perfplot 0.9.3

Performance plots for Python code snippets

perfplot extends Python's timeit by testing snippets with input parameters (e.g., the size of an array) and plotting the results.

For example, to compare different NumPy array concatenation methods, the script

import numpy as np
import perfplot

perfplot.show(
    setup=lambda n: np.random.rand(n),  # or setup=np.random.rand
    kernels=[
        lambda a: np.c_[a, a],
        lambda a: np.stack([a, a]).T,
        lambda a: np.vstack([a, a]).T,
        lambda a: np.column_stack([a, a]),
        lambda a: np.concatenate([a[:, None], a[:, None]], axis=1),
    ],
    labels=["c_", "stack", "vstack", "column_stack", "concat"],
    n_range=[2 ** k for k in range(25)],
    xlabel="len(a)",
    # More optional arguments with their default values:
    # logx="auto",  # set to True or False to force scaling
    # logy="auto",
    # equality_check=np.allclose,  # set to None to disable "correctness" assertion
    # show_progress=True,
    # target_time_per_measurement=1.0,
    # max_time=None,  # maximum time per measurement
    # time_unit="s",  # set to one of ("auto", "s", "ms", "us", or "ns") to force plot units
    # relative_to=1,  # plot the timings relative to one of the measurements
    # flops=lambda n: 3*n,  # FLOPS plots
)

produces

Clearly, stack and vstack are the best options for large arrays.

(By default, perfplot asserts the equality of the output of all snippets, too.)

If your plot takes a while to generate, you can also use

perfplot.live(
    # ...
)

with the same arguments as above. It will plot the updates live.

Benchmarking and plotting can be separated. This allows multiple plots of the same data, for example:

out = perfplot.bench(
    # same arguments as above (except the plot-related ones, like time_unit or log*)
)
out.show()
out.save("perf.png", transparent=True, bbox_inches="tight")

Other examples:

• Making a flat list out of list of lists in Python
• Most efficient way to map function over numpy array
• numpy: most efficient frequency counts for unique values in an array
• Most efficient way to reverse a numpy array
• How to add an extra column to an numpy array
• Initializing numpy matrix to something other than zero or one

Installation

perfplot is available from the Python Package Index, so simply do

pip install perfplot

to install.

Testing

To run the perfplot unit tests, check out this repository and type

pytest

License

This software is published under the GPLv3 license.