Creating Apps & Python

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)


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### 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)

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### 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>

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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>

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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

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What exactly is artificial intelligence? 
What are the four types of AI? 
How does artificial intelligence differ from machine learning? 
And what are the different types of ML?

In this White Paper, we will answer all of these questions and more about the topic.

Find out how you can use AI to improve and automate (engineering) processes within your organization and learn how to build a machine learning application yourself as we guide you through the following chapters:

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

Find out how you can use Artificial Intelligence and Machine Learning to automate engineering processes!

Download the White Paper and get INSPIRED

Artificial Intelligence in 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>

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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>

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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! 



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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

VIKTOR updates

## Run apps on new versions of Python!

It is now possible to run your published apps on Python versions 3.8, 3.9, and 3.10. Use the config file to specify your preferred Python version to benefit from all the improvements in functionality and performance that these versions offer!

Visit the [community topic](https://community.viktor.ai/t/support-for-python-3-7-3-10/546/3?utm_campaign=Newsletter+developers&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz--mrBIZAoTC_p-HmPbbHDPZFd6NqYerI0h2mlVq54bIZeRk2Z1f3hOV7Fbmh2GnnCwpi0OW) if you have any questions about this feature.

</br>

![python.png](https://cms.viktor.ai/uploads/python_bcc57b8e1d.png)

</br>

## Easier unit testing for views, file uploads, workers, and more

Testing your code has never been easier! We have added decorators that you can use to mock views, file uploads, workers, and more in VIKTOR - no more need to write these mocks yourself. 

More information about [mocking](https://docs.viktor.ai/docs/create-apps/development-tools-and-tips/automated-testing?_highlight=unit&_highlight=testing#mocking) can be found in the documentation.

</br>

![Newsletter_Aug_unittesting](https://cms.viktor.ai/uploads/Newsletter_Aug_unittesting_e0d963e059.png)

</br>

## Improved search functionality in the docs

In the developer survey, many of you told us you would like to see the search functionality improved in the documentation. So, we started working on it! The new search bar is now up and running. In the meantime, we will continue to improve the documentation further. 

Please visit the [documentation](https://docs.viktor.ai/docs/welcome/?utm_campaign=Newsletter%20developers&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz--mrBIZAoTC_p-HmPbbHDPZFd6NqYerI0h2mlVq54bIZeRk2Z1f3hOV7Fbmh2GnnCwpi0OW) to check out the new search bar and let us know what you think of it in the [community forum](https://community.viktor.ai/?utm_campaign=Newsletter%20developers&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz--mrBIZAoTC_p-HmPbbHDPZFd6NqYerI0h2mlVq54bIZeRk2Z1f3hOV7Fbmh2GnnCwpi0OW)!

</br>

![Newsletter_Aug_searchbar](https://cms.viktor.ai/uploads/Newsletter_Aug_searchbar_0a470ed0bf.png)

</br>

## Coming soon...

### Self-servicing of VIKTOR development accounts

Soon, admins will be able to assign developer rights and create a development workspace for new users when adding them to an environment. Existing users can also be given developer rights by going to users > edit user and ticking the 'developer' box!

</br>

![Newsletter_Aug_selfservicing_dev_acc](https://cms.viktor.ai/uploads/Newsletter_Aug_selfservicing_dev_acc_d50474d0fe.png)

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## Other changes

### SDK

- SDK v13.3.0 has been released

</br>

### PLATFORM

- Bugfix - Development Workspace was not deleted after developer was removed
- Bugfix - TableField scrollbar was overlapping with number cell text
- Bugfix - Create workspace button now only shown to environment administrators
- Bugfix - Privileged API requests were not working on Development Workspaces
- Bugfix - Assignment filter on workers page to work properly work with Development Workspaces
- Bugfix - Granting adminstrator access to a user would revoke their Development Workspace access, showing it to the other Admins
- Simplified workspaces search bar text to 'Search'
 
</br>

### CLI

- Updated `configure` command to only ask for Python path if isolation mode is venv 
- Added support to publish with Python version defined in viktor.config.toml 
- Fixed bug where `configure` command fails to prompt for Python path if Python not in PATH 
- Added support for simple app type 

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### 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

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How to create a succesful that people will adopt

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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!

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. 

This guide will provide actionable insights that are useful for anyone involved in creating custom software tools. For example:

- 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 interesting in the process of creating awesome software tools

Learn the process to build apps that provide real value to your work and that other people will definitely want to use as well.

Build successful applications

Learn how to build apps that provide real value to your work and that other people will definitely want to use as well.

Building Successful Applications

Support for running and publishing apps on Python 3.8, 3.9, and 3.10, easier unit testing by using mocks, and an improved search bar for the documentation!

VIKTOR platform updates | August 2022

What's new in VIKTOR (August 2022)

## Interactive debugging is here!

One of the features you have been requesting is here: interactive debugging! We now support debugging in PyCharm and Visual Studio Code. To automatically install the `viktor_debugging` module, all you have to do is reinstall your app and configure your IDE by following the steps described in the [documentation](https://docs.viktor.ai/docs/create-apps/development-tools-and-tips/interactive-debugging?_highlight=debug). After that, you can go on a bug hunt by setting breakpoints, stepping through your code, or by reading the value of variables.


