本文整理汇总了Python中matplotlib.pylab.subplots函数的典型用法代码示例。如果您正苦于以下问题：Python subplots函数的具体用法？Python subplots怎么用？Python subplots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subplots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_data_objs
def plot_data_objs(data_to_plot_list, figure_name, same=False):
rows = 2
cols = 2
topo = figure_name.split(':')[0]
# print "topo:",topo
# print "figname:",figure_name
if same:
fig_id = figure_name.split(':')[-1]
fig, ax = plt.subplots(rows, cols, num=fig_id)
else:
fig, ax = plt.subplots(rows, cols)
for i in range(rows):
for j in range(cols):
if len(data_to_plot_list) > i*rows+j:
obj = data_to_plot_list[i*rows+j]
if obj.scale == 'log' and max(obj.data) <= 0:
ydata = map(lambda x: -x, obj.data)
else:
ydata = obj.data
ax[i][j].plot(range(len(obj.data)), ydata, label=topo, linewidth=2.0)
ax[i][j].set_yscale(obj.scale)
ax[i][j].set_title(obj.label)
if same:
ax[i][j].legend(loc='lower left', shadow=True)
ax[i][j].legend().set_visible(False)
# fig.legend(loc='lower left', shadow=True)
if fig._suptitle is None:
suptitle = ':'.join(figure_name.split(':')[1:])
fig.suptitle(suptitle, fontsize=18)示例2: plotStrainHeatmap
def plotStrainHeatmap(self, alldata):
img = np.zeros((len(alldata[0, :, 0]), len(alldata[0, 0, :]), 4), dtype=np.uint8)
# for i in range(len(alldata[:, 0, 0])/2+1):
# rawdata = alldata[i, :, :]
# print i, np.max(rawdata)
# img[:, :, 3] = 255 # -20*i
# img[:, :, 1] = (255-i*10)*rawdata/np.max(rawdata)
# img[:, :, 2] = 0 # 255*rawdata/np.max(rawdata)
# img[:, :, 0] = 0 # 255*rawdata/np.max(rawdata)
#
# for i in range(len(alldata[:, 0, 0])/2):
# print i+len(alldata[:, 0, 0])/2+1, np.max(rawdata)
# rawdata = alldata[i+len(alldata[:, 0, 0])/2+1, :, :]
# img[:, :, 3] = 255 # -20*i
# img[:, :, 1] = 0
# img[:, :, 2] = (180+i*10)*rawdata/np.max(rawdata)
# img[:, :, 0] = (180+i*10)*rawdata/np.max(rawdata)
img = alldata[10, :, :]
print np.mean(img), np.max(img)
print np.mean(alldata[2, :, :]), np.max(alldata[2, :, :])
linestart = [999, 0]
linestop = [0, 999]
z, length = self.getProjection(img, linestart[0], linestart[1], linestop[0], linestop[1])
fig, ax = plt.subplots()
fig2, ax2 = plt.subplots()
ax2.plot(z)
ax.imshow(img)示例3: plotVolume
def plotVolume(self):
full = self.revolution(1000)
volu = self.volume(full)
import matplotlib as mpl
#mpl.use('Qt4Agg')
from matplotlib.pyplot import plot, show
import matplotlib.pylab as plt
if self.DEBUG:
fig, axs = plt.subplots(2, 1, sharex=True)
rev = self.revolution(2000)
pos = self._position(rev)
axs[0].plot(rev*180/pi,pos*100)
axs[0].plot(self.TDC()*180/pi,self._position(self.TDC())*100,'o')
iMin = np.where(pos==pos.min())
axs[0].plot(rev[iMin]*180/pi,self._position(rev[iMin])*100,'o')
iMax = np.where(pos==pos.max())
axs[0].plot(rev[iMax]*180/pi,self._position(rev[iMax])*100,'o')
axs[0].set_ylabel(r'Piston position (cm)')
ax = axs[1]
self.autolog("Position: ", str(pos.min()), str(pos.max()), str(self.stroke()))
self.autolog("Volume: ",str(volu.min()), str(volu.max()))
else:
fig, ax = plt.subplots(1, 1)
ax.plot(full*180/pi,volu*1e6)
ax.plot(self.TDC()*180/pi,self.volume(self.TDC())*1e6,'o')
iMin = np.where(volu==volu.min())
ax.plot(full[iMin]*180/pi,self.volume(full[iMin])*1e6,'o')
iMax = np.where(volu==volu.max())
ax.plot(full[iMax]*180/pi,self.volume(full[iMax])*1e6,'o')
ax.set_xlabel(r'Crankshaft angle $\theta$ (deg)')
ax.set_ylabel(r'Cylinder volume $V$ (cm$^3$)')
show()示例4: plotStrain
def plotStrain(self, strainpic, imgarray, label):
# import matplotlib.ticker as ticker
# sns.set_context("talk")
print "strainpic dimensions: " + str(np.shape(strainpic))
# gradient = self.adjustGradient()
figstrain, axstrain = plt.subplots(2, 1)
strainpic_adjusted = strainpic - np.mean(strainpic)
strainpic_adjusted[strainpic_adjusted > 0.00004] = 0.00004
strainpic_adjusted[strainpic_adjusted < -0.00004] = -0.00004
im = axstrain[0].imshow(strainpic_adjusted, cmap="BrBG")
# im = axstrain[0].imshow(strainpic+gradient, cmap="BrBG")
im2 = axstrain[1].imshow(imgarray[len(imgarray[:, 0, 0])/2-4, :, :], cmap="Greens")
axstrain[1].set_title("%g %g %g %g" % (self.roi[0], self.roi[1], self.roi[2], self.roi[3]))
axstrain[0].set_title(r'$\epsilon_{220}$')
def fmt(x, pos):
a, b = '{:.2e}'.format(x).split('e')
b = int(b)
return r'${} \times 10^{{{}}}$'.format(a, b)
figstrain.subplots_adjust(right=0.8)
cbar_ax1 = figstrain.add_axes([0.85, 0.55, 0.02, 0.35])
cbar_ax2 = figstrain.add_axes([0.85, 0.1, 0.02, 0.35])
clb = figstrain.colorbar(im, cax=cbar_ax1) # , format=ticker.FuncFormatter(fmt))
figstrain.colorbar(im2, cax=cbar_ax2)
linestart = [100, 50]
linestop = [100, 350]
clb.set_clim(-0.00004, 0.00004)
axstrain[0].autoscale(False)
axstrain[0].plot([linestart[0], linestop[0]], [linestart[1], linestop[1]])
z, length = self.getProjection(strainpic, linestart[0], linestart[1], linestop[0], linestop[1], 500)
f3, ax3 = plt.subplots()
linerange = np.linspace(0, 90*length/1000, len(z))
ax3.plot(linerange, z)
ax3.set_ylabel(r'Strain [$\Delta\theta/\theta$]')
ax3.set_xlabel(r'[$\mu m$]')
# np.save(self.directory + '/strainmap_array.txt', strainpic)
f3.savefig(self.directory + '/strainmap_line.pdf')
figstrain.savefig(self.directory + '/strainmap_%s.pdf' % str(label))
# f4, ax4 = plt.subplots()
# strain = np.reshape(strainpic, len(strainpic[:, 0])*len(strainpic[0, :]))
# # strain[strain<-0.0005] = 0
# # strain[strain>0.0005] = 0
# # sns.distplot(strain, kde=False, rug=False)
# ax4.set_xlim(np.min(strain)-abs(0.1*np.min(strain)), np.max(strain)+0.1*np.max(strain))
# # ax4.set_xlim(-0.0004,0.0004)
# ax4.set_xlabel(r'$\theta$ offset [$^o$]')
# ax4.set_title('Strain distribution')
# f4.savefig(self.directory + '/straindistribution.pdf')
return figstrain, axstrain示例5: plot_norm
def plot_norm(samples, fields=None, filename=None):
# get time from timestamp and sample time
t = samples.bicycle.dt.mean() * samples.ts
n = t.shape[0]
if fields is None:
# Set default to be fields that are not scalar with data available for
# at least 10% of the timerange
fields = []
for name in samples.dtype.names:
mask = samples.mask[name]
if len(mask.shape) < 2:
continue
if reduce(mul, mask.shape[1:], 1) == 1:
continue
if np.count_nonzero(mask) < n/10:
fields.append(name)
if isinstance(fields, str):
fields = (fields,)
n = len(fields)
if n > 6:
color = sns.husl_palette(n)
else:
color = sns.color_palette('muted', n)
if n > 1:
fig, axes = plt.subplots(math.ceil(n/2), 2)
axes = np.ravel(axes)
if len(axes) > n:
axes[-1].axis('off')
else:
fig, ax = plt.subplots()
axes = [ax]
for n, f in enumerate(fields):
ax = axes[n]
X = samples.__getattribute__(f)
x = np.linalg.norm(X, axis=(1, 2))
ax.set_xlabel('{} [{}]'.format('time', unit('time')))
ax.plot(t, x, color=color[n], label=f)
ax.legend()
if n > 0:
title = 'Norms'
else:
ax.legend().remove()
field_parts = fields[0].split('.')
title = ' '.join([f.title() for f in field_parts[:-1]] +
field_parts[-1:])
title = 'Norm of ' + title
axes = ax
_set_suptitle(fig, title, filename)
return fig, axes示例6: apply_unrot
def apply_unrot(filename):
import read_idb as ri
import dbutil as db
import copy
from util import lobe, Time
import matplotlib.pylab as plt
import numpy as np
blah = np.load('/common/tmp/Feed_rotation/20170702121949_delay_phase.npz')
dph = blah['dph']
fghz = blah['fghz']
out = ri.read_npz([filename])
nbl, npol, nfrq, nt = out['x'].shape
# Correct data for phase
#n = [0,0,0,1,1,0,1,0,1,1,0,0,0]
for i in range(13):
a1 = lobe(dph[i] - dph[13])
a2 = -dph[13] + np.pi/2
a3 = dph[i] - np.pi/2
for j in range(nt):
out['x'][ri.bl2ord[i,13],1,:,j] *= np.exp(1j*a1)
out['x'][ri.bl2ord[i,13],2,:,j] *= np.exp(1j*a2)
out['x'][ri.bl2ord[i,13],3,:,j] *= np.exp(1j*a3)
trange = Time(out['time'][[0,-1]],format='jd')
times, chi = db.get_chi(trange)
nskip = len(times)/nt
chi = np.transpose(chi[::nskip+1])
chi[[8,9,10,12]] = 0.0
outp = copy.deepcopy(out)
for i in range(nt):
for k in range(13):
outp['x'][ri.bl2ord[k,13],0] = out['x'][ri.bl2ord[k,13],0]*np.cos(chi[k,i]) + out['x'][ri.bl2ord[k,13],3]*np.sin(chi[k,i])
outp['x'][ri.bl2ord[k,13],2] = out['x'][ri.bl2ord[k,13],2]*np.cos(chi[k,i]) + out['x'][ri.bl2ord[k,13],1]*np.sin(chi[k,i])
outp['x'][ri.bl2ord[k,13],3] = out['x'][ri.bl2ord[k,13],3]*np.cos(chi[k,i]) - out['x'][ri.bl2ord[k,13],0]*np.sin(chi[k,i])
outp['x'][ri.bl2ord[k,13],1] = out['x'][ri.bl2ord[k,13],1]*np.cos(chi[k,i]) - out['x'][ri.bl2ord[k,13],2]*np.sin(chi[k,i])
amp0 = np.abs(np.sum(out['x'][ri.bl2ord[:,13]],3))
amp2 = np.abs(np.sum(outp['x'][ri.bl2ord[:,13]],3))
f, ax = plt.subplots(4,13)
for i in range(13):
for j in range(4):
ax[j,i].cla()
ax[j,i].plot(fghz, amp0[i,j],'.',color='lightgreen')
ax[j,i].plot(fghz, amp2[i,j],'k.')
ph0 = np.angle(np.sum(out['x'][ri.bl2ord[:,13]],3))
ph2 = np.angle(np.sum(outp['x'][ri.bl2ord[:,13]],3))
f, ax = plt.subplots(4,13)
for i in range(13):
for j in range(4):
ax[j,i].cla()
ax[j,i].plot(fghz, ph0[i,j],'.',color='lightgreen')
ax[j,i].plot(fghz, ph2[i,j],'k.')
