Parsed Data
What Is Parsing of Data? – Blog | Oxylabs
If you work with development (whether part of the team or work in a company where you need to communicate with the tech team often), you’ll most likely come across the term data parsing. Simply put, it’s a process when one data format is transformed into another, more readable data format. But that’s a rather straightforward explanation.
In this article we’ll dig a little deeper on what is parsing of data, and discuss whether building an in-house data parser is more beneficial to a business, or is it better to buy a data extraction solution that already does the parsing for you.
What is data parsing?
Data parsing is a widely used method for data structuring; thus, you may discover many different descriptions while trying to find out what exactly it is. To make understanding this concept easier, we’ve put it into a simple definition.
What is data parsing? Data parsing is a method where one string of data gets converted into a different type of data. So let’s say you receive your data in raw HTML, a parser will take the said HTML and transform it into a more readable data format that can be easily read and understood.
What does a parser do?
A well-made parser will distinguish which information of the HTML string is needed, and in accordance to the parsers pre-written code and rules, it will pick out the necessary information and convert it into JSON, CSV or a table, for example.
It’s important to mention that a parser itself is not tied to a data format. It’s a tool that converts one data format into another, how it converts it and into what depends on how the parser was built.
Parsers are used for many technologies, including:
Java and other programming languagesHTML and XMLInteractive data language and object definition languageSQL and other database languagesModeling languagesScripting languagesHTTP and other internet protocols
To build or to buy?
Now, when it comes to the business side of things, an excellent question to ask yourself is, “Should my tech team build their own parser, or should we simply outsource? ”
As a rule of thumb, it’s usually cheaper to build your own, rather than to buy a premade tool. However, this isn’t an easy question to answer, and a lot more things should be taken into consideration when deciding to build or to buy.
Let’s look into the possibilities and outcomes with both options.
Building a data parser
Let’s say you decide to build your own parser. There are a few distinct benefits if making this decision:
A parser can be anything you like. It can be tailor-made for any work (parsing) you require. It’s usually cheaper to build your own ’re in control whatever decisions need to be made when updating and maintaining your parser.
But, like with anything, there’s always a downside of building your own parser:
You’ll need to hire and train a whole in-house team to build the intaining the parser is necessary – meaning more in house expenses and time resources ’ll need to buy and build a server that will be fast enough to parse your data in the speed you in control isn’t necessarily easy or beneficial – you’ll need to work closely with the tech team to make the right decisions to create something good, spending a lot of your time planning and testing.
Building your own has its benefits – but it takes a lot of your resources and time. Especially if you need to develop a sophisticated parser for parsing large volumes. That will require more maintenance and human resources, and valuable human resources because building one will require a highly-skilled developer team.
Buying a data parser
So what about buying a tool that parses your data for you? Let’s start with the benefits:
You won’t need to spend any money on human resources, as everything will be done for you, including maintaining the parser and the issues that arise will be solved a lot faster, as the people you buy your tools from have extensive know-how and are familiarized with their technology. It’s also less likely that the parser will crash or experience issues in general, as it will be tested and perfected to fit the markets’ requirements. You’ll save a lot on human resources and your own time, as the decision making on how to build the best parser will come from the outsourcing.
Of course, there are a few downsides to buying a parser as well:
It will be slightly more won’t have too much control over it.
Now, it seems that there are a lot of benefits to simply just buy one. But one thing that might make things easier to choose is to consider what sort of parser you’ll need. An expert developer can make an easy parser probably within a week. But if it’s a complex one, it can take months – that’s a lot of time, and resources.
It also falls to whether you’re a big business that has a lot of time and resources on their hands to build and maintain a parser. Or you’re a smaller business that needs to get things done to be able to grow within the market.
How we do it: Real-Time Crawler
Here at Oxylabs, we have a data gathering tool called Real-Time Crawler. This product is specifically built to scrape search engines and e-commerce websites on a large scale. We covered what Real-Time Crawler is and how it works in great detail in one of our articles, so make sure to check it out. Also, here’s a video below:
But why are we bringing up this tool? Well, Real-Time Crawler not only gathers the data – it also has a built-in parser that turns your HTML into JSON. If you choose to use Real-Time Crawler Callback method, after every job request, you’ll be provided with a URL to download the results in HTML or parsed JSON format.
