Creating Apps & Python

VIKTOR updates

## Faster apps!

Dear developers, we've heard your feedback and are happy to let you know that we have improved the speed of the platform! This goes for both the navigation, as well as in the editor itself. Maybe you already noticed this, for example when loading your Workspace's users page. 

Please let us know if there is anything else you would like to see improved by leaving a message in the [community forum](https://community.viktor.ai/?utm_campaign=Newsletter%20developers&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-8PVlqkiKlNJFc1hzgFQMn7BTQbgS3EaoOPtQg2qMh-Q_iY13vLrsXhRA1LhdHc874b-pzm).

</br>


[![CTA_Join the community](https://cms.viktor.ai/uploads/CTA_Join_the_community_22d97b332e.svg)](https://community.viktor.ai/)

## Quick and easy visualization of lines

It is now possible to visualize 2D lines, arcs, and polylines in the (3D) GeometryView. This makes rendering a line a lot quicker and easier as you no longer have to create a 3D element with a very thin thickness to do so! 

Below, you can see the Python code needed to visualize a line, along with an example of how this looks in the editor. 

![python code line.png](https://cms.viktor.ai/uploads/python_code_line_0c515ac939.png)

</br>

![Blog_newsletter_Oct_2022](https://cms.viktor.ai/uploads/Blog_newsletter_Oct_2022_4e999a72db.png)


</br>

## Coming soon...

### The new 'editor' app type

Because some apps only require a single editor, we will soon introduce the 'editor' app type! This app type has no folder structure, meaning end-users immediately go to the editor after entering a workspace. This way, you can create dashboards or simple calculators in a more user-friendly manner!

![editor only app type.png](https://cms.viktor.ai/uploads/editor_only_app_type_3c8148f906.png)

</br>

### New 'Apps' page

Previously, you could already create your own Workspaces. Soon, it will also be possible to create and manage Apps! We are currently building a dedicated 'Apps' page where you can view all your organization's Apps and adjust your own. Additionally, admins will be able to assign more fine-grained publishing rights to developers to determine who is working on which App.

![Details Page.png](https://cms.viktor.ai/uploads/Details_Page_33e6495c30.png)

</br>


## Other changes

**SDK**

- SDK version v13.5.0 is now available
- Various classes now accept or can return a File object 
- Mock functions were added for all external analysis classes to facilitate easier testing

**WORKER**

- Worker v5.2.0 is now available
- SCIA installer has been simplified, major version is now retrieved from program directory
- More descriptive error message is returned when trying to parse non-existing files
- Excel worker now correctly evaluates automatically updating cells when running macros
- Added the possibility to add a description when creating a worker in the Administrator menu

**CLI**

- CLI v0.24.1 is available
- Improved auto-reload on Windows
- Added `--use-filesystem` flag to use the filesystem instead of git to gather files during publication of an app
- Improved structure of help by grouping commands
- Fixed bug where configure retrieved the wrong Python path 

**PLATFORM**

- Fixed wrong name used in Workspace invite email 
- Fixed incorrectly sending Developer Upgrade email to non-developers
- Fixed issue with updating the details of a user on SSO environments
- Fixed wrong translation in user creation modal
- Simplified Workspaces search bar text to 'Search'
- Fixed issue where OutputField doesn't take up the full width
- Fixed issue where the developer email notifications did not show correct setting







Blog_newsletter_October_2022

Discover the latest platform updates. Join our community and start building now! 

VIKTOR platform updates | October 2022

What's new in VIKTOR (October 2022)

### Content: 

1. An introduction to developing successful applications
2. Designing and planning your application
3. How to start coding
4. Gathering and processing feedback
5. Increasing the adoption of your app



How to create successful application that ensure adoption

medium_White Papers & guides - Building succesful applications.jpg

small_White Papers & guides - Building succesful applications.jpg

thumbnail_White Papers & guides - Building succesful applications.jpg

WP_pink

How to create a succesful that people will adopt

large_White Papers & Guides - Building Succesful applications

medium_White Papers & Guides - Building Succesful applications

small_White Papers & Guides - Building Succesful applications

thumbnail_White Papers & Guides - Building Succesful applications

WP_pink_2

Do you want to build a successful application? This guide is here to help you make apps that provide real value to your work and that other people will definitely want to use too!

This guide will provide actionable insights that are useful for anyone involved in creating custom software tools, such as:

- **People developing the tools** (developers, engineers, data scientists, etc..)
- **End-users**, who will use the tools when they are released
- **Project managers** who are interested in workflow automation/optimization
- **Program managers,** who oversee and stimulate digital transformation initiatives
- **Domain experts**, who provide their expertise to people creating software tools
- And **anyone else i in the process of creating awesome software tools**

Over the past years, we, the application developers at VIKTOR, have built many apps for many different industries. We have learned a lot from that and now want to share our experiences with you. 

The principles and processes we explain in this guide are the same as we apply daily. They have helped us build anything from simple apps designed to save time to advanced engineering applications that are used to automatically calculate underground tunnels, dikes, and other large structures. 





Learn how you (developer, engineer, end-user, domain expert, project manager, etc.) can contribute to the creation of apps that provide real value to your work.

Build successful applications

This guide provides actionable insights for anyone involved in the creation of apps, such as engineers, developers, end-users, project managers, and domain experts. Learn how you can contribute to creating apps that provide real value to your work!

Building Successful Applications

Artificial Intelligence

## What are distributed algorithms?

To enable communication between various, interconnected processors (computers) that do not have a shared memory, distributed algorithms were designed back in the 1960s. Distributed algorithms are a specific type of algorithm that simultaneously runs its different parts on interconnected computers to perform a task or solve a calculation. For the computer software to run properly, it is important that these computers are interconnected and able to communicate with each other. We call this a ‘distributed system’. 

Examples of areas in which distributed systems and algorithms are used are telecommunications (telephone, television), scientific computing (weather prediction), or [leader election](https://en.wikipedia.org/wiki/Leader_election) (the procedure of designating a single process as the organizer of a task distributed among several computers). 

## Distributed algorithms vs. regular algorithms

Distributed algorithms differ from ‘traditional’ algorithms regarding the number of processors that are used to complete a task. While a distributed algorithm runs on multiple processors, a traditional computer algorithm runs on just one. With distributed algorithms, each processor – at the same time – takes care of a different part of the ‘big’ algorithm, meaning they can draw on the 
capabilities of other computing devices and processors. 

![Distributed algorithm](https://cms.viktor.ai/uploads/Distributed_algorithm_56e7f1a576.svg)

</br>

## Why a distributed system? 

Distributed systems are beneficial in the way that many processors can be used to solve a single problem, and that they can be located anywhere. This provides scalability and an ensures for an improved performance compared to using individual processors. 

An example of a distributed system are the processors used to create NASA’s space picture of a black hole. This picture was not taken by just one telescope, but through a network of many telescopes responsible tasked to look at a certain point in space for a set number of seconds at a set time. 


![NASA black hole distributed system](https://cms.viktor.ai/uploads/NASA_black_hole_d328edf909.png)


</br>

### A simple distributed system

A simple example of a distributed system can be a ring network. Every processor (normally called node) in a ring network can only communicate with its direct neighbor. If a processor wants to communicate with any other processor, it must do this in X number of steps, in which X implicates the number of intermediate processors. 

