def __pow__(self, other):
shape = self.array.shape
if len(shape) != 2 or shape[0] != shape[1]:
raise TypeError, "matrix is not square"
if type(other) in (type(1), type(1L)):
if other == 0:
return Matrix(identity(shape[0]))
if other < 0:
result = Matrix(LinearAlgebra.inverse(self.array))
x = Matrix(result)
other = -other
else:
result = self
x = result
if other <= 3:
while (other > 1):
result = result * x
other = other - 1
return result
# binary decomposition to reduce the number of Matrix
# Multiplies for other > 3.
beta = _binary(other)
t = len(beta)
Z, q = x.copy(), 0
while beta[t - q - 1] == '0':
Z *= Z
q += 1
result = Z.copy()
for k in range(q + 1, t):
Z *= Z
if beta[t - k - 1] == '1':
result *= Z
return result
def __pow__(self, other):
shape = self.array.shape
if len(shape)!=2 or shape[0]!=shape[1]:
raise TypeError, "matrix is not square"
if type(other) in (type(1), type(1L)):
if other==0:
return Matrix(identity(shape[0]))
if other<0:
result=Matrix(LinearAlgebra.inverse(self.array))
x=Matrix(result)
other=-other
else:
result=self
x=result
if other <= 3:
while(other>1):
result=result*x
other=other-1
return result
# binary decomposition to reduce the number of Matrix
# Multiplies for other > 3.
beta = _binary(other)
t = len(beta)
Z,q = x.copy(),0
while beta[t-q-1] == '0':
Z *= Z
q += 1
result = Z.copy()
for k in range(q+1,t):
Z *= Z
if beta[t-k-1] == '1':
result *= Z
return result
else:
raise TypeError, "exponent must be an integer"
def eigenvector_for_largest_eigenvalue(matrix):
"""Returns eigenvector corresponding to largest eigenvalue of matrix.
Implements a numerical method for finding an eigenvector by repeated
application of the matrix to a starting vector. For a matrix A the
process w(k) <-- A*w(k-1) converges to eigenvector w with the largest
eigenvalue. Because distance matrix D has all entries >= 0, a theorem
due to Perron and Frobenius on nonnegative matrices guarantees good
behavior of this method, excepting degenerate cases where the
iteration oscillates. For distance matrices a remedy is to add the
identity matrix to A, permitting the iteration to converge to the
eigenvector. (From Sander and Schneider (1991), and Vingron and
Sibbald (1993))
Note: Only works on square matrices.
"""
#always add the identity matrix to avoid oscillating behavior
matrix = matrix + identity(len(matrix))
#v is a random vector (chosen as the normalized vector of ones)
v = ones(len(matrix))/len(matrix)
#iterate until convergence
for i in range(1000):
new_v = matrixmultiply(matrix,v)
new_v = new_v/sum(new_v) #normalize
if sum(map(abs,new_v-v)) > 1e-9:
v = new_v #not converged yet
continue
else: #converged
break
return new_v
def rotate(self, angle): m = identity(3, typecode = Float) s = sin(angle) c = cos(angle) m[0, 0] = c m[0, 1] = -s m[1, 0] = s m[1, 1] = c self.matrix = matrixmultiply(self.matrix, m)
def rotate(self, angle):
m = identity(3, typecode=Float)
s = sin(angle)
c = cos(angle)
m[0, 0] = c
m[0, 1] = -s
m[1, 0] = s
m[1, 1] = c
self.matrix = matrixmultiply(self.matrix, m)
def optimize(self):
"""Optimize the objective function with respect to varying params."""
