def test_neighbor_throats_with_nans(self):
net = op.network.Cubic(shape=[2, 2, 2])
net['throat.values'] = 1.0
net['throat.values'][0] = sp.nan
f = mods.from_neighbor_throats
with_nans = f(target=net,
throat_prop='throat.values',
ignore_nans=False,
mode='min')
assert sp.any(sp.isnan(with_nans))
no_nans = f(target=net,
throat_prop='throat.values',
ignore_nans=True,
mode='min')
assert sp.all(~sp.isnan(no_nans))
with_nans = f(target=net,
throat_prop='throat.values',
ignore_nans=False,
mode='max')
assert sp.any(sp.isnan(with_nans))
no_nans = f(target=net,
throat_prop='throat.values',
ignore_nans=True,
mode='max')
assert sp.all(~sp.isnan(no_nans))
with_nans = f(target=net,
throat_prop='throat.values',
ignore_nans=False,
mode='mean')
assert sp.any(sp.isnan(with_nans))
no_nans = f(target=net,
throat_prop='throat.values',
ignore_nans=True,
mode='mean')
assert sp.all(~sp.isnan(no_nans))
def computePCsPython(out_dir,k,bfile,ffile):
""" reading in """
RV = plink_reader.readBED(bfile,useMAFencoding=True)
X = np.ascontiguousarray(RV['snps'])
""" normalizing markers """
print('Normalizing SNPs...')
p_ref = X.mean(axis=0)/2.
X -= 2*p_ref
with warnings.catch_warnings():
warnings.simplefilter("ignore")
X /= sp.sqrt(2*p_ref*(1-p_ref))
hasNan = sp.any(sp.isnan(X),axis=0)
if sp.any(hasNan):
print(('%d SNPs have a nan entry. Exluding them for computing the covariance matrix.'%hasNan.sum()))
X = X[:,~hasNan]
""" computing prinicipal components """
U,S,Vt = ssl.svds(X,k=k)
U -= U.mean(0)
U /= U.std(0)
U = U[:,::-1]
""" saving to output """
np.savetxt(ffile, U, delimiter='\t',fmt='%.6f')
def test_RSA_mask_edge_3d(self):
im = sp.zeros([50, 50, 50], dtype=int)
im = ps.generators.RSA(im, radius=5, volume_fraction=0.5,
mode='contained')
coords = sp.argwhere(im == 2)
assert ~sp.any(coords < 5)
assert ~sp.any(coords > 45)
def _check_bounds(self, x_new):
# If self.bounds_error = 1, we raise an error if any x_new values
# fall outside the range of x. Otherwise, we return an array indicating
# which values are outside the boundary region.
# !! Needs some work for multi-dimensional x !!
below_bounds = less(x_new, self.x[0])
above_bounds = greater(x_new, self.x[-1])
# Note: sometrue has been redefined to handle length 0 arrays
# !! Could provide more information about which values are out of bounds
# RHC -- Changed these ValueErrors to PyDSTool_BoundsErrors
if self.bounds_error and any(sometrue(below_bounds)):
## print "Input:", x_new
## print "Bound:", self.x[0]
## print "Difference input - bound:", x_new-self.x[0]
raise PyDSTool_BoundsError, " A value in x_new is below the"\
" interpolation range."
if self.bounds_error and any(sometrue(above_bounds)):
## print "Input:", x_new
## print "Bound:", self.x[-1]
## print "Difference input - bound:", x_new-self.x[-1]
raise PyDSTool_BoundsError, " A value in x_new is above the"\
" interpolation range."
# !! Should we emit a warning if some values are out of bounds.
# !! matlab does not.
out_of_bounds = logical_or(below_bounds, above_bounds)
return out_of_bounds
def crop_pts(coords, box):
r'''
Drop all points lying outside the box
Parameters
----------
coords : array_like
An Np x ndims array off [x,y,z] coordinates
box : array_like
A 2 x ndims array of diametrically opposed corner coordintes
Returns
-------
coords : array_like
Inputs coordinates with outliers removed
Notes
-----
This needs to be made more general so that an arbitray cuboid with any
orientation can be supplied, using Np x 8 points
'''
coords = coords[_sp.any(coords < box[0], axis=1)]
coords = coords[_sp.any(coords > box[1], axis=1)]
return coords
def test_RSA_mask_edge_2d(self):
im = sp.zeros([100, 100], dtype=int)
im = ps.generators.RSA(im, radius=10, volume_fraction=0.5,
mode='contained')
coords = sp.argwhere(im == 2)
assert ~sp.any(coords < 10)
assert ~sp.any(coords > 90)
def crop_pts(coords, box):
r'''
Drop all points lying outside the box
Parameters
----------
coords : array_like
An Np x ndims array off [x,y,z] coordinates
box : array_like
A 2 x ndims array of diametrically opposed corner coordintes
Returns
-------
coords : array_like
Inputs coordinates with outliers removed
Notes
-----
This needs to be made more general so that an arbitray cuboid with any
orientation can be supplied, using Np x 8 points
'''
coords = coords[_sp.any(coords < box[0], axis=1)]
coords = coords[_sp.any(coords > box[1], axis=1)]
return coords
def _check_bounds(self,x_new):
# If self.bounds_error = 1, we raise an error if any x_new values
# fall outside the range of x. Otherwise, we return an array indicating
# which values are outside the boundary region.
# !! Needs some work for multi-dimensional x !!
below_bounds = less(x_new,self.x[0])
above_bounds = greater(x_new,self.x[-1])
# Note: sometrue has been redefined to handle length 0 arrays
# !! Could provide more information about which values are out of bounds
# RHC -- Changed these ValueErrors to PyDSTool_BoundsErrors
if self.bounds_error and any(sometrue(below_bounds)):
## print "Input:", x_new
## print "Bound:", self.x[0]
## print "Difference input - bound:", x_new-self.x[0]
raise PyDSTool_BoundsError, " A value in x_new is below the"\
" interpolation range."
if self.bounds_error and any(sometrue(above_bounds)):
## print "Input:", x_new
## print "Bound:", self.x[-1]
## print "Difference input - bound:", x_new-self.x[-1]
raise PyDSTool_BoundsError, " A value in x_new is above the"\
" interpolation range."
# !! Should we emit a warning if some values are out of bounds.
# !! matlab does not.
out_of_bounds = logical_or(below_bounds,above_bounds)
return out_of_bounds
def generate_simulation_data(n_classes=2,n_samples=[20,20],n_features=3,seed=0,scales=None):
#initial checks
assert n_classes>0, "n_classes has to be larger than 0"
assert n_features>2, "n_features has to be larger than 2"
assert n_classes==len(n_samples), "n_samples has to be an array with as many elements as n_classes"
if sp.any(scales)==None:
scales = sp.ones(n_features)
else:
assert len(scales)==n_features, "scales has to be an array with as many elements as features"
#set seed
sp.random.seed(seed)
feature_matrix = None
class_labels = None
#generate data for each class
for i in range(n_classes):
#generate random data for class i drawn from a multivariate gauss distribution
class_matrix = sp.random.multivariate_normal(sp.ones(n_features)*(i+1)*sp.random.randn(n_features),sp.eye(n_features),n_samples[i])
#store data
class_labels = sp.ones(n_samples[i])*(i+1) if (sp.any(class_labels)==None) else sp.concatenate([class_labels,sp.ones(n_samples[i])*(i+1)])
feature_matrix = class_matrix if (sp.any(feature_matrix)==None) else sp.vstack([feature_matrix,class_matrix])
#scale features
if sp.any(scales)!=None:
feature_matrix *= scales
#generate data dict
data = Data(data=feature_matrix,
target=class_labels,
n_samples=feature_matrix.shape[0],
n_features=feature_matrix.shape[1])
return data
def _apply_percolation(self, inv_val):
r"""
Determine which pores and throats are invaded at a given applied
capillary pressure. This method is called by ``run``.
"""
# Generate a list containing boolean values for throat state
Tinvaded = self['throat.entry_pressure'] <= inv_val
# Add residual throats, if any, to list of invaded throats
Tinvaded = Tinvaded + self['throat.residual']
# Find all pores that can be invaded at specified pressure
[pclusters, tclusters] = self._net.find_clusters2(mask=Tinvaded,
t_labels=True)
# Identify clusters connected to inlet sites
inv_clusters = sp.unique(pclusters[self['pore.inlets']])
inv_clusters = inv_clusters[inv_clusters >= 0]
# Find pores on the invading clusters
pmask = np.in1d(pclusters, inv_clusters)
# Store current applied pressure in newly invaded pores
pinds = (self['pore.inv_Pc'] == sp.inf) * (pmask)
self['pore.inv_Pc'][pinds] = inv_val
# Find throats on the invading clusters
tmask = np.in1d(tclusters, inv_clusters)
# Store current applied pressure in newly invaded throats
tinds = (self['throat.inv_Pc'] == sp.inf) * (tmask)
self['throat.inv_Pc'][tinds] = inv_val
# Set residual pores and throats, if any, to invaded
if sp.any(self['pore.residual']):
self['pore.inv_Pc'][self['pore.residual']] = 0
if sp.any(self['throat.residual']):
self['throat.inv_Pc'][self['throat.residual']] = 0
def computePCsPython(out_dir, k, bfile, ffile):
""" reading in """
RV = plink_reader.readBED(bfile, useMAFencoding=True)
X = np.ascontiguousarray(RV['snps'])
""" normalizing markers """
print('Normalizing SNPs...')
p_ref = X.mean(axis=0) / 2.
X -= 2 * p_ref
with warnings.catch_warnings():
warnings.simplefilter("ignore")
X /= sp.sqrt(2 * p_ref * (1 - p_ref))
hasNan = sp.any(sp.isnan(X), axis=0)
if sp.any(hasNan):
print((
'%d SNPs have a nan entry. Exluding them for computing the covariance matrix.'
% hasNan.sum()))
X = X[:, ~hasNan]
""" computing prinicipal components """
U, S, Vt = ssl.svds(X, k=k)
U -= U.mean(0)
U /= U.std(0)
U = U[:, ::-1]
""" saving to output """
np.savetxt(ffile, U, delimiter='\t', fmt='%.6f')
def _apply_percolation(self, inv_val):
r"""
Determine which pores and throats are invaded at a given applied
capillary pressure. This method is called by ``run``.
"""
# Generate a list containing boolean values for throat state
Tinvaded = self['throat.entry_pressure'] <= inv_val
# Add residual throats, if any, to list of invaded throats
Tinvaded = Tinvaded + self['throat.residual']
# Find all pores that can be invaded at specified pressure
[pclusters, tclusters] = self._net.find_clusters2(mask=Tinvaded,
t_labels=True)
# Identify clusters connected to inlet sites
inv_clusters = sp.unique(pclusters[self['pore.inlets']])
inv_clusters = inv_clusters[inv_clusters >= 0]
# Find pores on the invading clusters
pmask = np.in1d(pclusters, inv_clusters)
# Store current applied pressure in newly invaded pores
pinds = (self['pore.inv_Pc'] == sp.inf) * (pmask)
self['pore.inv_Pc'][pinds] = inv_val
# Find throats on the invading clusters
tmask = np.in1d(tclusters, inv_clusters)
# Store current applied pressure in newly invaded throats
tinds = (self['throat.inv_Pc'] == sp.inf) * (tmask)
self['throat.inv_Pc'][tinds] = inv_val
# Set residual pores and throats, if any, to invaded
if sp.any(self['pore.residual']):
self['pore.inv_Pc'][self['pore.residual']] = 0
if sp.any(self['throat.residual']):
self['throat.inv_Pc'][self['throat.residual']] = 0
def test_snow_partitioning_n(self):
im = self.im
snow = ps.filters.snow_partitioning_n(im + 1, r_max=4, sigma=0.4,
return_all=True, mask=True,
randomize=False, alias=None)
assert sp.amax(snow.regions) == 44
assert not sp.any(sp.isnan(snow.regions))
assert not sp.any(sp.isnan(snow.dt))
assert not sp.any(sp.isnan(snow.im))
def cylinders(shape: List[int],
radius: int,
nfibers: int,
phi_max: float = 0,
theta_max: float = 90):
r"""
Generates a binary image of overlapping cylinders. This is a good
approximation of a fibrous mat.
Parameters
----------
phi_max : scalar
A value between 0 and 90 that controls the amount that the fibers
lie out of the XY plane, with 0 meaning all fibers lie in the XY
plane, and 90 meaning that fibers are randomly oriented out of the
plane by as much as +/- 90 degrees.
theta_max : scalar
A value between 0 and 90 that controls the amount rotation in the
XY plane, with 0 meaning all fibers point in the X-direction, and
90 meaning they are randomly rotated about the Z axis by as much
as +/- 90 degrees.
Returns
-------
image : ND-array
A boolean array with ``True`` values denoting the pore space
"""
shape = sp.array(shape)
if sp.size(shape) == 1:
shape = sp.full((3, ), int(shape))
elif sp.size(shape) == 2:
raise Exception("2D fibers don't make sense")
im = sp.zeros(shape)
R = sp.sqrt(sp.sum(sp.square(shape)))
n = 0
while n < nfibers:
x = sp.rand(3) * shape
phi = sp.deg2rad(90 + 90 * (0.5 - sp.rand()) * phi_max / 90)
theta = sp.deg2rad(180 - 90 * (0.5 - sp.rand()) * 2 * theta_max / 90)
X0 = R * sp.array([
sp.sin(theta) * sp.cos(phi),
sp.sin(theta) * sp.sin(phi),
sp.cos(theta)
])
[X0, X1] = [X0 + x, -X0 + x]
crds = line_segment(X0, X1)
lower = ~sp.any(sp.vstack(crds).T < [0, 0, 0], axis=1)
upper = ~sp.any(sp.vstack(crds).T >= shape, axis=1)
valid = upper * lower
if sp.any(valid):
im[crds[0][valid], crds[1][valid], crds[2][valid]] = 1
n += 1
im = sp.array(im, dtype=bool)
dt = spim.distance_transform_edt(~im) < radius
return ~dt
def process_file(self, file_ind) :
params = self.params
file_middle = params['file_middles'][file_ind]
input_fname = (params['input_root'] + file_middle +
params['input_end'])
sub_input_fname = (params['subtracted_input_root'] + file_middle
+ params['input_end'])
output_fname = (params['output_root']
+ file_middle + params['output_end'])
sub_output_fname = (params['subtracted_output_root']
+ file_middle + params['output_end'])
Writer = fitsGBT.Writer(feedback=self.feedback)
SubWriter = fitsGBT.Writer(feedback=self.feedback)
# Read in the data, and loop over data blocks.
Reader = fitsGBT.Reader(input_fname, feedback=self.feedback)
SubReader = fitsGBT.Reader(sub_input_fname, feedback=self.feedback)
if (sp.any(Reader.scan_set != SubReader.scan_set)
or sp.any(Reader.IF_set != SubReader.IF_set)) :
raise ce.DataError("IFs and scans don't match signal subtracted"
" data.")
# Get the number of scans if asked for all of them.
scan_inds = params['scans']
if len(scan_inds) == 0 or scan_inds is None :
scan_inds = range(len(Reader.scan_set))
if_inds = params['IFs']
if len(if_inds) == 0 or scan_inds is None :
if_inds = range(len(Reader.IF_set))
if self.feedback > 1 :
print "New flags each block:",
# Loop over scans and IFs
for thisscan in scan_inds :
for thisIF in if_inds :
Data = Reader.read(thisscan, thisIF)
SubData = SubReader.read(thisscan, thisIF)
n_flags = ma.count_masked(Data.data)
# Now do the flagging.
flag(Data, SubData, params['thres'])
Data.add_history("Reflaged for outliers.", ("Used file: "
+ utils.abbreviate_file_path(sub_input_fname),))
SubData.add_history("Reflaged for outliers.")
Writer.add_data(Data)
SubWriter.add_data(SubData)
# Report the numbe of new flags.
n_flags = ma.count_masked(Data.data) - n_flags
if self.feedback > 1 :
print n_flags,
if self.feedback > 1 :
print ''
