From 10e1bac03854a48206f9fe2e202e65f30e374c65 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Noem=C3=AD=20Cervero?= Date: Thu, 9 Nov 2023 18:01:36 +0100 Subject: [PATCH] finished lab --- your-code/main.ipynb | 338 ++++++++++++++++++++++++++++++++++++++----- 1 file changed, 300 insertions(+), 38 deletions(-) diff --git a/your-code/main.ipynb b/your-code/main.ipynb index 11ab621..ef99356 100644 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -23,6 +23,19 @@ "Use either or both of the lbraries in this exercise." ] }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import uniform\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import seaborn as sns" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -35,11 +48,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2.25006954 2.36733315 2.47214325 2.19106213 2.28382575 2.97955895\n", + " 2.9962977 2.17769995 2.81955283 2.44111746]\n" + ] + } + ], "source": [ - "from scipy.stats import uniform\n", "x = uniform.rvs(size=10)\n", "a = 2\n", "b = 3\n", @@ -73,11 +94,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here" + "def uniformly_distributed(bottom, ceiling, count):\n", + " return np.random.uniform(bottom, ceiling, count)\n", + "\n", + "fig, ax = plt.subplots(1, 2, sharey=True)\n", + "\n", + "arr1 = uniformly_distributed(10, 15, 100)\n", + "ax[0].hist(arr1, bins=10)\n", + "ax[0].set_xticks(np.arange(10, 16, 1))\n", + "\n", + "arr2 = uniformly_distributed(10, 60, 1000)\n", + "ax[1].hist(arr2, bins=10)\n", + "ax[1].set_xticks(np.arange(10, max(arr2) + 10, 10))\n", + "\n", + "plt.show()" ] }, { @@ -114,11 +159,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 61, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here" + "def normal_distributed(mean, stdev, count):\n", + " return np.random.normal(mean, stdev, count)\n", + "\n", + "fig, ax = plt.subplots(1, 2, sharey=True)\n", + "\n", + "arr1 = normal_distributed(10, 1, 1000)\n", + "ax[0].hist(arr1, bins=50)\n", + "\n", + "arr2 = normal_distributed(10, 50, 1000)\n", + "ax[1].hist(arr2, bins=50)\n", + "\n", + "plt.show()" ] }, { @@ -152,17 +219,137 @@ "source": [ "First import vehicles.csv.\n", "Then plot the histograms for the following variables:\n", - "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
MakeModelYearEngine DisplacementCylindersTransmissionDrivetrainVehicle ClassFuel TypeFuel Barrels/YearCity MPGHighway MPGCombined MPGCO2 Emission Grams/MileFuel Cost/Year
25572MitsubishiLancer Sportback20132.44.0Auto(AV-S6)Front-Wheel DriveSmall Station WagonsRegular13.1844222925358.01350
\n", + "
" + ], + "text/plain": [ + " Make Model Year Engine Displacement Cylinders \\\n", + "25572 Mitsubishi Lancer Sportback 2013 2.4 4.0 \n", + "\n", + " Transmission Drivetrain Vehicle Class Fuel Type \\\n", + "25572 Auto(AV-S6) Front-Wheel Drive Small Station Wagons Regular \n", + "\n", + " Fuel Barrels/Year City MPG Highway MPG Combined MPG \\\n", + "25572 13.1844 22 29 25 \n", + "\n", + " CO2 Emission Grams/Mile Fuel Cost/Year \n", + "25572 358.0 1350 " + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vehicles = pd.read_csv('vehicles.csv')\n", + "vehicles.sample()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "1. Fuel Barrels/Year" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 89, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Years')" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here" + "vehicles['Fuel Barrels/Year']\n", + "sns.histplot(data=vehicles, x=\"Fuel Barrels/Year\", bins=50)\n", + "plt.axvline(vehicles['Fuel Barrels/Year'].mean(), c=\"r\")\n", + "plt.ylabel(\"Years\");" ] }, { @@ -174,11 +361,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 90, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here" + "sns.histplot(data=vehicles, x=\"CO2 Emission Grams/Mile\", bins=50)\n", + "plt.axvline(vehicles['CO2 Emission Grams/Mile'].mean(), c=\"r\")\n", + "plt.ylabel(\"Years\");" ] }, { @@ -190,11 +390,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 91, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkQAAAGwCAYAAABIC3rIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA3T0lEQVR4nO3dfXQU9d3//9cSyHK7gQC5K0kaigIBAgIKe6mUmzQBUoRCzyXCJVhRLzCxQijyzU9EBBELVcQaoVYUvQqCeAFWEEgAAZEAmhrBgLkEY4NlN+mRkk0iJJDM7w8PU1cgJphkN5nn45w5JzOfd2bfMyeVV2c+M2szDMMQAACAhTXzdQMAAAC+RiACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACW19zXDTQGVVVVOnPmjNq1ayebzebrdgAAQA0YhqGSkhJFRESoWbPqrwERiGrgzJkzioyM9HUbAADgOpw+fVpdunSptoZAVAPt2rWT9O0JdTgcPu4GjUJZmRQR8e3PZ85Ibdr4th8AsCCPx6PIyEjz3/HqEIhq4PJtMofDQSBCzQQE/Ptnh4NABAA+VJPpLkyqBgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAltfc1w0A/qRXXD+5XK5qa8LDw5V7NKdhGgIANAgCEfAdLpdLCYu3VFuT8ei4BukFANBwfHrLbOXKlYqLi5PD4ZDD4ZDT6dT27dvN8aFDh8pms3kt06dP99pHQUGBkpKS1Lp1a4WEhGjOnDm6dOmSV83evXvVv39/2e12devWTWvWrGmIwwMAAI2ET68QdenSRU8//bRuuOEGGYah1157TWPHjtXHH3+sXr16SZLuv/9+LVy40Pyd1q1bmz9XVlYqKSlJYWFhOnjwoFwul6ZMmaIWLVroqaeekiTl5+crKSlJ06dP19q1a7V7927dd999Cg8PV2JiYsMeMAAA8Es+DURjxozxWl+8eLFWrlypQ4cOmYGodevWCgsLu+rvZ2Rk6Pjx49q1a5dCQ0PVr18/LVq0SHPnztWCBQsUGBioVatWKSYmRs8884wkqWfPnjpw4ICWL19OIAIAAJL86CmzyspKrV+/XmVlZXI6neb2tWvXqlOnTurdu7fS0tL0zTffmGNZWVnq06ePQkNDzW2JiYnyeDzKzc01a+Lj470+KzExUVlZWdfspby8XB6Px2sBAABNl88nVR87dkxOp1MXLlxQ27ZttXnzZsXGxkqSJk2apOjoaEVEROjo0aOaO3eu8vLytGnTJkmS2+32CkOSzHW3211tjcfj0fnz59WqVasrelqyZImeeOKJOj9WAADgn3weiLp3766cnBwVFxfrrbfe0tSpU7Vv3z7FxsbqgQceMOv69Omj8PBwjRgxQqdOndLPfvazeuspLS1Nqamp5rrH41FkZGS9fR4AAPAtn98yCwwMVLdu3TRgwAAtWbJEffv21YoVK65aO2jQIEnSyZMnJUlhYWEqLCz0qrm8fnne0bVqHA7HVa8OSZLdbjeffLu8AACApsvngej7qqqqVF5eftWxnJwcSd++GE+SnE6njh07pqKiIrMmMzNTDofDvO3mdDq1e/dur/1kZmZ6zVMCAADW5tNbZmlpaRo1apSioqJUUlKidevWae/evdq5c6dOnTqldevWafTo0erYsaOOHj2qWbNmaciQIYqLi5MkJSQkKDY2VnfffbeWLl0qt9utefPmKTk5WXa7XZI0ffp0vfDCC3rkkUd07733as+ePXrzzTe1bds2Xx46AADwIz4NREVFRZoyZYpcLpeCgoIUFxennTt36he/+IVOnz6tXbt26bnnnlNZWZkiIyM1YcIEzZs3z/z9gIAAbd26VTNmzJDT6VSbNm00depUr/cWxcTEaNu2bZo1a5ZWrFihLl266OWXX+aRewAAYLIZhmH4ugl/5/F4FBQUpOLiYuYTNXHBnUNr9NUdZ/9ZWG2Nysqktm2//bm0VGrTpm4aBADUWG3+/fa7OUQAAAANjUAEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsr7mvGwAaG09JqYI7h1Zb0zU0RB81UD8AgB+PQATUklFVpYTFW6qtOfD/jW2YZgAAdYJbZgAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPL46g7AR3rF9ZPL5aq2Jjw8XLlHcxqmIQCwMAIR4CMul+sHvxMt49FxDdILAFgdt8wAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDl8ZQZUA88JaXmz12iY/SNzXaVmpKGbAkAUA0CEVAPjKoq8+cRj69Xub3VFTUbU4Y3ZEsAgGpwywwAAFgegQgAAFgegQgAAFieTwPRypUrFRcXJ4fDIYfDIafTqe3bt5vjFy5cUHJysjp27Ki2bdtqwoQJKiws9NpHQUGBkpKS1Lp1a4WEhGjOnDm6dOmSV83evXvVv39/2e12devWTWvWrGmIwwMAAI2ETwNRly5d9PTTTys7O1sfffSRhg8frrFjxyo3N1eSNGvWLL3zzjvauHGj9u3bpzNnzmj8+PHm71dWViopKUkVFRU6ePCgXnvtNa1Zs0bz5883a/Lz85WUlKRhw4YpJydHM2fO1H333aedO3c2+PECAAD/5NOnzMaMGeO1vnjxYq1cuVKHDh1Sly5dtHr1aq1bt07Dh3/7NM6rr76qnj176tChQxo8eLAyMjJ0/Phx7dq1S6GhoerXr58WLVqkuXPnasGCBQoMDNSqVasUExOjZ555RpLUs2dPHThwQMuXL1diYmKDHzMAAPA/fjOHqLKyUuvXr1dZWZmcTqeys7N18eJFxcfHmzU9evRQVFSUsrKyJElZWVnq06ePQkNDzZrExER5PB7zKlNWVpbXPi7XXN7H1ZSXl8vj8XgtAACg6fJ5IDp27Jjatm0ru92u6dOna/PmzYqNjZXb7VZgYKDat2/vVR8aGiq32y1JcrvdXmHo8vjlsepqPB6Pzp8/f9WelixZoqCgIHOJjIysi0MFAAB+yueBqHv37srJydHhw4c1Y8YMTZ06VcePH/dpT2lpaSouLjaX06dP+7QfAABQv3z+purAwEB169ZNkjRgwAB9+OGHWrFihe68805VVFTo3LlzXleJCgsLFRYWJkkKCwvTkSNHvPZ3+Sm079Z8/8m0wsJCORwOtWp15duDJclut8tut9fJ8QEAAP/n8ytE31dVVaXy8nINGDBALVq00O7du82xvLw8FRQUyOl0SpKcTqeOHTumoqIisyYzM1MOh0OxsbFmzXf3cbnm8j4AAAB8eoUoLS1No0aNUlRUlEpKSrRu3Trt3btXO3fuVFBQkKZNm6bU1FQFBwfL4XDooYcektPp1ODBgyVJCQkJio2N1d13362lS5fK7XZr3rx5Sk5ONq/wTJ8+XS+88IIeeeQR3XvvvdqzZ4/efPNNbdu2zZeHDgAA/IhPA1FRUZGmTJkil8uloKAgxcXFaefOnfrFL34hSVq+fLmaNWumCRMmqLy8XImJiXrxxRfN3w8ICNDWrVs1Y8YMOZ1OtWnTRlOnTtXChQvNmpiYGG3btk2zZs3SihUr1KVLF7388ss8cg8AAEw+DUSrV6+udrxly5ZKT09Xenr6NWuio6P17rvvVrufoUOH6uOPP76uHgEAQNPnd3OIAAAAGhqBCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWJ5PA9GSJUt08803q127dgoJCdG4ceOUl5fnVTN06FDZbDavZfr06V41BQUFSkpKUuvWrRUSEqI5c+bo0qVLXjV79+5V//79Zbfb1a1bN61Zs6a+Dw8AADQSPg1E+/btU3Jysg4dOqTMzExdvHhRCQkJKisr86q7//775XK5zGXp0qXmWGVlpZKSklRRUaGDBw/qtdde05o1azR//nyzJj8/X0lJSRo2bJhycnI0c+ZM3Xfffdq5c2eDHSsAAPBfzX354Tt27PBaX7NmjUJCQpSdna0hQ4aY21u3bq2wsLCr7iMjI0PHjx/Xrl27FBoaqn79+mnRokWaO3euFixYoMDAQK1atUoxMTF65plnJEk9e/bUgQMHtHz5ciUmJl6xz/LycpWXl5vrHo+nLg4XAAD4Kb+aQ1RcXCxJCg4O9tq+du1aderUSb1791ZaWpq++eYbcywrK0t9+vRRaGiouS0xMVEej0e5ublmTXx8vNc+ExMTlZWVddU+lixZoqCgIHOJjIysk+MDAAD+yadXiL6rqqpKM2fO1K233qrevXub2ydNmqTo6GhFRETo6NGjmjt3rvLy8rRp0yZJktvt9gpDksx1t9tdbY3H49H58+fVqlUrr7G0tDSlpqaa6x6Ph1AEAEAT5jeBKDk5WZ9++qkOHDjgtf2BBx4wf+7Tp4/Cw8M1YsQInTp1Sj/72c/qpRe73S673V4v+wYAAP7HL26ZpaSkaOvWrXrvvffUpUuXamsHDRokSTp58qQkKSwsTIWFhV41l9cvzzu6Vo3D4bji6hAAALAenwYiwzCUkpKizZs3a8+ePYqJifnB38nJyZEkhYeHS5KcTqeOHTumoqIisyYzM1MOh0OxsbFmze7du732k5mZKafTWUdHAgAAGjOfBqLk5GT95S9/0bp169SuXTu53W653W6dP39eknTq1CktWrRI2dnZ+vLLL/XXv/5VU6ZM0ZAhQxQXFydJSkhIUGxsrO6++2598skn2rlzp+bNm6fk5GTzttf06dP1xRdf6JFHHtFnn32mF198UW+++aZmzZrls2MHAAD+w6eBaOXKlSouLtbQoUMVHh5uLhs2bJAkBQYGateuXUpISFCPHj00e/ZsTZgwQe+88465j4CAAG3dulUBAQFyOp36r//6L02ZMkULFy40a2JiYrRt2zZlZmaqb9++euaZZ/Tyyy9f9ZF7AABgPT6dVG0YRrXjkZGR2rdv3w/uJzo6Wu+++261NUOHDtXHH39cq/4AAIA1+M1TZkB96xXXTy6Xq9oaT0lJA3UDAPAnBCJYhsvlUsLiLdXWbEwZ3jDNAAD8il88dg8AAOBLBCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