本文整理汇总了Python中pylab.subplot函数的典型用法代码示例。如果您正苦于以下问题:Python subplot函数的具体用法?Python subplot怎么用?Python subplot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subplot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_tracks
def plot_tracks(src, fakewcs, spa=None, **kwargs):
# NOTE -- MAGIC 61 = monthly; this is ASSUMEd below.
tt = np.linspace(2010., 2015., 61)
t0 = TAITime(None, mjd=TAITime.mjd2k + 365.25*10)
#rd0 = src.getPositionAtTime(t0)
#print 'rd0:', rd0
xx,yy = [],[]
rr,dd = [],[]
for t in tt:
#print 'Time', t
rd = src.getPositionAtTime(t0 + (t - 2010.)*365.25*24.*3600.)
ra,dec = rd.ra, rd.dec
rr.append(ra)
dd.append(dec)
ok,x,y = fakewcs.radec2pixelxy(ra,dec)
xx.append(x - 1.)
yy.append(y - 1.)
if spa is None:
spa = [None,None,None]
for rows,cols,sub in spa:
if sub is not None:
plt.subplot(rows,cols,sub)
ax = plt.axis()
plt.plot(xx, yy, 'k-', **kwargs)
plt.axis(ax)
return rr,dd,tt
示例2: plot_vm
def plot_vm(self):
"""Plot Vm for presynaptic compartment and soma - along with
the same in NEURON simulation if possible."""
pylab.subplot(211)
pylab.title('Soma Vm')
pylab.plot(self.tseries*1e3, self.somaVmTab.vec * 1e3,
label='Vm (mV) - moose')
pylab.plot(self.tseries*1e3, self.injectionTab.vec * 1e9,
label='Stimulus (nA)')
try:
nrn_data = np.loadtxt('../nrn/data/%s_soma_Vm.dat' % \
(self.celltype))
nrn_indices = np.nonzero(nrn_data[:, 0] <= self.tseries[-1]*1e3)[0]
pylab.plot(nrn_data[nrn_indices,0], nrn_data[nrn_indices,1],
label='Vm (mV) - neuron')
except IOError:
print 'No neuron data found.'
pylab.legend()
pylab.subplot(212)
pylab.title('Presynaptic Vm')
pylab.plot(self.tseries*1e3, self.presynVmTab.vec * 1e3,
label='Vm (mV) - moose')
pylab.plot(self.tseries*1e3, self.injectionTab.vec * 1e9,
label='Stimulus (nA)')
try:
nrn_data = np.loadtxt('../nrn/data/%s_presynaptic_Vm.dat' % \
(self.celltype))
nrn_indices = np.nonzero(nrn_data[:, 0] <= self.tseries[-1]*1e3)[0]
pylab.plot(nrn_data[nrn_indices,0], nrn_data[nrn_indices,1],
label='Vm (mV) - neuron')
except IOError:
print 'No neuron data found.'
pylab.legend()
pylab.show()
示例3: Xtest2
def Xtest2(self):
"""
Test from Kate Marvel
As the following code snippet demonstrates, regridding a
cdms2.tvariable.TransientVariable instance using regridTool='regrid2'
results in a new array that is masked everywhere. regridTool='esmf'
and regridTool='libcf' both work as expected.
This passes.
"""
import cdms2 as cdms
import numpy as np
filename = cdat_info.get_sampledata_path() + '/clt.nc'
a=cdms.open(filename)
data=a('clt')[0,...]
print data.mask #verify this data is not masked
GRID= data.getGrid() # input = output grid, passes
test_data=data.regrid(GRID,regridTool='regrid2')
# check that the mask does not extend everywhere...
self.assertNotEqual(test_data.mask.sum(), test_data.size)
if PLOT:
pylab.subplot(2, 1, 1)
pylab.pcolor(data[...])
pylab.title('data')
pylab.subplot(2, 1, 2)
pylab.pcolor(test_data[...])
pylab.title('test_data (interpolated data)')
pylab.show()
示例4: 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5: plot_Barycenter
def plot_Barycenter(dataset_name, feat, unfeat, repo):
if dataset_name==MNIST:
_, _, test=get_data(dataset_name, repo, labels=True)
xtest1,_,_, labels,_=test
else:
_, _, test=get_data(dataset_name, repo, labels=False)
xtest1,_,_ =test
labels=np.zeros((len(xtest1),))
# get labels
def bary_wdl2(index): return _bary_wdl2(index, xtest1, feat, unfeat)
n=xtest1.shape[-1]
num_class = (int)(max(labels)+1)
barys=[bary_wdl2(np.where(labels==i)) for i in range(num_class)]
pl.figure(1, (num_class, 1))
for i in range(num_class):
pl.subplot(1,10,1+i)
pl.imshow(barys[i][0,0,:,:],cmap='Blues',interpolation='nearest')
pl.xticks(())
pl.yticks(())
if i==0:
pl.ylabel('DWE Bary.')
