def label_data(prefix, size=100, savename=None):
from glob import glob
from os.path import basename
from PIL import Image
from os.path import isfile
if savename==None: savename=labelpath+'label_'+prefix+'.txt'
# We want to avoid labeling an image twice, so keep track
# of what we've labeled in previous labeling sessions.
if isfile(savename):
fileout = open(savename,'r')
already_seen = [line.split(',')[0] for line in fileout]
fileout.close()
else: already_seen = []
# Now reopen the file for appending.
fileout = open(savename,'a')
pl.ion()
pl.figure(1,figsize=(9,9))
files = glob(imgpath+prefix+'*.png')
for file in np.random.choice(files, size=size, replace=False):
if basename(file) in already_seen: continue
pl.clf()
pl.subplot(1,1,1)
pl.imshow(np.array(Image.open(file)))
pl.title(file)
pl.axis('off')
pl.draw()
label = get_one_char()
if label=='q': break
fileout.write(basename(file)+','+label+'\n')
print file,label
fileout.close()
return
def writeNudges(self, outfile='jitter.txt'):
counters = np.arange(len(self.x))
bjds = self.camera.counterToBJD(counters)
time = bjds - np.min(bjds)
plt.figure('jitter timeseries')
gs = gridspec.GridSpec(2, 1, hspace=0.15)
kw = dict(linewidth=2)
ax = None
for i, what in enumerate((self.x, self.y)):
ax = plt.subplot(gs[i], sharex=ax, sharey=ax)
ax.plot(time, what, **kw)
ax.set_ylabel(['dRA (arcsec)', 'dDec (arcsec)'][i])
if i == 0:
ax.set_title('Jitter Timeseries from\n{}'.format(self.basename))
plt.xlabel('Time from Observation Start (days)')
plt.xlim(np.min(time), np.max(time))
plt.draw()
plt.savefig(outfile.replace('.txt', '.pdf'))
data = [counters, bjds, self.x, self.y]
names = ['imagenumber', 'bjd', 'arcsecnudge_ra', 'arcsecnudge_dec']
t = astropy.table.Table(data=data, names=names)
t.write(outfile.replace('.txt', '_amplifiedby{}.txt'.format(self.amplifyinterexposurejitter)),
format='ascii.fixed_width', delimiter=' ')
logger.info("save jitter nudge timeseries to {0}".format(outfile))
def matplotlib_set_plot(ax, plotter, outfile, default_camera=(14, -120),
hide_x=False, hide_y=False):
ax.set_title(plotter.plot_title)
tsize = 'medium'
ax.set_xlabel(plotter.xaxis_label, fontsize=tsize)
ax.set_ylabel(plotter.yaxis_label, fontsize=tsize)
ax.set_zlabel(plotter.zaxis_label, fontsize=tsize)
ax.ticklabel_format(axis='both', labelpad=150, useOffset=False)
ax.set_xlim(*plotter.xaxis_range)
ax.set_ylim(*plotter.yaxis_range)
ax.set_zlim(*plotter.zaxis_range)
ax.legend(fontsize='small')
# getting a nice view over the whole mess in ppv
ax.view_init(*default_camera)
# hide axis-numbers:
if hide_x:
ax.get_xaxis().set_ticks([])
ax.xaxis.set_visible(False)
ax.get_xaxis().set_visible(False)
if hide_y:
ax.get_yaxis().set_ticks([])
ax.yaxis.set_visible(False)
ax.get_yaxis().set_visible(False)
plt.draw()
plt.savefig(outfile)
plt.show()
def find_gates(mag1, mag2, param):
col = mag1 - mag2
lines = open(param, 'r').readlines()
colmin, colmax = map(float, lines[4].split()[3:-1])
mag1min, mag1max = map(float, lines[5].split()[:-1])
#mag2min, mag2max = map(float, lines[5].split()[:-1])
# click around
fig, ax = plt.subplots()
ax.plot(col, mag2, ',', color='k', alpha=0.2)
ax.set_ylim(mag1max, mag1min)
ax.set_xlim(colmin, colmax)
ok = 1
while ok == 1:
print 'click '
pts = np.asarray(plt.ginput(n=4, timeout=-1))
exclude_gate = '1 {} 0 \n'.format(' '.join(['%.4f' % p for p in pts.flatten()]))
pts = np.append(pts, pts[0]).reshape(5,2)
ax.plot(pts[:,0], pts[:,1], color='r', lw=3, alpha=0.3)
plt.draw()
ok = move_on(0)
lines[7] = exclude_gate
# not so simple ... need them to be parallelograms.
# PASS!
# write new param file with exclude/include gate
os.system('mv {0} {0}_bkup'.format(param))
with open(param, 'w') as outp:
[outp.write(l) for l in lines]
print('wrote %s' % param)
def show_stat(net):
plt.clf()
f = plt.gcf()
f.add_subplot('211')
plt.title(net.checkpoint_name)
plt.plot(net.stat['epoch'], net.stat['train']['error'], label='train')
plt.plot(net.stat['epoch'], net.stat['val']['error'], label='val')
plt.plot(net.stat['epoch'], net.stat['test']['error'], label='test')
plt.legend(loc = 'lower left')
plt.ylabel('error')
plt.xlabel('epochs')
plt.grid()
f.add_subplot('212')
plt.plot(net.stat['epoch'], net.stat['train']['cost'], label='train')
plt.plot(net.stat['epoch'], net.stat['val']['cost'], label='val')
plt.plot(net.stat['epoch'], net.stat['test']['cost'], label='test')
plt.legend(loc = 'lower left')
plt.ylabel('cost')
plt.xlabel('epochs')
plt.grid()
plt.draw()
plt.savefig(net.output_dir + 'stat.png')
time.sleep(0.05)
def waterfall_plot(q,x,sampling=10,cmap=None,num_colors=100,outdir='./',outname='waterfall',format='eps',cbar_label='$|q| (a.u.)$'):
plt.figure()
plt.hold(True)
colorVal = 'b'
vmax = q[:,:].max()
print vmax,len(q)
for n in range(0,len(q),sampling):
if cmap is not None:
print q[n,:].max()
colorVal = get_color(value=q[n,:].max(),cmap=cmap,vmax=vmax+.1,num_colors=num_colors)
plt.plot(x,q[n,:]+n/10.0,label=str(n),color=colorVal,alpha=0.7)
ax = plt.gca()
for tic in ax.yaxis.get_major_ticks():
tic.tick1On = tic.tick2On = False
tic.label1On = tic.label2On = False
if cmap is not None:
scalar = get_smap(vmax=q[:,:].max()+.1,num_colors=sampling)
cbar = plt.colorbar(scalar)
plt.xlabel('$x\quad (a.u.)$')
cbar.set_label(cbar_label)
plt.draw()
plt.savefig(os.path.join(outdir,outname+'.'+format),format=format,dpi=320,bbox_inches='tight')
plt.close()
return
def graphical_test(satisfactory=0):
from matplotlib import cm, pylab
def cons():
return np.random.random(2)*4-2
def foo(x,y,a,b):
"banana function"
tmp=a-x
tmp*=tmp
out=-x*x
out+=y
out*=out
out*=b
out+=tmp
return out*(abs(np.cos((x-1)**2+(y-1)**2))+10.0/b)
def f(params):
return foo(params[0], params[1],1,100)
optimizer=optimize(f, cons, verbose=False,its=1, hillWalks=0, satisfactory=satisfactory, finalWalk=0)
bgx,bgy=np.mgrid[-2:2:1000j,-2:2:1000j]
bg=foo(bgx,bgy, 1,100)
for i in xrange(20):
pylab.clf()
pylab.imshow(bg, cmap=cm.RdBu,vmax=bg.mean()/10)
for x in optimizer.pool: pylab.plot((x[2]+2)/4*1000,(x[1]+2)/4*1000, ('gx'))
print optimizer.pool[0],optimizer.muterate
pylab.gca().set_xbound(0,1000)
pylab.gca().set_ybound(0,1000)
pylab.draw()
pylab.colorbar()
optimizer.run()
raw_input('enter to advance')
return optimizer
def ttplot(corfp,srp,slp,n,I1,I2):
global tplot_cum,dt,firstfile
tplot=time.time()
rchplot=int(ceil(log(n/chn)/log(2))+1)
normplot=zeros((1,rcr),dtype=float32)
for ir in xrange(rchplot):
if ir==0:
normplot[0,:chn]=1./arange(n-2,n-chn-2,-1)
else:
normplot[0,chn2*(ir+1.):chn2*(ir+2.)]=1./arange((n-1)/(2**ir)-chn2-1,(n-1)/(2**ir)-chn-1,-1)
indt=int(chn+chn2*log(n/chn)/log(2))-2
cc1=corfp[0,:indt]/(slp[0,:indt]*srp[0,:indt])/normplot[0,:indt]
cc2=corfp[-1,:indt]/(slp[-1,:indt]*srp[-1,:indt])/normplot[0,:indt]
t_axis=lag[0,:indt]
t_axis2=tI_avg[0,:n]
t_axis2b=tI_avg[0,:n]/dt+firstfile
lm1.set_data(t_axis,cc1)
lm2.set_data(t_axis2,I1)
lm1b.set_data(t_axis,cc2)
lm2b.set_data(t_axis2b,I2)
ax1.set_xlim(min(t_axis),max(t_axis))
ax1.set_ylim(min(cc1),max(cc1))
ax1b.set_ylim(min(cc2),max(cc2))
ax2.set_xlim(min(t_axis2),max(t_axis2))
ax2b.set_xlim(min(t_axis2b),max(t_axis2b))
ax2.set_ylim(min(I1),max(I1))
ax2b.set_ylim(min(I2),max(I2))
p.draw()
tplot_cum+=time.time()-tplot
return
def plot(self):
Ns = 1
h = 0.1
m = []
for i in range(len(self.l)):
for s in range(Ns):
m.append(ml_to_xy((i, float(s)/Ns, 0), self.kappa, self.l, self.x0, self.theta0))
m.append(ml_to_xy((len(self.l)-1, 1., 0), self.kappa, self.l, self.x0, self.theta0))
plt.clf()
plt.hold(True)
x=[p[0] for p in m]
y=[p[1] for p in m]
bp, angles, tangents= breakpoints(self.kappa, self.l, self.x0, self.theta0)
x=[p[0] for p in bp]
y=[p[1] for p in bp]
plt.plot(x[::10],y[::10],'k-')
# xm = [p[0] for i, p in enumerate(bp) if self.markers[i]==1]
# ym = [p[1] for i, p in enumerate(bp) if self.markers[i]==1]
# plt.plot(xm,ym,'kx')
plt.xlim((-100, 3000))
plt.ylim((-3000, 100))
plt.draw()
def plot(y, function):
""" Show an animation of Poincare plot.
--- arguments ---
y: A list of initial values
function: function which is argument of Runge-Kutta solver
"""
h = dt
fig = plt.figure()
ax = fig.add_subplot(111)
ax.grid()
time_text = ax.text(0.05, 0.9, '', transform=ax.transAxes)
plt.ion()
for i in range(nmax + 1):
for j in range(nstep):
rk4 = RK.RK4(function)
y = rk4.solve(y, j * h, h)
# -pi <= theta <= pi
while y[0] > pi:
y[0] = y[0] - 2 * pi
while y[0] < -pi:
y[0] = y[0] + 2 * pi
if ntransient <= i < nmax: # <-- draw the poincare plots
plt.scatter(y[0], y[1], s=2.0, marker='o', color='blue')
time_text.set_text('n = %d' % i)
plt.draw()
if i == nmax: # <-- to stop the interactive mode
plt.ioff()
plt.scatter(y[0], y[1], s=2.0, marker='o', color='blue')
time_text.set_text('n = %d' % i)
plt.show()
def transition_related_averaging_run(self, simulation_data, smoothing_kernel_width = 200, sampling_interval = [-50, 150], plot = True ): """docstring for transition_related_averaging""" transition_occurrence_times = self.transition_occurrence_times(simulation_data = simulation_data, smoothing_kernel_width = smoothing_kernel_width) # make sure only valid transition_occurrence_times survive transition_occurrence_times = transition_occurrence_times[(transition_occurrence_times > -sampling_interval[0]) * (transition_occurrence_times < (simulation_data.shape[0] - sampling_interval[1]))] # separated into on-and off periods: transition_occurrence_times_separated = [transition_occurrence_times[::2], transition_occurrence_times[1::2]] mean_time_course, std_time_course = np.zeros((2, sampling_interval[1] - sampling_interval[0], 5)), np.zeros((2, sampling_interval[1] - sampling_interval[0], 5)) if transition_occurrence_times_separated[0].shape[0] > 2: for k in [0,1]: averaging_interval_times = np.array([transition_occurrence_times_separated[k] + sampling_interval[0],transition_occurrence_times_separated[k] + sampling_interval[1]]).T interval_data = np.array([simulation_data[avit[0]:avit[1]] for avit in averaging_interval_times]) mean_time_course[k] = interval_data.mean(axis = 0) std_time_course[k] = (interval_data.std(axis = 0) / np.sqrt(interval_data.shape[0])) if plot: f = pl.figure(figsize = (10,8)) for i in range(simulation_data.shape[1]): s = f.add_subplot(simulation_data.shape[1], 1, 1 + i) for j in [0,1]: pl.plot(np.arange(mean_time_course[j].T[i].shape[0]), mean_time_course[j].T[i], ['r--','b--'][j], linewidth = 2.0 ) pl.fill_between(np.arange(mean_time_course[j].shape[0]), mean_time_course[j].T[i] + std_time_course[j].T[i], mean_time_course[j].T[i] - std_time_course[j].T[i], ['r','b'][j], alpha = 0.2) s.set_title(self.variable_names[i]) pl.draw() return (mean_time_course, std_time_course)
def runcand(doplot=False):
import time
l = os.listdir("BenLike/")
m = open("features.csv","w")
has_run = False
for f in l:
if f.find(".xml") != -1:
fname = "BenLike/" + f
print "working on", f
x0,y,dy, name = _load_dotastro_data(fname)
a = ebfeature(t=x0,m=y,merr=dy,name=name)
a.gen_orbital_period(doplot=doplot)
if doplot:
plt.draw()
if not has_run:
ff = a.features.keys()
ff.remove("run")
ff.remove("p_pulse_initial")
m.write("name," + ",".join(ff) + "\n")
has_run = True
m.write(os.path.basename(name) + "," + ",".join([str(a.features.get(s)) for s in ff]) + "\n")
time.sleep(1)
m.close()
def plot_data(self,data,plots):
"""
plot the parameters requested. In principle, this list of tuple pairs
can be very large and you can have multiple subplot views of your data.
