Update MoV code to use xCDAT

.pre-commit-config.yaml

@@ -25,7 +25,7 @@ repos:
  - id: black
 
  - repo: https://github.com/timothycrosley/isort
- rev: 5.12.0
+ rev: 5.13.2
  hooks:
  - id: isort
  args: ["--honor-noqa"]
@@ -34,7 +34,7 @@ repos:
  # Python linting
  # =======================
  - repo: https://github.com/pycqa/flake8
- rev: 6.0.0
+ rev: 7.0.0
  hooks:
  - id: flake8
  args: ["--config=setup.cfg"]

conda-env/dev.yml

@@ -18,10 +18,10 @@ dependencies:
  - genutil=8.2.1
  - cdutil=8.2.1
  - cdp=1.7.0
- - eofs=1.4.0
+ - eofs=1.4.1
  - seaborn=0.12.2
  - enso_metrics=1.1.1
- - xcdat>=0.6.1
+ - xcdat>=0.7.0
  - xmltodict=0.13.0
  - setuptools=67.7.2
  - netcdf4=1.6.3

pcmdi_metrics/graphics/portrait_plot/portrait_plot_lib.py

@@ -342,12 +342,12 @@ def portrait_plot(
 # ----------------------------------------------------------------------
 def prepare_data(data, xaxis_labels, yaxis_labels, debug=False):
  # In case data was given as list of arrays, convert it to numpy (stacked) array
- if type(data) == list:
+ if isinstance(data, list):
  if debug:
  print("data type is list")
  print("len(data):", len(data))
  if len(data) == 1: # list has only 1 array as element
- if (type(data[0]) == np.ndarray) and (len(data[0].shape) == 2):
+ if isinstance(data[0], np.ndarray) and (len(data[0].shape) == 2):
  data = data[0]
  num_divide = 1
  else:
@@ -366,7 +366,7 @@ def prepare_data(data, xaxis_labels, yaxis_labels, debug=False):
  if data.shape[-2] != len(yaxis_labels) and len(yaxis_labels) > 0:
  sys.exit("Error: Number of elements in yaxis_label mismatchs to the data")
 
- if type(data) == np.ndarray:
+ if isinstance(data, np.ndarray):
  # data = np.squeeze(data)
  if len(data.shape) == 2:
  num_divide = 1

pcmdi_metrics/io/__init__.py

@@ -1,12 +1,13 @@
 # init for pcmdi_metrics.io
 from .xcdat_openxml import xcdat_open # noqa # isort:skip
+from .string_constructor import StringConstructor, fill_template # noqa # isort:skip
 from . import base # noqa
 from .base import MV2Json # noqa
-from .default_regions_define import load_regions_specs # noqa
-from .default_regions_define import region_subset # noqa
-from .xcdat_dataset_io import ( # noqa
+from .xcdat_dataset_io import ( # noqa # isort:skip
+ da_to_ds,
  get_axis_list,
  get_data_list,
+ get_grid,
  get_latitude_bounds_key,
  get_latitude_key,
  get_latitude,
@@ -21,3 +22,4 @@
  get_time_key,
  select_subset,
 )
+from .regions import load_regions_specs, region_subset # noqa

pcmdi_metrics/io/base.py

@@ -19,7 +19,7 @@
 
 import pcmdi_metrics
 from pcmdi_metrics import LOG_LEVEL
-from pcmdi_metrics.utils import StringConstructor
+from pcmdi_metrics.io import StringConstructor
 
 value = 0
 cdms2.setNetcdfShuffleFlag(value) # where value is either 0 or 1

pcmdi_metrics/io/default_regions_define.py → pcmdi_metrics/io/regions.py

@@ -1,7 +1,12 @@
+from typing import Union
+
+import xarray as xr
 import xcdat as xc
 
