def find_section_coordinates(sec):
""" Determine xyz coordinates for a given section """
from numpy import array
from neuron import h
x=[];y=[];z=[];
n=h.n3d(sec=sec).__int__()
for ii in xrange(n):
x.append(h.x3d(ii,sec=sec))
y.append(h.y3d(ii,sec=sec))
z.append(h.z3d(ii,sec=sec))
return array([array(x),array(y),array(z)])
def find_mean_section_coordinates(sec):
""" Determine average coordinate for a given section """
from neuron import h
from numpy import array, mean
x=[];y=[];z=[];
n=h.n3d(sec=sec).__int__()
for ii in xrange(n):
x.append(h.x3d(ii,sec=sec))
y.append(h.y3d(ii,sec=sec))
z.append(h.z3d(ii,sec=sec))
return array([mean(x),mean(y),mean(z)])
def rotate_coordinates(X,Y,Z,v,axis='z'):
#print "NEW RUN: ",v
if axis=='z':
from numpy import array, c_, arctan,sin,cos,matrix,ones,pi,sqrt,sign
X = array(X)
Y = array(Y)
Z = array(Z)
assert X.shape==Y.shape==Z.shape
v = matrix(v)
dx = v[1,0]-v[0,0]
dy = v[1,1]-v[0,1]
dz = v[1,2]-v[0,2]
#print dx,dy,dz
length = sqrt(dx**2 + dy**2 + dz**2)
xyz = matrix(c_[X.flatten(),
Y.flatten(),
Z.flatten()])
# this is where we translate/rotate
T = matrix([[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[-v[1,0], -v[1,1], -v[1,2], 1]])
xyz = (c_[xyz,ones(X.flatten().shape)] * T)[:,:3]
v = (c_[v, ones(v[:,0].shape)] * T)[:,:3]
# Rotate about X axis
dx = v[1,0]-v[0,0]
dy = v[1,1]-v[0,1]
dz = v[1,2]-v[0,2]
#print dx,dy,dz
#assert length == sqrt(dx**2 + dy**2 + dz**2)
#print length, sqrt(dx**2 + dy**2 + dz**2)
if dz==0:
theta_x = -1 * sign(dy) * pi/2.0
else:
theta_x = -arctan(dy/dz)
#print 'THETA X: ',theta_x
#print v
Rx = matrix([[1,0,0],
[0,cos(theta_x),-sin(theta_x)],
[0,sin(theta_x),cos(theta_x)]])
xyz *= Rx
v *= Rx
# Rotate about Y axis
dx = v[1,0]-v[0,0]
dy = v[1,1]-v[0,1]
dz = v[1,2]-v[0,2]
#print dx,dy,dz
#assert length == sqrt(dx**2 + dy**2 + dz**2)
#print length, sqrt(dx**2 + dy**2 + dz**2)
if dz==0:
theta_y = sign(dx) * pi/2.0
else:
theta_y = arctan(dx/dz)
Ry = matrix([[cos(theta_y),0,sin(theta_y)],
[0,1,0],
[-sin(theta_y),0,cos(theta_y)]])
xyz *= Ry
v *= Ry
if sign((v[1,2]-v[0,2]))==-1: # aligned in the wrong direction
flip = matrix([[1,0,0],
[0,-1,0],
[0,0,-1]])
xyz *= flip
v *= flip
#assert length == sqrt(dx**2 + dy**2 + dz**2)
#print length, sqrt(dx**2 + dy**2 + dz**2)
#print "v", v
# Rotate about Z axis
## dx = v[1,0]-v[0,0]
## dy = v[1,1]-v[0,1]
## if dy != 0:
## theta_z = -arctan(dx/dy)
## print "THETA Z: ",theta_z
## print "v", v
## Rz = matrix([[cos(theta_z),-sin(theta_z),0],
## [sin(theta_z),cos(theta_z),0],
## [0,0,1]])
## xyz *= Rz
## v *= Rz
xyz = array(xyz)
v = array(v)
return xyz[:,0].reshape(X.shape),xyz[:,1].reshape(Y.shape),xyz[:,2].reshape(Z.shape),v
else:
raise StandardError,'Not implemented'
def find_cylindrical_coords(X,Y,Z,v):
" Align v with positive z axis "
from numpy import sqrt
X,Y,Z,v = rotate_coordinates(X,Y,Z,v)
R = sqrt(X**2 + Y**2)
return R,Z
def rotate_point_around_vector(xyz, uvw,angle,angle_units='degree'):
""" Rotate the point (x,y,z) around the vector (u,v,w) """
from numpy import cos, sin, array, pi, deg2rad
x = xyz[0]
y = xyz[1]
z = xyz[2]
u = uvw[0]
v = uvw[1]
w = uvw[2]
if angle_units=='degree':
angle = deg2rad(angle)
ux = u * x
uy = u * y
uz = u * z
vx = v * x
vy = v * y
vz = v * z
wx = w * x
wy = w * y
wz = w * z
sa = sin(angle)
ca = cos(angle)
new_x = u * (ux + vy + wz) + (x * (v * v + w * w) - u * (vy + wz)) * ca + (-wy + vz) * sa
new_y = v * (ux + vy + wz) + (y * (u * u + w * w) - v * (ux + wz)) * ca + (wx - uz) * sa
new_z = w * (ux + vy + wz) + (z * (u * u + v * v) - w * (ux + vy)) * ca + (-vx + uy) * sa
return array([new_x, new_y, new_z])
def point_along_vector(origin, direction, length):
from numpy import array
from numpy.linalg import norm
origin = array(origin).astype(float)
direction = array(direction).astype(float)
unit_direction = direction/norm(direction)
return origin + length * unit_direction
def _float_approx_equal(x, y, tol=1e-18, rel=1e-7):
## {{{ http://code.activestate.com/recipes/577124/ (r1)
if tol is rel is None:
raise TypeError('cannot specify both absolute and relative errors are None')
tests = []
if tol is not None: tests.append(tol)
if rel is not None: tests.append(rel*abs(x))
assert tests
return abs(x - y) <= max(tests)
def approx_equal(x, y, *args, **kwargs):
"""approx_equal(float1, float2[, tol=1e-18, rel=1e-7]) -> True|False
approx_equal(obj1, obj2[, *args, **kwargs]) -> True|False
Return True if x and y are approximately equal, otherwise False.
If x and y are floats, return True if y is within either absolute error
tol or relative error rel of x. You can disable either the absolute or
relative check by passing None as tol or rel (but not both).
For any other objects, x and y are checked in that order for a method
__approx_equal__, and the result of that is returned as a bool. Any
optional arguments are passed to the __approx_equal__ method.
__approx_equal__ can return NotImplemented to signal that it doesn't know
how to perform that specific comparison, in which case the other object is
checked instead. If neither object have the method, or both defer by
returning NotImplemented, approx_equal falls back on the same numeric
comparison used for floats.
>>> almost_equal(1.2345678, 1.2345677)
True
>>> almost_equal(1.234, 1.235)
False
"""
## {{{ http://code.activestate.com/recipes/577124/ (r1)
if not (type(x) is type(y) is float):
# Skip checking for __approx_equal__ in the common case of two floats.
methodname = '__approx_equal__'
# Allow the objects to specify what they consider "approximately equal",
# giving precedence to x. If either object has the appropriate method, we
# pass on any optional arguments untouched.
for a,b in ((x, y), (y, x)):
try:
method = getattr(a, methodname)
except AttributeError:
continue
else:
result = method(b, *args, **kwargs)
if result is NotImplemented:
continue
return bool(result)
# If we get here without returning, then neither x nor y knows how to do an
# approximate equal comparison (or are both floats). Fall back to a numeric
# comparison.
return _float_approx_equal(x, y, *args, **kwargs)
def make_legend(**extra_args):
from matplotlib import pyplot
leg = pyplot.legend(numpoints=1, labelspacing=0, scatterpoints=1,
**extra_args)
for t in leg.get_texts():
t.set_fontsize('small') # the legend text fontsize
def find_rho(radius=0.2,n=1.36,NA=0.37):
""" Rho is a measure of light spread
radius: (mm) In Gradinaru paper, they report the diameter as 400 um
n: index of refraction of gray matter
NA: numerical aperture of the optical fiber
"""
from numpy import sqrt
return radius * sqrt((n/(NA**2)) - 1)
def spreading(dist,radius=0.2,n=1.36,NA=0.37):
rho = find_rho(radius,n,NA)
return (rho ** 2) / ((rho + dist) ** 2)