Creating Apps & Python

Many thanks everyone who joined us for this edition of the Python & Pizza night at our office in Rotterdam. We had a blast eating pizza and developing apps with you, and we loved seeing your amazing work! 

![Python & Pizza header optie 1.jpg](https://cms.viktor.ai/uploads/Python_and_Pizza_header_optie_1_2c83eac8d3.jpg)


We loved having so many developers from different companies (Boskalis, Dura Vermeer, Heijmans, Mocean Offshore, Omexom, Port of Rotterdam, Royal HaskoningDHV, RPS, Smit) and with different levels of experiencing developing (VIKTOR) web apps together. Whether you just finished the tutorial, build your first app, or where there to continue working on your current project, it was great seeing such enthusiasm. 
It was especially remarkable that the guys from [MO4](https://mo4.online/) managed to onboard themselves and build a working application to place wind turbines on a map in just 3 hours! 

![Python & Pizza wind turbine 2.jpg](https://cms.viktor.ai/uploads/Python_and_Pizza_wind_turbine_2_d4bd7d742e.jpg)


As you can see from the pictures, they were even so nice to give us a presentation of their application at the end of the evening. This was followed by a short sneak peek of upcoming platform updates by our Product Owner Stijn. 

We are already looking forward to a next edition! Will we see you there as well? 


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Looking back at a successful night developing web apps with Python and VIKTOR

Python & Pizzas: Looking back at a successful night developing web apps

### 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!

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

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)

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

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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>

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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

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

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

WP_white

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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

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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

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

</br>

## Create apps with only an editor

We have created a new app type called *editor* that allows you to easily create applications with only one page! Implementing the *editor* app type will be the same as for the recently introduced *[simple](https://docs.viktor.ai/docs/create-apps/app-types/#simple-type-app)* app type. However, with the *editor* app type users will directly enter the editor after opening a workspace, making it easier to navigate. This is especially useful if you don't require any data saving, for example when you are creating a dashboard or calculator app. 
 
Find more information about the different app types in the [documentation](https://docs.viktor.ai/docs/create-apps/app-types/). 

<div style="padding:49.84% 0 0 0;position:relative;"><iframe src="https://player.vimeo.com/video/774246715?h=51e3072c9c&badge=0&autopause=0&player_id=0&app_id=58479/embed" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen frameborder="0" style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe></div>

</br>

## Dedicated page for apps 

We have added a new page to the environment where developers and admins can view, create, and update all their (organization’s) apps. When opening an app from here, you will find information such as a description of the app, its maintainers, and details about the most recent and previously published versions (date, developer). The new Apps page gives us a lot of opportunities to add additional features, such as fine-grained control over the maintainers of apps. Keep on reading in the next highlight for more details. 
 
For more information about the Apps page, please visit the [documentation](https://docs.viktor.ai/docs/manage-apps/workspaces_and_apps/#apps-in-the-interface).

![Blog_newsletter_nov22 Dedicated page for apps](https://cms.viktor.ai/uploads/Blog_newsletter_nov22_Dedicated_page_for_apps_c5fd3c374f.png)


</br>

### Apps vs. Workspaces

The difference between an app and a workspace can be difficult. To make sure all is clear, we will quickly explain both concepts. 

A normal user mainly interacts with workspaces: this is where calculations can be made. Developers and admins however also interact with apps. Apps contain all a programmer's logic and code: how the app is structured, how input is organized, what the output looks like, and what calculations should be performed. In short, a workspace is an app plus data (from users and calculations). 

You can find more information on the apps versus workspaces [here](https://docs.viktor.ai/docs/manage-apps/workspaces_and_apps/#workspace-vs-app). 


</br>

## Assign developers to apps

You can now assign maintainers to your apps. Maintainers are the only developers that have the rights to publish new versions of an app. They also receive error reports on workspaces using this app. This way, access to sensitive app logic can be limited.  

![Blog_newsletter_nov22_maintainers](https://cms.viktor.ai/uploads/Blog_newsletter_nov22_maintainers_9a32c3234a.png)

</br>

## Other changes


**SDK**

- SDK version 13.6.0 is now available


**PLATFORM**

- Fixed issue with editor locking by the same developer 
- Changed icons in entity tree to be more intuitive 
- Fix that sometimes the wrong browser language is selected in the interface 
- Fix null value of generic entity field not present in initial revision 
- Fix inactive app maintainers got error reports for apps 
- VIKTOR_APP_SECRET is now also evaluated during the installation of packages 
- Improved platform stability 


**WORKER**

- Worker version 5.3.0 is now available
- Support for IDEA RCS v22


**CLI**

- CLI version 0.25.0 is now available
- Better error messages when using CLI in CI/CD pipelines
- Support for editor app type



VIKTOR Blog

What's new in VIKTOR (November 2022)