{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tide Tuning Notebook ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "N36_Cin = {'North': {'S2': {'Pha': -9.2307801486176153, 'Amp': 1.038819257728969}, 'M2': {'Pha': 2.6888859885826322, 'Amp': 0.88935590323168601}, 'O1': {'Pha': -2.5, 'Amp': 0.96099403975860098}, 'Q1': {'Pha': -5, 'Amp': 0.955}, 'N2': {'Pha': -4.6638540853471788, 'Amp': 0.98340380585090903}, 'K1': {'Pha': 4, 'Amp': 1.0204698354704094}}, 'West': {'S2': {'Pha': 1.6977373796987445, 'Amp': 1.042595155967583}, 'M2': {'Pha': -7.2661477680807574, 'Amp': 0.87208325073801229}, 'O1': {'Pha': 2.0, 'Amp': 1.09}, 'Q1': {'Pha': 3.0, 'Amp': 0.99611855906827507}, 'N2': {'Pha': 0.8, 'Amp': 0.98014138690691721}, 'K1': {'Pha': -2.8032934372377518, 'Amp': 0.98438671203095895}}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "N36_Bin = {'North': {'Q1': {'Pha': -6, 'Amp': 0.95049054590546012}, 'K1': {'Pha': -4, 'Amp': 1.02}, 'M2': {'Pha': 2.3827828994834683, 'Amp': 0.88558971714596835}, 'N2': {'Pha': -4.6678925773866355, 'Amp': 0.98323714871353618}, 'S2': {'Pha': -8.8871723559264488, 'Amp': 1.0379680543704286}, 'O1': {'Pha': -3, 'Amp': 0.96353680059599056}}, 'West': {'Q1': {'Pha': 0, 'Amp': 0.9943368632471852}, 'K1': {'Pha': -4, 'Amp': 0.98682615564783738}, 'M2': {'Pha': -7.4948755798835851, 'Amp': 0.87901024019047747}, 'N2': {'Pha': -0.2, 'Amp': 0.9797124228733876}, 'S2': {'Pha': 1.6, 'Amp': 1.0447724256431379}, 'O1': {'Pha': 1.9, 'Amp': 1.06}}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TS13in = {'North': {'M2': {'Amp': 0.8845477202697829, 'Pha': 2.3403076176485551}, 'O1': {'Amp': 0.96272350844507903, 'Pha': 12.0}, 'S2': {'Amp': 1.0379680543704286, 'Pha': -9.2}, 'K1': {'Amp': 1.0330857501316655, 'Pha': -19.0}, 'Q1': {'Amp': 0.97916112578543213, 'Pha': 4.0}, 'N2': {'Amp': 0.9832161382284451, 'Pha': -4.6840045875437299}}, 'West': {'M2': {'Amp': 0.88170684906405461, 'Pha': -7.5434415446258862}, 'O1': {'Amp': 1.0309632498092482, 'Pha': 12.2}, 'S2': {'Amp': 1.0446321721304719, 'Pha': 1.73}, 'K1': {'Amp': 1.0243962888030014, 'Pha': -14.26898720916071}, 'Q1': {'Amp': 0.9943368632471852, 'Pha': 9.85}, 'N2': {'Amp': 0.9797124228733876, 'Pha': 1.0}}}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TS12in = {'West': {'K1': {'Pha': -14.26898720916071, 'Amp': 1.0243962888030014}, 'S2': {'Pha': 1.73, 'Amp': 1.0446178034256923}, 'Q1': {'Pha': 9.8, 'Amp': 0.9943368632471852}, 'O1': {'Pha': 12.050236466923987, 'Amp': 1.0305322478848729}, 'N2': {'Pha': 1.1, 'Amp': 0.9797124228733876}, 'M2': {'Pha': -7.5808775409993272, 'Amp': 0.883}}, 'North': {'K1': {'Pha': -15.0, 'Amp': 1.0}, 'S2': {'Pha': -9.132874394421526, 'Amp': 1.0379680543704286}, 'Q1': {'Pha': 1.6326019921444512, 'Amp': 0.99137048561193775}, 'O1': {'Pha': 12.399628661111066, 'Amp': 0.96285725026965896}, 'N2': {'Pha': -4.6862481353032051, 'Amp': 0.98321613822844511}, 'M2': {'Pha': 2.3120312919802783, 'Amp': 0.88373899355715846}}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TS11in = {'North': {'K1': {'Pha': -11.0, 'Amp': 1.0}, 'S2': {'Pha': -9.132874394421526, 'Amp': 1.0379680543704286}, 'Q1': {'Pha': 7.632601992144451, 'Amp': 1.023011793904681}, 'M2': {'Pha': 2.2779834668333558, 'Amp': 0.88270427351908831}, 'N2': {'Pha': -4.689607824516905, 'Amp': 0.98318244724240378}, 'O1': {'Pha': 18.399628661111066, 'Amp': 0.96361058463564087}}, 'West': {'K1': {'Pha': -14.26898720916071, 'Amp': 1.0243962888030014}, 'S2': {'Pha': 1.73, 'Amp': 1.0447177932216045}, 'Q1': {'Pha': 9.717984938287248, 'Amp': 0.99433686324718518}, 'M2': {'Pha': -7.62709254474166, 'Amp': 0.884}, 'N2': {'Pha': 1.4777129541142697, 'Amp': 0.9797124228733876}, 'O1': {'Pha': 12.050236466923987, 'Amp': 1.0299456999774774}}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TS10in = {'North': {'Q1': {'Amp': 1.0825116171662457, 'Pha': 12.632601992144451}, 'S2': {'Amp': 1.0379680543704286, 'Pha': -9.332874394421525}, 'N2': {'Amp': 0.98355159834393069, 'Pha': -4.6814662131266642}, 'O1': {'Amp': 0.96565598198952585, 'Pha': 28.399628661111066}, 'K1': {'Amp': 0.8200000000000001, 'Pha': -9.0}, 'M2': {'Amp': 0.88138318239218327, 'Pha': 2.2386464647335269}}, 'West': {'Q1': {'Amp': 0.99470837890616992, 'Pha': 9.517984938287249}, 'S2': {'Amp': 1.0446956705700003, 'Pha': 1.7247857894725884}, 'N2': {'Amp': 0.9797124228733876, 'Pha': 0.7610588345320388}, 'O1': {'Amp': 1.0290395717320522, 'Pha': 11.550236466923987}, 'K1': {'Amp': 1.05, 'Pha': -16.26898720916071}, 'M2': {'Amp': 0.88774830007038597, 'Pha': -7.6851903099544563}}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TS9in = {'North': {'O1': {'Amp': 0.9883470888743955, 'Pha': 23.399628661111066}, 'M2': {'Amp': 0.87832866964865763, 'Pha': 2.1477843647649064}, 'N2': {'Amp': 0.98342713711111474, 'Pha': -4.6896078245169051}, 'K1': {'Amp': 0.8200000000000001, 'Pha': -9.0}, 'Q1': {'Amp': 1.1482701669875641, 'Pha': 12.632601992144451}, 'S2': {'Amp': 1.0379680543704286, 'Pha': -8.4439557398943386}}, 'West': {'O1': {'Amp': 1.04, 'Pha': 12.550236466923987}, 'M2': {'Amp': 0.8906867359929187, 'Pha': -7.8166410932266306}, 'N2': {'Amp': 0.9797124228733876, 'Pha': 1.6187676872376835}, 'K1': {'Amp': 1.0243962888030014, 'Pha': -14.26898720916071}, 'Q1': {'Amp': 0.99162436129565101, 'Pha': 10.517984938287249}, 'S2': {'Amp': 1.0445332886627665, 'Pha': 1.7247857894725884}}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TS8in = {'North': {'Q1': {'Pha': 17.63260199214445, 'Amp': 1.2}, 'N2': {'Pha': -4.6764732709324885, 'Amp': 0.98307570791396637}, 'O1': {'Pha': 33.399628661111066, 'Amp': 0.96834708887439547}, 'K1': {'Pha': -7.0, 'Amp': 0.77}, 'M2': {'Pha': 1.9437797162850838, 'Amp': 0.87105935851246963}, 'S2': {'Pha': -8.3328743944215251, 'Amp': 1.0379680543704286}}, 'West': {'Q1': {'Pha': 10.517984938287249, 'Amp': 0.99510234222303384}, 'N2': {'Pha': 1.7610588345320388, 'Amp': 0.9797124228733876}, 'O1': {'Pha': 12.550236466923987, 'Amp': 1.04}, 'K1': {'Pha': -16.26898720916071, 'Amp': 1.05}, 'M2': {'Pha': -8.0785860056219647, 'Amp': 0.89475860223501025}, 'S2': {'Pha': 1.7247857894725884, 'Amp': 1.050516310380342}}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TS7in = {'West': {'S2': {'Amp': 1.0505163103803421, 'Pha': 3.7247857894725884}, 'N2': {'Amp': 0.96971242287338755, 'Pha': 1.7940575230642932}, 'O1': {'Amp': 