EAMxx variables

.github/workflows/build_workflow.yml

@@ -51,7 +51,7 @@ jobs:
     strategy:
       matrix:
         python-version: ["3.9", "3.10", "3.11", "3.12"]
-    container: 
+    container:
       image: ghcr.io/e3sm-project/containers-e3sm-diags-test-data:e3sm-diags-test-data-0.0.2
     steps:
       - id: skip_check

auxiliary_tools/cdat_regression_testing/892-bottleneck/debug_ref_u.py

@@ -0,0 +1,85 @@
+"""
+This script is used to debug the bottleneck issue in the reference u variable.
+"""
+
+# %%
+import timeit
+
+import xarray as xr
+
+# Perlmutter
+# ----------
+# filepaths = [
+#     "/global/cfs/cdirs/e3sm/diagnostics/observations/Atm/time-series/ERA5/ua_197901_201912.nc"
+# ]
+
+# LCRC
+# -----
+filepaths = [
+    "/lcrc/group/e3sm/diagnostics/observations/Atm/time-series/ERA5/ua_197901_201912.nc"
+]
+time_slice = slice("1996-01-15", "1997-01-15", None)
+
+# %%
+# Test case 1 - OPEN_MFDATASET() + "ua" dataset (76 GB) + subsetting + `.load()`
+# Result: .load() hangs when using `open_mfdataset`
+# ------------------------------------------------------------------------------
+ds_ua_omfd = xr.open_mfdataset(
+    filepaths,
+    decode_times=True,
+    use_cftime=True,
+    coords="minimal",
+    compat="override",
+)
+ds_ua_omfd_sub = ds_ua_omfd.sel(time=time_slice)
+
+# %%
+start_time = timeit.default_timer()
+ds_ua_omfd_sub.load()
+elapsed = timeit.default_timer() - start_time
+print(f"Time taken to load ds_xc_sub: {elapsed} seconds")
+
+# %%
+# Test case 2 - OPEN_DATASET() + "ua" dataset (76 GB) + subsetting + `.load()`
+# Result: load() works fine when using `open_dataset`
+# ------------------------------------------------------------------------------
+ds_ua_od = xc.open_dataset(
+    filepaths[0],
+    add_bounds=["X", "Y", "T"],
+    decode_times=True,
+    use_cftime=True,
+    # coords="minimal",
+    # compat="override",
+)
+ds_ua_od_sub = ds_ua_od.sel(time=time_slice)
+
+# %%
+start_time = timeit.default_timer()
+ds_ua_od_sub.load()
+elapsed = timeit.default_timer() - start_time
+print(f"Time taken to load ds_xc_sub: {elapsed} seconds")
+
+# %%
+# Test case 3 - OPEN_MFDATASET() + "pr" dataset (2 GB) + subsetting + `.load()`
+# Result: ds.load() works fine with pr variable, but not with ua variable
+# Notes: pr is 3D variable (time, lat, lon), ua is a 4D variable (time, lat, lon, plev).
+# ------------------------------------------------------------------------------
+filepaths_pr = [
+    "/global/cfs/cdirs/e3sm/diagnostics/observations/Atm/time-series/ERA5/pr_197901_201912.nc"
+]
+ds_pr = xc.open_mfdataset(
+    filepaths_pr,
+    add_bounds=["X", "Y", "T"],
+    decode_times=True,
+    use_cftime=True,
+    coords="minimal",
+    compat="override",
+)
+
+# %%
+# pr dataset is ~2 GB without subsetting. There is no need to subset.
+start_time = timeit.default_timer()
+ds_pr.load()
+elapsed = timeit.default_timer() - start_time
+print(f"Time taken to load ds_xc_sub_0: {elapsed} seconds")
+# %%

auxiliary_tools/cdat_regression_testing/892-bottleneck/run_script.cfg

@@ -0,0 +1,13 @@
+[#]
+sets = ["lat_lon"]
+case_id = "ERA5"
+variables = ["U"]
+ref_name = "ERA5"
+reference_name = "ERA5 Reanalysis"
+seasons = ["ANN", "01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12", "DJF", "MAM", "JJA", "SON"]
+plevs = [850.0]
+test_colormap = "PiYG_r"
+reference_colormap = "PiYG_r"
+contour_levels = [-20, -15, -10, -8, -5, -3, -1, 1, 3, 5, 8, 10, 15, 20]
+diff_levels = [-8, -6, -5, -4, -3, -2, -1, 1, 2, 3, 4, 5, 6, 8]
+regrid_method = "bilinear"

