Skip to content

Instantly share code, notes, and snippets.

@emiliom

emiliom/PacificHake_AWSrawdata_3days2017.ipynb

Created June 9, 2021 18:46
Show Gist options
  • Save emiliom/4bd2c05f62e9b0fb80a93e286fe0788c to your computer and use it in GitHub Desktop.
Save emiliom/4bd2c05f62e9b0fb80a93e286fe0788c to your computer and use it in GitHub Desktop.
Download ZIP
Pacific Hake survey EK60 echosounder data processing using echopype
Display the source blob
Display the rendered blob
Raw
PacificHake_AWSrawdata_3days2017.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pacific Hake Survey ship data echopype demo\n",
"https://github.com/emiliom/\n",
"\n",
"- 2021-6-9\n",
"- Uses the conda environment created by `environment.yml` in `echopype/.ci_helpers`\n",
"- The notebook currently assumes that these two directory paths already exist: `./hake2017data/convertednc` and `./hake2017data/calibratednc`. netcdf files will be exported there."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from pathlib import Path\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import fsspec\n",
"import numpy as np\n",
"import pandas as pd\n",
"import geopandas as gpd\n",
"import xarray as xr\n",
"\n",
"from shapely.geometry import box\n",
"import cartopy.crs as ccrs\n",
"import cartopy.io.img_tiles as cimgt\n",
"from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
"\n",
"import echopype as ep\n",
"\n",
"import warnings; warnings.simplefilter(\"ignore\", category=DeprecationWarning)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'0.5.0'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ep.__version__"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Establish AWS S3 file system connection\n",
"\n",
"EK60 `.raw` data files will be read from the AWS S3 bucket maintained by NCEI WCSD. S3 directory path: `s3://ncei-wcsd-archive/data/raw/Bell_M._Shimada/SH1707/EK60/`"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"fs = fsspec.filesystem('s3', anon=True)\n",
"\n",
"bucket = \"ncei-wcsd-archive\"\n",
"rawdirpath = \"data/raw/Bell_M._Shimada/SH1707/EK60\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generate list of target `.raw` files from S3 bucket based on dates"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use `fs.glob` to generate list of all raw files in the bucket, then filter on file names for target dates."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"s3rawfiles = fs.glob(f\"{bucket}/{rawdirpath}/*.raw\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['ncei-wcsd-archive/data/raw/Bell_M._Shimada/SH1707/EK60/Summer2017-D20170913-T180733.raw',\n",
" 'ncei-wcsd-archive/data/raw/Bell_M._Shimada/SH1707/EK60/Winter2017-D20170615-T002629.raw']"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s3rawfiles[-2:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Select files from 2017-07-28, 2017-07-29 and 2017-07-30.**"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"250"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s3rawfiles = [\n",
" s3path for s3path in s3rawfiles \n",
" if any([f\"2017{datestr}\" in s3path for datestr in ['0728', '0729', '0730']])\n",
"]\n",
"len(s3rawfiles)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## echopype processing: convert, calibrate and export\n",
"\n",
"Loop through all the selected raw files on S3 and convert, calibrate and generate MVBS. Save the converted and MVBS data to netcdf files."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Access, convert and save to local netcdf files"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def populate_metadata(ed, raw_fname):\n",
" \"\"\"\n",
" Manually populate additional metadata about the dataset and the platform\n",
" \"\"\"\n",
" \n",
" # -- SONAR-netCDF4 Top-level Group attributes\n",
" survey_name = \"2017 Pacific Hake Survey\"\n",
" ed.top.attrs['title'] = f\"{survey_name}, file {raw_fname}\"\n",
" # TODO: Expand this summary, including a link to the Hake Survey program\n",
" ed.top.attrs['summary'] = f\"EK60 raw file {raw_fname} from the {survey_name}, converted to a SONAR-netCDF4 file using echopype\"\n",
"\n",
" # -- SONAR-netCDF4 Platform Group attributes\n",
" # Per SONAR-netCDF4, for platform_type see https://vocab.ices.dk/?ref=311\n",
" ed.platform.attrs['platform_type'] = \"Research vessel\"\n",
" ed.platform.attrs['platform_name'] = \"Bell M. Shimada\"\n",
" # NOAA Bell M. Shimada (not the confirmed ship; just an example)\n",
" ed.platform.attrs['platform_code_ICES'] = \"315\""
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"01:08:58 parsing file Summer2017-D20170728-T000534.raw, time of first ping: 2017-Jul-28 00:05:34\n",
"01:09:01 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T000534.nc\n",
"01:09:07 parsing file Summer2017-D20170728-T002507.raw, time of first ping: 2017-Jul-28 00:25:07\n",
"01:09:09 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T002507.nc\n",
"01:09:15 parsing file Summer2017-D20170728-T004438.raw, time of first ping: 2017-Jul-28 00:44:38\n",
"01:09:17 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T004438.nc\n",
"01:09:23 parsing file Summer2017-D20170728-T010354.raw, time of first ping: 2017-Jul-28 01:03:54\n",
"01:09:25 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T010354.nc\n",
"01:09:31 parsing file Summer2017-D20170728-T012241.raw, time of first ping: 2017-Jul-28 01:22:41\n",
"01:09:33 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T012241.nc\n",
"01:09:38 parsing file Summer2017-D20170728-T014121.raw, time of first ping: 2017-Jul-28 01:41:21\n",
"01:09:41 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T014121.nc\n",
"01:09:47 parsing file Summer2017-D20170728-T015946.raw, time of first ping: 2017-Jul-28 01:59:46\n",
"01:09:48 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T015946.nc\n",
"01:09:51 parsing file Summer2017-D20170728-T021051.raw, time of first ping: 2017-Jul-28 02:10:51\n",
"01:09:54 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T021051.nc\n",
"01:09:59 parsing file Summer2017-D20170728-T022147.raw, time of first ping: 2017-Jul-28 02:21:47\n",
"01:10:01 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T022147.nc\n",
"01:10:07 parsing file Summer2017-D20170728-T023241.raw, time of first ping: 2017-Jul-28 02:32:41\n",
"01:10:09 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T023241.nc\n",
"01:10:15 parsing file Summer2017-D20170728-T024336.raw, time of first ping: 2017-Jul-28 02:43:36\n",
"01:10:17 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T024336.nc\n",
"01:10:22 parsing file Summer2017-D20170728-T025431.raw, time of first ping: 2017-Jul-28 02:54:31\n",
"01:10:25 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T025431.nc\n",
"01:10:30 parsing file Summer2017-D20170728-T030526.raw, time of first ping: 2017-Jul-28 03:05:26\n",
"01:10:32 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T030526.nc\n",
"01:10:38 parsing file Summer2017-D20170728-T031620.raw, time of first ping: 2017-Jul-28 03:16:20\n",
"01:10:40 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T031620.nc\n",
"01:10:45 parsing file Summer2017-D20170728-T032715.raw, time of first ping: 2017-Jul-28 03:27:15\n",
"01:10:48 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T032715.nc\n",
"01:10:53 parsing file Summer2017-D20170728-T033809.raw, time of first ping: 2017-Jul-28 03:38:09\n",
"01:10:56 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T033809.nc\n",
"01:11:01 parsing file Summer2017-D20170728-T034904.raw, time of first ping: 2017-Jul-28 03:49:04\n",
"01:11:03 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T034904.nc\n",
"01:11:09 parsing file Summer2017-D20170728-T035959.raw, time of first ping: 2017-Jul-28 03:59:59\n",
"01:11:11 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T035959.nc\n",
"01:11:17 parsing file Summer2017-D20170728-T041053.raw, time of first ping: 2017-Jul-28 04:10:53\n",
"01:11:19 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T041053.nc\n",
"01:11:25 parsing file Summer2017-D20170728-T042149.raw, time of first ping: 2017-Jul-28 04:21:49\n",
"01:11:27 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T042149.nc\n",
"01:11:33 parsing file Summer2017-D20170728-T043243.raw, time of first ping: 2017-Jul-28 04:32:43\n",
"01:11:36 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T043243.nc\n",
"01:11:41 parsing file Summer2017-D20170728-T044338.raw, time of first ping: 2017-Jul-28 04:43:38\n",
"01:11:44 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T044338.nc\n",
"01:11:49 parsing file Summer2017-D20170728-T045433.raw, time of first ping: 2017-Jul-28 04:54:33\n",
"01:11:52 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T045433.nc\n",
"01:11:57 parsing file Summer2017-D20170728-T050528.raw, time of first ping: 2017-Jul-28 05:05:28\n",
"01:12:00 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T050528.nc\n",
"01:12:05 parsing file Summer2017-D20170728-T051622.raw, time of first ping: 2017-Jul-28 05:16:22\n",
"01:12:07 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T051622.nc\n",
"01:12:13 parsing file Summer2017-D20170728-T052717.raw, time of first ping: 2017-Jul-28 05:27:17\n",
"01:12:15 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T052717.nc\n",
"01:12:21 parsing file Summer2017-D20170728-T053812.raw, time of first ping: 2017-Jul-28 05:38:12\n",
"01:12:23 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T053812.nc\n",
"01:12:28 parsing file Summer2017-D20170728-T054907.raw, time of first ping: 2017-Jul-28 05:49:07\n",
"01:12:31 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T054907.nc\n",
"01:12:36 parsing file Summer2017-D20170728-T060002.raw, time of first ping: 2017-Jul-28 06:00:02\n",
"01:12:39 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T060002.nc\n",
"01:12:44 parsing file Summer2017-D20170728-T061057.raw, time of first ping: 2017-Jul-28 06:10:57\n",
"01:12:47 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T061057.nc\n",
"01:12:52 parsing file Summer2017-D20170728-T062152.raw, time of first ping: 2017-Jul-28 06:21:52\n",
"01:12:55 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T062152.nc\n",
"01:13:00 parsing file Summer2017-D20170728-T063247.raw, time of first ping: 2017-Jul-28 06:32:47\n",
"01:13:03 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T063247.nc\n",
"01:13:08 parsing file Summer2017-D20170728-T064342.raw, time of first ping: 2017-Jul-28 06:43:42\n",
"01:13:11 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T064342.nc\n",
"01:13:16 parsing file Summer2017-D20170728-T065438.raw, time of first ping: 2017-Jul-28 06:54:38\n",
"01:13:19 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T065438.nc\n",
"01:13:24 parsing file Summer2017-D20170728-T070533.raw, time of first ping: 2017-Jul-28 07:05:33\n",
"01:13:27 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T070533.nc\n",
"01:13:32 parsing file Summer2017-D20170728-T071628.raw, time of first ping: 2017-Jul-28 07:16:28\n",
"01:13:35 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T071628.nc\n",
"01:13:41 parsing file Summer2017-D20170728-T072728.raw, time of first ping: 2017-Jul-28 07:27:28\n",
"01:13:43 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T072728.nc\n",
"01:13:49 parsing file Summer2017-D20170728-T073827.raw, time of first ping: 2017-Jul-28 07:38:27\n",
"01:13:51 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T073827.nc\n",
"01:13:56 parsing file Summer2017-D20170728-T074927.raw, time of first ping: 2017-Jul-28 07:49:27\n",
"01:13:59 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T074927.nc\n",
"01:14:05 parsing file Summer2017-D20170728-T080026.raw, time of first ping: 2017-Jul-28 08:00:26\n",
"01:14:07 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T080026.nc\n",
"01:14:13 parsing file Summer2017-D20170728-T081126.raw, time of first ping: 2017-Jul-28 08:11:26\n",
"01:14:15 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T081126.nc\n",
"01:14:20 parsing file Summer2017-D20170728-T082225.raw, time of first ping: 2017-Jul-28 08:22:25\n",
"01:14:23 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T082225.nc\n",
"01:14:28 parsing file Summer2017-D20170728-T083325.raw, time of first ping: 2017-Jul-28 08:33:25\n",
"01:14:31 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T083325.nc\n",
"01:14:36 parsing file Summer2017-D20170728-T084424.raw, time of first ping: 2017-Jul-28 08:44:24\n",
"01:14:39 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T084424.nc\n",
"01:14:44 parsing file Summer2017-D20170728-T085524.raw, time of first ping: 2017-Jul-28 08:55:24\n",
"01:14:46 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T085524.nc\n",
"01:14:52 parsing file Summer2017-D20170728-T090623.raw, time of first ping: 2017-Jul-28 09:06:23\n",
"01:14:54 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T090623.nc\n",
"01:15:00 parsing file Summer2017-D20170728-T091722.raw, time of first ping: 2017-Jul-28 09:17:22\n",
"01:15:02 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T091722.nc\n",
"01:15:08 parsing file Summer2017-D20170728-T092822.raw, time of first ping: 2017-Jul-28 09:28:22\n",
"01:15:10 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T092822.nc\n",
"01:15:16 parsing file Summer2017-D20170728-T093921.raw, time of first ping: 2017-Jul-28 09:39:21\n",
"01:15:18 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T093921.nc\n",
"01:15:24 parsing file Summer2017-D20170728-T095020.raw, time of first ping: 2017-Jul-28 09:50:20\n",
"01:15:27 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T095020.nc\n",
"01:15:33 parsing file Summer2017-D20170728-T100117.raw, time of first ping: 2017-Jul-28 10:01:17\n",
"01:15:35 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T100117.nc\n",
"01:15:41 parsing file Summer2017-D20170728-T101212.raw, time of first ping: 2017-Jul-28 10:12:12\n",
"01:15:44 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T101212.nc\n",
"01:15:50 parsing file Summer2017-D20170728-T102306.raw, time of first ping: 2017-Jul-28 10:23:06\n",
"01:15:53 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T102306.nc\n",
"01:15:58 parsing file Summer2017-D20170728-T103401.raw, time of first ping: 2017-Jul-28 10:34:01\n",
"01:16:00 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T103401.nc\n",
"01:16:06 parsing file Summer2017-D20170728-T104456.raw, time of first ping: 2017-Jul-28 10:44:56\n",
"01:16:08 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T104456.nc\n",
"01:16:14 parsing file Summer2017-D20170728-T105551.raw, time of first ping: 2017-Jul-28 10:55:51\n",
"01:16:17 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T105551.nc\n",
"01:16:22 parsing file Summer2017-D20170728-T110646.raw, time of first ping: 2017-Jul-28 11:06:46\n",
"01:16:25 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T110646.nc\n",
"01:16:31 parsing file Summer2017-D20170728-T111742.raw, time of first ping: 2017-Jul-28 11:17:42\n",
"01:16:34 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T111742.nc\n",
"01:16:40 parsing file Summer2017-D20170728-T112836.raw, time of first ping: 2017-Jul-28 11:28:36\n",
"01:16:43 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T112836.nc\n",
"01:16:48 parsing file Summer2017-D20170728-T113931.raw, time of first ping: 2017-Jul-28 11:39:31\n",
"01:16:51 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T113931.nc\n",
"01:16:57 parsing file Summer2017-D20170728-T115026.raw, time of first ping: 2017-Jul-28 11:50:26\n",
"01:17:00 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T115026.nc\n",
"01:17:05 parsing file Summer2017-D20170728-T120121.raw, time of first ping: 2017-Jul-28 12:01:21\n",
"01:17:08 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T120121.nc\n",
"01:17:13 parsing file Summer2017-D20170728-T121216.raw, time of first ping: 2017-Jul-28 12:12:16\n",
"01:17:16 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T121216.nc\n",
"01:17:21 parsing file Summer2017-D20170728-T122311.raw, time of first ping: 2017-Jul-28 12:23:11\n",
"01:17:21 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T122311.nc\n",
"01:17:23 parsing file Summer2017-D20170728-T122525.raw, time of first ping: 2017-Jul-28 12:25:25\n",
"01:17:26 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T122525.nc\n",
"01:17:31 parsing file Summer2017-D20170728-T124210.raw, time of first ping: 2017-Jul-28 12:42:10\n",
"01:17:34 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T124210.nc\n",
"01:17:40 parsing file Summer2017-D20170728-T130032.raw, time of first ping: 2017-Jul-28 13:00:32\n",
"01:17:42 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T130032.nc\n",
"01:17:48 parsing file Summer2017-D20170728-T131854.raw, time of first ping: 2017-Jul-28 13:18:54\n",
"01:17:51 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T131854.nc\n",
"01:17:57 parsing file Summer2017-D20170728-T133734.raw, time of first ping: 2017-Jul-28 13:37:34\n",
"01:17:59 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T133734.nc\n",
"01:18:05 parsing file Summer2017-D20170728-T135621.raw, time of first ping: 2017-Jul-28 13:56:21\n",
"01:18:08 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T135621.nc\n",
"01:18:13 parsing file Summer2017-D20170728-T141519.raw, time of first ping: 2017-Jul-28 14:15:19\n",
"01:18:16 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T141519.nc\n",
"01:18:22 parsing file Summer2017-D20170728-T143451.raw, time of first ping: 2017-Jul-28 14:34:51\n",
"01:18:25 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T143451.nc\n",
"01:18:31 parsing file Summer2017-D20170728-T145441.raw, time of first ping: 2017-Jul-28 14:54:41\n",
"01:18:34 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T145441.nc\n",
"01:18:40 parsing file Summer2017-D20170728-T151434.raw, time of first ping: 2017-Jul-28 15:14:34\n",
"01:18:43 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T151434.nc\n",
"01:18:48 parsing file Summer2017-D20170728-T153425.raw, time of first ping: 2017-Jul-28 15:34:25\n",
"01:18:51 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T153425.nc\n",
"01:18:56 parsing file Summer2017-D20170728-T155417.raw, time of first ping: 2017-Jul-28 15:54:17\n",
"01:18:59 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T155417.nc\n",
"01:19:04 parsing file Summer2017-D20170728-T161408.raw, time of first ping: 2017-Jul-28 16:14:08\n",
"01:19:07 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T161408.nc\n",
"01:19:12 parsing file Summer2017-D20170728-T163359.raw, time of first ping: 2017-Jul-28 16:33:59\n",
"01:19:16 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T163359.nc\n",
"01:19:21 parsing file Summer2017-D20170728-T165350.raw, time of first ping: 2017-Jul-28 16:53:50\n",
"01:19:24 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T165350.nc\n",
"01:19:29 parsing file Summer2017-D20170728-T171341.raw, time of first ping: 2017-Jul-28 17:13:41\n",
"01:19:31 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T171341.nc\n",
"01:19:37 parsing file Summer2017-D20170728-T173332.raw, time of first ping: 2017-Jul-28 17:33:32\n",
"01:19:39 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T173332.nc\n",
"01:19:45 parsing file Summer2017-D20170728-T175338.raw, time of first ping: 2017-Jul-28 17:53:38\n",
"01:19:47 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T175338.nc\n",
"01:19:53 parsing file Summer2017-D20170728-T181619.raw, time of first ping: 2017-Jul-28 18:16:19\n",
"01:19:56 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T181619.nc\n",
"01:20:01 parsing file Summer2017-D20170728-T184131.raw, time of first ping: 2017-Jul-28 18:41:31\n",
"01:20:04 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T184131.nc\n",
"01:20:10 parsing file Summer2017-D20170728-T190728.raw, time of first ping: 2017-Jul-28 19:07:28\n",
"01:20:13 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T190728.nc\n",
"01:20:18 parsing file Summer2017-D20170728-T193459.raw, time of first ping: 2017-Jul-28 19:34:59\n",
"01:20:20 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T193459.nc\n",
"01:20:23 parsing file Summer2017-D20170728-T195219.raw, time of first ping: 2017-Jul-28 19:52:19\n",
"01:20:26 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T195219.nc\n",
"01:20:32 parsing file Summer2017-D20170728-T202608.raw, time of first ping: 2017-Jul-28 20:26:08\n",
"01:20:36 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T202608.nc\n",
"01:20:41 parsing file Summer2017-D20170728-T205857.raw, time of first ping: 2017-Jul-28 20:58:57\n",
"01:20:44 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T205857.nc\n",
"01:20:50 parsing file Summer2017-D20170728-T212916.raw, time of first ping: 2017-Jul-28 21:29:16\n",
"01:20:53 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T212916.nc\n",
"01:20:58 parsing file Summer2017-D20170728-T215347.raw, time of first ping: 2017-Jul-28 21:53:47\n",
"01:21:01 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T215347.nc\n",
"01:21:07 parsing file Summer2017-D20170728-T221343.raw, time of first ping: 2017-Jul-28 22:13:43\n",
"01:21:10 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T221343.nc\n",
"01:21:15 parsing file Summer2017-D20170728-T223346.raw, time of first ping: 2017-Jul-28 22:33:46\n",
"01:21:18 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T223346.nc\n",
"01:21:24 parsing file Summer2017-D20170728-T225342.raw, time of first ping: 2017-Jul-28 22:53:42\n",
"01:21:27 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T225342.nc\n",
"01:21:33 parsing file Summer2017-D20170728-T231334.raw, time of first ping: 2017-Jul-28 23:13:34\n",
"01:21:36 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T231334.nc\n",
"01:21:41 parsing file Summer2017-D20170728-T233248.raw, time of first ping: 2017-Jul-28 23:32:48\n",
"01:21:44 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T233248.nc\n",
"01:21:49 parsing file Summer2017-D20170728-T235144.raw, time of first ping: 2017-Jul-28 23:51:44\n",
"01:21:52 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170728-T235144.nc\n",
"01:21:57 parsing file Summer2017-D20170729-T001030.raw, time of first ping: 2017-Jul-29 00:10:30\n",
"01:22:01 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T001030.nc\n",
"01:22:06 parsing file Summer2017-D20170729-T002909.raw, time of first ping: 2017-Jul-29 00:29:09\n",
"01:22:10 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T002909.nc\n",
"01:22:15 parsing file Summer2017-D20170729-T004732.raw, time of first ping: 2017-Jul-29 00:47:32\n",
"01:22:18 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T004732.nc\n",
"01:22:24 parsing file Summer2017-D20170729-T010556.raw, time of first ping: 2017-Jul-29 01:05:56\n",
"01:22:27 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T010556.nc\n",
"01:22:33 parsing file Summer2017-D20170729-T012420.raw, time of first ping: 2017-Jul-29 01:24:20\n",
"01:22:38 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T012420.nc\n",
"01:22:44 parsing file Summer2017-D20170729-T014242.raw, time of first ping: 2017-Jul-29 01:42:42\n",
"01:22:47 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T014242.nc\n",
"01:22:53 parsing file Summer2017-D20170729-T020106.raw, time of first ping: 2017-Jul-29 02:01:06\n",
"01:22:56 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T020106.nc\n",
"01:23:02 parsing file Summer2017-D20170729-T021938.raw, time of first ping: 2017-Jul-29 02:19:38\n",
"01:23:05 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T021938.nc\n",
"01:23:12 parsing file Summer2017-D20170729-T023826.raw, time of first ping: 2017-Jul-29 02:38:26\n",
"01:23:15 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T023826.nc\n",
"01:23:21 parsing file Summer2017-D20170729-T025714.raw, time of first ping: 2017-Jul-29 02:57:14\n",
"01:23:25 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T025714.nc\n",
"01:23:31 parsing file Summer2017-D20170729-T031607.raw, time of first ping: 2017-Jul-29 03:16:07\n",
"01:23:34 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T031607.nc\n",
"01:23:40 parsing file Summer2017-D20170729-T033503.raw, time of first ping: 2017-Jul-29 03:35:03\n",
"01:23:41 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T033503.nc\n",
"01:23:45 parsing file Summer2017-D20170729-T034649.raw, time of first ping: 2017-Jul-29 03:46:49\n",
"01:23:48 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T034649.nc\n",
"01:23:53 parsing file Summer2017-D20170729-T040618.raw, time of first ping: 2017-Jul-29 04:06:18\n",
"01:23:56 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T040618.nc\n",
"01:24:02 parsing file Summer2017-D20170729-T042604.raw, time of first ping: 2017-Jul-29 04:26:04\n",
"01:24:04 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T042604.nc\n",
"01:24:10 parsing file Summer2017-D20170729-T044550.raw, time of first ping: 2017-Jul-29 04:45:50\n",
"01:24:13 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T044550.nc\n",
"01:24:19 parsing file Summer2017-D20170729-T050541.raw, time of first ping: 2017-Jul-29 05:05:41\n",
"01:24:21 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T050541.nc\n",
"01:24:27 parsing file Summer2017-D20170729-T052531.raw, time of first ping: 2017-Jul-29 05:25:31\n",
"01:24:29 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T052531.nc\n",
"01:24:35 parsing file Summer2017-D20170729-T054522.raw, time of first ping: 2017-Jul-29 05:45:22\n",
"01:24:38 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T054522.nc\n",
"01:24:43 parsing file Summer2017-D20170729-T060513.raw, time of first ping: 2017-Jul-29 06:05:13\n",
"01:24:46 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T060513.nc\n",
"01:24:51 parsing file Summer2017-D20170729-T062501.raw, time of first ping: 2017-Jul-29 06:25:01\n",
"01:24:54 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T062501.nc\n",
"01:24:59 parsing file Summer2017-D20170729-T064450.raw, time of first ping: 2017-Jul-29 06:44:50\n",
"01:25:02 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T064450.nc\n",
"01:25:08 parsing file Summer2017-D20170729-T070438.raw, time of first ping: 2017-Jul-29 07:04:38\n",
"01:25:11 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T070438.nc\n",
"01:25:16 parsing file Summer2017-D20170729-T072426.raw, time of first ping: 2017-Jul-29 07:24:26\n",
"01:25:19 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T072426.nc\n",
"01:25:25 parsing file Summer2017-D20170729-T074413.raw, time of first ping: 2017-Jul-29 07:44:13\n",
"01:25:27 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T074413.nc\n",
"01:25:33 parsing file Summer2017-D20170729-T080357.raw, time of first ping: 2017-Jul-29 08:03:57\n",
"01:25:35 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T080357.nc\n",
"01:25:41 parsing file Summer2017-D20170729-T082342.raw, time of first ping: 2017-Jul-29 08:23:42\n",
"01:25:43 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T082342.nc\n",
"01:25:49 parsing file Summer2017-D20170729-T084328.raw, time of first ping: 2017-Jul-29 08:43:28\n",
"01:25:51 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T084328.nc\n",
"01:25:57 parsing file Summer2017-D20170729-T090314.raw, time of first ping: 2017-Jul-29 09:03:14\n",
"01:26:00 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T090314.nc\n",
"01:26:05 parsing file Summer2017-D20170729-T092259.raw, time of first ping: 2017-Jul-29 09:22:59\n",
"01:26:08 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T092259.nc\n",
"01:26:13 parsing file Summer2017-D20170729-T094245.raw, time of first ping: 2017-Jul-29 09:42:45\n",
"01:26:16 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T094245.nc\n",
"01:26:21 parsing file Summer2017-D20170729-T100230.raw, time of first ping: 2017-Jul-29 10:02:30\n",
"01:26:24 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T100230.nc\n",
"01:26:29 parsing file Summer2017-D20170729-T102215.raw, time of first ping: 2017-Jul-29 10:22:15\n",
"01:26:32 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T102215.nc\n",
"01:26:38 parsing file Summer2017-D20170729-T104201.raw, time of first ping: 2017-Jul-29 10:42:01\n",
"01:26:40 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T104201.nc\n",
"01:26:46 parsing file Summer2017-D20170729-T110147.raw, time of first ping: 2017-Jul-29 11:01:47\n",
"01:26:49 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T110147.nc\n",
"01:26:54 parsing file Summer2017-D20170729-T112132.raw, time of first ping: 2017-Jul-29 11:21:32\n",
"01:26:57 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T112132.nc\n",
