Simple Recipes in Python

William Park

March 1999

1    Introduction

This article describes few elementary numerical routines that I wrote in Python. They are loosely modelled after Numerical Recipes in C [1] because I needed, at the time, actual source codes which I can examine instead of just wrappers around Fortran libraries like NumPy [2] and Octave [3]. As evident from the documentations, the routines were written with emphasis on clarity rather than on runtime efficiency.

I use TeX notation whenever HTML markup becomes too complicated or impossible to do; so, this article should be readable in both graphical and text browsers. Also, I use my own HTMLtag package to generate LaTeX-like document structures, such as title, section numbers, footnotes, table of contents, and other HTML tags. To get an overview of this article, see Table of Contents at the bottom.

Since all the source codes are included, you can save this to a file, remove HTML, and use it as your own module --- it's called emath.py on my machine. In the Usage section, int denotes integer; real denotes integer or float, especially when there is possibility of complex; number denotes any integer, float, or complex.

2    Elementary Funtions

2.1    sinc()

Summary
Sinc function
Usage
real = sinc(real)
""" sinc(x) = sin(\pi x) / (\pi x), if x != 0 = 1, if x = 0 """ def sinc(x): from math import pi, sin try: x = pi * x return sin(x) / x except ZeroDivisionError: # sinc(0) = 1 return 1.0

The sinc() function usually comes up when doing Fourier Transform, since sinc(f) is the fourier transform of rect(t) = {1, if |t| < 1/2; 0, if |t| > 1/2}.

2.2    cbrt()

Summary
Cubic root
Usage
real = cbrt(real)
""" cbrt(x) = x^{1/3}, if x >= 0 = -|x|^{1/3}, if x < 0 """ def cbrt(x): from math import pow if x >= 0: return pow(x, 1.0/3.0) else: return -pow(abs(x), 1.0/3.0)

GNU C libm library has cubic root, hyperbolic, gamma, bessel, expontial, and other higher functions, but standard Python math module does not include them. So, I implement cbrt() using pow() which is defined as pow(x, n) = x^n for only x >= 0.

2.3    rect(), polar()

Summary
Conversion between rectangular and polar coordinates
Usage
real, real = rect(real, real [, deg=1])
real, real = polar(real, real [, deg=1])
""" Convert from polar (r,w) to rectangular (x,y) x = r cos(w) y = r sin(w) """ def rect(r, w, deg=0): # radian if deg=0; degree if deg=1 from math import cos, sin, pi if deg: w = pi * w / 180.0 return r * cos(w), r * sin(w) """ Convert from rectangular (x,y) to polar (r,w) r = sqrt(x^2 + y^2) w = arctan(y/x) = [-\pi,\pi] = [-180,180] """ def polar(x, y, deg=0): # radian if deg=0; degree if deg=1 from math import hypot, atan2, pi if deg: return hypot(x, y), 180.0 * atan2(y, x) / pi else: return hypot(x, y), atan2(y, x)

Normally, rect() and polar() uses radian for angle; but, if deg=1 specified (any non-zero will do), degree is used instead.

3    Polynomials

3.1    polyeval()

Summary
Evaluate n'th degree polynomial
Usage
number = polyeval(list, number)
""" p(x) = polyeval(a, x) = a[0] + a[1]x + a[2]x^2 +...+ a[n-1]x^{n-1} + a[n]x^n = a[0] + x(a[1] + x(a[2] +...+ x(a[n-1] + a[n]x)...) """ def polyeval(a, x): p = 0 a.reverse() for coef in a: p = p * x + coef a.reverse() return p

Evaluating n'th degree polynomial is simple loop, starting with highest coefficient a[n]. I wrote this because the polynomial module supplied with previous Python releases was brain-dead.

3.2    polyderiv()

Summary
Find the first derivative of polynomial
Usage
list = polyderiv(list)
""" p'(x) = polyderiv(a) = b[0] + b[1]x + b[2]x^2 +...+ b[n-2]x^{n-2} + b[n-1]x^{n-1} where b[i] = (i+1)a[i+1] """ def polyderiv(a): b = [] for i in range(1, len(a)): b.append(i * a[i]) return b

3.3    polyreduce()

