Houdini to Unity Star Data

2024-11-07 22:20 UTC gpt-4o Open in ChatGPT ↗

You

Modify this Houdini python code, so that it processes the stars BIN files in C# Unity and stores them in a texture (to be used by VFX graph),

import hou import os import struct import numpy

node = hou.pwd() geo = node.geometry()

COLUMNS = ‘appmag,absmag,color,size’.split(’,’) for col in COLUMNS: geo.addAttrib(hou.attribType.Point, col, 0.0)

def read_star(fp): pos = struct.unpack(‘>ddd’, fp.read(24)) pos = numpy.array(pos)/numpy.linalg.norm(pos) # Because Python has 64bit precision while Houdini only 32 by default, I normalize the pos here. Also I don’t care about the actual position, though some of you reading this might. # read bytes without even storing them to a variable just “skips” a chunk. fp.read(48) # 4 single-precision floats (32-bit * 4) – appmag, absmag, color, size - Magnitudes, colors (encoded), and size (a derived quantity, for rendering) appmag, absmag, color, size = struct.unpack(‘>ffff’, fp.read(16)) fp.read(24) # 1 single-precision integer (32-bit) – namelen -> Length of name name_length = struct.unpack(‘>i’, fp.read(4))[0] # namelen * char (16-bit * namelen) – Characters of the star name. if name_length > 0: fp.read(name_length*2)

# Create the Houdini points
point = geo.createPoint()
point.setPosition(hou.Vector3(*pos))
point.setAttribValue('appmag', appmag)
point.setAttribValue('color', color)
point.setAttribValue('size', size)

def read_bin(fp, maxrows): number_of_stars = struct.unpack(‘>l’, fp.read(4))[0] count = 0 while count < number_of_stars: if count >= maxrows: break read_star(fp) count += 1 return count

def main(): maxrows = node.parm(‘maxrows’).eval()
directory = node.parm(‘directory’).eval()

files = []
for name in os.listdir(directory):
    if name.endswith('.bin'):
        files.append(os.path.join(directory, name))
files.sort()

count = 0
with hou.InterruptableOperation('Reading BIN', 'Reading Files', True) as op:
    for fi, filename in enumerate(files):
        op.updateLongProgress(fi / float(len(files)-1), os.path.basename(filename))
        if count >= maxrows:
            break
        with open(filename, 'rb') as fp:
            count += read_bin(fp, maxrows - count)

Put this at the very end of your node’s code.

main()

ChatGPT

To modify the Houdini Python code for use in C# Unity, we need to read the star data from BIN files, process it, and store it in a texture. This involves adapting the file reading logic from Python to C#, creating and filling a texture with data for use in Unity’s VFX graph. Here’s how this translation and modification can be done:

1. Reading the binary data.
2. Normalizing the position data.
3. Creating a texture that stores star attributes.

Below is the modified code for Unity in C#:

using System;
using System.Collections.Generic;
using System.IO;
using UnityEngine;

public class StarProcessor : MonoBehaviour
{
    public string directoryPath; // Directory containing BIN files
    public int maxRows = 1000; // Max number of stars to process
    public Texture2D starTexture; // Texture to store star data

    private const int TextureWidth = 1024; // Define the texture width
    private const int TextureHeight = 1024; // Define the texture height

    void Start()
    {
        List<float> starData = new List<float>();

        if (Directory.Exists(directoryPath))
        {
            string[] files = Directory.GetFiles(directoryPath, "*.bin");
            Array.Sort(files); // Sorting files alphabetically
            int count = 0;

            foreach (string file in files)
            {
                if (count >= maxRows)
                    break;

                using (BinaryReader reader = new BinaryReader(File.Open(file, FileMode.Open)))
                {
                    count += ReadBinFile(reader, starData, maxRows - count);
                }
            }

            CreateTextureFromData(starData);
        }
        else
        {
            Debug.LogError("Directory does not exist: " + directoryPath);
        }
    }

    private int ReadBinFile(BinaryReader reader, List<float> starData, int remainingRows)
    {
        int numberOfStars = ReadIntBigEndian(reader);

        int count = 0;
        while (count < numberOfStars && count < remainingRows)
        {
            ReadStar(reader, starData);
            count++;
        }

        return count;
    }

    private void ReadStar(BinaryReader reader, List<float> starData)
    {
        // Read position (normalized)
        double x = ReadDoubleBigEndian(reader);
        double y = ReadDoubleBigEndian(reader);
        double z = ReadDoubleBigEndian(reader);
        Vector3 position = new Vector3((float)x, (float)y, (float)z).normalized;

        // Skip 48 bytes
        reader.BaseStream.Seek(48, SeekOrigin.Current);

        // Read attributes (appmag, absmag, color, size)
        float appmag = ReadFloatBigEndian(reader);
        float absmag = ReadFloatBigEndian(reader);
        float color = ReadFloatBigEndian(reader);
        float size = ReadFloatBigEndian(reader);

        // Skip 24 bytes
        reader.BaseStream.Seek(24, SeekOrigin.Current);

        // Read name length and skip the name
        int nameLength = ReadIntBigEndian(reader);
        if (nameLength > 0)
        {
            reader.BaseStream.Seek(nameLength * 2, SeekOrigin.Current);
        }

        // Store the star data (x, y, z, appmag, color, size)
        starData.Add(position.x);
        starData.Add(position.y);
        starData.Add(position.z);
        starData.Add(appmag);
        starData.Add(color);
        starData.Add(size);
    }

    private void CreateTextureFromData(List<float> starData)
    {
        int dataCount = starData.Count;
        int pixelCount = TextureWidth * TextureHeight;

        if (dataCount > pixelCount * 4)
        {
            Debug.LogWarning("Not enough texture space to store all star data, some data will be truncated.");
            dataCount = pixelCount * 4;
        }

        Color[] colors = new Color[TextureWidth * TextureHeight];
        for (int i = 0; i < colors.Length; i++)
        {
            if (i * 4 + 3 < dataCount)
            {
                colors[i] = new Color(
                    starData[i * 4],
                    starData[i * 4 + 1],
                    starData[i * 4 + 2],
                    starData[i * 4 + 3]
                );
            }
            else
            {
                colors[i] = Color.black; // Fill remaining texture space with black (no data)
            }
        }

        starTexture = new Texture2D(TextureWidth, TextureHeight, TextureFormat.RGBAFloat, false);
        starTexture.SetPixels(colors);
        starTexture.Apply();

        // Optionally, assign the texture to a material or use in a VFX graph
        Debug.Log("Star texture created with " + starData.Count / 4 + " stars.");
    }

    private int ReadIntBigEndian(BinaryReader reader)
    {
        byte[] bytes = reader.ReadBytes(4);
        if (BitConverter.IsLittleEndian)
        {
            Array.Reverse(bytes);
        }
        return BitConverter.ToInt32(bytes, 0);
    }

    private float ReadFloatBigEndian(BinaryReader reader)
    {
        byte[] bytes = reader.ReadBytes(4);
        if (BitConverter.IsLittleEndian)
        {
            Array.Reverse(bytes);
        }
        return BitConverter.ToSingle(bytes, 0);
    }

    private double ReadDoubleBigEndian(BinaryReader reader)
    {
        byte[] bytes = reader.ReadBytes(8);
        if (BitConverter.IsLittleEndian)
        {
            Array.Reverse(bytes);
        }
        return BitConverter.ToDouble(bytes, 0);
    }
}

Explanation:

1. Reading Binary Data: The code reads binary files similarly to the original Houdini script using BinaryReader in C#.
2. Normalization: Positions are normalized.
3. Texture Creation: A texture is created using Texture2D to store the star data.
4. Endian Handling: Since the original file format uses big-endian, the byte order is reversed if necessary.

Usage:

• Attach this script to a GameObject in your Unity scene.
• Set the directoryPath to the folder containing your BIN files.
• The generated Texture2D can be used in the VFX graph for rendering star data.

You

use nativearrays, nativeslices and parallel jobs whenever possible.

ChatGPT

Here’s a modified version of the Unity C# code that utilizes Unity’s NativeArray, NativeSlice, and parallel jobs for improved performance. This version will maximize the use of Unity’s job system, burst compilation, and memory safety features for better performance when processing large amounts of star data:

using System;
using System.IO;
using Unity.Collections;
using Unity.Jobs;
using Unity.Burst;
using UnityEngine;

public class StarProcessorWithJobs : MonoBehaviour
{
    public string directoryPath; // Directory containing BIN files
    public int maxRows = 1000; // Maximum number of stars to process
    public Texture2D starTexture; // Texture to store star data

    private const int TextureWidth = 1024;
    private const int TextureHeight = 1024;

    void Start()
    {
        if (!Directory.Exists(directoryPath))
        {
            Debug.LogError("Directory does not exist: " + directoryPath);
            return;
        }

        string[] files = Directory.GetFiles(directoryPath, "*.bin");
        Array.Sort(files); // Sort files alphabetically

        NativeArray<float> starData = new NativeArray<float>(maxRows * 6, Allocator.TempJob); // Each star has 6 values: x, y, z, appmag, color, size
        int totalStarsRead = 0;

        foreach (string file in files)
        {
            if (totalStarsRead >= maxRows)
                break;

            using (BinaryReader reader = new BinaryReader(File.Open(file, FileMode.Open)))
            {
                int starsRead = ReadBinFile(reader, starData, totalStarsRead, maxRows);
                totalStarsRead += starsRead;
            }
        }

        CreateTextureFromData(starData, totalStarsRead);
        starData.Dispose();
    }

    private int ReadBinFile(BinaryReader reader, NativeArray<float> starData, int startIdx, int maxRows)
    {
        int numberOfStars = ReadIntBigEndian(reader);

        int count = 0;
        while (count < numberOfStars && startIdx + count < maxRows)
        {
            ReadStar(reader, starData, startIdx + count);
            count++;
        }

        return count;
    }

    private void ReadStar(BinaryReader reader, NativeArray<float> starData, int index)
    {
        // Read position (normalized)
        double x = ReadDoubleBigEndian(reader);
        double y = ReadDoubleBigEndian(reader);
        double z = ReadDoubleBigEndian(reader);
        Vector3 position = new Vector3((float)x, (float)y, (float)z).normalized;

        // Skip 48 bytes
        reader.BaseStream.Seek(48, SeekOrigin.Current);

        // Read attributes (appmag, absmag, color, size)
        float appmag = ReadFloatBigEndian(reader);
        float absmag = ReadFloatBigEndian(reader);
        float color = ReadFloatBigEndian(reader);
        float size = ReadFloatBigEndian(reader);

        // Skip 24 bytes
        reader.BaseStream.Seek(24, SeekOrigin.Current);

        // Read name length and skip the name
        int nameLength = ReadIntBigEndian(reader);
        if (nameLength > 0)
        {
            reader.BaseStream.Seek(nameLength * 2, SeekOrigin.Current);
        }

        // Store the star data
        int baseIdx = index * 6;
        starData[baseIdx] = position.x;
        starData[baseIdx + 1] = position.y;
        starData[baseIdx + 2] = position.z;
        starData[baseIdx + 3] = appmag;
        starData[baseIdx + 4] = color;
        starData[baseIdx + 5] = size;
    }

    private void CreateTextureFromData(NativeArray<float> starData, int totalStars)
    {
        int pixelCount = TextureWidth * TextureHeight;
        Color[] colors = new Color[pixelCount];

        NativeArray<Color> nativeColors = new NativeArray<Color>(colors, Allocator.TempJob);

        ProcessStarDataJob job = new ProcessStarDataJob
        {
            StarData = starData,
            Colors = nativeColors,
            TotalStars = totalStars
        };

        JobHandle handle = job.Schedule(pixelCount, 64); // Schedule parallel processing in batches of 64
        handle.Complete();

        // Copy data back to managed array and create the texture
        nativeColors.CopyTo(colors);
        starTexture = new Texture2D(TextureWidth, TextureHeight, TextureFormat.RGBAFloat, false);
        starTexture.SetPixels(colors);
        starTexture.Apply();

        nativeColors.Dispose();
    }

    [BurstCompile]
    private struct ProcessStarDataJob : IJobParallelFor
    {
        [ReadOnly] public NativeArray<float> StarData;
        public NativeArray<Color> Colors;
        public int TotalStars;

        public void Execute(int index)
        {
            if (index * 4 < TotalStars * 6)
            {
                int starIndex = index * 6 / 4;
                Colors[index] = new Color(
                    StarData[starIndex * 6],
                    StarData[starIndex * 6 + 1],
                    StarData[starIndex * 6 + 2],
                    StarData[starIndex * 6 + 3] // Using appmag as alpha for demonstration
                );
            }
            else
            {
                Colors[index] = Color.black; // Fill remaining texture space with black (no data)
            }
        }
    }

    private int ReadIntBigEndian(BinaryReader reader)
    {
        byte[] bytes = reader.ReadBytes(4);
        if (BitConverter.IsLittleEndian)
        {
            Array.Reverse(bytes);
        }
        return BitConverter.ToInt32(bytes, 0);
    }

    private float ReadFloatBigEndian(BinaryReader reader)
    {
        byte[] bytes = reader.ReadBytes(4);
        if (BitConverter.IsLittleEndian)
        {
            Array.Reverse(bytes);
        }
        return BitConverter.ToSingle(bytes, 0);
    }

    private double ReadDoubleBigEndian(BinaryReader reader)
    {
        byte[] bytes = reader.ReadBytes(8);
        if (BitConverter.IsLittleEndian)
        {
            Array.Reverse(bytes);
        }
        return BitConverter.ToDouble(bytes, 0);
    }
}

Explanation:

1. NativeArray & Job System: Uses NativeArray<float> to store star data and IJobParallelFor to process data in parallel.
2. Burst Compilation: BurstCompile attribute optimizes the job execution.
3. Parallel Job Processing: The job processes pixel colors in parallel for better performance.
4. Memory Management: Ensures proper memory management by disposing of NativeArrays.

Key Improvements:

• The data processing is parallelized for better performance on large data sets.
• The use of NativeArray improves memory efficiency.
• BurstCompile offers performance optimizations for job execution.

You

use the raw texturedata instead of allocating a new nativearray for colors

ChatGPT

Here’s the updated code that directly modifies the raw texture data using Texture2D.GetRawTextureData<T>() instead of allocating a separate NativeArray for colors. This approach provides improved memory efficiency and better integration with the Unity rendering pipeline by avoiding redundant data copying.

using System;
using System.IO;
using Unity.Collections;
using Unity.Jobs;
using Unity.Burst;
using UnityEngine;

public class StarProcessorWithJobs : MonoBehaviour
{
    public string directoryPath; // Directory containing BIN files
    public int maxRows = 1000; // Maximum number of stars to process
    public Texture2D starTexture; // Texture to store star data

    private const int TextureWidth = 1024;
    private const int TextureHeight = 1024;

    void Start()
    {
        if (!Directory.Exists(directoryPath))
        {
            Debug.LogError("Directory does not exist: " + directoryPath);
            return;
        }

        string[] files = Directory.GetFiles(directoryPath, "*.bin");
        Array.Sort(files); // Sort files alphabetically

        NativeArray<float> starData = new NativeArray<float>(maxRows * 6, Allocator.TempJob); // Each star has 6 values: x, y, z, appmag, color, size
        int totalStarsRead = 0;

        foreach (string file in files)
        {
            if (totalStarsRead >= maxRows)
                break;

            using (BinaryReader reader = new BinaryReader(File.Open(file, FileMode.Open)))
            {
                int starsRead = ReadBinFile(reader, starData, totalStarsRead, maxRows);
                totalStarsRead += starsRead;
            }
        }

        CreateTextureFromData(starData, totalStarsRead);
        starData.Dispose();
    }

    private int ReadBinFile(BinaryReader reader, NativeArray<float> starData, int startIdx, int maxRows)
    {
        int numberOfStars = ReadIntBigEndian(reader);

        int count = 0;
        while (count < numberOfStars && startIdx + count < maxRows)
        {
            ReadStar(reader, starData, startIdx + count);
            count++;
        }

        return count;
    }

    private void ReadStar(BinaryReader reader, NativeArray<float> starData, int index)
    {
        // Read position (normalized)
        double x = ReadDoubleBigEndian(reader);
        double y = ReadDoubleBigEndian(reader);
        double z = ReadDoubleBigEndian(reader);
        Vector3 position = new Vector3((float)x, (float)y, (float)z).normalized;

        // Skip 48 bytes
        reader.BaseStream.Seek(48, SeekOrigin.Current);

        // Read attributes (appmag, absmag, color, size)
        float appmag = ReadFloatBigEndian(reader);
        float absmag = ReadFloatBigEndian(reader);
        float color = ReadFloatBigEndian(reader);
        float size = ReadFloatBigEndian(reader);

        // Skip 24 bytes
        reader.BaseStream.Seek(24, SeekOrigin.Current);

        // Read name length and skip the name
        int nameLength = ReadIntBigEndian(reader);
        if (nameLength > 0)
        {
            reader.BaseStream.Seek(nameLength * 2, SeekOrigin.Current);
        }

        // Store the star data
        int baseIdx = index * 6;
        starData[baseIdx] = position.x;
        starData[baseIdx + 1] = position.y;
        starData[baseIdx + 2] = position.z;
        starData[baseIdx + 3] = appmag;
        starData[baseIdx + 4] = color;
        starData[baseIdx + 5] = size;
    }

    private void CreateTextureFromData(NativeArray<float> starData, int totalStars)
    {
        // Create the texture
        starTexture = new Texture2D(TextureWidth, TextureHeight, TextureFormat.RGBAFloat, false);
        NativeArray<Color> textureData = starTexture.GetRawTextureData<Color>();

        ProcessStarDataJob job = new ProcessStarDataJob
        {
            StarData = starData,
            TextureData = textureData,
            TotalStars = totalStars
        };

        JobHandle handle = job.Schedule(textureData.Length, 64); // Schedule parallel processing in batches of 64
        handle.Complete();

        starTexture.Apply(); // Apply changes to the texture
    }

    [BurstCompile]
    private struct ProcessStarDataJob : IJobParallelFor
    {
        [ReadOnly] public NativeArray<float> StarData;
        public NativeArray<Color> TextureData;
        public int TotalStars;

        public void Execute(int index)
        {
            if (index * 4 < TotalStars * 6)
            {
                int starIndex = index * 6 / 4;
                TextureData[index] = new Color(
                    StarData[starIndex * 6],
                    StarData[starIndex * 6 + 1],
                    StarData[starIndex * 6 + 2],
                    StarData[starIndex * 6 + 3] // Using appmag as alpha for demonstration
                );
            }
            else
            {
                TextureData[index] = Color.black; // Fill remaining texture space with black (no data)
            }
        }
    }

    private int ReadIntBigEndian(BinaryReader reader)
    {
        byte[] bytes = reader.ReadBytes(4);
        if (BitConverter.IsLittleEndian)
        {
            Array.Reverse(bytes);
        }
        return BitConverter.ToInt32(bytes, 0);
    }

    private float ReadFloatBigEndian(BinaryReader reader)
    {
        byte[] bytes = reader.ReadBytes(4);
        if (BitConverter.IsLittleEndian)
        {
            Array.Reverse(bytes);
        }
        return BitConverter.ToSingle(bytes, 0);
    }

    private double ReadDoubleBigEndian(BinaryReader reader)
    {
        byte[] bytes = reader.ReadBytes(8);
        if (BitConverter.IsLittleEndian)
        {
            Array.Reverse(bytes);
        }
        return BitConverter.ToDouble(bytes, 0);
    }
}

Key Changes and Explanation:

1. Raw Texture Data: NativeArray<Color> textureData = starTexture.GetRawTextureData<Color>() is used to get a NativeArray that directly maps to the texture’s pixel data. This eliminates the need for a separate allocation and copying of data.
2. Memory Efficiency: Data is directly written to the texture data array, improving performance and reducing memory overhead.
3. Texture Apply: starTexture.Apply() is called to upload the modified pixel data to the GPU.

Benefits:

• Improved memory usage by avoiding redundant copying of color data.
• Better performance through direct manipulation of texture data.
• Streamlined data processing using Unity’s job system and NativeArray.

You

Ok, but I want to create 1 texture that contains XYZ positions, another one that contains color and another one that contains size and magnitude. also the color was encoded, must be decoded

ChatGPT

To accommodate your requirements, I’ll modify the code to create three separate textures:

1. One texture for storing XYZ positions.
2. Another texture for storing color (decoded).
3. A third texture for storing size and magnitude.

Additionally, I’ll handle color decoding according to your requirements (assuming some kind of color decoding logic based on your earlier description).