"""
self.fig = plt.figure()
## figure out the number plots to make and how to order them
if plots is None:
print len(data[0])
parameters = ["p%i" % i for i in range(len(data[0]))]
plots = []
for i,p in enumerate(parameters[:-1]):
for pp in parameters[i+1:]:
plots.append( (p,pp))
nrow = int(np.floor(np.sqrt(len(plots))))
ncols = int(np.ceil(len(plots)/nrow))
## loop over all the plots
self.axes = []
for i,p in enumerate(plots):
# add the axes and also save what's being plotted here
self.axes.append( (self.fig.add_subplot(nrow,ncols,i+1),p))
# set the color explicitly for each point
cols = np.ones( (len(data),4))
cols[:,0:2] = 0.4
plt.scatter(data[p[0]],data[p[1]],c=cols,edgecolors='none')
plt.xlabel( p[0] )
plt.ylabel( p[1] )
plt.draw()
def axes_animate_out(ax, iterations=10, max_animation_time=1):
bottom, top = ax.get_ylim()
alpha_objects = ax.findobj(has_alpha)
alpha_divider = np.power(float(iterations-1), 2)
def ease_out(cur_index, end_index, func):
return 1. / (func(cur_index) / float(func(end_index)))
start_time = time()
for i in range(0, iterations):
c = ease_out(iterations - i, iterations, roll_index_transformation)
ax.set_ylim([bottom, c*top])
for item in alpha_objects:
item.set_alpha(alpha_index_transformation(iterations-i-1) / alpha_divider)
if time() - start_time > max_animation_time:
for item in alpha_objects:
item.set_alpha(0)
pl.draw()
return
pl.draw()
def main():
# u_t = u_xx
dx = .1
dt = .5
timesteps = 100000
x = np.arange(-10,10,dx)
m = len(x)
kappa = 50
# u''(x) = (u(x + dx) - 2u(x) + u(x - dx)) / dx^2
ones = lambda x: np.ones(x)
A = np.diag(ones(m-1),k=-1) + -2*np.diag(ones(m)) + np.diag(ones(m-1),k=1)
A *= kappa*(dx**2)
U = 0*ones(m)
for i in xrange(0,m):
if x[i] > -2 and x[i] < 2:
U[i] = 1
p.ion()
lines, = p.plot(x,U)
for n in xrange(0,timesteps):
U = U + dt*dudt(U,A)
if n % 100 == 0:
lines.set_ydata(U)
p.draw()
p.show()
def histograma(hist):
hist=hist.histogram(255)
## hist.save("hola4Hist.txt")
pylab.plot(hist)
pylab.draw()
pylab.pause(0.0001)
def test():
GP = GaussianProcess(GaussianKernel_iso([0.2, 1.0]))
X = array([[0.2], [0.3], [0.5], [1.5]])
Y = [1, 0, 1, 0.75]
GP.addData(X, Y)
figure(1)
A = arange(0, 2, 0.01)
mu = array([GP.mu(x) for x in A])
sig2 = array([GP.posterior(x)[1] for x in A])
Ei = EI(GP)
ei = [-Ei.negf(x) for x in A]
Pi = PI(GP)
pi = [-Pi.negf(x) for x in A]
Ucb = UCB(GP, 1, T=2)
ucb = [-Ucb.negf(x) for x in A]
ax = subplot(1, 1, 1)
ax.plot(A, mu, "k-", lw=2)
xv, yv = poly_between(A, mu - sig2, mu + sig2)
ax.fill(xv, yv, color="#CCCCCC")
ax.plot(A, ei, "g-", lw=2, label="EI")
ax.plot(A, ucb, "g--", lw=2, label="UCB")
ax.plot(A, pi, "g:", lw=2, label="PI")
ax.plot(X, Y, "ro")
ax.legend()
draw()
show()
def test1():
x = [0.5]*3
xbounds = [(-5, 5) for y in x]
GA = GenAlg(fitcalc1, x, xbounds, popMult=100, bitsPerGene=9, mutation=(1./9.), crossover=0.65, crossN=2, direction='min', maxGens=60, hammingDist=False)
results = GA.run()
print "*** DONE ***"
#print results
plt.ioff()
#generate pareto frontier numerically
x1_ = np.arange(-5., 0., 0.05)
x2_ = np.arange(-5., 0., 0.05)
x3_ = np.arange(-5., 0., 0.05)
pfn = []
for x1 in x1_:
for x2 in x2_:
for x3 in x3_:
pfn.append(fitcalc1([x1,x2,x3]))
pfn.sort(key=lambda x:x[0])
plt.figure()
i = 0
for x in results:
plt.scatter(x[1][0], x[1][1], 20, c='r')
plt.scatter([x[0] for x in pfn], [x[1] for x in pfn], 1.0, c='b', alpha=0.1)
plt.xlim([-20,-1])
plt.ylim([-12, 2])
plt.draw()
def click(event):
print([event.key])
if event.key == 'm':
mode = raw_input('Enter new mode: ')
for k in plots:
try:
d = data_mode(plt_data[k], mode)
plots[k].set_data(d)
except(ValueError):
print('Unrecognized plot mode')
p.draw()
elif event.key == 'd':
max = raw_input('Enter new max: ')
try: max = float(max)
except(ValueError): max = None
drng = raw_input('Enter new drng: ')
try: drng = float(drng)
except(ValueError): drng = None
for k in plots:
_max,_drng = max, drng
if _max is None or _drng is None:
d = plots[k].get_array()
if _max is None: _max = d.max()
if _drng is None: _drng = _max - d.min()
plots[k].set_clim(vmin=_max-_drng, vmax=_max)
print('Replotting...')
p.draw()
def ttplot(corf,sr,sl,norm,n, I1, I2,lag):
global tplot_cum
firstfile=int(input_info['n_first_image'])
tplot=time.time()
rchplot=int(ceil(log(n/nchannels)/log(2))+1)
normplot=zeros((1,rcr),dtype=float32)
for ir in xrange(rchplot):
if ir==0:
normplot[0,:nchannels]=1./arange(n-2,n-nchannels-2,-1)
else:
normplot[0,nchannels2*(ir+1):nchannels2*(ir+2)]=1./arange((n-1)/(2**ir)-nchannels2-1,(n-1)/(2**ir)-nchannels-1,-1)
indt=int(nchannels+nchannels2*log(n/nchannels)/log(2))-2
cc1=corf[0,:indt]/(sl[0,:indt]*sr[0,:indt])/normplot[0,:indt]
cc2=corf[nq/2,:indt]/(sl[nq/2,:indt]*sr[nq/2,:indt])/normplot[0,:indt]
t_axis=lag[0,:indt]
t_axis2=tI_avg[0,:n]
t_axis2b=tI_avg[0,:n]/dt+firstfile
lm1.set_data(t_axis,cc1)
lm2.set_data(t_axis2,I1)
lm1b.set_data(t_axis,cc2)
lm2b.set_data(t_axis2b,I2)
ax1.set_xlim(min(t_axis),max(t_axis))
ax1.set_ylim(min(cc1),max(cc1))
ax1b.set_ylim(min(cc2),max(cc2))
ax2.set_xlim(min(t_axis2),max(t_axis2))
ax2b.set_xlim(min(t_axis2b),max(t_axis2b))
ax2.set_ylim(min(I1),max(I1))
ax2b.set_ylim(min(I2),max(I2))
p.draw()
tplot_cum+=time.time()-tplot
def update(self, train_dict_list, test_dict_list, folder):
if test_dict_list != []:
self.p1.set_xdata(np.append(self.p1.get_xdata(), test_dict_list[-1]['NumIters']))
self.p1.set_ydata(np.append(self.p1.get_ydata(), test_dict_list[-1]['PixelAccuracy']))
self.p2.set_xdata(np.append(self.p2.get_xdata(), test_dict_list[-1]['NumIters']))
self.p2.set_ydata(np.append(self.p2.get_ydata(), test_dict_list[-1]['MeanAccuracy']))
self.p3.set_xdata(np.append(self.p3.get_xdata(), test_dict_list[-1]['NumIters']))
self.p3.set_ydata(np.append(self.p3.get_ydata(), test_dict_list[-1]['IU']))
self.p4.set_xdata(np.append(self.p4.get_xdata(), test_dict_list[-1]['NumIters']))
self.p4.set_ydata(np.append(self.p4.get_ydata(), test_dict_list[-1]['FreqWeighMeanAcc']))
if train_dict_list != []:
self.p5.set_xdata(np.append(self.p5.get_xdata(), train_dict_list[-1]['NumIters']))
self.p5.set_ydata(np.append(self.p5.get_ydata(), train_dict_list[-1]['PixelAccuracy']))
self.p6.set_xdata(np.append(self.p6.get_xdata(), train_dict_list[-1]['NumIters']))
self.p6.set_ydata(np.append(self.p6.get_ydata(), train_dict_list[-1]['MeanAccuracy']))
self.p7.set_xdata(np.append(self.p7.get_xdata(), train_dict_list[-1]['NumIters']))
self.p7.set_ydata(np.append(self.p7.get_ydata(), train_dict_list[-1]['IU']))
self.p8.set_xdata(np.append(self.p8.get_xdata(), train_dict_list[-1]['NumIters']))
self.p8.set_ydata(np.append(self.p8.get_ydata(), train_dict_list[-1]['FreqWeighMeanAcc']))
self.ax2.relim()
self.ax2.autoscale_view()
plt.draw()
self.metrics_fig.savefig(os.path.join(os.path.join(folder, 'plots'), 'Metrics.png'), bbox_extra_artists=(self.lgd2,), bbox_inches="tight")
def histogramac(histc):
histc=histc.histogram(255)
B.show()
pylab.plot(histc)
pylab.draw()
pylab.pause(0.0001)
def callback(self,event): self.changed = True self.last_time+=1 if(self.last_time>6): self.last_time=0 self.update(0) plt.draw()
def __init__(self, folder, **kwargs):
if not os.path.isdir(os.path.join(folder, 'plots')):
os.mkdir(os.path.join(folder, 'plots'))
plt.ioff()
self.metrics_fig = plt.figure('Metrics')
self.ax2 = self.metrics_fig.add_subplot(111)
self.p1, = self.ax2.plot([], [], 'ro-', label='TEST: Pixel accuracy')
self.p5, = self.ax2.plot([], [], 'rv-', label='TRAIN: Pixel accuracy')
self.p2, = self.ax2.plot([], [], 'bo-', label='TEST: Mean-Per-Class accuracy')
self.p6, = self.ax2.plot([], [], 'bv-', label='TRAIN:Mean-Per-Class accuracy')
self.p3, = self.ax2.plot([], [], 'go-', label='TEST: Mean-Per-Class IU')
self.p7, = self.ax2.plot([], [], 'gv-', label='TRAIN:Mean-Per-Class IU')
self.p4, = self.ax2.plot([], [], 'ko-', label='TEST: Freq. weigh. mean IU')
self.p8, = self.ax2.plot([], [], 'kv-', label='TRAIN:Freq. weigh. mean IU')
plt.xlabel('iterations')
self.handles2, self.labels2 = self.ax2.get_legend_handles_labels()
self.lgd2 = self.ax2.legend(self.handles2, self.labels2, loc='upper center', bbox_to_anchor=(0.5,-0.2))
self.ax2.grid(True)
plt.draw()
def redrawall(self):
#Color all class one labeled pixels red
oneclazz = np.nonzero(self.mmc.classvec)[0]
col_row = self.collection[oneclazz]
rowcs, colcs = col_row[:, 1], col_row[:, 0]
red = np.array([255, 0, 0])
for i in range(-self.windowsize, self.windowsize + 1):
for j in range(-self.windowsize, self.windowsize + 1):
self.img[rowcs+i, colcs+j, :] = red
#Return the original color of the class zero labeled pixels
zeroclazz = np.nonzero(self.mmc.classvec - 1)[0]
col_row = self.collection[zeroclazz]
rowcs, colcs = col_row[:, 1], col_row[:, 0]
for i in range(-self.windowsize, self.windowsize + 1):
for j in range(-self.windowsize, self.windowsize + 1):
self.img[rowcs+i, colcs+j, :] = self.img_orig[rowcs+i, colcs+j, :]
self.imdata.set_data(self.img)
#Update the slider position according to labeling of the current working set
sliderval = 0
if len(mmc.working_set) > 0:
sliderval = len(np.nonzero(self.mmc.classvec_ws)[0]) / len(mmc.working_set)
self.in_selection_slider.set_val(sliderval)
#Update the RLS objective function display
self.objfun_display_axis.imshow(mmc.compute_steepness_vector()[np.newaxis, :], cmap=plt.get_cmap("Oranges"))
self.objfun_display_axis.set_aspect('auto')
#Final stuff
self.lasso.canvas.draw_idle()
plt.draw()
print_instructions()
def kmr_test_plot(data, k, end_thresh):
from matplotlib.pylab import ion, figure, draw, ioff, show, plot, cla
ion()
fig = figure()
ax = fig.add_subplot(111)
ax.grid(True)
# get k centroids
kmr = kmeans.kmeans_runner(k, end_thresh)
kmr.init_data(data)
print kmr.centroids
plot(data[:,0], data[:,1], 'o')
i = 0
while kmr.stop_flag is False:
kmr.iterate()
#print kmr.centroids, kmr.itr_count
plot(kmr.centroids[:, 0], kmr.centroids[:, 1], 'sr')
time.sleep(.2)
draw()
i += 1
print "N Iterations: %d" % (i)
plot(kmr.centroids[:, 0], kmr.centroids[:, 1], 'g^', linewidth=3)
ioff()
show()
print kmr.itr_count, kmr.centroids
def plot(self, outf=None, dosave=True, savedir="Plot/", show=True):
if outf is None:
outf = self.outf
# print outf
oo = mlab.csv2rec(outf, delimiter=" ")
# print oo
plt.errorbar(oo["time"] % self.period, oo["magnitude"], oo["error"], fmt="b.")
plt.plot(oo["time"] % self.period, oo["model"], "ro")
plt.title(
"#%i P=%f d (chisq/dof = %f) r1+r2=%f"
% (self.dotastro_id, self.period, self.outrez["chisq"], self.outrez.get("r1") + self.outrez.get("r2"))
)
ylim = plt.ylim()
# print ylim
if ylim[0] < ylim[1]:
plt.ylim(ylim[1], ylim[0])
plt.draw()
if show:
plt.show()
if dosave:
if not os.path.isdir(savedir):
os.mkdir(savedir)
plt.savefig("%splot%i.png" % (savedir, self.dotastro_id)) # ,self.period))
print("Saved", "%splot%i.png" % (savedir, self.dotastro_id)) # ,self.period)
plt.clf()
def _brush(self,event,region,inverse=False):
"""
This will loop through all the other subplots (without the brush region)
and change the opacity of the points not associated with that region.
when inverse is True, it will "unbrush" by resetting the opacity of the brushed points
"""
opacity_fraction = self.opac
# what variables are in the plot?
plot_vars = [x[1] for x in self.axis_info if x[0] == event.inaxes][0]
## figure out the min max of this region
minx, miny = region.get_xy()
maxx = minx + region.get_width()
maxy = miny + region.get_height()
## now query the data to get all the sources that are inside this range
if isinstance(self.data[plot_vars[0]][0],datetime.datetime):
maxx = datetime.datetime.fromordinal(maxx)
minx= datetime.datetime.fromordinal(minx)
elif isinstance(self.data[plot_vars[0]][0],datetime.date):
maxx = datetime.date.fromordinal(maxx)
minx= datetime.date.fromordinal(minx)
if isinstance(self.data[plot_vars[1]][0],datetime.datetime):
maxy = datetime.datetime.fromordinal(maxx)
miny= datetime.datetime.fromordinal(minx)
elif isinstance(self.data[plot_vars[1]][0],datetime.date):
maxy = datetime.date.fromordinal(maxy)
miny= datetime.date.fromordinal(miny)
inds = (self.data[plot_vars[0]]<= maxx) & (self.data[plot_vars[0]] > minx) & \
(self.data[plot_vars[1]] <= maxy) & (self.data[plot_vars[1]] > miny)
invinds = ~ inds # get all indicies of those records not inside the region
for a,pv in self.axis_info:
# dont self brush!
if a == event.inaxes:
continue
## get the scatterplot color and alpha channel data
self.t = a.collections[0]
fc = self.t.get_facecolor() # this will be a 2d array
'''Here we change the color and opacity of the points
fc[index,0] = Red
fc[index,1] = Green
fc[index,2] = Blue
fc[index,3] = Alpha
default is [ 0.4 , 0.4 , 1. , 1.0]
'''
if not inverse:
fc[invinds,2] /= 20. #reduce blue channel greatly
fc[invinds,3] /= opacity_fraction
else:
fc[invinds,2] *= 20.
fc[invinds,3] *= opacity_fraction
self.t.set_facecolor(fc)
plt.draw()
def pick(event) :
global data
global X
global Y
global numpy_container
if event.key == 'q' :
if len(X) == 0 : return
if not numpy_container :
data = VectorDataSet(X)
else :
data = PyVectorDataSet(numpy.array(X))
data.attachLabels(Labels(Y))
X = []
Y = []
print 'done creating data. close this window and use the decisionSurface function'
pylab.disconnect(binding_id)
if event.key =='1' or event.key == '2' :
if event.inaxes is not None:
print 'data coords', event.xdata, event.ydata
X.append([event.xdata, event.ydata])
Y.append(event.key)
pylab.plot([event.xdata], [event.ydata],
plotStr[int(event.key) - 1])
pylab.draw()
def plot_spikes(time,voltage,APTimes,titlestr):
"""
plot_spikes takes four arguments - the recording time array, the voltage
array, the time of the detected action potentials, and the title of your
plot. The function creates a labeled plot showing the raw voltage signal
and indicating the location of detected spikes with red tick marks (|)
"""
# Make a plot and markup
plt.figure()
plt.title(titlestr)
plt.xlabel("Time (s)")
plt.ylabel("Voltage (uV)")
plt.plot(time, voltage)
# Vertical positions for red marker
# The following attributes are configurable if required
vertical_markers_indent = 0.01 # 1% of Voltage scale height
vertical_markers_height = 0.03 # 5% of Voltage scale height
y_scale_height = 100 # Max of scale
marker_ymin = 0.5 + ( max(voltage) / y_scale_height / 2 ) + vertical_markers_indent
marker_ymax = marker_ymin + vertical_markers_height
# Drawing red markers for detected spikes
for spike in APTimes:
plt.axvline(spike, ymin=marker_ymin, ymax=marker_ymax, color='red')
plt.draw()
pylab.figure()
pylab.plot(mixdiag_info_dict['lap_history'],
mixdiag_info_dict['loss_history'],
'b.-',
label='mix + diag gauss')
pylab.plot(hmmdiag_info_dict['lap_history'],
hmmdiag_info_dict['loss_history'],
'k.-',
label='hmm + diag gauss')
pylab.plot(hmmfull_info_dict['lap_history'],
hmmfull_info_dict['loss_history'],
'r.-',
label='hmm + full gauss')
pylab.plot(hmmar_info_dict['lap_history'],
hmmar_info_dict['loss_history'],
'c.-',
label='hmm + ar gauss')
pylab.legend(loc='upper right')
pylab.xlabel('num. laps')
pylab.ylabel('loss')
pylab.xlim([4, 100]) # avoid early iterations
pylab.ylim([2.4, 3.7]) # handpicked
pylab.draw()
pylab.tight_layout()
pylab.show()
def Update(self):
plb.draw()
def extract_pseudo_lms(pos_list):
"""Determine pseudo-landmarks
Calculate the landmarks using the positions of the robot as anchor points.
