clean up, extract ref dataset info from json to include it in taylor …

…diagram

pcmdi_metrics/graphics/share/Metrics.py

@@ -54,6 +54,7 @@ def __init__(self, files):
  self.df_dict,
  self.var_list,
  self.var_unit_list,
+ self.var_ref_dict,
  self.regions,
  self.stats,
  ) = read_mean_clim_json_files(files)
@@ -145,6 +146,7 @@ def merge(self, metrics_obj):
 
  result.var_list = list(set(self.var_list + metrics_obj.var_list))
  result.var_unit_list = list(set(self.var_unit_list + metrics_obj.var_unit_list))
+ result.var_ref_dict = {**self.var_ref_dict, **metrics_obj.var_ref_dict}
  result.regions = list(set(self.regions + metrics_obj.regions))
  result.stats = list(set(self.stats + metrics_obj.stats))
 

pcmdi_metrics/graphics/share/read_json_mean_clim.py

@@ -24,33 +24,37 @@ def read_mean_clim_json_files(
  (Rows: models, Columns: variables (i.e., 2d array)
  - `var_list`: list of string, all variables from JSON files
  - `var_unit_list`: list of string, all variables and its units from JSON files
+ - `var_ref_list`: list of string, list for default reference dataset for each variable
  - `regions`: list of string, regions
  - `stats`: list of string, statistics
  """
  # Read JSON and get unit for each variable
  results_dict = {} # merged dict by reading all JSON files
  var_list = []
  var_unit_list = []
+ var_ref_dict = {}
  regions_all = []
 
  for json_file in json_list:
  if debug:
  print("json_file:", json_file)
  with open(json_file) as fj:
- dict_temp = json.load(fj)
- var = dict_temp["Variable"]["id"]
+ dict_temp = json.load(fj) # e.g., load contents of precipitation json file
+ var = dict_temp["Variable"]["id"] # e.g., 'pr'
  if "level" in list(dict_temp["Variable"].keys()):
  var += "-" + str(int(dict_temp["Variable"]["level"] / 100.0)) # Pa to hPa
  results_dict[var] = dict_temp
  unit = extract_unit(var, results_dict[var])
  var_unit = var + " [" + unit + "]"
  var_list.append(var)
  var_unit_list.append(var_unit)
+ var_ref_dict[var] = extract_ref(var, results_dict[var])
  regions_all.extend(extract_region(var, results_dict[var]))
  if stats is None:
  stats = extract_stat(var, results_dict[var])
  if debug:
  print("var_unit_list:", var_unit_list)
+ print("var_ref_dict:", var_ref_dict)
 
  if regions is None:
  regions = list(set(regions_all)) # Remove duplicates
@@ -81,7 +85,7 @@ def read_mean_clim_json_files(
  results_dict, var_list, region, stat, season, mip, debug
  )
 
- return df_dict, var_list, var_unit_list, regions, stats
+ return df_dict, var_list, var_unit_list, var_ref_dict, regions, stats
 
 
 def extract_unit(var, results_dict_var):
@@ -90,6 +94,12 @@ def extract_unit(var, results_dict_var):
  return units
 
 
+def extract_ref(var, results_dict_var):
+ model_list = sorted(list(results_dict_var["RESULTS"].keys()))
+ ref = results_dict_var["RESULTS"][model_list[0]]["default"]["source"]
+ return ref
+
+
 def extract_region(var, results_dict_var):
  region_list, stat_list = extract_region_stat(var, results_dict_var)
  return region_list

pcmdi_metrics/graphics/taylor_diagram/taylor_diagram.py

@@ -1,13 +1,15 @@
 def TaylorDiagram(
  stddev, corrcoef, refstd, fig, colors,
  normalize=True,
- labels=None, markers=None, markersizes=None, zorders=None):
+ labels=None, markers=None, markersizes=None, zorders=None,
+ ref_label=None, smax=None):
 
  """Plot a Taylor diagram.
-
- This code was adpated and revised from the ILAMB code by Nathan Collier found here:
+ This code was adpated from the ILAMB code by Nathan Collier found here:
  https://github.com/rubisco-sfa/ILAMB/blob/master/src/ILAMB/Post.py#L80,
- which was originally adapted from the code by Yannick Copin found here:
+ which was revised by Jiwoo Lee to enable more customization.
+
+ The original code was written by Yannick Copin that can be found here:
  https://gist.github.com/ycopin/3342888
 
  Parameters
@@ -32,6 +34,10 @@ def TaylorDiagram(
  list of integer for marker size
  zorders : list, optional
  list of integer for zorder
+ ref_label : str, optional
+ label for reference data
+ smax : int or float, optional
+ maximum of axis range for (normalized) standard deviation
 
  Return
  ------
@@ -59,7 +65,8 @@ def TaylorDiagram(
  stddev = stddev / refstd
  refstd = 1.
  smin = 0
- smax = max(2.0, 1.1 * stddev.max())
+ if smax is None:
+ smax = max(2.0, 1.1 * stddev.max())
 
  # add the curvilinear grid
  ghelper = FA.GridHelperCurveLinear(tr,
@@ -114,7 +121,7 @@ def TaylorDiagram(
  ax.plot(np.arccos(corrcoef[i]), stddev[i], marker, color=colors[i], mew=0, ms=ms, label=label, zorder=zorder)
 
  # Add reference point and stddev contour
- l, = ax.plot([0], refstd, 'k*', ms=12, mew=0)
+ l, = ax.plot([0], refstd, 'k*', ms=12, mew=0, label=ref_label)
  t = np.linspace(0, np.pi / 2)
  r = np.zeros_like(t) + refstd
  ax.plot(t, r, 'k--')