def forward(self, length):
if self._reset:
self.clear()
self._reset = False
fig = self.fig
ax = self.ax
dx = length * py.cos(py.radians(self.angle))
dy = length * py.sin(py.radians(self.angle))
if self.pen == 'down':
ax.plot([self.x, self.x + dx], [self.y, self.y + dy],
color=self.color,
linestyle='-')
self.data.append([
[self.x, self.x + dx],
[self.y, self.y + dy],
self.color,
])
else:
self.data.append([
[self.x, self.x + dx],
[self.y, self.y + dy],
None,
])
self.x += dx
self.y += dy
def boxForcing(bMin, bMax, radius, c,
angle): #confines the particles inside the sample
x = pl.uniform(bMin, bMax)
y = pl.uniform(bMin, bMax)
if x < (bMin + c * radius):
x = x + (radius) * (1 + c * pl.absolute(pl.sin(pl.radians(angle))))
if x > (bMax - c * radius):
x = bMin + c * radius
if x > (bMax - c * radius):
x = x - (radius) * (1 + c * pl.absolute(pl.sin(pl.radians(angle))))
if x < (bMin + c * radius):
x = bMax - c * radius
if y > (bMax - c * radius):
y = y - (radius) * (1 + c * pl.absolute(pl.cos(pl.radians(angle))))
if y < (bMin + c * radius):
y = bMax - c * radius
if y < (bMin + c * radius):
y = y + (radius) * (1 + c * pl.absolute(pl.cos(pl.radians(angle))))
if y > (bMax - c * radius):
y = bMin + c * radius
center = []
center.append([x, y])
return center
def animate(delay=0.05, skip=1, figsize=(5, 5)):
# delay will be per 100 units
global _t
from IPython.display import clear_output
import time
i = 0
interrupt_count = 0
while True:
try:
clear_output(wait=True)
fig = py.figure(figsize=_t.figsize)
ax = fig.add_subplot(111)
ax.clear()
ax.set_facecolor(_t.facecolor)
ax.axis('equal')
ax.axis(_t.limits)
for x, y, t, k in _t.texts:
ax.text(x, y, t, **k)
for x, y, c, a, ps in _t.data[:i]:
if c is not None:
ax.plot(x, y, color=c, linestyle='-', linewidth=ps)
x, y, c, a, ps = _t.data[i - 1]
ax.plot(
[x[1]],
[y[1]],
'g',
marker=(3, 0, a - 90),
markersize=20,
)
ax.plot([x[1] + 2 * py.cos(py.radians(a))],
[y[1] + 2 * py.sin(py.radians(a))], 'r.')
py.show()
# each step is a delay
time.sleep(delay)
if i == len(_t.data):
break
i += skip
if i > len(_t.data): # make sure to plot the last one
i = len(_t.data)
except KeyboardInterrupt:
interrupt_count += 1
if interrupt_count == 1:
delay = 0
else:
i = len(_t.data)
def __init__(self,obj,ref,epoch,*args):
'''
*args are either a,e,i,lan,aop,M0
or r,v
'''
self.obj = obj
self.ref = ref
self.shape2D = None
self.shape3D = None
self._a = None
self._e = None
if len(args) == 2:
self.initFromStateVectors(epoch,*args)
elif len(args) == 6:
self.epoch = float(epoch)
self._a = float(args[0])
self._e = float(args[1])
self.i = radians(float(args[2]))
self.lan = radians(float(args[3]))
self.aop = radians(float(args[4]))
self.M0 = float(args[5])
self.calculateH()
#if self.e < 1:
# self.h = sqrt(self.a*self.ref.mu*(1-self.e**2))
#elif self.e > 1:
# self.h = sqrt(-self.a*self.ref.mu*(self.e**2-1))
else:
print "error"
print args
print "len",len(args)
raise AttributeError("Unable to init"+str(args))
if len(args) == 0:
pass
else:
self.updateShape()
def circle(self, radius, extent=None, steps=50):
R = radius
n = steps
b = (n - 2) * 180 / n # polygon interior angle
