Continuing development of my C bitmap library, I added basic graphic primitives to augment the library’s functionality beyond simply reading and writing bitmaps and manually manipulating individual pixels. Source code can be seen below and also downloaded — bmp.c. The underlying implementation of the bitmap file format can be seen in my prior post BMP Implementation in C. Graphic primitives include drawing lines, rectangles, circles, ellipses; rotating, flipping, cropping, resizing and blitting images. A full list of defined graphic primitives can be seen below, together with a short functionality description.
Test image regarding drawing primitives.
/* === DRAWING PRIMITIVES === */
void hline (image *img, int x0, int x1, int y , int c ); // draw horizontal line
void vline (image *img, int x , int y0, int y1, int c ); // draw vertical line
void line (image *img, int x0, int y0, int x1, int y1, int c ); // draw line
void fillrect (image *img, int x0, int y0, int x1, int y1, int c ); // draw filled rectangle
void rect (image *img, int x0, int y0, int x1, int y1, int c ); // draw rectangle
void fillcircle (image *img, int x , int y , int r , int c ); // draw filled circle
void circle (image *img, int x , int y , int r , int t , int c ); // draw circle (with certain thickness)
void fillellipse(image *img, int x , int y , int rx, int ry, int c ); // draw filled ellipse
void ellipse (image *img, int x , int y , int rx, int ry, int t, int c); // draw ellipse (with certain thickness)
/* === TRANSFORMATION PRIMITIVES === */
image *resize (image *img, int w , int h ); // resize an image
image *hflip (image *img ); // flip horizontally
image *vflip (image *img ); // flip vertically
image *rrotate (image *img ); // rotate clockwise
image *lrotate (image *img ); // rotate counter-clockwise
image *hrotate (image *img ); // rotate half a revolution
image *crop (image *img, int x0, int y0, int x1, int y1 ); // crop an image
void blit (image *img, image*, int x , int y ); // blit an image onto another one
Test image regarding transformation primitives.
Future plans for this library include performance optimizations regarding the ellipse drawing primitives; circle drawing is already optimized as it uses the shape’s symmetry to save computational cost.Further primitives that may be added include a flood filling functionality as well as the ability to draw irregular polygons.
Today it is the fourteenth of March 2018. Today’s date — when written in the M/D/Y format —, 3/14/18, looks close enough to Archimedes’ constant’s decimal representation for it to be the constant’s celebratory day. As always on Pi Day, I have implemented an algorithm to generate 𝜋, albeit this year’s accuracy is not the greatest (Try it online [1]).
typedef double d;typedef long l;l f(l n
){l f=1;while(n>1)f*=n--;return f;}d ne(d v,
l p){d r=1;for(l k=0;k<p;k++)r*=v;return r;}d
ps(d(*c)(l),l i,d x){d s=0;for(l k=0;k<i;k++)s
+=c(k)* ne(x, k);return
s;} d exc ( l
n){ return 1./f (n)
; } d exp(d x
) { return
ps(exc ,20,x);}
d G( d x){return
exp(-x *x);}d I
(d a,d b,d g,d
(* f)(d )){d cs=
0;for( d x=a;x<=
b;x +=g) cs+=f(x)
*g;return cs ; } int
main( ) { d pi_root =I(
-2.5, 2.5 , 1e-4,G); d pi
= pi_root * pi_root+(0xf&0xf0
) ; printf( "%c%c%c%c%c%f%c"
,'p','i', ' ','=',' ',pi
,'\n' ) ; }
I use various methods of generating 𝜋 throughout the Pi Days; this time I chose to use an improper integral paired with a power series. 𝜋 is calculated using a famous identity involving infinite continuous sums, roots, e, statistics and — of course — 𝜋.
Furthermore, to compute 𝑒, the following identity is used.
Both formulae are combined, the approximated value of is squared and 𝜋 is printed to stdout.
You can download this program’s prettified (some call it obfuscated, see above) source code pi.c and also the (nearly, as #include is not missing so that the compiler does not need to guess my dependencies) equivalent code in a more traditional source layout tpi.c.
Sorting arrays of integers is an integral part of computing. Therefore, over the years, a lot of different approaches to sorting where found and resulted in a wide range of sorting algorithms existent today. Of these the most common algorithms include quicksort — maybe one of the best known algorithms by name —, merge sort, natural merge sort — my personal favourite out of this list — and insertion, selection and bubble sort — more trivial sorting approaches. In this post, I will look at the aforementioned algorithms alongside a C implementation. The full C source code can be seen below and also downloaded. All sorting algorithm implementations take an integer pointer and an array length as input; sorting the array in-place — manipulating the pointed to memory.
I) Quicksort
A fundamental observation used by most sorting algorithms is that a singleton array — an array which only consists of one element — is sorted. Thus one can write an algorithm which recursively operates on already sorted arrays, as every array has a sorted base (all array elements interpreted as singleton arrays). Quicksort first chooses one of the input array’s elements as its pivot element — my implementation always chooses the first element — and splits the remaining array into two subarrays, one containing all array elements less than or equal to the pivot, the other one containing those greater than the pivot. The array is rearranged such that the first subarray is followed by the pivot element which is followed by the second subarray. Both subarrays are recursively sorted; singleton arrays mark the end of recursion as they are already sorted. Quicksort — as its name implies is a rather efficient sorting algorithm, having an average time complexity of . In the following I show my C implementation. It first creates a swap array for storing intermediate values while permuting the array and then calls its recursive part which initiates the sorting.
