def subhplot(hplot): # create histogram plots
hh = hplot[0]
xmin = hplot[1][0]
xmax = hplot[1][1]
xtipos = hplot[1][2]
ymin = hplot[2][0]
ymaxf = hplot[2][1]
fmx = hplot[2][2]
ytiposf = hplot[2][3]
hname = hplot[3]
sbp = hplot[4]
nsub = numpy.mod(sbp,10)
pylab.plot(hh[1],hh[0],linestyle='None',linewidth=0.1 )
yyl = numpy.log10(max(hh[0]))
ylf = numpy.fix(yyl)
ymax = (numpy.round(10**(yyl - ylf)))*(10**ylf)
ystep=numpy.round(ymax-ymin)/4
ytipos= numpy.arange(ystep,4*ystep,ystep)
if fmx == True :
ymax = ymaxf
ytipos = ytiposf
pylab.text(xmin+0.1*(xmax-xmin),0.7*ymax,hname)
pylab.axis([xmin,xmax,ymin,ymax],linewidth=0.1)
#if numpy.mod(nsub,2)==1 :
if numpy.mod(nsub,2)<2 :
pylab.yticks(ytipos,weight='light',fontsize=9)
if nsub >= 5:
pylab.xticks(xtipos,weight='light',fontsize=9)
xtiblank=(((''),)*len(xtipos))
ytiblank=(((''),)*len(ytipos))
#if numpy.mod(nsub,2)==0 :
# pylab.yticks(ytipos,ytiblank)
if nsub < 5:
pylab.xticks(xtipos,xtiblank)
return
def plot_embedding(X, title=None):
x_min, x_max = np.min(X, 0), np.max(X, 0)
X = (X - x_min) / (x_max - x_min)
pl.figure()
ax = pl.subplot(111)
for i in range(digits.data.shape[0]):
pl.text(
X[i, 0],
X[i, 1],
str(digits.target[i]),
color=pl.cm.Set1(digits.target[i] / 10.0),
fontdict={"weight": "bold", "size": 9},
)
if hasattr(offsetbox, "AnnotationBbox"):
# only print thumbnails with matplotlib > 1.0
shown_images = np.array([[1.0, 1.0]]) # just something big
for i in range(digits.data.shape[0]):
dist = np.sum((X[i] - shown_images) ** 2, 1)
if np.min(dist) < 4e-3:
# don't show points that are too close
continue
shown_images = np.r_[shown_images, [X[i]]]
imagebox = offsetbox.AnnotationBbox(offsetbox.OffsetImage(digits.images[i], cmap=pl.cm.gray_r), X[i])
ax.add_artist(imagebox)
pl.xticks([]), pl.yticks([])
if title is not None:
pl.title(title)
def plotAllWarmJumps():
jumpAddrs = np.array(getAllWarmJumpsAddr()).reshape((8, 18))
figure()
pcolor(jumpAddrs)
for (x, y), v in np.ndenumerate(jumpAddrs):
text(y + 0.125, x + 0.5, "0x%03x" % v)
show()
def plot_cfl_results(results):
cfl = np.array([r.CFL_NUMBER for r in results.ANALYSES.values()])
its = np.array([r.ITERATIONS for r in results.ANALYSES.values()])
evl = np.arange(len(cfl))
i = np.argsort(cfl)
cfl = cfl[i]
its = its[i]
evl = evl[i]
its[its > 1000] = np.nan
plt.figure(1)
plt.clf()
plt.plot(cfl, its, "bo-", lw=2, ms=10)
plt.xlabel("CFL Number")
plt.ylabel("Iterations")
for e, x, y in zip(evl, cfl, its):
plt.text(x, y, "%i" % (e + 1))
plt.savefig("cfl_history.eps")
plt.close()
def cmap_plot(cmdLine):
pylab.figure(figsize=[5,10])
a=outer(ones(10),arange(0,1,0.01))
subplots_adjust(top=0.99,bottom=0.00,left=0.01,right=0.8)
maps=[m for m in cm.datad if not m.endswith("_r")]
maps.sort()
l=len(maps)+1
for i, m in enumerate(maps):
print m
subplot(l,1,i+1)
pylab.setp(pylab.gca(),xticklabels=[],xticks=[],yticklabels=[],yticks=[])
imshow(a,aspect='auto',cmap=get_cmap(m),origin="lower")
pylab.text(100.85,0.5,m,fontsize=10)
# render plot
if cmdLine:
pylab.show(block=True)
else:
pylab.ion()
pylab.plot([])
pylab.ioff()
status = 1
return status
def plot_density(self, plot_filename="out/density.png"):
x, y, labels = self.load_data()
figure(figsize=(self.fig_width, self.fig_height), dpi=80)