1.02153509818521, 'Pha': 14.550236466923987}, 'M2': {'Amp': 0.89853811467771461, 'Pha': -8.7976656174843022}, 'K1': {'Amp': 1.0800017009372505, 'Pha': -18.268987209160709}, 'Q1': {'Amp': 0.97358470939406028, 'Pha': 13.517984938287249}}, 'North': {'S2': {'Amp': 1.0479680543704286, 'Pha': -8.5775631728451902}, 'N2': {'Amp': 0.9899441635274836, 'Pha': -4.9011012456689809}, 'O1': {'Amp': 0.98667683685586627, 'Pha': 43.399628661111066}, 'M2': {'Amp': 0.86150074612567795, 'Pha': 0.63888680551324561}, 'K1': {'Amp': 0.75, 'Pha': -5.0}, 'Q1': {'Amp': 1.2518950248746064, 'Pha': 27.632601992144451}}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TS6in = {'North': {'N2': {'Pha': -5.0340458851303485, 'Amp': 0.99093746613836042}, 'Q1': {'Pha': 31.76446905720422, 'Amp': 1.29800213507216}, 'S2': {'Pha': -8.5545198974892447, 'Amp': 1.0490562377934676}, 'O1': {'Pha': 46.267709352580937, 'Amp': 0.99784655545890988}, 'M2': {'Pha': 2.729140847167737, 'Amp': 0.87131594055414263}, 'K1': {'Pha': -5.9289384852688727, 'Amp': 0.74675608275025374}}, 'West': {'N2': {'Pha': 1.8035783055008339, 'Amp': 0.96705193710790061}, 'Q1': {'Pha': 14.139475946283159, 'Amp': 0.96348802266580769}, 'S2': {'Pha': 4.0093996059906685, 'Amp': 1.0506161230881381}, 'O1': {'Pha': 15.072578656156807, 'Amp': 1.0190547114607584}, 'M2': {'Pha': -9.6711874443139667, 'Amp': 0.89769234884245885}, 'K1': {'Pha': -18.747764436328552, 'Amp': 1.0854844970422486}}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TS5in = {'North': {'M2': {'Amp': 0.8503826575559933, 'Pha': 1.4765232701548285}, 'S2': {'Amp': 1.060093886807302, 'Pha': -6.946359019746533}, 'Q1': {'Amp': 1.5368297096448922, 'Pha': 60.42364948634719}, 'K1': {'Amp': 0.773677834111803, 'Pha': -108.3072633495973}, 'N2': {'Amp': 0.9871188616242617, 'Pha': -4.530096424751415}, 'O1': {'Amp': 0.923875247607606, 'Pha': 90.88987314530584}}, 'West': {'M2': {'Amp': 0.8765426451574038, 'Pha': -6.766842787292333}, 'S2': {'Amp': 1.0035869299692508, 'Pha': 8.728134209383496}, 'Q1': {'Amp': 0.9358987350001928, 'Pha': 28.204796180164475}, 'K1': {'Amp': 1.128562033874093, 'Pha': -27.67836501066415}, 'N2': {'Amp': 0.9293430555315706, 'Pha': 1.9060762160877864}, 'O1': {'Amp': 0.9736846815080314, 'Pha': 27.43518571910129}}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TS4in = {'West': {'Q1': {'Amp': 0.95973008246671854, 'Pha': 11.726283958259742}, 'S2': {'Amp': 1.0560814538710048, 'Pha': 2.7883778835861137}, 'K1': {'Amp': 1.0836454717694404, 'Pha': -17.258576900678481}, 'M2': {'Amp': 0.91298101239070584, 'Pha': -12.883470958465448}, 'N2': {'Amp': 0.97616005994525723, 'Pha': 0.62431409913450864}, 'O1': {'Amp': 1.0224087908142427, 'Pha': 11.993685651677305}}, 'North': {'Q1': {'Amp': 1.2501178259629055, 'Pha': 22.116883975191229}, 'S2': {'Amp': 1.0453077941440578, 'Pha': -9.2572751948132552}, 'K1': {'Amp': 0.74541612168305771, 'Pha': -55.123000171765071}, 'M2': {'Amp': 0.93073892518672774, 'Pha': 5.8729325945818829}, 'N2': {'Amp': 0.99352352620481288, 'Pha': -5.3488189574785938}, 'O1': {'Amp': 0.93735590315618389, 'Pha': 33.986361779272698}}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TS3in = {'West': {'Q1': {'Amp': 1.0210325925510757, \n",
    "                       'Pha': 19.713188044831874}, \n",
    "                'S2': {'Amp': 1.0619670281785616, \n",
    "                       'Pha': 1.6225000000000023}, \n",
    "                'K1': {'Amp': 1.0418394160583937, \n",
    "                       'Pha': -16.731369863013693}, \n",
    "                'M2': {'Amp': 0.89799999999999947, \n",
    "                       'Pha': -13.180000000000003}, \n",
    "                'N2': {'Amp': 0.98119624254615978, \n",
    "                       'Pha': -0.95666666666667055}, \n",
    "                'O1': {'Amp': 1.0285514282124055, 'Pha': 11.929584148727976}}, 'North': {'Q1': {'Amp': 1.6950033029840668, 'Pha': -17.727035647279536}, 'S2': {'Amp': 0.99589688506981933, 'Pha': -27.76166666666667}, 'K1': {'Amp': 0.76388461538461572, 'Pha': -40.112195121951224}, 'M2': {'Amp': 0.93099999999999972, 'Pha': 5.9250000000000007}, 'N2': {'Amp': 0.96926862611073172, 'Pha': -24.561666666666667}, 'O1': {'Amp': 1.2414115152944531, 'Pha': 35.273504645760752}}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "TS2in = {'West': {'M2': {'Amp': 0.898, \n",
    "                        'Pha': -13.18},\n",
    "                  'K1': {'Amp': 1.076,\n",
    "                        'Pha': -18.0},\n",
    "                  'O1': {'Amp': 0.999,\n",
    "                        'Pha': 13.6},\n",
    "                  'S2': {'Amp': 1.037,\n",
    "                        'Pha': 0.9},\n",
    "                  'N2': {'Amp': 0.974,\n",
    "                        'Pha': 0.53},\n",
    "                  'Q1': {'Amp': 0.988,\n",
    "                         'Pha': 12.9}\n",
    "                  },\n",
    "          'North' : {'M2': {'Amp': 0.931, \n",
    "                        'Pha': 6},\n",
    "                  'K1': {'Amp': 0.769,\n",
    "                        'Pha': -24.6},\n",
    "                  'O1': {'Amp': 0.998,\n",
    "                        'Pha': 53.},\n",
    "                  'S2': {'Amp': 1.044,\n",
    "                        'Pha': -9.5},\n",
    "                  'N2': {'Amp': 0.993,\n",
    "                        'Pha': -5.5},\n",
    "                  'Q1': {'Amp': 1.344,\n",
    "                         'Pha': 35.3}\n",
    "                  }\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TS1in = {'West': {'M2': {'Amp': 0.909, \n",
    "                        'Pha': -13.47},\n",
    "                  'K1': {'Amp': 1.016,\n",
    "                        'Pha': -4.77},\n",
    "                  'O1': {'Amp': 1.055,\n",
    "                        'Pha': -1.88},\n",
    "                  'S2': {'Amp': 1.075,\n",
    "                        'Pha': -4.5},\n",
    "                  'N2': {'Amp': 1.032,\n",
    "                        'Pha': -0.27},\n",
    "                  'Q1': {'Amp': 1.047,\n",
    "                         'Pha': -3.28}\n",
    "                  },\n",
    "         'North': {'M2':  {'Amp': 0.953, \n",
    "                        'Pha': 5.7},\n",
    "                  'K1': {'Amp': 0.764,\n",
    "                        'Pha': 28.4},\n",
