semvec/pypi-packages huggingface dataset:
pyflush 1.0.0
A python library to remove content of a directory, or file in a safer manner. After running pyflush, it became much safer to delete the file/directory manually/programmatically.
Keywords: File Deletion,Deletion Utility,Directory Deletion,Delete Data,Data Deletion

# pyflush
This repository contains python code to remove content of a directory, or file in a safer manner. After running pyflush, it became much safer to delete the file manually/programmatically.

#### Developer: [Ravin Kumar](http://mr-ravin.github.io)

### Steps to use pyflush

- Removing a file

```python
import pyflush
pyflush.flushfile("filename.txt")
```

- Removing a directory

```python
import pyflush
pyflush.flushdir("directoryname")
```

#### Installation using PyPi

```
pip3 install pyflush
```
```python
Copyright (c) 2019 Ravin Kumar
Website: https://mr-ravin.github.io

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation 
files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, 
modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the 
Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the 
Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE 
WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR 
COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, 
ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
```
refkeys 0.0.2
This repository contains a tool for secret-sharing among two parties.
Keywords: Secret Sharing,Cryptography,Security Tools,Secret Key,Secret Key Generation

# Refkeys: A secure way for sharing secret (key).
#### Author & Developer: [Ravin Kumar](https://mr-ravin.github.io)
This repository contains a tool for secret-sharing among two parties.

For example, lets say two parties A and B want to share a secret_key, such that only if they both agree than only they should be able to get the secret.
Our software generates 3 individual keys, one for each party, and one for the mediator. When these keys are combined then only the secret_key can be revealed.

#### Advantage of our system:
- Even though the same secret is shared among G1={a,b,c} and G2={x,y,z}. The beauty of refkeys is that, even if some member of a group tries to contact other group members to generate the secret key, it can not be generated.
- Only when all parties of G1 agrees than only they can generate the secret_key.

#### Demonstration for generating individual keys:
```python
import refkeys
key=refkeys.get_keys("secure_key_passed_here")
person1_key=key[0]
person2_key=key[1]
mediator_key=key[2]
## note: person1_key,person2_key,mediator_key are of type-list.
```

#### Demonstration for generating individual filekeys:
```python
import refkeys
refkeys.get_keyfiles("secure_key_passed_here","person1_name","person2_name","mediator_name",path="./")
## note: names should be distinct, and mediator name is not mandatory.
```



#### Demonstration for generating secret key using individual keys:
```python
import refkeys
....
secret_key=refkeys.combine_keys(person1_key,person2_key,mediator_key)
## note: person1_key,person2_key,mediator_key are of type-list.
```


#### Demonstration for generating secret key using filekeys:
```python
import refkeys
....
secret_key=refkeys.combine_keyfiles(person1_key,person2_key,mediator_key)
## note: names should be distinct, and mediator name is not mandatory.
```

#### Installation using pip:
```python
pip install refkeys
```

```python
Copyright (c) 2019 Ravin Kumar
Website: https://mr-ravin.github.io

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation 
files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, 
modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the 
Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the 
Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE 
WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR 
COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, 
ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
```
dynasty 0.0.3
A Python class hierarchy analyzer

# dynasty

Dynasty extracts the class hierachy for your Python package, to track down lost or misplaced classes and help prevent [ravioli code](https://docs.python-guide.org/writing/structure).

**Installation**
```buildoutcfg
pip install dynasty
```

**Usage**
```python
import mypackage
from dynasty import Dynasty

mypackage_dynasty = Dynasty(mypackage)

# display collapsible class hierarchy (in jupyter notebook)
mypackage_dynasty.widget()

# print class hierarchy to screen
mypackage_dynasty.print()
```
<img src="https://github.com/migolan/dynasty/blob/main/widget_demo.gif" width="400">
<img src="https://github.com/migolan/dynasty/blob/main/print_demo.png" width="250">

**Author**

Michal Golan <[migolan@gmail.com](mailto:migolan@gmail.com)> [migolan@github](https://github.com/migolan)
nervatura-grpc 5.0.0
Nervatura Framework Python gRPC packages
Requires Python: >=3.6

Nervatura Framework Python gRPC packages
=========================

Open Source Business Management Framework

Please see more the [Nervatura Docs](https://nervatura.github.io/nervatura/)

Nervatura server-side Python example code: [nervatura-fastapi](https://github.com/nervatura/nervatura-fastapi)
beepbeep-txflowutils 0.0.2
Sync data transformation information from BigQuery to GitHub and create a dynamic Markdown documantation.
Requires Python: >=3.6

# beepbeep_txflowutils

This is an project to Sync data transformation information from BigQuery to GitHub and create a dynamic Markdown documantation.