# Perform a kernel density estimator on the coords in data.
# The following 10 lines can be commented out if density map not needed.
space_factor = 1.2
xmin = space_factor * x.min()
xmax = space_factor * x.max()
ymin = space_factor * y.min()
ymax = space_factor * y.max()
X, Y = mgrid[xmin:xmax:100j, ymin:ymax:100j]
positions = c_[X.ravel(), Y.ravel()]
values = c_[x, y]
kernel = stats.kde.gaussian_kde(values.T)
Z = reshape(kernel(positions.T).T, X.T.shape)
imshow(rot90(Z), cmap=cm.gist_earth_r, extent=[xmin, xmax, ymin, ymax])
# Plot the labels
num_labels_to_plot = min([len(labels), self.max_labels, len(x), len(y)])
if self.has_labels:
for i in range(num_labels_to_plot):
text(x[i], y[i], labels[i]) # assumes m size and order matches labels
else:
plot(x, y, "k.", markersize=1)
axis("equal")
axis("off")
savefig(plot_filename)
print "wrote %s" % (plot_filename)
def plotB3reg():
w=loadStanFit('revE2B3BHreg.fit')
printCI(w,'mmu')
printCI(w,'mr')
for b in range(2):
subplot(1,2,b+1)
plt.title('')
px=np.array(np.linspace(-0.5,0.5,101),ndmin=2)
a0=np.array(w['mmu'][:,b],ndmin=2).T
a1=np.array(w['mr'][:,b],ndmin=2).T
y=np.concatenate([sap(a0+a1*px,97.5,axis=0),sap(a0+a1*px[:,::-1],2.5,axis=0)])
x=np.squeeze(np.concatenate([px,px[:,::-1]],axis=1))
plt.plot(px[0,:],np.median(a0)+np.median(a1)*px[0,:],'red')
#plt.plot([-1,1],[0.5,0.5],'grey')
ax=plt.gca()
ax.set_aspect(1)
ax.add_patch(plt.Polygon(np.array([x,y]).T,alpha=0.2,fill=True,fc='red',ec='w'))
y=np.concatenate([sap(a0+a1*px,75,axis=0),sap(a0+a1*px[:,::-1],25,axis=0)])
ax.add_patch(plt.Polygon(np.array([x,y]).T,alpha=0.2,fill=True,fc='red',ec='w'))
man=np.array([-0.4,-0.2,0,0.2,0.4])
mus=[]
for m in range(len(man)):
mus.append(loadStanFit('revE2B3BH%d.fit'%m)['mmu'][:,b])
mus=np.array(mus).T
errorbar(mus,x=man)
ax.set_xticks(man)
plt.xlim([-0.5,0.5])
plt.ylim([-0.4,0.8])
#plt.xlabel('Manipulated Displacement')
if b==0:
plt.ylabel('Perceived Displacemet')
plt.gca().set_yticklabels([])
subplot_annotate()
plt.text(-1.1,-0.6,'Pivot Displacement',fontsize=8);
def plot_one_gene(self,gene , y_value=1, buffer=0.4): #ax: pylab.axis obj. """ 2008-09-29: Code largely borrowed from Yu Huang.. """ y_value = buffer+y_value/2.0 pylab.text(gene.startPos, -y_value+0.08, gene.tairID, size=8) pylab.plot([gene.startPos,gene.endPos],[-y_value,-y_value],"k",linewidth=2)
def _draw_V(self):
""" draw the V-cycle on our optional visualization """
xdown = numpy.linspace(0.0, 0.5, self.nlevels)
xup = numpy.linspace(0.5, 1.0, self.nlevels)
ydown = numpy.linspace(1.0, 0.0, self.nlevels)
yup = numpy.linspace(0.0, 1.0, self.nlevels)
pylab.plot(xdown, ydown, lw=2, color="k")
pylab.plot(xup, yup, lw=2, color="k")
pylab.scatter(xdown, ydown, marker="o", color="k", s=40)
pylab.scatter(xup, yup, marker="o", color="k", s=40)
if self.up_or_down == "down":
pylab.scatter(
xdown[self.nlevels - self.current_level - 1],
ydown[self.nlevels - self.current_level - 1],
marker="o",
color="r",
zorder=100,
s=38,
)
else:
pylab.scatter(xup[self.current_level], yup[self.current_level], marker="o", color="r", zorder=100, s=38)
pylab.text(0.7, 0.1, "V-cycle %d" % (self.current_cycle))
pylab.axis("off")
def add_bar(self, fname, cname, model):
chiT, dof = 0, 0
fname = cdir+fname
pnames = open(fname+'.paramnames').readlines()
if 'Neff' in fname:
loglsx = open(fname+'.maxlike').readlines()
logls2 = loglsx
else:
for i in range(3):
loglsx = open(fname+'_'+str(i+1)+'.maxlike').readlines()
if (i == 0):
logls2 = loglsx
else:
if float(loglsx[0].split()[1]) < float(logls2[0].split()[1]):
logls2 = loglsx
logls = logls2[0].split(' ')[2:]
chi2d = {}
print(' ')
print('++++' + model)
for pname, logl in zip(pnames, logls):
if "_like" in pname:
ppname = pname.split(' ')
if 'SPlanck' in ppname[0]:
chi2 = 0
else:
if 'Neff' in fname:
chi2 = float(logl)
if 'Betoule' in ppname[0]:
chi2 = chi2 - 692
else:
chi2 = -2*float(logl)
if 'Betoule' in ppname[0]:
chi2 = chi2 - 30
chiT += chi2
xname = ppname[0].replace('_like', '')
print(xname, chi2)
chi2d[xname] = chi2
print('Min_chi2 = ', chiT+30)
param = mdof[model]
for xname in nlist:
dof += defdof[xname]
left = 0
for xname in nlist:
chi2 = chi2d[xname]
color = colors[xname]
PP = pylab.barh(self.cy-0.25, chi2, left=left,
height=0.5, color=color, linewidth=0)
self.patches[xname] = PP[0]
left += chi2
if "SN" in dataset:
pylab.text(position, self.cy-0.25, r' %.2f / %s' %
(chiT + 30, dof-param + 30), fontsize=15)
else:
pylab.text(position, self.cy-0.25, r' %.2f / %s' %
(chiT, dof-param), fontsize=15)
self.ys.append(self.cy)
self.cy += 1
self.names.append(cname)
def plot_prob_effector(sens, fpr, xmax=1, baserate=0.1):
"""Plots a line graph of P(effector|positive test) against
the baserate of effectors in the input set to the classifier.
