def view_simple( self, stats, thetas ): # plotting params nbins = 20 alpha = 0.5 label_size = 8 linewidth = 3 linecolor = "r" # extract from states #thetas = states_object.get_thetas()[burnin:,:] #stats = states_object.get_statistics()[burnin:,:] #nsims = states_object.get_sim_calls()[burnin:] # plot sample distribution of thetas, add vertical line for true theta, theta_star f = pp.figure() sp = f.add_subplot(111) pp.plot( self.fine_theta_range, self.posterior, linecolor+"-", lw = 1) ax = pp.axis() pp.hist( thetas, self.nbins_coarse, range=self.range,normed = True, alpha = alpha ) pp.fill_between( self.fine_theta_range, self.posterior, color="m", alpha=0.5) pp.plot( self.posterior_bars_range, self.posterior_bars, 'ro') pp.vlines( thetas.mean(), ax[2], ax[3], color="b", linewidths=linewidth) #pp.vlines( self.theta_star, ax[2], ax[3], color=linecolor, linewidths=linewidth ) pp.vlines( self.posterior_mode, ax[2], ax[3], color=linecolor, linewidths=linewidth ) pp.xlabel( "theta" ) pp.ylabel( "P(theta)" ) pp.axis([self.range[0],self.range[1],ax[2],ax[3]]) set_label_fonsize( sp, label_size ) pp.show()
def shade_bands(edges, y_range=[-1e5,1e5],cmap='prism', **kwargs):
'''
Shades frequency bands.
when plotting data over a set of frequency bands it is nice to
have each band visually seperated from the other. The kwarg `alpha`
is useful.
Parameters
--------------
edges : array-like
x-values seperating regions of a given shade
y_range : tuple
y-values to shade in
cmap : str
see matplotlib.cm or matplotlib.colormaps for acceptable values
\*\* : key word arguments
passed to `matplotlib.fill_between`
Examples
-----------
>>> rf.shade_bands([325,500,750,1100], alpha=.2)
'''
cmap = plb.cm.get_cmap(cmap)
for k in range(len(edges)-1):
plb.fill_between(
[edges[k],edges[k+1]],
y_range[0], y_range[1],
color = cmap(1.0*k/len(edges)),
**kwargs)
def plot(self):
if not self.plot_state: return
pop,best = self.plot_state
with self.pylab_interface:
import pylab
pylab.clf()
n,p = pop.shape
iternum = numpy.arange(1,n+1)
tail = int(0.25*n)
pylab.hold(True)
c = coordinated_colors(base=(0.4,0.8,0.2))
if p==5:
pylab.fill_between(iternum[tail:], pop[tail:,1], pop[tail:,3],
color=c['light'], label='_nolegend_')
pylab.plot(iternum[tail:],pop[tail:,2],
label="80% range", color=c['base'])
pylab.plot(iternum[tail:],pop[tail:,0],
label="_nolegend_", color=c['base'])
else:
pylab.plot(iternum,pop, label="population",
color=c['base'])
pylab.plot(iternum[tail:], best[tail:], label="best",
color=c['dark'])
pylab.xlabel('iteration number')
pylab.ylabel('chisq')
pylab.legend()
#pylab.gca().set_yscale('log')
pylab.hold(False)
pylab.draw()
def plotWithVariance(x, y, variance, *args, **kwargs):
"""
Plot data with variance indicated by shading within one sigma.
"""
line = pylab.plot(x, y.flatten(), *args, **kwargs)[0]
sigma = np.sqrt(variance)
pylab.fill_between(x, y - sigma, y + sigma, color=line.get_color(), alpha=0.5)
def demoplot(theta,args):
colour=np.array([0,0,1.0])
faded = 1-(1-colour)/2.0
(X,y) = args
(n, D) = np.shape(X)
xrange = X.max() - X.min()
Xtest = np.arange(X.min()-xrange/2,X.max()+xrange/2,(X.max()-X.min())/100)
Xtest.shape = (len(Xtest),1)
k = kernel2(X,X,theta,wantderiv=False)
kstar = [kernel2(X,xs*np.ones((1,1)),theta,wantderiv=False,measnoise=False) for xs in Xtest]
kstar = np.squeeze(kstar)
kstarstar = [kernel2(xs*np.ones((1,1)),xs*np.ones((1,1)),theta,wantderiv=False,measnoise=False) for xs in Xtest]
kstarstar = np.squeeze(kstarstar)
L = np.linalg.cholesky(k)
invk = np.linalg.solve(L.transpose(),np.linalg.solve(L,np.eye(np.shape(X)[0])))
mean = np.dot(kstar,np.dot(invk,y))
var = kstarstar - np.diag(np.dot(kstar,np.dot(invk,kstar.T)))
#var = np.reshape(var,(100,1))
pl.ion()
fig = pl.figure()
#ax1 = fig.add_subplot(211)
#ax2 = fig.add_subplot(212,sharex=ax1,sharey=ax1)
pl.plot(Xtest,mean,'-k')
#pl.plot(xstar,mean+2*np.sqrt(var),'x-')
#pl.plot(xstar,mean-2*np.sqrt(var),'x-')
#print np.shape(xstar), np.shape(mean), np.shape(var)
pl.fill_between(np.squeeze(Xtest),np.squeeze(mean-2*np.sqrt(var)),np.squeeze(mean+2*np.sqrt(var)),color='0.75')
pl.plot(X,y,'ko')
def plotuttdpc():
pylab.figure(1)
auttdpc = uttdperc(always)
modauttdpc = uttdperc(modalways)
for name,pf,c in variables:
ivals = map(lambda x : uttdperc(x),pf)
imean,istd,imeanpstd,imeanmstd = multimeanstd(ivals)
pylab.fill_between(pallthedays, imeanpstd, imeanmstd, facecolor=c, alpha=0.3)
for name,pf,c in variables:
ivals = map(lambda x : uttdperc(x),pf)
imean,istd,imeanpstd,imeanmstd = multimeanstd(ivals)
mdiff = numpy.mean(imean)
pylab.plot(pallthedays,imean,color=c,label=("Mean (+-1std) UTTDpC of 30 \"%s\" users" % name))
pylab.plot(pallthedays,auttdpc,color='black',label="UTTDpC of \"Always Upgrade\" user")
pylab.plot(pallthedays,modauttdpc,color='red',label="UTTDpC of \"Progressive Always Upgrade\" user")
print "Last uttd always",auttdpc[-1]
print "Last uttd mod always",modauttdpc[-1]
pylab.legend(loc="upper left")
pylab.xlabel("Date")
pylab.ylabel("Uptodate Distance per Component")
pylab.title("Uptodate Distance per Component of Users")
pylab.ylim([0,1])
saveFigure("q4auttdperc")
def plotchange():
fig = pylab.figure(10)
chtalw = chtt(always)
chtmodalw= chtt(modalways)
for name,pf,c in variables:
ivals = map(lambda x : chtt(x),pf)
imean,istd,imeanpstd,imeanmstd = multimeanstd(ivals)
pylab.fill_between(pallthedays, imeanpstd, imeanmstd, facecolor=c, alpha=0.3)
for name,pf,c in variables:
ivals = map(lambda x : chtt(x),pf)
imean,istd,imeanpstd,imeanmstd = multimeanstd(ivals)
mdiff = numpy.mean(imean)
pylab.plot(pallthedays,imean,color=c,label=("Mean (+-1std) Total Change of 30 \"%s\" users" % name))
pylab.plot(pallthedays,chtalw, color="black", label="Total Change of \"Always Upgrade\" user")
pylab.plot(pallthedays,chtmodalw, color="red", label="Total Change of \"Progressive Always Upgrade\" user")
print "Last change always",chtalw[-1]
print "Last change mod always",chtmodalw[-1]
pylab.legend(loc="upper left")
pylab.xlabel("Date")
pylab.ylabel("Total Change")
pylab.title("Total Change of Users")
saveFigure("q4achange")
def plotnew():
fig = pylab.figure(20)
for name,pf,c in variables:
ivals = map(lambda x : nntt(x),pf)
imean,istd,imeanpstd,imeanmstd = multimeanstd(ivals)
pylab.fill_between(pallthedays, imeanpstd, imeanmstd, facecolor=c, alpha=0.3)
for name,pf,c in variables:
ivals = map(lambda x : nntt(x),pf)
imean,istd,imeanpstd,imeanmstd = multimeanstd(ivals)
mdiff = numpy.mean(imean)
pylab.plot(pallthedays,imean,color=c,label=(name +"+-1std "))
nntal = nntt(always)
nntmod =nntt(modalways)
pylab.plot(pallthedays,nntal, color="black", label="Always Upgrade Mean change")
pylab.plot(pallthedays,nntmod, color="red", label="Always Upgrade Mean change")
print "Last new always",nntal[-1]
print "Last new mod always",nntmod[-1]
pylab.legend(loc="upper left")
saveFigure("q4anew")
def plot_trend(date_map, filename):
x_data = []
y_data = []
for date in sorted(date_map):
