### Generate Pie Charts with Python

Source: https://context7.com/timothytickle/quickplots/llms.txt

Generates pie charts with automatic data normalization. Requires the pieChart module.

```python
import json
from pieChart import PieChart
```

--------------------------------

### Implement Custom Chart Extending QuickPlot Base Class

Source: https://context7.com/timothytickle/quickplots/llms.txt

This snippet shows how to create a custom chart type by extending the `QuickPlot` base class. Implement the `func_plot` method for custom plotting logic. It handles JSON data loading and command-line argument parsing.

```python
import abc
import json
from quickPlot import QuickPlot
import quickPlot as qp

class CustomChart(QuickPlot):

    def __init__(self):
        QuickPlot.__init__(self)

    def func_plot(self, json_data, str_output_file):
        """
        Implement custom plotting logic.

        json_data: dict with required 'data' key plus optional:
            - title (default: "Histogram")
            - x_axis (default: "X Axis")
            - y_axis (default: "Count")
            - color (default: "cyan")
        str_output_file: output file path (supports PDF, PNG, etc.)
        """
        # Access common JSON keys using constants
        title = json_data.get(qp.c_STR_TITLE, qp.c_STR_TITLE_DEFAULT)
        x_label = json_data.get(qp.c_STR_X_AXIS, qp.c_STR_X_AXIS_DEFAULT)
        y_label = json_data.get(qp.c_STR_Y_AXIS, qp.c_STR_Y_AXIS_DEFAULT)

        for data_series in json_data[qp.c_STR_DATA]:
            values = data_series[qp.c_STR_DATA]
            label = data_series.get(qp.c_STR_DATA_LABEL, None)
            color = data_series.get(qp.c_C_PLOT_COLOR, qp.c_C_PLOT_COLOR_DEFAULT)
            # Custom plotting implementation here

        return True

# Use from command line or programmatically
if __name__ == "__main__":
    CustomChart().func_make_figure()

# Or use func_plot_from_paths for file-based input
with open("data.json", "r") as f:
    chart = CustomChart()
    chart.func_plot_from_paths(f, "output.pdf")
```

--------------------------------

### Create Pie Chart from JSON Data

Source: https://context7.com/timothytickle/quickplots/llms.txt

Use this snippet to generate a pie chart. Provide data in JSON format and specify the output file path. Supports programmatic and command-line execution.

```python
json_data = {
    "title": "Market Share Distribution",
    "data": [
        {
            "data": [35, 25, 20, 15, 5],
            "label": ["Company A", "Company B", "Company C", "Company D", "Others"],
            "color": ["#2ecc71", "#3498db", "#e74c3c", "#f39c12", "#9b59b6"]
        }
    ]
}

# Programmatic usage
pie = PieChart()
pie.func_plot(json_data, "market_share.pdf")

# Command line usage:
# python pieChart.py input.json output.pdf
```

--------------------------------

### Generate Histograms with Python

Source: https://context7.com/timothytickle/quickplots/llms.txt

Generates histograms from raw data arrays with support for multiple overlapping series. Configurable bin counts allow for detailed distribution analysis.

```python
import json
from histogram import Histogram

# JSON input format for overlapping histograms
json_data = {
    "title": "Score Distribution",
    "x_axis": "Score",
    "y_axis": "Frequency",
    "bins": 20,  # Default is 40
    "data": [
        {
            "label": "Class A",
            "color": "blue",
            "data": [72, 85, 91, 78, 82, 88, 95, 71, 83, 90, 87, 76, 81, 89, 94]
        },
        {
            "label": "Class B",
            "color": "red",
            "data": [65, 70, 75, 68, 72, 77, 80, 62, 69, 74, 78, 66, 71, 73, 79]
        }
    ]
}

# Programmatic usage
hist = Histogram()
hist.func_plot(json_data, "score_distribution.pdf")

# Command line usage:
# python histogram.py input.json output.pdf
```

--------------------------------

### Generate Scatter Plots with Python

Source: https://context7.com/timothytickle/quickplots/llms.txt

Creates scatter plots requiring exactly two data series for x and y coordinates. The first series maps to the x-axis and the second to the y-axis.

```python
import json
from scatter import ScatterPlot

# JSON input format for scatter plot (requires exactly 2 data series)
json_data = {
    "title": "Height vs Weight Correlation",
    "color": "orange",
    "data": [
        {
            "label": "Height (inches)",
            "data": [62, 65, 68, 70, 72, 74, 67, 69, 71, 66]
        },
        {
            "label": "Weight (lbs)",
            "data": [130, 145, 160, 175, 185, 195, 155, 165, 180, 150]
        }
    ]
}

# Programmatic usage
scatter = ScatterPlot()
scatter.func_plot(json_data, "height_weight.pdf")

# Command line usage:
# python scatter.py input.json output.pdf
```

