diff --git a/icecream.csv b/icecream.csv
index 380e6da..945ff64 100644
--- a/icecream.csv
+++ b/icecream.csv
@@ -2,7 +2,7 @@ Github,Favourite Ice Cream,Favourite Colour,Height (cm)
 jdding,pistachio,green,160  # please use lower case letters for ice cream and colour
 abel4433291
 alecasat
-AnnavanHerwijnen
+AnnavanHerwijnen,cookie dough,pink,178
 Antonie11
 bakerman27
 bartligtenberg
diff --git a/lab7_3.ipynb b/lab7_3.ipynb
index 56fc1bb..596d67a 100644
--- a/lab7_3.ipynb
+++ b/lab7_3.ipynb
@@ -22,13 +22,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 4,
    "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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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -38,7 +38,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -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[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[4], line 30\u001b[0m\n\u001b[0;32m     28\u001b[0m \u001b[38;5;66;03m# Plot heights\u001b[39;00m\n\u001b[0;32m     29\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure()\n\u001b[1;32m---> 30\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhist\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mHeight (cm)\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medgecolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mblack\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m     31\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHeight Distribution\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     32\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHeight (cm)\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\annav\\anaconda3\\envs\\TIL6022_Y1Q1\\Lib\\site-packages\\matplotlib\\_api\\deprecation.py:453\u001b[0m, in \u001b[0;36mmake_keyword_only.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    447\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m name_idx:\n\u001b[0;32m    448\u001b[0m     warn_deprecated(\n\u001b[0;32m    449\u001b[0m         since, message\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPassing the \u001b[39m\u001b[38;5;132;01m%(name)s\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m%(obj_type)s\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    450\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpositionally is deprecated since Matplotlib \u001b[39m\u001b[38;5;132;01m%(since)s\u001b[39;00m\u001b[38;5;124m; the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    451\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter will become keyword-only in \u001b[39m\u001b[38;5;132;01m%(removal)s\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    452\u001b[0m         name\u001b[38;5;241m=\u001b[39mname, obj_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m()\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 453\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\annav\\anaconda3\\envs\\TIL6022_Y1Q1\\Lib\\site-packages\\matplotlib\\pyplot.py:3478\u001b[0m, in \u001b[0;36mhist\u001b[1;34m(x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, data, **kwargs)\u001b[0m\n\u001b[0;32m   3453\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mhist)\n\u001b[0;32m   3454\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mhist\u001b[39m(\n\u001b[0;32m   3455\u001b[0m     x: ArrayLike \u001b[38;5;241m|\u001b[39m Sequence[ArrayLike],\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   3476\u001b[0m     BarContainer \u001b[38;5;241m|\u001b[39m Polygon \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mlist\u001b[39m[BarContainer \u001b[38;5;241m|\u001b[39m Polygon],\n\u001b[0;32m   3477\u001b[0m ]:\n\u001b[1;32m-> 3478\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhist\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   3479\u001b[0m \u001b[43m        \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3480\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbins\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3481\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mrange\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3482\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdensity\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdensity\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3483\u001b[0m \u001b[43m        \u001b[49m\u001b[43mweights\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweights\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3484\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcumulative\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcumulative\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3485\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbottom\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbottom\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3486\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhisttype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhisttype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3487\u001b[0m \u001b[43m        \u001b[49m\u001b[43malign\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43malign\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3488\u001b[0m \u001b[43m        \u001b[49m\u001b[43morientation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morientation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3489\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrwidth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrwidth\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3490\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlog\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlog\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3491\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3492\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlabel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3493\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstacked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstacked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3494\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3495\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3496\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\annav\\anaconda3\\envs\\TIL6022_Y1Q1\\Lib\\site-packages\\matplotlib\\_api\\deprecation.py:453\u001b[0m, in \u001b[0;36mmake_keyword_only.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    447\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m name_idx:\n\u001b[0;32m    448\u001b[0m     warn_deprecated(\n\u001b[0;32m    449\u001b[0m         since, message\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPassing the \u001b[39m\u001b[38;5;132;01m%(name)s\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m%(obj_type)s\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    450\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpositionally is deprecated since Matplotlib \u001b[39m\u001b[38;5;132;01m%(since)s\u001b[39;00m\u001b[38;5;124m; the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    451\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter will become keyword-only in \u001b[39m\u001b[38;5;132;01m%(removal)s\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    452\u001b[0m         name\u001b[38;5;241m=\u001b[39mname, obj_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m()\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 453\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\annav\\anaconda3\\envs\\TIL6022_Y1Q1\\Lib\\site-packages\\matplotlib\\__init__.py:1524\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1521\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[0;32m   1522\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m   1523\u001b[0m     \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[1;32m-> 1524\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1525\u001b[0m \u001b[43m            \u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1526\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcbook\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1527\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m{\u001b[49m\u001b[43mk\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mcbook\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1529\u001b[0m     bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1530\u001b[0m     auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[0;32m   1531\u001b[0m                   \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n",
