/// <inheritdoc/>
public override object GetNormalisedValuePrecise(object originalValue, int identifier)
{
DimensionData.Metadata meta = this[identifier].MetaData;
// Determine which textualDimensionsList to use
Dictionary <string, Dictionary <string, int> > textualDimensionsListReverse;
if (nodeDimensionData.Select(x => x.Identifier).FirstOrDefault() != null)
{
textualDimensionsListReverse = nodeTextualDimensionsListReverse;
}
else
{
textualDimensionsListReverse = edgeTextualDimensionsListReverse;
}
if (meta.Type == IATKDataType.String)
{
int stringIdx = textualDimensionsListReverse[this[identifier].Identifier][originalValue.ToString()];
return(UtilMath.NormaliseValue(stringIdx, meta.Min, meta.Max, 0f, 1f));
}
else
{
return(UtilMath.NormaliseValue((float)originalValue, meta.Min, meta.Max, 0f, 1f));
}
}
/// <inheritdoc/>
public override object GetValuePrecise(float normalisedValue, string identifier)
{
DimensionData.Metadata meta = this[identifier].MetaData;
// Determine which textualDimensionsList to use
Dictionary <string, Dictionary <int, string> > textualDimensionsList;
if (nodeDimensionData.FirstOrDefault(x => x.Identifier == identifier) != null)
{
textualDimensionsList = nodeTextualDimensionsList;
}
else
{
textualDimensionsList = edgeTextualDimensionsList;
}
float normValue = UtilMath.NormaliseValue(normalisedValue, 0f, 1f, meta.Min, meta.Max);
// Dimensions of type String should return a string from the textual dimensions list
if (meta.Type == IATKDataType.String)
{
return(textualDimensionsList[identifier][(int)normValue]);
}
// Otherwise re can return the de-normalised value
else
{
return(normValue);
}
}
/// <inheritdoc/>
public override object GetValueApproximate(float normalisedValue, string identifier)
{
DimensionData.Metadata meta = this[identifier].MetaData;
// Determine which textualDimensionsList to use
Dictionary <string, Dictionary <int, string> > textualDimensionsList;
if (nodeDimensionData.FirstOrDefault(x => x.Identifier == identifier) != null)
{
textualDimensionsList = nodeTextualDimensionsList;
}
else
{
textualDimensionsList = edgeTextualDimensionsList;
}
// Dimensions of type String should return a string from the textual dimensions list
if (meta.Type == IATKDataType.String)
{
// Since this function allows for approximate input values, we need to find the value closest to the given one
float normValue = UtilMath.NormaliseValue(ValueClosestTo(this[identifier].Data, normalisedValue), 0f, 1f, meta.Min, meta.Max);
return(textualDimensionsList[identifier][(int)normValue]);
}
// Otherwise we can return a de-normalised value
else
{
return(UtilMath.NormaliseValue(normalisedValue, 0f, 1f, meta.Min, meta.Max));
}
}
/// <inheritdoc/>
public override object GetNormalisedValuePrecise(object originalValue, int identifier)
{
DimensionData.Metadata meta = this[identifier].MetaData;
if (meta.Type == IATKDataType.String)
{
int stringIdx = textualDimensionsListReverse[this[identifier].Identifier][originalValue.ToString()];
return(UtilMath.NormaliseValue(stringIdx, meta.Min, meta.Max, 0f, 1f));
}
else
{
return(UtilMath.NormaliseValue((float)originalValue, meta.Min, meta.Max, 0f, 1f));
}
}
/// <inheritdoc/>
public override object GetValuePrecise(float normalisedValue, string identifier)
{
DimensionData.Metadata meta = this[identifier].MetaData;
float normValue = UtilMath.NormaliseValue(normalisedValue, 0f, 1f, meta.Min, meta.Max);
// Dimensions of type String should return a string from the textual dimensions list
if (meta.Type == IATKDataType.String)
{
return(textualDimensionsList[this[identifier].Identifier][(int)normValue]);
}
// Otherwise re can return the de-normalised value
else
{
return(normValue);
}
}
/// <inheritdoc/>
public override object GetValueApproximate(float normalisedValue, string identifier)
{
DimensionData.Metadata meta = this[identifier].MetaData;
// Dimensions of type String should return a string from the textual dimensions list
if (meta.Type == IATKDataType.String)
{
// Since this function allows for approximate input values, we need to find the value closest to the given one
float normValue = UtilMath.NormaliseValue(ValueClosestTo(this[identifier].Data, normalisedValue), 0f, 1f, meta.Min, meta.Max);
return(textualDimensionsList[identifier][(int)normValue]);
}
// Otherwise we can return a de-normalised value
else
{
return(UtilMath.NormaliseValue(normalisedValue, 0f, 1f, meta.Min, meta.Max));
}
}
/// <summary>
/// Normalises a given column from a 2D array of float values within the range 0..1. This function also sets some metadata values.
