Transport.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import arrow\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import netCDF4 as nc\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import xarray as xr\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Grid ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mesh = nc.Dataset('/home/sallen/MEOPAR/grid/mesh_mask201702.nc')\n",
    "#mesh = nc.Dataset('../../../myResults/mesh_mask201702.nc')\n",
    "gdepw = mesh.variables['gdepw_1d'][0]\n",
    "bathy = nc.Dataset('/home/sallen/MEOPAR/grid/bathymetry_201702.nc')\n",
    "#bathy = nc.Dataset('../../../myResults/bathymetry_201702.nc')\n",
    "lats = bathy.variables['nav_lat'][:]\n",
    "lons = bathy.variables['nav_lon'][:]\n",
    "mesh.close()\n",
    "bathy.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def calculate_transport(basedir, dir1, section):\n",
    "    filename = 'ariane_positions_quantitative.nc'\n",
    "    with nc.Dataset(os.path.join('/data/sallen/results/Ariane',basedir, dir1, filename)) as dataset:\n",
    "        final_section = dataset.variables['final_section'][:]\n",
    "        final_transport = dataset.variables['final_transp'][:]\n",
    "        vic_transport = np.sum(np.where(final_section == section, final_transport, 0))\n",
    "    return (vic_transport/24.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def calculate_all(basedir, dir1, section, allv, loop=None):\n",
    "    final = {}\n",
    "    mean = {}    \n",
    "    filename = 'ariane_positions_quantitative.nc'\n",
    "    fullfile = os.path.join('/data/sallen/results/Ariane',basedir, dir1, filename)\n",
    "    try:\n",
    "        if os.stat(fullfile).st_size > 0:\n",
    "            filegood = True\n",
    "            with nc.Dataset(fullfile) as dataset:\n",
    "                final_section = dataset.variables['final_section'][:]\n",
    "                final_transport = dataset.variables['final_transp'][:]\n",
    "                final_age = dataset.variables['final_age'][:]\n",
    "                if loop == None:\n",
    "                    vic_transport = np.sum(np.where(final_section == section, final_transport, 0))\n",
    "                elif loop == 'Short':\n",
    "                    vic_transport = np.sum(np.where((final_section == section) & (final_age <= 24*3600), final_transport, 0))\n",
    "                elif loop == 'Long':\n",
    "                    vic_transport = np.sum(np.where((final_section == section) & (final_age > 24*3600), final_transport, 0))\n",
    "                for variable in allv:\n",
    "                    if variable == 'final_age':\n",
    "                        scale = 86400. # convert from seconds to days\n",
    "                    else:\n",
    "                        scale = 1.\n",
    "                    final[variable] = dataset.variables[variable][:]/scale\n",
    "                    if loop == None:\n",
    "                        mean[variable] = np.sum(np.where(final_section == section, final_transport*final[variable], 0))/vic_transport\n",
    "                    elif loop == 'Short':\n",
    "                        mean[variable] = np.sum(np.where((final_section == section) & (final_age <= 24*3600), \n",
    "                                                         final_transport*final[variable], 0))/vic_transport\n",
    "                    elif loop == 'Long':\n",
    "                        mean[variable] = np.sum(np.where((final_section == section) & (final_age > 24*3600), \n",
    "                                                         final_transport*final[variable], 0))/vic_transport\n",
    "                \n",
    "        else:\n",
    "            filegood = False\n",
    "            vic_transport = float('NaN')\n",
    "            print (\"empty file\", dir1)\n",
    "    except OSError:\n",
    "        filegood = False\n",
    "        vic_transport = float('NaN')\n",
    "        print ('Missing Data', dir1)\n",
    "    return filegood, vic_transport/24., mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
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   "outputs": [],
   "source": [
    "def get_data(start, endtime, basedir, section):\n",
    "    transport = np.zeros(365*2+1)\n",
    "    timerange = arrow.Arrow.range('day', start, endtime)\n",
    "    time = []\n",
    "    for i, r in enumerate(timerange):\n",
    "        dir1 = r.format('DDMMMYY').lower()\n",
    "        transport[i] = calculate_transport(basedir, dir1, section)\n",
    "        transport[i] = calculate_transport('./', './', section)\n",
    "        time.append(r.datetime)\n",
    "    return time, transport[:len(timerange)]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_data_all(start, endtime, basedir, section, allv, loop=None):\n",
    "    timerange = arrow.Arrow.range('day', start, endtime)\n",
    "    length = (endtime-start).days + 1\n",
    "    transport = np.zeros(length)\n",
    "    mean = {}\n",
    "    for variable in allv:\n",
    "        mean[variable] = np.zeros(length)\n",
    "    time = []\n",
    "    for i, r in enumerate(timerange):\n",
    "        dir1 = r.format('DDMMMYY').lower()\n",
    "        filegood, transport[i], meanday = calculate_all(basedir, dir1, section, allv, loop)\n",
    "        for variable in allv:\n",
    "            if filegood:\n",
    "                mean[variable][i] = meanday[variable]\n",
    "            else:\n",
    "                mean[variable][i] = float('NaN')\n",
    "        time.append(r.datetime)\n",
    "    return time, transport, mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def save_position(basedir, dir1, section, fi, ff):\n",
    "    print(fi, ff)\n",
    "    filename = 'ariane_positions_quantitative.nc'\n",
    "    with nc.Dataset(os.path.join('/data/sallen/results/Ariane',basedir, dir1, filename)) as dataset:\n",
    "        final_section = dataset.variables['final_section'][:]\n",
    "        final_transport = dataset.variables['final_transp'][:]\n",
    "        final_depth = dataset.variables['final_depth'][:]\n",
    "        final_lon = dataset.variables['final_lon'][:]\n",
    "        final_lat = dataset.variables['final_lat'][:]\n",
    "        init_depth = dataset.variables['init_depth'][:]\n",
    "        init_lat = dataset.variables['init_lat'][:]\n",
    "        init_lon = dataset.variables['init_lon'][:]\n",
    "        for part in range(final_section.shape[0]):\n",
    "            if final_section[part] == section:\n",
    "                fi.write('{0:4f} {1:4f} {2:4f} {3:4f} \\n'.format(\n",
    "                    init_depth[part], init_lat[part], init_lon[part], final_transport[part]))\n",
    "                ff.write('{0:4f} {1:4f} {2:4f} {3:4f} \\n'.format(\n",
    "                    final_depth[part], final_lat[part], final_lon[part], final_transport[part]))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the Data ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Meanders ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "years = [2015, 2016, 2017, 2018]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:        (ntraj: 40842)\n",
       "Dimensions without coordinates: ntraj\n",
       "Data variables: (12/25)\n",
       "    init_x         (ntraj) float64 178.0 178.0 178.0 178.0 ... 178.0 178.0 178.0\n",
       "    init_y         (ntraj) float64 234.5 235.5 236.5 237.5 ... 279.5 280.5 281.5\n",
       "    init_z         (ntraj) float64 1.5 1.5 1.5 1.5 1.5 ... 28.5 28.5 28.5 28.5\n",
       "    init_t         (ntraj) float64 1.0 1.0 1.0 1.0 1.0 ... 24.0 24.0 24.0 24.0\n",
       "    init_age       (ntraj) timedelta64[ns] 00:00:00 00:00:00 ... 00:00:00\n",
       "    init_transp    (ntraj) float64 70.94 125.7 111.4 176.9 ... 528.9 493.7 101.7\n",
       "    ...             ...\n",
       "    init_lon       (ntraj) float64 -123.3 -123.3 -123.3 ... -123.4 -123.4 -123.4\n",
       "    init_lat       (ntraj) float64 48.12 48.12 48.13 48.13 ... 48.3 48.3 48.31\n",
       "    init_depth     (ntraj) float64 -0.5 -0.5 -0.5 -0.5 ... -116.4 -115.9 -113.4\n",
       "    final_lon      (ntraj) float64 -123.3 -123.3 -123.3 ... -123.4 -123.4 -123.4\n",
       "    final_lat      (ntraj) float64 48.12 48.13 48.13 48.13 ... 48.29 48.3 48.3\n",
       "    final_depth    (ntraj) float64 -0.4399 -2.334 -4.07 ... -120.3 -123.9 -115.5\n",
       "Attributes: (12/105)\n",
       "    key_roms:                  .FALSE.\n",
       "    key_symphonie:             .FALSE.\n",
       "    key_B2C_grid:              .FALSE.\n",
       "    key_sequential:            .TRUE.\n",
       "    key_alltracers:            .TRUE.\n",
       "    key_ascii_outputs:         .TRUE.\n",
       "    ...                        ...\n",
       "    ind0_sa:                   1\n",
       "    indn_sa:                   30\n",
       "    maxsize_sa:                2\n",
       "    c_suffix_sa:               _grid_T.nc\n",
       "    nc_var_sa:                 vosaline\n",
