Displaying sensor readings on OLED in Raspberry Pi - Time & Space Complexity
When showing sensor readings on an OLED screen, the time it takes to update depends on how many readings we process and display.
We want to know how the time to update grows as we show more sensor data.
Analyze the time complexity of the following code snippet.
import board
import adafruit_ssd1306
i2c = board.I2C()
display = adafruit_ssd1306.SSD1306_I2C(128, 32, i2c)
sensor_readings = [23, 25, 22, 24, 26]
for i, reading in enumerate(sensor_readings):
display.text(f"Temp {i}: {reading}C", 0, i*10, 1)
display.show()
This code reads a list of sensor values and writes each one as text on the OLED screen, then updates the display.
Identify the loops, recursion, array traversals that repeat.
- Primary operation: Looping through each sensor reading to write text on the display.
- How many times: Once for each reading in the list.
Each new sensor reading adds one more text write operation before updating the display.
| Input Size (n) | Approx. Operations |
|---|---|
| 10 | 10 text writes + 1 display update |
| 100 | 100 text writes + 1 display update |
| 1000 | 1000 text writes + 1 display update |
Pattern observation: The time grows roughly in direct proportion to the number of readings.
Time Complexity: O(n)
This means the time to update the display grows linearly as you add more sensor readings.
[X] Wrong: "Updating the display takes the same time no matter how many readings I show."
[OK] Correct: Each reading adds a text write operation, so more readings mean more work before the display updates.
Understanding how display updates scale with data size helps you write efficient code for real devices, a skill valued in many projects.
"What if we buffered all readings and updated the display only once at the end? How would the time complexity change?"
