semvec/pypi-packages huggingface dataset:
cardec 1.0.3
A deep learning method for joint batch correction, denoting, and clustering of single-cell rna-seq data.
Requires Python: >=3.7

# CarDEC

CarDEC (**C**ount **a**dapted **r**egularized **D**eep **E**mbedded **C**lustering) is a joint deep learning computational tool that is useful for analyses of single-cell RNA-seq data. CarDEC can be used to:

1. Correct for batch effect in the full gene expression space, allowing the investigator to remove batch effect from downstream analyses like psuedotime analysis and coexpression analysis. Batch correction is also possible in a low-dimensional embedding space.
2. Denoise gene expression.
3. Cluster cells.

## Reproducibility

We described and introduced CarDEC in our [methodological paper](https://www.biorxiv.org/content/10.1101/2020.09.23.310003v1). To find code to reproduce the results we generated in that paper, please visit this separate [github repository](https://github.com/jlakkis/CarDEC_Codes), which provides all code (including that for other methods) necessary to reproduce our results.

## Installation

Recomended installation procedure is as follows. 

1. Install [Anaconda](https://www.anaconda.com/products/individual) if you do not already have it. 
2. Create a conda environment, and then activate it as follows in terminal.

```
$ conda create -n cardecenv
$ conda activate cardecenv
```

3. Install an appropriate version of python.

```
$ conda install python==3.7
```

4. Install nb_conda_kernels so that you can change python kernels in jupyter notebook.

```
$ conda install nb_conda_kernels
```

5. Finally, install CarDEC.

```
$ pip install CarDEC
```

Now, to use CarDEC, always make sure you activate the environment in terminal first ("conda activate cardecenv"). And then run jupyter notebook. When you create a notebook to run CarDEC, make sure the active kernel is switched to "cardecenv"

## Usage

A [tutorial jupyter notebook](https://drive.google.com/drive/folders/19VVOoq4XSdDFRZDou-VbTMyV2Na9z53O?usp=sharing), together with a dataset, is publicly downloadable.

## Software Requirements
    
- Python >= 3.7
- TensorFlow >= 2.0.1, <= 2.3.1
- scikit-learn == 0.22.2.post1
- scanpy == 1.5.1
- louvain == 0.6.1
- pandas == 1.0.1
- scipy == 1.4.1

## Trouble shooting

Installation on MacOS should be smooth. If installing on Windows Subsystem for Linux (WSL), the user must properly configure their g++ compiler to ensure that the louvain package can be built during installation. If the compiler is not properly configured, the user may encounter a following deprecation error similar to the following.

"DEPRECATION: Could not build wheels for louvain which do not use PEP 517. pip will fall back to legacy 'setup.py install' for these. pip 21.0 will remove support for this functionality. A possible replacement is to fix the wheel build issue reported above."

To fix this error, try to install the libxml2-dev package.
FTTCrypt 1.0.1
FTTCrypt, Encrypt a plain text to Cipher or any File to Cipher
Requires Python: >=3.8
Keywords: fttcrypt ecnryption fileencryption textencryption

FTTCrypt Encryption, Encrypt Text to Cipher or any File to Cipher
#
FTTCrypt stands for File to Text Encryptor, it is a software library built for text encryption and Files converting & encryption to a small ciphertext purposely to enhance file upload and transfer. Imagine sending a file from User A to user B, the file has to be uploaded into a server or network then the receiver to downloads the file. According to FTTCrypt, you are not uploading a File, but a small, encrypted cipherText that represents the file which means you can store the cipher in a database level and access
the file using the cipher anywhere the FTTCrypt runs. No need for a large file or blob storage for your next application. Save
your files within your database as a cipher.
##
```
from fttcrypt import FTTCryptor

key = "SecretKeyGoesHere"
text="Secured Text to to be encrypted"

# Encrypt a plain Text to Cipher
cipherText = FTTCryptor.encryptText(text, key)
print(cipherText)


# Decrypt Cipher to Plain Text
decryptedText = FTTCryptor.decryptText(cipherText, key)
print(decryptedText)


# Encrypt a file to Cipher
cipherText = FTTCryptor.encryptFile(filePath, key)
print(cipherText)

# Decrypt Cipher to File
filePath = FTTCryptor.decryptFile(cipherText, key)
print(filePath)
```
example-pkg-reinier-sterkenburg 0.0.7
A small example package
Requires Python: >=3.6

# Example Package

This is a simple - empty - example package.

to upload to PyPI:

(see also https://packaging.python.org/tutorials/packaging-projects/)

<code>py -m twine upload --repository testpypi dist/*</code>

Note: uploading to testpypi did not work for me.
Instead, I could upload to PyPI directly (so without the --repository parameter)