parse

parse.py

+import pickle
+
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
@@ -16,15 +18,16 @@ def mean_stats(dfs, mode='mutations'):
     return pd.concat(list(dfs)).groupby(mode).mean()
 
 def struc_plot(dfs, sse='multi', mode='mutations'):
-    dfs = (structure_info(df) for df in dfs)
-    strucs = pd.concat(dfs).groupby(mode).mean()
-    print(strucs)
+    # 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"))
     sse_mean = strucs[sse]
     sns.tsplot(sse_mean)
-    plt.xlabel("Mutations")
-    plt.ylabel("Multiloop frequency")
-    plt.title("Energy based mutation rate c=3 a=15")
-    plt.savefig("multis_adapt_a15_mut.pdf", format="pdf")
+    plt.xlabel("Multiloops")
+    plt.ylabel("Energy")
+    plt.title("Energy based mutation rate")
+    # plt.savefig("multi_lehm_mut.pdf", format="pdf")
     plt.show()
 
     # print(sse_mean)
@@ -37,7 +40,7 @@ def mut_plot():
     plt.show()
 if __name__ == "__main__":
     path = "maternal_50_adaptive_0.csv"
-    dfs = (pd.read_csv(f"adaptive_run/maternal_50_a10_{i}.csv") for i in range(20))
-    struc_plot(dfs, sse='multi', mode='generations')
+    dfs = (pd.read_csv(f"maternal_lehm_{i}.csv") for i in range(20))
+    struc_plot(dfs, sse='multi', mode='mutations')
     # mut_plot()
     pass