def plot(D,title):
im=plt.imshow(D,interpolation='nearest',cmap='Reds')
plt.gca().xaxis.tick_top()
x=np.arange(D.index.shape[0])
plt.colorbar(im)
plt.gca().tick_params(axis='both', which='major', labelsize=10)
plt.title(title,y=1.03)
def benchmark(clf_class, params, name):
print("parameters:", params)
t0 = time()
clf = clf_class(**params).fit(X_train, y_train)
print("done in %fs" % (time() - t0))
if hasattr(clf, 'coef_'):
print("Percentage of non zeros coef: %f"
% (np.mean(clf.coef_ != 0) * 100))
print("Predicting the outcomes of the testing set")
t0 = time()
pred = clf.predict(X_test)
print("done in %fs" % (time() - t0))
print("Classification report on test set for classifier:")
print(clf)
print()
print(classification_report(y_test, pred,
target_names=news_test.target_names))
cm = confusion_matrix(y_test, pred)
print("Confusion matrix:")
print(cm)
# Show confusion matrix
pl.matshow(cm)
pl.title('Confusion matrix of the %s classifier' % name)
pl.colorbar()
def display_image_from_array(nparray,colory='binary',roi=None):
"""
Produce a display of the nparray 2D matrix
@param nparray : image to display
@type nparray : numpy 2darray
@param colory : color mapping of the image (see http://www.scipy.org/Cookbook/Matplotlib/Show_colormaps)
@type colory : string
"""
#Set the region of interest to display :
# (0,0) is set at lower left corner of the image
if roi == None:
roi = ((0,0),nparray.shape)
nparraydsp = nparray
print roi
elif type(roi[0])==tuple and type(roi[1])==tuple:
# Case of 2 points definition of the domain : roi = integers index of points ((x1,y1),(x2,y2))
print roi
nparraydsp = nparray[roi[0][0]:roi[1][0],roi[0][1]:roi[1][1]]
elif type(roi[0])==int and type(roi[1])==int:
# Case of image centered domain : roi = integers (width,high)
nparraydsp = nparray[int(nparray.shape[0]/2)-int(roi[0])/2:int(nparray.shape[0]/2)+int(roi[0])/2,int(nparray.shape[1]/2)-int(roi[1])/2:int(nparray.shape[1]/2)+int(roi[1])/2]
fig = pylab.figure()
#Display array with grayscale intensity and no pixel smoothing interpolation
pylab.imshow(nparraydsp,cmap=colory,interpolation='nearest')#,origin='lower')
pylab.colorbar()
pylab.axis('off')
def connection_field_plot_continuous(self,index,afferent=True,density=30):
weights = self.proj.getWeights(format='array')
x = []
y = []
w = []
if afferent:
weights = weights[:,index].ravel()
p = self.proj.pre
else:
weights = weights[index,:].ravel()
p = self.proj.post
for (ww,i) in zip(weights,numpy.arange(0,len(weights),1)):
x.append(p.positions[0][i])
y.append(p.positions[1][i])
w.append(ww)
bx = min([min(p.positions[0]),min(p.positions[0])])
by = max([max(p.positions[1]),max(p.positions[1])])
xi = numpy.linspace(min(p.positions[0]),max(p.positions[0]),100)
yi = numpy.linspace(min(p.positions[1]),max(p.positions[1]),100)
zi = griddata(x,y,w,xi,yi)
pylab.figure()
pylab.imshow(zi)
pylab.title('Connection field from %s to %s of neuron %d' % (self.source.name,self.target.name,index))
pylab.colorbar()
def getOptCandGamma(cv_train, cv_label):
print "Finding optimal C and gamma for SVM with RBF Kernel"
C_range = 10.0 ** np.arange(-2, 9)
gamma_range = 10.0 ** np.arange(-5, 4)
param_grid = dict(gamma=gamma_range, C=C_range)
cv = StratifiedKFold(y=cv_label, n_folds=40)
# Use the svm.SVC() as the cost function to evaluate parameter choices
# NOTE: Perhaps we should run computations in parallel if needed. Does it
# do that already within the class?
grid = GridSearchCV(svm.SVC(), param_grid=param_grid, cv=cv)
grid.fit(cv_train, cv_label)
score_dict = grid.grid_scores_
scores = [x[1] for x in score_dict]
scores = np.array(scores).reshape(len(C_range), len(gamma_range))
pl.figure(figsize=(8,6))
pl.subplots_adjust(left=0.05, right=0.95, bottom=0.15, top=0.95)
pl.imshow(scores, interpolation='nearest', cmap=pl.cm.spectral)
pl.xlabel('gamma')
pl.ylabel('C')
pl.colorbar()
pl.xticks(np.arange(len(gamma_range)), gamma_range, rotation=45)
pl.yticks(np.arange(len(C_range)), C_range)
pl.show()
print "The best classifier is: ", grid.best_estimator_
def makeContourPlot(scores, average, HEIGHT, WIDTH, outputId, maskId, plt_title, outputdir, barcodeId=-1, vmaxVal=100):
pylab.bone()
#majorFormatter = FormatStrFormatter('%.f %%')
#ax = pylab.gca()
#ax.xaxis.set_major_formatter(majorFormatter)
pylab.figure()
ax = pylab.gca()
ax.set_xlabel(str(WIDTH) + ' wells')
ax.set_ylabel(str(HEIGHT) + ' wells')
ax.autoscale_view()
pylab.jet()
pylab.imshow(scores,vmin=0, vmax=vmaxVal, origin='lower')
pylab.vmin = 0.0
pylab.vmax = 100.0
ticksVal = getTicksForMaxVal(vmaxVal)
pylab.colorbar(format='%.0f %%',ticks=ticksVal)
print "'%s'" % average
if(barcodeId!=-1):
if(barcodeId==0): maskId = "No Barcode Match,"
else: maskId = "Barcode Id %d," % barcodeId
if plt_title != '': maskId = '%s\n%s' % (plt_title,maskId)
print "Checkpoint A"
pylab.title('%s Loading Density (Avg ~ %0.f%%)' % (maskId, average))
pylab.axis('scaled')
print "Checkpoint B"
pngFn = outputdir+'/'+outputId+'_density_contour.png'
print "Try save to", pngFn;
pylab.savefig(pngFn, bbox_inches='tight')
print "Plot saved to", pngFn;
def displayResults(self,res, cm=pylab.cm.gray, title='Specify a title'):
if self.display:
self.count=self.count+1
pylab.figure(self.count)
pylab.imshow(res, cm, interpolation='nearest')
pylab.colorbar()
pylab.title(title)
def correlation_matrix(data, size=8.0):
""" Calculates and shows the correlation matrix of the pandas data frame
'data' as a heat map.
Only the correlations between numerical variables are calculated!
"""
# calculate the correlation matrix
corr = data.corr()
#print corr
lc = len(corr.columns)
# set some settings for plottin'
pl.pcolor(corr, vmin = -1, vmax = 1, edgecolor = "black")
pl.colorbar()
pl.xlim([-5,lc])
pl.ylim([0,lc+5])
pl.axis('off')
# anotate the rows and columns with their corresponding variables
ax = pl.gca()
for i in range(0,lc):
ax.annotate(corr.columns[i], (-0.5, i+0.5), \
size='large', horizontalalignment='right', verticalalignment='center')
ax.annotate(corr.columns[i], (i+0.5, lc+0.5),\
size='large', rotation='vertical',\
horizontalalignment='center', verticalalignment='right')
# change the size of the image
fig = pl.figure(num=1)
fig.set_size_inches(size+(size/4), size)
pl.show()
def window_fn_matrix(Q,N,num_remov=None,save_tag=None,lms=None):
Q = n.matrix(Q); N = n.matrix(N)
Ninv = uf.pseudo_inverse(N,num_remov=None) # XXX want to remove dynamically
#print Ninv
info = n.dot(Q.H,n.dot(Ninv,Q))
M = uf.pseudo_inverse(info,num_remov=num_remov)
W = n.dot(M,info)
if save_tag!=None:
foo = W[0,:]
foo = n.real(n.array(foo))
foo.shape = (foo.shape[1]),
print foo.shape
p.scatter(lms[:,0],foo,c=lms[:,1],cmap=mpl.cm.PiYG,s=50)
p.xlabel('l (color is m)')
p.ylabel('W_0,lm')
p.title('First Row of Window Function Matrix')
p.colorbar()
p.savefig('{0}/{1}_W.pdf'.format(fig_loc,save_tag))
p.clf()
print 'W ',W.shape
p.imshow(n.real(W))
p.title('Window Function Matrix')
p.colorbar()
p.savefig('{0}/{1}_W_im.pdf'.format(fig_loc,save_tag))
p.clf()
return W
def __call__(self, n):
if len(self.f.shape) == 3:
# f = f[x,v,t], 2 dim in phase space
ft = self.f[n,:,:]
pylab.pcolormesh(self.X, self.V, ft.T, cmap = 'jet')
pylab.colorbar()
pylab.clim(0,0.38) # for Landau test case
pylab.grid()
pylab.axis([self.xmin, self.xmax, self.ymin, self.ymax])
pylab.xlabel('$x$', fontsize = 18)
pylab.ylabel('$v$', fontsize = 18)
pylab.title('$N_x$ = %d, $N_v$ = %d, $t$ = %2.1f' % (self.x.N, self.v.N, self.it*self.t.width))
pylab.savefig(self.path + self.filename)
pylab.clf()
return None
if len(self.f.shape) == 2:
# f = f[x], 1 dim in phase space
ft = self.f[n,:]
pylab.plot(self.x.gridvalues,ft,'ob')
pylab.grid()
pylab.axis([self.xmin, self.xmax, self.ymin, self.ymax])
pylab.xlabel('$x$', fontsize = 18)
pylab.ylabel('$f(x)$', fontsize = 18)
pylab.savefig(self.path + self.filename)
return None
def psfplots():
tpsf = wise.get_psf_model(1, pixpsf=True)
psfp = tpsf.getPointSourcePatch(0, 0)
psf = psfp.patch
psf /= psf.sum()
plt.clf()
plt.imshow(np.log10(np.maximum(1e-5, psf)), interpolation='nearest', origin='lower')
plt.colorbar()
ps.savefig()
h,w = psf.shape
cx,cy = w/2, h/2
X,Y = np.meshgrid(np.arange(w), np.arange(h))
R = np.sqrt((X - cx)**2 + (Y - cy)**2)
plt.clf()
plt.semilogy(R.ravel(), psf.ravel(), 'b.')
plt.xlabel('Radius (pixels)')
plt.ylabel('PSF value')
plt.ylim(1e-8, 1.)
ps.savefig()
plt.clf()
plt.loglog(R.ravel(), psf.ravel(), 'b.')
plt.xlabel('Radius (pixels)')
plt.ylabel('PSF value')
plt.ylim(1e-8, 1.)
ps.savefig()
print('PSF norm:', np.sqrt(np.sum(np.maximum(0, psf)**2)))
print('PSF max:', psf.max())
def savepng(pre, img, title=None, **kwargs):
fn = '%s-%s.png' % (pre, idstr)
print 'Saving', fn
plt.clf()
plt.imshow(img, **kwargs)
ax = plt.axis()
if debug:
print len(xplotx),len(allobjx)
for i,(objx,objy,objc) in enumerate(zip(allobjx,allobjy,allobjc)):
plt.plot(objx,objy,'-',c=objc)
tempx = []
tempx.append(xplotx[i])
tempx.append(objx[0])
tempy = []
tempy.append(xploty[i])
tempy.append(objy[0])
plt.plot(tempx,tempy,'-',c='purple')
plt.plot(pointx,pointy,'y.')
plt.plot(xplotx,xploty,'xg')
plt.axis(ax)
if title is not None:
plt.title(title)
plt.colorbar()
plt.gray()
plt.savefig(fn)
def plot_ch():
for job in jobs_orig:
print "plane of", job.path
pylab.clf()
x_center = int((job.position(0)[0] + job.position(1)[0])/2)
x_final = 50 + x_center
#plane = np.concatenate((job.plane(y=50)[:, x_final:],
# job.plane(y=50)[:, :x_final]), axis=1)
plane = job.plane(y=50)
myplane = plane[plane < 0.0]
p0 = myplane.min()
p12 = np.median(myplane)
p14 = np.median(myplane[myplane<p12])
p34 = np.median(myplane[myplane>p12])
p1 = myplane.max()
contour_values = (p0, p14, p12, p34, p1)
pylab.title(r'$u_x=%.4f,\ D_{-}=%.4f,\ D_{+}=%.4f,\ ch=%i$ ' %
(job.u_x, job.D_minus, job.D_plus, job.ch_objects))
car = pylab.imshow(plane, vmin=-0.001, vmax=0.0,
interpolation='nearest')
pylab.contour(plane, contour_values, linestyles='dashed',
colors='white')
pylab.grid(True)
pylab.colorbar(car)
#imgfilename = 'plane_r20-y50-u_x%.4fD%.4fch%03i.png' % \
# (job.u_x, job.D_minus, job.ch_objects)
imgfilename = 'plane_%s.png' % job.job_id
pylab.savefig(imgfilename)
def pressx(ifile, varkey, options, before = '', after = ''):
import pylab as pl
from matplotlib.colors import Normalize, LogNorm
outpath = getattr(options, 'outpath', '.')