+      "File \u001b[1;32mc:\\Users\\annav\\anaconda3\\envs\\TIL6022_Y1Q1\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:7053\u001b[0m, in \u001b[0;36mAxes.hist\u001b[1;34m(self, x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)\u001b[0m\n\u001b[0;32m   7051\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m orientation \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvertical\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m   7052\u001b[0m     convert_units \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_xunits\n\u001b[1;32m-> 7053\u001b[0m     x \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_process_unit_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mx\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[0;32m   7054\u001b[0m          \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mmap\u001b[39m(convert_units, x[\u001b[38;5;241m1\u001b[39m:])]\n\u001b[0;32m   7055\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:  \u001b[38;5;66;03m# horizontal\u001b[39;00m\n\u001b[0;32m   7056\u001b[0m     convert_units \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_yunits\n",
+      "File \u001b[1;32mc:\\Users\\annav\\anaconda3\\envs\\TIL6022_Y1Q1\\Lib\\site-packages\\matplotlib\\axes\\_base.py:2663\u001b[0m, in \u001b[0;36m_AxesBase._process_unit_info\u001b[1;34m(self, datasets, kwargs, convert)\u001b[0m\n\u001b[0;32m   2661\u001b[0m     \u001b[38;5;66;03m# Update from data if axis is already set but no unit is set yet.\u001b[39;00m\n\u001b[0;32m   2662\u001b[0m     \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\u001b[38;5;241m.\u001b[39mhave_units():\n\u001b[1;32m-> 2663\u001b[0m         \u001b[43maxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2664\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis_name, axis \u001b[38;5;129;01min\u001b[39;00m axis_map\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m   2665\u001b[0m     \u001b[38;5;66;03m# Return if no axis is set.\u001b[39;00m\n\u001b[0;32m   2666\u001b[0m     \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",
+      "File \u001b[1;32mc:\\Users\\annav\\anaconda3\\envs\\TIL6022_Y1Q1\\Lib\\site-packages\\matplotlib\\axis.py:1754\u001b[0m, in \u001b[0;36mAxis.update_units\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m   1752\u001b[0m neednew \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_converter \u001b[38;5;241m!=\u001b[39m converter\n\u001b[0;32m   1753\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_set_converter(converter)\n\u001b[1;32m-> 1754\u001b[0m default \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_converter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdefault_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1755\u001b[0m \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\u001b[38;5;241m.\u001b[39munits \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1756\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_units(default)\n",
+      "File \u001b[1;32mc:\\Users\\annav\\anaconda3\\envs\\TIL6022_Y1Q1\\Lib\\site-packages\\matplotlib\\category.py:106\u001b[0m, in \u001b[0;36mStrCategoryConverter.default_units\u001b[1;34m(data, axis)\u001b[0m\n\u001b[0;32m    104\u001b[0m \u001b[38;5;66;03m# the conversion call stack is default_units -> axis_info -> convert\u001b[39;00m\n\u001b[0;32m    105\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis\u001b[38;5;241m.\u001b[39munits \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 106\u001b[0m     axis\u001b[38;5;241m.\u001b[39mset_units(\u001b[43mUnitData\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m    107\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    108\u001b[0m     axis\u001b[38;5;241m.\u001b[39munits\u001b[38;5;241m.\u001b[39mupdate(data)\n",
+      "File \u001b[1;32mc:\\Users\\annav\\anaconda3\\envs\\TIL6022_Y1Q1\\Lib\\site-packages\\matplotlib\\category.py:182\u001b[0m, in \u001b[0;36mUnitData.__init__\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m    180\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_counter \u001b[38;5;241m=\u001b[39m itertools\u001b[38;5;241m.\u001b[39mcount()\n\u001b[0;32m    181\u001b[0m \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[1;32m--> 182\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\annav\\anaconda3\\envs\\TIL6022_Y1Q1\\Lib\\site-packages\\matplotlib\\category.py:217\u001b[0m, in \u001b[0;36mUnitData.update\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m    214\u001b[0m convertible \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m    215\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m val \u001b[38;5;129;01min\u001b[39;00m OrderedDict\u001b[38;5;241m.\u001b[39mfromkeys(data):\n\u001b[0;32m    216\u001b[0m     \u001b[38;5;66;03m# OrderedDict just iterates over unique values in data.\u001b[39;00m\n\u001b[1;32m--> 217\u001b[0m     \u001b[43m_api\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcheck_isinstance\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mbytes\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mval\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    218\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m convertible:\n\u001b[0;32m    219\u001b[0m         \u001b[38;5;66;03m# this will only be called so long as convertible is True.\u001b[39;00m\n\u001b[0;32m    220\u001b[0m         convertible \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_str_is_convertible(val)\n",
+      "File \u001b[1;32mc:\\Users\\annav\\anaconda3\\envs\\TIL6022_Y1Q1\\Lib\\site-packages\\matplotlib\\_api\\__init__.py:92\u001b[0m, in \u001b[0;36mcheck_isinstance\u001b[1;34m(types, **kwargs)\u001b[0m\n\u001b[0;32m     90\u001b[0m     names\u001b[38;5;241m.\u001b[39mremove(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     91\u001b[0m     names\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m---> 92\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[0;32m     93\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[38;5;124m must be an instance of \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m, not a \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[0;32m     94\u001b[0m         k,\n\u001b[0;32m     95\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(names[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]) \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m or \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m names[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m     96\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(names) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m names[\u001b[38;5;241m0\u001b[39m],\n\u001b[0;32m     97\u001b[0m         type_name(\u001b[38;5;28mtype\u001b[39m(v))))\n",
+      "\u001b[1;31mTypeError\u001b[0m: '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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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -62,7 +84,8 @@
     "import matplotlib.pyplot as plt\n",
     "\n",
     "# Load dataset\n",
-    "data = pd.read_csv(\"expanded_dataset.csv\")\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 +120,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "mude-base",
+   "display_name": "TIL6022_Y1Q1",
    "language": "python",
    "name": "python3"
   },