plotting script

fitness_landscape.py

+import sys
+
+import numpy as np
+import numpy
+from mayavi import mlab
+import matplotlib.pyplot as plt
+
+R = 0.0019872041
+T = 310.15
+
+def landscape_plot(beta=1, alpha=1):
+    x = np.linspace(-80, 0, 200)
+    y = np.linspace(0, 1, 200)
+    xx, yy = np.meshgrid(x, y)
+    z = np.exp((-1 * beta * xx) / (R*T) - (alpha * yy))
+    # z = (-1 * beta * xx) - (alpha * yy)
+    plt.plot(np.exp(-1 * beta * x / (R*T)))
+    plt.show()
+    plt.scatter(xx,yy, c=z)
+    plt.xlabel("Energy")
+    plt.ylabel("Unpaired")
+    plt.title("beta=0.01, alpha=1 unpaired fitness")
+    cb = plt.colorbar()
+    cb.set_label("Fitness")
+    plt.show()
+    sys.exit()
+    print(z)
+    mlab.figure(bgcolor=(1,1,1))
+    mlab.surf(x, y, z )
+    mlab.axes(xlabel='energy', ylabel='paired', zlabel='fitness', color=(0,0,0))
+    mlab.show()
+
+if __name__ == "__main__":
+    landscape_plot(beta=.01, alpha=1)
+    pass

parse.py

@@ -21,26 +21,31 @@ def struc_plot(dfs, sse='multi', mode='mutations'):
     # dfs = (structure_info(df) for df in dfs)
     # strucs = pd.concat(dfs).groupby(mode).mean()
     # pickle.dump(strucs, open("lehm_df_muts.csv", "wb"))
-    strucs = pickle.load(open("lehm_df_muts.csv", "rb"))
+    strucs = pickle.load(open(f"Data/lin2_df_{mode}.pickle", "rb"))
     sse_mean = strucs[sse]
+    # k = 100
+    # s_l = list(sse_mean)
+    # bins = [np.mean(s_l[i:i+k]) for i in range(0, len(s_l) - k)]
     sns.tsplot(sse_mean)
-    plt.xlabel("Multiloops")
-    plt.ylabel("Energy")
-    plt.title("Energy based mutation rate")
-    # plt.savefig("multi_lehm_mut.pdf", format="pdf")
+    # sns.tsplot(bins)
+    plt.xlabel(f"{mode}")
+    plt.ylabel(f"{sse}")
+    plt.title("linear mutation rate")
+    # plt.savefig(f"Figures/stnd1_{mode}_{sse}.pdf", format="pdf")
     plt.show()
 
     # print(sse_mean)
 
 def mut_plot():
-    sig = lambda e: 1 / (3 + np.exp(-e/15))
+    # sig = lambda e: 1 / (3 + np.exp(-e/15))
+    sig = lambda e: 1 / (80 + np.exp(-e/25))
     print(sig(-80))
     plt.plot([sig(x) for x in np.arange(-80, 0, 1)])
     plt.xticks(np.arange(0, 80, 10), np.arange(-80, 0, 10))
     plt.show()
 if __name__ == "__main__":
-    path = "maternal_50_adaptive_0.csv"
+    # path = "maternal_50_.adaptive_0.csv"
     dfs = (pd.read_csv(f"maternal_lehm_{i}.csv") for i in range(20))
-    struc_plot(dfs, sse='multi', mode='mutations')
+    struc_plot(dfs, sse='multi', mode='generation')
     # mut_plot()
     pass

plot.py

+import sys
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+plt.style.use('fivethirtyeight')
+
+labels = {'orig': 'original', 
+          'lin2': 'energy-mut',
+          'norm': 'gauss-fitness',
+          'nosel3': 'no-selecion',
+          'unp': 'unpaired-objective',
+          'beta01': 'beta=0.1'}
+
+def maternal_plot(exps, feature='multi', mode='mutations'):
+    for e in exps:
+        df = pd.read_csv(f"Data/{e}_df_{mode}_mean.csv")
+        plt.plot(df[feature], label=f'{labels[e]}')
+    plt.xlabel(mode)
+    plt.ylabel(feature)
+    plt.legend()
+    plt.tight_layout()
+    plt.show()
+    pass 
+
+def subfigs(exps, dim1, dim2, mode='mutations', feature='multi'):
+    count = 1
+    modes = ['mutations']
+    features = ['multi', 'energy']
+    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")
+                plt.plot(df[feature], label=f'{labels[e]}')
+            plt.ylabel(feature)
+            plt.xlabel(mode)
+    plt.legend()
+    plt.tight_layout()
+    plt.show()
+if __name__ == "__main__":
+    exps = ['orig', 'unp', 'nosel3', 'norm', 'beta01', 'lin2']
+    # maternal_plot(exps, feature='entropy')
+    subfigs(exps, 2, 3)
+    pass