</br>

## Create new workspaces whenever you want!

We have taken another step towards making the VIKTOR platform completely self-serviceable: where previously you had to contact us to create a new workspace, admins can now do this themselves. This means you can now create new projects and tools as you please, giving admins more control over the environment and making the process communication free! 

You can create a new workspace by pressing the blue button in the upright corner. 

**Please note:** Only admins are able to see the blue button and create new workspaces.

![MicrosoftTeams-image (12).png](https://cms.viktor.ai/uploads/Microsoft_Teams_image_12_af48b27d55.png)


</br>

## Build and use apps in a centralized environment

Some time ago, we introduced workspaces to browse and use all your company's VIKTOR apps from a centralized place. Now, to further improve user- and developer experience, we are also migrating your personal development environment into the environment of your company. The result: a single place to build, browse, and use all your VIKTOR apps! 

This means that, from now on, you can find your development workspace on the same URL as your Company Environment.

**Please note:** If your development account is on cloud.viktor.ai you have already been migrated. 

![MicrosoftTeams-image (13).png](https://cms.viktor.ai/uploads/Microsoft_Teams_image_13_b9c5193405.png)


</br>

## Coming soon...

### New ways to interact with geometries

Following last update’s extended map view interaction, we are now working on making it easier and more intuitive to interact with geometries as well! Soon, we will be extending the possibilities in which users can select and move geometries by clicking, dragging, and dropping them directly in the view.  

### Publish apps on the latest Python version

Right now, all published VIKTOR apps run on Python 3.7, but soon it will be possible to use newer Python versions (3.8, 3.9, 3.10) as well! All you have to do is specify the Python version you want to use in the config file. After that, you will be able to benefit from the improvements in functionality and performance the newer versions of the language have to offer. 

</br>

## Other changes

### PLATFORM

- Allowed listing of all storage keys, 'prefix' and 'key' parameters are optional 

- Fixed regression on file entity upload with empty group 

- Added support for separate URLs on each user profile settings tab for better referencing 

- Added a button in the editor to clear unsaved changes in the input fields 

- Added search functionality on the workspaces page 

- Made the filtered files tab visible when the file manager is opened through the file field 

- Updated the environment user search to be case-insensitive 

- Fixed issue with the input not being cleared upon selection on the modify user groups dialog 

- Fixed the assignment type filtering on the workers page 

- Corrected the link in the new developer token dialog to refer to the correct docs page 

- Improved platform transactional emails (e.g. account creation) 

- Fixed issue with copying an entity after a rename 


### CLI

- Added –path flag to 'test' command to run specific unit tests 

- Removed version check on 'upgrade' command 

- Added Python path to configuration file to prevent ‘Python not added to path' errors 

- 'check-system' now only checks virtual environment-related checks by default, use '--docker' flag to perform docker-related checks 

- The CLI now uses environment backend instead of central VIKTOR backend (required for moving developers to company environments) 

- Fixed broken links in demo app 

- Retry added to 'clear' command 


### WORKER

- Terms and conditions in installer updated to English 



Blog_newsletter_july2022

VIKTOR platform updates | July 2022

What's new in VIKTOR (July 2022)

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



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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

The adoption of parametric design grows exponentially thanks to friendly programming languages like Python or visual programming packages like Dynamo or Grasshopper.
 
Our perception is changing. We no longer see parametric design as a model to solve specific technical problems, but rather as a new way of working, in which stakeholders collaborate to find the best solution for a project.

Learn about the latest trends and the future of parametric design in this white paper

Collaborative parametric design models allow you to work together for better solutions

Download the white paper and get INSPIRED

Did you know parametric design models are no longer just used to solve specific technical problems, but rather as a new way of working? Read here how stakeholders use parametric design to collaborate and find the best solutions for their projects!

The Future of Parametric Design is COLLABORATION

## Transfer data between Dynamo and VIKTOR

With the new VIKTOR Dynamo nodes, you can easily transfer information between your VIKTOR app and Dynamo model. This way, you can automatically update your Dynamo models using information contained in VIKTOR, and the other way around. This gives you all the freedom of working directly in Dynamo and makes that everyone in your team always works with the correct version of the data stored in VIKTOR.

Visit [the documentation](https://docs.viktor.ai/docs/create-apps/software-integrations/dynamo-nodes?_highlight=dynamo&_highlight=librar) to learn more about the VIKTOR Dynamo library.

![dynamo-viktor-select-entity.png](https://cms.viktor.ai/uploads/dynamo_viktor_select_entity_f7d1950280.png)

</br>

## Select objects in your MapView

Map views are more interactive than ever! End users are now able to select objects (points, lines, areas, etc.) directly from the map, which can be used as input for calculations.

Developers can use `MapSelectInteraction` to create a button that allows users to start interacting with the map. This means that, for example, you no longer have to use drop-downs to let users select a MapPoint

For more info on the extended MapView interaction, visit [the documentation](https://docs.viktor.ai/docs/create-apps/results-and-visualizations/map?_highlight=mapview#mapview).