"01:27:02 parsing file Summer2017-D20170729-T114118.raw, time of first ping: 2017-Jul-29 11:41:18\n",
"01:27:05 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T114118.nc\n",
"01:27:10 parsing file Summer2017-D20170729-T120104.raw, time of first ping: 2017-Jul-29 12:01:04\n",
"01:27:13 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T120104.nc\n",
"01:27:19 parsing file Summer2017-D20170729-T122049.raw, time of first ping: 2017-Jul-29 12:20:49\n",
"01:27:19 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T122049.nc\n",
"01:27:20 parsing file Summer2017-D20170729-T122320.raw, time of first ping: 2017-Jul-29 12:23:20\n",
"01:27:20 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T122320.nc\n",
"01:27:22 parsing file Summer2017-D20170729-T122642.raw, time of first ping: 2017-Jul-29 12:26:42\n",
"01:27:25 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T122642.nc\n",
"01:27:30 parsing file Summer2017-D20170729-T124630.raw, time of first ping: 2017-Jul-29 12:46:30\n",
"01:27:33 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T124630.nc\n",
"01:27:38 parsing file Summer2017-D20170729-T130615.raw, time of first ping: 2017-Jul-29 13:06:15\n",
"01:27:41 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T130615.nc\n",
"01:27:47 parsing file Summer2017-D20170729-T132601.raw, time of first ping: 2017-Jul-29 13:26:01\n",
"01:27:50 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T132601.nc\n",
"01:27:55 parsing file Summer2017-D20170729-T134546.raw, time of first ping: 2017-Jul-29 13:45:46\n",
"01:27:58 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T134546.nc\n",
"01:28:04 parsing file Summer2017-D20170729-T140536.raw, time of first ping: 2017-Jul-29 14:05:36\n",
"01:28:06 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T140536.nc\n",
"01:28:11 parsing file Summer2017-D20170729-T142749.raw, time of first ping: 2017-Jul-29 14:27:49\n",
"01:28:15 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T142749.nc\n",
"01:28:20 parsing file Summer2017-D20170729-T144927.raw, time of first ping: 2017-Jul-29 14:49:27\n",
"01:28:23 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T144927.nc\n",
"01:28:29 parsing file Summer2017-D20170729-T150925.raw, time of first ping: 2017-Jul-29 15:09:25\n",
"01:28:32 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T150925.nc\n",
"01:28:38 parsing file Summer2017-D20170729-T152925.raw, time of first ping: 2017-Jul-29 15:29:25\n",
"01:28:40 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T152925.nc\n",
"01:28:46 parsing file Summer2017-D20170729-T154923.raw, time of first ping: 2017-Jul-29 15:49:23\n",
"01:28:48 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T154923.nc\n",
"01:28:54 parsing file Summer2017-D20170729-T160924.raw, time of first ping: 2017-Jul-29 16:09:24\n",
"01:28:56 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T160924.nc\n",
"01:29:02 parsing file Summer2017-D20170729-T162927.raw, time of first ping: 2017-Jul-29 16:29:27\n",
"01:29:04 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T162927.nc\n",
"01:29:10 parsing file Summer2017-D20170729-T165128.raw, time of first ping: 2017-Jul-29 16:51:28\n",
"01:29:13 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T165128.nc\n",
"01:29:19 parsing file Summer2017-D20170729-T172207.raw, time of first ping: 2017-Jul-29 17:22:07\n",
"01:29:22 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T172207.nc\n",
"01:29:27 parsing file Summer2017-D20170729-T175542.raw, time of first ping: 2017-Jul-29 17:55:42\n",
"01:29:31 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T175542.nc\n",
"01:29:36 parsing file Summer2017-D20170729-T182943.raw, time of first ping: 2017-Jul-29 18:29:43\n",
"01:29:39 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T182943.nc\n",
"01:29:45 parsing file Summer2017-D20170729-T185953.raw, time of first ping: 2017-Jul-29 18:59:53\n",
"01:29:47 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T185953.nc\n",
"01:29:53 parsing file Summer2017-D20170729-T192034.raw, time of first ping: 2017-Jul-29 19:20:34\n",
"01:29:55 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T192034.nc\n",
"01:30:00 parsing file Summer2017-D20170729-T194026.raw, time of first ping: 2017-Jul-29 19:40:26\n",
"01:30:03 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T194026.nc\n",
"01:30:09 parsing file Summer2017-D20170729-T200020.raw, time of first ping: 2017-Jul-29 20:00:20\n",
"01:30:11 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T200020.nc\n",
"01:30:17 parsing file Summer2017-D20170729-T202014.raw, time of first ping: 2017-Jul-29 20:20:14\n",
"01:30:20 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T202014.nc\n",
"01:30:25 parsing file Summer2017-D20170729-T204008.raw, time of first ping: 2017-Jul-29 20:40:08\n",
"01:30:28 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T204008.nc\n",
"01:30:33 parsing file Summer2017-D20170729-T210002.raw, time of first ping: 2017-Jul-29 21:00:02\n",
"01:30:36 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T210002.nc\n",
"01:30:42 parsing file Summer2017-D20170729-T211958.raw, time of first ping: 2017-Jul-29 21:19:58\n",
"01:30:45 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T211958.nc\n",
"01:30:50 parsing file Summer2017-D20170729-T213950.raw, time of first ping: 2017-Jul-29 21:39:50\n",
"01:30:53 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T213950.nc\n",
"01:30:59 parsing file Summer2017-D20170729-T215945.raw, time of first ping: 2017-Jul-29 21:59:45\n",
"01:31:02 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T215945.nc\n",
"01:31:07 parsing file Summer2017-D20170729-T221931.raw, time of first ping: 2017-Jul-29 22:19:31\n",
"01:31:11 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T221931.nc\n",
"01:31:16 parsing file Summer2017-D20170729-T223917.raw, time of first ping: 2017-Jul-29 22:39:17\n",
"01:31:19 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T223917.nc\n",
"01:31:25 parsing file Summer2017-D20170729-T225900.raw, time of first ping: 2017-Jul-29 22:59:00\n",
"01:31:28 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T225900.nc\n",
"01:31:33 parsing file Summer2017-D20170729-T231846.raw, time of first ping: 2017-Jul-29 23:18:46\n",
"01:31:36 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T231846.nc\n",
"01:31:42 parsing file Summer2017-D20170729-T233829.raw, time of first ping: 2017-Jul-29 23:38:29\n",
"01:31:45 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T233829.nc\n",
"01:31:50 parsing file Summer2017-D20170729-T235813.raw, time of first ping: 2017-Jul-29 23:58:13\n",
"01:31:53 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170729-T235813.nc\n",
"01:31:59 parsing file Summer2017-D20170730-T001758.raw, time of first ping: 2017-Jul-30 00:17:58\n",
"01:32:03 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T001758.nc\n",
"01:32:08 parsing file Summer2017-D20170730-T003744.raw, time of first ping: 2017-Jul-30 00:37:44\n",
"01:32:09 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T003744.nc\n",
"01:32:12 parsing file Summer2017-D20170730-T004614.raw, time of first ping: 2017-Jul-30 00:46:14\n",
"01:32:15 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T004614.nc\n",
"01:32:21 parsing file Summer2017-D20170730-T010601.raw, time of first ping: 2017-Jul-30 01:06:01\n",
"01:32:24 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T010601.nc\n",
"01:32:29 parsing file Summer2017-D20170730-T012546.raw, time of first ping: 2017-Jul-30 01:25:46\n",
"01:32:32 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T012546.nc\n",
"01:32:38 parsing file Summer2017-D20170730-T014532.raw, time of first ping: 2017-Jul-30 01:45:32\n",
"01:32:41 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T014532.nc\n",
"01:32:47 parsing file Summer2017-D20170730-T020517.raw, time of first ping: 2017-Jul-30 02:05:17\n",
"01:32:49 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T020517.nc\n",
"01:32:55 parsing file Summer2017-D20170730-T022501.raw, time of first ping: 2017-Jul-30 02:25:01\n",
"01:32:58 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T022501.nc\n",
"01:33:04 parsing file Summer2017-D20170730-T024444.raw, time of first ping: 2017-Jul-30 02:44:44\n",
"01:33:07 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T024444.nc\n",
"01:33:13 parsing file Summer2017-D20170730-T030349.raw, time of first ping: 2017-Jul-30 03:03:49\n",
"01:33:16 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T030349.nc\n",
"01:33:21 parsing file Summer2017-D20170730-T032000.raw, time of first ping: 2017-Jul-30 03:20:00\n",
"01:33:24 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T032000.nc\n",
"01:33:30 parsing file Summer2017-D20170730-T033604.raw, time of first ping: 2017-Jul-30 03:36:04\n",
"01:33:31 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T033604.nc\n",
"01:33:35 parsing file Summer2017-D20170730-T034646.raw, time of first ping: 2017-Jul-30 03:46:46\n",
"01:33:39 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T034646.nc\n",
"01:33:44 parsing file Summer2017-D20170730-T040253.raw, time of first ping: 2017-Jul-30 04:02:53\n",
"01:33:47 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T040253.nc\n",
"01:33:52 parsing file Summer2017-D20170730-T041906.raw, time of first ping: 2017-Jul-30 04:19:06\n",
"01:33:55 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T041906.nc\n",
"01:34:01 parsing file Summer2017-D20170730-T043546.raw, time of first ping: 2017-Jul-30 04:35:46\n",
"01:34:04 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T043546.nc\n",
"01:34:09 parsing file Summer2017-D20170730-T045239.raw, time of first ping: 2017-Jul-30 04:52:39\n",
"01:34:12 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T045239.nc\n",
"01:34:18 parsing file Summer2017-D20170730-T050854.raw, time of first ping: 2017-Jul-30 05:08:54\n",
"01:34:21 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T050854.nc\n",
"01:34:27 parsing file Summer2017-D20170730-T052743.raw, time of first ping: 2017-Jul-30 05:27:43\n",
"01:34:30 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T052743.nc\n",
"01:34:36 parsing file Summer2017-D20170730-T054831.raw, time of first ping: 2017-Jul-30 05:48:31\n",
"01:34:39 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T054831.nc\n",
"01:34:44 parsing file Summer2017-D20170730-T061115.raw, time of first ping: 2017-Jul-30 06:11:15\n",
"01:34:47 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T061115.nc\n",
"01:34:53 parsing file Summer2017-D20170730-T063458.raw, time of first ping: 2017-Jul-30 06:34:58\n",
"01:34:55 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T063458.nc\n",
"01:35:01 parsing file Summer2017-D20170730-T065403.raw, time of first ping: 2017-Jul-30 06:54:03\n",
"01:35:03 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T065403.nc\n",
"01:35:09 parsing file Summer2017-D20170730-T071156.raw, time of first ping: 2017-Jul-30 07:11:56\n",
"01:35:11 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T071156.nc\n",
"01:35:17 parsing file Summer2017-D20170730-T072937.raw, time of first ping: 2017-Jul-30 07:29:37\n",
"01:35:19 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T072937.nc\n",
"01:35:24 parsing file Summer2017-D20170730-T074418.raw, time of first ping: 2017-Jul-30 07:44:18\n",
"01:35:27 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T074418.nc\n",
"01:35:32 parsing file Summer2017-D20170730-T075856.raw, time of first ping: 2017-Jul-30 07:58:56\n",
"01:35:35 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T075856.nc\n",
"01:35:41 parsing file Summer2017-D20170730-T081349.raw, time of first ping: 2017-Jul-30 08:13:49\n",
"01:35:43 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T081349.nc\n",
"01:35:48 parsing file Summer2017-D20170730-T082849.raw, time of first ping: 2017-Jul-30 08:28:49\n",
"01:35:51 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T082849.nc\n",
"01:35:57 parsing file Summer2017-D20170730-T084418.raw, time of first ping: 2017-Jul-30 08:44:18\n",
"01:36:01 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T084418.nc\n",
"01:36:07 parsing file Summer2017-D20170730-T090134.raw, time of first ping: 2017-Jul-30 09:01:34\n",
"01:36:11 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T090134.nc\n",
"01:36:17 parsing file Summer2017-D20170730-T092110.raw, time of first ping: 2017-Jul-30 09:21:10\n",
"01:36:20 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T092110.nc\n",
"01:36:26 parsing file Summer2017-D20170730-T094101.raw, time of first ping: 2017-Jul-30 09:41:01\n",
"01:36:28 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T094101.nc\n",
"01:36:34 parsing file Summer2017-D20170730-T100052.raw, time of first ping: 2017-Jul-30 10:00:52\n",
"01:36:36 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T100052.nc\n",
"01:36:42 parsing file Summer2017-D20170730-T101802.raw, time of first ping: 2017-Jul-30 10:18:02\n",
"01:36:45 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T101802.nc\n",
"01:36:51 parsing file Summer2017-D20170730-T103246.raw, time of first ping: 2017-Jul-30 10:32:46\n",
"01:36:53 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T103246.nc\n",
"01:36:59 parsing file Summer2017-D20170730-T104638.raw, time of first ping: 2017-Jul-30 10:46:38\n",
"01:37:01 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T104638.nc\n",
"01:37:07 parsing file Summer2017-D20170730-T110014.raw, time of first ping: 2017-Jul-30 11:00:14\n",
"01:37:10 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T110014.nc\n",
"01:37:16 parsing file Summer2017-D20170730-T111349.raw, time of first ping: 2017-Jul-30 11:13:49\n",
"01:37:19 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T111349.nc\n",
"01:37:24 parsing file Summer2017-D20170730-T112724.raw, time of first ping: 2017-Jul-30 11:27:24\n",
"01:37:27 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T112724.nc\n",
"01:37:32 parsing file Summer2017-D20170730-T114100.raw, time of first ping: 2017-Jul-30 11:41:00\n",
"01:37:35 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T114100.nc\n",
"01:37:41 parsing file Summer2017-D20170730-T115435.raw, time of first ping: 2017-Jul-30 11:54:35\n",
"01:37:43 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T115435.nc\n",
"01:37:49 parsing file Summer2017-D20170730-T120811.raw, time of first ping: 2017-Jul-30 12:08:11\n",
"01:37:51 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T120811.nc\n",
"01:37:57 parsing file Summer2017-D20170730-T122152.raw, time of first ping: 2017-Jul-30 12:21:52\n",
"01:37:59 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T122152.nc\n",
"01:38:05 parsing file Summer2017-D20170730-T123529.raw, time of first ping: 2017-Jul-30 12:35:29\n",
"01:38:08 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T123529.nc\n",
"01:38:13 parsing file Summer2017-D20170730-T124907.raw, time of first ping: 2017-Jul-30 12:49:07\n",
"01:38:16 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T124907.nc\n",
"01:38:22 parsing file Summer2017-D20170730-T130509.raw, time of first ping: 2017-Jul-30 13:05:09\n",
"01:38:25 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T130509.nc\n",
"01:38:30 parsing file Summer2017-D20170730-T132102.raw, time of first ping: 2017-Jul-30 13:21:02\n",
"01:38:32 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T132102.nc\n",
"01:38:38 parsing file Summer2017-D20170730-T133158.raw, time of first ping: 2017-Jul-30 13:31:58\n",
"01:38:41 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T133158.nc\n",
"01:38:47 parsing file Summer2017-D20170730-T134618.raw, time of first ping: 2017-Jul-30 13:46:18\n",
"01:38:50 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T134618.nc\n",
"01:38:55 parsing file Summer2017-D20170730-T135814.raw, time of first ping: 2017-Jul-30 13:58:14\n",
"01:38:58 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T135814.nc\n",
"01:39:03 parsing file Summer2017-D20170730-T140906.raw, time of first ping: 2017-Jul-30 14:09:06\n",
"01:39:06 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T140906.nc\n",
"01:39:12 parsing file Summer2017-D20170730-T142018.raw, time of first ping: 2017-Jul-30 14:20:18\n",
"01:39:14 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T142018.nc\n",
"01:39:20 parsing file Summer2017-D20170730-T143110.raw, time of first ping: 2017-Jul-30 14:31:10\n",
"01:39:22 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T143110.nc\n",
"01:39:28 parsing file Summer2017-D20170730-T144200.raw, time of first ping: 2017-Jul-30 14:42:00\n",
"01:39:31 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T144200.nc\n",
"01:39:37 parsing file Summer2017-D20170730-T150043.raw, time of first ping: 2017-Jul-30 15:00:43\n",
"01:39:40 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T150043.nc\n",
"01:39:46 parsing file Summer2017-D20170730-T152008.raw, time of first ping: 2017-Jul-30 15:20:08\n",
"01:39:48 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T152008.nc\n",
"01:39:54 parsing file Summer2017-D20170730-T153933.raw, time of first ping: 2017-Jul-30 15:39:33\n",
"01:39:57 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T153933.nc\n",
"01:40:02 parsing file Summer2017-D20170730-T155858.raw, time of first ping: 2017-Jul-30 15:58:58\n",
"01:40:05 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T155858.nc\n",
"01:40:14 parsing file Summer2017-D20170730-T161839.raw, time of first ping: 2017-Jul-30 16:18:39\n",
"01:40:35 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T161839.nc\n",
"01:40:44 parsing file Summer2017-D20170730-T164023.raw, time of first ping: 2017-Jul-30 16:40:23\n",
"01:41:02 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T164023.nc\n",
"01:41:12 parsing file Summer2017-D20170730-T170849.raw, time of first ping: 2017-Jul-30 17:08:49\n",
"01:41:26 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T170849.nc\n",
"01:41:34 parsing file Summer2017-D20170730-T174159.raw, time of first ping: 2017-Jul-30 17:41:59\n",
"01:41:51 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T174159.nc\n",
"01:42:02 parsing file Summer2017-D20170730-T181459.raw, time of first ping: 2017-Jul-30 18:14:59\n",
"01:42:24 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T181459.nc\n",
"01:42:34 parsing file Summer2017-D20170730-T184532.raw, time of first ping: 2017-Jul-30 18:45:32\n",
"01:42:52 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T184532.nc\n",
"01:43:06 parsing file Summer2017-D20170730-T191309.raw, time of first ping: 2017-Jul-30 19:13:09\n",
"01:43:18 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T191309.nc\n",
"01:43:26 parsing file Summer2017-D20170730-T193651.raw, time of first ping: 2017-Jul-30 19:36:51\n",
"01:43:45 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T193651.nc\n",
"01:43:53 parsing file Summer2017-D20170730-T195647.raw, time of first ping: 2017-Jul-30 19:56:47\n",
"01:44:11 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T195647.nc\n",
"01:44:19 parsing file Summer2017-D20170730-T201642.raw, time of first ping: 2017-Jul-30 20:16:42\n",
"01:44:43 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T201642.nc\n",
"01:44:52 parsing file Summer2017-D20170730-T203637.raw, time of first ping: 2017-Jul-30 20:36:37\n",
"01:45:12 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T203637.nc\n",
"01:45:17 parsing file Summer2017-D20170730-T205631.raw, time of first ping: 2017-Jul-30 20:56:31\n",
"01:45:20 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T205631.nc\n",
"01:45:26 parsing file Summer2017-D20170730-T211625.raw, time of first ping: 2017-Jul-30 21:16:25\n",
"01:45:28 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T211625.nc\n",
"01:45:34 parsing file Summer2017-D20170730-T213619.raw, time of first ping: 2017-Jul-30 21:36:19\n",
"01:45:37 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T213619.nc\n",
"01:45:42 parsing file Summer2017-D20170730-T215614.raw, time of first ping: 2017-Jul-30 21:56:14\n",
"01:45:45 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T215614.nc\n",
"01:45:51 parsing file Summer2017-D20170730-T221612.raw, time of first ping: 2017-Jul-30 22:16:12\n",
"01:45:54 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T221612.nc\n",
"01:45:59 parsing file Summer2017-D20170730-T223608.raw, time of first ping: 2017-Jul-30 22:36:08\n",
"01:46:02 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T223608.nc\n",
"01:46:08 parsing file Summer2017-D20170730-T225603.raw, time of first ping: 2017-Jul-30 22:56:03\n",
"01:46:10 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T225603.nc\n",
"01:46:16 parsing file Summer2017-D20170730-T231557.raw, time of first ping: 2017-Jul-30 23:15:57\n",
"01:46:19 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T231557.nc\n",
"01:46:25 parsing file Summer2017-D20170730-T233546.raw, time of first ping: 2017-Jul-30 23:35:46\n",
"01:46:28 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T233546.nc\n",
"01:46:34 parsing file Summer2017-D20170730-T235529.raw, time of first ping: 2017-Jul-30 23:55:29\n",
"01:46:36 saving /usr/mayorgadat/workmain/acoustics/echopype/echopype_paper/hake2017data/convertednc/Summer2017-D20170730-T235529.nc\n",
"CPU times: user 27min 8s, sys: 1min 10s, total: 28min 19s\n",
"Wall time: 37min 43s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"for s3rawfpath in s3rawfiles:\n",
" raw_fname = Path(s3rawfpath).name\n",
" try:\n",
" ed = ep.open_raw(\n",
" f\"s3://{s3rawfpath}\",\n",
" sonar_model='EK60',\n",
" storage_options={'anon': True}\n",
" )\n",
" # Manually populate additional metadata about the dataset and the platform\n",
" populate_metadata(ed, raw_fname)\n",
"\n",
" # Save to converted netcdf, then use the EchoData ed object to generate \n",
" # calibrated and compute-MVBS files that will also be saved to netcdf\n",
" ed.to_netcdf(save_path='./hake2017data/convertednc')\n",
"\n",
" # Generate and save MVBS\n",
" ds_Sv = ep.calibrate.compute_Sv(ed)\n",
" ds_MVBS = ep.preprocess.compute_MVBS(\n",
" ds_Sv,\n",
" range_meter_bin=10, # in meters\n",
" ping_time_bin='20s' # in seconds\n",
" )\n",
" ds_MVBS.to_netcdf(Path('./hake2017data/calibratednc') / f\"MVBS_{raw_fname[:-4]}.nc\") \n",
" except Exception as e:\n",
" print(f\"Failed to process raw file {raw_fname}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Test for time reversals"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"def exist_reversed_time(ds, time_name):\n",
" # test for a negative np.diff\n",
" return (np.diff(ds[time_name]) < np.timedelta64(0, \"ns\")).any()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"for datapath in Path('./hake2017data/convertednc').glob('*'):\n",
" ed = ep.open_converted(datapath)\n",
" if exist_reversed_time(ed.beam, time_name='ping_time'):\n",
" print(f\"Reversed time in {datapath}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are no time reversals!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Examine the EchoData object for one of the data files"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <div>\n",
" <div class='xr-header'>\n",
" <div class=\"xr-obj-type\">EchoData: standardized raw data from hake2017data/convertednc/Summer2017-D20170730-T040253.nc</div>\n",
" </div>\n",
" <ul class=\"xr-sections group-sections\">\n",
" \n",
" <li class = \"xr-section-item\">\n",
" <input id=\"idata_topf40c36b6-cbb8-4918-8661-d243301e6225\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
" <label for=\"idata_topf40c36b6-cbb8-4918-8661-d243301e6225\" class = \"xr-section-summary\">top: (Top-level) contains metadata about the SONAR-netCDF4 file format.</label>\n",
" <div class=\"xr-section-inline-details\"></div>\n",
" <div class=\"xr-section-details\">\n",
" <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
" <div style=\"padding-left:2rem;\"><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.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;\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-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",
" 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",
" 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-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-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",
" 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: ()\n",
"Data variables:\n",
" *empty*\n",
"Attributes:\n",
" conventions: CF-1.7, SONAR-netCDF4-1.0, ACDD-1.3\n",
" keywords: EK60\n",
" sonar_convention_authority: ICES\n",
" sonar_convention_name: SONAR-netCDF4\n",
" sonar_convention_version: 1.0\n",
" summary: EK60 raw file Summer2017-D20170730-T040253.r...\n",
" title: 2017 Pacific Hake Survey, file Summer2017-D2...\n",
" date_created: 2017-07-30T04:02:53Z\n",
" survey_name: </pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-35cc62ff-42ac-49eb-9e77-25e6ef0810ff' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-35cc62ff-42ac-49eb-9e77-25e6ef0810ff' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-15d1df68-8490-4ce3-95bc-7e951c0fdcc2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-15d1df68-8490-4ce3-95bc-7e951c0fdcc2' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-c7ad2708-47df-446e-9ac3-f104c261a933' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c7ad2708-47df-446e-9ac3-f104c261a933' class='xr-section-summary' title='Expand/collapse section'>Data variables: <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-d74d0799-9ddb-48e0-ae55-253931e739f1' class='xr-section-summary-in' type='checkbox' checked><label for='section-d74d0799-9ddb-48e0-ae55-253931e739f1' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>conventions :</span></dt><dd>CF-1.7, SONAR-netCDF4-1.0, ACDD-1.3</dd><dt><span>keywords :</span></dt><dd>EK60</dd><dt><span>sonar_convention_authority :</span></dt><dd>ICES</dd><dt><span>sonar_convention_name :</span></dt><dd>SONAR-netCDF4</dd><dt><span>sonar_convention_version :</span></dt><dd>1.0</dd><dt><span>summary :</span></dt><dd>EK60 raw file Summer2017-D20170730-T040253.raw from the 2017 Pacific Hake Survey, converted to a SONAR-netCDF4 file using echopype</dd><dt><span>title :</span></dt><dd>2017 Pacific Hake Survey, file Summer2017-D20170730-T040253.raw</dd><dt><span>date_created :</span></dt><dd>2017-07-30T04:02:53Z</dd><dt><span>survey_name :</span></dt><dd></dd></dl></div></li></ul></div></div><br></div>\n",
" </ul>\n",
" </div>\n",
" </li>\n",
" \n",
" <li class = \"xr-section-item\">\n",
" <input id=\"idata_environment68a777d6-2b29-45a7-a01c-9fcf8d5a6543\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
" <label for=\"idata_environment68a777d6-2b29-45a7-a01c-9fcf8d5a6543\" class = \"xr-section-summary\">environment: (Environment) contains information relevant to acoustic propagation through water.</label>\n",
" <div class=\"xr-section-inline-details\"></div>\n",
" <div class=\"xr-section-details\">\n",
" <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
" <div style=\"padding-left:2rem;\"><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.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;\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-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",
" 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",
" 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-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-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",
" 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: (frequency: 3, ping_time: 536)\n",
"Coordinates:\n",
" * frequency (frequency) float64 1.8e+04 3.8e+04 1.2e+05\n",
" * ping_time (ping_time) datetime64[ns] 2017-07-30T04:02:53.48...\n",
"Data variables:\n",
" absorption_indicative (frequency, ping_time) float64 0.002822 ... 0.03259\n",
" sound_speed_indicative (frequency, ping_time) float64 1.481e+03 ... 1.48...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-19bc64b3-7416-4eef-9a84-1b3ce6a4ce3f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-19bc64b3-7416-4eef-9a84-1b3ce6a4ce3f' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>frequency</span>: 3</li><li><span class='xr-has-index'>ping_time</span>: 536</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5c6ae1dd-1a1b-4410-9d82-dc4e904689b7' class='xr-section-summary-in' type='checkbox' checked><label for='section-5c6ae1dd-1a1b-4410-9d82-dc4e904689b7' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>frequency</span></div><div class='xr-var-dims'>(frequency)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.8e+04 3.8e+04 1.2e+05</div><input id='attrs-e79188f8-a5a2-47e9-8379-54efd410fb0e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e79188f8-a5a2-47e9-8379-54efd410fb0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87c5d613-f8fc-43f5-8cd4-ed1cf43361aa' class='xr-var-data-in' type='checkbox'><label for='data-87c5d613-f8fc-43f5-8cd4-ed1cf43361aa' 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>units :</span></dt><dd>Hz</dd><dt><span>long_name :</span></dt><dd>Transducer frequency</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([ 18000., 38000., 120000.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ping_time</span></div><div class='xr-var-dims'>(ping_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-07-30T04:02:53.488999936 .....</div><input id='attrs-0ee95170-4b3a-40bc-a500-286e45f0c9c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ee95170-4b3a-40bc-a500-286e45f0c9c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e59f12a6-b529-4973-8293-752d6c5266bf' class='xr-var-data-in' type='checkbox'><label for='data-e59f12a6-b529-4973-8293-752d6c5266bf' 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>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>Timestamps for NMEA position datagrams</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-07-30T04:02:53.488999936&#x27;, &#x27;2017-07-30T04:02:55.312999936&#x27;,\n",
" &#x27;2017-07-30T04:02:57.117000192&#x27;, ..., &#x27;2017-07-30T04:19:00.534000128&#x27;,\n",
" &#x27;2017-07-30T04:19:02.398000128&#x27;, &#x27;2017-07-30T04:19:04.250999808&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-27e0a1d2-9c45-4731-8558-e5e50b5f6965' class='xr-section-summary-in' type='checkbox' checked><label for='section-27e0a1d2-9c45-4731-8558-e5e50b5f6965' class='xr-section-summary' >Data variables: <span>(2)</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>absorption_indicative</span></div><div class='xr-var-dims'>(frequency, ping_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1a9d9014-e930-4902-bbfa-d0f98f657b40' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a9d9014-e930-4902-bbfa-d0f98f657b40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c801e16-590f-4ffe-be42-94e63404a0b4' class='xr-var-data-in' type='checkbox'><label for='data-0c801e16-590f-4ffe-be42-94e63404a0b4' 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>Indicative acoustic absorption</dd><dt><span>units :</span></dt><dd>dB/m</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([[0.002822, 0.002822, 0.002822, ..., 0.002822, 0.002822, 0.002822],\n",
" [0.009855, 0.009855, 0.009855, ..., 0.009855, 0.009855, 0.009855],\n",
" [0.032594, 0.032594, 0.032594, ..., 0.032594, 0.032594, 0.032594]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sound_speed_indicative</span></div><div class='xr-var-dims'>(frequency, ping_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4c63d198-1743-4480-b947-e581a0461d4f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c63d198-1743-4480-b947-e581a0461d4f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5e0a36c-04d3-473b-8f8a-cbd94aee843f' class='xr-var-data-in' type='checkbox'><label for='data-a5e0a36c-04d3-473b-8f8a-cbd94aee843f' 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>Indicative sound speed</dd><dt><span>standard_name :</span></dt><dd>speed_of_sound_in_sea_water</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([[1480.625977, 1480.625977, 1480.625977, ..., 1480.625977, 1480.625977,\n",
" 1480.625977],\n",
" [1480.625977, 1480.625977, 1480.625977, ..., 1480.625977, 1480.625977,\n",
" 1480.625977],\n",
" [1480.625977, 1480.625977, 1480.625977, ..., 1480.625977, 1480.625977,\n",
" 1480.625977]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1d0637f2-75c6-4e9c-bde3-da0b0640dacf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1d0637f2-75c6-4e9c-bde3-da0b0640dacf' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div><br></div>\n",
" </ul>\n",
" </div>\n",
" </li>\n",
" \n",
" <li class = \"xr-section-item\">\n",
" <input id=\"idata_platforme43ba9ba-eda9-4a22-a209-3368dddcbff3\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
" <label for=\"idata_platforme43ba9ba-eda9-4a22-a209-3368dddcbff3\" class = \"xr-section-summary\">platform: (Platform) contains information about the platform on which the sonar is installed.</label>\n",
" <div class=\"xr-section-inline-details\"></div>\n",
" <div class=\"xr-section-details\">\n",
" <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
" <div style=\"padding-left:2rem;\"><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.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;\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-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",
" 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",
" 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-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-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",
" 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: (frequency: 3, location_time: 1382, ping_time: 536)\n",
"Coordinates:\n",
" * location_time (location_time) datetime64[ns] 2017-07-30T04:02:54.2870000...\n",
" * frequency (frequency) float64 1.8e+04 3.8e+04 1.2e+05\n",
" * ping_time (ping_time) datetime64[ns] 2017-07-30T04:02:53.488999936 ....\n",
"Data variables:\n",
" latitude (location_time) float64 44.31 44.31 44.31 ... 44.28 44.28\n",
" longitude (location_time) float64 -124.7 -124.7 ... -124.8 -124.8\n",
" sentence_type (location_time) object &#x27;GGA&#x27; &#x27;GLL&#x27; &#x27;GGA&#x27; ... &#x27;GGA&#x27; &#x27;GLL&#x27;\n",
" pitch (frequency, ping_time) float64 -0.6207 -0.4622 ... -0.6818\n",
" roll (frequency, ping_time) float64 -0.7396 -0.82 ... -1.508\n",
" heave (frequency, ping_time) float64 0.05022 -0.007819 ... -0.1022\n",
" water_level (frequency, ping_time) float64 9.15 9.15 9.15 ... 9.15 9.15\n",
"Attributes:\n",
" platform_type: Research vessel\n",
" platform_name: Bell M. Shimada\n",
" platform_code_ICES: 315</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-97b6f730-1981-4b1b-a1bf-d97906a5cfd2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-97b6f730-1981-4b1b-a1bf-d97906a5cfd2' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>frequency</span>: 3</li><li><span class='xr-has-index'>location_time</span>: 1382</li><li><span class='xr-has-index'>ping_time</span>: 536</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d7e3ea40-de24-4dc3-9b45-f811e099d327' class='xr-section-summary-in' type='checkbox' checked><label for='section-d7e3ea40-de24-4dc3-9b45-f811e099d327' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>location_time</span></div><div class='xr-var-dims'>(location_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-07-30T04:02:54.287000064 .....</div><input id='attrs-d4c26bfc-ba3b-43ae-bbae-d0b4895bda85' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4c26bfc-ba3b-43ae-bbae-d0b4895bda85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a98fc77e-c336-4581-b5af-9dc75770bcb2' class='xr-var-data-in' type='checkbox'><label for='data-a98fc77e-c336-4581-b5af-9dc75770bcb2' 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>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>Timestamps for NMEA position datagrams</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-07-30T04:02:54.287000064&#x27;, &#x27;2017-07-30T04:02:54.445000192&#x27;,\n",
" &#x27;2017-07-30T04:02:55.137999872&#x27;, ..., &#x27;2017-07-30T04:19:05.088000000&#x27;,\n",
" &#x27;2017-07-30T04:19:06.090000384&#x27;, &#x27;2017-07-30T04:19:06.248999936&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>frequency</span></div><div class='xr-var-dims'>(frequency)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.8e+04 3.8e+04 1.2e+05</div><input id='attrs-5f0880ad-8c52-4306-b1a1-a6b5ad915c4b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f0880ad-8c52-4306-b1a1-a6b5ad915c4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1467eff9-1d81-4646-a845-a2dde0d71c8c' class='xr-var-data-in' type='checkbox'><label for='data-1467eff9-1d81-4646-a845-a2dde0d71c8c' 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>units :</span></dt><dd>Hz</dd><dt><span>long_name :</span></dt><dd>Transducer frequency</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([ 18000., 38000., 120000.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ping_time</span></div><div class='xr-var-dims'>(ping_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-07-30T04:02:53.488999936 .....</div><input id='attrs-00a6d96a-ecf1-4244-a401-b18a19c9aa48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-00a6d96a-ecf1-4244-a401-b18a19c9aa48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6d25372-97f7-4dda-8944-910bbfa34294' class='xr-var-data-in' type='checkbox'><label for='data-b6d25372-97f7-4dda-8944-910bbfa34294' 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>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>Timestamps for position datagrams</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-07-30T04:02:53.488999936&#x27;, &#x27;2017-07-30T04:02:55.312999936&#x27;,\n",
" &#x27;2017-07-30T04:02:57.117000192&#x27;, ..., &#x27;2017-07-30T04:19:00.534000128&#x27;,\n",
" &#x27;2017-07-30T04:19:02.398000128&#x27;, &#x27;2017-07-30T04:19:04.250999808&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-49aaeccd-c7ae-4d3a-b853-9a52b7eedd55' class='xr-section-summary-in' type='checkbox' checked><label for='section-49aaeccd-c7ae-4d3a-b853-9a52b7eedd55' class='xr-section-summary' >Data variables: <span>(7)</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>latitude</span></div><div class='xr-var-dims'>(location_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-193be7bb-bfd8-457b-9157-c983eadb0bfe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-193be7bb-bfd8-457b-9157-c983eadb0bfe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b095a2ce-715b-41b9-84fc-df6037016ef8' class='xr-var-data-in' type='checkbox'><label for='data-b095a2ce-715b-41b9-84fc-df6037016ef8' 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>Platform latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_range :</span></dt><dd>[-90. 90.]</dd></dl></div><div class='xr-var-data'><pre>array([44.307337, 44.307333, 44.307315, ..., 44.2795 , 44.279472, 44.2795 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(location_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-51337777-c04b-4026-a143-119105602f99' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-51337777-c04b-4026-a143-119105602f99' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab786a34-f2d6-42c3-aa18-2986164a3723' class='xr-var-data-in' type='checkbox'><label for='data-ab786a34-f2d6-42c3-aa18-2986164a3723' 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>Platform longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_range :</span></dt><dd>[-180. 180.]</dd></dl></div><div class='xr-var-data'><pre>array([-124.733412, -124.733333, -124.733477, ..., -124.794755, -124.794817,\n",
" -124.794833])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sentence_type</span></div><div class='xr-var-dims'>(location_time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-079517b4-d286-4279-a4dc-192522817fa6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-079517b4-d286-4279-a4dc-192522817fa6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99ab5e36-e826-4ffc-a344-b4c2e971b019' class='xr-var-data-in' type='checkbox'><label for='data-99ab5e36-e826-4ffc-a344-b4c2e971b019' 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'></dl></div><div class='xr-var-data'><pre>array([&#x27;GGA&#x27;, &#x27;GLL&#x27;, &#x27;GGA&#x27;, ..., &#x27;GGA&#x27;, &#x27;GGA&#x27;, &#x27;GLL&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pitch</span></div><div class='xr-var-dims'>(frequency, ping_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fd35570f-1c89-4728-880d-f65410a690fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd35570f-1c89-4728-880d-f65410a690fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88231400-9df9-4037-80ac-53dfdb3ebfe9' class='xr-var-data-in' type='checkbox'><label for='data-88231400-9df9-4037-80ac-53dfdb3ebfe9' 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>Platform pitch</dd><dt><span>standard_name :</span></dt><dd>platform_pitch_angle</dd><dt><span>units :</span></dt><dd>arc_degree</dd><dt><span>valid_range :</span></dt><dd>[-90. 90.]</dd></dl></div><div class='xr-var-data'><pre>array([[-0.620658, -0.462181, -0.68 , ..., -0.534337, -0.850781, -0.681758],\n",
" [-0.620658, -0.462181, -0.68 , ..., -0.534337, -0.850781, -0.681758],\n",
" [-0.620658, -0.462181, -0.68 , ..., -0.534337, -0.850781, -0.681758]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roll</span></div><div class='xr-var-dims'>(frequency, ping_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cc8b8489-c96a-4eb1-b837-f90e716a45da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc8b8489-c96a-4eb1-b837-f90e716a45da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68085a08-c43b-4ec2-bfa2-79501a441d59' class='xr-var-data-in' type='checkbox'><label for='data-68085a08-c43b-4ec2-bfa2-79501a441d59' 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>Platform roll</dd><dt><span>standard_name :</span></dt><dd>platform_roll_angle</dd><dt><span>units :</span></dt><dd>arc_degree</dd><dt><span>valid_range :</span></dt><dd>[-90. 90.]</dd></dl></div><div class='xr-var-data'><pre>array([[-0.739562, -0.82 , -0.69 , ..., -0.731325, -0.703123, -1.508242],\n",
" [-0.739562, -0.82 , -0.69 , ..., -0.731325, -0.703123, -1.508242],\n",
" [-0.739562, -0.82 , -0.69 , ..., -0.731325, -0.703123, -1.508242]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heave</span></div><div class='xr-var-dims'>(frequency, ping_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7fdb99ef-83bf-41ba-b4b5-ce5cca5cf195' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7fdb99ef-83bf-41ba-b4b5-ce5cca5cf195' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f3b405b-3afe-44e7-906d-2b225e7f5513' class='xr-var-data-in' type='checkbox'><label for='data-7f3b405b-3afe-44e7-906d-2b225e7f5513' 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>Platform heave</dd><dt><span>standard_name :</span></dt><dd>platform_heave_angle</dd><dt><span>units :</span></dt><dd>arc_degree</dd><dt><span>valid_range :</span></dt><dd>[-90. 90.]</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.050219, -0.007819, 0.05 , ..., -0.061325, 0.129219, -0.102162],\n",
" [ 0.050219, -0.007819, 0.05 , ..., -0.061325, 0.129219, -0.102162],\n",
" [ 0.050219, -0.007819, 0.05 , ..., -0.061325, 0.129219, -0.102162]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>water_level</span></div><div class='xr-var-dims'>(frequency, ping_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8097322d-ada3-4a98-9c24-5b984c89fd59' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8097322d-ada3-4a98-9c24-5b984c89fd59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00e8f235-3527-4fec-962f-96fe80685c81' class='xr-var-data-in' type='checkbox'><label for='data-00e8f235-3527-4fec-962f-96fe80685c81' 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>z-axis distance from the platform coordinate system origin to the sonar transducer</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([[9.15, 9.15, 9.15, ..., 9.15, 9.15, 9.15],\n",
" [9.15, 9.15, 9.15, ..., 9.15, 9.15, 9.15],\n",
" [9.15, 9.15, 9.15, ..., 9.15, 9.15, 9.15]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1a85cb04-5f29-4922-92d6-668a9b587c73' class='xr-section-summary-in' type='checkbox' checked><label for='section-1a85cb04-5f29-4922-92d6-668a9b587c73' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>platform_type :</span></dt><dd>Research vessel</dd><dt><span>platform_name :</span></dt><dd>Bell M. Shimada</dd><dt><span>platform_code_ICES :</span></dt><dd>315</dd></dl></div></li></ul></div></div><br></div>\n",
" </ul>\n",
" </div>\n",
" </li>\n",
" \n",
" <li class = \"xr-section-item\">\n",
" <input id=\"idata_provenanceb0755709-42a2-435b-bc76-79df4dbe987b\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
" <label for=\"idata_provenanceb0755709-42a2-435b-bc76-79df4dbe987b\" class = \"xr-section-summary\">provenance: (Provenance) contains metadata about how the SONAR-netCDF4 version of the data were obtained.</label>\n",
" <div class=\"xr-section-inline-details\"></div>\n",
" <div class=\"xr-section-details\">\n",
" <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
" <div style=\"padding-left:2rem;\"><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.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;\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-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",
" 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",
" 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-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-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",
" 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: ()\n",
"Data variables:\n",
" *empty*\n",
"Attributes:\n",
" conversion_software_name: echopype\n",
" conversion_software_version: 0.5.0\n",
" conversion_time: 2021-06-09T08:33:46Z\n",
" src_filenames: s3://ncei-wcsd-archive/data/raw/Bell_M._Shi...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3808a17c-f270-4d57-ae46-18429779d95d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3808a17c-f270-4d57-ae46-18429779d95d' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c5694f3a-9e80-4ff8-a874-d8f68d1b0988' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c5694f3a-9e80-4ff8-a874-d8f68d1b0988' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-2dc947d1-d7db-4eda-a6a0-2b5f72dfd96c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2dc947d1-d7db-4eda-a6a0-2b5f72dfd96c' class='xr-section-summary' title='Expand/collapse section'>Data variables: <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-54a44e0d-e93b-4280-9438-8de58e4dd0d5' class='xr-section-summary-in' type='checkbox' checked><label for='section-54a44e0d-e93b-4280-9438-8de58e4dd0d5' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>conversion_software_name :</span></dt><dd>echopype</dd><dt><span>conversion_software_version :</span></dt><dd>0.5.0</dd><dt><span>conversion_time :</span></dt><dd>2021-06-09T08:33:46Z</dd><dt><span>src_filenames :</span></dt><dd>s3://ncei-wcsd-archive/data/raw/Bell_M._Shimada/SH1707/EK60/Summer2017-D20170730-T040253.raw</dd></dl></div></li></ul></div></div><br></div>\n",
" </ul>\n",
" </div>\n",
" </li>\n",
" \n",
" <li class = \"xr-section-item\">\n",
" <input id=\"idata_sonar2e9e68b4-3405-4263-b39a-af505a87bf88\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
" <label for=\"idata_sonar2e9e68b4-3405-4263-b39a-af505a87bf88\" class = \"xr-section-summary\">sonar: (Sonar) contains specific metadata for the sonar system.</label>\n",
" <div class=\"xr-section-inline-details\"></div>\n",
" <div class=\"xr-section-details\">\n",
" <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
" <div style=\"padding-left:2rem;\"><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.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;\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-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",
" 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",
" 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-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-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",
" 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: ()\n",
"Data variables:\n",
" *empty*\n",
"Attributes:\n",
" sonar_manufacturer: Simrad\n",
" sonar_model: ER60\n",
" sonar_serial_number: \n",
" sonar_software_name: \n",
" sonar_software_version: 2.4.3\n",
" sonar_type: echosounder</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-59285c03-def5-4a84-be70-12194890e903' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-59285c03-def5-4a84-be70-12194890e903' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f83b08c6-d72a-40db-a0ab-21825c43ffa3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f83b08c6-d72a-40db-a0ab-21825c43ffa3' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-64c7d3c1-23e6-4963-93f8-984f3d7bc5a5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-64c7d3c1-23e6-4963-93f8-984f3d7bc5a5' class='xr-section-summary' title='Expand/collapse section'>Data variables: <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-28538846-528a-4833-9c6b-16c7e19f760d' class='xr-section-summary-in' type='checkbox' checked><label for='section-28538846-528a-4833-9c6b-16c7e19f760d' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>sonar_manufacturer :</span></dt><dd>Simrad</dd><dt><span>sonar_model :</span></dt><dd>ER60</dd><dt><span>sonar_serial_number :</span></dt><dd></dd><dt><span>sonar_software_name :</span></dt><dd></dd><dt><span>sonar_software_version :</span></dt><dd>2.4.3</dd><dt><span>sonar_type :</span></dt><dd>echosounder</dd></dl></div></li></ul></div></div><br></div>\n",
" </ul>\n",
" </div>\n",
" </li>\n",
" \n",
" <li class = \"xr-section-item\">\n",
" <input id=\"idata_beam5aa5b51f-3710-432f-a80b-6d3d9528ce24\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
" <label for=\"idata_beam5aa5b51f-3710-432f-a80b-6d3d9528ce24\" class = \"xr-section-summary\">beam: (Beam) contains backscatter data and other beam or channel-specific data.</label>\n",
" <div class=\"xr-section-inline-details\"></div>\n",
" <div class=\"xr-section-details\">\n",
" <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
" <div style=\"padding-left:2rem;\"><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.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;\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-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",
" 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",
" 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-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-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",
" 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: (frequency: 3, ping_time: 536, range_bin: 3957)\n",
"Coordinates:\n",
" * frequency (frequency) float64 1.8e+04 3.8e+04 1.2e+05\n",
" * ping_time (ping_time) datetime64[ns] 2017-07-30T04:...\n",
" * range_bin (range_bin) int64 0 1 2 3 ... 3954 3955 3956\n",
"Data variables:\n",
" channel_id (frequency) object &#x27;GPT 18 kHz 009072058...\n",
" beam_type (frequency) int64 1 1 1\n",
" beamwidth_receive_alongship (frequency) float64 10.9 6.81 6.58\n",
" beamwidth_receive_athwartship (frequency) float64 10.82 6.85 6.52\n",
" beamwidth_transmit_alongship (frequency) float64 10.9 6.81 6.58\n",
" beamwidth_transmit_athwartship (frequency) float64 10.82 6.85 6.52\n",
" beam_direction_x (frequency) float64 0.0 0.0 0.0\n",
" beam_direction_y (frequency) float64 0.0 0.0 0.0\n",
" beam_direction_z (frequency) float64 0.0 0.0 0.0\n",
" angle_offset_alongship (frequency) float64 -0.18 -0.08 -0.05\n",
" angle_offset_athwartship (frequency) float64 0.25 0.0 0.37\n",
" angle_sensitivity_alongship (frequency) float64 13.89 21.97 23.12\n",
" angle_sensitivity_athwartship (frequency) float64 13.89 21.97 23.12\n",
" equivalent_beam_angle (frequency) float64 -17.37 -21.01 -20.47\n",
" transducer_offset_x (frequency) float64 0.0 0.0 0.0\n",
" transducer_offset_y (frequency) float64 0.0 0.0 0.0\n",
" transducer_offset_z (frequency) float64 0.0 0.0 0.0\n",
" gain_correction (frequency) float64 22.95 26.07 26.55\n",
" gpt_software_version (frequency) object &#x27;070413&#x27; ... &#x27;070413&#x27;\n",
" backscatter_r (frequency, ping_time, range_bin) float32 ...\n",
" sample_interval (frequency, ping_time) float64 0.000256 ....\n",
" transmit_bandwidth (frequency, ping_time) float64 1.574e+03 ...\n",
" transmit_duration_nominal (frequency, ping_time) float64 0.001024 ....\n",
" transmit_power (frequency, ping_time) float64 2e+03 ... ...\n",
" data_type (frequency, ping_time) float64 3.0 ... 3.0\n",
" count (frequency, ping_time) float64 3.957e+03 ...\n",
" offset (frequency, ping_time) float64 0.0 ... 0.0\n",
" transmit_mode (frequency, ping_time) float64 0.0 ... 0.0\n",
" angle_athwartship (frequency, ping_time, range_bin) float64 ...\n",
" angle_alongship (frequency, ping_time, range_bin) float64 ...\n",
"Attributes:\n",
" beam_mode: vertical\n",
" conversion_equation_t: type_3</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e86b50dd-7e9b-4214-a42d-d0f3ab0f5ccd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e86b50dd-7e9b-4214-a42d-d0f3ab0f5ccd' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>frequency</span>: 3</li><li><span class='xr-has-index'>ping_time</span>: 536</li><li><span class='xr-has-index'>range_bin</span>: 3957</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-60323942-9333-45fa-aa32-6ebb268d5bee' class='xr-section-summary-in' type='checkbox' checked><label for='section-60323942-9333-45fa-aa32-6ebb268d5bee' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>frequency</span></div><div class='xr-var-dims'>(frequency)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.8e+04 3.8e+04 1.2e+05</div><input id='attrs-1e4950be-44af-4628-bf74-28fcbb0104dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1e4950be-44af-4628-bf74-28fcbb0104dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d5c1321-2866-4797-92b3-93a04dc4c408' class='xr-var-data-in' type='checkbox'><label for='data-5d5c1321-2866-4797-92b3-93a04dc4c408' 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>units :</span></dt><dd>Hz</dd><dt><span>long_name :</span></dt><dd>Transducer frequency</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([ 18000., 38000., 120000.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ping_time</span></div><div class='xr-var-dims'>(ping_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-07-30T04:02:53.488999936 .....</div><input id='attrs-e98b48a8-c284-46d8-9bed-9908656958e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e98b48a8-c284-46d8-9bed-9908656958e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8e94089-cafc-4560-8cd0-5a72e21f479e' class='xr-var-data-in' type='checkbox'><label for='data-f8e94089-cafc-4560-8cd0-5a72e21f479e' 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>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>Timestamp of each ping</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-07-30T04:02:53.488999936&#x27;, &#x27;2017-07-30T04:02:55.312999936&#x27;,\n",
" &#x27;2017-07-30T04:02:57.117000192&#x27;, ..., &#x27;2017-07-30T04:19:00.534000128&#x27;,\n",
" &#x27;2017-07-30T04:19:02.398000128&#x27;, &#x27;2017-07-30T04:19:04.250999808&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range_bin</span></div><div class='xr-var-dims'>(range_bin)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 3953 3954 3955 3956</div><input id='attrs-7b9c489a-6b03-4cf0-b944-96324f4d84a7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7b9c489a-6b03-4cf0-b944-96324f4d84a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6445f97-ee60-43b3-aabf-46d802ee89a5' class='xr-var-data-in' type='checkbox'><label for='data-f6445f97-ee60-43b3-aabf-46d802ee89a5' 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'></dl></div><div class='xr-var-data'><pre>array([ 0, 1, 2, ..., 3954, 3955, 3956])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-547f4b3e-369e-48e2-b80f-8ab0b3f0f7bf' class='xr-section-summary-in' type='checkbox' ><label for='section-547f4b3e-369e-48e2-b80f-8ab0b3f0f7bf' class='xr-section-summary' >Data variables: <span>(30)</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>channel_id</span></div><div class='xr-var-dims'>(frequency)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-698f269b-353d-4dfd-96cf-e60f995d16f1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-698f269b-353d-4dfd-96cf-e60f995d16f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a84603a-d295-4eb9-8967-29750630b825' class='xr-var-data-in' type='checkbox'><label for='data-1a84603a-d295-4eb9-8967-29750630b825' 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'></dl></div><div class='xr-var-data'><pre>array([&#x27;GPT 18 kHz 009072058c8d 1-1 ES18-11&#x27;,\n",
" &#x27;GPT 38 kHz 009072058146 2-1 ES38B&#x27;,\n",