5vKkaTQJfywEA+DEIRGgS+FoOAMCPwS0zAABgeQQiAABgeQQiAABgeQQiAABgeUyqBvyYp6RUwZ1Dq60JDw9X7tGchmkIAJooAhH8npUfqTeqqn7w6bmMR8c1SC8A0JQRiOD3eKQeAFDfmEMEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsz6eBaMmSJbr55pvVrl07hYSEaNy4ccrLy/OquXDhgpKTk9WxY0e1bdtWEyZMUGFhoVdNQUGBkpKS1Lp1a4WEhGjOnDm6dOmSV83evXvVv39/2e12devWTWvWrKnvwwMAAI1ErQPRjh07dODAAXM9PT1d/fr106RJk/Svf/2rVvvat2+fkpOTdejQIWVmZurixYtKSEhQWVmZWTNr1iy988472rhxo/bt26czZ85o/Pjx5nhlZaWSkpJUUVGhgwcP6rXXXtOaNWs0f/58syY/P19JSUkaNmyYcnJyNHPmTN13333auXNnbQ8fAAA0QbUORHPmzJHH45EkHTt2TLNnz9bo0aOVn5+v1NTUWu1rx44duueee9SrVy/17dtXa9asUUFBgbKzsyVJxcXFWr16tZ599lkNHz5cAwYM0KuvvqqDBw/q0KFDkqSMjAwdP35cf/nLX9SvXz+NGjVKixYtUnp6uioqKiRJq1atUkxMjJ555hn17NlTKSkp+vWvf63ly5fX9vABAEATVOtAlJ+fr9jYWEnS//7v/+qXv/ylnnrqKaWnp2v79u0/qpni4mJJUnBwsCQpOztbFy9eVHx8vFnTo0cPRUVFKSsrS5KUlZWlPn36KDQ01KxJTEyUx+NRbm6uWfPdfVyuubyP7ysvL5fH4/FaAABA01XrQBQYGKhvvvlGkrRr1y4lJCRI+jbE/JjgUFVVpZkzZ+rWW29V7969JUlut1uBgYFq3769V21oaKjcbrdZ890wdHn88lh1NR6PR+fPn7+ilyVLligoKMhcIiMjr/u4AACA/6t1ILr11luVmpqqRYsW6ciRI0pKSpIk/d///Z+6dOly3Y0kJyfr008/1fr16697H3UlLS1NxcXF5nL69GlftwQAAOpRrQNRenq6WrRoobfeeksrV67UT37yE0nS9u3bNXLkyOtqIiUlRVu3btV7773nFarCwsJUUVGhc+fOedUXFhYqLCzMrPn+U2eX13+oxuFwqFWrVlf0Y7fb5XA4vBYAANB0Na9N8aVLl7R37179+c9/NsPGZdczQdkwDD300EPavHmz9u7dq5iYGK/xAQMGqEWLFtq9e7cmTJggScrLy1NBQYGcTqckyel0avHixSoqKlJISIgkKTMzUw6Hw5zr5HQ69e6773rtOzMz09wHAACwtlpdIWrevLmmT5+u8vLyOvnw5ORk/eUvf9G6devUrl07ud1uud1uc15PUFCQpk2bptTUVL333nvKzs7Wb37zGzmdTg0ePFiSlJCQoNjYWN1999365JNPtHPnTs2bN0/Jycmy2+2SpOnTp+uLL77QI488os8++0wvvvii3nzzTc2aNatOjgMAADRutbpCJEm33HKLPv74Y0VHR//oD1+5cqUkaejQoV7bX331Vd1zzz2Svr3y1KxZM02YMEHl5eVKTEzUiy++aNYGBARo69atmjFjhpxOp9q0aaOpU6dq4cKFZk1MTIy2bdumWbNmacWKFerSpYtefvllJSYm/uhjwI/TK66fXC5XtTWekpIG6gYAYFW1DkQPPvigZs+era+++koDBgxQmzZtvMbj4uJqvC/DMH6wpmXLlkpPT1d6evo1a6Kjo6+4JfZ9Q4cO1ccff1zj3tAwXC6XEhZvqbZmY8rwhmkGAGBZtQ5EEydOlCT99re/NbfZbDYZhiGbzabKysq66w4AAKAB1DoQ5efn10cfAAAAPlPrQFQXc4cAAAD8Sa0D0WXHjx9XQUGB+X1hl91xxx0/uikAAICGVOtA9MUXX+hXv/qVjh07Zs4dkr6dRySJOUQAAKDRqfWbqh9++GHFxMSoqKhIrVu3Vm5urvbv36+BAwdq79699dAiAABA/ar1FaKsrCzt2bNHnTp1UrNmzdSsWTPddtttWrJkiX7729/yaDsAAGh0an2FqLKyUu3atZMkderUSWfOnJH07WTrvLy8uu0OAACgAdT6ClHv3r31ySefKCYmRoMGDdLSpUsVGBiol156SV27dq2PHgEAAOpVrQPRvHnzVFZWJklauHChfvnLX+r2229Xx44dtWHDhjpvEAAAoL7VOhB99/u/unXrps8++0xnz55Vhw4dzCfNAAAAGpNazyG67OTJk9q5c6fOnz+v4ODguuwJAACgQdU6EH399dcaMWKEbrzxRo0ePdr8pvJp06Zp9uzZdd4gAABAfat1IJo1a5ZatGihgoICtW7d2tx+5513aseOHXXaHIAf5ikpVXDn0GqXXnH9fN0mAPi1Ws8hysjI0M6dO9WlSxev7TfccIP+/ve/11ljAGrGqKpSwuIt1dZkPDquQXoBgMaq1leIysrKvK4MXXb27FnZ7fY6aQoAAKAh1TgQXX4B4+23367XX3/d3G6z2VRVVaWlS5dq2LBhdd8hAABAPavxLbNevXopPT1dy5Yt0/Dhw/XRRx+poqJCjzzyiHJzc3X27Fl98MEH9dkrAABAvahxIFq8eLH++7//WyNHjtTx48e1atUqtWvXTqWlpRo/frySk5MVHh5en70CAADUixoHogcffFCjRo3StGnT1KtXL7300kt69NFH67M3AACABlGrp8xiYmK0Z88evfDCC5owYYJ69uyp5s29d/G3v/2tThsEAACob7V+7P7vf/+7Nm3apA4dOmjs2LFXBCIAAIDGplZp5s9//rNmz56t+Ph45ebmqnPnzvXVFwAAQIOpcSAaOXKkjhw5ohdeeEFTpkypz54AAAAaVI0DUWVlpY4ePXrFG6oBAAAauxoHoszMzPrsAwAAwGdq/dUdAAAATQ2BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWF6Nv9wVQOPlKSlVcOfQamvCw8OVezSnYRoCAD/j0ytE+/fv15gxYxQRESGbzaYtW7Z4jd9zzz2y2Wxey8iRI71qzp49q8mTJ8vhcKh9+/aaNm2aSktLvWqOHj2q22+/XS1btlRkZKSWLl1a34cG+BWjqkoJi7dUu7hcLl+3CQA+49NAVFZWpr59+yo9Pf2aNSNHjpTL5TKXN954w2t88uTJys3NVWZmprZu3ar9+/frgQceMMc9Ho8SEhIUHR2t7OxsLVu2TAsWLNBLL71Ub8cFAAAaF5/eMhs1apRGjRpVbY3dbldYWNhVx06cOKEdO3boww8/1MCBAyVJf/zjHzV69Gj94Q9/UEREhNauXauKigq98sorCgwMVK9evZSTk6Nnn33WKzh9V3l5ucrLy811j8dznUcIAAAaA7+fVL13716FhISoe/fumjFjhr7++mtzLCsrS+3btzfDkCTFx8erWbNmOnz4sFkzZMgQBQYGmjWJiYnKy8vTv/71r6t+5pIlSxQUFGQukZGR9XR0AADAH/h1IBo5cqRef/117d69W7///e+1b98+jRo1SpWVlZIkt9utkJAQr99p3ry5goOD5Xa7zZrQUO/JpJfXL9d8X1pamoqLi83l9OnTdX1oAADAj/j1U2YTJ040f+7Tp4/i4uL0s5/9THv37tWIESPq7XPtdrvsdnu97R8AAPgXv75C9H1du3ZVp06ddPLkSUlSWFiYioqKvGouXbqks2fPmvOOwsLCVFhY6FVzef1ac5MAAIC1NKpA9NVXX+nrr79WeHi4JMnpdOrcuXPKzs42a/bs2aOqqioNGjTIrNm/f78uXrxo1mRmZqp79+7q0KFDwx4AAADwSz4NRKWlpcrJyVFOTo4kKT8/Xzk5OSooKFBpaanmzJmjQ4cO6csvv9Tu3bs1duxYdevWTYmJiZKknj17auTIkbr//vt15MgRffDBB0pJSdHEiRMVEREhSZo0aZICAwM1bdo05ebmasOGDVqxYoVSU1N9ddgAAMDP+DQQffTRR7rpppt00003SZJSU1N10003af78+QoICNDRo0d1xx136MYbb9S0adM0YMAAvf/++17ze9auXasePXpoxIgRGj16tG677TavdwwFBQUpIyND+fn5GjBggGbPnq358+df85F7AABgPT6dVD106FAZhnHN8Z07d/7gPoKDg7Vu3bpqa+Li4vT+++/Xuj8AAGANjWoOEQAAQH0gEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMsjEAEAAMtr7usGAPgHT0mpgjuHVlsTHh6u3KM5DdMQADQgAhEASZJRVaWExVuqrcl4dFyD9AIADY1bZgAAwPK4QoR60yuun1wuV7U1npKSBuoGAIBrIxCh3rhcrh+8BbMxZXjDNAMAQDW4ZQYAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyvua8bANB4eEpKFdw5tNqa8PBw5R7NaZiGAKCOEIgA1JhRVaWExVuqrcl4dFyD9AIAdYlbZgAAwPJ8Goj279+vMWPGKCIiQjabTVu2bPEaNwxD8+fPV3h4uFq1aqX4+Hh9/vnnXjVnz57V5MmT5XA41L59e02bNk2lpaVeNUePHtXtt9+uli1bKjIyUkuXLq3vQwMAAI2ITwNRWVmZ+vbtq/T09KuOL126VM8//7xWrVqlw4cPq02bNkpMTNSFCxfMmsmTJys3N1eZmZnaunWr9u/frwceeMAc93g8SkhIUHR0tLKzs7Vs2TItWLBAL730Ur0fHwAAaBx8Oodo1KhRGjVq1FXHDMPQc889p3nz5mns2LGSpNdff12hoaHasmWLJk6cqBMnTmjHjh368MMPNXDgQEnSH//4R40ePVp/+MMfFBERobVr16qiokKvvPKKAgMD1atXL+Xk5OjZZ5/1Ck4AAMC6/HYOUX5+vtxut+Lj481tQUFBGjRokLKysiRJWVlZat++vRmGJCk+Pl7NmjXT4cOHzZohQ4YoMDDQrElMTFReXp7+9a9/XfWzy8vL5fF4vBYAANB0+W0gcrvdkqTQUO9HfENDQ80xt9utkJAQr/HmzZsrODjYq+Zq+/juZ3zfkiVLFBQUZC6RkZE//oAAAIDf8ttA5EtpaWkqLi42l9OnT/u6JQAAUI/8NhCFhYVJkgoLC722FxYWmmNhYWEqKiryGr906ZLOnj3rVXO1fXz3M77PbrfL4XB4LQAAoOny20AUExOjsLAw7d6929zm8Xh0+PBhOZ1OSZLT6dS5c+eUnZ1t1uzZs0dVVVUaNGiQWbN//35dvHjRrMnMzFT37t3VoUOHBjoaAADgz3waiEpLS5WTk6OcnBxJ306kzsnJUUFBgWw2m2bOnKknn3xSf/3rX3Xs2DFNmTJFERERGjdunCSpZ8+eGjlypO6//34dOXJEH3zwgVJSUjRx4kRFRERIkiZNmqTAwEBNmzZNubm52rBhg1asWKHU1FQfHTUAAPA3Pn3s/qOPPtKwYcPM9cshZerUqVqzZo0eeeQRlZWV6YEHHtC5c+d02223aceOHWrZsqX5O2vXrlVKSopGjBihZs2aacKECXr++efN8aCgIGVkZCg5OVkDBgxQp06dNH/+fB65BwAAJp8GoqFDh8owjGuO22w2LVy4UAsXLrxmTXBwsNatW1ft58TFxen999+/7j4BAEDT5rdziAAAABoKgQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFhec183gMapV1w/uVyuams8JSUN1A0AAD8OgQjXxeVyKWHxlmprNqYMb5hmAAD4kbhlBgAALI9ABAAALI9ABAAALI85RADqlKekVMGdQ6utCQ8PV+7RnIZpCABqgEAEoE4ZVVU/OOE+49FxDdILANQUt8wAAIDlEYgAAIDlEYgAAIDlEYgAAIDlMakaQIPjSTQA/oZABKDB8SQaAH/DLTMAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