![Blog_newsletter_June_MapView_interaction](https://cms.viktor.ai/uploads/Blog_newsletter_June_Map_View_interaction_01dc3423dc.png)

</br>

## Easier app development without manifest

With the latest platform update, the manifest can now be completely removed. We decided to deprecate the manifest because this:

- makes it easier for (new) developers to start development
- creates more consistency regarding where to define entity type information.

Originally, you had to define all entity types of an app and their relationships in the manifest. We noticed that some users found this difficult. Therefore, we have gradually been working on moving all information from the manifest to the corresponding Controllers. With this platform update, the manifest can be completely removed if you prefer so!

More details on the removal of the manifest can be found in [the documentation](https://docs.viktor.ai/sdk/upgrades?_highlight=manifest#U77).

</br>

## Coming soon...

### Single developer environments

Some time ago, we introduced Workspaces as a centralized location for all your VIKTOR apps. To further improve user- and developer experience, we are now also planning on migrating your personal development environment into the environment of your organization. The result: A single place where you can develop and use apps!

### Introducing 'simple' apps

Would you like to write less code when you want to create a very simple app? We have good news! Soon, the simple app, consisting of a single entity type, will be released. With the simple app, developers only need to define a single Controller and Parametrization, and we will take care of the rest. With a simplified user interface, not only developers but also end users will benefit from this update!


</br>

## Other changes

### SDK

- SDK version 13.2.0 is now available

### PLATFORM

- Added a dropdown filter for the user type in the users admin page
- Added dropdown filters for integration type and assignment to the workers page
- Updated the activity and dashboard pages to display previous entity name for rename operations
- Added the platform version to the environment sidebar
- We now allow file extensions to include capital letters in the file field
- Fixed page jumping on the environment activity on environments with many workspaces
- Updated the file upload queue to track files and update statuses more reliably
- Improved listing entities API performance
- Fixed issue with restriction on using the same user group name in multiple workspaces
- Added a button to easily hide or show all workspaces in the environment activity
- Renamed create new object to create in entity view
- Improved workspaces page loading time

### CLI

- Allow `create-app` command to create app directly within current working directory 

### WORKER

- Improved worker management by including name of integration in version info

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Read all about the latest VIKTOR platform updates.

VIKTOR platform updates | June 2022

What's new in VIKTOR? (June 2022)

## Easier debugging in production

When using your apps, users sometimes get a “something went wrong” message. However, it’s not always 100% clear what went wrong and how it can be solved. That is why from now on, users can report errors whenever a problem occurs. Consequently, a report including all relevant information (e.g. error stack, used parameters) is sent to the developer. This not only makes it easier to debug your apps in production but also ensures users that the issue will be worked on!

![Blog_newsletter_september_report_problem](https://cms.viktor.ai/uploads/Blog_newsletter_september_report_problem_edcdf1228c.png)

*The new error message, including 'report problem' option'* 

</br>

![Blog_newsletter_september_report_problem_information](https://cms.viktor.ai/uploads/Blog_newsletter_september_report_problem_information_3224ae3002.png)

*Example of a detailed overview of a reported problem*

</br>

## Add and remove developer accounts yourself

For some time now, we have been working on gradually giving admins more control over their environments. Previously, admins could already create workspaces and add users to their environment. Now, it’s also possible to add and remove developers and specify publishing rights!

This makes starting with development much quicker for new users after they’ve been added to an environment and assigned developer rights, and takes away any security risks in case of developer offboarding, as access and publishing rights can either be granted or revoked immediately.

![Blog_newsletter_september_developer_access](https://cms.viktor.ai/uploads/Blog_newsletter_september_developer_access_2f3fae6730.png)

</br>

## Coming soon...

### Input validation of steps

Soon, you can stop users from going to the next step in an application whenever they’ve entered invalid input. Additionally, you can annotate which fields are invalid to optimally guide users towards a solution and prevent any problems caused earlier on from occurring in later steps. Annotation of invalid fields can also be applied to views, leading to better guidance in case a view can not be calculated.

</br>

## Other changes

### SDK

- SDK v13.3.1 and v13.4.0 are now available


### PLATFORM

- Added the platform version information on all screens 
- Added the ability to select and deselect all options in a MultiFileField 
- Bugfix – data view items were not expanding when children were dynamically added to them 
- Bugfix – Some texts were not properly shown in the simple app type dashboard and notifications 
- Bugfix – Scrollbar wasn't reset to top on page changes in Step and Page editor 


### CLI 

- Added support for multiple accounts in configuration file 
- Updated demo app to refer to updated documentation structure 
- Improved descriptions of configure command 
- Fixed failing tests in demo template app 
- Fixed failing configure command if configuration file does not yet exist 
- Fixed configure command suggesting invalid Python installation path 

Blog_newsletter_September

Easier debugging in production and the possibility for admins to add and remove developer accounts from their environments.

VIKTOR Blog

What's new in VIKTOR (September 2022)