{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime as dt\n",
    "from salishsea_tools import evaltools as et, viz_tools\n",
    "import os\n",
    "import datetime as dt\n",
    "import gsw\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib as mpl\n",
    "import matplotlib.dates as mdates\n",
    "import cmocean as cmo\n",
    "import scipy.interpolate as sinterp\n",
    "import cmocean\n",
    "import json\n",
    "import f90nml\n",
    "from collections import OrderedDict\n",
    "\n",
    "fs=16\n",
    "mpl.rc('xtick', labelsize=fs)\n",
    "mpl.rc('ytick', labelsize=fs)\n",
    "mpl.rc('legend', fontsize=fs)\n",
    "mpl.rc('axes', titlesize=fs)\n",
    "mpl.rc('axes', labelsize=fs)\n",
    "mpl.rc('figure', titlesize=fs)\n",
    "mpl.rc('font', size=fs)\n",
    "mpl.rc('font', family='sans-serif', weight='normal', style='normal')\n",
    "\n",
    "import warnings\n",
    "#warnings.filterwarnings('ignore')\n",
    "from IPython.display import Markdown, display\n",
    "\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import IOS Zooplankton data and create dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/ocean/ksuchy/MOAD/observe/Data and Code files for KS 2020dec11/2020_05_21 1995-2011 SoG VNH.csv\n"
     ]
    }
   ],
   "source": [
    "ls '/ocean/ksuchy/MOAD/observe/Data and Code files for KS 2020dec11/2020_05_21 1995-2011 SoG VNH.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('/ocean/ksuchy/MOAD/observe/Data and Code files for KS 2020dec11/2020_05_21 2012-2015 SoG VNH.csv',\n",
    "               encoding = \"ISO-8859-1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Key', 'region_name', 'Station', 'PROJECT', 'lon', 'lat', 'Date',\n",
       "       'STN_TIME', 'Twilight', 'Net_Type', 'Mesh_Size(um)', 'Net_Mouth_Dia(m)',\n",
       "       'DEPTH_STRT1', 'DEPTH_END1', 'Bottom Depth(m)', 'Volume Filtered(m3)',\n",
       "       'CTD', 'NOTES', 'PI', 'Phylum:', 'Class:', 'Order:', 'Family:', 'Name',\n",
       "       'Abundance(#/m3)', 'Biomass(mg/m3)', 'NumberOfSpecies',\n",
       "       'Station Diversity', 'Station Equitability'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2200     5.51249\n",
       "2201     2.75624\n",
       "2202    33.07494\n",
       "2203    49.61240\n",
       "2204     5.51249\n",
       "          ...   \n",
       "2295    17.61982\n",
       "2296    22.42523\n",
       "2297    36.84145\n",
       "2298     1.60180\n",
       "2299     0.80090\n",
       "Name: Abundance(#/m3), Length: 100, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Abundance(#/m3)'][2200:2300]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert date to proper format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('6/14/2012', '7:32')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Date'][0],df['STN_TIME'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000    6/15/2012\n",
       "1001    6/15/2012\n",
       "1002    6/15/2012\n",
       "1003    6/15/2012\n",
       "1004    6/15/2012\n",
       "1005    6/15/2012\n",
       "1006    6/15/2012\n",
       "1007    6/15/2012\n",
       "1008    6/15/2012\n",
       "1009    6/15/2012\n",
       "1010    6/15/2012\n",
       "1011    6/15/2012\n",
       "1012    6/15/2012\n",
       "1013    6/15/2012\n",
       "1014    6/15/2012\n",
       "1015    6/15/2012\n",
       "1016    6/15/2012\n",
       "1017    6/15/2012\n",
       "1018    6/15/2012\n",
       "1019    6/15/2012\n",
       "Name: Date, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Date'][1000:1020]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['6', '14', '2012']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Date'][0].split('/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "dateslist=list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "for el in df['Date']:\n",
    "    dateslist.append(el.split('/'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "timeslist=list()\n",
    "for el in df['STN_TIME']:\n",
    "    timeslist.append(el.split(':'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dts=list()\n",
    "for ii,jj in zip(dateslist,timeslist):\n",
    "    dts.append(dt.datetime(int(ii[2]),int(ii[0]),int(ii[1]),int(jj[0]),int(jj[1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['7:32', '7:52', '3:30', '7:42', '7:49', '15:09', '16:30', '22:09',\n",
       "       '22:27', '19:36', '19:53', '9:21', '9:35', '13:29', '13:45',\n",
       "       '19:25', '19:43', '5:26', '5:43', '5:45', '5:47', '5:56', '5:59',\n",
       "       '8:38', '8:39', '8:40', '8:41', '8:57', '11:10', '11:44', '11:45',\n",
       "       '11:47', '11:48', '11:49', '12:40', '12:42', '15:14', '15:15',\n",
       "       '15:16', '15:17', '15:18', '17:54', '17:55', '17:57', '17:58',\n",
       "       '17:59', '5:39', '6:05', '14:47', '15:02', '19:13', '19:30',\n",
       "       '14:32', '14:49', '20:04', '20:24', '20:27', '20:47', '9:37',\n",
       "       '9:48', '13:57', '14:17', '19:41', '19:59', '7:18', '7:53', '9:25',\n",
       "       '12:57', '13:09', '7:30', '7:45', '13:33', '13:36', '13:38',\n",
       "       '10:00', '10:30', '11:00', '11:30', '12:30', '13:00', '13:30',\n",
       "       '16:00', '17:00', '9:29', '10:26', '11:19', '13:39', '9:16',\n",
       "       '10:04', '10:42', '11:03', '9:00', '9:41', '10:17', '10:40',\n",
       "       '12:00', '9:33', '10:13', '11:11', '12:02', '12:32', '13:22',\n",
       "       '9:57', '10:35', '10:02', '12:08', '13:18', '14:50', '17:45',\n",
       "       '18:45', '8:30', '9:30', '11:41', '12:37', '13:03', '14:05',\n",
       "       '9:20', '11:56', '13:43', '9:32', '11:22', '11:52', '7:27',\n",
       "       '19:08', '9:45', '16:38', '16:57', '16:04', '16:26', '13:31',\n",
       "       '13:35', '7:12', '16:12', '16:14', '16:18', '16:20', '16:22',\n",
       "       '3:48', '4:04', '15:20', '15:35', '21:28', '21:42', '4:33', '4:45',\n",
       "       '11:38', '11:55', '18:18', '18:29', '10:12', '12:55', '10:16',\n",
       "       '11:20', '13:08', '11:07', '12:19', '12:58', '14:42', '15:37',\n",
       "       '12:10', '12:23', '13:24', '14:33', '10:32', '12:39', '9:49',\n",
       "       '10:53', '12:49', '11:36', '14:11', '9:51', '11:24', '13:04',\n",
       "       '13:19', '9:24', '10:15', '11:53', '12:51', '12:50', '16:11',\n",
       "       '6:15', '17:15', '11:16', '12:17', '13:10', '13:59', '11:21',\n",
       "       '12:01', '12:35', '9:58', '10:41', '11:29', '9:05', '9:50', '8:49',\n",
       "       '9:55', '11:14', '12:14', '9:10', '10:20', '10:50', '11:25',\n",
       "       '11:50', '19:52', '20:32', '6:20', '5:58', '20:29', '5:20',\n",
       "       '20:10', '15:52', '6:43', '20:12', '7:22', '21:03', '21:55',\n",
       "       '14:56', '10:08', '20:01', '11:33', '15:22', '16:40', '23:15',\n",
       "       '8:51', '12:12', '15:12', '10:57', '7:03', '8:03', '14:22',\n",
       "       '23:06', '4:05', '6:25', '9:26', '11:28', '13:27', '15:11',\n",
       "       '15:53', '15:54', '15:56', '15:59', '18:06', '19:45', '9:40',\n",
       "       '20:11', '7:10', '14:34', '11:42', '13:28', '17:12', '9:04',\n",
       "       '14:10', '16:17', '8:02', '10:31', '15:27', '17:01', '16:43',\n",
       "       '10:46', '13:15', '14:43', '9:44', '13:55', '15:44', '16:59',\n",
       "       '18:41', '8:18', '11:13', '8:28', '15:43', '18:08', '7:40',\n",
       "       '10:56', '10:33', '12:52', '14:55', '7:43', '12:13', '13:44',\n",
       "       '7:41', '16:56', '8:17', '9:13', '11:12', '15:40', '18:27',\n",
       "       '10:07', '12:06', '16:07', '16:41', '10:52', '16:33', '17:48',\n",
       "       '7:48', '11:09', '15:08', '8:31', '12:22', '14:39', '12:03',\n",
       "       '14:30', '17:31', '14:15', '18:16', '7:55', '17:38', '6:36',\n",
       "       '18:22', '7:15', '9:17', '12:44', '18:54', '12:43', '18:44',\n",
       "       '7:08', '13:02', '7:19', '16:36', '8:25', '8:33', '13:25', '17:20',\n",
       "       '17:26', '13:42', '18:37', '18:43', '13:21', '14:53', '17:24',\n",
       "       '9:38', '7:06', '9:31', '12:20', '15:28', '11:01', '11:57',\n",
       "       '14:12', '15:10', '7:35', '14:38', '16:06', '8:08', '16:05',\n",
       "       '16:52', '17:47', '8:21', '9:19', '16:35', '8:10', '8:59', '10:21',\n",
       "       '14:41', '16:09', '18:26', '11:23', '8:14', '10:10', '11:27',\n",
       "       '13:26', '11:15', '13:11', '9:18', '12:04', '10:45', '12:25',\n",
       "       '13:56', '10:22', '13:53', '9:28', '11:06', '11:32', '10:34',\n",
       "       '14:08', '11:18', '12:48', '14:01', '12:07', '14:07', '11:08',\n",
       "       '14:04', '10:06', '11:43', '13:40', '10:18', '13:34', '16:27',\n",
       "       '10:28', '15:26', '9:14', '12:15', '15:23', '8:23', '14:58',\n",
       "       '9:08', '10:43', '14:29', '8:44', '9:52', '8:13', '14:27', '14:46',\n",
       "       '14:16', '10:11', '14:51', '8:32', '10:37'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.Twilight=='Daylight']['STN_TIME'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['dtUTC']=et.pac_to_utc(dts) #convert from Pac time to UTC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Diplostraca', 'Thecostraca', 'Amphipoda', 'Decapoda',\n",
       "       'Euphausiacea', 'Calanoida', 'Cyclopoida', 'Poecilostomatoida',\n",
       "       'Halocyprida', 'Aphragmophora', 'Copelata', 'Leptothecate',\n",
       "       'Siphonophorae', 'Trachylina', 'Cydippida', nan, 'Pholadomyoida',\n",
       "       'Neotaenioglossa', 'Thecosomata', 'Aciculata', 'Canalipalpata',\n",
       "       'Osmeriformes', 'Perciformes', 'Beroida', 'Teuthida',\n",
       "       'Gymnosomata', 'Isopoda', 'Siphonostomatoida', 'Anthoathecatae',\n",
       "       'Scorpaeniformes', 'Phragmophora', 'Clupeiformes', 'Ophiurida',\n",
       "       'Gadiformes', 'Semaeostomeae', 'Cumacea', 'Echinoida',\n",
       "       'Harpacticoida', 'Pleuronectiformes', 'Tricladida', 'Myodocopida',\n",
       "       'Phaeogromia', 'Noctilucales', 'Octopoda', 'Actiniaria',\n",
       "       'Foraminiferida', 'Monstrilloida', 'Oligotrichida', 'Mysida',\n",
       "       'Acariformes', 'Lophogastrida', 'Ophidiiformes',\n",
       "       'Thalassocalycida', 'Doliolida', 'Lepadomorpha', 'Cephalaspidea',\n",
       "       'Sygnathiformes'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Order:'].unique()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "SpeciesNames = df['Name'].unique()[1:1000]\n",
    "SpeciesNames.sort()\n",
    "print(SpeciesNames)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Start by creating a group of zooplankton taxa of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "colList=('Anomura *sp. megalops s1',\n",
    " 'Anomura *sp. zoea s1' ,'Anomura *sp. zoea s2', 'Axiidae *sp.  s1', 'Axiidae *sp. mysis s2',\n",
    " 'Axiidae *sp. zoea s1' ,'Axiidae *sp. zoea s2','Brachyura *sp. zoea s1',\n",
    "'Calanoida *sp.  1', 'Calanoida *sp.  2' ,'Calanoida *sp.  3',\n",
    " 'Calanoida *sp.  4' ,'Calanoida *sp.  6M', 'Calanus *sp.  1',\n",
    " 'Calanus *sp.  2', 'Calanus *sp.  3' ,'Calanus *sp.  4',\n",
    " 'Calanus *sp. nauplii s1', 'Calanus marshallae  2' ,'Calanus marshallae  3',\n",
    " 'Calanus marshallae  4' ,'Calanus marshallae  5' ,'Calanus marshallae  6F',\n",
    " 'Calanus marshallae  6M' ,'Calanus pacificus  2' ,'Calanus pacificus  3',\n",
    " 'Calanus pacificus  4' ,'Calanus pacificus  5', 'Calanus pacificus  6F',\n",
    " 'Calanus pacificus  6M','Calliopius *sp.  s1' ,'Calliopius *sp.  s2', 'Calliopius *sp.  s3',\n",
    " 'Calliopius pacificus  s1' ,'Calliopius pacificus  s2',\n",
    " 'Calliopius pacificus  s3', 'Cancer *sp. megalops s1',\n",
    " 'Cancer *sp. zoea s1' ,'Cancer *sp. zoea s2', 'Cancer magister megalops s2',\n",
    " 'Cancer magister zoea s1' ,'Cancer magister zoea s2',\n",
    " 'Cancer oregonensis megalops s1', 'Cancer oregonensis megalops s2',\n",
    " 'Cancer productus megalops s1' ,'Cancer productus megalops s2',\n",
    " 'Cancer productus zoea s1' ,'Cancer productus zoea s2','Caridea *sp.  s3' ,'Caridea *sp. mysis s1',\n",
    " 'Caridea *sp. mysis s2', 'Caridea *sp. mysis s3' ,'Caridea *sp. zoea s1',\n",
    " 'Caridea *sp. zoea s2', 'Chaetognatha *sp.  s3' ,'Chaetognatha *sp. juvenile s1',\n",
    " 'Chaetognatha *sp. juvenile s2','Chionoecetes *sp. megalops s1',\n",
    " 'Chionoecetes *sp. megalops s2', 'Chionoecetes *sp. zoea s1','Corycaeus anglicus  3',\n",
    " 'Corycaeus anglicus  4' ,'Corycaeus anglicus  5' ,'Corycaeus anglicus  6F',\n",
    " 'Corycaeus anglicus  6M' ,'Crangonidae *sp. mysis s1',\n",
    " 'Crangonidae *sp. mysis s2' ,'Crangonidae *sp. mysis s3',\n",
    " 'Crangonidae *sp. zoea s1', 'Crangonidae *sp. zoea s2','Cyphocaris *sp.  s1',\n",
    " 'Cyphocaris challengeri  s1' ,'Cyphocaris challengeri  s2',\n",
    " 'Cyphocaris challengeri  s3', 'Decapoda *sp. zoea s1','Eucalanus *sp.  1',\n",
    " 'Eucalanus *sp.  2' ,'Eucalanus *sp.  3' ,'Eucalanus *sp. nauplii s1',\n",
    " 'Eucalanus bungii  3', 'Eucalanus bungii  4', 'Eucalanus bungii  4F',\n",
    " 'Eucalanus bungii  4M', 'Eucalanus bungii  5F', 'Eucalanus bungii  5M',\n",
    " 'Eucalanus bungii  6F', 'Eucalanus bungii  6M',\n",
    " 'Eucalanus californicus  5F', 'Eucalanus californicus  6F','Euphausia pacifica  F',\n",
    " 'Euphausia pacifica  M' ,'Euphausia pacifica  s2', 'Euphausia pacifica  s3',\n",
    " 'Euphausia pacifica eggs s1' ,'Euphausia pacifica nauplii s1',\n",
    " 'Euphausia pacifica zoea s1', 'Euphausiacea *sp.  s2',\n",
    " 'Euphausiidae *sp. eggs s1', 'Euphausiidae *sp. nauplii s1',\n",
    " 'Euphausiidae *sp. protozoea (or calyptopis) s1',\n",
    " 'Euphausiidae *sp. zoea (or furcilia) s1','Galatheidae *sp. zoea s1', 'Galatheidae *sp. zoea s2',\n",
    " 'Gammaridea *sp.  s1', 'Gammaridea *sp.  s2','Grapsidae *sp. megalops s1', 'Grapsidae *sp. zoea s1','Hemigrapsus *sp. megalops s1'\n",
    " ,'Hemigrapsus *sp. zoea s1','Hyperia medusarum  F', 'Hyperia medusarum  M',\n",
    " 'Hyperia medusarum  s1', 'Hyperia medusarum  s2' ,'Limacina helicina  s0',\n",
    " 'Limacina helicina  s1' ,'Limacina helicina  s2','Lithodidae *sp. megalops s1',\n",
    " 'Lithodidae *sp. zoea s1', 'Lithodidae *sp. zoea s2','Lophopanopeus *sp. megalops s1','Majidae *sp. megalops s1',\n",
    " 'Majidae *sp. megalops s2', 'Majidae *sp. zoea s1','Metridia *sp.  1',\n",
    " 'Metridia *sp.  2', 'Metridia *sp.  3' ,'Metridia *sp.  4',\n",
    " 'Metridia *sp.  5', 'Metridia *sp.  6F', 'Metridia *sp.  6M',\n",
    " 'Metridia pacifica  3' ,'Metridia pacifica  4' ,'Metridia pacifica  5F',\n",
    " 'Metridia pacifica  5M' 'Metridia pacifica  6F' 'Metridia pacifica  6M'\n",
    " 'Metridia pseudopacifica  5', 'Metridia pseudopacifica  6F',\n",
    " 'Metridia pseudopacifica  6M' ,'Munida *sp. zoea s1' ,'Munida *sp. zoea s2',\n",
    " 'Munida quadrispina megalops s2', 'Munida quadrispina megalops s3','Neocalanus *sp.  4',\n",
    " 'Neocalanus *sp. eggs s1', 'Neocalanus *sp. nauplii s1',\n",
    " 'Neocalanus cristatus  2', 'Neocalanus cristatus  3',\n",
    " 'Neocalanus cristatus  4', 'Neocalanus cristatus  5',\n",
    " 'Neocalanus cristatus  6F' ,'Neocalanus cristatus  6M',\n",
    " 'Neocalanus plumchrus  1', 'Neocalanus plumchrus  2',\n",
    " 'Neocalanus plumchrus  3', 'Neocalanus plumchrus  4',\n",
    " 'Neocalanus plumchrus  5', 'Neocalanus plumchrus  6F',\n",
    " 'Neocalanus plumchrus  6M' ,'Neotrypaea *sp. mysis s1',\n",
    " 'Neotrypaea *sp. mysis s2', 'Neotrypaea *sp. zoea s1',\n",
    " 'Neotrypaea *sp. zoea s2', 'Oikopleura *sp.  s1', 'Oikopleura *sp.  s2',\n",
    " 'Oikopleura dioica  s1' ,'Oikopleura dioica  s2',\n",
    " 'Oikopleura labradoriensis  s1', 'Oikopleura labradoriensis  s2',\n",
    " 'Oikopleura labradoriensis  s3', 'Oregonia *sp. megalops s1','Paguridae *sp. megalops s1' ,'Paguridae *sp. megalops s2',\n",
    " 'Paguridae *sp. zoea s1', 'Paguridae *sp. zoea s2',\n",
    " 'Pandalidae *sp. mysis s2', 'Pandalidae *sp. mysis s3',\n",
    " 'Pandalidae *sp. zoea s1', 'Pandalidae *sp. zoea s2',\n",
    " 'Pandalopsis dispar  s2' ,'Pandalopsis dispar  s3' ,'Pandalus danae  s2',\n",
    " 'Pandalus danae  s3', 'Pandalus eous  s3', 'Pandalus jordani  s2',\n",
    " 'Pandalus jordani  s3', 'Pandalus stenolepis  s1',\n",
    " 'Pandalus stenolepis  s2', 'Pandalus stenolepis  s3',\n",
    " 'Pandalus tridens  s2' ,'Pandalus tridens  s3','Parasagitta elegans  s2', 'Parasagitta elegans  s3',\n",
    " 'Parasagitta elegans juvenile s1', 'Parasagitta euneritica  s2',\n",
    " 'Parasagitta euneritica  s3','Pinnixa *sp. megalops s1', 'Pinnotheres *sp. megalops s1',\n",
    " 'Pinnotheridae *sp. megalops s1', 'Pinnotheridae *sp. megalops s2',\n",
    " 'Pinnotheridae *sp. zoea s1' ,'Pinnotheridae *sp. zoea s2',\n",
    " 'Pisidae zoea zoea s1', 'Porcellanidae *sp. megalops s1', 'Porcellanidae *sp. zoea s1',\n",
    " 'Porcellanidae *sp. zoea s2', 'Primno *sp.  s1', 'Primno abyssalis  F',\n",
    " 'Primno abyssalis  M', 'Primno abyssalis  s1', 'Primno abyssalis  s2',\n",
    " 'Primno brevidens  s1' ,'Pugettia *sp. megalops s1', 'Themisto *sp.  s1',\n",
    " 'Themisto pacifica  F', 'Themisto pacifica  M', 'Themisto pacifica  s2',\n",
    " 'Themisto pacifica juvenile s1', 'Thysanoessa *sp.  s1',\n",
    " 'Thysanoessa *sp.  s2', 'Thysanoessa *sp. nauplii s1',\n",
    " 'Thysanoessa *sp. zoea s1', 'Thysanoessa longipes  F',\n",
    " 'Thysanoessa longipes  M', 'Thysanoessa longipes  s2',\n",
    " 'Thysanoessa longipes  s3' ,'Thysanoessa longipes zoea s1',\n",
    " 'Thysanoessa raschii  F', 'Thysanoessa raschii  M',\n",
    " 'Thysanoessa raschii  s2' ,'Thysanoessa raschii zoea s1',\n",
    " 'Thysanoessa spinifera  F', 'Thysanoessa spinifera  M',\n",
    " 'Thysanoessa spinifera  s2', 'Thysanoessa spinifera  s3',\n",
    " 'Thysanoessa spinifera eggs s1', 'Thysanoessa spinifera nauplii s1',\n",
    " 'Thysanoessa spinifera zoea s1','Xanthidae *sp. megalops s1',\n",
    " 'Xanthidae *sp. zoea s1')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Key', 'region_name', 'Station', 'PROJECT', 'lon', 'lat', 'Date',\n",
       "       'STN_TIME', 'Twilight', 'Net_Type', 'Mesh_Size(um)', 'Net_Mouth_Dia(m)',\n",
       "       'DEPTH_STRT1', 'DEPTH_END1', 'Bottom Depth(m)', 'Volume Filtered(m3)',\n",
       "       'CTD', 'NOTES', 'PI', 'Phylum:', 'Class:', 'Order:', 'Family:', 'Name',\n",
       "       'Abundance(#/m3)', 'Biomass(mg/m3)', 'NumberOfSpecies',\n",
       "       'Station Diversity', 'Station Equitability', 'dtUTC'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtsutc=et.pac_to_utc(dts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Key                                                      IOS2012005000901\n",
       "region_name                                    Northern Strait of Georgia\n",
       "Station                                                                22\n",
       "PROJECT                                                         Str. Geo.\n",
       "lon                                                              -124.272\n",
       "lat                                                                 49.67\n",
       "Date                                                            6/14/2012\n",
       "STN_TIME                                                             7:32\n",
       "Twilight                                                         Daylight\n",
       "Net_Type                                                         SCOR VNH\n",
       "Mesh_Size(um)                                                         236\n",
       "Net_Mouth_Dia(m)                                                     0.56\n",
       "DEPTH_STRT1                                                            50\n",
       "DEPTH_END1                                                              0\n",
       "Bottom Depth(m)                                                       352\n",
       "Volume Filtered(m3)                                                 10.47\n",
       "CTD                                                                   8.0\n",
       "NOTES                   Flowmeter reading may be incorrect. Value ente...\n",
       "PI                                                            Dave Mackas\n",
       "Phylum:                                                        Arthropoda\n",
       "Class:                                                       Branchiopoda\n",
       "Order:                                                        Diplostraca\n",
       "Family:                                                         Podonidae\n",
       "Name                                                       Podon *sp.  s1\n",
       "Abundance(#/m3)                                                  12.22541\n",
       "Biomass(mg/m3)                                                    0.06113\n",
       "NumberOfSpecies                                                        51\n",
       "Station Diversity                                                    2.63\n",
       "Station Equitability                                                 0.67\n",
       "dtUTC                                                 2012-06-14 14:32:00\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "towIDlist=['Key', 'region_name', 'Station',  'lon', 'lat','Date', 'dtUTC', 'Twilight', 'Net_Type', 'Mesh_Size(um)', 'DEPTH_STRT1', 'DEPTH_END1', 'Bottom Depth(m)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "towIDlist2=['Key', 'region_name', 'Station',  'lon', 'lat','Date', 'dtUTC', 'Twilight', 'Net_Type', 'Mesh_Size(um)', 'DEPTH_STRT1', 'DEPTH_END1', 'Bottom Depth(m)','CTD']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(654, 12694, 654)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.groupby(towIDlist)),len(df.groupby(towIDlist2)),len(df.groupby(['Key']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Key is a unique identifier for each tow\n",
    "# do not group by CTD due to NaN values"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "for el in df.loc[df['Order:']=='Calanoida','Name']:\n",
    "    print(el)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Month']=[ii.month for ii in df['dtUTC']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.loc[df.Month==2]['Key'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "test=df.loc[(df.Month==2)&(df['Order:']=='Calanoida')].groupby(['Key']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>Mesh_Size(um)</th>\n",
       "      <th>Net_Mouth_Dia(m)</th>\n",
       "      <th>DEPTH_STRT1</th>\n",
       "      <th>DEPTH_END1</th>\n",
       "      <th>Bottom Depth(m)</th>\n",
       "      <th>Volume Filtered(m3)</th>\n",
       "      <th>CTD</th>\n",
       "      <th>Abundance(#/m3)</th>\n",
       "      <th>Biomass(mg/m3)</th>\n",
       "      <th>NumberOfSpecies</th>\n",
       "      <th>Station Diversity</th>\n",
       "      <th>Station Equitability</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>IOS2015001000201A</th>\n",
       "      <td>-4067.085</td>\n",
       "      <td>1604.262</td>\n",
       "      <td>7788</td>\n",
       "      <td>18.48</td>\n",
       "      <td>6732</td>\n",
       "      <td>0</td>\n",
       "      <td>7491</td>\n",
       "      <td>1574.43</td>\n",
       "      <td>99.0</td>\n",
       "      <td>228.25407</td>\n",
       "      <td>5.45896</td>\n",
       "      <td>1452</td>\n",
       "      <td>65.34</td>\n",
       "      <td>17.16</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IOS2015001000701A</th>\n",
       "      <td>-6820.110</td>\n",
       "      <td>2656.500</td>\n",
       "      <td>12980</td>\n",
       "      <td>30.80</td>\n",
       "      <td>9735</td>\n",
       "      <td>0</td>\n",
       "      <td>10285</td>\n",
       "      <td>1885.95</td>\n",
       "      <td>275.0</td>\n",
       "      <td>80.92749</td>\n",
       "      <td>3.33723</td>\n",
       "      <td>2915</td>\n",
       "      <td>176.00</td>\n",
       "      <td>44.55</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IOS2015008000501</th>\n",
       "      <td>-5808.025</td>\n",
       "      <td>2292.190</td>\n",
       "      <td>9400</td>\n",
       "      <td>23.50</td>\n",
       "      <td>2773</td>\n",
       "      <td>0</td>\n",
       "      <td>2820</td>\n",
       "      <td>462.95</td>\n",
       "      <td>188.0</td>\n",
       "      <td>422.43662</td>\n",
       "      <td>3.71556</td>\n",
       "      <td>2538</td>\n",
       "      <td>94.00</td>\n",
       "      <td>23.97</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IOS2015008000801</th>\n",
       "      <td>-3955.936</td>\n",
       "      <td>1560.000</td>\n",
       "      <td>6400</td>\n",
       "      <td>16.00</td>\n",
       "      <td>960</td>\n",
       "      <td>0</td>\n",
       "      <td>960</td>\n",
       "      <td>158.08</td>\n",
       "      <td>224.0</td>\n",
       "      <td>398.78547</td>\n",
       "      <td>1.77838</td>\n",
       "      <td>1152</td>\n",
       "      <td>72.00</td>\n",
       "      <td>20.16</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IOS2015008001201</th>\n",
       "      <td>-5191.452</td>\n",
       "      <td>2047.416</td>\n",
       "      <td>8400</td>\n",
       "      <td>21.00</td>\n",
       "      <td>2226</td>\n",
       "      <td>0</td>\n",
       "      <td>2394</td>\n",
       "      <td>312.06</td>\n",
       "      <td>462.0</td>\n",
       "      <td>397.71195</td>\n",
       "      <td>4.57015</td>\n",
       "      <td>2016</td>\n",
       "      <td>77.28</td>\n",
       "      <td>20.16</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IOS2015008001601</th>\n",
       "      <td>-5437.256</td>\n",
       "      <td>2144.472</td>\n",
       "      <td>8800</td>\n",
       "      <td>22.00</td>\n",
       "      <td>3080</td>\n",
       "      <td>0</td>\n",
       "      <td>3212</td>\n",
       "      <td>473.44</td>\n",
       "      <td>660.0</td>\n",
       "      <td>461.98892</td>\n",
       "      <td>5.28456</td>\n",
       "      <td>2332</td>\n",
       "      <td>87.12</td>\n",
       "      <td>22.00</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IOS2015099000101</th>\n",
       "      <td>-6328.692</td>\n",
       "      <td>2528.733</td>\n",
       "      <td>12036</td>\n",
       "      <td>28.56</td>\n",
       "      <td>10200</td>\n",
       "      <td>0</td>\n",
       "      <td>13464</td>\n",
       "      <td>2135.37</td>\n",
       "      <td>102.0</td>\n",
