def infer_diag_post(self,X_ii,D_i):
X_i = dc(X_ii)
ns = len(D_i)
X_i.resize([ns,self.D])
[m,V] = self.infer_diag(X_i,D_i)
if sp.amin(V)<=-0.:
class MJMError(Exception):
pass
print "negative/eq variance"
print [m,V,X_i,D_i]
print "_______________"
#self.printc()
raise(MJMError)
if sp.amin(sp.var(m,axis=0))<-0.:
class MJMError(Exception):
pass
print "negativevar of mean"
print X_i.shape
print [m,V,sp.var(m,axis=0),X_i,D_i]
print "_______________"
#self.printc()
raise(MJMError)
return [sp.mean(m,axis=0).reshape([1,ns]),(sp.mean(V,axis=0)+sp.var(m,axis=0)).reshape([1,ns])]
def loadfile(self, skiprows=2):
self.d.efld = np.loadtxt(self.efile, skiprows=skiprows)
self.d.hfld = np.loadtxt(self.hfile, skiprows=skiprows)
erows, ecols = np.shape(self.d.efld)
hrows, hcols = np.shape(self.d.hfld)
if (erows != hrows) or (ecols != hcols):
raise TypeError('Input file size of E and H is inconsistent.')
exl = np.unique(self.d.efld[:, cx])
eyl = np.unique(self.d.efld[:, cy])
ezl = np.unique(self.d.efld[:, cz])
hxl = np.unique(self.d.hfld[:, cx])
hyl = np.unique(self.d.hfld[:, cy])
hzl = np.unique(self.d.hfld[:, cz])
if any(exl != hxl) or any(eyl != hyl) or any(eyl != hyl):
raise TypeError('Input data grid of E and H is inonsisitent.')
self.d.xmin = np.amin(exl)
self.d.xmax = np.amax(exl)
self.d.ymin = np.amin(eyl)
self.d.ymax = np.amax(eyl)
self.d.zmin = np.amin(ezl)
self.d.zmax = np.amax(ezl)
self.d.dsize = erows
self.d.xsize = len(exl) - 1
self.d.ysize = len(eyl) - 1
self.d.zsize = len(ezl) - 1
self.d.dx = (self.d.xmax - self.d.xmin) / float(self.d.xsize)
self.d.dy = (self.d.ymax - self.d.ymin) / float(self.d.ysize)
self.d.dz = (self.d.zmax - self.d.zmin) / float(self.d.zsize)
self.zyxsort()
def add_boundary_pores(self, labels=['top', 'bottom', 'front', 'back',
'left', 'right'], offset=None):
r"""
Add boundary pores to the specified faces of the network
Pores are offset from the faces of the domain.
Parameters
----------
labels : string or list of strings
The labels indicating the pores defining each face where boundary
pores are to be added (e.g. 'left' or ['left', 'right'])
offset : scalar or array_like
The spacing of the network (e.g. [1, 1, 1]). This must be given
since it can be quite difficult to infer from the network,
for instance if boundary pores have already added to other faces.
"""
offset = sp.array(offset)
if offset.size == 1:
offset = sp.ones(3)*offset
for item in labels:
Ps = self.pores(item)
coords = sp.absolute(self['pore.coords'][Ps])
axis = sp.count_nonzero(sp.diff(coords, axis=0), axis=0) == 0
ax_off = sp.array(axis, dtype=int)*offset
if sp.amin(coords) == sp.amin(coords[:, sp.where(axis)[0]]):
ax_off = -1*ax_off
topotools.add_boundary_pores(network=self, pores=Ps, offset=ax_off,
apply_label=item + '_boundary')
def merged_event_breakpoint_stats(mev):
bp1d, bp2d = [], []
bend1 = bend2 = None
reads = []
quals = []
for ev in mev.events:
bp1d.append(ev.bp1.pos)
bp2d.append(ev.bp2.pos)
reads.append(ev.reads)
quals.append(ev.qual)
bend1 = ev.bp1.breakend
bend2 = ev.bp2.breakend
bp1d = np.array(bp1d)
bp2d = np.array(bp2d)
if bend1 == "+":
bp1limit = scipy.amin(bp1d)
else:
bp1limit = scipy.amax(bp1d)
if bend2 == "+":
bp2limit = scipy.amin(bp2d)
else:
bp2limit = scipy.amax(bp2d)
reads_median = int(scipy.median(reads))
qual_median = int(scipy.median(quals))
return int(bp1limit), int(bp2limit), int(bp2limit - bp1limit), scipy.mean(
bp1d), scipy.amax(bp1d) - scipy.amin(bp1d), scipy.std(
bp1d), scipy.mean(bp2d), scipy.amax(bp2d) - scipy.amin(
bp2d), scipy.std(bp2d), reads_median, qual_median
def plot(self):
ex = self.ex
hy = self.hy
ngridx = self.ngridx
nSteps = self.numSteps
x = np.linspace(0, ngridx, ngridx)
ymin1 = S.amin(ex)
ymin2 = S.amin(hy)
ymax1 = S.amax(ex)
ymax2 = S.amax(hy)
yminimum = min(ymin1, ymin2)
ymaximum = max(ymax1, ymax2)
title1 = 'EX and Hy field in FDTD 1D simulation.'
fig = plt.figure()
ax1 = fig.add_subplot(121)
ax1.set_xlabel('FDTD Cells', fontsize=12)
ax1.plot(x, ex, 'tab:blue', label='Ex (Normalized)')
ax1.set_xlim([0, ngridx])
ax1.legend(loc='best', shadow=True, ncol=2)
# ax1.legend(loc = 'upper center', bbox_to_anchor=(0.5, 0.1), shadow=True, ncol=2)
ax2 = fig.add_subplot(122)
ax2.set_xlabel('FDTD Cells', fontsize=12)
ax2.plot(x, hy, 'tab:red', label='Hy')
ax2.set_xlim([0, ngridx])
ax2.legend(loc='best', shadow=True, ncol=2)
# ax2.legend(loc = 'upper center', bbox_to_anchor=(0.5, 0.1), shadow=True, ncol=2)
plt.suptitle(title1, fontsize=20)
plt.savefig('Figure.png')
plt.show()
def plot_curr(self):
"""Plotet anhand der eingebenen Zeilennummer"""
######## plot a 3D Current ########
fig = plt.figure(figsize=plt.figaspect(0.5))
canvas = FigureCanvasTkAgg(fig, master=page3)
canvas.get_tk_widget().pack(side='top', fill='both')
canvas._tkcanvas.pack(side='top', fill='both', expand=15)
toolbar = NavigationToolbar2TkAgg(canvas, page3)
canvas.get_tk_widget().grid(row=1,column=1)
toolbar.grid(row=50,column=1)
ax = fig.add_subplot(1, 2, 1, projection='3d')
X = sp.linspace(0,10,10)
Y = sp.linspace(0,10,10)
X, Y = sp.meshgrid(X, Y)
X, Y = X.ravel(), Y.ravel()
#R = sp.sqrt(X**2 + Y**2)
#Z = sp.sin(R)
width = depth = 1
bottom=X*0.
