fpipe 0.0.13

Library for working with file-likes as piped streams

fPipe

python framework for data manipulation and metadata extraction built around the python file-like api.

Disclaimer: framework is currently in Alpha, production use discouraged

Installing

for S3 support you need boto3

brew install python3
# apt, yum, apk...
pip3 install fpipe
# Optional
pip3 install boto3

Getting started

Simple example

Calculates size and md5 of stream, while storing stream to disk and prints content. When file is read finished, md5 is ready and printed

from fpipe.file import ByteFile
from fpipe.gen import LocalGen, MetaGen
from fpipe.meta import Path, SizeCalculated, MD5Calculated
from fpipe.workflow import WorkFlow

workflow = WorkFlow(
    LocalGen(pass_through=True),
    MetaGen(SizeCalculated, MD5Calculated)
)

for stream in workflow.compose(ByteFile(b'x' * 10, Path('x.dat')), ByteFile(b'y' * 20, Path('y.dat'))):
    print(f'\n{"-"*46}\n')
    print("Path name:", stream.meta(Path).value)
    print("Stream content: ", stream.file.read().decode('utf-8'))
    with open(stream.meta(Path).value) as f:
        print("File content:", f.read())
    print("Stream md5:", stream.meta(MD5Calculated).value)
    print("Stream size:", stream.meta(SizeCalculated).value)

Subprocess script example

Stores original stream, calculates md5, encrypts using cli, stores, calculates md5, decrypts using cli and stores. Using flush_iter() we know all files have been completely read(), so MD5Calculated will be readable.

from fpipe.file import ByteFile
from fpipe.gen import LocalGen, MetaGen, ProcessGen
from fpipe.meta import Path, MD5Calculated
from fpipe.workflow import WorkFlow

workflow = WorkFlow(
    MetaGen(MD5Calculated),
    LocalGen(pass_through=True),

    ProcessGen("gpg --batch --symmetric --passphrase 'secret'"),
    MetaGen(MD5Calculated),
    LocalGen(pass_through=True, pathname_resolver=lambda x: f'{x.meta(Path).value}.gpg'),

    ProcessGen("gpg --batch --decrypt --passphrase 'secret'"),
    MetaGen(MD5Calculated),
    LocalGen(pass_through=True, pathname_resolver=lambda x: f'{x.meta(Path).value}.decrypted')
)

for f in workflow.compose(ByteFile(b'x' * 10, Path('x.orig')), ByteFile(b'y' * 20, Path('y.orig'))).flush_iter():
    print(f'\n{"-"*46}\n')
    print("Original path:", f.meta(Path, 2).value)
    print("Original md5:", f.meta(MD5Calculated, 2).value, end='\n\n')

    print("Encrypted path:", f.meta(Path, 1).value)
    print("Encrypted md5:", f.meta(MD5Calculated, 1).value, end='\n\n')

    print("Decrypted path:", f.meta(Path).value)
    print("Decrypted md5:", f.meta(MD5Calculated).value)

See unittests for more examples

Run tests and verify pypi compatibility

To run tests install tox and twine with pip, go to project root and run tox

# python3 -m venv .venv
# Activate virtualenv
source .venv/bin/activate
# Run tests
tox -e py37
# Build distribution
python setup.py sdist bdist_wheel
# Validate distribution
twine check dist/*

Built With

• Travis CI
• Codecov
• Tox

Contributing

Bug-reports and pull requests on github

Versioning

Any version change could break the public API (until 1.0.0 release)

• Pypi
• Github

License

This project is licensed under the MIT License - see the LICENSE.txt file for details