如何忽略-inf并转入for循环中的下一次迭代

时间:2015-01-10 00:18:45

标签: python if-statement for-loop numpy continue

您好我希望得到一些帮助我对python很新,我想要一些关于如何设置条件的建议,以便在我的数据中遇到-inf然后程序将循环到下一次迭代

import numpy as np
import math
import matplotlib.pylab as plt
import pandas as pd
from scipy.interpolate import interp1d
from scipy.signal import butter, filtfilt
from scipy import interpolate
Ic = 400
lower_Ig = 720 #the lower limit of the generator current Ig
Upper_Ig = 1040 #Upper limit
Ix=range(-60,61,1)
for j in range(40, 80, 10):
    Var=(40000* j)/ 10000
    #print Var
    for c in range(lower_Ig, Upper_Ig+1, 40):
        #print c
        Names =['Vg','V3', 'V4']
        Data = pd.read_csv('/Documents/JTL_'+str(Var)+'/Ig='+str(c)+'/Grey_Zone.csv', names=Names)
        Vg = Data['Vg']
        V3 = Data['V3']
        V4 = Data ['V4']
        Prf = V4 / Vg
        #print Prf
        C = 0.802
        freq = 100
        b, a = butter(2, (5/C)/(freq/2), btype = 'low')
        yg = filtfilt(b, a, Vg)  # filter with phase shift correction
        y4 = filtfilt(b, a, V4)  # filter with phase shift correction
        SW = y4 / yg
        if SW == np.nan: #I need a condition here that if -inf is encountered then the programme should loop to next c value in for loop 
            continue 
            f = interp1d( SW, Ix )
            print f(0.25), f(0.5), f(0.75)
            print f(0.75)-f(0.25)

我尝试使用不同的numpy函数,但我总是得到相同的错误

The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() 

我不认为我可以使用any()all(),因为那只包含所有数据,我想忽略-inf。非常感谢任何帮助

2 个答案:

答案 0 :(得分:1)

假设我们在循环的一次迭代中有SW,如下所示:

>>> import numpy as np
>>> SW = [np.inf, -np.inf, np.nan, 0, 1]
>>> np.isfinite(SW)
[False, False, False, True, True]
>>> all(np.isfinite(SW))
False   # since one or more in the list is False

如果您想跳过SW的任何nan, inf, -inf,您可以使用

if not all(np.isfinite(SW)):
    continue

如果nan不是问题,只有-inf则可以使用

if any(np.isneginf(SW)):
   continue

如果SW的任何元素为-inf

,则会跳过迭代

请注意,您无法使用==np.nan

进行比较
>>> x = np.nan
>>> x == np.nan
False

而是使用isnan

>>> np.isnan(x)
True

答案 1 :(得分:0)

您可以filter删除不需要的元素(例如newlist=filter(lambda n: not numpy.isneginf(n), list_of_numbers)),或者只使用numpy.nan_to_num(...)转换为正确的数字列表。