data = date_map[date]
x_data.append(date)
y_data.append(data['total'])
fig = pylab.figure(figsize=(9, 6), dpi=80, facecolor='#ffffff', edgecolor='#333333')
fig.autofmt_xdate()
pylab.grid(True)
x_vals = pylab.np.array(list(range(len(x_data))))
y_vals = pylab.np.array(y_data)
pylab.plot(x_vals, y_vals, color='#555555', alpha=1.00)
pylab.fill_between(x_vals, y_vals, 0, where=y_vals>0, color='#5555ff',
alpha=.25, interpolate=True)
pylab.fill_between(x_vals, y_vals, 0, where=y_vals<0, color='#ff5555',
alpha=.25, interpolate=True)
pylab.xlim(0, len(x_data) - 1)
sep = int(len(x_vals) / 10)
ran = list(range(0, len(x_vals), sep))
pylab.xticks([x_vals[i] for i in ran], [x_data[i] for i in ran], rotation=45, ha='right')
pylab.ylim(-100, 100)
pylab.yticks(range(-100, 101, 25))
pylab.text(len(x_vals) - 2, 87, '/r/Christianity alignment', ha='right',
va='center', color="#333333", alpha=.55, fontsize=16)
pylab.savefig(filename)
def drawROC(points,zeTitle,zeFilename,visible,show_fig,save_fig=True,
special_point=None,special_value=None,special_label=None):
AUC=computeAUC(points)
import pylab
pylab.clf()
pylab.grid(color='#aaaaaa', linestyle='-', linewidth=1,alpha=0.5)
pylab.plot([x[0] for x in points], [y[1] for y in points], '-', linewidth=3,color="#000088",zorder=3)
pylab.fill_between([x[0] for x in points], [y[1] for y in points],0,color='0.9')
pylab.plot([0.0,1.0], [0.0, 1.0], '-',color="#AAAAAA")
pylab.ylim((-0.01,1.01))
pylab.xlim((-0.01,1.01))
pylab.xticks(pylab.arange(0,1.1,.1))
pylab.yticks(pylab.arange(0,1.1,.1))
pylab.grid(True)
ax=pylab.gca()
r = pylab.Rectangle((0,0), 1, 1, edgecolor='#444444', facecolor='none',zorder=1)
ax.add_patch(r)
[spine.set_visible(False) for spine in ax.spines.values()]
if len(points)<10:
for i in range(1,len(points)-1):
pylab.plot(points[i][0],points[i][1],'o',color="#000066",zorder=6)
pylab.xlabel('False positive rate')
pylab.ylabel('True positive rate')
if special_point is not None:
pylab.plot(special_point[0],special_point[1],'o',color="#DD9999",zorder=6)
if special_value is not None:
pylab.text(special_point[0]+0.01,special_point[1]-0.01, special_value,
{'color' : '#DD5555', 'fontsize' : 10},
horizontalalignment = 'left',
verticalalignment = 'top',
rotation = 0,
clip_on = False)
if special_label is not None:
if special_label!="":
labels=[special_label]
colors=['#DD9999']
circles=[pylab.Circle((0, 0), 1, fc=colors[0])]
legend_location = 'lower right'
pylab.legend(circles, labels, loc=legend_location)
pylab.text(0.5, 0.3,'AUC=%f'%AUC,
horizontalalignment='center',
verticalalignment='center',
fontsize=18)
pylab.title(zeTitle)
if save_fig:
pylab.savefig(zeFilename,dpi=300)
print("\n result in "+zeFilename)
if show_fig:
pylab.show()
def sun():
dur = 1000
cad = 29.4 / 60.0 / 24.0
X = numpy.genfromtxt('%ssun/sun_composite_tsi_20130930.txt' % root).T
date = X[0]
t = X[1]
irr = X[2]
l = (irr > 0) * (t > 5000)
t = t[l] - t[l].min()
date_ref = date[l][0]
irr = irr[l] / irr[l].max()
pylab.figure(1)
pylab.clf()
pylab.plot(t, irr, 'k-')
col = ['r','g','b','y','m']
tstart = [1000, 2100, 2600, 3800, 5000]
for i in numpy.arange(5):
time = numpy.r_[tstart[i]:tstart[i]+dur:cad]
if i == 0:
y1 = numpy.zeros(len(time)) + 1
y2 = y1 - 0.0045
g = scipy.interpolate.interp1d(t, irr, bounds_error = False)
dF = filter.boxcare(g(time), 10, fill = True)
pylab.plot(time, dF, c = col[i])
pylab.fill_between(time, y1, y2, color = col[i], alpha = 0.3)
X = numpy.zeros((2,len(time)))
X[0,:] = time - time[0]
X[1,:] = dF
numpy.savetxt('%ssun/sun_lightcurve_%02d.txt' % (root, i), X.T)
pylab.xlim(t.min(), t.max())
pylab.ylim(0.9955,1)
pylab.xlabel('Days since %d' % date_ref)
pylab.ylabel('Flux decrement')
pylab.savefig('%ssun/sun_lightcurves.png' % root)
return
def sampleplot_K(r,ylim=None,HDI_y=None):
from pylab import plot,fill_between,gca,text
x,y=histogram(r,plot=False)
plot(x,y,'-o')
fill_between(x,y,facecolor='blue', alpha=0.2)
if ylim:
gca().set_ylim(ylim)
dx=x[1]-x[0]
cs=np.cumsum(y)*dx
HDI=np.percentile(r,[2.5,50,97.5])
yl=gca().get_ylim()
dy=0.05*yl[1]
if HDI_y is None:
HDI_y=yl[1]*.1
text((HDI[0]+HDI[2])/2, HDI_y+dy,'95% HDI', ha='center', va='center',fontsize=12)
plot(HDI,[HDI_y,HDI_y,HDI_y],'k.-',linewidth=1)
for v in HDI:
text(v, HDI_y-dy,'%.3f' % v, ha='center', va='center',
fontsize=12)
xl=gca().get_xlim()
text(.05*(xl[1]-xl[0])+xl[0], 0.9*yl[1],r'$\tilde{x}=%.3f$' % np.median(r), ha='left', va='center')
def GP_plotpred(xpred, x, y, cov_par, cov_func = None, cov_typ = 'SE',
MF = None, MF_par = None, MF_args = None, MF_args_pred = None, \
WhiteNoise = False, plot_color = None):
'''
Wrapper for GP_predict that takes care of merging the
covariance and mean function parameters, and (optionally) plots
the predictive distribution (as well as returning it)
'''
if MF != None:
merged_par = scipy.append(cov_par, MF_par)
n_MF_par = len(MF_par)
else:
merged_par = cov_par[:]
n_MF_par = 0
fpred, fpred_err = GP_predict(merged_par, xpred, x, y, \
cov_func = cov_func, cov_typ = cov_typ, \
MF = MF, n_MF_par = n_MF_par, \
MF_args = MF_args, MF_args_pred = MF_args_pred, \
WhiteNoise = WhiteNoise)
xpl = scipy.array(xpred[:,0]).flatten()
if plot_color != None:
pylab.fill_between(xpl, fpred + 2 * fpred_err, fpred - 2 * fpred_err, \
color = plot_color, alpha = 0.1)
pylab.fill_between(xpl, fpred + fpred_err, fpred - fpred_err, \
color = plot_color, alpha = 0.1)
pylab.plot(xpl, fpred, '-', color = plot_color)
return fpred, fpred_err
def Draw(func1, func2):
# генирация точек графика
xlist = mlab.frange(a, b, 0.01)
ylist = [func1(x) for x in xlist]
ylist2 = [func2(x) for x in xlist]
# Генирирум ось
y0 = [0 for x in xlist]
pylab.plot(xlist, ylist)
#pylab.plot(xlist, y0, label='line1', color='blue')
pylab.plot(xlist, ylist2, label='$sin(x)/x)$', color='red')
pylab.legend()
# Включаем рисование сетки
pylab.grid(True)
pylab.fill_between(xlist, ylist, ylist2, color='green', alpha=0.25)
# если мало разбиений, то переопереляем сетку под шаг
if ((round((b - a) / h)) < 25):
pylab.xticks([a + i * h for i in range(round((b - a) / h) + 1)])
# рисуем корни, промерка того что корень не содержит ошибок
for i in range(1, len(table)):
if (table[i][4] != ':-('):
pylab.scatter(table[i][3], table[i][4])
# Рисуем фогрму с графиком
pylab.show()
def visualize(generation_list):
'''Generate pretty pictures using pylab and pygame'''
best = []
average = []
stddev = []
average_plus_stddev = []
average_minus_stddev = []
for pop in generation_list:
best += [most_fit(pop).fitness]
average += [avg_fitness(pop)]
stddev += [fitness_stddev(pop)]
average_plus_stddev += [average[-1] + stddev[-1]]
average_minus_stddev += [average[-1] - stddev[-1]]
pylab.figure(1)
pylab.fill_between(range(len(generation_list)), average_plus_stddev, average_minus_stddev, alpha=0.2, color='b', label="Standard deviation")
pylab.plot(range(len(generation_list)), best, color='r', label='Best')
pylab.plot(range(len(generation_list)), average, color='b', label='Average with std.dev.')