The baserate argument draws an annotation arrow
indicating P(pos|+ve) at that baserate
"""
assert 0.1 <= xmax <= 1, "Max x axis value must be in range [0,1]"
assert 0.01 <= baserate <= 1, "Baserate annotation must be in range [0,1]"
baserates = pylab.arange(0, 1.05, xmax * 0.005)
probs = [p_correct_given_pos(sens, fpr, b) for b in baserates]
pylab.plot(baserates, probs, 'r')
pylab.title("P(eff|pos) vs baserate; sens: %.2f, fpr: %.2f" % (sens, fpr))
pylab.ylabel("P(effector|positive)")
pylab.xlabel("effector baserate")
pylab.xlim(0, xmax)
pylab.ylim(0, 1)
# Add annotation arrow
xpos, ypos = (baserate, p_correct_given_pos(sens, fpr, baserate))
if baserate < xmax:
if xpos > 0.7 * xmax:
xtextpos = 0.05 * xmax
else:
xtextpos = xpos + (xmax-xpos)/5.
if ypos > 0.5:
ytextpos = ypos - 0.05
else:
ytextpos = ypos + 0.05
pylab.annotate('baserate: %.2f, P(pos|+ve): %.3f' % (xpos, ypos),
xy=(xpos, ypos),
xytext=(xtextpos, ytextpos),
arrowprops=dict(facecolor='black', shrink=0.05))
else:
pylab.text(0.05 * xmax, 0.95, 'baserate: %.2f, P(pos|+ve): %.3f' % \
(xpos, ypos))
def plot_different_versions(repo_name, pre_fetch_size, distance_to_fetch,
fig_name=None):
"""Sample plot of several different repos."""
x = range(100)
legend = []
fig, ax = plt.subplots()
for version in version_color:
csv_reader = _file_to_csv(
version=version, repo_name=repo_name,
pre_fetch_size=pre_fetch_size, distance_to_fetch=distance_to_fetch)
y = get_column(csv_reader, 'hit_rate')
if y is not None:
plt.plot(x, y, color=version_color[version])
line = mlines.Line2D(
[], [],
label=version, color=version_color[version], linewidth=2)
legend.append(line)
plt.title('Different version outputs of %s.git' % (repo_name,),
y=1.02)
plt.legend(handles=legend, loc=4)
plt.ylabel('hit rate')
plt.xlabel('cache size (%)')
legend_text = 'pfs = %s, dtf = %s\ncommit_num = %s' % (
pre_fetch_size, distance_to_fetch, REPO_DATA[repo_name]['commit_num'])
text(0.55, 0.07, legend_text, ha='center', va='center',
transform=ax.transAxes, multialignment='left',
bbox=dict(alpha=1.0, boxstyle='square', facecolor='white'))
plt.grid(True)
plt.autoscale(False)
plt.show()
def plot_frontier(self,frontier_only=False,plot_samples=True) :
""" Plot the frontier"""
frontier = self.frontier
frontier_energy = self.frontier_energy
feat1,feat2 = self.feats
pl.figure()
if not frontier_only :
ll_list1,ll_list2 = zip(*self.all_seq_energy)
pl.plot(ll_list1,ll_list2,'b*')
if plot_samples :
ll_list1,ll_list2 = zip(*self.sample_seq_energy)
pl.plot(ll_list1,ll_list2,'g*')
pl.plot(*zip(*sorted(frontier_energy)),color='magenta',\
marker='*', linestyle='dashed')
ctr = dict(zip(set(frontier_energy),[0]*
len(set(frontier_energy))))
for i,e in enumerate(frontier_energy) :
ctr[e] += 1
pl.text(e[0],e[1]+0.1*ctr[e],str(i),fontsize=10)
pl.text(e[0]+0.4,e[1]+0.1*ctr[e],frontier[i],fontsize=9)
pl.xlabel('Energy:'+feat1)
pl.ylabel('Energy:'+feat2)
pl.title('Energy Plot')
xmin,xmax = pl.xlim()
ymin,ymax = pl.ylim()
pl.xlim(xmin,xmax)
pl.ylim(ymin,ymax)
pic_dir = '../docs/tex/pics/'
pl.savefig(pic_dir+self.name+'.pdf')
pl.savefig(pic_dir+self.name+'.png')
def plotDirections(aabb=(),mask=0,bins=20,numHist=True,noShow=False,sphSph=False):
"""Plot 3 histograms for distribution of interaction directions, in yz,xz and xy planes and
(optional but default) histogram of number of interactions per body. If sphSph only sphere-sphere interactions are considered for the 3 directions histograms.
:returns: If *noShow* is ``False``, displays the figure and returns nothing. If *noShow*, the figure object is returned without being displayed (works the same way as :yref:`yade.plot.plot`).
"""
import pylab,math
from yade import utils
for axis in [0,1,2]:
d=utils.interactionAnglesHistogram(axis,mask=mask,bins=bins,aabb=aabb,sphSph=sphSph)
fc=[0,0,0]; fc[axis]=1.