+from pcmdi_metrics.io import da_to_ds, get_longitude, select_subset
+
 
-def load_regions_specs():
+def load_regions_specs() -> dict:
  regions_specs = {
  # Mean Climate
  "global": {},
@@ -35,7 +40,10 @@ def load_regions_specs():
  "NAO": {"domain": {"latitude": (20.0, 80), "longitude": (-90, 40)}},
  "SAM": {"domain": {"latitude": (-20.0, -90), "longitude": (0, 360)}},
  "PNA": {"domain": {"latitude": (20.0, 85), "longitude": (120, 240)}},
+ "NPO": {"domain": {"latitude": (20.0, 85), "longitude": (120, 240)}},
  "PDO": {"domain": {"latitude": (20.0, 70), "longitude": (110, 260)}},
+ "NPGO": {"domain": {"latitude": (20.0, 70), "longitude": (110, 260)}},
+ "AMO": {"domain": {"latitude": (0.0, 70), "longitude": (-80, 0)}},
  # Monsoon domains for Wang metrics
  # All monsoon domains
  "AllMW": {"domain": {"latitude": (-40.0, 45.0), "longitude": (0.0, 360.0)}},
@@ -45,7 +53,8 @@ def load_regions_specs():
  # South American Monsoon
  "SAMM": {"domain": {"latitude": (-45.0, 0.0), "longitude": (240.0, 330.0)}},
  # North African Monsoon
- "NAFM": {"domain": {"latitude": (0.0, 45.0), "longitude": (310.0, 60.0)}},
+ # "NAFM": {"domain": {"latitude": (0.0, 45.0), "longitude": (310.0, 60.0)}},
+ "NAFM": {"domain": {"latitude": (0.0, 45.0), "longitude": (-50.0, 60.0)}},
  # South African Monsoon
  "SAFM": {"domain": {"latitude": (-45.0, 0.0), "longitude": (0.0, 90.0)}},
  # Asian Summer Monsoon
@@ -70,55 +79,77 @@ def load_regions_specs():
  return regions_specs
 
 
-def region_subset(ds, regions_specs, region=None):
- """
- d: xarray.Dataset
- regions_specs: dict
- region: string
+def region_subset(
+ ds: Union[xr.Dataset, xr.DataArray],
+ region: str,
+ data_var: str = "variable",
+ regions_specs: dict = None,
+ debug: bool = False,
+) -> Union[xr.Dataset, xr.DataArray]:
+ """_summary_
+
+ Parameters
+ ----------
+ ds : Union[xr.Dataset, xr.DataArray]
+ _description_
+ region : str
+ _description_
+ data_var : str, optional
+ _description_, by default None
+ regions_specs : dict, optional
+ _description_, by default None
+ debug: bool, optional
+ Turn on debug print, by default False
+
+ Returns
+ -------
+ Union[xr.Dataset, xr.DataArray]
+ _description_
  """
+ if isinstance(ds, xr.DataArray):
+ is_dataArray = True
+ ds = da_to_ds(ds, data_var)
+ else:
+ is_dataArray = False
+
+ if regions_specs is None:
+ regions_specs = load_regions_specs()
+
+ if "domain" in regions_specs[region]:
+ if "latitude" in regions_specs[region]["domain"]:
+ lat0 = regions_specs[region]["domain"]["latitude"][0]
+ lat1 = regions_specs[region]["domain"]["latitude"][1]
+ # proceed subset
+ ds = select_subset(ds, lat=(min(lat0, lat1), max(lat0, lat1)))
+ if debug:
+ print("region_subset, latitude subsetted, ds:", ds)
+
+ if "longitude" in regions_specs[region]["domain"]:
+ lon0 = regions_specs[region]["domain"]["longitude"][0]
+ lon1 = regions_specs[region]["domain"]["longitude"][1]
+
+ # check original dataset longitude range
+ lon_min = get_longitude(ds).min().values.item()
+ lon_max = get_longitude(ds).max().values.item()
+
+ # Check if longitude range swap is needed
+ if min(lon0, lon1) < 0:
+ # when subset region lon is defined in (-180, 180) range
+ if min(lon_min, lon_max) < 0:
+ # if original data lon range is (-180, 180), no treatment needed
+ pass
+ else:
+ # if original data lon range is (0, 360), convert and swap lon
+ ds = xc.swap_lon_axis(ds, to=(-180, 180))
+
+ # proceed subset
+ # ds = select_subset(ds, lon=(min(lon0, lon1), max(lon0, lon1)))
+ ds = select_subset(ds, lon=(lon0, lon1))
+ if debug:
+ print("region_subset, longitude subsetted, ds:", ds)
 