pylab.title("Fitness plot - Beer-cog")
pylab.xlabel("Generation")
pylab.ylabel("Fitness")
pylab.legend(loc="upper left")
pylab.savefig("mincog_fitness.png")
best_index = best.index(max(best))
best_individual = most_fit(generation_list[-1])
with open('last.txt','w') as f:
f.write(str(best_individual.gtype))
print best_individual.gtype
game = min_cog_game.Game()
game.play(best_individual.ptype, True)
def addqqplotinfo(qnull,M,xl='-log10(P) observed',yl='-log10(P) expected',xlim=None,ylim=None,alphalevel=0.05,legendlist=None,fixaxes=False):
distr='log10'
pl.plot([0,qnull.max()], [0,qnull.max()],'k')
pl.ylabel(xl)
pl.xlabel(yl)
if xlim is not None:
pl.xlim(xlim)
if ylim is not None:
pl.ylim(ylim)
if alphalevel is not None:
if distr == 'log10':
betaUp, betaDown, theoreticalPvals = _qqplot_bar(M=M,alphalevel=alphalevel,distr=distr)
lower = -sp.log10(theoreticalPvals-betaDown)
upper = -sp.log10(theoreticalPvals+betaUp)
pl.fill_between(-sp.log10(theoreticalPvals),lower,upper,color="grey",alpha=0.5)
#pl.plot(-sp.log10(theoreticalPvals),lower,'g-.')
#pl.plot(-sp.log10(theoreticalPvals),upper,'g-.')
if legendlist is not None:
leg = pl.legend(legendlist, loc=4, numpoints=1)
# set the markersize for the legend
for lo in leg.legendHandles:
lo.set_markersize(10)
if fixaxes:
fix_axes()
def show_barlines(page):
import pylab
for i, barlines in enumerate(page.barlines):
sd = page.staves.staff_dist[i]
for j, barline_range in enumerate(barlines):
barline_x = int(barline_range.mean())
staff_y = page.staves.staff_y(i, barline_x)
repeats = page.repeats[i][j]
if repeats:
# Draw thick bar
pylab.fill_between([barline_x - sd/4,
barline_x + sd/4],
staff_y - sd*2,
staff_y + sd*2,
color='g')
for letter, sign in (('L', -1), ('R', +1)):
if letter in repeats:
# Draw thin bar
bar_x = barline_x + sign * sd/2
pylab.plot([bar_x, bar_x],
[staff_y - sd*2,
staff_y + sd*2],
color='g')
for y in (-1, +1):
circ = pylab.Circle((bar_x + sign*sd/2,
staff_y + y*sd/2),
sd/4,
color='g')
pylab.gcf().gca().add_artist(circ)
else:
pylab.plot([barline_x, barline_x],
[staff_y - sd*2,
staff_y + sd*2],
color='g')
def bootstrap(self, nBoot, nbins = 20):
pops = np.zeros((nBoot, nbins))
#medianpop = [[] for i in data.cat]
pylab.figure(figsize = (20,14))
for i in xrange(3):
pylab.subplot(1,3,i+1)
#if i ==0:
#pylab.title("Bootstrap on medians", fontsize = 20.)
pop = self.angles[(self.categories == i)]# & (self.GFP > 2000)]
for index in xrange(nBoot):
newpop = np.random.choice(pop, size=len(pop), replace=True)
#medianpop[i].append(np.median(newpop))
newhist, binedges = np.histogram(newpop, bins = nbins)
pops[index,:] = newhist/1./len(pop)
#pylab.hist(medianpop[i], bins = nbins, label = "{2} median {0:.1f}, std {1:.1f}".format(np.median(medianpop[i]), np.std(medianpop[i]), data.cat[i]), color = data.colors[i], alpha =.2, normed = True)
meanpop = np.sum(pops, axis = 0)/1./nBoot
stdY = np.std(pops, axis = 0)
print "width", binedges[1] - binedges[0]
pylab.bar(binedges[:-1], meanpop, width = binedges[1] - binedges[0], label = "mean distribution", color = data.colors[i], alpha = 0.6)
pylab.fill_between((binedges[:-1]+binedges[1:])/2., meanpop-stdY, meanpop+stdY, alpha = 0.3)
pylab.legend()
pylab.title(data.cat[i])
pylab.xlabel("Angle(degree)", fontsize = 15)
pylab.ylim([-.01, 0.23])
pylab.savefig("/users/biocomp/frose/frose/Graphics/FINALRESULTS-diff-f3/distrib_nBootstrap{0}_bins{1}_GFPsup{2}_{3}.png".format(nBoot, nbins, 'all', randint(0,999)))
def plotchange():
fig = pylab.figure(10)
for name,pf,c in variables:
ivals = map(lambda x : chtt(x),pf)
imean,istd,imeanpstd,imeanmstd = multimeanstd(ivals)
pylab.fill_between(pallthedays, imeanpstd, imeanmstd, facecolor=c, alpha=0.3)
for name,pf,c in variables:
ivals = map(lambda x : chtt(x),pf)
imean,istd,imeanpstd,imeanmstd = multimeanstd(ivals)
mdiff = numpy.mean(imean)
print "Final change",name,imean[-1]
pylab.plot(pallthedays,imean,color=c,label=("Mean (+-1std) Total Change of 30 \"%s\" users" % name))
pylab.legend(loc="upper left")
pylab.xlabel("Date")
pylab.ylabel("Total Change")
pylab.title("Total Change of Users")
pylab.ylim([0,1500])
saveFigure("q1cchange")
#This graph shows the range over which change can occur whn altering the probability to install.
# given the results from this supports the idea that the probability to install corrolates to the amount of packages installed, this is straight forward.
#An effect that did not occur, which was hypothesised might, was that over time the amount of installed packages decreases as common dependended packages are installed.
#This does not occur, but may be because of the randomness that packages are selected.
#It may still occur in reaility, as say a graphics designer will likely install graphics components that will require similar functionality.
#Another interesting point from that can be seen in this graphi is the variability created by the soft failures, as discussed previously.
# it can be seen that the std increased after a soft failure in install twice a week change curve.
uivals = zip(alll,map(lambda x : rempd(x),alll))
for name,ui in uivals:
for date, remv in zip(allthedays,ui):
if remv >= 100:
print date,datetime.date.fromtimestamp(date),name
def plotBkMeasure(bk, ek, vk, figurePath):
#print bk
#print ek
#print vk
k = list(range(len(bk)))
#for i,j in enumerate(bk):
pylab.ioff()
pylab.figure()
pylab.plot(k, bk, '.', label='Bk')
pylab.plot(k, ek, label='E(Bk)')
#pylab.plot(k, ek+2*np.sqrt(vk), '-.r', label='limit range')
#pylab.plot(k, ek-2*np.sqrt(vk), '-.r')
#for i in range(len(ek)):
pylab.fill_between(k, ek+4*np.sqrt(vk), ek-4*np.sqrt(vk), facecolor='red', interpolate=True )
# figure setting
pylab.xlim(2,k[-1])
pylab.ylim(0,1.0)
pylab.legend(loc='upper right')
pylab.xlabel('Number of Clusters')
pylab.ylabel('Bk')
# pylab.title('Bk measure between two algorithm')
# show result
pylab.savefig(figurePath, format='svg')
def plot_shaded_lines(my_xticks, y1, y2, error1, error2, ylab, xlab, filename):
plt.figure(figsize=(6,6))
from matplotlib import rcParams
rcParams.update({'figure.autolayout': True})
x = range(0, len(y1))
plt.plot(x, y1, 'k-', color="blue", label='men')
plt.fill_between(x, y1-error1, y1+error1, facecolor='blue', alpha=.2)
plt.plot(x, y2, 'k-', color="red", label='women')
plt.fill_between(x, y2-error2, y2+error2, facecolor='red', alpha=.2)
#if isinstance(x, (int, long, float, complex)):
# plt.xlim(np.min(x), np.max(x))
plt.gcf().subplots_adjust(bottom=0.3)
plt.xticks(x, my_xticks)
plt.xticks(rotation=70, fontsize=14)
plt.yticks(fontsize=14)
#plt.setp(ax.get_xticklabels(), rotation='vertical', fontsize=14)
plt.ylabel(ylab, fontsize=14)
plt.xlabel(xlab, fontsize=14)
plt.legend()
plt.savefig(filename)
def plotPhaseTransitionMd(dic):
M = dic['M']
S = dic['S']
C = dic['C']
pl.close("all")
colors = ['b','g','r','c','m','y']
pl.figure(1,(11,9))
for i,m in enumerate(np.unique(M)):
print m
if m == 4:#or m == 3:
continue
index = np.argwhere(M == m)
B = binning(S[index],C[index],50,confinter=0.01)
pl.fill_between(B['bins'],B['percUp'],B['percDown'],alpha=0.05,color = colors[i])
pl.plot(B['bins'],B['mean'],'x-',label="M = %s"%m,color = colors[i],lw=1)
pl.plot(B['bins'],B['percUp'],'-.',color = colors[i])
pl.plot(B['bins'],B['percDown'],'-.',color = colors[i])
pl.plot(S[index],C[index],"o",color = colors[i])
pl.xlabel("Property violation s")
pl.ylabel("Cooperation level c")
pl.legend(loc=0)
return B
def plotTorques(self):
print "Plotting torques..."
