semvec/pypi-packages huggingface dataset:
disparity-interpolation 1.0.5
Nearest neighbor interpolation for disparity images with missing parts.

[![Python 3.7](https://img.shields.io/badge/python-3.7-blue.svg)](https://www.python.org/downloads/release/python-370/)
[![Python 3.8](https://img.shields.io/badge/python-3.8-blue.svg)](https://www.python.org/downloads/release/python-380/)
[![Python 3.9](https://img.shields.io/badge/python-3.9-blue.svg)](https://www.python.org/downloads/release/python-390/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](./LICENSE)
![Linux CI](https://github.com/jhonykaesemodel/disparity_interpolation/actions/workflows/github-deploy.yml/badge.svg)

## Fast nearest-neighbor interpolation of disparity maps using Cython

Some stereo matching methods such as [Semi-Global Matching (SGM)](https://core.ac.uk/download/pdf/11134866.pdf) might provide sparse disparity maps, meaning that some pixels will not have valid disparity values. In those cases, an interpolation of the predicted disparity map might be needed for evaluation against semi-dense ground-truth, as in the [KITTI Stereo 2015 benchmark](http://www.cvlibs.net/datasets/kitti/eval_scene_flow.php?benchmark=stereo).

**Usage example:**
```python
import disparity_interpolation


def interpolate_disparity(disparity_map: np.array) -> np.array:
    """Intepolate disparity image to inpaint holes.
       The expected run time for a stereo image with 2056 Ã— 2464 pixels is ~50 ms.
    """
    # Set the invalid disparity values defined as "0" to -1.
    disparity_map[disparity_map == 0] = -1
    disparity_map_interp = disparity_interpolation.disparity_interpolator(disparity_map)

    return disparity_map_interp
```
MarkerCount-combio.dku 0.0.2
MarkerCount
Requires Python: >=3.6

## MarkerCount updated on June 22, 2021
Slight modification was made to improve the identification performance.

## MarkerCount: Brief introduction
MarkerCount is a python3 cell-type identification toolkit for single-cell RNA-Seq experiments.
MarkerCount works both in reference and marker-based mode, where the latter utilizes only the existing lists of markers, while the former required pre-annotated dataset to train the model. Please refer to the preprint manuscript "MarkerCount: A stable, count-based cell type identifier for single cell RNA-Seq experiments" available at https://www.researchsquare.com/article/rs-418249/v2 DOI:
https://doi.org/10.21203/rs.3.rs-418249/v2 

All the functions to implement MarkerCount are defined in the python3 script, `marker_count.py`, where the two key functions are 

1. `MarkerCount()`: marker-based cell-type identifier
1. `MarkerCount_Ref()`: reference-based cell-type identifier

One can import the function by adding a line in your script, i.e., `from marker_count import MarkerCount_Ref, MarkerCount`

## Example usage in Jupyter notebook

We provide example usage of MarkerCount in Jupyter notebook file `cell_id_example_v03.ipynb`, where you can see how to import and how to run MarkerCount, both in reference-based and marker-based mode. For quick overveiw of the usage of MarkerCount, simply click `cell_id_example_v03.ipynb` above in the file list.

To run the example, please download the script, Jupyter notebook file, maker matrix in `.csv` file and the two sample single-cell RNA-Seq data with `.h5ad` file extension (they are the two data we used in our paper mentioned above) and follow the instruction below.

1. Download all the files in ZIP format.
2. Decompress the files into a desired folder.
3. Run jupyter notebook and open the jupyter notebook file `cell_id_example_v03.ipynb`
4. You can run the codes step-by-step and can see the intermediate and final results.

To run MarkerCount, you need the python packages `Numpy`, `Pandas`, `sklearn` and `scipy`.
`scanpy` and `plotly` are required only to show the results, not for the MarkerCount itself.
All of them can be installed simply using `pip` command.

## Contact
Send email to syoon@dku.edu for any inquiry on the usages.
ve4as1 2.0.10
aownierewe
Requires Python: >=3.6, <4
Keywords: sample,setuptools,development

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pypwdgen 1.0.4
Complex password generator
Requires Python: >=3.6

# pypwdgen

![GitHub Workflow Status (branch)](https://img.shields.io/github/workflow/status/polluxtroy3758/pypwdgen/Build%20Python%20Package/main?label=main%20build&logo=githubactions&style=flat-square)
![PyPI](https://img.shields.io/pypi/v/pypwdgen?logo=pypi&style=flat-square)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pypwdgen?logo=python&style=flat-square)

Complex pasword generator

## Prerequisites
Python 3.6 minimum is required to use the `secrets` module (which is recomended instead of the `random` module to manipulate sensitive data).

## Installation
```shell
pip install pypwdgen
```

## Using the cli
```shell
python3 -m pypwdgen 
```

### Optional arguments
```text
-l, --length:
    Length of the password (default: 9)
-n, --number:
    Number of passwords to generate (default: 1)
-c, --complexity:
    Minimum number of character classes to use (default: 3)
```

### Example
```shell
# generates 20 passwords of 10 characters
python3 -m pypwdgen -l 10 -n 20
```

## Using in a script
You can easily use `pypwdgen` to generate password inside your scripts.

### Get only the password itself
```python
from pypwdgen.core import Password

passwd_string = str(Password(20, 4))  # Please note the usage of str()

# Print password
print(f"My password is: {passwd_string}")
```

### Get a Password object and use its properties
```python
from pypwdgen.core import Password

passwd_object = Password(20, 4)

# Print password complexity score from object attribute
print(f"My password complexity score is: {passwd_object.complexity}")

# Print password string from object attribute
print(f"My password is: {passwd_object.password}")
```
hbdl 0.0.6
Another humble bundle downloader

# hbdl the Humble Bundle downloader
A simple [Humble Bundle](https://www.humblebundle.com) downloader, takes a `Humble Bundle` product URI (e.g `https://www.humblebundle.com/downloads?key=xxxxxx`) and it takes care of the download from there.
## Installation
* ```pip install hbdl```
## Usage
```
Usage: hbdl [OPTIONS] URI
Options:
  --dest-dir PATH
  --downloader [wget|curl|python]
  --help                          Show this message and exit.
  ```
## Todo
 * Check the hash of the downloaded to avoid repetitive download
 * Better logging and error reporting
 * Standalone app / GUI
 * Automate the pypi publish
dicom-to-cnn 0.76
Python Library to handle Input / Output conversion in Dicom <=> Convolutional Neural Network
Requires Python: >=3.7

# Dicom-To-CNN

Install : pip install dicom-to-cnn 

Features : 
- Description of Series content in a huge dataset of DICOM (output JSON descriptor for each series containings main DICOM tags).
- Conversion  :
  - Dicom to Nifti
  - Mask to RTSS and DICOMSeg
  - RTSS to mask
  - DicomSeg to mask 
- PET/CT
  - Conversion Bqml/Counts to SUV and SUL
  - Fusion


#Maintainer  : Salim Kanoun
#Contributors : Thomas Trouillard, Wendy Revailler