![Distributed system](https://cms.viktor.ai/uploads/Distributed_system_2395a1e454.svg)

</br>

### Priority in distributed systems

When working with distributed systems, we want to achieve truly distributed solutions. Sometimes, however, a certain system might require priority over other systems. For example, to prioritize sending certain specific messages over others in message systems. With a truly distributed system, this priority is unknown. Luckily, with a selection algorithm we can overcome this issue. For example, the [Hirschberg-Sinclair algorithm](https://en.wikipedia.org/wiki/Hirschberg%E2%80%93Sinclair_algorithm) designed for leader election problems in ring networks.

### Leader election

Leader election is the procedure of designating a single process as the organizer of some task that is distributed among several computers. Before starting the task, all computers involved are either unaware of or unable to communicate with the leader of this task. By running the algorithm (election), each computer will recognize a specific computer as the task leader. Which computer this will be is decided based on a shared identity characteristic of all computers, such as a ranking in numbers. The computer with the ‘highest’ identity (in this case: the highest number) is ultimately chosen the leader. 


![Distributed system priority](https://cms.viktor.ai/uploads/Distributed_system_with_priority_69ed3f4c4b.svg)

</br>

## Distributed algorithms in a web app

In this application, the Hirschberg-Sinclair election algorithm is used in combination with a bidirectional ring network. This means that each processor in the ring can only communicate directly with its two neighbors. 

Each processor is assigned a random number, indicating priority (higher number equals higher priority). The algorithm then works in phases trying to figure out if it has the highest priority in its neighborhood. 

Every phase, all processors ask their neighbors whether they are the highest in priority, to which the neighbor returns a message either confirming or denying this. This way, all processors – except for one – gradually disappear from the network. Each processor that disappears, does no longer participate in the ranking, but still forwards the information from its neighbors. This process repeats itself for several rounds, which are also kept track of, so the network knows which processors should disappear and which should still be included.

<div style="padding:56.25% 0 0 0;position:relative;"><iframe src="https://player.vimeo.com/video/742652816?h=6d22fd2c59&&autoplay=1&loop=1&autopause=0amp;badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;" title="Distributed algorithms - 100 nodes"></iframe></div><script src="https://player.vimeo.com/api/player.js"></script>

</br>

There are a 6 states that the algorithm can be in every phase:

- **Idle**: The processors do nothing
- **Send out**: … or forwards other outgoing messages if it is not the original sender.
- **Send in**: The processor either responds to the outgoing message or forwards another ingoing message.
- **Receive out**: The processor is either asked to compare itself with the original sender or forward the message.
- **Receive in**: The processor is asked to forward and incoming message or if it is the original sender they will know if it is larger or smaller than their neighbors
- **Selected**: Processors that were larger than their neighbors stay, and others disappear from the network

<div style="padding:56.25% 0 0 0;position:relative;"><iframe src="https://player.vimeo.com/video/742654151?h=8b115f6cef&autoplay=1&loop=1&autopause=0&amp;badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;" title="Distributed algorithms - 100 nodes messages"></iframe></div><script src="https://player.vimeo.com/api/player.js"></script>

</br>

In the application, a local network is used – made with Pyro4. The network closely resembles a real distributed system, except for without the delay in time. Because it is an asynchronized algorithm, it is also possible to turn on a delay to resemble real time passing (i.e., a delay of 30 seconds to send information from a processor theoretically being in Australia to another oner one theoretically being in the Netherlands). 

## Try it yourself!

Are you interested in using this application for your own projects? You can now download the full Python code from our [GitHub repository](https://github.com/viktor-platform/sample-distributed-algorithms) or sign up for a [free VIKTOR account]( https://www.viktor.ai/start-building-apps) to start building!



Interactive visualization distributed algorithm

A web-based application showcasing an interactive distributed algorithm, implemented with nothing but Python

Interactive visualization of distributed algorithms with Python

1. An introduction to Artificial Intelligence
2. Artificial Intelligence vs Machine Learning
3. AI and ML in organizations
4. Building a Machine Learning application
5. About VIKTOR

artificial intelligence AI machine learning ML engineering construction

medium_A4 - White Paper COVERwp ai cover.png

small_A4 - White Paper COVERwp ai cover.png

thumbnail_A4 - White Paper COVERwp ai cover.png

WP_green

artificial intelligence machine learning engineering construction

large_artificial intelligence machine learning engineering construction

medium_artificial intelligence machine learning engineering construction

small_artificial intelligence machine learning engineering construction

thumbnail_artificial intelligence machine learning engineering construction

Blog_AI_and_Machine_learning_engineering_construction_1

Have you ever wondered how you can use AI to automate engineering processes? This white paper is for all engineers, managers, or directors that want to know more about applying AI-methods, such as machine learning, in their organization. 

Find out more about the different types of AI and how you can use them to improve and automate (engineering) processes within your organization. You will also see some examples of real AI apps used by organizations in the industry and learn what it takes to build a machine learning application yourself. 

Keep scrolling to download the white paper.

Find how you can use AI automate engineering processes in your organization.

Download the White Paper and get INSPIRED

Have you ever wondered how you can use AI to automate engineering processes? This white paper is for all engineers, managers, or directors that want to know more about applying AI-methods, such as machine learning, in their organization. 

Artificial Intelligence: Engineering and Construction

## The traveling salesman problem

The traveling salesman problem (TSP) is a popular [NP-hard problem](https://en.wikipedia.org/wiki/NP-hardness) that tries to answer the following question: “Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city exactly one time before returning to the city of origin?”. 

Being first introduced in 1930, the TSP has been an intensively studied optimization problem ever since. Even though the TSP is a computationally difficult problem, by now there are many exact algorithms known that can be used to solve instances with up to tens of thousands of cities completely. Even problems with millions of cities can be approximated with a small fraction of 1%.

Today, the TSP is applied in instances such as planning, logistics, and the manufacture of microchips. 

## TSP in a VIKTOR app

The goal of this application is to showcase some of the approaches to the TSP in the VIKTOR environment. While no new approach is developed in this application, the app gives you the opportunity to play around with different algorithms to try to get the best solution for a generated topology. 

### Two topologies

In the application, you can choose between two different topologies: 

1.	**Circle:** This topology chooses X number of points on a circle with a radius of Y based on your definition of X and Y. We have added this option so you can check whether the algorithm is able to determine the shortest possible route, which is always a circle.

<div style="padding:56.25% 0 0 0;position:relative;"><iframe src="https://player.vimeo.com/video/733567691?h=ae69e6b6a9&amp;badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;" title="Traveling salesman problem | Generic algorithm | Circle topology"></iframe></div><script src="https://player.vimeo.com/api/player.js"></script>

</br>

2.	**Random:** This topology has a grid of 0-1 and chooses pseudorandom X and Y locations. It is also possible to enter a Z seed to initialize the pseudorandom number generator. This allows you to recreate the same random topology, which you, for example, can use for different algorithms so you can compare them. 

<div style="padding:56.25% 0 0 0;position:relative;"><iframe src="https://player.vimeo.com/video/733569835?h=0e51409f2f&amp;badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;" title="Traveling salesman problem | Two-opt algorithm | Random topology"></iframe></div><script src="https://player.vimeo.com/api/player.js"></script>

</br>

For each topology that you generate, storage is produced. This is done by creating a hash with the coordinates of all the cities. In this storage, the shortest known distance over all your runs is saved as the topology high score. For each topology that is run, you can compare the determined distance with the high score.  

### Three algorithms

The application contains three different algorithms, some well-known, some lesser known, that have previously been used to solve the TSP. If you switch between the algorithms, you will see a different explanatory text appear that provides information on the specifics of the corresponding algorithm. Additionally, all the input variables contain a text box with a description matching the algorithms.

