I have an array that represents a grid
For the sake of this example we will start the array at 1 rather that 0 because I realized after doing the picture, and can't be bothered to edit it
In this example blue would have an index of 5, green an index of 23 and red 38
Each color represents an object and the array index represents where the object is. I have implemented very simple gravity, whereby if the grid underneath is empty x + (WIDTH * (y + 1)) then the grid below is occupied by this object, and the grid that the object was in becomes empty.
This all works well in its current form, but what I want to do is make it so that red is the gravity point, so that in this example, blue will move to array index 16 and then 27.
This is not too bad, but how would the object be able to work out dynamically where to move, as in the example of the green grid? How can I get it to move to the correct index?
Also, what would be the best way to iterate through the array to 'find' the location of red? I should also note that red won't always be at 38
Any questions please ask, also thank you for your help.
This sounds very similar to line rasterization. Just imagine the grid to be a grid of pixels. Now when you draw a line from the green point to the red point, the pixels/cells that the line will pass are the cells that the green point should travel along, which should indeed be the shortest path from the green point to the red point along the discrete grid cells. You then just stop once you encounter a non-empty grid cell.
Look for Bresenham's algorithm as THE school book algorithm for line rasterization.
And for searching the red point, just iterate over the array linearly until you have it and then keep track of its grid position, like William already suggested in his answer.
x = x position
y = y position
cols = number of columns across in your grid
(y * cols) + x = index in array absolute value for any x, y
you could generalize this in a function:
int get_index(int x, int y, int gridcols)
{
return (gridcols * y) + x;
}
It should be noted that this works for ZERO BASED INDICES.
This is assuming I am understanding what you're talking about at all...
As for the second question, for any colored element you have, you should keep a value in memory (possibly stored in a structure) that keeps track of its position so you don't have to search for it at all.
struct _THING {
int xpos;
int ypos;
};
Using the get_index() function, you could find the index of the grid cell below it by calling like this:
index_below = get_index(thing.x, thing.y + 1, gridcols);
thing.y++; // increment the thing's y now since it has moved down
simple...
IF YOU WANT TO DO IT IN REVERSE, as in finding the x,y position by the array index, you can use the modulus operator and division.
ypos = array_index / total_cols; // division without remainder
xpos = array_index % total_cols; // gives the remainder
You could generalize this in a function like this:
// x and y parameters are references, and return values using these references
void get_positions_from_index(int array_index, int total_columns, int& x, int& y)
{
y = array_index / total_columns;
x = array_index % total_columns;
}
Whenever you're referring to an array index, it must be zero-based. However, when you are referring to the number of columns, that value will be 1-based for the calculations. x and y positions will also be zero based.
Probably easiest would be to work entirely in a system of (x,y) coordinates to calculate gravity and switch to the array coordinates when you finally need to lookup and store objects.
In your example, consider (2, 4) (red) to be the center of gravity; (5, 1) (blue) needs to move in the direction (2-5, 4-1) == (-3, 3) by the distance _n_. You get decide how simple you want n to be -- it could be that you move your objects to an adjoining element, including diagonals, so move (blue) to (5-1, 1+1) == (4, 2). Or perhaps you could move objects by some scalar multiple of the unit vector that describes the direction you need to move. (Say, heavier objects move further because the attraction of gravity is stronger. Or, lighter objects move further because they have less inertia to overcome. Or objects move further the closer they are to the gravity well, because gravity is an inverse square law).
Once you've sorted out the virtual coordinates of your universe, then convert your numbers (4, 2) via some simple linear formulas: 4*columns + 2 -- or just use multidimensional arrays and truncate your floating-point results to get your array indexes.
Related
I'm making a game with C++ and SFML and was wondering if there's a way to iterate through specific elements in a vector. I have a vector of tiles which makes up the game world, but depending on the game map's size, (1000 x 1000 tiles) iterating through all of them seems very inefficient. I was wondering if there was a way to say "for each tile in vector of tiles that (fits a condition)". Right now, my code for drawing these tiles looks like this:
void Tile::draw()
{
for (const auto& TILE : tiles)
{
if (TILE.sprite.getGlobalBounds().intersects(Game::drawCuller.getGlobalBounds()))
{
Game::window.draw(TILE.sprite);
}
}
}
As you can see, I'm only drawing the tiles in the view (or drawculler). If the vector is too large, it will take a really long time to iterate through it. This greatly impacts my fps. When I have a 100 x 100 tile map, I get around 800 fps, but when I use a 1000 x 1000 tile map, I get roughly 25 fps due to the lengthy iteration. I know that I could separate my tiles into chunks and only iterate through the ones in the current chunk, but I wanted something a little easier to implement. Any help would be appreciated :)
Given the following assumptions:
Your tiles are likely arranged on a regular grid with a (column, row) index.
Your tiles are likely inserted into your vector in row-major order, and is also likely fully-populated. So the index of a tile in your vector is likely (row * numColumns + column).
Your view is likely axis-aligned to the grid (where you can't rotate your view - as is the case with many 2d tile-based games)
If those assumptions hold true, then you can easily iterate through the appropriate range of tiles with a nested loop.
for (int row = minRow; row <= maxRow; ++row) {
for( int column = numColumn; column <= maxColumn; ++column) {
int index = row * numColumns + column;
// Here you can...
doSomethingWith(tiles[index]);
}
}
This just requires that you can compute the minRow, maxRow, minColumn, and maxColumn from your Game::drawCuller.getGlobalBounds(). You haven't disclosed the details, but it's likely something like a rectangle in world coordinates (which might be in some units like meters). It's likely either a left, top, width, height style rectangle or a min, max style bounds rectangle. Assuming the latter:
minViewColumn = floor((bounds.minInMeters.x - originOfGridInMeters.x) / gridTileSizeInMeters);
maxViewColumn = ceil((bounds.maxInMeters.x - originOfGridInMeters.x) / gridTileSizeInMeters);
// similarly for rows
minViewRow = floor((bounds.minInMeters.y - originOfGridInMeters.y) / gridTileSizeInMeters);
maxViewRow = ceil((bounds.maxInMeters.y - originOfGridInMeters.y) / gridTileSizeInMeters);
The originOfGridInMeters is the global coordinates of top-left corner of the tile at (row=0, column=0), which may very well be (0, 0), conveniently, if you set up your world like that. And gridTileSizeInMeters is, well, just that; presumably your tiles have a square aspect ratio in world space.
If the view is permitted to go outside the extents of the tile array, minViewColumn, (and the other iterator ranges) may now be less than 0 or greater than or equal to the number of columns in your tile array. So, it would then be necessary to compute minColumn from minViewColumn by clipping it to the range of tiles stored in your grid. (Same goes for the other iteration extents.)
// Clip to the range of valid rows and columns.
minColumn = min(max(minViewColumn, 0), numColumns - 1);
maxColumn = min(max(maxViewColumn, 0), numColumns - 1);
minRow = min(max(minViewRow, 0), numRows - 1);
maxRow = min(max(maxViewRow, 0), numRows - 1);
Now do that loop I showed you above, and you're good to go!
I was wondering if there was a way to say "for each tile in vector of tiles that (fits a condition)
In general, no. The only way to know if an element fits a condition is to look at it and see if it fits the condition. You can't do that without iterating over all the elements and checking the condition for each.
The way to avoid this is to build some sort of index structure. For instance, if you have tiles with attributes that change rarely, you could pre-build vectors of pointers to all of your tiles with some attribute. That way you can check the condition only once (or rarely) instead of on each frame. For instance you could build separate vectors of all of your blue tiles, all of your red tiles, and all of your green tiles. Then if you want to iterate over all of the tiles of a certain color you could do "for each blue tile" directly instead of "for each tile, if it's blue". This generally trades storage/memory usage for execution speed.
The same concept applies to your specific situation, as you mentioned. You can pre-build caches of chunks, and quickly filter out whole chunks that aren't near your camera. This will prevent you from having to check every tile to see if it's in view.
I have an NxN grid with 2 points, the source and destination. I need to move step by step from the source to the destination (which is also moving). How do I determine what the next point is to move to?
One way is to assess all 8 points and see which yields the lowest distance using an Euclidian distance. However, I was hoping there is a cool (mathematical) trick which will yield more elegant results.
Your question statement allows moving diagonally, which is faster (since it's moving both horizontally and vertically in a single step): this solution will always do that unless it has the same x or y coordinate as the target.
using Position = pair<int,int>;
Position move(Position const ¤t, Position const &target) {
// horizontal and vertical distances
const int dx = target.first - current.first;
const int dy = target.second - current.second;
// horizontal and vertical steps [-1,+1]
const int sx = dx ? dx/abs(dx) : 0;
const int sy = dy ? dy/abs(dy) : 0;
return { current.first + sx, current.second + sy };
}
I'm not sure if this counts as a cool mathematical trick though, it just depends on knowing that:
dx = target.x-current.x is positive if you should move in the positive x-direction, negative if you should go in the negative direction, and zero if you should go straight up/down
dx/abs(dx) keeps the sign and removes the magnitude, so it's always one of -1,0,+1 (avoiding however division by zero)
I suppose that answer to your question is Bresenham's line algorithm. It allows to build sequence of integer points between start and end points in your grid. Anyway you can adapt ideas from it to your problem
For more information see https://www.cs.helsinki.fi/group/goa/mallinnus/lines/bresenh.html
I would simply use some vector math, take dest minus source as a vector, and then calculate the angle between that vector and some reference vector, e.g. <1, 0>, with standard methods.
Then you can simply divide the circle in 8 (or 4 if your prefer) sections and determine in which section your vector lies from the angle you obtained.
See euclidean space for how to calculate the angle between two vectors.
In advance I will tell you that I am starting to learn to program.
The problem is the following:
I have an array of length N and I want to find all the indices that lie inside a circle of radius R centered one index, say the jth.
I have an idea, but it might be very inefficient.
I would convert some kth index in [0,N-1] to Cartesian coordinates using:
int x = k / side;
int y= k % side;
where side is sqrt(N), and test whether it satisfies the equation of the circle:
(x_xC)*(x-xC)+ (y_yC)*(y-yC)<=R*R
where (xC, yC) are the coordinates of the jth element. If yes, I would store the index associated with (x, y) or else do it again for the next element until I cover up the whole array.
Is it a good idea or is it too inefficient for very large arrays?
There is a way to loop over not the entire array, but only the elements which do lie in the circle:
Compute xC and yC.
Let y loop from yC-R to yC+R (with proper clipping at the array boundaries, and with proper rounding in case R is not an integer).
For each such y, let r=sqrt(R*R-(y-yC)*(y-yC)) and let x loop from xC-r to xC+r with proper rounding (and, again, proper clipping at the array boundaries).
Convert x and y back to an array index.
I have a set of objects(each object contains a rectangle and a value assigned to it) which is kept in a vector container.
See picture below:
I need to create a matrix by drawing horizontal and vertical lines at each y/x lower left (LL) / upper right(UR) coordinate like below:
And I need to assign value = 0 to each new empty rectangle, and to other rectangles which are inside of initial rectangles, I need to assign their old values.
I've implemented this with some naive algorithm but it works too slow when I have huge number of rectangles. My algorithm basically does the following:
- Stores all rectangles in a map container. Each element of the map contains set of rectangles with the same LL Y coordinate and they are sorted by LL X coordinate, i.e. key is LL Y coordinate.
- Stores all X/Y coordinates in set containers.
- Iterates over Y/X coordinate containers, and for each new rectangle finds out if it exists in map or not, if exists-assigns existing value to it, otherwise-assigns 0 value. I.e, for each new rectangle it looks for its LL Y coordinate in map, if such Y exists, then searches through the corresponding value(set of rectangles), otherwise-it searches in a whole map.
