A 3D Structural Engineering Finite Element Llibrary for Python


An easy to use linear elastic 3D structural engineering finite element analysis library for Python.

Current Capabilities

  • 3D static analysis of elastic structures.
  • P-Δ analysis of frame type structures.
  • Member point loads, linearly varying distributed loads, and nodal loads are supported.
  • Classify loads by load case and create load combinations from load cases.
  • Produces shear, moment, and deflection results and diagrams for each member.
  • Tension-only and compression-only elements.
  • Springs: two-way, tension-only, and compression-only.
  • Quadrilateral plate elements (based on an isoparametric formulation).
  • Rectangular plate elements (based on a 12-term polynomial formulation).
  • Basic meshing algorithms for some common shapes.
  • Reports support reactions.
  • Rendering of model geometry, supports, load cases, load combinations, and deformed shapes.
  • Generates PDF reports for models and model results.

Project Objectives

As I’ve gotten into the structural engineering profession, I’ve found there’s a need for an easy to use open-source finite element package. I hope to help fill that need by prioritizing the following:

  1. Accuracy: There are no guarantees PyNite is error free, but accuracy and correctness are a priority. When bugs or errors are identified, top priority will be given to eliminate them. PyNite’s code is frequently reviewed, and its output is regularly being tested against problems with known solutions to isolate errors. If you find an error, please report it as an issue.

  2. Simplicity: There are other finite element alternatives out there with many more capabilities, but they are often lacking in documentation, written in outdated languages, or require extensive knowledge of finite element theory and/or element formulations to use. PyNite is not intended to be the most technically advanced solver out there. Rather, the goal is to provide a robust yet simple general purpose package.

  3. Improvement: I plan to continue supporting PyNite for many years to come. There are a lot of pieces I’d like to add to PyNite going forward: triangular plates, plate meshing and loading algorithms, dynamics, pushover anlysis, etc. There’s a lot of potential to create extensions as well to solve all kinds of engineering problems. There are more problems to solve than I have time for, so some priorities will have to be made. The plan is to keep PyNite mainstream, adding core functionality first. Occasionally however I may just add what interests me at the time.

  4. Collaboration: The intent is to keep PyNite free and open source. This will encourage future development and contributions. Keeping it open source will allow anyone to inspect and improve the code it runs on. If you see an area you think you can help PyNite improve in you are encouraged to contribute. I’d like to get PyNite doing a lot more. Don’t be offended if I’m a little slow to accept your contributions. FEA is a very technical subject and accuracy is extremely important to me. Sometimes I’m a little slow understanding both FEA and Python and it takes some time for me to comprehend what’s being proposed. I also have a young family to take care of that takes first priority.


PyNite depends on the following packages:

Required Dependencies

  • numpy: used for matrix algebra and dense matrix solver
  • scipy: used for sparse matrix solver to improve solution speed
  • matplotlib: used for plotting member diagrams
  • PrettyTable : used to format tabular output

Optional Dependencies

  • VTK: used for visualization - Note that VTK is a little picky about which version of Python you are running. You must run a 64 bit installation of Python, rather than a 32 bit version. VTK does not need to be installed if you don’t plan to use the visualization tools built into PyNite.
  • PDFKit: Used for generating pdf reports. In order to generate pdf reports, PDFKit requires you to have wkhtmltopdf installed on your computer. This is a free program available for download at https://wkhtmltopdf.org/downloads.html. Once installed, you’ll need to help PyNite find it. On Windows, this can be done by setting your PATH environment variable to include the path to “wkhtmltopdf.exe” after installation. For example, mine is installed at “C:\Program Files\wkhtmltopdf\bin”
  • jinja2: Used for templating reports into HTML prior to HTML-to-pdf conversion.
  • jupyterlab: Only needed if you want to view the derivations used to build PyNite.
  • sympy: Only needed if you want to view the derivations used to build PyNite.

Example Projects

Here’s a list of projects that run on PyNite:

What’s New?

Version 0.0.31

  • Added contour smoothing for better plate and quad contour plots.
  • Greatly reduced memory usage. The stiffness matrix is now stored as a sparse matrix by default. If the user opts not to use the sparse solver it is converted to a dense matrix later on.
  • Revised circular meshes so that the Y-axis is upward instead of the Z-axis.
  • Bug fix - Rendering springs was broken after the recent change to storing springs in dictionaries.

Version 0.0.30

  • Fixed a bug where quad and plate displacement contours weren’t being plotted correctly when user-defined load combinations were being used.
  • Added a few basic meshing features for quads.
  • Added the ability to merge duplicate nodes.

