scientific computing numerical differentiation

Scientific ComputingNumerical Differentiation

Dr. Guy Tel-Zur

Clouds. Picture by Peter Griffin, publicdomainpictures.net

Some Recent New Articles and Blog Posts

“MY SLICE OF PIZZA” Blog• ” - DIMACS Parallelism 2020: John Gustafson • “Throw out old Numerical Analysis textbooks! Algorithm

designers have historically "measured" algorithm run times by counting the number of floating point operations / additions / multiplications. This made sense decades ago, when floating point arithmetic took 100 times as long as reading a word from memory. Now, one multiplication takes about 1.3 nanoseconds (to go through the entire pipeline; this underestimates throughput), compared to 50-100 nanoseconds for the memory access. Why do our algorithms measure the wrong thing? We should be counting memory accesses; it isn't reasonable to ignore the constant factor of 50.”

• Slides are here

Computing in Science and Engineering

• March/April 2011 – A few papers about Python!

• Python for Scientists and Engineers• Python: An Ecosystem for Scientific Computing • Mayavi: 3D Visualization of Scientific Data• Cython: The Best of Both Worlds• The NumPy Array: A Structure for Efficient

Numerical Computation

Code examples from “Python for Scientists and

Engineers“

from sympy import expf=lambda x: exp(-x**2)integrate(f(x),(x,-inf,inf))

Another example (Guy):Demo: symbolicmath.py

See also: “From Equations to Code: Automated Scientific Computing”By Andy R. Terrel”, Computing in Science and Engineering, March-April 2011

Next Slides from Michael T. Heath – Scientific Computing

• Source: http://www.cse.illinois.edu/heath/scicomp/notes/chap01.pdf

MHJ Chapter 3: Numerical Differentiation

Should be f’c

“2 ”stands for two points

forward/backward 1st derivative: ±h

f(x)=a+bx^2 f(x)=a+bx

f’=2bx f’=b Exact f’

f2’=((a+b(x+h)^2)-(a+bx^2))/h = (a+bx^2+2bxh+bh^2)-(a+bx^2))/h=2bx+bh Bad!!!

f2’=((a+b(x+h))-(a+bx))/h=b

Good!

f2’

((a+b(x+h)^2)-(a+b(x-h)^2))/(2h)=(a+bx^2+2bxh+bh^2-a-bx^2+2bxh-bh^2)/(2h)=4bxh/2h=2bxGood!

(1/2)(f2L’+f2R’)=f2’=(f(x+h)-f(x-h))/(2h)

N-points stencil

Example code

• 2nd derivative of exp(x)• Code in C++, we will learn more of the

language features:– Pointers– Call by Value/Reference– Dynamic memory allocation– Files (I/O)

Call by value vs. call by reference

• printf(“speed= %d\n”, v); // this is a call by value as the value of v won’t be changed by the function (printf) – which is desired

• scanf(“%d\n”,&v); // this is a call by reference, we want to supply the address of v in order to set it’s value(s)

// This program module demonstrates memory allocation and data transfer // in between functions in C++

#include<stdio.h>#include<stdlib.h>

int main(int argc, char *argv[]) { int a; // line 1 int *b; // line 2 a = 10; // line 3 b = new int[10]; // line 4 for(i = 0; i < 10; i++) { b[i] = i; // line 5 } func( a,b); // line 6 return 0;} // End: function main()

void func( int x, int *y) // line 7{ x += 7; // line 8 *y += 10; // line 9 y[6] += 10; // line 10 return; // line 11} // End: function func()

Analyzing an example code

//// This program module// demonstrates memory allocation and data transfer in between functions in C++

