{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import os\n",
    "import re\n",
    "from matplotlib import pyplot as plt\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()\n",
    "import netCDF4 as nc\n",
    "\n",
    "from sqlalchemy.sql import select, and_, or_, not_, func\n",
    "from sqlalchemy import create_engine, Column, String, Integer, Boolean, MetaData, Table, case, between, ForeignKey, desc\n",
    "from sqlalchemy.orm import mapper, create_session, relationship\n",
    "from sqlalchemy.ext.declarative import declarative_base\n",
    "from sqlalchemy.ext.automap import automap_base\n",
    "import sqlalchemy.types as types\n",
    "from sqlalchemy.sql import select, and_, or_, not_, func\n",
    "from time import strptime\n",
    "import string\n",
    "import pandas as pd\n",
    "from dateutil.parser import parse as dutparse\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "basepath='/ocean/eolson/MEOPAR/obs/'\n",
    "basedir=basepath + 'ECBuoy/'\n",
    "dbname='ECBuoy'\n",
    "Base = automap_base()\n",
    "engine = create_engine('sqlite:///' + basedir + dbname + '.sqlite', echo = False)\n",
    "# reflect the tables in salish.sqlite:\n",
    "Base.prepare(engine, reflect=True)\n",
    "# mapped classes have been created\n",
    "FBuoyTBL=Base.classes.FBuoyTBL\n",
    "FlowTBL=Base.classes.FlowTBL\n",
    "session = create_session(bind = engine, autocommit = False, autoflush = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df=pd.DataFrame(session.query(FlowTBL.DecDay,FlowTBL.RateHope).filter(FlowTBL.RateHope>=0).all())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dts=[dt.datetime(1900,1,1)+dt.timedelta(days=ii) for ii in df['DecDay']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['YD']=[(ii-dt.datetime(ii.year-1,12,31)).days for ii in dts]\n",