a = 180 - b # polygon exterior angle
L = R * py.cos(py.radians(b / 2)) # length of one side
if not extent is None: # partial circle
n = n * extent // 360
for i in range(n):
self.forward(L)
if radius > 0:
self.left(a)
else:
self.right(a)
import os
os.chdir(os.path.expanduser("~/Desktop/reflectivity/dynXRD/"))
import pyasf
import reflectivity
import sympy as sp
import pylab as pl
data = pl.loadtxt("test6.dat")
data[:, 0] = pl.radians(data[:, 0])
#pl.ion()
R = 1, 1, 3
thickness = 1000
Energy = 10000
#struct = pyasf.unit_cell("1011117")
struct = pyasf.unit_cell("cif/MgO_52026.cif") #MgO
Sub = reflectivity.Substrate(struct)
v_par = sp.Matrix([1, -1, 0])
v_perp = sp.Matrix([1, 1, 0])
Sub.calc_orientation(v_par, v_perp)
layer1 = reflectivity.Epitaxial_Layer(struct, thickness)
layer1.calc_orientation(v_par, v_perp)
crystal = reflectivity.Sample(Sub, layer1)
crystal.set_Miller(R)
crystal.calc_g0_gH(Energy)
thBragg = float(
layer1.calc_Bragg_angle(Energy).subs(layer1.structure.subs).evalf())
angle = pl.linspace(0.995, 1.005, 501) * thBragg
import os
os.chdir(os.path.expanduser("~/Desktop/reflectivity/dynXRD/"))
import pyasf
import reflectivity
import sympy as sp
import pylab as pl
data = pl.loadtxt("test4.dat")
data[:,0] = pl.radians(data[:,0])
#pl.ion()
R = 0,0,4
thickness=1000
Energy=10000
#struct = pyasf.unit_cell("1011169")
struct = pyasf.unit_cell("1011117") #MgO
Sub=reflectivity.Substrate(struct)
v_par=sp.Matrix([1,-1,0])
v_perp=sp.Matrix([1,1,1])
Sub.calc_orientation(v_par, v_perp)
layer1=reflectivity.Epitaxial_Layer(struct, thickness)
layer1.calc_orientation(v_par, v_perp)
crystal=reflectivity.Sample(Sub, layer1)
crystal.set_Miller(R)
crystal.calc_g0_gH(Energy)
thBragg= float(layer1.calc_Bragg_angle(Energy).subs(layer1.structure.subs).evalf())
angle=pl.linspace(0.997, 1.003,501)*thBragg
crystal.calc_reflectivity(angle, Energy)
layer1.calc_amplitudes(angle, Energy)
sim.animArm = 1 # show arm animation
sim.graphsArm = 1 # plot arm graphs
sim.updateInterval = 20 # delay between arm updated (ms)
sim.initArmMovement = 50 # time at which to start moving arm (ms)
sim.armLen = [0.4634 - 0.173, 0.7169 - 0.4634
] # elbow - shoulder from MSM;radioulnar - elbow from MSM;
sim.startAng = [
0.62, 1.53
] # starting shoulder and elbow angles (rad) = natural rest position
sim.targetDist = 0.15 # target distance from center (15 cm)
# Propriocpetive encoding
allCellTags = sim._gatherAllCellTags()
sim.pop_sh = [gid for gid, tags in allCellTags.items() if tags['pop'] == 'Psh']
sim.pop_el = [gid for gid, tags in allCellTags.items() if tags['pop'] == 'Pel']
sim.minPval = radians(-30)
sim.maxPval = radians(135)
sim.minPrate = 0.01
sim.maxPrate = 100
# Motor encoding
sim.nMuscles = 4 # number of muscles
motorGids = [gid for gid, tags in allCellTags.items() if tags['pop'] == 'EM']
cellsPerMuscle = len(motorGids) / sim.nMuscles
sim.motorCmdCellRange = [
motorGids[i:i + cellsPerMuscle]
for i in range(0, len(motorGids), cellsPerMuscle)
] # cell gids of motor output to each muscle
sim.cmdmaxrate = 120 # value to normalize motor command num spikes
sim.cmdtimewin = 50 # window to sum spikes of motor commands
sim.antagInh = 1 # inhibition from antagonic muscle
def stereoplot(strike, dip, filename):
# Here is the stereonet plotting section
bigr = 1.2
phid = pylab.arange(2, 90, 2) # Angular range for
phir = phid * pylab.pi / 180
omegad = 90 - phid
omegar = pylab.pi / 2 - phir
# Set up for plotting great circles with centers along
# positive x-axis
x1 = bigr * pylab.tan(phir)
y1 = pylab.zeros(pylab.size(x1))
r1 = bigr / pylab.cos(phir)
theta1ad = (180 - 80) * pylab.ones(pylab.size(x1))
theta1ar = theta1ad * pylab.pi / 180
theta1bd = (180 + 80) * pylab.ones(pylab.size(x1))
theta1br = theta1bd * pylab.pi / 180
# Set up for plotting great circles
# with centers along the negative x-axis
x2 = -1 * x1
y2 = y1
r2 = r1
theta2ad = -80 * pylab.ones(pylab.size(x2))
theta2ar = theta2ad * pylab.pi / 180
theta2bd = 80 * pylab.ones(pylab.size(x2))
theta2br = theta2bd * pylab.pi / 180
# Set up for plotting small circles
# with centers along the positive y-axis
y3 = bigr / pylab.sin(omegar)
x3 = pylab.zeros(pylab.size(y3))
r3 = bigr / pylab.tan(omegar)
theta3ad = 3 * 90 - omegad
theta3ar = 3 * pylab.pi / 2 - omegar
theta3bd = 3 * 90 + omegad
theta3br = 3 * pylab.pi / 2 + omegar
# Set up for plotting small circles
# with centers along the negative y-axis
y4 = -1 * y3
x4 = x3
r4 = r3
theta4ad = 90 - omegad
theta4ar = pylab.pi / 2 - omegar
theta4bd = 90 + omegad
theta4br = pylab.pi / 2 + omegar
# Group all x, y, r, and theta information for great cricles
phi = pylab.append(phid, phid, 0)
x = pylab.append(x1, x2, 0)
y = pylab.append(y1, y2, 0)
r = pylab.append(r1, r2)
thetaad = pylab.append(theta1ad, theta2ad, 0)
thetaar = pylab.append(theta1ar, theta2ar, 0)
thetabd = pylab.append(theta1bd, theta2bd, 0)
thetabr = pylab.append(theta1br, theta2br, 0)
# Plot portions of all great circles that lie inside the
# primitive circle, with thick lines (1 pt.) at 10 degree increments
for i in range(0, len(x)):
thd = pylab.arange(thetaad[i], thetabd[i] + 1, 1)
thr = pylab.arange(thetaar[i], thetabr[i] + pylab.pi / 180, pylab.pi / 180)
xunit = x[i] + r[i] * pylab.cos(pylab.radians(thd))
yunit = y[i] + r[i] * pylab.sin(pylab.radians(thd))
# p = pylab.plot(xunit,yunit,'b',lw=.5) #commented out to remove small verticle lines
pylab.hold(True)
# Now "blank out" the portions of the great circle cyclographic traces
# within 10 degrees of the poles of the primitive circle.
rr = bigr / pylab.tan(80 * pylab.pi / 180)
ang1 = pylab.arange(0, pylab.pi + pylab.pi / 180, pylab.pi / 180)
xx = pylab.zeros(pylab.size(ang1)) + rr * pylab.cos(ang1)
yy = bigr / pylab.cos(10 * pylab.pi / 180) * pylab.ones(pylab.size(ang1)) - rr * pylab.sin(ang1)
p = pylab.fill(xx, yy, 'w')
yy = -bigr / pylab.cos(10 * pylab.pi / 180) * pylab.ones(pylab.size(ang1)) + rr * pylab.sin(ang1)
p = pylab.fill(xx, yy, 'w')
for i in range(1, len(x)):
thd = pylab.arange(thetaad[i], thetabd[i] + 1, 1)
thr = pylab.arange(thetaar[i], thetabr[i] + pylab.pi / 180, pylab.pi / 180)
xunit = x[i] + r[i] * pylab.cos(pylab.radians(thd))
yunit = y[i] + r[i] * pylab.sin(pylab.radians(thd))
if pylab.mod(phi[i], 10) == 0:
p = pylab.plot(xunit, yunit, 'b', lw=1)
angg = thetaad[i]
pylab.hold(True)
