semvec/pypi-packages huggingface dataset:
django-coalesce 0.0.1
Django accelerated web app development kit
Requires Python: >=3.10

# django-coalesce

Inspired by the awesome .NET [Coalesce](https://intellitect.github.io/Coalesce/) project for accelerated web app development.

### Build

```shell
python3 -m pip install --upgrade build twine
python3 -m build
```
uncertain 0.0.13
A Python 3 package for mathematical calculations with         uncertain numbers
Requires Python: >=3.6

UNCERTAIN
=========

Python module to keep track of uncertainties in mathematical calculations.


Usage example
-------------
- Define uncertain values
  
.. code:: python

	  import uncertain as uc

	  # Uncertain value between 3 and 8 and a normal distribution with
	  # mean=5 and standard deviation=1.
	  a = uc.UncertainValue(5, 3, 8, 'normal', [5, 1]) 

	  # Uncertain value with a uniform distribution between 0.1 and 4
	  b = uc.UncertainValue(1, 0.1, 4)

	  # Uncertain value from measured data points
	  c = uc.from_data([1, 2, 3, 4, 5])

- Perform mathematical calculations: addition, substraction, multiplication, power, sine, cosine, tangent. In order for the trigonometric functions to work, they need to be called from `numpy` (`math` does not work).

.. code:: python
  
	  d = -b+2*a**(b/3)


.. code:: python
  
	  import numpy as np
	  e = np.pi / 180 * uc.UncertainValue(40, 35, 45)
	  f = np.sin(e) + np.cos(e) + np.cos(e)

- Display properties and plot results in density plots or cumulative density plots

.. code:: python
	  
	  print(c.describe(),
	  "\n\nThe standard deviation of b is "+str(b.std),
	  "\n\nThe probability of /c/ being between 2 and 6 is " +
	  str(probability_in_interval(c, [2, 6])))

	  a.plot_distribution(title="Uncertain numbers", label="a")
	  b.plot_distribution(label="b", alpha=0.5)
	  d.plot_distribution(label="d", alpha=0.5)

	  d.plot_distribution(plot_type='cdf', new_figure=True)

Output:

::
   
    This variable is an uncertain value. It has the following properties:
  
  	- Nominal value: 2.4199518933533937
  
  	- Mean: 5.1973349566661415
  	- Median: 3.8063419262133795
  	- Variance: 13.086116036143682
  	- Standard deviation: 3.6174737091157527
  	- Skewness: 1.5519941650511524
  
  	- Lower bound: -1.9254016053940988
  	- Percentile 5: 2.0248565203431506
  	- Q1: 2.432100693608657
  	- Q3: 6.832833238201248
  	- Percentile 95: 12.808458201483177
  	- Upper bound: 31.899999999999995
  
  	- Probability distribution type: custom
  	- Number of samples: 100000
   
  
    The standard deviation of b is 1.1245368594834484 
  
    The probability of /c/ being between 2 and 6 is 0.67164


.. image:: https://gitlab.com/mnn/uncertain/-/raw/master/resources/density_plot.png

	   
.. image:: https://gitlab.com/mnn/uncertain/-/raw/master/master/resources/cdf_plot.png
Tanisha-102103372 0.2
A python package for implementing topsis

# Topsis-Tanisha-102103372

For : Assignment(UCS654)<br>
Submitted by: Tanisha Sood<br>
Roll no:102103372<br>
Group:3COE14

## Description

This is a python package used to implement TOPSIS(Technique of Order Preference Similarity to the Ideal Solution) for MCDA(Multiple criteria decision analysis)