" &#x27;GPT 120 kHz 00907205a6d0 4-1 ES120-7C&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beam_type</span></div><div class='xr-var-dims'>(frequency)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6246af26-205e-45e2-91d3-818d25dde0c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6246af26-205e-45e2-91d3-818d25dde0c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9d34f56-7e50-4b9b-b63e-a2cf14bc21e3' class='xr-var-data-in' type='checkbox'><label for='data-d9d34f56-7e50-4b9b-b63e-a2cf14bc21e3' 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 transducer (0-single, 1-split)</dd></dl></div><div class='xr-var-data'><pre>array([1, 1, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beamwidth_receive_alongship</span></div><div class='xr-var-dims'>(frequency)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0ec163ab-7456-4306-a2c7-f2a85e92f3dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ec163ab-7456-4306-a2c7-f2a85e92f3dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c68defce-3a67-488b-8ecb-c7e4c7e42174' class='xr-var-data-in' type='checkbox'><label for='data-c68defce-3a67-488b-8ecb-c7e4c7e42174' 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>Half power one-way receive beam width along alongship axis of beam</dd><dt><span>units :</span></dt><dd>arc_degree</dd><dt><span>valid_range :</span></dt><dd>[ 0. 360.]</dd></dl></div><div class='xr-var-data'><pre>array([10.9 , 6.81, 6.58])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beamwidth_receive_athwartship</span></div><div class='xr-var-dims'>(frequency)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f3dd5f3c-d47a-428e-9d0e-f9d8e3e4cc5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f3dd5f3c-d47a-428e-9d0e-f9d8e3e4cc5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b51958ed-b810-49ae-8897-a8a7fc434710' class='xr-var-data-in' type='checkbox'><label for='data-b51958ed-b810-49ae-8897-a8a7fc434710' 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>Half power one-way receive beam width along athwartship axis of beam</dd><dt><span>units :</span></dt><dd>arc_degree</dd><dt><span>valid_range :</span></dt><dd>[ 0. 360.]</dd></dl></div><div class='xr-var-data'><pre>array([10.82, 6.85, 6.52])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beamwidth_transmit_alongship</span></div><div class='xr-var-dims'>(frequency)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-96695fbb-1cb8-4ed0-816a-b5888e6dfb01' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-96695fbb-1cb8-4ed0-816a-b5888e6dfb01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6da6f5bf-6478-4386-9a75-3a772944b13b' class='xr-var-data-in' type='checkbox'><label for='data-6da6f5bf-6478-4386-9a75-3a772944b13b' 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>Half power one-way transmit beam width along alongship axis of beam</dd><dt><span>units :</span></dt><dd>arc_degree</dd><dt><span>valid_range :</span></dt><dd>[ 0. 360.]</dd></dl></div><div class='xr-var-data'><pre>array([10.9 , 6.81, 6.58])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beamwidth_transmit_athwartship</span></div><div class='xr-var-dims'>(frequency)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b9ed7b3c-202e-452b-b3cb-33c57b440670' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9ed7b3c-202e-452b-b3cb-33c57b440670' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2beb2794-40c6-4e75-9544-8951af16e814' class='xr-var-data-in' type='checkbox'><label for='data-2beb2794-40c6-4e75-9544-8951af16e814' 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>Half power one-way transmit beam width along athwartship axis of beam</dd><dt><span>units :</span></dt><dd>arc_degree</dd><dt><span>valid_range :</span></dt><dd>[ 0. 360.]</dd></dl></div><div class='xr-var-data'><pre>array([10.82, 6.85, 6.52])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beam_direction_x</span></div><div class='xr-var-dims'>(frequency)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7eaffa71-ec2f-4f28-ad4b-93804ab587ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7eaffa71-ec2f-4f28-ad4b-93804ab587ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2308635-1662-4720-bb13-af979189b3e8' class='xr-var-data-in' type='checkbox'><label for='data-f2308635-1662-4720-bb13-af979189b3e8' 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>x-component of the vector that gives the pointing direction of the beam, in sonar beam coordinate system</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_range :</span></dt><dd>[-1. 1.]</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beam_direction_y</span></div><div class='xr-var-dims'>(frequency)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d97dba3a-81e2-499f-9181-984ec8a09b02' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d97dba3a-81e2-499f-9181-984ec8a09b02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aaf9ef16-71f6-42a9-a981-f7f7fa30808b' class='xr-var-data-in' type='checkbox'><label for='data-aaf9ef16-71f6-42a9-a981-f7f7fa30808b' 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>y-component of the vector that gives the pointing direction of the beam, in sonar beam coordinate system</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_range :</span></dt><dd>[-1. 1.]</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beam_direction_z</span></div><div class='xr-var-dims'>(frequency)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-693a1605-6714-4f1c-837b-139ae2224e5d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-693a1605-6714-4f1c-837b-139ae2224e5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-febb6ef1-2bfd-4000-b130-d7e23e1e78d3' class='xr-var-data-in' type='checkbox'><label for='data-febb6ef1-2bfd-4000-b130-d7e23e1e78d3' 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>z-component of the vector that gives the pointing direction of the beam, in sonar beam coordinate system</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_range :</span></dt><dd>[-1. 1.]</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angle_offset_alongship</span></div><div class='xr-var-dims'>(frequency)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e7e1dae6-81bf-4b24-b3f1-5090457d85a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7e1dae6-81bf-4b24-b3f1-5090457d85a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c094b5c1-c613-490b-a52c-1d2a60180c18' class='xr-var-data-in' type='checkbox'><label for='data-c094b5c1-c613-490b-a52c-1d2a60180c18' 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>electrical alongship angle of the transducer</dd></dl></div><div class='xr-var-data'><pre>array([-0.18, -0.08, -0.05])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angle_offset_athwartship</span></div><div class='xr-var-dims'>(frequency)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-03277ce2-4485-466f-a654-e65db4252d4c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03277ce2-4485-466f-a654-e65db4252d4c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2622cd4-9aec-450c-bd0f-1b285b5390e6' class='xr-var-data-in' type='checkbox'><label for='data-d2622cd4-9aec-450c-bd0f-1b285b5390e6' 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>electrical athwartship angle of the transducer</dd></dl></div><div class='xr-var-data'><pre>array([0.25, 0. , 0.37])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angle_sensitivity_alongship</span></div><div class='xr-var-dims'>(frequency)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-455d51d9-8821-4dc6-8d60-0d955c5eb796' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-455d51d9-8821-4dc6-8d60-0d955c5eb796' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-081e3032-1e62-4108-b284-5a9633e4b9b5' class='xr-var-data-in' type='checkbox'><label for='data-081e3032-1e62-4108-b284-5a9633e4b9b5' 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>alongship sensitivity of the transducer</dd></dl></div><div class='xr-var-data'><pre>array([13.89 , 21.969999, 23.120001])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angle_sensitivity_athwartship</span></div><div class='xr-var-dims'>(frequency)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-35e6160a-033f-4ce3-98f8-53f5e35ab76c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-35e6160a-033f-4ce3-98f8-53f5e35ab76c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09c10c60-4f97-4213-bcec-6eea7fbacb32' class='xr-var-data-in' type='checkbox'><label for='data-09c10c60-4f97-4213-bcec-6eea7fbacb32' 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>athwartship sensitivity of the transducer</dd></dl></div><div class='xr-var-data'><pre>array([13.89 , 21.969999, 23.120001])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>equivalent_beam_angle</span></div><div class='xr-var-dims'>(frequency)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3602a5cf-4d32-46c9-9239-d657f3e2eb92' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3602a5cf-4d32-46c9-9239-d657f3e2eb92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b3d6c57-7ce7-441e-ac0e-0e12b3856c85' class='xr-var-data-in' type='checkbox'><label for='data-2b3d6c57-7ce7-441e-ac0e-0e12b3856c85' 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>Equivalent beam angle</dd><dt><span>units :</span></dt><dd>sr</dd><dt><span>valid_range :</span></dt><dd>[ 0. 12.56637061]</dd></dl></div><div class='xr-var-data'><pre>array([-17.370001, -21.01 , -20.469999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>transducer_offset_x</span></div><div class='xr-var-dims'>(frequency)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6d2986f0-b0d2-424d-a7d1-7e204dc5f9ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d2986f0-b0d2-424d-a7d1-7e204dc5f9ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a57b8da-edcb-401c-b2b7-bdd40351578d' class='xr-var-data-in' type='checkbox'><label for='data-4a57b8da-edcb-401c-b2b7-bdd40351578d' 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>x-axis distance from the platform coordinate system origin to the sonar transducer</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>transducer_offset_y</span></div><div class='xr-var-dims'>(frequency)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e7f7a0bd-420c-485a-a6b3-33b3de3064d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7f7a0bd-420c-485a-a6b3-33b3de3064d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-208e85d5-eba2-40b0-a358-c5ec07deb42b' class='xr-var-data-in' type='checkbox'><label for='data-208e85d5-eba2-40b0-a358-c5ec07deb42b' 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>y-axis distance from the platform coordinate system origin to the sonar transducer</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>transducer_offset_z</span></div><div class='xr-var-dims'>(frequency)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9217311f-f779-4730-97a2-6b7d9fd323ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9217311f-f779-4730-97a2-6b7d9fd323ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-796f3e82-fac4-4762-a473-9e50d380ad3f' class='xr-var-data-in' type='checkbox'><label for='data-796f3e82-fac4-4762-a473-9e50d380ad3f' 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>z-axis distance from the platform coordinate system origin to the sonar transducer</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gain_correction</span></div><div class='xr-var-dims'>(frequency)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cf0f25e2-5161-4e11-8706-93237be71b71' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf0f25e2-5161-4e11-8706-93237be71b71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37474f3e-6ae3-47b6-82cb-95938a36b867' class='xr-var-data-in' type='checkbox'><label for='data-37474f3e-6ae3-47b6-82cb-95938a36b867' 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>Gain correction</dd><dt><span>units :</span></dt><dd>dB</dd></dl></div><div class='xr-var-data'><pre>array([22.950001, 26.07 , 26.549999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gpt_software_version</span></div><div class='xr-var-dims'>(frequency)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4b41bf3c-f53d-4354-9481-fc05220cb346' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4b41bf3c-f53d-4354-9481-fc05220cb346' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cbe2eb7e-ba0f-4bf4-ad0e-760c95e38580' class='xr-var-data-in' type='checkbox'><label for='data-cbe2eb7e-ba0f-4bf4-ad0e-760c95e38580' 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'></dl></div><div class='xr-var-data'><pre>array([&#x27;070413&#x27;, &#x27;070413&#x27;, &#x27;070413&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>backscatter_r</span></div><div class='xr-var-dims'>(frequency, ping_time, range_bin)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3b38e21e-b419-4db5-a880-d3355fa932e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b38e21e-b419-4db5-a880-d3355fa932e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d6bdbf3-2de7-4225-aeda-feff3af5fc68' class='xr-var-data-in' type='checkbox'><label for='data-9d6bdbf3-2de7-4225-aeda-feff3af5fc68' 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>Backscatter power</dd><dt><span>units :</span></dt><dd>dB</dd></dl></div><div class='xr-var-data'><pre>[6362856 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sample_interval</span></div><div class='xr-var-dims'>(frequency, ping_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7468504c-b4cf-47fd-86bd-1683cf0e3548' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7468504c-b4cf-47fd-86bd-1683cf0e3548' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-739b0fa7-77cb-40fa-acf7-3760a0e2747a' class='xr-var-data-in' type='checkbox'><label for='data-739b0fa7-77cb-40fa-acf7-3760a0e2747a' 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>Interval between recorded raw data samples</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([[0.000256, 0.000256, 0.000256, ..., 0.000256, 0.000256, 0.000256],\n",
" [0.000256, 0.000256, 0.000256, ..., 0.000256, 0.000256, 0.000256],\n",
" [0.000256, 0.000256, 0.000256, ..., 0.000256, 0.000256, 0.000256]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>transmit_bandwidth</span></div><div class='xr-var-dims'>(frequency, ping_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7dd836a8-85ce-4b5e-9605-bd3a1605e2b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7dd836a8-85ce-4b5e-9605-bd3a1605e2b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fe94d96-dada-4ee2-a55d-be3dd61d63dd' class='xr-var-data-in' type='checkbox'><label for='data-0fe94d96-dada-4ee2-a55d-be3dd61d63dd' 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>Nominal bandwidth of transmitted pulse</dd><dt><span>units :</span></dt><dd>Hz</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([[1573.665527, 1573.665527, 1573.665527, ..., 1573.665527, 1573.665527,\n",
" 1573.665527],\n",
" [2425.149658, 2425.149658, 2425.149658, ..., 2425.149658, 2425.149658,\n",
" 2425.149658],\n",
" [3026.391602, 3026.391602, 3026.391602, ..., 3026.391602, 3026.391602,\n",
" 3026.391602]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>transmit_duration_nominal</span></div><div class='xr-var-dims'>(frequency, ping_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-40995773-4661-4d68-89b9-31589bb51007' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40995773-4661-4d68-89b9-31589bb51007' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c15665b-02c8-4af9-97b7-ac1329601403' class='xr-var-data-in' type='checkbox'><label for='data-8c15665b-02c8-4af9-97b7-ac1329601403' 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>Nominal bandwidth of transmitted pulse</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([[0.001024, 0.001024, 0.001024, ..., 0.001024, 0.001024, 0.001024],\n",
" [0.001024, 0.001024, 0.001024, ..., 0.001024, 0.001024, 0.001024],\n",
" [0.001024, 0.001024, 0.001024, ..., 0.001024, 0.001024, 0.001024]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>transmit_power</span></div><div class='xr-var-dims'>(frequency, ping_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9e47c1a4-40c5-4272-91a9-dc50b5e109a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9e47c1a4-40c5-4272-91a9-dc50b5e109a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e8476e5-9948-4e68-9486-34aedb0bedbb' class='xr-var-data-in' type='checkbox'><label for='data-2e8476e5-9948-4e68-9486-34aedb0bedbb' 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>Nominal transmit power</dd><dt><span>units :</span></dt><dd>W</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([[2000., 2000., 2000., ..., 2000., 2000., 2000.],\n",
" [2000., 2000., 2000., ..., 2000., 2000., 2000.],\n",
" [ 250., 250., 250., ..., 250., 250., 250.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>data_type</span></div><div class='xr-var-dims'>(frequency, ping_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1a1c6d2b-990c-426c-905e-aad31b72a21d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a1c6d2b-990c-426c-905e-aad31b72a21d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8fc434e9-d864-4989-b4f3-4421f7213e7c' class='xr-var-data-in' type='checkbox'><label for='data-8fc434e9-d864-4989-b4f3-4421f7213e7c' 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>recorded data type (1-power only, 2-angle only 3-power and angle)</dd></dl></div><div class='xr-var-data'><pre>array([[3., 3., 3., ..., 3., 3., 3.],\n",
" [3., 3., 3., ..., 3., 3., 3.],\n",
" [3., 3., 3., ..., 3., 3., 3.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>count</span></div><div class='xr-var-dims'>(frequency, ping_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fca0be16-9501-4e2e-902e-79a893beb12c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fca0be16-9501-4e2e-902e-79a893beb12c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a6861bd-9772-45da-9d23-65ca7612c4f2' class='xr-var-data-in' type='checkbox'><label for='data-2a6861bd-9772-45da-9d23-65ca7612c4f2' 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 samples </dd></dl></div><div class='xr-var-data'><pre>array([[3957., 3957., 3957., ..., 3957., 3957., 3957.],\n",
" [3957., 3957., 3957., ..., 3957., 3957., 3957.],\n",
" [3957., 3957., 3957., ..., 3957., 3957., 3957.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>offset</span></div><div class='xr-var-dims'>(frequency, ping_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9b1b7b3f-7334-48f4-bf60-c1e5b1324eea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b1b7b3f-7334-48f4-bf60-c1e5b1324eea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96316e53-7a31-40bf-a8d8-87008c68f732' class='xr-var-data-in' type='checkbox'><label for='data-96316e53-7a31-40bf-a8d8-87008c68f732' 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>Offset of first sample</dd></dl></div><div class='xr-var-data'><pre>array([[0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>transmit_mode</span></div><div class='xr-var-dims'>(frequency, ping_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9001e5ac-e419-42e7-be17-ceb602ae82de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9001e5ac-e419-42e7-be17-ceb602ae82de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0dfe22d8-d2fa-4405-afe6-d82818411752' class='xr-var-data-in' type='checkbox'><label for='data-0dfe22d8-d2fa-4405-afe6-d82818411752' 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>0 = Active, 1 = Passive, 2 = Test, -1 = Unknown</dd></dl></div><div class='xr-var-data'><pre>array([[0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angle_athwartship</span></div><div class='xr-var-dims'>(frequency, ping_time, range_bin)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-850d3e6a-97f0-4c02-bac1-6710811434ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-850d3e6a-97f0-4c02-bac1-6710811434ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11790ebc-bb5a-46a9-aa56-ae5c7a23bb12' class='xr-var-data-in' type='checkbox'><label for='data-11790ebc-bb5a-46a9-aa56-ae5c7a23bb12' 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>electrical athwartship angle</dd></dl></div><div class='xr-var-data'><pre>[6362856 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angle_alongship</span></div><div class='xr-var-dims'>(frequency, ping_time, range_bin)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-de22219e-d39d-4424-8885-4f6c2411e51c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de22219e-d39d-4424-8885-4f6c2411e51c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-360a752f-a0bb-4a52-8899-7f8357732e61' class='xr-var-data-in' type='checkbox'><label for='data-360a752f-a0bb-4a52-8899-7f8357732e61' 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>electrical alongship angle</dd></dl></div><div class='xr-var-data'><pre>[6362856 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5f82cca2-b98e-471d-8e0c-7b563d305a32' class='xr-section-summary-in' type='checkbox' checked><label for='section-5f82cca2-b98e-471d-8e0c-7b563d305a32' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>beam_mode :</span></dt><dd>vertical</dd><dt><span>conversion_equation_t :</span></dt><dd>type_3</dd></dl></div></li></ul></div></div><br></div>\n",
" </ul>\n",
" </div>\n",
" </li>\n",
" \n",
" <li class = \"xr-section-item\">\n",
" <input id=\"idata_vendor31941dc6-f9ee-4736-8e6f-3f3dd9c6bef4\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
" <label for=\"idata_vendor31941dc6-f9ee-4736-8e6f-3f3dd9c6bef4\" class = \"xr-section-summary\">vendor: (Vendor specific) contains vendor-specific information about the sonar and the data.</label>\n",
" <div class=\"xr-section-inline-details\"></div>\n",
" <div class=\"xr-section-details\">\n",
" <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
" <div style=\"padding-left:2rem;\"><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.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;\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-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",
" 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",
" 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-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-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",
" 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: (frequency: 3, pulse_length_bin: 5)\n",
"Coordinates:\n",
" * frequency (frequency) float64 1.8e+04 3.8e+04 1.2e+05\n",
" * pulse_length_bin (pulse_length_bin) int64 0 1 2 3 4\n",
"Data variables:\n",
" sa_correction (frequency, pulse_length_bin) float64 0.0 -0.7 ... -0.3\n",
" gain_correction (frequency, pulse_length_bin) float64 20.3 22.95 ... 26.55\n",
" pulse_length (frequency, pulse_length_bin) float64 0.000512 ... 0.00...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4760d963-8958-4ccc-ba7b-0ee6366678db' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4760d963-8958-4ccc-ba7b-0ee6366678db' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>frequency</span>: 3</li><li><span class='xr-has-index'>pulse_length_bin</span>: 5</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-20e60f2d-e6e2-45c1-a109-fad949eeb65e' class='xr-section-summary-in' type='checkbox' checked><label for='section-20e60f2d-e6e2-45c1-a109-fad949eeb65e' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>frequency</span></div><div class='xr-var-dims'>(frequency)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.8e+04 3.8e+04 1.2e+05</div><input id='attrs-e9f7e176-b1ce-4921-87ec-d99ac76e5f3e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9f7e176-b1ce-4921-87ec-d99ac76e5f3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e9cb6fc-12c2-4032-8fd1-c13eb419dfba' class='xr-var-data-in' type='checkbox'><label for='data-5e9cb6fc-12c2-4032-8fd1-c13eb419dfba' 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>units :</span></dt><dd>Hz</dd><dt><span>long_name :</span></dt><dd>Transducer frequency</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([ 18000., 38000., 120000.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pulse_length_bin</span></div><div class='xr-var-dims'>(pulse_length_bin)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4</div><input id='attrs-d7394edd-8076-4724-9d07-9dbc2616e9f7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d7394edd-8076-4724-9d07-9dbc2616e9f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e586f9db-a8a0-482d-9c4b-ef49d8c16e5a' class='xr-var-data-in' type='checkbox'><label for='data-e586f9db-a8a0-482d-9c4b-ef49d8c16e5a' 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'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-61cb2a44-459c-4aba-a595-503d587dfacf' class='xr-section-summary-in' type='checkbox' checked><label for='section-61cb2a44-459c-4aba-a595-503d587dfacf' class='xr-section-summary' >Data variables: <span>(3)</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>sa_correction</span></div><div class='xr-var-dims'>(frequency, pulse_length_bin)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7713ce2c-98c6-4c85-88d6-8bba2c1a6a4d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7713ce2c-98c6-4c85-88d6-8bba2c1a6a4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1494e2fe-435c-4b5c-887b-f752356fb8d6' class='xr-var-data-in' type='checkbox'><label for='data-1494e2fe-435c-4b5c-887b-f752356fb8d6' 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'></dl></div><div class='xr-var-data'><pre>array([[ 0. , -0.7 , 0. , 0. , 0. ],\n",
" [ 0. , 0. , -0.52, 0. , 0. ],\n",
" [ 0. , 0. , 0. , 0. , -0.3 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gain_correction</span></div><div class='xr-var-dims'>(frequency, pulse_length_bin)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c63ddf4a-77aa-4f8e-acc5-8663088efc15' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c63ddf4a-77aa-4f8e-acc5-8663088efc15' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-186cab9c-46bb-448e-89be-0b3f2f735d28' class='xr-var-data-in' type='checkbox'><label for='data-186cab9c-46bb-448e-89be-0b3f2f735d28' 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'></dl></div><div class='xr-var-data'><pre>array([[20.299999, 22.950001, 22.9 , 23. , 23. ],\n",
" [24. , 26. , 26.07 , 26.5 , 26.5 ],\n",
" [25.5 , 26.799999, 27. , 27. , 26.549999]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pulse_length</span></div><div class='xr-var-dims'>(frequency, pulse_length_bin)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-da109762-11db-4c0d-a19c-ea605144f246' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-da109762-11db-4c0d-a19c-ea605144f246' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-575b6907-1117-412a-be87-d85fc391022c' class='xr-var-data-in' type='checkbox'><label for='data-575b6907-1117-412a-be87-d85fc391022c' 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'></dl></div><div class='xr-var-data'><pre>array([[5.120e-04, 1.024e-03, 2.048e-03, 4.096e-03, 8.192e-03],\n",
" [2.560e-04, 5.120e-04, 1.024e-03, 2.048e-03, 4.096e-03],\n",
" [6.400e-05, 1.280e-04, 2.560e-04, 5.120e-04, 1.024e-03]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1a562710-2067-44f4-90f0-a3bbfde7f3a6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1a562710-2067-44f4-90f0-a3bbfde7f3a6' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div><br></div>\n",
" </ul>\n",
" </div>\n",
" </li>\n",
" \n",
" </ul>\n",
" </div>\n",
" <style> /* CSS stylesheet for displaying InferenceData 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.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;\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-sections.group-sections {\n",
" grid-template-columns: auto;\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-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",
" 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",
" 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-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-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, 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",
" 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",
".xr-wrap{width:700px!important;} </style>"
],
"text/plain": [
"EchoData: standardized raw data from hake2017data/convertednc/Summer2017-D20170730-T040253.nc\n",
" > top: (Top-level) contains metadata about the SONAR-netCDF4 file format.\n",
" > environment: (Environment) contains information relevant to acoustic propagation through water.\n",
" > platform: (Platform) contains information about the platform on which the sonar is installed.\n",
" > provenance: (Provenance) contains metadata about how the SONAR-netCDF4 version of the data were obtained.\n",
" > sonar: (Sonar) contains specific metadata for the sonar system.\n",
" > beam: (Beam) contains backscatter data and other beam or channel-specific data.\n",
" > vendor: (Vendor specific) contains vendor-specific information about the sonar and the data."
