Taylor Diagram

pcmdi_metrics/graphics/__init__.py

@@ -1,6 +1,6 @@
 # init file
 # isort: skip_file
-from .share.plot_utils import add_logo, download_archived_results # noqa
+from .share.plot_utils import add_logo, download_archived_results, combine_ref_dicts # noqa
 from .share.read_json_mean_clim import (
  read_mean_clim_json_files,
  normalize_by_median,
@@ -12,3 +12,4 @@
 from .portrait_plot.portrait_plot_lib import portrait_plot # noqa
 from .portrait_plot.portrait_plot_lib import prepare_data # noqa
 from .bar_chart.lib import BarChart # noqa
+from .taylor_diagram.taylor_diagram import TaylorDiagram # noqa

pcmdi_metrics/graphics/share/Metrics.py

@@ -5,7 +5,7 @@
 import numpy as np
 import pandas as pd
 
-from pcmdi_metrics.graphics import read_mean_clim_json_files
+from pcmdi_metrics.graphics import combine_ref_dicts, read_mean_clim_json_files
 
 
 class Metrics:
@@ -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 = combine_ref_dicts(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/plot_utils.py

@@ -1,5 +1,6 @@
 import os
 import urllib.request
+from collections import defaultdict
 
 import matplotlib.pyplot as plt
 import requests
@@ -74,3 +75,33 @@ def download_archived_results(path, local_dir):
  except Exception:
  print(path, 'not exist in ', url_head_github_repo)
  pass
+
+
+def combine_ref_dicts(d1, d2):
+ """
+ Combine two dictionaries for reference datasets of variables, raising warning message if different reference datasets were used.
+ Below code is revised from https://stackoverflow.com/a/5946322
+
+ Parameters
+ ----------
+ d1: dict
+ d2: dict
+
+ Return
+ ------
+ dd: merged dict
+ """
+ # Merge dicts
+ dd = defaultdict(list)
+ for d in (d1, d2): # you can list as many input dicts as you want here
+ for key, value in d.items():
+ dd[key].append(value)
+ # Check consistency in content
+ for key in dd:
+ if len(list(set(dd[key]))) == 1:
+ dd[key] = dd[key][0]
+ else:
+ print('Warning: differnt reference datasets detected for ' + key + ': ', dd[key])
+ # Convert outcome to normal dict and return
+ dd = dict(dd)
+ return dd

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/__init__.py

@@ -0,0 +1 @@
+# init file

pcmdi_metrics/graphics/taylor_diagram/taylor_diagram.py

@@ -0,0 +1,136 @@
+def TaylorDiagram(
+ stddev, corrcoef, refstd, fig, colors,
+ normalize=True,
+ labels=None, markers=None, markersizes=None, zorders=None,
+ ref_label=None, smax=None):
+
+ """Plot a Taylor diagram.
+ 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 revised by Jiwoo Lee to enable more flexible customization of the plot.
+
+ The original code was written by Yannick Copin that can be found here:
+ https://gist.github.com/ycopin/3342888
+
+ Parameters
+ ----------
+ stddev : numpy.ndarray
+ an array of standard deviations
+ corrcoeff : numpy.ndarray
+ an array of correlation coefficients
+ refstd : float
+ the reference standard deviation
+ fig : matplotlib figure
+ the matplotlib figure
+ colors : array
+ an array of colors for each element of the input arrays
+ normalize : bool, optional
+ disable to skip normalization of the standard deviation
+ labels : list, optional
+ list of text for labels
+ markers : list, optional
+ list of marker type
+ markersizes : list, optional
+ 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
+ ------
+ fig : matplotlib figure
+ the matplotlib figure
+ ax : matplotlib axis
+ the matplotlib axis
+ """
+ import mpl_toolkits.axisartist.floating_axes as FA
+ import mpl_toolkits.axisartist.grid_finder as GF
+ import numpy as np
+ from matplotlib.projections import PolarAxes
+
+ # define transform
+ tr = PolarAxes.PolarTransform()
+
+ # correlation labels
+ rlocs = np.concatenate((np.arange(10) / 10., [0.95, 0.99]))
+ tlocs = np.arccos(rlocs)
+ gl1 = GF.FixedLocator(tlocs)
+ tf1 = GF.DictFormatter(dict(zip(tlocs, map(str, rlocs))))
+
+ # standard deviation axis extent
+ if normalize:
+ stddev = stddev / refstd
+ refstd = 1.
+ smin = 0
+ if smax is None:
+ smax = max(2.0, 1.1 * stddev.max())
+
+ # add the curvilinear grid
+ ghelper = FA.GridHelperCurveLinear(tr,
+ extremes=(0, np.pi / 2, smin, smax),
+ grid_locator1=gl1,
+ tick_formatter1=tf1)
+ ax = FA.FloatingSubplot(fig, 111, grid_helper=ghelper)
+ fig.add_subplot(ax)
+
+ # adjust axes
+ ax.axis["top"].set_axis_direction("bottom")
+ ax.axis["top"].toggle(ticklabels=True, label=True)
+ ax.axis["top"].major_ticklabels.set_axis_direction("top")
+ ax.axis["top"].label.set_axis_direction("top")
+ ax.axis["top"].label.set_text("Correlation")
+ ax.axis["left"].set_axis_direction("bottom")
+ if normalize:
+ ax.axis["left"].label.set_text("Normalized standard deviation")
+ else:
+ ax.axis["left"].label.set_text("Standard deviation")
+ ax.axis["right"].set_axis_direction("top")
+ ax.axis["right"].toggle(ticklabels=True)
+ ax.axis["right"].major_ticklabels.set_axis_direction("left")
+ ax.axis["bottom"].set_visible(False)
+ ax.grid(True)
+
+ ax = ax.get_aux_axes(tr)
+ # Plot data
+ corrcoef = corrcoef.clip(-1, 1)
+ for i in range(len(corrcoef)):
+ # customize label
+ if labels is not None:
+ label = labels[i]
+ else:
+ label = None
+ # customize marker
+ if markers is not None:
+ marker = markers[i]
+ else:
+ marker = 'o'
+ # customize marker size
+ if markersizes is not None:
+ ms = markersizes[i]
+ else:
+ ms = 8
+ # customize marker order
+ if zorders is not None:
+ zorder = zorders[i]
+ else:
+ zorder = None
+ # customize end
+ 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, label=ref_label)
+ t = np.linspace(0, np.pi / 2)
+ r = np.zeros_like(t) + refstd
+ ax.plot(t, r, 'k--')
+
+ # centralized rms contours
+ rs, ts = np.meshgrid(np.linspace(smin, smax),
+ np.linspace(0, np.pi / 2))
+ rms = np.sqrt(refstd**2 + rs**2 - 2 * refstd * rs * np.cos(ts))
+ contours = ax.contour(ts, rs, rms, 5, colors='k', alpha=0.4)
+ ax.clabel(contours, fmt='%1.1f')
+
+ return fig, ax