# Now "blank out" the portions of the great circle cyclographic traces
# within 2 degrees of the poles of the primitive circle.
rr = bigr / pylab.tan(88 * pylab.pi / 180)
ang1 = pylab.arange(0, pylab.pi + pylab.pi / 180, pylab.pi / 180)
xx = pylab.zeros(pylab.size(ang1)) + rr * pylab.cos(ang1)
yy = bigr / pylab.cos(2 * pylab.pi / 180) * pylab.ones(pylab.size(ang1)) - rr * pylab.sin(ang1)
p = pylab.fill(xx, yy, 'w')
yy = -bigr / pylab.cos(2 * pylab.pi / 180) * pylab.ones(pylab.size(ang1)) + rr * pylab.sin(ang1)
p = pylab.fill(xx, yy, 'w')
# Group all x, y, r, and theta information for small circles
phi = pylab.append(phid, phid, 0)
x = pylab.append(x3, x4, 0)
y = pylab.append(y3, y4, 0)
r = pylab.append(r3, r4)
thetaad = pylab.append(theta3ad, theta4ad, 0)
thetaar = pylab.append(theta3ar, theta4ar, 0)
thetabd = pylab.append(theta3bd, theta4bd, 0)
thetabr = pylab.append(theta3br, theta4br, 0)
# Plot primitive circle
thd = pylab.arange(0, 360 + 1, 1)
thr = pylab.arange(0, 2 * pylab.pi + pylab.pi / 180, pylab.pi / 180)
xunit = bigr * pylab.cos(pylab.radians(thd))
yunit = bigr * pylab.sin(pylab.radians(thd))
p = pylab.plot(xunit, yunit)
pylab.hold(True)
# Plot portions of all small circles that lie inside the
# primitive circle, with thick lines (1 pt.) at 10 degree increments
for i in range(0, len(x)):
thd = pylab.arange(thetaad[i], thetabd[i] + 1, 1)
thr = pylab.arange(thetaar[i], thetabr[i] + pylab.pi / 180, pylab.pi / 180)
xunit = x[i] + r[i] * pylab.cos(pylab.radians(thd))
yunit = y[i] + r[i] * pylab.sin(pylab.radians(thd))
blug = pylab.mod(thetaad[i], 10)
if pylab.mod(phi[i], 10) == 0:
p = pylab.plot(xunit, yunit, 'b', lw=1)
angg = thetaad[i]
# else: #Commented out to remove the small horizontal lines
# p = pylab.plot(xunit,yunit,'b',lw=0.5)
pylab.hold(True)
# Draw thick north-south and east-west diameters
xunit = [-bigr, bigr]
yunit = [0, 0]
p = pylab.plot(xunit, yunit, 'b', lw=1)
pylab.hold(True)
xunit = [0, 0]
yunit = [-bigr, bigr]
p = pylab.plot(xunit, yunit, 'b', lw=1)
pylab.hold(True)
'''
This is the plotting part'''
trend1 = strike
plunge1 = pylab.absolute(dip)
# num = leng(lines1(:,1));
trendr1 = [foo * pylab.pi / 180 for foo in trend1]
plunger1 = [foo * pylab.pi / 180 for foo in plunge1]
rho1 = [bigr * pylab.tan(pylab.pi / 4 - ((foo) / 2)) for foo in plunger1]
# polarb plots ccl from 3:00, so convert to cl from 12:00
# pylab.polar(pylab.pi/2-trendr1,rho1,'o')
pylab.plot(9000, 90000, 'o', markerfacecolor="b", label='Positive Dip')
pylab.plot(9000, 90000, 'o', markerfacecolor="w", label='Negative Dip')
pylab.legend(loc=1)
for n in range(0, len(strike)):
if dip[n] > 0:
pylab.plot(rho1[n] * pylab.cos(pylab.pi / 2 - trendr1[n]), rho1[n] * pylab.sin(pylab.pi / 2 - trendr1[n]), 'o', markerfacecolor="b", label='Positive Dip')
else:
pylab.plot(rho1[n] * pylab.cos(pylab.pi / 2 - trendr1[n]), rho1[n] * pylab.sin(pylab.pi / 2 - trendr1[n]), 'o', markerfacecolor="w", label='Negative Dip')
'''above is self'''
pylab.axis([-bigr, bigr, -bigr, bigr])
sun_azimuth =[]
sun_elevation = []
day = []
for n in days:
d = datetime(2000,1,1,12)+timedelta(n-1)
day.append(d)
i3time = dataclasses.make_I3Time(d)
sun = astro.I3GetSunDirection(i3time)
sun_azimuth.append(90 - sun.azimuth/I3Units.degree)
sun_elevation.append(90 - sun.zenith/I3Units.degree)
plt.plot_date(day,
-4*plt.array(sun_azimuth)*plt.cos(plt.radians(sun_elevation)),
