MeanVelocitiesAllMonths.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "95ab64e6-253c-4bca-a747-d0753217a8e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "## check mean file ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "c1816805-1e35-46a3-bee4-988418ea7d99",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cmocean.cm as cm\n",
    "import copy\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.collections import PatchCollection\n",
    "from matplotlib.patches import Rectangle\n",
    "import numpy as np\n",
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "1888b4b8-2a40-4773-9546-a321fb252698",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.size'] = 15\n",
    "cm_balance = copy.copy(cm.balance)\n",
    "cm_balance.set_bad('grey')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6336ad35-7649-4b6b-b28b-4e72e9ea5550",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = xr.open_dataset('/data/sallen/results/Reshapr/output/SalishSeaCast_hour_vvelocity_20150725_20150804.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "413067c5-9e76-4029-858c-26beb502c847",
   "metadata": {},
   "outputs": [],
   "source": [
    "igrid = 386\n",
    "j1, j2 = 210, 320"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f6fe07be-1151-45b7-b750-69e416e882c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;vomecrty&#x27; (time: 264, depth: 40, gridY: 1, gridX: 110)&gt;\n",
       "[1161600 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2015-07-25T00:30:00 ... 2015-08-04T23:30:00\n",
       "  * depth    (depth) float32 0.5 1.5 2.5 3.5 4.5 ... 360.7 387.6 414.5 441.5\n",
       "  * gridY    (gridY) int64 386\n",
       "  * gridX    (gridX) int64 210 211 212 213 214 215 ... 314 315 316 317 318 319\n",
       "Attributes:\n",
       "    units:          m s-1\n",
       "    standard_name:  sea_water_y_velocity\n",
       "    long_name:      Ocean Current Along y-Axis</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'vomecrty'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 264</li><li><span class='xr-has-index'>depth</span>: 40</li><li><span class='xr-has-index'>gridY</span>: 1</li><li><span class='xr-has-index'>gridX</span>: 110</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-4d493d17-7830-4cba-9935-7279d1487831' class='xr-array-in' type='checkbox' checked><label for='section-4d493d17-7830-4cba-9935-7279d1487831' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[1161600 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-3eefbd98-de8f-494f-8662-189642e0232d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3eefbd98-de8f-494f-8662-189642e0232d' class='xr-section-summary' >Coordinates: <span>(4)</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 class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2015-07-25T00:30:00 ... 2015-08-...</div><input id='attrs-6501581e-063b-493d-99f2-6b64bc7d367b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6501581e-063b-493d-99f2-6b64bc7d367b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f90ee9fc-5182-4893-8478-2984283e70ab' class='xr-var-data-in' type='checkbox'><label for='data-f90ee9fc-5182-4893-8478-2984283e70ab' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time Axis</dd><dt><span>time_origin :</span></dt><dd>2007-01-01 00:30:00</dd><dt><span>comment :</span></dt><dd>time values are UTC at the centre of the intervals over which the calculated model results are averaged; e.g. the field average values for the first hour of 8 February 2022 have a time value of 2022-02-08 00:30:00Z</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2015-07-25T00:30:00.000000000&#x27;, &#x27;2015-07-25T01:30:00.000000000&#x27;,\n",
       "       &#x27;2015-07-25T02:30:00.000000000&#x27;, ..., &#x27;2015-08-04T21:30:00.000000000&#x27;,\n",
       "       &#x27;2015-08-04T22:30:00.000000000&#x27;, &#x27;2015-08-04T23:30:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>depth</span></div><div class='xr-var-dims'>(depth)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.5 1.5 2.5 ... 387.6 414.5 441.5</div><input id='attrs-6773c99e-4176-435d-a782-526e069fb3b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6773c99e-4176-435d-a782-526e069fb3b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a12302a-777f-49e4-9849-7a0d91c40ac3' class='xr-var-data-in' type='checkbox'><label for='data-5a12302a-777f-49e4-9849-7a0d91c40ac3' 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>long_name :</span></dt><dd>Sea Floor Depth</dd><dt><span>standard_name :</span></dt><dd>sea_floor_depth</dd><dt><span>units :</span></dt><dd>metres</dd><dt><span>positive :</span></dt><dd>down</dd></dl></div><div class='xr-var-data'><pre>array([  0.5     ,   1.500003,   2.500011,   3.500031,   4.500071,   5.500151,\n",
       "         6.50031 ,   7.500623,   8.501236,   9.502433,  10.504766,  11.509312,\n",
       "        12.518167,  13.535412,  14.568982,  15.634288,  16.761173,  18.007135,\n",
       "        19.481785,  21.389978,  24.100256,  28.229916,  34.685757,  44.517723,\n",
       "        58.484333,  76.58559 ,  98.06296 , 121.866516, 147.08946 , 173.11449 ,\n",
       "       199.57304 , 226.2603  , 253.06664 , 279.93454 , 306.8342  , 333.75018 ,\n",
       "       360.67453 , 387.6032  , 414.5341  , 441.4661  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>gridY</span></div><div class='xr-var-dims'>(gridY)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>386</div><input id='attrs-2fc4e42f-4896-41bc-a5eb-e4b2d7b36098' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2fc4e42f-4896-41bc-a5eb-e4b2d7b36098' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b1aeba4-9508-4be9-8d16-130f6a563a38' class='xr-var-data-in' type='checkbox'><label for='data-1b1aeba4-9508-4be9-8d16-130f6a563a38' 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>standard_name :</span></dt><dd>y</dd><dt><span>long_name :</span></dt><dd>Grid Y</dd><dt><span>units :</span></dt><dd>count</dd><dt><span>comment :</span></dt><dd>gridY values are grid indices in the model y-direction</dd></dl></div><div class='xr-var-data'><pre>array([386])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>gridX</span></div><div class='xr-var-dims'>(gridX)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>210 211 212 213 ... 316 317 318 319</div><input id='attrs-04bf9abf-cd85-4ae6-9ee7-de6bdd9b3ae5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04bf9abf-cd85-4ae6-9ee7-de6bdd9b3ae5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc4d82fd-6754-4ad0-845b-08b8ba5318f0' class='xr-var-data-in' type='checkbox'><label for='data-bc4d82fd-6754-4ad0-845b-08b8ba5318f0' 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>standard_name :</span></dt><dd>x</dd><dt><span>long_name :</span></dt><dd>Grid X</dd><dt><span>units :</span></dt><dd>count</dd><dt><span>comment :</span></dt><dd>gridX values are grid indices in the model x-direction</dd></dl></div><div class='xr-var-data'><pre>array([210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223,\n",
       "       224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237,\n",
       "       238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251,\n",
       "       252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265,\n",
       "       266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279,\n",
       "       280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293,\n",
       "       294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307,\n",
       "       308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c1e58a29-c6b9-4613-808a-a8ec47eea8c9' class='xr-section-summary-in' type='checkbox'  ><label for='section-c1e58a29-c6b9-4613-808a-a8ec47eea8c9' class='xr-section-summary' >Indexes: <span>(4)</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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-13582425-8bdd-4bd7-ac25-9134246b469e' class='xr-index-data-in' type='checkbox'/><label for='index-13582425-8bdd-4bd7-ac25-9134246b469e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2015-07-25 00:30:00&#x27;, &#x27;2015-07-25 01:30:00&#x27;,\n",
