tidal_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151.23 -22.99 108\n",
      "finding datasets for ls5_pq_albers in ('1986-01-01', '2017-01-01')\n",
      "finding datasets for ls7_pq_albers in ('1986-01-01', '2003-05-01')\n",
      "finding datasets for ls8_pq_albers in ('1986-01-01', '2017-01-01')\n",
      "tide post selected 151.23-22.99\n",
      "[['1986-08-21', 0.909], ['1987-09-25', 1.224], ['1987-10-11', 0.279], ['1987-11-12', -0.394], ['1987-11-28', -0.705], ['1987-12-14', -0.274], ['1987-12-30', 0.721], ['1988-01-15', 0.763], ['1988-01-31', 1.625], ['1988-02-16', 2.413], ['1988-03-19', 1.384], ['1988-04-20', -0.315], ['1988-05-06', -0.6], ['1988-05-22', -0.776], ['1988-06-07', -1.178], ['1988-06-23', -0.752], ['1988-07-09', -0.182], ['1988-07-25', -0.201], ['1988-08-10', 0.719], ['1988-08-26', 1.39], ['1988-09-11', 1.34], ['1988-09-27', 1.63], ['1988-10-13', 1.236], ['1988-10-29', 0.143], ['1988-11-14', -0.02], ['1988-11-30', -0.444], ['1989-01-01', -0.074], ['1989-01-17', 0.799], ['1989-02-18', 1.735], ['1989-03-06', 2.213], ['1989-03-22', 1.457], ['1989-06-10', -0.906], ['1989-06-26', -1.232], ['1989-07-28', -0.134], ['1989-08-13', 0.029], ['1989-08-29', 1.016], ['1989-09-14', 1.587], ['1989-09-30', 1.522], ['1989-10-16', 1.774], ['1989-11-17', -0.072], ['1989-12-03', -0.015], ['1989-12-19', -0.448], ['1990-02-21', 1.075], ['1990-03-09', 1.776], ['1990-05-12', 0.231], ['1990-05-28', -0.822], ['1990-06-13', -0.625], ['1990-06-29', -0.96], ['1990-07-15', -1.127], ['1990-07-31', -0.408], ['1990-08-16', -0.052], ['1990-09-01', 0.321], ['1990-09-17', 1.36], ['1990-10-03', 1.728], ['1990-11-04', 1.795], ['1990-11-20', 0.915], ['1990-12-06', -0.191], ['1990-12-22', -0.003], ['1991-01-07', -0.435], ['1991-01-23', -0.487], ['1991-02-24', 0.804], ['1991-03-12', 1.053], ['1991-03-28', 1.648], ['1991-04-13', 1.436], ['1991-04-29', 0.911], ['1991-05-15', 0.781], ['1991-05-31', 0.109], ['1991-07-02', -0.523], ['1991-07-18', -0.966], ['1991-08-03', -0.962], ['1991-08-19', -0.255], ['1991-09-04', -0.085], ['1991-09-20', 0.551], ['1991-10-06', 1.63], ['1991-10-22', 1.743], ['1991-11-07', 1.711], ['1991-11-23', 1.838], ['1991-12-25', -0.106], ['1992-01-10', 0.02], ['1992-01-26', -0.464], ['1992-02-11', -0.406], ['1992-02-27', 0.426], ['1992-03-30', 0.908], ['1992-04-15', 1.442], ['1992-05-01', 1.085], ['1992-05-17', 0.793], ['1992-06-02', 0.745], ['1992-06-18', 0.05], ['1992-07-04', -0.703], ['1992-07-20', -0.452], ['1992-08-05', -0.955], ['1992-08-21', -0.804], ['1992-09-06', -0.079], ['1992-09-22', -0.053], ['1992-10-08', 0.829], ['1992-10-24', 1.871], ['1992-11-09', 1.767], ['1992-11-25', 1.771], ['1992-12-11', 1.766], ['1992-12-27', 0.699], ['1993-01-12', -0.059], ['1993-01-28', -0.079], ['1993-03-01', -0.358], ['1993-03-17', 0.436], ['1993-04-02', 0.255], ['1993-04-18', 0.776], ['1993-05-04', 1.273], ['1993-05-20', 0.905], ['1993-06-05', 0.796], ['1993-06-21', 0.754], ['1993-07-07', 0.015], ['1993-07-23', -0.483], ['1993-08-08', -0.433], ['1993-08-24', -0.953], ['1993-09-09', -0.673], ['1993-09-25', 0.079], ['1993-10-27', 1.059], ['1993-11-12', 2.008], ['1993-11-28', 1.772], ['1993-12-14', 1.871], ['1993-12-30', 1.673], ['1994-01-15', 0.54], ['1994-01-31', 0.014], ['1994-03-20', -0.357], ['1994-04-05', 0.355], ['1994-04-21', 0.037], ['1994-05-07', 0.702], ['1994-05-23', 1.164], ['1994-06-08', 0.855], ['1994-06-24', 0.883], ['1994-07-10', 0.759], ['1994-07-26', -0.052], ['1994-08-11', -0.296], ['1994-08-27', -0.481], ['1994-09-12', -0.939], ['1994-09-28', -0.531], ['1994-10-14', 0.264], ['1994-10-30', 0.188], ['1994-11-15', 1.313], ['1994-12-01', 2.129], ['1994-12-17', 1.784], ['1995-01-02', 1.893], ['1995-02-03', 0.244], ['1995-02-19', -0.047], ['1995-03-07', -0.385], ['1995-03-23', -0.733], ['1995-04-24', 0.322], ['1995-05-26', 0.751], ['1995-06-11', 1.167], ['1995-06-27', 0.873], ['1995-07-13', 0.936], ['1995-07-29', 0.675], ['1995-08-14', -0.234], ['1995-08-30', -0.248], ['1995-09-15', -0.559], ['1995-10-01', -0.878], ['1995-10-17', -0.321], ['1995-11-02', 0.493], ['1995-11-18', 0.5], ['1995-12-04', 1.566], ['1995-12-20', 2.198], ['1996-01-21', 1.861], ['1996-02-06', 1.194], ['1996-02-22', -0.025], ['1996-03-09', -0.087], ['1996-03-25', -0.533], ['1996-04-10', -0.859], ['1996-05-12', 0.1], ['1996-05-28', -0.027], ['1996-06-29', 1.06], ['1996-07-15', 0.936], ['1996-07-31', 1.217], ['1996-08-16', 0.795], ['1996-09-01', -0.097], ['1996-09-17', 0.046], ['1996-10-03', -0.563], ['1996-10-19', -0.851], ['1996-11-04', -0.271], ['1996-12-22', 1.62], ['1997-01-07', 2.01], ['1997-01-23', 1.795], ['1997-02-08', 2.091], ['1997-02-24', 1.173], ['1997-03-12', -0.001], ['1997-03-28', -0.004], ['1997-04-13', -0.713], ['1997-04-29', -0.998], ['1997-05-31', -0.322], ['1997-06-16', -0.117], ['1997-07-02', 0.733], ['1997-07-18', 0.916], ['1997-08-03', 1.055], ['1997-08-19', 1.513], ['1997-09-04', 0.935], ['1997-09-20', 0.124], ['1997-10-06', 