I am trying to make a C program that calculates the value of Pi from the infinite series, aka Leibniz series, and display it to the user. My problem is that I need to display a special message that appears when the program hits the first 3.14, and the first 3.141. That special message should include in which iteration of the loop did the the number become 3.14 and 3.141. I am not lazy so a found a way to make the infinite series but the second part I couldn't figure out, so what should I add to my code to make it display the special message?
#include <stdio.h>
int main(void) {
int i, den; // denominator and counter
double pi = 4;
for (i = 0; i < 10000; i++) {
den = i * 2 + 3;
// (4 - 4/3 + 4/5 -4/7 + 4/9 -......)
if (i % 2 == 0) {
pi = pi - (4.0 / den);
}
else {
pi = pi + (4.0 / den);
}
printf("pi = %lf\n", pi);
}
}
Here's a possible solution:
#include<stdio.h>
#include <math.h>
int
main (void)
{
int i, den; //denominator and counter
int prec = 0;
double pi = 4;
for (i = 0; i < 10000; i++)
{
den = i * 2 + 3;
//(4 - 4/3 + 4/5 -4/7 + 4/9 -......)
if (i % 2 == 0)
pi -= 4.0 / den;
else
pi += 4.0 / den;
//printf ("pi = %lf\n", pi);
if (prec < 1 && trunc (100 * pi) == 314)
{
printf ("Found 3.14 at iteration %d\n", i);
prec++;
}
if (prec < 2 && (int)trunc (1000 * pi) == 3141)
{
printf ("Found 3.141 at iteration %d\n", i);
prec++;
}
}
}
The output is:
pi = 2.666667
pi = 3.466667
pi = 2.895238
...
pi = 3.150140
pi = 3.133118
pi = 3.149996
Found 3.14 at iteration 117
...
pi = 3.141000
pi = 3.142185
pi = 3.141000
Found 3.141 at iteration 1686
...
Here is a version that compares the first n digits of a double cmp_n(). Variables use minimal scope. The variable oracle holds the truncated pi to n decimals. The values of oracle must be stored in ascending order. I tweaked the pi formula to be a bit more compact format.
#include <math.h>
#include <stdio.h>
int cmp_n(double d1, double d2, size_t n) {
return fabs(trunc(pow(10, n) * d1) - trunc(pow(10, n) * d2)) < 1.0;
}
int main() {
double pi = 4;
size_t o = 0;
struct {
double pi[;
size_t n;
} oracle[] = {
{ 3.14, 2 },
{ 3.141, 3 }
};
for (int i = 0; i < 10000; i++) {
int den = i * 2 + 3;
//(4 - 4/3 + 4/5 -4/7 + 4/9 -......)
pi += ((i % 2) ? 4.0 : -4.0) / den;
int special = 0;
if(
o < sizeof(oracle) / sizeof(*oracle) &&
cmp_n(pi, oracle[o].pi, oracle[o].n)
) {
special = 1;
o++;
}
printf("pi = %.15f%2s\n", pi, special ? "*" : "");
}
}
and the relevant data (with line numbers);
$ ./a.out | nl -v0 | grep '*'
117 pi = 3.149995866593470 *
1686 pi = 3.141000236580159 *
Note: you need to add the "%.15lf" format string other the pi output is rounded. double only gives you about 15 digits, and the cmp_n() scales the number and this may not work as expected as you get close to the precision supported by double.
Okay, I want to make a C program that calculates pi accurately to 4th decimal place (3.1415...). I thought that double is more accurate than float type... Even with a trillion terms (n=trillion), the program cannot go past 3.1414... Can someone help? Am I using an incorrect data type to store my Pi value or is my loops incorrect?
#include <stdio.h>
#include <stdlib.h>
int main(int argc, char** argv) {
int n;
while(1){
printf("Please enter how many terms (n) you wish to add to approximate Pi: ");
scanf("%d", &n);
if(n>=1)
break;
}
int x;
int count =2;
double negSum=0;
double posSum=0;
double pi = 0;
for(x=1;x<=n;x++){
do{
if(x%2==1){
posSum += (4.0)/(2.0*x-1.0);
count++;
}
else{
negSum += (-4.0)/(2.0*x-1.0);
count++;
}
pi = negSum + posSum;
}
while(pi>3.1414999 && pi<3.14160000);
}
//pi = negSum + posSum;
printf("The value of Pi using your approximation is %f, and the iteration was %d", pi, count);
return (EXIT_SUCCESS);
}
Here is some of my sample input/output:
Please enter how many terms (n) you wish to add to approximate Pi: 98713485
The value of Pi using your approximation is 3.141407, and the iteration was 98713488
The series you are using:
pi = 4(1 - 1/3 + 1/5 - 1/7 + 1/9 ...)