       "               &#x27;2015-07-25 02:30:00&#x27;, &#x27;2015-07-25 03:30:00&#x27;,\n",
       "               &#x27;2015-07-25 04:30:00&#x27;, &#x27;2015-07-25 05:30:00&#x27;,\n",
       "               &#x27;2015-07-25 06:30:00&#x27;, &#x27;2015-07-25 07:30:00&#x27;,\n",
       "               &#x27;2015-07-25 08:30:00&#x27;, &#x27;2015-07-25 09:30:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2015-08-04 14:30:00&#x27;, &#x27;2015-08-04 15:30:00&#x27;,\n",
       "               &#x27;2015-08-04 16:30:00&#x27;, &#x27;2015-08-04 17:30:00&#x27;,\n",
       "               &#x27;2015-08-04 18:30:00&#x27;, &#x27;2015-08-04 19:30:00&#x27;,\n",
       "               &#x27;2015-08-04 20:30:00&#x27;, &#x27;2015-08-04 21:30:00&#x27;,\n",
       "               &#x27;2015-08-04 22:30:00&#x27;, &#x27;2015-08-04 23:30:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=264, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>depth</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f2dd1d7d-b969-46bf-bfd4-235c232a3428' class='xr-index-data-in' type='checkbox'/><label for='index-f2dd1d7d-b969-46bf-bfd4-235c232a3428' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.5000002980232239, 1.5000030994415283,  2.500011444091797,\n",
       "        3.500030517578125,  4.500070571899414,  5.500150680541992,\n",
       "         6.50031042098999, 7.5006232261657715,  8.501235961914062,\n",
       "        9.502432823181152, 10.504765510559082,  11.50931167602539,\n",
       "       12.518166542053223, 13.535411834716797, 14.568982124328613,\n",
       "        15.63428783416748, 16.761173248291016,  18.00713539123535,\n",
       "        19.48178482055664, 21.389978408813477, 24.100255966186523,\n",
       "       28.229915618896484,  34.68575668334961, 44.517723083496094,\n",
       "        58.48433303833008,  76.58558654785156,  98.06295776367188,\n",
       "       121.86651611328125, 147.08946228027344, 173.11448669433594,\n",
       "        199.5730438232422,  226.2602996826172, 253.06663513183594,\n",
       "        279.9345397949219,  306.8341979980469, 333.75018310546875,\n",
       "        360.6745300292969, 387.60321044921875,  414.5340881347656,\n",
       "        441.4660949707031],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;depth&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>gridY</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e8747ea2-29e9-4dec-af13-85ada242f0c5' class='xr-index-data-in' type='checkbox'/><label for='index-e8747ea2-29e9-4dec-af13-85ada242f0c5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([386], dtype=&#x27;int64&#x27;, name=&#x27;gridY&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>gridX</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-16a17307-4b8d-4d62-b67e-1cbc53b76dfa' class='xr-index-data-in' type='checkbox'/><label for='index-16a17307-4b8d-4d62-b67e-1cbc53b76dfa' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([210, 211, 212, 213, 214, 215, 216, 217, 218, 219,\n",
       "       ...\n",
       "       310, 311, 312, 313, 314, 315, 316, 317, 318, 319],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;gridX&#x27;, length=110))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-35055fd5-4956-425e-94b0-5ba2ba0c0765' class='xr-section-summary-in' type='checkbox'  checked><label for='section-35055fd5-4956-425e-94b0-5ba2ba0c0765' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>standard_name :</span></dt><dd>sea_water_y_velocity</dd><dt><span>long_name :</span></dt><dd>Ocean Current Along y-Axis</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'vomecrty' (time: 264, depth: 40, gridY: 1, gridX: 110)>\n",
       "[1161600 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2015-07-25T00:30:00 ... 2015-08-04T23:30:00\n",
       "  * depth    (depth) float32 0.5 1.5 2.5 3.5 4.5 ... 360.7 387.6 414.5 441.5\n",
       "  * gridY    (gridY) int64 386\n",
       "  * gridX    (gridX) int64 210 211 212 213 214 215 ... 314 315 316 317 318 319\n",
       "Attributes:\n",
       "    units:          m s-1\n",
       "    standard_name:  sea_water_y_velocity\n",
       "    long_name:      Ocean Current Along y-Axis"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.vomecrty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "698da4bd-ffab-4f74-8839-bb0ed61737ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.vomecrty[:].mean(axis=0).plot(yincrease=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "86ef0428-2fa4-419f-b023-f472d48571c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = xr.open_dataset('/data/sallen/results/Reshapr/output/SalishSeaCast_hour_vvelocity_20150601_20150901.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "de3038ee-f6e9-4627-bd8a-33818ada5f37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:   (time: 4, depth: 40, gridY: 1, gridX: 110)\n",
       "Coordinates:\n",
       "  * depth     (depth) float32 0.5 1.5 2.5 3.5 4.5 ... 360.7 387.6 414.5 441.5\n",
       "  * gridY     (gridY) int64 386\n",
       "  * gridX     (gridX) int64 210 211 212 213 214 215 ... 314 315 316 317 318 319\n",
       "  * time      (time) datetime64[ns] 2015-06-15 ... 2015-09-15\n",
       "Data variables:\n",
       "    vomecrty  (time, depth, gridY, gridX) float32 ...\n",
       "Attributes:\n",
       "    name:         SalishSeaCast_hour_vvelocity_20150601_20150901\n",
       "    description:  Hourly v velocity extracted from SalishSeaCast v202111\n",
       "    history:      2024-07-03 14:15 -07:00: Generated by `reshapr extract /dat...\n",
       "    Conventions:  CF-1.6</pre><div class='xr-wrap' style='display:none'><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-934ab91b-0c2d-418b-a1b2-771cd305df7d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-934ab91b-0c2d-418b-a1b2-771cd305df7d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4</li><li><span class='xr-has-index'>depth</span>: 40</li><li><span class='xr-has-index'>gridY</span>: 1</li><li><span class='xr-has-index'>gridX</span>: 110</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d9c9e981-88aa-4216-8775-2594ce7fb15d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d9c9e981-88aa-4216-8775-2594ce7fb15d' class='xr-section-summary' >Coordinates: <span>(4)</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 class='xr-has-index'>depth</span></div><div class='xr-var-dims'>(depth)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.5 1.5 2.5 ... 387.6 414.5 441.5</div><input id='attrs-4036124a-4259-4052-9973-9760c81914a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4036124a-4259-4052-9973-9760c81914a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acb62980-e09b-43d4-af28-90548c2b8bdc' class='xr-var-data-in' type='checkbox'><label for='data-acb62980-e09b-43d4-af28-90548c2b8bdc' 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>standard_name :</span></dt><dd>sea_floor_depth</dd><dt><span>long_name :</span></dt><dd>Sea Floor Depth</dd><dt><span>units :</span></dt><dd>metres</dd><dt><span>positive :</span></dt><dd>down</dd></dl></div><div class='xr-var-data'><pre>array([  0.5     ,   1.500003,   2.500011,   3.500031,   4.500071,   5.500151,\n",
       "         6.50031 ,   7.500623,   8.501236,   9.502433,  10.504766,  11.509312,\n",
       "        12.518167,  13.535412,  14.568982,  15.634288,  16.761173,  18.007135,\n",
       "        19.481785,  21.389978,  24.100256,  28.229916,  34.685757,  44.517723,\n",
       "        58.484333,  76.58559 ,  98.06296 , 121.866516, 147.08946 , 173.11449 ,\n",
       "       199.57304 , 226.2603  , 253.06664 , 279.93454 , 306.8342  , 333.75018 ,\n",