0.223], ['1997-10-22', -0.615], ['1997-11-07', -0.758], ['1997-11-23', -0.216], ['1997-12-09', 0.048], ['1997-12-25', 0.585], ['1998-01-26', 1.862], ['1998-02-27', 2.093], ['1998-03-15', 1.027], ['1998-03-31', -0.039], ['1998-05-02', -0.914], ['1998-05-18', -1.031], ['1998-06-03', -0.553], ['1998-06-19', -0.609], ['1998-07-05', -0.06], ['1998-08-06', 0.889], ['1998-08-22', 1.217], ['1998-09-07', 1.673], ['1998-09-23', 1.002], ['1998-10-09', 0.295], ['1998-10-25', 0.229], ['1998-11-10', -0.697], ['1998-11-26', -0.646], ['1998-12-28', -0.043], ['1999-01-13', 0.772], ['1999-02-14', 1.781], ['1999-03-02', 1.822], ['1999-03-18', 1.907], ['1999-04-03', 0.78], ['1999-04-19', -0.135], ['1999-05-05', -0.276], ['1999-05-21', -1.093], ['1999-06-06', -1.008], ['1999-06-22', -0.535], ['1999-07-08', -0.726], ['1999-07-16', 0.021], ['1999-07-24', 0.098], ['1999-08-09', 0.865], ['1999-08-17', -0.553], ['1999-08-25', 0.986], ['1999-09-02', -0.954], ['1999-09-10', 1.425], ['1999-09-18', -0.565], ['1999-09-26', 1.729], ['1999-10-04', -0.334], ['1999-10-28', 0.384], ['1999-11-13', 0.107], ['1999-11-29', -0.773], ['1999-12-07', 1.669], ['1999-12-23', 2.352], ['2000-01-08', 1.596], ['2000-01-24', 0.69], ['2000-02-09', 0.609], ['2000-02-25', -0.344], ['2000-03-12', -0.712], ['2000-03-28', -0.357], ['2000-04-13', -0.144], ['2000-04-29', -0.093], ['2000-05-15', 0.705], ['2000-05-31', 0.708], ['2000-06-16', 0.792], ['2000-08-03', 0.053], ['2000-08-19', 0.21], ['2000-09-04', -0.566], ['2000-09-20', -0.768], ['2000-10-06', -0.467], ['2000-10-22', -0.348], ['2000-11-07', 0.044], ['2000-11-23', 1.23], ['2000-12-09', 1.478], ['2000-12-25', 1.772], ['2001-01-10', 2.388], ['2001-01-26', 1.484], ['2001-02-11', 0.602], ['2001-02-27', 0.489], ['2001-03-31', -0.698], ['2001-04-16', -0.366], ['2001-05-18', -0.091], ['2001-06-03', 0.597], ['2001-06-19', 0.548], ['2001-07-21', 1.22], ['2001-08-06', 0.732], ['2001-08-22', 0.203], ['2001-09-23', -0.584], ['2001-10-25', -0.378], ['2001-12-28', 1.533], ['2002-01-13', 1.884], ['2002-01-29', 2.349], ['2002-02-14', 1.354], ['2002-03-02', 0.536], ['2002-03-18', 0.287], ['2002-04-03', -0.709], ['2002-04-19', -0.712], ['2002-05-05', -0.415], ['2002-07-24', 0.897], ['2002-09-10', 0.47], ['2002-09-26', 0.356], ['2002-10-12', -0.627], ['2002-10-28', -0.521], ['2002-11-13', -0.299], ['2002-11-29', -0.406], ['2002-12-15', 0.537], ['2002-12-31', 1.516], ['2003-01-16', 1.6], ['2003-02-01', 2.017], ['2003-02-17', 2.215], ['2003-03-05', 1.16], ['2003-03-21', 0.457], ['2003-04-06', 0.033], ['2003-04-22', -0.902], ['2003-07-03', -0.183], ['2003-07-19', -0.781], ['2003-08-20', -0.592], ['2003-09-05', -0.441], ['2003-09-21', -0.21], ['2003-10-07', 0.901], ['2003-10-23', 1.212], ['2003-11-08', 1.621], ['2003-11-24', 2.31], ['2003-12-10', 1.5], ['2003-12-26', 0.71], ['2004-01-11', 0.634], ['2004-01-27', -0.315], ['2004-02-12', -0.516], ['2004-02-28', -0.147], ['2004-03-15', 0.11], ['2004-03-31', 0.335], ['2004-04-16', 1.095], ['2004-05-02', 0.928], ['2004-05-18', 0.86], ['2004-06-03', 1.09], ['2004-06-19', 0.498], ['2004-07-05', -0.135], ['2004-07-21', 0.003], ['2004-08-06', -0.719], ['2004-08-22', -0.73], ['2004-09-07', -0.482], ['2004-09-23', -0.522], ['2004-10-09', -0.051], ['2004-10-25', 1.029], ['2004-11-10', 1.206], ['2004-11-26', 1.722], ['2004-12-12', 2.337], ['2004-12-28', 1.474], ['2005-01-13', 0.842], ['2005-01-29', 0.675], ['2005-02-14', -0.383], ['2005-03-02', -0.407], ['2005-03-18', -0.164], ['2005-04-03', -0.203], ['2005-04-19', 0.181], ['2005-05-05', 0.732], ['2005-05-21', 0.504], ['2005-06-06', 0.709], ['2005-06-22', 0.998], ['2005-07-08', 0.538], ['2005-07-24', 0.141], ['2005-08-09', 0.219], ['2005-08-25', -0.621], ['2005-09-10', -0.472], ['2005-09-26', -0.438], ['2005-10-12', -0.609], ['2005-10-28', 0.133], ['2005-11-13', 1.119], ['2006-02-01', 0.968], ['2006-02-17', 0.568], ['2006-03-05', -0.515], ['2006-04-22', -0.57], ['2006-05-08', -0.017], ['2006-05-24', 0.356], ['2006-06-25', 0.742], ['2006-07-11', 1.025], ['2006-08-12', 0.554], ['2006-08-28', 0.391], ['2006-09-13', -0.556], ['2006-09-29', -0.362], ['2006-10-15', -0.457], ['2006-10-31', -0.674], ['2006-11-16', 0.301], ['2006-12-02', 1.113], ['2006-12-18', 1.324], ['2007-01-03', 2.046], ['2007-01-19', 2.254], ['2007-02-04', 1.433], ['2007-02-20', 1.021], ['2007-03-08', 0.35], ['2007-03-24', -0.684], ['2007-04-09', -0.496], ['2007-04-25', -0.477], ['2007-05-11', -0.882], ['2007-05-27', -0.151], ['2007-06-12', 0.113], ['2007-06-28', 0.336], ['2007-07-14', 0.945], ['2007-07-30', 1.138], ['2007-08-15', 0.894], ['2007-08-31', 0.891], ['2007-09-16', 0.396], ['2007-10-02', -0.579], ['2008-01-22', 2.259], ['2008-02-07', 2.085], ['2008-02-23', 1.228], ['2008-03-10', 0.841], ['2008-03-26', 0.034], ['2008-04-11', -0.868], ['2008-09-02', 1.036], ['2008-09-18', 1.011], ['2008-10-04', 0.223], ['2008-10-20', -0.638], ['2008-12-07', -0.367], ['2008-12-23', 0.838], ['2009-01-08', 