if num_class >1:
pl.title('{}'.format(i))
pl.tight_layout(pad=0,h_pad=-2,w_pad=-2)
pl.savefig("imgs/{}_dwe_bary.pdf".format(dataset_name))
示例6: graphError
def graphError(predictedPercentChanges, actualPercentChanges, title):
# considering error and only considering it as error when the signs are different
def computeSignedError(pred, actual):
if (pred > 0 and actual > 0) or (pred < 0 and actual < 0):
return 0
else:
error = abs(pred - actual)
# print 'pred: {0}, actual: {1}, error: {2}'.format(pred, actual, error)
return error
signedError = map(
lambda pred, actual: computeSignedError(pred, actual), predictedPercentChanges, actualPercentChanges
)
pl.figure(2)
pl.title(title + " Error")
pl.subplot(211)
pl.plot(signedError)
pl.xlabel("Time step")
pl.ylabel("Error (0 if signs are same and normal error if signs are different)")
pl.figure(3)
pl.title(title + " Actual vs Predictions")
pl.subplot(211)
pl.plot(
range(len(predictedPercentChanges)),
predictedPercentChanges,
"ro",
range(len(actualPercentChanges)),
actualPercentChanges,
"bs",
)
示例7: T2_cpmg_process
def T2_cpmg_process(folder_to_process,plot='y'):
"""Given a folder of images will process cpmg data and return
fitted T2 values and associated uncertainties"""
data=img_roi_signal([folder_to_process],['EchoTime'])
rois=data[0][0]
TEs=data[2][0]
mean_signal_mat=data[3]
serr_signal_mat=data[4]
if plot=='y':
plt.figure()
spin_echo_fits=[]
for jj in np.arange(len(rois)-2):
mean_sig=mean_signal_mat[0,jj,:]
#serr_sig=serr_signal_mat[0,jj,:]
mean_noise=np.mean(mean_signal_mat[0,-2,:])
try:
spin_echo_fit = SE_fit_new( np.array(TEs[0:]), mean_sig[0:], mean_noise, 'n' )
if plot=='y':
TE_full=np.arange(0,400,1)
plt.subplot(4,4,jj+1)
plt.plot(np.array(TEs[0:]), mean_sig[0:],'o')
plt.plot(TE_full,spin_echo_fit(TE_full))
spin_echo_fits.append(spin_echo_fit)
except RuntimeError:
print 'RuntimeError'
spin_echo=fitting.model('M0*exp(-x/T2)+a',{'M0':0,'T2':0,'a':0})
spin_echo_fits.append(spin_echo)
return spin_echo_fits
示例8: main
def main():
star = StarBinary(90.0, 0.5, nside=64, limb_law=-1, limb_coeff=[0.8])
Flux0 = star.flux(0.0)
phases = np.linspace(-0.5, 0.5, 100)
flux1 = np.zeros(len(phases))
for i in range(len(phases)):
flux1[i] = star.flux(phases[i]) / Flux0
# for tt in np.arange(0,360,80):
# star.makeSpot(0,-45,10.,0.8)
# star.makeSpot(45,+00,10.,0.8)
# star.makeSpot(00, -90, 20., 0.)
# for theta in range(0,360,45):
# star.makeSpot(theta,65,10.,0.8)
# star.makeSpot(00,-90,20.,0.)
# star.makeSpot(0,+45,10.,0.8)
# star.makeSpot(270,-10.,10.,0.8)
# star.makeSpot(180,-45.,10.,0.8)
# star.makeSpot(tt,65.,10.,0.5)
flux2 = np.zeros(len(phases))
for i in range(len(phases)):
flux2[i] = star.flux(phases[i]) / Flux0
H.mollview(star.I, sub=211, rot=(-90, 90))
#ff = np.loadtxt('/tmp/cl.dat', unpack=True)
py.subplot(212)
py.plot(phases, flux1, '-')
# py.plot(phases,flux2,'-')
#py.plot(ff[0], ff[1], '.')