converges REALLY slowly to pi. It is the evaluation of a Taylor series for 4arctan(x) at x=1 and converges conditionally (it is right on edge of the interval of convergence). That's not going to be a very numerically efficient way to compute pi.
Beyond that, I haven't carefully checked your implementation, but some others have pointed out problems in the comments.
To compute Pi to 4th decimal place, you could use Gauss-Legendre algorithm:
#include <math.h>
#include <stdio.h>
int main(void) {
const double PI = acos(-1), SQRT2 = sqrt(2.0);
double a = 1, b = 1/SQRT2, t = .25, p = 1;
double an, piold, pi = 1, eps = 1e-6; /* use +2 decimal places */
int iteration_count = 0;
do {
++iteration_count;
an = .5 * (a + b);
b = sqrt(a * b);
t -= p * (a - an) * (a - an);
a = an;
p *= 2;
piold = pi;
pi = (a + b) * (a + b) / (4 * t);
} while (fabs(pi - piold) > eps);
printf("got pi=%f with rel. err=%.2e in %d iterations\n",
pi, (pi - PI) / PI, iteration_count);
return 0;
}
To run it:
$ gcc *.c -lm && ./a.out
Output
got pi=3.141593 with rel. err=2.83e-16 in 3 iterations
I have been using Ubuntu 12.04 LTS with GCC to compile my the codes for my assignment for a while. However, recently I have run into two issues as follows:
The following code calculates zero for a nonzero value with the second formula is used.
There is a large amount of error in the calculation of the integral of the standard normal distribution from 0 to 5 or larger standard deviations.
How can I remedy these issues? I am especially obsessed with the first one. Any help or suggestion is appreciated. thanks in advance.
The code is as follows:
#include <stdio.h>
#include <math.h>
#include <limits.h>
#include <stdlib.h>
#define N 599
long double
factorial(long double n)
{
//Here s is the free parameter which is increased by one in each step and
//pro is the initial product and by setting pro to be 0 we also cover the
//case of zero factorial.
int s = 1;
long double pro = 1;
//Here pro stands for product.
if (n < 0)
printf("Factorial is not defined for a negative number \n");
else {
while (n >= s) {
pro *= s;
s++;
}
return pro;
}
}
int main()
{
// Since the function given is the standard normal distribution
// probability density function we have mean = 0 and variance = 1.
// Hence we also have z = x; while dealing with only positive values of
// x and keeping in mind that the PDF is symmetric around the mean.
long double * summand1 = malloc(N * sizeof(long double));
long double * summand2 = malloc(N * sizeof(long double));
int p = 0, k, z[5] = {0, 3, 5, 10, 20};
long double sum1[5] = {0}, sum2[5] = {0} , factor = 1.0;
for (p = 0; p <= 4; p++)
{
for (k = 0; k <= N; k++)
{
summand1[k] = (1 / sqrtl(M_PI * 2) )* powl(-1, k) * powl(z[p], 2 * k + 1) / ( factorial(k) * (2 * k + 1) * powl(2, k));
sum1[p] += summand1[k];
}
//Wolfamalpha site gives the same value here
for (k = 0; k <= N; k++)
{
factor *= (2 * k + 1);
summand2[k] = ((1 / sqrtl(M_PI * 2) ) * powl(z[p], 2 * k + 1) / factor);
//printf("%Le \n", factor);
sum2[p] += summand2[k];
}
sum2[p] = sum2[p] * expl((-powl(z[p],2)) / 2);
}
for (p = 0; p < 4; p++)
{
printf("The sum obtained for z between %d - %d \
\nusing the first formula is %Lf \n", z[p], z[p+1], sum1[p+1]);
printf("The sum obtained for z between %d - %d \
\nusing the second formula is %Lf \n", z[p], z[p+1], sum2[p+1]);
}
return 0;
}
The working code without the outermost for loop is
#include <stdio.h>
#include <math.h>
#include <limits.h>
#include <stdlib.h>
#define N 1200
long double
factorial(long double n)
{
//Here s is the free parameter which is increased by one in each step and
//pro is the initial product and by setting pro to be 0 we also cover the
//case of zero factorial.
int s = 1;
long double pro = 1;
//Here pro stands for product.
if (n < 0)
printf("Factorial is not defined for a negative number \n");
else {
while (n >= s) {
pro *= s;
s++;
}
return pro;
}
}
int main()
{
// Since the function given is the standard normal distribution
// probability density function we have mean = 0 and variance = 1.
