semvec/pypi-packages huggingface dataset:
neuralfilter 0.1.9
neural attention filter
Requires Python: >=3
Keywords: neural-filter

Neural Filter
=====

## Functions
#### neuralfilter.generate(x, force=False)
- <strong>x</strong>: Array with resolution $(H, W, C)$  
The dimension $C$ is recommended as 1.  
- <strong>force</strong>: If you want to force this operation when the dimension $C$ is higher than 1,  set 'force' as True.  

#### neuralfilter.batch_generate(x, force=False)
- The batch processing mode of '<a href='https://github.com/YeongHyeon/NeuralFilter#neuralfiltergeneratex-forcefalse'>generate</a>'.  
- <strong>x</strong>: Array with resolution $(N, H, W, C)$  
The dimension $C$ is recommended as 1.  
- <strong>force</strong>: If you want to force this operation when the dimension $C$ is higher than 1,  set 'force' as True.  

batch_generate(x, force=False)



## Usage
``` python
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import neuralfilter

(x_tr, y_tr), (x_te, y_te) = tf.keras.datasets.mnist.load_data()

idx = 1
a = neuralfilter.generate(np.expand_dims(x_tr[idx], axis=-1))

plt.figure(figsize=(9, 3), dpi=100)
plt.subplot(1, 3, 1)
plt.imshow(x_tr[idx], cmap='gray')
plt.xticks([], [])
plt.yticks([], [])

plt.subplot(1, 3, 2)
plt.imshow(a, cmap='jet')
plt.xticks([], [])
plt.yticks([], [])

plt.subplot(1, 3, 3)
plt.imshow(x_tr[idx], cmap='gray')
plt.imshow(a, cmap='jet', alpha=0.5)
plt.xticks([], [])
plt.yticks([], [])

plt.tight_layout()
plt.savefig('sample.png')
plt.show()
```

<img src="https://github.com/YeongHyeon/NeuralFilter/blob/main/figures/sample.png" width="500">
pMTnet-Omni 0.0.3
Your one-stop TCR-pMHC affinity prediction algorithm
Requires Python: >=3.9

# pMTnet Omni: your one-stop TCR-pMHC affinity prediction algorithm :microscope:

![Logo](/assets/logo.png)

![forthebadge](/assets/tcr-pmhc.svg)
![forthebadge](/assets/deep-learning.svg)

[![Documentation Status](https://readthedocs.org/projects/tao-wang-pmtnet-omni/badge/?version=latest)](https://tao-wang-pmtnet-omni.readthedocs.io/en/latest/?badge=latest)
[![codecov](https://codecov.io/gh/Yuqiu-Yang/pMTnet_Omni/branch/main/graph/badge.svg?token=L59TPMM3VN)](https://codecov.io/gh/Yuqiu-Yang/pMTnet_Omni)

<b>pMTnet Omni</b> is a deep learning algorithm for affinity prediction based on TCR Va, Vb, CDR3a, CDR3b sequences, peptide sequence, and MHC allele types. The predictions can be made for human and mouse alleles, and for both CD8 T cells/MHC class I and CD4 T cells/MHC class II.

Please refer to our paper for more details: [pMTnet Omni paper link here](www.google.com)

We host the supporting data as well the online tool [here](http://lce-test.biohpc.swmed.edu/pmtnet), where you can find all the members of the pMTnet 
family, including [pMTnet V1](https://github.com/tianshilu/pMTnet). 

We have also built a detailed [online documentation](https://tao-wang-pmtnet-omni.readthedocs.io/en/latest/) where we walk you through every single step from installation to final affinity prediction.

## Model Overview 
![Model Overview](/docs/source/images/overview.png)

## Dependencies 
- numpy==1.22.4
- pandas==1.5.2
- matplotlib==3.6.2
- scikit-learn==1.0.2
- tqdm==4.64.1
- torch==1.13.1

## Enviroment Setup
```shell
conda env create -f pMTnet_Omni_env.yml
```

## Installation (Coming right up (not run now))
<b>PyPi</b>
```shell
conda activate pMTnet_Omni
pip install pMTnet_Omni
```
<b>Conda</b>
```shell
conda activate pMTnet_Omni
conda install pMTnet_Omni
```

## Quick Start Guide 
1. Go to [our website](http://lce-test.biohpc.swmed.edu/pmtnet) to download the supporting data to your local directory, say <i>./data</i>. 

<b>Caution: </b> the data file is around 30Gb. Make sure you have enough space on your hard drive. 

2. Prepare your dataset so that it looks somewhat like the following:
![Sample df](/docs/source/images/sample_df.png)

3. Say your dataset is under <i>./df.csv</i>. In your terminal, run 
```shell
python -m pMTnet_Omni --data_dir "./data" --user_data_path "./df.csv" --output_file_path "./df_results.csv"
```

4. An example output would look like this:
![Sample output](/docs/source/images/sample_output.png)

For a more in-depth exploration including the usage of interactive Python, check out our [online documentation](https://tao-wang-pmtnet-omni.readthedocs.io/en/latest/).
snapspam 0.2.0
Spam sendit, LMK, or NGL messages.
Requires Python: ~=3.6

# snapspam [![Docs Badge]](https://snapspam.readthedocs.io/en/stable/) [![Python Badge]](https://pypi.org/project/snapspam/)

Spam sendit, LMK, or NGL messages.