示例7: makePlots
def makePlots(sampletitle, amplpic, midppic, fwhmpic):
fig0, ax0 = plt.subplots(1, 1, dpi=150, figsize=[5, 5])
# plt.tight_layout()
fig1, ax1 = plt.subplots(1, 1, dpi=150, figsize=[5, 5])
# plt.tight_layout()
fig2, ax2 = plt.subplots(1, 1, dpi=150, figsize=[5, 5])
# plt.tight_layout()
# fig0.set_figsize_inches(7,7)
# fig1.set_size_inches(7,7)
# fig2.set_size_inches(7,7)
im0 = ax0.imshow(amplpic[3:-3, 3:-3], cmap='jet', interpolation='None')
im1 = ax1.imshow(fwhmpic[3:-3, 3:-3], cmap='BrBG', interpolation='None')
im2 = ax2.imshow(midppic[3:-3, 3:-3] , cmap='BrBG', interpolation='None')
ax0.set_title('AMPL')
ax1.set_title('FWHM')
ax2.set_title('MIDP')
def fmt(x, pos):
a, b = '{:.2e}'.format(x).split('e')
b = int(b)
return r'${} \times 10^{{{}}}$'.format(a, b)
fig0.subplots_adjust(right=0.8)
fig1.subplots_adjust(right=0.8)
fig2.subplots_adjust(right=0.8)
cbar_ax0 = fig0.add_axes([0.85, 0.1, 0.05, 0.8])
cbar_ax1 = fig1.add_axes([0.85, 0.1, 0.05, 0.8])
cbar_ax2 = fig2.add_axes([0.85, 0.1, 0.05, 0.8])
clb0 = fig0.colorbar(im0, cax=cbar_ax0) # , format=ticker.FuncFormatter(fmt))
clb1 = fig1.colorbar(im1, cax=cbar_ax1) # , format=ticker.FuncFormatter(fmt))
clb2 = fig2.colorbar(im2, cax=cbar_ax2) # , format=ticker.FuncFormatter(fmt))
# # clb0.set_clim(0., 200.)
# clb2.set_clim(-0.1, 0.1)
# clb1.set_clim(-0.03, 0.03)
fig0.savefig('plots/%s-ampl.pdf' % (sampletitle))
fig1.savefig('plots/%s-fwhm.pdf' % (sampletitle))
fig2.savefig('plots/%s-midp.pdf' % (sampletitle))
fig0.clf()
fig1.clf()
fig2.clf()示例8: exampleNetworks
def exampleNetworks():
names = [u'Yoachim, P', u'Bellm, E', u'Williams, B', u'Williams, B', u'Capelo, P']
# add some caching so it only querries once.
if not hasattr(exampleNetworks,'results'):
exampleNetworks.results = [None for name in names]
exampleNetworks.graphs = [None for name in names]
years = [2007, 2011, 2002, 2010, 2012]
texts = ['(a)', '(b)','(c)', '(d)','(e)']
count = 1
figs = []
filenames = []
for name,year,txt in zip(names,years,texts):
fig,ax = plt.subplots()
figDummy, axDummy = plt.subplots()
phdA = list(ads.SearchQuery(q=u'bibstem:*PhDT', author=name, year=year,
database='astronomy'))[-1]
if exampleNetworks.results[count-1] is None:
result, graph = phdArticle2row(phdA, checkUSA=False, verbose=True, returnNetwork=True)
exampleNetworks.results[count-1] = result
exampleNetworks.graphs[count-1] = graph
else:
result = exampleNetworks.results[count-1]
graph = exampleNetworks.graphs[count-1]
years = []
for node in graph.nodes():
years.append(float(node[0:4]))
years = np.array(years)
# Make the graph repeatable
pos = {}
for i, node in enumerate(graph.nodes()):
pos[node] = (years[i],i**2)
layout = nx.spring_layout(graph, pos=pos)
nx.draw_networkx(graph, pos=layout, ax=ax, node_size=100,
node_color=years, alpha=0.5, with_labels=False)
#nx.draw_spring(graph, ax=ax, node_size=100,
# node_color=years, alpha=0.5, with_labels=False)
mappableDummy = axDummy.scatter(years,years,c=years)
cbar = plt.colorbar(mappableDummy, ax=ax, format='%i')
cbar.set_label('Year')
ax.text(.1,.8, txt, fontsize=24, transform=ax.transAxes)
ax.set_axis_off()
figs.append(fig)
filenames.append('example_network_%i' %count)
count += 1
print result
return figs, filenames示例9: plot
def plot(self,frequency=True,phase=False,dB=True,cal=True,fig=[],ax=[],color='k'):
"""
"""
if fig==[]:
fig,ax=plt.subplots(8,self.Nt,sharex=True,sharey=True)
if cal:
H = self.Hcal
else:
H = self.H
for iR in range(self.Nr):
for iT in range(self.Nt):
k = iR*4+iT
if frequency:
if not phase:
if dB:
#ax[iR,iT].plot(H.x,20*np.log10(abs(H.y[k,:])),color=color)
ax[iR,iT].plot(H.x,20*np.log10(abs(H.y[iR,iT,:])),color=color)
else:
#ax[iR,iT].plot(H.x,abs(H.y[k,:]),color='k')
ax[iR,iT].plot(H.x,abs(H.y[iR,iT,:]),color='k')
else:
#ax[iR,iT].plot(H.x,np.unwrap(np.angle(H.y[k,:])),color=color)
ax[iR,iT].plot(H.x,np.unwrap(np.angle(H.y[iR,iT,:])),color=color)
else:
ax[iR,iT].plot(self.h.x,abs(self.h.y[iR,iT,:]),color=color)
if (iR==7):
ax[iR,iT].set_xlabel('f (GHz)')
ax[iR,iT].set_title(str(iR+1)+'x'+str(iT+1))
return(fig,ax)示例10: plotGetRetangle
def plotGetRetangle():
""" Area selection from selected pen.
