public void ChunkedArray_creates_appropriate_number_of_chunks()
{
int size = 16 * 1024 * 4 + 100;
int chunkCount = (int)Math.Ceiling((double)size * sizeof(int) / (16 * 1024));
var array = new ChunkedArray<int>(size);
Assert.AreEqual(chunkCount, array.ChunkCount);
}
public void ChunkedArray_reports_proper_length()
{
var length = 16 * 1024 * 4 + 100;
var array = new ChunkedArray <int>(length);
Assert.AreEqual(length, array.Length);
}
public void ChunkedArray_creates_appropriate_number_of_chunks()
{
int size = 16 * 1024 * 4 + 100;
int chunkCount = (int)Math.Ceiling((double)size * sizeof(int) / (16 * 1024));
var array = new ChunkedArray <int>(size);
Assert.AreEqual(chunkCount, array.ChunkCount);
}
public void Can_get_expected_value_via_indexer()
{
var n = 16 * 1024 * 2 + 1800;
var array = new ChunkedArray<int>(n);
for(var i = 0; i < n; i++) {
array[i] = 2 * i;
}
for(var i = 0; i < n; i++) {
Assert.AreEqual(2 * i, array[i]);
}
}
public void Can_enumerate_value_in_proper_order()
{
var n = 16 * 1024 * 2 + 1800;
var array1 = new int[n];
var array2 = new ChunkedArray<int>(n);
for(var i = 0; i < n; i++) {
array1[i] = i;
array2[i] = i;
}
Assert.AreEqual(array1, array2.ToArray());
}
private static void MyersDiff <T>(T[] before, T[] after, Equality <T> equal, int maxdelta, IList <Tuple <ArrayDiffKind, T> > result) where T : class
{
// NOTE (steveb): we mark items as 'Added' when they are actually 'Removed' and vice versa; this means the initial 'before' and 'after' arrays must be passed in reversed order as well;
// the reason for doing so is that it will show first 'Removed' items, and then the 'Added' ones, which is what the 3-way merge algorithm requires.
// initialize temp arrays required for search
int max = before.Length + after.Length + 1;
var down_array = new ChunkedArray <int>(2 * max + 2);
var up_array = new ChunkedArray <int>(2 * max + 2);
// run myers diff algorithm
MyersDiffRev(after, 0, after.Length - 1, before, 0, before.Length - 1, equal, maxdelta, result, max, down_array, up_array);
}
public void Can_enumerate_value_in_proper_order()
{
var n = 16 * 1024 * 2 + 1800;
var array1 = new int[n];
var array2 = new ChunkedArray <int>(n);
for (var i = 0; i < n; i++)
{
array1[i] = i;
array2[i] = i;
}
Assert.AreEqual(array1, array2.ToArray());
}
public void Can_get_expected_value_via_indexer()
{
var n = 16 * 1024 * 2 + 1800;
var array = new ChunkedArray <int>(n);
for (var i = 0; i < n; i++)
{
array[i] = 2 * i;
}
for (var i = 0; i < n; i++)
{
Assert.AreEqual(2 * i, array[i]);
}
}
private static void MyersDiffRev <T>(T[] before, int i_start, int i_end, T[] after, int j_start, int j_end, Equality <T> equal, int maxdelta, IList <Tuple <ArrayDiffKind, T> > result, int max, ChunkedArray <int> down_array, ChunkedArray <int> up_array) where T : class
{
// NOTE (steveb): we mark items as 'Added' when they are actually 'Removed' and vice versa; this means the initial 'before' and 'after' arrays must be passed in reversed order as well;
// the reason for doing so is that it will show first 'Removed' items, and then the 'Added' ones, which is what the 3-way merge algorithm requires.
// skip matching items at the beginning
while ((i_start <= i_end) && (j_start <= j_end) && equal(before[i_start], after[j_start]))
{
// add skipped item
result.Add(new Tuple <ArrayDiffKind, T>(ArrayDiffKind.Same, before[i_start]));
// move diagonally
++i_start;
++j_start;
}
// skip matching items at the end
int tailCopyCount = 0;
while ((i_start <= i_end) && (j_start <= j_end) && equal(before[i_end], after[j_end]))
{
// count skipped items
++tailCopyCount;
// move diagonally
--i_end;
--j_end;
}
// check if we found a terminal case or if new need to recurse
if (j_start > j_end)
{
// add remaining items as 'removed'
while (i_start <= i_end)
{
result.Add(new Tuple <ArrayDiffKind, T>(ArrayDiffKind.Added, before[i_start++]));
}
}
else if (i_start > i_end)
{
// add remaining items as 'added'
while (j_start <= j_end)
{
result.Add(new Tuple <ArrayDiffKind, T>(ArrayDiffKind.Removed, after[j_start++]));
}
}
else
{
// find the optimal path
int i_split;
int j_split;
ComputeMyersSplit(before, i_start, i_end, out i_split, after, j_start, j_end, out j_split, equal, maxdelta, max, down_array, up_array);
// solve sub-problem
MyersDiffRev(before, i_start, i_split - 1, after, j_start, j_split - 1, equal, maxdelta, result, max, down_array, up_array);
MyersDiffRev(before, i_split, i_end, after, j_split, j_end, equal, maxdelta, result, max, down_array, up_array);
}
// add tail items
for (int k = 1; k <= tailCopyCount; ++k)
{
result.Add(new Tuple <ArrayDiffKind, T>(ArrayDiffKind.Same, before[i_end + k]));
}
}
private static void ComputeMyersSplit <T>(T[] before, int i_start, int i_end, out int i_split, T[] after, int j_start, int j_end, out int j_split, Equality <T> equal, int maxdelta, int max, ChunkedArray <int> down_array, ChunkedArray <int> up_array)
{
// check if problem space is odd or even sized
bool odd = (((i_end - i_start + 1) - (j_end - j_start + 1)) & 1) != 0;
// the k-line to start the forward search
int down_k = i_start - j_start;
// the k-line to start the reverse search
int up_k = i_end - j_end;
// The vectors in the publication accepts negative indexes. the vectors implemented here are 0-based
// and are access using a specific offset: UpOffset UpVector and DownOffset for DownVektor
int down_offset = max - down_k;
int up_offset = max - up_k;
// initialize arrays
down_array[down_offset + down_k + 1] = i_start;
up_array[up_offset + up_k - 1] = i_end + 1;
// find furthest reaching D-path
int d_max = (((i_end - i_start + 1) + (j_end - j_start + 1) + 1) / 2);
if (d_max >= maxdelta)
{
throw new ExceededDeltaException();
}
for (int d = 0; d <= d_max; ++d)
{
// search forward
for (int k = down_k - d; k <= down_k + d; k += 2)
{
// find the only or better starting point
int x;
int y;
if (k == down_k - d)
{
x = down_array[down_offset + k + 1]; // down
}
else
{
x = down_array[down_offset + k - 1] + 1; // a step to the right
if ((k < down_k + d) && (down_array[down_offset + k + 1] >= x))
{
x = down_array[down_offset + k + 1]; // down
}
}
y = x - k;
// find the end of the furthest reaching forward D-path in diagonal k
while ((x <= i_end) && (y <= j_end) && equal(before[x], after[y]))
{
++x;
++y;
}
down_array[down_offset + k] = x;
// overlap ?
