Week 6 - Data analysis

Problem statement:

Data visualizer

• Write a 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 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

Compatability check

• 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 micro grams of fuel (per 1 stroke cycle). Calculate its specific fuel consumption.

Data Analysis:

Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.

Steps Followed:

1. Import the modules

2.Check the file for compatibility

3. Printing the data quantites as column number,quantites and its units

4. Plotting the two quantites and save it in .png format

5. Calculation of certain engine performance quantites as shown below

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

fname = input("Enter the filename:  ") # Input the filename

if fname == 'engine_data.out': # Condition to check for validity of the file 
	print('file is valid')

else:

	print('file is invalid')

	exit()
# Function to check for solver output file 
def compatibility_check(a): 

	if 'CONVERGE' not in a: 

		print('please provide converge output file to process')

		exit()

	else:

		print('compatibility check is successful')

		pass
# Array that stores data into the file 
FILE = [] 

for line in open(fname):

	if '#' in line:

		FILE.append(line.split())

compatibility_check(FILE[0])


noc = len(FILE[1]) # No of columns 
head = len(FILE[2]) # denotes the length of the column 
col_no = [] # Array that stores the column number
col_val = []# Array that stores the column name 
col_units = [] # array that stores the units in column 

z = FILE[1].index('column') # Variable that stores starting value in array
for i in range(z,noc-1):# loop that contains column range 
	col_no.append(i) # appends the column number


for j in FILE[2]: # loop that contains the column name 

	if j is not '#':

		col_val.append(j) # Column that
		


for k in FILE[3]: # loop that contains propeties units 

	if k is not '#':

		col_units.append(k) # Column that contains the units of the properties 


print('data available are:')

for a in range(0,len(col_no)): # Loop that 

	print(col_no[a],col_val[a],col_units[a]) # prints the column number,column value and column units 

xaxis = int(input('Enter xaxis column no:'))-1 # input  for the x axis column 
yaxis = int(input('Enter yaxis column no:'))-1 # input for the y axis column 

x = [] # array contains X axis value 
y = [] # array contains Y axis value 

for line in open(fname): # loop that reads the line in filename 

	if '#' not in line:

		x.append(float(line.split()[xaxis])) 
		y.append(float(line.split()[yaxis]))

plt.plot(x,y,linewidth=1.5) # Plot the x and y axis 
plt.grid('on')
plt.xlabel(col_val[xaxis]+col_units[xaxis]) # Plot the x label
plt.ylabel(col_val[yaxis]+col_units[yaxis]) # Plot the y label 
plt.savefig( col_val[xaxis]+'_Vs_'+col_val[yaxis] +'.png')
plt.show() # Shows the plot 


pressure = [] # Array that contains the pressure value 
vol = [] # Array that contains the volume value 

for line in open(fname):
	if '#' not in line:

		pressure.append(float(line.split()[1])) # Appends the pressure array
		vol.append(float(line.split()[7])) # Appends the volume array

# Calculation of Certain Quantities 
Pressure = 1e6*np.array(pressure)
Volume = np.array(vol)
area = np.trapz(Pressure,Volume) # Area Under P_V Diagram
print('workdone =',area, 'joules') # Prints the workdone
cyclepersec = 750/60
power = area*(cyclepersec/1000) # Calculates power
print('power=',power,'kw') # Print the power
mass_flow = 20e-6
sfc = mass_flow*3600/power # Calculates the specific fuel consumption
print('specific fuel consumption =',sfc,'g/kwh')

Output:

Conclusion

As data analysis for the given file has been carried out as per the data provided.