def cg(self,j11,m11,j22,m22,j,m):
# calculates a clebsch-gordan coef (j1,m1,j2,m2,j,m).
# uses general cg formula provided in many references.
# assume qf is index for fstates and qj for ijstates.
# fstates[qf] = sum_over_qj( C(qf,qj)*(ijstates[qj] )
fac = self.fac
if j11 < j22: #if j1<j2 switch them
j1 = j22
j2 = j11
m1 = m22
m2 = m11
else:
j1 = j11
j2 = j22
m1 = m11
m2 = m22
N1 = fac(j1+j2-j)*fac(j+j1-j2)*fac(j+j2-j1)*(2*j+1)
D1 = fac(j+j1+j2+1)
N2 = fac(j1+m1)*fac(j1-m1)*fac(j2+m2)*fac(j2-m2)*fac(j+m)*fac(j-m)
def D2(k):
return fac(k)*fac(j1+j2-j-k)*fac(j1-m1-k)*fac(j2+m2-k)*fac(j-j2+m1+k)*fac(j-j1-m2+k)
factor1 = self.delta(m1+m2-m)*scipy.sqrt(N1/float(D1))
#find range for k, largest k such that no factorials are negative
klist = scipy.rint(scipy.asarray([j1+j2-j,j1-m1,j2+m2]))
kmax = int(klist.min())
klist = scipy.rint(scipy.asarray([0,-(j-j2+m1),-(j-j1-m2)]))
kmin = int(klist.max())
factor2 = 0
for k in range(kmin,kmax+1):
factor2 += ((-1)**k)*scipy.sqrt(N2)/D2(k)
return factor1*factor2
def coordinates_to_voxel_idx(coords_xyz, masker): # transform to homogeneous coordinates coords_h_xyz = sp.append(coords_xyz, ones([1,coords_xyz.shape[1]]),axis=0) # apply inverse affine transformation to get homogeneous coordinates in voxel space inv_transf = sp.linalg.inv(masker.volume.get_affine()) coords_h_voxel_space = inv_transf.dot(coords_h_xyz) coords_h_voxel_space = sp.rint(coords_h_voxel_space).astype(int) # remove homogeneous dimension coords_voxel_space = coords_h_voxel_space[0:-1,:] # convert coordinates to idcs in a flattened voxel space flattened_idcs = sp.ravel_multi_index(coords_voxel_space, masker.dims) # check if there is any study data for the flattened idcs voxel_idcs = sp.zeros((1,len(flattened_idcs)),dtype=int64) for i in range(0,len(flattened_idcs)): idcs = find(masker.in_mask == flattened_idcs[i]) if len(idcs > 0): voxel_idcs[0,i] = find(masker.in_mask == flattened_idcs[i]) else: voxel_idcs[0,i] = nan return voxel_idcs
def get_hand_props(self):
"""Initializes self.handarea [sqmeters], self.handrad [m],
self.handslope [-], and self.handstage [ft]."""
handc = self.hand_props.variables['COMID']
handslope = self.hand_props.variables['Slope'] # So
handstage = self.hand_props.variables[
'StageHeight'] # h values for Aw and Hr
handarea = self.hand_props.variables['WetArea'] # Aw
handrad = self.hand_props.variables['HydraulicRadius'] # Hr
handlen = self.hand_props.variables['Length'] # Length
if handc[self.hand_props_idx] == self.comid:
self.handarea = handarea[self.hand_props_idx] * (
3.28084**2) # Convert sqm to sqft
self.handrad = handrad[
self.hand_props_idx] * 3.28084 # Convert m to ft
self.handslope = handslope[self.hand_props_idx] # unitless
self.handlen = handlen[
self.hand_props_idx] * 3.28084 # Convert m to ft
handstage = scipy.array(handstage) * 3.28084 # Convert m to ft
self.handstage = scipy.rint(handstage) # Round to nearest int
def get_handnetcdf(self):
"""Initializes self.handarea (sqmeters), self.handrad (m),
self.handslope (-), and self.handstage (ft) """
handc = self.handnetcdf.variables['COMID']
handslope = self.handnetcdf.variables['Slope']
handarea = self.handnetcdf.variables['WetArea']
handrad = self.handnetcdf.variables['HydraulicRadius']
handstage = self.handnetcdf.variables['StageHeight']
if handc[self.handnetcdfidx] == self.comid:
self.handarea = handarea[
self.handnetcdfidx] * 10.7639 # Convert sqm to sqft
self.handrad = handrad[
self.handnetcdfidx] * 3.28084 # Convert m to ft
self.handslope = handslope[self.handnetcdfidx]
