From 51f1582e6852e1056ec7d9163be70067843618f6 Mon Sep 17 00:00:00 2001 From: tsmith37 Date: Wed, 12 Nov 2025 19:04:46 -0500 Subject: [PATCH] Finished making the codes and bar graphs All that is left is the conclusion, write some comments and review it all before sending it out. excited to be almost done --- .ipynb_checkpoints/argument-checkpoint.ipynb | 343 +++++++++++++------ argument.ipynb | 343 +++++++++++++------ 2 files changed, 478 insertions(+), 208 deletions(-) diff --git a/.ipynb_checkpoints/argument-checkpoint.ipynb b/.ipynb_checkpoints/argument-checkpoint.ipynb index 3ee881e..9921764 100644 --- a/.ipynb_checkpoints/argument-checkpoint.ipynb +++ b/.ipynb_checkpoints/argument-checkpoint.ipynb @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 2, "id": "technical-evans", "metadata": {}, "outputs": [], @@ -59,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 3, "id": "overhead-sigma", "metadata": {}, "outputs": [], @@ -74,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 4, "id": "heated-blade", "metadata": {}, "outputs": [ @@ -213,7 +213,7 @@ "4 West " ] }, - "execution_count": 75, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -266,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 5, "id": "f7bba5f3-5911-4a76-ad43-f6ce78cd4fb3", "metadata": {}, "outputs": [ @@ -282,7 +282,7 @@ " dtype='object')" ] }, - "execution_count": 76, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -301,7 +301,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 6, "id": "basic-canadian", "metadata": {}, "outputs": [ @@ -311,7 +311,7 @@ "" ] }, - "execution_count": 77, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -363,7 +363,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 7, "id": "negative-highlight", "metadata": {}, "outputs": [ @@ -379,7 +379,7 @@ "Name: percentage_drivers_fatal_alcohol_impaired, dtype: float64" ] }, - "execution_count": 78, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -397,7 +397,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 8, "id": "victorian-burning", "metadata": {}, "outputs": [ @@ -407,13 +407,13 @@ "" ] }, - "execution_count": 88, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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QYYMbGTZsmOsO/uSTT9rhw4etcuXKrlFx0aJF3XoFN2qH07hxY1e11bNnT/f6YqGHDgAA4SXdg5vcuXPbCy+84B4pUddzdUEHAABIjdBrUAIAABAAghsAABBRCG4AAEBEIbgBAAARJU0bFGtsmi+//NI3HxQAAEDIBDf/NFnmuSbOLFasmP+pAwAASMuJM4M9WSYAAEC6BTepnSwTAAAgLNvcaNLLSZMm2fLly910CbGxsW50YU2bkNbzPgEAAAS1t9SuXbvcFAhTpkxx8z+VL1/ezer91ltvWaNGjezvv//2Z7MAACCIMkVp0mlvk5JM//c+8vlVcvPKK6+4YGbevHl2+eWX+5Zv27bN2rZta8OHD7fBgwcHM50AAOACZYqKsRyFytmx3evds95nBH6V3CxevNi6deuWLLARvVfD42+//TZY6QMAAAHIfXk1K3xDG/ecUfgV3CQkJLg2NinJnz+/HTlyJNB0AQAAXLzgpkyZMjZ37twU13300UdWunRp/1IDAAAQIL8q3zp16mSPPPKIHTp0yO644w4rWLCg7dmzxz755BNXZfX6668Hmi4AAICLF9zUqFHDNRh+9dVXk7WvueSSS2zgwIF26623+pcaAACAAPndbFpdvu+55x77/fffXQlO3rx5rUSJEoxiDAAA0lVAfcKOHTtmOXPmtOzZs7v3O3fu9K0rWrRo4KkDAAC4GMHNn3/+ad27d7d169ad8zPr16/3Z9MAAAAXP7h58cUX3YB9HTt2tMsuu8yiovzqdAUAABAawc2KFSvs+eefd+1uAAAAQolfRS5qZ6Pu3wAAABER3KiX1Ntvv+1GKgYAAAjLaqnevXv7XsfHx9uiRYvceDYVK1b09ZbyUndwjXcDAMjIM1F7MtRM1AjD4GbZsmXJ3hcpUsQ9r1mz5qzPMtYNAGRcGXUmaoSOVB9xCxcuTNuUAAAihmagzkizUCO0BBROx8XF2apVq+zw4cNuNvBrrrnGcuXKFbzUAUAqURUCIODgZsKECTZmzBg7ceKEb1mWLFmsQ4cO1rlzZ383CwB+oSoEgJdfZ/+HH35ow4YNs/vuu88aNmzoJszUrOAfffSRjRo1yk290LhxY382DQB+oyoEgN/BzeTJk+2hhx5yA/l5adLMG2+80bJly+a6iRPcAACAsBnnZuvWrVavXr0U19WtW9fNFA4AABA2wU3hwoVtx44dKa7bvn07jYoBAEB4BTd16tSxESNGnDXGzerVq23kyJFuPQAAQNi0uenatat999131rRpUytWrJhrULx3717766+/rGTJkvbUU08FP6UAAABpFdyo2mnmzJmu19QPP/xghw4dcmPctG3b1po0aeIaFafWwYMHXc+rr7/+2o4cOWJlypRxwVHlypXd+jZt2rhAKqmqVava1KlT/Uk6AACIcH4PBHHy5Em77LLLrFmzZr62Nt98842dPn36goKb7t27u27kCnAKFCjggpZHHnnEZs2a5Xpgbdy40V544YVkDZgzZ87sb7IBAECE86vNzebNm+3OO+90QYfXtm3bbNCgQXbvvfees7FxSr2ulixZ4rajkporr7zS+vbta4UKFbK5c+favn373KNSpUpWsGBB3yNfvnz+JBsAAGQAfgU3r7zyiusx9e677/qWVa9e3ZXcKPAYMmRIqrYTGxvrRjpWlVbSSTf10NQOKrXRawU9AAAAaVYttWLFCl+Ak5SqlTp27Gh9+vRJ1Xby5Mljt9xyS7Jln3/+uSvR0TZ+/fVXy507t/Xr18+V8OTIkcNuu+0269Spk5vqIRAxMeeP66Kj/Yr7LopwmUMnWPswlPMinARjP5IXwcG5ETrIi9ARzH3oV3Cj0pTjx4+nuC4+Pt61u/E3aOrdu7fVr1/fatWq5QIcte2pWLGia1i8fv16Vyqkaq/Ulg6lJCoqk8XG5rRwFS5z6OTJkz29k4AkyI/QQV6EDvIiMvPCr1/FKlWq2OjRo12vJc0GnrTn07hx49zyC7VgwQLr0aOHXX/99fbqq6+6ZSqx6dWrl+XNm9e9L126tGtM/OSTT1rPnj1dF3R/JCZ6LC7u2D9GkKF80IfDHDpxccctISEx4O2Eel6Ei2DkB3kRHJwboYO8CJ+80P5NbemOX8GNumo/8MADbqqFa6+91gU4Bw4csFWrVrnqoqFDh17Q9qZNm2YDBgxwVU4vv/yyr8opJibGF9h4lSpVyj3v2rXL7+BG4uMDP5hxfjpI2c+hg/wIHeRF6CAvIjMv/KrgUgPfjz/+2B588EE7duyYrV271jUAVsAze/bsC2oAPH36dHvppZesefPmrjt40rY0LVq0cNVUSf3888+u9KZ48eL+JB0AAEQ4vxtrqDGxqowCsWXLFhs4cKDdeuut1qFDBzfKsZfGymnQoIFbrzY3N910kwts1NZG4+AwfxUAAAgouFGJzIVo1KjRP35GPaPU+PiLL75wj6QaN25sgwcPdo2XNbCfghyNcdO6dWtr3779BaUFAABkHKkObp555plUb1QBSWqCG3Ub1+N8VF2lBwAAQFCDmy+//DK1HwUAAAj94Eazf6eWJsAEAAAImwbFp06dsilTptjy5cvda49HI+Wae1bvqU2bNtnq1auDnVYAAIC0CW7UY0lj02hQvf3791vWrFndWDeaLkENhLt06eLPZgEAANJnnJv58+e76RDmzJljDz/8sFWoUME++OADt1zVV4mJDIgEAADCKLhRaU3NmjXda5XeaPwZ79g36qY9b9684KYSAAAgLYMbzdSttjZyxRVX2M6dO32NiDVysN4DAACETXBTuXJlN7CeZgZXcJM9e3Y38aWsXLmS0YMBAEB4BTdqMKxJMlUFpcktmzVrZn379rUmTZrYiBEj3LQJAAAAYdNbqkyZMvbpp5+63lHeWcJVWrNixQqrU6cO0yMAAIDwmzhT8zzp4Z1uIaVpFNRrSnNB9evXj1m8AQBA6FZLpZYG9dNAf0ePHk3LrwEAALg4wQ0AAMDFRnADAAAiCsENEIBMUdH61/vu/94DANITwQ0QgExRMZajUDkX2OhZ7wEA6YsrMRCg3JdXcw8AQGig5AYAAEQUghsAABBR0jS40eB+RYsWtSxZsqTl1wAAAFx4m5sffvjBLkSVKlUsKirKFi5ceEH/DwAA4KIENy1atHAlMakZlVifW79+fUAJAwAASNPg5u233/brCwAAAEIyuKlatWqqN6rSGwAAgLAa52bevHluUsxTp075ghk9Hzt2zFatWmXffvttMNMJAACQdsHNqFGj3CN37twWHx9vmTNntpiYGNu/f79rRHz//ff7s1kAAID06Qo+a9Ysa9SokSu5ad26tdWuXdu+++47mzlzpuXLl89KlSoVeMoAAAAuVnDz999/29133+16RZUrV85WrlzplleoUME6duxoH3zwgT+bBQAASJ/gJkeOHL5u4VdccYVt377dTpw44d4r2NF7AACAsAlurrnmGps9e7Z7feWVV1p0dLQtXbrUvd+8eTMjEgMAgPBqUKyqpzZt2lhcXJyNGzfOGjZsaL169bIbb7zRFi9ebPXq1Qt+SgEAANIquNHUCmo8vHHjRvf+ueeec72kVqxYYbfddpv17t3bn80CAACkT3CzY8cOK1mypJUtW9a9z5o1q7300kvu9cmTJ23dunV2/fXXB546AACAi9Hmpm7duuecO2rNmjWuygoAACCkS25efvllO3jwoG8k4jFjxlhsbOxZn1PQo8H9AAAAQjq4KVGihI0dO9a9VjfwtWvXntUrSr2mFNhcSJsbBUzDhg2zr7/+2o4cOWJlypSxp556yipXruzWqxfWK6+84nphXXrppda1a1e78847U/8XAgCADCXVwY2mVPBOq1CnTh0bPXq0G9MmUN27d7c9e/a4AKdAgQI2depUe+SRR9woyCoh6tChg6vmUoCjAKhnz56WP39+q169esDfDQAAIo9fDYoXLlx43vUqgcmVK9c/bmfr1q22ZMkSmz59ut1www1uWd++fW3RokU2d+5c27dvnyvJefLJJ906NWL+5ZdfbOLEiQQ3AAAgeMGNZgKfMmXKOWcF37Rpk61evfoft6M2OxMmTHCDAnqpyksPjaHz448/njVmTrVq1WzAgAHuu7yjJAMAAAQU3AwZMsSmTZtmpUuXdjOBqyu4qop+/fVXO336tHXp0iVV28mTJ4/dcsstyZZ9/vnnrkSnT58+rmqqSJEiydYXKlTIjh8/bgcOHHDf6a+YmPN3FIuO9qsjGdJgH5IXFjL7kbwIDs6N0EFehI5g7kO/gpv58+e7djAalVgjFKuH1IgRI9yEmg8//LAlJib6lRgNAqjGyPXr17datWq5+arObLTsfa8SI39FRWWy2Nicfv9/pE6ePNnTOwlIgvwIHeRF6CAvIjMv/ApuVFpTs2ZN91qlN++//757XbhwYWvfvr299dZbqS698VqwYIH16NHDDf736quvumUqEToziPG+z57d/52QmOixuLhj/xhBctAHJi7uuCUk+BfoJkVehE5+kBfBwbkROsiL8MkL7d/Ulu74Fdyou7c3yNCs4Dt37vQ1Ii5evLh7fyFUxaV2NJq6QePpeEtn1PV79+7dyT6r95qVPNCxdOLjAz+YcX46SNnPoYP8CB3kReggLyIzL/yq4NIYNOqyrbYvCm5UiqKSF1m5cmWqekp5qaeUpm5o3ry56w6etBpK36NGy0l9//33rnRHc1kBAACcya8IoXPnzrZq1SpXBRUTE2PNmjVzXbibNGni2t40aNAgVdvZsmWLDRw40G699VY3ns3evXvdmDd6HD582Fq0aOGmc1A1lQbxmzRpkn322WfWrl07f5INAAAygFRXS2lCTLWBEU2Y+emnn7reUaIRhVVaowbBGuBPQU9qqGeUeld98cUX7pFU48aNbfDgwW6aBw3gp67nl112mXvNGDcAACDg4EZBy6hRo+y6665zzxqtuEaNGm6dxpvp2LGjXSj9n3/6f2q47G28DAAAELRqKVUTeRv3auoFdfsGAAAI25IbjSKs6if1ZtLowGp3c+YYNF4qyfE2MAYAAAjJ4EY9mSZPnuxm8Z49e7aVL18+oBGCAQAA0jW40QB9GpFYli1b5iazVMPif7Jjxw43ZYJ6VQEAAIRkV3DNCp6awCYhIcHq1q1rGzdu9OdrAAAALliaj4TnnTEcAADgYmCYXwAAEFEIbgAAQEQhuAEAABGF4AYAAEQUghsAABBRCG4AAEBEIbgBAAARJWjBTXx8vJuaIdnGo6KsS5cuboRiAACAkA1uFMiMGjXK5s6d65uOoUaNGla9enVr1aqVHTp0yDeBpoKbggULBjfVAAAAwQxuXn/9dRs7dqzFxcW59/3797d8+fJZ79697c8//7ShQ4f6s1kAAID0CW4++eQT6969uzVv3tw2b95sv/32mz322GPWsmVLN6Gm5p4CAAAIm+Bm9+7dVqlSJff666+/dm1ratas6d4XKVLEDh8+HNxUAgAApGVwowbC27dvd69VSlOuXDnLnz+/e79y5UoX4AAAAIRNcHPXXXfZoEGD7JFHHrGffvrJ7r33Xrd8wIABNnLkSLv77ruDnU4AAIBUiTE/PPHEE5YjRw774Ycf7KmnnrJmzZq55T///LO1bdvWOnXq5M9mAQAA0ie4UU+pBg0aWIcOHZItnzFjRuApAgAAuNjVUuPHj/e1uQEAAAj74Oaqq66yLVu2BD81AAAA6VEtVbt2bRs2bJgtWrTIypQp49rfJKWRiTt37hxo2gAAAC5OcKOpF2TJkiXucSaCGwAAEFbBzYYNG4KfEgAAgFCYFVyjEWsKhlOnTllCQkIw0gQAAHDxgxvNBH7//fdb1apV3aB9ml9KY94MHjzY/9QAAACkR3CzdOlSNzpxtmzZrEePHubxeNzysmXL2ttvv21vvfVWoOkCAAC4eMHNa6+9ZnXr1rWpU6daq1atfMFNx44drV27dvbBBx8EO50AAABpF9ysX7/eN5+UekYlVaNGDfvrr7/82SwAAED6BDe5c+e2PXv2pLhu586dbj0AAEDYBDeqkho+fLibKNNLJTi7du2ycePGWa1atYKZRgAAgLQd50a9olavXm0PPPCAXXLJJW5Z9+7dXXBz6aWXutf+0rxVixcvdu15vJ599tmz2vEUK1bMFi5c6Pf3AACAyORXcJM3b14XbMyePdu+//57O3jwoKuKatGihTVp0sSyZ8/uV2Leeecd11i5cuXKyZZv3LjRNVZ++OGHfcuio6P9+g4AABDZ/B6hWN2+VXKjR6D+/vtve/75593YOcWLF0+2Tj2xNm3aZO3bt7eCBQsG/F0AACCy+dXmplGjRm7gvjfffNN2794dcCLWrVtnmTNntjlz5lilSpWSrfvzzz/t2LFjVqJEiYC/BwAARD6/J878+OOPbeTIkW52cI1SfM8991j9+vXPmiE8NerUqeMeKfn111/ds9rgfPvttxYVFWU1a9a0J598MqBeWTEx54/roqMDnpkiwwvWPiQvLGT2I3kRHJwboYO8CB3B3Id+BTf16tVzD5WoLFiwwObNm+ca/b744otuecOGDe3mm28OSgIV3CigKVSokOuJpZKcIUOGuOkepkyZ4tZdqKioTBYbmzMo6cO55cnjX9srpA3yI3SQF6GDvIjMvPAruPFSKY0CGT3UqFglOu+++64r1dFAf8Hw2GOPWbNmzSw2Nta9L126tGt7o7Y+6op+ZjVWaiQmeiwu7tg/RpAc9IGJiztuCQmJAW+HvAid/CAvgoNzI3SQF+GTF9q/qS3dCSi4kbVr19onn3xin332mRvAr1y5cq6KKlhUMuMNbLxKlSrlntX13J/gRuLjAz+YcX46SNnPoYP8CB3kReggLyIzL/wKbtR7SQGNqqNUTaQqIzUwVlDjDTyCpWfPnq7R8uTJk33LvIMHXnXVVUH9LgAAEP78Cm7uuusuVyXVoEEDe+GFF6xatWpnzTEVLPqOTp06uSovVX9t2bLF+vXr59JQsmTJNPlOAACQwYKbV1991TUczpYtm6U1TfWggf0mTJhgb7zxhushpVKiJ554Is2/GwAARHBws2PHDteQV+PRXH/99bZ///7zfr5o0aJ+JWjw4MFnLbv99tvdAwAAIGjBjUpQ3nvvPatYsaIbk+afqqGC1VsKAAAgTYKbgQMH2uWXX+5eDxo06IK+BAAAIOSCm8aNG/teq8u3GvrSoBcAAIQav8Y6Hj9+vG3fvj34qQEAAEiP4Ebjy6hLNgAAQER0Ba9du7abMHPRokVWpkyZsybLVGPjzp07ByuNAAAAaT8ruCxZssQ9zkRwAwAAwiq42bBhQ/BTAgAAkF5tbgAAAMK+5KZ3794XtGHGwgEAACEd3CxbtizZe83UHR8f76ZZ0LQMBw8etG3btlmWLFmsbNmyaZFWAACA4AU3Cxcu9L2eO3eumzxz5MiRbjoGr02bNrkZvJkHCgAAhFWbm+HDh1v37t2TBTbe8W80W/fEiRODlT4AAIC0D24OHDhgefLkSXFdTEyMHTt2zJ/NAgAApE9wc+2119rYsWPt0KFDZ7XDUVXVjTfeGHjKAAAALtY4N7169bIWLVq4kYqvu+46y5cvn+3bt89WrlxpefPmdYEPAABA2JTcqDfUxx9/bE2bNrUjR47Y2rVr7cSJE9a2bVubM2eOXXbZZcFPKQAAQFqV3EjhwoVdCc75eDwe69Onj3Xt2tV1GQcAAAjrEYoTExNt9uzZrgEyAABAREy/oNIbAACAi4W5pQAAQEQhuAEAABGF4AYAAEQUghsAABBRCG4AAEBESfPgJlOmTGn9FQAAAD50BQcAABHF7xGKZfPmzbZkyRI3Yabmmtq2bZubmiFXrlxufXR0tG3YsCFYaQUAAEib4EYjDz/33HP24YcfupIZVT3dfvvtNmbMGPvzzz9t2rRpVqRIEX82DQAAcPGrpRTEzJ071/r37+9KbrxVT08//bQLfIYPHx5YqgAAAC5mcKMSm27dutm9995r+fLl8y0vV66cW66ABwAAIGyCm71797pA5lyzhcfFxQWaLgAAgIsX3FxxxRX2zTffpLhu+fLlbj0AAEDYNChu1aqVa1B8+vRpq127tmtQvHXrVlu2bJlNmjTJnnnmmeCnFAAAIK2Cm/vvv9/2799vY8eOtXfffdc1KO7evbtlzpzZ2rVrZw899JD5a/z48bZ48WKbOnWqb9n69ettwIABtnbtWsufP7+1bt3aWrZs6fd3AACAyOX3ODcdOnSw5s2b24oVK+zQoUOWJ08eq1SpUrIGxhfqnXfesddee80qV67sW3bgwAFr06aN1alTx1588UVbtWqVe86ZM6dr0AwAABC0Qfw0WF/NmjUtUH///bc9//zzrlqrePHiyda9//77rkSoX79+FhMTYyVLlnRVYBMmTCC4AQAAwQluVIpyrjmjoqKiLEeOHK5RsUYtrlKlyj9ub926dS6AmTNnjo0ePdr++usv37off/zRqlat6gIbr2rVqrnqK/XauuSSS/z5EwAAQITyq7fU3XffbXv27LFjx465wOOOO+6wG2+80U6ePGk7duxwpS87d+50DY+XLl2aqmBp5MiRdvnll5+1bteuXWeNdlyoUCH3rO8AAAAIuOTm4MGDVr58eXvzzTdd2xevEydOuLY4BQsWtBEjRlifPn3caMbVq1f352t828ySJUuyZVmzZnXPCqb8FRNz/rguOjrN5xSNeMHah+SFhcx+JC+Cg3MjdJAXoSOY+9Cv4Oazzz6zQYMGJQtsJFu2bK4nk7qCP/vss65E5/HHHw8ogdrmqVOnki3zBjWq/vJHVFQmi41NnnYEX5482dM7CUiC/Agd5EXoIC8iMy/8blB89OjRFJcfPnzY4uPj/7fxmJhzts1JLVVJadbxpLzvNRqyPxITPRYXd+wfI0gO+sDExR23hITEgLdDXoROfpAXwcG5ETrIi/DJC+3f1Jbu+BXc/Pvf/7Zhw4bZVVddlWwahg0bNriu3DVq1HDvv/jiC9e7KRBqkDxjxgxLSEiw6Ohot+z777+3K6+80goUKOD3duPjAz+YcX46SNnPoYP8CB3kReggLyIzL/yq4FJbGlUXNWnSxOrXr28PPvig3Xrrrda4cWPLnj27/ec//7H58+fb9OnT7ZFHHgkogerufeTIEbfNTZs22X//+1+bPHmya9sDAAAQlJIbNRj+6KOPXNdtjU2j0YpVQtO5c2fXk0olLCVKlLD33nvPKlasaIFQ6czEiRPdCMUKnvTdPXv2dK8BAACC1uZGPZjuu+8+9ziTpmNQlZU/Bg8efNYyBUgKlAAAANIsuJk3b56bAVw9mRTMiJ419o2mSPj222/93TQAAMDFDW5GjRrlHrlz53Y9ozS6sHpGqXpKIxRrYk0AAID04FeD4lmzZlmjRo1cyY3Gtaldu7Z99913NnPmTDdxZqlSpYKfUgAAgLQKbjTRpRoOawwbdQVfuXKlW16hQgXr2LGjffDBB/5sFgAAIH2CG40M7B2cTxNkbt++3U2TIAp29B4AACBsgptrrrnGZs+e7V5rMD11/fZOkLl58+az5oICAAAI6QbFqnpq06aNxcXF2bhx46xhw4bWq1cvNzP44sWLrV69esFPKQAAQFoFN5oSQY2HN27c6N4/99xzrpfUihUr7LbbbrPevXv7s1kAAID0CW527NjhRiQuW7ase581a1Z76aWXfDN2r1u3zq6//vrAUwcAAHAx2tzUrVvX1q9fn+K6NWvWuCorAACAkC65efnll+3gwYO+kYjHjBljsbGxZ31OQY8G9wMAAAjp4EYTYY4dO9a9VjfwtWvXntUrSr2mFNjQ5gYAAIR8cKMpFbzTKtSpU8eV3Hjb3AAAAIR1g+KFCxcGPyUAAADpFdyozY2mWPjqq6/s+PHjlpiYmGy9qq2mTJkSjPQBAACkfXAzdOhQmzhxol122WVWpEgR31QMSYMfAACAsAluNPWCuntrVGIAAICwH+fmyJEjVqtWreCnBgAAID2CmxtuuMFNtQAAABAR1VLt2rWzp59+2uLj461SpUqWPXv2FOefAgAACIvgxju9wujRo91z0gbFakys9+eangEAACDkgpu33347+CkBAABIr+CmatWqwfhuAACA0AhuZP/+/fbmm2/ad999Z3v27HHj3ixYsMBNyVCvXr3gphIAACAte0tt27bNGjZsaO+//74VLlzY9u3bZwkJCbZlyxbr1q2bff311/5sFgAAIH1Kbl5++WUrUKCATZ061XLkyGEVKlTwjVx88uRJGzduHOPgAACA8Cm5Wbp0qXXq1Mny5Mlz1tQLTZs2td9++y1Y6QMAAEj74EZiYlIu9Dl16tRZAQ8AAEBIBzeVK1e28ePH27Fjx3zLFNBodvB3333Xrr/++mCmEQAAIG3b3Dz11FP20EMPWf369e3GG290gY16Tm3evNm2bt1q06dP92ezAAAA6VNyU7p0aZs5c6YLbJYtW2bR0dGuS/i//vUvmzFjhpUrVy7wlAEAAFzMcW6uvPJKGzJkiAts5Pjx426uqdy5c/u7SQAAgPQpuTl9+rQ9//zz9sADD/iWrVy50qpXr+66iavtDQAAQNgENyNHjrQ5c+bYnXfe6VtWvnx569GjhxvYT6MVAwAAhE211Ny5c61Xr1724IMP+pbly5fPWrdu7bqIa2LN9u3bBzOdAAAAaRfcHDhwwC6//PIU15UoUcJ27dplwfT3339bzZo1z1o+aNAga9KkSVC/CwAAZMDgRgHM559/bjVq1Dhr3cKFC+2KK66wYNqwYYNlzZrVTcyZdIBAGi8DAICgBDctW7a0Z555xg4ePOhmANc8U5ol/KuvvrJPP/3UlagE06+//mrFixe3QoUKBXW7AAAg8vgV3DRq1MiOHj1qY8aMsfnz5/uWx8bGWt++fd36YNq4caOVLFkyqNsEAACRya/gRiMRN2/e3Jo1a2ZbtmxxJTiaRFPVVVFRfk9Xdd6SGwVO+k59n6q9HnvssRTb4aRWTMz50xkdHfy/I6MJ1j4kLyxk9iN5ERycG6GDvAgdwdyHfgU3Cmp69+7tSmgU0KQlDQz4+++/21VXXeWqwnLlymWffPKJ64311ltvubF1LlRUVCaLjc2ZJunF/5cnT/b0TgKSID9CB3kROsiLyMwLv4KbzJkzu5KUi0Fdy71TPGTLls0tq1Chgv32229uPit/gpvERI/Fxf3/ST/PFUFy0AcmLu64JSQEPqAjeRE6+UFeBAfnRuggL8InL7R/U1u641dw8/jjj7upFw4fPmxly5a1HDlynPWZokWLWrDkzHl2KUupUqVs8eLFfm8zPp5RlNOaDlL2c+ggP0IHeRE6yIvIzAu/gpsXXnjBEhIS7Omnnz7nZ9avX2/BoBKapk2b2tixY91EnV5r1651VVUAAAABBzf9+/e3i0W9pNSup1+/fvbiiy+66jBN8bBq1Sr78MMPL1o6AABABAc3jRs3totFva/GjRtnQ4cOtSeeeMLi4uLcPFZqTFy6dOmLlg4AABDBwY2cOnXKZs6cad99953t2bPHBg4caMuXL7err77aKlasGNREXnLJJUEfGBAAAEQmvzqVazTie++91wYMGGBbt261NWvW2IkTJ+zrr7+2Fi1a2MqVK4OfUgAAgLQKbtRTSiMUz5s3z2bNmmUej8ctf/311+2aa65xzwAAAGET3GgOKXUH10jBSSey1OSWbdu2tXXr1gUzjQAAAGkb3Jw8edLy5cuX4joNtnf69Gl/NgsAAJA+wY2qnqZPn57iurlz57oRhAEAANKD3yMUt27d2u655x675ZZbXNXUxx9/bCNHjnSjBk+cODH4KQUAAEirkpvKlSu7cWY07YICGTUonjx5susSPn78eKtWrZo/mwUAAEi/cW6qVKlinTp1ciU1x48fd4FO3bp13XIAAICwCm4OHTpkHTp0sNWrV7sGxGpcfPDgQZsyZYrVrFnTVU9lyZIl+KkFAABIi2opjUa8ZcsWF8T8/PPPrvRGA/mNGDHCzfk0fPhwfzYLAACQPsGNRiLu0aOH1atXzzfOjeaAql+/vj355JOuxxQAAEDYBDdqQKz5nlJy6aWX2rFjxwJNFwAAwMULbjQr+NixY90UDEnFx8fbtGnTLuqs4QAAAAE3KM6ePbv98ccfrneUHoULF7YDBw7YN998Y7t27bK8efNa79693WdVbaU2OgAAACEb3MyZM8dy5crlXi9dujTZuiJFitiKFSt875POPQUAABCSwc3ChQuDnxIAAID0anMDAAAQqghuAABARCG4AQAAEYXgBgAARBSCGwAAEFEIbgAAQEQhuAEAABGF4AYAAEQUghsAABBRCG4AAEBEIbgBAAARheAGAABEFIIbAAAQUQhuAABARCG4AQAAEYXgBgAARBSCGwAAEFEIbgAAQEQJi+AmMTHRXn/9dbv55pvt2muvtUcffdS2bduW3skCAAAhKCyCmzFjxtj06dPtpZdeshkzZrhgp127dnbq1Kn0ThoAAAgxIR/cKICZNGmSdevWzWrVqmVly5a14cOH265du2z+/PnpnTwAABBiQj642bBhgx09etSqV6/uW5YnTx4rX768/fDDD+maNgAAEHoyeTwej4Uwlc507drVVq9ebdmyZfMtf/zxx+3EiRM2fvz4C96m/uTExPP/2ZkymUVFRdmhIycsISHRr7RnVNHRUZY3VzZXfRiMo4u8CJ38IC8Cw7kROsiL8MuLqKhMlkk7OhViLMQdP37cPWfJkiXZ8qxZs9qhQ4f82qZ2TnR06naQdjj8oxM9mMiL0MkP8iIwnBuhg7yIzLwI+Wopb2nNmY2HT548admzZ0+nVAEAgFAV8sHNpZde6p53796dbLneFy5cOJ1SBQAAQlXIBzfqHZUrVy5btmyZb1lcXJz98ssvVqVKlXRNGwAACD0h3+ZGbW0efvhhe/XVVy1//vxWrFgxe+WVV6xIkSJWv3799E4eAAAIMSEf3IjGuImPj7dnn33W9ZBSic2bb75pmTNnTu+kAQCAEBPyXcEBAAAiqs0NAADAhSC4AQAAEYXgBgAARBSCGwAAEFEIbgAAQEQhuAEAABGF4AYAAEQUgpsAzJkzxx544AG79tpr7brrrrN7773XZsyYEdTvOHDggH3wwQe+9y1atLBnnnnG0pOGRpo1a5bt27fPIkGXLl3s/vvvP2u58rZMmTK2fPnys/Jd04IE+vefPn3aJk+ebJGkTp067nHkyJGz1um41fEbiK+++so2bdrkXmtKFuXP9u3bLRSEy3mh/NF+e+utt1Jc/9xzz7n1I0eOtP/+97/u9flovT6XHn766Sf78ccfLRxkxHNjx44d9sknn6TLdxPc+GnmzJn2/PPPux9AXdA+/PBDa9SokfXv399GjRoVtO8ZMmSI+zENJT/88IM7GY8fP26RoHr16rZ+/Xo3+rXXwYMH7eeff3YTty5atCjZ53UxVXBToECBgL73448/tkGDBlmk+euvv9xxmxbb7dixY8gGD+F0Xmh0988///ys5RoJfv78+ZYpUyb3/o477rDFixdbqGrWrJn9+eefFi4y2rnRq1evs66fFwvBjZ+mT5/uSmruu+8+u/LKK61EiRIu8m7durW9/fbbQfueUBxAOhTTFIhq1aq5UhQFM17fffedC16UxykFN//+978D/t5I249el19+ub333ntuHwZTqO+vUE/fmQH9qlWrbNeuXcmWf//995YjRw4X1Eu2bNmsYMGC6ZTKyJNRz430QHDjp6ioKFu5cqUdOnQo2fL27du7g1dUEvDaa69Z3bp17ZprrrF77rkn2d1SSkW+SZfpLlClQqoWSfq5o0ePWu/eva1y5cp2ww03uM8dO3bMt37z5s326KOPuqqym266yZ566inbs2ePb73SrHm6br75Zrv66qvdhU7vk95xau6uevXqWYUKFVxR6ujRo90JpOLOli1bus/o70qv4uhgKlmypBUuXNhWrFjhW6aARvtOjw0bNtjevXvd8v3797v9q+WnTp1yk7hqP2pfqxQv6V1uQkKCW3/LLbe4/XjbbbfZu+++69ZpvykPRXmbdNb7cNewYUN3TP3nP/9JsQjeWzL24osvun1TsWJFe/DBB5PtA1WJaMLcJ5980q6//np3V6rjTXT8ab3XN998Y3fddZfbx3feead9/fXXbvngwYPt7rvvTnbclytXzvr16+dbtnDhQpd3J0+edMf3G2+84b6nUqVK7nw9s9Q0Us4L7fOiRYvaZ599lmz5vHnz7Pbbb/eV3Jx5jVIw9Nhjj7l9VrNmTZs7d26yfakSTZ0jXirNVt4kzYPy5cv7qpJUldKkSROXnltvvdVdL3VeJc1brVd+6JjStc57zfWmS+dRelfVh9u5Iak53hcsWOCq7NX0Qr9hyoukN3t//PGHPfLII+53SMeEXm/cuNGt082+frv0G6Zz5aLT3FK4cJ9++qmnbNmynooVK3oeffRRz/jx4z2rV6/2JCYm+j7z2GOPeW655RbPV1995fn99989r7/+uqdMmTKeL774wq3/8MMPPaVLl0623aTL4uLiPI8//rinadOmnt27d7tlDz/8sFs/bNgwz9atWz0LFizwXHPNNZ7XXnvNrd+1a5enatWqnpdeesmzadMmz88//+xp3769p3bt2p6jR4+6z3Ts2NHTuHFjz6pVqzzbtm3zfPTRR56rr77a89Zbb7n1X375padKlSqexYsXe/766y/PJ5984tbPnj3bc/LkSc/nn3/u0qC/9/jx455I0LNnT0+HDh1872+66SbPnDlzPPHx8Z7KlSt7Zs2a5ZbPnz/f5fmJEyc83bt399xzzz2e77//3rNlyxbPpEmT3H5Sfsvbb7/tqVOnjuenn37ybN++3TN16lS333744Qe33yZPnuzeK2+1XyOBjjMd5/p7r7vuOs+zzz7rW9erVy93/Gqf6vi76667PMuWLfP89ttvnr59+7p9p2NKtA3tm/79+3v+/PNPz+bNm906LdPxd+TIEbff9b5+/frutc6xTp06eSpVqpRsvffc0Tmr8++OO+7wpUnf26VLF/d66NChLv3KP51bM2fOdH/DtGnTIuq88ObRkCFD3LXFS3+D/r5169b5PpP0enT69GnPnXfe6f7P2rVrPStWrHDHv9brc/qbdW5ov8i+ffvcNVLr9+7d65Z9/PHHnmrVqnkSEhI833zzjfv8u+++6/b3okWLXF5269bN9/8rVKjg9r+Opx9//NGdT3369HHrla/ats4jXStDXSidG6k53vXbofzT74K+55dffvE88sgjLv+81yultXfv3u76p7S2a9fOU69ePbfuwIED7ljRb5jy8mILi1nBQ5HuwosUKeKqoJYsWeIiZClevLgNHDjQ8uXLZ19++aWNGzfOatWq5dZ17drVlQJome7+/knu3LldsbDqx5MWDSuaV9Qu//rXv6xGjRq2du1a914lA0qXSmK8dDekqhfdpSny1uc1s7r3zueyyy6zadOm2a+//ureqw47S5YsVqxYMXd3p0ehQoXcs5bnzZvXfS5//vwufZFAd1Nq/6K7Gd15qKRL+yk6Otqt092K7kLVrkIlZrqDVZuZ2bNnu9IAadOmjctf3d0rz7UfVcSv/av9p7stVV+qGlP7TfkrkVjsr2NH9e1qnNqgQQNX0uWl0q1169a5u/7SpUu7ZbpTVbWg9t2IESN8n+3WrZtvP3kbR+r4y5kzp+8zffr0sRtvvNG97ty5s7vbVOma7ib1WZ2fyjtVBeguVetVEnfJJZfYt99+a0888YQr+VTj7mHDhvnOV51basugNDVv3jzizguV0Ohv+/vvv13JpfaT0q6SlZQsXbrUfvvtN/viiy/cvhGdM9q3or9Z54ryV211tL+1LZ1LKnnQMpUcaP+q5FvXQZV2qmRCtE0dB61atXJ5ffjwYVeKo/2rfa6H/o9KRJOeNzo+vMdIOAiFc+Oqq676x+Nd176+ffu6dk1eKhlSrYDa9qjqUueEquj1N+l3Sr99v//+uyUmJrrfQC3TcaHj6mIjuAmAiur0UEbqR00BjoIEZb4aFosusEkpqNABFQgFUEnpgNZBKb/88ou7AKmIMCkVu+ugFh2sKkJWcaGKFdXCXieHfni9RadqIK0TTyeBDl691kUmUumirOJgnZi6wOii7D0hFeR4G4mrOF3Fu9rPkvTEF7XdyZMnj3utC4QuJipeVgCk7ej/BtoQOVw0bdrUVcMq0FYg6KUgWhdl78VbVA2ioDFptZ72U2p+tBQsenn3vaqEY2JiXJWhfmT1A6wfb/2grF692v3Yqjpy9+7dLn90DugcURWufniTNrDVD6y2F2nnhaoq1AZEeaQfLVVJ6fg8F+WbrjXewEZ0XCcN5FT9MGbMGPda+1vnla5NasujG0LdJHivjTqH1qxZ4zpnnNl2RNcq5YuqVFTtokBG549+iFV9Fe7S+9zYlIrjXXmr/J4wYYK7Lm7dutX9zok3wNRNtgIatUGtWrWqO9+UZ0m3mV4Ibvygu/bx48dbhw4dXCmJMlI/hnqoRCZpHfOZdPLqonsu3oPmfBRRn4sCLZXSqCfXmXQyaL3SrQBI6dTdlNrdKEL30o/6Rx995NoU6QKlk0olVCp5UrfpSKQ7V10IvH9z0rspvdaPou6odHJ7707knXfeSXanJN4TW0Goep6o3lnb1F2r6rh1t9u4cWPLCPRDpnYvSXuFnavx45nnRmpLP1K6kHq/QyU1yi/dYaqEQjcXupNVcKOAXjcfsbGxvh43KuX0BvlJqWRG6Ym080KlNyrR1Y+tSpqTDjtxJv3I6vpxpqR5puBD54qCE5X0DBgwwFcaoKBS7foUpIi21a5duxTPBW+pzNChQ12Jg0rYFKQ+/fTTLs+mTJli4S49zw3P/33P+Y53XbfUhkZ5qn2utCr/lB9euoFT0Kobe+X366+/bmPHjnUl2ioZTU/pH16FIWW8LgIpddH2RscqrvaOw5CU7vx11ycqspOkDctUkpKUt2FfapUqVcpdWFRkeMUVV7iHom9d4HVXoC7PulCoeLNHjx7ublR3Yrq4ew94/V2q3tIBraLP999/3zUq052dP2kKF7oTV6Ni/Xh5L8CiIlcFKgpkFPipOk/7WVTk7t3PeqgBprcxqX74FNxoWz179nRFzbqTjfT9mJRKNdTYU3fn3kak2n+qcvBWg4qOPZ0r3nMjJf7sL91JqkROeaFGk6omVD6rJEGNWb0NMXWB14+HxuVImp+6aOuHWT8SkXheKLjRMa8SKZXiqDTrXHQnr3zTjVHS61XS65eueyoR0n5S1YX2lY55fU4dLbTvs2fP7j6rc2jLli3J9rduHNVVWp0mFAzpuqW8US9UlSDovfIu1Lo8h9u5USIVx/ukSZPcjYAaKGv/6zq2c+dOX5qUB2qcr9JqNXdQ5wmdI7omnjk2WHoguPGDfuB0x6EAYfjw4S5g2LZtm7tY6g5OB4SKEWvXru3qS3XHrpNYVRu6O2rbtq3bjqq0dFDq4NFd5KeffuqqipLSxVhF59p+aqiaRCeHAheVMuihokPV2aqoU9G0Dmp9l7ap5WpzoAPS20tBxZUvv/yyi76VLp14amvirepSmkTb1kUoUugirP2iPFEvhDN/JLVOn9F6XZiVvyohUxWf9qVKZVSi5y22V68RnfzKc929qkhex8qZ+1HtpZKOsRNpFACo9Mt7DOu1fihVJK6LoIJx7Sdd0NXe4ly8+0uf0zGeGiqt1LmoH1blnehZRez68fQGN/qc2n7onFbpjNKqHx1dsL03KpF4Xigf9KOmEpLzVUmJrmsKEBWoqxu5rh16fWbpgKqmtL91fVMJg4ImtTvTfvXub1H1vapmdF3U9VF3/ur5pLxVyU2uXLlcdYfyQPmlfFcgqRsNlbZ597mOHw12Go7S69zInYrjXTfIan+o41zHuwJgb5sf/Vboplm/bapa8/4GahBb3bQrwBWVauvad+aQAxfFRW/CHEHUg+ahhx7y3HDDDa41+6233up6MXl7JelZvZb+/e9/u1b/TZo0cb1tkpoxY4Zrsa71rVq1cttM2oNqzZo1nptvvtn1KlBPKLWoV8v6pLwt7b3U26Ft27aea6+91qVNvbnUkt1LvYCUVn2nvvvFF1/0DBw40NfKXSZMmOD7TI0aNTz9+vXzHDt2zK1TS3ltU3/zm2++6YkUhw4dcr0Dkvaa8lKPAuWLt9eUaH8MGDDA7R/tJ/XCUY8DL/UuUY+UWrVquX2lZ/VqU48IOXjwoOf+++936+bNm+eJBN4eIWfasWOHOxa9x6l6T6iHmnrnqAdH8+bNPcuXL/d9XtvQts6knhna1zqvvD1C1OPPS6+1TOu8vL3S1EvNS71I1CMlKeXXyJEj3fcqT3Q+6DxI2gMyEs6LM/No+PDhZ+3HlHpLyf79+10vQfWqqV69utu3ur7pc17r1693/2fUqFG+ZeoZpHPL22vKS8e9elxpn2l/qmeQzkOvhQsXuuumrmXXX3+96/Gjnj1eSqOujSmds6Em1M6N0/9wvCuv1ZNQadNDPZ/UG0v723sdVI9cHfPqoateuw888IBnyZIlya6bWqe/xXvdu1gy6Z+LH1IBAACkDaqlAABARCG4AQAAEYXgBgAARBSCGwAAEFEIbgAAQEQhuAEAABGF4AYAAEQUghsAEadFixbuASBjYhA/ABFHsx7L+ebjARC5CG4AAEBE+f9zqAPARaQJFuvVq+cm59NM7Hfffbc9/fTTNmzYMFuwYIGbAFCTCGriV++kl6JZqDVz9BdffOEmHK1Vq5ab0HHQoEFuW+Ktkpo6dapv0suJEye6mdk1kZ8mBbzvvvvcBLjeiR/1fzTpqSaS1ISNmvX46quvtj59+ljFihXTZR8B8A/BDYB0884771ibNm3cDNGaQVgzH+/du9cFNJqdWDMRKwBRYOINcDp16uRmIdZnihYt6gIRzWp9Liqc7tixo5vJukuXLla2bFlbtmyZvfbaa24m45deesn3Wc1SXbJkSTfTsf6fZgHv2rWrm/k9Ojr6ouwTAIEjuAGQbhSc9OjRw71+//33bcOGDe5ZJTFSs2ZNV6Ly6quvukBn6dKlLjAZOXKk1a9f3/eZu+66yzZv3pzid3z77bf23XffuRKhO++80y2rUaOGZcuWzUaMGGEtW7a0UqVKueXx8fH25ptvWq5cudz7o0ePWq9evVwwVaFChYuyTwAEjt5SANKNqp28FLgULFjQVQUpyNAjISHBateubWvXrrVDhw7Z999/b5kzZ3bVWV6qVrrjjjvO+R3Lly+3mJgYu+2225Itb9iwoW+9lxogewMbKVy4sHs+fvx4kP5iABcDJTcA0k2OHDl8rw8ePGh79uxxwU1KtO7AgQOWL18+XzsZrwIFCpzzOxQUxcbGnlWtpEBK1LbHK3v27Mk+4/2exMTEC/q7AKQvghsAISF37txWvHhxVwWVkssuu8yVpCjAUbCRNMBR499zyZs3r/s/KgVKGuDs3r3bPSvwARBZqJYCEBKqVq1qO3fudKUw11xzje+xZMkS16BYgYk+o+oqNfD1UsNf9a4633b1fz777LNky+fMmeOeb7jhhjT8qwCkB0puAISEJk2a2LRp01zvKfVuUndtNQR+44037OGHH3ZtbapUqeIaA//nP/9xvarUIHnmzJmuC3imTJlS3K4aHN94442uB9Tff//tekupnY2227hxYwb6AyIQwQ2AkGl/o67h6tb9yiuvuLYwxYoVs6eeesratm3r+9zw4cNt8ODB7nMqkalbt6499NBDNnv27BS3q6Bn/Pjx9vrrr9vkyZNt//79roqre/fuLpACEHkYoRhA2NAAfBqvRgGNunJ7devWzY1ZM2vWrHRNH4DQQMkNgLChRsTPPPOMC240wrDa4SxatMjmz5/vRigGAKHkBkBY0Vg3o0ePdgPrqVpKIwqrekkD+QGAENwAAICIQldwAAAQUQhuAABARCG4AQAAEYXgBgAARBSCGwAAEFEIbgAAQEQ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tY8aMibZ+wIAB0qBBA/dKAwAA4I2aGW1a6tixY7T12rvpwIED7uwSAADAe8FM9uzZZdeuXdHW67ocOXLEej+jR4+WSpUqSYYMGczzWrRoIYcPH3Z5TN26dU1376iLTqcAAADgdjNT586d5e2335YTJ05I9erVzboNGzbIJ598In369In1ftasWSNdu3Y1Ac29e/fk/fffl4YNG5ranXTp0rm83vDhw533/f39OXoAAMD9YGbIkCGmNuXzzz+XQYMGmXV58uSRoUOHSo8ePWK9n6VLl7rcnzFjhqmh2b59u9SuXdsleMmVK5c7RQUAAImcW81M2tSjCcBnzpyRq1evmkVva9fsuMygrftRWbJkcVn/448/SrZs2aR06dImeLpx48ZD93H79m0JDw93WQAAQOLlVs2MjgaszUJFixY1NTRRZ9PWeZoKFiz4xPuMjIyUXr16SY0aNUzQYnn11VfNgH1a86PzQ2lvKc2rWbhw4UPzcIYNG+bOnwUAAJJKMPPGG2+YnksazESlY818++23snr16ifep+bO7Nu3T9avX++yXnNzLDqpZe7cuaV+/fpm0L6YBujTmpuoeTtaMxMYGCh2xhw0AAB4uJlp586dpgblQVWrVo2xl9PjdOvWTZYsWSLBwcGSL1++Rz62SpUq5v9jx47FuN3Pz08yZszosiSWOWisRe8DAIA41MxoXsy1a9dizHl5khmzdZbt7t27y6JFi0xtTqFChR77HCtY0hoaAAAAt2pmtKeR5qZEDVz0tq6rWbPmEzUtzZo1S2bPnm1yby5cuGCWmzdvmu3alDRixAjTu+nkyZOyePFiad++vXl9nUIBAADArZoZHU9GA4pixYpJrVq1zLp169aZ/JRVq1bFej9TpkxxDowX1fTp001eTurUqWXFihVmQsuIiAiT+9KqVSsZPHiwO8UGAACJkFvBjE4yqT2LvvzyS9m9e7ekTZvW1Jho7suD3aof18z0KBq86MB6AAAAHg1mlHaVHjVqlLtPBwAA8G4wozUxsUU+CwAA8Llgpnz58qYX0+OahvQxT9KjCQAAwCvBjI76CwAAYNtgRqcUAAAASDQJwOrAgQNy+vRpuXPnjsv6Zs2axbVcAAAA8RfMnDhxQlq2bCl79+51yaOxZswmZwYAAPj0CMA9e/Y0Uw+EhYWJv7+/7N+/X9auXSsVK1Z0a5JJAAAAr9bMbNy40Yz0my1bNkmePLlZdBoDnc6gR48eZiJKAAAAn62Z0WYknUtJaUBz7tw5Z5Lw4cOHPVtCAAAAT9fMlC5d2kxjoE1NVapUkbFjx5p5lKZOnSqFCxd2Z5cAAADeC2Z0oked+FENHz5cXnzxRTPhZNasWWXu3LnulQQAAMBbwUyjRo2ct4OCguTQoUNy+fJlyZw5s7NHkzpz5oyZw0lzagAAAOKDx6IMnS07aiBjza598uRJT70EAABANPFaZfK4eZwAAADiivYfAACQdKczAJIaR4pUcrVsW5f7AICERTADPAmdviNl6oQuBQDAW81MDyYEAwAAeBoJwAAAwNbitZnpwIEDZpwZAACABA9mXnrppVjvdOHCheb/wMBA90oFAADg6WAmICAgtg8FAADwvWBm+vTp8VsSAAAANzBoHgAASJoJwPPnz5eff/5ZTp8+LXfu3HHZtmPHDk+UDQAAIH5qZiZNmiRvvvmm5MyZU3bu3CmVK1eWrFmzyokTJ6Rx48bu7BIAAMB7wcxXX30lU6dOlS+++EJSp04t/fv3l+XLl0uPHj3k6tWr7pUEAADAW8GMNi1Vr17d3E6bNq1cu3bN3G7Xrp389NNP7uwSAADAe8FMrly55PLly+Z2/vz5ZdOmTeZ2SEgIo/4CAADfD2bq1asnixcvNrc1d6Z3797SoEEDad26tbRs2dLTZQQAAPBsbybNl4mMjDS3u3btapJ///rrL2nWrJm888477uwSAADAe8HMmTNnXKYqaNOmjVm0iSk0NNQ0PQEAAPhsM1OhQoXkn3/+ibZe82h0GwAAgE8HM1oDkyxZsmjrr1+/LmnSpPFEuQAAADzfzNSnTx/zvwYyQ4YMEX9/f+e2+/fvy+bNm6V8+fKeLyUAAIAnamZ0tF9dtGZm7969zvu6HDp0SMqVKyczZsyI9f5Gjx4tlSpVkgwZMkiOHDmkRYsWcvjwYZfH3Lp1y5lknD59emnVqpX8/fffT1JsAACQiD1RzUxwcLCzO/bEiRMlY8aMcXrxNWvWmEBFA5p79+7J+++/Lw0bNpQDBw5IunTpzGO02/dvv/0m8+bNk4CAAOnWrZu89NJLsmHDhji9NgAASMK9maZPn+6RF1+6dKnLfa3V0Rqa7du3S+3atc3UCN99953Mnj3bjG1jvXaJEiXMQH1Vq1aNts/bt2+bxRIeHu6RsgIAgEQ2a/a2bdseOmv2woUL3dqnNa9TlixZzP8a1Ny9e1eee+4552OKFy9uun5v3LgxxmBGm66GDRvm1usDAIAk0ptpzpw5Zm6mgwcPyqJFi0zAsX//flm1apVpCnKHDsLXq1cvqVGjhpQuXdqsu3DhgpnIMlOmTC6P1dm6dVtMBg0aZIIia9FxbwAAQOLlVs3MqFGjZPz48SbfRZN3NX9Gx5fR0X9z587tVkF0X/v27ZP169dLXPj5+ZkFAAAkDW7VzBw/flxeeOEFc1trTiIiIkx3bU3W1akOnpQm9S5ZssQkGOfLl89lQkttwrpy5YrL47U3k24DAABwK5jJnDmzXLt2zdzOmzevqVFRGnTcuHEj1vvRLt4ayGhTlTZRPTh6cIUKFSRVqlSycuVK5zrtuq15OtWqVXOn6AAAIJFxq5lJexotX75cypQpIy+//LL07NnTBCO6rn79+k/UtKQ9lX799VfTXGXlwWjeTdq0ac3/HTt2NIP1aVKwdgXv3r27CWRiSv4FAABJj1vBzJdffmkGs1MffPCBqT3RWbN1QLvBgwfHej9Tpkwx/9etW9dlvXa/fuONN8xtzc1Jnjy52bd2uW7UqJF89dVX7hQbAAAk5WBGa0dGjBhhBrPTZiXtzaQ00Bg4cKBbL67NTI+jcz1NnjzZLAAAAG7nzHzxxRdmIkn17LPPmhmyAQAAbFMzU7BgQZk0aZKZbkBrVHTQOk0EflhODQAAgE8FM59++qm8++67ZoRd7YbdsmXLGB+n23QGbQAAAJ8KZnRGa120qUl7FWkXaZ1HCQAAwFbjzKRPn94MbqdjwmjX6ZgWy5gxY6INeAcAAJDgg+bVqVNHUqZMGatpD0gUBgAAPhfMeLLrNQAAgM8GMwAAAPGNYAYAANgawQwAALA1ghkAAGBr8RrM1KpVy8x+DQAA4FPBzI4dO2Tv3r3O+7/++qsZUO/999+XO3fuONf//vvvkjt3bs+UFAAAwFPBzDvvvCNHjhwxt0+cOCFt2rQRf39/mTdvnvTv39+dXQIAAHgvmNFApnz58ua2BjA6seTs2bNlxowZsmDBAvdKAgAA4K1gRgfDi4yMNLdXrFghTZo0MbcDAwPl4sWL7uwSAADAe8FMxYoVZeTIkfLDDz/ImjVr5IUXXjDrQ0JCJGfOnO6VBAAAwFvBzIQJE0wScLdu3eSDDz6QoKAgs37+/PlSvXp1d3YJAADglsfPFvmA+/fvm5mw165dK5kzZ3bZ9umnn0qKFCncKwkAAIA3amY0WGnYsKEJaB6UJk0aSZUqlTvlAAAA8F4zU+nSpU2XbAAAAFsGM5r8+95778mSJUvk/PnzEh4e7rIAAAD4bM6MsrpiN2vWTJIlS+bSZVvva14NAACAzwYzwcHBni8JAACAt4KZOnXquPM0AAAA35k1e926dfL666+bcWXOnj1r1ukgeuvXr/dk+QAAADwfzOj8S40aNZK0adOawfNu375t1l+9elVGjRrlzi4BAAC825vp66+/lm+++cZlXJkaNWqY4AYAAMCng5nDhw+bmbIfFBAQEONgegAAAD4VzOTKlUuOHTsWbb3myxQuXNgT5QIAAIi/YKZz587Ss2dP2bx5sxlX5ty5c/Ljjz+agfS6dOnizi4BAAC81zV74MCBEhkZKfXr15cbN26YJic/Pz8TzHTv3t29kgAAAHgrmNHamA8++ED69etnmpuuX78uJUuWlPTp07uzOwAAAO82M82aNcvUyKROndoEMZUrVyaQAQAA9glmevfuLTly5JBXX31Vfv/9d+ZiAgAA9gpmdKbsOXPmmOamV155RXLnzi1du3aVv/7664n2s3btWmnatKnkyZPH7OuXX35x2f7GG2+Y9VGX559/3p0iAwCARMqtYCZlypTy4osvmh5MYWFhMn78eDl58qQ8++yzUqRIkVjvJyIiQsqVKyeTJ09+6GM0eNHgyVp++uknd4oMAAASKbcSgKPy9/c3Uxv8+++/curUKTl48GCsn9u4cWOzPIr2ktJxbQAAADw60aQmAGvNTJMmTSRv3rwyYcIEadmypezfv188afXq1SY/p1ixYmYMm0uXLj3y8TpPVHh4uMsCAAASL7dqZtq0aSNLliwxtTKaMzNkyBCpVq2axwunTUwvvfSSFCpUSI4fPy7vv/++qcnZuHGjpEiRIsbnjB49WoYNG+bxsgAAgEQUzGgg8fPPP5vmpYcFFZ6gQZOlTJkyUrZsWZOTo7U1OmBfTAYNGiR9+vRx3teamcDAwHgrIwAAsGEwo81LCUHnfcqWLZsZqO9hwYzm2OgCAACShlgHM5MmTZK3335b0qRJY24/So8ePSQ+nDlzxuTMaFdwAACAJwpmtPv1a6+9ZoKZcePGmTFfYqLrYxvM6DQIUWffDgkJkV27dkmWLFnMorkvrVq1Mr2ZNGemf//+EhQUZJq3AAAAniiY0UDDomPKeMK2bdvM2DQWK9elQ4cOMmXKFNmzZ498//33cuXKFTOwXsOGDWXEiBE0IwEAAPdzZu7evSvFixc3vZlKlCghcVG3bl1xOBwP3b5s2bI47R8AACR+TzzOTKpUqeTWrVvxUxoAAABvDJqn8zB98skncu/ePXeeDgAAkLBds7du3SorV66UP//804z/ki5dOpftCxcu9FT5AAAAPB/MZMqUyfQyAgAAsGUwM336dM+XBAAAwJsTTQIAANiqZubpp59+6EB5D9qxY0dcygQAAOD5YKZFixbO29o1+6uvvpKSJUs6Z8vetGmT7N+/X/7zn//E/tUBAAC8Fcx89NFHztudOnUyUxboaLwPPiY0NDSuZQIAAIjfnJl58+ZJ+/bto61//fXXZcGCBe7sEgAAwHvBTNq0aWXDhg3R1us6nYgSAADAp7tm9+rVS7p06WISfStXrmzWbd68WaZNmyZDhgzxdBkBAAA8G8wMHDhQChcuLBMnTpRZs2aZdTrppI4/88orr7izSwAAAO8FM0qDlscFLj/99JM0a9Ys2nQHAAAAthg075133pG///47Pl8CAAAkcfEazDgcjvjcPQAAANMZAAAAeyOYAQAAtkYwAwAAbI1gBgAA2Fq8BjMFChSQVKlSxedLAACAJM7tYObKlSvy7bffyqBBg+Ty5ctmnY4IfPbsWedj9u3bJ4GBgZ4pKQAAgKcGzduzZ48899xzEhAQICdPnpTOnTtLlixZZOHChXL69GmZOXOmO7sFAADwTs1Mnz595I033pCjR4+6TCzZpEkTWbt2rTu7BAAA8F4ws3XrVjO674Py5s0rFy5ccK8kAAAA3gpm/Pz8JDw8PNr6I0eOSPbs2d3ZJQAAgPeCGZ08cvjw4XL37l1zP1myZCZXZsCAAdKqVSv3SgIAAOCtYObzzz+X69evS44cOeTmzZtSp04dCQoKkgwZMsjHH3/szi4BAAC815tJezEtX75c1q9fb3o2aWDzzDPPmB5OAAAAPh/MWGrWrGkWAAAAWwUzkyZNinG95s5oV21tcqpdu7akSJEiruUDAADwfDAzfvx4+eeff+TGjRuSOXNms+7ff/8Vf39/SZ8+vYSFhUnhwoUlODiYEYABAIDvJQCPGjVKKlWqZAbNu3Tpklm0W3aVKlVk4sSJpmdTrly5pHfv3p4vMQAAQFxrZgYPHiwLFiyQIkWKONdp09Jnn31mumafOHFCxo4dSzdtAADgmzUz58+fl3v37kVbr+usEYDz5Mkj165di3sJAQAAPB3MPPvss2Y6g507dzrX6e0uXbpIvXr1zP29e/dKoUKFHrkfncepadOmJvDR5OFffvnFZbvD4ZAPP/xQcufOLWnTpjVdv7VpCwAAIE7BzHfffWdmya5QoYKZ2kCXihUrmnW6TWkisA6u9ygRERFSrlw5mTx5cozbtalKe059/fXXsnnzZkmXLp00atRIbt265U6xAQBAIuRWzowm9+qgeYcOHTKJv6pYsWJmiVp78ziNGzc2S0y0VmbChAkmP6d58+Zm3cyZMyVnzpymBqdNmzbuFB0AACQycRo0r3jx4maJDyEhISb/JuqowjrysPaY2rhx40ODmdu3b5vFEtOEmAAAIPFwO5g5c+aMLF682HTDvnPnjsu2cePGxblgViKx1sREpfetbTEZPXq0DBs2LM6vDwAAEnEws3LlSjNztg6Mp01NpUuXlpMnT5qmIZ2jKSENGjRI+vTp41Izw8B9AAAkXsndDRjee+8902NJpy/QMWdCQ0PN7Nkvv/yyRwqmeTnq77//dlmv961tMdFk5IwZM7osAAAg8XIrmDl48KC0b9/e3E6ZMqXcvHnT9F4aPny4fPLJJx4pmHbr1qBFa4Gi1rJor6Zq1ap55DUAAEASbWbSLtJWnoyOAXP8+HEpVaqUuX/x4sVY7+f69ety7Ngxl6TfXbt2mS7e+fPnl169esnIkSOlaNGiJrgZMmSIGZOmRYsW7hQbAAAkQm4FM1WrVpX169dLiRIlpEmTJtK3b1/T5LRw4UKzLba2bdvm0oXbynXp0KGDzJgxQ/r372/Gonn77bflypUrUrNmTVm6dKlp2gIAAHA7mNHeSlqrorTnkN6eO3euqUF5kp5MdevWNUnDD6OjAmvTlS4AAAAeC2a0F1PUJicdoRcAAMA2CcAazFy6dCnaem0KihroAAAA+GQwo2PK3L9/P9p6HXn37NmznigXAACA55uZdMRfy7Jly8z0AhYNbrQbdcGCBZ9klwAAAN4LZqwu0ZqYqz2OokqVKpUJZB43UzYAAECCBTORkZHmfx3zZevWrZItWzaPFgYAAMArvZl0cDsAAABbz5qt+TG6hIWFOWtsLNOmTfNE2QAAAOInmNGB8nQgu4oVK5rpDDSHBgAAwDbBjA6Sp9MNtGvXzvMlAgAAiO9xZnSSyerVq7vzVAAAgIQPZjp16iSzZ8/2bEkAAAC81cx069YtmTp1qqxYsULKli1rxpiJ6kkmmwQAAPB6MLNnzx4pX768ub1v3z6XbSQDAwAAnw9mgoODPV8SAAAAb+XMWI4dO2bmaLp586a573A44rI7AAAA7wQzly5dkvr168tTTz0lTZo0kfPnz5v1HTt2lL59+7qzSwAAAO8FM7179zZJv6dPnxZ/f3/n+tatW8vSpUvdKwkAAIC3cmb+/PNP07yUL18+l/VFixaVU6dOubNLAAAA79XMREREuNTIWC5fvix+fn7ulQQAAMBbwUytWrVk5syZLt2xdbLJsWPHyrPPPuvOLgEAALzXzKRBiyYAb9u2zUxt0L9/f9m/f7+pmdmwYYN7JQEAAPBWzUzp0qXlyJEjUrNmTWnevLlpdnrppZdk586dUqRIEXd2CQAA4L2aGRUQECAffPCBu08HAABIuJqZ6dOny7x586Kt13Xff/+9J8oFAAAQf8HM6NGjJVu2bNHW58iRQ0aNGuXOLgEAALwXzOhgeYUKFYq2vkCBAmYbAACATwczWgOjM2c/aPfu3ZI1a1ZPlAsAACD+gpm2bdtKjx49zOzZ9+/fN8uqVaukZ8+e0qZNG3d2CQAA4L3eTCNGjJCTJ0+asWZSpvz/dqGD5rVv356cGQAA4NvBjMPhkAsXLsiMGTNk5MiRsmvXLkmbNq2UKVPG5MwAAAD4fDATFBRkRvzViSV1AQAAsE3OTPLkyU0Ac+nSpfgpEQAAQHwnAI8ZM0b69esn+/btc+fpAAAACZsArIm+N27ckHLlyknq1KlNzkxUOuEkAACAzwYzEyZM8HxJAAAAvBXMdOjQQbxl6NChMmzYMJd1xYoVk0OHDnmtDAAAIBHOmn38+HEz4aT+P3HiRDMq8B9//CH58+eXUqVKebSQur8VK1Y471tj2wAAALiVALxmzRozrszmzZtl4cKFcv36ded0Bh999JGny2iCl1y5cjmXmCa5tNy+fVvCw8NdFgAAkHi5FcwMHDjQDJi3fPlykwBsqVevnmzatEk87ejRo5InTx4pXLiwvPbaa4+czFJn9A4ICHAugYGBHi8PAACweTCzd+9eadmyZbT12tR08eJF8aQqVaqY0YaXLl0qU6ZMkZCQEKlVq5Zcu3YtxscPGjRIrl696lxCQ0M9Wh4AAOBb3Eo+yZQpk5w/f14KFSrksn7nzp2SN29e8aTGjRs7b5ctW9YENzptws8//ywdO3aM9ng/Pz+zAACApMGtmhmdGXvAgAFmjqZkyZKZSSY3bNgg7733nhmDJj5pIPXUU0/JsWPH4vV1AABAIg5mdGbs4sWLm3wUTf4tWbKkafqpXr26DB48WOKTvp72oMqdO3e8vg4AAEjEzUya9PvNN9/Ihx9+aPJnIiIi5OmnnzYTUHqa1vY0bdrUNC2dO3fO9JZKkSKFtG3b1uOvBQAA7MftAVu+++47GT9+vOlppHTyyV69ekmnTp08WT45c+aMCVx0Ysvs2bNLzZo1TY8pvQ0AAOBWMKM1MuPGjZPu3btLtWrVzLqNGzdK7969Tbfp4cOHe6yAc+bM8di+AABA4uNWMKNdpLWZKWpTT7NmzUxvIw1wPBnMAAAAeDwB+O7du1KxYsVo6ytUqCD37t1zZ5cAAADeC2batWtnamceNHXqVDNCLwAAgC0SgP/880+pWrWqua/zNGm+jI4z06dPH+fjNLcGAADAp4KZffv2yTPPPGNu65gvSid/1EW3WXRAPQAAAJ8LZoKDgz1fEgAAAG/lzAAAAPgKghkAAGBrBDMAAMDWCGYAAICtEcwAAABbI5gBAAC2RjADAABsjWAGAADYGsEMAACwNYIZAABgawQzAADA1ghmAACArRHMAAAAWyOYAQAAtkYwAwAAbI1gBgAA2BrBDAAAsDWCGQAAYGsEMwAAwNYIZgAAgK0RzAAAAFsjmAEAALZGMAMAAGyNYAYAANgawQwAALA1ghkAAGBrBDMAAMDWCGYAAICtEcwAAABbs0UwM3nyZClYsKCkSZNGqlSpIlu2bEnoIgEAAB/h88HM3LlzpU+fPvLRRx/Jjh07pFy5ctKoUSMJCwtL6KIBAAAf4PPBzLhx46Rz587y5ptvSsmSJeXrr78Wf39/mTZtWkIXDQAA+ICUCV2AR7lz545s375dBg0a5FyXPHlyee6552Tjxo0xPuf27dtmsVy9etX8Hx4e/tjXu3/7pkfKnRTF5v19EhwL3zkeHIu44dzwHRwLex0L6zEOh+PxO3T4sLNnz+pf4Pjrr79c1vfr189RuXLlGJ/z0UcfmeewsLCwsLCwiO2X0NDQx8YLPl0z4w6txdEcG0tkZKRcvnxZsmbNKsmSJRO70gg1MDBQQkNDJWPGjAldnCSNY+E7OBa+g2PhO8ITybHQGplr165Jnjx5HvtYnw5msmXLJilSpJC///7bZb3ez5UrV4zP8fPzM0tUmTJlksRCP5h2/nAmJhwL38Gx8B0cC9+RMREci4CAAPsnAKdOnVoqVKggK1eudKlp0fvVqlVL0LIBAADf4NM1M0qbjDp06CAVK1aUypUry4QJEyQiIsL0bgIAAPD5YKZ169byzz//yIcffigXLlyQ8uXLy9KlSyVnzpySlGjTmY6182ATGryPY+E7OBa+g2PhO/yS4LFIplnACV0IAAAAd/l0zgwAAMDjEMwAAABbI5gBAAC2RjDj41avXm0G+7ty5UpCFwVIcG+88Ya0aNEioYuRaNWtW1d69er1yMcULFjQ9CqF73kjCZ8fBDOxpD2qunTpIvnz5zcZ4jpon87evWHDBq9eSBJCYguodLLSDBkyyL1795zrrl+/LqlSpTLHIKa//fjx426/3smTJ80+du3aJYntwql/15gxY1zW//LLL3Eebdsu75kdvtit4/Tuu+9G29a1a1ezTR+jFi5cKCNGjBBfpWXVz5cdJNXz440ECqgIZmKpVatWsnPnTvn+++/lyJEjsnjxYvPFd+nSpYQuGp7Qs88+a4KXbdu2OdetW7fOBKibN2+WW7duOdcHBwebALZIkSIJVFrfliZNGvnkk0/k33//9egEs/AsHdp+zpw5cvPm/z8xon7OZ8+ebT7flixZsphAH57B+eE9BDOxoDUS+mWnH0r9IixQoIAZwE/ngWrWrJl5zOnTp6V58+aSPn16M3z0K6+84jINQ0zRqtbCWDUBun3NmjUyceJEE3HrotG3RWcP14ED/f39pXr16nL48GGXff3666/yzDPPmJOncOHCMmzYMJeah3HjxkmZMmUkXbp05sL2n//8x3yhW06dOiVNmzaVzJkzm8eUKlVKfv/9d1MG/ZuVbov6K86uihUrJrlz5za1Lha9rcevUKFCsmnTJpf1+vfryNOjR48229OmTSvlypWT+fPnOx+nF6vXXntNsmfPbrYXLVpUpk+fbrbpc9TTTz9t3r8Ha3/sTGew1yBQ35uHWbBggfk8aY2m1mR8/vnnLtt1ndYGtG/f3pw7b7/99mPfs88++8wcQ51zTWsX7t69a9Z/+eWXUrp06Wi/grU2LmqZBw8eHKtzR0euGDp0qLNGVueI6dGjh9mmZdLzpnfv3s5z1lfp36fnvda8WPS2/l36Hj+sdjgsLMxcF/Qzrcfkxx9/dNnve++9Jy+++KLzvtZS6fugY4FZgoKC5Ntvv3Xe19slSpQw73fx4sXlq6++cvmi7tatmzm2ul2vtdZnSz8nqmXLluY1rPu+zNfOD3X79m1z3PLmzWuu9VWqVHG5FuoP9LZt25rt+n2j3xs//fSTRKXXPl2vnwt9Df07dTBbPVf0B7+eU9Y5EXXf8cpTM1wnZnfv3nWkT5/e0atXL8etW7eibb9//76jfPnyjpo1azq2bdvm2LRpk6NChQqOOnXqOB/ToUMHR/PmzV2e17NnT+djrly54qhWrZqjc+fOjvPnz5vl3r17juDgYDNraJUqVRyrV6927N+/31GrVi1H9erVnftZu3atI2PGjI4ZM2Y4jh8/7vjzzz8dBQsWdAwdOtT5mPHjxztWrVrlCAkJcaxcudJRrFgxR5cuXZzbX3jhBUeDBg0ce/bsMfv43//+51izZo0pw4IFC0wZDh8+bMqlZbW7V1991dGwYUPn/UqVKjnmzZvnePfddx0ffvihWXfjxg2Hn5+feV9HjhzpKF68uGPp0qXm/Zk+fbrZpsdEde3a1XwGtm7dat7j5cuXOxYvXmy2bdmyxbx/K1asMO/fpUuXHImB9ZleuHChI02aNM6ZbRctWmT+XqXnQ/LkyR3Dhw83nx9939KmTWv+txQoUMB8fj/77DPHsWPHzPKw90xfUx+rx+ngwYPmc+rv7++YOnWq2a6f32TJkjnCwsLMfT1ns2XL5mjdurW5f+fOHfN4PT6xOXf0M6Hbf//9d8epU6ccmzdvdr6Wlilfvnzmb7POWV8+TuPGjXPUr1/fuV5v63VBt+ljlF6P9Lpkady4saNcuXKOjRs3mmOp1x09fvo8pZ/xgIAAc51QLVq0MO/3gAEDzP0zZ86Y43j06FFzf9asWY7cuXOba8qJEyfM/1myZDHvv/r0008dgYGB5ricPHnSsW7dOsfs2bPNNj2mui/97Oh7bR1jX+WL54fq1KmTOY76Huu+9D3Xa9mRI0ecx0zX7dy505wTkyZNcqRIkcJ89tW