本文整理汇总了Python中pylab.ylim函数的典型用法代码示例。如果您正苦于以下问题：Python ylim函数的具体用法？Python ylim怎么用？Python ylim使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了ylim函数的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: plot_frontier
def plot_frontier(self,frontier_only=False,plot_samples=True) :
""" Plot the frontier"""
frontier = self.frontier
frontier_energy = self.frontier_energy
feat1,feat2 = self.feats
pl.figure()
if not frontier_only :
ll_list1,ll_list2 = zip(*self.all_seq_energy)
pl.plot(ll_list1,ll_list2,'b*')
if plot_samples :
ll_list1,ll_list2 = zip(*self.sample_seq_energy)
pl.plot(ll_list1,ll_list2,'g*')
pl.plot(*zip(*sorted(frontier_energy)),color='magenta',\
marker='*', linestyle='dashed')
ctr = dict(zip(set(frontier_energy),[0]*
len(set(frontier_energy))))
for i,e in enumerate(frontier_energy) :
ctr[e] += 1
pl.text(e[0],e[1]+0.1*ctr[e],str(i),fontsize=10)
pl.text(e[0]+0.4,e[1]+0.1*ctr[e],frontier[i],fontsize=9)
pl.xlabel('Energy:'+feat1)
pl.ylabel('Energy:'+feat2)
pl.title('Energy Plot')
xmin,xmax = pl.xlim()
ymin,ymax = pl.ylim()
pl.xlim(xmin,xmax)
pl.ylim(ymin,ymax)
pic_dir = '../docs/tex/pics/'
pl.savefig(pic_dir+self.name+'.pdf')
pl.savefig(pic_dir+self.name+'.png')示例2: trace
def trace(data, name, format='png', datarange=(None, None), suffix='', path='./', rows=1, columns=1,
num=1, last=True, fontmap = None, verbose=1):
"""
Generates trace plot from an array of data.
:Arguments:
data: array or list
Usually a trace from an MCMC sample.
name: string
The name of the trace.
datarange: tuple or list
Preferred y-range of trace (defaults to (None,None)).
format (optional): string
Graphic output format (defaults to png).
suffix (optional): string
Filename suffix.
path (optional): string
Specifies location for saving plots (defaults to local directory).
fontmap (optional): dict
Font map for plot.
"""
if fontmap is None: fontmap = {1:10, 2:8, 3:6, 4:5, 5:4}
# Stand-alone plot or subplot?
standalone = rows==1 and columns==1 and num==1
if standalone:
if verbose>0:
print_('Plotting', name)
figure()
subplot(rows, columns, num)
pyplot(data.tolist())
ylim(datarange)
# Plot options
title('\n\n %s trace'%name, x=0., y=1., ha='left', va='top', fontsize='small')
# Smaller tick labels
tlabels = gca().get_xticklabels()
setp(tlabels, 'fontsize', fontmap[rows/2])
tlabels = gca().get_yticklabels()
setp(tlabels, 'fontsize', fontmap[rows/2])
if standalone:
if not os.path.exists(path):
os.mkdir(path)
if not path.endswith('/'):
path += '/'
# Save to file
savefig("%s%s%s.%s" % (path, name, suffix, format))示例3: param_set_averages_plot
def param_set_averages_plot(results):
averages_ocr = [
a[1] for a in sorted(
param_set_averages(results, metric='ocr').items(),
key=lambda x: int(x[0].split('-')[1]))
]
averages_q = [
a[1] for a in sorted(
param_set_averages(results, metric='q').items(),
key=lambda x: int(x[0].split('-')[1]))
]
averages_mse = [
a[1] for a in sorted(
param_set_averages(results, metric='mse').items(),
