Week 6 - Data analysis

OBJECTIVE:

1. To perform Data Analysis.
2. Plot a graph between any two Column vector.
3. Calculate the area under the P-V diagram.
4. Calculate the power output of this engine.
5. Calculate engine specific fuel consumption.

 

PROBLEM STATEMENT:

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 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

Compatibility check

• You 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.

THEORY:

 

Data analysis is a process of inspecting, cleansing, transforming and modelling data with the goal of discovering useful information, informing conclusions and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.

 

Data analysis is a somewhat abstract concept to understand without the help of examples. So to better illustrate how and why data analysis is important for businesses, here are the 4 types of data analysis and examples of each.

 

Descriptive Analysis: Descriptive data analysis looks at past data and tells what happened. This is often used when tracking Key Performance Indicators (KPIs), revenue, sales leads, and more.

 

Diagnostic Analysis: Diagnostic data analysis aims to determine why something happened. Once your descriptive analysis shows that something negative or positive happened, diagnostic analysis can be done to figure out the reason. A business may see that leads increased in the month of October and use diagnostic analysis to determine which marketing efforts contributed the most.

 

Predictive Analysis: Predictive data analysis predicts what is likely to happen in the future. In this type of research, trends are derived from past data which are then used to form predictions about the future. For example, to predict next year’s revenue, data from previous years will be analysed. If revenue has gone up 20% every year for many years, we would predict that revenue next year will be 20% higher than this year. This is a simple example, but predictive analysis can be applied to much more complicated issues such as risk assessment, sales forecasting, or qualifying leads.

 

Prescriptive Analysis: Prescriptive data analysis combines the information found from the previous 3 types of data analysis and forms a plan of action for the organization to face the issue or decision. This is where the data-driven choices are made.

 

These 4 types of data analysis can be applied to any issue with data related to it. And with the internet, data can be found about pretty much everything.

 

GOVERNING EQUATIONS:

1 Work done by the engine ():This can be found by plotting p-V diagram of the engine and calculating area under the curve.

2 Indicated (Total) Power Output without consideration of friction losses which is equal to Brake Power Output.

3 Specific Fuel Consumption.

 

 

EXPLANATION OF THE PROGRAM:

 

At first important modules such as math, matplotlib, numpy, scipy are imported for their various functions.

For checking compatibility of the program, 'try' and 'except' command is used according to their syntax and the given data le is opened using 'open' command within a for loop. If the file name is misspelled or is wrong, the code will yield error with the following message "File not recognized. Please provide a valid CONVERGE output file" and will not execute and exit.

Then we start with data extraction, we store the no of columns in list a, Store the names of variables in list b and Store the unit of variables in list c.

Then we store the data from the file in a 2D array calld ‘d’ which stores the data of each column in it.

Then we perform data visualisation, in which we print out the names of the column present in the file.

After that we ask the user to choose the variables between which they want to plot a graph. According to the variables chosen by the user the graph is plotted between them and the plot labels are extracted from the file. If the user wishes to save the plot, it gets saved with the appropriate name automatically. User can plot as many plots as he wishes to, then he can move on.

Then user is asked if he wants a plot for single variable. According to the variable chosen by the user the graph is and plot labels are extracted from the file. If the user wishes to save the plot, it gets saved with the appropriate name automatically. User can plot as many plots as he wishes to, then he can move on.

Then we calculate the basic performance of the engine. Where we ask user for the RPM of the engine crank and the micrograms of fuel consumed per stroke, and then calculate the Power output of the engine and the specific fuel consumption.

 

 

 

 

#Importing the basic libraries/modules

import numpy as np

import matplotlib.pyplot as plt

from scipy.integrate import simps

 

# File name

filename = 'engine_data.out'

 

# Compatability check

# Checking if the file is a valid CONVERGE output file

try:

    f = open(filename)

    f.close()

except FileNotFoundError:

    print(' This is not a valid CONVERGE output file.n')

    exit()

 

# Checking if data is present in the CONVERGE output file

i=0

for line in open(filename):

    if '#' not in line:

        break

    i = i+1

j=0

for line in open(filename):

    j=j+1

    if j>i:

        break

if j==i:

    print('No data present in the file.n')

    exit()

 

# Data extraction

# Getting number of columns i.e. number of variables

i=0

for line in open(filename):

    if i==2:

        c = line.split()        # Storing unit of variables in list c

        i = 0

        break

    if i==1:

        b=line.split()          # Storing names of variables in list b

        i = i + 1

    if 'column' in line.split():

        a = line.split()        # Storing column numbers of variables in list a

        i=1

 

a.remove("column")      # Removing column from list a

a.remove("#")           # Removing # from list a

a = np.array(a, dtype=int)       # Converting data to integer

b.remove("#")       # Removing # from list b

c.remove("#")       # Removing # from list c

number_of_column = len(a)   # Number of columns/Number of variables

# Getting Data

num_data = 0                # Initializing Number of data points counter

data = np.arange(0,number_of_column)

for line in open(filename):

    if '#' not in line:

        d = line.split()                        # Storing current data line as a list in d

        d = np.array(d, dtype=np.float32)       # Converting list data to float array

        data = np.vstack([data,d])              # Stacking the float array d in data matrix

        num_data = num_data + 1

data = np.delete(data,0,0)                      # Deleting the first row used to initilize the array

