semvec/pypi-packages huggingface dataset:
flask-help-tools 0.3.5
A library that provides examples and solutions for common tasks in Flask applications
Requires Python: >=3.6

# Flask Help Tools

Flask Help Tools is a Python library that provides examples and solutions for common tasks in Flask applications. This library aims to help developers by providing ready-to-use code snippets and documentation-like examples.

## Installation

You can install Flask Help Tools using pip:

```bash
pip install flask-help-tools
osESD 1.1.6
Anomaly detection algorithm for time series datasets
Requires Python: >=3.8


# Automated Online Sequential ESD (Python)

This package includes Python codes for online sequential ESD(osESD), a variation of GESD tests.
It is a accurate and efficient way to detect anomalies in an univariate time series dataset.


We provide the basic function `osESD` and an automated grid search method `auto-osESD`.
`auto-osESD` can be used to find the best parameters for a specific dataset,  
using basic parameters or parameters provided explicitly by the user.  


## Installation

### 1. Download library.
Download osESD library using pip.
```
pip install osESD
```


### 2. Import function.
Import function in python code.
`osESD` is for a single online sequential ESD test, and will return indices of the anomalies.
Also, it will produce a text file with these anomalies in the designated output file (default 'osESD_results').
`auto_osESD` is for convenient tuning of `osESD`, and will run with default parameters if not specified otherwise.

```
from osESD import osESD, auto_osESD
```


### 3. Run on dataset.
Read data and run it using `osESD`.
The model is designed to run on univarites time series dataset in a csv. fashion.
Imported csv. dataset and custom name of the dataset should be entered. Also, the name of the column with values should be entered to 'value_name' and the name of the column with anomaly values should be entered as 'anomaly_name'.

```
import os
import pandas as pd

from osESD import osESD, auto_osESD

os.chdir(os.path.dirname(os.path.abspath(__file__)))

df = pd.read_csv("ARIMA1_ber_1.csv")
anoms_1 = osESD(df,"A2_benchmark", value_name='value',anomaly_name='anomaly')
print(anoms_1)

anoms_2 = osESD(df,"A2_benchmark", value_name='value',anomaly_name='anomaly',size=100, dwin=5, rwin=5, maxr=6, alpha=0.01)
print(anoms_2)

auto_anoms_1 = auto_osESD(df,"A2_benchmark_auto")
print(auto_anoms_1)

auto_anoms_2 = auto_osESD(df,"A2_benchmark_auto", size=[20,50,100,150],conditions=[True,False],alphas=[0.001,0.01],weights=[0,0,1,0],learning_length=0.3)
print(auto_anoms_2)

```

## Versions
```
Python = 3.8.16    
argparse = 1.1  
numpy = 1.24.3  
pandas = 1.5.3  
torch = 1.13.1  
matplotlib = 3.7.0  
scikit-learn = 1.2.1  
scipy = 1.10.1  
rrcf = 0.4.4  
```

## License
```
License :: OSI Approved :: MIT License
Operating System :: OS Independent
```

## Acknowledgements
```
This work was supported by the Ministry of Education of the Republic of Korea 
and the National Research Foundation of Korea (NRF-2018R1A5A7059549). 
This work was also supported by `Human Resources Program in Energy Technology' 
of the Korea Institute of Energy, Technology Evaluation and Planning (KETEP), 
and was granted financial resources from the Ministry of Trade, 
Industry $\&$ Energy, Republic of Korea (No. 20204010600090).

Related research and tests were mainly done in 
Intelligent Data Systems Laboratory in Hanyang University, Seoul, South Korea.
```

## References
Paper (currently under revision).
nslookup 1.8.1
Sensible high-level DNS lookups in Python, using DNSpython resolver
Keywords: dns

# PyNslookup
[![PyPi package](https://img.shields.io/pypi/v/nslookup.svg)](https://pypi.python.org/pypi/nslookup)

Simple, sensible high-level DNS lookups in Python (on top of dnspython dns.resolver).

#### Purpose and scope
This library is a simple wrapper around [dnspython](https://github.com/rthalley/dnspython), 
to provide high level functions with good error/exception handling, for the most common basic DNS lookup cases.

- `A`, `AAAA` record lookups (typical DNS queries)
- SOA lookups

This is not intended to be a complete wrapper around dnspython library or to handle uncommon edge cases.
Features like DoH are out of scope, and should be done using other libraries or dnspython directly.

### Usage

Returns an object containing two arrays:
 - `response_full`: the full DNS response string(s)
 - `answer`: the parsed DNS answer (list of IPs or SOA string)

```python
from nslookup import Nslookup

domain = "example.com"

# Initialize Nslookup
dns_query = Nslookup()
# Alternatively, the Nslookup constructor supports optional
# arguments for setting custom dns servers (defaults to system DNS),
# verbosity (default: True) and using TCP instead of UDP (default: False)
dns_query = Nslookup(dns_servers=["1.1.1.1"], verbose=False, tcp=False)

ips_record = dns_query.dns_lookup(domain)
print(ips_record.response_full, ips_record.answer)

soa_record = dns_query.soa_lookup(domain)
print(soa_record.response_full, soa_record.answer)
```

### Note
This library is oriented around regular UDP DNS.

Using TCP modes in this simple library will create a separate TCP session for 
each query, which can be resource intensive for a large number of queries. 
For this it is recommended to use the more granular `dnspython` API directly.