Here’s the modified code:

using System;
using System.IO;
using Unity.Collections;
using Unity.Jobs;
using Unity.Burst;
using UnityEngine;

public class StarProcessorWithMultipleTextures : MonoBehaviour
{
    public string directoryPath; // Directory containing BIN files
    public int maxRows = 1000; // Maximum number of stars to process
    public Texture2D positionTexture; // Texture to store XYZ positions
    public Texture2D colorTexture; // Texture to store decoded color
    public Texture2D sizeMagnitudeTexture; // Texture to store size and magnitude

    private const int TextureWidth = 1024;
    private const int TextureHeight = 1024;

    void Start()
    {
        if (!Directory.Exists(directoryPath))
        {
            Debug.LogError("Directory does not exist: " + directoryPath);
            return;
        }

        string[] files = Directory.GetFiles(directoryPath, "*.bin");
        Array.Sort(files); // Sort files alphabetically

        NativeArray<float> starData = new NativeArray<float>(maxRows * 6, Allocator.TempJob); // Each star has 6 values: x, y, z, appmag, color, size
        int totalStarsRead = 0;

        foreach (string file in files)
        {
            if (totalStarsRead >= maxRows)
                break;

            using (BinaryReader reader = new BinaryReader(File.Open(file, FileMode.Open)))
            {
                int starsRead = ReadBinFile(reader, starData, totalStarsRead, maxRows);
                totalStarsRead += starsRead;
            }
        }

        CreateTexturesFromData(starData, totalStarsRead);
        starData.Dispose();
    }

    private int ReadBinFile(BinaryReader reader, NativeArray<float> starData, int startIdx, int maxRows)
    {
        int numberOfStars = ReadIntBigEndian(reader);

        int count = 0;
        while (count < numberOfStars && startIdx + count < maxRows)
        {
            ReadStar(reader, starData, startIdx + count);
            count++;
        }

        return count;
    }

    private void ReadStar(BinaryReader reader, NativeArray<float> starData, int index)
    {
        // Read position (normalized)
        double x = ReadDoubleBigEndian(reader);
        double y = ReadDoubleBigEndian(reader);
        double z = ReadDoubleBigEndian(reader);
        Vector3 position = new Vector3((float)x, (float)y, (float)z).normalized;

        // Skip 48 bytes
        reader.BaseStream.Seek(48, SeekOrigin.Current);

        // Read attributes (appmag, absmag, color, size)
        float appmag = ReadFloatBigEndian(reader);
        float absmag = ReadFloatBigEndian(reader);
        float encodedColor = ReadFloatBigEndian(reader);
        float size = ReadFloatBigEndian(reader);

        // Skip 24 bytes
        reader.BaseStream.Seek(24, SeekOrigin.Current);

        // Read name length and skip the name
        int nameLength = ReadIntBigEndian(reader);
        if (nameLength > 0)
        {
            reader.BaseStream.Seek(nameLength * 2, SeekOrigin.Current);
        }

        // Store the star data
        int baseIdx = index * 6;
        starData[baseIdx] = position.x;
        starData[baseIdx + 1] = position.y;
        starData[baseIdx + 2] = position.z;
        starData[baseIdx + 3] = appmag;
        starData[baseIdx + 4] = DecodeColor(encodedColor); // Decode the color
        starData[baseIdx + 5] = size;
    }

    private float DecodeColor(float encodedColor)
    {
        // Placeholder color decoding logic, replace with your actual decoding logic
        return Mathf.Clamp01(encodedColor); // Simple normalization for demonstration
    }

    private void CreateTexturesFromData(NativeArray<float> starData, int totalStars)
    {
        // Create the textures
        positionTexture = new Texture2D(TextureWidth, TextureHeight, TextureFormat.RGBAFloat, false);
        colorTexture = new Texture2D(TextureWidth, TextureHeight, TextureFormat.RFloat, false);
        sizeMagnitudeTexture = new Texture2D(TextureWidth, TextureHeight, TextureFormat.RGFloat, false);

        NativeArray<Color> positionTextureData = positionTexture.GetRawTextureData<Color>();
        NativeArray<float> colorTextureData = colorTexture.GetRawTextureData<float>();
        NativeArray<Vector2> sizeMagnitudeTextureData = sizeMagnitudeTexture.GetRawTextureData<Vector2>();

        ProcessStarDataJob job = new ProcessStarDataJob
        {
            StarData = starData,
            PositionTextureData = positionTextureData,
            ColorTextureData = colorTextureData,
            SizeMagnitudeTextureData = sizeMagnitudeTextureData,
            TotalStars = totalStars
        };

        JobHandle handle = job.Schedule(positionTextureData.Length, 64); // Schedule parallel processing
        handle.Complete();

        positionTexture.Apply(); // Apply changes to the position texture
        colorTexture.Apply(); // Apply changes to the color texture
        sizeMagnitudeTexture.Apply(); // Apply changes to the size-magnitude texture
    }

    [BurstCompile]
    private struct ProcessStarDataJob : IJobParallelFor
    {
        [ReadOnly] public NativeArray<float> StarData;
        public NativeArray<Color> PositionTextureData;
        public NativeArray<float> ColorTextureData;
        public NativeArray<Vector2> SizeMagnitudeTextureData;
        public int TotalStars;

        public void Execute(int index)
        {
            if (index < TotalStars)
            {
                int baseIdx = index * 6;

                // Write XYZ to position texture
                PositionTextureData[index] = new Color(
                    StarData[baseIdx],
                    StarData[baseIdx + 1],
                    StarData[baseIdx + 2],
                    1.0f // Alpha channel, can be used or ignored
                );

                // Write decoded color to color texture
                ColorTextureData[index] = StarData[baseIdx + 4];

                // Write size and magnitude to size-magnitude texture
                SizeMagnitudeTextureData[index] = new Vector2(
                    StarData[baseIdx + 5], // Size
                    StarData[baseIdx + 3]  // Appmag
                );
            }
            else
            {
                // Fill remaining texture space with default values
                PositionTextureData[index] = Color.black;
                ColorTextureData[index] = 0.0f;
                SizeMagnitudeTextureData[index] = Vector2.zero;
            }
        }
    }

    private int ReadIntBigEndian(BinaryReader reader)
    {
        byte[] bytes = reader.ReadBytes(4);
        if (BitConverter.IsLittleEndian)
        {
            Array.Reverse(bytes);
        }
        return BitConverter.ToInt32(bytes, 0);
    }

    private float ReadFloatBigEndian(BinaryReader reader)
    {
        byte[] bytes = reader.ReadBytes(4);
        if (BitConverter.IsLittleEndian)
        {
            Array.Reverse(bytes);
        }
        return BitConverter.ToSingle(bytes, 0);
    }

    private double ReadDoubleBigEndian(BinaryReader reader)
    {
        byte[] bytes = reader.ReadBytes(8);
        if (BitConverter.IsLittleEndian)
        {
            Array.Reverse(bytes);
        }
        return BitConverter.ToDouble(bytes, 0);
    }
}

Explanation of the Modifications:

1. Three Textures:

   • positionTexture stores the XYZ positions.
   • colorTexture stores the decoded color.
   • sizeMagnitudeTexture stores size and apparent magnitude.

2. Decoding Color:

   • A DecodeColor function is provided to demonstrate color decoding logic. Replace it with the actual decoding logic as required.

3. Raw Texture Data Access:

   • NativeArray<Color>, NativeArray<float>, and NativeArray<Vector2> are used to directly modify the raw texture data.

4. Job System:

   • The ProcessStarDataJob writes data to the three different textures in parallel.

This approach ensures efficient memory usage and parallel processing while splitting the data across three textures for use in your VFX graph.

You

i can only assume that the encoded color is an rgba 8bit color encoded into a float. lets turn the float into Color32

ChatGPT

To decode the float as an RGBA 8-bit color into a Color32, you can follow this approach. A float can be interpreted as 32 bits, where each byte represents a channel (R, G, B, A) packed into a float. Here’s how you can decode the float into a Color32 in Unity:

I’ll modify the decoding function and relevant code to convert the encoded color float into a Color32 and integrate it accordingly.

Here’s the updated code:

using System;
using System.IO;
using Unity.Collections;
using Unity.Jobs;
using Unity.Burst;
using UnityEngine;

public class StarProcessorWithMultipleTextures : MonoBehaviour
{
    public string directoryPath; // Directory containing BIN files
    public int maxRows = 1000; // Maximum number of stars to process
    public Texture2D positionTexture; // Texture to store XYZ positions
    public Texture2D colorTexture; // Texture to store decoded color (as Color32)
    public Texture2D sizeMagnitudeTexture; // Texture to store size and magnitude

    private const int TextureWidth = 1024;
    private const int TextureHeight = 1024;

    void Start()
    {
        if (!Directory.Exists(directoryPath))
        {
            Debug.LogError("Directory does not exist: " + directoryPath);
            return;
        }

        string[] files = Directory.GetFiles(directoryPath, "*.bin");
        Array.Sort(files); // Sort files alphabetically

        NativeArray<float> starData = new NativeArray<float>(maxRows * 6, Allocator.TempJob); // Each star has 6 values: x, y, z, appmag, encodedColor, size
        int totalStarsRead = 0;

        foreach (string file in files)
        {
            if (totalStarsRead >= maxRows)
                break;

            using (BinaryReader reader = new BinaryReader(File.Open(file, FileMode.Open)))
            {
                int starsRead = ReadBinFile(reader, starData, totalStarsRead, maxRows);
                totalStarsRead += starsRead;
            }
        }

        CreateTexturesFromData(starData, totalStarsRead);
        starData.Dispose();
    }

    private int ReadBinFile(BinaryReader reader, NativeArray<float> starData, int startIdx, int maxRows)
    {
        int numberOfStars = ReadIntBigEndian(reader);

        int count = 0;
        while (count < numberOfStars && startIdx + count < maxRows)
        {
            ReadStar(reader, starData, startIdx + count);
            count++;
        }

        return count;
    }

    private void ReadStar(BinaryReader reader, NativeArray<float> starData, int index)
    {
        // Read position (normalized)
        double x = ReadDoubleBigEndian(reader);
        double y = ReadDoubleBigEndian(reader);
        double z = ReadDoubleBigEndian(reader);
        Vector3 position = new Vector3((float)x, (float)y, (float)z).normalized;

        // Skip 48 bytes
        reader.BaseStream.Seek(48, SeekOrigin.Current);

        // Read attributes (appmag, absmag, encodedColor, size)
        float appmag = ReadFloatBigEndian(reader);
        float absmag = ReadFloatBigEndian(reader);
        float encodedColor = ReadFloatBigEndian(reader);
        float size = ReadFloatBigEndian(reader);

        // Skip 24 bytes
        reader.BaseStream.Seek(24, SeekOrigin.Current);

        // Read name length and skip the name
        int nameLength = ReadIntBigEndian(reader);
        if (nameLength > 0)
        {
            reader.BaseStream.Seek(nameLength * 2, SeekOrigin.Current);
        }

        // Store the star data
        int baseIdx = index * 6;
        starData[baseIdx] = position.x;
        starData[baseIdx + 1] = position.y;
        starData[baseIdx + 2] = position.z;
        starData[baseIdx + 3] = appmag;
        starData[baseIdx + 4] = encodedColor; // Store encoded color as a float
        starData[baseIdx + 5] = size;
    }

    private Color32 DecodeColor(float encodedColor)
    {
        uint colorBits = BitConverter.ToUInt32(BitConverter.GetBytes(encodedColor), 0);
        byte r = (byte)((colorBits >> 24) & 0xFF);
        byte g = (byte)((colorBits >> 16) & 0xFF);
        byte b = (byte)((colorBits >> 8) & 0xFF);
        byte a = (byte)(colorBits & 0xFF);
        return new Color32(r, g, b, a);
    }

    private void CreateTexturesFromData(NativeArray<float> starData, int totalStars)
    {
        // Create the textures
        positionTexture = new Texture2D(TextureWidth, TextureHeight, TextureFormat.RGBAFloat, false);
        colorTexture = new Texture2D(TextureWidth, TextureHeight, TextureFormat.RGBA32, false);
        sizeMagnitudeTexture = new Texture2D(TextureWidth, TextureHeight, TextureFormat.RGFloat, false);

        NativeArray<Color> positionTextureData = positionTexture.GetRawTextureData<Color>();
        NativeArray<Color32> colorTextureData = colorTexture.GetRawTextureData<Color32>();
        NativeArray<Vector2> sizeMagnitudeTextureData = sizeMagnitudeTexture.GetRawTextureData<Vector2>();

        ProcessStarDataJob job = new ProcessStarDataJob
        {
            StarData = starData,
            PositionTextureData = positionTextureData,
            ColorTextureData = colorTextureData,
            SizeMagnitudeTextureData = sizeMagnitudeTextureData,
            TotalStars = totalStars
        };

        JobHandle handle = job.Schedule(positionTextureData.Length, 64); // Schedule parallel processing
        handle.Complete();

        positionTexture.Apply(); // Apply changes to the position texture
        colorTexture.Apply(); // Apply changes to the color texture
        sizeMagnitudeTexture.Apply(); // Apply changes to the size-magnitude texture
    }

    [BurstCompile]
    private struct ProcessStarDataJob : IJobParallelFor
    {
        [ReadOnly] public NativeArray<float> StarData;
        public NativeArray<Color> PositionTextureData;
        public NativeArray<Color32> ColorTextureData;
        public NativeArray<Vector2> SizeMagnitudeTextureData;
        public int TotalStars;

        public void Execute(int index)
        {
            if (index < TotalStars)
            {
                int baseIdx = index * 6;

                // Write XYZ to position texture
                PositionTextureData[index] = new Color(
                    StarData[baseIdx],
                    StarData[baseIdx + 1],
                    StarData[baseIdx + 2],
                    1.0f // Alpha channel, can be used or ignored
                );

                // Decode and write color to color texture
                float encodedColor = StarData[baseIdx + 4];
                Color32 decodedColor = DecodeColor(encodedColor);
                ColorTextureData[index] = decodedColor;

                // Write size and magnitude to size-magnitude texture
                SizeMagnitudeTextureData[index] = new Vector2(
                    StarData[baseIdx + 5], // Size
                    StarData[baseIdx + 3]  // Appmag
                );
            }
            else
            {
                // Fill remaining texture space with default values
                PositionTextureData[index] = Color.black;
                ColorTextureData[index] = new Color32(0, 0, 0, 0);
                SizeMagnitudeTextureData[index] = Vector2.zero;
            }
        }

        private Color32 DecodeColor(float encodedColor)
        {
            uint colorBits = BitConverter.ToUInt32(BitConverter.GetBytes(encodedColor), 0);
            byte r = (byte)((colorBits >> 24) & 0xFF);
            byte g = (byte)((colorBits >> 16) & 0xFF);
            byte b = (byte)((colorBits >> 8) & 0xFF);
            byte a = (byte)(colorBits & 0xFF);
            return new Color32(r, g, b, a);
        }
    }

    private int ReadIntBigEndian(BinaryReader reader)
    {
        byte[] bytes = reader.ReadBytes(4);
        if (BitConverter.IsLittleEndian)
        {
            Array.Reverse(bytes);
        }
        return BitConverter.ToInt32(bytes, 0);
    }

    private float ReadFloatBigEndian(BinaryReader reader)
    {
        byte[] bytes = reader.ReadBytes(4);
        if (BitConverter.IsLittleEndian)
        {
            Array.Reverse(bytes);
        }
        return BitConverter.ToSingle(bytes, 0);
    }

    private double ReadDoubleBigEndian(BinaryReader reader)
    {
        byte[] bytes = reader.ReadBytes(8);
        if (BitConverter.IsLittleEndian)
        {
            Array.Reverse(bytes);
        }
        return BitConverter.ToDouble(bytes, 0);
    }
}

Explanation:

1. Decoding the Encoded Color:

   • The DecodeColor(float encodedColor) method decodes the float representation of the RGBA color into a Color32 using bitwise operations.
   • It assumes that the float is encoded in a way that can be interpreted as a 32-bit unsigned integer, where each byte represents one channel.

2. Separate Textures:

   • positionTexture stores XYZ positions.
   • colorTexture stores the decoded colors as Color32.
   • sizeMagnitudeTexture stores size and magnitude values.

3. NativeArray Usage:

   • Directly accesses the raw texture data for improved

You

Burst error BC1016: The managed function System.BitConverter.GetBytes(float value) is not supported

ChatGPT

To avoid using System.BitConverter.GetBytes(float) within a Burst-compatible job, you need to use a different approach to convert the float directly to a uint. Here’s how you can modify the code to use Unity.Mathematics utilities, which are compatible with Burst:

Updated Code with Unity.Mathematics for Burst Compatibility

using System;
using System.IO;
using Unity.Collections;
using Unity.Jobs;
using Unity.Burst;
using Unity.Mathematics;
using UnityEngine;

public class StarProcessorWithMultipleTextures : MonoBehaviour
{
    public string directoryPath; // Directory containing BIN files
    public int maxRows = 1000; // Maximum number of stars to process
    public Texture2D positionTexture; // Texture to store XYZ positions
    public Texture2D colorTexture; // Texture to store decoded color (as Color32)
    public Texture2D sizeMagnitudeTexture; // Texture to store size and magnitude

    private const int TextureWidth = 1024;
    private const int TextureHeight = 1024;

    void Start()
    {
        if (!Directory.Exists(directoryPath))
        {
            Debug.LogError("Directory does not exist: " + directoryPath);
            return;
        }

        string[] files = Directory.GetFiles(directoryPath, "*.bin");
        Array.Sort(files); // Sort files alphabetically

        NativeArray<float> starData = new NativeArray<float>(maxRows * 6, Allocator.TempJob); // Each star has 6 values: x, y, z, appmag, encodedColor, size
        int totalStarsRead = 0;

        foreach (string file in files)
        {
            if (totalStarsRead >= maxRows)
                break;

            using (BinaryReader reader = new BinaryReader(File.Open(file, FileMode.Open)))
            {
                int starsRead = ReadBinFile(reader, starData, totalStarsRead, maxRows);
                totalStarsRead += starsRead;
            }
        }

        CreateTexturesFromData(starData, totalStarsRead);
        starData.Dispose();
    }

    private int ReadBinFile(BinaryReader reader, NativeArray<float> starData, int startIdx, int maxRows)
    {
        int numberOfStars = ReadIntBigEndian(reader);

        int count = 0;
        while (count < numberOfStars && startIdx + count < maxRows)
        {
            ReadStar(reader, starData, startIdx + count);
            count++;
        }

        return count;
    }

    private void ReadStar(BinaryReader reader, NativeArray<float> starData, int index)
    {
        // Read position (normalized)
        double x = ReadDoubleBigEndian(reader);
        double y = ReadDoubleBigEndian(reader);
        double z = ReadDoubleBigEndian(reader);
        Vector3 position = new Vector3((float)x, (float)y, (float)z).normalized;

        // Skip 48 bytes
        reader.BaseStream.Seek(48, SeekOrigin.Current);

        // Read attributes (appmag, absmag, encodedColor, size)
        float appmag = ReadFloatBigEndian(reader);
        float absmag = ReadFloatBigEndian(reader);
        float encodedColor = ReadFloatBigEndian(reader);
        float size = ReadFloatBigEndian(reader);

        // Skip 24 bytes
        reader.BaseStream.Seek(24, SeekOrigin.Current);

        // Read name length and skip the name
        int nameLength = ReadIntBigEndian(reader);
        if (nameLength > 0)
        {
            reader.BaseStream.Seek(nameLength * 2, SeekOrigin.Current);
        }

        // Store the star data
        int baseIdx = index * 6;
        starData[baseIdx] = position.x;
        starData[baseIdx + 1] = position.y;
        starData[baseIdx + 2] = position.z;
        starData[baseIdx + 3] = appmag;
        starData[baseIdx + 4] = encodedColor; // Store encoded color as a float
        starData[baseIdx + 5] = size;
    }

    private void CreateTexturesFromData(NativeArray<float> starData, int totalStars)
    {
        // Create the textures
        positionTexture = new Texture2D(TextureWidth, TextureHeight, TextureFormat.RGBAFloat, false);
        colorTexture = new Texture2D(TextureWidth, TextureHeight, TextureFormat.RGBA32, false);
        sizeMagnitudeTexture = new Texture2D(TextureWidth, TextureHeight, TextureFormat.RGFloat, false);

        NativeArray<Color> positionTextureData = positionTexture.GetRawTextureData<Color>();
        NativeArray<Color32> colorTextureData = colorTexture.GetRawTextureData<Color32>();
        NativeArray<Vector2> sizeMagnitudeTextureData = sizeMagnitudeTexture.GetRawTextureData<Vector2>();

        ProcessStarDataJob job = new ProcessStarDataJob
        {
            StarData = starData,
            PositionTextureData = positionTextureData,
            ColorTextureData = colorTextureData,
            SizeMagnitudeTextureData = sizeMagnitudeTextureData,
            TotalStars = totalStars
        };

        JobHandle handle = job.Schedule(positionTextureData.Length, 64); // Schedule parallel processing
        handle.Complete();

        positionTexture.Apply(); // Apply changes to the position texture
        colorTexture.Apply(); // Apply changes to the color texture
        sizeMagnitudeTexture.Apply(); // Apply changes to the size-magnitude texture
    }

    [BurstCompile]
    private struct ProcessStarDataJob : IJobParallelFor
    {
        [ReadOnly] public NativeArray<float> StarData;
        public NativeArray<Color> PositionTextureData;
        public NativeArray<Color32> ColorTextureData;
        public NativeArray<Vector2> SizeMagnitudeTextureData;
        public int TotalStars;

        public void Execute(int index)
        {
            if (index < TotalStars)
            {
                int baseIdx = index * 6;

                // Write XYZ to position texture
                PositionTextureData[index] = new Color(
                    StarData[baseIdx],
                    StarData[baseIdx + 1],
                    StarData[baseIdx + 2],
                    1.0f // Alpha channel, can be used or ignored
                );

                // Decode and write color to color texture
                float encodedColor = StarData[baseIdx + 4];
                Color32 decodedColor = DecodeColor(encodedColor);
                ColorTextureData[index] = decodedColor;

                // Write size and magnitude to size-magnitude texture
                SizeMagnitudeTextureData[index] = new Vector2(
                    StarData[baseIdx + 5], // Size
                    StarData[baseIdx + 3]  // Appmag
                );
            }
            else
            {
                // Fill remaining texture space with default values
                PositionTextureData[index] = Color.black;
                ColorTextureData[index] = new Color32(0, 0, 0, 0);
                SizeMagnitudeTextureData[index] = Vector2.zero;
            }
        }

        private Color32 DecodeColor(float encodedColor)
        {
            // Use Unity.Mathematics for Burst-compatible float-to-uint conversion
            uint colorBits = math.asuint(encodedColor);
            byte r = (byte)((colorBits >> 24) & 0xFF);
            byte g = (byte)((colorBits >> 16) & 0xFF);
            byte b = (byte)((colorBits >> 8) & 0xFF);
            byte a = (byte)(colorBits & 0xFF);
            return new Color32(r, g, b, a);
        }
    }

    private int ReadIntBigEndian(BinaryReader reader)
    {
        byte[] bytes = reader.ReadBytes(4);
        if (BitConverter.IsLittleEndian)
        {
            Array.Reverse(bytes);
        }
        return BitConverter.ToInt32(bytes, 0);
    }

    private float ReadFloatBigEndian(BinaryReader reader)
    {
        byte[] bytes = reader.ReadBytes(4);
        if (BitConverter.IsLittleEndian)
        {
            Array.Reverse(bytes);
        }
        return BitConverter.ToSingle(bytes, 0);
    }

    private double ReadDoubleBigEndian(BinaryReader reader)
    {
        byte[] bytes = reader.ReadBytes(8);
        if (BitConverter.IsLittleEndian)
        {
            Array.Reverse(bytes);
        }
        return BitConverter.ToDouble(bytes, 0);
    }
}

Key Changes:

1. math.asuint() Usage:

   • The DecodeColor method uses math.asuint(encodedColor) to convert the float to uint without using BitConverter, making it compatible with Burst.
   • This approach ensures efficient conversion in a Burst-compatible manner.

2. Unity.Mathematics:

   • This library provides Burst-friendly mathematical operations and conversions, including asuint() to convert a float to a uint directly.

This change ensures that your code is Burst-compatible and still effectively decodes the float-encoded color into a Color32.

You

store the textures in the assets folder

ChatGPT

To store the textures in Unity’s Assets folder, you’ll need to save the generated Texture2D objects as PNG files or another suitable format. Unity’s Texture2D.EncodeToPNG() can be used to encode the textures, and System.IO.File.WriteAllBytes() will save them to a specified path.

Here’s how to modify the code to save the generated textures in the Assets folder:

using System;
using System.IO;
using Unity.Collections;
using Unity.Jobs;
using Unity.Burst;
using Unity.Mathematics;
using UnityEngine;

public class StarProcessorWithMultipleTextures : MonoBehaviour
{
    public string directoryPath; // Directory containing BIN files
    public int maxRows = 1000; // Maximum number of stars to process
    private const int TextureWidth = 1024;
    private const int TextureHeight = 1024;

    private Texture2D positionTexture; // Texture to store XYZ positions
    private Texture2D colorTexture; // Texture to store decoded color (as Color32)
    private Texture2D sizeMagnitudeTexture; // Texture to store size and magnitude

    void Start()
    {
        if (!Directory.Exists(directoryPath))
        {
            Debug.LogError("Directory does not exist: " + directoryPath);
            return;
        }

        string[] files = Directory.GetFiles(directoryPath, "*.bin");
        Array.Sort(files); // Sort files alphabetically

        NativeArray<float> starData = new NativeArray<float>(maxRows * 6, Allocator.TempJob); // Each star has 6 values: x, y, z, appmag, encodedColor, size
        int totalStarsRead = 0;

        foreach (string file in files)
        {
            if (totalStarsRead >= maxRows)
                break;

            using (BinaryReader reader = new BinaryReader(File.Open(file, FileMode.Open)))
            {
                int starsRead = ReadBinFile(reader, starData, totalStarsRead, maxRows);
                totalStarsRead += starsRead;
            }
        }

        CreateAndSaveTextures(starData, totalStarsRead);
        starData.Dispose();
    }

    private int ReadBinFile(BinaryReader reader, NativeArray<float> starData, int startIdx, int maxRows)
    {
        int numberOfStars = ReadIntBigEndian(reader);

        int count = 0;
        while (count < numberOfStars && startIdx + count < maxRows)
        {
            ReadStar(reader, starData, startIdx + count);
            count++;
        }

        return count;
    }

    private void ReadStar(BinaryReader reader, NativeArray<float> starData, int index)
    {
        // Read position (normalized)
        double x = ReadDoubleBigEndian(reader);
        double y = ReadDoubleBigEndian(reader);
        double z = ReadDoubleBigEndian(reader);
        Vector3 position = new Vector3((float)x, (float)y

You

they need to be stored as floating point textures, since they contain position data

ChatGPT

To save the textures in the Unity Assets folder as floating-point textures, Unity provides a way to save Texture2D in an uncompressed floating-point format, which is crucial for data like position vectors.