subp=pylab.subplot(220+axis+1,polar=True);
# 1.1 makes small gaps between values (but the column is a bit decentered)
pylab.bar(d[0],d[1],width=math.pi/(1.1*bins),fc=fc,alpha=.7,label=['yz','xz','xy'][axis])
#pylab.title(['yz','xz','xy'][axis]+' plane')
pylab.text(.5,.25,['yz','xz','xy'][axis],horizontalalignment='center',verticalalignment='center',transform=subp.transAxes,fontsize='xx-large')
if numHist:
pylab.subplot(224,polar=False)
nums,counts=utils.bodyNumInteractionsHistogram(aabb if len(aabb)>0 else utils.aabbExtrema())
avg=sum([nums[i]*counts[i] for i in range(len(nums))])/(1.*sum(counts))
pylab.bar(nums,counts,fc=[1,1,0],alpha=.7,align='center')
pylab.xlabel('Interactions per body (avg. %g)'%avg)
pylab.axvline(x=avg,linewidth=3,color='r')
pylab.ylabel('Body count')
if noShow: return pylab.gcf()
else:
pylab.ion()
pylab.show()
def plot_genome(out_file, data_file, samples, dpi=300, screen=False):
if screen: PL.rcParams.update(PLOT_PARAMS_SCREEN)
LOG.info("plot_genome - out_file=%s, data_file=%s, samples=%s, dpi=%d"%(out_file, data_file, str(samples), dpi))
colors = 'bgryckbgryck'
data = read_posterior(data_file)
if samples is None or len(samples) == 0: samples = data.keys()
if len(samples) == 0: return
PL.figure(None, [14, 4])
right_end = 0 # rightmost plotted base pair
for chrm in sort_chrms(data.values()[0]): # for chromosomes in ascending order
max_site = max(data[samples[0]][chrm]['L']) # length of chromosome
for s, sample in enumerate(samples): # plot all samples
I = SP.where(SP.array(data[sample][chrm]['SD']) < 0.3)[0] # at sites that have confident posteriors
PL.plot(SP.array(data[sample][chrm]['L'])[I] + right_end, SP.array(data[sample][chrm]['AF'])[I], alpha=0.4, color=colors[s], lw=2) # offset by the end of last chromosome
if right_end > 0: PL.plot([right_end, right_end], [0,1], 'k--', lw=0.4, alpha=0.2) # plot separators between chromosomes
new_right = right_end + max(data[sample][chrm]['L'])
PL.text(right_end + 0.5*(new_right - right_end), 0.9, str(chrm), horizontalalignment='center')
right_end = new_right # update rightmost end
PL.plot([0,right_end], [0.5,0.5], 'k--', alpha=0.3)
PL.xlim(0,right_end)
xrange = SP.arange(0,right_end, 1000000)
PL.xticks(xrange, ["%d"%(int(x/1000000)) for x in xrange])
PL.xlabel("Genome (Mb)"), PL.ylabel("Reference allele frequency")
PL.savefig(out_file, dpi=dpi)
def labelPlot(numFlips, numTrials, mean, sd):
pylab.title(str(numTrials) + ' trials of ' + str(numFlips) + ' flips each')
pylab.xlabel('Fraction of Heads')
pylab.ylabel('Number of Trials')
xmin, xmax = pylab.xlim()
ymin, ymax = pylab.ylim()
pylab.text(xmin + (xmax - xmin) * 0.02, (ymax - ymin) / 2, 'Mean = ' + str(round(mean, 4)) + '\nSD = ' + str(round(sd, 4)))
def bondlengths(Ea, dE):
"""Calculate bond lengths and write to bondlengths.csv file"""
B = []
E0 = []
csv = open('bondlengths.csv', 'w')
for formula, energies in dE:
bref = diatomic[formula][1]
b = np.linspace(0.96 * bref, 1.04 * bref, 5)
e = np.polyfit(b, energies, 3)
if not formula in Ea:
continue
ea, eavasp = Ea[formula]
dedb = np.polyder(e, 1)
b0 = np.roots(dedb)[1]
assert abs(b0 - bref) < 0.1
b = np.linspace(0.96 * bref, 1.04 * bref, 20)
e = np.polyval(e, b) - ea
if formula == 'O2':
plt.plot(b, e, '-', color='0.7', label='GPAW')
else:
plt.plot(b, e, '-', color='0.7', label='_nolegend_')
name = latex(data[formula]['name'])
plt.text(b[0], e[0] + 0.2, name)
B.append(bref)
E0.append(-eavasp)
csv.write('`%s`, %.3f, %.3f, %+.3f\n' %
(name[1:-1], b0, bref, b0 - bref))
plt.plot(B, E0, 'g.', label='reference')
plt.legend(loc='lower right')
plt.xlabel('Bond length $\mathrm{\AA}$')
plt.ylabel('Energy [eV]')
plt.savefig('bondlengths.png')
def bar_plot_1(data):
"""Generates bar plot from data"""
x_labels=[a for (a,b) in data]
y_data=[b for (a,b) in data]
# Create chart
pos=range(1,len(x_labels)+1)
P.figure(1,figsize=(11,7))
P.bar(left=pos,height=y_data,log=True,width=.6,color="lightgrey",edgecolor="#8094B6")
pos2=[a+.3 for a in pos]
P.xticks(pos2,x_labels)
P.title("Evolution of network data size over time",fontsize="x-large")
P.xlabel("Network data sets (year published)",fontsize="large")
P.ylabel("Number of vertices [log(N)]",fontsize="large")
text_color="black"
for i in range(len(y_data)):
if i<2:
P.text(pos[i]+0.01,y_data[i]+5,int_to_scinot(y_data[i]),color=text_color)
elif i==2:
P.text(pos[i]+0.01,y_data[i]+100,int_to_scinot(y_data[i]),color=text_color)
elif i==3:
P.text(pos[i]+0.01,y_data[i]+1000,int_to_scinot(y_data[i]),color=text_color)
elif i==4:
P.text(pos[i]+0.01,y_data[i]+100000,int_to_scinot(y_data[i]),color=text_color)
else:
P.text(pos[i]+0.01,y_data[i]+1000000,int_to_scinot(y_data[i]),color=text_color)
P.savefig("../../images/figures/net_size_evo.png",dpi=100,format="png")
def gui_repr(self):
"""Generate a GUI to represent the sentence alignments
"""
if __pylab_loaded__:
fig_width = max(len(self.text_e), len(self.text_f)) + 1
fig_height = 3
pylab.figure(figsize=(fig_width*0.8, fig_height*0.8), facecolor='w')
pylab.box(on=False)
pylab.subplots_adjust(left=0, right=1, bottom=0, top=1)
pylab.xlim(-1, fig_width - 1)
pylab.ylim(0, fig_height)
pylab.xticks([])
pylab.yticks([])
e = [0 for _ in xrange(len(self.text_e))]
f = [0 for _ in xrange(len(self.text_f))]
for (i, j) in self.align:
e[i] = 1
f[j] = 1
# draw the middle line
pylab.arrow(i, 2, j - i, -1, color='r')
for i in xrange(len(e)):
# draw e side line
pylab.text(i, 2.5, self.text_e[i], ha = 'center', va = 'center',
rotation=30)
if e[i] == 1:
pylab.arrow(i, 2.5, 0, -0.5, color='r', alpha=0.3, lw=2)
for i in xrange(len(f)):
# draw f side line
pylab.text(i, 0.5, self.text_f[i], ha = 'center', va = 'center',
rotation=30)
if f[i] == 1:
pylab.arrow(i, 0.5, 0, 0.5, color='r', alpha=0.3, lw=2)
pylab.draw()
def generateNetwork(aINDEX, dDIFF, AM, name):
R = 100
X = []
Y = []
Z = []
for i in range(len(aINDEX)):
#r = 100*R + sum(AM[i])*R
r = 1000*R + 10000*abs(dDIFF[aINDEX[i]])*R
t = 2*math.pi*random.random()
X.append(r*math.cos(t))
Y.append(r*math.sin(t))
Z.append(sum(AM[i]))
fig = P.figure()
P.axis('off')
P.scatter(X, Y, Z, edgecolor = '', c = 'lightblue', alpha = 0.5)
for i in range(len(aINDEX)):
for j in range(i):
if sum(AM[i]) > 200 and sum(AM[j]) > 200:
P.plot([X[i], X[j]], [Y[i], Y[j]],'k',lw = 0.01)
for i in range(len(aINDEX)):
if sum(AM[i])>200:
P.text(X[i], Y[i], aINDEX[i], fontsize = 8)
#P.show()
fig.savefig('figures/' + name + '.png')
return
def doSubplot(multiplier=1.0, layout=(-1, -1)):
if time_sec < 16200:
xs, ys = xs_1, ys_1
domain_bounds = bounds_1sthalf
grid = grid_1
else:
xs, ys = xs_2, ys_2
domain_bounds = bounds_2ndhalf
grid = grid_2
try:
mo = ARPSModelObsFile("%s/%s/KCYS%03dan%06d" % (base_path, exp, min_ens, time_sec))
except AssertionError:
mo = ARPSModelObsFile("%s/%s/KCYS%03dan%06d" % (base_path, exp, min_ens, time_sec), mpi_config=(2, 12))
except:
print "Can't load reflectivity ..."
mo = {'Z':np.zeros((1, 255, 255), dtype=np.float32)}
pylab.contour(xs, ys, wind[exp]['w'][wdt][domain_bounds], levels=np.arange(2, 102, 2), styles='-', colors='k')
pylab.contour(xs, ys, wind[exp]['w'][wdt][domain_bounds], levels=np.arange(-100, 0, 2), styles='--', colors='k')
pylab.quiver(xs[thin], ys[thin], wind[exp]['u'][wdt][domain_bounds][thin], wind[exp]['v'][wdt][domain_bounds][thin])
pylab.contourf(xs, ys, mo['Z'][0][domain_bounds], levels=np.arange(10, 85, 5), cmap=NWSRef, zorder=-10)
grid.drawPolitical(scale_len=10)
row, col = layout
if col == 1:
pylab.text(-0.075, 0.5, exp_names[exp], transform=pylab.gca().transAxes, rotation=90, ha='center', va='center', size=12 * multiplier)
def scatter_from_csv(self, filename, sand = 'sand', silt = 'silt', clay = 'clay', diameter = '', hue = '', tags = '', **kwargs):
"""Loads data from filename (expects csv format). Needs one header row with at least the columns {sand, silt, clay}. Can also plot two more variables for each point; specify the header value for columns to be plotted as diameter, hue. Can also add a text tag offset from each point; specify the header value for those tags.
Note! text values (header entries, tag values ) need to be quoted to be recognized as text. """
fh = file(filename, 'rU')
soilrec = csv2rec(fh)
count = 0
if (sand in soilrec.dtype.names):
count = count + 1
if (silt in soilrec.dtype.names):
count = count + 1
if (clay in soilrec.dtype.names):
count = count + 1
if (count < 3):
print "ERROR: need columns for sand, silt and clay identified in ', filename"
locargs = {'s': None, 'c': None}
for (col, key) in ((diameter, 's'), (hue, 'c')):
col = col.lower()
if (col != '') and (col in soilrec.dtype.names):
locargs[key] = soilrec.field(col)
else:
print 'ERROR: did not find ', col, 'in ', filename
for k in kwargs:
locargs[k] = kwargs[k]
values = zip(*[soilrec.field(sand), soilrec.field(clay), soilrec.field(silt)])
print values
(xs, ys) = self._toCart(values)
