def root_poly3(a1, a2, a3):
"""Roots for a cubic polinomy, x^3 + a1*x^2 + a2*x + a3"""
Q = (3 * a2 - a1**2) / 9.
L = (9 * a1 * a2 - 27 * a3 - 2 * a1**3) / 54.
D = Q**3 + L**2
if D < 0:
tita = acos(L / (-Q**3)**0.5)
z1 = 2 * (-Q)**0.5 * cos(tita / 3. + 2. * pi / 3) - a1 / 3.
z2 = 2 * (-Q)**0.5 * cos(tita / 3. + 4. * pi / 3) - a1 / 3.
z3 = 2 * (-Q)**0.5 * cos(tita / 3.) - a1 / 3.
z = [z1, z2, z3]
else:
S1 = cbrt((L + D**0.5))
S2 = cbrt((L - D**0.5))
if D > 0:
z = [S1 + S2 - a1 / 3.]
else:
z1 = cbrt(L) + cbrt(L) - a1 / 3.
z2 = -cbrt(L) - a1 / 3.
z = [z1, z2]
# print z[0]+z[1]+z[2], -a1
# print z[0]*z[1]+z[1]*z[2]+z[2]*z[0], a2
# print z[0]*z[1]*z[2], -a3
z.sort()
return z
def equivalent_diameter(target,
pore_volume='pore.volume',
pore_shape='sphere'):
r"""
Calculates the diameter of a sphere or edge-length of a cube with same
volume as the pore.
Parameters
----------
target : OpenPNM Geometry Object
The Geometry object which this model is associated with. This controls
the length of the calculated array, and also provides access to other
necessary geometric properties.
pore_volume : string
The dictionary key containing the pore volume values
pore_shape : string
The shape of the pore body to assume when back-calculating from
volume. Options are 'sphere' (default) or 'cube'.
"""
from scipy.special import cbrt
pore_vols = target[pore_volume]
if pore_shape.startswith('sph'):
value = cbrt(6 * pore_vols / _np.pi)
elif pore_shape.startswith('cub'):
value = cbrt(pore_vols)
return value
def nonlinear_resonator(f, f_0, Q, Q_e_real, Q_e_imag, a):
Q_e = Q_e_real + 1j * Q_e_imag
if np.isscalar(f):
fmodel = np.linspace(f * 0.9999, f * 1.0001, 1000)
scalar = True
else:
fmodel = f
scalar = False
y_0 = ((fmodel - f_0) / f_0) * Q
y = (y_0 / 3. + (y_0**2 / 9 - 1 / 12) / cbrt(a / 8 + y_0 / 12 + np.sqrt(
(y_0**3 / 27 + y_0 / 12 + a / 8)**2 -
(y_0**2 / 9 - 1 / 12)**3) + y_0**3 / 27) +
cbrt(a / 8 + y_0 / 12 + np.sqrt((y_0**3 / 27 + y_0 / 12 + a / 8)**2 -
(y_0**2 / 9 - 1 / 12)**3) +
y_0**3 / 27))
x = y / Q
s21 = (1 - (Q / Q_e) / (1 + 2j * Q * x))
mask = np.isfinite(s21)
if scalar or not np.all(mask):
s21_interp_real = np.interp(f, fmodel[mask], s21[mask].real)
s21_interp_imag = np.interp(f, fmodel[mask], s21[mask].imag)
s21new = s21_interp_real + 1j * s21_interp_imag
else:
s21new = s21
return s21new
def root_poly3(a1, a2, a3):
"""Roots for a cubic polinomy, x^3 + a1*x^2 + a2*x + a3"""
Q = (3*a2-a1**2)/9.
L = (9*a1*a2-27*a3-2*a1**3)/54.
D = Q**3+L**2
if D < 0:
tita = acos(L/(-Q**3)**0.5)
z1 = 2*(-Q)**0.5*cos(tita/3.+2.*pi/3)-a1/3.
z2 = 2*(-Q)**0.5*cos(tita/3.+4.*pi/3)-a1/3.
z3 = 2*(-Q)**0.5*cos(tita/3.)-a1/3.
z = [z1, z2, z3]
else:
S1 = cbrt((L+D**0.5))
S2 = cbrt((L-D**0.5))
if D > 0:
z = [S1+S2-a1/3.]
else:
z1 = cbrt(L)+cbrt(L)-a1/3.
z2 = -cbrt(L)-a1/3.
z = [z1, z2]
# print z[0]+z[1]+z[2], -a1
# print z[0]*z[1]+z[1]*z[2]+z[2]*z[0], a2
# print z[0]*z[1]*z[2], -a3
z.sort()
return z
def bifurcation_s21(params,f):
"""
Swenson paper:
Equation: y = yo + A/(1+4*y**2)
"""
A = (params['A_mag'].value *
np.exp(1j * params['A_phase'].value))
f_0 = params['f_0'].value
Q = params['Q'].value
Q_e = (params['Q_e_real'].value +
1j * params['Q_e_imag'].value)
a = params['a'].value
if np.isscalar(f):
fmodel = np.linspace(f*0.9999,f*1.0001,1000)
scalar = True
else:
fmodel = f
scalar = False
y_0 = ((fmodel - f_0)/f_0)*Q
y = (y_0/3. +
(y_0**2/9 - 1/12)/cbrt(a/8 + y_0/12 + np.sqrt((y_0**3/27 + y_0/12 + a/8)**2 - (y_0**2/9 - 1/12)**3) + y_0**3/27) +
cbrt(a/8 + y_0/12 + np.sqrt((y_0**3/27 + y_0/12 + a/8)**2 - (y_0**2/9 - 1/12)**3) + y_0**3/27))
x = y/Q
s21 = A*(1 - (Q/Q_e)/(1+2j*Q*x))
msk = np.isfinite(s21)
if scalar or not np.all(msk):
s21_interp_real = np.interp(f,fmodel[msk],s21[msk].real)
s21_interp_imag = np.interp(f,fmodel[msk],s21[msk].imag)
s21new = s21_interp_real+1j*s21_interp_imag
else:
s21new = s21
return s21new*cable_delay(params,f)
def bifurcation_s21(params, f):
"""
Swenson paper:
Equation: y = yo + A/(1+4*y**2)
"""
A = (params['A_mag'].value * np.exp(1j * params['A_phase'].value))
f_0 = params['f_0'].value
Q = params['Q'].value
Q_e = (params['Q_e_real'].value + 1j * params['Q_e_imag'].value)
a = params['a'].value
if np.isscalar(f):
fmodel = np.linspace(f * 0.9999, f * 1.0001, 1000)
scalar = True
else:
fmodel = f
scalar = False
y_0 = ((fmodel - f_0) / f_0) * Q
y = (y_0 / 3. + (y_0**2 / 9 - 1 / 12) / cbrt(a / 8 + y_0 / 12 + np.sqrt(
(y_0**3 / 27 + y_0 / 12 + a / 8)**2 -
(y_0**2 / 9 - 1 / 12)**3) + y_0**3 / 27) +
cbrt(a / 8 + y_0 / 12 + np.sqrt((y_0**3 / 27 + y_0 / 12 + a / 8)**2 -
(y_0**2 / 9 - 1 / 12)**3) +
y_0**3 / 27))
x = y / Q
s21 = A * (1 - (Q / Q_e) / (1 + 2j * Q * x))
mask = np.isfinite(s21)
if scalar or not np.all(mask):
s21_interp_real = np.interp(f, fmodel[mask], s21[mask].real)
s21_interp_imag = np.interp(f, fmodel[mask], s21[mask].imag)
s21new = s21_interp_real + 1j * s21_interp_imag
else:
s21new = s21
return s21new * cable_delay(params, f)
def equivalent_diameter(target, pore_volume='pore.volume',
pore_shape='sphere'):
r"""
Calculates the diameter of a sphere or edge-length of a cube with same
volume as the pore.
