Added types of maritime configurations

Author: mtomasini

configs/maritime_configs/frontConnection.csv

@@ -0,0 +1,8 @@
+0,1,0,0,1,0,0,0
+1,0,1,0,0,1,0,0
+0,1,0,1,0,0,1,0
+0,0,1,0,0,0,0,1
+1,0,0,0,0,1,0,0
+0,1,0,0,1,0,1,0
+0,0,1,0,0,1,0,1
+0,0,0,1,0,0,1,0

configs/maritime_configs/island.csv

@@ -0,0 +1,8 @@
+0,1,1,1,1,1,1,1
+1,0,1,1,1,1,1,1
+1,1,0,1,1,1,1,1
+1,1,1,0,1,1,1,1
+1,1,1,1,0,1,1,1
+1,1,1,1,1,0,1,1
+1,1,1,1,1,1,0,1
+1,1,1,1,1,1,1,0

configs/maritime_configs/singleConnection.csv

@@ -0,0 +1,8 @@
+0,1,0,0,0,0,0,0
+1,0,1,0,0,0,0,0
+0,1,0,1,0,0,1,0
+0,0,1,0,0,0,0,0
+0,0,0,0,0,1,0,0
+0,0,0,0,1,0,1,0
+0,0,1,0,0,1,0,1
+0,0,0,0,0,0,1,0

exploration.ipynb

@@ -1,7 +1,7 @@
 import matplotlib.pyplot as plt
 import pandas as pd
 
-def plot_comparison(dataset_1, dataset_2, title, legend_1, legend_2, output_file):
+def plot_comparison_set_counts(dataset_1, dataset_2, title, legend_1, legend_2, output_file):
     dataset_1_local = pd.read_csv(f"{dataset_1}_subpop_set_counts.csv", index_col=0)
     dataset_2_local = pd.read_csv(f"{dataset_2}_subpop_set_counts.csv", index_col=0)
     dataset_1_global = pd.read_csv(f"{dataset_1}_metapop_set_counts.csv", index_col=0)
@@ -28,10 +28,48 @@ def plot_comparison(dataset_1, dataset_2, title, legend_1, legend_2, output_file
     plt.savefig(output_file)
 
 
+def plot_comparison_gini(dataset_1, dataset_2, title, legend_1, legend_2, output_file):
+    dataset_1_local = pd.read_csv(f"{dataset_1}_subpop_gini.csv", index_col=0)
+    dataset_2_local = pd.read_csv(f"{dataset_2}_subpop_gini.csv", index_col=0)
+    dataset_1_global = pd.read_csv(f"{dataset_1}_metapop_gini.csv", index_col=0)
+    dataset_2_global = pd.read_csv(f"{dataset_2}_metapop_gini.csv", index_col=0)
 
-def metapopulation_plot_comparison(dataset_1, dataset_2, title, legend_1, legend_2, output_file, dataset_3 = None, legend_3 = None):
-    dataset_1_global = pd.read_csv(f"{dataset_1}_metapop_set_counts.csv", index_col=0)
-    dataset_2_global = pd.read_csv(f"{dataset_2}_metapop_set_counts.csv", index_col=0)
+    plt.plot(dataset_1_local.mean(axis=1), color = 'xkcd:sky blue')
+    plt.plot(dataset_1_global.mean(axis=1), color = 'xkcd:blue')
+    plt.plot(dataset_2_local.mean(axis=1), color = 'tan')
+    plt.plot(dataset_2_global.mean(axis=1), color = 'xkcd:puce')
+
+    plt.fill_between(dataset_1_local.index, dataset_1_local.mean(axis=1) - dataset_1_local.std(axis=1), dataset_1_local.mean(axis=1) + dataset_1_local.std(axis=1), color='xkcd:sky blue', alpha=0.3)
+    plt.fill_between(dataset_2_local.index, dataset_2_local.mean(axis=1) - dataset_2_local.std(axis=1), dataset_2_local.mean(axis=1) + dataset_2_local.std(axis=1), color='tan', alpha=0.3)
+    plt.fill_between(dataset_1_global.index, dataset_1_global.mean(axis=1) - dataset_1_global.std(axis=1), dataset_1_global.mean(axis=1) + dataset_1_global.std(axis=1), color='xkcd:blue', alpha=0.3)
+    plt.fill_between(dataset_2_global.index, dataset_2_global.mean(axis=1) - dataset_2_global.std(axis=1), dataset_2_global.mean(axis=1) + dataset_2_global.std(axis=1), color='xkcd:puce', alpha=0.3)
+
+    plt.axvline(500, color="black", linestyle='--', ymax=1)
+
+    plt.legend([f"{legend_1} subpop avg", f"{legend_1} whole metapop", f"{legend_2} subpop avg", f"{legend_2} whole metapop"])
+    plt.ylabel("Number of feature sets")
+    plt.xlabel("Generations (x100)")
+    plt.grid(linestyle=':', linewidth=0.5)
+    plt.title(title)
+
+    plt.savefig(output_file)
+
+
+def metapopulation_plot_comparison(dataset_1, dataset_2, title, legend_1, legend_2, output_file, what_measure, dataset_3 = None, legend_3 = None):
+    if what_measure == 'set_counts':
+        dataset_1_global = pd.read_csv(f"{dataset_1}_metapop_set_counts.csv", index_col=0)
+        dataset_2_global = pd.read_csv(f"{dataset_2}_metapop_set_counts.csv", index_col=0)
+    elif what_measure == 'shannon':
+        dataset_1_global == pd.read_csv(f"{dataset_1}_metapop_shannon.csv", index_col=0)
+        dataset_2_global = pd.read_csv(f"{dataset_2}_metapop_shannon.csv", index_col=0)
+    elif what_measure == 'simpson':
+        dataset_1_global = pd.read_csv(f"{dataset_1}_metapop_simpson.csv", index_col=0)
+        dataset_2_global = pd.read_csv(f"{dataset_2}_metapop_simpson.csv", index_col=0)
+    elif what_measure == 'gini':
+        dataset_1_global = pd.read_csv(f"{dataset_1}_metapop_gini.csv", index_col=0)
+        dataset_2_global = pd.read_csv(f"{dataset_2}_metapop_gini.csv", index_col=0)
+    else:
+        print("You need to decide what measure you will plot: 'set_counts', 'shannon', 'simpson' or 'gini'?")
 
     plt.plot(dataset_1_global.mean(axis=1), color = 'xkcd:blue')
     plt.plot(dataset_2_global.mean(axis=1), color = 'xkcd:puce')
@@ -42,6 +80,7 @@ def metapopulation_plot_comparison(dataset_1, dataset_2, title, legend_1, legend
     plt.axvline(500, color="black", linestyle='--', ymax=1)
 
     if dataset_3 is not None:
+        # TODO FIX HERE
         dataset_3_global = pd.read_csv(f"{dataset_3}_metapop_set_counts.csv", index_col=0)
         plt.plot(dataset_3_global.mean(axis=1), color = 'xkcd:light purple')
         plt.fill_between(dataset_3_global.index, dataset_3_global.mean(axis=1) - dataset_3_global.std(axis=1), dataset_3_global.mean(axis=1) + dataset_3_global.std(axis=1), color='xkcd:light purple', alpha=0.3)