Stop looking at your curves, start piloting them.

From detection to exploration

In the previous article, we took a crucial step: Aggregation. We learned to stop drowning in raw data and instead extract strategic indicators. It was thanks to this method that we unmasked the “traitor” in our test: Cycle 3. Remember, its average of 30.77 seemed perfect, but its peak of 37.02 screamed anomaly.

However, identifying a problem is one thing; understanding its root cause is another. A summary table tells you there’s a peak, but it doesn’t show you its shape. Is it a vibration? A sudden shock? Electrical interference? To find out, you need to be able to delve into the data.
Today, we’re leaving the world of static reports and entering the world of interactive management.

The problem: The “wall” of the static graph

In Excel or with classic libraries like Matplotlib, a graph is a static image. If a colleague asks you, “What is the exact value at 4.2 seconds?”, you have to go back to your spreadsheet, zoom in manually, and re-export an image.

This is a waste of time that disrupts the flow of your analysis. As a de facto engineer, your time should be spent on diagnosis, not layout.

The solution: Transform your data into a cockpit with Plotly

Plotly isn’t just a library of “pretty graphs.” It’s a rendering engine that transforms your dataframes into powerful digital measurement tools. Here are the three key features that will change your daily work:

1. Le “Unified Hover” (Survol intelligent)
   Forget about guessing on the axes. By hovering your mouse over the curve, a vertical line follows your movement and displays the precise values ​​(Time, Power, Torque) to the nearest thousandth. Say goodbye to “I think it’s around 35”.
   
2. Le Range Slider : Naviguez dans le temps
   Imagine a 2-hour test recorded at 100 Hz. That’s 720,000 lines. Displaying that on a standard screen is unreadable.
3. With the Range Slider, you have a navigation bar at the bottom of your graph. You maintain the overview while being able to isolate a 2-second window with a simple mouse swipe.
   
4. L’interactivité sans frontières (Le format HTML)
   This is the ultimate argument for your reports. Plotly allows you to export your graphs as .html files.

The result? You send a lightweight file to your client or management. They open it in their web browser (Chrome, Edge, Safari) and can zoom in and explore the data without even having Python installed. You’re giving them the cockpit, not just the picture.

🔍 The Expert’s Eye: Correlating Calculation and Visuals

The table in the previous article alerted us to an overshoot of 37.02 kW. But a number doesn’t tell the whole story. Thanks to the interactive feature, we can finally see the “head” of this peak: is it a sudden surge or a gradual increase? By hovering over the curve, we confirm the exact value. We’re no longer guessing; we’re connecting our statistics to the reality on the ground.

“Why is Cycle 3 our priority target?”

You will notice that the power peak of Cycle 3 is less impressive than that of Cycle 1. Mathematically, this is simply a phase shift effect between our 5s windows and the motor rotation.
But that is precisely why it is crucial.

A massive spike (Cycle 1) is immediately obvious to any technician. However, the Cycle 3 spike is “borderline.” It flirts with the red zone without actually crossing it. This is the silent anomaly, the one that doesn’t trip the circuit breaker right away but slowly wears down your machine.

By focusing on this visually “less important” cycle, we prove the power of our tool: Plotly allows us to uncover weak signals that the average and static graphs would have smoothed out.

Conclusion : 

With interactivity, you no longer passively receive your data, you actively query it. You now have all the tools at your disposal: rapid calculation, intelligent synthesis, and surgical visualization.

But one question remains: what if you don’t have a single test file, but 50 files from 5 different test benches? Are you going to run your script 50 times manually? Obviously not.

Next step: Industrialization.
In the next article, we’ll learn how to create a Mass Automation Loop. We’ll code a script capable of “scanning” an entire directory, processing each file one by one, and generating a consolidated global report while you enjoy your coffee.

➡️ Read the article: The Power of “Batch Processing” — From Craft to Industry.