Calculate the earliest sunset and the latest sunrise in winter.

plotting.py

"""
This file contains some code which is useful for plotting,
but if you're following the tutorials you shouldn't need to worry about it.
"""

from cycler import cycler

blueprint = {
    "xtick.labelsize":10,
    "xtick.major.size": 5,
    "xtick.minor.visible": True,
    "xtick.color": "k",
    "ytick.labelsize":10,
    "ytick.major.size": 5,
    "ytick.minor.visible": True,
    # Lines
    "lines.linewidth": 2,
    "axes.prop_cycle": cycler("color", ['#FFFFFF', '#CCCCCC', '#AAAAAA']),
    # Fonts
    "font.monospace": ["Source code pro"],
    "font.sans-serif": ["Source sans pro"],
    "font.family": "monospace",
    # Grid
    "axes.grid": True,
    "axes.grid.which": "both",
    "grid.color": "#CED8F7",
    "grid.alpha": 0.3,
    "grid.linestyle": "-",
    "grid.linewidth": 0.25,
    # Plot display
    "figure.dpi": 300,
    # Plot facecolors
    "axes.facecolor": "#11496f",
    "figure.facecolor": "#FFFFFFFF"
}

pastle = {
    "xtick.labelsize":10,
    "xtick.major.size": 5,
    "xtick.minor.visible": True,
    "xtick.color": "k",
    "ytick.labelsize":10,
    "ytick.major.size": 5,
    "ytick.minor.visible": True,
    # Lines
    "lines.linewidth": 2,
    "axes.prop_cycle": cycler("color", ['#8dd3c7', '#ffffb3', '#bebada',
                                        '#fb8072', '#80b1d3', '#fdb462',
                                        '#b3de69', '#fccde5', '#d9d9d9',
                                        '#bc80bd', '#ccebc5', '#ffed6f']),
    # Fonts
    "font.monospace": ["Source code pro"],
    "font.sans-serif": ["Source sans pro"],
    "font.family": "monospace",
    # Grid
    "grid.color": "#4298bd",
    "grid.alpha": 0.5,
    # Display
    "figure.dpi": 300,
    # Face colors
    "axes.facecolor": "#ecf5f8",
    "figure.facecolor": "#FFFFFFFF"
}

