This is just a save point for me to fall back on while I eliminate the copy pasta

src/config.py

@@ -11,6 +11,23 @@ NA_PRECIP_FILES = [
                 (DATA_DIR + 'pr_sresa2_2071-2099_percent_change.nc_hatched_north_america_ar5_white.nc', 'pr_sresa2_2071-2099_NA_percent_change.eps')
                 ]
 
+NA_ANNUAL_PRECIP_FILES = [
+                (DATA_DIR + 'pr_rcp26_2071-2099_percent_change-1.nc_hatched_global_ar5_white.nc', 'pr_rcp26_2071-2099_NA_annual_percent_change.eps'),
+                (DATA_DIR + 'pr_rcp85_2071-2099_percent_change-1.nc_hatched_global_ar5_white.nc', 'pr_rcp85_2071-2099_NA_annual_percent_change.eps'),                                 
+                 ]
+
+GLOBAL_ANNUAL_PRECIP_FILES = [
+                (DATA_DIR + 'pr_rcp26_2071-2099_percent_change-1.nc_hatched_global_ar5_white.nc', 'pr_rcp26_2071-2099_global_annual_percent_change.eps'),
+                (DATA_DIR + 'pr_rcp85_2071-2099_percent_change-1.nc_hatched_global_ar5_white.nc', 'pr_rcp85_2071-2099_global_annual_percent_change.eps')                                
+                 ]
+
+GLOBAL_CATEGORY_FILES = [
+                         
+                (DATA_DIR + 'pr_rcp26_2071-2099_percent_change-1.nc_hatched_global_ar5_white.nc', 'pr_rcp26_2071-2099_global_annual_categories.eps'),
+                (DATA_DIR + 'pr_rcp85_2071-2099_percent_change-1.nc_hatched_global_ar5_white.nc', 'pr_rcp85_2071-2099_global_annual_categories.eps')  
+                    ]
+
+
 NA_CATEGORY_FILES = [
                 (DATA_DIR + 'pr_rcp85_2071-2099_percent_change.nc_hatched_north_america_ar5_white.nc', 'pr_rcp85_2071-2099_NA_percent_change_categories.eps'),
                 (DATA_DIR + 'pr_rcp26_2071-2099_percent_change.nc_hatched_north_america_ar5_white.nc', 'pr_rcp26_2071-2099_NA_percent_change_categories.eps'),
@@ -18,8 +35,8 @@ NA_CATEGORY_FILES = [
                 ]
 
 GLOBAL_PRECIP_FILES = [
-                       (DATA_DIR + 'pr_rcp26_2071-2099_percent_change.nc_hatched_global_ar5_white.nc', 'pr_rcp26_2071-2099_global_percent_change_grads15.eps'),
-                       (DATA_DIR + 'pr_rcp85_2071-2099_percent_change.nc_hatched_global_ar5_white.nc', 'pr_rcp85_2071-2099_global_percent_change_grads15.eps')
+                       (DATA_DIR + 'pr_rcp26_2071-2099_percent_change-1.nc_hatched_global_ar5_white.nc', 'pr_rcp26_2071-2099_global_percent_change_grads15.eps'),
+                       (DATA_DIR + 'pr_rcp85_2071-2099_percent_change-1.nc_hatched_global_ar5_white.nc', 'pr_rcp85_2071-2099_global_percent_change_grads15.eps')
                        ]
 
 HI_PRECIP_FILES = [
@@ -29,6 +46,7 @@ HI_PRECIP_FILES = [
                 ]
 
 SEASONS = {
+           'Annual': ('annual_percent_change', 'annual_stipple'),
            'Winter': ('DJF_percent_change', 'DJF_stipple'),
            'Spring': ('MAM_percent_change', 'MAM_stipple'),
            'Summer': ('JJA_percent_change', 'JJA_stipple'),

src/plot_global_annual_precip.py

+'''
+Created on Nov 30, 2012
+
+@author: abuddenberg
+'''
+from scipy.io.netcdf import netcdf_file
+from mpl_toolkits.basemap import Basemap
+from numpy import meshgrid
+
+import matplotlib.pyplot as plt
+from config import GLOBAL_ANNUAL_PRECIP_FILES, SEASONS
+
+for infilename, outfilename in GLOBAL_ANNUAL_PRECIP_FILES:
+    nc = netcdf_file(infilename) 
+    
+    lat_data = nc.variables['lat'].data 
+    lon_data = nc.variables['lon'].data
+    
+    fig = plt.figure(figsize=(25,16), dpi=100, tight_layout=True)
+    
+    
+    data = nc.variables['annual_percent_change'].data
+    signif = nc.variables['annual_stipple'].data
+    
+    plt.title('Annual')
+    m = Basemap(
+        projection='eck4',
+                lon_0=0,
+        resolution='l',area_thresh=10000
+    )
+    
+    m.drawcoastlines()
+    m.drawcountries()
+    
+    lons, lats = meshgrid(lon_data, lat_data)
+    x,y = m(lons, lats)
+        
+
+    levels_15 = [-45, -30, -15, 0, 15, 30, 45]
+    levels_10 = [-30, -20, -10, 0, 10, 20, 30]
+    prcp = m.contourf(x, y, data, cmap=plt.get_cmap('BrBG'), levels=levels_10, extend='both')   
+    m.colorbar(prcp, location='bottom',pad="5%")
+
+    hatching = m.contourf(x,y, signif, 1, colors='none',hatches=[None, '//'])
+#        m.colorbar(hatching,location='right',pad="5%")
+
+    
+#    plt.savefig('../dist/' + outfilename, format='eps', dpi=200)
+    plt.show()
\ No newline at end of file

src/plot_global_categories.py

+'''
+Created on Nov 30, 2012
+
+@author: abuddenberg
+'''
+from scipy.io.netcdf import netcdf_file
+from mpl_toolkits.basemap import Basemap
+from numpy import meshgrid
+from numpy.ma import masked_equal
+import numpy as np
+
+import matplotlib.pyplot as plt
+from config import GLOBAL_CATEGORY_FILES
+
+for infilename, outfilename in GLOBAL_CATEGORY_FILES:
+    nc = netcdf_file(infilename) 
+    
+    lat_data = nc.variables['lat'].data 
+    lon_data = nc.variables['lon'].data
+    
+    fig = plt.figure(figsize=(25,16), dpi=100, tight_layout=True)
+    
+    
+    data = nc.variables['annual_percent_change'].data
+    signif = nc.variables['annual_stipple'].data
+    
+    plt.title('Annual')
+    m = Basemap(
+        projection='eck4',
+                lon_0=0,
+        resolution='l',area_thresh=10000
+    )
+    
+    m.drawcoastlines()
+    m.drawstates()
+    m.drawcountries()
+    
+    lons, lats = meshgrid(lon_data, lat_data)
+    x,y = m(lons, lats)
+
+    #Build boolean masks of the gridpoint for each category
+    stipples_mask = np.ma.getmask(np.ma.masked_equal(signif, 1.))
+    zeros_mask = np.ma.getmask(masked_equal(data, 0.0))
+
+    both_mask = np.ma.mask_or(stipples_mask, zeros_mask)
+    third_cat_mask = ~both_mask
+    
+    #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.mask = zeros_mask
+    data = np.ma.masked_array(data.filled(1.0)) #1.0 denotes areas little change
+            
+    data.mask = third_cat_mask
+    data = np.ma.masked_array(data.filled(2.0)) #2.0 denotes areas of statistical uncertainty
+    
+
+    weird = m.pcolor(x,y, data)
+    m.colorbar(weird,location='right',pad="5%")
+        
+    #Tests for overlap (There shouldn't be any)
+#    print np.any(np.logical_and(stipples_mask, zeros_mask))
+#    print np.any(np.logical_and(third_cat_mask, zeros_mask))
+#    print np.any(np.logical_and(third_cat_mask, stipples_mask))
+        
+    
+    plt.savefig('../dist/' + outfilename, format='eps', dpi=200)
+#    plt.show()
\ No newline at end of file

src/plot_global_precip.py

@@ -25,13 +25,13 @@ 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']):
+    for i, season in enumerate(['Winter', 'Spring', 'Summer', 'Fall', 'Annual']):
         data_var, signif_var = SEASONS[season]
         
         data = nc.variables[data_var].data
         signif = nc.variables[signif_var].data
         
