# set up things
%matplotlib inline
%load_ext autoreload
%autoreload 2
import warnings
warnings.filterwarnings('ignore')
import numpy as np
from nmc_met_io.read_satellite import read_awx_cloud
data = read_awx_cloud("./samples/ANI_IR1_R04_20220331_2100_FY2G.AWX")
data
<xarray.Dataset>
Dimensions: (channel: 1, lat: 1300, lon: 1900, time: 1)
Coordinates:
* time (time) datetime64[ns] 2022-03-31T13:00:00
* channel (channel) int16 1
* lat (lat) float64 -4.96 -4.91 -4.86 -4.81 ... 59.82 59.87 59.92 59.97
* lon (lon) float64 50.02 50.07 50.12 50.17 ... 144.8 144.9 144.9 145.0
Data variables:
image (time, channel, lat, lon) float64 293.0 293.5 293.5 ... 332.8 332.8
Attributes:
Conventions: CF-1.6
Origin: MICAPS Cassandra Serverarray(['2022-03-31T13:00:00.000000000'], dtype='datetime64[ns]')
array([1], dtype=int16)
array([-4.96 , -4.910015, -4.860031, ..., 59.870031, 59.920015, 59.97 ])
array([ 50.02, 50.07, 50.12, ..., 144.87, 144.92, 144.97])
array([[[[293.04, 293.48, 293.48, ..., 236.4 , 242.07, 245.12],
[292.61, 293.04, 293.04, ..., 245.12, 248.74, 250.14],
[292.17, 292.61, 292.61, ..., 252.88, 254.86, 254.86],
...,
[258.68, 258.68, 259.31, ..., 255.51, 254.86, 254.86],
[258.68, 259.31, 259.92, ..., 254.86, 254.86, 254.86],
[316.71, 308.91, 304.24, ..., 332.77, 332.77, 332.77]]]])