key=lambda x: int(x[0].split('-')[1]))
]
fig = plt.figure(figsize=(6, 4))
# plt.tight_layout()
plt.plot(averages_ocr, label='OCR', linewidth=2.0)
plt.plot(averages_q, label='Q', linewidth=2.0)
plt.plot(averages_mse, label='MSE', linewidth=2.0)
plt.ylim([0, 1])
plt.xlabel(u'Paslėptų neuronų skaičius')
plt.ylabel(u'Vidurinė Q įverčio pokyčio reikšmė')
plt.grid(True)
plt.tight_layout()
plt.legend(loc='lower right')
plt.show()示例4: plotB3reg
def plotB3reg():
w=loadStanFit('revE2B3BHreg.fit')
printCI(w,'mmu')
printCI(w,'mr')
for b in range(2):
subplot(1,2,b+1)
plt.title('')
px=np.array(np.linspace(-0.5,0.5,101),ndmin=2)
a0=np.array(w['mmu'][:,b],ndmin=2).T
a1=np.array(w['mr'][:,b],ndmin=2).T
y=np.concatenate([sap(a0+a1*px,97.5,axis=0),sap(a0+a1*px[:,::-1],2.5,axis=0)])
x=np.squeeze(np.concatenate([px,px[:,::-1]],axis=1))
plt.plot(px[0,:],np.median(a0)+np.median(a1)*px[0,:],'red')
#plt.plot([-1,1],[0.5,0.5],'grey')
ax=plt.gca()
ax.set_aspect(1)
ax.add_patch(plt.Polygon(np.array([x,y]).T,alpha=0.2,fill=True,fc='red',ec='w'))
y=np.concatenate([sap(a0+a1*px,75,axis=0),sap(a0+a1*px[:,::-1],25,axis=0)])
ax.add_patch(plt.Polygon(np.array([x,y]).T,alpha=0.2,fill=True,fc='red',ec='w'))
man=np.array([-0.4,-0.2,0,0.2,0.4])
mus=[]
for m in range(len(man)):
mus.append(loadStanFit('revE2B3BH%d.fit'%m)['mmu'][:,b])
mus=np.array(mus).T
errorbar(mus,x=man)
ax.set_xticks(man)
plt.xlim([-0.5,0.5])
plt.ylim([-0.4,0.8])
#plt.xlabel('Manipulated Displacement')
if b==0:
plt.ylabel('Perceived Displacemet')
plt.gca().set_yticklabels([])
subplot_annotate()
plt.text(-1.1,-0.6,'Pivot Displacement',fontsize=8);示例5: plot_sphere_x
def plot_sphere_x( s, fname ):
""" put plot of ionization fractions from sphere `s` into fname """
plt.figure()
s.Edges.units = 'kpc'
s.r_c.units = 'kpc'
xx = s.r_c
L = s.Edges[-1]
plt.plot( xx, np.log10( s.xHe1 ),
color='green', ls='-', label = r'$x_{\rm HeI}$' )
plt.plot( xx, np.log10( s.xHe2 ),
color='green', ls='--', label = r'$x_{\rm HeII}$' )
plt.plot( xx, np.log10( s.xHe3 ),
color='green', ls=':', label = r'$x_{\rm HeIII}$' )
plt.plot( xx, np.log10( s.xH1 ),
color='red', ls='-', label = r'$x_{\rm HI}$' )
plt.plot( xx, np.log10( s.xH2 ),
color='red', ls='--', label = r'$x_{\rm HII}$' )
plt.xlim( -L/20, L+L/20 )
plt.xlabel( 'r_c [kpc]' )
plt.ylim( -4.5, 0.2 )
plt.ylabel( 'log 10 ( x )' )
plt.grid()
plt.legend(loc='best', ncol=2)
plt.tight_layout()
plt.savefig( 'doc/img/x_' + fname )示例6: InitializePlot
def InitializePlot(self, goal_config):
self.fig = pl.figure()
lower_limits, upper_limits = self.boundary_limits
pl.xlim([lower_limits[0], upper_limits[0]])
pl.ylim([lower_limits[1], upper_limits[1]])
pl.plot(goal_config[0], goal_config[1], 'gx')
# Show all obstacles in environment
for b in self.robot.GetEnv().GetBodies():
if b.GetName() == self.robot.GetName():
continue
bb = b.ComputeAABB()
pl.plot([bb.pos()[0] - bb.extents()[0],
bb.pos()[0] + bb.extents()[0],
