Creating Apps & Python

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 try SkyCiv in VIKTOR for free in the [demo environment](https://www.viktor.ai/demo-environment). Or do you want 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!

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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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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

VIKTOR updates

## 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/guides/integrations/dynamo-nodes/) 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/guides/topics/views/interaction).

![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#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)

### 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

Artificial Intelligence

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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

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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artificial intelligence machine learning 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

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

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

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


Blog_sample_app_PLAXIS

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

PLAXIS in a web-based application

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

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

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

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

</br> 

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


PLAXIS in a web app

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

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

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

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


Automating in- and output

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

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

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

</br>

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


Centralizing data

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

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

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

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

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

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

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

If you are still looking for a way to:

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


Easy material parametrization

### Using the functionality

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

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

### Automating PLAXIS in 3 steps

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

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

</br> 

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

1.	**Define your materials used**

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

*Input in the material database*

</br>

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

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

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

</br>

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

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

*Results of the PLAXIS analysis in the application*

</br>

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


Open-source Python code for a PLAXIS web app

Save time by automating PLAXIS in the Cloud

## Why a hackathon?

Knowledge development is one reason for the IB decided to organize a hackathon. Addiotionally, besides getting acquainted with programming hands-on, they also wanted to experience what it is like working with VIKTOR. 

As some structural engineers, such as Lester Neijzing, are currenlty part of a pilot project by the Gemeente Amsterdam in which VIKTOR is also being used, some are already a bit familiar with the platform. Now, the hackathon offers a great opportunity for the other structural engineers to get introduced to VIKTOR as well, and for those who are already acquainted to improve their programming skills. 

> "Participating in the hackathon is a fun way to get to know VIKTOR and learn more about programming with Python." - Lester Neijzing

![blog_hackathon_amsterdam](https://cms.viktor.ai/uploads/blog_hackathon_amsterdam_ae737a634c.jpg)

</br>
</br>

## Quay wall applications

During the two-day hackathon, three teams used Python to develop applications on the VIKTOR platform. All of the apps are to some extend related to the parametric design application that Amsterdam is currenlty developing for the renovation of their quay walls. 

The stakes were high during the event: In total, three apps were developed, but only one could be the winner and would thus be further developed eventually. Next, each app will be described in more detail.

![kademuur renoveren.jpg](https://cms.viktor.ai/uploads/kademuur_renoveren_7f4840c190.jpg)

</br>
</br>

### Application 1

Currently, structural engineers at the IB gather data about the state of quay walls in Excel sheets. Even though this process works well for the people who work with these sheets on a daily basis, it is difficult for other to interpret the data. For that reason, team 1 developed an application to make this data more transparent and easily adjustable. 

Of course, the IB could ask their GIS-department to visualize their data for them, but even then it would still remain a static process. With this application, engineers can visualize and adjust project data using different colors on a graphical map themselves. This also helps make it easier to explain to others why the work of the structural engineers is important and how they, for example, estimate workload and project duration.

### Application 2

Justl ike team 1, team 2 also worked on an application for the visualization of project data. With their app, it becomes easier and faster to project geometries on a map view and translate these to the calculation program SCIA, and after that to Word to generate downloadable reports.

Because this app includes a rather far-reaching integration, it was not unfortunately yet at a usable level at the end of the hackathon. On the flip side, this does mean that there is still a lot of room to improve and expand the tool. 

### Application 3

Opposing team 1 and 2, team 3 developed an application not aimed at the visualization of project data, but for the automated design and modelling of foundation piles, channels, and the surrounding soil. For this, the team integrated an Excel calculation sheet with an export to the calculation program SCIA to their VIKTOR application.

Just like with team 2's application, there was still some work to be done before team 3's app was ready to be used on real projects. However, the results were promising so far and made for an interesting concept for further developments.

## Choosing a winner

Based on the judge's critiques, a winner was chosen at the end of the second hackathon day: Application 1. Where the other apps mainly focused on integrating with pre-existing calculation software, application 1 stood out due to its unique use of data-processing and visualization - a nice use of VIKTOR that had not been done that often yet. 

> "I think it was this tool that won the hackathon because the rough outlines were already finished - the application worked in such a way that it produced the results that we were looking for. According to the jury, it was a unique tool, especially thanks to the maps and the visualizations." - Lester Neijzing

## Putting the pieces together

According to team 1 participants Rick and Lester, even though they were part of the same team, they were constantly programming completely different parts of the application. Only within the last few hours of the hackathon, when they began putting all the parts together, it felt like "it magically turned into a working product".

The application consists of several different parts, For example, it shows a graphical map and Excel sheet with risk drilling and assessments of quay walls, using different colors to indicate wether a wall is still sufficient or needs reinforcement. Team 1: "The map view really makes the data come to life". Within the application, it is also possible to adjust source data, link it immediately back to Excel (or the other way around), and export overview reports.