p.scatter(xs, ys, label='_', **locargs)
if (tags != ''):
tags = tags.lower()
for (x, y, tag) in zip(*[xs, ys, soilrec.field(tags)]):
print x,
print y,
print tag
p.text(x + 1, y + 1, tag, fontsize=12)
fh.close()
def print_matplotlib(s):
pylab.figure()
pylab.text(0,0,s)
pylab.axis('off')
pylab.figure()
#pylab.show()
return
def scatter_stats(db, s1, s2, f1=None, f2=None, **kwargs):
if f1 == None:
f1 = lambda x: x # constant function
if f2 == None:
f2 = f1
x = []
xerr = []
y = []
yerr = []
for k in db:
x_k = [f1(x_ki) for x_ki in db[k].__getattribute__(s1).gettrace()]
y_k = [f2(y_ki) for y_ki in db[k].__getattribute__(s2).gettrace()]
x.append(pl.mean(x_k))
xerr.append(pl.std(x_k))
y.append(pl.mean(y_k))
yerr.append(pl.std(y_k))
pl.text(x[-1], y[-1], " %s" % k, fontsize=8, alpha=0.4, zorder=-1)
default_args = {"fmt": "o", "ms": 10}
default_args.update(kwargs)
pl.errorbar(x, y, xerr=xerr, yerr=yerr, **default_args)
pl.xlabel(s1)
pl.ylabel(s2)
def compare_models(db, stoch="itn coverage", stat_func=None, plot_type="", **kwargs):
if stat_func == None:
stat_func = lambda x: x
X = {}
for k in sorted(db.keys()):
c = k.split("_")[2]
X[c] = []
for k in sorted(db.keys()):
c = k.split("_")[2]
X[c].append([stat_func(x_ki) for x_ki in db[k].__getattribute__(stoch).gettrace()])
x = pl.array([pl.mean(xc[0]) for xc in X.values()])
xerr = pl.array([pl.std(xc[0]) for xc in X.values()])
y = pl.array([pl.mean(xc[1]) for xc in X.values()])
yerr = pl.array([pl.std(xc[1]) for xc in X.values()])
if plot_type == "scatter":
default_args = {"fmt": "o", "ms": 10}
default_args.update(kwargs)
for c in X.keys():
pl.text(pl.mean(X[c][0]), pl.mean(X[c][1]), " %s" % c, fontsize=8, alpha=0.4, zorder=-1)
pl.errorbar(x, y, xerr=xerr, yerr=yerr, **default_args)
pl.xlabel("First Model")
pl.ylabel("Second Model")
pl.plot([0, 1], [0, 1], alpha=0.5, linestyle="--", color="k", linewidth=2)
elif plot_type == "rel_diff":
d1 = sorted(100 * (x - y) / x)
d2 = sorted(100 * (xerr - yerr) / xerr)
pl.subplot(2, 1, 1)
pl.title("Percent Model 2 deviates from Model 1")
pl.plot(d1, "o")
pl.xlabel("Countries sorted by deviation in mean")
pl.ylabel("deviation in mean (%)")
pl.subplot(2, 1, 2)
pl.plot(d2, "o")
pl.xlabel("Countries sorted by deviation in std err")
pl.ylabel("deviation in std err (%)")
elif plot_type == "abs_diff":
d1 = sorted(x - y)
d2 = sorted(xerr - yerr)
pl.subplot(2, 1, 1)
pl.title("Percent Model 2 deviates from Model 1")
pl.plot(d1, "o")
pl.xlabel("Countries sorted by deviation in mean")
pl.ylabel("deviation in mean")
pl.subplot(2, 1, 2)
pl.plot(d2, "o")
pl.xlabel("Countries sorted by deviation in std err")
pl.ylabel("deviation in std err")
else:
assert 0, "plot_type must be abs_diff, rel_diff, or scatter"
return pl.array([x, y, xerr, yerr])
def PASTISConfMap(confmatrix):
norms = np.sum(confmatrix,axis=1)
for i in range(len(confmatrix[:,0])):
confmatrix[i,:] /= norms[i]
p.figure(12)
p.clf()
p.imshow(confmatrix,interpolation='nearest',origin='lower',cmap='YlOrRd')
#box labels
for x in range(len(confmatrix[:,0])):
for y in range(len(confmatrix[:,0])):
if confmatrix[y,x] > 0.05:
if confmatrix[y,x]>0.7:
p.text(x,y,str(np.round(confmatrix[y,x],decimals=3)),va='center',ha='center',color='w')
else:
p.text(x,y,str(np.round(confmatrix[y,x],decimals=3)),va='center',ha='center')
#plot grid lines (using p.grid leads to unwanted offset)
for x in [0.5,1.5,2.5,3.5,4.5]:
p.plot([x,x],[-0.5,6.5],'k--')
for y in [0.5,1.5,2.5,3.5,4.5]:
p.plot([-0.5,6.5],[y,y],'k--')
p.xlim(-0.5,5.5)
p.ylim(-0.5,5.5)
p.xlabel('Predicted Class')
p.ylabel('True Class')
#class labels
p.xticks([0,1,2,3,4,5],['Planet', 'EB', 'ET', 'PSB','BEB', 'BTP'],rotation='vertical')
p.yticks([0,1,2,3,4,5],['Planet', 'EB', 'ET', 'PSB','BEB', 'BTP'])
def suplabel(axis, label, label_prop=None, labelpad=3, ha='center', va='center'):
"""
Add super ylabel or xlabel to the figure
Similar to matplotlib.suptitle
axis - string: "x" or "y"
label - string
label_prop - keyword dictionary for Text
labelpad - padding from the axis (default: 5)
ha - horizontal alignment (default: "center")
va - vertical alignment (default: "center")
"""
fig = pylab.gcf()
xmin = []
ymin = []
for ax in fig.axes:
xmin.append(ax.get_position().xmin)
ymin.append(ax.get_position().ymin)
xmin, ymin = min(xmin), min(ymin)
dpi = fig.dpi
if axis.lower() == "y":
rotation = 90.
x = xmin-float(labelpad)/dpi
y = 0.5
elif axis.lower() == 'x':
rotation = 0.