# Finally write the data back to file.
utils.mkparents(output_fname)
utils.mkparents(sub_output_fname)
Writer.write(output_fname)
SubWriter.write(sub_output_fname)
def updateData(self, array = Array(sp.zeros((0,2)),scale=[0,0], Type = 0), action = 1, Type = None):
""" Запис в тимчасовий файл даних з масиву
action = {-1, 0, 1, 2}
-1 : undo
0 : reset
1 : add
"""
if Type is None:
if sp.any(array):
Type = array.Type
#print(sp.shape(array),array.Type)
emit = False
#print(len(self.dataStack[Type]),action)
# Запис в історію
if action == 1:
if sp.any(array) and sp.shape(array)[1] == 2 and sp.shape(array)[0] > 1:
self.dataStack[Type].append(array)
emit = True
else: print('updateData: arrayError',sp.any(array) , sp.shape(array)[1] == 2 , sp.shape(array)[0] > 1)
# Видалення останнього запису
elif action == -1 and len(self.dataStack[Type])>=2:
self.dataStack[Type].pop()
emit = True
#self.setActiveLogScale( Type)
# Скидання історії, або запис першого елемента історії
elif action == 0:
print(0)
if sp.any(array) and sp.shape(array)[1] == 2 and sp.shape(array)[0] > 1 and len(self.dataStack[Type])>=1:
self.dataStack[Type][0:] = []
self.dataStack[Type].append(array)
emit = True
if not sp.any(array) and len(self.dataStack[Type])>=2:
self.dataStack[Type][1:] = []
emit = True
#self.setActiveLogScale( Type)
else:
print("updateData: Error0",len(self.dataStack[Type]))
print(sp.shape(self.getData(Type)))
try:
for i in self.dataStack[Type]: print(i.scaleX, i.scaleY, i.shape)
except:
pass
# Емітувати повідомлення про зміу даних
if emit:
self.data_signal.emit(Type, action)
self.Plot(self.getData(Type) )
def test_ransohoff_snapoff_verts(self):
ws = op.Workspace()
ws.clear()
bp = sp.array([[0.25, 0.25, 0.25], [0.25, 0.75, 0.25],
[0.75, 0.25, 0.25], [0.75, 0.75, 0.25],
[0.25, 0.25, 0.75], [0.25, 0.75, 0.75],
[0.75, 0.25, 0.75], [0.75, 0.75, 0.75]])
scale = 1e-4
sp.random.seed(1)
p = (sp.random.random([len(bp), 3])-0.5)/1000
bp += p
fiber_rad = 2e-6
bp = op.topotools.reflect_base_points(bp, domain_size=[1, 1, 1])
prj = op.materials.VoronoiFibers(fiber_rad=fiber_rad,
resolution=1e-6,
shape=[scale, scale, scale],
points=bp*scale,
name='test')
net = prj.network
del_geom = prj.geometries()['test_del']
vor_geom = prj.geometries()['test_vor']
f = op.models.physics.capillary_pressure.ransohoff_snap_off
water = op.phases.GenericPhase(network=net)
water['pore.surface_tension'] = 0.072
water['pore.contact_angle'] = 45
phys1 = op.physics.GenericPhysics(network=net,
geometry=del_geom,
phase=water)
phys1.add_model(propname='throat.snap_off',
model=f,
wavelength=fiber_rad)
phys1.add_model(propname='throat.snap_off_pair',
model=f,
wavelength=fiber_rad,
require_pair=True)
phys2 = op.physics.GenericPhysics(network=net,
geometry=vor_geom,
phase=water)
phys2.add_model(propname='throat.snap_off',
model=f,
wavelength=fiber_rad)
phys2.add_model(propname='throat.snap_off_pair',
model=f,
wavelength=fiber_rad,
require_pair=True)
ts = ~net['throat.interconnect']
assert ~sp.any(sp.isnan(water['throat.snap_off'][ts]))
assert sp.any(sp.isnan(water['throat.snap_off_pair'][ts]))
assert sp.any(~sp.isnan(water['throat.snap_off_pair'][ts]))
def test_ransohoff_snapoff_verts(self):
ws = op.Workspace()
ws.clear()
bp = sp.array([[0.25, 0.25, 0.25], [0.25, 0.75, 0.25],
[0.75, 0.25, 0.25], [0.75, 0.75, 0.25],
[0.25, 0.25, 0.75], [0.25, 0.75, 0.75],
[0.75, 0.25, 0.75], [0.75, 0.75, 0.75]])
scale = 1e-4
sp.random.seed(1)
p = (sp.random.random([len(bp), 3]) - 0.5) / 1000
bp += p
fiber_rad = 2e-6
bp = op.topotools.reflect_base_points(bp, domain_size=[1, 1, 1])
prj = op.materials.VoronoiFibers(fiber_rad=fiber_rad,
resolution=1e-6,
shape=[scale, scale, scale],
points=bp * scale,
name='test')
net = prj.network
del_geom = prj.geometries()['test_del']
vor_geom = prj.geometries()['test_vor']
f = op.models.physics.capillary_pressure.ransohoff_snap_off
water = op.phases.GenericPhase(network=net)
water['pore.surface_tension'] = 0.072
water['pore.contact_angle'] = 45
phys1 = op.physics.GenericPhysics(network=net,
geometry=del_geom,
phase=water)
phys1.add_model(propname='throat.snap_off',
model=f,
wavelength=fiber_rad)
phys1.add_model(propname='throat.snap_off_pair',
model=f,
wavelength=fiber_rad,
require_pair=True)
phys2 = op.physics.GenericPhysics(network=net,
geometry=vor_geom,
phase=water)
phys2.add_model(propname='throat.snap_off',
model=f,
wavelength=fiber_rad)
phys2.add_model(propname='throat.snap_off_pair',
model=f,
wavelength=fiber_rad,
require_pair=True)
ts = ~net['throat.interconnect']
assert ~sp.any(sp.isnan(water['throat.snap_off'][ts]))
assert sp.any(sp.isnan(water['throat.snap_off_pair'][ts]))
assert sp.any(~sp.isnan(water['throat.snap_off_pair'][ts]))
def affine_transform(input, matrix, shift=None, offset=None, interptype=InterpolationType.CATMULL_ROM_CUBIC_SPLINE, fill=None):
"""
Applies an affine transformation to an image. This is the forward transform
so that (conceptually)::
idx = scipy.array((i,j,k), dtype="int32")
tidx = matrix.dot((idx-offset).T) + offset + shift
out[tuple(tidx)] = input[tuple(idx)]
This differs from the :func:`scipy.ndimage.interpolation.affine_transform` function
which does (conceptually, ignoring shift)::
idx = scipy.array((i,j,k), dtype="int32")
tidx = matrix.dot((idx-offset).T) + offset
out[tuple(idx)] = input[tuple(tidx)]
:type input: :obj:`mango.Dds`
:param input: Image to be transformed.
:type matrix: :obj:`numpy.ndarray`
:param matrix: A :samp:`(3,3)` shaped affine transformation matrix.
:type shift: 3-sequence
:param shift: The translation (number of voxels), can be :obj:`float` elements.
:type offset: 3-sequence
:param offset: The centre-point of the affine transformation (relative to input.origin).
If :samp:`None`, the centre of the image is used as the centre of affine transformation.
Elements can be :obj:`float`.
:type interptype: :obj:`mango.image.InterpolationType`
:param interptype: Interpolation type.
:type fill: numeric
:param fill: The value used for elements outside the image-domain.
If :samp:`None` uses the :samp:`input.mtype.maskValue()` or :samp:`0`
if there is no :samp:`input.mtype` attribute.
:rtype: :obj:`mango.Dds`
:return: Affine-transformed :obj:`mango.Dds` image.
"""
if (_mango_reg_core is None):
raise Exception("This mango build has not been compiled with registration support.")
if (sp.any(input.md.getVoxelSize() <= 0)):
raise Exception("Non-positive voxel size (%s) found in input, affine_transform requires positive voxel size to be set." % (input.md.getVoxelSize(),))
if (fill is None):
fill = 0
if (hasattr(input,"mtype") and (input.mtype != None)):
fill = input.mtype.maskValue()
if (offset is None):
# Set the default offset to be the centre of the image.
offset = sp.array(input.shape, dtype="float64")*0.5
# Convert from relative offset value to absolute global coordinate.
centre = sp.array(offset, dtype="float64") + input.origin
mangoFilt = _mango_reg_core._TransformApplier(matrix, centre, shift, interptype, fill)
filt = _DdsMangoFilterApplier(mangoFilt)
###trnsDds = filt(input, mode=mode, cval=cval)
trnsDds = filt(input, mode="constant", cval=fill)
return trnsDds
def geodesic(x, v, tmax, func, jacobian, Avv, args = (), lam = 0, dtd = None,
rtol = 1e-6, atol = 1e-6, maxsteps = 500, callback = None):
N = len(x)
y = scipy.empty((2*N,))
y[:N] = x[:]
y[N:] = v[:]
if dtd is None:
dtd = scipy.eye(N)
j = jacobian(x,*args)
M,N = j.shape
Acc = scipy.empty((M,))
r = ode(geodesic_rhs_ode,jac=None).set_f_params(lam, dtd, func, jacobian, Avv, args, j, Acc).set_integrator('vode',atol = atol, rtol=rtol).set_initial_value(y,0.0)
steps = 0
xs = []
vs = []
ts = []
stop = False
while r.successful() and steps < maxsteps and r.t < tmax and not(scipy.any(scipy.isnan(r.y))) and not stop:
try:
r.integrate(tmax,step = 1)
xs.append(r.y[:N])
vs.append(r.y[N:])
ts.append(r.t)
steps += 1
if callback is not None:
stop = callback(r.y[:N], r.y[N:], r.t, j, Acc, dtd)
except:
stop = True
return scipy.array(xs), scipy.array(vs), scipy.array(ts)
def main():
args = getArguments(getParser())
# prepare logger
logger = Logger.getInstance()
if args.debug: logger.setLevel(logging.DEBUG)
elif args.verbose: logger.setLevel(logging.INFO)
# load input image
data_input, header_input = load(args.input)
# transform to uin8
data_input = data_input.astype(scipy.uint8)
# reduce to 3D, if larger dimensionality
if data_input.ndim > 3:
for _ in range(data_input.ndim - 3): data_input = data_input[...,0]
# iter over slices (2D) until first with content is detected
for plane in data_input:
if scipy.any(plane):
# set pixel spacing
spacing = list(header.get_pixel_spacing(header_input))
spacing = spacing[1:3]
__update_header_from_array_nibabel(header_input, plane)
header.set_pixel_spacing(header_input, spacing)
# save image
save(plane, args.output, header_input, args.force)
break
logger.info("Successfully terminated.")
