semvec/pypi-packages huggingface dataset:
pyXhCustApp 0.0.4
Custom App Environment

# PyXHCustApp

A library provide functionality process chia plot log.

# Installation

```python_script
pip install pyXhCustApp
```

```python_script
pip3 install pyXhCustApp
```

# Demo

```python_script
from pyXhCustApp import CustApp
CustApp.appDefault("abc_app")
print(app.has_proxy())
app.set_kv('xxx', "xxx")
app.set_kv('xxx', "yyy")
print(app.get_kv('xxx'))
app.rm_kv('xxx')
print(app.has_kv('xxx'))
print(app.home)
```

```shell script
python -m pyXhCustApp {name_space} list #Show all all
python -m pyXhCustApp {name_space} exist {key} #return true if {key} exists
python -m pyXhCustApp {name_space} value {key} #return value of {key} or None
python -m pyXhCustApp {name_space} set {key} {value} #set {value} to {key}
python -m pyXhCustApp {name_space} remove {key} #remove {key}
```
getRemarks 1.0.4
Find grade scored and then remarks regarding the grade

#What is new in latest version:

Major update, fixed some major bugs and few exceptions.

Updated readme file!


#How to install:

Copy the command from pypi.org and paste onto terminal. 

#How to use:

1: This package will help you to find grade by providing marks obtained and total marks.

2: To get the grade, use the Grade() and supply marks obtained followed by total marks seperated by comma.

   say Grade(100,150). Note, follow obtained marks and then total marks.

3: Remember not to supply invalid details, if you do, you will get required error message at the end.

4: There are total 5 grades within the package.

   A grade is from 90-100

   B grade is from 75-89

   C grade is from 60-74

   D grade is from 45-59 

   F grade is from 0-44, obviously. To get the grade simply type objName.getGrade()

5: According to grades, conclusion/remarks are provided at the end. To get the remarks, type objName.remarks().


#Example for you:

   1: pip install getRemarks //this will install getRemarks package on your machine.


   2: import getRemarks as gr  //where gr can be anything depending upon you. This will btw import getRemarks package which was installed in step 1.


   3: gro= gr.Grade(x,y) //where gro is object and can be any thing. x,y are two int which denotes marks scored and total marks respectively. 

      Remember to seperate two numbers via comma. Say Grade(100,400)


   4: gro.getGrade() //this function will calculate grade, will find grade according to  #How to use Section point 4.


   5: gro.remarks() //this will find the conclusion of the grade scored and will provide necessary feedback

 
  	
       ##Note: I didn't create this to divide students. This is just a project to get hands dirty with. All students are same and I too am a student.
deepcase 1.0.1
DeepCASE: Semi-Supervised Contextual Analysis of Security Events

# DeepCASE
This repository contains the code for DeepCASE by the authors of the IEEE S&P DeepCASE [1] paper [[PDF]](https://vm-thijs.ewi.utwente.nl/static/homepage/papers/deepcase.pdf).
Please [cite](#References) DeepCASE when using it in academic publications. This `main` branch provides DeepCASE as an out of the box tool. For the original experiments from the paper, please checkout the `sp` branch.

## Introduction
DeepCASE introduces a semi-supervised approach for the contextual analysis of security events. This approach automatically finds correlations in sequences of security events and clusters these correlated sequences. The clusters of correlated sequences are then shown to security operators who can set policies for each sequence. Such policies can ignore sequences of unimportant events, pass sequences to a human operator for further inspection, or (in the future) automatically trigger response mechanisms. The main contribution of this work is to reduce the number of manual inspection security operators have to perform on the vast amounts of security events that they receive.

## Documentation
We provide an extensive documentation including installation instructions and reference at [deepcase.readthedocs.io](https://deepcase.readthedocs.io/en/latest/).

## References
[1] `van Ede, T., Aghakhani, H., Spahn, N., Bortolameotti, R., Cova, M., Continella, A., van Steen, M., Peter, A., Kruegel, C. & Vigna, G. (2022, May). DeepCASE: Semi-Supervised Contextual Analysis of Security Events. In 2022 Proceedings of the IEEE Symposium on Security and Privacy (S&P). IEEE.`

### Bibtex
```
@inproceedings{vanede2020deepcase,
  title={{DeepCASE: Semi-Supervised Contextual Analysis of Security Events}},
  author={van Ede, Thijs and Aghakhani, Hojjat and Spahn, Noah and Bortolameotti, Riccardo and Cova, Marco and Continella, Andrea and van Steen, Maarten and Peter, Andreas and Kruegel, Christopher and Vigna, Giovanni},
  booktitle={Proceedings of the IEEE Symposium on Security and Privacy (S&P)},
  year={2022},
  organization={IEEE}
}
```
shishua 0.2.0
SHISHUA: The fastest PRNG. A seedrandom-compatible random number generator.
Requires Python: >=3.6

# SHISHUA for Python: The fastest PRNG

The official page for SHISHUA is [here][SHISHUA].  
This [PyPI][] package provides bindings for Python.

```python
from shishua import SHISHUA
rng = SHISHUA(0)
buffer = rng.random_raw(1 << 30)  # 1 GiB
# It takes 1s on my laptop.
```

NumPy support:

```python
import numpy
from shishua import SHISHUA
buffer = numpy.random.Generator(SHISHUA()).bytes(1024)
print(buffer.hex())
```

## API

### `SHISHUA(seed=None)`

Returns a generator instance initialized with the given seed.

The `seed` determines the sequence of numbers that this function returns:

```python
print(SHISHUA('seed').random_raw(10).hex())
#> 1aa64a39f6949b969e97
print(SHISHUA('seed').random_raw(10).hex())
#> 1aa64a39f6949b969e97
print(SHISHUA('different seed, different values').random_raw(10).hex())
#> 216a0889a858eb57ae30
```

The seed can be either:

- A list of four 64-bit integers,
  to map exactly to the functionality provided
  by the underlying SHISHUA algorithm,
- A string, which is hashed into the four integers,
- A single integer, which sets the first of the four,
- If not provided (or undefined),
  a random seed is generated from the system's CSPRNG.

### Associated methods

The object returned by calling `SHISHUA()` also has the following methods,
which all tap into the stream generated by the seed:

- `.random_raw(size)`, which returns a `bytes` filled with random bytes.

[SHISHUA]: https://github.com/espadrine/shishua
[PyPI]: https://pypi.org/project/shishua/
geoframepy 0.0.5
A Python library for the GEOframe-NewAge system
Requires Python: >=3.6

# GEOframePy


GEOframePy is a Python library for doing Hydrology with the GEOframe-NewAge system.

Browse [GEOframe](https://geoframe.blogspot.com/) to discover the GEOframe-NewAge system.

You can find the source code of our tools [here](https://github.com/geoframecomponents), and [here](https://github.com/GEOframeOMSProjects) the compiled version.