semvec/pypi-packages huggingface dataset:
screenshot-assistant 0.1.0
A Wayland screenshot assistant with LLM integration
Requires Python: >=3.11

# Screenshot Assistant

A Wayland screenshot assistant with LLM integration. Capture screenshots and analyze them using Ollama's LLaVA model.

## Installation

```bash
pip install screenshot-assistant
```

## Requirements

- Wayland compositor (Sway, Hyprland, or other wlroots-based)
- grim (screenshot utility)
- slurp (area selection)
- imv (image viewer)
- Ollama with LLaVA model installed

## Usage

You can run the assistant in two ways:

1. As a command:

```bash
screenshot-assistant
```

2. Using the provided run script:

```bash
./run
```

## Configuration

Create a `.env` file with the following options:

```env
OLLAMA_URL=http://localhost:11434/api/generate
OLLAMA_MODEL=llava
WINDOW_TITLE=Screenshot Assistant
SCREENSHOT_MODE=active  # Options: all, active, select
```

## Development

To set up for development:

```bash
git clone https://github.com/tcsenpai/screenshot-assistant.git
cd screenshot-assistant
uv venv .venv
uv pip install -e .
```
PyFina 0.0.7
A numpy subclass to read emoncms PHPFINA feeds as numpy array
Requires Python: >=3.6

# PyFina

PyFina is a subclass of numpy np.ndarray to import emoncms PHPFINA feeds as numpy arrays

**The pip installer will install any missing requirements (numpy, matplotlib)**

PyFina brings the power of numpy to the PHPFINA timeseries, including basic operations and more :
addition, soustraction, multiplication, division, min, max, mean, sum, slicing with the bracket operator

Note : any operation on a PyFina object results to a standard numpy nd.array object.
The signature of the PyFina object is lost.

It does not prevent to add two PyFina objects of the same size but sampled with different intervals


## Installation

```
python3 -m pip install PyFina
```
or, for python on Windows
```
py -m pip install PyFina
```

## Post installation testing

copy the content of [test.py](https://raw.githubusercontent.com/Open-Building-Management/PyFina/main/tests/test.py), paste it in a blank file on your local machine and save it using the same name.

```
py test.py
```

## Getting Started

To retrieve metadatas for feed number 1 :

```
from PyFina import getMeta, PyFina
import matplotlib.pyplot as plt
# classic emoncms feed storage on linux
dir = "/var/opt/emoncms/phpfina"
meta = getMeta(1,dir)
print(meta)
```
To import the first 8 days of datas, with a sampling interval of 3600 s :

```
step = 3600
start = meta["start_time"]
window = 8*24*3600
length = meta["npoints"] * meta["interval"]
if window > length:
    window = length
nbpts = window // step
Text = PyFina(1,dir,start,step,nbpts)
```
To catch the signature of the created PyFina object :
```
# time start as a unixtimestamp expressed in seconds
print(Text.start)
# step/interval in seconds
print(Text.step)
```

To plot:
```
import datetime
import time
localstart = datetime.datetime.fromtimestamp(start)
utcstart = datetime.datetime.utcfromtimestamp(start)
title = "starting on :\nUTC {}\n{} {}".format(utcstart,time.tzname[0],localstart)
plt.subplot(111)
plt.title(title)
plt.ylabel("outdoor Temp °C")
plt.xlabel("time in hours")
plt.plot(Text)
plt.show()
```
With the above code, the xrange will be expressed in hour, so 192 points will be displayed

![](test.png)

To express the xrange in seconds :
```
xrange = Text.timescale()
plt.subplot(111)
plt.plot(xrange,Text)
plt.show()
```
Topsis-Anoushka-102203560 1.0.0
A Python package to perform TOPSIS analysis.
Keywords: python, topsis, anoushka, 102203560


# TOPSIS

TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is a Python package for multi-criteria decision analysis. This is an easy-to-use implementation of TOPSIS, allowing users to analyze and rank alternatives based on multiple criteria.

## Installation

```bash
pip install topsis-anoushka-102203560
Topsis-Anoushka-10223560 1.0.0
A Python package to perform TOPSIS analysis.
Keywords: python, topsis, anoushka, 102203560


# TOPSIS

TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is a Python package for multi-criteria decision analysis. This is an easy-to-use implementation of TOPSIS, allowing users to analyze and rank alternatives based on multiple criteria.

## Installation

```bash
pip install topsis-anoushka-102203560
topsis-angadSingh-102203314 1.0.0
A Python package to perform TOPSIS analysis on datasets.
Requires Python: >=3.6

# TOPSIS Implementation in Python

---

## Overview
This Python script implements the **TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)** method for multi-criteria decision-making. It calculates the performance score and rank for given data based on user-defined weights and impacts.

---

## Usage

Run the script with the following command:

```bash
python topsis.py <inputFileName> <weights> <impacts> <resultFileName>
```

- `<inputFileName>`: Path to the CSV file containing the dataset.
- `<weights>`: Comma-separated weights for the criteria (e.g., `1,2,3`).
- `<impacts>`: Comma-separated impacts for the criteria (`+` for benefit, `-` for cost).
- `<resultFileName>`: Name of the output file where results will be saved.

---

## Input File Requirements
1. Must be a `.csv` file with at least **3 columns**.
2. The first column can contain non-numeric values (e.g., IDs).
3. Columns from the 2nd to the last must contain **numeric values only**.

---

## Features
- **Validation**:
  - Ensures correct number of inputs.
  - Validates weights, impacts, and column counts.
  - Handles missing or invalid files.
- **Error Handling**:
  - Displays user-friendly messages for issues like file not found, non-numeric data, or mismatched input parameters.
- **Output**:
  - Adds two new columns: `Topsis Score` and `Rank`.
  - Saves the results in the specified output file.

