-
Notifications
You must be signed in to change notification settings - Fork 40
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge pull request #1062 from PCMDI/feature/lee1043-mov-modularize
Feature/lee1043 mov modularize
- Loading branch information
Showing
4 changed files
with
157 additions
and
148 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
147 changes: 147 additions & 0 deletions
147
pcmdi_metrics/variability_mode/lib/adjust_timeseries.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,147 @@ | ||
| import xarray as xr | ||
|
|
||
| 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, | ||
| ) | ||
|
|
||
|
|
||
| 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 | ||
|
|
||
|
|
||
| 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 | ||
|
|
||
|
|
||
| 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 | ||
|
|
||
|
|
||
| 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 |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters