Create calculate_prob_cdf.py

Author: azzatha

calculate_prob_cdf.py

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+import pandas as pd
+from sklearn import preprocessing
+import numpy as np
+import glob
+import math
+
+# Function to Convert the cM to probability of recombination
+def probability (genaticMaps):
+  prob = (1.0 - math.exp(-genaticMaps["cM"] / 50)) / 2.0
+  return prob
+
+
+# Read the files in the current directory that contains all the genetic map
+for filename in glob.glob('*.txt'):
+  genaticMaps = pd.read_csv(filename, sep="\t")
+
+  # 1.  Convert the cM to probability of recombination
+  genaticMaps["Recmb_Prob"] = genaticMaps.apply(probability, axis=1)
+
+  # 2. Normalize the probability
+  X_normalized = preprocessing.normalize(genaticMaps["Recmb_Prob"], norm='l1')
+  df_normalized = pd.DataFrame(X_normalized)
+  df1_transposed = df_normalized.T
+  df1_transposed['chr'] = genaticMaps["chr"]
+  df1_transposed['pos'] = genaticMaps["pos"]
+  df1_transposed['prob'] = df1_transposed[0]
+  del df1_transposed[0]
+
+  #3. Calculate CDF
+  df1_transposed['cdf'] = np.cumsum(df1_transposed["prob"])
+
+  # Save the final result to a new file
+  df1_transposed.to_csv("./genetic_map/CDF_"+filename, sep="\t", index=False)