For saving high-precision textures in Unity:

1. Convert the textures to a RenderTexture with a floating-point format.
2. Read the texture data back from the RenderTexture into a Texture2D that can save 16-bit or 32-bit precision.
3. Use AssetDatabase.CreateAsset to save the Texture2D as an asset in the Assets folder (requires Unity Editor scripting).

Here’s how you can implement it:

#if UNITY_EDITOR
using System.IO;
using Unity.Collections;
using Unity.Jobs;
using Unity.Burst;
using Unity.Mathematics;
using UnityEngine;
using UnityEditor;

public class StarProcessorWithMultipleTextures : MonoBehaviour
{
    public string directoryPath; // Directory containing BIN files
    public int maxRows = 1000; // Maximum number of stars to process
    private const int TextureWidth = 1024;
    private const int TextureHeight = 1024;

    private Texture2D positionTexture; // Texture to store XYZ positions
    private Texture2D colorTexture; // Texture to store decoded color (as Color32)
    private Texture2D sizeMagnitudeTexture; // Texture to store size and magnitude

    void Start()
    {
        if (!Directory.Exists(directoryPath))
        {
            Debug.LogError("Directory does not exist: " + directoryPath);
            return;
        }

        string[] files = Directory.GetFiles(directoryPath, "*.bin");
        Array.Sort(files); // Sort files alphabetically

        NativeArray<float> starData = new NativeArray<float>(maxRows * 6, Allocator.TempJob); // Each star has 6 values: x, y, z, appmag, encodedColor, size
        int totalStarsRead = 0;

        foreach (string file in files)
        {
            if (totalStarsRead >= maxRows)
                break;

            using (BinaryReader reader = new BinaryReader(File.Open(file, FileMode.Open)))
            {
                int starsRead = ReadBinFile(reader, starData, totalStarsRead, maxRows);
                totalStarsRead += starsRead;
            }
        }

        CreateAndSaveTextures(starData, totalStarsRead);
        starData.Dispose();
    }

    private int ReadBinFile(BinaryReader reader, NativeArray<float> starData, int startIdx, int maxRows)
    {
        int numberOfStars = ReadIntBigEndian(reader);

        int count = 0;
        while (count < numberOfStars && startIdx + count < maxRows)
        {
            ReadStar(reader, starData, startIdx + count);
            count++;
        }

        return count;
    }

    private void ReadStar(BinaryReader reader, NativeArray<float> starData, int index)
    {
        // Read position (normalized)
        double x = ReadDoubleBigEndian(reader);
        double y = ReadDoubleBigEndian(reader);
        double z = ReadDoubleBigEndian(reader);
        Vector3 position = new Vector3((float)x, (float)y, (float)z).normalized;

        // Skip 48 bytes
        reader.BaseStream.Seek(48, SeekOrigin.Current);

        // Read attributes (appmag, absmag, encodedColor, size)
        float appmag = ReadFloatBigEndian(reader);
        float absmag = ReadFloatBigEndian(reader);
        float encodedColor = ReadFloatBigEndian(reader);
        float size = ReadFloatBigEndian(reader);

        // Skip 24 bytes
        reader.BaseStream.Seek(24, SeekOrigin.Current);

        // Read name length and skip the name
        int nameLength = ReadIntBigEndian(reader);
        if (nameLength > 0)
        {
            reader.BaseStream.Seek(nameLength * 2, SeekOrigin.Current);
        }

        // Store the star data
        int baseIdx = index * 6;
        starData[baseIdx] = position.x;
        starData[baseIdx + 1] = position.y;
        starData[baseIdx + 2] = position.z;
        starData[baseIdx + 3] = appmag;
        starData[baseIdx + 4] = encodedColor; // Store encoded color as a float
        starData[baseIdx + 5] = size;
    }

    private void CreateAndSaveTextures(NativeArray<float> starData, int totalStars)
    {
        // Create the textures
        positionTexture = new Texture2D(TextureWidth, TextureHeight, TextureFormat.RGBAFloat, false);
        colorTexture = new Texture2D(TextureWidth, TextureHeight, TextureFormat.RGBA32, false);
        sizeMagnitudeTexture = new Texture2D(TextureWidth, TextureHeight, TextureFormat.RGFloat, false);

        NativeArray<Color> positionTextureData = positionTexture.GetRawTextureData<Color>();
        NativeArray<Color32> colorTextureData = colorTexture.GetRawTextureData<Color32>();
        NativeArray<Vector2> sizeMagnitudeTextureData = sizeMagnitudeTexture.GetRawTextureData<Vector2>();

        ProcessStarDataJob job = new ProcessStarDataJob
        {
            StarData = starData,
            PositionTextureData = positionTextureData,
            ColorTextureData = colorTextureData,
            SizeMagnitudeTextureData = sizeMagnitudeTextureData,
            TotalStars = totalStars
        };

        JobHandle handle = job.Schedule(positionTextureData.Length, 64); // Schedule parallel processing
        handle.Complete();

        positionTexture.Apply(); // Apply changes to the position texture
        colorTexture.Apply(); // Apply changes to the color texture
        sizeMagnitudeTexture.Apply(); // Apply changes to the size-magnitude texture

        // Save the textures to the Assets folder
        SaveTextureAsAsset(positionTexture, "Assets/PositionTexture.asset");
        SaveTextureAsAsset(colorTexture, "Assets/ColorTexture.asset");
        SaveTextureAsAsset(sizeMagnitudeTexture, "Assets/SizeMagnitudeTexture.asset");

        // Clean up native arrays
        positionTextureData.Dispose();
        colorTextureData.Dispose();
        sizeMagnitudeTextureData.Dispose();
    }

    private void SaveTextureAsAsset(Texture2D texture, string assetPath)
    {
        // Ensure the directory exists
        string directory = Path.GetDirectoryName(assetPath);
        if (!Directory.Exists(directory))
        {
            Directory.CreateDirectory(directory);
        }

        // Encode texture to a format and save as asset
        byte[] textureData = texture.EncodeToEXR(Texture2D.EXRFlags.OutputAsFloat);
        File.WriteAllBytes(assetPath, textureData);

        // Import the asset to Unity’s AssetDatabase
        AssetDatabase.ImportAsset(assetPath);
        TextureImporter importer = (TextureImporter)AssetImporter.GetAtPath(assetPath);
        importer.textureCompression = TextureImporterCompression.Uncompressed;
        importer.SaveAndReimport();
    }

    [BurstCompile]
    private struct ProcessStarDataJob : IJobParallelFor
    {
        [ReadOnly] public NativeArray<float> StarData;
        public NativeArray<Color> PositionTextureData;
        public NativeArray<Color32> ColorTextureData;
        public NativeArray<Vector2> SizeMagnitudeTextureData;
        public int TotalStars;

        public void Execute(int index)
        {
            if (index < TotalStars)
            {
                int baseIdx = index * 6;

                // Write XYZ to position texture
                PositionTextureData[index] = new Color(
                    StarData[baseIdx],
                    StarData[baseIdx + 1],
                    StarData[baseIdx + 2],
                    1.0f // Alpha channel, can be used or ignored
                );

                // Decode and write color to color texture
                float encodedColor = StarData[baseIdx + 4];
                Color32 decodedColor = DecodeColor(encodedColor);
                ColorTextureData[index] = decodedColor;

                // Write size and magnitude to size-magnitude texture
                SizeMagnitudeTextureData[index] = new Vector2(
                    StarData[baseIdx + 5], // Size
                    StarData[baseIdx + 3]  // Appmag
                );
            }
            else
            {
                // Fill remaining texture space with default values
                PositionTextureData[index] = Color.black;
                ColorTextureData[index] = new Color32(0, 0, 0, 0);
                SizeMagnitudeTextureData[index] = Vector2.zero;
            }
        }

        private Color32 DecodeColor(float encodedColor)
        {
            uint colorBits = math.asuint(encodedColor);
            byte r = (byte)((colorBits >> 24) & 0xFF);
            byte g = (byte)((colorBits >> 16) & 0xFF);
            byte b = (byte)((colorBits >> 8) & 0xFF);
            byte a = (byte)(colorBits & 0xFF);
            return new Color32(r, g, b, a);
        }
    }

    private int ReadIntBigEndian(BinaryReader reader)
    {
        byte[] bytes = reader.ReadBytes(4);
        if (BitConverter.IsLittleEndian)
        {
            Array.Reverse(bytes);
        }
        return BitConverter.ToInt32(bytes, 0);
    }

    private float ReadFloatBigEndian(BinaryReader reader)
    {
        byte[] bytes = reader.ReadBytes(4);
        if (BitConverter.IsLittleEndian)
        {
            Array.Reverse(bytes);
        }
        return BitConverter.ToSingle(bytes, 0);
    }

    private double ReadDoubleBigEndian(BinaryReader reader)
    {
        byte[] bytes = reader.ReadBytes(8);
        if (BitConverter.IsLittleEndian)
        {
            Array.Reverse(bytes);

You

can the file be read asynchronously so that Unity doesnt freeze?

ChatGPT

Yes, you can read the file asynchronously to prevent Unity from freezing. Unity’s job system and asynchronous file reading are good solutions for this. By reading the binary files in an async task and then processing the data in Unity’s main thread (or using jobs), you can improve performance and avoid blocking the main thread.

Here’s how to modify the code to asynchronously read files and process data without freezing Unity:

1. Use async and await in C# to read the binary files asynchronously.
2. Once the data is read, use Unity’s NativeArray to store the star data, and then use Unity’s job system to process this data without freezing the main thread.
3. For Unity-specific actions (like creating textures), ensure those run on the main thread by calling them after the async data processing is complete.

Here’s the modified code:

#if UNITY_EDITOR
using System;
using System.IO;
using System.Threading.Tasks;
using Unity.Collections;
using Unity.Jobs;
using Unity.Burst;
using Unity.Mathematics;
using UnityEngine;
using UnityEditor;

public class StarProcessorWithMultipleTextures : MonoBehaviour
{
    public string directoryPath; // Directory containing BIN files
    public int maxRows = 1000; // Maximum number of stars to process
    private const int TextureWidth = 1024;
    private const int TextureHeight = 1024;

    private Texture2D positionTexture; // Texture to store XYZ positions
    private Texture2D colorTexture; // Texture to store decoded color (as Color32)
    private Texture2D sizeMagnitudeTexture; // Texture to store size and magnitude

    async void Start()
    {
        if (!Directory.Exists(directoryPath))
        {
            Debug.LogError("Directory does not exist: " + directoryPath);
            return;
        }

        string[] files = Directory.GetFiles(directoryPath, "*.bin");
        Array.Sort(files); // Sort files alphabetically

        NativeArray<float> starData = new NativeArray<float>(maxRows * 6, Allocator.TempJob); // Each star has 6 values: x, y, z, appmag, encodedColor, size
        int totalStarsRead = 0;

        foreach (string file in files)
        {
            if (totalStarsRead >= maxRows)
                break;

            // Asynchronously read each BIN file without blocking the main thread
            int starsRead = await ReadBinFileAsync(file, starData, totalStarsRead, maxRows);
            totalStarsRead += starsRead;
        }

        CreateAndSaveTextures(starData, totalStarsRead);
        starData.Dispose();
    }

    private async Task<int> ReadBinFileAsync(string filePath, NativeArray<float> starData, int startIdx, int maxRows)
    {
        using (FileStream fileStream = new FileStream(filePath, FileMode.Open, FileAccess.Read, FileShare.Read, 4096, true))
        using (BinaryReader reader = new BinaryReader(fileStream))
        {
            int numberOfStars = ReadIntBigEndian(reader);

            int count = 0;
            while (count < numberOfStars && startIdx + count < maxRows)
            {
                await Task.Yield(); // Allows Unity to remain responsive

                // Read star data synchronously since BinaryReader does not support async read
                ReadStar(reader, starData, startIdx + count);
                count++;
            }

            return count;
        }
    }

    private void ReadStar(BinaryReader reader, NativeArray<float> starData, int index)
    {
        // Read position (normalized)
        double x = ReadDoubleBigEndian(reader);
        double y = ReadDoubleBigEndian(reader);
        double z = ReadDoubleBigEndian(reader);
        Vector3 position = new Vector3((float)x, (float)y, (float)z).normalized;

        // Skip 48 bytes
        reader.BaseStream.Seek(48, SeekOrigin.Current);

        // Read attributes (appmag, absmag, encodedColor, size)
        float appmag = ReadFloatBigEndian(reader);
        float absmag = ReadFloatBigEndian(reader);
        float encodedColor = ReadFloatBigEndian(reader);
        float size = ReadFloatBigEndian(reader);

        // Skip 24 bytes
        reader.BaseStream.Seek(24, SeekOrigin.Current);

        // Read name length and skip the name
        int nameLength = ReadIntBigEndian(reader);
        if (nameLength > 0)
        {
            reader.BaseStream.Seek(nameLength * 2, SeekOrigin.Current);
        }

        // Store the star data
        int baseIdx = index * 6;
        starData[baseIdx] = position.x;
        starData[baseIdx + 1] = position.y;
        starData[baseIdx + 2] = position.z;
        starData[baseIdx + 3] = appmag;
        starData[baseIdx + 4] = encodedColor; // Store encoded color as a float
        starData[baseIdx + 5] = size;
    }

    private void CreateAndSaveTextures(NativeArray<float> starData, int totalStars)
    {
        // Create the textures
        positionTexture = new Texture2D(TextureWidth, TextureHeight, TextureFormat.RGBAFloat, false);
        colorTexture = new Texture2D(TextureWidth, TextureHeight, TextureFormat.RGBA32, false);
        sizeMagnitudeTexture = new Texture2D(TextureWidth, TextureHeight, TextureFormat.RGFloat, false);

        NativeArray<Color> positionTextureData = positionTexture.GetRawTextureData<Color>();
        NativeArray<Color32> colorTextureData = colorTexture.GetRawTextureData<Color32>();
        NativeArray<Vector2> sizeMagnitudeTextureData = sizeMagnitudeTexture.GetRawTextureData<Vector2>();

        ProcessStarDataJob job = new ProcessStarDataJob
        {
            StarData = starData,
            PositionTextureData = positionTextureData,
            ColorTextureData = colorTextureData,
            SizeMagnitudeTextureData = sizeMagnitudeTextureData,
            TotalStars = totalStars
        };

        JobHandle handle = job.Schedule(positionTextureData.Length, 64); // Schedule parallel processing
        handle.Complete();

        positionTexture.Apply(); // Apply changes to the position texture
        colorTexture.Apply(); // Apply changes to the color texture
        sizeMagnitudeTexture.Apply(); // Apply changes to the size-magnitude texture

        // Save the textures to the Assets folder
        SaveTextureAsAsset(positionTexture, "Assets/PositionTexture.asset");
        SaveTextureAsAsset(colorTexture, "Assets/ColorTexture.asset");
        SaveTextureAsAsset(sizeMagnitudeTexture, "Assets/SizeMagnitudeTexture.asset");

        // Clean up native arrays
        positionTextureData.Dispose();
        colorTextureData.Dispose();
        sizeMagnitudeTextureData.Dispose();
    }

    private void SaveTextureAsAsset(Texture2D texture, string assetPath)
    {
        // Ensure the directory exists
        string directory = Path.GetDirectoryName(assetPath);
        if (!Directory.Exists(directory))
        {
            Directory.CreateDirectory(directory);
        }

        // Encode texture to a format and save as asset
        byte[] textureData = texture.EncodeToEXR(Texture2D.EXRFlags.OutputAsFloat);
        File.WriteAllBytes(assetPath, textureData);

        // Import the asset to Unity’s AssetDatabase
        AssetDatabase.ImportAsset(assetPath);
        TextureImporter importer = (TextureImporter)AssetImporter.GetAtPath(assetPath);
        importer.textureCompression = TextureImporterCompression.Uncompressed;
        importer.SaveAndReimport();
    }

    [BurstCompile]
    private struct ProcessStarDataJob : IJobParallelFor
    {
        [ReadOnly] public NativeArray<float> StarData;
        public NativeArray<Color> PositionTextureData;
        public NativeArray<Color32> ColorTextureData;
        public NativeArray<Vector2> SizeMagnitudeTextureData;
        public int TotalStars;

        public void Execute(int index)
        {
            if (index < TotalStars)
            {
                int baseIdx = index * 6;

                // Write XYZ to position texture
                PositionTextureData[index] = new Color(
                    StarData[baseIdx],
                    StarData[baseIdx + 1],
                    StarData[baseIdx + 2],
                    1.0f // Alpha channel, can be used or ignored
                );

                // Decode and write color to color texture
                float encodedColor = StarData[baseIdx + 4];
                Color32 decodedColor = DecodeColor(encodedColor);
                ColorTextureData[index] = decodedColor;

                // Write size and magnitude to size-magnitude texture
                SizeMagnitudeTextureData[index] = new Vector2(
                    StarData[baseIdx + 5], // Size
                    StarData[baseIdx + 3]  // Appmag
                );
            }
            else
            {
                // Fill remaining texture space with default values
                PositionTextureData[index] = Color.black;
                ColorTextureData[index] = new Color32(0, 0, 0, 0);
                SizeMagnitudeTextureData[index] = Vector2.zero;
            }
        }

        private Color32 DecodeColor(float encodedColor)
        {
            uint colorBits = math.asuint(encodedColor);
            byte r = (byte)((colorBits >> 24) & 0xFF);
            byte g = (byte)((colorBits >> 16) & 0xFF);
            byte b = (byte)((colorBits >> 8) & 0xFF);
            byte a = (byte)(colorBits & 0xFF);
            return new Color32(r, g, b, a);
        }
    }

    private int ReadIntBigEndian(BinaryReader reader)
    {
        byte[] bytes = reader.ReadBytes(4);
        if (BitConverter.IsLittleEndian)
        {
            Array

You

parse the file in python and generate .exr textures

ChatGPT

To parse the binary file using Python and generate .exr textures containing floating-point data, you’ll use the OpenEXR and numpy libraries. Here’s how to do it:

Step 1: Parse the Binary File

We’ll read the star data, extract relevant attributes, and store them in numpy arrays.

Step 2: Save the Data as .exr Files

We’ll use OpenEXR to save the numpy arrays as .exr textures.

Prerequisites:

• Install OpenEXR and numpy if you haven’t already:

  pip install OpenEXR numpy

Code

import struct
import os
import numpy as np
import OpenEXR
import Imath

def parse_bin_file(file_path, max_rows):
    positions = []
    colors = []
    size_magnitudes = []

    with open(file_path, 'rb') as f:
        # Read the number of stars (4 bytes as big-endian integer)
        number_of_stars = struct.unpack('>i', f.read(4))[0]

        count = 0
        while count < number_of_stars and count < max_rows:
            # Read position (3 doubles, big-endian)
            x, y, z = struct.unpack('>ddd', f.read(24))
            pos = np.array([x, y, z])
            pos /= np.linalg.norm(pos)  # Normalize the position
            positions.append(pos)

            # Skip 48 bytes
            f.read(48)

            # Read attributes (4 floats, big-endian: appmag, absmag, encoded color, size)
            appmag, absmag, encoded_color, size = struct.unpack('>ffff', f.read(16))

            # Decode the color (assuming RGBA encoded in a float)
            color_bits = struct.unpack('>I', struct.pack('>f', encoded_color))[0]
            r = (color_bits >> 24) & 0xFF
            g = (color_bits >> 16) & 0xFF
            b = (color_bits >> 8) & 0xFF
            a = color_bits & 0xFF
            colors.append([r / 255.0, g / 255.0, b / 255.0, a / 255.0])  # Normalize to [0, 1]

            # Store size and magnitude
            size_magnitudes.append([size, appmag])

            # Skip 24 bytes
            f.read(24)

            # Read and skip the name
            name_length = struct.unpack('>i', f.read(4))[0]
            if name_length > 0:
                f.read(name_length * 2)

            count += 1

    return np.array(positions), np.array(colors), np.array(size_magnitudes)

def save_exr(data, width, height, channels, file_name):
    header = OpenEXR.Header(width, height)
    FLOAT = Imath.PixelType(Imath.PixelType.FLOAT)

    # Create a channel list based on the number of channels (R, G, B, A or others)
    channel_names = ['R', 'G', 'B', 'A'][:channels]
    header['channels'] = {name: OpenEXR.Channel(FLOAT) for name in channel_names}

    # Reshape data and convert it to string buffers
    exr = OpenEXR.OutputFile(file_name, header)
    data_channels = [data[:, :, i].astype(np.float32).tostring() for i in range(channels)]
    exr.writePixels({name: channel for name, channel in zip(channel_names, data_channels)})
    exr.close()

def main():
    max_rows = 1000  # Maximum number of rows to read
    bin_directory = 'path/to/your/bin/files'
    output_directory = 'path/to/save/exr/files'

    # Create output directory if it doesn't exist
    if not os.path.exists(output_directory):
        os.makedirs(output_directory)

    # Read each .bin file and process
    for file_name in os.listdir(bin_directory):
        if file_name.endswith('.bin'):
            file_path = os.path.join(bin_directory, file_name)
            positions, colors, size_magnitudes = parse_bin_file(file_path, max_rows)

            # Assume square textures (for simplicity)
            width = height = int(np.ceil(np.sqrt(max_rows)))

            # Pad the data to fit into the texture size
            def pad_array(arr, channels):
                pad_size = width * height - len(arr)
                padded = np.pad(arr, ((0, pad_size), (0, 0)), mode='constant', constant_values=0)
                return padded.reshape((height, width, channels))

            positions = pad_array(positions, 3)
            colors = pad_array(colors, 4)
            size_magnitudes = pad_array(size_magnitudes, 2)

            # Save each texture as .exr
            save_exr(positions, width, height, 3, os.path.join(output_directory, f"{file_name}_positions.exr"))
            save_exr(colors, width, height, 4, os.path.join(output_directory, f"{file_name}_colors.exr"))
            save_exr(size_magnitudes, width, height, 2, os.path.join(output_directory, f"{file_name}_size_magnitudes.exr"))

if __name__ == "__main__":
    main()

Explanation:

1. Binary File Parsing: The function parse_bin_file() reads the binary data as described. It extracts positions, colors, and size-magnitude pairs.
2. Color Decoding: Assumes the encoded color is a float representing an RGBA color packed into 32 bits.
3. Padding: Pads the data to fit into a square texture for simplicity.
4. Saving as .exr: The save_exr() function saves data as .exr textures using OpenEXR.

Adjustments:

• Set bin_directory and output_directory paths as needed.
• Adjust max_rows and texture dimensions based on your specific needs.

You

utilize this data structure

Version 2

This version is much more compact, and it uses smaller data types when possible. The header contains a token integer (-1) marking the following version number, plus the number of stars.

1 single-precision integer (32-bit) – special token number -1, signaling the presence of a version number

1 single-precision integer (32-bit) – version number (2 in this case)

1 single-precision integer (32-bit) – number of stars in the file

For each star:

        3 double-precision floats (64-bit * 3) – X, Y, Z cartesian coordinates in internal units

        3 single-precision floats (32-bit * 3) – Vx, Vy, Vz - cartesian velocity vector in internal units per year

        3 single-precision floats (32-bit * 3) – mualpha, mudelta, radvel - proper motion

        4 single-precision floats (32-bit * 4) – appmag, absmag, color, size - Magnitudes, colors (encoded), and size (a derived quantity, for rendering)

        1 single-precision integer (32-bit) – HIP number (if any, otherwise negative)

        1 double-precision integer (64-bit) – Gaia SourceID

        1 single-precision integer (32-bit) – namelen -> Length of name

        namelen * char (16-bit * namelen) – Characters of the star name, where each character is encoded with UTF-16

The RGB color of stars uses 8 bits per channel in RGBA, and is encoded into a single float using the libgdx Color class.

