semvec/pypi-packages huggingface dataset:
ozzy-image-processing 0.0.3
Package to process images using Python
Requires Python: >=3.8

# ozzy_image_processing

## Description

The package `ozzy_image_processing` is used to:

Processing:
- Histogram matching
- Structural similarity
- Resize image

Utils:
- Read image
- Save image
- Plot image
- Plot result
- Plot histogram

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install `ozzy_image_processing`:

```bash
pip install ozzy_image_processing
```
## Autor
	Ozzy Azevedo

## License
	MIT
LiveTimer 0.2.0
A Python module to log multiple points in a repeating operation to get live statistics.
Requires Python: <4.0,>=3.09

# LiveTimer
A Python module to log multiple points in a repeating operation to get live statistics.

## Upfront Performance Notice
This module was not designed with per-cycle performance in mind. It was designed to time IO-bound operations with 
millisecond precision, and has not been benchmarked or optimized for highly performant applications. You are welcome
to submit pull requests that would improve performance.

## Usage
This example can be found in `demo.py`.
```python
>> with RichTimer().start as timer:
>>     for _ in range(100):
>>         with timer.cycle as cycle:
>>             time.sleep(randint(1, 8) * 0.001)
>>             cycle.log("Logpoint: 10-80ms")
>>             time.sleep(randint(1, 2) * 0.001)
>>             cycle.log("Logpoint: 10-20ms")
>>             time.sleep(0.1)
>>             cycle.log("Logpoint: 100ms")
```

Output:
```
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓
┃ Metric              ┃ Total ┃ Avg      ┃ Min      ┃ Max      ┃
┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩
│ Count               │ 100   │          │          │          │
│ Time per Iteration  │       │ 0.107448 │ 0.102287 │ 0.113916 │
│ - Logpoint: 10-80ms │       │ 0.004728 │ 0.001104 │ 0.010578 │
│ - Logpoint: 10-20ms │       │ 0.001943 │ 0.001042 │ 0.003702 │
│ - Logpoint: 100ms   │       │ 0.100768 │ 0.100067 │ 0.109139 │
└─────────────────────┴───────┴──────────┴──────────┴──────────┘
```
nuant.helpers 0.0.9
Nuant helpers
Requires Python: >=3.8

<div align="center">
  <img src="https://cdn.prod.website-files.com/6645c2bf1d8a88dfb078b41d/6645d629b58f71cda23ae53e_Logotype-Nuant.svg" alt="Logo">
  <h1>Nuant Helpers</h1>
</div>

# Introduction

This python library can help you manipulate Nuant data more easily.
This module is automatically available on your Nuant development environment (IDE / Dashboard).

# Helpers

List of all helpers available

## Simulation

### fetch_list_files

List all files available as result for this simulation

```python
from nuant.helpers import simulation

all_files = simulation.fetch_list_files("YOUR_SIMULATION_ID", api_key="YOUR_API_KEY")
```

### fetch_data

Fetch a result of the simulation with the timeseries preformatted with the merge of time and values files.

```python
from nuant.helpers import simulation

data = simulation.fetch_data("YOUR_SIMULATION_ID", "DATA_FILE_NAME", api_key="YOUR_API_KEY")
```

### fetch_parameters

Fetch parameters file of the simulation

```python
from nuant.helpers import simulation

parameters = simulation.fetch_parameters("YOUR_SIMULATION_ID", api_key="YOUR_API_KEY")
```

### convert_to_singletimeseries

Convert raw data from the simulator to Nuant Quantlib's SingleTimeseries

```python
from nuant.helpers import simulation

# Examples values but you probably want to pass simulator's data
timestamps = { 'time': [1724139765] }
values = { 'mean': [1] }

parameters = simulation.convert_to_singletimeseries(timestamps, values, target='mean')
```

### convert_from_singletimeseries

Convert Nuant Quantlib's SingleTimeseries to array of dict for the dashboard visualisation

```python
from nuant.helpers import simulation

my_rows = simulation.convert_from_singletimeseries(singletimeseries, target='mean')
```
Message-client-k2foxspb 0.1.1
mess_client

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inequalitychay 1.0.1
不等式库（含解一元一次不等式和随机一元一次不等式）
Requires Python: >=3.5

# Inequality-Including

不等式库

suijiine(dif:int) -> str
随机不等式，dif为难度系数

solveine(s:str) -> str
解不等式，s为一元一次不等式，形如ax+b>0的形式
utils_fastapi 0.0.7
Utils for FastApi. Filters, Pagination, Serializers, Schemas
Requires Python: >=3.10

# FastAPI-Utils

FastAPI-Utils is an utils library built upon the principles of Django designed for
seamless migration from Django to FastAPI.

## Features

* [X] Pagination
* [X] Filtering
* [X] Serializers

## Installation

```bash
pip install utils_fastapi
```

To use sqlalchemy model serializer

```bash
pip install utils_fastapi[sqlalchemy]
```

To use tortoise model serializer

```bash
pip install utils_fastapi[tortoise]
```
MercatorConverter 0.0.2
Mercator Coordinate Converter is a Python package designed to simplify the conversion of geographical projection coordinates, such as latitude and longitude, into Mercator coordinates, and vice versa.
Requires Python: >=3.8

# Mercator Coordinate Converter

**Mercator Coordinate Converter** is a Python package designed to simplify the conversion of geographical projection coordinates, such as latitude and longitude, into Mercator coordinates, and vice versa. This package is ideal for developers and GIS professionals who need a reliable and efficient tool for coordinate transformation.

## Installation

You can install the package using pip:

```bash
pip install MecatorConverter
```

## Usage

Here's a quick example of how to use the package:

```python
import MecatorConverter

# Convert from geographical to Mercator coordinates
latitude = 52.3676
longitude = 4.9041
mercator_x, mercator_y = MecatorConverter.coord_to_mercator(latitude, longitude)
print(f"Mercator Coordinates: X={mercator_x}, Y={mercator_y}")

# Convert from Mercator to geographical coordinates
mercator_x = 580000
mercator_y = 6800000
latitude, longitude = MecatorConverter.mercator_to_coord(mercator_x, mercator_y)
print(f"Geographical Coordinates: Latitude={latitude}, Longitude={longitude}")
```

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request or open an Issue if you have any suggestions or improvements.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.

## Contact

If you have any questions or need further assistance, please contact Daniil at donil858@gmail.com.
mmcr 0.1.0
MMCR Loss: Learning efficient coding of natural images with maximum manifold capacity representations
Keywords: torch, loss, mmcr, Maximum Manifold Capacity Representations

# Pytorch implementation of Maximum Manifold Capacity Representations Loss
> This is not an official implementation from the authors.
> [Official implementation from the authors](https://github.com/ThomasYerxa/mmcr).


Maximum Manifold Capacity Representation Loss (MMCR Loss) is a novel objective function for self-supervised learning (SSL) proposed by researchers in Center for Neural Science, NYU.

This repository aims to offer a convenient MMCR loss module for PyTorch, which can be easily integrated into your projects using `git clone` or `pip install`.

## How to install
```sh
pip3 install mmcr
```
or 
```sh
git clone https://github.com/skyil7/mmcr
cd mmcr
pip install -e .
```
## Usage
```python
import torch
from mmcr import MMCRLoss

loss = MMCRLoss()

input_tensor = torch.randn((8, 16, 128))  # batch_size, n_aug, feature_dim
loss_val = loss(input_tensor)

print(loss_val)
```

## How it works
```math
\mathcal{L} = \lambda\frac{\sum^{N}_{i=1}\lVert z_{i} \rVert_{*}}{N} - \lVert C\rVert_{*}
```

Where $\lambda$ is a trade-off parameter, $`\lVert z_i\rVert_*`$ is local nuclear norm of the $i$-th sample's augmented matrix, and $`\lVert C\rVert_*`$ is the global nuclear norm of centroid matrix $C$.

### Arguments
- `lmbda`: Trade-off parameter $\lambda$. default is 0.
- `n_aug`: number of augmented views. If your input tensor is 3-dimensional $(N, k, d)$, you don't need to specify it.

