Visualizing Data

We export a number of files that you can use to visualize and analyze your simulation and optimization results locally.

Mesh .parquet .csv

TBD.

Contacts .parquet .csv

TBD.

Report .json

TBD.

Plot .csv

This file is available in .csv format, and contains data required for plotting the quality of a 3D print.

The example below shows how to use this file to generate an interactive 3D plot of the quality:

1. Ensure dependencies

   #!/bin/bash
   uv init
   uv add polars plotly

2. Import dependencies

   from typing import List
   from dataclasses import dataclass
   import plotly.graph_objects as go
   import polars as pl

3. Load plot data and group by group

   df = pl.read_csv("plot.csv")
   df = df.rename({"meshIndex": "mesh_index"})  # Convenience using snake case
   grouped_df = df.group_by("group")

4. Write a convenience function to produce correct colors

   @dataclass
   class Color:
       r: float
       g: float
       b: float
   
   def get_gradient_rgb(raw_value: float | None) -> Color:
       if raw_value == None:
           return Color(r=0, g=0, b=0)
   
       clamped_value = max(-1, min(1, raw_value))
   
       if clamped_value <= 0:
           t = clamped_value + 1
           return Color(r=0, g=round(t * 255), b=round((1 - t) * 255))
       else:
           t = clamped_value
           return Color(r=round(t * 255), g=round((1 - t) * 255), b=0)

5. Iterate over the data & produce lines for the plot

   lines: List[go.Scatter3d]  = []
   for group_id, group_data in grouped_df:
       shifted_group_data = group_data.shift(-1)
   
       for previous_row, row in zip(group_data.iter_rows(named=True), shifted_group_data.iter_rows(named=True)):
           color = get_gradient_rgb(previous_row["quality"])
           line = go.Scatter3d(
               x=[previous_row["x"], row["x"]],
               y=[previous_row["y"], row["y"]],
               z=[previous_row["z"], row["z"]],
               mode="lines",
               line=dict(color=f"rgb({color.r},{color.g},{color.b})", width=5),
           )
           lines.append(line)

6. Plot the data

   layout = go.Layout(
       scene=dict(
           xaxis_title="X Axis",
           yaxis_title="Y Axis",
           zaxis_title="Z Axis",
           aspectmode="data"
       ),
       showlegend=False
   )
   fig = go.Figure(data=lines, layout=layout)
   fig.show()

7. View the result

   The browser should open and display an interactive 3D plot:

   

Working with Parquet Files

Many of our exported files are available in Apache Parquet format for efficient data processing. For more information about Parquet files and how to work with them, see the Data Formats guide.