bb.pos()[0] + bb.extents()[0],
bb.pos()[0] - bb.extents()[0],
bb.pos()[0] - bb.extents()[0]],
[bb.pos()[1] - bb.extents()[1],
bb.pos()[1] - bb.extents()[1],
bb.pos()[1] + bb.extents()[1],
bb.pos()[1] + bb.extents()[1],
bb.pos()[1] - bb.extents()[1]], 'r')
pl.ion()
pl.show()示例7: rmsdSpreadSubplot
def rmsdSpreadSubplot(multiplier=1.0, layout=(-1, -1)):
rmsd_data = dict( (e, rad_data[e]['innov'][quant]) for e in rad_data.iterkeys() )
spread_data = dict( (e, rad_data[e]['spread'][quant]) for e in rad_data.iterkeys() )
times = temp.getTimes()
n_t = len(times)
for exp, exp_name in exp_names.iteritems():
pylab.plot(sawtooth(times, times)[:(n_t + 1)], rmsd_data[exp][:(n_t + 1)], color=colors[exp], linestyle='-')
pylab.plot(times[(n_t / 2):], rmsd_data[exp][n_t::2], color=colors[exp], linestyle='-')
for exp, exp_name in exp_names.iteritems():
pylab.plot(sawtooth(times, times)[:(n_t + 1)], spread_data[exp][:(n_t + 1)], color=colors[exp], linestyle='--')
pylab.plot(times[(n_t / 2):], spread_data[exp][n_t::2], color=colors[exp], linestyle='--')
ylim = pylab.ylim()
pylab.plot(times, -1 * np.ones((len(times),)), color='#999999', linestyle='-', label="RMS Innovation")
pylab.plot(times, -1 * np.ones((len(times),)), color='#999999', linestyle='--', label="Spread")
pylab.axhline(y=7, color='k', linestyle=':')
pylab.axvline(x=14400, color='k', linestyle=':')
pylab.ylabel("RMS Innovation/Spread (dBZ)", size='large')
pylab.xlim(times[0], times[-1])
pylab.ylim(ylim)
pylab.legend(loc=4)
pylab.xticks(times[::2], [ "" for t in times[::2] ])
pylab.yticks(size='x-large')
return示例8: InitializePlot
def InitializePlot(self, goal_config): # default
self.fig = pl.figure()
pl.xlim([self.lower_limits[0], self.upper_limits[0]])
pl.ylim([self.lower_limits[1], self.upper_limits[1]])
pl.plot(goal_config[0], goal_config[1], "gx")
# Show all obstacles in environment
for b in self.robot.GetEnv().GetBodies():
if b.GetName() == self.robot.GetName():
continue
bb = b.ComputeAABB()
pl.plot(
[
bb.pos()[0] - bb.extents()[0],
bb.pos()[0] + bb.extents()[0],
bb.pos()[0] + bb.extents()[0],
bb.pos()[0] - bb.extents()[0],
bb.pos()[0] - bb.extents()[0],
],
[
bb.pos()[1] - bb.extents()[1],
bb.pos()[1] - bb.extents()[1],
bb.pos()[1] + bb.extents()[1],
bb.pos()[1] + bb.extents()[1],
bb.pos()[1] - bb.extents()[1],
],
"r",
)
pl.ion()
pl.show()示例9: RosenbrockTest
def RosenbrockTest():
nDim = 3
numOfParticles = 20
maxIteration = 200
minX = array([-5.0]*nDim)
maxX = array([5.0]*nDim)
maxV = 0.2*(maxX - minX)
minV = -1.0*maxV
numOfTrial = 20
gBest = array([0.0]*maxIteration)
for i in xrange(numOfTrial):
p1 = RPSO.PSOProblem(nDim, numOfParticles, maxIteration, minX, maxX, minV, maxV, RPSO.Rosenbrock)
p1.run()
gBest = gBest + p1.gBestArray[:maxIteration]
gBest = gBest / numOfTrial
pylab.title('$G_{best}$ over 20 trials')
pylab.xlabel('The $N^{th}$ Iteratioin')
pylab.ylabel('Average gBest over '+str(numOfTrial)+' runs (logscale)')
