I have this function to reach a certain 1 dimensional value accelerated and damped with overshoot. That is: given an inital value, a velocity and a acceleration (force/mass), the target value is attained by accelerating to it and gets increasingly damped while getting closer to the target value.
This all works fine, howver If i want to know what the TotalAngle is after time 't' I have to run this function say N steps with a 'small' dt to find the 'limit'.
I was wondering If i can (and how) to intergrate over dt so that the TotalAngle can be determined given a time 't' initially.
Regards, Tanks for any help.
dt = delta time step per frame
input = 1
TotalAngle = 0 at t=0
Velocity = 0 at t=0
void FAccelDampedWithOvershoot::Update(float dt, float input, float& Velocity, float& TotalAngle)
{
const float Force = 500000.f;
const float DampForce = 5000.f;
const float MaxAngle = 45.f;
const float InvMass = 1.f / 162400.f;
float target = MaxAngle * input;
float ratio = (target - TotalAngle) / MaxAngle;
float fMove = Force * ratio;
float fDamp = -Velocity * DampForce;
Velocity += (fMove + fDamp) * invMass * dt;
TotalAngle += Velocity * dt;
}
Updated with fixed bugs in math
Originally I've lost mass and MaxAngle a few times. This is why you should first solve it on a paper and then enter to the SO rather than trying to solve it in the text editor.
Anyway, I've fixed the math and now it seems to work reasonably well. I put fixed solution just over previous one.
Well, this looks like a Newtonian mechanics which means differential equations. Let's try to solve them.
SO is not very friendly to math formulas and I'm a bit bored to type characters so here is what I use:
F = Force
Fd = DampForce
MA = MaxAngle
A= TotalAngle
v = Velocity
m = 1 / InvMass
' for derivative i.e. something' is 1-st derivative of something by t and something'' is 2-nd derivative
if I divide you last two lines of code by dt and merge in all the other lines I can get (I also assume that input = 1 as other case is obviously symmetrical)
v' = ([F * (1 - A / MA)] - v * Fd) / m
and applying A' = v we get
m * A'' = F(1 - A/MA) - Fd * A'
or moving to one side we get a simple 2-nd order differential equation
m * A'' + Fd * A' + F/MA * A = F
IIRC, the way to solve it is to first solve characteristic equation which here is
m * x^2 + Fd * x + F/MA = 0
x[1,2] = (-Fd +/- sqrt(Fd^2 - 4*F*m/MA))/ (2*m)
I expect that part under sqrt i.e. (Fd^2 - 4*F*m/MA) is negative thus solution should be of the following form. Let
Dm = Fd/(2*m)
K = sqrt(F/MA/m - Dm^2)
(note the negated value under sqrt so it works now) then
A(t) = e^(-Dm*t) * [P * sin(K*t) + Q * cos(K*t)] + C
where P, Q and C are some constants.
The solution is easier to find as a sum of two solutions: some specific solution for
m * A'' + Fd * A' + F/MA * A = F
and a general solution for homogeneou
m * A'' + Fd * A' + F/MA * A = 0
that makes original conditions fit. Obviously specific solution A(t) = MA works and thus C = MA. So now we need to fit P and Q of general solution to match starting conditions. To find them we need
A(0) = - MA
A'(0) = V(0) = 0
Given that e^0 = 1, sin(0) = 0 and cos(0) = 1 you get something like
Q = -MA
P = 0
or
P = 0
Q = - MA
C = MA
thus
A(t) = MA * [1 - e^(-Dm*t) * cos(K*t)]
where
Dm = Fd/(2*m)
K = sqrt(F/MA/m - Dm^2)
which kind of makes sense given your task.
Note also that this equation assumes that everything happens in radians rather than degrees (i.e. derivative of [sin(t)]' is just cos(t)) so you should transform all your constants accordingly or transform the solution.
const float Force = 500000.f * M_PI / 180;
const float DampForce = 5000.f * M_PI / 180;
const float MaxAngle = M_PI_4;
which on my machine produces
Dm = 0.000268677541
K = 0.261568546
This seems to be similar to original funcion is I step with dt = 0.01f and the main obstacle seems to be precision loss because of float
Hope this helps!
This is not a full answer and I am sure someone else can work it out, but there is no room in the comments and it may help you find a better solution.