#plt.ion()
getrow=int(entryrowplot.get())
current_floats=sp.float64(self.data_str['current'][getrow].split(";")).reshape(10,10)
maxcurr= np.max(current_floats)
#print sp.mean(current_floats)
top=current_floats.ravel()
top_scaled=(top-sp.amin(top))/(sp.amax(top)-sp.amin(top))
mycolors = cm.jet(top_scaled)
ax.bar3d(X, Y, bottom, width, depth, top,color=mycolors, alpha=float(entryTrans.get()))
ax.set_title('Partial Currents @ '+self.data_str['sumcurr'][getrow]+' A'+'// @ '+self.data['voltage'][getrow]+' V')
ax.set_zlim(0,maxcurr)
ax.set_zlabel('Current in A', linespacing=10.4)
ax.view_init(elev=int(entryCurrEval.get()), azim=int(entryCurrAngle.get()))
#plt.show()
fig = plt.figure()
#plt.clf()
plt.show()
plt.gcf().canvas.draw()
currplot = ts.strftime("%Y.%m.%d. %H:%M:%S :")+"Current dargestellt"
self.displaystate.insert(tk.END, currplot+'\n')
def centroid_quadratic(curve, ratio):
num, div = 0, 0
top = scp.amin(curve) + (scp.amax(curve) - scp.amin(curve)) * ratio
for i in range(0, len(curve)):
if (curve[i] <= top):
num += (top - curve[i]) * (top - curve[i]) * (i + 1)
div += (top - curve[i]) * (top - curve[i])
centroid = num / div
return centroid
def __init__(self, evaluator, hof1, hof2, **args):
if 'symmetric' in args:
M = CiaoPlot.generateData(evaluator, hof1, hof2, symmetric = args['symmetric'])
del args['symmetric']
else:
M = CiaoPlot.generateData(evaluator, hof1, hof2)
M *= 1/(amin(M) - amax(M))
M -= amin(M)
self.relData = M
ColorMap.__init__(self, M, minvalue = 0, maxvalue = 1, **args)
def partition(self, midpoint):
"""
returns two lists forward and reverse who have length len(self)=len(forward) +len(reverse)
each index contains the distance of the closest coordinate of the other strand of the molecule as defined by midpoint
"""
forward = scipy.amin(self._d[0:midpoint + 1, midpoint + 1:len(self)],
axis=1)
reverse = scipy.amin(self._d[0:midpoint + 1, midpoint + 1:len(self)],
axis=0)
return forward, reverse
def compute(i, j):
if i == j:
return 1.0
elif trains[i].size <= 0 or trains[j].size <= 0:
return 0.0
else:
diff_matrix = sp.absolute(trains[i] - sp.atleast_2d(trains[j]).T)
return 0.5 * (
sp.sum(kernel(sp.amin(diff_matrix, axis=0))) / trains[i].size +
sp.sum(kernel(sp.amin(diff_matrix, axis=1))) / trains[j].size)
def compute(i, j):
if i == j:
return 1.0
elif trains[i].size <= 0 or trains[j].size <= 0:
return 0.0
else:
diff_matrix = sp.absolute(trains[i] - sp.atleast_2d(trains[j]).T)
return 0.5 * (
sp.sum(kernel(sp.amin(diff_matrix, axis=0))) / trains[i].size +
sp.sum(kernel(sp.amin(diff_matrix, axis=1))) / trains[j].size)
def __init__(self, evaluator, hof1, hof2, **args):
if "symmetric" in args:
M = CiaoPlot.generateData(evaluator, hof1, hof2, symmetric=args["symmetric"])
del args["symmetric"]
else:
M = CiaoPlot.generateData(evaluator, hof1, hof2)
M *= 1 / (amin(M) - amax(M))
M -= amin(M)
self.relData = M
ColorMap.__init__(self, M, minvalue=0, maxvalue=1, **args)
def interpolate(self, canvas, status=None):
# Clear the interpolated canvas
canvas.interpolated = sp.zeros_like(canvas.fringes_image) - 1024.0
if status is not None:
status.set("Performing the interpolation", 70)
else:
print("Performing the interpolation")
# Iterate over all the triangles in the triangulation
for triangle in self.triangles:
# Create a shortcut to the triangle's vertices
co = triangle.vert_coordinates
# Calculate a few constants for the Barycentric Coordinates
# More info: https://codeplea.com/triangular-interpolation
div = (co[1, 0] - co[2, 0]) * (co[0, 1] - co[2, 1]) + (
co[2, 1] - co[1, 1]) * (co[0, 0] - co[2, 0])
a0 = (co[1, 0] - co[2, 0])
a1 = (co[2, 1] - co[1, 1])
a2 = (co[2, 0] - co[0, 0])
a3 = (co[0, 1] - co[2, 1])
# Calculate the bounds of a rectangle that fully encloses
# the current triangle
xmin = int(sp.amin(triangle.vert_coordinates[:, 1]))
xmax = int(sp.amax(triangle.vert_coordinates[:, 1])) + 1
ymin = int(sp.amin(triangle.vert_coordinates[:, 0]))
ymax = int(sp.amax(triangle.vert_coordinates[:, 0])) + 1
# Take out slices of the x and y arrays,
# containing the points' coordinates
x_slice = canvas.x[ymin:ymax, xmin:xmax]
y_slice = canvas.y[ymin:ymax, xmin:xmax]
# Use Barycentric Coordinates and the magic of numpy (scipy in this
# case) to perform the calculations with the C backend, instead
# of iterating on pixels with Python loops.
# If you have not worked with numpy arrays befor dear reader,
# the idea is that if x = [[0 1]
# [2 3]],
# then x*3+1 is a completely valid operation, returning
# x = [[1 4]
# [7 10]]
# Basically, we can do maths on arrays as if they were variables.
# Convenient, and really fast!
w0 = (a0 * (x_slice - co[2, 1]) + a1 * (y_slice - co[2, 0])) / div
w1 = (a2 * (x_slice - co[2, 1]) + a3 * (y_slice - co[2, 0])) / div
w2 = sp.round_(1 - w0 - w1, 10)
# Calculate the values for a rectangle enclosing our triangle
slice = (self.values[triangle.vertices[0]] * w0 +
self.values[triangle.vertices[1]] * w1 +
self.values[triangle.vertices[2]] * w2)
# Make a mask (so that we only touch the points
# inside of the triangle).
# In Barycentric Coordinates the points outside of the triangle
# have at least one of the coefficients negative, so we use that
mask = sp.logical_and(sp.logical_and(w0 >= 0, w1 >= 0), w2 >= 0)
# Change the points in the actual canvas
canvas.interpolated[ymin:ymax, xmin:xmax][mask] = slice[mask]
canvas.interpolation_done = True
def test_largest_sphere(self):
net = OpenPNM.Network.Cubic(shape=[5, 5, 5], spacing=[0.1, 0.2, 0.3])
geo = OpenPNM.Geometry.GenericGeometry(network=net, pores=net.Ps,
throats=net.Ts)
geo.models.add(propname='pore.diameter',
model=mods.largest_sphere,
iters=1)
dmin = sp.amin(geo['pore.diameter'])
assert dmin <= 0.1
geo.models['pore.diameter']['iters'] = 5
geo.regenerate()
assert dmin < sp.amin(geo['pore.diameter'])
def plot_temp(self):
######## plot 3D Temperature ########
fig2 = plt.figure(figsize=plt.figaspect(0.5))
ax2 = fig2.add_subplot(1, 2, 1, projection='3d')
X2 = sp.linspace(0, 5, 5)
Y2 = sp.linspace(0, 5, 5)
X2, Y2 = sp.meshgrid(X2, Y2)
X2, Y2 = X2.ravel(), Y2.ravel()
#R = sp.sqrt(X**2 + Y**2)
#Z = sp.sin(R)
width = depth = 1
bottom = X2 * 0
#plt.ion()
p = int(entryrowplot.get())
temp_floats = sp.float64(self.data_str['temp'][p].split(";")).reshape(
5, 5)
mintemp = np.min(temp_floats)
mintempplot = mintemp
mintemptxt = str(round(mintemp, 1))
maxtemp = np.max(temp_floats)
maxtempplot = maxtemp
maxi = maxtempplot - mintempplot + 0.1
top2 = temp_floats.ravel() - mintemp
top_scaled2 = (top2 - sp.amin(top2)) / (sp.amax(top2) - sp.amin(top2))
mycolors = cm.jet(top_scaled2)
ax2.bar3d(X2,
Y2,
bottom,
width,
depth,
top2,
color=mycolors,
alpha=float(entryTrans.get()))
ax2.set_title('Temperature' + '\n' + '@ ' +
self.data_str['sumcurr'][p] + ' A' + ' // @ ' +
self.data['voltage'][p] + ' V')
ax2.set_zlim(0, maxi)
ax2.set_zlabel('Temperature +' + mintemptxt + '°C')
ax2.view_init(elev=25., azim=60)
plt.show()
tempplot = ts.strftime(
"%Y.%m.%d. %H:%M:%S :") + "Temperature dargestellt"
self.displaystate.insert(tk.END, tempplot + '\n')
def test_from_neighbor_throats_max(self):
self.geo.pop('pore.seed', None)
self.geo.models.pop('pore.seed', None)
self.geo.models.pop('throat.seed', None)
self.geo['throat.seed'] = sp.rand(self.net.Nt, )
self.geo.add_model(model=mods.from_neighbor_throats,
propname='pore.seed',
throat_prop='throat.seed',
mode='max')
assert sp.all(sp.in1d(self.geo['pore.seed'], self.geo['throat.seed']))
pmin = sp.amin(self.geo['pore.seed'])
tmin = sp.amin(self.geo['throat.seed'])
assert pmin >= tmin
def test_largest_sphere(self):
net = OpenPNM.Network.Cubic(shape=[5, 5, 5], spacing=[0.1, 0.2, 0.3])
geo = OpenPNM.Geometry.GenericGeometry(network=net,
pores=net.Ps,
throats=net.Ts)
geo.models.add(propname='pore.diameter',
model=mods.largest_sphere,
iters=1)
dmin = sp.amin(geo['pore.diameter'])
assert dmin <= 0.1
geo.models['pore.diameter']['iters'] = 5
geo.regenerate()
assert dmin < sp.amin(geo['pore.diameter'])
def test_neighbor_max(self):
catch = self.geo.pop('pore.seed', None)
catch = self.geo.models.pop('pore.seed', None)
catch = self.geo.models.pop('throat.seed', None)
mod = gm.pore_misc.neighbor
self.geo['throat.seed'] = sp.rand(self.net.Nt,)
self.geo.models.add(model=mod,
propname='pore.seed',
throat_prop='throat.seed',
mode='max')
assert sp.all(sp.in1d(self.geo['pore.seed'], self.geo['throat.seed']))
pmin = sp.amin(self.geo['pore.seed'])
tmin = sp.amin(self.geo['throat.seed'])
assert pmin >= tmin
def representative_elementary_volume(im, npoints=1000):
r"""
Calculates the porosity of the image as a function subdomain size. This
function extracts a specified number of subdomains of random size, then
finds their porosity.
Parameters
----------
im : ND-array
The image of the porous material
npoints : int
The number of randomly located and sized boxes to sample. The default
is 1000.
Returns
-------
A tuple containing the ND-arrays: The subdomain *volume* and its
*porosity*. Each of these arrays is ``npoints`` long. They can be
conveniently plotted by passing the tuple to matplotlib's ``plot`` function
using the \* notation: ``plt.plot(*the_tuple, 'b.')``. The resulting plot
is similar to the sketch given by Bachmat and Bear [1]
Notes
-----
This function is frustratingly slow. Profiling indicates that all the time
is spent on scipy's ``sum`` function which is needed to sum the number of
void voxels (1's) in each subdomain.
Also, this function is primed for parallelization since the ``npoints`` are
calculated independenlty.
