semvec/pypi-packages huggingface dataset:
dash-stylish-header-footer-hook 0.0.2
Stylish header and footer components for Dash applications using Dash Hooks
Requires Python: >=3.8

# Dash Hooks Demo

A stylish header and footer component for Dash applications using Dash Hooks.

## Installation

```bash
pip install dash-stylish-header-footer-hook
```

## Usage

```python
from dash import Dash
import dash_stylish_header_footer_hook  

# Register the header with custom title
dash_stylish_header_footer_hook.add_header(
    title="My Dash App",
    gradient="linear-gradient(90deg, black, red)"
)
dash_stylish_header_footer_hook.add_footer_hook(text="©2025 My Dash App",
    background="black", color="magenta", padding="20px 40px")


app = Dash(__name__)
# Rest of your app code...
```

## Example

Run the included example:

```bash
python example.py
```
correction-ea-vhr 0.2
Neural network correction tool for activation energy in VHR thermoluminescence
Requires Python: >=3.7

# Correction of Activation Energy Affected by Variable Heating Rate (EaVHRCorrector)
===================================================================================

This project presents the development of a tool based on artificial intelligence to assist researchers in the correction of kinetic parameters obtained through the deconvolution of thermoluminescence TL glow curves. The correction is particularly useful when the results are affected by anomalous behavior associated with the heating rate, mainly due to Thermal Quenching.

Overview
--------
The model takes as input the area under each TL emission peak, which is a magnitude easily obtained from the analysis of experimental data. The input to the model is a vector of differences in area:

    X ≡ [∆A_medium, ∆A_median, ∆A_maximum, ∆A_minimum]

Features
--------
- AI-based correction of activation energy (Ea) affected by variable heating rate
- Designed for thermoluminescence glow curve analysis
- Accepts Excel files as input
- Provides corrected Ea, uncertainty, and model prediction


Usage
-----
Here is a minimal example of how to use the tool:

    from correction_ea_vhr.corrector import EaVHRCorrector
    import pandas as pd

    # Load your data
    df = pd.read_excel("TLGC.xlsx")
    X = df.iloc[:, 0:4].to_numpy()
    Y = df.iloc[:, 4:5].to_numpy()

    # Use the corrector
    modelo = EaVHRCorrector()
    media, incertidumbre, pred = modelo.corregir(X, Y)

Input Format
------------
The input Excel file should contain 5 columns:
1. ∆A_medium  
2. ∆A_median  
3. ∆A_maximum  
4. ∆A_minimum  
5. Experimental activation energy (Ea)

Output
------
- media: Corrected activation energy (mean)
- incertidumbre: Associated uncertainty
- pred: Model prediction array
py-roma 1.0.0
A python development kit containing handy packages.
Requires Python: >=3.9

Py-Roma
----

This package provides handy tools for regular development in python.

> At the heart of the conquest of Rome's vast territory was a sophisticated 
  infrastructure.

- [console](##console)
    - [Fancy Text](###fancy-text)
    - [Progress Bar](###progress-bar)
- [spqr](##spqr)
    - [censor](###censor)
    - [atticus](###atticus)

## `console`  
`console` should be imported in the manner below:

```python
from roma import console
```

### Fancy Text
Demo:
```python
console.write_line('#{Hello}{red} #{World}{blue}!')
```

### Progress Bar
Demo:
```python
import time

total = 100
quarter = int(total / 4)
for i in range(1, total + 1):
  if i % quarter == 0:
    console.write_line('{}/{} done!'.format(i, total))
  console.print_progress(i, total)
  time.sleep(0.05)
```

## `spqr`
This package serves as a MISC package, which contains a bundle of common, useful tools.

### `censor`

`censor` module handles issues related to argument types.

Demo of `check_type()`:
```python
from roma import censor

assert all([
  censor.check_type(31, int) == 31,
  censor.check_type({'adh', 93, 19}, inner_type=str) 
    == {'adh', '93', '19'},
  censor.check_type([None, 12, 19.0], tuple, 
    inner_type=int, nullable=True) == (None, 12, 19),
])

```

### `atticus`

`atticus` provides tools for produce appropriate strings.

Demo of `ordinal()`:
```python
from roma import atticus

assert all([
  atticus.ordinal(1) == '1st', atticus.ordinal(2) == '2nd', atticus.ordinal(3) == '3rd',
  atticus.ordinal(8) == '8th', atticus.ordinal(11) == '11th', atticus.ordinal(12) == '12th',
  atticus.ordinal(13) == '13th', atticus.ordinal(21) == '21st', atticus.ordinal(112) == '112th',
])
```
s3-ls 0.0.5
Fast S3 object listing
Requires Python: >=3.10

# s3-ls

Fast S3 object listing

## Introduction

Listing S3 directory containing millions of objects is very painful because it has to be sequential, but maximum number of objects per one request is limited to 1000.

```python
# First 1k objects
response = s3.list_objects_v2(
    Bucket=bucket, Prefix=prefix, StartAfter=""
)
last_key = response["Contents"][-1]["Key"]

# Next 1k objects
response = s3.list_objects_v2(
    Bucket=bucket, Prefix=prefix, StartAfter=last_key
)
...
```

`s3-ls` parallelizes it by partitioning the prefix into multiple parts and listing them in parallel.

```
prefix = "path/to/dir/"

prefix_1 = "path/to/dir/a"
prefix_2 = "path/to/dir/b"
prefix_3 = "path/to/dir/c"
... (70 prefixes)
```

In this way, the listing process can be done **70x faster** in the ideal case.

## Quick Start

### Installation

```bash
pip install s3-ls
```

### Usage

```bash
s3-ls s3://bucket/prefix
```

```python
from s3_ls import list_objects

for obj in list_objects(bucket, prefix, **s3_kwargs):
    key = obj["Key"]
```