ctsm5.4.0xx: Fix years used by generate_gdds

cime_config/SystemTests/rxcropmaturity.py

@@ -492,15 +492,8 @@ def _run_interpolate_gdds(self):
         )
 
     def _get_conda_env(self):
-        conda_setup_commands = stu.cmds_to_setup_conda(self._get_caseroot())
-
-        # If npl conda environment is available, use that (It has dask, which
-        # enables chunking, which makes reading daily 1-degree netCDF files
-        # much more efficient.
-        if "npl " in os.popen(conda_setup_commands + "conda env list").read():
-            self._this_conda_env = "npl"
-        else:
-            self._this_conda_env = "ctsm_pylib"
+        stu.cmds_to_setup_conda(self._get_caseroot())
+        self._this_conda_env = "ctsm_pylib"
 
     def _append_to_user_nl_clm(self, additions):
         caseroot = self._get_caseroot()

cime_config/testdefs/ExpectedTestFails.xml

@@ -36,27 +36,6 @@
     </phase>
   </test>
 
-  <test name="RXCROPMATURITY_Lm61.f10_f10_mg37.IHistClm60BgcCrop.derecho_intel.clm-cropMonthOutput">
-    <phase name="RUN">
-      <status>FAIL</status>
-      <issue>#3740</issue>
-    </phase>
-  </test>
-
-  <test name="RXCROPMATURITY_Lm61.f09_t232.IHistClm60BgcCrop.derecho_intel.clm-cropMonthOutput">
-    <phase name="RUN">
-      <status>FAIL</status>
-      <issue>#3740</issue>
-    </phase>
-  </test>
-
-  <test name="RXCROPMATURITY_Lm61.f09_g17.IHistClm60BgcCrop.derecho_intel.clm-cropMonthOutput">
-    <phase name="RUN">
-      <status>FAIL</status>
-      <issue>#3740</issue>
-    </phase>
-  </test>
-
   <test name="SMS_Ld5.f09_g17.IHistClm50Sp.derecho_intel.clm-nofire">
     <phase name="SHAREDLIB_BUILD">
       <status>FAIL</status>

cime_config/testdefs/testlist_clm.xml

@@ -4510,22 +4510,14 @@
     </options>
   </test>
 
-  <test name="RXCROPMATURITY_Lm61" grid="f09_g17" compset="IHistClm60BgcCrop" testmods="clm/cropMonthOutput">
-    <machines>
-      <machine name="derecho" compiler="intel" category="ctsm_sci"/>
-    </machines>
-    <options>
-      <option name="wallclock">12:00:00</option>
-      <option name="comment">This test is designed to test the ability to prescribe crop sowing dates and maturity requirements. It first performs a GDD-generating run, then calls Python code to generate the maturity requirement file. This is then used in a sowing+maturity forced run, which finally is tested to ensure correct behavior.</option>
-    </options>
-  </test>
   <test name="RXCROPMATURITY_Lm61" grid="f09_t232" compset="IHistClm60BgcCropCrujra"  testmods="clm/cropMonthOutput">
     <machines>
       <machine name="derecho" compiler="intel" category="ctsm_sci"/>
+      <machine name="derecho" compiler="intel" category="rxcropmaturity"/>
     </machines>
     <options>
       <option name="wallclock">12:00:00</option>
-      <option name="comment">Repeat RXCROP test with t232 and Crujra while the defaults have not changed</option>
+      <option name="comment">This test is designed to test the ability to prescribe crop sowing dates and maturity requirements. It first performs a GDD-generating run, then calls Python code to generate the maturity requirement file. This is then used in a sowing+maturity forced run, which finally is tested to ensure correct behavior.</option>
     </options>
   </test>
 

python/ctsm/crop_calendars/check_constant_vars.py

@@ -147,8 +147,8 @@ def ensure_all_patches_checked(this_ds, this_da, ra_sp, incl_patches):
     incl_patches = np.sort(incl_patches)
     if not np.array_equal(incl_patches, np.unique(incl_patches)):
         raise RuntimeError("Patch(es) checked but also all-NaN??")
-    if not np.array_equal(incl_patches, np.arange(this_ds.dims["patch"])):
-        for patch in np.arange(this_ds.dims["patch"]):
+    if not np.array_equal(incl_patches, np.arange(this_ds.sizes["patch"])):
+        for patch in np.arange(this_ds.sizes["patch"]):
             if patch not in incl_patches:
                 raise RuntimeError(
                     f"Not all patches checked! E.g., {patch}: {this_da.isel(patch=patch).values}"
@@ -178,7 +178,7 @@ def check_one_constant_var_loop_through_timesteps(
     In check_one_constant_var(), loop through timesteps
     """
     found_in_rx = None
-    for timestep in np.arange(time_1 + 1, this_ds.dims[time_coord]):
+    for timestep in np.arange(time_1 + 1, this_ds.sizes[time_coord]):
         t_yr = this_ds[time_coord].values[timestep]
         t_vals = np.squeeze(this_da.isel({time_coord: timestep, "patch": these_patches}).values)
         ok_p = t1_vals == t_vals
@@ -267,7 +267,7 @@ def check_one_constant_var(
         bad_patches,
     ) = check_one_constant_var_setup(this_ds, case, var)
 