NOTE For now this is only a nice approach for square datasets.
pos_list : list.
List of robot positions.
"""
pos_arr = np.hstack(pos_list).T
fig = plt.figure()
plt.ion()
plt.axis("equal")
hull = ConvexHull(pos_arr[:, :2])
indices = hull.vertices
indices = np.hstack((indices, indices[0]))
# Visual check for triangulation
plt.gca().clear()
plt.scatter(*pos_arr[:, :2].T)
plt.plot(*pos_arr[indices, :2].T)
for i in indices:
plt.text(*pos_arr[i, :2].T, s=str(i))
plt.draw()
plt.pause(0.01)
while 1:
keep_str = input(
"Enter the IDs of landmarks to be keep (comma seperated, blank continues, order matters!): "
)
try:
keep_new = ast.literal_eval(keep_str)
# XXX incase something wrong is entered, just restart loop
if type(keep_new) is int:
continue
elif len(keep_new) != 3:
continue
else:
keep = keep_new
except Exception:
logger.exception("Error understanding input")
break
plt.gca().clear()
plt.scatter(*pos_arr[:, :2].T)
plt.plot(*pos_arr[keep[:2], :2].T)
plt.plot(*pos_arr[keep[::2], :2].T)
for i in indices:
plt.text(*pos_arr[i.T, :2], s=str(i))
plt.draw()
plt.pause(0.01)
if len(keep) != 3:
raise ValueError("Can only keep 3 landmarks")
lms = pos_arr[keep, :].reshape(3, 3, 1) # (N, d, 1)
# Enforce all lms in the same plane
lms[1:, 2] = 1 * lms[0, 2]
# Axis vectors
vec_x = lms[1, :] - lms[0, :]
axis_x = vec_x / np.linalg.norm(vec_x)
axis_z = np.array([[0, 0, 1]]).T
axis_y = np.cross(axis_x.flatten(), axis_z.flatten()).reshape(3, 1)
vec_2 = lms[2, :] - lms[0, :]
vec_y = axis_y.T.dot(vec_2) * axis_y
vec_x = vec_x.flatten()
vec_y = vec_y.flatten()
tri = Delaunay(lms[:, :2, 0])
simplices = tri.simplices
# Visual check for triangulation
plt.gca().clear()
plt.triplot(*lms[:, :2, 0].T, simplices)
plt.scatter(*pos_arr[:, :2].T)
plt.draw()
plt.pause(0.01)
indices = np.arange(0, lms.shape[1])
valid_indices = np.arange(0, lms.shape[1])
while 1:
div_str = input("Enter the number of divisions (>= 1): ")
try:
div = ast.literal_eval(div_str)
if (type(div) is not int) or (div < 0):
raise ValueError("Divisons must be integer and >=1.")
except Exception:
logger.exception("Error understanding input")
div = 0
break
N_lms = (div + 1)**2
spacing = np.linspace(0, 1, div + 1)
x, y = np.meshgrid(spacing, spacing)
coords = np.hstack(
(x.reshape(N_lms, 1), y.reshape(N_lms, 1), np.zeros(
(N_lms, 1)))) # (N_lms, 3)
pseudo_lms = np.zeros((N_lms, 3, 1), dtype=float)
pseudo_lms[:, :,
0] = coords[:, 0, None] * vec_x + coords[:, 1, None] * vec_y
pseudo_lms += lms[0, :]
triangulation = Delaunay(pseudo_lms[:, :2, 0])
# Check number of regions from triangulation matches the divisions
N = triangulation.simplices.shape[0]
# This shouldn't be an issue anymore
# assert (N == 2*div**2) , "Number of triangulation regions is
# inconsistent with number of divisions."
simplices = triangulation.simplices
# Visual check for triangulation
plt.gca().clear()
plt.triplot(*pseudo_lms[:, :2, 0].T, simplices)
plt.scatter(*pos_arr[:, :2].T)
plt.draw()
plt.pause(0.01)
plt.close(fig)
plt.ioff()
return pseudo_lms, triangulation
def propag(n, V, a):
'''Prend en argument un format de matrice carrée, la provenance du vent à choisir parmi 'N', 'S', 'E', 'O', 'NE','NO', 'SO', 'SE', 'A', un type d'arbre 'R' pour résineux et 'F' pour feuillu et renvoie la création d'une forêt, la mise en feu d'un arbre et la première étape de la propagation'''
cmap = colors.ListedColormap(['white', 'limegreen', 'red', 'black'])
anima = []
F, L = crea_foret_feu(n)
input()
plt.close()
p = 0.8
if a == 'R':
p = 1
P = liste(p)
k = 1
for (m, r) in L:
if m < n - 1 and r < n - 1 and m > 1 and r > 1:
if V == 'S': #vent du sud
if F[m - 1, r - 1] == 1:
F[m - 1, r - 1] = choice(P)
if F[m - 1, r - 1] == 2:
L.append((m - 1, r - 1))
if F[m - 1, r] == 1:
F[m - 1, r] = choice(P)
if F[m - 1, r] == 2:
L.append((m - 1, r))
if F[m - 1, r + 1] == 1:
F[m - 1, r + 1] = choice(P)
if F[m - 1, r + 1] == 2:
L.append((m - 1, r + 1))
if V == 'N': #vent du nord
if F[m + 1, r - 1] == 1:
F[m + 1, r - 1] = choice(P)
if F[m + 1, r - 1] == 2:
L.append((m + 1, r - 1))
if F[m + 1, r] == 1:
F[m + 1, r] = choice(P)
if F[m + 1, r] == 2:
L.append((m + 1, r))
if F[m + 1, r + 1] == 1:
F[m + 1, r + 1] = choice(P)
if F[m + 1, r + 1] == 2:
L.append((m + 1, r + 1))
if V == 'E': #vent d'ouest
if F[m - 1, r - 1] == 1:
F[m - 1, r - 1] = choice(P)
if F[m - 1, r - 1] == 2:
L.append((m - 1, r - 1))
if F[m, r - 1] == 1:
F[m, r - 1] = choice(P)
if F[m, r - 1] == 2:
L.append((m, r - 1))
if F[m + 1, r - 1] == 1:
F[m + 1, r - 1] = choice(P)
if F[m + 1, r - 1] == 2:
L.append((m + 1, r - 1))
if V == 'O': #vent d'est
if F[m - 1, r + 1] == 1:
F[m - 1, r + 1] = choice(P)
if F[m - 1, r + 1] == 2:
L.append((m - 1, r + 1))
if F[m, r + 1] == 1:
F[m, r + 1] = choice(P)
if F[m, r + 1] == 2:
L.append((m, r + 1))
if F[m + 1, r + 1] == 1:
F[m + 1, r + 1] = choice(P)
if F[m + 1, r + 1] == 2:
L.append((m + 1, r + 1))
if V == 'SO': #vent du sud ouest
if F[m - 1, r + 1] == 1:
F[m - 1, r + 1] = choice(P)
if F[m - 1, r + 1] == 2:
L.append((m - 1, r + 1))
if F[m - 1, r] == 1:
F[m - 1, r] = choice(P)
if F[m - 1, r] == 2:
L.append((m - 1, r))
if F[m, r + 1] == 1:
F[m, r + 1] = choice(P)
if F[m, r + 1] == 2:
L.append((m, r + 1))
if V == 'SE': #vent du sud est
if F[m - 1, r - 1] == 1:
F[m - 1, r - 1] = choice(P)
if F[m - 1, r - 1] == 2:
L.append((m - 1, r - 1))
if F[m - 1, r] == 1:
F[m - 1, r] = choice(P)
if F[m - 1, r] == 2:
L.append((m - 1, r))
if F[m, r - 1] == 1:
F[m, r - 1] = choice(P)
if F[m, r - 1] == 2:
L.append((m, r - 1))
if V == 'NE': #vent du nord est
if F[m, r - 1] == 1:
F[m, r - 1] = choice(P)
if F[m, r - 1] == 2:
L.append((m, r - 1))
if F[m + 1, r - 1] == 1:
F[m + 1, r - 1] = choice(P)
if F[m + 1, r - 1] == 2:
L.append((m + 1, r - 1))
if F[m + 1, r] == 1:
F[m + 1, r] = choice(P)
if F[m + 1, r] == 2:
L.append((m + 1, r))
if V == 'NO': #vent du nord ouest
if F[m + 1, r + 1] == 1:
F[m + 1, r + 1] = choice(P)
if F[m + 1, r + 1] == 2:
L.append((m + 1, r + 1))
if F[m + 1, r] == 1:
F[m + 1, r] = choice(P)
if F[m + 1, r] == 2:
L.append((m + 1, r))
if F[m, r + 1] == 1:
F[m, r + 1] = choice(P)
if F[m, r + 1] == 2:
L.append((m, r + 1))
if V == 'A': #Absence de vent
if F[m, r - 1] == 1:
F[m, r - 1] = choice(P)
if F[m, r - 1] == 2:
L.append((m, r - 1))
if F[m - 1, r] == 1:
F[m - 1, r] = choice(P)
if F[m - 1, r] == 2:
L.append((m - 1, r))
if F[m + 1, r] == 1:
F[m + 1, r] = choice(P)
if F[m + 1, r] == 2:
L.append((m + 1, r))
if F[m, r + 1] == 1:
F[m, r + 1] = choice(P)
if F[m, r + 1] == 2:
L.append((m, r + 1))
F[m,
r] = 3 #Arbre en cendres: détruit mais ne permet plus de propager de le feu
G = F[2:n - 2, 2:n - 2]
print(G)
anima.append([matshow(G, fignum=False, animated=True, cmap=cmap)])
plt.draw()
plt.show()
i = input()
plt.close()
if i == 's':
return ('Fin de la modélisation')
k = k + 1
print(k)
v[p] = 20.0
v[p1] = 15.0
v[p2] = -20.0
sp = findspikes(t, v, 0.0, dt=dt, mode='schmitt', interpolate=False)
print('findSpikes')
print('sp: ', sp)
f = MP.figure(1)
MP.plot(t, v, 'ro-')
si = (numpy.floor(sp / dt))
print('si: ', si)
spk = []
for k in si:
spk.append(numpy.argmax(v[k - 1:k + 1]) + k)
MP.plot(sp, v[spk], 'bs')
MP.ylim((0, 25))
MP.draw()
MP.show()
exit()
print("getSpikes")
y = [] * 5
for j in range(0, 1):
d = numpy.zeros((5, 1, len(v)))
for k in range(0, 5):
p = range(20 * k, 500, 50 + int(50.0 * (k / 2.0)))
vn = v.copy()
vn[p] = 20.0
d[k, 0, :] = numpy.array(vn) # load up the "spike" array
y.append(d)
tpts = range(0, len(t)) # numpy.arange(0, len(t)).astype(int).tolist()
#def findspikes(x, v, thresh, t0=None, t1= None, dt=1.0, mode=None, interpolate=False):
def main(unused_args):
print unused_args
#Generating some data
x, y = gen_seq()
n_steps = len(x) / 2
plt.plot(x, y)
plt.show()
seq_width = 10
num_hidden = 10
### Model initialiation
#random uniform initializer for the LSTM nodes
initializer = tf.random_uniform_initializer(-.1, .1)
#placeholders for input/target/sequence_length
seq_input = tf.placeholder(tf.float32, [n_steps, seq_width])
seq_target = tf.placeholder(tf.float32, [n_steps, 1.])
early_stop = tf.placeholder(tf.int32)
#making a list of timestamps for rnn input
inputs = [
tf.reshape(i, (1, seq_width)) for i in tf.split(0, n_steps, seq_input)
]
#LSTM cell
cell = rnn_cell.LSTMCell(num_hidden, seq_width, initializer=initializer)
initial_state = cell.zero_state(1, tf.float32)
#feeding sequence to the RNN
outputs, states = rnn(cell,
inputs,
initial_state=initial_state,
sequence_length=early_stop)
#outputs is a list, but we need a single tensor instead
outputs = tf.reshape(tf.concat(1, outputs), [-1, num_hidden])
#mapping to 1-D
W = tf.get_variable('W', [num_hidden, 1])
b = tf.get_variable('b', [1])
#final prediction
output = tf.matmul(outputs, W) + b
#squared error
error = tf.pow(tf.reduce_sum(tf.pow(tf.sub(output, seq_target), 2)), .5)
lr = tf.Variable(0., trainable=False, name='lr')
#optimizer setup
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(error, tvars), 5.)
optimizer = tf.train.AdamOptimizer(lr)
train_op = optimizer.apply_gradients(zip(grads, tvars))
### Model initialization DONE
###Let the training begin
init = tf.initialize_all_variables()
session = tf.Session()
session.run(init)
#training and testing data
train_input, train_target = gen_input(y,
n_steps,
offset=0,
seq_width=10,
lag=60)
test_input, test_target = gen_input(y,
n_steps,
offset=n_steps,
seq_width=10,
lag=60)
feed = {
early_stop: n_steps,
seq_input: train_input,
seq_target: train_target
}
#initial predictions on untrained model
outs = session.run(output, feed_dict=feed)
plt.figure(1)
plt.plot(x[:n_steps], train_target, 'b-', x[:n_steps], outs[:n_steps],
'r-')
plt.ion()
plt.show()
tf.get_variable_scope().reuse_variables()
session.run(tf.assign(lr, 1.))
saver = tf.train.Saver()
is_training = True
if is_training:
#Training for 100 epochs
for i in range(100):
new_lr = 1e-2
if i > 25:
new_lr = 1e-2
elif i > 50:
new_lr = 5e-3
elif i > 75:
new_lr = 1e-4
session.run(tf.assign(lr, new_lr))
err, outs, _ = session.run([error, output, train_op],
feed_dict=feed)
print('Epoch %d done. Error: %1.5f') % (i + 1, err)
plt.clf()
plt.plot(x[:n_steps], train_target, 'b-', x[:n_steps],
outs[:n_steps], 'r-')
plt.draw()
time.sleep(.1)
#saving the model variables
saver.save(session,
'sine-wave-rnn-' + str(num_hidden) + '-' + str(seq_width),
global_step=0)
if not is_training:
saver.restore(
session,
'sine-wave-rnn-' + str(num_hidden) + '-' + str(seq_width) + '-0')
plt.ioff()
plt.figure(1)
plt.clf()
#model prediction on training data
train_outs = session.run(output, feed_dict=feed)
plt.plot(x[:n_steps], train_target[:n_steps], 'b-', x[:n_steps],
train_outs[:n_steps], 'g--')
#model prediction on test data
feed = {
seq_input: test_input,
seq_target: test_target,
early_stop: n_steps
}
test_outs = session.run(output, feed_dict=feed)
#plotting
plt.plot(x[n_steps:2 * n_steps], test_outs, 'r--')
plt.plot(x[n_steps:2 * n_steps], test_target, 'b-')
plt.show()
def SingleStarReg(imfile,
ra,
dec,
wcsname='SINGLESTAR',
computesig=False,
refim=None,
threshmin=0.5,
peakmin=None,
peakmax=None,
searchrad=5.0,
nsigma=1.5,
fluxmin=None,
fluxmax=None,
verbose=True,
clobber=True):
""" Update the WCS of imfile so that it matches the WCS of the refimfile,
using a single star to define the necessary shift.
If xy or xyref are provided, then assume the star is located within
searchrad arcsec of that position in the imfile or the refimfile,
respectively. If either of those pixel coordinates are not provided, then
use the brightest star in the image.
"""
# noinspection PyUnresolvedReferences
from numpy import deg2rad, sqrt, cos, where, median
from astropy.io import ascii
from drizzlepac.updatehdr import updatewcs_with_shift
from numpy import unique
if refim is None: refim = imfile
if computesig == False:
skysigma = getskysigma(imfile)
if verbose:
print(("sndrizipipe.register.SingleStarReg: "
" Manually computed sky sigma for %s as %.5e" %
(imfile, skysigma)))
else:
skysigma = 0.0
# locate stars in imfile, pick out the brightest one
topdir = os.path.abspath('.')
imfiledir = os.path.dirname(os.path.abspath(imfile))
imfilebase = os.path.basename(imfile)
os.chdir(imfiledir)
# Iterate the source-finding algorithm with progressively smaller threshold
# values. This helps to ensure that we correctly locate the single bright
# source in the image
xycatfile = None
threshold = 200.
while threshold >= threshmin:
try:
xycatfile = mkSourceCatalog(imfilebase,
computesig=computesig,
skysigma=skysigma,
nsigma=nsigma,
threshold=threshold,
peakmin=peakmin,
peakmax=peakmax,
fluxmin=fluxmin,
fluxmax=fluxmax)[0]
# The source finder succeeded!
radeccatfile = xycatfile.replace('xy.coo', 'radec.coo')
break
except NameError:
# the source finder failed, try again with a lower threshold
threshold /= 2.
continue
if xycatfile is None:
print(( "Failed to generate a clean source catalog for %s"%imfile + \
" using threshmin = %.3f"%threshmin ))
import pdb
pdb.set_trace()
raise RuntimeError(
"Failed to generate a clean source catalog for %s"%imfile + \
" using threshmin = %.3f"%threshmin )
xycat = ascii.read(xycatfile)
radeccat = ascii.read(radeccatfile)
if verbose:
print(("Located %i sources with threshold=%.1f sigma" %
(len(xycat), threshold)))
os.chdir(topdir)
# compute the approximate separation in arcsec from the target ra,dec
# to each of the detected sources, then limit to those within searchrad
rasrc, decsrc = radeccat['col1'], radeccat['col2']
darcsec = sqrt(((rasrc - ra) * cos(deg2rad(dec)))**2 +
(decsrc - dec)**2) * 3600.
inear = where(darcsec <= searchrad)[0]
if verbose:
print((" %i of these sources are within %.1f arcsec of the target" %
(len(inear), searchrad)))
# identify the brightest source within searchrad arcsec of the target
ibrightest = inear[xycat['col3'][inear].argmax()]
xfnd, yfnd = [xycat['col1'][ibrightest], xycat['col2'][ibrightest]]
if verbose:
brightratio = xycat['col3'][ibrightest] / median(xycat['col3'][inear])
print((
" The brightest of these sources is %.1fx brighter than the median."
