从 python 中的数据文件读取和绘图

Question

I am a beginner in python and i have some question that i need assistance from you. I am sorry if the question is too basic.

I have a data file which looks like below (the file is long with more than 4000 lines);

4
0.1 0.1
1.5 0.1
0.5 0.6
1.5 0.6
0.0000000E+00 0.0000000E+00 0.0000000E+00 -1.3180656E-04
0.0000000E+00 0.0000000E+00 0.0000000E+00 -2.8582822E-05
0.0000000E+00 0.0000000E+00 0.0000000E+00 -1.5833791E-04
0.0000000E+00 0.0000000E+00 0.0000000E+00 -9.9146621E-05
2.5294579E-02 -3.7180660E-01 7.5958102E-02 6.4079851E-01
2.5294579E-02 7.1739070E+00 6.4493904E-01 6.6945873E-01
2.5294579E-02 3.1476634E+00 8.8396035E-01 -1.4551238E-02
2.5294579E-02 1.6825711E+00 5.1869466E+00 2.4610339E-02
3.2829473E-02 -5.1518883E+00 5.4573026E+00 6.7564747E-01
3.2829473E-02 1.9206643E+01 -1.2400739E+00 6.9728887E-01
3.2829473E-02 -1.5529481E+01 4.6126603E+00 -5.2802531E-03
3.2829473E-02 -4.3019722E+00 7.6228330E+00 5.4802021E-02
3.6500080E-02 -1.0096638E+01 1.1882060E+01 7.0272428E-01
3.6500080E-02 2.8727686E+01 -7.0275729E+00 6.5145852E-01
3.6500080E-02 -3.7448674E+01 -4.4589296E+00 2.2430999E-02
3.6500080E-02 -1.1295979E+01 5.3019553E+00 8.0316341E-02

I would like to write a python code that reads the 6th line, then skips the next three lines and reads the 10th line, then 14th line and so on (In other words the code should skip the first five lines, then reads the 6th line skips next 3 lines, read the next line and so on ).

Then the code should use these read lines to plot the second, third and fourth columns along the y-axis and the first column along x-axis.

I have tried to write the code below but it doesn't do extactly what i want.

import numpy as np 
import matplotlib.pyplot as plt 
from math import* 
import scipy.optimize as sci 
#loading data 
with open('data.his') as f: 
d=f.readlines()[5:] 
#assigning the columns 
time=d[:,0] 
vx=d[:,1] 
vy=d[:,2] 
temp=d[:,3] 
# plotting
plt.plot(time,temp, label='Temperature variation') 
plt.xlabel('Time [s]') 
plt.ylabel('Temperature [K]') 
plt.legend() 
plt.show() 

Thank you in advance for your assistance.

Answer 1

You can use the modulo operator % to pick only every fourth line.

time = []

for j, c in enumerate(d):
    if j % 4 == 0:
        time.append(c[0])

Same for vx , vy and temp (using the corresponding colum indexes).

Answer 2

TL;DR

data  = dict(zip(('x', 'y1', 'y2', 'y3', ...), # as many as your columns
                 (c for c in zip(*((float(s) for s in l.split())
                      for n, l in enumerate(open(...)) if n>4 and not (n-5)%4)))))
plt.plot(cols['x'], cols['y1'], label='Y1')
...
plt.legend() ; plt.show()

---

Unfolded, well commented initial code

You can do what you want in many different ways, personally I would start like this (please read the inline comments for the explanation of the code) and later...

# open the file and slurp its contents
with f as open(...):
    lines = f.readlines()

# throw away the uninteresting lines,
# using slice notation, [5::4] means:
#   from line #6 ("[5:", Python counts from 0) to the end ("::") with a step of 4 (":4]")
lines = lines[5::4]

# turn space separated strings into floats
# for every line in the interesting lines, split it and
# make a list containing the numerical values
# numbers is a list of lists, each list corresponding to a line in the file
numbers = [[float(s) for s in line.split()] for line in lines]

# transpose the numbers, i.e., turn columns into rows, so that now
# numbers is a list of lists, each list corresponding to a COLUMN of data
numbers = [column for column in zip(*numbers)]

# plot the relevant columns (remember that Python counts from 0)
plt.plot(numbers[0], numbers[1], label='1')
plt.plot(numbers[0], numbers[2], label='2')
...
plt.legend()

---

First, reasonable optimisation

In place of

lines = lines[5::3]
numbers = [[float(s) for s in line.split()] for line in lines]

you probably should coalesce the two statements into

numbers = [[float(s) for s in line.split()] for line in lines[5::3]]

I have used two statements in the example code because it was easier to comment appropriately.

---

A less reasonable optimisation

Further down into the narrowing path of coalescing,

cols = [c for c in zip(*((float(s)for s in l.split())for n,l in enumerate(open(...))if n>4 and not (n-5)%4))]
plt.plot(cols[0], cols[1], label='1')
...

---

Last optimisation, not unreasonable...

Use a dictionary!

data  = dict(zip(('x', 'y1', 'y2', 'y3', ...), # as many as your columns
                 (c for c in zip(*((float(s) for s in l.split())
                      for n, l in enumerate(open(...)) if n>4 and not (n-5)%4)))))
plt.plot(data['x'], data['y1'], label='Y1')
...
plt.legend() ; plt.show()