-    for time_1 in np.arange(this_ds.dims[time_coord] - 1):
+    for time_1 in np.arange(this_ds.sizes[time_coord] - 1):
         condn = ~np.isnan(ra_sp[time_1, ...])
         if time_1 > 0:
             condn = np.bitwise_and(condn, np.all(np.isnan(ra_sp[:time_1, ...]), axis=0))
@@ -324,12 +324,12 @@ def check_one_constant_var(
     if not any_bad:
         if any_bad_before_checking_rx:
             print(
-                f"✅ CLM output {var} do not vary through {this_ds.dims[time_coord]} growing "
+                f"✅ CLM output {var} do not vary through {this_ds.sizes[time_coord]} growing "
                 + "seasons of output (except for patch(es) with missing rx)."
             )
         else:
             print(
-                f"✅ CLM output {var} do not vary through {this_ds.dims[time_coord]} growing "
+                f"✅ CLM output {var} do not vary through {this_ds.sizes[time_coord]} growing "
                 + "seasons of output."
             )
 

python/ctsm/crop_calendars/convert_axis_time2gs.py

@@ -48,13 +48,13 @@ def convert_axis_time2gs_setup(this_ds, verbose):
     Various setup steps for convert_axis_time2gs_setup()
     """
     # How many non-NaN patch-seasons do we expect to have once we're done organizing things?
-    n_patch = this_ds.dims["patch"]
+    n_patch = this_ds.sizes["patch"]
     # Because some patches will be planted in the last year but not complete, we have to ignore any
     # finalyear-planted seasons that do complete.
-    n_gs = this_ds.dims["time"] - 1
+    n_gs = this_ds.sizes["time"] - 1
     expected_valid = n_patch * n_gs
 
-    mxharvests = this_ds.dims["mxharvests"]
+    mxharvests = this_ds.sizes["mxharvests"]
 
     if verbose:
         print(
@@ -377,7 +377,7 @@ def ignore_harvests_planted_in_final_year(
     )
     is_valid = ~np.isnan(hdates_pg2)
     is_fake = np.isneginf(hdates_pg2)
-    is_fake = np.reshape(is_fake[is_valid], (this_ds.dims["patch"], n_gs))
+    is_fake = np.reshape(is_fake[is_valid], (this_ds.sizes["patch"], n_gs))
     discrepancy = np.sum(is_valid) - expected_valid
     unique_n_seasons = np.unique(np.sum(is_valid, axis=1))
     if verbose:
@@ -435,8 +435,8 @@ def create_dataset(
 
             # Remove the nans and reshape to patches*growingseasons
             da_pyh = da_yhp.transpose("patch", "time", "mxharvests")
-            ar_pg = np.reshape(da_pyh.values, (this_ds.dims["patch"], -1))
-            ar_valid_pg = np.reshape(ar_pg[is_valid], (this_ds.dims["patch"], n_gs))
+            ar_pg = np.reshape(da_pyh.values, (this_ds.sizes["patch"], -1))
+            ar_valid_pg = np.reshape(ar_pg[is_valid], (this_ds.sizes["patch"], n_gs))
             # Change -infs to nans
             ar_valid_pg[is_fake] = np.nan
             # Save as DataArray to new Dataset, stripping _PERHARV from variable name

python/ctsm/crop_calendars/cropcal_module.py

@@ -42,7 +42,7 @@ def check_and_trim_years(year_1, year_n, ds_in):
 
     # Make sure you have the expected number of timesteps (including extra year)
     n_years_expected = year_n - year_1 + 2
-    if ds_in.dims["time"] != n_years_expected:
+    if ds_in.sizes["time"] != n_years_expected:
         raise RuntimeError(
             f"Expected {n_years_expected} timesteps in output but got {ds_in.dims['time']}"
         )
@@ -208,7 +208,7 @@ def import_max_gs_length(paramfile):
     Import maximum growing season length
     """
     # Get parameter file
-    paramfile_ds = xr.open_dataset(paramfile)
+    paramfile_ds = xr.open_dataset(paramfile, decode_timedelta=True)
 
     # Import max growing season length (stored in netCDF as nanoseconds!)
     paramfile_mxmats = paramfile_ds["mxmat"].values / np.timedelta64(1, "D")