---

## Dependencies
- Python 3.x
- Required libraries: `pandas`, `math`, `sys`

Install dependencies using:
```bash
pip install pandas
```

---

## License
This project is open-source and free to use under the MIT License.
TOPSIS-DishavSingla-102217004 0.4
A Python package for implementing the TOPSIS decision-making method.
Requires Python: >=3.6

ï»¿# **TOPSIS - Dishav_Singla-102217004**

A Python implementation of the **TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)** method, designed for decision-making in multi-criteria decision analysis (MCDA).

---

## **Project Description**

This project implements the **TOPSIS** technique, a popular method used in decision-making that evaluates multiple alternatives based on various criteria. The project is focused on applying **TOPSIS** to a given dataset with multiple alternatives and criteria. The objective is to rank and identify the best alternative by calculating the **relative closeness** of each alternative to the ideal solution.

### **Objective**

- To apply **TOPSIS** on a sample dataset.
- To rank different alternatives based on defined criteria.
- To generate a ranking list of the alternatives for decision-making purposes.

---

## **Features**

- **Data Preprocessing**: The dataset is cleaned and normalized.
- **TOPSIS Algorithm**: Implementation of the **TOPSIS** method, including the calculation of:
  - Ideal and negative-ideal solutions.
  - Separation measures.
  - Relative closeness to the ideal solution.
- **Ranking**: Rank the alternatives based on the relative closeness.
- **Visualizations**: Plot results (optional, if applicable).
  
---

## **Installation**

### Prerequisites

Ensure you have **Python 3.x** installed. You can download it from the official website:

- [Python Official Website](https://www.python.org/downloads/)

### Install Dependencies

Clone this repository and install the necessary dependencies using **pip**:

```bash
git clone https://github.com/DISHAVSINGLA/TOPSIS-Dishav_Singla-102217004.git
cd TOPSIS-Dishav_Singla-102217004
pip install -r requirements.txt
```

### Install Library

```bash
pip install TOPSIS-DishavSingla-102217004
```
Topsis-DishavSingla-102217004 0.2
A Python package for implementing the TOPSIS decision-making method.
Requires Python: >=3.6

ï»¿# **TOPSIS - Dishav_Singla-102217004**

A Python implementation of the **TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)** method, designed for decision-making in multi-criteria decision analysis (MCDA).

---

## **Project Description**

This project implements the **TOPSIS** technique, a popular method used in decision-making that evaluates multiple alternatives based on various criteria. The project is focused on applying **TOPSIS** to a given dataset with multiple alternatives and criteria. The objective is to rank and identify the best alternative by calculating the **relative closeness** of each alternative to the ideal solution.

### **Objective**

- To apply **TOPSIS** on a sample dataset.
- To rank different alternatives based on defined criteria.
- To generate a ranking list of the alternatives for decision-making purposes.

---

## **Features**

- **Data Preprocessing**: The dataset is cleaned and normalized.
- **TOPSIS Algorithm**: Implementation of the **TOPSIS** method, including the calculation of:
  - Ideal and negative-ideal solutions.
  - Separation measures.
  - Relative closeness to the ideal solution.
- **Ranking**: Rank the alternatives based on the relative closeness.
- **Visualizations**: Plot results (optional, if applicable).
  
---

## **Installation**

### Prerequisites

Ensure you have **Python 3.x** installed. You can download it from the official website:

- [Python Official Website](https://www.python.org/downloads/)

### Install Dependencies

Clone this repository and install the necessary dependencies using **pip**:

```bash
git clone https://github.com/your-username/TOPSIS-Dishav_Singla-102217004.git
cd TOPSIS-Dishav_Singla-102217004
pip install -r requirements.txt
Topsis-Balbir-Singh-102217078 1.1
A Python package for multi-criteria decision making using the TOPSIS method.
Requires Python: >=3.6
Keywords: topsis, decision analysis, MCDM, multi-criteria decision making, Python

# Topsis-Balbir-102217078

`Topsis-Balbir-102217078` is a Python package that implements the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method for multi-criteria decision making. This tool is ideal for evaluating and ranking alternatives based on multiple criteria, which is essential in fields like supply chain management, finance, and engineering.

## Installation

You can install `Topsis-Balbir-102217078` directly from the Python Package Index using pip:

```bash
pip install Topsis-Balbir-Singh-102217078
```

## Usage

To use Topsis-Balbir-102217078, you will need to prepare your data in a CSV format where the first column contains the names/labels of the alternatives, and the subsequent columns contain the criteria values. The command line interface can be used as follows:

```bash
topsis data.csv "1,2,3" "+,-,+" results.csv
```

Where:
- `data.csv` is your input file.
- `"1,2,3"` is a comma-separated string of weights for each criterion.
- `"+,-,+"` is a comma-separated string of impacts for each criterion, where `+` indicates that higher is better, and `-` that lower is better.
- `results.csv` will be the output file with the TOPSIS scores and rankings.

## Features

- Easy integration with Pandas DataFrames.
- Customizable weights and criteria impacts.
- Automatic normalization and ranking of alternatives.
- Command line interface for easy access and usage.
dbt-duckdb-udf 0.0.4
dbt-duckdb plugin enabling direct integration with DuckDB's Python interface
Requires Python: >=3.12

## A dbt-duckdb plugin enabling direct integration with DuckDB's Python interface

Check out this tutorial on how to use it: [[Part 2] DuckDB, DBT, and Python— The perfect combo for T in ELT](https://brucemen711.medium.com/part-2-duckdb-dbt-and-python-the-perfect-combo-for-t-in-elt-6a41039c3f81)