1.313], ['2009-01-24', 1.648], ['2009-02-25', 1.799], ['2009-03-13', 0.95], ['2009-03-29', 0.62], ['2009-04-30', -1.002], ['2009-05-16', -0.775], ['2009-07-03', -0.017], ['2009-07-19', 0.106], ['2009-08-04', 0.731], ['2009-08-20', 1.608], ['2009-09-05', 1.422], ['2009-09-21', 1.203], ['2009-10-07', 1.105], ['2009-10-23', 0.085], ['2009-12-10', -0.398], ['2010-01-11', 0.977], ['2010-01-27', 1.243], ['2010-02-12', 1.642], ['2010-04-01', 0.73], ['2010-04-17', 0.446], ['2010-05-03', -0.504], ['2010-06-04', -0.855], ['2010-08-23', 0.968], ['2010-12-13', -0.385], ['2011-01-14', 0.026], ['2011-02-15', 1.18], ['2011-04-04', 1.155], ['2011-04-20', 0.548], ['2011-05-06', 0.278], ['2011-06-07', -0.926], ['2011-06-23', -0.869], ['2011-07-09', -1.079], ['2011-07-25', -0.578], ['2011-08-10', 0.259], ['2011-09-11', 1.242], ['2011-09-27', 2.062], ['2013-04-04', -0.665], ['2013-04-09', 1.255], ['2013-04-25', 1.379], ['2013-05-27', 0.604], ['2013-06-12', 0.182], ['2013-06-28', -0.851], ['2013-07-14', -0.545], ['2013-07-30', -0.844], ['2013-08-15', -1.071], ['2013-08-31', -0.276], ['2013-11-03', 2.002], ['2013-12-05', 1.59], ['2013-12-21', 0.823], ['2014-01-06', -0.34], ['2014-01-22', -0.093], ['2014-02-07', -0.39], ['2014-02-23', -0.579], ['2014-03-11', 0.255], ['2014-03-27', 0.59], ['2014-04-12', 0.694], ['2014-04-28', 1.249], ['2014-05-14', 1.165], ['2014-05-30', 0.743], ['2014-06-15', 0.654], ['2014-07-01', 0.113], ['2014-07-17', -0.835], ['2014-08-02', -0.479], ['2014-08-18', -0.825], ['2014-09-03', -0.963], ['2014-09-19', -0.061], ['2014-10-05', 0.303], ['2014-10-21', 0.87], ['2014-11-06', 1.922], ['2014-11-22', 1.941], ['2014-12-08', 1.611], ['2014-12-24', 1.594], ['2015-01-09', 0.664], ['2015-01-25', -0.369], ['2015-02-10', -0.16], ['2015-02-26', -0.457], ['2015-03-14', -0.592], ['2015-03-30', 0.267], ['2015-04-15', 0.342], ['2015-05-01', 0.638], ['2015-05-17', 1.241], ['2015-06-02', 1.009], ['2015-06-18', 0.725], ['2015-07-04', 0.724], ['2015-07-20', 0.059], ['2015-08-05', -0.72], ['2015-08-21', -0.453], ['2015-09-06', -0.823], ['2015-09-22', -0.824], ['2015-10-24', 0.313], ['2015-11-09', 1.066], ['2015-11-25', 2.118], ['2015-12-11', 1.874], ['2015-12-27', 1.629], ['2016-01-12', 1.555], ['2016-01-28', 0.486], ['2016-02-13', -0.346], ['2016-02-29', -0.273], ['2016-03-16', -0.582], ['2016-04-01', -0.593], ['2016-04-17', 0.238], ['2016-05-03', 0.109], ['2016-05-19', 0.596], ['2016-06-04', 1.227], ['2016-06-20', 0.916], ['2016-07-06', 0.763], ['2016-07-22', 0.782], ['2016-08-07', 0.01], ['2016-08-23', -0.527], ['2016-09-08', -0.453], ['2016-09-24', -0.837], ['2016-10-10', -0.666]]\n"
     ]
    }
   ],
   "source": [
    "#Run for entire archive of datasets to extract observed tide height on those UTC time\n",
    "from shapely.geometry import Point, mapping, shape\n",
    "from fiona import collection\n",
    "import fiona\n",
    "import rasterio.features\n",
    "import datacube\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.gridspec import GridSpec\n",
    "from datacube.utils import geometry\n",
    "from datacube.api.grid_workflow import GridWorkflow\n",
    "from datacube.api.query import query_group_by\n",
    "import numpy as np\n",
    "import itertools\n",
    "from datetime import date, datetime, timedelta\n",
    "import otps\n",
    "import sys\n",
    "from otps import TimePoint, predict_tide\n",
    "from dateutil.relativedelta import relativedelta\n",
    "\n",
    "# Id 28 segmented centroid lon and lat 151.69-23.18\n",
    "#Id 108 segmented centroid lon and lat 151.23-22.99\n",
    "# 178 segmented centroid lon and lat 150.99-22.84\n",
    "#Id 221 segmented centroid lon and lat 151.55-22.93\n",
    "#Id 239 segmented centroid lon and lat 151.63-23.73\n",
    "#LON=\"--lon_range '(151.68, 151.70)'\"  # Gladstone\n",
    "#LAT=\"--lat_range '(-23.17, -23.19)'\"\n",
    "lon_range = ''\n",
    "# COMMENT OUT lon_range IN CASE DEALING WITH POLYGON\n",
    "#lon_range = '(151.0, 151.50)'\n",
    "lat_range = '(-22, -23)'\n",
    "\n",
    "time_period=('1986-01-01', '2017-01-01')\n",
    "filepath =''\n",
    "# COMMENT OUT IN CASE DEALING WITH LON/LAT AND MAKE SURE PASS lon AND lat as TIDE POST from csv model file.\n",
    "filepath = '/g/data/u46/users/bxb547/otps/vector_data/ecog_320_aus_albers.shp'\n",
    "dc=datacube.Datacube(app='test')\n",
    "#gw=GridWorkflow(dc.index)\n",
    "\n",
    "tp=list()\n",
    "all_times = list()\n",
    "lon = 151.23\n",
    "lat = -22.99\n",
    "products = ['ls5_pq_albers', 'ls7_pq_albers', 'ls8_pq_albers']\n",
    "#products = ['ls5_pq_albers', 'ls8_pq_albers']\n",
    "\n",
    "try:\n",
    "    with fiona.open(filepath) as Input:\n",
    "        crs = geometry.CRS(str(Input.crs_wkt))\n",
    "        for feature in Input:\n",
    "            lon = feature['properties']['lon']\n",
    "            lat = feature['properties']['lat']\n",
    "            Id = feature['properties']['Id']\n",