Summary
Factor out a root from n'th degree polynomial, and return the remaining (n-1)'th degree polynomial.
Usage
list = polyreduce(list, number)
""" Given x = r is a root of n'th degree polynomial p(x) = (x-r)q(x), divide p(x) by linear factor (x-r) using the same algorithm as polynomial evaluation. Then, return the (n-1)'th degree quotient q(x) = polyreduce(a, r) = c[0] + c[1]x + c[2]x^2 +...+ c[n-2]x^{n-2} + c[n-1]x^{n-1} """ def polyreduce(a, root): c, p = [], 0 a.reverse() for coef in a: p = p * root + coef c.append(p) a.reverse() c.reverse() return c[1:]

3.4    quadratic()

Summary
Solve quadratic equation with real coefficients
Usage
number, number = quadratic(real, real [, real])
""" x^2 + ax + b = 0 (or ax^2 + bx + c = 0) By substituting x = y-t and t = a/2, the equation reduces to y^2 + (b-t^2) = 0 which has easy solution y = +/- sqrt(t^2-b) """ def quadratic(a, b, c=None): import math, cmath if c: # (ax^2 + bx + c = 0) a, b = b / float(a), c / float(a) t = a / 2.0 r = t**2 - b if r >= 0: # real roots y1 = math.sqrt(r) else: # complex roots y1 = cmath.sqrt(r) y2 = -y1 return y1 - t, y2 - t

Normally, x^2 + ax + b = 0 is assumed with the 2 coefficients as arguments; but, if 3 arguments are present, then ax^2 + bx + c = 0 is assumed.

3.5    cubic()

Summary
Solve cubic equation with real coefficients
Usage
real, number, number = cubic(real, real, real [, real])
""" x^3 + ax^2 + bx + c = 0 (or ax^3 + bx^2 + cx + d = 0) With substitution x = y-t and t = a/3, the cubic equation reduces to y^3 + py + q = 0, where p = b-3t^2 and q = c-bt+2t^3. Then, one real root y1 = u+v can be determined by solving w^2 + qw - (p/3)^3 = 0 where w = u^3, v^3. From Vieta's theorem, y1 + y2 + y3 = 0 y1 y2 + y1 y3 + y2 y3 = p y1 y2 y3 = -q, the other two (real or complex) roots can be obtained by solving y^2 + (y1)y + (p+y1^2) = 0 """ def cubic(a, b, c, d=None): from math import cos if d: # (ax^3 + bx^2 + cx + d = 0) a, b, c = b / float(a), c / float(a), d / float(a) t = a / 3.0 p, q = b - 3 * t**2, c - b * t + 2 * t**3 u, v = quadratic(q, -(p/3.0)**3) if type(u) == type(0j): # complex cubic root r, w = polar(u.real, u.imag) y1 = 2 * cbrt(r) * cos(w / 3.0) else: # real root y1 = cbrt(u) + cbrt(v) y2, y3 = quadratic(y1, p + y1**2) return y1 - t, y2 - t, y3 - t

Normally, x^3 + ax^2 + bx + c = 0 is assumed with the 3 coefficients as arguments; but, if 4 arguments are present, then ax^3 + bx^2 + cx + d = 0 is assumed.

Even though both quadratic() and cubic() functions take real arguments, they can be modified to accept any real or complex coefficients because the method of solution does not make any assumptions. [4]

4    Zeros of Functions

4.1    newton()

Summary
Solve for a zero of function using Newton-Raphson method
Usage
real = func(real)
real = funcd(real)
real = newton(func, funcd, real [, TOL=real])
""" Ubiquitous Newton-Raphson algorithm for solving f(x) = 0 where a root is repeatedly estimated by x = x - f(x)/f'(x) until |dx|/(1+|x|) < TOL is achieved. This termination condition is a compromise between |dx| < TOL, if x is small |dx|/|x| < TOL, if x is large """ def newton(func, funcd, x, TOL=1e-6): # f(x)=func(x), f'(x)=funcd(x) f, fd = func(x), funcd(x) count = 0 while 1: dx = f / float(fd) if abs(dx) < TOL * (1 + abs(x)): return x - dx x = x - dx f, fd = func(x), funcd(x) count = count + 1 print "newton(%d): x=%s, f(x)=%s" % (count, x, f)

Even though it converges quadratically once a root has been "sighted", it does not guarantee global convergence. So, I use print statement to see intermediate results.