// Hence we also have z = x; while dealing with only positive values of
// x and keeping in mind that the PDF is symmetric around the mean.
long double * summand1 = malloc(N * sizeof(long double));
long double * summand2 = malloc(N * sizeof(long double));
int k, z = 3;
long double sum1 = 0, sum2 = 0, pro = 1.0;
for (k = 0; k <= N; k++)
{
summand1[k] = (1 / sqrtl(M_PI * 2) )* powl(-1, k) * powl(z, 2 * k + 1) / ( factorial(k) * (2 * k + 1) * powl(2, k));
sum1 += summand1[k];
}
//Wolfamalpha site gives the same value here
printf("The sum obtained for z between 0-3 using the first formula is %Lf \n", sum1);
for (k = 0; k <= N; k++)
{
pro *= (2 * k + 1);
summand2[k] = ((1 / sqrtl(M_PI * 2) * powl(z, 2 * k + 1) / pro));
//printf("%Le \n", pro);
sum2 += summand2[k];
}
sum2 = sum2 * expl((-powl(z,2)) / 2);
printf("The sum obtained for z between 0-3 using the second formula is %Lf \n", sum2);
return 0;
}
I'm quite certain that the problem is in factor not being set back to 1 in the outer loop..
factor *= (2 * k + 1); (in the loop that calculates sum2.)
In the second version provided the one that works it starts with z=3
However in the first loop since you do not clear it between iterations on p by the time you reach z[2] it already is a huge number.
EDIT: Possible help with precision..
Basically you have a huge number powl(z[p], 2 * k + 1) divided by another huge number factor. huge floating point numbers lose their precision. The way to avoid that is to perform the division as soon as possible..
Instead of first calculating powl(z[p], 2 * k + 1) and dividing by factor :
- (z[p]z[p] ... . * z[p]) / (1*3*5*...(2*k+1))`
rearrange the calculation: (z[p]/1) * (z[p]^2/3) * (z[p]^2/5) ... (z[p]^2/(2*k+1))
You can do this in sumand2 calculation and a similar trick in summand1
For my studies, I have to code an algorithm to calculate sin() with this function:
However, in my algorithm, I have to keep the value of X between 0 and Pi/2. So, I wrote my algorithm but all the results are wrong.
Here is my code:
double sinX(double x){
double resultat = 0;
int i;
if(x < 0 || x > M_PI_2)
x = fmod(x,M_PI_2);
for(i = 1;i<=30;i++){
resultat += -1 * ((x*x)/(2*i*(2*i+1)))*(pow(-1,i-1))*((pow(x,2*i-1))/(factorielle(2*i-1)));
}
return resultat;
}
I didn't find the reason. Can you help me?
Here the are few values of X and the result with fmod
1 / 1
2 / 0.429204
3 / 1.4292
4 / 0.858407
5 / 0.287611
6 / 1.28761
7 / 0.716815
8 / 0.146018
9 / 1.14602
10 / 0.575222
11 / 0.00442571
12 / 1.00443
13 / 0.433629
14 / 1.43363
15 / 0.862833
16 / 0.292037
17 / 1.29204
18 / 0.72124
19 / 0.150444
20 / 1.15044
and the result with the algorithm
1 / -0.158529
2 / -0.0130568
3 / -0.439211
4 / -0.101605
5 / -0.00394883
6 / -0.327441
7 / -0.0598281
8 / -0.000518332
9 / -0.234888
10 / -0.0312009
11 / -1.44477e-008
12 / -0.160572
13 / -0.0134623
14 / -0.443022
15 / -0.103145
16 / -0.00413342
17 / -0.330639
18 / -0.0609237
19 / -0.000566869
20 / -0.237499
Here is my "factorielle" definition
double factorielle(double x){
double resultat = 1;
int i;
if(x != 0){
for (i=2;i<=x;i++)
{
resultat *= i;
}
}
else{
resultat = 1;
}
return resultat;
}
And values :
1 / 1
2 / 2
3 / 6
4 / 24
5 / 120
6 / 720
7 / 5040
8 / 40320
9 / 362880
10 / 3.6288e+006
11 / 3.99168e+007
12 / 4.79002e+008
13 / 6.22702e+009
14 / 8.71783e+010
15 / 1.30767e+012
16 / 2.09228e+013
17 / 3.55687e+014
18 / 6.40237e+015
19 / 1.21645e+017
20 / 2.4329e+018
You're misunderstanding the purpose of the second formula you show. The idea is that you use that formula to compute each term in the sum from the preceding term, saving you from the need to use any pow or factorial calls.
