semvec/pypi-packages huggingface dataset:
dsa-stl 0.0.7
DSA Algorithm
Keywords: python,dsa_stl,data structure,algorithms

# dsa_stl
- ##### python package for Data Structure and Algorithms.
---
## Installation
```
pip install dsa_stl
```
---
## Testing

```
from dsa_stl import testdsa
testdsa.test()
```
##### Output

`
test successful...
`
---
## Available Libraries
- <a href="https://github.com/aman2000jaiswal14/dsa_stl/blob/main/Documentation/doc_graph_stl.md" > graph_stl </a>
- <a href="https://github.com/aman2000jaiswal14/dsa_stl/blob/main/Documentation/doc_directed_graph_stl.md" > directed_graph_stl </a>
- <a href="https://github.com/aman2000jaiswal14/dsa_stl/blob/main/Documentation/doc_dsu_stl.md" > dsu_stl </a>
- <a href="https://github.com/aman2000jaiswal14/dsa_stl/blob/main/Documentation/doc_lca_stl.md" > lca_graph_stl </a>
- <a href="https://github.com/aman2000jaiswal14/dsa_stl/blob/main/Documentation/doc_Balance_bst.md" > Balance_bst </a>

## Git Hub
- <a href = "https://github.com/aman2000jaiswal14/dsa_stl"> Git Hub Code </a>
genieparse-maercu 0.0.1
A wrapper to the genie parsers
Requires Python: >=3.6

## genieparse

### About
A simple wrapper to the [genie parsers](https://pubhub.devnetcloud.com/media/genie-feature-browser/docs/#/parsers), based on the code from [ckay584](https://github.com/clay584/parse_genie).
LTMN 0.0.2
Library used Normalize numerical time-series data

        
        This Library is used for normalizing data array with respect to long term memory retention. 
        Timeseries Data's have influence of long term memory retention on their current data. This phenomenon happens in the field of finance,sports and other live streaming timeseries datas.
        
        Developed by Philip Pankaj (c) 2021
        
        ##Example with Nifty 50 data
        
        <p align="center">
          <img src="static/LTMN_Nasdaq_Graph.png">
        </p>
        
        '''python
        #importing our LTMN
        from LTMN import Normalization
        
        #importing all required library
        from yahoo_fin.stock_info import get_data
        from datetime import date
        
        #getting nifty data and converyting to numpy array
        now=date.today().strftime("%d/%m/%Y")  
        from_date="5/5/2006" # m/d/year
        data= get_data("^NSEI", start_date=from_date, end_date=now, index_as_date = True, interval="1d")
        data=data['close'].dropna()
        data=data.to_numpy()
        
        #initializing ltmn and transforming data
        ltmn=Normalization(data,0.5)
        n_data=ltmn.transform()
        
        #importing matplotlib and plotting
        import matplotlib.pyplot as plt
        plt.figure(figsize=(40, 40))
        
        fig, ax1 = plt.subplots()
        ax1.set_ylabel('Nifty-50')
        ax1.plot(data[-1000:], color = 'tab:blue')
        
        ax2=ax1.twinx()
        ax2.set_ylabel('LTM-Normalized')
        ax2.plot(n_data[-1000:],color='tab:green')
        plt.show()
        '''
        
Keywords: python,normalization,standardization,long term memory,LTM,LTM Normalization
Platform: UNKNOWN
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Description-Content-Type: text/markdown