vert = getpresbnds(ifile)
var = ifile.variables[varkey]
dims = [(k, l) for l, k in zip(var[:].shape, var.dimensions) if l > 1]
if len(dims) > 2:
raise ValueError('Press-x can have 2 non-unity dimensions; got %d - %s' % (len(dims), str(dims)))
if options.logscale:
norm = LogNorm()
else:
norm = Normalize()
exec(before)
ax = pl.gca()
print(varkey, end = '')
vals = var[:].squeeze()
x = np.arange(vals.shape[1])
patches = ax.pcolor(x, vert, vals, norm = norm)
#ax.set_xlabel(X.units.strip())
#ax.set_ylabel(Y.units.strip())
pl.colorbar(patches)
ax.set_ylim(vert.max(), vert.min())
ax.set_xlim(x.min(), x.max())
fmt = 'png'
figpath = os.path.join(outpath + '_PRESX_' + varkey + '.' + fmt)
exec(after)
pl.savefig(figpath)
print('Saved fig', figpath)
return figpath
def plot_worker(jobq,mask,pid,lineshape,range):
'''
args[0] = array file name
args[1] = output figure name
if mask, where masked==0 is masked
'''
if lineshape:
lines = shapefile.load_shape_list(lineshape)
else:
lines = None
while True:
#--get some args from the queue
args = jobq.get()
#--check if this is a sentenial
if args == None:
break
#--load
if args[2]:
arr = np.fromfile(args[0],dtype=np.float32)
arr.resize(bro.nrow,bro.ncol)
else:
arr = np.loadtxt(args[0])
if mask != None:
arr = np.ma.masked_where(mask==0,arr)
#print args[0],arr.min(),arr.max(),arr.mean()
#--generic plotting
fig = pylab.figure()
ax = pylab.subplot(1,1,1,aspect='equal')
if range:
vmax = range[1]
vmin = range[0]
else:
vmax = arr.max()
vmin = arr.min()
#p = ax.imshow(arr,interpolation='none')
p = ax.pcolor(bro.X,bro.Y,np.flipud(arr),vmax=vmax,vmin=vmin)
pylab.colorbar(p)
if lines:
for line in lines:
ax.plot(line[0,:],line[1,:],'k-',lw=1.0)
#break
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_xlim(bro.plt_x)
ax.set_ylim(bro.plt_y)
ax.set_title(args[0])
fmt = args[1].split('.')[-1]
pylab.savefig(args[1],dpi=300,format=fmt)
pylab.close(fig)
#--mark this task as done
jobq.task_done()
print 'plot worker',pid,' finished',args[0]
#--mark the sentenial as done
jobq.task_done()
return
def plot_gc_distribution(pp, data):
names = data.keys()
# Plot the 2D histogram of coverage vs gc
for name in names:
x = [ i * 100 for i in data[name][GC_DISTRIBUTION_NAME]['gc_samples'] ]
y = data[name][GC_DISTRIBUTION_NAME]['cov_samples']
# Use the median to determine the range to show and round
# to nearest 100 to avoid aliasing artefacts
m = np.median(y)
y_limit = math.ceil( 2*m / 100) * 100
hist,xedges,yedges = np.histogram2d(x,y, bins=[20, 50], range=[ [0, 100.0], [0, y_limit] ])
# draw the plot
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1] ]
pl.imshow(hist.T,extent=extent,interpolation='nearest',origin='lower', aspect='auto')
pl.colorbar()
pl.title(name + ' GC Bias')
pl.xlabel("GC %")
pl.ylabel("k-mer coverage")
pl.savefig(pp, format='pdf')
pl.close()
def plot_plasma(self):
P.tricontourf(self.rzt[:, 0], self.rzt[:, 1],
self.tris, self.beta, 1001, zorder=0)
cticks = P.linspace(0.0, 0.2, 5)
P.colorbar(ticks=cticks, format='%.2f')
P.jet()
def dovis(self):
"""
Do runtime visualization.
"""
pylab.clf()
phi = self.cc_data.get_var("phi")
myg = self.cc_data.grid
pylab.imshow(numpy.transpose(phi[myg.ilo:myg.ihi+1,
myg.jlo:myg.jhi+1]),
interpolation="nearest", origin="lower",
extent=[myg.xmin, myg.xmax, myg.ymin, myg.ymax])
pylab.xlabel("x")
pylab.ylabel("y")
pylab.title("phi")
pylab.colorbar()
pylab.figtext(0.05,0.0125, "t = %10.5f" % self.cc_data.t)
pylab.draw()
def field_map_ndar(ndar_field,t,ar_coorx,ar_coory,X,image_out,variable):
ar_field=ndar_field[t,:]
max_val=int(np.max(ndar_field))
if variable==4:
max_val=100.
xmin=min(ar_coorx);xmax=max(ar_coorx)
ymin=min(ar_coory);ymax=max(ar_coory)
step=X
nx=(xmax-xmin)/step+1
ny=(ymax-ymin)/step+1
ar_indx=np.array((ar_coorx-xmin)/step,int)
ar_indy=np.array((ar_coory-ymin)/step,int)
ar_map=np.ones((ny,nx))*-99.9
ar_map[ar_indy,ar_indx]=ar_field
ar_map2 = M.masked_where(ar_map <0, ar_map)
ut.check_file_exist(image_out)
pl.clf()
pl.axes(axisbg='gray')
pl.imshow(ar_map2, cmap=pl.cm.RdBu,
interpolation='Nearest', origin='lower', vmax=max_val, vmin=0)
pl.title('time step= '+ut.string(t,len(str(t))))
pl.colorbar()
pl.savefig(image_out)
def VisualizeAlm(alm,figno=1,max_l=None):
""" Visualize a healpy a_lm vector """
lmax = hp.Alm.getlmax(f_lm.size)
l,m = hp.Alm.getlm(lmax)
mag = np.zeros([lmax+1,lmax+1])
phs = np.zeros([lmax+1,lmax+1])
mag[m,l] = np.abs(alm)
phs[m,l] = np.angle(alm)
cl = hp.alm2cl(alm)
# Decide the range of l to plot
if max_l != None:
max_l = (max_l if (max_l <= lmax) else lmax)
else:
max_l = lmax
print max_l
plt.figure(figno)
plt.clf()
plt.subplot(211)
plt.imshow(mag[0:max_l,0:max_l],interpolation='nearest',origin='lower')
plt.colorbar()
plt.subplot(212)
plt.imshow(phs[0:max_l,0:max_l],interpolation='nearest',origin='lower')
plt.colorbar()
# plt.subplot(313)
#plt.semilogy(cl[0:max_l])
return {'mag':mag,'phs':phs,'cl':cl}
def plotphicont(X,Y,Z):
from pylab import subplot, show, contourf, contour, colorbar, title
spl = subplot(111)
cpl = contour(X,Y,Z,100)
colorbar(cpl)
spl.set_aspect('equal','box')
title(r'Field potential $\varphi$')
def plot_box_data(field,redshift):
nf1 = get_box_data(field,redshift)
chosenIndex=200
nf1 = nf1.reshape((400,400,400))[chosenIndex,:,:]
plt.imshow(nf1)
plt.colorbar()
plt.show()
def draw_heat_graph(getgraph, opts):
# from pyevolve_graph script
stage_points = getgraph()
fg = pl.figure()
ax = fg.add_subplot(111)
pl.imshow(
stage_points, aspect="auto", interpolation="gaussian",
cmap=matplotlib.cm.__dict__["jet"])
pl.title("Population scores along the generations")
def labelfmt(x, pos=0):
# there is surely a better way to do that
return (float(x) == int(x)) and '%d' % (x) or ''
ax.xaxis.set_major_formatter(pl.FuncFormatter(labelfmt))
pl.xlabel('Generations -->')
pl.ylabel('Sorted Population Results')
pl.grid(True)
pl.colorbar()
if opts.outfile:
fg.savefig(opts.outfile)
if opts.show:
pl.show()
def plothistory(self):
a=self.a
b=self.b
plt.figure(figsize=(12,6))
I=np.concatenate([a.T,np.array(np.nansum(a[:,:3],1),ndmin=2),
np.array(np.nansum(a[:,3:6],1),ndmin=2),np.array(np.nansum(a[:,6:],1),ndmin=2)],axis=0)
plt.plot(range(b.size),b,'rx',ms=8,mew=2)
plt.plot([10.5,10.5],[-1,I.shape[1]],'r',lw=2)
plt.imshow(I,interpolation='nearest',cmap='winter')
plt.colorbar()
ax=plt.gca()
ax.set_yticks(range(I.shape[0]))
ax.set_yticklabels(['']*a.shape[0]+['color','rel len','abs len'])
c1=plt.Circle((-1.5,0),radius=0.4,color='blue',clip_on=False)
c2=plt.Circle((-1.5,1),radius=0.4,color='white',clip_on=False)
c3=plt.Circle((-1.5,2),radius=0.4,color='yellow',clip_on=False)
ax.add_patch(c1);ax.add_patch(c2);ax.add_patch(c3);
c1=plt.Rectangle((-2,3),1,0.2,color='white',clip_on=False)
c2=plt.Rectangle((-2.5,4),1.5,0.2,color='white',clip_on=False)
c3=plt.Rectangle((-3,5),2,0.2,color='white',clip_on=False)
ax.add_patch(c1);ax.add_patch(c2);ax.add_patch(c3);
c1=plt.Rectangle((-2,6),1,0.2,color='gray',clip_on=False)
c2=plt.Rectangle((-2.5,7),1.5,0.2,color='gray',clip_on=False)
c3=plt.Rectangle((-3,8),2,0.2,color='gray',clip_on=False)
c4=plt.Rectangle((-3.5,9),2.5,0.2,color='gray',clip_on=False)
ax.add_patch(c1);ax.add_patch(c2);ax.add_patch(c3);ax.add_patch(c4);
print I[-3,-1]
def plot_samples_distance(dataset, sortbyattr=None):
"""Plot the euclidean distances between all samples of a dataset.
Parameters
----------
dataset : Dataset
Providing the samples.
sortbyattr : None or str
If None, the samples distances will be in the same order as their
appearance in the dataset. Alternatively, the name of a samples
attribute can be given, which wil then be used to sort/group the
samples, e.g. to investigate the similarity samples by label or by
chunks.
"""
if sortbyattr is not None:
slicer = []
for attr in dataset.sa[sortbyattr].unique:
slicer += \
get_samples_by_attr(dataset, sortbyattr, attr).tolist()
samples = dataset.samples[slicer]
else:
samples = dataset.samples
ed = np.sqrt(squared_euclidean_distance(samples))
pl.imshow(ed, interpolation='nearest')
pl.colorbar()
def plot_dendrogram_and_matrix(linkage, matrix, color_threshold=None):
# Compute and plot dendrogram.
fig = pylab.figure(figsize=(20,20))
axdendro = fig.add_axes([0.09,0.1,0.2,0.8])
dendrogram = sch.dendrogram(linkage, color_threshold=color_threshold, orientation='right')
axdendro.set_xticks([])
axdendro.set_yticks([])
# Plot distance matrix.
axmatrix = fig.add_axes([0.3,0.1,0.6,0.8])
index = dendrogram['leaves']
D = matrix[:]
D = D[index,:]
D = D[:,index]
im = axmatrix.matshow(D, aspect='auto', origin='lower')
axmatrix.set_xticks([])
axmatrix.set_yticks([])
# Plot colorbar.
axcolor = fig.add_axes([0.91,0.1,0.02,0.8])
pylab.colorbar(im, cax=axcolor)
# Display and save figure.
fig.show()
raw_input()
return dendrogram
def plot_mtx(mtx=None, title=None, newfig=False, cbar=True, **kwargs):
"""
::
static method for plotting a matrix as a time-frequency distribution (audio features)
"""
if mtx is None or type(mtx) != np.ndarray:
raise ValueError('First argument, mtx, must be a array')
if newfig: P.figure()
dbscale = kwargs.pop('dbscale', False)
bels = kwargs.pop('bels',False)
norm = kwargs.pop('norm',False)
normalize = kwargs.pop('normalize',False)
origin=kwargs.pop('origin','lower')
aspect=kwargs.pop('aspect','auto')
interpolation=kwargs.pop('interpolation','nearest')
cmap=kwargs.pop('cmap',P.cm.gray_r)
clip=-100.