    "            \n",
    "        # Darwin test\n",
    "        #130.55,-12.20\n",
    "        #130.28,-12.50\n",
    "        #130.85,-12.60\n",
    "            if Id == 108:\n",
    "                print lon ,lat, Id\n",
    "                first_geometry = feature['geometry']\n",
    "            #print first_geometry\n",
    "                geom = geometry.Geometry(first_geometry, crs=crs)\n",
    "                                \n",
    "                for source in products:\n",
    "                    time_range = ('1986-01-01', '2003-05-01') if source == 'ls7_pq_albers' else time_period\n",
    "                    print \"finding datasets for \" + source + \" in \" + str(time_range)\n",
    "                    ds = dc.find_datasets(product=source, time=time_range, geopolygon=geom, group_by='solar_day')\n",
    "                    group_by = query_group_by(group_by='solar_day')\n",
    "                    sources = dc.group_datasets(ds, group_by)\n",
    "                    if len(ds) > 0 :\n",
    "                        all_times = all_times + [dd for dd in sources.time.data.astype('M8[s]').astype('O').tolist()] \n",
    "                    all_times = sorted(all_times)            \n",
    "                break\n",
    "except IOError as e:\n",
    "    print (\"Polygon File not found \")\n",
    "                \n",
    "  \n",
    "if len(lon_range) > 0:\n",
    "    for source in products:\n",
    "        time_range = ('1986-01-01', '2003-05-01') if source == 'ls7_pq_albers' else time_period\n",
    "        print \"finding datasets for \" + source + \" in \" + str(time_range)\n",
    "        ds = dc.find_datasets(product=source, time=time_range, x = eval(lon_range), y= eval(lat_range))\n",
    "        group_by = query_group_by(group_by='solar_day')\n",
    "        sources = dc.group_datasets(ds, group_by)\n",
    "        if len(ds) > 0 :\n",
    "            all_times = all_times + [dd for dd in sources.time.data.astype('M8[s]').astype('O').tolist()] \n",
    "            all_times = sorted(all_times)\n",
    "    \n",
    "\n",
    "print \"tide post selected \" + str(lon) + str(lat)\n",
    "for dt in all_times:\n",
    "    tp.append(TimePoint(lon, lat, dt))\n",
    "tides = predict_tide(tp)\n",
    "tide_dic = dict()\n",
    "\n",
    "for tt in tides:\n",
    "    tide_dic[datetime.strptime(tt.timepoint.timestamp.isoformat()[0:19], \"%Y-%m-%dT%H:%M:%S\")] = tt.tide_m\n",
    "\n",
    "my_data = sorted(tide_dic.items(), key=lambda x: x[1])\n",
    "print sorted([[x[0].strftime('%Y-%m-%d'), x[1]] for x in my_data])\n",
    "\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percentage accepted for high tide percentage 25 and for low tide percentage 25\n",
      "LOW LISTING with length 33 [['2010-05-03', -0.504], ['2010-06-04', -0.855], ['2010-12-13', -0.385], ['2011-06-07', -0.926], ['2011-06-23', -0.869], ['2011-07-09', -1.079], ['2011-07-25', -0.578], ['2013-04-04', -0.665], ['2013-06-28', -0.851], ['2013-07-14', -0.545], ['2013-07-30', -0.844], ['2013-08-15', -1.071], ['2014-01-06', -0.34], ['2014-02-07', -0.39], ['2014-02-23', -0.579], ['2014-07-17', -0.835], ['2014-08-02', -0.479], ['2014-08-18', -0.825], ['2014-09-03', -0.963], ['2015-01-25', -0.369], ['2015-02-26', -0.457], ['2015-03-14', -0.592], ['2015-08-05', -0.72], ['2015-08-21', -0.453], ['2015-09-06', -0.823], ['2015-09-22', -0.824], ['2016-02-13', -0.346], ['2016-03-16', -0.582], ['2016-04-01', -0.593], ['2016-08-23', -0.527], ['2016-09-08', -0.453], ['2016-09-24', -0.837], ['2016-10-10', -0.666]]\n",
      "HIGH LISTING with length 13 [['2010-02-12', 1.642], ['2011-09-27', 2.062], ['2013-04-25', 1.379], ['2013-11-03', 2.002], ['2013-12-05', 1.59], ['2014-11-06', 1.922], ['2014-11-22', 1.941], ['2014-12-08', 1.611], ['2014-12-24', 1.594], ['2015-11-25', 2.118], ['2015-12-11', 1.874], ['2015-12-27', 1.629], ['2016-01-12', 1.555]]\n",
      "LOW hight sorted [[-1.079, '2011-07-09'], [-1.071, '2013-08-15'], [-0.963, '2014-09-03'], [-0.926, '2011-06-07'], [-0.869, '2011-06-23'], [-0.855, '2010-06-04'], [-0.851, '2013-06-28'], [-0.844, '2013-07-30'], [-0.837, '2016-09-24'], [-0.835, '2014-07-17'], [-0.825, '2014-08-18'], [-0.824, '2015-09-22'], [-0.823, '2015-09-06'], [-0.72, '2015-08-05'], [-0.666, '2016-10-10'], [-0.665, '2013-04-04'], [-0.593, '2016-04-01'], [-0.592, '2015-03-14'], [-0.582, '2016-03-16'], [-0.579, '2014-02-23'], [-0.578, '2011-07-25'], [-0.545, '2013-07-14'], [-0.527, '2016-08-23'], [-0.504, '2010-05-03'], [-0.479, '2014-08-02'], [-0.457, '2015-02-26'], [-0.453, '2015-08-21'], [-0.453, '2016-09-08'], [-0.39, '2014-02-07'], [-0.385, '2010-12-13'], [-0.369, '2015-01-25'], [-0.346, '2016-02-13'], [-0.34, '2014-01-06']]\n",
      "HIGH hight sorted [[1.379, '2013-04-25'], [1.555, '2016-01-12'], [1.59, '2013-12-05'], [1.594, '2014-12-24'], [1.611, '2014-12-08'], [1.629, '2015-12-27'], [1.642, '2010-02-12'], [1.874, '2015-12-11'], [1.922, '2014-11-06'], [1.941, '2014-11-22'], [2.002, '2013-11-03'], [2.062, '2011-09-27'], [2.118, '2015-11-25']]\n"
