semvec/pypi-packages huggingface dataset:
easyerrors 1.0.0
a simple module for testing out functions and catching its errors
Requires Python: >=3.6

This module allows you to easily test simple functions. All errors will be catched and showed to you gracefully.

Note that this module is incredibly basic and does not work with any kind of indented code, so e.g. for loops are not an option.

code examples:
```
>>> from easyerrors import test
>>> 
>>> @test
>>> def tryout(msg, num):
... 	print(msg, num)
...
>>> tryout('hey', 3)

Testing code inside function 'tryout':

-------- Test started! --------

>>> print(msg, num,)
hey 3


-------- Test ended! --------
```	

```
>>> from easyerrors import test
>>> 
>>> @test
>>> def tryout(msg, num):
... 	print(msg, num)
... 	print(invalidargument)
...
>>> tryout('hey', 3)

Testing code inside function 'tryout':

-------- Test started! --------

>>> print(msg, num)
hey 3

>>> print(incorrectargument)
Error: name 'incorrectargument' is not defined


-------- Test ended! --------

 Errors found:
  - name 'incorrectargument' is not defined
  
```
marketools 1.0.1
Tools for stock market analysis.
Requires Python: >=3.8

marketools: tools for stock market analysis
===========================================

[![PyPI Latest Release](https://img.shields.io/pypi/v/marketools.svg)](https://pypi.org/project/marketools/)
[![License](https://img.shields.io/pypi/l/marketools.svg)](https://github.com/AlbertRtk/marketools/blob/main/LICENSE)
[![Python Versions](https://img.shields.io/pypi/pyversions/marketools.svg)]()

## About
marketools is a Python package for web scraping and analysis of stock market data. Project is under development. At the moment, analysis functionality is limited. You are welcomed to contribute to the project.

### Source of data
Data are scraped from [Stooq](http://stooq.com/) service.

## Support
You may report issues and functionality requests [here](https://github.com/AlbertRtk/marketools/issues).

## Installation
```bash
pip install marketools
```
or 
```bash
git clone https://github.com/AlbertRtk/marketools.git
```

## Documentation
* [Documentation](https://albertrtk.github.io/marketools/docs/html/marketools/index.html)
    * [analysis](https://albertrtk.github.io/marketools/docs/html/marketools/analysis/index.html)
    * [stock](https://albertrtk.github.io/marketools/docs/html/marketools/stock.html)
    * [stqscraper](https://albertrtk.github.io/marketools/docs/html/marketools/stqscraper/index.html)

## Example
Import Stock class
```python
from marketools import Stock
```
Create Stock instance for selected ticker, here [PKN](https://stooq.com/q/?s=pkn)
```python
pkn = Stock('PKN')
```
For not Polish stocks, you need to append country code to the ticker (after a dot), e.g.,
```python
apple = Stock('AAPL.US')
```
Get and print OHLC (open-high-low-close) data from the last 5 days
```python
pkn_ohlc = pkn.ohlc  # returns pandas.DataFrame
print(pkn_ohlc.tail(5))
```
Print available fundamental information
```python
print(pkn.fundamentals)
```
wcfkcenters 0.0.7
A python package for Weighted Cluster Fuzzy-k-Centers algorithm
Requires Python: >=3.6

Python implementations of the Weighted CLuster FuzzykCenters algorithms for fuzzy clustering categorical data:

## Installation:
### Using pip: 
```shell
pip install wcfkcenters
```

### Import the packages:
```shell
from WCFkCenters.WCFkCenters import WCFkCenters
from WCFkCenters import TDef
import numpy as np
```
### Generate a simple categorical dataset:

```shell
X = np.array([[0,0,0],[0,1,1],[0,0,0],[1,0,1],[2,2,2],[2,3,2],[2,3,2]])
y = np.array([0,0,0,0,1,1,1])
```

### LSHk-Representatives (Init): 

```shell
algo = WCFkCenters(X,y ,k=TDef.k, alpha=TDef.alpha)
algo.SetupMeasure("Overlap")
algo.DoCluster()
algo.CalcScore()


```

### Built-in evaluattion metrics:
```shell
algo.CalcFuzzyScore()
```

### Outcome:
```shell
SKIP LOADING distMatrix because: True bd=None yellow
Saving Overlap to: saved_dist_matrices/json/Overlap_None.json
>>> algo.DoCluster()
array([0, 0, 0, 0, 1, 1, 1], dtype=int64)
>>> algo.CalcScore()
Purity: 1.00 NMI: 1.00 ARI: 1.00 Sil:  0.59 Acc: 1.00 Recall: 1.00 Precision: 1.00
(1.0, 1.0, 1.0, 1.0, 1.0, 0.5873015873015872, 0.011969540000001189, -1, 1.0, 1.0, 1.0)
Fuzzy scores PC:1.00 NPC:1.00 FHVâ†“:0.02 FSâ†“:-2000.00 XBâ†“:0.11 BHâ†“:0.06 BWS:-2000.00 FPC:3.49 SIL_R:0.70 FSIL:0.70 MPO:12.18 NPE:0.01 PE:0.01 PEB:0.01
```

## Parameters:
X: Categorical dataset\
y: Labels of object (for evaluation only)\
n_init: Number of initializations \
n_clusters: Number of target clusters\
max_iter: Maximum iterations\
verbose: \
random_state: 

 
## Outputs:
cluster_representatives: List of final representatives\
labels_: Prediction labels\
u: Fuzzy membership
cost_: Final sum of squared distance from objects to their centroids\
n_iter_: Number of iterations\

## References:
*To be updated*