---
title: "Visualize simulation scalar fields in C#"
url: https://recursiveai.org/onescope/cookbook/visualize-scalar-fields/
description: "Show a scalar field in a OneScope module with FieldMesh and FieldPoints, a color range, a legend and entity or point reads. Runnable .NET example."
version: "1.0.0"
---

# Visualize simulation scalar fields in C#

**OneScope** is a free, open-source viewer for simulation recordings, with a desktop window or command-line control, from Recursive AI. MIT or Apache-2.0.

[NuGet](https://www.nuget.org/packages/RecursiveAI.OneScope.Tool) · [GitHub](https://github.com/recursive-ai-oss/onescope-csharp) · [Docs](https://recursiveai.org/onescope/docs/) · [Download example (.zip)](https://recursiveai.org/onescope/cookbook/visualize-scalar-fields/visualize-scalar-fields.zip)

A scalar is one number at each sample. A module is C# code that tells OneScope how to read and draw your simulation’s recorded run, called a replay.

```shell
dotnet tool install --global RecursiveAI.OneScope.Tool
```

Requires [.NET 10](https://dotnet.microsoft.com/download/dotnet/10.0) (net10.0); .NET 9 and earlier aren’t supported. The window and the no-window (`headless`) example both need a working graphics renderer; without one, OneScope cannot start.

Download and unzip the example first.

**Window path — build the module, then open the replay:**

The zip contains `sender.replay`.

```shell
dotnet build -o bin/demo
onescope open sender.replay \
  --module bin/demo/Example.dll
```

Opens a window with a colored triangle, a point and a Temperature legend. This window is at step 3.

![OneScope at step 3 showing the colored triangle, sample point and Temperature legend](data:image/png;base64,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)

**Console path — run the C# example without a window:**

Unzip [the example](https://recursiveai.org/onescope/cookbook/visualize-scalar-fields/visualize-scalar-fields.zip), then `dotnet run`.

## Add a field to your own module

Put the field declaration, drawing calls and value reads in your own module’s world. This focused example uses a constant temperature of 3 degrees; the full program uses the step number as the value. Include `TemperatureLayer.Heat` in the source’s `SourceInfo.Fields` list so the viewer can name the field and show its range and legend.

Add the SDK reference to your module’s project file:

```xml
<PackageReference Include="RecursiveAI.OneScope.Sdk" Version="1.0.0" />
```

```csharp
using System.Numerics;
using OneScope.Sdk;

// Add these members to your own module's world.
public sealed class TemperatureLayer
{
    public static readonly FieldInfo Heat = new(
        "heat", "Temperature", ColorMap.Heat, 0, 10, "degrees");

    public void Draw(DrawList draw)
    {
        draw.FieldMesh(
            [new(0, 0, 0), new(2, 0, 0), new(0, 2, 0)],
            [0, 1, 2], [3f, 3f, 3f], FieldDomain.Vertex,
            Heat, new DrawStyle(Color32.White));
        draw.FieldPoints([new Vector3(1, 1, 0)], [3f],
            Heat, 12, new DrawStyle(Color32.White));
    }

    public bool TryReadField(
        string field, EntityId entity, out double value)
    {
        value = 3;
        return field == "heat" && entity.Value == 1;
    }

    public bool TryReadFieldAt(
        string field, Vector3 point, out double value)
    {
        value = 3;
        return field == "heat";
    }
}
```

`FieldMesh` draws the triangle; `FieldPoints` draws a sample point. The read methods return the same temperature rather than asking a user to estimate it from a color.

## The full program

In your own module, add the field to `SourceInfo.Fields`, draw its values and implement the two numeric-read methods shown above. The complete program below connects those pieces to a small replay reader and opens it in OneScope.

