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apply landmask function added, clean up
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@lee1043
lee1043 committed Dec 17, 2023
1 parent 51434ef commit 69883ef
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Showing 3 changed files with 101 additions and 44 deletions.
36 changes: 34 additions & 2 deletions pcmdi_metrics/mean_climate/mean_climate_driver.py
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Expand Up @@ -14,7 +14,7 @@
load_and_regrid,
mean_climate_metrics_to_json,
)
from pcmdi_metrics.utils import create_land_sea_mask, create_target_grid
from pcmdi_metrics.utils import apply_landmask, create_land_sea_mask, create_target_grid
from pcmdi_metrics.variability_mode.lib import sort_human, tree

parser = create_mean_climate_parser()
Expand Down Expand Up @@ -143,7 +143,7 @@
print("--- prepare mean climate metrics calculation ---")

# generate target grid
t_grid = create_target_grid(target_grid)
t_grid = create_target_grid(target_grid_resolution=target_grid)

# generate land sea mask for the target grid
sft = create_land_sea_mask(t_grid)
Expand Down Expand Up @@ -333,19 +333,51 @@
ds_test_tmp = ds_test.copy(deep=True)
ds_ref_tmp = ds_ref.copy(deep=True)
if "land" in region.split("_"):
ds_test_tmp[varname] = apply_landmask(
ds_test,
data_var=varname,
landfrac=t_grid["sftlf"],
mask_land=False,
mask_ocean=True,
)
ds_ref_tmp[varname] = apply_landmask(
ds_ref,
data_var=varname,
landfrac=t_grid["sftlf"],
mask_land=False,
mask_ocean=True,
)
"""
ds_test_tmp[varname] = ds_test[varname].where(
t_grid["sftlf"] != 0.0
)
ds_ref_tmp[varname] = ds_ref[varname].where(
t_grid["sftlf"] != 0.0
)
"""
elif "ocean" in region.split("_"):
ds_test_tmp[varname] = apply_landmask(
ds_test,
data_var=varname,
landfrac=t_grid["sftlf"],
mask_land=True,
mask_ocean=False,
)
ds_ref_tmp[varname] = apply_landmask(
ds_ref,
data_var=varname,
landfrac=t_grid["sftlf"],
mask_land=True,
mask_ocean=False,
)
"""
ds_test_tmp[varname] = ds_test[varname].where(
t_grid["sftlf"] == 0.0
)
ds_ref_tmp[varname] = ds_ref[varname].where(
t_grid["sftlf"] == 0.0
)
"""
print("mask done")
else:
ds_test_tmp = ds_test
Expand Down
3 changes: 2 additions & 1 deletion pcmdi_metrics/utils/__init__.py
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@@ -1,2 +1,3 @@
from .create_land_sea_mask import create_land_sea_mask
from .create_land_sea_mask import apply_landmask, create_land_sea_mask
from .create_target_grid import create_target_grid
from .sort_human import sort_human
106 changes: 65 additions & 41 deletions pcmdi_metrics/utils/create_land_sea_mask.py
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Expand Up @@ -3,35 +3,34 @@
import xcdat as xc


def create_land_sea_mask(ds: xr.Dataset, boolean: bool = False) -> xr.DataArray:
"""A function generates land sea mask (1: land, 0: sea) for given xarray Dataset,
assuming the given xarray dataset and has latitude and longitude coordinates.
def create_land_sea_mask(ds: xr.Dataset, as_boolean: bool = False) -> xr.DataArray:
"""Generate a land-sea mask (1 for land, 0 for sea) for a given xarray Dataset.
Parameters
----------
ds : xr.Dataset
A Dataset object.
boolen : bool, optional
Set mask value to True (land) or False (ocean), by default False, thus 1 (land) abd 0 (ocean)
as_boolean : bool, optional
Set mask value to True (land) or False (ocean), by default False, thus 1 (land) and 0 (ocean).
Returns
-------
xr.DataArray
A DataArray of land sea mask (1|0 or True|False for land|sea)
A DataArray of land-sea mask (1 or 0 for land or sea, or True or False for land or sea).
Examples
--------
Import:
>>> from pcmdi_metrics.utils import create_land_sea_mask
Generate land-sea mask (land: 1, ocean: 0):
Generate land-sea mask (land: 1, sea: 0):
>>> mask = create_land_sea_mask(ds)
Generate land-sea mask (land: True, ocean: False):
Generate land-sea mask (land: True, sea: False):
>>> mask = create_land_sea_mask(ds, boolean=True)
>>> mask = create_land_sea_mask(ds, as_boolean=True)
"""
# Create a land-sea mask using regionmask
land_mask = regionmask.defined_regions.natural_earth_v5_0_0.land_110
Expand All @@ -46,77 +45,102 @@ def create_land_sea_mask(ds: xr.Dataset, boolean: bool = False) -> xr.DataArray:
# Mask the land-sea mask to match the dataset's coordinates
land_sea_mask = land_mask.mask(lon, lat)

