def ring(x, y, height, thickness, gaussian_width):
"""
Circular ring (annulus) with Gaussian fall-off after the solid ring-shaped region.
"""
radius = height / 2.0
half_thickness = thickness / 2.0
distance_from_origin = sqrt(x**2 + y**2)
distance_outside_outer_disk = distance_from_origin - radius - half_thickness
distance_inside_inner_disk = radius - half_thickness - distance_from_origin
ring = 1.0 - bitwise_xor(greater_equal(distance_inside_inner_disk, 0.0),
greater_equal(distance_outside_outer_disk, 0.0))
sigmasq = gaussian_width * gaussian_width
if sigmasq == 0.0:
inner_falloff = x * 0.0
outer_falloff = x * 0.0
else:
with float_error_ignore():
inner_falloff = exp(
divide(
-distance_inside_inner_disk * distance_inside_inner_disk,
2.0 * sigmasq))
outer_falloff = exp(
divide(
-distance_outside_outer_disk * distance_outside_outer_disk,
2.0 * sigmasq))
return maximum(inner_falloff, maximum(outer_falloff, ring))
def arc_by_radian(x, y, height, radian_range, thickness, gaussian_width):
"""
Radial arc with Gaussian fall-off after the solid ring-shaped
region with the given thickness, with shape specified by the
(start,end) radian_range.
"""
# Create a circular ring (copied from the ring function)
radius = height/2.0
half_thickness = thickness/2.0
distance_from_origin = sqrt(x**2+y**2)
distance_outside_outer_disk = distance_from_origin - radius - half_thickness
distance_inside_inner_disk = radius - half_thickness - distance_from_origin
ring = 1.0-bitwise_xor(greater_equal(distance_inside_inner_disk,0.0),greater_equal(distance_outside_outer_disk,0.0))
sigmasq = gaussian_width*gaussian_width
if sigmasq==0.0:
inner_falloff = x*0.0
outer_falloff = x*0.0
else:
with float_error_ignore():
inner_falloff = exp(divide(-distance_inside_inner_disk*distance_inside_inner_disk, 2.0*sigmasq))
outer_falloff = exp(divide(-distance_outside_outer_disk*distance_outside_outer_disk, 2.0*sigmasq))
output_ring = maximum(inner_falloff,maximum(outer_falloff,ring))
# Calculate radians (in 4 phases) and cut according to the set range)
# RZHACKALERT:
# Function float_error_ignore() cannot catch the exception when
# both dividend and divisor are 0.0, and when only divisor is 0.0
# it returns 'Inf' rather than 0.0. In x, y and
# distance_from_origin, only one point in distance_from_origin can
# be 0.0 (circle center) and in this point x and y must be 0.0 as
# well. So here is a hack to avoid the 'invalid value encountered
# in divide' error by turning 0.0 to 1e-5 in distance_from_origin.
distance_from_origin += where(distance_from_origin == 0.0, 1e-5, 0)
with float_error_ignore():
sines = divide(y, distance_from_origin)
cosines = divide(x, distance_from_origin)
arcsines = arcsin(sines)
phase_1 = where(logical_and(sines >= 0, cosines >= 0), 2*pi-arcsines, 0)
phase_2 = where(logical_and(sines >= 0, cosines < 0), pi+arcsines, 0)
phase_3 = where(logical_and(sines < 0, cosines < 0), pi+arcsines, 0)
phase_4 = where(logical_and(sines < 0, cosines >= 0), -arcsines, 0)
arcsines = phase_1 + phase_2 + phase_3 + phase_4
if radian_range[0] <= radian_range[1]:
return where(logical_and(arcsines >= radian_range[0], arcsines <= radian_range[1]),
output_ring, 0.0)
else:
return where(logical_or(arcsines >= radian_range[0], arcsines <= radian_range[1]),
output_ring, 0.0)
def arc_by_radian(x, y, height, radian_range, thickness, gaussian_width):
"""
Radial arc with Gaussian fall-off after the solid ring-shaped
region with the given thickness, with shape specified by the
(start,end) radian_range.
"""
# Create a circular ring (copied from the ring function)
radius = height/2.0
half_thickness = thickness/2.0
distance_from_origin = sqrt(x**2+y**2)
distance_outside_outer_disk = distance_from_origin - radius - half_thickness
distance_inside_inner_disk = radius - half_thickness - distance_from_origin
ring = 1.0-bitwise_xor(greater_equal(distance_inside_inner_disk,0.0),greater_equal(distance_outside_outer_disk,0.0))
sigmasq = gaussian_width*gaussian_width
if sigmasq==0.0:
inner_falloff = x*0.0
outer_falloff = x*0.0
else:
with float_error_ignore():
inner_falloff = exp(divide(-distance_inside_inner_disk*distance_inside_inner_disk, 2.0*sigmasq))
outer_falloff = exp(divide(-distance_outside_outer_disk*distance_outside_outer_disk, 2.0*sigmasq))
output_ring = maximum(inner_falloff,maximum(outer_falloff,ring))
