emileten

import numpy as np
import xarray as xr
from xclim import sdba

def _datafactory(
    x, start_time="1950-01-01", variable_name="fakevariable", lon=1.0, lat=1.0
):
    """Populate xr.Dataset with synthetic data for testing"""
    start_time = str(start_time)
    if x.ndim != 1:

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def censor(x):
  if x < 1:
    return np.random.uniform(low=0.5, high=1)
  else:
    return x
vcensor = np.vectorize(censor)

ds_corrected = xr.apply_ufunc(vcensor, ds, dask='parallelized') # rather, should we load the data before hand ?

emileten

import xarray as xr
import numpy as np

### Fake data ###

def spatio_temporal_gcm_factory(
    x=np.random.rand(1, 361, 721),
    start_date="1995-01-01",
    lat=np.arange(-90, 90.5, 0.5),
    lon=np.arange(-180, 180.5, 0.5),

emileten

from yaml import load, dump, Loader

fp = '/Users/emile/Desktop/testyaml.yaml'
out = {
    'a': 1
}
with open(fp, 'w') as f:
    dump(out, f)

with open(fp, 'r') as f:

emileten

### This works ##### 

FILEPATTERN = '...'
def func(ds):
    var = next(var for var in ds)
    fp = ds[var].encoding['source']
    coordds = ds.assign_coords(path=fp)
    dimds = coordds.expand_dims('path')
    return dimds
fs = fsspec.filesystem('gs')

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emileten

emileten

name: explore-sentinel1-rtc
channels:
    - conda-forge
    - defaults
dependencies:
    - python=3.10
    - ipykernel
    - pip
    - pip:
        - stackstac

emileten

emileten

# set ARN_MFA_DEVICE to the ARN of the MFA device associated with your SMCE account.
# and AWS_PROFILE to your AWS credentials profile associated with the SMCE MAAP account (in ~/.aws/credentials)
# first argument (positional) of this function is the MFA code that is displayed by your MFA app at the moment 
# you run the function (e.g. Google Auth) 
mfa_authentication_smce_maap () {

	export AWS_PROFILE="YOUR_SMCE_MAAP_PROFILE"
	export ARN_MFA_DEVICE="ARN_OF_MFA_DEVICE"

	tokens=$(aws sts get-session-token --serial-number $ARN_MFA_DEVICE --token-code $1 --output json)