pylab.fill_between(range(len(self.torque_est)), self.torque_sup,\
self.torque_inf, facecolor='blue', alpha=0.3)
pylab.plot(self.torque_est, 'b')
pylab.plot(self.torque_mes, 'r', alpha=0.1)
pylab.plot(self.torque_filt,'r')
pylab.show()
def plot(fldname):
self.histmoeller(fldname)
pl.fill_between(self.jdvec, self.jdvec*0, self.posmean,
facecolor='powderblue')
pl.fill_between(self.jdvec, self.negmean, self.jdvec*0,
facecolor='pink')
pl.plot(self.jdvec, self.negmean+self.posmean,lw=1,c='k')
pl.gca().xaxis.axis_date()
def plot_gp_pred(sigma, **fillargs):
# pdb.set_trace()
nugget = (sigma ** 2 / (0.1 + d.astype('float') ** 2))
gp = GaussianProcess(corr='squared_exponential', nugget=nugget)
gp.fit(np.atleast_2d(range(n)).T, np.atleast_2d(d).T)
x = np.atleast_2d(np.linspace(0, n - 1)).T
y_pred, MSE = gp.predict(x, eval_MSE=True)
pylab.plot(x, y_pred)
pylab.fill_between(x.T[0], y_pred + MSE, y_pred - MSE, **fillargs)
def masterPlot(timeSteps, concentration):
### First make a List which contains all logged values of c
logC = []
for c in concentration:
logC.append(np.log(c))
### compute k
k, e = np.polyfit(x, logC, 1)
### get the estimates
startC = concentration[0]
endTime = timeSteps[-1]
estTime = [i for i in range(endTime)]
estConc = myExp(startC, endTime, k)
###
exp = lambda x: startC*np.exp(k*x)
AUC, err = integrate.quad(exp, 0, np.inf)
text = 'AUC = ' + str(round(AUC/60))
text2 = '$y= %d \cdot e^{-%rt}$' % (startC, round((k*60), 1))
fig = pl.figure()
fig.subplots_adjust(bottom=0.025, left=0.025, top = 0.975, right=0.975)
pl.subplot(2, 1, 1)
pl.scatter(timeSteps, concentration)
pl.plot(estTime, estConc, color="green", label=text2)
pl.title('Medikament X: AUC und approx. Formel beziehen sich auf Stunden')
pl.ylabel('Konzentration c [mg/L]')
pl.xlim(0, endTime)
pl.grid()
pl.legend()
#pl.xticks(())
#pl.yticks(())
pl.subplot(2, 2, 3)
pl.scatter(timeSteps, concentration)
pl.ylabel('Linearisiert (log[c])')
pl.grid()
pl.xlim(0, endTime)
pl.semilogy()
pl.xlim(0)
#pl.xticks(())
#pl.yticks(())
pl.subplot(2, 2, 4)
pl.plot(estTime, estConc, label=text)
pl.xlim(0, endTime)
pl.ylabel('Regressionskurve')
pl.fill_between(estTime, estConc, color='green', alpha=.25)
pl.legend()
#pl.xticks(())
pl.yticks(())
pl.grid()
#pl.subplot(2, 3, 6)
#pl.xticks(())
#pl.yticks(())
pl.show()
def plotWithPercentiles(ltraces, color, name=None, whichp=25, plotall=False):
m = median(ltraces, axis=1)
lp = percentile(ltraces, whichp, axis=1)
up = percentile(ltraces, 100-whichp, axis=1)
if plotall:
for l in ltraces.T:
pylab.plot(l, color + '-', alpha=0.3)
pylab.plot(m, color + '-', label=name)
pylab.fill_between(range(len(m)), lp, up, facecolor=color, alpha=0.1)
def fill_windows( x, y0, y1, fill_xs, fill_c, fill_lab ):
for i, xx in enumerate( fill_xs ):
y0_clipped = y0
if type( y0 ) is np.ndarray: # if y_bot is a curve
x_clipped, y0_clipped, lims = wf.clip_xy( xx, x, y0 )
x_clipped, y1_clipped, lims = wf.clip_xy( xx, x, y1 )
pl.fill_between( x_clipped, y0_clipped, y1_clipped, facecolor=fill_c[0],
edgecolor=fill_c[1], label=fill_lab )
def Draw(func1, func2):
# генирация точек графиков
xlist = mlab.frange(a, b, 0.01)
ylist = [func1(x) for x in xlist]
ylist2 = [func2(x) for x in xlist]
# Генирирум ось
y0 = [0 for x in xlist]
#############################################################
max1Y = max(ylist)
min1Y = min(ylist)
max2Y = max(ylist2)
min2Y = min(ylist2)
minmaxarrayY = []
minmaxarrayX = []
for i in range(len(ylist)):
if ((max1Y == ylist[i]) or (min1Y == ylist[i])):
minmaxarrayY.append(ylist[i])
minmaxarrayX.append(xlist[i])
for i in range(len(ylist2)):
if ((max2Y == ylist2[i]) or (min2Y == ylist2[i])):
minmaxarrayY.append(ylist2[i])
minmaxarrayX.append(xlist[i])
################################################################
extremumX, extremumY = converter(korn, 0, 3, 4, func1)
inflectionX, inflectionY = converter(korn1, 0, 3, 4, func1)
kornsX, kornsY = converter(table, 1, 3, 4, func1)
pylab.plot(extremumX, extremumY, 'go', label='extremum', color='red')
pylab.plot(inflectionX, inflectionY, 'go', label='inflection point', color='yellow')
pylab.plot(minmaxarrayX, minmaxarrayY, 'go', label='min/max', color='green')
pylab.plot(kornsX, kornsY, 'go', label='Korn', color='black')
pylab.plot(xlist, ylist, label='$sin(x)/x$')
pylab.plot(xlist, y0, color='pink')
pylab.plot(xlist, ylist2, label='$0.02*x* x - 4$', color='pink')
pylab.legend()
# Включаем рисование сетки
pylab.grid(True)
xlist1 = mlab.frange(float(table2[0][3]), float(table2[len(table2) - 1][3]), 0.01)
pylab.fill_between(xlist1, [func1(x) for x in xlist1], [func2(x) for x in xlist1], color='green', alpha=0.25)
# если мало разбиений, то переопереляем сетку под шаг
if ((round((b - a) / h)) < 25):
pylab.xticks([a + i * h for i in range(round((b - a) / h) + 1)])
print()
print()
print("Минимумы и максимумы:")
print("X", "Y", sep="\t")
for i in range(len(minmaxarrayY)):
print('{:3.5g}'.format(minmaxarrayX[i]), '{:3.5g}'.format(minmaxarrayY[i]), sep='\t\t')
# Рисуем фогрму с графиком
pylab.show()
def parameter_autocor(sessions, population_fit, param = 'side'):
''' Evaluate within and cross subject variability in
specified parameter and autocorrelation across sessions.
'''
assert len(population_fit['MAP_fits']) == len(sessions), \
'Population fit does not match number of sessions.'