       "      <td>262.04919</td>\n",
       "      <td>15.79196</td>\n",
       "      <td>2754</td>\n",
       "      <td>131.58</td>\n",
       "      <td>32.64</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IOS2015099000201</th>\n",
       "      <td>-6558.644</td>\n",
       "      <td>2610.356</td>\n",
       "      <td>12508</td>\n",
       "      <td>29.68</td>\n",
       "      <td>13038</td>\n",
       "      <td>0</td>\n",
       "      <td>21200</td>\n",
       "      <td>3302.43</td>\n",
       "      <td>159.0</td>\n",
       "      <td>170.91952</td>\n",
       "      <td>14.82036</td>\n",
       "      <td>3286</td>\n",
       "      <td>126.67</td>\n",
       "      <td>30.74</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IOS2015099000301</th>\n",
       "      <td>-7981.952</td>\n",
       "      <td>3181.120</td>\n",
       "      <td>15104</td>\n",
       "      <td>35.84</td>\n",
       "      <td>16000</td>\n",
       "      <td>0</td>\n",
       "      <td>19136</td>\n",
       "      <td>3584.64</td>\n",
       "      <td>0.0</td>\n",
       "      <td>205.39196</td>\n",
       "      <td>8.40212</td>\n",
       "      <td>4608</td>\n",
       "      <td>159.36</td>\n",
       "      <td>37.12</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSF2015098000101</th>\n",
       "      <td>-3119.175</td>\n",
       "      <td>1237.075</td>\n",
       "      <td>6250</td>\n",
       "      <td>12.50</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>249975</td>\n",
       "      <td>173.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.14424</td>\n",
       "      <td>3.09494</td>\n",
       "      <td>800</td>\n",
       "      <td>64.00</td>\n",
       "      <td>18.25</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSF2015098000201</th>\n",
       "      <td>-4370.310</td>\n",
       "      <td>1736.140</td>\n",
       "      <td>8750</td>\n",
       "      <td>17.50</td>\n",
       "      <td>1400</td>\n",
       "      <td>0</td>\n",
       "      <td>1400</td>\n",
       "      <td>215.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1202.27643</td>\n",
       "      <td>12.38677</td>\n",
       "      <td>1470</td>\n",
       "      <td>78.40</td>\n",
       "      <td>21.00</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSF2015098000301</th>\n",
       "      <td>-4731.000</td>\n",
       "      <td>1879.746</td>\n",
       "      <td>9500</td>\n",
       "      <td>19.00</td>\n",
       "      <td>4940</td>\n",
       "      <td>0</td>\n",
       "      <td>379962</td>\n",
       "      <td>772.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>154.37984</td>\n",
       "      <td>7.44550</td>\n",
       "      <td>1862</td>\n",
       "      <td>105.26</td>\n",
       "      <td>26.98</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSF2015098000401</th>\n",
       "      <td>-4237.692</td>\n",
       "      <td>1686.128</td>\n",
       "      <td>8500</td>\n",
       "      <td>17.00</td>\n",
       "      <td>4420</td>\n",
       "      <td>0</td>\n",
       "      <td>339966</td>\n",
       "      <td>737.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>166.26730</td>\n",
       "      <td>4.97040</td>\n",
       "      <td>1088</td>\n",
       "      <td>80.24</td>\n",
       "      <td>23.12</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        lon       lat  Mesh_Size(um)  Net_Mouth_Dia(m)  \\\n",
       "Key                                                                      \n",
       "IOS2015001000201A -4067.085  1604.262           7788             18.48   \n",
       "IOS2015001000701A -6820.110  2656.500          12980             30.80   \n",
       "IOS2015008000501  -5808.025  2292.190           9400             23.50   \n",
       "IOS2015008000801  -3955.936  1560.000           6400             16.00   \n",
       "IOS2015008001201  -5191.452  2047.416           8400             21.00   \n",
       "IOS2015008001601  -5437.256  2144.472           8800             22.00   \n",
       "IOS2015099000101  -6328.692  2528.733          12036             28.56   \n",
       "IOS2015099000201  -6558.644  2610.356          12508             29.68   \n",
       "IOS2015099000301  -7981.952  3181.120          15104             35.84   \n",
       "PSF2015098000101  -3119.175  1237.075           6250             12.50   \n",
       "PSF2015098000201  -4370.310  1736.140           8750             17.50   \n",
       "PSF2015098000301  -4731.000  1879.746           9500             19.00   \n",
       "PSF2015098000401  -4237.692  1686.128           8500             17.00   \n",
       "\n",
       "                   DEPTH_STRT1  DEPTH_END1  Bottom Depth(m)  \\\n",
       "Key                                                           \n",
       "IOS2015001000201A         6732           0             7491   \n",
       "IOS2015001000701A         9735           0            10285   \n",
       "IOS2015008000501          2773           0             2820   \n",
       "IOS2015008000801           960           0              960   \n",
       "IOS2015008001201          2226           0             2394   \n",
       "IOS2015008001601          3080           0             3212   \n",
       "IOS2015099000101         10200           0            13464   \n",
       "IOS2015099000201         13038           0            21200   \n",
       "IOS2015099000301         16000           0            19136   \n",
       "PSF2015098000101          1000           0           249975   \n",
       "PSF2015098000201          1400           0             1400   \n",
       "PSF2015098000301          4940           0           379962   \n",
       "PSF2015098000401          4420           0           339966   \n",
       "\n",
       "                   Volume Filtered(m3)    CTD  Abundance(#/m3)  \\\n",
       "Key                                                              \n",
       "IOS2015001000201A              1574.43   99.0        228.25407   \n",
       "IOS2015001000701A              1885.95  275.0         80.92749   \n",
       "IOS2015008000501                462.95  188.0        422.43662   \n",
       "IOS2015008000801                158.08  224.0        398.78547   \n",
       "IOS2015008001201                312.06  462.0        397.71195   \n",
       "IOS2015008001601                473.44  660.0        461.98892   \n",
       "IOS2015099000101               2135.37  102.0        262.04919   \n",
       "IOS2015099000201               3302.43  159.0        170.91952   \n",
       "IOS2015099000301               3584.64    0.0        205.39196   \n",
       "PSF2015098000101                173.25    0.0        300.14424   \n",
       "PSF2015098000201                215.25    0.0       1202.27643   \n",
       "PSF2015098000301                772.16    0.0        154.37984   \n",
       "PSF2015098000401                737.80    0.0        166.26730   \n",
       "\n",
       "                   Biomass(mg/m3)  NumberOfSpecies  Station Diversity  \\\n",
       "Key                                                                     \n",
       "IOS2015001000201A         5.45896             1452              65.34   \n",
       "IOS2015001000701A         3.33723             2915             176.00   \n",
       "IOS2015008000501          3.71556             2538              94.00   \n",
       "IOS2015008000801          1.77838             1152              72.00   \n",
       "IOS2015008001201          4.57015             2016              77.28   \n",
       "IOS2015008001601          5.28456             2332              87.12   \n",
       "IOS2015099000101         15.79196             2754             131.58   \n",
       "IOS2015099000201         14.82036             3286             126.67   \n",
       "IOS2015099000301          8.40212             4608             159.36   \n",
       "PSF2015098000101          3.09494              800              64.00   \n",
       "PSF2015098000201         12.38677             1470              78.40   \n",
       "PSF2015098000301          7.44550             1862             105.26   \n",
       "PSF2015098000401          4.97040             1088              80.24   \n",
       "\n",
       "                   Station Equitability  Month  \n",
       "Key                                             \n",
       "IOS2015001000201A                 17.16     66  \n",
       "IOS2015001000701A                 44.55    110  \n",
       "IOS2015008000501                  23.97     94  \n",
       "IOS2015008000801                  20.16     64  \n",
       "IOS2015008001201                  20.16     84  \n",
       "IOS2015008001601                  22.00     88  \n",
       "IOS2015099000101                  32.64    102  \n",
       "IOS2015099000201                  30.74    106  \n",
       "IOS2015099000301                  37.12    128  \n",
       "PSF2015098000101                  18.25     50  \n",
       "PSF2015098000201                  21.00     70  \n",
       "PSF2015098000301                  26.98     76  \n",
       "PSF2015098000401                  23.12     68  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp=df.loc[(df.Month==2)&(df['Order:']=='Calanoida'),['Name','Biomass(mg/m3)']].groupby('Name',as_index=False).sum().sort_values(['Biomass(mg/m3)'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yes\n"
     ]
    }
   ],
   "source": [
    "if 'Neocalanus plumchrus  1' in colList:\n",
    "    print('yes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(np.isnan(df['Biomass(mg/m3)']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total: 91.05689\n",
      "Acartia *sp.  1 0.1524709442635258\n",
      "Acartia *sp.  3 0.29484666124661185\n",
      "Acartia *sp.  4 0.3680829643973125\n",
      "Acartia hudsonica  6F 0.4329025513610228\n",
      "Acartia hudsonica  6M 0.49399644551884\n",
      "Acartia longiremis  5 0.5431409967988144\n",
      "Acartia longiremis  6F 0.5886318981463128\n",
      "Acartia longiremis  6M 0.6190386032292559\n",
      "Aetideidae *sp.  1 0.6439498427850984\n",
      "Aetideidae *sp.  2 0.6671245855201072\n",
      "Aetideus divergens  4 0.6864790791778634\n",
      "Aetideus divergens  5 0.7047988351018797\n",
      "Aetideus divergens  6F 0.7226583293147834\n",
      "Aetideus divergens  6M 0.7390363321216001\n",
      "Calanoida *sp.  1 0.7541849935792889\n",
      "Calanoida *sp.  2 0.7665354044048726\n",
      "Calanus *sp.  1 0.7788231071805769\n",
      "Calanus *sp.  2 0.790237839223369\n",
      "Calanus *sp. nauplii s1 0.8015660319608983\n",
      "Calanus marshallae  5 0.8112372386098404\n",
      "Calanus marshallae  6F 0.8207127434288607\n",
      "Calanus marshallae  6M 0.829321427516358\n",
      "Calanus pacificus  4 0.8376354606444388\n",
      "Calanus pacificus  5 0.8458247366014807\n",
      "Calanus pacificus  6F 0.8535784606744201\n",
      "Calanus pacificus  6M 0.8607734131925657\n",
      "Candacia *sp.  1 0.8679344308816171\n",
      "Candacia columbiae  6F 0.8747362225966646\n",
      "Centropages abdominalis  1 0.880900720417752\n",
      "Centropages abdominalis  2 0.8866486654661718\n",
      "Centropages abdominalis  3 0.8916313746274446\n",
      "Centropages abdominalis  4 0.8963357962258539\n",
      "Centropages abdominalis  5 0.9007499597229819\n",
      "Centropages abdominalis  6F 0.9046806891823342\n",
      "Centropages abdominalis  6M 0.9085288329087452\n",
      "Chiridius *sp.  3 0.9121159310404736\n",
      "Chiridius gracilis  3 0.9155190782377918\n",
      "Chiridius gracilis  4 0.9188578700634294\n",
      "Chiridius gracilis  5 0.922149987771381\n",
      "Chiridius gracilis  6F 0.9253754438571316\n",
      "Clausocalanus arcuicornis  5 0.9284623052687172\n",
      "Clausocalanus arcuicornis  6F 0.9314462639784864\n",
      "Clausocalanus arcuicornis  6M 0.9343421458826454\n",
      "Clausocalanus parapergens  5 0.9371864117037162\n",
      "Clausocalanus parapergens  6F 0.9399879569794224\n",
      "Clausocalanus pergens  6F 0.9427879647547812\n",
      "Ctenocalanus vanus  4 0.9452954081783376\n",
      "Ctenocalanus vanus  5 0.9475569613677779\n",
      "Ctenocalanus vanus  6F 0.9498129136630957\n",
      "Epilabidocera *sp. nauplii s1 0.9518977641340486\n",
      "Epilabidocera longipedata  5 0.9538723538658086\n",
      "Eucalanus *sp. nauplii s1 0.9558418918107131\n",
      "Eucalanus bungii  5F 0.9577635476019443\n",
      "Eucalanus bungii  5M 0.9596218364145757\n",
      "Eucalanus bungii  6F 0.9614208216423822\n",
      "Eucalanus bungii  6M 0.9630263014693342\n",
      "Eucalanus californicus  5F 0.9645543571716539\n",
      "Eucalanus californicus  6F 0.9659662217762982\n",
      "Euchaetidae *sp.  1 0.9673497524459708\n",
      "Euchaetidae *sp.  2 0.9685823884387001\n",
      "Euchaetidae *sp.  3 0.9697042145849697\n",
      "Euchaetidae *sp. nauplii s1 0.9707633326813598\n",
      "Euchirella *sp.  4 0.9718003766656207\n",
      "Euchirella pseudopulchra  6M 0.9727906367107421\n",
      "Gaetanus minutus  4 0.973688427092118\n",
      "Gaetanus minutus  5 0.9745852290804133\n",
      "Gaetanus minutus  6F 0.9754191033759225\n",
      "Heterorhabdus *sp.  3 0.9762425446333606\n",
      "Heterorhabdus *sp.  4 0.9770157974866042\n",
      "Heterorhabdus tanneri  6F 0.9777780681945101\n",
      "Metridia *sp.  1 0.978522987112782\n",
      "Metridia *sp.  2 0.9792526408490341\n",
      "Metridia *sp.  3 0.979977901727151\n",
      "Metridia *sp.  4 0.9807012956405606\n",
      "Metridia *sp.  5 0.9813971243691719\n",
      "Metridia *sp.  6F 0.9820906468472622\n",
      "Metridia pacifica  5F 0.9827817532533781\n",
      "Metridia pacifica  5M 0.9834220123265796\n",
      "Metridia pacifica  6F 0.9839754026301578\n",
      "Metridia pacifica  6M 0.9845213250749064\n",
      "Metridia pseudopacifica  6F 0.9850584618033847\n",
      "Metridia pseudopacifica  6M 0.9855917547809946\n",
      "Microcalanus *sp.  3 0.9861073665046104\n",
      "Microcalanus *sp.  4 0.9865972800081355\n",
      "Microcalanus pusillus  4 0.9870838988680593\n",
      "Microcalanus pusillus  5 0.9875657954054877\n",
      "Microcalanus pusillus  6F 0.9880469231927423\n",
      "Microcalanus pusillus  6M 0.9885272822298237\n",
      "Microcalanus pygmaeus  3 0.9890037975160365\n",
      "Microcalanus pygmaeus  4 0.9894687815496445\n",
      "Microcalanus pygmaeus  5 0.9899276155818633\n",
      "Microcalanus pygmaeus  6F 0.9903856808639082\n",
      "Microcalanus pygmaeus  6M 0.9908161809611551\n",
      "Neocalanus *sp. eggs s1 0.9912303176618485\n",
      "Neocalanus cristatus  6F 0.991639292754233\n",
      "Neocalanus plumchrus  4 0.9920401410590676\n",
      "Neocalanus plumchrus  5 0.9924279206109499\n",
      "Neocalanus plumchrus  6F 0.9927969206943044\n",
      "Neocalanus plumchrus  6M 0.9931437368440763\n",
      "Paracalanus indicus  4 0.9934752878118285\n",
      "Paracalanus indicus  5 0.9937899262757602\n",
      "Paracalanus indicus  6F 0.9940953397376081\n",
      "Paracalanus indicus  6M 0.994397019270041\n",
      "Paracalanus parvus  5 0.9946788211194123\n",
      "Paracalanus parvus  6F 0.9949577676109959\n",
      "Paracalanus quasimodo  5 0.9952290266008427\n",
      "Paracalanus quasimodo  6F 0.9954999561263295\n",
      "Paracalanus quasimodo  6M 0.9957663829722276\n",
      "Paraeuchaeta *sp.  4 0.9960321508894056\n",
      "Paraeuchaeta elongata  4 0.9962944045200753\n",
      "Paraeuchaeta elongata  5F 0.9965557795791181\n",
      "Paraeuchaeta elongata  5M 0.9967816823087192\n",
      "Paraeuchaeta elongata  6F 0.9969847421760175\n",
      "Paraeuchaeta elongata  6M 0.9971824207921003\n",
      "Pleuromamma indica  6F 0.9973601119036681\n",
      "Pseudocalanus *sp.  1 0.9975172664034538\n",
      "Pseudocalanus *sp.  2 0.9976716753669052\n",
      "Pseudocalanus *sp.  3 0.9978228995082085\n",
      "Pseudocalanus *sp.  4 0.9979725861491645\n",
      "Pseudocalanus mimus  5F 0.9981151343956508\n",
      "Pseudocalanus mimus  5M 0.9982552665701628\n",
      "Pseudocalanus mimus  6F 0.9983936416014209\n",
      "Pseudocalanus minutus  5F 0.9985239996665821\n",
      "Pseudocalanus minutus  5M 0.9986469996943669\n",
      "Pseudocalanus minutus  6F 0.9987699997221517\n",
      "Pseudocalanus minutus  6M 0.9988843238551196\n",
      "Pseudocalanus moultoni  5F 0.9989867872711226\n",
      "Pseudocalanus moultoni  5M 0.9990847480075368\n",
      "Pseudocalanus moultoni  6F 0.9991825989224978\n",
      "Pseudocalanus moultoni  6M 0.9992677105488669\n",
      "Pseudocalanus newmani  5F 0.9993470016382066\n",
      "Pseudocalanus newmani  5M 0.9994149811178484\n",
      "Pseudocalanus newmani  6F 0.9994764811317408\n",
      "Pseudocalanus newmani  6M 0.9995352356092989\n",
      "Racovitzanus antarcticus  4 0.9995854240134933\n",
      "Rhincalanus nasutus  6F 0.9996324275955395\n",
      "Scaphocalanus brevicornis  6M 0.9996775642128783\n",
      "Scolecithricella globulosa  5 0.9997201749367896\n",
      "Scolecithricella minor  3 0.9997611383389\n",
      "Scolecithricella minor  4 0.9997937553105537\n",
      "Scolecithricella minor  5 0.9998251642462203\n",
      "Scolecithricella minor  6F 0.999854925860086\n",
      "Scolecithricella minor  6M 0.9998805142587233\n",
      "Spinocalanus brevicaudatus  5 0.9999061026573607\n",
      "Spinocalanus brevicaudatus  6F 0.9999260901618757\n",
      "Tortanus discaudatus  1 0.9999438812373231\n",
      "Tortanus discaudatus  2 0.9999592562407963\n",
      "Tortanus discaudatus  3 0.9999718857079348\n",
      "Tortanus discaudatus  4 0.9999794633882181\n",
      "Tortanus discaudatus  5 0.9999866017826877\n",
      "Tortanus discaudatus  6F 0.9999937401771575\n",
      "Tortanus discaudatus  6M 1.0\n",
      "included: 45.85667\n",
      "fraction included: 0.5036046146535424\n"
     ]
    }
   ],
   "source": [
    "temp=df.loc[(df.Month==2)&(df['Order:']=='Calanoida'),['Name','Biomass(mg/m3)']].groupby('Name',as_index=False).sum()\\\n",
    "        .sort_values(['Biomass(mg/m3)'],ascending=False)\n",
    "print('Total:',np.sum(temp['Biomass(mg/m3)']))\n",
    "bsum=0\n",
    "for i in range(0,len(temp)):\n",
    "    print(temp['Name'][i],np.sum(temp['Biomass(mg/m3)'][:(i+1)])/np.sum(temp['Biomass(mg/m3)']))\n",
    "    if temp['Name'][i] in colList:\n",
    "        bsum=bsum+temp['Biomass(mg/m3)'][(i+1)]\n",
    "print('included:',bsum)\n",
    "print('fraction included:',bsum/np.sum(temp['Biomass(mg/m3)']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Acartia *sp.  1'"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp['Name'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.784098014243127"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10**(np.sum(np.log10(test['Biomass(mg/m3)']))/13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "273.85192568125007"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10**(np.sum(np.log10(test['Abundance(#/m3)']))/13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.097374999675256"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10**(np.sum(np.log10(test['Abundance(#/m3)']))/13)*(4.13*(1.8**3.92))*0.45/1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05239622263482247"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10**(np.sum(np.log10(test['Abundance(#/m3)']))/13)*4.13*(0.5**3.28)*0.45/1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create an abundance dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomassDF=df.groupby(towIDlist,as_index=False).first()\\\n",
    "     .loc[:,towIDlist].copy(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Key</th>\n",
       "      <th>region_name</th>\n",
       "      <th>Station</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>Date</th>\n",
       "      <th>dtUTC</th>\n",
       "      <th>Twilight</th>\n",
       "      <th>Net_Type</th>\n",
       "      <th>Mesh_Size(um)</th>\n",
       "      <th>DEPTH_STRT1</th>\n",
       "      <th>DEPTH_END1</th>\n",
       "      <th>Bottom Depth(m)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IOS2012005000901</td>\n",
       "      <td>Northern Strait of Georgia</td>\n",
       "      <td>22</td>\n",
       "      <td>-124.272</td>\n",
       "      <td>49.670</td>\n",
       "      <td>6/14/2012</td>\n",
       "      <td>2012-06-14 14:32:00</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>SCOR VNH</td>\n",
       "      <td>236</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IOS2012005001001</td>\n",
       "      <td>Northern Strait of Georgia</td>\n",
       "      <td>22</td>\n",
       "      <td>-124.272</td>\n",
       "      <td>49.670</td>\n",
       "      <td>6/14/2012</td>\n",
       "      <td>2012-06-14 14:52:00</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>SCOR VNH</td>\n",
       "      <td>236</td>\n",
       "      <td>345</td>\n",
       "      <td>0</td>\n",
       "      <td>352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IOS2012005002101</td>\n",
       "      <td>Northern Strait of Georgia</td>\n",
       "      <td>11</td>\n",
       "      <td>-124.722</td>\n",
       "      <td>49.710</td>\n",
       "      <td>6/14/2012</td>\n",
       "      <td>2012-06-14 07:00:00</td>\n",
       "      <td>Night</td>\n",
       "      <td>SCOR VNH</td>\n",
       "      <td>236</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IOS2012005002201</td>\n",
       "      <td>Northern Strait of Georgia</td>\n",
       "      <td>11</td>\n",
       "      <td>-124.722</td>\n",
       "      <td>49.710</td>\n",
       "      <td>6/14/2012</td>\n",
       "      <td>2012-06-14 07:05:00</td>\n",
       "      <td>Night</td>\n",
       "      <td>SCOR VNH</td>\n",
       "      <td>236</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IOS2012005002901</td>\n",
       "      <td>Northern Strait of Georgia</td>\n",
       "      <td>CPF2</td>\n",
       "      <td>-124.499</td>\n",
       "      <td>49.466</td>\n",
       "      <td>6/15/2012</td>\n",
       "      <td>2012-06-15 10:00:00</td>\n",
       "      <td>Night</td>\n",
       "      <td>SCOR VNH</td>\n",
       "      <td>236</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>SOO2015095000101</td>\n",
       "      <td>Tidal Mixed</td>\n",
       "      <td>CLO-42</td>\n",
       "      <td>-123.345</td>\n",
       "      <td>48.394</td>\n",
       "      <td>4/27/2015</td>\n",
       "      <td>2015-04-27 17:37:00</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>SCOR VNH</td>\n",
       "      <td>236</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>SOO2015095000401</td>\n",
       "      <td>Tidal Mixed</td>\n",
       "      <td>CB01</td>\n",
       "      <td>-123.318</td>\n",
       "      <td>48.344</td>\n",
       "      <td>4/30/2015</td>\n",
       "      <td>2015-04-30 19:00:00</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>SCOR VNH</td>\n",
       "      <td>236</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651</th>\n",
       "      <td>SOO2015095000501</td>\n",
       "      <td>Tidal Mixed</td>\n",
       "      <td>CLO-41</td>\n",
       "      <td>-123.345</td>\n",
       "      <td>48.395</td>\n",
       "      <td>7/9/2015</td>\n",
       "      <td>2015-07-09 19:25:00</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>SCOR VNH</td>\n",
       "      <td>236</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>SOO2015095000701</td>\n",
       "      <td>Tidal Mixed</td>\n",
       "      <td>CB01</td>\n",
       "      <td>-123.318</td>\n",
       "      <td>48.344</td>\n",
       "      <td>7/27/2015</td>\n",
       "      <td>2015-07-27 19:06:00</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>SCOR VNH</td>\n",
       "      <td>236</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>SOO2015095000801</td>\n",
       "      <td>Tidal Mixed</td>\n",
       "      <td>MAC41</td>\n",
       "      <td>-123.409</td>\n",
       "      <td>48.402</td>\n",
       "      <td>7/27/2015</td>\n",
       "      <td>2015-07-27 20:00:00</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>SCOR VNH</td>\n",
       "      <td>236</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>654 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Key                 region_name Station      lon     lat  \\\n",
       "0    IOS2012005000901  Northern Strait of Georgia      22 -124.272  49.670   \n",
       "1    IOS2012005001001  Northern Strait of Georgia      22 -124.272  49.670   \n",
       "2    IOS2012005002101  Northern Strait of Georgia      11 -124.722  49.710   \n",
       "3    IOS2012005002201  Northern Strait of Georgia      11 -124.722  49.710   \n",
       "4    IOS2012005002901  Northern Strait of Georgia    CPF2 -124.499  49.466   \n",
       "..                ...                         ...     ...      ...     ...   \n",
       "649  SOO2015095000101                 Tidal Mixed  CLO-42 -123.345  48.394   \n",
       "650  SOO2015095000401                 Tidal Mixed    CB01 -123.318  48.344   \n",
       "651  SOO2015095000501                 Tidal Mixed  CLO-41 -123.345  48.395   \n",
       "652  SOO2015095000701                 Tidal Mixed    CB01 -123.318  48.344   \n",
       "653  SOO2015095000801                 Tidal Mixed   MAC41 -123.409  48.402   \n",
       "\n",
       "          Date               dtUTC  Twilight  Net_Type  Mesh_Size(um)  \\\n",
       "0    6/14/2012 2012-06-14 14:32:00  Daylight  SCOR VNH            236   \n",
       "1    6/14/2012 2012-06-14 14:52:00  Daylight  SCOR VNH            236   \n",
       "2    6/14/2012 2012-06-14 07:00:00     Night  SCOR VNH            236   \n",
       "3    6/14/2012 2012-06-14 07:05:00     Night  SCOR VNH            236   \n",
       "4    6/15/2012 2012-06-15 10:00:00     Night  SCOR VNH            236   \n",
       "..         ...                 ...       ...       ...            ...   \n",
       "649  4/27/2015 2015-04-27 17:37:00  Daylight  SCOR VNH            236   \n",
       "650  4/30/2015 2015-04-30 19:00:00  Daylight  SCOR VNH            236   \n",
       "651   7/9/2015 2015-07-09 19:25:00  Daylight  SCOR VNH            236   \n",
       "652  7/27/2015 2015-07-27 19:06:00  Daylight  SCOR VNH            236   \n",
       "653  7/27/2015 2015-07-27 20:00:00  Daylight  SCOR VNH            236   \n",
       "\n",
       "     DEPTH_STRT1  DEPTH_END1  Bottom Depth(m)  \n",
       "0             50           0              352  \n",
