semvec/pypi-packages huggingface dataset:
shortprint 0.0.5
The module to help you understand your data
Requires Python: >=3.6

# ShortPrint, the module to help you understand your data in an instant

## How to use Shortprint

First, you need to install our module
```
pip install shortprint
```

Then, you simply need to do the following in your code:
```python
from shortprint import shortprint

from requests import Request()
your_object = Request()

# Print a preview of the result of you function
# Without having hundred lines filled with text

shortprint(your_object)
```
And the result could be the following:
```
List[]
  headers: Dict[]
  hooks: Dict[
    (1) str: List[]
  ]
  json: None
  method: None
  params: Dict[]
  url: None
)
```


## How to contribute

1. Clone the repository
1. Run the `make install-dev` command to install the development dependencies
1. Enjoy !
ReduceMonomsRBC 0.2
Reduce monomial basis for SDP computations for ROM of Rayleigh–Bénard convection.

# ReduceMonomsRBC
ReduceMonomsRBC is a Python package that reduces the list of monomials for sum-of-squares (SOS) optimization of reduced-order models (ROMS). The monomials are reduced by applying highest-degree cancellation and symmetry conditions derived from the structure of the ROM. This package is built for ROMs of 2D Rayleigh&ndash;BÃ©nard convection, but can be adapted to any ROM whose general structure is known.

## Features:
- Reduce list of monomials and save list for use in SOS optimization
- Generate ouptut file with number of monomials at each reduction step.

# Package Requirements
- numpy (Version 1.5 or later)
- sympy (Version 1.6 or later)
- csv (any version)
- scipy (Version 0.10 or later)

# Installation
To install the package, either:
- download or clone this repository and use `from ReduceMonomsRBC import monom_reduction` from the directory containing the package, OR
- install package directly using `pip install ReduceMonomsRBC` then `from ReduceMonomsRBC import monom_reduction`

# Instructions
To construct a system of ROMs, use the command `monom_reduction(*args)`. Options can be passed as function arguments as detailed below.

## Options:
  - `ode_name` : Name of ODE
  - `num_vars` : Number of variables in ODE
  - `monom_deg` : Maximum degree of auxilary functions. Typically an even number
  - `hk_hier` : If `True`, uses HK hierarchy for Rayleigh&ndash;BÃ©nard
  - `hier_num` : Model number in the HK hierarchy. Only matters if `hk_hier=True`
  - `monom_stats` : If `True`, outputs stats on number of monomials after each step
  - `out_file` : Name of output file
  - `out_dir` : Specify output directory
        
## Examples:
`monom_reduction('HK4', 4, 6, hk_hier=True, hier_num=1)`  
Generates and reduces list of monomials of degree 6 for the HK4 model (in the HK hierarchy of ROMs)