semvec/pypi-packages huggingface dataset:
codedharmony 0.1.4
A collection of utility functions for harmonizing code.

# codedharmony

A collection of utility functions to make your coding life easier.

## Installation

```bash
pip install codedharmony
```



## Python

```bash
import codedharmony 
```

#### or

```bash
from codedharmony import merge_dicts, safe_division, flatten_list, chunk_list
```

## Examples

```
# Merging dictionaries
merged_dict = merge_dicts({'a': 1}, {'b': 2})
print(merged_dict)  # Output: {'a': 1, 'b': 2}

# Safe division
safe_result = safe_division(10, 0, default=0)
print(safe_result)  # Output: 0

# Flattening a nested list
nested_list = [1, [2, 3, [4, 5]], 6]
flat = flatten_list(nested_list)
print(flat)  # Output: [1, 2, 3, 4, 5, 6]

# Chunking a list
chunked_list = chunk_list([1, 2, 3, 4, 5], 2)
print(chunked_list)  # Output: [[1, 2], [3, 4], [5]]

# Finding duplicates in a list
duplicates = find_duplicates([1, 2, 2, 3, 4, 4, 5])
print(duplicates)  # Output: [2, 4]

# Getting unique elements
unique = unique_elements([1, 2, 2, 3, 4, 4])
print(unique)  # Output: [1, 2, 3, 4]

# Converting from camelCase
snake = camel_to_snake('camelCaseString')
print(snake)  # Output: camel_case_string

# Converting from snake_case to camelCase
camel = snake_to_camel('snake_case_string')
print(camel)  # Output: SnakeCaseString

# Checking if a string is a palindrome
is_pal = is_palindrome('A man, a plan, a canal: Panama')
print(is_pal)  # Output: True


# Counting occurrences
count = count_occurrences([1, 2, 2, 3, 3, 3, 4])
print(count)  # Output: {1: 1, 2: 2, 3: 3, 4: 1}

# Sorting dictionary by value
sorted_dict = sort_dict_by_value({'a': 3, 'b': 1, 'c': 2})
print(sorted_dict)  # Output: [('b', 1), ('c', 2), ('a', 3)]

# Removing None values from dictionary
clean_dict = remove_none_values({'a': 1, 'b': None, 'c': 3})
print(clean_dict)  # Output: {'a': 1, 'c': 3}

# Converting list to dictionary
data = ['apple', 'banana', 'cherry']
fruit_dict = list_to_dict(data, lambda x: x.upper())
print(fruit_dict)  # Output: {'APPLE': 'apple', 'BANANA': 'banana', 'CHERRY': 'cherry'}

```
md-dlp 0.2.3
A tool for markdown data loss prevention
Requires Python: >=3.6

# md-dlp

A tool for markdown data loss prevention.

## Installation

### Using pip (Recommended)

```bash
pip install md-dlp
```

### Using Homebrew (Coming Soon)

> ⚠️ Homebrew installation is currently under development and not yet available. Please use pip installation for now.

The following will be available once the Homebrew formula is ready:
```bash
# Add the tap
brew tap markshawn2020/tools

# Install md-dlp
brew install md-dlp
```

## Usage

```bash
md-dlp [options] <file>
```

## Features

- Prevent data loss in markdown files
- Easy to use command-line interface
- Cross-platform support
- Available through both Homebrew and pip

## License

MIT License

## Author

MarkShawn (shawninjuly@gmail.com)
ebg 0.13.3
Tool for estimating the Felsenstein Bootstrap support of phylogenetic trees

# EBG: Educated Bootstrap Guesser
[![image](https://img.shields.io/pypi/v/ebg.svg)](https://pypi.python.org/pypi/ebg)
[![image](https://img.shields.io/conda/vn/conda-forge/ebg.svg)](https://anaconda.org/conda-forge/ebg)
[![image](https://img.shields.io/badge/License-GPL3-yellow.svg)](https://opensource.org/licenses/GPL-3-0)

Documentation: https://github.com/wiegertj/EBG/wiki
## Description

EBG is a Python tool for predicting the Felsenstein Bootstrap Support of phylogenies inferred by RAxML-NG.
It was trained on empirical datasets from TreeBASE and can use both AA and DNA data.


## Installation
### Using conda
The latest version of EBG can easily be installed via conda:
```
conda install ebg -c conda-forge
```
### Using pip
```
pip install ebg
```
## Usage Example - Light Mode
EBG now offers a faster, lightweight alternative to the standard EBG mode by omitting the most computationally expensive feature calculations while maintaining performance comparable to the standard mode. This mode is particularly useful for MSAs with a large number of sites.

In our experiments, performance decreases by 6% in light mode.

You can enable light mode in EBG using the ```-light``` flag.

## Usage Example - Standard Mode
A simple command line call of EBG looks like this:
```
ebg -msa /test/example.fasta -tree /test/example.bestTree -model /test/example.bestModel -t b -o test 
```
This command will use the MSA in fasta format, and the best tree inferred with RAxML-NG and the model.
By selecting ```-t b```(oth) EBG will output the bootstrap predictions as well as the probabilities for exceeding different bootstrap thresholds (70, 75, 80, 85). 
The results will be stored in a folder called test.

Please keep in mind that EBG requires an installation of RAxML-NG. By default, it uses the command ```raxml-ng```. 
If your RAxML-NG installation is not part of the PATH variable, you can specify the path to the RAxML-NG binary file with the parameter ```-raxmlng PATH_TO_RAXMLNG```.

### Citation
If you are using EBG for your publication, please cite our published paper in Molecular Biology and Evolution: 
[EBG Paper](https://academic.oup.com/mbe/article/41/10/msae215/7825466)
### References
* A. M. Kozlov, D. Darriba, T. Flouri, B. Morel, and A. Stamatakis (2019) 
**RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference** 
*Bioinformatics*, 35(21): 4453–4455. 
[https://doi.org/10.1093/bioinformatics/btz305](https://doi.org/10.1093/bioinformatics/btz305)
mongodb-data-layer 1.2
A data layer for mongodb

# MongoDB Data Layer

A data modeling **MongoDB** library for **Python**

<hr />

### Installation
1. Install the python package via pip:

```bash
pip install mongodb-data-layer
```
**Note**: It is better to create a python virtual environment...

2. Setup an environment variables:
   
   - To create an environment variables, simply create a file named **".env"**.
    - Then, add these following variables to a created file:

    ```bash
    MONGO_DB_URL="mongodb://localhost:27020" # The default mongodb localhost but you can also put a connection string from atlas server.
    MONGO_DB_NAME="my_database" # Your created database
    ```

3. To create a model you can simply enter this command on a command promt:

```bash
generate-model /models/test_model.py  # where '/models/test_model.py' is your file path and file name
```

4. It will generate a file like this:

```python
#A MongoDB model for the tests collection
from mongodb_data_layer.base import BaseMongoModel

class Test(BaseMongoModel):
    
    collection_name = 'tests' # Create a collection with this name in the database
```

<hr />

### Notes

<p>This library is very useful to lessen the configuration for mongodb database. </p>

<p>This package is build on top of MongoDB official package/drivers.</p>

<p>This is also works well in fastapi.</p>

<p>This library is currently in development. Feel free to contribute.</p>

<hr />

Official repository: <a href="https://github.com/nojram00/mongodb-data-layer">https://github.com/nojram00/mongodb-data-layer</a>
python-docx-symbol 0.1.2
Convert docx Symbol characters to unicode characters.
Requires Python: >=3.9

# python-docx-symbol

Convert docx Symbol characters to unicode characters.

## Installation

`pip install python-docx-symbol`

## Example

```python
from docx import Document
from python_docx_symbol import convert_symbols

doc = Document("/path/to/input.docx")
result = convert_symbols(doc)
print(result.converted)
print(result.unconverted)

doc.save("/path/to/output.docx")
```
cg-r-pathway 0.1.6
A comprehensive protein reaction coordinate optimization framework
Requires Python: >=3.6

# cg_r_pathway

`cg_r_pathway` is a comprehensive protein reaction coordinate optimization framework that integrates:

- **Physics-based Modeling:** Residue-specific parameters (mass, charge, ε, σ, hydrophobicity), bonds, angles, nonbonded interactions.
- **Advanced Optimization:** Global path optimization using Adaptive PSO+SA and local refinement using Double-Nested Simulated Annealing (DSA-ASA).
- **Self-Learning ML Model:** A placeholder neural network force field for continuous improvement.
- **Coarse-Graining and Backmapping:** Converting full-atom PDB structures to a coarse-grained model and reconstructing all-atom models via rigid-body transformation.
- **Advanced Visualization:** Reaction coordinate (RMSD vs Energy) plots and 3D path visualization.

## Installation

Clone the repository and install with pip:

```bash
pip install .