Cleaned up erroneous commit of conflict artifacts. Moved old data files into their own directory

plot_precip_maps.iml

@@ -4,7 +4,7 @@
     <output url="file://$MODULE_DIR$/bin" />
     <exclude-output />
     <content url="file://$MODULE_DIR$" />
-    <orderEntry type="jdk" jdkName="Python 2.7.3 (/Library/Frameworks/Python.framework/Versions/2.7/bin/python)" jdkType="Python SDK" />
+    <orderEntry type="inheritedJdk" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>

src/config.py

@@ -6,7 +6,6 @@ Created on Nov 20, 2012
 
 DATA_DIR = '/Users/abuddenberg/workspace/plot_precip_maps/data/'
 NA_PRECIP_FILES = [
-<<<<<<< HEAD
                 (DATA_DIR + 'pr_rcp85_1970-1999_2071-2099_percent_change.nc_hatched_North_America_AR5_white.nc', 'pr_rcp85_2071-2099_north_american_{}.eps'),
                 (DATA_DIR + 'pr_rcp26_1970-1999_2071-2099_percent_change.nc_hatched_North_America_AR5_white.nc', 'pr_rcp26_2071-2099_north_american_{}.eps'),
                 (DATA_DIR + 'pr_sresa2_1970-1999_2071-2099_percent_change.nc_hatched_North_America_AR5_white.nc', 'pr_sresa2_2071-2099_north_american_{}.eps'),
@@ -30,17 +29,6 @@ NA_SCENARIOS_FILES = {
 GLOBAL_PRECIP_FILES = [
                        (DATA_DIR + 'pr_rcp26_1970-1999_2071-2099_percent_change.nc_hatched_Global_AR5_white.nc', 'pr_rcp26_2071-2099_global_{}.eps'),
                        (DATA_DIR + 'pr_rcp85_1970-1999_2071-2099_percent_change.nc_hatched_Global_AR5_white.nc', 'pr_rcp85_2071-2099_global_{}.eps')
-=======
-                (DATA_DIR + 'pr_rcp85_1970-1999_2071-2099_percent_change.nc_hatched_North_America_AR5_white.nc', 'pr_rcp85_2071-2099_NA_{}.eps'),
-                (DATA_DIR + 'pr_rcp26_1970-1999_2071-2099_percent_change.nc_hatched_North_America_AR5_white.nc', 'pr_rcp26_2071-2099_NA_{}.eps'),
-                (DATA_DIR + 'pr_sresa2_1970-1999_2071-2099_percent_change.nc_hatched_North_America_AR5_white.nc', 'pr_sresa2_2071-2099_NA_{}.eps')
-                ]
-
-
-GLOBAL_PRECIP_FILES = [
-                       (DATA_DIR + 'pr_rcp26_2071-2099_percent_change-1.nc_hatched_global_ar5_white.nc', 'pr_rcp26_2071-2099_global_{}.eps'),
-                       (DATA_DIR + 'pr_rcp85_2071-2099_percent_change-1.nc_hatched_global_ar5_white.nc', 'pr_rcp85_2071-2099_global_{}.eps')
->>>>>>> dbd7e5f7b2a0c4d96dda5d2c140efbb6a052e6ad
                        ]
 
 

src/plot_global_categories.py

@@ -36,7 +36,6 @@ for infilename, outfilename in GLOBAL_PRECIP_FILES:
     m.drawcountries()
     
     lons, lats = meshgrid(lon_data, lat_data)
-<<<<<<< HEAD
 
     #pcolor coordinates should refer to the lower left corner of the grid box;
     # the data refers to the center of the grid box.  Shift the lon and lat south and west by half the height and
@@ -49,9 +48,6 @@ for infilename, outfilename in GLOBAL_PRECIP_FILES:
 
     x, y = m(lons, lats)
     x_shifted, y_shifted = m(lons_shifted, lats_shifted)
-=======
-    x,y = m(lons, lats)
->>>>>>> dbd7e5f7b2a0c4d96dda5d2c140efbb6a052e6ad
 
     #Build boolean masks of the gridpoint for each category
     stipples_mask = np.ma.getmask(np.ma.masked_equal(signif, 1.))
@@ -72,26 +68,16 @@ for infilename, outfilename in GLOBAL_PRECIP_FILES:
     data = np.ma.masked_array(data.filled(2.0)) #2.0 denotes areas of statistical uncertainty
     
