semvec/pypi-packages huggingface dataset:
scmlab367104703 0.1.0
Basic number theory tools: GCD calculation, solving linear Diophantine equations, and listing primes..
Requires Python: >=3.8

# scmlab367104703

`scmlab367104703` This is a basic number theory module consisting of three functions `euclidean()`,`extended()`,`eratosthenes()`

---

## Features
- **Euclidean Algorithm**: Use `euclidean()` to find the greatest common divisor (gcd) of two integers
- **Extended Euclidean Algorithm**: Use `extended()` to find 
- The greatest common divisor (gcd) of two numbers `a` and `b`
- And two numbers `x` and `y` such that:

\[
a \times x + b \times y = \gcd(a, b)
\]
- **Sieve of Eratosthenes**:Use `eratosthenes(n)` to find all prime numbers up to a certain  `n`.
---

## Installation

You can install the package directly from PyPI:

```
pip install scmlab367104703

```
---
## Example1(Euclidean Algorithm)
```
#input
>>>import scmlab367104703 as scm
>>> scm.euclidean(807, 481)

#output
gcd(807, 481)
807 = 481 x 1 + 326
481 = 326 x 1 + 155
326 = 155 x 2 + 16
155 = 16 x 9 + 11
16 = 11 x 1 + 5
11 = 5 x 2 + 1
5 = 1 x 5 + 0
gcd is 1
```
## Example2(Extended Euclidean Algorithm)
```
#input
>>> import scmlab367104703 as scm
>>> scm.extended(120, 23)

#output
120*(-9) + 23*(47) = 1
x = -9, y = 47
```
## Example3(Sieve of Eratosthenes)
```
#input
>>> import scmlab367104703 as scm
>>> scm.eratosthenes(50)

#output
+------+-----+------+----+-----+----+------+----+------+----+
|  1   | *2* | *3*  | 4  | *5* | 6  | *7*  | 8  |  9   | 10 |
| *11* |  12 | *13* | 14 |  15 | 16 | *17* | 18 | *19* | 20 |
|  21  |  22 | *23* | 24 |  25 | 26 |  27  | 28 | *29* | 30 |
| *31* |  32 |  33  | 34 |  35 | 36 | *37* | 38 |  39  | 40 |
| *41* |  42 | *43* | 44 |  45 | 46 | *47* | 48 |  49  | 50 |
+------+-----+------+----+-----+----+------+----+------+----+
```
scm203lab367101691 0.1.0
Find the greatest common divisor and find integers x,y such that ax + by = gcd(a,b).
Requires Python: >=3.7

`scm203lab367101691` is a lightweight Python package that provides simple utility functions
for **the greatest common divisor** and **extended euclidean algorithm**.
It is designed mainly for learning purposes and as an example of Python packaging.

---

## Features
- **gcd(a,b)**: This function returns the greatest common divisor of a and b using the Euclidean algorithm.
- **extended_gcd(a,b)**: This function returns the coefficients x and y such that ax + by = gcd(a, b).

---

## Installation

You can install the package directly from PyPI:

```bash
pip install scm203lab367101691
```

## Example1 (`gcd(a,b)`)
-Input

```bash
import scm203lab367101691 as scm
a = 234
b = 42
d=scm.gcd(a,b)
print(f"The gcd of 234 and 42 is {d}")

```
-Output
```
234 = 5*42 + 24
42 = 1*24 + 18
24 = 1*18 + 6
18 = 3*6
The gcd of 234 and 42 is 6
```

## Example2 (`extended_gcd(a,b)`)
-Input
```bash
import scm203lab367101691 as scm
a = 7920
b = 4536
print(scm.extended_gcd(a,b))
```
-Output
```
72 = 72*1 + 1080*0
72 = 1080*-1 + 1152*1
72 = 1152*3 + 3384*-1
72 = 3384*-4 + 4536*3
72 = 4536*7 + 7920*-4
72 = 1080*-1 + 1152*1
72 = 1152*3 + 3384*-1
72 = 3384*-4 + 4536*3
72 = 1080*-1 + 1152*1
72 = 1152*3 + 3384*-1
72 = 1080*-1 + 1152*1
72 = 1080*-1 + 1152*1
72 = 1080*-1 + 1152*1
72 = 1152*3 + 3384*-1
72 = 3384*-4 + 4536*3
72 = 3384*-4 + 4536*3
72 = 4536*7 + 7920*-4
72 = 7920*-4 + 4536*7
(72, -4, 7)  #gcd(a,b) = 72,x = -4,y = 7
```
scm203lab367112755 0.1.0
Number theory: Euclidean Algorithm ,Extended Euclidean Algorithm and Sieve of Eratosthenes.
Requires Python: >=3.7

# mypackage = scm203lab367112755

`scm203lab367112755` This is an example of Python packaging that combines important Number Theory algorithms into an easy-to-use format: **Euclidean Algorithm**, **Extended Euclidean Algorithm**, and **Sieve of Eratosthenes**.It is designed mainly for learning purposes and as an example of Python packaging.