       "       360.67453 , 387.6032  , 414.5341  , 441.4661  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>gridY</span></div><div class='xr-var-dims'>(gridY)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>386</div><input id='attrs-4d7c1f48-e654-49ab-9c21-7f72ef72cae3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d7c1f48-e654-49ab-9c21-7f72ef72cae3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c3183f6-b049-4b2c-8c73-766e7b8bcb59' class='xr-var-data-in' type='checkbox'><label for='data-0c3183f6-b049-4b2c-8c73-766e7b8bcb59' 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>long_name :</span></dt><dd>Grid Y</dd><dt><span>standard_name :</span></dt><dd>y</dd><dt><span>comment :</span></dt><dd>gridY values are grid indices in the model y-direction</dd><dt><span>units :</span></dt><dd>count</dd></dl></div><div class='xr-var-data'><pre>array([386])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>gridX</span></div><div class='xr-var-dims'>(gridX)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>210 211 212 213 ... 316 317 318 319</div><input id='attrs-3aa23e94-65c6-41f7-8b8d-c79188506579' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3aa23e94-65c6-41f7-8b8d-c79188506579' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f4de06f-38a7-4c63-ae4d-453d687aab5b' class='xr-var-data-in' type='checkbox'><label for='data-1f4de06f-38a7-4c63-ae4d-453d687aab5b' 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>long_name :</span></dt><dd>Grid X</dd><dt><span>standard_name :</span></dt><dd>x</dd><dt><span>comment :</span></dt><dd>gridX values are grid indices in the model x-direction</dd><dt><span>units :</span></dt><dd>count</dd></dl></div><div class='xr-var-data'><pre>array([210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223,\n",
       "       224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237,\n",
       "       238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251,\n",
       "       252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265,\n",
       "       266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279,\n",
       "       280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293,\n",
       "       294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307,\n",
       "       308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2015-06-15 ... 2015-09-15</div><input id='attrs-8e44e34e-5666-4cfd-a2fa-33d34d3360ca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e44e34e-5666-4cfd-a2fa-33d34d3360ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-358f1df2-7fcd-432e-9d2d-f2e6bfe4bf8c' class='xr-var-data-in' type='checkbox'><label for='data-358f1df2-7fcd-432e-9d2d-f2e6bfe4bf8c' 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>long_name :</span></dt><dd>Time Axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>comment :</span></dt><dd>time values are UTC at the centre of the intervals over which the calculated model results are averaged; e.g. the field average values for January 2022 have a time value of 2022-01-15 12:00:00Z, and those for April 2022 have a time value of 2022-04-15 00:00:00Z</dd><dt><span>time_origin :</span></dt><dd>2007-01-01 12:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2015-06-15T00:00:00.000000000&#x27;, &#x27;2015-07-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;2015-08-15T12:00:00.000000000&#x27;, &#x27;2015-09-15T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-27f5f99c-ddec-445b-a4c1-d8bf397f2372' class='xr-section-summary-in' type='checkbox'  checked><label for='section-27f5f99c-ddec-445b-a4c1-d8bf397f2372' class='xr-section-summary' >Data variables: <span>(1)</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>vomecrty</span></div><div class='xr-var-dims'>(time, depth, gridY, gridX)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3209c7a3-9327-4e68-8546-aefac5e0c2ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3209c7a3-9327-4e68-8546-aefac5e0c2ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f65045c-56bd-459f-941e-f0e93b23acce' class='xr-var-data-in' type='checkbox'><label for='data-9f65045c-56bd-459f-941e-f0e93b23acce' 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>standard_name :</span></dt><dd>sea_water_y_velocity</dd><dt><span>long_name :</span></dt><dd>Ocean Current Along y-Axis</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><pre>[17600 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6aad97bf-82ad-42bd-88bc-7ff8fae0cbb8' class='xr-section-summary-in' type='checkbox'  ><label for='section-6aad97bf-82ad-42bd-88bc-7ff8fae0cbb8' class='xr-section-summary' >Indexes: <span>(4)</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-index-name'><div>depth</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-751c831c-5fe5-4d64-93c6-5ba60b44a901' class='xr-index-data-in' type='checkbox'/><label for='index-751c831c-5fe5-4d64-93c6-5ba60b44a901' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.5000002980232239, 1.5000030994415283,  2.500011444091797,\n",
       "        3.500030517578125,  4.500070571899414,  5.500150680541992,\n",
       "         6.50031042098999, 7.5006232261657715,  8.501235961914062,\n",
       "        9.502432823181152, 10.504765510559082,  11.50931167602539,\n",
       "       12.518166542053223, 13.535411834716797, 14.568982124328613,\n",
       "        15.63428783416748, 16.761173248291016,  18.00713539123535,\n",
       "        19.48178482055664, 21.389978408813477, 24.100255966186523,\n",
       "       28.229915618896484,  34.68575668334961, 44.517723083496094,\n",
       "        58.48433303833008,  76.58558654785156,  98.06295776367188,\n",
       "       121.86651611328125, 147.08946228027344, 173.11448669433594,\n",
       "        199.5730438232422,  226.2602996826172, 253.06663513183594,\n",
       "        279.9345397949219,  306.8341979980469, 333.75018310546875,\n",
       "        360.6745300292969, 387.60321044921875,  414.5340881347656,\n",
       "        441.4660949707031],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;depth&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>gridY</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7fcff085-1d6d-42aa-bd36-547aedf2f099' class='xr-index-data-in' type='checkbox'/><label for='index-7fcff085-1d6d-42aa-bd36-547aedf2f099' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([386], dtype=&#x27;int64&#x27;, name=&#x27;gridY&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>gridX</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d17e478d-61b2-457c-b91d-eeaf0f72fb5f' class='xr-index-data-in' type='checkbox'/><label for='index-d17e478d-61b2-457c-b91d-eeaf0f72fb5f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([210, 211, 212, 213, 214, 215, 216, 217, 218, 219,\n",
       "       ...\n",
       "       310, 311, 312, 313, 314, 315, 316, 317, 318, 319],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;gridX&#x27;, length=110))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2a129029-fdbe-4a20-879d-5a2090d99051' class='xr-index-data-in' type='checkbox'/><label for='index-2a129029-fdbe-4a20-879d-5a2090d99051' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2015-06-15 00:00:00&#x27;, &#x27;2015-07-15 12:00:00&#x27;,\n",
       "               &#x27;2015-08-15 12:00:00&#x27;, &#x27;2015-09-15 00:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-892457cb-c259-47d5-9a5e-2928bfe8153a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-892457cb-c259-47d5-9a5e-2928bfe8153a' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>SalishSeaCast_hour_vvelocity_20150601_20150901</dd><dt><span>description :</span></dt><dd>Hourly v velocity extracted from SalishSeaCast v202111</dd><dt><span>history :</span></dt><dd>2024-07-03 14:15 -07:00: Generated by `reshapr extract /data/sallen/results/Reshapr/mean_v_velocity_PR.yaml`</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:   (time: 4, depth: 40, gridY: 1, gridX: 110)\n",
       "Coordinates:\n",
       "  * depth     (depth) float32 0.5 1.5 2.5 3.5 4.5 ... 360.7 387.6 414.5 441.5\n",