Our built-in parser handles quite a lot of data daily. On February, 12 billion requests were made! And that’s back in February! Based on our 2019, Q1 statistics, the total requests grew by 7. 02% in comparison to Q4 2018. And these numbers continue to rise in accordance in Q2, 2019.
Our tech team has been working with this project for a few years now, and having this much experience we can say with confidence that the parser we built can handle any volume of data one might request.
So – to build or to buy? Well, building several years of experience, improvements, and maintenance of a tool that does its job to perfection – honestly, quite expensive.
Wrapping up
Hopefully, now you have a decent understanding of what is parsing of data. Taking everything into account, keep in mind whether you’re building a very sophisticated parser or not. If you are parsing large volumes of data, you will need good developers on your team to develop and maintain the parser. But, if you need a less complicated, smaller parser – probably best to build your own.
Also be mindful if you are a large company with a lot of resources, or a smaller one, that needs the right tools to keep things growing.
Oxylabs’ clients have significantly increased growth with Real-Time Crawler! If you are also looking for ways to improve your business, register here to start using our tools. Also, if you have more questions about data parsing, book a call with our sales team!
People also ask
What tools are required for data parsing?
After web scraping tools provide the required data, there are several options for data parsing. BeautifulSoup and LXML are two commonly used data parsing tools.
How to use a data parser?
Every data parsing tool will come with its own manual. Most of them will require some technical knowledge such as understanding Python and data from a web scraper.
What is data scraping?
Data scraping is the process of acquiring large amounts of data from the web through the use of automation and rotating IP address.
Gabija Fatenaite is a Product Marketing Manager at Oxylabs. Having grown up on video games and the internet, she grew to find the tech side of things more and more interesting over the years. So if you ever find yourself wanting to learn more about proxies (or video games), feel free to contact her – she’ll be more than happy to answer you.
All information on Oxylabs Blog is provided on an “as is” basis and for informational purposes only. We make no representation and disclaim all liability with respect to your use of any information contained on Oxylabs Blog or any third-party websites that may be linked therein. Before engaging in scraping activities of any kind you should consult your legal advisors and carefully read the particular website’s terms of service or receive a scraping license.
What is data parsing? – ScrapingBee
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07 June, 2021
10 min read
Kevin worked in the web scraping industry for 10 years before co-founding ScrapingBee. He is also the author of the Java Web Scraping Handbook.
Data parsing is the process of taking data in one format and transforming it to another format. You’ll find parsers used everywhere. They are commonly used in compilers when we need to parse computer code and generate machine code.
This happens all the time when developers write code that gets run on hardware. Parsers are also present in SQL engines. SQL engines parse a SQL query, execute it, and return the results.
In the case of web scraping, this usually happens after data has been extracted from a web page via web scraping. Once you’ve scraped data from the web, the next step is making it more readable and better for analysis so that your team can use the results effectively.
A good data parser isn’t constrained to particular formats. You should be able to input any data type and output a different data type. This could mean transforming raw HTML into a JSON object or they might take data scraped from JavaScript rendered pages and change that into a comprehensive CSV file.
Parsers are heavily used in web scraping because the raw HTML we receive isn’t easy to make sense of. We need the data changed into a format that’s interpretable by a person. That might mean generating reports from HTML strings or creating tables to show the most relevant information.
Even though there are multiple uses for parsers, the focus of this blog post will be about data parsing for web scraping because it’s an online activity that thousands of people handle every day.
How to build a data parser
Regardless of what type of data parser you choose, a good parser will figure out what information from an HTML string is useful and based on pre-defined rules. There are usually two steps to the parsing process, lexical analysis and syntactic analysis.
Lexical analysis is the first step in data parsing. It basically creates tokens from a sequence of characters that come into the parser as a string of unstructured data, like HTML. The parser makes the tokens by using lexical units like keywords and delimiters. It also ignores irrelevant information like whitespaces and comments.
After the parser has separated the data between lexical units and the irrelevant information, it discards all of the irrelevant information and passes the relevant information to the next step.
The next part of the data parsing process is syntactic analysis. This is where parse tree building happens. The parser takes the relevant tokens from the lexical analysis step and arranges them into a tree. Any further irrelevant tokens, like semicolons and curly braces, are added to the nesting structure of the tree.
Once the parse tree is finished, then you’re left with relevant information in a structured format that can be saved in any file type. There are several different ways to build a data parser, from creating one programmatically to using existing tools. It depends on your business needs, how much time you have, what your budget is, and a few other factors.
