memscope 0.1.1

Analyze and simulate dynamic memory usage in C/C++ code

memscope

🔍 Analyze and visualize memory allocation and release operations in C/C++ code.

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🚀 Features

• Detects malloc, calloc, realloc, new, delete, free
• Counts allocation vs. deallocation operations
• Detects potential memory leaks
• Visualizes heap behavior over time using Matplotlib

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📦 Installation

pip install memscope
Or from source:

bash
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git clone https://github.com/YOUR_USER/memscope
cd memscope
pip install -e .
🧠 Quick Start
Analyze C/C++ Source Code
python
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from memscope import analyze_source

result = analyze_source("examples/sample.c")
print(result)
Example output:

python
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{
    "allocs": 2,
    "frees": 1,
    "unfreed_allocations": 1,
    "lines": {
        "alloc": [4, 7],
        "free": [9]
    }
}
Visualize Memory Operations
python
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from memscope.visualizer import plot_memory_timeline, plot_memory_bars

events = [
    {"step": 1, "allocated": 128, "freed": 0},
    {"step": 2, "allocated": 256, "freed": 64},
    {"step": 3, "allocated": 0,   "freed": 320},
]

plot_memory_timeline(events)
plot_memory_bars(events)
🧩 API Reference
analyze_source(filepath: str) -> dict
Analyze a C/C++ source file and detect allocation/deallocation operations.

Parameters

filepath: path to .c or .cpp file.

Returns

python
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{
    "allocs": int,
    "frees": int,
    "unfreed_allocations": int,
    "lines": {"alloc": list[int], "free": list[int]}
}
plot_memory_timeline(events: list[dict])
Plots total heap size over time.

python
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plot_memory_timeline([
    {"step": 1, "allocated": 128, "freed": 0},
    {"step": 2, "allocated": 0, "freed": 128},
])
plot_memory_bars(events: list[dict])
Plots allocation (green) and free (red) deltas.

python
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plot_memory_bars([
    {"step": 1, "allocated": 200, "freed": 0},
    {"step": 2, "allocated": 0, "freed": 100},
])