Histogram.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Used for data manipulation\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import math as mt\n",
    "#Others\n",
    "import time\n",
    "import warnings\n",
    "#Data visualization\n",
    "import datetime\n",
    "import matplotlib.font_manager as font\n",
    "import matplotlib.dates as mdates\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "import matplotlib.ticker as ticker\n",
    "from matplotlib.ticker import MaxNLocator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Time to get the data from AWS; download locally***\n",
    "file = 'gdp_v2.00.nc'\n",
    "#url_path = 'https://noaa-oar-hourly-gdp-pds.s3.amazonaws.com/latest/'\n",
    "#ds = xr.open_dataset(url_path+file+'#mode=bytes')\n",
    "url_path = '/Users/19082/Desktop/Drifter/'   # Change this for jupyterLab\n",
    "ds = xr.open_dataset(url_path+file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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:                (traj: 17324, obs: 165754333)\n",
       "Coordinates:\n",
       "    ID                     (traj) int64 ...\n",
       "    longitude              (obs) float32 ...\n",
       "    latitude               (obs) float32 ...\n",
       "    time                   (obs) datetime64[ns] ...\n",
       "    ids                    (obs) int64 ...\n",
       "Dimensions without coordinates: traj, obs\n",
       "Data variables: (12/53)\n",
       "    rowsize                (traj) int64 ...\n",
       "    location_type          (traj) bool ...\n",
       "    WMO                    (traj) int32 ...\n",
       "    expno                  (traj) int32 ...\n",
       "    deploy_date            (traj) datetime64[ns] ...\n",
       "    deploy_lon             (traj) float32 ...\n",
       "    ...                     ...\n",
       "    err_sst                (obs) float32 ...\n",
       "    err_sst1               (obs) float32 ...\n",
       "    err_sst2               (obs) float32 ...\n",
       "    flg_sst                (obs) int8 ...\n",
       "    flg_sst1               (obs) int8 ...\n",
       "    flg_sst2               (obs) int8 ...\n",
       "Attributes: (12/15)\n",
       "    title:             Global Drifter Program hourly drifting buoy collection\n",
       "    history:           Version 2.00.  Metadata from dirall.dat and deplog.dat\n",
       "    Conventions:       CF-1.6\n",
       "    date_created:      2022-01-14T11:16:01.998226\n",
       "    publisher_name:    GDP Drifter DAC\n",
       "    publisher_email:   aoml.dftr@noaa.gov\n",
       "    ...                ...\n",
       "    metadata_link:     https://www.aoml.noaa.gov/phod/dac/dirall.html\n",
       "    contributor_name:  NOAA Global Drifter Program\n",
       "    contributor_role:  Data Acquisition Center\n",
       "    institution:       NOAA Atlantic Oceanographic and Meteorological Laboratory\n",
       "    acknowledgement:   Elipot et al. (2022) to be submitted. Elipot et al. (2...\n",
       "    summary:           Global Drifter Program hourly data</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-f01b3722-2c56-4fee-b4d3-ec5c2797a407' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f01b3722-2c56-4fee-b4d3-ec5c2797a407' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>traj</span>: 17324</li><li><span>obs</span>: 165754333</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b11e4bc1-7bf0-4d5f-8cf8-ee6294d16c65' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b11e4bc1-7bf0-4d5f-8cf8-ee6294d16c65' class='xr-section-summary' >Coordinates: <span>(5)</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>ID</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-35c25928-de10-4d31-a4b1-39797e37a0ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-35c25928-de10-4d31-a4b1-39797e37a0ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-910303e3-d71e-4467-a087-e8c1836f334f' class='xr-var-data-in' type='checkbox'><label for='data-910303e3-d71e-4467-a087-e8c1836f334f' 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>Global Drifter Program Buoy ID</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-13e13949-f3ba-457e-af70-bd64ae207513' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13e13949-f3ba-457e-af70-bd64ae207513' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18c4f615-5447-439d-9312-8bc406727f04' class='xr-var-data-in' type='checkbox'><label for='data-18c4f615-5447-439d-9312-8bc406727f04' 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>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[165754333 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7834ea0e-6516-4c04-896c-622d330858a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7834ea0e-6516-4c04-896c-622d330858a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d48d9acd-fdf8-4695-a052-6f360865316c' class='xr-var-data-in' type='checkbox'><label for='data-d48d9acd-fdf8-4695-a052-6f360865316c' 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>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[165754333 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-37dd225e-4283-4854-a9af-382dcff16430' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37dd225e-4283-4854-a9af-382dcff16430' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e8f6667-1da5-49a3-911f-7a512639d368' class='xr-var-data-in' type='checkbox'><label for='data-5e8f6667-1da5-49a3-911f-7a512639d368' 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</dd></dl></div><div class='xr-var-data'><pre>[165754333 values with dtype=datetime64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ids</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-958529c5-2ef4-4691-87ad-69bad25490c5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-958529c5-2ef4-4691-87ad-69bad25490c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0e3235d-e021-4b4f-a4b1-11e6fca3c415' class='xr-var-data-in' type='checkbox'><label for='data-c0e3235d-e021-4b4f-a4b1-11e6fca3c415' 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>Trajectory index of vars[&#x27;traj&#x27;] for all observations</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>[165754333 values with dtype=int64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0d4cdc9a-269b-471f-a996-0c5db73711f1' class='xr-section-summary-in' type='checkbox'  ><label for='section-0d4cdc9a-269b-471f-a996-0c5db73711f1' class='xr-section-summary' >Data variables: <span>(53)</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>rowsize</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6cf6db07-26ee-451f-b9b5-6320ebabdf50' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6cf6db07-26ee-451f-b9b5-6320ebabdf50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9b3b4e8-329f-43be-bdb8-14063e079e86' class='xr-var-data-in' type='checkbox'><label for='data-a9b3b4e8-329f-43be-bdb8-14063e079e86' 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>Number of observations per trajectory</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>location_type</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7763133f-bb39-4e88-848f-04857f19ab34' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7763133f-bb39-4e88-848f-04857f19ab34' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a3bd2de-28db-46fe-99b9-724e4209a275' class='xr-var-data-in' type='checkbox'><label for='data-3a3bd2de-28db-46fe-99b9-724e4209a275' 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>Satellite-based location system</dd><dt><span>units :</span></dt><dd>-</dd><dt><span>comments :</span></dt><dd>0 (Argos), 1 (GPS)</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=bool]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>WMO</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4f28cac5-5a8c-4cd9-973a-3e89fb0fb73a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f28cac5-5a8c-4cd9-973a-3e89fb0fb73a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ae2aa29-d42e-4012-8f9c-f5c88e95a247' class='xr-var-data-in' type='checkbox'><label for='data-9ae2aa29-d42e-4012-8f9c-f5c88e95a247' 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>World Meteorological Organization buoy identification number</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>expno</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0429ae54-d133-46e2-86e1-b618e799fe80' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0429ae54-d133-46e2-86e1-b618e799fe80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c85bfad6-9158-4b18-b9de-7d54a463c57d' class='xr-var-data-in' type='checkbox'><label for='data-c85bfad6-9158-4b18-b9de-7d54a463c57d' 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>Experiment number</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>deploy_date</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5d26cc90-8b23-485e-83ee-0ada98a0eaf7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5d26cc90-8b23-485e-83ee-0ada98a0eaf7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e30f7c67-eada-4d51-8c02-907033e83cda' class='xr-var-data-in' type='checkbox'><label for='data-e30f7c67-eada-4d51-8c02-907033e83cda' 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>Deployment date and time</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=datetime64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>deploy_lon</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4a7f0fcf-ac87-4944-bed3-f1b9dbf45595' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a7f0fcf-ac87-4944-bed3-f1b9dbf45595' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c16ca569-9849-412b-859f-418a301bba8b' class='xr-var-data-in' type='checkbox'><label for='data-c16ca569-9849-412b-859f-418a301bba8b' 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>Deployment longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>deploy_lat</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-045784a8-701a-4c7d-a97a-571b79f27f06' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-045784a8-701a-4c7d-a97a-571b79f27f06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e50c3cbb-7947-4e74-ae2c-d06a165c462b' class='xr-var-data-in' type='checkbox'><label for='data-e50c3cbb-7947-4e74-ae2c-d06a165c462b' 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>Deployment latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>end_date</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d0398be2-db7d-4532-9965-975ab8d8877e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d0398be2-db7d-4532-9965-975ab8d8877e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e964bba-9e8a-435a-ab66-bb14a8b9802c' class='xr-var-data-in' type='checkbox'><label for='data-6e964bba-9e8a-435a-ab66-bb14a8b9802c' 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>End date and time</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=datetime64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>end_lat</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7aa030b2-58db-412b-89f2-f3acc4fd705c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7aa030b2-58db-412b-89f2-f3acc4fd705c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8262411c-2d40-44f8-b258-7994ea05ca16' class='xr-var-data-in' type='checkbox'><label for='data-8262411c-2d40-44f8-b258-7994ea05ca16' 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>End latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>end_lon</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-aef3e2a7-1639-4756-975c-bc154cdfbeea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aef3e2a7-1639-4756-975c-bc154cdfbeea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d08ccd76-f7ba-41c9-ad90-0e7169d10831' class='xr-var-data-in' type='checkbox'><label for='data-d08ccd76-f7ba-41c9-ad90-0e7169d10831' 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>End longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>drogue_lost_date</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5217b367-3079-46b3-af02-99d120be88f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5217b367-3079-46b3-af02-99d120be88f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87c36b73-7c08-4dfc-8835-833a95b2c262' class='xr-var-data-in' type='checkbox'><label for='data-87c36b73-7c08-4dfc-8835-833a95b2c262' 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>Date and time of drogue loss</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=datetime64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>type_death</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f054507d-2049-4ddd-be9e-597f79c3872e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f054507d-2049-4ddd-be9e-597f79c3872e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a1d5af4-eb80-441e-92c7-752363fd352f' class='xr-var-data-in' type='checkbox'><label for='data-1a1d5af4-eb80-441e-92c7-752363fd352f' 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>Type of death</dd><dt><span>units :</span></dt><dd>-</dd><dt><span>comments :</span></dt><dd>0 (buoy still alive), 1 (buoy ran aground), 2 (picked up by vessel), 3 (stop transmitting), 4 (sporadic transmissions), 5 (bad batteries), 6 (inactive