def execute(self):
self.power_mat, self.thermal_expectation = self.full_calculation()
n_chan = self.power_mat.shape[1]
n_freq = self.power_mat.shape[0]
# Calculate the the mean channel correlations at low frequencies.
low_f_mat = sp.mean(self.power_mat[1:4 * n_chan + 1, :, :], 0).real
# Factorize it into preinciple components.
e, v = linalg.eigh(low_f_mat)
self.low_f_mode_values = e
# Make sure the eigenvalues are sorted.
if sp.any(sp.diff(e) < 0):
raise RuntimeError("Eigenvalues not sorted.")
self.low_f_modes = v
# Now subtract out the noisiest channel modes and see what is left.
n_modes_subtract = 10
mode_subtracted_power_mat = sp.copy(self.power_mat.real)
mode_subtracted_auto_power = sp.empty((n_modes_subtract, n_freq))
for ii in range(n_modes_subtract):
mode = v[:, -ii]
amp = sp.sum(mode[:, None] * mode_subtracted_power_mat, 1)
amp = sp.sum(amp * mode, 1)
to_subtract = amp[:, None, None] * mode[:, None] * mode
mode_subtracted_power_mat -= to_subtract
auto_power = mode_subtracted_power_mat.view()
auto_power.shape = (n_freq, n_chan**2)
auto_power = auto_power[:, ::n_chan + 1]
mode_subtracted_auto_power[ii, :] = sp.mean(auto_power, -1)
self.subtracted_auto_power = mode_subtracted_auto_power
def generate_matrices(model, col_map, c, r, m, t):
noBrPointsPerEpoch = model.nbreakpoints
nleaves = model.nleaves
all_time_breakpoints, time_breakpoints = default_bps(model, c, r, t)
M = []
for e in xrange(len(noBrPointsPerEpoch)):
newM = identity(nleaves)
newM[:] = m[e]
M.append(newM)
pi, T, E = model.run(r, c, time_breakpoints, M, col_map=col_map)
assert not any(isnan(pi))
assert not any(isnan(T))
assert not any(isnan(E))
return pi, T, array(E)
def _initMean(self, Y, F=None, tol=1e-6):
"""
initialize the mean term
Args:
F: sample design of the fixed effect
"""
if F is not None:
R = LA.qr(F, mode='r')[0][:F.shape[1], :]
I = (abs(R.diagonal()) > tol)
if SP.any(~I):
warnings.warn(
'cols ' + str(SP.where(~I)[0]) +
' have been removed because linearly dependent on the others'
)
self.F = F[:, I]
else:
self.F = None
#dimensions
self.N, self.P = Y.shape
#get F and Y
self.Y = Y
# build mean
self.mean = mean(Y)
if F is not None:
A = SP.eye(self.P)
self.mean.addFixedEffect(F=self.F, A=A)
def makehist(testpath, npulses):
"""
This functions are will create histogram from data made in the testpath.
Inputs
testpath - The path that the data is located.
npulses - The number of pulses in the sim.
"""
sns.set_style("whitegrid")
sns.set_context("notebook")
params = ['Ne', 'Te', 'Ti', 'Vi']
pvals = [1e11, 2.1e3, 1.1e3, 0.]
histlims = [[4e10, 2e11], [1200., 3000.], [300., 1900.], [-250., 250.]]
erlims = [[-2e11, 2e11], [-1000., 1000.], [-800., -800], [-250., 250.]]
erperlims = [[-100., 100.]] * 4
lims_list = [histlims, erlims, erperlims]
errdict = makehistdata(params, testpath)[:4]
ernames = ['Data', 'Error', 'Error Percent']
sig1 = sp.sqrt(1. / npulses)
# Two dimensiontal histograms
pcombos = [i for i in itertools.combinations(params, 2)]
c_rows = int(math.ceil(float(len(pcombos)) / 2.))
(figmplf, axmat) = plt.subplots(c_rows,
2,
figsize=(12, c_rows * 6),
facecolor='w')
axvec = axmat.flatten()
for icomn, icom in enumerate(pcombos):
curax = axvec[icomn]
str1, str2 = icom
_, _, _ = make2dhist(testpath, PARAMDICT[str1], PARAMDICT[str2],
figmplf, curax)
filetemplate = str(Path(testpath).joinpath('AnalysisPlots', 'TwoDDist'))
plt.tight_layout()
plt.subplots_adjust(top=0.95)
figmplf.suptitle('Pulses: {0}'.format(npulses), fontsize=20)
fname = filetemplate + '_{0:0>5}Pulses.png'.format(npulses)
plt.savefig(fname)
plt.close(figmplf)
# One dimensiontal histograms
for ierr, iername in enumerate(ernames):
filetemplate = str(Path(testpath).joinpath('AnalysisPlots', iername))
(figmplf, axmat) = plt.subplots(2, 2, figsize=(20, 15), facecolor='w')
axvec = axmat.flatten()
for ipn, iparam in enumerate(params):
plt.sca(axvec[ipn])
if sp.any(sp.isinf(errdict[ierr][iparam])):
continue
binlims = lims_list[ierr][ipn]
bins = sp.linspace(binlims[0], binlims[1], 100)
histhand = sns.distplot(errdict[ierr][iparam],
bins=bins,
kde=True,
rug=False)
axvec[ipn].set_title(iparam)
figmplf.suptitle(iername + ' Pulses: {0}'.format(npulses), fontsize=20)
fname = filetemplate + '_{0:0>5}Pulses.png'.format(npulses)
plt.savefig(fname)
plt.close(figmplf)
def has_complex(arr):
try:
imag = arr.imag
except AttributeError:
return False
else:
return sp.any(imag != 0)
def refractory_correct_SpikeTrain(SpikeTrain, ref_per=2 * pq.ms):
"""
checks for spike duplets in the SpikeTrain, removes the latter if both
spikes are closer in time than a refractory period.
Args:
SpikeTrain (neo.core.SpikeTrain): the SpikeTrain
ref_per (quantities.Quantity): the refractory period - minimum time
between two spikes
Returns:
neo.core.SpikeTrain: the corrected SpikeTrain
"""
ind = 0
next_ind = 0
good_inds = []
try:
while sp.any(
sp.argmax(SpikeTrain.times - SpikeTrain.times[ind] > ref_per)):
next_ind = sp.argmax(
SpikeTrain.times - SpikeTrain.times[ind] > ref_per)
good_inds.append(next_ind)
ind = next_ind
except IndexError:
# when empty
return SpikeTrain
return SpikeTrain[good_inds]
def have_same_subd_decomp(dds0, dds1):
"""
Returns :samp:`True` if pairs of non-halo-sub-domains on all processes have the
same (global) non-halo-sub-domain origin index and
same non-halo-sub-domain shape. Note: performs an MPI *allreduce* operation.
:type dds0: :obj:`mango.Dds`
:param dds0: Array.
:type dds1: :obj:`mango.Dds`
:param dds1: Array.
:rtype: :obj:`bool`
:return: :samp:`True` if MPI non-halo-subdomain decomposition is the
same for :samp:`dds0` and :samp:`dds1`.
"""
numDiff = 0
if (sp.any(sp.logical_or(dds0.subd.origin != dds1.subd.origin, (dds0.subd.shape != dds1.subd.shape)))):
numDiff = 1
mpiComm = None
if (hasattr(dds0, "mpi") and hasattr(dds0.mpi, "comm") and (dds0.mpi.comm != None)):
mpiComm = dds0.mpi.comm
numDiff = mpiComm.allreduce(numDiff, op=mango.mpi.SUM)
return (numDiff == 0)
def is_symmetric(a, rtol=1e-10):
r"""
Is ``a`` a symmetric matrix?
Parameters
----------
a : ndarray, sparse matrix
Object to check for being a symmetric matrix.
rtol : float
Relative tolerance with respect to the smallest entry in ``a`` that
is used to determine if ``a`` is symmetric.
Returns
-------
bool
``True`` if ``a`` is a symmetric matrix, ``False`` otherwise.
"""
if type(a) != _sp.ndarray and not _sp.sparse.issparse(a):
raise Exception("'a' must be either a sparse matrix or an ndarray.")
if a.shape[0] != a.shape[1]:
raise Exception("'a' must be a square matrix.")
atol = _sp.amin(_sp.absolute(a.data)) * rtol
if _sp.sparse.issparse(a):
issym = False if ((a - a.T) > atol).nnz else True
elif type(a) == _sp.ndarray:
issym = False if _sp.any((a - a.T) > atol) else True
return issym
def dispersion_relation_extraordinary(kx, ky, k, nO, nE, c):
"""Dispersion relation for the extraordinary wave.
NOTE
See eq. 16 in Glytsis, "Three-dimensional (vector) rigorous
coupled-wave analysis of anisotropic grating diffraction",
JOSA A, 7(8), 1990 Always give positive real or negative
imaginary.
"""
if kx.shape != ky.shape or c.size != 3:
raise ValueError('kx and ky must have the same length and c must have 3 components')
kz = S.empty_like(kx)
for ii in xrange(0, kx.size):
alpha = nE**2 - nO**2
beta = kx[ii]/k * c[0] + ky[ii]/k * c[1]
# coeffs
C = S.array([nO**2 + c[2]**2 * alpha, \
2. * c[2] * beta * alpha, \
nO**2 * (kx[ii]**2 + ky[ii]**2) / k**2 + alpha * beta**2 - nO**2 * nE**2])
# two solutions of type +x or -x, purely real or purely imag
tmp_kz = k * S.roots(C)
# get the negative imaginary part or the positive real one
if S.any(S.isreal(tmp_kz)):
kz[ii] = S.absolute(tmp_kz[0])
else:
kz[ii] = -1j * S.absolute(tmp_kz[0])
return kz
def main(testpath,npulse = 1400 ,functlist = ['spectrums','radardata','fitting','analysis']):
""" This function will call other functions to create the input data, config
file and run the radar data sim. The path for the simulation will be
created in the Testdata directory in the SimISR module. The new
folder will be called BasicTest. The simulation is a long pulse simulation
will the desired number of pulses from the user.
Inputs
npulse - Number of pulses for the integration period, default==100.
functlist - The list of functions for the SimISR to do.
"""
curloc = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
if not os.path.isdir(testpath):
os.mkdir(testpath)
functlist_default = ['spectrums','radardata','fitting']
check_list = sp.array([i in functlist for i in functlist_default])
check_run =sp.any( check_list)
functlist_red = sp.array(functlist_default)[check_list].tolist()
configfilesetup(testpath,npulse)
config = os.path.join(testpath,'stats.ini')
(sensdict,simparams) = readconfigfile(config)
makedata(testpath,simparams['Tint'])
if check_run :
runsim(functlist_red,testpath,config,True)
if 'analysis' in functlist:
analysisdump(testpath,config)
def dataListener(self,Type, action):
"""Обробка зміни даних"""
Buttons = ( ('cUndo', 'cReset'), ('sUndo', 'sReset'),
('rUndo', 'rReset'))
Types = ['c','s','r']
active = self.getData(Type)
self.mprint("dataChanged: scaleX : %d, scaleY : %d, type : %d, len : %d, action : %d" %\
(active.scaleX, active.scaleY ,active.Type, sp.shape(active)[0],action))
#for i in self.dataStack[Type]:
# print(i.scale)
if sp.any(active):
#intervalCheck = ['cAutoInterval', 'sAutoInterval', 'rAutoInterval']
b_splineSCheck = ['cAutoB_splineS', 'sAutoB_splineS', 'rAutoB_splineS']
#intervalObj = self.findChild(QtGui.QCheckBox,intervalCheck[Type])
b_splineSObj = self.findChild(QtGui.QCheckBox,b_splineSCheck[Type])
#self.AutoInterval(intervalObj.checkState(), isSignal = False, senderType = Type)
if getattr(self.ui,Types[Type] + 'AllSliceConcat').currentIndex() == 0:
getattr(self.ui,Types[Type] + 'Start').setValue(active[:,0].min())
getattr(self.ui,Types[Type] + 'End').setValue(active[:,0].max())
self.AutoB_splineS(b_splineSObj.checkState(), isSignal = False, senderType = Type )
##### Undo/Reset
hist = self.dataStack[Type]
state = False
if len(hist)>=2:
state = True
buttons = self.findChilds(QtGui.QPushButton,Buttons[Type])
buttons[0].setEnabled(state)
buttons[1].setEnabled(state)
def main(npulse=100,
functlist=['spectrums', 'radardata', 'fitting', 'analysis']):
""" This function will call other functions to create the input data, config
file and run the radar data sim. The path for the simulation will be
created in the Testdata directory in the SimISR module. The new
folder will be called BasicTest. The simulation is a long pulse simulation
will the desired number of pulses from the user.
Inputs
npulse - Number of pulses for the integration period, default==100.
functlist - The list of functions for the SimISR to do.
"""
curloc = Path(__file__).resolve()
testpath = curloc.parent.parent / 'Testdata' / 'BasicTest'
if not testpath.is_dir():
testpath.mkdir(parents=True)
functlist_default = ['spectrums', 'radardata', 'fitting']
check_list = sp.array([i in functlist for i in functlist_default])
check_run = sp.any(check_list)
functlist_red = sp.array(functlist_default)[check_list].tolist()
configfilesetup(str(testpath), npulse)
config = testpath.joinpath('stats.ini')
(sensdict, simparams) = readconfigfile(str(config))
makedata(testpath, simparams['Tint'])
if check_run:
runsim(functlist_red, str(testpath), config, True)
if 'analysis' in functlist:
analysisdump(str(testpath), config)
def isequal(A,B,tol=1e-15):
"""Determines if two qobj objects are equal to within given tolerance.
Parameters
----------
A : qobj
Qobj one
B : qobj
Qobj two
tol : float
Tolerence for equality to be valid
Returns
-------
isequal : bool
True if qobjs are equal, False otherwise.
"""
if A.dims!=B.dims:
return False
else:
Adat=A.data
Bdat=B.data
elems=(Adat-Bdat).data
if any(abs(elems)>tol):
return False
else:
return True
def delayedsignalF(x, t0_pts):
#==============================================================
"""
Delay a signal with a non integer value
(computation in frequency domain)
Synopsis:
y=delayedsignalF(x,t0_pts)
Inputs: x vector of length N
t0_pts is a REAL delay
expressed wrt the sampling time Ts=1:
t0_pts = 1 corresponds to one time dot
t0_pts may be positive, negative, non integer
t0_pts>0: shift to the right
t0_pts<0: shift to the left
Rk: the length of FFT is 2^(nextpow2(N)+1
"""
#
# M. Charbit, Jan. 2010
#==============================================================
N = len(x)
p = ceil(log2(N)) + 1
Lfft = int(2.0**p)
Lffts2 = Lfft / 2
fftx = fft(x, Lfft)
ind = concatenate((range(Lffts2 + 1), range(Lffts2 + 1 - Lfft, 0)), axis=0)
fftdelay = exp(-2j * pi * t0_pts * ind / Lfft)
fftdelay[Lffts2] = real(fftdelay[Lffts2])
ifftdelay = ifft(fftx * fftdelay)
y = ifftdelay[range(N)]
if isreal(any(x)):
y = real(y)
return y
def computeCovarianceMatrixPython(out_dir, bfile, cfile, sim_type='RRM'):
print "Using python to create covariance matrix. This might be slow. We recommend using plink instead."
if sim_type is not 'RRM':
raise Exception('sim_type %s is not known' % sim_type)
""" loading data """
data = plink_reader.readBED(bfile, useMAFencoding=True)
iid = data['iid']
X = data['snps']
N = X.shape[1]
print '%d variants loaded.' % N
print '%d people loaded.' % X.shape[0]
""" normalizing markers """
print 'Normalizing SNPs...'
p_ref = X.mean(axis=0) / 2.
X -= 2 * p_ref
with warnings.catch_warnings():
warnings.simplefilter("ignore")
X /= sp.sqrt(2 * p_ref * (1 - p_ref))
hasNan = sp.any(sp.isnan(X), axis=0)
print '%d SNPs have a nan entry. Exluding them for computing the covariance matrix.' % hasNan.sum(
)
""" computing covariance matrix """
print 'Computing relationship matrix...'
K = sp.dot(X[:, ~hasNan], X[:, ~hasNan].T)
K /= 1. * N
print 'Relationship matrix calculation complete'
print 'Relationship matrix written to %s.cov.' % cfile
print 'IDs written to %s.cov.id.' % cfile
""" saving to output """
np.savetxt(cfile + '.cov', K, delimiter='\t', fmt='%.6f')
np.savetxt(cfile + '.cov.id', iid, delimiter=' ', fmt='%s')
def main():
args = getArguments(getParser())
# prepare logger
logger = Logger.getInstance()
if args.debug: logger.setLevel(logging.DEBUG)
elif args.verbose: logger.setLevel(logging.INFO)
# load input image
data_input, header_input = load(args.input)
# transform to uin8
data_input = data_input.astype(scipy.uint8)
# reduce to 3D, if larger dimensionality
if data_input.ndim > 3:
for _ in range(data_input.ndim - 3): data_input = data_input[...,0]
# iter over slices (2D) until first with content is detected
for plane in data_input:
if scipy.any(plane):
# set pixel spacing
spacing = list(header.get_pixel_spacing(header_input))
spacing = spacing[1:3]
__update_header_from_array_nibabel(header_input, plane)
header.set_pixel_spacing(header_input, spacing)
# save image
save(plane, args.output, header_input, args.force)
break
logger.info("Successfully terminated.")
def _get_Voronoi_edges(vor):
r"""
Given a Voronoi object as produced by the scipy.spatial.Voronoi class,
this function calculates the start and end points of eeach edge in the
Voronoi diagram, in terms of the vertex indices used by the received
Voronoi object.
Parameters
----------
vor : scipy.spatial.Voronoi object
Returns
-------
A 2-by-N array of vertex indices, indicating the start and end points of
each vertex in the Voronoi diagram. These vertex indices can be used to
index straight into the ``vor.vertices`` array to get spatial positions.
"""
edges = [[], []]
for facet in vor.ridge_vertices:
# Create a closed cycle of vertices that define the facet
edges[0].extend(facet[:-1]+[facet[-1]])
edges[1].extend(facet[1:]+[facet[0]])
edges = sp.vstack(edges).T # Convert to scipy-friendly format
mask = sp.any(edges == -1, axis=1) # Identify edges at infinity
edges = edges[~mask] # Remove edges at infinity
edges = sp.sort(edges, axis=1) # Move all points to upper triangle
# Remove duplicate pairs
edges = edges[:, 0] + 1j*edges[:, 1] # Convert to imaginary
edges = sp.unique(edges) # Remove duplicates
edges = sp.vstack((sp.real(edges), sp.imag(edges))).T # Back to real
edges = sp.array(edges, dtype=int)
return edges
def isherm(Q):
"""Determines if given operator is Hermitian.
Parameters
----------
Q : qobj
Quantum object
Returns
-------
isherm : bool
True if operator is Hermitian, False otherwise.
Examples
--------
>>> a=destroy(4)
>>> isherm(a)
False
"""
if Q.dims[0] != Q.dims[1]:
return False
else:
dat = Q.data
elems = (dat.transpose().conj() - dat).data
if any(abs(elems) > 1e-15):
return False
else:
return True
def ismember(element, array, rows=False):
"""Check if element is member of array"""
if rows:
return sp.any([sp.all(array[x, :] == element) for x in range(array.shape[0])])
else:
return sp.all([element[i] in array for i in element.shape[0]])
def isherm(Q):
"""Determines if given operator is Hermitian.
Parameters
----------
Q : qobj
Quantum object
Returns
-------
isherm : bool
True if operator is Hermitian, False otherwise.
Examples
--------
>>> a=destroy(4)
>>> isherm(a)
False
"""
if Q.dims[0]!=Q.dims[1]:
return False
else:
dat=Q.data
elems=(dat.transpose().conj()-dat).data
if any(abs(elems)>1e-15):
return False
else:
return True
def generate_matrices(model, col_map, c, r, m, t):
noBrPointsPerEpoch = model.nbreakpoints
nleaves = model.nleaves
all_time_breakpoints, time_breakpoints = default_bps(model, c, r, t)
M = []
for e in xrange(len(noBrPointsPerEpoch)):
newM = identity(nleaves)
newM[:] = m[e]
M.append(newM)
pi, T, E = model.run(r, c, time_breakpoints, M, col_map=col_map)
assert not any(isnan(pi))
assert not any(isnan(T))
assert not any(isnan(E))
return pi, T, array(E)
def main(npulse=100, functlist=['spectrums', 'radardata', 'fitting', 'analysis'],radar='pfisr'):
""" This function will call other functions to create the input data, config
file and run the radar data sim. The path for the simulation will be
created in the Testdata directory in the SimISR module. The new
folder will be called BasicTest. The simulation is a long pulse simulation
will the desired number of pulses from the user.
Inputs
npulse - Number of pulses for the integration period, default==100.
functlist - The list of functions for the SimISR to do.
"""
curloc = Path(__file__).resolve()
testpath = curloc.parent.parent/'Testdata'/'BasicTest'
if not testpath.is_dir():
testpath.mkdir(parents=True)
functlist_default = ['spectrums', 'radardata', 'fitting']
check_list = sp.array([i in functlist for i in functlist_default])
check_run = sp.any(check_list)
functlist_red = sp.array(functlist_default)[check_list].tolist()
config = testpath.joinpath('stats.yml')
if not config.exists():
configfilesetup(str(testpath), npulse, radar)
(_, simparams) = readconfigfile(str(config))
makedata(testpath, simparams['Tint'])
if check_run:
runsim(functlist_red, str(testpath), config, True)
if 'analysis' in functlist:
analysisdump(str(testpath), config)
def compactness(target,
throat_perimeter='throat.perimeter',
throat_area='throat.area'):
r"""
Mortensen et al. have shown that the Hagen-Poiseuille hydraluic resistance
is linearly dependent on the compactness. Defined as perimeter^2/area.
The dependence is not universal as shapes with sharp corners provide more
resistance than those that are more elliptical. Count the number of
vertices and apply the right correction.
Parameters
----------
target : OpenPNM Object
The object which this model is associated with. This controls the
length of the calculated array, and also provides access to other
necessary properties.
throat_perimeter : string
The dictionary key of the array containing the throat perimeter values.
throat_area : string
The dictionary key of the array containing the throat area values.
Returns
-------
alpha : NumPy ndarray
Array containing throat compactness values.
References
----------
Mortensen N.A, Okkels F., and Bruus H. Reexamination of Hagen-Poiseuille
flow: Shape dependence of the hydraulic resistance in microchannels.
Physical Review E, v.71, pp.057301 (2005).
"""
# Only apply to throats with an area
ts = target.throats()[target[throat_area] > 0]
P = target[throat_perimeter]
A = target[throat_area]
C = _sp.ones(target.num_throats())
C[ts] = P[ts]**2 / A[ts]
alpha = _sp.ones_like(C) * 8 * _sp.pi
if 'throat.offset_vertices' in target.props():
verts = target['throat.offset_vertices']
for i in ts:
if ~_sp.any(_sp.isnan(verts[i])):
if len(verts[i]) == 3:
# Triangular Correction
alpha[i] = C[i] * (25 / 17) + (40 * _sp.sqrt(3) / 17)
elif len(verts[i]) == 4:
# Rectangular Correction
alpha[i] = C[i] * (22 / 7) - (65 / 3)
elif len(verts[i]) > 4:
# Approximate Elliptical Correction
alpha[i] = C[i] * (8 / 3) - (8 * _sp.pi / 3)
# For a perfect circle alpha = 8*pi so normalize by this
alpha /= 8 * _sp.pi
# Very small throats could have values less than one
alpha[alpha < 1.0] = 1.0
return alpha
def test_late_pore_and_throat_filling(self):
mip = op.algorithms.Porosimetry(network=self.net)
mip.setup(phase=self.hg)
mip.set_inlets(pores=self.net.pores('left'))
# Run without late pore filling
mip.run()
data_no_lpf = mip.get_intrusion_data()
# Now run with late pore filling
self.phys['pore.pc_star'] = 2/self.net['pore.diameter']
self.phys.add_model(propname='pore.partial_filling',
pressure='pore.pressure',
Pc_star='pore.pc_star',
model=op.models.physics.multiphase.late_filling)
mip.reset()
mip.set_inlets(pores=self.net.pores('left'))
mip.set_partial_filling(propname='pore.partial_filling')
mip.run()
self.phys.regenerate_models()
data_w_lpf = mip.get_intrusion_data()
assert sp.all(sp.array(data_w_lpf.Snwp) < sp.array(data_no_lpf.Snwp))
# Now run with late throat filling
self.phys['throat.pc_star'] = 2/self.net['throat.diameter']
self.phys.add_model(propname='throat.partial_filling',
pressure='throat.pressure',
Pc_star='throat.pc_star',
model=op.models.physics.multiphase.late_filling)
mip.reset()
mip.set_inlets(pores=self.net.pores('left'))
mip.set_partial_filling(propname='throat.partial_filling')
mip.run()
data_w_ltf = mip.get_intrusion_data()
assert sp.any(sp.array(data_w_ltf.Snwp) < sp.array(data_w_lpf.Snwp))
def execute(self):
self.power_mat, self.thermal_expectation = self.full_calculation()
n_chan = self.power_mat.shape[1]
n_freq = self.power_mat.shape[0]
# Calculate the the mean channel correlations at low frequencies.
low_f_mat = sp.mean(self.power_mat[1:4 * n_chan + 1,:,:], 0).real
# Factorize it into preinciple components.
e, v = linalg.eigh(low_f_mat)
self.low_f_mode_values = e
# Make sure the eigenvalues are sorted.
if sp.any(sp.diff(e) < 0):
raise RuntimeError("Eigenvalues not sorted.")
self.low_f_modes = v
# Now subtract out the noisiest channel modes and see what is left.
n_modes_subtract = 10
mode_subtracted_power_mat = sp.copy(self.power_mat.real)
mode_subtracted_auto_power = sp.empty((n_modes_subtract, n_freq))
for ii in range(n_modes_subtract):
mode = v[:,-ii]
amp = sp.sum(mode[:,None] * mode_subtracted_power_mat, 1)
amp = sp.sum(amp * mode, 1)
to_subtract = amp[:,None,None] * mode[:,None] * mode
mode_subtracted_power_mat -= to_subtract
auto_power = mode_subtracted_power_mat.view()
auto_power.shape = (n_freq, n_chan**2)
auto_power = auto_power[:,::n_chan + 1]
mode_subtracted_auto_power[ii,:] = sp.mean(auto_power, -1)
self.subtracted_auto_power = mode_subtracted_auto_power
def isequal(A, B, tol=1e-15):
"""Determines if two qobj objects are equal to within given tolerance.
Parameters
----------
A : qobj
Qobj one
B : qobj
Qobj two
tol : float
Tolerence for equality to be valid
Returns
-------
isequal : bool
True if qobjs are equal, False otherwise.