The image below shows the velocity (blue) as your function integrates at time steps 1. The red shows the function below that calculates the value for time t
The function F(t)
F(t) = sin((t / f) * pi * 2) * (1 / (((t / f) + a) ^ c)) * b
With f = 23.7, a = 1.4, c = 2, and b= 50 that give the red plot in the image above
All the values are just approximation.
f determines the frequency and is close to a match,
a,b,c control the falloff in amplitude and are a by eye guestimate.
If it does not matter that you have a perfect match then this will work for you. totalAngle uses the same function but t has 0.25 added to it. Unfortunately I did not get any values for a,b,c for totalAngle and I did notice that it was offset so you will have to add the offset value d (I normalised everything so have no idea what the range of totalAngle was)
Function F(t) for totalAngle
F(t) = sin(((t+0.25) / f) * pi * 2) * (1 / ((((t+0.25) / f) + a) ^ c)) * b + d
Sorry only have f = 23.7, c= 2, a~1.4 nothing for b=? d=?
I need to make a simple bandpass audio filter.
Now I've used this simple C++ class: http://www.cardinalpeak.com/blog/a-c-class-to-implement-low-pass-high-pass-and-band-pass-filters
It works well and cut off the desired bands. But when I try to change upper or lower limit with small steps, on some values of limit I hear the wrong result - attenuated or shifted in frequency (not corresponding to current limits) sound.
Function for calculating impulse response:
void Filter::designBPF()
{
int n;
float mm;
for(n = 0; n < m_num_taps; n++){
mm = n - (m_num_taps - 1.0) / 2.0;
if( mm == 0.0 ) m_taps[n] = (m_phi - m_lambda) / M_PI;
else m_taps[n] = ( sin( mm * m_phi ) -
sin( mm * m_lambda ) ) / (mm * M_PI);
}
return;
}
where
m_lambda = M_PI * Fl / (Fs/2);
m_phi = M_PI * Fu / (Fs/2);
Fs - sample rate (44.100)
Fl - lower limit
Fu - upper limit
And simple filtering function:
float Filter::do_sample(float data_sample)
{
int i;
float result;
if( m_error_flag != 0 ) return(0);
for(i = m_num_taps - 1; i >= 1; i--){
m_sr[i] = m_sr[i-1];
}
m_sr[0] = data_sample;
result = 0;
for(i = 0; i < m_num_taps; i++) result += m_sr[i] * m_taps[i];
return result;
}
Do I need to use any window function (Blackman, etc.)? If yes, how do I do this?
I have tried to multiply my impulse response to Blackman window:
m_taps[n] *= 0.42 - 0.5 * cos(2.0 * M_PI * n / double(N - 1)) +
0.08 * cos(4.0 * M_PI * n / double(N - 1));
but the result was wrong.
And do I need to normalize taps?
I found a good free implementation of FIR filter:
http://www.iowahills.com/A7ExampleCodePage.html
...This Windowed FIR Filter C Code has two parts, the first is the
calculation of the impulse response for a rectangular window (low
pass, high pass, band pass, or notch). Then a window (Kaiser, Hanning,
etc) is applied to the impulse response. There are several windows to
choose from...
y[i] = waveform[i] × (0.42659071 – 0.49656062cos(w) + 0.07684867cos(2w))
where w = (2)i/n and n is the number of elements in the waveform
Try this I got the code from:
http://zone.ni.com/reference/en-XX/help/370592P-01/digitizers/blackman_window/
I hope this helps.
My question is not how to filter an image using the laplacian of gaussian (basically using filter2D with the relevant kernel etc.).
What I want to know is how I generate the NxN kernel.
I'll give an example showing how I generated a [Winsize x WinSize] Gaussian kernel in openCV.
In Matlab:
gaussianKernel = fspecial('gaussian', WinSize, sigma);
In openCV:
cv::Mat gaussianKernel = cv::getGaussianKernel(WinSize, sigma, CV_64F);
cv::mulTransposed(gaussianKernel,gaussianKernel,false);
Where sigma and WinSize are predefined.
I want to do the same for a Laplacian of Gaussian.
In Matlab:
LoGKernel = fspecial('log', WinSize, sigma);
How do I get the exact kernel in openCV (exact up to negligible numerical differences)?
I'm working on a specific application where I need the actual kernel values and simply finding another way of implementing LoG filtering by approximating Difference of gaussians is not what I'm after.