 
-<<<<<<< HEAD
     weird = m.pcolor(x_shifted, y_shifted, data)
     m.colorbar(weird,location='right',pad="5%")
 
     m.scatter(x, y, 3, marker='o')
-=======
-    weird = m.pcolor(x,y, data)
-    m.colorbar(weird,location='right',pad="5%")
->>>>>>> dbd7e5f7b2a0c4d96dda5d2c140efbb6a052e6ad
         
     #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))
         
-    
-<<<<<<< HEAD
+
     plt.savefig('../dist/' + outfilename.format('categories'), format='eps', dpi=200)
     # plt.show()
-=======
-#    plt.savefig('../dist/' + outfilename.format('categories'), format='eps', dpi=200)
-    plt.show()
->>>>>>> dbd7e5f7b2a0c4d96dda5d2c140efbb6a052e6ad

src/plot_global_precip.py

@@ -44,28 +44,14 @@ for infilename, outfilename in GLOBAL_PRECIP_FILES:
         
         lons, lats = meshgrid(lon_data, lat_data)
         x,y = m(lons, lats)
-<<<<<<< HEAD
     
         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')
-=======
-            
-    
-        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_15, extend='both')   
->>>>>>> dbd7e5f7b2a0c4d96dda5d2c140efbb6a052e6ad
         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%")
 
-<<<<<<< HEAD
     plt.savefig('../dist/' + outfilename.format('percent_change'), format='eps', dpi=200)
     # plt.show()
-=======
-    
-#    plt.savefig('../dist/' + outfilename.format('percent_change'), format='eps', dpi=200)
-    plt.show()
->>>>>>> dbd7e5f7b2a0c4d96dda5d2c140efbb6a052e6ad

src/plot_hawaii_precip.py

@@ -5,16 +5,11 @@ Created on Nov 27, 2012
 """
 from scipy.io.netcdf import netcdf_file
 from mpl_toolkits.basemap import Basemap
-<<<<<<< HEAD
 import numpy as np
-=======
-from numpy import meshgrid
->>>>>>> dbd7e5f7b2a0c4d96dda5d2c140efbb6a052e6ad
 
 import matplotlib.pyplot as plt
 from config import NA_PRECIP_FILES, SEASONS
 
-<<<<<<< HEAD
 map_proj = {
     'projection': 'aea',
     'lon_0': -96,
@@ -144,53 +139,3 @@ def plot_category_map():
         # plt.show()
 
 main()
-=======
-for infilename, outfilename in NA_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)
-    
-    for i, season in enumerate(['Winter', 'Spring', 'Summer', 'Fall']):
-        data_var, signif_var = SEASONS[season]
-        
-        data = nc.variables[data_var].data
-        signif = nc.variables[signif_var].data
-        
-        ax = fig.add_subplot(221 + i)
-        plt.title(season)
-        m = Basemap(
-            projection='aea',
-                    lon_0=-96,
-                    lat_0=37.5,
-                    lat_1=29.5,
-                    lat_2=45.5,
-        #            lat_ts=median(lats),
-            llcrnrlat=10,
-            urcrnrlat=40,
-            llcrnrlon=-160,
-            urcrnrlon=-158,
-            resolution='l',area_thresh=1
-        )
-        
-        m.drawcoastlines()
-        m.drawstates()
-        m.drawcountries()
-        
-        lons, lats = meshgrid(lon_data, lat_data)
-        x,y = m(lons, lats)
-            
-        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('HI_percent_change'), format='eps', dpi=200)
-#    plt.show()
->>>>>>> dbd7e5f7b2a0c4d96dda5d2c140efbb6a052e6ad

src/plot_north_american_categories.py

@@ -31,26 +31,15 @@ for infilename, outfilename in NA_PRECIP_FILES:
     
     lat_data = nc.variables['lat'].data 
     lon_data = nc.variables['lon'].data
-<<<<<<< HEAD
 
     fig = plt.figure(figsize=(25,16), dpi=100, tight_layout=True)
     
     for i, season in enumerate(['Winter', 'Spring', 'Summer', 'Fall']): #['Winter', 'Spring', 'Summer', 'Fall']
-=======
-    
-    fig = plt.figure(figsize=(25,16), dpi=100, tight_layout=True)
-    
-    for i, season in enumerate(['Winter', 'Spring', 'Summer', 'Fall']):
->>>>>>> dbd7e5f7b2a0c4d96dda5d2c140efbb6a052e6ad
         data_var, signif_var = SEASONS[season]
         
         data = nc.variables[data_var].data
         signif = nc.variables[signif_var].data
-<<<<<<< HEAD
 
