ric_sun_times.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# source: https://dwr.virginia.gov/wp-content/uploads/media/sunrise-sunset.pdf\n",
    "# surely there's a more readable source of sunrise/sunset times..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "df = pd.read_csv('ric_sun_rise_set.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>jan_rise</th>\n",
       "      <th>jan_set</th>\n",
       "      <th>feb_rise</th>\n",
       "      <th>feb_set</th>\n",
       "      <th>mar_rise</th>\n",
       "      <th>mar_set</th>\n",
       "      <th>apr_rise</th>\n",
       "      <th>apr_set</th>\n",
       "      <th>may_rise</th>\n",
       "      <th>...</th>\n",
       "      <th>aug_rise</th>\n",
       "      <th>aug_set</th>\n",
       "      <th>sept_rise</th>\n",
       "      <th>sept_set</th>\n",
       "      <th>oct_rise</th>\n",
       "      <th>oct_set</th>\n",
       "      <th>nov_rise</th>\n",
       "      <th>nov_set</th>\n",
       "      <th>dec_rise</th>\n",
       "      <th>dec_set</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>7:25</td>\n",
       "      <td>5:02</td>\n",
       "      <td>7:14</td>\n",
       "      <td>5:33</td>\n",
       "      <td>6:41</td>\n",
       "      <td>6:03</td>\n",
       "      <td>6:56</td>\n",
       "      <td>7:32</td>\n",
       "      <td>6:15</td>\n",
       "      <td>...</td>\n",
       "      <td>6:14</td>\n",
       "      <td>8:17</td>\n",
       "      <td>6:41</td>\n",
       "      <td>7:38</td>\n",
       "      <td>7:06</td>\n",
       "      <td>6:52</td>\n",
       "      <td>6:35</td>\n",
       "      <td>5:11</td>\n",
       "      <td>7:06</td>\n",
       "      <td>4:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>7:25</td>\n",
       "      <td>5:03</td>\n",
       "      <td>7:13</td>\n",
       "      <td>5:34</td>\n",
       "      <td>6:40</td>\n",
       "      <td>6:04</td>\n",
       "      <td>6:54</td>\n",
       "      <td>7:33</td>\n",
       "      <td>6:14</td>\n",
       "      <td>...</td>\n",
       "      <td>6:15</td>\n",
       "      <td>8:16</td>\n",
       "      <td>6:41</td>\n",
       "      <td>7:37</td>\n",
       "      <td>7:07</td>\n",
       "      <td>6:51</td>\n",
       "      <td>6:36</td>\n",
       "      <td>5:10</td>\n",
       "      <td>7:07</td>\n",
       "      <td>4:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>7:25</td>\n",
       "      <td>5:04</td>\n",
       "      <td>7:12</td>\n",
       "      <td>5:35</td>\n",
       "      <td>6:39</td>\n",
       "      <td>6:05</td>\n",
       "      <td>6:53</td>\n",
       "      <td>7:34</td>\n",
       "      <td>6:12</td>\n",
       "      <td>...</td>\n",
       "      <td>6:16</td>\n",
       "      <td>8:15</td>\n",
       "      <td>6:42</td>\n",
       "      <td>7:35</td>\n",
       "      <td>7:08</td>\n",
       "      <td>6:49</td>\n",
       "      <td>6:37</td>\n",
       "      <td>5:09</td>\n",
       "      <td>7:08</td>\n",
       "      <td>4:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>7:25</td>\n",
       "      <td>5:04</td>\n",
       "      <td>7:11</td>\n",
       "      <td>5:37</td>\n",
       "      <td>6:37</td>\n",
       "      <td>6:06</td>\n",
       "      <td>6:51</td>\n",
       "      <td>7:35</td>\n",
       "      <td>6:11</td>\n",
       "      <td>...</td>\n",
       "      <td>6:17</td>\n",
       "      <td>8:14</td>\n",
       "      <td>6:43</td>\n",
       "      <td>7:34</td>\n",
       "      <td>7:08</td>\n",
       "      <td>6:48</td>\n",
       "      <td>6:38</td>\n",
       "      <td>5:08</td>\n",
       "      <td>7:09</td>\n",
       "      <td>4:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>7:25</td>\n",
       "      <td>5:05</td>\n",
       "      <td>7:10</td>\n",
       "      <td>5:38</td>\n",
       "      <td>6:36</td>\n",
       "      <td>6:07</td>\n",
       "      <td>6:50</td>\n",
       "      <td>7:36</td>\n",
       "      <td>6:10</td>\n",
       "      <td>...</td>\n",
       "      <td>6:18</td>\n",
       "      <td>8:13</td>\n",
       "      <td>6:44</td>\n",
       "      <td>7:32</td>\n",
       "      <td>7:09</td>\n",
       "      <td>6:46</td>\n",
       "      <td>6:40</td>\n",
       "      <td>5:07</td>\n",
       "      <td>7:10</td>\n",
