How to Convert UFF Files to Excel

A practical comparison of every method for getting Universal File Format data into Excel spreadsheets — from Python scripts to VBA macros to dedicated conversion tools.

You have a stack of .uff files from a vibration test, a modal analysis campaign, or a legacy data archive, and you need the data in Excel. Maybe it's for a report, maybe your colleague doesn't have access to the test software, or maybe you just want to plot some FRFs without firing up a full analysis package. Whatever the reason, getting UFF data into a spreadsheet should be simple — but anyone who's tried knows it rarely is.

This guide walks through every practical method for converting UFF files to Excel, with honest pros and cons for each approach so you can pick the one that fits your situation.

Why UFF to Excel Is Harder Than It Sounds

Before diving into methods, it helps to understand why you can't just open a .uff file in Excel directly. Although UFF files are technically plain ASCII text, they aren't delimited in any way that Excel can interpret automatically. The format uses fixed-width fields across 80-character lines, with a complex structure of header records, metadata, and data values that vary depending on the dataset type. (For a full breakdown of how UFF files are structured, see our companion guide on understanding the UFF file format.)

A typical Dataset 58 block (the most common type, containing frequency response functions or time histories) has 11 header records packed with metadata — node identifiers, DOF directions, function type codes, frequency spacing, number of data points — followed by the actual measurement values. Simply importing the raw text into Excel gives you an unreadable mess of interleaved headers and data.

On top of that, UFF files often contain complex-valued data (real and imaginary components of FRFs), multiple datasets stacked in sequence, and sometimes a mix of different dataset types (geometry, measurements, and metadata all in one file). Any conversion approach needs to handle all of this correctly.

Method 1: Manual Text Parsing in Excel

The most basic approach is to open the UFF file as a text file in Excel and try to parse it manually using Excel's text-to-columns feature, formulas, or manual copy-paste.

This can technically work for very simple cases — a single Dataset 58 with a small number of data points. You would open the file, identify where the data records begin (after the 11 header lines following each -1 delimiter), and manually extract the columns you need.

In practice, this method is only viable for quick one-off checks on small files. It falls apart immediately with multiple datasets, large files, complex-valued data, or any need for repeatability. You'll spend more time figuring out which lines are headers versus data than you would on the actual analysis. That said, it's worth knowing how to eyeball a UFF file in a text editor to verify that other conversion methods are producing correct results.

Method 2: Python with pyuff

Python is the most popular scripting approach for reading UFF files, primarily through the pyuff library available on PyPI. The library provides a UFF class that can parse a UFF file and return the contents of each dataset as a Python dictionary.

A basic workflow looks something like this: you install pyuff, read the file to get a list of dataset dictionaries, filter for the dataset type you want (typically 58 for FRF data), extract the data arrays, and then write them to an Excel file using a library like openpyxl or pandas.

The advantage of Python is flexibility. You have full programmatic control over which datasets to extract, how to organize the output, and what transformations to apply. If you need to process UFF files as part of a larger automated pipeline, Python is the natural choice.

The downsides are meaningful, though. You need a working Python environment, which not every engineer has set up. The pyuff library handles standard ASCII Dataset 58 well but can struggle with binary 58b files, unusual dataset types, or files with formatting quirks from certain acquisition systems. Error handling for malformed files requires additional coding. And if you're not comfortable writing Python, the learning curve is a real barrier — especially when you just need data in a spreadsheet.

For engineers who already use Python in their workflow, this is a solid option. For everyone else, it's more effort than it's worth for a straightforward conversion task.

Method 3: MATLAB

MATLAB is widely used in the vibration testing and modal analysis community, and there are several community-contributed functions on the MATLAB File Exchange for reading UFF files. The readuff function is the most well-known.

The workflow is similar to Python: load the file, iterate through the datasets, extract the data you need, and write it to an Excel file using xlswrite or writematrix. MATLAB's matrix-oriented nature makes it natural for handling the numerical data in UFF files.

The pros are that many test engineers already have MATLAB on their machines and are comfortable with the syntax. The cons mirror Python: you need a MATLAB license (which is expensive), the community functions don't cover every edge case, and binary UFF support is inconsistent. It's also overkill if all you need is a format conversion.

Method 4: VBA Excel Macro (ReadUFF.xlsm)

There is a macro-enabled Excel workbook that has circulated in the vibration testing community for years, originally created for importing UFF Dataset 58 data directly into Excel using VBA. You open the workbook, run the macro, select your UFF file, and the data gets pulled into the spreadsheet.

The appeal is obvious — it works entirely within Excel, requires no external software, and is free. For simple ASCII UFF files with a single or small number of Dataset 58 blocks, it can get the job done.