status)</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=int8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>type_buoy</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>|S15</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-030273e7-56cc-43e7-8775-d68ad59b36cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-030273e7-56cc-43e7-8775-d68ad59b36cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01d11ebc-c06c-487b-958c-037b854404b8' class='xr-var-data-in' type='checkbox'><label for='data-01d11ebc-c06c-487b-958c-037b854404b8' 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>Buoy type (see https://www.aoml.noaa.gov/phod/dac/dirall.html)</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=|S15]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DeploymentShip</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>|S15</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b9f146a4-3839-4c56-bfcf-75569d6089b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9f146a4-3839-4c56-bfcf-75569d6089b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-872f0692-8f0d-4dd1-9e6f-806f0e21ac0e' class='xr-var-data-in' type='checkbox'><label for='data-872f0692-8f0d-4dd1-9e6f-806f0e21ac0e' 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>Name of deployment ship</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=|S15]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DeploymentStatus</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>|S15</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-164a73dd-4e4a-4c55-b262-7d5491b14b0b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-164a73dd-4e4a-4c55-b262-7d5491b14b0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2b23816-e750-4329-b401-8cd45837b3ee' class='xr-var-data-in' type='checkbox'><label for='data-d2b23816-e750-4329-b401-8cd45837b3ee' 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>Deployment status</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=|S15]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BuoyTypeManufacturer</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>|S15</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3e17e503-7d55-4a12-afc9-b028ae7ce011' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e17e503-7d55-4a12-afc9-b028ae7ce011' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50b17246-c6a2-4c9b-8f0e-78731c62cfe3' class='xr-var-data-in' type='checkbox'><label for='data-50b17246-c6a2-4c9b-8f0e-78731c62cfe3' 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>Buoy type manufacturer</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=|S15]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BuoyTypeSensorArray</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>|S15</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-da69ae14-6eb0-487d-bd6c-246c90aa6a78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da69ae14-6eb0-487d-bd6c-246c90aa6a78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea0a68ce-5cf5-42ee-a330-feed730cfc1f' class='xr-var-data-in' type='checkbox'><label for='data-ea0a68ce-5cf5-42ee-a330-feed730cfc1f' 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>Buoy type sensor array</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=|S15]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CurrentProgram</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-57c24f76-b652-4588-8a85-73f7b56b8324' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57c24f76-b652-4588-8a85-73f7b56b8324' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4405459b-9296-44b2-9813-3d2d06e831fe' class='xr-var-data-in' type='checkbox'><label for='data-4405459b-9296-44b2-9813-3d2d06e831fe' 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>Current Program</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PurchaserFunding</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>|S15</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d2c9c00e-d341-416b-a193-3ee4c741334c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2c9c00e-d341-416b-a193-3ee4c741334c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c145c8b9-dda9-4bf8-b2be-09c91e58e704' class='xr-var-data-in' type='checkbox'><label for='data-c145c8b9-dda9-4bf8-b2be-09c91e58e704' 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>Purchaser funding</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=|S15]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SensorUpgrade</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>|S15</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c94ebf91-5a5e-4475-964c-1f67038d143f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c94ebf91-5a5e-4475-964c-1f67038d143f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6749df08-2755-4756-a286-32ec2f247c45' class='xr-var-data-in' type='checkbox'><label for='data-6749df08-2755-4756-a286-32ec2f247c45' 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>Sensor upgrade</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=|S15]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Transmissions</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>|S15</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d4346365-c706-43b4-b9a9-1891432d6ad6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4346365-c706-43b4-b9a9-1891432d6ad6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69c108bf-5afa-48dc-b48e-dba010b080d6' class='xr-var-data-in' type='checkbox'><label for='data-69c108bf-5afa-48dc-b48e-dba010b080d6' 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>Transmissions</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=|S15]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DeployingCountry</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>|S15</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5318e065-2b86-4fb4-9f12-2d5f2731c94d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5318e065-2b86-4fb4-9f12-2d5f2731c94d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9dd1ce9c-ad6d-4857-947e-2e6bbb2bf781' class='xr-var-data-in' type='checkbox'><label for='data-9dd1ce9c-ad6d-4857-947e-2e6bbb2bf781' 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>Deploying country</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=|S15]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DeploymentComments</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0ac5b1e0-bb31-45fb-90f9-8133706d139a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ac5b1e0-bb31-45fb-90f9-8133706d139a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee8ce7c3-b20a-4f2f-bead-29289a38dff4' class='xr-var-data-in' type='checkbox'><label for='data-ee8ce7c3-b20a-4f2f-bead-29289a38dff4' 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>Deployment comments</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=object]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ManufactureYear</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6b723bda-e0ab-4692-b104-a52a0c4b1d41' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b723bda-e0ab-4692-b104-a52a0c4b1d41' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30d4a9f9-fb60-4267-849d-7aa0c69c3530' class='xr-var-data-in' type='checkbox'><label for='data-30d4a9f9-fb60-4267-849d-7aa0c69c3530' 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>Manufacture year</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ManufactureMonth</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-83f48fd8-5758-46c0-922d-49e73b955253' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83f48fd8-5758-46c0-922d-49e73b955253' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-846f119f-0fb9-4f79-84f2-b7afba2e47ac' class='xr-var-data-in' type='checkbox'><label for='data-846f119f-0fb9-4f79-84f2-b7afba2e47ac' 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>Manufacture month</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ManufactureSensorType</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>|S5</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1dd01a05-fbc8-4b5c-a06e-d43b54ab7524' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1dd01a05-fbc8-4b5c-a06e-d43b54ab7524' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-043789d5-a879-4051-890d-c67225cb3cf3' class='xr-var-data-in' type='checkbox'><label for='data-043789d5-a879-4051-890d-c67225cb3cf3' 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>Manufacture Sensor Type</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=|S5]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ManufactureVoltage</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f6afd294-5b63-4f3c-b265-09fc4520f96b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f6afd294-5b63-4f3c-b265-09fc4520f96b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd86d0a7-eddd-44e3-8923-590f2deddd20' class='xr-var-data-in' type='checkbox'><label for='data-fd86d0a7-eddd-44e3-8923-590f2deddd20' 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>Manufacture voltage</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>FloatDiameter</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-94c23a71-6ce6-48e6-9679-a43332987edf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94c23a71-6ce6-48e6-9679-a43332987edf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33c4364c-06bb-48fc-8a79-dfe147689251' class='xr-var-data-in' type='checkbox'><label for='data-33c4364c-06bb-48fc-8a79-dfe147689251' 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>Diameter of surface floater</dd><dt><span>units :</span></dt><dd>cm</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SubsfcFloatPresence</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1d2cd243-3597-41b5-a42b-c651f8412445' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d2cd243-3597-41b5-a42b-c651f8412445' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0befc1a1-18f3-40cd-b4f5-bbcbc69b819c' class='xr-var-data-in' type='checkbox'><label for='data-0befc1a1-18f3-40cd-b4f5-bbcbc69b819c' 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>Subsurface Float Presence</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=bool]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DrogueType</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>|S15</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-995dae2e-4de0-4d97-b052-66b1250d3cff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-995dae2e-4de0-4d97-b052-66b1250d3cff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17c3e21a-5985-4017-84f3-a44025ff4a7b' class='xr-var-data-in' type='checkbox'><label for='data-17c3e21a-5985-4017-84f3-a44025ff4a7b' 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>drogue_type :</span></dt><dd>Drogue Type</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=|S15]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DrogueLength</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c80e23fd-9827-4b24-bd57-ced1e41d7b73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c80e23fd-9827-4b24-bd57-ced1e41d7b73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0718fa0-ea89-4fbf-8487-084d3f84d9c8' class='xr-var-data-in' type='checkbox'><label for='data-b0718fa0-ea89-4fbf-8487-084d3f84d9c8' 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>Length of drogue.</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DrogueBallast</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9073a01c-3d26-455f-a74d-28e51213e61c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9073a01c-3d26-455f-a74d-28e51213e61c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a2bc563-941a-4f2c-adbe-ed941d486e3c' class='xr-var-data-in' type='checkbox'><label for='data-6a2bc563-941a-4f2c-adbe-ed941d486e3c' 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>Weight of the drogue&#x27;s ballast.</dd><dt><span>units :</span></dt><dd>kg</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DragAreaAboveDrogue</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dd7cb66d-aead-4112-b52d-45f4f9ab599b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd7cb66d-aead-4112-b52d-45f4f9ab599b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd5a3a53-9d18-401e-8250-1e90bdb3fbff' class='xr-var-data-in' type='checkbox'><label for='data-cd5a3a53-9d18-401e-8250-1e90bdb3fbff' 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>Drag area above drogue.</dd><dt><span>units :</span></dt><dd>m^2</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DragAreaOfDrogue</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0230cf79-16f6-4aa5-bbf6-77a88be62a8d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0230cf79-16f6-4aa5-bbf6-77a88be62a8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c5aba0a-a049-482b-ab42-cbcc341203fa' class='xr-var-data-in' type='checkbox'><label for='data-7c5aba0a-a049-482b-ab42-cbcc341203fa' 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>Drag area drogue.</dd><dt><span>units :</span></dt><dd>m^2</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DragAreaRatio</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e0abc90c-d02d-4c52-88db-1570c3260a45' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0abc90c-d02d-4c52-88db-1570c3260a45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0009106e-a8c0-4959-96fc-944b6783ef9f' class='xr-var-data-in' type='checkbox'><label for='data-0009106e-a8c0-4959-96fc-944b6783ef9f' 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>Drag area ratio</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DrogueCenterDepth</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-12029721-0e66-48bb-89fd-80ce1db77c96' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12029721-0e66-48bb-89fd-80ce1db77c96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba2cd960-b94b-4030-b4c6-827c804b04d8' class='xr-var-data-in' type='checkbox'><label for='data-ba2cd960-b94b-4030-b4c6-827c804b04d8' 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>Average depth of the drogue.</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DrogueDetectSensor</span></div><div class='xr-var-dims'>(traj)</div><div class='xr-var-dtype'>|S15</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-55b07979-7515-49da-b54d-c04f6ec2d82d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-55b07979-7515-49da-b54d-c04f6ec2d82d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d77bf9ac-9ef4-46de-99f5-31da3cb053e0' class='xr-var-data-in' type='checkbox'><label for='data-d77bf9ac-9ef4-46de-99f5-31da3cb053e0' 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>Drogue detection sensor</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>[17324 values with dtype=|S15]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ve</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-03a74fb2-584a-4e2b-8898-cbf0d3dbb839' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03a74fb2-584a-4e2b-8898-cbf0d3dbb839' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83da6b49-b861-4090-ad29-07de548314c5' class='xr-var-data-in' type='checkbox'><label for='data-83da6b49-b861-4090-ad29-07de548314c5' 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>Eastward velocity</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><pre>[165754333 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vn</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8c632382-a5d9-40ab-86d4-366f266d1428' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c632382-a5d9-40ab-86d4-366f266d1428' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96e9ea29-203e-409f-ad15-e3f574742057' class='xr-var-data-in' type='checkbox'><label for='data-96e9ea29-203e-409f-ad15-e3f574742057' 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>Northward velocity</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><pre>[165754333 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gap</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4da3fed8-fc82-4089-bf97-84e287a6ed1e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4da3fed8-fc82-4089-bf97-84e287a6ed1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c70563b-a163-4517-9d54-7ed3a9253dca' class='xr-var-data-in' type='checkbox'><label for='data-6c70563b-a163-4517-9d54-7ed3a9253dca' 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 interval between previous and next location</dd><dt><span>units :</span></dt><dd>s</dd></dl></div><div class='xr-var-data'><pre>[165754333 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>err_lat</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cfd3f541-d46c-42bb-a152-95b1135e2033' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cfd3f541-d46c-42bb-a152-95b1135e2033' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a46f7065-cdb5-4ad3-ba79-10d157b2b315' class='xr-var-data-in' type='checkbox'><label for='data-a46f7065-cdb5-4ad3-ba79-10d157b2b315' 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>Standard error in latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[165754333 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>err_lon</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-09558769-7e31-4afa-bebc-73fdd67cacec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-09558769-7e31-4afa-bebc-73fdd67cacec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14f1addf-8f0f-4f7e-9ad1-6fc8fba0798b' class='xr-var-data-in' type='checkbox'><label for='data-14f1addf-8f0f-4f7e-9ad1-6fc8fba0798b' 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>Standard error in longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[165754333 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>err_ve</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c05f29b3-a017-4bb2-8e0b-85b0784b4615' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c05f29b3-a017-4bb2-8e0b-85b0784b4615' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac73616e-8ec0-4005-938c-1439d259b290' class='xr-var-data-in' type='checkbox'><label for='data-ac73616e-8ec0-4005-938c-1439d259b290' 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>Standard error in eastward velocity</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><pre>[165754333 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>err_vn</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1df821ef-d719-41dd-969a-84b56bff49db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1df821ef-d719-41dd-969a-84b56bff49db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-351b47b7-6019-4a0f-ae6e-ee7535266ed1' class='xr-var-data-in' type='checkbox'><label for='data-351b47b7-6019-4a0f-ae6e-ee7535266ed1' 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>Standard error in northward velocity</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><pre>[165754333 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a0e6a702-dfde-4522-85d9-f9ba33e2abd1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0e6a702-dfde-4522-85d9-f9ba33e2abd1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4642de77-be41-41f7-8f27-bdaae9c5ebca' class='xr-var-data-in' type='checkbox'><label for='data-4642de77-be41-41f7-8f27-bdaae9c5ebca' 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>Fitted sea water temperature</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>comments :</span></dt><dd>Estimated near-surface sea water temperature from drifting buoy measurements. It is the sum of the fitted near-surface non-diurnal sea water temperature and fitted diurnal sea water temperature anomaly. Discrepancies may occur because of rounding.