Parameters
----------
target : OpenPNM Geometry Object
The Geometry object which this model is associated with. This controls
the length of the calculated array, and also provides access to other
necessary geometric properties.
pore_volume : string
The dictionary key containing the pore volume values
pore_shape : string
The shape of the pore body to assume when back-calculating from
volume. Options are 'sphere' (default) or 'cube'.
Returns
-------
D : NumPy ndarray
Array containing pore diameter values.
"""
from scipy.special import cbrt
pore_vols = target[pore_volume]
if pore_shape.startswith('sph'):
value = cbrt(6*pore_vols/_np.pi)
elif pore_shape.startswith('cub'):
value = cbrt(pore_vols)
return value
def setUp(self):
"""Verify environment is setup properly
data taken from the fitted params of the sweep 2013-12-03_174822.nc
swps[0], swp.atten = 21.0, swp.power_dbm=-51 """
f = np.linspace(82.85, 82.90, 10000)
f_0 = 82.881569344696032
A_mag = 0.045359757228080611
A_phase = 0
A = A_mag * np.exp(1j * A_phase)
Q = 32253.035141948105
Q_e_real = 120982.33962292993
Q_e_imag = 51324.601136160083
Q_e = Q_e_real + 1j * Q_e_imag
delay = 0.05255962136531532
phi = -1.3313606721103854
f_min = f[0]
a = 0.56495480690974931
self.Q_i = (Q ** -1 - np.real(Q_e ** -1)) ** -1
y_0 = ((f - f_0) / f_0) * Q
y = (
y_0 / 3.0
+ (y_0 ** 2 / 9 - 1 / 12)
/ cbrt(
a / 8
+ y_0 / 12
+ np.sqrt((y_0 ** 3 / 27 + y_0 / 12 + a / 8) ** 2 - (y_0 ** 2 / 9 - 1 / 12) ** 3)
+ y_0 ** 3 / 27
)
+ cbrt(
a / 8
+ y_0 / 12
+ np.sqrt((y_0 ** 3 / 27 + y_0 / 12 + a / 8) ** 2 - (y_0 ** 2 / 9 - 1 / 12) ** 3)
+ y_0 ** 3 / 27
)
)
x = y / Q
s21 = A * (1 - (Q / Q_e) / (1 + 2j * Q * x))
msk = np.isfinite(s21)
if not np.all(msk):
s21_interp_real = np.interp(f, f[msk], s21[msk].real)
s21_interp_imag = np.interp(f, f[msk], s21[msk].imag)
s21new = s21_interp_real + 1j * s21_interp_imag
else:
s21new = s21
cable_delay = np.exp(1j * (-2 * np.pi * (f - f_min) * delay + phi))
bifurcation = s21new * cable_delay
self.f_0 = f_0
self.f = f
self.Q = Q
self.bifurcation = bifurcation
self.res = resonator.Resonator(
self.f, self.bifurcation, model=khalil.bifurcation_s21, guess=khalil.bifurcation_guess
)
def _distort_tsai(self, metric_image_coord):
"""
Distort centered metric image coordinates following Tsai model.
See: Devernay, Frederic, and Olivier Faugeras. "Straight lines have to be straight."
Machine vision and applications 13.1 (2001): 14-24. Section 2.1.
(only for illustration, the formulas didn't work for me)
http://www.cvg.rdg.ac.uk/PETS2009/sample.zip :CameraModel.cpp:CameraModel::undistortedToDistortedSensorCoord
Analytical inverse of the undistort_tsai() function.
:param metric_image_coord: centered metric image coordinates
(metric image coordinate = image_xy * f / z)
:type metric_image_coord: numpy.ndarray, shape=(2, n)
:return: distorted centered metric image coordinates
:rtype: numpy.ndarray, shape=(2, n)
"""
assert metric_image_coord.shape[0] == 2
x = metric_image_coord[0, :] # vector
y = metric_image_coord[1, :] # vector
r_u = np.sqrt(x**2 + y**2) # vector
c = 1.0 / self.tsai_kappa # scalar
d = -c * r_u # vector
# solve polynomial of 3rd degree for r_distorted using Cardan method:
# https://proofwiki.org/wiki/Cardano%27s_Formula
# r_distorted ** 3 + c * r_distorted + d = 0
q = c / 3. # scalar
r = -d / 2. # vector
delta = q**3 + r**2 # polynomial discriminant, vector
positive_mask = delta >= 0
r_distorted = np.zeros((metric_image_coord.shape[1]))
# discriminant > 0
s = cbrt(r[positive_mask] + np.sqrt(delta[positive_mask]))
t = cbrt(r[positive_mask] - np.sqrt(delta[positive_mask]))
r_distorted[positive_mask] = s + t
# discriminant < 0
delta_sqrt = np.sqrt(-delta[~positive_mask])
s = cbrt(np.sqrt(r[~positive_mask]**2 + delta_sqrt**2))
# s = cbrt(np.sqrt(r[~positive_mask] ** 2 + (-delta[~positive_mask]) ** 2))
t = 1. / 3 * np.arctan2(delta_sqrt, r[~positive_mask])
r_distorted[
~positive_mask] = -s * np.cos(t) + s * np.sqrt(3) * np.sin(t)
return metric_image_coord * r_distorted / r_u
def _distort_tsai(self, metric_image_coord):
"""
Distort centered metric image coordinates following Tsai model.
See: Devernay, Frederic, and Olivier Faugeras. "Straight lines have to be straight."
Machine vision and applications 13.1 (2001): 14-24. Section 2.1.
(only for illustration, the formulas didn't work for me)
http://www.cvg.rdg.ac.uk/PETS2009/sample.zip :CameraModel.cpp:CameraModel::undistortedToDistortedSensorCoord
Analytical inverse of the undistort_tsai() function.