Is there an effective algorithm to get needed results?
For n rectangles this can be solved easily in O(n^3) time (or just O(n^2) time if at most a bounded number of rectangles intersect) by looking at the problem a different way. This should be adequate for handling up to thousands of rectangles in a few seconds.
Also, unless some other constraints are added to the problem, the latter time bound is optimal: that is, there exist inputs consisting of n non-intersecting rectangles for which O(n^2) smaller grid rectangles will need to be output (which of course requires O(n^2) time). An example such input is n width-1 rectangles, all having equal bottommost y co-ord and having heights 1, 2, ..., n.
Grid size bounds
First of all, notice that there can be at most 2n vertical lines, and at most 2n horizontal lines, since each input rectangle introduces at most 2 of each kind (it may introduce less if one or both vertical lines are also the edge(s) for some already-considered rectangle, and likewise for horizontal lines). So there can be at most (2*n - 1)^2 = O(n^2) cells in the grid defined by these lines.
The grid cell co-ordinate system
We can invent a co-ordinate system for grid cells in which each cell is identified by its lower-left corner, and the co-ordinates of an intersection of two grid lines is given simply by the number of horizontal grid lines below it and the number of vertical grid lines to its left (so that the bottommost, leftmost grid cell has co-ords (0, 0), the cell to its right has co-ords (1, 0), the cell two cells above that cell has co-ords (1, 2), etc.)
The algorithm
For each input rectangle having LL co-ords (x1, y1) and UR co-ords (x2, y2), we determine the horizontal and vertical intervals that it occupies within the new grid co-ordinate system, and then simply iterate through every cell (i, j) belonging to this rectangular region (i.e., every grid cell (i, j) such that toGridX(x1) <= i < toGridX(x2) and toGridY(y1) <= j < toGridY(y2)) with a nested for loop, recording in a hashtable that the ID (colour?) for the cell at (i, j) should be the colour of the current input rectangle. Input rectangles should be processed in decreasing z-order (implicitly at least there seems to be such an order, from your example) so that for any cell covered by more than one input rectangle, the hashtable will wind up recording whatever the "nearest" rectangle's colour is. Finally, iterate through the hash table, converting each grid co-ord pair (i, j) back to the LL and UR co-ords of the input-space rectangle that corresponds to this grid cell, and output this rectangle with the ID given by the value for this hash key.
Preprocessing
In order to accomplish the above, we need two things: a way to map input-space co-ordinates to grid co-ordinates (to determine the horizontal and vertical grid intervals for a given input rectangle), and a way to map grid co-ordinates back to input-space co-ordinates (to generate the output rectangles in the final step). Both operations are easy to do via that old workhorse, sorting.
Given any corner (x, y) of some input rectangle, the grid x co-ordinate corresponding to x, toGridX(x), is simply the rank position of x within the sorted list of all distinct x positions of vertical edges that are present among the input rectangles. Similarly, toGridY(y) is just the rank position of y within the sorted list of all distinct y positions of horizontal edges that are present among the input rectangles. In the other direction, for any grid co-ordinate (i, j), the corresponding input-space x co-ordinate, fromGridX(i), is simply the i-th smallest x co-ord (ignoring duplicates) of any vertical edge among the input rectangles, and similarly for fromGridY(j). These can all be computed as follows (all array indices start at 0, and I show only how to do it for x co-ords; y co-ords are similar):
For each rectangle i in the input having LL co-ords (x1, y1) and (x2, y2):
Append the two-element array [x1, i] to the list-of-arrays VERT.
Append the two-element array [x2, i] to the list-of-arrays VERT.
Sort the list VERT in increasing order by its first item.
Combine elements in VERT having identical x co-ords. Specifically:
Set j = 0.
For i from 1 to n-1:
If VERT[i][0] == VERT[j][0] then append VERT[i][1] to VERT[j] (thereby forming an array of length 3 or more at position j), otherwise set j = j + 1 and overwrite VERT[j] with the two-element array VERT[i].