#include<stdio.h>#include<stdlib.h>void func( int x, int *y);int main(int argc, char *argv[]) { int a; // line 1 int *b; // line 2 a = 10; // line 3 b = new int[10]; // line 4 for(int i = 0; i < 10; i++) { b[i] = i; // line 5 } func( a,b); // line 6 printf("a=%d\n",a); printf("b[0]=%d\n",b[0]); printf("b[1]=%d\n",b[1]); printf("b[2]=%d\n",b[2]); printf("b[3]=%d\n",b[3]); printf("b[4]=%d\n",b[4]); printf("b[5]=%d\n",b[5]); printf("b[6]=%d\n",b[6]); printf("b[7]=%d\n",b[7]); printf("b[8]=%d\n",b[8]); printf("b[9]=%d\n",b[9]); return 0;} // End: function main()

void func( int x, int *y) // line 7{ x += 7; // line 8 *y += 10; // line 9 y[6] += 10; // line 10 return; // line 11} // End: function func()

תכנית משופרת

Check program: demo1.cppUnder /lectures/02/code

Topics from MHJ Chapter 3

• Program 1: 2nd derivative of exp(x) in C++• Program 2: Working with files• Program 3: Same as program #1 plus working

with files• Program 4: The same in Fortran 90• Error Estimation• Plotting the error using gnuplot

! • A reminder for myself: open DevC++ for the demos!)

• I slightly modified “program1.cpp” from MHJ section 3.2.1 (2009 Fall edition): http://www.fys.uio.no/compphys/cp/programs/FYS3150/chapter03/cpp/program1.cpp

• Install DevC++ on you personal laptop/computer and try it!

Explain program1.cppWorking directory:c:\Users\telzur\Documents\Weizmann\ScientificComputing\SC2011B\Lectures\02\code>

Open DevC++ IDE for the demo

Program description:// Program to compute the second derivative of exp(x). // Three calling functions are included// in this version. In one function we read in the data from screen,// the next function computes the second derivative// while the last function prints out data to screen. // The variable h is the step size. We also fix the total number// of divisions by 2 of h. The total number of steps is read from// screen

Usage: > program1<user input> 0.01 10 100Examine the output:> type out.dat

program2.cpp• The book mentions program2.cpp which is in

cpp and the URL is indeed a cpp code, but the listing below the URL is in C.

• This demonstrates the I/O differences between C and C++

using namespace std;#include <iostream>

int main(int argc, char *argv[]) { FILE *in_file, *out_file; if( argc < 3) { printf("The programs has the following structure :\n"); printf("write in the name of the input and output files \n"); exit(0); } in_file = fopen( argv[1], "r"); // returns pointer to the input file if( in_file == NULL ) { // NULL means that the file is missing printf("Can't find the input file %s\n", argv[1]); exit(0); } out_file = fopen( argv[2], "w"); // returns a pointer to the output file if( out_file == NULL ) { // can't find the file printf("Can't find the output file%s\n", argv[2]); exit(0); } fclose(in_file); fclose(out_file); return 0; }

Working with files in C++

program2.cpp

program3.cpp

• Usage: > program3 outfile_name• All the rest is like in program1.cpp

Now lets check the f90 version

• Open in SciTE program1.f90• In the image below: compilation and execution demo:

MHJ section3.2.2 Error analysis

Content of exp(10)’’ computation

• See MHJ section 3.2.2 and Fig. 3.2 (Fall 2009 Edition)• Text output with 4 columns:

h, computed_derivative, log(h),ε >program1 Input: 0.1 10. 10>more out.dat1.00000E-001 2.72055E+000 -1.00000E+000 -3.07904E+0005.00000E-002 2.71885E+000 -1.30103E+000 -3.68121E+0002.50000E-002 2.71842E+000 -1.60206E+000 -4.28329E+0001.25000E-002 2.71832E+000 -1.90309E+000 -4.88536E+0006.25000E-003 2.71829E+000 -2.20412E+000 -5.48742E+0003.12500E-003 2.71828E+000 -2.50515E+000 -6.08948E+0001.56250E-003 2.71828E+000 -2.80618E+000 -6.69162E+0007.81250E-004 2.71828E+000 -3.10721E+000 -7.29433E+0003.90625E-004 2.71828E+000 -3.40824E+000 -7.89329E+0001.95313E-004 2.71828E+000 -3.70927E+000 -8.44284E+000