-=======
-        
->>>>>>> dbd7e5f7b2a0c4d96dda5d2c140efbb6a052e6ad
         ax = fig.add_subplot(231 + i)
         plt.title(season)
         m = Basemap(
@@ -70,7 +59,6 @@ for infilename, outfilename in NA_PRECIP_FILES:
         m.drawcoastlines()
         m.drawstates()
         m.drawcountries()
-<<<<<<< HEAD
 
         lons, lats = meshgrid(lon_data, lat_data)
 
@@ -85,11 +73,6 @@ for infilename, outfilename in NA_PRECIP_FILES:
 
         x, y = m(lons, lats)
         x_shifted, y_shifted = m(lons_shifted, lats_shifted)
-=======
-        
-        lons, lats = meshgrid(lon_data, lat_data)
-        x,y = m(lons, lats)
->>>>>>> dbd7e5f7b2a0c4d96dda5d2c140efbb6a052e6ad
     
         #Build boolean masks of the gridpoint for each category
         stipples_mask = np.ma.getmask(np.ma.masked_equal(signif, 1.))
@@ -105,7 +88,6 @@ for infilename, outfilename in NA_PRECIP_FILES:
         
         data.mask = zeros_mask
         data = np.ma.masked_array(data.filled(1.0)) #1.0 denotes areas little change; blue
-<<<<<<< HEAD
 
         data.mask = third_cat_mask
         data = np.ma.masked_array(data.filled(2.0)) #2.0 denotes areas of statistical uncertainty; green
@@ -115,26 +97,12 @@ for infilename, outfilename in NA_PRECIP_FILES:
 
         m.scatter(x, y, 3, marker='o')
 
-=======
-                
-        data.mask = third_cat_mask
-        data = np.ma.masked_array(data.filled(2.0)) #2.0 denotes areas of statistical uncertainty; green
-        
- 
-        weird = m.pcolor(x,y, data)
-        m.colorbar(weird,location='right',pad="5%")
-        
->>>>>>> dbd7e5f7b2a0c4d96dda5d2c140efbb6a052e6ad
         #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))
         
-    
-<<<<<<< HEAD
+
     # plt.savefig('../dist/' + outfilename.format('categories'), format='eps', dpi=200)
-=======
-    # plt.savefig('../dist/' + outfilename.format('north_american_categories'), format='eps', dpi=200)
->>>>>>> dbd7e5f7b2a0c4d96dda5d2c140efbb6a052e6ad
     plt.show()
 

src/plot_north_american_precip.py

@@ -24,12 +24,8 @@ for infilename, outfilename in NA_PRECIP_FILES:
     lon_data = nc.variables['lon'].data
     
     fig = plt.figure(figsize=(25,16), dpi=100, tight_layout=True)
-    
-<<<<<<< HEAD
-    for i, season in enumerate(['Winter', 'Spring', 'Summer', 'Fall']): #['Winter', 'Spring', 'Summer', 'Fall']
-=======
+
     for i, season in enumerate(['Winter', 'Spring', 'Summer', 'Fall']):
->>>>>>> dbd7e5f7b2a0c4d96dda5d2c140efbb6a052e6ad
         data_var, signif_var = SEASONS[season]
         
         data = nc.variables[data_var].data
@@ -57,7 +53,6 @@ for infilename, outfilename in NA_PRECIP_FILES:
         
         lons, lats = meshgrid(lon_data, lat_data)
         x,y = m(lons, lats)
-<<<<<<< HEAD
 
         levels_10 = [-30, -20, -10, 0, 10, 20, 30]
 
@@ -65,15 +60,7 @@ for infilename, outfilename in NA_PRECIP_FILES:
         m.colorbar(prcp, location='bottom',pad="5%")
 
         hatching = m.contourf(x, y, signif, 1, colors='none', hatches=[None, '//'])
-=======
-            
-        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, '//'])
->>>>>>> dbd7e5f7b2a0c4d96dda5d2c140efbb6a052e6ad
 #        m.colorbar(hatching,location='right',pad="5%")