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ed"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Open `platform` group as multi-file dataset and extract GPS locations\n",
"\n",
"NOTE: Look into applying a resampling to the lat-lon GPS data, so that all subsequent plotting and operations are much faster."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 1min 50s, sys: 1.96 s, total: 1min 52s\n",
"Wall time: 1min 53s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"# platform_ds = xr.open_mfdataset('./hake2017data/convertednc/*.nc', group='Platform', combine='nested')\n",
"platform_ds = xr.open_mfdataset(\n",
" './hake2017data/convertednc/*.nc', group='Platform', \n",
" concat_dim=[\"location_time\", \"ping_time\"],\n",
" data_vars='minimal', coords='minimal'\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"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.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;\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-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",
" 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",
" 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-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-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",
" 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: (frequency: 3, location_time: 366800, ping_time: 131243)\n",
"Coordinates:\n",
" * location_time (location_time) datetime64[ns] 2017-07-28T00:05:36.1030000...\n",
" * frequency (frequency) float64 1.8e+04 3.8e+04 1.2e+05\n",
" * ping_time (ping_time) datetime64[ns] 2017-07-28T00:05:34.896999936 ....\n",
"Data variables:\n",
" latitude (location_time) float64 dask.array&lt;chunksize=(366800,), meta=np.ndarray&gt;\n",
" longitude (location_time) float64 dask.array&lt;chunksize=(366800,), meta=np.ndarray&gt;\n",
" sentence_type (location_time) object dask.array&lt;chunksize=(366800,), meta=np.ndarray&gt;\n",
" pitch (frequency, ping_time) float64 dask.array&lt;chunksize=(3, 534), meta=np.ndarray&gt;\n",
" roll (frequency, ping_time) float64 dask.array&lt;chunksize=(3, 534), meta=np.ndarray&gt;\n",
" heave (frequency, ping_time) float64 dask.array&lt;chunksize=(3, 534), meta=np.ndarray&gt;\n",
" water_level (frequency, ping_time) float64 dask.array&lt;chunksize=(3, 534), meta=np.ndarray&gt;\n",
"Attributes:\n",
" platform_type: Research vessel\n",
" platform_name: Bell M. Shimada\n",
" platform_code_ICES: 315</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a4c03050-9c9e-4d17-a990-54778758c049' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a4c03050-9c9e-4d17-a990-54778758c049' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>frequency</span>: 3</li><li><span class='xr-has-index'>location_time</span>: 366800</li><li><span class='xr-has-index'>ping_time</span>: 131243</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-60c4d258-db85-4f68-9401-b43737a06bd4' class='xr-section-summary-in' type='checkbox' checked><label for='section-60c4d258-db85-4f68-9401-b43737a06bd4' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>location_time</span></div><div class='xr-var-dims'>(location_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-07-28T00:05:36.103000064 .....</div><input id='attrs-4142ee35-243e-484b-82b0-3bcfdc4e6c32' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4142ee35-243e-484b-82b0-3bcfdc4e6c32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e5773a9-a02a-4daa-b8ad-76e57ad9504d' class='xr-var-data-in' type='checkbox'><label for='data-9e5773a9-a02a-4daa-b8ad-76e57ad9504d' 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>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>Timestamps for NMEA position datagrams</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-07-28T00:05:36.103000064&#x27;, &#x27;2017-07-28T00:05:37.511000064&#x27;,\n",
" &#x27;2017-07-28T00:05:37.669000192&#x27;, ..., &#x27;2017-07-31T00:15:14.024000000&#x27;,\n",
" &#x27;2017-07-31T00:15:14.182000128&#x27;, &#x27;2017-07-31T00:15:15.426999808&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>frequency</span></div><div class='xr-var-dims'>(frequency)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.8e+04 3.8e+04 1.2e+05</div><input id='attrs-8797c931-1c8a-463b-ab22-798535456faa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8797c931-1c8a-463b-ab22-798535456faa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-afc5d9de-51e5-497d-9694-f303987b0c43' class='xr-var-data-in' type='checkbox'><label for='data-afc5d9de-51e5-497d-9694-f303987b0c43' 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>units :</span></dt><dd>Hz</dd><dt><span>long_name :</span></dt><dd>Transducer frequency</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([ 18000., 38000., 120000.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ping_time</span></div><div class='xr-var-dims'>(ping_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-07-28T00:05:34.896999936 .....</div><input id='attrs-8b2c42b3-9211-4782-95f3-095c41aece76' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b2c42b3-9211-4782-95f3-095c41aece76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3c68ea4-c2fd-4d70-ac19-42950d549a1c' class='xr-var-data-in' type='checkbox'><label for='data-a3c68ea4-c2fd-4d70-ac19-42950d549a1c' 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>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>Timestamps for position datagrams</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-07-28T00:05:34.896999936&#x27;, &#x27;2017-07-28T00:05:37.161999872&#x27;,\n",
" &#x27;2017-07-28T00:05:38.403999744&#x27;, ..., &#x27;2017-07-31T00:15:08.787999744&#x27;,\n",
" &#x27;2017-07-31T00:15:11.004000256&#x27;, &#x27;2017-07-31T00:15:13.221000192&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e9f3ba5e-6996-4481-86eb-b3b35555c478' class='xr-section-summary-in' type='checkbox' checked><label for='section-e9f3ba5e-6996-4481-86eb-b3b35555c478' class='xr-section-summary' >Data variables: <span>(7)</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>latitude</span></div><div class='xr-var-dims'>(location_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(366800,), meta=np.ndarray&gt;</div><input id='attrs-e43aea88-844c-48a6-b508-f3371ffdfda5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e43aea88-844c-48a6-b508-f3371ffdfda5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3329c4fb-4929-4dae-a1e6-bbd733ee4ecb' class='xr-var-data-in' type='checkbox'><label for='data-3329c4fb-4929-4dae-a1e6-bbd733ee4ecb' 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>Platform latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_range :</span></dt><dd>[-90. 90.]</dd></dl></div><div class='xr-var-data'><table>\n",
"<tr>\n",
"<td>\n",
"<table>\n",
" <thead>\n",
" <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr><th> Bytes </th><td> 2.80 MiB </td> <td> 2.80 MiB </td></tr>\n",
" <tr><th> Shape </th><td> (366800,) </td> <td> (366800,) </td></tr>\n",
" <tr><th> Count </th><td> 2490 Tasks </td><td> 1 Chunks </td></tr>\n",
" <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
" </tbody>\n",
"</table>\n",
"</td>\n",
"<td>\n",
"<svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
" <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Vertical lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
" <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Colored Rectangle -->\n",
" <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
"\n",
" <!-- Text -->\n",
" <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >366800</text>\n",
" <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
"</svg>\n",
"</td>\n",
"</tr>\n",
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(location_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(366800,), meta=np.ndarray&gt;</div><input id='attrs-191eb6a2-73b0-4e39-9d79-a946e78dd1e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-191eb6a2-73b0-4e39-9d79-a946e78dd1e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9abd292-30d8-41f9-b36a-c09947ae0dc8' class='xr-var-data-in' type='checkbox'><label for='data-d9abd292-30d8-41f9-b36a-c09947ae0dc8' 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>Platform longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_range :</span></dt><dd>[-180. 180.]</dd></dl></div><div class='xr-var-data'><table>\n",
"<tr>\n",
"<td>\n",
"<table>\n",
" <thead>\n",
" <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr><th> Bytes </th><td> 2.80 MiB </td> <td> 2.80 MiB </td></tr>\n",
" <tr><th> Shape </th><td> (366800,) </td> <td> (366800,) </td></tr>\n",
" <tr><th> Count </th><td> 2490 Tasks </td><td> 1 Chunks </td></tr>\n",
" <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
" </tbody>\n",
"</table>\n",
"</td>\n",
"<td>\n",
"<svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
" <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Vertical lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
" <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Colored Rectangle -->\n",
" <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
"\n",
" <!-- Text -->\n",
" <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >366800</text>\n",
" <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
"</svg>\n",
"</td>\n",
"</tr>\n",
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sentence_type</span></div><div class='xr-var-dims'>(location_time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(366800,), meta=np.ndarray&gt;</div><input id='attrs-13468f60-895b-47c1-9941-72de85988f39' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-13468f60-895b-47c1-9941-72de85988f39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fb4bbde-4e7e-4582-bb25-7029270e5724' class='xr-var-data-in' type='checkbox'><label for='data-1fb4bbde-4e7e-4582-bb25-7029270e5724' 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'></dl></div><div class='xr-var-data'><table>\n",
"<tr>\n",
"<td>\n",
"<table>\n",
" <thead>\n",
" <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr><th> Bytes </th><td> 2.80 MiB </td> <td> 2.80 MiB </td></tr>\n",
" <tr><th> Shape </th><td> (366800,) </td> <td> (366800,) </td></tr>\n",
" <tr><th> Count </th><td> 2490 Tasks </td><td> 1 Chunks </td></tr>\n",
" <tr><th> Type </th><td> object </td><td> numpy.ndarray </td></tr>\n",
" </tbody>\n",
"</table>\n",
"</td>\n",
"<td>\n",
"<svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
" <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Vertical lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
" <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Colored Rectangle -->\n",
" <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
"\n",
" <!-- Text -->\n",
" <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >366800</text>\n",
" <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
"</svg>\n",
"</td>\n",
"</tr>\n",
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pitch</span></div><div class='xr-var-dims'>(frequency, ping_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 534), meta=np.ndarray&gt;</div><input id='attrs-7d16c94d-69e9-4469-baf0-d39fb9334ef7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d16c94d-69e9-4469-baf0-d39fb9334ef7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a667ef73-2dbc-4c58-a663-543df32f42ed' class='xr-var-data-in' type='checkbox'><label for='data-a667ef73-2dbc-4c58-a663-543df32f42ed' 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>Platform pitch</dd><dt><span>standard_name :</span></dt><dd>platform_pitch_angle</dd><dt><span>units :</span></dt><dd>arc_degree</dd><dt><span>valid_range :</span></dt><dd>[-90. 90.]</dd></dl></div><div class='xr-var-data'><table>\n",
"<tr>\n",
"<td>\n",
"<table>\n",
" <thead>\n",
" <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr><th> Bytes </th><td> 3.00 MiB </td> <td> 12.68 kiB </td></tr>\n",
" <tr><th> Shape </th><td> (3, 131243) </td> <td> (3, 541) </td></tr>\n",
" <tr><th> Count </th><td> 750 Tasks </td><td> 250 Chunks </td></tr>\n",
" <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
" </tbody>\n",
"</table>\n",
"</td>\n",
"<td>\n",
"<svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
" <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Vertical lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
" <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"25\" />\n",
" <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n",
" <line x1=\"19\" y1=\"0\" x2=\"19\" y2=\"25\" />\n",
" <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" />\n",
" <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"25\" />\n",
" <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
" <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
" <line x1=\"50\" y1=\"0\" x2=\"50\" y2=\"25\" />\n",
" <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
" <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
" <line x1=\"68\" y1=\"0\" x2=\"68\" y2=\"25\" />\n",
" <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n",
" <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"25\" />\n",
" <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
" <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
" <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n",
" <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n",
" <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
" <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Colored Rectangle -->\n",
" <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
"\n",
" <!-- Text -->\n",
" <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >131243</text>\n",
" <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">3</text>\n",
"</svg>\n",
"</td>\n",
"</tr>\n",
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roll</span></div><div class='xr-var-dims'>(frequency, ping_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 534), meta=np.ndarray&gt;</div><input id='attrs-6fc72e38-0ef5-4fed-ab82-d2fcb50fef77' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6fc72e38-0ef5-4fed-ab82-d2fcb50fef77' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1312307-b58b-400f-92fb-082c4e483100' class='xr-var-data-in' type='checkbox'><label for='data-b1312307-b58b-400f-92fb-082c4e483100' 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>Platform roll</dd><dt><span>standard_name :</span></dt><dd>platform_roll_angle</dd><dt><span>units :</span></dt><dd>arc_degree</dd><dt><span>valid_range :</span></dt><dd>[-90. 90.]</dd></dl></div><div class='xr-var-data'><table>\n",
"<tr>\n",
"<td>\n",
"<table>\n",
" <thead>\n",
" <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr><th> Bytes </th><td> 3.00 MiB </td> <td> 12.68 kiB </td></tr>\n",
" <tr><th> Shape </th><td> (3, 131243) </td> <td> (3, 541) </td></tr>\n",
" <tr><th> Count </th><td> 750 Tasks </td><td> 250 Chunks </td></tr>\n",
" <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
" </tbody>\n",
"</table>\n",
"</td>\n",
"<td>\n",
"<svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
" <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Vertical lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
" <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"25\" />\n",
" <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n",
" <line x1=\"19\" y1=\"0\" x2=\"19\" y2=\"25\" />\n",
" <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" />\n",
" <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"25\" />\n",
" <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
" <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
" <line x1=\"50\" y1=\"0\" x2=\"50\" y2=\"25\" />\n",
" <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
" <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
" <line x1=\"68\" y1=\"0\" x2=\"68\" y2=\"25\" />\n",
" <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n",
" <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"25\" />\n",
" <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
" <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
" <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n",
" <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n",
" <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
" <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Colored Rectangle -->\n",
" <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
"\n",
" <!-- Text -->\n",
" <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >131243</text>\n",
" <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">3</text>\n",
"</svg>\n",
"</td>\n",
"</tr>\n",
