Thomas-Moore-Creative

import matplotlib.pyplot as plt

# Define the x and y coordinates
x = [0, 1, 2, 3]
y = [0, 1, 2, 3]

# Define the u and v components of the currents
u = [1, 2, 3, 4]
v = [0, 1, 0, -1]

Thomas-Moore-Creative

import xarray as xr
file = 'path/to/file.nc'
data = xr.open_dataset(file)
data.OHC_0_200_emean.isel(time=0).plot()

Thomas-Moore-Creative

# define preprocess drop function - this unfortunately removes useful information like areau that's missing from 2015 files
def drop_not_in_2015(ds):
    if 'latu_bounds' in ds.data_vars:
        ds = ds.drop(['latu_bounds'])
    if 'lonu_bounds' in ds.data_vars:
        ds = ds.drop(['lonu_bounds'])
    if 'latv_bounds' in ds.data_vars:
        ds = ds.drop(['latv_bounds'])
    if 'lonv_bounds' in ds.data_vars:
        ds = ds.drop(['lonv_bounds'])

Thomas-Moore-Creative

def calc_EKE(u, v):
    '''
    Author1 = {"name": "Thomas Moore", "affiliation": "CSIRO", "email": "thomas.moore@csiro.au", "orcid": "0000-0003-3930-1946"}
    u,v are x and y currents as an xarray data array
    '''
    u_mean = u.mean('time')
    v_mean = v.mean('time')
    MKE = 0.5*(u_mean**2 + v_mean**2).rename('MKE') # currents
    EKE = ( 0.5 * ((u-u_mean)**2 + (v-v_mean)**2) ).rename('EKE') # eddies
    return EKE, MKE

Thomas-Moore-Creative

import re
file_list =[]
# get regex range from web generator - https://3widgets.com/
# find range of files from filename_1981.nc to filename_2018.nc
regexp = re.compile('filename_(198[1-9]|199[0-9]|200[0-9]|201[0-8]).nc')
ROOT_DIR ='/root/path/to/files/'
for root, dirs, files in os.walk(ROOT_DIR):
    for file in files:
        if regexp.search(file):
            file_list.append(os.path.join(root, file))

Thomas-Moore-Creative

# this is specific to needs outlined in this repo:
# https://github.com/Thomas-Moore-Creative/NCI-CCiA-ARD
import xarray as xr
import numpy as np
import datetime
import intake
NCI_catalog = intake.open_catalog('/g/data/hh5/public/apps/nci-intake-catalogue/catalogue.yaml')
# build CCiA catalog subset
CCiA_catalog = NCI_catalog.esgf.cmip5.search(variable=['psl','pr'],
            realm=['atmos'],

Thomas-Moore-Creative

import intake
import xarray as xr
NCI_catalog = intake.open_catalog('/g/data/hh5/public/apps/nci-intake-catalogue/catalogue.yaml')
CCiA_catalog = NCI_catalog.esgf.cmip5.search(variable=['psl','pr'],
            realm=['atmos'],
            time_frequency='mon',
            experiment=['rcp85','historical'],
            ensemble=['r1i1p1'],
            model=['ACCESS1.0',
                   'CESM1(CAM5)',