The discussion revolves around the quantification of noise in 1D DEEP2 DEIMOS fits files, focusing on defining exclusion criteria for data analysis in Python 2.7. Participants explore methods to identify and handle noisy and unpredictable data behaviors.
Participants do not reach a consensus on a specific method for quantifying noise, and multiple competing views regarding data handling and theory development are present.
Participants express uncertainty regarding the nature of the noise and its effects on data interpretation, highlighting the complexity of distinguishing between instrumental noise and actual measurements.
Calculate the RMS value?Curtnos said:I'm currently working with the 1d DEEP2 DEIMOS fits files (see http://deep.ps.uci.edu/deep3/specprimer.html) and am trying to define some exclusion criteria in Python 2.7 for the data based on the noise. What's the best way to quantify the noise in order to do this?
Thank you!
I should have added, a lot of the data is showing very odd and unpredictable behaviour. I'm unsure how to edit the original question, however I was hoping to find a more general method to both discount noisy data and bad data which fluctuates unpredictably. Some have sudden, repeated drops to zero flux, some have fluxes which appear symmetric about the flux = 0 line, some look like step functions, etc.berkeman said:Calculate the RMS value?