py.show()
示例9: sim_results
def sim_results(obs, modes, stars, model, data):
synth = model.generate_data(modes)
synth_stats = model.summary_stats(synth)
obs_stats = model.summary_stats(obs)
f = plt.figure(figsize=(15,3))
plt.suptitle('Obs Cand.:{}; Sim Cand.:{}'.format(obs.size, synth.size))
plt.rc('legend', fontsize='xx-small', frameon=False)
plt.subplot(121)
bins = opt_bin(obs_stats[0],synth_stats[0])
plt.hist(obs_stats[0], bins=bins, histtype='step', label='Data', lw=2)
plt.hist(synth_stats[0], bins=bins, histtype='step', label='Simulation', lw=2)
plt.xlabel(r'$\xi$')
plt.legend()
plt.subplot(122)
bins = opt_bin(obs_stats[1],synth_stats[1])
plt.hist(obs_stats[1], bins=np.arange(bins.min()-0.5, bins.max()+1.5,
1),
histtype='step', label='Data', log=True, lw=2)
plt.hist(synth_stats[1], bins=np.arange(bins.min()-0.5, bins.max()+1.5,
1),
histtype='step', label='Simulation', log=True, lw=2)
plt.xlabel(r'$N_p$')
plt.legend()
示例10: graphSimpleResults
def graphSimpleResults(resultsDir):
x=[]
y=[]
files=[open("%s/objectiveFunctionReport.txt" % resultsDir),
open("%s/fitnessReport.txt" % resultsDir)]
for f in files:
x.append([])
y.append([])
i=len(x)-1
for line in f:
line=line.split(',')
if line[0] != "gen":
x[i].append(int(line[0]))
y[i].append(float(line[1]))
ylen=len(y[0])
pl.subplot(2,1,1)
pl.plot(x[0],y[0],'bo')
pl.ylabel('Maximum x')
pl.title('Maximizing x**2 with SGA')
pl.annotate("{0:,}".format(y[0][0]),xy=(x[0][0],y[0][0]), xycoords='data',
xytext=(50, 30), textcoords='offset points',
arrowprops=dict(arrowstyle="->") )
pl.annotate("{0:,}".format(y[0][ylen-1]),xy=(x[0][ylen-1],y[0][ylen-1]), xycoords='data',
xytext=(-30, -30), textcoords='offset points',
arrowprops=dict(arrowstyle="->") )
pl.subplot(2,1,2)
pl.plot(x[1],y[1],'go')
pl.xlabel('Generation')
pl.ylabel('Fitness')
pl.savefig("%s/simple_result.png" % resultsDir)
示例11: graphTSPResults
def graphTSPResults(resultsDir,numberOfTours):
x=[]
y=[]
files=[open("%s/objectiveFunctionReport.txt" % resultsDir),
open("%s/fitnessReport.txt" % resultsDir)]
for f in files:
x.append([])
y.append([])
i=len(x)-1
for line in f:
line=line.split(',')
if line[0] != "gen":
x[i].append(int(line[0]))
y[i].append(float(line[1] if i==1 else line[2]))
ylen=len(y[0])
pl.subplot(2,1,1)
pl.plot(x[0],y[0],'bo')
pl.ylabel('Minimum Distance')
pl.title("TSP with a %s City Tour" % numberOfTours)
pl.annotate("{0:,}".format(y[0][0]),xy=(x[0][0],y[0][0]), xycoords='data',
xytext=(30, -30), textcoords='offset points',
arrowprops=dict(arrowstyle="->") )
pl.annotate("{0:,}".format(y[0][ylen-1]),xy=(x[0][ylen-1],y[0][ylen-1]), xycoords='data',
xytext=(-30,30), textcoords='offset points',
arrowprops=dict(arrowstyle="->") )
pl.subplot(2,1,2)
pl.plot(x[1],y[1],'go')
pl.xlabel('Generation')
pl.ylabel('Fitness')
pl.savefig("%s/tsp_result.png" % resultsDir)
pl.clf()
示例12: main
def main():
src_cv_img_1 = cv.LoadImage("data/gorskaya/images/1/g126.jpg")
src_cv_img_gray_1 = cv.LoadImage("data/gorskaya/images/1/g126.jpg",
cv.CV_LOAD_IMAGE_GRAYSCALE)
src_cv_img_2 = cv.LoadImage("data/gorskaya/images/1/g127.jpg")
src_cv_img_gray_2 = cv.LoadImage("data/gorskaya/images/1/g127.jpg",
cv.CV_LOAD_IMAGE_GRAYSCALE)
(keypoints_1, descriptors_1) = \
cv.ExtractSURF(src_cv_img_gray_1, None, cv.CreateMemStorage(),
