Scaling / normalization / log transform inputs as `sample_pipeline` step

[PeterDSteinberg]

Several of the example machine learning projects mentioned by our contact as GSFC use normalization steps before learning. Here are some of the use cases we should support:

• z-score taking mean and std dev from config or from function in config that returns the right mean/ std dev for a given pixel
• log: log base e
• log10
• anomaly_probs:
  • caclulate time-mean for each space grid cell
  • calculate the deviation from time-mean for each space grid cell
  • bin / round the deviation into increments of X
  • calculate log10 probability as log10(np.bincount(i)/ total_num_obs) where i is the binned anomaly and total_num_obs is the # of obs for a given space grid cell
• further notes on anomaly_probs:
  • Allow anomaly_probs as described above, but within time windows, such as calendar months
  • Instead of finding the anomaly based on the time-mean for each space grid cell, compare to some other zonal stat, such as the mean of a polygon in which a pixel falls.
• other methods from sklearn for scaling such as StandardScaler, MinMaxScaler, etc.