Sunset plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ephem\n",
    "import datetime\n",
    "import pandas\n",
    "import matplotlib.pyplot as plt\n",
    "import plotting\n",
    "plt.style.use(plotting.pastle)\n",
    "\n",
    "# I've written this for Glasgow, Scotland, but you can adapt it to anywhere by getting the coordinates for your town. \n",
    "# You can normally get these from Wikipedia - there's a link in the top right hand corner of most articles for places; \n",
    "# click that link, and alongside lots of other useful stuff it'll give you a decimal representation you can use here.\n",
    "\n",
    "glasgow = ephem.Observer()\n",
    "glasgow.lat, glasgow.lon = \"55.860916\", \"-4.251433\"\n",
    "\n",
    "# This just makes an array of 60 dates to use to calculate the sunrise and set times\n",
    "start = datetime.datetime.strptime(\"2021-12-01\", \"%Y-%m-%d\")\n",
    "numdays=50\n",
    "date_list = [start + datetime.timedelta(days=x) for x in range(numdays)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.dates as mdates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sunset import rise_set_times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = rise_set_times(date_list, glasgow)\n",
    "data = data.applymap(lambda x: pandas.to_datetime(x.strftime('%Y-%m-%d %H:%M:%S')).time())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.dates as mdates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
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urpy5c9s4QgAAgP+Q6AAAAAAAAJKk6OhoLVu8UJPHj9fNG9clSS+9XEWDRo/TXZ9iNo4OAAAgYSQ64FA6t/dTeHh4vPqOnbqoY5euNogIAAAAAByfwWDQ5o0bNG7UCAWePClJKlykiEaOHa/Wb7TV/YgobTp93cZRAgAAJIxEBxzKgYAAhYSExKuvUau2DaIBAAAAAMf31+FDGjNimPbs2iVJ8vTy0sChw9WtR09lypTJxtEBAAAkj0QHHEqVqlUTHNHh61vIBtEAAAAAgOO6eOFfTfhktFau+FmSlClTJvV6/wN9+PFAZc+Rw7bBAQAAWIBEBxzKEv8VcnF1tXUYAAAAAOCw7t65o8+mTNb8b+YoKipKTk5OatfxLQ0b/YkKFPS1dXgAAAAWI9EBAAAAAMBzICIiQt/Nma2Z06cq5N49SVLd+g30yYRJqlCxom2DAwAASAUSHQAAAAAAPMNiY2O1wv9HTRr7ia5cvixJKle+gsZMnKR6DRvZODoAAIDUI9EBAAAAAMAzatfv2/XJiOE6+vcRSVK+/Pk1fPQYvdnxLTk7O9s2OAAAACsh0QEAAAAAwDPmxLFjGjtyuLZt2SxJypotmz76eJDefb+v3NzcbBwdAACAdZHogEPp3N5P4eHh8eo7duqijl262iAiAAAAALAf14Ku6tNxY7V8yWIZDAa5uLioe89eGjBkqHxy5rR1eAAAAGmCRAccyoGAAIWEhMSrr1Grtg2iAQAAAAD7EHr/vr6cMV1zv/5KDx8+lCS1fqOtRowZp6LFitk4OgAAgLRFogMOpUrVqgmO6PD1LWSDaAAAAADAtqKiorRw/jxN+3Sibt+6JUmqWr26xk6arMpVXrFxdAAAAOmDRAccyhL/FXJxdbV1GAAAAABgUwaDQb+tXqXxn4zS+bNnJUnFS5bU6PET1ax5Czk5Odk4QgAAgPRDogMAAAAAAAfy5769GjNiuA78GSBJypkzlwaPHKXOb3eTK18MAwAAz6EMtg4gMbdv39asWbNUr149+fr6KlOmTMqfP7+qV6+uKVOm6OrVq6k+R1BQkMaPH6/69esrT548ypgxo3LkyKFSpUqpa9eu+uWXXxQTE2OFT5O20uNapcc5AAAAAACJO3vmjN7u2F7NG9bXgT8DlCVLFg0cNlz7jx7X/3r0JMkBAACeW3Y5osPf31+9e/fW3bt3TeqDgoIUFBSkffv2acyYMZo+fbr69Olj8ZDcyMhIjR8/XpMnT1Z0dLTJvpCQEIWEhOj06dNavHixSpUqpSVLlujll19O9edKC2l9rdLrHAAAAACAhAXfvKlpn07UwvnzFBMTowwZMqjT2900eMRI5c2bz9bhAQAA2JzdjeiYNm2aOnToYPJSPUOGDMqVK5dcXP7Ly0RERKhv37764IMPZDAYzO4/KipK7du314QJE+IlOXx8fJQhg+klCQwMVI0aNbR79+4UfqK0k9bXKr3OAQAAAACILzw8XJ9N+VQvVyir77/9RjExMWrc7DXt+vOgPv96NkkOAACA/2dXiY6VK1dq8ODBxnKRIkXk7++v8PBw3bhxQw8fPtT69ev14osvGtvMmjVLX375pdnnmDZtmlatWmUsV65cWRs3blRISIiCg4MVFhamI0eO6L333jOOTIiMjJSfn59CQ0NT/RmtJT2uVXqcw1Kd2/upVdNG8X6WL16UZucEAAAAgPQUExOjJQsX6JUXyuvTcWMV9uCBXnypslZt2KRlK35V6bJlbR0iAACAXXEy2MnX7x8+fKiSJUvqypUrkqRKlSpp27Zt8vT0TLDt66+/rs2bN0uS3N3dde7cOeXOnTvJc4SFhSl37twKCwuTJLVq1UorV66MN4rjiW3btqlRo0bGEQpTpkwxefFvK+lxrdLjHMmJjIyMN5Kmbdu2CgkJidd20PARGjJiVKrOB1hLyMNIbTp93VhuUjKPsrtltGFEgH3iWQHMx/MCPB8MBoO2btqksSOH69TJE5Ik30KFNHLseL3e1i/R/7umB34PAebjeQHMw7PybImOitKxg3+a1NWqVUsZM6bPPbWbER2zZs0yvlR3c3PTihUrEnyp/mT/smXLlCdPHkmPExgTJ05M9hxbt241Jjnc3Ny0YMGCJP+h2KBBA/Xv399YXr16tdmfJy2lx7VKj3OkRJWqVVW9Vq14P76+hdLkfAAAAACQHo78dVhtXmuqjm1f16mTJ5TD01PjJ0/Vvr/+0RtvtrNpkgMAAMDe2c2/lBYuXGjc7tmzp4oWLZpke29vb3388cfG8tKlSxUVFZXkMefOnTNuV6pUSV5eXsnG1a5dO+N2YGBgsu0Ts27dOlWuXFkeHh6qXLmy1q1bl+K+0uNapcc5UmKJ/wqt2bgl3k/HLl2tfi4AAAAASGuXLl5Qr/+9rYY1q+uPXTuVKVMm9f1ogA4ePaHeH/RTpkyZbB0iAACA3bOLRMepU6d07NgxY7lXr15mHffOO+/I2dlZknTnzh1t3749yfZP2kpSvnzmLdpWNs7cpw8fPjTrmKft3LlTLVq00OHDhxUWFqbDhw+rRYsW2rVrl8V9pce1Sq/7AQAAAADPq3t372r0sKGq+uIL+uUnf0nSmx06KuDIPxozcZJyJDKiHgAAAPHZRaJj27Ztxu1ChQqpTJkyZh3n6empatWqJdhPQkqXLm3cPnr0qMxZniQ4ONi4bW5cT5sxY4ZF9UlJj2uVXvcDAAAAAJ43jx490qwvZurlCmU1+8uZioyMVK06dbXtj32aM3+BCjItLwAAgMXsItERd/RA9erV5eTkZPax1atXN24fP348ybbVqlUzTlcVGBioFStWJNv/kwW2JalRo0ZmxxXX2bNnLapPSnpcq/S6HwAAAADwvIiNjdUK/x9VrdIL+mT4UN27e1dlypbTj7+u1q/rNqhipUq2DhEAAMBhudg6AOnx6IonSpUqZdGxcdvH7Sch2bJl06BBgzRs2DBJUvfu3eXm5qYWLVok2P7BgweaNm2aJMnd3V0DBgywKLYnihUrphMnTiRYb6n0uFbpdT9S4q+jxyQn6+TnvLx95JMzp1X6AuK6HxGliOgYkzKA+HhWAPPxvAC2s2X9Oh0I2KsBw0Yqs5tbivrYt3uXJo8ZpWN//yVJyp0nrwYMH6U27TvK2dnZIZ5pfg8B5uN5AczDs/JsiYm27f2zi0RHUFCQcbtw4cIm+4KDg9WzZ09t27ZNL7/8subPn2+yMHaRIkVM+jEYDEmOQBg4cKB2796t9evX68GDB2rZsqVatWqlSZMmqVy5csZ2sbGx6t69u86fPy9Jmj17tnKm8KX4Rx99pLVr18ar79+/v8V9pce1Ss/7YakOLZoqJCTEKn291r2vWvToZ5W+gLgeRcXo3O0HxrKTQcrk6pzEEcDziWcFMB/PC2AbV88FavI7XRUTFaWb0S5q1q23RccHnT+jVbOn6djeHZKkzFnc1bjLu6rfvpsyZnbT1nPBSXdgR/g9BJiP5wUwD8/Ks8XZECPLv9ZvPXYxdVVoaKhxO1u2bCb7+vfvr9WrV+vBgwfasWOHunXrZrI/a9asxu2YmBhFREQkeS4XFxf5+/urdevWxro1a9aoQoUKaty4sRYuXKibN2+qXbt2+vnnnyVJo0ePVteuXVP68VS/fn2tWbNGlSpVkru7uypVqqQ1a9aobt26FveVHtcqPe8HAAAAANijqMhILRw3SDFRj7+duGXJtwq9c9usY+8F39CST4drYteWOrZ3hzI4u6iOX2eN/Wmrmr7dWxkzp2xkCAAAABJmFyM64r5Yz5Ili8m+DRs2mJR3796t0NBQ4wt1Dw8Pk/3379+XWzLDiT08PLRy5Uq1aNFC69evlyQZDAZt2bJFW7ZsMbZzcnLS3Llz9e6771r+oZ7SsmVLtWzZMtX9pMe1Su/7AQAAAAD2Zt38L3XlzCljOSI8TOsXfK32H3+S6DERYQ+0Zek8bV3+vaIePf7SV6W6TdTqvQHK7Vsk0eMAAACQOnaR6IiOjjZuOzubDk/KkCH+oJO4dU+3j9tXYo4fP65+/fpp+/btkqQ6dero8OHDJi/4pcfJj/3796tNmzYpnrbK2tLjWqX3/bDEj79tZI0O2L37EVEyxJmxrW7RXMqW2dV2AQF2imcFMB/PC5C+Dv4ZoK1L58Wr37PaX6MGDVCR4iVM6qOiouS/+Ad9MfVT3bl1S5L00iuvauiYCar8yqvpEnNa4/cQYD6eF8A8PCvPlpjoKF06dtVm57eLRIeHh4dx3YXw8HCTfc2aNdPixYuN5Tp16sjd3d1YDgsLM2kfd+qkhKxcuVKdOnXSw4cPVbBgQc2bN0+NGzdWRESENmzYoOXLl2vlypXGF/Tz58/XL7/8ouXLl6tp06ap+pzWkB7XKj3vh6UqVSgvF1d+4cH+ZXb5L+mXLbOrsrtltGE0gP3iWQHMx/MCpI/Q0FAN6dtLsbGx8fZFR0dr5qfj9cOyHyU9/nLc+rVrNG70SJ07c0aSVLR4cY0eN0HNW7W26nqF9oDfQ4D5eF4A8/CsPDuio2z77x67WKMj7svwp1+Uz5gxQ61atZKHh4fq1KmjBQsWmOx/uv3TUyfFtWTJErVt21YPHz5U+fLlFRAQoMaNG0uSMmfOrDZt2uinn37ShQsXNHjwYGXPnl2SdO/ePbVo0UJLlixJ1ee0hvS4Vul1PwAAAADA3oweNkQX/v030f2/rV6lP/ft1YE/A9SiUX293bG9zp05Ix+fnJoyY6b2HPxLLVq//swlOQAAAOyZXYzo8P4/9u49vsn67v/4+0qaNmlT2rSl5VCBgihyw+2YgsxDPTJREXc7NmWKc1NvJ3N6u6mb093TbTpRt9/mdNt9T0Wnc7I5nUxF5aDiCcHDvGEqIAe19gA9SpukaZPr90dtaEgPaZNcSdrXcw8fy3Xlyvf7zam01+f6fD7FxaqqqpIk1dTURNxXUlKiJ554os/HVldXh297PJ5eSyt1H3f55ZfLNE2NGjVKzz33nMaOHdvrsePHj9eyZct01VVXafHixXrhhRcUDAZ16aWX6sQTT9T48eMH+xQTxorXyoo5AAAAAAxvpmmqwwwpZJqyGYYchi3pJ//jnfOZp5/Sg8vvG/C487+6SE2NjZIkl8ulS79zhS694koVFBQoK0GldvuTitcWAAAgnaVFoGP69Ol65513JEnbtm0b1GN7Hj99+vQ+j/vhD38YLsf0ox/9qM8gR09jxozRP/7xDx111FF699135fV69eMf/1j33BNdq9UqVrxWVswBAAAAYPhpCvi0q7VJ9e1tamj3KRAKhu/LttlVnONSSU6eKtweebJdaTVn/d69umrpZbHN2dgowzB0/KL/0MmXfVO5o4v1dNOHUlP6P8+hMk1TpkKSIcns2gYAAEgXaRHomDFjRvh29wn2WPU8vuc4PYVCIT3++OPh7e5yVbFwu9265ppr9I1vfEOS9OKLLw5qfYmW7NfKqjmG6vxzFkX1DZGkxect0eIlFyR8PgAAAGA4SHYGQJW3RZub67TH39bnMYFQUDW+VtX4WrW5uU6lzjzNLCxTeW5Byuc0TVPfu+Jy7d27J+b5C8pKdfp1V8nuiPyzOp2f52D1DK7U+33qyN8fXHm67lOVOJMbXJHIXgEAALFJi0DHUUcdFb69ceNGNTU1yePxDPi4zs5OrVmzJrw9Z86cXo9raGjQp59+Gt6eNGnSoNY3a9as8O2dO3eqvb1dOTk5gxojUZL9Wlk1x1Bt2rAhnJnT0zHHVSZ8LgAAACAZrDpxa0UGgD/YqY31VdrV1jTox+7xt2lt7U5VuD2aU1wupz22P0+TMeeKPz2kp1b2XaK3N821dXrtr4/r2K99ZUhzDiQVr223WIIrHWZygitS6rNXAABA5kmLQEdlZaWKiorU2NioUCikxx57TBdddNGAj1u3bp2amrp+6bPb7Vq4cGGvx9nt9ojt//u//9Oxxx4b8/oaGhrCt0OhkFpaWlRaWhrz46WuIMCqVau0detWHXrooTr99NOj1hWLZL9WVs0xVLPnzu01o2PChIkJnwsAAABIFCtP3FqVAdDY7tOa2h3yBTviWu+u1ibV+lo1b+yUAZ97MuZsrd2jH1z93SGN89zv79ORC0+X0503qDlT8Txj+VylMrgipTZ7pSeySAAAyDxpEehwOBw6++yzw70vbr/9dl144YX9BgJM09SyZcvC2yeddJJKSkp6Pdbj8aigoCCcCfDUU08NKtDx2muvhW87nc4+5+lLW1ubTjnlFG3YsCG8b+7cuVqzZo3y8gb+hbinZL9WVs0xVA+teFRZDkfCxwUAAMDINJxKOll5krqx3adna7ZHBGzi4Qt26Jnq7Zo/bmqfJ+STMefTVVv18GVXq3XfviGN0dbUrHX3PajTr/hWzHOm4nkONGf3vKkIrkipD7BIZJEAAJDpbKleQLdrr71WWVldv5Bs3bpVP/nJT/o9/r777tO6devC2zfccEOfxxqGof/4j/8Ib99222169tlnY1rXtm3bdNttt4W3TzvtNNlsg3vZbrnllogghyRt2LBBP//5zwc1TrdkvlZWzgEAAACkQlPAp7caq/VczXY98uFm/Xn3/2nFZ///yIeb9VzNdr3VWK2mgG/Ic/iDnVpft1tra3f2G+ToTfeJ2/V7dssf7IzpMY3tPq2sen9IJ4p72tXapJVV7/f73P3BTq2p3ZGwE/HdAqGgVtfs6PU5J2vOtQ88rNdffjmuMV7845/VXBt7b49UPM/+5pT2B1fiDXJ06w6uxPIdsvKz25sqb4tWVW/Tyqr3tbm5TjW+1qjXvzsYubm5Tiur3teq6m2q8kaXVI6HaZoKhILyBzsVCAVp9g4AwCClTaBj6tSp+ta39l8F89Of/lTLli1TKBSKOM40Td1///1aunRpeN+CBQtUWdl/j4bvf//7ys7OltRVfmrRokW6+eabI3p3HDjPP/7xD51++unhYwzD0DXXXDPo57Z27dpB7R9Isl8rq+YAAAAArGTVCU2rT9xafZJ6Y31Vwubqbe6NDVWWzFmzfYee+vVv4x6nsz2gZ+7+30E9xsrnOdCcqQquSKkNsFgdjOyNFUFXAABGCsNMo8sEfD6fTjjhBG3cuDG879BDD9WiRYtUXl6uPXv2aOXKlXrzzTfD90+ePFmvv/56TGWS7rnnHl1yySUR+4qKinT88cfr4IMP1qRJk9Tc3KydO3dq48aN2rx5c8SxP/7xj3XjjTcO+nnNnj1bb7zxRq/7ez7XwUj2a2XVHP0JBAJ66aWXIvbNOPIoSlch7bX4Anp2W214+9RDxqjAlZ3CFQHpie8KELuR8n1JVhmpeMridEtVSSepq2xOX2WH/MFOrax6Pyknx112hxaWT4t4zlXeFq2t3ZnwuQ508pjJ4dJdyZizIxDQLad9WZ/uqU/IeIZh6Ier/qai8WMH9bhkP8+B5pSk9XW74w7K9afC7VFl6aSo/VZ/dntKVJmu7rkGU6ZLiq2k3YGS0YsEyTNS/t0G4sV3ZXjp7OjQljdej9h33HHHhZMPki0tenR0c7lceuKJJ3T22WeH+2Js3bpVN998c6/HH3bYYfr73/8e80n1iy++WG63W0uXLg03zW5sbNTjjz/e7+Oys7N1880363vf+94gns1+J510Uq+BjpNOOmlI40nJf62smgMAAAAjW7Lr4lvZdyDZV8b3duLWigyAniepNzfXJWWuA21p3hM+oZuMOZ+9+56EBTkkyV3kkd0x+D+vk/08B5qzytuS1CCH1PXdmez2RJ2gt/qz2y1VPVCk9OhF0hNN1wEAw0laBTokacyYMVq/fr1uv/123XXXXaquro46pqioSBdddJFuvPFG5ebmDmr8c889VyeeeKLuvPNOLV++XDU1NX0em5+fr6997Wv67ne/q0MOOWTQz6XbDTfcoOeff16bNm0K75szZ46uv/76IY8pJf+1smqOwTj/nEXyer1R+xeft0SLl1yQ1LkBAACQOFY06bb6hKbVJ26tPkndFPANusTPUNX5W8PlepIx5+fP+KLeeXatnO5clUycoByXSw5njg4bPU6u3FztbN+nbJdTDqdT2c4cOZw5n93u2udw5nx2Oyd83GB7OUrJf579zenJdqUkuCKlLsCSimBkt1Q2e++JpusAgOEqrUpXHSgYDOqVV17RBx98oLq6OhUXF6uiokKVlZXKycmJe3zTNLV9+3a9+eab2rt3rz799FPl5uaqqKhI//7v/66ZM2fKkaAySZ2dnXrqqae0bds2HXLIITrjjDPCzb4TIdmvlVVz9NRb6aovf/nLammJrpF8zQ+v1/ev/1HC1wAMBamXQGz4rgCxS8X3JdPLSI2Ekk6rqrdZcnK8zOnW/HFT9VZjtWUnxiVpZmGZJOsyHUbanBVuj1ZWvW/ZnAvLp4VPnFv92e2WqjJdVpe06w3lsqzF77lAbPiuDC+UruqH3W5XZWVl0hpbG4ahQw45JK5sjVhlZWXprLPOStr4yX6trJpjILPnzu01o2PChIkpWA0AAMDwMpzKSA33kk6pyK6ob7dmvm717V5J1l6XN7LmtNau1iZ5ilwp+ex6sl0jMouke37KZQEARoK0DnQAB3poxaM0IwcAAEiw4VZGaiSUdNrVmtznd6Cd+xrV0O6zdM56f5vlJ0BHypwN7V5ZXdyhO7hi9We3O8CSqjJdqepFIlEuCwAwshDoAAAAANKcaZoyFZIMSaYSdoLSqit9rb6i2eoTmqk4cWt1dsXedm/C37+BdJghqxMdRsycgVBQjQFrA1fdwZVUZAaNtCwSKbVN17tZEUQHAKAbgQ4AAAAgDfW8Arbe71NH/v6TVU/XfaoSJ2WkpOgrmkdCSae9/jbLT1I3pqDUEZLL6sBVIBRURyhoeWZQQ7tXO/c1WjpnqrNIKJcFABiJ+BcDAAAASCOxXAHbYVJGqqeeVzSPhJJODe3eriwAC1k9H4an9lAwJQGWvRYH6lKZRSJRLutA9AUBgJGBQAcAAACQBigjFZ/uK5pHTEmnFMi22S19rt0nI5kzOXOOpOBVUwrKdKUqi2Skl8vqRl8QABh5CHQgo5x/ziJ5vdFX4yw+b4kWL7kgBSsCAAAjTTKuDKWMVPzq/K1qbPdanl0xkko6ebJdqvO3WjZfiTNPkqkaH3MmWnFOrhoDPkuDK9k2u7Jtdsvm62mkZJFI1geXu6W6XFY3+oIAwMhFoAMZZdOGDWppaYnaf8xxlSlYDQAAGCmSeWUoZaQSZ0drI9kVSZxvdE6utYGOnFxJsjboMELmHO3Mk2FYO2dxTm442DESsldSkUXS2O4dkeWyJPqCAAAIdCDDzJ47t9eMjgkTJqZgNQAAYLhL9pWhlJFKrIYUZVekIuhQlO1SrYVBh+KcXE3OL9KWlj2WzVnh9kiy7nM7Eue0OqBjGIaKc1yWzltkcYCuWyqySHa0jrxyWVL69gUxFZIMSWbXNgAguQh0IKM8tOJRZTkcqV4GAAAY5qy6MpQyUonVaPF83awu6VSck6uSnFxLAx0lObnyZLtU6syz5HNU5nSHTzQyZ5LmdKcmuFKSk2dt9kpOrposLtOVqiwSq4O9qS6XJaVvX5B6v08d+fvX9HTdpypx0hcEAJLJluoFAAAAAOmksd2nlVXvx3116q7WJq2ser/P8iVWXQFb5W0J37ZSqspIWd0DoLukk5VKcnLDJ42t0j3fzMIyS+abUVgavs2cyZmzO3BlhZ4BHas/u5Pzi1ScY+1J5SKLfyZ0awr4LZ0vVeWyuiU7K9If7Izp+Cpvi1ZVb9PKqve1ublONb5WdZiRa+owg+HMz5VV72tV9bbwv88AgMQg0AEAAICMZZqmAqGg/MFOBULBuEtDdF8Zmqgsi+4rQ3sLdlh5BaykEVNGypPttHS+7pJOVuq+GjgVJ6nLcwtUkZfcE9UVB5SnYc7kzZmKgE4qPrslOdbM1230Z/1IrOQwbCOmXFY3K7Ii++MPdmp93W6trd056GBPd/bn+j27Yw6oAAD6R+kqAAAAZJRkNQa3sl8GZaSSpzgnV3UWvbZSaks6zSws09ranUmfs+dJakmaU1KuWn9rUk4wuuwOzSkuj9rPnMmZszu4kszssgODK5L1n90Kt8fSMl2T84vUEPBa3IvEZenPvm6pKpdFX5BopmmqwwwpZJqyGYYchk2GYcQ1JgBkEgIdAAAAyAjJbgxuZb+MkVRGyuom3VPcRXq3Za9lc/Ys6WR10CFVJ6md9izNGztFz1Qnri6+1PX+zRs7pde+NsyZvDlTEdCx+rObimCk1b1IrA7ydktFuSzTNOkL8plkXfwBAJmI0lXIKOefs0gL58+L+u/PD/4x1UsDAABJYkVpCKv7ZVBGKjmKc3JVlJM7Yko6SV0nqV12R1Lm6+sktdR14nj+uKkJm9tldwx4ko85kzNnd3Al0aWW+guuSNZ/dq0u02V1L5Ip7qIRUy6rvr2NviC99AU5cE3dF3/QFwTASEGgAxll04YNevWll6L+++ijD1O9NAAARrxE98uQrGsMbuWVoaZpjqgyUlYq+Wy+VPQdkFITdEjVSWqp64T8wvJpcZ/QrXB7tLB8WkxXGzNncuZMRUDH6s+u1cFIq3uRFOXkpqDpemoyBHa1Nls8H31BACATULoKGWX23LnyeqOvSJwwYWIKVgMAAJJZMsGq0hBW98vY295GGakk6T4pPJJKOkn7T1Kvrom/XrzUdZI61nrxTnuWKksnabLboy3Ne1Tnj71UT5nTrRmFpTGVlmPO5M/ZHVzZ2FAVV3m9CrdHc4rL+w2S9ZzTys+u1WW6rC5pN1LKZTX2cdFCstAXBAAyA4EOZJSHVjyqLEdyrpIDAACxS3a/DCsbg1vdL2O3xVeidvNkOy09IdWzjJTVTbql1PQdkFIXdEjFSeqeynMLVJ5b0CP46VVDu7eX4GeuSnJyE1IvnjkTP2cqAjpWfnatDkZaHXS1uun6FHeRPthnbQ8oh2HrMzszWegL0jcaoANIJwQ6AAAAEDN/sFMb66uGdNKmu2RCLCejrGwMbnW/DKuvRO1m9ZW3PctIWd2kW0pddoWUuqBDKk5SH8iT7ZKnqOukmWma6jRDCpqm7IahrCSdAGPOxM/ZW3Cl3u9Vh7n/u+Qw7CpxJiagY+VndzhnkVjddL27XJaVWSRFObmD+nwkQqr6gnR/pqy8+CMWNEAHkK4IdAAAACAmVpVMsLI0xHjXqBT0y/BSRioJc/V2knOklXTqloqsg94YhiGHYZeV+djMmVg9gyvN3nY9u71WMkzJNHTq1DEqzM1J6HxWfXaHcxbJcC+XVZTtsjzQIaWmL0j3d8/Kiz/6k+xsXgCIF4EOAAAADMjKkglWloYodbpT0i+jLMdt6YmakVpGShqZJZ26pSLrAMOXYRgyZEimEd5OFis+u8M1i2S4l8ua5C7Ue59aF7TvNlL7gkjWZfMCQLz4CQMAADAMJbJmspUlE6xuDN742YkEq1l9RepILiPVPf9ILOnUUyqyDoBESPZndzhmkQznclmjc/Isz4ocqX1BJBqgA8gsBDoAAACGiWTVTLayZILVjcE/9rZYOl+3CouvSB3pZaS6jeSSTgD6N5yySIZzuSzDMOgLkgQH9gWR0rcBOgD0hUAHMsr55yyS1xt95eXi85Zo8ZILUrAiAABSL5k1k60umWB5Y/B2X0r6ZZTk5FFGKgVlpHqug5JOAPoyHLJIhnO5LPqCJGu+/X1B0q0Bek+JzFoGMLwQ6EBG2bRhg1paoq+8POa4yhSsBgCA1LKiZrKVJRNS0hg84FNRtku1FvfLMAyDMlIpLCPVE9kVAFIl2UHX4Voui74gyVHfo5xnujRA75asrGUAwwuBDmSU2XPn9prRMWHCxBSsBgCA1LGiZrLV/TL2trdZ3hg8EAqqKMfaQEd3vwzKSKVHGSkASAfJCroOx3JZ9AVJju6+IJ/4Pk15A/RuycxaBjD8EOhARnloxaPKcnC9HQAgsyQ6xd6qmslW98vYbXGJhm4H5Rbq3Rbr+2VIlJHqXgdlpAAguYZbuSz6giReIBRUpxlKeQN0yZqsZQDDD992AACAJEhWir2VNZMt75dh8ZWL3YpzrL0ytOf7TRmpSJSRAoDkGi7lsugLkhwN7dZm8x7YAF2yJmsZwPBEoAMAACCBkp1ib1XNZNM0re+X0e5NSWPwLMOWsn4ZEmWkAACpkenlsugLkngfe5stna9nA3TJuqxlAMMTgQ4AAIAEsCLFvsrbYlnN5FKn2/J+GR1mSGU5bkuvYOxuDJ7KfhkSZaQAAMNPsoPo9AVJrGybXY0WX+TSswG6lVnLg5XoMrQAkoNABwAAQJysSrG3smbyCWV5lsx1IKtLNXQ3BpdS2y9DoowUAGB4SmYQnb4giVOU7bK8jGh3A3TDMCzLWo5VssrQAkgeAh3IKOefs0herzdq/+LzlmjxkgtSsCIAwEhnVYp9U8DamsmfdvgtmetAFRaXaujZGDzV/TK69XYFbL3fqw5z/5ochl0lTspIAQAySzKC6PQFSYyiHJdqU9QAvc7XalnW8kAXhSS7DC2A5CHQgYyyacMGtbS0RO0/5rjKFKwGADDSWZliH88f7kPxcVtLSkomlOTkpawxuJQe/TJ6rqX7Cthmb7ue3V4rGaZkGjp16hgV5ubEvT4AAIYD+oLE76DcAr3bYn1fkKBpWpq13Nf7bEUZWgDJxTcPGWX23Lm9ZnRMmDAxBasBAGSaRNfXtTLFvr7dmmyObg0Bn+UlE7r7ZaSyMbiUPv0yejIMQ4YMyTTC2wAAIBJ9QYamzOlWUY9ynlba1+G3NGu5KeCLes+tKkMLILkIdCCjPLTiUWU5qBQNAIhdsurrWtkYfLxrlBosbg7Z0O7VIfnFlgY6uvtlpLoxuJR+/TIAAEDs6AsyODMKS+UwbCnJ5v2oLbpqRzLtam0KfzYk68rQAkg+Ah0AAGBYSnZ9XStT7Eudbkv/6JS6XpsJeQXa0rLHsjl79stIdWPwbsm+MhQAACQXfUEGXkP3776pyOZtCERX7Uim+vb981lZhnawEp2JDowEBDoAAMCwYkV9Xasbgze2W/sHYLd8hzNl/TLSpTF4t2ReGQoAADLPcOwLYnUD9OJsl7bta7BsPqkra9k0TRmGYWkZ2lgkKxMdGCkIdAAAgGHDqvq6ljcG91qb0t/NnuJ+GenUGLynZFwZCgAAMlNv2Z/1fq86zP0nqR2GXSXO9O8LYnkDdIuzh6WubItOM6Q6X6tlZWgHCmglOxMdGCkIdAAAgGHByvq6VjcGb2z3paRmcpZhS3m/jHRsDA4AAHCgntmfzd52Pbu9VjJMyTR06tQxKszNiWtsKy7+sLoB+iiHM+nz9CZompaWoe3r91wrMtGBkYRvATLK+ecsktcbXb5j8XlLtHjJBSlYEQBgqEzTlKmQZEgyu7aHysr6uqZpWt4YvDHgU1G2S7WDKIkQr+Kc3HA5plT3y6AxOAAAyCSGYciQIZlGeDteVl38YWU2ry1FpT/3dfgtLUPbFPBFBZWsykQHRhICHcgomzZsUEtLdPmOY46rTMFqAACD1bPubL3fp478/YGJp+s+VYlzaHVnrayv22GGUtIYvCjH2kBHSU5u+Ha69MugMTgAABjJrLj4w8psXtM0U5K1/FGbtWVhd7U2hbN9JGsz0YGRhEAHMsrsuXN7zeiYMGFiClYDAIhVLHVnO8yh1Z2t8rZYWl83FEfmSTwOyi3Uuy17LZuvwu2J2E6nfhk0BgcAACNZsi/+sCqb1zAMFee4rG2AnpOrhkD0eaVkqm/fP5+VmejASMMnHxnloRWPKstB608AyBRW1J21ur5uqlLsi3OsrZnc2x/D6dgvg8bgAABgpErWxR9WZvOW5ORZG+jIdmnbvgbL5pOkhnavTNOUYRiWZqIPlmma6jBDCpmmbIYhBxcQIcMQ6AAAAElhRd3ZpoDP8vq6hQ5nyhqDW1kzuS/0ywAAAEg/ib74w6ps3gq3x7ILlyTpoLwCbWnZY9l8Ule2RacZUp2v1dJM9Fj0LC3c0O7rJStoaKWFgVQg0AEAABLOqrqz8WQVDMWu1iZ9vmhcSlLsDcOwtGbyQOiXAQAAMLxZkc3rybY2a3mUw5n0eXoTNE3LM9H7E0tp4UBoaKWFgVQh0AEAABLKyrqz9e3WZHN0666va3WKfc/G4FbVTI4V/TIAAACGLyuyea3MWk5VGdp9HX7LM9F7u8jIitLCQKrwiQQAAAmtx2pV3VnTNNXQ7kvKPH3prq9rdYp9z8bgVtZMHiz6ZQAAAAxPyczmtTJr2TTNlJSh/aitxbL5pK5M9O6LkbpZUVoYSCUCHcgo55+zSF6vN2r/4vOWaPGSC1KwIgDIXMmox1rlbbGs7myp023pHyjS/vq6VqfYH/j6W1UzGQAAAOgpWdm8VmUtG4aRkjK0DYHoc1nJ1J2J3s2q0sJAKhHoQEbZtGGDWlqio+DHHFeZgtUAQGZKZj1WK+vOnlCWZ8lcBwqaphyyNsW+N1bUTAYAAAD6kshsXiuzlq0uQ1uc7dK2fQ2WzSftz0Q3DMPS0sJAKvEpREaZPXdurxkdEyZMTMFqACCzJLsea1PAZ2nd2U87/JbMdSD7Z1eppUNjcCtqJgMAAABWsCpr2eoytAflFWhLyx7L5pP2Z6I7DLtlpYWHwjRNmQpJhiSzaxsYKgIdyCgPrXhUWQ4qfwPAYFlRjzWerIKh+LitJSX1dbMMW3g7XRqDJ7NmMgAAAGAVK7KWrS5DO8rhTPo8vQmapuosLC0c6wVUPUso1/t96sjf/zfL03WfqsQ5+BLKgESgAwCAYc+qeqz17dZkc3RrCPhSUl+3Z93hdGsMnqyayQAAAIBVrMhatrIMrS1Fv4PbDcPS0sIDveaxlFDuMIdWQhmQJNvAh6RGQ0OD7r77bp144omaMGGCcnJyNH78eB199NFatmyZPvnkk1QvcUTh/QCAzJTseqz+YKekrpPqDe2+hM4xkIZ2r4qzcy2dsyQner7uFHuXPTEZhy67IyFN/QzDkMNml9OeJYfNTpADAAAAGaU8t0Dzx03VwvJpmllYprGufGXb7BHHZNvsGuvK18zCMi0sn6b546bGdFK8uwxtMnWXoXUYtqh1J1u2za59He2WlhZuCvT+96A/2Kn1dbu1tnbnoNfTXUJ5/Z7d4b89gb6kZUbHihUrdNlll6mpKTK1qrq6WtXV1Xrttdd044036o477tDSpUsH/MO9qqpKxx57bMLW9/LLL6u8PLZSEskQ7/O54447tGjRopiPT/T7AQCwjlX1WDvMkKUlpKSuYMsEi+vdVrh7/2OIxuAAAABAciQra9mqMrSGYaQkE313W7Nl80ldJay636duVpRQBrql3V/Rt99+u6699tqIfTabTSUlJWpsbFRnZ1f0zu/36/LLL9d7772n3/zmN/3+UOvs7NSHH36YsDV2ryFV4n0+ra2x/2BNxvsBAOifaZrqMEMKmaZshiHHEH95r7KwHmtJTl5S5+lLvsNpaX3d/n6ppjE4AAAAkFyGYchh2JWIXGory9CW5ORZGugoycm1vLRwfbs3YtuqEspAt7QKdDz++OMRJ9UrKip066236qyzzlJOTo46Ozu1evVq/fCHP9Q///lPSdLdd9+tqVOn6sorr7RkjTNnzlRZWZklc6VaOr4f55+zSF6vN2r/4vOWaPGSC5IyJwBYoWdDtoZ2Xy9NpAffkM3KeqwnjZlsyVwHshuGpfV1Y0FjcAAAACAzdJehXV0Tf9aB1JXJ0VvWQYXbY9nfZ5I0Ka9QWz+tt2w+qau0sGmaMgwj6SWUF5ZPIwseUdLmE+Hz+XTFFVeEt2fNmqW1a9fK49lfIiIrK0unnXaaTjjhBH3pS1/Sc889J0m6/vrrde655/YZgJg0aZJM0xzSut5++23NnTtXgUBAJSUlWrlypVyu9DoZ8Ze//EVf+cpXEjpmMt+PeGzasEEtLS1R+485rjLhcwGAFWJpyBYIDb4hW1PAZ2k91taOdmXb7JaWr8q22ZVl2ML1dZOZvdJdX3cwaAwOAAAApD8rytB6sl2WZqK7HTkpKS3caYbkMOyWlVAGekqbZuR33323qqqqJEkul0uPPvpoxEn1nlwulx5++GGNGTNGktTW1qabb7454Wtqa2vT4sWLFQgE5HA49Nhjj2nSpEkJnydepaWxXWE6GOn4fkjS7LlzdfRxx0X9N2HCxKTMBwDJkuyGbPH8gj4Uu9uaVZxj7YUAxTm54WDBnJLyhDUDP1DP+rpDRWNwAAAAIH11l6E9ecxklTndg3psmdOtk8dMVmXppH6zDGYWWlMhZkZhqUJDvOA7XkHTtKyEcpU3+kLoWJimqUAoKH+wU4FQcMgXxyP9pE1GxwMPPBC+fckll2jy5P5LYBQXF+t73/uerrnmGknSn/70J/3iF7+Qw5G4kxxXXHGFtm7dKqmrV8Vxxx035LGeeuop/fd//7e2bt2qQw89VD/5yU90xhlnJGSdyQh0pOP7IUkPrXhUWQkeEwCsZkVDtlTUY01F3dluVtbXBQAAADA8JbMMrZWZ6FZnc3SzG4alJZRjzbpPRqlopJ+0+Kv9/fff15YtW8Lbl156aUyPu+iii/SDH/xAwWBQjY2NWrdunU499dSErGnFihW67777JEnz5s3Td77znSGP9eKLL2rBggXh7bfeeksLFizQiy++qMrK+EsuJTrQkY7vBwAMF1Y0ZDNNUw3tvoSMH6uGdq+OLBpnad3ZCndkpqFV9XUBAAAADG/JKkM7p6Rctf7WpJR16pmJ7jBsKSktvK+j3dISyk0BX79/ryWrVDTSU1qUrlq7dm349sSJE3XYYYfF9DiPx6MvfOELvY4Tj927d4dP7hcWFmr58uWy2Yb+Uv3yl78c1P7BsNvtfZaUGqp0ez8AYLhIdkO27jJWHWYoJfVY8x05KnXmWTJfmdPd6y+03fV1DwyCDFaF26OF5dMIcgAAAAAjXCLL0HZnomfb7AlcYXQmumEYKSktvLut2dI5+yrZnOxS0UhPaRHo6Jk9cPTRRw/qB8bRRx8dvv2vf/0r7rWYpqklS5aEG17fcsstGj9+fFxjfvDBB4PaPxijR4+OKwjTm3R6PwAg1RJZv9OKhmySUlqP1cq6s32xor4uAAAAAAxFdyZ6onoMuuyOiAz/biU51lyEtn++3JSUUD5QY7tPK6vej7tE2K7WJq2sel9NAWurJWDo0uIv+M2bN4dvH3rooYN6bM/je44zVH/961/18ssvS5KmTZumSy65JO4xp0yZonfffbfX/fFKRn+OdHo/DvT25i2SkZjATlFxiUpGj07IWEBPn/o75O8MRmwjs7R0+PWJr0VNHT41d/jVYe5/Px2GXYUOpzwOl8pdBRrlcMY0Zq1/nyUN2coc+SrOzh344CRo9Xco3+ZSubNAVf6hNYaLRbmzQPmGSy2+QL/H5RsufcEzUZ92+FU1yPdzoLEBq/FvC4BU4+cQEDu+LxiITXadWDJZ/9dSG9ffTuXOAv17wRjZgvaov2FKstySrCstXGLP0/v+esvmk6R6v1fN3vbwRdotHX693LA74m++ePiCHVr1yXYdWzxJBTH+7T+SBTtT+7MuLQId1dXV4duTJk2KuG/v3r265JJLtHbtWh155JG69957IxpjV1RURIxjmuaQU8gCgYCuu+668PZtt92mrKz4X6L/+q//0j/+8Y+o/VdddVXcY5eWlmr37t26//779dJLL2n37t2qqqqSy+VSaWmpZs+erVNPPVVf+cpX5HLFlrKWLu9Hb85dMD+cbROv0795uRZcfEVCxgJ6au8IakfD/obMhinlOBKblorkCNoD6sz2KZTVd3pqhxnU3kCb9gbatK2tXrbOLGUFXLIHs/sdu93VYsm/uhv3VivbN0pyG10fPquYhtZ+sEeGDJmySXmGZEvC/CFD9XttenZv7RAe7JRdObJLXa+N2fXvU6sMtcrUx2pO4EKBxOLfFgCpxs8hIHZ8XxC7LGXb8wf8O/RA3X+HNuzL0vN7+w4u2FxZgxp3qGydWXptd5M68q0todxhBvXs9trP/g4NyZ/XnPC/QzvMoJ7fs1POtkIZ6VEcKW3ZzaDiv6x/6NLi3dm3b1/49qhRoyLuu+qqq/TEE0+otbVVL7zwgi688MKI+/Pz88O3g8Gg/H7/kNdx//33a+fOnZKk6dOnRzQQj8dJJ52klStXatasWcrLy9OsWbO0cuVKnXDCCXGP/eqrr6qiokI33XST1q1bp507dyoQCKilpUXbt2/Xww8/rK9//euaMGGCHn744ZhKrqTL+wEAVjEVUsC5T4HcfYP+JTCU1alA7j4FnPtkKtT7MbZOS3657F6PaQvKFrT2WgZbMEuGugIHhmzK8Y0KBxISxjSU4xsV1y+XRvf/TFv4NgAAAACkij2YrRxfgXLaCpTV7pKt0xH9t5RpyNbpUFa7SzltBcrxFQx4sZ0kZQWs6dORFXApZX9afXaBX4ezLTkX20mSzewaH2ktLTI6ep5Yz82NLLexatWqiO2XXnpJ+/btC59Qd7sja29/+umnMWcu9GSapn7zm9+Et5cuXZrQTIQzzzxTZ555ZsLG6+b1RtaiKy4uVktLizo7I0+o1dfX67zzztP//d//6dZbb+13zHR4PwDAKiFbp9pdn8b9C1HQEVDQ3qEc3yjZQpH/vAazrC2DFMwKyBbMUijLurTRAwMrtlCWcryjEvLaSpJCRq+vLQAAAAAMB7ZQlmyBrr93TH32N1SPTPShXKRlD2bL3pGtoCN5f5PaO7JlD2b3eeFf0pmGgvZAUp+j9Nnf/B2BmAJMB+p6P82uYJApiYvukiItzhb0PClvt0em8vXWaLvnvgOPP/AEf6xefPHFcBPu3NxcLVmyZEjjWKG4uFizZs3S22+/rTFjxuiKK67QvHnzNHXqVBUUFKizs1O7d+/W66+/rptuuknbt28PP3bZsmWaMWOGzj///D7HT4f3oy+PPPkMPTqQ9j71d0RcfHHC5FKNciamyRgSq7t+pxLVvNtmKuRu1dEH1O98pWG39loY6/Dk2zRj1Bg9X7/Dsjkrx4/rtV9JINSZsLqz2ba0+LUFSAn+bQGQavwcAmLH9wXpJBAq0bq9O+QPJb7KgNOWpZPKK5Rty5Jpmnq67tOE9ceIhcOw69SpY/Ry4241WPA3d35hUMcVj4np2GT0/kx3wc4OfbTlk5TNnxZnDNxud7jvwoEZCqeddpoefPDB8Pbxxx+vvLy88HZbW2TaUM/SSYPxxz/+MXx73rx5USWb0kl+fr5efvllPfnkk1q4cKGczsgvQ1ZWlg4++GAdfPDB+upXv6qf//zn+vGPfxy+/5prrtGXv/zlPjMt0uH96MusmTOU5eCXA6Q/Z9b+oN8op0MFrsFH/JFc/mCnNuz5KOG/hHWYQW1o+kgLy6fJae/6Za+l09oyfi2dfh2Un6/S1jzt8Sc/vbbM6dZBff67ma2T8yarytuiLc17VOdv7eO43sedUViq8tyCxCwUyHD82wIg1fg5BMSO7wvSR7a+6DhYz1RvVyCUuL9/s212fXHcwfJk7z+/WOJ0qcYX+9988Spx5srMCqkh4B344ARoCHgVsgcjnvOBqrwt2txc1+/f4gf2/ix15mlmYVnG/+3b2ZHaLJW06NHR82T4gSfKf/nLX2rhwoVyu906/vjjtXz58oj7Dzz+wNJJsQgGg3ryySfD26effvqgx7Babm6uvvrVr0YFOQ7kcDj03//93/r6178e3ldbW6t169b1+ZhUvx8AYIWN9VXyBZNT2skX7NDGhipJUocZSugvk7EIhILqNEOaWVhmyXwzCksHPKY8t0Dzx03VwvJpmllYprGufDmMyCxAh2HXWFe+ZhaWaWH5NM0fNzXjf9EDAAAAgFTzZLs0f9xUueyJuXjYZXdo/ripUSf8S3Ly+nhEcpTk5GpXa5Olc/Y1nz/YqfV1u7W2duegLzjc42/T2tqdWr9nt/xBa/p7DkdpkdFRXFysqqquE0I1NTUR95WUlOiJJ57o87HV1dXh2x6Pp9fSSgPZsGGD9u7dG96eP3/+oMdId1dffbUeeOCB8PaaNWt0xhln9Hpsqt8PAEi2Km+LdrUl95ehXa1Nmuz2WP6LXregaao8t0AVeZ6kPtcKt2dQwQhPtkueoq5fhpu97Xp2e2247uypU8eoMDcnWUsFAAAAgBHLk+3SwvJp2thQFVdwoMLt0Zzicjnt0aeVK9webW6ui2eZg17Lps8uMrRKfXt09khju09ranfEfTHlrtYm1fpaNW/slH6zRtC7tDgLPX369PDtbdu2DeqxPY/vOc5grF+/Pny7pKREBx100JDGSWeHHXZYRKmqTz7pu15aqt8PAOiLaZoKhILyBzsVCAVlDrG3hlW/eG1p3iObkZrUTftn884pKU/YVTsHctkdmlNcPuTHG0ZXAzbDtHX9f4peKwAAAAAYCZz2LFWWTtLJYyarzDm4KixlTrdOHjNZlaWTeg1ySF3BlFKnNRf7lTndKnQ41dDus2S+bg3t3ohzEY3tPj1bsz1hFSN8wQ49U71dTQFrn9dwkBYZHTNmzAjffueddwb12J7