x = 0.5
y = ymin - float(labelpad)/dpi
else:
raise Exception("Unexpected axis: x or y")
if label_prop is None:
label_prop = dict()
pylab.text(x, y, label, rotation=rotation,
transform=fig.transFigure,
ha=ha, va=va,
**label_prop)
def _show_rates(rate, wo, wt, attenuator, tau_NP, tau_P):
import pylab
#pylab.figure()
pylab.errorbar(rate, wt[0], yerr=wt[1], fmt='g.', label='attenuated')
pylab.errorbar(rate, wo[0], yerr=wo[1], fmt='b.', label='unattenuated')
pylab.xscale('log')
pylab.yscale('log')
pylab.xlabel('incident rate (counts/second)')
pylab.ylabel('observed rate (counts/second)')
pylab.legend(loc='best')
pylab.grid(True)
pylab.plot(rate, rate/attenuator, 'g-', label='target')
pylab.plot(rate, rate, 'b-', label='target')
Ipeak, Rpeak = peak_rate(tau_NP=tau_NP, tau_P=tau_P)
if rate[0] <= Ipeak <= rate[-1]:
pylab.axvline(x=Ipeak, ls='--', c='b')
pylab.text(x=Ipeak, y=0.05, s=' %g'%Ipeak,
ha='left', va='bottom',
transform=pylab.gca().get_xaxis_transform())
if False:
pylab.axhline(y=Rpeak, ls='--', c='b')
pylab.text(y=Rpeak, x=0.05, s=' %g\n'%Rpeak,
ha='left', va='bottom',
transform=pylab.gca().get_yaxis_transform())
def plotVowelProportionHistogram(wordList, numBins=15):
"""
Plots a histogram of the proportion of vowels in each word in wordList
using the specified number of bins in numBins
"""
vowels = 'aeiou'
vowelProportions = []
for word in wordList:
vowelsCount = 0.0
for letter in word:
if letter in vowels:
vowelsCount += 1
vowelProportions.append(vowelsCount / len(word))
meanProportions = sum(vowelProportions) / len(vowelProportions)
print "Mean proportions: ", meanProportions
pylab.figure(1)
pylab.hist(vowelProportions, bins=15)
pylab.title("Histogram of Proportions of Vowels in Each Word")
pylab.ylabel("Count of Words in Each Bucket")
pylab.xlabel("Proportions of Vowels in Each Word")
ymin, ymax = pylab.ylim()
ymid = (ymax - ymin) / 2
pylab.text(0.03, ymid, "Mean = {0}".format(
str(round(meanProportions, 4))))
pylab.vlines(0.5, 0, ymax)
pylab.text(0.51, ymax - 0.01 * ymax, "0.5", verticalalignment = 'top')
pylab.show()
def _plot(self, attribute_name, file_path=None):
clf() # Clear existing plot
a = self._values[0] * 100
b = self._values[1] * 100
ax = subplot(111)
plot(a, a, "k--", a, b, "r")
ax.set_ylim([0, 100])
ax.grid(color="0.5", linestyle=":", linewidth=0.5)
xlabel("population")
ylabel(attribute_name)
title("Lorenz curve")
font = {"fontname": "Courier", "color": "r", "fontweight": "bold", "fontsize": 11}
box = {"pad": 6, "facecolor": "w", "linewidth": 1, "fill": True}
text(5, 90, "Gini coefficient: %(gini)f" % {"gini": self._ginicoeff}, font, color="k", bbox=box)
majorLocator = MultipleLocator(20)
majorFormatter = FormatStrFormatter("%d %%")
minorLocator = MultipleLocator(5)
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_major_formatter(majorFormatter)
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_major_locator(majorLocator)
ax.yaxis.set_major_formatter(majorFormatter)
ax.yaxis.set_minor_locator(minorLocator)
if file_path:
savefig(file_path)
close()
else:
show()
def plotMatches(self, loader, timespread=14.0):
"""This function plots the matched events across all the loggers, so the quality
of matching can be visually inspected. Supply a loader function which then
loads up the correct audio, given a logger index and timestamp. Optional
timespread is the time around the central event"""
# sort out our plotting context (bounds)
fmin = 0
fmax = 3000
fsize = 16 # label font size
# first load up all the audio files and plot spectrograms
pylab.figure(figsize=(16, 12))
numplots = len(self.matchedEvents)
axislist = []
plotindex = 1
# This is the start time of the event
centre_time = self.referenceEvent.event_time
for event in self.matchedEvents:
stream = loader(event[0].logger, event[0].coarse_timestamp,
centre_time, timespread)
ax = pylab.subplot(numplots, 1, plotindex)
axislist.append(ax)
pylab.specgram(stream, Fs=44100, NFFT=4096, noverlap=3000)
# relabel the x axis
pylab.xticks(range(0, int(timespread), 2), [
"{:0.2f}".format(float(l) + centre_time - timespread / 2)
for l in range(0, int(timespread), 2)
])
pylab.ylim(fmin, fmax)
pylab.grid(True)
# Add annotation to each panel
pylab.text((timespread) * 0.02,
fmax * 0.8,
"Logger:" + str(event[0].logger.logger_id),
fontsize=fsize,
style='normal')
# add box to each panel
delta_time = event[0].event_time - centre_time + timespread / 2
if event[
0].logger.logger_id == self.referenceEvent.logger.logger_id:
rect_color = 'red'
else:
rect_color = 'black'
ax.add_patch(
patches.Rectangle(
((delta_time, (fmax - fmin) * 0.01)),
self.referenceEvent.event_length,
(fmax - fmin) * 0.98,
edgecolor=rect_color,
fill=False # remove background
))
str_time = "t={:.2f}s SS={:.2e}".format(event[0].event_time,
event[0].SS)
ax.text((timespread) * 0.11,
fmax * 0.8,
str_time,
fontsize=fsize,
style='normal')
plotindex += 1
if self.referenceEvent.classLabel is not None:
pylab.suptitle(self.referenceEvent.classLabel)
pylab.tight_layout()
print('=' * 80)
print("LinearSVC with L1-based feature selection")
results.append(benchmark(L1LinearSVC()))
# make some plots
indices = np.arange(len(results))
results = [[x[i] for x in results] for i in range(4)]
clf_names, score, training_time, test_time = results
training_time = np.array(training_time) / np.max(training_time)
test_time = np.array(test_time) / np.max(test_time)
pl.figure(figsize=(12, 8))
pl.title("Score")
pl.barh(indices, score, .2, label="score", color='r')
pl.barh(indices + .3, training_time, .2, label="training time", color='g')
pl.barh(indices + .6, test_time, .2, label="test time", color='b')
pl.yticks(())
pl.legend(loc='best')
pl.subplots_adjust(left=.25)
pl.subplots_adjust(top=.95)
pl.subplots_adjust(bottom=.05)
for i, c in zip(indices, clf_names):
pl.text(-.3, i, c)
pl.show()
# solve and return W, H, x, y, w, h
sol = solvers.cpl(c, F, G, h)
return sol['x'][0], sol['x'][1], sol['x'][2:7], sol['x'][7:12], \
sol['x'][12:17], sol['x'][17:]
solvers.options['show_progress'] = False
pylab.figure(facecolor='w')
pylab.subplot(221)
Amin = matrix([100., 100., 100., 100., 100.])
