def _logticks(self, lower, upper):
#lower,upper = map(_Numeric.log10,[lower,upper])
#print 'logticks',lower,upper
ticks = []
mag = _Numeric.power(10,_Numeric.floor(lower))
if upper-lower > 6:
t = _Numeric.power(10,_Numeric.ceil(lower))
base = _Numeric.power(10,_Numeric.floor((upper-lower)/6))
def inc(t):
return t*base-t
else:
t = _Numeric.ceil(_Numeric.power(10,lower)/mag)*mag
def inc(t):
return 10**int(_Numeric.floor(_Numeric.log10(t)+1e-16))
majortick = int(_Numeric.log10(mag))
while t <= pow(10,upper):
if majortick != int(_Numeric.floor(_Numeric.log10(t)+1e-16)):
majortick = int(_Numeric.floor(_Numeric.log10(t)+1e-16))
ticklabel = '1e%d'%majortick
else:
if upper-lower < 2:
minortick = int(t/pow(10,majortick)+.5)
ticklabel = '%de%d'%(minortick,majortick)
else:
ticklabel = ''
ticks.append((_Numeric.log10(t), ticklabel))
t += inc(t)
if len(ticks) == 0:
ticks = [(0,'')]
return ticks
def processBuffer(self, bounds):
self.param1 = bounds.height/65536.0
self.param2 = bounds.height/2.0
if(self.stop==False):
Fs = 48000
nfft= 65536
self.newest_buffer=self.newest_buffer[0:256]
self.fftx = fft(self.newest_buffer, 256,-1)
self.fftx=self.fftx[0:self.freq_range*2]
self.draw_interval=bounds.width/(self.freq_range*2.)
NumUniquePts = ceil((nfft+1)/2)
self.buffers=abs(self.fftx)*0.02
self.y_mag_bias_multiplier=0.1
self.scaleX = "hz"
self.scaleY = ""
if(len(self.buffers)==0):
return False
# Scaling the values
val = []
for i in self.buffers:
temp_val_float = float(self.param1*i*self.y_mag) + self.y_mag_bias_multiplier * self.param2
if(temp_val_float >= bounds.height):
temp_val_float = bounds.height-25
if(temp_val_float <= 0):
temp_val_float = 25
val.append( temp_val_float )
self.peaks = val
def __init__(self, shp, ptlist, origin, selSense, **opts):
"""
ptlist is a list of 3d points describing a selection.
origin is the center of view, and normal gives the direction
of the line of light. Form a structure for telling whether
arbitrary points fall inside the curve from the point of view.
"""
# bruce 041214 rewrote some of this method
simple_shape_2d.__init__( self, shp, ptlist, origin, selSense, opts)
# bounding rectangle, in integers (scaled 8 to the angstrom)
ibbhi = array(map(int, ceil(8 * self.bboxhi)+2))
ibblo = array(map(int, floor(8 * self.bboxlo)-2))
bboxlo = self.bboxlo
# draw the curve in these matrices and fill it
# [bruce 041214 adds this comment: this might be correct but it's very
# inefficient -- we should do it geometrically someday. #e]
mat = zeros(ibbhi - ibblo)
mat1 = zeros(ibbhi - ibblo)
mat1[0,:] = 1
mat1[-1,:] = 1
mat1[:,0] = 1
mat1[:,-1] = 1
pt2d = self.pt2d
pt0 = pt2d[0]
for pt in pt2d[1:]:
l = ceil(vlen(pt - pt0)*8)
if l<0.01: continue
v=(pt - pt0)/l
for i in range(1 + int(l)):
ij = 2 + array(map(int, floor((pt0 + v * i - bboxlo)*8)))
mat[ij]=1
pt0 = pt
mat1 += mat
fill(mat1, array([1, 1]),1)
mat1 -= mat #Which means boundary line is counted as inside the shape.