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heave</span></div><div class='xr-var-dims'>(frequency, ping_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 534), meta=np.ndarray&gt;</div><input id='attrs-7decdcd8-9f40-4a20-9f7b-62a8d9a32048' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7decdcd8-9f40-4a20-9f7b-62a8d9a32048' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e43a1f0-7f7c-46bf-ab6d-f4709bec03a1' class='xr-var-data-in' type='checkbox'><label for='data-1e43a1f0-7f7c-46bf-ab6d-f4709bec03a1' 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>Platform heave</dd><dt><span>standard_name :</span></dt><dd>platform_heave_angle</dd><dt><span>units :</span></dt><dd>arc_degree</dd><dt><span>valid_range :</span></dt><dd>[-90. 90.]</dd></dl></div><div class='xr-var-data'><table>\n",
"<tr>\n",
"<td>\n",
"<table>\n",
" <thead>\n",
" <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr><th> Bytes </th><td> 3.00 MiB </td> <td> 12.68 kiB </td></tr>\n",
" <tr><th> Shape </th><td> (3, 131243) </td> <td> (3, 541) </td></tr>\n",
" <tr><th> Count </th><td> 750 Tasks </td><td> 250 Chunks </td></tr>\n",
" <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
" </tbody>\n",
"</table>\n",
"</td>\n",
"<td>\n",
"<svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
" <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Vertical lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
" <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"25\" />\n",
" <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n",
" <line x1=\"19\" y1=\"0\" x2=\"19\" y2=\"25\" />\n",
" <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" />\n",
" <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"25\" />\n",
" <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
" <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
" <line x1=\"50\" y1=\"0\" x2=\"50\" y2=\"25\" />\n",
" <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
" <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
" <line x1=\"68\" y1=\"0\" x2=\"68\" y2=\"25\" />\n",
" <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n",
" <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"25\" />\n",
" <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
" <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
" <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n",
" <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n",
" <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
" <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Colored Rectangle -->\n",
" <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
"\n",
" <!-- Text -->\n",
" <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >131243</text>\n",
" <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">3</text>\n",
"</svg>\n",
"</td>\n",
"</tr>\n",
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>water_level</span></div><div class='xr-var-dims'>(frequency, ping_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 534), meta=np.ndarray&gt;</div><input id='attrs-61ddba83-94c2-41f8-87ff-aadfef58a3a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-61ddba83-94c2-41f8-87ff-aadfef58a3a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f831b485-884d-46fa-9fa7-fa6cc18a3c10' class='xr-var-data-in' type='checkbox'><label for='data-f831b485-884d-46fa-9fa7-fa6cc18a3c10' 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>z-axis distance from the platform coordinate system origin to the sonar transducer</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
"<tr>\n",
"<td>\n",
"<table>\n",
" <thead>\n",
" <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr><th> Bytes </th><td> 3.00 MiB </td> <td> 12.68 kiB </td></tr>\n",
" <tr><th> Shape </th><td> (3, 131243) </td> <td> (3, 541) </td></tr>\n",
" <tr><th> Count </th><td> 750 Tasks </td><td> 250 Chunks </td></tr>\n",
" <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
" </tbody>\n",
"</table>\n",
"</td>\n",
"<td>\n",
"<svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
" <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Vertical lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
" <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"25\" />\n",
" <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n",
" <line x1=\"19\" y1=\"0\" x2=\"19\" y2=\"25\" />\n",
" <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" />\n",
" <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"25\" />\n",
" <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
" <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
" <line x1=\"50\" y1=\"0\" x2=\"50\" y2=\"25\" />\n",
" <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
" <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
" <line x1=\"68\" y1=\"0\" x2=\"68\" y2=\"25\" />\n",
" <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n",
" <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"25\" />\n",
" <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"25\" />\n",
" <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
" <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n",
" <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n",
" <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
" <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Colored Rectangle -->\n",
" <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
"\n",
" <!-- Text -->\n",
" <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >131243</text>\n",
" <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">3</text>\n",
"</svg>\n",
"</td>\n",
"</tr>\n",
"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-f82bf0ef-4614-48fd-aa7a-a57efe98a6f4' class='xr-section-summary-in' type='checkbox' checked><label for='section-f82bf0ef-4614-48fd-aa7a-a57efe98a6f4' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>platform_type :</span></dt><dd>Research vessel</dd><dt><span>platform_name :</span></dt><dd>Bell M. Shimada</dd><dt><span>platform_code_ICES :</span></dt><dd>315</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (frequency: 3, location_time: 366800, ping_time: 131243)\n",
"Coordinates:\n",
" * location_time (location_time) datetime64[ns] 2017-07-28T00:05:36.1030000...\n",
" * frequency (frequency) float64 1.8e+04 3.8e+04 1.2e+05\n",
" * ping_time (ping_time) datetime64[ns] 2017-07-28T00:05:34.896999936 ....\n",
"Data variables:\n",
" latitude (location_time) float64 dask.array<chunksize=(366800,), meta=np.ndarray>\n",
" longitude (location_time) float64 dask.array<chunksize=(366800,), meta=np.ndarray>\n",
" sentence_type (location_time) object dask.array<chunksize=(366800,), meta=np.ndarray>\n",
" pitch (frequency, ping_time) float64 dask.array<chunksize=(3, 534), meta=np.ndarray>\n",
" roll (frequency, ping_time) float64 dask.array<chunksize=(3, 534), meta=np.ndarray>\n",
" heave (frequency, ping_time) float64 dask.array<chunksize=(3, 534), meta=np.ndarray>\n",
" water_level (frequency, ping_time) float64 dask.array<chunksize=(3, 534), meta=np.ndarray>\n",
"Attributes:\n",
" platform_type: Research vessel\n",
" platform_name: Bell M. Shimada\n",
" platform_code_ICES: 315"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"platform_ds"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(numpy.datetime64('2017-07-28T00:05:36.103000064'),\n",
" numpy.datetime64('2017-07-31T00:15:15.426999808'))"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"platform_ds.location_time.values.min(), platform_ds.location_time.values.max()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Use `Platform` group `latitude` and `longitude` to plot GPS locations on a map\n",
"\n",
"Create a Pandas DataFrame, then use it to create a GeoPandas GeoDataFrame."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"lat_lon_df = platform_ds.latitude.to_dataframe().join(platform_ds.longitude.to_dataframe())"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>latitude</th>\n",
" <th>longitude</th>\n",
" </tr>\n",
" <tr>\n",
" <th>location_time</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2017-07-28 00:05:36.103000064</th>\n",
" <td>43.533072</td>\n",
" <td>-124.683998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-28 00:05:37.511000064</th>\n",
" <td>43.533080</td>\n",
" <td>-124.684005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-28 00:05:37.669000192</th>\n",
" <td>43.533167</td>\n",
" <td>-124.684000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" latitude longitude\n",
"location_time \n",
"2017-07-28 00:05:36.103000064 43.533072 -124.683998\n",
"2017-07-28 00:05:37.511000064 43.533080 -124.684005\n",
"2017-07-28 00:05:37.669000192 43.533167 -124.684000"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lat_lon_df.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"lat_lon_gdf = gpd.GeoDataFrame(\n",
" lat_lon_df,\n",
" geometry=gpd.points_from_xy(lat_lon_df['longitude'], lat_lon_df['latitude']), \n",
" crs=\"epsg:4326\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define bounding boxes around two of the N-S transects, using `shapely.box`."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAdIAAAHdCAYAAACpNvjjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAABN3ElEQVR4nO3dedxc4/3/8dfnvhNRIRUSJQsJQS0lJXZqj7WWolLrr9VGlVYFQbVFVSuWxE5bVbTUWpX6IkLEviUkIWJJIshSW2KtJbnn8/tjzsw9M/fsc+Y+c595Pz2O+8w157rmc+a+M5+5rnOdc8zdERERkeq0RB2AiIhIV6ZEKiIiUgMlUhERkRookYqIiNRAiVRERKQGSqQiIiI1UCIVEcnDzA4xs5lmljCzYUW2m2dmL5rZNDObklG+iplNNLPXg5+9g/JVzexhM/vUzK7IaWvzoK3ZZnaZmVlQ3sPMbg3KnzGzQRl1jg5e43UzOzqjfHCw7etB3eWCcgvanm1mM8xss4w6e5rZq8Fzp5falxLv3xgzeylYDi21fVemRCoiTc/MdjKz63OKXwK+BzxaRhM7u/tQd89MuKcDD7n7usBDwWOAL4DfAKfkaedqYCSwbrDsGZQfAyxx9yHAOGBMEPcqwFnAVsCWwFkZSW4MMC54/SVBGwB7ZbQ/MnhNzKwVuDJ4fkPgB2a2YYl9ycvM9gE2A4YGsZ1qZr2K1enKlEhFRPJw91nu/moNTewP3BCs3wAcELT7mbs/TjKhppnZGkAvd3/Kk1fKuTFVJ6etO4Bdg97qHsBEd1/s7kuAicCewXO7BNtmvX7Q1o2e9DSwcvDaWwKz3X2uu38F3BJsW3BfzKzVzC40s+eC3u2xwTYbAo+4+zJ3/wyYTvuXgthRIhURqY0DD5jZVDMbmVH+DXdfBBD8XK1EO/2B+RmP5wdlqefeDtpaBnwErJpZnlNnVeDDYNuCbeU8V6i82L4cA3zk7lsAWwA/MbPBJBPnXma2gpn1AXYGBpbY/y6rW9QBiIhExcyeAXoAKwKrmNm04KnT3H1Cmc1s5+4LzWw1YKKZveLu5QwHdwgnT5mXeK7S8mraKmY4sImZHRw8/jqwrrs/YGZbAE8C7wFPAcsKtNHlqUcqIk3L3bdy96HAj4HxwXHOoRUkUdx9YfDzXeAukkOkAO8EQ6apYdt3SzQ1HxiQ8XgAsDDjuYFBW91IJqzFmeU5dd4nOWTbrVhbOc8VKi+2Lwb8PON9G+zuDwTvx3lB2e7Bdq+X2P8uS4lURKRKZtbTzFZKrZPsob0UPD0eSM2iPRq4u1hbwZDpJ2a2dXCM86iMOpltHQxMCo6jTgCGm1nvYJLRcGBC8NzDwba5rz8eOCqYvbs1yaHZRcBzwLrBbN/lgBHBtsX2ZQJwnJl1D96D9YL3pNXMVg3KNgE2AR4otv9dmrtr0aJFS1MvwE7A9TllB5LspX0JvEMyQQH0A+4N1tcmeTxwOjATODOj/qokZ7i+HvxcJeO5eSR7lJ8Gr7FhUD6MZCKeA1wBWFC+PHA7MBt4Flg7o60fBeWzgR9mlK8dbDs7qNsjKDeSs3PnAC8CwzLq7A28FjxXcl9Idsb+ELTzEsnk/fUg3peD5WlgaNS/43ouqV+SiIiIVEFDuyIiIjVQIhUREalBQ57+0qdPHx80aFDUYYiIdAlTp0593937ht3uHjv39A8Wt4XdLFNnfDnB3WNzgYaGTKSDBg1iypQppTcUERHM7M16tPvB4jaenbBm6O22rvF6n9AbjVBDJlIREYmeAwkSUYfR8JRIRUSkAKfNlUhL0WQjERGRGqhHKiIieSWHdnWtgVLUIxUREamBeqQiIlKQJhuVpkQqIiJ5OU6bLiNbkoZ2RUREaqAeqYiIFKTJRqWpRyoiIlID9UhFRCQvB9rUIy1JPVIREWk4ZtZqZi+Y2T3B47PNbIGZTQuWvTO2PcPMZpvZq2a2R0b55mb2YvDcZWZmQXkPM7s1KH/GzAZl1DnazF4PlqPLiVU9UhERKSjCY6QnArOAXhll49z9osyNzGxDYASwEdAPeNDM1nP3NuBqYCTwNHAvsCdwH3AMsMTdh5jZCGAMcKiZrQKcBQwj2SGfambj3X1JsUDVIxURkbwcaHMPfSnFzAYA+wDXlhHm/sAt7v6lu78BzAa2NLM1gF7u/pS7O3AjcEBGnRuC9TuAXYPe6h7ARHdfHCTPiSSTb1FKpCIi0mguAUZDh6tBnGBmM8zsOjPrHZT1B97O2GZ+UNY/WM8tz6rj7suAj4BVi7RVlBKpiIgUlKjDAvQxsykZy8jU65nZvsC77j41J5SrgXWAocAi4OJUlTxhe5HyausUpGOkEisfLhwIHMTK/S6JOhQRKex9dx9W4LntgP2CyUTLA73M7B/ufkRqAzP7C3BP8HA+MDCj/gBgYVA+IE95Zp35ZtYN+DqwOCjfKafO5FI7ox6pxMaHCwfiOM4dUYciEguO01aHpehrup/h7gPcfRDJSUST3P2I4JhnyoHAS8H6eGBEMBN3MLAu8Ky7LwI+MbOtg+OfRwF3Z9RJzcg9OHgNByYAw82sdzB0PDwoK0o9UomlDxYOYNV+80tvKCKFObQ1zmmkF5jZUJJDrfOAYwHcfaaZ3Qa8DCwDjg9m7AIcB1wPfI3kbN37gvK/An83s9kke6IjgrYWm9m5wHPBdr9z98WlAjNvwAsSDxs2zKdMmVJ1/Wfe/AXwn4yS5D625Bv9zitBa9WvnsmDdlYEPi2wTXdgJWAtksP/m5P8AtQKfA58GdT9ghZbgeQIRCuwOrAUmAR8UlFMBC3ULsy2ksr/HXXUx5IDLG047s4neb75rgR0o4UECdqA5Vu6B88YrenXXh5YgeQcgzPoucZ21Qcl0gnMbGqRodKqbbJJdx9/b5+wm2XwwP/WJd6oxKpH+sKbd/AVp3Yor+yzOUELhY86V6K9nUJJFJLJcHGwvAAFhiUNxx2sxqCM2verHm2l2qt2H1fObcsMd8dIffVoxTIabqEl64+/Jf3+GvBFsCwGDuOzRZXHY8G7E+b70xJSay0YWHixWfBfaPrOoqU1zK9nUq3kjb2llFgl0nxJFMJJitVI0HgHoYtNWaumrTDfV6f6HmlrEEnCE7Ra8oO9j+X/8054IvnR3yFjR/FXUlrYX1bCFO5XhZTGGyUTKSZWiTTTVmu9EXUI0ok+XDgQA1qsvSeT70O+V7+3mDs/mLPgsHb/KrqbFfh80VskD8fcRWVD8O2SaaVYyloXWB94Aih6ARYccCy04XhPfv2pMJ3uCPyGltXXDCkKqR+jrUG/YDaS2CTSZ97cOb2uJNrcUjMNE7TSt99bkcbytTXWBM4JFpGuxYGEBghKik0iTU7ikma1cr/2i5G8tzB1IZK2/BvTF+O9usckIs2h0Q7hidTd2gNmRB2CSJfRFgzvhrnETWwS6UpcF3UI0jB6ldwiNeN43vw1Sm0qIlJUbBLphmvtXHojaQp9+81Kr7+38PgIIxHp2pI39laPtJTYJNJMc958PuoQpGH8O+oARLq0hFvoS9zEMpG+z0FRhyANwN0141BE6i5Gs3ZFMq0IfIIVObm/BV21RaSY1NCuFBfLHqlI336vpu99+N8Fx3R4Xh8NIhKWWCXSgd0fT68/8+aPIoxEGst9HUpakpebzbhQvYjkcow2WkJf4iZWe9SvX/+MRw9HFoeIiDSPWCXSFA+WN998M+pQJFIrRB2ASJenWbulxS6RbrXWG+ljYwvQuaXNbPX+s9Pr/12wS4SRiHRNOo+0PDGdtRu/X5TU6pWoAxCRmIpdjxRg27XmpteffHPtCCMREenKjDZvCX2Jm/jtkUiGvqu33xXmvwv6RRiJiMRVbBNpT/6UXn/yzc0ijESi1Noa1i2sRZqPAwlaQl/iJn57FNh0rd0zHn0YVRjSAHr3ab/Re2avNLZ//CIh0mSj0prms2TBggVRhyAR6dGjR8Hn4vdPWkQ6W6wTaeakozeX7RBhJBK11uWmp9f/u6AfrRiG0aJUKlKQuyYblSN+eySSR9++faMOQURiKvaJdKsBr6fXdSpMs3sq6gBEupwEFvoSNzG9IEM7zdqUlNX7r8V/g0PlBkVusCYikLqyUez7WzVrkndoQHpt+puTIoxDorZ6/4VRhyAiMdMUiXTbtR5Nr3/GjyOMRBpJ/AaYRMKmyUbliN8eiZSwev+FGtYVkdA0TSLV9Xcl24FKpiIl6MpG5Yn9ZCORfPr1vxK4MuowRCQG4vfVoAjjmPT6k28eEWEkIiJdQ5tb6EvclJ1IzazVzF4ws3tyyk8xMzezPpXW7WzbrHUmAO7Q5k9GGYqISMNzjDZaQl/ippI9OhGYlVlgZgOB3YG3Kq0bmeDAWPy+E4mISBTKSqRmNgDYB7g256lxwGiKnNtepG4kth00lwQtOC08Nm/PqMMREWloCW8JfYmbcvfoEpIJM5EqMLP9gAXuPr1QpUJ1G8fsqAMQEZEuruSsXTPbF3jX3aea2U5B2QrAmcDwSusW2XYkMBJgzTXXLCN0ERGpJ10isDzlvEPbAfuZ2TzgFmAX4O/AYGB6UD4AeN7MVi9V18z+ke9F3P3P7j7M3YfV+04d3+D/0uvvvfdeXV9LRKSrcsKfsduUs3bd/Qx3H+Dug4ARwCR3P8jdV3P3QUH5fGAzd/9vGXUjP+9kvUHrp9df+WybCCMREZGuLvQ+u5n1M7N7w25XREQ6n65sVFpFVzZy98nA5DzlgzLWFwJ7l1tXRESkK4vfV4My7TCofcbuY/M2jDASEZHGlLx4je7+UoqutQvAV1EHICLSgIyELl9TUvy+GoiIiHSipk6k6yzXfsPvjz76KMJIREQaj6Oh3XLEb48q0K9fv/T6jCWbRxiJiIh0VTpGKiIiBenKRqXpHRIREalB0yfS7NNgNo4wEhGRxuIYCQ9/iRsN7Wb5IuoAREQaioZ2S1MiDRS8oaqIiEgR+qoBwFVAMpm+Mu+VaEMREWkQjm7sXY747VEVdhg0HKcFaOEd9os6HBER6UI0tCsiIgUYbbpEYElKpCIikldqaFeK0zsU2HHQa+n1R+atF2EkzeM/czdh/NxNuHvuJlGHIiJSNSVSiUwiY/3uuUOjCkNEimgLhnfDXOJGiVQioz8+EYkDfZZl2GHN9lNfHpl3bISRNI/M76bqlYo0FnfT6S9l0GSjDC0tmb/ghyOLo/m04LoihkhDiuNtz8Kmd0giMWXuv9Lr+689DSc5Q/Bfc74dWUwiItVQIu1g//SH+jvvvBN1MLG1iLOzHjuGx3ASgkhX5kACC32JGyXSHDsOujC9PvPzHSKMJL7+o9NdRCRGdIw0j/bTMuL3zalezpzxPVKX/m/p8LY5m7TOZbnlwHKem/U/ePbFA9mkB7S0oGOlVZgw605e5k9ZZcmeBJyywQORxCRxYTpGWgYl0rzOBs4BYNK89atuJeHp1BJCTBnt0gr0oQeHsXmfH7DSSiuF2n4hf399B2Bp3ud68yktJD+8BwDdu0NrawstBXY9kYC5X6yIA715h6/aYPkWaAMueHFbHGg1WAY4LbQA3YGPAVi1zIiTg8W1fx3qw09WPY811lij5pYqcekru+UtT33ZSDiU+tsaM3MPJr3fAlT/d7wq3TiCvdlq401ZddVy3/vm8cEHH/Dqq6/y+lcLubPt6eBvNFvyy3nyL/Gsvoex6yaaCxAn5g3YBRg2bJhPmTIl0hhqSaAp6Q+8EBNp6vhtWL3l1G+/VIzV/JnkfpN1h/8l4EtWzLP1l6wQbP6/RI+cdqDSLyOZH1xhqCaGlGTvsHDdZELMH2sbhV83XyL14Mtb8t7JyTY//6KFJz6p7e/ZPeS/u+DvqT5thvR7dzJur1h9m571M7udR3e9kDCY2VR3HxZKYxnW2Ki3/+ifO4fdLH/Y9K66xBsV9UgL2GXQq8ya9xcWcVHVbSRKbxJbiUTyg6PN4ROAVPK00h9JmcO/7h2Hg9uf7Br3ka3tY92SScyTf0/LSP1dLQc4rYWqeXJKhwPPfFJTAF1KqH8PqTcwhGa68meBbuxdmhJpERsM+gkb8JOow2gI97x+DUu4Ps8zyU+aofYXvjVkE0ZPPyT9zAWb3l7Ra9w+Z3MAjh7yZLVh1tV+j/08z+fqB8DHDAZ6LvcJX2uF5Vph+WBMeanBl6R6iEH/sQWMrxX+gpCP5V0lAVnJdGuO5TsbHpBV9bQKXkbq7zsPnQqE1xuV6CmRSln2XfenwE9ZtmwZ/3xj+3R56rvqi/yEF1+DDbrBrGVrVpxEG9n3Hj+BpekMmp39/rPDzZ0ej3RdixYtijqEijhGwsM7RBJXSqRSkW7dunHkuk8DMOn1v7GAa9LJ1A2WWw42Xe4tbp69FQCHDXmm4te4fc5POWSda8IKuWr7P35Cej13lO8/O1ze6fFI13fUy2OjDkHqQIlUqrbLuj8EfgjArbNP4KvEcx1m6aYSai/2ZN8h5xRtr32yyHNhh1qRzASakkqiSqBSiy+jDqAKYU6WjCslUgnFoUOuSB8fHcxbfL1n9vMfcz83z74/ue3gJ2lt7ThNxon2zN18CRSgP324aoezOzcYkQbgDm0a2i1JiVRCkTnJ6LhN24dzUz3STLe+sW16PXvotwUHDh3SuT3SQgn0mnXO7PRzR0Wk61EilbpKJcqnZt/MG1za4fmbZ28BUPg0jjr66KOPOOrFMzuU3739FRFEI9KYNNmoNCVS6RTbDDmMbTgMyO6l5rvy0Ny5L7L22t+qazz5eqFjVvsF31xvvbq+rojEj44iS80+//zz9Pro/leX3P6wIc+kl/YrNbWrZxL98MMP8ybRu7e/QklU6m4wvaIOoSLJ01+iubG3mbWa2Qtmdk/weBUzm2hmrwc/e2dse4aZzTazV81sj4zyzc3sxeC5y8ySZ2+bWQ8zuzUof8bMBmXUOTp4jdfN7OhyYlWPVGp21mtHpScK9enTp6K6h3Xi8dB8CfSKtU5j4MCBnRaDNLfrd/k1O04aHXUYFWmLbgrgicAsSH/7OB14yN3PN7PTg8enmdmGwAhgI6Af8KCZrefubcDVwEjgaeBeYE/gPuAYYIm7DzGzEcAY4FAzWwU4CxhG8jv+VDMb7+5LigWqHqnULJHzs9HMmDWzYC9USVQ6k1V0OavmZWYDgH2AazOK9wduCNZvAA7IKL/F3b909zeA2cCWZrYG0Mvdn/LkReVvzKmTausOYNegt7oHMNHdFwfJcyLJ5FuUeqQSika95u3+jx8frLV/gP1rm0vznn4j0pmemjWLbTbYIOowinIim2x0CTAayLy11TfcfRGAuy8ys9WC8v4ke5wp84OypcF6bnmqzttBW8vM7COSt5VKl+epU5B6pBKCFqCFXw38U8ktO8ukF57mwCeO7zCZ6e7tr1ASlUilbkJw2sLrog4lSn3MbErGMjL1hJntC7zr7lPLbCtfpi90WnrqO381dQpSj1Rqcsr0Q9Prq6yySoSRtDvwieOzHjtw17aX0VLo5qginSh9W7UucVqJlT05qELvF7mN2nbAfma2N7A80MvM/gG8Y2ZrBL3RNYB3g+3nA5nHaAYAC4PyAXnKM+vMN7NuwNeBxUH5Tjl1JpfaGX2ySGwc+MTxHZKoGdy9/ZVKotJAwrndfFy5+xnuPsDdB5GcRDTJ3Y8AxgOpWbRHA3cH6+OBEcFM3MHAusCzwTDwJ2a2dXD886icOqm2Dg5ew4EJwHAz6x3MCh4elBWlHql0ea/Pfp3R71zSofyu7a7s/GBEYibROEn/fOA2MzsGeAs4BMDdZ5rZbcDLJG/Ze3wwYxfgOOB64GskZ+veF5T/Ffi7mc0m2RMdEbS12MzOhfQFv3/n7otLBaZEKqEYxt6RvG5uDxRgn+478uMtvx9BNCLxEvW1dt19MsHQqrt/AOxaYLvzgPPylE8BNs5T/gVBIs7z3HVARQewlUilapnHR0dsWtZ5y6HJl0BBvVAR6XxKpNKlnPvE5TzPKx3KlUClq9iaQTzNvKjDKFudJhvFit4h6RLeevstDnzi+A5J9Je9fqgkKl3KRbv9LOoQJGTqkUrNtma/urafbxi3O3CbEqhIXSWvtdswk40alhKpVCXz+OjBmx5el9fQcVCJu3feeYdvfOMbUYdRVAPN2m1YSqTScP727K2MX/oIuefaKYFK3Bz04sU8/o0Log5DaqREKg3loCd/hnsyhXpwxa6Tex3D9t/aLOrQRJpOhNfa7VKUSKViD0+bkL7M2cVDbw2lzVdeeYUzF18GJK9GlAheQL1QEWl0SqRSsfH8LdT2bn7qdu70h7PK7truqlBfQ0Sqo9NfSlMilSqFM9wz6smzeJP3ssru3FZJVKQhuGbtlkOJVCJz0JPZ59N9g95ctW2Hq3yJiDQ0JVKpyJRpz6bXxw29pep2cpPovq078sOtDi2wtYhEwdHpL+VQIpWK3MTYmtvITaK/X+XnbPDNDWpuV0QkCkqk0qlyk+gd21xJ8laBIs1jM9bgeRZFHUZZdIy0NE3Hkk6Tm0Tv3PYqJVFpSpftdlLUIUiI1COVqpzY+/yKts+XREWamXvpbaKmCzKUR4lUynbStBHp9UFrDSq7npKoSNelRFqahnalbl6f/bqSqEhBRljnY0u01COVuvjTkzfxAE9klSmJinT0xGsz2W69jaIOIy/dRq086pFK6EY+OZoHeDyrTElUJL/Rb94YdQhSI/VIpSzjpv26fb3IhRgOefK44J4tybu3rMlqjNv2nPoHKCJ1oQsylKZEKmWZ57NL/nM65MnjgFQShWN7HsTwTXerd2giUi+uyUblUCKVihSasZ9KoinjBv6KgQMH1j8gEZGIKZFKmazsJHr7tlfXPxwRqTudR1oeTTaSmiiJikizU49UqqYkKhJ/6pGWVnaP1MxazewFM7snp/wUM3Mz65OnzkAze9jMZpnZTDM7MYygJXpKoiLxlzqPNOwlbirpkZ4IzAJ6pQrMbCCwO/BWgTrLgJPd/XkzWwmYamYT3f3lagOW6CmJioi0K6tHamYDgH2Aa3OeGgeMpsBkTndf5O7PB+ufkEzE/auOViKXSGQ/VhIVqc4KUQdQJncLfYmbcod2LyGZMNMfo2a2H7DA3aeX04CZDQK+DTxTWYhSDxvcdQ4b3FXBhRK8490qlERFqnfTpidHHYKEpGQiNbN9gXfdfWpG2QrAmcBvy3kRM1sRuBP4pbt/XGCbkWY2xcymvPfee2UFL7UrJ5me++QlLGvLLlMSFanNaqutFnUIZUlgoS9xU06PdDtgPzObB9wC7AL8HRgMTA/KBwDPm9nquZXNrDvJJHqTu/+r0Iu4+5/dfZi7D+vbt2/FOyKV86CXeeq/fldwm0OePI4ZvJpVpiQqItKuZCJ19zPcfYC7DwJGAJPc/SB3X83dBwXl84HN3P2/mXXNzIC/ArPcfWz44UsY/lOgPHdSkQEtOvNYpGl4cIlAzdotLvSPRTPrZ2b3Bg+3A44EdjGzacGyd9ivKbXqOFcsN4l264ZunShSJ+dMzJ3H2Tg02ai0ihKpu092933zlA9y9/eD9YXuvnew/ri7m7tv4u5Dg+Xe3PrS+WYdeBaW8ff8zYzh3UOf+mlwQcBkgr16vXM7OTqR5vIAr0cdgtRAVzYSIHnLM0gm0Uyp46GJt9rv6iIi4XEn60ttY4nnUGzYdMSric068Kx0YjTg0Mfak6gZ3L7tNZHEJdIsEp79U7om9UibXrKfufFqi/CW9sOgt26Tm0Rb1BsVCZ0Fo0GN2+uL4zHNsCmRChuvtig9G3fZMrhzB/VERTpP4yYq3UatPBrabXJD11iYlURffn91Nvz32ZHGJCLSlSiRNqlly5ZlTSxaujSZRFOUTEUkdWnQsJe4USJtQq+8+gqHP3dC+vEXX8DM9/uR8Nas2YNKpiL19R0GRx2ChEDHSMu0yphVWPLFkqLb9F6+N4tPW9xJEVXnlqf+zV3cn1X2ypL2G/LM3P8sNrq7/fq7G/77bF4+4OzOCk+kqYwZfizbPHB61GEUFcdr44ZNibRMS75Ygp9VfEzCzmnsP7ifP3U67/JhVtmt21zDene2X3DBzJgyfDTDHrggXbbhv89m1zU7K0oRaRSOZu2WQ0O7TeLQp36aN4kCZFzECIAVVliBR7c/IWvbd3RDHpG6uvyxO6MOQaqkHmkILn/qIca9kbzN6sRZz7L7BlsW3f6Oxx9m9NtPZZX1A9ZleZbRSg9aWI0VOHKHXdlgwKCKYjn62R+ScEjQAhg3bXVth6sVrc/a/G6b0R0rZyTTPn368NBWx7LrM38CYObnsFrw/M+eP5yrNruporhEpLibP3+On3NQ1GHk0JWNyqFEWqO1b/5D1uNjX3gQXniwdMWcv82FwEK+yCj5hFseu7l0MzntbD8YWg1ag3uw/79nf8jyLe0z5RIJmJ14kZHPHZmcQQd8Z832cH7y7JFZ7W3XL7XWQuq+7u5w7HNH8Oj8Ie27khNHtZc8O3O1nThsm52qq1zCokWLuOP5pxifmManwDKgLfhZmeSbGfZl3cJtz7FQ2wx/ny3VZnhNJtsLcZ9TTYXTZn3+biR6SqRdWL5/kIkEtLZ23C61bUtL8peeWVaupQ7drONr57suS7XXDz3v3cmcd/fDISaBjA/rENtrdE39Wd3YFwrqcuJ4ukrYlEhrkNkbnXvYr7BzzmTuYb+KMKJsP3j6h0Dyl9wtlVwTsCy4+ny+A+T5/tG0pv/XQltQtoENY+JXn6a3mXPomen1p59/ntFvj+eDWncgTKF9sCbfvMbujSZzSYvBysD+DGOjPv3YefPNac39liUiNVMijbF/bv23srZb7472WbuvHfybsuqsc+t5BZ/berPNeHSzzcpqR0Qam2btlqZEWoNU762ZjnksWrSI7R+9LqssszcqIvGRvBJRE33AVUmnv4SgWY4h3D71CbZ/9K9ZZUqiItLs1COtSTy+qZXzRWCT237PZ+ntkrM5lERF4k+nv5SmHmkIvhZ1AHU25Lbf8z/ah7B7oZ6oiEiKeqQhOKTn2lGHUKP83zgTiQTr3ZF9nuzl6+3F3t/evDOCEpEG0CyHrmqhRBqCcw44NOoQQrfdbefxTs45k7O//+uIohGRqGiyUWlKpNLhG+eQ237fYRslURGR/JRIq/TJJ59EHUJd5CbRo/quz293PiSiaEQkSo6pR1oGJdIqfWf8FVGHUHfqhYqIlKZEWqUPow6gzpRERQS6ytWlo6XTXyQraf57k4OVREUkKbiyUdhL3KhHKoB6oCIi1VIiFRGRwjS2W5KGdkVERGqgHqmIiBQUx2OaYVMiFRGRgnSJwNI0tCsiIlID9UhFRCQvR0O75VCPVEREpAbqkYqISH4OqEdaknqkIiIiNVCPtFqaySYiTUCzdktTIhURkcKUSEvS0K6IiEgN1CMVEZEC4nm3lrCpRyoiIg3DzJY3s2fNbLqZzTSzc4Lys81sgZlNC5a9M+qcYWazzexVM9sjo3xzM3sxeO4yM7OgvIeZ3RqUP2NmgzLqHG1mrwfL0eXErB6piIgU1vnHSL8EdnH3T82sO/C4md0XPDfO3S/K3NjMNgRGABsB/YAHzWw9d28DrgZGAk8D9wJ7AvcBxwBL3H2ImY0AxgCHmtkqwFnAMJJ7PtXMxrv7kmIBq0cqIiL5RXBjb0/6NHjYPViKpfP9gVvc/Ut3fwOYDWxpZmsAvdz9KXd34EbggIw6NwTrdwC7Br3VPYCJ7r44SJ4TSSbfopRIRUSks/UxsykZy8jMJ82s1cymAe+STGzPBE+dYGYzzOw6M+sdlPUH3s6oPj8o6x+s55Zn1XH3ZcBHwKpF2ipKiVRERArzOizwvrsPy1j+nPWS7m3uPhQYQLJ3uTHJYdp1gKHAIuDiYPN8XVwvUl5tnYKUSEVEpCG5+4fAZGBPd38nSLAJ4C/AlsFm84GBGdUGAAuD8gF5yrPqmFk34OvA4iJtFaVEKiIiRVgdliKvZtbXzFYO1r8G7Aa8EhzzTDkQeClYHw+MCGbiDgbWBZ5190XAJ2a2dXD88yjg7ow6qRm5BwOTguOoE4DhZtY7GDoeHpQVpVm7VfomLbxCIuowRETqq/Nn7a4B3GBmrSQ7e7e5+z1m9nczGxpENA84FsDdZ5rZbcDLwDLg+GDGLsBxwPXA10jO1k3N/v0r8Hczm02yJzoiaGuxmZ0LPBds9zt3X1wqYCXSKl209YHs+/SdUYchIhIr7j4D+Hae8iOL1DkPOC9P+RRg4zzlXwCHFGjrOuC6CkLW0G61NhqyXtQhiIjUX30mG8WKEqmIiEgNNLQrIiL56cbeZVGPVEREpAbqkYqISEG6sXdpSqQiIlKYEmlJGtoVERGpgXqkIiJSmCYblaREWgMdOxARESVSEREpyNRhKEmJtCYa8hCRGIvplYjCpslGTW749WOYPn161GGIiHRZSqRNbND1Y3gN2P+F+xl0/Rja2tpK1hGRZmLJyUZhLzGjRCpJDuvceFHUUYiIdDlKpCFYunRp1CGEZtDfLog6BBFpJLr7S0lKpCE47+5bow5BRKQ+lEhLUiINwV2fz486hIqd/p9b0uuj196c5Azk+B27EBGpNyXSEHwUdQBVuGXxm+nc+bPv7IY76UVEJE090pKUSEVERGqgCzI0oUE3jEmvzzv6tAgjEZGGpht7l6XsHqmZtZrZC2Z2T075KWbmZtanQL09zexVM5ttZqfXGrDUx6uH/SLqEEREuqRKhnZPBGZlFpjZQGB34K18FcysFbgS2AvYEPiBmW1YXagShsze6MPf+X56ffnll08fv7joussjiExEGpF5+EvclJVIzWwAsA9wbc5T44DRFD58vCUw293nuvtXwC3A/lXGKjXKTKIAgwcPzrvd5f55Z4QjIl2BJhuVVG6P9BKSCTORKjCz/YAF7l7sQq39gbczHs8PyuKhC/9R6NioiEg4SiZSM9sXeNfdp2aUrQCcCfy2VPU8ZXlTj5mNNLMpZjblvffeKxVWY+kCyTSzN/rb9beOMBIRkXgpp0e6HbCfmc0jOTS7C/B3YDAwPSgfADxvZqvn1J0PDMx4PABYmO9F3P3P7j7M3Yf17du3op2Q4g6/6c9Zj3+09Y4RRRIPgy+/mMGXXxx1GCLSIEqe/uLuZwBnAJjZTsAp7n5Q5jZBMh3m7u/nVH8OWNfMBgMLgBHAYTVHLWU78s6/8cSyJenHxYZ0H9h+f4Y/fndnhNUl5SbPzMdv/PzkTolhxowZHDB5YuEN2g++MPeXowD48MMP+cXlf+OFJQVrdbBFv68z+sAd+eY661QdaxydeMn1TH6jgjcyJRi1mn7ZSeEG1AniODkobKGfR2pm/YBr3X1vd19mZicAE4BW4Dp3nxn2azaCQTeen/WzPCGen5X6Y099kOZr+gsYdE2JO7wEfxFrXX0BVsv1OsL+x5duL6T3zIu3dsiAtbjggIP51qnjcOCzAYn28Zs8+zb4siCpJoA2YEnwszuwYrBR6l+bFfiZLyCvcI9TGydg7QvHwv+g50eVv2fPLfyIQ64cX7D5mtXp7yPUMx7rkEA2/cW4rpdMdR5pSRUlUnefDEzOUz4oY30hsHfG43uBe6sNsJHts9I3+L9P3ok6jGyFjkp/CcnvMlKO2+e/ye2XXwxr5jzRBj3nt2DAp/0Sybc08z1vCZbVOitSkr9fy3kMyX/dveCzlTyZUBfrA7Fs6oVJBXRloxpceeAPuTLjsZ1zBvOOar/mxKeff86cDz7g2eefZ8FHH/GBf848FvNiPYIx2hOmkTHEBxUl0ERQv9EuYJ/av85qL/e54O1445f5h3CvufdexswJTrNOUP40u8yZ3w4sC+p/AnwBK1YwKrBmd/jTCYfxnTtuyt4/A3rCZz2duSeNKru9Uma+MY/rb7uXCe98GVqblVqtG+yw4SqM2H5n1l8391tPbT7++EtWWmk5zGr/t7DpL8YBcMtxe5fYssF04TMTOpN5A16lfNiwYT5lypSow8hi5xh+VvH3qpxt6i2RSLD2X8Zmlc079pSy66917YXp9Td/fGpocXUlL82axYjr7m9/fGEXG4rL8Pbbb7PjHbdnla0GPB1iQpXiUkkU6neM1MymuvuwsNvtMXCg9z85/JjfOOnkusQbFV20PmZaWlqYtPv3ssq++acSx0UlS2YSPXO3rSKMpHYDBw7s0At9F1h73Nj8FURy6YIMJSmRlqn38r2xc6zo0ijWXnttbti8/RSXL4B9/nRFdAF1IRufOi7r8Q/22DaiSMI196RRHa6Esva4sSxcmPdsNJE0XSKwNB0jLdPi0xZHHUJFdhy2BSd+9AGXzn4JgJl8wfG3/Z0rv39kxJE1rtwk2pWHdPN5LOiZZvZGt781eYP3MI+dSn7jT/5e6Y2kS1KPNMZO2nVPdl9p5fTj/1vyDn969OHoAmpgP7vi71mP45ZEM+VLmhrqrY/r7nk0vb7WWmtFGEkNNLRbkhJpzP3lsB+zTsYU0j/OmsoDxSZyxfiPvZhH32y/lkick2jK3JNGcdq3vpVVtva4sZx1660RRRRPlz4wtfRG0uUpkTaBh449me4Zj0dOncwzL7wQWTyNJnNId+O+KxbZMl6O3W337N6pw98XLGCdseqdSgb1SEtSIm0Sr+ecAnPosw/x4syOF5myjKUZ5B4XvWX0TyKKJDr5hnrXGTuWRCKRZ2sRyaVE2kRyzyf97uP3MWPWrAJbN59mGNItZO5Jo5hz0klZX6DWveQS9U6bXD1m7MZx1q4SaZPJTqbOfo/ew/0NdvGLzpLZG737p/tFGEljMDPmjMrfO5XaTLv0l1GHUD238JeYUSJtQrk9058+/zCPNfkx03V0l5O0OaNGcfrGG2eVrTN2LL/VRKSKnPOXO9LrYVxmUBqXEmmTmnfsKVkHQ4987sG8x0zjauPR7b3R72+qJJrrJ8OHd+id3qSJSBX514tvRx1CODTZqCQl0iY2b2T2tXS/+8S9sf5jL+S3R2hYtxAN9YqUpkTa5HKTaTPYOtUbNRjat1e0wXQBc0aNYvZJ2ROx1hk7lo2UUJuCJhuVpkQqTZdMP81Y/8foYyKLoyvJNxHpC9Q7LSouIzsa2i1JiVSA5kmm+551WXq9t+Z/VGzOqFFs1yvoxQcfiutcPJZFixZFGlcjq9et06Rx6KL1ktYMyXTe523p9cfG6AOuGjf++MdAMoGmbH/zPwGYc7Iufg+w/5nj4nFRk5gOxYZNPVJpGlfdNSHqEGIlX9LMTK7N7M2Po45AOpMSqTSNq556Ob3+0gXqjYZhzsmj+OOWW2SVrXPxWKbPmBFRRBI6HSMtSYlUmsIjM16MOoTY+v4OO3TonX5v4oPqncaFEmlJSqTSFI7/x4PpdfVG60NDvR1Nu1x/a81AiVRib8GCBVGH0DTmnDyK/+y9V1bZOhePZdqLzTMiMPTn40pv1IXoPNLSlEgl9va49Lb0unqj9bfhBht06J0e9MDEpu+dSnwpkYpIXWioV5qFEqnEWubF6dUb7XxzTh7FI4d+P6tsnYvH8tprr0UUkUj4lEhFpK4GDBjQoXe61/h7Yt87jc1EI83aLUmJVGIrszf6wnknRBiJQJ6hXod1LopXMt3pF/GaaESRCUOabNROiVRiabczsz/QunfvHlEkkmnOyaO497v7ZpWtc9FYJk2bFk1AIfswhklCSlMildj59d9uY9HS9hEkHRttLOuvtx5zTsnunf7kwUmx653GhoZ2S1IilVh5bNp07nql/bzRY7ZaL8JopJjcZArxG+qV5qBEKrHxxhtv8NN/Tko+MNh01Z6cdNA+0QYlRc05ZRRTf/yjrLJ1LhrL9kqojUM90pKUSCUW2tra2Peaf2eV3XzayGiCkYqsvPLKHXqni1DvtBEYmmxUDiVSiYVNfnVZ1uOZutdolzPnlFH87JvrZ5Wtc9FY3n777YgiqkJMe1xSnBKpdHkbnZY9Q1dJtOs6ed99OvROd7r1dr7ZxXqnI7f6ZtQhhEdDuyUpkUqXpiQaT7nJdClda6j3Z0fsVXojiQ0lUumylETjbc4po7hm+G5ZZetcNJbPP/88ooiakC7IUBYlUumSlESbw+6bbNKhd7rxlVez88XjCtSIztw33oo6hPrQ0G5JSqTS5SiJNp/cZPqWe8MN9X7v4jujDkEiokQqXYqSaPOac8oo/t966yYfBD2bIRc2VjKNJfVIS1IilS5DSVR+s993O/ROh1w4luNu+mdEEYkokUoXoSQqmeacOgrLeDxx4SL1TutEk41KUyKVhqckKvnMPnUUu6y+WlaZkqlEQYlUGlZbW5uSqBT15yOP4LWTf5lVNuTCscyfPz+agOJIx0hLUiKVhvTWW2+xyZm67J+U1tLSwuxTc66G9M/b+KZ6p7WrRxJVIhXpHHtdlXEqgSmJSmm5yXQZGurtisxseTN71symm9lMMzsnKF/FzCaa2evBz94Zdc4ws9lm9qqZ7ZFRvrmZvRg8d5mZWVDew8xuDcqfMbNBGXWODl7jdTM7upyYlUil4Wx0esZwrsHM85VEpTyzTx3F+TvukFU25MKxJBKJiCLq+iKYbPQlsIu7bwoMBfY0s62B04GH3H1d4KHgMWa2ITAC2AjYE7jKzFqDtq4GRgLrBsueQfkxwBJ3HwKMA8YEba0CnAVsBWwJnJWZsAtRIpWGkpVEURKVyh285RYdeqfrXXwJe1xyWYEa0kg86dPgYfdgcWB/4Iag/AbggGB9f+AWd//S3d8AZgNbmtkaQC93f8rdHbgxp06qrTuAXYPe6h7ARHdf7O5LgIm0J9+ClEilYSiJSphyk+mcpcs01FuNCI6RmlmrmU0D3iWZ2J4BvuHuiwCCn6kp2/2BzHvtzQ/K+gfrueVZddx9GfARsGqRtopSIpWGcMgfrs56rCQqYZh96iiOWn/drLIhF44l2UGRctRpaLePmU3JWEZmvqa7t7n7UGAAyd7lxsVCzFPmRcqrrVOQEqk0hJc//iK9riQqYfrtft/t0Dtd96JxbFWP3qnyc7ned/dhGcuf823k7h8Ck0kOr74TDNcS/Hw32Gw+MDCj2gBgYVA+IE95Vh0z6wZ8HVhcpK2ilEglcplDutsO/HqEkUicZSVThw8chlwwrnCFCm3QqyXddqx08tCumfU1s5WD9a8BuwGvAOOB1Czao4G7g/XxwIhgJu5gkpOKng2Gfz8xs62D459H5dRJtXUwMCk4jjoBGG5mvYNJRsODsqKUSCVSucdF/3L8jyKKRJrB7FNHMTJ3qPeCcSxevLjmtq8988c1tyEArAE8bGYzgOdIHiO9Bzgf2N3MXgd2Dx7j7jOB24CXgfuB4929LWjrOOBakhOQ5gD3BeV/BVY1s9nAKIIZwO6+GDg3eN3ngN8FZUV1q3WPRaqlyUUShdH7fZfR+2X3Rre8NjmBc/bo6v8Ge/bsmfcAW5cWwQUU3H0G8O085R8Auxaocx5wXp7yKUCH46vu/gVwSIG2rgOuqyRm9UglEkOVRCVi+ZJmmEO9cWB1WuJGiVQ63ZQZM1ia8VhJVKIye/RJXLrrjlllQy4YxxUTHogoIumKlEil0x1980Pp9RO3+1aEkYjAPptv1qF3esn0meqdpkRwHmlXo0QqnSr3uOjI7+4WUSQi2WaPPonlc8qUTKUcSqTlWmUVMCu+rLJK1FE2NE0ukkb30uiTeP3UX2aVDblgHHtfdmU0ATUA3di7NCXSci1ZAu7FlyVLoo6yYW10hpKodA1m1mGo97UvvlLvVApSIpW6UxKVrmj26JM4aM01ssqGXDCOayZNLll3zF//VaeoIqBjpCXpPFKp2Qa/zkiUuXPbl5H13HXf274zQhIJxZgRIxhD9rHSi6a8wEVTXih6zuktz7/Jacd0QoCdIYaJL2xKpBKu3H90rRnrBj8a/ziMfzyrLLSXDOkEtd/stjWH77BNTW3c8tAjXPTPqenHDsnj6BkeOP9oVl1Vx9W7gtmjT+owtDtkTPLx7NM0wtLslEhDsNWRyYtfP5OxXlAVH/ZhJ4qwk9eKwKd9ab9roGe8RubP3CSbr6xaIbZ17oNPce6DT5XeMPf1POOnA31J9sgTwGfAMgczegabDT/9Bj5NXVK71O+k1GulltTrfZizvQErk/wdtYC3kv/ATubvLvc1EsBSoI3kTadI/u6LqsfZ92W0+UlqpQXoFfzsnlGWaqfQFQJy34PMIqP9C6LDkD+MS743gZVKh9d1xHRyUNiUSGtUMnE2oswPy5Cs+F4yV9CH5F9VIniJNljh/TIbqSCmTzPWDfDUX3LP4PVTH3aZH5SWU6nQa5f7/hRrI2W5oDzj0/WzMpqu6rVSrwelM1yh+oXer9z2eyV/fJpnkw5y389q39+M6qH+/RZrK89zBngi40EL7cm5H7AQDtt+YMeKEltKpJ1oJYMHb2y/A0UikWD69Ol8+9vJy0o+8+yznHrF43yZr3KRf+xBJ4OlZH0xLsxg09WX44cH78BXny5h/fXXZ401kpMqPv30U6ZPnw7A559/zhX/msHC/xVuJ1NPgCBp/g9YoZxYCrRVSipPnLD/Zuy1zYbc+uBkbn5oPks/rqydSmJKJ42v055QelQQeKFNgx6fJcAWZfwOv07yy0B32j+wM9sp1pMqJLeXCckd6077F5AWyv8CUo7cnm+tSbDa+sV6VuX2uiw7BE99KUgV/BfWWB5O/cHB1UTYmNQjLcka8Qa3w4YN8ylTpkQdRjaz4F9Ntq2OSPZId9psdcacfHjebUSayfl33Ml1r76VXVhj8lwFOG6zjTl6z91rayimzGyquw8Lu90VVhvo6x8yqvSGFZp21ai6xBsV9UhDMmbUYXDy4VGHIRK50w8+KHlPKpEmoURag1v+9Wgs72QgIpKmQbaSdEGGGlzyrwYbfhYRkU5XdiI1s1Yze8HM7gken2tmM8xsmpk9YGb9CtQ7ycxmmtlLZvZPM8u9LrSIiDQoXWu3tEp6pCcCszIeX+jum7j7UOAe4Le5FcysP/ALYJi7b0xyDuKI6sMVEZFOU4/LAzZrIjWzAcA+wLWpMnfPPNGgJ4Xfnm7A18ysG8kzIhZWF6qIiEjjKXey0SXAaHIu2mFm5wFHkbzOyc65ldx9gZldBLwFfA484O55bz1vZiOBkQBrrrlmmWE1hh5RByAiUi8x7EGGrWSP1Mz2Bd5196m5z7n7me4+ELgJOCFP3d7A/sBgktf86GlmR+R7HXf/s7sPc/dhffv2rXA3ovXIP8I/z0pERLqGcoZ2twP2M7N5wC3ALmb2j5xtbgYOylN3N+ANd3/P3ZcC/wK2rSHehjHxiRlRhyAiUleGJhuVo2Qidfcz3H2Auw8iOVFokrsfYWbrZmy2H/BKnupvAVub2QpmZsCuZE9Y6rJ+c/WDUYcgIiINoJYLMpxvZuuTvDTom8BPAYLTYK51973d/RkzuwN4nuR9KV4A/lxjzCIi0lli2IMMW0WJ1N0nA5OD9XxDubj7QmDvjMdnAWdVHaGIiETGdP3wknRlIxERkRroWrs1OuHATaMOQUSkPmJ6AYWwqUdaoyMO2jXqEEREJELqkVZh6+AepCIicRfH01XCpkQqIiKFKZGWpKFdERGRGqhHWi13MN3WW0TiTUO7palHWo3UeVU6v0pEpOmpRyoiIoWpv1CSEqmIiOQX04vMh01DuyIiIjVQj1RERApTj7Qk9UhFRERqoB6piIjklbqxtxSnRCoiIoXpNL+SNLQrIiJSA/VIRUSkIA3tlqYeqYiISA3UIxURkfx0Y++yqEcqIiJSA/VIRUSkIEtEHUHjUyKtwYAV1aEXkZjT0G5JygQ1+OUxO0UdgoiIREw90hpsv+W3ow5BRKSudPpLaeqRioiI1EA90mroG5qINANHlwgsgxKpiIgUpKHd0pRIK7R06VIs6iBERKRhKJFW6Ge/uirqEEREOo96pCVpslGFXlqwLOoQRESkgahHKhKSL7/8kt2/fxkAj959asTRiNRON/YujxKpSEh2C5KoSGy4a9ZuGTS0KyIiUgP1SEVEpCAN7ZamHqlICHY44MKoQxCRiKhHKlKjrCTq6DxjiRf1SEtSj1SkBvmS6KPjNWNXpJmoRypSpdzhXPVEJY50jLQ0JVKRKnRIom0RBSJSTw4klElL0dBupRwdM2hi9z80tUMSfezfGsoVaWbqkVZp7M+3jzoE6WQ7HnAhiZyyVBLVsK7EljoOJSmRVmmbbbaKOgTpRPlOb1FPVERAiVSkpNwkespxO7L/HltGFI1I59Jko9KUSEUKaGtrY6eDxmaV5euF7vjd9kT7yH/US5WY0bV2S1IircA2P7g46hCkk5z82+t59sX3s8oKDuUmghNI9Xkj0pSUSEVy7HDgRe0PLDmN6LG7TokoGpFoaWi3NJ3+IpIhK4kCm260atEkmkgE83j1YSPStNQjFQGWLVvGzodcklX26L9Oxqz4iS07f7c98T7yf6PrEZpIdHTefFmUSKXp5fZCoXGGcpcuXcoFF97BQw+/2V6YezJrCVtvtRa/+MXOrL766uEG1yR22+G85IrBg4+eGW0wncwA02SjkpRIq5GAbQ69uMNJ+E8C2x5a4YSksP5Gg3YqvjBALf9Iqn3NEu2FJu+dWBLJRFSop/lFgm4J2Hn4mPJeI/UvaFmROu6VvUelfidGRcn06afn8fTTfyv5mmXHmIrPPViAZdmbtJYfXnuzqZUSowBVqaXJ1AEwN3b7TjKpNltCleJ0jLQacbmMTZy/aXrWj4xyy/9B7Q6fJ5NohS8BFPmTqMd7XGGPNHQWvIctLdDaCt1aYfnspW35Vtp6lLEs15JeEqmluyWXzLLc5ypduhmJVkhY8u2r6C2suELMJOqwFGFmA83sYTObZWYzzezEoPxsM1tgZtOCZe+MOmeY2Wwze9XM9sgo39zMXgyeu8yCYzVm1sPMbg3KnzGzQRl1jjaz14Pl6HLeIvVIK3DXuMM58KSb4nPMwKymD/otNunF4XvvxOabD6GlpbLvZMuWLePeSU/x99ufYdHiVDyE994GbXVIcG3Bv+Kl7a+V9Y+gghg8/b/av1vtvut6nHH69wDYY4tz2nt7gQlTz6nxFSr31ltLePWVuVhLd1ZddTlOOfmu8is75b0p5fY+U+9FLb1Vs6yuQyLzd9eWKPi7TP9JxOULdONbBpzs7s+b2UrAVDObGDw3zt2zjsWY2YbACGAjoB/woJmt5+5twNXASOBp4F5gT+A+4BhgibsPMbMRwBjgUDNbBTgLGEby1z7VzMa7+5JiAZs3YK9k2LBhPmXKlKjDyFZO0qkxMUn97bxH9vDr6aN2YY89tqiqrR33viC9/si94U402mPzs9LrUSTRevj3XZO56sqnaKv2Tjl5/m0NGbIi++73bXbZdTN69uyZt9q5v/srkye9k6e95I9K8+PW263J7/9wRIW16svMprr7sLDb7dVrgG8x7Piwm2XSw78qO14zuxu4AtgO+DRPIj0DwN3/GDyeAJwNzAMedvdvBuU/AHZy92NT27j7U2bWDfgv0JdkQt7J3Y8N6vwJmOzu/ywWo3qk5erduz7HbqRTPTzhtKxkev7YSZw/dhIPTzgtwqiawwEH7sQBB+7U6a/7m98ew29+27H8s88+Y/+9L624vUZLonEWDLl+G3iGZCI9wcyOAqaQ7LUuAfqT7HGmzA/KlgbrueUEP98GcPdlZvYRsGpmeZ46BSmRlmvx4tLbSJeQm0wh2VN96L5TKx6irodL//iXqENoCj179uTBR36V97knn3yZ357x7/Tjf9z6o+ac9Vy/01/6mFnmsOOf3f3PmRuY2YrAncAv3f1jM7saODeI6FzgYuBH5B9UKHRwIbU31dQpSIlUmtLDE07jww8/5MBD/5Qu23WvC9PPlfLDY9t7MmEP6957x/z0P+m4DOt2NdtuuyEPPrJh1GE0AK/X4ar3iw3tmll3kkn0Jnf/F4C7v5Px/F+Ae4KH84GBGdUHAAuD8gF5yjPrzA+Gdr8OLA7Kd8qpM7nUzkT/9VskIiuvvHLepLnTHqVPfZn79pf1CClJh9mliQUza/8KzHL3sRnla2RsdiDwUrA+HhgRzMQdDKwLPOvui4BPzGzroM2jgLsz6qRm5B4MTPLkhKEJwHAz621mvYHhQVlR6pFK03t4wmn8333PcNElk9M5bKc9x7Dxhitzxdhj81cqNkBUgz2+nTHJ6Hn1RiV6EVxrdzvgSOBFM5sWlP0K+IGZDSX5r28ecCyAu880s9uAl0nO+D0+mLELcBxwPfA1krN17wvK/wr83cxmk+yJjgjaWmxm5wLPBdv9zt1LHtdTIhUB9tlrK/bZa6us3uhLL3/ITnuOYfL92b3Wnfa6ILe6iITE3R8n/1fUe4vUOQ84L0/5FGDjPOVfAIcUaOs64Lpy4wUN7YpkmTzhNFb7RvY/i532HMOEiS9klVmwhHl89H//+19obYmEJn0FqxCXmFGPVCTHbTcm7zu6057tvdM/XvwAf7z4gewNQz4d6sDt2l9vwgsa1pUG4GDNfFWnMqlHKlJA7pBuh+fv091eREQ9UpGiUsk0s3eaWR6WzElG/2/0t0JtW6QmMRyKDZt6pCJlCDtxFvODHxzcaa8lIrVTj1SkTPVKpolEIpyLsovUgzqkJSmRikRs783OTq9rkpE0Gt3YuzQlUpEGoH6oSNelRCoSoT2Htt+W5PCT1o8wEpEC1CMtSZONRBrEkUcfHnUIIlKFshOpmbWa2Qtmdk/w+Fwzm2Fm08zsATPrV6DeymZ2h5m9YmazzGybsIIX6cq++uqrqEMQKc6BRB2WmKmkR3oiMCvj8YXuvom7DyV5O5s8t84F4FLg/uAu5ZvmtCHStPbb8vfp9fun/S7CSESkFmUlUjMbAOwDXJsqc/ePMzbpSZ5J0mbWC/gOySvt4+5fufuHNcQrIiKdxHDMw1/iptzJRpcAo4GVMgvN7DyS93j7CNg5T721gfeAv5nZpsBU4ER3/6zagEXiYM+hv01O4jDjjEt3jTockcJimPjCVrJHamb7Au+6+9Tc59z9THcfCNwEnJCnejdgM+Bqd/828BlweoHXGWlmU8xsynvvvVfJPoh0PakPJ3d23HHHaGMRkZqUM7S7HbCfmc0DbgF2MbN/5GxzM3BQnrrzgfnu/kzw+A6SibUDd/+zuw9z92F9+/YtK3iRrujBBx+MOgSR8uk2aiWVTKTufoa7D3D3QSTvIj7J3Y8ws3UzNtsPeCVP3f8Cb5tZ6gS5XUnexVykaV108iPp9funnxthJCIShlouyHB+kCATwJvATwGC02Cudfe9g+1+DtxkZssBc4Ef1vCaIiLSWVKnv0hRFSVSd58MTA7W8w3l4u4Lgb0zHk8DhlUboEic7Lnpb9LrY27UXV6k8cVxlm3YdGUjkYhsuummUYcgIiHQtXZFOsmsWboWiXRB6pGWpB6pSCc5acTN6XVNMhKJD/VIRTqLbt4tXU48T1cJmxKpSCfYc5Nfp9fVG5Uuw1EiLYOGdkXqbMqUKVGHICJ1pB6pSJ39+kf/Tq/fP+P3hTcUaUQ6j7Qk9UhF6ujsUy+OOgQRqTP1SEXq6OkJS9Lr6o1KV6QLMpSmHqlInew59NelNxKRLk89UpF6yTi2pN6odFnqkZakRCpSB3t+68z2BzpvVLoqBxJKpKVoaFekztQbFYk39UhFQpbVGxXp0nRlo3KoRypSR/e/eF7UIYhInalHKhIi9UYldtQjLUmJVKRO1BuVWFAiLUlDuyJV+se1t7F7yyHs3nIIoN6oSLNSIhWp0g0jb0+vp5JpinqjEgup01/CXmJGiVQkDK0tuHvyg0dEmooSqUiVJiZSPVLDhqwd3LtRvVGJEwdPhL/EjBKpSA2SydQxM3Bo+/jjqEMSCZd7+EvMKJGK1Kh1g/UAkkO7C99l99ZDI45IRDqTEqlIDX566B8AMDP888/T5UqmEguabFQWJVKRGsx76bP0+sR5l2Q9p2Qq0hyUSEWqdN4ZV3Uom9h2a9ZjJVPp8nSMtCQlUpEqPTZ+QXr9/pntM3WVTEWaixKpSBUmTnyw6LfrB5bdkvVYyVS6LPVIS1IiFanC2BMnpdcze6MpZsb1b43NKvtuv8PrHpdIuOqQRJVIReTxxx8HwIKlkP79+3Ph1DPSj794Zxkjtzu1vsGJSKdTIhWp0Hkj702v3/fyH4puO3ToUH52zRHpx288/RbjTr+mbrGJhMqBRCL8JWaUSEUq8Mgjj1Rc58CffJe9Tt41/fjeCx/moYceCzMsEYmQEqlIBc4/bkJ6vVRvNNOoC0YyaIv+7e0Mv4LXXnst1NhE6kLHSEtSIhUp0/PPP19T/b88PZaWFdsfH7/Bb/g842pIIg1JibSkblEHINJVnHnEHen1SnqjmSZ8dGvWqTD7rfj/Opx3KuW75JJLuf+s6RV9OO93+s787FdH1TEqaTZKpCJlaGtra/+wtmJzdUub2BYk06CZ4d1H8MDSW4pXajLuzl6r/CS7sMZJKh78/u7+w0NKpGWL57Vxw6ZEKlKGfTf+dXsiTTh7rXda1vP3vTamovYmtt3K8O4j0o8z11MGb/cN9vjh7lzzs3/AV+3l+/1+V5646SmW/Pd/JD4BlgFW4CjNiiskf376v/ICazXo2TN5W7jMLwyZw3Jtbe1LOR+yhWLL1a0VWlvbXze3l5kqz5dQyx02bNHRLAmfEqlICHITazlDja0DB6R7Se6O0b4O8MacBNec9n9ZSRRg/K8f6tiYe3biW3EF6NGDllTiWH75Dtt7kJAst4edL/bM5Nbamn+HiiRVL3Yz51LJODOeRAKWtbWfwFvp6EAMT72oKy/xuxNAiVSkLPfN+iNffvklB2xyVjgNukOLYQ6k84SBJ9KJzVqDHlq/1fO3USgB5SbCfMnDrGMCTVf3ILHTMUGnq9c2vF1QIgFfLS29nVP8ahgSHg3tlqREKlKmHj16cN+r55e17Tkn/4mnx88pu+1Ub7TDcGpuWarcC5Rnbh88vm/hFelN9ur/8+RKItGh/lb7rs3Zfzm57JhFJEmJVKQOzrr4WLg46ig6um/B5VGHIF1NDE9XCZuOvIuIiNRAPVIREcnPXRO0yqAeqYiISA3UIxURkcJ0jLQkJVIRESnINbRbkoZ2RUREaqAeqYiIFBDPu7WETT1SERGRGqhHKiIi+Tm6RGAZlEhFRKQwXbS+JA3tioiI1EA9UhERycsB19BuSeqRioiI1EA9UhERyc9dx0jLoEQqIiIFaWi3NA3tioiI1EA9UhERKUxDuyWpRyoiIlID8wa8jqKZvQe82ckv2wd4v5NfszNp/7q+uO+j9q96a7l737AbNbP7ScYdtvfdfc86tBuJhkykUTCzKe4+LOo46kX71/XFfR+1f9JVaWhXRESkBkqkIiIiNVAibffnqAOoM+1f1xf3fdT+SZekY6QiIiI1UI9URESkBrFPpGZ2iJnNNLOEmQ3LKN/dzKaa2YvBz10ynptsZq+a2bRgWS1PuwXrd6Z67V+w3RlmNjvYdo/O2J88MVS8fxnbjDezlwq0293MbgjqzzKzM+q5H4XUa/+C5zcxs6eC9l80s+XrtR9FYqjb/gXbrGlmn5rZKfWIv5Q6/n02xOeLlKcZrmz0EvA94E855e8D33X3hWa2MTAB6J/x/OHuPqVIu6Xqd5a67J+ZbQiMADYC+gEPmtl67t4WavSlVbV/ZvY94NMi7R4C9HD3b5nZCsDLZvZPd58XavSl1WX/zKwb8A/gSHefbmarAkvDDr4M9fr9pYwD7gsp1mrUa/8a5fNFyhD7ROruswDMLLf8hYyHM4HlzayHu39ZZrs11Q9LvfYP2B+4Jdj+DTObDWwJPFV71OWrZv/MbEVgFDASuK1Q00DPIOF8DfgK+Djk8Euq4/4NB2a4+/SgvQ/Cjr0cddw/zOwAYC7wWchhl61e+9cony9SntgP7ZbpIOCFnD/SvwXDnr+x3H8l5dVvJNXsX3/g7YzH82ncb8S5+3cucDHwvyJ17iD5AbwIeAu4yN0X1zXK6lWzf+sBbmYTzOx5Mxtd7yBrUPH+mVlP4DTgnPqHV7Nqfn/F6kuDiUWP1MweBFbP89SZ7n53ibobAWNIfoNPOdzdF5jZSsCdwJHAjRXUD1VE+5cvudZlineY+2dmQ4Eh7n6SmQ0qUnVLoI3ksHVv4DEze9Dd51a+B8VFtH/dgO2BLUh+YD9kZlPd/aHK96C4iPbvHGCcu39a+ntubSLav7z1pTHFIpG6+27V1DOzAcBdwFHuPiejvQXBz0/M7GaSH7odEmmh+mGLaP/mAwMzHg8AFlYTRykh7982wOZmNo/k3/dqZjbZ3XfKqX4YcL+7LwXeNbMngGEkhwpDFdH+zQcecff3g7buBTYDQk+kEe3fVsDBZnYBsDKQMLMv3P2KamIpJqL967TPFwmBuzfFAkwGhmU8XhmYDhyUs103oE+w3p3kEOBP87SXt36M9m+joH4PYDDJBNPa6PuXU2cQ8FKB504D/kay590TeBnYJEb71xt4Hlgh+J0/COwTl/3L2e5s4JSo9q1Ov7+G+nzRUuL3H3UAdd9BOJDkt/MvgXeACUH5r0keI5uWsawWfKhOBWaQPMh/aSqBAPsBvytWPy77Fzw+E5gDvArs1RV+fzl1sz6ocn5/KwK3B+/By8Cpcdq/4PERwf69BFwQt/3LKD+biBJpHf8+G+LzRUt5i65sJCIiUgPN2hUREamBEqmIiEgNlEhFRERqoEQqIiJSAyVSERGRGiiRioiI1ECJVEREpAZKpCIiIjX4/2EpylszuK2TAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 576x576 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"lat_lon_gdf['location_time_epoch'] = (lat_lon_gdf.index - pd.Timestamp(\"1970-01-01\")) // pd.Timedelta('1s')\n",