References
----------
[1] Bachmat and Bear. On the Concept and Size of a Representative
Elementary Volume (Rev), Advances in Transport Phenomena in Porous Media
(1987)
"""
im_temp = sp.zeros_like(im)
crds = sp.array(sp.rand(npoints, im.ndim) * im.shape, dtype=int)
pads = sp.array(sp.rand(npoints) * sp.amin(im.shape) / 2 + 10, dtype=int)
im_temp[tuple(crds.T)] = True
labels, N = spim.label(input=im_temp)
slices = spim.find_objects(input=labels)
porosity = sp.zeros(shape=(N, ), dtype=float)
volume = sp.zeros(shape=(N, ), dtype=int)
for i in tqdm(sp.arange(0, N)):
s = slices[i]
p = pads[i]
new_s = extend_slice(s, shape=im.shape, pad=p)
temp = im[new_s]
Vp = sp.sum(temp)
Vt = sp.size(temp)
porosity[i] = Vp / Vt
volume[i] = Vt
profile = namedtuple('profile', ('volume', 'porosity'))
profile.volume = volume
profile.porosity = porosity
return profile
def test_spatially_correlated_zero_weights(self):
f = OpenPNM.Geometry.models.pore_seed.spatially_correlated
self.geo.models.add(propname='pore.seed',
model=f,
weights=[0, 0, 0])
assert sp.amin(self.geo['pore.seed'] > 0)
assert sp.amax(self.geo['pore.seed'] < 1)
def late_pore_filling(physics,
phase,
network,
Pc,
Swp_star=0.2,
eta=3,
wetting_phase=False,
pore_occupancy='pore.occupancy',
throat_capillary_pressure='throat.capillary_pressure',
**kwargs):
r'''
Applies a late pore filling model to calculate fractional pore filling as
a function of applied capillary pressure.
Parameters
----------
Pc : float
The capillary pressure in the non-wetting phase (Pc > 0)
Swp_star : float
The residual wetting phase in an invaded pore immediately after
nonwetting phase invasion
eta : float
Exponent to control the rate at which wetting phase is displaced
wetting_phase : boolean
Indicates whether supplied phase is the wetting or non-wetting phase
'''
pores = phase.pores(physics.name)
prop = phase[throat_capillary_pressure]
neighborTs = network.find_neighbor_throats(pores,flatten=False)
Pc_star = sp.array([sp.amin(prop[row]) for row in neighborTs])
Swp = Swp_star*(Pc_star/Pc)**eta
if wetting_phase:
values = Swp*phase[pore_occupancy]*(Pc_star<Pc)
else:
values = (1-Swp)*(1-phase[pore_occupancy])*(Pc_star<Pc)
return values
def domain_length(self,face_1,face_2):
r'''
Calculate the distance between two faces of the network
Parameters
----------
face_1 and face_2 : array_like
Lists of pores belonging to opposite faces of the network
Returns
-------
The length of the domain in the specified direction
Notes
-----
- Does not yet check if input faces are perpendicular to each other
'''
#Ensure given points are coplanar before proceeding
if misc.iscoplanar(self['pore.coords'][face_1]) and misc.iscoplanar(self['pore.coords'][face_2]):
#Find distance between given faces
x = self['pore.coords'][face_1]
y = self['pore.coords'][face_2]
Ds = misc.dist(x,y)
L = sp.median(sp.amin(Ds,axis=0))
else:
self._logger.warning('The supplied pores are not coplanar. Length will be approximate.')
f1 = self['pore.coords'][face_1]
f2 = self['pore.coords'][face_2]
distavg = [0,0,0]
distavg[0] = sp.absolute(sp.average(f1[:,0]) - sp.average(f2[:,0]))
distavg[1] = sp.absolute(sp.average(f1[:,1]) - sp.average(f2[:,1]))
distavg[2] = sp.absolute(sp.average(f1[:,2]) - sp.average(f2[:,2]))
L = max(distavg)
return L
def find_acceleromoeter_scale_bias_priors(measures):
maxZ = sp.amax(measures)
minZ = sp.amin(measures)
bias = minZ + (maxZ - minZ) / 2.0
scale = 1 / (maxZ - bias)
return scale, bias
def neighbor(geometry, network, pore_prop='pore.seed', mode='min', **kwargs):
r"""
Adopt a value based on the values in the neighboring pores
Parameters
----------
mode : string
Indicates how to select the values from the neighboring pores. The
options are:
- min : (Default) Uses the minimum of the value found in the neighbors
- max : Uses the maximum of the values found in the neighbors
- mean : Uses an average of the neighbor values
pore_prop : string
The dictionary key containing the pore property to be used.
"""
throats = network.throats(geometry.name)
P12 = network.find_connected_pores(throats)
pvalues = network[pore_prop][P12]
if mode == 'min':
value = _sp.amin(pvalues, axis=1)
if mode == 'max':
value = _sp.amax(pvalues, axis=1)
if mode == 'mean':
value = _sp.mean(pvalues, axis=1)
return value
def scale(self, x, M=None, m=None): # TODO: DO IN PLACE SCALING
"""!@brief Function that standardize the data
Input:
x: the data
M: the Max vector
m: the Min vector
Output:
x: the standardize data
M: the Max vector
m: the Min vector
"""
[n, d] = x.shape
if not sp.issubdtype(x.dtype, float):
x = x.astype('float')
# Initialization of the output
xs = sp.empty_like(x)
# get the parameters of the scaling
if M is None:
M, m = sp.amax(x, axis=0), sp.amin(x, axis=0)
den = M - m
for i in range(d):
if den[i] != 0:
xs[:, i] = 2 * (x[:, i] - m[i]) / den[i] - 1
else:
xs[:, i] = x[:, i]
return xs
def run(self, N=100):
r'''
'''
im = self.image
# Create a list of N random points to use as box centers
pad = [0.1,0.1,0.45] # Ensure points are near middle
Cx = sp.random.randint(pad[0]*sp.shape(im)[0],(1-pad[0])*sp.shape(im)[0],N)
Cy = sp.random.randint(pad[1]*sp.shape(im)[1],(1-pad[1])*sp.shape(im)[1],N)
Cz = sp.random.randint(pad[2]*sp.shape(im)[2],(1-pad[2])*sp.shape(im)[2],N)
C = sp.vstack((Cx,Cy,Cz)).T
# Find maximum radius allowable for each point
Rmax = sp.array(C>sp.array(sp.shape(im))/2)
Rlim = sp.zeros(sp.shape(Rmax))
Rlim[Rmax[:,0],0] = sp.shape(im)[0]
Rlim[Rmax[:,1],1] = sp.shape(im)[1]
Rlim[Rmax[:,2],2] = sp.shape(im)[2]
R = sp.absolute(C-Rlim)
R = R.astype(sp.int_)
Rmin = sp.amin(R,axis=1)
vol = []
size = []
porosity = []
for i in range(0,N):
for r in sp.arange(Rmin[i],1,-10):
imtemp = im[C[i,0]-150:C[i,0]+150,C[i,1]-150:C[i,1]+150:,C[i,2]-r:C[i,2]+r]
vol.append(sp.size(imtemp))
size.append(2*r)
porosity.append(sp.sum(imtemp==1)/(sp.size(imtemp)))
vals = namedtuple('REV', ('porosity', 'size'))
vals.porosity = porosity
vals.size = size
return vals
def overf_power_spectrum(amp, index, f0, dt, n, cut_off=0):
"""Calculates the theoretical f**`index` power spectrum.
