Added comments describing what the color mean.

src/plot_north_american_categories.py

@@ -24,9 +24,9 @@ from numpy.ma import masked_equal
 import numpy as np
 
 import matplotlib.pyplot as plt
-from config import GLOBAL_PRECIP_FILES, SEASONS
+from config import GLOBAL_PRECIP_FILES, NA_PRECIP_FILES, SEASONS
 
-for infilename, outfilename in GLOBAL_PRECIP_FILES:
+for infilename, outfilename in NA_PRECIP_FILES:
     nc = netcdf_file(infilename) 
     
     lat_data = nc.variables['lat'].data 
@@ -34,7 +34,7 @@ for infilename, outfilename in GLOBAL_PRECIP_FILES:
     
     fig = plt.figure(figsize=(25,16), dpi=100, tight_layout=True)
     
-    for i, season in enumerate(['Winter', 'Spring', 'Summer', 'Fall', 'Annual']):
+    for i, season in enumerate(['Winter', 'Spring', 'Summer', 'Fall']):
         data_var, signif_var = SEASONS[season]
         
         data = nc.variables[data_var].data
@@ -73,13 +73,13 @@ for infilename, outfilename in GLOBAL_PRECIP_FILES:
         #There's got to be a better way of doing this than copying the array
         data = np.ma.masked_array(data)
         data.mask = stipples_mask
-        data = np.ma.masked_array(data.filled(3.0)) #3.0 denotes areas of statistical significance
+        data = np.ma.masked_array(data.filled(3.0)) #3.0 denotes areas of statistical significance; red
         
         data.mask = zeros_mask
-        data = np.ma.masked_array(data.filled(1.0)) #1.0 denotes areas little change
+        data = np.ma.masked_array(data.filled(1.0)) #1.0 denotes areas little change; blue
                 
         data.mask = third_cat_mask
-        data = np.ma.masked_array(data.filled(2.0)) #2.0 denotes areas of statistical uncertainty
+        data = np.ma.masked_array(data.filled(2.0)) #2.0 denotes areas of statistical uncertainty; green
         
  
         weird = m.pcolor(x,y, data)
@@ -91,6 +91,6 @@ for infilename, outfilename in GLOBAL_PRECIP_FILES:
 #        print np.any(np.logical_and(third_cat_mask, stipples_mask))
         
     
-    plt.savefig('../dist/' + outfilename.format('north_american_categories'), format='eps', dpi=200)
-#    plt.show()
+    # plt.savefig('../dist/' + outfilename.format('north_american_categories'), format='eps', dpi=200)
+    plt.show()