       "      <td>4:51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   day jan_rise jan_set feb_rise feb_set mar_rise mar_set apr_rise apr_set  \\\n",
       "0    1     7:25    5:02     7:14    5:33     6:41    6:03     6:56    7:32   \n",
       "1    2     7:25    5:03     7:13    5:34     6:40    6:04     6:54    7:33   \n",
       "2    3     7:25    5:04     7:12    5:35     6:39    6:05     6:53    7:34   \n",
       "3    4     7:25    5:04     7:11    5:37     6:37    6:06     6:51    7:35   \n",
       "4    5     7:25    5:05     7:10    5:38     6:36    6:07     6:50    7:36   \n",
       "\n",
       "  may_rise  ... aug_rise aug_set sept_rise sept_set oct_rise oct_set nov_rise  \\\n",
       "0     6:15  ...     6:14    8:17      6:41     7:38     7:06    6:52     6:35   \n",
       "1     6:14  ...     6:15    8:16      6:41     7:37     7:07    6:51     6:36   \n",
       "2     6:12  ...     6:16    8:15      6:42     7:35     7:08    6:49     6:37   \n",
       "3     6:11  ...     6:17    8:14      6:43     7:34     7:08    6:48     6:38   \n",
       "4     6:10  ...     6:18    8:13      6:44     7:32     7:09    6:46     6:40   \n",
       "\n",
       "  nov_set dec_rise dec_set  \n",
       "0    5:11     7:06    4:52  \n",
       "1    5:10     7:07    4:52  \n",
       "2    5:09     7:08    4:51  \n",
       "3    5:08     7:09    4:51  \n",
       "4    5:07     7:10    4:51  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "month_index = {m.replace('_rise', ''): i for i, m in enumerate(df.loc[:, df.columns.str.endswith('_rise')].columns)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stack \n",
    "rises = df.loc[:, df.columns.str.endswith('_rise')]\n",
    "rises.columns = [i+1 for i in range(len(rises.columns))] # switch back to month numbers for indexing\n",
    "rises = rises.stack().swaplevel(0,1).sort_index()\n",
    "rises.index.set_names(['month','day'], inplace=True)\n",
    "rises = rises.apply(lambda x: float(x[0:x.find(':')]) + float(x[x.find(':')+1:])/60.0)\n",
    "\n",
    "sets = df.loc[:, df.columns.str.endswith('_set')]\n",
    "sets.columns = [i+1 for i in range(len(sets.columns))] \n",
    "sets = sets.stack().swaplevel(0,1).sort_index()\n",
    "sets.index.set_names(['month','day'], inplace=True)\n",
    "sets = sets.apply(lambda x: float(x[0:x.find(':')])+12 + float(x[x.find(':')+1:])/60.0) # convert to PM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>rises</th>\n",
       "      <th>sets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.416667</td>\n",
       "      <td>17.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7.416667</td>\n",
       "      <td>17.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7.416667</td>\n",
       "      <td>17.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7.416667</td>\n",
       "      <td>17.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>7.416667</td>\n",
       "      <td>17.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   day_of_year  month  day     rises       sets\n",
       "0            1      1    1  7.416667  17.033333\n",
       "1            2      1    2  7.416667  17.050000\n",
       "2            3      1    3  7.416667  17.066667\n",
       "3            4      1    4  7.416667  17.066667\n",
       "4            5      1    5  7.416667  17.083333"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame(rises)\n",
    "df2.columns = ['rises']\n",
    "df2['sets'] = sets\n",
    "df2 = df2.reset_index().reset_index()\n",
    "df2.rename(columns={\"index\":\"day_of_year\"}, inplace=True)\n",
    "df2['day_of_year'] = df2.day_of_year + 1\n",
    "df2['day'] = df2.day + 1\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.to_csv('ric_sun_times.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Ric sunrise/sets')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1 plot\n",
    "fig, ax1 = plt.subplots(1, 1)\n",
    "\n",
    "# set figure size in inches\n",
    "fig.set_figheight(10)\n",
    "fig.set_figwidth(20)\n",
    "\n",
    "\n",
    "ax1.plot(df2.day_of_year, df2.sets, c=\"red\")\n",
    "ax1.plot(df2.day_of_year, df2.rises, c=\"red\")\n",
    "ax1.fill_between(df2.day_of_year, df2.rises, df2.sets, alpha=0.2)\n",
    "\n",
    "\n",
    "ax1.set_title('Ric sunrise/sets', fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>rises</th>\n",
       "      <th>sets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>91</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6:56</td>\n",
