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Following is the python script to generate a plot using matplotlib. 

#!/usr/bin/python

import matplotlib.pyplot as plt
import time
import numpy as np
from scipy.interpolate import spline

# Local variables
x = []
y = []

# Open the data file for reading lines
datafile = open('testdata1.txt', 'r')
sepfile = datafile.read().split('\n')
datafile.close()

# Create a canvas to place the subgraphs
canvas = plt.figure()
rect = canvas.patch
rect.set_facecolor('white')

# Iterate through the lines and parse them
for datapair in sepfile:
    if datapair:
        xypair = datapair.split(' ')
        x.append(int(xypair[1]))
        y.append(int(xypair[3]))

# Define the matrix of 1x1 to place subplots
# Placing the plot1 on 1x1 matrix, at pos 1
sp1 = canvas.add_subplot(1,1,1, axisbg='w')
sp1.plot(x, y, 'red', linewidth=2)

# Colorcode the tick tabs 
sp1.tick_params(axis='x', colors='red')
sp1.tick_params(axis='y', colors='red')

# Colorcode the spine of the graph
sp1.spines['bottom'].set_color('r')
sp1.spines['top'].set_color('r')
sp1.spines['left'].set_color('r')
sp1.spines['right'].set_color('r')

# Put the title and labels
sp1.set_title('matplotlib example 1', color='red')
sp1.set_xlabel('matplot x label', color='red')
sp1.set_ylabel('matplot y label', color='red')

# Show the plot/image
plt.tight_layout()
plt.grid(alpha=0.8)
plt.savefig("example6.eps")
plt.show()

It generates the plot as enter image description here

I am trying to generate a SMOOTH graph instead of lines, but failed to achieve the result. I was trying to follow this video: https://www.youtube.com/watch?v=uSB8UBrbMfk

Can someone please suggest me changes?

makeyourownmaker
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AnilJ
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    You didn't do the things that the video suggests. For instance, look at the part starting around 3:20 where he creates smoothed versions of the data. – BrenBarn Sep 13 '14 at 17:51
  • (a) you do not define x_smooth and y_smooth (b) when you are trying to implement a numerical method, refrain from beautifying the plot in the first initial implementation i.e. keep graphics simple or default – Jagte Sep 13 '14 at 18:02
  • Correlating the example, my arrays x[] and y[] are dynamic in nature (I am reading data from file). I do not know how to use np.array in the same manner. In video example they are statically used. – AnilJ Sep 13 '14 at 18:12
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    possible duplicate of [Plot smooth line with PyPlot](http://stackoverflow.com/questions/5283649/plot-smooth-line-with-pyplot) – tommy.carstensen Jul 20 '15 at 11:04

1 Answers1

40

I got this working! Thanks for the comments. Here is the updated code.

#!/usr/bin/python

import matplotlib.pyplot as plt
import time
import numpy as np
from scipy.interpolate import spline

# Local variables
x = []
y = []

# Open the data file for reading lines
datafile = open('testdata1.txt', 'r')
sepfile = datafile.read().split('\n')
datafile.close()

# Create a canvas to place the subgraphs
canvas = plt.figure()
rect = canvas.patch
rect.set_facecolor('white')

# Iterate through the lines and parse them
for datapair in sepfile:
    if datapair:
        xypair = datapair.split(' ')
        x.append(int(xypair[1]))
        y.append(int(xypair[3]))

x_sm = np.array(x)
y_sm = np.array(y)

x_smooth = np.linspace(x_sm.min(), x_sm.max(), 200)
y_smooth = spline(x, y, x_smooth)

# Define the matrix of 1x1 to place subplots
# Placing the plot1 on 1x1 matrix, at pos 1
sp1 = canvas.add_subplot(1,1,1, axisbg='w')
#sp1.plot(x, y, 'red', linewidth=2)
sp1.plot(x_smooth, y_smooth, 'red', linewidth=1)

# Colorcode the tick tabs 
sp1.tick_params(axis='x', colors='red')
sp1.tick_params(axis='y', colors='red')

# Colorcode the spine of the graph
sp1.spines['bottom'].set_color('r')
sp1.spines['top'].set_color('r')
sp1.spines['left'].set_color('r')
sp1.spines['right'].set_color('r')

# Put the title and labels
sp1.set_title('matplotlib example 1', color='red')
sp1.set_xlabel('matplot x label', color='red')
sp1.set_ylabel('matplot y label', color='red')

# Show the plot/image
plt.tight_layout()
plt.grid(alpha=0.8)
plt.savefig("example6.eps")
plt.show()

New plot looks like this.

enter image description here

AnilJ
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