/// </summary>
/// <param name="dataArray">A 2D float array of data.</param>
/// <param name="col">An integer index of the column to normalise.</param>
/// <returns>A normalised float array in the range 0..1.</returns>
private float[] NormaliseColumn(float[,] dataArray, int col, ref List <DimensionData> dimensionData)
{
float[] result = GetColumn(dataArray, col);
float minValue = result.Min();
float maxValue = result.Max();
if (minValue == maxValue)
{
// where there are no distinct values, need the dimension to be distinct
// otherwise lots of maths breaks with division by zero, etc.
// this is the most elegant hack I could think of, but should be fixed properly in future
minValue -= 1.0f;
maxValue += 1.0f;
}
// Populate metadata values
DimensionData.Metadata metadata = dimensionData[col].MetaData;
metadata.Min = minValue;
metadata.Max = maxValue;
metadata.Categories = result.Distinct().Select(x => UtilMath.NormaliseValue(x, minValue, maxValue, 0.0f, 1.0f)).ToArray();
metadata.CategoryCount = metadata.Categories.Count();
metadata.BinCount = (int)(maxValue - minValue + 1);
dimensionData[col].SetMetadata(metadata);
for (int j = 0; j < result.Length; j++)
{
if (minValue < maxValue)
{
result[j] = UtilMath.NormaliseValue(result[j], minValue, maxValue, 0f, 1f);
}
else
{
// Avoid NaNs or nonsensical normalization
result[j] = 0;
}
}
return(result);
}
protected virtual void DrawMinMaxSlider(Rect rect, SerializedProperty minFilterProp, SerializedProperty maxFilterProp, string attributeid, DataSource dataSource)
{
bool isUndefined = dataSource == null || attributeid == "Undefined";
int idx = Array.IndexOf(dataSource.Select(m => m.Identifier).ToArray(), attributeid);
// get the normalized value
float minValue = !isUndefined ? dataSource[attributeid].MetaData.Min : 0.0f;
float maxValue = !isUndefined ? dataSource[attributeid].MetaData.Max : 1.0f;
// calculate the real value
float min = UtilMath.NormaliseValue(minFilterProp.floatValue, 0, 1, minValue, maxValue);
float max = UtilMath.NormaliseValue(maxFilterProp.floatValue, 0, 1, minValue, maxValue);
// get the string representation
string minLogical = isUndefined ? "" : dataSource.GetValueApproximate(minFilterProp.floatValue, idx).ToString();
string maxLogical = isUndefined ? "" : dataSource.GetValueApproximate(maxFilterProp.floatValue, idx).ToString();
EditorGUI.TextField(new Rect(rect.x, rect.y, 75, rect.height), minLogical);
EditorGUI.MinMaxSlider(new Rect(rect.x + 75, rect.y, rect.width - 150, rect.height), GUIContent.none, ref min, ref max, minValue, maxValue);
EditorGUI.TextField(new Rect(rect.x + rect.width - 78, rect.y, 75, rect.height), maxLogical);
minFilterProp.floatValue = UtilMath.NormaliseValue(min, minValue, maxValue, 0, 1);
maxFilterProp.floatValue = UtilMath.NormaliseValue(max, minValue, maxValue, 0, 1);
}
/// <summary>
/// Creates an array of positions that are aggregated based on the given aggregation type.
/// This MUST be called AFTER each time the other dimensions change.