</dd></dl></div><div class='xr-var-data'><pre>[165754333 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst1</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-71c2d1ee-805b-4721-9172-9a1adf03074b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71c2d1ee-805b-4721-9172-9a1adf03074b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cb78df0-265c-40dd-b204-b3776d69a385' class='xr-var-data-in' type='checkbox'><label for='data-5cb78df0-265c-40dd-b204-b3776d69a385' 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>Fitted non-diurnal sea water temperature</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>comments :</span></dt><dd>Estimated near-surface non-diurnal sea water temperature from drifting buoy measurements</dd></dl></div><div class='xr-var-data'><pre>[165754333 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst2</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a60aea39-5535-4c24-acc1-12428da7b4cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a60aea39-5535-4c24-acc1-12428da7b4cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98e06f78-e2ae-4294-b898-5dfbc6e0f5ea' class='xr-var-data-in' type='checkbox'><label for='data-98e06f78-e2ae-4294-b898-5dfbc6e0f5ea' 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>Fitted diurnal sea water temperature anomaly</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>comments :</span></dt><dd>Estimated near-surface diurnal sea water temperature anomaly from drifting buoy measurements</dd></dl></div><div class='xr-var-data'><pre>[165754333 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>err_sst</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cd6b3621-75c8-4c2f-81d4-55eebd72f901' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd6b3621-75c8-4c2f-81d4-55eebd72f901' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26b48336-d752-4b47-9e96-31a757e37456' class='xr-var-data-in' type='checkbox'><label for='data-26b48336-d752-4b47-9e96-31a757e37456' 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>Standard uncertainty of fitted sea water temperature</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>comments :</span></dt><dd>Estimated one standard error of near-surface sea water temperature estimate from drifting buoy measurements</dd></dl></div><div class='xr-var-data'><pre>[165754333 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>err_sst1</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5e746458-6dd6-433b-a19e-416265d11d37' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e746458-6dd6-433b-a19e-416265d11d37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba8d0c05-70f2-42c5-aa77-9ce8f20c7fcc' class='xr-var-data-in' type='checkbox'><label for='data-ba8d0c05-70f2-42c5-aa77-9ce8f20c7fcc' 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>Standard uncertainty of fitted non-diurnal sea water temperature</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>comments :</span></dt><dd>Estimated one standard error of near-surface non-diurnal sea water temperature estimate from drifting buoy measurements</dd></dl></div><div class='xr-var-data'><pre>[165754333 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>err_sst2</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-05b34c12-a33c-4f44-94d2-44ba37fe5b10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-05b34c12-a33c-4f44-94d2-44ba37fe5b10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64eb7b43-9a7d-40e1-a835-415afc270bc9' class='xr-var-data-in' type='checkbox'><label for='data-64eb7b43-9a7d-40e1-a835-415afc270bc9' 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>Standard uncertainty of fitted diurnal sea water temperature anomaly</dd><dt><span>units :</span></dt><dd>Kelvin</dd><dt><span>comments :</span></dt><dd>Estimated one standard error of near-surface diurnal sea water temperature anomaly estimate from drifting buoy measurements</dd></dl></div><div class='xr-var-data'><pre>[165754333 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>flg_sst</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b8df22f0-3daf-4d80-a42d-69e9ca0d8afa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8df22f0-3daf-4d80-a42d-69e9ca0d8afa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a5da72c-fc8f-4210-accd-9fb550857a77' class='xr-var-data-in' type='checkbox'><label for='data-4a5da72c-fc8f-4210-accd-9fb550857a77' 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>Fitted sea water temperature quality flag</dd><dt><span>units :</span></dt><dd>-</dd><dt><span>flag_values :</span></dt><dd>0, 1, 2, 3, 4, 5</dd><dt><span>flag_meanings :</span></dt><dd>no-estimate, no-uncertainty-estimate, estimate-not-in-range-uncertainty-not-in-range, estimate-not-in-range-uncertainty-in-range estimate-in-range-uncertainty-not-in-range, estimate-in-range-uncertainty-in-range</dd></dl></div><div class='xr-var-data'><pre>[165754333 values with dtype=int8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>flg_sst1</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-64a09376-2f3b-4b5b-8023-35811eee9dfb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64a09376-2f3b-4b5b-8023-35811eee9dfb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-521189bf-6fd9-4847-8982-7f581817a425' class='xr-var-data-in' type='checkbox'><label for='data-521189bf-6fd9-4847-8982-7f581817a425' 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>Fitted non-diurnal sea water temperature quality flag</dd><dt><span>units :</span></dt><dd>-</dd><dt><span>flag_values :</span></dt><dd>0, 1, 2, 3, 4, 5</dd><dt><span>flag_meanings :</span></dt><dd>no-estimate, no-uncertainty-estimate, estimate-not-in-range-uncertainty-not-in-range, estimate-not-in-range-uncertainty-in-range estimate-in-range-uncertainty-not-in-range, estimate-in-range-uncertainty-in-range</dd></dl></div><div class='xr-var-data'><pre>[165754333 values with dtype=int8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>flg_sst2</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-050e0f02-028c-48ee-9f8b-9eb06f93a1e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-050e0f02-028c-48ee-9f8b-9eb06f93a1e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0439eed3-e645-43e7-ac55-6dc39c7011db' class='xr-var-data-in' type='checkbox'><label for='data-0439eed3-e645-43e7-ac55-6dc39c7011db' 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>Fitted diurnal sea water temperature anomaly quality flag</dd><dt><span>units :</span></dt><dd>-</dd><dt><span>flag_values :</span></dt><dd>0, 1, 2, 3, 4, 5</dd><dt><span>flag_meanings :</span></dt><dd>no-estimate, no-uncertainty-estimate, estimate-not-in-range-uncertainty-not-in-range, estimate-not-in-range-uncertainty-in-range estimate-in-range-uncertainty-not-in-range, estimate-in-range-uncertainty-in-range</dd></dl></div><div class='xr-var-data'><pre>[165754333 values with dtype=int8]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ec8ff6fe-a60b-424e-bf11-4ae2a088a0df' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ec8ff6fe-a60b-424e-bf11-4ae2a088a0df' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-3c399495-8646-4b80-86d9-8c7c693a053f' class='xr-section-summary-in' type='checkbox'  ><label for='section-3c399495-8646-4b80-86d9-8c7c693a053f' class='xr-section-summary' >Attributes: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>Global Drifter Program hourly drifting buoy collection</dd><dt><span>history :</span></dt><dd>Version 2.00.  