5c+ccKVOmNJ8nvdbpOTd58mTHtWvXzPLKK684nn/+eec5cfv2bYc3EMzE0vz58x2ZM2c2H0r9IAwaNMixe/dus00vgHqwT58+7Xy8Bh36YdMPXWyCmZguJMoKZvRDa/ntt9/Mups3bzovSqNGjXJ53g8//GAuGg+jF+msWbM675cpU8Yl+ImpDP/++68jsfjmm28c6dKlM4FqeHi4OTn14qgXztq1a5vHaNCnf7deVPWC8Ndff7nso2PHjo62bdua202bNnW8+eabMb6WnvC6H704JCZRP9NVq1Z1vPXWW9Eu1ho0apAcVb9+/RwlS5Z0uVjrl2Bs3jN9TX289eWpXn75ZWewEhkZaT7X+vlWGmCOHj3akStXLnN//fr1jlSpUjkiIiJide58/vnnjqeeesoEQTHRslhf7L5+nPTzrV9a+nnWRa9l//zzz0ODGf1yjXoNU/oFqeusv1mvCfplrEG8vvcamOj7rT++rOAlb968zucXKVLEGZxYRowYYX7Iqe7duzvq1atn9hUTfW39fNmBL54fp06dMt9VZ8+edXmengf6nfYw+mO3b9++5vb27dud18XH/d3eRDPTE+TMnDt3zuTKPP/886bqTKtuZ8yYIQcPHjRVuLpYSpYsaWbr1m2eULZsWedtrT60qoDV7t27Zfjw4aaJy1o6d+4s58+flxs3bpjHrFixQurXr2+qDrVNvF27dqY60dquVecjR46UGjVqmGGw9+zZI4mZVstqtejWrVtNE+JTTz1lmojq1KnjzJvRY6zNDtocp+9TgwYNXN7jmTNnOhODNTlccxJ0uo3+/fvLX3/9JUmJNsFq9fKDn3e9r5+pqPT+0aNH5f79+8512oQaW1olnyJFCpfzwToXtFq7du3a5thp8/CBAwdMk6pWrR86dMg05VaqVMlUn8fm3Hn55ZdNnol+DnT9okWLXJpv7UQ/3y+88IK5ZmkTqN7Oli3bQx+vxy5lypRmsl+LNgvpdc2it7XJVd/vvXv3msmBtRlE8wv1vNH3W88ppeebni8dO3Z0eb/1umOdR9qErUmt2hSs16Q///xTEgNfOT/27t1r9qvXu6jHQI+TdQx0uzZraTOS5lDp9mXLlplUCqXHW79LdLueH998841Hc4LcRTDzBLQNV7/QhgwZYr6s9MTTL/7YSJ48uWl/jypqO+bjaE8bi9U2r3kcSi8a2s6vFwFr0Q+tnhBaZs170XZtDYi0fVbzbyZPnuySTNapUyc5ceKECXL0uXryfPHFF5JYaTt+vnz5TIKvLtYFV3MiNCjV46vr69Wr58wt+u2331zeY/2itPJmGjdu7Myf0KBXT3Ztl04qNIDQ3n2aR+YObbt351ywzgfrXLACVf1y1SBVcwo0z8AKcKJ+ucbm3NHPguanaV6H5gdoYKT7epJz15e89dZbJpjRL1a97QnW+229t/oFqDkx69evd3m/rfNIv/yivt/79u1z5qnpD8SQkBDzZapBpOYe/t///Z/Yna+cH9evXzeBjn4HRD0GGlRpvqb69NNPze0BAwaYa6Bu17Jb3xX6/OXLl8sff/xhfrTr94QGn3rcEpLPTzTpy/RAaoKhnrihoaFmsWpn9ItOfxnqY6xfRXrSRqUfkqgfPP1VEzUajy29AOgFV7+gY6IfXP0wa2KZBlXq559/jvY4Lbt239RFTzq96HTv3t2US7lTNl+mib16EdZfFf369XO58OiJumXLFlPjosdQk/P0l0nUL8IH6THWGd51qVWrltmnJuIl1vfvQdoFVWum9MJm0XPjweEL9L7+Moz66/FBcXnP9BhpEuu8efOciZH6v9ZO6mv37ds31ueO0iBGk2B10WRKrZ3QgEef6+45m1C0Vlm/lPQLTr+gHkX/Tq2F0uuH1mYpfa8eHKJB3+9p06aZWhzdv/V+a9Ko9vy0joFODqw/FvRHkybLP4wGnzrBsC4ayOg+L1++bIIkvV7a6f32tfPj6aefNs/Rmhq9RsVEX187Q7z++uvmvn536HG0vsuUfn60BkkXnQRaE7W11rJPnz4Jdk4QzMSCNsdodZr+ktHaDW2m0W69Y8eONQddM7m1yk1PUM3m1wuA/oLTk9yqHtRf+BrxatNEtWrVZNasWSa4idqTQLPWtYlDa1K0ak9P3tjQD5PWvGjPBD35NWDR6nPdv1bh6oVaf0lqBK0XZP2wRu3dofTir7ULehLpl7tG5HqiKf2g6od3yZIl0qRJE3Nx1/IlhmDGyvSPGqTobe1RoRd9fYweb61l0VoXPbFr1qwpV69eNe+jXng1eNFjoNXxWsWrTRr6XlnvX44cOcx7pj08tDZIf/EHBARIYmOdA5MmTXKu08BBvwj1l7Z+OW3cuNH0OIragyUmcXnP9BzVnnfa7ViPg9IvVD2G1kU4tueO1mLohVl7fGjTlJ63Wi49J6xzdu3atdKmTRsT8D6q2cYX6Bek1dTxqC9LpV+6Gki88847MmXKFBOs6HVC//6oNPi/du2aea+tMVX0/db3U5s49Jpi0VowbT7SY6n71nNFr6V6zdEvQu11qc/R66IeCw1ItTeQ1bSl7/fKlSvNMdT3W4+zXfjC+fHUU0+ZMmjPKP1xq++zjqGm76meN9r0qD0xtcZZa6f1/dVjoj1zrWBGv6P08Q0bNjTl0Pu6D+t6p8dIm6U08NWeTlquB2uL4oXXs3RsSHswDRw40PHMM8+YzH1NBtXeQIMHDzY9XqzEqmbNmpmk0gwZMpikqwsXLrjsR3vJ5MyZ0+yjd+/ejm7durkkAGvCnSaKaTa7HhpN8oop+VaTvqztFu1lY/U00Gz2ypUru2Swa+a5JjXq9kaNGjlmzpzpsl8tiybnaYJg9uzZHe3atXNcvHjR+XzNttckSu0pYiUL2p2VRKe9lKLSxDZdr8fYogmJEyZMMOs0gVTfI30ftceXlcRYokQJ8/5qEqQmwGlvjagJx9pLQ5Mlox5zO4sp0U/f09SpUzsTHK3keU1o1Pctf/78pqdEbJJoY3rPYpNIr/QxmtStvSusHoeawK/n14Mede5osqYms+p6Pbf1+VGT8bWXT9myZc1546uX08clZD6qN5P2RtHkT/379NjpdSOm46U9nqwka6W9a/Ra0aZNm2iv9+OPP5rEbP2c6DHRhHvt8aP0fddt+l7re66JqTt27HA+V3tPBQUFmWOr5fBlvnp+3Llzx3wXaa89fU39XmjZsqXplWQdO92H9uDNkSOH+Z5r3769c78HDhww1z69BurnQhPkv/jiC+f+NdFck5r1+fp36neYNyTTf+I/ZAIAAIgfJAADAABbI5gBAAC2RjADAABsjWAGAADYGsEMAACwNYIZAABgawQzAADA1ghmAACArRHMALA9HUJdpxIBkDQxAjAA29O5YXRmYZ0/CUDSQzADIMHoZJ7WDMAA4C6amQB4jc6mrDOS6+zLOsN0o0aNzAzVOmO7zsSeM2dOadeunVy8eNH5HJ2RWWf61ZoXnVF5/PjxZj+6j4c1M50+fdrMaK/71JnNX3nlFTPzr2Xo0KFSvnx5+eGHH8xzdWZfnflaXwuA/RDMAPCq77//3tTGbNiwQcaMGSP16tWTp59+WrZt2yZLly41QYcGH5Y+ffqYxy5evFiWL18u69atkx07djx0/5GRkSaQuXz5sqxZs8Y858SJE9K6dWuXxx0/flx++eUXWbJkiVn0sVoeAPaTMqELACBpKVq0qIwdO9bcHjlypAlkRo0a5dw+bdo0CQwMlCNHjpiaGA1+Zs+eLfXr1zfbp0+fLnny5Hno/leuXCl79+6VkJAQsx81c+ZMKVWqlGzdulUqVarkDHpmzJghGTJkMPe1Rkif+/HHH8fr3w/A8whmAHhVhQoVnLd3794twcHBpjnoQVpzcvPmTbl7965UrlzZuV6bhIoVK/bQ/R88eNAEMVYgo0qWLCmZMmUy26xgRpuXrEBGaeAUFhbmkb8RgHcRzADwKs19sVy/fl2aNm0qn3zySbTHaXBx7NixeCtHqlSpXO4nS5bM1NYAsB9yZgAkmGeeeUb2799vakmCgoJcFg16ChcubIIObR6yXL161TRBPUyJEiUkNDTULJYDBw7IlStXTA0NgMSHYAZAgunatatJ1G3btq0JWLRpadmyZfLmm2/K/fv3TTNQhw4dpF+/fqY5SgOfjh07SvLkyU1NSkyee+45KVOmjOkBpYnCW7Zskfbt20udOnWkYsWKXv8bAcQ/ghkACUYTebWnkgYuDRs2NEGIdrnW/BYNWNS4ceOkWrVq8uKLL5pApUaNGqb2JU2aNDHuU4OcX3/9VTJnziy1a9c2z9Eanrlz53r5rwPgLQyaB8BWIiIiJG/evPL555+bWhoAIAEYgE/buXOnHDp0yPRo0nyZ4cOHm/U6lgwAKIIZAD7vs88+k8OHD5vB9rRrtw6cpyMIA4CimQkAANgaCcAAAMDWCGYAAICtEcwAAABbI5gBAAC2RjADAABsjWAGAADYGsEMAACwNYIZAAAgdvb/AISBVf7XSJnXAAAAAElFTkSuQmCC", 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" ] @@ -467,7 +467,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 9, "id": "pursuant-surrey", "metadata": {}, "outputs": [ @@ -483,7 +483,7 @@ "Name: car_insurance_premiums, dtype: float64" ] }, - "execution_count": 85, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -500,7 +500,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 10, "id": "located-night", "metadata": {}, "outputs": [ @@ -510,13 +510,13 @@ "" ] }, - "execution_count": 87, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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VLVo0VOsHAIhRM2dOsSVLFtkVV1xlnTt3y+vVQQwJKhy1b98+9GsCAMB/paam2uLFi8zny3QB6eabO1mBAnQaQZiFI1WVdejQwcqWLfuH1WZcWw0AkBvp6WkuGElmZqZ7TDhCWIajZs2aEY4AAEBUSwzmYrNceBYAAESr8LuuBQAAQKQ1yD569KjNnj3bPvnkE0tJScmxWm3mzJmhWD8AAIDwD0eDBw+2efPmWZUqVdyFZnMa7wgIN7pOm27hJjEx/pjH2aflNV3fjmvcAYgVQYWjd999111DjUbXiBQKRSVKFHIXAg43+fJlDR0lShS2IkUKWzjJyMi05ORDBCQAMSGocBQfH2916tQJ/doAf2I4UjAa/9IqdyHgcJKediTL48ETl1piviQLFxXKFLf7bmnitiHhCEAsCCoctW3b1lWrNWrUyAUlIFIoGH23Y7+Fk8z01CyPf9iZbPGJjOcCABEVjnr16uUC0hVXXGHVq1e3ggULHtMgW5cYAQAAiIlwNHLkSNu+fbsLRZs2bTpmfk4XpQUAAIjacLRw4UK7/fbbrX///lSrAQCAqBJUssnIyLAWLVoQjAAAQNQJKt20atXK3nnnndCvDQAAQCRWq9WqVcu1O9I11tSlv3DhrGOycOFZAAAQU+Fo4MCB7u/GjRvdLTvCEQAAiKlwpBKjUyk9Pd3Gjx9vCxYssOTkZLvgggvswQcftNq1a7v5W7ZssaFDh9oXX3xhpUqVco3Fb7vttsDzMzMz7fnnn7dXX33VfvvtN6tfv7498cQTdtZZZ53S9wEAAMJfrltUK2xs27bNXYxWDbX/DP/4xz9csBkyZIgLSJUqVbJu3brZnj17bP/+/XbHHXfY2Wefba+99porsVKVn+77TZgwwebMmeOeP3fuXBeW9HytMwAAQEjC0dq1a61Dhw7WoEEDu+aaa+zrr7+2vn372vDhwy3Uli1bZldffbVdcsklds4559jDDz/sQpmq9F555RXLly+fuxhu5cqV7frrr3clR5MnT3bPVQCaNm2a9ezZ05o3b27VqlWz0aNH265du2zp0qUhX1cAABCD4Wj16tXWtWtXS0pKsn79+pnP95/rLSl4vPDCCzZ9+vSQrmTp0qXtvffes59++smVTr388suWP39+9/9t2LDBBbTExP+vIdRlTb777jvbu3evqwI8ePCgNW7cODC/WLFirmpu/fr1IV1PAAAQo+FozJgxdtlll9msWbOsc+fOgXDUvXt3V12lKrBQeuyxx1zpkP7PGjVquJKfsWPHuqo0lQCVK1cuy/JlypRxf3/++Wc3X8qXL3/MMv55AAAAuWqQrQbQ/t5o2S8V0qRJE5s5c6aF0jfffGNFixZ1jbLLli3rwpdKrGbPnm1HjhxxpUheBQr856KdqampdvjwYXc/p2UOHMjd1dkTE0+cLXUVeOROqLYh+yL32BfhJdq3Y/bzqx7/0Tk30kX7Po2kbRhUOFJQ+eWXX3Kcp9IazQ8VvZ7aMs2YMcPq1avnpqn0SIFp3Lhxrmove8NqhSIpVKiQmy9axn/fv0z2C+b+L+Lj46xkyazjOyH0ihULfh8htNgX4SXa90e+fP+pkfArUaKwFSnCORen5rgIKhypektVW+edd55ru+MvQVI11cSJE13D51D57LPPLC0tzQWi7ANRfvDBB3bGGWe4Xmte/scqZdIwAP5pqobzLlO1atWg1ysz02cpKYf+MMFG+wnsz5aSctgyMjJz/Trsi9xjX0Tn/ghXaivqlZx80NLSovui5hwbf/5xoe17MqVLQYUjleQotNx444122mmnuWl9+vRx4Uhte3Q/VPztib766iurWbNmYPq///1vq1ixogtJ6p6vhtoJCQlu3po1a1x3fzXkVilWkSJFXO86fzhKSUmxzZs3W8eOHXO1bunp0XtiChf6kLOdwwP7IrxE+/7I/t70OJrfL8LruAgqHBUvXty1+9GYQwoiGphRIaRTp07Wvn37XFVXZadAVLduXXvooYfsySefdGFJ/696zL300kt25pln2pQpU1yjbTUG37Rpk6uCGzRoUKCtkUKQxj7SAJEVKlSwESNGuNdp3bp1yNYTAABEh6DC0eOPP2433HCDKznS7c8UHx/vBoFUD7lHHnnENaJWdZ4CkEqNROFII2S3a9fOTj/9dOvfv7+776cxjlS9NmDAANeAWyNkT5061fWAAwAAyHU4Wrhwof31r3+1U0UlVSo10u14pUsa++h4VN2my43oBgAAcCJB9XmrU6eOa8MDAAAQbYIqOVIvL1VLLV682I1SrS7zXuq5NmzYsFCtIwAAQHiHo3fffdeNMK0u9p9//vkx87MPDAkAABDV4WjFihWhXxMAAIBIDUfeQbo2btzoepBpTCH1HvOOQg0AABAT4UgXmh01apS7hpr30h0a30jXXNN4QwhvcfEaMFPVnxqiP+6/jwHEGl0KSbdwEynXVtPVEnRDdAkqHGncITXI1uCKGkhRpUa//vqra6Cty4oUK1bsTx//CLkTF59ohcqcb4f2bHF/9RhAbFEoKlGiUFhe8DRSrq2mEZmTkw8RkKJMUN+IGh377rvvtgceeCAwTZfr0IVh1XNt+vTphKMIUPSsRu4GIHbDkYLR+JdW2Y49ByycpKcdyfJ48MSllpgvvJptVChT3O67pYnbjoSj6BJUONq/f7+7pEdOGjZsaC+88EJu1wsAcIooGH23Y7+Fk8z01CyPf9iZbPGJBfJsfRBbgipLbdSokRslOyfvv//+cYMTAABAVJYcXXvtte7Crl27dnX3y5Yt60qTli1b5todqbpNF4f1a9u2bSjXGYgqNI4HEM7iYvAcFVQ46tOnj/u7atUqd8tOPdm8A0ISjoDjo3E8gHAWF4PnqKDe4fLly0O/JkAMo3E8gHBWNMbOUUGFowoVKpzUcpmZmdaqVSubOHGiValSJZj/CgAA4JT6Uwe30GCRO3bsyDJQJAAAQDgLv5G/AAAA8hDhCAAAwINwBAAA4EE4AgAA8CAcAQAAeBCOAAAAPAhHAAAApyoc6dIh9evXt8KFC/+Z/w0AAEDIBH2BFA3sOG/ePPvoo4/sl19+sWHDhtm6deusevXqVrNmTbdMfHy8zZo1K3RrCwAAEI4lR/v27bPrr7/ehg4dat9//71t2rTJjhw5Yv/617+sU6dO9umnn4Z+TQEAAMI1HD377LN28OBBW7Rokc2fP99dJkTGjh1rNWrUcH8BAABiJhy999579sADD9g555zj2hX5FShQwLp06WJffvllKNcRAAAgvMNRamqqlShRIsd5CQkJlpaWltv1AgAAiJxwpKqzOXPm5DjvzTfftAsvvDC36wUAABA5vdVUpXb77bfbddddZ5deeqmrWnvrrbds3LhxtnLlSpsyZUro1xQAACBcS47q1atn06dPt4IFC7ogpAbZM2bMcF36J02aZI0aNQr9mgIAAITzOEca3HHu3LmuC/+BAwesSJEiDPYIAABid4TsyZMn21133WVJSUlWtmxZ++KLL+ySSy6x2bNnh3YNAQAAwj0cTZs2zcaMGWMVK1YMTDv77LPtyiuvtOHDh9urr74aynUEAAAI72o1Vaf16tXLlRz5lS9f3gYMGGCnnXaaa3/UoUOHUK4nAABA+JYc7d6923Xnz0mtWrXsp59+yu16AQAARE44qlChgq1evTrHeevXr7dy5crldr0AAAAip1rtxhtvtBEjRriRsC+//HIrXbq0uxitLiuiLv59+/YN/ZoCAACEazjSAJCqWps1a5ZrX+S9dEjnzp3tjjvuCOU6AgAAhP84Rw899JDde++99umnn7pxjooVK2Y1a9a0kiVLhnYNAQAAIiEcSdGiRa1Zs2ahWxsAAMwsLj5B/5qZz/39z2MgjMORRsX+xz/+4doYHT582DIzM7PM17XWli1bFqp1BADEmLj4RCtU5nw7tGeL+6vHwKkS1Kdt6NChNm/ePGvQoIGdf/75Fh8f9EDbAADkqOhZjdwNiIhwtHTpUuvdu3eWQSABAACiQVBFPurCr8bXp9KCBQvsqquucoNPtmnTxt55553APA06effdd9tFF13kru+mS5tkZGRkef6LL75ol112mVvvW2+91TZv3nxK1x8AAERxOFIA+eCDD+xUeeONN+yxxx6zv/3tb/b222/b1VdfbX369HE95RTUunbtGrisycCBA+2ll16y8ePHB54/f/58e/bZZ+2BBx6w119/3c4880w33IDGZgIAAMh1tZpKcJ588kkXLnS5kIIFCx6zTNu2bS0UfD6f/f3vf7fbbrvNhSO55557bMOGDbZu3TrbsWOH7dy501555RUrXry4nXfeefbrr7+6MNS9e3fLnz+/TZw40Tp27GjXXnute/6wYcPc4JW6QK5KnAAAAHIVjnTRWX9Vl27ZqbdaqMLR9u3bXQC65pprskyfOnWq+6uSourVq7tg5NeoUSP7/fffbcuWLa6U6LvvvrPGjRsH5icmJlq9evXcpU4IRwAAINfhaPny5XaqKBzJoUOHXPWZ2gop8Kj0qGXLlrZr165jruVWpkwZ9/fnn392QUjKly9/zDJbt27N1bolJp64VjIhgV58uRWqbci+yD32RXgJxXZkX4QGx0b4CNU2TAz2wrN/VBUWKioB8o/I3aNHD+vXr58tWbLEjc6t67hpzCWNzu1VoEAB9zc1NdWNwySqXsu+jOYHKz4+zkqWLBz083FyihU7tsoWeYN9EV7YH+GDfRF9+yLoUbUWLVrk2vwcPXo0EIb0VyU8GzduDFmD7Xz58rm/KjVq166du6+xlVSCpHCUlJTk1sHLH3oKFSrk5ktOy+TUVupkZWb6LCXl0B8mWA6a3ElJOWwZGVkHGQ0G+yL32BfRtz/YF6HBsRE5+0Lb92RKl4IKR88//7y76fIh6enpLsCo+koNtDUgZIcOHSxUypYt6/6qobXXX/7yF/vXv/7lBqL897//nWXenj17As/1V6dpWuXKlbMs43/tYKWn5/5gwInpQ852Dg/si/DC/ggf7Ivo2xdBVc6pa7waXKvk6Pbbb7cWLVrYRx995EbNLlGihFWpUsVCRY2tCxcubJ999lmW6QpEZ599ttWvX9+VIvmr32TNmjXuOdWqVbPSpUtbpUqVbO3atYH5CnTq7abnAgAA5Doc7d692/UeU680VXFpvCG58MILXfd5dZEPFVWLdevWzY1b9NZbb9kPP/zgruu2atUqN1aRuuSffvrprgedGljrmm6jRo2yLl26BNoZ6b6q4BTqvvnmG3v00UddW6UbbrghZOsJAACiQ1DVamrLo2Ak55xzjhuhWmFDQUZhSY9DSY2v1T5o9OjRLpipemzcuHHWsGFDN3/KlCk2aNAgu/HGG12Xfo2Aref4afpvv/3mRs5OTk52IU5hqVSpUiFdTwAAEKPhSJfw0PhGF198sauySkhIsNWrV7vqtW3bth3TMywUVEqkW04U0KZNm3bC56tBt38kbQAAgJCGI1WdKaikpKS40ac18rS62qskZ+XKla6qCwAAIGbCkRoyq/H1V1995R4/8cQTrpfaJ598YldeeaU9/PDDoV5PAACA8A1HEyZMsCuuuMKuu+66wICKQ4YMCfW6AQAAREZvtUmTJoW80TUAAEDEhiMNwOi/5hkAAIDFerWaeqVpLKEPP/zQqlat6rr2e6mb/3333ReqdQQAADhlgr58iGggRt2yIxwBAICYCkcaiRoAACAaBdXmCAAAIFoFVXL0yCOP/OEyTz/9dDAvDQAAEHnhyHuFe79Dhw6565aVKFHCXV4EAAAgZsLRihUrcpyu66r16NHD2rZtm9v1AgAAiPw2R5UrV7b7778/0JsNAADAYr1BdpEiRWzHjh2hflkAAIDwrVbbuXPnMdMyMjJs9+7dNnbsWFeCBAAAEDPhqGXLlm6gx+x8Pp8lJSVRrQYAAGIrHA0bNuyYcKTHqlJr2LChFS1aNFTrBwAAEP7hqH379qFfEwAAgEhukD1//nx7//33A5cTueaaa+yiiy6yRx991I4ePRrKdQQAAAjvcDRt2jQXgjZv3uweDxw40Pbv328dOnSwZcuWuUbZAAAAMROOXn31VevWrZvdc8899tNPP9nGjRvt3nvvdZcV6du3r7399tuhX1MAAIBwDUcKRM2aNXP3VbWmxtjqwSbnnnuu/frrr6FdSwAAgHAOR6VKlbK9e/cGwpECUbly5dzjr776yk477bTQriUAAEA491Zr0aKFPffcc7Z69Wr74IMPrHfv3m769OnTbfz48fRmAwAAsVVypLZFF198sa1fv95uvvlm69Kli5s+d+5cu/TSS61Xr16hXk8AAIDwLTkqUKCADR48+JjpCxcudPMAAABiKhz5LxWyZcsWO3TokLufXf369XO7bgAAAJERjjZt2mQPPPCA7dq165h5CkrqvabgBAAAEBPh6Omnn7bExET3V73U4uODHmgbAAAg8sPRl19+aaNGjbLLL7889GsEAACQh4Iq8ildurQlJCSEfm0AAAAiMRzdeuutNmnSJNcYGwAAwGK9Wu3777+3bdu2WZMmTaxKlSqWlJSUZb4aZM+cOTNU6wgAABD+4ahatWqBx9m78ufUtR8AACBqw9GsWbNCvyYAAABhgD74AAAAwZQcnX/++fbyyy9bzZo1XZWa2hUdj+Zt3rz5ZF8aAAAg8sLRfffdZ2XLlg3cP1E4AgAAiPpw1KNHj8D9+++//6Seo4bZ48ePt5tuuslOP/304NYQAAAgWtocZWZmunC0Z8+eP/O/AQAAiJwG2XTrBwAAkYTeagAAAB6EIwAAAA/CEQAAQCSHo+3bt1udOnXs9ddfD0zbsmWLdezY0WrXrm0tW7a0F1544ZiG4WPHjrWmTZu6Ze6880778ccf82DtAQBAuIuocJSWlmb9+vWzQ4cOBabt37/f7rjjDjv77LPttddec2MwjRw50t33mzBhgs2ZM8eGDBlic+fOdWGpW7dudvTo0Tx6JwAAIFxFVDgaN26cFSlSJMu0V155xfLly2eDBw+2ypUr2/XXX2+33367TZ482c1XAJo2bZr17NnTmjdv7kb3Hj16tO3atcuWLl2aR+8EAABEVTjatm2bnWrr1693ly8ZPnx4lukbNmywBg0aWGLi/49n2ahRI/vuu+9s7969tnXrVjt48KA1btw4ML9YsWJ2wQUXuNcEAAAIaoRsr1tvvdUeeeQRa9u27QmXS0hIsOXLl1uZMmUsN1JSUqx///42YMAAK1++fJZ5KgE677zzskzz/38///yzmy/Zn6dl/POClZh44myZkBBRBXNhKVTbkH2Re+yL8BKK7ci+CA2OjfARqm0YVDhSNVbJkiVPatkKFSpYbg0cONA1wr7mmmuOmXfkyBHLnz9/lmkFChRwf1NTU+3w4cPufk7LHDhwIOh1io+Ps5IlCwf9fJycYsUK5vUq4L/YF+GF/RE+2BfRty+CCkcPPPCAPfvss/bbb7+5NjyFChU6ZpkzzjgjFOtnCxYscFVnb775Zo7zk5KSjmlYrVAkWi/NFy3jv+9fpmDB4DdiZqbPUlL+v2H48RIsB03upKQctoyMzFy/Dvsi99gX0bc/2BehwbEROftC2/dkSpcSgy3JycjIsAcffPC4y6h7fSio19mvv/7qGlN7Pfnkk7Zo0SIrV67cMddu8z8uW7aspaenB6apR5t3mapVq+Zq3dLTc38w4MT0IWc7hwf2RXhhf4QP9kX07YugwtFTTz1lp4q65avqzKt169au99m1115rb7zxhuuer7CmNk6yZs0aq1SpkpUuXdqKFi3qeritXbs2EI7Uhmnz5s1ubCQAAIBch6N27drZqaLSn5wo+Gieuu5PmTLFHnvsMTd20aZNm2zGjBk2aNCgQFsjhSCFrFKlSrk2UCNGjHAlTgpZAAAAuQ5Hsnv3bvv444+ztPfR4IpqAK02QhpL6FRQSFI4Gjp0qAttp59+uuvZ5g1wKmVS9Zp6u6kUqn79+jZ16lTXsBwAACDX4Wjx4sVupGoFjri4ODfN5/MF7p977rn2Z/rqq6+yPK5Zs6YbA+l4VN2m9lEnaiMFAAAgQQ0IMHHiRKtevbq7vln79u3tuuuus7ffftuFDwWRRx99lK0LAABip+RIF3997rnn3CjTDRs2dJfn0KU7dNOo1ApPTZo0Cf3aAgAAhGPJUXx8vBUvXtzdP+ecc+zbb7917Y2kWbNm9s0334R2LQEAAMI5HKlN0SeffBK4r0bZuoaZv5s8V7sHAAAxVa128803u0EYDx06ZL1793YXetW11m644QabPXu2a48EAAAQMyVHHTp0cOMK+UuIBg8e7ILSsGHD3GCMmgcAABCJgr587cGDB23btm3uvkaeVkBSd/7OnTvn+rIcAAAAERWO1DttzJgxVrFixcA03ddI1M8884y9+uqroVxHAACA8G5zpGuZ9erVy+66667AtPLly7sRqE877TR3+Q5VvQEAAMREyZEuHVKjRo0c59WqVct++umn3K4XAABA5IQjXbx19erVOc5bv369u6grAABAzFSr3Xjjje7K9mlpaXb55Ze7i7/u27fP3nvvPZs+fbr17ds39GsKAAAQruHo9ttvd1Vrs2bNcu2L/HRdNfVWu+OOO0K5jgAAAOEdjuShhx6ye++91zZu3GjJyclWrFgxq1mzppUsWTK0awgAABAJ4UiKFi1qTZs2Dd3aAAAAROogkAAAANGIcAQAAOBBOAIAAPAgHAEAAHgQjgAAADwIRwAAAB6EIwAAAA/CEQAAgAfhCAAAwINwBAAA4EE4AgAA8CAcAQAAeBCOAAAAPAhHAAAAHoQjAAAAD8IRAACAB+EIAADAg3AEAADgQTgCAADwIBwBAAB4EI4AAAA8CEcAAAAehCMAAAAPwhEAAIAH4QgAAMCDcAQAAOBBOAIAAPAgHAEAAERaOEpOTrYnnnjCmjVrZhdddJHdcssttmHDhsD81atXW/v27a1WrVp25ZVX2ttvv53l+ampqTZo0CBr3Lix1alTx/r27Wv79u3Lg3cCAADCXUSEoz59+tinn35qo0aNstdee83OP/9869q1q3377be2bds2u/vuu61p06b2+uuvW4cOHax///4uMPkNHDjQVq5caePGjbOZM2e65/Xs2TNP3xMAAAhPiRbmvv/+e1u1apXNmTPH6tat66Y9/vjj9uGHH9qbb75pv/76q1WtWtV69+7t5lWuXNk2b95sU6ZMcSVFu3fvtgULFtjEiROtXr16bhmFLJUwKXCpJAkAACBiSo5KlixpkydPtho1agSmxcXFuVtKSoqrXlMI8mrUqJF9/PHH5vP53F//NL9KlSpZ2bJlbf369afwnQAAgEgQ9uGoWLFidumll1r+/PkD05YsWeJKlFSVtmvXLitXrlyW55QpU8YOHz5s+/fvdyVHClgFChQ4Zhk9FwAAIKKq1bL75JNP7JFHHrHWrVtb8+bN7ciRI1mCk/gfHz161IWk7PNFYUkNtXMjMfHE2TIhIeyzZ9gL1TZkX+Qe+yK8hGI7si9Cg2MjfIRqG0ZUOFq2bJn169fP9VgbOXJkIOQoBHn5HxcsWNCSkpKOmS8KRpofrPj4OCtZsnDQz8fJKVYs+H2E0GJfhBf2R/hgX0TfvoiYcDR79mwbOnSoa0j9zDPPBEqDypcvb3v27MmyrB4XKlTIihYt6qrcNBSAApK3BEnLqN1RsDIzfZaScugPEywHTe6kpBy2jIzMXL8O+yL32BfRtz/YF6HBsRE5+0Lb92RKlyIiHKmn2pAhQ6xTp0722GOPucbYfuqBtm7duizLr1mzxpUuxcfHux5umZmZrmG2v+H29u3bXVuk+vXr52q90tNzfzDgxPQhZzuHB/ZFeGF/hA/2RfTti7Cv4FSQGTZsmLVq1cqNZ7R371775Zdf3O23335zgWnTpk2umk1jHk2bNs0WL15s3bp1c89X6VCbNm1swIABtnbtWresxk1q0KCB1a5dO6/fHgAACDNhX3KknmlpaWn27rvvuptXu3btbPjw4TZhwgQbMWKEG+DxzDPPdPe93ftV6qSA1aNHD/dYI20rLAEAAERcOOrevbu7nYjCjm7Ho/ZHTz31lLsBAABEdLUaAADAqUQ4AgAA8CAcAQAAeBCOAAAAPAhHAAAAHoQjAAAAD8IRAACAB+EIAADAg3AEAADgQTgCAADwIBwBAAB4EI4AAAA8CEcAAAAehCMAAAAPwhEAAIAH4QgAAMCDcAQAAOBBOAIAAPAgHAEAAHgQjgAAADwIRwAAAB6EIwAAAA/CEQAAgAfhCAAAwINwBAAA4EE4AgAA8CAcAQAAeBCOAAAAPAhHAAAAHoQjAAAAD8IRAACAB+EIAADAg3AEAADgQTgCAADwIBwBAAB4EI4AAAA8CEcAAAAehCMAAAAPwhEAAIAH4QgAAMCDcAQAAOBBOAIAAPAgHAEAAHgQjgAAAGIxHGVmZtrYsWOtadOmVrt2bbvzzjvtxx9/zOvVAgAAYSZmwtGECRNszpw5NmTIEJs7d64LS926dbOjR4/m9aoBAIAwEhPhSAFo2rRp1rNnT2vevLlVq1bNRo8ebbt27bKlS5fm9eoBAIAwEhPhaOvWrXbw4EFr3LhxYFqxYsXsggsusPXr1+fpugEAgPAS5/P5fBblVDp0//3322effWZJSUmB6Q888IAdOXLEJk2a9D+/pjZbZuaJN11cnFl8fLwd+P2IZWRkBrXusSohId6KF0ly1Z+h+ISyL4LHvoje/cG+yB2OjcjbF/HxcRanDf0HEi0GHD582P3Nnz9/lukFChSwAwcOBPWa2rgJCX+8gUU7DMHRiSKU2BfBY19E7/5gX+QOx0b07YuYqFbzlxZlb3ydmppqBQsWzKO1AgAA4SgmwlH58uXd3z179mSZrsdly5bNo7UCAADhKCbCkXqnFSlSxNauXRuYlpKSYps3b7b69evn6boBAIDwEhNtjtTWqGPHjjZy5EgrVaqUVahQwUaMGGHlypWz1q1b5/XqAQCAMBIT4Ug0xlF6eroNGDDA9VBTidHUqVMtX758eb1qAAAgjMREV34AAICTFRNtjgAAAE4W4QgAAMCDcAQAAOBBOAIAAPAgHAEAAHgQjgAAADwIRwAAAB6Eozy2cOFCu/HGG6127dpWp04du/76623u3Lkh/T/2799vr776auBxp06d7OGHH7a8pOG15s+fb7/++qtFgx49eliHDh2Oma59W7VqVVu3bt0x+12Xtcnt+09LS7MZM2ZYNGnZsqW7/f7778fM0+dWn9/ceO+99+ybb75x93VJIe2fn376ycJBpBwX2j/abtOnT89x/hNPPOHmjxs3zl5//XV3/0Q0X8vlhY8//tg2bNhgkSAWj42dO3fa22+/fcr/X8JRHpo3b549+eST7gtUJ8TXXnvN2rZta0899ZQ9//zzIft/nn32WfdlHE7Wr1/vDubDhw9bNGjcuLFt2bLFjb7ul5ycbJ9//rm78PGHH36YZXmdjBWOSpcunav/96233rKnn37aos2OHTvc5/bPeN3u3buHbfiIpONCVxdYsmTJMdN1JYKlS5daXFyce3zVVVfZypUrLVzdeuut9sMPP1ikiLVj46GHHjrm/HkqEI7y0Jw5c1xJ0Q033GCVKlWyc8891yX/22+/3V544YWQ/T/hOAh6OK5TbjRq1MiV4igM+X300Ucu/Ggf5xSOLr744lz/v9G2Hf3OOusse/nll902DKVw317hvn7ZfxBs3LjRdu3alWX6mjVrrFChQu5HgSQlJdnpp5+eR2sZfWL12DjVCEd5KD4+3j799FM7cOBAlul33XWX+/CLSiLGjBljl112mdWoUcOuu+66LL/Wciqy9k7Tr1CVSqlax7vcwYMH7ZFHHrF69epZ3bp13XKHDh0KzN+2bZvdeeedrqrvkksusb59+9ovv/wSmK911nXqmjZtatWrV3cnSj32/uLVtesuv/xyu/DCC11R8Pjx490BqOLa2