H9xxnMN56663w7ZkzZw7LEy12u13Tp0/Xm2++KUnhHhy9SfX70Z/zz1kU1TdEkhaft0SLl1yQ8PkApF5TwKddrU2qb29TQ7svogxUts2u4hyXSnLyVOH2xHTFQ1PAZ0nfCkmq87eqtaNd2Ta7peWrsm12ZRld1zI47VmaN3ZKUuqxzhs7pc9fcAEAAAAA6ak8t0DluQU9/t72qqHd28vf27kqycmN+e9tSZpZWKa1tTuTtfSwGYWlKS0V7TDs8gc7taZ2R8LXEAgFtbpmR7j3J2KTFq/UUUcdFb69ceNGNTU1yePxDPi4zs5OrVmzJrw9Z86cIc3fffJfkv7t3/5tSGNkgtbW/c2Aysr6rtue6vejP5s2bOg1SHPMcZUJnwtAasXSwCsQCqrG16oaX6s2N9fF1MDL6vqdu9uaVZxjbUO24pzciKB9dz3W1TXxp9JKXZkcpNICAAAAQGbrWVrYNE11miEFTVN2w1CWYRvSxeBWllBOVT+LoGnKIWt6f1aWTkrK+MNRWgQ6KisrVVRUpMbGRoVCIT322GO66KKLBnzcunXr1NTU9aWx2+1auHDhoOcOBALatWtXeHvs2LGDHiMWnZ2dWrVqlbZu3apDDz1Up59+uux2+8APTJC6ujpt3749vD1lypQ+j03l+zGQ2XPn9prRMWHCxITPBSA1/MFObayvGtIvRd0NvPqrGVrfbk02x/75vCrJybM00FGSkxu1z4p6rAAAAACAzGQYhhyGXYkofDynpFy1/takBAF6llBOZaloK3t/DqYvZk+maarDDClkmrIZhhxDDF5lirQ4S+FwOHT22WfrnnvukSTdfvvtuvDCC/sNBJimqWXLloW3TzrpJJWUlAx67gObbY8aNWrQYwykra1Np5xyijZs2BDeN3fuXK1Zs0Z5eYOrW/fpp5/K5/P1m5HRmzvvvFOhUCi8/aUvfanPY1P5fgzkoRWPKsuRnFrzAFIv2Q28TNNMSf3OI4vGWd6QrTfd9Vgnuz3a0rxHdf7Ygy9lTrdmFJYO+RcsAAAAAMDIYFUJZYdhS1mpaCt7fw7m7/BEl//OJGnRjFySrr32WmVldX1It27dqp/85Cf9Hn/fffdp3bp14e0bbrhhSPMe2JQ7GYGOW265JSLIIUkbNmzQz3/+80GNY5qmvvnNb+rII4/Ua6+9FvPjnn32Wd1+++3h7blz5+rf//3f+31Mqt4PACOXFQ28UlW/M9+RY2lDtoF+WSnPLdD8cVO1sHyaZhaWaawrX9m2yGB2ts2usa58zSws08LyaZo/bipBDgAAAABATLpLKLvsiblg2WV3aP64qRF/7xqGoeIca0/WF+fkqrnDb2nvz1gak1d5W7SqeptWVr2vzc11qvG1Rp3/6C7/vbm5Tiur3teq6m2q8vbdxznTpE2gY+rUqfrWt74V3v7pT3+qZcuWRWQhSF0n+++//34tXbo0vG/BggWqrBxaj4a6usjoWzICHWvXrh3U/r789re/1d/+9jdVVVXpmGOO0Te/+U1t27atz+MbGhr0X//1X1qwYIE6OrpOHNrtdv32t78dME0pVe8HgJEp2Q28uut2hkwzoePHKmiamlk4uEy8oZpRWBrzsZ5slz5fNE5fHHuwzp04U1+b9O8657P/P3fiTH1x7MH6fNG4YXeVBwAAAAAg+bpLKPdVdSBWFW6PFpZP6/Vv05Icay4q3D9fruW9P/ubzx/s1Pq63Vpbu3PQwZfu8t/r9+xOWb+TREqL0lXdbrvtNm3cuFEbN26UaZr6wQ9+oOXLl2vRokUqLy/Xnj17tHLlyojm4ZMnT9by5cuHPKfPFxkRS0agIxjs/cRdX/v78qUvfUkPPvigXn/9dZmmqeXLl2v58uU6/PDDdcwxx2j8+PHyeDzavXu33nvvPa1fvz6icbdhGHrwwQc1a9asmOZLxfsBYGSyqoFXKut3WtmQbSgSWY8VAAAAAAAp+SWUK9wey0tFb2qosmw+qav3Z2+SXf4706RVoMPlcumJJ57Q2WefHS7NtHXrVt188829Hn/YYYfp73//e1y9INrb2yO2kxHoOOmkk/TGG2/0un8wxo8frxdffFG33367br31VrW1dUXp3nnnHb3zzjv9PrasrEz33nuvzjjjjJjnS8X7ASBzJKqplZUNvMa7RqWsfqdkXUM2AAAAAADSSXlugcpzC3r0kPCq3u9Vh7n/73OHYVeJM1clObkx95DwZLtU6syzpJRUmdOtQoczJb0/TdOMOOfSXf47Uec3ust/H1geLJOkVaBDksaMGaP169fr9ttv11133aXq6uqoY4qKinTRRRfpxhtvVG5ublzz+f3+iO38/Py4xuvNDTfcoOeff16bNm0K75szZ46uv/76QY+Vk5OjG264QRdffLF+//vf67HHHtPmzZv7PH7s2LH61re+pW9/+9sqLi4e9HxWvx8DOf+cRfJ6o6OYi89bosVLLkjq3ACS09TK6gZexTku1fhiv4IkXsU5ueFfRqxqyAYAAAAAQDryZLvkKeo6X9Dsbdez22slw5RMQ6dOHaPC3JxBjzmzsExra3cmeqlRZhSWpqz3Z6cZksPo6q2Z7PLfC8unZeT5hbRccVZWlq677jpde+21euWVV/TBBx+orq5OxcXFqqioUGVlpXJyBv+h781ll12myy67LCFj9SU/P1+vvvqqnnrqKW3btk2HHHKIzjjjjHCz76EYM2aMbrzxRt14443atWuXtm/frpqaGtXW1sput2v06NE64ogjNH36dNls8bVisfL9GMimDRsiynF1O+Y4eoIAyVTlbdHm5rp+r5DobmrV3diq1JmnmYVl/aaYNgV8ljfwKsnJszTQUZITGQDubsi2uib+9FKpK5Mj09NLAQAAAAAjj2EYMmRIphHeHgorS0WnqpdF0DTDpaatKv+dadIy0NHNbrersrJyWDS2zsrK0llnnZWUsSsqKlRRUZGUsXtKh/dj9ty5vWZ0TJgwMQWrAYY/f7BTG+urhvTLQndTqwq3R3OKy3u9GiAVDbxSUb/zQN0N2TY2VMX1GvT32gIAAAAAMFJYVSo6lb0/JWvLfw+1B2iqcGYEGeWhFY8qy0GrXMAKVjS1qm+3Jptj/3xefb5onKX1O/vKtEh2QzYAAAAAAEYKq0pFOwxbSnt/Wl3+O5MQ6AAARLGiqZVpmilr4GVl/c6B9NaQraHd20v/k8E1ZAMAAAAAYCSxolS0YRgp6/2ZivLfsZx/6D6f0eDdp9EWrK0vBDoAABGsamqVygZeVtbvjFXPhmymaarTDClomrIbhrIM25BrlQIAAAAAMFJYUSo6Vb0/U1H+u/s8RW8O7OdqD5kpDXTE16UaADDsWNHUSpJCppmUOQYS/GzeOSXlctmTUwqvZ/3OoTAMQw6bXU57lhw2O0EOAAAAAABi1F0q+uQxk1XmdA/qsWVOt04eM1mVpZP67IfZWy/OZOqeLxXlv3vjD3Zqfd1ura3daVmGSSzI6AAAhFnZ1Kp0kL9sJEp3Ay+r6ncCAAAAAADrJatUtCfbZXnvz1SW/+558WWi+rkmA2dhkFHOP2eRvN7oaOLi85Zo8ZILUrAiID2YpqkOM6SQacpmGHIMsdSRlU2tTh07KqUNvCRr6ncCAAAAAIDUSUapaKt7f6ay/LfDsEtKfD/XRCPQgYyyacMGtbS0RO0/5rjKFKwGSK39VyS0qaHd18sVCS6V5OTFfEWC1U2tmjv8KWvg1ZMV9TsBAAAAAEDqGYYhh2FXvIWsre79mcry3w4lr59rInE2Bhll9ty5vWZ0TJgwMQWrAVLjwGZPvQmEgqrxtarG16rNzXUqdeZpZmFZv82xU9HUKlUNvA7UXb9zstujLc17VOePfU1lTrdmFJYOqvE4AAAAAADIbHNKylXrb01KGacDe3/aUtS7s7v8dzL7uSYKgQ5klIdWPKosR3KaBwPpzh/s1Mb6qiFdLbDH36a1tTv7zTpIRVOr2cXjLSuXJQ3cMCxZ9TsBAAAAAMDwYmXvT4dhS1n5byv6uSYCgQ4AyACJava0q7VJtb7WqD4SqWpqVehwWt7AKxbJqN8JAAAAAACGF6t6fxqGkbLy31ZeoBoP28CHAABSqbvZU6JSBH3BDj1TvV1Ngf2BjVQ2tZpZWGbJfN0NvAbLMAw5bHY57Vly2OwEOQAAAAAAQFh378+BqkgMpMLt0cLyaX1epFmSkxfX+INVkpNraT/XeBHoAIA0lqxmT4FQUKtrdsgf7JSU2qZW3Q28kqlnAy8AAAAAAIBE6u79efKYySpzugf12DKnWyePmazK0km9lhrvFm8gZbAq3B7L+7nGg9JVAJDGktnsyRfs0MaGKlWWTkp5UysrG3gBAAAAAAAkQzJ7f3qyXZaX/97UXpX0uRKFQAcyyvnnLJLX643av/i8JVq85IIUrAhIHiuaPe1qbdJ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\n",
      "text/plain": [
       "<Figure size 1800x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax2, ax1) = plt.subplots(2,1 , sharex=True, dpi=300)\n",
    "g = ax1.plot_date(data.index, data.set.apply(lambda x: datetime.datetime.combine(datetime.datetime(2021, 12, 1), x)))\n",
    "h = ax2.plot_date(data.index, data.rise.apply(lambda x: datetime.datetime.combine(datetime.datetime(2021, 12, 1), x)))\n",
    "\n",
    "ax2.format_xdata = mdates.DateFormatter('%m-%d')\n",
    "ax2.format_ydata = mdates.DateFormatter('%h-%m')\n",
    "\n",
    "\n",
    "ax2.yaxis.set_minor_locator(mdates.MinuteLocator())\n",
    "ax2.yaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))\n",
    "\n",
    "ax1.yaxis.set_minor_locator(mdates.MinuteLocator())\n",
    "ax1.yaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))\n",
    "\n",
    "ax1.set_xlim(data.index[1], data.index[-1])\n",
    "#ax2.set_ylim([h[0].get_ydata()[1:].max(), h[0].get_ydata()[1:].min()])\n",
    "#ax1.set_ylim([g[0].get_ydata()[1:].max(), g[0].get_ydata()[1:].min()])\n",