1.	**2-opt:** The first and easiest algorithm to be implemented in this app is the 2-opt local search algorithm. This algorithm detects if the route crosses over itself and, if it does, it tries to reorder itself so that it does not. The improvement threshold of this algorithm checks how much your route has improved. If it can no longer be improved, it quits trying. 

<div style="padding:56.25% 0 0 0;position:relative;"><iframe src="https://player.vimeo.com/video/733569411?h=e6c8a73680&amp;badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;" title="Traveling salesman problem | Two-opt algorithm | Circle topology"></iframe></div><script src="https://player.vimeo.com/api/player.js"></script>

</br>

2.	**Genetical algorithm:** The Genetical Algorithm (GA) is an evolutionary algorithm, based on Charles Darwin's theory of natural evolution. After initializing the population (which is defined as different possible routes), we select the fittest individuals and combine them with each other to get new, and hopefully better, solutions. We can also implement random mutations in the population. This is helpful as otherwise there is a chance that the solution will never improve because we keep having the same individuals when breeding.

<div style="padding:56.25% 0 0 0;position:relative;"><iframe src="https://player.vimeo.com/video/733568236?h=d9aca7601d&amp;badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;" title="Traveling salesman problem | Generic algorithm | Random topology"></iframe></div><script src="https://player.vimeo.com/api/player.js"></script>

 </br>

3.	**Self-organizing maps:** The self-organizing maps algorithm implements a neural network that is closely related to the topology we generate. The purpose of the technique is to represent the model with a lower number of dimensions while maintaining the relations of similarity of the nodes contained in it. The algorithm used in this app generates a circular array of neurons, behaving as an elastic ring. During the execution of the algorithm, the neurons are searching for cities closest to their neighborhood. The ring then warps around the cities. To ensure convergence, we can add a learning rate to the algorithm. The higher the rate, the more the neurons will move. However, we might want to make the algorithm less aggressive the longer it runs. Because of this, we can also add decay to the learning rate.

<div style="padding:56.25% 0 0 0;position:relative;"><iframe src="https://player.vimeo.com/video/733568419?h=6863c4ecdc&amp;badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;" title="Traveling salesman problem | Self-organizing maps"></iframe></div><script src="https://player.vimeo.com/api/player.js"></script>

</br>

Here, you can see a small part of the Python code for the self-organizing maps: 

```python
def self_organizing_maps(
    cities: np.ndarray, iterations: int, learning_rate: float = 0.8, popSize: int = 100, decay: float = 0.0003
) -> Union[list, list, list]:
    """Solve the TSP using a Self-Organizing Map."""
    problem = _read_tsp(cities)

    # Obtain the normalized set of cities (w/ coord in [0,1])
    cities = problem.copy()

    cities[["x", "y"]] = _normalize(cities[["x", "y"]])

    # The population size
    n = popSize

    # Generate an adequate network of neurons:
    network = _generate_network(n)

    routes = [_get_route(cities, network).values.tolist()]
    distances = [path_distance(problem[["x", "y"]].to_numpy())]

    for i in range(iterations):
        # Choose a random city
        city = cities.sample(1)[["x", "y"]].values
        winner_idx = _select_closest(network, city)
        # Generate a filter that applies changes to the winner's gaussian
        gaussian = _get_neighborhood(winner_idx, n // 10, network.shape[0])
        # Update the network's weights (closer to the city)
        network += gaussian[:, np.newaxis] * learning_rate * (city - network)
        # Decay the variables
        learning_rate = learning_rate * (1 - decay)
        n = n * (1 - decay)

        # Adding data for the frames
        route = _get_route(cities, network)
        problem = problem.reindex(route)
        distances.append(path_distance(problem[["x", "y"]].to_numpy()))
        route = route.values.tolist()
        routes.append(route)

    return routes, np.arange(0, iterations + 1, 1).tolist(), distances
```

## If you want to use the application...

You can now download the full Python code from our [GitHub repository](https://github.com/viktor-platform/sample-travelling-salesman) or sign up for a [free VIKTOR account](https://www.viktor.ai/start-building-apps) to use it in your own applications! 



Blog_traveling_salesman_problem

Use one of three algorithms for solving the traveling salesman problem in a free web-based application on the VIKTOR platform.

Traveling salesman problem: Three algorithms in a web application

Three algorithms to solve the traveling salesman problem in a web app

Parametric design

## What is SkyCiv?

If you are a structural or civil engineer, you might know SkyCiv. SkyCiv is a powerful cloud-based structural design software focusing on efficiency and innovation for the modern engineer. On top of that, it also has a fantastic API, supporting many modern programming languages, including python. Today, professionals in more than 160 countries use SkyCiv structural analysis and design software, and API technology to solve their projects. 

Visit the [SkyCiv](https://skyciv.com/) website for more information.


### What we like most about SkyCiv

With the same level of functionality as traditional structural analysis software, or maybe more, SkyCiv has many things we are enthusiastic about. However, we want to highlight these functionalities in particular:

- All calculations are done in the cloud, making it super easy to integrate SkyCiv into your web application. You don't need software running on your computer or company's servers, making company-wide licensing and distribution issues a thing of the past. We really liked that! 
- Having a Python-enabled API is great! It enables you to use the complete Python ecosystem and powerful libraries such as NumPy, Plotly, and Pandas for your calculations.
- The model you make using the SkyCiv API is also a Python object and not just a text input file, meaning you can interact directly with it, read, and change its properties, and more!  
- You can ask SkyCiv to render your model and results in HTML, making it possible to embed the completely interactive 3D model in your website or VIKTOR app. Enabling you, for example, to adjust the view, get information about the materials or sections, and calculate the distance between nodes inside your app. 
- SkyCiv has a database with all kinds of section and material properties, and much cooler; you can also get the wind and snow load based on a GPS location. 


## SkyCiv and VIKTOR: A powerful combination

You hear us being quite positive about SkyCiv. So, why should you want to use it in combination with VIKTOR?

Like any FEM software, SkyCiv is powerful but requires specialist knowledge to make the most of it. Many processes can be automated, but that means working with an elaborate Python script using the SkyCiv API. Will all your colleagues be able to? Maybe, but probably not.

If you are looking for a way to make custom tools people can pick up and use quickly, you need a user-friendly interface on top of SkyCiv. Yes, we know you can make a product configurator directly with SkyCiv, but for this, you need to be quite handy with HTML and JavaScript. Here is where VIKTOR comes in handy.

With VIKTOR, you can quickly create a complete web-based user interface using nothing but Python, and it is easy too! On top of that, VIKTOR allows you to automate a larger part of your workflow by integrating your SkyCiv model with all kinds of other software packages. And what about the hosting? VIKTOR takes care of that too.     
     
<div style="padding:56.25% 0 0 0;position:relative;"><iframe src="https://player.vimeo.com/video/729982382?h=305d479fc4&autoplay=1&loop=1&autopause=0&amp;badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;" title="SkyCiv &amp;lt; &amp;gt; VIKTOR"></iframe></div><script src="https://player.vimeo.com/api/player.js"></script>

*Select fullscreen for a better view of the application*

</br>

### The benefits of SkyCiv and VIKTOR

Integrating SkyCiv with VIKTOR brings out the best features of both software. Below, we have listed why we think SkyCiv and VIKTOR complement each other perfectly: 

**Accessibility:** VIKTOR has an intuitive user interface, which makes it easy for other people to interact with your app. This makes building your SkyCiv model in an automated way much more accessible for people without (lots of) coding experience.

For example, this is a VIKTOR interface:

![MicrosoftTeams-image (11).png](https://cms.viktor.ai/uploads/Microsoft_Teams_image_11_9b22b4f850.png)

</br>

And this is the code you need to build a SkyCiv model:

```python
def _build_model_from_parameters(num_floors,
            grid_num_width, grid_num_length, 
            nodes_per_plain, corner_positions, 
            neighbours, grid_size_length, 
            grid_size_width, add_braces
        ) -> skyciv.Model:
    """Builds a SkyCiv model with the chosen parameters"""
    model = skyciv.Model("metric")  # Initialise an empty model

    # Member ids:
    # 1: Column
    # 2: Beams
    # 3: Braces

    current_y = 0  # Would be the current height
    for current_floor in range(num_floors + 1):
        current_z = 0  # Would be the current "width"
        for _ in range(grid_num_width):
            current_x = 0  # Would be the current "length"
            for _ in range(grid_num_length):
                uid = int(
                    model.nodes.add(current_x, current_y, current_z)
                )  # Add the node to the model and record the id tag
                # If the node ID is bigger then the nodes per plain
                #   then there is a node below it
                if uid > nodes_per_plain:  
                    # Connect the node to the node below
                    model.members.add(uid - nodes_per_plain, uid, 1)  
                    # If the column is not the first in its row
                    if (uid - 1) % grid_num_length > 0:
                        # Connect beam in x direction  
                        model.members.add(uid, uid - 1, 2)  
                    # The id on its own floor
                    floor_position = (uid - 1) % nodes_per_plain
                    # If the column is not in the in the first row
                    if floor_position >= grid_num_length:  
                        # Connect beam in z direction
                        model.members.add(uid, uid - grid_num_length, 2)  
                    if add_braces:  # Enable braces on the model
                        # Check for corners
                        for c, n in zip(corner_positions, neighbours): 
                            if floor_position == c:  # This is a corner
                                for sn in n: # Check every neighbour of corner
                                    # Get the id of the neighbour
                                    nid = sn+current_floor*nodes_per_plain+1  
                                    model.members.add(
                                        nid, uid - nodes_per_plain, 3
                                    )# Brace from neighbour to column of corner
                                    model.members.add(
                                        nid - nodes_per_plain, uid, 3
                                    )# Brace from column of neighbour to corner
                else:  # These nodes are on the floor
                    # Add support to the floor node
                    model.supports.add(uid, "FFFFFF")  
                current_x += grid_size_length  # Increment on the x-axis
            current_z += grid_size_width  # Increment on the z-axis
        current_y += FLOOR_HEIGHT  # Increment on the y-axis
    return model
```

</br>

**Speed:** VIKTOR is like a box of digital building blocks you can assemble to create your own apps. With Python, you can use the full functionality of SkyCiv and VIKTOR, making it easy to build powerful and user-friendly applications quickly.

<div style="padding:56.25% 0 0 0;position:relative;"><iframe src="https://player.vimeo.com/video/729945922?h=557e8fb532&autoplay=1&loop=1&autopause=0&amp;badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;" title="SkyCiv &amp;amp; VIKTOR - Adding parameter"></iframe></div><script src="https://player.vimeo.com/api/player.js"></script>

*Adding a parameter*

</br>

<div style="padding:56.25% 0 0 0;position:relative;"><iframe src="https://player.vimeo.com/video/729945794?h=811668fa56&autoplay=1&loop=1&autopause=0&amp;badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;" title="SkyCiv &amp;amp; VIKTOR - Adding control"></iframe></div><script src="https://player.vimeo.com/api/player.js"></script>

*Adding a control*


</br>


**Integrate with other software:** VIKTOR makes it easy to use SkyCiv in combination with (multiple) different ready-to-use integrations with industry-leading software, Like Excel sheets, Geotechnical Software, 3D modeling software, and much more. You can also generate custom reports based on your own templates.

**Interactive visualizations:** You can embed a completely interactive SkyCiv model into your VIKTOR app. Select, for example, which parts of the design you would like to show and which parts you would like to hide. You can also see information about the materials used for each member, forces, and calculate the distance between nodes. On top of that, you can use VIKTOR Views to enrich your app with maps, interactive graphs, other 3D models, and more. 

For example, this is the web view in your VIKTOR app:

<div style="padding:56.3% 0 0 0;position:relative;"><iframe src="https://player.vimeo.com/video/731261594?h=23f0446916& autoplay=1&loop=1&autopause=0&amp;badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;" title="SkyCiv results and interactive renderer"></iframe></div><script src="https://player.vimeo.com/api/player.js"></script>

</br>

And this is the code used to create an HTML page for your VIKTOR web view:

```python
def get_html_render(model, mode: str = "model", results: str = None):
        """The SkyCiv render is written in javascript. We can use the 
        webview to use it. However the webview only uses a single
        html file. We therefor use the jinja utility to build the html 
        file.
        """

        # Build the html file
        url = (
            "https://api.skyciv.com/dist/v3/javascript/skyciv-renderer-dist-2.0.0.js" 
        ) # The url to the skyciv renderer
        renderer = get_renderer(url)  # Request the renderer
        context = {
            "renderer": renderer,
            "model": model.get(),
            "mode": mode,
            "results": results,
        }  # Create the context to use with jinja
        path = Path(__file__).parent.parent / "lib" # Template location
        # Open the html file to be used as jinja template 
        with open(path / "renderer.html.jinja", "rb") as template:
            # Build the html file using the template and context  
            filedata = render_jinja_template(template, context)  

        # The return type of the jinja utility is already an 
        # viktor.core.File so it can be directly used in WebResult
        return filedata  
```

</br>

**Easy parametrization:** It is super easy to create parametric structures with SkyCiv in VIKTOR. Simply connect the InputField, Sliders, and other features to your SkyCiv model, and see it change at the click of a button.  

<div style="padding:56.3% 0 0 0;position:relative;"><iframe src="https://player.vimeo.com/video/731261579?h=88632d1b91&autoplay=1&loop=1&autopause=0&amp;badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;" title="SkyCiv model in VIKTOR"></iframe></div><script src="https://player.vimeo.com/api/player.js"></script>

</br>

**Total flexibility:** You can create a parametric model in VIKTOR, but what happens if you want to add some specific details for a project? No worries, The SkyCiv API lets you save the models you send in their own cloud environment. So, if you want to edit details after designing your model, you can continue inside the SkyCiv editor.

<div style="padding:56.25% 0 0 0;position:relative;"><iframe src="https://player.vimeo.com/video/729946100?h=665a8c0a89&autoplay=1&loop=1&autopause=0&amp;badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;" title="SkyCiv &amp;amp; VIKTOR - Dashboard"></iframe></div><script src="https://player.vimeo.com/api/player.js"></script>

*The dashboard*

</br>


## Try SkyCiv in VIKTOR

In this video, you can see the SkyCiv API integrated with a VIKTOR web application. With only a handful of input parameters, a structural model is created and analyzed in no time.

<div style="padding:56.25% 0 0 0;position:relative;"><iframe src="https://player.vimeo.com/video/729982382?h=305d479fc4&autoplay=1&loop=1&autopause=0&amp;badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;" title="SkyCiv &amp;lt; &amp;gt; VIKTOR"></iframe></div><script src="https://player.vimeo.com/api/player.js"></script>

</br>

There are three steps to using the application:

1. **Design the building:** First, insert the input parameters. The input parameters for materials are aligned with the materials in the SkyCiv database. 

2. **Map view:** Second, go to the map view to select a point on the map that you can later use to calculate the snow load through the SkyCiv API.

3. **Analyze:** For this third step, you can choose for yourself which loads you use. There are two things you can do with the API request here: 1) Generate an analysis report from the model that you created that shows typical FEA results like nodal displacements, support reactions, and member forces. 2) View the deformations on the model inside the SkyCiv renderer. These deformations are based on the parameters you have set. This model is then also saved to your SkyCiv dashboard (see: 'Recent files'). 


