(but not just 96 well plates)¶
Purpose¶
ninetysix provides a method of combining well-value data pairs and efficiently adding additional information (e.g., controls, well conditions) and processing and visualizing the results.
This primarily works via the Plate class, but visualization tools are available for pandas DataFrame objects as well through ninetysix.viz.
Documentation¶
See the Getting Started tutorial for basic info on how to use this package, or find more detailed documentation on specific uses from the following pages:
1. The Plate class¶
2. Basic data visualization¶
3. Advanced data visualization¶
4. Multi-Plate support¶
Install¶
pip install ninetysixAlthough jupyter lab is not a strict dependency for ninetysix, much of the visualization functionality benefits from being run in a notebook. If your jupyter lab and other packages are up to date, the above pip install should suffice. If you have issues, the following conda environment should work:
# Create the environment with python and jupyterlab installed
conda create -n ns_env python jupyterlab
# Activate the environment
conda activate ns_env
# Install ninetysix and its dependencies
pip install ninetysix
# Open jupyter lab
jupyter labGenerate processed data and interactive plots with code as simple as:¶
import ninetysix as ns
# well-activity dataset
data = 'example_data.csv'
# Well conditions in the plate
control_mapping = {
'default': 'experiment',
'[A-D]10': 'standard',
'[E-H]10': 'negative',
}
# Declarative colormapping
cmap = {
'standard': ns.Colors.green,
'negative': ns.Colors.orange,
'experiment': ns.Colors.blue,
}
# Annotate, process, and visualize
ns.Plate(
data,
annotate={'controls': control_mapping}
).normalize(
to='controls=standard',
zero='controls=negative',
update_value=True,
).plot_scatter(
color='controls',
cmap=cmap,
ranked=True,
)