The limitations are significant, though. VBA macros are constrained by Excel's row limits (just over one million rows, which large UFF files can exceed) and memory management (VBA loads everything into memory, which can crash Excel on large files). The macro typically handles only Dataset 58, so if you need geometry or mode shape data, you're out of luck. There's no support for binary 58b files, and the macro hasn't been actively maintained to handle the wide variety of UFF formatting variations that different acquisition systems produce.

This method works best as a quick-and-dirty solution for small, standard ASCII files when you don't want to install anything.

Method 5: Export from Source Software

If you have access to the software that originally created the UFF files — Siemens Simcenter Testlab, Brüel & Kjær PULSE, Dewesoft, m+p international, or similar — you can often export the data directly to Excel or CSV from within that application, bypassing the UFF format entirely.

This is worth trying first because the source software understands its own data better than any third-party parser. The export will typically include all metadata, proper channel labeling, and correct units.

The reasons this often isn't an option: you may not have a license for the software, the software may not be installed on your current machine, the UFF files may come from a different organization or a legacy system that's no longer available, or the original project files may have been lost and all you have are the exported UFF files.

Method 6: Dedicated Converter Tool (UFF Converter Pro)

This is the approach we built UFF Converter Pro to address. It's a standalone desktop application that reads UFF files and exports them to Excel (.xlsx), CSV, or raw CSV format.

The workflow is straightforward: drag and drop your files (or folders for batch processing), choose your output format and options (complex number representation, phase units, whether to include metadata headers), and click convert. The output is a properly formatted Excel file with the data organized into sheets, headers preserved, and — for Dataset 58 FRF data — optional Bode and Nyquist charts generated automatically.

We built it specifically to handle the things that trip up the other methods. It supports both ASCII and binary 58b files natively, uses a streaming Excel engine that writes directly to disk (so it handles million-row datasets without running out of memory), processes all the common dataset types (15, 55, 58, 58b, 82, 151, 164, and the 2411–2467 I-deas series), and runs entirely offline with no cloud processing. You can configure the complex number format (real/imaginary or magnitude/phase), choose degrees or radians for phase data, and output either a single combined file or separate files per dataset.

The tradeoff is that it's a paid tool ($199/year), so it makes the most sense for engineers who work with UFF files regularly or need reliable batch processing. There's a 14-day free trial with full functionality if you want to test it against your own files before committing.

Choosing the Right Method

The best approach depends on your situation.

If you have a single small ASCII UFF file and just need a quick look at the data, the VBA macro or manual text parsing might be sufficient.

If you're a Python or MATLAB user and need to integrate UFF conversion into an automated analysis pipeline, scripting gives you the most flexibility — just be prepared to handle edge cases.

If you can go back to the source software that created the files, export directly from there for the cleanest results.

If you work with UFF files regularly, deal with large files or binary 58b format, need batch processing, or want built-in charting without writing code, a dedicated converter tool will save you significant time over the scripting approaches.

Tips for a Clean Conversion

Regardless of which method you choose, a few practices will help ensure your conversion goes smoothly.

Verify units first. Check whether the UFF file contains a Dataset 164 (Units) record. If it does, make sure your conversion tool or script is interpreting the units correctly. If it doesn't, check with whoever created the file — assuming the wrong unit system is one of the most common and costly mistakes in test data analysis.

Spot-check against the source. After conversion, compare a few data points against the original software (if available) or against what you'd expect from the test setup. Catch errors early before they propagate into reports or analysis.

Decide on complex number format upfront. FRF data is complex-valued. Some workflows need real and imaginary components; others need magnitude and phase. Pick the format that matches your downstream analysis to avoid an extra conversion step later.

Watch for multiple datasets. A single UFF file can contain dozens or hundreds of individual measurements. Make sure your conversion method handles them all and organizes the output in a way you can navigate — otherwise you'll end up with thousands of rows of interleaved data and no way to tell which measurement is which.

Keep the original files. UFF is a lossless format. Once you convert to Excel, you've potentially lost precision (Excel has floating-point limitations) and metadata. Always keep the original UFF files as your source of truth.

Summary

Converting UFF files to Excel is a common need in the vibration testing and modal analysis world, but the UFF format's complexity means there's no single "open with Excel" solution. Your options range from manual text parsing (tedious, error-prone) through Python or MATLAB scripting (flexible, requires coding) to VBA macros (free, limited) and dedicated converter tools (easiest, handles edge cases). For most engineers, the right choice comes down to how often you need to do this and how complex your files are.

If you'd like to see UFF Converter Pro in action, we have a full tutorial video on YouTube walking through the entire conversion process, and you can download the free trial to test with your own files.