267zS2j95VXxemhVLlyZStbtqx98skngWkKRNp2um3dutX27t3rpu/bt89tX00/evSouwiytqO2tUoRvb+yMzIy3PxLL73Ubccrr7zSXnrpJTdP2037ULRvtV2jxbXXXus+U4899liOVQj+krlBgwa5bVOzZk27+eabs2wDVenogtO9e/e2iy66yP0q1udN9PnTfL/333/frr76areN27RpY//617/c9OHDh9s111yT5XN//vnn2+DBgwPTVqxY4fZdamqq+3z/85//dP9PrVq13PGavdQ2Wo4LbfMzzjjDFi9enGX6okWL7K9//Wug5Cj7OUph6p577nHbrFmzZvbmm29m2ZYqUdUx4qfSdO0b7z644IILAlVhqgpq3769W59WrVq586WOK+++1XztD32mdK7zn3P966XjKK+bGkTasSEn83lftmyZa3KgpiP6DtO+8P5Y/O6776xr167ue0ifCd3/6quv3DwVFui7S99hOlZOKV1bDXnjnXfe8VWrVs1Xs2ZN35133umbNGmS77PPPvNlZmYGlrnnnnt8l156qe+9997zffvtt76xY8f6qlat6nv33Xfd/Ndee8133nnnZXld77SUlBTfAw884Lvpppt8e/bscdM6duzo5o8aNcr3/fff+5YtW+arUaOGb8yYMW7+rl27fA0aNPANGTLE98033/g+//xz31133eVr0aKF7+DBg26Z7t27+9q1a+fbuHGj78cff/S98cYbvurVq/umT5/u5i9fvtxXv35938qVK307duzwvf32227+ggULfKmpqb4lS5a4ddD7PXz4sC8a9O/f33f33XcHHl9yySW+hQsX+tLT03316tXzzZ8/301funSp2+dHjhzx9enTx3fdddf51qxZ49u+fbtv2rRpbjtpf8sLL7zga9mype/jjz/2/fTTT75Zs2a57bZ+/Xq33WbMmOEea99qu0YDfc70Odf7rVOnjm/AgAGBeQ899JD7/Gqb6vN39dVX+9auXev7+uuvfY8//rjbdvpMiV5D2+app57y/fDDD75t27a5eZqmz9/vv//utrset27d2t3XMXbvvff6atWqlWW+/9jRMavj76qrrgqsk/7fHj16uPvPPfecW3/tPx1b8+bNc+9h9uzZUXVc+PfRs88+684tfnoPen9ffvllYBnv+SgtLc3Xpk0b95wvvvjC98knn7jPv+ZrOb1nHRvaLvLrr7+6c6Tm792710176623fI0aNfJlZGT43n//fbf8Sy+95Lb3hx9+6PZlz549A8+/8MIL3fbX52nDhg3ueHr00UfdfO1XvbaOI50rw104HRsn83nXd4f2n74X9P9s3rzZ17VrV7f//Ocrresjjzzizn9a127duvkuv/xyN2///v3us6LvMO3LUynx1EYxeKkUoFy5cq4KbdWqVS6hS8WKFW3YsGFWokQJW758uU2cONGaN2/u5t1///2uFELT9OvzjxQtWtQVa6t9gLdoW78m9KtBzj77bGvSpIl98cUX7rFKJrReKgny068xVR3pV6KSv5avX79+4JfXmWeeabNnz7Z///vf7rHq8PPnz28VKlRwvy51K1OmjPur6cWLF3fLlSpVyq1fNNCvObX/0a8p/fJRSZu2U0JCgpunX0v6Fax2JSqx0y9otRlasGCBK42QO+64w+1flS5on2s7qopC21fbT7/2VP2qalhtN+1ficZqC3121N5AjXuvuOIKV9Lmp9K1L7/80pU6nHfeeW6afimrWlPb7u9//3tg2Z49ewa2k79xqT5/hQsXDizz6KOPWsOGDd39++67z/3aVemefs1qWR2f2neqytCvZM1XSeBpp51mH3zwgfXq1cuVvKpx/KhRowLHq44tteXQOv3tb3+LuuNCJUR6b7t373Ylp9pOWneV7ORk9erV9vXXX9u7777rto3omNG2Fb1nHSvav2qrpO2t19KxpJIPTVPJhbavSt51HlRpq0pGRK+pz0Hnzp3dvv7tt99cKZK2r7a5bnqOSmS9x40+H/7PSCQIh2PjL3/5yx9+3nXue/zxx127Lj+VTKlWQm2bVPWqY0JNDPSe9D2l775vv/3WMjMz3Xegpulzoc/VqUQ4ymMqatRNHwR9KSogKWTow6OG2aITtJdCiT6QuaEA5qUDQh9q2bx5szuBqYjTS9UGOihEH3YVgau4U8Wi6uGgg0tf3P6iXzUw14Grg0gfft3XSSpa6aSu4mwd2DpB6aTuP6AVkvyN7FUdoOJpbWfxnjhEbZeKFSvm7usEo5ORiscVoPQ6em5uG3JHiptuuslVIyuoK0j6KYTrpO4/+YuqcRQ6vdWS2k4n86WnsOnn3/aq0k5MTHRVnvqS1he4vvz1hfTZZ5+5L2tVp+7Zs8ftHx0DOkZUBa0vbm8DZX1B6/Wi7bhQVYvawGgf6UtPVWr6fB6P9pvONf5gJPpce4Ogqk8mTJjg7mt767jSuUltmfSDUj8y/OdGHUObNm1ynVuyt53RuUr7RVVCqjZSENLxoy9yVb9Furw+Nr45ic+79q329+TJk9158fvvv3ffc+IPqPqRrkCkNrgNGjRwx5v2mfc18wLhKI+o1GDSpEl29913u1IafRD0ZaqbSoS8dezZ6eDXSft4/B+6E1GiPx4FNZUSqSdddjqYNF/rrQCl9dSvObU70i8EP4WCN954w7Wp0glOB6VKyFTypW7v0Ui/nHUi8b9n76853deXqn7R6eTg/3UkL774YpZfauI/MSjEqueP6t31mvrVrDp+/dpu166dxQJ9Eardj7dX3vEaj2Y/Nk629CWnE7H//1BJkfaXfuGqhEQ/TvRLWuFIPwj046VkyZKBHk8qZfX/SPBSyZDWJ9qOC5UeqURZX9Yq6fYOG5KdvqR1/sjOu88UXnSsKNyopGno0KGB0giFUrVrVMgRvVa3bt1yPBb8pULPPfecK/FQCZ9C7oMPPuj22cyZMy3S5eWx4fvv/3Oiz7vOW2pDpH2qba511f7T/vDTD0CFXhUMaH+PHTvW/vGPf7gSdZXM5hUaZOcRfXB0Esmpi70/nau43T8Oh5dKHvSrU1TkKN6GeSrJ8fI3jDxZVapUcScmFXmec8457qb0ry8I/SpRl3WdaFQ8269fP/drWL8E9eXgP2D0vlQ9pwNCRbevvPKKa5SnX5bBrFOkUEmAGmXry89/AhcVGSvoKAgpOKo6UttZVGXg3866qQGrvzGuvjgVjvRa/fv3d0Xl+iUd7dvRS6Uqaiyr0gF/I1xtP1WZ+KtxRZ89HSv+YyMnwWwv/ZJViaD2hRqdqppT+1klGWoM7G/Iqi8IffloXBbv/tRJX1/s+pKJxuNC4UifeZWIqRRJpWnHo5IE7Tf9sPKer7znL533VCKl7aSqF20rfea1nDqqaNsXLFjQLatjaPv27Vm2t354qqu7Op0oTOm8pX2jXsAqwdBj7btw67IeacfGuSfxeZ82bZr7IaEG3tr+Oo/9/PPPgXXSPlDnBpWWq7mGOp/oGNE5MfvYcKca4SiP6AtSv3gUMEaPHu0Cx48//uhOtvoFqQ+UikFbtGjh6otVYqCTgKpm9OusS5cu7nVUJacPtT58+hX7zjvvuKouL53MVfSv1z8ZqubRwaXgo1IO3VT0qTprFdUqzeug0P+l19R0tbnQB9rfS0TFrc8884xL/1ovHbhqa+OvqtM6iV5bJ7FooZO4tov2iXqBZP+S1Twto/k6sWv/qoROVZTalioVUomiv9pBvXZ08tA+169nVSnos5J9O6q9mHeMpWijAKHSN/9nWPf1RasifZ1EFea1nfSFoPYmx+PfXlpOn/GTodJSHYv6Yta+E/1VFYG+fP3hSMup7YuOaZUOaV31paUTvv+HTjQeF9oP+lJUCc2JqtRE5zUFTAV9DQOgc4fuZy+dUNWatrfObyrhUOhSuzttV//2FjU/UNWSzos6P6rkQT3PtG9VclSkSBFXXaN9oP2l/a4gqh8qKu3zb3N9fjRYbiTKq2Oj6El83vUDW+0v9TnX510B2t/mSd8V+tGt7zZVDfq/AzUIsn70KyCLStV17ss+ZMSf7pQ2/8Yx1IPplltu8dWtW9f1JmjVqpXrRebvFaa/6jV28cUXu14X7du3d72dvObOnet6DGh+586d3Wt6e7Bt2rTJ17RpU9erQz3R1KNBPRu8/D0d/NTbpEuXLr7atWu7dVNvOvUk8FMvLK2r/k/934MGDfINGzYs0MtAJk+eHFimSZMmvsGDB/sOHTrk5qmngl5T73nq1Km+aHHgwAHXO8Pba81PPTq0X/y91kTbY+jQoW77aDupF5R6fPipd496BDVv3txtK/1Vr0L1SJHk5GRfhw4d3LxFixb5ooG/R052O3fudJ9F/+dUvVfUQ1C9o9SD5m9/+5tv3bp1geX1Gnqt7NQzRttax5W/R456XPrpvqZpnp+/V6B6CfqpF496BHlpf40bN879v9onOh50HHh7oEbDcZF9H40ePfqY7ZhTbzXZt2+f66WpXk2NGzd221bnNy3nt2XLFvec559/PjBNPbN0bPl7rfnpc68eb9pm2p7qmaXj0G/FihXuvKlz2UUXXeR6XKlnlZ/WUefGnI7ZcBNux0baH3zeta/Vk1Prppt6nqk3nLa3/zyoHtH6zKuHtHpN33jjjb5Vq1ZlOW9qnt6L/7x3KsTpn1MbxwAAAMIX1WoAAAAehCMAAAAPwhEAAIAH4QgAAMCDcAQAAOBBOAIAAPAgHAEAAHgQjgAgB506dXI3ALGHQSABIAe66ric6HpUAKIT4QgAAMAj0fsAACKJLlB6+eWXu4tbfvrpp3bNNdfYgw8+aKNGjbJly5a5C2jqIpy6cLL/orGiq8Dryu3vvvuuu2Bv8+bN3QVRn376afda4q9SmzVrVuCisVOmTLE333zTXQhTF9W84YYb3AWk/RdO1XN00WBdiFUXPNVVx6tXr26PPvqo1axZM0+2EYD/HeEIQER78cUX7Y477nBXaNcVvHXl8b1797pApKuD60rgCjAKNv6AdO+997qrgGuZM844wwUZXVX+eFTA3r17d3cl+R49eli1atVs7dq1NmbMGHcl8SFDhgSW1VXiK1eu7K40ruc988wzdv/999uKFSssISHhlGwTALlDOAIQ0RRu+vXr5+6/8sortnXrVvdXJUHSrFkzV6IzcuRIF5RWr17tgs24ceOsdevWgWWuvvpq27ZtW47/xwcffGAfffSRK5Fq06aNm9akSRNLSkqyv//973bbbbdZlSpV3PT09HSbOnWqFSlSxD0+ePCgPfTQQy6MXXjhhadkmwDIHXqrAYhoqjbzU/A5/fTTXVWWQopuGRkZ1qJFC/viiy/swIEDtmbNGsuXL5+rjvNTtdhVV1113P9j3bp1lpiYaFdeeWWW6ddee21gvp8acPuDkZQtW9b9PXz4cIjeMYA/GyVHACJaoUKFAveTk5Ptl19+ceEoJ5q3f/9+K1GiRKCdkF/p0qWP+38oVJUsWfKYajEFMVHbJr+CBQtmWcb//2RmZv5P7wtA3iEcAYgaRYsWtYoVK7oqtJyceeaZriRHAUlhxRuQ1Hj6eIoXL+6eo1Iob0Das2eP+6vgBCB6UK0GIGo0aNDAfv75Z1cKVKNGjcBt1apVrkG2go2WUXWbGkj7qeG0ered6HX1nMWLF2eZvnDhQve3bt26f+K7AnCqUXIEIGq0b9/eZs+e7XqvqXeZuturIfU///lP69ixo2trVL9+fdeY+rHHHnO92tSge968ea4Lf1xcXI6vqwbbDRs2dD3Qdu/e7XqrqZ2RXrddu3YMFAlEGcIRgKhqf6Su/eqWP2LECNcWqEKFCta3b1/r0qVLYLnRo0fb8OHD3XIqEbrsssvslltusQULFuT4ugpNkyZNsrFjx9qMGTNs3759roquT58+LogBiC6MkA0gpmgAR41XpECkrvh+PXv2dGMWzZ8/P0/XD0Deo+QIQExRI+yHH37YhSONcK12SB9++KEtXbrUjZANAJQcAYg5Guto/PjxbmBGVatpRGtVj2kgSAAgHAEAAHjQlR8AAMCDcAQAAOBBOAIAAPAgHAEAAHgQjgAAADwIRwAAAB6EIwAAAA/CEQAAgAfhCAAAwP7f/wG06w4Vrc6yIwAAAABJRU5ErkJggg==", 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", "text/plain": [ "
" ] @@ -562,7 +562,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 24, "id": "096fe314-2953-4644-86e0-cd717f77eb8f", "metadata": {}, "outputs": [ @@ -644,112 +644,38 @@ "West 91.727273 " ] }, - "execution_count": 94, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "region = df.groupby(\"region\")\n", - "region[[\"percentage_drivers_fatal_not_distracted\", \"percentage_drivers_fatal_no_previous_accidents\"]].mean().sort_values(\"region\")" + "region_mean = region[[\"percentage_drivers_fatal_not_distracted\", \"percentage_drivers_fatal_no_previous_accidents\"]].mean().sort_values(\"region\")\n", + "region_mean" ] }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 25, "id": "3777e6d1-08a5-4747-9100-9af86f88e90a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 98, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Error in callback (for post_execute), with arguments args (),kwargs {}:\n" - ] - }, - { - "ename": "ConversionError", - "evalue": "Failed to convert value(s) to axis units: masked_array(data=[--, --, --, --, --, --, --, --, --, --, --, --, --, --,\n --, --, --, --, --, --, --, --, --, --, --, --, --, --,\n --, --, --, --, --, --, --, --, --, --, --, --, --, --,\n --, --, --, --, --, --, --, --, --],\n mask=[ True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True],\n fill_value=1e+20,\n dtype=float64)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/axis.py:1822\u001b[0m, in \u001b[0;36mAxis.convert_units\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 1821\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1822\u001b[0m ret \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[43mconvert\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munits\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1823\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/category.py:57\u001b[0m, in \u001b[0;36mStrCategoryConverter.convert\u001b[0;34m(value, unit, axis)\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[38;5;66;03m# force an update so it also does type checking\u001b[39;00m\n\u001b[0;32m---> 57\u001b[0m \u001b[43munit\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 58\u001b[0m s \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mvectorize(unit\u001b[38;5;241m.\u001b[39m_mapping\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__getitem__\u001b[39m, otypes\u001b[38;5;241m=\u001b[39m[\u001b[38;5;28mfloat\u001b[39m])(values)\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/category.py:217\u001b[0m, in \u001b[0;36mUnitData.update\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;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[1;32m 216\u001b[0m \u001b[38;5;66;03m# OrderedDict just iterates over unique values in data.\u001b[39;00m\n\u001b[0;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[1;32m 218\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m convertible:\n\u001b[1;32m 219\u001b[0m \u001b[38;5;66;03m# this will only be called so long as convertible is True.\u001b[39;00m\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/_api/__init__.py:92\u001b[0m, in \u001b[0;36mcheck_isinstance\u001b[0;34m(types, **kwargs)\u001b[0m\n\u001b[1;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[0;32m---> 92\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;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[1;32m 94\u001b[0m k,\n\u001b[1;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[1;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[1;32m 97\u001b[0m type_name(\u001b[38;5;28mtype\u001b[39m(v))))\n", - "\u001b[0;31mTypeError\u001b[0m: 'value' must be an instance of str or bytes, not a float", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mConversionError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/pyplot.py:279\u001b[0m, in \u001b[0;36m_draw_all_if_interactive\u001b[0;34m()\u001b[0m\n\u001b[1;32m 277\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_draw_all_if_interactive\u001b[39m() \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 278\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m matplotlib\u001b[38;5;241m.\u001b[39mis_interactive():\n\u001b[0;32m--> 279\u001b[0m \u001b[43mdraw_all\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/_pylab_helpers.py:131\u001b[0m, in \u001b[0;36mGcf.draw_all\u001b[0;34m(cls, force)\u001b[0m\n\u001b[1;32m 129\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m manager \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mget_all_fig_managers():\n\u001b[1;32m 130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m force \u001b[38;5;129;01mor\u001b[39;00m manager\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39mstale:\n\u001b[0;32m--> 131\u001b[0m \u001b[43mmanager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw_idle\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/backend_bases.py:1891\u001b[0m, in \u001b[0;36mFigureCanvasBase.draw_idle\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1889\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_idle_drawing:\n\u001b[1;32m 1890\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_idle_draw_cntx():\n\u001b[0;32m-> 1891\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\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[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/backends/backend_agg.py:382\u001b[0m, in \u001b[0;36mFigureCanvasAgg.draw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 379\u001b[0m \u001b[38;5;66;03m# Acquire a lock on the shared font cache.\u001b[39;00m\n\u001b[1;32m 380\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtoolbar\u001b[38;5;241m.\u001b[39m_wait_cursor_for_draw_cm() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtoolbar\n\u001b[1;32m 381\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m nullcontext()):\n\u001b[0;32m--> 382\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 383\u001b[0m \u001b[38;5;66;03m# A GUI class may be need to update a window using this draw, so\u001b[39;00m\n\u001b[1;32m 384\u001b[0m \u001b[38;5;66;03m# don't forget to call the superclass.\u001b[39;00m\n\u001b[1;32m 385\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mdraw()\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/artist.py:94\u001b[0m, in \u001b[0;36m_finalize_rasterization..draw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m 92\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[1;32m 93\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mdraw_wrapper\u001b[39m(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 94\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\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\u001b[1;32m 95\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[1;32m 96\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/artist.py:71\u001b[0m, in \u001b[0;36mallow_rasterization..draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \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 69\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 71\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \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", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/figure.py:3257\u001b[0m, in \u001b[0;36mFigure.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 3254\u001b[0m \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[1;32m 3256\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[0;32m-> 3257\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3258\u001b[0m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3260\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfigure\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 3261\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/image.py:134\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m 132\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m 133\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 134\u001b[0m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 136\u001b[0m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m 137\u001b[0m image_group \u001b[38;5;241m=\u001b[39m []\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/artist.py:71\u001b[0m, in \u001b[0;36mallow_rasterization..draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \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 69\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 71\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \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", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/axes/_base.py:3181\u001b[0m, in \u001b[0;36m_AxesBase.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 3178\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artists_rasterized:\n\u001b[1;32m 3179\u001b[0m _draw_rasterized(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_figure(root\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m), artists_rasterized, renderer)\n\u001b[0;32m-> 3181\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3182\u001b[0m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mroot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3184\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maxes\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 3185\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/image.py:134\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m 132\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m 133\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 134\u001b[0m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 136\u001b[0m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m 137\u001b[0m image_group \u001b[38;5;241m=\u001b[39m []\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/artist.py:71\u001b[0m, in \u001b[0;36mallow_rasterization..draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \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 69\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 71\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \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", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/collections.py:1017\u001b[0m, in \u001b[0;36m_CollectionWithSizes.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 1014\u001b[0m \u001b[38;5;129m@artist\u001b[39m\u001b[38;5;241m.\u001b[39mallow_rasterization\n\u001b[1;32m 1015\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mdraw\u001b[39m(\u001b[38;5;28mself\u001b[39m, renderer):\n\u001b[1;32m 1016\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_sizes(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sizes, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_figure(root\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\u001b[38;5;241m.