ls='-',marker=None
)
plt.title("Equation of Time")
plt.ylabel("Sundial Faster [minutes]")
plt.figure()
plt.plot_date(day,sun_elevation,ls='-',marker=None)
plt.ylabel("Sun Elevation")
plt.title("Sun Elevation")
plt.axes().yaxis.set_major_formatter(FormatStrFormatter(u'%d\u00b0'))
plt.figure()
ax = plt.axes()
plt_info = ax.plot(sun_azimuth,sun_elevation)
# Arm parameters f.useArm = 1 # include arm in simulation f.animArm = 1 # show arm animation f.graphsArm = 1 # plot arm graphs f.updateInterval = 20 # delay between arm updated (ms) f.initArmMovement = 50 # time at which to start moving arm (ms) f.armLen = [0.4634 - 0.173, 0.7169 - 0.4634] # elbow - shoulder from MSM;radioulnar - elbow from MSM; f.startAng = [0.62,1.53] # starting shoulder and elbow angles (rad) = natural rest position f.targetDist = 0.15 # target distance from center (15 cm) # Propriocpetive encoding allCellTags = f.sim.gatherAllCellTags() f.pop_sh = [gid for gid,tags in allCellTags.iteritems() if tags['popLabel'] == 'Psh'] f.pop_el = [gid for gid,tags in allCellTags.iteritems() if tags['popLabel'] == 'Pel'] f.minPval = radians(-30) f.maxPval = radians(135) f.minPrate = 0.01 f.maxPrate = 100 # Motor encoding f.nMuscles = 4 # number of muscles motorGids = [gid for gid,tags in allCellTags.iteritems() if tags['popLabel'] == 'EM'] cellsPerMuscle = len(motorGids) / f.nMuscles f.motorCmdCellRange = [motorGids[i:i+cellsPerMuscle] for i in xrange(0, len(motorGids), cellsPerMuscle)] # cell gids of motor output to each muscle f.cmdmaxrate = 120 # value to normalize motor command num spikes f.cmdtimewin = 50 # window to sum spikes of motor commands f.antagInh = 1 # inhibition from antagonic muscle # RL f.useRL = 1
observed['ra'] = observed.apply(lambda x: -1*float(x['ra']) if float(x['ra']) < 0 else float(x['ra']), axis = 1) #SB Queries and data manipulation sboffset = getSBOffsets(sbUID) sb = getSBSummary(asdm.asdmDict['SBSummary']) sbUID = sb.values[0][0] target = getSBTargets(sbUID) science = getSBScience(sbUID) partId = target[target['ObsParameter'] == science.entityPartId.values[0]].FieldSource.values[0] predicted = sboffset[sboffset['partId'] == partId][['latitude','longitude']] longitude, lat = sbfield[sbfield['entityPartId'] == partId][['longitude','latitude']].values[0] predicted[['raoff','decoff']] = predicted[['longitude','latitude']].astype(float) RA0 = float(longitude)*pl.pi/180. Dec0 = float(lat)*pl.pi/180. predicted['dRA'] = predicted.apply(lambda x: pl.radians(x['raoff']/3600.), axis = 1) predicted['dDec'] = predicted.apply(lambda x: pl.radians(x['decoff']/3600.), axis = 1) predicted['Pl'] = predicted.apply(lambda x: list((pl.cos(x['dRA'])*pl.cos(x['dDec']), pl.sin(x['dRA'])*pl.cos(x['dDec']), pl.sin(x['dDec']))) , axis = 1) predicted['Ps'] = predicted.apply(lambda x: rot(x['Pl'],RA0,Dec0), axis = 1) predicted['otcoor'] = predicted.apply(lambda x: list((pl.arctan2(x['Ps'][1], x['Ps'][0]) % (2.*pl.pi), pl.arcsin(x['Ps'][2]))), axis =1) predicted['otcoor_ra'] = predicted.apply(lambda x: x['otcoor'][0], axis =1 ) predicted['otcoor_dec'] = predicted.apply(lambda x: x['otcoor'][1], axis =1 ) predictedList = list() #predictedList.append((RA0,Dec0)) pred = pd.DataFrame(predictedList, columns = ['ra','dec']) ot = predicted[['otcoor_ra','otcoor_dec']] ot.columns= ['ra','dec'] pred = pd.concat([pred,ot]) pred['series'] = 'SchedBlock' comb = combinations(geo[['lat','lon']].values, 2)
