{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright CNES\n",
"\n",
"## Read and plot a SWOT-HR Pixel Cloud netcdf products \n",
"In this notebook, we show how to read the SWOT-HR pixel cloud products with xarray and how to represent a variable on a map with cartopy."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Libraries\n",
"Please note that apart from the libraries listed in the cell below, you need to install the h5netcdf library (conda install -c conda-forge h5netcdf). This will be used by th xarray.open_dataset function to read the netcdf files."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import xarray as xr\n",
"import os\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1. Read a SWOT-HR Pixel Cloud netcdf product\n",
"Note this is an extraction of the original file for demonstration purpose. It does not contain all variables and groups"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"dir_swot = \"../docs/data/swot\"\n",
"file_swot_pxc = os.path.join(dir_swot, \"SWOT_L2_HR_PIXC\", \"SWOT_L2_HR_PIXC_015_033_163R_20240509T115817_20240509T115828_PIC0_01_extract.nc\")\n",
"# read data with xarray, specifying a group in the netcdf structure\n",
"xr_pxc = xr.open_dataset(file_swot_pxc, group=\"pixel_cloud\")\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"
<xarray.Dataset>\n",
"Dimensions: (points: 10001)\n",
"Coordinates:\n",
" latitude (points) float64 4.597 4.597 4.597 ... 4.653 4.652 4.652\n",
" longitude (points) float64 -53.06 -53.06 -53.06 ... -53.39 -53.39\n",
"Dimensions without coordinates: points\n",
"Data variables:\n",
" classification (points) float32 nan nan nan nan nan ... nan nan nan nan nan\n",
" coherent_power (points) float32 nan nan nan nan nan ... nan nan nan nan nan\n",
" cross_track (points) float32 nan nan nan nan nan ... nan nan nan nan nan\n",
" geoid (points) float32 nan nan nan nan nan ... nan nan nan nan nan\n",
" height (points) float32 nan nan nan nan nan ... nan nan nan nan nan\n",
" sig0 (points) float32 nan nan nan nan nan ... nan nan nan nan nan\n",
" wse (points) float32 nan nan nan nan nan ... nan nan nan nan nan\n",
"Attributes:\n",
" description: cloud of geolocated interferogram pixels\n",
" interferogram_size_azimuth: 3277\n",
" interferogram_size_range: 4694\n",
" looks_to_efflooks: 1.55135648150391\n",
" num_azimuth_looks: 7.0\n",
" azimuth_offset: 7
xarray.Dataset
points: 10001
latitude
(points)
float64
4.597 4.597 4.597 ... 4.652 4.652
long_name :
latitude (positive N, negative S)
standard_name :
latitude
units :
degrees_north
quality_flag :
geolocation_qual
valid_min :
-80.0
valid_max :
80.0
comment :
Geodetic latitude [-80,80] (degrees north of equator) of the pixel.
coherent power combination of minus_y and plus_y channels
units :
1
quality_flag :
interferogram_qual
valid_min :
0.0
valid_max :
1e+20
comment :
Power computed by combining the plus_y and minus_y channels coherently by co-aligning the phases (arbitrary units that give sigma0 when noise subtracted and normalized by the X factor).
Geoid height above the reference ellipsoid with a correction to refer the value to the mean tide system, i.e. includes the permanent tide (zero frequency). This value is reported for reference but is not applied to the reported height.