       "1            345           0              352  \n",
       "2             50           0              307  \n",
       "3            300           0              307  \n",
       "4             50           0              325  \n",
       "..           ...         ...              ...  \n",
       "649           58           0               72  \n",
       "650           54           0               64  \n",
       "651           42           0               64  \n",
       "652           28           0               60  \n",
       "653           49           0               62  \n",
       "\n",
       "[654 rows x 13 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biomassDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Get abundance column to input into biomassDF as these abundance values will be multiplied by carbon conversion equations"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "def getabundance(colname,key,origdf,matchcol):        \n",
    "    abundanceArray=origdf.loc[(origdf.Key==key)&(origdf[matchcol]==colname),\n",
    "                    ['Abundance(#/m3)']]\n",
    "    abundance=np.nansum(abundanceArray)\n",
    "    \n",
    "    return abundance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for icol in colList:\n",
    "#    biomassDF[icol]=[getabundance(icol,ikey,df,'Name') for ikey in biomassDF['Key']]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "def getabundance(colname,key,origdf):        \n",
    "    abundanceArray=df.loc[(origdf.Key==key)&(origdf['Order:']==colname)&(origdf['Family:']==colname)&(origdf['Name']==colname),\n",
    "                    ['Abundance(#/m3)']]\n",
    "    abundance=np.nansum(abundanceArray)\n",
    "    \n",
    "    return abundance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for icol in colList:\n",
    "#    biomassDF[icol]=[getabundance(icol,ikey,df) for ikey in biomassDF['Key']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getabundance(colname,key,origdf):        \n",
    "    abundanceArray=origdf.loc[(origdf.Key==key)&(origdf['Name']==colname),\n",
    "                    ['Abundance(#/m3)']]\n",
    "    if len(abundanceArray)>1:\n",
    "        print(len(abundanceArray),colname,key)\n",
    "    abundance=np.nansum(abundanceArray)\n",
    "    \n",
    "    return abundance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "for icol in colList:\n",
    "    biomassDF[icol]=[getabundance(icol,ikey,df) for ikey in biomassDF['Key']]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>Calanus *sp.  1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.05635</td>\n",
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       "</table>\n",
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      "text/plain": [
       "   Calanus *sp.  1\n",
       "0          3.05635"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biomassDF.loc[biomassDF.Key=='IOS2012005000901',['Calanus *sp.  1']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Key</th>\n",
       "      <th>region_name</th>\n",
       "      <th>Station</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>Date</th>\n",
       "      <th>dtUTC</th>\n",
       "      <th>Twilight</th>\n",
       "      <th>Net_Type</th>\n",
       "      <th>Mesh_Size(um)</th>\n",
       "      <th>...</th>\n",
       "      <th>Thysanoessa raschii zoea s1</th>\n",
       "      <th>Thysanoessa spinifera  F</th>\n",
       "      <th>Thysanoessa spinifera  M</th>\n",
       "      <th>Thysanoessa spinifera  s2</th>\n",
       "      <th>Thysanoessa spinifera  s3</th>\n",
       "      <th>Thysanoessa spinifera eggs s1</th>\n",
       "      <th>Thysanoessa spinifera nauplii s1</th>\n",
       "      <th>Thysanoessa spinifera zoea s1</th>\n",
       "      <th>Xanthidae *sp. megalops s1</th>\n",
       "      <th>Xanthidae *sp. zoea s1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IOS2012005000901</td>\n",
       "      <td>Northern Strait of Georgia</td>\n",
       "      <td>22</td>\n",
       "      <td>-124.272</td>\n",
       "      <td>49.670</td>\n",
       "      <td>6/14/2012</td>\n",
       "      <td>2012-06-14 14:32:00</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>SCOR VNH</td>\n",
       "      <td>236</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IOS2012005001001</td>\n",
       "      <td>Northern Strait of Georgia</td>\n",
       "      <td>22</td>\n",
       "      <td>-124.272</td>\n",
       "      <td>49.670</td>\n",
       "      <td>6/14/2012</td>\n",
       "      <td>2012-06-14 14:52:00</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>SCOR VNH</td>\n",
       "      <td>236</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IOS2012005002101</td>\n",
       "      <td>Northern Strait of Georgia</td>\n",
       "      <td>11</td>\n",
       "      <td>-124.722</td>\n",
       "      <td>49.710</td>\n",
       "      <td>6/14/2012</td>\n",
       "      <td>2012-06-14 07:00:00</td>\n",
       "      <td>Night</td>\n",
       "      <td>SCOR VNH</td>\n",
       "      <td>236</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IOS2012005002201</td>\n",
       "      <td>Northern Strait of Georgia</td>\n",
       "      <td>11</td>\n",
       "      <td>-124.722</td>\n",
       "      <td>49.710</td>\n",
       "      <td>6/14/2012</td>\n",
       "      <td>2012-06-14 07:05:00</td>\n",
       "      <td>Night</td>\n",
       "      <td>SCOR VNH</td>\n",
       "      <td>236</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IOS2012005002901</td>\n",
       "      <td>Northern Strait of Georgia</td>\n",
       "      <td>CPF2</td>\n",
       "      <td>-124.499</td>\n",
       "      <td>49.466</td>\n",
       "      <td>6/15/2012</td>\n",
       "      <td>2012-06-15 10:00:00</td>\n",
       "      <td>Night</td>\n",
       "      <td>SCOR VNH</td>\n",
       "      <td>236</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>SOO2015095000101</td>\n",
       "      <td>Tidal Mixed</td>\n",
       "      <td>CLO-42</td>\n",
       "      <td>-123.345</td>\n",
       "      <td>48.394</td>\n",
       "      <td>4/27/2015</td>\n",
       "      <td>2015-04-27 17:37:00</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>SCOR VNH</td>\n",
       "      <td>236</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>SOO2015095000401</td>\n",
       "      <td>Tidal Mixed</td>\n",
       "      <td>CB01</td>\n",
       "      <td>-123.318</td>\n",
       "      <td>48.344</td>\n",
       "      <td>4/30/2015</td>\n",
       "      <td>2015-04-30 19:00:00</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>SCOR VNH</td>\n",
       "      <td>236</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651</th>\n",
       "      <td>SOO2015095000501</td>\n",
       "      <td>Tidal Mixed</td>\n",
       "      <td>CLO-41</td>\n",
       "      <td>-123.345</td>\n",
       "      <td>48.395</td>\n",
       "      <td>7/9/2015</td>\n",
       "      <td>2015-07-09 19:25:00</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>SCOR VNH</td>\n",
       "      <td>236</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>SOO2015095000701</td>\n",
       "      <td>Tidal Mixed</td>\n",
       "      <td>CB01</td>\n",
       "      <td>-123.318</td>\n",
       "      <td>48.344</td>\n",
       "      <td>7/27/2015</td>\n",
       "      <td>2015-07-27 19:06:00</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>SCOR VNH</td>\n",
       "      <td>236</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>SOO2015095000801</td>\n",
       "      <td>Tidal Mixed</td>\n",
       "      <td>MAC41</td>\n",
       "      <td>-123.409</td>\n",
       "      <td>48.402</td>\n",
       "      <td>7/27/2015</td>\n",
       "      <td>2015-07-27 20:00:00</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>SCOR VNH</td>\n",
       "      <td>236</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>654 rows × 250 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Key                 region_name Station      lon     lat  \\\n",
       "0    IOS2012005000901  Northern Strait of Georgia      22 -124.272  49.670   \n",
       "1    IOS2012005001001  Northern Strait of Georgia      22 -124.272  49.670   \n",
       "2    IOS2012005002101  Northern Strait of Georgia      11 -124.722  49.710   \n",
       "3    IOS2012005002201  Northern Strait of Georgia      11 -124.722  49.710   \n",
       "4    IOS2012005002901  Northern Strait of Georgia    CPF2 -124.499  49.466   \n",
       "..                ...                         ...     ...      ...     ...   \n",
       "649  SOO2015095000101                 Tidal Mixed  CLO-42 -123.345  48.394   \n",
       "650  SOO2015095000401                 Tidal Mixed    CB01 -123.318  48.344   \n",
       "651  SOO2015095000501                 Tidal Mixed  CLO-41 -123.345  48.395   \n",
       "652  SOO2015095000701                 Tidal Mixed    CB01 -123.318  48.344   \n",
       "653  SOO2015095000801                 Tidal Mixed   MAC41 -123.409  48.402   \n",
       "\n",
       "          Date               dtUTC  Twilight  Net_Type  Mesh_Size(um)  ...  \\\n",
       "0    6/14/2012 2012-06-14 14:32:00  Daylight  SCOR VNH            236  ...   \n",
       "1    6/14/2012 2012-06-14 14:52:00  Daylight  SCOR VNH            236  ...   \n",
       "2    6/14/2012 2012-06-14 07:00:00     Night  SCOR VNH            236  ...   \n",
       "3    6/14/2012 2012-06-14 07:05:00     Night  SCOR VNH            236  ...   \n",
       "4    6/15/2012 2012-06-15 10:00:00     Night  SCOR VNH            236  ...   \n",
       "..         ...                 ...       ...       ...            ...  ...   \n",
       "649  4/27/2015 2015-04-27 17:37:00  Daylight  SCOR VNH            236  ...   \n",
       "650  4/30/2015 2015-04-30 19:00:00  Daylight  SCOR VNH            236  ...   \n",
       "651   7/9/2015 2015-07-09 19:25:00  Daylight  SCOR VNH            236  ...   \n",
       "652  7/27/2015 2015-07-27 19:06:00  Daylight  SCOR VNH            236  ...   \n",
       "653  7/27/2015 2015-07-27 20:00:00  Daylight  SCOR VNH            236  ...   \n",
       "\n",
       "     Thysanoessa raschii zoea s1  Thysanoessa spinifera  F  \\\n",
       "0                            0.0                       0.0   \n",
       "1                            0.0                       0.0   \n",
       "2                            0.0                       0.0   \n",
       "3                            0.0                       0.0   \n",
       "4                            0.0                       0.0   \n",
       "..                           ...                       ...   \n",
       "649                          0.0                       0.0   \n",
       "650                          0.0                       0.0   \n",
       "651                          0.0                       0.0   \n",
       "652                          0.0                       0.0   \n",
       "653                          0.0                       0.0   \n",
       "\n",
       "     Thysanoessa spinifera  M  Thysanoessa spinifera  s2  \\\n",
       "0                         0.0                        0.0   \n",
       "1                         0.0                        0.0   \n",
       "2                         0.0                        0.0   \n",
       "3                         0.0                        0.0   \n",
       "4                         0.0                        0.0   \n",
       "..                        ...                        ...   \n",
       "649                       0.0                        0.0   \n",
       "650                       0.0                        0.0   \n",
       "651                       0.0                        0.0   \n",
       "652                       0.0                        0.0   \n",
       "653                       0.0                        0.0   \n",
       "\n",
       "     Thysanoessa spinifera  s3  Thysanoessa spinifera eggs s1  \\\n",
       "0                          0.0                            0.0   \n",
       "1                          0.0                            0.0   \n",
       "2                          0.0                            0.0   \n",
       "3                          0.0                            0.0   \n",
       "4                          0.0                            0.0   \n",
       "..                         ...                            ...   \n",
       "649                        0.0                            0.0   \n",
       "650                        0.0                            0.0   \n",
       "651                        0.0                            0.0   \n",
       "652                        0.0                            0.0   \n",
       "653                        0.0                            0.0   \n",
       "\n",
       "     Thysanoessa spinifera nauplii s1  Thysanoessa spinifera zoea s1  \\\n",
       "0                                 0.0                            0.0   \n",
       "1                                 0.0                            0.0   \n",
       "2                                 0.0                            0.0   \n",
       "3                                 0.0                            0.0   \n",
       "4                                 0.0                            0.0   \n",
       "..                                ...                            ...   \n",
       "649                               0.0                            0.0   \n",
       "650                               0.0                            0.0   \n",
       "651                               0.0                            0.0   \n",
       "652                               0.0                            0.0   \n",
       "653                               0.0                            0.0   \n",
       "\n",
       "     Xanthidae *sp. megalops s1  Xanthidae *sp. zoea s1  \n",
       "0                           0.0                     0.0  \n",
       "1                           0.0                     0.0  \n",
       "2                           0.0                     0.0  \n",
       "3                           0.0                     0.0  \n",
       "4                           0.0                     0.0  \n",
       "..                          ...                     ...  \n",
       "649                         0.0                     0.0  \n",
       "650                         0.0                     0.0  \n",
       "651                         0.0                     0.0  \n",
       "652                         0.0                     0.0  \n",
       "653                         0.0                     0.0  \n",
       "\n",
       "[654 rows x 250 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biomassDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['region_name', 'Station', 'Lon', 'Lat', 'Date', 'dtUTC', 'Twilight',\n",
       "       'Net_Type', 'Mesh_Size(um)', 'Z_lower', 'Z_upper', 'Bottom Depth(m)',\n",
       "       'Anomura *sp. megalops s1', 'Anomura *sp. zoea s1',\n",
       "       'Anomura *sp. zoea s2', 'Axiidae *sp.  s1', 'Axiidae *sp. mysis s2',\n",
       "       'Axiidae *sp. zoea s1', 'Axiidae *sp. zoea s2',\n",
       "       'Brachyura *sp. zoea s1', 'Calanoida *sp.  1', 'Calanoida *sp.  2',\n",
       "       'Calanoida *sp.  3', 'Calanoida *sp.  4', 'Calanoida *sp.  6M',\n",
       "       'Calanus *sp.  1', 'Calanus *sp.  2', 'Calanus *sp.  3',\n",
       "       'Calanus *sp.  4', 'Calanus *sp. nauplii s1', 'Calanus marshallae  2',\n",
       "       'Calanus marshallae  3', 'Calanus marshallae  4',\n",
       "       'Calanus marshallae  5', 'Calanus marshallae  6F',\n",
       "       'Calanus marshallae  6M', 'Calanus pacificus  2',\n",
       "       'Calanus pacificus  3', 'Calanus pacificus  4', 'Calanus pacificus  5',\n",
       "       'Calanus pacificus  6F', 'Calanus pacificus  6M', 'Calliopius *sp.  s1',\n",
       "       'Calliopius *sp.  s2', 'Calliopius *sp.  s3',\n",
       "       'Calliopius pacificus  s1', 'Calliopius pacificus  s2',\n",
       "       'Calliopius pacificus  s3', 'Cancer *sp. megalops s1',\n",
       "       'Cancer *sp. zoea s1', 'Cancer *sp. zoea s2',\n",
       "       'Cancer magister megalops s2', 'Cancer magister zoea s1',\n",
       "       'Cancer magister zoea s2', 'Cancer oregonensis megalops s1',\n",
       "       'Cancer oregonensis megalops s2', 'Cancer productus megalops s1',\n",
       "       'Cancer productus megalops s2', 'Cancer productus zoea s1',\n",
       "       'Cancer productus zoea s2', 'Caridea *sp.  s3', 'Caridea *sp. mysis s1',\n",
       "       'Caridea *sp. mysis s2', 'Caridea *sp. mysis s3',\n",
       "       'Caridea *sp. zoea s1', 'Caridea *sp. zoea s2', 'Chaetognatha *sp.  s3',\n",
       "       'Chaetognatha *sp. juvenile s1', 'Chaetognatha *sp. juvenile s2',\n",
       "       'Chionoecetes *sp. megalops s1', 'Chionoecetes *sp. megalops s2',\n",
       "       'Chionoecetes *sp. zoea s1', 'Corycaeus anglicus  3',\n",
       "       'Corycaeus anglicus  4', 'Corycaeus anglicus  5',\n",
       "       'Corycaeus anglicus  6F', 'Corycaeus anglicus  6M',\n",
       "       'Crangonidae *sp. mysis s1', 'Crangonidae *sp. mysis s2',\n",
       "       'Crangonidae *sp. mysis s3', 'Crangonidae *sp. zoea s1',\n",
       "       'Crangonidae *sp. zoea s2', 'Cyphocaris *sp.  s1',\n",
       "       'Cyphocaris challengeri  s1', 'Cyphocaris challengeri  s2',\n",
       "       'Cyphocaris challengeri  s3', 'Decapoda *sp. zoea s1',\n",
       "       'Eucalanus *sp.  1', 'Eucalanus *sp.  2', 'Eucalanus *sp.  3',\n",
       "       'Eucalanus *sp. nauplii s1', 'Eucalanus bungii  3',\n",
       "       'Eucalanus bungii  4', 'Eucalanus bungii  4F', 'Eucalanus bungii  4M',\n",
       "       'Eucalanus bungii  5F', 'Eucalanus bungii  5M', 'Eucalanus bungii  6F',\n",
       "       'Eucalanus bungii  6M'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biomassDF.keys()[1:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define log transform function\n",
    "def log(x):\n",
    "  return np.log10(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert Abundances to new Biomass values using Puget Sound LW Regressions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Copepod Conversions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Neocalanus conversion C (ug):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomassDF['Neocalanus plumchrus  1']=(biomassDF['Neocalanus plumchrus  1']*(4.13*(0.9**3.28))*0.45)/1000\n",
    "biomassDF['Neocalanus plumchrus  2']=(biomassDF['Neocalanus plumchrus  2']*(4.13*(1.1**3.28))*0.45)/1000\n",
    "biomassDF['Neocalanus plumchrus  3']=(biomassDF['Neocalanus plumchrus  3']*(4.13*(1.75**3.28))*0.45)/1000\n",
    "biomassDF['Neocalanus plumchrus  4']=(biomassDF['Neocalanus plumchrus  4']*(4.13*(2.6**3.28))*0.45)/1000\n",
    "biomassDF['Neocalanus plumchrus  5']=(biomassDF['Neocalanus plumchrus  5']*(2.00*(3.8**3.92))*0.5)/1000\n",
    "biomassDF['Neocalanus plumchrus  6F']=(biomassDF['Neocalanus plumchrus  6F']*(2.00*(4.5**3.92))*0.5)/1000\n",
    "biomassDF['Neocalanus plumchrus  6M']=(biomassDF['Neocalanus plumchrus  6M']*(2.00*(4.5**3.92))*0.5)/1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calanus Conversions C (ug):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#check these lengths/regression equations\n",
    "biomassDF['Calanoida *sp.  1']=(biomassDF['Calanoida *sp.  1']*(4.13*(0.5**3.28))*0.45)/1000\n",
    "biomassDF['Calanoida *sp.  2']=(biomassDF['Calanoida *sp.  2']*(4.13*(0.8**3.28))*0.45)/1000\n",
    "biomassDF['Calanoida *sp.  3']=(biomassDF['Calanoida *sp.  3']*(4.13*(1.2**3.28))*0.45)/1000\n",
    "biomassDF['Calanoida *sp.  4']=(biomassDF['Calanoida *sp.  4']*(4.13*(1.4**3.28))*0.45)/1000\n",
    "biomassDF['Calanoida *sp.  6M']=(biomassDF['Calanoida *sp.  6M']*(2.00*(2.1**3.92))*0.45)/1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#check these lengths/regression equations\n",
    "biomassDF['Calanus *sp.  1']=(biomassDF['Calanus *sp.  1']*(4.13*(0.5**3.28))*0.45)/1000\n",
    "biomassDF['Calanus *sp.  2']=(biomassDF['Calanus *sp.  2']*(4.13*(0.8**3.28))*0.45)/1000\n",
    "biomassDF['Calanus *sp.  3']=(biomassDF['Calanus *sp.  3']*(4.13*(1.2**3.28))*0.45)/1000\n",
    "biomassDF['Calanus *sp.  4']=(biomassDF['Calanus *sp.  4']*(4.13*(1.4**3.28))*0.45)/1000\n",
    "biomassDF['Calanus *sp. nauplii s1']=(biomassDF['Calanus *sp. nauplii s1']*(4.13*(0.4**3.28))*0.45)/1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomassDF['Calanus pacificus  2']=(biomassDF['Calanus pacificus  2']*(4.13*(0.8**3.28))*0.45)/1000\n",
    "biomassDF['Calanus pacificus  3']=(biomassDF['Calanus pacificus  3']*(4.13*(1.1**3.28))*0.45)/1000\n",
    "biomassDF['Calanus pacificus  4']=(biomassDF['Calanus pacificus  4']*(4.13*(1.4**3.28))*0.45)/1000\n",
    "biomassDF['Calanus pacificus  5']=(biomassDF['Calanus pacificus  5']*(2.00*(1.8**3.92))*0.45)/1000\n",
    "biomassDF['Calanus pacificus  6F']=(biomassDF['Calanus pacificus  6F']*(2.00*(2.2**3.92))*0.45)/1000\n",
    "biomassDF['Calanus pacificus  6M']=(biomassDF['Calanus pacificus  6M']*(2.00*(2.1**3.92))*0.45)/1000\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomassDF['Calanus marshallae  2']=(biomassDF['Calanus marshallae  2']*(4.13*(0.8**3.28))*0.45)/1000\n",
    "biomassDF['Calanus marshallae  3']=(biomassDF['Calanus marshallae  3']*(4.13*(1.6**3.28))*0.45)/1000\n",
    "biomassDF['Calanus marshallae  4']=(biomassDF['Calanus marshallae  4']*(4.13*(2.0**3.28))*0.45)/1000\n",
    "biomassDF['Calanus marshallae  5']=(biomassDF['Calanus marshallae  5']*(2.00*(2.5**3.92))*0.45)/1000\n",
    "biomassDF['Calanus marshallae  6F']=(biomassDF['Calanus marshallae  6F']*(2.00*(2.8**3.92))*0.45)/1000\n",
    "biomassDF['Calanus marshallae  6M']=(biomassDF['Calanus marshallae  6M']*(2.00*(2.6**3.92))*0.45)/1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Metridia conversions C (ug):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#need lengths for these groups. Should they be included?\n",
    "'Metridia *sp.  2', 'Metridia *sp.  3' ,'Metridia *sp.  4',\n",
    " 'Metridia *sp.  5', 'Metridia *sp.  6F', 'Metridia *sp.  6M',\n",
    " 'Metridia pseudopacifica  5', 'Metridia pseudopacifica  6F',\n",
    " 'Metridia pseudopacifica  6M'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomassDF['Metridia *sp.  2']=(biomassDF['Metridia *sp.  2']*(4.13*(0.4**3.28))*0.45)/1000\n",
    "biomassDF['Metridia *sp.  3']=(biomassDF['Metridia *sp.  3']*(4.13*(0.7**3.28))*0.45)/1000\n",
    "biomassDF['Metridia *sp.  4']=(biomassDF['Metridia *sp.  4']*(4.13*(1.1**3.28))*0.45)/1000\n",
    "biomassDF['Metridia *sp.  5']=(biomassDF['Metridia *sp.  5']*(4.13*(1.3**3.28))*0.45)/1000\n",
    "biomassDF['Metridia *sp.  6F']=(biomassDF['Metridia *sp.  6F']*(4.13*(2.0**3.28))*0.45)/1000\n",
    "biomassDF['Metridia *sp.  6M']=(biomassDF['Metridia *sp.  6M']*(4.13*(1.4**3.28))*0.45)/1000\n",
    "biomassDF['Metridia pacifica  3']=(biomassDF['Metridia pacifica  3']*(4.13*(0.7**3.28))*0.45)/1000\n",
    "biomassDF['Metridia pacifica  4']=(biomassDF['Metridia pacifica  4']*(4.13*(1.1**3.28))*0.45)/1000\n",
    "biomassDF['Metridia pacifica  5F']=(biomassDF['Metridia pacifica  5F']*(4.13*(1.5**3.28))*0.45)/1000\n",
    "#biomassDF['Metridia pacifica  5M']=biomassDF['Metridia pacifica  5M']*(4.13*(1.3**3.28))*0.45\n",
    "#biomassDF['Metridia pacifica  6F']=biomassDF['Metridia pacifica  6F']*(4.13*(2.0**3.28))*0.45\n",
    "#biomassDF['Metridia pacifica  6M']=biomassDF['Metridia pacifica  6M']*(4.13*(1.4**3.28))*0.45"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00260935, 0.02090429, 0.0086791 , 0.01314684, 0.00035168,\n",
       "       0.01081596, 0.00156882, 0.01201997, 0.00216238, 0.02722169,\n",
       "       0.00376946, 0.0137952 , 0.0034529 , 0.00916302, 0.00267991,\n",
       "       0.01073472, 0.00145261, 0.00274524, 0.00100394])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biomassDF['Metridia *sp.  2'].unique()[1:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Eucalanus conversions C (ug):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#need lengths for these groups if I will use them. Not including them for now.\n",
    "'Eucalanus *sp.  1',\n",
    " 'Eucalanus *sp.  2' ,'Eucalanus *sp.  3' ,'Eucalanus *sp. nauplii s1',\n",
    " 'Eucalanus californicus  5F', 'Eucalanus californicus  6F'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomassDF['Eucalanus bungii  3']=(biomassDF['Eucalanus bungii  3']*(4.13*(2.6**3.28))*0.45)/1000\n",
    "biomassDF['Eucalanus bungii  4']=(biomassDF['Eucalanus bungii  4']*(4.13*(3.2**3.28))*0.45)/1000\n",
    "biomassDF['Eucalanus bungii  4F']=(biomassDF['Eucalanus bungii  4F']*(4.13*(3.2**3.28))*0.45)/1000\n",
    "biomassDF['Eucalanus bungii  4M']=(biomassDF['Eucalanus bungii  4M']*(4.13*(3.2**3.28))*0.45)/1000\n",
    "biomassDF['Eucalanus bungii  5F']=(biomassDF['Eucalanus bungii  5F']*(4.13*(4.6**3.28))*0.45)/1000\n",
    "biomassDF['Eucalanus bungii  5M']=(biomassDF['Eucalanus bungii  5M']*(4.13*(4.4**3.28))*0.45)/1000\n",
    "biomassDF['Eucalanus bungii  6F']=(biomassDF['Eucalanus bungii  6F']*(4.13*(6.2**3.28))*0.45)/1000\n",
    "biomassDF['Eucalanus bungii  6M']=(biomassDF['Eucalanus bungii  6M']*(4.13*(6.1**3.28))*0.45)/1000\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.03194719, 0.20475132, 0.05426117, 0.0515078 , 0.        ,\n",
       "       0.02195154, 0.02452752, 0.02647487, 0.56557544, 0.08391082,\n",
       "       0.1501332 , 0.13528053, 0.04986061, 0.00547231, 0.02444432,\n",
       "       0.00898015, 0.03032253, 0.00576887, 0.01201845])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biomassDF['Calanus pacificus  5'].unique()[1:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Euphausiid Conversions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Euphausia pacifica"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Include these groups too?\n",
    " 'Euphausiacea *sp.  s2',\n",
    " \n",
    " 'Thysanoessa *sp.  s1',\n",
    " 'Thysanoessa *sp.  s2',\n",
    " 'Thysanoessa *sp. zoea s1', \n",
    " \n",
    " 'Thysanoessa longipes  F',\n",
    " 'Thysanoessa longipes  M', 'Thysanoessa longipes  s2',\n",
    " 'Thysanoessa longipes  s3' ,'Thysanoessa longipes zoea s1',\n",
    " \n",
    " 'Thysanoessa raschii  F', 'Thysanoessa raschii  M',\n",
