Question

这是我正在阅读的我的peaks_ef.xpk文件中的一个示例。

label dataset sw sf
1H 1H_2
NOESY_F1eF2f.nv
4807.69238281 4803.07373047
600.402832031 600.402832031
1H.L 1H.P 1H.W 1H.B 1H.E 1H.J 1H.U 1H_2.L 1H_2.P 1H_2.W 1H_2.B 1H_2.E 1H_2.J 1H_2.U vol int stat comment flag0 flag8 flag9
0 {1.H2'} 4.93607 0.05000 0.10000 ++ {0.0} {} {1.H1'} 5.82020 0.05000 0.10000 ++ {0.0} {} 0.0 100.0000 0 {} 0 0 0
1 {1.H2'} 4.93607 0.05000 0.10000 ++ {0.0} {} {1.H1'} 5.82020 0.05000 0.10000 ++ {0.0} {} 0.0 100.0000 0 {} 0 0 0
2 {1.H3'} 4.70891 0.05000 0.10000 ++ {0.0} {} {1.H8} 8.13712 0.05000 0.10000 ++ {0.0} {} 0.0 100.0000 0 {} 0 0 0
3 {1.H2'} 4.93607 0.05000 0.10000 ++ {0.0} {} {1.H8} 8.13712 0.05000 0.10000 ++ {0.0} {} 0.0 100.0000 0 {} 0 0 0
4 {2.H2'} 4.55388 0.05000 0.10000 ++ {0.0} {} {2.H1'} 5.90291 0.05000 0.10000 ++ {0.0} {} 0.0 100.0000 0 {} 0 0 0
5 {2.H2'} 4.55388 0.05000 0.10000 ++ {0.0} {} {2.H1'} 5.90291 0.05000 0.10000 ++ {0.0} {} 0.0 100.0000 0 {} 0 0 0
6 {2.H3'} 4.60420 0.05000 0.10000 ++ {0.0} {} {2.H8} 7.61004 0.05000 0.10000 ++ {0.0} {} 0.0 100.0000 0 {} 0 0 0
7 {2.H2'} 4.55388 0.05000 0.10000 ++ {0.0} {} {2.H8} 7.61004 0.05000 0.10000 ++ {0.0} {} 0.0 100.0000 0 {} 0 0 0
8 {1.H3'} 4.70891 0.05000 0.10000 ++ {0.0} {} {2.H8} 7.61004 0.05000 0.10000 ++ {0.0} {} 0.0 100.0000 0 {} 0 0 0
9 {1.H2'} 4.93607 0.05000 0.10000 ++ {0.0} {} {2.H8} 7.61004 0.05000 0.10000 ++ {0.0} {} 0.0 100.0000 0 {} 0 0 0
10 {3.H5} 5.20481 0.05000 0.10000 ++ {0.0} {} {2.H8} 7.61004 0.05000 0.10000 ++ {0.0} {} 0.0 100.0000 0 {} 0 0 0

我想获取1H.P和1H_2.P列中的值并将它们写出到另一个文件，但我只想包含一定范围内的值。我以为我是为我的代码做的。 mask变量应该“过滤”这些值吗？

这是我的代码：

import pandas as pd
import os
import sys
import re

i=0;
contents_peak=[]
peak_lines=[]
with open ("ee_pinkH1.xpk","r") as peakPPM:
    for PPM in peakPPM.readlines():
        float_num = re.findall("[\s][1-9]{1}\.[0-9]+",PPM)
        if (len(float_num)>1):
            i=i+1
            value = ('Peak '+ str(i) + ' ' + str(float_num[0]) + ' 0.05 ' + str(float_num[1]) + ' 0.05' + '\n')
            peak_lines.append(value)
tclust_peak = open("tclust.txt","w+")
tclust_peak.write("rbclust \n")
for value in peak_lines:
    tclust_peak.write(value)
tclust_peak.close()

result={}
text = 'ee'
filename= 'ee_pinkH1.xpk'

if text == 'ee':
    df=pd.read_csv("peaks_ee.xpk",sep=" ", skiprows=5)

    shift1 = df["1H.P"]
    shift2 = df["1H_2.P"]

    if filename=='ee_pinkH1.xpk':
        mask = ((shift1>5.1) & (shift1<6)) & ((shift2>7) & (shift2<8.25))
    elif filename == 'ee_pinkH2.xpk':
        mask = ((shift1>3.25)&(shift1<5))&((shift2>7)&(shift2<8.5))

if text == 'ef':
    df = pd.read_csv('peaks_ef.xpk',sep = " ",skiprows=5)
    shift1=df["1H.P"]
    shift2=df["1H_2.P"]

