Optimizing for Import Speed

As SeqLike forms the basis of downstream tooling, import timing becomes important as well. As such, we have implemented a few tricks to enable fast importing of the library.

Lazy Loading

In accordance with SPEC-001, we use lazy_loader to lazily load Python packages into our modules. Doing so ensures that we do not incur the time penalty associated with importing large packages, such as:

• pandas
• NumPy
• matplotlib
• bokeh

The general pattern we use here is to do:

import lazy_loader as lazy

pd = lazy.load("pandas") # equivalent to `import pandas as pd`

This pattern is generally useful only for top-level packages.

Nesting imports

The second strategy for deferring imports is to import them within functions and class methods. We do this when there are submodules or specific functions/classes that we need to access. One example is Bokeh:

def some_bokeh_function():
    from bokeh.plotting import figure
    ...
    p1 = figure(...)

Try/Excepts

The third pattern we use is the try/except pattern. This one is particularly useful for distinguishing between notebook and shell environments. For example, in draw_utils.py, we use the following pattern:

try:
    get_ipython
    from bokeh.io import output_notebook

    output_notebook()

This way, we avoid executing output_notebook() if we are in a CLI/shell environment and not in a Jupyter notebook environment.