line colors

plot.py

@@ -6,12 +6,24 @@ import pandas as pd
 
 plt.style.use('fivethirtyeight')
 
-labels = {'orig': 'original', 
+fivethirtyeight = [c['color'] for c in list(plt.rcParams['axes.prop_cycle'])]
+color_wheel = fivethirtyeight
+
+labels = {'orig2': r'$\beta=1$',
+          'gamma_b0_b0': r'$\beta=0$',
+          'gamma_002': r'$\beta=1$',
           'lin2': 'energy-mut',
+          'gamma_emut': 'energy-mut',
           'norm': 'gauss-fitness',
-          'nosel3': 'no-selecion',
+          'gamma_gauss': 'gauss-fitness',
+          'nosel3': r'$\beta=0$',
           'unp': 'unpaired-objective',
-          'beta01': 'beta=0.1'}
+          'beta01': r'$\beta=0.1$',
+          'beta001': r'$\beta=0.01$',
+          'beta05': r'$\beta=0.05$',
+          'unp10': 'unpaired fitness'}
+
+gc_labels = {g: g[::-1] for g in ['01', '03', '05', '07', '09']}
 
 def maternal_plot(exps, feature='multi', mode='mutations'):
     for e in exps:
@@ -22,29 +34,61 @@ def maternal_plot(exps, feature='multi', mode='mutations'):
     plt.legend()
     plt.tight_layout()
     plt.show()
-    pass 
+    pass
+
+def generation_window(feat, window_size=20):
+    binned = [np.mean(feat[i:i+window_size]) for i in np.arange(0, len(feat), window_size)]
+    return binned
 
 def subfigs(exps, dim1, dim2, mode='mutations', feature='multi'):
     count = 1
     modes = ['mutations', 'generation']
     features = ['multi', 'energy', 'entropy']
+    ax = plt.subplot(111)
     for mode in modes:
         for feature in features:
             plt.subplot(len(modes), len(features), count)
             count += 1
-            for e in exps:
-                df = pd.read_csv(f"Data/{e}_df_{mode}_mean.csv")
+            for i, e in enumerate(exps):
+                gc = e.split('_')[-1]
+                df = pd.read_csv(f"betas/{e}_df_{mode}_mean.csv")
                 if mode == 'generation':
-                    plt.plot(df[feature][:1000], label=f'{labels[e]}')
+                    xx = generation_window(df[feature][:1000])
+                    # plt.plot(xx, label=f'{labels[e[:-3]]}')
+                    plt.plot(xx, label=f'{e}')
+                    # plt.plot(xx, label=gc_labels[gc])
+                    locs = np.linspace(0, len(xx), 3)
+                    print(locs)
+                    plt.xticks(locs, np.arange(0, 1001, 500))
                 else:
-                    plt.plot(df[feature], label=f'{labels[e]}')
+                    # plt.plot(df[feature], label=f'{labels[e[:-3]]}')
+                    plt.plot(df[feature], label=f'{e}')
+                    # plt.plot(df[feature], label=gc_labels[gc])
+                    plt.xticks([0, 50], [0, 50])
             plt.ylabel(feature)
             plt.xlabel(mode)
-    plt.legend()
+    # plt.legend(bbox_to_anchor=(-1.5,-.2), ncol=5, prop={'size': 7})
+    g = exps[0][-2:][::-1]
+    # plt.suptitle(f"GC: {g}")
+    plt.suptitle(f"GC: 90")
+    plt.legend(prop={'size': 8} )
     plt.tight_layout()
+    # plt.savefig(f'gamma_{g}.pdf', format='pdf')
+    plt.savefig("betas_09_trans.pdf", format="pdf")
     plt.show()
 if __name__ == "__main__":
-    exps = [ 'beta01', 'unp', 'nosel3', 'norm', 'lin2']
-    # maternal_plot(exps, feature='entropy')
+    # exps = [ 'beta01', 'orig2', 'nosel3', 'norm']
+    # exps = ['nosel3', 'norm', 'lin2', 'orig2']
+    # exps = ['gamma_b0_b0_01', 'gamma_b0_b0_03', 'gamma_b0_b0_05', 'gamma_b0_b0_07', 'gamma_b0_b0_09']
+    # exps = ['gamma_gauss_01', 'gamma_gauss_03', 'gamma_gauss_05', 'gamma_gauss_07', 'gamma_gauss_09']
+    # exps = ['gamma_002_01', 'gamma_002_03', 'gamma_002_05', 'gamma_002_07', 'gamma_002_09']
+    exps = [f'exp_bs09_{b}' for b in ['001', '002', '003', '004', '005']]
     subfigs(exps, 2, 3)
+    # for g in ['01', '03', '05', '07', '09']:
+        # exps = [f'gamma_002_{g}', f'gamma_b0_b0_{g}',
+            # f'gamma_gauss_{g}', f'gamma_emut_{g}']
+        # subfigs(exps, 2, 3)
+    # maternal_plot(exps, feature='entropy')
+    # maternal_plot(exps, feature='entropy')
+    # subfigs(exps, 2, 3)
     pass