Thanks!
You can generate it manually, using formula
LoG(x,y) = (1/(pi*sigma^4)) * (1 - (x^2+y^2)/(sigma^2))* (e ^ (- (x^2 + y^2) / 2sigma^2)
http://homepages.inf.ed.ac.uk/rbf/HIPR2/log.htm
cv::Mat kernel(WinSize,WinSize,CV_64F);
int rows = kernel.rows;
int cols = kernel.cols;
double halfSize = (double) WinSize / 2.0;
for (size_t i=0; i<rows;i++)
for (size_t j=0; j<cols;j++)
{
double x = (double)j - halfSize;
double y = (double)i - halfSize;
kernel.at<double>(j,i) = (1.0 /(M_PI*pow(sigma,4))) * (1 - (x*x+y*y)/(sigma*sigma))* (pow(2.718281828, - (x*x + y*y) / 2*sigma*sigma));
}
If function above is not OK, you can simply rewrite matlab version of fspecial:
case 'log' % Laplacian of Gaussian
% first calculate Gaussian
siz = (p2-1)/2;
std2 = p3^2;
[x,y] = meshgrid(-siz(2):siz(2),-siz(1):siz(1));
arg = -(x.*x + y.*y)/(2*std2);
h = exp(arg);
h(h<eps*max(h(:))) = 0;
sumh = sum(h(:));
if sumh ~= 0,
h = h/sumh;
end;
% now calculate Laplacian
h1 = h.*(x.*x + y.*y - 2*std2)/(std2^2);
h = h1 - sum(h1(:))/prod(p2); % make the filter sum to zero
I want to thank old-ufo for nudging me in the correct direction.
I was hoping I won't have to reinvent the wheel by doing a quick matlab-->openCV conversion but guess this is the best solution I have for a quick solution.
NOTE - I did this for square kernels only (easy to modify otherwise, but I have no need for that so...).
Maybe this can be written in a more elegant form but is a quick job I did so I can carry on with more pressing matters.
From main function:
int WinSize(7); int sigma(1); // can be changed to other odd-sized WinSize and different sigma values
cv::Mat h = fspecialLoG(WinSize,sigma);
And the actual function is:
// return NxN (square kernel) of Laplacian of Gaussian as is returned by Matlab's: fspecial(Winsize,sigma)
cv::Mat fspecialLoG(int WinSize, double sigma){
// I wrote this only for square kernels as I have no need for kernels that aren't square
cv::Mat xx (WinSize,WinSize,CV_64F);
for (int i=0;i<WinSize;i++){
for (int j=0;j<WinSize;j++){
xx.at<double>(j,i) = (i-(WinSize-1)/2)*(i-(WinSize-1)/2);
}
}
cv::Mat yy;
cv::transpose(xx,yy);
cv::Mat arg = -(xx+yy)/(2*pow(sigma,2));
cv::Mat h (WinSize,WinSize,CV_64F);
for (int i=0;i<WinSize;i++){
for (int j=0;j<WinSize;j++){
h.at<double>(j,i) = pow(exp(1),(arg.at<double>(j,i)));
}
}
double minimalVal, maximalVal;
minMaxLoc(h, &minimalVal, &maximalVal);
cv::Mat tempMask = (h>DBL_EPSILON*maximalVal)/255;
tempMask.convertTo(tempMask,h.type());
cv::multiply(tempMask,h,h);
if (cv::sum(h)[0]!=0){h=h/cv::sum(h)[0];}
cv::Mat h1 = (xx+yy-2*(pow(sigma,2))/(pow(sigma,4));
cv::multiply(h,h1,h1);
h = h1 - cv::sum(h1)[0]/(WinSize*WinSize);
return h;
}
There is some difference between your function and the matlab version:
http://br1.einfach.org/tmp/log-matlab-vs-opencv.png.
Above is matlab fspecial('log', 31, 6) and below is the result of your function with the same parameters. Somehow the hat is more 'bent' - is this intended and what is the effect of this in later processing?