The C# launcher installs OneScope.Tool 1.0.0 in its build folder and loads the module from its own assembly.

```csharp
using System.Buffers;
using System.Buffers.Binary;
using System.Numerics;
using System.Diagnostics;
using System.Text.Json;
using OneScope.Sdk;

// CNTR + little-endian role: 0 stays, 1 sends, 2 receives; ten empty steps.
File.WriteAllBytes("sender.replay", [67, 78, 84, 82, 0, 0, 0, 0]);
File.WriteAllBytes("receiver.replay", [67, 78, 84, 82, 2, 0, 0, 0]);
string assembly = typeof(CounterModule).Assembly.Location;
string toolDir = Path.GetFullPath("obj/onescope-tool");
string tool = Path.Combine(toolDir, OperatingSystem.IsWindows() ? "onescope.exe" : "onescope");
if (!File.Exists(tool))
    Run("dotnet", "tool", "install", "RecursiveAI.OneScope.Tool", "--version", "1.0.0", "--tool-path", toolDir);
var r = Open("seek {\"step\":3}", "field", "field {\"field\":\"heat\",\"entity\":1}",
    "field {\"field\":\"heat\",\"point\":[1,1,0]}");
Console.WriteLine($"Fields: {r[2].GetProperty("fields").GetArrayLength()}");
Console.WriteLine($"Entity temperature: {r[3].GetProperty("value")}");
Console.WriteLine($"Point temperature: {r[4].GetProperty("value")}");

JsonElement[] Open(params string[] operations)
{
    var args = new List<string> { "open", "sender.replay", "--module", assembly,
        "--headless", "--size", "640x480" };
    foreach (string operation in operations) args.AddRange(["--run", operation]);
    return Run(tool, args.ToArray()).Split('\n', StringSplitOptions.RemoveEmptyEntries)
        .Select(s => JsonDocument.Parse(s).RootElement.Clone()).ToArray();
}
string Run(string command, params string[] arguments)
{
    var start = new ProcessStartInfo(command) { RedirectStandardOutput = true, RedirectStandardError = true };
    foreach (string arg in arguments) start.ArgumentList.Add(arg);
    using var process = Process.Start(start)!;
    var output = process.StandardOutput.ReadToEndAsync();
    var errors = process.StandardError.ReadToEndAsync();
    process.WaitForExit();
    if (process.ExitCode != 0) throw new InvalidOperationException(output.Result + errors.Result);
    return output.Result;
}

public sealed class CounterModule : IScopeModule
{
    public string Id => "counter";
    public string Name => "Counter";
    public IScopeSource Open(ReadOnlyMemory<byte> data, string name)
    {
        if (data.Length != 8 || !data.Span[..4].SequenceEqual("CNTR"u8))
            throw new ScopeFormatException(name, "Expected CNTR and an int32 role (8 bytes).");
        int steps = 10;
        int role = data.Span[4];
        if (role < 0 || role > 2 || data.Span[5..].IndexOfAnyExcept((byte)0) >= 0)
            throw new ScopeFormatException(name, "Expected role 0..2 and zero padding at offset 4.");
        var inputs = new ReadOnlyMemory<byte>[steps];
        return new CounterSource(name, Recording.Create(ReadOnlyMemory<byte>.Empty, inputs), role);
    }
}

public sealed class CounterSource(string name, Recording recording, int role) : IScopeSource
{
    public SourceInfo Info { get; } = new()
    {
        ModuleId = "counter", Name = name, Dimensions = 2,
        Unit = LengthUnit.Metre,
        Fields = [DemoField.Info],
        Axes = [new AxisInfo(AxisIds.Time, "Time", 0)],
        EntityKinds = [new EntityKindInfo("counter", "Counter")],
        Overlays = [new OverlayInfo(0, "Counter", true)]
    };
    public Recording Recording { get; } = recording;
    public IScopeWorld CreateWorld() => new CounterWorld(role);
    public IScopeSource WithRecording(Recording other) => new CounterSource(name, other, role);
    public void WriteReplay(IBufferWriter<byte> writer)
    {
        Span<byte> bytes = writer.GetSpan(8)[..8];
        "CNTR"u8.CopyTo(bytes);
        BinaryPrimitives.WriteInt32LittleEndian(bytes[4..], role);
        writer.Advance(8);
    }
}

public sealed class CounterWorld(int role) : IScopeWorld
{
    public long Step { get; private set; }
    public ulong Hash() => (ulong)Step; // This toy's entire state is its counter.
    public void Advance(ReadOnlySpan<byte> input, TimelineSink sink)
    {
        if (!input.IsEmpty) throw new ScopeFormatException("counter", "Expected empty step input.");
        Step = checked(Step + 1);
        if (Step == 3) sink.Finding("warm", Severity.Warning, [new EntityId(1)], "Temperature reached 3.", "Check the heat input.");
        if (Step == 7) sink.Finding("hot", Severity.Error, [new EntityId(1)], "Temperature reached 7.");
    }
    public void AdvanceAxis(string axis, int position, ReadOnlySpan<byte> input) =>
        throw new ArgumentException("No secondary axes.", nameof(axis));
    public void WriteSnapshot(IBufferWriter<byte> writer)
    {
        BinaryPrimitives.WriteInt64LittleEndian(writer.GetSpan(8), Step);
        writer.Advance(8);
    }
    public void Restore(ReadOnlySpan<byte> snapshot)
    {
        if (snapshot.Length != 8 || BinaryPrimitives.ReadInt64LittleEndian(snapshot) < 0)
            throw new ScopeFormatException("counter", "Expected a nonnegative int64 snapshot.");
        Step = BinaryPrimitives.ReadInt64LittleEndian(snapshot);
    }
    public void CollectEntities(List<EntityInfo> into)
    {
        into.Clear();
        if (Present) into.Add(new(new EntityId(1), default, "counter", "Counter"));
    }
    public bool Inspect(EntityId entity, PropertyList into)
    {
        into.Clear();
        if (entity.Value != 1 || !Present) return false;
        into.Add("State", "Value", ScopeValue.OfInteger(Step));
        return true;
    }
    bool Present => role == 0 || (role == 1 ? Step < 5 : Step >= 5);
    public bool TryReadField(string field, EntityId entity, out double value)
    {
        value = Step;
        return field == "heat" && entity.Value == 1 && Present;
    }
    public bool TryReadFieldAt(string field, Vector3 point, out double value)
    {
        value = Step;
        return field == "heat";
    }
    public void Draw(DrawList into, in DrawRequest request)
    {
        if (!Present) return;
        into.FieldMesh([new(0, 0, 0), new(2, 0, 0), new(0, 2, 0)],
            [0, 1, 2], [(float)Step, (float)Step, (float)Step],
            FieldDomain.Vertex, DemoField.Info,
            new DrawStyle(Color32.White, Entity: new EntityId(1)));
        into.FieldPoints([new Vector3(1, 1, 0)], [(float)Step], DemoField.Info,
            12, new DrawStyle(Color32.White, Entity: new EntityId(1)));
    }
    public void EncodePoke(in PokeCall call, IBufferWriter<byte> into) =>
        throw new ArgumentException("This counter declares no pokes.");
    public Difference? FirstDifference(IScopeWorld other)
    {
        if (other is not CounterWorld counter) throw new ArgumentException("Expected a counter.");
        return Step == counter.Step ? null : new Difference(new EntityId(1), "value",
            Step.ToString(System.Globalization.CultureInfo.InvariantCulture),
            counter.Step.ToString(System.Globalization.CultureInfo.InvariantCulture));
    }
}

public static class DemoField
{
    public static readonly FieldInfo Info = new("heat", "Temperature", ColorMap.Heat, 0, 10, "degrees");
}
```