To get started, let’s take a look at HTML parsing libraries.
HTML parsing libraries
HTML parsing libraries are great for adding automation to your web scraping flow. You can connect many of these libraries to your web scraper via API calls and parse data as you receive it.
Here are a few popular HTML parsing libraries:
Scrapy or BeautifulSoup
These are libraries written in Python. BeautifulSoup is a Python library for pulling data out of HTML and XML files. Scrapy is a data parser that can also be used for web scraping. When it comes to web scraping with Python, there are a lot of options available and it depends on how hands-on you want to be.
Cheerio
If you’re used to working with Javascript, Cheerio is a good option. It parses markup and provides an API for manipulating the resulting data structure. You could also use Puppeteer. This can be used to generate screenshots and PDFs of specific pages that can be saved and further parsed with other tools. There are many other JavaScript-based web scrapers and web parsers.
JSoup
For those that work primarily with Java, there are options for you as well. JSoup is one option. It allows you to work with real-world HTML through its API for fetching URLs and extracting and manipulating data. It acts as both a web scraper and a web parser. It can be challenging to find other Java options that are open-source, but it’s definitely worth a look.
Nokogiri
There’s an option for Ruby as well. Take a look at Nokogiri. It allows you to work with HTML and HTML with Ruby. It has an API similar to the other packages in other languages that lets you query the data you’ve retrieved from web scraping. It adds an extra layer of security because it treats all documents as untrusted by default. Data parsing in Ruby can be tricky as it can be harder to find gems you can work with.
Regular expression
Now that you have an idea of what libraries are available for your web scraping and data parsing needs, let’s address a common issue with HTML parsing, regular expressions. Sometimes data isn’t well-formatted inside of an HTML tag and we need to use regular expressions to extract the data we need.
You can build regular expressions to get exactly what you need from difficult data. Tools like regex101 can be an easy way to test out whether you’re targeting the correct data or not. For example, you might want to get your data specifically from all of the paragraph tags on a web page. That regular expression might look something like this:
/
(. *)<\/p>/
The syntax for regular expressions changes slightly depending on which programming language you’re working with. Most of the time, if you’re working with one of the libraries we listed above or something similar, you won’t have to worry about generating regular expressions.
If you aren’t interested in using one of those libraries, you might consider building your own parser. This can be challenging, but potentially worth the effort if you’re working with extremely complex data structures.
Building your own parser
When you need full control over how your data is parsed, building your own tool can be a powerful option. Here are a few things to consider before building your own parser.
A custom parser can be written in any programming language you like. You can make it compatible with other tools you’re using, like a web crawler or web scraper, without worrying about integration issues.
In some cases, it might be cost-effective to build your own tool. If you already have a team of developers in-house, it might not too big of a task for them to accomplish.
You have granular control over everything. If you want to target specific tags or keywords, you can do that. Any time you have an update to your strategy, you won’t have many problems with updating your data parser.
Although on the other hand, there are a few challenges that come with building your own parser.
The HTML of pages is constantly changing. This could become a maintenance issue for your developers. Unless you foresee your parsing tool becoming of huge importance to your business, taking that time from product development might not be effective.
It can be costly to build and maintain your own data parser. If you don’t have a developer team, contracting the work is an option but that could lead to step bills based on developers’ hourly rates. There’s also the cost of ramping up developers that are new to the project as they figure out how things work.
You will also need to buy, build, and maintain a server to host your custom parser on. It has to be fast enough to handle all of the data that you send through it or else you might run into issues with parsing data consistently. You’ll also have to make sure that server stays secure since you might be parsing sensitive data.
Having this level of control can be nice if data parsing is a big part of your business, otherwise, it could add more complexity than is necessary. There are plenty of reasons for wanting a custom parser, just make sure that it’s worth the investment over using an existing tool.
Parsing meta data
There’s also another way to parse web data through a website’s schema. Web schema standards are managed by, a community that promotes schema for structured data on the web. Web schema is used to help search engines understand information on web pages and provide better results.
There are many practical reasons people want to parse schema metadata. For example, companies might want to parse schema for an e-commerce product to find updated prices or descriptions. Journalists could parse certain web pages to get information for their news articles. There are also website that might aggregate data like recipes, how-to guides, and technical articles.
Schema comes in different formats. You’ll hear about JSON-LD, RDFa, and Microdata schema. These are the formats you’ll likely be parsing.