#include <stdio.h>
double sinX(double x) {
double term, total_so_far;
int i;
term = x; /* First term in the expansion. */
total_so_far = 0.0;
for (i = 1; i <= 30; i++) {
/* Add current term to sum. */
total_so_far += term;
/* Compute next term from the current one. */
term *= -(x * x) / (2*i) / (2*i + 1);
}
return total_so_far;
}
int main(void) {
/* testing */
double x;
int i;
for (i = 0; i <= 10; i++) {
x = i / 10.0;
printf("sin(%f) is %f\n", x, sinX(x));
}
return 0;
}
And the results of running this code, on my machine:
sin(0.000000) is 0.000000
sin(0.100000) is 0.099833
sin(0.200000) is 0.198669
sin(0.300000) is 0.295520
sin(0.400000) is 0.389418
sin(0.500000) is 0.479426
sin(0.600000) is 0.564642
sin(0.700000) is 0.644218
sin(0.800000) is 0.717356
sin(0.900000) is 0.783327
sin(1.000000) is 0.841471
That should give you reasonable results for the range 0 to pi / 2. Outside that range you'll need to be a bit cleverer about the reduction you're using: simply reducing modulo pi / 2 won't give correct results. (Hint: it's safe to reduce modulo 2 * pi, since the sin function is periodic with period 2 * pi. Now use symmetries of the sin function to reduce to the range 0 to pi / 2.)
EDIT An explanation of why the current code is giving incorrect results: apart from the flawed reduction step, in your sum you start with the term i = 1. But the first term should be for i = 0 (that's the x term, while the i=1 term is the -x^3 / 3! term). A quick and dirty fix is to remove the reduction step, and to initialise your resultat variable to x rather than 0. That should give you good results for small x, and then you can figure out how to replace the reduction step. I'd be surprised if you were really intended to compute the answer using explicit factorial and power calls, though - I'm almost sure that you're expected to compute each term from the preceding one as described above.
There are two problems with your code:
sin(x+k*π/2) does not necessarily equal sin(x)
Your expression for the term is a little bit messed up. The instructions seem to suggest that you calculate the next term in the series from the previous term. Start with the value for i=0 and then use the equation in your question to compute the next term in each iteration.
Finally, I followed yours indications. Here is my final code :
double sinX(double x)
{
double result = 1.0;
double term_i = 1.0;
int i = 2;
x = fmod(x, 2*M_PI);
for(i = 2; i<= 30; i+=2)
{
term_i = (-term_i * (x*x)) / (i*(i+1));
result += term_i;
}
return x * result;
}
Idea about the number of terms with OP's posted answer.
As long as one performs some range limitation first, like fmod(), the number of terms needed can be reasonably determined dynamically. (Uses 1 to 23 iterations for x: 0 to 2*pi.)
double sinX1(double x)
{
double result = 1.0;
double term_i = 1.0;
int i = 2;
x = fmod(x, 2*M_PI);
// for(i = 2; i<= 30; i+=2)
for(i = 2; ((1.0 + term_i) != 1.0); i+=2)
{
term_i = (-term_i * (x*x)) / (i*(i+1));
result += term_i;
}
return x * result;
}
I'm looking for some nice C code that will accomplish effectively:
while (deltaPhase >= M_PI) deltaPhase -= M_TWOPI;
while (deltaPhase < -M_PI) deltaPhase += M_TWOPI;
What are my options?
Edit Apr 19, 2013:
Modulo function updated to handle boundary cases as noted by aka.nice and arr_sea:
static const double _PI= 3.1415926535897932384626433832795028841971693993751058209749445923078164062862089986280348;
static const double _TWO_PI= 6.2831853071795864769252867665590057683943387987502116419498891846156328125724179972560696;
// Floating-point modulo
// The result (the remainder) has same sign as the divisor.
// Similar to matlab's mod(); Not similar to fmod() - Mod(-3,4)= 1 fmod(-3,4)= -3
template<typename T>
T Mod(T x, T y)
{
static_assert(!std::numeric_limits<T>::is_exact , "Mod: floating-point type expected");
if (0. == y)
return x;
double m= x - y * floor(x/y);
// handle boundary cases resulted from floating-point cut off:
if (y > 0) // modulo range: [0..y)
{
if (m>=y) // Mod(-1e-16 , 360. ): m= 360.
return 0;
if (m<0 )
{
if (y+m == y)
return 0 ; // just in case...
else
return y+m; // Mod(106.81415022205296 , _TWO_PI ): m= -1.421e-14
}
}
else // modulo range: (y..0]
{
if (m<=y) // Mod(1e-16 , -360. ): m= -360.
return 0;
if (m>0 )
{
if (y+m == y)
return 0 ; // just in case...
else
return y+m; // Mod(-106.81415022205296, -_TWO_PI): m= 1.421e-14
}
}
return m;
}
// wrap [rad] angle to [-PI..PI)
inline double WrapPosNegPI(double fAng)
{
return Mod(fAng + _PI, _TWO_PI) - _PI;
}
// wrap [rad] angle to [0..TWO_PI)
inline double WrapTwoPI(double fAng)
{
return Mod(fAng, _TWO_PI);
}
// wrap [deg] angle to [-180..180)
inline double WrapPosNeg180(double fAng)
{
return Mod(fAng + 180., 360.) - 180.;
}
// wrap [deg] angle to [0..360)
inline double Wrap360(double fAng)
{
return Mod(fAng ,360.);
}
One-liner constant-time solution:
Okay, it's a two-liner if you count the second function for [min,max) form, but close enough — you could merge them together anyways.
/* change to `float/fmodf` or `long double/fmodl` or `int/%` as appropriate */
/* wrap x -> [0,max) */
double wrapMax(double x, double max)
{
/* integer math: `(max + x % max) % max` */
return fmod(max + fmod(x, max), max);
}
/* wrap x -> [min,max) */
double wrapMinMax(double x, double min, double max)
{
return min + wrapMax(x - min, max - min);
}
Then you can simply use deltaPhase = wrapMinMax(deltaPhase, -M_PI, +M_PI).
The solutions is constant-time, meaning that the time it takes does not depend on how far your value is from [-PI,+PI) — for better or for worse.
Verification:
Now, I don't expect you to take my word for it, so here are some examples, including boundary conditions. I'm using integers for clarity, but it works much the same with fmod() and floats:
Positive x:
wrapMax(3, 5) == 3: (5 + 3 % 5) % 5 == (5 + 3) % 5 == 8 % 5 == 3
wrapMax(6, 5) == 1: (5 + 6 % 5) % 5 == (5 + 1) % 5 == 6 % 5 == 1
Negative x:
Note: These assume that integer modulo copies left-hand sign; if not, you get the above ("Positive") case.
wrapMax(-3, 5) == 2: (5 + (-3) % 5) % 5 == (5 - 3) % 5 == 2 % 5 == 2
wrapMax(-6, 5) == 4: (5 + (-6) % 5) % 5 == (5 - 1) % 5 == 4 % 5 == 4
Boundaries:
wrapMax(0, 5) == 0: (5 + 0 % 5) % 5 == (5 + 0) % 5 == 5 % 5 == 0
wrapMax(5, 5) == 0: (5 + 5 % 5) % 5 == (5 + 0) % 5== 5 % 5 == 0
wrapMax(-5, 5) == 0: (5 + (-5) % 5) % 5 == (5 + 0) % 5 == 5 % 5 == 0
Note: Possibly -0 instead of +0 for floating-point.
The wrapMinMax function works much the same: wrapping x to [min,max) is the same as wrapping x - min to [0,max-min), and then (re-)adding min to the result.
I don't know what would happen with a negative max, but feel free to check that yourself!
