Hello, thank you very much for your contribution to enable me to use Python for wavelet analysis. I have a question that I would like to ask, and I wonder if you are comfortable answering it.
In your example data, the time granularity is seasonal, i.e. 0.25 years, and it is continuous full year data. Now I have a time series with a daily granularity, but only observations are available from 1 February to 30 November each year. There are two ways I can think of to do wavelet analysis on such data.

1. fill all the days of the year for which there is no data with zeros, and then perform a wavelet analysis.
2. Define the number of days in the year and define the period for which data is actually available as a year, i.e. 303 days. Afterwards perform a wavelet analysis and apply the definition of a year to all cycles of the analysis.
   My personal preference is to use the second approach, but is this theoretically feasible?