本文整理汇总了Python中pylab.subplot2grid函数的典型用法代码示例。如果您正苦于以下问题：Python subplot2grid函数的具体用法？Python subplot2grid怎么用？Python subplot2grid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subplot2grid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: test2_filtersb_test_real
def test2_filtersb_test_real(self):
"""Test if filter_sideband() on real data for inspection"""
# NB: jpgs are required for correct [0,1] data range. PNG data range
# [0, 255] gives weird results?
files = ['fringe_130622_154235Z_000082_img.jpg',
'fringe_130622_154235Z_000139_img.jpg',
'fringe_130622_154235Z_000220_img.jpg',
'fringe_130622_154235Z_000330_img.jpg',
'fringe_130622_154235Z_000412_img.jpg',
'fringe_130622_154235Z_000505_img.jpg']
fringes = [tim.file.read_file(pjoin(TESTDATAPATH,f)) for f in files]
cfreq = fringe_cal(fringes, store_pow=False, do_embed=True)
apt_mask = tim.im.mk_rad_mask(*fringes[0].shape) < 1
self.fcache = {}
phasepow_l = [filter_sideband(i, cfreq, 0.5, method='spectral', apt_mask=apt_mask, unwrap=True, wsize=-0.5, wfunc='cosine', do_embed=True, cache=self.fcache, ret_pow=True, get_complex=False, verb=0) for i in fringes]
for im, phase in zip(fringes, phasepow_l):
plt.figure(400, figsize=(4,4)); plt.clf()
ax0 = plt.subplot2grid((2,2),(0, 0))
ax0.set_title("fringes")
ax0.imshow(im)
ax1 = plt.subplot2grid((2,2),(0, 1))
ax1.set_title("log side band power")
ax1.imshow(np.log(phase[2]))
ax2 = plt.subplot2grid((2,2),(1, 0))
ax2.set_title("phase")
ax2.imshow(phase[0])
ax3 = plt.subplot2grid((2,2),(1, 1))
ax3.set_title("amplitude")
ax3.imshow(phase[1])
if (raw_input("Continue [b=break]...") == 'b'): break
plt.close()示例2: plot_simulation_results_initial_controls
def plot_simulation_results_initial_controls(time_points, y_sim):
pl.rc("text", usetex = True)
pl.rc("font", family="serif")
pl.subplot2grid((4, 2), (0, 0))
pl.plot(time_points, y_sim[:,0])
pl.title("Simulation results for initial controls")
pl.xlabel("t")
pl.ylabel("X", rotation = 0, labelpad = 20)
pl.subplot2grid((4, 2), (1, 0))
pl.plot(time_points, y_sim[:,1])
pl.xlabel("t")
pl.ylabel("Y", rotation = 0, labelpad = 15)
pl.subplot2grid((4, 2), (2, 0))
pl.plot(time_points, y_sim[:,2])
pl.xlabel("t")
pl.ylabel(r"\phi", rotation = 0, labelpad = 15)
pl.subplot2grid((4, 2), (3, 0))
pl.plot(time_points, y_sim[:,3])
pl.xlabel("t")
pl.ylabel("v", rotation = 0, labelpad = 20)
pl.subplot2grid((4, 2), (0, 1), rowspan = 4)
pl.plot(y_sim[:,0], y_sim[:, 1])
pl.title("Simulated race car path for initial controls")
pl.xlabel("X")
pl.ylabel("Y", rotation = 0, labelpad = 20)
pl.show()示例3: plot_measurements
def plot_measurements(time_points, ydata):
pl.rc("text", usetex = True)
pl.rc("font", family="serif")
pl.subplot2grid((4, 2), (0, 0))
pl.plot(time_points, ydata[:,0])
pl.title("Considered measurement data")
pl.xlabel("t")
pl.ylabel("X", rotation = 0, labelpad = 20)
pl.subplot2grid((4, 2), (1, 0))
pl.plot(time_points, ydata[:,1])
pl.xlabel("t")
pl.ylabel("Y", rotation = 0, labelpad = 15)
