semvec/pypi-packages huggingface dataset:
meshutility 0.0.2
A collection of python utilities for mesh processing
Requires Python: >=3.6

[![Build](https://github.com/zishun/MeshUtility/actions/workflows/wheels.yml/badge.svg?branch=main)](https://github.com/zishun/MeshUtility/actions/workflows/wheels.yml)
# MeshUtility

> A collection of python utilities for mesh processing.

Install with
```
pip install meshutility==0.0.2
```

A simple tutorial is available [here](https://zishun.github.io/projects/MeshUtility/).


## Functions & Modules
- ```colormap_vertex_color```: assign vertex color to visualize a scalar field defined on mesh.
- ```cut_along_curve```: cut a mesh along a vertex chain.
- ```ff_graph```: face-face graph of mesh.
- ```get_scalar_field_on_resampled_points```: given a scalar field defined on a triangular mesh, get field values on resampled points.
- ```IsoCurve``` module: extract isocurves on a scalar field defined on a manifold triangular mesh.
- ```pygeodesic``` module (C++): geodesic for triangular meshes.
    - exact geodesic by Danil Kirsanov.
    - fast marching, may use different speed on each face.
- ```pyisocurve``` module (C++): almost the same as ```IsoCurve``` above.
- ```pyremesh``` module (C++): incremental isotropic remeshing.
- ```pyshapeop``` module (C++): a partial binding of ShapeOp.
- ```read_obj_lines```: read polyline from a [Wavefront .obj file](https://en.wikipedia.org/wiki/Wavefront_.obj_file#Line_elements).
- ```remove_unreferenced_vertices```: remove unreferenced vertices.
- ```sphere_cvt```: iteratively approximate centroidal Voronoi tessellation (CVT) on the unit sphere (kind of uniform sampling).
- ```split_connected_components```: split connected components.
- ```split_mesh, split_mesh_complete```: split a mesh by inserting new vertices defined on mesh edges.
- ```write_obj_lines```: write polyline as a Wavefront .obj file that can be open with MeshLab.
- ```vv_graph```: vertex-vertex graph of mesh.


## Build from Source

see [```build.md```](https://github.com/zishun/MeshUtility/blob/main/build.md)
ControlBurn 0.1.3
Feature Selection by Sparse Forests
Requires Python: >=3.6


## ControlBurn v0.1.3

This package implements ControlBurn in python. ControlBurn is a feature selection algorithm that uses a weighted LASSO-based feature selection to prune unnecessary features from tree ensembles. The algorithm is efficient and only requires a single training iteration to run.

Tree ensembles distribute feature importance scores evenly amongst groups of correlated features. The average feature ranking of the correlated group is suppressed, which reduces interpretability and complicates feature selection. Like the linear LASSO, ControlBurn assigns all the feature importance of a correlated group of features to a single feature. The algorithm is able to quickly select a subset of important independent features for further analysis.


### Installation

The easiest way to install ControlBurn is through pip.
```sh
!pip install ControlBurn==0.1.3
```


#### Dependencies

ControlBurn works on python 3.7 or above. The following packages are required.

-   numpy (1.20.1)
-   pandas (1.2.4)
-   sklearn (0.24.1)
-   mosek (9.2.47)
-   cvxpy (1.1.13)

### Quick Start
```python
from ControlBurn.ControlBurnModel import ControlBurnClassifier
cb = ControlBurnClassifier(alpha = 0.1)
cb.fit(X,y)
print(cb.features_selected_) #print selected features
print(cb.feature_importances_) #print feature importances

pred = cb.predict(X) #return predictions of polished model using selected features
```
### Reference Paper

ControlBurn: Feature Selection by Sparse Forests B. Liu, M. Xie, and M. Udell  
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021
SetPrecision 0.2.0
Python module providing an easy way to set the precision of a floating-point number/decimal to the desired amount of significant figures.
Keywords: SetPrecision,float,decimal,precision,sig,figs,significant,digits,scientific,notation,math,conversion,script,point,floating,scientific-notation

# SetPrecision

## About

- _**`SetPrecision`**_ is a small module providing a simple way to set the precision of a floating point number or decimal to the desired amount of digits following the decimal point.

