first add

Tools.py

@@ -0,0 +1,180 @@
+import numpy as np
+def blosum62():
+    f1 = open("BLOSUM62","r")
+    l_AAS = []
+    AAs = []
+    scores = {}
+    for line in f1.readlines():
+        sp = line.split()
+        aa = sp[0]
+        AAs.append(aa)
+    num = 0
+    f1 = open("BLOSUM62","r")
+    for line in f1.readlines():
+        sp = line.split()
+        for i in range(len(sp)):
+            if i == 0:
+                continue
+            else:
+                score = float(sp[i])
+                aas = AAs[num] + "_" + AAs[i-1]
+                aas2 = AAs[i-1] + "_" + AAs[num]
+                if aas not in l_AAS and aas2 not in l_AAS:
+                    l_AAS.append(aas)
+                    scores[aas] = score
+        num += 1
+    return scores,l_AAS,AAs
+
+
+def getWeightScoreType(pos, neg, matrix, AAs,length):
+    scores = []
+    for i in range(length*2+1):
+        pos_score = []
+        for j in range(len(AAs)):
+            aa1 = AAs[j]
+            score = 0.0
+            for oth in pos:
+                aa2 = oth[i:i + 1]
+                aas = aa1 + "_" + aa2
+                aas2 = aa2 + "_" + aa1
+                if aas in matrix:
+                    score += matrix[aas]
+                else:
+                    score += matrix[aas2]
+            pos_score.append(score)
+        scores.append(pos_score)
+
+    l_scores = []
+    l_type = []
+    l_peps = []
+
+    for pep in pos:
+        score = []
+        for i in range(len(pep)):
+            aa = pep[i:i + 1]
+            index = AAs.index(aa)
+            aascore = (scores[i][index] - matrix[aa + "_" + aa]) / (len(pos) - 1)
+            score.append(aascore)
+        l_scores.append(score)
+        l_type.append(1)
+        l_peps.append(pep)
+
+    # num = 0
+    for pep in neg:
+        score = []
+        for i in range(len(pep)):
+            aa = pep[i:i + 1]
+            index = AAs.index(aa)
+            aascore = scores[i][index] / len(pos)
+            score.append(aascore)
+        l_scores.append(score)
+        l_type.append(0)
+        l_peps.append(pep)
+    return l_scores, l_type,l_peps
+
+def getMMScoreType(pos, neg, matrix, weights, l_aas, AAs, length):
+    scorespos = []
+    scoresneg = []
+    for i in range(length * 2 + 1):
+        score_pos = []
+        score_neg = []
+        for j in range(len(AAs)):
+            aa1 = AAs[j]
+            score = []
+            for z in range(len(l_aas)):
+                score.append(0.0)
+            for oth in pos:
+                aa2 = oth[i:i + 1]
+                aas1 = aa1 + "_" + aa2
+                aas2 = aa2 + "_" + aa1
+                if aas1 in l_aas:
+                    index = l_aas.index(aas1)
+                    score[index] += matrix[aas1] * weights[i]
+                elif aas2 in l_aas:
+                    index = l_aas.index(aas2)
+                    score[index] += matrix[aas2] * weights[i]
+            scoreneg = np.array(score)
+            index2 = l_aas.index(aa1 + "_" + aa1)
+            score[index2] -= matrix[aa1 + "_" + aa1] * weights[i]
+            scorepos = np.array(score)
+
+            score_pos.append(scorepos)
+            score_neg.append(scoreneg)
+        scorespos.append(score_pos)
+        scoresneg.append(score_neg)
+
+    l_scores = []
+    l_type = []
+    l_peps = []
+
+    for pep in pos:
+        score = getArray(l_aas)
+        for i in range(len(pep)):
+            aa = pep[i:i + 1]
+            index = AAs.index(aa)
+            scoreary = scorespos[i][index]
+            score += scoreary
+        score = (score / (len(pos) - 1)).tolist()
+        l_scores.append(score)
+        l_type.append(1)
+        l_peps.append(pep)
+
+    for pep in neg:
+        score = getArray(l_aas)
+        for i in range(len(pep)):
+            aa = pep[i:i + 1]
+            index = AAs.index(aa)
+            scoreary = scoresneg[i][index]
+            score += scoreary
+        score = (score / len(pos)).tolist()
+        l_scores.append(score)
+        l_type.append(0)
+        l_peps.append(pep)
+    return l_scores, l_type, l_peps
+
+def getArray(l_aas):
+    score = []
+    for i in range(len(l_aas)):
+        score.append(0.0)
+    scoreary = np.array(score)
+
+    return scoreary
+
+def WeightAndMatrix(path):
+    f1 = open(path, "r")
+    weights = []
+    l_AAS = []
+    AAs = []
+    scores = {}
+    for line in f1.readlines():
+        if line.startswith(" A "):
+            sp = line.strip().split()
+            for i in range(len(sp)):
+                aa = sp[i]
+                AAs.append(aa)
+        if line.startswith("@weight"):
+            sp = line.strip().split("\t")
+            for i in range(len(sp))[1:]:
+                w = float(sp[i])
+                weights.append(w)
+
+    num = 0
+    f1 = open(path, "r")
+    t = False
+    for line in f1.readlines():
+        if t:
+            sp = line.strip().split()
+            for i in range(len(sp)):
+                if i == 0:
+                    continue
+                else:
+                    score = float(sp[i])
+                    aas = AAs[num] + "_" + AAs[i - 1]
+                    aas2 = AAs[i - 1] + "_" + AAs[num]
+                    if aas not in l_AAS and aas2 not in l_AAS:
+                        l_AAS.append(aas)
+                    scores[aas] = score
+            num += 1
+        if line.startswith(" A "):
+            t = True
+    return scores, l_AAS, weights, AAs