Delete VERT[j+1] and all later elements from VERT.
By this time, for any i, VERT[i] is an array that contains (in its second and subsequent positions) the IDs of every input rectangle that uses, as either its left or right edge, the ith-leftmost distinct vertical line used by any input rectangle -- or in other words, the rank-i vertical line. We now "invert" this:
For i from 0 to n-1:
For j from 1 to length(VERT[i])-1:
Set toGridX[VERT[i][j]] = i.
For i from 0 to length(VERT)-1:
Set fromGridX[i] = VERT[i][0].
Running time
As previously established, there are at most O(n^2) grid cells. Each of the n input rectangles can occupy at most all of these cells, each of which is visited once per input rectangle, for a time bound of O(n^3). Note that this is an extremely pessimistic time bound, and for example if none (or none but a bounded number) of your rectangles overlap, then it drops to O(n^2) since no grid cell will ever be visited more than once.
I suspect the lookups and iterations are not fast enough. Things like 'otherwise it searches the whole map' point out that you do very heavy computations.
What I think you need is to use a 2d datastructure. A k-d tree or a BSP would work but the easiest to understand and implement would be a quad tree.
In a quad tree each node represents a rectangle in your space. Each node can be split into 4 children by selecting the mid point along the 2 dimensions and having the children represent the 4 resulting rectangles. Each node also holds the value that you want to assign to the area and an extra flag if the value is uniform.
To mark a rectangle with some value, you start from the root and recursively:
If the input rectangle covers the node rectangle you set the value to that node, mark it as uniform and return.
If the input rectangle and the node rectangle don't touch just return.
If the node is marked as uniform, copy the value to it's children and mark the node not uniform.
Recursively call for the 4 children (you might have to create them).
On the way back, check if the 4 children have the same value and are all marked as uniform and if so mark the node as uniform and set the same value as the children.
The main advantage of this approach is that you get to mark large areas of your map quickly. You can also prove that marking a area is O(logN) where N is the size of your map (with a larger constant than the usual tree).
You can find a more detailed explanation and some helpful images on wikipedia.
Assuming you know the top- and bottom-most y and the left- and right-most x, extend the four vectors belonging to each rectangle to the respective max and min x and y points. Keep a set of extended vertical vectors and a set of extended horizontal ones. Whenever an extended vector is added, it will necessarily intersect with each vector in the perpendicular list - the intersections are the cell coordinates of the matrix.
Once the list of cell coordinates is made, iterate over them and assign values appropriately, looking up if they are in or out of an original rectangle. I'm not too versed in data structures for rectangles, but it seems to me that two interval trees, one for horizontal, the other for vertical could find that answer in O(log n) time per query, where n is the number of intervals in the tree.
All together, this method seems to be O(n * log m) time, where n is the number of cell coordinates in the resultant matrix and m is the number of original rectangles.
I have a 2D matrix represented as a vector of values, an index representing the first cell and a pair of coordinate representing the second cell.
vector<double> matrix;
auto index = 10;
auto x1 = index % width;
auto y1 = index / width;
auto x2 = ...
auto y2 = ...
I need to find the distance between these two cells, where the distance is equals to 1 for the first "ring" of the 8 neighbor cells, 2 for the second ring, and so on.
Is there a way faster than the euclidean distance?
What you need is something like a modified Manhattan Distance. I think there may be a specific name for your use case, but I don't know it. Anyway, this is how I'd do it.
Suppose the two points are x rows away and y columns away. Then x+y is the Manhattan Distance. But in your case, diagonal movements are also allowed. So, if you moved diagonally towards the point initially, you'd cover the smaller of x and y, with some amount remaining in the other. You can then move horizontally/vertically to cover the remaining distance. Hence, the distance by your metric would be max(x,y).
Given points (x1,y1) and (x2,y2), the answer would be max(|x1-x2|,|y1-y2|)