<br>

## How to use this package:

## Installation

pip install Topsis-Tanisha-102103372

## Example:

## Sample dataset

| Fund Name | P1   | P2   | P3  | P4   | P5    |
| --------- | ---- | ---- | --- | ---- | ----- |
| M1        | 0.78 | 0.61 | 5.5 | 34.7 | 10.4  |
| M2        | 0.88 | 0.77 | 5   | 58.4 | 16.26 |
| M3        | 0.61 | 0.37 | 5.9 | 39.9 | 11.7  |
| M4        | 0.76 | 0.58 | 4.2 | 57.7 | 15.81 |
| M5        | 0.84 | 0.71 | 3.2 | 48   | 13.19 |
| M6        | 0.76 | 0.58 | 4   | 68.8 | 18.54 |
| M7        | 0.81 | 0.66 | 6.5 | 38.2 | 11.54 |
| M8        | 0.81 | 0.66 | 3.2 | 32.8 | 9.37  |

## Input

### In Command Prompt

Enter filename followed by .csv or .xlsx extension, then enter values of weights separated by commas like "1,1,1,2,2",then enter values of impacts separated by commas like "+,+,-,-,+" without giving space in between comma value, then enter name of file where you want to save output followed by .csv extension

```
python -m Topsis_Tanisha-102103372 data.xlsx "1,1,1,2,2" "+,+,-,-,+" output.csv
```

## Output

This will be in our Output csv file
Fund Name | P1 | P2 | P3 | P4 | P5 | Topsis Score | Rank
------------ | ------------- | ------------ | ------------- | ------------ | ------------ | ------------ | ------------
M1 | 0.78 | 0.61 | 5.5 | 34.7 | 10.4 | 0.5303740545041122 | 4
M2 | 0.88 | 0.77 | 5 | 58.4 | 16.26 | 0.5372510220778413 | 3
M3 | 0.61 | 0.37 | 5.9 | 39.9 | 11.7 | 0.4715707210914604 | 8
M4 | 0.76 | 0.58 | 4.2 | 57.7 | 15.81 | 0.5099483054760279 | 6
M5 | 0.84 | 0.71 | 3.2 | 48 | 13.19 | 0.57723478293325 | 1
M6 | 0.76 | 0.58 | 4 | 68.8 | 18.54 | 0.49447887833737925 | 7
M7 | 0.81 | 0.66 | 6.5 | 38.2 | 11.54 | 0.5244107252631429 | 5
M8 | 0.81 | 0.66 | 3.2 | 32.8 | 9.37 | 0.5576533672285703 | 2
dice-roll 0.1.1
Train's Dice Stats Package
Requires Python: >=3.8
Keywords: python,dice

A simple dice roll probability package inspired by anydice.com. There are many like it, but this one is mine.

# Usage

Dice rolls use the `dice_roll` class. The most basic dice roll is consist of a number of dice and a number of sides. For example, to roll three six sided dice or 3d6:

```
dnd = dice_roll((3,6))
```

You can drop the lowest rolls by using the drop option. So, to get the 4d6 drop lowest D&D stat roll

```
dndDrop = dice_roll((4,6),drop=1)
print(dnd)
```

```
             pmf       cmf
domain                    
3       0.000611  0.000611
4       0.001833  0.002443
5       0.003665  0.006109
6       0.008312  0.014421
7       0.015774  0.030195
8       0.026050  0.056245
9       0.041685  0.097930
10      0.062678  0.160608
11      0.082418  0.243026
12      0.108365  0.351391
13      0.127389  0.478780
14      0.139489  0.618268
15      0.135985  0.754254
16      0.123489  0.877743
17      0.079540  0.957283
18      0.042717  1.000000
```

We can also apply a function to the dice roll afterwards. This can be done in the dice_roll constructor or with the `apply_function` method. For example, we can get the D&D stat modifier distribution

```
dndDrop_modifiers = dnd.apply_function(lambda x: (x-10)//2)
```
Note, the function is always cast to an integer.