pylab.grid(True)
# pylab.yscale('log')
ymin, ymax = -1.5, 2.5
ystep = 0.5
pylab.ylim(ymin, ymax)
yticks = linspace(ymin, ymax, (ymax-ymin)/ystep+1)
pylab.yticks(tuple(yticks),tuple(map(str,yticks)))
pylab.plot(range(maxIteration), log10(gBest),'-', label='Global best')
pylab.legend()
pylab.show()示例10: tracks_movie
def tracks_movie(base, skip=1, frames=500, size=10):
"""
A movie of each particle as a point
"""
conf, track, pegs = load(base)
fig = pl.figure(figsize=(size,size*conf['top']/conf['wall']))
plot = None
for t in xrange(1,max(frames, track.shape[1]/skip)):
tmp = track[:,t*skip,:]
if not ((tmp[:,0] > 0) & (tmp[:,1] > 0) & (tmp[:,0] < conf['wall']) & (tmp[:,1] < conf['top'])).any():
continue
if plot is None:
plot = pl.plot(tmp[:,0], tmp[:,1], 'k,', alpha=1.0, ms=0.1)[0]
pl.xticks([])
pl.yticks([])
pl.xlim(0,conf['wall'])
pl.ylim(0,conf['top'])
pl.tight_layout()
else:
plot.set_xdata(tmp[:,0])
plot.set_ydata(tmp[:,1])
pl.draw()
pl.savefig(base+'-movie-%05d.png' % (t-1))示例11: test_seq
def test_seq(self):
sleep(1)
self.cmd_logger(0)
self.takeoffpub.publish(Empty()) ;print 'takeoff' #takeoff
sleep(12); print '4'; sleep(1); print '3'; sleep(1); print '2'; sleep(1); print '1'; sleep(1)
self.cmd_logger(0)
self.twist.linear.z = self.vzcmd
self.cmd_logger(self.vzcmd)
self.cmdpub.publish(self.twist) ;print 'vzcmd' #set vzcmd
sleep(self.vzdur) #wait for vzdur
self.cmd_logger(self.vzcmd)
self.clear_twist()
self.cmd_logger(0)
self.cmdpub.publish(self.twist) ;print 'clear vz' #clear vz
sleep(4)
self.cmd_logger(0)
self.landpub.publish(Empty()) ;print 'land' #land
sleep(1)
if not raw_input('show and save?') == 'n':
pl.xlabel('time (s)')
pl.ylim(0,2400)
pl.plot(self.cmd_log['tm'],self.cmd_log['cmd'], 'b-s')
pl.plot(self.nd_log['tm'],self.nd_log['vz'], 'g-+')
pl.plot(self.nd_log['tm'],self.nd_log['al'], 'r-+')
pl.grid(True)
pl.show()示例12: plot_file_color
def plot_file_color(base, thin=True, start=0, size=14, save=False):
conf, track, pegs = load(base)
fig = pl.figure(figsize=(size,size*conf['top']/conf['wall']))
track = track[start:]
x = track[:,0]; y = track[:,1]
t = np.linspace(0,1,x.shape[0])
points = np.array([x,y]).transpose().reshape(-1,1,2)
segs = np.concatenate([points[:-1],points[1:]],axis=1)
lc = LineCollection(segs, linewidths=0.25, cmap=pl.cm.coolwarm)
lc.set_array(t)
pl.gca().add_collection(lc)
#pl.scatter(x, y, c=np.arange(len(x)),linestyle='-',cmap=pl.cm.coolwarm)
#pl.plot(track[-1000000:,0], track[-1000000:,1], '-', linewidth=0.0125, alpha=0.8)
for peg in pegs:
pl.gca().add_artist(pl.Circle(peg, conf['radius'], color='k', alpha=0.3))
pl.xlim(0, conf['wall'])
pl.ylim(0, conf['top'])
pl.xticks([])
pl.yticks([])
pl.tight_layout()
pl.show()
if save:
pl.savefig(base+".png", dpi=200)示例13: plot_prob_effector
def plot_prob_effector(sens, fpr, xmax=1, baserate=0.1):
"""Plots a line graph of P(effector|positive test) against
the baserate of effectors in the input set to the classifier.