Metadata from dirall.dat and deplog.dat</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>date_created :</span></dt><dd>2022-01-14T11:16:01.998226</dd><dt><span>publisher_name :</span></dt><dd>GDP Drifter DAC</dd><dt><span>publisher_email :</span></dt><dd>aoml.dftr@noaa.gov</dd><dt><span>publisher_url :</span></dt><dd>https://www.aoml.noaa.gov/phod/gdp</dd><dt><span>licence :</span></dt><dd>MIT License</dd><dt><span>processing_level :</span></dt><dd>Level 2 QC by GDP drifter DAC</dd><dt><span>metadata_link :</span></dt><dd>https://www.aoml.noaa.gov/phod/dac/dirall.html</dd><dt><span>contributor_name :</span></dt><dd>NOAA Global Drifter Program</dd><dt><span>contributor_role :</span></dt><dd>Data Acquisition Center</dd><dt><span>institution :</span></dt><dd>NOAA Atlantic Oceanographic and Meteorological Laboratory</dd><dt><span>acknowledgement :</span></dt><dd>Elipot et al. (2022) to be submitted. Elipot et al. (2016). Global Drifter Program quality-controlled hourly interpolated data from ocean surface drifting buoys, version 2.00. NOAA National Centers for Environmental Information. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2016JC011716TBA. Accessed [date].</dd><dt><span>summary :</span></dt><dd>Global Drifter Program hourly data</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                (traj: 17324, obs: 165754333)\n",
       "Coordinates:\n",
       "    ID                     (traj) int64 ...\n",
       "    longitude              (obs) float32 ...\n",
       "    latitude               (obs) float32 ...\n",
       "    time                   (obs) datetime64[ns] ...\n",
       "    ids                    (obs) int64 ...\n",
       "Dimensions without coordinates: traj, obs\n",
       "Data variables: (12/53)\n",
       "    rowsize                (traj) int64 ...\n",
       "    location_type          (traj) bool ...\n",
       "    WMO                    (traj) int32 ...\n",
       "    expno                  (traj) int32 ...\n",
       "    deploy_date            (traj) datetime64[ns] ...\n",
       "    deploy_lon             (traj) float32 ...\n",
       "    ...                     ...\n",
       "    err_sst                (obs) float32 ...\n",
       "    err_sst1               (obs) float32 ...\n",
       "    err_sst2               (obs) float32 ...\n",
       "    flg_sst                (obs) int8 ...\n",
       "    flg_sst1               (obs) int8 ...\n",
       "    flg_sst2               (obs) int8 ...\n",
       "Attributes: (12/15)\n",
       "    title:             Global Drifter Program hourly drifting buoy collection\n",
       "    history:           Version 2.00.  Metadata from dirall.dat and deplog.dat\n",
       "    Conventions:       CF-1.6\n",
       "    date_created:      2022-01-14T11:16:01.998226\n",
       "    publisher_name:    GDP Drifter DAC\n",
       "    publisher_email:   aoml.dftr@noaa.gov\n",
       "    ...                ...\n",
       "    metadata_link:     https://www.aoml.noaa.gov/phod/dac/dirall.html\n",
       "    contributor_name:  NOAA Global Drifter Program\n",
       "    contributor_role:  Data Acquisition Center\n",
       "    institution:       NOAA Atlantic Oceanographic and Meteorological Laboratory\n",
       "    acknowledgement:   Elipot et al. (2022) to be submitted. Elipot et al. (2...\n",
       "    summary:           Global Drifter Program hourly data"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obtain and plot SST Data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "bin1 = '2000-09-03'\n",
    "bin2 = '2001-03-03'"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subset Method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mask_var(var: xr.DataArray, value):\n",
    "    \"\"\"Return the mask of a subset of the data matching a test criterion.\n",
    "    Args:\n",
    "        var (xr.DataArray): ak.Array\n",
    "        value: tuple, list or scalar defining a test criterion\n",
    "    Returns:\n",
    "        mask (xr.DataArray): where values corresponding to the criterion are True\n",
    "    \"\"\"\n",
    "\n",
    "    if isinstance(value, tuple):  # min/max defining range\n",
    "        mask = np.logical_and(var >= min(value), var <= max(value))\n",
    "    elif isinstance(value, list):  # select multiples\n",
    "        mask = xr.zeros_like(var)\n",
    "        for v in value:\n",
    "            mask = np.logical_or(mask, var == v)\n",
    "    else:  # select one\n",
    "        mask = var == value\n",
    "    return mask\n",
    "\n",
    "\n",
    "def subset(ds: xr.Dataset, criteria: dict) -> xr.Dataset:\n",
    "    \"\"\"Subset the dataset as a function of one or many criteria. The criteria are passed as a dictionary, where\n",
    "    a variable to subset is assigned to either a range (valuemin, valuemax), a list [value1, value2, valueN],\n",
    "    or a single value.\n",
    "    Args:\n",
    "        ds (xr.Dataset): dataset\n",
    "        criteria (dict): dictionary containing the variables and the ranges/values to retrieve\n",
    "    Returns:\n",
    "        ds_subset: subset xr.Dataset\n",
    "    Usage:\n",
    "        Operation can be combined, and any data or metadata variables part of the Dataset can\n",
    "        be used as a criterion. Criterion can be defined using three datatypes:\n",
    "        Tuple to subset between a range of values:\n",
    "        >>> subset(ds, {\"lon\": (min_lon, max_lon), \"lat\": (min_lat, max_lat)})  # extract a region\n",
    "        >>> subset(ds, {\"time\": (min_time, max_time)})  # extract a temporal range\n",
    "        A list to select multiples values.\n",
    "        >>> subset(ds, {\"ID\": [1, 2, 3]})  # different IDs\n",
    "        A scalar for selecting a specific value.\n",
    "        >>> subset(ds, {\"drogue_status\": True})  # extract segment of trajectory with drogue\n",
    "    \"\"\"\n",
    "    mask_traj = xr.DataArray(data=np.ones(ds.dims[\"traj\"], dtype=\"bool\"), dims=[\"traj\"])\n",
    "    mask_obs = xr.DataArray(data=np.ones(ds.dims[\"obs\"], dtype=\"bool\"), dims=[\"obs\"])\n",
    "\n",
    "    for key in criteria.keys():\n",
    "        if key in ds:\n",
    "            if ds[key].dims == (\"traj\",):\n",
    "                mask_traj = np.logical_and(mask_traj, mask_var(ds[key], criteria[key]))\n",
    "            elif ds[key].dims == (\"obs\",):\n",
    "                mask_obs = np.logical_and(mask_obs, mask_var(ds[key], criteria[key]))\n",
    "        else:\n",