"\n",
"# These are horizontal boxes\n",
"# track1_bbox = gpd.GeoSeries(box(-125.3, 44.14, -124.1, 44.17), crs=lat_lon_gdf.crs)\n",
"# track2_bbox = gpd.GeoSeries(box(-125.3, 44.31, -124.1, 44.34), crs=lat_lon_gdf.crs)\n",
"\n",
"track1_bbox = gpd.GeoSeries(box(-125.17, 43.65, -125.14, 43.84), crs=lat_lon_gdf.crs)\n",
"track2_bbox = gpd.GeoSeries(box(-125.17, 43.98, -125.14, 44.17), crs=lat_lon_gdf.crs)\n",
"\n",
"ax=lat_lon_gdf.plot(figsize=(8,8), markersize=0.5, column='location_time_epoch', legend=True)\n",
"track1_bbox.plot(ax=ax,\n",
" edgecolor=\"red\", facecolor='none'\n",
")\n",
"track2_bbox.plot(ax=ax,\n",
" edgecolor=\"green\", facecolor='none'\n",
");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot below includes the OOI shelf mooring (star). See http://nvs.nanoos.org/Explorer?action=oiw:fixed_platform:OOI_CE02SHSM"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 504x504 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"bnd = lat_lon_gdf.geometry.bounds\n",
"basemap = cimgt.OSM() # cimgt.Stamen('terrain')\n",
"dx, dy = 1, 2\n",
"\n",
"_, ax = plt.subplots(\n",
" figsize=(7, 7), subplot_kw={\"projection\": basemap.crs}\n",
")\n",
"ax.set_extent([bnd.minx.min() - dx, bnd.maxx.max() + dx, bnd.miny.min() - dy, bnd.maxy.max() + dy])\n",
"ax.add_image(basemap, 7)\n",
"ax.gridlines(draw_labels=True, xformatter=LONGITUDE_FORMATTER, yformatter=LATITUDE_FORMATTER)\n",
"lat_lon_gdf.plot(ax=ax, transform=ccrs.PlateCarree(), markersize=2, color='red')\n",
"# OOI shelf mooring\n",
"plt.plot(-124.31, 44.64, marker='*', color='yellow', markersize=13, transform=ccrs.PlateCarree());"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read MVBS and plot echograms for the time periods corresponding to the two tracks above"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 2.13 s, sys: 64.9 ms, total: 2.19 s\n",
"Wall time: 2.23 s\n"
]
}
],
"source": [
"%%time\n",
"MVBS_ds = xr.open_mfdataset(\n",
" './hake2017data/calibratednc/MVBS_*.nc', \n",
" combine='by_coords'\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"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.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;\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-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",
" 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",
" 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-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-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",
" 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: (frequency: 3, ping_time: 13201, range: 75)\n",
"Coordinates:\n",
" * ping_time (ping_time) datetime64[ns] 2017-07-28T00:05:20 ... 2017-07-31T...\n",
" * frequency (frequency) float64 1.8e+04 3.8e+04 1.2e+05\n",
" * range (range) float64 0.0 10.0 20.0 30.0 ... 710.0 720.0 730.0 740.0\n",
"Data variables:\n",
" Sv (frequency, ping_time, range) float64 dask.array&lt;chunksize=(3, 60, 75), meta=np.ndarray&gt;\n",
"Attributes:\n",
" binning_mode: physical units\n",
" range_meter_interval: 10m\n",
" ping_time_interval: 20s</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-64860374-47f1-414e-8d33-c9a9b4200226' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-64860374-47f1-414e-8d33-c9a9b4200226' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>frequency</span>: 3</li><li><span class='xr-has-index'>ping_time</span>: 13201</li><li><span class='xr-has-index'>range</span>: 75</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-195a37d1-e979-44e0-a193-7fd23bcca2d2' class='xr-section-summary-in' type='checkbox' checked><label for='section-195a37d1-e979-44e0-a193-7fd23bcca2d2' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ping_time</span></div><div class='xr-var-dims'>(ping_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-07-28T00:05:20 ... 2017-07-...</div><input id='attrs-249ee6a3-4be8-4883-ac9e-6ac05bd47625' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-249ee6a3-4be8-4883-ac9e-6ac05bd47625' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9643ae1-49a6-4713-bdb4-6c17ac886525' class='xr-var-data-in' type='checkbox'><label for='data-a9643ae1-49a6-4713-bdb4-6c17ac886525' 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'></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-07-28T00:05:20.000000000&#x27;, &#x27;2017-07-28T00:05:40.000000000&#x27;,\n",
" &#x27;2017-07-28T00:06:00.000000000&#x27;, ..., &#x27;2017-07-31T00:14:20.000000000&#x27;,\n",
" &#x27;2017-07-31T00:14:40.000000000&#x27;, &#x27;2017-07-31T00:15:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>frequency</span></div><div class='xr-var-dims'>(frequency)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.8e+04 3.8e+04 1.2e+05</div><input id='attrs-b88a8e6f-152f-4da4-9727-cb14391b5756' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b88a8e6f-152f-4da4-9727-cb14391b5756' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c05326b-496c-47e2-aacb-cf645fe17354' class='xr-var-data-in' type='checkbox'><label for='data-2c05326b-496c-47e2-aacb-cf645fe17354' 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>units :</span></dt><dd>Hz</dd><dt><span>long_name :</span></dt><dd>Transducer frequency</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([ 18000., 38000., 120000.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 10.0 20.0 ... 720.0 730.0 740.0</div><input id='attrs-05edc3d2-d5c7-4438-a2aa-ebdd206135c1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-05edc3d2-d5c7-4438-a2aa-ebdd206135c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef4dc903-20ae-4369-9e70-e25e1770641a' class='xr-var-data-in' type='checkbox'><label for='data-ef4dc903-20ae-4369-9e70-e25e1770641a' 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'></dl></div><div class='xr-var-data'><pre>array([ 0., 10., 20., 30., 40., 50., 60., 70., 80., 90., 100., 110.,\n",
" 120., 130., 140., 150., 160., 170., 180., 190., 200., 210., 220., 230.,\n",
" 240., 250., 260., 270., 280., 290., 300., 310., 320., 330., 340., 350.,\n",
" 360., 370., 380., 390., 400., 410., 420., 430., 440., 450., 460., 470.,\n",
" 480., 490., 500., 510., 520., 530., 540., 550., 560., 570., 580., 590.,\n",
" 600., 610., 620., 630., 640., 650., 660., 670., 680., 690., 700., 710.,\n",
" 720., 730., 740.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-db789f0c-0771-40a7-93db-7684008c3a76' class='xr-section-summary-in' type='checkbox' checked><label for='section-db789f0c-0771-40a7-93db-7684008c3a76' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Sv</span></div><div class='xr-var-dims'>(frequency, ping_time, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 60, 75), meta=np.ndarray&gt;</div><input id='attrs-0bb0f59b-53d8-490f-ae23-c41e12b086ba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0bb0f59b-53d8-490f-ae23-c41e12b086ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6d71b93-84fc-4500-9e41-847000d394f6' class='xr-var-data-in' type='checkbox'><label for='data-a6d71b93-84fc-4500-9e41-847000d394f6' 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>binning_mode :</span></dt><dd>physical units</dd><dt><span>range_meter_interval :</span></dt><dd>10m</dd><dt><span>ping_time_interval :</span></dt><dd>20s</dd></dl></div><div class='xr-var-data'><table>\n",
"<tr>\n",
"<td>\n",
"<table>\n",
" <thead>\n",
" <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr><th> Bytes </th><td> 22.66 MiB </td> <td> 181.05 kiB </td></tr>\n",
" <tr><th> Shape </th><td> (3, 13201, 75) </td> <td> (3, 103, 75) </td></tr>\n",
" <tr><th> Count </th><td> 750 Tasks </td><td> 250 Chunks </td></tr>\n",
" <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
" </tbody>\n",
"</table>\n",
"</td>\n",
"<td>\n",
"<svg width=\"100\" height=\"184\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
" <line x1=\"10\" y1=\"5\" x2=\"24\" y2=\"20\" />\n",
" <line x1=\"10\" y1=\"9\" x2=\"24\" y2=\"24\" />\n",
" <line x1=\"10\" y1=\"13\" x2=\"24\" y2=\"28\" />\n",
" <line x1=\"10\" y1=\"17\" x2=\"24\" y2=\"32\" />\n",
" <line x1=\"10\" y1=\"21\" x2=\"24\" y2=\"36\" />\n",
" <line x1=\"10\" y1=\"28\" x2=\"24\" y2=\"43\" />\n",
" <line x1=\"10\" y1=\"37\" x2=\"24\" y2=\"52\" />\n",
" <line x1=\"10\" y1=\"44\" x2=\"24\" y2=\"59\" />\n",
" <line x1=\"10\" y1=\"51\" x2=\"24\" y2=\"65\" />\n",
" <line x1=\"10\" y1=\"58\" x2=\"24\" y2=\"73\" />\n",
" <line x1=\"10\" y1=\"64\" x2=\"24\" y2=\"79\" />\n",
" <line x1=\"10\" y1=\"73\" x2=\"24\" y2=\"87\" />\n",
" <line x1=\"10\" y1=\"80\" x2=\"24\" y2=\"95\" />\n",
" <line x1=\"10\" y1=\"86\" x2=\"24\" y2=\"101\" />\n",
" <line x1=\"10\" y1=\"93\" x2=\"24\" y2=\"108\" />\n",
" <line x1=\"10\" y1=\"99\" x2=\"24\" y2=\"114\" />\n",
" <line x1=\"10\" y1=\"103\" x2=\"24\" y2=\"118\" />\n",
" <line x1=\"10\" y1=\"112\" x2=\"24\" y2=\"127\" />\n",
" <line x1=\"10\" y1=\"120\" x2=\"24\" y2=\"134\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Vertical lines -->\n",
" <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n",
" <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"134\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Colored Rectangle -->\n",
" <polygon points=\"10.0,0.0 24.9485979497544,14.948597949754403 24.9485979497544,134.9485979497544 10.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
" <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Vertical lines -->\n",
" <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
" <line x1=\"35\" y1=\"0\" x2=\"50\" y2=\"14\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Colored Rectangle -->\n",
" <polygon points=\"10.0,0.0 35.41261651458248,0.0 50.36121446433688,14.948597949754403 24.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" style=\"stroke-width:2\" />\n",
" <line x1=\"24\" y1=\"20\" x2=\"50\" y2=\"20\" />\n",
" <line x1=\"24\" y1=\"24\" x2=\"50\" y2=\"24\" />\n",
" <line x1=\"24\" y1=\"28\" x2=\"50\" y2=\"28\" />\n",
" <line x1=\"24\" y1=\"32\" x2=\"50\" y2=\"32\" />\n",
" <line x1=\"24\" y1=\"36\" x2=\"50\" y2=\"36\" />\n",
" <line x1=\"24\" y1=\"43\" x2=\"50\" y2=\"43\" />\n",
" <line x1=\"24\" y1=\"52\" x2=\"50\" y2=\"52\" />\n",
" <line x1=\"24\" y1=\"59\" x2=\"50\" y2=\"59\" />\n",
" <line x1=\"24\" y1=\"65\" x2=\"50\" y2=\"65\" />\n",
" <line x1=\"24\" y1=\"73\" x2=\"50\" y2=\"73\" />\n",
" <line x1=\"24\" y1=\"79\" x2=\"50\" y2=\"79\" />\n",
" <line x1=\"24\" y1=\"87\" x2=\"50\" y2=\"87\" />\n",
" <line x1=\"24\" y1=\"95\" x2=\"50\" y2=\"95\" />\n",
" <line x1=\"24\" y1=\"101\" x2=\"50\" y2=\"101\" />\n",
" <line x1=\"24\" y1=\"108\" x2=\"50\" y2=\"108\" />\n",
" <line x1=\"24\" y1=\"114\" x2=\"50\" y2=\"114\" />\n",
" <line x1=\"24\" y1=\"118\" x2=\"50\" y2=\"118\" />\n",
" <line x1=\"24\" y1=\"127\" x2=\"50\" y2=\"127\" />\n",
" <line x1=\"24\" y1=\"134\" x2=\"50\" y2=\"134\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Vertical lines -->\n",
" <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"134\" style=\"stroke-width:2\" />\n",
" <line x1=\"50\" y1=\"14\" x2=\"50\" y2=\"134\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Colored Rectangle -->\n",
" <polygon points=\"24.9485979497544,14.948597949754403 50.36121446433688,14.948597949754403 50.36121446433688,134.9485979497544 24.9485979497544,134.9485979497544\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
"\n",
" <!-- Text -->\n",
" <text x=\"37.654906\" y=\"154.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >75</text>\n",
" <text x=\"70.361214\" y=\"74.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,70.361214,74.948598)\">13201</text>\n",
" <text x=\"7.474299\" y=\"147.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,147.474299)\">3</text>\n",
"</svg>\n",
"</td>\n",
"</tr>\n",
"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-a3908af1-94ad-438f-8d21-984574b97493' class='xr-section-summary-in' type='checkbox' checked><label for='section-a3908af1-94ad-438f-8d21-984574b97493' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>binning_mode :</span></dt><dd>physical units</dd><dt><span>range_meter_interval :</span></dt><dd>10m</dd><dt><span>ping_time_interval :</span></dt><dd>20s</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (frequency: 3, ping_time: 13201, range: 75)\n",
"Coordinates:\n",
" * ping_time (ping_time) datetime64[ns] 2017-07-28T00:05:20 ... 2017-07-31T...\n",
" * frequency (frequency) float64 1.8e+04 3.8e+04 1.2e+05\n",
" * range (range) float64 0.0 10.0 20.0 30.0 ... 710.0 720.0 730.0 740.0\n",
"Data variables:\n",
" Sv (frequency, ping_time, range) float64 dask.array<chunksize=(3, 60, 75), meta=np.ndarray>\n",
"Attributes:\n",
" binning_mode: physical units\n",
" range_meter_interval: 10m\n",
" ping_time_interval: 20s"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"MVBS_ds"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Clip the GPS locations GeoPandas GeoDataFrame `lat_lon_gdf` generated from the `Platform` group with the two rectangular regions defined earlier for \"Track 1\" and \"Track 2\". Then extract from those clipped GeoDataFrames (`track1gps_gdf` and `track2gps_gdf`) the minimum and maximum `location_time` values for each track, and apply those values to filter `MVBS_ds` on `ping_time` slices."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"track1gps_gdf = gpd.clip(lat_lon_gdf, track1_bbox)\n",
"track1_MVBS_ds = MVBS_ds.sel(\n",
" ping_time=slice(track1gps_gdf.index.min(), track1gps_gdf.index.max())\n",
")\n",
"\n",
"track2gps_gdf = gpd.clip(lat_lon_gdf, track2_bbox)\n",
"track2_MVBS_ds = MVBS_ds.sel(\n",
" ping_time=slice(track2gps_gdf.index.min(), track2gps_gdf.index.max())\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot MVBS echograms for the two N-S tracks, for all 3 frequencies."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1296x1296 with 12 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"freq_len = len(MVBS_ds.frequency)\n",
"\n",
"fig, axes = plt.subplots(nrows=freq_len, ncols=2, constrained_layout=True, figsize=(18, 18))\n",
"\n",
"track1dt = track1gps_gdf.index\n",
"track2dt = track2gps_gdf.index\n",
"fig.suptitle(\n",
" f\"\"\"left: Track 1, {track1dt.min().strftime(\"%b-%d %H:%MZ\")} to {track1dt.max().strftime(\"%b-%d %H:%MZ\")} \\\n",
" right: Track 2, {track2dt.min().strftime(\"%b-%d %H:%MZ\")} to {track2dt.max().strftime(\"%b-%d %H:%MZ\")}\n",
" \"\"\",\n",
" fontsize=16);\n",
"\n",
"for f in range(freq_len):\n",
" track1_MVBS_ds.Sv.isel(frequency=f).plot(\n",
" ax=axes[f][0], \n",
" x='ping_time',\n",
" y='range',\n",
" yincrease=False,\n",
" vmin=-80,\n",
" vmax=-50,\n",
" )\n",
" if f < 2:\n",
" axes[f][0].set_xlabel(None)\n",
" axes[f][0].set_ylabel('range (meters)')\n",
" \n",
"for f in range(freq_len):\n",
" track2_MVBS_ds.Sv.isel(frequency=f).plot(\n",
" ax=axes[f][1], \n",
" x='ping_time',\n",
" y='range',\n",
" yincrease=False,\n",
" vmin=-80,\n",
" vmax=-50,\n",
" )\n",
" if f < 2:\n",
" axes[f][1].set_xlabel(None)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:echopype_test]",
"language": "python",
"name": "conda-env-echopype_test-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
},
"toc-autonumbering": true
},
"nbformat": 4,
"nbformat_minor": 4
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment