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Merge pull request #1062 from PCMDI/feature/lee1043-mov-modularize
Feature/lee1043 mov modularize
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pcmdi_metrics/variability_mode/lib/adjust_timeseries.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,147 @@ | ||
| import xarray as xr | ||
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|
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| from pcmdi_metrics.io import ( | ||
| get_latitude_bounds, | ||
| get_latitude_bounds_key, | ||
| get_longitude_bounds, | ||
| get_longitude_bounds_key, | ||
| get_time_key, | ||
| region_subset, | ||
| select_subset, | ||
| ) | ||
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| def adjust_timeseries( | ||
| ds: xr.Dataset, | ||
| data_var: str, | ||
| mode: str, | ||
| season: str, | ||
| regions_specs: dict = None, | ||
| RmDomainMean: bool = True, | ||
| ) -> xr.Dataset: | ||
| """ | ||
| Remove annual cycle (for all modes) and get its seasonal mean time series if | ||
| needed. Then calculate residual by subtraction domain (or global) average. | ||
| Input | ||
| - ds: array (t, y, x) | ||
| Output | ||
| - timeseries_season: array (t, y, x) | ||
| """ | ||
| if not isinstance(ds, xr.Dataset): | ||
| raise TypeError( | ||
| "The first parameter of adjust_timeseries must be an xarray Dataset" | ||
| ) | ||
| # Reomove annual cycle (for all modes) and get its seasonal mean time series if needed | ||
| ds_anomaly = get_anomaly_timeseries(ds, data_var, season) | ||
| # Calculate residual by subtracting domain (or global) average | ||
| ds_residual = get_residual_timeseries( | ||
| ds_anomaly, data_var, mode, regions_specs, RmDomainMean=RmDomainMean | ||
| ) | ||
| # return result | ||
| return ds_residual | ||
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| def get_anomaly_timeseries(ds: xr.Dataset, data_var: str, season: str) -> xr.Dataset: | ||
| """ | ||
| Get anomaly time series by removing annual cycle | ||
| Input | ||
| - timeseries: variable | ||
| - season: string | ||
| Output | ||
| - timeseries_ano: variable | ||
| """ | ||
| if not isinstance(ds, xr.Dataset): | ||
| raise TypeError( | ||
| "The first parameter of get_anomaly_timeseries must be an xarray Dataset" | ||
| ) | ||
| # Get anomaly field by removing annual cycle | ||
| ds_anomaly = ds.temporal.departures(data_var, freq="month", weighted=True) | ||
| # Get temporal average if needed | ||
| if season == "yearly": | ||
| # yearly time series | ||
| ds_anomaly = ds_anomaly.temporal.group_average( | ||
| data_var, freq="year", weighted=True | ||
| ) | ||
| # restore bounds (especially time bounds) | ||
| ds_anomaly = ds_anomaly.bounds.add_missing_bounds() | ||
| # get overall average | ||
| ds_ave = ds_anomaly.temporal.average(data_var) | ||
| # anomaly | ||
| ds_anomaly[data_var] = ds_anomaly[data_var] - ds_ave[data_var] | ||
| elif season.upper() in ["DJF", "MAM", "JJA", "SON"]: | ||
| ds_anomaly_all_seasons = ds_anomaly.temporal.departures( | ||
| data_var, | ||
| freq="season", | ||
| weighted=True, | ||
| season_config={"dec_mode": "DJF", "drop_incomplete_djf": True}, | ||
| ) | ||
| ds_anomaly = select_by_season(ds_anomaly_all_seasons, season) | ||
| # return result | ||
| return ds_anomaly | ||
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| def select_by_season(ds: xr.Dataset, season: str) -> xr.Dataset: | ||
| time_key = get_time_key(ds) | ||
| ds_subset = ds.where(ds[time_key].dt.season == season, drop=True) | ||
| # Preserve original spatial bounds info | ||
| # Extract original bounds | ||
| lat_bnds_key = get_latitude_bounds_key(ds) | ||
| lon_bnds_key = get_longitude_bounds_key(ds) | ||
| # Assign back to the new dataset | ||
| ds_subset[lat_bnds_key] = get_latitude_bounds(ds) | ||
| ds_subset[lon_bnds_key] = get_longitude_bounds(ds) | ||
| return ds_subset | ||
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| def get_residual_timeseries( | ||
| ds_anomaly: xr.Dataset, | ||
| data_var: str, | ||
| mode: str, | ||
| regions_specs: dict = None, | ||
| RmDomainMean: bool = True, | ||
| ) -> xr.Dataset: | ||
| """ | ||
| Calculate residual by subtracting domain average (or global mean) | ||
| Input | ||
| - ds_anomaly: anomaly time series, array, 3d (t, y, x) | ||
| - mode: string, mode name, must be defined in regions_specs | ||
| - RmDomainMean: bool (True or False). | ||
| If True, remove domain mean of each time step. | ||
| Ref: | ||
| Bonfils and Santer (2011) | ||
| https://doi.org/10.1007/s00382-010-0920-1 | ||
| Bonfils et al. (2015) | ||
| https://doi.org/10.1175/JCLI-D-15-0341.1 | ||
| If False, remove global mean of each time step for PDO, or | ||
| do nothing for other modes | ||
| Default is True for this function. | ||
| - region_subdomain: lat lon range of sub domain for given mode, which was | ||
| extracted from regions_specs -- that is a dict contains domain | ||
| lat lon ragne for given mode | ||
| Output | ||
| - ds_residual: array, 3d (t, y, x) | ||
| """ | ||
| if not isinstance(ds_anomaly, xr.Dataset): | ||
| raise TypeError( | ||
| "The first parameter of get_residual_timeseries must be an xarray Dataset" | ||
| ) | ||
| ds_residual = ds_anomaly.copy() | ||
| if RmDomainMean: | ||
| # Get domain mean | ||
| ds_anomaly_region = region_subset( | ||
| ds_anomaly, mode, data_var=data_var, regions_specs=regions_specs | ||
| ) | ||
| ds_anomaly_mean = ds_anomaly_region.spatial.average(data_var) | ||
| # Subtract domain mean | ||
| ds_residual[data_var] = ds_anomaly[data_var] - ds_anomaly_mean[data_var] | ||
| else: | ||
| if mode in ["PDO", "NPGO", "AMO"]: | ||
| # Get global mean (latitude -60 to 70) | ||
| ds_anomaly_subset = select_subset(ds_anomaly, lat=(-60, 70)) | ||
| ds_anomaly_subset_mean = ds_anomaly_subset.spatial.average(data_var) | ||
| # Subtract global mean | ||
| ds_residual[data_var] = ( | ||
| ds_anomaly[data_var] - ds_anomaly_subset_mean[data_var] | ||
| ) | ||
| # return result | ||
| return ds_residual |
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