param_index = population_fit['param_names'].index(param)
for i, MAP_fit in enumerate(population_fit['MAP_fits']):
sessions[i].side_loading = MAP_fit['params_U'][param_index]
sIDs = list(set([s.subject_ID for s in sessions]))
p.figure(1)
p.clf()
p.subplot2grid((2,2),(0,0), colspan = 2)
subject_means = []
subject_SDs = []
cor_len = 20
subject_autocorrelations = np.zeros([len(sIDs), 2 * cor_len + 1])
subject_shuffled_autocor = np.zeros([len(sIDs), 2 * cor_len + 1, 1000])
for i, sID in enumerate(sIDs):
a_sessions = sorted([s for s in sessions if s.subject_ID == sID],
key = lambda s:s.day)
sl = [s.side_loading for s in a_sessions]
p.plot(sl)
subject_means.append(np.mean(sl))
subject_SDs.append(np.std(sl))
sl = (np.array(sl) - np.mean(sl)) / np.std(sl)
autocor = np.correlate(sl, sl, 'full') / len(sl)
subject_autocorrelations[i,:] = autocor[autocor.size/2 - cor_len:
autocor.size/2 + cor_len + 1]
for j in range(1000):
shuffle(sl)
autocor = np.correlate(sl, sl, 'full') / len(sl)
subject_shuffled_autocor[i,:,j] = autocor[autocor.size/2 - cor_len:
autocor.size/2 + cor_len + 1]
mean_shuffled_autocors = np.mean(subject_shuffled_autocor,0)
mean_shuffled_autocors.sort(1)
p.xlabel('Day')
p.ylabel('Subject rotational bias')
p.subplot2grid((2,2),(1,0))
p.fill_between(range(-cor_len, cor_len + 1),mean_shuffled_autocors[:,10],
mean_shuffled_autocors[:,-10], color = 'k', alpha = 0.2)
p.plot(range(-cor_len, cor_len + 1),np.mean(subject_autocorrelations,0),'b.-', markersize = 5)
p.xlabel('Lag (days)')
p.ylabel('Correlation')
p.subplot2grid((2,2),(1,1))
p.bar([0.5,1.5], [np.mean(subject_SDs), np.sqrt(np.var(subject_means))])
p.xticks([1,2], ['Within subject', 'Cross subject'])
p.xlim(0.25,2.5)
p.ylabel('Standard deviation')
def plot_one(data):
kwargs = dict(**data['style'])
setdefaults(kwargs,DEFAULTS)
mass,limit = get_mass_limit(data)
plt.fill_between(mass, limit, y2=1.0,
edgecolor=COLORS['blue'],
facecolor=COLORS['blue'],
alpha=kwargs['alpha'])
plot_text(data)
def make_plot(x, y, x_tst, y_tst, y_tst_pred, y_tst_samples, lc, uc, textstr):
pylab.figure()
pylab.scatter(x, y, color='r')
pylab.plot(x_tst, y_tst)
pylab.plot(x_tst,y_tst_pred)
# pylab.plot(x_tst, y_tst_samples)
pylab.fill_between(x_tst.squeeze(), lc.squeeze(), uc.squeeze(), color='gray', alpha=0.5)
pylab.xlim([-7,7])
pylab.ylim([-250,250])
pylab.text(-6.8, 150., textstr, fontsize=12)
def shadedplot(x, y, fill=True, label='', color='b'):
# y[0,:] mean, median etc; in the middle
# y[1,:] lower
# y[2,:] upper
p = plt.plot(x, y[0, :], label=label, color=color)
c = p[-1].get_color()
#plt.plot(x, y[1,:], color=c, alpha=0.25)
#plt.plot(x, y[2,:], color=c, alpha=0.25)
if fill:
plt.fill_between(x, y[1, :], y[2, :], color=c, alpha=0.25)
def plot_eb_mode_test_residuals(self, YLIM=[-5, 30], add_title='toto'):
print("eb mode plot test residuals")
plt.figure(figsize=(12, 8))
plt.subplots_adjust(bottom=0.12, top=0.88, right=0.99)
mean_e, std_e = mean_check_finite(self.e_mode_residuals)
mean_b, std_b = mean_check_finite(self.b_mode_residuals)
Filtre0 = ((np.exp(self.logr[0]) > 6e-3) &
(np.exp(self.logr[0]) < 1e-2))
Title = '$\\xi_E(0) = (%.0f \pm %.0f)$ mas$^2$ | $\\xi_B(0) = (%.0f \pm %.0f)$ mas$^2$' % (
(np.mean(mean_e[Filtre0]), np.mean(std_e[Filtre0]),
np.mean(mean_b[Filtre0]), np.mean(std_b[Filtre0])))
plt.plot(np.exp(self.logr[0]) * 60.,
mean_e,
'b',
lw=3,
label='mean E-mode (test, after GP)')
plt.fill_between(np.exp(self.logr[0]) * 60.,
mean_e - std_e,
mean_e + std_e,
color='b',
alpha=0.4,
label='$\pm 1 \sigma$ E-mode')
plt.plot(np.exp(self.logr[0]) * 60.,
mean_b,
'r',
lw=3,
label='mean B-mode (test, after GP)')
plt.fill_between(np.exp(self.logr[0]) * 60.,
mean_b - std_b,
mean_b + std_b,
color='r',
alpha=0.4,
label='$\pm 1 \sigma$ B-mode')
plt.plot(np.exp(self.logr[0]) * 60.,
np.zeros_like(self.logr[0]),
'k--',
lw=3)
plt.ylim(YLIM[0], YLIM[1])
plt.xlim(0.005 * 60., 1.5 * 60.)
plt.xscale('log')
plt.xticks(size=20)
plt.yticks(size=20)
plt.xlabel('$\Delta \\theta$ (arcmin)', fontsize=24)
plt.ylabel('$\\xi_{E/B}$ (mas$^2$)', fontsize=24)
plt.legend(fontsize=20)
if add_title is not None:
plt.title(add_title + '\n' + Title, fontsize=24)
else:
plt.title(Title, fontsize=16)
def phase__1():
"""Constant phase lag, no binning, no noise, RED NOISE LEAK
TEST: phases from segments without noise, i.e with leak
SUMMARY:
1. taper helps alot! both in the scatter and bias
"""
n = 2**12
dt = 1.0
mu = 100.0
lag = 0.5
nsim = 200
sim = az.SimLC(seed=463284)
sim.add_model('powerlaw', [1e-2, -2.])
sim.add_model('constant', lag, lag=True)
lag = []
for isim in range(nsim):
az.misc.print_progress(isim, nsim, isim == nsim - 1)
sim.simulate(n * 4, dt, mu, norm='var')
sim.apply_lag(phase=True)
l1 = az.LCurve.calculate_lag(sim.y[:n], sim.x[:n], dt, phase=True)
l2 = az.LCurve.calculate_lag(sim.y[:n],
sim.x[:n],
dt,
phase=True,
taper=True)
lag.append([l1, l2])
lag = np.array(lag)
fq = lag[0, 0, 0]
l = lag[:, :, 1].mean(0)
lp = np.percentile(lag[:, :, 1], [50, 16, 100 - 16], 0)
plt.rcParams['figure.figsize'] = [8, 4]
plt.rcParams['font.size'] = 7
ax = plt.subplot(121)
plt.plot(fq, lp[0, 0], color='C0')
plt.title('no-taper')
plt.fill_between(fq, lp[1, 0], lp[2, 0], alpha=0.5, color='C1')
plt.plot(sim.normalized_lag[0, 1:], sim.normalized_lag[1, 1:], color='C2')
ax.set_xscale('log')
ax = plt.subplot(122)
plt.plot(fq, lp[0, 1], color='C0')
plt.title('taper')
plt.fill_between(fq, lp[1, 1], lp[2, 1], alpha=0.5, color='C1')
plt.plot(sim.normalized_lag[0, 1:], sim.normalized_lag[1, 1:], color='C2')
ax.set_xscale('log')
plt.savefig('png/phase__1.png')
def phase__2():
"""Constant phase lag, no binning, NOISE, RED NOISE LEAK
TEST: phases from segments (i.e. leak) with noise,
SUMMARY:
1. again, taper helps alot! The scatter is very small in intermediate frequencies
not affected by noise, unlike in no-tpaer case.
"""
n = 2**12
dt = 1.0
mu = 100.0
lag = 0.5
nsim = 200
sim = az.SimLC(seed=4634)
sim.add_model('powerlaw', [1e-2, -2.])
sim.add_model('constant', lag, lag=True)
lag = []
for isim in range(nsim):
az.misc.print_progress(isim, nsim, isim == nsim - 1)
sim.simulate(n * 4, dt, mu, norm='var')
sim.apply_lag(phase=True)
scale_fac = 100
x = np.random.poisson(sim.x[:n] * scale_fac) / scale_fac
y = np.random.poisson(sim.y[:n] * scale_fac) / scale_fac
l1 = az.LCurve.calculate_lag(y, x, dt, phase=True)
l2 = az.LCurve.calculate_lag(y, x, dt, phase=True, taper=True)
lag.append([l1, l2])
lag = np.array(lag)
fq = lag[0, 0, 0]
l = lag[:, :, 1].mean(0)
lp = np.percentile(lag[:, :, 1], [50, 16, 100 - 16], 0)
plt.rcParams['figure.figsize'] = [8, 4]
plt.rcParams['font.size'] = 7
ax = plt.subplot(121)
plt.plot(fq, lp[0, 0], color='C0')
plt.title('no-taper')
plt.fill_between(fq, lp[1, 0], lp[2, 0], alpha=0.5, color='C1')
plt.plot(sim.normalized_lag[0, 1:], sim.normalized_lag[1, 1:], color='C2')
ax.set_xscale('log')
ax = plt.subplot(122)
plt.plot(fq, lp[0, 1], color='C0')
plt.title('taper')
plt.fill_between(fq, lp[1, 1], lp[2, 1], alpha=0.5, color='C1')
plt.plot(sim.normalized_lag[0, 1:], sim.normalized_lag[1, 1:], color='C2')
ax.set_xscale('log')
plt.savefig('png/phase__2.png')
def plot_SandI(susceptible, infected, Title):
pl.figure(figsize=(5,3))
pl.plot(time, susceptible,label="Susceptible")
pl.fill_between(time, susceptible, 0, alpha=0.30)
pl.plot(time, infected,label="Infected")
pl.fill_between(time, infected, 0, alpha=0.30)
pl.ticklabel_format(axis="y", style="sci", scilimits=(0,0))
pl.xlabel('Time in days', fontsize=14)
pl.ylabel('Individuals', fontsize=14)
pl.title(Title)
pl.legend()
def graph_maxmin_fitness(pop, identify, minimize=False, filesave=None):
x = []
max_y = []
min_y = []
avg_y = []
for it in pop:
x.append(it["generation"])
max_y.append(it["fitMax"])
min_y.append(it["fitMin"])
avg_y.append(it["fitAve"])
pylab.figure()
pylab.plot(x, max_y, "g", label="Max fitness")
pylab.plot(x, min_y, "r", label="Min fitness")
pylab.plot(x, avg_y, "b", label="Avg fitness")
pylab.fill_between(x,
min_y,
max_y,
color="g",
alpha=0.1,
label="Diff max/min")
if minimize: raw_max = min(min_y)
else: raw_max = max(max_y)
if minimize: gen_max = x[min_y.index(raw_max)]
else: gen_max = x[max_y.index(raw_max)]
if minimize: annot_label = "Minimum (%.2f)" % (raw_max, )
else: annot_label = "Maximum (%.2f)" % (raw_max, )
pylab.annotate(
annot_label,
xy=(gen_max, raw_max),
xycoords='data',
xytext=(8, 15),
textcoords='offset points',
arrowprops=dict(arrowstyle="->", connectionstyle="arc"),
)
pylab.xlabel("Generation (#)")
pylab.ylabel("Fitness score")
pylab.title("Plot of evolution identified by '%s' (fitness scores)" %
(identify))
pylab.grid(True)
pylab.legend(prop=FontProperties(size="smaller"), loc=0)
if filesave:
pylab.savefig(filesave)
print("Graph saved to %s file !" % (filesave, ))
else:
pylab.show()
def plot_sausage(X,
mean,
std,
alpha=None,
format_fill={
'alpha': 0.3,
'facecolor': 'k'
},
format_line=dict(alpha=1, color='g', lw=3, ls='dashed')):
"""
plot saussage plot of GP. I.e:
.. image:: ../images/sausage.png
:height: 8cm
**returns:** : [fill_plot, line_plot]
The fill and the line of the sausage plot. (i.e. green line and gray fill of the example above)
**Parameters:**
X : [double]
Interval X for which the saussage shall be plottet.