% brightratio))
# The TweakReg imagefind algorithm sometimes misses the true center
# catastrophically. Here we use a centroiding algorithm to re-center the
# position on the star, checking for a large offset.
# Note that we are using numpy arrays, so the origin is (0,0) and we need
# to correct the cntrd output to the (1,1) origin used by pyfits and drizzlepac.
imdat = pyfits.getdata(imfile)
fwhmpix = getfwhmpix(imfile)
xcntrd, ycntrd = cntrd(imdat, xfnd, yfnd, fwhmpix)
if xcntrd == -1:
if verbose: print('Recentering within a 5-pixel box')
xcntrd, ycntrd = cntrd(imdat, xfnd, yfnd, fwhmpix, extendbox=5)
assert ((xcntrd > 0) &
(ycntrd > 0)), "Centroid recentering failed for %s" % imfile
xcntrd += 1
ycntrd += 1
dxcntrd = xfnd - xcntrd
dycntrd = yfnd - ycntrd
if verbose > 9:
# plot the centroid shift and save a .png image
from matplotlib import pylab as pl
from matplotlib import cm
pl.clf()
vmin = imdat[ycntrd - 10:ycntrd + 10, xcntrd - 10:xcntrd + 10].min()
vmax = imdat[ycntrd - 10:ycntrd + 10, xcntrd - 10:xcntrd + 10].max()
pl.imshow(imdat, cmap=cm.Greys, vmin=vmin, vmax=vmax)
pl.plot(xfnd - 1,
yfnd - 1,
'r+',
ms=12,
mew=1.5,
label='drizzlepac.ndfind source position: %.3f, %.3f' %
(xfnd, yfnd))
pl.plot(xcntrd - 1,
ycntrd - 1,
'gx',
ms=12,
mew=1.5,
label='register.cntrd recentered position: %.3f, %.3f' %
(xcntrd, ycntrd))
pl.title("Centroiding shift : dx = %.3f dy = %.3f" %
(dxcntrd, dycntrd))
ax = pl.gca()
ax.set_xlim(xcntrd - 10, xcntrd + 10)
ax.set_ylim(ycntrd - 10, ycntrd + 10)
pl.colorbar()
ax.set_xlabel('X (pixels)')
ax.set_ylabel('Y (pixels)')
ax.legend(loc='upper right', numpoints=1, frameon=False)
pl.draw()
outpng = imfile.replace('.fits', '_recenter.png')
pl.savefig(outpng)
print(("Saved a recentering image as " + outpng))
# locate the appropriate extensions for updating
hdulist = pyfits.open(imfile)
sciextlist = [hdu.name for hdu in hdulist if 'WCSAXES' in hdu.header]
# convert the target position from ra,dec to x,y
if len(sciextlist) > 0:
imwcs = stwcs.wcsutil.HSTWCS(hdulist, ext=(sciextlist[0], 1))
else:
sciextlist = ['PRIMARY']
imwcs = stwcs.wcsutil.HSTWCS(hdulist, ext=None)
xref, yref = imwcs.wcs_world2pix(ra, dec, 1)
# If the new centroid position differs substantially from the original
# ndfind position, then update the found source position
# (i.e. in cases of catastrophic ndfind failure)
if (abs(dxcntrd) > 0.5) or (abs(dycntrd) > 0.5):
xfnd = xcntrd
yfnd = ycntrd
# compute the pixel shift from the xy position found in the image
# to the reference (target) xy position
xshift = xfnd - xref
yshift = yfnd - yref
# apply that shift to the image wcs
for sciext in unique(sciextlist):
print(("Updating %s ext %s with xshift,yshift = %.5f %.5f" %
(imfile, sciext, xshift, yshift)))
updatewcs_with_shift(imfile,
refim,
wcsname=wcsname,
rot=0.0,
scale=1.0,
xsh=xshift,
ysh=yshift,
fit=None,
xrms=None,
yrms=None,
verbose=verbose,
force=clobber,
sciext=sciext)
hdulist.close()
return (wcsname)
def my_visualization_function(**kwargs):
print("\r{}".format(kwargs), end="")
plot_data["iterations"].append(kwargs['iterations'])
plot_data["loss"].append(kwargs['loss'])
plot_data["axis_00"].append(kwargs['axis_00'])
plot_data["axis_01"].append(kwargs['axis_01'])
plot_data["axis_02"].append(kwargs['axis_02'])
plot_data["axis_03"].append(kwargs['axis_03'])
plot_data["axis_04"].append(kwargs['axis_04'])
axes[0, 0].clear()
axes[0, 0].scatter(plot_data["axis_00"],
plot_data["loss"],
c=plot_data["loss"],
cmap="jet",
marker='.')
axes[0, 0].set_ylabel("loss")
axes[0, 0].set_xlabel("axis_00")
axes[0, 1].clear()
axes[0, 1].scatter(plot_data["axis_01"],
plot_data["loss"],
c=plot_data["loss"],
cmap="jet",
marker='.')
axes[0, 1].set_xlabel("axis_01")
axes[0, 2].clear()
axes[0, 2].scatter(plot_data["axis_02"],
plot_data["loss"],
c=plot_data["loss"],
cmap="jet",
marker='.')
axes[0, 2].set_xlabel("axis_02")
axes[1, 0].clear()
axes[1, 0].scatter(plot_data["axis_03"],
plot_data["loss"],
c=plot_data["loss"],
cmap="jet",
marker='.')
axes[1, 0].set_ylabel("loss")
axes[1, 0].set_xlabel("axis_03")
axes[1, 1].clear()
axes[1, 1].scatter(plot_data["axis_04"],
plot_data["loss"],
c=plot_data["loss"],
cmap="jet",
marker='.')
axes[1, 1].set_xlabel("axis_04")
axes[1, 2].clear()
axes[1, 2].plot(plot_data["iterations"],
plot_data["loss"],
"--",
c=(0.8, 0.8, 0.8, 0.5))
axes[1, 2].scatter(plot_data["iterations"],
plot_data["loss"],
marker='.',
c=(0.2, 0.2, 0.2))
axes[1, 2].set_xlabel("iterations")
plt.draw()
plt.tight_layout()
plt.pause(0.001)
def kohonen(targetdigits, size_k=6, sigma=10.0, eta=0.9, tmax=5000, threshold=1000, plot_errors=False):
"""Example for using create_data, plot_data and som_step.
:param targetdigits: Set of labels to take into account for this algorithm
:param size_k: Size of the Kohonen map. In this case it will be 6 X 6 by default
:param sigma: Width of the neighborhood via the width of the gaussian that describes it, 10.0 by default
:param eta: Learning rate, 0.9 by default
:param tmax: Maximal iteration count. 5000 by default
:param threshold: threshold of the error for convergence criteria. 1000 by default
:param plot_errors: Plot the errors
"""
plb.close('all')
dim = 28 * 28
data_range = 255.0
unit_of_mean = 400
# load in data and labels
data = np.array(np.loadtxt('data.txt'))
labels = np.loadtxt('labels.txt')
# this selects all data vectors that corresponds to one of the four digits
data = data[np.logical_or.reduce([labels == x for x in targetdigits]), :]
# filter the labels
labels = labels[np.logical_or.reduce([labels == x for x in targetdigits])]
dy, dx = data.shape
# initialise the centers randomly
centers = np.random.rand(size_k ** 2, dim) * data_range
# build a neighborhood matrix
neighbor = np.arange(size_k ** 2).reshape((size_k, size_k))
# set the random order in which the datapoints should be presented
i_random = np.arange(tmax) % dy
np.random.shuffle(i_random)
# Converge step
last_centers = np.copy(centers)
errors = []
mean_errors = []
last_errors = [0.0]
etas = [eta]
for t, i in enumerate(i_random):
sigma = som_step(centers, data[i, :], neighbor, eta, sigma)
eta = max(0.9999 * eta, 0.1)
etas.append(eta)
err = np.sum(np.sum((last_centers - centers) ** 2, 1)) * 0.01
if t > unit_of_mean:
if len(last_errors) >= unit_of_mean:
last_errors.pop(0)
last_errors.append(err)
# Update the mean error term
tmp_error = np.mean(last_errors)
mean_errors.append(tmp_error)
if tmp_error < threshold:
print('The algorithm converges after', t, 'iterations')
break
errors.append(err)
last_centers = np.copy(centers)
# Digit assignment given labels.txt
digit_assignment = []
for i in range(0, size_k ** 2):
index = np.argmin(np.sum((data[:] - centers[i, :]) ** 2, 1))
digit_assignment.append(labels[index])
print('Digit assignment: \n')
print(np.resize(digit_assignment, (size_k, size_k)))
# for visualization, you can use this:
for i in range(size_k ** 2):
plb.subplot(size_k, size_k, i + 1)
plb.imshow(np.reshape(centers[i, :], [28, 28]), interpolation='bilinear')
plb.axis('off')
# leave the window open at the end of the loop
plb.show()
plb.draw()
if(plot_errors):
plb.plot(errors)
plb.title('Square of the errors over iterations', fontsize=20)
plb.ylabel('Squared errors', fontsize=18)
plb.xlabel('Iterations', fontsize=18)
plb.ylim([0, 600000])
plb.show()
plb.plot(mean_errors)
plb.title('Mean squared errors of the last 400 terms iterations', fontsize=20)
plb.ylabel('Mean squared error', fontsize=18)
plb.xlabel('Iterations', fontsize=18)
plb.show()
plb.plot(etas)
plb.title('Learning rates decrease over iterations', fontsize=20)
plb.ylabel('Learning rates', fontsize=18)
plb.xlabel('Iterations', fontsize=18)
plb.show()
def update_plot(val, feature, length):
global fig, gs
global p_feat, p_svd_lpf_feat, p_clusters_feat
global p_clusters_svd_lpf_feat, p_segmentation
beat_sync = True
# set parameters
if beat_sync:
n_fft = 8
hop_length = 4
window = 16
step_size = 4
kernel_size = 4
else:
n_fft = 32
hop_length = 16
window = 4
step_size = 2
kernel_size = 4
# extract features
file_path, file_ext = os.path.splitext(open_file_dialog())
feats, beat_times = extract_features(file_path,
file_ext,
feature,
beat_sync=beat_sync,
n_fft=n_fft,
hop_length=hop_length)
# convert beat_times to nicely formatted strings
beat_times_str = np.array(
['{}:{}'.format(int(x / 60), int(x % 60)) for x in beat_times])
# perform segmentation
print '\tSegmentation'
flip = True
peaks = {}
distances = {}
distances_filtered = {}
segments = {}
for k, v in feats.items():
peaks[k], distances[k], distances_filtered[k] = compute_segmentation(
v, window, step_size, kernel_size, flip=flip)
# computes segments
segments[k] = get_segments(v.T, peaks[k])
# compute pair-wise distances using DTW
print '\tDTW'
dtw_distances = {}
for k, v in segments.items():
dists = np.zeros((len(v), len(v)))
for i in xrange(len(v)):
for j in xrange(len(v)):
if i == j:
dists[i, j] = 0.0
else:
dists[i, j] = compute_dtw_distance(v[i], v[j], 'cosine')
dtw_distances[k] = dists
# perform clustering using DTW distances
print '\tClustering'
clustering = {}
for k, v in dtw_distances.items():
if np.isnan(v).any():
pdb.bset_trace()
clustering[k] = compute_clusters_dtw(v, 'dbscan', segments[k], False)
# clean axes and update data individually
plt.suptitle(ntpath.basename(file_path))
ax_plt = fig.add_subplot(gs[0, 0])
ax_plt.set_title('Original {}'.format(feature))
ax_plt.imshow(feats[feature],
interpolation='nearest',
origin='low',
aspect='auto',
cmap=plt.cm.plasma)
for p in peaks[feature]:
ax_plt.axvline(p, color='k', linestyle='--', alpha=0.5)
ax_plt.set_xticks(peaks[feature])
ax_plt.set_xticklabels(beat_times_str[peaks[feature]], rotation=70)
ax = fig.add_subplot(gs[0, 1])
ax.imshow(clustering[feature][0].T,
interpolation='nearest',
origin='low',
aspect='auto',
cmap=plt.cm.plasma)
ax.set_xticks(clustering[feature][2])
ax.set_xticklabels(xrange(1, max(clustering[feature][1]) + 2))
ax = fig.add_subplot(gs[1, 0], sharex=ax_plt)
ax.plot(distances[feature])
for p in peaks[feature]:
ax.axvline(p, color='k', linestyle='--', alpha=0.5)
ax.set_xticks(peaks[feature])
ax.set_xticklabels(beat_times_str[peaks[feature]], rotation=70)
ax_plt = fig.add_subplot(gs[2, 0])
ax_plt.set_title('FFT({})'.format(feature))
ax_plt.imshow(feats['FFT({})'.format(feature)],
interpolation='nearest',
origin='low',
aspect='auto',
cmap=plt.cm.plasma)
for p in peaks['FFT({})'.format(feature)]:
ax_plt.axvline(p, color='k', linestyle='--', alpha=0.5)
ax_plt.set_xticks(peaks['FFT({})'.format(feature)])
ax_plt.set_xticklabels(beat_times_str[peaks['FFT({})'.format(feature)] *
hop_length],
rotation=70)
ax = fig.add_subplot(gs[2, 1])
ax.imshow(clustering['FFT({})'.format(feature)][0].T,
interpolation='nearest',
origin='low',
aspect='auto',
cmap=plt.cm.plasma)
ax.set_xticks(clustering['FFT({})'.format(feature)][2])
ax.set_xticklabels(
xrange(1, max(clustering['FFT({})'.format(feature)][1] + 2)))
ax = fig.add_subplot(gs[3, 0], sharex=ax_plt)
ax.plot(distances['FFT({})'.format(feature)])
for p in peaks['FFT({})'.format(feature)]:
ax.axvline(p, color='k', linestyle='--', alpha=0.5)
ax.set_xticks(peaks['FFT({})'.format(feature)])
ax.set_xticklabels(beat_times_str[peaks['FFT({})'.format(feature)] *
hop_length],
rotation=70)
plt.tight_layout()
plt.draw()
def image(sim,
qty='rho',
width="10 kpc",
resolution=500,
units=None,
log=True,
vmin=None,
vmax=None,
av_z=False,
filename=None,
z_camera=None,
clear=True,
cmap=None,
title=None,
qtytitle=None,
show_cbar=True,
subplot=False,
noplot=False,
ret_im=False,
fill_nan=True,
fill_val=0.0,
linthresh=None,
kernel_type='spline',
**kwargs):
"""
Make an SPH image of the given simulation.
**Keyword arguments:**
*qty* (rho): The name of the array to interpolate
*width* (10 kpc): The overall width and height of the plot. If
``width`` is a float or an int, then it is assumed to be in units
of ``sim['pos']``. It can also be passed in as a string
indicating the units, i.e. '10 kpc', in which case it is
converted to units of ``sim['pos']``.
*resolution* (500): The number of pixels wide and tall
*units* (None): The units of the output
*av_z* (False): If True, the requested quantity is averaged down
the line of sight (default False: image is generated in
the thin plane z=0, unless output units imply an integral
down the line of sight). If a string, the requested quantity
is averaged down the line of sight weighted by the av_z
array (e.g. use 'rho' for density-weighted quantity;
the default results when av_z=True are volume-weighted).
*z_camera* (None): If set, a perspective image is rendered. See
:func:`pynbody.sph.image` for more details.
*filename* (None): if set, the image will be saved in a file
*clear* (True): whether to call clf() on the axes first
*cmap* (None): user-supplied colormap instance
*title* (None): plot title
*qtytitle* (None): colorbar quantity title
*show_cbar* (True): whether to plot the colorbar
*subplot* (False): the user can supply a AxesSubPlot instance on
which the image will be shown
*noplot* (False): do not display the image, just return the image array
*ret_im* (False): return the image instance returned by imshow
*num_threads* (None) : if set, specify the number of threads for
the multi-threaded routines; otherwise the pynbody.config default is used
*fill_nan* (True): if any of the image values are NaN, replace with fill_val
*fill_val* (0.0): the fill value to use when replacing NaNs
*linthresh* (None): if the image has negative and positive values
and a log scaling is requested, the part between `-linthresh` and
`linthresh` is shown on a linear scale to avoid divergence at 0
*kernel_type* ('spline'): SPH kernel to use for smoothing. Defualts to a
cubic spline, but can also be set to 'wendlandC2'
"""
if not noplot:
import matplotlib.pylab as plt
global config
if not noplot:
if subplot:
p = subplot
else:
p = plt
if qtytitle is None:
qtytitle = qty
if isinstance(units, str):
units = _units.Unit(units)
if isinstance(width, str) or issubclass(width.__class__, _units.UnitBase):
if isinstance(width, str):
width = _units.Unit(width)
width = width.in_units(sim['pos'].units, **sim.conversion_context())
width = float(width)
kernel = sph.Kernel(type=kernel_type)
perspective = z_camera is not None
if perspective and not av_z:
kernel = sph.Kernel2D()
is_projected = False
if units is not None:
is_projected = _units_imply_projection(sim, qty, units)
if is_projected:
kernel = sph.Kernel2D(type=kernel_type)
if av_z:
if isinstance(kernel, sph.Kernel2D):
raise _units.UnitsException(
"Units already imply projected image; can't also average over line-of-sight!"
)
else:
kernel = sph.Kernel2D(type=kernel_type)
if units is not None:
aunits = units * sim['z'].units
else:
aunits = None
if isinstance(av_z, str):
if units is not None:
aunits = units * sim[av_z].units * sim['z'].units
sim["__prod"] = sim[av_z] * sim[qty]
qty = "__prod"
else:
av_z = "__one"
sim["__one"] = np.ones_like(sim[qty])
sim["__one"].units = "1"
im = sph.render_image(sim,
qty,
width / 2,
resolution,
out_units=aunits,
kernel=kernel,
z_camera=z_camera,
**kwargs)
im2 = sph.render_image(sim,
av_z,
width / 2,
resolution,
kernel=kernel,
z_camera=z_camera,
**kwargs)
top = sim.ancestor
try:
del top["__one"]
except KeyError:
pass
try:
del top["__prod"]
except KeyError:
pass
im = im / im2
else:
im = sph.render_image(sim,
qty,
width / 2,
resolution,
out_units=units,
kernel=kernel,
z_camera=z_camera,
**kwargs)
if fill_nan:
im[np.isnan(im)] = fill_val
if not noplot:
# set the log or linear normalizations
if log:
try:
im[np.where(im == 0)] = abs(im[np.where(abs(im != 0))]).min()
except ValueError:
raise ValueError(
"Failed to make a sensible logarithmic image. This probably means there are no particles in the view."
)
# check if there are negative values -- if so, use the symmetric
# log normalization
if (vmin is None and
(im < 0).any()) or ((vmin is not None) and vmin < 0):
# need to set the linear regime around zero -- set to by
# default start at 1/1000 of the log range
if linthresh is None:
linthresh = np.nanmax(abs(im)) / 1000.
norm = matplotlib.colors.SymLogNorm(linthresh,
vmin=vmin,
vmax=vmax)
else:
norm = matplotlib.colors.LogNorm(vmin=vmin, vmax=vmax)
else:
norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)
#
# do the actual plotting
#
if clear and not subplot:
p.clf()
if ret_im:
return p.imshow(im[::-1, :].view(np.ndarray),
extent=(-width / 2, width / 2, -width / 2,
width / 2),
vmin=vmin,
vmax=vmax,
cmap=cmap,
norm=norm)
ims = p.imshow(im[::-1, :].view(np.ndarray),
extent=(-width / 2, width / 2, -width / 2, width / 2),
vmin=vmin,
vmax=vmax,
cmap=cmap,
norm=norm)
u_st = sim['pos'].units.latex()
if not subplot:
plt.xlabel("$x/%s$" % u_st)
plt.ylabel("$y/%s$" % u_st)
else:
p.set_xlabel("$x/%s$" % u_st)
p.set_ylabel("$y/%s$" % u_st)
if units is None:
units = im.units
if units.latex() == "":
units = ""
else:
units = "$" + units.latex() + "$"
if show_cbar:
plt.colorbar(ims).set_label(qtytitle + "/" + units)
# colorbar doesn't work wtih subplot: mappable is NoneType
# elif show_cbar:
# import matplotlib.pyplot as mpl
# if qtytitle: mpl.colorbar().set_label(qtytitle)
# else: mpl.colorbar().set_label(units)
if title is not None:
if not subplot:
p.title(title)
else:
p.set_title(title)
if filename is not None:
p.savefig(filename)
plt.draw()
# plt.show() - removed by AP on 30/01/2013 - this should not be here as
# for some systems you don't get back to the command prompt
return im
def plot_hist(self, par_list):
""" Plots histograms for samples of parameters, model results, residuals, and test data
:param par_list: list of string for parameter names
:return: None; creates and saves plots in main class output directory
"""
print("Plotting histograms and graphs...")
outdir = self.outmcmc+'histograms/'
if not os.path.exists(outdir):
os.makedirs(outdir)
# plotting histograms for parameters
for i in range(self.ndim):
plt.figure(i)
dist = self.samples_i[:, i]
plt.hist(dist, 30, facecolor='blue', alpha=0.5)
if i ==0: plt.title('Alpha')
if i ==1: plt.title('Sigma')
if (i > 1) and (i<self.ndim_blr + 2): plt.title(par_list[i])
if i >= self.ndim_blr + 2:
plt.title('GP par: '+str(i-int(self.ndim_blr) - 1))
plt.axvline(self.params_fit[i], c='r', ls='-')
plt.axvline(self.params_mean[i], c='b', ls='-')
plt.axvline(self.params_mean[i] + self.errors_fit[i], c='b', ls='--')
plt.axvline(self.params_mean[i] - self.errors_fit[i], c='b', ls='--')
plt.axvline(0., c='k', ls='--')
plt.draw()
plt.savefig(outdir+'hist_'+par_list[i]+'.png')
# plot observed vs modeled
xmin = np.min(np.concatenate([self.y,self.y_test]))
xmax = np.max(np.concatenate([self.y,self.y_test]))
xdiff = xmax - xmin
ymin = np.min(np.concatenate([self.y_model,self.y_model_test]))
ymax = np.max(np.concatenate([self.y_model,self.y_model_test]))
ydiff = ymax - ymin
x_range = [xmin - xdiff*0.2, xmax + xdiff*0.2]
y_range = [ymin - ydiff*0.2, ymax + ydiff*0.2]
plt.clf()
plt.plot(x_range, y_range, '.', alpha=0.0)
plt.plot(self.y, self.y_model, 'o', c='b',label='Train', alpha=0.5)
plt.plot(self.y_test, self.y_model_test, 'o', c='r',label='Test', alpha=0.5)
plt.xlabel('Observed y')
plt.ylabel('Predicted y')
plt.title('Predicted vs Ground Truth')
# plt.title('Predicted vs Ground Truth')
plt.legend(loc='upper left', numpoints=1)
ax = plt.axes([0.63, 0.15, 0.25, 0.25], frameon=True)
ax.hist(self.residual, alpha=0.5, bins=16, color='b', stacked=True, normed=True)
ax.hist(self.residual_test, alpha=0.5, bins=16, color='r', stacked=True, normed=True)
#ax.xaxis.set_ticks(np.arange(-2, 2, 1))
ax.set_title('Residual')
plt.draw()
plt.savefig(self.outmcmc + 'Data_vs_Model_1d.png')
# plot more histograms:
# plt.clf()
# plt.hist(self.residual_i, 30, facecolor='blue', alpha=0.5)
# plt.title('Sum Residual y-model')
# plt.ylabel('N Samples')
# plt.draw()
# plt.savefig(outdir + 'hist_residual.png')
plt.clf()
plt.plot(self.y, self.mu_blr, 'o')
plt.xlabel('Log Crime Rate')
plt.ylabel('Predicted Crime Rate')
plt.title('Predicted from Demographics vs Ground Truth')
plt.draw()
plt.savefig(self.outmcmc + 'Data_ModelBLR_1d.png')
plt.clf()
plt.plot(self.y_model, self.residual, 'o')
plt.xlabel('Predicted Crime Rate')
plt.ylabel('Residual')
plt.draw()
plt.savefig(self.outmcmc + 'Model_Residual_1d.png')
def plot(self, fig_number=322):
"""plot the stored data in figure `fig_number`.
Dependencies: `matlabplotlib.pylab`
"""
from matplotlib import pylab
from matplotlib.pylab import (gca, figure, plot, xlabel, grid,
semilogy, text, draw, show, subplot,
tight_layout, rcParamsDefault, xlim,
ylim)
def title_(*args, **kwargs):
kwargs.setdefault('size', rcParamsDefault['axes.labelsize'])
pylab.title(*args, **kwargs)
def subtitle(*args, **kwargs):
kwargs.setdefault('horizontalalignment', 'center')
text(0.5 * (xlim()[1] - xlim()[0]), 0.9 * ylim()[1], *args,
**kwargs)
def legend_(*args, **kwargs):
kwargs.setdefault('framealpha', 0.3)
kwargs.setdefault('fancybox', True)
kwargs.setdefault('fontsize', rcParamsDefault['font.size'] - 2)
pylab.legend(*args, **kwargs)
figure(fig_number)
dat = self._data # dictionary with entries as given in __init__
if not dat:
return
try: # a hack to get the presumable population size lambda
strpopsize = ' (evaluations / %s)' % str(dat['eval'][-2] -
dat['eval'][-3])
except IndexError:
strpopsize = ''
# plot fit, Delta fit, sigma
subplot(221)
gca().clear()
if dat['fit'][0] is None: # plot is fine with None, but comput-
dat['fit'][0] = dat['fit'][1] # tations need numbers
# should be reverted later, but let's be lazy
assert dat['fit'].count(None) == 0
fmin = min(dat['fit'])
imin = dat['fit'].index(fmin)
dat['fit'][imin] = max(dat['fit']) + 1
fmin2 = min(dat['fit'])
dat['fit'][imin] = fmin
semilogy(dat['iter'],
[f - fmin if f - fmin > 1e-19 else None for f in dat['fit']],
'c',
linewidth=1,
label='f-min(f)')
semilogy(dat['iter'], [
max((fmin2 - fmin, 1e-19)) if f - fmin <= 1e-19 else None
for f in dat['fit']
], 'C1*')
semilogy(dat['iter'], [abs(f) for f in dat['fit']],
'b',
label='abs(f-value)')
semilogy(dat['iter'], dat['sigma'], 'g', label='sigma')
semilogy(dat['iter'][imin], abs(fmin), 'r*', label='abs(min(f))')
if dat['more_data']:
gca().twinx()
plot(dat['iter'], dat['more_data'])
grid(True)
legend_(*[
[v[i] for i in [1, 0, 2, 3]] # just a reordering
for v in gca().get_legend_handles_labels()
])
# plot xmean
subplot(222)
gca().clear()
plot(dat['iter'], dat['xmean'])
for i in range(len(dat['xmean'][-1])):
text(dat['iter'][0], dat['xmean'][0][i], str(i))
text(dat['iter'][-1], dat['xmean'][-1][i], str(i))
subtitle('mean solution')
grid(True)
# plot squareroot of eigenvalues
subplot(223)
gca().clear()
semilogy(dat['iter'], dat['D'], 'm')
xlabel('iterations' + strpopsize)
title_('Axis lengths')
grid(True)
# plot stds
subplot(224)
# if len(gcf().axes) > 1:
# sca(pylab.gcf().axes[1])
# else:
# twinx()
gca().clear()
semilogy(dat['iter'], dat['stds'])
for i in range(len(dat['stds'][-1])):
text(dat['iter'][-1], dat['stds'][-1][i], str(i))
title_('Coordinate-wise STDs w/o sigma')
grid(True)
xlabel('iterations' + strpopsize)
_stdout.flush()
tight_layout()
draw()
show()
CMAESDataLogger.plotted += 1
simplices = np.array([valid_lms])
notHappy = False
break
else:
triangles = Delaunay(last_lms_valid, incremental=True)
simplices = triangles.simplices # Indices of the points in each triangulation
# Remap simplices to valid landmark ids
remap = lambda x: valid_lms_arr[x]
simplices = np.apply_along_axis(remap, 0, simplices)
# Visual check for triangulation
plt.gca().clear()
plt.triplot(last_lms[:, 0], last_lms[:, 1], simplices.copy())
for i in valid_lms:
plt.text(*last_lms[i, :], s=str(i))
plt.draw()
plt.pause(0.01)
if check_triangulation:
remove_str = input("Enter the IDs of landmarks to be removed: ")
try:
remove = ast.literal_eval(remove_str)
except Exception as e:
print("Error understanding input:", e)
remove = ()
notHappy = False
# If only one number entered
if type(remove) is int:
remove = (remove, )
new_valid = sorted(list(set(valid_lms) - set(remove)))
def plotxy_old(beam,cols1,cols2,nbins=25,nbins_h=None,level=5,xrange=None,yrange=None,nolost=0,title='PLOTXY',xtitle=None,ytitle=None,noplot=0,calfwhm=0,contour=0):
'''
Draw the scatter or contour or pixel-like plot of two columns of a Shadow.Beam instance or of a given shadow file, along with histograms for the intensity on the top and right side.
Inumpy.ts:
beam : str instance with the name of the shadow file to be loaded, or a Shadow.Beam initialized instance.
cols1 : first column.
cols2 : second column.
Optional Inumpy.ts:
nbins : int for the size of the grid (nbins x nbins). It will affect the plot only if non scatter.
nbins_h : int for the number of bins for the histograms
level : int number of level to be drawn. It will affect the plot only if contour.
xrange : tuple or list of length 2 describing the interval of interest for x, the data read from the chosen column.
yrange : tuple or list of length 2 describing the interval of interest for y, counts or intensity depending on ref.
nolost :
0 All rays
1 Only good rays
2 Only lost rays
title : title of the figure, it will appear on top of the window.
xtitle : label for the x axis.
ytitle : label for the y axis.
noplot :
0 plot the histogram
1 don't plot the histogram
calfwhm :
0 don't compute the fwhm
1 compute the fwhm and draw it
2 in addition to calfwhm=1, it computes now the intensity in a
slit of FWHM_h x FWHM_v
contour :
0 scatter plot
1 contour, black & white, only counts (without intensity)
2 contour, black & white, with intensity.
3 contour, colored, only counts (without intensity)
4 contour, colored, with intensity.
5 pixelized, colored, only counts (without intensity)
6 pixelized, colored, with intensity.
Outputs:
ShadowTools.Histo1_Ticket instance.
Error:
if an error occurs an ArgsError is raised.
Possible choice for col are:
1 X spatial coordinate [user's unit]
2 Y spatial coordinate [user's unit]
3 Z spatial coordinate [user's unit]
4 X' direction or divergence [rads]
5 Y' direction or divergence [rads]
6 Z' direction or divergence [rads]
7 X component of the electromagnetic vector (s-polariz)
8 Y component of the electromagnetic vector (s-polariz)
9 Z component of the electromagnetic vector (s-polariz)
10 Lost ray flag
11 Energy [eV]
12 Ray index
13 Optical path length
14 Phase (s-polarization)
15 Phase (p-polarization)
16 X component of the electromagnetic vector (p-polariz)
17 Y component of the electromagnetic vector (p-polariz)
18 Z component of the electromagnetic vector (p-polariz)
19 Wavelength [A]
20 R= SQRT(X^2+Y^2+Z^2)
21 angle from Y axis
22 the magnituse of the Electromagnetic vector
23 |E|^2 (total intensity)
24 total intensity for s-polarization
25 total intensity for p-polarization
26 K = 2 pi / lambda [A^-1]
27 K = 2 pi / lambda * col4 [A^-1]
28 K = 2 pi / lambda * col5 [A^-1]
29 K = 2 pi / lambda * col6 [A^-1]
30 S0-stokes = |Es|^2 + |Ep|^2
31 S1-stokes = |Es|^2 - |Ep|^2
32 S2-stokes = 2 |Es| |Ep| cos(phase_s-phase_p)
33 S3-stokes = 2 |Es| |Ep| sin(phase_s-phase_p)
'''
if nbins_h==None: nbins_h=nbins+1
try:
stp.plotxy_CheckArg(beam,cols1,cols2,nbins,nbins_h,level,xrange,yrange,nolost,title,xtitle,ytitle,noplot,calfwhm,contour)
except stp.ArgsError as e:
raise e
#plot_nicc.ioff()
plt.ioff()
col1,col2,col3,col4 = getshcol(beam,(cols1,cols2,10,23,))
nbins=nbins+1
if xtitle==None: xtitle=(stp.getLabel(cols1-1))[0]
if ytitle==None: ytitle=(stp.getLabel(cols2-1))[0]
if nolost==0: t = numpy.where(col3!=-3299)
if nolost==1: t = numpy.where(col3==1.0)
if nolost==2: t = numpy.where(col3!=1.0)
if xrange==None: xrange = stp.setGoodRange(col1[t])
if yrange==None: yrange = stp.setGoodRange(col2[t])
#print xrange
#print yrange
tx = numpy.where((col1>xrange[0])&(col1<xrange[1]))
ty = numpy.where((col2>yrange[0])&(col2<yrange[1]))
tf = set(list(t[0])) & set(list(tx[0])) & set(list(ty[0]))
t = (numpy.array(sorted(list(tf))),)
if len(t[0])==0:
print ("no point selected")
return None
#figure = pylab.plt.figure(figsize=(12,8),dpi=96)
figure = plt.figure(figsize=(12,8),dpi=96)
ratio = 8.0/12.0
left, width = 0.1*ratio, 0.65*ratio
bottom, height = 0.1, 0.65
bottom_h = bottom+height+0.02
left_h = left+width+0.02*ratio
rect_scatter = [0.10*ratio, 0.10, 0.65*ratio, 0.65]
rect_histx = [0.10*ratio, 0.77, 0.65*ratio, 0.20]
rect_histy = [0.77*ratio, 0.10, 0.20*ratio, 0.65]
rect_text = [1.00*ratio, 0.10, 1.20*ratio, 0.65]
axScatter = figure.add_axes(rect_scatter)
axScatter.set_xlabel(xtitle)
axScatter.set_ylabel(ytitle)
if contour==0:
axScatter.scatter(col1[t],col2[t],s=0.5)
if contour>0 and contour<7:
if contour==1 or contour==3 or contour==5: w = numpy.ones( len(col1) )
if contour==2 or contour==4 or contour==6: w = col4
grid = numpy.zeros(nbins*nbins).reshape(nbins,nbins)
for i in t[0]:
indX = stp.findIndex(col1[i],nbins,xrange[0],xrange[1])
indY = stp.findIndex(col2[i],nbins,yrange[0],yrange[1])
try:
grid[indX][indY] = grid[indX][indY] + w[i]
except IndexError:
pass
X, Y = numpy.mgrid[xrange[0]:xrange[1]:nbins*1.0j,yrange[0]:yrange[1]:nbins*1.0j]
L = numpy.linspace(numpy.amin(grid),numpy.amax(grid),level)
if contour==1 or contour==2: axScatter.contour(X, Y, grid, colors='k', levels=L)
if contour==3 or contour==4: axScatter.contour(X, Y, grid, levels=L)
if contour==5 or contour==6: axScatter.pcolor(X, Y, grid)
#axScatter.set_xlim(xrange)
#axScatter.set_ylim(yrange)
#axScatter.axis(xmin=xrange[0],xmax=xrange[1])
#axScatter.axis(ymin=yrange[0],ymax=yrange[1])
for tt in axScatter.get_xticklabels():
tt.set_size('x-small')
for tt in axScatter.get_yticklabels():
tt.set_size('x-small')
#if ref==0: col4 = numpy.ones(len(col4),dtype=float)
axHistx = figure.add_axes(rect_histx, sharex=axScatter)
axHisty = figure.add_axes(rect_histy, sharey=axScatter)
binx = numpy.linspace(xrange[0],xrange[1],nbins_h)
biny = numpy.linspace(yrange[0],yrange[1],nbins_h)
if contour==0 or contour==1 or contour==3 or contour==5:
hx, binx, patchx = axHistx.hist(col1[t],bins=binx,range=xrange,histtype='step',color='k')
hy, biny, patchy = axHisty.hist(col2[t],bins=biny,range=yrange,orientation='horizontal',histtype='step',color='k')
if contour==2 or contour==4 or contour==6:
hx, binx, patchx = axHistx.hist(col1[t],bins=binx,range=xrange,weights=col4[t],histtype='step',color='b')
hy, biny, patchy = axHisty.hist(col2[t],bins=biny,range=yrange,weights=col4[t],orientation='horizontal',histtype='step',color='b')
for tl in axHistx.get_xticklabels(): tl.set_visible(False)
for tl in axHisty.get_yticklabels(): tl.set_visible(False)
for tt in axHisty.get_xticklabels():
tt.set_rotation(270)
tt.set_size('x-small')
for tt in axHistx.get_yticklabels():
tt.set_size('x-small')
intensityinslit = 0.0
if calfwhm>=1:
fwhmx,txf, txi = stp.calcFWHM(hx,binx[1]-binx[0])
fwhmy,tyf, tyi = stp.calcFWHM(hy,biny[1]-biny[0])
axHistx.plot([binx[txi],binx[txf+1]],[max(hx)*0.5,max(hx)*0.5],'x-')
axHisty.plot([max(hy)*0.5,max(hy)*0.5],[biny[tyi],biny[tyf+1]],'x-')
print ("fwhm horizontal: %g" % fwhmx)
print ("fwhm vertical: %g" % fwhmy)
if calfwhm>=2:
xx1 = binx[txi]
xx2 = binx[txf+1]
yy1 = biny[tyi]
yy2 = biny[tyf+1]
print ("limits horizontal: %g %g " % (binx[txi],binx[txf+1]))
print ("limits vertical: %g %g " % (biny[tyi],biny[tyf+1]))
axScatter.plot([xx1,xx2,xx2,xx1,xx1],[yy1,yy1,yy2,yy2,yy1])
#fwhmx,txf, txi = stp.calcFWHM(hx,binx[1]-binx[0])
#fwhmy,tyf, tyi = stp.calcFWHM(hy,biny[1]-biny[0])
#calculate intensity in slit
if nolost==0: tt = numpy.where(col3!=-3299)
if nolost==1: tt = numpy.where(col3==1.0)
if nolost==2: tt = numpy.where(col3!=1.0)
ttx = numpy.where((col1>=xx1)&(col1<=xx2))
tty = numpy.where((col2>=yy1)&(col2<=yy2))
ttf = set(list(tt[0])) & set(list(ttx[0])) & set(list(tty[0]))
tt = (numpy.array(sorted(list(ttf))),)
if len(tt[0])>0:
intensityinslit = col4[tt].sum()
print ("Intensity in slit: %g ",intensityinslit)
if title!=None:
axHistx.set_title(title)
axText = figure.add_axes(rect_text)
ntot = len(numpy.where(col3!=3299)[0])
ngood = len(numpy.where(col3==1)[0])
nbad = ntot - ngood
if nolost==0: axText.text(0.0,0.8,"ALL RAYS")
if nolost==1: axText.text(0.0,0.8,"GOOD RAYS")
if nolost==2: axText.text(0.0,0.8,"LOST RAYS")
tmps = "intensity: "+str(col4[t].sum())
if calfwhm == 2:
tmps=tmps+" (in slit:"+str(intensityinslit)+") "
axText.text(0.0,0.7,tmps)
axText.text(0.0,0.6,"total number of rays: "+str(ntot))
axText.text(0.0,0.5,"total good rays: "+str(ngood))
axText.text(0.0,0.4,"total lost rays: "+str(ntot-ngood))
if calfwhm>=1:
axText.text(0.0,0.3,"fwhm H: "+str(fwhmx))
axText.text(0.0,0.2,"fwhm V: "+str(fwhmy))
if isinstance(beam,str): axText.text(0.0,0.1,"FILE: "+beam)
if isinstance(beam,sd.Beam): axText.text(0.0,0.1,"from Shadow3 Beam instance")
axText.text(0.0,0.0,"DIR: "+os.getcwd())
axText.set_axis_off()
#pylab.plt.draw()
plt.draw()
if noplot==0: figure.show()
ticket = plotxy_Ticket()
ticket.figure = figure
ticket.xrange = xrange
ticket.yrange = yrange
ticket.xtitle = xtitle
ticket.ytitle = ytitle
ticket.title = title
if calfwhm>=1:
ticket.fwhmx = fwhmx
ticket.fwhmy = fwhmy
ticket.intensity = col4[t].sum()
ticket.averagex = numpy.average( col1[t] )
ticket.averagey = numpy.average( col2[t] )
ticket.intensityinslit = intensityinslit
return ticket
def draw_ortho(self, im, g, cmap=None, vmin=0, vmax=1):
slices = self.slices
int_slice = np.clip(np.round(slices), 0,
np.array(im.shape) - 1).astype('int')
g['xy'].cla()
g['yz'].cla()
g['xz'].cla()
g['in'].cla()
g['xy'].imshow(im[int_slice[0], :, :], vmin=vmin, vmax=vmax, cmap=cmap)
g['xy'].hlines(slices[1],
0,
im.shape[2],
colors='y',
linestyles='dashed',
lw=1)
g['xy'].vlines(slices[2],
0,
im.shape[1],
colors='y',
linestyles='dashed',
lw=1)
self._format_ax(g['xy'])
g['yz'].imshow(im[:, int_slice[1], :], vmin=vmin, vmax=vmax, cmap=cmap)
g['yz'].hlines(slices[0],
0,
im.shape[2],
colors='y',
linestyles='dashed',
lw=1)
g['yz'].vlines(slices[2],
0,
im.shape[0],
colors='y',
linestyles='dashed',
lw=1)
self._format_ax(g['yz'])
g['xz'].imshow(im[:, :, int_slice[2]].T,
vmin=vmin,
vmax=vmax,
cmap=cmap)
g['xz'].hlines(slices[1],
0,
im.shape[0],
colors='y',
linestyles='dashed',
lw=1)
g['xz'].vlines(slices[0],
0,
im.shape[1],
colors='y',
linestyles='dashed',
lw=1)
self._format_ax(g['xz'])
if self.inset == 'exposure':
m = im * self.state.image_mask
self.pix = np.r_[m[slices[0], :, :], m[:, :, slices[2]],
m[:, slices[1], :]].ravel()
self.pix = self.pix[self.pix != 0.]
g['in'].hist(self.pix, bins=300, histtype='step')
g['in'].semilogy()
g['in'].set_xlim(0, 1)
g['in'].set_ylim(9e-1, 1e3)
if self.view == 'diff' and g == self.gr:
g['in'].set_xlim(-0.3, 0.3)
self._format_ax(g['in'])
pl.draw()
print 'Generating plots... this may take a while...'
AntNos = np.array(AntNos)
figcnt = 0
for pol in pols:
pl.figure(figcnt)
DDD = np.zeros((Nant,Nant))
for i,ant1 in enumerate(AntNos):
for j,ant2 in enumerate(AntNos):
if not (ant1,ant2) in G_ij[pol].keys(): continue
DDD[i,j] = 10**G_ij[pol][(ant1,ant2)]/flx
DDD = np.ma.array(DDD,mask=np.where(DDD==0,1,0))
pl.imshow(DDD,aspect='auto',interpolation='nearest')
pl.colorbar()
pl.title('Gains by baseline, polarization %s'%pol[0])
pl.draw()
figcnt += 1
if opts.wgt != 'equal':
for pol in pols:
pl.figure(figcnt)
WWW = np.zeros((Nant,Nant))
for i,ant1 in enumerate(AntNos):
for j,ant2 in enumerate(AntNos):
if not (ant1,ant2) in W_ij[pol].keys(): continue
WWW[i,j] = W_ij[pol][(ant1,ant2)]
WWW = np.ma.array(WWW/np.sum(WWW),mask=np.where(WWW==0,1,0))
pl.imshow(WWW,aspect='auto',interpolation='nearest')
pl.colorbar()
pl.title('Weights for each baseline, polarization %s'%pol[0])
pl.draw()
def draw_ortho(self, im, g, cmap=None, vmin=0, vmax=1):
im, vmin, vmax, cmap = self.field, self.vmin, self.vmax, self.cmap
if self.fourier:
im = np.abs(im)
slices = self.slices
int_slice = np.clip(np.round(slices), 0,
np.array(im.shape[:3]) - 1).astype('int')
# if vmin is None:
# vmin = im.min()
# if vmax is None:
# vmax = im.max()
# if self.cmap == 'RdBu_r':
# val = np.max(np.abs([vmin, vmax]))
# vmin = -val
# vmax = val
self.g['xy'].cla()
self.g['yz'].cla()
self.g['xz'].cla()
self.g['in'].cla()
self.g['xy'].imshow(im[int_slice[0], :, :], cmap=cmap)
self.g['xy'].hlines(slices[1],
0,
im.shape[2],
colors='y',
linestyles='dashed',
lw=1)
self.g['xy'].vlines(slices[2],
0,
im.shape[1],
colors='y',
linestyles='dashed',
lw=1)
self._format_ax(self.g['xy'])
self.g['yz'].imshow(im[:, int_slice[1], :],
vmin=vmin,
vmax=vmax,
cmap=cmap)
self.g['yz'].hlines(slices[0],
0,
im.shape[2],
colors='y',
linestyles='dashed',
lw=1)
self.g['yz'].vlines(slices[2],
0,
im.shape[0],
colors='y',
linestyles='dashed',
lw=1)
self._format_ax(self.g['yz'])
self.g['xz'].imshow(np.rollaxis(im[:, :, int_slice[2]], 1), cmap=cmap)
self.g['xz'].hlines(slices[1],
0,
im.shape[0],
colors='y',
linestyles='dashed',
lw=1)
self.g['xz'].vlines(slices[0],
0,
im.shape[1],
colors='y',
linestyles='dashed',
lw=1)
self._format_ax(self.g['xz'])
if self.dohist:
tt = np.real(self.field).ravel()
c, s = tt.mean(), 5 * tt.std()
y, x = np.histogram(tt,
bins=np.linspace(c - s, c + s, 700),
normed=True)
x = (x[1:] + x[:-1]) / 2
self.g['in'].plot(x, y, 'k-', lw=1)
self.g['in'].fill_between(x, y, 1e-10, alpha=0.5)
self.g['in'].set_yscale('log', nonposy='clip')
self.g['in'].set_xlim(c - s, c + s)
self.g['in'].set_ylim(1e-3 * y.max(), 1.4 * y.max())
self._format_ax(self.g['in'])
pl.draw()
def plot_deviations(
self,
figsize=None,
savefig=None,
plots=["data", "strangeness", "pvalue", "deviation", "threshold"],
debug=False):
'''Plots the anomaly score, deviation level and p-value, over time.'''