pl.subplot2grid((4, 2), (2, 0))
pl.plot(time_points, ydata[:,2])
pl.xlabel("t")
pl.ylabel(r"\phi", rotation = 0, labelpad = 15)
pl.subplot2grid((4, 2), (3, 0))
pl.plot(time_points, ydata[:,3])
pl.xlabel("t")
pl.ylabel("v", rotation = 0, labelpad = 20)
pl.subplot2grid((4, 2), (0, 1), rowspan = 4)
pl.plot(ydata[:,0], ydata[:, 1])
pl.title("Considered racecar track (measured)")
pl.xlabel("X")
pl.ylabel("Y", rotation = 0, labelpad = 20)
pl.show()示例4: weightedAffineInvCompWarpCost
def weightedAffineInvCompWarpCost(targetIMG, templateIMG, templateWeight, curParameters, displayStuff, targetIMGMask = None):
#function [ImageWarpedToTemplate, TX, TY, ErrorIMG, CostValue] = lk_weighted_run_affine_inv_comp_warpcost(targetIMG, templateIMG, TemplateW, CurParameters, displayStuff)
# [ImageWarpedToTemplate, TX, TY] = lk_warp_image_affine(targetIMG, size(templateIMG), CurParameters);
targetIMGToTemplate, TX, TY = warpImageAffine(targetIMG, templateIMG.shape, curParameters)
if displayStuff == True:
pylab.subplot(4, 3, 5); CCSegUtils.showIMG(targetIMGToTemplate); pylab.title('template coordinates warped to image');
pylab.subplot2grid((4, 3), (1, 2), rowspan = 2, colspan = 1); pylab.cla(); CCSegUtils.showIMG(targetIMG);
pylab.plot(TX[:, 0], TY[:, 0], 'b-')
pylab.plot(TX[0, :], TY[0, :], 'b-')
pylab.plot(TX[:, -1], TY[:, -1], 'b-')
pylab.plot(TX[-1, :], TY[-1, :], 'b-')
pylab.title('Coordinates on target')
#print "oiajfdoijadsf"
errorIMG = targetIMGToTemplate - templateIMG
LKCost = numpy.sum(errorIMG * templateWeight * errorIMG)
# find out if any coordinates are not in the mask
if targetIMGMask != None:
T = CCSegUtils.interp2q(numpy.arange(1, targetIMG.shape[1] + 1), numpy.arange(1, targetIMG.shape[0] + 1), targetIMGMask, TX, TY, extrapval = 0)
if numpy.any(T == 0):
LKCost = numpy.inf
return (targetIMGToTemplate, TX, TY, errorIMG, LKCost)示例5: plot_xin
def plot_xin(viking,data,fit,output="plot.png",startrow=1,stoprow=-1,baseline=None):
values=odict()
#for fit functions
values["vl1"] = odict()
values["vl2"] = odict()
for name in ["viking","data","fit"]:
fname = getattr(args,name)
try:
values["vl1"][name]=read_data(fname, "vl1", args.startrow, args.stoprow)
values["vl2"][name]=read_data(fname, "vl2", args.startrow, args.stoprow)
except AttributeError as e:
pass
for v in ["vl1","vl2"]:
values[v]["diff"] = dict(values[v]["viking"])
values[v]["diff"]["y"] = values[v]["diff"]["y"] - values[v]["fit"]["y"]
pylab.figure(figsize=(6,6))
ylim=None
plots = odict()
top_plots = [ ["vl1",values["vl1"]["data"], "grey","-",1],
[None,values["vl1"]["fit"], "magenta","--",2],
[None,values["vl1"]["viking"], "blue","--",2],
["vl2",values["vl2"]["data"], "black","-",1],
[None,values["vl2"]["fit"], "green","--",2],
[None,values["vl2"]["viking"], "red","--",2]]
bottom_plots = [ ["vl1",values["vl1"]["diff"], "blue","-",2],
["vl2",values["vl2"]["diff"], "red","-",2] ]
ax = pylab.subplot2grid((9,1),(0, 0),rowspan=6)
handles = odict()
for n,p,c,l,w in top_plots:
ylim,handle=segmented_plot(p["x"],p["y"],ylim=ylim, color=c, ls=l,lw=w, return_handle=True,alpha=1)
if n:
handles[n] = handle
pylab.legend(handles,loc='upper left', framealpha=0.5)
pylab.xlabel("Ls") ; pylab.xlim(0,360) ; pylab.xticks([0,60,120,180,240,300,360])
pylab.ylabel("Pressure (Pa)") ; pylab.ylim(ylim)
pylab.grid()
ax = pylab.subplot2grid((9,1),(7, 0),rowspan=2)
ylim=None
handles = odict()
for n,p,c,l,w in bottom_plots:
ylim,handle=segmented_plot(p["x"],p["y"],ylim=ylim, color=c, ls=l,lw=w, return_handle=True,alpha=1)
if n:
handles[n] = handle
pylab.legend(handles, loc="upper left", framealpha=0.5)
pylab.xlabel("Ls") ; pylab.xlim(0,360) ; pylab.xticks([0,60,120,180,240,300,360])
pylab.ylabel("P (Pa)") ; pylab.ylim(ylim)
pylab.grid()
pylab.savefig(filename=args.output, dpi=150)示例6: main
def main( ):
global loc_
nGames = 20
prevRows, prevCells = np.zeros( shape=(10,10) ), np.zeros( shape=(100,100))
cellTransitionProbMean = [ ]
rowTransitionProbMean = [ ]
for i in range( nGames ):
loc_ = 0
while True:
r1, c1 = int(loc_ / 10), loc_
loc_ += roll_dice( )
loc_ = check_for_snakes_and_ladders( loc_ )
r2, c2 = int(loc_ / 10), loc_
if loc_ < 100:
cells_[c1,c2] += 1.0
rows_[r1,r2] += 1.0
if loc_ > 100:
print( "Game %d is over" % i )
loc_ = 0
thisRows = renormalize( rows_ )
thisCells = renormalize( cells_ )
diffRow = thisRows - prevRows
diffCell = thisCells - prevCells
rowTransitionProbMean.append( diffRow.mean( ) )
cellTransitionProbMean.append( diffCell.mean( ) )
prevRows, prevCells = thisRows, thisCells
# drawNow( img )
break
pCells = cells_ / cells_.sum( axis = 0 )
pRows = rows_ / rows_.sum( axis = 0 )
with np.errstate(divide='ignore', invalid='ignore'):
pCells = np.true_divide(cells_, cells_.sum( axis = 0 ) )
pRows = np.true_divide( rows_, rows_.sum( axis = 0 ) )
pCells = np.nan_to_num( pCells )
pRows = np.nan_to_num( pRows )
pylab.figure( figsize=(10,6) )
gridSize = (2, 2)
ax1 = pylab.subplot2grid( gridSize, (0,0), colspan = 1 )
ax2 = pylab.subplot2grid( gridSize, (0,1), colspan = 1 )
ax3 = pylab.subplot2grid( gridSize, (1,0), colspan = 2 )
ax1.set_title( 'Cell to cell transitions' )
img1 = ax1.imshow( pCells, interpolation = 'none' )
pylab.colorbar( img1, ax = ax1 ) #orientation = 'horizontal' )
img2 = ax2.imshow( pRows, interpolation = 'none' )
pylab.colorbar( img2, ax = ax2 ) # orientation = 'horizontal' )
ax2.set_title( 'Row to row transitions' )
ax3.plot( rowTransitionProbMean, '-o', label = 'Row probs' )
ax3.plot( cellTransitionProbMean, '-*', label = 'Cell probs' )
ax3.set_ylabel( 'diff of transition probs' )
ax3.set_xlabel( 'Number of games completed' )
ax3.legend( )
pylab.suptitle( 'Total games %d' % nGames )
# pylab.tight_layout( )
pylab.savefig( '%s.png' % sys.argv[0] )示例7: _run_interface
def _run_interface(self, runtime):
import matplotlib
matplotlib.use("Agg")
import pylab as plt
realignment_parameters = np.loadtxt(self.inputs.realignment_parameters)
title = self.inputs.title
F = plt.figure(figsize=(8.3, 11.7))
