d4data 0.1.1

Python Boilerplate contains all the boilerplate you need to create a Python package.

D4Data

Data Engineered with python

Proof of concept project for python data engineering. Envisioned use cases:

• Data access and sharing with data defined as code.

• Data catologing and discovery.

• Data transfer and partitioning for distributed computing.

• Go from remote data sources to model training with simple and expressive python.

Installation

git clone [e-mail]:kforti/D4Data.git
cd D4Data
pip install .

Example API:

Define data as code

from d4data.storage_clients import FTPStorageClient
from d4data.sources import CSVDataSource

class NIHChromosomeSNPS38(CSVDataSource):
    def __init__(self, chromosome, output_path):
        # define data that is specific to your data source
        self.chromosome = chromosome

        # give your data source a name, file name, local paths to save to and uri
        self.name = "NIH_Chromose_{}_SNPS38".format(self.chromosome)
        self.file_name = "bed_chr_{}.bed.gz".format(self.chromosome)
        self.uri = "https://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b151_GRCh38p7/BED/" + self.file_name
        self.local_paths = [os.path.join(output_path, self.file_name)]

        # assign a storage client
        self.client = FTPStorageClient()

• Download data programmatically

data = NIHChromosomeSNPS38(chromosome=1, local_path="./datasources")

# calls client.download(uri=self.uri)
data.to_disk()

• Process data

dataset = data.to_dataset()
for i in range(len(dataset)):
    some_func(dataset[i])

• Compose DataSources dynamically with a DataStrategy:

from d4data.storage_clients import HTTPStorageClient
from d4data.core import DataStrategy, CompositeDataSource

# Define the DataSource
class HaploRegSource(CSVDataSource):
    def __init__(self, population, local_path):
        self.name = "LD_{}".format(population.upper())
        self.file_name = self.name + ".tsv.gz"
        self.uri = "https://pubs.broadinstitute.org/mammals/haploreg/data/" + self.file_name
        self.local_paths = [os.path.join(local_path, self.file_name)]

        self.client = HTTPStorageClient()

# Define the DataStrategy
# Data Strategies contain logic for building data sources from some higher level data about the data, e.g list of s3 urls.
# Data Strategies can also contain a partition strategy where logic for partitioning data sources can be implemented- you may want to partition based on compute resources available.
class HaploRegStrategy(DataStrategy):
    def __init__(self, populations, local_path):
        self.populations = populations
        self.local_path = local_path

        self._sources = {
            "haplo_reg": HaploRegSource
        }

    def create_sources(self):
        comp_source = CompositeDataSource()
        source = self._sources["haplo_reg"]
        for population in self.populations:
            ds = source(population, self.local_path)
            comp_source.add(ds)
        return comp_source

pops = ["afr", "eur", "amr]
haplo_strategy = HaploRegStrategy(pops, local_path="./data_sources")
comp_source = haplo_strategy.create_sources()
for source in comp_source:
    # Download sources to in-memory file system
    d = s.to_memfs()

• Prefect Integration: TODO

• Pytorch Integration: TODO

• Free software: Apache Software License 2.0

• Documentation: https://d4data.readthedocs.io.

Features

• TODO