Unpin Xarray: dataArray -> dataArray.variable

build_envs/asv-bench.yml

@@ -14,7 +14,7 @@ dependencies:
   - pint
   - setuptools
   - setuptools_scm
-  - xarray<=2023.02.0
+  - xarray
   - xskillscore
   - pip
   - pip:

build_envs/docs.yml

@@ -6,7 +6,7 @@ dependencies:
     - pre_commit
     - geocat-datafiles
     - geocat-viz
-    - xarray<=2023.02.0 #pin per issue #381
+    - xarray
     - netcdf4
     - pint
     - ipykernel

build_envs/environment.yml

@@ -12,7 +12,7 @@ dependencies:
   - numba
   - numpy<1.24  # 1.24 not yet fully compatible with numba
   - pint
-  - xarray<=2023.02.0 #pin per issue #381
+  - xarray
   - xskillscore
 # Packages listed below are for testing
   - geocat-datafiles

docs/release-notes.rst

@@ -23,12 +23,14 @@ Documentation
 Bug Fixes
 ^^^^^^^^^
 * Fix Python version in upstream CI by `Philip Chmielowiec`_ in (:pr:`436`)
+* Unpin xarray in enviroment builds with changes to interpolation.py (specify dims in xr.DataArray) and climatologies.py (replace loffset with to_offset) by `Cora Schneck`_ in (:pr:`381`)
 
 Internal Changes
 ^^^^^^^^^^^^^^^^
 * Add benchmarking to commits to main and tagged releases by `Anissa Zacharias`_ in (:pr:`496`)
 * Fix benchmarking workflow failures by `Anissa Zacharias`_ in (:pr:`499`)
 
+
 v2023.10.0 (Oct 3, 2023)
 -----------------------
 This release adds a code of conduct, minor edits to our contributor's guide, and

geocat/comp/climatologies.py

@@ -1,6 +1,7 @@
 import cf_xarray
 import cftime
 import numpy as np
+import pandas as pd
 import typing
 import xarray as xr
 import warnings
@@ -12,8 +13,8 @@ def _contains_datetime_like_objects(d_arr):
     """Check if a variable contains datetime like objects (either
     np.datetime64, or cftime.datetime)"""
     return np.issubdtype(
-        d_arr.dtype,
-        np.datetime64) or xr.core.common.contains_cftime_datetimes(d_arr)
+        d_arr.dtype, np.datetime64) or xr.core.common.contains_cftime_datetimes(
+            d_arr.variable)
 
 
 def _validate_freq(freq):
@@ -302,7 +303,19 @@ def month_to_season(
     # Group the months into three and take the mean
     means = data_filter.resample({
         time_coord_name: quarter
-    }, loffset='MS').mean(keep_attrs=keep_attrs)
+    }).mean(keep_attrs=keep_attrs)
+    # Set offset for supported array formats
+    if isinstance(means.indexes[time_coord_name],
+                  xr.coding.cftimeindex.CFTimeIndex):
+        means[time_coord_name] = means.indexes[
+            time_coord_name] + xr.coding.cftime_offsets.to_offset(freq="MS")
+    elif isinstance(means.indexes[time_coord_name], pd.DatetimeIndex):
+        means[time_coord_name] = means.indexes[
+            time_coord_name] + pd.tseries.frequencies.to_offset(freq="MS")
+    else:
+        raise ValueError(
+            f"unsupported array type - {type(means.indexes[time_coord_name])}. Valid types include: (xr.coding.cftimeindex.CFTimeIndex, pandas.core.indexes.datetimes.DatetimeIndex)"
+        )
 
     # The line above tries to take the mean for all quarters even if there is not data for some of them
     # Therefore, we must filter out the NaNs

geocat/comp/interpolation.py

@@ -593,19 +593,19 @@ def interp_sigma_to_hybrid(data: xr.DataArray,
         output = data_stacked[:, :len(hyam)].copy()
 
         for idx, (d, s) in enumerate(zip(data_stacked, sigma_stacked)):
-            output[idx, :] = xr.DataArray(
-                _vertical_remap(func_interpolate, s.data, sig_coords.data,
-                                d.data))
+            output[idx, :] = xr.DataArray(_vertical_remap(
+                func_interpolate, s.data, sig_coords.data, d.data),
+                                          dims=[lev_dim])
 
         # Make output shape same as data shape
         output = output.unstack().transpose(*data.dims)
     else:
         h_coords = sigma
 
         output = data[:len(hyam)].copy()
-        output[:len(hyam)] = xr.DataArray(
-            _vertical_remap(func_interpolate, sigma.data, sig_coords.data,
-                            data.data))
+        output[:len(hyam)] = xr.DataArray(_vertical_remap(
+            func_interpolate, sigma.data, sig_coords.data, data.data),
+                                          dims=[lev_dim])
 
     # Set output dims and coords
     output = output.rename({lev_dim: 'hlev'})

requirements.txt

@@ -9,6 +9,6 @@ eofs
 metpy
 numpy
 scipy
-xarray<=2023.02.0 #pin per issue #381
+xarray
 xskillscore
 packaging

setup.cfg

@@ -39,7 +39,7 @@ install_requires =
     eofs
     metpy
     scipy
-    xarray<=2023.02.0 #pin per issue #381
+    xarray
     xskillscore
     packaging
 tests_require =