4.2    secant()

Summary
Solve for a zero of function using Secant method
Usage
real = func(real)
real = secant(func, real, real [, TOL=real])
""" Similar to Newton's method, but the derivative is estimated by divided difference using only function calls. A root is estimated by x = x - f(x) (x - oldx)/(f(x) - f(oldx)) where oldx = x[i-1] and x = x[i]. """ def secant(func, oldx, x, TOL=1e-6): # f(x)=func(x) oldf, f = func(oldx), func(x) if (abs(f) > abs(oldf)): # swap so that f(x) is closer to 0 oldx, x = x, oldx oldf, f = f, oldf count = 0 while 1: dx = f * (x - oldx) / float(f - oldf) if abs(dx) < TOL * (1 + abs(x)): return x - dx oldx, x = x, x - dx oldf, f = f, func(x) count = count + 1 print "secant(%d): x=%s, f(x)=%s" % (count, x, f)

This routine is used when f'() is difficult or impossible to evaluate. It requires 2 initial points to start the iteration, and the order of convergence is 1.6, which is better than linear method like bisection but not as fast as quadratic method like Newton.

5    Integration

5.1    closedpoints()

Summary
Closed integration of function using extended Simpson's rule
Usage
real = func(real)
real = closedpoints(func, real, real [, TOL=real])
""" Closed Simpson's rule for \int_a^b f(x) dx Divide [a,b] iteratively into h, h/2, h/4, h/8, ... step sizes; and, for each step size, evaluate f(x) at a+h, a+3h, a+5h, a+7h, ..., b-3h, b-h, noting that other points have already been sampled. At each iteration step, data are sampled only where necessary so that the total data is represented by adding sampled points from all previous steps: step 1: h a---------------b step 2: h/2 a-------^-------b step 3: h/4 a---^-------^---b step 4: h/8 a-^---^---^---^-b total: a-^-^-^-^-^-^-^-b So, for step size of h/n, there are n intervals, and the data are sampled at the boundaries including the 2 end points. If old = Trapezoid formula for an old step size 2h, then Trapezoid formula for the new step size h is obtained by new = old/2 + h{f(a+h) + f(a+3h) + f(a+5h) + f(a+7h) +...+ f(b-3h) + f(b-h)} Also, Simpson formula for the new step size h is given by simpson = (4 new - old)/3 """ def closedpoints(func, a, b, TOL=1e-6): # f(x)=func(x) h = b - a old2 = old = h * (func(a) + func(b)) / 2.0 count = 0 while 1: h = h / 2.0 x, sum = a + h, 0 while x < b: sum = sum + func(x) x = x + 2 * h new = old / 2.0 + h * sum new2 = (4 * new - old) / 3.0 if abs(new2 - old2) < TOL * (1 + abs(old2)): return new2 old = new # Trapezoid old2 = new2 # Simpson count = count + 1 print 'closedpoints(%d): Trapezoid=%s, Simpson=%s' % (count, new, new2)

5.2    openpoints()

Summary
Open integration of function using extended Simpson's rule
Usage
real = func(real)
real = openpoints(func, real, real [, TOL=real])
""" Open Simpson's rule (excluding end points) for \int_a^b f(x) dx Divide [a,b] iteratively into h, h/3, h/9, h/27, ... step sizes; and, for each step size, evaluate f(x) at a+h/2, a+2h+h/2, a+3h+h/2, a+5h+h/2, a+6h+h/2, ... , b-3h-h/2, b-2h-h/2, b-h/2, noting that other points have already been sampled. At each iteration step, data are sampled only where necessary so that the total data is represented by adding sampled points from all previous steps: step 1: h a-----------------^-----------------b step 2: h/3 a-----^-----------------------^-----b step 3: h/9 a-^-------^---^-------^---^-------^-b total: a-^---^---^---^---^---^---^---^---^-b So, for step size of h/n, there are n intervals, and the data are sampled at the midpoints. If old = Trapezoid formula for an old step size 3h, then Trapezoid formula for the new step size h is obtained by new = old/3 + h{f(a+h/2) + f(a+2h+h/2) + f(a+3h+h/2) + f(a+5h+h/2) + f(a+6h+h/2) +...+ f(b-3h-h/2) + f(b-2h-h/2) + f(b-h/2)} Also, Simpson formula for the new step size h is given by simpson = (9 new - old)/8 """ def openpoints(func, a, b, TOL=1e-6): # f(x)=func(x) h = b - a old2 = old = h * func((a + b) / 2.0) count = 0 while 1: h = h / 3.0 x, sum = a + 0.5 * h, 0 while x < b: sum = sum + func(x) + func(x + 2 * h) x = x + 3 * h new = old / 3.0 + h * sum new2 = (9 * new - old) / 8.0 if abs(new2 - old2) < TOL * (1 + abs(old2)): return new2 old = new # Trapezoid old2 = new2 # Simpson count = count + 1 print 'openpoints(%d): Trapezoid=%s, Simpson=%s' % (count, new, new2)

Both closedpoints() and openpoints() routines use Trapezoid formula to calculate Simpson's formula, as opposed to more direct summation. And, because the discrete integration is over uniformly sampled points, it can take advantage of already sampled data taken at previous iterations.