mean : [double]
The mean of to be plottet.
std : [double]
Pointwise standard deviation.
format_fill : {format}
The format of the fill. See http://matplotlib.sourceforge.net/ for details.
format_line : {format}
The format of the mean line. See http://matplotlib.sourceforge.net/ for details.
"""
X = X.squeeze()
Y1 = (mean + 2 * std)
Y2 = (mean - 2 * std)
if (alpha is not None):
old_alpha_fill = min(1, format_fill['alpha'] * 2)
for i, a in enumerate(alpha[:-2]):
format_fill['alpha'] = a * old_alpha_fill
hf = PL.fill_between(X[i:i + 2],
Y1[i:i + 2],
Y2[i:i + 2],
lw=0,
**format_fill)
i += 1
hf = PL.fill_between(X[i:], Y1[i:], Y2[i:], lw=0, **format_fill)
else:
hf = PL.fill_between(X, Y1, Y2, **format_fill)
hp = PL.plot(X, mean, **format_line)
return [hf, hp]
def plotfill(time, csfr, csfrhi, csfrlo, color, alpha=1.0):
plt.fill_between(10**np.append(time, 10.15) / 10**9,
np.append(csfr, 0),
np.append(csfr + csfrhi, 0),
alpha=alpha,
color=color)
plt.fill_between(10**np.append(time, 10.15) / 10**9,
np.append(csfr, 0),
np.append(csfr - csfrlo, 0),
alpha=alpha,
color=color)
def mixing_stats():
pylab.figure(num=2, figsize=(16.5, 11.5))
pylab.suptitle('Mixing')
times = []
mixing_stats_lower = []
mixing_stats_mixed = []
mixing_stats_upper = []
stat_files, time_index_end = le_tools.GetstatFiles('./')
for sf in stat_files:
stat = stat_parser(sf)
for i in range(
time_index_end[pylab.find(numpy.array(stat_files) == sf)]):
times.append(stat['ElapsedTime']['value'][i])
mixing_stats_lower.append(stat['fluid']['Temperature']
['mixing_bins%cv_normalised'][0][i])
mixing_stats_mixed.append(stat['fluid']['Temperature']
['mixing_bins%cv_normalised'][1][i])
mixing_stats_upper.append(stat['fluid']['Temperature']
['mixing_bins%cv_normalised'][2][i])
pylab.plot(times, mixing_stats_lower, label='T < -0.4')
pylab.plot(times, mixing_stats_mixed, label='-0.4 < T < 0.4')
pylab.plot(times, mixing_stats_upper, label='0.4 < T')
time = le_tools.ReadLog('diagnostics/logs/time.log')
X_ns = [x - 0.4 for x in le_tools.ReadLog('diagnostics/logs/X_ns.log')]
X_fs = [0.4 - x for x in le_tools.ReadLog('diagnostics/logs/X_fs.log')]
try:
index = pylab.find(numpy.array(X_ns) < 0.4 - 1E-3)[-1]
pylab.fill_between([time[index], time[index + 1]], [0, 0], [0.5, 0.5],
color='0.3')
index = pylab.find(numpy.array(X_fs) < 0.4 - 1E-3)[-1]
pylab.fill_between([time[index], time[index + 1]], [0, 0], [0.5, 0.5],
color='0.6')
except IndexError:
print 'not plotting shaded regions on mixing plot as front has not reached end wall'
pylab.axis([0, times[-1], 0, 0.5])
pylab.grid("True")
pylab.legend(loc=0)
pylab.text(
times[-1] - (times[-1] / 5),
0.005,
'shaded regions show when \nfronts near the end wall \ndark: free-slip, light: no-slip',
bbox=dict(facecolor='white', edgecolor='black'))
pylab.xlabel('time (s)')
pylab.ylabel('domain fraction')
pylab.savefig('diagnostics/plots/mixing.png')
return
def plotWithVariance(x, y, variance, *args, **kwargs):
"""
Plot data with variance indicated by shading within one sigma.
"""
line = pylab.plot(x, y.flatten(), *args, **kwargs)[0]
sigma = np.sqrt(variance)
pylab.fill_between(x,
y - sigma,
y + sigma,
color=line.get_color(),
alpha=0.5)
def show_plot(t_array, th_array):
""" Display theta vs t plot. """
th_mean = gv.mean(th_array)
th_sdev = gv.sdev(th_array)
thp = th_mean + th_sdev
thm = th_mean - th_sdev
plt.fill_between(t_array, thp, thm, color='0.8')
plt.plot(t_array, th_mean, linewidth=0.5)
plt.xlabel('$t$')
plt.ylabel(r'$\theta(t)$')
plt.savefig('pendulum.pdf', bbox_inches='tight')
plt.show()
def plot(fldname):
self.histmoeller(fldname)
pl.fill_between(self.jdvec,
self.jdvec * 0,
self.posmean,
facecolor='powderblue')
pl.fill_between(self.jdvec,
self.negmean,
self.jdvec * 0,
facecolor='pink')
pl.plot(self.jdvec, self.negmean + self.posmean, lw=1, c='k')
pl.gca().xaxis.axis_date()
def make_unit_plots(file_dir, fs):
'''Makes waveform plots for sorted unit in unit_waveforms_plots
Parameters
----------
file_dir : str, full path to recording directory
fs : float, smapling rate in Hz
'''
h5_name = h5io.get_h5_filename(file_dir)
h5_file = os.path.join(file_dir, h5_name)
plot_dir = os.path.join(file_dir, 'unit_waveforms_plots')
if os.path.exists(plot_dir):
shutil.rmtree(plot_dir, ignore_errors=True)
os.mkdir(plot_dir)
fs_str = '(%g samples per ms)' % (fs / 1000.0)
unit_numbers = get_unit_numbers(h5_file)
with tables.open_file(h5_file, 'r+') as hf5:
units = hf5.list_nodes('/sorted_units')
for i, unit in zip(unit_numbers, units):
# plot all waveforms
waveforms = unit.waveforms[:]
descriptor = hf5.root.unit_descriptor[i]
fig, ax = blech_waveforms_datashader.waveforms_datashader(
waveforms)
ax.set_xlabel('Samples (%s)' % fs_str)
ax.set_ylabel('Voltage (microvolts)')
unit_title = (
('Unit %i, total waveforms = %i\nElectrode: %i, '
'Single Unit: %i, RSU: %i, FSU: %i') %
(i, waveforms.shape[0], descriptor['electrode_number'],
descriptor['single_unit'], descriptor['regular_spiking'],
descriptor['fast_spiking']))
ax.set_title(unit_title)
fig.savefig(os.path.join(plot_dir, 'Unit%i.png' % i))
plt.close('all')
# Plot mean and SEM of waveforms
# Downsample by 10 to remove upsampling from de-jittering
fig = plt.figure()
mean_wave = np.mean(waveforms[:, ::10], axis=0)
std_wave = np.std(waveforms[:, ::10], axis=0)
mean_x = np.arange(mean_wave.shape[0]) + 1
plt.plot(mean_x, mean_wave, linewidth=4.0)
plt.fill_between(mean_x,
mean_wave - std_wave,
mean_wave + std_wave,
alpha=0.4)
plt.xlabel('Samples (%s)' % fs_str)
plt.ylabel('Voltage (microvolts)')
plt.title(unit_title)
fig.savefig(os.path.join(plot_dir, 'Unit%i_mean_sd.png' % i))
plt.close('all')
def plot_mean_std(mn, std, title, plt_num=[1, 1, 1], legend=True):
"""plot the mean +/- std as timeline """
ax = plt.subplot(plt_num[0], plt_num[1], plt_num[2])
plt.hold(True)
plt.fill_between(range(len(std)), mn - std, mn + std, facecolor=clr2)
plt.plot(range(len(std)), mn, color=clr1, label='Trial Mean', linewidth=2)
if legend:
plt.legend(bbox_to_anchor=(0.55, 0.95, 0.4, 0.1),
frameon=False,
ncol=2)
adjust_spines(ax, ['bottom', 'left'])
plt.title(title)
def plot_count_spikes(
filepath,
signal_map): #FIXME integrate this with count_spikes properly
""" useful for manual review """
raw, block, segments, header = load_abf(filepath)
if list(raw.read_header()['nADCSamplingSeq'][:2]) != [1, 2]: #FIXME
print('Not a ledstim file')
return None, None
nseg = len(segments)
gains = header['fTelegraphAdditGain'] #TODO
zero = transform_maker(0, 20 * gains[0]) #cell
one = transform_maker(0, 5) #led
plt.figure()
counts = []
for seg, n in zip(segments, range(nseg)):
if len(seg.analogsignals) != 2:
print('No led channel found.')