    "            raise ValueError(f\"Unknown variable '{key}'.\")\n",
    "\n",
    "    # remove data when trajectories are filtered\n",
    "    traj_idx = np.insert(np.cumsum(ds[\"rowsize\"].values), 0, 0)\n",
    "    for i in np.where(~mask_traj)[0]:\n",
    "        mask_obs[slice(traj_idx[i], traj_idx[i + 1])] = False\n",
    "\n",
    "    # remove trajectory completely filtered in mask_obs\n",
    "    mask_traj = np.logical_and(\n",
    "        mask_traj, np.in1d(ds[\"ID\"], np.unique(ds[\"ids\"].isel({\"obs\": mask_obs})))\n",
    "    )\n",
    "\n",
    "    if not any(mask_traj):\n",
    "        warnings.warn(\"Empty set.\")\n",
    "        return xr.Dataset()\n",
    "    else:\n",
    "        # update rowsize\n",
    "        id_count = np.bincount(ds.ids[mask_obs])\n",
    "        ds[\"rowsize\"].values[mask_traj] = [id_count[i] for i in ds.ID[mask_traj]]\n",
    "        # apply the filtering for both dimensions\n",
    "        return ds.isel({\"traj\": mask_traj, \"obs\": mask_obs})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "criteria = {\n",
    "    \"time\": (np.datetime64(bin1), np.datetime64(bin2)),\n",
    "    \"flg_sst\": 5,\n",
    "}\n",
    "data_subset_sst = subset(ds, criteria)\n",
    "data_subset_sst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_subset_sst = ds[['time']].where((ds.time > np.datetime64(bin1)) & \n",
    "                                     (ds.time < np.datetime64(bin2)) & \n",
    "                                     (ds.flg_sst ==5), drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot()\n",
    "xticks = pd.date_range(bin1, bin2, freq= \"2M\") \n",
    "bins = pd.date_range(bin1, bin2, freq= \"10D\")  # 2 year bins\n",
    "data_subset_sst['time'].plot.hist(xticks = xticks, bins = bins)\n",
    "scale_y = 1e4\n",
    "ticks_y = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x/scale_y)) # scale y-axis\n",
    "ax.yaxis.set_major_formatter(ticks_y)\n",
    "ax.set_ylabel(\"Count in 10 day bins (10$^4$)\")\n",
    "ax.set_xlabel(\"\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obtain and plot sst2 data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_subset_sst2 = ds[['time']].where((ds.time > np.datetime64(bin1)) & \n",
    "                                      (ds.time < np.datetime64(bin2)) & \n",
    "                                      (ds.flg_sst2 ==5), drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot()\n",
    "xticks = pd.date_range(bin1, bin2, freq= \"2M\") \n",
    "bins = pd.date_range(bin1, bin2, freq= \"10D\")  # 10 day bins\n",
    "data_subset_sst2['time'].plot.hist(xticks = xticks, bins = bins)\n",
    "scale_y = 1e4\n",
    "ticks_y = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x/scale_y)) # scale y-axis\n",
    "ax.yaxis.set_major_formatter(ticks_y)\n",
    "ax.set_ylabel(\"Count in 10 day bins (10$^4$)\")\n",
    "ax.set_xlabel(\"\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obtain and plot pos+velo data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_subset_vn = ds[['time']].where((ds.time > np.datetime64(bin1)) & \n",
    "                                            (ds.time < np.datetime64(bin2)) &\n",
    "                                            (ds.vn.isnull() == False), drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot()\n",
    "xticks = pd.date_range(bin1, bin2, freq= \"2M\") #\n",
    "bins = pd.date_range(bin1, bin2, freq= \"10D\")  # 10 day bins\n",
    "data_subset_vn['time'].plot.hist(xticks = xticks, bins = bins)\n",
    "scale_y = 1e4\n",
    "ticks_y = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x/scale_y)) # scale y-axis\n",
    "ax.yaxis.set_major_formatter(ticks_y)\n",
    "ax.set_ylabel(\"Count in 10 day bins (10$^4$)\")\n",
    "ax.set_xlabel(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([11230., 11261., 11291., 11322., 11353., 11381.]),\n",
       " [Text(11230.0, 0, '2000-09-30'),\n",
       "  Text(11261.0, 0, '2000-10-31'),\n",
       "  Text(11291.0, 0, '2000-11-30'),\n",
       "  Text(11322.0, 0, '2000-12-31'),\n",
       "  Text(11353.0, 0, '2001-01-31'),\n",
       "  Text(11381.0, 0, '2001-02-28')])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xticks = pd.date_range(bin1, bin2, freq= \"1M\") # xticks\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot()\n",
    "bins = pd.date_range(bin1, bin2, freq= \"10D\")  # 10 day bins\n",
    "data_subset_vn['time'].plot.hist(xticks = xticks, bins = bins, label = \"Position and Velocity\", color = \"dodgerblue\")\n",
    "data_subset_sst['time'].plot.hist(xticks = xticks, bins = bins, label = \"$\\hat{s}$$_{m}$\", color = \"firebrick\", alpha = 0.8)\n",
    "data_subset_sst2['time'].plot.hist(xticks = xticks, bins = bins, label = \"$\\hat{s}$$_{D}$\", color = \"darkgoldenrod\", alpha = 0.8)\n",
    "scale_y = 1e4\n",
    "ticks_y = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x/scale_y)) # scale y-axis\n",
    "ax.yaxis.set_major_formatter(ticks_y)\n",
    "plt.suptitle(\"Measurement Density Histogram\")\n",
    "plt.legend(loc='upper left')\n",
    "ax.set_ylabel(\"Count in 10 day bins (10$^4$)\")\n",
    "ax.set_xlabel(\"\")\n",
    "plt.xticks(rotation = 45) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([11230., 11261., 11291., 11322., 11353., 11381.]),\n",
       " [Text(11230.0, 0, '2000-09-30'),\n",
       "  Text(11261.0, 0, '2000-10-31'),\n",
       "  Text(11291.0, 0, '2000-11-30'),\n",
       "  Text(11322.0, 0, '2000-12-31'),\n",
       "  Text(11353.0, 0, '2001-01-31'),\n",
       "  Text(11381.0, 0, '2001-02-28')])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xticks = pd.date_range(bin1, bin2, freq= \"1M\") # xticks\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot()\n",
    "bins = pd.date_range(bin1, bin2, freq= \"2D\")  # 10 day bins\n",
    "data_subset_vn['time'].plot.hist(xticks = xticks, bins = bins, label = \"Position and Velocity\", color = \"dodgerblue\")\n",
    "data_subset_sst['time'].plot.hist(xticks = xticks, bins = bins, label = \"$\\hat{s}$$_{m}$\", color = \"firebrick\", alpha = 0.8)\n",
    "data_subset_sst2['time'].plot.hist(xticks = xticks, bins = bins, label = \"$\\hat{s}$$_{D}$\", color = \"darkgoldenrod\", alpha = 0.8)\n",
    "scale_y = 1e3\n",
    "ticks_y = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x/scale_y)) # scale y-axis\n",
    "ax.yaxis.set_major_formatter(ticks_y)\n",
    "plt.suptitle(\"Measurement Density Histogram\")\n",
    "plt.legend(loc='upper left')\n",
    "ax.set_ylabel(\"Count in 2 day bins (10$^3$)\")\n",
    "ax.set_xlabel(\"\")\n",
    "plt.xticks(rotation = 45) \n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}