I need to make a program for the Taylor expansion of sin(x).
I made something but I don't know if it is good. I don't know how to check it.
Could you please check it?
double mein_sinus (double x,long n)
{
double p;
double px;
double fakto;
double resultat;
long i;
for (i=0; i <= n; i++)
{
p = pow(-1, i);
px = pow(x,(2*i + 1));
fakto = faktoriel (2*i + 1);
resultat = p * px / fakto;
}
return resultat;
}
Related
I have made an implementation of the Reaction-Diffusion algorithm on Processing 3.1.1, following a video tutorial. I have made some adaptations on my code, like implementing it on a torus space, instead of a bounded box, like the video.
However, I ran into this annoying issue, that the code runs really slow, proportional to the canvas size (larger, slower). With that, I tried optmizing the code, according to my (limited) knowledge. The main thing I did was to reduce the number of loops running.
Even then, my code still ran quite slow.
Since I have noticed that with a canvas of 50 x 50 in size, the algorithm ran at a good speed, I tried making it multithreaded, in such a way that the canvas would be divided between the threads, and each thread would run the algorithm for a small region of the canvas.
All threads read from the current state of the canvas, and all write to the future state of the canvas. The canvas is then updated using Processing's pixel array.
However, even with multithreading, I didn't see any performance improvement. By the contrary, I saw it getting worse. Now sometimes the canvas flicker between a rendered state and completely white, and in some cases, it doesn't even render.
I'm quite sure that I'm doing something wrong, or I may be taking the wrong approach to optimizing this algorithm. And now, I'm asking for help to understand what I'm doing wrong, and how I could fix or improve my code.
Edit: Implementing ahead of time calculation and rendering using a buffer of PImage objects has removed flickering, but the calculation step on the background doesn't run fast enough to fill the buffer.
My Processing Sketch is below, and thanks in advance.
ArrayList<PImage> buffer = new ArrayList<PImage>();
Thread t;
Buffer b;
PImage currentImage;
Point[][] grid; //current state
Point[][] next; //future state
//Reaction-Diffusion algorithm parameters
final float dA = 1.0;
final float dB = 0.5;
//default: f = 0.055; k = 0.062
//mitosis: f = 0.0367; k = 0.0649
float feed = 0.055;
float kill = 0.062;
float dt = 1.0;
//multi-threading parameters to divide canvas
int threadSizeX = 50;
int threadSizeY = 50;
//red shading colors
color red = color(255, 0, 0);
color white = color(255, 255, 255);
color black = color(0, 0, 0);
//if redShader is false, rendering will use a simple grayscale mode
boolean redShader = true;
//simple class to hold chemicals A and B amounts
class Point
{
float a;
float b;
Point(float a, float b)
{
this.a = a;
this.b = b;
}
}
void setup()
{
size(300, 300);
//initialize matrices with A = 1 and B = 0
grid = new Point[width][];
next = new Point[width][];
for (int x = 0; x < width; x++)
{
grid[x] = new Point[height];
next[x] = new Point[height];
for (int y = 0; y < height; y++)
{
grid[x][y] = new Point(1.0, 0.0);
next[x][y] = new Point(1.0, 0.0);
}
}
int a = (int) random(1, 20); //seed some areas with B = 1.0
for (int amount = 0; amount < a; amount++)
{
int siz = 2;
int x = (int)random(width);
int y = (int)random(height);
for (int i = x - siz/2; i < x + siz/2; i++)
{
for (int j = y - siz/2; j < y + siz/2; j++)
{
int i2 = i;
int j2 = j;
if (i < 0)
{
i2 = width + i;
} else if (i >= width)
{
i2 = i - width;
}
if (j < 0)
{
j2 = height + j;
} else if (j >= height)
{
j2 = j - height;
}
grid[i2][j2].b = 1.0;
}
}
}
initializeThreads();
}
/**
* Divide canvas between threads
*/
void initializeThreads()
{
ArrayList<Reaction> reactions = new ArrayList<Reaction>();
for (int x1 = 0; x1 < width; x1 += threadSizeX)
{
for (int y1 = 0; y1 < height; y1 += threadSizeY)
{
int x2 = x1 + threadSizeX;
int y2 = y1 + threadSizeY;
if (x2 > width - 1)