JSON-LD is JavaScript Object Notation for Linked Data. This is made of multi-dimensional arrays. It’s implemented using the standards in terms of SEO. JSON-LD is generally more simple to implement because you can paste the markup directly in an HTML document.
RDFa (Resource Description Framework in Attributes) is recommended by the World Wide Web Consortium (W3C). It’s used to embed RDF statements in XML and HTML. One big difference between this and the other schema types is that RDFa only defines the metasyntax for semantic tagging.
Microdata is a WHATWG HTML specification that’s used to nest metadata inside existing content on web pages. Microdata standards allow developers to design a custom vocabulary or use others like
All of these schema types are easily parsable with a number of tools across different languages. There’s a library from ScrapingHub, another from RDFLib.
We’ve covered a number of existing tools, but there are other great services available. For example, the ScrapingBee Google Search API. This tool allows you to scrape search results in real-time without worrying about server uptime or code maintainance. You only need an API key and a search query to start scraping and parsing web data.
There are many other web scraping tools, like JSoup, Puppeteer, Cheerio, or BeautifulSoup.
A few benefits of purchasing a web parser include:
Using an existing tool is low maintenance.
You don’t have to invest a lot of time with development and configurations.
You’ll have access to support that’s trained specifically to use and troubleshoot that particular tool.
Some of the downsides of purchasing a web parser include:
You won’t have granular control over everything the way your parser handles data. Although you will have some options to choose from.
It could be an expensive upfront cost.
Handling server issues will not be something you need to worry about.
Final thoughts
Parsing data is a common task handling everything from market research to gathering data for machine learning processes. Once you’ve collected your data using a mixture of web crawling and web scraping, it will likely be in an unstructured format. This makes it hard to get insightful meaning from it.
Using a parser will help you transform this data into any format you want whether it’s JSON or CSV or any data store. You could build your own parser to morph the data into a highly specified format or you could use an existing tool to get your data quickly. Choose the option that will benefit your business the most.
File Parsing and Data Analysis in Python Part I (Interactive Parsing …
Definition:
Parse essentially 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. ”Within computational linguistics the term is used to refer to the formal analysis by a computer of a sentence or other string of words into its constituents, resulting in a parse tree showing their syntactic relation to each other, which may also contain semantic and other information. ” (Wikipedia). A parser is a program that parses the text files.
Converge File:
A converge file is usually some thermodynamic properties file containing data points related to various properties. In this project, I will be parsing an Engine output file. The file contains 17 thermodynamic properties like crank angle, pressure, temperature, volume, etc. There are thousands of data points for each property.
The Converge file that I will use in this project is named ” and can be found here:
1. 1 Data Pre-Processing
Before one use information given in a file, it is very important to understand the given file, find the patterns and meaningful ways of data extraction. This is a part of data pre-processing. Rigorously speaking, Data preprocessing is a technique that involves transforming raw data into an understandable format. Real-world data is often incomplete, inconsistent, and/or lacking in certain behaviours or trends, and is likely to contain many errors. Data preprocessing prepares raw data for further processing. (Techopedia)
In data preprocessing, techniques like data cleansing, data integration, data transformation etc. are used. The first two techniques deal with missing and inconsistent data. Data transformation involves transforming the raw data into meaningful and usable formats.
Now, looking at the file in Fig. 1. 1 below, it can be seen that the first line in the file contains Converge release name and date. The second line contains the column numbers. The third and fourth lines contain properties and their units. The fifth line is blank and the data points for each property occur from the 6th line up till the last line of the file. It is also clear that lines which do not contain data points start with the ‘#’ symbol. (I opened the data file in WordPad).
Fig. 1: An Excerpt From The Converge File
1. 2 File Reading, Data Extraction
In python, one of the best ways to parse a file is to use a for-loop and read a file line by line as shiwn in the following code.
#Reading and extracting data from file
#Extraction
engine_lines =[] #preallocation
for line in open(”, ‘r’): #r stands for read only
(line) #appends the lines in a list
#Printing Information
print(‘No of lines = ‘, len(engine_lines), ‘n’) #number of lines in the list
print(‘n First two lines: n’)
print(engine_lines[0:2], ) #first 2 lines
print(‘n First data-points line: n’)
print(engine_lines[5]) #first data points line
print(‘nClass Typen’)
print(‘Class of the engine_lines variable = ‘, type(engine_lines))
print(‘Class of elements = ‘, type(engine_lines[0]), type(engine_lines[5]))
Output 1. File Parsing Line-by-Line
From Output 1. 1, we see that there are 8670 lines in the file. With the above code, all lines are individual entries stored in the list named engine_lines. It can also be seen that each element in the engine_lines list is a string. However, I need to store each data point for a particular property separately as a number and not as a string.