`dotnet run` prints:

```text
Fields: 1
Entity temperature: 3
Point temperature: 3
```

The included eight-byte inputs are `43 4E 54 52 00 00 00 00` (sender) and `43 4E 54 52 02 00 00 00` (receiver): `CNTR` plus a file-role number. The reader creates ten simulation steps.

A field read returns `no_value` when the module has no value at that entity or point.

## Variations

### Read a field at a point

Run from the same example folder:

```shell
dotnet build -o bin/demo
onescope open sender.replay \
  --module bin/demo/Example.dll \
  --run 'seek {"step":3}' \
  --run 'field {"field":"heat","point":[1,1,0]}'
```

The final operation returns:

```json
{"source":0,"step":3,"field":"heat","point":[1,1,0],"value":3,"unit":"degrees","min":0,"max":10}
```

## Related recipes

- [Track an entity across replay sources](https://recursiveai.org/onescope/cookbook/track-entity-between-sources/)
- [Inspect simulation replay entity properties](https://recursiveai.org/onescope/cookbook/inspect-replay-entity-properties/)
- [Filter simulation replay diagnostic findings](https://recursiveai.org/onescope/cookbook/filter-replay-findings/)

---

## The short answer

Declare the field in `SourceInfo.Fields`, then draw it with `DrawList.FieldMesh` or `FieldPoints`. A scalar field is one numeric value at each sample location. The shell maps those values to colors using the declared minimum, maximum and color map, and draws a legend in views that draw the field. Geometry alone is not a scalar-field declaration.

The example makes temperature equal to the step index. Its triangle and point therefore both read 3 at step 3. This is a small SDK example, not a heat solver: replace that value with your simulation’s temperature, density or other measurement. The [field contract](https://recursiveai.org/onescope/docs/fields-links-ghosts/#scalar-fields) describes how a module supplies the data.

## How do I visualize a scalar field in C#?

Choose the geometry that matches where your simulation stores values. `FieldMesh` accepts a value per vertex or per triangle. Per-vertex values blend across the triangle; per-triangle values produce a flat color. `FieldPoints` accepts a value per position. A point cloud does not require triangle connectivity, but it does need the same number of values as positions.

In the full program, `DemoField.Info` names the field `heat`, gives it the display name Temperature and sets a range from 0 to 10 degrees. The source declares that exact descriptor, and its world passes it to both drawing calls. The mesh has three vertices, one triangle and three values with `FieldDomain.Vertex`. The point draw has one position and one value. Those counts follow the storage domain rather than the number of entities.

Use a fixed declared range when comparing steps. Otherwise a color could represent a different value after each seek. The example’s range stays fixed while the numeric value changes. The shell uses the metadata for its legend, including the name, unit and endpoints; you do not need to draw a separate legend in the module.

## How do color maps handle missing and out-of-range values?

The SDK supplies Viridis, Heat, CoolWarm and Grayscale. Values outside the declared range clamp to its endpoints. NaN means no data and draws gray. A missing measurement should not silently become zero if zero has physical meaning in your field.

`ColorMaps.Evaluate` is the shared mapping function used by the renderer and legend. The drawing contract also specifies bounded storage: a draw list accepts 1,024 field draws and 8M values. A wrong value count drops the mesh and increments `DrawList.Dropped`. If a field does not appear, check the domain, counts and dropped count before changing the camera.

## How do I read the scalar value at an entity or point?

The `field` operation without a field ID lists the source’s declarations. With an ID, supply either `entity` or `point`, not both. The world implements `TryReadField` and `TryReadFieldAt` to return the values it paints. The example returns its step-based temperature from both methods, so the two reads agree.

The default implementations return no value. Adding only a drawing call does not create a queryable field. Likewise, the viewer does not infer interpolation from a mesh for your module: implement the point-read rule your simulation actually uses. An unknown field or an invalid combination of arguments returns `bad_args`; a declared field without a value at that location returns `no_value`.