> "We knew what our vision was but did not yet realize how incredible it would be until we put everything together and it actually started working the way we envisioned." - Rick Voortman

![hackathon artikel 2.jpg](https://cms.viktor.ai/uploads/hackathon_artikel_2_b764fb081e.jpg)


</br>
</br>

## What's next?

Following the hackathon, two things are certain:

1. The further development of team 1's winning application
2. A second edition of the hackathon

**Are you also interested in organizing a hackathon with your organization? Don't hesitate to contact us at contact@viktor.ai!**



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Structural engineers at the engineering firm of the municipality of Amsterdam organized a hackathon to boost their programming skills and learn about building apps with VIKTOR.

Develop programming skills through hackathon | VIKTOR.ai

Hackathon stimulates knowledge development

## Create basic apps with a CLI command

We have added a create-app commmand to the CLI that generates an empty app, including all necessary components, that you can use as a quick starting point for your development.

Additionally, you can create a demo app that showcases the most important SDK functionalities by adding the flag --demo (create-app folder_name --demo). If you are new to VIKTOR, make sure to check out the demo app to discover new and exciting possibilities!

Visit the [documentation](https://docs.viktor.ai/docs/cli/?utm_campaign=Newsletter%20developers&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-8GqSiZlKGUm5w0beUfa7YAy-Ii8SPDU8kWMMxkqDPQAhFjUeRgw112Tn8qjEZxR9dXQxAS#create-app) for more information.

![Blog_newsletter_may_map_view_interaction](https://cms.viktor.ai/uploads/Blog_newsletter_may_map_view_interaction_b2096b5f54.png)


</br>


## Extended SCIA binding

We have extended the SCIA binding in the SDK with a couple of new features. Developers can now: 

- Add a description on the (non-linear) load combination 
- Specify solver settings
- Use cross-sections from the SCIA library 
- Add basic project data 
- Add cross-links 
- Apply a line load on a circular plane edge without raising an error 

Visit the [documentation](https://docs.viktor.ai/sdk/api/external/scia/?utm_campaign=Newsletter%20developers&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-8GqSiZlKGUm5w0beUfa7YAy-Ii8SPDU8kWMMxkqDPQAhFjUeRgw112Tn8qjEZxR9dXQxAS) for more information on the extended SCIA binding.  

![Blog_newsletter_may_scia](https://cms.viktor.ai/uploads/Blog_newsletter_may_scia_441fd8b7f8.webp)


</br>

## Assign dedicated workers to a workspace

We have added a new feature that allows admins to assign workers to either one workspace, multiple workspaces, or the whole environment.

This gives admins the flexibility to decide the number and type of workers they want to assign to a workspace. For example, you can assign more workers to your most important workspaces, or create separate workers for staging and production workspaces.

This prevents that end-users experience delays when running tasks, because a worker is busy executing a task triggered from another workspace. 

![Blog_newsletter_workspace-specific_workers](https://cms.viktor.ai/uploads/Blog_newsletter_workspace_specific_workers_bb502a092f.png)

</br>

## Coming soon...

### Map view interaction

We have good news! Next platform update, we are extending the possibilities that users have of interacting with the map. You will soon be able to implement a button on the input side of your app that allows users to start interacting with the map and simultaneously change multiple input parameters. With this new feature, you can improve user experience by making the input process more visual and intuitive!

</br>

## Other Changes
 

### SDK

- New version v13.1.0 is now available!

### PLATFORM

- Added search functionality in the environment users page 
- Updated the job progress dialog in the editor to use light theme 
- Updated the dates throughout the interface to be relative to current time, e.g. 1 hour ago 
- Updated the field states of the BooleanField to be more visually explicit 
- Adjusted the file manager functionality in the editor to only show on supported entity types 
- Updated the activity dashboard x-axis to match in all diagrams 
- Fixed an issue with wrong filename when downloading a file entity 
- Fixed an occasional mismatch of the end date in the activity dashboard 
- Added filtering functionality in the table field 

### CLI

- Create-app command added, including demo app for onboarding purposes 
- Validation of credentials before creating the credentials file when configuring CLI 
- Trim whitespaces from email address and token to prevent misconfiguration 
- Added check of Python installation in check-system command 
- Improved messages throughout CLI commands 
- Always print possible causes on a failed check-system check, not only when verbose flag is added 

### WORKER

- Improved error message when a integration that uses COM cannot start 

Blog_newsletter_may_2022

Discover what is new in the VIKTOR platorm for May 2022.

VIKTOR platform updates | May 2022

What's new in VIKTOR (May 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/guides/tools/debugging). 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 



VIKTOR Blog

What's new in VIKTOR (July 2022)