semvec/pypi-packages huggingface dataset:
toga-view 0.0.1
xml/json view support for toga
Requires Python: >=3.8
Keywords: beeware, toga, toga-echosystem, ui

<h1 align="center">
    Toga-View
</h1>
<p align="center">
    xml/json view support for toga
</p>
<br/>

<div align="center">
    <img src="https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10%20%7C%203.11%20%7C%203.12-blue" />
    <br>
    <a href="https://github.com/somehitDev/toga-view/blob/main/LICENSE">
        <img src="https://img.shields.io/github/license/somehitDev/toga-view.svg" alt="MIT License" />
    </a>
    <a href="https://pypi.org/project/toga-view/">
        <img src="https://img.shields.io/pypi/v/toga-view.svg" alt="pypi" />
    </a>
</div><br><br>

## 🛠️ Features
- **Toga View Format**: support .ui.xml/.ui.json format file.
- **Parse/Archive**
  - support parse ui file and archive python to ui file
  - support parse ui file into python file
  - support transpile ui file between .ui.xml and .ui.json

<br>

## 💿 Installation
- install from git(latest)
```bash
pip install git@github.com:somehitDev/toga-view.git
```
- pypi
```bash
pip install toga-view
```

<br>

## 📑 Usage
- cli
```bash
# parse
toga_view_cli parse {ui_file} -o {py_file}
# transpile
toga_view_cli transpile {ui_file} -o {ui_file of different ext}
```
- python
```python
# parse(into toga.Widget)
XmlParser.parse_file("{ui_file}")
JsonParser.parse_file("{ui_file}")
# parse(into python script)
XmlParser.parse_file_to_script("{ui_file}", "{widget_class_name}")
JsonParser.parse_file_to_script("{ui_file}", "{widget_class_name}")

# archive(into element)
XmlArchiver.archive({toga.Widget})
JsonArchiver.archive({toga.Widget})
# archive(into ui file)
XmlArchiver.archive_to_file({toga.Widget}, "{ui_file}")
JsonArchiver.archive_to_file({toga.Widget}, "{ui_file}")
```
<br>

##  Project Structure
- `toga_view/`: toga-view source.
- `tests/`: examples.
ta-bitwarden-cli 0.14.0
Thoughtful BitWarden CLI Package
Requires Python: >=3.8
Keywords: ta_bitwarden_cli

================
ta-bitwarden-cli
================


.. image:: https://img.shields.io/pypi/v/ta_bitwarden_cli.svg
        :target: https://pypi.python.org/pypi/ta_bitwarden_cli

.. image:: https://img.shields.io/travis/macejiko/ta_bitwarden_cli.svg
        :target: https://travis-ci.com/macejiko/ta_bitwarden_cli

.. image:: https://readthedocs.org/projects/ta-bitwarden-cli/badge/?version=latest
        :target: https://ta-bitwarden-cli.readthedocs.io/en/latest/?version=latest
        :alt: Documentation Status

|

Thoughtful Automation BitWarden CLI installation package

|

Installation
------------

For correct work please use python virtualenv approach!
Also use *--no-cache-dir* option.
Otherwise it would cache package wheel and wont download **bw** CLI binary anymore

::

   python3 -m virtualenv venv
   source venv/bin/activate
   pip install --no-cache-dir ta-bitwarden-cli

Code above will additionally install **bw** CLI binary to a first available folder in the $PATH

|

Example Usage
-------------

.. code:: python

        import os
        from ta_bitwarden_cli import ta_bitwarden_cli as ta

        bitwarden_credentials = {
            "password": os.getenv("BW_PASSWORD"),
            "client_id": os.getenv("BW_CLIENTID"),
            "client_secret": os.getenv("BW_CLIENTSECRET"),
        }
        creds = {
            "my_vault_item": "Google Maps API Key",
        }
        bw = ta.Bitwarden(bitwarden_credentials)
        assert bw.get_credentials(creds)["my_vault_item"]["password"] == "XXXXXXX"

|

Troubleshooting
---------------

If you use Windows during code execution you could face with something like:

::

   FileNotFoundError: [WinError 2] The system cannot find the file specified

This means that no binary is available. In that case please manually download BitWarden CLI binary from https://vault.bitwarden.com/download/?app=cli&platform=windows
and put it to any folder from $PATH. This approach is similar to chromedriver

|

Development
-----------

**1. Prepare local dev env**

::

   python3 -m virtualenv venv
   source venv/bin/activate
   pip install -r requirements.txt
   pre-commit install

**2. Test**

::

   BW_PASSWORD=YYY BW_CLIENTID=XXX BW_CLIENTSECRET=ZZZ pytest

**3. Commit your code using https://www.conventionalcommits.org/en/v1.0.0/ style commit messages**

**4. Submit PR**
rocauc-comparison 0.1
A package for comparing ROC AUC scores using DeLong's method.
Requires Python: >=3.6

# rocauc_comparison

`rocauc_comparison` is a Python package designed for comparing Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) scores using DeLong's method. This package provides a statistical test to determine if the difference between two ROC AUC scores is statistically significant.