## Installation
Run the following to install:
```python
pip install beepbeep_txflowutils
```

## Usage
```python
git clone https://github.com/beepbeeptechnology/sync_from_bigquery_to_github.git
```

# Create venv
```python
python -m venv <venv_name>
source <venv_name>/Scripts/activate for wondows os
```

# Genarate streamlit app
```
streamlit run app.py
```

# Developing beepbeep_txflowutils
To install beepbeep_txflowutils, along with the tool need to develop and run test, run the following in your virtualenv:

```bash
$pip install -e .[dev] TODO
```
example-pkg-AGAIN-3 0.0.1
Sync data transformation information from BigQuery to GitHub and create a dynamic Markdown documantation.
Requires Python: >=3.6

# beepbeep_txflowutils

This is an project to Sync data transformation information from BigQuery to GitHub and create a dynamic Markdown documantation.

## Installation
Run the following to install:
```python
pip install beepbeep_txflowutils
```

## Usage
```python
git clone https://github.com/beepbeeptechnology/sync_from_bigquery_to_github.git
```

# Create venv
```python
python -m venv <venv_name>
source <venv_name>/Scripts/activate for wondows os
```

# Genarate streamlit app
```
streamlit run app.py
```

# Developing beepbeep_txflowutils
To install beepbeep_txflowutils, along with the tool need to develop and run test, run the following in your virtualenv:

```bash
$pip install -e .[dev] TODO
```
bracu-cgpa-calc 1.0.6
A CGPA calculator module made for BracU Students.
Requires Python: >=3.0
Keywords: bracu cgpa calculator,cg calculator bracu,bracu cg

# BracU CGPA Calculator Module

[![forthebadge made-with-python](http://ForTheBadge.com/images/badges/made-with-python.svg)](https://www.python.org/) 

BracU CGPA Module is a Python library for easier calculation of CGPA in [Brac University](https://www.bracu.ac.bd/). 

Developed by [Joyanta J. Mondal](https://github.com/lepotatoguy) from BracU (C) 2021

Email: hello@joyantamondal.com

## Functionality:

- Adding Courses
- Removing Courses
- Checking Courses Attempted 
- Get CGPA
- Number of Credits Attempted
- Read from Device (PC/Google Colab)
- Save to Device (PC/Google Colab)

- Template of Excel File (Read from PC)
![Template of Excel File (Read from PC)](https://i.postimg.cc/1R7q78nt/getfrompc.png)

- Flush Info
- Check Completed Course. 
- Check Retake Course. 
- Credits Completed.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install bracu_cgpa_calc.

```
pip install bracu_cgpa_calc
```



## Usage

```python
from bracu_cgpa_calc import *
#import bracu_cgpa_calc as bcc #this is another alternative import

add("CSE110", 4, 3) #adding courses
add("CSE111", 3.7, 3) #adding courses
add("CSE220", 3.3, 3) #adding courses
add("MAT215", 0, 3) #adding courses
#bcc.add("MAT215", 0, 3) #adding courses using alternative import
remove("CSE220") #removing courses
check_attempted_course() #returns list of courses attempted.
check_completed_course() #returns list of courses completed.
check_retake_course() #returns list of courses needs retake.
credits_attempted() #returns number of credits attempted.
credits_completed() #returns number of credits completed.
get_cgpa() #returns CGPA
readme() #prints out readme
docs() #prints out documentation
save_to_pc() #self-explanatory
read_from_pc("CGPA.xlsx") #reads "CGPA.xlsx" from device
flush() #flushes all the data of course list and CGPA and everything from the code
```
## Note 
- I have tried to implement all the functionalities, it might have some bugs also. Please ignore that or please contact me email to notify me about the bug. I will try to give proper credits to that too. And you are welcome to give your valuable opinion and suggestions.

## License
[MIT](https://choosealicense.com/licenses/mit/)


## CHANGELOG:

1.0.6:
- Fixed: Course Remove was not working. 
- Fixed: Get CGPA was not working. 

1.0.5:
- Fixed: Credits Attempted was not adding number of credits. 
- Fixed: Flush was not removing Credits Completed. 

1.0.4:
- Added: Check Completed Course. 
- Added: Check Retake Course. 
- Added: Credits Completed.