F.text(0.5, 0.96, self.inputs.title, horizontalalignment="center")
ax1 = plt.subplot2grid((2, 2), (0, 0), colspan=2)
handles = ax1.plot(realignment_parameters[:, 0:3])
ax1.legend(handles, ["x translation", "y translation", "z translation"], loc=0)
ax1.set_xlabel("image #")
ax1.set_ylabel("mm")
ax1.set_xlim((0, realignment_parameters.shape[0] - 1))
ax1.set_ylim(bottom=realignment_parameters[:, 0:3].min(), top=realignment_parameters[:, 0:3].max())
ax2 = plt.subplot2grid((2, 2), (1, 0), colspan=2)
handles = ax2.plot(realignment_parameters[:, 3:6] * 180.0 / np.pi)
ax2.legend(handles, ["pitch", "roll", "yaw"], loc=0)
ax2.set_xlabel("image #")
ax2.set_ylabel("degrees")
ax2.set_xlim((0, realignment_parameters.shape[0] - 1))
ax2.set_ylim(
bottom=(realignment_parameters[:, 3:6] * 180.0 / np.pi).min(),
top=(realignment_parameters[:, 3:6] * 180.0 / np.pi).max(),
)
if isdefined(self.inputs.outlier_files):
try:
outliers = np.loadtxt(self.inputs.outlier_files)
except IOError as e:
if e.args[0] == "End-of-file reached before encountering data.":
pass
else:
raise
else:
if outliers.size > 0:
ax1.vlines(outliers, ax1.get_ylim()[0], ax1.get_ylim()[1])
ax2.vlines(outliers, ax2.get_ylim()[0], ax2.get_ylim()[1])
if title != "":
filename = title.replace(" ", "_") + ".pdf"
else:
filename = "plot.pdf"
F.savefig(filename, papertype="a4", dpi=self.inputs.dpi)
plt.clf()
plt.close()
del F
self._plot = filename
runtime.returncode = 0
return runtime示例8: SE_spectrum_analysis
def SE_spectrum_analysis(sim_exp):
#load matlab file where timeserie is stored
#f = h5py.File('C:/Users/Squirel/Desktop/data_antoine/status_epilepticus/timeserie_SE_4j_rat8l_sr250Hz.mat', 'r')
if sim_exp=='exp':
f = h5py.File('C:/Users/Squirel/Desktop/data_antoine/status_epilepticus/EEG_rat8l.mat', 'r')
data = f.get('EEG/data')
data = pb.array(data) #transform to numpy array --> now each element of data is an array of one float
sf = 250. # sampling freq (Hz)
beg=120000; lgth = 20.; #stating time (s) and length (s) of the selected period
spectrum_ymax = 0.2 #maximum value displayed in plot (to have all the same scale)
ts_ylim = (-0.0020, 0.0010)
else: #sim_exp=='sim'
#radical='_40_HMR_40_ML_CpES1_0_CpES2_0_x0_35_noise3_0_noise2_30_noise1_60_gx1x2_20_gx2x1_20_gx1x1_20_gx2x2_20_r40_20s' #phase I
#radical='_40_HMR_40_ML_CpES1_20_CpES2_20_x0_20_noise3_0_noise2_30_noise1_60_gx1x2_20_gx2x1_20_gx1x1_20_gx2x2_20_r40_20s' # phase II
#radical='_40_HMR_40_ML_CpES1_80_CpES2_80_x0_20_noise3_0_noise2_30_noise1_60_gx1x2_20_gx2x1_20_gx1x1_20_gx2x2_20_r40_20s' #phase III
radical='_40_HMR_40_ML_CpES1_40_CpES2_40_x0_35_noise3_0_noise2_30_noise1_60_gx1x2_20_gx2x1_20_gx1x1_20_gx2x2_20_r40_100s' #phase IV !!! set beg=20 instead of 10s
x1_plot = np.load('./traces_dec13/x1plot'+radical+'.npy')
x2_plot = np.load('./traces_dec13/x2plot'+radical+'.npy')
#downsampling to 1kHz
x1_plot = x1_plot[:,0:-1:10]
x2_plot = x2_plot[:,0:-1:10]
data = -(0.75*x1_plot.mean(axis=0) + 0.25*x2_plot.mean(axis=0)) #transform to numpy array --> now each element of data is an array of one float