6    FFT

6.1    nextpow2()

Summary
Find 2^n that is equal to or greater than
Usage
int = nextpow2(int)
""" Find 2^n that is equal to or greater than. """ def nextpow2(i): n = 2 while n < i: n = n * 2 return n

This is internal function used by fft(), because the FFT routine requires that the data size be a power of 2.

6.2    bitrev()

Summary
Return bit-reversed array for FFT
Usage
list = bitrev(list)
""" Return bit-reversed list, whose length is assumed to be 2^n: eg. 0111 <--> 1110 for N=2^4. """ def bitrev(x): N, x = len(x), x[:] if N != nextpow2(N): raise ValueError, 'N is not power of 2' for i in range(N): k, b, a = 0, N>>1, 1 while b >= a: if b & i: k = k | a if a & i: k = k | b b, a = b>>1, a<<1 if i < k: # important not to swap back x[i], x[k] = x[k], x[i] return x

6.3    fft(), ifft()

Summary
FFT and inverse FFT for 2^n data size [5]
Usage
list = fft(list [, sign=1])
list = ifft(list)
""" FFT using Cooley-Tukey algorithm where N = 2^n. The choice of e^{-j2\pi/N} or e^{j2\pi/N} is made by 'sign=-1' or 'sign=1' respectively. Since I prefer Engineering convention, I chose 'sign=-1' as the default. FFT is performed as follows: 1. bit-reverse the array. 2. partition the data into group of m = 2, 4, 8, ..., N data points. 3. for each group with m data points, 1. divide into upper half (section A) and lower half (section B), each containing m/2 data points. 2. divide unit circle by m. 3. apply "butterfly" operation |a| = |1 w||a| or a, b = a+w*b, a-w*b |b| |1 -w||b| where a and b are data points of section A and B starting from the top of each section, and w is data points along the unit circle starting from z = 1+0j. FFT ends after applying "butterfly" operation on the entire data array as whole, when m = N. """ def fft(x, sign=-1): from cmath import pi, exp N, W = len(x), [] for i in range(N): # exp(-j...) is default W.append(exp(sign * 2j * pi * i / N)) x = bitrev(x) m = 2 while m <= N: for s in range(0, N, m): for i in range(m/2): n = i * N / m a, b = s + i, s + i + m/2 x[a], x[b] = x[a] + W[n % N] * x[b], x[a] - W[n % N] * x[b] m = m * 2 return x """ Inverse FFT with normalization by N, so that x == ifft(fft(x)) within round-off errors. """ def ifft(X): N, x = len(X), fft(X, sign=1) # e^{j2\pi/N} for i in range(N): x[i] = x[i] / float(N) return x

I calculate N points along the unit circle at the beginning instead of calculating as needed; so, this should be more efficient than the FFT routine given in Numerical Recipes in C, but not by much.

6.4    dft(), idft()

Summary
DFT and inverse DFT using direct summation
Usage
list = dft(list [, sign=1])
list = idft(list)
""" DFT using discrete summation X(n) = \sum_k W^{nk} x(k), W = e^{-j2\pi/N} where N need not be power of 2. The choice of e^{-j2\pi/N} or e^{j2\pi/N} is made by "sign=-1" or "sign=1" respectively. """ def dft(x, sign=-1): from cmath import pi, exp N, W = len(x), [] for i in range(N): # exp(-j...) is default W.append(exp(sign * 2j * pi * i / N)) X = [] for n in range(N): sum = 0 for k in range(N): sum = sum + W[n * k % N] * x[k] X.append(sum) return X """ Inverse DFT with normalization by N, so that x == idft(dft(x)) within round-off errors. """ def idft(X): N, x = len(X), dft(X, sign=1) # e^{j2\pi/N} for i in range(N): x[i] = x[i] / float(N) return x

I wrote these functions to test FFT routines. DFT should only be used for small data size (less than 64).