5fh2IFixYIJvN5rX06NHDHL9w4YKSk5PVsWNHtW3bVhMmTFBhYaHXPgoKCpSUlKTWrVsrJCREc+bM0aVLlxr6UAAAgB/z+zdV9+rVS7t27TLXmzf/d8uzZs3Stm3btHHjRgUFBSklJUXjx4/XBx98IEmqrKxUUlKSwsLCdPDgQblcLk2ZMkUtWrTQU0891eDHAgAA/JPfB6LmzZsrLCzsiu3FxcVavXq11q1bp+HDh0uSXn31VfXs2VOHDh3S4MGDlZGRoePHj2vXrl0KDQ1Vv379tGjRIs2dO1cLFixQYGDgVT+zvLxc5eXl5rrH46mfgwMAAH7Br2+ZSdLnn3+uiIgIde3aVZMnT1ZBQYEkKTs7WxcvXlR8fLxZ26NHD0VFRSkrK0uSlJWVpT59+ig09N9fIpmYmCiPx6Pc3NxrfuaSJUsUFBRkLpGRkfV0dAAAwB/4dSAaNGiQ1qxZox07dmjlypXKz8/X7bffrpKSErndbgUGBqp9+/ZevxMaGiq32y1JcrvdXmHo8vjlsWtJS0tTcXGxuZw+fbpuDwwAAPgVv75lNmrUKPPnuLg4DRo0SNHR0XrzzTfVqlWrevtcu90uu91eb/sHAAD+xa+vEH1f+/btdeONN+rkyZMKCwtTRUWFzp0751VTWFhozjkKCwu74qmzy+tXm5cEAACsqVEFotLSUp06dUrh4eEaMGCAWrRood27d5vjeXl5KigokNPplCQ5nU4dO3ZMRUVFZk1mZqYcDodiY2MbvH8AAOCf/PqW2e9+9zuNGTNG0dHROnPmjB5//HEFBATorrvuUlBQkKZNm6bU1FQFBwfL4XDooYcektPp1ODBgyVJCQkJio2N1d13362lS5fK7XZr3rx5Sk5O5pYY4Oc8JaUK7hxabU14eLhyj+Y0TEMAmjS/DkRfffWV7rrrLn399dfq3LmzbrvtNh06dEidO3eWJC1fvlzNmjXThAkTVF5ersTERL344ovm7wcEBGjr1q2aMWOGnE6n2rRpo6lTp2rhwoW+OqRGoVdcP7lcrmprPCUlDdQNrMqoqlLC4i3V1mQ8Oq5BegHQ9Pl1IFq/fn214y1btlR6errS09OvWRMdHa133323rltr0lwu1w/+Q7QxZXjDNAMAQANoVHOIAAAA6gOBCAAAWB6BCAAAWB6BCAAAWB6BCAAAWJ5fP2UGANXhXUUA6gqBCECjxbuKANQVbpkBAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADLIxABAADL403VAJo0vt4DQE0QiAA0aXy9B4Ca4JYZAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPB67B4Aa6BXXTy6Xq9oa3mcENF4EIgCWV5OXN3pKSvTr53dXW8P7jIDGi0AEwPJq8vLGjSnDf3A/vBUbaLwIRABQR3grNtB4MakaAABYHleILKYmE0M9JSUN1A0AAP6BQGQxLperTuZKAADQlHDLDAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6TqgGgATXWlzfy1SVo6ghEANCAavLyxrd+G+93oakmT6jy0kk0ZgQiAPAzdfnGa67sADVjqUCUnp6uZcuWye12q2/fvvrjH/+oW265xddt1RleughYR01uvX1b13BfSluTnr45f0GtW7WstoaABl+wTCDasGGDUlNTtWrVKg0aNEjPPfecEhMTlZeXp5CQEF+3Vyd46SJgHTW5iiTV3ZfS1uT/TNX0S3ITnt1RbQ233uALlglEzz77rO6//3795je/kSStWrVK27Zt0yuvvKL/9//+n4+7+2Fc/QFQX2oaZKyqIW87covTdywRiCoqKpSdna20tDRzW7NmzRQfH6+srKwr6svLy1VeXm6uFxcXS5I8Hk+99HeL81YVut3V1nhKSzVu6TvV1mz+3S918XxZtTWGYVDTQDWX/1oqzpfpYlWVz/uhxno1/thTTWqKPSXq0LFztTXfXChX65b2BqmpyX9/9yy8q07+jTjzj39o+Pw3fvRn1eTflZoce2hYmI5kfVBtjT+7fJ4Mw/jhYsMC/vGPfxiSjIMHD3ptnzNnjnHLLbdcUf/4448bklhYWFhYWFiawHL69OkfzAqWuEJUW2lpaUpNTTXXq6qqdPbsWXXs2FE2m82Hnf14Ho9HkZGROn36tBwOh6/bafI43w2L892wON8Ni/Nde4ZhqKSkRBERET9Ya4lA1KlTJwUEBKiwsNBre2FhocLCwq6ot9vtstu9LyO2b9++PltscA6Hg/9BNSDOd8PifDcsznfD4nzXTlBQUI3qLPHVHYGBgRowYIB27/73o6dVVVXavXu3nE6nDzsDAAD+wBJXiCQpNTVVU6dO1cCBA3XLLbfoueeeU1lZmfnUGQAAsC7LBKI777xT//znPzV//ny53W7169dPO3bsUGjoD7/YrCmx2+16/PHHr7gliPrB+W5YnO+GxfluWJzv+mUzjJo8iwYAANB0WWIOEQAAQHUIRAAAwPIIRAAAwPIIRAAAwPIIRE3Q/v37NWbMGEVERMhms2nLli1e44ZhaP78+QoPD1erVq0UHx+vzz//3DfNNgFLlizRzTffrHbt2ikkJETjxo1TXl6eV82FCxeUnJysjh07qm3btpowYcIVLwpFzaxcuVJxcXHmy+mcTqe2b99ujnOu69fTTz8tm82mmTNnmts453VnwYIFstlsXkuPHj3Mcc51/SEQNUFlZWXq27ev0tPTrzq+dOlSPf/881q1apUOHz6sNm3aKDExURcuXGjgTpuGffv2KTk5WYcOHVJmZqYuXryohIQElZX9+wssZ82apXfeeUcbN27Uvn37dObMGY0fP96HXTdeXbp00dNPP63s7Gx99NFHGj58uMaOHavc3FxJnOv69OGHH+pPf/qT4uLivLZzzutWr1695HK5zOXAgQPmGOe6HtXJt6fCb0kyNm/ebK5XVVUZYWFhxrJly8xt586