# Calculate radians (in 4 phases) and cut according to the set range)
# RZHACKALERT:
# Function float_error_ignore() cannot catch the exception when
# both dividend and divisor are 0.0, and when only divisor is 0.0
# it returns 'Inf' rather than 0.0. In x, y and
# distance_from_origin, only one point in distance_from_origin can
# be 0.0 (circle center) and in this point x and y must be 0.0 as
# well. So here is a hack to avoid the 'invalid value encountered
# in divide' error by turning 0.0 to 1e-5 in distance_from_origin.
distance_from_origin += where(distance_from_origin == 0.0, 1e-5, 0)
with float_error_ignore():
sines = divide(y, distance_from_origin)
cosines = divide(x, distance_from_origin)
arcsines = arcsin(sines)
phase_1 = where(logical_and(sines >= 0, cosines >= 0), 2*pi-arcsines, 0)
phase_2 = where(logical_and(sines >= 0, cosines < 0), pi+arcsines, 0)
phase_3 = where(logical_and(sines < 0, cosines < 0), pi+arcsines, 0)
phase_4 = where(logical_and(sines < 0, cosines >= 0), -arcsines, 0)
arcsines = phase_1 + phase_2 + phase_3 + phase_4
if radian_range[0] <= radian_range[1]:
return where(logical_and(arcsines >= radian_range[0], arcsines <= radian_range[1]),
output_ring, 0.0)
else:
return where(logical_or(arcsines >= radian_range[0], arcsines <= radian_range[1]),
output_ring, 0.0)
def erf(x):
"""
Approximation to the erf-function with fractional error
everywhere less than 1.2e-7
@param x: value
@type x: float
@return: value
@rtype: float
"""
if x > 10.: return 1.
if x < -10.: return -1.
z = abs(x)
t = 1. / (1. + 0.5 * z)
r = t * N.exp(-z * z - 1.26551223 + t * (1.00002368 + t * (0.37409196 + \
t * (0.09678418 + t * (-0.18628806 + t * (0.27886807 + t * \
(-1.13520398 + t * (1.48851587 + t * (-0.82215223 + t * \
0.17087277)))))))))
if x >= 0.:
return 1. - r
else:
return r - 1.
def FugaM(self, Z, A_i, B_i, A, B):
L = (1 / (2 * sqrt(2))) * log(
(Z + B * (1 + sqrt(2))) / (Z + B * (1 - sqrt(2))))
LogFug = B_i / B * (Z - 1) - log(Z - B) + A / B * (B_i / B -
2 * A_i / A) * L
Fug = exp(LogFug)
return Fug
def FugaP(self, Z, A, B):
""" Fugacity Coefficient of Pure Substances"""
L = (1 / (2 * sqrt(2))) * log(
(Z + B * (1 + sqrt(2))) / (Z + B * (1 - sqrt(2))))
LogFug = Z - 1 - log(Z - B) - A / B * L
Fug = exp(LogFug)
return Fug
def function(self,params):
"""Hyperbolic function."""
aspect_ratio = params['aspect_ratio']
x = self.pattern_x/aspect_ratio
y = self.pattern_y
thickness = params['thickness']
gaussian_width = params['smoothing']
size = params['size']
half_thickness = thickness / 2.0
distance_from_vertex_middle = fmod(sqrt(absolute(x**2 - y**2)),size)
distance_from_vertex_middle = minimum(distance_from_vertex_middle,size - distance_from_vertex_middle)
distance_from_vertex = distance_from_vertex_middle - half_thickness
hyperbola = 1.0 - greater_equal(distance_from_vertex,0.0)
sigmasq = gaussian_width*gaussian_width
with float_error_ignore():
falloff = exp(divide(-distance_from_vertex*distance_from_vertex, 2.0*sigmasq))
return maximum(falloff, hyperbola)
def concentricrings(x, y, white_thickness, gaussian_width, spacing):
"""
Concetric rings with the solid ring-shaped region, then Gaussian fall-off at the edges.
"""
# To have zero value in middle point this pattern calculates zero-value rings instead of
# the one-value ones. But to be consistent with the rest of functions the parameters
# are connected to one-value rings - like half_thickness is now recalculated for zero-value ring:
half_thickness = ((spacing-white_thickness)/2.0)*greater_equal(spacing-white_thickness,0.0)
distance_from_origin = sqrt(x**2+y**2)
distance_from_ring_middle = fmod(distance_from_origin,spacing)
distance_from_ring_middle = minimum(distance_from_ring_middle,spacing - distance_from_ring_middle)
distance_from_ring = distance_from_ring_middle - half_thickness
ring = 0.0 + greater_equal(distance_from_ring,0.0)
sigmasq = gaussian_width*gaussian_width
with float_error_ignore():
falloff = exp(divide(-distance_from_ring*distance_from_ring, 2.0*sigmasq))
return maximum(falloff,ring)
def erf(x):
"""
Approximation to the erf-function with fractional error
everywhere less than 1.2e-7
@param x: value
@type x: float
@return: value
@rtype: float
"""
if x > 10.: return 1.
if x < -10.: return -1.
z = abs(x)
t = 1. / (1. + 0.5 * z)
r = t * N.exp(-z * z - 1.26551223 + t * (1.00002368 + t * (0.37409196 + \
t * (0.09678418 + t * (-0.18628806 + t * (0.27886807 + t * \
(-1.13520398 + t * (1.48851587 + t * (-0.82215223 + t * \
0.17087277)))))))))
if x >= 0.:
return 1. - r
else:
return r - 1.
def FugaP(self,Z,A,B):
""" Fugacity Coefficient of Pure Substances"""
## print Z-B
L = ( 1/( 2*sqrt(2) ) ) * log( ( Z +B*(1+sqrt(2) ) ) / ( Z +B*(1-sqrt(2) ) ) )
LogFug = Z-1 - log(Z-B) - A/B*L
Fug = exp( LogFug )
return Fug
def function(self,params):
"""Archemidean spiral function."""
aspect_ratio = params['aspect_ratio']
x = self.pattern_x/aspect_ratio
y = self.pattern_y
thickness = params['thickness']
gaussian_width = params['smoothing']
size = params['size']
half_thickness = thickness/2.0
spacing = size*2*pi
distance_from_origin = sqrt(x**2+y**2)
distance_from_spiral_middle = fmod(spacing + distance_from_origin - size*arctan2(y,x),spacing)
distance_from_spiral_middle = minimum(distance_from_spiral_middle,spacing - distance_from_spiral_middle)
distance_from_spiral = distance_from_spiral_middle - half_thickness
spiral = 1.0 - greater_equal(distance_from_spiral,0.0)
sigmasq = gaussian_width*gaussian_width
with float_error_ignore():
falloff = exp(divide(-distance_from_spiral*distance_from_spiral, 2.0*sigmasq))
return maximum(falloff, spiral)
def function(self,params):
"""Concentric rings."""
aspect_ratio = params['aspect_ratio']
x = self.pattern_x/aspect_ratio
y = self.pattern_y
thickness = params['thickness']
gaussian_width = params['smoothing']
size = params['size']
half_thickness = thickness / 2.0
distance_from_origin = sqrt(x**2+y**2)
distance_from_ring_middle = fmod(distance_from_origin,size)
distance_from_ring_middle = minimum(distance_from_ring_middle,size - distance_from_ring_middle)
distance_from_ring = distance_from_ring_middle - half_thickness
ring = 1.0 - greater_equal(distance_from_ring,0.0)
sigmasq = gaussian_width*gaussian_width
with float_error_ignore():
falloff = exp(divide(-distance_from_ring*distance_from_ring, 2.0*sigmasq))
return maximum(falloff, ring)
def sigmoid(axis, slope):
"""
Sigmoid dividing axis into a positive and negative half,
with a smoothly sloping transition between them (controlled by the slope).
At default rotation, axis refers to the vertical (y) axis.
"""
with float_error_ignore():
return (2.0 / (1.0 + exp(-2.0 * slope * axis))) - 1.0
def sigmoid(axis, slope):
"""
Sigmoid dividing axis into a positive and negative half,
with a smoothly sloping transition between them (controlled by the slope).
At default rotation, axis refers to the vertical (y) axis.
"""
with float_error_ignore():
return (2.0 / (1.0 + exp(-2.0*slope*axis))) - 1.0
def exponential(x, y, xscale, yscale):
"""
Two-dimensional oriented exponential decay pattern.
"""
if xscale == 0.0 or yscale == 0.0:
return x * 0.0
with float_error_ignore():
x_w = divide(x, xscale)
y_h = divide(y, yscale)
return exp(-sqrt(x_w * x_w + y_h * y_h))
def logMean(alpha, beta):
"""
@param alpha: mean of log-transformed distribution
@type alpha: float
@param beta: standarddev of log-transformed distribution
@type beta: float
@return: mean of the original lognormal distribution
@rtype: float
"""
return N.exp(alpha + (beta**2) / 2.)
def hyperbola(x, y, thickness, gaussian_width, axis):
"""
Two conjugate hyperbolas with Gaussian fall-off which share the same asymptotes.
abs(x^2/a^2 - y^2/b^2) = 1
As a = b = axis, these hyperbolas are rectangular.
"""
difference = absolute(x**2 - y**2)
hyperbola = 1.0 - bitwise_xor(greater_equal(axis**2,difference),greater_equal(difference,(axis + thickness)**2))
distance_inside_hyperbola = sqrt(difference) - axis
distance_outside_hyperbola = sqrt(difference) - axis - thickness
sigmasq = gaussian_width*gaussian_width
with float_error_ignore():
inner_falloff = exp(divide(-distance_inside_hyperbola*distance_inside_hyperbola, 2.0*sigmasq))
outer_falloff = exp(divide(-distance_outside_hyperbola*distance_outside_hyperbola, 2.0*sigmasq))
return maximum(hyperbola,maximum(inner_falloff,outer_falloff))
def exponential(x, y, xscale, yscale):
"""
Two-dimensional oriented exponential decay pattern.
"""
if xscale==0.0 or yscale==0.0:
return x*0.0
with float_error_ignore():
x_w = divide(x,xscale)
y_h = divide(y,yscale)
return exp(-sqrt(x_w*x_w+y_h*y_h))
def logSigma(alpha, beta):
"""
@param alpha: mean of log-transformed distribution
@type alpha: float
@param beta: standarddev of log-transformed distribution
@type beta: float
@return: 'standard deviation' of the original lognormal distribution
@rtype: float
"""
return logMean(alpha, beta) * N.sqrt(N.exp(beta**2) - 1.)
def logMean( alpha, beta ):
"""
@param alpha: mean of log-transformed distribution
@type alpha: float
@param beta: standarddev of log-transformed distribution
@type beta: float
@return: mean of the original lognormal distribution
@rtype: float
"""
return N.exp( alpha + (beta**2)/2. )
def logMedian( alpha, beta=None ):
"""
@param alpha: mean of log-transformed distribution
@type alpha: float
@param beta: not needed
@type beta: float
@return: median of the original lognormal distribution
@rtype: float
"""
return N.exp( alpha )
def smooth_rectangle(x, y, rec_w, rec_h, gaussian_width_x, gaussian_width_y):
"""
Rectangle with a solid central region, then Gaussian fall-off at the edges.
"""
gaussian_x_coord = abs(x)-rec_w/2.0
gaussian_y_coord = abs(y)-rec_h/2.0
box_x=less(gaussian_x_coord,0.0)
box_y=less(gaussian_y_coord,0.0)
sigmasq_x=gaussian_width_x*gaussian_width_x
sigmasq_y=gaussian_width_y*gaussian_width_y
with float_error_ignore():
falloff_x=x*0.0 if sigmasq_x==0.0 else \
exp(divide(-gaussian_x_coord*gaussian_x_coord,2*sigmasq_x))
falloff_y=y*0.0 if sigmasq_y==0.0 else \
exp(divide(-gaussian_y_coord*gaussian_y_coord,2*sigmasq_y))
return minimum(maximum(box_x,falloff_x), maximum(box_y,falloff_y))
def smooth_rectangle(x, y, rec_w, rec_h, gaussian_width_x, gaussian_width_y):
"""
Rectangle with a solid central region, then Gaussian fall-off at the edges.
"""
gaussian_x_coord = abs(x) - rec_w / 2.0
gaussian_y_coord = abs(y) - rec_h / 2.0
box_x = less(gaussian_x_coord, 0.0)
box_y = less(gaussian_y_coord, 0.0)
sigmasq_x = gaussian_width_x * gaussian_width_x
sigmasq_y = gaussian_width_y * gaussian_width_y
with float_error_ignore():
falloff_x=x*0.0 if sigmasq_x==0.0 else \
exp(divide(-gaussian_x_coord*gaussian_x_coord,2*sigmasq_x))
falloff_y=y*0.0 if sigmasq_y==0.0 else \
exp(divide(-gaussian_y_coord*gaussian_y_coord,2*sigmasq_y))
return minimum(maximum(box_x, falloff_x), maximum(box_y, falloff_y))
def logMedian(alpha, beta=None):
"""
@param alpha: mean of log-transformed distribution
@type alpha: float
@param beta: not needed
@type beta: float
@return: median of the original lognormal distribution
@rtype: float
"""
return N.exp(alpha)
def logSigma( alpha, beta ):
"""
@param alpha: mean of log-transformed distribution
@type alpha: float
@param beta: standarddev of log-transformed distribution
@type beta: float
@return: 'standard deviation' of the original lognormal distribution
@rtype: float
"""
return logMean( alpha, beta ) * N.sqrt( N.exp(beta**2) - 1.)
def gabor(x, y, xsigma, ysigma, frequency, phase):
"""
Gabor pattern (sine grating multiplied by a circular Gaussian).
"""
if xsigma==0.0 or ysigma==0.0:
return x*0.0
with float_error_ignore():
x_w = divide(x,xsigma)
y_h = divide(y,ysigma)
p = exp(-0.5*x_w*x_w + -0.5*y_h*y_h)
return p * 0.5*cos(2*pi*frequency*y + phase)
def gabor(x, y, xsigma, ysigma, frequency, phase):
"""
Gabor pattern (sine grating multiplied by a circular Gaussian).
"""
if xsigma == 0.0 or ysigma == 0.0:
return x * 0.0
with float_error_ignore():
x_w = divide(x, xsigma)
y_h = divide(y, ysigma)
p = exp(-0.5 * x_w * x_w + -0.5 * y_h * y_h)
return p * 0.5 * cos(2 * pi * frequency * y + phase)
def gaussian(x, y, xsigma, ysigma):
"""
Two-dimensional oriented Gaussian pattern (i.e., 2D version of a
bell curve, like a normal distribution but not necessarily summing
to 1.0).
"""
if xsigma==0.0 or ysigma==0.0:
return x*0.0
with float_error_ignore():
x_w = divide(x,xsigma)
y_h = divide(y,ysigma)
return exp(-0.5*x_w*x_w + -0.5*y_h*y_h)
def P(self,T,m):
P_j= array( exp( m["HAR_A"]+m["HAR_B"] / T + m["HAR_C"]*log(T) ) )
P = []
T1 = log(T)
T2 = power(T,2)
## print m["HAR_D"]
for j in range(len(m["HAR_A"])):
P_r = P_j[j]
i= 1
## print j,P_r,T2
while i<=20:
P_i = m["HAR_A"][j]+m["HAR_B"][j]/ T + m["HAR_C"][j]*T1 + m["HAR_D"][j]*P_r/T2
## print P_i
P_i = exp(P_i)
## print P_i*0.13332236
i +=1
if abs(P_i-P_r)<=1:
P.append(P_i)
break
P_r = P_i
return array(P) * self.Factor
def gaussian(x, y, xsigma, ysigma):
"""
Two-dimensional oriented Gaussian pattern (i.e., 2D version of a
bell curve, like a normal distribution but not necessarily summing
to 1.0).
"""
if xsigma == 0.0 or ysigma == 0.0:
return x * 0.0
with float_error_ignore():
x_w = divide(x, xsigma)
y_h = divide(y, ysigma)
return exp(-0.5 * x_w * x_w + -0.5 * y_h * y_h)
def log_gaussian(x, y, x_sigma, y_sigma, mu):
"""
Two-dimensional oriented Log Gaussian pattern (i.e., 2D version of a
bell curve with an independent, movable peak). Much like a normal
distribution, but not necessarily placing the peak above the center,
and not necessarily summing to 1.0).
"""
if x_sigma == 0.0 or y_sigma == 0.0:
return x * 0.0
with float_error_ignore():
x_w = divide(log(x) - mu, x_sigma * x_sigma)
y_h = divide(log(y) - mu, y_sigma * y_sigma)
return exp(-0.5 * x_w * x_w + -0.5 * y_h * y_h)
def line(y, thickness, gaussian_width):
"""
Infinite-length line with a solid central region, then Gaussian fall-off at the edges.
"""
distance_from_line = abs(y)
gaussian_y_coord = distance_from_line - thickness/2.0
sigmasq = gaussian_width*gaussian_width
if sigmasq==0.0:
falloff = x*0.0
else:
with float_error_ignore():
falloff = exp(divide(-gaussian_y_coord*gaussian_y_coord,2*sigmasq))
return where(gaussian_y_coord<=0, 1.0, falloff)
def leftDrag(self, event):
"""
Event handler for LMB drag event.
@param event: A Qt mouse event.
@type event: U{B{QMouseEvent}<http://doc.trolltech.com/4/qmouseevent.html>}
"""
dScale = 0.025 # This works nicely. Mark 2008-01-29.
delta = self.prevY - event.y()
self.prevY = event.y()
factor = exp(dScale * delta)
# print "y, py =", event.y(), self.prevY, ", delta =", delta, ", factor=", factor
self.glpane.rescale_around_point(factor)
self.glpane.gl_update()
return
def log_gaussian(x, y, x_sigma, y_sigma, mu):
"""
Two-dimensional oriented Log Gaussian pattern (i.e., 2D version of a
bell curve with an independent, movable peak). Much like a normal
distribution, but not necessarily placing the peak above the center,
and not necessarily summing to 1.0).
"""
if x_sigma==0.0 or y_sigma==0.0:
return x * 0.0
with float_error_ignore():
x_w = divide(log(x)-mu, x_sigma*x_sigma)
y_h = divide(log(y)-mu, y_sigma*y_sigma)
return exp(-0.5*x_w*x_w + -0.5*y_h*y_h)
def line(y, thickness, gaussian_width):
"""
Infinite-length line with a solid central region, then Gaussian fall-off at the edges.
"""
distance_from_line = abs(y)
gaussian_y_coord = distance_from_line - thickness/2.0
sigmasq = gaussian_width*gaussian_width
if sigmasq==0.0:
falloff = y*0.0
else:
with float_error_ignore():
falloff = exp(divide(-gaussian_y_coord*gaussian_y_coord,2*sigmasq))
return where(gaussian_y_coord<=0, 1.0, falloff)
def ring(x, y, height, thickness, gaussian_width):
"""
Circular ring (annulus) with Gaussian fall-off after the solid ring-shaped region.
"""
radius = height/2.0
half_thickness = thickness/2.0
distance_from_origin = sqrt(x**2+y**2)
distance_outside_outer_disk = distance_from_origin - radius - half_thickness
distance_inside_inner_disk = radius - half_thickness - distance_from_origin
ring = 1.0-bitwise_xor(greater_equal(distance_inside_inner_disk,0.0),greater_equal(distance_outside_outer_disk,0.0))
sigmasq = gaussian_width*gaussian_width
if sigmasq==0.0:
inner_falloff = x*0.0
outer_falloff = x*0.0
else:
with float_error_ignore():
inner_falloff = exp(divide(-distance_inside_inner_disk*distance_inside_inner_disk, 2.0*sigmasq))
outer_falloff = exp(divide(-distance_outside_outer_disk*distance_outside_outer_disk, 2.0*sigmasq))
return maximum(inner_falloff,maximum(outer_falloff,ring))
def leftDrag(self, event):
"""
Event handler for LMB drag event.
@param event: A Qt mouse event.
@type event: U{B{QMouseEvent}<http://doc.trolltech.com/4/qmouseevent.html>}
"""
dScale = .025 # This works nicely. Mark 2008-01-29.
delta = self.prevY - event.y()
self.prevY = event.y()
factor = exp(dScale * delta)
#print "y, py =", event.y(), self.prevY, ", delta =", delta, ", factor=", factor
self.glpane.rescale_around_point(factor)
self.glpane.gl_update()
return
def test_MatrixPlot(self):
"""MatrixPlot test"""
n = 30
z = N.zeros((n, n), N.Float)
for i in range(N.shape(z)[0]):
for j in range(N.shape(z)[1]):
z[i, j] = N.exp(-0.01 * ((i - n / 2)**2 + (j - n / 2)**2))
self.p = MatrixPlot(z, palette='sausage', legend=1)
if self.local or self.VERBOSITY > 2:
self.p.show()
self.assert_(self.p is not None)
def von_mises( self, pars, x ):
"""
Compute a simplified von Mises function.
Original formulation in Richard von Mises, "Wahrscheinlichkeitsrechnung
und ihre Anwendungen in der Statistik und theoretischen Physik", 1931,
Deuticke, Leipzig; see also Mardia, K.V. and Jupp, P.E., " Directional
Statistics", 1999, J. Wiley, p.36;
http://en.wikipedia.org/wiki/Von_Mises_distribution
The two differences are that this function is a continuous probability
distribution on a semi-circle, while von Mises is on the full circle,
and that the normalization factor, which is the inverse of the modified
Bessel function of first kind and 0 degree in the original, is here a fit parameter.
"""
a, k, t = pars
return a * exp( k * ( cos( 2 * ( x - t ) ) - 1 ) )
def von_mises(self, pars, x):
"""
Compute a simplified von Mises function.
Original formulation in Richard von Mises, "Wahrscheinlichkeitsrechnung
und ihre Anwendungen in der Statistik und theoretischen Physik", 1931,
Deuticke, Leipzig; see also Mardia, K.V. and Jupp, P.E., " Directional
Statistics", 1999, J. Wiley, p.36;
http://en.wikipedia.org/wiki/Von_Mises_distribution
The two differences are that this function is a continuous probability
distribution on a semi-circle, while von Mises is on the full circle,
and that the normalization factor, which is the inverse of the modified
Bessel function of first kind and 0 degree in the original, is here a fit parameter.
"""
a, k, t = pars
return a * exp(k * (cos(2 * (x - t)) - 1))
def disk(x, y, height, gaussian_width):
"""
Circular disk with Gaussian fall-off after the solid central region.
"""
disk_radius = height/2.0
distance_from_origin = sqrt(x**2+y**2)
distance_outside_disk = distance_from_origin - disk_radius
sigmasq = gaussian_width*gaussian_width
if sigmasq==0.0:
falloff = x*0.0
else:
with float_error_ignore():
falloff = exp(divide(-distance_outside_disk*distance_outside_disk,
2*sigmasq))
return where(distance_outside_disk<=0,1.0,falloff)
def disk(x, y, height, gaussian_width):
"""
Circular disk with Gaussian fall-off after the solid central region.
"""
disk_radius = height/2.0
distance_from_origin = sqrt(x**2+y**2)
distance_outside_disk = distance_from_origin - disk_radius
sigmasq = gaussian_width*gaussian_width
if sigmasq==0.0:
falloff = x*0.0
else:
with float_error_ignore():
falloff = exp(divide(-distance_outside_disk*distance_outside_disk,
2*sigmasq))
return where(distance_outside_disk<=0,1.0,falloff)
def radial(x, y, wide, gaussian_width):
"""
Radial grating - A sector of a circle with Gaussian fall-off.
Parameter wide determines in wide of sector in radians.
"""
angle = absolute(arctan2(y,x))
half_wide = wide/2
radius = 1.0 - greater_equal(angle,half_wide)
distance = angle - half_wide
sigmasq = gaussian_width*gaussian_width
with float_error_ignore():
falloff = exp(divide(-distance*distance, 2.0*sigmasq))
return maximum(radius,falloff)
def T(self,P,m):
P= P/self.Factor
T = []
T_j = array( m["HAR_B"] /( log(P)-m["HAR_A"] ) )
## print m["HAR_D"]
for j in range(len(m["HAR_A"])):
T_r = T_j[j]
i= 1
while i<=20:
fP_i = log(P_i) - exp( m["HAR_A"][j]+m["HAR_B"][j] / ( T_r ) + m["HAR_C"][j]*log(T_r) + m["HAR_D"][j]*P/power(T_r,2) )
dP_i = m["HAR_C"][j]/T_r- m["HAR_B"][j]/power(T_r,2) - 2*m["HAR_D"][j]*P/power(T_r,3)
i +=1
if abs(fP_i)<=1e-3:
T.append(T_r)
break
T = T-fP_i/dPi
return T
def vector_sum(self, d):
"""
Return the vector sum of the distribution as a tuple (magnitude, avgbinnum).
Each bin contributes a vector of length equal to its value, at
a direction corresponding to the bin number. Specifically,
the total bin number range is mapped into a direction range
[0,2pi].
For a cyclic distribution, the avgbinnum will be a continuous
measure analogous to the max_value_bin() of the distribution.
But this quantity has more precision than max_value_bin()
because it is computed from the entire distribution instead of
just the peak bin. However, it is likely to be useful only
for uniform or very dense sampling; with sparse, non-uniform
sampling the estimates will be biased significantly by the
particular samples chosen.
The avgbinnum is not meaningful when the magnitude is 0,
because a zero-length vector has no direction. To find out
whether such cases occurred, you can compare the value of
undefined_vals before and after a series of calls to this
function.
"""
# vectors are represented in polar form as complex numbers
h = d._data
r = h.values()
theta = d._bins_to_radians(array(h.keys()))
v_sum = innerproduct(r, exp(theta * 1j))
magnitude = abs(v_sum)
direction = arg(v_sum)
if v_sum == 0:
d.undefined_vals += 1
direction_radians = d._radians_to_bins(direction)
# wrap the direction because arctan2 returns principal values
wrapped_direction = wrap(d.axis_bounds[0], d.axis_bounds[1],
direction_radians)
return (magnitude, wrapped_direction)
def vector_sum(self, d ):
"""
Return the vector sum of the distribution as a tuple (magnitude, avgbinnum).
Each bin contributes a vector of length equal to its value, at
a direction corresponding to the bin number. Specifically,
the total bin number range is mapped into a direction range
[0,2pi].
For a cyclic distribution, the avgbinnum will be a continuous
measure analogous to the max_value_bin() of the distribution.
But this quantity has more precision than max_value_bin()
because it is computed from the entire distribution instead of
just the peak bin. However, it is likely to be useful only
for uniform or very dense sampling; with sparse, non-uniform
sampling the estimates will be biased significantly by the
particular samples chosen.
The avgbinnum is not meaningful when the magnitude is 0,
because a zero-length vector has no direction. To find out
whether such cases occurred, you can compare the value of
undefined_vals before and after a series of calls to this
function.
"""
# vectors are represented in polar form as complex numbers
h = d._data
r = h.values()
theta = d._bins_to_radians(array( h.keys() ))
v_sum = innerproduct(r, exp(theta*1j))
magnitude = abs(v_sum)
direction = arg(v_sum)
if v_sum == 0:
d.undefined_vals += 1
direction_radians = d._radians_to_bins(direction)
# wrap the direction because arctan2 returns principal values
wrapped_direction = wrap(d.axis_bounds[0], d.axis_bounds[1], direction_radians)
return (magnitude, wrapped_direction)
def function(self,params):
"""Radial function."""
aspect_ratio = params['aspect_ratio']
x = self.pattern_x/aspect_ratio
y = self.pattern_y
gaussian_width = params['smoothing']
angle = absolute(arctan2(y,x))
half_length = params['arc_length']/2
radius = 1.0 - greater_equal(angle,half_length)
distance = angle - half_length
sigmasq = gaussian_width*gaussian_width
with float_error_ignore():
falloff = exp(divide(-distance*distance, 2.0*sigmasq))
return maximum(radius, falloff)
def spiral(x, y, thickness, gaussian_width, density):
"""
Archemidean spiral with Gaussian fall-off outside the spiral curve.
"""
half_thickness = thickness/2.0
spacing = density*2*pi
distance_from_origin = sqrt(x**2+y**2)
distance_from_spiral_middle = fmod(spacing + distance_from_origin - density*arctan2(y,x),spacing)
distance_from_spiral_middle = minimum(distance_from_spiral_middle,spacing - distance_from_spiral_middle)
distance_from_spiral = distance_from_spiral_middle - half_thickness
spiral = 1.0 - greater_equal(distance_from_spiral,0.0)
sigmasq = gaussian_width*gaussian_width
with float_error_ignore():
falloff = exp(divide(-distance_from_spiral*distance_from_spiral, 2.0*sigmasq))
return maximum(falloff,spiral)
def logArea(x, alpha, beta):
"""
Area of the smallest interval of a lognormal distribution that still
includes x.
@param x: border value
@type x: float
@param alpha: mean of log-transformed distribution
@type alpha: float
@param beta: standarddev of log-transformed distribution
@type beta: float
@return: probability that x is NOT drawn from the given distribution
@rtype: float
"""
r_max = N.exp(alpha - beta**2)
if x < r_max: x = r_max**2 / x
upper = (N.log(x) - alpha) / beta
return 0.5 * (erf(upper / N.sqrt(2)) - erf(-(upper + 2*beta) / N.sqrt(2)))
def logArea(x, alpha, beta):
"""
Area of the smallest interval of a lognormal distribution that still
includes x.
@param x: border value
@type x: float
@param alpha: mean of log-transformed distribution
@type alpha: float
@param beta: standarddev of log-transformed distribution
@type beta: float
@return: probability that x is NOT drawn from the given distribution
@rtype: float
"""
r_max = N.exp(alpha - beta**2)
if x < r_max: x = r_max**2 / x
upper = (N.log(x) - alpha) / beta
return 0.5 * (erf(upper / N.sqrt(2)) -
erf(-(upper + 2 * beta) / N.sqrt(2)))
def test_preference(self):
for i in range(3):
for j in range(2):
self.assertAlmostEqual(self.fm1.weighted_average()[i,j], 0.5)
self.assertAlmostEqual(self.fm2.weighted_average()[i,j], 0.5)
# To test the update function
self.fm1.update(self.a1,0.7)
self.fm2.update(self.a1,0.7)
for i in range(3):
for j in range(2):
self.assertAlmostEqual(self.fm1.weighted_average()[i,j], 0.6)
vect_sum = wrap(0,1,arg(exp(0.7*2*pi*1j)+exp(0.5*2*pi*1j))/(2*pi))
self.assertAlmostEqual(self.fm2.weighted_average()[i,j],vect_sum)
# To test the keep_peak=True
self.fm1.update(self.a1,0.7)
self.fm2.update(self.a1,0.7)
for i in range(3):
for j in range(2):
self.assertAlmostEqual(self.fm1.weighted_average()[i,j], 0.6)
vect_sum =wrap(0,1,arg(exp(0.7*2*pi*1j)+exp(0.5*2*pi*1j))/(2*pi))
self.assertAlmostEqual(self.fm2.weighted_average()[i,j],vect_sum)
self.fm1.update(self.a2,0.7)
self.fm2.update(self.a2,0.7)
for i in range(3):
for j in range(2):
self.assertAlmostEqual(self.fm1.weighted_average()[i,j], 0.65)
vect_sum =wrap(0,1,arg(3*exp(0.7*2*pi*1j)+exp(0.5*2*pi*1j))/(2*pi))
self.assertAlmostEqual(self.fm2.weighted_average()[i,j],vect_sum)
# to even test more....
self.fm1.update(self.a3,0.9)
self.fm2.update(self.a3,0.9)
for i in range(3):
self.assertAlmostEqual(self.fm1.weighted_average()[i,0], 0.65)
self.assertAlmostEqual(self.fm1.weighted_average()[i,1], 0.7)
vect_sum = wrap(0,1,arg(3*exp(0.7*2*pi*1j)+exp(0.5*2*pi*1j))/(2*pi))
self.assertAlmostEqual(self.fm2.weighted_average()[i,0],vect_sum)
vect_sum = wrap(0,1,arg(3*exp(0.7*2*pi*1j)+exp(0.5*2*pi*1j)+exp(0.9*2*pi*1j))/(2*pi))
self.assertAlmostEqual(self.fm2.weighted_average()[i,1],vect_sum)
def test_selectivity(self):
for i in range(3):
for j in range(2):
# when only one bin the selectivity is 1 (from C code)
self.assertAlmostEqual(selectivity(self.fm1)[i, j], 1.0)
self.assertAlmostEqual(selectivity(self.fm2)[i, j], 1.0)
# To test the update function
self.fm1.update(self.a1, 0.7)
self.fm2.update(self.a1, 0.7)
for i in range(3):
for j in range(2):
proportion = 1.0 / 2.0
offset = 1.0 / 2.0
relative_selectivity = (proportion - offset) / (
1.0 - offset) ## gives 0 ..?
self.assertAlmostEqual(
selectivity(self.fm1)[i, j], relative_selectivity)
vect_sum = abs(
exp(0.7 * 2 * pi * 1j) + exp(0.5 * 2 * pi * 1j)) / 2.0
self.assertAlmostEqual(selectivity(self.fm2)[i, j], vect_sum)
# To test the keep_peak=True
self.fm1.update(self.a1, 0.7)
self.fm2.update(self.a1, 0.7)
for i in range(3):
for j in range(2):
proportion = 1.0 / 2.0
offset = 1.0 / 2.0
relative_selectivity = (proportion - offset) / (1.0 - offset)
self.assertAlmostEqual(
selectivity(self.fm1)[i, j], relative_selectivity)
vect_sum = abs(
exp(0.7 * 2 * pi * 1j) + exp(0.5 * 2 * pi * 1j)) / 2.0
self.assertAlmostEqual(selectivity(self.fm2)[i, j], vect_sum)
self.fm1.update(self.a2, 0.7)
self.fm2.update(self.a2, 0.7)
for i in range(3):
for j in range(2):
proportion = 3.0 / 4.0
offset = 1.0 / 2.0
relative_selectivity = (proportion - offset) / (1.0 - offset)
#self.assertAlmostEqual( selectivity( self.fm1 )[i,j],relative_selectivity)
vect_sum = abs(3 * exp(0.7 * 2 * pi * 1j) +
exp(0.5 * 2 * pi * 1j)) / 4.0
self.assertAlmostEqual(selectivity(self.fm2)[i, j], vect_sum)