handstagenew = scipy.array([])
for i in handstage:
handstagenew = scipy.append(handstagenew, handstage)
self.handstage = handstagenew
self.handstage = self.handstage[:49] * 3.28084 # Convert m to ft
self.handstage = scipy.rint(
self.handstage) # Round to nearest int, to clean up conversion
def get_radial_edges(rwidth, drange):
nbins = scipy.rint((dmax - dmin) / rwidth).astype(int)
return scipy.linspace(dmin, dmax, nbins + 1)
# perform kmeans clustering on pixel rgb values
ar = ar.astype(float)
codes, dist = scipy.cluster.vq.kmeans(ar, NUM_CLUSTERS)
# Getting main colors
vecs, dist = scipy.cluster.vq.vq(ar, codes)
counts, bins = scipy.histogram(vecs, len(codes))
# Get hexes, rgbs, names, pantones
color_data = []
hexes = []
rgbs = []
c_names = []
cmyks = []
text_colors = []
pantone_names = []
codes = scipy.rint(codes).astype(int)
# For each "primary" color
for i in range(0, len(codes)):
single_color_data = {}
# hexes
hexes.append(''.join(chr(c) for c in codes[i]).encode('hex'))
# rgbs
cur = ""
for j in range(0, len(codes[i])):
cur = cur + str(codes[i][j]) + ", "
cur = cur[:-2]
rgbs.append(cur)
# Closest CSS3 name
cur = tuple(map(int, cur.split(",")))
c_names.append(get_colour_name(cur))
# CMYK
def mod(n, m):
x = n - m * rint(n / m)
if x < 0:
return x + abs(m)
return x
def get_radial_density(self,
catalogue,
iaddbins=None,
rwidth=2.,
redges=None,
normalize=True,
density=True,
power_weight=1):
iaddbins = utils.tolist(iaddbins)
normalize = utils.tolist(normalize, n=len(iaddbins), fill=-1)
density = utils.tolist(density, n=len(iaddbins), fill=-1)
power_weight = utils.tolist(power_weight, n=len(iaddbins), value=1)
distance = catalogue.distance()
dmin, dmax = distance.min(), distance.max()
self.logger.info('Comoving distances: {:.1f} - {:.1f}.'.format(
dmin, dmax))
if redges is not None:
radialedges = scipy.array(redges)
rwidth = scipy.mean(scipy.diff(radialedges))
rmin, rmax = radialedges.min(), radialedges.max()
if (rmin > dmin) or (rmax < dmax):
raise ValueError(
'Provided radial-edges ({:.1f} - {:.1f}) do not encompass the full survey ({:.1f} - {:.1f}).'
.format(rmin, rmax, dmin, dmax))
self.logger.info(
'Provided radial-edges of width: {:.1f} and range: {:.1f} - {:.1f}.'
.format(rwidth, rmin, rmax))
nbins = len(radialedges) - 1
else:
self.logger.info('Provided radial-width: {:.1f}.'.format(rwidth))
nbins = scipy.rint((dmax - dmin) / rwidth).astype(int)
radialedges = scipy.linspace(dmin, dmax + 1e-9, nbins + 1)
self.logger.info(
'There are {:d} radial-bins with an average of {:.1f} objects.'.
format(nbins,
len(catalogue) * 1. / nbins))
def radial_density(distance, weight, normalize=True, density=True):
toret = stats.binned_statistic(distance,
values=weight,
statistic='sum',
bins=radialedges)[0]
if density: toret /= radial_volume(radialedges)
if normalize: toret /= toret.sum()
return toret
radial = (radialedges[:-1] + radialedges[1:]) / 2.
densities, weights = [], []
for iaddbin_, normalize_, density_, power_ in zip(
iaddbins, normalize, density, power_weight):
if iaddbin_ is not None:
mask = catalogue['iaddbin'] == iaddbin_
self.logger.info('Additional cut {:d} ({:d} objects).'.format(
iaddbin_, mask.sum()))
else:
mask = catalogue.trues()
weight = catalogue['Weight'][mask]
if power_ != 1:
self.logger.info(
'Raising weights to power {:.4f}.'.format(power_))
weight **= power_
densities.append(
radial_density(distance[mask],
weight,
normalize=normalize_,
density=density_))
weights.append(weight)
self.params['catalogue'] = None
return radial, densities, weights