introduce party color mapping to make colors more consistent

de_analysis.py

@@ -4,6 +4,7 @@
 import voting_lib.load_data as ld
 import voting_lib.voting_analysis as va
 import voting_lib.political_compass as pc
+from voting_lib.party_colors import de_name_color
 import numpy as np
 
 # Training Paramters
@@ -30,5 +31,5 @@ for period, df in dataset.items():
     model = va.train_model(X, grid_h, grid_w, radius, step, ep)
 
     # Predict and visualize output
-    va.predict(model, data, grid_h, grid_w, pc.get_compass_parties(year=period_to_compass_year[period], country='de'))
+    va.predict(model, data, grid_h, grid_w, de_name_color, pc.get_compass_parties(year=period_to_compass_year[period], country='de'))
 

uk_analysis.py

@@ -6,6 +6,7 @@
 import voting_lib.load_data as ld
 import voting_lib.voting_analysis as va
 import voting_lib.political_compass as pc
+from voting_lib.party_colors import uk_name_color
 import numpy as np
 import pandas as pd
 import os
@@ -33,4 +34,4 @@ for dirname, _, filenames in os.walk(main_directory):
 
             model = va.train_model(X, grid_h, grid_w, radius, step, ep)
             # Predict and visualize output
-            va.predict(model, data, grid_h, grid_w, pc.get_compass_parties(year=period_to_compass_year[dirname.split('/')[-1]], country='uk'))
+            va.predict(model, data, grid_h, grid_w, uk_name_color, pc.get_compass_parties(year=period_to_compass_year[dirname.split('/')[-1]], country='uk'))

voting_lib/party_colors.py

@@ -0,0 +1,23 @@
+de_name_color = {
+    'AfD': 'blue', 
+    'BÜ90/GR': 'green', 
+    'CDU/CSU': 'black', 
+    'DIE LINKE.': 'purple', 
+    'FDP': 'yellow', 
+    'SPD': 'red',
+}
+
+
+uk_name_color = {
+    'Conservative': 'blue', 
+    'Democratic Unionist Party': 'salmon',
+    'Green Party': 'limegreen', 
+    'Labour': 'red',
+    'Liberal Democrat': 'darkorange', 
+    'Plaid Cymru': 'darkgreen', 
+    'Scottish National Party': 'yellow', 
+    'Sinn Féin': 'yellowgreen', 
+    'Social Democratic & Labour Party': 'cyan', 
+    'UK Independence Party': 'purple',
+    'Ulster Unionist Party': 'lightskyblue',
+}

voting_lib/voting_analysis.py

@@ -8,6 +8,7 @@ import matplotlib.pyplot as plt
 import pandas as pd
 import seaborn as sn
 import re
+from matplotlib.colors import ListedColormap
 
 def train_model(X, grid_h, grid_w, radius, step, ep):
 
@@ -27,7 +28,7 @@ def train_model(X, grid_h, grid_w, radius, step, ep):
     sofmnet.train(X, epochs=ep)
     return sofmnet
 
-def predict(model, data, grid_h, grid_w, comparison_data=pd.DataFrame()):
+def predict(model, data, grid_h, grid_w, party_colors, comparison_data=pd.DataFrame()):
     # default tight layout
     plt.rcParams["figure.autolayout"] = True
 
@@ -46,7 +47,7 @@ def predict(model, data, grid_h, grid_w, comparison_data=pd.DataFrame()):
 
     # plotting mps
     party_affiliation = data[:,1]
-    plot_mps(data[:,0], xs, ys, party_affiliation, randomize_positions=True)
+    plot_mps(data[:,0], xs, ys, party_affiliation, party_colors, randomize_positions=True)
     plt.title("Members of Parliament")
     plt.show()    
 
@@ -64,7 +65,7 @@ def predict(model, data, grid_h, grid_w, comparison_data=pd.DataFrame()):
     plt.colorbar()
     
     # plotting parties
-    plot_parties(party_pos, randomize_positions=False, new_plot=False)
+    plot_parties(party_pos, party_colors, randomize_positions=False, new_plot=False)
     plt.title('Node distance plot with parties')
 
     # plotting party distances in output space
@@ -74,7 +75,7 @@ def predict(model, data, grid_h, grid_w, comparison_data=pd.DataFrame()):
     plt.show()
 
     if not comparison_data.empty:
-       plot_parties(comparison_data, randomize_positions=False, new_plot=True)
+       plot_parties(comparison_data, party_colors, randomize_positions=False, new_plot=True)
        plt.title("Political Compass")
        plt.ylabel("libertarian - authoritarian")
        plt.xlabel("left < economic > right")
@@ -173,7 +174,7 @@ def plot_hoverscatter(x, y, categories, hover_labels, colors, cmap = plt.cm.RdYl
 
     fig.canvas.mpl_connect("motion_notify_event", hover)
 
-def plot_mps(names, xs, ys, party_affiliation, randomize_positions=True):
+def plot_mps(names, xs, ys, party_affiliation, party_colors, randomize_positions=True):
     # converting parties to numeric format 
     party_index_mapping, party_ids = np.unique(party_affiliation, return_inverse=True)
 
@@ -186,7 +187,9 @@ def plot_mps(names, xs, ys, party_affiliation, randomize_positions=True):
         ys_disp = ys
     
     parties = party_index_mapping[party_ids]
-    plot_hoverscatter(xs_disp, ys_disp, party_index_mapping, names + " (" + parties + ")", party_ids)
+
+    colormap = ListedColormap(list(map(lambda x: party_colors[x], party_index_mapping)))
+    plot_hoverscatter(xs_disp, ys_disp, party_index_mapping, names + " (" + parties + ")", party_ids, cmap=colormap)
 
 def calc_party_pos(members_of_parliament, party_affiliation):
     party_index_mapping, party_ids = np.unique(party_affiliation, return_inverse=True)
@@ -203,10 +206,13 @@ def calc_party_pos(members_of_parliament, party_affiliation):
 
     return pd.DataFrame(data=party_pos, index=party_index_mapping)
 
-def plot_parties(parties, randomize_positions=False, new_plot=True):
+def plot_parties(parties, party_colors, randomize_positions=False, new_plot=True):
     
     party_index_mapping = parties.index
 
+    colors = list(map(lambda x: party_colors[x], party_index_mapping))
+
+    
     if new_plot:
         plt.figure()
     
@@ -219,7 +225,7 @@ def plot_parties(parties, randomize_positions=False, new_plot=True):
     
     for i, party in enumerate(party_index_mapping):
         print("Party ", party, " x = ", xs_disp[i], "y = ", ys_disp[i])
-        plt.scatter(xs_disp[i], ys_disp[i], label=party, zorder=2)
+        plt.scatter(xs_disp[i], ys_disp[i], label=party, zorder=2, c=colors[i], edgecolors='black')
         
     plt.legend(title='Parties', bbox_to_anchor=(1.3, 1), loc='upper left')