// quick sort (recursive part)
void _qsort(int *Arr, int *Swp, int len) {
// split array at pivot (first element) and store in swap
int l = 0, r = len-1;
for (int j = 1; j < len; j++)
if (Arr[j] < Arr[0]) Swp[l++] = Arr[j];
else Swp[r--] = Arr[j];
// move swap to array
Swp[l] = Arr[0];
for (int j = 0; j < len; j++)
Arr[j] = Swp[j];
// recursively sort split arrays
if (l > 1) _qsort(Arr, Swp, l);
if (len-r-1 > 1) _qsort(Arr+l+1, Swp+l+1, len-r-1);
}
// quick sort (initialization)
void QSort(int *Arr, int len) {
if (len < 2) return;
int *Swp = malloc(len*sizeof(int));
_qsort(Arr, Swp, len);
free(Swp);
}
II) Merge Sort
As quicksort, merge sort also fundamentally uses the inherent sorted nature of singleton arrays. However, in contrast to quicksort, merge sort does not split the input array into a correctly placed pivot and two arrays left to sort, but rather uses a merging algorithm to merge two already sorted arrays into one sorted array — conceptually moving from the bottom up instead of from the top down. To merge two sorted subarrays, simply take the smallest of the first elements of both subarrays to create a new array; repeating until both subarrays are empty. Once a merging function is implemented, simply recursively split the input array and merge all singleton arrays together to sort the entire array. As quicksort, merge sort also has an average time complexity of .
// merge two arrays located in memory next to each other
void merge(int *Arr, int *Swp, int llen, int rlen) {
// build array by choosing the smallest of both
// array's first elements, merging both arrays
int len = llen+rlen, l = 0, r = 0;
for (int j = 0; j < len; j++) {
if (l < llen && r < rlen)
if (Arr[l] < Arr[llen+r]) Swp[j] = Arr[l++];
else Swp[j] = Arr[llen+r++];
else if (l < llen) Swp[j] = Arr[l++];
else if (r < rlen) Swp[j] = Arr[llen+r++];
}
// move swap to array
for (int j = 0; j < len; j++)
Arr[j] = Swp[j];
}
// merge sort (recursive part)
void _msort(int *Arr, int *Swp, int len) {
// split arrays' lenghts, sort split arrays
int llen = len/2, rlen = len-llen;
if (llen > 1) _msort(Arr, Swp, llen);
if (rlen > 1) _msort(Arr+llen, Swp, rlen);
// merge arrays
merge(Arr, Swp, llen, rlen);
}
// merge sort (initialization)
void MSort(int *Arr, int len) {
if (len < 2) return;
int *Swp = malloc(len*sizeof(int));
_msort(Arr, Swp, len);
free(Swp);
}
III) Natural Merge Sort
Instead of relying on splitting the input array into singleton lists, natural merge sort searches subarrays which naturally appear sorted and merges them to form one sorted list. The same merging algorithm as in traditional merge sort is used; as merge sort, natural merge sort also has an average time complexity of [1].
// natural merge sort
void NMSort(int *Arr, int len) {
if (len < 2) return;
int *Swp = malloc(len*sizeof(int));
for (int k = 0, j = 0; j < len; k = j+1) {
for (j = k; j < len-1 && Arr[j] <= Arr[j+1];) j++;
if (j < len) merge(Arr, Swp, k, j-k+1);
}
free(Swp);
}
IV) Insertion Sort / Selection Sort
Being a rather trivial sorting algorithm, insertion sort builds up a new array by always removing the input array’s smallest element. Equivalently, selection sort always selects the input array’s smallest element. Thus I have only implemented insertion sort, not using any swap memory but only swapping array elements with each other. Insertion sort is a trivially brute force approach to sorting, making it a rather inefficient algorithm with an average time complexity of .
// insertion sort
void ISort(int *Arr, int len) {
if (len < 2) return;
// loop through array elements
for (int j = 0; j < len; j++) {
// find minimum element
int m = j;
for (int i = j+1; i < len; i++)
if (Arr[i] < Arr[m]) m = i;
// swap minimum element with current element
if (j != m) {
int swp = Arr[j];
Arr[j] = Arr[m];
Arr[m] = swp;
}
}
}
V) Bubble Sort
Bubble sort works by iteratively finding neighbouring elements which are misaligned, swapping them to sort the entire list. Presumably, the rather amusing name comes from observing how elements behave while they are being sorted, bubbling to the top like a pop’s fizz. Its average time complexity is fairly inefficient at .
// bubble sort
void BSort(int *Arr, int len) {
while (len-- > 1)
for (int j = 0; j < len; j++)
if (Arr[j] > Arr[j+1]) {
int swp = Arr[j];
Arr[j] = Arr[j+1];
Arr[j+1] = swp;
}
}
Conclusion
In conclusion, one most likely will not have to worry about implementing sorting algorithms, as most modern languages supply an essential tool belt to the user, complete with various sorting methods and predefined data structures dependent on sorting. Nevertheless, the theory and diversity of sorting algorithm fascinates me, as it shows how widely different tactics can be used to solve the same seemingly mundane problem; sorting an array of integers. All implementations shown in this post have been tested several thousands of times on arrays with varying lengths — to see the test function take a look at the full source code.
is squared and 
.
.