       "  * gridY     (gridY) int64 386\n",
       "  * gridX     (gridX) int64 210 211 212 213 214 215 ... 314 315 316 317 318 319\n",
       "  * time      (time) datetime64[ns] 2015-06-15 ... 2015-09-15\n",
       "Data variables:\n",
       "    vomecrty  (time, depth, gridY, gridX) float32 ...\n",
       "Attributes:\n",
       "    name:         SalishSeaCast_hour_vvelocity_20150601_20150901\n",
       "    description:  Hourly v velocity extracted from SalishSeaCast v202111\n",
       "    history:      2024-07-03 14:15 -07:00: Generated by `reshapr extract /dat...\n",
       "    Conventions:  CF-1.6"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3f5b7692-52b6-4bdc-9bf7-0a1ac21b208d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 2, figsize=(10,10))\n",
    "data.vomecrty[0].plot(ax=axs[0,0], yincrease=False);\n",
    "data.vomecrty[1].plot(ax=axs[0,1], yincrease=False);\n",
    "data.vomecrty[2].plot(ax=axs[1,0], yincrease=False);\n",
    "data.vomecrty[0:3].mean(axis=0).plot(ax=axs[1,1], yincrease=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ed0c3052-d864-46ad-bfd7-a6766585fcd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "mesh = xr.open_dataset('/home/sallen/MEOPAR/grid/mesh_mask202108.nc')\n",
    "vmask = 1 - mesh.vmask[0]\n",
    "e3v = mesh.e3v_0[0]\n",
    "e1v = mesh.e1v[0]\n",
    "mesh.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "31231229-b1f7-4961-9aae-4353fdadac03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos_values = np.array(data.vomecrty[0:3, :, 0].mean(axis=0))\n",
    "neg_values = np.array(data.vomecrty[0:3, :, 0].mean(axis=0))\n",
    "depth = np.array(data.depth)\n",
    "pos_values[pos_values < 0] = 0\n",
    "neg_values[neg_values > 0] = 0\n",
    "xx, yy = np.meshgrid(depth, range(110))\n",
    "\n",
    "fig, axs = plt.subplots(1, 2, figsize=(10, 4))\n",
    "colours = axs[0].pcolormesh(range(110), depth, pos_values, cmap='bwr', vmax=0.4, vmin=-0.4, shading='nearest')\n",
    "axs[0].invert_yaxis()\n",
    "fig.colorbar(colours, ax=axs[0])\n",
    "colours = axs[1].pcolormesh(range(110), depth, neg_values, cmap='bwr', vmax=0.4, vmin=-0.4, shading='nearest')\n",
    "axs[1].invert_yaxis()\n",
    "fig.colorbar(colours, ax=axs[1]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "6ccaccec-4026-4b2c-b4b7-6ed5fddae6d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray ()>\n",
      "array(80727.6382259)\n"
     ]
    }
   ],
   "source": [
    "print ((pos_values * e3v[:, igrid, j1:j2] * e1v[igrid, j1:j2]).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "9ef66c22-0917-4aaf-a604-fff860dfdbd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray ()>\n",
      "array(-90961.69763315)\n"
     ]
    }
   ],
   "source": [
    "print ((neg_values * e3v[:, igrid, j1:j2] * e1v[igrid, j1:j2]).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "310523fa-8078-42f4-8e62-c242c34b81af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:   (time: 48, depth: 40, gridY: 1, gridX: 110)\n",
       "Coordinates:\n",
       "  * depth     (depth) float32 0.5 1.5 2.5 3.5 4.5 ... 360.7 387.6 414.5 441.5\n",
       "  * gridY     (gridY) int64 386\n",
       "  * gridX     (gridX) int64 210 211 212 213 214 215 ... 314 315 316 317 318 319\n",
       "  * time      (time) datetime64[ns] 2015-01-15T12:00:00 ... 2018-12-15T12:00:00\n",
       "Data variables:\n",
       "    vomecrty  (time, depth, gridY, gridX) float32 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
       "Attributes:\n",
       "    name:         SalishSeaCast_hour_vvelocity_20150101_20151231\n",
       "    description:  Hourly v velocity extracted from SalishSeaCast v202111\n",
       "    history:      2024-07-03 16:05 -07:00: Generated by `reshapr extract /dat...\n",
       "    Conventions:  CF-1.6</pre><div class='xr-wrap' style='display:none'><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-0addcc8d-80dd-4f84-b4aa-6993664dd68a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0addcc8d-80dd-4f84-b4aa-6993664dd68a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 48</li><li><span class='xr-has-index'>depth</span>: 40</li><li><span class='xr-has-index'>gridY</span>: 1</li><li><span class='xr-has-index'>gridX</span>: 110</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2debc0a9-676b-4a8b-bf40-d771b2d64349' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2debc0a9-676b-4a8b-bf40-d771b2d64349' class='xr-section-summary' >Coordinates: <span>(4)</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 class='xr-has-index'>depth</span></div><div class='xr-var-dims'>(depth)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.5 1.5 2.5 ... 387.6 414.5 441.5</div><input id='attrs-75ca998a-e4a0-4716-8d43-1b0593b620ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75ca998a-e4a0-4716-8d43-1b0593b620ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74f4b623-174b-45f5-97c1-9769eae38cf4' class='xr-var-data-in' type='checkbox'><label for='data-74f4b623-174b-45f5-97c1-9769eae38cf4' 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>standard_name :</span></dt><dd>sea_floor_depth</dd><dt><span>long_name :</span></dt><dd>Sea Floor Depth</dd><dt><span>units :</span></dt><dd>metres</dd><dt><span>positive :</span></dt><dd>down</dd></dl></div><div class='xr-var-data'><pre>array([  0.5     ,   1.500003,   2.500011,   3.500031,   4.500071,   5.500151,\n",
       "         6.50031 ,   7.500623,   8.501236,   9.502433,  10.504766,  11.509312,\n",
       "        12.518167,  13.535412,  14.568982,  15.634288,  16.761173,  18.007135,\n",
       "        19.481785,  21.389978,  24.100256,  28.229916,  34.685757,  44.517723,\n",
       "        58.484333,  76.58559 ,  98.06296 , 121.866516, 147.08946 , 173.11449 ,\n",
       "       199.57304 , 226.2603  , 253.06664 , 279.93454 , 306.8342  , 333.75018 ,\n",
       "       360.67453 , 387.6032  , 414.5341  , 441.4661  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>gridY</span></div><div class='xr-var-dims'>(gridY)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>386</div><input id='attrs-dd1ffe31-6368-4d20-8612-4b05c256643e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd1ffe31-6368-4d20-8612-4b05c256643e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24ec727f-dfaa-46fb-93a4-91ec7a830ecb' class='xr-var-data-in' type='checkbox'><label for='data-24ec727f-dfaa-46fb-93a4-91ec7a830ecb' 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>long_name :</span></dt><dd>Grid Y</dd><dt><span>standard_name :</span></dt><dd>y</dd><dt><span>comment :</span></dt><dd>gridY values are grid indices in the model y-direction</dd><dt><span>units :</span></dt><dd>count</dd></dl></div><div class='xr-var-data'><pre>array([386])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>gridX</span></div><div class='xr-var-dims'>(gridX)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>210 211 212 213 ... 316 317 318 319</div><input id='attrs-4876432c-da6f-4f72-b153-8a755b905038' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4876432c-da6f-4f72-b153-8a755b905038' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5cafa4a-093a-4749-a7c8-a5d801bf90d4' class='xr-var-data-in' type='checkbox'><label for='data-a5cafa4a-093a-4749-a7c8-a5d801bf90d4' 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>long_name :</span></dt><dd>Grid X</dd><dt><span>standard_name :</span></dt><dd>x</dd><dt><span>comment :</span></dt><dd>gridX values are grid indices in the model x-direction</dd><dt><span>units :</span></dt><dd>count</dd></dl></div><div class='xr-var-data'><pre>array([210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223,\n",
       "       224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237,\n",
       "       238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251,\n",
       "       252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265,\n",
       "       266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279,\n",
