add multiple version

pcmdi_metrics/sea_ice/sea_ice_driver.py

@@ -228,14 +228,14 @@
  end_year = meyear
 
  real_clim = {
- "arctic": {"model_mean": {}},
- "ca": {"model_mean": {}},
- "na": {"model_mean": {}},
- "np": {"model_mean": {}},
- "antarctic": {"model_mean": {}},
- "sp": {"model_mean": {}},
- "sa": {"model_mean": {}},
- "io": {"model_mean": {}},
+ "arctic": {"model_mean": None},
+ "ca": {"model_mean": None},
+ "na": {"model_mean": None},
+ "np": {"model_mean": None},
+ "antarctic": {"model_mean": None},
+ "sp": {"model_mean": None},
+ "sa": {"model_mean": None},
+ "io": {"model_mean": None},
  }
  real_mean = {
  "arctic": {"model_mean": 0},
@@ -309,7 +309,15 @@
  "%(model_version)": model,
  "%(realization)": run,
  }
- test_data_full_path = os.path.join(test_data_path, filename_template)
+ test_data_tmp = lib.replace_multi(test_data_path, tags)
+ if "*" in test_data_tmp:
+ # Get the most recent version for last wildcard
+ ind = test_data_tmp.split("/")[::-1].index("*")
+ tmp1 = "/".join(test_data_tmp.split("/")[0:-ind])
+ globbed = glob.glob(tmp1)
+ globbed.sort()
+ test_data_tmp = globbed[-1]
+ test_data_full_path = os.path.join(test_data_tmp, filename_template)
  test_data_full_path = lib.replace_multi(test_data_full_path, tags)
  test_data_full_path = glob.glob(test_data_full_path)
  test_data_full_path.sort()
@@ -365,20 +373,29 @@
  # Running sum of all realizations
  for rgn in clims:
  real_clim[rgn][run] = clims[rgn]
+ if real_clim[rgn]["model_mean"] is None:
+ real_clim[rgn]["model_mean"] = clims[rgn]
+ else:
+ real_clim[rgn]["model_mean"][var] = (
+ real_clim[rgn]["model_mean"][var] + clims[rgn][var]
+ )
  real_mean[rgn][run] = means[rgn]
+ real_mean[rgn]["model_mean"] = (
+ real_mean[rgn]["model_mean"] + means[rgn]
+ )
 
  print("\n-------------------------------------------")
  print("Calculating model regional average metrics \nfor ", model)
  print("--------------------------------------------")
  for rgn in real_clim:
  print(rgn)
  # Get model mean
- datalist = [real_clim[rgn][r][var].data for r in list_of_runs]
- real_clim[rgn]["model_mean"][var] = np.nanmean(
- np.array(datalist), axis=0
+ real_clim[rgn]["model_mean"][var] = real_clim[rgn]["model_mean"][
+ var
+ ] / len(list_of_runs)
+ real_mean[rgn]["model_mean"] = real_mean[rgn]["model_mean"] / len(
+ list_of_runs
  )
- datalist = [real_mean[rgn][r] for r in list_of_runs]
- real_mean[rgn]["model_mean"] = np.nanmean(np.array(datalist))
 
  for run in real_clim[rgn]:
  # Set up metrics dictionary