semvec/pypi-packages huggingface dataset:
tf-datachain 0.1.0
A local dataset loader based on tf.data input pipeline

# tf-datachain

`tf-datachain` is a local dataset loader based on `tf.data` input pipeline. It handles the job of reading and encoding data direct in your disk and simplify the processing by providing several predefined methods.

## Object Detection

```python
from tf_datachain import ObjectDetection as od
```

Before using `ObjectDetection` functions, you have to define some basic information, like folder path and class name list.

```python
od.imageFolder = "data/images"

# hard-code class names
od.classNames = ["class1", "class2", "class3"]
# or read them from csv file
import pandas as pd
od.classNames = pd.read_csv("class.csv", header=None).iloc[:,0].values.tolist()
```

Then, a ready-to-use `tf.data` input pipeline can be built within 3 steps:

- Preparation: prepare the list to process without reading content.
- Data Loading: load data from prepared list via `tf.data`.
- Augmentation: shuffle, batch, and resize.

The best practices to load dataset with different format are shown below.

### Pascal VOC XML Format

```python
from tf_datachain.utils import split

BATCH_SIZE = 4
# read .xml file within data/annotaions folder
# then split them with the ratio of 6:2:2
trainDataset, validationDataset, testDataset = split(od.prepareAnnotation("data/annotations", ".xml"), 6, 2, 2)

trainDataset = tf.data.Dataset.from_tensor_slices(trainDataset)
trainDataset = trainDataset.map(lambda data: od.loadData(data, "Pascal VOC XML", "xyxy"), num_parallel_calls=tf.data.AUTOTUNE)
# shuffle, ragged batch, and jittered resize
trainDataset = od.datasetProcessing(trainDataset, BATCH_SIZE, "Jittered Resize", (960, 960), "xyxy")
trainDataset = trainDataset.prefetch(tf.data.AUTOTUNE)
```

### Visualize Dataset

```python
# visualize single data
for data in dataset.take(1):
  visualizeData(data, "xyxy")

# visualize dataset shown in 2x2 grid
visualizeDataset(dataset, "xyxy", rows=2, cols=2)
```
chemnlp 2023.7.1
chemnlp
Requires Python: >=3.7

[![name](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/ChemNLP_Example.ipynb)
![alt text](https://github.com/usnistgov/chemnlp/actions/workflows/main.yml/badge.svg)
[![DOI](https://zenodo.org/badge/523320947.svg)](https://zenodo.org/badge/latestdoi/523320947)


# ChemNLP

# Table of Contents
* [Introduction](#intro)
* [Installation](#install)
* [Examples](#example)
* [Web-app](#webapp)
* [Reference](#reference)

<a name="intro"></a>
Introduction
-------------------------
ChemNLP is a software-package to process chemical information from the scientific literature.

<a name="install"></a>
Installation
-------------------------
First create a conda environment:
Install miniconda environment from https://conda.io/miniconda.html
Based on your system requirements, you'll get a file something like 'Miniconda3-latest-XYZ'.

Now,

```
bash Miniconda3-latest-Linux-x86_64.sh (for linux)
bash Miniconda3-latest-MacOSX-x86_64.sh (for Mac)
```
Download 32/64 bit python 3.8 miniconda exe and install (for windows)
Now, let's make a conda environment, say "chemnlp", choose other name as you like::
```
conda create --name chemnlp python=3.8
source activate chemnlp
```
#### Method 1 (using setup.py):

Now, let's install the package:
```
git clone https://github.com/usnistgov/chemnlp.git
cd chemnlp
python setup.py develop
cde data download
```

#### Method 2 (using pypi):

As an alternate method, ChemNLP can also be installed using `pip` command as follows:
```
pip install chemnlp
cde data download
```

<a name="example"></a>
Examples
---------
#### Parse chemical formula 

```
run_chemnlp.py --file_path="chemnlp/tests/XYZ"
```

#### Text classification example

```
python chemnlp/classification/scikit_class.py --csv_path chemnlp/sample_data/cond_mat_small.csv
```

[Google Colab example for installation and text classification](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/ChemNLP_Example.ipynb)

[Google Colab example for Text Generation with HuggingFace](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/ChemNLP_TitleToAbstract.ipynb)


<a name="webapp"></a>
Using the webapp
---------
The webapp is available at: https://jarvis.nist.gov/jarvischemnlp

![JARVIS-ChemNLP](https://github.com/usnistgov/chemnlp/blob/develop/chemnlp/PTable.PNG)

<a name="reference"></a>
Reference
---------


[ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data](https://arxiv.org/abs/2209.08203)
ssl-sniffer 0.1.0
A command line tool that checks if hosts have valid SSL certificates for port 443.

# SSL-Sniffer
SSL Sniffer is a command line tool that checks if hosts have valid SSL certificates for port 443. It is a Python tool that can be installed using pip.

## Installation

To install SSL Sniffer, you can run the following command:

pip install ssl_sniffer


## Usage

To use SSL Sniffer, you can run the following command:

ssl_sniffer [hosts]

The hosts argument is a list of hosts that you want to check. For example, to check the hosts www.example.com, www.google.com, and www.yahoo.com, you would run the following command:

ssl_sniffer www.example.com www.google.com www.yahoo.com

Output
SSL Sniffer will print a message for each host that it checks. If the host has a valid SSL certificate, the message will say "The host <host> has a valid SSL certificate for port 443.". If the host does not have a valid SSL certificate, the message will say "The host <host> does not have a valid SSL certificate for port 443.".

Author
SSL Sniffer was created by @thatonesecguy.

License
SSL Sniffer is licensed under the MIT License.
dgarbsutils 1.6.2
A set of curated utils
Requires Python: >=3.7

# Utils

## Description

I found myself using the same code over and over across projects so I created a package to import my own code faster.

## Install

```bash
pip install dgarbsutils
```

## Usage

```python
from dgarbsutils import utils, awsUtils
```

## Build

```bash
python -m build
```

## Upload

```bash
python3 -m twine upload --repository pypi dist/*
python3 -m twine upload --repository nexus dist/*
```