Data Parsing and Analysis using Python

AIM: To create a scipt for the following operations

• Data visualizer

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

1. Each file should be saved by the name of the column
2. The plot labels should be extracted from the file. If, I request for 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

• Basic performance calculation

1. Calculate the area under the P-V diagram. P is in column 2 and V is in column 8.
2. Calculate the power output of this engine. Assume that RPM is 1500
3. This engine consumed 20 micro grams of fuel (per 1 stroke cycle). Calculate its specific fuel consumption.

PROCEDURE:

• Import the neccesay modules.
• Create empty list for arrays to be appended.
• use open file and input command to enable the suer to input the required file name and open it for parsing
• Use try and except command to check if the file is compatible
• Afte the file is opened in reading mode, We need to use a counter variable for line count.
• using if statement we can seperate the lines containing # and lines not containing # and using ' ' as delimiter we can seperate the words on each line
• The split command command will convert data into the form of strings.
• Use float command to convert these strings into floating-point numbers and then append them to the respective list.
• Use this command in a for loop to perform the same task for each line.
• Create an aray of various columns with respective to its columns numbers.
• Using for loop specify different filename for each columns specified by the user.
• Again use input comand such that the user can specify the columns they want to use for plotting.
• Create the plots for Columns[x] and Columns[y] with respective to the user's desire.
• After the plotting is done and saved, We can use these data to find the area under the graph.
• For this, we need to import trapz from numpy to perform the calculation.
• From the value of area and the given parameters we can calculate the power and specific fuel consumption.
• NOTE: We need to use SublimeREPL to run the command because input command does not work in sublime BUILD.

CODE:

import math
import numpy as np
import matplotlib.pyplot as plt


#Defining the empty arrays
Crank=[]
Pressure=[]
Max_Pressure=[]
Min_Pressure=[]
Mean_Temp=[]
Max_Temp=[]
Min_Temp=[]
Volume=[]
Mass=[]
Density=[]
Integrated_HR= []
HR_Rate=[]
C_p=[]
C_v=[]
Gamma=[]
Kin_Visc=[]
Dyn_Visc=[]

#Enabling the user to enter the required filename
file=input('Enter the name of the input file: \n')
print(file)
#Command to open the file
try:
	file_data=open(file,'r')  
	print('\n Data file is successfully opened')

except filenotfounderror:
	print('\n Please enter a valid file name')
#Reading the data from the data file by each line using the for loop	
#Initialising a counter variable
line_count=1

for line in file_data:
	if line_count==3:
		columns=line.split()
	if line_count==4:
			units=line.split()
	line_count=line_count+1
	if '#' not in line:
	#Appending the data into the empty arrays
		Crank.append(float(line.split()[0]))
		Pressure.append(float(line.split()[1]))
		Max_Pressure.append(float(line.split()[2]))
		Min_Pressure.append(float(line.split()[3]))
		Mean_Temp.append(float(line.split()[4]))
		Max_Temp.append(float(line.split()[5]))
		Min_Temp.append(float(line.split()[6]))
		Volume.append(float(line.split()[7]))
		Mass.append(float(line.split()[8]))
		Density.append(float(line.split()[9]))
		Integrated_HR.append(float(line.split()[10]))
		HR_Rate.append(float(line.split()[11]))
		C_p.append(float(line.split()[12]))
		C_v.append(float(line.split()[13]))
		Gamma.append(float(line.split()[14]))
		Kin_Visc.append(float(line.split()[15]))
		Dyn_Visc.append(float(line.split()[16]))
#Assigning the column numbers for each column
print("Please select the columns no. from the list below:")
for i in range(1,17+1):
	filename=str(columns[i])+str(units[i])
	print(filename)
Columns=np.array([[0],Crank,Pressure,Max_Pressure,Min_Pressure,Mean_Temp,Max_Temp,Min_Temp,Volume,Mass,Density,Integrated_HR,HR_Rate,C_p,C_v,Gamma,Kin_Visc,Dyn_Visc])

#Enabling the user to input value fo column numbers
x=int(input('Enter the column number you want on x-axis:  '))
y=int(input('Enter the column number you want on y-axis:  '))

#Plotting the graph with respective to the columns values
plt.plot(Columns[x],Columns[y])
#Specifying varying title for each plot
plt.title(str(columns[x])+'  '+'VS'+'  '+str(columns[y]))
plt.xlabel(str(columns[x])+' '+str(units[x]))
plt.ylabel(str(columns[y])+' '+str(units[y]))
plt.savefig(str(columns[x])+' '+'vs'+' '+str(columns[y]))
plt.show()

#For finding the Area under the graph, Power output and Specific Fuel Consumption

if x==8 and y==2:
        Area=np.trapz(Columns[x],Columns[y])
        Workdone= Area*1000
        RPM=1500
        Power= 2*math.pi*RPM*Workdone/60
        #To calcualte Specific Fuel Consumption use the formula BSFC= Fuel_Consumption/power
        Fuel_per_stroke=20*pow(10,-6)  
        Fuel_per_sec=Fuel_per_stroke*RPM*(0.5/60)
        Fuel_per_hour=Fuel_per_sec*3600
        BSFC=Fuel_per_hour/Power

        print('Area under the PV-graph: ',Area)
        print('Workdone: ',Workdone,'KJ')
        print('Power Output of the Engine: ',Power,'KW')
        print('Specific Fuel Consumption: ',BSFC,'g/KWh')

              

OUTPUT:

#Compatibility Check

RESULT

#IDLE

#SublimeREPL

PLOTS:

CALCULATION OUTPUT:

Area under the PV-graph: -0.0002590377238248494
Workdone: -0.2590377238248494 KJ
Power Output of the Engine: -40.68955050853843 KW
Specific Fuel Consumption: -0.022118700962576152 g/KWh