:param metric_image_coord: centered metric image coordinates
(metric image coordinate = image_xy * f / z)
:type metric_image_coord: numpy.ndarray, shape=(2, n)
:return: distorted centered metric image coordinates
:rtype: numpy.ndarray, shape=(2, n)
"""
assert metric_image_coord.shape[0] == 2
x = metric_image_coord[0, :] # vector
y = metric_image_coord[1, :] # vector
r_u = np.sqrt(x ** 2 + y ** 2) # vector
c = 1.0 / self.tsai_kappa # scalar
d = -c * r_u # vector
# solve polynomial of 3rd degree for r_distorted using Cardan method:
# https://proofwiki.org/wiki/Cardano%27s_Formula
# r_distorted ** 3 + c * r_distorted + d = 0
q = c / 3. # scalar
r = -d / 2. # vector
delta = q ** 3 + r ** 2 # polynomial discriminant, vector
positive_mask = delta >= 0
r_distorted = np.zeros((metric_image_coord.shape[1]))
# discriminant > 0
s = cbrt(r[positive_mask] + np.sqrt(delta[positive_mask]))
t = cbrt(r[positive_mask] - np.sqrt(delta[positive_mask]))
r_distorted[positive_mask] = s + t
# discriminant < 0
delta_sqrt = np.sqrt(-delta[~positive_mask])
s = cbrt(np.sqrt(r[~positive_mask] ** 2 + delta_sqrt ** 2))
# s = cbrt(np.sqrt(r[~positive_mask] ** 2 + (-delta[~positive_mask]) ** 2))
t = 1. / 3 * np.arctan2(delta_sqrt, r[~positive_mask])
r_distorted[~positive_mask] = -s * np.cos(t) + s * np.sqrt(3) * np.sin(t)
return metric_image_coord * r_distorted / r_u
def usr_moments(coords):
"""
Calculates the USR moments for a set of input coordinates as well as the four
USR reference atoms.
:param coords: numpy.ndarray
"""
# centroid of the input coordinates
ctd = coords.mean(axis=0)
# get the distances to the centroid
dist_ctd = distance_to_point(coords, ctd)
# get the closest and furthest coordinate to/from the centroid
cst, fct = coords[dist_ctd.argmin()], coords[dist_ctd.argmax()]
# get the distance distributions for the points that are closest/furthest
# to/from the centroid
dist_cst = distance_to_point(coords, cst)
dist_fct = distance_to_point(coords, fct)
# get the point that is the furthest from the point that is furthest from
# the centroid
ftf = coords[dist_fct.argmax()]
dist_ftf = distance_to_point(coords, ftf)
# calculate the first three moments for each of the four distance distributions
moments = concatenate([(ar.mean(), ar.std(), cbrt(skew(ar)))
for ar in (dist_ctd, dist_cst, dist_fct, dist_ftf)])
# return the USR moments as well as the four points for later re-use
return (ctd, cst, fct, ftf), moments
def __call__(self, coords):
# generate ellipsoid values based on parameters
(mcx, mcy, mcz) = coords
# start with random radius-like values
x0 = randint(1, 4)
y0 = randint(1, 4)
z0 = randint(1, 4)
v0 = 4/3 * pi * x0 * y0 * z0
# scale to match volume and round up
scale = cbrt(self.size / v0)
x1 = int(round(scale * x0))
y1 = int(round(scale * y0))
z1 = int(round(scale * z0))
# pre-calculate squares
x2 = x1 * x1
y2 = y1 * y1
z2 = z1 * z1
# generate ranges
xr = xrange(-1 * x1, x1)
yr = xrange(-1 * y1, y1)
zr = xrange(-1 * z1, z1)
# calculate ellipsoid
oreCoords = [[mcx+x, mcy+y, mcz+z] for x, y, z in product(xr, yr, zr) if x*x/x2+y*y/y2+z*z/z2 <= 1]
# if len(oreCoords) > self.size+2:
# print "warning: oreCoords larger than self.size -- %d > %d" % (len(oreCoords), self.size)
# if len(oreCoords) < self.size-2:
# print "warning: oreCoords smaller than self.size -- %d < %d" % (len(oreCoords), self.size)
return oreCoords
def model_normalized(params, f):
f_r = params['f_r'].value
i = params['i'].value
c = params['c'].value
c_imag = params['c_imag'].value
a = params['a'].value
Q_r = (i + c)**-1
y_0 = (f / f_r - 1) * Q_r
y = (y_0 / 3 +
(y_0**2 / 9 - 1 / 12) / cbrt(a / 8 + y_0 / 12 + np.sqrt(
(y_0**3 / 27 + y_0 / 12 + a / 8)**2 - (y_0**2 / 9 - 1 / 12)**3) + y_0**3 / 27) +
cbrt(a / 8 + y_0 / 12 + np.sqrt(
(y_0**3 / 27 + y_0 / 12 + a / 8)**2 - (y_0**2 / 9 - 1 / 12)**3) + y_0**3 / 27))
x = y / Q_r
return (1 - ((c + 1j * c_imag) /
(i + c + 2j * x)))
def direct_from_centered(mean, cov, skew):
""" Follow the advice of Arellano-Valle, R. B. & Azzalini, A. The
centred parametrization for the multivariate skew-normal
distribution. J. Multivar. Anal. 99, 1362-1382 (2008).
Given centered parameters, return direct parameters
"""
# Calculate mu_z from Eq. 5
c = cbrt((2 * skew) / (4 - np.pi))
u_z = c / np.sqrt(1 + c**2)
# Calculate delta_z
b = np.sqrt(2. / np.pi)
delta_z = u_z / b
sigma_z = np.diag(np.sqrt(1 - (b * delta_z)**2))
sigma = np.diag(np.sqrt(np.diag(cov)))
w = sigma.dot(np.linalg.pinv(sigma_z))
wdotu_z = w.dot(u_z)
dp_mean = mean - wdotu_z
dp_cov = cov + np.outer(wdotu_z, u_z).dot(w)
winv = np.linalg.pinv(w)
omega_hat = winv.dot(dp_cov).dot(winv)
omega_hat_inv = np.linalg.pinv(omega_hat)
ohi_dot_delta = omega_hat_inv.dot(delta_z)
dp_skew = ohi_dot_delta / np.sqrt(1 - ohi_dot_delta.dot(delta_z))
assert is_pos_def(dp_cov), "Covariance matrix must be pos-def"
assert np.all(np.isfinite(dp_skew)), "Skew must be finite"
return dp_mean, dp_cov, dp_skew
def _calc_epsTE01_int(self, Itke, theta):
"""
The integral, equation A13, in [TE01].
Parameters
----------
Itke : |np.ndarray|
(beta in TE01) is the turbulence intensity ratio:
\sigma_u / V
theta : |np.ndarray|
is the angle between the mean flow, and the primary axis of
velocity fluctuations.
"""
x = np.arange(-20, 20, 1e-2) # I think this is a long enough range.
out = np.empty_like(Itke.flatten())
for i, (b, t) in enumerate(zip(Itke.flatten(), theta.flatten())):
out[i] = np.trapz(
cbrt(x ** 2 - 2 / b * np.cos(t) * x + b ** (-2)) *
np.exp(-0.5 * x ** 2), x)
return out.reshape(Itke.shape) * \
(2 * np.pi) ** (-0.5) * Itke ** (2 / 3)
def _calc_epsTE01_int(self, Itke, theta):
"""
The integral, equation A13, in [TE01].
Parameters
----------
Itke : |np.ndarray|
(beta in TE01) is the turbulence intensity ratio:
\sigma_u / V
theta : |np.ndarray|
is the angle between the mean flow, and the primary axis of
velocity fluctuations.
"""
x = np.arange(-20, 20, 1e-2) # I think this is a long enough range.
out = np.empty_like(Itke.flatten())
for i, (b, t) in enumerate(zip(Itke.flatten(), theta.flatten())):
out[i] = np.trapz(
cbrt(x ** 2 - 2 / b * np.cos(t) * x + b ** (-2)) *
np.exp(-0.5 * x ** 2), x)
return out.reshape(Itke.shape) * \
(2 * np.pi) ** (-0.5) * Itke ** (2 / 3)
def compute_exchage_slater(rho):
"""Slater exchange energy functional."""
from scipy.special import cbrt
cx = (3. / 4) * (3 / np.pi)**(1. / 3)
ex = -cx * (np.power(rho, 4. / 3))
vx = -(4. / 3) * cx * cbrt(np.fabs(rho))
return ex, vx
def D(z, omega0):
"""
Growth Function
"""
a = 1/(1+z)
v = scs.cbrt(omega0/(1.-omega0))/a
return a*d1(v)
def scale(network,
scale_factor=[1, 1, 1],
preserve_vol=False,
linear_scaling=[False, False, False]):
r"""
A method for scaling the coordinates and vertices to create anisotropic networks
The original domain volume can be preserved by setting preserve_vol = True
Example
---------
>>> import OpenPNM
>>> import OpenPNM.Utilities.vertexops as vo
>>> import numpy as np
>>> pn = OpenPNM.Network.Delaunay(num_pores=100, domain_size=[3,2,1])
>>> pn.add_boundaries()
>>> B1 = pn.pores("left_boundary")
>>> B2 = pn.pores("right_boundary")
>>> Vol = vo.vertex_dimension(pn,B1,B2)
>>> vo.scale(network=pn,scale_factor=[2,1,1],preserve_vol=True)
>>> Vol2 = vo.vertex_dimension(pn,B1,B2)
>>> np.around(Vol-Vol2,5) == 0.0
True
>>> vo.scale(network=pn,scale_factor=[2,1,1],preserve_vol=False)
>>> Vol3 = vo.vertex_dimension(pn,B1,B2)
>>> np.around(Vol3/Vol,5) == 2.0
True
"""
from scipy.special import cbrt
import scipy as sp
minmax = np.around(
vertex_dimension(network=network, face1=network.pores(),
parm='minmax'), 10)
scale_factor = np.asarray(scale_factor)
if preserve_vol is True:
scale_factor = scale_factor / (cbrt(sp.prod(scale_factor)))
lin_scale = _linear_scale_factor(network["pore.coords"], minmax,
scale_factor, linear_scaling)
network["pore.coords"] = network["pore.coords"] * lin_scale
# Cycle through all vertices of all pores updating vertex values
for pore in network.pores():
for i, vert in network['pore.vert_index'][pore].items():
vert_scale = _linear_scale_factor(vert, minmax, scale_factor,
linear_scaling)
network["pore.vert_index"][pore][i] = vert * vert_scale
# Cycle through all vertices of all throats updating vertex values
for throat in network.throats():
for i, vert in network['throat.vert_index'][throat].items():
vert_scale = _linear_scale_factor(vert, minmax, scale_factor,
linear_scaling)
network["throat.vert_index"][throat][i] = vert * vert_scale
# Scale the vertices on the voronoi diagram stored on the network
# These are used for adding boundaries on the Delaunay network class
vert = network._vor.vertices
vert_scale = _linear_scale_factor(vert, minmax, scale_factor,
linear_scaling)
network._vor.vertices = vert * vert_scale
def placeoreintile(tile):
# strictly speaking, this should be in class Tile somehow
oreobjs = dict([(ore.name, ore) for ore in oreObjs])
tile.ores = dict([(name, list()) for name in oreobjs])
for ore in oreobjs:
extent = cbrt(oreobjs[ore].size)*2
maxy = pow(2, oreobjs[ore].depth)
numrounds = int(oreobjs[ore].rounds * (tile.size/16) * (tile.size/16))
oreID = materialNamed(oreobjs[ore].name)
for dummy in xrange(numrounds):
orex = randint(0, tile.size)
orey = randint(0, maxy)
orez = randint(0, tile.size)
coords = [orex+tile.mcoffsetx, orey, orez+tile.mcoffsetz]
if (orex < extent or (tile.size-orex) < extent or orez < extent or (tile.size-orez) < extent):
try:
tile.ores[ore]
except KeyError:
tile.ores[ore] = []
tile.ores[ore].append(coords)
else:
for x, y, z in oreobjs[ore](coords):
if tile.world.blockAt(x, y, z) == Ore.stoneID:
tile.world.setBlockAt(x, y, z, oreID)
def __call__(self, coords):
# generate ellipsoid values based on parameters
(mcx, mcy, mcz) = coords
# start with random radius-like values
x0 = randint(1, 4)
y0 = randint(1, 4)
z0 = randint(1, 4)
v0 = 4 / 3 * pi * x0 * y0 * z0
# scale to match volume and round up
scale = cbrt(self.size / v0)
x1 = int(round(scale * x0))
y1 = int(round(scale * y0))
z1 = int(round(scale * z0))
# pre-calculate squares
x2 = x1 * x1
y2 = y1 * y1
z2 = z1 * z1
# generate ranges
xr = xrange(-1 * x1, x1)
yr = xrange(-1 * y1, y1)
zr = xrange(-1 * z1, z1)
# calculate ellipsoid
oreCoords = [[mcx + x, mcy + y, mcz + z]
for x, y, z in product(xr, yr, zr)
if x * x / x2 + y * y / y2 + z * z / z2 <= 1]
# if len(oreCoords) > self.size+2:
# print "warning: oreCoords larger than self.size -- %d > %d" % (len(oreCoords), self.size)
# if len(oreCoords) < self.size-2:
# print "warning: oreCoords smaller than self.size -- %d < %d" % (len(oreCoords), self.size)
return oreCoords
def placeoreintile(tile):
# strictly speaking, this should be in class Tile somehow
oreobjs = dict([(ore.name, ore) for ore in oreObjs])
tile.ores = dict([(name, list()) for name in oreobjs])
for ore in oreobjs:
extent = cbrt(oreobjs[ore].size) * 2
maxy = pow(2, oreobjs[ore].depth)
numrounds = int(oreobjs[ore].rounds * (tile.size / 16) *
(tile.size / 16))
oreID = materialNamed(oreobjs[ore].name)
for dummy in xrange(numrounds):
orex = randint(0, tile.size)
orey = randint(0, maxy)
orez = randint(0, tile.size)
coords = [orex + tile.mcoffsetx, orey, orez + tile.mcoffsetz]
if (orex < extent or (tile.size - orex) < extent
or orez < extent or (tile.size - orez) < extent):
try:
tile.ores[ore]
except KeyError:
tile.ores[ore] = []
tile.ores[ore].append(coords)
else:
for x, y, z in oreobjs[ore](coords):
if tile.world.blockAt(x, y, z) == Ore.stoneID:
tile.world.setBlockAt(x, y, z, oreID)
def __init__(self, path_to_arc, rootUEP='/', **kwargs):
"""
:Parameters:
path_to_arc : path
Path to the HDF5 archive.
rootUEP : str
root in the archive used as the user entry path (UEP).
:Keywords:
see NeuronData
:Exceptions:
IOError:
Error finding or reading in the archive.
NosuchNodeError:
Error finding a data node in the archive.
see NeuronData
"""
# super
super(SampledND, self).__init__(**kwargs)
# read in data - may raise IOError or NoSuchNodeError
arc = openFile(path_to_arc, mode='r', rootUEP=rootUEP)
soma_v = arc.getNode('/soma_v').read()
ap_phase = xrange(0, soma_v.size)
# ap_phase = xrange(*get_eap_range(soma_v))
self.intra_v = arc.getNode('/soma_v').read()[..., ap_phase]
# self.extra_v = arc.getNode('/LFP').read().T[..., ap_phase]
self.extra_v = arc.getNode('/LFP').read()[..., ap_phase]
# read temporary data
x_pos = arc.getNode('/el_pos_x').read()
y_pos = arc.getNode('/el_pos_y').read()
z_pos = arc.getNode('/el_pos_z').read()
params = {}
for item in arc.getNode('/parameters'):
params[item.name] = item.read()
# close and delete archive
arc.close()
del arc
# horizon calculation
if not (
abs(x_pos.min()) ==
abs(x_pos.max()) ==
abs(y_pos.min()) ==
abs(y_pos.max()) ==
abs(z_pos.min()) ==
abs(z_pos.max())
):
raise ValueError('spatial shape is not cubic!')
self.horizon = x_pos.max()
# sample rate
if 'timeres_python' in params:
self.sample_rate = 1000.0 / params['timeres_python']
# spatial info - spatial resolution: x > y > z
self.grid_step = abs(z_pos[1] - z_pos[0])
self.grid_size = cbrt(self.extra_v.shape[0])
# filename stuff
self.description = 'Einevoll::%s' % osp.basename(path_to_arc)
def _ctd_alpha(Coordinates):
ctd = np.array([np.mean(Coordinates, axis=0)])
ctd_distance = cdist(ctd, Coordinates)[0]
ctd_alpha = {}
ctd_alpha['ctd_1'] = round(np.mean(ctd_distance), 3)
ctd_alpha['ctd_2'] = round(np.std(ctd_distance), 3)
ctd_alpha['ctd_3'] = round(cbrt(stats.skew(ctd_distance)), 3)
return ctd_alpha
def voronoi(geometry, pore_volume='pore.volume', **kwargs):
r"""
Calculate pore diameter from equivalent sphere - volumes must be calculated first
"""
from scipy.special import cbrt
pore_vols = geometry[pore_volume]
value = cbrt(6 * pore_vols / _sp.pi)
return value
def growthrate(z,omega0):
"""
growth rate f = dln(D(a))/dln()
"""
a = 1/(1+z)
v = scs.cbrt(omega0/(1.-omega0))/a
return (omega0/(((1.-omega0)*a**3)+omega0))*((2.5/d1(v))-1.5)
def scale(network, scale_factor=[1, 1, 1], preserve_vol=False,
linear_scaling=[False, False, False]):
r"""
A method for scaling the coordinates and vertices to create anisotropic networks
The original domain volume can be preserved by setting preserve_vol = True
Example
---------
>>> import OpenPNM
>>> import OpenPNM.Utilities.vertexops as vo
>>> import numpy as np
>>> pn = OpenPNM.Network.Delaunay(num_pores=100, domain_size=[3,2,1])
>>> pn.add_boundaries()
>>> B1 = pn.pores("left_boundary")
>>> B2 = pn.pores("right_boundary")
>>> Vol = vo.vertex_dimension(pn,B1,B2)
>>> vo.scale(network=pn,scale_factor=[2,1,1],preserve_vol=True)
>>> Vol2 = vo.vertex_dimension(pn,B1,B2)
>>> np.around(Vol-Vol2,5) == 0.0
True
>>> vo.scale(network=pn,scale_factor=[2,1,1],preserve_vol=False)
>>> Vol3 = vo.vertex_dimension(pn,B1,B2)
>>> np.around(Vol3/Vol,5) == 2.0
True
"""
from scipy.special import cbrt
import scipy as sp
minmax = np.around(vertex_dimension(network=network,
face1=network.pores(), parm='minmax'), 10)
scale_factor = np.asarray(scale_factor)
if preserve_vol is True:
scale_factor = scale_factor/(cbrt(sp.prod(scale_factor)))
lin_scale = _linear_scale_factor(network["pore.coords"], minmax,
scale_factor, linear_scaling)
network["pore.coords"] = network["pore.coords"]*lin_scale
# Cycle through all vertices of all pores updating vertex values
for pore in network.pores():
for i, vert in network['pore.vert_index'][pore].items():
vert_scale = _linear_scale_factor(vert, minmax,
scale_factor, linear_scaling)
network["pore.vert_index"][pore][i] = vert*vert_scale
# Cycle through all vertices of all throats updating vertex values
for throat in network.throats():
for i, vert in network['throat.vert_index'][throat].items():
vert_scale = _linear_scale_factor(vert, minmax,
scale_factor, linear_scaling)
network["throat.vert_index"][throat][i] = vert*vert_scale
# Scale the vertices on the voronoi diagram stored on the network
# These are used for adding boundaries on the Delaunay network class
vert = network._vor.vertices
vert_scale = _linear_scale_factor(vert, minmax, scale_factor, linear_scaling)
network._vor.vertices = vert*vert_scale
def scott(data, ddof=0):
"""
Scott's rule for the number of bins in a histogram.
"""
if np.std(data, ddof=ddof) == 0:
return sturges_rule(data)
h = 3.5 * np.std(data, ddof=ddof) / cbrt(len(data))
return int((np.max(data) - np.min(data)) / h)
def voronoi(geometry,
pore_volume='pore.volume',
**kwargs):
r"""
Calculate pore diameter from equivalent sphere - volumes must be calculated first
"""
from scipy.special import cbrt
pore_vols = geometry[pore_volume]
value = cbrt(6*pore_vols/_sp.pi)
return value
def cubeRatio(uspdO, uspdS, debug=False, plot=False):
"""
Estimates the cubed-ratio and returns it.
"""
ratio = sps.cbrt(np.mean(np.power(uspdO,3))/np.mean(np.power(uspdS,3)))
if debug:
print 'speed ratio is {}'.format(ratio)
return ratio
def scott(data, ddof=0):
"""
Scott's rule for the number of bins in a histogram.
"""
if np.std(data, ddof=ddof) == 0:
return sturges_rule(data)
h = 3.5*np.std(data, ddof=ddof)/cbrt(len(data))
return int((np.max(data) - np.min(data))/h)
def __init__(self):
u0 = cbrt(1 + sqrt(2)) / sqrt(3)
r0 = u0 + 1 / (3*u0)
lmbd = 2 * r0**2 - r0**4 + 8 * sqrt(2/27.) * r0
self.__norm_alpha = 2 / lmbd
self.__norm_beta = 1 / lmbd
self.__norm_kappa = -8 * sqrt(2/27.) / lmbd
self.__phi_b = pi / 4.
self.__qh_b = - sqrt(1/27.)
def diff_gaussian_offset_1d( self, sigma_e, sigma_i, size=7, origin=0 ):
"""Builds a 1D difference of gaussian kernel centered at the origin given"""
f = empty( size )
x = arange( size ) - origin
center = math.floor( size / 2 )
f = 1.0 / (sigma_e*math.sqrt(2*pi)) * \
exp( -square(x-center) / (2*sigma_e**2)) - \
1.0 / (sigma_i*math.sqrt(2*pi)) * \
exp( -square(x-center) / (2*sigma_i**2))
f /= abs(sum(f.ravel())) # normalize
f = cbrt( f ) # The result of this filter and a 2D filter should be normalized
return f
def Gtilde(x):
"""
AKP10 Eq. D7
Factor ~2 performance gain in using cbrt(x)**n vs x**(n/3.)
Invoking crbt only once reduced time by ~40%
"""
cb = cbrt(x)
gt1 = 1.808 * cb / np.sqrt(1 + 3.4 * cb**2.)
gt2 = 1 + 2.210 * cb**2. + 0.347 * cb**4.
gt3 = 1 + 1.353 * cb**2. + 0.217 * cb**4.
return gt1 * (gt2 / gt3) * np.exp(-x)
def __init__(self, radiiInitializer):
# tunable parameters
self.p1 = 2e6 # upstream pressure
self.numberOfPipes = 10 # spatial resolution
totalTime = 3.6e3*6 # simulation duration
# physical parameters
self.c0 = 0.0 # upstream concentration
sampleLength = 2.62e-2 # sample length
eta = 1.07e-3 # viscosity
L = sampleLength/self.numberOfPipes # pipe length
self.L = L # reference length
self.K0 = np.pi*L*L*L/8/eta # reference proportional constant
self.P0 = 1e5 # reference pressure
self.p0 = 0.0 # downstream pressure
self.V0 = np.pi*L*L*L # reference volume
self.C0 = 35.0 # reference/saturation concentration
self.T0 = self.V0/self.K0/self.P0 # reference time
self.dt = 0.0 # time step
nu = 9.0e-7 # kinetic viscosity
kS = 1.87e-3/self.C0 # reaction constant
M = 100.0e-3 # molar mass of CaCO3
rho = 2.7e3 # density of CaCO3
Dm = 7.9e-10; # molecular diffusivity
Sc = nu/Dm
u0 = self.K0 * self.P0 / (np.pi * L * L)
Re0 = 2*u0*L/nu
self.Sh_inf = 3.66 # Sherwood number
self.Sh_0 = 0.7 * np.sqrt(Re0) * cbrt(Sc) # Sherwood number
self.k_SC_0 = 2 * kS * L / Dm # reaction speed
self.F0 = 2 * kS * self.T0 / L
self.C_bound = rho/M/self.C0 # nondimensional concentration bound
# state variables
self.time = 0.0
self.maxTime = totalTime/self.T0
self.stepCount = 0
self.concentration = np.zeros(self.numberOfPipes)
self.radii = np.empty(self.numberOfPipes)
self.pressure = np.empty(self.numberOfPipes+1)
self.pressure[self.numberOfPipes] = self.p0 / self.P0
# initialzation
radiiInitializer.initialize(self.radii)
self.flux = (self.p1 - self.p0) / \
np.sum(8*eta*L/np.pi/np.power(self.radii, 4))
print "flux", self.flux
self.radii /= self.L
# log simulation settings
totalVolume = self.V0*np.sum(self.radii*self.radii)*1e6
logging.info("Total volume: %f cm3", totalVolume)
injectVolume = self.flux*totalTime*1e6
logging.info("Volume injected: %f cm3 in %f hours",
injectVolume, totalTime/3600)
logging.info("Volume ratio: %d", injectVolume/totalVolume)
def _ctd_alpha(coords):
"""
:param coords: n * 3
:return:
"""
ctd = np.array([np.mean(coords, axis=0)])
ctd_distance = cdist(ctd, coords)[0]
ctd_alpha = {}
ctd_alpha['ctd_1'] = round(np.mean(ctd_distance), 3)
ctd_alpha['ctd_2'] = round(np.std(ctd_distance), 3)
ctd_alpha['ctd_3'] = round(cbrt(stats.skew(ctd_distance)), 3)
return ctd_alpha
def _fct_alpha(Coordinates):
ctd = np.array([np.mean(Coordinates, axis=0)])
ctd_distance = cdist(ctd, Coordinates)[0]
fct_index = np.argmax(ctd_distance)
fct = np.array([Coordinates[fct_index]])
fct_distance = cdist(fct, Coordinates)[0]
fct_alpha = {}
fct_alpha['fct_1'] = round(np.mean(fct_distance), 3)
fct_alpha['fct_2'] = round(np.std(fct_distance), 3)
fct_alpha['fct_3'] = round(cbrt(stats.skew(fct_distance)), 3)
return fct_alpha
def _cst_alpha(Coordinates):
ctd = np.array([np.mean(Coordinates, axis=0)])
ctd_distance = cdist(ctd, Coordinates)[0]
cst_index = np.argmin(ctd_distance)
cst = np.array([Coordinates[cst_index]])
cst_distance = cdist(cst, Coordinates)[0]
cst_alpha = {}
cst_alpha['cst_1'] = round(np.mean(cst_distance), 3)
cst_alpha['cst_2'] = round(np.std(cst_distance), 3)
cst_alpha['cst_3'] = round(cbrt(stats.skew(cst_distance)), 3)
return cst_alpha
def diff_gaussian_offset_2d( self, sigma_e, sigma_i, shape=( 7, 7 ), origin=( 0, 0 ) ):
"""Builds a 2D difference of gaussian kernel centered at the origin given"""
f = empty( shape )
x, y = meshgrid( arange( shape[0] ) - origin[0], arange( shape[1] ) - origin[1] )
center = ( math.floor( shape[0] / 2 ), math.floor( shape[1] / 2 ) )
f = 1.0 / ( 2* sigma_e**2 * pi ) * \
exp( (-( x - center[0])**2 - (y - center[1])**2 ) / (2*sigma_e**2)) - \
1.0 / ( 2 * sigma_i**2 * pi ) * \
exp( (-( x - center[0])**2 - (y - center[1])**2 ) / (2*sigma_i**2))
f /= abs(sum(f.ravel())) # normalize
#f = square( cbrt( f ) ) # The result of this filter and a 1D filter should be normalized
f = cbrt( f ) # TEMP
return f
def _Gtilde(self, x):
"""
Useful equation. Aharonian, Kelner, Prosekin 2010 Eq. D7
Taken from Naima.
Factor ~2 performance gain in using cbrt(x)**n vs x**(n/3.)
Invoking crbt only once reduced time by ~40%
"""
cb = cbrt(x) # x**1/3
gt1 = 1.808 * cb / np.sqrt(1 + 3.4 * cb**2.0)
gt2 = 1 + 2.210 * cb**2.0 + 0.347 * cb**4.0
gt3 = 1 + 1.353 * cb**2.0 + 0.217 * cb**4.0
return gt1 * (gt2 / gt3) * np.exp(-x)
def json2png(fname):
root, ext = os.path.splitext(fname)
fopen = open
if ext == '.gz':
fopen = gzip.open
root, ext = os.path.splitext(root)
if ext != '.json':
raise ValueError('Not a json file (%s): %s'%(ext, fname))
with fopen(fname, 'r') as injson:
histo = np.array(json.load(injson))
histo = cbrt(histo)
image = toimage(histo, high=65536, low=0, mode='I')
image = image.transpose(Image.FLIP_TOP_BOTTOM)
image.save(root+'.png', format='PNG', bits=16)
def __global_minimum_radius(self,phi):
qh = self.__norm_kappa * abs(sin(phi)) / (8 * self.__norm_beta)
signum_phi = np.sign(sin(phi))
if signum_phi == 0:
signum_phi = 1
if (qh <= self.__qh_b):
u = cbrt(-signum_phi*qh + sqrt(qh*qh - 1 / 27))
r0 = u + 1 / (3*u)
else:
r0 = signum_phi * sqrt(4/3) * cos(1/3 * arccos(-qh*sqrt(27)) )
return r0
def nonlinear_resonator(f,f_0,Q,Q_e_real,Q_e_imag,a):
Q_e = Q_e_real + 1j*Q_e_imag
if np.isscalar(f):
fmodel = np.linspace(f * 0.9999, f * 1.0001, 1000)
scalar = True
else:
fmodel = f
scalar = False
y_0 = ((fmodel - f_0) / f_0) * Q
y = (y_0 / 3. +
(y_0 ** 2 / 9 - 1 / 12) / cbrt(a / 8 + y_0 / 12 + np.sqrt(
(y_0 ** 3 / 27 + y_0 / 12 + a / 8) ** 2 - (y_0 ** 2 / 9 - 1 / 12) ** 3) + y_0 ** 3 / 27) +
cbrt(a / 8 + y_0 / 12 + np.sqrt(
(y_0 ** 3 / 27 + y_0 / 12 + a / 8) ** 2 - (y_0 ** 2 / 9 - 1 / 12) ** 3) + y_0 ** 3 / 27))
x = y / Q
s21 = (1 - (Q / Q_e) / (1 + 2j * Q * x))
mask = np.isfinite(s21)
if scalar or not np.all(mask):
s21_interp_real = np.interp(f, fmodel[mask], s21[mask].real)
s21_interp_imag = np.interp(f, fmodel[mask], s21[mask].imag)
s21new = s21_interp_real + 1j * s21_interp_imag
else:
s21new = s21
return s21new
def diff_gaussian_separable( self, dim_e, dim_i, sigma_e, sigma_i ):
dim = max(dim_e, dim_i)
f = empty( ( dim ) )
center = math.floor( dim / 2 )
for x in xrange( dim ):
f[x] = ( 1 if x <= center + dim_e and x >= center - dim_e else 0 ) * \
1.0 / (sigma_e*math.sqrt(2*pi)) * \
math.exp( -(x-center)**2 / (2*sigma_e**2)) - \
( 1 if x <= center + dim_i and x >= center - dim_i else 0 ) * \
1.0 / (sigma_i*math.sqrt(2*pi)) * \
math.exp( -(x-center)**2 / (2*sigma_i**2))
f /= abs(sum(f.ravel())) # normalize
f = cbrt( f ) # Take the cubed root, so the filter applied 3 times will be normalized
return f
def Gtilde(x):
"""
AKP10 Eq. D7
Factor ~2 performance gain in using cbrt(x)**n vs x**(n/3.)
"""
gt1 = 1.808 * cbrt(x) / np.sqrt(1 + 3.4 * cbrt(x) ** 2.)
gt2 = 1 + 2.210 * cbrt(x) ** 2. + 0.347 * cbrt(x) ** 4.
gt3 = 1 + 1.353 * cbrt(x) ** 2. + 0.217 * cbrt(x) ** 4.
return gt1 * (gt2 / gt3) * np.exp(-x)
def fwhm(anat_file, mask_file, out_vox=False):
"""Calculate the FWHM of the input image using AFNI's 3dFWHMx.
- Uses AFNI 3dFWHMx. More details here:
https://afni.nimh.nih.gov/pub/dist/doc/program_help/3dFWHMx.html
:type anat_file: str
:param anat_file: The filepath to the anatomical image NIFTI file.
:type mask_file: str
:param mask_file: The filepath to the binary head mask NIFTI file.
:type out_vox: bool
:param out_vox: (default: False) Output the FWHM as number of voxels
instead of mm (the default).
:rtype: tuple
:return: A tuple of the FWHM values (x, y, z, and combined).
"""
import nibabel as nib
import numpy as np
from scipy.special import cbrt
import subprocess
fwhm_string_list = ["3dFWHMx", "-combined", "-mask", mask_file,
"-input", anat_file]
try:
retcode = subprocess.check_output(fwhm_string_list)
except Exception as e:
err = "\n\n[!] Something went wrong with AFNI's 3dFWHMx. Error " \
"details: %s\n\n" % e
raise Exception(err)
# extract output
vals = np.array(retcode.split(), dtype=np.float)
if out_vox:
# get pixel dimensions
img = nib.load(anat_file)
hdr = img.get_header()
pixdim = hdr['pixdim'][1:4]
# convert to voxels
pixdim = np.append(pixdim, cbrt(pixdim.prod()))
# get the geometrix mean
vals = vals / pixdim
return tuple(vals)
def fwhm(anat_file, mask_file, out_vox=False):
"""
Calculate the FWHM of the input image.
Parameters
----------
anat_file: str
path to anatomical file
mask_file: str
path to brain mask
out_vox: bool
output the FWHM as # of voxels (otherwise as mm)
Returns
-------
fwhm: tuple (x,y,z,combined)
FWHM in the x, y, x, and combined direction
"""
import commands
import nibabel as nib
import numpy as np
from scipy.special import cbrt
# call AFNI command to get the FWHM in x,y,z and combined
cmd = "3dFWHMx -combined -mask %s -input %s" % (mask_file, anat_file)
out = commands.getoutput(cmd)
# extract output
line = out.splitlines()[-1].strip()
vals = np.array(line.split(), dtype=np.float)
if out_vox:
# get pixel dimensions
img = nib.load(anat_file)
hdr = img.get_header()
pixdim = hdr['pixdim'][1:4]
# convert to voxels
pixdim = np.append(pixdim, cbrt(pixdim.prod()))
# get the geometrix mean
vals = vals / pixdim
return tuple(vals)
def equivalent_cube(geometry, pore_volume='pore.volume', **kwargs):
r"""
Calculate pore diameter as the width of a cube with an equivalent volume.