dM+x2u/HGG2/4oMOmp6ioyJBk7Nu3zzCMb89vixYtjI0bN5o1J06cMCQZWVlZvmqzSenQoYPx8ssvc67rUUlJiXHDDTcYmZmZxs9//nPj4YcfNgyDv++69vjjjxt9+/a96hjnun5xhchi8vPz5Xa7FR8fb24LCgrSoEGDlJWV5cPOmo7i4mJJUnBwsCQpOztbFy9e9DrnPXr0UFRUFOf8R6qsrNT69etVVlYmp9PJua5HycnJSkpK8jq3En/f9eHzzz9XRESEunbtqsmTJ6ugoEAS57q+WeZN1fiW2+2WpCve0B0aGmqO4fpVVVVp5syZuvXWW9W7d29J357zwMDAK74gmHN+/Y4dOyan06kLFy6obdu22rx5s2JjY5WTk8O5rgfr16/X3/72N3344YdXjPH3XbcGDRqkNWvWqHv37nK5XHriiSd0++2369NPP+Vc1zMCEVCHkpOT9emnn3rd80fd6969u3JyclRcXKy33npLU6dO1b59+3zdVpN0+vRpPfzww8rMzFTLli193U6TN2rUKPPnuLg4DRo0SNHR0XrzzTfVqlUrH3bW9HHLzGLCwsIk6YqnEgoLC80xXJ+UlBRt3bpV7733nrp06WJuDwsLU0VFhc6dO+dVzzm/foGBgerWrZsGDBigJUuWqG/fvlqxYgXnuh5kZ2erqKhI/fv3V/PmzdW8eXPt27dPzz//vJo3b67Q0FDOeT1q3769brzxRp08eZK/73pGILKYmJgYhYWFaffu3eY2j8ejw4cPy+l0+rCzxsswDKWkpGjz5s3as2ePYmJivMYHDBigFi1aeJ3zvLw8FRQUcM7rSFVVlcrLyznX9WDEiBE6duyYcnJyzGXgwIGaPHmy+TPnvP6Ulpbq1KlTCg8P5++7nnHLrAkqLS3VyZMnzfX8/Hzl5OQoODhYUVFRmjlzpp588kndcMMNiomJ0WOPPaaIiAiNGzfOd003YsnJyVq3bp3efvtttWvXzryXHxQUpFatWikoKEjTpk1TamqqgoOD5XA49NBDD8npdGrw4ME+7r7xSUtL06hRoxQVFaWSkhKtW7dOe/fu1c6dOznX9aBdu3bmfLjL2rRpo44dO5rbOed153e/+53GjBmj6OhonTlzRo8//rgCAgJ011138fdd33z9mBvq3nvvvWdIumKZOnWqYRjfPnr/2GOPGaGhoYbdbjdGjBhh5OXl+bbpRuxq51qS8eqrr5o158+fNx588EGjQ4cORuvWrY1f/epXhsvl8l3Tjdi9995rREdHG4GBgUbnzp2NESNGGBkZGeY457r+ffexe8PgnNelO++80wgPDzcCAwONn/zkJ8add95pnDx50hznXNcfm2EYho+yGAAAgF9gDhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhEAALA8AhGARsdms2nLli3XHP/yyy9ls9mUk5Pj814ANA4EIgDXxe1266GHHlLXrl1lt9sVGRmpMWPGeH3xpK9ERkbK5XJd8R1cvnDPPffIZrNp+vTpV4wlJyfLZrPpnnvuuaLeZrMpMDBQ3bp108KFC3Xp0iWzxjAM/fnPf5bT6ZTD4VDbtm3Vq1cvPfzww17fYwig5ghEAGrtyy+/1IABA7Rnzx4tW7ZMx44d044dOzRs2DAlJyf7uj0FBAQoLCxMzZv7x/dXR0ZGav369Tp//ry57cKFC1q3bp2ioqKuqB85cqRcLpc+//xzzZ49WwsWLNCyZcskfRuGJk2apN/+9rcaPXq0MjIydPz4ca1evVotW7bUk08+2WDHBTQlBCIAtfbggw/KZrPpyJEjmjBhgm688Ub16tVLqampOnTokFlXUFCgsWPHqm3btnI4HPrP//xPFRYWmuMLFixQv3799MorrygqKkpt27bVgw8+qMrKSi1dulRhYWEKCQnR4sWLr+jB5XJp1KhRatWqlbp27aq33nrLHPv+LbO9e/fKZrNp9+7dGjhwoFq3bq3/+I//UF5entc+3377bfXv318tW7ZU165d9cQTT3hdmfn88881ZMgQtWzZUrGxscrMzKzR+erfv78iIyO1adMmc9umTZsUFRWlm2666Yp6u92usLAwRUdHa8aMGYqPj9df//pXSdKGDRu0fv16bdiwQY899pgGDx6sqKgoDR48WL///e/16quv1qgnAN4IRABq5ezZs9qxY4eSk5PVpk2bK8bbt28vSaqqqtLYsWN19uxZ7du3T5mZmfriiy905513etWfOnVK27dv144dO/TGG29o9erVSkpK0ldffaV9+/bp97//vebNm6fDhw97/d5jjz2mCRMm6JNPPtHkyZM1ceJEnThxotreH330UT3zzDP66KOP1Lx5c917773m2Pvvv68pU6bo4Ycf1vHjx/WnP/1Ja9asMcNYVVWVxo8fr8DAQB0+fFirVq3S3Llza3ze7r33Xq+w8sorr+g3v/lNjX63VatWqqiokCS98cYb6t69u+64446r1tpsthr3BOA7DACohcOHDxuSjE2bNlVbl5GRYQQEBBgFBQXmttzcXEOSceTIEcMwDOPxxx83WrdubXg8HrMmMTHR+OlPf2pUVlaa27p3724sWbLEXJdkTJ8+3evzBg0aZMyYMcMwDMPIz883JBkff/yxYRiG8d577xmSjF27dpn127ZtMyQZ58+fNwzDMEaMGGE89dRTXvv8n//5HyM8PNwwDMPYuXOn0bx5c+Mf//iHOb59+3ZDkrF58+ZrnoepU6caY8eONYqKigy73W58+eWXxpdffmm0bNnS+Oc//2mMHTvWmDp16hX1hmEYVVVVRmZmpmG3243f/e53hmEYRo8ePYw77rjD6zMefvhho02bNkabNm2Mn/zkJ9fsBcC1+ccNdgCNhmEYNao7ceKEIiMjFRkZaW6LjY1V+/btdeLECd18882SpJ/+9Kdq166dWRMaGqqAgAA1a9bMa1tRUZHX/p1O5xXrP/RUWVxcnPlzeHi4JKmoqEhRUVH65JNP9MEHH3jdnqusrNSFCxf0zTffmMcTERFxzR6q07lzZyUlJWnNmjUyDENJSUnq1KnTVWu3bt2qtm3b6uLFi6qqqtKkSZO0YMGCa+770UcfVUpKijZt2qSnnnqqxj0B+DcCEYBaueGGG2Sz2fTZZ5/Vyf5atGjhtW6z2a66raqqqk4/6/Ktpcv7LS0t1RNPPKHx48df8XstW7b80Z8tfXvbLCUlRZKUnp5+zbphw4Zp5cqVCgwMVEREhNfk8BtuuOGKuU+dO3dW586dFRISUid9AlbEHCIAtRIcHKzExESlp6errKzsivFz585Jknr27KnTp0/r9OnT5tjx48d17tw5xcbG/ug+vjt5+/J6z549r3t//fv3V15enrp163bF0qxZM/N4XC7XNXv4ISNHjlRFRYUuXryoxMTEa9a1adNG3bp1U1RU1BVPyt11113Ky8vT22+/XbsDBFAtrhABqLX09HTdeuutuuWWW7Rw4ULFxcXp0qVLyszM1MqVK3XixAnFx8erT58+mjx5sp577jldunRJDz74oH7+859r4MCBP7qHjRs3auDAgbrtttu0du1aHTlyRKtXr77u/c2fP1+//OUvFRUVpV//+tdq1qyZPvnkE3366ad68sknFR8frxtvvFFTp07VsmXL5PF49Oijj9bqMwICAsyJ3wEBAdfV58SJE7Vp0yZNnDhRaWlpSkxMVGhoqP7+979rw4YN171fwOq4QgSg1rp27aq//e1vGjZsmGbPnq3evXvrF7/4hXbv3q2VK1dK+vaW1Ntvv60OHTpoyJAhio+PV9euXbVhw4Y66eGJJ57Q+vXrFRcXp9dff11vvPHGj7rylJiYqK1btyojI0M333yzBg8erOXLlys6OlqS1KxZM23evFnnz5/XLbfcovvuu++qrwP4IQ6HQw6H47r7tNls2rBhg5577jm9++67GjFihLp37657771XkZGROnDgwHXvG7Aym1HTGZIAAABNFFeIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5RGIAACA5f3/udSgPjh0tY4AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here" + "sns.histplot(data=vehicles, x=\"Combined MPG\", bins=50)\n", + "plt.axvline(vehicles['Combined MPG'].mean(), c=\"r\")\n", + "plt.ylabel(\"Years\");" ] }, { @@ -210,7 +423,9 @@ "metadata": {}, "outputs": [], "source": [ - "# your answer here" + "'''\n", + "They all are normally distributed because the mean it's always around the middle of the distributed data.\n", + "'''" ] }, { @@ -237,11 +452,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 71, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here" + "def exp_distributed(mean, count):\n", + " return np.random.exponential(mean, count)\n", + "\n", + "fig, ax = plt.subplots(1, 2, sharey=True)\n", + "\n", + "arr1 = exp_distributed(2, 1000)\n", + "ax[0].hist(arr1, bins=100)\n", + "ax[0].set_xticks(np.arange(0, max(arr1), 2))\n", + "\n", + "arr2 = exp_distributed(200, 1000)\n", + "ax[1].hist(arr2, bins=100)\n", + "ax[1].set_xticks(np.arange(0, max(arr2), 300))\n", + "\n", + "plt.show()" ] }, { @@ -273,12 +512,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 93, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.7768698398515702" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your answer here\n", - "# Hint: This is same as saying P(x<15)" + "def exponential_cdf(x, rate):\n", + " return 1 - np.exp(-rate * x)\n", + "\n", + "mean_time = 10\n", + "rate = 1 / mean_time\n", + "time_threshold = 15\n", + "\n", + "probability_less_than_15 = exponential_cdf(time_threshold, rate)\n", + "probability_less_than_15" ] }, { @@ -290,19 +547,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 94, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.2231301601484298" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your answer here" + "probability_more_than_15 = 1 - probability_less_than_15\n", + "probability_more_than_15" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -321,7 +583,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.11.5" } }, "nbformat": 4,