7    Convolution and Correlation

7.1    conv(), corr()

Summary
Discrete convolution and correlation [5]
Usage
list = conv(list, list)
list = corr(list, list)
""" Convolution of 2 casual signals, x(t<0) = y(t<0) = 0, using discrete summation. x*y(t) = \int_{u=0}^t x(u) y(t-u) du = y*x(t) where the size of x[], y[], x*y[] are P, Q, N=P+Q-1 respectively. """ def conv(x, y): P, Q, N = len(x), len(y), len(x)+len(y)-1 z = [] for k in range(N): t, lower, upper = 0, max(0, k-(Q-1)), min(P-1, k) for i in range(lower, upper+1): t = t + x[i] * y[k-i] z.append(t) return z """ Correlation of 2 casual signals, x(t<0) = y(t<0) = 0, using discrete summation. Rxy(t) = \int_{u=0}^{\infty} x(u) y(t+u) du = Ryx(-t) where the size of x[], y[], Rxy[] are P, Q, N=P+Q-1 respectively. The Rxy[i] data is not shifted, so relationship with the continuous Rxy(t) is preserved. For example, Rxy(0) = Rxy[0], Rxy(t) = Rxy[i], and Rxy(-t) = Rxy[-i]. The data are ordered as follows: t: -(P-1), -(P-2), ..., -3, -2, -1, 0, 1, 2, 3, ..., Q-2, Q-1 i: N-(P-1), N-(P-2), ..., N-3, N-2, N-1, 0, 1, 2, 3, ..., Q-2, Q-1 """ def corr(x, y): P, Q, N = len(x), len(y), len(x)+len(y)-1 z1=[] for k in range(Q): t, lower, upper = 0, 0, min(P-1, Q-1-k) for i in range(lower, upper+1): t = t + x[i] * y[i+k] z1.append(t) # 0, 1, 2, ..., Q-1 z2=[] for k in range(1,P): t, lower, upper = 0, k, min(P-1, Q-1+k) for i in range(lower, upper+1): t = t + x[i] * y[i-k] z2.append(t) # N-1, N-2, ..., N-(P-2), N-(P-1) z2.reverse() return z1 + z2

7.2    fftconv(), fftcorr()

Summary
FFT convolution and correlation [5]
Usage
list = fftconv(list, list)
list = fftcorr(list, list)
""" FFT convolution using relation x*y <==> XY where x[] and y[] have been zero-padded to length N, such that N >= P+Q-1 and N = 2^n. """ def fftconv(x, y): N, X, Y, x_y = len(x), fft(x), fft(y), [] for i in range(N): x_y.append(X[i] * Y[i]) return ifft(x_y) """ FFT correlation using relation Rxy <==> X'Y where x[] and y[] have been zero-padded to length N, such that N >= P+Q-1 and N = 2^n. """ def fftcorr(x, y): N, X, Y, Rxy = len(x), fft(x), fft(y), [] for i in range(N): Rxy.append(X[i].conjugate() * Y[i]) return ifft(Rxy)

Due to the nature of FFT routines, fftconv() and fftcorr() assume that input data have been zero-padded to a length that is big enough to hold the output size P+Q-1. However, conv() and corr() can accept any length, because they use direction summation.

8    Vector Operations

8.1    vdot()

Summary
Vector dot
Usage
real = vdot(list, list)
""" vdot(x, y) = <x|y> = \sum_i x_i y_i """ def vdot(x, y): if len(x) != len(y): raise ValueError, 'unequal length' sum = 0 for a, b in map(None, x, y): sum = sum + a * b return sum

8.2    vnorm()

Summary
Vector norms
Usage
real = vnorm(list, normtype=1)
real = vnorm(list)
real = vnorm(list, normtype=3)
""" Various vector norms of x[]: ||x||1 = \sum_i |x_i| ||x||2 = sqrt(\sum_i x_i^2) = sqrt(<x|x>) ||x||oo = \max |x_i| """ def vnorm(x, normtype=2): from math import sqrt if normtype == 1: # ||x||1 sum = 0 for a in x: sum = sum + abs(a) return sum elif normtype == 2: # ||x||2 is default return sqrt(vdot(x, x)) elif normtype == 'oo': # ||x||oo return max( abs(min(x)), abs(max(x)) ) else: raise ValueError, 'unknown option'

8.3    meanstdv()

Summary
Mean and standard deviation of data
Usage
real, real = meanstdv(list)
""" Calculate mean and standard deviation of data x[]: mean = {\sum_i x_i \over n} std = sqrt(\sum_i (x_i - mean)^2 \over n-1) """ def meanstdv(x): from math import sqrt n, mean, std = len(x), 0, 0 for a in x: mean = mean + a mean = mean / float(n) for a in x: std = std + (a - mean)**2 std = sqrt(std / float(n-1)) return mean, std