"""
if cut_off < 0:
raise ValueError("Low frequency cut off must not be negative.")
# Sometimes the fitting routines do something weird that causes
# an overflow from a ridiculous index. Limit the index.
index = max(index, -20)
# Get the frequencies represented in the FFT.
df = 1.0/dt/n
freq = sp.arange(n, dtype=float)
freq[n//2+1:] -= freq[-1] + 1
freq = abs(freq)*df
# 0th (mean) mode is meaningless has IR divergence. Deal with it later (in
# the cut off.
freq[0] = 1
# Make the power spectrum.
power = (freq/f0)**index
power *= amp
# Restore frequency of mean mode.
freq[0] = 0
# Find the power just above the cut off frequency.
p_cut = power[sp.amin(sp.where(freq > cut_off)[0])]
# Flatten off the power spectrum.
power[freq <= cut_off] = p_cut
return power
def compX(self,xtrue):
n = self.X.shape[0]
R = sp.empty([2,n])
for i in xrange(n):
R[0,i] = spl.norm(self.X[i,:]-xtrue)
R[1,i] = sp.amin(R[0,:i+1])
return R
def domain_length(self,face_1,face_2):
r'''
Calculate the distance between two faces of the network
Parameters
----------
face_1 and face_2 : array_like
Lists of pores belonging to opposite faces of the network
Returns
-------
The length of the domain in the specified direction
Notes
-----
- Does not yet check if input faces are perpendicular to each other
'''
#Ensure given points are coplanar before proceeding
if misc.iscoplanar(self['pore.coords'][face_1]) and misc.iscoplanar(self['pore.coords'][face_2]):
#Find distance between given faces
x = self['pore.coords'][face_1]
y = self['pore.coords'][face_2]
Ds = misc.dist(x,y)
L = sp.median(sp.amin(Ds,axis=0))
else:
logger.warning('The supplied pores are not coplanar. Length will be approximate.')
f1 = self['pore.coords'][face_1]
f2 = self['pore.coords'][face_2]
distavg = [0,0,0]
distavg[0] = sp.absolute(sp.average(f1[:,0]) - sp.average(f2[:,0]))
distavg[1] = sp.absolute(sp.average(f1[:,1]) - sp.average(f2[:,1]))
distavg[2] = sp.absolute(sp.average(f1[:,2]) - sp.average(f2[:,2]))
L = max(distavg)
return L
def plot_pairwise_velocities_r(case,color,all_radial_distances,all_radial_velocities):
dr = 0.3 # Mpc/h
rmin, rmax = sp.amin(all_radial_distances), sp.amax(all_radial_distances)
rrange = rmax-rmin
N = int(sp.ceil(rrange/dr))
rs = sp.linspace(rmin,rmax,N)
v12_of_r = [[] for index in range(N)]
for r,v12 in zip(all_radial_distances,all_pairwise_velocities):
index = int(sp.floor((r-rmin)/dr))
v12_of_r[index].append(v12)
sigma_12s = sp.zeros(N)
v12_means = sp.zeros(N)
for index in range(len(sigma_12s)):
v12_of_r_index = sp.array(v12_of_r[index])
print "number of counts in the", index,"th bin:", len(v12_of_r_index)
sigma_12 = sp.sqrt(sp.mean(v12_of_r_index**2))
v12_mean = -sp.mean(v12_of_r_index)
sigma_12s[index] = sigma_12
v12_means[index] = v12_mean
plt.plot(rs,sigma_12s,color=color,label='$\sigma_{12}$')
plt.plot(rs,v12_means,color=color,label='$|v_{12}|$')
plt.xlabel('r [Mpc/h]')
plt.ylabel('[km/s]')
plt.xscale('log')
plt.axis([0.5,100,0,600])
def print_all_stats(ctx, series):
ftime = get_ftime(series)
start = 0
end = ctx.interval
print('start-time, samples, min, avg, median, 90%, 95%, 99%, max')
while (start < ftime): # for each time interval
end = ftime if ftime < end else end
sample_arrays = [ s.get_samples(start, end) for s in series ]
samplevalue_arrays = []
for sample_array in sample_arrays:
samplevalue_arrays.append(
[ sample.value for sample in sample_array ] )
#print('samplevalue_arrays len: %d' % len(samplevalue_arrays))
#print('samplevalue_arrays elements len: ' + \
#str(map( lambda l: len(l), samplevalue_arrays)))
# collapse list of lists of sample values into list of sample values
samplevalues = reduce( array_collapser, samplevalue_arrays, [] )
#print('samplevalues: ' + str(sorted(samplevalues)))
# compute all stats and print them
myarray = scipy.fromiter(samplevalues, float)
mymin = scipy.amin(myarray)
myavg = scipy.average(myarray)
mymedian = scipy.median(myarray)
my90th = scipy.percentile(myarray, 90)
my95th = scipy.percentile(myarray, 95)
my99th = scipy.percentile(myarray, 99)
mymax = scipy.amax(myarray)
print( '%f, %d, %f, %f, %f, %f, %f, %f, %f' % (
start, len(samplevalues),
mymin, myavg, mymedian, my90th, my95th, my99th, mymax))
# advance to next interval
start += ctx.interval
end += ctx.interval
def test_random(self):
self.geo.models.add(propname='throat.seed',
model=OpenPNM.Geometry.models.throat_seed.random,
seed=0,
num_range=[0.1, 2])
assert sp.amax(self.geo['throat.seed']) > 1.9
assert sp.amin(self.geo['throat.seed']) > 0.1
def test_random_with_range(self):
mod = gm.throat_misc.random
self.geo.models.add(model=mod,
propname='throat.seed',
num_range=[0.1, 0.9])
assert sp.amax(self.geo['throat.seed']) <= 0.9
assert sp.amin(self.geo['throat.seed']) >= 0.1
def GetStat(filename,Nsamp=1):
hfile=[]
if type(filename)==list:
for i,f in enumerate(filename):
hfile.append(h5py.File(f,'r'))
elif type(filename)==str:
hfile.append(h5py.File(filename,'r'))
filename=[filename]
stats=[]
datapath=[]
for ih,h in enumerate(hfile):
for r in h:
for d in h[r]:
try:
stats.append(int(h[r][d].attrs['statistics'][0]))
except KeyError as err:
stats.append(1)
datapath.append((filename[ih],"/{0}/{1}".format(r,d)))
bunches,args=vln.bunch(stats,Nsamp,indices=True)
addstat=sc.array([sum(bunches[i]) for i in range(Nsamp)])
print("Average statistics of {0} (min: {1}, max: {2})"\
.format(sc.mean(addstat),sc.amin(addstat),sc.amax(addstat)))
for f in hfile:
f.close()
return datapath,args
def _do_one_outer_iteration(self, **kwargs):
r"""
One iteration of an outer iteration loop for an algorithm
(e.g. time or parametric study)
"""
# Checking for the necessary values in Picard algorithm
nan_tol = sp.isnan(self['pore.source_tol'])
nan_max = sp.isnan(self['pore.source_maxiter'])
self._tol_for_all = sp.amin(self['pore.source_tol'][~nan_tol])
self._maxiter_for_all = sp.amax(self['pore.source_maxiter'][~nan_max])
if self._guess is None:
self._guess = sp.zeros(self._coeff_dimension)
t = 1
step = 0
# The main Picard loop
while t > self._tol_for_all and step <= self._maxiter_for_all:
X, t, A, b = self._do_inner_iteration_stage(guess=self._guess,
**kwargs)
logger.info('tol for Picard source_algorithm in step ' +
str(step) + ' : ' + str(t))
self._guess = X
step += 1
# Check for divergence
self._steps = step
if t >= self._tol_for_all and step > self._maxiter_for_all:
raise Exception('Iterative algorithm for the source term reached '
'to the maxiter: ' + str(self._maxiter_for_all) +
' without achieving tol: ' +
str(self._tol_for_all))
logger.info('Picard algorithm for source term converged!')