    "                  'O1': {'Amp': 0.968,\n",
    "                        'Pha': 0.4},\n",
    "                  'S2': {'Amp': 1.020,\n",
    "                        'Pha': -11.2},\n",
    "                  'N2': {'Amp': 0.990,\n",
    "                        'Pha': -5.3},\n",
    "                  'Q1': {'Amp': 1.071,\n",
    "                         'Pha': -0.6}\n",
    "                  }\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def initialize_dict():\n",
    "    TS = {'West': {'M2': {'Amp': 0, \n",
    "                        'Pha': 0},\n",
    "                  'K1': {'Amp': 0,\n",
    "                        'Pha': 0},\n",
    "                  'O1': {'Amp': 0,\n",
    "                        'Pha': 0},\n",
    "                  'S2': {'Amp': 0,\n",
    "                        'Pha': 0},\n",
    "                  'N2': {'Amp': 0,\n",
    "                        'Pha': 0},\n",
    "                  'Q1': {'Amp': 0,\n",
    "                         'Pha': 0}\n",
    "                  },\n",
    "         'North': {'M2': {'Amp': 0, \n",
    "                        'Pha': 0},\n",
    "                  'K1': {'Amp': 0,\n",
    "                        'Pha': 0},\n",
    "                  'O1': {'Amp': 0,\n",
    "                        'Pha': 0},\n",
    "                  'S2': {'Amp': 0,\n",
    "                        'Pha': 0},\n",
    "                  'N2': {'Amp': 0,\n",
    "                        'Pha': 0},\n",
    "                  'Q1': {'Amp': 0,\n",
    "                         'Pha': 0}\n",
    "                  }\n",
    "        }\n",
    "    return TS\n",
    "TSNin = initialize_dict()\n",
    "TSN2in = initialize_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feb0b792668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for TS in (TS1in, TS2in, TS3in, TS4in, TS5in, TS6in, TS7in, TS8in, TS9in, TS10in, TS11in, TS12in, TS13in, \n",
    "           N36_Bin, N36_Cin):\n",
    "    for const, symbol in zip(('M2', 'S2', 'N2'), ('o','s','^')):\n",
    "        plt.plot(TS['West'][const]['Amp'], TS['West'][const]['Pha'],symbol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feb094e6780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for TS in (TS1in, TS2in, TS3in, TS4in, TS5in, TS6in, TS7in, TS8in, TS9in, TS10in, TS11in, TS12in, TS13in, \n",
    "           N36_Bin, N36_Cin):\n",
    "    for const, symbol in zip(('K1', 'O1', 'Q1'), ('o','s','^')):\n",
    "        plt.plot(TS['West'][const]['Amp'], TS['West'][const]['Pha'],symbol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Ts1out = {'West': {'Q1': {'Amp': 1.0002572547617277, 'Pha': 3.0075447504032695}, 'S2': {'Amp': 0.95307125754457123, 'Pha': 5.7198115533444138}, 'K1': {'Amp': 1.0585313055637906, 'Pha': -13.200757777125908}, 'M2': {'Amp': 0.98799240608799199, 'Pha': 0.29234517194957516}, 'N2': {'Amp': 0.93318917930521317, 'Pha': 1.0654603431878371}, 'O1': {'Amp': 1.0029590524663352, 'Pha': 2.3038937822556562}}, 'North': {'Q1': {'Amp': 1.263438995383616, 'Pha': -17.096293558632283}, 'S2': {'Amp': 1.0004497332114852, 'Pha': 1.9782821187744162}, 'K1': {'Amp': 1.0063487360512036, 'Pha': -52.899421715538068}, 'M2': {'Amp': 0.97668547636460912, 'Pha': 0.30302472717710316}, 'N2': {'Amp': 0.97999881464282712, 'Pha': 0.082189463385645922}, 'O1': {'Amp': 1.0365442709803026, 'Pha': -0.42241125887312592}}}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Ts2out = {'West': {'Q1': {'Amp': 0.98356590441914449, 'Pha': -1.4334345475063373}, 'S2': {'Amp': 1.0216419632851881, 'Pha': 0.90779104737045202}, 'K1': {'Amp': 0.99266606835333504, 'Pha': 1.3508016934226554}, 'M2': {'Amp': 1.0015851836909175, 'Pha': -0.058748044962015362}, 'N2': {'Amp': 0.99537214954697717, 'Pha': 0.47459245342846879}, 'O1': {'Amp': 1.0336150491261633, 'Pha': -0.48588841857058185}}, 'North': {'Q1': {'Amp': 0.90960982150305669, 'Pha': -28.835321790582896}, 'S2': {'Amp': 1.0051059524147195, 'Pha': 0.76024295755041749}, 'K1': {'Amp': 1.2659586959029192, 'Pha': -11.966162418417269}, 'M2': {'Amp': 1.0001080402583795, 'Pha': -0.12727645118474129}, 'N2': {'Amp': 1.0024693099628155, 'Pha': 0.21684096723782886}, 'O1': {'Amp': 1.1317630850593585, 'Pha': -34.12635889300995}}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#TS3o\n",
    "Ts3out = {'West': {'Q1': {'Amp': 0.95110110499464851, 'Pha': -7.6426784791072464}, 'S2': {'Amp': 1.0202025932595069, 'Pha': 1.0144847613380019}, 'K1': {'Amp': 1.0272219086326759, 'Pha': 0.13292497155501992}, 'M2': {'Amp': 1.0008871425553614, 'Pha': -0.026401458001528565}, 'N2': {'Amp': 1.0045383235506544, 'Pha': 2.8578421947387653}, 'O1': {'Amp': 1.0284224577447008, 'Pha': 0.15177275062089279}}, 'North': {'Q1': {'Amp': 1.0491593153643064, 'Pha': 11.38432262097092}, 'S2': {'Amp': 1.0633942810638697, 'Pha': 22.092814038315055}, 'K1': {'Amp': 1.399915987512546, 'Pha': -14.492809784225329}, 'M2': {'Amp': 0.99934510071026761, 'Pha': -0.1857003075421062}, 'N2': {'Amp': 1.0354092178535133, 'Pha': 22.631314821886065}, 'O1': {'Amp': 1.0853916220804463, 'Pha': -25.230049427371426}}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#TS3\n",
    "TSRout = {'West': {'Q1': {'Amp': 0.9615240748671523, 'Pha': -8.0186552500333903}, 'S2': {'Amp': 1.0314281994044106, 'Pha': 1.5245866303987459}, 'K1': {'Amp': 1.0423440174621237, 'Pha': -0.42662190481817891}, 'M2': {'Amp': 1.0148414225529525, 'Pha': 0.30699215045163986}, 'N2': {'Amp': 1.0190109589854979, 'Pha': 3.1444151751516358}, 'O1': {'Amp': 1.0403371684399034, 'Pha': -0.3682571354704578}}, 'North': {'Q1': {'Amp': 1.0402483648893954, 'Pha': 9.9466892870592574}, 'S2': {'Amp': 1.0644803022440763, 'Pha': 23.072834208666109}, 'K1': {'Amp': 1.3417137185150854, 'Pha': -11.210779203809977}, 'M2': {'Amp': 0.97345565972896475, 'Pha': 1.1010105205821503}, 'N2': {'Amp': 1.0179979559630259, 'Pha': 24.008950313318181}, 'O1': {'Amp': 1.1091799512735716, 'Pha': -19.851697664023504}}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#TS4\n",