sun_elevation = []
day = []
for n in days:
d = datetime(2000, 1, 1, 12) + timedelta(n - 1)
day.append(d)
i3time = dataclasses.make_I3Time(d)
sun = astro.I3GetSunDirection(i3time)
sun_azimuth.append(90 - sun.azimuth / I3Units.degree)
sun_elevation.append(90 - sun.zenith / I3Units.degree)
plt.plot_date(day,
-4 * plt.array(sun_azimuth) *
plt.cos(plt.radians(sun_elevation)),
ls='-',
marker=None)
plt.title("Equation of Time")
plt.ylabel("Sundial Faster [minutes]")
plt.figure()
plt.plot_date(day, sun_elevation, ls='-', marker=None)
plt.ylabel("Sun Elevation")
plt.title("Sun Elevation")
plt.gca().yaxis.set_major_formatter(FormatStrFormatter(u'%d\u00b0'))
plt.figure()
ax = plt.gca()
plt_info = ax.plot(sun_azimuth, sun_elevation)
def main(args):
angles = args.haversine_distance
affinity = args.affinity
string = args.spectral_parameter
nside = args.nside
sky = get_sky(nside, 's1d1')
if string == 'Bs':
param = (sky.components[0].pl_index)
elif string == 'Bd':
param = (sky.components[1].mbb_index)
elif string == 'Td':
param = (sky.components[1].mbb_temperature).value
sigmaparam = hp.read_map(args.parameter_uncertainties, verbose=False)
Galmask = hp.read_map(args.galmask, verbose=False)
Galmask = cu.check_nside(nsideout=nside, mapin=Galmask)
sigmaparam = cu.check_nside(nsideout=nside, mapin=sigmaparam)
Galmask = pl.ma.masked_not_equal(Galmask, 0).mask
ones = pl.ma.masked_greater(Galmask, 0).mask
fsky = (Galmask[ones].shape[0] / Galmask.shape[0])
if args.verbose: print(f' Clustering on fsky = {fsky:.2f}% ')
#param[~Galmask]=0
#sigmaparam [~Galmask ] =0
noisestring = ''
if args.add_noise:
noise_param = np.random.normal(loc=pl.zeros_like(param),
scale=sigmaparam / 10.)
param += noise_param
param = hp.smoothing(param,
fwhm=pl.radians(args.parameter_resolution),
verbose=False)
noisestring = '_noise'
save_affinity = True
anglestring = ''
if angles:
anglestring = '_haversine'
fmap = (
'/Users/peppe/work/adaptive_compsep/clusterpatches/' +
f'clusters_{affinity}{anglestring}_galmask_{string}_{nside}{noisestring}_{args.optimization}.fits'
)
file_affinity = (
f'/Users/peppe/work/adaptive_compsep/affinities/' +
f'{affinity}{anglestring}_galmask_{string}_{nside}{noisestring}.npy')
Cluster = ClusterData([param, sigmaparam],
nfeatures=2,
nside=nside,
affinity=affinity,
file_affinity=file_affinity,
include_haversine=angles,
verbose=args.verbose,
save_affinity=save_affinity,
scaler=None,
feature_weights=[1, 1],
galactic_mask=Galmask)
Kmin = 2
Kmax = 200
Cluster(nvals=args.num_cluster_evaluation,
Kmax=Kmax - 1,
Kmin=Kmin,
minimize=args.optimization,
parameter_string=string)
if args.optimization == 'partition':
label1 = r'Under partition '
label2 = r'Over partition '
ylabel = 'Partition measure '
elif args.optimization == 'residuals':
label1 = r'Syst. residuals'
label2 = r'Stat. residuals'
ylabel = r'Residuals [ $\mu K^2$ ] '
pl.title(string)
pl.plot(Cluster.Kvals, Cluster.Vu, '.', label=label1)
pl.plot(Cluster.Kvals, Cluster.Vo, '.', label=label2)
pl.plot(Cluster.Kvals,
pl.sqrt(Cluster.Vo**2 + Cluster.Vu**2),
'-',
label=r'Root Squared Sum ')
pl.legend()
pl.xlabel('K', fontsize=15)
pl.ylabel(ylabel, fontsize=15)
pl.show()
patches = pl.zeros_like(param, dtype=pl.int_)
patches[Galmask] = pl.int_(Cluster.clusters.labels_) + 1
hp.mollview(patches, cmap=pl.cm.tab20)
pl.show()
hp.write_map(fmap, patches, overwrite=True)