LightningChart teamed up with industry leaders, in ore processing, and machine learning – with Metso Plc, VTT, Aalto University, Quva Ltd and Business Finland – in AIMODE project. The project aimed at boosting recovery of minerals, by using machine learning, digital twins, surrogate models, advanced data visualization and artificial intelligence. LightningChart developed new data visualization technologies, and created data visualization projects, to support the important research within this project group.

LightningChart teamed up with industry leaders, in ore processing, and machine learning – with Metso Plc, VTT, Aalto University, Quva Ltd and Business Finland – in AIMODE project.

The project aimed at boosting recovery of minerals, by using machine learning, digital twins, surrogate models, advanced data visualization and artificial intelligence.

LightningChart developed new data visualization technologies, and created data visualization projects, to support the important research within this project group.

The amount of data in this project was huge, and it provided real challenges to be able to visualize it efficiently. The project group used various tools, and Python was one of the most used ones. When joining the project in 2022, it became apparent LightningChart needs to develop a high-performance Python charting toolkit for data visualization, as project researchers don’t use JavaScript or C# much in the advanced research.

LightningChart Python played an important role in the development of ore processing optimization work conducted by VTT and Metso. The research team needed to visualize complex optimization results from their digital models that simulated different ore blending scenarios and plant configurations. LightningChart Python enabled them to create complex visualizations including stacked bar charts showing ore composition changes over hundreds of optimization trials.

Examples of complex visualizations were scatter plots, line charts that track profit maximization progress, and most importantly, interactive parallel coordinates charts that displayed relationships between multiple input variables and outputs. Visualization techniques used in this project shows that LightningChart is not only the optimal high-performance charting technology ,but also offers a wide range of features to fully customize the charts.

The run-time performance has been very important for all LightningChart® products – making LightningChart® Python no exception. The performance comparisons made in Q2/2025, for LightningChart Python vs. the “default data visualization library” MatplotLib”, and “high-performance data visualization library” Plotly, reveal LightningChart is magnitudes faster than either of these –  In real-time streaming data tests, LightningChart Python performed ~7100 times faster than MatplotLib and ~190 times  faster than Plotly. In static data tests, LightningChart Python performed approximately 44000 times faster than MatplotLib and about 96 times faster than Plotly.

LightningChart Python has been made publicly available just lately. The Python charts will be introduced to universities, in the end of 2025, to get global visibility and starting of adoption of this new, super-efficient data visualization library.

The amount of data in this project was huge, and it provided real challenges to be able to visualize it efficiently. The project group used various tools, and Python was one of the most used ones.

When joining the project in 2022, it became apparent LightningChart needs to develop a high-performance Python charting toolkit for data visualization, as project researchers don’t use JavaScript or C# much in the advanced research.

LightningChart Python played an important role in the development of ore processing optimization work conducted by VTT and Metso.

The research team needed to visualize complex optimization results from their digital models that simulated different ore blending scenarios and plant configurations. LightningChart Python enabled them to create complex visualizations including stacked bar charts showing ore composition changes over hundreds of optimization trials.

Examples of complex visualizations were scatter plots, line charts that track profit maximization progress, and most importantly, interactive parallel coordinates charts that displayed relationships between multiple input variables and outputs.

Visualization techniques used in this project shows that LightningChart is not only the optimal high-performance charting technology ,but also offers a wide range of features to fully customize the charts.

The run-time performance has been very important for all LightningChart® products – making LightningChart® Python no exception.

The performance comparisons made in Q2/2025, for LightningChart Python vs. the “default data visualization library” MatplotLib”, and “high-performance data visualization library” Plotly, reveal LightningChart is magnitudes faster than either of these.

In real-time streaming data tests, LightningChart Python performed ~7100 times faster than MatplotLib and ~190 times  faster than Plotly. In static data tests, LightningChart Python performed approximately 44000 times faster than MatplotLib and about 96 times faster than Plotly.

LightningChart Python has been made publicly available just lately. The Python charts will be introduced to universities, in the end of 2025, to get global visibility and starting of adoption of this new, super-efficient data visualization library.

Figure 1. Illustrative LightningChart Python dashboard of ore refining to gold (data from public sources).

In 2022, LightningChart had a new cloud-based data analytics platform, Dashtera, already under development, which has been now introduced in beta stage, in Q3/2025, after years of intensive development. Dashtera was presented to the project group in different stages of development during the project, and feedback and improvement suggestions were collected.

Dashtera is an ultra-high-performance, no-code platform, with easy drag-drop interface for building immersive dashboards, for Business Intelligence, Science & Engineering, and Finance & Trading. It covers large enterprise data visualization needs for all operations, as one unified solution. Truly a Big Data dashboard solution, with the database and data lake integrations. Dashtera will be positioned to compete against multi-billion-dollar products, e.g. Grafana, Tableau, Microsoft Power BI and some others – being more performant, next generation, data visualization solution.

In 2022, LightningChart had a new cloud-based data analytics platform, Dashtera, already under development, which has been now introduced in beta stage, in Q3/2025, after years of intensive development.

Dashtera was presented to the project group in different stages of development during the project, and feedback and improvement suggestions were collected.

Dashtera is an ultra-high-performance, no-code platform, with easy drag-drop interface for building immersive dashboards, for Business Intelligence, Science & Engineering, and Finance & Trading.

It covers large enterprise data visualization needs for all operations, as one unified solution. Truly a Big Data dashboard solution, with the database and data lake integrations.

Dashtera will be positioned to compete against multi-billion-dollar products, e.g. Grafana, Tableau, Microsoft Power BI and some others – being more performant, next generation, data visualization solution.

Figure 2. Process parameters and statistics in a Dashtera dashboard project (data by Metso).