ChatGPT

To process data according to this new structure (Version 2), I’ll adapt the Python code to handle the updated file structure, extracting additional attributes like velocities, proper motion, HIP number, Gaia SourceID, and star names. We will also decode the color using the provided encoding approach and save relevant textures.

Here’s how you can process this more compact data structure and generate .exr files accordingly:

Updated Python Code

import struct
import os
import numpy as np
import OpenEXR
import Imath

def parse_bin_file_v2(file_path, max_rows):
    positions = []
    velocities = []
    proper_motions = []
    colors = []
    size_magnitudes = []

    with open(file_path, 'rb') as f:
        # Read the header
        token = struct.unpack('>i', f.read(4))[0]
        if token != -1:
            raise ValueError("Invalid token. Expected -1 for versioning.")

        version = struct.unpack('>i', f.read(4))[0]
        if version != 2:
            raise ValueError(f"Unsupported version: {version}. Expected version 2.")

        number_of_stars = struct.unpack('>i', f.read(4))[0]

        count = 0
        while count < number_of_stars and count < max_rows:
            # Read position (3 doubles)
            x, y, z = struct.unpack('>ddd', f.read(24))
            positions.append([x, y, z])

            # Read velocity (3 floats)
            vx, vy, vz = struct.unpack('>fff', f.read(12))
            velocities.append([vx, vy, vz])

            # Read proper motion (3 floats)
            mualpha, mudelta, radvel = struct.unpack('>fff', f.read(12))
            proper_motions.append([mualpha, mudelta, radvel])

            # Read magnitudes, color, and size (4 floats)
            appmag, absmag, encoded_color, size = struct.unpack('>ffff', f.read(16))

            # Decode color (assuming RGBA encoded in a float)
            color_bits = struct.unpack('>I', struct.pack('>f', encoded_color))[0]
            r = (color_bits >> 24) & 0xFF
            g = (color_bits >> 16) & 0xFF
            b = (color_bits >> 8) & 0xFF
            a = color_bits & 0xFF
            colors.append([r / 255.0, g / 255.0, b / 255.0, a / 255.0])  # Normalize to [0, 1]

            # Store size and magnitude
            size_magnitudes.append([size, appmag])

            # Read HIP number (int)
            hip_number = struct.unpack('>i', f.read(4))[0]

            # Read Gaia SourceID (double-precision int, 8 bytes)
            gaia_source_id = struct.unpack('>q', f.read(8))[0]

            # Read star name length and star name (UTF-16)
            name_length = struct.unpack('>i', f.read(4))[0]
            if name_length > 0:
                star_name = f.read(name_length * 2).decode('utf-16')

            count += 1

    return np.array(positions), np.array(velocities), np.array(proper_motions), np.array(colors), np.array(size_magnitudes)

def save_exr(data, width, height, channels, file_name):
    header = OpenEXR.Header(width, height)
    FLOAT = Imath.PixelType(Imath.PixelType.FLOAT)

    # Create a channel list based on the number of channels (R, G, B, A or others)
    channel_names = ['R', 'G', 'B', 'A'][:channels]
    header['channels'] = {name: OpenEXR.Channel(FLOAT) for name in channel_names}

    # Reshape data and convert it to string buffers
    exr = OpenEXR.OutputFile(file_name, header)
    data_channels = [data[:, :, i].astype(np.float32).tobytes() for i in range(channels)]
    exr.writePixels({name: channel for name, channel in zip(channel_names, data_channels)})
    exr.close()

def main():
    max_rows = 1000  # Maximum number of rows to read
    bin_directory = 'path/to/your/bin/files'
    output_directory = 'path/to/save/exr/files'

    # Create output directory if it doesn't exist
    if not os.path.exists(output_directory):
        os.makedirs(output_directory)

    # Read each .bin file and process
    for file_name in os.listdir(bin_directory):
        if file_name.endswith('.bin'):
            file_path = os.path.join(bin_directory, file_name)
            positions, velocities, proper_motions, colors, size_magnitudes = parse_bin_file_v2(file_path, max_rows)

            # Assume square textures (for simplicity)
            width = height = int(np.ceil(np.sqrt(max_rows)))

            # Pad the data to fit into the texture size
            def pad_array(arr, channels):
                pad_size = width * height - len(arr)
                padded = np.pad(arr, ((0, pad_size), (0, 0)), mode='constant', constant_values=0)
                return padded.reshape((height, width, channels))

            positions = pad_array(positions, 3)
            velocities = pad_array(velocities, 3)
            proper_motions = pad_array(proper_motions, 3)
            colors = pad_array(colors, 4)
            size_magnitudes = pad_array(size_magnitudes, 2)

            # Save each texture as .exr
            save_exr(positions, width, height, 3, os.path.join(output_directory, f"{file_name}_positions.exr"))
            save_exr(velocities, width, height, 3, os.path.join(output_directory, f"{file_name}_velocities.exr"))
            save_exr(proper_motions, width, height, 3, os.path.join(output_directory, f"{file_name}_proper_motions.exr"))
            save_exr(colors, width, height, 4, os.path.join(output_directory, f"{file_name}_colors.exr"))
            save_exr(size_magnitudes, width, height, 2, os.path.join(output_directory, f"{file_name}_size_magnitudes.exr"))

if __name__ == "__main__":
    main()

Explanation:

1. Header Parsing: The code first reads and verifies the header (token, version, and number of stars).
2. Data Parsing: For each star, it reads and extracts positions, velocities, proper motions, color, size/magnitude, HIP number, and Gaia SourceID.
3. Color Decoding: The RGBA color encoded as a float is decoded into individual channels.
4. Data Padding: Arrays are padded to fit into a square texture size for simplicity.
5. Saving as .exr: Data is saved as .exr files using the OpenEXR library. Adjust the padding and texture dimensions as necessary.

You

dont save a texture for each file. but a big texture for all files

ChatGPT

To create one large texture for all files combined, you need to aggregate the data from each .bin file into a single array before saving it as a large .exr texture. This approach avoids creating separate textures for each file, which can be inefficient for handling large datasets.

Adjusted Python Code

import struct
import os
import numpy as np
import OpenEXR
import Imath

def parse_bin_file_v2(file_path, max_rows, positions, velocities, proper_motions, colors, size_magnitudes):
    count = 0

    with open(file_path, 'rb') as f:
        # Read the header
        token = struct.unpack('>i', f.read(4))[0]
        if token != -1:
            raise ValueError("Invalid token. Expected -1 for versioning.")

        version = struct.unpack('>i', f.read(4))[0]
        if version != 2:
            raise ValueError(f"Unsupported version: {version}. Expected version 2.")

        number_of_stars = struct.unpack('>i', f.read(4))[0]

        while count < number_of_stars and len(positions) < max_rows:
            # Read position (3 doubles)
            x, y, z = struct.unpack('>ddd', f.read(24))
            positions.append([x, y, z])

            # Read velocity (3 floats)
            vx, vy, vz = struct.unpack('>fff', f.read(12))
            velocities.append([vx, vy, vz])

            # Read proper motion (3 floats)
            mualpha, mudelta, radvel = struct.unpack('>fff', f.read(12))
            proper_motions.append([mualpha, mudelta, radvel])

            # Read magnitudes, color, and size (4 floats)
            appmag, absmag, encoded_color, size = struct.unpack('>ffff', f.read(16))

            # Decode color (assuming RGBA encoded in a float)
            color_bits = struct.unpack('>I', struct.pack('>f', encoded_color))[0]
            r = (color_bits >> 24) & 0xFF
            g = (color_bits >> 16) & 0xFF
            b = (color_bits >> 8) & 0xFF
            a = color_bits & 0xFF
            colors.append([r / 255.0, g / 255.0, b / 255.0, a / 255.0])  # Normalize to [0, 1]

            # Store size and magnitude
            size_magnitudes.append([size, appmag])

            # Skip additional data (HIP number, Gaia SourceID, star name)
            f.read(4)  # HIP number
            f.read(8)  # Gaia SourceID
            name_length = struct.unpack('>i', f.read(4))[0]
            if name_length > 0:
                f.read(name_length * 2)

            count += 1

def save_exr(data, width, height, channels, file_name):
    header = OpenEXR.Header(width, height)
    FLOAT = Imath.PixelType(Imath.PixelType.FLOAT)

    # Create a channel list based on the number of channels (R, G, B, A or others)
    channel_names = ['R', 'G', 'B', 'A'][:channels]
    header['channels'] = {name: OpenEXR.Channel(FLOAT) for name in channel_names}

    # Reshape data and convert it to string buffers
    exr = OpenEXR.OutputFile(file_name, header)
    data_channels = [data[:, :, i].astype(np.float32).tobytes() for i in range(channels)]
    exr.writePixels({name: channel for name, channel in zip(channel_names, data_channels)})
    exr.close()

def main():
    max_rows = 1000000  # Maximum total rows to process across all files
    bin_directory = 'path/to/your/bin/files'
    output_directory = 'path/to/save/exr/files'

    # Initialize lists to hold aggregated data
    positions = []
    velocities = []
    proper_motions = []
    colors = []
    size_magnitudes = []

    # Process each .bin file and aggregate data
    for file_name in os.listdir(bin_directory):
        if file_name.endswith('.bin'):
            file_path = os.path.join(bin_directory, file_name)
            parse_bin_file_v2(file_path, max_rows, positions, velocities, proper_motions, colors, size_magnitudes)

    # Convert lists to numpy arrays
    positions = np.array(positions)
    velocities = np.array(velocities)
    proper_motions = np.array(proper_motions)
    colors = np.array(colors)
    size_magnitudes = np.array(size_magnitudes)

    # Determine texture dimensions (assume square for simplicity)
    width = height = int(np.ceil(np.sqrt(len(positions))))

    # Pad the data to fit the texture size
    def pad_array(arr, channels):
        pad_size = width * height - len(arr)
        padded = np.pad(arr, ((0, pad_size), (0, 0)), mode='constant', constant_values=0)
        return padded.reshape((height, width, channels))

    positions = pad_array(positions, 3)
    velocities = pad_array(velocities, 3)
    proper_motions = pad_array(proper_motions, 3)
    colors = pad_array(colors, 4)
    size_magnitudes = pad_array(size_magnitudes, 2)

    # Save each large texture as .exr
    if not os.path.exists(output_directory):
        os.makedirs(output_directory)

    save_exr(positions, width, height, 3, os.path.join(output_directory, "all_positions.exr"))
    save_exr(velocities, width, height, 3, os.path.join(output_directory, "all_velocities.exr"))
    save_exr(proper_motions, width, height, 3, os.path.join(output_directory, "all_proper_motions.exr"))
    save_exr(colors, width, height, 4, os.path.join(output_directory, "all_colors.exr"))
    save_exr(size_magnitudes, width, height, 2, os.path.join(output_directory, "all_size_magnitudes.exr"))

if __name__ == "__main__":
    main()

Explanation:

1. Aggregate Data: The code aggregates data from all .bin files into a single list for each data type (positions, velocities, etc.).
2. Maximum Rows: The max_rows variable limits the total number of stars processed across all files.
3. Padding and Reshaping: The aggregated data is padded and reshaped into square textures for simplicity.
4. Single .exr Textures: The code saves one large .exr texture for each data type, containing data from all files combined.

Adjustments:

• Set bin_directory and output_directory paths as needed.
• Adjust max_rows based on your specific needs.

You

the header construction is wrong

ChatGPT

Thank you for pointing that out. It seems I misunderstood some parts of the data structure header construction for version 2. Let’s carefully reconstruct and process the header and data according to the specified structure:

Corrected Header Parsing

Here’s how you should correctly handle the header and data parsing according to the specified format for Version 2.

import struct
import os
import numpy as np
import OpenEXR
import Imath

def parse_bin_file_v2(file_path, max_rows, positions, velocities, proper_motions, colors, size_magnitudes):
    count = 0

    with open(file_path, 'rb') as f:
        # Read and validate the special token (-1)
        token = struct.unpack('>i', f.read(4))[0]
        if token != -1:
            raise ValueError(f"Invalid token {token}. Expected -1 for versioning.")

        # Read the version number (should be 2)
        version = struct.unpack('>i', f.read(4))[0]
        if version != 2:
            raise ValueError(f"Unsupported version: {version}. Expected version 2.")

        # Read the number of stars
        number_of_stars = struct.unpack('>i', f.read(4))[0]

        while count < number_of_stars and len(positions) < max_rows:
            # Read position (3 doubles, 64 bits each)
            x, y, z = struct.unpack('>ddd', f.read(24))
            positions.append([x, y, z])

            # Read velocity (3 floats, 32 bits each)
            vx, vy, vz = struct.unpack('>fff', f.read(12))
            velocities.append([vx, vy, vz])

            # Read proper motion (3 floats, 32 bits each)
            mualpha, mudelta, radvel = struct.unpack('>fff', f.read(12))
            proper_motions.append([mualpha, mudelta, radvel])

            # Read magnitudes, color, and size (4 floats, 32 bits each)
            appmag, absmag, encoded_color, size = struct.unpack('>ffff', f.read(16))

            # Decode color (assuming RGBA encoded in a float)
            color_bits = struct.unpack('>I', struct.pack('>f', encoded_color))[0]
            r = (color_bits >> 24) & 0xFF
            g = (color_bits >> 16) & 0xFF
            b = (color_bits >> 8) & 0xFF
            a = color_bits & 0xFF
            colors.append([r / 255.0, g / 255.0, b / 255.0, a / 255.0])  # Normalize to [0, 1]

            # Store size and magnitude
            size_magnitudes.append([size, appmag])

            # Read HIP number (32-bit integer)
            hip_number = struct.unpack('>i', f.read(4))[0]

            # Read Gaia SourceID (64-bit integer)
            gaia_source_id = struct.unpack('>q', f.read(8))[0]

            # Read star name length (32-bit integer) and name (UTF-16 encoded)
            name_length = struct.unpack('>i', f.read(4))[0]
            if name_length > 0:
                star_name = f.read(name_length * 2).decode('utf-16')

            count += 1

def save_exr(data, width, height, channels, file_name):
    header = OpenEXR.Header(width, height)
    FLOAT = Imath.PixelType(Imath.PixelType.FLOAT)

    # Create a channel list based on the number of channels (R, G, B, A or others)
    channel_names = ['R', 'G', 'B', 'A'][:channels]
    header['channels'] = {name: OpenEXR.Channel(FLOAT) for name in channel_names}

    # Reshape data and convert it to string buffers
    exr = OpenEXR.OutputFile(file_name, header)
    data_channels = [data[:, :, i].astype(np.float32).tobytes() for i in range(channels)]
    exr.writePixels({name: channel for name, channel in zip(channel_names, data_channels)})
    exr.close()

def main():
    max_rows = 1000000  # Maximum total rows to process across all files
    bin_directory = 'path/to/your/bin/files'
    output_directory = 'path/to/save/exr/files'

    # Initialize lists to hold aggregated data
    positions = []
    velocities = []
    proper_motions = []
    colors = []
    size_magnitudes = []

    # Process each .bin file and aggregate data
    for file_name in os.listdir(bin_directory):
        if file_name.endswith('.bin'):
            file_path = os.path.join(bin_directory, file_name)
            parse_bin_file_v2(file_path, max_rows, positions, velocities, proper_motions, colors, size_magnitudes)

    # Convert lists to numpy arrays
    positions = np.array(positions)
    velocities = np.array(velocities)
    proper_motions = np.array(proper_motions)
    colors = np.array(colors)
    size_magnitudes = np.array(size_magnitudes)

    # Determine texture dimensions (assume square for simplicity)
    width = height = int(np.ceil(np.sqrt(len(positions))))

    # Pad the data to fit the texture size
    def pad_array(arr, channels):
        pad_size = width * height - len(arr)
        padded = np.pad(arr, ((0, pad_size), (0, 0)), mode='constant', constant_values=0)
        return padded.reshape((height, width, channels))

    positions = pad_array(positions, 3)
    velocities = pad_array(velocities, 3)
    proper_motions = pad_array(proper_motions, 3)
    colors = pad_array(colors, 4)
    size_magnitudes = pad_array(size_magnitudes, 2)

    # Save each large texture as .exr
    if not os.path.exists(output_directory):
        os.makedirs(output_directory)

    save_exr(positions, width, height, 3, os.path.join(output_directory, "all_positions.exr"))
    save_exr(velocities, width, height, 3, os.path.join(output_directory, "all_velocities.exr"))
    save_exr(proper_motions, width, height, 3, os.path.join(output_directory, "all_proper_motions.exr"))
    save_exr(colors, width, height, 4, os.path.join(output_directory, "all_colors.exr"))
    save_exr(size_magnitudes, width, height, 2, os.path.join(output_directory, "all_size_magnitudes.exr"))

if __name__ == "__main__":
    main()

Explanation of Corrected Header Parsing:

1. Special Token Verification: Ensures the first integer in the file is -1.
2. Version Check: Confirms the file is using version 2.
3. Data Parsing: Handles each star’s data in the order specified:
   • 3 doubles for position.
   • 3 floats for velocity.
   • 3 floats for proper motion.
   • 4 floats for magnitudes, color, and size.
   • 1 integer for HIP number.
   • 1 long integer (64-bit) for Gaia SourceID.
   • UTF-16 encoded star name with length specified as a 32-bit integer.

Adjustments:

• Adjust bin_directory and output_directory paths as needed.
• max_rows can be adjusted based on the number of rows you want to process across all files.

You

header[‘channels’] = {name: OpenEXR.Channel(FLOAT) for name in channel_names}

this doesnt work

ChatGPT

You are correct; I apologize for the mistake. In OpenEXR, the Header’s channels field should be a dictionary where each key corresponds to a channel name and the value is an OpenEXR.Channel object. However, setting it this way is not directly valid for OpenEXR. Instead, you’ll need to properly configure the header and write the pixel data.

Here’s a corrected way to configure and use OpenEXR:

Corrected save_exr function

def save_exr(data, width, height, channels, file_name):
    header = OpenEXR.Header(width, height)
    FLOAT = Imath.PixelType(Imath.PixelType.FLOAT)

    # Create a channel list based on the number of channels (R, G, B, A or others)
    channel_names = ['R', 'G', 'B', 'A'][:channels]
    header['channels'] = {name: OpenEXR.Channel(FLOAT) for name in channel_names}

    # Open the file for writing
    exr = OpenEXR.OutputFile(file_name, header)

    # Split the data into channels and write them
    data = data.reshape(height, width, channels)
    data_channels = [data[:, :, i].astype(np.float32).tobytes() for i in range(channels)]
    exr.writePixels({name: channel for name, channel in zip(channel_names, data_channels)})
    exr.close()

Important Points:

1. Header Setup: We create the header and define the channels using OpenEXR.Channel(FLOAT).
2. Data Handling: The data is reshaped to the expected texture dimensions and split into individual channels. The reshape call ensures that the data is treated correctly as per the specified width, height, and channel count.
3. Writing Data: writePixels requires a dictionary mapping channel names to data bytes, which is generated correctly.

Example Usage:

Ensure that the data passed to save_exr is correctly shaped and aligned with the specified width, height, and channel count.

Full Code Context:

Make sure to replace your existing save_exr function with the corrected version and verify that the input data is correctly shaped to match the specified dimensions. This correction should help avoid issues with writing .exr files using OpenEXR.

You

Traceback (most recent call last): File “C:\Users\Uncharted Limbo\Documents\star_parser\main.py”, line 127, in main() File “C:\Users\Uncharted Limbo\Documents\star_parser\main.py”, line 120, in main save_exr(positions, width, height, 3, os.path.join(output_directory, “Positions.exr”)) File “C:\Users\Uncharted Limbo\Documents\star_parser\main.py”, line 64, in save_exr header[‘channels’] = {name: OpenEXR.Channel(FLOAT) for name in channel_names} ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File “C:\Users\Uncharted Limbo\Documents\star_parser\main.py”, line 64, in header[‘channels’] = {name: OpenEXR.Channel(FLOAT) for name in channel_names} ^^^^^^^^^^^^^^^^^^^^^^ TypeError: init(): incompatible constructor arguments. The following argument types are supported: 1. OpenEXR.Channel() 2. OpenEXR.Channel(xSampling: int, ySampling: int, pLinear: bool = False) 3. OpenEXR.Channel(pixels: numpy.ndarray) 4. OpenEXR.Channel(pixels: numpy.ndarray, xSampling: int, ySampling: int, pLinear: bool = False) 5. OpenEXR.Channel(name: str) 6. OpenEXR.Channel(name: str, xSampling: int, ySampling: int, pLinear: bool = False) 7. OpenEXR.Channel(name: str, pixels: numpy.ndarray) 8. OpenEXR.Channel(name: str, pixels: numpy.ndarray, xSampling: int, ySampling: int, pLinear: bool = False)

ChatGPT

Thank you for pointing this out, and I apologize for the confusion earlier. It seems I used the incorrect approach when defining channels in the OpenEXR header. The OpenEXR.Channel() constructor does not work as described earlier. Instead, we need to use Imath.Channel() for specifying channel data types.

Here’s the corrected approach using Imath.Channel():

Corrected save_exr Function

def save_exr(data, width, height, channels, file_name):
    header = OpenEXR.Header(width, height)
    FLOAT = Imath.PixelType(Imath.PixelType.FLOAT)

    # Create channel names and specify each channel using Imath.Channel
    channel_names = ['R', 'G', 'B', 'A'][:channels]
    header['channels'] = {name: Imath.Channel(FLOAT) for name in channel_names}

    # Open the file for writing
    exr = OpenEXR.OutputFile(file_name, header)

    # Split the data into channels and write them
    data = data.reshape(height, width, channels)
    data_channels = [data[:, :, i].astype(np.float32).tobytes() for i in range(channels)]
    exr.writePixels({name: data_channels[i] for i, name in enumerate(channel_names)})
    exr.close()

Explanation and Corrections:

1. Imath.Channel Usage: When creating the header['channels'], use Imath.Channel(FLOAT) instead of OpenEXR.Channel(). This correctly specifies the pixel type for each channel.
2. Data Writing: When writing data with writePixels, ensure that each channel is correctly mapped to its corresponding name.

Testing and Usage:

Ensure your data passed into save_exr() is a numpy array of appropriate shape (height, width, channels). The function will correctly handle splitting the channels and writing them to the .exr file.

You

use the closest power of two

ChatGPT

To adjust your code to pad the data to the closest power of two dimensions for the .exr textures, you can modify the logic that determines the texture dimensions. This will ensure that your output textures have dimensions that are powers of two, which is often beneficial for compatibility and performance reasons.

Updated Code with Power-of-Two Padding

Here’s how you can modify the main() function to ensure the texture dimensions are the closest power of two:

import struct
import os
import numpy as np
import OpenEXR
import Imath

def parse_bin_file_v2(file_path, max_rows, positions, velocities, proper_motions, colors, size_magnitudes):
    count = 0

    with open(file_path, 'rb') as f:
        # Read and validate the special token (-1)
        token = struct.unpack('>i', f.read(4))[0]
        if token != -1:
            raise ValueError(f"Invalid token {token}. Expected -1 for versioning.")

        # Read the version number (should be 2)
        version = struct.unpack('>i', f.read(4))[0]
        if version != 2:
            raise ValueError(f"Unsupported version: {version}. Expected version 2.")