    " 'Thysanoessa raschii  s2' ,'Thysanoessa raschii zoea s1',\n",
    " \n",
    " 'Thysanoessa spinifera  F', 'Thysanoessa spinifera  M',\n",
    " 'Thysanoessa spinifera  s2', 'Thysanoessa spinifera  s3',\n",
    " 'Thysanoessa spinifera zoea s1'\n",
    " \n",
    " ##### multiply by 3 to adjust for nighttime abundance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Double check these regressions for the calyptopis and furcilia with BethElLee(4.13*[Length (mm)^3.28)*46.9%\n",
    "biomassDF['Euphausiidae *sp. protozoea (or calyptopis) s1']=(biomassDF['Euphausiidae *sp. protozoea (or calyptopis) s1']*(4.13*(1.3**3.28)*.469))/1000\n",
    "biomassDF['Euphausiidae *sp. zoea (or furcilia) s1']=(biomassDF['Euphausiidae *sp. zoea (or furcilia) s1']*(4.13*(3.0**3.28)*.469))/1000\n",
    "\n",
    "biomassDF['Euphausia pacifica zoea s1']=(biomassDF['Euphausia pacifica zoea s1']*(4.13*(4.7**3.28)*.469))/1000\n",
    "biomassDF['Euphausia pacifica  s2']=(biomassDF['Euphausia pacifica  s2']*(4.13*(8.0**3.28)*.469))/1000\n",
    "biomassDF['Euphausia pacifica  s3']=(biomassDF['Euphausia pacifica  s3']*(4.13*(12.0**3.28)*.469))/1000\n",
    "biomassDF['Euphausia pacifica  F']=(biomassDF['Euphausia pacifica  F']*(4.13*(18.1**3.28)*.469))/1000\n",
    "biomassDF['Euphausia pacifica  M']=(biomassDF['Euphausia pacifica  M']*(4.13*(15.1**3.28)*.469))/1000\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Amphipod Conversions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Decide later if we include Calliopius (not in Puget Sound?) \n",
    " 'Calliopius *sp.  s1' ,'Calliopius *sp.  s2', 'Calliopius *sp.  s3',\n",
    " 'Calliopius pacificus  s1' ,'Calliopius pacificus  s2',\n",
    " 'Calliopius pacificus  s3',\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Hyperiids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomassDF['Themisto *sp.  s1']=biomassDF['Themisto *sp.  s1']*(0.0049*(3.5**2.957)*1000)*0.37/1000\n",
    "biomassDF['Themisto pacifica juvenile s1']=biomassDF['Themisto pacifica juvenile s1']*(0.0049*(4.3**2.957)*1000)*0.37/1000\n",
    "biomassDF['Themisto pacifica  s2']=biomassDF['Themisto pacifica  s2']*(0.0049*(6.2**2.957)*1000)*0.37/1000\n",
    "biomassDF['Themisto pacifica  F']=biomassDF['Themisto pacifica  F']*(0.0049*(7.5**2.957)*1000)*0.37/1000\n",
    "biomassDF['Themisto pacifica  M']=biomassDF['Themisto pacifica  M']*(0.0049*(5.9**2.957)*1000)*0.37/1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomassDF['Hyperia medusarum  s1']=biomassDF['Hyperia medusarum  s1']*(0.0049*(3.0**2.957)*1000)*0.37/1000\n",
    "biomassDF['Hyperia medusarum  s2']=biomassDF['Hyperia medusarum  s2']*(0.0049*(6.0**2.957)*1000)*0.37/1000\n",
    "biomassDF['Hyperia medusarum  F']=biomassDF['Hyperia medusarum  F']*(0.0049*(10.8**2.957)*1000)*0.37/1000\n",
    "biomassDF['Hyperia medusarum  M']=biomassDF['Hyperia medusarum  M']*(0.0049*(10.4**2.957)*1000)*0.37/1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomassDF['Primno *sp.  s1']=biomassDF['Primno *sp.  s1']*(0.0049*(3.0**2.957)*1000)*0.37/1000\n",
    "biomassDF['Primno brevidens  s1']=biomassDF['Primno brevidens  s1']*(0.0049*(3.0**2.957)*1000)*0.37/1000\n",
    "biomassDF['Primno abyssalis  s1']=biomassDF['Primno abyssalis  s1']*(0.0049*(3.0**2.957)*1000)*0.37/1000\n",
    "biomassDF['Primno abyssalis  s2']=biomassDF['Primno abyssalis  s2']*(0.0049*(7.5**2.957)*1000)*0.37/1000\n",
    "biomassDF['Primno abyssalis  F']=biomassDF['Primno abyssalis  F']*(0.0049*(12.3**2.957)*1000)*0.37/1000\n",
    "biomassDF['Primno abyssalis  M']=biomassDF['Primno abyssalis  M']*(0.0049*(11.8**2.957)*1000)*0.37/1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Gammariids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomassDF['Gammaridea *sp.  s1']=biomassDF['Gammaridea *sp.  s1']*(0.0049*(4.0**2.957)*1000)*0.37/1000\n",
    "biomassDF['Gammaridea *sp.  s2']=biomassDF['Gammaridea *sp.  s2']*(0.0049*(6.5**2.957)*1000)*0.37/1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Cyphocariids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomassDF['Cyphocaris *sp.  s1']=biomassDF['Cyphocaris *sp.  s1']*((0.02 * (3.3)**2.1)*1000)*0.37/1000\n",
    "biomassDF['Cyphocaris challengeri  s1']=biomassDF['Cyphocaris challengeri  s1']*((0.02 * (3.2)**2.1)*1000)*0.37/1000\n",
    "biomassDF['Cyphocaris challengeri  s2']=biomassDF['Cyphocaris challengeri  s2']*((0.02 * (8.6)**2.1)*1000)*0.37/1000\n",
    "biomassDF['Cyphocaris challengeri  s3']=biomassDF['Cyphocaris challengeri  s3']*((0.02 * (11.3)**2.1)*1000)*0.37/1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chaetognath Conversions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomassDF['Chaetognatha *sp. juvenile s1']=biomassDF['Chaetognatha *sp. juvenile s1']*0.0956*(3.5**2.9093)/1000\n",
    "biomassDF['Chaetognatha *sp. juvenile s2']=biomassDF['Chaetognatha *sp. juvenile s2']*0.0956*(6.0**2.9093)/1000\n",
    "biomassDF['Chaetognatha *sp.  s3']=biomassDF['Chaetognatha *sp.  s3']*0.0956*(15.0**2.9093)/1000\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomassDF['Parasagitta elegans juvenile s1']=biomassDF['Parasagitta elegans juvenile s1']*0.0956*(3.5**2.9093)/1000\n",
    "biomassDF['Parasagitta elegans  s2']=biomassDF['Parasagitta elegans  s2']*0.0956*(7.5**2.9093)/1000\n",
    "biomassDF['Parasagitta elegans  s3']=biomassDF['Parasagitta elegans  s3']*0.0956*(20.0**2.9093)/1000\n",
    "biomassDF['Parasagitta euneritica  s2']=biomassDF['Parasagitta euneritica  s2']*0.0956*(7.5**2.9093)/1000\n",
    "biomassDF['Parasagitta euneritica  s3']=biomassDF['Parasagitta euneritica  s3']*0.0956*(20.0**2.9093)/1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gastropod - Limacina Conversions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomassDF['Limacina helicina  s0']=biomassDF['Limacina helicina  s0']*(2.6*(1.0**2.659))*0.22/1000\n",
    "biomassDF['Limacina helicina  s1']=biomassDF['Limacina helicina  s1']*(2.6*(2.0**2.659))*0.22/1000                                                                       \n",
    "biomassDF['Limacina helicina  s2']=biomassDF['Limacina helicina  s2']*(2.6*(6.0**2.659))*0.22/1000    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Larvaceans - Oikopleura Conversions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Need length for this category: 'Oikopleura *sp.  s2',\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomassDF['Oikopleura *sp.  s1']=biomassDF['Oikopleura *sp.  s1']*((38.8*(1.3**2.574))*0.46)/1000\n",
    "biomassDF['Oikopleura dioica  s1']=biomassDF['Oikopleura dioica  s1']*((38.8*(4.6**2.574))*0.46)/1000\n",
    "biomassDF['Oikopleura dioica  s2']=biomassDF['Oikopleura dioica  s2']*((38.8*(8.5**2.574))*0.46)/1000\n",
    "biomassDF['Oikopleura labradoriensis  s1']=biomassDF['Oikopleura labradoriensis  s1']*((38.8*(4.6**2.574))*0.46)/1000\n",
    "biomassDF['Oikopleura labradoriensis  s2']=biomassDF['Oikopleura labradoriensis  s2']*((38.8*(8.5**2.574))*0.46)/1000\n",
    "biomassDF['Oikopleura labradoriensis  s3']=biomassDF['Oikopleura labradoriensis  s3']*((38.8*(18.5**2.574))*0.46)/1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import model data for comparison with observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "import netCDF4 as nc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "ftemp=nc.Dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/mesh_mask201702.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['nav_lon', 'nav_lat', 'time_counter', 'tmask', 'umask', 'vmask', 'fmask', 'tmaskutil', 'umaskutil', 'vmaskutil', 'fmaskutil', 'glamt', 'glamu', 'glamv', 'glamf', 'gphit', 'gphiu', 'gphiv', 'gphif', 'e1t', 'e1u', 'e1v', 'e1f', 'e2t', 'e2u', 'e2v', 'e2f', 'ff', 'mbathy', 'misf', 'isfdraft', 'e3t_0', 'e3u_0', 'e3v_0', 'e3w_0', 'gdept_0', 'gdepu', 'gdepv', 'gdepw_0', 'gdept_1d', 'gdepw_1d', 'e3t_1d', 'e3w_1d'])"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ftemp.variables.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'netCDF4._netCDF4.Variable'>\n",
       "float64 e3t_0(t, z, y, x)\n",
       "    _FillValue: nan\n",
       "    standard_name: e3t_0\n",
       "    long_name: grid spacing on T-grid in w direction\n",
       "    units: m\n",
       "unlimited dimensions: t\n",
       "current shape = (1, 40, 898, 398)\n",
       "filling on"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ftemp.variables['e3t_0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(data=[[ 1.00000115,  1.00000501,  1.00001253,  1.00002718,\n",
       "                     1.0000557 ,  1.00011125,  1.00021946,  1.0004302 ,\n",
       "                     1.00084067,  1.00164012,  1.0031971 ,  1.00622914,\n",
       "                     1.01213271,  1.02362358,  1.04597551,  1.08940061,\n",
       "                     1.17356428,  1.33592899,  1.64636781,  2.22990285,\n",
       "                     3.29248567,  5.11998508,  7.97451506, 11.8252972 ,\n",
       "                    16.10792044, 19.95870258, 22.81323256, 24.64073198,\n",
       "                    25.70331479, 26.28684983, 26.59728865, 26.75965336,\n",
       "                    26.84381704, 26.88724213, 26.90959407, 26.92108493,\n",
       "                    26.9269885 , 26.93002054, 26.93157752, 26.93237697]],\n",
       "             mask=False,\n",
       "       fill_value=1e+20)"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ftemp.variables['e3t_1d'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "fdict={'ptrc_T':1,'grid_T':1}\n",
    "start_date = dt.datetime(2014,1,1)\n",
    "end_date = dt.datetime(2016,12,31)\n",
    "flen=1 # number of days per model output file. always 1 for 201905 and 201812 model runs\n",
    "namfmt='nowcast' # for 201905 and 201812 model runs, this should always be 'nowcast'\n",
    "# filemap is dictionary of the form variableName: fileType, where variableName is the name\n",
    "# of the variable you want to extract and fileType designates the type of \n",
    "# model output file it can be found in (usually ptrc_T for biology, grid_T for temperature and \n",
    "# salinity)\n",
    "filemap={'microzooplankton':'ptrc_T','mesozooplankton':'ptrc_T'}\n",
    "# fdict is a dictionary mappy file type to its time resolution. Here, 1 means hourly output\n",
    "# (1h file) and 24 means daily output (1d file). In certain runs, multiple time resolutions \n",
    "# are available\n",
    "fdict={'ptrc_T':1,'grid_T':1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH= '/results2/SalishSea/nowcast-green.201905/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomassDF.rename(columns={'lon':'Lon','lat':'Lat'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Key', 'region_name', 'Station', 'Lon', 'Lat', 'Date', 'dtUTC',\n",
       "       'Twilight', 'Net_Type', 'Mesh_Size(um)',\n",
       "       ...\n",
       "       'OtherCalanoids', 'Calanoids', 'CalanoidsDiaRemoved', 'Euphausiids',\n",
       "       'Themisto', 'Hyperia', 'Primno', 'Hyperiids', 'Gammariids', 'Total'],\n",
       "      dtype='object', length=272)"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biomassDF.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomassDF.rename(columns={'DEPTH_STRT1':'Z_lower','DEPTH_END1':'Z_upper'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomassDF['Year']=[ii.year for ii in biomassDF['dtUTC']]\n",
    "biomassDF['Month']=[ii.month for ii in biomassDF['dtUTC']]\n",
    "biomassDF['YD']=et.datetimeToYD(biomassDF['dtUTC'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Need to convert biomass from mg C to N using a known C:N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "#biomassDF['Amphipoda']=biomassDF['Amphipoda']*0.45/8.5\n",
    "\n",
    "#biomassDF['Euphausiacea']=biomassDF['Euphausiacea']*0.45/8.5\n",
    "#biomassDF['Calanoida']=biomassDF['Calanoida']*0.45/8.5\n",
    "\n",
    "\n",
    "\n",
    "#biomassDF['Cyclopoida']=biomassDF['Cyclopoida']*0.45/8.5\n",
    "#biomassDF['Poecilostomatoida']=biomassDF['Poecilostomatoida']*0.45/8.5\n",
    "#biomassDF['Copelata']=biomassDF['Copelata']*0.45/8.5\n",
    "#biomassDF['Harpacticoida']=biomassDF['Harpacticoida']*0.45/8.5\n",
    "#biomassDF['Decapoda']=biomassDF['Decapoda']*0.45/8.5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomassDF['Neocalanus']=(biomassDF['Neocalanus plumchrus  1']+biomassDF['Neocalanus plumchrus  2']+biomassDF['Neocalanus plumchrus  3']+biomassDF['Neocalanus plumchrus  4']+biomassDF['Neocalanus plumchrus  5']+biomassDF['Neocalanus plumchrus  6F']+biomassDF['Neocalanus plumchrus  6M'])\n",
    "biomassDF['NeocalanusDiaRemoved']=(biomassDF['Neocalanus plumchrus  1']+biomassDF['Neocalanus plumchrus  2']+biomassDF['Neocalanus plumchrus  3']+biomassDF['Neocalanus plumchrus  4'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.16228376e+00, 4.25664753e+00, 6.76832999e+00, 1.67322568e-02,\n",
       "       5.19182122e+00, 1.56416337e-02, 5.70440844e-02, 5.50394930e+00,\n",
       "       5.24904520e-02, 2.44223301e+00, 9.88737895e-02, 2.72848784e-01,\n",
       "       2.11242203e+01, 1.60857534e-02, 3.26710947e-01, 1.80099161e+00,\n",
       "       2.83673986e+01, 3.99770194e+01, 3.16960664e+01])"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biomassDF['Neocalanus'].unique()[1:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomassDF['CalPacificus']=(biomassDF['Calanus pacificus  2']+biomassDF['Calanus pacificus  3']+biomassDF['Calanus pacificus  4']+biomassDF['Calanus pacificus  5']+biomassDF['Calanus pacificus  6F']+biomassDF['Calanus pacificus  6M'])\n",
    "biomassDF['CalPacificusDiaRemoved']=(biomassDF['Calanus pacificus  2']+biomassDF['Calanus pacificus  3']+biomassDF['Calanus pacificus  4'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomassDF['CalMarsh']=(biomassDF['Calanus marshallae  2']+biomassDF['Calanus marshallae  3']+biomassDF['Calanus marshallae  4']+biomassDF['Calanus marshallae  5']+biomassDF['Calanus marshallae  6F']+biomassDF['Calanus marshallae  6M'])\n",
    "biomassDF['CalMarshDiaRemoved']=(biomassDF['Calanus marshallae  2']+biomassDF['Calanus marshallae  3']+biomassDF['Calanus marshallae  4']+biomassDF['Calanus marshallae  5'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomassDF['Metridia']=(biomassDF['Metridia *sp.  2']+biomassDF['Metridia *sp.  3']+biomassDF['Metridia *sp.  4']+biomassDF['Metridia *sp.  5']+biomassDF['Metridia *sp.  6F']+biomassDF['Metridia *sp.  6M']+biomassDF['Metridia pacifica  3']+biomassDF['Metridia pacifica  4']+biomassDF['Metridia pacifica  5F'])\n",
    "biomassDF['Eucalanus']=(biomassDF['Eucalanus bungii  3']+biomassDF['Eucalanus bungii  4']+biomassDF['Eucalanus bungii  4F']+biomassDF['Eucalanus bungii  4M']+biomassDF['Eucalanus bungii  5F']+biomassDF['Eucalanus bungii  5M']+biomassDF['Eucalanus bungii  6F']+biomassDF['Eucalanus bungii  6M'])\n",
    "biomassDF['EucalanusDiaRemoved']=(biomassDF['Eucalanus bungii  3']+biomassDF['Eucalanus bungii  4']+biomassDF['Eucalanus bungii  4F']+biomassDF['Eucalanus bungii  4M'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.05328713, 0.15333201, 0.15656956, 0.47845137, 0.04713698,\n",
       "       0.11257754, 0.0756802 , 0.11441692, 0.0672129 , 0.2762964 ,\n",
       "       0.09263692, 0.07144549, 0.01643963, 0.07175813, 0.03725966,\n",
       "       0.06370595, 0.03816424, 0.08821852, 0.04425084])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biomassDF['Metridia'].unique()[1:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomassDF['OtherCalanoids']=(biomassDF['Calanoida *sp.  1']+biomassDF['Calanoida *sp.  2']+biomassDF['Calanoida *sp.  3']+biomassDF['Calanoida *sp.  4']+biomassDF['Calanoida *sp.  6M']+biomassDF['Calanus *sp.  1']+biomassDF['Calanus *sp.  2']+biomassDF['Calanus *sp.  3']+biomassDF['Calanus *sp.  4'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomassDF['Calanoids']=biomassDF['Neocalanus']+biomassDF['CalPacificus']+biomassDF['CalMarsh']+biomassDF['Metridia']+biomassDF['Eucalanus']+biomassDF['OtherCalanoids']\n",
    "biomassDF['CalanoidsDiaRemoved']=biomassDF['NeocalanusDiaRemoved']+biomassDF['CalPacificusDiaRemoved']+biomassDF['CalMarshDiaRemoved']+biomassDF['EucalanusDiaRemoved']+biomassDF['OtherCalanoids']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomassDF['Euphausiids']=(biomassDF['Euphausiidae *sp. protozoea (or calyptopis) s1']+biomassDF['Euphausiidae *sp. zoea (or furcilia) s1']+biomassDF['Euphausia pacifica zoea s1']+biomassDF['Euphausia pacifica  s2']+biomassDF['Euphausia pacifica  s3']+biomassDF['Euphausia pacifica  F']+biomassDF['Euphausia pacifica  M'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomassDF['Themisto']=(biomassDF['Themisto *sp.  s1']+biomassDF['Themisto pacifica juvenile s1']+biomassDF['Themisto pacifica  s2']+biomassDF['Themisto pacifica  F']+biomassDF['Themisto pacifica  M'])\n",
    "biomassDF['Hyperia']=(biomassDF['Hyperia medusarum  s1']+biomassDF['Hyperia medusarum  s2']+biomassDF['Hyperia medusarum  F']+biomassDF['Hyperia medusarum  M'])\n",
    "biomassDF['Primno']=(biomassDF['Primno *sp.  s1']+biomassDF['Primno brevidens  s1']+biomassDF['Primno abyssalis  s1']+biomassDF['Primno abyssalis  s2']+biomassDF['Primno abyssalis  F']+biomassDF['Primno abyssalis  M'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomassDF['Hyperiids']=biomassDF['Themisto']+biomassDF['Hyperia']+biomassDF['Primno']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomassDF['Gammariids']=(biomassDF['Gammaridea *sp.  s1']+biomassDF['Gammaridea *sp.  s2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "biomassDF['Total']=biomassDF['Calanoids']+biomassDF['Euphausiids']+biomassDF['Hyperiids']+biomassDF['Gammariids']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.36245896, 1.87300085, 3.0219189 , 8.89761441, 0.88179773,\n",
       "       2.20092374, 1.05748946, 2.39226072, 0.14470814, 1.42228345,\n",
       "       0.81166803, 0.79332675, 0.66087864, 0.17989049, 0.37087584,\n",
       "       1.05846975, 0.57754293, 0.05944203, 0.2487363 ])"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biomassDF['Hyperiids'].unique()[1:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>Key</th>\n",
       "      <th>region_name</th>\n",
       "      <th>Station</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>Date</th>\n",
       "      <th>dtUTC</th>\n",
       "      <th>Twilight</th>\n",
       "      <th>Net_Type</th>\n",
       "      <th>Mesh_Size(um)</th>\n",
       "      <th>...</th>\n",
       "      <th>OtherCalanoids</th>\n",
       "      <th>Calanoids</th>\n",
       "      <th>CalanoidsDiaRemoved</th>\n",
       "      <th>Euphausiids</th>\n",
       "      <th>Themisto</th>\n",
       "      <th>Hyperia</th>\n",
       "      <th>Primno</th>\n",
       "      <th>Hyperiids</th>\n",
       "      <th>Gammariids</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IOS2012005000901</td>\n",
       "      <td>Northern Strait of Georgia</td>\n",
       "      <td>22</td>\n",
       "      <td>-124.272</td>\n",
       "      <td>49.670</td>\n",
       "      <td>6/14/2012</td>\n",
       "      <td>2012-06-14 14:32:00</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>SCOR VNH</td>\n",
       "      <td>236</td>\n",
       "      <td>...</td>\n",
       "      <td>0.115408</td>\n",
       "      <td>0.445351</td>\n",
       "      <td>0.237637</td>\n",
       "      <td>0.448822</td>\n",
       "      <td>0.827555</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.827555</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.721729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IOS2012005001001</td>\n",
       "      <td>Northern Strait of Georgia</td>\n",
       "      <td>22</td>\n",
       "      <td>-124.272</td>\n",
       "      <td>49.670</td>\n",
       "      <td>6/14/2012</td>\n",
       "      <td>2012-06-14 14:52:00</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>SCOR VNH</td>\n",
       "      <td>236</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015486</td>\n",
       "      <td>3.666233</td>\n",
       "      <td>0.604391</td>\n",
       "      <td>0.172039</td>\n",
       "      <td>0.278596</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083863</td>\n",
       "      <td>0.362459</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.200731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IOS2012005002101</td>\n",
       "      <td>Northern Strait of Georgia</td>\n",
       "      <td>11</td>\n",
       "      <td>-124.722</td>\n",
       "      <td>49.710</td>\n",
       "      <td>6/14/2012</td>\n",
       "      <td>2012-06-14 07:00:00</td>\n",
       "      <td>Night</td>\n",
       "      <td>SCOR VNH</td>\n",
       "      <td>236</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004346</td>\n",
       "      <td>12.477846</td>\n",
       "      <td>1.859686</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.635304</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.237697</td>\n",
       "      <td>1.873001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.350847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IOS2012005002201</td>\n",
       "      <td>Northern Strait of Georgia</td>\n",
       "      <td>11</td>\n",
       "      <td>-124.722</td>\n",
       "      <td>49.710</td>\n",
       "      <td>6/14/2012</td>\n",
       "      <td>2012-06-14 07:05:00</td>\n",
       "      <td>Night</td>\n",
       "      <td>SCOR VNH</td>\n",
       "      <td>236</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010369</td>\n",
       "      <td>14.173214</td>\n",
       "      <td>2.698241</td>\n",
       "      <td>12.270955</td>\n",
       "      <td>2.562381</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.459538</td>\n",
       "      <td>3.021919</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.466088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IOS2012005002901</td>\n",
       "      <td>Northern Strait of Georgia</td>\n",
       "      <td>CPF2</td>\n",
       "      <td>-124.499</td>\n",
       "      <td>49.466</td>\n",
       "      <td>6/15/2012</td>\n",
       "      <td>2012-06-15 10:00:00</td>\n",
       "      <td>Night</td>\n",
       "      <td>SCOR VNH</td>\n",
       "      <td>236</td>\n",
       "      <td>...</td>\n",
       "      <td>0.051333</td>\n",
       "      <td>0.747524</td>\n",
       "      <td>0.051333</td>\n",
       "      <td>39.755211</td>\n",
       "      <td>7.895820</td>\n",
       "      <td>0.197026</td>\n",
       "      <td>0.804768</td>\n",
       "      <td>8.897614</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.400349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>SOO2015095000101</td>\n",
       "      <td>Tidal Mixed</td>\n",
       "      <td>CLO-42</td>\n",
       "      <td>-123.345</td>\n",
       "      <td>48.394</td>\n",
       "      <td>4/27/2015</td>\n",
       "      <td>2015-04-27 17:37:00</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>SCOR VNH</td>\n",
       "      <td>236</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000146</td>\n",
       "      <td>0.496019</td>\n",
       "      <td>0.056654</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.304123</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.304123</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.800142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>SOO2015095000401</td>\n",
       "      <td>Tidal Mixed</td>\n",
       "      <td>CB01</td>\n",
       "      <td>-123.318</td>\n",
       "      <td>48.344</td>\n",
       "      <td>4/30/2015</td>\n",
       "      <td>2015-04-30 19:00:00</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>SCOR VNH</td>\n",
       "      <td>236</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006763</td>\n",
       "      <td>1.091657</td>\n",
       "      <td>0.180086</td>\n",
       "      <td>0.023474</td>\n",
       "      <td>1.761051</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.761051</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.876183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651</th>\n",
       "      <td>SOO2015095000501</td>\n",
       "      <td>Tidal Mixed</td>\n",
       "      <td>CLO-41</td>\n",
       "      <td>-123.345</td>\n",
       "      <td>48.395</td>\n",
       "      <td>7/9/2015</td>\n",
       "      <td>2015-07-09 19:25:00</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>SCOR VNH</td>\n",
       "      <td>236</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010097</td>\n",
       "      <td>0.141755</td>\n",
       "      <td>0.075749</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.141755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>SOO2015095000701</td>\n",
       "      <td>Tidal Mixed</td>\n",
       "      <td>CB01</td>\n",
       "      <td>-123.318</td>\n",
       "      <td>48.344</td>\n",
       "      <td>7/27/2015</td>\n",
       "      <td>2015-07-27 19:06:00</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>SCOR VNH</td>\n",
       "      <td>236</td>\n",
       "      <td>...</td>\n",
       "      <td>0.029189</td>\n",
       "      <td>0.029189</td>\n",
       "      <td>0.029189</td>\n",
       "      <td>0.003526</td>\n",
       "      <td>0.168176</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.168176</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.200890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>SOO2015095000801</td>\n",
       "      <td>Tidal Mixed</td>\n",
       "      <td>MAC41</td>\n",
       "      <td>-123.409</td>\n",
       "      <td>48.402</td>\n",
       "      <td>7/27/2015</td>\n",
       "      <td>2015-07-27 20:00:00</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>SCOR VNH</td>\n",
       "      <td>236</td>\n",
       "      <td>...</td>\n",
       "      <td>0.399796</td>\n",
       "      <td>0.893605</td>\n",
       "      <td>0.655242</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.731182</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.731182</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.624787</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>654 rows × 269 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Key                 region_name Station      lon     lat  \\\n",
       "0    IOS2012005000901  Northern Strait of Georgia      22 -124.272  49.670   \n",
       "1    IOS2012005001001  Northern Strait of Georgia      22 -124.272  49.670   \n",
       "2    IOS2012005002101  Northern Strait of Georgia      11 -124.722  49.710   \n",
