CDAT Migration: Refactor annual_cycle_zonal_mean set

auxiliary_tools/cdat_regression_testing/669-annual_cycle_zonal_mean/669-annual_cycle_zonal_mean.cfg

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+[#]
+sets = ["annual_cycle_zonal_mean"]
+case_id = "AOD_550"
+variables = ["AODVIS"]
+ref_name = "AOD_550"
+reference_name = "AERONET-composite AOD"
+test_colormap = "Oranges"
+reference_colormap = "Oranges"
+diff_colormap = "BrBG_r"
+contour_levels = [0., 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2]
+diff_levels = [-0.5, -0.4, -0.3, -0.2, -0.1, -0.05, -0.02, 0.02, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5]
+
+# [#]
+# sets = ["annual_cycle_zonal_mean"]
+# case_id = "OMI-MLS"
+# variables = ["SCO"]
+# ref_name = "OMI-MLS"
+# reference_name = "OMI-MLS"
+# test_colormap = "WhiteBlueGreenYellowRed.rgb"
+# reference_colormap = "WhiteBlueGreenYellowRed.rgb"
+# diff_colormap = "diverging_bwr.rgb"
+# contour_levels = [180,200,220,240,260,280,300,320,340,360,380]
+# diff_levels = [ -40, -30, -20, -15, -10, -5,-2,2, 5, 10, 15, 20, 30,40]
+
+# [#]
+# sets = ["annual_cycle_zonal_mean"]
+# case_id = "OMI-MLS"
+# variables = ["TCO"]
+# ref_name = "OMI-MLS"
+# reference_name = "OMI-MLS"
+# test_colormap = "WhiteBlueGreenYellowRed.rgb"
+# reference_colormap = "WhiteBlueGreenYellowRed.rgb"
+# diff_colormap = "diverging_bwr.rgb"
+# contour_levels = [12,16,20,24,28,32,36,40,44]
+# diff_levels = [-20,-15,-10,-5,-2,2,5,10,15,20]
+
+# [#]
+# sets = ["annual_cycle_zonal_mean"]
+# case_id = "SST_CL_HadISST"
+# variables = ["SST"]
+# ref_name = "HadISST_CL"
+# reference_name = "HadISST (Climatology)"
+# contour_levels = [-1, 0, 1, 3, 6, 9, 12, 15, 18, 20, 22, 24, 26, 28, 29]
+# diff_levels = [-5, -4, -3, -2, -1, -0.5, -0.2, 0.2, 0.5, 1, 2, 3, 4, 5]
+
+
+# [#]
+# sets = ["annual_cycle_zonal_mean"]
+# case_id = "GPCP_v3.2"
+# variables = ["PRECT"]
+# ref_name = "GPCP_v3.2"
+# reference_name = "GPCP_v3.2"
+# test_colormap = "WhiteBlueGreenYellowRed.rgb"
+# reference_colormap = "WhiteBlueGreenYellowRed.rgb"
+# diff_colormap = "BrBG"
+# contour_levels = [0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16]
+# diff_levels = [-5, -4, -3, -2, -1, -0.5, 0.5, 1, 2, 3, 4, 5]

auxiliary_tools/cdat_regression_testing/669-annual_cycle_zonal_mean/669-annual_cycle_zonal_mean_run_script.py

@@ -0,0 +1,14 @@
+# %%
+from auxiliary_tools.cdat_regression_testing.base_run_script import run_set
+
+SET_NAME = "annual_cycle_zonal_mean"
+SET_DIR = (
+    "/global/cfs/cdirs/e3sm/www/cdat-migration-fy24/669-annual_cycle_zonal_mean"
+)
+# CFG_PATH = "auxiliary_tools/cdat_regression_testing/669-annual_cycle_zonal_mean/669-annual_cycle_zonal_mean.cfg"
+CFG_PATH = "e3sm_diags/driver/default_diags/annual_cycle_zonal_mean_model_vs_obs.cfg"
+MULTIPROCESSING = True
+
+run_set(SET_NAME, SET_DIR, CFG_PATH, MULTIPROCESSING)
+
+# %%

auxiliary_tools/cdat_regression_testing/669-annual_cycle_zonal_mean/debug/debug_AODVIS.py