auxiliary_tools/cdat_regression_testing/892-bottleneck/run_script.py

@@ -0,0 +1,39 @@
+import sys
+import os
+from e3sm_diags.parameter.core_parameter import CoreParameter
+from e3sm_diags.run import runner
+
+param = CoreParameter()
+
+
+param.reference_data_path = (
+    "/global/cfs/cdirs/e3sm/diagnostics/observations/Atm/time-series"
+)
+param.test_data_path = "/global/cfs/cdirs/e3sm/chengzhu/eamxx/post/data/rgr"
+param.test_name = "eamxx_decadal"
+param.seasons = ["ANN"]
+# param.save_netcdf = True
+
+param.ref_timeseries_input = True
+# Years to slice the ref data, base this off the years in the filenames.
+param.ref_start_yr = "1996"
+param.ref_end_yr = "1996"
+
+prefix = "/global/cfs/cdirs/e3sm/www/cdat-migration-fy24/892-bottleneck"
+param.results_dir = os.path.join(prefix, "eamxx_decadal_1996_1107_edv3")
+
+cfg_path = "auxiliary_tools/cdat_regression_testing/892-bottleneck/run_script.cfg"
+sys.argv.extend(["--diags", cfg_path])
+
+runner.sets_to_run = [
+    "lat_lon",
+    "zonal_mean_xy",
+    "zonal_mean_2d",
+    "zonal_mean_2d_stratosphere",
+    "polar",
+    "cosp_histogram",
+    "meridional_mean_2d",
+    "annual_cycle_zonal_mean",
+]
+
+runner.run_diags([param])

auxiliary_tools/cdat_regression_testing/892-bottleneck/xr_mvce_e3sm_data.py

@@ -0,0 +1,20 @@
+# %%
+import timeit
+
+import xarray as xr
+
+filepaths = [
+    "/lcrc/group/e3sm/diagnostics/observations/Atm/time-series/ERA5/ua_197901_201912.nc"
+]
+
+ds = xr.open_mfdataset(filepaths)
+
+ds_sub = ds.sel(time=slice("1996-01-15", "1997-01-15", None))
+
+# %%
+start_time = timeit.default_timer()
+ds_sub.ua.load()
+elapsed = timeit.default_timer() - start_time
+print(f"Time taken to load ds_xc_sub: {elapsed} seconds")
+
+# %%

auxiliary_tools/cdat_regression_testing/892-bottleneck/xr_mvce_gh.py

@@ -0,0 +1,48 @@
+# %%
+import numpy as np
+import pandas as pd
+import xarray as xr
+import timeit
+
+import dask.array as da
+
+# %%
+# Define the dimensions
+time = 12
+plev = 37
+lat = 721
+lon = 1440
+
+# Create the data arrays using dask.
+data = da.random.random(size=(time, plev, lat, lon), chunks=(12, 37, 721, 1440)).astype(
+    np.float32
+)
+
+# Create the coordinates.
+times = pd.date_range("2000-01-01", periods=time)
+plevs = np.linspace(100000, 10, plev)
+lats = np.linspace(-90, 90, lat)
+lons = np.linspace(0, 360, lon, endpoint=False)
+
+# Create the dataset and write out to a file.
+ds = xr.Dataset(
+    {"data": (["time", "plev", "lat", "lon"], data)},
+    coords={"time": times, "plev": plevs, "lat": lats, "lon": lons},
+)
+# %%
+ds.to_netcdf("dask_bottleneck.nc")
+
+# %%
+# Open the dataset.
+ds_open = xr.open_mfdataset("dask_bottleneck.nc")
+
+# %%
+# Load the dataset into memory
+start_time = timeit.default_timer()
+ds.load()
+end_time = timeit.default_timer()
+
+print(f"Time taken to load the dataset: {end_time - start_time} seconds")
+
+
+# %%