analemma_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 2018-01-01 00:00:00+00:00\n",
      "120 2018-01-11 00:00:00+00:00\n",
      "240 2018-01-21 00:00:00+00:00\n",
      "360 2018-02-01 00:00:00+00:00\n",
      "480 2018-02-11 00:00:00+00:00\n",
      "600 2018-02-21 00:00:00+00:00\n",
      "720 2018-03-01 00:00:00+00:00\n",
      "840 2018-03-11 00:00:00+00:00\n",
      "960 2018-03-21 00:00:00+00:00\n",
      "1080 2018-04-01 00:00:00+00:00\n",
      "1200 2018-04-11 00:00:00+00:00\n",
      "1320 2018-04-21 00:00:00+00:00\n",
      "1440 2018-05-01 00:00:00+00:00\n",
      "1560 2018-05-11 00:00:00+00:00\n",
      "1680 2018-05-21 00:00:00+00:00\n",
      "1800 2018-06-01 00:00:00+00:00\n",
      "1920 2018-06-11 00:00:00+00:00\n",
      "2040 2018-06-21 00:00:00+00:00\n",
      "2160 2018-07-01 00:00:00+00:00\n",
      "2280 2018-07-11 00:00:00+00:00\n",
      "2400 2018-07-21 00:00:00+00:00\n",
      "2520 2018-08-01 00:00:00+00:00\n",
      "2640 2018-08-11 00:00:00+00:00\n",
      "2760 2018-08-21 00:00:00+00:00\n",
      "2880 2018-09-01 00:00:00+00:00\n",
      "3000 2018-09-11 00:00:00+00:00\n",
      "3120 2018-09-21 00:00:00+00:00\n",
      "3240 2018-10-01 00:00:00+00:00\n",
      "3360 2018-10-11 00:00:00+00:00\n",
      "3480 2018-10-21 00:00:00+00:00\n",
      "3600 2018-11-01 00:00:00+00:00\n",
      "3720 2018-11-11 00:00:00+00:00\n",
      "3840 2018-11-21 00:00:00+00:00\n",
      "3960 2018-12-01 00:00:00+00:00\n",
      "4080 2018-12-11 00:00:00+00:00\n",
      "4200 2018-12-21 00:00:00+00:00\n"
     ]
    }
   ],
   "source": [
    "d = get_dates(2018)\n",
    "for i,_d in enumerate(d):\n",
    "    if i % 120 == 0:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pysolar.solar import *\n",
    "def get_dates(year):\n",
    "    months = list(range(1,13))\n",
    "    days = list(range(1,30,10))\n",
    "    hours = list(range(0,24))\n",
    "    minutes = list(range(0,61,15))\n",
    "    minutes[-1] = minutes[-1]-1\n",
    "    dates = []\n",
    "    for m in months:\n",
    "        for d in days:\n",
    "            for h in hours:\n",
    "                for _min in minutes:\n",
    "                    dates.append(datetime.datetime(year,m,d,h,_min,tzinfo=datetime.timezone.utc))\n",
    "    return dates\n",
    "def max_day_alt(alt):\n",
    "    return alt.index(max(alt))\n",
    "def format_datetime(d):\n",
    "    return str(d).split('dsf').pop(0)\n",
    "def solar_alt_azi(lat,lon,year=2019,verb=True):\n",
    "    dates = get_dates(year)\n",
    "    _alt,_azi = [],[]\n",
    "    alt,azi,dat = [],[],[]\n",
    "    i,I=0,0\n",
    "    for d in dates:\n",
    "        _alt.append(get_altitude(lat,lon,d))\n",
    "        _azi.append(get_azimuth(lat,lon,d))\n",
    "        if i % 120 ==0 and i>0:\n",
    "            max_ind = max_day_alt(_alt)\n",
    "            d_fmt = format_datetime(dates[I+max_ind])\n",
    "            \n",
    "            alt.append(_alt[max_ind])\n",
    "            azi.append(_azi[max_ind])\n",
    "            dat.append(d_fmt)\n",
    "            \n",
    "            _alt, _azi = [],[]\n",
    "            I+=120\n",
    "        i+=1\n",
    "    max_ind = max_day_alt(_alt)\n",
    "    d_fmt = format_datetime(dates[I+max_ind])\n",
    "    alt.append(_alt[max_ind])\n",
    "    azi.append(_azi[max_ind])\n",
    "    dat.append(d_fmt)\n",
    "    return alt,azi,dat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tacitus/anaconda3/envs/basemap/lib/python3.6/site-packages/pysolar/solartime.py:112: UserWarning: I don't know about leap seconds after 2018\n",
      "  (leap_seconds_base_year + len(leap_seconds_adjustments) - 1)\n"
     ]
    }
   ],
   "source": [
    "sd_lat, sd_lon = 32.753555, -117.160313\n",
    "alt,azi,dates = solar_alt_azi(sd_lat,sd_lon)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11ac7e588>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(azi,alt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2019-01-01 19:59:00+00:00',\n",
       " '2019-01-11 19:45:00+00:00',\n",
       " '2019-01-21 19:59:00+00:00',\n",
       " '2019-02-01 19:59:00+00:00',\n",
       " '2019-02-11 19:59:00+00:00',\n",
       " '2019-02-21 19:59:00+00:00',\n",
       " '2019-03-01 19:59:00+00:00',\n",
       " '2019-03-11 19:45:00+00:00',\n",
       " '2019-03-21 19:45:00+00:00',\n",
       " '2019-04-01 19:45:00+00:00',\n",
       " '2019-04-11 19:30:00+00:00',\n",
       " '2019-04-21 19:30:00+00:00',\n",
       " '2019-05-01 19:30:00+00:00',\n",
       " '2019-05-11 19:30:00+00:00',\n",
       " '2019-05-21 19:30:00+00:00',\n",
       " '2019-06-01 19:30:00+00:00',\n",
       " '2019-06-11 19:30:00+00:00',\n",
       " '2019-06-21 19:30:00+00:00',\n",
       " '2019-07-01 19:45:00+00:00',\n",
       " '2019-07-11 19:45:00+00:00',\n",
       " '2019-07-21 19:45:00+00:00',\n",
       " '2019-08-01 19:45:00+00:00',\n",
       " '2019-08-11 19:45:00+00:00',\n",
       " '2019-08-21 19:30:00+00:00',\n",
       " '2019-09-01 19:30:00+00:00',\n",
       " '2019-09-11 19:30:00+00:00',\n",
       " '2019-09-21 19:30:00+00:00',\n",
       " '2019-10-01 19:30:00+00:00',\n",
       " '2019-10-11 19:15:00+00:00',\n",
       " '2019-10-21 19:15:00+00:00',\n",
       " '2019-11-01 19:15:00+00:00',\n",
       " '2019-11-11 19:15:00+00:00',\n",
       " '2019-11-21 19:15:00+00:00',\n",
       " '2019-12-01 19:30:00+00:00',\n",
       " '2019-12-11 19:30:00+00:00',\n",
       " '2019-12-21 19:30:00+00:00']"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}