{
x2 = width - 1;
}
if (y2 > height - 1)
{
y2 = height - 1;
}
Reaction r = new Reaction(x1, y1, x2, y2);
reactions.add(r);
}
}
b = new Buffer(reactions);
t = new Thread(b);
t.start();
}
void draw()
{
if (buffer.size() == 0)
{
return;
}
PImage i = buffer.get(0);
image(i, 0, 0);
buffer.remove(i);
//println(frameRate);
println(buffer.size());
//saveFrame("output/######.png");
}
/**
* Faster than calling built in pow() function
*/
float pow5(float x)
{
return x * x * x * x * x;
}
class Buffer implements Runnable
{
ArrayList<Reaction> reactions;
boolean calculating = false;
public Buffer(ArrayList<Reaction> reactions)
{
this.reactions = reactions;
}
public void run()
{
while (true)
{
if (buffer.size() < 1000)
{
calculate();
if (isDone())
{
buffer.add(currentImage);
Point[][] temp;
temp = grid;
grid = next;
next = temp;
calculating = false;
}
}
}
}
boolean isDone()
{
for (Reaction r : reactions)
{
if (!r.isDone())
{
return false;
}
}
return true;
}
void calculate()
{
if (calculating)
{
return;
}
currentImage = new PImage(width, height);
for (Reaction r : reactions)
{
r.calculate();
}
calculating = true;
}
}
class Reaction
{
int x1;
int x2;
int y1;
int y2;
Thread t;
public Reaction(int x1, int y1, int x2, int y2)
{
this.x1 = x1;
this.x2 = x2;
this.y1 = y1;
this.y2 = y2;
}
public void calculate()
{
Calculator c = new Calculator(x1, y1, x2, y2);
t = new Thread(c);
t.start();
}
public boolean isDone()
{
if (t.getState() == Thread.State.TERMINATED)
{
return true;
} else
{
return false;
}
}
}
class Calculator implements Runnable
{
int x1;
int x2;
int y1;
int y2;
//weights for calculating the Laplacian for A and B
final float[][] laplacianWeights = {{0.05, 0.2, 0.05},
{0.2, -1, 0.2},
{0.05, 0.2, 0.05}};
/**
* x1, x2, y1, y2 delimit a rectangle. The object will only work within it
*/
public Calculator(int x1, int y1, int x2, int y2)
{
this.x1 = x1;
this.x2 = x2;
this.y1 = y1;
this.y2 = y2;
//println("x1: " + x1 + ", y1: " + y1 + ", x2: " + x2 + ", y2: " + y2);
}
#Override
public void run()
{
reaction();
show();
}
public void reaction()
{
for (int x = x1; x <= x2; x++)
{
for (int y = y1; y <= y2; y++)
{
float a = grid[x][y].a;
float b = grid[x][y].b;
float[] l = laplaceAB(x, y);
float a2 = reactionDiffusionA(a, b, l[0]);
float b2 = reactionDiffusionB(a, b, l[1]);
next[x][y].a = a2;
next[x][y].b = b2;
}
}
}
float reactionDiffusionA(float a, float b, float lA)
{
return a + ((dA * lA) - (a * b * b) + (feed * (1 - a))) * dt;
}
float reactionDiffusionB(float a, float b, float lB)
{
return b + ((dB * lB) + (a * b * b) - ((kill + feed) * b)) * dt;
}
/**
* Calculates Laplacian for both A and B at same time, to reduce amount of loops executed
*/
float[] laplaceAB(int x, int y)
{
float[] l = {0.0, 0.0};
for (int i = x - 1; i < x + 2; i++)
{
for (int j = y - 1; j < y + 2; j++)
{
int i2 = i;
int j2 = j;
if (i < 0)
{
i2 = width + i;
} else if (i >= width)
{
i2 = i - width;
}
if (j < 0)
{
j2 = height + j;
} else if (j >= height)
{
j2 = j - height;
}
int weightX = (i - x) + 1;
int weightY = (j - y) + 1;
l[0] += laplacianWeights[weightX][weightY] * grid[i2][j2].a;
l[1] += laplacianWeights[weightX][weightY] * grid[i2][j2].b;
}
}
return l;
}
public void show()
{
currentImage.loadPixels();
//renders the canvas using the pixel array
for (int x = 0; x < width; x++)
{
for (int y = 0; y < height; y++)
{
float a = next[x][y].a;
float b = next[x][y].b;
int pix = x + y * width;
float diff = (a - b);
color c;
if (redShader) //aply red shading
{
float thresh = 0.5;
if (diff < thresh)
{
float diff2 = map(pow5(diff), 0, pow5(thresh), 0, 1);
c = lerpColor(black, red, diff2);
} else
{
float diff2 = map(1 - pow5(-diff + 1), 1 - pow5(-thresh + 1), 1, 0, 1);
c = lerpColor(red, white, diff2);
}
} else //apply gray scale shading
{
c = color(diff * 255, diff * 255, diff * 255);
}
currentImage.pixels[pix] = c;
}
}
currentImage.updatePixels();
}
}
A programmer had a problem. He thought “I know, I’ll solve it with threads!”. has Now problems. two he
Processing uses a single rendering thread.
It does this for good reason, and most other renderers do the same thing. In fact, I don't know of any multi-threaded renderers.
You should only change what's on the screen from Processing's main rendering thread. In other words, you should only change stuff from Processing's functions, not your own thread. This is what's causing the flickering you're seeing. You're changing stuff as it's being drawn to the screen, which is a horrible idea. (And it's why Processing uses a single rendering thread in the first place.)
You could try to use your multiple threads to do the processing, not the rendering. But I highly doubt that's going to be worth it, and like you saw, it might even make things worse.
If you want to speed up your sketch, you might also consider doing the processing ahead of time instead of in real time. Do all your calculations at the beginning of the sketch, and then just reference the results of the calculations when it's time to draw the frame. Or you could draw to a PImage ahead of time, and then just draw those.
I'm having a strange issue that I can't resolve. I made this as a simple example that demonstrates the problem. I have a sine wave defined between [0, 2*pi]. I take the Fourier transform using FFTW. Then I have a for loop where I repeatedly take the inverse Fourier transform. In each iteration, I take the average of my solution and print the results. I expect that the average stays the same with each iteration because there is no change to solution, y. However, when I pick N = 256 and other even values of N, I note that the average grows as if there are numerical errors. However, if I choose, say, N = 255 or N = 257, this is not the case and I get what is expect (avg = 0.0 for each iteration).
Code:
#include <stdio.h>
#include <stdlib.h>
#include <fftw3.h>
#include <math.h>
int main(void)
{
int N = 256;
double dx = 2.0 * M_PI / (double)N, dt = 1.0e-3;
double *x, *y;
x = (double *) malloc (sizeof (double) * N);
y = (double *) malloc (sizeof (double) * N);
// initial conditions
for (int i = 0; i < N; i++) {
x[i] = (double)i * dx;
y[i] = sin(x[i]);
}
fftw_complex yhat[N/2 + 1];
fftw_plan fftwplan, fftwplan2;
// forward plan
fftwplan = fftw_plan_dft_r2c_1d(N, y, yhat, FFTW_ESTIMATE);
fftw_execute(fftwplan);
// set N/2th mode to zero if N is even
if (N % 2 < 1.0e-13) {
yhat[N/2][0] = 0.0;
yhat[N/2][1] = 0.0;
}
// backward plan
fftwplan2 = fftw_plan_dft_c2r_1d(N, yhat, y, FFTW_ESTIMATE);
for (int i = 0; i < 50; i++) {
// yhat to y
fftw_execute(fftwplan2);
// rescale
for (int j = 0; j < N; j++) {
y[j] = y[j] / (double)N;
}
double avg = 0.0;
for (int j = 0; j < N; j++) {
avg += y[j];
}
printf("%.15f\n", avg/N);
}
fftw_destroy_plan(fftwplan);
fftw_destroy_plan(fftwplan2);
void fftw_cleanup(void);
free(x);
free(y);
return 0;
}
Output for N = 256:
0.000000000000000
0.000000000000000
0.000000000000000
-0.000000000000000
0.000000000000000
0.000000000000022
-0.000000000000007
-0.000000000000039
0.000000000000161
-0.000000000000314
0.000000000000369
0.000000000004775
-0.000000000007390
-0.000000000079126
-0.000000000009457
-0.000000000462023
0.000000000900855
-0.000000000196451
0.000000000931323
-0.000000009895302
0.000000039348379
0.000000133179128
0.000000260770321
-0.000003233551979
0.000008285045624
-0.000016331672668
0.000067450106144
-0.000166893005371
0.001059055328369
-0.002521514892578
0.005493164062500
-0.029907226562500
0.093383789062500
-0.339111328125000
1.208251953125000
-3.937500000000000
13.654296875000000
-43.812500000000000
161.109375000000000
-479.250000000000000
1785.500000000000000
-5369.000000000000000
19376.000000000000000
-66372.000000000000000
221104.000000000000000
-753792.000000000000000
2387712.000000000000000
-8603776.000000000000000
29706240.000000000000000
-96833536.000000000000000
Any ideas?
libfftw has the odious habit of modifying its inputs. Back up yhat if you want to do repeated inverse transforms.
OTOH, it's perverse, but why are you repeating the same operation if you don't expect it give different results? (Despite this being the case)
As indicated in comments: "if you want to keep the input data unchanged, use the FFTW_PRESERVE_INPUT flag. Per http://www.fftw.org/doc/Planner-Flags.html"
For example:
// backward plan
fftwplan2 = fftw_plan_dft_c2r_1d(N, yhat, y, FFTW_ESTIMATE | FFTW_PRESERVE_INPUT);
I'm trying to implement this code on a 8 core cluster. It has 2 sockets each with 4 cores. I am trying to create 8 threads and set affinity using pthread_attr_setaffinity_np function. But when I look at my performance in VTunes , it shows me that 3969 odd threads are being created. I don't understand why and how! Above all, my performance is exactly the same as it was when no affinity was set (OS thread scheduling). Can someone please help me debug this problem? My code is running perfectly fine but I have no control over the threads! Thanks in advance.
--------------------------------------CODE-------------------------------------------
const int num_thrd=8;
bool RCTAlgorithmBackprojection(RabbitCtGlobalData* r)
{
float O_L = r->O_L;
float R_L = r->R_L;
double* A_n = r->A_n;
float* I_n = r->I_n;
float* f_L = r->f_L;*/
cpu_set_t cpu[num_thrd];
pthread_t thread[num_thrd];
pthread_attr_t attr[num_thrd];
for(int i =0; i< num_thrd; i++)
{
threadCopy[i].L = r->L;
threadCopy[i].O_L = r->O_L;
threadCopy[i].R_L = r->R_L;
threadCopy[i].A_n = r->A_n;
threadCopy[i].I_n = r->I_n;
threadCopy[i].f_L = r->f_L;
threadCopy[i].slice= i;
threadCopy[i].S_x = r->S_x;
threadCopy[i].S_y = r->S_y;
pthread_attr_init(&attr[i]);
CPU_ZERO(&cpu[i]);
CPU_SET(i, &cpu[i]);
pthread_attr_setaffinity_np(&attr[i], CPU_SETSIZE, &cpu[i]);
int rc=pthread_create(&thread[i], &attr[i], backProject, (void*)&threadCopy[i]);
if (rc!=0)
{
cout<<"Can't create thread\n"<<endl;
return -1;
}
// sleep(1);
}
for (int i = 0; i < num_thrd; i++) {
pthread_join(thread[i], NULL);
}
//s_rcgd = r;
return true;
}
void* backProject (void* parm)
{
copyStruct* s = (copyStruct*)parm; // retrive the slice info
unsigned int L = s->L;
float O_L = s->O_L;
float R_L = s->R_L;
double* A_n = s->A_n;
float* I_n = s->I_n;
float* f_L = s->f_L;
int slice1 = s->slice;
//cout<<"The size of volume is L= "<<L<<endl;
int from = (slice1 * L) / num_thrd; // note that this 'slicing' works fine
int to = ((slice1+1) * L) / num_thrd; // even if SIZE is not divisible by num_thrd
//cout<<"computing slice " << slice1<< " from row " << from<< " to " << to-1<<endl;
for (unsigned int k=from; k<to; k++)
{
double z = O_L + (double)k * R_L;
for (unsigned int j=0; j<L; j++)
{
double y = O_L + (double)j * R_L;
for (unsigned int i=0; i<L; i++)
{
double x = O_L + (double)i * R_L;
double w_n = A_n[2] * x + A_n[5] * y + A_n[8] * z + A_n[11];
double u_n = (A_n[0] * x + A_n[3] * y + A_n[6] * z + A_n[9] ) / w_n;
double v_n = (A_n[1] * x + A_n[4] * y + A_n[7] * z + A_n[10]) / w_n;
f_L[k * L * L + j * L + i] += (float)(1.0 / (w_n * w_n) * p_hat_n(u_n, v_n));
}
}
}
//cout<<" finished slice "<<slice1<<endl;
return NULL;
}
Alright, so I found out the reason was because of CPU_SETSIZE that I was using as an argument in pthread_attr_setaffinity_np. I replaced it with num_thrd . Apparently CPU_SETSIZE which will be declared inside #define __USE_GNU was not included in my file.!! Sorry if I bothered any of y'all who were trying to debug this thanks again!