--------------------------------

### Generate Bar Charts with Python

Source: https://context7.com/timothytickle/quickplots/llms.txt

Creates grouped bar charts with support for error bars and custom sorting. Requires a JSON object defining the data series and axis labels.

```python
import json
from barChart import BarChart

# JSON input format for a grouped bar chart with error bars
json_data = {
    "title": "Sales Comparison",
    "x_axis": "Quarter",
    "y_axis": "Revenue ($K)",
    "sort": "lexical",  # Options: "lexical", "numeric", or omit
    "y_limit": 15,  # Optional: set max y-axis value
    "data": [
        {
            "data": [5, 8, 12, 10],
            "error": [0.5, 0.8, 1.2, 0.9],
            "color": "blue",
            "label": "Product A",
            "x_ticks": ["Q1", "Q2", "Q3", "Q4"]
        },
        {
            "data": [3, 6, 9, 11],
            "error": [0.3, 0.5, 0.7, 0.6],
            "color": "green",
            "label": "Product B"
        }
    ]
}

# Programmatic usage
chart = BarChart()
chart.func_plot(json_data, "sales_comparison.pdf")

# Command line usage:
# python barChart.py input.json output.pdf
```

--------------------------------

### Generate Box Plots with Python

Source: https://context7.com/timothytickle/quickplots/llms.txt

Creates grouped box plots for comparing distributions across multiple categories. Input data is provided as a list of lists.

```python
import json
from boxPlot import BoxPlot

# JSON input format for box plots
json_data = {
    "title": "Performance by Category",
    "x_axis": "Category",
    "y_axis": "Score",
    "label": ["High", "Medium", "Low"],
    "data": [
        [0.9, 0.85, 0.88, 0.92, 0.87, 0.91],
        [0.6, 0.55, 0.58, 0.62, 0.57, 0.61],
        [0.2, 0.15, 0.18, 0.22, 0.17, 0.21]
    ]
}

# Programmatic usage
box = BoxPlot()
box.func_plot(json_data, "performance_boxplot.pdf")

# Command line usage:
# python boxPlot.py input.json output.pdf
```

--------------------------------

### Create Venn Diagram from JSON Data

Source: https://context7.com/timothytickle/quickplots/llms.txt

Use this snippet to generate a two-set Venn diagram visualizing the overlap between data sets. The library computes intersections automatically. Supports programmatic and command-line execution.

```python
import json
from vennDiagram import VennDiagram

# JSON input format for 2-set Venn diagram
json_data = {
    "title": "Feature Overlap Analysis",
    "data": [
        {
            "label": "Dataset A",
            "color": "blue",
            "data": ["feature1", "feature2", "feature3", "feature4", "feature5"]
        },
        {
            "label": "Dataset B",
            "color": "red",
            "data": ["feature3", "feature4", "feature5", "feature6", "feature7"]
        }
    ]
}

# Programmatic usage
venn = VennDiagram()
venn.func_plot(json_data, "feature_overlap.pdf")

# Command line usage:
# python vennDiagram.py input.json output.pdf
```

--------------------------------

### Generate ROC Curve from JSON Data

Source: https://context7.com/timothytickle/quickplots/llms.txt

This snippet demonstrates how to create an ROC curve for evaluating binary classifier performance. Data points should be in the format [actual, predicted, threshold_value]. The AUC is automatically calculated and displayed. Supports programmatic and command-line execution.

```python
import json
from roc import PositiveROC

# JSON input format for ROC curves
# Each data point: [actual_class (bool), predicted_class (bool), threshold_value]
json_data = {
    "title": "Classifier Performance Comparison",
    "data": [
        {
            "label": "Model A",
            "color": "green",
            "data": [
                [True, True, 1], [True, True, 1],
                [True, True, 2], [True, False, 2],
                [True, True, 3], [True, False, 3],
                [False, False, 4], [False, False, 4],
                [False, False, 5], [False, True, 5]
            ]
        },
        {
            "label": "Model B",
            "color": "red",
            "data": [
                [True, False, 1], [True, True, 1],
                [True, False, 2], [True, True, 2],
                [False, True, 3], [False, False, 3],
                [False, True, 4], [False, False, 4],
                [True, True, 5], [False, False, 5]
            ]
        }
    ]
}

# Programmatic usage
roc = PositiveROC()
roc.func_plot(json_data, "roc_comparison.pdf")

# Command line usage:
# python roc.py input.json output.pdf
```

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