    "ax2.xaxis.set_major_formatter(mdates.DateFormatter('%d/%m'))\n",
    "\n",
    "ax1.set_title(\"↓ Sunset\")\n",
    "ax2.set_title(\"↓ Sunrise\")\n",
    "\n",
    "latest_sunrise = data.rise[1:].apply(lambda x: x.hour*3600+x.minute*60+x.second).idxmax()\n",
    "earliest_sunset = data.set[1:].apply(lambda x: x.hour*3600+x.minute*60+x.second).idxmin()\n",
    "ax2.annotate(f\"Latest ({latest_sunrise:%d/%m}) \\n {data.rise[1:].max()}\", h[0].get_xydata()[h[0].get_ydata()[1:].argmax()], xytext=(15, 25), \n",
    "            textcoords='offset points', arrowprops=dict(arrowstyle='-|>', color='k'))\n",
    "ax1.annotate(f\"Earliest ({earliest_sunset:%d/%m}) \\n {data.set[1:].min()}\", g[0].get_xydata()[g[0].get_ydata()[1:].argmin()], xytext=(15, -35), \n",
    "            textcoords='offset points', arrowprops=dict(arrowstyle='-|>', color='k'), )\n",
    "f.text(0.05,0, \"Latest sunrise, earliest sunset for 2021/22   // Daniel Williams (@daniel_williams)\", fontsize=7)\n",
    "\n",
    "f.savefig(\"sunset2021.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Invalid object type at position 0",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m~/sandbox/_environment/lib/python3.8/site-packages/pandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.maybe_convert_numeric\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Invalid object type",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_77253/3247937028.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numeric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'rise'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/sandbox/_environment/lib/python3.8/site-packages/pandas/core/tools/numeric.py\u001b[0m in \u001b[0;36mto_numeric\u001b[0;34m(arg, errors, downcast)\u001b[0m\n\u001b[1;32m    181\u001b[0m         \u001b[0mcoerce_numeric\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"raise\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 183\u001b[0;31m             values, _ = lib.maybe_convert_numeric(\n\u001b[0m\u001b[1;32m    184\u001b[0m                 \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoerce_numeric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcoerce_numeric\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m             )\n",
      "\u001b[0;32m~/sandbox/_environment/lib/python3.8/site-packages/pandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.maybe_convert_numeric\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Invalid object type at position 0"
     ]
    }
   ],
   "source": [
    "pandas.to_numeric(data['rise'][1:]).argmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "arrays used as indices must be of integer or boolean type",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_77253/1190037432.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrise\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrise\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrise\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/sandbox/_environment/lib/python3.8/site-packages/pandas/core/indexes/extension.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    277\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    278\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 279\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    280\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    281\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/sandbox/_environment/lib/python3.8/site-packages/pandas/core/arrays/datetimelike.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    320\u001b[0m         \u001b[0monly\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mlikes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0minteger\u001b[0m \u001b[0mscalars\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    321\u001b[0m         \"\"\"\n\u001b[0;32m--> 322\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    323\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    324\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/sandbox/_environment/lib/python3.8/site-packages/pandas/core/arrays/_mixins.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    204\u001b[0m             \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextract_numpy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m         )\n\u001b[0;32m--> 206\u001b[0;31m         \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    207\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ndarray\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/sandbox/_environment/lib/python3.8/site-packages/pandas/core/indexers.py\u001b[0m in \u001b[0;36mcheck_array_indexer\u001b[0;34m(array, indexer)\u001b[0m\n\u001b[1;32m    571\u001b[0m             ) from err\n\u001b[1;32m    572\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 573\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"arrays used as indices must be of integer or boolean type\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    574\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    575\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: arrays used as indices must be of integer or boolean type"
     ]
    }
   ],
   "source": [
    "data.index[data.where(data.rise==data.rise[1:].max()).rise.values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.where?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "mimetype": "text/x-python",
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sunset.py