You can plot the distribution with the `plot` method.

```
dndDrop_modifiers.plot(title="4d6 Drop Lowest Stat Modifiers")
```

![4d6 Drop Lowest Stat Modifiers Plot](https://github.com/jtrainrva/dice_roller/blob/main/dndarray_modifiers.png?raw=True)

`dice_roll` supports mean, median, and mode.

```
[dndDrop_modifiers.mean,dndDrop_modifiers.median,dndDrop_modifiers.mode]
```

```
[1.4318137860082305, 2, 2]
```

Quantiles are also availible with the `quantile` method.

```
dndDrop_modifiers.quantile([.05,.95])
```

```
array([-1,  3])
```

You can add and subtract any two dice_roll distributions.

```
dice_roll((1,6))+dice_roll((1,6))-dice_roll((2,6))
```

```
             pmf       cmf
domain                    
-10     0.000772  0.000772
-9      0.003086  0.003858
-8      0.007716  0.011574
-7      0.015432  0.027006
-6      0.027006  0.054012
-5      0.043210  0.097222
-4      0.061728  0.158951
-3      0.080247  0.239198
-2      0.096451  0.335648
-1      0.108025  0.443673
 0      0.112654  0.556327
 1      0.108025  0.664352
 2      0.096451  0.760802
 3      0.080247  0.841049
 4      0.061728  0.902778
 5      0.043210  0.945988
 6      0.027006  0.972994
 7      0.015432  0.988426
 8      0.007716  0.996142
 9      0.003086  0.999228
 10     0.000772  1.000000
```

You can compute the sum of a sample of size `k` from a dice_roll object using thr `sum` method. This supports dropping the lowest roll.

```
dndDrop.sum(6,drop=2).plot(title="D&D Stat Array Total Modifiers with Two Dump Stats")
```

![D&D Stat Array Total Modifiers with Two Dump](https://github.com/jtrainrva/dice_roller/blob/main/dndarray_modifiers_dumpstats.png?raw=True)

Lastly, you can export the table of support, pmf and cmf to a Pandas datafram with the `df` method.

```
dndDrop_modifiers.df
```
hk-bus-eta 2.1.6
Query the ETA (Estimated Time of Arrival) of HK Bus/Minibus/MTR/Lightrail
Keywords: python,hongkong,eta,estimated time of arrival,kmb,nlb,mtr,ctb,minibus,lightrail


# HK Bus Crawling (a.k.a. hk-bus-eta)

[![Python 3.8.8](https://img.shields.io/badge/python-3.8.8-blue.svg)](https://www.python.org/downloads/release/python-388/) ![Data fetching status](https://github.com/hkbus/hk-bus-crawling/actions/workflows/fetch-data.yml/badge.svg) 

This project is to fetch the bus route information of KMB, CTB, NLB, minibus, MTR, lightrail into one single JSON. It is daily synced to data.gov.hk and launched in gh-pages.

## Fetching Transport ETA

The package is a python vertion for the npm package [hk-bus-eta](https://www.npmjs.com/package/hk-bus-eta).

### Installation
To install the package,

```sh
pip install hk-bus-eta
```

## Usage

**Fetch ETAs of a route**
```python
from hk_bus_eta import HKEta

hketa = HKEta()
etas = hketa.getEtas(route_id = "TCL+1+Hong Kong+Tung Chung", seq=0, language="en")
print (etas)

"""
[{'eta': '2023-09-12T11:43:00+08:00', 'remark': {'zh': '1號月台', 'en': 'Platform 1'}, 'co': 'mtr'}, {'eta': '2023-09-12T11:51:00+08:00', 'remark': {'zh': '1號月台', 'en': 'Platform 1'}, 'co': 'mtr'}, {'eta': '2023-09-12T11:58:00+08:00', 'remark': {'zh': '1號月台', 'en': 'Platform 1'}, 'co': 'mtr'}, {'eta': '2023-09-12T12:05:00+08:00', 'remark': {'zh': '1號月台', 'en': 'Platform 1'}, 'co': 'mtr'}]
"""
```

**List Route IDs**
```python
from hk_bus_eta import HKEta

hketa = new HKEta()
route_ids = list( hketa.route_list.keys() )
print( route_ids )

"""
['1+1+CHUK YUEN ESTATE+STAR FERRY', '1+1+Central (Hong Kong Station Public Transport Interchange)+The Peak (Public Transport Terminus)', '1+1+Felix Villas+Happy Valley (Upper)', '1+1+Happy Valley (Upper)+Felix Villas', '1+1+Kowloon Bay (Telford Gardens)+Sai Kung', '1+1+Mui Wo Ferry Pier+Tai O', '1+1+STAR FERRY+CHUK YUEN ESTATE', '1+1+Sai Kung+Kowloon Bay (Telford Gardens)', '1+1+Tai O+Mui Wo Ferry Pier', '1+1+The Peak (Public Transport Terminus)+Central (Hong Kong Station Public Transport Interchange)']
"""
```