1. 3 Splitting Lines and Data Integration
Most of the data files are text files and contain some characteristics which are used to separate data points from each other. If a file is a Comma Separated Values file, then the data points are separated by commas. Looking at the file, it is clear that the data points are separated by spaces. On the first glance, when checking the first data lines, one finds that there are three spaces between each data point. So, while coding, one can use this feature of the file and extract all the data points.
For splitting the data points, using the inbuilt function () can be very useful. Now say, if the converge file was a CSV file, the split function could be used by writing (‘, ‘). As the data points are separated by (seemingly three) spaces, I could use (‘ ‘). [Note the three spaces between the single quotes]. However, the three-space criterion for all lines was only my assumption. While looking keenly at the file, there are certain lines, where there are more or less than three spaces in between the data points. (of course, I realised it only after getting errors). The best way is to input nothing in the split function. This way, the function automatically finds meaningful separators (at least in my case). Also, the ‘non-data’ lines contain the ‘#’ symbol at the beginning. Using, the two properties of the converge file, the code to integrate data points from it is shown below.
Raw Data Extraction Code:
#Creating different lists for properties with respective data points
#Defining variable types
Crank = []
Pressure = []
Max_Pres = []
Min_Pres = []
Mean_Temp = []
Max_Temp = []
Min_Temp = []
Volume = []
Mass = []
Density = []
Integrated_HR = []
HR_Rate = []
C_p = []
C_v = []
Gamma = []
Kin_Visc = []
Dyn_Visc = []
”’the above variables can be names anything like A, B, C etc. But, for clarity in code, it is better to write the property name for each variable”’
for line in open(”, ‘r’):
if “#” not in line:
(float(()[0])) #python counts from 0 and not 1
(float(()[1]))
(float(()[2]))
(float(()[3]))
(float(()[4]))
(float(()[5]))
(float(()[6]))
(float(()[7]))
(float(()[8]))
(float(()[9]))
(float(()[10]))
(float(()[11]))
(float(()[12]))
(float(()[13]))
(float(()[14]))
(float(()[15]))
(float(()[16]))
(Volume, Pressure)
()
Pressure-Volume Plot from the above code is given in figure 1. 2 below:
Fig. 2 Pressure-Volume Plot From Raw Data Extraction
The code given in Section 1. 3 works, but it is in no way interactive or appealing. Often, one would like to enter a given Converge engine file, select two desired properties to be plotted with proper labels and titles and be able to save the figures. That would require some complexity in coding. I will explain that step by step in the next sections.
The code given in section in 1. 3 is not versatile at all. In order to make an interactive program, there are many considerations and shortcomings that may lead to the crashing of the program. For example, if a user enters an invalid file, enters a column number that doesn’t exist, the program won’t work and is likely to crash. I will discuss each situation along with the solutions. A perfectly working program would be one which will:
Let the user input the name of the file
Check the existence and validity of the file
Parse the file, extract Converge version, release date, property names, and units from the first 4 lines, and
Let the user select (two) properties to be plotted.
Above all, it is important that the program
doesn’t crash any time
re-plots unless the user decides to exit on will
reruns unless the user decides to exit on will
The program can crash when following cases occur:
While Inputting file:
The user enters a file which doesn’t exist
The user enters a file which exists but is not readable
The user enters a file, is readable but is not a converge file
While Inputting wrong column numbers
While Inputting a wrong response where ever some response is needed
In short, the program must be crash-proof.
The criteria to determine whether a given file is a valid Converge engine file or not depends on some unique characteristic that must exist in all such files. I have selected the existence of the word ‘CONVERGE’ (after the # symbol) in the first line of the file as the criteria for a valid file. With the below pseudo code, it is possible to make a ‘software’ solution for reading, extracting and plotting data from similar files. However, there are some assumptions that need to be considered. Assuming all Converge Engine files contain:
hash symbols in the first 5 lines
the word ‘CONVERGE’ as the second element in the first line
version name and release in the first line after the word ‘CONVERGE’
column numbers in the second line
properties in the third line
units in the fourth line
an empty 5th line (with # at the beginning)
data points for each property after the 6th line
data points are convertible to numbers
2. 1 Pseudo-Code:
Based on the above assumptions, the following pseudo code illustrates the program idea.