---

## Installation

### Using PIP _(Recommended)_

> _Easiest_ method. Highly recommended over manual installation.

- To install _**`SetPrecision`**_ using `pip`, enter the following:

  ```python
  python -m pip install SetPrecision
  ```

- Done!

---

### Manual Installation

> _Not_ recommended.

1. Before use, navigate to intended installation location, and create a new directory.

2. Clone repository with the git client of your preference using the following command:

   - ```bash
     git clone https://github.com/schlopp96/SetPrecision/releases/latest
     ```

3. Install all dependencies for this package within said directory using:

   - ```bash
     pip install -r requirements.txt
     ```

- **(Optional)**: move installation directory to `"path/to/Python/Libs/site_packages"` to be able to import this package to a Python program like any other importable package.

- Done!

---

## Usage

- In order to use _**`SetPrecision`**_, start by importing the module to your Python environment:

  ```python
  from SetPrecision import set_precision
  ```

- Now, simply call the `set_precision` method and enter your desired number to be formatted as the `number` parameter, and the level of precision as the `precision` parameter:

  ```python

  >>> testA = 3.141592653589793 # Not necessary to set number as variable.

  >>> testA = set_precision(testA, 2)

  >>> print(testA)

  '3.15'

  >>> testB = 3.141592653589793

  >>> testB = set_precision(testB, 4)

  >>> print(testB)

  '3.1416'
  ```

> Note that the output is automatically rounded up when `number >= 5`, and down when `number < 5`.

- Both params can be entered in string format, and will output successfully assuming that both parameters can be cast to their appropriate types.
  - This is done automatically.

---

## Contact the Author

- If you have any questions, comments, or concerns that cannot be alleviated through the [project's GitHub repository](https://github.com/schlopp96/SetPrecision), please feel free to contact me through my email address:
  - `schloppdaddy@gmail.com`
setPrecision 0.1.0
`setPrecision` is a small module providing a simple way to set the precision of a floating point number or decimal to the desired amount of significant digits.
Keywords: setPrecision,float,decimal,precision,sig,figs,significant,digits,scientific,notation,math,conversion,script,point,floating

# setPrecision

## About

- _**`setPrecision`**_ is a small module providing a simple way to set the precision of a floating point number or decimal to the desired amount of digits following the decimal point.

---

## Installation

### Using PIP _(Recommended)_

> _Easiest_ method. Highly recommended over manual installation.

- To install _**`setPrecision`**_ using `pip`, enter the following:

  ```python
  pip install setPrecision
  ```

- You should now be able to import/run _**`setPrecision`**_ within your python environment by entering the following:

  ```python
  >>> from setPrecision import set
  ...
  ```

- Done!

---

### Manual Installation

> _Not_ recommended.

1. Before use, navigate to intended installation location, and create a new directory.
2. Install all dependencies for this package within said directory using:

   ```python
     pip install -r requirements.txt
   ```

3. Clone repository with the git client of your preference.
   - (Optional) move installation directory to `"path/to/Python/Libs/site_packages"` to be able to import this package to a Python program like any other importable package.

- Done!

---

## Usage

- In order to use _**`setPrecision`**_, start by importing the module to your Python environment:

  ```python
  from setPrecision import set
  ```

- Now, simply call the `set` method and enter your desired number to be formatted as the `digit` parameter, and the level of precision as the `precision` parameter:

  ```python
  >>> myNumber = 3.141592653589793 # Not necessary to set number as variable.
  >>> myNumber = set(myNumber, 2)
  >>> print(myNumber)
  '3.15'
  ```

> Note that the output is automatically rounded up when `digit >= 5`, and down when `digit < 5`.

- Both params can be entered in string format, and will output successfully assuming that both paramaters can be cast to their appropriate types.
  - This is done automatically.

---

## Contact the Author

- If you have any questions, comments, or concerns that cannot be alleviated through the [project's GitHub repository](https://github.com/schlopp96/setPrecision), please feel free to contact me through my email address:
  - `schloppdaddy@gmail.com`