    if filename == 'ef_blue.xpk':
        mask = ((shift1>5) & (shift1<6)) & ((shift2>7.25) & (shift2<8.25))
    elif filename == 'ef_green.xpk':
        mask = ((shift1>7) & (shift1<9)) & ((shift2>5.25) & (shift2<6.2))
    elif filename == 'ef_orange.xpk':
        mask = ((shift1>3) & (shift1<5)) & ((shift2>5.2) & (shift2<6.25))

if text == 'fe':
    df = pd.read_csv('peaks_fe.xpk', sep=" ",skiprows=5)

    shift1= df["1H.P"]
    shift2= df["1H_2.P"]

    if filename == 'fe_yellow.xpk':
        mask = ((shift1>3) & (shift1<5)) & ((shift2>5) & (shift2<6))
    elif filename == 'fe_green.xpk':
        mask = ((shift1>5.1) & (shift1<6)) & ((shift2>7) & (shift2<8.25))

result = df[mask]
result = result[["1H.L","1H_2.L"]]

for col in result.columns:
    result[col] = result[col].str.strip("{} ")
result.drop_duplicates(keep='first', inplace=True)
result = result.set_index([['Atom '+str(i) for i in range(1,len(result)+1)]])
tclust_atom=open("tclust.txt","a")
result.to_string(tclust_atom, header = False)
df1 = df.copy()[['1H.L','1H.P']]
df2 = df.copy()[['1H_2.L','1H_2.P']]

df2.rename(columns={'1H_2.L': '1H.L', '1H_2.P': '1H.P'}, inplace=True)
df = pd.concat([df1,df2])
df['1H.L']=df['1H.L'].apply(lambda row: row.strip('{}'))
df['new']=0.3
df.drop_duplicates(keep='first',inplace=True)

tclust_atom=open("tclust_ppm.txt","w+")
df.to_csv("tclust_ppm.txt",sep=" ", index=False, header=False)

我的输出样本是：

5.H3' 4.43488 0.3
6.H2' 4.49744 0.3
7.H1' 5.95115 0.3
6.H3' 4.51612 0.3
8.H5 5.39709 0.3
7.H3' 4.62099 0.3
7.H2 7.67414 0.3
8.H2' 4.31783 0.3
9.H1' 5.91813 0.3
8.H3' 4.45577 0.3
10.H5 5.17157 0.3
9.H3' 4.66179 0.3

根据我的代码，过滤器或“mask”变量位于if语句中：

if text == 'ef':
df = pd.read_csv('peaks_ef.xpk',sep = " ",skiprows=5)
shift1=df["1H.P"]
shift2=df["1H_2.P"]

if filename == 'ef_blue.xpk':
    mask = ((shift1>5) & (shift1<6)) & ((shift2>7.25) & (shift2<8.25))
elif filename == 'ef_green.xpk':
    mask = ((shift1>7) & (shift1<9)) & ((shift2>5.25) & (shift2<6.2))
elif filename == 'ef_orange':
    mask = ((shift1>3) & (shift1<5)) & ((shift2>5.2) & (shift2<6.25))

它应该来自elif filename =='ef_orange':并且shift1和shift2都不应该大于6.25，但在我的输出中我得到的答案是7.67414。为什么我的过滤无法正常工作？如何解决？

Answer 1

使用

<ViewSwitcher
    android:layout_width="match_parent"
    android:layout_height="match_parent">

    <include
        layout="@layout/my_product"/>

    <include
        layout="@layout/about_us"/>

    <include
        layout="@layout/thank_you"/>

</ViewSwitcher>

你正在将你的过滤器缩小到只有一个系列，那就是你的专栏，而你想要在整个数据帧上过滤，为了你的利益，它会更容易被看作是它自己的功能。

shift1=df["1H.P"]
shift2=df["1H_2.P"]

使用完整代码进行修改

def fil(df,oneLow,oneHigh,twoLow,twoHigh):
    df = df[((df['1H.P'] > oneLow) & (df['1H.P'] < oneHigh)) & ((df['1H_2.P'] > twoLow) & (df['1H_2.P'] < twoHigh))]
    return df


if text == 'ef':
    df = pd.read_csv('peaks_ef.xpk',sep = " ",skiprows=5)
    #shift1=df["1H.P"] remove
    #shift2=df["1H_2.P"] remove

    if filename == 'ef_blue.xpk':
        #mask = ((shift1>5) & (shift1<6)) & ((shift2>7.25) & (shift2<8.25))
        df = fil(df,5,6,7.25,8.25)
    elif filename == 'ef_green.xpk':
        #mask = ((shift1>7) & (shift1<9)) & ((shift2>5.25) & (shift2<6.2))
        df = fil(df,7,9,5.25,6.2)
    elif filename == 'ef_orange':
        #mask = ((shift1>3) & (shift1<5)) & ((shift2>5.2) & (shift2<6.25))
        df = fil(df,3,5,5.2,6.25)

如何从文件中读入，只有在某些值在某个范围内时才写入另一个文件？

1 个答案: