semvec/pypi-packages huggingface dataset:
timeitpoj 0.1.11
yet another random library for measuring python performance
License: MIT
Requires Python: >=3.8,<4.0
Keywords: timeit,performance,benchmark,time,measure

# Timeitpoj

yet another random library for measuring python performance

[![Build and Test](https://github.com/jvanmelckebeke/timeitpoj/actions/workflows/run-tests.yaml/badge.svg)](https://github.com/jvanmelckebeke/timeitpoj/actions/workflows/run-tests.yaml)
[![PyPI version](https://badge.fury.io/py/timeitpoj.svg)](https://badge.fury.io/py/timeitpoj)

## Usage in production

if you do not need to measure performance of your code in a production environment, simply set the environment
variable `TIME_IT` to`false`

#### Example

```python
from timeitpoj.timeit import TimeIt
from time import sleep

BASE_TIME = 0.1

with TimeIt("my timer") as timer:
    print("Executing my timer....")
    sleep(BASE_TIME)

    with timer("my subtimer"):
        print("Executing my subtimer....")
        sleep(BASE_TIME)

        with timer("my nested subtimer"):
            print("Executing my nested subtimer....")
            sleep(BASE_TIME)

            for _ in range(2):
                with timer("my super nested subtimer 2"):
                    sleep(BASE_TIME)

        for _ in range(5):
            with timer("my nested subtimer 2"):
                sleep(BASE_TIME)

    with timer("my subtimer 992"):
        print("Executing my subtimer 2....")
        sleep(BASE_TIME)

    with timer("my subtimer 4"):
        print("Executing my subtimer 4....")
        sleep(BASE_TIME)

    # Something that is not covered by the timer
    sleep(BASE_TIME * 2)
```

#### Output

```
================= [my timer] TIMEIT REPORT =================
[TIMEIT] my timer took 1.403 seconds
└── [81.80%] my subtimer ; 0.902 seconds
    ├── [37.49%] my nested subtimer ; 0.300 seconds
        └── my super nested subtimer 2 ; 0.200 seconds ; 2 times ; avg 0.100 seconds
    └── [62.51%] my nested subtimer 2 ; 0.501 seconds ; 5 times ; avg 0.100 seconds
├── [ 9.12%] my subtimer 992 ; 0.101 seconds
└── [ 9.08%] my subtimer 4   ; 0.100 seconds
└── [ 0.01%] internal time: 0.112 milliseconds
[78.56%% COVERAGE] time accounted for: 1.102 seconds, time unaccounted for: 0.301 seconds
[TIMEIT] report generation took 0.190 milliseconds
================= [my timer] TIMEIT REPORT =================
```
tinygp 0.3.0
The tiniest of Gaussian Process libraries
License: MIT
Requires Python: >=3.9

<p align="center">
  <img src="https://raw.githubusercontent.com/dfm/tinygp/main/docs/_static/zap.png" width="50"><br>
  <strong>tinygp</strong><br>
  <i>the tiniest of Gaussian Process libraries</i>
  <br>
  <br>
  <a href="https://github.com/dfm/tinygp/actions/workflows/tests.yml">
    <img alt="GitHub Workflow Status" src="https://img.shields.io/github/actions/workflow/status/dfm/tinygp/tests.yml?branch=main">
  </a>
  <a href="https://tinygp.readthedocs.io">
    <img alt="Read the Docs" src="https://img.shields.io/readthedocs/tinygp">
  </a>
  <a href="https://doi.org/10.5281/zenodo.6389737">
    <img alt="Zenodo DOI" src="https://zenodo.org/badge/DOI/10.5281/zenodo.6389737.svg">
  </a>
</p>

`tinygp` is an extremely lightweight library for building Gaussian Process (GP)
models in Python, built on top of [`jax`](https://github.com/google/jax). It has
a [nice interface][api-ref], and it's [pretty fast][benchmarks]. Thanks to
`jax`, `tinygp` supports things like GPU acceleration and automatic
differentiation.

Check out the docs for more info: [tinygp.readthedocs.io][docs]

[api-ref]: https://tinygp.readthedocs.io/en/latest/api/index.html
[benchmarks]: https://tinygp.readthedocs.io/en/latest/benchmarks.html
[docs]: https://tinygp.readthedocs.io