    "TS4out = {'West': {'Q1': {'Amp': 1.021710281717962, 'Pha': -1.2818051360898437}, 'S2': {'Amp': 1.0048287114712622, 'Pha': -0.77931142110978868}, 'K1': {'Amp': 1.0077723235798881, 'Pha': 0.050270335765687305}, 'M2': {'Amp': 1.0019637630738421, 'Pha': -0.084921979605779865}, 'N2': {'Amp': 0.99488881155208686, 'Pha': 0.31632214863880626}, 'O1': {'Amp': 1.0160040214143051, 'Pha': 0.28326329135653339}}, 'North': {'Q1': {'Amp': 1.1396837053873892, 'Pha': -22.343216315967823}, 'S2': {'Amp': 1.01031637074638, 'Pha': 0.61309589110754814}, 'K1': {'Amp': 1.4993965264182494, 'Pha': -19.036640927224198}, 'M2': {'Amp': 0.99847670218981277, 'Pha': -0.033309406172094214}, 'N2': {'Amp': 1.0048771725470016, 'Pha': 0.16683054836840938}, 'O1': {'Amp': 1.3692040838013475, 'Pha': -29.098224761850673}}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#TS4 w Hollingsworth\n",
    "TSHout = {'West': {'Q1': {'Amp': 1.0119383657601555, 'Pha': -1.9438847794043141}, 'S2': {'Amp': 0.99559468002265994, 'Pha': -1.4019991460643482}, 'K1': {'Amp': 0.98832300736547773, 'Pha': 0.28881171540387657}, 'M2': {'Amp': 0.98854451467568982, 'Pha': -0.5788869325603514}, 'N2': {'Amp': 0.98697982726560152, 'Pha': -0.052406117338830827}, 'O1': {'Amp': 0.99971651564967901, 'Pha': 0.2006417619422507}}, 'North': {'Q1': {'Amp': 1.1336904122250575, 'Pha': -22.262549937009737}, 'S2': {'Amp': 1.019390426248554, 'Pha': 1.0650283890524292}, 'K1': {'Amp': 1.4689939087715977, 'Pha': -19.71301519371076}, 'M2': {'Amp': 1.0095401002831588, 'Pha': 0.5060034580093884}, 'N2': {'Amp': 1.0141608691246866, 'Pha': 0.66414894517527046}, 'O1': {'Amp': 1.3401176934857579, 'Pha': -29.105986664905771}}}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TS4newBout = {'North': {'M2': {'Pha': -4.3964093244270543, 'Amp': 0.91366400882544685}, 'K1': {'Pha': -53.184263177832236, 'Amp': 1.0379140075008491}, 'O1': {'Pha': 3.7192481882009076, 'Amp': 1.0229871678905673}, 'Q1': {'Pha': -14.877497666676277, 'Amp': 1.2759573935003046}, 'S2': {'Pha': -2.0854931493603317, 'Amp': 0.92658797320536079}, 'N2': {'Pha': -3.5776867916998754, 'Amp': 0.90777415180494747}}, 'West': {'M2': {'Pha': 6.1166281711731152, 'Amp': 0.96008858153809173}, 'K1': {'Pha': -10.419788109985671, 'Amp': 1.041449499190275}, 'O1': {'Pha': 5.0217119574383133, 'Amp': 0.99181798223604978}, 'Q1': {'Pha': 6.0587241119190622, 'Amp': 1.0155889522120538}, 'S2': {'Pha': 12.056384496970498, 'Amp': 0.9123655647133796}, 'N2': {'Pha': 7.398390288126393, 'Amp': 0.91404237128654797}}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TS5out = {'North': {'M2': {'Pha': 0.42758785421409584, 'Amp': 1.011625002682464}, 'K1': {'Pha': -29.472163528321573, 'Amp': 1.5805376110939042}, 'O1': {'Pha': -83.573669377494497, 'Amp': 1.9482260093882917}, 'Q1': {'Pha': -61.734484942181325, 'Amp': 1.0241774045384158}, 'S2': {'Pha': -1.4895386482369872, 'Amp': 0.99513722966584894}, 'N2': {'Pha': 0.10773931037238071, 'Amp': 1.0111376898211519}}, 'West': {'M2': {'Pha': -1.5309396078788888, 'Amp': 1.0378637494752101}, 'K1': {'Pha': 12.035886712521583, 'Amp': 0.94974179875911324}, 'O1': {'Pha': -3.7224212917422079, 'Amp': 1.0325092803506841}, 'Q1': {'Pha': -8.3677928244752877, 'Amp': 0.99273824278749245}, 'S2': {'Pha': -6.9463119651399552, 'Amp': 1.1154505816612328}, 'N2': {'Pha': -1.3598703918715493, 'Amp': 1.0890526878512701}}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TS6out = {'West': {'O1': {'Amp': 0.99917259570573957, 'Pha': -0.26278166115388046}, 'N2': {'Amp': 1.0244990697827043, 'Pha': 1.224306456276878}, 'Q1': {'Amp': 1.0176497900134822, 'Pha': -1.4806862994981884}, 'S2': {'Amp': 1.0087992347785939, 'Pha': 0.1336154074221394}, 'M2': {'Amp': 1.009684365613287, 'Pha': 1.2434341458076474}, 'K1': {'Amp': 0.97410589754902577, 'Pha': 3.0078799229218589}}, 'North': {'O1': {'Amp': 1.1099589622402357, 'Pha': -27.838619895114306}, 'N2': {'Amp': 0.98180311923347008, 'Pha': -0.58195399212740995}, 'Q1': {'Amp': 0.91915208049571451, 'Pha': -25.717717368800777}, 'S2': {'Amp': 0.98467109778763395, 'Pha': -1.4704736261345488}, 'M2': {'Amp': 0.99011883203452811, 'Pha': -1.3444498805239675}, 'K1': {'Amp': 1.1904807255499437, 'Pha': -6.1957349307586469}}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TS7out = {'North': {'Q1': {'Pha': -22.886425235514594, 'Amp': 0.93055076235820455}, 'M2': {'Pha': 1.8789511310803277, 'Amp': 1.0423735647108798}, 'K1': {'Pha': -7.0524623691283921, 'Amp': 1.1619354362342378}, 'N2': {'Pha': 2.2311317939641242, 'Amp': 1.0312508215460159}, 'O1': {'Pha': -25.60358248760835, 'Amp': 1.0881114343042568}, 'S2': {'Pha': 1.6180611113048258, 'Amp': 1.0326339034344933}}, 'West': {'Q1': {'Pha': -0.34469260821630598, 'Amp': 0.98149555239898412}, 'M2': {'Pha': 1.3234744539132743, 'Amp': 0.97796155111043792}, 'K1': {'Pha': 3.8762994934517199, 'Amp': 0.95946938123484593}, 'N2': {'Pha': 0.89717076965282549, 'Amp': 0.98678259304211158}, 'O1': {'Pha': 1.0664646301858483, 'Amp': 0.97995456282236715}, 'S2': {'Pha': -0.27450001446825567, 'Amp': 0.97941497283467949}}}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TS8out = {'West': {'K1': {'Amp': 0.97294394809883511, 'Pha': 2.7579367053216881}, 'Q1': {'Amp': 0.96966233066943985, 'Pha': 1.9150525736966344}, 'S2': {'Amp': 0.95472229971458944, 'Pha': 1.0372218360624021}, 'N2': {'Amp': 0.95752188775425451, 'Pha': 0.40262808850771226}, 'M2': {'Amp': 0.95858932366390581, 'Pha': 1.1601428772132749}, 'O1': {'Amp': 0.97183294662054664, 'Pha': 2.007318956120443}}, 'North': {'K1': {'Amp': 1.1547120834449451, 'Pha': -7.6129201656615066}, 'Q1': {'Amp': 0.96681474235607734, 'Pha': -15.124112334094548}, 'S2': {'Amp': 1.0513147965750143, 'Pha': 0.20788982904463182}, 'N2': {'Amp': 1.0526603636580936, 'Pha': 1.0521057205134241}, 'M2': {'Amp': 1.0521929700885222, 'Pha': 1.1130570825965833}, 'O1': {'Amp': 1.0868904745440655, 'Pha': -19.161141807354142}}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TS9out = {'North': {'K1': {'Amp': 1.141806975674275, 'Pha': -6.8066122155455275}, 'O1': {'Amp': 1.0790183141135039, 'Pha': -12.693489398286005}, 'N2': {'Amp': 1.0276135396079655, 'Pha': 0.69195097514989357}, 'S2': {'Amp': 1.0267195882687952, 'Pha': -0.21023995360576464}, 'M2': {'Amp': 1.0273131680911955, 'Pha': 0.6410257630467413}, 'Q1': {'Amp': 0.99349042997534653, 'Pha': -11.450128659908728}}, 'West': {'K1': {'Amp': 1.0005922446358808, 'Pha': 0.30581150821401393}, 'O1': {'Amp': 0.99691301051231362, 'Pha': -0.27067321788786103}, 'N2': {'Amp': 0.97009487763399349, 'Pha': 0.079806302494018655}, 'S2': {'Amp': 0.97355520330980305, 'Pha': 0.52786277421644989}, 'M2': {'Amp': 