7mOQVpwsjQJH4XTouIsGGzOo6XrSEMqNYvvL1BHpLaiYqqnQ+l0rdRc7CiNx29BI7iOwalK195IqrTiOgYxUX/G07KRbUXFVA9qVo2YzBiuY5BW2FCXgTjvXlzH4MxZETXxkqbdLqjCrbwKrmOYJSqmeriSfwVF8iKuY5BWqFPXYZm1GjkO3lxH4cQXBT24jkBM3F8JNPl9W1Ax1cPhjMNcRyBtUFRbgqXevqgWWXMdpV1VO4fhcpkN1zGIidt/LZfrCGaJimkbsSyLw5lUTM3VjYoMvBYyFCwYrqO0mwvSh7iOQMzAjdwKpBVWcR3D7FAxbaOEwgTkVtEnOHN2qOQ6Pu9APXy3FoZyHYGYib+vUVNva1ExbSNq4rUMX5YlYH+3SK5jGJ3cMQinS+y4jkHMBDX1th4V0zai6QMtxxpVNq57hnAdw6guWw/hOgIxI/F3y3C3pJrrGGaFimkb3Ci+gbuVd7mOQQxErqrFUjsRCmzduY5iNNtLwriOQMwMnZ22DhXTNjiYfpDrCMTA8uWFeMGvO2oFljdnbZ19AA4VOnIdg5iZv6mYtgoV0zagXryWKaH8NtaFDec6hsFdtRnKdQRihi5nliCvXM51DLNBxbSVUktTkVaWxnUMYiT/K7mGr3uO4TqGQX1b1pPrCMQMsSxw4DqdneqKimkrUccjy/dpeSKOBlrGmEylbWf8ke/CdQxipk4m0xqnuqJi2kqns05zHYEYGQsWr6IQt9zMfwHt6/aWP+yHGM/F9GKwLMt1DLNAxbQVFCoFEosSuY5B2kG1shpLnW1QYu3EdRS97KnouJP6E/2VVCuQnF/JdQyzQMW0FRKLE1GnruM6BmknWdV5WBbYEwqekOsobaKSeeKnXDeuYxAzdz6N1mvWBRXTVriaf5XrCKSdXS5LwZvhD3Mdo01uOkSCZTvO3MPEOGKpmOqEimkrXC2gYtoR/VKSgO9CR3Mdo9V+rIrgOgKxALHpVEx1QcW0FeIK4riOQDjyQdVNnPEfwHUMnamtXPBdjgfXMYgFyCmT404xTS3YEiqmOsqtykV+dT7XMQhHVKwKLwvKkebShesoOkl1ioKKpT9vYhh03bRl9NemIzorJRWKSixxc0GZ1J7rKC3aW0NNvMRwLqQVcR3B5FEx1RF1PiIAkFGVjVe694WK4XMdpUlqiQO2Z3tzHYNYkNj0Eq4jmDwqpjqKL4jnOgIxEWdLb+K9cNPtkJThEolaNf1pE8NJK6xCfgXN09sc+ovTQa2qFknFSVzHICZkT2kCfu4xiusYjfqttg/XEYgFik2js9PmUDHVQWJRIhRqBdcxiIl5W56KWB/TKlys2AZbs3y5jkEs0OVMKqbNoWKqA7peShqjVCuxXFKLu46duY6icddlKKpU9GdNDC+FphVsFv3V6YAmayBNKa0rwxIvb1SJbbiOAgD4n6Iv1xGIhaJi2jwqpjqgYkqak1J5B6/2GAQ1w+2fEyu0xpZsf04zEMuVXVaDmjoV1zFMFhXTFhTWFKKgpoDrGMTEHStJwsfh3C4qnuv6EMoUAk4zEMvFskBqAZ2dNoWKaQsyyjO4jkDMxPbSBPwRNJyz4+9X9+Ps2KRjoGLaNCqmLcgsz+Q6AjEj6+sycdW7Z7sfl+WLsSU7oN2PSzoWum7aNCqmLcisoGJKdFenrsMyawa59l7tetxCt8HIrzXPdVeJ+aBi2jQqpi2gZl7SWoW1xVjauQtqRFbtdsxD6N9uxyIdFzXzNo2KaQvuVNzhOgIxQ0kV6VgdEgkWxl+cm+UJ8Xl2V6Mfh5D0wmqo1CzXMUwSFdMW0DVT0laHSq5jS0/j9/AtcRuALLnY6MchpE6lRkZRFdcxTBIV02aUVhbCrUbEdQxixr4ov46D3YYa9RjHeOazaDkxf6kFVEwbQ4PSmiFOzcK7HxSCkUgAN2fUutqh3EmKQnsesmwUuC2txA1xMe4IyriOSkwUCxavqXLh7RGMoJxEw++f4ePznO4G3y8hTUnJr8SoYDeuY5gcKqbNUOTkAgBYuRzIuAtRxl04A3AG0B3AiH+2Y6yswLo7o9bFDuVOEhTYAVkyBVKtKpEkLkQOny7ad2Q1KjmWOrjg+0o3OFfkGXTf5a59kZohNeg+CWlOVmk11xFMUpuKqb+/P2JjY+Hk5KR1e2lpKSIiInD79m2DhOOaMi9Xp+3Y6mrgdibEtwEX1H8FA7i3QBcjswbr7gK5iw3KHOuL7R1ZHW5bVSBJVIx8KrYWL7emAC/4B2P7tVKIVLUG2+9J4SCD7YsQXZRU0QpajWlTMU1PT4dK1XCOxtraWmRlZekdylQocg1zFsFWVgEpVZCkABIAbgBC7rufsbEB6+6MGldblDmIkG8H3LGpQ4qkDIniIpTwagySg3ArvjwV63uOxNuX/2eQ/bFgsCUvyCD7IkRXRVWG+zBoSVpVTH///XfN/w8cOAA7OzvN9yqVCjExMfD19TVYOK7pemaqL7aiAqiogDQZkAJwBxB23/2MvR3Ubk6ocbFBqaMYebZqTbFNEhehjJG3S06ivz9KEhAQNgbz4v/We19VrhG4nmltgFSE6I7OTBvXqmI6efJkAADDMJg9e7bWfUKhEL6+vti4caPBwnHNUGem+mJLy8CUlsHqJmAFwBNAr/vuZxwdoHJzQo2LDCUOIuTZqXHHuhbJ0jIkiQpRydRxlJw05pPKJHQJGIzIlNN67eesaLCBEhGiu+Jqej9pTKuKqVqtBgD4+fkhNjYWzs7ORgllKpS57XNmqi+2uAS84hJYJwHWADoB6H3vToYB41RfbKudZShxECLXTo0M6xqkSMqRKCqAnFFyF74DUrNqrGSK8J1bNwTk3WzzfrYU9DBgKkJ0U0rFtFFtumaalpZm6BwmSVFgAUuvsSzYwmLwCoshAyAD4A1As4Q0jwfG2RFKN0dUOctQ4iBArm19sb0lKcENYRHqGFrD0NCqlNVY7OyGHyocYV9d3OrHVzuH4fJd01iQnHQsChWLshoF7KQ0F/T92jw0JiYmBjExMcjPz9ecsd6zbds2vYNxjVUoAEUHuDagVoPNLwQ/vxC2AGwB+AD/zvTK54NxcYLSzQGVztYothcgx1aFDOsa3BSX4JawCEpG3eTuSdOyqvOwvGsvfBl/AkJ1615rF6QPGSkVIS0rqaqjYvqANhXT119/HRs2bECfPn3g4eEBhjH+/KPtTV1LTRkAAJUKbG4++Ln5sANgB8APgGZAhkAAxtUZCjcHVDpZociBjxwbJdKtanBTUoIUYRFUoLk8mxJbloy3wx/Gulb28P26KNRIiQhpWXF1HXxBnd/u16Zi+sUXX2DHjh2YOXOmofOYDLaOun/rRKkEm50LQXYu7AHYA+gCQHPeJBSCcXNBnZsDKp2kKLLnI8tGiTSrKtwSlyBVUAzW8j6Ltcp/SxIQEBqNGQkHdNpe7tgdp7LtWt6QECMpqaKTjQe1qZjW1dVh0CDLHizO1lIxNQiFAuzdHAjv5sABgAOAAACR/9zNiMWAmzPqXO01UzVm2yiQZlWNJHERMgSlnEVvT+9X3YKfX38MSjvf4raXrY071y8hLSmiYtpAm4rp/PnzsWfPHqxZs8bQeUwGFdP2wdbWAplZEGZmwQmAE4BuAIb9cz8jlYB1c0Gtqx0qnCQosOMhy6YOt62qcENUhLuCcu7CG5CKVeEVUSX2OPvDp7D5GcS2l4Q1ez8hxkZnpg21qZjK5XJ89dVXOHz4MMLCwiAUal+I/vDDDw0Sjkt0zdQ0sDVyIP0OxOl3IEb9vMj3z/lTPy+yC2pdbVHmKEaBPQ9ZsjrcllYiUVyIXDOaqrG8rgKL3b2wu6oItjWNL55QZ98Fh3Id2zkZIdporGlDbSqm8fHxCA8PBwBcu3ZN6z5L6YxE10zNQ/28yBkQ3wZcUf91/+hLxkYG1s0Zcpd/iu0/UzWmSiuQKC5CIc+0lpNKr8rCiu798XlcDPhswyFJ8baRgHkMfyYWTF5Hw+Ue1KZievToUUPnMDnUzGsZ2IpKoKKy6XmRbW2hdneC/J+pGvPtgLuyWqRIK3BNVIAyXvtP1Xi69AY+CB+NlVca9vDdVUZNvIR7SjX10H8QLcHWBDUV0w6BLS8HU14O6a36eZE9APS8737Gwb5+XmRnGUqdxMizZZEpkyNFWo5EYSEqeMZ5nXxXmoDA4JGYknhYc5vStjN+y3M1yvEIaQ01S8X0QW0qpsOGDWu2OffIkSNtDmQq6MyUAABbUgqmpBRWaGZeZPf6YlviIESeHZApq5+q8bqwANW8tk/88WZtGnw690bvzEsAgOt2kUC+Pj8NIYahojPTBtpUTO9dL71HoVAgLi4O165dazABvrmiYkp0oZkXGU3Ni+wIlZsjqlz+Kba2amTI5LglKcUNYWGz8yIr1Aost1Jgj2NneBVnYk9lrya3JaQ9UTNvQ20qph999FGjt69fvx6VlebTe7I51MxL9MayYAuLwCssgg0AGwCd8cC8yC5OULo61M+L7ChAjo0KGdZy3JKU4KawCMW1pVji1Rk7IMJPOW6c/SiE3E9NxbQBhmUN1/idkpKCfv36obi49RN3m5qSH39C7rp1XMcgHdk/8yLXebkif/hEFHkVwtoqDaDpGQnHbG17Ymj4fK5jmBSDdkA6e/YsJBKJIXfJGWrmJZxTqVAndUVSlyUQ519FV3koLovl6BKQAx7/HBSKIq4Tkg7KSSjiOoLJaVMxnTJlitb3LMsiJycHFy9etJhZkVgFDUom3CqLfgYJTG/U5SthbZ2MCKsI9FP0x//OXYBCMRYhISq4ut2EXH4FAK3cQ9oPw/C5jmBy2lRM7ey0J9nm8Xjo1q0bNmzYgIcfftggwTjHpxcL4YZaZo/MietwO1sEQAWGYVGadwfZ7rfRqcgXE1364y/+RVy9WgUgGC4uIQgKLoJAcB4KhQWswUtMHhXThtpUTLdv327oHCaHb2PLdQTSAdWGDUVCl6dQnv3vkBobxyrkFysQfzMGnVznw7ZAgPEOffG37RWUlZehoECNguMOYJhohISycHdPRq38EljQLDXEOBgeTVHwIL2ekUuXLiEpKQkMwyA4OBi9ellO132erQ3XEUgHwjIMiiavwLVyH6hLtMemiqX18/RWVBSCDeGByVVBVsLHeNte2G8fj6LS+g5/LMsgIZ5BQnw3ODkFIbhHCUSiC6iro/kHiWHRmWlDbSqm+fn5eOKJJ3Ds2DHY29uDZVmUlZVh2LBh+OGHH+Di4mLonO2OzkxJe1G5dkbKiFeRlcOi0Z667L9Nt3mqDLijEwBAWs7HOGVPHHRORG5hntZDiorUOHnCDgwzCsHBLDw9U1FbFwuWbXpcKyG6Yhg6M30Qry0PWrJkCcrLy3H9+nUUFxejpKQE165dQ3l5OZYuXWrojJzg05kpaQfVg6cgtt/qfwpp42orczT/v3rrCHDf5GOiah6iC4PRydWz0ceyLHD9OoNDhwIQf3UGWPVkiEReBstPOiY6M22oTR8v9u/fj8OHDyMo6N/FsIKDg/H5559bTAckni2dmRLjUYvEyJ2yFjfy7IHK5s8Wy/IzNf8vLc0BugmA/H8fI5TzMCqnO453EuJ2TkaT+yktVePUKRsAw9C9O9DJOw11dRfAsm2f8pB0THy+NdcRTE6biqlarW6whikACIVCqNWW0UWfT8WUGInCPwxJfZ5HYW7LTa7WdnIUpWsvE5ePu3CFu9ZtfAWDqMwuEPoKcDMrtYW9MrhxA7hxwx+2tl0QEloJK6tLqK1tuhATcj+RyJnrCCanTc28w4cPxwsvvIDs7GzNbVlZWXjxxRcxYsQIg4XjEk8mAyxkbVZiOspGL8C5wOdQmK/btUuprOEi4QmpR7Waeu/hqRgMTvNBaKfuOucpL2dx5rQ1Dh8airzcmRCLIsHjiXV+POmYxCLz7xdjaG0qpps2bUJFRQV8fX3RpUsXBAQEwM/PDxUVFfjss88MnZETDI9XX1AJMQC1zB5p0z/GJXlPKGp1b73hMYUNbisszAScG29U4qkZ9Ev1RG/vkEbvb86tW8Dhw51xMXYalMpHIRb7t3ofpGMQiWkpwAe1qZnX29sbly9fxqFDh3Djxg2wLIvg4GCMHDnS0Pk4xbexgbqigusYxMzJw4fhmt+TWmNHdVVX0/iwliJeDpzQ+NkBwzLolewGUaAQZ+9cafUxq6pYnD0jBTAYXboMhq/fXSiV56BW17R6X8Qy0ZlpQ62a6P7IkSNYvHgxzp07B9sHrimWlZVh0KBB+OKLLzBkyBCDB+XC7cmPoPbGDa5jEDPFMgwKH3kV18u8oVa1bXJ6PrsDVaUNF45wdw9ApHRqi49PDijD8bsX23Ts+0mtGISF1cDW5irktbf03h8xb8OiksDj0fy892tVM+/HH3+MZ555pkEhBeqnGFy4cCE+/PBDg4XjGt+GhseQtlG6+eDmE58jobhTmwup2ErRaCEFgNzcFMC5YSfABwWm2GGkZ38wel7/r6lmcf6cBIcO9cedzJkQCUdQj84OSiCwo0LaiFYV06tXr2L06NFN3v/www/j0qVLeocyFTQ8hrRF1ZDHENvn/5DdzNhRXVjblTd7f4kgX6f9+N6WYbTbQPANNN90ejoQE+OJc2enQi5/HBJJUIuPIZZDRE28jWpVMc3Ly2t0SMw9AoEABQWWM9E2nZmS1mBFEmQ98Q7OC6JQU6X/TEMCYfNLrCVmntJ5X17pUox1HNjs329ryeUsYi+IcehgH6Snz4ZQMAp8Pv3NWDqxmIppY1pVTL28vJCQkNDk/fHx8fDw8NA7lKmg+XmJrhRdwhE/6RPczLU12NrdSnles/ffzUoE46h7cXS7I8Z42UCIxYYf+nInU40jR9xx5vRk1FRPg0TS+t7ExDzQmWnjWlVMx44di7Vr10Iulze4r6amBuvWrcP48eMNFo5rAkcnriMQM1A65jmcC1iIogLDzntbVZLV8rHFrVsg3ClHiAniAbCSWrU1VrPq6oCLF0U4dLAXbqfOhoA/GgKBvVGORbhBPXkb16revHl5eYiIiACfz8fixYvRrVs3MAyDpKQkfP7551CpVLh8+TLc3NyMmbndlO8/gKxly7iOQUyU2sYR6RPWIT3b8JN+84UqVBd8BpZtfkyqb+ee6M9vuh9DUyqdVPiLdxnlFc1flzUEgQAIDVPC2TkRcvlVox+PGFdAl5Xw8VnAdQyT06piCgAZGRl47rnncODAAdx7KMMwiI6OxubNm+Hr62uMnJyQ37qFtImTuI5BTJA8fDiu+T2B8hLjzGtr71qG3Jvf6LTtE71eA1va+hzVdmrsl8ShuKyk1Y9tK3d3Bt27F4LhnYNS2XhPZWLagoM3wsN9MtcxTE6rP1L7+Pjgr7/+QklJCVJSUsCyLAIDA+Hg4GCMfJwS+/oCfD6gokWWST2WYVA4ZRWul3ZqsO6oIYnEuheacqtS2JS2fpiKVRkPY5XhOOh4DfnF7dNxMDeXRW6uE/j8cQgNVcHF9Sbk8ssw2IVmYnTUzNu4NrdPOTg4oG/fvobMYnIYkQjCTl5QZGS2vDGxeEoPXyRHrUBOU+uOGpBaqXtxS865gAgMa9NxJFU8jFaF4rBLErILclp+gIGoVEBcHB9AMFxceiA4uBh8wXkoFJYzGsBS0ST3jWvT3Lwdidi/C9cRiAmoHPo4LkSs+qeQGl912V2dt02+fQGMTduv24rkDB7OC4KPu3eb96GPggIWx4874OiRaJSWzoBE0g8MaL1MUyUWu7e8UQdExbQFIn8/riMQDt0bO3qBHwm5AcaO6oJhWJTlt9yT936VNvrNIS2oYzD8biACPbmb3J5lGSTE83DoYDdcv/4UGEyESEhv3KZEJHKBUGjHdQyTRMW0BXRm2nEpAnoZfOyoLmwcq6FStO56bEq+/jOP8ZUMhqT7IrhTV733pa+iIjVOnLDDkSOjUFw0AxLxADCM4XtNk9aRWXfjOoLJomLaAjoz7ZhKxy7CWf8FBh87qguJtPW9a2+knAEj07/Y8NQMBqZ2Qrh3sN77MgSWBa5f5+HQoUDEX50OVj0ZIpEX17E6LGsZ9x+0TBUV0xaIu9CZaUeitnXC7emf4HJ1DyjrdF931JBYtg2dcFgW1XbVBjk+wzLok+yBft5hBtmfoZSWsjh1ygYxh4ehsGAmxOLBYBjDTY9IWmYKZ6Y7duyAvb091zEaoGLaAr6tLfjO1HutI5BHjMSl4e8YZRKGVuWozG7T424XtX7t0uaEJbvgIe/eBt2nYTBISgIOH/JH3JUnoVZNgVjUmetQHYLMgGemd+7cwdNPPw1PT0+IRCL4+PjghRdeQFHRv7N6+fr64uOPPzbYMY2JiqkOxP7cdcogxsfy+Mifuhrn7Kegog2THxg0C1iU5d1p02MTb54EIzVsL9juyfYY7tVP7yXcjKW8nMXp09Y4fDgSebkzIRYPBY9n+LmHCQDwYG0daJA93b59G3369MGtW7fw/fffIyUlBV988QViYmIwcOBAFBe3/4Qeilb2U3gQFVMd0HVTy6Xy8MONaZtwrcgTajX3EwfI7OSoq2lbc61arUKNY62BEwH+qTZ42H0AeDzTfru4dQs4fMgHly5Og1I5FWIxfQg2JKnUG3y+1CD7ev755yESiXDw4EFERkaic+fOGDNmDA4fPoysrCysXr0aUVFRyMjIwIsvvgiGYRp8oDtw4ACCgoIgk8kwevRo5ORoj5Pevn07goKCIJFI0L17d2zevFlzX3p6OhiGwU8//YSoqChIJBJ89913ev1Mpv3XYSKoR69lqop8AucjXkVODjfXRhtjJdNvrtz0kqZXddKHd5oVxjgPhEBg+j1qKytZnD1jhcOHBiM7ayZEomHg8QxTBDoyG5lhOqUVFxfjwIEDWLRoEaRS7d+Lu7s7ZsyYgR9//BF79+5Fp06dsGHDBuTk5GgVy+rqanzwwQfYtWsXTpw4gczMTLz88sua+7du3YrVq1fjrbfeQlJSEt5++22sWbMGO3fu1DreypUrsXTpUiQlJSE6Olqvn8v0/zJMgIiaeS0KK5Yia8o63MqxAdpp7KiuGKZQr8dfu3EMQV37gJUbfgpMj0wJxnkOxN+V51FXV2fw/RtDaiqQmtoJVlaPITSsGrY2VyGvTeY6llmytQ01yH6Sk5PBsiyCghpfVD4oKAglJSVQqVTg8/mwsbGBu7v2eGOFQoEvvvgCXf7pILp48WJs2LBBc/8bb7yBjRs3YsqUKQAAPz8/JCYm4ssvv8Ts2bM12y1btkyzjb6omOpAHEBnppaiLjACieELUZxjWkX0nroa/ab0U6nqUOukgCjLOI1OLtkiTHAbgL/4saipqTHKMYyhuprF+XNSAAPg5zcQ/v5ZUKnOQqU2TA/ojsDGtn16d9+/gEpTrKysNIUUADw8PJCfnw8AKCgo0HRueuaZZzTbKJVK2NlpTzjRp08fg+WmYqoDobs7BO7uUObmch2F6KFk3PNIUIRAycHYUV2VF7St89H9MssTEQDjLc7tkCfEBOf++Nv6IiqqKo12HGNJS2ORluYJieRRhIXJYWcfD7n8BtexTBwDWxvDvKYCAgLAMAwSExMxefLkBvffuHEDDg4OcG5mFIVQqD0kimEYTRFWq+sv22zduhX9+/fX2o7P1+6gZ23d+gUimkLXTHVk1dsUhwgQXajtnJE6/VNcqQrmbOyoLsTWClQbYDm0hKQjYETG/dO2LeRjvKov7G3Nd2o5uZzFhQtiHDrYF+npsyAUjgKfb8N1LJNkZeUPgcAwz42TkxNGjRqFzZs3N2jdyM3Nxe7duzFt2jQwDAORSARVK1ftcnNzg5eXF27fvo2AgACtLz8/43UmpWKqI6u+hmsOIO1H3vthXIx6GxnZpj9xuszWMAt11ylqUOds/GUDrUt5GFcTAWcHJ6Mfy9juZLI4EuOOs2ceQU31NEgkPbiOZFIMdb30nk2bNqG2thbR0dE4ceIE7ty5g/3792PUqFHw8vLCW2+9BaB+nOmJEyeQlZWFwkLd+xOsX78e77zzDj755BPcunULCQkJ2L59Oz788EOD/hz3o2KqIzozNS8sj4/8R9fgnN1kVJZxO3ZUV3xBUcsb6ehu1U2D7as50goexpaGwcPZrV2OZ2y1tSwuXhTh0MEI3E6dDQF/NAQC8z37NhRbG8NeLw0MDMTFixfRpUsXTJs2DV26dMGCBQswbNgwnD17Fo6OjgCADRs2ID09HV26dIGLi+7rqM6fPx9ff/01duzYgdDQUERGRmLHjh1GPTNl2HsNzSaKYRj8+uuvmDx5MtLT0+Hn54crV64gPDwcx44dw7Bhw1BSUmL06aVYlkXywEFQlZYa9ThEf0pPf9wa+gpyc023Sbcxjq4nkX0z1iD7kkhkmNR5CaBon+dAIWZx1P0mMvNat9qNORAIgLAwJZycEyGXX+U6Dif69N4LO7twrmOYtDafmU6YMAEjR45s9L6zZ8+CYRhcvny5zcHuycnJwZgxY/Tej74YhoGUzk5NXmXUdFwIX2F2hRQAKot1X8O0JXJ5JVQu7fc5WVjLYER2N/h7+LbbMduLUglcvizAoYNhSEmeDT5/LAQCB65jtRuBwM7gzbyWqM3F9Omnn8aRI0eQkZHR4L5t27YhPDwcEREReoUD6gfxisWmMT2YlQG7URPDYsVS3HnyXVzAYMirjX+90NAEIhXKCw3bWzyrpn3HU/IVDKIy/dG9U0C7Hrc95eSoceyoE04cH4+K8umQSCIAmOZUi4bi6PgQGMb0+xxwrc3FdPz48XB1dcWOHTu0bq+ursaPP/6IyZMn48knn0SnTp1gZWWF0NBQfP/991rbRkVFYenSpVixYgUcHR3h7u6O9evXa23DMAz27dunU6aioqIWj6kP6/79DLYvYjh13fri6oSPkZwj4zpKm9nYV9SvN2ZA8TePAPz2faPnqRgMTu2MMO/u7Xrc9qZSAXFxfBw62AM3b8wCjxkPoVD3a3rmxNlpGNcRzEKbi6lAIMCsWbOwY8cO3H/Z9eeff0ZdXR3mz5+P3r17488//8S1a9ewYMECzJw5E+fPn9faz86dO2FtbY3z58/jvffew4YNG3Do0KE2ZZLL5Tods63EQUHg/3NhnJiGkvFLcM5nHooLTXfsqC6EYv2HxDyoqqoEatf2P2tiWAb9kr3Qp3PHaBrMz1fj+HEHHDs6GqWl0yGR9IXl9O3kwclpKNchzIJev/F58+YhPT0dx44d09y2bds2TJkyBV5eXnj55ZcRHh4Of39/LFmyBNHR0fj555+19hEWFoZ169YhMDAQs2bNQp8+fRATE9OmPLoes60YhoH1wIEG2RfRj8rBtX7saGV3kx47qiu1Mt8o+82pSzPKfnURfssVg7x7cXb89qZWAwnxfBw62B2J158CMAEioXuLjzNltrahEInMf+hTe9CrmHbv3h2DBg3Ctm3bAACpqak4efIk5s2bB5VKhbfeegthYWFwcnKCTCbDwYMHkZmZqbWPsDDtLtf3TwvVWroeUx/WgwcbbF+kbWr6ROPS0DfMYuyorqrLjNML9uqtGIDH3TW94GRHRHXqy9nxuVJUxOLkCXscOTIKxcVPQSIeAIYxvwnnnKiJV2d6t0U8/fTT2Lt3L8rLy7F9+3b4+PhgxIgR2LhxIz766COsWLECR44cQVxcHKKjoxtMkN3YtFD3poNqLV2PqQ8qptxheXzkPboW52wnorLMvJt178fw1CjLM1xP3vtVlBeAdeW2yTEgxRajPAeY7JqoxsSywPVrDA4dCkT81ekAOxkikRfXsXTm7BTJdQSzofdf2eOPPw4+n489e/Zg586dmDt3LhiGwcmTJzFp0iQ89dRT6NmzJ/z9/ZGcbNzehe1xTKGbK8SBlttb0VQpvQKQ9PjnuF7oBtb8W3W12DhWQ6U03oeDPFXDHvftzee2Nca4DmowN2pHUlrK4uRJG8QcHobCgqcgEQ8GwwhbfiBHRCJn2Nh0jOvehqB3MZXJZJg2bRr+7//+D9nZ2ZgzZw6A+smMDx06hDNnziApKQkLFy5ErpEnim+vY1oPorPT9lQx/Clc6PkycnPNb8iLLsQSw3c+ut/VW0dMYvSGZ4YE4xwGNmiN6ngYJCUxOHTIH1fjpkOtegRikTfXoRpwcozskK0JbWWQ9p+nn34aJSUlGDlyJDp37gwAWLNmDSIiIhAdHY2oqCi4u7s3ukKAIbXXMa2HDDH4PklDaok17jz5HmLVA81y7KjOWP3WMG1JaWkO4GIa1+tc74oxQTYQEomE6ygmoaxMjdOnZTh8OAr5eTMhFg8Fw4i4jgUAcHKO4jqCWTH56QRNEatUInloJFTFxVxHsVh13fviesgzKCk0j3l19WFj+z8UZBh3Lt3h/WfDJd90epaWuyjxP9VFVFVXcR3F5MhkDMLCqmBlfRm1tdz0xmYYAYYOuWiwlWI6AksZDNWuGIEAtiYwxaGlKp7wAs55z+sQhZQFi9I8w/U2b0pC6nGTaOq9x7ZAgAnoC1sbW66jmJzKShZnzljh8KGHkJM9E2JRFHi89j2Tt7OLoELaSlRM28hu0kSuI1gclaMbUqZ/hriKrlC20wTtXJPZyaGQ17S8oZ4KCtMBZ9No6r1HVszHhNrecLKniVCakpICHD7sjdgLj0NR9ygk4vbp/OjsFNUux7EkVEzbSBoWBpGvL9cxLEZNv7G4NGQDMrM71ktSKitrt2MV8XLa7Vi6kpbzMLaiJ9ycXLmOYtKqq1mcOyfFoUMDcffOLIiEw8HnWRnteE5UTFutY71zGZjthPFcRzB7LF+AvMfW4ZxsnEWNHdUVwxi389H9rqWfaLdjtYa4iofoohB4uXpyHcUspKWxiInxwrlzj6JW/jgkEsPOgyyRdIJM1s2g++wIqJjqwW4iNfXqQ+ndDYmPbcL1AleLGzuqK0WNcYeL3S83NwWMk2kOSxHJGTyc0x1+7p25jmI25HIWFy6IcehgX2RkzIJQMBJ8vv6LPbi7T9Y/XAdkWhdRzIzI2xvS8HDUxMVxHcXsVIyYiXjxYNRa6NhRXZXnG7/z0f1KRPmwh2muxclXMIi6EwChrwC3sm5zHcesZGawyMzwgFg8BaFhtXBwuAa5/Hob9sTA02OqwfN1BHRmqifqiNQ6aok1Mp98H7GqAai15LGjOpBY16G6vLRdj5mYcbpdj9dafBWDh9J8EdKJmhnboraWxcVYEQ4djMDt1FkQCEZDILDT+fH29v0glVLrQFtQMdWT7ZgxQIef0UU3dUH9cXXch0jJMV7HCXNibVve7se8k3UdjINpv155agb9U73Qy7sH11HMWlYWi6NHXHDq5ERUVT0BiaRni4+hs9K2o2KqJ769PWQ0I1KLiicuw7lOc1BS1PE6GTWFLyji5LilUm6O2xoMy6B3sjv6e7dcAEjzFArg8iUhDh0MQ0ryLPD5YyAQNGzq5/Ot4epK4+fbioqpAdhNnMB1BJOlcnRD8vRNiCsP7DBjR3WlrM3j5Lg3757l5LhtEZrsjKGd+nAdw2Lk5LA4dtQZJ0+MR2XFdEgkvXBvNg9X1zHg86nVqK1oOkEDUNfWIvmhIVBXVHAdxaTU9BuHePeJqCqns9HGiAR7UF7Qfr157/dE+Gtgy8xnhqk0/0ocybkAersyPFdXHoKCCtGz52w4OvbmOo7ZojNTA+CJxbCJfpjrGCaD5QuQ+9h6nJONpULaBKFIifJCbs5MAaDcupSzY7eF320Zot0HgMejtyxDy89XIzGxGxVSPdEr00DsJlCvXgBQdO6GxEc3IbHApcOOHdWFzKGyfuVojiTnXODs2G3VKc0K45wHQiCgEX2G1qcPNaXri4qpgVj16wuRnx/XMThVMXI2LvR4EXl5HXvIiy6EIm5XHEq+fQGMjfkVJbdMCcbbDoRYLOY6isUQiUTo2ZM6eumLiqmBMAwDp6fncR2DE2qpDJlPfoBYZT/U1lAh1YVKkc91BFTaVHIdoU2cs0WYIBkAKyl1ljGEsLAwWl/WAKiYGpDdxIkQuLlxHaNd1QUPRNzYD5GSI+U6ilmpLsviOgJSCy5xHaHN7PMEmMDrCxsZLROmr759+3IdwSJQMTUgRiSC45w5XMdoN8WTluOs1yyUFplPr1BTwPDVKMvjvpjeSD4Dxtr8mnrvsSkSYIKiNxzs7LmOYrY6d+4Mtw52AmAsVEwNzOHxx8C30336LnOkdnSvHzta1gUqGjvaajYOVVCruO/lzLJqVNtVcx1DL1ZlfIyr6gUXR2euo5glOis1HCqmBsaztobDjBlcxzCamv7jEfvQ67iTzXAdxWxJpKVcR9C4XXyF6wh6k1TyMKYkFJ4u7lxHMSsODg4IDg7mOobFoGJqBA4znwIjtaxriCxfgJzHX8c5qzE0dlRPrLqA6wgaiTdPgpHyuY6hN1ENDw/nBaOzWyeuo5iNyMhI8Pnm/7s3FVRMjUDg4AD7xx7lOobBKDt3x/VHNyEp35nLoZEWQ16RzXUEDbVaBbljLdcxDEJQx2B4VlcEeHbsIWq6cHJyQlhYGNcxLAoVUyNxmjvXIlaTKR81F+d6LEM+jR01CBYsSvPadw3TlqSXJHAdwWAESgZD0/0Q1CmQ6ygmLTIykmaTMjB6No1E6OEBu/HjuY7RZmprW2Q8uREXFX1QR2NHDUZmL4dCLuc6hpZrN4+BEVtOcx9PzWBQqjd6etP1wMa4uLggJCSE6xgWh4qpETk9Mx9gzK+jTm2PQYgb/QFSc2ggt6FZycq4jtCAUlmHWifLGt7EsAz6Jnugrzc1ZT4oKiqKzkqNgJ5RIxL7+0M2YjjXMVqlaNJLOOfxFI0dNRIGptP56H6ZFYlcRzCKnskuGOwdwXUMk+Hm5kY9eI2EiqmROS9YwHUEnaicPHBr+ue4WuYPlZJ6GRlLbTU3S661JCHpCBiRZb4dBCU7YFinflzHMAlRUVFgzLC1zBxY5l+PCZGGhcGqf3+uYzSreuBExA5aj7um08nUYpXnm1bno3vqFDVQOFvuBBxdUmzwsEfHXsLNw8MDQUFBXMewWB33ldWOXF5YynWERqkFIuRM24DzkmhUV9DYUWOTWteipsL0rpnec6fqJtcRjKpzmjXGOA/ssGMrhw0bxnUEi0bFtB1YRUTAdsIErmNoUfoE4/rUT5GU50RjR9uJlV051xGaFX/zCCC07LcEj0wJxjsMhEgk4jpKu/Ly8kLXrl25jmHRLPsvx4S4vvIyeNbWXMcA8M/Y0aClKKCxo+2Kz+d2DdOWyGvKoXKx/E9WLnfFmGA1oEMtO0ZnpcZHxbSdCF1d4bzoOU4zqK3tkDH9w/qxo3IqpO1NKTfNzkf3y6pJ4TpCu3DIFWKisD9kJvIB15i8vb0REBDAdQyLR8W0HTnOmgWRvz8nx64NHYIr0e8jNVvMyfEJUFF8l+sILYq/GQPwO0ZvT9sCAcar+8LO1rJXeRo+3LyG55krKqbtiBEK4fZ//9fuxy2c/ArOuT2JsmIaO8oVoViFiqJ8rmO0qKqqBGrXjlFMAUBWwsd4eS842TtyHcUogoOD4edHcxW3Byqm7Uz20GDIRo5ol2OpnL1wc/rniC/1pbGjHJPZl8Ncenrl1KVxHaFdScv5GFfeE+7OlrVItkgkwujRo7mO0WFQMeWA26urwBi580P1oMmIHbgWWTR21CQIRabd+eh+V2/FALyOc3YKAKJqHqILesDb1YvrKAYzbNgw2Nrach2jw6BiygFRJy84Pf20UfZdP3b0TZwXj6KxoyZErTD9Jt57KsoLwLp2vLcGYS2DkTnd4O/hw3UUvbm7u6O/iU8WY2k63l+MiXB6Zj6EXob9FKzw7YHrUz5DUp6DubQodhhVpVlcR2iVPJVpztRkbHwFg6jMLujm1YXrKHoZN25ch57tiQv0bHOEJ5HA9dWVBttf+cNP43z3JSjIp7NRU8Pjq1GWb17FNCH5CNCxWno1eCoGg9N8ENqpO9dR2qR3797w9vbmOkaHQ8WUQ7ajRsF68GC99qGW2SF9+oe4WBdBY0dNlI1DFdQq8/rdFJdkAy4CrmNwhqdm0C/VE729zWvdT5lMhpEjR3Ido0OiYsoxt9WrAaGwTY+Vhw3BlYffx20aO2rSxNISriO0SQFMf1ysMTEsg17Jbhjo3YvrKDobN24cpFIp1zE6JCqmHBP7+8FpzpxWPYZlGBQ+sgLnXGjsqDlgVaa5hmlLElKPd9im3vv1SHZEZKc+XMdoUXBwsEWsCsMwDPbt28d1jFajYmoCXJYshri7btdnVC6dcOvJTYgv8YFaRb2MzEFNhXmOTyooTAecO25T7/0CU+ww0nOAya4FamVlhbFjx+q9nzlz5oBhGDz77LMN7lu0aBEYhsEcHT/8p6eng2EYxMXF6Z3LHFAxNQGMSASv998DI26+ubZ68COI7b+Gxo6aFRZleXe4DtFmRXzTn0+4vfjetsZoN9Ncwm3MmDGQyWQG2Ze3tzd++OEH1NTUaG6Ty+X4/vvv0blzZ4McwxJRMTUR4sBAuL60vNH71CIxsqe9iXOikaiupN665kRmL4eiVs51jDa7nnaC6wgmxStdirGOAyFsYz8HY+jWrRtCQ0MNtr+IiAh07twZv/zyi+a2X375Bd7e3ujV69/rx/v378dDDz0Ee3t7ODk5Yfz48UhNTdXcf28aw169eoFhGERFRWnu27ZtG3r06AGxWAwPDw8sXrxYK0NhYSEeeeQRWFlZITAwEL///rvmPpVKhaeffhp+fn6QSqXo1q0bPvnkE4P9/G1FxdSEOMyc2aB3r8IvBNcmf4obeQ4AteqaHamslOsIesnJTQbjZDqFwxS43RFjvGwgxC20JLUHiUSC8ePHG3y/c+fOxfbt2zXfb9u2DfPmzdPapqqqCsuXL0dsbCxiYmLA4/HwyCOPQK1WAwAuXLgAADh8+DBycnI0xXnLli14/vnnsWDBAiQkJOD3339vsKrN66+/jscffxzx8fEYO3YsZsyYgeLi+lnE1Go1OnXqhJ9++gmJiYlYu3Yt/u///g8//fSTwZ+H1mBYlob3mxJFXj7SJk6EqqwMZdHPIB4RUNSquY5F2si1UwIyEw5xHUMv0YMWwD7HgesYJqfUVYm/FLGorqnmLMP06dMNuuj3nDlzUFpaiq+//hqdOnXCjRs3wDAMunfvjjt37mD+/Pmwt7fHjh07Gjy2oKAArq6uSEhIQEhICNLT0+Hn54crV64gPDxcs52Xlxfmzp2LN998s9EMDMPgtddewxtvvAGgvmjb2Njgr7/+anKu4eeffx55eXn473//q/dz0FZ0ZmpihG6ucH/zTaRP/wiXasOpkJq52irzv8CdmHGa6wgmyT5fgIm8frC14Wb+26FDhxq0kN7P2dkZ48aNw86dO7F9+3aMGzcOzs7OWtukpqZi+vTp8Pf3h62traZZNzOz6dmz8vPzkZ2djREjml/sIywsTPN/a2tr2NjYID//3yk5v/jiC/Tp0wcuLi6QyWTYunVrs8dtD1RMTZDtqJGQBtCySZagvMB8Ox/dcyfrOhgHauptjKyIj/F1veFo175n7v7+/lrXII1h3rx52LFjB3bu3NmgiRcAJkyYgKKiImzduhXnz5/H+fPnAQB1dXVN7lPXMbAPXpNmGEbTfPzTTz/hxRdfxLx583Dw4EHExcVh7ty5zR63PVAxNVFDHguEs7dheucRbkhkdaipKOc6hkGUSYq4jmCyrMp4GFsZDldHl3Y5np2dHaZOnWr0uXdHjx6Nuro61NXVITo6Wuu+oqIiJCUl4bXXXsOIESMQFBSEkhLtyUlEIhGA+g5D99jY2MDX1xcxMTFtznXy5EkMGjQIixYtQq9evRAQEKDV8YkrVExNFF/IQ/QzIRBJTK8bPtGNzLaM6wgGcyP7HNcRTJqkiofRxaHwcvEw6nH4fD4ee+wxWFtbG/U4946VlJSEpKSkBsOBHBwc4OTkhK+++gopKSk4cuQIli/XHo3g6uoKqVSK/fv3Iy8vD2Vl9X8P69evx8aNG/Hpp58iOTkZly9fxmeffaZzroCAAFy8eBEHDhzArVu3sGbNGsTGxur/A+uJiqkJs3e1QtRT5jnZNgF4fMs5m0tLvwLGjpp6myOSMxiVFwQfd+NNMj969Gh06tTJaPt/kK2tbaNrovJ4PPzwww+4dOkSQkJC8OKLL+L999/X2kYgEODTTz/Fl19+CU9PT0yaNAkAMHv2bHz88cfYvHkzevTogfHjxyM5OVnnTM8++yymTJmCadOmoX///igqKsKiRYv0+0ENgHrzmoHj39/EtePmteoIARxcjiHn1mWuYxjM2MGLYZNt/DMic6cSsDjVOR3J2bcNut+wsDBMmTLFoPskhkNnpmbgoUcD4dLZhusYpJUqisy/89H9knO5b0ozB3wlgyHpvgjuZLietm5ubpgwYYLB9kcMj4qpGeALeRjzbCis7URcRyE6EkmUqCwyzwnum5J8+wIYG5qrVxc8NYOBqZ0Q7h2s977EYjEef/xxk5p1iTRExdRM2DhKMG5xTwipQ5JZkNlXcB3B8FgWVbaVXKcwGwzLoE+yB/p5h7W8cTMeeeQRODk5GSgVMRYqpmbExdsGYxaEgsc3zZUryL8EwmKuIxhFSv4lriOYnbBkFzzk3btNjx08eDC667iiFOEWFVMz4x3siGHUw9fkqRR5XEcwihvJZ8BYU1Nva3VPtsdwr36tWsLN19e3xZmCiOmgYmqGug/0QL8JNEOSKasqscze1yyrRrU9d3PRmjP/VBs87D5Ap8kWXFxc8Pjjjxt9YgZiOPSbMlN9x/kheLBxB4iTtuEJ1CgvMP85eZtyuyiO6whmyzvNCmOdB0EgaPrs3tbWFk899RSsrKzaMRnRFxVTMxY5vRs696COCabGxqEK6vumULM0SbdOgpFSR7i2cs8UY5ztQM10e/eTSqWYOXMm7OzsOEhG9EHF1Izx+DyMXhBCY1BNjFhS0vJGZkylUkLuWMt1DLPmki3CBOkArYnfhUIhpk+fDheX9pnjlxgWFVMzJxTzMX5xT9g4SbiOQv7BqvJb3sjMpZckcB3B7DnkCTGB3x821jLweDw89thj8PY23lSExLiomFoAK1sRJizpCTH1sjQJNeWWe730nms3j4ERU1OvvmwL+Riv7oupj0wx2tqkpH1QMbUQDu7WGPdcGPhC+pVyi0VpnmVNI9gYpbIOtc4KrmNYBK/IQPQIDeE6BtETvfNaEI8Ae4yaFwwejyZ14IrMoQbKuo5xPTGzPJHrCGbPbqwfZAM8uY5BDICKqYXp0ssVoxeGgC+gXy0XpNaWs4ZpSxJuHAVDLSFtZjuyM2yGtt9yasS46C/BAvn1dMH4xWEQ0jUtDljW5PbNqaurhsJFzXUMs2QzzBu2I324jkEMiIqpherU3RETl4VDbEWdktpTXVUO1xHa1Z2qm1xHMDu2o31hF+3LdQxiYFRMLZi7nx0eeSkCVra0dFt7KcvP5DpCu4q/eQQQ0DV6nTCA/SMBsI2i4S+WiIqphXPykmHKKxE0DrUdSG1qIa+0wKXXmiGvKYeK5hhoGY+B47RukPWnKUAtFRXTDsDOxQpTXo6AgzvN9WlMVjblXEfgRLY8hesIpk3Ag9PMIFiFu3KdhBgRFdMOQuYgwSMvR9DUg0bE5xdyHYET8bdiAFpjt1GMmA+XeT0gDaI5tC0dFdMORCoTYfKLveARQJNoG4NCbplrmLaksrIEahcqpg/iWQng8kwoxP72XEch7YCKaQcjkgowcWk4Ovdw5DqKxakotPyZj5qSo0jjOoJJ4duL4bIwDKJO1BLUUVAx7YAEIj7GLgpDlwi6hmMoQqkClcUdZ4zpgxJuHaF3k3+IfG3hujgcQjdrrqOQdkQv/w6Kz+chen4P9Hq4M9dRLILMrpLrCJwqK88H60qThFj1cYPL/FDwZTQcraOhYtqBMTwGg6YEYMzCUIgk9EaoD6GwiOsInMtTddxmbvAAu3F+cHy0KxiayrNDot86gX8vFzy2qi+cvKhZqq1UCstfw7QlCSlHgA7YD4kR8+E0uwdshtA8ux0ZFVMCALB3s8LUlX3QtZ8b11HMUlXJXa4jcK64OAtw6VjTVwqcJHB9PhzSbtShr6OjYko0hCI+Rs3rgaFPdAWPpojTGV+gQlm+5S8IrosCJovrCO1GHGAP1+fDIXSlyVAIFVPSiNCoTnjkpQjIHMRcRzELMocqsGpaPQUAElKPcR3B+Jj6VV+c54aAZyXkOg0xEVRMSaPc/ezw+Oq+6NTdgesoJk8sKeE6gskoKEi36KZevq0IzvNDYRftC4ZmfSL3oWJKmiSViTBxaTh6j/bpkB1LdKVWddzxpY0p4lvmTFCSIEe4vhABSRd7rqMQE0TFlDSL4TEYMLkLxj4XRmujNqGmvONcJ9RFYvoJriMYloAH+4ld4Dy7B/jW1KxLGkfFlOjEL8wZj63qC1cfmh5NC8OiLK8Dj69sRHbOLTBOllF0BK5SuD4fDtkgT66jEBNHxZTozM5Fiqkr+2DAZH/waWA6AEDmUANlXR3XMUxOicj8m76t+7nDdXEviDxo/DVpGb0jklbh8Rj0Hu2Laa/1hbu/LddxOCe1KuU6gklKyjzNdYQ249uK4DQrGA5TAsET0cxgRDdUTEmbOLhbY8rLvTH40QAIRB35ZWT+Z2DGkHn3GhgHM2vqZerPRt2W94Y0mNYfJa1DPUpImzE8BuEjO8OvpzOO7rqBrFulXEdqd7WVOVxHMFllkiLYwjxaL/hOEjhMCaSeuqTNGJZlWa5DEPPHsixunM3FmV9SIK9UcB2n3bB1X6G2qmOvGNMUP99e6Mc8zHWM5vEZ2AztBNvh3mCE1KRL2o6KKTEoeZUCZ39NReLpbMDCX1lSGzlKMjdzHcOkPRH+Gtgy0/xwJfKzhcMjgTQdIDEIauYlBiWxFmLYU90RNMgDx/bcRNFdyz1rs7YtB8191LwK61LIykyrNyxPJoTdaD9Y96FFHYjh0JkpMRq1mkXC0bs4/8dtKOQqruMYnKt3EjLj/+Y6hknr2mUAeqkjuY4BAGBEPMiGdILN0E7gialJlxgWFVNidDUVdbh8MBPXjt2FUmE5E8I7OB9BTnIc1zFMG8PgidD/A1uh5C4DD7Du4w7bkT7g24q4y0EsGhVT0m6qympx+UAGrp/MhsoCiiof36KqpJDrGCZvwuAXYJUt4eTYku6OsBvjC6GbaTU1E8tDxZS0u8qSWlzan47E09lQK83z5SeUKlCR/RnXMcxCUNchCFMMatdjCr1ksBvrR0NdSLvpyKPtm8QwDPbt29fk/b6+vvj444/bLU97OHbsGBiGQWlpqdGPJXMQI/LJbnhqw0AEP+QJnhkuZWVjX8F1BLNxI/k0GOv26esocJHC8YlucF0czmkhbek9pD3MmTMHkydP5jRDR2JWxTQ3NxdLliyBv78/xGIxvL29MWHCBMTExLRrjtjYWCxYsECvfTAMo/mytrZGYGAg5syZg0uXLhkopemzcZRg2FPdMeP1Aeg+0B0Mz3yKqkBQzHUEs8GyatTYVxv1GMJOMjjOCILbi71hFe4KhjHua8lU3osMKT09HQzDIC4ujusoZslshsakp6dj8ODBsLe3x3vvvYewsDAoFAocOHAAzz//PG7cuNFuWVxcXAyyn+3bt2P06NGQy+W4desWvvrqK/Tv3x/btm3DrFmzDHIMc2DrLMWI2cHoPdoXsf9LQ3JsHkz94oOqzjLX7DSW20VXEYy+Bt+vOMAeNpGdIAlsv0XsTem9iJgOszkzXbRoERiGwYULF/Doo4+ia9eu6NGjB5YvX45z585ptsvMzMSkSZMgk8lga2uLxx9/HHl52m98W7ZsQZcuXSASidCtWzfs2rWr2WNv2LABbm5umk9sDzbzlpWVYcGCBXB1dYWtrS2GDx+Oq1evtvgz2dvbw93dHb6+vnj44Yfx3//+FzNmzMDixYtRUvLvCMa9e/eiR48eEIvF8PX1xcaNG7X201iTkr29PXbs2KH5/syZMwgPD4dEIkGfPn2wb9++Rj+FXrp0CX369IGVlRUGDRqEmzdvtvhzGIq9mxVGzeuBJ9b2R0AfV5NekLyy5C7XEcxK4q0TYKQGGo7CANIeTnB9Phwu80PbtZACur8XFRYW4pFHHoGVlRUCAwPx+++/a+3n+PHj6NevH8RiMTw8PPDqq69Cqfy317Narca7776LgIAAiMVidO7cGW+99Zbm/oSEBAwfPhxSqRROTk5YsGABKiubHte9f/9+PPTQQ7C3t4eTkxPGjx+P1NRUzf1+fn4AgF69eoFhGERFRWnu2759O4KCgiCRSNC9e3ds3kyTlTzILIppcXEx9u/fj+effx7W1g175dnb2wOon9Ju8uTJKC4uxvHjx3Ho0CGkpqZi2rRpmm1//fVXvPDCC3jppZdw7do1LFy4EHPnzsXRo0cb7JdlWbzwwgv45ptvcOrUKYSHhze6zbhx45Cbm4u//voLly5dQkREBEaMGIHi4tY3Bb744ouoqKjAoUOHANQXt8cffxxPPPEEEhISsH79eqxZs0arULakoqICEyZMQGhoKC5fvow33ngDK1eubHTb1atXY+PGjbh48SIEAgHmzZvX6p9BX44e1oieH4KZbwxE7zE+sLYzreEMfKEK5fk0J29rqFRKyB1r9dsJn4FVbze4vdgbTjODIfJu/7V1dX0vAoDXX38djz/+OOLj4zF27FjMmDFD856QlZWFsWPHom/fvrh69Sq2bNmCb775Bm+++abm8atWrcK7776LNWvWIDExEXv27IGbW/1EE9XV1Rg9ejQcHBwQGxuLn3/+GYcPH8bixYubzF5VVYXly5cjNjYWMTEx4PF4eOSRR6BW1/esv3DhAgDg8OHDyMnJwS+//AIA2Lp1K1avXo233noLSUlJePvtt7FmzRrs3LlTvyfTwphFM29KSgpYlkX37t2b3e7w4cOIj49HWloavL29AQC7du1Cjx49EBsbi759++KDDz7AnDlzsGjRIgDQfJr84IMPMGzYMM2+lEolZs2ahYsXL+L06dPo1KlTo8c8evQoEhISkJ+fD7FYDAD44IMPsG/fPvz3v/9t9bXVez9jeno6AODDDz/EiBEjsGbNGgBA165dkZiYiPfffx9z5szRaZ+7d+8GwzDYunUrJBIJgoODkZWVhWeeeabBtm+99RYiI+sH2b/66qsYN24c5HI5JJL2H9pg6yzFgEld0G+CP9LjC5F4OhuZ14vBqrltA7ZxqEJVvvkP7Wlv6aXX0A3hrX6cwEkCq77usO7tBr4Ntx+sdH0vAuo7AD355JMAgLfffhufffYZLly4gNGjR2Pz5s3w9vbGpk2bwDAMunfvjuzsbKxcuRJr165FVVUVPvnkE2zatAmzZ88GAHTp0gUPPfQQgPq/6ZqaGnz77beaor5p0yZMmDAB7777rqbo3m/q1Kla33/zzTdwdXVFYmIiQkJCNJevnJyc4O7urtnujTfewMaNGzFlyhQA9WewiYmJ+PLLLzXZiJmcmd4bvdNSp4KkpCR4e3trCikABAcHw97eHklJSZptBg8erPW4wYMHa+6/58UXX8TZs2dx8uTJJgspUH/mWFlZCScnJ8hkMs1XWlqaVhOKrh78WZvKm5ycDJVKt1mFbt68ibCwMK2C2K9fv0a3DQsL0/zfw8MDAJCfn6/7D2AEPB4D/3AXjH++J2a9NRB9x/tB5ijmLI9ITJMItsW1G0fB6DrzkICBtKcLnOeHwu3lPrCN8ua8kAK6vxcB2n9L1tbWsLGx0fwtJSUlYeDAgVr7GTx4MCorK3H37l0kJSWhtrYWI0aMaHTfSUlJ6Nmzp9bZ8eDBg6FWq5u8NJOamorp06fD398ftra2mmbdzMzMJn+GgoIC3LlzB08//bTW+9ubb77Zpvc3S2YWZ6aBgYFgGAZJSUnNdvVmWbbRF/mDtz+4TWOPGzVqFL7//nscOHAAM2bMaPKYarUaHh4eOHbsWIP77m/y0dW9on7vhd5YtgeHBjMM0+A2hUKhtX1L+7hHKPx3Dcp7j7nXDGQKZA4S9Bvvh75jfZGZWIzEU9lIjy+Euh3PVtUqbj9cmCulsg51TgoIs5v+DC9wkcK6nzusItzAtza99VB1fS8CtP+WgPq/p3t/S839TTIMA6lU2uy+m3qvu/f4xkyYMAHe3t7YunUrPD09oVarERISgrq6uiaPcy/v1q1b0b9/f637+HyakvF+ZnFm6ujoiOjoaHz++eeoqqpqcP+9sZHBwcHIzMzEnTt3NPclJiairKwMQUFBAICgoCCcOnVK6/FnzpzR3H/PxIkTsWfPHsyfPx8//PBDk9kiIiKQm5sLgUCAgIAArS9nZ+dW/6wff/wxbG1tMXLkSM3P1Fjerl27al7MLi4uyMn59xpecnIyqqv/HYrQvXt3xMfHo7b232tWFy9ebHU2U8LwGPiEOGHMs6GY9c4gDJjsD1vn9mmKrinLapfjWKLMisQGtzEiPqx6ucJlYRjcX+oDmyGdTLKQArq/F7UkODgYZ86c0fpQe+bMGdjY2MDLywuBgYGQSqVNDrUJDg5GXFycVobTp0+Dx+Oha9euDbYvKipCUlISXnvtNYwYMQJBQUFanRwBQCSqP/O/v8XLzc0NXl5euH37doP3t3sf+Ek9syimALB582aoVCr069cPe/fuRXJyMpKSkvDpp59i4MCBAICRI0ciLCwMM2bMwOXLl3HhwgXMmjULkZGR6NOnDwDglVdewY4dO/DFF18gOTkZH374IX755Re8/PLLDY75yCOPYNeuXZg7dy7++9//Nppr5MiRGDhwICZPnowDBw4gPT0dZ86cwWuvvdZiwSotLUVubi4yMjJw6NAhPProo9izZw+2bNmiOat96aWXEBMTgzfeeAO3bt3Czp07sWnTJq28w4cPx6ZNm3D58mVcvHgRzz77rNan4unTp0OtVmPBggVISkrCgQMH8MEHHwDQrbnK1FnbidF7tC+eemMgJi4LR2BfN4itjNPowjAsSvOoJ29bxd84CkbIAyPkQRrqDMcZQfBc0x+O07pB7GfHdTyd6PJe1JJFixbhzp07WLJkCW7cuIHffvsN69atw/Lly8Hj8SCRSLBy5UqsWLEC3377LVJTU3Hu3Dl88803AIAZM2ZAIpFg9uzZuHbtGo4ePYolS5Zg5syZjV4vdXBwgJOTE7766iukpKTgyJEjWL58udY2rq6ukEql2L9/P/Ly8lBWVgYAWL9+Pd555x188sknuHXrFhISErB9+3Z8+OGHej6TlsUsmnmB+mbPy5cv46233sJLL72EnJwcuLi4oHfv3tiyZQuAf4eILFmyBEOHDgWPx8Po0aPx2Wf/Tvs2efJkfPLJJ3j//fexdOlS+Pn5Yfv27VrdwO/36KOPQq1WY+bMmeDxeJqL8PcwDIO//voLq1evxrx581BQUAB3d3cMHTq00Rf1/ebOnQsAkEgk8PLywkMPPYQLFy4gIiJCs01ERAR++uknrF27Fm+88QY8PDywYcMGrc5HGzduxNy5czF06FB4enrik08+0Zr8wdbWFn/88Qeee+45hIeHIzQ0FGvXrsX06dM56VhkLAzDwLu7I7y7O0KtZpF7uwwZCUXIuFaEoizDLAUnc6hGQXHTzWKkaSKpFfzCe8NquDfsunuBJzLPZkJd3ota4uXlhb/++guvvPIKevbsCUdHRzz99NN47bXXNNusWbMGAoEAa9euRXZ2Njw8PPDss88CAKysrHDgwAG88MIL6Nu3L6ysrDB16tQmCxyPx8MPP/yApUuXIiQkBN26dcOnn36q9b4nEAjw6aefYsOGDVi7di2GDBmCY8eOYf78+bCyssL777+PFStWwNraGqGhoVi2bFmbn0NLRHPzdlC7d+/G3LlzUVZW1uL1GUtQWSJHxrUipCcU4e7NEihr27YknIt3Fu7E/2jgdJbL2sERXXr3Q0DfgegcEga+wDSbbwnRl9mcmRL9fPvtt/D394eXlxeuXr2KlStX4vHHH+8QhRSo77jUY4gXegzxgkqhRlZyCTKuFSEjoQhlBTU674dhaZWY5kisZegUHArvHmHoHBIGZ28friMR0i6omHYQubm5WLt2LXJzc+Hh4YHHHntMazaVjoQv5KFzsBM6BzthyONAaV51fWG9Vojs5DKolE33XpZXZrdjUtMnkkrh1b1HffHsEQZXX38wPLPpikGIwVAzLyH3USnVKMqqRH56OfIyKpCfXo6SnCrNXMFs3VeorTLM9VdzZOfqBlffLnDzD4B3j1C4d+kKHg2RIISKKSEtUdSqUJBZgfzMYmQn/YqCjDQUZ92FWqVs+cFmisfnw9HLG64+fnD16wJXX3+4+PpDYi3jOhohJomKKSFtoFIqUZx1BwWZ6SjJyUJZfh7KC/JQVpCPquJisKzpTHTRHKmtHexcXGHr4gZbF1c4eHjC1bcLnL19IBBxP+MQIeaCiikhBqZSKlBeWIDy/HyUFeSivCAfZfl5qCwpQm1lJeTVVaitqkKdvAbGWmuO4fEgkdlAKrOBRGYDawcH2Lq4wc7VDXb/FE47FzcILWhoFCFcomJKCEdYtRq11dWora6EvKq+wNZWV6KupgasLlM4MgyEYjFEEilEUiuIpFKIpFKIrWUQW1lbxIQchJgLKqaEEEKInqgPOyGEEKInKqaEEEKInqiYEkIIIXqiYkoIIYToiYopIYQQoicqpoQQQoieqJgSQggheqJiSggBAERFRXG64PP69esRHh7O2fEBID09HQzDIC4ujtMczdHleeL6d9kRUTElxELNmTMHDMPg2WefbXDfokWLwDAM5syZo7ntl19+wRtvvKHz/ltbePbu3YuoqCjY2dlBJpMhLCwMGzZsQHFxsc7HNEVz5szB5MmTDbY/QzxPrf1dEv1RMSXEgnl7e+OHH35ATc2/C6DL5XJ8//336Ny5s9a2jo6OsLGxMUqO1atXY9q0aejbty/+/vtvXLt2DRs3bsTVq1exa9cuoxzTHBnqeTLm75I0gSWEWKTZs2ezkyZNYkNDQ9nvvvtOc/vu3bvZ0NBQdtKkSezs2bM1t0dGRrIvvPCC5nsfHx/2rbfeYufOncvKZDLW29ub/fLLLzX3A9D6ioyMbDTH+fPnWQDsxx9/3Oj9JSUlLMuy7Lp169iePXuy3377Levj48Pa2tqy06ZNY8vLyzXbyuVydsmSJayLiwsrFovZwYMHsxcuXNDa37Vr19ixY8eyNjY2rEwmYx966CE2JSWFZVmWValU7Ouvv856eXmxIpGI7dmzJ/v3339rHpuWlsYCYK9cucKyLMsqlUp23rx5rK+vLyuRSNiuXbtq/Rzr1q1r8DwcPXqUZVmWvXv3Lvv444+z9vb2rKOjIztx4kQ2LS2t0efA0M9TY7/LDRs2sE8++SRrbW3Nenh4sJ9++mmTWUjr0ZkpIRZu7ty52L59u+b7bdu2Yd68eTo9duPGjejTpw+uXLmCRYsW4bnnnsONGzcAABcuXAAAHD58GDk5Ofjll18a3cfu3bshk8mwaNGiRu+3t7fX/D81NRX79u3Dn3/+iT///BPHjx/Hf/7zH839K1aswN69e7Fz505cvnwZAQEBiI6O1jSBZmVlYejQoZBIJDhy5AguXbqEefPmQamsX3v2k08+wcaNG/HBBx8gPj4e0dHRmDhxIpKTkxvNplar0alTJ/z0009ITEzE2rVr8X//93/46aefAAAvv/wyHn/8cYwePRo5OTnIycnBoEGDUF1djWHDhkEmk+HEiRM4deoUZDIZRo8ejbq6OqM/T415//33ERYWhsuXL2PVqlV48cUXcejQoWYfQ1qB62pOCDGOe2emBQUFrFgsZtPS0tj09HRWIpGwBQUFOp2ZPvXUU5rv1Wo16+rqym7ZsoVl2YZncU0ZM2YMGxYW1mLedevWsVZWVlpnWK+88grbv39/lmVZtrKykhUKhezu3bs199fV1bGenp7se++9x7Isy65atYr18/Nj6+rqGj2Gp6cn+9Zbb2nd1rdvX3bRokU6/0yLFi1ip06dqvn+3vN8v2+++Ybt1q0bq1arNbfV1tayUqmUPXDgQKP7NdTzxLKN/y5Hjx6ttZ9p06axY8aMafF4RDcCros5IcS4nJ2dMW7cOOzcuRMsy2LcuHFwdnbW6bFhYWGa/zMMA3d3d+Tn57fq+CzL6rwcnK+vr9a1Pg8PD83xUlNToVAoMHjwYM39QqEQ/fr1Q1JSEgAgLi4OQ4YMgVAobLDv8vJyZGdnaz0eAAYPHoyrV682memLL77A119/jYyMDNTU1KCurq7F3rSXLl1CSkpKg+uWcrkcqampjT7GUM9TUwYOHNjg+48//lin45GWUTElpAOYN28eFi9eDAD4/PPPdX7cg0WJYRiodVlr9T5du3bFqVOnoFAoGi1yuh6P/We1yAcLzv1FSCqVtpinucc/6KeffsKLL76IjRs3YuDAgbCxscH777+P8+fPN3sMtVqN3r17Y/fu3Q3uc3FxafQxhnqeWoPWvDUcumZKSAdw71pdXV0doqOjDbJPkUgEAFCpVM1uN336dFRWVmLz5s2N3l9aWqrT8QICAiASiXDq1CnNbQqFAhcvXkRQUBCA+jPpkydPQqFQNHi8ra0tPD09tR4PAGfOnNE8/kEnT57EoEGDsGjRIvTq1QsBAQENzixFIlGD5yAiIgLJyclwdXVFQECA1pednV2jxzLU89SUc+fONfi+e/fueu2T/IuKKSEdAJ/PR1JSEpKSksDn8w2yT1dXV0ilUuzfvx95eXkoKytrdLv+/ftjxYoVeOmll7BixQqcPXsWGRkZiImJwWOPPYadO3fqdDxra2s899xzeOWVV7B//34kJibimWeeQXV1NZ5++mkAwOLFi1FeXo4nnngCFy9eRHJyMnbt2oWbN28CAF555RW8++67+PHHH3Hz5k28+uqriIuLwwsvvNDoMQMCAnDx4kUcOHAAt27dwpo1axAbG6u1ja+vL+Lj43Hz5k0UFhZCoVBgxowZcHZ2xqRJk3Dy5EmkpaXh+PHjeOGFF3D37l2jPk9NOX36NN577z3cunULn3/+OX7++ecmf27SelRMCekgbG1tYWtra7D9CQQCfPrpp/jyyy/h6emJSZMmNbntu+++iz179uD8+fOIjo5Gjx49sHz5coSFhWH27Nk6H/M///kPpk6dipkzZyIiIgIpKSk4cOAAHBwcAABOTk44cuQIKisrERkZid69e2Pr1q2aZtGlS5fipZdewksvvYTQ0FDs378fv//+OwIDAxs93rPPPospU6Zg2rRp6N+/P4qKihr0tn3mmWfQrVs39OnTBy4uLjh9+jSsrKxw4sQJdO7cGVOmTEFQUBDmzZuHmpqaZn8HhnqeGvPSSy/h0qVL6NWrF9544w1s3LjRYK0UBGDYexciCCGEWCRfX18sW7aMphg0IjozJYQQQvRExZQQQgjREzXzEkIIIXqiM1NCCCFET1RMCSGEED1RMSWEEEL0RMWUEEII0RMVU0IIIURPVEwJIYQQPVExJYQQQvRExZQQQgjRExVTQgghRE9UTAkhhBA9UTElhBBC9ETFlBBCCNETFVNCCCFET1RMCSGEED1RMSWEEEL0RMWUEEII0RMVU0IIIURPVEwJIYQQPVExJYQQQvRExZQQQgjRExVTQgghRE9UTAkhhBA9UTElhBBC9ETFlBBCCNETFVNCmjFnzhxMnjzZ6MfZu3cvoqKiYGdnB5lMhrCwMGzYsAHFxcVGPzYhRH9UTAnh2OrVqzFt2jT07dsXf//9N65du4aNGzfi6tWr2LVrV6OPUSgU7ZySENIcKqaEtIJarca7776LgIAAiMVidO7cGW+99Zbm/qysLEybNg0ODg5wcnLCpEmTkJ6e3uT+Lly4gLfffhsbN27E+++/j0GDBsHX1xejRo3C3r17MXv2bADA+vXrER4ejm3btsHf3x9isRgsy6KsrAwLFiyAq6srbG1tMXz4cFy9elWz/9TUVEyaNAlubm6QyWTo27cvDh8+rJXB19cXb775JmbNmgWZTAYfHx/89ttvKCgowKRJkyCTyRAaGoqLFy8a9skkxIJQMSWkFVatWoV3330Xa9asQWJiIvbs2QM3NzcAQHV1NYYNGwaZTIYTJ07g1KlTkMlkGD16NOrq6hrd3+7duyGTybBo0aJG77e3t9f8PyUlBT/99BP27t2LuLg4AMC4ceOQm5uLv/76C5cuXUJERARGjBihaR6urKzE2LFjcfjwYVy5cgXR0dGYMGECMjMztY7z0UcfYfDgwbhy5QrGjRuHmTNnYtasWXjqqadw+fJlBAQEYNasWWBZVs9nkBALxRJCmjR79mx20qRJLMuybHl5OSsWi9mtW7c2uu0333zDduvWjVWr1ZrbamtrWalUyh44cKDRx4wZM4YNCwtrMce6detYoVDI5ufna26LiYlhbW1tWblcrrVtly5d2C+//LLJfQUHB7OfffaZ5nsfHx/2qaee0nyfk5PDAmDXrFmjue3s2bMsADYnJ6fFrIR0RAKuizkh5iIpKQm1tbUYMWJEo/dfunQJKSkpsLGx0bpdLpcjNTW10cewLAuGYXQ6vo+PD1xcXLSOV1lZCScnJ63tampqNMerqqrC66+/jj///BPZ2dlQKpWoqalpcGYaFham+f+9M+3Q0NAGt+Xn58Pd3V2nvIR0JFRMCdGRVCpt9n61Wo3evXtj9+7dDe67vwjer2vXrjh16hQUCgWEQmGz+7e2tm5wPA8PDxw7dqzBtveah1955RUcOHAAH3zwAQICAiCVSvHoo482aHa+/9j3intjt6nV6mYzEtJR0TVTQnQUGBgIqVSKmJiYRu+PiIhAcnIyXF1dERAQoPVlZ2fX6GOmT5+O/2/fDnkTB+M4jv9IKhbEVEUJktkZJggIDHgYAQNmqcIhSFoxwE9NjWAmxgtALkEgoAkgxwtYgIQsJBgCaHYOc3C5u+fOfT+2ydMnNd/06b+Hw0Htdvvs9e12e3E/8Xhc6/ValmX9dD/btiVJQRDo4eFB9/f3ur29leM4vxyIAvB3iCnwm66uruT7vjzPU7fb1efnp6bTqV5fXyVJlUpFtm0rl8spCALN53MNh0PVajWtVquzayYSCXmep3q9Ls/zNJlMtFwuNRgMVCqV9Pb2dnE/2WxWyWRS+Xxe/X5fi8VC4/FYjUbjNHl7c3OjXq+nj48PzWYzlctl3i6B/4BjXuAPNJtNWZalVqulr68vRSIRVatVSVI4HNZoNJLv+yoUCtrv94pGo8pkMrq+vr645tPTk+7u7vTy8qJOp6Pj8ahYLKZisXj6NeacUCik9/d3PT4+ynVdbTYbOY6jdDp9+sb5/Pws13WVSqVk27Z839dut/u3DwWAQt/fzLoDAGCCY14AAAwRUwAADBFTAAAMEVMAAAwRUwAADBFTAAAMEVMAAAwRUwAADBFTAAAMEVMAAAwRUwAADBFTAAAM/QDRAQfZ+rowcwAAAABJRU5ErkJggg==", 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", 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EZ599ttvHpqSkID09HW+88Qbef/992NnZITIyEk8++SSAG4c4f/75Z/zzn//E7Nmzcf36dXh7e2PkyJHw8vIy10skVk7AcRzHughCCCGEFTo0SgghhNcoCAkhhPAaBSEhhBBeoyAkhBDCaxSEhBBCeI0unyDEjLRaLaqqqqBWqzv8b01NDVpaWtDS0oLW1tYOP+236XQ6SKXS2/7I5XK4ubl1+JFKpaxfNiEWjYKQECPR6XQoKSlBYWEhCgoK9D8lJSVQq9VQq9Wora2Fua9YcnJy6hCM3t7eCAoKQlBQEFQqFQIDA2ndP8JrdB0hIQaqq6vD+fPncf78eVy8eBH5+fn6wGtpaWFdnsFEIhF8fX31wahSqRAWFoa4uDi4urqyLo8Qk6MgJOQ2NBoNLl68iAsXLiAvLw+7du1CYWGh2Ud1rPj6+iI2NhaxsbGIi4tDbGws3N3dWZdFiFFREBJyk5KSEpw8eRInTpzA6dOnUVpa2uH+pqYmlJWVMarOMvj4+CA2NhYDBw7EkCFDEBcXB7GYzrIQ60VBSHituLgYJ06cwMmTJ3Hy5Mk7rlHn5eWFI0eOmKk66yCXyzFw4EAMHToUw4cPR3x8PIRCmpBOrAcFIeGVxsZGZGZm4tChQzh27BiuX79u0OMjIiKQkZFhoupsg0KhwNChQ3Hvvfdi1KhR8PHxYV0SIbdFQUhsXmVlJQ4ePIg//vgDx44dQ2tra6/3FR4ejp07dxqxOtsXHx+P+++/HykpKd2uK0gISxSExCbl5ubi4MGDOHjwIC5cuGC0yS0eHh7Iysoyyr74KCQkBCkpKbj//vvRv39/CAQC1iURQkFIbEdFRQV27dqFXbt2ITc312TPU1VVBY1GY7L984WPjw9SUlLwyCOPIDY2lnU5hMcoCIlVa25uxoEDB5CRkYFjx45Bp9OZ/DmVSiWOHz9u8ufhk8jISDz22GOYMGEC3NzcWJdDeIaC0Mbk5eUhODgYx48fR0JCAutyTILjOJw8eRI7d+7Eb7/9hvr6erM+f79+/bBr1y6zPidf2NnZ4b777sMjjzyC5ORkuiyDmAX9lpnIzJkz8cUXXwAAxGIxAgIC8PDDD2Pp0qWQy+WMq7NO9fX1+OWXX7BlyxYUFxczq4MuDTCd1tZWZGRkICMjA+7u7pg4cSKmTZuGwMBA1qURG0ZBaEJjx45FWloaWltbceDAATz55JOor6/H6tWrDd5XS0sLJBKJCaq0fEVFRfjhhx+QkZGBhoYG1uWgtraWdQm8UFFRgc8++wzr1q3D6NGjMXv2bAwZMoR1WcQG0VdbE5JKpfD29kZAQACmTJmCqVOnYsuWLZg5cyYmTpzYYdsFCxYgKSlJ/++kpCTMnTsXL7zwAtzd3TFmzBgAgEAgwOrVqzFu3Dg4ODggODgY33333W3rOHfuHB544AE4OjrCy8sL06ZNQ0VFhbFfrtFlZWXh73//O6ZPn44ff/zRIkIQAMrLy2lFBzPS6XTYuXMnHn/8cfzpT3/Cli1b+nQJDCG3oiA0IwcHB4P+gL/44guIxWIcPHgQa9as0d/+2muv4ZFHHsHJkyfxxBNP4C9/+QtycnK63EdpaSkSExORkJCAo0ePYseOHSgvL8ekSZP6/HpMoaWlBVu3bsXMmTPx8ssv4/DhwxbX17OtrY2uh2Pk9OnTWLhwIUaOHInVq1ejurqadUnEBtChUTPJzMzE119/jVGjRvX4MWFhYXj//fc73f7YY4/hySefBAC8+eab2LVrFz755BOkpqZ22nb16tUYOHAgli1bpr9t3bp1CAgIwMWLFxEeHt6LV2N8LS0t2L59OzZt2mRwtxcWPD09cfbsWdZl8FZZWRnef/99fPLJJ5gyZQr++te/wsPDg3VZxErRiNCE0tPT4ejoCHt7ewwbNgwjR47EJ5980uPHDxo0qMvbhw0b1unf3Y0Is7OzsXfvXjg6Oup/IiMjAQBXrlzpcS2m0tLSgh9//BFTp07Fxx9/bBUhCIAOjVqIxsZGfP7550hMTMS7776Lqqoq1iURK0QjQhNKTk7G6tWrYWdnB19fX/3ip0KhsNPhvq4OmRoyu7S7Dh06nQ7jx4/He++91+k+lj0g20eAX3/9tVWcr7xVY2Mj6xLITRobG7FmzRps3LgR06dPx9NPPw2FQsG6LGIlKAhNSC6Xd3kuycPDA2fOnOlw24kTJ3q8Svjhw4cxffr0Dv8eMGBAl9sOHDgQ33//PVQqlUVck6XVarFt2zarDcB2ZWVlEAqFZrmAn/RcXV0dUlNTsWHDBsyePRuzZ8+Gs7Mz67KIhaNDowzcd999OHr0KL788ktcunQJixcv7hSMt/Pdd99h3bp1uHjxIhYvXozMzEzMnTu3y22fe+45qNVq/OUvf0FmZiauXr2KnTt3Yvbs2WhrazPWS+qRQ4cOYfbs2fj444+tOgSBGx1tVCoV6zJIN2pra7Fy5UqMHDkSa9eupVmm5LYoCBlISUnBa6+9hpdffhmDBw9GbW1thxHenSxduhTffPMN4uPj8cUXX2Djxo2Ijo7ucltfX18cPHgQbW1tSElJQWxsLJ5//nkoFAqzXRiem5uLl156Cf/4xz9QWFholuc0Bz8/P9YlkDvQaDRYtmwZUlJSsHfvXtblEAtFLdasjEAgwI8//tjpOkRLpNFosG7dOqSnp9vkIcTIyEjs2LGDdRnEAMnJyXj11VcREhLCuhRiQdifNCI2R6vV4ocffsCGDRtQV1fHuhyTocNt1mfv3r34/fffMWPGDMyfPx9OTk6sSyIWgA6NEqM6deoUZs+ejdWrV9t0CALAtWvXWJdAeqG1tRWfffYZkpOTsXnzZotr2EDMjw6NEqNoaGjAmjVrsG3bNl59sDQ3N6O0tJR1GaQPhgwZgvfeew9BQUGsSyGM0IiQ9NmhQ4cwc+ZMbN26lVchCIA+PG3AkSNHMG7cOHz22Wc2eS6b3BmNCEmvVVdX45NPPsGvv/7KuhRmIiIikJGRwboMYiQDBgzA+++/T71keYZGhKRXdu3ahZkzZ/I6BAHwbgRs644fP44HH3wQqamp0Gq1rMshZkIjQmIQjUaD5cuX4/fff2ddikVwd3fH0aNHWZdBTCAuLg7vv/++vjcvsV0UhKTHsrOz8c4776CyspJ1KRZFo9FQs2cbJZVK8c9//hPTpk1jXQoxIQpCckft082/++47OhTYBVdXVxw7dox1GcSExo4di/fee4/6ltooOkdIbqukpATz5s3Dt99+SyHYDTc3N9YlEBPbsWMHHnzwQRw/fpx1KcQEKAhJt/bu3Yunn34aFy5cYF2KRTNXz1bCVlFRESZPnow1a9bQl0IbQ4dGSSdarRarVq3CTz/9xLoUq+Dr64s//viDdRnEjBITE7FixQo6GmAjKAhJB9XV1Vi8eDFOnTrFuhSrIRQKUVJSgqamJtalEDPy8vLCp59+ioSEBNalkD6iYzpE7/Lly/jb3/5GIWggnU6H0NBQ1mUQMysvL8fkyZPx/fffsy6F9BEFIQFw43zgvHnzUF5ezroUq+Tl5cW6BMJAS0sLFi1ahDfffNPsC10T46Eg5DmO4/DZZ5/hjTfeoEN7fSCRSFiXQBg6dOgQJkyYgJqaGtalkF6g9Qh5rKGhAW+//TZN9DCChoYG1iUQRnx9fXH48GFUVVVh2LBhSE9PR3BwMOuyiAFoRMhTlZWVmDdvHoWgkZSXl0MkErEug5iZo6MjKioq9J2Fzp07h7vvvptaEFoZCkIeKiwsxNy5c3H16lXWpdiM5uZmGgXwjEgkgouLCy5evNjh9oqKCowZM4YuP7IiFIQ8c+HCBcyfPx9lZWWsS7E5vr6+rEsgZhQVFdXtyK+pqQmPPPIIvvjiCzNXRXqDgpBHjh49ioULF6K6upp1KTZJJpOxLoGYSUJCAtLT02+7TVtbG2bNmoUPP/zQTFWR3qIg5Ilff/0V//jHP9DY2Mi6FJvV0tLCugRiBuHh4fj55597tC3HcXjhhRfwz3/+08RVkb6gzjI88MMPP2DVqlXUH9HEHB0dcebMGdZlEBPy8vLC1atXcf36dYMf+9e//hWpqanUm9YCURDauI0bN+Kzzz5jXQZvtLS0oKSkhHUZxAQcHBwgFApx9uzZXu9j0qRJ2LBhA113amHoq4kN27RpE4WgmQUFBbEugZiAQCCAl5dXn0IQAL799ls89thjaG1tNVJlxBgoCG3Ut99+i//85z+sy+AdWrjVNsXFxWHfvn1G2dfWrVsxZcoUaslmQSgIbdD333+P1atXsy6Dl3Q6HesSiJHFx8dj69atRt3nf//7X8yYMYN+XywEBaGN2bJlC1atWsW6DN5Sq9WsSyBGFBoaip07d5pk3xs3bsRTTz1Fk9gsAAWhDdm6dSs+/vhj1mXwWmVlJZRKJesyiBG4u7sjJyfHpM3o161bh+eee85k+yc9Q0FoI3bs2IGPPvqIvl1agJCQENYlkD6yt7eHVqs1ywzg1atXY+HChSZ/HtI9CkIbcOTIESxfvpxC0EK4urqyLoH0kb+/P06cOGG25/voo4/w2muvme35SEcUhFbu4sWLWLp0Kc1AsyB0wbR1S0hIwO7du83+vG+99RbWrl1r9uclFIRWraysDH//+9+pbZqFocVZrVdcXBzTVSOeffZZZGRkMHt+vqLOMlaqpqYG8+bNQ0FBAetSyC2EQiFKSkpMOsmCGJ9KpUJWVhbq6+uZ1uHk5IQDBw6gf//+TOvgExoRWqGWlha8+uqrFIIWSqfTISwsjHUZxABKpRJXr15lHoIAUFtbiwcffBDFxcWsS+ENCkIrw3Ecli1bhtOnT7MuhdyGp6cn6xJID9nZ2UEkElnUF8vi4mI8+OCDqK2tZV0KL1AQWpm1a9di//79rMsgd0BNla1HaGgojh49yrqMTk6ePIlJkyZBq9WyLsXmURBakf3792PTpk2syyA90NDQwLoE0gMJCQnYsWMH6zK6tWPHDixYsIB1GTaPJstYiby8PDz77LM0Q9RKSCQSXLlyhS5rsWAxMTHYsWOHVfw32rBhA5544gnWZdgsCkIrUF9fj2eeeQaFhYWsSyEGEIvFuHz5MusySBcCAgJw6tQpaDQa1qX0iEwmw+HDhxEXF8e6FJtEh0YtHMdxeOeddygErZCvry/rEkgXFAoFSkpKrCYEgRuH2h955BG6RtVEKAgt3MaNG3Hw4EHWZZBekMlkrEsgtxCLxZDJZLhy5QrrUgx26dIlzJw5k3UZNomC0IJlZmYiLS2NdRmkl1paWliXQG4RHh6OQ4cOsS6j13788Ue8//77rMuwOXSO0EJdv34dTz75JB0KsWKOjo44c+YM6zLI/5eQkIAtW7awLqPPRCIRdu/ejaSkJNal2AwKQgvEcRxefPFFHD9+nHUppI9aW1upQ4gFiIyMxO7du23mmjwvLy+cPHkSXl5erEuxCXRo1AJ9++23FII2IigoiHUJvOfr64vMzEybCUEAKC8vx1NPPcW6DJtBQWhhrly5gs8//5x1GcRInJ2dWZfAa46OjlCr1VCr1axLMbpt27bRZ4WRUBBakJaWFrz99ttobW1lXQoxEmu4WNtWCYVCKJVKnD9/nnUpJrNw4ULk5uayLsPqURBakP/85z/0S21jKisrWZfAWzExMThw4ADrMkyqtrYW06dPh06nY12KVaMgtBBHjx7FDz/8wLoMYmRqtRpubm6sy+CdhIQEbNu2jXUZZvH7779j+fLlrMuwajRr1ALU1tZi9uzZqKioYF0KMQE3NzdkZ2ezLoM3+vXrh/379/PqOk6JRIKsrCzEx8ezLsUq0YjQAqxevZpC0Ia5urqyLoE3vLy8cOrUKV6FIHBjfsETTzzBu9dtLBSEjJ06dcqil4EhfScU0p+ZOTg4OKC+vh7l5eWsS2Hi9OnT1HWml+gvlKHW1lZ88MEHoKPTts2amjtbK4FAAG9vb9538nn77bdx9epV1mVYHQpChr755hvk5+ezLoOY2LVr16gBt4nFx8dj7969rMtgrqmpCXPnzmVdhtWhIGSkuLgYX331FesyiBnodDqEhoayLsNmxcfH46effmJdhsX45ZdfaAa6gSgIGfnwww/pxDaPeHp6si7BJoWEhGDXrl2sy7A4zz//POrq6liXYTUoCBnYtWsXTafnGTs7O9Yl2Bx3d3dcuHABjY2NrEuxOEVFRViyZAnrMqwGBaGZ1dXVYfXq1azLIGZWX1/PugSbIpVK0dbWRit73MbKlStx+vRp1mV0KSkpCQsWLND/W6VS4aOPPmJWDwWhmW3cuBFVVVWsyyBmVlZWBrFYzLoMmxEYGEgrtNyBVqvFM888Y5J9cxyH0aNHIyUlpdN9qampUCgUKCgoMMlzmwIFoRmVlZXh+++/Z10GYaC1tRXBwcGsy7AJCQkJdF6whw4ePIgff/zR6PsVCARIS0vDkSNHsGbNGv3tubm5eOWVV7By5UoEBgYa/XlNhYLQjNasWUMrS/CYr68v6xKsXmxsLM0QNdA//vEPk6yCEhAQgJUrV2LRokXIzc0Fx3GYM2cORo0ahbvvvhsPPPAAHB0d4eXlhWnTphnUPaugoAATJkyAo6MjnJ2dMWnSJH2jBI1GA5FIpJ9nwXEcXF1dMXjwYP3jN23aBB8fnx4/HwWhmVy9epUurOY5BwcH1iVYtaCgIOzbt48aUBjo/PnzWLdunUn2PWPGDIwaNQqzZs3CqlWrcObMGaxcuRKJiYlISEjA0aNHsWPHDpSXl2PSpEk92ifHcZg4cSLUajX279+PXbt24cqVK5g8eTIAQKFQICEhAfv27QNwoztX+//W1NQAAPbt24fExMQevw4KQjP57rvvUFVVhXvvvRdhYWGsyyEM0OUyvadUKpGfn0+XBPTSkiVL0NDQYJJ9/+c//8G5c+ewYMECrFmzBp9//jkGDhyIZcuWITIyEgMGDMC6deuwd+9eXLx48Y772717N06dOoWvv/4ad911F4YMGYINGzZg//79yMrKAnBjsk17EO7btw+jRo1CbGwsfv/9d/1tSUlJPX4NFIRmcObMGf3ioEVFRWhra0NSUhK8vb0ZV0bMia89MPvKzs4OdnZ2yMvLY12K1SopKTHZrExPT088/fTTiIqKwp///GdkZ2dj7969cHR01P9ERkYCAK5cuXLH/eXk5CAgIAABAQH626Kjo+Hi4oKcnBwAN4LwwIED0Ol02L9/P5KSkpCUlIT9+/ejrKwMFy9epBGhJeE4rtMEGY7jkJeXB0dHRyQlJcHJyYlRdcSc6uvrO/xxk54JCwtDZmYm6zKs3vvvv2+yhaLFYrF+VrROp8P48eNx4sSJDj+XLl3CyJEj77gvjuMgEAhue/vIkSNRW1uLY8eO4cCBA0hKSkJiYiL279+PvXv3wtPTE1FRUT2un4LQxI4ePdrtN1mtVou8vDwEBARgxIgRdNE1D1jTTDpLkJCQgF9++YV1GTZBo9Hg7bffNvnzDBw4EGfPnoVKpUJYWFiHH7lcfsfHR0dHo6CgAIWFhfrbzp07B41Gow+39vOEq1atgkAgQHR0NEaMGIHjx48jPT3doNEgQEFoUm1tbT3q+dfQ0IDCwkLExcV1mPlEbI+joyPrEqxGdHQ0b1aZN5fU1NQOAWMKzz33HNRqNf7yl78gMzMTV69exc6dOzF79uwezV4dPXo04uPjMXXqVBw7dgyZmZmYPn06EhMTMWjQIP12SUlJ+Oqrr5CYmAiBQAClUono6Ghs3rzZoPODAAWhSf3+++8oKyvr8fZqtRrXr1/HsGHDEB0dbcLKCCummMZui/z9/XHw4EF6v4ysubnZ5GsW+vr66v/bpaSkIDY2Fs8//zwUCkWP1uYUCATYsmULlEolRo4cidGjRyMkJASbN2/usF1ycrJ+vkW7xMREtLW1GTwiFHA0F9kkdDodXn755T4dkw8MDEReXp5VdWggt+fq6opjx46xLsOiOTs7o6GhAZcvX2Zdik2yt7dHXl4evLy8WJdiMWhEaCLHjh3r84npgoIC2NnZITk5Ga6urkaqjLCkVqvh7u7OugyLJRaL4ejoSCFoQk1NTVixYgXrMiwKBaGJGKsFVFtbG3Jzc+Hh4YHExES6KNsGUKu17kVEROCPP/5gXYbNW716NfU8vgkFoQnk5+f36MJRQzQ3NyM/Px/9+vXD8OHDe3SsnVgmpVLJugSLlJCQgO3bt7Mugxfq6uqQmprKugyLQZ+mJmDKhsA1NTUoKSnBXXfdhYSEBJM9DzGdrq6R4ruIiAgKQTP75JNP0NTUxLoMi0BBaGQ1NTU4cuSIyZ/n+vXrqK6uppZtVqi6upp1CRbFx8cHR48epYb0ZlZeXo4vv/ySdRkWgYLQyPbt2wetVmu256OWbdbn+vXrkMlkrMuwCHK5HNXV1SbreEJub8WKFdTEHBSERqXVarF3716zPy+1bLMuOp2ORvEAhEIh3Nzc9P0jifldvHiR1nYEBaFRZWVlMT3sdXPLtnvvvZdatlkwDw8P1iUwFxsbi99++411Gbx388K6fEVBaES7d+9mXQKAGy3bioqKEBsbi7vvvpsmZ1ggvn9J6d+/P7Zu3cq6DAJg69atKC0tZV0GUxSERnLlyhVcvXqVdRkdVFVV4dq1axg6dCi1bLMw9fX1rEtgpl+/ftixYwfrMsj/p9Vq8fnnn7MugykKQiOx5OPspaWlaGhowMiRIxEUFMS6HAKgrKyMl6NCT09PnDp1Cs3NzaxLITdZu3YtdDod6zKYoSA0gqqqKhw9epR1GXdUUFAAkUiEpKQkatnGWGtrK+86zDg4OKCxsZEWKLZABQUF+Pnnn1mXwQwFoRH8+uuvVtMlX6fTIS8vj1q2WQBfX1/WJZiNQCCAj48PTp8+zboU0g0+T5qhIOyjtrY27N+/n3UZBru5ZduwYcOoZRsDfPoSEh8fj19//ZV1GeQ2fv75Z96udEOffn104cIF1NbWsi6j12pqalBaWoq77roLAwYMYF0Or/ClvVV8fDzNELUCOp0OGzZsYF0GExSEfWQN5wZ74vr166iqqqKWbWZUXl5u85e2hISEYPfu3dS9xErcuvgtX1AQ9oFOp0N2djbrMoyqvWVbYmIifHx8WJdj0xoaGhAQEMC6DJNxc3PDxYsX0dDQwLoU0kOnT5/GuXPnWJdhdhSEfXD58mXU1NSwLsPoOI5Dfn4+5HI5kpOT4ezszLokm+Xv78+6BJOQSCTgOA5FRUWsSyEG+uabb1iXYHYUhH1gK4dFu6PVapGbmwt/f3+MGDGCl9e9mZqtfslQqVQ4duwY6zJIL/Dx8CgFYS9xHGdzh0W709DQgMLCQmrZZgLmXKnEXBISErBz507WZZBeunjxIo4fP866DLOiIOylq1evQq1Wsy7DrNpbtg0ZMgQxMTGsy7EJtrb8UGxsLM0QtQF8GxVSEPYSX0aDXSkrK0N9fT21bDOCqqoqm1mJIigoCPv37+d1qy5bQUFIesTWzw/2xM0t29zc3FiXY7VUKhXrEvrMxcUF+fn5Vn1NLfmfvLw8ZGZmsi7DbCgIe6GgoADXr19nXYZFaG/Z5u7uTi3bekmpVLIuoU/s7OwglUqRl5fHuhRiRNu3b2ddgtlQEPZCVlYW6xIsTnvLtrCwMAwfPpxatvFIWFgYjhw5wroMYmR8WiqLPq16gc/nB++ktrYWJSUlGDhwIAYOHMi6HKug0WhYl9BrCQkJ+OWXX1iXQUzg6NGjNjeZqzsUhAYqLi7m/WrOPVFRUQG1Wo177rkH/fr1Y12ORbt27RqcnJxYl2GwqKgobNu2jXUZxER0Oh1vLoOhIDQQjQYNU1xcDK1Wi6SkJGrZ1g2O4xASEsK6DIP4+/vj8OHDVrP8GOkdvoz2KQgNROupGY7jOOTl5UEulyMpKclmu6n0hbu7O+sSeszJyQnl5eWoqqpiXQoxsZ07d/KiYToFoQG0Wi3NjOuD9vfP398fI0eOpJZtN7GW90IkEsHZ2RmXLl1iXQoxg/Lycl50maEgNEB+fr5NtsQyt4aGBhQUFFDLtpvU1dWxLqFHoqKicPDgQdZlEDPiw+xRCkIDXL58mXUJNuXmlm2xsbGsy2GqvLzc4keFCQkJSE9PZ10GMbPdu3ezLsHkKAgNcOXKFdYl2KSysjLU1dXxumVba2srQkNDWZfRrYiICF5dYE3+JzMz0+YnRVEQGoBGhKZ1c8s2a5o8Yize3t6sS+iSt7c3srOz0drayroUwkB9fb3NTxKkIOyhyspKmiVnBu0t29zc3JCYmAiZTMa6JLOxt7dnXUIncrkcGo0GFRUVrEshDP3xxx+sSzApCsIeosOi5tXesi00NJQ3Lduam5tZl9CBUCiEu7s7cnJyWJdCGDt06BDrEkzK9j9djISCkA0+tWwrKyuzqBm0sbGx2L9/P+syiAWgICQA6Pwgaze3bAsPD2ddjkk0NjZazGSh/v370wK7RO/KlSs2veIOBWEPtLa2oqCggHUZBDdatrW2tiIxMdEmW7b5+/uzLgFhYWHIyMhgXQaxMLY8KqQg7IG8vDy6kN6CcByH/Px8fcs2hULBuiSjcXR0ZPr8np6eOHPmDJqampjWQSwPBSHP0WFRy9Tess3X1xcjRoyw+AvSe4LlFy57e3s0NzejrKyMWQ3Ech07dox1CSZDQdgDNFHGsjU2NqKwsBAxMTEYMmSIRU04MRTLyxT8/Pxw8uRJZs9PLNvZs2dZl2AyZg3CvLw8CAQCnDhxwmj7FAgE2LJli9H21xUKQutQXV2N8vJyq27ZVl1dDU9PT7M/b//+/bFnzx6zPy+xHsXFxVa9iPTtGDUIZ86cCYFAoP9xc3PD2LFjcerUKWM+jVlpNBpUV1ezLoMY4OaWbSqVinU5BjN3zXFxcTRDlPTIuXPnWJdgEkYfEY4dOxalpaUoLS3Fnj17IBaL8dBDDxn7acymvLycdQmklwoKCiAUCpGUlAQPDw/W5fSYi4uL2Z4rODgYe/bs4cWac6TvbPXwqNGDUCqVwtvbG97e3khISMArr7yCwsLCLq9BaWtrw5w5cxAcHAwHBwdERERg5cqVnbZbt24dYmJiIJVK4ePjg7lz53b7/G+88Qa8vLyMdviVJg5Yt/aWbUqlknct2+7E1dUVV65cQUNDA+tSiJWw1SAUm3LndXV12LhxI8LCwuDm5ob6+voO9+t0Ovj7++Pbb7+Fu7s7/vjjDzz99NPw8fHBpEmTAACrV6/GCy+8gHfffRfjxo2DRqPpcj00juOwYMECbNmyBb///jv69etnlNdw7do1o+yHsNXS0oL8/HyEhIRAoVDg8OHDFttR3xyH4iUSCQQCAV0fSwxiq4dGjR6E6enp+muh6uvr4ePjg/T09C57RdrZ2WHp0qX6fwcHB+OPP/7At99+qw/Ct956Cy+++CKef/55/XaDBw/usB+tVovp06fj6NGjOHjwoFEvSqZDo7alrq4OdXV1GDBgAHQ6nUVOCb9+/TqcnZ1RU1NjsucIDg6mi+aJwWx1RGj0Q6PJyck4ceIETpw4gSNHjuD+++/HuHHjkJ+f3+X2n376KQYNGgQPDw84Ojpi7dq1+m+p165dQ0lJCUaNGnXb51y4cCEOHTqEAwcOGL0zB40IbdPNLdsiIiJYl9MBx3EIDg422f4TEhIoBEmv2OrMUaMHoVwuR1hYGMLCwnD33Xfj888/R319PdauXdtp22+//RYLFy7E7NmzsXPnTpw4cQKzZs1CS0sLAMDBwaFHzzlmzBgUFxeb5I+bgtC2FRcXo7m5GYmJifD19WVdjp6pJvfExMTQDFHSJ1evXmVdgtGZ/DpCgUAAoVCIxsbGTvcdOHAAw4cPx7PPPosBAwYgLCyswzV7Tk5OUKlUd7y+6U9/+hO+/vprPPnkk/jmm2+MVrtGo6FWUzyRn58PmUxmMS3bxGLjn74PDAzEgQMHoNPpjL5vwh9FRUWsSzA6o/+13dyiqaqqCqtWrUJdXR3Gjx/faduwsDB8+eWXyMjIQHBwMDZs2ICsrKwOh4WWLFmCv/3tb/D09MS4ceNQW1uLgwcPYt68eR329ec//xkbNmzAtGnTIBaL8eijj/b5tdBipPxyc8u2/v374/Dhw/qjE+ZWW1tr1P0pFAoUFRWZ9Lwj4QcKwh7YsWOHflUAJycnREZG4rvvvkNSUhLy8vI6bPu3v/0NJ06cwOTJkyEQCPCXv/wFzz77LH755Rf9NjNmzEBTUxM+/PBDLFq0CO7u7t2G3KOPPgqdTodp06ZBKBTi4Ycf7tNrUavVfXo8sU6NjY0oKChAdHQ0pFIpMjMzzX6dXXl5OSQSiVGC2M7ODvb29tQ+jRhFYWEh6xKMTsD14i88JCQEWVlZcHNz63B7dXU1Bg4caDPHkHfu3IlNmzaxLoMw5uXlhYaGBpw+fdqsz2tvb4/z58/3eT/R0dH4+eefjVARIcC0adPw5Zdfsi7DqHp1jjAvL6/La7Cam5tRXFzc56IsBY0ICXBjdFZbW4sRI0aYtf2ZMdZbTEhIoBAkRsX7Q6M3zzbLyMjoMKmgra0Ne/bsscrejt2pqqpiXQKxIIWFhfqWbWfPnjX5it1SqbRPj4+KikJ6erqRqiHkBls8NGpQEE6cOBHAjZmgM2bM6HCfnZ0dVCoVVqxYYbTiWKMgJLe6uWVbTEwMsrKyOnVMMpa+zFj29fXF4cOHaUFpYnS2dNSvnUFB2D7tOjg4GFlZWXB3dzdJUZaCDo2S7rS0tCAvLw/BwcEma9lWXl4OoVBo8OUOjo6OqKiooC9yxCQaGxtRWVnZaY6INevVOcLc3FybD0HAPD0fiXWrq6tDcXExBgwYgLvuusuo+25sbERgYKBBjxGJRHBxccHFixeNWgshN7O1S8t6ffnEnj17sGfPHly7dq3TN9Z169b1uTDWtFqtxTZlJpan/YPhnnvuQUVFBS5cuGCU/fr7+3e67Oh26LwgMQdbux61VyPCpUuX4v7778eePXv0h2Bu/rEFra2trEsgVsjYLdvaG9j3REJCAoUgMQtbC8JejQg//fRTrF+/HtOmTTN2PRaDgpD0xc0t244fP97rRsU9/T0MDw+nyySI2dhaEPZqRNjS0oLhw4cbuxaLQkFI+urmlm0jR46ERCIxeB89ORfj5eWF48ePM2sHR/iHghDAk08+ia+//trYtVgUCkJiLO0t26KiojB06FAIBIIeP1aj0cDLy6vb+2UyGerq6kx+TSMhN7O1IOzVodGmpib85z//we7duxEfHw87O7sO93/wwQdGKY4lCkJibBqNBhqNBnfffbdBLdtUKlWXC0QLBAJ4enpi3759Rq6UkNujIARw6tQpJCQkAADOnDnT4T5Dvu1aMgpCYirtoTZixAgUFRUhNzf3ttt3tyxUXFwcrS1ImKAgBLB3715j12FxqCMHMbXCwkIIBIJetWyLj4+nECTM2FoQmnxhXmtFI0JiDhzH6Vu2JSUlQS6Xd9rm1kuSQkNDsXPnTnOVSEgnzc3NrEswql6NCJOTk297CPTXX3/tdUGWgmbgEXO6Xcu2iooKODs7o6amBu7u7sjJyelTH1JC+srQtn+WrldB2H5+sF1raytOnDiBM2fOdGrGba1oREhYqKurQ11dnf5vLDs7GxzHITQ0FDk5OdBqtSgpKWFbJOE9W+u61asg/PDDD7u8fcmSJairq+tTQZaCgpCwVFlZCQAYPnw41Go12tra4O/vj927dzOujBDbGxH2aoX67ly+fBl33323TazasG/fPnzxxResyyA85+TkBJVKhbCwMHh5eUGhUNjMzGxivTiOQ2RkJOsyjKbXTbe7cujQIdjb2xtzl8zQiJCwIJPJoFKp4OzsjJaWFqjVatjZ2UGhUMDT0xMODg5wcXGBVCqlQCTESHoVhA8//HCHf3Mch9LSUhw9ehSvvfaaUQpjjS6fIOYglUqhUqmgVCrR2toKtVqNhoYGNDQ06LcJDAxES0sLCgoKEBISor8O0cXFBY6OjhAKhRSKhPRBr4Lw1gt8hUIhIiIi8MYbb+D+++83SmGsCYV0ZQkxPrFYjODgYLi6ukKn00GtVqO5uRllZWVdbh8ZGYnq6mqIxWJIJBL9zNK2tjZUV1ejuroaEokESqWSRomE9FKvgjAtLc3YdVgcmUzGugRiA4RCIYKCguDh4QEAUKvVaG1t7bJl2q3kcrl+UkL75Tw6nQ7FxcXw9fXtcF/7/hQKBZycnGiUSIgB+nSOMDs7Gzk5ORAIBIiOjsaAAQOMVRdzFISkNwQCAQICAuDl5QWhUIiqqiq0tLTg2rVrBu8rPDxcfzH9zRcwNzU1obKyEq6urrh1rlt7P1OJRAIXFxfY29tTIBJyB70KwmvXruHxxx/Hvn374OLiAo7joNFokJycjG+++Ub/7deaURCSnvL19YWPjw/EYjE0Gg2ampr6vBpEcHBwh44ytzZ40Gg0cHBw6HZy2s3h6+zsDCcnJ4hEIgpFQrrQqxNh8+bNQ01NDc6ePQu1Wo2qqiqcOXMGNTU1mD9/vrFrZIKCkHTHy8sLAwcOxNChQxEdHQ2JRILKykqUl5cbpeOLRCKBg4NDh9s4joNIJOpwW3fnFW9VU1OD4uJilJaWorGxsdMokhC+69V1hAqFArt378bgwYM73J6ZmYn7778f1dXVxqqPmevXr+Pll19mXQaxAK6urggMDISDgwPq6upQW1tr0udLSEjo8m+oX79+XV7WExoaanCnDxolEvI/vTo0qtPpOq1BCAB2dnY203Ggq+bHhB8UCgUCAwMhl8vR0NCAmpoa/QxNU/Px8YFGo+nyPpFI1GUQ5uXlQaVSGRSGNTU1qKmpgVgshlKphIODAwUi4a1eBeF9992H559/Hps2bYKvry8AoLi4GAsXLsSoUaOMWiAr7ZMM6DCS7ZPL5VCpVHByckJTUxOqq6tRW1tr8pHfrQQCATw8PLpd4qa7oGpra0NJSQl8fHwM/iKq1Wr15zMdHR3h7OwMsVhMoUh4pVdBuGrVKkyYMAEqlQoBAQEQCAQoKChAXFwcvvrqK2PXyIRQKIS9vT0aGxtZl0KMzMHBASqVCgqFAi0tLaiqqkJ9fT3q6+uZ1hUbG3vbdd5uF06NjY2orq6Gs7Nzr5+/veG3SCTSjxLpelrCB70KwoCAABw7dgy7du3C+fPnwXEcoqOjMXr0aGPXx5RMJqMgtAF2dnb6i9i1Wi3UajUaGxst6r+tUqnscz1qtRr29vaQSCR92k9bWxsqKioA0CiR8INBk2V+/fVXzJ07F4cPH+70zVOj0WD48OH49NNPMWLECKMXysLrr7+OwsJC1mUQA4lEIqhUKri7u+u7t1j6sjHdTZC5WVRUVI/CUqVSGaeomwiFQiiVSshkMholEptj0Ijwo48+wlNPPdXl4ReFQoG//vWv+OCDD2wmCOkSCusgEAgQFBQET09PADdWdO9p9xZLEBER0aOJOD09/5eXl9ermaR3eu7KykpUVlZCLpfD2dkZdnZ2NEokNsGgIDx58iTee++9bu+///77sXz58j4XZSkoCC2TQCCAn58fvL29IRaLUVVVhebm5l51b2Ht1usFb8eQiTD5+fkICgoyyUi4/XwqjRKJrTAoCMvLy7u8bEK/M7G4zx01LAkFoeXw8fGBr68v7OzsoNFo0NjYqD+PZc0iIyM7dJC5HUNWRNFqtSgrK4Onp6fJZj7fPEpsXx6KRonEGhkUhH5+fjh9+jTCwsK6vP/UqVPw8fExSmGWgIKQHXd3dwQEBMDe3h41NTWor6/Xr9puK1QqVY9DELgRbrd2l7md+vp61NbWwtHRsTflGaR98pFQKISLiwvkcjmNEonVMCgIH3jgAbz++usYN25cpx6HjY2NWLx4MR566CGjFshSX6aiE8MolUoEBgZCJpOhvr4eNTU1BoWEtRGLxZDL5QZdstHW1mZQEAJARUUFpFLpbY/kGFP75CS1Wg0HBwcoFApIJBIaJRKLZtCs0fLycgwcOBAikQhz585FREQEBAIBcnJy8O9//xttbW04duwYvLy8TFmz2WRlZSE1NZV1GTbJyckJKpUKjo6OaGho6Labiq3q37+/wa/Z3d2916O74OBgps0hlEol5HK5wUFOiDkY3Gs0Pz8fzzzzDDIyMvR/WAKBACkpKUhNTTXJ1G1WioqK8Nprr7EuwybIZDKoVCo4OzujubnZpkd7d+Ll5dVhrcGecnZ2hqura6+eUyAQICQkhPllJFKpFEqlkkaJxKL0quk2cGOK+uXLl8FxHPr16welUmns2pjTarX461//ajP9U81JKpVCpVJBqVSitbUVarWa2tXhRiDFxcXdtoNMd6RSaZ/OwdvZ2SEwMJB5GLZzcXGBo6MjLSJMmOt1EPLFK6+8YpXT8s1NLBbru7dYy0XsLMTGxqKurq7Xj+/rERcnJye4u7tb1JcSqVQKFxcXSKVSCkTCRJ9WqOcDX19fCsIuiEQiBAUFwd3dHcCN9l7WdBE7CwqFos/rFYpEoj59waitrYW9vb1Fra7S3Nys/71RKBRwcnKiUSIxKwrCO/Dx8cGJEydYl8GcQCBAQEAAvLy8IBQKUVVV1WEVdHJnISEhfT432tcgBG6stWlvb2+RE1c0Gg00Gg0kEglcXFz0q8AQYkoUhHdgS9dFGsrX1xc+Pj4Qi8XQaDRoamqyqYYJ5hQeHm6UCULGujavsLAQISEhFnv+++YvWbSIMDE1CsI74FMQenl5wc/PDxKJBDU1NWhoaLC5i9hZMOZyRsYMgtzcXIuYSXon7YsI29nZQalU0iiRGB0F4R20Lzxsi1xdXREYGAgHBwfU1dWhtrYWarWadVk2x5A2andizADgOA5FRUXw8/Oz2JHhzVpbW2mUSLqUl5eH4OBgHD9+HAkJCQY/nnog3YFMJrOZDjMKhQLx8fEYNmwY4uPj4ezsjOrqapSWlpp9NXa+CAwMtOhrJpubm1FRUWF1YVJTU4Pi4mKUlJSgoaHBombBktubOXMmBAIBBAIBxGIxAgMD8cwzzzD9O6ERYQ/4+vr26rov1hwdHaFSqeDk5KRfwbz9MBMxPZFIBCcnJ4PaqN2JKT7wa2pq4ODgYNBKGJZCq9Xqz1vTIsLWY+zYsUhLS4NWq8W5c+cwe/ZsVFdXY9OmTUzqoRFhD1jLeUIHBwdERUVh6NChGDhwIDw8PFBXV4fS0tIerXdHjCs2NtaoIQgYthSTIcrLy63i8Ojt1NXVoaSkBCUlJaivr7f612PLpFIpvL294e/vj/vvvx+TJ0/Gzp079fenpaUhKioK9vb2iIyM7NTqMjMzEwMGDIC9vT0GDRqE48eP96keGhH2gKUGoZ2dHYKDg6FUKtHW1ga1Wq1fBYCw5eHhYZLDzab8cC8oKDD6gr4saLVa/RJdNEq0fFevXsWOHTv0jeHXrl2LxYsXY9WqVRgwYACOHz+Op556CnK5HDNmzEB9fT0eeugh3Hffffjqq6+Qm5uL559/vk81UBD2gKUEoUgkgkql0ncGqaysREtLC13EboF8fX1N0kjckDUJeyM3NxfBwcFWH4bt6urqUFdXR4sIW5j09HQ4Ojqira1N32Tigw8+AAC8+eabWLFiBR5++GEANxrGnzt3DmvWrMGMGTOwceNGtLW1Yd26dZDJZIiJiUFRURGeeeaZXtdDQdgDrGaOCoVCBAYGwsPDAwKBAFVVVdS9xQrExMSYbDUNQ9ckNJROp0NxcTF8fX1t6tDizYsIy+VyODs70yLCDCUnJ2P16tVoaGjAZ599hosXL2LevHm4fv06CgsLMWfOHDz11FP67bVaLRQKBQAgJycH/fv377Be7LBhw/pUDwVhD7i6ukKpVJp8VpNAIIC/vz+8vLwgEolQXV2N5uZmuojdijg5OaGlpcVk+zd1EAJAU1MT1Go1lEqlTc7GrK+vR319PY0SGZLL5foF3j/++GMkJydj6dKlmDt3LoAbh0eHDBnS4THtv/em+J2kIOyh8PBwHDlyxOj79fHxga+vL+zs7KDRaNDY2Kg/v0GsT1hYmEm/MLW0tEAqlZps/+2qq6vh4OBgludi5eZRooODA1xcXGiUyMjixYsxbtw4PPPMM/Dz88PVq1cxderULreNjo7Ghg0b0NjYqJ/pfPjw4T49PwVhD0VERBglCD08PODv7w+pVIra2lrU19dT9xYbYeoQBGDS0eatSktLbWp90dtpn2QmFArh4uICuVxOo0QzSkpKQkxMDJYtW4YlS5Zg/vz5cHZ2xrhx49Dc3IyjR4+iqqoKL7zwAqZMmYJ//vOfmDNnDl599VXk5eVh+fLlfXp+CsIeCg8P79XjlEolAgMDIZPJ9N1bLPkCa9I7UqkUdnZ2Jp/M0tzcbNL93yovL88mZpL2VPsSYmq1Gg4ODlAoFLSIsJm88MILmDVrFi5fvozPPvsM//rXv/Dyyy9DLpcjLi4OCxYsAHBjJvC2bdvwt7/9DQMGDEB0dDTee+89PPLII71+blqPsIc4jsP8+fPvuJack5MTVCoVHB0d0dDQYLJJE8SyDBgwwGxfcMw9SmufrcyXMOyKUqmEXC63yBU7SN/RiLCHBAIB+vXr1+nCTZlMhuDgYDg5OaG5uRlVVVWora2llmU84u/vb9aGBcZYiskQbW1tKC0thbe3t03NJDVEVVUVqqqqYG9vDxcXFxol2hgKQgNERETg3LlzUKlUUCqVaG1thVqt1s9CI/zTfk6pL6vOG8rcQQgADQ0NqK6utpm+u73V1NSEsrIyAICLiwscHR1pEWEbQGeDDRAfHw9/f380NzejrKwMlZWVNjm9nPRcXFycWUMQMN6ahIZSq9VobW1l8tyWqLq6GkVFRSgvL0dTUxN9FlgxCkIDeHt72/R0cmIYd3d3JofAWY4+iouLafRzi+bmZpSXl6OgoADV1dVoa2ujULQyFIQGEAgECAkJYV0GsRCs1vFjHUS5ubk0aaQbGo0GRUVFKCsro1GiFaEgNFBoaCjrEogFiIqKYjYj2BI+XPPz8ykMb6O9B3D7KFGr1VrEfzfSNQpCA1EQkvZmwaxYwgeqVqtFeXk589GpNdBoNCguLkZpaSkaGxst4r8f6YiC0EBOTk7w8PBgXQZhqF+/fmbt8HIrS/kgbW8QQXqmtbUV165dQ0FBAaqqqmiUaEEoCHuBRoX8FRoayrwzkCVd2F5RUWHybjq2qKamBsXFxSgpKUFDQwMFImMUhL3Q3jWd8ItEIoFEImFdhkUFIQAUFRVRX85e0mq1uH79OgoKCqBWq2mUyAj99vZCcHBwh7WwCD9ER0ejsbGRdRkWOQKjmaR9V1tbqx8l1tfX87aLDwsUhL0gFAoRExPDugxiRn5+fhbTN9YSL2rnOA4FBQUUhkag1WpRUVGBwsJCVFZWorW1lUaJJkZB2Ev9+/dnXQIxE6FQCFdXV4v5MGI5Ued22ieD0ExS46mrq0NJSQmKiopQV1dHo0QToSDsJT8/P7i5ubEug5hBTEyMRc2OtNQgBKBfY5MYV/siwoWFhaioqEBLS4vFfDGzBRSEfRAXF8e6BGJirq6uaGhoYF1GB+Zek9BQ165ds7gJPbakvr4epaWlNEo0IgrCPoiPj2ddAjGxwMBAi/tQt8RzhLcqLCykmaQmdvMo8dq1azRK7AP6Te0DpVIJf39/1mUQE4mMjDTrOoOGEIstfwU1mklqPo2NjfpRYm1tLY0SDURB2Ec0KrRNcrncoj9MrGG0xXEcXWNoZjqdDmq1Wj9KbG5uplFiD9BvaB/FxsbSH7oNCg8Pt+hJKdbyO9fc3IzKykqaScpAY2MjysrKUFhYSKPEO7COvyYL5uDggH79+rEugxhRcHAw8zZqd2ItQQjcaDptaROO+ITjOP0osby8nEaJXbCevyYLRrNHbYdEIoGDgwPrMmxOeXk5jUgsQFNTE8rKylBQUICamhpaRPj/oyA0goiICFq53kZER0dbxejFGj+8qPOMZamqqkJRURHKy8t5v4gwBaERiMViREdHsy6D9JGPj4/FtFG7E2v90MrLy6MwtDDNzc1Qq9W8Po9LQWgkNHvUugkEAnh4eFhNwFjrYca2tjYUFxdb1TlOPnB0dGRdAlP022gkQUFB1HLNisXGxqKmpoZ1GT1mrUEI3DhPVVVVxesRiCURCAQUhKwLsBUCgQDDhw9nXQbpBaVSaRHLKxnC0rrdGKqqqgpNTU2syyAAZDIZ70fo/H71Rta/f384OTmxLoMYKCgoyCLX+Lsda6u3K6WlpaxLIAB9ZoGC0KhEIhGGDRvGugxigIiICItto3Y71tBvtCdo8gxb9vb2NOMdFIRGd9ddd9F1aFZCJpOxLqHXLLnrjaHy8/MpDBlxdnZmXYJFoCA0MolEgsGDB7Mug/RARESExS9p1B1bCkKtVouysjLen6cyN2oe8T/0m2cCQ4YMgZ2dHesyyG2oVCqLb6N2O9Ya4N2pr6+3mms4bQWNBv+HgtAEZDIZBg4cyLoM0g2xWAy5XM66jD5pbW21ucsPKisrbebcp6UTi8VWfWrA2CgITWTYsGF0qMdCxcTEoL6+nnUZfWaL59WKi4ttLuAtkbOzM73PN6FPahNRKBTUbcYCeXl5oba2lnUZRmGLQQjQgr6mJhKJeH8B/a0oCE3onnvuYV0CuYlAIICXl5dVd2W5mS1/o6cG3aZDo8HOKAhNyN3dHZGRkazLIP9fTEyMVbVRuxNb/jBrbW1FeXm5Tb9GFoRCIY0Gu0BBaGL33nsv6xIIbhyqtrWWXrYeEnV1dairq2Ndhk1xdnamuQtdoHfExPz8/KBSqViXwXshISE20ZbsZtayUkZfXL9+3er7qloKkUhEl0x0g4LQDJKTk1mXwGvh4eFWfc1gd/gQhABQWFhIoxgjUCqVNn8Uobfot8sMAgMDERcXx7oMXnJwcLDZD1FbmfTTEzSTtG8kEgldN3gbtvkJYYHGjBkDiUTCugzeiYyMtLlzg+34FIQcx9HIsA9oNHh79FtlJk5OThg5ciTrMnglMDDQJg+JtrO1c5530tLSgoqKCvpAN5CDgwPs7e1Zl2HRKAjNaOjQoXB3d2ddBi+IRCKbX2eNb0EIADU1NTbRFcicXFxcWJdg8SgIzUgkEmHs2LGsy+CF2NhYm//A5GtfzmvXrvHqsHBfODo60imZHqAgNLPQ0FC6yN7EPDw8bKaN2u3Y0lJMhiooKKDzhXcgEAigUChYl2EV6DeJgZSUFIjFYtZl2CxfX19ejBj4HIQAzSS9E4VCQZ8zPURByICLiwv1ITWRmJgY3qxrZ2trEhqK4zgUFxfTyLALEomELp43AP0GMXLPPffQSWwjc3Jy4tUoSavV8n4GZVNTEyorK3n/PtzK1dWV3hMDUBAyYmdnh/vvv591GTYlLCyMdxNI6NAgoNFo0NDQwLoMi+Hk5ASpVMq6DKtCQchQVFQUQkNDWZdhE8LCwmz6msHuUBDeUF5ezpuWc7cjEonoSFMvUBAyNnbsWDrH0UdSqRR2dnasy2CCDn/9T35+Pu+/GLi5udHnSS/QO8aYu7s7hg0bxroMqxYdHY3GxkbWZTBBQdhRXl4eb8NQJpPBwcGBdRlWiYLQAiQnJ8PLy4t1GVbJ398f1dXVrMtghoKwo7a2NpSUlPBuVCQUCuHq6sq6DKvFr98WCyUSifDwww/TNT8GEgqFcHFx4fW5IT6/9u40Njby7nyxUqnk7UjYGCgILYSnpydGjRrFugyrEhcXx/sVzCkIu1ZVVcWbS2lkMhkcHR1Zl2HVKAgtyJAhQ2gWaQ+5u7vzoo3anfChg05vlZSUsC7B5EQiER0SNQIKQgsiEAgwYcIEOuHdA35+fhQCuHFOjHTP1ifPuLm52fTrMxcKQgvj5OSEhx56iHUZFi0qKoo3bdTuhILwzmz1sgpnZ2f60mwkFIQWKDo6GnfddRfrMiySo6MjffjfhG+ddHpDq9WirKzMpmaSSiQSunDeiGznN8PGjB07Ft7e3qzLsDj9+vXjzSSInqAg7Jn6+nqbOYogEAjg7u5Ol84YEQXhTfLy8iAQCHDixAnWpUAsFuOxxx6jnoE3CQ0N5d20+DuhLwU9V1lZaRNfHNzc3HjbSclUeBWEM2fOhEAg0P+4ublh7NixOHXqFOvSuuTq6orx48ezLsMiSCQSWmm7CxSEhikuLrbqkZSjoyPkcrlR9lVYWIg5c+bA19cXEokEQUFBeP7551FZWWmU/VsTXgUhcOOQY2lpKUpLS7Fnzx6IxWKLnpwSExODQYMGsS6DOT63UbudpqYm1iVYHWtd0FcqlRrtUomrV69i0KBBuHjxIjZt2oTLly/j008/xZ49ezBs2DCo1eouH2erX7x4F4RSqRTe3t7w9vZGQkICXnnlFRQWFuL69eudtl2/fn2nE9Jbtmzp9I1y27ZtuOuuu2Bvb4+QkBAsXboUWq3WaDWnpKTAx8fHaPuzNn5+fjZzfsfYdDqdVY9wWCkoKLCqMBSJRPDw8DDaf+vnnnsOEokEO3fuRGJiIgIDAzFu3Djs3r0bxcXF+Oc//wkAUKlUeOuttzBz5kwoFAo89dRTAIBXXnkF4eHhkMlkCAkJwWuvvdbhsPOSJUuQkJCADRs2QKVSQaFQ4PHHH+9w7W9tbS2mTp0KuVwOHx8ffPjhh0hKSsKCBQv027S0tODll1+Gn58f5HI5hgwZgn379hnlPbgZ74LwZnV1ddi4cSPCwsLg5ubWq31kZGTgiSeewPz583Hu3DmsWbMG69evx9tvv220OsViMSZPngwnJyej7dNatPdQpA4q3aPWfIZrbW1FeXm5VXyJEAgE8PT0NFpwq9VqZGRk4Nlnn+10+YW3tzemTp2KzZs36//m/vWvfyE2NhbZ2dl47bXXANy4zGv9+vU4d+4cVq5cibVr1+LDDz/ssK8rV65gy5YtSE9PR3p6Ovbv3493331Xf/8LL7yAgwcPYuvWrdi1axcOHDiAY8eOddjHrFmzcPDgQXzzzTc4deoUHnvsMYwdOxaXLl0yynvRjndBmJ6eDkdHRzg6OsLJyQlbt27F5s2bez21+u2338b//d//YcaMGQgJCcGYMWPw5ptvYs2aNUatW6FQYMqUKbw7TxYTE0MdZO7Ali4LMKe6ujqraNHn7u5u1L/7S5cugeM4REVFdXl/VFQUqqqq9EfJ7rvvPixatAhhYWEICwsDALz66qsYPnw4VCoVxo8fjxdffBHffvtth/3odDqsX78esbGxGDFiBKZNm4Y9e/YAuDEa/OKLL7B8+XKMGjUKsbGxSEtL63Bp1JUrV7Bp0yZ89913GDFiBEJDQ7Fo0SLce++9SEtLM9r7AQC8+yqZnJyM1atXA7jxzSg1NRXjxo1DZmZmr/aXnZ2NrKysDiPAtrY2NDU1oaGhATKZzCh1Aze+rU2aNAlff/01L7qquLq60srjPUBB2HvXr1+Hvb29xR4mVSgURv0M6Yn2kWD7aLmrOQr//e9/8dFHH+Hy5cuoq6uDVquFs7Nzh21UKlWHo1g+Pj64du0agBvnKFtbW3H33Xfr71coFIiIiND/+9ixY+A4DuHh4R3229zc3OsjeN3hXRDK5XL9txoAuOuuu6BQKLB27Vo8+eSTHbYVCoWdDsndOv1ap9Nh6dKlePjhhzs9l729vRErvyE0NBTjx4/HTz/9ZPR9W5rAwEBeL7FEzKOwsBAhISEW9+VSJpOZ5KL5sLAwCAQCnDt3DhMnTux0//nz56FUKuHu7g4AnWapHj58GI8//jiWLl2KlJQUKBQKfPPNN1ixYkWH7W69xEMgEOjf41vDtt3Nn7c6nQ4ikQjZ2dmdvqgYu8k474LwVgKBAEKhsMsZiR4eHqitrUV9fb3+l+HWawwHDhyICxcudAhXU0tISIBGozHJSWNLERkZSSHYQ9ZwnsvS5ebmIiQkxGK6FkkkEqOPetq5ublhzJgxSE1NxcKFCzucJywrK8PGjRsxffr0bn+vDh48iKCgIP2EGuBGGztDhIaGws7ODpmZmQgICAAA1NTU4NKlS0hMTAQADBgwAG1tbbh27RpGjBhh6Ms0CO+OqTQ3N6OsrAxlZWXIycnBvHnzUFdX1+X1ekOGDIFMJsM//vEPXL58GV9//TXWr1/fYZvXX38dX375JZYsWYKzZ88iJycHmzdvxquvvmrS15GYmIgBAwaY9DlYkcvlFvft3JLRRKK+4zgORUVFFnGYuX2GqClrWbVqFZqbm5GSkoLffvsNhYWF2LFjB8aMGQM/P7/bTvYLCwtDQUEBvvnmG1y5cgUff/wxfvzxR4Oe38nJCTNmzMBLL72EvXv34uzZs5g9ezaEQqE+gMPDwzF16lRMnz4dP/zwA3Jzc5GVlYX33nsPP//8c59e/63Y/1c3sx07dsDHxwc+Pj4YMmQIsrKy8N133yEpKanTtq6urvjqq6/w888/Iy4uDps2bcKSJUs6bJOSkoL09HTs2rULgwcPxtChQ/HBBx8gKCjI5K/loYceMutI1FzCw8Nt9nolU6AgNI7m5mZUVFQwHWELhUJ4enqafCZwv379cPToUYSGhmLy5MkIDQ3F008/jeTkZBw6dOi21ytOmDABCxcuxNy5c5GQkIA//vhDP5vUEB988AGGDRuGhx56CKNHj8Y999yDqKioDqeU0tLSMH36dLz44ouIiIjAn/70Jxw5ckQ/ijQWAUd/RVatpaUF69evR2lpKetSjMISz9VYOvriYFyenp5mn6AC3DjE7eXlxdu2ivX19fDz88OKFSswZ84csz4370aEtkYikWDKlCk20YleIpGYZIKRrbOU81q24tq1a0y+jHl4ePAqBI8fP45NmzbhypUrOHbsGKZOnQrgxojT3CgIbYCjoyOmTp1q9WuTRUdH0+USvUBBaHwFBQVmPV/o4eFh9X+/vbF8+XL0798fo0ePRn19PQ4cOKCfrWpOdGjUhhQUFGDDhg1Gbe9mLj4+PrC3t6fzXb3g5+dHqxGYgFAoRHBwsMm/aLi5uRn9cgBiGBoR2pDAwEA8/PDDFjHzzRACgQAeHh4Ugr1kC0sLWSKdTofi4mKT/j25urpSCFoA6/rEJHcUFRWFSZMmWWynjK7ExsaipqaGdRlWiybKmE5TUxMqKytNMpPUxcWFl/2DLREFoQ2KiIjA1KlTraIvqVKppOWV+oiC0LQ0Go3Rf0cVCgUUCoVR90l6j4LQRgUHB2PatGkWPwszKCjIKs9pWhJak9D0ysrKjLYvFxcXm5jlbUsoCG2Yv78/Zs6cabHnICIiIqiNmhHodDqrOy9sjfLy8vp8ysHV1ZVGghaI/npsnJeXF2bNmmVx30BZXLBsy6zpnLA160sYuru70zlBC0VByAOurq6YNWsWk+tzuhMREYHm5mbWZdgMGhGaR1tbG0pKSgx6v9tnRd+6igOxHPTXwxPOzs6YNWsWfHx8WJcClUqFqqoq1mXYFApC82lsbOzxIf321eXpCIhlo78eHpHJZJgxYwYCAwOZ1SAWi+mbsQnQUkzmpVar7zhbVygUwsvLy+InrBEKQt6RSqV44oknmK1aERsbi/r6eibPTYgxlZSUdHufSCTidQNta0NByEN2dnZ4/PHHER0dbdbn9fLyogvnTYS68rDR1eQZiUQCHx8fq7iOl9xAQchTIpEIjz76KIYPH26W52tfYoaWWDINCkJ28vPz9WEok8ng7e1Ns3itDAUhjwkEAowZMwaTJk0y+SGcmJgYGg2aEK1AwY5Wq0V5eTkUCgXc3d3pfK0VoiAkiIqKwlNPPQVPT0+T7F+hUFD3ExOjkTY7QqEQgYGBcHFxoRC0UhSEBMCNpWCefPJJxMXFGX3fISEh1EbNxOj9ZcPe3h4DBgyAh4cH61JIH9B6hKSTrKwsZGRkGOVwW3h4ODWFNgNfX1+anGFmLi4uiI6OprUgbQCNCEkngwcPxsyZM+Hs7Nyn/Tg4ONCF3mZCaxKal7+/P+Lj4ykEbQSNCEm3Ghoa8P333+Pq1au9evyAAQOog4yZKBQKKJVK1mXYPDs7O0RERMDNzY11KcSIKAjJbXEch19//RW///67QY8LDAyk0aAZOTg4wMvLi3UZNs3NzQ3h4eF0CNoG0ScVuS2BQIBRo0bh8ccf73GrKJFIRF32zYxm5ZqOUChEv379EBsbSyFoo2hESHqsqqoK//3vf2/bWgoA+vfvD41GY6aqSLuQkBC6jMLInJycEBkZSU2zbRwFITGITqfDH3/8gX379nU5q9TDwwNOTk70gcxAv379aNKMEQUGBkKlUtG1gTxAQUh6paKiAj/99BOKioo63E6jQXZojUfjsLe3R2RkJK0kzyMUhKTXOI7D4cOHsXfvXrS2tiImJoZWlmAoKioKjY2NrMuwat7e3ggLC6NeoTxDQUj6TK1WY8+ePWhtbaVDcwxFR0ejoaGBdRlWyd7eHmFhYXRZBE9REBKj4DgOly9fRnZ2Nh2eY4SC0HDtfUIDAgLoch8eoyAkRtXU1ITs7GxcvnyZdSm8Q4dGDePu7o7Q0FBaQZ5QEBLTKC8vx+HDh1FdXc26FN6gyTI9I5PJEBYWRp14iB4FITEZnU6H8+fP4/Tp03TBtxnQ5RO3JxKJEBQUBD8/PzoMSjqgICQm19rairNnz+LcuXP0QW1CoaGhtEBvN7y8vBASEkKdYUiXKAiJ2TQ1NeH06dM4f/48XXBvAtTftTMnJyeEhobSNYHktigIidnV19fjxIkTuHLlCujXz3hoTcL/cXJyQlBQEF0OQXqEgpAwo9FocPz4ceTn57MuxSa4u7vD0dGRdRlMOTs7IygoCK6urqxLIVaEgpAwV1FRgWPHjqG0tJR1KVbN2dmZtwGgUCgQFBREM0FJr1AQEotRVlaGs2fPdupfSnrG3t4e3t7erMswK6VSicDAQLi4uLAuhVgxCkJicWpqanD+/HlcvnyZZpkaQCAQICgoiHUZZuHq6oqgoCA4OzuzLoXYAApCYrFaW1tx+fJl5OTkoLa2lnU5VsGW1yQUCATw8PCAv78/LfxMjIqCkFg8juNQVFSEnJwcOo94B7Z4Ub1UKoWvry+8vb1pViwxCQpCYlWqq6uRk5ODq1evQqvVsi7H4kRGRtpMFx9XV1f4+vrC1dWVFsclJkVBSKxSc3MzLl++jNzcXFRWVrIux2JYe+Pt9gk/Xl5e1AybmA0FIbF6NTU1yMvLQ25uLu+bfFvjUkxCoRAeHh7w9vaGQqGg0R8xOwpCYlOqq6uRm5uLvLw81NTUsC7H7KwlCEUiEVxdXeHm5gZ3d3daEZ4wRUFIbFZlZaV+pFhfX8+6HLOw5EOjUqkUbm5ucHNzg4uLC/VFJRaDgpDwwvXr15GXl4fi4mJoNBrW5ZiMpa1JKJfL4e7uDjc3N7rkgVgsCkLCO/X19SgpKUFJSQlKS0stKjj6ivXlEwKBAAqFQh9+NOGFWAMKQsJrHMdBrVajrKwM5eXluHbtmlUHo7kvqBeLxXB2doazszMUCgWcnJzofB+xOhSEhNyE4zhUV1ejvLwc5eXlqKioQF1dHeuyeszUaxLK5XJ98Dk7O0Mmk5nsuQgxFwpCQu6gtbUV1dXVqKqq0v9vVVWVRY4cjbkmoVQq7RB8Tk5OEIvFRtk3a+vXr8eCBQt4f7kNucE2fqsJMSE7Ozt4eHjAw8Ojw+2NjY36UGwPSI1Gw7TjTUtLi0FBKJVK4eDg0OnH3t7eKg5xFhYWYsmSJfjll19QUVEBHx8fTJw4Ea+//rp+UV6VSoUFCxZgwYIFbIslFouCkJBeag8NX19f/W0cx6G5uRmNjY13/GlpaTF6Te37FAgEEIvFsLOzg52dHcRiMSQSSafAs+ZLGK5evYphw4YhPDwcmzZtQnBwMM6ePYuXXnoJv/zyCw4fPmz29RlbW1thZ2dn1uckfUeHRglhpK2tDY2NjWhqakJbWxt0Oh04juvR/wLQB92tgefg4ACRSGTzHVrGjRuHM2fO4OLFi3BwcNDfXlZWhtDQUEyfPh05OTnYv39/h8dxHKc/NLp582YsWLAAhYWFuPfee5GWlgYfHx/9tmlpaXj//feRm5sLlUqF+fPn49lnnwUA5OXlITg4GJs3b0ZqaioOHz6M1atXY9asWeZ5A4jxcIQQYmUqKys5gUDALVu2rMv7n3rqKU6pVHIVFRWcv78/98Ybb3ClpaVcaWkpx3Ecl5aWxtnZ2XGjR4/msrKyuOzsbC4qKoqbMmWKfh//+c9/OB8fH+7777/nrl69yn3//fecq6srt379eo7jOC43N5cDwKlUKv02xcXFpn/xxOjo0CghxOpcunQJHMchKiqqy/ujoqJQVVWFtrY2iEQiODk5wdvbu8M2ra2t+PTTTxEaGgoAmDt3Lt544w39/W+++SZWrFiBhx9+GAAQHByMc+fOYc2aNZgxY4Z+uwULFui3IdaJgpAQYnO4/3/G53aHh2UymT4EAcDHxwfXrl0DcKMTUWFhIebMmYOnnnpKv41Wq4VCoeiwn0GDBhmzdMKA9Z4pJ7yxZMkSJCQkGPQYlUqFjz76yCT1EPbCwsIgEAhw7ty5Lu8/f/48lEol3N3du93HrZNaBAKBPkDbz8OuXbsWJ06c0P+cOXMGhw8f7vA4uVzel5dCLAAFIbEIM2fOhEAggEAggJ2dHUJCQrBo0SLU19dj0aJF2LNnD+sSiQVxc3PDmDFjkJqa2qnJeFlZGTZu3IjJkydDIBBAIpGgra3NoP17eXnBz88PV69eRVhYWIef4OBgY74UYgEoCInFGDt2LEpLS3H16lW89dZbSE1NxaJFi+Do6Ki/JoyQdqtWrUJzczNSUlLw22+/obCwEDt27MCYMWPg5+eHt99+G8CNowO//fYbiouLUVFR0eP9L1myBO+88w5WrlyJixcv4vTp00hLS8MHH3xgqpdEGKEgJBZDKpXC29sbAQEBmDJlCqZOnYotW7Z0OjQ6c+ZMTJw4EcuXL4ePjw/c3Nzw3HPP3bbZdFpaGhQKBXbt2mWGV0LMoV+/fjh69ChCQ0MxefJkhIaG4umnn0ZycjIOHTqkv4bwjTfeQF5eHkJDQzs1RbidJ598Ep999hnWr1+PuLg4JCYmYv369TQitEE0WYZYLAcHh27Dbe/evfDx8cHevXtx+fJlTJ48GQkJCR0mNrRbvnw53nnnHWRkZGDo0KGmLpuYUVBQENLS0m67zdChQ3Hy5MkOt82cORMzZ87scNvEiRP15wjbTZkyBVOmTOlyvyqVqtP2xDpREBKLlJmZia+//hqjRo3q8n6lUolVq1ZBJBIhMjISDz74IPbs2dMpCP/+97/jiy++wL59+xAXF2eO0gkhVoaCkFiM9PR0ODo6QqvVorW1FRMmTMAnn3yC1NTUTtvGxMR06IXp4+OD06dPd9hmxYoVqK+vx9GjRxESEmLy+gkh1onOERKLkZycjBMnTuDChQtoamrCDz/8AE9Pzy637Wrq+63r8I0YMQJtbW349ttvTVYzIcT60YiQWAy5XI6wsDCj7e/uu+/GvHnzkJKSApFIhJdeeslo+yaE2A4KQmLThg0bhl9++QVjx46FWCzGwoULWZdECLEwFITE5t1zzz3Yvn07HnjgAYhEIsyfP591SYQQC0LLMBFCCOE1mixDCCGE1ygICSGE8BoFISGEEF6jICSEEMJrFISEEEJ4jYKQEEIIr1EQEkII4TUKQkIIIbxGQUgIIYTXKAgJIYTwGgUhIYQQXqMgJIQQwmsUhIQQQniNgpAQQgivURASQgjhNQpCQgghvEZBSAghhNcoCAkhhPAaBSEhhBBeoyAkhBDCaxSEhBBCeI2CkBBCCK9REBJCCOE1CkJCCCG8RkFICCGE1ygICSGE8BoFISGEEF6jICSEEMJrFISEEEJ4jYKQEEIIr1EQEkII4TUKQkIs1JIlS5CQkMC6DEJsHgUhISZSVlaGefPmISQkBFKpFAEBARg/fjz27NnDujRCyE3ErAsgxBbl5eXhnnvugYuLC95//33Ex8ejtbUVGRkZeO6553D+/HnWJQIA2traIBAIIBTSd2LCX/TbT4gJPPvssxAIBMjMzMSjjz6K8PBwxMTE4IUXXsDhw4cBAAUFBZgwYQIcHR3h7OyMSZMmoby8vNt96nQ6vPHGG/D394dUKkVCQgJ27Nihv3/fvn0QCASorq7W33bixAkIBALk5eUBANavXw8XFxekp6cjOjoaUqkU+fn5JnkPCLEWFISEGJlarcaOHTvw3HPPQS6Xd7rfxcUFHMdh4sSJUKvV2L9/P3bt2oUrV65g8uTJ3e535cqVWLFiBZYvX45Tp04hJSUFf/rTn3Dp0iWD6mtoaMA777yDzz77DGfPnoWnp6fBr5EQW0KHRgkxssuXL4PjOERGRna7ze7du3Hq1Cnk5uYiICAAALBhwwbExMQgKysLgwcP7vSY5cuX45VXXsHjjz8OAHjvvfewd+9efPTRR/j3v//d4/paW1uRmpqK/v37G/jKCLFNNCIkxMg4jgMACASCbrfJyclBQECAPgQBIDo6Gi4uLsjJyem0fU1NDUpKSnDPPfd0uP2ee+7pcvvbkUgkiI+PN+gxhNgyCkJCjKxfv34QCAS3DSiO47oMyu5ub3frfTdv3z7hpT2IgRujv1s5ODjc9jkI4RsKQkKMzNXVFSkpKfj3v/+N+vr6TvdXV1cjOjoaBQUFKCws1N9+7tw5aDQaREVFdXqMs7MzfH198fvvv3e4/Y8//tBv7+HhAQAoLS3V33/ixAljvCRCbBoFISEmkJqaira2Ntx99934/vvvcenSJeTk5ODjjz/GsGHDMHr0aMTHx2Pq1Kk4duwYMjMzMX36dCQmJmLQoEFd7vOll17Ce++9h82bN+PChQv4v//7P5w4cQLPP/88ACAsLAwBAQFYsmQJLl68iO3bt2PFihXmfNmEWCWaLEOICQQHB+PYsWN4++238eKLL6K0tBQeHh646667sHr1aggEAmzZsgXz5s3DyJEjIRQKMXbsWHzyySfd7nP+/PmoqanBiy++iGvXriE6Ohpbt25Fv379AAB2dnbYtGkTnnnmGfTv3x+DBw/GW2+9hccee8xcL5sQqyTgbj6hQAghhPAMHRolhBDCaxSEhBBCeI2CkBBCCK9REBJCCOE1CkJCCCG8RkFICCGE1ygICSGE8BoFISGEEF6jICSEEMJrFISEEEJ4jYKQEEIIr1EQEkII4bX/B2cgD4e8JXuEAAAAAElFTkSuQmCC", + "image/png": 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", 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" ] @@ -46,9 +46,31 @@ "metadata": {}, "output_type": "display_data" }, + { + "ename": "TypeError", + "evalue": "'value' must be an instance of str or bytes, not a float", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 29\u001b[39m\n\u001b[32m 27\u001b[39m \u001b[38;5;66;03m# Plot heights\u001b[39;00m\n\u001b[32m 28\u001b[39m plt.figure()\n\u001b[32m---> \u001b[39m\u001b[32m29\u001b[39m plt.hist(data[\u001b[33m\"\u001b[39m\u001b[33mHeight (cm)\u001b[39m\u001b[33m\"\u001b[39m], bins=\u001b[32m10\u001b[39m, edgecolor=\u001b[33m\"\u001b[39m\u001b[33mblack\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 30\u001b[39m plt.title(\u001b[33m\"\u001b[39m\u001b[33mHeight Distribution\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 31\u001b[39m plt.xlabel(\u001b[33m\"\u001b[39m\u001b[33mHeight (cm)\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\vermeej\\AppData\\Local\\anaconda3\\envs\\TIL6022-25\\Lib\\site-packages\\matplotlib\\_api\\deprecation.py:453\u001b[39m, in \u001b[36mmake_keyword_only..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 447\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) > name_idx:\n\u001b[32m 448\u001b[39m warn_deprecated(\n\u001b[32m 449\u001b[39m since, message=\u001b[33m\"\u001b[39m\u001b[33mPassing the \u001b[39m\u001b[38;5;132;01m%(name)s\u001b[39;00m\u001b[33m \u001b[39m\u001b[38;5;132;01m%(obj_type)s\u001b[39;00m\u001b[33m \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 450\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mpositionally is deprecated since Matplotlib \u001b[39m\u001b[38;5;132;01m%(since)s\u001b[39;00m\u001b[33m; the \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 451\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mparameter will become keyword-only in \u001b[39m\u001b[38;5;132;01m%(removal)s\u001b[39;00m\u001b[33m.\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 452\u001b[39m name=name, obj_type=\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc.\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m()\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m453\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m func(*args, **kwargs)\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\vermeej\\AppData\\Local\\anaconda3\\envs\\TIL6022-25\\Lib\\site-packages\\matplotlib\\pyplot.py:3478\u001b[39m, in \u001b[36mhist\u001b[39m\u001b[34m(x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, data, **kwargs)\u001b[39m\n\u001b[32m 3453\u001b[39m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes.hist)\n\u001b[32m 3454\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mhist\u001b[39m(\n\u001b[32m 3455\u001b[39m x: ArrayLike | Sequence[ArrayLike],\n\u001b[32m (...)\u001b[39m\u001b[32m 3476\u001b[39m BarContainer | Polygon | \u001b[38;5;28mlist\u001b[39m[BarContainer | Polygon],\n\u001b[32m 3477\u001b[39m ]:\n\u001b[32m-> \u001b[39m\u001b[32m3478\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m gca().hist(\n\u001b[32m 3479\u001b[39m x,\n\u001b[32m 3480\u001b[39m bins=bins,\n\u001b[32m 3481\u001b[39m \u001b[38;5;28mrange\u001b[39m=\u001b[38;5;28mrange\u001b[39m,\n\u001b[32m 3482\u001b[39m density=density,\n\u001b[32m 3483\u001b[39m weights=weights,\n\u001b[32m 3484\u001b[39m cumulative=cumulative,\n\u001b[32m 3485\u001b[39m bottom=bottom,\n\u001b[32m 3486\u001b[39m histtype=histtype,\n\u001b[32m 3487\u001b[39m align=align,\n\u001b[32m 3488\u001b[39m orientation=orientation,\n\u001b[32m 3489\u001b[39m rwidth=rwidth,\n\u001b[32m 3490\u001b[39m log=log,\n\u001b[32m 3491\u001b[39m color=color,\n\u001b[32m 3492\u001b[39m label=label,\n\u001b[32m 3493\u001b[39m stacked=stacked,\n\u001b[32m 3494\u001b[39m **({\u001b[33m\"\u001b[39m\u001b[33mdata\u001b[39m\u001b[33m\"\u001b[39m: data} \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m {}),\n\u001b[32m 3495\u001b[39m **kwargs,\n\u001b[32m 3496\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\vermeej\\AppData\\Local\\anaconda3\\envs\\TIL6022-25\\Lib\\site-packages\\matplotlib\\_api\\deprecation.py:453\u001b[39m, in \u001b[36mmake_keyword_only..