"""
if A.dims != B.dims:
return False
else:
Adat = A.data
Bdat = B.data
elems = (Adat - Bdat).data
if any(abs(elems) > tol):
return False
else:
return True
def delayedsignalF(x,t0_pts):
#==============================================================
"""
Delay a signal with a non integer value
(computation in frequency domain)
Synopsis:
y=delayedsignalF(x,t0_pts)
Inputs: x vector of length N
t0_pts is a REAL delay
expressed wrt the sampling time Ts=1:
t0_pts = 1 corresponds to one time dot
t0_pts may be positive, negative, non integer
t0_pts>0: shift to the right
t0_pts<0: shift to the left
Rk: the length of FFT is 2^(nextpow2(N)+1
"""
#
# M. Charbit, Jan. 2010
#==============================================================
N = len(x)
p = ceil(log2(N))+1;
Lfft = int(2.0**p);
Lffts2 = Lfft/2;
fftx = fft(x, Lfft);
ind = concatenate((range(Lffts2+1),
range(Lffts2+1-Lfft,0)),axis=0)
fftdelay = exp(-2j*pi*t0_pts*ind/Lfft);
fftdelay[Lffts2] = real(fftdelay[Lffts2]);
ifftdelay = ifft(fftx*fftdelay);
y = ifftdelay[range(N)];
if isreal(any(x)):
y=real(y)
return y
def straight(network,
geometry,
pore_diameter='pore.diameter',
L_negative = 1e-9,
**kwargs):
r"""
Calculate throat length
Parameters
----------
L_negative : float
The default throat length to use when negative lengths are found. The
default is 1 nm. To accept negative throat lengths, set this value to
``None``.
"""
#Initialize throat_property['length']
throats = network.throats(geometry.name)
pore1 = network['throat.conns'][:,0]
pore2 = network['throat.conns'][:,1]
C1 = network['pore.coords'][pore1]
C2 = network['pore.coords'][pore2]
E = _sp.sqrt(_sp.sum((C1-C2)**2,axis=1)) #Euclidean distance between pores
D1 = network[pore_diameter][pore1]
D2 = network[pore_diameter][pore2]
value = E-(D1+D2)/2.
value = value[throats]
if _sp.any(value<0) and (L_negative is not None):
print('Negative throat lengths are calculated. Arbitrary positive length assigned: '+str(L_negative))
Ts = _sp.where(value<0)[0]
value[Ts] = L_negative
return value
def test_late_pore_and_throat_filling(self):
mip = op.algorithms.Porosimetry(network=self.net)
mip.setup(phase=self.hg)
mip.set_inlets(pores=self.net.pores('left'))
# Run without late pore filling
mip.run()
data_no_lpf = mip.get_intrusion_data()
# Now run with late pore filling
self.phys['pore.pc_star'] = 2 / self.net['pore.diameter']
self.phys.add_model(propname='pore.partial_filling',
pressure='pore.pressure',
Pc_star='pore.pc_star',
model=op.models.physics.multiphase.late_filling)
mip.reset()
mip.set_inlets(pores=self.net.pores('left'))
mip.set_partial_filling(propname='pore.partial_filling')
mip.run()
self.phys.regenerate_models()
data_w_lpf = mip.get_intrusion_data()
assert sp.all(sp.array(data_w_lpf.Snwp) < sp.array(data_no_lpf.Snwp))
# Now run with late throat filling
self.phys['throat.pc_star'] = 2 / self.net['throat.diameter']
self.phys.add_model(propname='throat.partial_filling',
pressure='throat.pressure',
Pc_star='throat.pc_star',
model=op.models.physics.multiphase.late_filling)
mip.reset()
mip.set_inlets(pores=self.net.pores('left'))
mip.set_partial_filling(propname='throat.partial_filling')
mip.run()
data_w_ltf = mip.get_intrusion_data()
assert sp.any(sp.array(data_w_ltf.Snwp) < sp.array(data_w_lpf.Snwp))
def __call__(self, Xi, Xj, ni, nj, hyper_deriv=None, symmetric=False):
"""Evaluate the covariance between points `Xi` and `Xj` with derivative order `ni`, `nj`.
Parameters
----------
Xi : :py:class:`Matrix` or other Array-like, (`M`, `D`)
`M` inputs with dimension `D`.
Xj : :py:class:`Matrix` or other Array-like, (`M`, `D`)
`M` inputs with dimension `D`.
ni : :py:class:`Matrix` or other Array-like, (`M`, `D`)
`M` derivative orders for set `i`.
nj : :py:class:`Matrix` or other Array-like, (`M`, `D`)
`M` derivative orders for set `j`.
hyper_deriv : Non-negative int or None, optional
The index of the hyperparameter to compute the first derivative
with respect to. If None, no derivatives are taken. Hyperparameter
derivatives are not supported at this point. Default is None.
symmetric : bool
Whether or not the input `Xi`, `Xj` are from a symmetric matrix.
Default is False.
Returns
-------
Kij : :py:class:`Array`, (`M`,)
Covariances for each of the `M` `Xi`, `Xj` pairs.
Raises
------
NotImplementedError
If the `hyper_deriv` keyword is not None.
"""
if hyper_deriv is not None:
raise NotImplementedError(
"Hyperparameter derivatives have not been implemented!")
if scipy.any(scipy.sum(ni, axis=1) > 1) or scipy.any(
scipy.sum(nj, axis=1) > 1):
raise ValueError(
"Matern52Kernel only supports 0th and 1st order derivatives")
Xi = scipy.asarray(Xi, dtype=scipy.float64)
Xj = scipy.asarray(Xj, dtype=scipy.float64)
ni = scipy.array(ni, dtype=scipy.int32)
nj = scipy.array(nj, dtype=scipy.int32)
var = scipy.square(self.params[-self.num_dim:])
value = _matern52(Xi, Xj, ni, nj, var)
return self.params[0]**2 * value
def readSRI_h5(fn,params,timelims = None):
assert isinstance(params,(tuple,list))
h5fn = Path(fn).expanduser()
'''This will read the SRI formated h5 files for RISR and PFISR.'''
coordnames = 'Spherical'
# Set up the dictionary to find the data
pathdict = {'Ne':('/FittedParams/Ne', None),
'dNe':('/FittedParams/Ne',None),
'Vi':('/FittedParams/Fits', (0,3)),
'dVi':('/FittedParams/Errors',(0,3)),
'Ti':('/FittedParams/Fits', (0,1)),
'dTi':('/FittedParams/Errors',(0,1)),
'Te':('/FittedParams/Fits', (-1,1)),
'Ti':('/FittedParams/Errors',(-1,1))}
with h5py.File(str(h5fn),'r',libver='latest') as f:
# Get the times and time lims
times = f['/Time/UnixTime'].value
# get the sensor location
sensorloc = np.array([f['/Site/Latitude'].value,
f['/Site/Longitude'].value,
f['/Site/Altitude'].value])
# Get the locations of the data points
rng = f['/FittedParams/Range'].value / 1e3
angles = f['/BeamCodes'][:,1:3]
nt = times.shape[0]
if timelims is not None:
times = times[(times[:,0]>= timelims[0]) & (times[:,1]<timelims[1]) ,:]
nt = times.shape[0]
# allaz, allel corresponds to rng.ravel()
allaz = np.tile(angles[:,0],rng.shape[1])
allel = np.tile(angles[:,1],rng.shape[1])
dataloc =np.vstack((rng.ravel(),allaz,allel)).T
# Read in the data
data = {}
with h5py.File(str(h5fn),'r',libver='latest') as f:
for istr in params:
if not istr in pathdict.keys(): #list() NOT needed
logging.error('{} is not a valid parameter name.'.format(istr))
continue
curpath = pathdict[istr][0]
curint = pathdict[istr][-1]
if curint is None: #3-D data
tempdata = f[curpath]
else: #5-D data -> 3-D data
tempdata = f[curpath][:,:,:,curint[0],curint[1]]
data[istr] = np.array([tempdata[iT,:,:].ravel() for iT in range(nt)]).T
# remove nans from SRI file
nanlog = sp.any(sp.isnan(dataloc),1)
keeplog = sp.logical_not(nanlog)
dataloc = dataloc[keeplog]
for ikey in data.keys():
data[ikey]= data[ikey][keeplog]
return (data,coordnames,dataloc,sensorloc,times)
def compactness(target, throat_perimeter='throat.perimeter',
throat_area='throat.area'):
r"""
Mortensen et al. have shown that the Hagen-Poiseuille hydraluic resistance
is linearly dependent on the compactness. Defined as perimeter^2/area.
The dependence is not universal as shapes with sharp corners provide more
resistance than those that are more elliptical. Count the number of
vertices and apply the right correction.
Parameters
----------
target : OpenPNM Object
The object which this model is associated with. This controls the
length of the calculated array, and also provides access to other
necessary properties.
throat_perimeter : string
The dictionary key of the array containing the throat perimeter values.
throat_area : string
The dictionary key of the array containing the throat area values.
Returns
-------
alpha : NumPy ndarray
Array containing throat compactness values.