0.97177309878878693, 'Pha': 0.65317168768398026}, 'Q1': {'Amp': 1.005747559953059, 'Pha': -0.38741079347094853}}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TS10out = {'West': {'Q1': {'Amp': 0.98050924008569673, 'Pha': 2.1640812550494957}, 'N2': {'Amp': 0.98497707891758624, 'Pha': 0.73188917599328818}, 'S2': {'Amp': 0.98386804368385228, 'Pha': 0.49094103412041257}, 'M2': {'Amp': 0.9835814140583149, 'Pha': 0.33283810199147285}, 'K1': {'Amp': 0.98125945152519567, 'Pha': 2.0121855104044792}, 'O1': {'Amp': 0.98879469627003824, 'Pha': 2.423226052588376}}, 'North': {'Q1': {'Amp': 1.0262218515128376, 'Pha': -11.636416402243867}, 'N2': {'Amp': 1.0121869970770876, 'Pha': 0.25638965602854569}, 'S2': {'Amp': 1.0127597355406559, 'Pha': 0.59505848045751009}, 'M2': {'Amp': 1.0133909064729139, 'Pha': 0.33157708793285678}, 'K1': {'Amp': 1.1311741337398815, 'Pha': -6.2684264923129263}, 'O1': {'Amp': 1.0807892207906737, 'Pha': -15.033773286125751}}}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TS11out = {'West': {'N2': {'Amp': 0.9861002244759608, 'Pha': -0.10566829682310153}, 'K1': {'Amp': 0.99899667510872814, 'Pha': -0.036503272822104603}, 'M2': {'Amp': 0.98567279493454218, 'Pha': 0.3057391725200202}, 'Q1': {'Amp': 1.0010194819454052, 'Pha': 0.047263663169253789}, 'S2': {'Amp': 0.98418635692484369, 'Pha': 0.34645353156939507}, 'O1': {'Amp': 1.0045736271018535, 'Pha': -0.035061767485288442}}, 'North': {'N2': {'Amp': 1.0118494949360155, 'Pha': 0.37538197471974399}, 'K1': {'Amp': 1.0011334075203402, 'Pha': -4.4039853987503932}, 'M2': {'Amp': 1.0117183935668721, 'Pha': 0.33110599860091838}, 'Q1': {'Amp': 0.95470825319238906, 'Pha': -7.1123004897670796}, 'S2': {'Amp': 1.0103376571119917, 'Pha': 0.31215195736308488}, 'O1': {'Amp': 0.98265530252292388, 'Pha': -7.7938163393311299}}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TS12out = {'North': {'Q1': {'Pha': -2.8028207797105438, 'Amp': 1.0067196858504108}, 'O1': {'Pha': -3.4538665865347014, 'Amp': 1.0238409720567998}, 'N2': {'Pha': 0.33510031771822923, 'Amp': 1.0091900611944493}, 'M2': {'Pha': 0.31120580423186084, 'Amp': 1.0100886881011024}, 'K1': {'Pha': -4.3189261967383743, 'Amp': 1.0276037991916289}, 'S2': {'Pha': 0.20258067707970895, 'Amp': 1.010827726447741}}, 'West': {'Q1': {'Pha': 0.23364280047611174, 'Amp': 1.0034113035505845}, 'O1': {'Pha': 0.16957879223932082, 'Amp': 1.0042583168341883}, 'N2': {'Pha': 0.17550044986570212, 'Amp': 0.98473045540527915}, 'M2': {'Pha': 0.28090946947771794, 'Amp': 0.98647781863541684}, 'K1': {'Pha': 0.0016394684952055845, 'Amp': 0.99968355103521234}, 'S2': {'Pha': 0.32145241386463219, 'Amp': 0.98671884010077271}}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TS13out = {'West': {'O1': {'Pha': 0.04680136479113628, 'Amp': 1.0028414117632114}, 'N2': {'Pha': 0.28407505636140273, 'Amp': 0.98666343884396468}, 'S2': {'Pha': 0.30991534737121995, 'Amp': 0.98748279768091618}, 'M2': {'Pha': 0.2517691311556558, 'Amp': 0.9878931090830384}, 'K1': {'Pha': 0.014731604514469591, 'Amp': 0.99954343378996247}, 'Q1': {'Pha': 0.0071499783811859174, 'Amp': 1.0018607902885381}}, 'North': {'O1': {'Pha': -3.4702531096732798, 'Amp': 1.0239069789801176}, 'N2': {'Pha': 0.35918217891162385, 'Amp': 1.0087207644351472}, 'S2': {'Pha': 0.27295659468495614, 'Amp': 1.0097792960221685}, 'M2': {'Pha': 0.29874565143435916, 'Amp': 1.0083006574774827}, 'K1': {'Pha': -4.0578237330762477, 'Amp': 1.0270972397817333}, 'Q1': {'Pha': -4.235818999980836, 'Amp': 0.99737542293651305}}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "N36_Aout = {'West': {'S2': {'Pha': 0.024968663334318464, 'Amp': 0.99337155681265432}, 'Q1': {'Pha': 3.3569984413439684, 'Amp': 0.95663330904750743}, 'K1': {'Pha': 10.525916755897864, 'Amp': 0.95627620722297868}, 'O1': {'Pha': -0.019006280510751594, 'Amp': 1.0095477235303318}, 'N2': {'Pha': -0.0012874180538793301, 'Amp': 0.99344078977697381}, 'M2': {'Pha': 0.20672969828430446, 'Amp': 0.99007316601581041}}, 'North': {'S2': {'Pha': 0.11378081255770667, 'Amp': 1.0070620543538167}, 'Q1': {'Pha': 0.88814591096328854, 'Amp': 0.95394468894565598}, 'K1': {'Pha': 10.085614670042702, 'Amp': 0.99230295956348635}, 'O1': {'Pha': -2.8850242464157532, 'Amp': 1.022596420254793}, 'N2': {'Pha': 0.42669693943682319, 'Amp': 1.007929667620058}, 'M2': {'Pha': 0.27507562700012045, 'Amp': 1.0073131602562162}}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "N36_Bout = {'North': {'N2': {'Pha': 0.228619922684004, 'Amp': 1.0044197820734821}, 'O1': {'Pha': 8.4089357005164231, 'Amp': 0.98525392629581676}, 'S2': {'Pha': -0.46028630594918241, 'Amp': 1.0108745392383118}, 'Q1': {'Pha': 9.0291992812070987, 'Amp': 0.97501550156148509}, 'K1': {'Pha': 7.8443759889432698, 'Amp': 0.95588820757114035}, 'M2': {'Pha': 0.19904203096535866, 'Amp': 1.0024480929027397}}, 'West': {'N2': {'Pha': 1.0231581697589434, 'Amp': 0.99485890137137734}, 'O1': {'Pha': 0.51036973879503478, 'Amp': 1.0090380271121973}, 'S2': {'Pha': 0.19181980227444306, 'Amp': 0.98490801519221349}, 'Q1': {'Pha': 3.5029764974067277, 'Amp': 0.99309398972124918}, 'K1': {'Pha': 0.45180641855848502, 'Amp': 0.98606684406471978}, 'M2': {'Pha': 0.14578991784822209, 'Amp': 0.99131929120194784}}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "N36_Cout = {'North': {'O1': {'Pha': 7.3854138207617837, 'Amp': 0.94085326688379123}, 'Q1': {'Pha': 6.4006241776399975, 'Amp': 0.95897973076356524}, 'K1': {'Pha': 7.1413981914269868, 'Amp': 0.91658971870546968}, 'S2': {'Pha': -0.3595232949933802, 'Amp': 1.0060087070702608}, 'M2': {'Pha': -0.07795186160888079, 'Amp': 0.99633800734625722}, 'N2': {'Pha': -0.063366454852484821, 'Amp': 0.99810122970525916}}, 'West': {'O1': {'Pha': -0.79300780292307138, 'Amp': 0.98074493235008808}, 'Q1': {'Pha': -0.6739014812033588, 'Amp': 0.98943179187765928}, 'K1': {'Pha': -0.72383160181509798, 'Amp': 0.98614869389993487}, 'S2': {'Pha': 0.069163430058537756, 'Amp': 0.99327610629011942}, 'M2': {'Pha': -0.038238175514344983, 'Amp': 0.99773471593815255}, 'N2': {'Pha': -0.01545786753853351, 'Amp': 1.0017159403672811}}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "diurnals = ('K1', 'O1', 'Q1')\n",
    "semis = ('M2', 'S2', 'N2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "West 7.53873802668 0.0260001399284\n",
      "West 8.73191361202 0.0348112683801\n",