\u001b[39mdpi)\n\u001b[0;32m-> 1017\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/artist.py:71\u001b[0m, in \u001b[0;36mallow_rasterization..draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \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 69\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 71\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \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", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/collections.py:360\u001b[0m, in \u001b[0;36mCollection.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 356\u001b[0m renderer\u001b[38;5;241m.\u001b[39mopen_group(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_gid())\n\u001b[1;32m 358\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mupdate_scalarmappable()\n\u001b[0;32m--> 360\u001b[0m transform, offset_trf, offsets, paths \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_prepare_points\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 362\u001b[0m gc \u001b[38;5;241m=\u001b[39m renderer\u001b[38;5;241m.\u001b[39mnew_gc()\n\u001b[1;32m 363\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_set_gc_clip(gc)\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/collections.py:332\u001b[0m, in \u001b[0;36mCollection._prepare_points\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 330\u001b[0m ys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_yunits(ys)\n\u001b[1;32m 331\u001b[0m paths\u001b[38;5;241m.\u001b[39mappend(mpath\u001b[38;5;241m.\u001b[39mPath(np\u001b[38;5;241m.\u001b[39mcolumn_stack([xs, ys]), path\u001b[38;5;241m.\u001b[39mcodes))\n\u001b[0;32m--> 332\u001b[0m xs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconvert_xunits\u001b[49m\u001b[43m(\u001b[49m\u001b[43moffsets\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 333\u001b[0m ys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_yunits(offsets[:, \u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m 334\u001b[0m offsets \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mma\u001b[38;5;241m.\u001b[39mcolumn_stack([xs, ys])\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/artist.py:278\u001b[0m, in \u001b[0;36mArtist.convert_xunits\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 276\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m ax\u001b[38;5;241m.\u001b[39mxaxis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 277\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m x\n\u001b[0;32m--> 278\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxaxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconvert_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/axis.py:1824\u001b[0m, in \u001b[0;36mAxis.convert_units\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 1822\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_converter\u001b[38;5;241m.\u001b[39mconvert(x, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munits, \u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m 1823\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m-> 1824\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m munits\u001b[38;5;241m.\u001b[39mConversionError(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFailed to convert value(s) to axis \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 1825\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124munits: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m 1826\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ret\n", - "\u001b[0;31mConversionError\u001b[0m: Failed to convert value(s) to axis units: masked_array(data=[--, --, --, --, --, --, --, --, --, --, --, --, --, --,\n --, --, --, --, --, --, --, --, --, --, --, --, --, --,\n --, --, --, --, --, --, --, --, --, --, --, --, --, --,\n --, --, --, --, --, --, --, --, --],\n mask=[ True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True],\n fill_value=1e+20,\n dtype=float64)" - ] - }, - { - "ename": "ConversionError", - "evalue": "Failed to convert value(s) to axis units: masked_array(data=[--, --, --, --, --, --, --, --, --, --, --, --, --, --,\n --, --, --, --, --, --, --, --, --, --, --, --, --, --,\n --, --, --, --, --, --, --, --, --, --, --, --, --, --,\n --, --, --, --, --, --, --, --, --],\n mask=[ True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True],\n fill_value=1e+20,\n dtype=float64)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/axis.py:1822\u001b[0m, in \u001b[0;36mAxis.convert_units\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 1821\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1822\u001b[0m ret \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[43mconvert\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munits\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1823\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/category.py:57\u001b[0m, in \u001b[0;36mStrCategoryConverter.convert\u001b[0;34m(value, unit, axis)\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[38;5;66;03m# force an update so it also does type checking\u001b[39;00m\n\u001b[0;32m---> 57\u001b[0m \u001b[43munit\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 58\u001b[0m s \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mvectorize(unit\u001b[38;5;241m.\u001b[39m_mapping\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__getitem__\u001b[39m, otypes\u001b[38;5;241m=\u001b[39m[\u001b[38;5;28mfloat\u001b[39m])(values)\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/category.py:217\u001b[0m, in \u001b[0;36mUnitData.update\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;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[1;32m 216\u001b[0m \u001b[38;5;66;03m# OrderedDict just iterates over unique values in data.\u001b[39;00m\n\u001b[0;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[1;32m 218\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m convertible:\n\u001b[1;32m 219\u001b[0m \u001b[38;5;66;03m# this will only be called so long as convertible is True.\u001b[39;00m\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/_api/__init__.py:92\u001b[0m, in \u001b[0;36mcheck_isinstance\u001b[0;34m(types, **kwargs)\u001b[0m\n\u001b[1;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[0;32m---> 92\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;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[1;32m 94\u001b[0m k,\n\u001b[1;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[1;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[1;32m 97\u001b[0m type_name(\u001b[38;5;28mtype\u001b[39m(v))))\n", - "\u001b[0;31mTypeError\u001b[0m: 'value' must be an instance of str or bytes, not a float", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mConversionError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/IPython/core/formatters.py:402\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 400\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m 401\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 402\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mprinter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 403\u001b[0m \u001b[38;5;66;03m# Finally look for special method names\u001b[39;00m\n\u001b[1;32m 404\u001b[0m method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/IPython/core/pylabtools.py:170\u001b[0m, in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m 167\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_bases\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m FigureCanvasBase\n\u001b[1;32m 168\u001b[0m FigureCanvasBase(fig)\n\u001b[0;32m--> 170\u001b[0m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbytes_io\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[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 171\u001b[0m data \u001b[38;5;241m=\u001b[39m bytes_io\u001b[38;5;241m.\u001b[39mgetvalue()\n\u001b[1;32m 172\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fmt \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msvg\u001b[39m\u001b[38;5;124m'\u001b[39m:\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/backend_bases.py:2155\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m 2152\u001b[0m \u001b[38;5;66;03m# we do this instead of `self.figure.draw_without_rendering`\u001b[39;00m\n\u001b[1;32m 2153\u001b[0m \u001b[38;5;66;03m# so that we can inject the orientation\u001b[39;00m\n\u001b[1;32m 2154\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(renderer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_draw_disabled\u001b[39m\u001b[38;5;124m\"\u001b[39m, nullcontext)():\n\u001b[0;32m-> 2155\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2156\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches:\n\u001b[1;32m 2157\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtight\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/artist.py:94\u001b[0m, in \u001b[0;36m_finalize_rasterization..draw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m 92\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[1;32m 93\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mdraw_wrapper\u001b[39m(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 94\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\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\u001b[1;32m 95\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[1;32m 96\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/artist.py:71\u001b[0m, in \u001b[0;36mallow_rasterization..draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \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 69\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 71\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \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", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/figure.py:3257\u001b[0m, in \u001b[0;36mFigure.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 3254\u001b[0m \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[1;32m 3256\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[0;32m-> 3257\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3258\u001b[0m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3260\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfigure\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 3261\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/image.py:134\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m 132\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m 133\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 134\u001b[0m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 136\u001b[0m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m 137\u001b[0m image_group \u001b[38;5;241m=\u001b[39m []\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/artist.py:71\u001b[0m, in \u001b[0;36mallow_rasterization..draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \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 69\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 71\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \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", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/axes/_base.py:3181\u001b[0m, in \u001b[0;36m_AxesBase.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 3178\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artists_rasterized:\n\u001b[1;32m 3179\u001b[0m _draw_rasterized(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_figure(root\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m), artists_rasterized, renderer)\n\u001b[0;32m-> 3181\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3182\u001b[0m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mroot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3184\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maxes\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 3185\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/image.py:134\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m 132\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m 133\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 134\u001b[0m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 136\u001b[0m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m 137\u001b[0m image_group \u001b[38;5;241m=\u001b[39m []\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/artist.py:71\u001b[0m, in \u001b[0;36mallow_rasterization..draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \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 69\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 71\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \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", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/collections.py:1017\u001b[0m, in \u001b[0;36m_CollectionWithSizes.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 1014\u001b[0m \u001b[38;5;129m@artist\u001b[39m\u001b[38;5;241m.\u001b[39mallow_rasterization\n\u001b[1;32m 1015\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mdraw\u001b[39m(\u001b[38;5;28mself\u001b[39m, renderer):\n\u001b[1;32m 1016\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_sizes(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sizes, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_figure(root\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\u001b[38;5;241m.\u001b[39mdpi)\n\u001b[0;32m-> 1017\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/artist.py:71\u001b[0m, in \u001b[0;36mallow_rasterization..draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \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 69\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 71\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \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", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/collections.py:360\u001b[0m, in \u001b[0;36mCollection.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 356\u001b[0m renderer\u001b[38;5;241m.\u001b[39mopen_group(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_gid())\n\u001b[1;32m 358\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mupdate_scalarmappable()\n\u001b[0;32m--> 360\u001b[0m transform, offset_trf, offsets, paths \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_prepare_points\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 362\u001b[0m gc \u001b[38;5;241m=\u001b[39m renderer\u001b[38;5;241m.\u001b[39mnew_gc()\n\u001b[1;32m 363\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_set_gc_clip(gc)\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/collections.py:332\u001b[0m, in \u001b[0;36mCollection._prepare_points\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 330\u001b[0m ys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_yunits(ys)\n\u001b[1;32m 331\u001b[0m paths\u001b[38;5;241m.\u001b[39mappend(mpath\u001b[38;5;241m.\u001b[39mPath(np\u001b[38;5;241m.\u001b[39mcolumn_stack([xs, ys]), path\u001b[38;5;241m.\u001b[39mcodes))\n\u001b[0;32m--> 332\u001b[0m xs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconvert_xunits\u001b[49m\u001b[43m(\u001b[49m\u001b[43moffsets\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 333\u001b[0m ys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_yunits(offsets[:, \u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m 334\u001b[0m offsets \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mma\u001b[38;5;241m.\u001b[39mcolumn_stack([xs, ys])\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/artist.py:278\u001b[0m, in \u001b[0;36mArtist.convert_xunits\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 276\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m ax\u001b[38;5;241m.\u001b[39mxaxis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 277\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m x\n\u001b[0;32m--> 278\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxaxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconvert_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/axis.py:1824\u001b[0m, in \u001b[0;36mAxis.convert_units\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 1822\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_converter\u001b[38;5;241m.\u001b[39mconvert(x, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munits, \u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m 1823\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m-> 1824\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m munits\u001b[38;5;241m.