sf = 1000. # sampling freq (Hz)
beg=20.; lgth = 80.; #stating time (s) and length (s) of the selected period
spectrum_ymax = 300 #maximum value displayed in plot (to have all the same scale)
ts_ylim = (-0.5, 1.75)
minfreq=1.; maxfreq=50.; #Hz
#pb.plot(range(lgth*sf), data[beg*sf:beg*sf+lgth*sf]); pb.show() #if splot needed
signal = data[beg*sf:beg*sf+lgth*sf]
#signal = pb.sin(2*pb.pi*pb.arange(0,15,0.004))
#pb.plot(signal); pb.show()
spectrum = fft.fftn(signal)
delta_f = 1.0 / ((lgth*sf)/2) * lgth
#pb.plot(pb.arange(minfreq, maxfreq, delta_f),abs(spectrum[minfreq*delta_f:maxfreq*delta_f]), "."); pb.show()
#pb.plot(pb.arange(minfreq, maxfreq, 1/lgth), abs(spectrum[minfreq*lgth:maxfreq*lgth]), "."); pb.show()
spec = pb.arange(minfreq,maxfreq,1.0)
for i in range(len(spec)):
spec[i] = pb.mean(abs(spectrum[(minfreq+i)*lgth:(minfreq+i)*lgth + lgth]))
#pb.plot(spec)
#pb.show()
#plots:
fig = pb.figure()
ax1 = pb.subplot2grid((2,2),(0,0),colspan=2)
ax2 = pb.subplot2grid((2,2),(1,0))
ax3 = pb.subplot2grid((2,2),(1,1))
ax1.plot(signal)
ax1.set_ylim(ts_ylim)
ax2.plot(pb.arange(minfreq, maxfreq, 1/lgth), abs(spectrum[minfreq*lgth:maxfreq*lgth]), ".")
ax2.set_ylim(0,spectrum_ymax)
ax3.plot(spec)
pb.show()示例9: plotPendulumWithThetaAndPhi
def plotPendulumWithThetaAndPhi(animate = True, \
title = "PendulumNormalModes.png", t0 = p.t0, p0 = p.p0, nsteps = 120, massRat = rm):
global rm
old = rm
rm = massRat
fig = pylab.figure()
if animate:
ax1 = pylab.subplot2grid((1, 2), (0, 1), aspect = 1.0) # Pendulum
ax2 = pylab.subplot2grid((1, 2), (0, 0)) # Theta/Phi plots
else:
ax1 = fig.gca(aspect = 1.0)
fig2 = pylab.figure()
ax2 = fig2.gca()
pendSetup(ax1, titleHigh = True)
ax1.set_axis_on()
line1, = ax1.plot([], [], 'k', linestyle = '-', marker = 'o', \
markeredgecolor = 'k', markerfacecolor = (0.5, 0.5, 1.0), markersize = 10)
line1.set_markevery((1, 1))
line2, = ax2.plot([], [], 'b-', label = r"$\theta$")
line3, = ax2.plot([], [], 'r-', label = r"$\phi$")
ax2.set_xlim((0, nsteps * p.dt))
ax2.set_ylim((-30 * np.pi / 180.0, 30 * np.pi / 180))
ax2.set_xlabel(r"simulation time $\tau$", fontsize = 14)
ax2.set_ylabel("pendula angles", fontsize = 14)
pendAnimator1 = PendulumAnimate(line1)
angleAnimator = AngleAnimator(line2, line3)
simulation = DopeSimulation([pendAnimator1, angleAnimator], \
x0 = np.array([t0, p0, 0.0, 0.0]), nsteps = nsteps)
if animate:
anim = animation.FuncAnimation(fig, simulation, \
blit = True, init_func = simulation.reset, \
interval = 10, repeat = True)
simulation.reset()
for i in range(nsteps):
simulation(i)
pylab.legend(loc = 'lower right')
pylab.tight_layout()
if animate:
pylab.show()
else:
fig.savefig("pendulum_" + title)
fig2.savefig("angles_" + title)
rm = old示例10: PlotMCMC
def PlotMCMC(params, EPIC, sampler, lc, GP=False, KepName=''):
import corner
if type(sampler)!=np.ndarray:
samples = sampler.chain[:, -10000:, :].reshape((-1, len(params)))
else:
samples=sampler
#Turning b into absolute...
samples[:,1]=abs(samples[:,1])
p.figure(1)
#Clipping extreme values (top.bottom 0.1 percentiles)
toclip=np.array([(np.percentile(samples[:,t],99.9)>samples[:,t]) // (samples[:,t]>np.percentile(samples[:,t],0.1)) for t in range(len(params))]).all(axis=0)
samples=samples[toclip]
#Earmarking the difference between GP and non
labs = ["$T_{c}$","$b$","$v$","$Rp/Rs$","$u1$","$u2$","$\sigma_{white}$","$tau$","$a$"] if GP\
else ["$T_{c}$","$b$","$v$","$Rp/Rs$","$F_0$","$u1$","$u2$"]
#This plots the corner:
fig = corner.corner(samples, label=labs, quantiles=[0.16, 0.5, 0.84], plot_datapoints=False)
#Making sure the lightcuvre plot doesnt overstep the corner
ndim=np.shape(samples)[1]
rows=(ndim-1)/2
cols=(ndim-1)/2
#Printing Kepler name on plot
p.subplot(ndim,ndim,ndim+3).axis('off')
if KepName=='':
if str(int(EPIC)).zfill(9)[0]=='2':
KepName='EPIC'+str(EPIC)
else:
KepName='KIC'+str(EPIC)
p.title(KepName, fontsize=22)
#This plots the model on the same plot as the corner
ax = p.subplot2grid((ndim,ndim), (0, ndim-cols), rowspan=rows-1, colspan=cols)
modelfits=PlotModel(lc, samples, scale=1.0, GP=True)
#plotting residuals beneath:
ax = p.subplot2grid((ndim,ndim), (rows-1, ndim-cols), rowspan=1, colspan=cols)
modelfits=PlotModel(lc, samples, modelfits, residuals=True, GP=True, scale=1.0)
#Saving as pdf. Will save up to 3 unique files.
if os.path.exists(Namwd+'/Outputs/Corner_'+str(EPIC)+'_1.pdf'):
if os.path.exists(Namwd+'/Outputs/Corner_'+str(EPIC)+'_2.pdf'):
fname=Namwd+'/Outputs/Corner_'+str(EPIC)+'_3.pdf'
else:
fname=Namwd+'/Outputs/Corner_'+str(EPIC)+'_2.pdf'
else:
fname=Namwd+'/Outputs/Corner_'+str(EPIC)+'_1.pdf'
p.savefig(fname,Transparent=True,dpi=300)
return modelfits示例11: __init__
def __init__(self,overview):
self.overview = overview
self.fig = plt.figure(figsize=[14,12])
self.prof_ax = plt.subplot2grid([5,9],[0,1],colspan=2)
self.fold_ax = plt.subplot2grid([5,9],[1,1],colspan=2,rowspan=2,sharex=self.prof_ax)
self.subs_ax = plt.subplot2grid([5,9],[1,0],rowspan=2,sharey=self.fold_ax)
self.table_ax = plt.subplot2grid([5,9],[0,3],colspan=3,rowspan=3,frameon=False)
self.dm_ax = plt.subplot2grid([5,9],[0,6],colspan=2)
self.acc_ax = plt.subplot2grid([5,9],[1,8],colspan=1,rowspan=2)
self.dm_acc_ax = plt.subplot2grid([5,9],[1,6],colspan=2,rowspan=2,sharex=self.dm_ax,sharey=self.acc_ax)
self.all_ax = plt.subplot2grid([6,9],[4,0],colspan=9,rowspan=3)
self._plot_all_cands(self.all_ax)
self.timers = {
"read":Timer(),
"prof":Timer(),
"fold":Timer(),
"stat":Timer(),
"table":Timer(),
"dm":Timer(),
"acc":Timer(),
"dmacc":Timer(),
"write":Timer(),
"clear":Timer()
}
self.header = self.overview._xml.find("header_parameters")
self.fig.suptitle("Source name: %s"%self.header.find("source_name").text,fontsize=16)示例12: plot_rfs_and_theta
def plot_rfs_and_theta(sim,neurons,connections):
pre,post=neurons
c=connections[0]
weights=c.weights
num_neurons=len(weights)
fig=py.figure(figsize=(16,4*num_neurons))
for i,w in enumerate(weights):
try: # check for channel
neurons=pre.neuron_list
except AttributeError:
neurons=[pre]
num_channels=len(neurons)
count=0
vmin,vmax=w.min(),w.max()
for c,ch in enumerate(neurons):
try:
rf_size=ch.rf_size
if rf_size<0:
rf_size=py.sqrt(ch.N)
assert rf_size==int(rf_size)
rf_size=int(rf_size)
except AttributeError:
rf_size=py.sqrt(ch.N)
assert rf_size==int(rf_size)
rf_size=int(rf_size)
py.subplot2grid((num_neurons,num_channels+1),(i, c),aspect='equal')
subw=w[count:(count+rf_size*rf_size)]
#py.pcolor(subw.reshape((rf_size,rf_size)),cmap=py.cm.gray)
py.pcolormesh(subw.reshape((rf_size,rf_size)),cmap=py.cm.gray,
vmin=vmin,vmax=vmax)
py.xlim([0,rf_size]);
py.ylim([0,rf_size])
py.axis('off')
count+=rf_size*rf_size
py.subplot2grid((num_neurons,num_channels+1),(0, num_channels))
sim.monitors['theta'].plot()
return fig示例13: setup_main_screen
def setup_main_screen(self):
self.fig = pylab.figure(figsize=(8,8))
pylab.subplots_adjust(top=.93, bottom=0.1, left=.15, right=.97, hspace=.01, wspace=.15)
pylab.suptitle('Filter explore')
self.ax = pylab.subplot2grid((2, 5), (0, 0), colspan=4) #Amplitude response
self.ax2 = pylab.subplot2grid((2, 5), (1, 0), colspan=4) #Phase response
meax = self.menuax = pylab.subplot2grid((2, 5), (0, 4))
menu_names = ['butterworth', 'chebyshev I', 'chebyshev II', 'elliptic', 'bessel']
self.filter_types = ['butter', 'cheby1', 'cheby2', 'ellip', 'bessel']
self.menu_h = []
for n, item in enumerate(menu_names):
self.menu_h.append(meax.text(0.1, n, item, picker=5))
self.menu_h[0].set_weight('bold')
pylab.setp(meax, 'xticks', [], 'yticks', [], 'xlim',[0,1], 'ylim', [-.5, len(menu_names)-.5])示例14: pyplot
def pyplot(f, table, columns, resonance):
import pylab
global LINENUM
first = LINENUM < 0
LINENUM += 1
if LINENUM >= len(colors):
showplot(show=False)
LINENUM = 0
if LINENUM == 0:
if not first: FIGURES.append(pylab.gcf())
pylab.figure(figsize=(18,3), tight_layout={'pad':0})
pylab.subplot2grid((1,5),(0,0),colspan=4)
#pylab.figure(figsize=(8,2.3), tight_layout={'pad':0})
#pylab.subplot2grid((1,3),(0,0),colspan=2)
#print "shape",table.shape
name = os.path.splitext(os.path.basename(f))[0]
if '-' in name: name = "-".join(name.split('-')[1:])
label=name
p = abundance(f)
if p: label += " %.1f%%"%p
#label += " res: %.2fA"%wavelength(resonance)
color=colors[LINENUM]
for i in (2,): #range(1,table.shape[0]):
pylab.loglog(table[0,:].T*1e3, table[i,:], label=label,
linestyle=lines[i-1], color=color)
label='_nolegend_'
# Table of relative total cross section
if False:
if first:
print " "*(8*table.shape[0])+"%7s %7s %7s %7s"%("0.5A","6A","15A","20A")
TARGET=V2200
y0 = [np.interp(TARGET,table[0,:],table[i,:])
for i in range(1,table.shape[0])]
b_c = np.sqrt(y0[0]/(0.01*4*np.pi))
b_cL = np.sqrt(np.interp([L0p1,L0p2,L0p5,L6,L15,L20],table[0,:],table[1,:])/(0.01*4*np.pi))
delta = (b_cL-b_c)/b_c*100
#y0[0] = np.sqrt(y0[0]/(4*np.pi))
print "%7s"%name," ".join("%7.3f"%vi for vi in y0)," ".join("%6.1f%%"%vi for vi in delta)
else:
if first:
print "%7s %7s %15s"%("name","%","res. onset Ang/meV")
print "%7s %7s %7.2f %9.2f"%(
name, ("%.3f"%p if p else "-"),
wavelength(resonance), resonance*1e3)示例15: singular
def singular(Ytra,Ytes,sdata):
m=Ytra.shape[0]
mt=Ytes.shape[0]
xmean=np.mean(Ytra,axis=0)
ytra0=Ytra-np.outer(np.ones(m),xmean)
xmean=np.mean(Ytes,axis=0)
ytes0=Ytes-np.outer(np.ones(mt),xmean)
xnorm=np.sqrt(np.sum(ytra0**2,axis=0))
Ctra=np.dot(ytra0.T,ytra0)
xnorm=xnorm+(xnorm==0)
Ctra=Ctra/np.outer(xnorm,xnorm)
xnorm=np.sqrt(np.sum(ytes0**2,axis=0))
Ctes=np.dot(ytes0.T,ytes0)
xnorm=xnorm+(xnorm==0)
Ctes=Ctes/np.outer(xnorm,xnorm)
stra=np_lin.svd(Ctra)[1]
stes=np_lin.svd(Ctes)[1]
xlinewidth=3
fig1=lab.figure(figsize=(12,6))
ax1=lab.subplot2grid((10,2),(1,0), rowspan=9)
## ax=fig1.add_subplot(1,2,1)
ax1.plot(stra,linewidth=xlinewidth, \
linestyle='-',color='b')
ax1.set_xlabel('Label indexes', fontsize=14)
ax1.set_ylabel('Eigen values',fontsize=14)
ax1.set_title('Training:',fontsize=16)
ax1.grid(True)
## ax=fig1.add_subplot(1,2,2)
ax2=lab.subplot2grid((10,2),(1,1), rowspan=9)
ax2.plot(stes,linewidth=xlinewidth, \
linestyle='-',color='b')
ax2.set_xlabel('Label indexes', fontsize=14)
ax2.set_ylabel('Eigen values',fontsize=14)
ax2.set_title('Test:',fontsize=16)
ax2.grid(True)
fig1.suptitle('Eigen values of correlation matrixes: '+sdata,fontsize=18 )
lab.show()
return