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 447\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) > name_idx:\n\u001b[32m 448\u001b[39m warn_deprecated(\n\u001b[32m 449\u001b[39m since, message=\u001b[33m\"\u001b[39m\u001b[33mPassing the \u001b[39m\u001b[38;5;132;01m%(name)s\u001b[39;00m\u001b[33m \u001b[39m\u001b[38;5;132;01m%(obj_type)s\u001b[39;00m\u001b[33m \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 450\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mpositionally is deprecated since Matplotlib \u001b[39m\u001b[38;5;132;01m%(since)s\u001b[39;00m\u001b[33m; the \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 451\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mparameter will become keyword-only in \u001b[39m\u001b[38;5;132;01m%(removal)s\u001b[39;00m\u001b[33m.\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 452\u001b[39m name=name, obj_type=\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc.\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m()\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m453\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m func(*args, **kwargs)\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\vermeej\\AppData\\Local\\anaconda3\\envs\\TIL6022-25\\Lib\\site-packages\\matplotlib\\__init__.py:1524\u001b[39m, in \u001b[36m_preprocess_data..inner\u001b[39m\u001b[34m(ax, data, *args, **kwargs)\u001b[39m\n\u001b[32m 1521\u001b[39m \u001b[38;5;129m@functools\u001b[39m.wraps(func)\n\u001b[32m 1522\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34minner\u001b[39m(ax, *args, data=\u001b[38;5;28;01mNone\u001b[39;00m, **kwargs):\n\u001b[32m 1523\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1524\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m func(\n\u001b[32m 1525\u001b[39m ax,\n\u001b[32m 1526\u001b[39m *\u001b[38;5;28mmap\u001b[39m(cbook.sanitize_sequence, args),\n\u001b[32m 1527\u001b[39m **{k: cbook.sanitize_sequence(v) \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m kwargs.items()})\n\u001b[32m 1529\u001b[39m bound = new_sig.bind(ax, *args, **kwargs)\n\u001b[32m 1530\u001b[39m auto_label = (bound.arguments.get(label_namer)\n\u001b[32m 1531\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m bound.kwargs.get(label_namer))\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\vermeej\\AppData\\Local\\anaconda3\\envs\\TIL6022-25\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:7053\u001b[39m, in \u001b[36mAxes.hist\u001b[39m\u001b[34m(self, x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)\u001b[39m\n\u001b[32m 7051\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m orientation == \u001b[33m\"\u001b[39m\u001b[33mvertical\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m 7052\u001b[39m convert_units = \u001b[38;5;28mself\u001b[39m.convert_xunits\n\u001b[32m-> \u001b[39m\u001b[32m7053\u001b[39m x = [*\u001b[38;5;28mself\u001b[39m._process_unit_info([(\u001b[33m\"\u001b[39m\u001b[33mx\u001b[39m\u001b[33m\"\u001b[39m, x[\u001b[32m0\u001b[39m])], kwargs),\n\u001b[32m 7054\u001b[39m *\u001b[38;5;28mmap\u001b[39m(convert_units, x[\u001b[32m1\u001b[39m:])]\n\u001b[32m 7055\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m: \u001b[38;5;66;03m# horizontal\u001b[39;00m\n\u001b[32m 7056\u001b[39m convert_units = \u001b[38;5;28mself\u001b[39m.convert_yunits\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\vermeej\\AppData\\Local\\anaconda3\\envs\\TIL6022-25\\Lib\\site-packages\\matplotlib\\axes\\_base.py:2663\u001b[39m, in \u001b[36m_AxesBase._process_unit_info\u001b[39m\u001b[34m(self, datasets, kwargs, convert)\u001b[39m\n\u001b[32m 2661\u001b[39m \u001b[38;5;66;03m# Update from data if axis is already set but no unit is set yet.\u001b[39;00m\n\u001b[32m 2662\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m axis.have_units():\n\u001b[32m-> \u001b[39m\u001b[32m2663\u001b[39m axis.update_units(data)\n\u001b[32m 2664\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m axis_name, axis \u001b[38;5;129;01min\u001b[39;00m axis_map.items():\n\u001b[32m 2665\u001b[39m \u001b[38;5;66;03m# Return if no axis is set.\u001b[39;00m\n\u001b[32m 2666\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\vermeej\\AppData\\Local\\anaconda3\\envs\\TIL6022-25\\Lib\\site-packages\\matplotlib\\axis.py:1754\u001b[39m, in \u001b[36mAxis.update_units\u001b[39m\u001b[34m(self, data)\u001b[39m\n\u001b[32m 1752\u001b[39m neednew = \u001b[38;5;28mself\u001b[39m._converter != converter\n\u001b[32m 1753\u001b[39m \u001b[38;5;28mself\u001b[39m._set_converter(converter)\n\u001b[32m-> \u001b[39m\u001b[32m1754\u001b[39m default = \u001b[38;5;28mself\u001b[39m._converter.default_units(data, \u001b[38;5;28mself\u001b[39m)\n\u001b[32m 1755\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m default \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m.units \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 1756\u001b[39m \u001b[38;5;28mself\u001b[39m.set_units(default)\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\vermeej\\AppData\\Local\\anaconda3\\envs\\TIL6022-25\\Lib\\site-packages\\matplotlib\\category.py:106\u001b[39m, in \u001b[36mStrCategoryConverter.default_units\u001b[39m\u001b[34m(data, axis)\u001b[39m\n\u001b[32m 104\u001b[39m \u001b[38;5;66;03m# the conversion call stack is default_units -> axis_info -> convert\u001b[39;00m\n\u001b[32m 105\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m axis.units \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m106\u001b[39m axis.set_units(UnitData(data))\n\u001b[32m 107\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 108\u001b[39m axis.units.update(data)\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\vermeej\\AppData\\Local\\anaconda3\\envs\\TIL6022-25\\Lib\\site-packages\\matplotlib\\category.py:182\u001b[39m, in \u001b[36mUnitData.__init__\u001b[39m\u001b[34m(self, data)\u001b[39m\n\u001b[32m 180\u001b[39m \u001b[38;5;28mself\u001b[39m._counter = itertools.count()\n\u001b[32m 181\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m182\u001b[39m \u001b[38;5;28mself\u001b[39m.update(data)\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\vermeej\\AppData\\Local\\anaconda3\\envs\\TIL6022-25\\Lib\\site-packages\\matplotlib\\category.py:217\u001b[39m, in \u001b[36mUnitData.update\u001b[39m\u001b[34m(self, data)\u001b[39m\n\u001b[32m 214\u001b[39m convertible = \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m 215\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m val \u001b[38;5;129;01min\u001b[39;00m OrderedDict.fromkeys(data):\n\u001b[32m 216\u001b[39m \u001b[38;5;66;03m# OrderedDict just iterates over unique values in data.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m217\u001b[39m _api.check_isinstance((\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mbytes\u001b[39m), value=val)\n\u001b[32m 218\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m convertible:\n\u001b[32m 219\u001b[39m \u001b[38;5;66;03m# this will only be called so long as convertible is True.\u001b[39;00m\n\u001b[32m 220\u001b[39m convertible = \u001b[38;5;28mself\u001b[39m._str_is_convertible(val)\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\vermeej\\AppData\\Local\\anaconda3\\envs\\TIL6022-25\\Lib\\site-packages\\matplotlib\\_api\\__init__.py:92\u001b[39m, in \u001b[36mcheck_isinstance\u001b[39m\u001b[34m(types, **kwargs)\u001b[39m\n\u001b[32m 90\u001b[39m names.remove(\u001b[33m\"\u001b[39m\u001b[33mNone\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 91\u001b[39m names.append(\u001b[33m\"\u001b[39m\u001b[33mNone\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m92\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[32m 93\u001b[39m \u001b[33m\"\u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[33m must be an instance of \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[33m, not a \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[33m\"\u001b[39m.format(\n\u001b[32m 94\u001b[39m k,\n\u001b[32m 95\u001b[39m \u001b[33m\"\u001b[39m\u001b[33m, \u001b[39m\u001b[33m\"\u001b[39m.join(names[:-\u001b[32m1\u001b[39m]) + \u001b[33m\"\u001b[39m\u001b[33m or \u001b[39m\u001b[33m\"\u001b[39m + names[-\u001b[32m1\u001b[39m]\n\u001b[32m 96\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(names) > \u001b[32m1\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m names[\u001b[32m0\u001b[39m],\n\u001b[32m 97\u001b[39m type_name(\u001b[38;5;28mtype\u001b[39m(v))))\n", + "\u001b[31mTypeError\u001b[39m: 'value' must be an instance of str or bytes, not a float" + ] + }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -62,7 +84,7 @@ "import matplotlib.pyplot as plt\n", "\n", "# Load dataset\n", - "data = pd.read_csv(\"expanded_dataset.csv\")\n", + "data = pd.read_csv(\"icecream.csv\")\n", "\n", "# Plot icecream\n", "plt.figure()\n", @@ -97,7 +119,7 @@ ], "metadata": { "kernelspec": { - "display_name": "mude-base", + "display_name": "TIL6022-25", "language": "python", "name": "python3" }, @@ -111,7 +133,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.11" + "version": "3.13.5" } }, "nbformat": 4,