## Features

- **Compare Two ROC AUC Scores**: Use DeLong's test to compare the ROC AUC scores of two models.
- **Calculate AUC Variance**: Compute the variance of a single ROC AUC score using DeLong's method.
- **Fast and Efficient**: Implemented with optimized numpy operations for efficient computation.

## Installation

You can install the package using `pip`:

```sh
pip install rocauc_comparison

import numpy as np
from rocauc_comparison import delong_roc_test

# Ground truth labels
ground_truth = np.array([0, 1, 0, 1, 0, 1])

# Predictions from the first model
predictions_one = np.array([0.1, 0.4, 0.35, 0.8, 0.5, 0.9])

# Predictions from the second model
predictions_two = np.array([0.05, 0.45, 0.3, 0.7, 0.6, 0.85])

# Calculate the p-value for the difference in AUC
p_value = delong_roc_test(ground_truth, predictions_one, predictions_two)
print(f"p-value: {p_value}")
lib-ml-remla-team14-a2 5.0.0
A ML preprocessing library.
Requires Python: >=3.7

# lib_ml_remla_team14_a2

lib_ml_remla_team14_a2 contains pre-processing logic for data used in our ML training pipeline.

## Features

- Tokenization of text data at a character level.
- Padding of text sequences to uniform lengths.

## Installation

Install lib_ml_remla_team14_a2 using pip:

```bash
pip install lib_ml_remla_team14_a2
```

## Usage

### Importing the Class

```bash
from lib_ml.preprocessing import TextPreprocessor

```

### Initializing the Preprocessor'

You can customize the behavior of the `TextPreprocessor` by adjusting its configuration. Here's how to initialize it with the default settings:

```bash
preprocessor = TextPreprocessor()

```

Or customize its behavior:

```bash
config = {
    'lower': True,
    'char_level': True,
    'oov_token': '-n-',  # Token used for out-of-vocabulary characters
    'sequence_length': 150  # Adjust sequence length as needed
}
preprocessor = TextPreprocessor(config=config)

```

### Preprocessing Text Data

Use the `fit_text` method to fit the tokenizer on your text data, and `transform_text` to convert text data into padded sequences:

```bash
texts = ["hello", "world"]
preprocessor.fit_text(texts)
processed_texts = preprocessor.transform_text(texts)

```


### Output

The `transform_text` method returns an array of tokenized and padded sequences, ready for use in training or inference:

```bash
print(processed_texts)  # Output will be a NumPy array of processed text data

```
team-14-lib-version 5.0.1
version aware library
Requires Python: >=3.8

# lib-version
A version-aware library consumed by the URL fishing detection application service
The library parses the version from the metadata in the package.

Uses semantic versioning `vMAJOR.MINOR.PATCH`

Automatic Bumping on Github: If no #major, #minor or #patch tag is contained in the merge commit message, 
it will bump whichever DEFAULT_BUMP is set to (which is minor by default). Right now it increments the minor version with each PR.

# usage

In the file you want to use this in, do `from libversion import VersionUtil`.
Then you can call `VersionUtil.VersionUtil.get_version()` to get the library version.
Tihi-spectral-fitter 0.0.4
A peak identification and signal decomposition tools for chemical signals
Requires Python: >=3.8

![alt text](https://raw.githubusercontent.com/kyunghoon-han/Tihi_spectral_fitter/688758941fd63fb8998e23d9e3ef9c84c95aecbb/logo_small.png)

# Peak finder GUI tool for chemical or biological signals

## Installation  of the required libraries
One simply needs to run the following to install the required libraries.
```
pip install -r requirements
```
If `pip` is not installed on your machine, you can run either
```
python get-pip.py
```
or
```
python -m ensurepip --upgrade
```
on your machine.
coolcsv 0.0.1
rapid database build using csv
Requires Python: >=3.8

"""
project is provides a fast way to make simple insecure database for any 
startups 

only uses csv inbuilt module , so no external dependacies

code usage ->

'''
from mydb import *

# Create a CSVDatabase object for a file named 'inventory.csv'
inventory_db = mydb('inventory.csv')

# Sample data (assuming the CSV file has headers like 'name', 'price', 'quantity')
new_item = ['Laptop', 599.99, 3]

# Insert the new item
inventory_db.insert(new_item)
print("Item inserted successfully!")

# Update the price of the first item (assuming the data is loaded)
inventory_db.update(0, ['Headphones', 79.95, 10])  # Assuming the first row is headphones
print("Item price updated!")

# Delete the third item (assuming the data is loaded)
inventory_db.delete(2)
print("Item deleted!")

# Find items with 'price' greater than 100
expensive_items = inventory_db.find(search_term=None, column_index=1)  # Search price column
print("Expensive items:")
for item in expensive_items:
    print(item)

# Get the second row (assuming the data is loaded)
second_item = inventory_db.get_row(1)
if second_item:
    print("Second item:", second_item)
else:
    print("Invalid row index.")

''' 

"""