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package org.opentripplanner.common.geometry;
import java.util.HashMap;
import java.util.Iterator;
import java.util.Map;
/**
* A fast sparse 2D matrix holding elements of type T.
* The x and y indexes into the sparse matrix are _signed_ 32-bit integers (negative indexes are allowed).
* Square sub-chunks of size chunkSize x chunkSize are stored in a hashmap,
* keyed on a combination of the x and y coordinates.
* Does not implement the collection interface for simplicity and speed.
* Not thread-safe!
*
* @author laurent
*/
public class SparseMatrix implements Iterable {
private int shift; // How many low order bits to shift off to get the index of a chunk.
private int mask; // The low order bits to retain when finding the index within a chunk.
private Map chunks;
int size = 0; // The number of elements currently stored in this matrix (number of cells containing a T).
int matSize; // The capacity of a single chunk TODO rename
int chunkSize; // The dimension of a single chunk in each of two dimensions TODO rename
public int xMin, xMax, yMin, yMax; // The maximum and minimum indices where an element is stored.
/**
* @param chunkSize Must be a power of two so chunk indexes can be determined by shifting off low order bits.
* Keep it small (8, 16, 32...). Chunks are square, with this many elements in each of two dimensions,
* so the number of elements in each chunk will be the square of this value.
* @param totalSize Estimated total number of elements to be stored in the matrix (actual use, not capacity).
*/
public SparseMatrix(int chunkSize, int totalSize) {
shift = 0;
this.chunkSize = chunkSize;
mask = chunkSize - 1; // all low order bits below the given power of two
this.matSize = chunkSize * chunkSize; // capacity of a single chunk
/* Find log_2 chunkSize, the number of low order bits to shift off an index to get its chunk index. */
while (chunkSize > 1) {
if (chunkSize % 2 != 0)
throw new IllegalArgumentException("Chunk size must be a power of 2");
chunkSize /= 2;
shift++;
}
// We assume here that each chunk will be filled at ~25% (thus the x4)
this.chunks = new HashMap(totalSize / matSize * 4);
this.xMin = Integer.MAX_VALUE;
this.yMin = Integer.MAX_VALUE;
this.xMax = Integer.MIN_VALUE;
this.yMax = Integer.MIN_VALUE;
}
public final T get(int x, int y) {
T[] ts = chunks.get(new Key(x, y, shift));
if (ts == null) {
return null;
}
int index = ((x & mask) << shift) + (y & mask);
return ts[index];
}
@SuppressWarnings("unchecked")
public final T put(int x, int y, T t) {
/* Keep a bounding box around all matrix cells in use. */
if (x < xMin)
xMin = x;
if (x > xMax)
xMax = x;
if (y < yMin)
yMin = y;
if (y > yMax)
yMax = y;
Key key = new Key(x, y, shift);
T[] ts = chunks.get(key);
if (ts == null) {
ts = (T[]) (new Object[matSize]); // Java does not allow arrays of generics.
chunks.put(key, ts);
}
/* Find index within chunk: concatenated low order bits of x and y. */
int index = ((x & mask) << shift) + (y & mask);
if (ts[index] == null)
size++;
ts[index] = t;
return t;
}
public int size() {
return size;
}
/*
* We rely on the map iterator for checking for concurrent modification exceptions.
*/
private class SparseMatrixIterator implements Iterator {
private Iterator mapIterator;
private int chunkIndex = -1;
private T[] chunk = null;
private SparseMatrixIterator() {
mapIterator = chunks.values().iterator();
moveToNext();
}
@Override
public boolean hasNext() {
boolean hasNext = chunk != null;
return hasNext;
}
@Override
public T next() {
T t = chunk[chunkIndex];
moveToNext();
return t;
}
@Override
public void remove() {
throw new UnsupportedOperationException("remove");
}
private void moveToNext() {
if (chunk == null) {
chunk = mapIterator.hasNext() ? mapIterator.next() : null;
if (chunk == null)
return; // End
}
while (true) {
chunkIndex++;
if (chunkIndex == matSize) {
chunkIndex = 0;
chunk = mapIterator.hasNext() ? mapIterator.next() : null;
if (chunk == null)
return; // End
}
if (chunk[chunkIndex] != null)
return;
}
}
}
@Override
public Iterator iterator() {
return new SparseMatrixIterator();
}
/**
* A public representation of the internal structure of the sparse matrix, i.e. a map of chunks
* (each of which is a flattened two-dimensional array).
* We need to publish the internal structure to be able to efficiently export it through web-services.
*/
public class SparseMatrixChunk {
public int x0, y0; // the coordinates of the minimum corner of this chunk (i.e. with the low bits)
public T[] ts;
private SparseMatrixChunk(int x0, int y0, T[] ts) {
this.x0 = x0;
this.y0 = y0;
this.ts = ts;
}
public T getT(int x, int y) {
return ts[x * chunkSize + y];
}
}
public Iterable getChunks() {
return new Iterable() {
@Override
public Iterator iterator() {
// Again, we rely on the indexIterator to check for concurrent modification.
final Iterator keyIterator = chunks.keySet().iterator();
return new Iterator() {
@Override
public boolean hasNext() {
return keyIterator.hasNext();
}
@Override
public SparseMatrixChunk next() {
Key key = keyIterator.next();
T[] ts = chunks.get(key);
if (ts == null) {
return null;
}
// extract the indexes for the minimum corner of this chunk
int x0 = key.x << shift;
int y0 = key.y << shift;
return new SparseMatrixChunk(x0, y0, ts);
}
@Override
public void remove() {
throw new UnsupportedOperationException("remove");
}
};
}
};
}
/**
* We were previously bit-shifting two 32 bit integers into a long. These are used as map keys, so they had to be
* Long objects rather than primitive long ints. This purpose-built key object should be roughly the same in terms
* of space and speed, and more readable.
*/
static class Key {
int x, y;
public Key(int x, int y, int shift) {
this.x = x >>> shift; // shift off low order bits (index within chunk) retaining only the chunk number
this.y = y >>> shift; // same for y coordinate
}
@Override public int hashCode() {
return x ^ y;
}
@Override public boolean equals(Object other) {
return other instanceof Key && ((Key)other).x == x && ((Key)other).y == y;
}
}
}