---

## Features
- **Euclidean Algorithm**: 
Purpose: Compute the greatest common divisor (GCD) of two integers $a$ and $b$.
Idea: Uses repeated division with remiander.
\[
\gcd(a, b) = \gcd(b, \; a \bmod b)
\]
Complexity: $O(\log(\min(a,b)))$.
- **Extended Euclidean Algorithm**: 
Purpose: find greatest integer $x$ and $y$ such that
\[
ax+by = gcd(a, b)
\]
Application: Solving Diophantine equations modular inverse.
Complexity: $O(\log(\min(a,b)))$.
- **Sieve of Eratosthenes**: 
Purpose: Generates all prime numbers up to $n$.
Idea: Iteratively marks multiples of primes as composite.
Complexity: $O(n\log(\log(n)))$.
---
## Installation
You can install the package directly from PyPI:
```bash
pip install scm203lab367112755
```
### Example 1 **Euclidean Algorithm**
```bash
gcd(234, 42)
```
### Example 2 **Extended Euclidean Algorithm**
```bash
extended_gcd_steps(7920, 4536)
```
### Example 3 **Sieve of Eratosthenes**
```bash
sieve_steps(49)
```
PySujanam 2.0.0
A Python package for talking to Sujanam

# PySujanam

Use this Python package to say hello to Sujanam.

## Installation

You can install the package using pip:

```bash
pip install pysujanam
```

## Usage
Here's a simple example of how to use the package:

```python
import sujanam
sujanam.hello() 
sujanam.hello("Alice")
```
swarmsim 1.2.2
An agent-based swarm simulator.
Requires Python: >=3.12
Keywords: agent-based, robotics, simulator, swarm

# Robot Swarm Simulator
Contributors: Connor Mattson, Jeremy Clark, Daniel S. Brown, Kevin Zhu

[<img src="https://gist.githubusercontent.com/cxmeel/0dbc95191f239b631c3874f4ccf114e2/raw/documentation.svg" alt="Documentation" height="40" />](https://kenblu24.github.io/RobotSwarmSimulator/)


## Setup
Install RSS with pip
    
    pip install --upgrade swarmsim

or, for faster install with uv (`pip install uv`) preface any pip commands with `uv`, i.e. `uv pip install...`

To install as editable:

    git clone https://github.com/kenblu24/RobotSwarmSimulator.git
    cd RobotSwarmSimulator
    pip install -e .

See also:
* [Installation Guide](https://kenblu24.github.io/RobotSwarmSimulator/guide/install.html)
* [Development Installation](https://kenblu24.github.io/RobotSwarmSimulator/devel/install.html)

<!-- Test Simulation

    python -m demo.simulation.cyclic_pursuit

Test Evolution (Novelty Search) - Will take a long time to evolve.

    python -m demo.evolution.novelty_search -->


## Demos

**Code for novelty search has moved to https://github.com/kenblu24/novelswarms-es**

We invite you to augment cautiously and carefully test output validity.
marray 0.0.12
Masked versions of array API compatible arrays
Requires Python: >=3.10

<a href="https://mdhaber.github.io/marray">
  <img src="https://raw.githubusercontent.com/mdhaber/marray/main/mybook/logo.svg" alt="Logo. The MArray logo is a nod to NumPy's logo, but MArray is not affiliated with the NumPy project." width="110" height="110" align="left" />
</a>

[![PyPI Downloads](https://img.shields.io/pypi/dm/marray.svg?label=Pypi%20downloads)](https://pypi.org/project/marray/)

MArray adds masks to your favorite
[Python Array API Standard compatible](https://data-apis.org/array-api/latest/)
array library.

Install with `pip`:

```shell
pip install marray
```

or `conda`:

```shell
conda install -c conda-forge marray-python
```

Use the `from...import...as` syntax to get a masked array namespace.

```python3
# use with any Array API compatible library, installed separately
from marray import numpy as mxp
import numpy as xp  # optional (if the non-masked namespace is desired)
```

The resulting `mxp` namespace has all the features of `xp` that are specified
in the Array API standard, but they are modified to be mask-aware. Typically, the
signatures of functions in the `mxp` namespace match those in the standard;
the one notable exception is the addition of a `mask` keyword argument of `asarray`.

```python3
mxp.asarray([1, 2, 3], mask=[False, True, False])
# MArray(array([1, 2, 3]), array([False,  True, False]))
```

In the spirit of the [Zen of Python](https://peps.python.org/pep-0020/), this is the one
and only obvious way to set the mask of an array.

Documentation provided by attributes of `xp` are exposed in the `mxp`
namespace and are accessible via `help`. For more information, please see
[the tutorial](https://mdhaber.github.io/marray/tutorial.html).