Merge pull request #466 from NCAR/taylor_8

Taylor 8

Gallery/TaylorDiagrams/NCL_taylor_8.py

@@ -0,0 +1,94 @@
+"""
+NCL_taylor_8.py
+===============
+This script illustrates the following concepts:
+   - Creating a taylor diagram
+   - Plotting percent bias of each case for each variable in a taylor diagram
+   - Handling negative pattern correlation coefficients by adding text information
+
+See following URLs to see the reproduced NCL plot & script:
+    - Original NCL script: https://www.ncl.ucar.edu/Applications/Scripts/taylor_8.ncl
+    - Original NCL plot: https://www.ncl.ucar.edu/Applications/Images/taylor_8_lg.png
+
+Note: Due to limitations of matplotlib's axisartist toolkit, we cannot include minor tick marks
+        between 0.9 and 0.99, as seen in the original NCL plot.
+"""
+
+###############################################################################
+# Import packages:
+
+import matplotlib.pyplot as plt
+
+import geocat.viz as gv
+
+###############################################################################
+# Create dummy data:
+
+# Case A
+CA_std = [1.230, 0.988, 1.092, 1.172, 1.064, 0.990]  # standard deviation
+CA_corr = [0.958, 0.973, -0.740, 0.743, 0.922, 0.950]  # correlation coefficient
+BA = [2.7, -1.5, 17.31, -20.11, 12.5, 8.341]  # bias (%)
+
+# Case B
+CB_std = [1.129, 0.996, 1.016, 1.134, 1.023, 0.962]
+CB_corr = [0.963, 0.975, 0.801, 0.814, -0.946, 0.984]
+BB = [1.7, 2.5, -17.31, 20.11, 19.5, 7.341]
+
+###############################################################################
+# Plot:
+
+# Create a list of model names
+namearr = ["Globe", "20S-20N", "Land", "Ocean", "N. America", "Africa"]
+
+# Create figure and TaylorDiagram instance
+dia = gv.TaylorDiagram()
+
+# Add model sets
+modelTextsA, _ = dia.add_model_set(
+    CA_std,
+    CA_corr,
+    fontsize=13,
+    xytext=(-5, 13),  # marker label position
+    model_outlier_on=
+    True,  # plots models with negative correlations and/or standard deviations at bottom of diagram
+    percent_bias_on=True,  # model marker and size plotted based on bias array
+    bias_array=BA,
+    edgecolors='red',
+    facecolors='none',
+    linewidths=0.5,
+    label='Data A')
+
+modelTextsB, _ = dia.add_model_set(CB_std,
+                                   CB_corr,
+                                   fontsize=13,
+                                   xytext=(-5, 13),
+                                   model_outlier_on=True,
+                                   percent_bias_on=True,
+                                   bias_array=BB,
+                                   edgecolors='blue',
+                                   facecolors='none',
+                                   linewidths=0.5,
+                                   label='Data B')
+
+# Customize model labels: add background color
+for txt in modelTextsA:
+    txt.set_bbox(
+        dict(facecolor='red', edgecolor='none', pad=0.05, boxstyle='square'))
+    txt.set_color('white')
+
+for txt in modelTextsB:
+    txt.set_bbox(
+        dict(facecolor='blue', edgecolor='none', pad=0.05, boxstyle='square'))
+    txt.set_color('white')
+
+# Add legend
+dia.add_legend()
+
+# Add model name text
+dia.add_model_name(namearr, 0.06, 0.24, fontsize=12)
+
+# Add bias legend
+dia.add_bias_legend()
+
+# Show the plot
+plt.show()