I can create a kernel very similar to the matlab one with these functions, which just directly reflect the LoG formula:
float LoG(int x, int y, float sigma) {
float xy = (pow(x, 2) + pow(y, 2)) / (2 * pow(sigma, 2));
return -1.0 / (M_PI * pow(sigma, 4)) * (1.0 - xy) * exp(-xy);
}
static Mat LOGkernel(int size, float sigma) {
Mat kernel(size, size, CV_32F);
int halfsize = size / 2;
for (int x = -halfsize; x <= halfsize; ++x) {
for (int y = -halfsize; y <= halfsize; ++y) {
kernel.at<float>(x+halfsize,y+halfsize) = LoG(x, y, sigma);
}
}
return kernel;
}
Here's a NumPy version that is directly translated from the fspecial function in MATLAB.
import numpy as np
import sys
def get_log_kernel(siz, std):
x = y = np.linspace(-siz, siz, 2*siz+1)
x, y = np.meshgrid(x, y)
arg = -(x**2 + y**2) / (2*std**2)
h = np.exp(arg)
h[h < sys.float_info.epsilon * h.max()] = 0
h = h/h.sum() if h.sum() != 0 else h
h1 = h*(x**2 + y**2 - 2*std**2) / (std**4)
return h1 - h1.mean()
The code below is the exact equivalent to fspecial('log', p2, p3):
def fspecial_log(p2, std):
siz = int((p2-1)/2)
x = y = np.linspace(-siz, siz, 2*siz+1)
x, y = np.meshgrid(x, y)
arg = -(x**2 + y**2) / (2*std**2)
h = np.exp(arg)
h[h < sys.float_info.epsilon * h.max()] = 0
h = h/h.sum() if h.sum() != 0 else h
h1 = h*(x**2 + y**2 - 2*std**2) / (std**4)
return h1 - h1.mean()
I wrote exact Implementation of Matlab fspecial function in OpenCV
function:
Mat C_fspecial_LOG(double* kernel_size,double sigma)
{
double size[2]={ (kernel_size[0]-1)/2 , (kernel_size[1]-1)/2};
double std = sigma;
const double eps = 2.2204e-16;
cv::Mat kernel(kernel_size[0],kernel_size[1],CV_64FC1,0.0);
int row=0,col=0;
for (double y = -size[0]; y <= size[0]; ++y,++row)
{
col=0;
for (double x = -size[1]; x <= size[1]; ++x,++col)
{
kernel.at<double>(row,col)=exp( -( pow(x,2) + pow(y,2) ) /(2*pow(std,2)));
}
}
double MaxValue;
cv::minMaxLoc(kernel,nullptr,&MaxValue,nullptr,nullptr);
Mat condition=~(kernel < eps*MaxValue)/255;
condition.convertTo(condition,CV_64FC1);
kernel = kernel.mul(condition);
cv::Scalar SUM = cv::sum(kernel);
if(SUM[0]!=0)
{
kernel /= SUM[0];
}
return kernel;
}
usage of this function :
double kernel_size[2] = {4,4}; // kernel size set to 4x4
double sigma = 2.1;
Mat kernel = C_fspecial_LOG(kernel_size,sigma);
compare OpenCV result with Matlab:
opencv result:
[0.04918466596701741, 0.06170341496034986, 0.06170341496034986, 0.04918466596701741;
0.06170341496034986, 0.07740850411228289, 0.07740850411228289, 0.06170341496034986;
0.06170341496034986, 0.07740850411228289, 0.07740850411228289, 0.06170341496034986;
0.04918466596701741, 0.06170341496034986, 0.06170341496034986, 0.04918466596701741]
Matlab result for fspecial('gaussian', 4, 2.1) :
0.0492 0.0617 0.0617 0.0492
0.0617 0.0774 0.0774 0.0617
0.0617 0.0774 0.0774 0.0617
0.0492 0.0617 0.0617 0.0492
Just for the sake of reference, here is a Python implementation which creates the LoG filter kernel to detect blobs of a pre-defined radius in pixels.
def create_log_filter_kernel(r_in_px: float):
"""
Creates a LoG filter-kernel to detect blobs of a given radius r_in_px.
\[
LoG(x,y) = \frac{-1}{\pi\sigma^4}\left(1 - \frac{x^2 + y^2}{2\sigma^2}\right)e^{\frac{-(x^2+y^2)}{2\sigma^2}}
\]
Look for maxima if blob is black, minima if blob is white.
:param r_in_px:
:return: filter kernel
"""
# sigma from radius: LoG has zero-crossing at $1 - \frac{x^2 + y^2}{2\sigma^2} = 0$
# i.e. r^2 = 2\sigma^2$ and thus $sigma = r / \sqrt{2}$
sigma = r_in_px/np.sqrt(2)
# ksize such that filter covers $3\sigma$
ksize = int(np.round(sigma*3))*2 + 1
# setup filter
xgv = np.arange(0, ksize) - ksize / 2
ygv = np.arange(0, ksize) - ksize / 2
x, y = np.meshgrid(xgv, ygv)
kernel = -1 / (np.pi * sigma**4) * (1 - (x**2 + y**2) / (2*sigma**2)) * np.exp(-(x**2 + y**2) / (2 * sigma**2))
#normalize to sum zero (does not change zero crossing, I tried it out for r < 100)
kernel -= np.sum(kernel) / ksize**2
#this is important: normalize such that positive/negative parts are comparable over different scales
kernel /= np.sum(kernel[kernel>0])
return kernel
I am working on a home project where I need to closely solve an equation by iteration.
M = E - e* sin(E) or another way (E - e*sin(E))/M = 1.
M is previously solved for and e is given in the data message.
So would you plug in a number for E and check to see how close the value ends up to 1, then continue to adjust the plug in value of E untill the expression is within a set value to 1.00000?
Is there an "ideal" method to solving something like this in software?
The rest of my calculation function is shown as
FP64 is defined as double
bool SV_pos_GAL_L1(int chan, FP64* x, FP64* y, FP64* z) //finds SV ECEF position in orbit at ref GAL system time
{
FP64 smaxis = pow(GALChannel[chan].L1galData.sqrrtA, 2); //semi major axis
FP64 nc = sqrt( MU/(pow(smaxis, 3)) ) + GALChannel[chan].L1galData.delta_n; //n corrected
FP64 Tk = GALChannel[chan].L1galData.TOW - GALChannel[chan].L1galData.Toe; //time since ephemeris
FP64 M = GALChannel[chan].L1galData.M0 + nc * Tk; //mean anomaly
FP64 E;
FP64 v = atan( ((sqrt(1-pow(GALChannel[chan].L1galData.e,2)) * sin(E)) / (1-(GALChannel[chan].L1galData.e*cos(E)))) / ((cos(E)-GALChannel[chan].L1galData.e) / (1-(cos(E)*GALChannel[chan].L1galData.e))) );//true anomaly
FP64 Omega = GALChannel[chan].L1galData.Omega0 + (Tk * (GALChannel[chan].L1galData.dot_Omega - GALChannel[chan].L1galData.w)) - ( GALChannel[chan].L1galData.w * GALChannel[chan].L1galData.Toe); //corrected longitude of ascinding node
FP64 ArgLat = v + Omega; //argument of latitude
FP64 Su = (GALChannel[chan].L1galData.Cus * sin(2*ArgLat)) + ( GALChannel[chan].L1galData.Cuc * cos(2*ArgLat)); //argument of latitude correction
FP64 Sr = (GALChannel[chan].L1galData.Crs * sin(2*ArgLat)) + ( GALChannel[chan].L1galData.Crc * cos(2*ArgLat)); //radius correction
FP64 Si = (GALChannel[chan].L1galData.Cis * sin(2*ArgLat)) + ( GALChannel[chan].L1galData.Cic * cos(2*ArgLat)); //inclination correction
FP64 u = ArgLat + Su; //corrected arg latitude
FP64 r = smaxis * (1 - (GALChannel[chan].L1galData.e * cos(E))) + Sr; //corrected radius
FP64 i = GALChannel[chan].L1galData.i0 + Si + (GALChannel[chan].L1galData.dot_i * Tk); //corrected inclination
FP64 x1 = r * cos(u);
FP64 y1 = r * sin(u);
x = (x1 * cos(Omega)) - (y1 * cos(i) * sin(Omega));
y = (x1 * sin(Omega)) - (y1 * cos(i) * cos(Omega));
z = y1 * sin(i);
return true;
}
I am trying to write a .oct function for Octave that, given a single sine wave value, between -1 and 1, and sine wave period, returns a sine wave vector of period length with the last value in the vector being the given sine wave value. My code so far is:
#include <octave/oct.h>
#include <octave/dColVector.h>
#include <math.h>
#define PI 3.14159265
DEFUN_DLD (sinewave_recreate, args, , "args(0) sinewave value, args(1) is period")
{
octave_value_list retval;
double sinewave_value = args(0).double_value ();
double period = args(1).double_value ();
ColumnVector output_sinewave(period);
double degrees_inc = 360 / period;
double output_sinewave_degrees;
output_sinewave_degrees = asin( sinewave_value ) * 180 / PI;
output_sinewave(period-1) = sin( output_sinewave_degrees * PI / 180 );
for (octave_idx_type ii (1); ii < period; ii++) // Start the loop
{
output_sinewave_degrees = output_sinewave_degrees - degrees_inc;
if ( output_sinewave_degrees < 0 )
{
output_sinewave_degrees += 360 ;
}
output_sinewave( period-1-ii ) = sin( output_sinewave_degrees * PI / 180 );
}
retval(0) = output_sinewave;
return retval;
}
but is giving patchy results. By this I mean that it sometimes recreates the sine wave quite accurately and other times it is way off. I have determined this simply by creating a given sine wave, taking the last value in time and plugging this into the function to recreate the sine wave backwards through time and then comparing plots of the two. Obviously I am doing something wrong, but I can't seem to identify what.
