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s3.create_bucket("MyBucket") |
If you're interested in streaming netCDF files directly from S3 buckets, give this example a look:
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import netCDF4 as nc
import os
import glob
import xarray as xr
import boto3
from botocore.session import Session
from botocore.handlers import validate_bucket_name
import tempfile
def load_S3():
project_id = 'FILL ME IN'
bucketname = 'FILL ME IN'
access_key = 'FILL ME IN'
secret_access_key = 'FILL ME IN'
endpoint = 'https://s3.waw3-1.cloudferro.com'
bucketname = project_id + ':' + bucketname
botocore_session = Session()
botocore_session.unregister('before-parameter-build.s3', validate_bucket_name)
boto3.setup_default_session(botocore_session = botocore_session)
s3 = boto3.client('s3', endpoint_url=endpoint,
aws_access_key_id = access_key,
aws_secret_access_key = secret_access_key)
files = []
#List the objects in our bucket
response = s3.list_objects(Bucket=bucketname)
for item in response['Contents']:
files.append(item["Key"])
return s3, files, bucketname
def load_s3_file(filename):
s3, files, bucketname = load_S3()
tmp = tempfile.NamedTemporaryFile()
with open(tmp.name, 'wb') as f:
s3.download_fileobj(bucketname, filename, f)
dataSet = xr.open_dataset(tmp.name, engine='netcdf4')
return dataSet
def main():
netCDFfile = load_s3_file("myfile.nc")
main() |
If you're interested in more, I recommend taking a look at this article, which gives you a more detailed view into boto3's functionality (although it does emphasize on Amazon Web Services specifically, you can take a look at the Python code involved):
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