X = scale_mtx(mtx, normalize=normalize, dbscale=dbscale, norm=norm, bels=bels)
i_min, i_max = np.where(X.mean(1))[0][[0,-1]]
X = X[i_min:i_max+1].copy()
if dbscale or bels:
if bels: clip/=10.
P.imshow(P.clip(X,clip,0),origin=origin, aspect=aspect, interpolation=interpolation, cmap=cmap, **kwargs)
else:
P.imshow(X,origin=origin, aspect=aspect, interpolation=interpolation, cmap=cmap, **kwargs)
if title:
P.title(title,fontsize=16)
if cbar:
P.colorbar()
P.yticks(np.arange(0,i_max+1-i_min,3),pc_labels[i_min:i_max+1:3],fontsize=14)
P.xlabel('Tactus', fontsize=14)
P.ylabel('MIDI Pitch', fontsize=14)
P.grid()
def plot_C_gamma_grid_search(grid, C_range, gamma_range, score):
'''
Plots the scores computed on a grid.
Arguments:
grid - the grid search object created using GridSearchCV()
C_range - the C parameter range
gamma_range - the gamma parameter range
score - the scoring function
'''
# grid_scores_ contains parameter settings and scores
# We extract just the scores
scores = [x[1] for x in grid.grid_scores_]
scores = np.array(scores).reshape(len(C_range), len(gamma_range))
# draw heatmap of accuracy as a function of gamma and C
pl.figure(figsize=(8, 6))
pl.subplots_adjust(left=0.05, right=0.95, bottom=0.15, top=0.95)
pl.imshow(scores, interpolation='nearest', cmap=pl.cm.spectral)
pl.title("Grid search on C and gamma for best %s" % score)
pl.xlabel('gamma')
pl.ylabel('C')
pl.colorbar()
pl.xticks(np.arange(len(gamma_range)), gamma_range, rotation=45)
pl.yticks(np.arange(len(C_range)), C_range)
pl.show()
def plot_vel_vs_h3(maps, out_suffix=''):
xaxis = (np.arange(len(maps.velocity[0])) - ppxf_m31.bhpos_pix[0]) * 0.05
yaxis = (np.arange(len(maps.velocity)) - ppxf_m31.bhpos_pix[1]) * 0.05
yy, xx = np.meshgrid(xaxis, yaxis)
radius = np.hypot(xx, yy)
good = np.where((np.abs(yy) < 0.5) & (np.abs(xx) < 1.0))
plt.scatter(maps.velocity[good], maps.h3[good], c=maps.sigma[good], s=5,
marker='o', vmin=0, vmax=450)
plt.xlim(-700, 0)
plt.ylim(-0.5, 0.5)
plt.colorbar(label='Sigma (km/s)')
plt.axhline(linestyle='--', color='grey')
plt.xlabel('Velocity (km/s)')
plt.ylabel('h3')
plt.savefig(plot_dir + 'vel_vs_h3' + out_suffix + '.png')
plt.clf()
plt.scatter(maps.sigma[good], maps.h3[good], c=maps.velocity[good], s=5,
marker='o', vmin=-700, vmax=0)
plt.xlim(0, 450)
plt.ylim(-0.5, 0.5)
plt.colorbar(label='Velocity (km/s)')
plt.axhline(linestyle='--', color='grey')
plt.xlabel('Sigma (km/s)')
plt.ylabel('h3')
plt.savefig(plot_dir + 'sig_vs_h3' + out_suffix + '.png')
return
def main():
base_path = "/caps2/tsupinie/1kmf-control/"
temp = goshen_1km_temporal(start=14400, end=14400)
grid = goshen_1km_grid()
n_ens_members = 40
np.seterr(all='ignore')
ens = loadEnsemble(base_path, [ 11 ], temp.getTimes(), ([ 'pt', 'p' ], computeDensity))
ens = ens[0, 0]
zs = decompressVariable(nio.open_file("%s/ena001.hdfgrdbas" % base_path, mode='r', format='hdf').variables['zp'])
xs, ys = grid.getXY()
xs = xs[np.newaxis, ...].repeat(zs.shape[0], axis=0)
ys = ys[np.newaxis, ...].repeat(zs.shape[0], axis=0)
eff_buoy = effectiveBuoyancy(ens, (zs, ys, xs), plane={'z':10})
print eff_buoy
pylab.figure()
pylab.contourf(xs[0], ys[0], eff_buoy[0], cmap=matplotlib.cm.get_cmap('RdBu_r'))
pylab.colorbar()
grid.drawPolitical()
pylab.suptitle("Effective Buoyancy")
pylab.savefig("eff_buoy.png")
pylab.close()
return
def plot_coef_corr_seismic(offsets, trace_synt_ar_int, seismicData, T,
tmp_time_max, data_KK, int_time1, int_time2,
data_short, sgyname):
"""
Draws coef. correlation betweem real and synthetic seismic data
Parameters
----------
Input:
offsets - array of offsets (m)
trace_synt_ar_int - synthetic data interpolate on seismic grid
seismicData - seismic data class
T - time axs for seismic
tmp_time_max - picking of max amplitude near explored horizon
data_KK - array with correlation coefficients
int_time1 - upper time near explored horizon
int_time2 - lower time near explored horizon
data_short - interval real data
sgy_name - name of current segy
Output:
subplot figure with synthetic and real data, color shows the correlation coefficient between them
"""
#допилить граничные значения
#T_plot = np.arange(int_time1, int_time2, seismicData.dt)
fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(35, 12))
plt.sca(axs[0])
for ii in range(trace_synt_ar_int.shape[0]):
plt.plot(
trace_synt_ar_int[ii, :] / np.max(trace_synt_ar_int[ii, :]).T +
ii + 1,
T,
'k',
alpha=1,
zorder=1,
lw=1)
x = trace_synt_ar_int[ii, :] / np.max(
trace_synt_ar_int[ii, :]).T + ii + 1
T_tmp_plot = np.arange(int_time1, int_time2, seismicData.dt / 4)
y = T_tmp_plot
x2 = np.interp(T_tmp_plot, T, x)
plt.fill_betweenx(y, ii + 1, x2, where=(x2 > ii + 1), color='k')
#plt.plot(ii+1,pick[ii],'c.',alpha=1,zorder=1,lw=1,markersize = 10)
x1 = np.arange(1, ii + 1)
plt.xticks(
x1,
(np.round(offsets / 1000, decimals=1)),
fontsize=10,
)
plt.gca().invert_yaxis()
plt.xlabel('Offset,km')
plt.ylabel('T, ms')
plt.title('Synthetic')
plt.plot(tmp_time_max, '.r', markersize=25)
plt.imshow(
np.zeros_like(data_KK[:, :len(offsets)]),
alpha=1,
extent=[1, len(offsets) + 1, int_time2, int_time1],
aspect='auto',
cmap='Pastel1_r',
vmin=0,
vmax=0,
)
plt.sca(axs[1])
for ii in range(data_short.shape[0]):
plt.plot(data_short[ii, :] / np.max(data_short[ii, :]).T + ii + 1,
T,
'k',
alpha=1,
zorder=1,
lw=1)
x = data_short[ii, :] / np.max(data_short[ii, :]).T + ii + 1
T_tmp_plot = np.arange(int_time1, int_time2, seismicData.dt / 4)
y = T_tmp_plot
x2 = np.interp(T_tmp_plot, T, x)
plt.fill_betweenx(y, ii + 1, x2, where=(x2 > ii + 1), color='k')
#plt.plot(ii+1,pick[ii],'c.',alpha=1,zorder=1,lw=1,markersize = 10)
x1 = np.arange(1, ii + 1)
plt.xticks(
x1,
(np.round(offsets[:-2] / 1000, decimals=1)),
fontsize=10,
)
plt.gca().invert_yaxis()
plt.xlabel('Offset,km')
plt.ylabel('T, ms')
plt.title(sgyname)
im = plt.imshow(
data_KK[:, :len(offsets) + 1],
alpha=1,
extent=[1, len(offsets) + 1, int_time2, int_time1],
aspect='auto',
cmap='rainbow',
vmin=0,
vmax=1,
)
divider = make_axes_locatable(axs[1])
cax = divider.append_axes("right", size="5%", pad=0.05)
cbar = plt.colorbar(im, cax=cax)
cbar.set_label('Coef. correlation', rotation=270)
return
def run(self):
"""
2007-03-29
2007-04-03
2007-05-01
--db_connect()
--FilterStrainSNPMatrix_instance.read_data()
if self.comparison_only:
--FilterStrainSNPMatrix_instance.read_data()
else:
--get_SNPpos2index()
--create_SNP_matrix_2010()
--get_align_length_from_fname()
--get_positions_to_be_checked_ls()
--get_align_matrix_from_fname()
--get_positions_to_be_checked_ls()
--get_mapping_info_regarding_strain_acc()
--shuffle_data_matrix_according_to_strain_acc_ls()
--FilterStrainSNPMatrix_instance.write_data_matrix()
--extract_sub_data_matrix()
if self.sub_justin_output_fname:
--FilterStrainSNPMatrix_instance.write_data_matrix()
--compare_two_SNP_matrix()
--outputDiffType()
"""
from FilterStrainSNPMatrix import FilterStrainSNPMatrix
FilterStrainSNPMatrix_instance = FilterStrainSNPMatrix()
header, src_strain_acc_list, category_list, data_matrix = FilterStrainSNPMatrix_instance.read_data(
self.input_fname)
if self.comparison_only:
header, strain_acc_ls, abbr_name_ls_sorted, SNP_matrix_2010_sorted = FilterStrainSNPMatrix_instance.read_data(
self.output_fname)
SNP_matrix_2010_sorted = Numeric.array(SNP_matrix_2010_sorted)
else:
(conn, curs) = db_connect(self.hostname, self.dbname, self.schema)
#extract data from alignment
snp_acc_ls = header[2:]
SNPpos2index = self.get_SNPpos2index(curs, snp_acc_ls,
self.snp_locus_table)
abbr_name_ls, SNP_matrix_2010 = self.create_SNP_matrix_2010(
SNPpos2index, self.data_dir_2010)
strain_acc_ls, strain_acc2abbr_name, strain_acc2index = self.get_mapping_info_regarding_strain_acc(
curs, self.strain_info_table, self.strain_info_2010_table,
abbr_name_ls)
SNP_matrix_2010_sorted = self.shuffle_data_matrix_according_to_strain_acc_ls(
SNP_matrix_2010, strain_acc_ls, strain_acc2index)
abbr_name_ls_sorted = []
for strain_acc in strain_acc_ls:
abbr_name_ls_sorted.append(strain_acc2abbr_name[strain_acc])
FilterStrainSNPMatrix_instance.write_data_matrix(
SNP_matrix_2010_sorted, self.output_fname, header,
strain_acc_ls, abbr_name_ls_sorted)
#comparison
data_matrix = Numeric.array(data_matrix)
sub_data_matrix = self.extract_sub_data_matrix(src_strain_acc_list,
data_matrix,
strain_acc_ls)
if self.sub_justin_output_fname:
FilterStrainSNPMatrix_instance.write_data_matrix(
sub_data_matrix, self.sub_justin_output_fname, header,
strain_acc_ls, abbr_name_ls_sorted)
diff_matrix, diff_tag_dict, diff_tag2counter = self.compare_two_SNP_matrix(
SNP_matrix_2010_sorted, sub_data_matrix)
if self.diff_output_fname:
self.outputDiffType(diff_matrix, SNP_matrix_2010_sorted,
sub_data_matrix, diff_tag_dict,
self.diff_type_to_be_outputted,
abbr_name_ls_sorted, header[2:],
self.diff_output_fname)
summary_result_ls = []
for tag, counter in diff_tag2counter.iteritems():
summary_result_ls.append('%s(%s):%s' %
(tag, diff_tag_dict[tag], counter))
print '\t%s(%s)\t%s' % (tag, diff_tag_dict[tag], counter)
import pylab
pylab.clf()
diff_matrix_reverse = list(diff_matrix)
diff_matrix_reverse.reverse()
diff_matrix_reverse = Numeric.array(diff_matrix_reverse)
pylab.imshow(diff_matrix_reverse, interpolation='nearest')
pylab.title(' '.join(summary_result_ls))
pylab.colorbar()
pylab.show()
#2007-11-01 do something as CmpAccession2Ecotype.py