## What we can conclude

So, what we can conclude is that SkyCiv and VIKTOR together is a powerful combination. Linking SkyCiv's advanced FEM software - which often requires specialist knowledge to use to its full potential - to the user-friendly and intuitive interface of VIKTOR, enables not only specialists but also other (structural) engineers to automate and optimize their workflows! 

With easy accessibility, design and calculation speed, the ability to integrate with other software, interactive visualizations, easy parametrization, and total design flexibility, we invite you to build your own SkyCiV - VIKTOR apps. Download the full code from our [GitHub repository](https://github.com/viktor-platform) and sign up for a [free VIKTOR account](https://www.viktor.ai/start-building-apps) to start building awesome apps!

Blog_skyciv_VIKTOR

Combine SkyCiv and VIKTOR to create powerful and user-friendly parametric design tools. 

Automate and optimize structural workflows with SkyCiv and VIKTOR

SkyCiv and VIKTOR to automate and optimize structural workflows

1. What is parametric design, and how is it evolving
2. Collaborative parametric design: working together for better results
3. How to make tools available to others
4. Active collaboration on parametric design tools
5. Parametric design in a billion-euro project
6. What collaborative parametric design means for you



large_Viktor White Paper The future of parametric design is_ COLLABORATION.jpg

medium_Viktor White Paper The future of parametric design is_ COLLABORATION.jpg

small_Viktor White Paper The future of parametric design is_ COLLABORATION.jpg

thumbnail_Viktor White Paper The future of parametric design is_ COLLABORATION.jpg

WP_white

large_parametric_steel_structure.png

medium_parametric_steel_structure.png

small_parametric_steel_structure.png

thumbnail_parametric_steel_structure.png

WP_white_2

Parametric design is a design approach that uses flexible models that can be adjusted and solved quickly. The goal is to see the effect of specific choices on your design by just changing the input variables. 

The benefits are:

- **Flexibility:** Parametric design allows you to react quickly to changes at a low cost. Always generate the best solution for your project no matter the time pressure.

- **Better decisions:** Automatically calculate thousands of different solutions, get deep insight, and mitigate risks. Make better decisions and provide more added value to clients.

- **Digital Assets:** Create models and data that are reusable in new projects. Improve them over time, building up digital capital

This white paper is for engineers, architects, project leaders, and innovation and business managers. You will learn how collaborative parametric design in the cloud helps organizations get better results with real study cases such as a billion-euro infrastructure project.
 
Additionally, we talk about how you can transform Python, Dynamo and Grasshopper scripts into user-friendly web apps and significantly impact your organization performance. 

Learn how collaborative parametric design models allow you to work together for better solutions.

Download the white paper and get INSPIRED

This white paper is for engineers, project leaders, and innovation and business managers that want to learn more about what collaborative parametric design can mean for them. Discover more about this new way of working in which stakeholders collaborate to find the best solution for a project.

The Future of Parametric Design is COLLABORATION

large_Blog_sample_app_data_science

medium_Blog_sample_app_data_science

small_Blog_sample_app_data_science

thumbnail_Blog_sample_app_data_science

Blog_sample_app_data_science

Build clear and interactive dashboards to display your data. More info on VIKTOR.ai.

Display your data on through interactive Dashboards with VIKTOR

Due to the continuously growing amount of data, it is becoming more and more important for organizations to find ways to quickly extract useful information from large datasets. There are lots of software packages, Python libraries, and tools that you can use for this (think: Matlab, Excel, Numpy, Plotly, Matplotlib, Pandas). If you manage to make this complex data more accessible, usable, and understandable, you can not only obtain a lot of valuable insights, but also enhance decision-making, anticipate errors, compare results, and do much more to improve your organization’s performance.

The hard part often lies not with the extraction of information, but with the ability to make this information useful by, for example, visualizing it in the form of clear, effective, and interactive graphs, plots, tables, and infographics. For this, a user-friendly Graphical User Interface (GUI) is unmissable. And that is where VIKTOR comes into play. 

Turning data into valuable information

VIKTOR provides a user-friendly user interface with a rich collection of Python digital building blocks that can be used to quickly create all kinds of interactive visualizations, using your preferred library (Matplotlib, Plotly). With a VIKTOR application, you can turn your data into understandable insights through an interactive and customizable dashboard.

![visualizing data with VIKTOR](https://cms.viktor.ai/uploads/visualizing_data_with_VIKTOR_613259694c.svg)

</br>

In this article, we discuss how you can turn your data into valuable information with VIKTOR by integrating different Python libraries. The application shown in this article is a VIKTOR app that consists of four different pages, all doing something different with data. 

![Top navigation data science app](https://cms.viktor.ai/uploads/Top_navigation_data_science_app_95cefbe701.png)

</br>

In the app, users can easily switch between the pages by clicking the corresponding tab in the top navigation:

- CSV to Matplotlib visualization
- Plotly 
- NumPy interpolation
- Pokémon with Pandas

![Matplotlib, Plotly, Pandas, Numpy, Python](https://cms.viktor.ai/uploads/Matplotlib_Plotly_Pandas_Numpy_Python_0ab138fe50.png)




Visualizing data with VIKTOR

In this first page of the application, it is possible to upload CSV files to create Matplotlib visualizations. Matplotlib is an open-source plotting library for Python that can be used to create static, animated, and interactive visualizations. 

In the application, users can download an example CSV file about Pokémons by pressing the dark grey button ‘Download CSV example’. You can find the Pokémon data [here](https://abichat.github.io/minesnancy-visu.html).

After uploading and selecting the desired CSV file, you can plot its data to a Matplotlib using the parameters ‘X axis’ and ‘Y axis’. After defining the parameters, VIKTOR automatically plots the data and visualizes it in the view on the right.

<div style="padding:50.63% 0 0 0;position:relative;"><iframe src="https://player.vimeo.com/video/728818379?h=0e43fd25cf?autoplay=1&loop=1&autopause=0"&amp;badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;" title="Visualize data with Matplotlib and VIKTOR"></iframe></div><script src="https://player.vimeo.com/api/player.js"></script>

</br>

Here, you can see how the data from the CSV file is visualized in the scatterplot in VIKTOR with a few lines of Python code:

```python
try:
	buffer = params.csv_page.file_link.file.open_binary()
	df = pd.read_csv(buffer)
	df.plot(kind='scatter', x=params.csv_page.options_x, y=params.csv_page.options_y)
except ValueError as err:
	raise UserException(err)

buffer.close()
```

CSV to Matplotlib visualization

With VIKTOR, it is super easy to use integrations with software like Plotly. Plotly is an open-source, browser-based data visualization library for Python. If you are looking for a way to build a clear and intuitive GUI for your data visualization, you can easily combine Plotly with VIKTOR in just 3 lines of Python code.

```python
@PlotlyView(''Results'', duration_gues=3)
def plotly_visualization(self, params, **kwargs):
	''''''Use the build-in gapminder dataset from plotly to create a plot. ''''''
	dataframe = px.data.gapminder()
	fig = px.scatter(dataframe, x=''gdpPercap'', y=''lifeExp'', animation_frame=''year'', animation_group=''country'', log_x=True, size_max=45, range_x=[100, 100000], range_y=25, 90])
	return PlotlyResult(fig.to_json())
```

![data science plotly python](https://cms.viktor.ai/uploads/data_science_plotly_python_199013058a.gif)

</br>

Plotly

This example shows how you can easily create interactive apps using NumPy and VIKTOR. 
NumPy is an open-source Python library that offers comprehensive mathematical functions, random number generators, linear algebra routines, and more. 