8.4    getv(), getv2()

Summary
Read vector from stdin or file
Usage
list = getv([string])
list, list = getv2([string])
""" Read 1 number/line using eval() from <stdin> or 'file' if specified. If the first non-whitespace is not valid number characters '(+-.0123456789', then the line will be skipped. """ def getv(s=''): import sys, string if s == '': f = sys.stdin else: f = open(s, 'r') x = [] for a in f.readlines(): a = string.strip(a) if a != '' and a[0] in '(+-.0123456789': x.append(eval(a)) return x """ Read 2 numbers/line using eval() from <stdin> or 'file' if specified. If the first non-whitespace is not valid number characters '(+-.0123456789', then the line will be skipped. """ def getv2(s=''): import sys, string if s == '': f = sys.stdin else: f = open(s, 'r') x, y = [], [] for a in f.readlines(): a = string.strip(a) if a != '' and a[0] in '(+-.0123456789': b = string.split(a) x.append(eval(b[0])) y.append(eval(b[1])) return x, y

8.5    printv()

Summary
Print vector to stdout or file
Usage
printv([string])
""" Write 1 number/line to <stdout> or 'file' if specified. """ def printv(x, s=''): import sys out = '' for a in x: out = out + `a` + '\n' if s == '': sys.stdout.write(out) else: open(s, 'w').write(out)

8.6    linreg()

Summary
Linear regression of y = ax + b
Usage
real, real = linreg(list, list)
""" Returns coefficients to the regression line "y=ax+b" from x[] and y[]. Basically, it solves Sxx a + Sx b = Sxy Sx a + N b = Sy where Sxy = \sum_i x_i y_i, Sx = \sum_i x_i, and Sy = \sum_i y_i. The solution is a = (Sxy N - Sy Sx)/det b = (Sxx Sy - Sx Sxy)/det where det = Sxx N - Sx^2. In addition, Var|a| = s^2 |Sxx Sx|^-1 = s^2 | N -Sx| / det |b| |Sx N | |-Sx Sxx| s^2 = {\sum_i (y_i - \hat{y_i})^2 \over N-2} = {\sum_i (y_i - ax_i - b)^2 \over N-2} = residual / (N-2) R^2 = 1 - {\sum_i (y_i - \hat{y_i})^2 \over \sum_i (y_i - \mean{y})^2} = 1 - residual/meanerror It also prints to <stdout> few other data, N, a, b, R^2, s^2, which are useful in assessing the confidence of estimation. """ def linreg(X, Y): from math import sqrt if len(X) != len(Y): raise ValueError, 'unequal length' N = len(X) Sx = Sy = Sxx = Syy = Sxy = 0.0 for x, y in map(None, X, Y): Sx = Sx + x Sy = Sy + y Sxx = Sxx + x*x Syy = Syy + y*y Sxy = Sxy + x*y det = Sxx * N - Sx * Sx a, b = (Sxy * N - Sy * Sx)/det, (Sxx * Sy - Sx * Sxy)/det meanerror = residual = 0.0 for x, y in map(None, X, Y): meanerror = meanerror + (y - Sy/N)**2 residual = residual + (y - a * x - b)**2 RR = 1 - residual/meanerror ss = residual / (N-2) Var_a, Var_b = ss * N / det, ss * Sxx / det print "y=ax+b" print "N= %d" % N print "a= %g \\pm t_{%d;\\alpha/2} %g" % (a, N-2, sqrt(Var_a)) print "b= %g \\pm t_{%d;\\alpha/2} %g" % (b, N-2, sqrt(Var_b)) print "R^2= %g" % RR print "s^2= %g" % ss return a, b

Only the coefficients of regression line are returned, since they are usually what I want. Other informations are sent to stdout to be read later.

9    Footnotes

[1]
W H Press, B P Flannery, S A Teukolsky, W T Vetterling, Numerical Recipes in C: The Art Of Scientific Computing, 1988. http://www.nr.com/
[2]
NumPy is numerical array extension to Python. ftp://ftp-icf.llnl.gov/pub/python/
[3]
Octave is GNU clone of Matlab. http://www.che.wisc.edu/octave/
[4]
W Gellert, H Kustner, M Hellwich, H Kastner, The VNR Concise Encyclopedia of Mathematics, 1975, sections 4.5-4.6
[5]
E O Brigham, The Fast Fourier Transform, 1974

10    Table of Contents


This file was generated using HTMLtag package.
William Park opengeometry@yahoo.ca
March 1999