semvec/pypi-packages huggingface dataset:
Pred-in-Open-Env 1.0.1
A model to predict pedestrian flow in open environment
Requires Python: >=3.8

`Pred_in_Open_Env`是一个基于RNN的开放街区人流量预测模型  
可进行三个任务：

--全点位未来 10/20/30 分钟人数预测及人数流向接口

参数1为currentTime,其含义为当前时间，需按照以下格式（yyyy-MM-dd HH:mm:ss）

参数2为forecastDuration，含义为预测的时间，可选择（10，20，30）三个时段

可变参数:  
step:预测时间步数  
to_GPU:是否加载到GPU训练  
update:本次训练是否更新并保存历史平均值与模型参数  
运行示例:
```bash
python run.py task1 --currentTime "2024-10-22 10:20:00" --forecastDuration 10
```
或者在run.py中添加语句
```python
task1("2024-10-22 10:20:00",10)
```

--全点位在指定时间范围内实际人数与预测人数趋势

参数1为startTime,其含义为预测开始时间，需按照以下格式（yyyy-MM-dd HH:mm:ss）

参数2为endTime,其含义为预测结束时间，需按照以下格式（yyyy-MM-dd HH:mm:ss）

参数3为duration，含义为指定数据时间间隔，10/20/30，表示返回数据的时间间隔为10/20/30分钟

可变参数:  
to_GPU:是否加载到GPU训练  
update:本次训练是否更新并保存历史平均值与模型参数  
运行示例:  
```bash
python run.py task2 --startTime "2024-10-22 10:20:00" --endTime "2024-10-22 15:20:00" --duration 10
```
或者在run.py中添加语句
```python
task2("2024-10-22 10:20:00","2024-10-22 15:20:00",10)
```

--指定点位指定时间范围内实际人数与预测人数趋势

参数1为startTime,其含义为预测开始时间，需按照以下格式（yyyy-MM-dd HH:mm:ss）

参数2为endTime,其含义为预测结束时间，需按照以下格式（yyyy-MM-dd HH:mm:ss）

参数3为duration，含义为指定数据时间间隔，10/20/30，表示返回数据的时间间隔为10/20/30分钟

参数4为id,其含义为指定点位序号，在1～10选择

可变参数:  
to_GPU:是否加载到GPU训练  
update:本次训练是否更新并保存历史平均值与模型参数  
运行示例:  
```bash
python run.py task3 --startTime "2024-10-22 10:20:00" --endTime "2024-10-22 15:20:00" --duration 10 --id 1
```
或者在run.py中添加语句
```python
task3("2024-10-22 10:20:00","2024-10-22 15:20:00",10,1)
pythonexercises 1.0.0
exercises and quiz for beginner-intermediate level Python learners
Requires Python: >=3.9

# Python Exercises

This interactive TUI app includes exercises and multiple-choice questions intended to test the understanding of beginner to intermediate level Python learners.

# Screenshots

![Sample screenshot for Python exercises](https://raw.githubusercontent.com/learnbyexample/TUI-apps/main/PythonExercises/python_exercises.png)

![Sample screenshot for Python quiz](https://raw.githubusercontent.com/learnbyexample/TUI-apps/main/PythonExercises/python_quiz.png)

# Guide

See [app_guide.md](https://github.com/learnbyexample/TUI-apps/blob/main/PythonExercises/app_guide.md) for instructions.

# License

Code snippets are licensed under [MIT LICENSE](https://github.com/learnbyexample/TUI-apps/blob/main/LICENSE).

Exercise questions and associated files (like `exercises.json` and `quiz.txt`) are licensed under [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/).
ars-dev 0.1.0
A JAX/numpy implementation of Adaptive Rejection Sampling (ARS) introduced by Gilks and Wild in 1992
Requires Python: >=3.6

# Ars-dev
Ars-dev is a Python package designed for adaptive rejection sampling(ARS). This implementation references Adaptive Rejection Sampling for Gibbs Sampling(Gilks et al. 1992).


## Installation

First clone this project locally with `git`
```
git clone https://github.berkeley.edu/yoshi-chiu/ars-dev.git
```

Then you can install the package via `pip`. First move in the directory, then run `pip`.

```
cd ars-dev/
python -m pip install .
```

## Quick Start

Here is an example on how to run the package.
```
from ars import ars
from scipy.stats import norm
import numpy as np

domain = (-np.inf, np.inf)
n_samples = 10000
samples = ars(pdf=norm.pdf, domain=domain, n_samples=n_samples, is_logpdf=False, verbose=True)
```

The function also accepts pdf functions built with JAX. In that case, jax automatic differentiation is used in the sampling
```
from ars import ars
import jax.numpy as jnp

target_pdf = lambda x: jnp.exp(-x**2 / 2)
domain = (-jnp.inf, jnp.inf)

samples = ars(pdf=target_pdf, domain=domain, n_samples=1000, is_logpdf=False, verbose=True)
```

A function `test_ars` is also provided to run tests on easy to compute pdfs
```
from ars import test_ars
from scipy.stats import norm
import numpy as np

domain = (-np.inf, np.inf)
n_samples = 10000
samples, obj, fig = test_ars(pdf=norm.pdf, domain=domain, n_samples=n_samples, verbose=False)
```

## Running Unit tests
To run all the unit tests, first move in the project directory then run `pytest`
```
pytest
```

## Documentation
Documentation can be found at https://github.berkeley.edu/yoshi-chiu/ars-dev/blob/main/docs/report.pdf

## License
This project is licensed under the MIT License.

---
---
---

# (To be deleted)

## Setup (For us)

Run this to install the package in editable mode

```
python -m pip install -e .
```

## Running the function

## TODO 
- [ ] JIT ? (TBD)
- [x] write testing for all non-trivial functions (Jordan)
- [ ] Setup as a package (Hongyu)
- [ ] write a README on the easy setup and initial simple examples (Hongyu)
- [x] Write documentation, especially the main function with some simple examples (Yoshi)
- [x] write a report documet explaining the implementation approach (Jordan for a good part of it)
- [x] Add tests explanation and examples in report (Yoshi)
- [x] Add in results plots and examples in report (Jordan, Yoshi)
- [x] Improve the update of the arrays to avoid recomputing everything (not faster)
- [x] Update code for non-jax functions (Jordan)