from CmpAccession2Ecotype import CmpAccession2Ecotype
CmpAccession2Ecotype_ins = CmpAccession2Ecotype()
nt_number2diff_matrix_index = CmpAccession2Ecotype_ins.get_nt_number2diff_matrix_index(
nt2number)
dc_placeholder = dict(
zip(range(sub_data_matrix.shape[0]),
range(sub_data_matrix.shape[1])))
diff_matrix_ls = CmpAccession2Ecotype_ins.cmp_two_matricies(
SNP_matrix_2010_sorted, sub_data_matrix,
nt_number2diff_matrix_index, dc_placeholder, dc_placeholder,
dc_placeholder)
print diff_matrix_ls
for ch in xrange(eor1.shape[0]):
eor1[ch] = n.convolve(eor1[ch], fringe_filter, mode='same')
else: # this one is the exact one
ij = a.miriad.bl2ij(bls_master[0])
#beam_w_fr = capo.frf_conv.get_beam_w_fr(aa, bl)
#t, firs, frbins,frspace = capo.frf_conv.get_fringe_rate_kernels(beam_w_fr, inttime, FRF_WIDTH)
frp, bins = fringe.aa_to_fr_profile(aa, ij, 100)
timebins, firs = fringe.frp_to_firs(frp, bins, aa.get_freqs(), fq0=aa.get_freqs()[100])
for cnt,ch in enumerate(chans):
eor1[cnt] = n.convolve(eor1[cnt], firs[ch], mode='same')
#eor2 = eor.values()[0] * INJECT_SIG
eor = eor1 * INJECT_SIG
for k in days:
for bl in x[k]: x[k][bl] += eor
if False and PLOT:
p.subplot(211); capo.arp.waterfall(eor1, mode='real'); p.colorbar()
p.subplot(212); capo.arp.waterfall(eor2, mode='real'); p.colorbar(); p.show()
#Q = {} # Create the Q's that extract power spectrum modes
#for i in xrange(nchan):
# Q[i] = get_Q(i, nchan)
Q = [get_Q(i,nchan) for i in xrange(nchan)]
# Compute baseline auto-covariances and apply inverse to data
I,_I,_Ix = {},{},{}
C,_C,_Cx = {},{},{}
for k in days:
I[k],_I[k],_Ix[k] = {},{},{}
C[k],_C[k],_Cx[k] = {},{},{}
for bl in x[k]:
C[k][bl] = cov(x[k][bl])
figdir1 = rootdir+'/fig/'
if not(os.path.isdir(figdir1)):
os.mkdir(figdir1)
figdir=figdir1+'/'+config+'/'
if not(os.path.isdir(figdir)):
os.mkdir(figdir)
if DISPLAYX:
figx1 = pl.figure(2)
#norm = colors.LogNorm(vmin=1, vmax=Ampx.max())
norm = colors.Normalize(vmin=Ampx.min(),vmax=Ampx.max())
pl.scatter(x0,y0,c=Ampx,cmap='jet',s=10,norm=norm)
#pl.title('Amplitude Ex [$\mu$V/m]')
pl.xlabel(xlbl)
pl.ylabel(ylbl)
cbar = pl.colorbar()
cbar.set_label('Amplitude Vx [$\mu$V]')
#pl.gca().set_aspect(aspect=4) #'equal'
#pl.axis('equal')
if lowcut==0 and highcut==0:
figname = figdir+'/'+showerID+'_ampl_Vx_lin.png'
else:
figname = figdir+'/'+showerID+'_ampl_Vx_'+str(lowcut)+"-"+str(highcut)+"MHz_lin.png"
pl.savefig(figname,dpi=500)
#pl.show()
#raw_input()
#pl.close(figx)
try:
figx2 = pl.figure(3)
norm = colors.LogNorm(vmin=1, vmax=Ampx.max())
def RunAnimation(arg):
import os, sys, time
import subprocess
import psutil
import pylab as pl
from IPython import display
import matplotlib.gridspec as gridspec
import seaborn as sns
import pandas as pd
import numpy as np
print("RunAnimation")
sys.stdout.flush()
deviceCount = arg
# Need this only for animation of GPU usage to be consistent with
#from py3nvml.py3nvml import *
import py3nvml
maxNGPUS = int(subprocess.check_output("nvidia-smi -L | wc -l",
shell=True))
print("\nNumber of GPUS:", maxNGPUS)
py3nvml.py3nvml.nvmlInit()
total_deviceCount = py3nvml.py3nvml.nvmlDeviceGetCount()
if deviceCount == -1:
deviceCount = total_deviceCount
#for i in range(deviceCount):
# handle = nvmlDeviceGetHandleByIndex(i)
# print("Device {}: {}".format(i, nvmlDeviceGetName(handle)))
#print ("Driver Version:", nvmlSystemGetDriverVersion())
print("Animation deviceCount=%d" % (deviceCount))
file = os.getcwd() + "/error.txt"
print("opening %s" % (file))
fig = pl.figure(figsize=(9, 9))
pl.rcParams['xtick.labelsize'] = 14
pl.rcParams['ytick.labelsize'] = 14
gs = gridspec.GridSpec(3, 2, wspace=0.3, hspace=0.4)
ax1 = pl.subplot(gs[0, -2])
ax2 = pl.subplot(gs[0, 1])
ax3 = pl.subplot(gs[1:, :])
fig.suptitle('H2O.ai Machine Learning $-$ Generalized Linear Modeling',
size=18)
pl.gcf().subplots_adjust(bottom=0.2)
#cb = False
from matplotlib.colors import ListedColormap
cm = ListedColormap(sns.color_palette("RdYlGn", 10).as_hex())
cc = ax3.scatter([0.001, 0.001], [0, 0], c=[0, 1], cmap=cm)
cb = pl.colorbar(cc, ax=ax3)
os.system("mkdir -p images")
i = 0
while (True):
#try:
#print("In try i=%d" % i)
#sys.stdout.flush()
#cpu
snapshot = psutil.cpu_percent(percpu=True)
cpu_labels = range(1, len(snapshot) + 1)
plot_cpu_perf(ax1, cpu_labels, snapshot)
#gpu
gpu_snapshot = []
gpu_labels = list(range(1, deviceCount + 1))
import py3nvml
for j in range(deviceCount):
handle = py3nvml.py3nvml.nvmlDeviceGetHandleByIndex(j)
util = py3nvml.py3nvml.nvmlDeviceGetUtilizationRates(handle)
gpu_snapshot.append(util.gpu)
gpu_snapshot = gpu_snapshot
plot_gpu_perf(ax2, gpu_labels, gpu_snapshot)
res = pd.read_csv(file,
sep="\s+",
header=None,
names=[
'time', 'pass', 'fold', 'a', 'i', 'alpha',
'lambda', 'trainrmse', 'ivalidrmse', 'validrmse'
])
res['rel_acc'] = ((42665 - res['validrmse']) / (42665 - 31000))
res['alpha_prime'] = res['alpha'] + res['fold'].apply(
lambda x: new_alpha(x))
best = res.loc[res['rel_acc'] == np.max(res['rel_acc']), :]
plot_glm_results(ax3, res, best.tail(1), cb)
# flag for colorbar to avoid redrawing
#cb = True
# Add footnotes
footnote_text = "*U.S. Census dataset (predict Income): 45k rows, 10k cols\nParameters: 5-fold cross-validation, " + r'$\alpha = \{\frac{i}{7},i=0\ldots7\}$' + ", "\
'full $\lambda$-' + "search"
#pl.figtext(.05, -.04, footnote_text, fontsize = 14,)
pl.annotate(footnote_text, (0, 0), (-30, -50),
fontsize=12,
xycoords='axes fraction',
textcoords='offset points',
va='top')
#update the graphics
display.display(pl.gcf())
display.clear_output(wait=True)
time.sleep(0.01)
#save the images
saveimage = 0
if saveimage:
file_name = './images/glm_run_%04d.png' % (i, )
pl.savefig(file_name, dpi=200)
i = i + 1
grid_x = np.arange(n_grid[0] + 1) * grid_size + pos_min[0]
grid_y = np.arange(n_grid[1] + 1) * grid_size + pos_min[1]
#f = plt.figure(figsize=(12, 5))
f = plt.figure(figsize=(6, 5))
plt.scatter(x[w],
y[w],
c=catalog['vel'][w],
cmap='seismic',
s=2,
vmin=-1000,
vmax=1000)
f.axes[0].set_aspect('equal')
plt.xlabel(r'x [$h^{-1}$ Mpc]')
plt.ylabel(r'y [$h^{-1}$ Mpc]')
cbar = plt.colorbar()
cbar.set_label('Velocity [km/s]', rotation=270)
if add_grid:
for g in grid_x:
plt.axvline(g, color='k', ls='--', lw=1, alpha=0.5)
for g in grid_y:
if g >= -grid_size:
plt.axhline(g, color='k', ls='--', lw=1, alpha=0.5)
wg = (yg > -grid_size / 2) & (zg > -grid_size / 2) & (zg < grid_size / 2)
plt.autoscale(False)
plt.scatter(xg[wg],
yg[wg],
c=vg[wg],
s=1400,
# Training the SOM
from minisom import MiniSom
# STEP 2 CREATE A GRID COMPOSED OF NODES EACH ONE HAVING A WEIGHT VECTOR OF N_FEATURE ELEMENTS
som = MiniSom(x=10, y=10, input_len=15, sigma=1.0, learning_rate=0.5)
# STEP 3 RANDOMLY INITIALIZE THE VALUES OF THE WEIGHT VECTORS TO SMALL NUMBERS CLOSER TO 0 (BUT NOT 0)
som.random_weights_init(X)
som.train_random(data=X, num_iteration=100)
# Visualizing the results
from pylab import bone, pcolor, colorbar, plot, show
bone()
pcolor(som.distance_map().T)
colorbar()
markers = ['o', 's']
colors = ['r', 'g']
for i, x in enumerate(X):
w = som.winner(x)
plot(w[0] + 0.5,
w[1] + 0.5,
markers[y[i]],
markeredgecolor=colors[y[i]],
markerfacecolor='None',
markersize=10,
markeredgewidth=2)
show()
# Finding the frauds
mappings = som.win_map(X)
print Ts.shape
for i in xrange(Ts.shape[0]):
if n.random.uniform() > .5:
Ts[i] *= -1
Ns[i] *= -1
print Ts.shape
print Ns.shape
#print times[300], times[500]
#print ' '.join(['%d_%d' % a.miriad.bl2ij(bl) for bl in bls])
#sys.stdout.flush()
if PLOT:
#capo.arp.waterfall(cov(Ts), mode='log', drng=2); p.show()
p.subplot(141)
capo.arp.waterfall(Ts, mode='log', mx=1, drng=2)
p.colorbar(shrink=.5)
p.title('Vis in K. bls X ints.', fontsize=8)
p.subplot(142)
capo.arp.waterfall(Ns, mode='log') #, mx=1, drng=2); p.colorbar(shrink=.5)
p.title('FRF eor_model.', fontsize=8)
p.subplot(143)
capo.arp.waterfall(Ws)
p.colorbar(shrink=0.5)
p.title('Weights in samples bls x ints', fontsize=8)
p.subplot(144)
capo.arp.waterfall(cov(Ts), mode='log', drng=3)
p.colorbar(shrink=.5)
print cov(Ts).shape
p.title('cov(Ts)', fontsize=8)
p.show()
p.subplot(121)
def search(tile):
if os.path.exists('rogue-%s-02.png' %
tile) and not os.path.exists('rogue-%s-03.png' % tile):
print 'Skipping', tile
return
fn = os.path.join(tile[:3], tile, 'unwise-%s-w2-%%s-m.fits' % tile)
try:
II = [fitsio.read(os.path.join('e%i' % e, fn % 'img')) for e in [1, 2]]
PP = [fitsio.read(os.path.join('e%i' % e, fn % 'std')) for e in [1, 2]]
wcs = Tan(os.path.join('e%i' % 1, fn % 'img'))
except:
import traceback
print
print 'Failed to read data for tile', tile
traceback.print_exc()
print
return
H, W = II[0].shape
ps = PlotSequence('rogue-%s' % tile)
aa = dict(interpolation='nearest', origin='lower')
ima = dict(interpolation='nearest', origin='lower', vmin=-100, vmax=500)
plt.clf()
plt.imshow(II[0], **ima)
plt.title('Epoch 1')
ps.savefig()
plt.clf()
plt.imshow(II[1], **ima)
plt.title('Epoch 2')
ps.savefig()
# X = gaussian_filter(np.abs((II[0] - II[1]) / np.hypot(PP[0], PP[1])), 1.0)
# plt.clf()
# plt.imshow(X, interpolation='nearest', origin='lower')
# plt.title('Blurred abs difference / per-pixel-std')
# ps.savefig()
# Y = (II[0] - II[1]) / reduce(np.hypot, [PP[0], PP[1], np.hypot(100,II[0]), np.hypot(100,II[1]) ])
Y = (II[0] - II[1]) / reduce(np.hypot, [PP[0], PP[1]])
X = gaussian_filter(np.abs(Y), 1.0)
xthresh = 3.