\u001b[39mConversionError(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFailed to convert value(s) to axis \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 1825\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124munits: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m 1826\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ret\n", - "\u001b[0;31mConversionError\u001b[0m: Failed to convert value(s) to axis units: masked_array(data=[--, --, --, --, --, --, --, --, --, --, --, --, --, --,\n --, --, --, --, --, --, --, --, --, --, --, --, --, --,\n --, --, --, --, --, --, --, --, --, --, --, --, --, --,\n --, --, --, --, --, --, --, --, --],\n mask=[ True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True],\n fill_value=1e+20,\n dtype=float64)" - ] - }, { "data": { + "image/png": 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WrVtLjhw5TGuoTz75xL1eO+E7ceKEubVk0R6Gq1atKqtXr45zm6NHjzbPsSYNlgAAQPJlq2ZGA4RNmzbJ4sWLZdeuXWaZ5s9EDzriY//+/fLhhx9Kv3795L///a+sX7/eJBBrB3za/FsDGRVzsEp9bK2LafDgwWZ7Fg2wCGgAAEi+bAUzSoc0aNSokZns0vwarZkZNWqUeaw1M9oqSvNjrL5s7le6dOnMBAAAUgbbwcySJUvMpH3OxEz6nTFjRry2oS2UtBVUdFrDM3fuXDOfK1cu8//JkyfNcy36uFy5cnaLDgAAUnrOzNtvvy2NGzc2wcyZM2dMq6ToU3xpS6bdu3d7LNuzZ4/kz5/fzGsejgY0+j7Rbxtpq6bq1avbKToAAEhmbNXM6G2gmTNnSocOHRL05tr6qUaNGuY2U5s2bWTdunXy8ccfm8m6ldWnTx8ZOXKkFC1a1AQ3Q4cONS2orEEvAQBAymYrmLl586YJQhKqcuXKMm/ePJO0O3z4cBOsaFPsZ5991v2cgQMHypUrV+TFF180QybUqlXLdNrHIJcAAMB2MNO1a1eZPXu2qSVJqMcff9xMd6O1Mxro6AQAAOCVYEZ739VbQdo0u0yZMrF6A54wYYKdzQIAAPgmmNmyZYu7NZE2pY5ZkwIAAODXwczSpUvj9bwjR46YZN3AQFuNpgAAAO4pUaMM7UPm4MGDifkWAAAghUvUYEYHigQAAEhM3P8BAACORjADAAAcjWAGAAA4WqIGMzTTBgAAiY0EYAAAkPL6mYmvHTt2mH5mAAAAkjyYeeqpp+K90fDwcPN/aGiovVIBAAB4O5gJCQmJ71MBAAD8L5gJCwtL3JIAAADYQNNsAACQMhOAv/vuO/n222/l8OHDcvPmTY91mzZt8kbZAAAAEqdmZvLkydK5c2fJmTOnbN68WapUqSLZsmWT/fv3y2OPPWZnkwAAAL4LZqZNmyYff/yxTJkyRdKmTSsDBw6URYsWSa9eveTChQv2SgIAAOCrYEZvLdWoUcPMp0+fXi5dumTmO3ToIF999ZWdTQIAAPgumMmVK5ecO3fOzOfLl0/WrFlj5g8cOECvvwAAwP+Dmfr168v8+fPNvObO9O3bVxo1aiRt27aVli1beruMAAAA3m3NpPkyUVFRZr5Hjx4m+XfVqlXSvHlzeemll+xsEgAAwHfBzJEjRzyGKmjXrp2Z9BZTRESEufUEAADgt7eZChYsKKdPn461XPNodB0AAIBfBzNaAxMQEBBr+eXLlyUoKMgb5QIAAPD+baZ+/fqZ/zWQGTp0qAQHB7vX3blzR9auXSvlypW7n00CAAD4LpjR3n6tmpmtW7eaDvMsOl+2bFnp379/wkoEAACQWMHM0qVL3c2x33//fcmcOfP9vBwAAMA/WjOFhYV5vyQAAAC+HDV7w4YNdx01Ozw83O5mAQAAEr8109dff23GZtq5c6fMmzdPbt26Jdu3b5fff/9dQkJC7GwSAADAd8HMqFGjZOLEifLjjz+axF/Nn9m1a5e0adOGDvMAAID/BzP79u2TZs2amXkNZq5cuWKaa+sYTTrUAQAAgF8HM1mzZpVLly6Z+bx588q2bdvMfGRkpFy9etW7JQQAAPB2AnCdOnVk0aJFUrp0aWndurX07t3b5MvosgYNGni/lAAAAN4MZj744AO5fv26mX/jjTckTZo0ZtTsVq1ayZAhQ+xsEgAAIHGDGR3KYMSIEZIhQwZzW0lbM6nAwEAZNGiQvXcHAADwVc7MlClTzECSql69emaEbAAAAMfUzBQoUEAmT54sjRs3NmMzrV692iQC3y2nBgAAwK+CmXfffVdefvllGT16tGmG3bJlyzifp+t0BG0AAAC/CmZatGhhJr3VpANM7t69W3LkyJG4pQMAAPB2PzMZM2Y0o2cXLFjQDF0Q12QZM2aM6XsGAADArzrNq1u3rqROnTpewx6QKAwAAPwumIkvTRQGAABwbDADAACQ2AhmAACAoxHMAAAARyOYAQAAjpaowUzt2rUlffr0ifkWAAAghbMVzGzatEm2bt3qfvzDDz+YDvX++9//ys2bN93Lf/nlF8mdO7d3SgoAAOCtYOall16SPXv2mPn9+/dLu3btJDg4WObMmSMDBw60s0kAAADfBTMayJQrV87MawCjA0vOnj1bZs6cKXPnzrVXEgAAAF8FM9oZXlRUlJlfvHixNG3a1MyHhobKmTNn7GwSAADAd8FMpUqVZOTIkfL555/L8uXLpVmzZmb5gQMHJGfOnPZKAgAA4KtgZtKkSSYJ+NVXX5U33nhDihQpYpZ/9913UqNGDTubBAAAsOXeo0XGcOfOHTMS9h9//CFZs2b1WPfuu+9KqlSp7JUEAADAFzUzGqw0btzYBDQxBQUFSZo0aeyUAwAAwHe3mUqVKmWaZAMAADgymNHk3/79+8tPP/0kx48fl4sXL3pMAAAAfpszo6ym2M2bN5eAgACPJtv6WPNqAAAA/DaYWbp0qfdLAgAA4Ktgpm7dunZeBgAA4D+jZq9YsUKee+4506/M0aNHzTLtRG/lypXeLB8AAID3gxkdf6lJkyaSPn1603nejRs3zPILFy7IqFGj7GwSAADAt62Zpk+fLp988olHvzI1a9Y0wQ0AAIBfBzO7d+82I2XHFBISEmdnegAAAH4VzOTKlUv++eefWMs1X6ZQoUK2CzNmzBjTtLtPnz7uZdevX5cePXpItmzZJGPGjNKqVSs5efKk7fcAAADJi61gplu3btK7d29Zu3atCT6OHTsmX375pelIr3v37rYKsn79evnoo4+kTJkyHsv79u0rP/74o8yZM8eM0K3v9dRTT9l6DwAAkPzYapo9aNAgiYqKkgYNGsjVq1fNLad06dKZYKZnz573vb3Lly/Ls88+a3JwNB/HognFn376qcyePVvq169vloWFhUmJEiVkzZo1Uq1aNTvFBwAAKb1mRmtj3njjDTl37pxs27bNBBanT5+WESNG2CqE3kZq1qyZNGzY0GP5xo0b5datWx7LixcvLvny5ZPVq1fHuS1tWcXwCgAApBy2ama++OILc6snODhYSpYsmaACfP3116YFlN5miunEiROSNm1ayZIli8fynDlzmnVxGT16tLz99tsJKhMAAEjmNTOax5IjRw555pln5JdffrE9FlNERITJvdF8m6CgIPGGwYMHm9tT1qTvAQAAki9bwYyOlK01Knq7qU2bNpI7d25zq2jVqlX3tR29jXTq1CmpUKGCpE6d2kya5Dt58mQzrzUwN2/ejNXcW1szaYuquGjuTubMmT0mAACQfNkKZjTQePzxx02NigYjEydOlIMHD0q9evWkcOHC8d6OJhBv3bpV/vrrL/dUqVIlkwxszWunfEuWLPHo4+bw4cNSvXp1O0UHAADJjK2cmeg0b0aHNjh//rwcOnRIdu7cGe/XZsqUSUqVKuWxLEOGDKZPGWt5ly5dpF+/fvLAAw+YWhZtLaWBDC2ZAABAgoIZbZI9b948UzujNSehoaHSvn17+e6777y6Z7XWJzAw0HSWpy2VNHCaNm2aV98DAACksGCmXbt28tNPP5laGc2ZGTp0qNdu+yxbtszjsSYGT5061UwAAABeCWZSpUol3377rakl0XkAAABHBTN6awkAAMBRwYw2l37xxRfNbR+d/ze9evXyRtkAAAC8F8xoIq42mdZgZsKECaaPmbjocoIZAADgd8HMgQMH3PPapwwAAIAjO83TgR+1Y7z76U8GAADAb4IZ7ZH3+vXriVMaAAAAXwxnoOMwjR07Vm7fvm3n5QAAAEnbNHv9+vWm19+FCxdK6dKlzRAE0YWHh3urfAAAAN4PZrJkyWKGFwAAAHBkMBMWFub9kgAAAPgqZwYAAMBxNTPly5e/a0d5MW3atCkhZQIAAPB+MNOiRQv3vDbNnjZtmpQsWdI9WvaaNWtk+/bt8sorr8T/3QEAAHwVzAwbNsw937VrVzNkwYgRI2I9JyIiIqFlAgAASNycmTlz5kjHjh1jLX/uuedk7ty5djYJAADgu2Amffr08ueff8Zarst0IEoAAAC/bprdp08f6d69u0n0rVKlilm2du1amTFjhgwdOtTbZQQAAPBuMDNo0CApVKiQvP/++/LFF1+YZSVKlDD9z7Rp08bOJgEAAHwXzCgNWu4VuHz11VfSvHnzWMMdAAAAOKLTvJdeeklOnjyZmG8BAABSuEQNZlwuV2JuHgAAgOEMAACAsxHMAAAARyOYAQAAjkYwAwAAHC1Rg5n8+fNLmjRpEvMtAABACmc7mImMjJT//e9/MnjwYDl37pxZpj0CHz161P2cbdu2SWhoqHdKCgAA4K1O87Zs2SINGzaUkJAQOXjwoHTr1k0eeOABCQ8Pl8OHD8usWbPsbBYAAMA3NTP9+vWT559/Xvbu3esxsGTTpk3ljz/+sLNJAAAA3wUz69evN737xpQ3b145ceKEvZIAAAD4KphJly6dXLx4MdbyPXv2yIMPPmhnkwAAAL4LZnTwyOHDh8utW7fM44CAAJMr8/rrr0urVq3slQQAAMBXwcz48ePl8uXLkiNHDrl27ZrUrVtXihQpIpkyZZJ33nnHziYBAAB815pJWzEtWrRIVq5caVo2aWBToUIF08IJAADA74MZS61atcwEAADgqGBm8uTJcS7X3Bltqq23nOrUqSOpUqVKaPkAAAC8H8xMnDhRTp8+LVevXpWsWbOaZefPn5fg4GDJmDGjnDp1SgoVKiRLly6lB2AAAOB/CcCjRo2SypUrm07zzp49ayZtll21alV5//33TcumXLlySd++fb1fYgAAgITWzAwZMkTmzp0rhQsXdi/TW0vvvfeeaZq9f/9+GTduHM20AQCAf9bMHD9+XG7fvh1ruS6zegDOkyePXLp0KeElBAAA8HYwU69ePTOcwebNm93LdL579+5Sv35983jr1q1SsGBBO5sHAABI3GDm008/NaNkV6xY0QxtoFOlSpXMMl2nNBFYO9cDAADwu5wZTe7VTvN27dplEn9VsWLFzBS99gYAAMCvO80rXry4mQAAABwXzBw5ckTmz59vmmHfvHnTY92ECRO8UTYAAIDECWaWLFliRs7WjvH0VlOpUqXk4MGD4nK5zBhNAAAAfp0APHjwYOnfv79psaTDF2ifMxEREWb07NatW3u/lAAAAN4MZnbu3CkdO3Y086lTp5Zr166Z1kvDhw+XsWPH2tkkAACA74KZDBkyuPNkcufOLfv27XOvO3PmjL2SAAAA+Cpnplq1arJy5UopUaKENG3aVF577TVzyyk8PNysAwAA8OtgRlsrXb582cy//fbbZv6bb76RokWL0pIJAAD4fzCjrZii33KaPn26N8sEAACQuDkzGsycPXs21vLIyEiPQAcAAMAvgxntU+bOnTuxlt+4cUOOHj3qjXIBAAB4/zaT9vhr+e233yQkJMT9WIMb7UyvQIEC97NJAAAA3wUzLVq0MP8HBARIp06dPNalSZPGBDKMlA0AAPw2mImKijL/FyxYUNavXy/Zs2dPrHIBAAAkXmumAwcO2HkZAACA/4yarfkxOp06dcpdY2OZMWOGN8oGAACQOMGMdpSn4zBVqlTJDGegOTQAAACOCWa0k7yZM2dKhw4dvF8iAACAxO5nRgeZrFGjhp2XAgAAJH0w07VrV5k9e7Z3SwIAAOCr20zXr1+Xjz/+WBYvXixlypQxfcxEx2CTAADAr4OZLVu2SLly5cz8tm3bPNaRDAwAAPw+mFm6dKlX3nz06NESHh4uu3btkvTp05s8nLFjx0qxYsU8aoFee+01+frrr83YT02aNJFp06ZJzpw5vVIGAACQAnNmLP/8848Zo+natWvmscvluq/XL1++XHr06CFr1qyRRYsWya1bt6Rx48Zy5coV93P69u0rP/74o8yZM8c8/9ixY/LUU08lpNgAACCl18ycPXtW2rRpY2po9LbS3r17pVChQtKlSxfJmjVrvMdnWrBggcdjbe6dI0cO2bhxo9SpU0cuXLggn376qUk2rl+/vnlOWFiYlChRwgRA1apVs1N8AACQ0mtmtLZEk34PHz4swcHB7uVt27aNFaDcDw1e1AMPPGD+16BGa2saNmzofk7x4sUlX758snr16ji3obeiLl686DEBAIDky1Yws3DhQpPb8tBDD3ksL1q0qBw6dMhWQXRIhD59+kjNmjWlVKlSZtmJEyckbdq0kiVLFo/nar6MrrtbHk5ISIh7Cg0NtVUeAACQjIMZzWmJXiNjOXfunKRLl85WQTR3RltGaaJvQgwePNjU8FhTREREgrYHAACSYTBTu3ZtmTVrlvux5s1ozcq4ceOkXr169729V199VX766SeTgxO9tidXrlymt+HIyEiP5588edKsi4sGU5kzZ/aYAABA8mUrAViDlgYNGsiGDRtMsDFw4EDZvn27qZn5888/470dbf3Us2dPmTdvnixbtkwKFizosb5ixYomN0dH527VqpVZtnv3bpOrU716dTtFBwAAyYytYEZzWvbs2SMffPCBZMqUSS5fvmyaS+utIh1FO770+dpS6YcffjDbsfJgNNdF+53R/7WFVL9+/UxSsNayaPCjgQwtmQAAgO1gRmmg8cYbbyRoL3744Yfm/0ceecRjuTa/fv755838xIkTJTAw0NTMRO80DwAAwHYwo8FGxowZpXXr1h7LtWO7q1evSqdOneK1nfh0shcUFCRTp041EwAAgFcSgLX5c/bs2WMt1w7vRo0aZWeTAAAAvgtmNAE3ZrKuyp8/v1kHAADg18GM1sDoyNkx/f3335ItWzZvlAsAACDxgpn27dtLr169TL8wd+7cMdPvv/8uvXv3lnbt2tnZJAAAgO8SgEeMGCEHDx40fc2kTv1/N6Gd5nXs2JGcGQAA4N/BjLZA0v5gdITrkSNHyl9//WX6hCldurTJmQEAAPD7YKZIkSKmx18dWFInAAAAx+TMaAd2GsCcPXs2cUoEAACQ2AnAY8aMkQEDBphRrgEAAByXAKyJvtrTb9myZSVt2rQmZyY6HXASAADAb4OZSZMmeb8kAAAAvgpm4jv2EgAAgF/mzKh9+/bJkCFDTAd6p06dMst+/fVX08oJAADAr4OZ5cuXm35l1q5dK+Hh4XL58mX3cAbDhg3zdhkBAAC8G8wMGjTIdJi3aNEikwBsqV+/vqxZs8bOJgEAAHwXzGzdulVatmwZ5wCUZ86csVcSAAAAXwUzWbJkkePHj8davnnzZsmbN6+dTQIAAPgumNGRsV9//XUzRlNAQIAZZPLPP/+U/v37mz5oAAAA/DqY0ZGxixcvLqGhoSb5t2TJklK7dm2pUaOGaeEEAADg1/3MaNLvJ598Im+++abJn7ly5YqUL1/eDEAJAADg98GM+vTTT2XixImyd+9e81gHn+zTp4907drVm+UDAADwfjCjNTITJkyQnj17SvXq1c2y1atXS9++feXw4cMyfPhwO5sFAADwTTDz4YcfmttM2vuvpXnz5lKmTBkT4BDMAAAAv04AvnXrllSqVCnW8ooVK8rt27e9US4AAIDEC2Y6dOhgamdi+vjjj+XZZ5+1s0kAAADfJwAvXLhQqlWrZh7rOE2aL6P9zPTr18/9PM2tAQAA8KtgZtu2bVKhQgX36Nkqe/bsZtJ1Fu1QDwAAwO+CmaVLl3q/JAAAAL7KmQEAAPAXBDMAAMDRCGYAAICjEcwAAABHI5gBAACORjADAAAcjWAGAAA4GsEMAABwNIIZAADgaAQzAADA0QhmAACAoxHMAAAARyOYAQAAjkYwAwAAHI1gBgAAOBrBDAAAcDSCGQAA4GgEMwAAwNEIZgAAgKMRzAAAAEcjmAEAAI5GMAMAAByNYAYAADgawQwAAHA0ghkAAOBoBDMAAMDRCGYAAICjEcwAAABHI5gBAACORjADAAAcjWAGAAA4GsEMAABwNIIZAADgaAQzAADA0QhmAACAoxHMAAAARyOYAQAAjkYwAwAAHM0RwczUqVOlQIECEhQUJFWrVpV169YldZEAAICf8Ptg5ptvvpF+/frJsGHDZNOmTVK2bFlp0qSJnDp1KqmLBgAA/IDfBzMTJkyQbt26SefOnaVkyZIyffp0CQ4OlhkzZiR10QAAgB9IndQF+Dc3b96UjRs3yuDBg93LAgMDpWHDhrJ69eo4X3Pjxg0zWS5cuGD+v3jx4j3f786Na14pd0oUn/17PzgW/nM8OBYJw7nhPzgWzjoW1nNcLte9N+jyY0ePHtW/wLVq1SqP5QMGDHBVqVIlztcMGzbMvIaJiYmJiYlJHD9FRETcM17w65oZO7QWR3NsLFFRUXLu3DnJli2bBAQEiFNphBoaGioRERGSOXPmpC5Oisax8B8cC//BsfAfF5PJsdAamUuXLkmePHnu+Vy/DmayZ88uqVKlkpMnT3os18e5cuWK8zXp0qUzU3RZsmSR5EI/mE7+cCYnHAv/wbHwHxwL/5E5GRyLkJAQ5ycAp02bVipWrChLlizxqGnRx9WrV0/SsgEAAP/g1zUzSm8ZderUSSpVqiRVqlSRSZMmyZUrV0zrJgAAAL8PZtq2bSunT5+WN998U06cOCHlypWTBQsWSM6cOSUl0Vtn2tdOzFto8D2Ohf/gWPgPjoX/SJcCj0WAZgEndSEAAADs8uucGQAAgHshmAEAAI5GMAMAAByNYMZHHnnkEenTp8+/PkdHBtfWWvA/zz//vLRo0SKpi5HicRz8z7Jly0yHpJGRkUldFKRgBDMJvLDqSfzyyy/HWtejRw+zTp+jwsPDZcSIEeKvtKzff/+9OGm/jxkzxmO5lj+hvTwfPHjQbOOvv/4Sf+NvX+Qp9Tj46w8QbfXZvXt3yZcvn2nFoh2LNmnSRP7880+f/ihLCskxoNJBlTNlyiS3b992L7t8+bKkSZPGHIe4/v59+/Yl+3PubghmEki7jP7666/l2rX/P+DY9evXZfbs2eaiYnnggQfMBxPeERQUJGPHjpXz5897dWBT3B+Og/9o1aqVbN68WT777DPZs2ePzJ8/33zpnT17NqmLBhvq1atngpcNGza4l61YscIEqWvXrjXfM5alS5ea75vChQtLSkUwk0AVKlQwAY3WvFh0Xj9Y5cuXv+svmlOnTskTTzwh6dOnl4IFC8qXX37psd3+/fvL448/7n6sv/40atY+dixFihSR//3vf+7HOl+iRAnzBVO8eHGZNm2axxfEq6++Krlz5zbr8+fPL6NHj3b/ulQtW7Y072E99mc6crqe1NbfEJe5c+fKww8/bH6l6t80fvx4j/W6TGvLOnbsaLr8fvHFF82xUHrsdF/E/AX03nvvmX2oY31p7dutW7fc63S0dj1uefPmlQwZMkjVqlXNLyaLfqm0b9/erA8ODpbSpUvLV1995bH97777zizXz4W+h/6d2knkW2+9Zb6kfvjhB1MunaJvO6n423H44IMPpFSpUrFqifRXbvQyDxkyxP1Y96mex3peFCpUSN5++233r2HtuUL3vVXboWPE9OrVy6zTMh06dEj69u3rPiZJRWsk9ItOA0v9EtTzWzsZ1bHqmjdvbp5z+PBhefLJJyVjxoxmP7dp08ZjqJi4av70mmXte12/fPlyef/9991/r/6at2zcuNF0bqqf7Ro1asju3bs9tvVv+1lNmDDBfPb13NFr6iuvvGK+zC26r/WamTVrVvMc/Uz98ssvpgz6NytdF71G3MmKFStmPuPRz3Od12Oo58eaNWs8lterV8/0kK/noq7Xa0jZsmXNNcWiPzqeffZZefDBB836okWLSlhYmFl3r3PO73lrhOuUqFOnTq4nn3zSNWHCBFeDBg3cy3V+4sSJZp0+R9WtW9fVu3dv93Mee+wxV9myZV2rV692bdiwwVWjRg1X+vTpzevU/PnzXSEhIa7bt2+bxy1atHBlz57d9frrr5vHR44cMaOJ7t271zz+4osvXLlz53bNnTvXtX//fvP/Aw884Jo5c6ZZ/+6777pCQ0Ndf/zxh+vgwYOuFStWuGbPnm3WnTp1ymwrLCzMdfz4cfPYCfs9PDzcFRQU5B5Rdd68eebvULpPAwMDXcOHD3ft3r3b/G26f/V/S/78+V2ZM2d2vffee65//vnHTOvWrTPbWLx4sdkXZ8+edb+nPvfll1927dy50/Xjjz+6goODXR9//LF7e127djXHUfexbkv3ebp06Vx79uxxHzNdtnnzZte+fftckydPdqVKlcq1du1as/7YsWOu1KlTm8/TgQMHXFu2bHFNnTrVdenSJTO1adPG9eijj5py6XTjxg1XUvLH46D7LCAgwP0Z7tOnjzlv2rZtax7fvHnTPH/RokXmsR4r3Z6eJ3pMFi5c6CpQoIDrrbfeMuvnzJlj1v/yyy+uQ4cOmWNlvZeW6aGHHjJ/m3VMksqtW7dcGTNmNH/v9evXY62/c+eOq1y5cq5atWqZY7JmzRpXxYoVzXUp5vGMTq9Z1nMiIyNd1atXd3Xr1s399+r1aenSpeZYVa1a1bVs2TLX9u3bXbVr1zbnguVe+1npte/33383n/0lS5a4ihUr5urevbt7fbNmzVyNGjUyx1i3ocd++fLlpgx6vdMy6GdMy6VlTQ6eeeYZV+PGjd2PK1eubD6T+vl/8803zbKrV6+a64zu25EjR7qKFy/uWrBggdlHep7pOj0uqkePHuZzsH79erOf9TzQ7xp1t3POKQhmEsA6+fXCqR8YDRJ00gv76dOn7xrM6AmnHxr98Fj0wqzLrGDm/Pnz5ktAP3RRUVEmMBk9erS5YFjBS968ed2vL1y4sDs4sYwYMcJcfFTPnj1d9evXN9uKi763fgk5QfSLbrVq1VwvvPBCrC9RvQjohS+6AQMGuEqWLOnxJapBYnR6gus2NOCI+Z76fCu4VK1bt3Z/SeoXnQYmR48e9XidBraDBw++69+iF+jXXnvNzG/cuNG8t36G7vV3+wN/PA76+c6WLZu54Cu9cOt5kytXLvN45cqVrjRp0riuXLniPj6jRo3yeI/PP//c/DBQ48ePd/3nP/8xQVBctCzWOZvUvvvuO1fWrFnN9UcDCf3c/f3332adBg/6+Tx8+LD7+Rp0RL8O3SuYietHmbKCGf0StPz8889m2bVr1+K1n+Oix1CPpaV06dIewU9cZdDrZnLyySefuDJkyGCC1YsXL5ofO/p9o9f6OnXqmOdo4Cf/77qhgfqqVas8ttGlSxdX+/btzfwTTzzh6ty5c5zvdbdzzim4zeQFWmXXrFkzmTlzpqmy03kd8ftudu7cKalTpzaDaFr0tlD00b11XqsItfpw69atZtBNrX7Xe+Ja9arVvXXr1jXP1dsQmvjVpUsXU4VsTSNHjnQnhGm1qyZ2adWlVpMvXLhQkgOtVtfbL7pPo9PHNWvW9Fimj/fu3St37txxL9Nq8fjSam0dxd2iVcB6u1DpMdLt/uc///E4BnqcrGOg6/V2ilalaw6Vrv/tt99M9b/S492gQQOzvnXr1vLJJ594NRclJRwHrR6vU6eOOW/01suOHTvM7Qq9Bbhr1y5zPCpXrmxuhai///5bhg8f7nHMunXrJsePH5erV6+a46D5cHpbRJfPmzfP49aIv+XMHDt2zOTKPProo2Yf6G0dvS7pcdBbNzpZSpYsaa4zMY+ZXWXKlPE4Jso6Lvfaz2rx4sXm86+3YTW/sEOHDubWrLVer1t6TdPPj3bVv2XLFknu9FaPXt/Xr19vbiPq9UW/b/Tab+XN6HEuVKiQ+V7QfdWoUSOP/Txr1iz3NUgTxDXHU4cFGjhwoKxatUqSC4IZL3nhhRfMRUMv6DrvrQ+yflCtwEW/ADUnZuXKlR7BjHVfWb/8NGCxpm3btrnvq+pF7cCBA+bLVC/Oer/86aefFqfTLy5tsaG5AXbovff40lYE0ekXp96jto6BfsFq3kD0Y6BfFJpjoN59910z//rrr5uEPV2vZbcSXvX1ixYtkl9//dV80UyZMsUEn3rc/J2/HIfo541e/PX+v+aHWAFO9PPGOm6auxH9mGlgqsGW5nbol7/mfmj+meYYaGCk24qeK+VPtMz6ZTZ06FDzRaU/YvSLPz4CAwNNjlB09/N3Rj8uVv5Q9PPj3/az5r1ojqAGRJpjpefR1KlTzWut86Nr166yf/9+E+ToazUA1nMkOdO8yIceeshcL3SyPruau6WfTT3Gurx+/fru74Gff/7ZYz9rQG/lzTz22GPuPC8NfDV41Dy/5MDvB5p0Cv0lpCednsR6Uf83Wgujv+70hNVfiUovmDGbFeoHd8aMGaYWR7dvXag1aVRbK1gJWjropn649UTX5K670Yu6DtypkwYyus1z586ZIEkvRNF/KTuJNg3WXxr6xW/RoC9mk1R9rL9sov+qj0lrwNT97gv90tTX6C/R2rVrx/kcfX9N3nvuuefcF3o9jhq4WPTzo788ddLBVTWRU2sDdPR4LZs/HyN/OA7WeaOJq3PmzHGfI/q//vLX937ttdfcz9UgX889/dK4Gw1iNPFUJ0021vNXv0z1tf5+TPSzpUnQehwiIiLMZNXO6JecXnOsz5/+4tcfQNHpl2H0IMXu33uv/azXQj0fNDlcgyr17bffxnqell27wtBJA2f9AdezZ88EfV78nSb2aiCutbQDBgxwL9egWn/4rFu3ztS46HHUJHWt6Y0esMekx7lTp05m0muVblMT6p2+DwlmvEQvzFZ17b9dpJVe7DWQeOmll+TDDz80wYpefPWiGZ1+WC9duiQ//fSTuy8PvShrIKLVuPqFYNFfPVoNGxISYrat1erapE9PAP0i1JYC+hr90tWLhV7otRWKdWtLW5QsWbLEfInqCaGtApxCb8toEDd58mT3Mv3C0kBRa6I0eFu9erVp6RK9hVdccuTIYY6DthrTX0T6q1H36b3osdAyaIscvSDrftZ+P3Sf6q9NvfWoLQf0F5L+mtL9q8dEW5NYXyZabazPb9y4sSmHPtZt6BeRdYz0tpR+KWgrHi1XzFqKlH4clO5v3b/aPYKeO9Z5o79ArWDRogGj1ghoayU9r/Tc0Fsi+qWutzS0tlUv7toyTW9NffHFF6ZcGmRax+SPP/6Qdu3amfPm324vJya9HaO3xLRWWP9+vU2j5/+4ceNMAK0tuKzjoy0j9ceU1jLpl551i09/3Wvtod6WqF69uvlbdT9Eb5Wpf69+LrUmRW9h6A+h+LjXftYgR2uBtKZFg0YNOqO3QFN6jdSaBT3X9LqmNRLWuaHHQ4+tHu+mTZuaY6TlSy7BjNViL3qQovPaQlV/RNerV88cc/2Ma62LBoa1atWSCxcumH2pP2Q1eNHjoOkNeqtWvyN0f1n7MCHnnF9I6qQdJ7tXQua/tWbSbHFN/tTE4Xz58rlmzZoVZzKhtniykheVZphra4127drFer8vv/zSJDymTZvWJAJqgpi2NFHaAkPXaTKZtirQhLxNmza5X6sZ7UWKFDEJZloOp+13TV7Tvzv6R1oTIjXRVBM+dR9rS6L4JG9q0p22/NIEbCv5MT7JkZokqi0MtJWGvqcmN7Zs2dK0vrCOnW5DW53kyJHDNWTIEFfHjh3d292xY4erSZMmrgcffNB8LjTxdMqUKe7ta+KfJtPq6/Xv1KTHpOSvx0Hpc/SzrK3ArNY8ek5oonJM2vLDak2o50aVKlXcLZY0mVmT7nW5njv6+uiJrtoasUyZMuZ4JeXlVFswDRo0yFWhQgXTClITQbU1kH7GtLWLlaTevHlz83dkypTJJE6fOHHCYzv6+c2ZM6fZRt++fV2vvvqqx77Vxgu6D3Rf6d+rxzuu5FtNIrXWx2c/K23Fp+eMrtfzQK+J0berZdGGDrqv9Rzp0KGD68yZM+7Xa6syvVbq9dG67iYHVmKutlKKThN+dbkeZ4smwE+aNMks0/NN95PuS231ZTUKKVGihNnH2qhEzxNt/fpv55xTBOg/SR1QAQAA2EUCMAAAcDSCGQAA4GgEMwAAwNEIZgAAgKMRzAAAAEcjmAEAAI5GMAMAAByNYAYAADgawQwAx9Nu9rWbfgApEz0AA3A8HcNKR97W8ZMApDwEMwCSjA6SZ43WCwB2cZsJgM/o6NU60q+OgKwjTDdp0sSMnKyjIesoxzlz5pQOHTrImTNn3K/RkeN1tGetedGR3ydOnGi2o9u4222mw4cPm9GidZs6YnCbNm3MCOWWt956S8qVKyeff/65ea2ODqwjX+t7AXAeghkAPvXZZ5+Z2pg///xTxowZI/Xr15fy5cvLhg0bZMGCBSbo0ODD0q9fP/Pc+fPny6JFi2TFihWyadOmu24/KirKBDLnzp2T5cuXm9fs379f2rZt6/G8ffv2yffffy8//fSTmfS5Wh4AzpM6qQsAIGUpWrSojBs3zsyPHDnSBDKjRo1yr58xY4aEhobKnj17TE2MBj+zZ8+WBg0amPVhYWGSJ0+eu25/yZIlsnXrVjlw4IDZjpo1a5Y8/PDDsn79eqlcubI76Jk5c6ZkypTJPNYaIX3tO++8k6h/PwDvI5gB4FMVK1Z0z//999+ydOlSczsoJq05uXbtmty6dUuqVKniXq63hIoVK3bX7e/cudMEMVYgo0qWLClZsmQx66xgRm8vWYGM0sDp1KlTXvkbAfgWwQwAn9LcF8vly5fliSeekLFjx8Z6ngYX//zzT6KVI02aNB6PAwICTG0NAOchZwZAkqlQoYJs377d1JIUKVLEY9Kgp1ChQibo0NtDlgsXLphbUHdTokQJiYiIMJNlx44dEhkZaWpoACQ/BDMAkkyPHj1Mom779u1NwKK3ln777Tfp3Lmz3Llzx9wG6tSpkwwYMMDcjtLAp0uXLhIYGGhqUuLSsGFDKV26tGkBpYnC69atk44dO0rdunWlUqVKPv8bASQ+ghkASUYTebWlkgYujRs3NkGINrnW/BYNWNSECROkevXq8vjjj5tApWbNmqb2JSgoKM5tapDzww8/SNasWaVOnTrmNVrD88033/j4rwPgK3SaB8BRrly5Innz5pXx48ebWhoAIAEYgF/bvHmz7Nq1y7Ro0nyZ4cOHm+XalwwAKIIZAH7vvffek927d5vO9rRpt3acpz0IA4DiNhMAAHA0EoABAICjEcwAAABHI5gBAACORjADAAAcjWAGAAA4GsEMAABwNIIZAADgaAQzAABAnOz/AOY/Z8fE9/3HAAAAAElFTkSuQmCC", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -757,9 +683,218 @@ } ], "source": [ - "favorite_types = df[df.region.isin([\"percentage_drivers_fatal_not_distracted\", \"percentage_drivers_fatal_no_previous_accidents\"])]\n", - "sns.relplot(data=df, x=\"region\", )\n", - "\n" + "sns.barplot(data=region_mean, x=\"region\", y=\"percentage_drivers_fatal_not_distracted\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "21d5122b-9973-43f9-b4f0-7db7a6c936d9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.barplot(data=region_mean, x=\"region\", y=\"percentage_drivers_fatal_no_previous_accidents\")" + ] + }, + { + "cell_type": "markdown", + "id": "d66967db-fe78-4889-824e-f7bce4e02cc8", + "metadata": {}, + "source": [ + "## Fourth Research Question: Is there a connection between the average Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding and the region with the most expensive car insurance premiums?" + ] + }, + { + "cell_type": "markdown", + "id": "9661b6d4-3c4f-42a2-8916-b9df38375760", + "metadata": {}, + "source": [ + "### Methods" + ] + }, + { + "cell_type": "markdown", + "id": "cc44ade7-3ae9-44a0-b821-29ecb1b66385", + "metadata": {}, + "source": [ + "Explain how you will approach this research question below. Consider the following:\n", + "\n", + "Which aspects of the dataset will you use?\n", + "How will you reorganize/store the data?\n", + "What data science tools/functions will you use and why?\n", + "✏️ Write your answer below:\n", + "\n", + "To answer this question, I will organize the data for each state by the region it is in. Then, compare the average Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding to see if there is a connection with the region with the highest car insurance." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "b921b74c-951a-4f30-a42e-292f011fd61a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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percentage_drivers_fatal_speedingcar_insurance_premiums