       "3    IOS2012005002201  Northern Strait of Georgia      11 -124.722  49.710   \n",
       "4    IOS2012005002901  Northern Strait of Georgia    CPF2 -124.499  49.466   \n",
       "..                ...                         ...     ...      ...     ...   \n",
       "649  SOO2015095000101                 Tidal Mixed  CLO-42 -123.345  48.394   \n",
       "650  SOO2015095000401                 Tidal Mixed    CB01 -123.318  48.344   \n",
       "651  SOO2015095000501                 Tidal Mixed  CLO-41 -123.345  48.395   \n",
       "652  SOO2015095000701                 Tidal Mixed    CB01 -123.318  48.344   \n",
       "653  SOO2015095000801                 Tidal Mixed   MAC41 -123.409  48.402   \n",
       "\n",
       "          Date               dtUTC  Twilight  Net_Type  Mesh_Size(um)  ...  \\\n",
       "0    6/14/2012 2012-06-14 14:32:00  Daylight  SCOR VNH            236  ...   \n",
       "1    6/14/2012 2012-06-14 14:52:00  Daylight  SCOR VNH            236  ...   \n",
       "2    6/14/2012 2012-06-14 07:00:00     Night  SCOR VNH            236  ...   \n",
       "3    6/14/2012 2012-06-14 07:05:00     Night  SCOR VNH            236  ...   \n",
       "4    6/15/2012 2012-06-15 10:00:00     Night  SCOR VNH            236  ...   \n",
       "..         ...                 ...       ...       ...            ...  ...   \n",
       "649  4/27/2015 2015-04-27 17:37:00  Daylight  SCOR VNH            236  ...   \n",
       "650  4/30/2015 2015-04-30 19:00:00  Daylight  SCOR VNH            236  ...   \n",
       "651   7/9/2015 2015-07-09 19:25:00  Daylight  SCOR VNH            236  ...   \n",
       "652  7/27/2015 2015-07-27 19:06:00  Daylight  SCOR VNH            236  ...   \n",
       "653  7/27/2015 2015-07-27 20:00:00  Daylight  SCOR VNH            236  ...   \n",
       "\n",
       "     OtherCalanoids  Calanoids  CalanoidsDiaRemoved  Euphausiids  Themisto  \\\n",
       "0          0.115408   0.445351             0.237637     0.448822  0.827555   \n",
       "1          0.015486   3.666233             0.604391     0.172039  0.278596   \n",
       "2          0.004346  12.477846             1.859686     0.000000  1.635304   \n",
       "3          0.010369  14.173214             2.698241    12.270955  2.562381   \n",
       "4          0.051333   0.747524             0.051333    39.755211  7.895820   \n",
       "..              ...        ...                  ...          ...       ...   \n",
       "649        0.000146   0.496019             0.056654     0.000000  0.304123   \n",
       "650        0.006763   1.091657             0.180086     0.023474  1.761051   \n",
       "651        0.010097   0.141755             0.075749     0.000000  0.000000   \n",
       "652        0.029189   0.029189             0.029189     0.003526  0.168176   \n",
       "653        0.399796   0.893605             0.655242     0.000000  0.731182   \n",
       "\n",
       "      Hyperia    Primno  Hyperiids  Gammariids      Total  \n",
       "0    0.000000  0.000000   0.827555         0.0   1.721729  \n",
       "1    0.000000  0.083863   0.362459         0.0   4.200731  \n",
       "2    0.000000  0.237697   1.873001         0.0  14.350847  \n",
       "3    0.000000  0.459538   3.021919         0.0  29.466088  \n",
       "4    0.197026  0.804768   8.897614         0.0  49.400349  \n",
       "..        ...       ...        ...         ...        ...  \n",
       "649  0.000000  0.000000   0.304123         0.0   0.800142  \n",
       "650  0.000000  0.000000   1.761051         0.0   2.876183  \n",
       "651  0.000000  0.000000   0.000000         0.0   0.141755  \n",
       "652  0.000000  0.000000   0.168176         0.0   0.200890  \n",
       "653  0.000000  0.000000   0.731182         0.0   1.624787  \n",
       "\n",
       "[654 rows x 269 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biomassDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomassDF['Month']=[ii.month for ii in biomassDF['dtUTC']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Calanoids</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>0.231678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>0.262995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>0.045065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>0.011634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>0.263927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>0.210876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>0.910112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>1.208902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>1.319525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>0.150697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>0.294798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>0.441176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>0.343120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Calanoids\n",
       "239   0.231678\n",
       "240   0.262995\n",
       "241   0.045065\n",
       "242   0.011634\n",
       "243   0.263927\n",
       "244   0.210876\n",
       "457   0.910112\n",
       "458   1.208902\n",
       "459   1.319525\n",
       "589   0.150697\n",
       "590   0.294798\n",
       "591   0.441176\n",
       "592   0.343120"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biomassDF.loc[biomassDF.Month==2,['Calanoids']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: lower limit is not an ocean value:  i=265, j=388, k_upper=0, k_lower=27, k_seafloor=27 Lon=-123.311, Lat=48.898, dtUTC=2014-03-09 18:57:00\n",
      "Warning: lower limit is not an ocean value:  i=265, j=388, k_upper=0, k_lower=27, k_seafloor=27 Lon=-123.311, Lat=48.898, dtUTC=2014-03-09 18:57:00\n",
      "Warning: lower limit is not an ocean value:  i=159, j=645, k_upper=0, k_lower=34, k_seafloor=34 Lon=-124.72200000000001, Lat=49.706, dtUTC=2014-04-06 07:14:00\n",
      "Warning: lower limit is not an ocean value:  i=159, j=645, k_upper=0, k_lower=34, k_seafloor=34 Lon=-124.72200000000001, Lat=49.706, dtUTC=2014-04-06 07:14:00\n",
      "Warning: lower limit is not an ocean value:  i=201, j=382, k_upper=0, k_lower=22, k_seafloor=11 Lon=-123.62299999999999, Lat=48.751000000000005, dtUTC=2014-04-23 18:49:00\n",
      "Warning: lower limit is not an ocean value:  i=201, j=382, k_upper=0, k_lower=22, k_seafloor=11 Lon=-123.62299999999999, Lat=48.751000000000005, dtUTC=2014-04-23 18:49:00\n",
      "Warning: lower limit is not an ocean value:  i=202, j=382, k_upper=0, k_lower=22, k_seafloor=17 Lon=-123.62100000000001, Lat=48.751000000000005, dtUTC=2014-04-29 18:20:00\n",
      "Warning: lower limit is not an ocean value:  i=202, j=382, k_upper=0, k_lower=22, k_seafloor=17 Lon=-123.62100000000001, Lat=48.751000000000005, dtUTC=2014-04-29 18:20:00\n",
      "Warning: lower limit is not an ocean value:  i=205, j=381, k_upper=0, k_lower=23, k_seafloor=23 Lon=-123.605, Lat=48.755, dtUTC=2014-04-29 19:40:00\n",
      "Warning: lower limit is not an ocean value:  i=205, j=381, k_upper=0, k_lower=23, k_seafloor=23 Lon=-123.605, Lat=48.755, dtUTC=2014-04-29 19:40:00\n",
      "Warning: lower limit is not an ocean value:  i=203, j=378, k_upper=0, k_lower=22, k_seafloor=22 Lon=-123.602, Lat=48.74100000000001, dtUTC=2014-04-29 20:36:00\n",
      "Warning: lower limit is not an ocean value:  i=203, j=378, k_upper=0, k_lower=22, k_seafloor=22 Lon=-123.602, Lat=48.74100000000001, dtUTC=2014-04-29 20:36:00\n",
      "Warning: lower limit is not an ocean value:  i=201, j=382, k_upper=0, k_lower=19, k_seafloor=11 Lon=-123.62299999999999, Lat=48.751000000000005, dtUTC=2014-05-07 18:07:00\n",
      "Warning: lower limit is not an ocean value:  i=201, j=382, k_upper=0, k_lower=19, k_seafloor=11 Lon=-123.62299999999999, Lat=48.751000000000005, dtUTC=2014-05-07 18:07:00\n",
      "Warning: lower limit is not an ocean value:  i=211, j=382, k_upper=0, k_lower=24, k_seafloor=24 Lon=-123.574, Lat=48.77, dtUTC=2014-05-07 21:42:00\n",
      "Warning: lower limit is not an ocean value:  i=211, j=382, k_upper=0, k_lower=24, k_seafloor=24 Lon=-123.574, Lat=48.77, dtUTC=2014-05-07 21:42:00\n",
      "Warning: lower limit is not an ocean value:  i=202, j=382, k_upper=0, k_lower=18, k_seafloor=17 Lon=-123.62299999999999, Lat=48.751999999999995, dtUTC=2014-05-15 18:20:00\n",
      "Warning: lower limit is not an ocean value:  i=202, j=382, k_upper=0, k_lower=18, k_seafloor=17 Lon=-123.62299999999999, Lat=48.751999999999995, dtUTC=2014-05-15 18:20:00\n",
      "Warning: lower limit is not an ocean value:  i=205, j=381, k_upper=0, k_lower=23, k_seafloor=23 Lon=-123.605, Lat=48.755, dtUTC=2014-05-15 20:24:00\n",
      "Warning: lower limit is not an ocean value:  i=205, j=381, k_upper=0, k_lower=23, k_seafloor=23 Lon=-123.605, Lat=48.755, dtUTC=2014-05-15 20:24:00\n",
      "Warning: lower limit is not an ocean value:  i=211, j=382, k_upper=0, k_lower=24, k_seafloor=24 Lon=-123.572, Lat=48.771, dtUTC=2014-05-28 16:49:00\n",
      "Warning: lower limit is not an ocean value:  i=211, j=382, k_upper=0, k_lower=24, k_seafloor=24 Lon=-123.572, Lat=48.771, dtUTC=2014-05-28 16:49:00\n",
      "Warning: lower limit is not an ocean value:  i=202, j=382, k_upper=0, k_lower=21, k_seafloor=17 Lon=-123.62200000000001, Lat=48.751999999999995, dtUTC=2014-05-28 19:49:00\n",
      "Warning: lower limit is not an ocean value:  i=202, j=382, k_upper=0, k_lower=21, k_seafloor=17 Lon=-123.62200000000001, Lat=48.751999999999995, dtUTC=2014-05-28 19:49:00\n",
      "Warning: lower limit is not an ocean value:  i=207, j=376, k_upper=0, k_lower=25, k_seafloor=25 Lon=-123.575, Lat=48.736999999999995, dtUTC=2014-06-05 21:11:00\n",
      "Warning: lower limit is not an ocean value:  i=207, j=376, k_upper=0, k_lower=25, k_seafloor=25 Lon=-123.575, Lat=48.736999999999995, dtUTC=2014-06-05 21:11:00\n",
      "Warning: lower limit is not an ocean value:  i=122, j=628, k_upper=0, k_lower=23, k_seafloor=23 Lon=-124.86, Lat=49.568999999999996, dtUTC=2014-06-20 21:38:00\n",
      "Warning: lower limit is not an ocean value:  i=122, j=628, k_upper=0, k_lower=23, k_seafloor=23 Lon=-124.86, Lat=49.568999999999996, dtUTC=2014-06-20 21:38:00\n",
      "Warning: lower limit is not an ocean value:  i=120, j=621, k_upper=0, k_lower=23, k_seafloor=23 Lon=-124.84200000000001, Lat=49.537, dtUTC=2014-06-20 23:06:00\n",
      "Warning: lower limit is not an ocean value:  i=120, j=621, k_upper=0, k_lower=23, k_seafloor=23 Lon=-124.84200000000001, Lat=49.537, dtUTC=2014-06-20 23:06:00\n",
      "Warning: lower limit is not an ocean value:  i=123, j=606, k_upper=0, k_lower=23, k_seafloor=19 Lon=-124.77600000000001, Lat=49.483999999999995, dtUTC=2014-06-21 16:45:00\n",
      "Warning: lower limit is not an ocean value:  i=123, j=606, k_upper=0, k_lower=23, k_seafloor=19 Lon=-124.77600000000001, Lat=49.483999999999995, dtUTC=2014-06-21 16:45:00\n",
      "Warning: lower limit is not an ocean value:  i=136, j=606, k_upper=0, k_lower=23, k_seafloor=22 Lon=-124.713, Lat=49.50899999999999, dtUTC=2014-06-22 19:39:00\n",
      "Warning: lower limit is not an ocean value:  i=136, j=606, k_upper=0, k_lower=23, k_seafloor=22 Lon=-124.713, Lat=49.50899999999999, dtUTC=2014-06-22 19:39:00\n",
      "Warning: lower limit is not an ocean value:  i=134, j=639, k_upper=0, k_lower=23, k_seafloor=23 Lon=-124.831, Lat=49.635, dtUTC=2014-06-22 23:04:00\n",
      "Warning: lower limit is not an ocean value:  i=134, j=639, k_upper=0, k_lower=23, k_seafloor=23 Lon=-124.831, Lat=49.635, dtUTC=2014-06-22 23:04:00\n",
      "Warning: lower limit is not an ocean value:  i=125, j=635, k_upper=0, k_lower=23, k_seafloor=23 Lon=-124.866, Lat=49.604, dtUTC=2015-02-18 08:00:00\n",
      "Warning: lower limit is not an ocean value:  i=125, j=635, k_upper=0, k_lower=23, k_seafloor=23 Lon=-124.866, Lat=49.604, dtUTC=2015-02-18 08:00:00\n",
      "Warning: lower limit is not an ocean value:  i=211, j=382, k_upper=0, k_lower=24, k_seafloor=24 Lon=-123.575, Lat=48.77, dtUTC=2015-02-19 19:16:00\n",
      "Warning: lower limit is not an ocean value:  i=211, j=382, k_upper=0, k_lower=24, k_seafloor=24 Lon=-123.575, Lat=48.77, dtUTC=2015-02-19 19:16:00\n",
      "Warning: lower limit is not an ocean value:  i=201, j=381, k_upper=0, k_lower=21, k_seafloor=18 Lon=-123.62299999999999, Lat=48.75, dtUTC=2015-02-19 20:17:00\n",
      "Warning: lower limit is not an ocean value:  i=201, j=381, k_upper=0, k_lower=21, k_seafloor=18 Lon=-123.62299999999999, Lat=48.75, dtUTC=2015-02-19 20:17:00\n",
      "Warning: lower limit is not an ocean value:  i=207, j=376, k_upper=0, k_lower=25, k_seafloor=25 Lon=-123.574, Lat=48.738, dtUTC=2015-02-19 21:59:00\n",
      "Warning: lower limit is not an ocean value:  i=207, j=376, k_upper=0, k_lower=25, k_seafloor=25 Lon=-123.574, Lat=48.738, dtUTC=2015-02-19 21:59:00\n",
      "Warning: lower limit is not an ocean value:  i=202, j=379, k_upper=0, k_lower=22, k_seafloor=22 Lon=-123.60700000000001, Lat=48.74100000000001, dtUTC=2015-03-12 19:01:00\n",
      "Warning: lower limit is not an ocean value:  i=202, j=379, k_upper=0, k_lower=22, k_seafloor=22 Lon=-123.60700000000001, Lat=48.74100000000001, dtUTC=2015-03-12 19:01:00\n",
      "Warning: lower limit is not an ocean value:  i=202, j=382, k_upper=0, k_lower=22, k_seafloor=17 Lon=-123.62200000000001, Lat=48.751000000000005, dtUTC=2015-03-12 20:36:00\n",
      "Warning: lower limit is not an ocean value:  i=202, j=382, k_upper=0, k_lower=22, k_seafloor=17 Lon=-123.62200000000001, Lat=48.751000000000005, dtUTC=2015-03-12 20:36:00\n",
      "Warning: lower limit is not an ocean value:  i=124, j=605, k_upper=0, k_lower=24, k_seafloor=24 Lon=-124.76799999999999, Lat=49.483000000000004, dtUTC=2015-03-17 22:27:00\n",
      "Warning: lower limit is not an ocean value:  i=124, j=605, k_upper=0, k_lower=24, k_seafloor=24 Lon=-124.76799999999999, Lat=49.483000000000004, dtUTC=2015-03-17 22:27:00\n",
      "Warning: lower limit is not an ocean value:  i=211, j=382, k_upper=0, k_lower=24, k_seafloor=24 Lon=-123.573, Lat=48.769, dtUTC=2015-03-19 16:58:00\n",
      "Warning: lower limit is not an ocean value:  i=211, j=382, k_upper=0, k_lower=24, k_seafloor=24 Lon=-123.573, Lat=48.769, dtUTC=2015-03-19 16:58:00\n",
      "Warning: lower limit is not an ocean value:  i=202, j=381, k_upper=0, k_lower=23, k_seafloor=23 Lon=-123.618, Lat=48.75, dtUTC=2015-03-19 19:51:00\n",
      "Warning: lower limit is not an ocean value:  i=202, j=381, k_upper=0, k_lower=23, k_seafloor=23 Lon=-123.618, Lat=48.75, dtUTC=2015-03-19 19:51:00\n",
      "Warning: lower limit is not an ocean value:  i=149, j=594, k_upper=0, k_lower=25, k_seafloor=25 Lon=-124.60600000000001, Lat=49.485, dtUTC=2015-03-21 17:18:00\n",
      "Warning: lower limit is not an ocean value:  i=149, j=594, k_upper=0, k_lower=25, k_seafloor=25 Lon=-124.60600000000001, Lat=49.485, dtUTC=2015-03-21 17:18:00\n",
      "Warning: lower limit is not an ocean value:  i=211, j=382, k_upper=0, k_lower=24, k_seafloor=24 Lon=-123.575, Lat=48.769, dtUTC=2015-03-26 16:05:00\n",
      "Warning: lower limit is not an ocean value:  i=211, j=382, k_upper=0, k_lower=24, k_seafloor=24 Lon=-123.575, Lat=48.769, dtUTC=2015-03-26 16:05:00\n",
      "Warning: lower limit is not an ocean value:  i=211, j=382, k_upper=0, k_lower=24, k_seafloor=24 Lon=-123.574, Lat=48.769, dtUTC=2015-04-02 15:49:00\n",
      "Warning: lower limit is not an ocean value:  i=211, j=382, k_upper=0, k_lower=24, k_seafloor=24 Lon=-123.574, Lat=48.769, dtUTC=2015-04-02 15:49:00\n",
      "Warning: lower limit is not an ocean value:  i=203, j=378, k_upper=0, k_lower=22, k_seafloor=22 Lon=-123.60600000000001, Lat=48.74, dtUTC=2015-04-02 17:41:00\n",
      "Warning: lower limit is not an ocean value:  i=203, j=378, k_upper=0, k_lower=22, k_seafloor=22 Lon=-123.60600000000001, Lat=48.74, dtUTC=2015-04-02 17:41:00\n",
      "Warning: lower limit is not an ocean value:  i=202, j=382, k_upper=0, k_lower=20, k_seafloor=17 Lon=-123.62299999999999, Lat=48.751999999999995, dtUTC=2015-04-02 19:14:00\n",
      "Warning: lower limit is not an ocean value:  i=202, j=382, k_upper=0, k_lower=20, k_seafloor=17 Lon=-123.62299999999999, Lat=48.751999999999995, dtUTC=2015-04-02 19:14:00\n",
      "Warning: lower limit is not an ocean value:  i=202, j=382, k_upper=0, k_lower=19, k_seafloor=17 Lon=-123.62200000000001, Lat=48.751000000000005, dtUTC=2015-04-16 19:10:00\n",
      "Warning: lower limit is not an ocean value:  i=202, j=382, k_upper=0, k_lower=19, k_seafloor=17 Lon=-123.62200000000001, Lat=48.751000000000005, dtUTC=2015-04-16 19:10:00\n",
      "Warning: lower limit is not an ocean value:  i=126, j=636, k_upper=0, k_lower=23, k_seafloor=23 Lon=-124.867, Lat=49.608000000000004, dtUTC=2015-05-13 18:06:00\n",
      "Warning: lower limit is not an ocean value:  i=126, j=636, k_upper=0, k_lower=23, k_seafloor=23 Lon=-124.867, Lat=49.608000000000004, dtUTC=2015-05-13 18:06:00\n",
      "Warning: lower limit is not an ocean value:  i=149, j=594, k_upper=0, k_lower=25, k_seafloor=25 Lon=-124.605, Lat=49.485, dtUTC=2015-05-17 15:59:00\n",
      "Warning: lower limit is not an ocean value:  i=149, j=594, k_upper=0, k_lower=25, k_seafloor=25 Lon=-124.605, Lat=49.485, dtUTC=2015-05-17 15:59:00\n",
      "Warning: lower limit is not an ocean value:  i=202, j=382, k_upper=0, k_lower=19, k_seafloor=17 Lon=-123.62200000000001, Lat=48.751999999999995, dtUTC=2015-05-19 18:50:00\n",
      "Warning: lower limit is not an ocean value:  i=202, j=382, k_upper=0, k_lower=19, k_seafloor=17 Lon=-123.62200000000001, Lat=48.751999999999995, dtUTC=2015-05-19 18:50:00\n",
      "Warning: lower limit is not an ocean value:  i=159, j=646, k_upper=0, k_lower=34, k_seafloor=34 Lon=-124.72399999999999, Lat=49.707, dtUTC=2015-06-28 06:15:00\n",
      "Warning: lower limit is not an ocean value:  i=159, j=646, k_upper=0, k_lower=34, k_seafloor=34 Lon=-124.72399999999999, Lat=49.707, dtUTC=2015-06-28 06:15:00\n",
      "Warning: lower limit is not an ocean value:  i=239, j=329, k_upper=0, k_lower=31, k_seafloor=29 Lon=-123.249, Lat=48.615, dtUTC=2015-08-20 02:52:00\n",
      "Warning: lower limit is not an ocean value:  i=239, j=329, k_upper=0, k_lower=31, k_seafloor=29 Lon=-123.249, Lat=48.615, dtUTC=2015-08-20 02:52:00\n",
      "Warning: lower limit is not an ocean value:  i=149, j=594, k_upper=0, k_lower=25, k_seafloor=25 Lon=-124.605, Lat=49.485, dtUTC=2015-09-14 17:11:00\n",
      "Warning: lower limit is not an ocean value:  i=149, j=594, k_upper=0, k_lower=25, k_seafloor=25 Lon=-124.605, Lat=49.485, dtUTC=2015-09-14 17:11:00\n",
      "Warning: lower limit is not an ocean value:  i=251, j=457, k_upper=0, k_lower=28, k_seafloor=28 Lon=-123.61399999999999, Lat=49.141999999999996, dtUTC=2015-10-04 07:32:00\n",
      "Warning: lower limit is not an ocean value:  i=251, j=457, k_upper=0, k_lower=28, k_seafloor=28 Lon=-123.61399999999999, Lat=49.141999999999996, dtUTC=2015-10-04 07:32:00\n"
     ]
    }
   ],
   "source": [
    "data=et.matchData(biomassDF,filemap,fdict,start_date,end_date,'nowcast',PATH,1,quiet=False,method='vertNet');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "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>Key</th>\n",
       "      <th>region_name</th>\n",
       "      <th>Station</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Date</th>\n",
       "      <th>dtUTC</th>\n",
       "      <th>Twilight</th>\n",
       "      <th>Net_Type</th>\n",
       "      <th>Mesh_Size(um)</th>\n",
       "      <th>...</th>\n",
       "      <th>Primno</th>\n",
       "      <th>Hyperiids</th>\n",
       "      <th>Gammariids</th>\n",
       "      <th>Total</th>\n",
       "      <th>j</th>\n",
       "      <th>i</th>\n",
       "      <th>mod_microzooplankton</th>\n",
       "      <th>mod_mesozooplankton</th>\n",
       "      <th>k_upper</th>\n",
       "      <th>k_lower</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PBS2014016000801</td>\n",
       "      <td>Juan de Fuca</td>\n",
       "      <td>JF03</td>\n",
       "      <td>-124.051</td>\n",
       "      <td>48.388</td>\n",
       "      <td>3/7/2014</td>\n",
       "      <td>2014-03-07 19:01:00</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>Bongo VNH</td>\n",
       "      <td>253</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.092604</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.554291</td>\n",
       "      <td>342</td>\n",
       "      <td>94</td>\n",
       "      <td>0.049066</td>\n",
       "      <td>0.286660</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PBS2014016004401</td>\n",
       "      <td>Central Strait of Georgia</td>\n",
       "      <td>GS04</td>\n",
       "      <td>-123.311</td>\n",
       "      <td>48.898</td>\n",
       "      <td>3/9/2014</td>\n",
       "      <td>2014-03-09 18:57:00</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>Bongo VNH</td>\n",
       "      <td>253</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.025194</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.335986</td>\n",
       "      <td>388</td>\n",
       "      <td>265</td>\n",
       "      <td>0.065740</td>\n",
       "      <td>0.263313</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PBS2014016005201</td>\n",
       "      <td>Central Strait of Georgia</td>\n",
       "      <td>GS08</td>\n",
       "      <td>-123.580</td>\n",
       "      <td>49.045</td>\n",
       "      <td>3/9/2014</td>\n",
       "      <td>2014-03-09 23:57:00</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>Bongo VNH</td>\n",
       "      <td>253</td>\n",
       "      <td>...</td>\n",
       "      <td>0.158000</td>\n",
       "      <td>0.261419</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.316260</td>\n",
       "      <td>436</td>\n",
       "      <td>244</td>\n",
       "      <td>0.045737</td>\n",
       "      <td>0.193986</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IOS2014003000101</td>\n",
       "      <td>Central Strait of Georgia</td>\n",
       "      <td>GEO1</td>\n",
       "      <td>-123.744</td>\n",
       "      <td>49.250</td>\n",
       "      <td>3/9/2014</td>\n",
       "      <td>2014-03-10 02:08:00</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>SCOR VNH</td>\n",
       "      <td>236</td>\n",
       "      <td>...</td>\n",
       "      <td>0.068851</td>\n",
       "      <td>0.068851</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.469906</td>\n",
       "      <td>487</td>\n",
       "      <td>246</td>\n",
       "      <td>0.021994</td>\n",
       "      <td>0.094057</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PBS2014016011401</td>\n",
       "      <td>Central Strait of Georgia</td>\n",
       "      <td>GS20</td>\n",
       "      <td>-123.484</td>\n",
       "      <td>49.068</td>\n",
       "      <td>3/13/2014</td>\n",
       "      <td>2014-03-13 21:12:00</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>Bongo VNH</td>\n",
       "      <td>253</td>\n",
       "      <td>...</td>\n",
       "      <td>0.118801</td>\n",
       "      <td>0.286148</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.654707</td>\n",
       "      <td>433</td>\n",
       "      <td>261</td>\n",
       "      <td>0.059742</td>\n",
       "      <td>0.211711</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>PSF2015097005001</td>\n",
       "      <td>Northern Strait of Georgia</td>\n",
       "      <td>IS-3</td>\n",
       "      <td>-124.287</td>\n",
       "      <td>49.655</td>\n",
       "      <td>10/5/2015</td>\n",
       "      <td>2015-10-05 18:43:00</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>Ring VNH</td>\n",
       "      <td>250</td>\n",
       "      <td>...</td>\n",
       "      <td>0.289586</td>\n",
       "      <td>0.623782</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.103821</td>\n",
       "      <td>604</td>\n",
       "      <td>217</td>\n",
       "      <td>0.057682</td>\n",
       "      <td>0.424223</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>PSF2015098005701</td>\n",
       "      <td>Northern Strait of Georgia</td>\n",
       "      <td>BS-3</td>\n",
       "      <td>-124.666</td>\n",
       "      <td>49.692</td>\n",
       "      <td>10/5/2015</td>\n",
       "      <td>2015-10-05 19:15:00</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>Ring VNH</td>\n",
       "      <td>250</td>\n",
       "      <td>...</td>\n",
       "      <td>0.667453</td>\n",
       "      <td>1.180838</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.616760</td>\n",
       "      <td>639</td>\n",
       "      <td>166</td>\n",
       "      <td>0.058821</td>\n",
       "      <td>0.396426</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463</th>\n",
       "      <td>PSF2015098005801</td>\n",
       "      <td>Baynes Sound</td>\n",
       "      <td>BS-1</td>\n",
       "      <td>-124.867</td>\n",
       "      <td>49.608</td>\n",
       "      <td>10/5/2015</td>\n",
       "      <td>2015-10-05 20:38:00</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>Ring VNH</td>\n",
       "      <td>250</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.075827</td>\n",
       "      <td>636</td>\n",
       "      <td>126</td>\n",
       "      <td>0.194869</td>\n",
       "      <td>1.209480</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>PSF2015097005101</td>\n",
       "      <td>Nearshore-North East</td>\n",
       "      <td>IS-2</td>\n",
       "      <td>-124.083</td>\n",
       "      <td>49.637</td>\n",
       "      <td>10/5/2015</td>\n",
       "      <td>2015-10-05 20:40:00</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>Ring VNH</td>\n",
       "      <td>250</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.332255</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.409816</td>\n",
       "      <td>587</td>\n",
       "      <td>245</td>\n",
       "      <td>0.159315</td>\n",
       "      <td>1.060016</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>PSF2015098005901</td>\n",
       "      <td>Baynes Sound</td>\n",
       "      <td>BS-7</td>\n",
       "      <td>-124.767</td>\n",
       "      <td>49.483</td>\n",
       "      <td>10/5/2015</td>\n",
       "      <td>2015-10-05 21:51:00</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>Ring VNH</td>\n",
       "      <td>250</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.026601</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.921206</td>\n",
       "      <td>605</td>\n",