Parameters
----------
geometry : OpenPNM Geometry Object
The Geometry object which this model is associated with. This controls
the length of the calculated array, and also provides access to other
necessary geometric properties.
pore_volume : string
The dictionary key containing the pore volume values
"""
from scipy.special import cbrt
pore_vols = geometry[pore_volume]
value = cbrt(pore_vols)
return value
def SimData_Breiman1(n, sigma=1):
"""
Breiman 1 model
This is a 1-d model.
y = exp(x^3 + \epsilon)
\epsilon ~ N(0,1)
x^3 ~ N(0,1)
:param n: sample size
:param sigma:
:return:
"""
epsilon = sigma * np.random.randn(n)
x3 = np.random.randn(n)
y = np.exp(x3 + epsilon)
x = cbrt(x3)
return y, x
def neutron_radius():
data = np.genfromtxt("data/cross_section/neutron_rad.tsv", usecols=(0,1,2))
sigma = data[:,0]
dsigma = data[:,1]
A = data[:,2]
ydata = np.sqrt(sigma/(2*np.pi))
xdata = cbrt(A)
dy = np.sqrt(dsigma/(2*np.pi))
#print ydata
def sim_data(nruns):
r0 = []
dbw = [] # de broglie wavelength
for i in range(0, nruns):
yd = np.random.randn(len(ydata))*np.min(dy) + ydata
popt = np.polyfit(xdata, yd, 1)
r0.append(popt[0])
dbw.append(popt[1])
return r0, dbw
r0, dbw = sim_data(1500)
popt = [np.average(r0), np.average(dbw)]
pcov = [np.std(r0), np.std(dbw)]
yFit = linear(xdata, *popt)
yFit_max = linear(xdata, *np.add(popt,pcov))
yFit_min = linear(xdata, *np.subtract(popt,pcov))
plt.figure(figsize=(10, 10))
plt.errorbar(xdata, ydata, yerr=dy, fmt='o')
plt.plot(xdata, yFit, label="Best Fit")
plt.plot(xdata, yFit_max, label="Plus 1$\sigma$")
plt.plot(xdata, yFit_min, label="Minus 1$\sigma$")
plt.text(xdata[0], ydata[0] + 1e-12, "$r_0A^{1/3} + \lambda$ \n $r_{0} = %.1f\,\pm\,%.1f\,fm$ \n $\lambda = %.0f\,\pm\,%.0f\,fm$" % (popt[0]*1e13, pcov[0]*1e13, popt[1]*1e13, pcov[1]*1e13))
plt.xlabel("$A^{1/3}$ $(g/mol)^{1/3}$")
plt.ylabel(r"$\sqrt{\frac{\sigma}{2 \pi}}$ $(\sqrt{\frac{cm^2}{rad}})$")
plt.title("Radius and deBroglie Wavelength of the Neutron")
plt.legend(loc=3)
plt.savefig("plots/neutron_radius.pdf")
plt.show()
def SimData_Breiman1(n, sigma=1):
"""
Breiman 1 model
This is a 1-d model.
y = exp(x^3 + \epsilon)
\epsilon ~ N(0,1)
x^3 ~ N(0,1)
:param n: sample size
:param sigma:
:return:
"""
epsilon = sigma * np.random.randn(n)
x3 = np.random.randn(n)
y = np.exp(x3 + epsilon)
x = cbrt(x3)
# Standard data structure is a matrix
x = np.matrix(x).T
y = np.matrix(y).T
return y, x
def scale(network,scale_factor=[1,1,1],preserve_vol=True):
r"""
A method for scaling the coordinates and vertices to create anisotropic networks
The original domain volume can be preserved by setting preserve_vol = True
Example
---------
>>> import OpenPNM
>>> import OpenPNM.Utilities.vertexops as vo
>>> import numpy as np
>>> pn = OpenPNM.Network.Delaunay(num_pores=100, domain_size=[3,2,1])
>>> pn.add_boundaries()
>>> B1 = pn.pores("left_boundary")
>>> B2 = pn.pores("right_boundary")
>>> Vol = vo.vertex_dimension(pn,B1,B2)
>>> vo.scale(network=pn,scale_factor=[2,1,1])
>>> Vol2 = vo.vertex_dimension(pn,B1,B2)
>>> np.around(Vol-Vol2,5)
0.0
>>> vo.scale(network=pn,scale_factor=[2,1,1],preserve_vol=False)
>>> Vol3 = vo.vertex_dimension(pn,B1,B2)
>>> np.around(Vol3/Vol,5)
2.0
"""
from scipy.special import cbrt
import scipy as sp
scale_factor = np.asarray(scale_factor)
if preserve_vol == True:
scale_factor = scale_factor/(cbrt(sp.prod(scale_factor)))
network["pore.coords"]=network["pore.coords"]*scale_factor
#Cycle through all vertices of all pores updating vertex values
for pore in network.pores():
for i,vert in network["pore.vert_index"][pore].items():
network["pore.vert_index"][pore][i] = network["pore.vert_index"][pore][i]*scale_factor
#Cycle through all vertices of all throats updating vertex values
for throat in network.throats():
for i,vert in network["throat.vert_index"][throat].items():
network["throat.vert_index"][throat][i] = network["throat.vert_index"][throat][i]*scale_factor
def ecef2geodetic(x, y, z, degrees=True):
"""ecef2geodetic(x, y, z)
[m][m][m]
Convert ECEF coordinates to geodetic.
J. Zhu, "Conversion of Earth-centered Earth-fixed coordinates \
to geodetic coordinates," IEEE Transactions on Aerospace and \
Electronic Systems, vol. 30, pp. 957-961, 1994."""
r = sqrt(x * x + y * y)
Esq = a * a - b * b
F = 54 * b * b * z * z
G = r * r + (1 - esq) * z * z - esq * Esq
C = (esq * esq * F * r * r) / (pow(G, 3))
S = cbrt(1 + C + sqrt(C * C + 2 * C))
P = F / (3 * pow((S + 1 / S + 1), 2) * G * G)
Q = sqrt(1 + 2 * esq * esq * P)
r_0 = -(P * esq * r) / (1 + Q) + sqrt(0.5 * a * a*(1 + 1.0 / Q) - \
P * (1 - esq) * z * z / (Q * (1 + Q)) - 0.5 * P * r * r)
U = sqrt(pow((r - esq * r_0), 2) + z * z)
V = sqrt(pow((r - esq * r_0), 2) + (1 - esq) * z * z)
Z_0 = b * b * z / (a * V)
h = U * (1 - b * b / (a * V))
lat = arctan((z + e1sq * Z_0) / r)
lon = arctan2(y, x)
return rad2deg(lat), rad2deg(lon), z
scaling = ''
for o, a in myopts:
if o == '-x':
mx=a
elif o == '-s':
scaling = a
else:
print("Usage: %s -x cells -s scaling" % sys.argv[0])
size = PETSc.Comm.getSize(PETSc.COMM_WORLD)
rank = PETSc.Comm.getRank(PETSc.COMM_WORLD)
solver = 'classic'
if scaling=='weak':
mx = int(96 * cbrt(size/4))
tfinal_reduction = cbrt(size/4)
# Initialize grid
my = mz = mx
out_folder = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'scaling_3d_{0}'.format(solver))
processList = [0, 3]
if rank==0 and not os.path.isdir(out_folder):
os.mkdir(out_folder)
# Initialize log file
logfile = MPI.File.Open(MPI.COMM_WORLD, os.path.join(out_folder,"results_"+str(size)+'_'+str(mx)+".log"), MPI.MODE_CREATE|MPI.MODE_WRONLY)
tb1call = MPI.Wtime()
reduced_mx = cbrt(size/4) * 8
from __future__ import division
from numpy import sqrt
from scipy.special import cbrt
cardano = lambda a, b, c: cbrt(a + b * sqrt(c)) + cbrt(a - b * sqrt(c)) == 1
n = 1001
m = 0
for a in range(n):
for b in range(n - a):
for c in range(n - a - b):
if cardano(a, b, c):
print a, b, c
print m