      "West 1.94154815802 0.0180988418334\n",
      "West 2.32965359117 0.0282073583198\n",
      "West 2.25853718309 0.0285527991679\n",
      "West 0.32499835504 0.00378218205184\n",
      "West 2.20636675016 0.0168982909476\n",
      "West 0.0399819556054 0.00276671099209\n",
      "West 0.166681945482 0.00315543943701\n",
      "West 0.0286269692699 0.00197860338035\n",
      "West 6.3787335035 0.0359796190622\n",
      "West 2.06037623195 0.0103844715219\n",
      "West 0.731878125021 0.0149922572661\n"
     ]
    }
   ],
   "source": [
    "for TS in (TS4newBout, TS5out, TS6out, TS7out, TS8out, TS9out, TS10out, TS11out, TS12out, TS13out, \n",
    "           N36_Aout, N36_Bout, N36_Cout):\n",
    "    for side in ('West', 'North'):\n",
    "        phaerror = 0\n",
    "        amperror = 0\n",
    "        for const in diurnals:\n",
    "            phaerror += TS[side][const]['Pha']**2\n",
    "            amperror += (TS[side][const]['Amp'] - 1)**2\n",
    "        if side=='West':\n",
    "            print (side, np.sqrt(phaerror/3.), np.sqrt(amperror/3.) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "correction = {'S2': {'Pha' : ('M2', False, True),\n",
    "                    'Amp': ('M2', True, False)},\n",
    "              'N2': {'Pha' : ('M2', False, True),\n",
    "                    'Amp': ('M2', True, False)},\n",
    "             'O1': {'Pha' : ('K1', False, True),\n",
    "                   'Amp': ('K1', True, False)},\n",
    "             'Q1': {'Pha' : ('K1', False, True),\n",
    "                   'Amp': ('K1', True, False)},}\n",
    "\n",
    "def correctionf(TS):\n",
    "    TSc = initialize_dict()\n",
    "    for side in ('West', 'North'):\n",
    "        for const in ('M2', 'K1'):\n",
    "            for pa in ('Pha', 'Amp'):\n",
    "                TSc[side][const][pa] = TS[side][const][pa]\n",
    "        for const in ('O1', 'Q1', 'S2', 'N2'):\n",
    "            TSc[side][const]['Pha'] = (TS[side][const]['Pha'] - \n",
    "                                       TS[side][correction[const]['Pha'][0]]['Pha'])\n",
    "            TSc[side][const]['Amp'] = (TS[side][const]['Amp'] / \n",
    "                                       TS[side][correction[const]['Amp'][0]]['Amp'])  \n",
    "    return TSc\n",
    "TS3corr = correctionf(Ts3out)\n",
    "TS3Rcorr = correctionf(TSRout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "numruns = 7; x = np.zeros(numruns); y = np.zeros_like(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def make_comparison(freq, side):\n",
    "    fig, ax = plt.subplots(1,2,figsize=(15,10))\n",
    "    for ipa, pa in enumerate(('Amp', 'Pha')):\n",
    "        for const in freq:\n",
    "            for irun, (Tin, Tout) in enumerate(zip( (TS10in, \n",
    "                                                     TS11in, TS12in, TS13in, TS13in, N36_Bin, N36_Cin), \n",
    "                                                   (TS10out, TS11out, TS12out, TS13out, N36_Aout,\n",
    "                                                   N36_Bout, N36_Cout))):                \n",
    "                x[irun] = Tin[side][const][pa]\n",
    "                y[irun] = Tout[side][const][pa]\n",
    "     #           print const, x[irun]\n",
    "                if const in ('O1', 'Q1', 'S2', 'N2'):\n",
    "                    divisor = (correction[const][pa][1] * Tout[side][correction[const][pa][0]][pa] +\n",
    "                    (1-correction[const][pa][1]))\n",
    "                    y[irun] = (Tout[side][const][pa] / divisor\n",
    "                                       - correction[const][pa][2] * \n",
    "                                                       Tout[side][correction[const][pa][0]][pa])\n",
    "                    print (side, const, pa, y[irun])\n",
    "                    \n",
    "            \n",
    "            ax[ipa].plot(x[:-3],y[:-3],'o', label=const)\n",
    "            ax[ipa].plot(x[-3], y[-3], '<', label=const)\n",
    "            ax[ipa].plot(x[-2], y[-2], '>', label=const)\n",
    "            ax[ipa].plot(x[-1],y[-1],'^', label=const)   \n",
    "\n",
    "            m, b = np.polyfit(x, y, 1)\n",
    "            \n",
    "            if pa == 'Pha':\n",
    "                TSNin[side][const]['Pha'] = -b/m\n",
    "                ax[ipa].plot(TSNin[side][const]['Pha'], 0., '*')\n",
    "            else:\n",
    "                TSNin[side][const]['Amp'] = (1-b)/m\n",
    "                ax[ipa].plot(TSNin[side][const]['Amp'], 1., '*')\n",
    "            \n",
    "            \n",
    "            if pa == 'Amp':\n",
    "                if side == 'North' and const in diurnals:\n",
    "                    exes = np.arange(0.7, 1.8, 0.1)\n",
    "                else:\n",
    "                    exes = np.arange(0.88,1.1,0.05)\n",
    "            elif side == 'West':\n",
    "                if const in diurnals:\n",
    "                    exes = np.arange(-20, 21, 1)\n",
    "                else:\n",
    "                    exes = np.arange(-15,5,1)\n",
    "            else:\n",
    "                if const in diurnals:\n",
    "                    exes = np.arange(-60, 60, 2)\n",
    "                else:\n",
    "                    exes = np.arange(-30, 10, 1)\n",
    "            ax[ipa].plot(exes, m*exes+b)\n",
    "\n",
    "        ax[ipa].set_xlabel('Forcing')\n",
    "        ax[ipa].set_ylabel('Suggested Correction')\n",
    "        ax[ipa].grid()\n",
    "        ax[ipa].legend()\n",
    "        if side == 'North' and pa == 'Amp' and const in diurnals:\n",
    "            ax[ipa].set_ylim(0.5,2)\n",
    "            ax[ipa].set_xlim(0.7, 1.2)\n",
    "        elif side == 'North' and pa == 'Pha' and const in diurnals:\n",
    "            ax[ipa].set_xlim(-30,10)\n",
    "            ax[ipa].set_ylim(-14,11)\n",
    "        elif side == 'West' and pa == 'Pha' and const in semis:\n",
    "            ax[ipa].set_xlim(0,5)\n",
    "            ax[ipa].set_ylim(-2.5,2.5)\n",
    "        elif side== 'North' and pa == 'Pha' and const in semis:\n",
    "            ax[ipa].set_xlim(-12,7)\n",
    "            ax[ipa].set_ylim(-8,5)\n",
    "\n",
    "    print (side, freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "West S2 Amp 1.00029141424\n",
      "West S2 Amp 0.998491955934\n",
      "West S2 Amp 1.00024432528\n",
      "West S2 Amp 0.999584660123\n",
      "West S2 Amp 1.00333146166\n",
      "West S2 Amp 0.99353258222\n",
      "West S2 Amp 0.995531267403\n",
      "West N2 Amp 1.00141896221\n",
      "West N2 Amp 1.00043364243\n",
      "West N2 Amp 0.998228684723\n",
      "West N2 Amp 0.998755259827\n",
      "West N2 Amp 1.00340138878\n",
      "West N2 Amp 1.00357060556\n",
      "West N2 Amp 1.00399026351\n",
      "West S2 Pha 0.158102932129\n",
      "West S2 Pha 0.0407143590494\n",
      "West S2 Pha 0.0405429443869\n",
      "West S2 Pha 0.0581462162156\n",
      "West S2 Pha -0.18176103495\n",
      "West S2 Pha 0.0460298844262\n",
      "West S2 Pha 0.107401605573\n",
      "West N2 Pha 0.399051074002\n",
      "West N2 Pha -0.411407469343\n",
      "West N2 Pha -0.105409019612\n",
      "West N2 Pha 0.0323059252057\n",
      "West N2 Pha -0.208017116338\n",
      "West N2 Pha 0.877368251911\n",
      "West N2 Pha 0.0227803079758\n",
      "West ('M2', 'S2', 'N2')\n",
      "West N2 Pha\n",
      "1.0 -0.2 0.8 0.958753937874\n"
     ]
    },
    {
     "data": {
      "image/png": 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SK9ZLDeuljjUjMg++H9WwXmpYLzWsl3dwkkdERERERGQh3JNHRGQS3HPg0s49eW5iRhKR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fjtBrXe36rY7pjGnTOu7wNUZvIBKgPlmc2bdVljo2rnzNEwrgkEJASaPDdRPUdicH2hsVGL2tv1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\nf+jcEA0IsHKPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feb09133eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_comparison(semis,'West')\n",
    "side = 'West'; const = 'N2'; pa = 'Pha'\n",
    "print (side, const, pa)\n",
    "print (TS13in[side][const][pa], N36_Bin[side][const][pa], N36_Cin[side][const][pa], TSNin[side][const][pa])\n",
    "#print (N36_Aout[side][const][pa], N36_Bout[side][const][pa], N36_Cout[side][const][pa])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "West O1 Amp 1.00767915635\n",
      "West O1 Amp 1.00558255311\n",
      "West O1 Amp 1.00457621394\n",
      "West O1 Amp 1.00329948441\n",
      "West O1 Amp 1.0557072485\n",
      "West O1 Amp 1.0232957666\n",
      "West O1 Amp 0.994520337974\n",
      "West Q1 Amp 0.999235460674\n",
      "West Q1 Amp 1.00202483841\n",
      "West Q1 Amp 1.00372893253\n",
      "West Q1 Amp 1.00231841501\n",
      "West Q1 Amp 1.00037342958\n",
      "West Q1 Amp 1.00712643945\n",
      "West Q1 Amp 1.00332921191\n",
      "West O1 Pha 0.411040542184\n",
      "West O1 Pha 0.00144150533682\n",
      "West O1 Pha 0.167939323744\n",
      "West O1 Pha 0.0320697602767\n",
      "West O1 Pha -10.5449230364\n",
      "West O1 Pha 0.0585633202365\n",
      "West O1 Pha -0.069176201108\n",
      "West Q1 Pha 0.151895744645\n",
      "West Q1 Pha 0.0837669359914\n",
      "West Q1 Pha 0.232003331981\n",
      "West Q1 Pha -0.00758162613328\n",
      "West Q1 Pha -7.16891831455\n",
      "West Q1 Pha 3.05117007885\n",
      "West Q1 Pha 0.0499301206117\n",
      "West ('K1', 'O1', 'Q1')\n",
      "West Q1 Pha\n",
      "9.85 0 3.0 6.0579575156\n"
     ]
    },
    {
     "data": {
      "image/png": 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uW8czH33kdSQREWkHmgw6Lshrs4NcOwS7/o6uPfXWVEKdQnz0e/1BLMFhjGFK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54LcW1zp5Brh7DrD7l2UP0SDp+3dZ+zg9/5fc4Ofuf3OTv4nz8ZmgyKiIiI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\n4L7llg9OnW4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feb09470198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_comparison(diurnals, 'West')\n",
    "side = 'West'; const = 'Q1'; pa = 'Pha'\n",
    "print (side, const, pa)\n",
    "print (TS13in[side][const][pa], N36_Bin[side][const][pa], N36_Cin[side][const][pa], TSNin[side][const][pa])\n",
    "#print (N36_Aout[side][const][pa], N36_Bout[side][const][pa], N36_Cout[side][const][pa])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "North S2 Amp 0.999377169335\n",
      "North S2 Amp 0.998635256151\n",
      "North S2 Amp 1.00073165689\n",
      "North S2 Amp 1.00146646591\n",
      "North S2 Amp 0.999750717143\n",
      "North S2 Amp 1.00840586799\n",
      "North S2 Amp 1.00970624392\n",
      "North N2 Amp 0.998811999014\n",
      "North N2 Amp 1.00012958287\n",
      "North N2 Amp 0.99911034851\n",
      "North N2 Amp 1.0004166485\n",
      "North N2 Amp 1.00061203148\n",
      "North N2 Amp 1.00196687408\n",
      "North N2 Amp 1.001769703\n",
      "North S2 Pha 0.263481392525\n",
      "North S2 Pha -0.0189540412378\n",
      "North S2 Pha -0.108625127152\n",
      "North S2 Pha -0.0257890567494\n",
      "North S2 Pha -0.161294814442\n",
      "North S2 Pha -0.659328336915\n",
      "North S2 Pha -0.281571433384\n",
      "North N2 Pha -0.0751874319043\n",
      "North N2 Pha 0.0442759761188\n",
      "North N2 Pha 0.0238945134864\n",
      "North N2 Pha 0.0604365274773\n",
      "North N2 Pha 0.151621312437\n",
      "North N2 Pha 0.0295778917186\n",
      "North N2 Pha 0.0145854067564\n",
      "North ('M2', 'S2', 'N2')\n",
      "North N2 Pha\n",
      "-4.68400458754373 -4.6678925773866355 -4.663854085347179 -4.65488757361\n"
     ]
    },
    {
     "data": {
      "image/png": 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C5NTz6frabyEBGPVtpCnquEKpVCIsLAypqamYPXu23vNr1qzB0qVLsXv3bgQE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\nT5IkSZIG7g8aKa0rnljmzQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feb096eeb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_comparison(semis,'North')\n",
    "side = 'North'; const = 'N2'; pa = 'Pha'\n",
    "print (side, const, pa)\n",
    "print (TS13in[side][const][pa], N36_Bin[side][const][pa], N36_Cin[side][const][pa], TSNin[side][const][pa])\n",
    "#print (N36_Aout[side][const][pa], N36_Bout[side][const][pa], N36_Cout[side][const][pa])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "North O1 Amp 0.955457863253\n",
      "North O1 Amp 0.981542814515\n",
      "North O1 Amp 0.996338251048\n",
      "North O1 Amp 0.996893905778\n",
      "North O1 Amp 1.03052843932\n",
      "North O1 Amp 1.03072087143\n",
      "North O1 Amp 1.02647154739\n",
      "North Q1 Amp 0.907218279576\n",
      "North Q1 Amp 0.95362740472\n",
