semvec/pypi-packages huggingface dataset:
hours 0.3.1
Log working hours
License: MIT
Requires Python: >=3.8,<3.13

<div align="center" markdown>

# ⏳ Hours

[![Build](https://github.com/oroszgy/hours/actions/workflows/build.yml/badge.svg)](https://github.com/oroszgy/hours/actions/workflows/build.yml)
![PyPI - Wheel](https://img.shields.io/pypi/wheel/hours)
[![PyPI version](https://badge.fury.io/py/hours.svg)](https://pypi.org/project/hours/)
[![Downloads](https://static.pepy.tech/personalized-badge/hours?period=total&units=international_system&left_color=grey&right_color=green&left_text=Downloads)](https://pepy.tech/project/hours)

A minimalistic work time logger for the command line.
</div>

<hr/>

# Installation

```bash
pip install hours
```

## Usage

Let's add a client first and set the hourly rate:

```bash
hours clients add -n BigCorporate --rate 100 --currency €
hours clients list
```

Then log your work for today:

```bash
hours log -c BigCorporate -h 8.0 -p "ML pipeline" -t "fixing bugs"
```

Get a report for the current month (or any other period):

```bash
hours report 
```

Create a timesheet to Excel file:

```bash
hours export
```

For more information, see the [documentation](https://oroszgy.github.io/hours).
micropython-rp2-pimoroni_picolipo_16mb-stubs 1.22.0.post1
MicroPython stubs
License: MIT
Requires Python: >=3.8,<4.0

# micropython-rp2-pimoroni_picolipo_16mb-stubs


This is a stub-only package for MicroPython.
It is intended to be installed in a projects virtual environment to allow static type checkers and intellisense features to be used while writing Micropython code.

The version of this package is alligned the the version of the MicroPython firmware.
 - Major, Minor and Patch levels are alligned to the same version as the firmware.  
 - The post release level is used to publish new releases of the stubs.

For `Micropython 1.17` the stubs are published as `1.17.post1` ... `1.17.post2`  
for `Micropython 1.18` the stubs are published as `1.18.post1` ... `1.18.post2`  

To install the latest stubs:  
`pip install -I  micropython-<port>-stubs` where port is the port of the MicroPython firmware.

To install the stubs for an older version, such as MicroPython 1.17:  
`pip install micropython-stm32-stubs==1.17.*` which will install the last post release of the stubs for MicroPython 1.17.


As the creation of the stubs, and merging of the different types is still going though improvements, the stub packages are marked as Beta.
To upgrade stubs to the latest stubs for a specific version use `pip install micropython-stm32-stubs==1.17.* --upgrade`

If you have suggestions or find any issues with the stubs, please report them in the [MicroPython-stubs Discussions](https://github.com/Josverl/micropython-stubs/discussions)

For an overview of  Micropython Stubs please see: https://micropython-stubs.readthedocs.io/en/main/ 
 * List of all stubs : https://micropython-stubs.readthedocs.io/en/main/firmware_grp.html

Included stubs:
* Merged stubs from `stubs/micropython-v1_22_0-rp2-PIMORONI_PICOLIPO_16MB-merged`
* Core stubs from `stubs/micropython-core`


origin | Family | Port | Board | Version
-------|--------|------|-------|--------
Documentation | micropython | - | - | v1.22.0
mfpml 1.0.0
Probabilistic machine learning methods
License: MIT
Requires Python: <3.12,>=3.10
Keywords: multi-fidelity machine learning,Bayesian Optimization

<p align="center">
    <img src="docs/source/figures/logo.png" width="70%" align="center">
</p>

---

[**Documentation**](https://jiaxiangyi96.github.io/mfpml/)
| [**Installation**](https://jiaxiangyi96.github.io/mfpml/get_started.html)
| [**GitHub**](https://github.com/JiaxiangYi96/mfpml)
| [**Tutorials**](https://github.com/JiaxiangYi96/mfpml/tree/main/tutorials)

## Summary

This repository aims to provide a package for multi-fidelity probabilistic machine learning. The package is developed by Jiaxiang Yi and Ji Cheng based on their learning curve on multi-fidelity probabilistic machine learning, and multi-fidelity Bayesian optimization, and multi-fidelity reliability analysis.

Overall, this `mfpml` package has two main goals, the first one is to provide basic code on implement typical methods in modeling, optimization, and reliability analysis field. Then, based on the basic code, we also provide some advanced methods based on our publications.

---

(1) Basic methods

**Models**

- Kriging model
- Multi-fidelity Kriging model

**Optimizations**

- Evolutionary algorithms
- Single fidelity Bayesian optimization
- Multi-fidelity Bayesian optimization

**Reliability analysis**

- Active learning reliability analysis
- Multi-fidelity reliability analysis

(2) Advanced methods

For the advanced methods, we will provide code based on our publications.
please check out those papers:

- Jiang, Ping, et al. "Variable-fidelity lower confidence bounding approach for engineering optimization problems with expensive simulations." AIAA Journal 57.12 (2019): 5416-5430.
- Cheng, Ji, Qiao Lin, and Jiaxiang Yi. "An enhanced variable-fidelity optimization approach for constrained optimization problems and its parallelization." Structural and Multidisciplinary Optimization 65.7 (2022): 188.
- Yi, Jiaxiang, et al. "Efficient adaptive Kriging-based reliability analysis combining new learning function and error-based stopping criterion." Structural and Multidisciplinary Optimization 62 (2020): 2517-2536.
- Yi, Jiaxiang, et al. "An active-learning method based on multi-fidelity Kriging model for structural reliability analysis." Structural and Multidisciplinary Optimization 63 (2021): 173-195.
- Yi, Jiaxiang, Yuansheng Cheng, and Jun Liu. "A novel fidelity selection strategy-guided multifidelity kriging algorithm for structural reliability analysis." Reliability Engineering & System Safety 219 (2022): 108247.

## Authorship

**Authors**:

- Jiaxiang Yi(yagafighting@gmail.com)[1]
- Ji Cheng (jicheng9617@gmail.com)[2]

**Authors affiliation:**

- [1] Delft University of Technology

- [2] City University of Hong Kong

## Community Support

If you find any issues, bugs or problems with this package, you can raise an issue
on the github page, or contact the authors directly.

## License

Copyright 2023, Jiaxiang Yi and Ji Cheng

All rights reserved.