Updated simulations Kiel

Author: mtomasini

exploration.ipynb

@@ -103,7 +103,6 @@ def plot_comparison_gini_pulses(dataset_1, dataset_2, title, legend_1, legend_2,
     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)
 
-
     line1, = plt.plot(dataset_1_local.mean(axis=1), color = 'xkcd:sky blue', label=f"{legend_1} subpop avg")
     line2, = plt.plot(dataset_2_local.mean(axis=1), color = 'tan', label=f"{legend_2} subpop avg")
     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)
@@ -130,4 +129,34 @@ def plot_comparison_gini_pulses(dataset_1, dataset_2, title, legend_1, legend_2,
     plt.grid(linestyle=':', linewidth=0.5)
     plt.title(title)
 
-    plt.savefig(output_file)
+    plt.savefig(output_file)
+
+
+def plot_subpopulation_mean(dataset, subpop_id, output_file, number_of_pulses = 5, length_of_pulses = 1, settling_period = 99):
+    # deme = pd.Series(dataset[dataset["Replicate"] == 0][f"{subpop_id}"])
+    for replicate in dataset["Replicate"].unique():
+        if replicate == 0:
+            deme = pd.Series(dataset[dataset["Replicate"] == replicate][f"{subpop_id}"])
+
+        deme = pd.concat([deme, pd.Series(dataset[dataset["Replicate"] == replicate][f"{subpop_id}"])], axis=1)
+
+    fig = plt.plot(deme.mean(axis=1), color="tab:orange")
+    std_plus = deme.mean(axis=1) + deme.std(axis=1)
+    std_minus = deme.mean(axis=1) - deme.std(axis=1)
+    # std_plus cannot be larger than 100 in the beginning (but values can as it can be below 0 and this is a symetric +/-!)
+    std_plus[std_plus > 100] = 100
+    std_minus[std_minus < 0] = 0
+    plt.fill_between(deme.index, std_minus, std_plus, alpha = 0.3, color="tab:orange")
+    plt.ylabel("Individuals with extra trait")
+    plt.xlabel("Generations (x100)")
+    plt.ylim([0, 100])
+    plt.grid(linestyle=':', linewidth=0.5)
+    plt.title(f"Extra trait in subpopulation {subpop_id}")
+    
+    plt.axvline(500, color="black", linestyle='--', ymax=1)
+    for i in range(1, number_of_pulses + 1):
+        plt.axvline(500 + i*(length_of_pulses + settling_period), color="dimgrey", linestyle='--', ymax=1)
+
+    plt.savefig(output_file)
+    
+    return fig, deme

img/metapopulation_stepping-stone.odg

@@ -10,23 +10,23 @@
 measure_timing = 100
 
 total_population = 800
-replicates = 1
+replicates = 50
 save_output = True
-interactions = ['neutral_interaction'] # 'axelrod_interaction'
+interactions = ['axelrod_interaction'] # 'axelrod_interaction'
 number_of_subpopulations = 8
 migration_rates = [0.0001]
 mutation_rates = 0.0005
 
 burn_in = 50000
-pulse_length = 0
+pulse_length = 100
 number_of_pulses = 5
 settling_period = 10000 - pulse_length # 10000
 
 start_time = time.time()
 
 count = 0
 
-title = "frontConnection"
+title = "singleConnection"
 migration_config = np.genfromtxt(f"./configs/maritime_configs/{title}.csv", delimiter=",")
 
 for interaction in interactions:
@@ -104,7 +104,10 @@
 
                     t += 1
 
-            extra_traits_counts_df = pd.concat([extra_traits_counts_df, pd.Series(extra_traits_in_metapopulation, name=replicate_id)], axis=1)
+            current_df = pd.DataFrame(extra_traits_in_metapopulation)
+            current_df['Replicate'] = replicate_id
+            extra_traits_counts_df = pd.concat([extra_traits_counts_df, current_df])
+            
 
             if verbose:
                 print(f"Generation reached: {t}")