If ever your input angle can reach arbitrarily high values, and if continuity matters, you can also try
atan2(sin(x),cos(x))
This will preserve continuity of sin(x) and cos(x) better than modulo for high values of x, especially in single precision (float).
Indeed, exact_value_of_pi - double_precision_approximation ~= 1.22e-16
On the other hand, most library/hardware use a high precision approximation of PI for applying the modulo when evaluating trigonometric functions (though x86 family is known to use a rather poor one).
Result might be in [-pi,pi], you'll have to check the exact bounds.
Personaly, I would prevent any angle to reach several revolutions by wrapping systematically and stick to a fmod solution like the one of boost.
There is also fmod function in math.h but the sign causes trouble so that a subsequent operation is needed to make the result fir in the proper range (like you already do with the while's). For big values of deltaPhase this is probably faster than substracting/adding `M_TWOPI' hundreds of times.
deltaPhase = fmod(deltaPhase, M_TWOPI);
EDIT:
I didn't try it intensively but I think you can use fmod this way by handling positive and negative values differently:
if (deltaPhase>0)
deltaPhase = fmod(deltaPhase+M_PI, 2.0*M_PI)-M_PI;
else
deltaPhase = fmod(deltaPhase-M_PI, 2.0*M_PI)+M_PI;
The computational time is constant (unlike the while solution which gets slower as the absolute value of deltaPhase increases)
I would do this:
double wrap(double x) {
return x-2*M_PI*floor(x/(2*M_PI)+0.5);
}
There will be significant numerical errors. The best solution to the numerical errors is to store your phase scaled by 1/PI or by 1/(2*PI) and depending on what you are doing store them as fixed point.
Instead of working in radians, use angles scaled by 1/(2π) and use modf, floor etc. Convert back to radians to use library functions.
This also has the effect that rotating ten thousand and a half revolutions is the same as rotating half then ten thousand revolutions, which is not guaranteed if your angles are in radians, as you have an exact representation in the floating point value rather than summing approximate representations:
#include <iostream>
#include <cmath>
float wrap_rads ( float r )
{
while ( r > M_PI ) {
r -= 2 * M_PI;
}
while ( r <= -M_PI ) {
r += 2 * M_PI;
}
return r;
}
float wrap_grads ( float r )
{
float i;
r = modff ( r, &i );
if ( r > 0.5 ) r -= 1;
if ( r <= -0.5 ) r += 1;
return r;
}
int main ()
{
for (int rotations = 1; rotations < 100000; rotations *= 10 ) {
{
float pi = ( float ) M_PI;
float two_pi = 2 * pi;
float a = pi;
a += rotations * two_pi;
std::cout << rotations << " and a half rotations in radians " << a << " => " << wrap_rads ( a ) / two_pi << '\n' ;
}
{
float pi = ( float ) 0.5;
float two_pi = 2 * pi;
float a = pi;
a += rotations * two_pi;
std::cout << rotations << " and a half rotations in grads " << a << " => " << wrap_grads ( a ) / two_pi << '\n' ;
}
std::cout << '\n';
}}
Here is a version for other people finding this question that can use C++ with Boost:
#include <boost/math/constants/constants.hpp>
#include <boost/math/special_functions/sign.hpp>
template<typename T>
inline T normalizeRadiansPiToMinusPi(T rad)
{
// copy the sign of the value in radians to the value of pi
T signedPI = boost::math::copysign(boost::math::constants::pi<T>(),rad);
// set the value of rad to the appropriate signed value between pi and -pi
rad = fmod(rad+signedPI,(2*boost::math::constants::pi<T>())) - signedPI;
return rad;
}
C++11 version, no Boost dependency:
#include <cmath>
// Bring the 'difference' between two angles into [-pi; pi].
template <typename T>
T normalizeRadiansPiToMinusPi(T rad) {
// Copy the sign of the value in radians to the value of pi.