continue
plt.subplot(nseg, 1, n + 1)
nas = seg.size()['analogsignals']
signal = zero(seg.analogsignals[0])
led = one(seg.analogsignals[1])
led_on_index, base, maxV, minV = detect_led(
led) #FIXME move this to count_spikes
if not len(led_on_index):
print('No led detected maxV: %s minV: %s' % (maxV, minV))
continue
sig_on = signal.base[led_on_index]
sig_mean = np.mean(sig_on)
sig_std = np.std(sig_on)
#plt.plot(sig_std+sig_mean)
sm_arr = base * sig_mean
std_arr = base * sig_std
thresh = 3.3
s_list = detect_spikes(sig_on, thresh, 5)
counts.append(len(s_list))
[plt.plot(s, sig_on[s], 'ro') for s in s_list] #plot all the spikes
plt.plot(sig_on, 'k-')
plt.plot(sm_arr, 'b-')
plt.fill_between(np.arange(len(sm_arr)),
sm_arr - std_arr * thresh,
sm_arr + std_arr * thresh,
color=(.8, .8, 1))
plt.xlim((0, len(sig_on)))
plt.title('%s spikes %s' % (len(s_list), block.file_origin))
#plt.title(block.file_origin)
#plt.title('%s Segment %s'%(block.file_origin,n))
return np.mean(counts), np.var(counts), counts
def plot_limit_fill(data, low=False):
mass,limit = get_mass_limit(data)
kwargs = dict(**data['style'])
setdefaults(kwargs,DEFAULTS)
plt.fill_between(mass, limit, y2 = 1 if not low else 0,
edgecolor=kwargs['color'],
facecolor=kwargs['color'],
alpha=kwargs['alpha'],
)
plot_text(data)
def get_filters(plotit=True):
filters = prep_filters()
if plotit:
for i, band in enumerate(['r']):
pl.fill_between(filters[band]['ls'],
5. * filters[band]['Ts'],
alpha=0.3,
label=filters[band]['ppkey'],
color='g')
return filters
def plot_summary(x,
s,
interval=95,
num_samples=100,
sample_color='k',
sample_alpha=0.4,
interval_alpha=0.25,
color='r',
legend=True,
title="",
plot_mean=True,
plot_median=False,
label=""):
b = 0.5 * (100 - interval)
lower = np.percentile(s, b, axis=0).T
upper = np.percentile(s, 100 - b, axis=0).T
if plot_median:
median = np.percentile(s, [50], axis=0).T
lab = 'Median'
if len(label) > 0:
lab += " %s" % label
plt.plot(x.ravel(), median, label=lab, color=color, linewidth=4)
if plot_mean:
mean = np.mean(s, axis=0).T
lab = 'Mean'
if len(label) > 0:
lab += " %s" % label
plt.plot(x.ravel(), mean, '--', label=lab, color=color, linewidth=4)
plt.fill_between(x.ravel(),
lower.ravel(),
upper.ravel(),
color=color,
alpha=interval_alpha,
label='%d%% Interval' % interval)
if num_samples > 0:
idx_samples = np.random.choice(range(len(s)),
size=num_samples,
replace=False)
plt.plot(x,
s[idx_samples, :].T,
color=sample_color,
alpha=sample_alpha)
if legend:
plt.legend(loc='best')
if len(title) > 0:
plt.title(title, fontweight='bold')
def plotTiming(dataPath, figurePath):
rawdata = np.loadtxt(dataPath)
#item_count adding_time twisting_time
sorteddata = sorted(zip(rawdata[:, 0], rawdata[:, 1], rawdata[:, 2]))
adding = {}
twisting = {}
for i in sorteddata:
if i[0] in adding:
adding[i[0]].append(i[1])
twisting[i[0]].append(i[2])
else:
adding[i[0]] = [i[1]]
twisting[i[0]] = [i[2]]
d = []
for i in adding.iterkeys():
averageAdding = np.average(adding[i]) / 1000.0
averageTwisting = np.average(twisting[i]) / 1000.0
d.append((i, averageAdding, averageTwisting))
data = np.array(sorted(d))
#print data
# plot
pylab.figure()
pylab.plot(data[:, 0], data[:, 1], 'ob')
pylab.plot(data[:, 0], data[:, 1] + data[:, 2], 'ob')
pylab.fill_between(data[:, 0],
data[:, 1],
data[:, 1] + data[:, 2],
facecolor='red',
interpolate=True)
pylab.fill_between(data[:, 0], [0 for i in range(len(adding))],
data[:, 1],
facecolor='green',
interpolate=True)
p1 = pylab.Rectangle((0, 0), 1, 1, fc="green")
p2 = pylab.Rectangle((0, 0), 1, 1, fc="red")
pylab.legend([p1, p2], ['adding time', 'twisting time'], loc='upper left')
pylab.xlabel("size")
pylab.ylabel('time used (seconds)')
pylab.xlim([data[0, 0], data[-1, 0]])
pylab.savefig(figurePath, format='svg')
def doTheWork(algorithm, r, generations, gene_length, population):
print('Running {}'.format(algorithm.__name__))
runs = []
parameters = [
(r, generations, 0.15, 0.6),
# (r, generations, 0.06, 0.3),
# (r, generations, 0.4, 0.0),
# (r, generations, 0.0, 0.5)
]
with tqdm(total=sum(map(lambda item: item[0], parameters))) as bar:
for (r, generations, p_mutate, p_crossover) in parameters:
algs = []
for _ in range(r):
# Prepare genes
chromosomes = ga.chromosomes.Chromosome.create_random(
gene_length=gene_length, n=population)
# Init alg
alg = algorithm(chromosomes)
# Simulate generations
alg.run(generations, p_mutate, p_crossover)
algs.append((alg, generations, p_mutate, p_crossover))
bar.update(1)
runs.append((r, generations, p_mutate, p_crossover, algs))
for (r, generations, p_mutate, p_crossover, algs) in runs:
data = []
for (alg, _, _, _) in algs:
d = np.array(
[v for k, v in sorted(alg.overall_fittest_fit.items())],
dtype=np.float64)
if not any(data):
data = d
else:
data += d
data /= len(algs)
py.plot(data,
label='{} ; {} ; {}'.format(generations, p_mutate,
p_crossover))
py.fill_between(data,
np.percentile(data, q=0.25, axis=0),
np.percentile(data, q=0.75, axis=0),
alpha=0.3)
py.rcParams['figure.figsize'] = [10, 8]
py.xlabel('generation')
py.ylabel('fitness')
py.legend(loc='best')
py.show()
def plot_regression(x, y, smoothing=.3):
fit = sm.nonparametric.lowess(y, x, frac=smoothing)
df = (fit[:, 1] - y)**2
fit_var = sm.nonparametric.lowess(df, x, frac=smoothing)
isheld = pl.ishold()
pl.hold(1)
pl.plot(fit[:, 0], fit[:, 1])
pl.fill_between(fit_var[:, 0],
fit[:, 1] - 1 * pl.sqrt(fit_var[:, 1]),
fit[:, 1] + 1 * pl.sqrt(fit_var[:, 1]),
color=((0, 0, .99, .2), ))
pl.plot(x, y, '.')
