semvec/pypi-packages huggingface dataset:
PyMT64 2.0
Python version of the Mersenne Twister 64-bit pseudorandom number generator

# PyMT64 Package

                     PyMT64
                     
PyMT64 is a Python version of the Mersenne Twister (MT) 64-bit pseudorandom number generator by Takuji Nishimura and Makoto Matsumoto (see http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt64.html and the references below).

This customised version is thread safe and was interfaced from C to Python (see pymt64.c)

This module provides the following methods:
- init : initialization of the state vector (mt) used by the pseudorandom number generator (PNG)
- uniform : generation of an uniform distribution
- normal : generation of two Normal distributions
- poisson : generation of a Poisson distribution

The period of the PNG is 2**19937-1.

Example:
    import pymt64
    seed = 143439545 seed # the initial seed
    mt = pymt64.init(seed) # initialisation of the state vector of MT
    u = pymt64.uniform(mt,10) # generation of an uniform distribution
    print (u)
    [0.12444525 0.22084376 0.31059967 0.45578589 0.84743752 0.28825306
    0.83320389 0.05085032 0.48682253 0.17667076]

For a complete example, see pymt64_test.py

Note: the state vector 'mt' returned by pymt64.init has 313 elements instead of the 312 elements of the original C code. This is because the 313th element store the associated counter (mti).

Change history:
- 2.0 (23/05/2023):  suppress posible occurence of warning message or error due to the declaration of variable of type np.int
- 1.9 : correct bug associated with the normal distribution
- 1.8 : include a missing file
- 1.7 : corrected data package contain
- 1.6 : correct wrong size of the state vector 
- 1.5 : module interface is now based on Cython, module now compatible with python 3
- 1.4 : link problem fixed
- 1.3 : fixe a compilation problem regading the Numpy include directory
- 1.2 : the previous implementation of the poisson distribution was not thread safe
- 1.1 : fix a problem with the initialization of the seed  (in the previous version the seed set by init() was not taken into account such that the results were not reproductible)
- 1.0 : initial version


R. Samadi (LESIA, Observatoire de Paris), 22 Dec. 2012	    

References:
   T. Nishimura, ``Tables of 64-bit Mersenne Twisters''
     ACM Transactions on Modeling and 
     Computer Simulation 10. (2000) 348--357.
   M. Matsumoto and T. Nishimura,
     ``Mersenne Twister: a 623-dimensionally equidistributed
       uniform pseudorandom number generator''
     ACM Transactions on Modeling and 
     Computer Simulation 8. (Jan. 1998) 3--30.
nexus-databoard 0.0.2
An oppinionated data science project template for the Nexus platform. An abstraction over Streamlit.
Requires Python: >=3.7

# nexus-databoard

This application provides the boilerplate to rapidly build data applications on the streamlit framework (which is already rapid!).  


## Quick Start
There are a few options to get started rapidly.  

### Clone
Clone the repository, and do `pip install -r requirements.txt`  

You can add your own files to the [./pages](./pages) directory to add/replace the example pages.    
Then do `./run` or `streamlit run ./src/nexus_databoard/Home.py`  

### Docker Compose
Run the docker compose file to spin up the examples.  
You can also mount your own files in the `pages` directory in the app.  
For easy development, copy the requirements into your workspace and get autocompletion for the dependencies.  
`pip install -r requirements.txt`  

Example Compose file that mounts a local `pages` directory  
```
services:
  nexus_analytics:
    image: savantly/nexus-databoard:latest
    container_name: my-data-tools
    ports:
      - 8501:8501
    environment: []
    volumes:
      - ./pages:/app/pages
```

### Extend the image
Extend the image and add your page files to the `pages` directory.  

Example - 
```
FROM savantly/nexus-databoard
COPY ./pages /app/pages/
```