PyAnIML

Library that integrates the AnIML data model into an object oriented interface.
PyAnIML allows setting up valid AnIML documents and supports export/import to and from JSON

⚡️ Quick start

Get started with PyEnzyme by running the following command

# Using PyPI
python -m pip install pyaniml

Or build by source

git clone https://github.com/FAIRChemistry/pyAnIML.git
cd pyAniML
python3 setup.py install

⚙️ Example code

import pyaniml as animl

# Initialize AnIML document
animldoc = animl.AnIMLDocument()

# Create and add Sample
sample = animl.Sample(id="S1", name="Sample1")
sample_param = animl.Parameter(name="param1", parameter_type="String", value="Lol")
sample.add_property(sample_param)
animldoc.add_sample(sample)

# Create and add experiment step
exp_step = animl.ExperimentStep(name="Exp_Step", experiment_step_id="ID1")

# Add sample reference
exp_step.add_sample_reference(
    sample=sample, role="Role", sample_purpose="Purpose"
)

# Create method
device = animl.Device(name="Device", firmware_version="1.0", serial_number="123")
exp_step.add_method(device)
# Create result
data = animl.IndividualValueSet(data=[1, 2, 3, 4, 5])
series = animl.Series(name="Series", id="Series1", data=data,
                data_type="Int32", dependency="dependent", plot_scale="none"
                )
exp_step.add_result(series)

# Finally, add exp step to document
animldoc.add_experiment_step(exp_step)
xml_string = animldoc.toXML()
print(animl.AnIMLDocument.fromXMLString(xml_string))

_{(Code should run as it is)}

⚠️ License

PyAnIML is free and open-source software licensed under the MIT License.