References
----------
Mortensen N.A, Okkels F., and Bruus H. Reexamination of Hagen-Poiseuille
flow: Shape dependence of the hydraulic resistance in microchannels.
Physical Review E, v.71, pp.057301 (2005).
"""
# Only apply to throats with an area
ts = target.throats()[target[throat_area] > 0]
P = target[throat_perimeter]
A = target[throat_area]
C = _sp.ones(target.num_throats())
C[ts] = P[ts]**2/A[ts]
alpha = _sp.ones_like(C)*8*_sp.pi
if 'throat.offset_vertices' in target.props():
verts = target['throat.offset_vertices']
for i in ts:
if ~_sp.any(_sp.isnan(verts[i])):
if len(verts[i]) == 3:
# Triangular Correction
alpha[i] = C[i]*(25/17) + (40*_sp.sqrt(3)/17)
elif len(verts[i]) == 4:
# Rectangular Correction
alpha[i] = C[i]*(22/7) - (65/3)
elif len(verts[i]) > 4:
# Approximate Elliptical Correction
alpha[i] = C[i]*(8/3) - (8*_sp.pi/3)
# For a perfect circle alpha = 8*pi so normalize by this
alpha /= 8*_sp.pi
# Very small throats could have values less than one
alpha[alpha < 1.0] = 1.0
return alpha
def test_dump_and_fetch_data(self):
proj = self.ws.copy_project(self.proj)
proj._dump_data()
# Ensure only pore.coords and throat.conns are found
assert sum([len(item.props()) for item in proj]) == 2
proj._fetch_data()
assert sp.any([len(item.props()) for item in proj])
os.remove(proj.name+'.hdf5')
def test_add_boundary_pores(self):
net = op.Network.CubicDual(shape=[5, 5, 5], label_1='primary',
label_2='secondary')
Ps = net.pores(labels=['surface', 'bottom'], mode='intersection')
net.add_boundary_pores(pores=Ps, offset=[0, 0, -0.5])
Ps2 = net.pores(labels=['boundary'], mode='intersection')
assert Ps.size == Ps2.size
assert ~sp.any(sp.in1d(Ps, Ps2))
def preProcess(self,
periodF0 = 0.06,
deltaF_div_F0 = True,
max_threshold = None,
min_threshold = None,
nan_to_zeros = True,
detrend = False,
#~ band_filter = None,
gaussian_filter = None,
f1 = None,
f2 = None,
**kargs):
images = self.images
if deltaF_div_F0:
ind = self.t()<=self.t_start+periodF0
m0 = mean(images[ind,:,:] , axis = 0)
images = (images-m0)/m0*1000.
if max_threshold is not None:
#~ images[images>max_threshold] = max_threshold
images[images>max_threshold] = nan
if min_threshold is not None:
#~ images[images<min_threshold] = min_threshold
images[images<min_threshold] = nan
if nan_to_zeros:
images[isnan(images) ] = 0.
if detrend and not nan_to_zeros:
m = any(isnan(images) , axis = 0)
images[isnan(images) ] = 0.
images = signal.detrend( images , axis = 0)
images[:,m] = nan
elif detrend and nan_to_zeros:
images = signal.detrend( images , axis = 0)
if gaussian_filter is not None:
images = ndimage.gaussian_filter( images , (0 , gaussian_filter , gaussian_filter))
if f1 is not None or f2 is not None:
from ..computing.filter import fft_passband_filter
if f1 is None: f1=0.
if f2 is None: f1=inf
nq = self.sampling_rate/2.
images = fft_passband_filter(images, f_low = f1/nq , f_high = f2/nq , axis = 0)
return images
def ex3(exclude=sc.array([1,2,3,4]),plotfilename='ex3.png', bovyprintargs={}):
"""ex3: solve exercise 3
Input:
exclude - ID numbers to exclude from the analysis
plotfilename - filename for the output plot
Output:
plot
History:
2009-05-27 - Written - Bovy (NYU)
"""
#Read the data
data= read_data('data_yerr.dat')
ndata= len(data)
nsample= ndata- len(exclude)
#Put the dat in the appropriate arrays and matrices
Y= sc.zeros(nsample)
A= sc.ones((nsample,3))
C= sc.zeros((nsample,nsample))
yerr= sc.zeros(nsample)
jj= 0
for ii in range(ndata):
if sc.any(exclude == data[ii][0]):
pass
else:
Y[jj]= data[ii][1][1]
A[jj,1]= data[ii][1][0]
A[jj,2]= data[ii][1][0]**2.
C[jj,jj]= data[ii][2]**2.
yerr[jj]= data[ii][2]
jj= jj+1
#Now compute the best fit and the uncertainties
bestfit= sc.dot(linalg.inv(C),Y.T)
bestfit= sc.dot(A.T,bestfit)
bestfitvar= sc.dot(linalg.inv(C),A)
bestfitvar= sc.dot(A.T,bestfitvar)
bestfitvar= linalg.inv(bestfitvar)
bestfit= sc.dot(bestfitvar,bestfit)
#Now plot the solution
plot.bovy_print(**bovyprintargs)
#plot bestfit
xrange=[0,300]
yrange=[0,700]
nsamples= 1001
xs= sc.linspace(xrange[0],xrange[1],nsamples)
ys= sc.zeros(nsamples)
for ii in range(nsamples):
ys[ii]= bestfit[0]+bestfit[1]*xs[ii]+bestfit[2]*xs[ii]**2.
plot.bovy_plot(xs,ys,'k-',xrange=xrange,yrange=yrange,
xlabel=r'$x$',ylabel=r'$y$',zorder=2)
#Plot data
errorbar(A[:,1],Y,yerr,marker='o',color='k',linestyle='None',zorder=1)
#Put in a label with the best fit
text(5,30,r'$y = ('+'%4.4f \pm %4.4f)\,x^2 + ( %4.2f \pm %4.2f )\,x+ ( %4.0f\pm %4.0f' % (bestfit[2], m.sqrt(bestfitvar[2,2]),bestfit[1], m.sqrt(bestfitvar[1,1]), bestfit[0],m.sqrt(bestfitvar[0,0]))+r')$')
plot.bovy_end_print(plotfilename)
return 0
def SRIparams2iono(filename):
fullfile = h5file(filename)
fullfiledict = fullfile.readWholeh5file()
#Size = Nrecords x Nbeams x Nranges x Nions+1 x 4 (fraction, temperature, collision frequency, LOS speed)
fits = fullfiledict['/FittedParams']['Fits']
(nt,nbeams,nrng,nspecs,nstuff) = fits.shape
nlocs = nbeams*nrng
fits = fits.transpose((1,2,0,3,4))
fits = fits.reshape((nlocs,nt,nspecs,nstuff))
# Nrecords x Nbeams x Nranges
Ne = fullfiledict['/FittedParams']['Ne']
Ne = Ne.transpose((1,2,0))
Ne = Ne.reshape((nlocs,nt))
param_lists =sp.zeros((nlocs,nt,nspecs,2))
param_lists[:,:,:,0] = fits[:,:,:,0]
param_lists[:,:,:,1] = fits[:,:,:,1]
param_lists[:,:,-1,0]=Ne
Velocity = fits[:,:,0,3]
if fullfiledict['/FittedParams']['IonMass']==16:
species = ['O+','e-']
pnames = sp.array([['Ni','Ti'],['Ne','Te']])
time= fullfiledict['/Time']['UnixTime']
time = time
rng = fullfiledict['/FittedParams']['Range']
bco = fullfiledict['/']['BeamCodes']
angles = bco[:,1:3]
(nang,nrg) = rng.shape
allang = sp.tile(angles[:,sp.newaxis],(1,nrg,1))
all_loc = sp.column_stack((rng.flatten(),allang.reshape(nang*nrg,2)))
lkeep = ~ sp.any(sp.isnan(all_loc),1)
all_loc = all_loc[lkeep]
Velocity = Velocity[lkeep]
param_lists = param_lists[lkeep]
all_loc[:,0]=all_loc[:,0]*1e-3
iono1 = IonoContainer(all_loc,param_lists,times=time,ver = 1,coordvecs = ['r','theta','phi'],
paramnames = pnames,species=species,velocity=Velocity)
# MSIS
tn = fullfiledict['/MSIS']['Tn']
tn = tn.transpose((1,2,0))
tn = tn.reshape((nlocs,nt))
startparams = sp.ones((nlocs,nt,2,2))
startparams[:,:,0,1] = tn
startparams[:,:,1,1] = tn
startparams = startparams[lkeep]
ionoS = IonoContainer(all_loc,startparams,times=time,ver = 1,coordvecs = ['r','theta','phi'],
paramnames = pnames,species=species)
return iono1,ionoS
def isOrthogonal(A, tol=1e-13):
'''
Test whether matrix A is orthogonal, upto
numerical tolerance. If A is orthogonal then
sp.dot(A.T,A) will be the identity matrix.
'''
(_,p) = A.shape
Ix = sp.dot( A.T, A) - sp.eye(p)
return not sp.any(Ix > tol)