## Crawling by yourself

### Usage
Daily fetched JSON is in [gh-pages](https://github.com/hkbus/hk-bus-crawling/tree/gh-pages) or direct download [here](https://hkbus.github.io/hk-bus-crawling/routeFareList.min.json)

### Installation

To install the dependencies,
```
pip install -r ./crawling/requirements.txt
```

### Data Fetching

To fetch data, run the followings,
```
python ./crawling/parseHoliday.py
python ./crawling/ctb.py
python ./crawling/kmb.py
python ./crawling/nlb.py
python ./crawling/lrtfeeder.py
python ./crawling/lightRail.py
python ./crawling/mtr.py
python ./crawling/parseJourneyTime.py
python ./crawling/parseGtfs.py
python ./crawling/gmb.py
python ./crawling/matchGtfs.py
python ./crawling/cleansing.py
python ./crawling/mergeRoutes.py
```

## Citing 

Please kindly state you are using this app as
`
HK Bus Crawling@2021, https://github.com/hkbus/hk-bus-crawling
`

## Waypoint data

You may refer to the repository [HK Bus WayPoints Crawling](https://github.com/hkbus/route-waypoints)

## Contributors
[ChunLaw](http://github.com/chunlaw/)
breads-ad 1.1.3
Breaking Active Directory Security (BREADS) is a tool design to attack Active Directory environments
Requires Python: >=3.10.12

<div align="center">
  <h1> 🍞 BREADS </h1>
  <img src="https://i.imgur.com/uGdGDeo.png" width=690>
  <p> <b>BREADS (BREaking Active Directory Security) </b> is a project created by security researchers focused on attacking Active Directory environments </p>
</div>

<p align="center">
    <img src="https://img.shields.io/github/license/inferigang/breads?color=pink&logo=github&style=for-the-badge">
    <img src="https://img.shields.io/github/issues/inferigang/breads?color=pink&logo=github&style=for-the-badge">
    <img src="https://img.shields.io/github/stars/inferigang/breads?color=pink&logo=github&style=for-the-badge">
    <img src="https://img.shields.io/github/forks/inferigang/breads?color=pink&logo=github&style=for-the-badge">
    <img src="https://img.shields.io/github/languages/code-size/inferigang/breads?color=pink&logo=github&style=for-the-badge">
</p>

___

<br>

## ⚡ Installing / Getting Started

<p> A quick guide on how to install and use BREADS. </p>

### Install with pipx (recommended)
- `pipx install git+https://github.com/inferigang/breads --include-deps`

### Not recommended
1. Clone the repository with: `git clone https://github.com/inferigang/breads.git`
2. Install the libraries with: `python3 setup.py install`
3. Run BREADS with: `breads-ad`

<br>

## ✨ Features
- Profile creation
- +6 Modules (Enum Users, Disabled Accounts, Domain Controllers and more...)
- Low RAM and CPU usage
- Open-Source
- Python ❤️

<br>

## 🎥 Preview
[![asciicast](https://asciinema.org/a/630089.svg)](https://asciinema.org/a/630089)

<br>

## 📚 To-Do
- [ ] Improve profile creation
- [ ] Store data on SGBD
- [ ] Create a UI

### Modules
- Enum ADCS (i don't have an lab with ADCS to test this yet)

<br>

## 🙏 Credits / Inspiration
- CrackMapExec
- Impacket
- BloodHound
- NetExec

<br>

## 🤡 And a good meme...
<img src="https://i.imgur.com/uPrUvi5.png" width=512>

## ⚠️ Warning
- The developer is not responsible for any malicious use of this tool.