register_matplotlib_converters()
plots, nb_axs, i = list(set(plots)), 0, 0
if "data" in plots:
nb_axs += 1
if "strangeness" in plots:
nb_axs += 1
if any(s in ["pvalue", "deviation", "threshold"] for s in plots):
nb_axs += 1
fig, axes = plt.subplots(nb_axs, sharex="row", figsize=figsize)
if not isinstance(axes, (np.ndarray)):
axes = np.array([axes])
if "data" in plots:
axes[i].set_xlabel("Time")
axes[i].set_ylabel("Feature 0")
axes[i].plot(self.df.index, self.df.values[:, 0], label="Data")
if debug:
axes[i].plot(self.T,
np.array(self.representatives)[:, 0],
label="Representative")
axes[i].legend()
i += 1
if "strangeness" in plots:
axes[i].set_xlabel("Time")
axes[i].set_ylabel("Strangeness")
axes[i].plot(self.T, self.S, label="Strangeness")
if debug:
axes[i].plot(self.T,
np.array(self.diffs)[:, 0],
label="Difference")
axes[i].legend()
i += 1
if any(s in ["pvalue", "deviation", "threshold"] for s in plots):
axes[i].set_xlabel("Time")
axes[i].set_ylabel("Deviation")
axes[i].set_ylim(0, 1)
if "pvalue" in plots:
axes[i].scatter(self.T,
self.P,
alpha=0.25,
marker=".",
color="green",
label="p-value")
if "deviation" in plots:
axes[i].plot(self.T, self.M, label="Deviation")
if "threshold" in plots:
axes[i].axhline(y=self.dev_threshold,
color='r',
linestyle='--',
label="Threshold")
axes[i].legend()
fig.autofmt_xdate()
if savefig is None:
plt.draw()
plt.show()
else:
figpathname = utils.create_directory_from_path(savefig)
plt.savefig(figpathname)
print("Found %d time series" % nCells)
# #### Plot one time series
# In[18]:
n = 200
gr.figure(figsize=(11, 4))
gr.plot(sampTimes, fMinData[n])
# #### Plot several time series in a single graph
# In[27]:
# Extract a random sample of nWaves integers to pick the waves
nWaves = 5
wNumbers = sc.random.randint(0, nCells, nWaves)
print("Ploting waves:")
print(wNumbers)
gr.figure(figsize=(11, 4))
gr.ioff()
for n in wNumbers:
gr.plot(sampTimes, fMinData[n], label="w%d" % n)
gr.xlim(0, sampTimes.max())
gr.legend(ncol=nWaves)
gr.ion()
gr.draw()
# In[ ]:
def onkeycazzo(event):
global _pixel,_flux, idd, _lampixel, _refpeak, _deg, nonincl, fig, ax1, _params5, _num, _line, _line3,\
ax2, ax3, _skyref_wav, _skyref_flux, _line2, _line4, _line5
xdata, ydata = event.xdata, event.ydata
_params5 = np.polyfit(_lampixel[idd], _refpeak[idd], _deg)
p2 = np.poly1d(_params5)
dist = np.sqrt((xdata - np.array(p2(_lampixel)))**2 +
(ydata - (np.array(_refpeak) - p2(_lampixel)))**2)
ii = np.argmin(dist)
_num = ii
if event.key == 'a':
idd.append(idd[-1] + 1)
__lampixel = list(_lampixel)
__lampixel.append(xdata)
_lampixel = np.array(__lampixel)
__refpeak = list(_refpeak)
__refpeak.append(ydata)
_refpeak = np.array(__refpeak)
ax1.plot(xdata, ydata, 'ob')
if event.key == 'd':
idd.remove(ii)
_num = ii
for i in range(len(_lampixel)):
if i not in idd: nonincl.append(i)
if event.key == 'c':
_num = ii
if event.key in ['1', '2', '3', '4', '5', '6', '7', '8', '9']:
_deg = int(event.key)
_params5 = np.polyfit(_lampixel[idd], _refpeak[idd], _deg)
p2 = np.poly1d(_params5)
_line.pop(0).remove()
_line3.pop(0).remove()
_line = ax1.plot(p2(_lampixel), _refpeak - p2(_lampixel), '.r')
_line3 = ax1.plot(
np.array(p2(_lampixel))[nonincl], (_refpeak - p2(_lampixel))[nonincl],
'oc')
ax1.set_xlim(np.min(p2(_pixel)), np.max(p2(_pixel)))
ax2.plot(_skyref_wav, _skyref_flux, '-b')
ax2.plot(p2(_pixel), _flux, '-r')
ax2.plot(p2(_lampixel), np.ones(len(_lampixel)), '|b')
ax2.plot(_refpeak, np.ones(len(_lampixel)), '|r')
ax2.set_ylim(0, 1.1)
ax2.set_xlim(np.min(p2(_pixel)), np.max(p2(_pixel)))
_line2.pop(0).remove()
_line4.pop(0).remove()
_line5.pop(0).remove()
ax3.plot(_skyref_wav, _skyref_flux, '-b')
_line2 = ax3.plot(p2(_pixel), _flux, '-r')
ax3.set_xlim(p2(_lampixel[_num]) - 50, p2(_lampixel)[_num] + 50)
ax3.set_ylim(0, 1.1)
_line4 = ax3.plot(p2(_lampixel[_num]), [1],
'|b',
label='ref. lamp detection')
_line5 = ax3.plot(_refpeak[_num], [1], '|r', label='lamp detection')
plt.legend()
plt.draw()
def move(self):
""" Move the Person
"""
if self.pdshow:
fig = plt.gcf()
fig, ax = self.L.showG('w',
labels=False,
alphacy=0.,
edges=False,
fig=fig)
plt.draw()
plt.ion()
while True:
if self.moving:
if self.sim.verbose:
print 'meca: updt ag ' + self.ID + ' @ ', self.sim.now()
# if np.allclose(conv_vecarr(self.destination)[:2],self.L.Gw.pos[47]):
# import ipdb
# ipdb.set_trace()
while self.cancelled:
yield passivate, self
print "Person.move: activated after being cancelled"
checked = []
for zone in self.world.zones(self):
if zone not in checked:
checked.append(zone)
zone(self)
# updating acceleration
acceleration = self.steering_mind(self)
acceleration = acceleration.truncate(self.max_acceleration)
self.acceleration = acceleration
# updating velocity
velocity = self.velocity + acceleration * self.interval
self.velocity = velocity.truncate(self.max_speed)
if velocity.length() > 0.2:
# record direction only when we've really had some
self.localy = velocity.normalize()
self.localx = vec3(self.localy.y, -self.localy.x)
# updating position
self.position = self.position + self.velocity * self.interval
# self.update()
self.position.z = 0
self.world.update_boid(self)
self.net.update_pos(self.ID, conv_vecarr(self.position),
self.sim.now())
p = conv_vecarr(self.position).reshape(3, 1)
v = conv_vecarr(self.velocity).reshape(3, 1)
a = conv_vecarr(self.acceleration).reshape(3, 1)
# fill panda dataframe 2D trajectory
self.df = self.df.append(
pd.DataFrame(
{
't': pd.Timestamp(self.sim.now(), unit='s'),
'x': p[0],
'y': p[1],
'vx': v[0],
'vy': v[1],
'ax': a[0],
'ay': a[1]
},
columns=['t', 'x', 'y', 'vx', 'vy', 'ax', 'ay']))
if self.pdshow:
ptmp = np.array([p[:2, 0], p[:2, 0] + v[:2, 0]])
if hasattr(self, 'pl'):
self.pl[0].set_data(self.df['x'].tail(1),
self.df['y'].tail(1))
self.pla[0].set_data(ptmp[:, 0], ptmp[:, 1])
circle = plt.Circle(
(self.df['x'].tail(1), self.df['y'].tail(1)),
radius=self.radius,
alpha=0.3)
ax.add_patch(circle)
else:
self.pl = ax.plot(self.df['x'].tail(1),
self.df['y'].tail(1),
'o',
color=self.color,
ms=self.radius * 10)
self.pla = ax.plot(ptmp[:, 0], ptmp[:, 1], 'r')
circle = plt.Circle(
(self.df['x'].tail(1), self.df['y'].tail(1)),
radius=self.radius,
alpha=0.3)
ax.add_patch(circle)
# try:
# fig,ax=plu.displot(p[:2],p[:2]+v[:2],'r')
# except:
# pass
# import ipdb
# ipdb.set_trace()
plt.draw()
plt.pause(0.0001)
if 'mysql' in self.save:
self.db.writemeca(self.ID, self.sim.now(), p, v, a)
if 'txt' in self.save:
pyu.writemeca(self.ID, self.sim.now(), p, v, a)
# new target when arrived in poi
if self.arrived and\
(self.L.pt2ro(self.position) ==\
self.L.Gw.node[self.rooms[1]]['room']):
self.arrived = False
if self.endpoint:
self.endpoint = False
self.roomId = self.nextroomId
# remove the remaining waypoint which correspond
# to current room position
del self.waypoints[0]
del self.rooms[0]
# del self.dlist[0]
#
# If door lets continue
#
#
# ig destination --> next room
#
#adjroom = self.L.Gr.neighbors(self.roomId)
#Nadjroom = len(adjroom)
if self.cdest == 'random':
# self.nextroomId = int(np.floor(random.uniform(0,self.L.Gr.size())))
self.nextroomId = random.sample(
self.L.Gr.nodes(), 1)[0]
# test 1 ) next != actualroom
# 2 ) nextroom != fordiden room
# 3 ) room not share without another agent
while self.nextroomId == self.roomId or (
self.nextroomId in self.forbidroomId
): # or (self.nextroomId in self.sim.roomlist):
# self.nextroomId = int(np.floor(random.uniform(0,self.L.Gr.size())))
self.nextroomId = random.sample(
self.L.Gr.nodes(), 1)[0]
elif self.cdest == 'file':
self.room_counter = self.room_counter + 1
if self.room_counter >= self.nb_room:
self.room_counter = 0
self.nextroomId = self.room_seq[self.room_counter]
self.wait = self.room_wait[self.room_counter]
#self.sim.roomlist.append(self.nextroomId) # list of all destiantion of all nodes in object sim
self.rooms, wp = self.L.waypointGw(
self.roomId, self.nextroomId)
# self.dlist = [i in self.L.Gw.ldo for i in self.rooms]
for tup in wp[1:]:
self.waypoints.append(vec3(tup))
#nextroom = adjroom[k]
# print "room : ",self.roomId
# print "nextroom : ",self.nextroomId
#p_nextroom = self.L.Gr.pos[self.nextroomId]
#setdoors1 = self.L.Gr.node[self.roomId]['doors']
#setdoors2 = self.L.Gr.node[nextroom]['doors']
#doorId = np.intersect1d(setdoors1,setdoors2)[0]
#
# coord door
#
#unode = self.L.Gs.neighbors(doorId)
#p1 = self.L.Gs.pos[unode[0]]
#p2 = self.L.Gs.pos[unode[1]]
#print p1
#print p2
#pdoor = (np.array(p1)+np.array(p2))/2
self.destination = self.waypoints[0]
if self.sim.verbose:
print 'meca: ag ' + self.ID + ' wait ' + str(
self.wait) #*self.interval)
yield hold, self, self.wait
else:
del self.waypoints[0]
del self.rooms[0]
# del self.dlist[0]
#print "wp : ", self.waypoints
if len(self.waypoints) == 1:
self.endpoint = True
self.destination = self.waypoints[0]
#print "dest : ", self.destination
else:
yield hold, self, self.interval
else:
# self.update()
self.world.update_boid(self)
self.net.update_pos(self.ID, conv_vecarr(self.position),
self.sim.now())
yield hold, self, self.interval
def kohonen(data,
labels,
filter_size=6,
neighbourhood_width=3.0,
display_label=False,
n_epochs=100,
lr=0.001):
"""Example for using create_data, plot_data and som_step.
"""
plb.close('all')
dim = 28 * 28
data_range = 255.0
dy, dx = data.shape
#set the size of the Kohonen map. In this case it will be 6 X 6
size_k = filter_size
#set the width of the neighborhood via the width of the gaussian that
#describes it
sigma = neighbourhood_width
#initialise the centers randomly
centers = np.random.rand(size_k**2, dim) * data_range
#build a neighborhood matrix
neighbor = np.arange(size_k**2).reshape((size_k, size_k))
#set the learning rate
eta = lr # HERE YOU HAVE TO SET YOUR OWN LEARNING RATE
num_epochs = n_epochs
#set the maximal iteration count
quantization_errors = np.zeros(num_epochs)
for epoch in range(num_epochs):
#set the random order in which the datapoints should be presented
i_random = np.arange(np.shape(data)[0])
np.random.shuffle(i_random)
mean_error = 0
for t, i in enumerate(i_random):
mean_error += som_step(centers, data[i, :], neighbor, eta, sigma)
mean_error /= np.shape(data)[0]
print(mean_error)
quantization_errors[epoch] = mean_error
# for visualization, you can use this:
# for i in range(1, size_k**2+1):
# ax = plb.subplot(size_k,size_k,i)
# plb.imshow(np.reshape(centers[i-1,:], [28, 28]),interpolation='bilinear')
# if display_label:
# ax.set_title('label ' + str(assign_label(centers[i-1,:],data,labels)))
# plb.axis('off')
#
# if display_label:
# plb.subplots_adjust(hspace=1.0)
# plb.savefig('labeled_proto_sigma_'+str(int(sigma))+'_k_'+str(size_k)+'.png')
# else:
# plb.savefig('proto_sigma_'+str(int(sigma))+'_k_'+str(size_k)+'.png')
# plb.figure()
# plb.plot(range(1,num_epochs+1),quantization_errors)
# plb.xlabel('Number of epochs')
# plb.ylabel('Mean weighted quantization error')
# plb.savefig('weights_sigma_'+str(int(sigma))+'_k_'+str(size_k)+'.png')
# leave the window open at the end of the loop
plb.show()
plb.draw()
return centers, quantization_errors
def test():
import matplotlib.pylab as pyb
G=nx.path_graph(10,create_using=nx.DiGraph())
draw(G)
pyb.draw()
pyb.show()
def gen_outlier_stat_features(self,doplot=False,sig_features=[30,20,15,8,5],\
min_freq=10.0,dosave=True,max_pulse_period=400.0):
"""here we generate outlier features and refine the initial pulsational period
by downweighting those outliers.
"""
res2 = self._get_pulsational_period(doplot=doplot,min_freq=min_freq)
## now sigclip
offs = (self.y - res2['model'])/self.dy0
moffs = np.median(offs)
offs -= moffs
## do some feature creation ... find the statistics of major outliers
for i,s in enumerate(sig_features):
rr = (np.inf,s) if i == 0 else (sig_features[i-1],s)
tmp = (offs < rr[0]) & (offs > rr[1])
nlow = float(tmp.sum())/self.nepochs
tmp = (offs > -1*rr[0]) & (offs < -1*rr[1])
nhigh = float(tmp.sum())/self.nepochs
if self.verbose:
print "%i: low = %f high = %f feature-%i-ratio-diff = %f" % (s,nlow,nhigh,s,nhigh - nlow)
self.features.update({"feature-%i-ratio-diff" % s: (nhigh - nlow)*100.0})
tmp = np.where(abs(offs) > 4)
self.dy_orig = copy.copy(self.merr)
dy = copy.copy(self.merr)
dy[tmp] = np.sqrt(dy[tmp]**2 + res2['model_error'][tmp]**2 + (8.0*(1 - np.exp(-1.0*abs(offs[tmp])/4)))**2)
dy0 = np.sqrt(dy**2+self.sys_err**2)
#Xmax = self.x0.max()
#f0 = 1.0/max_pulse_period; df = 0.1/Xmax; fe = min_freq
#numf = int((fe-f0)/df)
#refine around original period
## Josh's original calcs, which fail for sources like: 221205
##df = 0.1/self.x0.max()
##f0 = res2['freq']*0.95
##fe = res2['freq']*1.05
##numf = int((fe-f0)/df)
df = 0.1/self.x0.max()
f0 = res2['freq']*0.95
fe = res2['freq']*1.05
numf = int((fe-f0)/df)
if numf == 0:
## Josh's original calcs, which fail for sources like: 221205
numf = 100 # kludge / fudge / magic number
df = (fe-f0) / float(numf)
psdr,res = lombr(self.x0,self.y,dy0,f0,df,numf,detrend_order=1)
period=1./res['freq']
self.features.update({"p_pulse": period})
if self.allow_plotting and doplot:
try:
tt=(self.x0*res2['freq']) % 1.; s=tt.argsort()
plt.errorbar (tt[tmp],self.y[tmp],self.dy_orig[tmp],fmt='o',c="r")
tt=(self.x0*res['freq']) % 1.; s=tt.argsort()
plt.plot(tt[s],res['model'][s],c="r")
if dosave:
plt.savefig("pulse-%s-p=%f.png" % (os.path.basename(self.name),period))
if self.verbose:
print "saved...", "pulse-%s-p=%f.png" % (os.path.basename(self.name),period)
plt.draw()
except:
pass
return offs, res2
def updateplot(struct, x_change):
"""
Update the plot after the drag event of a stationary point (struct),
move all related objects by x_change in the x direction,
and regenerate the corresponding lines
"""
global xlow, xhigh, xmargin, ylow, yhigh, ymargin
# set the new sizes of the figure
get_sizes()
plt.gca().set_xlim([xlow - xmargin, xhigh + xmargin])
plt.gca().set_ylim([ylow - ymargin, yhigh + ymargin])
# generate new coordinates for the images
if struct in imgsd:
old_extent = imgsd[struct].get_extent()
extent_change = (x_change, x_change, 0, 0)
extent = [old_extent[i] + extent_change[i] for i in range(0, 4)]
imgsd[struct].set_extent(extent=extent)
# end if
# generate new coordinates for the text
if struct in textd:
old_pos = textd[struct].get_position()
new_pos = (old_pos[0] + x_change, old_pos[1])
textd[struct].set_position(new_pos)
# generate new coordinates for the lines
for t in tss:
if (struct == t or struct == t.reactant or struct == t.product):
t.lines[0] = line(t.x,
t.y,
t.reactant.x,
t.reactant.y, [t, t.reactant],
col=t.color)
t.lines[1] = line(t.x,
t.y,
t.product.x,
t.product.y, [t, t.product],
col=t.color)
for i in range(0, 2):
li = t.lines[i]
if li.straight_line:
print('straight line')
else:
xlist = np.arange(li.xmin, li.xmax,
(li.xmax - li.xmin) / 1000)
a = li.coeffs
y = a[0] * xlist**3 + a[1] * xlist**2 + a[2] * xlist + a[3]
linesd[t][i][0].set_xdata(xlist)
linesd[t][i][0].set_ydata(y)
# end if
# end for
# end if
# end for
for b in barrierlesss:
if (struct == b.reactant or struct == b.product):
b.line = line(b.reactant.x,
b.reactant.y,
b.product.x,
b.product.y, [b.reactant, b.product],
col=b.color)
li = b.line
if li.straight_line:
print('straight line')
else:
xlist = np.arange(li.xmin, li.xmax, (li.xmax - li.xmin) / 1000)
a = li.coeffs
y = a[0] * xlist**3 + a[1] * xlist**2 + a[2] * xlist + a[3]
linesd[b][0][0].set_xdata(xlist)
linesd[b][0][0].set_ydata(y)
# end if
# end if
# end for
plt.draw()
def plotCurrentW(ww, currentData, delay):
# Plot the randomly generated data
fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")
ax.scatter(currentData["x1"],
currentData["x2"],
currentData["yp"],
marker=".")
ax.set_xlim(-1, 1)
ax.set_ylim(-1, 1)
ax.set_zlim(-1, 1)
ax.set_xlabel("x1")
ax.set_ylabel("x2")
ax.set_zlabel("yp")
# Create data points corresponding the the weights so that they can be plotted on the graph
wDict = {'x1': [], 'x2': [], 'yp': []}
# y = yIntercept + gradient*x
# = w[0] + w[1]*x
# To plot a straight line, we need the x and y at the far left (x=-1) and far right (x=+1)
#0 = w[0] + w[1]*x1 + w[2]*x2
#-w[0] = w[1]*x1 + w[2]*x2
#x2 = (-w[0] -w[1]*x1)/w[2]
#draw a plane
#X1,X2 = numpy.meshgrid(x1, x2)
#h = ww[0] + ww[1]*x1 + ww[2]*x2
##possibility??
