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Calcium imaging with miniscopes allows researchers to record the activity of many neurons over long periods in freely moving animals. While data collection has become easier, analysis have not. Typical calcium imaging analysis requires many processing steps, uses multiple software tools, and depends heavily on parameter choices. In practice, these details are often poorly recorded, rely on proprietary software, or are lost when large intermediate files are deleted to save disk space. As a result, analyses are hard to reproduce, compare, or rerun reliably. This paper describes an open, Python-based framework designed to make calcium imaging analysis clearer, more reproducible, and easier to manage. The framework treats each analysis step as an explicit, recorded operation with defined inputs, outputs, parameters, and software providers. All steps are logged in lightweight trace files saved to disk, allowing analyses to be resumed, audited, or exactly reproduced later, even if large intermediate data have been removed. Algorithm-specific code is isolated behind standardized wrappers, so users can switch between proprietary and open-source tools without changing the overall workflow.The framework also supports branching to compare different methods, batch processing across multiple animals or sessions, controlled cleanup to reduce disk usage, and a modular design. The result is a practical system that makes calcium imaging analyses easier to follow, repeat, and reuse.