The baserate argument draws an annotation arrow
indicating P(pos|+ve) at that baserate
"""
assert 0.1 <= xmax <= 1, "Max x axis value must be in range [0,1]"
assert 0.01 <= baserate <= 1, "Baserate annotation must be in range [0,1]"
baserates = pylab.arange(0, 1.05, xmax * 0.005)
probs = [p_correct_given_pos(sens, fpr, b) for b in baserates]
pylab.plot(baserates, probs, 'r')
pylab.title("P(eff|pos) vs baserate; sens: %.2f, fpr: %.2f" % (sens, fpr))
pylab.ylabel("P(effector|positive)")
pylab.xlabel("effector baserate")
pylab.xlim(0, xmax)
pylab.ylim(0, 1)
# Add annotation arrow
xpos, ypos = (baserate, p_correct_given_pos(sens, fpr, baserate))
if baserate < xmax:
if xpos > 0.7 * xmax:
xtextpos = 0.05 * xmax
else:
xtextpos = xpos + (xmax-xpos)/5.
if ypos > 0.5:
ytextpos = ypos - 0.05
else:
ytextpos = ypos + 0.05
pylab.annotate('baserate: %.2f, P(pos|+ve): %.3f' % (xpos, ypos),
xy=(xpos, ypos),
xytext=(xtextpos, ytextpos),
arrowprops=dict(facecolor='black', shrink=0.05))
else:
pylab.text(0.05 * xmax, 0.95, 'baserate: %.2f, P(pos|+ve): %.3f' % \
(xpos, ypos))示例14: pseudoSystem
def pseudoSystem():
#The corrolations discovered when answering this question shows the emergent effects of component evolution on CSE.
#We can further study these correlations by creating an example component system consisting of many components where a a different component has a new version released every day.
#By looking at users who Upgrade the system at different frequencies over 100 days, we present two graphs, Upgrade frequency to uttd and change.
l = 100
uttdxy = []
chxy = []
for uf in range(1,20):
uttd = range(uf)*(l*2/uf)
uttd = uttd[1:l+1]
uttdxy.append((uf,numpy.mean(uttd)))
sh = [0]*(uf-1) + [uf]
sh = sh*l
sh = sh[:l]
chxy.append((uf,sum(sh)))
pylab.figure(20)
x,y = zip(*sorted(uttdxy))
pylab.plot(x,y)
pylab.scatter(x,y)
saveFigure("q1bpseudouttd")
pylab.figure(21)
x,y = zip(*sorted(chxy))
pylab.plot(x,numpy.array(y))
pylab.scatter(x,numpy.array(y))
pylab.ylim([0,l+10])
saveFigure("q1bpseudochange")示例15: plotFeatureImportance
def plotFeatureImportance(featureImportance, title, originalImage=None, lim=0.06, colorate=None):
"""
originalImage : the index of the original image. If None, ignore
"""
indices = featureImportanceIndices(len(featureImportance), originalImage)
pl.figure()
pl.title(title)
if colorate is not None:
nbType = len(colorate)
X = [[] for i in range(nbType)]
Y = [[] for i in range(nbType)]
for j, f in enumerate(featureImportance):
X[j % nbType].append(j)
Y[j % nbType].append(f)
for i in range(nbType):
pl.bar(X[i], Y[i], align="center", label=colorate[i][0], color=colorate[i][1])
pl.legend()
else:
pl.bar(range(len(featureImportance)), featureImportance, align="center")
#pl.xticks(pl.arange(len(indices)), indices, rotation=-90)
pl.xlim([-1, len(indices)])
pl.ylabel("Feature importance")
pl.xlabel("Filter indices")
pl.ylim(0, lim)
pl.show()