p, d = [], []
for param in self.varyingParams:
p.append(param.getValue())
d.append(param.delta)
p = array(p)
d = identity(len(d)) * array(d,Float)
return powell(self._objective, p, drxns=d)
def AddPlane(self,origin): from Avatars.vtkGrid3D import vtkGrid3DPlane from Numeric import identity # Does the plane avatar already exist ? if hasattr(self,'plane'): # Yes, use it self.plane.SetOrigin(origin) else: # No, add it normal=identity(3)[self.GetSTMTool().GetNormalAxis()] self.plane=vtkGrid3DPlane(normal,origin,vtkstructuredgrid=self.GetVTKData().GetvtkStructuredGrid(),parent=self) self.plane.SetColorTable(self.GetColorTable())
def AddPlane(self,origin): from Visualization.Avatars.vtkGrid3D import vtkGrid3DPlane from Numeric import identity # Does the plane avatar already exist ? if hasattr(self,'plane'): # Yes, use it self.plane.SetOrigin(origin) else: # No, add it normal=identity(3)[self.GetSTMTool().GetNormalAxis()] self.plane=vtkGrid3DPlane(normal,origin,vtkstructuredgrid=self.GetVTKData().GetvtkStructuredGrid(),parent=self) self.plane.SetColorTable(self.GetColorTable())
def GetCoordinates(self): """ Returns a NumPy array with the scaled coordinates of the grid points. The array will have the shape, (< *dim*>,< *N1* , *N2* ,..., *Ndim* >) where *dim* is the dimension of the space. """ from ArrayTools import CoordinateArrayFromUnitVectors from Numeric import identity,Float shape=self.GetSpatialShape() gridunitcell=identity(len(shape)).astype(Float)/shape return CoordinateArrayFromUnitVectors(shape,gridunitcell)
def kfupdate(self, dt, rs):
self.ab = array((rs.ax, -rs.ay, -rs.az))
ph = self.phi
th = self.theta
P = self.pmat
A = array(((-rs.q*cos(ph)*tan(th)+rs.r*sin(ph)*tan(th), (-rs.q*sin(ph)+rs.r*cos(ph))/(cos(th)*cos(th))),
(rs.q*sin(ph)-rs.r*cos(ph) , 0)))
dph = rs.p - rs.q*sin(ph)*tan(th) - rs.r*cos(ph)*tan(th)
dth = - rs.q*cos(ph) - rs.r*sin(ph)
dP = dot(A, P) + dot(P, transpose(A)) + self.Q
ph = ph + dph * dt
th = th + dth * dt
P = P + dP * dt
Cx = array((0 , cos(th)))
Cy = array((-cos(th)*cos(ph), sin(th)*sin(ph)))
Cz = array((cos(th)*sin(ph) , sin(th)*cos(ph)))
C = array((Cx, Cy, Cz))
L = dot(dot(P, transpose(C)), inverse(self.R + dot(dot(C, P), transpose(C))))
h = array((sin(th), -cos(th)*sin(ph), -cos(th)*cos(ph)))
P = dot(identity(2) - dot(L, C), P)
ph = ph + dot(L[0], self.ab - h)
th = th + dot(L[1], self.ab - h)
ph = ((ph+pi) % (2*pi)) - pi;
th = ((th+pi) % (2*pi)) - pi;
self.pmat = P
self.phi = ph
self.theta = th
psidot = rs.q * sin(ph) / cos(th) + rs.r * cos(ph) / cos(th);
self.psi += psidot * dt;
self.quat = eul2quat(ph,th,self.psi)
# self.dcmb2e = quat2dcm(quatinv(self.quat))
# self.ae = dot(self.dcmb2e, self.ab)
self.ae = quatrotate(quatinv(self.quat), self.ab)
self.ae[2] = -self.ae[2]-1
self.ae[1] = -self.ae[1]
self.ve += self.ae * dt * 9.81
self.xe += self.ve * dt
ymin = height*j/float(m)
ymax = height*(j+1)/float(m)
color = get_color(A[i,j],mina,maxa)
if do_outline:
draw.rectangle((xmin,ymin,xmax,ymax),fill=color,
outline=(0,0,0))
else:
draw.rectangle((xmin,ymin,xmax,ymax),fill=color)
img.save(fname)
return
def get_color(a,cmin,cmax):
"""\
Convert a float value to one of a continuous range of colors.
Rewritten to use recipe 9.10 from the Python Cookbook.
"""
import math
try: a = float(a-cmin)/(cmax-cmin)
except ZeroDivisionError: a=0.5 # cmax == cmin
blue = min((max((4*(0.75-a),0.)),1.))
red = min((max((4*(a-0.25),0.)),1.))
green = min((max((4*math.fabs(a-0.5)-1.,0)),1.))
return '#%1x%1x%1x' % (int(15*red),int(15*green),int(15*blue))
from Numeric import identity,Float
a = identity(10,Float)
spy_matrix_pil(a)
pcolor_matrix_pil(a,'tmp2.png')
maxa = A[0,0]
for i in range(n):
for j in range(m):
mina = min(A[i,j], mina)
maxa = max(A[i,j], maxa)
if n>width or m>height:
raise "Rectangle too big %d %d %d %d" % (n,m,width,height)
for i in range(n):
xmin = width*i/float(n)
xmax = width*(i+1)/float(n)
for j in range(m):
ymin = height*j/float(m)
ymax = height*(j+1)/float(m)
color = colorfunc(A[i,j],mina,maxa)
if do_outline:
draw.rectangle((xmin,ymin,xmax,ymax),fill=color,
outline=(0,0,0))
else:
draw.rectangle((xmin,ymin,xmax,ymax),fill=color)
img.save(fname)
return
if __name__ == "__main__":
from Numeric import identity, Float
a = identity(10,Float)
spy_matrix_pil(a)
pcolor_matrix_pil(a,'tmp2.png')
def reset(self): self.matrix = identity(3, typecode = Float)
def scale(self, sx, sy):
m = identity(3, typecode=Float)
m[0, 0] = sx
m[1, 1] = sy
self.matrix = matrixmultiply(self.matrix, m)
def reset(self):
self.matrix = identity(3, typecode=Float)
def scale(self, sx, sy): m = identity(3, typecode = Float) m[0, 0] = sx m[1, 1] = sy self.matrix = matrixmultiply(self.matrix, m)
import string