     ]
    }
   ],
   "source": [
    "#LOW HIGH analysis\n",
    "EPOCH=\"--epoch 40\" #default epoch 2\n",
    "DEB=\"--debug\"   # To get detail of date/time and tide heights\n",
    "# LS7FL=\"--ls7fl\"  To include LS7 datasets\n",
    "YEAR=\"--year_range 2010-2017\"\n",
    "date_ranges=(datetime(2010,1,1) , datetime(2017,1,1))\n",
    "LR=\"--date_all_1 \"  # This is set for other than 50 % AND for low tides\n",
    "HR=\" --date_all_2 \"  # This is used for 50 % and also used for high percentage of LOW/HIGH\n",
    "QT=\"'\"\n",
    "#CRS=\"--crs \" + CRS\n",
    "#print CRSVAR\n",
    "\n",
    "PERCC=\"25\"  #for high percentage\n",
    "LOW_PER=\"25\" #For low percentage\n",
    "ODIR=\"--odir /g/data/u46/users/bxb547/otps/testdata2/\"\n",
    "PER =\"--per \" + PERCC #Default percentage 10\n",
    "EBBFLOW=\"--ebb_flow\"\n",
    "if \"50\" in PERCC:\n",
    "    PERC = \"25\"\n",
    "else:\n",
    "    PERC = PERCC\n",
    "print (\"percentage accepted for high tide percentage \" + PERCC + \" and for low tide percentage \" + LOW_PER)\n",
    "# Decide the epoch for lowest and highest\n",
    "\n",
    "sub_data = [[x[0], x[1]] for x  in my_data if (x[0] >= date_ranges[0]) \\\n",
    "        & (x[0] <= date_ranges[1])]\n",
    "# print sub_data\n",
    "max_height=sub_data[-1][1]\n",
    "min_height=sub_data[0][1]\n",
    "dr = float(max_height)-float(min_height)\n",
    "\n",
    "lmr = float(min_height) + dr*float(LOW_PER)*0.01   # low tide max range\n",
    "hlr = float(max_height) - dr*float(PERC)*0.01   # high tide range\n",
    "\n",
    "if \"50\" in PERCC:\n",
    "    date_middle = sorted([[x[0].strftime('%Y-%m-%d'), x[1]] for x  in my_data if (x[1] >= lmr) & (x[1] <= hlr) ])\n",
    "    print (\" 50 PERCENTAGE sorted date tide list \" + str(len(date_middle)))\n",
    "    print date_middle\n",
    "    print \"\"\n",
    "    st_middle = ['\"{0}, {1}\"'.format(w[0], w[1]) for w in date_middle]\n",
    "    st_middle = '{0}'.format(', '.join(st_middle))\n",
    "    # print st_middle\n",
    "    #%run inter_tidal_wf.py $LON $LAT $YEAR $HR $QT$st_middle$QT $PER $EPOCH $DEB\n",
    "    #%run inter_tidal_wf.py $POLY $TIDE_POST $YEAR $HR $QT$st_middle$QT $PER $EPOCH $DEB\n",
    "    #%run inter_tidal_poly.py $POLY $TIDE_POST $YEAR $HR $QT$st_middle$QT $PER $EPOCH $DEB $ODIR\n",
    "else:\n",
    "    #date_low = sorted([[x[0][0].strftime('%Y-%m-%d'), x[0][1]] for x in my_data if x[0][1] <= lmr])\n",
    "    #date_high = sorted([[x[0][0].strftime('%Y-%m-%d'), x[0][1]] for x in my_data if x[0][1] >= hlr])\n",
    "    date_low = sorted([[x[0].strftime('%Y-%m-%d'), x[1]] for x in sub_data if (x[1] <= lmr)])\n",
    "    date_high = sorted([[x[0].strftime('%Y-%m-%d'), x[1]] for x in sub_data if (x[1] >= hlr)])\n",
    "    #print (\"LOW TIDE sorted date list \" + str(len(date_low)) + \"\\n \" + str(date_low))\n",
    "    #print (\"LOW TIDE in sorted height \" + str([[x[0].strftime('%Y-%m-%d'), x[1]] for x  in my_data if x[1] <= lmr]))\n",
    "    #print (\"HIGH TIDE sorted date list \" + str(len(date_high)) + \"\\n \" + str(date_high))\n",
    "    #print (\"HIGH TIDE in sorted height \" + str([[x[0].strftime('%Y-%m-%d'), x[1]] for x  in my_data if x[1] >= hlr]))\n",
    "    st_low = ['\"{0}, {1}\"'.format(w[0], w[1]) for w in date_low]\n",
    "    st_low = '{0}'.format(', '.join(st_low))\n",
    "    st_high = ['\"{0}, {1}\"'.format(w[0], w[1]) for w in date_high]\n",
    "    st_high = '{0}'.format(', '.join(st_high))\n",
    "    \n",
    "    #%run inter_tidal_wf.py $LON $LAT $YEAR $LR $QT$st_low$QT $HR $QT$st_high$QT $PER $EPOCH $DEB\n",
    "    #%run inter_tidal_wf.py $POLY $TIDE_POST $YEAR $LR $QT$st_low$QT $HR $QT$st_high$QT $PER $EPOCH $DEB\n",
    "    #%run inter_tidal_poly.py $POLY $EBBFLOW $CRSVAR $TIDE_POST $YEAR $LR $QT$st_low$QT $HR $QT$st_high$QT $PER $EPOCH $DEB $ODIR\n",
    "    date_low = sorted([[x[0], x[1]] for x in my_data if (x[1] <= lmr) &\n",
    "                                (x[0] >= date_ranges[0]) & (x[0] <= date_ranges[1])])\n",
    "    date_high = sorted([[x[0], x[1]] for x in my_data if (x[1] >= hlr) &\n",
    "                                (x[0] >= date_ranges[0]) & (x[0] <= date_ranges[1])])\n",
    "    print \"LOW LISTING with length \" +  str(len(date_low)) + \" \" + str([[x[0].strftime('%Y-%m-%d'), x[1]] for x  in date_low])\n",
    "    print \"HIGH LISTING with length \" + str(len(date_high)) + \" \" + str([[x[0].strftime('%Y-%m-%d'), x[1]] for x  in date_high])\n",
    "    \n",
    "    print \"LOW hight sorted \" + str(sorted([[x[1], x[0].strftime('%Y-%m-%d')] for x  in date_low]))\n",