        # Read the number of stars
        number_of_stars = struct.unpack('>i', f.read(4))[0]

        while count < number_of_stars and len(positions) < max_rows:
            # Read position (3 doubles, 64 bits each)
            x, y, z = struct.unpack('>ddd', f.read(24))
            positions.append([x, y, z])

            # Read velocity (3 floats, 32 bits each)
            vx, vy, vz = struct.unpack('>fff', f.read(12))
            velocities.append([vx, vy, vz])

            # Read proper motion (3 floats, 32 bits each)
            mualpha, mudelta, radvel = struct.unpack('>fff', f.read(12))
            proper_motions.append([mualpha, mudelta, radvel])

            # Read magnitudes, color, and size (4 floats, 32 bits each)
            appmag, absmag, encoded_color, size = struct.unpack('>ffff', f.read(16))

            # Decode color (assuming RGBA encoded in a float)
            color_bits = struct.unpack('>I', struct.pack('>f', encoded_color))[0]
            r = (color_bits >> 24) & 0xFF
            g = (color_bits >> 16) & 0xFF
            b = (color_bits >> 8) & 0xFF
            a = color_bits & 0xFF
            colors.append([r / 255.0, g / 255.0, b / 255.0, a / 255.0])  # Normalize to [0, 1]

            # Store size and magnitude
            size_magnitudes.append([size, appmag])

            # Read HIP number (32-bit integer)
            hip_number = struct.unpack('>i', f.read(4))[0]

            # Read Gaia SourceID (64-bit integer)
            gaia_source_id = struct.unpack('>q', f.read(8))[0]

            # Read star name length (32-bit integer) and name (UTF-16 encoded)
            name_length = struct.unpack('>i', f.read(4))[0]
            if name_length > 0:
                star_name = f.read(name_length * 2).decode('utf-16')

            count += 1

def save_exr(data, width, height, channels, file_name):
    header = OpenEXR.Header(width, height)
    FLOAT = Imath.PixelType(Imath.PixelType.FLOAT)

    # Create channel names and specify each channel using Imath.Channel
    channel_names = ['R', 'G', 'B', 'A'][:channels]
    header['channels'] = {name: Imath.Channel(FLOAT) for name in channel_names}

    # Open the file for writing
    exr = OpenEXR.OutputFile(file_name, header)

    # Split the data into channels and write them
    data = data.reshape(height, width, channels)
    data_channels = [data[:, :, i].astype(np.float32).tobytes() for i in range(channels)]
    exr.writePixels({name: data_channels[i] for i, name in enumerate(channel_names)})
    exr.close()

def next_power_of_two(n):
    """ Returns the next power of two greater than or equal to n """
    return 2**((n - 1).bit_length())

def main():
    max_rows = 1000000  # Maximum total rows to process across all files
    bin_directory = 'path/to/your/bin/files'
    output_directory = 'path/to/save/exr/files'

    # Initialize lists to hold aggregated data
    positions = []
    velocities = []
    proper_motions = []
    colors = []
    size_magnitudes = []

    # Process each .bin file and aggregate data
    for file_name in os.listdir(bin_directory):
        if file_name.endswith('.bin'):
            file_path = os.path.join(bin_directory, file_name)
            parse_bin_file_v2(file_path, max_rows, positions, velocities, proper_motions, colors, size_magnitudes)

    # Convert lists to numpy arrays
    positions = np.array(positions)
    velocities = np.array(velocities)
    proper_motions = np.array(proper_motions)
    colors = np.array(colors)
    size_magnitudes = np.array(size_magnitudes)

    # Determine texture dimensions (find closest power of two)
    total_count = len(positions)
    width = height = next_power_of_two(int(np.ceil(np.sqrt(total_count))))

    # Pad the data to fit the texture size
    def pad_array(arr, channels):
        pad_size = width * height - len(arr)
        padded = np.pad(arr, ((0, pad_size), (0, 0)), mode='constant', constant_values=0)
        return padded.reshape((height, width, channels))

    positions = pad_array(positions, 3)
    velocities = pad_array(velocities, 3)
    proper_motions = pad_array(proper_motions, 3)
    colors = pad_array(colors, 4)
    size_magnitudes = pad_array(size_magnitudes, 2)

    # Save each large texture as .exr
    if not os.path.exists(output_directory):
        os.makedirs(output_directory)

    save_exr(positions, width, height, 3, os.path.join(output_directory, "all_positions.exr"))
    save_exr(velocities, width, height, 3, os.path.join(output_directory, "all_velocities.exr"))
    save_exr(proper_motions, width, height, 3, os.path.join(output_directory, "all_proper_motions.exr"))
    save_exr(colors, width, height, 4, os.path.join(output_directory, "all_colors.exr"))
    save_exr(size_magnitudes, width, height, 2, os.path.join(output_directory, "all_size_magnitudes.exr"))

if __name__ == "__main__":
    main()

Key Changes:

1. Power-of-Two Dimensions: Added a next_power_of_two function to compute the next power of two for the texture dimensions.
2. Updated Texture Dimensions: The dimensions of the textures are calculated using the next_power_of_two function to ensure they are a power of two.
3. Data Padding: Padding ensures the data fills the entire texture while maintaining the power-of-two dimensions.

Explanation:

• next_power_of_two(n) computes the smallest power of two greater than or equal to n.
• The main() function uses this to determine the texture width and height, ensuring that each texture is a power-of-two size.

You

also the color doesnot need to be exr float, since its 24bit RGBA

ChatGPT

Since the color data is represented as 24-bit RGBA, you can save it as an 8-bit per channel texture instead of a floating-point .exr format. This adjustment reduces memory usage and matches the intended data format. We can use the OpenEXR library for the other data types, while saving color data with a more efficient format using PIL (Python Imaging Library, also known as Pillow).

Here’s how you can adjust the code to handle 8-bit color textures separately:

Updated Code

import struct
import os
import numpy as np
import OpenEXR
import Imath
from PIL import Image  # For saving 8-bit RGBA textures

def parse_bin_file_v2(file_path, max_rows, positions, velocities, proper_motions, colors, size_magnitudes):
    count = 0

    with open(file_path, 'rb') as f:
        # Read and validate the special token (-1)
        token = struct.unpack('>i', f.read(4))[0]
        if token != -1:
            raise ValueError(f"Invalid token {token}. Expected -1 for versioning.")

        # Read the version number (should be 2)
        version = struct.unpack('>i', f.read(4))[0]
        if version != 2:
            raise ValueError(f"Unsupported version: {version}. Expected version 2.")

        # Read the number of stars
        number_of_stars = struct.unpack('>i', f.read(4))[0]

        while count < number_of_stars and len(positions) < max_rows:
            # Read position (3 doubles, 64 bits each)
            x, y, z = struct.unpack('>ddd', f.read(24))
            positions.append([x, y, z])

            # Read velocity (3 floats, 32 bits each)
            vx, vy, vz = struct.unpack('>fff', f.read(12))
            velocities.append([vx, vy, vz])

            # Read proper motion (3 floats, 32 bits each)
            mualpha, mudelta, radvel = struct.unpack('>fff', f.read(12))
            proper_motions.append([mualpha, mudelta, radvel])

            # Read magnitudes, color, and size (4 floats, 32 bits each)
            appmag, absmag, encoded_color, size = struct.unpack('>ffff', f.read(16))

            # Decode color (assuming RGBA encoded in a float)
            color_bits = struct.unpack('>I', struct.pack('>f', encoded_color))[0]
            r = (color_bits >> 24) & 0xFF
            g = (color_bits >> 16) & 0xFF
            b = (color_bits >> 8) & 0xFF
            a = color_bits & 0xFF
            colors.append([r, g, b, a])  # Store as integers in [0, 255]

            # Store size and magnitude
            size_magnitudes.append([size, appmag])

            # Read HIP number (32-bit integer)
            hip_number = struct.unpack('>i', f.read(4))[0]

            # Read Gaia SourceID (64-bit integer)
            gaia_source_id = struct.unpack('>q', f.read(8))[0]

            # Read star name length (32-bit integer) and name (UTF-16 encoded)
            name_length = struct.unpack('>i', f.read(4))[0]
            if name_length > 0:
                star_name = f.read(name_length * 2).decode('utf-16')

            count += 1

def save_exr(data, width, height, channels, file_name):
    header = OpenEXR.Header(width, height)
    FLOAT = Imath.PixelType(Imath.PixelType.FLOAT)

    # Create channel names and specify each channel using Imath.Channel
    channel_names = ['R', 'G', 'B', 'A'][:channels]
    header['channels'] = {name: Imath.Channel(FLOAT) for name in channel_names}

    # Open the file for writing
    exr = OpenEXR.OutputFile(file_name, header)

    # Split the data into channels and write them
    data = data.reshape(height, width, channels)
    data_channels = [data[:, :, i].astype(np.float32).tobytes() for i in range(channels)]
    exr.writePixels({name: data_channels[i] for i, name in enumerate(channel_names)})
    exr.close()

def save_png(data, width, height, file_name):
    # Reshape data for saving as 8-bit RGBA image
    data = data.reshape((height, width, 4)).astype(np.uint8)
    image = Image.fromarray(data, mode='RGBA')
    image.save(file_name)

def next_power_of_two(n):
    """ Returns the next power of two greater than or equal to n """
    return 2**((n - 1).bit_length())

def main():
    max_rows = 1000000  # Maximum total rows to process across all files
    bin_directory = 'path/to/your/bin/files'
    output_directory = 'path/to/save/textures'

    # Initialize lists to hold aggregated data
    positions = []
    velocities = []
    proper_motions = []
    colors = []
    size_magnitudes = []

    # Process each .bin file and aggregate data
    for file_name in os.listdir(bin_directory):
        if file_name.endswith('.bin'):
            file_path = os.path.join(bin_directory, file_name)
            parse_bin_file_v2(file_path, max_rows, positions, velocities, proper_motions, colors, size_magnitudes)

    # Convert lists to numpy arrays
    positions = np.array(positions)
    velocities = np.array(velocities)
    proper_motions = np.array(proper_motions)
    colors = np.array(colors)
    size_magnitudes = np.array(size_magnitudes)

    # Determine texture dimensions (find closest power of two)
    total_count = len(positions)
    width = height = next_power_of_two(int(np.ceil(np.sqrt(total_count))))

    # Pad the data to fit the texture size
    def pad_array(arr, channels):
        pad_size = width * height - len(arr)
        padded = np.pad(arr, ((0, pad_size), (0, 0)), mode='constant', constant_values=0)
        return padded.reshape((height, width, channels))

    positions = pad_array(positions, 3)
    velocities = pad_array(velocities, 3)
    proper_motions = pad_array(proper_motions, 3)
    colors = pad_array(colors, 4)
    size_magnitudes = pad_array(size_magnitudes, 2)

    # Save each large texture
    if not os.path.exists(output_directory):
        os.makedirs(output_directory)

    save_exr(positions, width, height, 3, os.path.join(output_directory, "all_positions.exr"))
    save_exr(velocities, width, height, 3, os.path.join(output_directory, "all_velocities.exr"))
    save_exr(proper_motions, width, height, 3, os.path.join(output_directory, "all_proper_motions.exr"))
    save_png(colors, width, height, os.path.join(output_directory, "all_colors.png"))
    save_exr(size_magnitudes, width, height, 2, os.path.join(output_directory, "all_size_magnitudes.exr"))

if __name__ == "__main__":
    main()

Key Changes:

1. Color Data: Color data is treated as an 8-bit RGBA image and saved using PIL’s Image class as a .png file.
2. Separate save_png Function: Handles saving 8-bit RGBA data using PIL.
3. Data Padding: Arrays are padded to match the power-of-two dimensions.

Explanation:

• The colors array is saved as an 8-bit RGBA image using PIL instead of using .exr floating-point format.
• The other data (positions, velocities, etc.) are saved as .exr files using OpenEXR. This approach keeps the high precision for the necessary data while optimizing storage for the color data.

You

Sometimes it’s more economical for the width to be different from the height. prioritize larger widths, and make this change

ChatGPT

To modify the code so that the width can be different from the height and to prioritize larger widths, we’ll adjust how we calculate the dimensions to minimize wasted space while still ensuring they are powers of two.

Here’s how you can modify the main() function to implement this change:

Updated main() Function with Prioritized Larger Widths

def next_power_of_two(n):
    """ Returns the next power of two greater than or equal to n """
    return 2**((n - 1).bit_length())

def find_optimal_dimensions(total_count):
    """ Find the optimal width and height (powers of two) to fit the total count of elements, prioritizing larger width """
    width = next_power_of_two(int(np.sqrt(total_count)))
    height = next_power_of_two((total_count + width - 1) // width)

    # Adjust if reducing height can still fit the data
    while width // 2 >= height and width * height >= total_count:
        width //= 2
        height = next_power_of_two((total_count + width - 1) // width)

    return width, height

def main():
    max_rows = 1000000  # Maximum total rows to process across all files
    bin_directory = 'path/to/your/bin/files'
    output_directory = 'path/to/save/textures'

    # Initialize lists to hold aggregated data
    positions = []
    velocities = []
    proper_motions = []
    colors = []
    size_magnitudes = []

    # Process each .bin file and aggregate data
    for file_name in os.listdir(bin_directory):
        if file_name.endswith('.bin'):
            file_path = os.path.join(bin_directory, file_name)
            parse_bin_file_v2(file_path, max_rows, positions, velocities, proper_motions, colors, size_magnitudes)

    # Convert lists to numpy arrays
    positions = np.array(positions)
    velocities = np.array(velocities)
    proper_motions = np.array(proper_motions)
    colors = np.array(colors)
    size_magnitudes = np.array(size_magnitudes)

    # Determine optimal texture dimensions
    total_count = len(positions)
    width, height = find_optimal_dimensions(total_count)

    # Pad the data to fit the texture size
    def pad_array(arr, channels):
        pad_size = width * height - len(arr)
        padded = np.pad(arr, ((0, pad_size), (0, 0)), mode='constant', constant_values=0)
        return padded.reshape((height, width, channels))

    positions = pad_array(positions, 3)
    velocities = pad_array(velocities, 3)
    proper_motions = pad_array(proper_motions, 3)
    colors = pad_array(colors, 4)
    size_magnitudes = pad_array(size_magnitudes, 2)

    # Save each large texture
    if not os.path.exists(output_directory):
        os.makedirs(output_directory)

    save_exr(positions, width, height, 3, os.path.join(output_directory, "all_positions.exr"))
    save_exr(velocities, width, height, 3, os.path.join(output_directory, "all_velocities.exr"))
    save_exr(proper_motions, width, height, 3, os.path.join(output_directory, "all_proper_motions.exr"))
    save_png(colors, width, height, os.path.join(output_directory, "all_colors.png"))
    save_exr(size_magnitudes, width, height, 2, os.path.join(output_directory, "all_size_magnitudes.exr"))

if __name__ == "__main__":
    main()

Key Changes:

1. Optimal Dimension Calculation:

   • The find_optimal_dimensions function determines the width and height by prioritizing a larger width while ensuring the dimensions are powers of two.
   • It calculates the width as the next power of two greater than or equal to the square root of the total number of elements and adjusts the height accordingly.
   • The loop further optimizes the dimensions to minimize the height if possible, while still fitting all the data.

2. Flexible Width and Height:

   • This approach ensures that the width is prioritized to be larger when possible, reducing the height to avoid wasting space.

Explanation:

• The function find_optimal_dimensions finds dimensions that are powers of two, with a preference for a larger width.
• The padding logic remains unchanged but adapts to the computed width and height dimensions.
• This approach ensures efficient use of texture space while adhering to power-of-two constraints.

You

can this be made much faster by a library like numba or sth else?

ChatGPT

Yes, you can significantly speed up parts of the code, especially loops and array manipulations, using libraries such as NumPy, Numba, or parallel processing techniques. Here’s how you can enhance the code for performance improvements:

Key Suggestions for Performance Improvement:

1. Use Numba for Loop Optimization: Numba can compile parts of your Python code into optimized machine code at runtime using just-in-time (JIT) compilation. It works well with NumPy arrays and loops, making it an excellent choice for speeding up data parsing and transformations.

2. Vectorized Operations with NumPy: Whenever possible, prefer vectorized operations using NumPy rather than loops. NumPy operations are generally much faster due to their internal optimizations.

3. Multithreading or Parallel Processing: Use concurrent.futures or other parallel processing libraries if you have multiple files to process concurrently.

Here’s how you can modify the code using Numba to accelerate loop-heavy parts, such as parsing and padding:

Optimized Code with Numba

First, you need to install numba if you haven’t already:

pip install numba

Applying Numba for Parsing and Padding

import struct
import os
import numpy as np
import OpenEXR
import Imath
from PIL import Image
from numba import jit, njit

@njit
def parse_bin_file_v2(file_path, max_rows, positions, velocities, proper_motions, colors, size_magnitudes):
    count = 0
    with open(file_path, 'rb') as f:
        # Read and validate the special token (-1)
        token = struct.unpack('>i', f.read(4))[0]
        if token != -1:
            raise ValueError(f"Invalid token {token}. Expected -1 for versioning.")

        # Read the version number (should be 2)
        version = struct.unpack('>i', f.read(4))[0]
        if version != 2:
            raise ValueError(f"Unsupported version: {version}. Expected version 2.")

        # Read the number of stars
        number_of_stars = struct.unpack('>i', f.read(4))[0]

        while count < number_of_stars and len(positions) < max_rows:
            # Read position (3 doubles, 64 bits each)
            x, y, z = struct.unpack('>ddd', f.read(24))
            positions.append((x, y, z))

            # Read velocity (3 floats, 32 bits each)
            vx, vy, vz = struct.unpack('>fff', f.read(12))
            velocities.append((vx, vy, vz))

            # Read proper motion (3 floats, 32 bits each)
            mualpha, mudelta, radvel = struct.unpack('>fff', f.read(12))
            proper_motions.append((mualpha, mudelta, radvel))

            # Read magnitudes, color, and size (4 floats, 32 bits each)
            appmag, absmag, encoded_color, size = struct.unpack('>ffff', f.read(16))

            # Decode color (assuming RGBA encoded in a float)
            color_bits = struct.unpack('>I', struct.pack('>f', encoded_color))[0]
            r = (color_bits >> 24) & 0xFF
            g = (color_bits >> 16) & 0xFF
            b = (color_bits >> 8) & 0xFF
            a = color_bits & 0xFF
            colors.append((r, g, b, a))

            # Store size and magnitude
            size_magnitudes.append((size, appmag))

            # Read and skip remaining data
            f.read(4 + 8)  # HIP number and Gaia SourceID
            name_length = struct.unpack('>i', f.read(4))[0]
            if name_length > 0:
                f.read(name_length * 2)  # Read and skip star name

            count += 1

@njit
def pad_array(arr, width, height, channels):
    padded = np.zeros((height, width, channels), dtype=np.float32)
    arr_len = len(arr)
    for i in range(arr_len):
        padded[i // width, i % width, :channels] = arr[i]
    return padded

def next_power_of_two(n):
    """ Returns the next power of two greater than or equal to n """
    return 2**((n - 1).bit_length())

def find_optimal_dimensions(total_count):
    """ Find the optimal width and height (powers of two) to fit the total count of elements, prioritizing larger width """
    width = next_power_of_two(int(np.sqrt(total_count)))
    height = next_power_of_two((total_count + width - 1) // width)

    # Adjust if reducing height can still fit the data
    while width // 2 >= height and width * height >= total_count:
        width //= 2
        height = next_power_of_two((total_count + width - 1) // width)

    return width, height

def main():
    max_rows = 1000000  # Maximum total rows to process across all files
    bin_directory = 'path/to/your/bin/files'
    output_directory = 'path/to/save/textures'

    # Initialize lists to hold aggregated data
    positions = []
    velocities = []
    proper_motions = []
    colors = []
    size_magnitudes = []

    # Process each .bin file and aggregate data
    for file_name in os.listdir(bin_directory):
        if file_name.endswith('.bin'):
            file_path = os.path.join(bin_directory, file_name)
            parse_bin_file_v2(file_path, max_rows, positions, velocities, proper_motions, colors, size_magnitudes)

    # Convert lists to numpy arrays
    positions = np.array(positions, dtype=np.float32)
    velocities = np.array(velocities, dtype=np.float32)
    proper_motions = np.array(proper_motions, dtype=np.float32)
    colors = np.array(colors, dtype=np.uint8)
    size_magnitudes = np.array(size_magnitudes, dtype=np.float32)

    # Determine optimal texture dimensions
    total_count = len(positions)
    width, height = find_optimal_dimensions(total_count)

    # Pad the data to fit the texture size
    positions = pad_array(positions, width, height, 3)
    velocities = pad_array(velocities, width, height, 3)
    proper_motions = pad_array(proper_motions, width, height, 3)
    colors = pad_array(colors, width, height, 4)
    size_magnitudes = pad_array(size_magnitudes, width, height, 2)

    # Save each large texture
    if not os.path.exists(output_directory):
        os.makedirs(output_directory)

    save_exr(positions, width, height, 3, os.path.join(output_directory, "all_positions.exr"))
    save_exr(velocities, width, height, 3, os.path.join(output_directory, "all_velocities.exr"))
    save_exr(proper_motions, width, height, 3, os.path.join(output_directory, "all_proper_motions.exr"))
    save_png(colors, width, height, os.path.join(output_directory, "all_colors.png"))
    save_exr(size_magnitudes, width, height, 2, os.path.join(output_directory, "all_size_magnitudes.exr"))

if __name__ == "__main__":
    main()

Key Optimizations:

1. Numba Acceleration:

   • The @njit decorator from numba speeds up the parsing and padding functions, which are often bottlenecks due to loop-heavy operations.
   • numba compiles the functions to machine code, providing significant performance boosts for numeric processing.

2. NumPy Optimizations:

   • The data is converted into NumPy arrays with specific data types (np.float32, np.uint8), which ensures efficient memory usage.

Additional Notes:

• This code prioritizes large widths while using Numba for faster array processing.
• You may further enhance performance by parallelizing file processing with concurrent.futures or similar parallel processing techniques if you have many .bin files.

You

dont place the pixelsfrom the top. unity reads textures from the bottom left

ChatGPT

To accommodate Unity’s convention of reading textures from the bottom-left corner instead of the top-left (which is the default for many image processing libraries), we need to ensure that the pixel data is flipped vertically before writing it out.

Here’s how you can modify the padding and flipping logic to ensure compatibility with Unity’s bottom-left texture orientation:

Updated Code with Bottom-Left Orientation

import struct
import os
import numpy as np
import OpenEXR
import Imath
from PIL import Image
from numba import njit

@njit
def parse_bin_file_v2(file_path, max_rows, positions, velocities, proper_motions, colors, size_magnitudes):
    count = 0
    with open(file_path, 'rb') as f:
        # Read and validate the special token (-1)
        token = struct.unpack('>i', f.read(4))[0]
        if token != -1:
            raise ValueError(f"Invalid token {token}. Expected -1 for versioning.")

        # Read the version number (should be 2)
        version = struct.unpack('>i', f.read(4))[0]
        if version != 2:
            raise ValueError(f"Unsupported version: {version}. Expected version 2.")

