I was asked to elaborate a little on my plot. I have attached the Python script to the first post and here is what I do:
I do assume there is a linear dependency on the temperature and time. Writing the whole thing in R would probably make more sene, but Python has a nice API to use R-style formulas:
from statsmodels.formula.api import ols
model = ols("value ~ numeric_date + temp_deviation", data).fit()
print(model.summary())
Here value is the value of the CAL?72 variable, numeric_date is the number of days since the start of the measurement, temp_deviation is the deviation from the mean temperature. Now, the model is a multiple linear regression, because the value should depend on both temperature and time. This is important, one cannot do a simple linear regression and either extract the tempco or drift, they have to be extracted at once. The formula simply states, that the column value (left) is related to both the columns numeric_date and temp_deviation (right), and then we want to run ordinary least squares (ols) on it. The next line then prints the summary of the fit.
The script I have attached, can typically easily be extended to work with any data source. Those data sources can be found in the file_parser.py file. Feel free to add your own.
Toggle the boolean plot_tempco to show either the drift plot or the tempco plot.
Edit: A bit of trivia: The A3 board died 3 months later @24/7 usage with a convergence error. So this plot can be considered end of life.