In this example, users can pick samples form a sin function and use NumPy to estimate an interpolation with given polynomial and check the error at a certain X value. In the application, you can use the slider to first define the number of samples. Then users can select ‘show interpolation’ and use another slider to define the polynomial. Finally, by using a third slider, users can check the error on certain locations. The error is shown on the data view on the right side of the application. 

<div style="padding:50.63% 0 0 0;position:relative;"><iframe src="https://player.vimeo.com/video/728819958?h=7c4110e808&amp;badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;" title="Visualize data with Numpy and VIKTOR"></iframe></div><script src="https://player.vimeo.com/api/player.js"></script>

</br>

Here you can see how NumPy is used to plot an interpolation with a given order polynomial in VIKTOR:

```python
# Interpolate over sinus curve
polyfit = np.polyfit(x_value, np.sin(x_value), params.numpy_interp.polynomial)
polyfit_function = np.poly1d(polyfit)
y_interpolated = polyfit_function(params.numpy_interp.x)
```


NumPy interpolation

This last example shows how easy it is to manipulate data using a combination of Pandas and VIKTOR. 

In this page, we use the same Pokémon database as in the first example (Matplotlib). Here, the Pandas open-source Python library is used to manipulate data by first removing all data-entries that are missing a value for its type. Then, VIKTOR automatically creates a correlation matrix that is used to plot a heatmap of the % of Pokémon that have two (or more) types. 

The input field at the left side of the applications allows users to define the types they want to compare. By clicking on ‘select all’, a full plot can be generated after pressing the update button. 

<div style="padding:50.63% 0 0 0;position:relative;"><iframe src="https://player.vimeo.com/video/728820038?h=c33c73ca6a&amp;badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;" title="Visualize data with Pandas and VIKTOR"></iframe></div><script src="https://player.vimeo.com/api/player.js"></script>

</br>

Here you can see how easy it is to read a CSV file with Pandas in VIKTOR:

```python
dataframe = pd.read_csv(Path(__file__).parent / 'datasets' / 'pokemon.csv').dropna()
```

Pokémon with Pandas

If these examples sparked your interest, there are two ways in which you can use them yourself:

1. By downloading the open-source code from the VIKTOR GitHub repository and integrating it with your own Python code. You can find the entire code [here](https://github.com/viktor-platform/sample-data-science). 
2. By requesting an account for the VIKTOR demo environment. The demo environment offers a collection of 'sample' applications like this that you can use to better understand VIKTOR or as inspiration for your own VIKTOR applications. You can request an account for the demo environment [here](https://www.viktor.ai/demo-environment).

Would you like to build VIKTOR applications instead? No problem! Request a free VIKTOR account [here](https://www.viktor.ai/start-building-apps). 

Open-source Python code

Data science adds value to a business on all kind of aspects. It is only important that an organization has the tools to use their data well. To maximize the value of your data, it is crucial that you are able to interpret the extracted information clearly. By visualizing your data by means of interactive graphs, tables, and dashboards, you can communicate your findings and make them usable and understandable to anyone.


VIKTOR is an application development platform that enables people to rapidly build their own applications with a Python Software Development Kit (SDK) with digital buildings blocks that can be used to create all kinds of interactive visualizations, using your preferred Python library. With a VIKTOR app, you can interpret data quick and easy by means of understandable insights, provided on an interactive and customizable dashboard which enables you to turn your knowledge into assets!


Conclusion

Visualize your data with Python and VIKTOR

Due to this wide range of options, setting all parameters using PLAXIS’ Graphical User Interface (GUI) can be a cumbersome task. Especially if you do not need most parameters for your specific purpose. Also, if you need to set up a model in the same fashion multiple times, this must be done manually, even though a computer could easily do this for you.

This is where VIKTOR comes into play. In this article, we will explain how you can achieve the best results with PLAXIS, while discussing the following:

- PLAXIS in a web app
- Automating in- and output
- Centralizing data
- Easy material parametrization
- Open-source Python code for a PLAXIS web app


Blog_sample_app_PLAXIS

Automate PLAXIS in- and output, centralize data, and easily parametrize materials with this open-source Python code by VIKTOR. 

PLAXIS in a web-based application

VIKTOR offers the possibility to integrate PLAXIS’ Python API with a web-based application that engineers can easily use to:

- Parametrically generate, update, and edit their PLAXIS models, 
- export results from PLAXIS easily to the Cloud, 
- and integrate PLAXIS models with other third-party software, such as D-Series.

Here, you can see PLAXIS integrated with such a web app on the VIKTOR platform:

<div style="padding:56.25% 0 0 0;position:relative;"><iframe src="https://player.vimeo.com/video/715587375?h=9017847dd2&amp;badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;" title="Sample app PLAXIS"></iframe></div><script src="https://player.vimeo.com/api/player.js"></script>

</br> 

In this video you can see how the materials that are used for a project are defined, as well as the layout of the soil, embankment and drains. Then, a 2D model of the embankment is generated and automatically analysed with PLAXIS.


PLAXIS in a web app

As mentioned, setting up the appropriate geometry and models of a PLAXIS analysis can be a lengthy process, especially as projects continue to grow. Additionally, modifying your calculation input must be done using the wide range of options that PLAXIS’ GUI offers. By automating the generation of models and integrating the analysis and visualization of results within a single application, a significant increase in efficiency can be achieved. For example, by reducing the number of options to a few necessary input parameters that can easily be re-used in other projects as well. 

In this video, you can see how a 2D embankment is analysed in a VIKTOR web app. In the app, you can adjust the necessary input parameters for the embankment (width, height, material), soil, and drains. By simply clicking the ‘update’ button in the down-right corner of the application, the results of the analysis are calculated in PLAXIS and returned to the VIKTOR app. From here on, you can see if any adjustments are needed and if so, run the analysis again at the push of a button. 

<div style="padding:56.25% 0 0 0;position:relative;"><iframe src="https://player.vimeo.com/video/715587462?h=878e4de30f&amp;badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;" title="Sample app PLAXIS - automation"></iframe></div><script src="https://player.vimeo.com/api/player.js"></script>
</br> 

The video also shows all the calculations that are happening in the background while the application is running the analysis. As you may know, the output files can have a substantial size. Therefore, only the relevant information is being taken out and sent back to the VIKTOR app. A clever tip is to store the output files on the server so they can always be inspected by a geotechnical engineer later. 


Automating in- and output

The advantage of integrating PLAXIS’ Python API with a VIKTOR web app does not end at the ability to automate processes and visualize results. Often, in a team of people working with PLAXIS, everyone has their own Excel sheet to define materials. Because these sheets are isolated, this can get confusing in case someone changes something in their Excel sheet and someone else does not in theirs. With a VIKTOR web app, everyone in a company can access a central database to define materials.

In the video, you can see how in the left menu underneath ‘materials’ a database application can be found in which all material parameters can be defined and edited. Whenever something is changed within the application, this change is automatically processed for everyone who works with the app. 

<div style="padding:57.08% 0 0 0;position:relative;"><iframe src="https://player.vimeo.com/video/715587998?h=2297ac11d1&amp;badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;" title="Sample app PLAXIS - material database"></iframe></div><script src="https://player.vimeo.com/api/player.js"></script>

</br>

Because VIKTOR applications are hosted in the Cloud, all data is stored centrally and can be accessed through the internet with the right credentials. VIKTOR apps automatically include user management and version control, enabling you to collaborate on projects with everyone you want. It is possible to assign roles and permissions to determine who can view and change parts of a project.


