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)