Mc2FOR demo: A tool for automatically translating MATLAB to FORTRAN 95
Bibliographic record
Abstract
MATLAB is a dynamic numerical scripting language widely used by scientists, engineers and students. While MATLAB'S high-level syntax and dynamic types makes it ideal for prototyping, programmers often prefer using high-performance static languages such as FORTRAN for their final distributable code. Rather than requiring programmers to rewrite their code by hand, our solution is to provide a tool that automatically translates the original MATLAB program to an equivalent FORTRAN program. There are several important challenges for automatically translating MATLAB to FORTRAN, such as correctly estimating the static type characteristics of all the variables in a MATLAB program, mapping MATLAB built-in functions, and effectively mapping MATLAB constructs to FORTRAN constructs. In this tool demonstration, we introduce the tool Mc2FOR, a mature prototype which automatically translates MATLAB programs to FORTRAN. This tool takes as input a MATLAB entry point function file of a program with corresponding information of its input parameters, then automatically finds all functions reachable directly or indirectly from the entry point, loads the necessary files, and translates all the reachable MATLAB functions to equivalent FORTRAN. The output of the tool is a collection of FORTRAN function files, which can be compiled with any FORTRAN 95-compliant compiler. Mc2FOR is open source, and has been implemented in Java using the McLab framework, which means that the tool runs on any system supporting Java.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.019 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".