With S3 buckets, accessing data is even easier than before. For those who want to use Python (encouraged), it is easy as pie.
Make sure you have both Python 3 and the access keys to your S3 bucket ready. Typically you'll find your access key and secret key in Morpheus under Tools -> Cypher.
Take a look at this code segment, which allows you to access the bucket, list its objects and upload/download files from it.
Before running any Python code, install the boto3 library:
python3 -m pip install boto3
Start by declaring some initial values for boto3 to know where your bucket is located at. Feel free to copy paste this segment and fill in with your own values.
If you're connecting to buckets hosted at the EUMETSAT side of the European Weather Cloud, the endpoint is: https://s3.waw3-1.cloudferro.com
import os import io import boto3 #Initializing some values project_id = '123' #Fill this in bucketname = 'MyFancyBucket123' #Fill this in access_key = '123asdf' #Fill this in secret_access_key = '123asdf111' #Fill this in endpoint = 'https://my-s3-endpoint.com' #Fill this in
Lets start by initializing the S3 client with our access keys and endpoint:
#Initialize the S3 client s3 = boto3.client('s3', endpoint_url=endpoint, aws_access_key_id = access_key, aws_secret_access_key = secret_access_key)
As a first step, and to confirm we have successfully connected, lets list the objects inside our bucket (up to a 1.000 objects).
#List the objects in our bucket response = s3.list_objects(Bucket=bucketname) for item in response['Contents']: print(item['Key'])
If you'd want to list more than 1000 objects in a bucket, you can use paginator:
#List objects with paginator (not constrained to a 1000 objects) paginator = s3.get_paginator('list_objects_v2') pages = paginator.paginate(Bucket=bucketname) #Lets store the names of our objects inside a list objects = [] for page in pages: for obj in page['Contents']: objects.append(obj["Key"]) print('Number of objects: ', len(objects))
Where an obj looks like this:
{'Key': 'MyFile.txt', 'LastModified': datetime.datetime(2021, 11, 11, 0, 39, 23, 320000, tzinfo=tzlocal()), 'ETag': '"2e22f62675cea3445f7e24818a4f6ba0d6-1"', 'Size': 1013, 'StorageClass': 'STANDARD'}
Now lets try to read a file from a bucket into Python's memory, so we can work with it inside Python without ever saving the file to our local computer:
#Read a file into Python's memory and open it as a string filename = '/folder1/folder2/myfile.txt' #Fill this in obj = s3.get_object(Bucket=bucketname, Key=filename) myObject = obj['Body'].read().decode('utf-8') print(myObject)
But if you'd want to download the file instead of reading it into memory, here's how you'd do that:
#Downloading a file from the bucket with open('myfile', 'wb') as f: #Fill this in s3.download_fileobj(bucketname, 'myfile', f)
And similarly you can upload files to the bucket (given that you have write access to the bucket):
#Uploading a file to the bucket (make sure you have write access) response = s3.upload_file('myfile', bucketname, 'myfile') #Fill this in
And lastly, creating a bucket (this could take some time):
s3.create_bucket(Bucket="MyBucket")
If you're interested in streaming netCDF files directly from S3 buckets, give these two examples a look:
import netCDF4 as nc import xarray as xr import boto3 import tempfile s3 = boto3.client('s3', endpoint_url=endpoint, aws_access_key_id = access_key, aws_secret_access_key = secret_access_key) tmp = tempfile.NamedTemporaryFile() tc = boto3.s3.transfer.TransferConfig(io_chunksize=2621440) with open(tmp.name, 'wb') as f: s3.download_fileobj(bucketname, filename, f, Config=tc) dataSet = xr.open_dataset(tmp.name, engine='netcdf4')
And an alternative and shorter version:
import smart_open bucketpath=smart_f = f"s3://{access_key}:{secret_access_key}@{endpoint}@{bucketname}/{obj_name}" smart_f = smart_open.open(bucketpath, 'rb') import h5py h=h5py.File(smart_f) print(h.keys())
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):
https://dashbird.io/blog/boto3-aws-python/
Check out a full code example at the official boto3 website:
https://boto3.amazonaws.com/v1/documentation/api/latest/guide/s3-examples.html
You can also see a differently styled tutorial at:
https://towardsdatascience.com/introduction-to-pythons-boto3-c5ac2a86bb63