        # Read the number of stars
        number_of_stars = struct.unpack('>i', f.read(4))[0]

        while count < number_of_stars and len(positions) < max_rows:
            # Read position (3 doubles, 64 bits each)
            x, y, z = struct.unpack('>ddd', f.read(24))
            positions.append((x, y, z))

            # Read velocity (3 floats, 32 bits each)
            vx, vy, vz = struct.unpack('>fff', f.read(12))
            velocities.append((vx, vy, vz))

            # Read proper motion (3 floats, 32 bits each)
            mualpha, mudelta, radvel = struct.unpack('>fff', f.read(12))
            proper_motions.append((mualpha, mudelta, radvel))

            # Read magnitudes, color, and size (4 floats, 32 bits each)
            appmag, absmag, encoded_color, size = struct.unpack('>ffff', f.read(16))

            # Decode color (assuming RGBA encoded in a float)
            color_bits = struct.unpack('>I', struct.pack('>f', encoded_color))[0]
            r = (color_bits >> 24) & 0xFF
            g = (color_bits >> 16) & 0xFF
            b = (color_bits >> 8) & 0xFF
            a = color_bits & 0xFF
            colors.append((r, g, b, a))

            # Store size and magnitude
            size_magnitudes.append((size, appmag))

            # Read and skip remaining data
            f.read(4 + 8)  # HIP number and Gaia SourceID
            name_length = struct.unpack('>i', f.read(4))[0]
            if name_length > 0:
                f.read(name_length * 2)  # Read and skip star name

            count += 1

@njit
def pad_and_flip_array(arr, width, height, channels):
    padded = np.zeros((height, width, channels), dtype=np.float32)
    arr_len = len(arr)
    for i in range(arr_len):
        # Calculate target position
        row = (height - 1) - (i // width)  # Flip vertically
        col = i % width
        padded[row, col, :channels] = arr[i]
    return padded

def next_power_of_two(n):
    """ Returns the next power of two greater than or equal to n """
    return 2**((n - 1).bit_length())

def find_optimal_dimensions(total_count):
    """ Find the optimal width and height (powers of two) to fit the total count of elements, prioritizing larger width """
    width = next_power_of_two(int(np.sqrt(total_count)))
    height = next_power_of_two((total_count + width - 1) // width)

    # Adjust if reducing height can still fit the data
    while width // 2 >= height and width * height >= total_count:
        width //= 2
        height = next_power_of_two((total_count + width - 1) // width)

    return width, height

def main():
    max_rows = 1000000  # Maximum total rows to process across all files
    bin_directory = 'path/to/your/bin/files'
    output_directory = 'path/to/save/textures'

    # Initialize lists to hold aggregated data
    positions = []
    velocities = []
    proper_motions = []
    colors = []
    size_magnitudes = []

    # Process each .bin file and aggregate data
    for file_name in os.listdir(bin_directory):
        if file_name.endswith('.bin'):
            file_path = os.path.join(bin_directory, file_name)
            parse_bin_file_v2(file_path, max_rows, positions, velocities, proper_motions, colors, size_magnitudes)

    # Convert lists to numpy arrays
    positions = np.array(positions, dtype=np.float32)
    velocities = np.array(velocities, dtype=np.float32)
    proper_motions = np.array(proper_motions, dtype=np.float32)
    colors = np.array(colors, dtype=np.uint8)
    size_magnitudes = np.array(size_magnitudes, dtype=np.float32)

    # Determine optimal texture dimensions
    total_count = len(positions)
    width, height = find_optimal_dimensions(total_count)

    # Pad and flip the data to fit the texture size
    positions = pad_and_flip_array(positions, width, height, 3)
    velocities = pad_and_flip_array(velocities, width, height, 3)
    proper_motions = pad_and_flip_array(proper_motions, width, height, 3)
    colors = pad_and_flip_array(colors, width, height, 4)
    size_magnitudes = pad_and_flip_array(size_magnitudes, width, height, 2)

    # Save each large texture
    if not os.path.exists(output_directory):
        os.makedirs(output_directory)

    save_exr(positions, width, height, 3, os.path.join(output_directory, "all_positions.exr"))
    save_exr(velocities, width, height, 3, os.path.join(output_directory, "all_velocities.exr"))
    save_exr(proper_motions, width, height, 3, os.path.join(output_directory, "all_proper_motions.exr"))
    save_png(colors, width, height, os.path.join(output_directory, "all_colors.png"))
    save_exr(size_magnitudes, width, height, 2, os.path.join(output_directory, "all_size_magnitudes.exr"))

if __name__ == "__main__":
    main()

Key Changes:

1. Vertical Flipping:

   • The pad_and_flip_array function pads the data and flips it vertically, ensuring that the data is correctly oriented for Unity’s bottom-left texture convention.
   • The row index is adjusted to (height - 1) - (i // width) to flip the data vertically.

2. Efficient Padding and Flipping:

   • This function uses Numba’s @njit decorator to accelerate padding and flipping operations.

Explanation:

• By flipping the data vertically when placing it in the padded array, we ensure that Unity reads the texture correctly from the bottom-left corner.
• This approach works for both 8-bit and floating-point data, ensuring consistent orientation.

You

with numba “cannot compute fingerprint on empty list”

ChatGPT

The error you’re seeing ("cannot compute fingerprint on empty list") arises because Numba’s JIT-compiled functions, decorated with @njit, cannot handle Python lists that change size dynamically or are passed as empty lists. Numba works best with fixed-size arrays, typically numpy arrays.

Solution

To fix this issue, you should:

1. Initialize positions, velocities, proper_motions, colors, and size_magnitudes as fixed-size NumPy arrays.
2. Use NumPy array operations rather than list appends within @njit functions.
3. If necessary, dynamically resize arrays using numpy.resize() or other techniques outside the @njit function.

Here’s how you can adjust the code:

Modified Code to Use NumPy Arrays

import struct
import os
import numpy as np
import OpenEXR
import Imath
from PIL import Image
from numba import njit

@njit
def parse_bin_file_v2(file_path, max_rows, positions, velocities, proper_motions, colors, size_magnitudes, current_index):
    count = 0
    with open(file_path, 'rb') as f:
        # Read and validate the special token (-1)
        token = struct.unpack('>i', f.read(4))[0]
        if token != -1:
            raise ValueError(f"Invalid token {token}. Expected -1 for versioning.")

        # Read the version number (should be 2)
        version = struct.unpack('>i', f.read(4))[0]
        if version != 2:
            raise ValueError(f"Unsupported version: {version}. Expected version 2.")

        # Read the number of stars
        number_of_stars = struct.unpack('>i', f.read(4))[0]

        while count < number_of_stars and current_index < max_rows:
            # Read position (3 doubles, 64 bits each)
            x, y, z = struct.unpack('>ddd', f.read(24))
            positions[current_index] = (x, y, z)

            # Read velocity (3 floats, 32 bits each)
            vx, vy, vz = struct.unpack('>fff', f.read(12))
            velocities[current_index] = (vx, vy, vz)

            # Read proper motion (3 floats, 32 bits each)
            mualpha, mudelta, radvel = struct.unpack('>fff', f.read(12))
            proper_motions[current_index] = (mualpha, mudelta, radvel)

            # Read magnitudes, color, and size (4 floats, 32 bits each)
            appmag, absmag, encoded_color, size = struct.unpack('>ffff', f.read(16))

            # Decode color (assuming RGBA encoded in a float)
            color_bits = struct.unpack('>I', struct.pack('>f', encoded_color))[0]
            r = (color_bits >> 24) & 0xFF
            g = (color_bits >> 16) & 0xFF
            b = (color_bits >> 8) & 0xFF
            a = color_bits & 0xFF
            colors[current_index] = (r, g, b, a)

            # Store size and magnitude
            size_magnitudes[current_index] = (size, appmag)

            # Read and skip remaining data
            f.read(4 + 8)  # HIP number and Gaia SourceID
            name_length = struct.unpack('>i', f.read(4))[0]
            if name_length > 0:
                f.read(name_length * 2)  # Read and skip star name

            count += 1
            current_index += 1

    return current_index

@njit
def pad_and_flip_array(arr, width, height, channels):
    padded = np.zeros((height, width, channels), dtype=np.float32)
    arr_len = len(arr)
    for i in range(arr_len):
        # Calculate target position
        row = (height - 1) - (i // width)  # Flip vertically
        col = i % width
        padded[row, col, :channels] = arr[i]
    return padded

def next_power_of_two(n):
    """ Returns the next power of two greater than or equal to n """
    return 2**((n - 1).bit_length())

def find_optimal_dimensions(total_count):
    """ Find the optimal width and height (powers of two) to fit the total count of elements, prioritizing larger width """
    width = next_power_of_two(int(np.sqrt(total_count)))
    height = next_power_of_two((total_count + width - 1) // width)

    # Adjust if reducing height can still fit the data
    while width // 2 >= height and width * height >= total_count:
        width //= 2
        height = next_power_of_two((total_count + width - 1) // width)

    return width, height

def main():
    max_rows = 1000000  # Maximum total rows to process across all files
    bin_directory = 'path/to/your/bin/files'
    output_directory = 'path/to/save/textures'

    # Initialize fixed-size NumPy arrays
    positions = np.zeros((max_rows, 3), dtype=np.float32)
    velocities = np.zeros((max_rows, 3), dtype=np.float32)
    proper_motions = np.zeros((max_rows, 3), dtype=np.float32)
    colors = np.zeros((max_rows, 4), dtype=np.uint8)
    size_magnitudes = np.zeros((max_rows, 2), dtype=np.float32)

    # Track the current index for filling data
    current_index = 0

    # Process each .bin file and aggregate data
    for file_name in os.listdir(bin_directory):
        if file_name.endswith('.bin'):
            file_path = os.path.join(bin_directory, file_name)
            current_index = parse_bin_file_v2(file_path, max_rows, positions, velocities, proper_motions, colors, size_magnitudes, current_index)

    # Trim arrays to actual size
    positions = positions[:current_index]
    velocities = velocities[:current_index]
    proper_motions = proper_motions[:current_index]
    colors = colors[:current_index]
    size_magnitudes = size_magnitudes[:current_index]

    # Determine optimal texture dimensions
    total_count = len(positions)
    width, height = find_optimal_dimensions(total_count)

    # Pad and flip the data to fit the texture size
    positions = pad_and_flip_array(positions, width, height, 3)
    velocities = pad_and_flip_array(velocities, width, height, 3)
    proper_motions = pad_and_flip_array(proper_motions, width, height, 3)
    colors = pad_and_flip_array(colors, width, height, 4)
    size_magnitudes = pad_and_flip_array(size_magnitudes, width, height, 2)

    # Save each large texture
    if not os.path.exists(output_directory):
        os.makedirs(output_directory)

    save_exr(positions, width, height, 3, os.path.join(output_directory, "all_positions.exr"))
    save_exr(velocities, width, height, 3, os.path.join(output_directory, "all_velocities.exr"))
    save_exr(proper_motions, width, height, 3, os.path.join(output_directory, "all_proper_motions.exr"))
    save_png(colors, width, height, os.path.join(output_directory, "all_colors.png"))
    save_exr(size_magnitudes, width, height, 2, os.path.join(output_directory, "all_size_magnitudes.exr"))

if __name__ == "__main__":
    main()

Key Adjustments:

1. Fixed-Size NumPy Arrays:

   • Instead of using Python lists, the data is stored in pre-allocated fixed-size NumPy arrays.
   • current_index is used to track the position in the array for inserting data.
   • After processing, the arrays are trimmed to the actual size using slicing.

2. NumPy Array Operations:

   • parse_bin_file_v2 uses NumPy arrays instead of lists to store data, which is compatible with Numba’s @njit.

Advantages:

• This approach leverages the speed of NumPy arrays and the optimization capabilities of Numba.
• The use of pre-allocated arrays avoids the overhead of dynamic list resizing, making the code more efficient.

You

numba.core.errors.UnsupportedError: Failed in nopython mode pipeline (step: analyzing bytecode) The ‘with (context manager) as (variable):’ construct is not supported.

ChatGPT

The error you encountered indicates that Numba’s nopython mode does not support the use of with statements, such as with open(file) as f:. This is because context managers are not yet supported in nopython mode.

To work around this issue, you can open files outside of @njit-decorated functions and pass the file data (like byte arrays) into the function for processing. Here is a modified version of the code that avoids using with statements inside Numba-optimized functions:

Updated Code without with Statements in Numba

import struct
import os
import numpy as np
import OpenEXR
import Imath
from PIL import Image
from numba import njit

@njit
def parse_bin_data(data, max_rows, positions, velocities, proper_motions, colors, size_magnitudes, current_index):
    count = 0
    offset = 0

    # Read and validate the special token (-1)
    token = struct.unpack('>i', data[offset:offset + 4])[0]
    offset += 4
    if token != -1:
        raise ValueError(f"Invalid token {token}. Expected -1 for versioning.")

    # Read the version number (should be 2)
    version = struct.unpack('>i', data[offset:offset + 4])[0]
    offset += 4
    if version != 2:
        raise ValueError(f"Unsupported version: {version}. Expected version 2.")

    # Read the number of stars
    number_of_stars = struct.unpack('>i', data[offset:offset + 4])[0]
    offset += 4

    while count < number_of_stars and current_index < max_rows:
        # Read position (3 doubles, 64 bits each)
        x, y, z = struct.unpack('>ddd', data[offset:offset + 24])
        offset += 24
        positions[current_index] = (x, y, z)

        # Read velocity (3 floats, 32 bits each)
        vx, vy, vz = struct.unpack('>fff', data[offset:offset + 12])
        offset += 12
        velocities[current_index] = (vx, vy, vz)

        # Read proper motion (3 floats, 32 bits each)
        mualpha, mudelta, radvel = struct.unpack('>fff', data[offset:offset + 12])
        offset += 12
        proper_motions[current_index] = (mualpha, mudelta, radvel)

        # Read magnitudes, color, and size (4 floats, 32 bits each)
        appmag, absmag, encoded_color, size = struct.unpack('>ffff', data[offset:offset + 16])
        offset += 16

        # Decode color (assuming RGBA encoded in a float)
        color_bits = struct.unpack('>I', struct.pack('>f', encoded_color))[0]
        r = (color_bits >> 24) & 0xFF
        g = (color_bits >> 16) & 0xFF
        b = (color_bits >> 8) & 0xFF
        a = color_bits & 0xFF
        colors[current_index] = (r, g, b, a)

        # Store size and magnitude
        size_magnitudes[current_index] = (size, appmag)

        # Read and skip remaining data
        offset += 4 + 8  # HIP number (4 bytes) and Gaia SourceID (8 bytes)
        name_length = struct.unpack('>i', data[offset:offset + 4])[0]
        offset += 4
        if name_length > 0:
            offset += name_length * 2  # Read and skip star name (UTF-16 encoding)

        count += 1
        current_index += 1

    return current_index

def process_bin_file(file_path, max_rows, positions, velocities, proper_motions, colors, size_magnitudes, current_index):
    with open(file_path, 'rb') as f:
        data = f.read()  # Read the entire file into a byte array
    return parse_bin_data(np.frombuffer(data, dtype=np.uint8), max_rows, positions, velocities, proper_motions, colors, size_magnitudes, current_index)

def next_power_of_two(n):
    """ Returns the next power of two greater than or equal to n """
    return 2**((n - 1).bit_length())

def find_optimal_dimensions(total_count):
    """ Find the optimal width and height (powers of two) to fit the total count of elements, prioritizing larger width """
    width = next_power_of_two(int(np.sqrt(total_count)))
    height = next_power_of_two((total_count + width - 1) // width)

    # Adjust if reducing height can still fit the data
    while width // 2 >= height and width * height >= total_count:
        width //= 2
        height = next_power_of_two((total_count + width - 1) // width)

    return width, height

def main():
    max_rows = 1000000  # Maximum total rows to process across all files
    bin_directory = 'path/to/your/bin/files'
    output_directory = 'path/to/save/textures'

    # Initialize fixed-size NumPy arrays
    positions = np.zeros((max_rows, 3), dtype=np.float32)
    velocities = np.zeros((max_rows, 3), dtype=np.float32)
    proper_motions = np.zeros((max_rows, 3), dtype=np.float32)
    colors = np.zeros((max_rows, 4), dtype=np.uint8)
    size_magnitudes = np.zeros((max_rows, 2), dtype=np.float32)

    # Track the current index for filling data
    current_index = 0

    # Process each .bin file and aggregate data
    for file_name in os.listdir(bin_directory):
        if file_name.endswith('.bin'):
            file_path = os.path.join(bin_directory, file_name)
            current_index = process_bin_file(file_path, max_rows, positions, velocities, proper_motions, colors, size_magnitudes, current_index)

    # Trim arrays to actual size
    positions = positions[:current_index]
    velocities = velocities[:current_index]
    proper_motions = proper_motions[:current_index]
    colors = colors[:current_index]
    size_magnitudes = size_magnitudes[:current_index]

    # Determine optimal texture dimensions
    total_count = len(positions)
    width, height = find_optimal_dimensions(total_count)

    # Pad and flip the data to fit the texture size
    positions = pad_and_flip_array(positions, width, height, 3)
    velocities = pad_and_flip_array(velocities, width, height, 3)
    proper_motions = pad_and_flip_array(proper_motions, width, height, 3)
    colors = pad_and_flip_array(colors, width, height, 4)
    size_magnitudes = pad_and_flip_array(size_magnitudes, width, height, 2)

    # Save each large texture
    if not os.path.exists(output_directory):
        os.makedirs(output_directory)

    save_exr(positions, width, height, 3, os.path.join(output_directory, "all_positions.exr"))
    save_exr(velocities, width, height, 3, os.path.join(output_directory, "all_velocities.exr"))
    save_exr(proper_motions, width, height, 3, os.path.join(output_directory, "all_proper_motions.exr"))
    save_png(colors, width, height, os.path.join(output_directory, "all_colors.png"))
    save_exr(size_magnitudes, width, height, 2, os.path.join(output_directory, "all_size_magnitudes.exr"))

if __name__ == "__main__":
    main()

Key Changes:

1. Moved File Reading Outside @njit:

   • The process_bin_file function reads the file data outside of Numba-optimized functions.
   • The data is passed as a numpy array to parse_bin_data, which is JIT-compiled by Numba.

2. NumPy Buffer Conversion:

   • The file data is read into a byte array and converted into a NumPy array using np.frombuffer(data, dtype=np.uint8) for better performance with Numba.

This modification allows you to leverage Numba’s speed for parsing and data extraction without encountering unsupported constructs.

You

any alternative to numba?

ChatGPT

Yes, if Numba’s limitations or compatibility issues are causing challenges, there are alternative approaches and libraries you can consider for optimizing your code. Here are some recommended options:

1. Cython

• Description: Cython is a superset of Python that allows you to compile Python code into C, offering significant performance improvements. Unlike Numba, Cython supports a broader range of Python features and can handle more complex code structures, such as context managers (with statements).
• Pros:
  • Works well with both Python and C/C++ code.
  • Can optimize loops, type declarations, and other operations.
  • Handles more general Python features than Numba.
• Cons:
  • Requires writing .pyx files and building them.
  • More setup and boilerplate than Numba.
• Usage Example: You can create a .pyx file and use cythonize to build it.

  parser.pyx

  def parse_bin_data(bytes data, int max_rows):
      cdef int i
      # Loop and other parsing logic here
      return

  Compile using:

  cythonize -i parser.pyx

  You can then import and use it like a regular Python module.

2. NumPy Vectorization

• Description: Often, the best way to speed up Python code is to replace loops with vectorized operations using NumPy. This approach is highly effective for numerical data processing.
• Pros:
  • Easy to use.
  • Highly optimized for numerical operations.
• Cons:
  • Not suitable for code that cannot be expressed as vectorized operations.
  • May require substantial code refactoring.
• Usage Example: Convert your data processing loops into vectorized operations, e.g., using np.where, np.sum, or other array-wide operations.

3. Multiprocessing and Parallelization

• Description: Utilize Python’s multiprocessing module to parallelize tasks, especially if you are processing multiple files or can split tasks independently.
• Pros:
  • Can speed up I/O-bound and CPU-bound tasks.
  • Works with standard Python.
• Cons:
  • Involves inter-process communication overhead.
  • More complex than NumPy-only optimizations.
• Usage Example:

  import multiprocessing
  
  def process_file(file_path):
      # Parse a single file
      return
  
  if __name__ == "__main__":
      with multiprocessing.Pool() as pool:
          pool.map(process_file, list_of_files)

4. Pythran

• Description: Pythran is a compiler for numerical kernels written in Python. It works well with NumPy code and can generate highly optimized C++ code from Python functions.
• Pros:
  • No need to rewrite code; annotations are often sufficient.
  • Can achieve performance similar to Cython or Numba for numerical computations.
• Cons:
  • Limited support compared to NumPy or Numba.
  • Requires installation and setup.
• Usage Example: Annotate your function and use the pythran command to compile.

  # pythran export my_function(float64[:])
  def my_function(arr):
      return arr.sum()

5. JAX

• Description: JAX is a high-performance library for numerical computing that offers just-in-time compilation via XLA (Accelerated Linear Algebra). It provides an API similar to NumPy but with additional capabilities like automatic differentiation and parallelization.
• Pros:
  • Highly optimized for numerical operations.
  • Easy-to-use NumPy-like API.
  • GPU and TPU acceleration support.
• Cons:
  • Not a direct drop-in replacement for Numba in all use cases.
  • Geared towards numerical computing (matrix operations, etc.).
• Usage Example:

  import jax.numpy as jnp
  from jax import jit
  
  @jit
  def process_data(data):
      return jnp.sum(data)

Conclusion

• For the broadest compatibility and general-purpose optimization, consider Cython.
• If vectorized operations can achieve the optimization you need, stick with NumPy.
• For parallel processing, leverage the multiprocessing module.
• For GPU acceleration or high-performance numerical tasks, JAX is a good option.

Select the tool that best suits your needs based on the complexity of your code, required speedup, and compatibility. If you need assistance with implementing any of these alternatives, let me know!

You

colors are only 24bit. why save as exr?

ChatGPT

You’re absolutely right—since the colors are only 24-bit (8 bits per channel for RGB), saving them as an .exr file, which is primarily designed for high dynamic range (HDR) images and 32-bit floating-point values per channel, is overkill and inefficient. It would be better to save them in a more suitable format like PNG, which supports 24-bit color (plus an optional 8-bit alpha channel if you need it).

Recommended Approach: Save Colors as PNG

1. Format Choice: Use formats like PNG or JPEG for 24-bit color data to save space and avoid unnecessary precision.
2. Efficient Storage: This will be faster, more storage-efficient, and well-supported across platforms, including Unity.

Here’s how you can modify your existing code to save the 24-bit color data using PNG:

Adjusted Code for Saving Colors as PNG

import struct
import os
import numpy as np
import OpenEXR
import Imath
from PIL import Image  # For saving 8-bit images like PNG

def save_png(data, width, height, file_name):
    """ Save the 24-bit color data as a PNG image """
    # Reshape data for saving as 8-bit RGB image
    data = data.reshape((height, width, 4)).astype(np.uint8)  # Assuming data includes RGBA channels
    image = Image.fromarray(data[:, :, :3], mode='RGB')  # Only use RGB channels if alpha is not needed
    image.save(file_name)

def main():
    max_rows = 1000000  # Maximum total rows to process across all files
    bin_directory = 'path/to/your/bin/files'
    output_directory = 'path/to/save/textures'

    # Initialize data arrays...
    # (similar to the earlier example)

    # Process and pad the data...

    # Save color data as PNG (since it's 24-bit RGB)
    save_png(colors, width, height, os.path.join(output_directory, "all_colors.png"))

    # Save other data as .exr...

Key Points:

• save_png Function: Uses PIL (Python Imaging Library, also known as Pillow) to save the data as a PNG.
• RGB Data: If you do not need the alpha channel, you can drop it by using data[:, :, :3].
• PNG Efficiency: PNG is well-suited for 24-bit RGB data, offering lossless compression and compatibility with Unity and other graphics tools.

Why Use PNG Instead of .exr for Colors?

1. Storage Efficiency: PNG files are more compact and efficient for storing 24-bit RGB data compared to .exr, which is optimized for high-precision, HDR content.
2. Compatibility: PNG files are widely supported by game engines like Unity, web browsers, and image processing tools.
3. Simpler Data Handling: 8-bit per channel data is directly supported by PNG without requiring conversions or excess precision.

Summary:

• Save color data (24-bit RGB) as PNG files.
• Use .exr files only for high-precision data like positions, velocities, and other floating-point data that benefit from a 32-bit per channel format. This keeps your storage efficient and your data format choices well-aligned with the data characteristics.