Download and install Gnuplothttp://www.gnuplot.info/

Visualization: Gnuplot

• Reconstruct result from MHJ - Figure 3.2 using gnuplot

• Gnuplot is included in Python(x,y) package!• Gnuplot tutorial:

http://www.duke.edu/~hpgavin/gnuplot.html• Example:

http://www.physics.ohio-state.edu/~ntg/780/handouts/gnuplot_quadeq_example.pdf

• 3D Examples: http://www.physics.ohio-state.edu/~ntg/780/handouts/gnuplot_3d_example_v2.pdf

Using gnuplot

Can we explain this behavior?

Computed for x=10

Error Analysisεro = Round-Off error

The approximation error:

Recall Eq. 3.4:

The leading term in the error (j=1) is therefore:

The Round-Off Error (εro )

• εro depends on the precision level of the chosen variables (single or double precision)

Single precision

Double precisionIf the terms are

very close to each other, the

difference is at the level of the round off error

hmin = 10-4 is therefore the step size that gives the minimal error in our case.If h>hmin the round-off error term will dominate

Summary

• Next 3 slides are from: Michael T. Heath Scientific Computing

• http://www.cse.illinois.edu/heath/scicomp/notes/chap08.pdf

Let’s upgrade our visualization skills!

• Mayavi• Included in the Python(x,y) package• 2D/3D• User guide:

http://code.enthought.com/projects/mayavi/docs/development/html/mayavi/index.html

http://code.enthought.com/projects/mayavi/

Mayavi

Mayavi is a general purpose, open source 3D scientific visualization package that is tightly integrated with the rich ecosystem of Python scientific packages. Mayavi provides a continuum of tools for developing scientific applications, ranging from interactive and script-based data visualization in Python to full-blown custom end-user applications.

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Mayavi demos

from enthought.mayavi import mlabfrom numpy import ogridx, y, z = ogrid[-10:10:100j, -10:10:100j,-10:10:100j]ctr = mlab.contour3d(0.5*x**2 + y**2 + 2*z**2)mlab.show()

cont.py

To execute:run cont.py

import numpy from enthought.mayavi.mlab import * x, y, z = numpy.mgrid[-2:3, -2:3, -2:3] r = numpy.sqrt(x**2 + y**2 + z**4) u = y*numpy.sin(r)/(r+0.001) v = -x*numpy.sin(r)/(r+0.001) w = numpy.zeros_like(z) quiver3d(x, y, z, u, v, w, line_width=3, scale_factor=1)

vector.py

import numpyfrom enthought.mayavi.mlab import *

"""Test surf on regularly spaced co-ordinates like MayaVi."""def f(x, y): sin, cos = numpy.sin, numpy.cos return sin(x+y) + sin(2*x - y) + cos(3*x+4*y)

x, y = numpy.mgrid[-7.:7.05:0.1, -5.:5.05:0.05]surf(x, y, f)

surface.py

• http://code.enthought.com/projects/mayavi/docs/development/html/mayavi/mlab.html#id5

# Create the data.from numpy import pi, sin, cos, mgriddphi, dtheta = pi/250.0, pi/250.0[phi,theta] = mgrid[0:pi+dphi*1.5:dphi,0:2*pi+dtheta*1.5:dtheta]m0 = 4; m1 = 3; m2 = 2; m3 = 3; m4 = 6; m5 = 2; m6 = 6; m7 = 4;r = sin(m0*phi)**m1 + cos(m2*phi)**m3 + sin(m4*theta)**m5 + cos(m6*theta)**m7x = r*sin(phi)*cos(theta)y = r*cos(phi)z = r*sin(phi)*sin(theta)

# View it.from enthought.mayavi import mlabs = mlab.mesh(x, y, z)mlab.show()

nice_mesh.py

Mayavi environment

Move the object and zoom with the mouse

In one of the next lectures we will also learn visualization with VisIt