@@ -0,0 +1,62 @@
+import numpy as np
+import xcdat as xc
+
+dev_path = "/global/cfs/cdirs/e3sm/www/cdat-migration-fy24/669-annual_cycle_zonal_mean-debug/annual_cycle_zonal_mean/AOD_550/AOD_550-AODVIS-ANNUALCYCLE-global_ref.nc"
+main_path = "/global/cfs/cdirs/e3sm/www/cdat-migration-fy24/main/annual_cycle_zonal_mean/AOD_550/AOD_550-AODVIS-Annual-Cycle_test.nc"
+
+
+var_a = xc.open_dataset(dev_path)["AODVIS"]
+var_b = xc.open_dataset(main_path)["AODVIS"]
+
+"""
+Floating point comparison
+
+AssertionError:
+Not equal to tolerance rtol=1e-07, atol=0
+
+Mismatched elements: 1808 / 2160 (83.7%)
+Max absolute difference: 0.12250582
+Max relative difference: 91.14554689
+ x: array([[0., 0., 0., ..., 0., 0., 0.],
+       [0., 0., 0., ..., 0., 0., 0.],
+       [0., 0., 0., ..., 0., 0., 0.],...
+ y: array([[0., 0., 0., ..., 0., 0., 0.],
+       [0., 0., 0., ..., 0., 0., 0.],
+       [0., 0., 0., ..., 0., 0., 0.],...
+"""
+np.testing.assert_allclose(var_a, var_b)
+
+# Get the max of all values
+# -------------------------
+# 0.28664299845695496
+print(var_a.max().item())
+# 0.2866430557436412
+print(var_b.max().item())
+
+# Get the min of all values
+# -------------------------
+# 0.0
+print(var_a.min().item())
+# 0.0
+print(var_b.min().item())
+
+# Get the sum of all values
+# -------------------------
+# 224.2569122314453
+print(var_a.sum().item())
+# 224.25691348856003
+print(var_b.sum().item())
+
+# Get the mean of all values
+# -------------------------
+# 0.10382264107465744
+print(var_a.mean().item())
+# 0.1038226451335926
+print(var_b.mean().item())
+
+
+# %%
+# Get the max absolute diff
+# -------------------------
+# 0.12250582128763199
+print((var_a - var_b).max().item())

auxiliary_tools/cdat_regression_testing/669-annual_cycle_zonal_mean/debug/debug_decode_times.py

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+# %%
+from glob import glob
+
+import cftime
+import xcdat as xc
+
+args = {
+    "add_bounds": ["X", "Y"],
+    "coords": "minimal",
+    "compat": "override",
+    "chunks": "auto",
+}
+
+filepath = "/global/cfs/cdirs/e3sm/diagnostics/observations/Atm/climatology/MERRA2_Aerosols/MERRA2_Aerosols_[0-1][0-9]_*climo.nc"
+paths = sorted(glob(filepath))
+
+# Check time coordinates are in ascending order with wildcard and sorted glob
+ds_mf = xc.open_mfdataset(filepath, decode_times=True, **args)
+ds_mf_raw = xc.open_mfdataset(paths, **args, decode_times=True)
+
+# %%
+
+# filepath 1: '/global/cfs/cdirs/e3sm/diagnostics/observations/Atm/climatology/MERRA2_Aerosols/MERRA2_Aerosols_01_198001_202101_climo.nc'
+ds_raw_time = xc.open_mfdataset(paths[0], **args, decode_times=False)
+
+# 10782720
+time_int = ds_raw_time.time.values.item()
+# 'minutes since 1980-01-01 00:30:00'
+units = ds_raw_time.time.units
+# None, so "standard"
+calendar = ds_raw_time.time.attrs.get("calendar", "standard")
+
+# cftime.DatetimeGregorian(2000, 7, 2, 0, 30, 0, 0, has_year_zero=False)
+cftime.num2date(time_int, units, calendar=calendar)
+
+# %%
+import datetime
+
+first_step = datetime.datetime(1980, 1, 1, hour=0, minute=30)
+time_delta = datetime.timedelta(minutes=10782720)
+
+# datetime.datetime(2000, 7, 2, 0, 30)
+print(first_step + time_delta)

auxiliary_tools/cdat_regression_testing/669-annual_cycle_zonal_mean/debug/debug_nco_open_mf_dataset_issue_1.py