# boolean raster of filled-in shape
self.matrix = mat1 ## For any element inside the matrix, if it is 0, then it's inside.
# where matrix[0, 0] is in x, y space
self.matbase = ibblo
# axes of the plane; only used for debugging
self.x = self.right
self.y = self.up
self.z = self.normal
def lowess2(x, y, xest, f=2./3., iter=3):
"""Returns estimated values of y in data points xest (or None if estimation fails).
Lowess smoother: Robust locally weighted regression.
The lowess function fits a nonparametric regression curve to a scatterplot.
The arrays x and y contain an equal number of elements; each pair
(x[i], y[i]) defines a data point in the scatterplot. The function returns
the estimated (smooth) values of y.
The smoothing span is given by f. A larger value for f will result in a
smoother curve. The number of robustifying iterations is given by iter. The
function will run faster with a smaller number of iterations."""
x = Numeric.asarray(x, 'd')
y = Numeric.asarray(y, 'd')
xest = Numeric.asarray(xest, 'd')
n = len(x)
nest = len(xest)
r = min(int(Numeric.ceil(f*n)),n-1) # radius: num. of points to take into LR
h = [Numeric.sort(abs(x-x[i]))[r] for i in range(n)] # distance of the r-th point from x[i]
w = Numeric.clip(abs(([x]-Numeric.transpose([x]))/h),0.0,1.0)
w = 1-w*w*w
w = w*w*w
hest = [Numeric.sort(abs(x-xest[i]))[r] for i in range(nest)] # r-th min. distance from xest[i] to x
west = Numeric.clip(abs(([xest]-Numeric.transpose([x]))/hest),0.0,1.0) # shape: (len(x), len(xest)
west = 1-west*west*west
west = west*west*west
yest = Numeric.zeros(n,'d')
yest2 = Numeric.zeros(nest,'d')
delta = Numeric.ones(n,'d')
try:
for iteration in range(iter):
# fit xest
for i in range(nest):
weights = delta * west[:,i]
b = Numeric.array([sum(weights*y), sum(weights*y*x)])
A = Numeric.array([[sum(weights), sum(weights*x)], [sum(weights*x), sum(weights*x*x)]])
beta = LinearAlgebra.solve_linear_equations(A,b)
yest2[i] = beta[0] + beta[1]*xest[i]
# fit x (to calculate residuals and delta)
for i in range(n):
weights = delta * w[:,i]
b = Numeric.array([sum(weights*y), sum(weights*y*x)])
A = Numeric.array([[sum(weights), sum(weights*x)], [sum(weights*x), sum(weights*x*x)]])
beta = LinearAlgebra.solve_linear_equations(A,b)
yest[i] = beta[0] + beta[1]*x[i]
residuals = y-yest
s = MLab.median(abs(residuals))
delta = Numeric.clip(residuals/(6*s),-1,1)
delta = 1-delta*delta
delta = delta*delta
except LinearAlgebra.LinAlgError:
print "Warning: NumExtn.lowess2: LinearAlgebra.solve_linear_equations: Singular matrix"
yest2 = None
return yest2
def processBuffer(self, bounds):
self.param1 = bounds.height / 65536.0
self.param2 = bounds.height / 2.0
if (self.stop == False):
Fs = 48000
nfft = 65536
self.newest_buffer = self.newest_buffer[0:256]
self.fftx = fft(self.newest_buffer, 256, -1)
self.fftx = self.fftx[0:self.freq_range * 2]
self.draw_interval = bounds.width / (self.freq_range * 2.)
NumUniquePts = ceil((nfft + 1) / 2)
self.buffers = abs(self.fftx) * 0.02
self.y_mag_bias_multiplier = 0.1
self.scaleX = "hz"
self.scaleY = ""
if (len(self.buffers) == 0):
return False
# Scaling the values
val = []
for i in self.buffers:
temp_val_float = float(
self.param1 * i *
self.y_mag) + self.y_mag_bias_multiplier * self.param2
if (temp_val_float >= bounds.height):
temp_val_float = bounds.height - 25
if (temp_val_float <= 0):
temp_val_float = 25
val.append(temp_val_float)
self.peaks = val
def lowessW(x, y, xest, f=2./3., iter=3, dWeights=None, callback=None):
"""Returns estimated values of y in data points xest (or None if estimation fails).