       "       280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293,\n",
       "       294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307,\n",
       "       308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2015-01-15T12:00:00 ... 2018-12-...</div><input id='attrs-883fd03c-7ff8-4e61-94ff-daf924f12edb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-883fd03c-7ff8-4e61-94ff-daf924f12edb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-abea8401-4fda-4d7c-a7bb-6c5df924ec4d' class='xr-var-data-in' type='checkbox'><label for='data-abea8401-4fda-4d7c-a7bb-6c5df924ec4d' 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>long_name :</span></dt><dd>Time Axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>comment :</span></dt><dd>time values are UTC at the centre of the intervals over which the calculated model results are averaged; e.g. the field average values for January 2022 have a time value of 2022-01-15 12:00:00Z, and those for April 2022 have a time value of 2022-04-15 00:00:00Z</dd><dt><span>time_origin :</span></dt><dd>2007-01-01 12:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2015-01-15T12:00:00.000000000&#x27;, &#x27;2015-02-14T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-03-15T12:00:00.000000000&#x27;, &#x27;2015-04-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-05-15T12:00:00.000000000&#x27;, &#x27;2015-06-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-07-15T12:00:00.000000000&#x27;, &#x27;2015-08-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;2015-09-15T00:00:00.000000000&#x27;, &#x27;2015-10-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;2015-11-15T00:00:00.000000000&#x27;, &#x27;2015-12-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-01-15T12:00:00.000000000&#x27;, &#x27;2016-02-14T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-03-15T12:00:00.000000000&#x27;, &#x27;2016-04-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-05-15T12:00:00.000000000&#x27;, &#x27;2016-06-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-07-15T12:00:00.000000000&#x27;, &#x27;2016-08-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-09-15T00:00:00.000000000&#x27;, &#x27;2016-10-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-11-15T00:00:00.000000000&#x27;, &#x27;2016-12-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;2017-01-15T12:00:00.000000000&#x27;, &#x27;2017-02-14T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-03-15T12:00:00.000000000&#x27;, &#x27;2017-04-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-05-15T12:00:00.000000000&#x27;, &#x27;2017-06-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-07-15T12:00:00.000000000&#x27;, &#x27;2017-08-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;2017-09-15T00:00:00.000000000&#x27;, &#x27;2017-10-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;2017-11-15T00:00:00.000000000&#x27;, &#x27;2017-12-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;2018-01-15T12:00:00.000000000&#x27;, &#x27;2018-02-14T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-03-15T12:00:00.000000000&#x27;, &#x27;2018-04-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-05-15T12:00:00.000000000&#x27;, &#x27;2018-06-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-07-15T12:00:00.000000000&#x27;, &#x27;2018-08-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;2018-09-15T00:00:00.000000000&#x27;, &#x27;2018-10-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;2018-11-15T00:00:00.000000000&#x27;, &#x27;2018-12-15T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-73208e72-9a8a-4dd8-8699-208318e605c4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-73208e72-9a8a-4dd8-8699-208318e605c4' class='xr-section-summary' >Data variables: <span>(1)</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>vomecrty</span></div><div class='xr-var-dims'>(time, depth, gridY, gridX)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-7b19f12a-c075-4a56-9ad8-e90c96237c72' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b19f12a-c075-4a56-9ad8-e90c96237c72' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85945ea8-974b-4a58-a27b-914d1457a425' class='xr-var-data-in' type='checkbox'><label for='data-85945ea8-974b-4a58-a27b-914d1457a425' 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>standard_name :</span></dt><dd>sea_water_y_velocity</dd><dt><span>long_name :</span></dt><dd>Ocean Current Along y-Axis</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><pre>array([[[[0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.]]],\n",
       "\n",
       "\n",
       "       [[[0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.]],\n",
       "...\n",
       "        [[0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.]]],\n",
       "\n",
       "\n",
       "       [[[0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.]]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e2c9a8f3-f441-4e69-9a5d-5e691783641f' class='xr-section-summary-in' type='checkbox'  ><label for='section-e2c9a8f3-f441-4e69-9a5d-5e691783641f' class='xr-section-summary' >Indexes: <span>(4)</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-index-name'><div>depth</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-315604aa-1cc3-4805-a729-e6679d49645b' class='xr-index-data-in' type='checkbox'/><label for='index-315604aa-1cc3-4805-a729-e6679d49645b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.5000002980232239, 1.5000030994415283,  2.500011444091797,\n",
       "        3.500030517578125,  4.500070571899414,  5.500150680541992,\n",
       "         6.50031042098999, 7.5006232261657715,  8.501235961914062,\n",
       "        9.502432823181152, 10.504765510559082,  11.50931167602539,\n",
       "       12.518166542053223, 13.535411834716797, 14.568982124328613,\n",
       "        15.63428783416748, 16.761173248291016,  18.00713539123535,\n",
       "        19.48178482055664, 21.389978408813477, 24.100255966186523,\n",
       "       28.229915618896484,  34.68575668334961, 44.517723083496094,\n",
       "        58.48433303833008,  76.58558654785156,  98.06295776367188,\n",
       "       121.86651611328125, 147.08946228027344, 173.11448669433594,\n",
       "        199.5730438232422,  226.2602996826172, 253.06663513183594,\n",
       "        279.9345397949219,  306.8341979980469, 333.75018310546875,\n",
       "        360.6745300292969, 387.60321044921875,  414.5340881347656,\n",
       "        441.4660949707031],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;depth&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>gridY</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e76ade61-3a67-4dc3-a5c3-571b3c940d22' class='xr-index-data-in' type='checkbox'/><label for='index-e76ade61-3a67-4dc3-a5c3-571b3c940d22' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([386], dtype=&#x27;int64&#x27;, name=&#x27;gridY&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>gridX</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6a4e1dae-b976-493a-b26a-c5e236990beb' class='xr-index-data-in' type='checkbox'/><label for='index-6a4e1dae-b976-493a-b26a-c5e236990beb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([210, 211, 212, 213, 214, 215, 216, 217, 218, 219,\n",
       "       ...\n",
       "       310, 311, 312, 313, 314, 315, 316, 317, 318, 319],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;gridX&#x27;, length=110))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9c35d938-2316-44bd-a813-0c3636c56c0b' class='xr-index-data-in' type='checkbox'/><label for='index-9c35d938-2316-44bd-a813-0c3636c56c0b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2015-01-15 12:00:00&#x27;, &#x27;2015-02-14 00:00:00&#x27;,\n",
       "               &#x27;2015-03-15 12:00:00&#x27;, &#x27;2015-04-15 00:00:00&#x27;,\n",
       "               &#x27;2015-05-15 12:00:00&#x27;, &#x27;2015-06-15 00:00:00&#x27;,\n",