import ephem
import datetime
import pandas
import matplotlib.pyplot as plt

# I've written this for Glasgow, Scotland, but you can adapt it to anywhere by getting the coordinates for your town. 
# You can normally get these from Wikipedia - there's a link in the top right hand corner of most articles for places; 
# click that link, and alongside lots of other useful stuff it'll give you a decimal representation you can use here.

glasgow = ephem.Observer()
glasgow.lat, glasgow.lon = "55.860916", "-4.251433"

# This just makes an array of 60 dates to use to calculate the sunrise and set times
start = datetime.datetime.strptime("2019-11-15", "%Y-%m-%d")
numdays=60
date_list = [start + datetime.timedelta(days=x) for x in range(numdays)]

def rise_set_times(dates, observatory):
  """
  Calculate the local sunrise, sunset, and local noon (sun transit) times for each day in an array, and return a DataFrame.
  
  Parameters
  ----------
  dates: list of `datetime.datetime` objects
     A list of dates where the sunrise and set should be computed
  observatory: `ephem.Observer`
     An ephem object describing the location of the observatory.
     
  Returns
  -------
  results : `pandas.DataFrame`
     A dataframe with the various times, with the date as the index.
  """
  
  results = []
  for date in dates:
    observatory.date = date.strftime("%Y-%m-%d")
    data = {
      "rise": observatory.next_rising(ephem.Sun()).datetime(),
      "date": date,
      "set": observatory.next_setting(ephem.Sun()).datetime(),
      "transit": observatory.next_transit(ephem.Sun()).datetime()
    }
    observatory.horizon = '-6'
    data['civil start'] = observatory.next_rising(ephem.Sun(), use_center=True).datetime()
    data['civil end'] = observatory.next_setting(ephem.Sun(), use_center=True).datetime()
    results.append(data)
  return pandas.DataFrame(results).set_index("date")
  
results = rise_set_times(date_list, glasgow)