# to even test more....
self.fm1.update(self.a3, 0.9)
self.fm2.update(self.a3, 0.9)
for i in range(3):
proportion = 3.0 / 4.0
offset = 1.0 / 3.0 ### Carefull, do not create bins when it is 0
### Check with Bednar what is num_bins in the original C-file and see what he wants
### now for the selectivity ....
relative_selectivity = (proportion - offset) / (1.0 - offset)
self.assertAlmostEqual(
selectivity(self.fm1)[i, 0], relative_selectivity)
proportion = 3.0 / 5.0
offset = 1.0 / 3.0
relative_selectivity = (proportion - offset) / (1.0 - offset)
### to fix this test as well
#self.assertAlmostEqual( selectivity( self.fm1 )[i,1], relative_selectivity)
vect_sum = abs(3 * exp(0.7 * 2 * pi * 1j) +
exp(0.5 * 2 * pi * 1j)) / 4.0
self.assertAlmostEqual(selectivity(self.fm2)[i, 0], vect_sum)
vect_sum = abs(3 * exp(0.7 * 2 * pi * 1j) +
exp(0.5 * 2 * pi * 1j) +
exp(0.9 * 2 * pi * 1j)) / 5.0
self.assertAlmostEqual(selectivity(self.fm2)[i, 1], vect_sum)
def test_preference(self):
for i in range(3):
for j in range(2):
self.assertAlmostEqual(weighted_average(self.fm1)[i, j], 0.5)
self.assertAlmostEqual(weighted_average(self.fm2)[i, j], 0.5)
# To test the update function
self.fm1.update(self.a1, 0.7)
self.fm2.update(self.a1, 0.7)
for i in range(3):
for j in range(2):
self.assertAlmostEqual(weighted_average(self.fm1)[i, j], 0.6)
vect_sum = wrap(
0, 1,
arg(exp(0.7 * 2 * pi * 1j) + exp(0.5 * 2 * pi * 1j)) /
(2 * pi))
self.assertAlmostEqual(
weighted_average(self.fm2)[i, j], vect_sum)
# To test the keep_peak=True
self.fm1.update(self.a1, 0.7)
self.fm2.update(self.a1, 0.7)
for i in range(3):
for j in range(2):
self.assertAlmostEqual(weighted_average(self.fm1)[i, j], 0.6)
vect_sum = wrap(
0, 1,
arg(exp(0.7 * 2 * pi * 1j) + exp(0.5 * 2 * pi * 1j)) /
(2 * pi))
self.assertAlmostEqual(
weighted_average(self.fm2)[i, j], vect_sum)
self.fm1.update(self.a2, 0.7)
self.fm2.update(self.a2, 0.7)
for i in range(3):
for j in range(2):
self.assertAlmostEqual(weighted_average(self.fm1)[i, j], 0.65)
vect_sum = wrap(
0, 1,
arg(3 * exp(0.7 * 2 * pi * 1j) + exp(0.5 * 2 * pi * 1j)) /
(2 * pi))
self.assertAlmostEqual(
weighted_average(self.fm2)[i, j], vect_sum)
# to even test more....
self.fm1.update(self.a3, 0.9)
self.fm2.update(self.a3, 0.9)
for i in range(3):
self.assertAlmostEqual(weighted_average(self.fm1)[i, 0], 0.65)
self.assertAlmostEqual(weighted_average(self.fm1)[i, 1], 0.7)
vect_sum = wrap(
0, 1,
arg(3 * exp(0.7 * 2 * pi * 1j) + exp(0.5 * 2 * pi * 1j)) /
(2 * pi))
self.assertAlmostEqual(weighted_average(self.fm2)[i, 0], vect_sum)
vect_sum = wrap(
0, 1,
arg(3 * exp(0.7 * 2 * pi * 1j) + exp(0.5 * 2 * pi * 1j) +
exp(0.9 * 2 * pi * 1j)) / (2 * pi))
self.assertAlmostEqual(weighted_average(self.fm2)[i, 1], vect_sum)
def rand_log_normal(alpha, beta, shape):
return N.exp(R.normal(alpha, beta, shape))
def ln(r, alpha, beta):
return N.exp(-0.5/beta**2 * (N.log(r) - alpha)**2 \
- 0.5*N.log(2*N.pi)-N.log(beta*r))
def P(self,T,m):
logP = m["ANT_A"]-(m["ANT_B"]/ ( T + m["ANT_C"] ))
for i in range(len(logP)):
if logP[i]<-36:
logP[i]= -18.420680743952367
return array( exp( logP )*self.Factor)
def FugaP(self, Z, A, B):
""" Fugacity Coefficient of Pure Substances"""
LogFug = Z - 1 - log(Z - B) - A / B * log(1 + B / Z)
Fug = exp(LogFug)
return Fug
def FugaM(self, Z, A_i, B_i, A, B):
LogFug = B_i / B * (Z - 1) - log(
Z - B) + A / B * (B_i / B - 2 * A_i / A) * log(1 + B / Z)
Fug = exp(LogFug)
return Fug
