Refactor.

edges.txt

@@ -5,8 +5,8 @@
 63	64	0.00376827912568
 64	69	0.0114938796716
 69	70	0.00240415262502
-70	19	0.0913179402432
 70	42	0.095761467563
+70	19	0.0913179402432
 69	6	0.068928283445
 64	74	0.0138257703957
 74	43	0.0701951689325
@@ -85,8 +85,8 @@
 83	91	0.013850279425
 91	102	0.00423269087876
 102	104	0.00461971797755
-104	51	0.0825808319509
 104	13	0.0802724654919
+104	51	0.0825808319509
 102	105	0.00656803437777
 105	110	0.0177473177689
 110	50	0.0824905753015

main.py

@@ -1,6 +1,6 @@
 from node import Node
 import sys
-from neighbour_joining import nei_saitou
+import neighbour_joining
 import utils
 from random import randint
 import bootstrap
@@ -18,7 +18,8 @@ def main(filename):
     #This is used to number the sequences/nodes in the tree to be constructed
     seqCounter = 120
 
-    root = nei_saitou(ids, distMatrix, seqCounter)
+    Neighbour_Joining_instance = neighbour_joining.Neighbour_Joining()
+    root = Neighbour_Joining_instance.nei_saitou(ids, distMatrix, seqCounter)
 
     # Writing the edge file
     utils.write_edge_file(root)

neighbour_joining.py

@@ -3,85 +3,88 @@
 import random
 import utils
 
-#Applies nei_saitou to calculte the distances recursively and get the phylogeny tree
-def nei_saitou(original_ids, original_distMatrix, seqCounter):
-    #Using a global root holder
-    global root
-    root = None
-    # Maintaining a map for the relationships between child and parent
-    relationship_map = {}
+# Holds the methods to join neighbours
+class Neighbour_Joining:
 
-    # Using a mapping for seqId and new ids.
-    seqId_orderId_map = {}
-    for i, id in enumerate(original_ids):
-        seqId_orderId_map[id] = str(i + 1)
-
-    def calculate_next_neighbours(ids, distMatrix, seqCounter, seqId_orderId_map):
+    #Applies nei_saitou to calculte the distances recursively and get the phylogeny tree
+    def nei_saitou(self, original_ids, original_distMatrix, seqCounter):
+        #Using a global root holder
         global root
-        N  = len(distMatrix)
+        root = None
+        # Maintaining a map for the relationships between child and parent
+        relationship_map = {}
 
-        # Base case for recursion
-        if N <= 2:
-            # Assigning the ids to these nodes according to the requirements
-            if ids[0] in seqId_orderId_map:
-                tip_1 = seqId_orderId_map[ids[0]]
+        # Using a mapping for seqId and new ids.
+        seqId_orderId_map = {}
+        for i, id in enumerate(original_ids):
+            seqId_orderId_map[id] = str(i + 1)
+
+        def calculate_next_neighbours(ids, distMatrix, seqCounter, seqId_orderId_map):
+            global root
+            N  = len(distMatrix)
+
+            # Base case for recursion
+            if N <= 2:
+                # Assigning the ids to these nodes according to the requirements
+                if ids[0] in seqId_orderId_map:
+                    tip_1 = seqId_orderId_map[ids[0]]
+                else:
+                    tip_1 = ids[0]
+                if ids[1] in seqId_orderId_map:
+                    tip_2 = seqId_orderId_map[ids[1]]
+                else:
+                    tip_2 = ids[1]
+                # Adding their relationship to the tree
+                relationship_map[tip_1] = (tip_2, distMatrix[0][1])
+                # Since these are the last two nodes, any of these can be chosen to be the root
+                root = tip_2
+                return
+
+            #First step is to get the closest two tips from the distance matrix
+            mini, minj, minVal = utils.construct_QMatrix(distMatrix)
+
+            edge_i, edge_j = utils.calculate_edge_lengths(distMatrix, mini, minj, N)
+            #print(edge_i, edge_j)
+            tip_1 = tip_2 = None
+            # Setting the new ids to the tips/ internal node
+            if ids[mini] in seqId_orderId_map:
+                tip_1 = seqId_orderId_map[ids[mini]]
             else:
-                tip_1 = ids[0]
-            if ids[1] in seqId_orderId_map:
-                tip_2 = seqId_orderId_map[ids[1]]
+                tip_1 = ids[mini]
+            if ids[minj] in seqId_orderId_map:
+                tip_2 = seqId_orderId_map[ids[minj]]
             else:
-                tip_2 = ids[1]
-            # Adding their relationship to the tree
-            relationship_map[tip_1] = (tip_2, distMatrix[0][1])
-            # Since these are the last two nodes, any of these can be chosen to be the root
-            root = tip_2
-            return
-
-        #First step is to get the closest two tips from the distance matrix
-        mini, minj, minVal = utils.construct_QMatrix(distMatrix)
-
-        edge_i, edge_j = utils.calculate_edge_lengths(distMatrix, mini, minj, N)
-        #print(edge_i, edge_j)
-        tip_1 = tip_2 = None
-        # Setting the new ids to the tips/ internal node
-        if ids[mini] in seqId_orderId_map:
-            tip_1 = seqId_orderId_map[ids[mini]]
-        else:
-            tip_1 = ids[mini]
-        if ids[minj] in seqId_orderId_map:
-            tip_2 = seqId_orderId_map[ids[minj]]
-        else:
-            tip_2 = ids[minj]
-
-        # Add them to the tree relationships
-        relationship_map[tip_1] = (str(seqCounter), edge_i)
-        relationship_map[tip_2] = (str(seqCounter), edge_j)
+                tip_2 = ids[minj]
+
+            # Add them to the tree relationships
+            relationship_map[tip_1] = (str(seqCounter), edge_i)
+            relationship_map[tip_2] = (str(seqCounter), edge_j)
 