       "      <td>125</td>\n",
       "      <td>0.183379</td>\n",
       "      <td>1.091250</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>466 rows × 278 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Key                 region_name Station      Lon     Lat  \\\n",
       "0     PBS2014016000801                Juan de Fuca    JF03 -124.051  48.388   \n",
       "1     PBS2014016004401   Central Strait of Georgia    GS04 -123.311  48.898   \n",
       "2     PBS2014016005201   Central Strait of Georgia    GS08 -123.580  49.045   \n",
       "3     IOS2014003000101   Central Strait of Georgia    GEO1 -123.744  49.250   \n",
       "4    PBS2014016011401    Central Strait of Georgia    GS20 -123.484  49.068   \n",
       "..                 ...                         ...     ...      ...     ...   \n",
       "461   PSF2015097005001  Northern Strait of Georgia    IS-3 -124.287  49.655   \n",
       "462   PSF2015098005701  Northern Strait of Georgia    BS-3 -124.666  49.692   \n",
       "463   PSF2015098005801                Baynes Sound    BS-1 -124.867  49.608   \n",
       "464   PSF2015097005101        Nearshore-North East    IS-2 -124.083  49.637   \n",
       "465   PSF2015098005901                Baynes Sound    BS-7 -124.767  49.483   \n",
       "\n",
       "          Date               dtUTC  Twilight   Net_Type  Mesh_Size(um)  ...  \\\n",
       "0     3/7/2014 2014-03-07 19:01:00  Daylight  Bongo VNH            253  ...   \n",
       "1     3/9/2014 2014-03-09 18:57:00  Daylight  Bongo VNH            253  ...   \n",
       "2     3/9/2014 2014-03-09 23:57:00  Daylight  Bongo VNH            253  ...   \n",
       "3     3/9/2014 2014-03-10 02:08:00  Daylight   SCOR VNH            236  ...   \n",
       "4    3/13/2014 2014-03-13 21:12:00  Daylight  Bongo VNH            253  ...   \n",
       "..         ...                 ...       ...        ...            ...  ...   \n",
       "461  10/5/2015 2015-10-05 18:43:00  Daylight   Ring VNH            250  ...   \n",
       "462  10/5/2015 2015-10-05 19:15:00  Daylight   Ring VNH            250  ...   \n",
       "463  10/5/2015 2015-10-05 20:38:00  Daylight   Ring VNH            250  ...   \n",
       "464  10/5/2015 2015-10-05 20:40:00  Daylight   Ring VNH            250  ...   \n",
       "465  10/5/2015 2015-10-05 21:51:00  Daylight   Ring VNH            250  ...   \n",
       "\n",
       "       Primno  Hyperiids  Gammariids      Total    j    i  \\\n",
       "0    0.000000   0.092604         0.0  10.554291  342   94   \n",
       "1    0.000000   0.025194         0.0   0.335986  388  265   \n",
       "2    0.158000   0.261419         0.0   1.316260  436  244   \n",
       "3    0.068851   0.068851         0.0   1.469906  487  246   \n",
       "4    0.118801   0.286148         0.0   1.654707  433  261   \n",
       "..        ...        ...         ...        ...  ...  ...   \n",
       "461  0.289586   0.623782         0.0   3.103821  604  217   \n",
       "462  0.667453   1.180838         0.0  76.616760  639  166   \n",
       "463  0.000000   0.000000         0.0   0.075827  636  126   \n",
       "464  0.000000   4.332255         0.0   4.409816  587  245   \n",
       "465  0.000000   1.026601         0.0   1.921206  605  125   \n",
       "\n",
       "     mod_microzooplankton  mod_mesozooplankton  k_upper  k_lower  \n",
       "0                0.049066             0.286660        0       23  \n",
       "1                0.065740             0.263313        0       27  \n",
       "2                0.045737             0.193986        0       28  \n",
       "3                0.021994             0.094057        0       37  \n",
       "4                0.059742             0.211711        0       28  \n",
       "..                    ...                  ...      ...      ...  \n",
       "461              0.057682             0.424223        0       28  \n",
       "462              0.058821             0.396426        0       28  \n",
       "463              0.194869             1.209480        0       22  \n",
       "464              0.159315             1.060016        0       21  \n",
       "465              0.183379             1.091250        0       23  \n",
       "\n",
       "[466 rows x 278 columns]"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm1=cmocean.cm.thermal\n",
    "with nc.Dataset('/ocean/ksuchy/MOAD/NEMO-forcing/grid/bathymetry_201702.nc') as bathy:\n",
    "    bathylon=np.copy(bathy.variables['nav_lon'][:,:])\n",
    "    bathylat=np.copy(bathy.variables['nav_lat'][:,:])\n",
    "    bathyZ=np.copy(bathy.variables['Bathymetry'][:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define log transform function with slight shift to accommodate zero values\n",
    "def logt(x):\n",
    "  return np.log10(x+.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define inverse log transform with same shift\n",
    "def logt_inv(y):\n",
    "    return 10**y-.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.5717944 , 4.38464013, 1.8619691 , 0.77795756, 0.07166609,\n",
       "       4.9216666 , 1.30426199, 0.04374841, 0.03221038, 1.42433184,\n",
       "       3.62682488, 6.55181683, 0.08520765, 1.18366988, 1.02939106,\n",
       "       0.29600866, 0.85211712, 1.79831119, 0.50258672, 0.07633187])"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Euphausiids'].unique()[20:40]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['L10Calanoids']=logt(data['Calanoids'])\n",
    "data['L10CalanoidsDiaRemoved']=logt(data['CalanoidsDiaRemoved'])\n",
    "data['L10Euphausiids']=logt(data['Euphausiids'])\n",
    "data['L10Hyperiids']=logt(data['Hyperiids'])\n",
    "data['L10Gammariids']=logt(data['Gammariids'])\n",
    "data['L10Neocalanus']=logt(data['Neocalanus'])\n",
    "\n",
    "data['L10Total']=logt(data['Total'])\n",
    "\n",
    "data['L10mod_mesozooplankton']=logt(data['mod_mesozooplankton']*5.7*14)\n",
    "data['L10mod_microzooplankton']=logt(data['mod_microzooplankton']*5.7*14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.52201674, -1.2195666 , -1.11382969, -1.38807898, -1.43340323,\n",
       "       -0.85816102, -1.08101945, -0.58318385, -0.48369823,  0.01373667,\n",
       "        0.08996821, -0.60699648, -0.23719985,  0.25433288, -0.02657255,\n",
       "       -2.14076674, -1.40852969, -1.66178233, -1.95689915])"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['L10Neocalanus'].unique()[1:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure showing observation locations of IOS zooplankton sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff1a5e72310>"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1,figsize = (6,6))\n",
    "with nc.Dataset('/ocean/ksuchy/MOAD/NEMO-forcing/grid/bathymetry_201702.nc') as grid:\n",
    "    viz_tools.plot_coastline(ax, grid, coords = 'map',isobath=.1)\n",
    "colors=('teal','green','firebrick','darkorange','darkviolet','fuchsia',\n",
    "        'royalblue','darkgoldenrod','mediumspringgreen','deepskyblue')\n",
    "datreg=dict()\n",
    "for ind, iregion in enumerate(data.region_name.unique()):\n",
    "    datreg[iregion] = data.loc[data.region_name==iregion]\n",
    "    ax.plot(datreg[iregion]['Lon'], datreg[iregion]['Lat'],'.',\n",
    "            color = colors[ind], label=iregion)\n",
    "ax.set_ylim(47,51)\n",
    "ax.legend(bbox_to_anchor=[1,.6,0,0])\n",
    "ax.set_xlim(-126, -120);\n",
    "ax.set_title('Observation Locations');\n",
    "ax.legend(bbox_to_anchor=(1.1, 1.05))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "#look at data for individual years\n",
    "View2012=data.loc[data.Year==2012]\n",
    "View2013=data.loc[data.Year==2013]\n",
    "View2014=data.loc[data.Year==2014]\n",
    "View2015=data.loc[data.Year==2015]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define log transform function with slight shift to accommodate zero values\n",
    "def logt(x):\n",
    "  return np.log10(x+.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "#look at data for a specific region\n",
    "ViewCentralSoG=data.loc[data.region_name=='Central Strait of Georgia']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "#look at data for an individual station\n",
    "ViewGEO1=data.loc[data.Station=='GEO1']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate Mean and SEMs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "#monthly mean and SEM for entire SoG\n",
    "monthlymean=data.groupby(['Month']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "monthlysem=data.groupby(['Month']).sem()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "#monthly mean and SEM for Central SoG Only\n",
    "monthlymeanCentral=ViewCentralSoG.groupby(['Month']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "monthlysemCentral=ViewCentralSoG.groupby(['Month']).sem()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "#monthly mean and SEM for station GEO1 only\n",
    "monthlymeanGEO1=ViewGEO1.groupby(['Month']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "monthlysemGEO1=ViewGEO1.groupby(['Month']).sem()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "#look at data for an individual station\n",
    "ViewBaynes=data.loc[data.Station=='BS-1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "#monthly mean and SEM for Baynes Sound Only\n",
    "monthlymeanBaynes=ViewBaynes.groupby(['Month']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "monthlymean2012=View2012.groupby(['Month']).mean()\n",
    "monthlymean2013=View2013.groupby(['Month']).mean()\n",
    "monthlymean2014=View2014.groupby(['Month']).mean()\n",
    "monthlymean2015=View2015.groupby(['Month']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "monthlysem2012=View2012.groupby(['Month']).sem()\n",
    "monthlysem2013=View2013.groupby(['Month']).sem()\n",
    "monthlysem2014=View2014.groupby(['Month']).sem()\n",
    "monthlysem2015=View2015.groupby(['Month']).sem()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff1a5007850>"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iIiIiIiIikpcKRyIiIiIiIiIikpcKRyIiIiIiIiIikpcKRyIiIiIiIiIikpcKR4KZbWRm/zazb81shpmNN7OnzexgM2vbhOMMNDMvZawFxtHdzNzMDilg29FmdkvpoxJpmmK9LpNj6bVZQmY2JLkuN7PZZvajmb1jZleZ2ep5tq+I50OkNTGzQ7Jep7m3iSU872gzu6NUx28uM9siufYtspYNMbMhBeyrHCZSZGZ2Y/KavDzFGG4xs68L2C6TT7uXIazs8xb8PlKKr13aAUi6zOw44HLgWeBk4AugM7AdcC0wEXg4pfCa6ztgI+DTtAMRaY4afV1Cbb823wN+l/y8MLAGcBhwlJkd6+7XZG17I/BkmeMTkbA3kPvBaFYagaTsLSIff5i17A8pxSLSqpnZfERuAjjQzE5y90rOS48R+eO7tAOR8lHhqBUzs82ID6dXu/sxOasfTireC5Q/spZx91+AV9OOQ6Q5avV1CTX/2pzs7tnX9pSZXQXcDVxlZm+4+xsA7v41835wbbXMrGPytyFSDu+4+6i0g0ibu/9ETj529w/r2VxESmt34kunx4Edge2BR1ONqAHuPhYYm3YcUl7qqta6nQJMAE7Kt9LdP3X398ysi5ldb2afmNk0M/vKzO4ys+UaO4GZ/cnMXjGzCWY20cxeNbOdcrbJNDv8nZmda2bfJdv+18yWz9m2vZmdnzT9npHcn29m7fMc75CcfY9Ntp9uZsPMbNM88S5tZrcm3YN+SWJ51MyWbOxaRYqkoNclgF6bjb82k/Odb2bHmNnnZjbZzIZaThcyC382sxFJ/N+Z2dVmtnBjv8v6uPtM4hv8WcD/ioCWp5tHE5+PP5jZ5WY2JnneH7Wc5uJmtp+ZPWtmY81sipm9bWYH13O8Q3KW5+vC0s/MXjazScnxRpjZWTn7rW1mj1h01fvZzF7KfS4taQZv0RXzZTP7Gfhrsu6AJM4pyXneN7PfIVIm+V6byfJbzGx01uOCX4tZ++xnZh+Z2dQkz22Ss34DM7sveX38nLzGLrRoiZC9Xd5uvEk8A7Mer2xmDyaxTTezL83sP2bWLllfUFc1M1vXzF5IjvGNmZ0JWJ7zH5tc389JDhhmZrvn+12IyDwOBn4EDgF+BgZkr8zkJjNb1cwGJXnkSzM7NFl/kJl9nPz/fM7MVsrZf7SZ3WFmR5jZqOT1/JaZbZkvmKzX/TQzG2lmR+Wsn6erWlPOYWb9zezdZJtxZna7mS2Ts838ZnaNxVANU8zsEWD5PMfawGIoh/FJvJ+Z2TW520nLqcVRK2UxRsoWwEPuPr2RzRcDpgOnEtXlZYETgJfMbNVG9u9OdMsYTfy97QI8amY7uvsTOdueCrxMdO9YErgMuBPYPGubW4F9gAuBF4lmkmcAPYADGrje3wJ/B24B7gV6Ei0BFsrZ9HagG/AX4CtgKWBrYP4GrlGkKJr4ugS9Ngt9bfYHRgDHAh2AvxGtt1bNagp+QXKd/wT+C/QGzgPWNrPN3X1OAeeZh7uPMbNhwMaNbNqdpj0f7wCHEs/HhUQLp9WTYhXE7/0+4GJgDrAZcKOZzefu1zXlGsysB/BIcrxzgRlAr+QcmW3WA14A3gaOAKYBRwGDzezX7v5m1iEXAe4BLgVOA35OPkTfAfyDeI7bAKsCizYlVpECtM0UT7LMaeZrvJDXIsCmwCrAmUTOPo94fXd394nJNl2TY90CTAZWB84iXmf7NSO2R4luzb8HxgHLES0ZCv7S2MyWILpMf098sP2FeH12zdnuQOL/wrlEHpgPWIv4HyUiDTCzZYFtgBvcfayZPQTsYWad3f3HnM3/A/yL+P/5B+BmM+tFvHc8BWgPXAncBWyYs+/mwPrA6cRr+WTgCTNb291HZG23cLL/34nX9KHAtWY2wt2fa+RyGj2HmR0JXE+85zuVeO96IbChma3n7lOSY10P7AucA7wBbJvElf27WxAYBLxOFN0mE++nft1InNIc7q5bK7wRH7ocuKgZ+7YFVkj23z1r+cD4k6p3vzbEB6KngIezlndPjjU0Z/sTk+XLJo/XSB4PzNnujGT5WjnHOyTrvF8BT+bst2+y3S1Zy6YAx6T9/OjWOm8teV0m++u1Oe+1OTASaJ+1bK9k+a+Tx5kC3C05+/ZPtvtNI+cYArzYwPq7gZ+L+Hx8CLTJWr5xsvy3jRzvX8C7eY53SM72WyTLt8j5fS3cQMzPAB8BHXL+Hj8iCqGZZbckx9o1z9/UhFK8rnTTzd0hPlR4PbdHk23yvjaTv9vRWY8Lfi0SxeAfgc5Zy/ok2x1QT6yWvGb7E4XfxXOOd0ueff6Xg4ElGstdua/zZNkQYEjW4wuIQnHXrGULEIUoz1p2NfBW2s+xbrpV440orjiwUfK4X/L4qKxtBibLBmQt60y0aB6f/f+ZaOHsQLesZaPzvJYXIlq43561LPM/esusZR2T1/wNWcsy+bR7U85BvC/4AXgu53ewSXK8Y5LHqwCzgVNytruWud9HZnLpWmk/j63hpq5qUhAz+33SpHAKkaS+TFat0sh+61s03f4h2W8mUTHOt99jOY/fT+4z32xtltznzk6Sebx5PWEsn9z+nbP8fuYdEPMN4C9Jk+s1zWye5tgilUSvzYI87XN/+58bf1/ijVFu/PckcdQXf6GMeGNT/wZNez7u86zWEe7+EjFm0kZZx+tlZneb2TfJsWYCh9dzvMa8k+x/j5ntZTndAy260mxOfBM6x8zaJS06DBhM3d9HxizmHbvhDaBz0sx9ZzNbtBlxihRid2CDnNtxzTxWo6/FxCs+d8uB3ByEmS1sZpeY2afEN/UziZaWRrTwa4rxwGfAxUm3kabun7ER8Kq7Z/6v4O5TiVaZ2d4A1rGYSXIbM1MrbZHCDQBGuvsryePBwLfkdFdL/K8FcpJTxhCv0Z+ytvk4uV8hZ9/c1/Jk6ga5zjbNs1oWeYxBOJKclob1aOwcqxCtM+/M3sndXyQmgsm839qQ+NIr9/3hPTmPRxItK69Pur/lXrMUkQpHrdd4og9tt8Y2NLOjgWuIRLYH8CvigxZApwb2W4H4Fnox4Gii2eAGxGxC+fabkPM4M1hqZttMk+fcEfy/z1mfK9Nn9ofshR5dVMbnbLsv0SXjJGKWpG/M7Cwz02tFyqHg1yXotdmE12az4s+Ko6XdLVbIPXa2ZjwfP9SzbLnkeAsCTwNrE03XN02OdzNRIGsSj4GE+xHvGW4Hvjez18ws8wZvMeJbxDOpK1Jlbn8iCkLZz9MYd5+dc46hxIwyKwAPAmPNbLCZrdXUeEUaMdzdh+XcmjtYdoOvxSxz5SCvGww++/X9f0T3zn8QReMNgD/m2a5RHl/FbwsMAy4CPknG/fh9U45D5Oj6rjHbbUSXuA2JbiMTzOwBK/NU3SLVxsw2ILrGP2BmiyZfmiwEPABsZGYr5+zyY87jGfUsg3nzRqH5Kvd4EO+bCslDjZ2jvveLEO8ZM+vzvj/Mfezuk4AtiULbNcCXZjbczPYsIFZpIo1x1Eq5+yyLARC3tcZntNkPeMbdT8gsMLMVCzjN9sRYFvt4zCKU2be530Rl3ngtzdzTeS+d3Od+0MzIJKelshcm34gvnr3M3ccQb9T+aGarEH36zyHGj7m2mXGLFKSJr0vQa7NYr83s+D/IE0d98TcqaZ3Th3m/JcvW1OdjqXqWvZP8vBFRfNw0+RYvc7zc//mZMbA65CxfPOcxybePz5lZR6I7zrnAY8kHw4lEd5p/Eh8g5+Fzjx+Tt/WVu98H3JcUvrYALgGeNLPlvZljTIk00XQAM+vg7jOyls/zmkg09losiJl1AnYluppdmbV8zXpinOs1a2bzFLfd/TNgQNI6c22iiHuNmY32ecdNq8931H+N2edyYjyS682sM7AdMebRvcw7zoqI1Dk4uT85ueUaQHT7L4b6XsvfFOn4hZwj+/1WrqWJYjfM/f7ws4aO7+7vAHsm73H6EOMm/TsZV2l4k6KXBqkVRet2MfFm6G/5VprZism3vfMT3xxnO7SA42c+9Pxv36Ry3tggsfUZmtznDhJ5YHL/fD37fU2Mo7JPzvI9aaB46u4j3P00ovK+RtNCFWm2Ql+XoNdmsV6brxLfpuXGv28Sx9B59iiAxYxy1yTH+EcDmzb1+dgruwWPmW1MdPnLNHPPd7zOxAfTbD8Q1537O9yJerj7L+7+LDET2gLAiknXlReID6dv5WnNMay+49Vzjinu/ijxQXQZ6v/QLlJsXyT3/3tNJC0A6htotbHXYqE6Eq32cvP5IfXEmPua3bm+A3t4Bzg+WdSUnPkK0De7+4eZLUAM3l/f+X5093uJLiZ67yRSDzPrQLzveI1oNZN7ewc4qIjDZuS+lhci/t83NV+15BwjiPcec73fMrNfE194Zd5vvUZ8IZX7/rDeiQLcfZa7v0q0fm4DrNb8y5B81OKoFXP3583seOByM1uNGBDtS2Kwta2J8TAOILpLnGxmpxGj1m9FDJbamMHEWBa3mdllxAeAc5JzNLlo6e4fmNndwMCkqvwy8c36mcDdnkxRnme/OWZ2DjGj0P8R3/z3JCrS/+sTbGaLJDHfSfQPnkl80OpMDFIrUnJNeF2+h16bRXltuvsEM7scONXMpgKPE284zidmiMsd4ymfhcws001wIWBNooi3CvAHn3tWsVxNfT4WAh4ys+uBLkRXlJHUtfZ5mfj9/dPMziYKPGcQg1suknXdbmb3Ar81s0+IN3Q7Ea19/sdiGt7NiN/LV8TAu6cSTcMz3+YdTxQIB5nZTcS3hUsA6wFt3f2UBq4fMzuX+CbxueS4yxMDfL7j7mMb2lekidaxmC0s1zBi/JBJwL+S105HonvslDzbQ+OvxYK4+yQzexU4wcy+I16rhzFvFxKIPHmzmV1BjBW2NjkFpuTLhSuJFj+jiKLUIUSeebYJoV1BzNz0lJkNpG5WtZ9zzncDMZvRK8SYKysDB6H3TiIN2Zn4YuQEdx+SuzLJK9eS8z+5BX5g7tfyycT7g/OKdPxGz+Hus83sLKJ14h3E2JLLEQPxjyS67OLuI8zsLuDcpDifmVVtx+yTmdnOwJHAQ8DnybmOoS4fSRGpcNTKufvfzex14M/E1I5LEC+2YcDviAEQnyKmRP4z0b91KDHexWd5Dpl97A8spmg9lxib5FNivI3taX4SPDg572HEB6Fvie4M5zQSy01J94fjgf2JDzv7MfdguNOBt4ippLsRle4RwIHu/nAz4xVpsgJflxCvrUXRa7MYTie6vR1FfFAaT3z4O7XAblJrEW9SnHiuPidmKNrP3T9oYL/mPB8XEQW2W4g3Sc8Bf8oMAO4xne/uRFeR+4jn4kpi7ICzc451LFGcGkjdQJRHM/fg1e8COyTnXZJoav4i8fv/OTnnW8lYDWcTrasWIX6fbwHXNXT9ideIN3tXJHGOIf73nFnAviJN8Z96lndx93HJB5EriNfC18Trchua8Vpsov2JD4n/JAoz/yZen7kDyd9KjAX2W+L/wQvEgN/Z4zR9TxSejyeKsNOJAbl3bqSIPZfk97E1kT9uJfLidcTnh7OyNn2JKJQfRLz2vyVyeG6+EZE6BxPvF+rLSXcDlyfbjS7C+YYS70suJPLCh8AO7v5JEY5d8Dnc/QYzm0YUoR8mCvOPAye5e3aR/nfJuhOJ7rnPEl+cvpi1zUgiX55JfOk2maTIlN31X4rDoltymU5mtgXxTzXXJHdftGyBiEirp3wk1SYZT+hz4Ah3vzHlcKRIlIuqj16LUouUi2qXmY0GXnT3/tV8DklXWi2OjiGqgRm50y6LiJSL8pGIVALlIhGpBMpFIjKPtApHHyWDV4mIpE35SEQqgXKRiFQC5SIRmYfGOBIREakC7j4aKNbsKiLSTHotikg1cffutXAOSVeTZ88pkjvNbLaZjTezu8ysa0pxiIgoH4lIJVAuEpFKoFwkIvMo9+DY6wIHEiOu/wSsC5xGTK28rruPKVswItKqKR+JSCVQLhKRSqBcJCINKWvhKG8AZusBrwMXu/sZedYfCRwJsMACC6y/6qqrljlCESm2N998c5y7d0k7jlwN5SPlIpHaU425KFmvfCRSQ5SLRKQSNJSLUi8cAZjZh8BX7t6voe369Onjw4YNK1NUIlIqZvamu/dJO458CslHykUitaHacxEoH4nUAuUiEakEDeWitMY4ymVA+hUsERHlIxGpDMpFIlIJlItEJP3CkZn1AVYGXks7FhFp3ZSPRKQSKBeJSCVQLhKRjHblPJmZ3Ql8DrwFTCQGXTsV+Aa4qpyxiEjrpnwkIpVAuUhEKoFykYg0pKyFI2A4sD9wNDA/8D3wAHC2u48rcywi0ropH4lIJVAuEpFKoFwkIvUqa+HI3S8CLirnOUVE8lE+EpFKoFwkIpVAuUhEGpL6GEciIiIiIiIiIlKZVDgSEREREREREZG8VDgSEREREREREZG8VDgSEREREREREZG8VDgSEREREREREZG8VDgSEREREREREZG8VDgSEREREREREZG8VDgSEREREREREZG8VDgSEREREREREZG8VDgSEREREREREZG8VDgSEREREREREZG8VDgSEREREREREZG8VDgSEREREREREZG8VDgSEREREREREZG8VDgSEREREREREZG8VDgSEREREREREZG8VDgSEREREREREZG8VDgSEREREREREZG8VDgSEREREREREZG8VDgSEREREREREZG8VDgSEREREREREZG82hW6oZl1B/oCywLzAeOAEcCr7j69JNGJiIiIiIiIiEhqGiwcmdmiwOHJrRdgeTabYWaPANe4+5BiBygiIiIiIiIiIumot6uamZ0AfAYcDwwC9gF6AosAHYClgY2Ak4FFgafNbLCZrVLimEVEREREREREpAwaanF0IHAY8Ii7z8mzfkxyew240syWBU4EdiG6sImIiIiIiIiISBWrt3Dk7us15UDu/i3ROklERERERERERGqAZlUTEREREREREZG8mlU4MrNlzOzXZrZ0sQMSEREREREREZHK0NDg2G3M7Aozm2Rm483stGT5+cCXwAvA12Z2dZliFRERERERERGRMmpocOw/AMcA9wITgFPMbH7gL8D5wDBgC+A4M3vR3e8pcawiIiIiIiIiIlJGDRWODgcud/e/AJjZc0QR6WJ3PyfZ5jEzW5AoMqlwJCIiIiKt0owZcOihcOKJsO66aUcjIiJSPA2NcbQSMCjr8eBk+2dytnsc6FXkuEREREREqsZzz8Fdd8HxmmNYRERqTEOFozmAZT2ektxPytluMrBoEWMSEREREakqzyRfrfbvn24cIiIixdZQ4egbotURAO4+G9gFGJmzXVdgTPFDExERERGpDoMGwVZbwW9/m3YkIiIixdVQ4eg1YLPsBe7+mLtPztluF+CNYgcmIiIiIlINZs2C3r1hr73gp5/APe2IREREiqfewpG7H+ruBxRwjP8DTi9eSCIiIiIi1aNdO7j77vh5kUVg3Lh04xERESmmhlocFcTdH3f3Ec3d38yeNDM3s/NbGouISHMpF4lIJVAuqk7jx8d9165xP2pUerGIFINykYhka1LhyMKyZtYj99ack5vZ/sDazdlXRKRYlItEpBIoF1Wn2bNhlVXgL3+Bnj1j2cjcEUFFqohykYjkKqhwZGaLm9k9wHTgK2KA7Nxbk5jZosAVgCYtFZHUKBeJSCVQLqpeb74ZLY7WWw9WXBHatFGLI6leykUikk+7Are7CdgSuBr4GJhRhHP/FfjA3e82s7uKcDwRkeZQLhKRSqBcVKUGDQIz2HZb6NABundXiyOpaspFIjKPQgtHWwLHuvstxTipmW0CDEBNIEUkRcpFIlIJlIuq25NPQp8+sMQS8fikk+p+FqkmykUiUp9CxziaAPxQjBOaWXvgeuDSQgbVNrMjzWyYmQ0bO3ZsMUIQEVEuEpGK0NRclOyjfFQhfvwRXn0V+vWrW/a738Gee6YXk0hzKBeJSEMKLRxdBRxlZlaEc54MzAdcUMjG7n6Du/dx9z5dunQpwulFRADlIhGpDE3KRaB8VEk6doTbb4cDD6xb9ssv8OGH8PPP6cUl0gzKRSJSr4K6qrn75Wa2LPChmQ0Gfpx3Ez+7seOYWVfgdOBwoKOZdcxa3TEZjG2yu88uKHoRkWZQLhKRSqBcVP3mnx8OOGDuZYMHw847w8svw0YbpROXSFMoF4lIYwqdVW1H4I/AKsn9GXluhegBdALuIIpPmRvAicnPaxZ4LBGR5lIuEpFKoFxUxdzhuutg9Oi5l/fqFfcaIFuqiHKRiDSo0MGxLwfeIIpGH7v7zGae7x1ioO1czxGJ6iZAE5iKSKm9g3KRiKTvHZSLqtZHH8Hvfw833ABHHFG3vHt3aNMGRumZk+rxDspFItKAQgtHXYFj3P39lpzM3ScCQ3KXJ0MnfeHu86wTESk25SIRqQTKRdVt0KC4zx4YG6BDhygeqcWRVAvlIhFpTKGDY78NLFvKQEREREREqsWgQbDaatC167zrevZU4UhERGpHoS2OjgFuNbOR7v5SsYNw92LM1iYi0iLKRSJSCZSLKt/PP8PQoXDUUfnXn3wyzJpV3phEik25SEQyCi0cPQQsDDxvZlOBiTnr3d27FTEuEREREZGK9O67MGPGvN3UMrbaqrzxiIiIlFKhhaNnAC9lICIiIiIi1aBvXxg3DhZYIP/6qVPhxRdhzTVhWQ32ICIiVa6gwpG7H1LiOEREREREqkbnzvWv++Yb2H57uPVWGDCgfDGJiIiUQr2DY5vZIDM7ysz0PYmIiIiICPDVV9EV7Y036t+me3do00YDZIuISG1oaFa10cCZwFdm9pqZnWpmq5UnLBERERGRyjNoEDz3XP3d1AA6dIji0ahRZQtLRESkZOotHLn779x9OWAT4DlgADDczEaY2SVm1rdcQYqIiIiIVIInn4Tll4fVGvk6tWdPtTgSEZHa0FCLIwDc/RV3P8XdVwNWB24BNgdeMrPvzOx6M9vezNqXOFYRERERkdTMmgWDB8dsatbIROW9ekWLI9f0MiIiUuUaLRxlc/eP3f0id+8LLAecC3QDHgLGFT88EREREZHK8NprMGlSDHzdmOOOgyFDSh2RiIhI6RU0q1o+7v49cC1wrZktDOxQtKhERERERCrMnDkxMPbWWze+bc+epY9HRESkHJrU4qg+7v6Tu99bjGOJiIiIiFSiTTeFZ56Bzp0b33b6dLjxRnjzzdLHJSIiUkoFFY7MbI6Zza7nNsvMxpvZU2a2XakDFhEREREpt2nTYOLEwrdv0waOOgoeeqhUEYmIiJRHoS2OzgO+AsYSg2NfAtyaPP4auB1YEnjCzHYufpgiIiIiIul55BFYYgkYPryw7Tt0gG7dNLOaiIhUv0LHOJoOfA7s4O7TMwvNbD7gCaKAtB7wGHAa8GiR4xQRERERSc2TT8Iii8BqqxW+T69eKhyJiEj1K7TF0VHAFdlFIwB3/xm4AjjK3ecANwJrFTdEEREREZH0uMOgQbDtttC2beH79eoFo0bF/iIiItWq0MLRkkD7etZ1ABZPfh4HWEuDEhERERGpFO+9B99/D/36NW2/Xr3gp59g7NjSxCUiIlIOhRaOhgEDzWyZ7IVmtixwdrIeoBvwbfHCExERERFJ16BBcd/UwtGAAVE06tKl+DGJiIiUS6FjHB0LPAN8bmavAGOIVkgbAdOA/sl2PYG7ih2kiIiIiEha9toLllwSll22afstumhJwhERESmrggpH7v6WmfUETgA2BNYEvgMuAy539/HJdmeVKlARERERkTT06BG35jj/fFhlFdh77+LGJCIiUi6FtjgiKQ6dVsJYREREREQqyttvw0cfwR57QKdOTd//lltg/fVVOBIRkepV6BhHIiIiIiKtzs03wxFHgDVz+pfMzGoiIiLVqqAWR2bWBjgS2BtYAcj9vsXdvVuRYxMRERERSdWgQbDlltCxY/P279kTXnoJ3JtffBIREUlToV3V/gocD7wNvAHMKFlEIiIiIiIV4LPPYORIOPro5h+jVy+YPDlmV1tyyeLFJiIiUi6FFo76A+e5+9mlDEZEREREpFIMGhT3/fo1/xi9esF888HXX6twJCIi1anQwlE74PlSBiIiIiIiUkneeQe6d4/iT3Nttx1MmQJtNLKoiIhUqUILR/cB/YBnShiLiIiIiEjFuP56+PHHlo1N1LZt8eIRERFJQ6GFo+OBO83sBmAQ8GPuBu7+bDEDExERERFJW+fOLT/GGWdA+/ZwtgZ9EBGRKlRo4WgZoAewK3B41nIHLLnX9ykiIiIiUhMuuQSGD4fbbmv5bGhvvgljxqhwJCIi1anQwtH/AUsAxwIfo1nVRERERKSG3XsvLLRQy4tGEGMkvfQSuBfneCIiIuVUaOGoDzDA3e8rZTAiIiIiImn74Qd4+2244ILiHK9nT5g8OVodLbVUcY4pIiJSLoXO7/AlamUkIiIiIq3AU0/F/fbbF+d4mVnZRo0qzvFERETKqdDC0fnAyWa2YCmDERERERFJ25NPQpcusM46xTneyitH8WjatOIcT0REpJwK7arWD1geGG1mrzDvrGru7gcXNTIRERERkRSsthqsuCK0KfQr1kastBJ88klxjiUiIlJuhRaONgHmAJOBNfKs96JFJCIiIiKSojPOSDsCERGRylFQ4cjdVyx1ICIiIiIiafvmG1hySWjfvrjHPeMMeO89eOSR4h5XRESk1AptcSQiIiIiUvP23z/un3++uMf96ScYMgTcway4xxYRESmlentum9kyzTmgmS3dwLp+ZvasmX1vZr+Y2ddm9m8z692cc4mINJfykYhUAuWiyjJpErz8MmyySfGP3bMnTJ4MY8YU/9giLaVcJCINaWjIv1FmdqWZrdrYQcxsPjM7wMzeAQ5vYNPFgDeBPwHbAacCqwOvmlm3wsMWEWkx5SMRqQTKRRXk2Wdh9mzo16/4x+7VK+5HjSr+sUWKQLlIROrVUFe1zYC/Ah+Y2XvAC8C7wFjgF6Az0AP4FbAVMXj2X4HL6zugu98N3J29zMxeBz4G9gIua+6FiIg0hfKRiFQC5aLKMmgQLLQQbLRR8Y+dKRyNHAkbb1z844u0hHKRiDSk3sKRu78JbG1m6wFHADsTFehs04HXgJOAO919cjNiGJ/cz2zGviIixaR8JCKVQLkoBe5RONpqK+jQofjH79YNtt4aFl20+McWKRHlIhEBChgc293fAn4PYGZLAssCnYhEMtrdm5xIzKwt0BboBlwMfA/c09TjiIi0lPKRiFQC5aLKcMst0LFjaY7dvj0MHlyaY4sUi3KRiOTTpFnV3H0MUIwh/V4D1k9+HgVslRxbRKTclI9EpBIoF6XMDDbfvPTn0axqUuGUi0RkHg0Njl1KBwF9gQOAn4Cnzax7vg3N7EgzG2Zmw8aOHVvGEEWklSgoHykXiUiJ6b1Rym68EV55pbTnOP98WHLJKB6JVCjlIhGZRyqFI3f/yN1fSwZh2xpYEDilnm1vcPc+7t6nS5cuZY1TRGpfoflIuUhESknvjdI1fToccwzcdVdpz7PIIjBuHIxR+w2pUMpFIpJPWi2O/sfdJxLNIHumHIqItHLKRyJSCZSLyu/FF+Hnn2H77Ut7np7JMzpyZGnPI1IMykUikpF64cjMlgJWBT5NOxYRad2Uj0SkEigXld+TT8ZMaltsUdrz9OoV9yocSTVQLhKRjCYNjt1SZvYg8BbwHtFndmXgz8As4LJyxiIirZvykYhUAuWiyjBoEGy6KSywQGnP060btG2rwpFUHuUiEWlIQYUjM2sDtHH3WVnL+gFrAM+6+9sFnu9VYB/gBKAD8BUwBLjI3UcXHraISIspH4lIJVAuStmkSfDNNzBgQOnP1b49/PnP0KdP6c8l0kTKRSJSr0JbHN0N/AIMADCzo4BrknUzzWwndx/c2EHc/RLgkuYEKiJSTMpHIlIJlIvSt8giMVj1jBnlOd/f/lae84g0hXKRiDSk0DGO+gKPZz3+C3AjsAjwAHB6keMSERERESmLdu1g/vnLcy53+P77uBcREakGhRaOlgS+ATCznsCKwNXuPhn4P2DN0oQnIiIiIlIas2dD375w113lO+c118Ayy8APP5TvnCIiIi1RaOHoJ2Dx5OctgHHu/l7yeDbQqchxiYiIiIiU1BtvwGuvxYDV5dKjR9xrgGwREakWhRaOXgZOMbOdgeOYu9taT+DrIsclIiIiIlJSgwaBGWyzTfnO2atX3I8aVb5zioiItEShhaOTgMWAR4jWRQOz1u0LvFLcsERERERESmvQINhgA1h88ca3LZbu3WNMJbU4EhGRalHQrGruPhJY2cwWd/fxOauPBb4vemQiIiIiIiXy44/RTe2MM8p73nbtYMUV1eJIRESqR0GFo4zsopGZLUYMkj3c3X8pdmAiIiIiIqUyYQL07w+77lr+c59xBiy2WPnPKyIi0hwFFY7M7AxgAXc/NXm8GfAosADwjZltnbRKEhERERGpeCutBLfems65BwxI57wiIiLNUegYR/2Bz7Ie/xV4F9gN+AE4r7hhiYiIiIiUzsSJ4J7OuadNixndpk5N5/wiIiJNUWjhaDlgJICZdQE2AM509/8CFwObliY8EREREZHi22QT2G+/dM79/PPwq1/BW2+lc34REZGmKLRwNBvokPy8GTAdeCl5PJaYcU1EREREpOLNmAEjRkDPnumcv1evuNcA2SIiUg0KLRx9APQ3swWBw4Ch7j4zWbcCMKYUwYmIiIiIFNuIETBrFqyxRjrn79YtZlcbqRFCRUSkChQ6q9q5wMPAgcBMoF/Wuh0BNbQVERERkaowfHjcp1U4atcOVlxRhSMREakOBRWO3H2Qma0GrAe84+6fZq1+nhgoW0RERESk4g0fHsWbVVZJL4ZevdRVTUREqkOhLY5w98+Bz/Msv76oEYmIiIiIlFC/frD44tChQ+Pblsppp8Hs2emdX0REpFAFF44AzKwz0AvolLvO3Z8vVlAiIiIiIqWy2WZxS9PGG6d7fhERkUIVVDgys07AzcA+gNWzWdtiBSUiIiIiUgrTp8O778Kaa8L886cXx+TJMHgwrL8+dO2aXhwiIiKNKXRWtTOBLYCDicLRn4DDgReBT4GdSxGciIiIiEgxvf8+9O0LTz2VbhxjxsAee0TxSEREpJIVWjjak5hZ7Z7k8Wvu/n/uvjkxMPb2pQhORERERKSY0p5RLaNbtxigWwNki4hIpSu0cNQV+MDdZwMzgQWy1t0M7FvswEREREREim34cJhvPlhxxXTjaNcuYhg5Mt04REREGlNo4Wg8sGDy81fA2lnrlgDmK2ZQIiIiIiKlMHw49O4NbStgdM5evdTiSEREKl+hs6q9CqwLPAHcD5xnZgsBs4ATiLGOREREREQq2vDhsO22aUcRevWCoUPBHay+6WdERERSVmjh6BKiuxrA+UBPYsyjtkRR6ffFD01EREREpHjc4a67YOGF044k/PnP8Ic/pB2FiIhIwwoqHLn7MGBY8vNkYE8z6wh0dPefShifiIiIiEhRmMHmm6cdRZ1u3dKOQEREpHGFjnE0D3f/RUUjEREREakWb7wBDzwAs2enHUmYPh3+8Q949dW0IxEREalfoV3VMLPFgJ2AFYBOOavd3c8uZmAiIiIiIsV0881w993w449pRxLatYMTToC//AX69k07GhERkfwKKhyZ2XbEoNgL1LOJAyociYiIiEjFGj4c1lijcgaibtcOevSAkSPTjkRERKR+hXZVuxx4G1ibGNeoTc6tAiY0FRERERHJzz0KR2uumXYkc+vZE0aNSjsKERGR+hVaOOoOnOfu77v7zBLGIyIiIiJSdN9+CxMnRoujStKrV7Q4ck87EhERkfwKLRy9DSxbykBEREREREpl+PC4r7TCUc+eMHUqjBmTdiQiIiL5FVo4Oh44ycw2KmUwIiIiIiKlsM020bLnV79KO5K5HXIITJkCSy2VdiQiIiL5FTqr2pvAM8CLZjYVmJiz3t29WzEDExEREREplrZto3VPpVlwwbQjEBERaVihhaNLgT8RXdY+BmaULCIRERERkSK74ILoprbrrmlHMq/TT4fVVoP+/dOOREREZF6FFo4OIQbHPruEsYiIiIiIFN2cOVE4Ouqoyiwc3XcfrLWWCkciIlKZCh3jyIHnSxmIiIiIiEgpfP45/Pxz5Q2MndGzZ4y/JCIiUokKLRz9B9ihlIGIiIiIiJTC++/HfaUWjnr1glGjwD3tSEREROZVaOHoCWAPM/uXme1pZlvl3go5iJntZWb3m9kXZvazmY0ws4vMbKHmX4KISNMoF4lIpVA+Ko/hw+O+d+9046hPz54wdSp8913akUhrpVwkIg0pdIyjB5P73ya3DAcsuW9bwHFOBL4ETgO+BtYFBgJbmtmv3X1OgfGIiLSEcpGIVArlozL44Qfo0aNyZzDr1QsWXxy+/x6WXTbtaKSVUi4SkXoVWjjaskjn28Xdx2Y9HmpmE4BbgS2AZ4t0HhGRhigXiUilUD4qg6uugssuSzuK+m23HYwbl3YU0sopF4lIvQoqHLn70GKcLCcZZbyR3C9XjHOIiDRGuUhEKoXyUfl06JB2BPUzSzsCae2Ui0SkIYWOcQSAmS1mZjuZ2UFmtqOZLVaEGDZP7j8qwrFERJpLuUhEKoXyURGNGAG77ALvvpt2JA078UQ45ZS0oxCZi3KRiABNKByZ2fnAN8AjRJPFR4FvzOy85p7czJYDzgUGu/uw5h5HRKQllItEpFIoHxXf22/Do49CmyZ9XVp+H38MTzyRdhQiQblIRLIV9C/UzI4jBkq7A9gKWI0Y9+gO4DQzO6apJzazBYGHgVnAoQ1sd6SZDTOzYWPH5mtBKSLSfMpFIlIplI9K4/33oV07WGWVtCNpWM+eMGoUuKcdibR2ykUikqvQ716OAq509yPcfai7j0jujwD+AfyhKSc1s05Ey6UeQD93/7q+bd39Bnfv4+59unTp0pTTiIg0SLlIRCqF8lHpDB8OK69c2WMcQcysNm0afPdd2pFIa6ZcJCL5FFo46g48Vs+6x5L1BTGz9sD9wK+AHd39/UL3FREpFuUiEakUykelNXw4rLFG2lE0rlevuB85Mt04pPVSLhKR+hRaOBoP1Pcvd/VkfaPMrA1wJ7A1sKu7v1rg+UVEika5SEQqhfJRac2eDcsvD337ph1J41ZZBdZeG2bNSjsSaY2Ui0SkIe0K3O5B4DwzGw/c4+4zzawdsDcxaNqtBR7nn8k+FwBTzSz73/jXDTWFFBEpIuUiEakUykcl1LYtDB2adhSF6dYN3nkn7SikFVMuEpF6Fdri6FTgHaJANM3MfgB+JqrS7xIDZxdih+T+dOCVnNvhBR5DRKSllItEpFIoH4lIJVAuEpF6FdTiyN0nm9lmwE7ApsBiwARgKPCEe2HzP7h792bGKSJSNMpFIlIplI9K6/TTYcgQePFFMEs7msadfDK8+SYMHpx2JNLaKBeJSEMK7apGUhx6NLmJiIiIiFS0YcPgl1+qo2gEMGMGvPIKuFdPzCIiUvsK7aomIiIiIlJV3n+/OmZUy+jVC6ZNg+++SzsSERGROvUWjsxstpn9Kvl5TvK4vpvmfxARERGRijF+fBRgqqlw1LNn3I8cmW4cIiIi2RrqqnYu8HXWzwWNYyQiIiIikrYPPoj7aioc9eoV96NGweabpxuLiIhIRr2FI3c/J+vngWWJRkRERESkCBZYAPbdF9ZaK+1ICte1K+y6Kyy1VNqRiIiI1Cl4cGwRERERkWqx/vpwzz1pR9E0bdvCQw+lHYWIiMjcGhwc28wWMrN+ZrazmS2YLFvFzO42sw/MbIiZ7VGeUEVERERECjNpUtoRNN/MmWlHICIiUqehwbFXBj4AHgceAT4xs/WBF4BtgCnAGsB/zGybMsQqIiIiItIod+jeHY4/Pu1Imu6cc2CxxeIaREREKkFDLY7OA6YD2wF9gQ+Bh4C3gRXcfUOgKzAUOKW0YYqIiIiIFObbb2HiRFhppbQjabouXWDKlJgRTkREpBI0VDjaGDjX3Z9x99eBo4HlgKvdfTqAu08DriJaHomIiIiIpG748LivphnVMjIzq40cmW4cIiIiGQ0VjpYGPs16nPn525ztvgO6FDMoEREREZHmyhSOVl893Tiao2fPuFfhSEREKkVDhaM2wOysx5mfc3tcqwe2iIiIiFSM4cNh6aVhiSXSjqTpunaF9u1h1Ki0IxEREQntGlm/nJn1SH5um7VsYtY2yxc9KhERERGRZtprL9hww7SjaJ62beHUU6FPn7QjERERCY0Vju7Ls+yhnMeGWh1Vheuug0cfhQcegA4d0o5GREREpDR22intCFrmnHPSjiAd7vDqq1H0a9NQvwgRESmrhgpHh5YtCim599+HY46BmTPhiivg5JPTjkhERESk+CZNgs8+i/GNqvWLslmz4MsvYcUVwSztaMrn3/+G/faD22+H/v3TjkZERDLqLRy5+63lDERKZ+ZMOOQQWHRR+OMfYf/9045IREREpDSGDIHddoPXXoNf/SrtaJrnhhviPdvXX8Nyy6UdTXnMmQMDB8bPt96qwpGISCVRI9BW4PHH4a234Npr4eyzY9BFV+dCERERqUGZGdV69043jpbo1SvuW9MA2W3awB13wC67wDPPRNFMREQqgwpHrcCuu8Lrr8Oee8bjH36Afv1g0KB04xIREREptvffjy5eCy6YdiTN17Nn3I8cmW4c5bb++jGkwtprw3ffpR2NiIhkqHBUw2bNgo8+ip832KBu+aKLwujRcPTR8MsvaUQmIiIiUhrDh8Maa6QdRct07RrjM7WWFkc33hjDKkybBiutBG+/Pfd7VxERSZcKRzXsb3+Lb2xGjJh7eceOcNVV8S3WZZelE5uIiIhIsc2YEe97qr1w1LYt9OjROlocTZ8eYxuNHAnzzVe3fPJkGDs2tbBERCRLQ7OqSRX74IP4J7z77rDKKvOu79cvuq6dfz4ceCB061b2EEVERESKyizGdlx++bQjablzzoHOndOOovSuvx6++SZmUsvMIDd9OnTvDgMGRNc1ERFJl1oc1aBZs6K578ILw9VX17/d5ZfHP+jzzitbaCIiIiIl0749bLstrLZa2pG03D77xLXUsqlT4cILYcst45bRqVM8vuuueF8rIiLpKrjFkZktDOwIdAU65ax2d1f5oUJceikMGwb//jd06VL/dl27wn//qz7kIiIiUhuefz6KETvskHYkLffTTzHWz3rrwUILpR1NaVx9NYwZAw88MO+6AQPg/vvhqadgxx3LH5uIiNQpqHBkZhsD/wUWrWcTB1Q4qhAzZsD++8Peeze+7VZb1e3jHuMfiYiIiFSjyy+PsXJqoXD02muw3XYwZAhsvnna0ZTGgQfCAgvAxhvPu2777WHxxeG221Q4EhFJW6Fd1f4OjAY2ADq5e5ucW9tSBShNd9ZZcOedhW//00+wzjrw17+WLCQRERGRknv//eofGDujV6+4r+UBspdfHv70p/zrOnSIL0IfeigGyhYRkfQUWjhaDTjD3d909xmlDEia74Yb4Omn4+fM4IKFWHhhWHPN6GP++eeliU1ERESklKZOhc8+q53C0QorRPGkFgtHEybALrtEoa8hxx8Pr79eu131RESqRaGFoy8BdWKqYB9+CEcfHTNTNMdll8XUr8cdV9SwRERERMriww/jvlYKR23bwtprw8MP194A0ZdeCo891vh2K64Ia61V+nhERKRhhRaOzgFOSQbIlgozaxYcemh8G/PPfzbvGMsvD2efDY88Ao8+Wtz4REREREpt+PC4r5XCEcBpp8GIETB4cNqRFM+YMXDllbDfftHivTGffgoHHQSjR5c8NBERqUehs6rtDCwFfG5mrwATcta7ux9c1MikYJdfHs14774bllqq+cc59lj4v/+Dm2+GnXcuXnwiIiIipda/P2y4IfTokXYkxbPrrvDmmzGzWq24+GKYPh0GDixs+7Zt4Y47YJVV4IwzShqaiIjUo9DC0SbEzGk/AavnWe9Fi0ia5LPPYjDsPfaAffdt2bE6dIBBg2DZZYsTm4iIiEi5tG8PvXunHUVxmdUVjaZOjRnIqtk338A118CAAbDyyoXt0717zCp3221w+ulNG8dTRESKo6Cuau6+YiO3Gvpup7p07w7/+Ef8Ey7GP9IVVohvdiZMiH/uteCqq2DUqLSjEBERkVI67bSYur4WXXddjPfz449pR9Iyiy4aLY3OOqtp+w0YEIOEv/56KaISkWo3enS0ZpyhabxKptAxjqQCTZ8ObdrAkUe2rItarlmzYIMN4rhehW3Jpk2LqVsBvv0WTjwxvtXabTcYOrQ6r0lERETqN2ECXHQRDBuWdiSlsdFGMG4c/PWvaUfSMgssAKecEkWwpthrL+jUKVodiYhku/feGC/thx+iB42URpMLR2a2pJl1zb2VIjip38cfR2ujZ54p/rHbtYM//hEefzwGy64W7vDgg9FMfY89YjDFZZeNCvTpp8OLL8IWW8D668MHH6QdrYiIiBRLLQ6MnW3tteGAA2JQ6W+/TTua5jnnHPjPf5q378ILw+9+B8ssU9yYRKR6TZkChx0WA+2vsQYcc0wsnzo13bhqVUGFIzNrY2YXmtl44Dvg8zw3KZPZs2MWtZkzYfV8I04VwdFHx7GPPTZa8FS6kSNhhx2iYLTwwtFUfaWVYt0yy8B558GXX8L110dXvOWWi3Xvvw/jx6cWtoiIiBRBrReOIN7LzJoVBZhq88knEf/LLzf/GH//uwbHFpHwxRcx/tstt0QDgeefj5aM990XEyR8rupE0RXa4ug44I/AZYABFwLnEwWjT4EjShGc5HfFFfDqqzF2z9JLl+Yc7dvDP/8ZL8qLLirNOYpl2jTo2xdeeSXeVLz1Fmy22bzbzT9/dL97443oY+8OBx8c4zr9/vcx3a2IiIhUn+HDYZFF6r4YqkUrrghHHRUzjE3Ind+4wp1zDnTsGN3UWmL2bHj33eLEJCLVa9ll44uCZ5+F88+Pz64AffrEcC4HHhiFdimeQgtHhwLnApckjx9097OB1YBvgIK7qpnZ8mZ2lZm9YmbTzMzNrHtTgm7NPv44vm3ZbTfYf//SnmvzzWNq208+qbxxgdyjVZF7FIRuuy0KP8ceG13tCmEGt94av8ebb4ZVV4Wdd46iXKm9914U5PbaK2bDq/bBLquRcpGIVALlouL4/vv4EFHrM26ddVYUyRZbLO1ICjd8ONx9d3QjaemYnGefHeNwVlvhrBooF0ml+/77GCh//PgoFD3wQAxDkq17d7jhhmhQUI2tMytZoYWjHsAwd58NzALmA3D3mcDfgcOacM6ewD7Aj8ALTdhPgIcfjoEFr722PG+ObropBhyrpDdin3wC228PW24Jjz4ay3baqXmtr9ZcM67xyy/jzcjrr8NHH8W6n3+GX35pWazffQePPQbnnhvFvi++iOVDh8bsL+++GwN577JLnE/KSrlIRCqBclERPPBAacZ9rDRLLFE3sHQ1DCUA8f5qoYXgL39p+bH23DOGarj33pYfS+ahXCQV6/HHYa21Ypy0xiZB2HffGNblggtqd6bNNBRaOJoEdEp+/hZYJWtdO6Ap33s87+5LufuOQDOHyGu9Tj45BnYuVRe1XJmR6UeOhBdS/hcydWoUW9ZYI1oFXXlljGtUDEstFdPDfvllNG2EmPq2e/do/jhuXMP7u8e+mfGSXnghxlZadtloxTRwYLSI+uGHWD9gQLQyGjkympy//DJcfXVxrkUKplwkIpVAuahIOnZMO4LyGTAAfvObtKMozF57wSWXFKeV1DrrxPtAza5WEspFUnGmT4fjjqtrJPDmm9CvX+P7/eMfsMoqtTvTZhoKLRy9DfROfh4EnGNm+5vZ3sBFwFuFntDd5zQtRIEoMLz3XvxcrqJRhnsUUw48ML1R6t1hu+2ie9f++0cR5phjCu+WVqhOneqKZX36xBuUM8+McZCOPBI+/DDW/fxzVLxPPTWS15JLQrducM89sb5r11h+5ZUxm9tPP0VLpl/9KtYvskiMswSw997xLenxxxf3WqRhykUiUgmUi1ruxRdjcoxMq97WYP31473D00+nHUnj9t8/xmYqBjM46KD4AnHkyOIcU4JykVSik06Kz1NHHx09Q3r3bnwfgAUXjHFvTzyxtPG1JoUWjv4OZBrEng18D9wJ3Au0B/5U9Mjkf2bPhkMOicLJ9OnlP78ZXHYZfPVVNPkrp5EjYcaMiOGss6Ilz623lqd4tumm8MQT0cJrwAC4/Xb44x9j3axZ0Qzy0kthzBjYddcYTHy77WJ9t24xyv8xx8DGG0fyasiWW8Zsb998A9dcU9LLEhERqSmvvQYPPhhd+VuLo46K9xqnnAJzKvTj/muvRUujYnfFP/DAeF/4738X97giUhncYcqU+Pm002Jokn/8I77gb4r55ov7IUPic5y0TEGFI3d/2t2vT37+HvgVsDKwDrCyu79XsgiFK6+MrkyXXdb0F0yxbLppFE8uvbQ8s49NnRqteVZfPa4fogXPJpuU/ty5eveG66+PrmjXXhvLFlooxieaMgXefhtuvBH+8Afo1atl5/rnP6M4peKRiIhIYYYPjy+Ullgi7UjKp2PHGD/xrbdi+ulKdPrpcPnlxS9sLbdctDg6+eTiHldE0jdhQvTG2GmnaDyx9NLxc0tcemn0HBk+vDgxtlaFtjiai4dR7v5eMkB2yZjZkWY2zMyGjR07tpSnqkiffBL/eH/zGzjggHRj+etfYwazo48u3Sxr7tEFbNVV4eKL45oHDCjNuZqqS5eIK2PNNYs/nsK558Zz/ac/Ve4bwdaqteciEakcykdzGz48xr1pbQ48MK77iivSjmRezz0XXelOPbU0LcF+9aviD1cgTadcJMX0/POw9toxGdTOOxdvcqabbophQvbbT5MRtUTBhSMzW87MLk+Sw+dmtkay/Dgz27BUAbr7De7ex937dOnSpVSnqUizZ8Nhh0Uro+uuS39ms6WWijGGeveOGS2KZcqUuFaIYtE++8S3hi+9FN29Wjp1azVp1y7GSfr1r+MN4XPPpR2RZLTmXCQilUX5qM6cOdGlvDUWjtq2jdnFnnwy7Ujm5h7jQy63XPHGNsrnvPPgjDNKd3xpnHKRFMOsWTEkyZZbxufeV16JWRjbNKuJy7yWWioG1P/gA4151BIFPR1mtjrwPnAQMataVyAZQphuwLElia6VmzMHttgiZttaZpm0owm//z38/e91A0g31ezZ8c3gTTdFk8G1144KcKbp4BprwFVXwRtvRPGkNZpvPnjkEejZM954lap1l4iISLX78cdoAdynT9qRpKN373gfNXt2jAlZCQYNii//Tj+9tEMsjBoV75HVgkCkuk2bFrNMH3RQdL8tRT7fbjs44YQYDiTtmcKrVaGNPC8DPgL6AdOB7H9NLwOXFDkuAdq3j6ngK9HQofD++9GlqiHffls3Av7KK8PgwbD99rFu0UWjqfGuu9bNMLbLLqWMunosthg89VR0DUy7pZmIiEilWnzxGIS5NZs8Ob5s23//GEg2bUssEbH89relPc9BB0Urgv/+N1qri0h1efRR2GYbWHhhGDYsPv+U0oUXRiOFjTcu7XlqVaENwDYBLnb3KUBu+4cfgCbNcWVme5nZXsD6yaIdkmWbN+U4tey44yqv6XG2W2+FP/85ppjPNnUq/O1vsNdeMYX9csvB7rvXzXzRt2/s+/HHMH58fCt17rkxM4jMbbnloHPnmEnvuONi9jYpLuUiEakEykXSEgstBCuuGDOYjR+fdjTRWuCuu5rfOr1QW24Z75U0W1LxKBdJuZx/fjQYyEwIVOqiEUROOuSQ6AL37bd1Q6VIYQptcdTQfAhLAE1tJPqfnMeZOaSGAls08Vg15/HHYyaxpZeua51TaS65JKa+PeQQWGedaE10wgnxgjzrrOhat8km0aJoww1h3XVjv0UWqZzBrqvFxx/DDTdEs+9nn403iFI0ykUiUgmUi5rpiCOiu1prn1DiwgthrbViLMpLL00nhjlz4IILoqXRssuW/nxt28Z4kJddFl+uLblk6c/ZCigXSck98kgMx3HggTHpUrl99VUMl3L88RonrSkKLRy9DhwK/DfPun2Al5pyUndX55t6/PxzvIBWXTX+mCtVly5RPPrd72DEiCggQXSv++67uq5n0nLrrBMttnbbDfbcM5p1lvpbvNZCuUhEKoFyUfO9+mq0tmnt1lgjvpi7+mo45hjo2rX8MfznP/HlYc+e0VWtHAYMgK+/jhbv0nLKRVJqH38M/ftHy8Qbb4zPjuW2/PKwww4wcCBsvTVstFH5Y6hGhXZVOw/YxcyeIgbIdmAbM7sV2B24oETxtToXXwyffRbN9iq9OHDkkfDNNzBhQgyYnaGiUfHtvDP861/w9NNw6KHxrZ6IiEhrNmNGfAhpjTOq5XPOOTGhxrXXlv/cs2bB2WfHc7HvvuU77+qrw513qngoUg3co9jbqRM88EBpB89viFnkya5do8g9cWI6cVSbglocuftQM9sN+Dtwc7L4YmA0sJu7t/JhCYvj88+jcHTAAdFvuxqUoymyhEMPhe+/jyboo0dDjx5pRyQiIpKeTz6JgoUKR6Fbt5i8JI0Z5u68M1qgP/BA8abQbooPPojhEJZfvvznFpHCmMEtt0ShZoUV0o1l4YVjLLZNNoGjjoK779aERI0pOLW7+2Pu3gtYmRgsezV37+HuT5QsulamW7doaZRW33SpfKecEm+OVDQSEZHWbvjwuFfhqE7fvtCuXUysUS4zZkRrp/XWi2715TZ+fIxXcvXV5T+3iBTmjTeixVHv3jELZCXo2zcmaWrXDmbOTDuaytfk7wTcfZS7v+zuI0oRUGs1Z058Q/Pb38bA0iL5mMWg6e6R6G67Le2IRERE0tGlC+yxB6yyStqRVJY33ogvI199tTznmzIFNt00ZklK4xv7xRePyWTuvFOzJIlUokcfjQmTbr658W3L7dRT4Y47Kn+ImEpQb+HIzLZqyq2cQdeayZNhzTU1I4gUbtYseOEFOOywmIVPRESktdl6a7j/fujYMe1IKstqq0UB55RT4oumUltsMbj11hhsNi0HHRSDZA8Zkl4MIjKvESNi9rT11ovhWCpNptj90UeRR375Jd14KllDLY4GA08nt8H13J7OupdmGjgw/ljT7usp1aN9+xhHYO21Ye+94TWNMiYiIq3MTz+lHUFlWnDBmOp66FB48snSnefFF2NMznfeKd05CvWb38SYJbffnnYkIpLx00/RfbVDB3jwQZhvvrQjqt+oUdHy6NRT046kcjXWVW0ycAuwM7BlnttWWffSDO+/D1deCYcfDhtumHY0Uk0WWihaGy2zDOy0U8wsIyIi0hpMnRqzuF52WdqRVKYjjojxEE89tfgzsQ4bFq2LNt00vvj8+uviHr855psvvkh7/HGNVSJSKQ49FEaOhH//O2Ywq2S77AJ//CNccQU8oRGc82qocLQlcD+wJ3APcBjQ1t2H5ruVI9haM2cO/P738cbnoovSjkaq0VJLwaBBMZ1lJXzjJyIiUg4ffhjdsDRZRH4dOsB558G778LTRewXMGAAbLABvP46XHIJfPop7Lxz8Y7fEuedF60G2rdPOxIRgSgcXXVV9cwW/re/xfAxBx8cM1nL3NrVtyIpBg01sz8CewAHAYPM7DvgTuA2d/+oPGHWpuefh5degptuioH9RJpjpZWi//ACC8Rjd00nKSIitU0zqjVuv/1iGIRNN23ZcT7/HLp3j/cWvXvHDGrHHRddwyqJJpcRqQyTJsEii1ROUblQ880H99wD668PF14I//hH2hFVlkZnVXP36e5+l7vvAHQFrgR2BIabmSa+bIEttoBXXoFDDkk7Eql2maLRww9Ht7VyDIYpIiKSluHDo7WtWhzVr02buqJRcwZ8HT06JuHo2RMeeyyWnXIKnHVW5RWNMl55JWZv+vbbtCMRaZ1Gjowvte+8M+1Imqd3bxg8OFofydwaLRzlGA+MTm4OdC5yPK1Gpvlb377xj12kGH75BX73O7U4EhGR2jZ8eLzBb9s27Ugq37/+BSuvDFOmFLb9t9/CH/4Q+9x1FxxzTHRPqwaLLw5vvBFxi0h5TZ4cg2EDbLxxqqG0yMYbx2ydEybA8cfD5ZfHpERvvQU//th6v6Cvt6taNjPbmOiqtjfQEXgY2AnNptYsr70Gm20G990XA3GJFMs++6QdgYiISOkdfDDMnp12FNVhrbXgyy/jw89ZZzW8rXu0iP/885i45fTTYfnlyxJmUay8ckw2c9ttcOKJaUcj0nq4Ry+ajz+OcdW6d087opY75RS49955Z/D8+mtYbjm4/3544QVYccW43sx9pbbIbKl6C0dm1pMoFvUHugPPAycC/3H3Ar+zkFyzZ8eA2EssEf+YRURERKRpDjgg7Qiqx4Ybwu67w6WXxnvQLl3mXv/jj3DddfHNeseOcP310K1b9XYDHDAgZkd6911Ye+20oxFpHS6+OFrlXHYZbFUj863fcEPkw4kTo+vu55/HfWY8tffegxtvjFk+M9q1g59/jvt//SvGoe3eva6wtPrqZb+MommoxdEnwE/AA8DhwBfJ8iXNbMncjd39s+KHV3uuvRbefjuqlwstlHY0IiIiItVlzBgYNy5al7QrqO28XHBBjIN44YUx3TREt5Irr4yC0qRJsM46sMMO1TMDUn323TcG777ttvgQKyKl17YtHHQQ/PnPaUdSXGbQuXPc1l137nXnnAMDB8L48XVFpXHj6v4vvfEG3H47TJ8ej5daqrpna2vs3+3CwCHAwQUcS73MG/H999Hkd9ttYe+9045GREREpPrcd1+0KPnqq+rqRpWm1VaLqbGvvx5OOy2KKhdfHB9yfvMbOPfc2mmds/jicY3V/M2+SLXIzOZ80kmtc2Zns+hJtMQS844Fl2mxNGZMFJYmTUonxmJpqHB0aNmiaCVefBHmzIGrr259LyoRERGRYhg+PKZ6Xm65tCOpLueeCyecEB9w7r4b1lsPzjsvZiGrNQMHph2BSO2bMiVmcz7zTNhmG32+zccsWhottVTakbRcvYUjd7+1nIG0BnvtFS+qRRdNOxIRERGR6jR8OKy5pj6kNNWyy8YN4Nlna3cA14wxY2IWpO23TzsSkdrjHq0YX3wx7UikXDQRfBnMmBH/oEFFIxERiDccmT7fIiKFco/C0RprpB1Jdav1ohFEq6M99ph3RiQRablLLoluw5dcEg0jpPapcFQGl18OW28Nw4alHYmISLpmzoz7Rx6JgW3vuy8+CIqIFOK772IWMBWOpDEDBsTsRvffn3YkIrXlySdjHLF9943ur9I6qHBUYl98EX3Kd98d+vRJOxoRkXTMmROz+my2WbQ0WmqpmKFi771hu+3go4/SjlBEqsGii8Kjj8LOO6cdiVS6DTeEXr1iViMRKZ5HHonuwjfdpC7DrYkKRyV27LHxgvr739OOREQkHZMmRXeBM86AHj2iiNS3L7z5ZkwWMGwYrLUWXHRR2pGKSKWbf/4YjLVbt7QjkUpnBv37w3PPxRe5IlIc//wnDBkCCyyQdiRSTiocldB//wsPPwxnnw1du6YdjYhI+X34YczY89hjcOWVcMcd8cEPoF27mFJ7xAg4+OC6abVnz1b3NRHJb9AgeOGFtKOQatG/f3wx8d13aUciUt3co3vayJFRlO3cOe2IpNzqnVVNWu7nn2HTTeG449KORESk/NzhkEOixdGzz0Y+zGfJJeHGG+seX3UVPPBAtEZaa62yhCoiVeKMM6K72tNPpx2JVIMePeDdd9OOQqR6zZgBr7wSXT5vugkWX1zjGrVWanFUQvvsA0OHQocOaUciIlI+s2bFOEZmcOedMR1yfUWjfJZYIloqrbsuHHMMTJxYslBFpIrMmQMffKCBsUVEyuH88+M92RZbwK23wlFHwfHHpx2VpEWFoxL45BO49trobqEBw0SkNRkzJga7PvLIaHHUqxcsu2zTjtG/f+TRo46KfvQrrxwDMYpI6/b559GaW4UjEZHimT4dnnoqikKrrx7v5SCGWjngAHjwQRg/Pj7f6rNt66XCUZG5x5gdp54aLzARkdbi9ddh/fWjSfPWW7fszcVii0XRaNiwKBxl+tJr7COR1mv48LhX4UhEpOXefx923DHec/XrB9dcE+NNTpgQ6wcMgOuug912g4UXTjVUqQAa46jI/v1vGDw4xuZYcsm0oxERKY9//Qv+9KdoXfTyy9HNrBjWXTcGws0UoY4/HqZNgwsuiObTItJ6TJwYuWD11dOORESkukyZEjMMPvkkbLMN7L47LLggjBoFRxwB228Pm29eN4GJSC4Vjorop5/gz3+G9daLLhYiIq3B2LFw0knRB/6uu2LgxGLKFI3coX37GJzxvvuieHTEEdC2bXHPJyKVwz2mUu/ePb79Xmih+LAjIiINmzMHLrssikUvvAAzZ8ICC0C3brF+xRVjaACRQqirWhGdfTZ8/3006dMHGRGpdWPHxoe6Ll2ildHjjxe/aJTNDP7615ghZ+214fe/hw02gPfeK9050+Ieb/BmzYrH06fHt4LDh0f3vRdfjC6BkyalG6dIKf3wA+y1V7zev/kmcsAee6QdlYhIZZo9G55/Hm65JR63aQO33Rbv1447Dp55JoZSOemkNKOUaqUWR0W0007xAWqDDdKORESktJ57DvbdN8Zz+/OfYbXVynfu1VePNz//+Q+cdlp8ewbxZmniRFh6aVhqqbh16lS+uCDetI0dC99+C999F1NBr7ZafOj9y1+iAJS5/fJLvJHbe++YRW7bbedeP2dOzGIyYEAUi/LNTHfffbDnnlG4+8tfYiDLFVaIW9eusNlmdeNDiVQL92i9eMwxMHUqnHtuvJ5FRGRuM2fCkCFw//3w0ENRcF9iiZhopF07ePXVuvdJIi2hwlERbbNN3EREapV7NHs++WRYZZUYVDENZrDPPlE0ybTwvOSSaPWUba21ooUSRNe2H36ID6CZ4lK3brDmmo2fz72uGJS5/+47WGedGDTyp5+iQPTDD1E8yjjrLDjnHOjQAd54A+abL4pZnTrFG7kOHWK7RReN32VmXadO0LFjtLSA+F3ffvvc62fMiMHIIc6ZOccDD8Q6iDeMG24Id98NAwfWFZQy93vtBYssEkWqNmqDLBVg5sz4u3zkEdhoI7j5Zlh11bSjEhGpHL/8Eu992rWLwvr558d7ip12ilaZO+4Y60BFIykeFY5ERKQgU6bAYYdFS5+99ooPdAstlG5M2d2Cb7oJvv46ugz/8EPcZ7c4euON+FYuu3vXJptEv3+AjTeOmUSWXjomN5gyJVqQDhwY63v2jJZAGWYxIPhuu8XvYccdoxi1zDIxSPgyy8BKK8W2XbrAyJH1X8eyy8YA4/Xp0iW+PazPpptGKzCIItDYsfDVV9C7dyxbYokoQn31FQwaFEUvd9hhhygcXXgh/P3vsf2aa8ZtjTXig7u6Xks5tW8fs/pcdhkce6z+/kREIFpfPvFEtCx67LFocbzddtEquU+f+Hm++dKOUmqZCkciIlKQt96KZtB/+xuccELdoNWVYuml41afhx6K++nTo7D0ww9zt7LZbLMYR+j77+Gdd2IA3kzrIbMo7Cy8cF1haMkl40Nu9vpK0KZNXVe9jG23jVvGzJnRcirz++rTJ76l/OCDaNk0eXK0YJoyJT64X3cdjB4dxaQ114wWIB07lvWypIZ9/TX88Y/ROm+ddeCf/0w7IhGRyjB+fEwE8uST8PPPMZbkPvvU/f/u1StuIqWmwpGIiNRrwoQYT2jvvaOw8tln0RqgmnXqFF3UMrOKZFx0UcP7NdTip9q0bz/39W+/fdwgWiJ9+SV8/nldYez11+GOO6LgBNEEfost4Omn69YvsUTMfKUub1Io92gpeMIJMRD8AQdE4UhEpLUaPz666s6eDYcfHl3ZR4+GQw+N7vmbbVbXDU2knPRnJyIic5kxI5pD33YbPPpoPB41KrpdVXvRSBpnNm9h7eab4frrY9re99+P2d0y4zMBHHhg/I0ssEAMXr7mmtCvXxQcRfL54ov4Fv3pp6MIeeONdV07RURak08+gcGD4cEHo9v57NnRlf7ww6PV71tvpR2hiApHIiKS5aWXYNdd4xuvJZeMKe8HDIjZwaR1a98+ikKrrz7vujvuiIJS5vbww9HySIUjqc+tt8ZsgP/8Jxx1lFqqiUjr8NNP8Npr0VL31FMj9/31r9H6cuWV4aSTomXReuulHanI3MpeODKzFYArgG0BAwYDx7n7l+WORURaL+WiMHp0fOhfeeXoM9+7dwyw2L9/jImT6aok0pANN4xbtszMbtKw1pSLPv88xhDbaCM45RQ4+OB5u4yKSHpaUz4qpzffjHEQX345Wuy6R+ve/faLlpannBKz1fbsWXnjR4pklPX7HTObH3gWWBU4GDgI6AU8Z2aaLFBEyqK156JJk6JbyOabw4orwplnwvPPx7rOneGuu2KGMBWNpCWyu7JJfq0lF82ZA1dfHV0YjzgiHnfooKKRSCVpLfmolKZNg6FD4eKL4Te/iVZFAN98A3ffHZNrnH02PPUU/PhjXffcnj1jgGsVjaSSlbvF0RFAD2AVdx8FYGbvASOB3wGXlzkeEWmdWl0umjOnrivI7rtHH/qVV4bzz4/xabp3TzU8kdaq5nPRqFHw299GcXr77eGGG9QtTaRC1Xw+Kib3mDCiQ4dovb333jEj66xZsX6VVWDcuPh5xx2jUKTcJ9Ws3IWj3wCvZpIRgLt/bmYvAbuihCQi5dEqcpF7DKh4++1w//3xhmbxxeHcc+ONzgYb6NstkZTVdC764IPIMx06wP/9X3RNU84RqVg1nY+uvBJ++CEKO7NmRdFn9dVjjDWAP/whxnfMXr/55tGFDGIQ/ylTYvmsWTHr7IABcMklsPTSMfvZSSfBr38NffvG+60MzYImtaDcf8arAw/nWf4BoCE0RaRcajoXjRsXXdFuvx0+/DA+tO2ySwzIuPjiMVOHiFSEms5FvXvHB6kjjoDllks7GhFpRE3no2uugc8+i2747drF/fbb1xWO3nknikGZ9e3awdSpdft37gwLLli3bsEFo0AE0KlTzBApUsvKXThaDPgxz/IJQOcyxyIirVdN56JJk2Kmjo03huuui0GvO1f9VYnUpJrORWYwcGDaUYhIgWo6H338ccMtHl9+ueH9H3ywuPGIVJs0Gs55nmX1vozN7EjgyOThL2Y2vCRRld8SwLi0gyiCWrkOqJ1rqYbrqIQhUWs+F730Utwy36bVoxr+XgpVK9dSK9cBlX8tVZeLoDrzUQEq/W+lKWrlWmrlOqDyr6USchG0gvdGBaj0v5WmqJVrqZXrgMq/lnpzUbkLRz8S1excnclf4cbdbwBuADCzYe7ep3ThlU+tXEutXAfUzrXUynWUmHJRQtdSeWrlOqC2rqVEmpyLoDbzUa1cB9TOtdTKdUBtXUsJ6b0RtXMdUDvXUivXAdV9LeUe2/0Dov9srt7Ah2WORURaL+UiEakEykUiUimUj0SkXuUuHD0C9DWzHpkFZtYd2DhZJyJSDspFIlIJlItEpFIoH4lIvcpdOPoXMBp42Mx2NbPfEKP3fwVcX8D+N5QwtnKrlWupleuA2rmWWrmOUlIuqqNrqTy1ch1QW9dSCi3NRVA7v+NauQ6onWupleuA2rqWUtF7o1Ar1wG1cy21ch1Qxddi7vnGQCvhCc26AlcA2xKDrT0DHOfuo8saiIi0aspFIlIJlItEpFIoH4lIfcpeOBIRERERERERkepQ7q5qeZnZCmZ2n5lNMrOfzOyBpOJdyL6dzOxvZvadmf1sZq+Y2WaljjlPHHuZ2f1m9kUSxwgzu8jMFipgX6/ntk4ZQs8Xzxb1xDOxgH0r4vnIimdIA7/fJxvZN5XnxcyWN7Orkt/dtOSc3fNs19nMbjSzcWY21cwGm9maBZ6jjZmdamajzWy6mb1rZnsW/WKqjHKRclGpVGMuSs6tfJSCWshFSSw1kY+Ui/63r3JRK6NcpFxUKspFDZ6jInNRu7QDMLP5gWeBX4CDAQfOB54zs7XcfWojh7gJ2An4C/AZ8EdgkJlt5O7vlCzweZ0IfAmcBnwNrAsMBLY0s1+7+5xG9r+FefsPf1LkGJvqGOCNrMezCtinUp6PjD8AC+cs2wi4nMIG+ruF8j8vPYF9gDeBF4DtcjcwMyPiXxE4mpgm9VTidbOOu3/dyDnOI/5mT0/Osx/wHzPb2d0fL9aFVBPlov+5BeWiUqjGXATKR2VXQ7kIai8fKRcpF7UaykVzuQXlomJTLqpfZeYid0/1BhwLzAZ6Zi1bkXgBHN/IvmsTSezQrGXtgBHAI2W+ji55lg1I4tuqkX0dOD/t5yIrni2SmLZp4n4V83w0EudNxD/BxSrxeQHaZP18eBJH95xtdk2Wb5m1bBFgAvCPRo6/ZHL95+QsfwZ4L+3nJ8W/C+Ui5aJyX19F56Lk3MpH5f+d10QuSs5dE/lIuSj950S5KJXfuXKRKxeV+fqUiyo4F1VCV7XfAK+6+6jMAnf/HHiJ+KU3tu9M4N6sfWcB9wD9zKxj8cPNz93H5lmcqQQvV644UlYxz0d9zGw+YG/gv+4+Ie148vHGv/WA+F1/6+7PZe03Cfgvjb9u+gEdgDtylt8BrGlmKzYh3FqiXFQ7Kub5qE815CJQPkpJTeSi5NytPR9V1PORj3LR/ygXzUu5qHZU1PORj3LR/1RsLqqEwtHqwPA8yz8Aehew7+fuPi3Pvh2IpmRp2jy5/6iAbX9vZr8kfSWfNbNNSxlYge40s9lmNt7M7rLG+zRX+vMBsAewEHBrgdtX4vMCDb9uuprZgo3s+wswKmf5B8l9Y6+7WqVcFCrxb165qDKflwzlo+Kq5VwE1Z2PlIsq7znJplxUXMpFdSrt7165qPKek2w1mYsqoXC0GNHvL9cEoHML9s2sT4WZLQecCwx292GNbH4H0c9zG+BIYHHgWTPbopQxNmAScBnR/G4rop/lNsArZrZkA/tV7PORZQAwBniigG0r7XnJ1tjvuqHXzmLARE/aPebZtxKepzQoF1Xe37xyUai05yWX8lFx1WQugqrOR8pFoZKek3yUi4pLuShU0t+9clGopOckn5rMRakPjp3I/cUAWAH7WQv2LZmkivgw0Qf40Ma2d/eDsh6+YGYPE1XK84FNShJkw/G8DbydtWiomT0PvE4MxnZGPbtW5PORYWbLEgnmyqR5ZoMq7XnJ0ZLfdUU/Tymrqd+pclHe5amrsVwEykelUHO/z2rOR8pFoZKek3ooFxVfzf0+lYvmWZ465aKi7VtSldDi6EfyV846k79Sl21CA/tm1peVmXUiRlHvAfTzxkdNn4e7TwYeAzYocnjN5u5vESPVNxRTxT0fOfoTf/OFNoGcS4U9L439rht67UwAOicj/ufbN+3nKS3KRTkq7G8eUC6CinxelI+Kq6ZyEdRmPlIuqrznBOWiYlMuyqPS/u6ViyrvOaFGc1ElFI4+IPry5eoNfFjAvitaTBeZu+8M5u0bWFJm1h64H/gVsKO7v9+Sw5G/2pimxmKqqOcjjwHAu+7+bguOUSnPS0Ovmy/dfUoj+3YEVsqzLzT+uqtVykX1HI7K+JvPplxUWc+L8lFx1UwugprPR8pFlfWcKBcVl3JRA4ejcv7uQbkIKus5qclcVAmFo0eAvmbWI7PAzLoDGyfrGtu3PTECe2bfdsC+wFPu/kvRo62HmbUB7gS2BnZ191dbcKyFgZ2A14oUXouZWR9gZRqOqWKej1xJ/KvTzEp2coxKel4eAZYzs8zAfpn4dqHx182TxD+JA3OW9weGJzNmtEbKRfMeq5L+5gHlouQYlfa8KB8VV03kouTcNZuPlIsq7zlBuajYlIvyH6ui/u6ViyrvOaFWc5G7p3oDFiCqnO8T09P9BngX+AxYMGu7bkRf1LNy9r+HaO51OJEM7gOmA+uV+TquJaqc5wN9c27L13cNwInAv4ADgC2Ag5PfxQxg05SekzuT69iDGHjtBGAc8CWwRDU8H3mu6R/ENJRL5VlXcc8LsFdyy/xd/T55vHmyvg3wMvAVsB8xdeMQovniCjnHmgXclLPs4uR5OT65vmuBOcAuaT5PKf+NKBcpF5XjmqoqFyUxKB+V92+kJnJREktN5CPlosp4TpSLyv43olxUAX/3OdehXFQBz0lrzUWpnTjnl9OVaDr4EzAZeAjonrNN9+SJGZizfD7gcuD75Bf8GrBFCtcwOokv321gfddAVB5fSl70M4HxRCXyVyk+H6cC7xEj989M/uhvAJaplucjJ6b2wFjgv/Wsr7jnpYG/pSFZ2ywG3JwkoWnAM8Da9RzrlpxlbYkB9L4gpnx8D9grzeepEm7KRcpFJb6eqstFSQzKR+X/W6n6XJTEUhP5SLmoMp4T5aJU/laUi5SLSnk9ykVVlIssCU5ERERERERERGQulTDGkYiIiIiIiIiIVCAVjkREREREREREJC8VjkREREREREREJC8VjkREREREREREJC8VjkREREREREREJC8VjkREREREREREJC8VjkREREREREREJC8VjkREREREREREJC8VjkREREREREREJK//B5FriGcKDVZ9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(1,4,figsize=(20,5))\n",
    "fig.suptitle('Strait of Georgia', fontsize=24)\n",
    "fig.subplots_adjust(top=0.8)\n",
    "#ax[0].errorbar(monthlymean.index,logt_inv(monthlymean['L10Calanoids']),\n",
    "#                  yerr=logt_inv(np.array([monthlymean['L10Calanoids']-monthlysem['L10Calanoids'],\n",
    "#                 monthlymean['L10Calanoids']+monthlysem['L10Calanoids']])),\n",
    "#               fmt='ro',capsize=5)\n",
    "ax[0].plot(logt_inv(monthlymean['L10Calanoids']),'b--')\n",
    "#ax[1].errorbar(monthlymean.index,logt_inv(monthlymean['L10CalanoidsDiaRemoved']),\n",
    "#               yerr=logt_inv(np.array([monthlymean['L10CalanoidsDiaRemoved']-monthlysem['L10CalanoidsDiaRemoved'],\n",
    "#                 monthlymean['L10CalanoidsDiaRemoved']+monthlysem['L10CalanoidsDiaRemoved']])),\n",
    "#               fmt='ro',capsize=5)\n",
    "ax[1].plot(logt_inv(monthlymean['L10CalanoidsDiaRemoved']),'b--')\n",
    "#ax[2].errorbar(monthlymean.index,logt_inv(monthlymean['L10Euphausiids']),\n",
    "#               yerr=logt_inv(np.array([monthlymean['L10Euphausiids']-monthlysem['L10Euphausiids'],\n",
    "#                 monthlymean['L10Euphausiids']+monthlysem['L10Euphausiids']])),\n",
    "#               fmt='ro',capsize=5)\n",
    "ax[2].plot(logt_inv(monthlymean['L10Euphausiids']),'b--')\n",
    "#ax[3].errorbar(monthlymeanCentral.index,logt_inv(monthlymeanCentral['L10Hyperiids']),\n",
    "#               yerr=logt_inv(np.array([monthlymeanCentral['L10Hyperiids']-monthlysemCentral['L10Hyperiids'],\n",
    "#                 monthlymeanCentral['L10Hyperiids']+monthlysemCentral['L10Hyperiids']])),\n",
    "#               fmt='ro',capsize=5)\n",
    "ax[3].plot(logt_inv(monthlymean['L10Hyperiids']),'b--')\n",
    "ax[0].set_title('Calanoids')\n",
    "ax[1].set_title('Calanoids no Diapausers')\n",
    "ax[2].set_title('Euphausiids')\n",
    "ax[3].set_title('Amphipods')\n",
    "ax[0].set_ylabel('Mean Biomass (mg/m3)')\n",
    "ax[0].set_xlim(0,12)\n",
    "ax[1].set_xlim(0,12)\n",
    "ax[2].set_xlim(0,12)\n",
    "ax[3].set_xlim(0,12)\n",
    "ax[0].set_ylim(0,5)\n",
    "ax[1].set_ylim(0,5)\n",
    "ax[2].set_ylim(0,5)\n",
    "ax[3].set_ylim(0,5)\n",
    "\n",
    "fig,ax=plt.subplots(1,1,figsize=(12,2.5))\n",
    "ax.plot(logt_inv(monthlymean['L10Total']),'--',color='blue',label='Total')\n",
    "ax.plot(logt_inv(monthlymean['L10CalanoidsDiaRemoved']),'--',color='lightblue',label='Calanoids No Diapause')\n",
    "ax.set_ylim(0,20)\n",
    "ax.set_xlim(0,12)\n",
    "ax.set_title('Strait of Georgia Observation Zooplankton Seasonal Cycle')\n",
    "ax.set_ylabel('Mean Biomass (mg/m3)',fontsize=10)\n",
    "ax.legend(fontsize=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 5.0)"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(1,4,figsize=(20,5))\n",
    "fig.suptitle('Central', fontsize=24)\n",
    "fig.subplots_adjust(top=0.8)\n",
    "ax[0].plot(logt_inv(monthlymeanCentral['L10Calanoids']),'b--')\n",
    "ax[1].plot(logt_inv(monthlymeanCentral['L10CalanoidsDiaRemoved']),'b--')\n",
    "ax[2].plot(logt_inv(monthlymeanCentral['L10Euphausiids']),'b--')\n",
    "ax[3].plot(logt_inv(monthlymeanCentral['L10Hyperiids']),'b--')\n",
    "ax[0].set_title('Calanoids')\n",
    "ax[1].set_title('Calanoids no Diapausers')\n",
    "ax[2].set_title('Euphausiids')\n",
    "ax[3].set_title('Amphipods')\n",
    "ax[0].set_ylabel('Mean Biomass (mg/m3)')\n",
    "ax[0].set_xlim(0,12)\n",
    "ax[1].set_xlim(0,12)\n",
    "ax[2].set_xlim(0,12)\n",
    "ax[3].set_xlim(0,12)\n",
    "ax[0].set_ylim(0,5)\n",
    "ax[1].set_ylim(0,5)\n",
    "ax[2].set_ylim(0,50)\n",
    "ax[3].set_ylim(0,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 20.0)"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(1,4,figsize=(20,5))\n",
    "fig.suptitle('Baynes', fontsize=24)\n",
    "fig.subplots_adjust(top=0.8)\n",
    "ax[0].plot(logt_inv(monthlymeanBaynes['L10Calanoids']),'b--')\n",
    "ax[1].plot(logt_inv(monthlymeanBaynes['L10CalanoidsDiaRemoved']),'b--')\n",
    "ax[2].plot(logt_inv(monthlymeanBaynes['L10Euphausiids']),'b--')\n",
    "ax[3].plot(logt_inv(monthlymeanBaynes['L10Hyperiids']),'b--')\n",
    "ax[0].set_title('Calanoids')\n",
    "ax[1].set_title('Calanoids no Diapausers')\n",
    "ax[2].set_title('Euphausiids')\n",
    "ax[3].set_title('Amphipods')\n",
    "ax[0].set_ylabel('Mean Biomass (mg/m3)')\n",
    "ax[0].set_xlim(0,12)\n",
    "ax[1].set_xlim(0,12)\n",
    "ax[2].set_xlim(0,12)\n",
    "ax[3].set_xlim(0,12)\n",
    "ax[0].set_ylim(0,20)\n",
    "ax[1].set_ylim(0,20)\n",
    "ax[2].set_ylim(0,20)\n",
    "ax[3].set_ylim(0,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Mean Biomass (mg/m3)')"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kfN2dEj3m4D+mMcnWTzaV2ybeOlLbN98C+pjZngnTT8TPXnyZSuApmIifrToxhFDbuO5v4b8lifvPSfh7+iCu3rD4g8zoAO844NMQwuqE1yfbxoX4xWnJ1Gdd10vUxefg6PH3hGS0pu+VVNfLBjO/YVe1Vt+odXYLKg+sU/0uqffnOWp1/wfeTSj2+foAXy+J30nH4d8p9f3+bOht+yCeMN8TrasqzKydme0f+zvqSnI//hk/H3g9VB0C9Bv8s39m1GiQqlfw/uMzatguiWdNpJVQy7eky3Qzm4S38H6HnwI9BL+A7p8hhFgf7S+i50vM7GWgLIRQV6vyK8DlZnYVnmyNwi/QqcsivKXheDObhl889V0IoVorTAihzMyuxVt2Hsf7LvbGT3POxEelqJcQwjozOxpv5Z1qZvfifSfX4S1Ex0RVV0f1V5nZ74B7o2T9Zbw/cW+8z+TkEMLfQggzzOxJYGyUFLyHt2D9H/BkCGFaHXGVm9l1+BjsD+J9v7fGTzsXUPvNj97D+0jfa2a/x0fiuAb/YctPYbVcgCfYb5rZOCpvsnMM3s/4oRBCsnV9iJndho9asAfexeSv0bUGmNnzeAvVJ3jL9hD8jMP90XuebWa34D++O+DJQDGesPwM77s5qa7go3X3BN4ilo0PSRbfSv4lfo3AjWZWhicPNd244wvgUDN7JYr5hyQt/uBJ8W+AZ8zsajz5PymK+9e1tFCnzMwuxq/ReApYY2bDklRbFUL4At8v38XHqe+Ot3TGRge6KYQQaxW8E295fS3aV1bho2psj3frSHRW1N3gI3wUiDPxi4pX1hB2fdZ1vYUQ3jG/O+/LwD/M7PjoDEvsO+w8M3s0Wu40Ul8vG2ME8Gfzuya+g4/WshX+ueqCn+1J+buEFD/PZjYB/556Hz/g2x5PTP8bLW+5md0BXGlma/CW453wLiPvUr1/e10adNtG8R2Dd2X5xMzupvImO3vgv1NP4QfsMQ/hFwPvQuV3dWx+wcwuwvuAv2lmf8H7hO8E9Agh/L6GUK7Ff8PeNrN78AS+M34Qs3UI4YwNfY/SzCVegamHHg3xwL+8XsC/MIvxRPdTfHzVNnH1MvE+pYvxJC9E0/tSw8gkeAvTn/Evt9X46eB+VB+KbGxsfnHTjsJ/LEtJMhJBkmXFxvkuwRP3KuN8R3VSGu0krn534Cb8y74oWj+zgUeBfZLUPwRvYV2FDz82C++DuXNcndg433Oj9zaXmsf5TjraC3BR3Pb6GO9KsYK4MW5JPtTgqGjbro3ex4XJ1n0t66MdPrzZ59E8VuM/0CcnqRtb/j74BaGF+CnuxHG+L8Fb35ZF8/w6iik7YX6nRPXWRPP6Eh/JoU9cnUCSUUjiyvtTOapPtZFb8BFQ3o229Xx86LbY0I7x63FPYEq0/iv2ZWoe5/sxUhvnO3EUjjq3Dd4lKNTxmBxXPzae9Y/4weQ31DzO93N44ldM7eN8D8D3+7X4mbI/UPc436mu6znA40ned+J3yESqj/P90yj+54i+y/CDvwX4qBgVy0plvcS9j/1r2H59E+OMq9MH/y75H/4dWop/L74EjNrA75I6P894V6vJ0TJL8AaWO4kbZpXk43zfSw3jfCdM60vC93M9t+3EFL97toq2z+zofRTiB3tXJMYZ1X8V7wqSVcP8RkXrtzB6fAacXltsVA65uyBuPb1Gku8/PVrPIzakWdqYWWz8z13x5Oob4LIQwptReWf84oKj8KTqfeC3IYTPk85QRBpF1BXkQ+DUEMJjddUXaQhmNhZPZrNDI91FVKQuUa7yPTA+hPB/TR2PtGxp7XZiZr/GjyrvIWq1wI9e20XlhreO9sNPk63Ab2IwycwGB7/FsYikWXTB6nn4qetV+OnSq/AWLd1GXEQ2SVE3nR3wrl4Z+MgnIhslbcl3dFHNeOB3IYTxcUXxoxocgZ/aHhWi/pVm9j7+g38ZfrpLRNJvLX6a/1S8z+EKvL/jFcGH1BMR2RQdil/j8z1wWqj94mORlKSt24mZXY/3u+waojFvk9R5CO/v1zth+qN4P76t0hKciIiIiEgTSOdQg3vhd8463sxmm9l6M5tlZufF1elP8mGjZgBbRgP+i4iIiIi0CulMvjfHb6RyG3AzPgj/a/jQXr+J6nQh+Q0uYgP0p3oXRBERERGRZi+dF1xm4HeBGh1CeCaa9mbUF/xKM7sLH4ooWb+XWgepN7MxwBiA9u3b77bjjjvWVl1EREREZKNNmTJlaQghlRvl1SidyfcyvOX7tYTp/8VvdtGL6Pa7SV4ba/FO1ipOCGEC0a1dhw4dGj7+eEPu9C0iIiIikjozm7ux80hnt5MZNUyPtWqXR3X6J6mzM/B9CKEwHYGJiIiIiDSFdCbfz0bPByZMPxC/Y9hCfIzv3ma2b6zQzPKAw6MyEREREZFWI53dTl7Cb7N6v5l1A74FjsUvvDw9qvMCfkfLx83sd1TeZMeAW9MYm4iIiIhIo0tb8h1CCGZ2FHATcB3ej/sr4KQQwt+iOuVmdhgwDr9rVC6ejI8MIcxLV2wiIiLSOpSWljJ//nyKi5PeUkRkg+Tm5tKnTx+ys7MbfN5pu8lOY9EFlyIiIpuu7777jo4dO9K1a1fMah0sTSQlIQSWLVvG6tWr6devX5UyM5sSQhi6MfNPZ59vERERkbQqLi5W4i0Nyszo2rVr2s6mKPkWERGRFk2JtzS0dO5TSr5FRERENsCyZcsYPHgwgwcPpmfPnvTu3bvi73Xr1lWpO378eIqKiuqc54gRI1B32tat1gsuzawPcDywN367+LXAdOA/wMshhPK0RygiIiLSDHXt2pWpU6cCMHbsWDp06MCll16atO748eM5+eSTadeuXSNGKM1RjS3fZvYI8DCwDrgFOAE4F3gdv0Plu2a2T2MEKSIiItISvPHGGwwZMoSBAwdyxhlnUFJSwl133cUPP/zAyJEjGTlyJADnnHMOQ4cOpX///vz+979v4qilMdXW8n17CGF6kunTgWfMrA2wZXrCEhEREam/ESOqT/vlL+Hcc6GoCA45pHr56NH+WLoUjj22atnkyakvu7i4mNGjR/PGG2+w/fbbc+qpp/LnP/+Ziy66iDvuuINJkybRrVs3AG688Ua6dOlCWVkZ++23H9OmTWPQoEGpL0xarBpbvmtIvOPL14UQZjV8SCIiIiItT1lZGf369WP77bcH4LTTTuPtt99OWvef//wnu+66K0OGDGHGjBl88cUXjRmqNKEaW77NrCfwe6AcuBa4ADgG+BL4TQjhx0aJUERERCRFtbVUt2tXe3m3bvVr6U7Uvn37lOp99913jBs3jo8++ojOnTszevRo3SRoE1LbaCcTgS+Aefht4tcChwLvAH9Je2QiIiIiLUhxcTFz5sxh1izvGPDYY4+x7777AtCxY0dWr14NwKpVq2jfvj35+fksWrSIl19+uclilsZXW5/vzUIIdwOY2bkhhFui6Xeb2a/SH5qIiIhIy5Gbm8sjjzzCL37xC9avX8/uu+/O2WefDcCYMWM4+OCD6dWrF5MmTWLIkCH079+frbfemj333LOJI5fGVOPt5c3ssxDCLtH/bwghXBNXNi2E0CyuCtDt5UVERDZdX375JTvttFNThyGtULJ9K923l3/ezDoAJCTe2wLfbMxCRUREREQ2RTV2OwkhXFvD9FnAscnKRERERESkZrXe4RLAzDoBpwJ94+uHEC5MW1QiIiIiIq1Qnck38BLwAfA5PuygiIiIiIhsgFSS79wQwsVpj0REREREpJWr7YLLmMfM7Cwz62VmXWKPtEcmIiIiItLKpJJ8rwNuA94HpkQPje0nIiIiAixcuJDjjz+ebbbZhp133plDDjmEb76pfWC4Dh06NHgc1157La+//nq16ZMnT+awww5LeT5mxiWXXFLx97hx4xg7dmzKr584cSLdu3dnyJAhbLfddhx44IG89957dca5qUil28nFwLYhhKXpDkZERESkJQkh8POf/5zTTjuNv//97wBMnTqVRYsWsf322zdqLNdff32DzCcnJ4dnnnmGK6+8km7dum3QPI477jjuueceACZNmsTRRx/NpEmT2GmnnRoszpYqlZbvGUBRugMRERERaWkmTZpEdnZ2xZ0sAQYPHszee+9NYWEh++23H7vuuisDBw7k+eefr/b6murMmTOHnXbaibPOOov+/ftzwAEHsHbtWsCT+2HDhjFo0CB+/vOfs2LFCgBGjx7NU089BcArr7zCjjvuyF577cUzzzxTsby33nqLwYMHM3jwYIYMGVJxy/t4WVlZjBkzhjvvvLNa2dy5c9lvv/0YNGgQ++23H99//32d62jkyJGMGTOGCRMmVIvz+uuvZ/fdd2fAgAGMGTOG2M0fR4wYwUUXXcTw4cMZMGAAH374IQAffvghw4cPZ8iQIQwfPpyvv/4a8Nb2888/v2KZhx12GJMnT6asrIzRo0czYMAABg4cWPGeZs+ezUEHHcRuu+3G3nvvzVdffVXn+2goqSTfZcBUM7vfzO6KPdIdmIiIiEh9vf39smqP2SvWALC+PCQtn1vgbYwl68urldVl+vTp7LbbbknLcnNzefbZZ/nkk0+YNGkSl1xyCYl3Fq+tzsyZMznvvPOYMWMGnTp14umnnwbg1FNP5ZZbbmHatGkMHDiQ6667rso8i4uLOeuss3jxxRd55513WLhwYUXZuHHjuPfee5k6dSrvvPMObdu2TRr7eeedxxNPPEFBQUGV6eeffz6nnnoq06ZN46STTuLCC1MbeXrXXXdNmuCef/75fPTRR0yfPp21a9fy73//u6JszZo1vPfee9x3332cccYZAOy44468/fbbfPrpp1x//fVcddVVtS536tSpLFiwgOnTp/P5559z+umnAzBmzBjuvvtupkyZwrhx4zj33HNTeh8NIZVuJ89FDxERERFJUQiBq666irfffpuMjAwWLFjAokWL6NmzZ511APr168fgwYMB2G233ZgzZw4FBQWsXLmSfffdF4DTTjuNX/ziF1WW+9VXX9GvXz+22247AE4++eSKVuc999yTiy++mJNOOomjjz6aPn36JI09Ly+PU089lbvuuqtKgv7+++9XtKSfcsopXHbZZSmvi2QmTZrErbfeSlFREcuXL6d///4cfvjhAJxwwgkA7LPPPqxatYqVK1eyevVqTjvtNGbOnImZUVpaWutyt956a7799lsuuOACDj30UA444AAKCwt57733qqy3kpKSlN5HQ6gx+TazCcDLwDMhhOrnJERERESamX227FpjWVaG1Vqek5VRa3ky/fv3r+hCkeiJJ55gyZIlTJkyhezsbPr27UtxcXHKdXJycirqZWZmVnQ7SYWZJZ1+xRVXcOihh/LSSy8xbNgwXn/9dXbcccekdS+66CJ23XXXitbi+iwn0aeffspOO+1UZVpxcTHnnnsuH3/8MVtssQVjx46tsn4S521m/N///R8jR47k2WefZc6cOYwYMQLwrjLl5eVV5g3QuXNnPvvsM1599VXuvfde/vnPfzJ+/Hg6derE1KlTU4q9odXW7eRhYBfgJTN7w8wuN7NdGikuERERkWZv1