print 'Value at rogue:', X[1452, 1596]
print 'pp at rogue:', [pp[1452, 1596] for pp in PP]
plt.clf()
plt.imshow(X, interpolation='nearest', origin='lower')
plt.title('X')
ps.savefig()
# plt.clf()
# plt.hist(np.minimum(100, PP[0].ravel()), 100, range=(0,100),
# histtype='step', color='r')
# plt.hist(np.minimum(100, PP[1].ravel()), 100, range=(0,100),
# histtype='step', color='b')
# plt.title('Per-pixel std')
# ps.savefig()
#Y = ((II[0] - II[1]) / np.hypot(PP[0], PP[1]))
#Y = gaussian_filter(
# (II[0] - II[1]) / np.hypot(100, np.hypot(II[0], II[1]))
# , 1.0)
#I = np.argsort(-X.ravel())
#yy,xx = np.unravel_index(I[:25], X.shape)
#print 'xx', xx
#print 'yy', yy
hot = (X > xthresh)
peak = find_peaks(hot, X)
dilate = 2
hot = binary_dilation(hot, structure=np.ones((3, 3)), iterations=dilate)
blobs, nblobs = label(hot, np.ones((3, 3), int))
blobslices = find_objects(blobs)
# Find maximum pixel within each blob.
BX, BY = [], []
BV = []
for b, slc in enumerate(blobslices):
sy, sx = slc
y0, y1 = sy.start, sy.stop
x0, x1 = sx.start, sx.stop
bl = blobs[slc]
i = np.argmax((bl == (b + 1)) * X[slc])
iy, ix = np.unravel_index(i, dims=bl.shape)
by = iy + y0
bx = ix + x0
BX.append(bx)
BY.append(by)
BV.append(X[by, bx])
BX = np.array(BX)
BY = np.array(BY)
BV = np.array(BV)
I = np.argsort(-BV)
xx, yy = BX[I], BY[I]
keep = []
S = 15
for i, (x, y) in enumerate(zip(xx, yy)):
#print x,y
if x < S or y < S or x + S >= W or y + S >= H:
continue
slc = slice(y - S, y + S + 1), slice(x - S, x + S + 1)
slc2 = slice(y - 3, y + 3 + 1), slice(x - 3, x + 3 + 1)
mx = np.max((II[0][slc] + II[1][slc]) / 2.)
#print 'Max within slice:', mx
#if mx > 5e3:
if mx > 2e3:
continue
mx2 = np.max((II[0][slc2] + II[1][slc2]) / 2.)
print 'Flux near object:', mx2
if mx2 < 250:
continue
#miny = np.min(Y[slc2])
#maxy = np.max(Y[slc2])
keep.append(i)
keep = np.array(keep)
if len(keep) == 0:
print 'No objects passed cuts'
return
xx = xx[keep]
yy = yy[keep]
plt.clf()
plt.imshow(X, interpolation='nearest', origin='lower', cmap='gray')
plt.title('X')
ax = plt.axis()
plt.plot(xx, yy, 'r+')
plt.plot(1596, 1452, 'o', mec=(0, 1, 0), mfc='none')
plt.axis(ax)
ps.savefig()
ylo, yhi = [], []
for i in range(min(len(xx), 100)):
x, y = xx[i], yy[i]
slc2 = slice(y - 3, y + 3 + 1), slice(x - 3, x + 3 + 1)
ylo.append(np.min(Y[slc2]))
yhi.append(np.max(Y[slc2]))
plt.clf()
plt.plot(ylo, yhi, 'r.')
plt.axis('scaled')
ps.savefig()
for i, (x, y) in enumerate(zip(xx, yy)[:50]):
print x, y
rows, cols = 2, 3
ra, dec = wcs.pixelxy2radec(x + 1, y + 1)
slc = slice(y - S, y + S + 1), slice(x - S, x + S + 1)
slc2 = slice(y - 3, y + 3 + 1), slice(x - 3, x + 3 + 1)
mx = max(np.max(II[0][slc]), np.max(II[1][slc]))
print 'Max within slice:', mx
miny = np.min(Y[slc2])
maxy = np.max(Y[slc2])
plt.clf()
plt.subplot(rows, cols, 1)
plt.imshow(II[0][slc], **ima)
plt.xticks([])
plt.yticks([])
plt.colorbar()
plt.title('epoch 1')
plt.subplot(rows, cols, 2)
plt.imshow(II[1][slc], **ima)
plt.xticks([])
plt.yticks([])
plt.colorbar()
plt.title('epoch 2')
plt.subplot(rows, cols, 3)
plt.imshow(PP[0][slc], **aa)
plt.xticks([])
plt.yticks([])
plt.colorbar()
plt.title('std 1')
plt.subplot(rows, cols, 6)
plt.imshow(PP[1][slc], **aa)
plt.xticks([])
plt.yticks([])
plt.colorbar()
plt.title('std 2')
plt.subplot(rows, cols, 4)
plt.imshow(X[slc], **aa)
plt.xticks([])
plt.yticks([])
plt.colorbar()
plt.title('X')
plt.subplot(rows, cols, 5)
plt.imshow(Y[slc], **aa)
plt.xticks([])
plt.yticks([])
plt.colorbar()
plt.title('Y')
#plt.suptitle('Tile %s, Flux: %4.0f, Range: %.2g %.2g' % (tile,mx,miny,maxy))
plt.suptitle('Tile %s, RA,Dec (%.4f, %.4f)' % (tile, ra, dec))
ps.savefig()
#ax.imshow(grid, extent=extent)
ax.pcolormesh(np.log10(densities), temperatures, grid)
ax.set_title('$%s$ o-H$_2$CO %s GHz' % (label, freq))
ax.set_xlabel('log Density')
ax.set_ylabel('Temperature')
pl.figure(2)
pl.clf()
ax = pl.subplot(2, 1, 1)
#ax.imshow(grid, extent=extent)
cax = ax.pcolormesh(np.log10(densities),
temperatures,
taugrid_140 / taugrid_150,
vmax=1.3,
vmin=0.8)
pl.colorbar(cax)
ax.set_title('$\\tau$ o-H$_2$CO 140/150 GHz')
ax.set_xlabel('log Density')
ax.set_ylabel('Temperature')
ax = pl.subplot(2, 1, 2)
#ax.imshow(grid, extent=extent)
cax = ax.pcolormesh(np.log10(densities),
temperatures,
texgrid_140 / texgrid_150,
vmax=1.3,
vmin=0.8)
pl.colorbar(cax)
ax.set_title('$T_{ex}$ o-H$_2$CO 140/150 GHz')
ax.set_xlabel('log Density')
ax.set_ylabel('Temperature')
def plot_composite_matrix(D,
labeltext,
show_labels=True,
show_indices=True,
vmax=1.0,
vmin=0.0,
force=False):
"""Build a composite plot showing dendrogram + distance matrix/heatmap.
Returns a matplotlib figure."""
if D.max() > 1.0 or D.min() < 0.0:
error(
'This matrix doesn\'t look like a distance matrix - min value {}, max value {}',
D.min(), D.max())
if not force:
raise ValueError("not a distance matrix")
else:
notify('force is set; scaling to [0, 1]')
D -= D.min()
D /= D.max()
if show_labels:
show_indices = True
fig = pylab.figure(figsize=(11, 8))
ax1 = fig.add_axes([0.09, 0.1, 0.2, 0.6])
# plot dendrogram
Y = sch.linkage(D, method='single') # centroid
dendrolabels = labeltext
if not show_labels:
dendrolabels = [str(i) for i in range(len(labeltext))]
Z1 = sch.dendrogram(Y,
orientation='left',
labels=dendrolabels,
no_labels=not show_indices)
ax1.set_xticks([])
xstart = 0.45
width = 0.45
if not show_labels:
xstart = 0.315
scale_xstart = xstart + width + 0.01
# plot matrix
axmatrix = fig.add_axes([xstart, 0.1, width, 0.6])
# (this reorders D by the clustering in Z1)
idx1 = Z1['leaves']
D = D[idx1, :]
D = D[:, idx1]
# show matrix
im = axmatrix.matshow(D,
aspect='auto',
origin='lower',
cmap=pylab.cm.YlGnBu,
vmin=vmin,
vmax=vmax)
axmatrix.set_xticks([])
axmatrix.set_yticks([])
# Plot colorbar.
axcolor = fig.add_axes([scale_xstart, 0.1, 0.02, 0.6])
pylab.colorbar(im, cax=axcolor)
return fig
def scatterplot(self , xvars , yvars , colorvars , colormap = None ):
cm = pl.cm.get_cmap(colormap)
sc = self.fig.axes[self.axnum].scatter(xvars ,yvars, c=colorvars, cmap = cm )
pl.colorbar(sc)
#test_netcdf: create a netcdf file with bogus data
#Paul Kushner
#See netCDF4 documentation at http://unidata.github.io/netcdf4-python/
import netCDF4
import numpy
import pylab
pylab.ion()
file=netCDF4.Dataset('temperature_time_record.nc')
xval = file.variables['x']
yval = file.variables['y']
tval = file.variables['t']
data = file.variables['temperature']
print 'shape xval, yval, data',numpy.shape(xval),numpy.shape(yval),numpy.shape(data)
pylab.figure(1)
pylab.subplot(2,1,1)
pylab.plot(xval,data[0,2,:],'r')
pylab.plot(xval,data[1,2,:],'b')
pylab.legend(('time %3.1f s'%tval[0],'time %3.1f'%tval[1]))
pylab.title('temperature for y=%3.1f %s'%(yval[2],yval.units))
pylab.xlabel('x(%s)'%xval.units)
pylab.ylabel('t(%s)'%tval.units)
pylab.subplot(2,1,2)
pylab.contourf(xval,yval,data[2,:,:])
pylab.title('Temperature(%s) at time %3.1f %s'%(data.units,tval[2],tval.units))
pylab.xlabel('x(%s)'%xval.units)
pylab.ylabel('y(%s)'%yval.units)
pylab.colorbar()
pylab.show()
def single_value_visualization(simulation_name,master_results_dir,query,value_names=None,filename=None,resolution=None,treat_nan_as_zero=False,ranges={},cols=4):
"""
Visualizes all single values (or those whose names match ones in `value_names` argument)
present in the datastores of parameter search over a fixed set of parameters.
Parameters
----------
simulation_name : str
The name of the simulation.
master_results_dir : str
The directory where the parameter search results are stored.
query : ParamFilterQuery
ParamFilterQuery filter query instance that will be applied to each datastore before records are retrieved.
value_names : list(str)
List of value names to visualize.
file_name : str
The file name into which to save the resulting figure. If None figure is just displayed.
resolution : int
If not None data will be plotted on a interpolated grid of size (resolution,...,resolution)
ranges : dict
A dictionary with value names as keys, and tuples of (min,max) ranges as values indicating what range of values should be displayed.
cols : int
The number of columns in which to show plots, default is 4.