    "    print \"HIGH hight sorted \" + str(sorted([[x[1], x[0].strftime('%Y-%m-%d')] for x  in date_high]))\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "low ebb flow [['2010-05-03', 'f'], ['2010-06-04', 'e'], ['2010-12-13', 'e'], ['2011-06-07', 'f'], ['2011-06-23', 'e'], ['2011-07-09', 'e'], ['2011-07-25', 'e'], ['2013-04-04', 'e'], ['2013-06-28', 'f'], ['2013-07-14', 'f'], ['2013-07-30', 'e'], ['2013-08-15', 'e'], ['2014-01-06', 'f'], ['2014-02-07', 'e'], ['2014-02-23', 'e'], ['2014-07-17', 'f'], ['2014-08-02', 'f'], ['2014-08-18', 'e'], ['2014-09-03', 'e'], ['2015-01-25', 'f'], ['2015-02-26', 'e'], ['2015-03-14', 'e'], ['2015-08-05', 'f'], ['2015-08-21', 'f'], ['2015-09-06', 'e'], ['2015-09-22', 'e'], ['2016-02-13', 'f'], ['2016-03-16', 'e'], ['2016-04-01', 'e'], ['2016-08-23', 'f'], ['2016-09-08', 'f'], ['2016-09-24', 'e'], ['2016-10-10', 'e']]\n",
      "high ebb flow [['2010-02-12', 'e'], ['2011-09-27', 'e'], ['2013-04-25', 'e'], ['2013-11-03', 'e'], ['2013-12-05', 'f'], ['2014-11-06', 'e'], ['2014-11-22', 'e'], ['2014-12-08', 'f'], ['2014-12-24', 'f'], ['2015-11-25', 'e'], ['2015-12-11', 'e'], ['2015-12-27', 'f'], ['2016-01-12', 'f']]\n"
     ]
    }
   ],
   "source": [
    "#Implementing EBB/FLOW/PEAK HIGH AND PEAK LOW \n",
    "from datetime import timedelta\n",
    "from operator import itemgetter\n",
    "\n",
    "tp = list()\n",
    "tide_dict = dict()\n",
    "dates=list()\n",
    "#lon = 151.69\n",
    "#lat = -23.18\n",
    "\n",
    "\n",
    "for x in date_low:\n",
    "    dates.append(x[0])\n",
    "for x in date_high:\n",
    "    dates.append(x[0])\n",
    "#print time_low\n",
    "#print dates\n",
    "ndate_list=list()\n",
    "mnt=timedelta(minutes=15)\n",
    "for dt in dates:\n",
    "    #dt = datetime.strptime(dt, \"%Y-%m-%d %H:%M:%S\")\n",
    "    ndate_list.append(dt-mnt)\n",
    "    ndate_list.append(dt)\n",
    "    ndate_list.append(dt+mnt)\n",
    "for dt in ndate_list:\n",
    "    tp.append(TimePoint(lon, lat, dt))\n",
    "tides = predict_tide(tp)\n",
    "\n",
    "for tt in tides:\n",
    "    tide_dict[datetime.strptime(tt.timepoint.timestamp.isoformat()[0:19], \"%Y-%m-%dT%H:%M:%S\")] = tt.tide_m\n",
    "\n",
    "tmp_lt = sorted(tide_dict.items(), key=lambda x: x[0])\n",
    "\n",
    "tmp_lt = [[tmp_lt[i+1][0].strftime(\"%Y-%m-%d\"), 'ph'] \\\n",
    "         if tmp_lt[i][1] < tmp_lt[i+1][1] and tmp_lt[i+2][1] <  tmp_lt[i+1][1]  else \\\n",
    "        [tmp_lt[i+1][0].strftime(\"%Y-%m-%d\"), 'pl'] if tmp_lt[i][1] > tmp_lt[i+1][1] and \\\n",
    "        tmp_lt[i+2][1] >  tmp_lt[i+1][1]  else [tmp_lt[i+1][0].strftime(\"%Y-%m-%d\"),'f'] \\\n",
    "        if tmp_lt[i][1] < tmp_lt[i+2][1] else [tmp_lt[i+1][0].strftime(\"%Y-%m-%d\"),'e'] \\\n",
    "         for i in range(0, len(tmp_lt), 3)]\n",
    "\n",
    "ebb_flow = str([tt for tt in tmp_lt ])                  \n",
    "#print tmp_lt\n",
    "#print \"for entire low high ebb flow\" + ebb_flow\n",
    "key = set(e[0].strftime(\"%Y-%m-%d\") for e in date_low)\n",
    "\n",
    "ebb_flow_low = [ff for ff in eval(ebb_flow) if ff[0] in key]\n",
    "key = set(e[0].strftime(\"%Y-%m-%d\") for e in date_high)\n",
    "#print sorted(key)\n",
    "#print date_low\n",
    "#print date_high\n",
    "ebb_flow_high = [ff for ff in eval(ebb_flow) if ff[0] in key]\n",
    "print \"low ebb flow \" + str(ebb_flow_low)\n",
    "print \"high ebb flow \" + str(ebb_flow_high)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting 2017-04-18 14:24:57.845154\n",
      "added to the list 2017-04-18 14:25:00.666421\n",
      "received from predict tides 2017-04-18 14:25:12.411754\n",
      "finished 2017-04-18 14:25:12.599889\n"
     ]
    }
   ],
   "source": [
    "#This is for model run\n",
    "import numpy as np\n",
    "import itertools\n",
    "from datetime import date, datetime, timedelta\n",
    "import otps\n",
    "import sys\n",
    "from otps import TimePoint, predict_tide\n",
    "from dateutil.relativedelta import relativedelta\n",
    "\n",
    "#lon = 151.23\n",
    "#lat = -22.99\n",
    "\n",
    "def date_range(start_date, end_date, increment, period):\n",
    "    result = []\n",
    "    nxt = start_date\n",
    "    delta = relativedelta(**{period:increment})\n",
    "    while nxt <= end_date:\n",
    "        result.append(nxt)\n",
    "        nxt += delta\n",
    "    return result\n",
    "\n",
    "start = datetime(2010,1,1,0,22,16)\n",
    "end = datetime(2017,1,1)\n",
    "\n",
    "tp = list()\n",
    "\n",
    "tide_det = list()\n",
    "print \"starting\", str(datetime.now())\n",
    "for string_date in date_range(start, end, 1, 'hours'):\n",
    "    dt = datetime.strptime(str(string_date), \"%Y-%m-%d %H:%M:%S\")\n",
    "    tp.append(TimePoint(lon, lat, dt))\n",
    "\n",
    "print \"added to the list\", str(datetime.now())\n",
    "tides = predict_tide(tp)\n",
    "print \"received from predict tides\", str(datetime.now())\n",
    "for tt in tides:\n",
    "    tide_det.append([tt.timepoint.timestamp.isoformat()[0:10], tt.tide_m])\n",
    "print \"finished\", str(datetime.now())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-2.138, '2015-03-20']\n"
     ]
    }
   ],
   "source": [
    "print str(sorted([[x[1], x[0]] for x  in tide_det])[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DATE LOW[['2010-05-03', -0.504], ['2010-06-04', -0.855], ['2010-12-13', -0.385], ['2011-06-07', -0.926], ['2011-06-23', -0.869], ['2011-07-09', -1.079], ['2011-07-25', -0.578], ['2013-04-04', -0.665], ['2013-06-28', -0.851], ['2013-07-14', -0.545], ['2013-07-30', -0.844], ['2013-08-15', -1.071], ['2014-01-06', -0.34], ['2014-02-07', -0.39], ['2014-02-23', -0.579], ['2014-07-17', -0.835], ['2014-08-02', -0.479], ['2014-08-18', -0.825], ['2014-09-03', -0.963], ['2015-01-25', -0.369], ['2015-02-26', -0.457], ['2015-03-14', -0.592], ['2015-08-05', -0.72], ['2015-08-21', -0.453], ['2015-09-06', -0.823], ['2015-09-22', -0.824], ['2016-02-13', -0.346], ['2016-03-16', -0.582], ['2016-04-01', -0.593], ['2016-08-23', -0.527], ['2016-09-08', -0.453], ['2016-09-24', -0.837], ['2016-10-10', -0.666]]\n",
      "\n",
      "DATE HIGH[['2010-02-12', 1.642], ['2011-09-27', 2.062], ['2013-04-25', 1.379], ['2013-11-03', 2.002], ['2013-12-05', 1.59], ['2014-11-06', 1.922], ['2014-11-22', 1.941], ['2014-12-08', 1.611], ['2014-12-24', 1.594], ['2015-11-25', 2.118], ['2015-12-11', 1.874], ['2015-12-27', 1.629], ['2016-01-12', 1.555]]\n",
      "['e', 'e', 'e', 'e', 'f', 'e', 'e', 'f', 'f', 'e', 'e', 'f', 'f']\n",
      "EBB FLOW LOW count 33 and [['2010-05-03', 'f'], ['2010-06-04', 'e'], ['2010-12-13', 'e'], ['2011-06-07', 'f'], ['2011-06-23', 'e'], ['2011-07-09', 'e'], ['2011-07-25', 'e'], ['2013-04-04', 'e'], ['2013-06-28', 'f'], ['2013-07-14', 'f'], ['2013-07-30', 'e'], ['2013-08-15', 'e'], ['2014-01-06', 'f'], ['2014-02-07', 'e'], ['2014-02-23', 'e'], ['2014-07-17', 'f'], ['2014-08-02', 'f'], ['2014-08-18', 'e'], ['2014-09-03', 'e'], ['2015-01-25', 'f'], ['2015-02-26', 'e'], ['2015-03-14', 'e'], ['2015-08-05', 'f'], ['2015-08-21', 'f'], ['2015-09-06', 'e'], ['2015-09-22', 'e'], ['2016-02-13', 'f'], ['2016-03-16', 'e'], ['2016-04-01', 'e'], ['2016-08-23', 'f'], ['2016-09-08', 'f'], ['2016-09-24', 'e'], ['2016-10-10', 'e']]\n",
      "EBB FLOW HIGH count 13 and [['2010-02-12', 'e'], ['2011-09-27', 'e'], ['2013-04-25', 'e'], ['2013-11-03', 'e'], ['2013-12-05', 'f'], ['2014-11-06', 'e'], ['2014-11-22', 'e'], ['2014-12-08', 'f'], ['2014-12-24', 'f'], ['2015-11-25', 'e'], ['2015-12-11', 'e'], ['2015-12-27', 'f'], ['2016-01-12', 'f']]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff5009ead10>"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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psZJJURRFUZT6iW0JsgHjUndfvOUJhphg7gUY96YP4iuNoijxRN3EFEWpV4hI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dZhWbQI7r9fXn11ugNxlfDPdP6RWcwV2lh+u6U7FLlnlqQISO/Z0/C9r6F4KZyAjh2lubNaPTp2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\nkt91rahcmb4ta9oMTYVRM6fZ00+zXUs02tzpvvvIq1n1OTNEVLLVywLQjCe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W+bl2iIh6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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff4efc10f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Displaying model tide, observed tide, low and high tides in a period\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "from datetime import datetime\n",
    "import itertools\n",
    "\n",
    "\n",
    "print \"DATE LOW\" + str([[e[0].strftime(\"%Y-%m-%d\"), e[1]] for e in date_low])\n",
    "print \"\\nDATE HIGH\" + str([[e[0].strftime(\"%Y-%m-%d\"), e[1]] for e in date_high])\n",
    "model_date=[x[0] for x in tide_det]\n",
    "model_height=[x[1] for x in tide_det]\n",
    "all_data = sorted([[x[0].strftime('%Y-%m-%d'), x[1]] for x in my_data if (x[0] >= date_ranges[0]) &\n",
    "                                 (x[0] <= date_ranges[1])])\n",
    "all_date = sorted([x[0] for x in all_data])\n",
    "all_height = [x[1] for x in all_data]\n",
    "low_date = [x[0].strftime(\"%Y-%m-%d\") for x in date_low]\n",
    "low_height = [x[1] for x in date_low]\n",
    "high_date = [x[0].strftime(\"%Y-%m-%d\") for x in date_high]\n",
    "high_height = [x[1] for x in date_high]\n",
    "efl = [x[1] for x in ebb_flow_low]\n",
    "efh = [x[1] for x in ebb_flow_high]\n",
    "labl = efh\n",
    "print efh\n",
    "col_dic = {'f':'red', 'e':'green', 'ph':'purple', 'pl':'yellow'}\n",
    "#colr= ['red' if 'f' in l else 'green' if 'e' in l else 'purple' if 'ph' in l else 'yellow' for l in labl]\n",
    "#ebb_date = [x[0] for x in eval(ebb_flow)]\n",
    "#print len(ebb_date)\n",
    "ebb_class = [x[1] for x in eval(ebb_flow)]\n",
    "print \"EBB FLOW LOW count \" + str(len(ebb_flow_low)) + \" and \" + str(ebb_flow_low)\n",
    "print \"EBB FLOW HIGH count \" + str(len(ebb_flow_high)) + \" and \" + str(ebb_flow_high)\n",
    "#df_obs = pd.DataFrame({'Observerdheight' : HT}, index=pd.DatetimeIndex(LT_DATE))\n",