/// </summary>
/// <param name="data"></param>
/// <param name="dimension"></param>
/// <param name="aggregation"></param>
/// <returns></returns>
public float[] SetAggregatedDimension(float[] yData, IATKBarAggregation aggregation)
{
// Extract independent arrays of the position values for the x and z dimensions
Vector3[] vertices = View.GetVertices();
float[] xData = new float[vertices.Length];
float[] zData = new float[vertices.Length];
for (int i = 0; i < DataSource.DataCount; i++)
{
xData[i] = vertices[i].x;
zData[i] = vertices[i].z;
}
// Get the unique "categories" of the x and z dimensions (these are technically floats)
var xCategories = xData.Distinct();
var zCategories = zData.Distinct();
// LAZY HACK: Set a value in the mesh's normal.y value to designate whether to show or hide the point to prevent z-fighting and mass lag
float[] masterBars = new float[DataSource.DataCount];
// Create a dictionary that will store the values assocatied with each (x, z) pairs of aggregating values (x bins * z bins = n lists)
Dictionary <float, Dictionary <float, List <float> > > aggGroups = new Dictionary <float, Dictionary <float, List <float> > >();
// Iterate through each position and assign the data values to the respective (x, z) pair
for (int i = 0; i < DataSource.DataCount; i++)
{
Dictionary <float, List <float> > innerDict;
if (!aggGroups.TryGetValue(xData[i], out innerDict))
{
innerDict = new Dictionary <float, List <float> >();
aggGroups[xData[i]] = innerDict;
}
List <float> innerList;
if (!innerDict.TryGetValue(zData[i], out innerList))
{
innerList = new List <float>();
innerDict[zData[i]] = innerList;
masterBars[i] = 1;
}
// If the aggregation type is count, we don't need to use the y axis values
if (aggregation == IATKBarAggregation.Count || yData == null)
{
innerList.Add(0);
}
else
{
innerList.Add(yData[i]);
}
}
// LAZY HACK: Send the master values to the mesh now
View.SetUVs(masterBars, IATKDimension.Y);
// Create another dictionary that will store the aggregated value for each (x, z) pair group
float max = 0;
Dictionary <float, Dictionary <float, float> > aggregatedValues = new Dictionary <float, Dictionary <float, float> >();
foreach (float xCategory in xCategories)
{
foreach (float zCategory in zCategories)
{
// Calculate final aggregated value
if (!aggGroups[xCategory].ContainsKey(zCategory))
{
continue;
}
List <float> values = aggGroups[xCategory][zCategory];
float aggregated = 0;
switch (aggregation)
{
case IATKBarAggregation.Count:
aggregated = values.Count;
break;
case IATKBarAggregation.Average:
aggregated = values.Average();
break;
case IATKBarAggregation.Sum:
aggregated = values.Sum();
break;
case IATKBarAggregation.Median:
values.Sort();
float mid = (values.Count - 1) / 2f;
aggregated = (values[(int)(mid)] + values[(int)(mid + 0.5f)]) / 2;
break;
case IATKBarAggregation.Min:
aggregated = values.Min();
break;
case IATKBarAggregation.Max:
aggregated = values.Max();
break;
}
// Set value
Dictionary <float, float> innerDict;
if (!aggregatedValues.TryGetValue(xCategory, out innerDict))
{
innerDict = new Dictionary <float, float>();
aggregatedValues[xCategory] = innerDict;
}
innerDict[zCategory] = aggregated;
// We need to normalise back into 0..1 for these specific aggregations, so we collect the max value
if (aggregation == IATKBarAggregation.Count || aggregation == IATKBarAggregation.Sum)
{
if (max < aggregated)
{
max = aggregated;
}
}
}
}
// Set y position based on newly aggregated values
float[] positions = new float[DataSource.DataCount];
for (int i = 0; i < DataSource.DataCount; i++)
{
// For specific aggregations, normalise
if (aggregation == IATKBarAggregation.Count || aggregation == IATKBarAggregation.Sum)
{
positions[i] = UtilMath.NormaliseValue(aggregatedValues[xData[i]][zData[i]], 0, max, 0, 1);
}
else
{
positions[i] = aggregatedValues[xData[i]][zData[i]];
}
}
return(positions);
}