       "      <td>19:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>92</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6:54</td>\n",
       "      <td>19:33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>93</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>6:53</td>\n",
       "      <td>19:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>94</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6:51</td>\n",
       "      <td>19:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>95</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6:50</td>\n",
       "      <td>19:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>96</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>6:48</td>\n",
       "      <td>19:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>97</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>6:47</td>\n",
       "      <td>19:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>98</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>6:45</td>\n",
       "      <td>19:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>99</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>6:44</td>\n",
       "      <td>19:39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>100</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>6:42</td>\n",
       "      <td>19:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>101</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>6:41</td>\n",
       "      <td>19:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>102</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>6:39</td>\n",
       "      <td>19:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>103</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>6:38</td>\n",
       "      <td>19:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>104</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>6:37</td>\n",
       "      <td>19:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>105</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>6:35</td>\n",
       "      <td>19:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>106</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>6:34</td>\n",
       "      <td>19:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>107</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>6:32</td>\n",
       "      <td>19:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>108</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>6:31</td>\n",
       "      <td>19:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>109</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>6:30</td>\n",
       "      <td>19:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>110</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>6:28</td>\n",
       "      <td>19:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>111</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>6:27</td>\n",
       "      <td>19:50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     day_of_year  month  day rises   sets\n",
       "90            91      4    1  6:56  19:32\n",
       "91            92      4    2  6:54  19:33\n",
       "92            93      4    3  6:53  19:34\n",
       "93            94      4    4  6:51  19:35\n",
       "94            95      4    5  6:50  19:36\n",
       "95            96      4    6  6:48  19:37\n",
       "96            97      4    7  6:47  19:37\n",
       "97            98      4    8  6:45  19:38\n",
       "98            99      4    9  6:44  19:39\n",
       "99           100      4   10  6:42  19:40\n",
       "100          101      4   11  6:41  19:41\n",
       "101          102      4   12  6:39  19:42\n",
       "102          103      4   13  6:38  19:43\n",
       "103          104      4   14  6:37  19:44\n",
       "104          105      4   15  6:35  19:45\n",
       "105          106      4   16  6:34  19:46\n",
       "106          107      4   17  6:32  19:46\n",
       "107          108      4   18  6:31  19:47\n",
       "108          109      4   19  6:30  19:48\n",
       "109          110      4   20  6:28  19:49\n",
       "110          111      4   21  6:27  19:50"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.loc[90:110,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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