self.A = A
self.b = b
self._tol_reached = t
return X
def __init__(self, mat, cmap=None, pixelspervalue=20, minvalue=None, maxvalue=None):
""" Make a colormap image of a matrix or sequence of Matrix/Connection objects
:key mat: the matrix to be used for the colormap.
"""
if isinstance(mat, (ParameterContainer, Connection)):
mat = reshape(mat.params, (mat.outdim, mat.indim))
if not isinstance(mat, ndarray):
raise ValueError("Don't know how to display a ColorMap for a matrix of type {}".format(type(mat)))
if minvalue == None:
minvalue = amin(mat)
if maxvalue == None:
maxvalue = amax(mat)
if isinstance(cmap, basestring) and cmap.strip():
cmap = getattr(cm, cmap.lower().strip())
if not cmap:
cmap = cm.hot
figsize = (array(mat.shape) / 100. * pixelspervalue)[::-1]
self.fig = figure(figsize=figsize)
axes([0, 0, 1, 1]) # Make the plot occupy the whole canvas
axis('off')
self.fig.set_size_inches(figsize)
imshow(mat, cmap=cmap, clim=(minvalue, maxvalue), interpolation='nearest')
def neighbor(geometry, pore_prop='pore.seed', mode='min', **kwargs):
r"""
Adopt a value based on the values in neighboring pores
Parameters
----------
geometry : OpenPNM Geometry Object
The object containing the ``pore_prop`` to be used.
pore_prop : string
The dictionary key to the array containing the pore property to be
used in the calculation. Default is 'pore.seed'.
mode : string
Controls how the throat property is calculated. Options are 'min',
'max' and 'mean'.
"""
network = geometry._net
throats = network.throats(geometry.name)
P12 = network.find_connected_pores(throats)
pvalues = network[pore_prop][P12]
if mode == 'min':
value = _sp.amin(pvalues, axis=1)
if mode == 'max':
value = _sp.amax(pvalues, axis=1)
if mode == 'mean':
value = _sp.mean(pvalues, axis=1)
return value
def plotDigit(self, adigit, nvals=False):
"""Plots an image specified by adigit. If nvals is true,
then it uses a colormap to distinguish negative and
positive values, as opposed to plotting a monochrome image."""
# Set max/min of scale equal to +/- of largest value found in adigit
scale = max(abs(scipy.amax(adigit)), abs(scipy.amin(adigit)))
# Reshape adigit to square image
dims = int(math.sqrt(len(adigit)))
adigit = adigit.reshape(dims, dims)
# Plot using interpolation
if nvals:
plt.imshow(
adigit,
cmap='RdBu_r',
vmin=-scale,
vmax=scale,
interpolation='nearest') # show image in red-blue colorscale
# Plot grey if nvals is false
else:
plt.imshow(adigit, cmap='Greys_r') # show image in greyscale
# Add colorbar and show plot
plt.colorbar()
plt.show()
def test_generic(self):
import OpenPNM.Geometry.models.pore_diameter as mods
func = spst.gamma(a=2, loc=0.001, scale=0.0001)
self.geo.models.add(propname="throat.diameter", model=mods.generic, func=func, seeds="throat.seed")
assert sp.amin(self.geo["throat.diameter"]) > 0.001
del self.geo["throat.diameter"]
def averageLogDistribution(values, log_step=0.2, first_point=None, last_point=None):
"""
calculates the <values> vs. xVariable in log scale
Parameters:
---------------
values : dict
A dictionary where the keys are the xValues
and each element contains an array-like sequence of data
Example:
[1: array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),
2: array([1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2]),
values : ndarray
Two columns array of xValues and yValues,
to be rearranged as above
Returns:
center point of the bin, average value within the bin
"""
# Check the list of Values
if not checkIfVoid(values):
print("Error")
if isinstance(values, dict):
xValues = np.asarray(values.keys())
yValues = np.asarray(values.values())
elif isinstance(values, np.ndarray):
xValues = np.unique(values[:, 0])
yValues = []
for xVal in xValues:
index = values[:, 0] == xVal
yValues.append(values[index, 1])
yValues = scipy.array(yValues)
else:
print("Values shape not recognized")
return
if not first_point:
first_point = scipy.amin(xValues) * 0.99
if not last_point:
last_point = scipy.amax(xValues) * 1.01
xbins, bins = getLogBins(first_point, last_point, log_step)
yAverage = []
for i, j in zip(bins[:-1], bins[1:]):
q1, q2 = np.greater_equal(xValues, i), np.less(xValues, j)
q = np.logical_and(q1, q2)
if sum(q) == 0:
averageValue = np.NaN
else:
allElements = [val for val in itertools.chain(*yValues[q])]
averageValue = sum(allElements) / float(len(allElements))
# print averageValue, allElements
yAverage.append(averageValue)
yAverage = np.asanyarray(yAverage)
# Check if there are NaN values
iNan = np.isnan(yAverage)
x = xbins[~iNan]
y = yAverage[~iNan]
return x, y
def scale(x, M=None, m=None, REVERSE=None):
""" Function that standardize the data
Input:
x: the data
M: the Max vector
m: the Min vector
Output:
x: the standardize data
M: the Max vector
m: the Min vector
"""
if not sp.issubdtype(x.dtype, float):
do_convert = 1
else:
do_convert = 0
if REVERSE is None:
if M is None:
M = sp.amax(x, axis=0)
m = sp.amin(x, axis=0)
if do_convert:
xs = 2 * (x.astype("float") - m) / (M - m) - 1
else:
xs = 2 * (x - m) / (M - m) - 1
return xs, M, m
else:
if do_convert:
xs = 2 * (x.astype("float") - m) / (M - m) - 1
else:
xs = 2 * (x - m) / (M - m) - 1
return xs
else:
return (1 + x) / 2 * (M - m) + m
def test_late_pore_and_throat_filling():
phys.models.add(propname='pore.fractional_filling',
model=OpenPNM.Physics.models.multiphase.late_pore_filling,