Lowess smoother: Robust locally weighted regression.
The lowess function fits a nonparametric regression curve to a scatterplot.
The arrays x and y contain an equal number of elements; each pair
(x[i], y[i]) defines a data point in the scatterplot. The function returns
the estimated (smooth) values of y.
The smoothing span is given by f. A larger value for f will result in a
smoother curve. The number of robustifying iterations is given by iter. The
function will run faster with a smaller number of iterations.
Data points may be assigned weights; if None, all weights equal 1.
"""
x = Numeric.asarray(x, 'd')
y = Numeric.asarray(y, 'd')
xest = Numeric.asarray(xest, 'd')
n = len(x)
if n <> len(y):
raise AttributeError, "Error: lowessW(x,y,xest,f,iter,dWeights): len(x)=%i not equal to len(y)=%i" % (len(x), len(y))
nest = len(xest)
# weights of data points (optional)
if dWeights <> None:
dWeights = Numeric.asarray(dWeights, 'd')
if len(dWeights) <> n:
raise AttributeError, "Error: lowessW(x,y,xest,f,iter,dWeights): len(dWeights)=%i not equal to len(x)=%i" % (len(dWeights), len(x))
## dWeights = dWeights.reshape((n,1))
else:
## dWeights = Numeric.ones((n,1))
dWeights = Numeric.ones((n,))
r = min(int(Numeric.ceil(f*n)),n-1) # radius: num. of points to take into LR
h = [Numeric.sort(abs(x-x[i]))[r] for i in range(n)] # distance of the r-th point from x[i]
w = Numeric.clip(abs(([x]-Numeric.transpose([x]))/h),0.0,1.0)
w = 1-w*w*w
w = w*w*w
hest = [Numeric.sort(abs(x-xest[i]))[r] for i in range(nest)] # r-th min. distance from xest[i] to x
west = Numeric.clip(abs(([xest]-Numeric.transpose([x]))/hest),0.0,1.0) # shape: (len(x), len(xest))
west = 1-west*west*west
west = west*west*west
yest = Numeric.zeros(n,'d')
yest2 = Numeric.zeros(nest,'d')
delta = Numeric.ones(n,'d')
try:
for iteration in range(int(iter)):
# fit xest
for i in range(nest):
## print delta.shape, west[:,i].shape, dWeights.shape
weights = delta * west[:,i] * dWeights
b = Numeric.array([sum(weights*y), sum(weights*y*x)])
A = Numeric.array([[sum(weights), sum(weights*x)], [sum(weights*x), sum(weights*x*x)]])
beta = LinearAlgebra.solve_linear_equations(A,b)
yest2[i] = beta[0] + beta[1]*xest[i]
# fit x (to calculate residuals and delta)
for i in range(n):
weights = delta * w[:,i] * dWeights
b = Numeric.array([sum(weights*y), sum(weights*y*x)])
A = Numeric.array([[sum(weights), sum(weights*x)], [sum(weights*x), sum(weights*x*x)]])
beta = LinearAlgebra.solve_linear_equations(A,b)
yest[i] = beta[0] + beta[1]*x[i]
residuals = y-yest
s = MLab.median(abs(residuals))
delta = Numeric.clip(residuals/(6*s),-1,1)
delta = 1-delta*delta
delta = delta*delta
if callback: callback()
except LinearAlgebra.LinAlgError:
print "Warning: NumExtn.lowessW: LinearAlgebra.solve_linear_equations: Singular matrix"
yest2 = None
return yest2