if not boolean:
if not as_boolean:
# Convert the land-sea mask to a boolean mask
land_sea_mask = xr.where(land_sea_mask, 0, 1)

return land_sea_mask


def find_max(da: xr.DataArray) -> float:
"""Find the maximum value in a given xarray DataArray.
Parameters
----------
da : xr.DataArray
Input DataArray.
Returns
-------
float
Maximum value in the DataArray.
"""
return float(da.max().values)


def find_min(da: xr.DataArray) -> float:
"""Find the minimum value in a given xarray DataArray.
Parameters
----------
da : xr.DataArray
Input DataArray.
Returns
-------
float
Minimum value in the DataArray.
"""
return float(da.min().values)


def apply_landmask(
ds: xr.Dataset,
data_var: str,
landfrac: xr.DataArray,
maskland: bool = True,
maskocean: bool = False,
land_criteria=0.8,
ocean_criteria=0.2,
mask_land: bool = True,
mask_ocean: bool = False,
land_criteria: float = 0.8,
ocean_criteria: float = 0.2,
) -> xr.DataArray:
"""_summary_
"""Apply a land-sea mask to a given DataArray in an xarray Dataset.
Parameters
----------
ds : xr.Dataset
Dataset that inlcudes a DataArray to apply land-sea mask
Dataset that includes a DataArray to apply a land-sea mask.
data_var : str
name of DataArray in the Dataset
Name of DataArray in the Dataset.
landfrac : xr.DataArray
data array for land fraction that consists 0 for ocean and 1 for land (fraction for grid along coastline)
maskland : bool, optional
mask out land region (thus value will exist over ocean only), by default True
maskocean : bool, optional
mask out ocean region (thus value will exist over land only), by default False
Data array for land fraction that consists of 0 for ocean and 1 for land (fraction for grid along coastline).
mask_land : bool, optional
Mask out land region (thus value will exist over ocean only), by default True.
mask_ocean : bool, optional
Mask out ocean region (thus value will exist over land only), by default False.
land_criteria : float, optional
when fraction is equal to land_criteria or larger, grid will be considered as land, by default 0.8
When the fraction is equal to land_criteria or larger, the grid will be considered as land, by default 0.8.
ocean_criteria : float, optional
when fraction is equal to ocean_criteria or smaller, grid will be considered as ocean, by default 0.2
When the fraction is equal to ocean_criteria or smaller, the grid will be considered as ocean, by default 0.2.
Returns
-------
xr.DataArray
land-sea mask applied DataArray
Land-sea mask applied DataArray.
Examples
--------
Import:
>>> from pcmdi_metrics.utils import apply_landmask
Mask over land (values over ocean only):
Mask over land (keep values over ocean only):
>>> da_masked = apply_landmask(ds, data_var="ts", landmask=mask, maskland=True, maskocean=False)
>>> da_masked = apply_landmask(ds, data_var="ts", landfrac=mask, mask_land=True, mask_ocean=False)
Mask over ocean (values over land only):
Mask over ocean (keep values over land only):
>>> da_masked = apply_landmask(ds, data_var="ts", landmask=mask, maskland=False, maskocean=True)
>>> da_masked = apply_landmask(ds, data_var="ts", landfrac=mask, mask_land=False, mask_ocean=True)
"""
da = ds[data_var].copy()
# if land = 100 instead of 1, divides landmask by 100
if (find_min(landfrac) == 0 and find_max(landfrac) == 100) or (
"units" in list(landfrac.attrs.keys()) and landfrac.units == "%"
):
landfrac = landfrac / 100.0
if maskland is True:
da = da.where(landfrac <= ocean_criteria)
if maskocean is True:
da = da.where(landfrac >= land_criteria)

return da
data_array = ds[data_var].copy()

# Convert landfrac to a fraction if it's in percentage form
if landfrac.units == "%" or (find_min(landfrac) == 0 and find_max(landfrac) == 100):
landfrac /= 100.0

# Apply land and ocean masks
if mask_land:
data_array = data_array.where(landfrac <= ocean_criteria)
if mask_ocean:
data_array = data_array.where(landfrac >= land_criteria)

return data_array

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