region
Midwest27.166667756.630833
Northeast32.444444975.038889
Northwest39.3333331160.163333
Southeast27.687500905.472500
West39.909091855.624545
\n", + "
" + ], + "text/plain": [ + " percentage_drivers_fatal_speeding car_insurance_premiums\n", + "region \n", + "Midwest 27.166667 756.630833\n", + "Northeast 32.444444 975.038889\n", + "Northwest 39.333333 1160.163333\n", + "Southeast 27.687500 905.472500\n", + "West 39.909091 855.624545" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "region = df.groupby(\"region\")\n", + "region_speed_insur = region[[\"percentage_drivers_fatal_speeding\", \"car_insurance_premiums\"]].mean().sort_values(\"region\")\n", + "region_speed_insur" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "c242ede2-9440-4d49-8967-e6dfce650ec1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.barplot(data=region_speed_insur, x=\"region\", y=\"car_insurance_premiums\")" ] }, { diff --git a/argument.ipynb b/argument.ipynb index 3ee881e..9921764 100644 --- a/argument.ipynb +++ b/argument.ipynb @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 2, "id": "technical-evans", "metadata": {}, "outputs": [], @@ -59,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 3, "id": "overhead-sigma", "metadata": {}, "outputs": [], @@ -74,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 4, "id": "heated-blade", "metadata": {}, "outputs": [ @@ -213,7 +213,7 @@ "4 West " ] }, - "execution_count": 75, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -266,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 5, "id": "f7bba5f3-5911-4a76-ad43-f6ce78cd4fb3", "metadata": {}, "outputs": [ @@ -282,7 +282,7 @@ " dtype='object')" ] }, - "execution_count": 76, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -301,7 +301,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 6, "id": "basic-canadian", "metadata": {}, "outputs": [ @@ -311,7 +311,7 @@ "" ] }, - "execution_count": 77, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -363,7 +363,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 7, "id": "negative-highlight", "metadata": {}, "outputs": [ @@ -379,7 +379,7 @@ "Name: percentage_drivers_fatal_alcohol_impaired, dtype: float64" ] }, - "execution_count": 78, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -397,7 +397,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 8, "id": "victorian-burning", "metadata": {}, "outputs": [ @@ -407,13 +407,13 @@ "" ] }, - "execution_count": 88, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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" ] @@ -467,7 +467,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 9, "id": "pursuant-surrey", "metadata": {}, "outputs": [ @@ -483,7 +483,7 @@ "Name: car_insurance_premiums, dtype: float64" ] }, - "execution_count": 85, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -500,7 +500,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 10, "id": "located-night", "metadata": {}, "outputs": [ @@ -510,13 +510,13 @@ "" ] }, - "execution_count": 87, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -562,7 +562,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 24, "id": "096fe314-2953-4644-86e0-cd717f77eb8f", "metadata": {}, "outputs": [ @@ -644,112 +644,38 @@ "West 91.727273 " ] }, - "execution_count": 94, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "region = df.groupby(\"region\")\n", - "region[[\"percentage_drivers_fatal_not_distracted\", \"percentage_drivers_fatal_no_previous_accidents\"]].mean().sort_values(\"region\")" + "region_mean = region[[\"percentage_drivers_fatal_not_distracted\", \"percentage_drivers_fatal_no_previous_accidents\"]].mean().sort_values(\"region\")\n", + "region_mean" ] }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 25, "id": "3777e6d1-08a5-4747-9100-9af86f88e90a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 98, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Error in callback (for post_execute), with arguments args (),kwargs {}:\n" - ] - }, - { - "ename": "ConversionError", - "evalue": "Failed to convert value(s) to axis units: masked_array(data=[--, --, --, --, --, --, --, --, --, --, --, --, --, --,\n --, --, --, --, --, --, --, --, --, --, --, --, --, --,\n --, --, --, --, --, --, --, --, --, --, --, --, --, --,\n --, --, --, --, --, --, --, --, --],\n mask=[ True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True],\n fill_value=1e+20,\n dtype=float64)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/axis.py:1822\u001b[0m, in \u001b[0;36mAxis.convert_units\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 1821\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1822\u001b[0m ret \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[43mconvert\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munits\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1823\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/category.py:57\u001b[0m, in \u001b[0;36mStrCategoryConverter.convert\u001b[0;34m(value, unit, axis)\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[38;5;66;03m# force an update so it also does type checking\u001b[39;00m\n\u001b[0;32m---> 57\u001b[0m \u001b[43munit\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 58\u001b[0m s \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mvectorize(unit\u001b[38;5;241m.\u001b[39m_mapping\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__getitem__\u001b[39m, otypes\u001b[38;5;241m=\u001b[39m[\u001b[38;5;28mfloat\u001b[39m])(values)\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/category.py:217\u001b[0m, in \u001b[0;36mUnitData.update\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;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[1;32m 216\u001b[0m \u001b[38;5;66;03m# OrderedDict just iterates over unique values in data.\u001b[39;00m\n\u001b[0;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[1;32m 218\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m convertible:\n\u001b[1;32m 219\u001b[0m \u001b[38;5;66;03m# this will only be called so long as convertible is True.\u001b[39;00m\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/_api/__init__.py:92\u001b[0m, in \u001b[0;36mcheck_isinstance\u001b[0;34m(types, **kwargs)\u001b[0m\n\u001b[1;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[0;32m---> 92\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;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[1;32m 94\u001b[0m k,\n\u001b[1;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[1;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[1;32m 97\u001b[0m type_name(\u001b[38;5;28mtype\u001b[39m(v))))\n", - "\u001b[0;31mTypeError\u001b[0m: 'value' must be an instance of str or bytes, not a float", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mConversionError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/pyplot.py:279\u001b[0m, in \u001b[0;36m_draw_all_if_interactive\u001b[0;34m()\u001b[0m\n\u001b[1;32m 277\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_draw_all_if_interactive\u001b[39m() \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 278\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m matplotlib\u001b[38;5;241m.\u001b[39mis_interactive():\n\u001b[0;32m--> 279\u001b[0m \u001b[43mdraw_all\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/_pylab_helpers.py:131\u001b[0m, in \u001b[0;36mGcf.draw_all\u001b[0;34m(cls, force)\u001b[0m\n\u001b[1;32m 129\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m manager \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mget_all_fig_managers():\n\u001b[1;32m 130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m force \u001b[38;5;129;01mor\u001b[39;00m manager\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39mstale:\n\u001b[0;32m--> 131\u001b[0m \u001b[43mmanager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw_idle\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/backend_bases.py:1891\u001b[0m, in \u001b[0;36mFigureCanvasBase.draw_idle\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1889\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_idle_drawing:\n\u001b[1;32m 1890\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_idle_draw_cntx():\n\u001b[0;32m-> 1891\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\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[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/backends/backend_agg.py:382\u001b[0m, in \u001b[0;36mFigureCanvasAgg.draw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 379\u001b[0m \u001b[38;5;66;03m# Acquire a lock on the shared font cache.\u001b[39;00m\n\u001b[1;32m 380\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtoolbar\u001b[38;5;241m.\u001b[39m_wait_cursor_for_draw_cm() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtoolbar\n\u001b[1;32m 381\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m nullcontext()):\n\u001b[0;32m--> 382\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 383\u001b[0m \u001b[38;5;66;03m# A GUI class may be need to update a window using this draw, so\u001b[39;00m\n\u001b[1;32m 384\u001b[0m \u001b[38;5;66;03m# don't forget to call the superclass.\u001b[39;00m\n\u001b[1;32m 385\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mdraw()\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/artist.py:94\u001b[0m, in \u001b[0;36m_finalize_rasterization..draw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m 92\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[1;32m 93\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mdraw_wrapper\u001b[39m(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 94\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\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\u001b[1;32m 95\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[1;32m 96\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/artist.py:71\u001b[0m, in \u001b[0;36mallow_rasterization..draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \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 69\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 71\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \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", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/figure.py:3257\u001b[0m, in \u001b[0;36mFigure.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 3254\u001b[0m \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[1;32m 3256\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[0;32m-> 3257\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3258\u001b[0m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3260\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfigure\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 3261\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/image.py:134\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m 132\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m 133\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 134\u001b[0m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 136\u001b[0m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m 137\u001b[0m image_group \u001b[38;5;241m=\u001b[39m []\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/artist.py:71\u001b[0m, in \u001b[0;36mallow_rasterization..draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \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 69\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 71\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \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", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/axes/_base.py:3181\u001b[0m, in \u001b[0;36m_AxesBase.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 3178\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artists_rasterized:\n\u001b[1;32m 3179\u001b[0m _draw_rasterized(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_figure(root\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m), artists_rasterized, renderer)\n\u001b[0;32m-> 3181\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3182\u001b[0m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mroot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3184\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maxes\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 3185\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/image.py:134\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m 132\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m 133\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 134\u001b[0m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 136\u001b[0m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m 137\u001b[0m image_group \u001b[38;5;241m=\u001b[39m []\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/artist.py:71\u001b[0m, in \u001b[0;36mallow_rasterization..draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \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 69\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 71\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \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", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/collections.py:1017\u001b[0m, in \u001b[0;36m_CollectionWithSizes.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 1014\u001b[0m \u001b[38;5;129m@artist\u001b[39m\u001b[38;5;241m.\u001b[39mallow_rasterization\n\u001b[1;32m 1015\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mdraw\u001b[39m(\u001b[38;5;28mself\u001b[39m, renderer):\n\u001b[1;32m 1016\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_sizes(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sizes, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_figure(root\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\u001b[38;5;241m.\u001b[39mdpi)\n\u001b[0;32m-> 1017\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/artist.py:71\u001b[0m, in \u001b[0;36mallow_rasterization..draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \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 69\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 71\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \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", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/collections.py:360\u001b[0m, in \u001b[0;36mCollection.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 356\u001b[0m renderer\u001b[38;5;241m.\u001b[39mopen_group(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_gid())\n\u001b[1;32m 358\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mupdate_scalarmappable()\n\u001b[0;32m--> 360\u001b[0m transform, offset_trf, offsets, paths \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_prepare_points\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 362\u001b[0m gc \u001b[38;5;241m=\u001b[39m renderer\u001b[38;5;241m.\u001b[39mnew_gc()\n\u001b[1;32m 363\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_set_gc_clip(gc)\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/collections.py:332\u001b[0m, in \u001b[0;36mCollection._prepare_points\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 330\u001b[0m ys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_yunits(ys)\n\u001b[1;32m 331\u001b[0m paths\u001b[38;5;241m.\u001b[39mappend(mpath\u001b[38;5;241m.\u001b[39mPath(np\u001b[38;5;241m.\u001b[39mcolumn_stack([xs, ys]), path\u001b[38;5;241m.\u001b[39mcodes))\n\u001b[0;32m--> 332\u001b[0m xs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconvert_xunits\u001b[49m\u001b[43m(\u001b[49m\u001b[43moffsets\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 333\u001b[0m ys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_yunits(offsets[:, \u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m 334\u001b[0m offsets \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mma\u001b[38;5;241m.\u001b[39mcolumn_stack([xs, ys])\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/artist.py:278\u001b[0m, in \u001b[0;36mArtist.convert_xunits\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 276\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m ax\u001b[38;5;241m.\u001b[39mxaxis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 277\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m x\n\u001b[0;32m--> 278\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxaxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconvert_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/axis.py:1824\u001b[0m, in \u001b[0;36mAxis.convert_units\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 1822\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_converter\u001b[38;5;241m.\u001b[39mconvert(x, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munits, \u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m 1823\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m-> 1824\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m munits\u001b[38;5;241m.