       "    nc_att_mask_sa:            NONE</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4c977d44-cd1d-41ad-839d-0b2c3bdbde79' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4c977d44-cd1d-41ad-839d-0b2c3bdbde79' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>ntraj</span>: 40842</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-17bf1b34-df26-418f-8cf7-c15e0502649d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-17bf1b34-df26-418f-8cf7-c15e0502649d' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-a7285d64-07d9-4bfb-9ddd-ef848f2bcf88' class='xr-section-summary-in' type='checkbox'  ><label for='section-a7285d64-07d9-4bfb-9ddd-ef848f2bcf88' class='xr-section-summary' >Data variables: <span>(25)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>init_x</span></div><div class='xr-var-dims'>(ntraj)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bf7a8495-5768-4fbe-b0d9-134fc926deb3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf7a8495-5768-4fbe-b0d9-134fc926deb3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02b1a10e-5380-402e-8884-2eb3db19346a' class='xr-var-data-in' type='checkbox'><label for='data-02b1a10e-5380-402e-8884-2eb3db19346a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>What is init_x ?</dd><dt><span>longname :</span></dt><dd>Initial position in i</dd><dt><span>units :</span></dt><dd>No dimension</dd></dl></div><div class='xr-var-data'><pre>array([178., 178., 178., ..., 178., 178., 178.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>init_y</span></div><div class='xr-var-dims'>(ntraj)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-96a8f423-7917-40d6-98df-25b58c7d48c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-96a8f423-7917-40d6-98df-25b58c7d48c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3815a2d9-4581-4ade-bb34-9e0dac6faaf5' class='xr-var-data-in' type='checkbox'><label for='data-3815a2d9-4581-4ade-bb34-9e0dac6faaf5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>What is init_y ?</dd><dt><span>longname :</span></dt><dd>Initial position in j</dd><dt><span>units :</span></dt><dd>No dimension</dd></dl></div><div class='xr-var-data'><pre>array([234.5, 235.5, 236.5, ..., 279.5, 280.5, 281.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>init_z</span></div><div class='xr-var-dims'>(ntraj)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-af0e8aec-910b-4859-b95a-91645db77b37' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af0e8aec-910b-4859-b95a-91645db77b37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3495f266-f526-45a4-9780-fb9b661d8e2b' class='xr-var-data-in' type='checkbox'><label for='data-3495f266-f526-45a4-9780-fb9b661d8e2b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>What is init_z ?</dd><dt><span>longname :</span></dt><dd>Initial position in k</dd><dt><span>units :</span></dt><dd>No dimension</dd></dl></div><div class='xr-var-data'><pre>array([ 1.5,  1.5,  1.5, ..., 28.5, 28.5, 28.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>init_t</span></div><div class='xr-var-dims'>(ntraj)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f49306b0-8033-4876-92c7-1ee8d36d97fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f49306b0-8033-4876-92c7-1ee8d36d97fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-329ace69-822e-476c-810b-659f41713f28' class='xr-var-data-in' type='checkbox'><label for='data-329ace69-822e-476c-810b-659f41713f28' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>What is init_t ?</dd><dt><span>longname :</span></dt><dd>Initial position in l (time)</dd><dt><span>units :</span></dt><dd>See global attributes...</dd></dl></div><div class='xr-var-data'><pre>array([ 1.,  1.,  1., ..., 24., 24., 24.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>init_age</span></div><div class='xr-var-dims'>(ntraj)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1caed90b-cd78-4d39-b114-549bd20a7f96' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1caed90b-cd78-4d39-b114-549bd20a7f96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f17133f9-3058-4dbb-a5d6-881d2352ccea' class='xr-var-data-in' type='checkbox'><label for='data-f17133f9-3058-4dbb-a5d6-881d2352ccea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>What is init_age ?</dd><dt><span>longname :</span></dt><dd>Initial age (time)</dd></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>init_transp</span></div><div class='xr-var-dims'>(ntraj)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d6abb3cd-cc70-4061-8bd2-e8c182d56a27' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6abb3cd-cc70-4061-8bd2-e8c182d56a27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea837dfe-afcb-4a97-b8d1-aa7870c4cb41' class='xr-var-data-in' type='checkbox'><label for='data-ea837dfe-afcb-4a97-b8d1-aa7870c4cb41' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>What is init_transp ?</dd><dt><span>longname :</span></dt><dd>Initial transport</dd><dt><span>units :</span></dt><dd>m3/s</dd></dl></div><div class='xr-var-data'><pre>array([ 70.936137, 125.736613, 111.442549, ..., 528.89877 , 493.702598,\n",
       "       101.680164])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>init_temp</span></div><div class='xr-var-dims'>(ntraj)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-62832e38-5a8e-43d8-a7ee-893523712b8d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-62832e38-5a8e-43d8-a7ee-893523712b8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa80c24e-b6f7-4bf8-8d6d-cd487be61698' class='xr-var-data-in' type='checkbox'><label for='data-fa80c24e-b6f7-4bf8-8d6d-cd487be61698' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>What is init_temp ?</dd><dt><span>longname :</span></dt><dd>Initial temperature</dd><dt><span>units :</span></dt><dd>degres</dd></dl></div><div class='xr-var-data'><pre>array([10.205647, 10.110914, 10.04063 , ...,  9.009487,  9.024521,  9.015411],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>init_salt</span></div><div class='xr-var-dims'>(ntraj)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a068408c-903f-4f35-b4a4-1e2c502316fc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a068408c-903f-4f35-b4a4-1e2c502316fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90ad9978-fd46-4c49-8eec-7c0251bc3cc0' class='xr-var-data-in' type='checkbox'><label for='data-90ad9978-fd46-4c49-8eec-7c0251bc3cc0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>What is init_salt ?</dd><dt><span>longname :</span></dt><dd>Initial salinity</dd><dt><span>units :</span></dt><dd>psu</dd></dl></div><div class='xr-var-data'><pre>array([31.098911, 31.24799 , 31.73251 , ..., 32.79876 , 32.781776, 32.789715],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>init_dens</span></div><div class='xr-var-dims'>(ntraj)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-17ecd227-afa9-4a54-9366-74b8ce524722' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-17ecd227-afa9-4a54-9366-74b8ce524722' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3643b1e6-e16f-4461-a383-9558cb515db0' class='xr-var-data-in' type='checkbox'><label for='data-3643b1e6-e16f-4461-a383-9558cb515db0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>What is init_dens ?</dd><dt><span>longname :</span></dt><dd>Initial density</dd><dt><span>units :</span></dt><dd>...</dd></dl></div><div class='xr-var-data'><pre>array([24.32661 , 24.458504, 24.847664, ..., 25.847467, 25.831812, 25.83946 ],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>final_x</span></div><div class='xr-var-dims'>(ntraj)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9c34b000-6958-4750-af32-0a205559ff85' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9c34b000-6958-4750-af32-0a205559ff85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f74a109-3499-4bd4-a833-63ca6ffaf6a4' class='xr-var-data-in' type='checkbox'><label for='data-1f74a109-3499-4bd4-a833-63ca6ffaf6a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>What is final_x ?</dd><dt><span>longname :</span></dt><dd>Final position in x (or i)</dd><dt><span>units :</span></dt><dd>No dimension</dd></dl></div><div class='xr-var-data'><pre>array([178., 178., 178., ..., 178., 178., 178.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>final_y</span></div><div class='xr-var-dims'>(ntraj)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-56f9d30d-9e7b-424c-b813-d4e4e24056a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-56f9d30d-9e7b-424c-b813-d4e4e24056a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b835c0ae-616b-466e-9c54-3e3f8e4d3931' class='xr-var-data-in' type='checkbox'><label for='data-b835c0ae-616b-466e-9c54-3e3f8e4d3931' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>What is final_y ?</dd><dt><span>longname :</span></dt><dd>Final position in y (or j)</dd><dt><span>units :</span></dt><dd>No dimension</dd></dl></div><div class='xr-var-data'><pre>array([235.450553, 236.703355, 236.97978 , ..., 277.94714 , 279.058188,\n",
       "       280.4133  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>final_z</span></div><div class='xr-var-dims'>(ntraj)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6a7c14e3-fe33-4566-8571-2862e4544ee2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a7c14e3-fe33-4566-8571-2862e4544ee2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9eb18737-ddaa-4322-afaf-a3de094fc9f5' class='xr-var-data-in' type='checkbox'><label for='data-9eb18737-ddaa-4322-afaf-a3de094fc9f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>What is final_z ?</dd><dt><span>longname :</span></dt><dd>Final position in z (or k)</dd><dt><span>units :</span></dt><dd>No dimension</dd></dl></div><div class='xr-var-data'><pre>array([ 1.439877,  3.334215,  5.070437, ..., 28.574117, 28.779154, 28.429596])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>final_t</span></div><div class='xr-var-dims'>(ntraj)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-01a774e7-4642-4095-b21a-f5623ebc3175' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01a774e7-4642-4095-b21a-f5623ebc3175' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75268f23-1d7c-4165-b39f-c2d128cab9f9' class='xr-var-data-in' type='checkbox'><label for='data-75268f23-1d7c-4165-b39f-c2d128cab9f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>What is final_t ?</dd><dt><span>longname :</span></dt><dd>Final position in t (time)</dd><dt><span>units :</span></dt><dd>See global attributes...</dd></dl></div><div class='xr-var-data'><pre>array([ 4.813784,  5.299366,  5.20379 , ..., 30.589475, 30.7155  , 30.084686])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>final_age</span></div><div class='xr-var-dims'>(ntraj)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-53c811f0-79cb-4457-ba15-94f0df9066f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53c811f0-79cb-4457-ba15-94f0df9066f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10dac6e7-ced6-4bde-9e5e-64af63ca5512' class='xr-var-data-in' type='checkbox'><label for='data-10dac6e7-ced6-4bde-9e5e-64af63ca5512' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>What is fial_age ?</dd><dt><span>longname :</span></dt><dd>Final Age.</dd></dl></div><div class='xr-var-data'><pre>array([13729623292829, 15477719061538, 15133645620675, ..., 23722110025696,\n",