You

we are turning 64bit floats to 32bit. can you scale them so that we don’t get errors?

ChatGPT

When converting 64-bit floats to 32-bit floats, you may experience precision loss, especially when dealing with very large or very small numbers. To minimize errors due to this conversion, you can scale and normalize your values appropriately before converting them. This approach ensures that the data fits within the representable range of 32-bit floats while maintaining as much precision as possible.

Here’s a step-by-step approach to implement scaling:

1. Determine the Range of Values: Compute the minimum and maximum values of your 64-bit float data.
2. Scale the Values: Transform the data into a normalized range (e.g., [0, 1]) or a range that best suits your use case.
3. Apply the Scaling During Conversion.

Updated Code for Scaling 64-bit to 32-bit Values

Here’s how you can integrate this into your existing workflow for positions (similar logic can be applied to other data arrays if needed):

import numpy as np

def scale_and_convert_to_32bit(values, scale_min=None, scale_max=None):
    """ Scales and converts 64-bit float values to 32-bit floats """
    # Determine scale_min and scale_max if not provided
    if scale_min is None:
        scale_min = values.min()
    if scale_max is None:
        scale_max = values.max()

    # Avoid division by zero if all values are the same
    if scale_max == scale_min:
        return np.zeros_like(values, dtype=np.float32)

    # Scale values to [0, 1] and then to the original range in 32-bit
    scaled_values = (values - scale_min) / (scale_max - scale_min)
    return scaled_values.astype(np.float32), scale_min, scale_max

def main():
    # Example data (64-bit positions)
    positions = np.array([[1.0e12, -2.0e12, 3.0e12], [4.0e12, 5.0e12, -6.0e12]], dtype=np.float64)

    # Scale and convert positions to 32-bit
    scaled_positions = np.zeros_like(positions, dtype=np.float32)
    scale_min, scale_max = None, None
    for i in range(positions.shape[1]):  # Scale each dimension separately
        scaled_positions[:, i], scale_min, scale_max = scale_and_convert_to_32bit(positions[:, i], scale_min, scale_max)

    print("Scaled Positions (32-bit):")
    print(scaled_positions)

    # Save scale_min and scale_max for later usage (e.g., for inverse scaling)

Explanation:

1. Scaling: The function scale_and_convert_to_32bit scales the input values to the range [0, 1] before converting them to 32-bit floats.
   • This normalization minimizes precision loss during conversion.
2. Custom Scaling Range: If you have a known range for your data (e.g., positions should be within certain bounds), you can pass scale_min and scale_max as arguments.
3. Handling Edge Cases: If all values are the same, the function handles it by setting all scaled values to zero.

Usage Considerations:

• Preserve Scaling Factors: Save scale_min and scale_max so you can reverse the scaling operation later if needed.
• Different Scaling per Dimension: This example scales each dimension (e.g., X, Y, Z) separately. If you want a uniform scale across all dimensions, compute the scale_min and scale_max globally.

Applying to Your Workflow:

• Before converting 64-bit positions, velocities, or other data, apply this scaling approach.
• After conversion, store scale_min and scale_max to later reconstruct the original values if needed.
• This approach minimizes precision loss and ensures that your 32-bit representation retains as much detail as possible.

You

apply this to the full example

ChatGPT

Here is the full example with the scaling applied to 64-bit floats before converting them to 32-bit floats. This ensures minimal precision loss during the conversion of position values (or any other large-range data) from 64-bit to 32-bit.

Full Example with Scaling for 64-bit to 32-bit Conversion

import struct
import os
import numpy as np
import OpenEXR
import Imath
from PIL import Image

def scale_and_convert_to_32bit(values, scale_min=None, scale_max=None):
    """ Scales and converts 64-bit float values to 32-bit floats """
    # Determine scale_min and scale_max if not provided
    if scale_min is None:
        scale_min = values.min()
    if scale_max is None:
        scale_max = values.max()

    # Avoid division by zero if all values are the same
    if scale_max == scale_min:
        return np.zeros_like(values, dtype=np.float32), scale_min, scale_max

    # Scale values to [0, 1] and then convert to 32-bit
    scaled_values = (values - scale_min) / (scale_max - scale_min)
    return scaled_values.astype(np.float32), scale_min, scale_max

def parse_bin_data(data, max_rows, positions, velocities, proper_motions, colors, size_magnitudes, current_index):
    count = 0
    offset = 0

    # Read and validate the special token (-1)
    token = struct.unpack('>i', data[offset:offset + 4])[0]
    offset += 4
    if token != -1:
        raise ValueError(f"Invalid token {token}. Expected -1 for versioning.")

    # Read the version number (should be 2)
    version = struct.unpack('>i', data[offset:offset + 4])[0]
    offset += 4
    if version != 2:
        raise ValueError(f"Unsupported version: {version}. Expected version 2.")

    # Read the number of stars
    number_of_stars = struct.unpack('>i', data[offset:offset + 4])[0]
    offset += 4

    while count < number_of_stars and current_index < max_rows:
        # Read position (3 doubles, 64 bits each) and scale them
        x, y, z = struct.unpack('>ddd', data[offset:offset + 24])
        offset += 24
        positions[current_index] = (x, y, z)

        # Read velocity (3 floats, 32 bits each)
        vx, vy, vz = struct.unpack('>fff', data[offset:offset + 12])
        offset += 12
        velocities[current_index] = (vx, vy, vz)

        # Read proper motion (3 floats, 32 bits each)
        mualpha, mudelta, radvel = struct.unpack('>fff', data[offset:offset + 12])
        offset += 12
        proper_motions[current_index] = (mualpha, mudelta, radvel)

        # Read magnitudes, color, and size (4 floats, 32 bits each)
        appmag, absmag, encoded_color, size = struct.unpack('>ffff', data[offset:offset + 16])
        offset += 16

        # Decode color (assuming RGBA encoded in a float)
        color_bits = struct.unpack('>I', struct.pack('>f', encoded_color))[0]
        r = (color_bits >> 24) & 0xFF
        g = (color_bits >> 16) & 0xFF
        b = (color_bits >> 8) & 0xFF
        a = color_bits & 0xFF
        colors[current_index] = (r, g, b, a)

        # Store size and magnitude
        size_magnitudes[current_index] = (size, appmag)

        # Read and skip remaining data
        offset += 4 + 8  # HIP number (4 bytes) and Gaia SourceID (8 bytes)
        name_length = struct.unpack('>i', data[offset:offset + 4])[0]
        offset += 4
        if name_length > 0:
            offset += name_length * 2  # Read and skip star name (UTF-16 encoding)

        count += 1
        current_index += 1

    return current_index

def process_bin_file(file_path, max_rows, positions, velocities, proper_motions, colors, size_magnitudes, current_index):
    with open(file_path, 'rb') as f:
        data = f.read()  # Read the entire file into a byte array
    return parse_bin_data(np.frombuffer(data, dtype=np.uint8), max_rows, positions, velocities, proper_motions, colors, size_magnitudes, current_index)

def main():
    max_rows = 1000000  # Maximum total rows to process across all files
    bin_directory = 'path/to/your/bin/files'
    output_directory = 'path/to/save/textures'

    # Initialize fixed-size NumPy arrays
    positions = np.zeros((max_rows, 3), dtype=np.float64)  # Use 64-bit initially for scaling
    velocities = np.zeros((max_rows, 3), dtype=np.float32)
    proper_motions = np.zeros((max_rows, 3), dtype=np.float32)
    colors = np.zeros((max_rows, 4), dtype=np.uint8)
    size_magnitudes = np.zeros((max_rows, 2), dtype=np.float32)

    # Track the current index for filling data
    current_index = 0

    # Process each .bin file and aggregate data
    for file_name in os.listdir(bin_directory):
        if file_name.endswith('.bin'):
            file_path = os.path.join(bin_directory, file_name)
            current_index = process_bin_file(file_path, max_rows, positions, velocities, proper_motions, colors, size_magnitudes, current_index)

    # Trim arrays to actual size
    positions = positions[:current_index]
    velocities = velocities[:current_index]
    proper_motions = proper_motions[:current_index]
    colors = colors[:current_index]
    size_magnitudes = size_magnitudes[:current_index]

    # Scale and convert positions to 32-bit floats
    for i in range(positions.shape[1]):  # Scale each dimension separately
        positions[:, i], scale_min, scale_max = scale_and_convert_to_32bit(positions[:, i])

    # Determine optimal texture dimensions
    total_count = len(positions)
    width, height = find_optimal_dimensions(total_count)

    # Pad and flip the data to fit the texture size
    positions = pad_and_flip_array(positions, width, height, 3)
    velocities = pad_and_flip_array(velocities, width, height, 3)
    proper_motions = pad_and_flip_array(proper_motions, width, height, 3)
    colors = pad_and_flip_array(colors, width, height, 4)
    size_magnitudes = pad_and_flip_array(size_magnitudes, width, height, 2)

    # Save each large texture
    if not os.path.exists(output_directory):
        os.makedirs(output_directory)

    save_exr(positions, width, height, 3, os.path.join(output_directory, "all_positions.exr"))
    save_exr(velocities, width, height, 3, os.path.join(output_directory, "all_velocities.exr"))
    save_exr(proper_motions, width, height, 3, os.path.join(output_directory, "all_proper_motions.exr"))
    save_png(colors, width, height, os.path.join(output_directory, "all_colors.png"))
    save_exr(size_magnitudes, width, height, 2, os.path.join(output_directory, "all_size_magnitudes.exr"))

if __name__ == "__main__":
    main()

Key Changes:

1. Initial 64-bit Storage for Positions: positions are initially stored as 64-bit floats to preserve precision during scaling.
2. Scaling Function: The scale_and_convert_to_32bit function scales and converts values to 32-bit floats.
3. Separate Scaling for Each Dimension: Each dimension (X, Y, Z) of the positions is scaled independently to minimize precision loss.

Explanation:

• Scaling ensures that values are normalized within a range that minimizes precision loss when converting to 32-bit.
• Storage of Scaling Factors: Consider storing scale_min and scale_max for each dimension if you need to reconstruct the original 64-bit values later.

You

make this work with this format.

Version 0

The version 0 is specified below. It contains a header with the number of stars and then a bunch of data for each star. It contains a 3-integer set which is the Tycho identifier, mainly for compatibility with TGAS.

1 single-precision integer (32-bit) – number of stars in the file

For each star:

        3 double-precision floats (64-bit * 3) – X, Y, Z cartesian coordinates in internal units

        3 double-precision floats (64-bit * 3) – Vx, Vy, Vz - cartesian velocity vector in internal units per year

        3 double-precision floats (64-bit * 3) – mualpha, mudelta, radvel - proper motion

        4 single-precision floats (32-bit * 4) – appmag, absmag, color, size - Magnitudes, colors (encoded), and size (a derived quantity, for rendering)

        1 single-precision integer (32-bit) – HIP number (if any, otherwise negative)

        3 single-precision integer (32-bit * 3) – Tycho identifiers

        1 double-precision integer (64-bit) – Gaia SourceID

        1 single-precision integer (32-bit) – namelen -> Length of name

        namelen * char (16-bit * namelen) – Characters of the star name, where each character is encoded with UTF-16

ChatGPT

Here is an adapted version of your code to handle data in the specified Version 0 format. The code reads the data according to the specified structure, performs the necessary scaling of 64-bit floats to 32-bit, and handles various fields appropriately.

Full Example for Version 0

import struct
import os
import numpy as np
import OpenEXR
import Imath
from PIL import Image

def scale_and_convert_to_32bit(values, scale_min=None, scale_max=None):
    """ Scales and converts 64-bit float values to 32-bit floats """
    if scale_min is None:
        scale_min = values.min()
    if scale_max is None:
        scale_max = values.max()

    if scale_max == scale_min:
        return np.zeros_like(values, dtype=np.float32), scale_min, scale_max

    scaled_values = (values - scale_min) / (scale_max - scale_min)
    return scaled_values.astype(np.float32), scale_min, scale_max

def parse_bin_data(data, max_rows, positions, velocities, proper_motions, magnitudes, hip_numbers, tycho_ids, gaia_ids, names, current_index):
    count = 0
    offset = 0

    # Read the number of stars
    number_of_stars = struct.unpack('>i', data[offset:offset + 4])[0]
    offset += 4

    while count < number_of_stars and current_index < max_rows:
        # Read position (3 doubles, 64 bits each)
        x, y, z = struct.unpack('>ddd', data[offset:offset + 24])
        offset += 24
        positions[current_index] = (x, y, z)

        # Read velocity (3 doubles, 64 bits each)
        vx, vy, vz = struct.unpack('>ddd', data[offset:offset + 24])
        offset += 24
        velocities[current_index] = (vx, vy, vz)

        # Read proper motion (3 doubles, 64 bits each)
        mualpha, mudelta, radvel = struct.unpack('>ddd', data[offset:offset + 24])
        offset += 24
        proper_motions[current_index] = (mualpha, mudelta, radvel)

        # Read magnitudes and size (4 floats, 32 bits each)
        appmag, absmag, encoded_color, size = struct.unpack('>ffff', data[offset:offset + 16])
        offset += 16
        magnitudes[current_index] = (appmag, absmag, encoded_color, size)

        # Read HIP number (32-bit integer)
        hip_numbers[current_index] = struct.unpack('>i', data[offset:offset + 4])[0]
        offset += 4

        # Read Tycho identifiers (3 integers, 32 bits each)
        tycho_ids[current_index] = struct.unpack('>iii', data[offset:offset + 12])
        offset += 12

        # Read Gaia SourceID (64-bit integer)
        gaia_ids[current_index] = struct.unpack('>q', data[offset:offset + 8])[0]
        offset += 8

        # Read star name length (32-bit integer)
        name_length = struct.unpack('>i', data[offset:offset + 4])[0]
        offset += 4
        if name_length > 0:
            star_name = data[offset:offset + name_length * 2].decode('utf-16')
            names[current_index] = star_name
            offset += name_length * 2
        else:
            names[current_index] = ""

        count += 1
        current_index += 1

    return current_index

def process_bin_file(file_path, max_rows, positions, velocities, proper_motions, magnitudes, hip_numbers, tycho_ids, gaia_ids, names, current_index):
    with open(file_path, 'rb') as f:
        data = f.read()
    return parse_bin_data(np.frombuffer(data, dtype=np.uint8), max_rows, positions, velocities, proper_motions, magnitudes, hip_numbers, tycho_ids, gaia_ids, names, current_index)

def main():
    max_rows = 1000000  # Maximum total rows to process across all files
    bin_directory = 'path/to/your/bin/files'
    output_directory = 'path/to/save/textures'

    # Initialize fixed-size NumPy arrays
    positions = np.zeros((max_rows, 3), dtype=np.float64)
    velocities = np.zeros((max_rows, 3), dtype=np.float64)
    proper_motions = np.zeros((max_rows, 3), dtype=np.float64)
    magnitudes = np.zeros((max_rows, 4), dtype=np.float32)
    hip_numbers = np.zeros(max_rows, dtype=np.int32)
    tycho_ids = np.zeros((max_rows, 3), dtype=np.int32)
    gaia_ids = np.zeros(max_rows, dtype=np.int64)
    names = np.zeros(max_rows, dtype=object)

    # Track the current index for filling data
    current_index = 0

    # Process each .bin file and aggregate data
    for file_name in os.listdir(bin_directory):
        if file_name.endswith('.bin'):
            file_path = os.path.join(bin_directory, file_name)
            current_index = process_bin_file(file_path, max_rows, positions, velocities, proper_motions, magnitudes, hip_numbers, tycho_ids, gaia_ids, names, current_index)

    # Trim arrays to actual size
    positions = positions[:current_index]
    velocities = velocities[:current_index]
    proper_motions = proper_motions[:current_index]
    magnitudes = magnitudes[:current_index]
    hip_numbers = hip_numbers[:current_index]
    tycho_ids = tycho_ids[:current_index]
    gaia_ids = gaia_ids[:current_index]
    names = names[:current_index]

    # Scale and convert positions to 32-bit floats
    for i in range(positions.shape[1]):
        positions[:, i], scale_min, scale_max = scale_and_convert_to_32bit(positions[:, i])

    # Scale velocities and proper motions similarly if needed

    # Determine optimal texture dimensions
    total_count = len(positions)
    width, height = find_optimal_dimensions(total_count)

    # Pad and flip the data to fit the texture size
    positions = pad_and_flip_array(positions, width, height, 3)
    velocities = pad_and_flip_array(velocities, width, height, 3)
    proper_motions = pad_and_flip_array(proper_motions, width, height, 3)
    magnitudes = pad_and_flip_array(magnitudes, width, height, 4)

    # Save each large texture
    if not os.path.exists(output_directory):
        os.makedirs(output_directory)

    save_exr(positions, width, height, 3, os.path.join(output_directory, "all_positions.exr"))
    save_exr(velocities, width, height, 3, os.path.join(output_directory, "all_velocities.exr"))
    save_exr(proper_motions, width, height, 3, os.path.join(output_directory, "all_proper_motions.exr"))
    save_exr(magnitudes, width, height, 4, os.path.join(output_directory, "all_magnitudes.exr"))

if __name__ == "__main__":
    main()

Key Changes for Version 0 Format:

1. Additional Fields:

   • Tycho identifiers (tycho_ids), HIP numbers (hip_numbers), and Gaia SourceIDs (gaia_ids) are handled.
   • The star names are stored in a names array, which uses an object array since string lengths vary.

2. 64-bit to 32-bit Scaling:

   • Positions are scaled from 64-bit to 32-bit, with similar logic applicable to velocities and proper motions if needed.

3. Padding and Flipping:

   • Data is padded and flipped as required for texture generation.

4. Structure Handling:

   • The parsing logic adheres to the specified structure for Version 0.

Feel free to adjust and add any additional data handling or saving logic as needed!

You

AttributeError: ‘numpy.ndarray’ object has no attribute ‘decode’

ChatGPT

The error you encountered, AttributeError: 'numpy.ndarray' object has no attribute 'decode', occurs because you’re trying to decode a NumPy array directly, but string decoding methods (decode('utf-16')) only work on bytes or strings.

To fix this issue, you need to first convert the specific portion of the array representing the star name into bytes before applying the decode method. Here’s how you can fix this:

Correcting the Decoding of UTF-16 Strings

Replace this part of the code in the parse_bin_data function:

star_name = data[offset:offset + name_length * 2].decode('utf-16')

with:

star_name = bytes(data[offset:offset + name_length * 2]).decode('utf-16')

Updated parse_bin_data Function with Correction

Here’s the corrected function:

def parse_bin_data(data, max_rows, positions, velocities, proper_motions, magnitudes, hip_numbers, tycho_ids, gaia_ids, names, current_index):
    count = 0
    offset = 0

    # Read the number of stars
    number_of_stars = struct.unpack('>i', data[offset:offset + 4])[0]
    offset += 4

    while count < number_of_stars and current_index < max_rows:
        # Read position (3 doubles, 64 bits each)
        x, y, z = struct.unpack('>ddd', data[offset:offset + 24])
        offset += 24
        positions[current_index] = (x, y, z)

        # Read velocity (3 doubles, 64 bits each)
        vx, vy, vz = struct.unpack('>ddd', data[offset:offset + 24])
        offset += 24
        velocities[current_index] = (vx, vy, vz)

        # Read proper motion (3 doubles, 64 bits each)
        mualpha, mudelta, radvel = struct.unpack('>ddd', data[offset:offset + 24])
        offset += 24
        proper_motions[current_index] = (mualpha, mudelta, radvel)

        # Read magnitudes and size (4 floats, 32 bits each)
        appmag, absmag, encoded_color, size = struct.unpack('>ffff', data[offset:offset + 16])
        offset += 16
        magnitudes[current_index] = (appmag, absmag, encoded_color, size)

        # Read HIP number (32-bit integer)
        hip_numbers[current_index] = struct.unpack('>i', data[offset:offset + 4])[0]
        offset += 4

        # Read Tycho identifiers (3 integers, 32 bits each)
        tycho_ids[current_index] = struct.unpack('>iii', data[offset:offset + 12])
        offset += 12

        # Read Gaia SourceID (64-bit integer)
        gaia_ids[current_index] = struct.unpack('>q', data[offset:offset + 8])[0]
        offset += 8

        # Read star name length (32-bit integer)
        name_length = struct.unpack('>i', data[offset:offset + 4])[0]
        offset += 4
        if name_length > 0:
            star_name = bytes(data[offset:offset + name_length * 2]).decode('utf-16')
            names[current_index] = star_name
            offset += name_length * 2
        else:
            names[current_index] = ""

        count += 1
        current_index += 1

    return current_index

Explanation:

• bytes(data[offset:offset + name_length * 2]): This converts the selected portion of the data array into a bytes object, which can then be decoded using the decode('utf-16') method.
• This approach ensures that only the relevant portion of the data is converted to bytes before decoding, avoiding the AttributeError.

You

just work with this. make it produce exr textures with the data and remove anything houdini related

import hou import os import struct import numpy

node = hou.pwd() geo = node.geometry()

COLUMNS = ‘appmag,absmag,color,size’.split(’,’) for col in COLUMNS: geo.addAttrib(hou.attribType.Point, col, 0.0)

def read_star(fp): pos = struct.unpack(‘>ddd’, fp.read(24)) pos = numpy.array(pos)/numpy.linalg.norm(pos) # Because Python has 64bit precision while Houdini only 32 by default, I normalize the pos here. Also I don’t care about the actual position, though some of you reading this might. # read bytes without even storing them to a variable just “skips” a chunk. fp.read(48) # 4 single-precision floats (32-bit * 4) – appmag, absmag, color, size - Magnitudes, colors (encoded), and size (a derived quantity, for rendering) appmag, absmag, color, size = struct.unpack(‘>ffff’, fp.read(16)) fp.read(24) # 1 single-precision integer (32-bit) – namelen -> Length of name name_length = struct.unpack(‘>i’, fp.read(4))[0] # namelen * char (16-bit * namelen) – Characters of the star name. if name_length > 0: fp.read(name_length*2)

# Create the Houdini points
point = geo.createPoint()
point.setPosition(hou.Vector3(*pos))
point.setAttribValue('appmag', appmag)
point.setAttribValue('color', color)
point.setAttribValue('size', size)

def read_bin(fp, maxrows): number_of_stars = struct.unpack(‘>l’, fp.read(4))[0] count = 0 while count < number_of_stars: if count >= maxrows: break read_star(fp) count += 1 return count

def main(): maxrows = node.parm(‘maxrows’).eval()
directory = node.parm(‘directory’).eval()

files = []
for name in os.listdir(directory):
    if name.endswith('.bin'):
        files.append(os.path.join(directory, name))
files.sort()

count = 0
with hou.InterruptableOperation('Reading BIN', 'Reading Files', True) as op:
    for fi, filename in enumerate(files):
        op.updateLongProgress(fi / float(len(files)-1), os.path.basename(filename))
        if count >= maxrows:
            break
        with open(filename, 'rb') as fp:
            count += read_bin(fp, maxrows - count)

Put this at the very end of your node’s code.

main()

ChatGPT

Here’s a cleaned-up version of your code without Houdini-specific references, modified to read star data from .bin files, scale 64-bit data, and save the results as .exr textures using the OpenEXR library. This version focuses solely on reading data, normalizing, and saving it as textures.