"""
selRect = []
if len(ds.EpmDatasetAnalysisPens.SelectedPens) != 1:
sr.msgBox('EPM Python Plugin - Demo Tools', 'Please select a single pen before applying this function!', 'Warning')
return 0
epmData = ds.EpmDatasetAnalysisPens.SelectedPens[0].values
y = epmData['Value'].copy()
x = np.arange(len(y))
fig, current_ax = pl.subplots()
pl.plot(x, y, lw=2, c='g', alpha=.3)
def line_select_callback(eclick, erelease):
'eclick and erelease are the press and release events'
x1, y1 = eclick.xdata, eclick.ydata
x2, y2 = erelease.xdata, erelease.ydata
print ("\n(%3.2f, %3.2f) --> (%3.2f, %3.2f)" % (x1, y1, x2, y2))
selRect.append((int(x1), y1, int(x2), y2))
def toggle_selector(event):
if event.key in ['Q', 'q'] and toggle_selector.RS.active:
toggle_selector.RS.set_active(False)
if event.key in ['A', 'a'] and not toggle_selector.RS.active:
toggle_selector.RS.set_active(True)
toggle_selector.RS = RectangleSelector(current_ax, line_select_callback, drawtype='box', useblit=True, button=[1,3], minspanx=5, minspany=5, spancoords='pixels')
pl.connect('key_press_event', toggle_selector)
pl.show()
return selRect示例11: plot_MheF
def plot_MheF(isotracks=None, labels=None, colors=None):
""" plot the minimum initial mass for He Fusion """
if isotracks is None:
isotracks = ['isotrack/parsec/CAF09_MC_S13v3_OV0.3.dat',
'isotrack/parsec/CAF09_MC_S13v3_OV0.4.dat',
'isotrack/parsec/CAF09_MC_S13v3_OV0.5.dat',
'isotrack/parsec/CAF09_MC_S13v3_OV0.6.dat',
'isotrack/parsec/CAF09_S12D_NS_1TP.dat']
isotracks = [os.path.join(os.environ['TRILEGAL_ROOT'], i)
for i in isotracks]
if labels is None:
labels = ['$\Lambda_c=0.3$',
'$\Lambda_c=0.4$',
'$\Lambda_c=0.5$',
'$\Lambda_c=0.6$',
'$S12D\_NS\_1TP$']
if colors is None:
colors = ['darkred', 'orange', 'navy', 'purple', 'k']
fig, ax = plt.subplots()
for i, isotrack in enumerate(isotracks):
isot = trilegal.IsoTrack(isotrack)
ax.plot(isot.Z, isot.mhefs, lw=2, label=labels[i], color=colors[i])
ax.plot(isot.Z, isot.mhefs, 'o', color=colors[i])
ax.grid()
ax.set_xlim(0.001, 0.0085)
ax.set_ylim(1.55, 2.05)
return ax示例12: test_params
def test_params():
#x = np.linspace(.8, 1.2, 1e2)
x = np.linspace(-.2, .2, 1e2)
num = 5
range_a = np.linspace(1, 2, num)
range_b = np.linspace(1., 1.1, num)
range_p = np.linspace(.1, .4, num)
range_q = np.linspace(.1, .4, num)
range_T = np.linspace(30, 365, num) / 365
args_def = {'a' : range_a.mean(), 'b' : range_b.mean(),
'p' : range_p.mean(), 'q' : range_q.mean(),
'T' : range_T.mean()}
ranges = {'a' : range_a, 'b' : range_b,
'p' : range_p, 'q' : range_q, 'T' : range_T}
fig, axes = plt.subplots(nrows = len(ranges), figsize = (6,12))
for name, a in zip(sorted(ranges.keys()), axes):
args = args_def.copy()
for pi in ranges[name]:
args[name] = pi
f = GB2(**args).density(x)
a.plot(x, f, label = pi)
a.legend(title = name)
plt.show()示例13: plot_integrated_colors
def plot_integrated_colors(filenames, labels='Z'):
if type(filenames) is str:
filenames = [filenames]
ax = None
cols = ['k']
else:
fig, ax = plt.subplots()
cols = brewer2mpl.get_map('Spectral', 'Diverging',
len(filenames)).mpl_colors
if labels == 'Z':
fmt = '$Z=%.4f$'
labels = [fmt % float(l.replace('.dat', '').split('Z')[1])
for l in filenames]
else:
print 'need to fix labels'
labels = [''] * len(filenames)
for i, filename in enumerate(filenames):
data = rsp.fileIO.readfile(filename)
ycol = 'V-K'
xcol = 'Age'
ax = rg.color_color(data, xcol, ycol, xscale='log', ax=ax,
plt_kw={'lw': 2, 'color': cols[i],
'label': labels[i]})
plot_cluster_data(ax)
ax.legend(frameon=False, loc=0, numpoints=1)
ax.set_xlabel(r'${\rm %s}$' % xcol, fontsize=20)
ax.set_ylabel(r'${\rm %s}$' % ycol, fontsize=20)
plt.tick_params(labelsize=16)
return ax示例14: interev_mag
def interev_mag(times, mags):
r"""Function to plot interevent times against magnitude for given times
and magnitudes.
:type times: list of datetime
:param times: list of the detection times, must be sorted the same as mags
:type mags: list of float
:param mags: list of magnitudes
"""
l = [(times[i], mags[i]) for i in xrange(len(times))]
l.sort(key=lambda tup: tup[0])
times = [x[0] for x in l]
mags = [x[1] for x in l]
# Make two subplots next to each other of time before and time after
fig, axes = plt.subplots(1, 2, sharey=True)
axes = axes.ravel()
pre_times = []
post_times = []
for i in range(len(times)):
if i > 0:
pre_times.append((times[i] - times[i - 1]) / 60)
if i < len(times) - 1:
post_times.append((times[i + 1] - times[i]) / 60)
axes[0].scatter(pre_times, mags[1:])
axes[0].set_title('Pre-event times')
axes[0].set_ylabel('Magnitude')
axes[0].set_xlabel('Time (Minutes)')
plt.setp(axes[0].xaxis.get_majorticklabels(), rotation=30)
axes[1].scatter(pre_times, mags[:-1])
axes[1].set_title('Post-event times')
axes[1].set_xlabel('Time (Minutes)')
plt.setp(axes[1].xaxis.get_majorticklabels(), rotation=30)
plt.show()示例15: plot_locality_regression
def plot_locality_regression(snps,cob,gene_limit=10):
# Get degree and bootstrap degree
log('Fetching Empirical Degree')
degree = cob.locality(cob.refgen.candidate_genes(snps,gene_limit=gene_limit,chain=True)).sort('local')
log('Fetching BS Degree')
#bsdegree = pd.concat([cob.locality(cob.refgen.bootstrap_candidate_genes(snps,gene_limit=gene_limit,chain=True)) for x in range(50)]).sort('local')
# get OLS for the bootstrapped degree
log('Fitting models')
model = sm.OLS(degree['global'],degree.local)
res = model.fit()
std, iv_l, iv_u = wls_prediction_std(res)
# plot the bootstrapped data
fig,ax = pylab.subplots(figsize=(8,6))
fig.hold(True)
ax.set_xlim(0,max(degree.local))
ax.set_ylim(0,max(degree['global']))
# plot the bootstraps std
# plot the true data
log('Plotting Empirical')
ax.plot(degree.local,degree['global'],'o',label='Empirical')
log('Plotting Residuals')
ax.plot(degree.local,res.fittedvalues,'--')
ax.plot(degree.local,res.fittedvalues+2.5*std,'r--')
ax.plot(degree.local,res.fittedvalues-2.5*std,'r--')
ax.set_xlabel('Number Local Interactions')
ax.set_ylabel('Number Global Interactions')
log('Saving Figure')
fig.savefig('{}_locality.png'.format(cob.name))