       "       24175801791627, 21904868276763], dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>final_transp</span></div><div class='xr-var-dims'>(ntraj)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-33684de4-2787-4885-8351-ad0f7399a6c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-33684de4-2787-4885-8351-ad0f7399a6c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f56681b2-0151-4f24-8554-223cb98caf0a' class='xr-var-data-in' type='checkbox'><label for='data-f56681b2-0151-4f24-8554-223cb98caf0a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>What is final_transp ?</dd><dt><span>longname :</span></dt><dd>Final transport</dd><dt><span>units :</span></dt><dd>m3/s</dd></dl></div><div class='xr-var-data'><pre>array([ 70.936137, 125.736613, 111.442549, ..., 528.89877 , 493.702598,\n",
       "       101.680164])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>final_section</span></div><div class='xr-var-dims'>(ntraj)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ac5dacf2-7934-4663-9448-1618b6a82ccb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac5dacf2-7934-4663-9448-1618b6a82ccb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad5ae5a8-14f5-4ed9-a18d-1e5e6d72d0f7' class='xr-var-data-in' type='checkbox'><label for='data-ad5ae5a8-14f5-4ed9-a18d-1e5e6d72d0f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>What is final_section ?</dd><dt><span>longname :</span></dt><dd>Final section</dd><dt><span>units :</span></dt><dd>No dimension</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>final_temp</span></div><div class='xr-var-dims'>(ntraj)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0a009d49-d7e3-4d17-b58b-496d505a6015' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a009d49-d7e3-4d17-b58b-496d505a6015' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d3e7b31-359e-42e0-a1bc-93148f996a62' class='xr-var-data-in' type='checkbox'><label for='data-7d3e7b31-359e-42e0-a1bc-93148f996a62' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>What is final_temp ?</dd><dt><span>longname :</span></dt><dd>Final temperature</dd><dt><span>units :</span></dt><dd>degres</dd></dl></div><div class='xr-var-data'><pre>array([10.134648, 10.032112, 10.014908, ...,  9.108043,  9.094237,  9.017263],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>final_salt</span></div><div class='xr-var-dims'>(ntraj)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b0681bd2-f3f8-451e-aec2-1c8a7b5861c5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0681bd2-f3f8-451e-aec2-1c8a7b5861c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99c2f089-0601-4638-9878-9bddc37d0576' class='xr-var-data-in' type='checkbox'><label for='data-99c2f089-0601-4638-9878-9bddc37d0576' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>What is final_salt ?</dd><dt><span>longname :</span></dt><dd>Final salinity</dd><dt><span>units :</span></dt><dd>psu</dd></dl></div><div class='xr-var-data'><pre>array([30.96328 , 31.599962, 31.698166, ..., 32.686836, 32.699505, 32.788967],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>final_dens</span></div><div class='xr-var-dims'>(ntraj)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-874d127b-5171-4a91-a575-fd468f36a0de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-874d127b-5171-4a91-a575-fd468f36a0de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f1774ac-d48f-47d9-93a5-5e7df7a7daaa' class='xr-var-data-in' type='checkbox'><label for='data-8f1774ac-d48f-47d9-93a5-5e7df7a7daaa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>What is final_dens ?</dd><dt><span>longname :</span></dt><dd>Final density</dd><dt><span>units :</span></dt><dd>...</dd></dl></div><div class='xr-var-data'><pre>array([24.232826, 24.745808, 24.825188, ..., 25.74434 , 25.756437, 25.838583],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>init_lon</span></div><div class='xr-var-dims'>(ntraj)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5dec1030-846d-46f1-b5e6-e2beb96b3ac8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5dec1030-846d-46f1-b5e6-e2beb96b3ac8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-161d3e0a-e673-4a23-b837-ec55a6c1367e' class='xr-var-data-in' type='checkbox'><label for='data-161d3e0a-e673-4a23-b837-ec55a6c1367e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>What is init_lon ?</dd><dt><span>longname :</span></dt><dd>Initial position in longitude</dd><dt><span>units :</span></dt><dd>No dimension</dd></dl></div><div class='xr-var-data'><pre>array([-123.255348, -123.258667, -123.261986, ..., -123.404854, -123.40818 ,\n",
       "       -123.411507])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>init_lat</span></div><div class='xr-var-dims'>(ntraj)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a3fb8788-af6b-4a59-bb81-0e76d3dfa503' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3fb8788-af6b-4a59-bb81-0e76d3dfa503' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e64b3c6c-4864-4a4b-82ff-248b63be26ca' class='xr-var-data-in' type='checkbox'><label for='data-e64b3c6c-4864-4a4b-82ff-248b63be26ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>What is init_lat ?</dd><dt><span>longname :</span></dt><dd>Initial position in latitude</dd><dt><span>units :</span></dt><dd>No dimension</dd></dl></div><div class='xr-var-data'><pre>array([48.120514, 48.12448 , 48.128446, ..., 48.298944, 48.302908, 48.306871])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>init_depth</span></div><div class='xr-var-dims'>(ntraj)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-41f4e0a2-4e39-429f-b2e0-734810417b6a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41f4e0a2-4e39-429f-b2e0-734810417b6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06b4794d-2625-4e6a-a8dd-26f3bad75950' class='xr-var-data-in' type='checkbox'><label for='data-06b4794d-2625-4e6a-a8dd-26f3bad75950' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>What is init_depth?</dd><dt><span>longname :</span></dt><dd>Initial position in depth</dd><dt><span>units :</span></dt><dd>No dimension</dd></dl></div><div class='xr-var-data'><pre>array([  -0.500001,   -0.500001,   -0.500001, ..., -116.431034, -115.868534,\n",
       "       -113.399784])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>final_lon</span></div><div class='xr-var-dims'>(ntraj)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cf27b886-97c5-458a-b68f-183ea57df41b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf27b886-97c5-458a-b68f-183ea57df41b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c984e5e-88a0-4216-b852-f95b54eb7cbb' class='xr-var-data-in' type='checkbox'><label for='data-4c984e5e-88a0-4216-b852-f95b54eb7cbb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>What is final_lon ?</dd><dt><span>longname :</span></dt><dd>Final position in longitude</dd><dt><span>units :</span></dt><dd>No dimension</dd></dl></div><div class='xr-var-data'><pre>array([-123.258503, -123.262661, -123.263578, ..., -123.399688, -123.403384,\n",
       "       -123.407892])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>final_lat</span></div><div class='xr-var-dims'>(ntraj)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a3a9d41f-ed7f-4cb6-ac02-32bf07d722e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3a9d41f-ed7f-4cb6-ac02-32bf07d722e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7af7c09-6500-4c99-b754-afa682c6bb83' class='xr-var-data-in' type='checkbox'><label for='data-d7af7c09-6500-4c99-b754-afa682c6bb83' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>What is final_lat ?</dd><dt><span>longname :</span></dt><dd>Final position in latitude</dd><dt><span>units :</span></dt><dd>No dimension</dd></dl></div><div class='xr-var-data'><pre>array([48.124284, 48.129252, 48.130348, ..., 48.29279 , 48.297193, 48.302564])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>final_depth</span></div><div class='xr-var-dims'>(ntraj)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1a5cd798-90c5-4106-80cb-66f7c6fa3cfd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a5cd798-90c5-4106-80cb-66f7c6fa3cfd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f31bcb7c-9fa3-4297-b50d-1d6edecd6785' class='xr-var-data-in' type='checkbox'><label for='data-f31bcb7c-9fa3-4297-b50d-1d6edecd6785' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>What is final_depth?</dd><dt><span>longname :</span></dt><dd>Final position in depth</dd><dt><span>units :</span></dt><dd>No dimension</dd></dl></div><div class='xr-var-data'><pre>array([  -0.439878,   -2.334226,   -4.070488, ..., -120.293894, -123.869309,\n",