if (odd && (up_k - d < k) && (k < up_k + d))
{
if (up_array[up_offset + k] <= down_array[down_offset + k])
{
i_split = down_array[down_offset + k];
j_split = down_array[down_offset + k] - k;
return;
}
}
}
// search backwards
for (int k = up_k - d; k <= up_k + d; k += 2)
{
// find the only or better starting point
int x;
int y;
if (k == up_k + d)
{
x = up_array[up_offset + k - 1]; // up
}
else
{
x = up_array[up_offset + k + 1] - 1; // left
if ((k > up_k - d) && (up_array[up_offset + k - 1] < x))
{
x = up_array[up_offset + k - 1]; // up
}
}
y = x - k;
while ((x > i_start) && (y > j_start) && equal(before[x - 1], after[y - 1]))
{
--x;
--y; // diagonal
}
up_array[up_offset + k] = x;
// overlap ?
if (!odd && (down_k - d <= k) && (k <= down_k + d))
{
if (up_array[up_offset + k] <= down_array[down_offset + k])
{
i_split = down_array[down_offset + k];
j_split = down_array[down_offset + k] - k;
return;
}
}
}
}
throw new InvalidOperationException("this should never happen");
}
public void ChunkedArray_reports_proper_length()
{
var length = 16 * 1024 * 4 + 100;
var array = new ChunkedArray<int>(length);
Assert.AreEqual(length, array.Length);
}
void Update()
{
var ms = MusicManager.MusicState;
while (_thoughts.Count < ThoughtCount)
{
var newThought = Instantiate(ThoughtPrefab.gameObject);
newThought.transform.SetParent(transform);
var newT = new Thought()
{
Transform = newThought.transform,
};
newT.Destination = newThought.transform.localPosition;
_thoughts.Add(newT);
}
for (var i = 0; i < _thoughts.Count; ++i)
{
var t = _thoughts[i];
if (Vector3.Distance(t.Destination, t.Transform.localPosition) < .1f)
{
Random.InitState(Time.frameCount + i);
t.Destination = new Vector3(Random.Range(-CellRadius, CellRadius) * GridSize,
Random.Range(-CellRadius, CellRadius) * GridSize,
Random.Range(-CellRadius, CellRadius) * GridSize);
}
t.Transform.localPosition = Vector3.MoveTowards(t.Transform.localPosition, t.Destination, ThoughtSpeed * ms.Peak);
}
var cellDiameter = CellRadius * 2;
var totalRadius = (CellRadius * GridSize) / 2f;
var arraySize = (cellDiameter + 1) * (cellDiameter + 1) * (cellDiameter + 1);
if (_matrices == null || _matrices.Count != arraySize)
{
_matrices = new ChunkedArray <Matrix4x4>(arraySize, 1023);
}
bool lastPhase = false;
int counter = 0;
for (var x = -CellRadius; x < CellRadius; ++x)
{
lastPhase = !lastPhase;
for (var y = -CellRadius; y < CellRadius; ++y)
{
lastPhase = !lastPhase;
for (var z = -CellRadius; z < CellRadius; ++z)
{
var phase = GetPhase(lastPhase ? 1 : 0) * GridSize;
lastPhase = !lastPhase;
var pos = new Vector3(x, y, z) * GridSize + phase;
int magicInt = ((x + y) * z);
Random.InitState(magicInt);
var wl = Mathf.Lerp(ms.Wavelength[Random.Range(0, ms.Wavelength.Length - 1)] % 1, 1, ScaleMultiplier);
var s = Scale.x + (Scale.y - Scale.x) * wl;
var mat = Matrix4x4.TRS(pos, Quaternion.identity, Vector3.one * s * GridSize);
mat = transform.localToWorldMatrix * mat;
_matrices[counter] = mat;
counter++;
}
}
}
foreach (Matrix4x4[] matrixArray in _matrices)
{
Graphics.DrawMeshInstanced(GridMesh, 0, GridMaterial, matrixArray, matrixArray.Length, null, ShadowCastingMode.Off, false, 0, null);
}
}