prl900

prl900

prl900

prl900

import TensorFlow

// Custom differentiable type.
struct LinearRegressor: Differentiable {
    var w: Tensor<Float>
    var b: Tensor<Float>
    func callAsFunction(_ x: Tensor<Float>) -> Tensor<Float> {
        return matmul(x, w) + b
    }
}

prl900

import json
import numpy as np
import matplotlib.pyplot as plt
from pyproj import Proj, transform

wgs84_proj = "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs "
au_albers = "+proj=aea +lat_1=-18 +lat_2=-36 +lat_0=0 +lon_0=132 +x_0=0 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs "
# Canberra (Australia)
lat = -35.28
lon = 149.13

prl900

prl900

prl900

module load gdal/1.9.2
module load cdo
module load nco

YEAR=2017
DATE=$(date -u -d $YEAR"-01-01")
END=$(date -u -d "2018-01-01")
COUNTER=0

prl900

module load cdo
module load nco
module load gdal/2.2.2

cdo -b I16 select,name=maxPV /g/data/ub8/au/LandCover/DEA_ALC/17_-25/fc_metrics_17_-25_2014.nc out.nc
ncpdq -a y,x out.nc out_swap.nc
gdal_translate -ot Byte NETCDF:"out_swap.nc":maxPV test.tiff -co TILED=YES -co COMPRESS=DEFLATE
gdaladdo test.tiff 2 4 8 16 32

prl900

import xarray as xr
import numpy as np
import matplotlib.pyplot as plt

print("Jan")
ds = xr.open_dataset("era5_prec_2018H1.nc")
prec = ds.tp.sel(dict(time=slice('2018-01-01', '2018-01-31'))).data * 1000
ds.close()
print(prec.shape)