       "               &#x27;2015-07-15 12:00:00&#x27;, &#x27;2015-08-15 12:00:00&#x27;,\n",
       "               &#x27;2015-09-15 00:00:00&#x27;, &#x27;2015-10-15 12:00:00&#x27;,\n",
       "               &#x27;2015-11-15 00:00:00&#x27;, &#x27;2015-12-15 12:00:00&#x27;,\n",
       "               &#x27;2016-01-15 12:00:00&#x27;, &#x27;2016-02-14 12:00:00&#x27;,\n",
       "               &#x27;2016-03-15 12:00:00&#x27;, &#x27;2016-04-15 00:00:00&#x27;,\n",
       "               &#x27;2016-05-15 12:00:00&#x27;, &#x27;2016-06-15 00:00:00&#x27;,\n",
       "               &#x27;2016-07-15 12:00:00&#x27;, &#x27;2016-08-15 12:00:00&#x27;,\n",
       "               &#x27;2016-09-15 00:00:00&#x27;, &#x27;2016-10-15 12:00:00&#x27;,\n",
       "               &#x27;2016-11-15 00:00:00&#x27;, &#x27;2016-12-15 12:00:00&#x27;,\n",
       "               &#x27;2017-01-15 12:00:00&#x27;, &#x27;2017-02-14 00:00:00&#x27;,\n",
       "               &#x27;2017-03-15 12:00:00&#x27;, &#x27;2017-04-15 00:00:00&#x27;,\n",
       "               &#x27;2017-05-15 12:00:00&#x27;, &#x27;2017-06-15 00:00:00&#x27;,\n",
       "               &#x27;2017-07-15 12:00:00&#x27;, &#x27;2017-08-15 12:00:00&#x27;,\n",
       "               &#x27;2017-09-15 00:00:00&#x27;, &#x27;2017-10-15 12:00:00&#x27;,\n",
       "               &#x27;2017-11-15 00:00:00&#x27;, &#x27;2017-12-15 12:00:00&#x27;,\n",
       "               &#x27;2018-01-15 12:00:00&#x27;, &#x27;2018-02-14 00:00:00&#x27;,\n",
       "               &#x27;2018-03-15 12:00:00&#x27;, &#x27;2018-04-15 00:00:00&#x27;,\n",
       "               &#x27;2018-05-15 12:00:00&#x27;, &#x27;2018-06-15 00:00:00&#x27;,\n",
       "               &#x27;2018-07-15 12:00:00&#x27;, &#x27;2018-08-15 12:00:00&#x27;,\n",
       "               &#x27;2018-09-15 00:00:00&#x27;, &#x27;2018-10-15 12:00:00&#x27;,\n",
       "               &#x27;2018-11-15 00:00:00&#x27;, &#x27;2018-12-15 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3c8cf81c-a9b1-4ee4-927e-bb478052e0b6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3c8cf81c-a9b1-4ee4-927e-bb478052e0b6' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>SalishSeaCast_hour_vvelocity_20150101_20151231</dd><dt><span>description :</span></dt><dd>Hourly v velocity extracted from SalishSeaCast v202111</dd><dt><span>history :</span></dt><dd>2024-07-03 16:05 -07:00: Generated by `reshapr extract /data/sallen/results/Reshapr/mean_v_velocity_PR.yaml`</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:   (time: 48, depth: 40, gridY: 1, gridX: 110)\n",
       "Coordinates:\n",
       "  * depth     (depth) float32 0.5 1.5 2.5 3.5 4.5 ... 360.7 387.6 414.5 441.5\n",
       "  * gridY     (gridY) int64 386\n",
       "  * gridX     (gridX) int64 210 211 212 213 214 215 ... 314 315 316 317 318 319\n",
       "  * time      (time) datetime64[ns] 2015-01-15T12:00:00 ... 2018-12-15T12:00:00\n",
       "Data variables:\n",
       "    vomecrty  (time, depth, gridY, gridX) float32 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
       "Attributes:\n",
       "    name:         SalishSeaCast_hour_vvelocity_20150101_20151231\n",
       "    description:  Hourly v velocity extracted from SalishSeaCast v202111\n",
       "    history:      2024-07-03 16:05 -07:00: Generated by `reshapr extract /dat...\n",
       "    Conventions:  CF-1.6"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data_2015 = xr.open_dataset('/data/sallen/results/Reshapr/output/SalishSeaCast_hour_vvelocity_20150101_20151231.nc')\n",
    "\n",
    "data_2016 = xr.open_dataset('/data/sallen/results/Reshapr/output/SalishSeaCast_hour_vvelocity_20160101_20161231.nc')\n",
    "\n",
    "data_2017 = xr.open_dataset('/data/sallen/results/Reshapr/output/SalishSeaCast_hour_vvelocity_20170101_20171231.nc')\n",
    "\n",
    "data_2018 = xr.open_dataset('/data/sallen/results/Reshapr/output/SalishSeaCast_hour_vvelocity_20180101_20181231.nc')\n",
    "\n",
    "data = xr.concat([data_2015, data_2016, data_2017, data_2018], dim='time')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0fac3c2f-a109-4973-ba6b-334d3cf2eb05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_2015.vomecrty.mean(axis=0).plot(yincrease=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8741fb6a-ea78-477f-baee-a5b1227fc6e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def process(data):\n",
    "    pos_values = np.array(data.vomecrty[:, :, 0].mean(axis=0))\n",
    "    neg_values = np.array(data.vomecrty[:, :, 0].mean(axis=0))\n",
    "    depth = np.array(data.depth)\n",
    "    pos_values[pos_values < 0] = 0\n",
    "    neg_values[neg_values > 0] = 0\n",
    "\n",
    "    fig, axs = plt.subplots(1, 2, figsize=(10, 4))\n",
    "    colours = axs[0].pcolormesh(range(110), depth, pos_values, cmap='bwr', vmax=0.4, vmin=-0.4, shading='nearest')\n",
    "    axs[0].invert_yaxis()\n",
    "    fig.colorbar(colours, ax=axs[0])\n",
    "    colours = axs[1].pcolormesh(range(110), depth, neg_values, cmap='bwr', vmax=0.4, vmin=-0.4, shading='nearest')\n",
    "    axs[1].invert_yaxis()\n",
    "    fig.colorbar(colours, ax=axs[1]);\n",
    "\n",
    "    print ('Positive Flux', (pos_values * e3v[:, igrid, j1:j2] * e1v[igrid, j1:j2]).sum())\n",
    "    print ('Negative Flux', (neg_values * e3v[:, igrid, j1:j2] * e1v[igrid, j1:j2]).sum())\n",
    "    return pos_values, neg_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "587e797b-f2b8-41a5-8ef6-cc0884974f4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positive Flux <xarray.DataArray ()>\n",
      "array(62208.90137442)\n",
      "Negative Flux <xarray.DataArray ()>\n",
      "array(-70296.12312508)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos_values, neg_values = process(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "d7ae4917-6d70-44e8-8294-b3fea7710c7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_lateral_avg(data, cut):\n",
    "    pos_values = np.array(data.vomecrty[:, :, 0, cut:].mean(axis=0))\n",
    "    pos_values = (pos_values * e1v[igrid, j1+cut:j2].values \n",
    "                  * e3v[:, igrid, j1+cut:j2].values).sum(axis=1)\n",
    "    print (pos_values.shape)\n",
    "    neg_values = np.array(data.vomecrty[:, :, 0, cut:].mean(axis=0))\n",
    "    neg_values = (neg_values * e1v[igrid, j1+cut:j2].values \n",
    "                  * e3v[:, igrid, j1+cut:j2].values).sum(axis=1)\n",
    "    depth = np.array(data.depth)\n",
    "    pos_values[pos_values < 0] = 0\n",
    "    neg_values[neg_values > 0] = 0\n",
    "\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(5, 4))\n",
    "    colours = ax.plot(depth, pos_values, c='tab:blue')\n",
    "    colours = ax.plot(depth, neg_values, c='tab:red')\n",
    "\n",
    "    print ('Positive Flux', pos_values.sum())\n",
    "    print ('Negative Flux', neg_values.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "9e39ad40-8dfd-4d66-8cd3-2030adf6a289",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(40,)\n",
      "Positive Flux 36649.558997214444\n",
      "Negative Flux -41839.67339921515\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "process_lateral_avg(data, cut=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "8e85033b-6206-4ada-9983-0405b22ef461",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(40,)\n",
      "Positive Flux 37717.51127859701\n",
      "Negative Flux -45804.73302926108\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "process_lateral_avg(data, cut=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a5fbb99f-8a70-4e3a-88a2-b8127e54598e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:   (time: 24, depth: 40, gridY: 1, gridX: 110)\n",
       "Coordinates:\n",
       "  * depth     (depth) float32 0.5 1.5 2.5 3.5 4.5 ... 360.7 387.6 414.5 441.5\n",
       "  * gridY     (gridY) int64 386\n",
       "  * gridX     (gridX) int64 210 211 212 213 214 215 ... 314 315 316 317 318 319\n",
       "  * time      (time) datetime64[ns] 2015-01-15T12:00:00 ... 2015-12-15T12:00:00\n",
