-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathaverage_distance.py
99 lines (40 loc) · 1.11 KB
/
average_distance.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd
import numpy as np
import argparse
# In[2]:
parser = argparse.ArgumentParser(description = "Calculador de media y desviacion estandar de una matriz simetrica")
parser.add_argument("matrix", help = "Matriz simetrica de distancias geneticas (tsv, recomendacion: output de snp-dists)")
args=parser.parse_args()
matrix = pd.read_csv(args.matrix, sep= "\t")
# In[3]:
df = pd.DataFrame(matrix)
# In[4]:
diago = len(matrix)
# In[10]:
df.columns[0]
# In[11]:
df_1=df.drop(df.columns[0], axis=1)
# In[12]:
sumatorio = df_1.sum().sum()
# In[13]:
total = diago*diago-diago
# In[14]:
txt = open("2.average_distance%s.txt" %diago, "w")
media = sumatorio/total
print ("Distancia genetica media: %s" %media)
txt.write("Distancia genetica media: %s\n" %media)
# In[20]:
## desviacion estandar
# In[21]:
df_2=np.triu(df_1, k=1)
# In[22]:
updf=list(df_2[np.triu_indices(len(df_2), k=1)])
# In[23]:
np.mean(updf)
# In[24]:
print ("Desviacion estandar: %s" %np.std(updf))
txt.write("Desviacion estandar: %s" %np.std(updf))
# In[ ]: