Data Analysis Using Python

Aim

• Data Analysis of given data

Objective

• Data visualizer:

Your script should take column numbers as the input and plot the respective columns as separate images

Each file should be saved by the name of the column

The plot labels should be extracted from the file. If I request a plot between column 1 (crank angle) and column 8(volume), then the label information should automatically be extracted and must appear as labels in the plots. THIS SHOULD NOT BE DONE MANUALLY.

• Compatibility check:

Your code should exit gracefully if a non-compatible file is provided as an input. It should say something like "File not recognized. Please provide a valid CONVERGE output file"

• Basic performance calculation:

Calculate the area under the P-V diagram. P is in column 2 and V is in column 8.

Calculate the power output of this engine. Assume that RPM is 1500

This engine consumed 20 micrograms of fuel (per 1 stroke cycle). Calculate its specific fuel consumption.

Python Script

"""
File parsing in python

"""

import matplotlib.pyplot as plt
import numpy as np 
from numpy import trapz as trp

#Compatability check
try:
  f=open('engine_data.out','r')

except:
  print('File not recognized. Please provide a valid CONVERGE output file')
  sys.exit()

#starting file parsing
file = 'engine_data.out'
count_rows = 0
count_columns = 0
count_header=0
column_title = []
column_units = []

for line in open(file,'r'):
	if '#' not in line:
		count_rows = count_rows + 1
		count_columns = len(str.split(line))
	else:
		count_header = count_header +1

		if count_header == 3:
			for i in range(0,len(str.split(line))):
				column_title.append(str.split(line)[i])

		elif count_header == 4:
			line_content = str.split(line)
			for i in range(0,len(str.split(line))):
				column_units.append(str.split(line)[i])

#Compatability check

if (count_header < 4 or count_rows < 1 or count_columns < 8):
	print('File not recognized. Please provide a valid CONVERGE output file')

else:
	#Plotting of all columns

 	for i in range(0,count_columns):
 		data_array=[]
 		for j in open(file,'r'):
 			if '#' not in j:
 				data_array.append(float(j.split()[i]))

 		plt.plot(data_array)
 		plt.ylabel(column_title[i+1] + column_units[i+1])
 		plt.savefig(column_title[i+1]+'.png')
 		plt.figure()

 		del data_array

 	#Plotting of given data columns

 	x_axis_column_num=8
 	y_axis_column_num=2
 	x_array=[]
 	y_array=[]


 	for k in open(file,'r'):
 		if '#' not in k:
 			y_array.append(float(k.split()[y_axis_column_num-1]))
 			x_array.append(float(k.split()[x_axis_column_num-1]))

 	plt.plot(x_array,y_array)
 	plt.xlabel(column_title[x_axis_column_num]+column_units[x_axis_column_num])
 	plt.ylabel(column_title[y_axis_column_num]+column_units[y_axis_column_num])
 	plt.savefig(column_title[x_axis_column_num] +' VS '+ column_title[y_axis_column_num]+'.png')
 	plt.figure()

    #Calculating area under the pv curve
 	d = x_array[1] - x_array[0]
 	work = []  
 	for i in range(0,len(x_array), 2):
 		work.append((y_array[i]*2*d))
 	Area = 0
 	for w in work:
 	    Area = Area + w 
 	print('Area under PV diagram:', Area, 'MJ')


    #Calculating power output of the engine
 	RPM = 1500
 	power = Area * 1e+06*RPM/(9550)
 	print('Power Output of the Engine:',power,'kw')

 	#Calculatig specific fuel consumption
 	fuel_per_stroke = 20*1e-06
 	fuel_per_sec = fuel_per_stroke*RPM*60
 	SFC = fuel_per_sec/ (power * 1e+03)
 	print('Specific Fuel Consumption:',SFC,'g/J')

Explanation of code

1. Parse means to ''resolve (a sentence) into its component parts and describe their syntactic roles''. In computing, parsing is 'an act of parsing a string or a text'. [Google Dictionary]File parsing in computer language means to give a meaning to the characters of a text file as per the formal grammar. A parser is a program that parses the text files.
2. In this challenge, we first open the output data file then we calculate the total numbers of columns and rows.
3. In that file first 5 rows is the header of the file. Row 3 and 4 having engine parameters title and its units.
4. Remaining rows contain values of engine parameters. First, we read the row 3 and split the titles separately and stored in column_titles.
5. Similarly, read row 4 and split the units and stored in column_units.
6. Here, we checking the compatibility of the file, if the file is not compatible it shows like this "File not recognized. Please provide a valid CONVERGE output file".
7. Now, we read the engine parameters values and separate each column and stored in data_array. Then plot the data_array and label them from column_titles(Not using manually).
8. Similarly, delete the data_array and stored the second column and plot the data_array, this is repeated for all the columns.
9. The plotting of all the columns is stored in the file as shown in the output.
10. Next, we plot the graph between column 8 and column 2. So, split each line and stored only column 8 and column 2 values in two separate arrays.
11. Now, plot the two arrays and label from column_titles and column_units instead of labeling manually.
12. Finally calculate the area under PV diagram, power output, and specific fuel consumption of the engine as shown in the coding.

Results Obtained

• plots auto-generated

• OUTPUT