For循环索引

Question

我试图在目录列表上运行for循环，以便从每个目录中的文件行中提取特定值。目录结构如下：

# 'sourcedir' --> main directory
#   |
#   |___ alineamiento1 --> directory
#   |       |
#   |       |___ modelo1 --> sub-directory with the file
#   |       |___ modelo2
#   |
#   |___ alineamiento2
#           |
#           |___ modelo1
#           |___ modelo2

我已经在一个包含15个目录的主目录上运行了这个脚本，效果非常好。但是，当我尝试在更多目录（~100）上运行它时，for循环会因为索引器错误而崩溃。你知道吗

这是期望的结果：

>similarity_group_0202
-lnL m0 = -3491.164041
-lnL m1 = -3442.220417
Likelihood ratio = 97.887248, df = 26
El mejor modelo es m1. P-value = 1.09405887112e-10

这是索引器输出：

>similarity_group_0217
Traceback (most recent call last):
  File "LRTb.py", line 52, in <module>
    path_m1 = os.path.join(os.getcwd(), modelos[2])
IndexError: list index out of range

有时，此错误发生在第8个目录之后，有时发生在第10个目录之后，以此类推。因此，基本上这不是目录中的文件的问题。你知道吗

这是我的剧本：

import os
import sys
import re
from scipy import stats

sourcedir = '<path/to/main/directory>'

sg=os.listdir(sourcedir)

sg[:] = [dirname for dirname in sg if dirname.startswith('s')]

n=len(sg)

grupos_m0=[]

grupos_m1=[]

for dirname in sg[0:n]:
    os.chdir(os.path.join(sourcedir, dirname))
    dir=os.getcwd()
    print(">" + dir.split(os.path.sep)[9])
    modelos = os.listdir(os.getcwd())
    path_m0 = os.path.join(os.getcwd(), modelos[1])
    path_m1 = os.path.join(os.getcwd(), modelos[2])
    os.chdir(path_m0)
    with open('out', 'r') as f:
        for line in f:
            if line.startswith('lnL'):
                Lm0 = float(line.split()[4])
                p0 = line.split()[3]
                p0 = re.findall('\d+', p0 )
                npm0 = int(p0[0])
        print("-lnL m0 = " + str(Lm0))
    os.chdir(path_m1)
    with open('out', 'r') as f:
        for line in f:
            if line.startswith('lnL'):
                Lm1 = float(line.split()[4])
                p1 = line.split()[3]
                p1 = re.findall('\d+', p1 )
                npm1 = int(p1[0])
        print("-lnL m1 = " + str(Lm1))
    LR = 2*(Lm1-(Lm0)) # Likelihood-ratio (2*delta(-lnL))
    df = npm1-npm0 
    print('Likelihood ratio = ' + str(LR) + ', ' + 'df = ' + str(df))
    p = stats.chi2.pdf(LR,df)
    if p < 0.05:
        print('El mejor modelo es m1. P-value = ' + str(p) + "\n")
        grupos_m1.append(dir.split(os.path.sep)[9])
    else:
        print('El mejor modelo es m0. P-value > 0.05)' + "\n")
        grupos_m0.append(dir.split(os.path.sep)[9])
print("Alineamientos donde el mejor modelo es m0: " + str(len(grupos_m0)) + "\n" + str(grupos_m0))

print("Alineamientos donde el mejor modelo es m1: " + str(len(grupos_m1)) + "\n" + str(grupos_m1))

任何帮助都将不胜感激！ 谢谢！你知道吗