KhRlJSU8MADD1RM++ijj3jrrbcoKCigR48eZGdnM2nSJObOnVvt9anUiZefn0/nzp155513AHjssccqWsFjdtxxR7777jtmz54NwJNPPllRNnv2bAYOHMjll1/O0KFDa+3r3KVLF375y1/y0EMPVUwbPnx4xYWlTzzxBHvttVet8YL3M58wYQJnnXVWlemxBLlbt24UFhZWO4j5xz/+AcC7775Lfn4++fn5FBQU0Lt3b8D7ecf07duXqVOnUl5ezrx58yr6iC9dupTy8nKOOeYY/vCHP/DJJ5+Ql5dHv379+Ne//gV4q/xnn31W5/toKDW2fIcQPsDvbDnWzLoCBwCXmNkg4BPglRDCPxsnTBEREZHmx8x49tlnueiii7j55pvJzc2lb9++jB8/vqILxdChQxk8eHDSFuaTTjqpzjqJHn30Uc4++2yKiorYeuuteeSRR6qU5+bmMmHCBA499FC6devGXnvtxfTp0wEYP348kyZNIjMzk5133pmDDz641mVdcsklFaOWANx1112cccYZ3HbbbXTv3r3asmP+8Y9/8O6771JUVES/fv14+umnq7V8d+rUibPOOouBAwfSt29fdt999yrlnTt3Zvjw4axatYqHH34YgMsuu4zTTjuNO+64g1GjRlXU3XPPPenXrx8DBw5kwIAB7LrrrgAsWLCA008/vaJV/KabbgL8wOGcc87hhhtuoLS0lOOPP55ddmmcNmarqQ9OrS8y2w04KIRwY8OHVD9Dhw4NH3+sYcdFREQ2RV9++WW1pE5avhEjRjBu3DiGDh3aZDEk27fMbEoIYaOCqvOCSzNLdmv5AuA/G7NgEREREZFNTSqjnQyNHi9Gfx8KfAScbWb/CiHcmq7gRERERGTTM3ny5KYOIW1SSb67AruGEAoBzOz3wFPAPvit5pV8i4iIiIikIJWb7GwJrIv7uxTYKoSwFmi8QRFFREREktiQ69dEapPOfSqVlu+/AR+YWeyeqIcDT5pZe+CLtEUmIiIiUofc3FyWLVtG165dUx5zWqQ2IQSWLVtGbm5uWuZfZ/IdQviDmb0E7AUYcHYIITa8yElpiUpEREQkBX369GH+/PksWbKkqUORViQ3N7fGu39urFRavgG+xbubZAGY2a4hhE/quzAzewU4ELgxhHBN3PTOwG3AUUBb4H3gtyGEz+u7DBEREdl0ZGdn069fv6YOQyRlqQw1+AdgNDAbiHWACcComl5Tw3xOwO+YmTjdgBeAfsAFwArgSmCSmQ0OIcyvz3JERERERJqrVFq+fwlsE0JYV2fNGphZJ+BO4Ld4H/J4R+BdWkaFECZF9d8HvgMuAy7c0OWKiIiIiDQnqYx2Mh3otJHLuRWYEUJ4MknZEcAPscQbIIRQgI8rfuRGLldEREREpNlIpeX7JuBTM5tO3NCCIYQjUlmAme0FnEqSLieR/niCn2gGcKqZdYiNMS4iIiIi0pKlknw/CtwCfA6U12fmZpYN3A+MCyF8XUO1LsCcJNOXR8+dASXfIiIiItLipZJ8Lw0h3LWB878cH73kxlrqGJUXciZOT/4CszHAGIAtt9xyA0MTEREREWlcqSTfU8zsJnxEkvhuJ7UONWhmWwJXA2cCOWaWE1ecE12EuRpv4e6SZBado+cViQUhhAnABIChQ4fqtlYiIiIi0iKkknwPiZ6HxU1LZajBrYFc4PEkZZdGjyF43+4DktTZGfhe/b1FREREpLVI5Q6XIzdw3lOBZK+dhCfkDwGz8Bb1081s3xDCWwBmloffxj5xWEIRERERkRarxuTbzE4G/hZCSHqRpZltA/QKIbybrDyEsBKYnOR1AHNDCJOjv1/A72j5uJn9jsqb7Bg+RKGIiIiISKtQW8t3V3yIwSnAFGAJ3o1kW2BfYClwxcYGEEIoN7PDgHHAfdEy3gdGhhDmbez8RURERESaCwuh5usVzSwT79u9J9ALWAt8CbwcQvi+USKsw9ChQ8PHH3/c1GGIiIiISCtnZlNCCEM3Zh619vkOIZQBr0UPERERERHZCKncXl5ERERERBqAkm8RERERkUai5FtEREREpJHUmXyb2W/MLM/cQ2b2iZkluymOiIiIiIjUIpWW7zNCCKvwu1B2B04Hbk5rVCIiIiIirVAqybdFz4cAj4QQPoubJiIiIiIiKUol+Z5iZv/Fk+9XzawjkPSulyIiIiIiUrNax/mO/AoYDHwbQigysy541xMREREREamHVFq+fwp8HUJYaWYnA9cABekNS0RERESk9Ukl+f4zUGRmuwCXAXOBv6Y1KhERERGRViiV5Ht9CCEARwJ/CiH8CeiY3rBERERERFqfVPp8rzazK4GTgX3MLBPITm9YIiIiIiKtTyot38cBJcCvQggLgd7AbWmNSkRERESkFaqz5TtKuO+I+/t71OdbRERERKTeUrm9/DAz+8jMCs1snZmVmZlGOxERERERqadUup3cA5wAzATaAmcC96YzKBERERGR1iiVCy4JIcwys8wQQhnwiJm9l+a4RERERERanVSS7yIzawNMNbNbgR+B9ukNS0RERESk9Uml28kpQCZwPrAG2AI4Jp1BiYiIiIi0RqmMdjI3+u9a4Lr0hiMiIiIi0nqlMtrJYWb2qZktN7NVZrbazFY1RnAiIiIiIq1JKn2+xwNHA59Ht5kXEREREZENkEqf73nAdCXeIiIiIiIbJ5WW78uAl8zsLfw28wCEEO6o+SUiIiIiIpIoleT7RqAQyAXapDccEREREZHWK5Xku0sI4YC0RyIiIiIi0sql0uf7dTNT8i0iIiIispFSSb7PA14xs+JomEENNSgiIiIisgFSuclOx8YIRERERESktUulzzdmdgSwT/Tn5BDCv9MXkoiIiIhI65TKHS5vBn4DfBE9fhNNExERERGRekil5fsQYHAIoRzAzB4FPgWuSGdgIiIiIiKtTSoXXAJ0ivt/fiovMLNjzexpM5trZmvN7Gszu8nMOibU62xmD5rZUjNbY2avm9nAFOMSEREREWkxUmn5vgn41MwmAYb3/b4yhdddCnwPXAXMB4YAY4GRZjY8hFBuZga8APQDLgBWRPOeZGaDQwjz6/l+RERERESarVRGO3nSzCYDu+PJ9+UhhIUpzPvwEMKSuL/fMrPlwKPACOBN4AhgL2BUCGESgJm9D3yH39b+wtTfioiIiIhI81ZjtxMz2zF63hXohbdezwM2j6bVKiHxjvkoeu4dPR8B/BBLvKPXFQAvAkem8gZERERERFqK2lq+LwbGALcnKQvAqA1Y3r7R85fRc39gepJ6M4BTzaxDCKFwA5YjIiIiItLs1Jh8hxDGRM8jG2JBZtYbuB54PYTwcTS5CzAnSfXl0XNnQMm3iIiIiLQKtY52YmZbmVm36P/DzOxSMzuqvgsxsw7A88B64PT4IrwVvdpL6pjfGDP72Mw+XrIkWe8WEREREZHmp7Y+39fiF0V+YGY3AOOBbvhNdsanugAzy8VHNNkaODBhBJPleOt3os7R84pk8wwhTAghDA0hDO3evXuqoYiIiIiINKna+nwfD+wEtMOHDOwZQigysyxgaiozN7Ns4GlgD2D/EMLnCVVmAAckeenOwPfq7y0iIiIirUlt3U6KQwjrQggrgdkhhCKAEMJ6YF1dMzazDOAJYD/gyBDCB0mqvQD0NrN9416XBxwelYmIiIiItBq1tXx3MrOj8f7XedH/if5O5S6X9wK/AG4E1pjZsLiy+VH3kxeA94HHzex3VN5kx4Bb6/VORERERESaudqS77fwFmiAt+P+H/u7LgdHz1dHj3jXAWOju1weBowD7gNy8WR8ZAhhXgrLEBERERFpMWobavD0mspSEULom2K95cAZ0UNEREREpNWqdahBERERERFpOEq+RUREREQaiZJvEREREZFGUtsFlxXMbDjQN75+COGvaYpJRERERKRVqjP5NrPHgG3wG+uURZMDoORbRERERKQeUmn5HgrsHEII6Q5GRERERKQ1S6XP93SgZ7oDERERERFp7VJp+e4GfGFmHwIlsYkhhCPSFpWIiIiISCuUSvI9Nt1BiIiIiIhsCupMvkMIbzVGICIiIiIirV2dfb7NbJiZfWRmhWa2zszKzGxVYwQnIiIiItKapHLB5T3ACcBMoC1wZjRNRERERETqIaWb7IQQZplZZgihDHjEzN5Lc1wiIiIiIq1OKsl3kZm1Aaaa2a3Aj0D79IYlIiIirc26dbBsGSxd6s+9esEOO0BBAVx3nU9buRL694eRI2H4cGivjENaGavr3jlmthWwCGgD/BbIB+4LIcxKf3h1Gzp0aPj444+bOgwREZFNytq1UFICnTr53//6FyxZUplYL10Ke+8NZ5/tSXfXrlBYWHUel14Kt90Gq1bB5pt7nQ4d4JtvYP16uP12uPhiWLECpkzxZLxdu0Z/qyIVzGxKCGHoxswjldFO5ppZW6BXCOG6jVmYiIiINE/z58OiRVVbprt0gZNO8vITT4SvvqosKyqCY46Bp57y8nPO8ekAeXnQrRtss43/3aaNl+fleYLdrZs/b7ttZf34xLywEN57D3bayf9+7TU47jjIzoaf/ARGjPDHXntBTk6614xIw0ql5ftwYBzQJoTQz8wGA9c3l5vsqOVbRETEheBdOGIJdEkJ7LOPlz34oLcex7dM9+4NL7/s5bvvDok/pz/9qSfBACef7POOT54HDoTDDvPymTM9ie7SxZPkhlRYCO++C5MmweTJHmd5Ocya5Qn+lCneej5sGLRt27DLFonXKC3f+E129gAmA4QQpppZ341ZqIhIcxGC/2hnZvrp7mXL4NlnPcno0gV69IDNNoPttoP8/KaOVjYFIXirckGBP3bcEczgww9h6lSftny5J89r18Ljj/vrzjoLJk707hoxm20GCxf6/19+Gd55pzJ53nprn3fMDTdAcXFlYt2tG3TuXFkeW05NttuuId59ch06wEEH+QP8M/v++/4eAO68E554wlvBhw3zVvFRoyoPPESak1SS7/UhhAIzS3swIiIbKwRYswYWL/b+p7HnAQNgjz3871NOqVq2bh386U9w4YV+2v2ss6rPd+JEOO00b3E7+eTKpDz2OOEEP4W+erXPc7PNdKHYpigET2BjiXNBgbcOt20Ln34Kb7xRtaygAB591A/sbrsNbr7Zp5WVVc6zqMhf/7e/+X4K3rLctSt07+4twBkZfoFi9+5Vk+fu3Svn89RTnsTX5MAD07NO0iEvr2q8997rn8FYy/gf/uB90GfM8PInn/RW/p/8RN1UpOmlknxPN7MTgUwz2w64ENBQgyLSaIqK/PR5rBXu0Uc9SY5PoPff3y/MKi6Gjh2rz+Oyyzz5zs310RQ23xwGD/bkpEcPvzAMvPXu++/9x335cl/OokWw665e3qYNDBrk0z7/HF5/3ef305968v3qq/CLX3jd9u0rk/MJE/wA4PPP4e23qybum23my1MbR9MrKfHkd+XKyuR48GBPZL/8Ev7+9+rJ8913w847w1//CmeeCaWlVec5fbqP3vHOO/C73/l2zsvzhDs/3w8W8/O9FfqEEyqnxx4Z0R05rr7aX5+f7/tW4v5y4om1v7fWvH/l58Ohh/oDfPvNn+//Ly+H887zizZzc/2izREj4PDDfduKNLZU+ny3A64GDgAMeBX4QwihOP3h1U19vkVanuLi6i3TeXlw1FFeftppfmFXrGzNGk9o//lPL+/SpfKHtEcPT6B/+UtPsAHuuKOyy0gsue7RI319QUtKPEHKzoa5c+HNNz05X7y4MnmfMMFPkf/pT3DRRdXnMXOmJ+9PPOGtdPGJeY8ecPTR3mJXXOwHABmp3CJtE1Be7q3NmZne3WL+fN8ehYWVSfSgQb5u58/3fSM+cV65Eq6/Hg4+2LfbfvtVX8aLL3q/5hdfhCOOqJo45+fDPfd4EjdlircuJybPe+/trykq8hg7dND2a2wrVvjBT6xl/LPP4PLL4aab/DN1++2ekO++u3++RGrSEH2+60y+mzsl3yJNb906T2J69PC/X30VvviianLdtSs8/LCX77YbfPJJ1XkMHw7/7//5/487zhOj+OR50CBPkADmzfNW8GStf81dWZn31Y0l5bEk/eyzfQi1Bx+E++6rnB7rv7t2rR9sXHSRJ3vdu1cm5716wSOPVPYLXrGisqxbt4a/+C3+vRQXe/LboYNP++YbP1gqKfGy4mKPY8gQL3/ggcozGbHyPfbwA6/SUhg9urIs9nziiT5SxvLlnuTGXldS4vvejTfCVVfBnDnQr1/1OGNdir74wrsdJCbHl17qSfe8efDYY9XLBwzwg7myMl/HSpxbvuXL/bPVowf873/eTxz8M7jnnp6In3wybLllk4YpzVBak28ze6G2F2q0E5FNy5o1lX2Y777bW/h+/NETxIICTwB/+MHLjzjCWwmzsioT6IEDPbEBP3W/enX1lum8vKZ5b81Vebkf1CxeXHlh3CuveAtefMv6unXenxjg2GPh6aerzmfnnSv7vt5+uyeZeXn+uuJi33aXX+7lF1/sZx3ik9/Bg+Ghh7x8990ry2MHBkceCc895//fbDOPK96JJ3qLPvg+VFRUWZaV5Qced9/tSfx22/lBRm6ut/Tn5sLxx3s//LVrvftAbHrsMWqUty6vWeNnR3JzPYmKJc9bbeXJs0hNli2Dt97yVvHJk7172HvveXey99/3shEjvOEgXQez0jKkO/leAswDngT+h3c5qRBCeGtjFtxQlHyLNLz58/2HZ9o0/xGaNs2nFRZ64nPttX6Kvk+fysS5Vy/41a/89YsX+6nb/PyW1zLd0s2b511f4pPzrCy45hovP/ZY+O9//eCnTRtPVHfbzbcneKI8c2bV5HaXXXwkDPAL2VaurJoAb799ZZehF1/0g4ZYeU6OJ+SxUSkWLqxcbk6OdxcRaW6WLPGza1lZ3jXlqqt8eocOPrb4iBF+FkoXb2560p18ZwI/A04ABgH/AZ4MIczYmAU2NCXfIhtu8WJPrGNJ9o03+oWI48b5hV2ZmX7r54EDvdvHBRckv5hRWp4QdGAkkqrFi6u2jC9d6geSZn7WprjYR5sZPNgTdmm9Gq3Pt5nl4En4bfgNdu7emIU2JCXfInUrLvb+rrGW6jff9BbORYsq6/Ts6V0HfvITWLDAW3523NFbKEVEpFJhYeV1Dgcf7N3BwLtz7b23n2EaPbrJwpM0SvtNdqKk+1A88e4L3AU8szELFJH0W74c/vznylbtmTP9YrEHHvCh0LbYAg45xFuzBw3ylu348YB79/aHiIhUF0u8wW9etHBhZav4pEl+N87Ro/0M00knwdCh3lVll13U1Upq73byKDAAeBn4ewhhemMGliq1fMumqqDAxw+O7zZy1FE+csPy5T66yNZbV3YZGTjQr+LffPOmjlxEpHUrLfULMxcu9Ltszpzp0zt18u59557bpOHJRkh3y/cpwBpge+DCuDtcGhBCCBqXQKQRrF/vX9zTpvnFPUcd5Re09e7tozuAf6EPHOjP4CM7rF5dtXVGREQaR2xElJ49ffjN+fMr+4wnGw5TNi0a51ukGVm1qnK4vSuu8FEpvvjCh3yDqmNhP/JI5fjXffro4jkREZF0S3ufbxFJn6++gg8+qOw2Mm2a34Fx7lwvX77ck+v99qvsmx0b6xng9NObJm4RERHZcEq+RdIoBE+mY8n1jBl+o5msLB+e6r77POEeMAAOP9wT7NgQcBMmNHX0IiIi0tCaRfJtZlsAd+LjihvwOnBRCOH7Jg1MBE+G16zxLiEFBT5SSIcO8O238MYbPi3+cd110Lev38VxzBjvex2z9dY+vF/v3nDJJfCb38A22+jqdxERkU1FkyffZtYOeBMoAU4DAnADMMnMBoUQ1jRlfNKylZf7eKwFBZXJc79+lbdCf/zx6snz5Zf7qCBvvuljta5a5cP0xfz3v/Czn8GUKZ5cg7dU5+X5HR2XL/fku0cPOPXUyi4j/ftXvUFN7I5/IiIisulo8uQbOAvYGtghhDALwMymATOBXwN3NGFs0oRCqJ4Yr1rliW3//v7/m26qXufss31c1a+/hp128vnE+8tf4Ne/9iGgLr/cW51jiXN+vifr4K3TJ55YOT32GDjQyw8+2G/lnZ8P7dtDRkbV5Ywa5Q8RERGRmOaQfB8BfBBLvAFCCN+Z2f8DjkTJd6t23nneUhyfPB97LIwd6yN8dO5c/TWXXw433+yt0bffXpkUxxLoWBeOHj3g6qurJ88DBnj5oEGeaLdrl3ykkB12gHvuqTn2Dh02vaH8QgjEhh0tKw8EAoZh5v3FAEzDroiIiNSoOSTf/YHnk0yfAfyikWORRvb6694yHUuMe/b0PtUAOTmBO8d7V438fMiLEuwttwAw8vMDq4sCZrHW7UAAsjIMyCC/U+DKa8t9etT6HYA2mRlABpYRKMtaz+p1Pj1Wp21WJjlZGawvL2dVyfqK18XKO7TJJDcrk3Vl5awsLiXWsB4IEKBTbja5WZkUry9j2dpSQrT8EDy+zdrnkJuVyep161m8poQQoDxWB+ib35bcrEyWr13HD4XFFdNDCJQDO3ftQE5WJgsLi5m3urhivl4vsFvPTrTJzGBuQRHfr1pLedyyQ4B9t+xKZobx9bJC5hYUVUwvj97JIdtsBsDURQXMLVhbGX+0bo/YricAHy9cyYLVxVW2Z25mBods669/f8FyFq8pwfDM3IAObbIYuVU3AD5YsIIVxeswMwwvz8vJZlhvP+L68IcVFK5bD3HlnXOz2WWzfF/+jyspXl9ekfibQZfcNuzQ1Y+IPl1YwPry8oqDATPomtuGvp3aAfD54lUV2y7+9Zt3zCWEwFfLCiveV+x4oktuG3q0z6GsPDBzRWWPuNjhRte2bejWrg2lZeV8u7KoShkG3du1oXNuG0rKyvm+oIhEPdrnkJ+TTfH6Muavilu30Uw2a59DxzZZFJWW8WNhcbXX9+yQQ/vsLNasW8/CNSXV4uvVMZe20b63pCgqjzsz1LtjLjlZmRSUlLK0aF21+W+R15Y2mRmsKC5l2dq48mgefTu1JSsjg2VF61heXP3123RuT4YZi9eUsLKktOryDbbv4ttuYWExBdFnL1Yl02C7qHzB6rWsXhd9NqPXZ2dksG2X9gDMW7XW9504bTIz2Kazl88pKKKotKxKedusTPpF+8a3K9ZQXFZepbx9diZb5Xv5rOVrWFdetbxjmyy2yGsLwNfLClmfcMqtU04WvTt6+RdLV5M4zG/nuH1vxtLVJOrWtg09O+RSVh74clnVcsP3ne7tcigtK+ebaN+MPwzerH0OXdu2oWR9WcW+Ga9Xh1w65Wazdn0Zcyv2Tav4t1eHHPJysikqXc+8aN+M37d7d8ilfZssCtetT7pv9u7YlnbZmawuWc+iopIqZQb0qWHfs+ifLTq2JTszg5XFpawoLk1anplhrCwupaAkoRzok9eWDDNWFJdW2Tdir+8TbZsVxaUUlcbvO0aG+fqJlRevr7rvZJrRo31ORfm6hH0nK8Po2rZNRXlOZgbtsnWhz6asOSTfXYAVSaYvB5K0e0prcvt/FrG+vLxKctw3vx3gCdaWBy2sqFsYPdpltmdz8igj8J/Zi6rNc8euHdi5W0dKysp55dvF1coHdu/Idl06ULS+jNfnLK1WPmSzfPp1asfqdeuZ/P2yauW79+rEFnltKSgp5d35y6uV/7R3Z3p1yGRFcSn/+6H6rr33Fl3IzcpkZXEpny1eVa08lpwXlKxn1oo1VVqWzYztOrcnB1i7vpxla9eRAVG5RQcivjJDgPLgr8vIsIrymNysDDrlZlfMN7aMWOt2t7ZtyLSqy86Mm8GWeW3pnJPtyXuUoPuBj+vVIZcO2VkV5YTYgY/rnJtdUT92cBD/g5STmcH6rMyKA48AZMQtv6w8UFoeHTLEXp9V+aNYUFJKSVl5lQOT+PjnrVrL+hAqygD65gc27+g/sl/GJd8x23VuT4/2OZSHwBdJEqSdunbw5Ls8eQI1sHtHT77Xl/H5kurlQzIyyM/Jpqi0jGlLqu8be/TqRMcowUm277TP7kz77CwKSpKX5+Vk0zYrkxVr1zF1UfXyLm3bkJOVybKidTXum20yM1iypoTpSd5fn7xcsjJgYVEJXydZf/06tSPDjIVrSpi1ovrlPLHke0FhMXML1lYpy8qwiuR7/uri6gd+WZXJ9/er1rJoTdUEr2ObzIrke27B2qoHD/hBcyz5/q6gqEryD37gFEu+Z69cw5qE5L1Xh5yK5HvWijWUJCRgW+S1rUi+Z61YQ1l51eS7X6fKfW/m8iSXOnWBnh1yKQ8haXlmhnnyXR6SrvvsKAEsKStPum/nZmV68l1axhdLq5e3z84kLyebwnVlSfftvDZZtG+TxaqS9Un37c65bWiXncmK4nVMS7Jvda1j3+vRLofszAwW17Dv9WyfQ2ZGJgsKi5O+/807tiXD4PtVRcxeUf3go88Ovm2+Xbmm2r6XnWEcHjU6fLO8MOm+F2u0+GLp6qT73s/69QBg2uICNu+QW7Evy6apOSTfUKXtpUKN567NbAwQXepGiZlNT0tU0hi6AdUzYGkptP1aLm27lk3br+XStmvZdtjYGTSH5HsF3vqdqDPJW8QJIUwAJgCY2ccbe6chaTrafi2btl/LpW3Xsmn7tVzadi2bmW30bdUz6q6SdjPwft+Jdga+aORYRERERETSpjkk3y8Aw8ysYtRjM+sL7BmViYiIiIi0Cs0h+X4AmAM8b2ZHmtkR+Ogn84D7U3i9bsLdsmn7tWzafi2Xtl3Lpu3XcmnbtWwbvf0scbijpmBmW1L19vJv4LeXn9OUcYmIiIiINKRmkXyLiIiIiGwKmkO3k3ozsy3M7CkzKzCzVWb2TNR6Ls2cmR1rZk+b2VwzW2tmX5vZTWbWsaljk/ozs1fMLJjZDU0di6TGzA4xs7fNrDD6/vzYzEY1dVxSNzPb08z+a2aLo233iZmd0dRxSVVm1sfM7jaz982sKPqO7JukXmcze9DMlprZGjN73cwGNkHIEieV7Wdm+5nZ42Y2O8plZpvZn82sRyrLaHHJt5m1A94EdgROA04BtgMmmVn7poxNUnIpUAZcBRwE/Bk4B3jNzFrc/rgpM7MTgF2aOg5JnZn9Gr+mZgrwc/wuwv8C2jVlXFI3MxsEvA5kA2cBxwAfAQ+Z2TlNGZtUsy3wS3y45HeSVTC/9e4L+O/gBfj2zMZzmT6NFKckV+f2A84GugI34NvwJuAI4AMzq/MOSi2u24mZ/Qa4A9ghhDArmtYPmAlcFkK4oynjk9qZWfcQwpKEaacCjwL7hRDebJrIpD7MrBPwFfBb4G/AjSGEa5o0KKlV1HLzJXBlCGF800Yj9WVmf8QbL7qEEArjpn8AhBDCT5ssOKnCzDJCCOXR/8/EB5boF38dm5kdCTwHjAohTIqm5QPfAY+HEC5s7LjFpbj9kuUy+wBvAb8KITxc2zJaYkvjEcAHscQbIITwHfD/gCObLCpJSeLOGvkoeu7dmLHIRrkVmBFCeLKpA5GUnQGUA39p6kBkg7QBSoG1CdNX0jJ/y1utWOJWhyOAH2KJd/S6AuBFlMs0qVS238bmMi3xA9sfSHY7+Rn4jXmk5dk3ev6ySaOQlJjZXsCpwLlNHYvUy1742Yrjo/6J681slpmd19SBSUomRs93mdnmZtbJzM4C9sNHC5OWpbZcZstUui5Is5NyLtMcbi9fX11Iftv55fgt6aUFMbPewPXA6yGEjb5lq6SXmWXj4++PCyF83dTxSL1sHj1uw6+5mI33+b7HzLJCCH9qyuCkdiGE6WY2AniWygPfUuDsEMLfmyou2WBd8HucJFoePXcGCpOUSzMUDRoxHk+8n6urfktMvgGSdVS3Ro9CNkp0ZP88sB44vYnDkdRcDrQFbmzqQKTeMoCOwOgQwjPRtDejvuBXmtldoaVdBLQJMbPtgKfxltGz8e4nRwJ/MbPiEMITTRmf1JuhXKZVMLMs4Em8u8meIYT1db2mJSbfK/AjxkSdSd4iLs2QmeXiV3pvDewbQpjfxCFJHaLhPK8GzgRyzCwnrjgnughzdQihrCnikzotw0eGei1h+n/xq/V7AT80dlCSsj/iLd2HhRBKo2lvmFlX4E9m9mSKfY2leVhOzbkMKJ9pEaJR2h4F9gcODSFMS+V1LbHP9wy8r1SinYEvGjkW2QBR14WngT2AQ0IInzdxSJKarYFc4HH8hyH2AB+FYQWgMWqbrxk1TI+1tClxa94GAp/FJd4xH+JDnqU0vrA0G7XlMt/Hj2gjzdpfgOOA40MIb6T6opaYfL8ADDOzrWMTotOme0Zl0oxFR4lP4BcJHRlC+KCJQ5LUTQVGJnmAJ+QjgVlJXynNwbPR84EJ0w8E5ocQFjZyPFI/C4HBZtYmYfpPgGIq+wpLy/AC0NvMYhfpYWZ5wOEol2kRzOx2/Ezw6SGE5+rz2pbY7eQB4HzgeTO7Bu8z9QdgHn4hmDRv9+IXed0IrDGzYXFl89X9pPkKIawEJidO93tFMDeEUK1MmpWXgEnA/WbWDfgWOBY4AF1z0RLcg98Q6UUzuw/v830EcAJwZwhhXVMGJ1WZ2bHRf3eLng82syXAkhDCW3iC/T7wuJn9Dj9zeCV+JurWxo5Xqqpr+5nZ5cDFwMPAzIRcZkkIYXat82+J19dEfU/vBH6G76hvABfFD4AuzZOZzQG2qqH4uhDC2MaLRhqCmQV0k50WIWpZuwlPujvjQw/eHEL4W5MGJikxs4Pxi577413AZgMTgPt1rUXzEn0vJvNWCGFEVKcLMA44Ct+e7wMXhxA+a4wYpWZ1bT8zm0zl0IKJHg0hjK51/i0x+RYRERERaYlaYp9vEREREZEWScm3iIiIiEgjUfItIiIiItJIlHyLiIiIiDQSJd8iIiIiIo1EybeIiIiISCNR8i0iIiIi0kiUfIuIiIiINBIl3yIiIiIijeT/A3BiWPT8AjWzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(1,1,figsize=(12,2.5))\n",
    "ax.plot(logt_inv(monthlymean['L10Total']),'--',color='blue',label='Total')\n",
    "ax.plot(logt_inv(monthlymean['L10CalanoidsDiaRemoved']),'--',color='lightblue',label='Calanoids No Diapause')\n",
    "ax.set_ylim(0,60)\n",
    "ax.set_xlim(0,12)\n",
    "ax.set_title('Strait of Georgia Observation Zooplankton Seasonal Cycle')\n",
    "ax.set_ylabel('Mean Biomass (mg/m3)',fontsize=10)\n",
    "ax.legend(fontsize=10)\n",
    "\n",
    "fig,ax=plt.subplots(1,1,figsize=(12,2.5))\n",
    "ax.plot(logt_inv(monthlymean['L10mod_mesozooplankton']),'--',color='red',label='Model Microzoop')\n",
    "ax.set_ylim(0,60)\n",
    "ax.set_xlim(0,12)\n",
    "ax.set_title('Model Mesozooplankton Seasonal Cycle')\n",
    "ax.set_ylabel('Mean Biomass (mg/m3)',fontsize=10)\n",
    "\n",
    "fig,ax=plt.subplots(1,1,figsize=(12,2.5))\n",
    "ax.plot(logt_inv(monthlymean['L10mod_microzooplankton']),'--',color='darkorange',label='Model Microzoop')\n",
    "ax.set_ylim(0,60)\n",
    "ax.set_xlim(0,12)\n",
    "ax.set_title('Model Microzooplankton Seasonal Cycle')\n",
    "ax.set_ylabel('Mean Biomass (mg/m3)',fontsize=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Key', 'region_name', 'Station', 'Lon', 'Lat', 'Date', 'dtUTC',\n",
       "       'Twilight', 'Net_Type', 'Mesh_Size(um)',\n",
       "       ...\n",
       "       'k_lower', 'L10Calanoids', 'L10CalanoidsDiaRemoved', 'L10Euphausiids',\n",
       "       'L10Hyperiids', 'L10Gammariids', 'L10Neocalanus', 'L10Total',\n",
       "       'L10mod_mesozooplankton', 'L10mod_microzooplankton'],\n",
       "      dtype='object', length=287)"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1.157396\n",
       "1      0.310793\n",
       "2      0.292791\n",
       "3      1.391521\n",
       "4      0.855453\n",
       "         ...   \n",
       "461    0.346093\n",
       "462    0.390772\n",
       "463    0.075827\n",
       "464    0.077561\n",
       "465    0.894605\n",
       "Name: Calanoids, Length: 466, dtype: float64"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Calanoids']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Calanoida'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/envs/py39/lib/python3.9/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2897\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-> 2898\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\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   2899\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Calanoida'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-253-3e1f8b5907be>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhspace\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[1;32m      3\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten\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----> 4\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Calanoida'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Euphausiacea'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Amphipoda'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mod_mesozooplankton'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'k.'\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      5\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Copepods Euphausiids Amphipods ($\\mu$M)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Obs'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py39/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2904\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\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[1;32m   2905\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\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[0;32m-> 2906\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\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   2907\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\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   2908\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py39/lib/python3.9/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2898\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2899\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2900\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2902\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Calanoida'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(2,2,figsize=(10,10))\n",
    "fig.subplots_adjust(hspace=1)\n",
    "ax=ax.flatten()\n",
    "ax[0].plot(data['Calanoida']+data['Euphausiacea']+data['Amphipoda'],data['mod_mesozooplankton'],'k.')\n",
    "ax[0].set_title('Copepods Euphausiids Amphipods ($\\mu$M)')\n",
    "ax[0].set_xlabel('Obs')\n",
    "ax[0].set_ylabel('Model')\n",
    "ax[0].plot((0,10),(0,10),'r-',alpha=.3)\n",
    "\n",
    "ax[1].plot(logt(data['Calanoida']+data['Euphausiacea']+data['Amphipoda']),logt(data['mod_mesozooplankton']),'k.')\n",
    "ax[1].set_title('Log10 Copepods Euphausiids Amphipods ($\\mu$M)')\n",
    "ax[1].set_xlabel('Obs')\n",
    "ax[1].set_ylabel('Model')\n",
    "ax[1].plot((-3,3),(-3,3),'r-',alpha=.3)\n",
    "\n",
    "ax[2].plot(data['Calanoida']+data['Euphausiacea']+data['Amphipoda']+data['Decapoda']+data['Copelata'],data['mod_mesozooplankton'],'k.')\n",
    "ax[2].set_title('Total ($\\mu$M)')\n",
    "ax[2].set_xlabel('Obs')\n",
    "ax[2].set_ylabel('Model')\n",
    "ax[2].plot((0,10),(0,10),'r-',alpha=.3)\n",
    "\n",
    "ax[3].plot(logt(data['Calanoida']+data['Euphausiacea']+data['Amphipoda']+data['Decapoda']+data['Copelata']),logt(data['mod_mesozooplankton']),'k.')\n",
    "ax[3].set_title('Log10 Total ($\\mu$M)')\n",
    "ax[3].set_xlabel('Obs')\n",
    "ax[3].set_ylabel('Model')\n",
    "ax[3].plot((-3,3),(-3,3),'r-',alpha=.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fig,ax=plt.subplots(1,1,figsize=(10,10))\n",
    "#ax.plot((data['L10Calanoida']),(data['L10mod_mesozooplankton']),'k.')\n",
    "#ax.set_title('Log10 Total ($\\mu$M)')\n",
    "#ax.set_xlabel('Obs')\n",
    "#ax.set_ylabel('Model')\n",
    "#ax.plot((-3,3),(-3,3),'r-',alpha=.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def yd(idt):\n",
    "    if type(idt)==dt.datetime:\n",
    "        yd=(idt-dt.datetime(idt.year-1,12,31)).days\n",
    "    else: # assume array or pandas\n",
    "        yd=[(ii-dt.datetime(ii.year-1,12,31)).days for ii in idt]\n",
    "    return yd\n",
    "\n",
    "data['yd']=yd(data['dtUTC'])\n",
    "data['Year']=[ii.year for ii in data['dtUTC']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### By Day of Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig,ax=plt.subplots(1,1,figsize=(7,7))\n",
    "m=ax.scatter(logt(data['Calanoida']+data['Euphausiacea']+data['Amphipoda']),logt(data['mod_mesozooplankton']),\n",
    "          c=data['yd'],s=5,cmap=cmocean.cm.phase)\n",
    "\n",
    "ax.set_title('log10[ Copepods Amphipods Euphausiids + 0.001] By Year Day')\n",
    "ax.set_xlabel('Obs')\n",
    "ax.set_ylabel('Model')\n",
    "ax.plot((-6,5),(-6,5),'r-',alpha=.3)\n",
    "ax.set_xlim(-1.5,1.5)\n",
    "ax.set_ylim(-1.5,1.5);\n",
    "fig.colorbar(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Month']=[ii.month for ii in data['dtUTC']]\n",
    "JF=data.loc[(data.Month==1)|(data.Month==2)]\n",
    "MAM=data.loc[(data.Month==3)|(data.Month==4)|(data.Month==5)]\n",
    "JJA=data.loc[(data.Month==6)|(data.Month==7)|(data.Month==8)]\n",
    "SOND=data.loc[(data.Month==9)|(data.Month==10)|(data.Month==11)|(data.Month==12)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def byRegion(ax,obsvar,modvar,lims):\n",
    "    SoG=[]\n",
    "    for ind, iregion in enumerate(data.region_name.unique()):\n",
    "        #ax.plot(datreg[iregion]['Lon'], datreg[iregion]['Lat'],'.',\n",
    "                #color = colors[ind], label=iregion)\n",
    "        SoG0=et.varvarPlot(ax,datreg[iregion],obsvar,modvar,\n",
    "                          cols=(colors[ind],),lname=iregion)\n",
    "        SoG.append(SoG0)\n",
    "    l=ax.legend(handles=[ip[0][0] for ip in SoG])\n",
    "    ax.set_xlabel('Obs')\n",
    "    ax.set_ylabel('Model')\n",
    "    ax.plot(lims,lims,'k-',alpha=.5)\n",
    "    ax.set_xlim(lims)\n",
    "    ax.set_ylim(lims)\n",
    "    ax.set_aspect(1)\n",
    "    return SoG,l\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bySeason(ax,obsvar,modvar,lims):\n",
    "    for axi in ax:\n",
    "        axi.plot(lims,lims,'k-')\n",
    "        axi.set_xlim(lims)\n",
    "        axi.set_ylim(lims)\n",
    "        axi.set_aspect(1)\n",
    "        axi.set_xlabel('Obs')\n",
    "        axi.set_ylabel('Model')\n",
    "    SoG=et.varvarPlot(ax[0],JF,obsvar,modvar,cols=('crimson','darkturquoise','navy'))\n",
    "    ax[0].set_title('Winter')\n",
    "    SoG=et.varvarPlot(ax[1],MAM,obsvar,modvar,cols=('crimson','darkturquoise','navy'))\n",
    "    ax[1].set_title('Spring')\n",
    "    SoG=et.varvarPlot(ax[2],JJA,obsvar,modvar,cols=('crimson','darkturquoise','navy'))\n",
    "    ax[2].set_title('Summer')\n",
    "    SoG=et.varvarPlot(ax[3],SOND,obsvar,modvar,cols=('crimson','darkturquoise','navy'))\n",
    "    ax[3].set_title('Autumn')\n",
    "    return "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#obsvar='L10Calanoida'\n",
    "#modvar='L10mod_mesozooplankton'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots of Calanoid copepods vs. model mesozooplankton 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1,1,figsize = (16,7))\n",
    "\n",
    "             \n",
    "SoG,l=byRegion(ax,'L10Calanoida','L10mod_mesozooplankton',(-1.5,1.5))\n",
    "ax.set_title('Log 10 Calanoida + 0.001 ($\\mu$M) By Region')\n",
    "ax.legend(bbox_to_anchor=(1.1, 1.05))\n",
    "\n",
    "fig, ax = plt.subplots(1,4,figsize = (16,3.3))\n",
    "bySeason(ax,'L10Calanoida','L10mod_mesozooplankton',(-1.5,1.5))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#obsvar='Euphausiacea'\n",
    "#modvar='mod_mesozooplankton'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots of Euphausiids vs. model mesozooplankton 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1,1,figsize = (16,7))\n",
    "SoG,l=byRegion(ax,'L10Euphausiacea','L10mod_mesozooplankton',(-1.5,1.5))\n",
    "ax.set_title('Log 10 Euphausiacea + 0.001 ($\\mu$M) By Region');\n",
    "ax.legend(bbox_to_anchor=(1.1, 1.05))\n",
    "\n",
    "fig, ax = plt.subplots(1,4,figsize = (16,3.3))\n",
    "bySeason(ax,'L10Euphausiacea','L10mod_mesozooplankton',(-1.5,1.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots of Amphipods vs. model mesozooplankton 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1,1,figsize = (16,7))\n",
    "SoG,l=byRegion(ax,'L10Amphipoda','L10mod_mesozooplankton',(-1.5,1.5))\n",
    "ax.set_title('Log 10 Amphipoda + 0.001 ($\\mu$M) By Region');\n",
    "ax.legend(bbox_to_anchor=(1.1, 1.05))\n",
    "\n",
    "fig, ax = plt.subplots(1,4,figsize = (16,3.3))\n",
    "bySeason(ax,'L10Amphipoda','L10mod_mesozooplankton',(-1.5,1.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decapods vs. model mesozooplankton 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1,1,figsize = (16,7))\n",
    "SoG,l=byRegion(ax,'L10Decapoda','L10mod_mesozooplankton',(-1.5,1.5))\n",
    "ax.set_title('Log 10 Decapoda + 0.001 ($\\mu$M) By Region');\n",
    "ax.legend(bbox_to_anchor=(1.1, 1.05))\n",
    "\n",
    "fig, ax = plt.subplots(1,4,figsize = (16,3.3))\n",
    "bySeason(ax,'L10Decapoda','L10mod_mesozooplankton',(-1.5,1.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Larvaceans vs. model mesozooplankton 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1,1,figsize = (16,7))\n",
    "SoG,l=byRegion(ax,'L10Copelata','L10mod_mesozooplankton',(-1.5,1.5))\n",
    "ax.set_title('Log 10 Larvaceans + 0.001 ($\\mu$M) By Region');\n",
    "ax.legend(bbox_to_anchor=(1.1, 1.05))\n",
    "\n",
    "fig, ax = plt.subplots(1,4,figsize = (16,3.3))\n",
    "bySeason(ax,'L10Copelata','L10mod_mesozooplankton',(-1.5,1.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots of Cyclopoid copepods vs. model mesozooplankton 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1,1,figsize = (16,7))\n",
    "\n",
    "SoG,l=byRegion(ax,'L10Cyclopoida','L10mod_mesozooplankton',(-1.5,1.5))\n",
    "ax.set_title('Log 10 Cyclopoida + 0.001 ($\\mu$M) By Region');\n",
    "ax.legend(bbox_to_anchor=(1.1, 1.05))\n",
    "\n",
    "fig, ax = plt.subplots(1,4,figsize = (16,3.3))\n",
    "bySeason(ax,'L10Cyclopoida','L10mod_mesozooplankton',(-1.5,1.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calanoid copepods vs. model microzooplankton from Mar-Jun 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1,1,figsize = (16,7))\n",
    "\n",
    "SoG,l=byRegion(ax,'L10Calanoida','L10mod_microzooplankton',(-1.5,1.5))\n",
    "ax.set_title('Log10 Calanoida +0.001 ($\\mu$M) By Region');\n",
    "ax.legend(bbox_to_anchor=(1.1, 1.05))\n",
    "\n",
    "fig, ax = plt.subplots(1,4,figsize = (16,3.3))\n",
    "bySeason(ax,'L10Cyclopoida','L10mod_mesozooplankton',(-1.5,1.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1,1,figsize = (16,7))\n",
    "\n",
    "SoG,l=byRegion(ax,'L10Copelata','L10mod_microzooplankton',(-1.5,1.5))\n",
    "ax.set_title('Log10 Larvaceans +0.001 ($\\mu$M) By Region');\n",
    "ax.legend(bbox_to_anchor=(1.1, 1.05))\n",
    "\n",
    "fig, ax = plt.subplots(1,4,figsize = (16,3.3))\n",
    "bySeason(ax,'L10Copelata','L10mod_microzooplankton',(-1.5,1.5))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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