[![Open in Cloud Shell](https://gstatic.com/cloudssh/images/open-btn.svg)](https://shell.cloud.google.com/cloudshell/editor?cloudshell_git_repo=https://github.com/MysteryBlokHed/snapspam.git)

## Installation

You can use snapspam without installing it, but installing it
will allow you to run it anywhere.

### From PyPI

To install from PyPI, [pipx](https://pypi.org/project/pipx/) is recommended.
With it installed, run:

```sh
pipx install snapspam
```

To just use pip, run:

```sh
pip install snapspam
```

### From Cloned Repository

```sh
python setup.py install
```

## Use

To get help from the CLI, run:

```sh
snapspam --help
```

or

```sh
python snapspam.py --help
```

Some information about how to use the app will be returned.

To get help for a specific target app (eg. sendit), run:

```sh
snapspam sendit --help
```

or

```sh
python snapspam.py sendit --help
```

Here's an example usage of the app to spam a sendit sticker:

```sh
snapspam sendit cd06ec9a-2879-1afa-5108-fed08b1ecaa0 'Spammed'
```

The ID can also be replaced by the full URL, like this:

```sh
snapspam sendit https://web.getsendit.com/s/cd06ec9a-2879-1afa-5108-fed08b1ecaa0 'Spammed'
```

## Documentation

Documentation of the CLI is contained in the help menu.
Library documentation is hosted on [Read the Docs].

## License

snapspam is licensed under the GNU General Public License, Version 3.0
([LICENSE](LICENSE) or <https://www.gnu.org/licenses/gpl-3.0.en.html>).

[docs badge]: https://readthedocs.org/projects/snapspam/badge/?version=latest
[python badge]: https://img.shields.io/pypi/pyversions/snapspam
[read the docs]: https://snapspam.readthedocs.io/en/stable/
Tienc 0.0.2
package for encrypting text and images using Hybrid encryption, asymmetric encryption or symmetric encryption.
Requires Python: >=3.7


# Tienc

Tienc is a simple library that allows you to 
encrypt and decrypt text and images Hybrid encryption, 
additionally tienc allow standalone encryption 
using either AES encryption or RSA encryption. 
Tienc is based on the ```Cryptography``` library.
Tienc contains 3 classes,
AESEncryption, RSAEncryption and HybridEncryption.

### AESEncryption

AES encryption class with two methods: ```encrypt``` and ```decrypt```.
encrypt takes data as a parameter and returns the ciphertext.
Decrypt takes ciphertext as a parameter and returns the decrypted data.

### RSAEncryption 

RSA encryption class with two methods: ```encrypt``` and ```decrypt```.
Encrypt takes data as a parameter and returns the ciphertext.
Decrypt takes ciphertext as a parameter and returns the decrypted data.

### HybridEncryption 

Hybrid encryption class with four methods: ```encrypt_text```, ```decrypt_text```, ```encrypt_image``` and ```decrypt_image```.
Encrypt_text takes text as a parameter and returns the ciphertext and the encrypted key.
Decrypt_text takes ciphertext and encrypted key as parameters and returns the decrypted text.
Encrypt_image takes image as a parameter and returns the ciphertext and the encrypted key.
Decrypt_image takes ciphertext and encrypted key as parameters and returns the decrypted image.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install Tienc.

```bash
pip install Tienc
```

## Usage

```python
hybrid_encryption = HybridEncryption()

# Encrypt text
ciphertext, encrypted_key = hybrid_encryption.encrypt_text("Hello, World!")

# Decrypt text
plaintext = hybrid_encryption.decrypt_text(ciphertext, encrypted_key)
print(plaintext) # "Hello, World!"

# Encrypt image
encrypted_chunks, encrypted_key = hybrid_encryption.encrypt_image("image.jpg")

# Decrypt image
image = hybrid_encryption.decrypt_image(encrypted_chunks, encrypted_key)
```
It's important to note that the key used in 
the ```AESEncryption``` class is generated randomly 
every time the class is instantiated, 
so the key used to encrypt the data must be 
stored in a secure location, 
if needed for future decryption.

## Contributing

Pull requests are welcome. For major changes, please open an issue first
to discuss what you would like to change.

Please make sure to update tests as appropriate.

## License

[MIT](https://choosealicense.com/licenses/mit/)
aoc-cli 0.0.8
Advent of Code scaffolding CLI
Requires Python: >=3.10

# aoc-cli

An opinionated scaffolder for Advent of Code

## Installation

`pip install aoc-cli`

Optionally install language-specific plugins such as `pip install aoc-cli-deno`.

## Usage

Scaffold a project with
`aoc init --language <python> --year <current year if december, else previous year> --location .`

All other subcommands are directory aware:

\*Note: plugins (specified with the --language flag) may alter the functionality below but in general...

`aoc get` downloads the input for the day
`aoc run <part>` executes the function specified in <part> (1 or 2) file for the day
`aoc submit <part>` executes the function specified in <part> (1 or 2) file for the day, then submits the answer to the form
`aoc open` opens the current day in the browser