-        ax = fig.add_subplot(221 + i)
+        ax = fig.add_subplot(231 + i)
         plt.title(season)
         m = Basemap(
             projection='eck4',
@@ -55,5 +55,5 @@ for infilename, outfilename in GLOBAL_PRECIP_FILES:
 #        m.colorbar(hatching,location='right',pad="5%")
 
     
-    plt.savefig('../dist/' + outfilename, format='eps', dpi=200)
-#    plt.show()
\ No newline at end of file
+#    plt.savefig('../dist/' + outfilename, format='eps', dpi=200)
+    plt.show()
\ No newline at end of file

src/plot_north_american_annual.py

+'''
+Created on Nov 30, 2012
+
+@author: abuddenberg
+'''
+from scipy.io.netcdf import netcdf_file
+from mpl_toolkits.basemap import Basemap
+from numpy import meshgrid
+
+import matplotlib.pyplot as plt
+from config import NA_ANNUAL_PRECIP_FILES, SEASONS
+
+for infilename, outfilename in NA_ANNUAL_PRECIP_FILES:
+    nc = netcdf_file(infilename) 
+    
+    lat_data = nc.variables['lat'].data 
+    lon_data = nc.variables['lon'].data - 360.
+    
+    fig = plt.figure(figsize=(25,16), dpi=100, tight_layout=True)
+
+    
+    data = nc.variables['annual_percent_change'].data
+    signif = nc.variables['annual_stipple'].data
+    
+    plt.title('Annual')
+    m = Basemap(
+        projection='aea',
+                lon_0=-96,
+                lat_0=37.5,
+                lat_1=29.5,
+                lat_2=45.5,
+    #            lat_ts=median(lats),
+        llcrnrlat=12,
+        urcrnrlat=80,
+        llcrnrlon=-135,
+        urcrnrlon=-25,
+        resolution='l',area_thresh=10000
+    )
+    
+    m.drawcoastlines()
+    m.drawstates()
+    m.drawcountries()
+    
+    lons, lats = meshgrid(lon_data, lat_data)
+    x,y = m(lons, lats)
+        
+
+    levels_15 = [-45, -30, -15, 0, 15, 30, 45]
+    levels_10 = [-30, -20, -10, 0, 10, 20, 30]
+    
+    prcp = m.contourf(x, y, data, cmap=plt.get_cmap('BrBG'), levels=levels_10, extend='both')   
+    m.colorbar(prcp, location='bottom',pad="5%")
+
+    hatching = m.contourf(x,y, signif, 1, colors='none',hatches=[None, '//'])
+#        m.colorbar(hatching,location='right',pad="5%")
+
+    
+    plt.savefig('../dist/' + outfilename, format='eps', dpi=200)
+#    plt.show()
\ No newline at end of file

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 NA_CATEGORY_FILES, SEASONS
+from config import GLOBAL_ANNUAL_PRECIP_FILES, SEASONS
 
-for infilename, outfilename in NA_CATEGORY_FILES:
+for infilename, outfilename in GLOBAL_ANNUAL_PRECIP_FILES:
     nc = netcdf_file(infilename) 
     
     lat_data = nc.variables['lat'].data 
@@ -34,13 +34,13 @@ for infilename, outfilename in NA_CATEGORY_FILES:
     
     fig = plt.figure(figsize=(25,16), dpi=100, tight_layout=True)
     
-    for i, season in enumerate(['Winter', 'Spring', 'Summer', 'Fall']):
+    for i, season in enumerate(['Winter', 'Spring', 'Summer', 'Fall', 'Annual']):
         data_var, signif_var = SEASONS[season]
         
         data = nc.variables[data_var].data
         signif = nc.variables[signif_var].data
         
-        ax = fig.add_subplot(221 + i)
+        ax = fig.add_subplot(231 + i)
         plt.title(season)
         m = Basemap(
             projection='aea',
@@ -91,5 +91,6 @@ for infilename, outfilename in NA_CATEGORY_FILES:
 #        print np.any(np.logical_and(third_cat_mask, stipples_mask))
         
     
-    plt.savefig('../dist/' + outfilename, format='eps', dpi=200)
-#    plt.show()
\ No newline at end of file
+    plt.savefig('../dist/' + outfilename + '.test.eps', format='eps', dpi=200)
+#    plt.show()
+