"""
(parameters,datastores,n) = load_fixed_parameter_set_parameter_search(simulation_name,master_results_dir,filter=ParamFilterQuery(ParameterSet({'ads_unique' : False, 'rec_unique' : False, 'params' : ParameterSet({'identifier' : 'SingleValue'})})))
# print(parameters)
# Lets first filter out parameters that do not vary
todelete=[];
for i in range(0,len(parameters)):
vals = set([v[0][i] for v in datastores])
# print(vals)
if len(vals) == 1:
todelete.append(i)
# print(todelete)
for k in range(0,len(datastores)):
datastores[k] = ([i for j, i in enumerate(datastores[k][0]) if j not in todelete],datastores[k][1])
parameters = [i for j, i in enumerate(parameters) if j not in todelete]
# print(parameters)
# Lets first filter out stuff we were asked by user
datastores = [(a,query.query(b)) for a,b in datastores]
sorted_parameter_indexes = zip(*sorted(enumerate(parameters), key=lambda x: x[1]))[0]
# if value_names is None lets set it to set of value_names in the first datastore
if value_names == None:
value_names = set([])
for d in datastores:
value_names.update(set([ads.value_name for ads in param_filter_query(d[1],identifier='SingleValue').get_analysis_result()]))
value_names = set(sorted(value_names))
# print("Value names:")
# print(value_names)
# Lets first make sure that the value_names uniqly identify a SingleValue ADS in each DataStore and
# that they exist in each DataStore.
for (param_values,datastore) in datastores:
for v in value_names:
if len(param_filter_query(datastore,identifier='SingleValue',value_name=v).get_analysis_result()) > 1:
param_filter_query(datastore,identifier='SingleValue',value_name=v).print_content(full_ADS=True)
#assert len(param_filter_query(datastore,identifier='SingleValue',value_name=v).get_analysis_result()) == 1, "Error, %d ADS with value_name %s found for parameter combination: %s" % (len(param_filter_query(datastore,identifier='SingleValue',value_name=v).get_analysis_result()),v, str([str(a) + ':' + str(b) for (a,b) in zip(parameters,param_values)]))
rows = math.ceil(1.0*len(value_names)/cols)
pylab.figure(figsize=(12*cols, 6*rows), dpi=300, facecolor='w', edgecolor='k')
# print(rows)
# print(cols)
# print("Plotting")
res = {}
for i,value_name in enumerate(value_names):
pylab.subplot(rows,cols,i+1)
if len(parameters) == 1:
x = []
y = []
for (param_values,datastore) in datastores:
x.append(param_values[0])
y.append(float(adss[0].value))
pylab.plot(x,y)
pylab.plot(x,y,marker='o')
pylab.xlabel(parameters[sorted_parameter_indexes[0]])
pylab.ylabel(value_name)
elif len(parameters) == 2:
# print('*****************************')
# print(i)
# print(len(datastores))
x = []
y = []
z = []
for (param_values,datastore) in datastores:
adss = param_filter_query(datastore,identifier='SingleValue',value_name=value_name).get_analysis_result()
if len(adss)>0:
x.append(param_values[sorted_parameter_indexes[0]])
y.append(param_values[sorted_parameter_indexes[1]])
z.append(float(adss[0].value))
if treat_nan_as_zero:
z = numpy.nan_to_num(z)
if value_name in ranges:
vmin,vmax = ranges[value_name]
else:
# print(value_name)
# print(z)
# print(min(z))
# print(max(z))
vmin = min(z)
vmax = max(z)
if resolution != None:
xi = numpy.linspace(numpy.min(x),numpy.max(x),resolution)
yi = numpy.linspace(numpy.min(y),numpy.max(y),resolution)
gr = griddata((x,y),z,(xi[None, :], yi[:, None]),method='cubic')
pylab.imshow(gr,interpolation='none',vmin=vmin,vmax=vmax,aspect='auto',cmap=cm.gray,origin='lower',extent=[numpy.min(x),numpy.max(x),numpy.min(y),numpy.max(y)])
else:
pylab.scatter(x,y,marker='o',s=300,c=z,cmap=cm.jet,vmin=vmin,vmax=vmax)
pylab.xlim(min(x)-0.1*(max(x)-min(x)),max(x)+0.1*(max(x)-min(x)))
pylab.ylim(min(y)-0.1*(max(y)-min(y)),max(y)+0.1*(max(y)-min(y)))
pylab.colorbar()
res[value_name]=((parameters[sorted_parameter_indexes[0]],parameters[sorted_parameter_indexes[1]]),x,y,z)
f = open(v+'.pickle','w')
pickle.dump((value_name,parameters[sorted_parameter_indexes[0]],parameters[sorted_parameter_indexes[1]],x,y,z),f)
f.close()
pylab.xlabel(parameters[sorted_parameter_indexes[0]])
pylab.ylabel(parameters[sorted_parameter_indexes[1]])
else:
raise ValueError("Currently cannot handle more than 2D data")
pylab.title(value_name)
import scipy
f = open('ps_res.pickle','w')
pickle.dump(res,f)
scipy.io.savemat('ps_res.mat', res)
f.close()
if filename != None:
pylab.savefig(master_results_dir+'/'+filename, bbox_inches='tight')
a[4][4] = data_sheet.cell_value(i + 1, 14)
#norm1 = plt.colors.Normalize(vmin=0,vmax=maximum/4)
pylab.figure()
cmap = pylab.cm.jet
im = pylab.imshow(a, cmap=cmap, interpolation='nearest')
pylab.title(str(data_sheet.cell_value(i + 1, 0)))
for ind1 in range(5):
for ind2 in range(5):
pylab.text(ind1,
ind2,
int(b[ind1][ind2]),
va='center',
ha='center')
pylab.axis('off')
pylab.colorbar(im)
pylab.savefig("heatmaps_jet/" + str(data_sheet.cell_value(i + 1, 0)) +
".png")
pylab.show()
pylab.figure()
cmap = pylab.cm.PiYG
im = pylab.imshow(a, cmap=cmap, interpolation='nearest')
pylab.title(str(data_sheet.cell_value(i + 1, 0)))
for ind1 in range(5):
for ind2 in range(5):
pylab.text(ind1,
ind2,
int(b[ind1][ind2]),
va='center',
ha='center')
plotInterval = N / 10
for t in xrange(N):
i = np.random.randn(1,cfg.layerTestSizes[layer],cfg.layerTestSizes[layer])
cv = float(np.squeeze(n.run(i,channel,layer)))
# av += cv
# r = cv / av
ab += i[:] * cv #/ N #ab * (1-r) + i * r
cvs.append(float(cv))
if (not t % testInterval):
testVals.append(float(np.squeeze(n.run(ab[:],channel,layer))))
if t and (not (t % plotInterval)):
pl.figure()
pl.imshow(ab[0])
pl.title("t = %i" % t)
pl.colorbar()
pl.savefig('%s/stim_%i.png' % (outDir, t))
pl.close()
pl.figure()
pl.plot(testVals)
pl.savefig('%s/progress_%i.png' % (outDir, t))
pl.close()
# pl.show()
pl.imsave('%s/optimal_%i.png' % (outDir, t), ab[0], cmap=pl.cm.gray)
pl.figure()
pl.imshow(ab[0])
pl.title("t = %i" % t)
pl.colorbar()
def plot(self,valueRange = None,\
show = True,\
saveFig = None,\
colorMapName = 'jet',\
colBarLabel = None,\
colBarOrient ='vertical',\
colBarShrink = 1.0,\
useImagePlot = False,\
**kwd_args):
"""
@brief Plots a liteMap using astLib.astPlots.ImagePlot.
The axes can be marked in either sexagesimal or decimal celestial coordinates.
If RATickSteps or decTickSteps are set to "auto", the appropriate axis scales will
be determined automatically from the size of the image array and associated WCS.
The tick step sizes can be overidden.
If the coordinate axes are in sexagesimal format a dictionary in the format
{'deg', 'unit'}. If the coordinate axes are in
decimal format, the tick step size is specified simply in RA, dec decimal degrees.
@param valueRange A tuple e.g. [-300,300] specifying the limits of the colorscale
@param show Show the plot instead of saving
@param saveFig save to a file
@param colorMapName name of pylab.cm colorMap
@param colBarLabel add label to the colorBar
@param colBarOrient orientation of the colorbar (can be 'vertical'(default) or 'horizontal'
@param colBarShrink shrink the color
@param kwd_args all keywords accepted by astLib.astPlots.ImagePlot:
@type axesLabels: string
@param axesLabels: either "sexagesimal" (for H:M:S, D:M:S), "decimal" (for decimal degrees)
or None (for no coordinate axes labels)
@type axesFontFamily: string
@param axesFontFamily: matplotlib fontfamily, e.g. 'serif', 'sans-serif' etc.
@type axesFontSize: float
@param axesFontSize: font size of axes labels and titles (in points)
@param RATickSteps See docstring above
@param decTickSteps See docstring above
"""
if valueRange != None:
vmin = valueRange[0]
vmax = valueRange[1]
else:
vmin = self.data.min()
vmax = self.data.max()
# Convert name to a matplotlib.cm.Colormap instance
try:
cmap = pylab.cm.__dict__[colorMapName]
except KeyError:
cmap = pylab.cm.hsv
if not (useImagePlot):
pylab.imshow(self.data,origin="down",vmin=vmin,vmax=vmax,\
extent=[self.x0,self.x1,self.y0,self.y1],\
aspect=1./(np.cos(0.5*np.pi/180.*(self.y0+self.y1))),\
cmap=cmap)
else:
astLib.astPlots.ImagePlot(self.data,self.wcs,colorMapName=colorMapName,\
cutLevels=[vmin,vmax],colorBar=False,**kwd_args)
cb = pylab.colorbar(orientation=colBarOrient, shrink=colBarShrink)
if colBarLabel != None:
cb.set_label(colBarLabel)
#pylab.xlabel('Ra (degrees)')
#pylab.ylabel('Dec (degrees)')
#pylab.title(title)
if saveFig != None:
pylab.savefig(saveFig)
if show:
pylab.show()
def plot_it(self):
d = self.data[:, :, 180]
d[d < 0.2] = 0
im = plt.imshow(d, cmap='hot')
plt.colorbar(im, orientation='horizontal')
plt.show()
use_bias=False)(zero_pad1)
batchnorm1=tkl.BatchNormalization()(conv)
leaky_relu=tkl.LeakyReLU()(batchnorm1)
zero_pad2=tkl.ZeroPadding2D()(leaky_relu)
last=tkl.Conv2D(
1,4,strides=1,kernel_initializer=initializer)(zero_pad2)
return tf.keras.Model(inputs=[inp,tar],outputs=last)
discriminator=Discriminator()
tku.plot_model(discriminator,show_shapes=True,dpi=48)
disc_output_img=discriminator(
[input_img[tf.newaxis,...],gen_output_img],training=False)
pl.imshow(disc_output_img[0,...,-1],
vmin=-20,vmax=20,cmap='jet')
pl.colorbar(); pl.tight_layout();
def discriminator_loss(disc_real_output,disc_generated_output):
real_loss=loss_object(
tf.ones_like(disc_real_output),disc_real_output)
generated_loss=loss_object(
tf.zeros_like(disc_generated_output),disc_generated_output)