1.0.3:
- Added: Flush.

1.0.2:
- Added: Save to Device. (Tested on PC and Google Colab)
- Added: Read from Device. (Tested on PC and Google Colab)

1.0.1:
- Fixed: Removing Courses: Wrong course was getting removed.
- Fixed: Checking Courses Attempted: It was showing random course instead of a list.

1.0.0:

- Added: Adding Courses
- Added: Removing Courses
- Added: Checking Courses Attempted 
- Added: Get CGPA
- Added: Number of Credits Attempted
example-pkg-AGAIN-2 0.0.5
Sync data transformation information from BigQuery to GitHub and create a dynamic Markdown documantation.
Requires Python: >=3.6

# beepbeep_txflowutils

This is an project to Sync data transformation information from BigQuery to GitHub and create a dynamic Markdown documantation.

## Installation
Run the following to install:
```python
pip install beepbeep_txflowutils
```

## Usage
```python
git clone https://github.com/beepbeeptechnology/sync_from_bigquery_to_github.git
```

# Create venv
```python
python -m venv <venv_name>
source <venv_name>/Scripts/activate for wondows os
```

# Genarate streamlit app
```
streamlit run app.py
```

# Developing beepbeep_txflowutils
To install beepbeep_txflowutils, along with the tool need to develop and run test, run the following in your virtualenv:

```bash
$pip install -e .[dev] TODO
```
citationhelper 0.4
Tool for determining packages used in a particular project that should be cited.

citationhelper

==============



This is a utility that generates a list of all packages imported into python scripts (\*.py) in a given list of directories.  It is intended to be used to help keep track of community software used in research projects so that the packages can be properly cited in publications.  The utility works by first walking the named directories and identifying \*.py files, then searching these files for import statements.



Installation

************



citationhelper can be installed via pip



    pip install citationhelper



Alternatively, citationhelper can be installed from github



    pip install git+https://github.com/EarthCubeInGeo/citationhelper.git



After installing, set the environment variable CITEHELP_REFFILE if you want to use a custom JSON library of full citations.  The utility will work without this, but it can only generate a list of package names with no citation information.



    export CITEHELP_REFFILE=filename



Usage

*****

Run citationhelper with the `citehelp` command followed by a list of the directories to search for import statements and print a report to the screen.



    citehelp project_directory





**WARNING**: This tool was developed to assist researchers in keeping track of software packages, but it is HIGHLY recommended users review the list of packages produced.
example-pkg-AGAIN 0.0.3
Sync data transformation information from BigQuery to GitHub and create a dynamic Markdown documantation.
Requires Python: >=3.6

# beepbeep_txflowutils

This is an project to Sync data transformation information from BigQuery to GitHub and create a dynamic Markdown documantation.

## Installation
Run the following to install:
```python
pip install beepbeep_txflowutils
```

## Usage
```python
git clone https://github.com/beepbeeptechnology/sync_from_bigquery_to_github.git
```

# Create venv
```python
python -m venv <venv_name>
source <venv_name>/Scripts/activate for wondows os
```

# Genarate streamlit app
```
streamlit run app.py
```

# Developing beepbeep_txflowutils
To install beepbeep_txflowutils, along with the tool need to develop and run test, run the following in your virtualenv:

```bash
$pip install -e .[dev] TODO
```
lgp-client 0.6.0
A very simple LGP command line client written in Python

# LGP-Client

A very simple [LGP](https://github.com/LadueCS/LGP) command line client written in Python. The only dependency is the [requests](https://docs.python-requests.org/en/master/index.html) library. Tested with Linux only. Please report bugs with other operating systems on our [issues page](https://github.com/LadueCS/LGP-Client/).


## Usage

You can get started with 
```
pip install lgp-client
```

Now register an account for your team on the appropriate LGP server with
```
lgp-client register -S https://server.exozy.me -N your_names -E your_emails -U your_username -P your_password
```

You can save your username, password, and server with
```
lgp-client save -S https://server.exozy.me -U your_username -P your_password
```

You can view the contests on the server with
```
lgp-client contests -c some_contest
```

Don't forget to check the contest info with
```
lgp-client info -c some_contest
```

And save the problem statements to `problems.pdf`
```
lgp-client problems -c some_contest
```

Now you can submit with
```
lgp-client submit -c some_contest -p some_problem -c your_code_file
```

And check your status with
```
lgp-client status -c some_contest
```

You can query your submission history for a contest
```
lgp-client history -c some_contest
```