Centralizing data

Have you already automated your PLAXIS in- and output, but are you still looking for a way to easily define your materials? Look no further, as we have figured this out for you!

When you try to define materials yourself using the Python API, you may find that some inputs are not copied correctly. One way to copy properties is by placing the entire string from the PLAXIS command window into your Python application. However, because this string is very long and has no documented ending, it is difficult to properly parametrize your materials from within Python.

An alternative option is to instantiate the material and then select all the properties on the material. In this fashion, all the keywords will be updated correctly. However, you have to do this in a very specific order for it to work.

Defining this specific order is difficult and time-consuming. That is why we have done this for you instead! 

With the following function you can now properly set your materials:

```python
# Add soil materials. This must be done in a specific order to ensure the materials are set correctly
materials = {}  # Make a dict to be able to refer to the materials by name
for i, params_material in enumerate(params_materials):
    material_zip_list = []
    material_flattened_dict = get_deepest_dict(
        params_material
    )  # Get the deepest items from the material parameters dict
    for (
        material_key
    ) in ORDERED_MATERIAL_KEYS:  # Use a specific material key order such that the settings are set correctly
        if material_key in material_flattened_dict:
            material_zip_list.append((material_key, material_flattened_dict[material_key]))
    material = g_i.soilmat(material_zip_list[0])
    for material_zip in material_zip_list[1:]:
        material.setproperties(*material_zip)  # Set all the properties of the material in an iterative fashion
    materials.update({material_flattened_dict["MaterialName"]: material})
```

Throughout this article, we have shown multiple videos from a PLAXIS application on the VIKTOR platform. In our [GitHub repository](https://github.com/viktor-platform/sample-plaxis), you can find the entire open-source code of this app. Feel free to download the code and use it in your own Python code.

If you are still looking for a way to:

- Easily integrate software,
- automate and optimize processes,
- centrally store data,
- and quickly visualize results,
 
If you first want to see the PLAXIS sample application from this article in action, you can access the app (and many more) through the [demo environment] (https://www.viktor.ai/demo-environment). Continue reading for more information about the sample app.


Easy material parametrization

### Using the functionality

If you have been looking for a way to easily automate and optimize your PLAXIS analyses, we can help you with in two ways:

1. With VIKTOR, nearly-out-of-the-box. Here, the parametrization of materials, embankment, and drains, as well as an output of the visualization is taken care of for you. Do you want to try VIKTOR? Request a [free version](https://www.viktor.ai/start-building-apps).
2. Without VIKTOR, in that case you integrate with your own Python code and create a parametrization of materials, embankment, and drains yourself, as well as an output of the visualization. You can follow the instructions as described in the [readme](https://github.com/viktor-platform/sample-plaxis) in the VIKTOR GitHub repository, under the section: “Specific script use”. 

### Automating PLAXIS in 3 steps

In the video below, you can see how the functionality is being used in a VIKTOR application, just as with the videos shown before. 

<div style="padding:56.25% 0 0 0;position:relative;"><iframe src="https://player.vimeo.com/video/715587375?h=9017847dd2&amp;badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;" title="Sample app PLAXIS"></iframe></div><script src="https://player.vimeo.com/api/player.js"></script>

</br> 

As you can see, the process of defining a PLAXIS run and output extraction consists of 3 steps. 

1.	**Define your materials used**

![Blog_sample_app_PLAXIS_materials](https://cms.viktor.ai/uploads/Blog_sample_app_PLAXIS_materials_40f55fe121.png)

*Input in the material database*

</br>

2.	**Define the layout of the soil, embankment and the drains**

![soil, embankment, drains pLAXIS.png](https://cms.viktor.ai/uploads/soil_embankment_drains_p_LAXIS_acef92fbbe.png)

*Input (left) and corresponing output (right) of layout*

</br>

3.	**Click update in the PLAXIS analysis view and review your results!**

![results PLAXIS analysis.png](https://cms.viktor.ai/uploads/results_PLAXIS_analysis_b714fd4977.png)

*Results of the PLAXIS analysis in the application*

</br>

Use the [free version](https://www.viktor.ai/start-building-apps) or apply for a [demo account](https://www.viktor.ai/demo-environment) to try the functionality yourself!


Open-source Python code for a PLAXIS web app

Save time by automating PLAXIS in the Cloud

## The Complexity of the AEC Industry

The AEC industry is known for its complexity, involving various stakeholders, intricate building designs, and evolving project requirements. Coordinating different disciplines and managing modifications throughout the project lifecycle can be challenging, especially when it comes to aligning HVAC drawings with ongoing design changes from other disciplines (such as: architects, structural engineers, etc.).  

Solving the challenges of AEC work nature often boils down to enabling better coordination between teams. An improvement that often translates into valuable time and cost savings! In this article we will discuss one of AEC's main challenges and introduce a potential solution!  

## The Challenge of Aligning HVAC Drawings and Modifications in AEC 

During the design and construction phases, modifications to the building design are inevitable. However, incorporating these modifications into HVAC drawings can be time-consuming and prone to errors. Ensuring that HVAC systems are accurately represented in the drawings and aligning them with evolving project requirements pose significant challenges for AEC professionals. 

## HVAC-Specific Software Solutions 

To address the complexities of HVAC design in the AEC industry, several software solutions are available. These tools, such as AutoCAD MEP, Revit MEP, and DesignBuilder, provide specialized functionalities for HVAC design, simulation, and documentation. They help streamline the creation of HVAC drawings and facilitate coordination with other building systems. Despite the usefulness of various software tools for HVAC analysis and calculations, effectively coordinating with other specialists on a project remains a significant challenge. This aspect of the AEC industry underscores the necessity of adopting a holistic approach, where all specialists can collaborate and align their efforts as early as possible. 

## Necessity is the mother of invention: BIM LOD and Early-Stage Optimization 

Building Information Modeling [(BIM)](https://www.viktor.ai/blog/100/how-BIM-innovations-transform-AEC) is a collaborative process that enables the creation and management of digital representations and information of buildings. BIM Level of Development/ Detail (LOD) defines the level of detail and accuracy of the BIM model at different project stages. Early-stage optimization in BIM involves leveraging low LOD models and simulations to make informed decisions that enhance HVAC performance while considering cost and energy efficiency. Within low LOD it is feasible to have efficient [early-stage optimization](https://www.viktor.ai/blog/106/unlocking-early-stage-design-optimizations-with-topologic-and-bim). Especially, as it is still easy to solve HVAC problems by alternating layouts and deploying optimization plans without redoing a previously produced tedious workload.

![HVAC picture 1.png](https://cms.viktor.ai/uploads/HVAC_picture_1_34490cb4c0.png)

It is more feasible to fix problems and optimize solutions in the early phases of the project when it is in the design phase. On the other hand, fixing HVAC problems in the late stages of the building is extremely hard and expensive as physical installation had begun. Luckily enough, there is a new technology to enable HVAC engineers to optimize their solutions from the early stages, In addition, running efficient simulations of their systems as well as communicating easily with low LOD BIM models! 

## A New Promising Technology: Topologic by Prof. Wassim Jabi

Topologic, developed by Wassim Jabi, is an innovative software tool that offers advanced geometric and topological analysis capabilities. It allows AEC professionals, including HVAC engineers, to explore complex spatial relationships, optimize design layouts, and assess the impact of modifications more efficiently. By incorporating Topologic into their workflow, HVAC engineers can enhance coordination and improve the integration of HVAC systems within the low LOD models. Topologic automates HVAC simulation, calculation and layout of building geometries. As it understands the whole 3d mass of the BIM model it can quickly understand the shortest and most efficient path for the ducts while being accessible to technicians. 