total_disc_loss=real_loss+generated_loss
return total_disc_loss
# Commented out IPython magic to ensure Python compatibility.
# %ch1 Model Optimizers, Callbacks & Visualizations
generator_optimizer=tf.keras.optimizers.Adam(2e-4,beta_1=.5)
discriminator_optimizer=tf.keras.optimizers.Adam(2e-4,beta_1=.5)
def plot(self, labels=False, show=True):
'''
This function plots 2D and 3D models
:param labels:
:param show: If True, the plots are displayed at the end of this call. If False, plt.show() should be called outside this function
:return:
'''
if self.k == 3:
import mayavi.mlab as mlab
predictFig = mlab.figure(figure='predict')
errorFig = mlab.figure(figure='error')
if self.testfunction:
truthFig = mlab.figure(figure='test')
dx = 1
pts = 25j
X, Y, Z = np.mgrid[0:dx:pts, 0:dx:pts, 0:dx:pts]
scalars = np.zeros(X.shape)
errscalars = np.zeros(X.shape)
for i in range(X.shape[0]):
for j in range(X.shape[1]):
for k1 in range(X.shape[2]):
errscalars[i][j][k1] = self.predicterr_normalized(
[X[i][j][k1], Y[i][j][k1], Z[i][j][k1]])
scalars[i][j][k1] = self.predict_normalized(
[X[i][j][k1], Y[i][j][k1], Z[i][j][k1]])
if self.testfunction:
tfscalars = np.zeros(X.shape)
for i in range(X.shape[0]):
for j in range(X.shape[1]):
for k1 in range(X.shape[2]):
tfplot = tfscalars[i][j][k1] = self.testfunction(
[X[i][j][k1], Y[i][j][k1], Z[i][j][k1]])
plot = mlab.contour3d(tfscalars,
contours=15,
transparent=True,
figure=truthFig)
plot.compute_normals = False
# obj = mlab.contour3d(scalars, contours=10, transparent=True)
plot = mlab.contour3d(scalars,
contours=15,
transparent=True,
figure=predictFig)
plot.compute_normals = False
errplt = mlab.contour3d(errscalars,
contours=15,
transparent=True,
figure=errorFig)
errplt.compute_normals = False
if show:
mlab.show()
if self.k == 2:
fig = pylab.figure(figsize=(8, 6))
samplePoints = zip(*self.X)
# Create a set of data to plot
plotgrid = 61
x = np.linspace(self.normRange[0][0],
self.normRange[0][1],
num=plotgrid)
y = np.linspace(self.normRange[1][0],
self.normRange[1][1],
num=plotgrid)
x = np.linspace(0, 1, num=plotgrid)
y = np.linspace(0, 1, num=plotgrid)
X, Y = np.meshgrid(x, y)
# Predict based on the optimized results
zs = np.array([
self.predict([x, y]) for x, y in zip(np.ravel(X), np.ravel(Y))
])
Z = zs.reshape(X.shape)
# Z = (Z*(self.ynormRange[1]-self.ynormRange[0]))+self.ynormRange[0]
#Calculate errors
zse = np.array([
self.predict_var([x, y])
for x, y in zip(np.ravel(X), np.ravel(Y))
])
Ze = zse.reshape(X.shape)
spx = (self.X[:, 0] * (self.normRange[0][1] - self.normRange[0][0])
) + self.normRange[0][0]
spy = (self.X[:, 1] * (self.normRange[1][1] - self.normRange[1][0])
) + self.normRange[1][0]
contour_levels = 25
ax = fig.add_subplot(222)
CS = pylab.contourf(X, Y, Ze, contour_levels)
pylab.colorbar()
pylab.plot(spx, spy, 'ow')
ax = fig.add_subplot(221)
if self.testfunction:
# Setup the truth function
zt = self.testfunction(np.array(zip(np.ravel(X), np.ravel(Y))))
ZT = zt.reshape(X.shape)
CS = pylab.contour(X,
Y,
ZT,
contour_levels,
colors='k',
zorder=2)
# contour_levels = np.linspace(min(zt), max(zt),50)
if self.testfunction:
contour_levels = CS.levels
delta = np.abs(contour_levels[0] - contour_levels[1])
contour_levels = np.insert(contour_levels, 0,
contour_levels[0] - delta)
contour_levels = np.append(contour_levels,
contour_levels[-1] + delta)
CS = plt.contourf(X, Y, Z, contour_levels, zorder=1)
pylab.plot(spx, spy, 'ow', zorder=3)
pylab.colorbar()
ax = fig.add_subplot(212, projection='3d')
# fig = plt.gcf()
#ax = fig.gca(projection='3d')
ax.plot_surface(X, Y, Z, rstride=3, cstride=3, alpha=0.4)
if self.testfunction:
ax.plot_wireframe(X, Y, ZT, rstride=3, cstride=3)
if show:
pylab.show()
def plotField(field, target_fields=None, completed_fields=None, options_basemap={}, **kwargs):
"""
Plot a specific target field.
Parameters:
-----------
field : The specific field of interest.
target_fields : The fields that will be observed
completed_fields : The fields that have been observed
options_basemap : Keyword arguments to the basemap constructor
kwargs : Keyword arguments to the matplotlib.scatter function
Returns:
--------
basemap : The basemap object
"""
if isinstance(field,np.core.records.record):
tmp = FieldArray(1)
tmp[0] = field
field = tmp
band = field[0]['FILTER']
cmap = matplotlib.cm.get_cmap(CMAPS[band])
defaults = dict(marker='H',s=100,edgecolor='',vmin=-1,vmax=4,cmap=cmap)
#defaults = dict(edgecolor='none', s=50, vmin=0, vmax=4, cmap='summer_r')
#defaults = dict(edgecolor='none', s=50, vmin=0, vmax=4, cmap='gray_r')
setdefaults(kwargs,defaults)
msg="%s: id=%10s, "%(datestring(field['DATE'][0],0),field['ID'][0])
msg +="ra=%(RA)-6.2f, dec=%(DEC)-6.2f, secz=%(AIRMASS)-4.2f"%field[0]
logging.info(msg)
defaults = dict(date=field['DATE'][0], name='ortho')
options_basemap = dict(options_basemap)
setdefaults(options_basemap,defaults)
fig, basemap = makePlot(**options_basemap)
plt.subplots_adjust(left=0.03,right=0.97,bottom=0.03,top=0.97)
# Plot target fields
if target_fields is not None and len(target_fields):
sel = target_fields['FILTER']==band
x,y = basemap.proj(target_fields['RA'], target_fields['DEC'])
kw = dict(kwargs,c='w',edgecolor='0.6',s=0.8*kwargs['s'])
basemap.scatter(x[sel], y[sel], **kw)
kw = dict(kwargs,c='w',edgecolor='0.8',s=0.8*kwargs['s'])
basemap.scatter(x[~sel], y[~sel], **kw)
# Plot completed fields
if completed_fields is not None and len(completed_fields):
sel = completed_fields['FILTER']==band
x,y = basemap.proj(completed_fields['RA'],completed_fields['DEC'])
kw = dict(kwargs)
basemap.scatter(x[~sel], y[~sel], c='0.6', **kw)
basemap.scatter(x[sel], y[sel], c=completed_fields['TILING'][sel], **kw)
# Try to draw the colorbar
try:
if len(fig.axes) == 2:
# Draw colorbar in existing axis
colorbar = plt.colorbar(cax=fig.axes[-1])
else:
colorbar = plt.colorbar()
colorbar.set_label('Tiling (%s-band)'%band)
except TypeError:
pass
plt.sca(fig.axes[0])
# Show the selected field
x,y = basemap.proj(field['RA'], field['DEC'])
kw = dict(kwargs,edgecolor='k')
basemap.scatter(x,y,c=COLORS[band],**kw)
return basemap
scaler = MinMaxScaler(feature_range=(0,1))
x = scaler.fit_transform(x) #should we even transform the ID?
#10x10 grid output with 15 dim input and sigma of 1
som = MiniSom(x=10,y=10, input_len=15,sigma=1.0, learning_rate=0.2)
som.random_weights_init(x)
som.train_random(x, num_iteration=200)
from pylab import bone, pcolor, colorbar, plot, show
bone()
#using colors instead of bones to show the map
pcolor(som.distance_map().T) #distance_map will return a matrix of mean interneuron distances (transposed)
colorbar() #legend, intensity of color. Dark = low mid, light = high mid, frauds
#to tell if the customers associated to the light color = approved or not
markers = ['o', 's'] #circles and squares. O for non-approval, and s for approval
colors = ['r', 'b']
for i,j in enumerate(x):
#get the winning node in the customer i
winning_node = som.winner(j)
plot(winning_node[0]+0.5, winning_node[1]+0.5, markers[y[i]],
markeredgecolor=colors[y[i]], markerfacecolor='None', markersize=10, markeredgewidth=2)
#plot(winning_node[1]+0.5)
#plot(markers[y[i]]) #if the customer was approved or not, based on y vector of our dataset
#plot(markeredgecolor = colors[y[i]], markerfacecolor = None, markersize = 10, markeredgewidth = 3)
show()
def stage_psfplots(T=None,
sedsn=None,
coimgs=None,
cons=None,
detmaps=None,
detivs=None,
blobsrcs=None,
blobflux=None,
blobslices=None,
blobs=None,
tractor=None,
cat=None,
targetrd=None,
pixscale=None,
targetwcs=None,
W=None,
H=None,
brickid=None,
bands=None,
ps=None,
tims=None,
plots=False,
**kwargs):
tim = tims[0]
tim.psfex.fitSavedData(*tim.psfex.splinedata)
spl = tim.psfex.splines[0]
print 'Spline:', spl
knots = spl.get_knots()
print 'knots:', knots
tx, ty = knots
k = 3
print 'interior knots x:', tx[k + 1:-k - 1]
print 'additional knots x:', tx[:k + 1], 'and', tx[-k - 1:]
print 'interior knots y:', ty[k + 1:-k - 1]
print 'additional knots y:', ty[:k + 1], 'and', ty[-k - 1:]
for itim, tim in enumerate(tims):
psfex = tim.psfex
psfex.fitSavedData(*psfex.splinedata)
if plots:
print
print 'Tim', tim
print
pp, xx, yy = psfex.splinedata
ny, nx, nparams = pp.shape
assert (len(xx) == nx)
assert (len(yy) == ny)
psfnil = psfex.psfclass(*np.zeros(nparams))
names = psfnil.getParamNames()
xa = np.linspace(xx[0], xx[-1], 50)
ya = np.linspace(yy[0], yy[-1], 100)
#xa,ya = np.meshgrid(xa,ya)
#xa = xa.ravel()
#ya = ya.ravel()
print 'xa', xa
print 'ya', ya
for i in range(nparams):
plt.clf()
plt.subplot(1, 2, 1)
dimshow(pp[:, :, i])
plt.title('grid fit')
plt.colorbar()
plt.subplot(1, 2, 2)
sp = psfex.splines[i](xa, ya)
sp = sp.T
print 'spline shape', sp.shape
assert (sp.shape == (len(ya), len(xa)))
dimshow(sp, extent=[xx[0], xx[-1], yy[0], yy[-1]])
plt.title('spline')
plt.colorbar()
plt.suptitle('tim %s: PSF param %s' % (tim.name, names[i]))
ps.savefig()
def main():
"""
In this simple tutorial example, the main function does all the work:
it sets the parameter values, creates and initializes a grid, sets up
the state variables, runs the main loop, and cleans up.
"""
# INITIALIZE
# User-defined parameter values
dem_name = 'ExampleDEM/west_bijou_gully.asc'
outlet_row = 6
outlet_column = 38
next_to_outlet_row = 7
next_to_outlet_column = 38
n = 0.06 # roughness coefficient (Manning's n)
h_init = 0.001 # initial thin layer of water (m)
g = 9.8 # gravitational acceleration (m/s2)
alpha = 0.2 # time-step factor (ND; from Bates et al., 2010)
run_time = 2400 # duration of run, seconds
rainfall_mmhr = 100 # rainfall rate, in mm/hr
rain_duration = 15*60 # rainfall duration, in seconds
# Derived parameters
rainfall_rate = (rainfall_mmhr/1000.)/3600. # rainfall in m/s
ten_thirds = 10./3. # pre-calculate 10/3 for speed
elapsed_time = 0.0 # total time in simulation
report_interval = 5. # interval to report progress (seconds)
next_report = time.time()+report_interval # next time to report progress
DATA_FILE = os.path.join(os.path.dirname(__file__), dem_name)
# Create and initialize a raster model grid by reading a DEM
print('Reading data from "'+str(DATA_FILE)+'"')
(mg, z) = read_esri_ascii(DATA_FILE)
print('DEM has ' + str(mg.number_of_node_rows) + ' rows, ' +
str(mg.number_of_node_columns) + ' columns, and cell size ' + str(mg.dx)) + ' m'
# Modify the grid DEM to set all nodata nodes to inactive boundaries
mg.set_nodata_nodes_to_closed(z, 0) # set nodata nodes to inactive bounds
# Set the open boundary (outlet) cell. We want to remember the ID of the
# outlet node and the ID of the interior node adjacent to it. We'll make
# the outlet node an open boundary.
outlet_node = mg.grid_coords_to_node_id(outlet_row, outlet_column)
node_next_to_outlet = mg.grid_coords_to_node_id(next_to_outlet_row,
next_to_outlet_column)
mg.set_fixed_value_boundaries(outlet_node)
# Set up state variables
h = mg.add_zeros('node', 'Water_depth') + h_init # water depth (m)
q = mg.create_active_link_array_zeros() # unit discharge (m2/s)
# Get a list of the core nodes
core_nodes = mg.core_nodes
# To track discharge at the outlet through time, we create initially empty
# lists for time and outlet discharge.
q_outlet = []
t = []
q_outlet.append(0.)
t.append(0.)
outlet_link = mg.get_active_link_connecting_node_pair(outlet_node, node_next_to_outlet)
# Display a message
print( 'Running ...' )
start_time = time.time()
# RUN
# Main loop
while elapsed_time < run_time:
# Report progress
if time.time()>=next_report:
print('Time = '+str(elapsed_time)+' ('
+str(100.*elapsed_time/run_time)+'%)')
next_report += report_interval
# Calculate time-step size for this iteration (Bates et al., eq 14)
dtmax = alpha*mg.dx/np.sqrt(g*np.amax(h))
# Calculate the effective flow depth at active links. Bates et al. 2010
# recommend using the difference between the highest water-surface
# and the highest bed elevation between each pair of cells.
zmax = mg.max_of_link_end_node_values(z)
w = h+z # water-surface height
wmax = mg.max_of_link_end_node_values(w)
hflow = wmax - zmax
# Calculate water-surface slopes
water_surface_slope = mg.calculate_gradients_at_active_links(w)
# Calculate the unit discharges (Bates et al., eq 11)
q = (q-g*hflow*dtmax*water_surface_slope)/ \
(1.+g*hflow*dtmax*n*n*abs(q)/(hflow**ten_thirds))
# Calculate water-flux divergence at nodes
dqds = mg.calculate_flux_divergence_at_nodes(q)
# Update rainfall rate
if elapsed_time > rain_duration:
rainfall_rate = 0.
# Calculate rate of change of water depth
dhdt = rainfall_rate-dqds
# Second time-step limiter (experimental): make sure you don't allow
# water-depth to go negative
if np.amin(dhdt) < 0.:
shallowing_locations = np.where(dhdt<0.)
time_to_drain = -h[shallowing_locations]/dhdt[shallowing_locations]
dtmax2 = alpha*np.amin(time_to_drain)
dt = np.min([dtmax, dtmax2])
else:
dt = dtmax
# Update the water-depth field
h[core_nodes] = h[core_nodes] + dhdt[core_nodes]*dt
h[outlet_node] = h[node_next_to_outlet]
# Update current time
elapsed_time += dt
# Remember discharge and time
t.append(elapsed_time)
q_outlet.append(abs(q[outlet_link]))
# FINALIZE
# Set the elevations of the nodata cells to the minimum active cell
# elevation (convenient for plotting)
z[np.where(z<=0.)] = 9999 # temporarily change their elevs ...
zmin = np.amin(z) # ... so we can find the minimum ...
z[np.where(z==9999)] = zmin # ... and assign them this value.
# Get a 2D array version of the water depths and elevations
# Clear previous plots
pylab.figure(1)
pylab.close()
pylab.figure(2)
pylab.close()
# Plot discharge vs. time
pylab.figure(1)
pylab.plot(np.array(t), np.array(q_outlet)*mg.dx)
pylab.xlabel('Time (s)')
pylab.ylabel('Q (m3/s)')
pylab.title('Outlet discharge')
# Plot topography
pylab.figure(2)
pylab.subplot(121)
imshow_grid(mg, z, allow_colorbar=False)
pylab.xlabel(None)
pylab.ylabel(None)
im = pylab.set_cmap('RdBu')
cb = pylab.colorbar(im)
cb.set_label('Elevation (m)', fontsize=12)
pylab.title('Topography')
# Plot water depth
pylab.subplot(122)
imshow_grid(mg, h, allow_colorbar=False)
im2 = pylab.set_cmap('RdBu')
pylab.clim(0, 0.25)
cb = pylab.colorbar(im2)
cb.set_label('Water depth (m)', fontsize=12)
pylab.title('Water depth')
#
## Display the plots
pylab.show()
print('Done.')
print('Total run time = '+str(time.time()-start_time)+' seconds.')
print("%0.3fs" % delta)
omp[i_f, i_s] = delta
results['time(LARS) / time(OMP)\n (w/ Gram)'] = (lars_gram / omp_gram)
results['time(LARS) / time(OMP)\n (w/o Gram)'] = (lars / omp)
return results
if __name__ == '__main__':
samples_range = np.linspace(1000, 5000, 5).astype(np.int)
features_range = np.linspace(1000, 5000, 5).astype(np.int)
results = compute_bench(samples_range, features_range)
max_time = max(np.max(t) for t in results.values())
import pylab as pl
fig = pl.figure('scikit-learn OMP vs. LARS benchmark results')
for i, (label, timings) in enumerate(sorted(results.iteritems())):
ax = fig.add_subplot(1, 2, i)
vmax = max(1 - timings.min(), -1 + timings.max())
pl.matshow(timings, fignum=False, vmin=1 - vmax, vmax=1 + vmax)
ax.set_xticklabels([''] + map(str, samples_range))
ax.set_yticklabels([''] + map(str, features_range))
pl.xlabel('n_samples')
pl.ylabel('n_features')
pl.title(label)
pl.subplots_adjust(0.1, 0.08, 0.96, 0.98, 0.4, 0.63)
ax = pl.axes([0.1, 0.08, 0.8, 0.06])
pl.colorbar(cax=ax, orientation='horizontal')
pl.show()
def plot_topo_file(topoplotdata):
"""
Read in a topo or bathy file and produce a pcolor map.
"""
import os
import pylab
from pyclaw.data import Data
fname = topoplotdata.fname
topotype = topoplotdata.topotype
if topoplotdata.climits:
# deprecated option
cmin = topoplotdata.climits[0]
cmax = topoplotdata.climits[1]
else:
cmin = topoplotdata.cmin
cmax = topoplotdata.cmax
figno = topoplotdata.figno
addcolorbar = topoplotdata.addcolorbar
addcontour = topoplotdata.addcontour
contour_levels = topoplotdata.contour_levels
xlimits = topoplotdata.xlimits
ylimits = topoplotdata.ylimits
coarsen = topoplotdata.coarsen
imshow = topoplotdata.imshow
gridedges_show = topoplotdata.gridedges_show
neg_cmap = topoplotdata.neg_cmap
pos_cmap = topoplotdata.pos_cmap
print_fname = topoplotdata.print_fname
if neg_cmap is None:
neg_cmap = colormaps.make_colormap({
cmin: [0.3, 0.2, 0.1],
0: [0.95, 0.9, 0.7]
})
if pos_cmap is None:
pos_cmap = colormaps.make_colormap({
0: [.5, .7, 0],
cmax: [.2, .5, .2]
})
if abs(topotype) == 1:
X, Y, topo = topotools.topofile2griddata(fname, topotype)
topo = pylab.flipud(topo)
Y = pylab.flipud(Y)
x = X[0, :]
y = Y[:, 0]
xllcorner = x[0]
yllcorner = y[0]
cellsize = x[1] - x[0]
elif abs(topotype) == 3:
file = open(fname, 'r')
lines = file.readlines()
ncols = int(lines[0].split()[0])
nrows = int(lines[1].split()[0])
xllcorner = float(lines[2].split()[0])
yllcorner = float(lines[3].split()[0])
cellsize = float(lines[4].split()[0])
NODATA_value = int(lines[5].split()[0])
print "Loading file ", fname
print " nrows = %i, ncols = %i" % (nrows, ncols)
topo = pylab.loadtxt(fname, skiprows=6, dtype=float)
print " Done loading"
if 0:
topo = []
for i in range(nrows):
topo.append(pylab.array(lines[6 + i], ))
print '+++ topo = ', topo
topo = pylab.array(topo)
topo = pylab.flipud(topo)
x = pylab.linspace(xllcorner, xllcorner + ncols * cellsize, ncols)
y = pylab.linspace(yllcorner, yllcorner + nrows * cellsize, nrows)
print "Shape of x, y, topo: ", x.shape, y.shape, topo.shape
else:
raise Exception("*** Only topotypes 1 and 3 supported so far")
if coarsen > 1:
topo = topo[slice(0, nrows, coarsen), slice(0, ncols, coarsen)]
x = x[slice(0, ncols, coarsen)]
y = y[slice(0, nrows, coarsen)]
print "Shapes after coarsening: ", x.shape, y.shape, topo.shape
if topotype < 0:
topo = -topo
if figno:
pylab.figure(figno)
if topoplotdata.imshow:
color_norm = Normalize(cmin, cmax, clip=True)
xylimits = (x[0], x[-1], y[0], y[-1])
#pylab.imshow(pylab.flipud(topo.T), extent=xylimits, \
pylab.imshow(pylab.flipud(topo), extent=xylimits, \
cmap=cmap, interpolation='nearest', \
norm=color_norm)
else:
neg_topo = ma.masked_where(topo > 0., topo)
all_masked = (ma.count(neg_topo) == 0)
if not all_masked:
pylab.pcolormesh(x, y, neg_topo, cmap=neg_cmap)
pylab.clim([cmin, 0])
if addcolorbar:
pylab.colorbar()
pos_topo = ma.masked_where(topo < 0., topo)
all_masked = (ma.count(pos_topo) == 0)
if not all_masked:
pylab.pcolormesh(x, y, pos_topo, cmap=pos_cmap)
pylab.clim([0, cmax])
if addcolorbar:
pylab.colorbar()
pylab.axis('scaled')
if addcontour:
pylab.contour(x, y, topo, levels=contour_levels, colors='k')
if gridedges_show:
pylab.plot([x[0], x[-1]], [y[0], y[0]], 'k')
pylab.plot([x[0], x[-1]], [y[-1], y[-1]], 'k')
pylab.plot([x[0], x[0]], [y[0], y[-1]], 'k')
pylab.plot([x[-1], x[-1]], [y[0], y[-1]], 'k')
if print_fname:
fname2 = os.path.splitext(fname)[0]
pylab.text(xllcorner + cellsize,
yllcorner + cellsize,
fname2,
color='m')
topodata = Data()
topodata.x = x
topodata.y = y
topodata.topo = topo
return topodata
import pylab as plt
import numpy as np
a = np.arange(-4, 4, 0.1)
x, y = np.meshgrid(a, a)
matrix = np.sin(x**2 + y**2)
plt.figure()
plt.imshow(matrix, cmap=plt.cm.plasma) # gray, hot, magma, ..
plt.colorbar()
plt.title("Heatmap der Funktion $\sin{(x^2 + y^2)}$")
plt.savefig("heatmap.png")
plt.savefig("heatmap.svg")
mags = []
for cnt, t in enumerate(times):
print 'Working on integration %i of %i' % (cnt + 1, obs_duration)
for i in xrange(nants):
#print i
for j in xrange(nants):
if i == j: continue #don't bother with autocorrelations
#if i > j: continue #don't double count
aa.set_jultime(t)
lst = aa.sidereal_time()
obs_zen.compute(aa)
u, v, w = aa.gen_uvw(i, j, src=obs_zen)
_beam = beamgridder(sigma=sigma,
xcen=u / dish_size_in_lambda,
ycen=v / dish_size_in_lambda,
size=dim)
#print sigma, u/dish_size_in_lambda,v/dish_size_in_lambda, dim
uv += _beam
#uv[:,:dim/2] = 0
#uv[dim/2:,dim/2] = 0
n.save('uvcov.npy', uv)
print 'there are %i minutes of integration in the uv plane' % n.sum(uv)
p.imshow(uv, interpolation='nearest')
p.colorbar()
p.show()
#本例红色被关联为最高的正相关，可以看出最强相关是：
#“花瓣宽度”petal width和“花瓣长度”petal length这两个变量。
from numpy import corrcoef
corr = corrcoef(data.T) # .T gives the transpose
print(corr)
#output:[[ 1. -0.10936925 0.87175416 0.81795363]
#output: [-0.10936925 1. -0.4205161 -0.35654409]
#output: [ 0.87175416 -0.4205161 1. 0.9627571 ]
#output: [ 0.81795363 -0.35654409 0.9627571 1. ]]
from pylab import pcolor, colorbar, xticks, yticks
from numpy import arange
pcolor(corr) #添加相关性矩阵，4个属性所以是4x4
colorbar() #添加彩色注释条
#添加X,Y轴注释，默认一个属性是1，坐标是1,2,3,4，对应四个属性name如下。
xticks(arange(1, 5),
['sepal length', 'sepal width', 'petal length', 'petal width'],
rotation=-20)
yticks(arange(1, 5),
['sepal length', 'sepal width', 'petal length', 'petal width'],
rotation=-45)
show()
###########################
#（6）成分分析（降维）
# 涉及算法之一PCA
###########################
from sklearn.decomposition import PCA
bg = nibabel.load(os.path.join('bg.nii.gz'))
pl.figure(figsize=(8, 8))
ax1 = pl.axes([0., 0., 1., 1.])
pl.imshow(bg.get_data()[:, :, 10].T,
interpolation="nearest",
cmap='gray',
origin='lower')
pl.imshow(np.ma.masked_less(sbrain_ridge[:, :, 10].T, 1e-6),
interpolation="nearest",
cmap='hot',
origin="lower")
plot_lines(contour[:, :, 10].T)
pl.axis('off')
ax2 = pl.axes([.08, .5, .05, .47])
cb = pl.colorbar(cax=ax2, ax=ax1)
cb.ax.yaxis.set_ticks_position('left')
cb.ax.yaxis.set_tick_params(labelcolor='white')
cb.ax.yaxis.set_tick_params(labelsize=20)
cb.set_ticks(np.arange(0., .8, .2))
pl.savefig(os.path.join('output', 'encoding_scores_ridge.pdf'))
pl.savefig(os.path.join('output', 'encoding_scores_ridge.png'))
pl.savefig(os.path.join('output', 'encoding_scores_ridge.eps'))
pl.clf()
sbrain_lasso = masking.unmask(np.array(scores_lasso).mean(0), dataset.mask)
pl.figure(figsize=(8, 8))
ax1 = pl.axes([0., 0., 1., 1.])
pl.imshow(bg.get_data()[:, :, 10].T,
interpolation="nearest",