feat: adding code for Issue_4

src/PrimingProb.py

+"""Imports."""
+import numpy as np
+import scipy.constants
+
+
+class Probability:
+    """Calculates the probability of priming and write the gff file."""
+
+    def InterPara():
+        """Open the RIblast output file and read only the parameter lines.
+
+        Args:
+            None
+
+        Returns:
+            my_list (list): Contains all the paramter lines from RIblast
+        """
+        myfile = open("./Docker-Files/Energies.txt", "r")  # ouput of RIblast
+
+        mylist = []  # all lines of Energies starting with an ID-number
+
+        for myline in myfile:  # Read lines containing needed data
+            if myline[0].isdigit():
+                mylist.append(myline)
+            else:
+                continue
+        myfile.close()
+
+        return(mylist)
+
+    data = InterPara()
+
+    def InterProb(data_list):
+        """Calculate the prob. and make the gff file.
+
+        Args:
+            data_list (list): Contains all parameters of RIblast
+
+        Returns:
+            gff (file): Gff file contains all the output information
+        """
+        para_list = []
+
+        for i in range(0, len(data_list)):
+            x = data_list[i].split(",")
+            para_list.append(x)
+        # splitting each list item by the "," this results in a 2-D list
+
+        for j in range(0, len(para_list)):
+            del para_list[j][1:-2]
+        # only keeps the ID-numer, the interaction
+        # energy, and interaction site of both sequences. (still a 2D-list)
+
+        for d in range(0, len(para_list)):  # Optimize location output
+            a = para_list[d][2].split(":")
+            a[1] = a[1].replace(") ", "")
+            a[1] = a[1].replace("\n", "")
+            a[1] = a[1].replace("-", " ")
+            a[1] = a[1].split(" ")
+            para_list[d][2] = a[1]
+
+        for k in range(0, len(para_list)):  # type-conversion of ID and E
+            for l in range(0, 2):
+                para_list[k][l] = float(para_list[k][l])
+
+        for z in range(0, len(para_list)):  # from kcal/mol to Joule/mol
+            para_list[z][1] = para_list[z][1] * 4184
+
+        kT = scipy.constants.R * 300.15  # calculating gas constant R * T
+
+        for u in range(0, len(para_list)):  # calculating -E / RT
+            para_list[u][1] = (-(para_list[u][1])/kT)
+
+        prob_list = []  # List containing all the prob.
+
+        for h in range(0, len(para_list)):  # calculating the e^(-E/kT)
+            probab = np.exp(para_list[h][1])
+            prob_list.append(probab)
+            para_list[h][1] = probab
+
+        prob_list_sum = sum(prob_list)  # Sum of all probabilities
+        real_prob = []
+
+        for v in range(0, len(para_list)):  # Normalized probabilities
+            prob_linear = (para_list[v][1])/prob_list_sum
+            real_prob.append(prob_linear)
+            para_list[v][1] = prob_linear
+
+        gff = open("./output/Potential_Priming_sites.txt", "w+")  # gff file
+
+        for i in range(0, len(para_list)):
+            gff.write("Interactions: "+str(para_list[i][0]) +
+                      "\tRIblast\ttranscript\t"+str(para_list[i][2][1])+"\t" +
+                      str(para_list[i][2][0])+"\t" +
+                      str(para_list[i][1])+"\t.\t.\t.\n")
+
+        gff.close
+
+        return gff
+
+    InterProb(data)