      "North Q1 Amp 0.979676881929\n",
      "North Q1 Amp 0.971062314556\n",
      "North Q1 Amp 0.961344194081\n",
      "North Q1 Amp 1.02000996962\n",
      "North Q1 Amp 1.04624753169\n",
      "North O1 Pha -8.76534679381\n",
      "North O1 Pha -3.38983094058\n",
      "North O1 Pha 0.865059610204\n",
      "North O1 Pha 0.587570623403\n",
      "North O1 Pha -12.9706389165\n",
      "North O1 Pha 0.564559711573\n",
      "North O1 Pha 0.244015629335\n",
      "North Q1 Pha -5.36798990993\n",
      "North Q1 Pha -2.70831509102\n",
      "North Q1 Pha 1.51610541703\n",
      "North Q1 Pha -0.177995266905\n",
      "North Q1 Pha -9.19746875908\n",
      "North Q1 Pha 1.18482329226\n",
      "North Q1 Pha -0.740774013787\n",
      "North ('K1', 'O1', 'Q1')\n",
      "North Q1 Pha\n",
      "4.0 -6 -5 -4.12707636008\n"
     ]
    },
    {
     "data": {
      "image/png": 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CYGBgq9slIiLyhNrqI6DOGskBn5/i3GrP3NxfljgL9+JrAY8vz7Wlz4QQGHv5Mhbv3t3q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RXyZEICezIy+Fl4OLcUFZGSk8OrlZU0d/xwnzZ33B/Fnblze5kDzfhG+tK8z33KOt25vdyVtJlH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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feb0b756dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_comparison(diurnals,'North')\n",
    "side = 'North'; const = 'Q1'; pa = 'Pha'\n",
    "print (side, const, pa)\n",
    "print (TS13in[side][const][pa], N36_Bin[side][const][pa], N36_Cin[side][const][pa], TSNin[side][const][pa])\n",
    "#print (N36_Aout[side][const][pa], N36_Bout[side][const][pa], N36_Cout[side][const][pa])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'West': {'Q1': {'Pha': 3.05, 'Amp': 0.997}, 'N2': {'Pha': 0.95875393787416763, 'Amp': 0.981}, 'S2': {'Pha': 1.7, 'Amp': 1.041}, 'K1': {'Pha': -3.5, 'Amp': 0.98}, 'O1': {'Pha': 1.95, 'Amp': 1.08}, 'M2': {'Pha': -7.3073751949072303, 'Amp': 0.86960814836411737}}, 'North': {'Q1': {'Pha': -5.4, 'Amp': 0.9684715727604003}, 'N2': {'Pha': -4.6548875736091935, 'Amp': 0.98348114549493537}, 'S2': {'Pha': -9.25, 'Amp': 1.039}, 'K1': {'Pha': 8, 'Amp': 0.99976471206600859}, 'O1': {'Pha': -2, 'Amp': 0.96332838609454297}, 'M2': {'Pha': 2.6134333646455961, 'Amp': 0.88765397392577172}}}\n"
     ]
    }
   ],
   "source": [
    "TSNin['West']['S2']['Amp'] = 1.041\n",
    "TSNin['West']['N2']['Amp'] = 0.981\n",
    "\n",
    "TSNin['West']['S2']['Pha'] = 1.7\n",
    "\n",
    "TSNin['West']['K1']['Amp'] = 0.98\n",
    "TSNin['West']['O1']['Amp'] = 1.08\n",
    "TSNin['West']['Q1']['Amp'] = 0.997\n",
    "\n",
    "TSNin['West']['K1']['Pha'] = -3.5\n",
    "TSNin['West']['O1']['Pha'] = 1.95\n",
    "TSNin['West']['Q1']['Pha'] = 3.05\n",
    "\n",
    "TSNin['North']['S2']['Amp'] = 1.039\n",
    "\n",
    "TSNin['North']['S2']['Pha'] = -9.25\n",
    "\n",
    "TSNin['North']['K1']['Pha'] = 8\n",
    "TSNin['North']['O1']['Pha'] = -2\n",
    "TSNin['North']['Q1']['Pha'] = -5.4\n",
    "\n",
    "\n",
    "print (TSNin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "Missing parentheses in call to 'print' (<ipython-input-44-2951823aa52f>, line 4)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-44-2951823aa52f>\"\u001b[1;36m, line \u001b[1;32m4\u001b[0m\n\u001b[1;33m    print side\u001b[0m\n\u001b[1;37m             ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m Missing parentheses in call to 'print'\n"
     ]
    }
   ],
   "source": [
    "#for side in ('West', 'North'):          # don't correct North because we are missing River and Kelsey Bay\n",
    "TSN2in['North'] = TSNin['North']\n",
    "side = 'West'\n",
    "print side\n",
    "for const in semis + diurnals:\n",
    "        print const, TSNin[side][const]\n",
    "        print TS3Rcorr[side][const], 'River'\n",
    "        print TS3corr[side][const], 'No River'\n",
    "        differ = {key: TS3Rcorr[side][const][key] - TS3corr[side][const][key] for key in ('Pha','Amp')}\n",
    "        print differ, 'Difference'\n",
    "        TSN2in[side][const] = {key: TSNin[side][const][key] + differ[key] for key in ('Pha', 'Amp')}\n",
    "        print TSN2in[side][const], 'Proposal'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print TSN2in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for side in ('West', 'North'):\n",
    "    for const in semis + diurnals:\n",
    "        print (side, const)\n",
    "        print (TS4newBout[side][const]['Pha'] + TS4in[side][const]['Pha'], TS4in[side][const]['Pha'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "correction = {'S2': {'Pha' : ('M2', False, True),\n",
    "                    'Amp': ('M2', True, False)},\n",
    "              'N2': {'Pha' : ('M2', False, True),\n",
    "                    'Amp': ('M2', True, False)},\n",
    "             'O1': {'Pha' : ('K1', False, True),\n",
    "                   'Amp': ('K1', True, False)},\n",
    "             'Q1': {'Pha' : ('K1', False, True),\n",
    "                   'Amp': ('K1', True, False)},}\n",
    "\n",
    "def correction2(TSprevin, TSnewout):\n",
    "    TSc = initialize_dict()\n",
    "    for side in ('West', 'North'):\n",
    "        for const in ('M2', 'K1'):\n",
    "            TSc[side][const]['Pha'] = TSprevin[side][const]['Pha'] + TSnewout[side][const]['Pha']\n",
    "            TSc[side][const]['Amp'] = TSprevin[side][const]['Amp'] * TSnewout[side][const]['Amp']\n",
    "        for const in ('O1', 'Q1', 'S2', 'N2'):\n",
    "            TSc[side][const]['Pha'] = (TSprevin[side][const]['Pha'] -\n",
    "                                       TSnewout[side][correction[const]['Pha'][0]]['Pha'] +\n",
    "                                      TSnewout[side][const]['Pha'])\n",
    "            TSc[side][const]['Amp'] = (TSprevin[side][const]['Amp'] / \n",
    "                                       TSnewout[side][correction[const]['Amp'][0]]['Amp'] *\n",
    "                                      TSnewout[side][const]['Amp'])  \n",
    "    return TSc\n",
    "\n",
    "TS5in = correction2(TS4in, TS4newBout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print (TS5in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print (TS4in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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