The final loop collects 400 voltage readings and 400 current readings. TOTAL_SAMPLES is 40 times 10, and each entry requests A0 followed by A1. That settles what the count means in this file: 400 software pairs and 800 conversions, rather than 400 simultaneous observations. The comment still calls the window 200 ms. At the stated 860 conversions per second, the conversion arithmetic alone gives roughly 0.93 seconds, which makes the comment worth separating from the actual program.
The first calculation is simple enough to do without the full program. At a nominal rate of 860 conversions per second, one conversion corresponds to about 1 divided by 860 seconds, or roughly 1.16 milliseconds. Four hundred conversions would then take about 0.465 seconds. That’s already longer than 200 milliseconds. I haven't measured the loop by doing this division. I've found a reason that the description needs closer attention. A quick calculation can reveal a conflict without telling me which part of the description should be changed.
In this implementation, the word pair describes two successive calls at the same array index. The voltage array and current array each contain TOTAL_SAMPLES entries. That’s why the total number of conversions is twice the array length. The count tells me how much acquisition work is requested, while the time interval tells me how long that collection actually occupied in the running system.
A complete pass through the acquisition loop reads voltage, reads current and adds each raw value to its channel's running mean total. It does not print inside that collection. The calculation pass comes afterward, using the stored arrays. Reading the actual calls makes the unit of work much clearer than inferring it from a comment about samples.
The nominal chip rate isn't automatically the rate of the whole program either. There can be communication work, waiting and processing around the conversions. The code's operating mode matters. I shouldn't replace the disputed 200 milliseconds with 0.465 seconds and call that a new measured result. The calculation is useful because it establishes a comparison and exposes an inconsistency. Measuring the actual elapsed interval is a separate task. Those two kinds of information can help each other without becoming interchangeable.
The waveform gives the time window a physical meaning. A 50 Hz cycle lasts 20 milliseconds. A 200-millisecond window would cover ten cycles. A nominal interval around 465 milliseconds corresponds to a different number of cycles. I don't need to pretend the exact relationship is settled to see why the distinction matters. The samples don't exist only as a collection in memory. They occupy a period during which voltage and current keep changing. Describing the collection inaccurately can change the explanation of what the average or RMS calculation represents.
There’s a timing boundary in the code as well. start_time is recorded with micros before the acquisition loop and end_time immediately after it. Their difference becomes sampling_duration_sec. The one-second wait comes later, after calculation and display. I can therefore distinguish three things: a count chosen in the definitions, a duration comment and an elapsed interval the program obtains while running. I compared the nominal-rate calculation with the acquisition boundary in the code.
A drawing helps me keep this straight. Imagine putting a mark on a time axis for each completed conversion. Now imagine alternating the labels V and I on those marks. One voltage-current pair spans two marks rather than one. The marks show the sequence of conversions. It becomes easier to ask whether the code's count refers to marks or pairs. It also shows why the spacing between the two channel readings is another question, separate from the total duration of the window.
There’s a tempting way to hide this problem: keep the final number and stop discussing the timing. I don't think that would help me understand the meter. RMS and power calculations depend on the data they receive, and the timing describes part of that data. If the write-up gives the wrong picture, a reader may believe the program collected information at moments it never did. I want to fix the description by understanding the collection, rather than preserve a neat sentence that doesn't fit the rest of the information.
Another temptation is to guess a history that makes the numbers fit. Perhaps an earlier version used a different count, or perhaps a number was copied from another example. Those are possibilities, but I don't have the records to choose one. A plausible story isn't the same as a recovered explanation. The useful next step is to identify the actual configuration and trace the loop. If the report needs a correction after that, the correction should say what the count and interval really describe.
This is also making me more careful about averages. An average of N values tells me about those N values; the elapsed time tells me where they came from in a changing process. Two collections with the same count can cover different durations. Two collections with the same duration can use different spacing. I can understand the mathematical expression perfectly and still misunderstand the measurement if I collapse all of that into the word samples. The time information isn't an optional detail added after the formula.
The program already provides the count and channel order needed for the basic consistency check. What its source does not supply is a recorded value of the elapsed acquisition interval. I can explain that distinction without deciding that a plausible display settles the timing. The micros boundary shows the interval used for the runtime.
The useful answer is now specific: 400 sequential pairs, an elapsed acquisition timer and a duration comment that does not follow from the stated rate. Timing has become part of explaining the data.