import libraries
start main Loop:
ask for the file name from the Users
if the name does not exist:
prompt the user to enter an existing file
if the name exists:
try parsing the file
if not able to read file:
promp the user to enter a valid file
if able to read an parse:
check validity by finding ‘CONVERGE’ in the first line
if ‘CONVERGE’ not in first line:
prompt the user to enter valid file
if ‘CONVERGE’ in first line:
extract labels and units
extract data columns
extract Converge release
Begin Particular-File-Loop:
print the converge release
print the column names with numbers
prompt the user to enter a valid column for x axis
print the selected column
prompt the user to enter a valid column for y axis
plot the graph with labels and titles
try:
to create a folder in the current directory
if folder exists:
save the plot in the folder
else:
create the folder and then save the plot
ask the user whether to re-run with the same file or a different file
if rerun:
then stay in Particular-File-Loop
go to Main Loop
ask the user whether to enter a new converge file or to exit the Programs
if new file:
stay in the Main Loop
exit the program
2. 2 Python-Code
The Python code for an interactive file parsing and data visualisation is given below
### Program to Parse a Converge Engine Data Thermodynamic File###
#This code checks the existence and validity of a Converge Engine Data file,
#Then parses the valid file, extracts release version, labels, units and data points
#Stores data points in each coloumn (a property) in separate arrays/lists
#The arrays/lists can then be used to plot graphs between two properties at a time
#The plots are saved in proper folders with proper names
#User will always be prompted to enter valid inputs
#1) Importing libraries/modules:
import as plt #for plotting
import numpy as np #for creating arrays
from time import sleep #for interactive time delays
from pathlib import Path #creats path name class
import os #Files and directory module
exit = ‘y’ #defining variable and setting default condition
while exit == ‘y’: #Main-Program-Loop
#2) Entering and Checking the existance File:
# 2. 1) Inputting the file name:
file_input = input(‘ nn Enter the name of the Converge Engine Data File: ‘)
cwd = () #gets the current working directory
path = cwd + ‘\’ #double backslash is interpreted as single
file_path = path + file_input #full path name
file_path_find = Path(file_path) #class = dowsPath
# 2. 2) Checking the existance of file in the directory:
exist = _file() _file method returns boolean values
# 2. 2. 1) Code for non existing file:
if exist == False:
print((“n No such file ‘%s’ exists in the directory: ‘%s’ n
Make sure you enter the file name (case sensitive) along with the exstenstion “%(file_input, path)))
sleep(2) #for an interactive pause
”’A backslash is used to indicate the compiler a line continuity”’
# 2. 2) Code for existing file:
if exist == True: #here, ‘if exist == True:’ can be replaced by an ‘else:’ statement
all_columns = [] #defining the type/preallocation
#3) Extacting and Checking Validity of file:
try: #try whether the file is readable or not
# 3. 1) Reading file and Extracting content:
for line in open(file_input, ‘r’): #read (‘r’) file line by line
separate = () #splits the line automatically
(separate) #store each
# 3. 2) Checking the validity of converge file:
# 3. 1) Invalidity Test
if ‘CONVERGE’ not in all_columns[0][1]:
print(‘nn You have entered an invalid or corrupt file. Please enter a valid CONVERGE Enigne Data Output File. n’)
sleep(2)
print(‘Itried not’)
”’
The key concept here is to identify a certian unique word or words that will only be
containedin a converge release file at a particular location. The above criteria is,
of course valid only if we assume that all Converge engine data files are similar in
format and, that all such files have 17 columns, first line contains the word CONVERGE
and then the Release etc and lastly that the data points start from the 6th line.
There are four cases that may arise:
1) Entered file is valid and passes the validity test. This is desired.
2) Entered file is invalid and doesn’t pass the validity test. This is also desired.
3) Entered file is invalid and passed the validity test.
4) Entered file is not readable, like an image file.
