semvec/pypi-packages huggingface dataset:
aegish 0.1.2
Natural language to Linux command converter
Requires Python: >=3.7

# aegish
Write linux commands in natural language using the power of local ai!

You can use aegish in two modes: Local or Cloud. Local uses ollama to run models on your device, whereas cloud uses your api key to make a request to a cloud provider.

Local providers: Ollama

Cloud Providers: ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic) 



## Table of Contents

- [Installation](#installation)
- [Usage](#usage)


## Installation

### Install using pip

```
pip install aegish
```

After installation, use the `ag` configure command to choose local or cloud ai, to choose models, and to save api keys:

```
ag --configure
```

Make sure you know the api usage prices per model before selecting the model. The AI company will bill you based on usage and models used. 

## Usage

Generate a Linux command from natural language:

```
ag list files in this directory
```

Print the generated command without running it:

```
ag --print-only show disk usage
```

Configure the default model and system prompt:

```
ag --configure
```
timeseriesviz 0.0.1
A package for visualizing time series model performance with a focus on spatio-temporal datasets.
Requires Python: >=3.9
Keywords: time series, plotting, forecasting

# timeseriesviz

**timeseriesviz** is a lightweight Python package for **visualizing time series model performance**, with a focus on **spatio-temporal datasets** (e.g., multiple locations, stations, or sensors). It helps researchers and practitioners quickly assess the accuracy of time series forecasts and compare real vs. predicted values across multiple locations.

---

## ✨ Features

- Plot **aggregated performance** across all locations.  
- Generate **detailed subplots** with zoomed-in sections for better error analysis. 
- Plot **error** calculated by (error = actual - forecasted) 
- Support for:
  - **Numpy arrays** (`plot_numpy`)
  - **Pandas DataFrames** from [Nixtla’s NeuralForecast](https://github.com/Nixtla/neuralforecast) (`plot_neuralforecast`) 
- Customizable (`splitsize`) parameter to specify number of detailed plots to generate.
- Option to **save plots to disk**.  

---

## 📦 Installation

```bash
pip install timeseriesviz
```

---

## 🚀 Usage

### Example 1: With Numpy arrays
```python
import numpy as np
from timeseriesviz import plot_numpy

# Simulated data: 100 time steps, 5 locations
y = np.random.rand(100, 5)
pred = y + np.random.normal(0, 0.1, size=y.shape)

fig, axs = plot_numpy(y, pred, title="Forecast vs Actual", splitsize=6)
```

---

### Example 2: With NeuralForecast DataFrame
```python
import pandas as pd
from timeseriesviz import plot_neuralforecast

# Example NeuralForecast output DataFrame
df = pd.DataFrame({
    "unique_id": ["loc1"]*100 + ["loc2"]*100,
    "ds": list(range(100))*2,
    "y": np.random.rand(200),
    "my_model": np.random.rand(200)
})

fig, axs = plot_neuralforecast(df, model_name="my_model", title="NeuralForecast Results", splitsize=6)
```

---

## 📊 Output Example

The generated plots contain:
- **Main Plot**: Entire aggregated time series (real, predicted, and error).  
- **Detailed Plots**: Split into smaller chunks for clearer inspection.  

---

## ⚠️ Requirements

- `pandas`
- `numpy`
- `matplotlib`

---

## 📄 License

MIT License © 2025