Lets start with some trigonometric identities:
sin(x)^2 + cos(x)^2 == 1
sin(x+y) == sin(x)*cos(y) + sin(y)*cos(x)
cos(x+y) == cos(x)*cos(y) - sin(x)*sin(y)
Given the sine and cosine at a point x, we can exactly calculate the values after a step of size d, after precalculating sd = sin(d) and cd = cos(d):
sin(x+d) = sin(x)*cd + cos(x)*sd
cos(x+d) = cos(x)*cd - sin(x)*sd
Given the initial sine value, you can calculate the initial cosine value:
cos(x) = sqrt(1 - sin(x)^2)
Note that there are two possible solutions, corresponding to the two possible square-root values. Also note that all the angles in these identities are in radians, and d needs to be negative if you're going back through the wave.
Mike's note that there are two possible solutions for cos(x) made me realise that I would need to resolve the phase ambiguity of the sine wave. My second, successful attempt at this function is:
#include <octave/oct.h>
#include <octave/dColVector.h>
#include <math.h>
#define PI 3.14159265
DEFUN_DLD (sinewave_recreate_3, args, , "args(0) sinewave value, args(1) is period, args(2) is the phase")
{
octave_value_list retval;
double sinewave_value = args(0).double_value ();
double period = args(1).double_value ();
double phase = args(2).double_value ();
ColumnVector output_sinewave(period);
double X0 = asin(sinewave_value);
if (sinewave_value < 0 & phase > 180 & phase < 270)
{
X0 = PI + (0 - X0);
}
if (sinewave_value < 0 & phase >= 270)
{
X0 = X0 + 2 * PI;
}
if (sinewave_value > 0 & phase > 90)
{
X0 = PI - X0;
}
if (sinewave_value > 0 & phase < 0)
{
X0 = X0 + PI / 2;
}
double dx = PI / 180 * (360/period);
for (octave_idx_type ii (0); ii < period; ii++) // Start the loop
{
output_sinewave(period-1-ii) = sin(X0 - dx * ii);
}
retval(0) = output_sinewave;
return retval;
}
Thanks are also due to Keynslug.
There is simple formula. Here is the example in Python:
import math
import numpy as np
# We are supposing step is equal to 1degree
T = math.radians(1.0/360.0)
PrevBeforePrevValue = np.sin(math.radians(49.0)) # y(t-2)
PrevValue = np.sin(math.radians(50.0)) # y(t-1)
ValueNowRecursiveFormula = ((2.0*(4.0-T*T))/(4.0+T*T))*PrevValue - PrevBeforePrevValue
print("From RECURSIVE formula - " + str(ValueNowRecursiveFormula))
The details can be found here:
http://howtodoit.com.ua/en/on-the-way-of-developing-recursive-sinewave-generator/
You might try an easier way to go through.
Just recall that if
y = sin(x)
then first derivative of y will be equal to
dy/dx = cos(x)
So at every step of computation you add to the current value of y some delta equal to
dy = cos(x) * dx
But that might cut your accuracy down as a side-effect. You could probe it whatever. HTH.
It seems that slightly improved equation tend to be more accurate:
dy = cos(x + dx/2) * dx
Take a look at this.