In 3) and 4) cases, the program again prompts to enter a valid file
# 3. 2) Validity Test:
”’ if the file isn’t invalid, it is, consequently valid”’
#print(‘n You have entered a valid Converge Engine Data File. n’)
#4) Extraction:
# 4. 1) Labels/Property names and Units
label_columns = all_columns[2:4]
#Property name and units are in the 3rd and 4th lines respectively
del(label_columns[0][0], label_columns[1][0]) #deleting the pound symbols
”’Note: The names and units are contained in lines while the data points
for each property in columns”’
# 4. 2) Data points (lines)
data_columns = all_columns[6:] #selects the only lines with data points
#5) Grouping:
# 5. 1) Defining/Preallocating (lists)
”’ list variables can be named anything Like A, B, C etc for simplicity
but, it is obvious naming them properly makes code understandable more easily”’
# 5. 2) Grouping data into respective columns:
for i in range(len(data_columns)):
(float(data_columns[i][0]))
(float(data_columns[i][1]))
(float(data_columns[i][2]))
(float(data_columns[i][3]))
(float(data_columns[i][4]))
(float(data_columns[i][5]))
(float(data_columns[i][6]))
(float(data_columns[i][7]))
(float(data_columns[i][8]))
(float(data_columns[i][9]))
(float(data_columns[i][10]))
(float(data_columns[i][11]))
(float(data_columns[i][12]))
(float(data_columns[i][13]))
(float(data_columns[i][14]))
(float(data_columns[i][15]))
(float(data_columns[i][16]))
”’The key idea here is that for each iteration (for each line, denoted by
‘i’), the loop appends to each (property) list a data point. For example
data_columns[i][7] will always append the 8th entry from each line. This way,
data points belonging to Volume only will ve stored in the Volume array.
DATA = [Crank, Pressure, Max_Pres, Min_Pres, Mean_Temp, Max_Temp, Min_Temp, Volume, Mass,
Density, Integrated_HR, HR_Rate, C_p, C_v, Gamma, Kin_Visc, Dyn_Visc] #GRoups each file column into a list
# 5. 3) Converting Arrays into absolute units
”’ The three columns belonging to pressure in the file are in MPa, while other are
in absolute units. Also, if needed, the Crank angles can be convertred to raidans
with np. radians() command. The other way is to multiply pressure arrays in Section
5. 2 e. g., (float(data_columns[i][2])*10e6)
#for i in range(1, 4):
# DATA[i] = 1e6*(DATA[i])
”’by converting a list to numpy array, elementwise operations can be done
Mega = 10^6 which in python is written as 1e^6
However, when plotting, the units for pressures will be in MPa already.
Only while calculating, shall we need to multiply 10^6 to pressure arrays
# 6) Prompting Columns to be plotted from the user:
rerun = ‘r’ #defining variable and setting default condition
while rerun == ‘r’: #Particular-File-Loop (let’s call it that)
# 6. 1) Creating converge file name, version and column values for display:
version = ‘ ‘ #defining variable
for i in range(1, 5):
version = version + ‘ ‘ + all_columns[0][i]
print(‘nnn’, (‘ ‘*10 + ‘*’*10)*5)
print(‘nttt Current File:%s’%(file_input+version))
#n creates new line and t creates a tab space
for i in range(len(label_columns[0])):
print(‘t’, label_columns[0][i], ‘=’, i+1)
Note: the variable ‘i’ used in the loops can be reused to save memory.
However, if the value of i is to be used after the loop ends, say as a
counter etc, then different loops should use different variables
# 6. 2) Prompting for the first column, X-axis:
If the user enters a float, char or string, the program can crash. Also,
the input() accepts strings by default. Using try-execpt this can be fixed
x =’anything’ #anything but an integer between 1 and 17
while x not in list(range(1, 18)): #because there are only 17 columns
x = input(‘nn Please Enter the column number (X-axis): ‘)
x = int(x)
if x not in list(range(1, 18)):
print(‘ Invalid Number. Accepted Values (1-17)’)
except:
The above code will try if the input value can be converted into an interger.
Then, test if the integer lies 1 and 17. If yes, this condition will satisfy
both ‘try condition’ as well as while loop. Otherwise, it will keep displaying
the error message and keep prompting for a valid number from the user
x = x – 1 #because python counts from 0:)
print(‘t’, label_columns[0][x]) #prints the selected column
# 6. 2) Prompting for the second column, Y-axis:
y = ‘anything’
while y not in list(range(1, 18)):
y = input(‘n Please Enter the column number (Y-axis): ‘)
y = int(y)
if y not in list(range(1, 18)):
y = y – 1
print(‘t’, label_columns[0][y])
# 7) Plotting:
# 7. 1) Creating Title, axes labels
title = ‘Plot of ‘ + label_columns[0][y] + ‘ Vs ‘ + label_columns[0][x]
x_lab = label_columns[0][x] + label_columns[1][x]
y_lab = label_columns[0][y] + label_columns[1][y]