pl.hold(isheld)
def plotting(input_file, savefile):
with open(input_file, 'r') as myfile:
results = json.load(myfile)
res_IE_1D = results['IE_1D']
thetas = np.linspace(res_IE_1D['theta_start'], res_IE_1D['theta_stop'],
1000)
p = Problem_FSI_1D(res_IE_1D['n'], **res_IE_1D['parameters'])
dt = res_IE_1D['tf'] / res_IE_1D['N_steps']
conv_rates_lims = [p.theoretical_conv_rate(dt, th) for th in thetas]
pl.figure()
for res, label, marker, ls in [(results['IE_1D'], 'IE 1D', None, '-'),
(results['IE_2D'], 'IE 2D', None, '-'),
(results['S2_1D'], 'SD2 1D', 'o', ''),
(results['S2_2D'], 'SD2 2D', '*', '')]:
conv_rates = []
for updates in res['updates']:
x = np.array(updates[:-1])
conv_rates.append(np.mean(x[1:] / x[:-1]))
pl.semilogy(res['theta'],
conv_rates,
label=label,
marker=marker,
linestyle=ls)
pl.semilogy(thetas,
conv_rates_lims,
label=r'$\Sigma(\Theta)$',
ls='--',
linewidth=3)
pl.axvline(res_IE_1D['theta_opt'],
ls='-',
color='k',
label=r'$\Theta_{opt}$')
a, b = res_IE_1D['theta_CFL_inf'], res_IE_1D['theta_CFL_zero']
pl.xlim(res_IE_1D['theta_start'], res_IE_1D['theta_stop'])
pl.ylim(1e-7, 2)
pl.fill_between([min(a, b), max(a, b)], [min(pl.ylim()) / 100] * 2,
[max(pl.ylim()) * 100] * 2,
alpha=0.2)
pl.xlabel(r'$\Theta$', labelpad=-20, position=(1.08, -1), fontsize=20)
lp = -50 if label == 'Air-Steel' else -70
pl.ylabel('Conv. rate',
rotation=0,
labelpad=lp,
position=(2., 1.05),
fontsize=20)
pl.legend(loc=3)
pl.savefig(savefile, dpi=100)
def main():
Gamma0, Omega, Sigma, T, Y_obs, amp, mu0, tec, freqs = generate_data()
hmm = NonLinearDynamicsSmoother(TecLinearPhaseNestedSampling(freqs))
hmm = jit(
partial(hmm,
tol=1.,
maxiter=2,
omega_window=None,
sigma_window=None,
momentum=0.,
omega_diag_range=(0, jnp.inf),
sigma_diag_range=(0, jnp.inf)))
#
# with disable_jit():
keys = random.split(random.PRNGKey(0), T)
# with disable_jit():
res = hmm(Y_obs, Sigma, mu0, Gamma0, Omega, amp, keys)
print(res.converged, res.niter)
plt.plot(tec, label='true tec')
plt.plot(res.post_mu[:, 0], label='infer tec')
plt.fill_between(jnp.arange(T),
res.post_mu[:, 0] - jnp.sqrt(res.post_Gamma[:, 0, 0]),
res.post_mu[:, 0] + jnp.sqrt(res.post_Gamma[:, 0, 0]),
alpha=0.5)
plt.legend()
plt.show()
plt.plot(jnp.sqrt(res.post_Gamma[:, 0, 0]))
plt.title("Uncertainty tec")
plt.show()
plt.plot(tec - res.post_mu[:, 0], label='infer')
plt.fill_between(
jnp.arange(T),
(tec - res.post_mu[:, 0]) - jnp.sqrt(res.post_Gamma[:, 0, 0]),
(tec - res.post_mu[:, 0]) + jnp.sqrt(res.post_Gamma[:, 0, 0]),
alpha=0.5)
plt.title("Residual tec")
plt.legend()
plt.show()
plt.plot(jnp.sqrt(res.Omega[:, 0, 0]))
plt.title("omega")
plt.show()
plt.plot(
jnp.mean(jnp.sqrt(jnp.diagonal(res.Sigma, axis2=-2, axis1=-1)),
axis=-1))
plt.title("mean sigma")
plt.show()
def histmoellerplot(self, fldname="Dchl"):
self.histmoeller(fldname)
figpref.current()
pl.close(1)
fig = pl.figure(1)
pl.subplots_adjust(hspace=0, right=0.85, left=0.15)
def subplot(sp, mat, vec, xvec):
ax = pl.subplot(2, 1, sp, axisbg='0.8')
im = pl.pcolormesh(self.jdvec,
xvec,
miv(mat.T),
rasterized=True,
norm=LogNorm(vmin=1, vmax=10000),
cmap=WRY())
pl.plot(self.jdvec, vec, lw=2)
pl.gca().xaxis.axis_date()
pl.setp(ax, xticklabels=[])
return im
im = subplot(1, self.posmat, self.posmean, self.hpos)
im = subplot(2, self.negmat, self.negmean, -self.hpos)
pl.figtext(0.08,
0.5,
r'%s d$^{-1}$)' % fldname,
rotation='vertical',
size=16,
va='center')
cbar_ax = fig.add_axes([0.87, 0.15, 0.025, 0.7])
cb = fig.colorbar(im, cax=cbar_ax)
cb.set_label('Number of grid cells')
pl.savefig('figs/liege/histmoeller_%s.pdf' % fldname)
pl.close(2)
fig = pl.figure(2)
pl.fill_between(self.jdvec,
self.jdvec * 0,
self.posmean,
facecolor='powderblue')
pl.fill_between(self.jdvec,
self.negmean,
self.jdvec * 0,
facecolor='pink')
pl.plot(self.jdvec, self.negmean + self.posmean, lw=1, c='k')
#pl.title(fldname)
pl.gca().xaxis.axis_date()
pl.savefig('figs/liege/meantime_%s.pdf' % fldname)
def _plot(self, kde, X, data_type, filename, bandwidth):
if self.plot:
if data_type == MZ_INTENSITY_RT:
self.logger.debug('3D plotting for %s not implemented' % MZ_INTENSITY_RT)
else:
fname = 'All' if filename is None else filename
title = '%s density estimation for %s - bandwidth %.3f' % (data_type, fname, bandwidth)
X_plot = np.linspace(np.min(X), np.max(X), 1000)[:, np.newaxis]
log_dens = kde.score_samples(X_plot)
plt.figure()
plt.fill_between(X_plot[:, 0], np.exp(log_dens), alpha=0.5)
plt.plot(X[:, 0], np.full(X.shape[0], -0.01), '|k')
plt.title(title)
plt.show()
def fightSizeDistributionPlot(samples,
color='k',
plotConfInt=True,
plotErrorBars=False,
log=False,
alpha=0.4,
makePlot=True,
confIntP=0.95,
removeZeros=False,
removeOnes=False,
multiple=1,
verbose=True,
maxSize=None,
**kwargs):
"""
multiple (1) : Multiply probabilities by this
factor. Useful for plotting expected
number rather than probability.
"""
#ell = len(samples[0])
dist,confIntList = fightSizeDistribution(samples, \
confIntP=confIntP,removeZeros=removeZeros,removeOnes=removeOnes,
maxSize=maxSize)
ell = len(dist)
dist, confIntList = multiple * dist, multiple * confIntList
if makePlot:
if plotConfInt:
#for confInt in confIntList:
# if confInt[0] == 0.: confInt[0] = zeroEquiv
#firstZero = pylab.find(dist==0)[2]
firstZero = len(dist)
pylab.fill_between(range(1,firstZero), \
confIntList[:,0][1:firstZero], \
confIntList[:,1][1:firstZero],color=color,alpha=alpha)
if plotErrorBars:
yerr = scipy.empty_like(confIntList.T)
yerr[0] = dist - confIntList[:, 1]
yerr[1] = confIntList[:, 0] - dist
pylab.errorbar(range(ell), dist, yerr=yerr, color=color)
pylab.plot(range(ell), dist, color=color, **kwargs)
if log: plotFn = pylab.yscale('log')
if verbose:
print("sum(dist) =", sum(dist))
return dist
def animate(i):
line1.set_data(GenerData[i, 0], GenerData[i, 1])
ax2.cla()
ax2.axis([-1.0, 1.0, 0, 1.0])
ax2.set_xlabel('environmental conditions ($ x $)', fontsize=FontSize)
ax2.set_ylabel('average uptake efficiency ($ U(x) $)', fontsize=FontSize)
p.fill_between(x,
genMeans[i, :] + genSTD[i, :],
genMeans[i, :] - genSTD[i, :],
color=(0.75, 0.75, 0.75, 0.75))
p.plot(x, genMeans[i, :], linewidth=2, color='k')
p.grid(True)
p.tight_layout(h_pad=Hpad)
return line1
def plot(self,
color_line='r',
bgcolor='grey',
color='yellow',
lw=4,
hold=False,
ax=None):
xmax = self.xmax + 1
if ax:
pylab.sca(ax)
pylab.fill_between([0, xmax], [0, 0], [20, 20], color='red', alpha=0.3)
pylab.fill_between([0, xmax], [20, 20], [30, 30],
color='orange',
alpha=0.3)
pylab.fill_between([0, xmax], [30, 30], [41, 41],
color='green',
alpha=0.3)
if self.X is None:
X = range(1, self.xmax + 1)
pylab.fill_between(X,
self.df.mean() + self.df.std(),
self.df.mean() - self.df.std(),
color=color,
interpolate=False)
pylab.plot(X, self.df.mean(), color=color_line, lw=lw)
pylab.ylim([0, 41])
pylab.xlim([0, self.xmax + 1])
pylab.title("Quality scores across all bases")
pylab.xlabel("Position in read (bp)")
pylab.ylabel("Quality")
pylab.grid(axis='x')
