# **Plotter Module**
A Python module to generate plots from a CSV configuration file using `seaborn`. This module allows you to create custom plots by reading plot configurations directly from a CSV file and automatically generating the plots according to the specified settings.
## Features
- **CSV-driven Plot Generation**: Read plot configurations from a CSV file and generate plots accordingly.
- **Customizable Plot Settings**: Control plot features such as axis labels, titles, themes, color palettes, and more.
- **Multiple Plot Sizes**: Choose from predefined aspect ratios (`small`, `medium`, `big`, `wide`).
- **Save Plots**: Optionally save plots as PNG, SVG, or PDF with custom resolutions.
- **Flexible Theme**: Select from light or dark themes.
- **Seaborn-powered**: Built on top of `seaborn`, making it easy to work with beautiful, statistical plots.
## Installation
To use this module, you need Python 3.x and the following libraries:
- `seaborn`
- `matplotlib`
- `pandas`
### Install via pip:
```bash
pip install seaborn matplotlib pandas
```
## Usage
### 1. **Importing the Module**:
```python
from plotter import Plotter
import pandas as pd
```
### 2. **Prepare Your Data**:
Ensure you have a `DataFrame` ready with the data to plot. For example:
```python
data = pd.DataFrame({
'time': [1, 2, 3, 4, 5],
'value': [10, 20, 15, 30, 25],
'category': ['A', 'B', 'A', 'B', 'A']
})
```
### 3. **Prepare Your Configuration CSV File**:
Create a CSV file (`plot_config.csv`) with the following columns (or your own as needed):
| x_var | y_var | x_label | y_label | hue | style | size | theme | palette | aspect | xlim | ylim | title |
|-------|-------|---------|---------|------|-------|------|-------|---------|--------|------|------|-------|
| time | value | Time | Value | category | | | light | deep | wide | 0,35 | 0,40 | Sample Plot 1 |
| time | value | Time | Value | category | | | dark | pastel | small | 0,40 | 0,50 | Sample Plot 2 |
### 4. **Generate Plots from CSV**:
Call the `plot_from_csv` function to read the configuration and generate plots:
```python
plotter = Plotter(data)
plotter.plot_from_csv('plot_config.csv', data, save_folder="plots")
```
### Parameters:
- **csv_file**: Path to the CSV file containing plot configurations.
- **data**: DataFrame containing the data to plot.
- **save_folder** (optional): Folder where the plots will be saved. If not specified, plots will be shown without saving.
- **dpi** (optional): Resolution for saving plots (default is 300).
### 5. **Plot Customization**:
You can customize the plot appearance in the CSV:
- **x_var** and **y_var**: Column names for the X and Y axes.
- **x_label** and **y_label**: Labels for the X and Y axes.
- **hue**: Variable to use for color encoding.
- **style**: Variable to use for line style.
- **size**: Variable to use for size encoding.
- **theme**: Select between `'light'` or `'dark'` for plot background theme.
- **palette**: Choose color palette (e.g., `'deep'`, `'pastel'`, `'muted'`).
- **aspect**: Set plot aspect ratio (`'small'`, `'medium'`, `'big'`, `'wide'`).
- **xlim** and **ylim**: Set axis limits as tuples.
- **title**: Add a title to the plot.
### 6. **Example Code**:
```python
import pandas as pd
from plotter import Plotter
# Sample Data
data = pd.DataFrame({
'time': [1, 2, 3, 4, 5],
'value': [10, 20, 15, 30, 25],
'category': ['A', 'B', 'A', 'B', 'A']
})
# Create the Plotter instance
plotter = Plotter(data)
# Plot the configurations from the CSV file
plotter.plot_from_csv('plot_config.csv', data, save_folder="plots")
```
### 7. **Saving the Plot**:
If you specify a folder in `save_folder`, the plots will be saved with the name `plot_1.png`, `plot_2.png`, etc. You can also specify custom file formats (e.g., PNG, SVG) and DPI.
## Example CSV (`plot_config.csv`):
```csv
x_var,y_var,x_label,y_label,hue,style,size,theme,palette,aspect,xlim,ylim,title
time,value,Time,Value,category,,medium,light,deep,wide,0,35,Sample Plot 1
time,value,Time,Value,category,,big,dark,pastel,small,0,40,Sample Plot 2
```
## Advanced Usage
You can extend the `Plotter` class to include more specific functionalities such as:
- Tooltips or zooming features (for future versions).
- Error handling for missing data or invalid configurations.
---
### Contributing
Feel free to open issues, create pull requests, or suggest improvements. This module is open for contributions and enhancements.
---
### License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
---
You can modify and expand this template as your project evolves! It should give users a clear guide on how to get started with your module.