\u001b[39mConversionError(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFailed to convert value(s) to axis \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 1825\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124munits: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m 1826\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ret\n", - "\u001b[0;31mConversionError\u001b[0m: Failed to convert value(s) to axis units: masked_array(data=[--, --, --, --, --, --, --, --, --, --, --, --, --, --,\n --, --, --, --, --, --, --, --, --, --, --, --, --, --,\n --, --, --, --, --, --, --, --, --, --, --, --, --, --,\n --, --, --, --, --, --, --, --, --],\n mask=[ True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True],\n fill_value=1e+20,\n dtype=float64)" - ] - }, - { - "ename": "ConversionError", - "evalue": "Failed to convert value(s) to axis units: masked_array(data=[--, --, --, --, --, --, --, --, --, --, --, --, --, --,\n --, --, --, --, --, --, --, --, --, --, --, --, --, --,\n --, --, --, --, --, --, --, --, --, --, --, --, --, --,\n --, --, --, --, --, --, --, --, --],\n mask=[ True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True],\n fill_value=1e+20,\n dtype=float64)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/axis.py:1822\u001b[0m, in \u001b[0;36mAxis.convert_units\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 1821\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1822\u001b[0m ret \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[43mconvert\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munits\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1823\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/category.py:57\u001b[0m, in \u001b[0;36mStrCategoryConverter.convert\u001b[0;34m(value, unit, axis)\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[38;5;66;03m# force an update so it also does type checking\u001b[39;00m\n\u001b[0;32m---> 57\u001b[0m \u001b[43munit\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 58\u001b[0m s \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mvectorize(unit\u001b[38;5;241m.\u001b[39m_mapping\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__getitem__\u001b[39m, otypes\u001b[38;5;241m=\u001b[39m[\u001b[38;5;28mfloat\u001b[39m])(values)\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/category.py:217\u001b[0m, in \u001b[0;36mUnitData.update\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;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[1;32m 216\u001b[0m \u001b[38;5;66;03m# OrderedDict just iterates over unique values in data.\u001b[39;00m\n\u001b[0;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[1;32m 218\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m convertible:\n\u001b[1;32m 219\u001b[0m \u001b[38;5;66;03m# this will only be called so long as convertible is True.\u001b[39;00m\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/_api/__init__.py:92\u001b[0m, in \u001b[0;36mcheck_isinstance\u001b[0;34m(types, **kwargs)\u001b[0m\n\u001b[1;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[0;32m---> 92\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;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[1;32m 94\u001b[0m k,\n\u001b[1;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[1;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[1;32m 97\u001b[0m type_name(\u001b[38;5;28mtype\u001b[39m(v))))\n", - "\u001b[0;31mTypeError\u001b[0m: 'value' must be an instance of str or bytes, not a float", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mConversionError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/IPython/core/formatters.py:402\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 400\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m 401\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 402\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mprinter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 403\u001b[0m \u001b[38;5;66;03m# Finally look for special method names\u001b[39;00m\n\u001b[1;32m 404\u001b[0m method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/IPython/core/pylabtools.py:170\u001b[0m, in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m 167\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_bases\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m FigureCanvasBase\n\u001b[1;32m 168\u001b[0m FigureCanvasBase(fig)\n\u001b[0;32m--> 170\u001b[0m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbytes_io\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[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 171\u001b[0m data \u001b[38;5;241m=\u001b[39m bytes_io\u001b[38;5;241m.\u001b[39mgetvalue()\n\u001b[1;32m 172\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fmt \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msvg\u001b[39m\u001b[38;5;124m'\u001b[39m:\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/backend_bases.py:2155\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m 2152\u001b[0m \u001b[38;5;66;03m# we do this instead of `self.figure.draw_without_rendering`\u001b[39;00m\n\u001b[1;32m 2153\u001b[0m \u001b[38;5;66;03m# so that we can inject the orientation\u001b[39;00m\n\u001b[1;32m 2154\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(renderer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_draw_disabled\u001b[39m\u001b[38;5;124m\"\u001b[39m, nullcontext)():\n\u001b[0;32m-> 2155\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2156\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches:\n\u001b[1;32m 2157\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtight\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/artist.py:94\u001b[0m, in \u001b[0;36m_finalize_rasterization..draw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m 92\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[1;32m 93\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mdraw_wrapper\u001b[39m(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 94\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\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\u001b[1;32m 95\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[1;32m 96\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/artist.py:71\u001b[0m, in \u001b[0;36mallow_rasterization..draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \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 69\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 71\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \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", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/figure.py:3257\u001b[0m, in \u001b[0;36mFigure.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 3254\u001b[0m \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[1;32m 3256\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[0;32m-> 3257\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3258\u001b[0m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3260\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfigure\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 3261\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/image.py:134\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m 132\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m 133\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 134\u001b[0m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 136\u001b[0m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m 137\u001b[0m image_group \u001b[38;5;241m=\u001b[39m []\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/artist.py:71\u001b[0m, in \u001b[0;36mallow_rasterization..draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \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 69\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 71\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \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", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/axes/_base.py:3181\u001b[0m, in \u001b[0;36m_AxesBase.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 3178\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artists_rasterized:\n\u001b[1;32m 3179\u001b[0m _draw_rasterized(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_figure(root\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m), artists_rasterized, renderer)\n\u001b[0;32m-> 3181\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3182\u001b[0m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mroot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3184\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maxes\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 3185\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/image.py:134\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m 132\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m 133\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 134\u001b[0m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 136\u001b[0m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m 137\u001b[0m image_group \u001b[38;5;241m=\u001b[39m []\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/artist.py:71\u001b[0m, in \u001b[0;36mallow_rasterization..draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \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 69\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 71\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \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", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/collections.py:1017\u001b[0m, in \u001b[0;36m_CollectionWithSizes.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 1014\u001b[0m \u001b[38;5;129m@artist\u001b[39m\u001b[38;5;241m.\u001b[39mallow_rasterization\n\u001b[1;32m 1015\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mdraw\u001b[39m(\u001b[38;5;28mself\u001b[39m, renderer):\n\u001b[1;32m 1016\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_sizes(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sizes, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_figure(root\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\u001b[38;5;241m.\u001b[39mdpi)\n\u001b[0;32m-> 1017\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/artist.py:71\u001b[0m, in \u001b[0;36mallow_rasterization..draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \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 69\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 71\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \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", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/collections.py:360\u001b[0m, in \u001b[0;36mCollection.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 356\u001b[0m renderer\u001b[38;5;241m.\u001b[39mopen_group(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_gid())\n\u001b[1;32m 358\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mupdate_scalarmappable()\n\u001b[0;32m--> 360\u001b[0m transform, offset_trf, offsets, paths \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_prepare_points\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 362\u001b[0m gc \u001b[38;5;241m=\u001b[39m renderer\u001b[38;5;241m.\u001b[39mnew_gc()\n\u001b[1;32m 363\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_set_gc_clip(gc)\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/collections.py:332\u001b[0m, in \u001b[0;36mCollection._prepare_points\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 330\u001b[0m ys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_yunits(ys)\n\u001b[1;32m 331\u001b[0m paths\u001b[38;5;241m.\u001b[39mappend(mpath\u001b[38;5;241m.\u001b[39mPath(np\u001b[38;5;241m.\u001b[39mcolumn_stack([xs, ys]), path\u001b[38;5;241m.\u001b[39mcodes))\n\u001b[0;32m--> 332\u001b[0m xs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconvert_xunits\u001b[49m\u001b[43m(\u001b[49m\u001b[43moffsets\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 333\u001b[0m ys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_yunits(offsets[:, \u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m 334\u001b[0m offsets \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mma\u001b[38;5;241m.\u001b[39mcolumn_stack([xs, ys])\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/artist.py:278\u001b[0m, in \u001b[0;36mArtist.convert_xunits\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 276\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m ax\u001b[38;5;241m.\u001b[39mxaxis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 277\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m x\n\u001b[0;32m--> 278\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxaxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconvert_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/lab-pokemon-8CYd8AD7-py3.13/lib/python3.13/site-packages/matplotlib/axis.py:1824\u001b[0m, in \u001b[0;36mAxis.convert_units\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 1822\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_converter\u001b[38;5;241m.\u001b[39mconvert(x, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munits, \u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m 1823\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m-> 1824\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m munits\u001b[38;5;241m.\u001b[39mConversionError(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFailed to convert value(s) to axis \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 1825\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124munits: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m 1826\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ret\n", - "\u001b[0;31mConversionError\u001b[0m: Failed to convert value(s) to axis units: masked_array(data=[--, --, --, --, --, --, --, --, --, --, --, --, --, --,\n --, --, --, --, --, --, --, --, --, --, --, --, --, --,\n --, --, --, --, --, --, --, --, --, --, --, --, --, --,\n --, --, --, --, --, --, --, --, --],\n mask=[ True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True, True, True, True, True, True,\n True, True, True],\n fill_value=1e+20,\n dtype=float64)" - ] - }, { "data": { + "image/png": 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -757,9 +683,218 @@ } ], "source": [ - "favorite_types = df[df.region.isin([\"percentage_drivers_fatal_not_distracted\", \"percentage_drivers_fatal_no_previous_accidents\"])]\n", - "sns.relplot(data=df, x=\"region\", )\n", - "\n" + "sns.barplot(data=region_mean, x=\"region\", y=\"percentage_drivers_fatal_not_distracted\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "21d5122b-9973-43f9-b4f0-7db7a6c936d9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.barplot(data=region_mean, x=\"region\", y=\"percentage_drivers_fatal_no_previous_accidents\")" + ] + }, + { + "cell_type": "markdown", + "id": "d66967db-fe78-4889-824e-f7bce4e02cc8", + "metadata": {}, + "source": [ + "## Fourth Research Question: Is there a connection between the average Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding and the region with the most expensive car insurance premiums?" + ] + }, + { + "cell_type": "markdown", + "id": "9661b6d4-3c4f-42a2-8916-b9df38375760", + "metadata": {}, + "source": [ + "### Methods" + ] + }, + { + "cell_type": "markdown", + "id": "cc44ade7-3ae9-44a0-b821-29ecb1b66385", + "metadata": {}, + "source": [ + "Explain how you will approach this research question below. Consider the following:\n", + "\n", + "Which aspects of the dataset will you use?\n", + "How will you reorganize/store the data?\n", + "What data science tools/functions will you use and why?\n", + "✏️ Write your answer below:\n", + "\n", + "To answer this question, I will organize the data for each state by the region it is in. Then, compare the average Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding to see if there is a connection with the region with the highest car insurance." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "b921b74c-951a-4f30-a42e-292f011fd61a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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percentage_drivers_fatal_speedingcar_insurance_premiums
region
Midwest27.166667756.630833
Northeast32.444444975.038889
Northwest39.3333331160.163333
Southeast27.687500905.472500
West39.909091855.624545
\n", + "
" + ], + "text/plain": [ + " percentage_drivers_fatal_speeding car_insurance_premiums\n", + "region \n", + "Midwest 27.166667 756.630833\n", + "Northeast 32.444444 975.038889\n", + "Northwest 39.333333 1160.163333\n", + "Southeast 27.687500 905.472500\n", + "West 39.909091 855.624545" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "region = df.groupby(\"region\")\n", + "region_speed_insur = region[[\"percentage_drivers_fatal_speeding\", \"car_insurance_premiums\"]].mean().sort_values(\"region\")\n", + "region_speed_insur" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "c242ede2-9440-4d49-8967-e6dfce650ec1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.barplot(data=region_speed_insur, x=\"region\", y=\"car_insurance_premiums\")" ] }, {