@@ -0,0 +1,76 @@
+# %%
+from glob import glob
+
+import xarray as xr
+import xcdat as xc
+
+args = {
+    "decode_times": True,
+    "add_bounds": ["X", "Y"],
+    "coords": "minimal",
+    "compat": "override",
+    "chunks": "auto",
+}
+
+filepath = "/global/cfs/cdirs/e3sm/diagnostics/observations/Atm/climatology/MERRA2_Aerosols/MERRA2_Aerosols_[0-1][0-9]_*climo.nc"
+paths = sorted(glob(filepath))
+
+# filepath 1: '/global/cfs/cdirs/e3sm/diagnostics/observations/Atm/climatology/MERRA2_Aerosols/MERRA2_Aerosols_01_198001_202101_climo.nc'
+ds_fp1 = xc.open_mfdataset(paths[0], **args)
+# filepath 2: '/global/cfs/cdirs/e3sm/diagnostics/observations/Atm/climatology/MERRA2_Aerosols/MERRA2_Aerosols_02_198002_202102_climo.nc'
+ds_fp2 = xc.open_mfdataset(paths[1], **args)
+
+# Check the units -- different
+# ----------------------------
+# 'minutes since 1980-01-01 00:30:00'
+ds_fp1.time.attrs["units"]
+# 'minutes since 1980-02-01 00:30:00'
+ds_fp2.time.attrs["units"]
+
+# Check the time values -- same
+# ----------------------------
+# 10782720
+ds_fp1.time.values[0]
+# 10782720
+ds_fp2.time.values[0]
+
+
+# %%
+import cftime
+
+
+def _get_encoded_time(var: xr.DataArray) -> xr.DataArray:
+    """Convert `cftime` datetime objects to encoded float objects.
+
+    This function is useful for plotters that plot the time axis, which
+    requires time coordinates to be encoded as floats if arithmetic is
+    performed.
+
+    Parameters
+    ----------
+    var : xr.DataArray
+        The variable.
+
+    Returns
+    -------
+    xr.DataArray
+        The encoded time coordinates.
+    """
+    time_coords = xc.get_dim_coords(var, axis="T")
+
+    for index, time_val in enumerate(time_coords):
+        new_time_val = cftime.date2num(
+            time_val.item(),
+            time_val.encoding["units"],
+            calendar=time_val.encoding["calendar"],
+        )
+        time_coords.values[index] = new_time_val
+        time_coords[time_coords.name].values[index] = new_time_val
+
+    return time_coords
+
+
+# %%
+new_time = _get_encoded_time(ds_fp1)
+
+# %%

auxiliary_tools/cdat_regression_testing/669-annual_cycle_zonal_mean/debug/debug_nco_open_mf_dataset_issue_2.py

@@ -0,0 +1,31 @@
+# %%
+from glob import glob
+
+import xarray as xr
+import xcdat as xc
+
+args = {
+    "decode_times": False,
+    "add_bounds": ["X", "Y"],
+    "coords": "minimal",
+    "compat": "override",
+    "chunks": "auto",
+}
+
+# %%
+filepath = "/global/cfs/cdirs/e3sm/diagnostics/observations/Atm/climatology/AOD_550/AOD_550_[0-1][0-9]_*climo.nc"
+filepaths = glob(filepath)
+ds = xc.open_mfdataset(filepaths, **args)
+
+# array([ 15.5101,  46.03  ,  76.5498, 107.57  , 137.5895, 168.6097,
+#       198.6292, 229.6494, 259.6689, 290.6891, 321.7142, 351.2334])
+ds.time.values
+
+# %%
+ds1 = xc.open_mfdataset(filepaths[0], **args)
+ds2 = xc.open_mfdataset(filepaths[1], **args)
+
+# 'days since 2000-03-01'
+ds1.time.units
+# 'days since 2000-03-01'
+ds2.time.units