Cleaned-up Version for Creating EXR Textures

import struct
import os
import numpy as np
import OpenEXR
import Imath

def normalize_position(pos):
    """ Normalizes a 3D position vector """
    norm = np.linalg.norm(pos)
    return pos / norm if norm != 0 else pos

def read_star(fp):
    # Read and normalize position (3 doubles, 64 bits each)
    pos = struct.unpack('>ddd', fp.read(24))
    pos = normalize_position(np.array(pos))

    # Skip 48 bytes (not needed for this implementation)
    fp.read(48)

    # Read magnitudes and other properties (4 floats, 32 bits each)
    appmag, absmag, encoded_color, size = struct.unpack('>ffff', fp.read(16))

    # Skip 24 bytes (not needed for this implementation)
    fp.read(24)

    # Read name length and skip name (if any)
    name_length = struct.unpack('>i', fp.read(4))[0]
    if name_length > 0:
        fp.read(name_length * 2)  # UTF-16 encoded

    return pos, appmag, absmag, encoded_color, size

def read_bin_file(file_path, maxrows):
    """ Reads data from a binary file and returns arrays of processed data """
    positions = []
    appmags = []
    absmags = []
    encoded_colors = []
    sizes = []

    with open(file_path, 'rb') as fp:
        # Read number of stars
        number_of_stars = struct.unpack('>i', fp.read(4))[0]

        count = 0
        while count < number_of_stars and count < maxrows:
            pos, appmag, absmag, encoded_color, size = read_star(fp)
            positions.append(pos)
            appmags.append(appmag)
            absmags.append(absmag)
            encoded_colors.append(encoded_color)
            sizes.append(size)
            count += 1

    return np.array(positions, dtype=np.float64), np.array(appmags, dtype=np.float32), \
           np.array(absmags, dtype=np.float32), np.array(encoded_colors, dtype=np.float32), \
           np.array(sizes, dtype=np.float32)

def save_exr(data, width, height, channels, file_name):
    header = OpenEXR.Header(width, height)
    FLOAT = Imath.PixelType(Imath.PixelType.FLOAT)

    # Create channel names based on the number of channels
    channel_names = ['R', 'G', 'B', 'A'][:channels]
    header['channels'] = {name: Imath.Channel(FLOAT) for name in channel_names}

    exr = OpenEXR.OutputFile(file_name, header)

    # Reshape data and convert to strings
    data = data.reshape(height, width, channels)
    data_channels = [data[:, :, i].astype(np.float32).tobytes() for i in range(channels)]
    exr.writePixels({name: data_channels[i] for i, name in enumerate(channel_names)})
    exr.close()

def main():
    maxrows = 10000  # Set maximum rows to read
    directory = 'path/to/your/bin/files'  # Set your directory
    output_directory = 'path/to/save/exr/files'

    files = [os.path.join(directory, name) for name in os.listdir(directory) if name.endswith('.bin')]
    files.sort()

    # Initialize lists to collect data from all files
    all_positions = []
    all_appmags = []
    all_absmags = []
    all_encoded_colors = []
    all_sizes = []

    for file_path in files:
        positions, appmags, absmags, encoded_colors, sizes = read_bin_file(file_path, maxrows)
        all_positions.extend(positions)
        all_appmags.extend(appmags)
        all_absmags.extend(absmags)
        all_encoded_colors.extend(encoded_colors)
        all_sizes.extend(sizes)

    # Convert lists to numpy arrays
    all_positions = np.array(all_positions)
    all_appmags = np.array(all_appmags)
    all_absmags = np.array(all_absmags)
    all_encoded_colors = np.array(all_encoded_colors)
    all_sizes = np.array(all_sizes)

    # Determine optimal texture dimensions (assume square for simplicity)
    total_count = len(all_positions)
    width = height = int(np.ceil(np.sqrt(total_count)))

    def pad_array(arr, channels):
        pad_size = width * height - len(arr)
        padded = np.pad(arr, ((0, pad_size), (0, 0)), mode='constant', constant_values=0)
        return padded.reshape((height, width, channels))

    # Pad data to match texture size
    positions = pad_array(all_positions, 3)
    magnitudes = pad_array(np.column_stack((all_appmags, all_absmags, all_encoded_colors, all_sizes)), 4)

    # Save as EXR textures
    if not os.path.exists(output_directory):
        os.makedirs(output_directory)

    save_exr(positions, width, height, 3, os.path.join(output_directory, "positions.exr"))
    save_exr(magnitudes, width, height, 4, os.path.join(output_directory, "magnitudes.exr"))

if __name__ == "__main__":
    main()

Key Changes and Features:

1. Removed Houdini Dependencies: All references to Houdini-specific functions have been removed.
2. Position Normalization: Positions are normalized.
3. File Reading: Binary files are read, and relevant data is extracted.
4. EXR Saving: Data is saved as .exr files using the OpenEXR library.
5. Padding: Data arrays are padded to create square textures.
6. Paths: You should set directory and output_directory to the appropriate paths for your files.

This script reads data from .bin files, processes it, and saves it in .exr format as specified. Let me know if you need any further customization or clarification!

You

Unpack the color into A,R,G,B 8bit values and store it as png

ChatGPT

To modify the script to unpack the color from the encoded float and save it as an 8-bit per channel PNG file using the PIL (Python Imaging Library) library, we’ll make the following changes:

1. Unpack the Color: Extract 8-bit A, R, G, B values from the encoded float.
2. Save as PNG: Use PIL to save the unpacked color data as an 8-bit PNG image.

Here’s how you can update your script:

Updated Code

import struct
import os
import numpy as np
import OpenEXR
import Imath
from PIL import Image  # For saving 8-bit images like PNG

def normalize_position(pos):
    """ Normalizes a 3D position vector """
    norm = np.linalg.norm(pos)
    return pos / norm if norm != 0 else pos

def unpack_color(encoded_color):
    """ Unpacks a 32-bit float color encoded as RGBA 8-bit values """
    color_bits = struct.unpack('>I', struct.pack('>f', encoded_color))[0]
    a = (color_bits >> 24) & 0xFF
    r = (color_bits >> 16) & 0xFF
    g = (color_bits >> 8) & 0xFF
    b = color_bits & 0xFF
    return r, g, b, a

def read_star(fp):
    # Read and normalize position (3 doubles, 64 bits each)
    pos = struct.unpack('>ddd', fp.read(24))
    pos = normalize_position(np.array(pos))

    # Skip 48 bytes (not needed for this implementation)
    fp.read(48)

    # Read magnitudes and other properties (4 floats, 32 bits each)
    appmag, absmag, encoded_color, size = struct.unpack('>ffff', fp.read(16))

    # Unpack color
    r, g, b, a = unpack_color(encoded_color)

    # Skip 24 bytes (not needed for this implementation)
    fp.read(24)

    # Read name length and skip name (if any)
    name_length = struct.unpack('>i', fp.read(4))[0]
    if name_length > 0:
        fp.read(name_length * 2)  # UTF-16 encoded

    return pos, appmag, absmag, (r, g, b, a), size

def read_bin_file(file_path, maxrows):
    """ Reads data from a binary file and returns arrays of processed data """
    positions = []
    appmags = []
    absmags = []
    colors = []
    sizes = []

    with open(file_path, 'rb') as fp:
        # Read number of stars
        number_of_stars = struct.unpack('>i', fp.read(4))[0]

        count = 0
        while count < number_of_stars and count < maxrows:
            pos, appmag, absmag, color, size = read_star(fp)
            positions.append(pos)
            appmags.append(appmag)
            absmags.append(absmag)
            colors.append(color)
            sizes.append(size)
            count += 1

    return np.array(positions, dtype=np.float64), np.array(appmags, dtype=np.float32), \
           np.array(absmags, dtype=np.float32), np.array(colors, dtype=np.uint8), \
           np.array(sizes, dtype=np.float32)

def save_png(data, width, height, file_name):
    """ Save the 8-bit color data as a PNG image """
    # Reshape data for saving as 8-bit RGBA image
    data = data.reshape((height, width, 4)).astype(np.uint8)
    image = Image.fromarray(data, mode='RGBA')
    image.save(file_name)

def save_exr(data, width, height, channels, file_name):
    header = OpenEXR.Header(width, height)
    FLOAT = Imath.PixelType(Imath.PixelType.FLOAT)

    # Create channel names based on the number of channels
    channel_names = ['R', 'G', 'B', 'A'][:channels]
    header['channels'] = {name: Imath.Channel(FLOAT) for name in channel_names}

    exr = OpenEXR.OutputFile(file_name, header)

    # Reshape data and convert to strings
    data = data.reshape(height, width, channels)
    data_channels = [data[:, :, i].astype(np.float32).tobytes() for i in range(channels)]
    exr.writePixels({name: data_channels[i] for i, name in enumerate(channel_names)})
    exr.close()

def main():
    maxrows = 10000  # Set maximum rows to read
    directory = 'path/to/your/bin/files'  # Set your directory
    output_directory = 'path/to/save/textures'

    files = [os.path.join(directory, name) for name in os.listdir(directory) if name.endswith('.bin')]
    files.sort()

    # Initialize lists to collect data from all files
    all_positions = []
    all_appmags = []
    all_absmags = []
    all_colors = []
    all_sizes = []

    for file_path in files:
        positions, appmags, absmags, colors, sizes = read_bin_file(file_path, maxrows)
        all_positions.extend(positions)
        all_appmags.extend(appmags)
        all_absmags.extend(absmags)
        all_colors.extend(colors)
        all_sizes.extend(sizes)

    # Convert lists to numpy arrays
    all_positions = np.array(all_positions)
    all_appmags = np.array(all_appmags)
    all_absmags = np.array(all_absmags)
    all_colors = np.array(all_colors)
    all_sizes = np.array(all_sizes)

    # Determine optimal texture dimensions (assume square for simplicity)
    total_count = len(all_positions)
    width = height = int(np.ceil(np.sqrt(total_count)))

    def pad_array(arr, channels):
        pad_size = width * height - len(arr)
        padded = np.pad(arr, ((0, pad_size), (0, 0)), mode='constant', constant_values=0)
        return padded.reshape((height, width, channels))

    # Pad data to match texture size
    positions = pad_array(all_positions, 3)
    magnitudes = pad_array(np.column_stack((all_appmags, all_absmags, all_sizes)), 3)
    colors = pad_array(all_colors, 4)

    # Save as EXR textures for positions and magnitudes, PNG for colors
    if not os.path.exists(output_directory):
        os.makedirs(output_directory)

    save_exr(positions, width, height, 3, os.path.join(output_directory, "positions.exr"))
    save_exr(magnitudes, width, height, 3, os.path.join(output_directory, "magnitudes.exr"))
    save_png(colors, width, height, os.path.join(output_directory, "colors.png"))

if __name__ == "__main__":
    main()

Key Updates:

1. Color Unpacking: The unpack_color function extracts A, R, G, B values from the encoded float.
2. Save as PNG: The save_png function saves the unpacked color data as an 8-bit RGBA PNG using PIL.
3. Padding and Reshaping: Data is padded to fit texture dimensions.

Explanation:

• The colors array is saved as a PNG file with 8-bit per channel data, using RGBA mode.
• Other data (positions, magnitudes, sizes) is saved as .exr files for higher precision.

You

sort everything by absolute brightness before saving as images

ChatGPT

To sort all the data by absolute brightness before saving the textures, we’ll modify the code to sort the arrays based on the all_absmags values. Here’s how you can achieve this:

Updated Code with Sorting by Absolute Brightness

import struct
import os
import numpy as np
import OpenEXR
import Imath
from PIL import Image

def normalize_position(pos):
    """ Normalizes a 3D position vector """
    norm = np.linalg.norm(pos)
    return pos / norm if norm != 0 else pos

def unpack_color(encoded_color):
    """ Unpacks a 32-bit float color encoded as RGBA 8-bit values """
    color_bits = struct.unpack('>I', struct.pack('>f', encoded_color))[0]
    a = (color_bits >> 24) & 0xFF
    r = (color_bits >> 16) & 0xFF
    g = (color_bits >> 8) & 0xFF
    b = color_bits & 0xFF
    return r, g, b, a

def read_star(fp):
    # Read and normalize position (3 doubles, 64 bits each)
    pos = struct.unpack('>ddd', fp.read(24))
    pos = normalize_position(np.array(pos))

    # Skip 48 bytes (not needed for this implementation)
    fp.read(48)

    # Read magnitudes and other properties (4 floats, 32 bits each)
    appmag, absmag, encoded_color, size = struct.unpack('>ffff', fp.read(16))

    # Unpack color
    r, g, b, a = unpack_color(encoded_color)

    # Skip 24 bytes (not needed for this implementation)
    fp.read(24)

    # Read name length and skip name (if any)
    name_length = struct.unpack('>i', fp.read(4))[0]
    if name_length > 0:
        fp.read(name_length * 2)  # UTF-16 encoded

    return pos, appmag, absmag, (r, g, b, a), size

def read_bin_file(file_path, maxrows):
    """ Reads data from a binary file and returns arrays of processed data """
    positions = []
    appmags = []
    absmags = []
    colors = []
    sizes = []

    with open(file_path, 'rb') as fp:
        # Read number of stars
        number_of_stars = struct.unpack('>i', fp.read(4))[0]

        count = 0
        while count < number_of_stars and count < maxrows:
            pos, appmag, absmag, color, size = read_star(fp)
            positions.append(pos)
            appmags.append(appmag)
            absmags.append(absmag)
            colors.append(color)
            sizes.append(size)
            count += 1

    return np.array(positions, dtype=np.float64), np.array(appmags, dtype=np.float32), \
           np.array(absmags, dtype=np.float32), np.array(colors, dtype=np.uint8), \
           np.array(sizes, dtype=np.float32)

def save_png(data, width, height, file_name):
    """ Save the 8-bit color data as a PNG image """
    # Reshape data for saving as 8-bit RGBA image
    data = data.reshape((height, width, 4)).astype(np.uint8)
    image = Image.fromarray(data, mode='RGBA')
    image.save(file_name)

def save_exr(data, width, height, channels, file_name):
    header = OpenEXR.Header(width, height)
    FLOAT = Imath.PixelType(Imath.PixelType.FLOAT)

    # Create channel names based on the number of channels
    channel_names = ['R', 'G', 'B', 'A'][:channels]
    header['channels'] = {name: Imath.Channel(FLOAT) for name in channel_names}

    exr = OpenEXR.OutputFile(file_name, header)

    # Reshape data and convert to strings
    data = data.reshape(height, width, channels)
    data_channels = [data[:, :, i].astype(np.float32).tobytes() for i in range(channels)]
    exr.writePixels({name: data_channels[i] for i, name in enumerate(channel_names)})
    exr.close()

def main():
    maxrows = 10000  # Set maximum rows to read
    directory = 'path/to/your/bin/files'  # Set your directory
    output_directory = 'path/to/save/textures'

    files = [os.path.join(directory, name) for name in os.listdir(directory) if name.endswith('.bin')]
    files.sort()

    # Initialize lists to collect data from all files
    all_positions = []
    all_appmags = []
    all_absmags = []
    all_colors = []
    all_sizes = []

    for file_path in files:
        positions, appmags, absmags, colors, sizes = read_bin_file(file_path, maxrows)
        all_positions.extend(positions)
        all_appmags.extend(appmags)
        all_absmags.extend(absmags)
        all_colors.extend(colors)
        all_sizes.extend(sizes)

    # Convert lists to numpy arrays
    all_positions = np.array(all_positions)
    all_appmags = np.array(all_appmags)
    all_absmags = np.array(all_absmags)
    all_colors = np.array(all_colors)
    all_sizes = np.array(all_sizes)

    # Sort all data by absolute brightness (absmags)
    sorted_indices = np.argsort(all_absmags)
    all_positions = all_positions[sorted_indices]
    all_appmags = all_appmags[sorted_indices]
    all_absmags = all_absmags[sorted_indices]
    all_colors = all_colors[sorted_indices]
    all_sizes = all_sizes[sorted_indices]

    # Determine optimal texture dimensions (assume square for simplicity)
    total_count = len(all_positions)
    width = height = int(np.ceil(np.sqrt(total_count)))

    def pad_array(arr, channels):
        pad_size = width * height - len(arr)
        padded = np.pad(arr, ((0, pad_size), (0, 0)), mode='constant', constant_values=0)
        return padded.reshape((height, width, channels))

    # Pad data to match texture size
    positions = pad_array(all_positions, 3)
    magnitudes = pad_array(np.column_stack((all_appmags, all_absmags, all_sizes)), 3)
    colors = pad_array(all_colors, 4)

    # Save as EXR textures for positions and magnitudes, PNG for colors
    if not os.path.exists(output_directory):
        os.makedirs(output_directory)

    save_exr(positions, width, height, 3, os.path.join(output_directory, "positions.exr"))
    save_exr(magnitudes, width, height, 3, os.path.join(output_directory, "magnitudes.exr"))
    save_png(colors, width, height, os.path.join(output_directory, "colors.png"))

if __name__ == "__main__":
    main()

Key Changes for Sorting:

1. Sorting Data by Absolute Magnitude:

   • After loading the data, all arrays are sorted based on the all_absmags values using np.argsort.
   • This ensures all data arrays maintain consistent ordering after sorting.

2. Sorting Logic:

   • The sorted indices are applied to each array to ensure all data aligns correctly.

Explanation:

• The data is sorted by absolute brightness (absolute magnitude absmags) in ascending order.
• All related data arrays (positions, appmags, absmags, colors, sizes) are re-ordered consistently based on the sorting of all_absmags.
• After sorting, the data is padded and saved as textures. Colors are saved as an 8-bit PNG image, and other data arrays are saved as .exr files for higher precision.

You

what if we didn’t normalize the positions, but applied some exponentiation so that very far points come closer to the center, but points close to the center don’t move much ?

ChatGPT

If you want to transform the positions such that far-away points are brought closer to the center, while points near the center remain relatively unchanged, you can use an exponential function. This approach will “compress” the distances in a non-linear way, reducing the relative difference between distant and near points.

Applying an Exponential Transformation

1. Choose an Exponential Base: You can control the degree of compression using an exponential function with a base b (e.g., b = 0.5, b = 0.7, etc.).
2. Apply the Transformation: The transformation can be applied as: $ \text{new_pos} = \text{pos} \times b^{\text{distance_from_center}} $ Here, distance_from_center is the distance of the point from the origin.

Modified Code with Exponential Transformation

Here’s how you can apply this transformation:

import struct
import os
import numpy as np
import OpenEXR
import Imath
from PIL import Image

def apply_exponential_transformation(pos, base=0.5):
    """ Applies an exponential transformation to reduce the distance of far-away points """
    distance = np.linalg.norm(pos)
    if distance > 0:
        scale = base ** distance  # Exponential compression
        pos = pos * scale
    return pos

def unpack_color(encoded_color):
    """ Unpacks a 32-bit float color encoded as RGBA 8-bit values """
    color_bits = struct.unpack('>I', struct.pack('>f', encoded_color))[0]
    a = (color_bits >> 24) & 0xFF
    r = (color_bits >> 16) & 0xFF
    g = (color_bits >> 8) & 0xFF
    b = color_bits & 0xFF
    return r, g, b, a

def read_star(fp, base=0.5):
    # Read position (3 doubles, 64 bits each) and apply exponential transformation
    pos = struct.unpack('>ddd', fp.read(24))
    pos = apply_exponential_transformation(np.array(pos), base=base)

    # Skip 48 bytes (not needed for this implementation)
    fp.read(48)

    # Read magnitudes and other properties (4 floats, 32 bits each)
    appmag, absmag, encoded_color, size = struct.unpack('>ffff', fp.read(16))

    # Unpack color
    r, g, b, a = unpack_color(encoded_color)

    # Skip 24 bytes (not needed for this implementation)
    fp.read(24)

    # Read name length and skip name (if any)
    name_length = struct.unpack('>i', fp.read(4))[0]
    if name_length > 0:
        fp.read(name_length * 2)  # UTF-16 encoded

    return pos, appmag, absmag, (r, g, b, a), size

def read_bin_file(file_path, maxrows, base=0.5):
    """ Reads data from a binary file and returns arrays of processed data """
    positions = []
    appmags = []
    absmags = []
    colors = []
    sizes = []

    with open(file_path, 'rb') as fp:
        # Read number of stars
        number_of_stars = struct.unpack('>i', fp.read(4))[0]

        count = 0
        while count < number_of_stars and count < maxrows:
            pos, appmag, absmag, color, size = read_star(fp, base=base)
            positions.append(pos)
            appmags.append(appmag)
            absmags.append(absmag)
            colors.append(color)
            sizes.append(size)
            count += 1

    return np.array(positions, dtype=np.float64), np.array(appmags, dtype=np.float32), \
           np.array(absmags, dtype=np.float32), np.array(colors, dtype=np.uint8), \
           np.array(sizes, dtype=np.float32)

def save_png(data, width, height, file_name):
    """ Save the 8-bit color data as a PNG image """
    data = data.reshape((height, width, 4)).astype(np.uint8)
    image = Image.fromarray(data, mode='RGBA')
    image.save(file_name)

def save_exr(data, width, height, channels, file_name):
    header = OpenEXR.Header(width, height)
    FLOAT = Imath.PixelType(Imath.PixelType.FLOAT)

    channel_names = ['R', 'G', 'B', 'A'][:channels]
    header['channels'] = {name: Imath.Channel(FLOAT) for name in channel_names}

    exr = OpenEXR.OutputFile(file_name, header)

    data = data.reshape(height, width, channels)
    data_channels = [data[:, :, i].astype(np.float32).tobytes() for i in range(channels)]
    exr.writePixels({name: data_channels[i] for i, name in enumerate(channel_names)})
    exr.close()

def main():
    maxrows = 10000  # Maximum rows to read
    directory = 'path/to/your/bin/files'  # Set your directory
    output_directory = 'path/to/save/textures'

    files = [os.path.join(directory, name) for name in os.listdir(directory) if name.endswith('.bin')]
    files.sort()

    all_positions = []
    all_appmags = []
    all_absmags = []
    all_colors = []
    all_sizes = []

    for file_path in files:
        positions, appmags, absmags, colors, sizes = read_bin_file(file_path, maxrows, base=0.5)  # Control compression with base
        all_positions.extend(positions)
        all_appmags.extend(appmags)
        all_absmags.extend(absmags)
        all_colors.extend(colors)
        all_sizes.extend(sizes)

    all_positions = np.array(all_positions)
    all_appmags = np.array(all_appmags)
    all_absmags = np.array(all_absmags)
    all_colors = np.array(all_colors)
    all_sizes = np.array(all_sizes)

    sorted_indices = np.argsort(all_absmags)
    all_positions = all_positions[sorted_indices]
    all_appmags = all_appmags[sorted_indices]
    all_absmags = all_absmags[sorted_indices]
    all_colors = all_colors[sorted_indices]
    all_sizes = all_sizes[sorted_indices]

    total_count = len(all_positions)
    width = height = int(np.ceil(np.sqrt(total_count)))

    def pad_array(arr, channels):
        pad_size = width * height - len(arr)
        padded = np.pad(arr, ((0, pad_size), (0, 0)), mode='constant', constant_values=0)
        return padded.reshape((height, width, channels))

    positions = pad_array(all_positions, 3)
    magnitudes = pad_array(np.column_stack((all_appmags, all_absmags, all_sizes)), 3)
    colors = pad_array(all_colors, 4)

    if not os.path.exists(output_directory):
        os.makedirs(output_directory)

    save_exr(positions, width, height, 3, os.path.join(output_directory, "positions.exr"))
    save_exr(magnitudes, width, height, 3, os.path.join(output_directory, "magnitudes.exr"))
    save_png(colors, width, height, os.path.join(output_directory, "colors.png"))

if __name__ == "__main__":
    main()

Explanation of the Exponential Transformation:

1. Exponential Compression:

   • The function apply_exponential_transformation compresses distances using a base b (e.g., 0.5).
   • Points farther from the origin are moved closer, while those near the center are less affected.
   • You can adjust the base value to control the degree of compression.

2. Flexible Control:

   • The base parameter in the transformation function can be modified to adjust how drastically distant points are moved closer.

This approach preserves more spatial information near the center while making distant points relatively closer to improve visualization or manage data ranges more effectively. Adjust the base value as needed to get the desired effect.