## Original Implementation from the author
- This repository was developed with reference to the [official implementation](https://github.com/ThomasYerxa/mmcr) provided by the authors.
ImageHawk 0.0.3
A package for text to image search using embeddings


### _ImageHawk_



`This package generates embeddings for the given photos and saves them in the qdrant vector store`
`It also generates embeddings for your text query`
`It provides  a function to find an image based on its semantic similarityÂ to the text query`
`Currently, it supports the Qdrant Vector Store, but other stores will be added soon....`

> Author:Junaid Bashir
numberandchinese 1.0.2
数字中文转换器
Requires Python: >=3.5

# numberandchinese-Including

中文、数字转换库

```python
Help on module numberandchinese:

NAME
    numberandchinese

DESCRIPTION
    # -*- coding:UTF-8 -*-
    # @Author:Chay
    # @TIME:2024/07/24 09:45
    # @FILE:numbertochinese.py
    # @version:1.0.0
    # @Software:Visual Studio Code

FUNCTIONS
    No2Cn(number: int) -> str

    chinese2digits(uchars_chinese)

    maxdigit(number: int, count: int) -> tuple

DATA
    common_used_numerals = {'一': 1, '万': 10000, '三': 3, '九': 9, '二': 2, '五...
    digitdict = {1: '十', 2: '百', 3: '千', 4: '万'}
    numdict = {0: '零', 1: '一', 2: '二', 3: '三', 4: '四', 5: '五', 6: '六', 7: ...

FILE
    d:\chay\project\numberandchinese-including\src\numberandchinese\numberandchinese.py
```
docfusion 0.1.0
Doc Fusion is a Data Sourcing framework capable of parsing various data types such as pdf, txt, md, docx, xlsx, csv and even a webpage url.
Requires Python: >=3.9

Doc Fusion is a Data Sourcing framework capable of parsing various data types such as pdf, txt, md, docx, xlsx, csv and even a webpage url. It can handle several types of data such as multi columnar, tabular and invoices. The framework uses an LLM (Large Language Model) Agentic approach, where each data type is managed by a dedicated LLM Agent.
streamlit-mui 1.0.4
Streamlit module which provides implementation of various Material UI components
Requires Python: >=3.9.0

# Welcome to Streamlit MUI

<img src="https://user-images.githubusercontent.com/7164864/217935870-c0bc60a3-6fc0-4047-b011-7b4c59488c91.png" alt="Streamlit logo" style="margin-top:50px"></img>


This Python library provides a flexible way to integrate Material UI components in your streamlit application. As of now this includes single bar and grouped bar charts. In near future a lot of MUI components will be provided by this library.

## Installation

Open a terminal and run

```cmd
$ pip install streamlit_mui
$ streamlit_mui_hello
```

## Usage

### Creating a Single Series Bar Chart

To create a bar chart, use the DefaultBarChartConfig class to define the chart's configuration, then pass it to the bar_chart function to render the chart.

```python
from streamlit_mui import bar_chart, DefaultBarChartConfig, BarData

default_bar_chart_config = DefaultBarChartConfig(data=[
    BarData("Jan", 400),
    BarData("Feb", 100),
    BarData("March", 100),
    BarData("April", 400),
    BarData("May", 100),
    BarData("June", 140),
    BarData("July", -100),
    BarData("August", 100),
    BarData("September", 100),
    BarData("October", 100),
    BarData("November", 80),
    BarData("December", 100),
],
    bar_color='#004b85',
    x_axis_label="Month", y_axis_label="Rain count")

bar_chart(key="bar_chart_unique_key", config=default_bar_chart_config)
```

## Usage

### Creating a Grouped Series Bar Chart

For a grouped bar chart, use the GroupBarChartConfig class. Define the data for each group using GroupSeriesData and GroupBarData.

```python
from streamlit_mui import bar_chart, GroupBarChartConfig, GroupBarData, GroupSeriesData

group_bar_chart_config = GroupBarChartConfig(
    x_axis_label="Month",
    y_axis_label="Score",
    data=[
        GroupSeriesData("Jan", data=[
            GroupBarData("London", 100),
            GroupBarData("Paris", 40)
        ]),
        GroupSeriesData("Feb", data=[
            GroupBarData("London", 90),
            GroupBarData("Paris", 60),
            GroupBarData("New York", 160)
        ]),
        GroupSeriesData("March", data=[
            GroupBarData("London", 30),
            GroupBarData("Paris", 90)
        ])
    ],
    series=["London", "Paris", "New York"])

bar_chart(key="bar_chart_unique_key", config=group_bar_chart_config)
```

![](https://github.com/user-attachments/assets/9cb2ad3c-8f9a-4f86-a3d6-8440cda87ebb)

## Author
- Dilip Thakkar [dilip.thakkar.eng@gmail.com]
- LinkedIn [https://www.linkedin.com/in/dilip-thakkar-465898194/]