semvec/pypi-packages huggingface dataset:
TOPSIS-Nikhal-101816034 0.0.1
Python Package Implementing TOPSIS (for finding ideal and anti-ideal solution)
Requires Python: >=3.6

# Topsis-Package
# What is TOPSIS
TOPSIS is an acronym that stands for â€˜Technique of Order Preference Similarity to the Ideal Solutionâ€™ and is a pretty straightforward MCDA method. As the name implies, the method is based on finding an ideal and an anti-ideal solution and comparing the distance of each one of the alternatives to those. It was presented in Hwang and Yoon (Multiple attribute decision making: methods and applications. Springer, Berlin, 1981) and Chen and Hwang (Fuzzy multiple attribute decision making methods. Springer, Berlin, 1992), and can be considered as one of the classical MCDA methods that has received a lot of attention from scholars and researchers. It has been successfully applied in various instances.

# How to ise this Package:
TOPSIS-Nikhal-101816034 can be run by typing the following code snippet:

# In Command Prompt
python topsis.py "data.csv" "1,1,1,2" "+,+,-,+" "Result.csv"

# Sample Dataset
The decison-matrix should be constructed with each row representing a model alternative and each column representing criterian such as accuracy,R-Sqaure,Root Mean Squared error,Correlation etc.

| Model | Corr | Rseq | RMSE | Accuracy |
|-------|------|------|------|----------|
| M1    | 0.79 | 0.62 | 1.25 | 60.89    |
| M2    | 0.66 | 0.44 | 2.89 | 63.07    |
| M3    | 0.56 | 0.31 | 1.57 | 62.87    |
| M4    | 0.82 | 0.67 | 2.68 | 70.19    |
| M5    | 0.75 | 0.56 | 1.3  | 80.39    |

# Output
| Model | Score             | Rank |
|-------|-------------------|------|
| 1     | 0.476957713840877 | 2    |
| 2     | 0.476572577796742 | 3    |
| 3     | 0.477495853743771 | 1    |
| 4     | 0.475616911138615 | 5    |
| 5     | 0.475948812224928 | 4    |

_The Ranking are displayed in the result csv file, with 1st rank offering the best decision and last rank offering the worst decision according to the TOPSIS method._
emptydrops 0.0.5
Python implementation of emptydrops() in CellRanger v3.0.2

# emptydrops
Python implementation of emptydrops-like cell calling as in CellRanger v3.0.2

## Disclaimer:
All code originally comes from https://github.com/10XGenomics/cellranger with
minimal modifications for packaging and running under python3.

## Usage:
```
from emptydrops import find_nonambient_barcodes
from emptydrops.matrix import CountMatrix

matrix = CountMatrix.from_legacy_mtx(mtx_dir)

find_nonambient_barcodes(
    matrix,          # Full expression matrix
    orig_cell_bcs,   # (iterable of str): Strings of initially-called cell barcodes
    min_umi_frac_of_median=0.01,
    min_umis_nonambient=500,
    max_adj_pvalue=0.01
)
```

Returns:
```
[
    'eval_bcs',      # Candidate barcode indices in addition to those in `orig_cell_bcs` (n)
    'log_likelihood',# Ambient log likelihoods (n)
    'pvalues',       # pvalues (n)
    'pvalues_adj',   # B-H adjusted pvalues (n)
    'is_nonambient', # Boolean nonambient calls (n)
]
```
latis-image-proccessing 0.0.19
LATIS image processing utilities for python
Requires Python: >=3.0

LATIS image processing utilities for python


## installation

#### Windows
```
pip install latis-gdcm-win
pip install latis-image-proccessing
```
#### Linux
```
sudo apt-get install python3-gdcm
( pip / pip3 ) install latis-image-proccessing
```



### Compiling The Package

```bash
# Build the package 
python setup.py bdist_wheel

# Deploy to pip  
python -m twine upload dist/*
```


# Release History