- if (region is None) or (
- (region is not None) and (region not in list(regions_specs.keys()))
- ):
- print("Error: region not defined")
+ # return the same type
+ if is_dataArray:
+ return ds[data_var]
  else:
- if "domain" in list(regions_specs[region].keys()):
- if "latitude" in list(regions_specs[region]["domain"].keys()):
- lat0 = regions_specs[region]["domain"]["latitude"][0]
- lat1 = regions_specs[region]["domain"]["latitude"][1]
- # proceed subset
- if "latitude" in (ds.coords.dims):
- ds = ds.sel(latitude=slice(lat0, lat1))
- elif "lat" in (ds.coords.dims):
- ds = ds.sel(lat=slice(lat0, lat1))
-
- if "longitude" in list(regions_specs[region]["domain"].keys()):
- lon0 = regions_specs[region]["domain"]["longitude"][0]
- lon1 = regions_specs[region]["domain"]["longitude"][1]
-
- # check original dataset longitude range
- if "longitude" in (ds.coords.dims):
- lon_min = ds.longitude.min()
- lon_max = ds.longitude.max()
- elif "lon" in (ds.coords.dims):
- lon_min = ds.lon.min()
- lon_max = ds.lon.max()
-
- # longitude range swap if needed
- if (
- min(lon0, lon1) < 0
- ): # when subset region lon is defined in (-180, 180) range
- if (
- min(lon_min, lon_max) < 0
- ): # if original data lon range is (-180, 180) no treatment needed
- pass
- else: # if original data lon range is (0, 360), convert swap lon
- ds = xc.swap_lon_axis(ds, to=(-180, 180))
-
- # proceed subset
- if "longitude" in (ds.coords.dims):
- ds = ds.sel(longitude=slice(lon0, lon1))
- elif "lon" in (ds.coords.dims):
- ds = ds.sel(lon=slice(lon0, lon1))
-
- return ds
+ return ds

pcmdi_metrics/io/string_constructor.py

@@ -0,0 +1,99 @@
+import warnings
+
+
+class StringConstructor:
+ """
+ This class aims at spotting keywords in a string and replacing them.
+ """
+
+ def __init__(self, template=None):
+ """
+ Instantiates a StringConstructor object.
+ """
+ self.template = template
+ # Generate the keys and set them to empty
+ keys = self.keys()
+ for k in keys:
+ setattr(self, k, "")
+
+ def keys(self, template=None):
+ if template is None:
+ template = self.template
+ if template is None:
+ return []
+ # Determine the keywords in the template
+ keys = []
+ template_split = template.split("%(")[1:]
+ if len(template_split) > 0:
+ for k in template_split:
+ sp = k.split(")")
+ if sp[0] not in keys:
+ keys.append(sp[0])
+ return keys
+
+ def construct(self, template=None, **kw):
+ """
+ Accepts a string with an unlimited number of keywords to replace.
+ """
+ if template is None:
+ template = self.template
+ # Replace the keywords with their values
+ for k in self.keys():
+ if k not in kw:
+ warnings.warn(f"Keyword '{k}' not provided for filling the template.")
+ template = template.replace("%(" + k + ")", kw.get(k, getattr(self, k, "")))
+ return template
+
+ def reverse(self, name, debug=False):
+ """
+ The reverse function attempts to take a template and derive its keyword values based on name parameter.
+ """
+ out = {}
+ template = self.template
+ for k in self.keys():
+ sp = template.split("%%(%s)" % k)
+ i1 = name.find(sp[0]) + len(sp[0])
+ j1 = sp[1].find("%(")
+ if j1 == -1:
+ if sp[1] == "":
+ val = name[i1:]
+ else:
+ i2 = name.find(sp[1])
+ val = name[i1:i2]
+ else:
+ i2 = name[i1:].find(sp[1][:j1])
+ val = name[i1 : i1 + i2]
+ template = template.replace("%%(%s)" % k, val)
+ out[k] = val
+ if self.construct(self.template, **out) != name:
+ raise ValueError("Invalid pattern sent")
+ return out
+
+ def __call__(self, *args, **kw):
+ """default call is construct function"""
+ return self.construct(*args, **kw)
+
+
+def fill_template(template: str, **kwargs) -> str:
+ """
+ Fill in a template string with keyword values.
+
+ Parameters
+ ----------
+ - template (str): The template string containing keywords of the form '%(keyword)'.
+ - kwargs (dict): Keyword arguments with values to replace in the template.
+
+ Returns
+ -------
+ - str: The filled-in string with replaced keywords.
+
+ Examples
+ --------
+ >>> from pcmdi_metrics.utils import fill_template
+ >>> template = "This is a %(adjective) %(noun) that %(verb)."
+ >>> filled_string = fill_template(template, adjective="great", noun="example", verb="works")
+ >>> print(filled_string) # It will print "This is a great example that works."
+ """
+ filler = StringConstructor(template)
+ filled_template = filler.construct(**kwargs)
+ return filled_template