-        # Remove the used ids and add the new seqCounter
-        ids = [id for id in ids if id not in (ids[mini], ids[minj])] + [str(seqCounter)]
+            # Remove the used ids and add the new seqCounter
+            ids = [id for id in ids if id not in (ids[mini], ids[minj])] + [str(seqCounter)]
 
-        seqCounter -=1
-        new_distMatrix = utils.calculate_new_distMatrix(distMatrix, mini, minj, N)
+            seqCounter -=1
+            new_distMatrix = utils.calculate_new_distMatrix(distMatrix, mini, minj, N)
 
-        # recursively call with the new distance matrix
-        calculate_next_neighbours(ids, new_distMatrix, seqCounter, seqId_orderId_map)
+            # recursively call with the new distance matrix
+            calculate_next_neighbours(ids, new_distMatrix, seqCounter, seqId_orderId_map)
 
-    calculate_next_neighbours(original_ids, original_distMatrix, seqCounter, seqId_orderId_map)
+        calculate_next_neighbours(original_ids, original_distMatrix, seqCounter, seqId_orderId_map)
 
-    # construct tree from the relationship_map relations
-    rootNode = Node(root)
-    queue = [rootNode]
-    # bfs for tree construction
-    while len(queue) > 0:
-        nodeList = []
-        for node in queue:
-            for child, (parent, distance) in relationship_map.items():
-                if parent == node.id:
-                    childNode = Node(child)
-                    node.add_child(childNode, distance)
-                    nodeList.append(childNode)
-        for node in nodeList:
-            del relationship_map[node.id]
-        queue = nodeList
-    return rootNode
+        # construct tree from the relationship_map relations
+        rootNode = Node(root)
+        queue = [rootNode]
+        # bfs for tree construction
+        while len(queue) > 0:
+            nodeList = []
+            for node in queue:
+                for child, (parent, distance) in relationship_map.items():
+                    if parent == node.id:
+                        childNode = Node(child)
+                        node.add_child(childNode, distance)
+                        nodeList.append(childNode)
+            for node in nodeList:
+                del relationship_map[node.id]
+            queue = nodeList
+        return rootNode
 