       "Data variables:\n",
       "    vomecrty  (time, depth, gridY, gridX) float32 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
       "Attributes:\n",
       "    name:         SalishSeaCast_hour_vvelocity_20150101_20151231\n",
       "    description:  Hourly v velocity extracted from SalishSeaCast v202111\n",
       "    history:      2024-07-03 16:05 -07:00: Generated by `reshapr extract /dat...\n",
       "    Conventions:  CF-1.6</pre><div class='xr-wrap' style='display:none'><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-b6b89f28-b0df-41c7-95fd-6d63a08bc692' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b6b89f28-b0df-41c7-95fd-6d63a08bc692' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 24</li><li><span class='xr-has-index'>depth</span>: 40</li><li><span class='xr-has-index'>gridY</span>: 1</li><li><span class='xr-has-index'>gridX</span>: 110</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-eedd0e6f-e61a-435e-bb22-1c1cca21411c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eedd0e6f-e61a-435e-bb22-1c1cca21411c' class='xr-section-summary' >Coordinates: <span>(4)</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 class='xr-has-index'>depth</span></div><div class='xr-var-dims'>(depth)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.5 1.5 2.5 ... 387.6 414.5 441.5</div><input id='attrs-3128de31-3991-4b4a-9cdc-7ce53ab2c5ca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3128de31-3991-4b4a-9cdc-7ce53ab2c5ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47126b7b-fb9b-44fe-94c2-773f8b0f6432' class='xr-var-data-in' type='checkbox'><label for='data-47126b7b-fb9b-44fe-94c2-773f8b0f6432' 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>standard_name :</span></dt><dd>sea_floor_depth</dd><dt><span>long_name :</span></dt><dd>Sea Floor Depth</dd><dt><span>units :</span></dt><dd>metres</dd><dt><span>positive :</span></dt><dd>down</dd></dl></div><div class='xr-var-data'><pre>array([  0.5     ,   1.500003,   2.500011,   3.500031,   4.500071,   5.500151,\n",
       "         6.50031 ,   7.500623,   8.501236,   9.502433,  10.504766,  11.509312,\n",
       "        12.518167,  13.535412,  14.568982,  15.634288,  16.761173,  18.007135,\n",
       "        19.481785,  21.389978,  24.100256,  28.229916,  34.685757,  44.517723,\n",
       "        58.484333,  76.58559 ,  98.06296 , 121.866516, 147.08946 , 173.11449 ,\n",
       "       199.57304 , 226.2603  , 253.06664 , 279.93454 , 306.8342  , 333.75018 ,\n",
       "       360.67453 , 387.6032  , 414.5341  , 441.4661  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>gridY</span></div><div class='xr-var-dims'>(gridY)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>386</div><input id='attrs-b6c6f669-0b07-4d54-8b2c-904b029ea744' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b6c6f669-0b07-4d54-8b2c-904b029ea744' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc7129bb-9196-4f50-b2e1-9e32f655a4e7' class='xr-var-data-in' type='checkbox'><label for='data-cc7129bb-9196-4f50-b2e1-9e32f655a4e7' 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>long_name :</span></dt><dd>Grid Y</dd><dt><span>standard_name :</span></dt><dd>y</dd><dt><span>comment :</span></dt><dd>gridY values are grid indices in the model y-direction</dd><dt><span>units :</span></dt><dd>count</dd></dl></div><div class='xr-var-data'><pre>array([386])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>gridX</span></div><div class='xr-var-dims'>(gridX)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>210 211 212 213 ... 316 317 318 319</div><input id='attrs-41b67a9c-f05a-4d62-95ae-d2badcb434a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41b67a9c-f05a-4d62-95ae-d2badcb434a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b538d8c-c42a-440e-b08d-6e1a5cd6c814' class='xr-var-data-in' type='checkbox'><label for='data-6b538d8c-c42a-440e-b08d-6e1a5cd6c814' 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>long_name :</span></dt><dd>Grid X</dd><dt><span>standard_name :</span></dt><dd>x</dd><dt><span>comment :</span></dt><dd>gridX values are grid indices in the model x-direction</dd><dt><span>units :</span></dt><dd>count</dd></dl></div><div class='xr-var-data'><pre>array([210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223,\n",
       "       224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237,\n",
       "       238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251,\n",
       "       252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265,\n",
       "       266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279,\n",
       "       280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293,\n",
       "       294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307,\n",
       "       308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2015-01-15T12:00:00 ... 2015-12-...</div><input id='attrs-4fedc700-18c5-4558-8d3e-d654430b9168' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4fedc700-18c5-4558-8d3e-d654430b9168' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-caa2e196-cb3f-4201-9e5a-4df6170287ea' class='xr-var-data-in' type='checkbox'><label for='data-caa2e196-cb3f-4201-9e5a-4df6170287ea' 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>long_name :</span></dt><dd>Time Axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>comment :</span></dt><dd>time values are UTC at the centre of the intervals over which the calculated model results are averaged; e.g. the field average values for January 2022 have a time value of 2022-01-15 12:00:00Z, and those for April 2022 have a time value of 2022-04-15 00:00:00Z</dd><dt><span>time_origin :</span></dt><dd>2007-01-01 12:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2015-01-15T12:00:00.000000000&#x27;, &#x27;2015-02-14T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-03-15T12:00:00.000000000&#x27;, &#x27;2015-04-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-05-15T12:00:00.000000000&#x27;, &#x27;2015-06-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-07-15T12:00:00.000000000&#x27;, &#x27;2015-08-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;2015-09-15T00:00:00.000000000&#x27;, &#x27;2015-10-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;2015-11-15T00:00:00.000000000&#x27;, &#x27;2015-12-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;2015-01-15T12:00:00.000000000&#x27;, &#x27;2015-02-14T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-03-15T12:00:00.000000000&#x27;, &#x27;2015-04-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-05-15T12:00:00.000000000&#x27;, &#x27;2015-06-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-07-15T12:00:00.000000000&#x27;, &#x27;2015-08-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;2015-09-15T00:00:00.000000000&#x27;, &#x27;2015-10-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;2015-11-15T00:00:00.000000000&#x27;, &#x27;2015-12-15T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3f3848fc-0318-4214-8e4c-62eaa7be2f0b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3f3848fc-0318-4214-8e4c-62eaa7be2f0b' class='xr-section-summary' >Data variables: <span>(1)</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>vomecrty</span></div><div class='xr-var-dims'>(time, depth, gridY, gridX)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-93f5c305-3965-49a4-924b-df842ef77191' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93f5c305-3965-49a4-924b-df842ef77191' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e02911c8-0f0a-4019-bd08-b490484da4c0' class='xr-var-data-in' type='checkbox'><label for='data-e02911c8-0f0a-4019-bd08-b490484da4c0' 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>standard_name :</span></dt><dd>sea_water_y_velocity</dd><dt><span>long_name :</span></dt><dd>Ocean Current Along y-Axis</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><pre>array([[[[0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.]]],\n",