 

# Data visualizer ( printing the columns present in the file )

print('\n**********************************************************************************')

print('Name of Columns present in the File')

 

for i in a:

    print('{} for {}{}'.format(i,b[i-1],c[i-1]))

x='n'

print('**********************************************************************************')

 

# Plotting graphs between two variables in the file

x = input("\n Do you want a plot between any two vriables? (y/n) : ")

while(x.lower()=='y'):

    x_var = input(" Enter the column number of the variable along x-axis(Refer List of the Columns above): ")

    y_var = input(" Enter the column number of the variable along y-axis(Refer List of the Columns above): ")

    x_var = int(x_var)

    y_var = int(y_var)

    plt.figure(1)

    plt.plot(data[:,x_var-1],data[:,y_var-1])

    plt.xlabel('{} {}'.format(b[x_var-1],c[x_var-1]))

    plt.ylabel('{} {} '.format(b[y_var-1],c[y_var-1]))

    plt.title('{} {} vs {} {}'.format(b[y_var-1],c[y_var-1],b[x_var-1],c[x_var-1]))

   

    z = "n"

    z = input(" Do you want to save the plot? (y/n):" )

    if(z.lower()=='y'):

        plt.savefig("{}-{}.png".format(b[y_var-1],b[x_var-1]))

    plt.show()

   

    print('\n**********************************************************************************')

    x = input("\n Do you want to plot any other two variables? (y/n): (press n to move on) ")

 

print('\n**********************************************************************************')

 

# Plotting single variable in the file

x='n'

x = input("\n Do you want a single vriable plot? (y/n): ")

while(x.lower()=='y'):

    column_to_plot = input("Enter the column number of the variable (Refer List of the Columns above): ")

    column_to_plot = int(column_to_plot)

    plt.figure(1)

    plt.plot(np.arange(0,num_data),data[:,column_to_plot-1])

    plt.xlabel('Data point number')

    plt.ylabel('{} {} '.format(b[column_to_plot-1],c[column_to_plot-1]))

   

    z = "n"

    z = input("Do you want to save the plot? (y/n):" )

    if(z.lower()=='y'):

        plt.savefig("{}.png".format(b[column_to_plot-1]))

 

    plt.show()

 

    print('\n**********************************************************************************')

    x = input("\n Do you want any other single variable plot? (y/n) (press n to move on):")

print('\n**********************************************************************************')

 

# Basic performance calculation

x='n'

x = input("\n Do you want to calculate perfomance of the engine? (y/n): ")

while(x.lower()=='y'):

    print('PERFORMANCE OF ENGINE')

    b1 = [i.lower() for i in b]

    pressure_column = b1.index('pressure')

    volume_column = b1.index('volume')

    pressure = data[:,pressure_column]

    volume = data[:,volume_column]

    A = 0           # Area under the P-V curve

    A = simps(pressure,volume,even='first') * np.power(10,6)            # = Work per cycle (J)

    RPM = input('Enter the RPM of engine at which data was collected: ')        # Rotations per min.

    f = input('Enter the value of fuel consumed per cycle in micro-grams: ')    # fuel consumtion micro-gram per cycle

    RPM = float(RPM)

    f = float(f)

    n = RPM/(2*60)          # Number of cycles per second for 4-stroke engine

    P = A * n           # Power output (W) at speed N rad/s

    f = (f*n*3600)/np.power(10,9) # fuel consumtion kg per hr

    sfc = (f*1000)/P # Specific fuel consumtion (kg per hr/kW)

    print('Area under the P-V curve i.e. work done per cycle = {} J'.format(A))

    print('Power output at {} RPM = {} W'.format(RPM,P))

    print('Specific fuel consumption = {} kg/kW.hr'.format(sfc))

 

    print('\n**********************************************************************************')

    x = input("\nDo you want to calculate perfomance of the engine again? (y/n): (Press n to exit) ")

    print('\n**********************************************************************************')

   

 

   

 

  

 

 

#Importing the basic libraries/modules
import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import simps

# File name 
filename = 'engine_data.out'

# Compatability check
# Checking if the file is a valid CONVERGE output file
try:
	f = open(filename)
	f.close()
except FileNotFoundError:
	print(' This is not a valid CONVERGE output file.n')
	exit()

# Checking if data is present in the CONVERGE output file
i=0
for line in open(filename):
	if '#' not in line:
		break
	i = i+1
j=0
for line in open(filename):
	j=j+1
	if j>i:
		break
if j==i:
	print('No data present in the file.n')
	exit()