       "       -115.488465])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1ee270b8-8b5c-4aea-8c2d-a15191d45bfd' class='xr-section-summary-in' type='checkbox'  ><label for='section-1ee270b8-8b5c-4aea-8c2d-a15191d45bfd' class='xr-section-summary' >Attributes: <span>(105)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>key_roms :</span></dt><dd>.FALSE.</dd><dt><span>key_symphonie :</span></dt><dd>.FALSE.</dd><dt><span>key_B2C_grid :</span></dt><dd>.FALSE.</dd><dt><span>key_sequential :</span></dt><dd>.TRUE.</dd><dt><span>key_alltracers :</span></dt><dd>.TRUE.</dd><dt><span>key_ascii_outputs :</span></dt><dd>.TRUE.</dd><dt><span>key_iU_jV_kW :</span></dt><dd>.FALSE.</dd><dt><span>key_read_age :</span></dt><dd>.FALSE.</dd><dt><span>mode :</span></dt><dd>quantitative</dd><dt><span>forback :</span></dt><dd>forward</dd><dt><span>bin :</span></dt><dd>nobin</dd><dt><span>init_final :</span></dt><dd>init</dd><dt><span>nmax :</span></dt><dd>60000</dd><dt><span>tunit :</span></dt><dd>3600.0</dd><dt><span>ntfic :</span></dt><dd>1</dd><dt><span>tcyc :</span></dt><dd>2592000.0</dd><dt><span>key_approximatesigma :</span></dt><dd>.FALSE.</dd><dt><span>key_computesigma :</span></dt><dd>.TRUE.</dd><dt><span>zsigma :</span></dt><dd>100.0</dd><dt><span>memory_log :</span></dt><dd>.TRUE.</dd><dt><span>output_netcdf_large_file :</span></dt><dd>.FALSE.</dd><dt><span>key_interp_temporal :</span></dt><dd>.FALSE.</dd><dt><span>maxcycles :</span></dt><dd>1</dd><dt><span>key_2dquant :</span></dt><dd>.FALSE.</dd><dt><span>key_eco :</span></dt><dd>.TRUE.</dd><dt><span>key_reducmem :</span></dt><dd>.TRUE.</dd><dt><span>imt_reg_start :</span></dt><dd>177</dd><dt><span>imt_reg_end :</span></dt><dd>369</dd><dt><span>imt_reg :</span></dt><dd>193</dd><dt><span>jmt_reg_start :</span></dt><dd>220</dd><dt><span>jmt_reg_end :</span></dt><dd>400</dd><dt><span>jmt_reg :</span></dt><dd>181</dd><dt><span>kmt_reg_start :</span></dt><dd>1</dd><dt><span>kmt_reg_end :</span></dt><dd>40</dd><dt><span>kmt_reg :</span></dt><dd>40</dd><dt><span>key_unitm3 :</span></dt><dd>.TRUE.</dd><dt><span>key_nointerpolstats :</span></dt><dd>.FALSE.</dd><dt><span>max_transport :</span></dt><dd>1000.0</dd><dt><span>lmin :</span></dt><dd>1</dd><dt><span>lmax :</span></dt><dd>24</dd><dt><span>imt :</span></dt><dd>398</dd><dt><span>jmt :</span></dt><dd>898</dd><dt><span>kmt :</span></dt><dd>40</dd><dt><span>lmt :</span></dt><dd>720</dd><dt><span>key_computew :</span></dt><dd>.FALSE.</dd><dt><span>w_surf_option :</span></dt><dd></dd><dt><span>key_partialsteps :</span></dt><dd>.TRUE.</dd><dt><span>key_jfold :</span></dt><dd>.FALSE.</dd><dt><span>pivot :</span></dt><dd>T</dd><dt><span>key_periodic :</span></dt><dd>.FALSE.</dd><dt><span>dir_mesh :</span></dt><dd>/home/sallen/MEOPAR/grid/</dd><dt><span>fn_mesh :</span></dt><dd>mesh_mask201702.nc</dd><dt><span>nc_var_xx_tt :</span></dt><dd>glamt</dd><dt><span>nc_var_xx_uu :</span></dt><dd>glamu</dd><dt><span>nc_var_zz_ww :</span></dt><dd>gdepw_0</dd><dt><span>nc_var_e2u :</span></dt><dd>e2u</dd><dt><span>nc_var_e1v :</span></dt><dd>e1v</dd><dt><span>nc_var_e1t :</span></dt><dd>e1t</dd><dt><span>nc_var_e2t :</span></dt><dd>e2t</dd><dt><span>nc_var_e3t :</span></dt><dd>e3t_0</dd><dt><span>nc_var_tmask :</span></dt><dd>tmask</dd><dt><span>nc_mask_val :</span></dt><dd>0.0</dd><dt><span>c_dir_zo :</span></dt><dd>Links</dd><dt><span>c_prefix_zo :</span></dt><dd>SalishSea_1h_</dd><dt><span>ind0_zo :</span></dt><dd>1</dd><dt><span>indn_zo :</span></dt><dd>30</dd><dt><span>maxsize_zo :</span></dt><dd>2</dd><dt><span>c_suffix_zo :</span></dt><dd>_grid_U.nc</dd><dt><span>nc_var_zo :</span></dt><dd>vozocrtx</dd><dt><span>nc_var_eivu :</span></dt><dd>NONE</dd><dt><span>nc_att_mask_zo :</span></dt><dd>NONE</dd><dt><span>c_dir_me :</span></dt><dd>Links</dd><dt><span>c_prefix_me :</span></dt><dd>SalishSea_1h_</dd><dt><span>ind0_me :</span></dt><dd>1</dd><dt><span>indn_me :</span></dt><dd>30</dd><dt><span>maxsize_me :</span></dt><dd>2</dd><dt><span>c_suffix_me :</span></dt><dd>_grid_V.nc</dd><dt><span>nc_var_me :</span></dt><dd>vomecrty</dd><dt><span>nc_var_eivv :</span></dt><dd>NONE</dd><dt><span>nc_att_mask_me :</span></dt><dd>NONE</dd><dt><span>c_dir_ve :</span></dt><dd>Links</dd><dt><span>c_prefix_ve :</span></dt><dd>SalishSea_1h_</dd><dt><span>ind0_ve :</span></dt><dd>1</dd><dt><span>indn_ve :</span></dt><dd>30</dd><dt><span>maxsize_ve :</span></dt><dd>2</dd><dt><span>c_suffix_ve :</span></dt><dd>_grid_W.nc</dd><dt><span>nc_var_ve :</span></dt><dd>vovecrtz</dd><dt><span>nc_var_eivw :</span></dt><dd>NONE</dd><dt><span>nc_att_mask_ve :</span></dt><dd>NONE</dd><dt><span>c_dir_te :</span></dt><dd>Links</dd><dt><span>c_prefix_te :</span></dt><dd>SalishSea_1h_</dd><dt><span>ind0_te :</span></dt><dd>1</dd><dt><span>indn_te :</span></dt><dd>30</dd><dt><span>maxsize_te :</span></dt><dd>2</dd><dt><span>c_suffix_te :</span></dt><dd>_grid_T.nc</dd><dt><span>nc_var_te :</span></dt><dd>votemper</dd><dt><span>nc_att_mask_te :</span></dt><dd>NONE</dd><dt><span>c_dir_sa :</span></dt><dd>Links</dd><dt><span>c_prefix_sa :</span></dt><dd>SalishSea_1h_</dd><dt><span>ind0_sa :</span></dt><dd>1</dd><dt><span>indn_sa :</span></dt><dd>30</dd><dt><span>maxsize_sa :</span></dt><dd>2</dd><dt><span>c_suffix_sa :</span></dt><dd>_grid_T.nc</dd><dt><span>nc_var_sa :</span></dt><dd>vosaline</dd><dt><span>nc_att_mask_sa :</span></dt><dd>NONE</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:        (ntraj: 40842)\n",
       "Dimensions without coordinates: ntraj\n",
       "Data variables: (12/25)\n",
       "    init_x         (ntraj) float64 ...\n",
       "    init_y         (ntraj) float64 ...\n",
       "    init_z         (ntraj) float64 ...\n",
       "    init_t         (ntraj) float64 ...\n",
       "    init_age       (ntraj) timedelta64[ns] ...\n",
       "    init_transp    (ntraj) float64 ...\n",
       "    ...             ...\n",
       "    init_lon       (ntraj) float64 ...\n",
       "    init_lat       (ntraj) float64 ...\n",
       "    init_depth     (ntraj) float64 ...\n",
       "    final_lon      (ntraj) float64 ...\n",
       "    final_lat      (ntraj) float64 ...\n",
       "    final_depth    (ntraj) float64 ...\n",
       "Attributes: (12/105)\n",
       "    key_roms:                  .FALSE.\n",
       "    key_symphonie:             .FALSE.\n",
       "    key_B2C_grid:              .FALSE.\n",
       "    key_sequential:            .TRUE.\n",
       "    key_alltracers:            .TRUE.\n",
       "    key_ascii_outputs:         .TRUE.\n",
       "    ...                        ...\n",
       "    ind0_sa:                   1\n",
       "    indn_sa:                   30\n",
       "    maxsize_sa:                2\n",
       "    c_suffix_sa:               _grid_T.nc\n",
       "    nc_var_sa:                 vosaline\n",
       "    nc_att_mask_sa:            NONE"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_case = xr.open_dataset('/data/sallen/results/Ariane/FullSouth/01nov15/ariane_positions_quantitative.nc')\n",
    "one_case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray ()>\n",
      "array(0.)\n"
     ]
    }
   ],
   "source": [
    "print ((one_case.init_transp - one_case.final_transp).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7287 nan\n",
      "nan\n"
     ]
    }
   ],
   "source": [
    "for ii in range(7300):\n",
    "    if np.isnan(one_case.final_section[ii]):\n",
    "        print (ii, one_case.final_section[ii].values)\n",
    "print (one_case.final_section[7287].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 24.0)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.hist((one_case.final_age[one_case.final_section == 0].values/1e9/3600))\n",
    "plt.plot(one_case.final_age[one_case.final_section == 0].values/np.timedelta64(1, 'h'), 'o')\n",
    "plt.ylim(0, 24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "myvalues = (one_case.final_age[one_case.final_section == 0].values/np.timedelta64(1, 'h'))\n",
    "sortvalues= np.sort(myvalues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sortvalues)\n",
    "plt.ylim(0, 48);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Victoria Sill Meanders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-4-4ab44c4032cd>:18: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    }
   ],
   "source": [
    "# Full South Vic Meanders\n",
    "year = 2015\n",
    "allv = ['final_age', 'final_depth', 'final_salt', 'final_temp', 'final_lon', \n",
    "        'init_depth', 'init_salt', 'init_temp', 'init_lat']\n",
    "start = datetime.datetime(year, 1, 1)\n",
    "endtime = datetime.datetime(year, 12, 31)\n",
    "time, transport, mean = get_data_all(start, endtime, 'FullSouth', 0, allv)\n",
    "fullsouth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "for variable in allv:\n",
    "    fullsouth_all = fullsouth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "    fullsouth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "fullsouth_all\n",
    "fullsouth_all.to_csv('Vic_meanders_'+str(year)+'_v3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-58-8c8f3492929a>:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015\n",
      "2016\n",
      "2017\n",
      "Missing Data 04sep18\n",
      "2018\n"
     ]
    }
   ],
   "source": [
    "# Full South Vic Tidal Pumping\n",
    "for year in years:\n",
    "    allv = ['final_age', 'final_depth', 'final_salt', 'final_temp', 'final_lon', \n",
    "            'init_depth', 'init_salt', 'init_temp', 'init_lat']\n",
    "    start = datetime.datetime(year, 1, 1)\n",
    "    endtime = datetime.datetime(year, 12, 31)\n",
    "    time, transport, mean = get_data_all(start, endtime, 'FullSouth', 0, allv, loop='Short')\n",
    "    fullsouth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "    for variable in allv:\n",
    "        fullsouth_all = fullsouth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "        fullsouth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "    fullsouth_all\n",
    "    fullsouth_all.to_csv('Vic_tidalpump_'+str(year)+'_v3.csv')\n",
    "    print (year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-58-8c8f3492929a>:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015\n",
      "2016\n",
      "2017\n",
      "Missing Data 04sep18\n",
