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{
"cell_type": "markdown",
"id": "edea84d1-7afd-4b54-b8c7-a68fe85ca6a2",
"metadata": {},
"source": [
"# Bathymetry data from the Caribbean \n",
"\n",
"This dataset is a compilation of several single-beam bathymetry surveys downloaded from the [NOAA NCEI](https://ngdc.noaa.gov/mgg/geodas/trackline.html). The data are originally in MGD77 format and include a header with metadata on each survey ([`MGD77_921744.h77t`](MGD77_921744.h77t)). The original data file was compressed with LZMA to save space and make it possible to upload it to this GitHub repository.\n",
"\n",
"License: Public domain\n",
"\n",
"Original source: https://ngdc.noaa.gov/mgg/geodas/trackline.html (region selected from the web interface; no direct download link)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "6f3df718-0b5e-458b-b63c-2f6af1f5366b",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import lzma\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import pyproj\n",
"import verde as vd\n",
"import pooch"
]
},
{
"cell_type": "markdown",
"id": "a6d25c2d-a8f1-4c85-862d-c00af6363562",
"metadata": {},
"source": [
"## Load and clean the data\n",
"\n",
"The dataset are LZMA compressed and are basically a tab delimited table. We can use pandas to read this directly."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d05412f1-20d7-42b1-bd42-e65a43893bb7",
"metadata": {},
"outputs": [],
"source": [
"data_full = pd.read_csv(\n",
" \"MGD77_921744.m77t.xz\", \n",
" sep=\"\\t\", \n",
" usecols=[0, 4, 5, 9],\n",
" dtype=dict(SURVEY_ID=\"str\", LON=\"float64\", LAT=\"float64\", CORR_DEPTH=\"float64\"),\n",
").dropna().reset_index(drop=True)"
]
},
{
"cell_type": "markdown",
"id": "9c5aab55-2592-49ce-beed-cd110904c1d8",
"metadata": {},
"source": [
"Rename the columns to something easier to type, use full names, and include units."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "e85b4449-db66-4dfc-92e6-95c6fb6f9dc9",
"metadata": {},
"outputs": [
{
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],
"source": [
"plt.figure(figsize=(16, 8))\n",
"plt.title(\"Bathymetry data for the Caribbean\")\n",
"plt.scatter(*coordinates, c=data.depth_m, s=1)\n",
"plt.colorbar(label=\"bathymetry (m)\", pad=0)\n",
"plt.axis(\"scaled\")\n",
"plt.xlim(*region[:2])\n",
"plt.ylim(*region[2:])\n",
"plt.xlabel(\"easting (m)\")\n",
"plt.ylabel(\"northing (m)\")\n",
"plt.savefig(\"preview.jpg\", dpi=200, bbox_inches=\"tight\")"
]
},
{
"cell_type": "markdown",
"id": "1122ceed-22aa-4916-8296-6ff9f54420a7",
"metadata": {},
"source": [
"## Export"
]
},
{
"cell_type": "markdown",
"id": "c3c07e22-1fac-45bd-870f-829056a1550a",
"metadata": {},
"source": [
"Set the export precision for each column by converting them to strings prior to saving the CSV."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "290522bf-c174-4dd5-adde-ef699d50f3e4",
"metadata": {},
"outputs": [
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"plt.figure(figsize=(16, 16))\n",
"plt.subplot(2, 1, 1)\n",
"plt.scatter(*coordinates_mean, c=bathymetry_mean, s=3)\n",
"plt.colorbar(label=\"bathymetry (m)\", pad=0)\n",
"plt.axis(\"scaled\")\n",
"plt.xlim(*region[:2])\n",
"plt.ylim(*region[2:])\n",
"plt.subplot(2, 1, 2)\n",
"plt.scatter(*coordinates_mean, c=bathymetry_std, s=3, vmax=1000)\n",
"plt.colorbar(label=\"standard deviation (m)\", pad=0)\n",
"plt.axis(\"scaled\")\n",
"plt.xlim(*region[:2])\n",
"plt.ylim(*region[2:])"
]
}
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