Pc=0,
Swp_star=0.2,
eta=1)
mod = OpenPNM.Physics.models.multiphase.late_throat_filling
phys.models.add(propname='throat.fractional_filling',
model=mod,
Pc=0,
Swp_star=0.2,
eta=1)
phys.regenerate()
drainage.setup(invading_phase=water, defending_phase=air,
pore_filling='pore.fractional_filling',
throat_filling='throat.fractional_filling')
drainage.set_inlets(pores=pn.pores('boundary_top'))
drainage.run()
data = drainage.get_drainage_data()
assert sp.amin(data['invading_phase_saturation']) == 0.0
assert sp.amax(data['invading_phase_saturation']) < 1.0
drainage.return_results(Pc=5000)
assert 'pore.occupancy' in water.keys()
assert 'throat.occupancy' in water.keys()
assert 'pore.partial_occupancy' in water.keys()
assert 'throat.partial_occupancy' in water.keys()
def _add_labels(self):
pind = self.get_pore_indices('all')
Tn = self.find_neighbor_throats(pnums=pind, flatten=False)
Tmax = sp.amax(self.num_neighbors(pnums=pind, flatten=False))
for i in sp.arange(0, sp.shape(Tn)[0]):
if sp.shape(Tn[i])[0] < Tmax:
self.set_pore_info(label='surface', locations=i)
else:
self.set_pore_info(label='internal', locations=i)
coords = self.get_pore_data(prop='coords')
self.set_pore_info(label='left',locations=coords[:,0]<=(sp.amin(coords[:,0])))
self.set_pore_info(label='right',locations=coords[:,0]>=(sp.amax(coords[:,0])))
self.set_pore_info(label='front',locations=coords[:,1]<=(sp.amin(coords[:,1])))
self.set_pore_info(label='back',locations=coords[:,1]>=(sp.amax(coords[:,1])))
self.set_pore_info(label='bottom',locations=coords[:,2]<=(sp.amin(coords[:,2])))
self.set_pore_info(label='top',locations=coords[:,2]>=(sp.amax(coords[:,2])))
def test_residual_and_lpf():
phys.models.add(propname='pore.fractional_filling',
model=OpenPNM.Physics.models.multiphase.late_pore_filling,
Pc=0,
Swp_star=0.2,
eta=1)
phys.models.add(propname='throat.fractional_filling',
model=OpenPNM.Physics.models.multiphase.late_throat_filling,
Pc=0,
Swp_star=0.2,
eta=1)
phys.regenerate()
drainage.setup(invading_phase=water, defending_phase=air,
pore_filling='pore.fractional_filling',
throat_filling='throat.fractional_filling')
drainage.set_inlets(pores=pn.pores('boundary_top'))
resPs = pn.pores('internal')[sp.random.random(len(pn.pores('internal')))<0.1]
resTs = pn.throats('internal')[sp.random.random(len(pn.throats('internal')))<0.1]
drainage.set_residual(pores=resPs, throats=resTs)
drainage.run()
drainage.return_results(Pc=5000)
data = drainage.get_drainage_data()
assert sp.all(water["pore.partial_occupancy"][resPs] == 1.0)
assert sp.all(water["throat.partial_occupancy"][resTs] == 1.0)
assert sp.amin(data['invading_phase_saturation']) > 0.0
assert sp.amax(data['invading_phase_saturation']) < 1.0
assert sp.all(water["pore.occupancy"]+air["pore.occupancy"] == 1.0)
total_pp = water["pore.partial_occupancy"]+air["pore.partial_occupancy"]
assert sp.all(total_pp == 1.0)
assert sp.all(water["throat.occupancy"]+air["throat.occupancy"] == 1.0)
total_pt = water["throat.partial_occupancy"]+air["throat.partial_occupancy"]
assert sp.all(total_pt == 1.0)
def main(database):
#Commits per committer limited to the 30 first with the highest accumulated activity
query = "select count(*) from scmlog group by committer_id order by count(*) desc limit 40"
#Connecting to the data base and retrieving data
connector = connect(database)
results = int(connector.execute(query))
if results > 0:
results_aux = connector.fetchall()
else:
print("Error when retrieving data")
return
#Moving data to a list
commits = []
for commit in results_aux[5:]:
# for commits in results_aux:
commits.append(int(commit[0]))
#Calculating basic statistics
print "max: " + str(sp.amax(commits))
print "min: " + str(sp.amin(commits))
print "mean: " + str(sp.mean(commits))
print "median: " + str(sp.median(commits))
print "std: " + str(sp.std(commits))
print ".25 quartile: " + str(sp.percentile(commits, 25))
print ".50 quartile: " + str(sp.percentile(commits, 50))
print ".75 quartile: " + str(sp.percentile(commits, 75))
def main(database):
# Commits per committer limited to the 30 first with the highest accumulated activity
query = "select count(*) from scmlog group by committer_id order by count(*) desc limit 40"
# Connecting to the data base and retrieving data
connector = connect(database)
results = int(connector.execute(query))
if results > 0:
results_aux = connector.fetchall()
else:
print ("Error when retrieving data")
return
# Moving data to a list
commits = []
for commit in results_aux[5:]:
# for commits in results_aux:
commits.append(int(commit[0]))
# Calculating basic statistics
print "max: " + str(sp.amax(commits))
print "min: " + str(sp.amin(commits))
print "mean: " + str(sp.mean(commits))
print "median: " + str(sp.median(commits))
print "std: " + str(sp.std(commits))
print ".25 quartile: " + str(sp.percentile(commits, 25))
print ".50 quartile: " + str(sp.percentile(commits, 50))
print ".75 quartile: " + str(sp.percentile(commits, 75))
def run(self, npts=25, inv_points=None, access_limited=True, **kwargs):
r"""
Parameters
----------
npts : int (default = 25)
The number of pressure points to apply. The list of pressures
is logarithmically spaced between the lowest and highest throat
entry pressures in the network.
inv_points : array_like, optional
A list of specific pressure point(s) to apply.