![hv tiny.gif](https://cms.viktor.ai/uploads/hv_tiny_03a51eaa8d.gif)

*Topologic compartments and fire simulations*

Additionally, Topologic offers a Python binding that enhances its flexibility and integration capabilities. This allows HVAC engineers to leverage the power of Topologic in conjunction with other powerful software tools such as EnergyPlus and OpenStudio. EnergyPlus is a widely-used simulation engine for energy analysis and thermal performance evaluations in buildings, while OpenStudio is a platform that enables whole-building energy modeling and analysis. The integration of Topologic with EnergyPlus and OpenStudio opens up new possibilities for HVAC engineers to conduct more comprehensive simulations, analyses, optimize energy efficiency, and fine-tune HVAC system performance based on advanced geometric and topological insights provided by Topologic. 

![hv 33.gif](https://cms.viktor.ai/uploads/hv_33_aece7c93f3.gif)

*Building Energy Analysis with Topologic*

In addition, Topologic specializes in handling low Level of Development (LOD) models, particularly conceptual masses. This allows for the development of a generative design logic that can provide initial layouts of HVAC systems without disrupting the drawings of other specialists. What's more, the integration of Topologic with machine learning is a crucial advancement. By training it with diverse cases, Topologic can offer improved solutions and provide valuable assistance to engineers through its generative design capabilities.

![hv 4444444.gif](https://cms.viktor.ai/uploads/hv_4444444_dbb24b69a0.gif)

*Topological awareness of spatial and structural elements in 3d model based on wall function, dimensions, U and R-value*

To leverage the full potential of the promising technology of Topologic, the logical progression is to convert these code components into applications that enable engineers to efficiently carry out their required tasks. This entails developing an optimized workflow that relies on input from user-engineers, allowing for seamless execution of their tasks while saving the time of building the logic each time. Overall, apps are easier to operate and can host Topologic algorithms for multiple users simultaneously!

## Web Apps to assist engineers 

VIKTOR aims to unlock the full powers of engineering by automating boring tasks, saving time and effort for creative engineering, and offering the power of advanced algorithms and data-driven processes to enhance design efficiency! As a  cloud-based platform designed specifically for engineers in the architecture, engineering, and construction (AEC) industry. It empowers engineers to rapidly develop custom web apps using Python. In view of emerging engineering complexities like interoperability, VIKTOR apps integrate with all engineering software and offer a range of tools to streamline and optimize engineering workflows.  Additionally,  enabling knowledge sharing through cloud accessibility!

## VIKTOR: Web Apps for Topologic

VIKTOR integrates with Topologic and provides web applications specifically tailored for AEC professionals. With VIKTOR, HVAC engineers can leverage the power of Topologic through an intuitive interface, enabling them to simulate, analyse, and optimize HVAC designs more effectively. 

![HVAC 5 Resized.png](https://cms.viktor.ai/uploads/HVAC_5_Resized_a56cc74f0e.png)

### 1. Initial layout and estimations system generation

The combination of Topologic and VIKTOR offers enhanced capabilities for early-stage design optimization, automatic layout of HVAC systems, and collaboration among project stakeholders.

Within low LOD (Level of Development) models, engineers have the opportunity to create a VIKTOR app utilizing Topologic that automates the generation of HVAC system layouts and calculations across the entire 3D model. This approach proves more efficient compared to configuring individual floor plans separately, resulting in significant time savings for the design process.

### 2. Data and geometry-aware system generation

Furthermore, apart from generating layouts, a VIKTOR app integrating Topologic has the capability to interpret various spatial data elements, including compartments, the structural skeleton of the building, and the U and R values of building components. Additionally, it can incorporate HVAC calculations such as Load Calculations, Airflow Calculations, and Duct Sizing Calculations, among others. This comprehensive approach enables the generative process of the VIKTOR and Topologic app to effectively comprehend the interplay between different disciplines, facilitating the determination of optimal strategies for HVAC system generation.

### 3. Smart UI to assist decision-making

Additionally, being a cloud-based application, the user interface of the app is specifically designed to support end-user engineers in making well-informed decisions. Engineers can obtain real-time simulations and results based on their input, presented in visually engaging graphs that provide a clear visualization of data besides the 3D geometry. Moreover, the app offers the convenience of automating reporting processes and facilitating easy sharing of results with just a few clicks. Additionally, the underlying logic of the app is accessible to user-developer engineers, allowing them to further enhance and expand the app's functionality with additional features.

### 4. All disciplines in one platform

To summarize, the application facilitates the integration of inputs from various (Architectural, structural and HVAC) disciplines, and It employs advanced algorithms to analyze the inputs and generate solutions that avoid clashes between disciplines’ inputs. Moreover, the application is designed to dynamically adapt to new inputs from any team while being easily accessible by authorized professionals to make essential modifications to app logic! This empowers innovative engineers to explore new tools and continuously enhance the performance of their apps to meet evolving project requirements.

## Conclusion

In the rapidly evolving AEC industry, HVAC design and coordination are critical for achieving comfortable and energy-efficient buildings. The complexity of the industry and the challenges associated with aligning HVAC drawings and modifications necessitate innovative solutions. Topologic and its integration with VIKTOR hold immense promise in addressing these challenges, empowering HVAC engineers with advanced tools for optimized design, coordination, and collaboration throughout the AEC process. By embracing these technologies, the industry can enhance the delivery of high-performing HVAC systems that meet the evolving needs of modern buildings.


medium_shutterstock_1516710845 (1).jpg

small_shutterstock_1516710845 (1).jpg

thumbnail_shutterstock_1516710845 (1).jpg

shutterstock_1516710845 (1).jpg

The AEC industry heavily depends on HVAC systems to create comfortable and healthy indoor environments in buildings. These systems regulate temperature, humidity, air quality, and ventilation, which are vital for occupant well-being and energy efficiency. To aid HVAC professionals, it is essential to leverage cutting-edge technologies and develop efficient tools.

Digital Solutions in HVAC Design: Benefits of Topologic and Early-stage Optimization

- What is Building Information Modeling (BIM)? 
- BIM for Architecture, Engineering, and Construction (AEC)
- Trends in BIM
- So, what's next? BIM level 3?
- Solve your BIM challenge with VIKTOR!
- Conclusion


medium_A4 - White Paper COVER.jpg

small_A4 - White Paper COVER.jpg

thumbnail_A4 - White Paper COVER.jpg

A4 - White Paper COVER.jpg

small_pexels-pixabay-302769 (3).png

thumbnail_pexels-pixabay-302769 (3).png

pexels-pixabay-302769 (3).png

In the world of Architecture, Engineering, and Construction, Computer-Aided Design (CAD) has been a go-to technology for decades. While it was a significant breakthrough in its time, it has its limitations, and many industries now find it inefficient and lacking in collaboration. This has led to the rise of Building Information Modeling (BIM), which has brought many benefits to the table. Real-life projects such as hospitals, schools, and high-rise buildings have implemented BIM and have seen significant improvements in their project lifecycle across all AEC disciplines. In fact, industry leaders have also recognized the potential of BIM and have embraced it as a way to enhance their performance.

In this White Paper, we will explain VIKTOR's vision in BIM: Efficiently pioneering today's & next-gen BIM cloud-based Automation! We will provide information on the meaning, benefits, challenges, and future of BIM!

Efficiently pioneering today's & next-gen BIM cloud-based Automation! We will provide information on the meaning, benefits, challenges, and future of BIM!

VIKTOR Blog