      "2018\n"
     ]
    }
   ],
   "source": [
    "# Full South Vic Loops\n",
    "for year in years:\n",
    "    allv = ['final_age', 'final_depth', 'final_salt', 'final_temp', 'final_lon', \n",
    "            'init_depth', 'init_salt', 'init_temp', 'init_lat']\n",
    "    start = datetime.datetime(year, 1, 1)\n",
    "    endtime = datetime.datetime(year, 12, 31)\n",
    "    time, transport, mean = get_data_all(start, endtime, 'FullSouth', 0, allv, loop='Long')\n",
    "    fullsouth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "    for variable in allv:\n",
    "        fullsouth_all = fullsouth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "        fullsouth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "    fullsouth_all\n",
    "    fullsouth_all.to_csv('Vic_loops_'+str(year)+'_v3.csv')\n",
    "    print (year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-4-4ab44c4032cd>:18: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    }
   ],
   "source": [
    "# BackSouth Vic Meanders\n",
    "year = 2015\n",
    "allv = ['final_age', 'final_depth', 'final_salt', 'final_temp', 'final_lon', \n",
    "        'init_depth', 'init_salt', 'init_temp', 'init_lat']\n",
    "start = datetime.datetime(year, 1, 1)\n",
    "endtime = datetime.datetime(year, 12, 31)\n",
    "time, transport, mean = get_data_all(start, endtime, 'BackSouth', 0, allv)\n",
    "fullsouth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "for variable in allv:\n",
    "    fullsouth_all = fullsouth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "    fullsouth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "fullsouth_all.to_csv('Vic_back_meanders_'+str(year)+'_v3.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PR and GI together meanders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-4-4ab44c4032cd>:18: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    }
   ],
   "source": [
    "# Full North PR Meanders\n",
    "year = 2015\n",
    "allv = ['final_age', 'final_depth', 'final_salt', 'final_temp', 'final_lon', \n",
    "        'init_depth', 'init_salt', 'init_temp', 'init_lat']\n",
    "start = datetime.datetime(year, 1, 1)\n",
    "endtime = datetime.datetime(year, 12, 31)\n",
    "time, transport, mean = get_data_all(start, endtime, 'FullNorth', 0, allv)\n",
    "fullnorth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "for variable in allv:\n",
    "    fullnorth_all = fullnorth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "    fullnorth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "fullnorth_all\n",
    "fullnorth_all.to_csv('PR_meanders_'+str(year)+'_v3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1042277/2938400006.py:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1042277/2938400006.py:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1042277/2938400006.py:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Data 12aug17\n",
      "2017\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1042277/2938400006.py:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018\n"
     ]
    }
   ],
   "source": [
    "# Full North PR Tidal Pumping\n",
    "for year in years:\n",
    "    allv = ['final_age', 'final_depth', 'final_salt', 'final_temp', 'final_lon', \n",
    "            'init_depth', 'init_salt', 'init_temp', 'init_lat']\n",
    "    start = datetime.datetime(year, 1, 1)\n",
    "    endtime = datetime.datetime(year, 12, 31)\n",
    "    time, transport, mean = get_data_all(start, endtime, 'FullNorth', 0, allv, loop='Short')\n",
    "    fullnorth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "    for variable in allv:\n",
    "        fullnorth_all = fullnorth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "        fullnorth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "    fullnorth_all.to_csv('PR_tidalpump_'+str(year)+'_v3.csv')\n",
    "    print (year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1042277/2938400006.py:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1042277/2938400006.py:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1042277/2938400006.py:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Data 12aug17\n",
      "2017\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1042277/2938400006.py:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018\n"
     ]
    }
   ],
   "source": [
    "# Full North PR Loops\n",
    "for year in years:\n",
    "    allv = ['final_age', 'final_depth', 'final_salt', 'final_temp', 'final_lon', \n",
    "            'init_depth', 'init_salt', 'init_temp', 'init_lat']\n",
    "    start = datetime.datetime(year, 1, 1)\n",
    "    endtime = datetime.datetime(year, 12, 31)\n",
    "    time, transport, mean = get_data_all(start, endtime, 'FullNorth', 0, allv, loop='Long')\n",
    "    fullnorth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "    for variable in allv:\n",
    "        fullnorth_all = fullnorth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "        fullnorth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "    fullnorth_all.to_csv('PR_loops_'+str(year)+'_v3.csv')\n",
    "    print (year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-4-4ab44c4032cd>:18: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Data 12aug17\n"
     ]
    }
   ],
   "source": [
    "# Full North PR -> GI Meanders\n",
    "for year in years:\n",
    "    allv = ['final_age', 'final_depth', 'final_salt', 'final_temp', 'final_lon', \n",
    "            'init_depth', 'init_salt', 'init_temp', 'init_lat']\n",
    "    start = datetime.datetime(year, 1, 1)\n",
    "    endtime = datetime.datetime(year, 12, 31)\n",
    "    time, transport, mean = get_data_all(start, endtime, 'FullNorth', 3, allv)\n",
    "    fullnorth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "    for variable in allv:\n",
    "        fullnorth_all = fullnorth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "        fullnorth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "    fullnorth_all\n",
    "    fullnorth_all.to_csv('PR_to_GI_meanders_'+str(year)+'_v3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1042277/2938400006.py:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1042277/2938400006.py:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1042277/2938400006.py:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Data 12aug17\n",
      "2017\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1042277/2938400006.py:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018\n"
     ]
    }
   ],
   "source": [
    "# Full North PR -> GI Tidal Pumping\n",
    "for year in years:\n",
    "    allv = ['final_age', 'final_depth', 'final_salt', 'final_temp', 'final_lon', \n",
    "            'init_depth', 'init_salt', 'init_temp', 'init_lat']\n",
    "    start = datetime.datetime(year, 1, 1)\n",
    "    endtime = datetime.datetime(year, 12, 31)\n",
    "    time, transport, mean = get_data_all(start, endtime, 'FullNorth', 3, allv, loop='Short')\n",
    "    fullnorth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "    for variable in allv:\n",
    "        fullnorth_all = fullnorth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "        fullnorth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "    fullnorth_all.to_csv('PR_to_GI_tidalpump_'+str(year)+'_v3.csv')\n",
    "    print (year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1042277/2938400006.py:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1042277/2938400006.py:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1042277/2938400006.py:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Data 12aug17\n",
      "2017\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1042277/2938400006.py:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018\n"
     ]
    }
   ],
   "source": [
    "# Full North PR -> GI Tidal Pumping\n",
    "for year in years:\n",
    "    allv = ['final_age', 'final_depth', 'final_salt', 'final_temp', 'final_lon', \n",
    "            'init_depth', 'init_salt', 'init_temp', 'init_lat']\n",
    "    start = datetime.datetime(year, 1, 1)\n",
    "    endtime = datetime.datetime(year, 12, 31)\n",
    "    time, transport, mean = get_data_all(start, endtime, 'FullNorth', 3, allv, loop='Long')\n",
    "    fullnorth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "    for variable in allv:\n",
    "        fullnorth_all = fullnorth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "        fullnorth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "    fullnorth_all.to_csv('PR_to_GI_loops_'+str(year)+'_v3.csv')\n",
    "    print (year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-4-4ab44c4032cd>:18: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Data 20aug17\n"
     ]
    }
   ],
   "source": [
    "# South G Islands GI meanders\n",
    "for year in years:\n",
    "    allv = ['final_age', 'final_depth', 'final_salt', 'final_temp', 'final_lon', \n",
    "            'init_depth', 'init_salt', 'init_temp', 'init_lat']\n",
    "    start = datetime.datetime(year, 1, 1)\n",
    "    endtime = datetime.datetime(year, 12, 31)\n",
    "    time, transport, mean = get_data_all(start, endtime, 'SouthGIslands', 0, allv)\n",
    "    fullsouth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "    for variable in allv:\n",
    "        fullsouth_all = fullsouth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "        fullsouth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "    fullsouth_all\n",
    "    fullsouth_all.to_csv('GI_meanders_'+str(year)+'_v3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1042277/2938400006.py:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1042277/2938400006.py:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1042277/2938400006.py:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Data 20aug17\n",
      "2017\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1042277/2938400006.py:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018\n"
     ]
    }
   ],
   "source": [
    "# South G Islands Tidal Pumping\n",
    "for year in years:\n",
    "    allv = ['final_age', 'final_depth', 'final_salt', 'final_temp', 'final_lon', \n",
    "            'init_depth', 'init_salt', 'init_temp', 'init_lat']\n",
    "    start = datetime.datetime(year, 1, 1)\n",
    "    endtime = datetime.datetime(year, 12, 31)\n",
    "    time, transport, mean = get_data_all(start, endtime, 'SouthGIslands', 0, allv, loop='Short')\n",
    "    fullsouth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "    for variable in allv:\n",
    "        fullsouth_all = fullsouth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "        fullsouth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "    fullsouth_all.to_csv('GI_tidalpump_'+str(year)+'_v3.csv')\n",
    "    print (year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1042277/2938400006.py:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1042277/2938400006.py:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1042277/2938400006.py:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Data 20aug17\n",
      "2017\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1042277/2938400006.py:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018\n"
     ]
    }
   ],
   "source": [
    "# South G Islands Loops\n",
    "for year in years:\n",
    "    allv = ['final_age', 'final_depth', 'final_salt', 'final_temp', 'final_lon', \n",
    "            'init_depth', 'init_salt', 'init_temp', 'init_lat']\n",
    "    start = datetime.datetime(year, 1, 1)\n",
    "    endtime = datetime.datetime(year, 12, 31)\n",
    "    time, transport, mean = get_data_all(start, endtime, 'SouthGIslands', 0, allv, loop='Long')\n",
    "    fullsouth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "    for variable in allv:\n",
    "        fullsouth_all = fullsouth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "        fullsouth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "    fullsouth_all.to_csv('GI_loops_'+str(year)+'_v3.csv')\n",
    "    print (year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-4-4ab44c4032cd>:18: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Data 20aug17\n"
     ]
    }
   ],
   "source": [
    "# South G Islands GI -> PR meanders\n",
    "for year in years:\n",
    "    allv = ['final_age', 'final_depth', 'final_salt', 'final_temp', 'final_lon', \n",
    "            'init_depth', 'init_salt', 'init_temp', 'init_lat']\n",
    "    start = datetime.datetime(year, 1, 1)\n",
    "    endtime = datetime.datetime(year, 12, 31)\n",
    "    time, transport, mean = get_data_all(start, endtime, 'SouthGIslands', 3, allv)\n",
    "    fullsouth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "    for variable in allv:\n",
    "        fullsouth_all = fullsouth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "        fullsouth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "    fullsouth_all\n",
    "    fullsouth_all.to_csv('GI_to_PR_meanders_'+str(year)+'_v3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1042277/2938400006.py:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1042277/2938400006.py:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n",
      "/tmp/ipykernel_1042277/2938400006.py:28: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  mean[variable] = np.sum(np.where((final_section == section) & (final_age <= 24*3600),\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1042277/2938400006.py:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Data 20aug17\n",
      "2017\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1042277/2938400006.py:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018\n"
     ]
    }
   ],
   "source": [
    "# South G Islands -> PR Tidal Pumping\n",
    "for year in years:\n",
    "    allv = ['final_age', 'final_depth', 'final_salt', 'final_temp', 'final_lon', \n",
    "            'init_depth', 'init_salt', 'init_temp', 'init_lat']\n",
    "    start = datetime.datetime(year, 1, 1)\n",
    "    endtime = datetime.datetime(year, 12, 31)\n",
    "    time, transport, mean = get_data_all(start, endtime, 'SouthGIslands', 3, allv, loop='Short')\n",
    "    fullsouth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "    for variable in allv:\n",
    "        fullsouth_all = fullsouth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "        fullsouth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "    fullsouth_all.to_csv('GI_to_PR_tidalpump_'+str(year)+'_v3.csv')\n",
    "    print (year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1042277/2938400006.py:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1042277/2938400006.py:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1042277/2938400006.py:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Data 20aug17\n",
      "2017\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1042277/2938400006.py:24: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018\n"
     ]
    }
   ],
   "source": [
    "# South G Islands -> PR Tidal Pumping\n",
    "for year in years:\n",
    "    allv = ['final_age', 'final_depth', 'final_salt', 'final_temp', 'final_lon', \n",
    "            'init_depth', 'init_salt', 'init_temp', 'init_lat']\n",
    "    start = datetime.datetime(year, 1, 1)\n",
    "    endtime = datetime.datetime(year, 12, 31)\n",
    "    time, transport, mean = get_data_all(start, endtime, 'SouthGIslands', 3, allv, loop='Long')\n",
    "    fullsouth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "    for variable in allv:\n",
    "        fullsouth_all = fullsouth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "        fullsouth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "    fullsouth_all.to_csv('GI_to_PR_loops_'+str(year)+'_v3.csv')\n",
    "    print (year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-4-4ab44c4032cd>:18: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    }
   ],
   "source": [
    "# Back North PR meanders\n",
    "for year in years:\n",
    "    allv = ['final_age', 'final_depth', 'final_salt', 'final_temp', 'final_lon', \n",
    "            'init_depth', 'init_salt', 'init_temp', 'init_lat']\n",
    "    start = datetime.datetime(year, 1, 1)\n",
    "    endtime = datetime.datetime(year, 12, 31)\n",
    "    time, transport, mean = get_data_all(start, endtime, 'BackNorth', 0, allv)\n",
    "    fullsouth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "    for variable in allv:\n",
    "        fullsouth_all = fullsouth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "        fullsouth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "    fullsouth_all\n",
    "    fullsouth_all.to_csv('PR_back_meanders_'+str(year)+'_v3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-4-4ab44c4032cd>:18: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    }
   ],
   "source": [
    "# Back PR from GI meanders\n",
    "for year in years:\n",
    "    allv = ['final_age', 'final_depth', 'final_salt', 'final_temp', 'final_lon', \n",
    "            'init_depth', 'init_salt', 'init_temp', 'init_lat']\n",
    "    start = datetime.datetime(year, 1, 1)\n",
    "    endtime = datetime.datetime(year, 12, 31)\n",
    "    time, transport, mean = get_data_all(start, endtime, 'BackNorth', 3, allv)\n",
    "    fullsouth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "    for variable in allv:\n",
    "        fullsouth_all = fullsouth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "        fullsouth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "    fullsouth_all\n",
    "    fullsouth_all.to_csv('PR_from_GI_meanders_'+str(year)+'_v3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-4-4ab44c4032cd>:18: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    }
   ],
   "source": [
    "# In GIslands into GI from Vic\n",
    "for year in years:\n",
    "    allv = ['final_age', 'final_depth', 'final_salt', 'final_temp', 'final_lon', \n",
    "            'init_depth', 'init_salt', 'init_temp', 'init_lat']\n",
    "    start = datetime.datetime(year, 1, 1)\n",
    "    endtime = datetime.datetime(year, 12, 31)\n",
    "    time, transport, mean = get_data_all(start, endtime, 'InGIslands', 0, allv)\n",
    "    fullsouth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "    for variable in allv:\n",
    "        fullsouth_all = fullsouth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "        fullsouth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "    fullsouth_all\n",
    "    fullsouth_all.to_csv('GI_back_meanders_'+str(year)+'_v3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-4-4ab44c4032cd>:18: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  final[variable] = dataset.variables[variable][:]/scale\n"
     ]
    }
   ],
   "source": [
    "# In GIslands into GI from PR\n",
    "for year in years:\n",
    "    allv = ['final_age', 'final_depth', 'final_salt', 'final_temp', 'final_lon', \n",
    "            'init_depth', 'init_salt', 'init_temp', 'init_lat']\n",
    "    start = datetime.datetime(year, 1, 1)\n",
    "    endtime = datetime.datetime(year, 12, 31)\n",
    "    time, transport, mean = get_data_all(start, endtime, 'InGIslands', 3, allv)\n",
    "    fullsouth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "    for variable in allv:\n",
    "        fullsouth_all = fullsouth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "        fullsouth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "    fullsouth_all\n",
    "    fullsouth_all.to_csv('GI_from_PI_meanders_'+str(year)+'_v3.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Original, Transect to Transect Fluxes ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sallen/anaconda/envs/py38/lib/python3.7/site-packages/ipykernel_launcher.py:19: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Data 04sep18\n"
     ]
    }
   ],
   "source": [
    "# Full South Vic -> PR (2)\n",
    "year = 2018\n",
    "allv = ['final_age', 'final_depth', 'final_salt', 'final_temp', 'final_lon', \n",
    "        'init_depth', 'init_salt', 'init_temp', 'init_lat']\n",
    "start = datetime.datetime(year, 1, 1)\n",
    "endtime = datetime.datetime(year, 12, 31)\n",
    "time, transport, mean = get_data_all(start, endtime, 'FullSouth', 2, allv)\n",
    "fullsouth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "for variable in allv:\n",
    "    fullsouth_all = fullsouth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "    fullsouth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "fullsouth_all\n",
    "fullsouth_all.to_csv('Vic_to_PR_'+str(year)+'_v3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sallen/anaconda/envs/py38/lib/python3.7/site-packages/ipykernel_launcher.py:19: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Data 04sep18\n"
     ]
    }
   ],
   "source": [
    "# Full South Vic -> GI (3)\n",
    "year = 2018\n",
    "allv = ['final_age', 'final_depth', 'final_salt', 'final_temp', 'final_lon', \n",
    "        'init_depth', 'init_salt', 'init_temp', 'init_lat']\n",
    "start = datetime.datetime(year, 1, 1)\n",
    "endtime = datetime.datetime(year, 12, 31)\n",
    "time, transport, mean = get_data_all(start, endtime, 'FullSouth', 3, allv)\n",
    "fullsouth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "for variable in allv:\n",
    "    fullsouth_all = fullsouth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "    fullsouth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "fullsouth_all\n",
    "fullsouth_all.to_csv('Vic_to_GI_'+str(year)+'_v3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sallen/anaconda/envs/py38/lib/python3.7/site-packages/ipykernel_launcher.py:19: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n"
     ]
    }
   ],
   "source": [
    "# Back North into PR from Vic\n",
    "year = 2018\n",
    "allv = ['final_age', 'final_depth', 'final_salt', 'final_temp', 'final_lon', \n",
    "        'init_depth', 'init_salt', 'init_temp', 'init_lat']\n",
    "start = datetime.datetime(year, 1, 1)\n",
    "endtime = datetime.datetime(year, 12, 31)\n",
    "time, transport, mean = get_data_all(start, endtime, 'BackNorth', 2, allv)\n",
    "fullsouth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "for variable in allv:\n",
    "    fullsouth_all = fullsouth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "    fullsouth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "fullsouth_all\n",
    "fullsouth_all.to_csv('PR_from_Vic_'+str(year)+'_v3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sallen/anaconda/envs/py38/lib/python3.7/site-packages/ipykernel_launcher.py:19: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n"
     ]
    }
   ],
   "source": [
    "# In GIslands into GI from Vic\n",
    "year = 2018\n",
    "allv = ['final_age', 'final_depth', 'final_salt', 'final_temp', 'final_lon', \n",
    "        'init_depth', 'init_salt', 'init_temp', 'init_lat']\n",
    "start = datetime.datetime(year, 1, 1)\n",
    "endtime = datetime.datetime(year, 12, 31)\n",
    "time, transport, mean = get_data_all(start, endtime, 'InGIslands', 2, allv)\n",
    "fullsouth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "for variable in allv:\n",
    "    fullsouth_all = fullsouth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "    fullsouth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "fullsouth_all\n",
    "fullsouth_all.to_csv('GI_from_Vic_'+str(year)+'_v3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sallen/anaconda/envs/py38/lib/python3.7/site-packages/ipykernel_launcher.py:19: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n"
     ]
    }
   ],
   "source": [
    "# Full North PR to Vic\n",
    "year = 2018\n",
    "allv = ['final_age', 'final_depth', 'final_salt', 'final_temp', 'final_lon', \n",
    "        'init_depth', 'init_salt', 'init_temp', 'init_lat']\n",
    "start = datetime.datetime(year, 1, 1)\n",
    "endtime = datetime.datetime(year, 12, 31)\n",
    "time, transport, mean = get_data_all(start, endtime, 'FullNorth', 2, allv)\n",
    "fullnorth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "for variable in allv:\n",
    "    fullnorth_all = fullnorth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "    fullnorth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "fullnorth_all\n",
    "fullnorth_all.to_csv('PR_to_Vic_'+str(year)+'_v3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sallen/anaconda/envs/py38/lib/python3.7/site-packages/ipykernel_launcher.py:19: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n"
     ]
    }
   ],
   "source": [
    "# South G Islands GI to Vic\n",
    "year = 2018\n",
    "allv = ['final_age', 'final_depth', 'final_salt', 'final_temp', 'final_lon', \n",
    "        'init_depth', 'init_salt', 'init_temp', 'init_lat']\n",
    "start = datetime.datetime(year, 1, 1)\n",
    "endtime = datetime.datetime(year, 12, 31)\n",
    "time, transport, mean = get_data_all(start, endtime, 'SouthGIslands', 2, allv)\n",
    "fullsouth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "for variable in allv:\n",
    "    fullsouth_all = fullsouth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "    fullsouth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "fullsouth_all\n",
    "fullsouth_all.to_csv('GI_to_Vic_'+str(year)+'_v3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sallen/anaconda/envs/py38/lib/python3.7/site-packages/ipykernel_launcher.py:19: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n"
     ]
    }
   ],
   "source": [
    "# BackSouth Vic from PR\n",
    "year = 2018\n",
    "allv = ['final_age', 'final_depth', 'final_salt', 'final_temp', 'final_lon', \n",
    "        'init_depth', 'init_salt', 'init_temp', 'init_lat']\n",
    "start = datetime.datetime(year, 1, 1)\n",
    "endtime = datetime.datetime(year, 12, 31)\n",
    "time, transport, mean = get_data_all(start, endtime, 'BackSouth', 2, allv)\n",
    "fullsouth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "for variable in allv:\n",
    "    fullsouth_all = fullsouth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "    fullsouth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "fullsouth_all\n",
    "fullsouth_all.to_csv('Vic_from_PR_'+str(year)+'_v3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sallen/anaconda/envs/py38/lib/python3.7/site-packages/ipykernel_launcher.py:19: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n"
     ]
    }
   ],
   "source": [
    "# BackSouth Vic from GI\n",
    "year = 2018\n",
    "allv = ['final_age', 'final_depth', 'final_salt', 'final_temp', 'final_lon', \n",
    "        'init_depth', 'init_salt', 'init_temp', 'init_lat']\n",
    "start = datetime.datetime(year, 1, 1)\n",
    "endtime = datetime.datetime(year, 12, 31)\n",
    "time, transport, mean = get_data_all(start, endtime, 'BackSouth', 3, allv)\n",
    "fullnorth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "for variable in allv:\n",
    "    fullnorth_all = fullnorth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "    fullnorth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "fullnorth_all\n",
    "fullnorth_all.to_csv('Vic_from_GI_'+str(year)+'_v3.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "fi = open('south_init.txt', 'a')\n",
    "ff = open('south_final.txt', 'a')\n",
    "save_position('./', './', 2, fi, ff)\n",
    "fi.close() \n",
    "ff.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Victoria_sill_j = 178-1;\n",
    "#    Victoria_sill_i = np.arange(235,302+1)-1\n",
    "fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
    "with open('south_init.txt', 'r') as fi:\n",
    "    mydata = np.loadtxt(fi)\n",
    "my_lats_edges = lats[235-1:302-1, 178-1] - (lats[235, 178] - lats[234, 178])/2.\n",
    "my_deps_edges = -gdepw[28::-1]\n",
    "H, xs, ys = np.histogram2d(mydata[:, 0], mydata[:, 1],  bins = [my_deps_edges, my_lats_edges],\n",
    "                        weights=mydata2[:,3])\n",
    "mesh = ax[0].pcolormesh(my_lats_edges[:-1], my_deps_edges[:-1], H)\n",
    "fig.colorbar(mesh, ax=ax[0])\n",
    "\n",
    "with open('south_final.txt', 'r') as ff:\n",
    "    mydata2 = np.loadtxt(ff)\n",
    "my_lons_edges = lons[387-1, 262-1:309-1] - (lons[387, 300] - lons[387, 301])/2.\n",
    "H, xs, ys = np.histogram2d(mydata2[:, 0], mydata2[:, 2],  bins = [my_deps_edges, my_lons_edges],\n",
    "                        weights=mydata2[:,3])\n",
    "mesh = ax[1].pcolormesh(my_lons_edges[:-1], my_deps_edges[:-1], H)\n",
    "fig.colorbar(mesh, ax=ax[1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sallen/anaconda/envs/py37/lib/python3.7/site-packages/ipykernel_launcher.py:19: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Data 04sep18\n"
     ]
    }
   ],
   "source": [
    "# to Puget Sound from Victoria Sill (#4)  (Discovery is #5)\n",
    "number = 4\n",
    "time, transport, mean = get_data_all(start, endtime, 'FullSouth', number, allv)\n",
    "fullnorth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "for variable in allv:\n",
    "    fullnorth_all = fullnorth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "    fullnorth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "fullnorth_all\n",
    "fullnorth_all.to_csv('Puget_from_VS_'+str(year)+'_v3.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sallen/anaconda/envs/py37/lib/python3.7/site-packages/ipykernel_launcher.py:19: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Data 04sep18\n"
     ]
    }
   ],
   "source": [
    "# to Discovery from Victoria Sill (#5)\n",
    "number = 5\n",
    "time, transport, mean = get_data_all(start, endtime, 'FullSouth', number, allv)\n",
    "fullnorth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "for variable in allv:\n",
    "    fullnorth_all = fullnorth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "    fullnorth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "fullnorth_all\n",
    "fullnorth_all.to_csv('Discovery_from_VS_'+str(year)+'_v3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sallen/anaconda/envs/py37/lib/python3.7/site-packages/ipykernel_launcher.py:19: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n"
     ]
    }
   ],
   "source": [
    "# to Puget Sound from Point Roberts (#4)  (Discovery is #5)\n",
    "number = 4\n",
    "time, transport, mean = get_data_all(start, endtime, 'FullNorth', number, allv)\n",
    "fullnorth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "for variable in allv:\n",
    "    fullnorth_all = fullnorth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "    fullnorth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "fullnorth_all\n",
    "fullnorth_all.to_csv('Puget_from_PR_'+str(year)+'_v3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sallen/anaconda/envs/py37/lib/python3.7/site-packages/ipykernel_launcher.py:19: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n"
     ]
    }
   ],
   "source": [
    "# to Discovery from Point Roberts (#5)\n",
    "number = 5\n",
    "time, transport, mean = get_data_all(start, endtime, 'FullNorth', number, allv)\n",
    "fullnorth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "for variable in allv:\n",
    "    fullnorth_all = fullnorth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "    fullnorth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "fullnorth_all\n",
    "fullnorth_all.to_csv('Discovery_from_PR_'+str(year)+'_v3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(14, 5))\n",
    "ax2 = ax.twinx()\n",
    "lag = -3\n",
    "ii, jj = 190, 300\n",
    "ax.plot(range(ii, jj),fullsouth_transport['transport'][ii+lag:jj+lag])\n",
    "ax2.plot(range(ii, jj), -low_pass_tide['uVelocity'][ii:jj], 'r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to Puget Sound from Gulf Islands (#4)  (Discovery is #5)\n",
    "number = 4\n",
    "time, transport, mean = get_data_all(start, endtime, 'SouthGIslands', number, allv)\n",
    "fullnorth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "for variable in allv:\n",
    "    fullnorth_all = fullnorth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "    fullnorth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "fullnorth_all\n",
    "fullnorth_all.to_csv('Puget_from_GI_'+str(year)+'_v3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(14, 5))\n",
    "ax2 = ax.twinx()\n",
    "lag = -3\n",
    "ii, jj = 100, 190\n",
    "ax.plot(range(ii, jj),fullsouth_transport['transport'][ii+lag:jj+lag])\n",
    "ax2.plot(range(ii, jj), -low_pass_tide['uVelocity'][ii:jj], 'r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to Discovery from Gulf Islands (#5)\n",
    "number = 5\n",
    "time, transport, mean = get_data_all(start, endtime, 'SouthGIslands', number, allv)\n",
    "fullnorth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "for variable in allv:\n",
    "    fullnorth_all = fullnorth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "    fullnorth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "fullnorth_all\n",
    "fullnorth_all.to_csv('Discovery_from_GI_'+str(year)+'_v3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sallen/anaconda/envs/py37/lib/python3.7/site-packages/ipykernel_launcher.py:19: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n"
     ]
    }
   ],
   "source": [
    "# from Puget Sound to Victoria Sill (#4)  (Discovery is #5)\n",
    "number = 4\n",
    "time, transport, mean = get_data_all(start, endtime, 'BackSouth', number, allv)\n",
    "fullnorth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "for variable in allv:\n",
    "    fullnorth_all = fullnorth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "    fullnorth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "fullnorth_all\n",
    "fullnorth_all.to_csv('Puget_to_VS_'+str(year)+'_v3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sallen/anaconda/envs/py37/lib/python3.7/site-packages/ipykernel_launcher.py:19: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n"
     ]
    }
   ],
   "source": [
    "# from Discovery to Victoria Sill (#5)\n",
    "number = 5\n",
    "time, transport, mean = get_data_all(start, endtime, 'BackSouth', number, allv)\n",
    "fullnorth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "for variable in allv:\n",
    "    fullnorth_all = fullnorth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "    fullnorth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "fullnorth_all\n",
    "fullnorth_all.to_csv('Discovery_to_VS_'+str(year)+'_v3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sallen/anaconda/envs/py37/lib/python3.7/site-packages/ipykernel_launcher.py:19: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n"
     ]
    }
   ],
   "source": [
    "# from Puget Sound to Point Roberts (#4)  (Discovery is #5)\n",
    "number = 4\n",
    "time, transport, mean = get_data_all(start, endtime, 'BackNorth', number, allv)\n",
    "fullnorth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "for variable in allv:\n",
    "    fullnorth_all = fullnorth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "    fullnorth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "fullnorth_all\n",
    "fullnorth_all.to_csv('Puget_to_PR_'+str(year)+'_v3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sallen/anaconda/envs/py37/lib/python3.7/site-packages/ipykernel_launcher.py:19: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "/home/sallen/anaconda/envs/py37/lib/python3.7/site-packages/ipykernel_launcher.py:20: RuntimeWarning: invalid value encountered in double_scalars\n"
     ]
    }
   ],
   "source": [
    "# from Discovery to Point Roberts (#5)\n",
    "number = 5\n",
    "time, transport, mean = get_data_all(start, endtime, 'BackNorth', number, allv)\n",
    "fullnorth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "for variable in allv:\n",
    "    fullnorth_all = fullnorth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "    fullnorth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "fullnorth_all\n",
    "fullnorth_all.to_csv('Discovery_to_PR_'+str(year)+'_v3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sallen/anaconda/envs/py37/lib/python3.7/site-packages/ipykernel_launcher.py:19: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n"
     ]
    }
   ],
   "source": [
    "# from Puget Sound to Gulf Islands (#4)  (Discovery is #5)\n",
    "number = 4\n",
    "time, transport, mean = get_data_all(start, endtime, 'InGIslands', number, allv)\n",
    "fullnorth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "for variable in allv:\n",
    "    fullnorth_all = fullnorth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "    fullnorth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "fullnorth_all\n",
    "fullnorth_all.to_csv('Puget_to_GI_'+str(year)+'_v3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sallen/anaconda/envs/py37/lib/python3.7/site-packages/ipykernel_launcher.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/home/sallen/anaconda/envs/py37/lib/python3.7/site-packages/ipykernel_launcher.py:19: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n"
     ]
    }
   ],
   "source": [
    "# from Discovery to Gulf Islands (#5)\n",
    "number = 5\n",
    "time, transport, mean = get_data_all(start, endtime, 'InGIslands', number, allv)\n",
    "fullnorth_all = pd.DataFrame(data=transport, index=time, columns=['transport'])\n",
    "for variable in allv:\n",
    "    fullnorth_all = fullnorth_all.assign(mycol=pd.Series(mean[variable]).values)\n",
    "    fullnorth_all.rename(columns={'mycol':variable}, inplace=True)\n",
    "fullnorth_all\n",
    "fullnorth_all.to_csv('Discovery_to_GI_'+str(year)+'_v3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "fs = open('gulfisland_fullsouth_early2015', 'wb')\n",
    "pickle.dump(gulfislands, fs)\n",
    "fs.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "fs = open('fullsouth_early2015', 'wb')\n",
    "pickle.dump(fullsouth_transport, fs)\n",
    "fs.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "vic_transport_2015 = pickle.load('vic_transport_2015')\n",
    "plt.plot(vic_transport_2015)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "fs = open('vic_transport_2015', 'wb')\n",
    "pickle.dump(vic_transport, fs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "vsq = pd.read_csv('../day_avg_tide_pd.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1, 5, figsize=(18, 5))\n",
    "for i, lag in enumerate(range(-7, 7, 3)):\n",
    "    ax[i].plot(vsq[99+lag:99+365+lag].uVelocity, vic_transport, '*')\n",
    "#    print (np.correlate(vsq[99+lag:99+365+lag], vic_transport[:]), lag, mode='valid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "print (final_section[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "print (final_transport[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  },
  "toc": {
   "nav_menu": {
    "height": "12px",
    "width": "252px"
   },
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": "block",
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}