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"description": "# **Plotter Module**\n\nA Python module to generate plots from a CSV configuration file using `seaborn`. This module allows you to create custom plots by reading plot configurations directly from a CSV file and automatically generating the plots according to the specified settings.\n\n## Features\n- **CSV-driven Plot Generation**: Read plot configurations from a CSV file and generate plots accordingly.\n- **Customizable Plot Settings**: Control plot features such as axis labels, titles, themes, color palettes, and more.\n- **Multiple Plot Sizes**: Choose from predefined aspect ratios (`small`, `medium`, `big`, `wide`).\n- **Save Plots**: Optionally save plots as PNG, SVG, or PDF with custom resolutions.\n- **Flexible Theme**: Select from light or dark themes.\n- **Seaborn-powered**: Built on top of `seaborn`, making it easy to work with beautiful, statistical plots.\n\n## Installation\n\nTo use this module, you need Python 3.x and the following libraries:\n- `seaborn`\n- `matplotlib`\n- `pandas`\n\n### Install via pip:\n\n```bash\npip install seaborn matplotlib pandas\n```\n\n## Usage\n\n### 1. **Importing the Module**:\n\n```python\nfrom plotter import Plotter\nimport pandas as pd\n```\n\n### 2. **Prepare Your Data**:\nEnsure you have a `DataFrame` ready with the data to plot. For example:\n\n```python\ndata = pd.DataFrame({\n 'time': [1, 2, 3, 4, 5],\n 'value': [10, 20, 15, 30, 25],\n 'category': ['A', 'B', 'A', 'B', 'A']\n})\n```\n\n### 3. **Prepare Your Configuration CSV File**:\n\nCreate a CSV file (`plot_config.csv`) with the following columns (or your own as needed):\n\n| x_var | y_var | x_label | y_label | hue | style | size | theme | palette | aspect | xlim | ylim | title |\n|-------|-------|---------|---------|------|-------|------|-------|---------|--------|------|------|-------|\n| time | value | Time | Value | category | | | light | deep | wide | 0,35 | 0,40 | Sample Plot 1 |\n| time | value | Time | Value | category | | | dark | pastel | small | 0,40 | 0,50 | Sample Plot 2 |\n\n### 4. **Generate Plots from CSV**:\n\nCall the `plot_from_csv` function to read the configuration and generate plots:\n\n```python\nplotter = Plotter(data)\nplotter.plot_from_csv('plot_config.csv', data, save_folder=\"plots\")\n```\n\n### Parameters:\n- **csv_file**: Path to the CSV file containing plot configurations.\n- **data**: DataFrame containing the data to plot.\n- **save_folder** (optional): Folder where the plots will be saved. If not specified, plots will be shown without saving.\n- **dpi** (optional): Resolution for saving plots (default is 300).\n\n### 5. **Plot Customization**:\nYou can customize the plot appearance in the CSV:\n- **x_var** and **y_var**: Column names for the X and Y axes.\n- **x_label** and **y_label**: Labels for the X and Y axes.\n- **hue**: Variable to use for color encoding.\n- **style**: Variable to use for line style.\n- **size**: Variable to use for size encoding.\n- **theme**: Select between `'light'` or `'dark'` for plot background theme.\n- **palette**: Choose color palette (e.g., `'deep'`, `'pastel'`, `'muted'`).\n- **aspect**: Set plot aspect ratio (`'small'`, `'medium'`, `'big'`, `'wide'`).\n- **xlim** and **ylim**: Set axis limits as tuples.\n- **title**: Add a title to the plot.\n\n### 6. **Example Code**:\n\n```python\nimport pandas as pd\nfrom plotter import Plotter\n\n# Sample Data\ndata = pd.DataFrame({\n 'time': [1, 2, 3, 4, 5],\n 'value': [10, 20, 15, 30, 25],\n 'category': ['A', 'B', 'A', 'B', 'A']\n})\n\n# Create the Plotter instance\nplotter = Plotter(data)\n\n# Plot the configurations from the CSV file\nplotter.plot_from_csv('plot_config.csv', data, save_folder=\"plots\")\n```\n\n### 7. **Saving the Plot**:\nIf you specify a folder in `save_folder`, the plots will be saved with the name `plot_1.png`, `plot_2.png`, etc. You can also specify custom file formats (e.g., PNG, SVG) and DPI.\n\n## Example CSV (`plot_config.csv`):\n\n```csv\nx_var,y_var,x_label,y_label,hue,style,size,theme,palette,aspect,xlim,ylim,title\ntime,value,Time,Value,category,,medium,light,deep,wide,0,35,Sample Plot 1\ntime,value,Time,Value,category,,big,dark,pastel,small,0,40,Sample Plot 2\n```\n\n## Advanced Usage\n\nYou can extend the `Plotter` class to include more specific functionalities such as:\n- Tooltips or zooming features (for future versions).\n- Error handling for missing data or invalid configurations.\n\n---\n\n### Contributing\n\nFeel free to open issues, create pull requests, or suggest improvements. This module is open for contributions and enhancements.\n\n---\n\n### License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n---\n\nYou can modify and expand this template as your project evolves! It should give users a clear guide on how to get started with your module.\n\n",
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