semvec/pypi-packages huggingface dataset:
confsmooth 1.0.0
The algorithm smooths functions preserving local peaks. To distinguish peaks from noise it needs noise standard deviation

# Confidence Based Smoothing
The algorithm smooths functions preserving local peaks. To distinguish peaks from noise it needs noise standard deviation. 

![Demo](./example.png)

## Installation

pip install --upgrade confsmooth

## Usage

```python
import os, shutil, statsmodels
import matplotlib.pyplot as plt
import numpy as np
from statsmodels.nonparametric.kernel_regression import KernelReg
from confsmooth import confsmooth
from scipy.signal import savgol_filter

x = np.linspace(-2,2,500)
real_noise_level = 0.002
y = 1 - np.power(np.abs(x)+0.01,0.01) + np.random.normal(0, real_noise_level, len(x))

# estimate noise standard deviation
savgol = savgol_filter(y,21,1)
noise_level = np.std(y - savgol)
print('Estimated noise level =', noise_level, 'Real noise level =', real_noise_level)

smoothed_conf = confsmooth(y, noise_level, confidence=0.995, deg=2)

kr = KernelReg(endog=y, exog=x, var_type='c', bw=[0.1])
smoothed_kr, _ = kr.fit(x)

fig,ax = plt.subplots()
ax.plot(x, y, label='initial')
ax.plot(x, savgol, label='savgol')
ax.plot(x, smoothed_kr, label='Kernel regr')
ax.plot(x, smoothed_conf, label='confsmooth')
ax.legend()
plt.show()
```
procaine 0.4.1
A REST client library for SAP AI Core API.
Requires Python: >=3.7
Keywords: AI Core,AICore,AI API,ai-api

Procaine
========

**Procaine** is a REST client library for SAP AI Core API.  Reduces the pain.


Installation
------------

To use *Procaine*, first install it using pip:

.. code-block:: console

   $ pip install procaine


Usage
-----

Run a flow::

  >>> from procaine import aicore

  >>> auth = {"url": AUTH_URL, "clientid": CLIENT_ID, "clientsecret": CLIENT_SECRET}
  >>> api = aicore.Client(AI_API_URL, auth)

  >>> hello = api.create_execution("hello-world")
  >>> hello
  {'id': 'e96bc32ee9bf9e63', 'message': 'Execution scheduled', 'status': 'UNKNOWN', 'targetStatus': 'COMPLETED'}

  >>> api.execution(hello)
  { ... 'status': 'COMPLETED', ... 'targetStatus': 'COMPLETED'}
  
  >>> logs = api.execution_logs(hello)
  >>> print(logs)
   _____________ 
  < hello world >
   ------------- 
      \
       \
	\     
		      ##        .            
		## ## ##       ==            
	     ## ## ## ##      ===            
	 /""""""""""""""""___/ ===        
    ~~~ {~~ ~~~~ ~~~ ~~~~ ~~ ~ /  ===- ~~~   
	 \______ o          __/            
	  \    \        __/             
	    \____\______/   


Documentation
-------------

More usage examples and detailed documentation could be found on https://procaine.readthedocs.io/