       "\n",
       "\n",
       "       [[[0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.]],\n",
       "...\n",
       "        [[0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.]]],\n",
       "\n",
       "\n",
       "       [[[0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.]]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4d06e283-79dc-43bf-9d31-dd6e656ce015' class='xr-section-summary-in' type='checkbox'  ><label for='section-4d06e283-79dc-43bf-9d31-dd6e656ce015' class='xr-section-summary' >Indexes: <span>(4)</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-index-name'><div>depth</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e3ae50dd-55ff-4065-8420-6e69efccbaa2' class='xr-index-data-in' type='checkbox'/><label for='index-e3ae50dd-55ff-4065-8420-6e69efccbaa2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.5000002980232239, 1.5000030994415283,  2.500011444091797,\n",
       "        3.500030517578125,  4.500070571899414,  5.500150680541992,\n",
       "         6.50031042098999, 7.5006232261657715,  8.501235961914062,\n",
       "        9.502432823181152, 10.504765510559082,  11.50931167602539,\n",
       "       12.518166542053223, 13.535411834716797, 14.568982124328613,\n",
       "        15.63428783416748, 16.761173248291016,  18.00713539123535,\n",
       "        19.48178482055664, 21.389978408813477, 24.100255966186523,\n",
       "       28.229915618896484,  34.68575668334961, 44.517723083496094,\n",
       "        58.48433303833008,  76.58558654785156,  98.06295776367188,\n",
       "       121.86651611328125, 147.08946228027344, 173.11448669433594,\n",
       "        199.5730438232422,  226.2602996826172, 253.06663513183594,\n",
       "        279.9345397949219,  306.8341979980469, 333.75018310546875,\n",
       "        360.6745300292969, 387.60321044921875,  414.5340881347656,\n",
       "        441.4660949707031],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;depth&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>gridY</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-41bcb4c8-6b8b-43ca-9a7e-f2b8cba6b5f0' class='xr-index-data-in' type='checkbox'/><label for='index-41bcb4c8-6b8b-43ca-9a7e-f2b8cba6b5f0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([386], dtype=&#x27;int64&#x27;, name=&#x27;gridY&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>gridX</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a3e8e03c-4840-4703-9d8f-5d0d9aa13978' class='xr-index-data-in' type='checkbox'/><label for='index-a3e8e03c-4840-4703-9d8f-5d0d9aa13978' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([210, 211, 212, 213, 214, 215, 216, 217, 218, 219,\n",
       "       ...\n",
       "       310, 311, 312, 313, 314, 315, 316, 317, 318, 319],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;gridX&#x27;, length=110))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1a9b498a-d4ba-415c-b86e-ed26e23999cb' class='xr-index-data-in' type='checkbox'/><label for='index-1a9b498a-d4ba-415c-b86e-ed26e23999cb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2015-01-15 12:00:00&#x27;, &#x27;2015-02-14 00:00:00&#x27;,\n",
       "               &#x27;2015-03-15 12:00:00&#x27;, &#x27;2015-04-15 00:00:00&#x27;,\n",
       "               &#x27;2015-05-15 12:00:00&#x27;, &#x27;2015-06-15 00:00:00&#x27;,\n",
       "               &#x27;2015-07-15 12:00:00&#x27;, &#x27;2015-08-15 12:00:00&#x27;,\n",
       "               &#x27;2015-09-15 00:00:00&#x27;, &#x27;2015-10-15 12:00:00&#x27;,\n",
       "               &#x27;2015-11-15 00:00:00&#x27;, &#x27;2015-12-15 12:00:00&#x27;,\n",
       "               &#x27;2015-01-15 12:00:00&#x27;, &#x27;2015-02-14 00:00:00&#x27;,\n",
       "               &#x27;2015-03-15 12:00:00&#x27;, &#x27;2015-04-15 00:00:00&#x27;,\n",
       "               &#x27;2015-05-15 12:00:00&#x27;, &#x27;2015-06-15 00:00:00&#x27;,\n",
       "               &#x27;2015-07-15 12:00:00&#x27;, &#x27;2015-08-15 12:00:00&#x27;,\n",
       "               &#x27;2015-09-15 00:00:00&#x27;, &#x27;2015-10-15 12:00:00&#x27;,\n",
       "               &#x27;2015-11-15 00:00:00&#x27;, &#x27;2015-12-15 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-40de6db3-8bb2-408a-9899-a8c99ca2f1c0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-40de6db3-8bb2-408a-9899-a8c99ca2f1c0' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>SalishSeaCast_hour_vvelocity_20150101_20151231</dd><dt><span>description :</span></dt><dd>Hourly v velocity extracted from SalishSeaCast v202111</dd><dt><span>history :</span></dt><dd>2024-07-03 16:05 -07:00: Generated by `reshapr extract /data/sallen/results/Reshapr/mean_v_velocity_PR.yaml`</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:   (time: 24, depth: 40, gridY: 1, gridX: 110)\n",
       "Coordinates:\n",
       "  * depth     (depth) float32 0.5 1.5 2.5 3.5 4.5 ... 360.7 387.6 414.5 441.5\n",
       "  * gridY     (gridY) int64 386\n",
       "  * gridX     (gridX) int64 210 211 212 213 214 215 ... 314 315 316 317 318 319\n",
       "  * time      (time) datetime64[ns] 2015-01-15T12:00:00 ... 2015-12-15T12:00:00\n",
       "Data variables:\n",
       "    vomecrty  (time, depth, gridY, gridX) float32 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
       "Attributes:\n",
       "    name:         SalishSeaCast_hour_vvelocity_20150101_20151231\n",
       "    description:  Hourly v velocity extracted from SalishSeaCast v202111\n",
       "    history:      2024-07-03 16:05 -07:00: Generated by `reshapr extract /dat...\n",
       "    Conventions:  CF-1.6"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "972ca342-8479-4be4-af59-efda2464efc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 398)"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mymesh = xr.open_dataset('/home/sallen/MEOPAR/grid/mesh_mask202108.nc')\n",
    "\n",
    "\n",
    "vmask = mymesh.vmask[0, :, figrid]\n",
    "u_lons = mymesh.glamv[0, figrid]    # note switch to v with the switch from t to f\n",
    "#w_depths = mymesh.gdepw_1d\n",
    "w_depths = mymesh.gdepw_0[0, :, igrid]\n",
    "w_depths.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "4bd00689-425a-4195-a70e-269ba4a4e021",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1372334/3745285282.py:6: UserWarning: The input coordinates to pcolormesh are interpreted as cell centers, but are not monotonically increasing or decreasing. This may lead to incorrectly calculated cell edges, in which case, please supply explicit cell edges to pcolormesh.\n",
      "  colours = ax.pcolormesh(np.array(u_lons[j1:j2]), np.array(w_depths[:, j1:j2]), plotfield, cmap=cm_balance,\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(7, 4))\n",
    "#data.vomecrty.mean(axis=0).plot(ax=ax, yincrease=False, cmap=cm.balance,);\n",
    "\n",
    "plotfield = np.ma.array(data.vomecrty.mean(axis=0)[:,0], mask= 1-mymesh.vmask[0, :, igrid, j1:j2])\n",
    "\n",
    "colours = ax.pcolormesh(np.array(u_lons[j1:j2]), np.array(w_depths[:, j1:j2]), plotfield, cmap=cm_balance, \n",
    "                            vmax = 0.2, vmin = -0.2)\n",
    "\n",
    "cb = fig.colorbar(colours, ax=ax)\n",
    "cb.set_label('Velocity [m s$^{-1}$]')\n",
    "\n",
    "#pc = draw_patches(fmask, j1, j2, np.array(u_lons), np.array(w_depths))\n",
    "#ax.add_collection(pc)\n",
    "\n",
    "ax.set_ylim(250, 0)\n",
    "ax.set_ylabel('Depth [m]')\n",
    "ax.set_xlabel('Longitude [$^o$E]')\n",
    "ax.set_title(\"Mean velocity across Point Roberts Transect\");\n",
    "\n",
    "plt.savefig('meanvelocity.pdf')\n",
    "plt.savefig('meanvelocity.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e9d82b6-05b7-40d4-ae5a-8a56b18e6252",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "3.11 mocsy",
   "language": "python",
   "name": "py311_mocsy"
  },
  "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}