    "df['Year']=[ii.year for ii in dts]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dDThYy/ohDRwc7R72xrB9GKyqur21trLbdnpqYZlZ4AXGR7vnsPX9RIp5qzB6Qi0AnEnfD/b3Ykz7yklD6OU0bB9GT6gFgJ0M4WB/WmPaVx5qCL2c5q3C6Fn9GIDh2NhIrr12crlfY1qJeEz7ykMNZXXe1dXk6qsFWhgpPbUADMO8exvHNKRxTPs6pmHW0+yrXk5gAITagzCmD0yGx+uXvpr3MMpFHewvog0OIdhM838byjDree+rxcmAnhNq520oH5iMk9cvfXYQvY2HfbC/yDbY52Az7f9tCPNHx7SvAFMyp3bezEuiz7x++2eec0j7bqu38VWvOvwTMvN6HrTB2Uz7fxvC/NEx7SvAlPTUztuY5iUxPGN7/fZ9qLWe9dMtordxns/D2NrgvEz7fxvCMOsx7SvAlITaefMhQp+N6fU7hEBoeOFymOfzMKY2OE97+b/1eZh1Mq59XZS+n/CEERJqD4IPEfpsLK/fIQRCvXrLYd7Pw1ja4LyN6f82hH1d1uA4hBOeMEJCLTBOQwiEevWWw1Ceh2UNGQzPMgfHIZzwhBESaoFxGkoQGUKPzRD0/XlY5pDB8CxzcBzCCU8YoX2tflxVP1lVn6iqj1fVW6vqr1XVpVX1gaq6o6p+raqOdts+ovv9zu72S7Y9ztVd+aer6hn72yWAKa2uJldfvTwHUwzbMq9UbdVlDtMyr8y8yFXUgZnN3FNbVRck+ZdJHt9a+/OqenuS5yd5VpLXttbeVlVvTPLCJNd1l19orf3tqnp+kl9I8k+q6vHd/Z6Q5JuS/O+q+pbW2oP72jPgYBmqCNNbZE/oNG1V7xSHaSgjZabl8xIO3H6HH5+d5Gur6itJHpnkniRPS/JPu9tvSvLvMwm1V3bXk+QdSX65qqorf1tr7ctJPltVdyZ5cpIlPJUNJDFUkek4kDtpUcMtp22rYwsZ7N282/OyDtmf9+fbtI/n/RL2ZeZQ21r7w6r6pSSfS/LnSd6b5PYkf9pa+2q32YkkF3TXL0hyV3ffr1bVF5M8piu/bdtDb78PsIyWeT4Uy8GJj4daVE/oXtrqsoYMFm9M7Xnen2/TPN6Y/r9wQGaeU1tV52bSy3ppJsOGvy7JM3fYtG3d5Qy3nal8p795rKo2q2rzvvvu23ulgflY5vlQLAdzNB9qUfP09tJWl3nOL4s1pvY878+3aR5vTP9fOCD7GX78j5J8trV2X5JU1a8n+XtJzqmqs7ve2guT3N1tfyLJRUlOVNXZSb4+yf3byrdsv89DtNauT3J9kqysrOwYfIFDsJehioZUjdNeeiaH8BqZZh8W0RM6bVvd2Ei+53tOPl/ve19/nwvmb0xzruc9FH+axxvT/xcOSLU2WzasqqckuSHJd2Uy/PjGJJtJvjvJzdsWivpoa+1XquolSb6ttfaibqGof9xa+4GqekKS/57JPNpvSnI8yWW7LRS1srLSNjc3Z6o7cEgMqRq3aYLeEF4jQ9iHF784eeMbT/7+ohcl1123uPqwfIZw8mmZ+f/Cjqrq9tbaym7b7WdO7Qeq6h1JPpTkq0l+J5Ne1P+V5G1V9XNd2Zu6u7wpyX/rFoK6P5MVj9Na+0S3cvInu8d5iZWPYSDMvR23aXomh/AaGcI+zJsD9OEx5/pg+f/Cvuxr9ePW2jVJrjml+DOZ9Lqeuu1fJHneGR7n55P8/H7qAkzhsA80DaliN0N4jQxhH17wguSGG5KvfCX5mq+Z/D6rg+i5FpIBeBj7/UofoC8WMUTS14SwmyG8RoayD+vr0w0X322befdcD2F4NwAHSqiFsVjUEElDqtjNEF4jY9iHacPlvHuuDe9eDnrLgSUm1MJYDGGIJLA404bLefdce+9aPL3lwJITamEs5n2g6aw9jMtewuU8e66X/b1rDO+FesuBJSfUwpjM60DTWXsYn0XOHV7W965FvRda9A/gIYRaYO+ctYdx6vvc4Xm/dy3ivdCifwCnEWqBvXPWHvpjDMNjpzXv965FvBda9A/gNEItsHfO2jMNYWrxTBV4qHm/dy3ivdBJRYDTCLXAbJy1P90yh7jDrpswtRzW15Mvfzn5y7+cXJoqMP/3rsN+L3RSEeA0Qi3APCxziFtE3cy7Xg6Pecwk0CaTy8c85nD+7jKf4BkCJxWB/RrY+7RQCzAPyxziFlE3QySXw5/8SXLWWZNAe9ZZk98P2kGcRBnYwRfAQi3zifgZCbUA87CoEDfNwf4i6maI5HJYW0se8Yh+L2Q0wIMvgIVa5hPxMxJqYZnpneiPRYS4aQ/2FxUwDZFcvCEsZDTAgy+AhRrgaCqhFpaV3on+OewQt5eDfQHzYC3zCai+L2Q0wIMvgIUa4GgqoRaWld4JduNgfzk4AXW6eQbpAR58ASzcwE52C7WwrAQWduNgfzn42pyDN7CDLwDmS6iFZSWwjNu0w1kd7C/eor42BwBIItTCchNYxslw1n5ZxNfmJMs9jxcADpFQC7BsFjWfWkiazSK+NseJDwD4K0ItwLJZxHxqIWl2854qMM3JhbEtJOeECwAPQ6gFWDaLmE89tpA0b/OaKjDtyYUxLSTnhAsAuxBqAZbRYc+nHlNIWmbTnlwY00JyTrgAsAuhFoBxhaRltpeTC2NZSM4JFwB2Ua21RddhJisrK21zc3PR1QCA+TJ/9HT+JwCjVFW3t9ZWdt1OqAUAAGDZTBtqzzqMygAAAMBBEGoBAADoLaEWAACA3hJqAQAA6C2hFgAAgN7aV6itqnOq6h1V9btV9amqWq2qR1fVrVV1R3d5brdtVdUbqurOqvpoVT1p2+Nc1W1/R1Vdtd+dAgBYKhsbybXXTi4BmKuz93n/1yf5zdba91fV0SSPTPIzSY631l5dVS9P8vIkL0vyzCSXdT9PSXJdkqdU1aOTXJNkJUlLcntV3dJa+8I+60Yf+S5CYJl4T2IeNjaSyy9PHnggOXo0OX7c6wlgjmYOtVX1qCTfneSHk6S19kCSB6rqyiRr3WY3JVnPJNRemeTNbfLFuLd1vbyP67a9tbV2f/e4tya5IslbZ60bh2ieB3w+9IFl4j2JeVlfn7yOHnxwcrm+7rUEMEf7GX78t5Lcl+S/VtXvVNWvVtXXJfmG1to9SdJdPrbb/oIkd227/4mu7EzlLLutA75XvnJyud8hVTt96AMsivck5mVtbXJi5MiRyeXa2qJrtHeGTwNLbD/Dj89O8qQkP95a+0BVvT6TocZnUjuUtYcpP/0Bqo4lOZYkF1988d5qy/zN+8zz1of+Vq9IHz/0geHwnsS8rK5Oevr7OpTdqAVgye0n1J5IcqK19oHu93dkEmr/uKoe11q7pxtefO+27S/adv8Lk9zdla+dUr6+0x9srV2f5PokWVlZ2TH4cojmfcDX9w99YFi8JzFPq6v9fQ0ZPg0suZlDbWvtj6rqrqr61tbap5NcnuST3c9VSV7dXb6zu8stSX6sqt6WyUJRX+yC73uS/MetVZKTPD3J1bPWi0N0EAd8ff7QB4bHexIYtcBiWKiPPdjv6sc/nuQt3crHn0nyI5nM0317Vb0wyeeSPK/b9l1JnpXkziRf6rZNa+3+qnpVkg922/3s1qJR9IADPgAYNqMWOGyGvLNH+wq1rbUPZ/JVPKe6fIdtW5KXnOFxbkhyw37qAgDAAXESm8NkyDt7tJ/VjwEAAOZrCCuGc6j2O/wYAABgfgx5Z4+EWgAAYLkY8s4eGH4MAABAbwm1AAAA9JZQCwAAQG8JtQAAAPSWUAsAAEBvCbUAAAD0llALAABAbwm1AMBy29hIrr12cgkApzh70RUAADijjY3k8suTBx5Ijh5Njh9PVlcXXSsAloieWgBgea2vTwLtgw9OLtfXF10jAJaMUAsALK+1tUkP7ZEjk8u1tUXXCIAlY/gxALC8VlcnQ47X1yeB1tBjAE4h1AIAy211VZgF4IwMPwYAAKC3hFoAAAB6S6gFAACgt4RaAAAAekuoBQAAoLeEWgAAAHqrWmuLrsNMquq+JH+woD9/XpLPL+hvwzLRFuAk7QEmtAU4SXvYn7/ZWjt/t416G2oXqao2W2sri64HLJq2ACdpDzChLcBJ2sPhMPwYAACA3hJqAQAA6C2hdjbXL7oCsCS0BThJe4AJbQFO0h4OgTm1AAAA9JaeWgAAAHpLqD1FVd1QVfdW1ce3lb2mqn63qj5aVb9RVedsu+3qqrqzqj5dVc9YTK3hYOzUHrbd9lNV1arqvO73qqo3dO3ho1X1pMOvMRyMM7WFqvrx7v3/E1X1i9vKfTYwWGc4VvqOqrqtqj5cVZtV9eSu3GcDg1VVF1XV+6rqU93nwE905Y+uqlur6o7u8tyuXHs4IELt6W5McsUpZbcmeWJr7duT/F6Sq5Okqh6f5PlJntDd51eq6sjhVRUO3I05vT2kqi5K8r1JPret+JlJLut+jiW57hDqB4flxpzSFqrqe5JcmeTbW2tPSPJLXbnPBobuxpz+2fCLSf5Da+07kvy77vfEZwPD9tUk/7q19neSPDXJS7rPgJcnOd5auyzJ8e73RHs4MELtKVpr709y/yll722tfbX79bYkF3bXr0zyttbal1trn01yZ5InH1pl4YDt1B46r03y00m2T8q/Msmb28RtSc6pqscdQjXhwJ2hLbw4yatba1/utrm3K/fZwKCdoT20JI/qrn99kru76z4bGKzW2j2ttQ911/8syaeSXJDJ6/6mbrObkjynu649HBChdu9+NMm7u+sXJLlr220nujIYrKp6dpI/bK195JSbtAfG5luS/IOq+kBV/Z+q+q6uXFtgjF6a5DVVdVcmoxau7sq1B0ahqi5J8neTfCDJN7TW7kkmwTfJY7vNtIcDItTuQVW9IpNhBm/ZKtphM8tJM1hV9cgkr8hkaNlpN+9Qpj0wZGcnOTeTIWf/Jsnbq6qiLTBOL07yk621i5L8ZJI3deXaA4NXVX89yc1JXtpa+38Pt+kOZdrDHAi1U6qqq5J8X5Ifaie/B+lEkou2bXZhTg63gSH65iSXJvlIVf1+Jq/5D1XVN0Z7YHxOJPn1bhjZbyf5yyTnRVtgnK5K8uvd9f+Rk0PutQcGraq+JpNA+5bW2lYb+OOtYcXd5db0FO3hgAi1U6iqK5K8LMmzW2tf2nbTLUmeX1WPqKpLM5n0/duLqCMchtbax1prj22tXdJauySTN+cntdb+KJP28IJuZb+nJvni1tAbGKj/meRpSVJV35LkaJLPx2cD43R3kn/YXX9akju66z4bGKxudM6bknyqtfaftt10SyYnetJdvnNbufZwAM5edAWWTVW9NclakvOq6kSSazKZF/KIJLdOXru5rbX2otbaJ6rq7Uk+mcmw5Je01h5cTM1h/nZqD621N51h83cleVYmi+J8KcmPHEol4RCc4bPhhiQ3dF9r8kCSq7qRPD4bGLQztId/nuT1VXV2kr/IZGXXxGcDw/b3k/yzJB+rqg93ZT+T5NWZTEl5YSbfFPG87jbt4YDUyZG0AAAA0C+GHwMAANBbQi0AAAC9JdQCAADQW0ItAAAAvSXUAgAA0FtCLQAAAL0l1AIAANBbQi0AAAC99f8BPM2az+3YqUgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(1,1,figsize=(16,4))\n",
    "for yr in np.unique(df['Year']):\n",
    "    df2=df.loc[df.Year==yr]\n",
    "    maxval=np.max(df2['RateHope'])\n",
    "    ax.plot(df2.loc[df2.RateHope==maxval,['YD']].values[0],maxval,'r.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "121 2015-05-01 00:00:00\n",
      "140 2015-05-20 00:00:00\n",
      "160 2015-06-09 00:00:00\n",
      "180 2015-06-29 00:00:00\n",
      "200 2015-07-19 00:00:00\n",
      "135 2015-05-15 00:00:00\n",
      "190 2015-07-09 00:00:00\n"
     ]
    }
   ],
   "source": [
    "for iid in (121,140,160,180,200,135,190):\n",
    "    print(iid, dt.datetime(2014,12,31)+dt.timedelta(days=iid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "session.close()\n",
    "engine.dispose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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