I have written an image resizer using Lanczos re-sampling. I've taken the implementation straight from the directions on wikipedia. The results look good visually, but for some reason it does not match the result from Matlab's resize with Lanczos very well (in pixel error).
Does anybody see any errors? This is not my area of expertise at all...
Here is my filter (I'm using Lanczos3 by default):
double lanczos_size_ = 3.0;
inline double sinc(double x) {
double pi = 3.1415926;
x = (x * pi);
if (x < 0.01 && x > -0.01)
return 1.0 + x*x*(-1.0/6.0 + x*x*1.0/120.0);
return sin(x)/x;
}
inline double LanczosFilter(double x) {
if (std::abs(x) < lanczos_size_) {
double pi = 3.1415926;
return sinc(x)*sinc(x/lanczos_size_);
} else {
return 0.0;
}
}
And my code to resize the image:
Image Resize(Image& image, int new_rows, int new_cols) {
int old_cols = image.size().cols;
int old_rows = image.size().rows;
double col_ratio =
static_cast<double>(old_cols)/static_cast<double>(new_cols);
double row_ratio =
static_cast<double>(old_rows)/static_cast<double>(new_rows);
// Apply filter first in width, then in height.
Image horiz_image(new_cols, old_rows);
for (int r = 0; r < old_rows; r++) {
for (int c = 0; c < new_cols; c++) {
// x is the new col in terms of the old col coordinates.
double x = static_cast<double>(c)*col_ratio;
// The old col corresponding to the closest new col.
int floor_x = static_cast<int>(x);
horiz_image[r][c] = 0.0;
double weight = 0.0;
// Add up terms across the filter.
for (int i = floor_x - lanczos_size_ + 1; i < floor_x + lanczos_size_; i++) {
if (i >= 0 && i < old_cols) {
double lanc_term = LanczosFilter(x - i);
horiz_image[r][c] += image[r][i]*lanc_term;
weight += lanc_term;
}
}
// Normalize the filter.
horiz_image[r][c] /= weight;
// Strap the pixel values to valid values.
horiz_image[r][c] = (horiz_image[r][c] > 1.0) ? 1.0 : horiz_image[r][c];
horiz_image[r][c] = (horiz_image[r][c] < 0.0) ? 0.0 : horiz_image[r][c];
}
}
// Now apply a vertical filter to the horiz image.
Image new_image(new_cols, new_rows);
for (int r = 0; r < new_rows; r++) {
double x = static_cast<double>(r)*row_ratio;
int floor_x = static_cast<int>(x);
for (int c = 0; c < new_cols; c++) {
new_image[r][c] = 0.0;
double weight = 0.0;
for (int i = floor_x - lanczos_size_ + 1; i < floor_x + lanczos_size_; i++) {
if (i >= 0 && i < old_rows) {
double lanc_term = LanczosFilter(x - i);
new_image[r][c] += horiz_image[i][c]*lanc_term;
weight += lanc_term;
}
}
new_image[r][c] /= weight;
new_image[r][c] = (new_image[r][c] > 1.0) ? 1.0 : new_image[r][c];
new_image[r][c] = (new_image[r][c] < 0.0) ? 0.0 : new_image[r][c];
}
}
return new_image;
}
Here is Lanczosh in one single loop. no errors.