#mn = np.min(currentData, axis=0)
#mx = np.max(currentData, axis=0)
#X,Y = np.meshgrid(np.linspace(mn[0], mx[0], 20), np.linspace(mn[1], mx[1],20))
leftleftY = ww[0] + ww[1] * (-1.0) + ww[2] * (-1.0)
leftrightY = ww[0] + ww[1] * (-1.0) + ww[2] * (1.0)
rightleftY = ww[0] + ww[1] * (1.0) + ww[2] * (-1.0)
rightrightY = ww[0] + ww[1] * (1.0) + ww[2] * (1.0)
wDict["x1"].append(-1.0)
wDict["x2"].append(-1.0)
wDict["yp"].append(leftleftY)
wDict["x1"].append(-1.0)
wDict["x2"].append(1.0)
wDict["yp"].append(leftrightY)
wDict["x1"].append(1.0)
wDict["x2"].append(-1.0)
wDict["yp"].append(rightleftY)
wDict["x1"].append(1.0)
wDict["x2"].append(1.0)
wDict["yp"].append(rightrightY)
# Convert to a dataframe so it can be plotted like the other data
resultW = pd.DataFrame(wDict)
xx1, xx2 = numpy.meshgrid(resultW.x1, resultW.x2)
z = ww[0] + ww[1] * xx1 + ww[2] * xx2
# Plot the corresponding classification separating line
#plt.plot(resultW.x, resultW.yp)
ax.plot_surface(xx1, xx2, z, alpha=0.2)
plt.draw()
plt.pause(delay)
plt.clf()
def kohonen():
"""Example for using create_data, plot_data and som_step.
"""
plb.close('all')
dynamicEta = True
dynamicSigma = True
Equilibrate = False
dim = 28 * 28
data_range = 255.0
# load in data and labels
data = np.array(np.loadtxt('data.txt'))
labels = np.loadtxt('labels.txt')
# select 4 digits
name = 'Lorkowski' # REPLACE BY YOUR OWN NAME
targetdigits = name2digits(
name) # assign the four digits that should be used
print targetdigits # output the digits that were selected
# this selects all data vectors that corresponds to one of the four digits
data = data[np.logical_or.reduce([labels == x for x in targetdigits]), :]
# filter the label
labels = labels[np.logical_or.reduce([labels == x for x in targetdigits])]
dy, dx = data.shape
#set the size of the Kohonen map. In this case it will be 6 X 6
size_k = 8
#set the width of the neighborhood via the width of the gaussian that
#describes it
#initial_sigma = 5
initial_sigma = float(size_k / 2)
sigma = [initial_sigma]
#initialise the centers randomly
centers = np.random.rand(size_k**2, dim) * data_range
#build a neighborhood matrix
neighbor = np.arange(size_k**2).reshape((size_k, size_k))
#set the learning rate
eta = [0.1] # HERE YOU HAVE TO SET YOUR OWN LEARNING RATE
#set the maximal iteration count
tmax = (
500 * size_k * size_k
) + 1000 # this might or might not work; use your own convergence criterion
#tmax = 20000
#set the random order in which the datapoints should be presented
i_random = np.arange(tmax) % dy
np.random.shuffle(i_random)
#convergence criteria
tol = 0.1
previousCenters = np.copy(centers)
errors = []
mErrors = []
logError = []
finalErrors = []
tailErrors = [0.0]
# 500 last errors
if ((dynamicEta == True) & (dynamicSigma == True)):
filename = 'k' + str(size_k) + 'dynamicEta' + str(
eta[0]) + 'dynamicSigma' + str(sigma[0]) + '_tmax' + str(tmax)
print filename
elif ((dynamicEta == True) & (dynamicSigma == False)):
filename = 'k' + str(size_k) + 'dynamicEta' + str(
eta[0]) + 'sigma' + str(sigma[0]) + '_tmax' + str(tmax)
print filename
elif ((dynamicEta == False) & (dynamicSigma == True)):
filename = 'k' + str(size_k) + 'eta' + str(
eta[0]) + 'dynamicSigma' + str(sigma[0]) + '_tmax' + str(tmax)
print filename
else:
filename = 'k' + str(size_k) + 'eta' + str(eta[0]) + 'sigma' + str(
sigma[0]) + '_tmax' + str(tmax)
print filename
#convergedList=[]
#numConverged=0
#holdConvergedLabelsCount=0
#t=-1
for t, i in enumerate(i_random):
'''
if ( labels[i] in convergedList ):
holdConvergedLabelsCount += 1
if (holdConvergedLabelsCount >= len(targetdigits)):
del convergedList[:]
holdConvergedLabelsCount = 0
numConverged = 0
print "releasing labels"
continue
t+=1 # If you use this with t in the iterator to tn
'''
if dynamicEta == True:
new_eta = eta[0] * exp(-float(t) / float(tmax))
'''
C = tmax/100
new_eta = C * eta[0] / (C+t)
'''
eta.append(new_eta)
if dynamicSigma == True:
if sigma[0] == 1:
new_sigma = sigma[0]
else:
mlambda = tmax / log(sigma[0])
new_sigma = sigma[0] * exp(-float(t / mlambda))
sigma.append(new_sigma)
# Change to sigma[0] for static and sigma[t] for dynamic neighborhood function
if ((dynamicEta == True) & (dynamicSigma == True)):
som_step(centers, data[i, :], neighbor, eta[t], sigma[t])
elif ((dynamicEta == False) & (dynamicSigma == True)):
som_step(centers, data[i, :], neighbor, eta[0], sigma[t])
elif ((dynamicEta == True) & (dynamicSigma == False)):
som_step(centers, data[i, :], neighbor, eta[t], sigma[0])
else:
som_step(centers, data[i, :], neighbor, eta[0], sigma[0])
# convergence check
e = sum(sum((centers - previousCenters)**2))
tailErrors.append(e)
# Since this is an online method, the centers will most likely change in
# the future even though the current iteration has a residual of 0.
# Basing the convergence on the residual of the mean of the last 500 errors
# may be a better convergence criterion.
if (t > 500):
if (len(tailErrors) >= 500):
tailErrors.pop(0)
tailErrors.append(e)
# Update the mean error term
tmpError = sum(tailErrors) / len(tailErrors)
mErrors.append(tmpError)
if t > (500 * size_k * size_k):
tolerance_check = np.abs(mErrors[-1] - mErrors[-501])
logError.append(tolerance_check)
data_print_static("Tol Error Minimum Is: {0}, "
"Iteration: {1}, Current Error: {2}".format(
np.min(logError), t, tolerance_check))
if logError[-1] < tol:
print ""
print "Converage after ", t, " iterations"
break
"""
future_tolerance_check = np.sum(mErrors[-500])/500
past_tolerance_check = np.sum(mErrors[-1000:-500])/500
tolerance_check = np.abs((future_tolerance_check -
past_tolerance_check))
logError.append(tolerance_check)
if np.size(logError) > 2:
log_d_v = ((np.sqrt((logError[-2] - logError[-1])**2)))
# plb.scatter(t, log_d_v, color='red')
# plb.pause(0.1)
finalErrors.append(log_d_v)
data_print_static("Tol Error Minimum Is: {0}, "
"Iteration: {1}, Current Error: {2}".
format(np.min(finalErrors), t,
log_d_v))
if log_d_v < tol:
print ""
print "Converage after ", t, " iterations"
break
"""
"""
if (len(mErrors) >= 2):
tolerance_check = np.abs(mErrors[-1] - mErrors[-2])
finalErrors.append(tolerance_check)
data_print_static("Tol Error Minimum Is: {0}, "
"Iteration: {1}, Current Error: {2}".
format(np.min(finalErrors), t, tolerance_check))
if ((tolerance_check < tol) & (t >= 500*size_k*size_k)):
'''
numConverged +=1
convergedList.append(labels[i])
print "Holding "+str(labels[i])
if (numConverged == 4):
print ""
print "Converage after ", t, " iterations"
break
'''
print ""
print "Converage after ", t, " iterations"
break
"""
errors.append(e)
previousCenters = np.copy(centers)
if Equilibrate == True:
old_eta = eta[-1]
new_tmax = 0.10 * tmax
i_random = np.arange(new_tmax) % dy
np.random.shuffle(i_random)
for t, i in enumerate(i_random):
new_eta = old_eta
eta.append(new_eta)
new_sigma = 1.0
sigma.append(new_sigma)
# Change to sigma[0] for static and sigma[t] for dynamic neighborhood function
if ((dynamicEta == True) & (dynamicSigma == True)):
som_step(centers, data[i, :], neighbor, eta[-1], sigma[-1])
elif ((dynamicEta == False) & (dynamicSigma == True)):
som_step(centers, data[i, :], neighbor, eta[0], sigma[-1])
elif ((dynamicEta == True) & (dynamicSigma == False)):
som_step(centers, data[i, :], neighbor, eta[-1], sigma[0])
else:
som_step(centers, data[i, :], neighbor, eta[0], sigma[0])
# convergence check
e = sum(sum((centers - previousCenters)**2))
tailErrors.append(e)
# Since this is an online method, the centers will most likely change in
# the future even though the current iteration has a residual of 0.
# Basing the convergence on the residual of the mean of the last 500 errors
# may be a better convergence criterion.
if (len(tailErrors) >= 500):
tailErrors.pop(0)
tailErrors.append(e)
# Update the mean error term
tmpError = sum(tailErrors) / len(tailErrors)
mErrors.append(tmpError)
if (len(mErrors) >= 2):
tolerance_check = np.abs(mErrors[-1] - mErrors[-501])
logError.append(tolerance_check)
data_print_static("Tol Error Minimum Is: {0}, "
"Iteration: {1}, Current Error: {2}".format(
np.min(logError), t, tolerance_check))
if logError[-1] < tol:
print ""
print "Converage after ", t, " iterations"
break
errors.append(e)
previousCenters = np.copy(centers)
# Find the digit assigned to each center
index = 0
digits = []
for i in range(0, size_k**2):
index = np.argmin(np.sum((data[:] - centers[i, :])**2, 1))
digits.append(labels[index])
print "Digit assignement to the clusters: \n"
print np.resize(digits, (size_k, size_k))
np.savetxt('data/' + filename + '_cluster.txt',
np.resize(digits, (size_k, size_k)),
fmt='%i')
# for visualization, you can use this:
for i in range(size_k**2):
plb.subplot(size_k, size_k, i + 1)
plb.imshow(np.reshape(centers[i, :], [28, 28]),
interpolation='bilinear')
plb.axis('off')
plb.draw()
# leave the window open at the end of the loop
plb.savefig('data/' + filename + '_kohonen.pdf')
plb.show()
# plb.draw()
import seaborn as sb
if dynamicSigma == True:
plb.plot(sigma)
plb.ylabel('Sigma values')
plb.xlabel('Iterations')
plb.savefig('data/sigma.pdf')
plb.show()
if dynamicEta == True:
plb.plot(eta)
plb.ylabel('Learning Rate')
plb.xlabel('Iterations')
plb.savefig('data/eta.pdf')
plb.show()
plb.plot(errors)
plb.ylabel('Sum of the Squared Errors')
plb.xlabel('Iterations')
plb.savefig('data/' + filename + '_sqerrors.pdf')
plb.show()
plb.plot(mErrors)
plb.ylabel('Mean of last 500 errors')
plb.xlabel('Iterations')
plb.savefig('data/' + filename + '_mean500.pdf')
plb.show()
plb.plot(logError)
plb.ylabel('Convergence Criteria')
plb.xlabel('Iterations')
plb.savefig('data/' + filename + '_convergence.pdf')
plb.show()
def montage(nifti,
anat,
roi_dict,
thr=2,
fig=None,
out_file=None,
feature_dict=None,
target_stat=None,
target_value=None):
if isinstance(anat, str):
anat = load_image(anat)
assert nifti is not None
assert anat is not None
assert roi_dict is not None
texcol = 1
bgcol = 0
iscale = 2
weights = nifti.get_data()
#weights = weights / weights.std(axis=3)
features = weights.shape[-1]
indices = [0]
y = 8
x = int(ceil(1.0 * features / y))
font = {"size": 8}
rc("font", **font)
if fig is None:
fig = plt.figure(figsize=[iscale * y, iscale * x / 2.5])
plt.subplots_adjust(left=0.01,
right=0.99,
bottom=0.01,
top=0.99,
wspace=0.1,
hspace=0)
for f in xrange(features):
roi = roi_dict.get(f, None)
if roi is None:
continue
coords = roi["top_clust"]["coords"]
assert coords is not None
feat = weights[:, :, :, f]
feat = feat / feat.std()
imax = np.max(np.absolute(feat))
imin = -imax
imshow_args = {"vmax": imax, "vmin": imin}
coords = ([-coords[0], -coords[1], coords[2]])
ax = fig.add_subplot(x, y, f + 1)
plt.axis("off")
try:
plot_map(feat,
xyz_affine(nifti),
anat=anat.get_data(),
anat_affine=xyz_affine(anat),
threshold=thr,
figure=fig,
axes=ax,
cut_coords=coords,
annotate=False,
cmap=cmap,
draw_cross=False,
**imshow_args)
except Exception as e:
logger.exception(e)
plt.text(0.05,
0.8,
str(f),
transform=ax.transAxes,
horizontalalignment="center",
color=(texcol, texcol, texcol))
pos = [(0.05, 0.05), (0.4, 0.05), (0.8, 0.05)]
colors = ["purple", "yellow", "green"]
if feature_dict is not None and feature_dict.get(f, None) is not None:
d = feature_dict[f]
for i, key in enumerate([k for k in d if k != "real_id"]):
plt.text(pos[i][0],
pos[i][1],
"%s=%.2f" % (key, d[key]),
transform=ax.transAxes,
horizontalalignment="left",
color=colors[i])
if key == target_stat:
assert target_value is not None
if d[key] >= target_value:
p_fancy = FancyBboxPatch((0.1, 0.1),
2.5 - .1,
1 - .1,
boxstyle="round,pad=0.1",
ec=(1., 0.5, 1.),
fc="none")
ax.add_patch(p_fancy)
elif d[key] <= -target_value:
p_fancy = FancyBboxPatch((0.1, 0.1),
iscale * 2.5 - .1,
iscale - .1,
boxstyle="round,pad=0.1",
ec=(0., 0.5, 0.),
fc="none")
ax.add_patch(p_fancy)
# stdout.write("\rSaving montage: DONE\n")
if out_file is not None:
plt.savefig(out_file,
transparent=True,
facecolor=(bgcol, bgcol, bgcol))
else:
plt.draw()
def click(event):
if not event.inaxes: return
global whatx, fit_1, fit_3, startpos, endpos, fits_1, xs_1, x1, y1, x3, y3
startpos = event.xdata, event.ydata
# find closest existing pt
if fit_1 == None:
# print "no fit_1?!"
x1 = []
y1 = []
d1 = pl.array([1e10])
else:
x1, y1 = fit_1.get_data()
d1 = abs(event.xdata - x1)
if fit_3 == None:
# print "no fit_3?!"
x3 = []
y3 = []
d3 = pl.array([1e10])
else:
x3, y3 = fit_3.get_data()
d3 = abs(event.xdata - x3)
# todo: for deletions, make sure we have all avail wavelength pts
# i suppose that the flux combination step that creates fit_wave
# will do that...
# print "x1=",x1
# print "x3=",x3
if len(d1) <= 0:
d1 = pl.array([1e10])
if len(d3) <= 0:
d3 = pl.array([1e10])
if d1.min() <= d3.min():
whatpoint = pl.where(d1 == d1.min())[0][0]
whatx = x1[whatpoint]
print "deleting detection %d @ " % whatpoint, whatx
fit_1.set_data(pl.delete(x1, whatpoint),
pl.delete(y1, whatpoint))
# delete the uncert error line too
# ds_1=abs(event.xdata-xs_1)
# k=pl.where(ds_1==ds_1.min())[0][0]
k = whatpoint
fits_1[k].remove()
fits_1 = pl.delete(fits_1, k)
xs_1 = pl.delete(xs_1, k)
else:
whatpoint = pl.where(d3 == d3.min())[0][0]
whatx = x3[whatpoint]
print "deleting UL %d @ " % whatpoint, whatx
x3 = pl.delete(x3, whatpoint)
y3 = pl.delete(y3, whatpoint)
fit_3.set_data(x3, y3)
if event.button == 3: #R-click
x3 = pl.append(x3, whatx)
y3 = pl.append(y3, startpos[1])
if fit_3 == None:
fit_3 = pl.plot(x3, y3, 'rv')[0]
else:
fit_3.set_data(x3, y3)
pl.draw()