# Data extraction
# Getting number of columns i.e. number of variables
i=0
for line in open(filename):
	if i==2:
		c = line.split() 		# Storing unit of variables in list c
		i = 0
		break
	if i==1:
		b=line.split() 			# Storing names of variables in list b
		i = i + 1
	if 'column' in line.split():
		a = line.split() 		# Storing column numbers of variables in list a
		i=1

a.remove("column") 		# Removing column from list a
a.remove("#") 			# Removing # from list a
a = np.array(a, dtype=int)		 # Converting data to integer
b.remove("#") 		# Removing # from list b
c.remove("#") 		# Removing # from list c
number_of_column = len(a) 	# Number of columns/Number of variables



# Getting Data
num_data = 0 				# Initializing Number of data points counter
data = np.arange(0,number_of_column)
for line in open(filename):
	if '#' not in line:
		d = line.split() 						# Storing current data line as a list in d
		d = np.array(d, dtype=np.float32) 		# Converting list data to float array
		data = np.vstack([data,d]) 				# Stacking the float array d in data matrix
		num_data = num_data + 1
data = np.delete(data,0,0)						# Deleting the first row used to initilize the array

# Data visualizer ( printing the columns present in the file )
print('\n**********************************************************************************')
print('Name of Columns present in the File')

for i in a:
	print('{} for {}{}'.format(i,b[i-1],c[i-1]))
x='n'
print('**********************************************************************************')

# Plotting graphs between two variables in the file
x = input("\n Do you want a plot between any two vriables? (y/n) : ")
while(x.lower()=='y'):
	x_var = input(" Enter the column number of the variable along x-axis(Refer List of the Columns above): ")
	y_var = input(" Enter the column number of the variable along y-axis(Refer List of the Columns above): ")
	x_var = int(x_var)
	y_var = int(y_var)
	plt.figure(1)
	plt.plot(data[:,x_var-1],data[:,y_var-1])
	plt.xlabel('{} {}'.format(b[x_var-1],c[x_var-1]))
	plt.ylabel('{} {} '.format(b[y_var-1],c[y_var-1]))
	plt.title('{} {} vs {} {}'.format(b[y_var-1],c[y_var-1],b[x_var-1],c[x_var-1]))
	
	z = "n"
	z = input(" Do you want to save the plot? (y/n):" )
	if(z.lower()=='y'):
		plt.savefig("{}-{}.png".format(b[y_var-1],b[x_var-1]))
	plt.show()
	
	print('\n**********************************************************************************')
	x = input("\n Do you want to plot any other two variables? (y/n): (press n to move on) ")

print('\n**********************************************************************************')

# Plotting single variable in the file
x='n'
x = input("\n Do you want a single vriable plot? (y/n): ")
while(x.lower()=='y'):
	column_to_plot = input("Enter the column number of the variable (Refer List of the Columns above): ")
	column_to_plot = int(column_to_plot)
	plt.figure(1)
	plt.plot(np.arange(0,num_data),data[:,column_to_plot-1])
	plt.xlabel('Data point number')
	plt.ylabel('{} {} '.format(b[column_to_plot-1],c[column_to_plot-1]))
	
	z = "n"
	z = input("Do you want to save the plot? (y/n):" )
	if(z.lower()=='y'):
		plt.savefig("{}.png".format(b[column_to_plot-1]))

	plt.show()

	print('\n**********************************************************************************')
	x = input("\n Do you want any other single variable plot? (y/n) (press n to move on):")
print('\n**********************************************************************************')


# Basic performance calculation
x='n'
x = input("\n Do you want to calculate perfomance of the engine? (y/n): ")
while(x.lower()=='y'):
	print('PERFORMANCE OF ENGINE')
	b1 = [i.lower() for i in b]
	pressure_column = b1.index('pressure')
	volume_column = b1.index('volume')
	pressure = data[:,pressure_column]
	volume = data[:,volume_column]
	A = 0 			# Area under the P-V curve
	A = simps(pressure,volume,even='first') * np.power(10,6) 			# = Work per cycle (J)
	RPM = input('Enter the RPM of engine at which data was collected: ') 		# Rotations per min.
	f = input('Enter the value of fuel consumed per cycle in micro-grams: ') 	# fuel consumtion micro-gram per cycle
	RPM = float(RPM)
	f = float(f)
	n = RPM/(2*60) 			# Number of cycles per second for 4-stroke engine
	P = A * n 			# Power output (W) at speed N rad/s
	f = (f*n*3600)/np.power(10,9) # fuel consumtion kg per hr
	sfc = (f*1000)/P # Specific fuel consumtion (kg per hr/kW)
	print('Area under the P-V curve i.e. work done per cycle = {} J'.format(A))
	print('Power output at {} RPM = {} W'.format(RPM,P))
	print('Specific fuel consumption = {} kg/kW.hr'.format(sfc))

	print('\n**********************************************************************************')
	x = input("\nDo you want to calculate perfomance of the engine again? (y/n): (Press n to exit) ")
	print('\n**********************************************************************************')