def calcDistanceMatrix(seqs1, seqs2): #calculate distance matrix from the 1-step list arr = [] hdist = hamming(seqs1, seqs2, ignore_gaps=False) for i in hdist: for j in i: arr.append(j[0]) return arr
def calcDistanceMatrix_fast(finalSeqs): #BROKEN l=len(finalSeqs) arr=np.zeros([l,l]) hdist=hamming(finalSeqs,finalSeqs,ignore_gaps=False) for id in range(len(hdist)): item=hdist[id] arr[:,id]=item[:,0] return arr
def calcDistanceMatrix(seqs1, seqs2): l1 = len(seqs1) l2 = len(seqs2) arr = np.zeros([l1, l2]) hdist = hamming(seqs1, seqs2, ignore_gaps=False) for id in range(len(hdist)): item = hdist[id] arr[:, id] = item[:, 0] return arr
def calcDistanceMatrix(finalSeqs): #calculate distance matrix from list of sequences l=len(finalSeqs) arr=np.zeros([l,l]) hdist=hamming(finalSeqs,finalSeqs,ignore_gaps=False) for id in range(len(hdist)): item=hdist[id] arr[:,id]=item[:,0] return arr
def calcDistanceMatrix(seqs1, seqs2): #calculate distance matrix from the 1-step list hdist = hamming(seqs1, seqs2, ignore_gaps=False) l = len(seqs1) w = len(seqs2) arr = np.zeros([l, w]) for id in range(len(hdist)): item = hdist[id] arr[:, id] = item[:, 0] return arr
def calc_distance_matrix( finalSeqs): #calculate distance matrix from the 1-step list from ghost.util.distance import hamming l = len(finalSeqs) arr = np.zeros([l, l]) hdist = hamming(finalSeqs, finalSeqs, ignore_gaps=False) for id in range(len(hdist)): item = hdist[id] arr[:, id] = item[:, 0] return arr
def doHdistReturnProp( seqs1, seqs2 ): #calculate hamming proportions between two sets of sequences, return matrix keylen = len(seqs1[0]) l1 = len(seqs1) l2 = len(seqs2) hdist = hamming(seqs1, seqs2, ignore_gaps=False) arr = np.zeros([l1, l2]) for id in range(len(hdist)): item = hdist[id] arr[:, id] = item[:, 0] return np.divide(arr, keylen, dtype=float)