# 7. 2) Creating The Folders and filename for the plot.
folder = path + ‘File Parsing\Plot Figures\’
”’Needed to create the folder for the first time. If the folder already
exists, it will move to except”’
kedirs(folder)
#makedirs makes folders and subfolders. mkdir, only a single folder
plot_filename = folder + title + ”
#’png’ or any image format can be used
# 7. 2) Plotting the figure:
(DATA[x], DATA[y])
(x_lab)
(y_lab)
(title)
vefig(plot_filename)
#Note: savefig() must be placed before show(), else, a blank image is saved
#8) Rerunning the program with the current converge file:
If the user wants to plot again with the same file, the program should not ask
again for the converge file. Thus, the user explicitly has to declare whether
the current file is to be used again or another file is to be used
rerun = input(‘n Press R to rerun or H to exit to home (R/H): ‘)
rerun = ()
#lower() converts string to lower case. User can enter R, r, H or h
while rerun! = ‘r’ and rerun! = ‘h’:
print(‘Invalid Input’)
prompting the user to enter either R, r or H, h only
if rerun == ‘h’:
#entering h will satisfy the ‘Particular-File-Loop’ and break it
print(‘n Exiting to Home… nn’)
sleep(1)
”’entering h will satisfy the ‘Particular-File-Loop’ and break it, and
thus, return to the Main-Program-Loop”’
print(‘n You have entered an invalid or corrupt file. nn’)
#9) Rerunning the program with a new file:
exit = input(‘nn Press Y to enter Converge file or N to exit (Y/N): ‘)
exit = ()
while exit! = ‘n’ and exit! = ‘y’:
print(‘ Invalid Input’)
exit = input(‘nn Press Y to enter Converge file or N to exit (Y/N):’)
”’#entering n will satisfy the ‘Main-Program-Loop’ and break it and thus, terminate, or exit the program”’
if exit == ‘n’:
print(‘n Exiting Program… ‘)
”’The (Y/N) prompt will be dispalyed everytime when non-existing file case arises, invalid converge file is input
or when the user returns to home after plotting”’
The program has freedom of the number of data lines in the file. Also, the program is independent of the file location, as long as the converge file also exists in the same folder.
The only limitation of this program is that a user will not be able to plot properties which have a column number higher then 17 (if there are any). This is because I will be using only 17 variables to store the columns and, any column higher than 17 will get parsed but not stored in any separate variable. Also, I have set valid column numbers between 1-17 only.
2. 1 Types of files
I have stored the python file named ” in a particular folder named Data Analysis. Along with it, I have copied the valid Converge file ”, an image file named image_png, a pdf file named ”, a copy of the converge file named ” with ‘CONVERGE’ erased from the first line and a copy of Converge engine file named ” with an additional incomplete 18th column. Fig. 1 – 2. 3 shows the various files in the directory.
Fig. 1: Contents In The Folder Where Python Program Is Stored.
Fig. 2: Changing Converge To Diverge (rest of the data is the same as that of the valid file)
Fig. 3: File Containing and Extra Column (rest of the data is the same as that of the valid file)
2. 2 Working
I will run the program through the following steps:
enter a non-existing file
enter an image file
enter a wrong response
enter a pdf file
enter the non_converge file
enter the valid converge file
enter Crank as the x-axis and Volume as the y-axis Fig. 4: Plot of Volume Vs Crank
re-run with the same file
try to plot PV diagram Fig. 4: Volume Vs Pressure Plot
go to home (Main Program Loop)
enter the more_columns file
try to plot the 18th column
plot Pressure Vs Volume plot correctly
go home
exit the program(the program exits just after hitting n)
The program should have created a folder named File Parsing. In it, a sub-folder named Plot Figures and in it the figures that I generated from the program.
Fig. 5: Creation of Folder ‘File Parsing’
Fig. 6: Creation of Sub Folder
Fig 2. 7: Plot Figures
I have made a video of the working of the program (below)
NOTE: The final PV plot was created from the file, which even though is an invalid file, nonetheless contains valid data for 17 columns.
For the Engine Performance, check out the second part of this project:
File Parsing and Data Analysis in Python Part II (Area Under Curve and Engine Performance).
[link:
***END***
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