fathomdata 0.0.8

Python package to make interacting with life sciences manufacturing data quick and intuitive.

fathomdata

Python package to make interacting with life sciences manufacturing data quick and intuitive. Getting the data should be the easy part.

Usage

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API setup

import fathomdata as fd

fd.set_api_key('xxx')

Get structured dataframes for documents that have been ingested

documents = fd.available_documents()
for index, row in documents.iterrows():
    document = fd.get_document(row['DocumentId'])
    print(document.get_materials_df())
    print(document.get_steps_df())
    print(document.get_parameters_df())

Ingest a new document into the dataset

new_document_id = fd.ingest_document(path="/path/to/document.pdf")

Create control charts for continuous process validation

import matplotlib.pyplot as plt

document_ids = documents['DocumentId'].tolist()

actuals = fd.get_parameter_actuals_across_documents(document_ids)
print(actuals)

titer_actuals = actuals.loc['Titer']
yield_actuals = actuals.loc['Yield']

first_document_params_df = fd.get_document(document_ids[0]).get_parameters_df()

titer_operating_limits = {
    'lower': first_document_params_df.at['Titer', 'Lower Operating Limit'],
    'upper': first_document_params_df.at['Titer', 'Upper Operating Limit']
}

yield_operating_limits = {
    'lower': first_document_params_df.at['Yield', 'Lower Operating Limit'],
    'upper': first_document_params_df.at['Yield', 'Upper Operating Limit']
}

fig, axes = plt.subplots(2, 1, sharex=True, figsize=(8,12))
titer_control_chart = fd.create_control_chart(axes[0], titer_actuals, titer_operating_limits['lower'], titer_operating_limits['upper'])
yield_control_chart = fd.create_control_chart(axes[1], yield_actuals, yield_operating_limits['lower'], yield_operating_limits['upper'])