Version 0.0.29

  • Fixed a bug where load combinations were being ignored on plates and quadrilaterals. The default load combination was being used for these items instead of user defined load combinations.
  • Fixed a bug where auxiliary nodes were causing renderings to crash in some cases.
  • Fixed a bug where area loads were always pointing in the positive direction during rendering. The loads were being applied properly in the model even though they were showing up incorrectly in the rendering.
  • Quad membrane stress contours can now be rendered.
  • A few meshing algorithms have been added to simplify building models from quadrilaterals. Rings, annulusses and frustrums can now be meshed automatically. This feature will continue to be expanded and examples will follow.
  • Added an option to turn off rendering of labels in the RenderModel method. This can greatly speed up interaction on large plate/quad models.

Version 0.0.28

  • Issues with quadrilateral elements have been fixed. Membrane stiffness terms were being placed in the wrong location in the element’s global stifness matrix.
  • Nodes, members, plates, quads, springs, and auxiliary nodes are now stored in dictionaries for faster computing and easier user access. For example, instead of using the syntax FEModel3D.GetNode('node_name') to retrieve a node, you can now alternatively access a node directly from the Nodes dictionary using the syntax FEModel3D.Nodes['node_name'].

Download Details:

Author: JWock82
Download Link: Download The Source Code
Official Website: https://github.com/JWock82/PyNite
License: MIT


What is GEEK

Buddha Community

A 3D Structural Engineering Finite Element Llibrary for Python
Shardul Bhatt

Shardul Bhatt


Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.


Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

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Art  Lind

Art Lind


Python Tricks Every Developer Should Know

Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

Let’s get started

Swapping value in Python

Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead

>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName 
>>> print(FirstName, LastName)
('Jordan', 'kalebu')

#python #python-programming #python3 #python-tutorials #learn-python #python-tips #python-skills #python-development

Art  Lind

Art Lind


How to Remove all Duplicate Files on your Drive via Python

Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.


In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.

Heres a solution

Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.

But How do we do it?

If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?

The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.

There’s a variety of hashing algorithms out there such as

  • md5
  • sha1
  • sha224, sha256, sha384 and sha512

#python-programming #python-tutorials #learn-python #python-project #python3 #python #python-skills #python-tips

How To Compare Tesla and Ford Company By Using Magic Methods in Python

Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc…

You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like init, call, str etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).

Now there are a number of such special methods, which you might have come across too, in Python. We will just be taking an example of a few of them to understand how they work and how we can use them.

1. init

class AnyClass:
    def __init__():
        print("Init called on its own")
obj = AnyClass()

The first example is _init, _and as the name suggests, it is used for initializing objects. Init method is called on its own, ie. whenever an object is created for the class, the init method is called on its own.

The output of the above code will be given below. Note how we did not call the init method and it got invoked as we created an object for class AnyClass.

Init called on its own

2. add

Let’s move to some other example, add gives us the ability to access the built in syntax feature of the character +. Let’s see how,

class AnyClass:
    def __init__(self, var):
        self.some_var = var
    def __add__(self, other_obj):
        print("Calling the add method")
        return self.some_var + other_obj.some_var
obj1 = AnyClass(5)
obj2 = AnyClass(6)
obj1 + obj2

#python3 #python #python-programming #python-web-development #python-tutorials #python-top-story #python-tips #learn-python

Arvel  Parker

Arvel Parker


Basic Data Types in Python | Python Web Development For Beginners

At the end of 2019, Python is one of the fastest-growing programming languages. More than 10% of developers have opted for Python development.

In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.

Table of Contents  hide

I Mutable objects

II Immutable objects

III Built-in data types in Python

Mutable objects

The Size and declared value and its sequence of the object can able to be modified called mutable objects.

Mutable Data Types are list, dict, set, byte array

Immutable objects

The Size and declared value and its sequence of the object can able to be modified.

Immutable data types are int, float, complex, String, tuples, bytes, and frozen sets.

id() and type() is used to know the Identity and data type of the object







Built-in data types in Python

a**=str(“Hello python world”)****#str**














Numbers (int,Float,Complex)

Numbers are stored in numeric Types. when a number is assigned to a variable, Python creates Number objects.

#signed interger




Python supports 3 types of numeric data.

int (signed integers like 20, 2, 225, etc.)

float (float is used to store floating-point numbers like 9.8, 3.1444, 89.52, etc.)

complex (complex numbers like 8.94j, 4.0 + 7.3j, etc.)

A complex number contains an ordered pair, i.e., a + ib where a and b denote the real and imaginary parts respectively).


The string can be represented as the sequence of characters in the quotation marks. In python, to define strings we can use single, double, or triple quotes.

# String Handling

‘Hello Python’

#single (') Quoted String

“Hello Python”

# Double (") Quoted String

“”“Hello Python”“”

‘’‘Hello Python’‘’

# triple (‘’') (“”") Quoted String

In python, string handling is a straightforward task, and python provides various built-in functions and operators for representing strings.

The operator “+” is used to concatenate strings and “*” is used to repeat the string.


output**:****‘Hello python’**

"python "*****2

'Output : Python python ’

#python web development #data types in python #list of all python data types #python data types #python datatypes #python types #python variable type