    "low_df=pd.DataFrame(date_low)\n",
    "cols=['date', 'height']\n",
    "low_df.columns = cols\n",
    "all_data=pd.DataFrame(all_data)\n",
    "all_data.columns = cols\n",
    "#print all_data\n",
    "df10 = pd.DataFrame({'ModelHeight' : model_height}, index=pd.DatetimeIndex(model_date))\n",
    "df1 = pd.DataFrame({'LowTidalHeight' : low_height}, index=pd.DatetimeIndex(low_date))\n",
    "df3 = pd.DataFrame({'HighTidalHeight' : high_height}, index=pd.DatetimeIndex(high_date))\n",
    "df2 = pd.DataFrame({'Observedheight' : all_height}, index=pd.DatetimeIndex(all_date))\n",
    "df4 = pd.DataFrame(dict(high_date=high_date, high=high_height, efh=efh))\n",
    "df5 = pd.DataFrame(dict(low_date=low_date, low=low_height, efl=efl))\n",
    "\n",
    "marker = itertools.cycle((',', '*', '.', 'o', '+', 'x'))\n",
    "color = itertools.cycle(('b', 'r', 'g', 'c', 'm', 'y', 'k'))\n",
    "fig = plt.figure()\n",
    "fig.set_size_inches(20,8)\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(df10, marker=marker.next(), color=next(color), label='$ModelData$')\n",
    "ax.plot(df2, marker=marker.next(), color=next(color), markersize=20, label='$Observedheight$')\n",
    "ax.plot(df1, marker=marker.next(), color=next(color), markersize=35, label='$LowTidalHeight$')\n",
    "ax.plot(df3, marker=marker.next(), color=next(color), markersize=15, label='$HighTidalHeight$')\n",
    "#ax.scatter(high_date, high_height, label=labl, s=150, linewidth=0.2, c=colr)\n",
    "ax.set_title('Tidal data for polygon feature ' + str(Id) + ' from  ' + date_ranges[0].strftime(\"%Y-%m-%d\") + \n",
    "             ' TO ' +date_ranges[1].strftime(\"%Y-%m-%d\") + ' for lowest ' +LOW_PER +\n",
    "             ' and highest percentile ' + PERCC, fontsize= 15)\n",
    "ax.legend()\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ydZAtVVLNzTRyrup8KkDPnj2ZOXMmXbt25Z577uHWW2+ladOmvPHG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\nPmRlZdGnTx8+//zzEukLCwsZO3YsBx10EJmZmbR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aaqVlXVLTNxLoynrnpsa+PAozrstb2q6gdV9aP+OH83U+fEeOnyNbP/+15V/bCqrhj1uTC+On5v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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff4ee987590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff4f27f9090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Displaying EBB FLOW attributes in LOW and High percentages\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "import numpy as np\n",
    "import matplotlib.patches as mpatches\n",
    "\n",
    "\n",
    "#todayTime=datetime.datetime.now();\n",
    "#df = pd.DataFrame({'x': [todayTime+datetime.timedelta(hours=i) for i in range(10)], 'y': range(10),'z' : [2*j for j in range(10)]});\n",
    "#xAlt=[0.5*i for i in range(10)];\n",
    "fig = plt.figure()\n",
    "fig.set_size_inches(20,8)\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "classes = ['flow','ebb','peak high', 'peak low']\n",
    "class_colours = ['r', 'g', 'purple', 'y']\n",
    "recs = []\n",
    "\n",
    "df5.low_date = pd.to_datetime(df5.low_date)\n",
    "plt.scatter([x for x in df5.low_date], df5['low'], s=300, c=df5['efl'].apply(lambda x: col_dic[x]),\n",
    "               label=df5['efl'].apply(lambda x: col_dic[x]), cmap = cm.jet)\n",
    "\n",
    "for i in range(0,len(class_colours)):\n",
    "    recs.append(mpatches.Rectangle((0,0),1,1,fc=class_colours[i]))\n",
    "plt.legend(recs,classes,loc=2, fontsize = 'xx-large')\n",
    "#ax.scatter(df4['high_date'], df4['high'])\n",
    "#plt.legend((ll), loc='upper left', numpoints=1, ncol=3, fontsize=8, scatterpoints=1)\n",
    "plt.title('Ebb flow for polygon feature ' + str(Id) + ' from  ' + date_ranges[0].strftime(\"%Y-%m-%d\") + ' TO ' + \n",
    "          date_ranges[1].strftime(\"%Y-%m-%d\") + ' for lowest percentile ' + LOW_PER , fontsize=20)\n",
    "plt.show()\n",
    "\n",
    "fig = plt.figure()\n",
    "fig.set_size_inches(20,8)\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "df4.high_date = pd.to_datetime(df4.high_date)\n",
    "plt.scatter([x for x in df4.high_date], df4['high'], s=300, c=df4['efh'].apply(lambda x: col_dic[x]),\n",
    "               label=df4['efh'].apply(lambda x: col_dic[x]), cmap = cm.jet)\n",
    "\n",
    "for i in range(0,len(class_colours)):\n",
    "    recs.append(mpatches.Rectangle((0,0),1,1,fc=class_colours[i]))\n",
    "#plt.legend(recs,classes,loc=2, fontsize = 'xx-large')\n",
    "#ax.scatter(df4['high_date'], df4['high'])\n",
    "#plt.legend((ll), loc='upper left', numpoints=1, ncol=3, fontsize=8, scatterpoints=1)\n",
    "plt.title('Ebb flow for polygon feature ' + str(Id) + ' from  ' + date_ranges[0].strftime(\"%Y-%m-%d\") + ' TO ' +\n",
    "          date_ranges[1].strftime(\"%Y-%m-%d\") + ' for highest percentile ' + PERCC, fontsize=20)\n",
    "plt.show()"
   ]
  }
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