Uses mentioned at top procedures.
void ResizeDD(
double* const pixelsSrc,
const int old_cols,
const int old_rows,
double* const pixelsTarget,
int const new_rows, int const new_cols)
{
double col_ratio =
static_cast<double>(old_cols) / static_cast<double>(new_cols);
double row_ratio =
static_cast<double>(old_rows) / static_cast<double>(new_rows);
// Now apply a filter to the image.
for (int r = 0; r < new_rows; ++r)
{
const double row_within = static_cast<double>(r)* row_ratio;
int floor_row = static_cast<int>(row_within);
for (int c = 0; c < new_cols; ++c)
{
// x is the new col in terms of the old col coordinates.
double col_within = static_cast<double>(c)* col_ratio;
// The old col corresponding to the closest new col.
int floor_col = static_cast<int>(col_within);
double& v_toSet = pixelsTarget[r * new_cols + c];
v_toSet = 0.0;
double weight = 0.0;
for (int i = floor_row - lanczos_size_ + 1; i <= floor_row + lanczos_size_; ++i)
{
for (int j = floor_col - lanczos_size_ + 1; j <= floor_col + lanczos_size_; ++j)
{
if (i >= 0 && i < old_rows && j >= 0 && j < old_cols)
{
const double lanc_term = LanczosFilter(row_within - i + col_within - j);
v_toSet += pixelsSrc[i * old_rows + j] * lanc_term;
weight += lanc_term;
}
}
}
v_toSet /= weight;
v_toSet = (v_toSet > 1.0) ? 1.0 : v_toSet;
v_toSet = (v_toSet < 0.0) ? 0.0 : v_toSet;
}
}
}
The line
for (int i = floor_x - lanczos_size_ + 1; i < floor_x + lanczos_size_; i++)
should be
for (int i = floor_x - lanczos_size_ + 1; i <= floor_x + lanczos_size_; i++)
Do not know but perhaps other mistakes linger too.
I think there is a mistake in your sinc function. Below the fraction bar you have to square pi and x. Additional you have to multiply the function with lanczos size
L(x) = **a***sin(pi*x)*sin(pi*x/a) * (pi**²**x**²**)^-1
Edit: My mistake, there is all right.
What I'm doing is testing to see what the level of intersection between a circle and rectangle. I would like to find whether the rectangle is completely inside the circle, partially intersecting it, or if there is no intersection at all.
I've attached the code that I've come up with today, it simply checks the distances from the center of the circle to the corners of the rectangle to determine the level of intersection.
What I'm wondering is there a more efficient way of doing this?
EDIT:
Here is my updated, working code. fullIntersect is my own, I found the partialIntersect snippet on Circle-Rectangle collision detection (intersection). I'm going to leave this open, as I'm still curious as to whether there is a better way of doing this.
public boolean fullIntersect(float circleX, float circleY, float radius)
{
float radsq = radius * radius;
double xsq = Math.pow(circleX - xPosition, 2);
double xpwsq = Math.pow(circleX - (xPosition + width), 2);
double ysq = Math.pow(circleY - yPosition, 2);
double yphsq = Math.pow(circleY - (yPosition + height), 2);
if(xsq + ysq > radsq || xsq + yphsq > radsq || xpwsq + yphsq > radsq || xpwsq + ysq > radsq)
return false;
return true;
/* this is what the one if statement does
double disBotLeft = xsq + ysq;
double disTopLeft = xsq + yphsq;
double disTopRight = xpwsq + yphsq;
double disBotRight = xpwsq + ysq;
if(disBotRight > radsq) return false;
if(disBotLeft > radsq) return false;
if(disTopLeft > radsq) return false;
if(disTopRight > radsq) return false;
return true;
*/
}
public int intersects(float circleX, float circleY, float radius)
{
if(!enabled) return 0;
double wo2 = width / 2.0d;
double ho2 = height / 2.0d;
double circleDistanceX = Math.abs(circleX - xPosition - wo2);
double circleDistanceY = Math.abs(circleY - yPosition - ho2);
if (circleDistanceX > (wo2 + radius)) { return 0; }
if (circleDistanceY > (ho2 + radius)) { return 0; }
if(fullIntersect(circleX, circleY, radius)) { return 2; }
if (circleDistanceX <= (wo2)) { return 1; }
if (circleDistanceY <= (ho2)) { return 1; }
double cornerDistance_sq = Math.pow(circleDistanceX - wo2,2) +
Math.pow(circleDistanceY - ho2,2);
return cornerDistance_sq <= (radius*radius) ? 1 : 0;
}
I think your code does not consider these intersections:
I'll delete this answer as soon as you enhance your code/question.