"""
if 'inlets' in kwargs.keys():
logger.info('Inlets recieved, passing to set_inlets')
self.set_inlets(pores=kwargs['inlets'])
if 'outlets' in kwargs.keys():
logger.info('Outlets recieved, passing to set_outlets')
self.set_outlets(pores=kwargs['outlets'])
self._AL = access_limited
if inv_points is None:
logger.info('Generating list of invasion pressures')
min_p = sp.amin(self['throat.entry_pressure']) * 0.98 # nudge down
max_p = sp.amax(self['throat.entry_pressure']) * 1.02 # bump up
inv_points = sp.logspace(sp.log10(min_p), sp.log10(max_p), npts)
self._npts = sp.size(inv_points)
# Execute calculation
self._do_outer_iteration_stage(inv_points)
def test_calculates_frequencies(self) :
self.Data.calc_freq()
self.assertTrue(hasattr(self.Data, 'freq'))
self.assertEqual(len(self.Data.freq), self.nfreq)
self.assertAlmostEqual(self.Data.field['BANDWID'],
sp.amax(self.Data.freq) -
sp.amin(self.Data.freq), -5)
def test_random(self):
self.geo.models.add(propname='throat.seed',
model=OpenPNM.Geometry.models.throat_seed.random,
seed=0,
num_range=[0.1, 2])
assert sp.amax(self.geo['throat.seed']) > 1.9
assert sp.amin(self.geo['throat.seed']) > 0.1
def _do_one_outer_iteration(self, **kwargs):
r"""
One iteration of an outer iteration loop for an algorithm
(e.g. time or parametric study)
"""
# Checking for the necessary values in Picard algorithm
nan_tol = sp.isnan(self['pore.source_tol'])
nan_max = sp.isnan(self['pore.source_maxiter'])
self._tol_for_all = sp.amin(self['pore.source_tol'][~nan_tol])
self._maxiter_for_all = sp.amax(self['pore.source_maxiter'][~nan_max])
if self._guess is None:
self._guess = sp.zeros(self._coeff_dimension)
t = 1
step = 0
# The main Picard loop
while t > self._tol_for_all and step <= self._maxiter_for_all:
X, t, A, b = self._do_inner_iteration_stage(guess=self._guess,
**kwargs)
logger.info('tol for Picard source_algorithm in step ' +
str(step) + ' : ' + str(t))
self._guess = X
step += 1
# Check for divergence
self._steps = step
if t >= self._tol_for_all and step > self._maxiter_for_all:
raise Exception('Iterative algorithm for the source term reached '
'to the maxiter: ' + str(self._maxiter_for_all) +
' without achieving tol: ' +
str(self._tol_for_all))
logger.info('Picard algorithm for source term converged!')
self.A = A
self.b = b
self._tol_reached = t
return X
def run(self, npts=25, inv_points=None, access_limited=True, **kwargs):
r"""
Parameters
----------
npts : int (default = 25)
The number of pressure points to apply. The list of pressures
is logarithmically spaced between the lowest and highest throat
entry pressures in the network.
inv_points : array_like, optional
A list of specific pressure point(s) to apply.
"""
if 'inlets' in kwargs.keys():
logger.info('Inlets recieved, passing to set_inlets')
self.set_inlets(pores=kwargs['inlets'])
if 'outlets' in kwargs.keys():
logger.info('Outlets recieved, passing to set_outlets')
self.set_outlets(pores=kwargs['outlets'])
self._AL = access_limited
if inv_points is None:
logger.info('Generating list of invasion pressures')
min_p = sp.amin(self['throat.entry_pressure']) * 0.98 # nudge down
max_p = sp.amax(self['throat.entry_pressure']) * 1.02 # bump up
inv_points = sp.logspace(sp.log10(min_p),
sp.log10(max_p),
npts)
self._npts = sp.size(inv_points)
# Execute calculation
self._do_outer_iteration_stage(inv_points)
def neighbor(geometry, throat_prop='throat.seed', mode='min', **kwargs):
r"""
Adopt a value from the values found in neighboring throats
Parameters
----------
geometry : OpenPNM Geometry Object
The object containing the ``throat_prop`` to be used.
throat_prop : string
The dictionary key of the array containing the throat property to be
used in the calculation. The default is 'throat.seed'.
mode : string
Controls how the pore property is calculated. Options are 'min',
'max' and 'mean'.
"""
network = geometry._net
Ps = geometry.pores()
data = geometry[throat_prop]
neighborTs = network.find_neighbor_throats(pores=Ps,
flatten=False,
mode='intersection')
values = _sp.ones((_sp.shape(Ps)[0], )) * _sp.nan
if mode == 'min':
for pore in Ps:
values[pore] = _sp.amin(data[neighborTs[pore]])
if mode == 'max':
for pore in Ps:
values[pore] = _sp.amax(data[neighborTs[pore]])
if mode == 'mean':
for pore in Ps:
values[pore] = _sp.mean(data[neighborTs[pore]])
return values
def neighbor(geometry, network, pore_prop='pore.seed', mode='min', **kwargs):
r"""
Adopt a value based on the values in the neighboring pores
Parameters
----------
mode : string
Indicates how to select the values from the neighboring pores. The
options are:
- min : (Default) Uses the minimum of the value found in the neighbors
- max : Uses the maximum of the values found in the neighbors
- mean : Uses an average of the neighbor values
pore_prop : string
The dictionary key containing the pore property to be used.
"""
throats = network.throats(geometry.name)
P12 = network.find_connected_pores(throats)
pvalues = network[pore_prop][P12]
if mode == 'min':
value = _sp.amin(pvalues, axis=1)
if mode == 'max':
value = _sp.amax(pvalues, axis=1)
if mode == 'mean':
value = _sp.mean(pvalues, axis=1)
return value
def __init__(self,
mat,
cmap=None,
pixelspervalue=20,
minvalue=None,
maxvalue=None):
""" Make a colormap image of a matrix
:key mat: the matrix to be used for the colormap.
"""
if minvalue == None:
minvalue = amin(mat)
if maxvalue == None:
maxvalue = amax(mat)
if not cmap:
cmap = cm.hot
figsize = (array(mat.shape) / 100. * pixelspervalue)[::-1]
self.fig = figure(figsize=figsize)
axes([0, 0, 1, 1]) # Make the plot occupy the whole canvas
axis('off')
self.fig.set_size_inches(figsize)
imshow(mat,
cmap=cmap,
clim=(minvalue, maxvalue),
interpolation='nearest')