And examine a particular submission with
```
lgp-client code -c some_contest -n some_submission_number
```

And view the number of solves of each problem with
```
lgp-client solves -c some_contest
```


General usage:
```
usage: lgp-client [-h] [-C CONFIG] [-S SERVER] [-U USERNAME] [-P PASSWORD] [-N NAMES] [-E EMAILS] [-c CONTEST] [-p PROBLEM] [-f FILE] [-n NUMBER]
               {save,about,contests,register,info,problems,solves,submit,status,history,code}

A very simple LGP command line client written in Python

positional arguments:
  {save,about,contests,register,info,problems,solves,submit,status,history,code}
                        the command you want to do

optional arguments:
  -h, --help            show this help message and exit
  -C CONFIG, --config CONFIG
                        path of your config file
  -S SERVER, --server SERVER
                        the URL of the LGP server that you are connecting to
  -U USERNAME, --username USERNAME
                        your username
  -P PASSWORD, --password PASSWORD
                        your username
  -N NAMES, --names NAMES
                        your names for registering an account
  -E EMAILS, --emails EMAILS
                        your emails for registering an account
  -c CONTEST, --contest CONTEST
                        the contest you are participating in
  -p PROBLEM, --problem PROBLEM
                        the problem you are submitting to
  -f FILE, --file FILE  the file you are submitting
  -n NUMBER, --number NUMBER
                        the submission number you are querying
```
classification-reportzr 0.0.1b9
Automate machine learning classification task report for Pak Zuherman
Requires Python: >=3.6
Keywords: classification report,laporan klasifikasi,zuherman,zr

# Classification Reportzr

Automate machine learning classification task report for Pak Zuherman

## Install

```bash
pip install -U classification-reportzr
```

## Test

```bash
pytest -v
```

## Usage

### Setting-up the experiment

```python
from sklearn import datasets
from sklearn.svm import SVC

from reporterzr import Reporterzr

iris = datasets.load_iris()
samples, labels = iris.data[:-1], iris.target[:-1]

param_grid = {
    'C': [10,50,100],
    'gamma': [0.005,0.05,0.5],
    'kernel': ['poly', 'rbf', 'linear']
}
svc_reporter = Reporterzr(SVC, param_grid)
```

### Run The Experiment

```python
# `test_sizes` defaults to [0.1, ..., 0.9]
# `repetition` defaults to 10
report = svc_reporter.run_experiment(samples, labels, test_sizes=[0.1, 0.2], repetition=5)
print(report)
```

prints

```
   Test Size   C  gamma  kernel                     Train Accuracies  \
0        0.1  10  0.005    poly  [0.881, 0.888, 0.873, 0.888, 0.881]
1        0.1  10  0.005     rbf   [0.978, 0.955, 0.955, 0.955, 0.97]
2        0.1  10  0.005  linear    [0.978, 0.97, 0.985, 0.978, 0.97]
3        0.1  10  0.050    poly  [0.985, 0.978, 0.978, 0.978, 0.985]
4        0.1  10  0.050     rbf  [0.985, 0.993, 0.993, 0.993, 0.993]

   Max Train Acc  Mean Train Acc  Stdev Train Acc  \
0          0.888           0.882            0.006
1          0.978           0.963            0.010
2          0.985           0.976            0.006
3          0.985           0.981            0.003
4          0.993           0.991            0.003

                       Test Accuracies  Max Test Acc  Mean Test Acc  \
0      [0.867, 0.867, 1.0, 0.8, 0.933]         1.000          0.893
1  [0.933, 0.933, 0.933, 0.867, 0.933]         0.933          0.920
2            [1.0, 1.0, 1.0, 1.0, 1.0]         1.000          1.000
3          [1.0, 1.0, 1.0, 1.0, 0.933]         1.000          0.987
4        [0.933, 1.0, 1.0, 0.867, 1.0]         1.000          0.960

   Stdev Test Acc                         Experiment Times (sec)
0           0.068  [0.00095, 0.00077, 0.00072, 0.00077, 0.00074]
1           0.026   [0.00079, 0.0008, 0.00082, 0.00082, 0.00081]
2           0.000   [0.0005, 0.00052, 0.00045, 0.00049, 0.00049]
3           0.027  [0.00052, 0.00055, 0.00052, 0.00054, 0.00053]
4           0.053  [0.00062, 0.00062, 0.00064, 0.00061, 0.00065]
```