Python control of an electrothermal atomizer system.
The system consists of an Ocean Optics (Ocean Insight) compact spectrometer and a power supply controlled via RS-232 serial connection. The particular power supply is controlled by a Cypress PSoC 5 chip with custom text codes sent via UART. Other power supplies that can accept serial connection can be controlled by modifying those commands. Update: A version for controlling a BK Precision 1696 is included as a separate file. Eventual integration of the two control systems is a goal.
This project manages control of a tungsten electrothermal atomizer from a single Python GUI. Control of hardware and data processing with user interaction are all parts of the goal. This solution includes:
- Serial communication (bidirectional) for hardware control
- Spectrometer interaction via SeaBreeze
- GUI that shows experiment conditions
- Implement the 2-line background correction method in the GUI
- Animated Matplotlib plot showing live spectrometer results
The data processing was separated from the instrument control for simplicity. It includes:
- Mouse pointer selection of data from Matplotlib figure
- Saving incremental data in human-readable formats with informational headers
- Post-processing for both absorbance data and temperature data
MatPlotLib is (not surprisingly) the slowest part of the program. Real-time data display is the limiting factor for the fastest data acquisition speed.
If I turn off refreshing the plot during the time-series acquisition, then data can be recorded near the hardware limit of the spectrometer. For example, a Raspberry PI 3B was able to record data with a 7-ms integration time with correct timing from an Ocean Optics spectrometer.
With real-time data displayed, the minimum time is very hardware and Python version, dependent and also depends on plotting choices. An Intel i5-9500 running Windows 10 was the test system. Real-time plotting with lines yielded minimum stable integration times of 25 ms (Python 3.6.8) and 22 ms (Python 3.9.10) using the same Matplotlib library and plotting lines. Hooray for progress, but beware of the speed limitations of older Python versions. A Raspberry Pi 3B achieved a minimum stable integration time of about 65 ms and an older Windows 10 PC achieved about 40 ms. The hardware influence is very clear. There may be a RAM influence, too, but that is a more subtle variable. The minimum stable integration time was tested with a 90-second acquisition period. Shorter acquisition can be stable at faster rates (again, likely a memory issue).
Plotting choices within Matplotlib can affect results, too. The plotting speed slowest to fastest is: line (lw=1), circle ('o'), point ('.'), and pixel (','). Again, not a surprise since calculating a line inevitably is more resource intensive. Plotting pixel may make the real-time result difficult to see. However, using point or pixel I was able to achieve a 15-ms integration time while plotting three trends at the same time on the test system.
Another plotting choice to achieve faster integration times is to use the Matplotlib markevery option in the line definition. On long results (e.g., total time between 30 and 90 seconds) the integration time may show doubling sporadically or continuously after about 10 seconds. I assume that this is a memory issue. Using markevery=2 plots everyother point, and so on. It seems to actually reduce the number of items that Matplotlib is managing, so it can help to avoid the doubling effect and make real-time plotting more stable.