semvec/pypi-packages huggingface dataset:
ConformalImpact 0.0.3
Confrmal Based Impact Analysis.
Keywords: forecasting,time series,seasonality,trend

# Conformal Impact
Take Causal Impact and replace the Bayesian Structural Time Series Model with MFLES and the Basyesian posterior with Conformal Prediction Intervals.

## Quick Examnple an comparison to Causal Impact
```
intervention_effect = 400
np.random.seed(42)
series = np.random.random((130, 1)) * 400
x_series = series * .4 + np.random.random((130, 1)) * 50 + 1000
trend = (np.arange(1, 131)).reshape((-1, 1))
series += 10 * trend
series[-30:] = series[-30:] + intervention_effect

data = pd.DataFrame(np.column_stack([series, x_series]), columns=['y', 'x1'])

import matplotlib.pyplot as plt

plt.plot(series)
plt.plot(x_series)
plt.show()


from ConformalImpact.Model import CI


conformal_impact = CI(opt_size=20,
                      opt_steps=10,
                      opt_step_size=3)
impact_df = conformal_impact.fit(data,
                              n_windows=30,
                              intervention_index=100,
                              seasonal_period=None)

conformal_impact.summary()
conformal_impact.plot()





from causalimpact import CausalImpact

impact = CausalImpact(data, [0, 99], [100, 130])
impact.run()
impact.plot()
print(impact.summary())
output = impact.inferences
np.mean(output['point_effect'].values[-30:])
```
file-helper-utils 0.1.0
Collection of exported functions for performning file-based tasks.
Requires Python: >=3.6
Keywords: file_helper_utils

===============================
File Helper Utils
===============================


Collection of exported functions for performing file-based tasks.


The following exported scripts are available:
----------------------------------------------

- calculate_md5
- check_indir_status
- check_infile_status
- check_outdir_status
- check_outfile_status
- get_file_creation_date
- get_file_list_from_directory
- get_file_size
- get_line_count
- is_binary_file


Credits
-------

This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage


=======
History
=======

0.1.0 (2024-02-10)
------------------

* First release on PyPI.
pyverify-xnz233 1.1.2
A small verify project
Requires Python: >=3.6

# pyverify
![PyPI](https://img.shields.io/pypi/v/pyverify-xnz233)

## 简介

pyverify包是一个基于`tkinter`库实现的GUI身份验证库
### 主要功能
- 为您的`py`文件能够快速使用TOTP认证的功能
- 提供一个简易的二维码生成器，可生成手机app可用的二维码

![](img/utils.jpg "生成器")

Tips:秘钥存储在运行目录下的`secdata.pass`文件中  
删除该文件会导致无法验证

## 使用教程

### 安装
你可以在终端执行pip来安装：  
`pip install pyverify-xnz233`

### 生成器


- 生成秘钥
  - 方法一：运行包中的简易生成器`utils.py`
  - 方法二：调用包中`manage()`函数:
```python
import pyverify
pyverify.manage()
```

- 读取秘钥
  - 方法一：手机app扫码
  - 方法二：查看运行目录下`secdata.pass`文件

### 验证程序
- 在任意py文件中的头部调用包中的`gui_verify()`:
```python
import pyverify
pyverify.gui_verify()

# Your code...
```