tree.txt

@@ -1 +1 @@
-(((4439229:0.0994636314408,(164216:0.0805450227767,146154:0.0648116394037):0.0142644977651):0.00735813002246,((720729:0.097762461851,(((((100149:0.0486748871823,(4468812:0.0261929524043,166838:0.0343723235042):0.0135727575014):0.0312959330949,348167:0.0893299080895):0.0107871348319,(286686:0.0867460556338,((779409:0.04678825286,593544:0.060210401245):0.00697756765851,4387271:0.0431570218435):0.0165513871656):0.00338682209946):0.0117075956228,(11136:0.104185713798,(5364:0.0459632162084,(3148512:0.0387720577239,106543:0.0419816434874):0.0112373221496):0.024851971263):0.00389111736513):0.00387210426656,(((1920715:0.120428860031,(260976:0.0822133814411,646961:0.0759292834311):0.0141606419883):0.0116483249952,((((4378332:0.0506567246683,523025:0.0846057248606):0.0147056847708,(511006:0.0515193738372,(((273179:0.0226913456411,188674:0.020377295005):0.000565372968216,319214:0.0219783686199):0.0109189939435,197222:0.0380379374159):0.0254489976298):0.0132847257272):0.00561348628869,(806723:0.0722060713208,(1009440:0.0708603722105,((954340:0.0772832314566,947627:0.0727840632945):0.00484880367808,(4164932:0.0605401041029,(565897:0.0476464546292,144260:0.0337801940922):0.0454491287369):0.00415186926943):0.00135530746646):0.00572966219195):0.0100726310145):0.00375090334222,4397763:0.0781236103481):0.0106890635478):0.00388775124005,((((822879:0.0825808319509,664158:0.0802724654919):0.00461971797755,((3906666:0.0824905753015,(816087:0.0812659224188,4448694:0.0762037949433):0.0255175000687):0.0177473177689,(4431405:0.0543373959424,809189:0.0614095757938):0.0302843107641):0.00656803437777):0.00423269087876,((2034243:0.094206517777,208595:0.0686467796658):0.00510333767336,996519:0.0891093137398):0.025745017735):0.013850279425,((152801:0.115587289276,3855581:0.124654971827):0.0215198171439,4345542:0.132248688913):0.188580743455):0.0120725313848):0.0120473718793):0.00536520381635):0.00336721222622,((2263183:0.0648454483874,((586332:0.0726342497929,1136782:0.103677997852):0.00763606407822,2963287:0.0704258470927):0.0074964089477):0.00238916628989,(555079:0.0352536797343,553272:0.0313681237651):0.0385765133871):0.0120786458936):0.00753624506626):0.000959080610108);
+(((4439229:0.0994636314408,(164216:0.0805450227767,146154:0.0648116394037):0.0142644977651):0.00735813002246,((720729:0.097762461851,(((((100149:0.0486748871823,(4468812:0.0261929524043,166838:0.0343723235042):0.0135727575014):0.0312959330949,348167:0.0893299080895):0.0107871348319,(286686:0.0867460556338,((779409:0.04678825286,593544:0.060210401245):0.00697756765851,4387271:0.0431570218435):0.0165513871656):0.00338682209946):0.0117075956228,(11136:0.104185713798,(5364:0.0459632162084,(3148512:0.0387720577239,106543:0.0419816434874):0.0112373221496):0.024851971263):0.00389111736513):0.00387210426656,(((1920715:0.120428860031,(260976:0.0822133814411,646961:0.0759292834311):0.0141606419883):0.0116483249952,((((4378332:0.0506567246683,523025:0.0846057248606):0.0147056847708,(511006:0.0515193738372,(((273179:0.0226913456411,188674:0.020377295005):0.000565372968216,319214:0.0219783686199):0.0109189939435,197222:0.0380379374159):0.0254489976298):0.0132847257272):0.00561348628869,(806723:0.0722060713208,(1009440:0.0708603722105,((954340:0.0772832314566,947627:0.0727840632945):0.00484880367808,(4164932:0.0605401041029,(565897:0.0476464546292,144260:0.0337801940922):0.0454491287369):0.00415186926943):0.00135530746646):0.00572966219195):0.0100726310145):0.00375090334222,4397763:0.0781236103481):0.0106890635478):0.00388775124005,((((664158:0.0802724654919,822879:0.0825808319509):0.00461971797755,((3906666:0.0824905753015,(816087:0.0812659224188,4448694:0.0762037949433):0.0255175000687):0.0177473177689,(4431405:0.0543373959424,809189:0.0614095757938):0.0302843107641):0.00656803437777):0.00423269087876,((2034243:0.094206517777,208595:0.0686467796658):0.00510333767336,996519:0.0891093137398):0.025745017735):0.013850279425,((152801:0.115587289276,3855581:0.124654971827):0.0215198171439,4345542:0.132248688913):0.188580743455):0.0120725313848):0.0120473718793):0.00536520381635):0.00336721222622,((2263183:0.0648454483874,((586332:0.0726342497929,1136782:0.103677997852):0.00763606407822,2963287:0.0704258470927):0.0074964089477):0.00238916628989,(555079:0.0352536797343,553272:0.0313681237651):0.0385765133871):0.0120786458936):0.00753624506626):0.000959080610108);