(0, 30000, 3, 1))
(keypoints_2, descriptors_2) = \
cv.ExtractSURF(src_cv_img_gray_2, None, cv.CreateMemStorage(),
(0, 30000, 3, 1))
print("Found {0} and {1} keypoints".format(
len(keypoints_1), len(keypoints_2)))
src_arr_1 = array(src_cv_img_1[:, :])[:, :, ::-1]
src_arr_2 = array(src_cv_img_2[:, :])[:, :, ::-1]
pylab.rc('image', interpolation='nearest')
pylab.subplot(121)
pylab.imshow(src_arr_1)
pylab.plot(*zip(*[k[0] for k in keypoints_1]),
marker='.', color='r', ls='')
pylab.subplot(122)
pylab.imshow(src_arr_2)
pylab.plot(*zip(*[k[0] for k in keypoints_2]),
marker='.', color='r', ls='')
pylab.show()
示例13: display_coeff
def display_coeff(data=None):
betaAll,betaErrAll, R2adjAll = measure_stamp_coeff(data = data, zernike_max_order=20)
ind = np.arange(len(betaAll[0]))
momname = ('M20','M22.Real','M22.imag','M31.real','M31.imag','M33.real','M33.imag')
fmtarr = ['bo-','ro-','go-','co-','mo-','yo-','ko-']
pl.figure(figsize=(17,13))
for i in range(7):
pl.subplot(7,1,i+1)
pl.errorbar(ind,betaAll[i],yerr = betaErrAll[i],fmt=fmtarr[i])
pl.grid()
pl.xlim(-1,21)
if i ==0:
pl.ylim(-10,65)
elif i ==1:
pl.ylim(-5,6)
elif i ==2:
pl.ylim(-5,6)
elif i == 3:
pl.ylim(-0.1,0.1)
elif i == 4:
pl.ylim(-0.1,0.1)
elif i ==5:
pl.ylim(-100,100)
elif i == 6:
pl.ylim(-100,100)
pl.xticks(ind,('','','','','','','','','','','','','','','','','','','',''))
pl.ylabel(momname[i])
pl.xticks(ind,('0','1','2','3','4','5','6','7','8','9','10','11','12','13','14','15','16','17','18','19'))
pl.xlabel('Zernike Coefficients')
return '--- done ! ----'
示例14: traceplot
def traceplot(traces, thin, burn):
'''
Plot parameter estimates for different levels of the model
into the same plots. Black lines are individual observers
and red lines are mean estimates.
'''
variables = ['Slope1', 'Slope2', 'Offset', 'Split']
for i, var in enumerate(variables):
plt.subplot(2, 2, i + 1)
vals = get_values(traces, var, thin, burn)
dim = (vals.min() - vals.std(), vals.max() + vals.std())
x = plt.linspace(*dim, num=1000)
for v in vals.T:
a = gaussian_kde(v)
y = a.evaluate(x)
y = y / y.max()
plt.plot(x, y, 'k', alpha=.5)
try:
vals = get_values(traces, 'Mean_' + var, thin, burn)
a = gaussian_kde(vals)
y = a.evaluate(x)
y = y / y.max()
plt.plot(x, y, 'r', alpha=.75)
except KeyError:
pass
plt.ylim([0, 1.1])
plt.yticks([0])
sns.despine(offset=5, trim=True)
plt.title(var)
示例15: display_orbit
def display_orbit(input, amplitude=0.000001):
import matplotlib.pyplot
import pylab
# Compare the power spectral density functions of the system with and
# without the input sequence.
x = [0.4, 0.6]
X = []
for i in range(warmups):
x = network(x)
x[0] = x[0] + ( amplitude * input[0][i % len(input[0])] )
x[1] = x[1] + ( amplitude * input[1][i % len(input[1])] )
for i in range(measure):
X.append(x[0])
x = network(x)
x[0] = x[0] + ( amplitude * input[0][i % len(input[0])] )
x[1] = x[1] + ( amplitude * input[1][i % len(input[1])] )
pylab.subplot(2,1,1)
matplotlib.pyplot.psd(X,1024,32)
x = [0.4, 0.6]
X = []
for i in range(warmups):
x = network(x)
# x[0] = x[0] + ( amplitude * input[i % len(input)] )
for i in range(measure):
X.append(x[0])
x = network(x)
# x[0] = x[0] + ( amplitude * input[i % len(input)] )
pylab.subplot(2,1,2)
matplotlib.pyplot.psd(X,1024,32)
pylab.show()