T signed_pi = std::copysign(M_PI,rad);
// Set the value of difference to the appropriate signed value between pi and -pi.
rad = std::fmod(rad + signed_pi,(2 * M_PI)) - signed_pi;
return rad;
}
I encountered this question when searching for how to wrap a floating point value (or a double) between two arbitrary numbers. It didn't answer specifically for my case, so I worked out my own solution which can be seen here. This will take a given value and wrap it between lowerBound and upperBound where upperBound perfectly meets lowerBound such that they are equivalent (ie: 360 degrees == 0 degrees so 360 would wrap to 0)
Hopefully this answer is helpful to others stumbling across this question looking for a more generic bounding solution.
double boundBetween(double val, double lowerBound, double upperBound){
if(lowerBound > upperBound){std::swap(lowerBound, upperBound);}
val-=lowerBound; //adjust to 0
double rangeSize = upperBound - lowerBound;
if(rangeSize == 0){return upperBound;} //avoid dividing by 0
return val - (rangeSize * std::floor(val/rangeSize)) + lowerBound;
}
A related question for integers is available here:
Clean, efficient algorithm for wrapping integers in C++
A two-liner, non-iterative, tested solution for normalizing arbitrary angles to [-π, π):
double normalizeAngle(double angle)
{
double a = fmod(angle + M_PI, 2 * M_PI);
return a >= 0 ? (a - M_PI) : (a + M_PI);
}
Similarly, for [0, 2π):
double normalizeAngle(double angle)
{
double a = fmod(angle, 2 * M_PI);
return a >= 0 ? a : (a + 2 * M_PI);
}
In the case where fmod() is implemented through truncated division and has the same sign as the dividend, it can be taken advantage of to solve the general problem thusly:
For the case of (-PI, PI]:
if (x > 0) x = x - 2PI * ceil(x/2PI) #Shift to the negative regime
return fmod(x - PI, 2PI) + PI
And for the case of [-PI, PI):
if (x < 0) x = x - 2PI * floor(x/2PI) #Shift to the positive regime
return fmod(x + PI, 2PI) - PI
[Note that this is pseudocode; my original was written in Tcl, and I didn't want to torture everyone with that. I needed the first case, so had to figure this out.]
deltaPhase -= floor(deltaPhase/M_TWOPI)*M_TWOPI;
The way suggested you suggested is best. It is fastest for small deflections. If angles in your program are constantly being deflected into the proper range, then you should only run into big out of range values rarely. Therefore paying the cost of a complicated modular arithmetic code every round seems wasteful. Comparisons are cheap compared to modular arithmetic (http://embeddedgurus.com/stack-overflow/2011/02/efficient-c-tip-13-use-the-modulus-operator-with-caution/).
In C99:
float unwindRadians( float radians )
{
const bool radiansNeedUnwinding = radians < -M_PI || M_PI <= radians;
if ( radiansNeedUnwinding )
{
if ( signbit( radians ) )
{
radians = -fmodf( -radians + M_PI, 2.f * M_PI ) + M_PI;
}
else
{
radians = fmodf( radians + M_PI, 2.f * M_PI ) - M_PI;
}
}
return radians;
}
If linking against glibc's libm (including newlib's implementation) you can access
__ieee754_rem_pio2f() and __ieee754_rem_pio2() private functions:
extern __int32_t __ieee754_rem_pio2f (float,float*);
float wrapToPI(float xf){
const float p[4]={0,M_PI_2,M_PI,-M_PI_2};
float yf[2];
int q;
int qmod4;
q=__ieee754_rem_pio2f(xf,yf);
/* xf = q * M_PI_2 + yf[0] + yf[1] /
* yf[1] << y[0], not sure if it could be ignored */
qmod4= q % 4;
if (qmod4==2)
/* (yf[0] > 0) defines interval (-pi,pi]*/
return ( (yf[0] > 0) ? -p[2] : p[2] ) + yf[0] + yf[1];
else
return p[qmod4] + yf[0] + yf[1];
}
Edit: Just realised that you need to link to libm.a, I couldn't find the symbols declared in libm.so
I have used (in python):
def WrapAngle(Wrapped, UnWrapped ):
TWOPI = math.pi * 2
TWOPIINV = 1.0 / TWOPI
return UnWrapped + round((Wrapped - UnWrapped) * TWOPIINV) * TWOPI
c-code equivalent:
#define TWOPI 6.28318531
double WrapAngle(const double dWrapped, const double dUnWrapped )
{
const double TWOPIINV = 1.0/ TWOPI;
return dUnWrapped + round((dWrapped - dUnWrapped) * TWOPIINV) * TWOPI;
}
notice that this brings it in the wrapped domain +/- 2pi so for +/- pi domain you need to handle that afterward like:
if( angle > pi):
angle -= 2*math.pi