W, H, x, y, w, h = floorplan(Amin)
for k in xrange(5):
pylab.fill([x[k], x[k], x[k]+w[k], x[k]+w[k]],
[y[k], y[k]+h[k], y[k]+h[k], y[k]], facecolor = '#D0D0D0')
pylab.text(x[k]+.5*w[k], y[k]+.5*h[k], "%d" %(k+1))
pylab.axis([-1.0, 26, -1.0, 26])
pylab.xticks([])
pylab.yticks([])
pylab.subplot(222)
Amin = matrix([20., 50., 80., 150., 200.])
W, H, x, y, w, h = floorplan(Amin)
for k in xrange(5):
pylab.fill([x[k], x[k], x[k]+w[k], x[k]+w[k]],
[y[k], y[k]+h[k], y[k]+h[k], y[k]], facecolor = '#D0D0D0')
pylab.text(x[k]+.5*w[k], y[k]+.5*h[k], "%d" %(k+1))
pylab.axis([-1.0, 26, -1.0, 26])
pylab.xticks([])
pylab.yticks([])
# cbar_ax.set_ylabel('Synchronization to the 1st REST')
# fig.savefig('sync'+desc, dpi=200)
# plt.show()
# plt.close()
fig = plt.figure(figsize=(8, 5))
ax = plt.pcolormesh(t/60, f, np.median(specs, 0), cmap='RdBu_r', vmin=-80, vmax=80)
plt.xlabel('Time $t$ [s]')
plt.ylabel('Frequency $f$ [Hz]')
for j, (x, y) in enumerate(blocks_intervals):
plt.text((x+y)/2, 35, blocks_list[j], ha='center', va='bottom', weight='bold')
plt.axvline(y, color='k', linestyle='--', alpha=0.6)
for (name, band) in bands:
plt.text(1 , np.mean(band), name, ha='center', va='center', weight='bold')
plt.axhline(band[0], color='k', linestyle='--', alpha=0.6)
plt.title('Experimental' if not control else 'Control')
plt.ylim(0, 40)
plt.xlim(0, 13)
fig.subplots_adjust(right=0.8)
cbar_ax = fig.add_axes([0.85, 0.10, 0.05, 0.8])
cb = fig.colorbar(ax, cax=cbar_ax)
cbar_ax.set_ylabel('$S(t, f)$ [%]')
def _normalized_text(X, Y, rx, ry, msg, **kwargs):
pylab.text(
min(X) * (1.0 - rx) + max(X) * rx,
min(Y) * (1.0 - ry) + max(Y) * ry, msg, **kwargs)
def plot(self, filename=None):
"""
Plot vrep and derivative together with fit info.
parameters:
===========
filename: graphics output file name
"""
try:
import pylab as pl
except:
raise AssertionError('pylab could not be imported')
r=np.linspace(0,self.r_cut)
v=[self(x,der=0) for x in r]
vp=[self(x,der=1) for x in r]
rmin=0.95*min([d[0] for d in self.deriv])
rmax = 1.1*self.r_cut
fig=pl.figure()
pl.subplots_adjust(wspace=0.25)
# Vrep
pl.subplot(1,2,1)
pl.ylabel(r'$V_{rep}(r)$ (eV)')
pl.xlabel(r'$r$ ($\AA$)')
if self.r_dimer!=None:
pl.axvline(x=self.r_dimer,c='r',ls=':')
pl.axvline(x=self.r_cut,c='r',ls=':')
pl.plot(r,v)
pl.ylim(ymin=0,ymax=self(rmin))
pl.xlim(xmin=rmin, xmax=self.r_cut)
# Vrep'
pl.subplot(1,2,2)
pl.ylabel(r'$dV_{rep}(r)/dr$ (eV/$\AA$)')
pl.xlabel(r'$r$ ($\AA$)')
pl.plot(r,vp,label=r'$dV_{rep}(r)/dr$')
for s in self.deriv:
pl.scatter( [s[0]],[s[1]],s=100*s[2],c=s[3],label=s[4])
pl.axvline(x=self.r_cut,c='r',ls=':')
if self.r_dimer!=None:
pl.axvline(x=self.r_dimer,c='r',ls=':')
ymin = 0
for point in self.deriv:
if rmin<=point[0]<=rmax: ymin = min(ymin,point[1])
ymax = np.abs(ymin)*0.1
pl.axhline(0,ls='--',c='k')
if self.r_dimer!=None:
pl.text(self.r_dimer, ymax, r'$r_{dimer}$')
pl.text(self.r_cut, ymax, r'$r_{cut}$')
pl.xlim(xmin=rmin, xmax=rmax)
pl.ylim(ymin=ymin, ymax=ymax)
#pl.subtitle('Fitting for %s and %s' % (self.sym1, self.sym2))
pl.rc('font', size=10)
pl.rc('legend',fontsize=8)
pl.legend(loc=4)
file = '%s_%s_repulsion.pdf' % (self.sym1, self.sym2)
if filename!=None:
file=filename
pl.savefig(file)
pl.clf()
def plot(wavekernel_out, wavekernel_out_path, to_show_msd, highlight_i,
energy_min, energy_max, ymin, ymax, is_log, to_label, title,
out_filename):
condition = wavekernel_out['condition']
eigenvalues = condition['eigenvalues']
eigenstate_msd = condition['eigenstate_mean_z']
eigenstate_msd = map(lambda x: x * kAngstromPerAu, eigenstate_msd)
fst_filter = wavekernel_out['setting']['fst_filter']
pylab.figure(figsize=(16, 12))
if is_log:
pylab.yscale('log')
pylab.plot(eigenvalues, eigenstate_msd, 'o')
if not highlight_i is None:
j = highlight_i - fst_filter
xs = [eigenvalues[j]]
ys = [eigenstate_msd[j]]
pylab.plot(xs,
ys,
'o',
color='red',
label=str(highlight_i),
markersize=10)
pylab.legend(numpoints=1)
if energy_min is None:
energy_min = pylab.xlim()[0]
if energy_max is None:
energy_max = pylab.xlim()[1]
if ymin is None:
ymin = pylab.ylim()[0]
if ymax is None:
ymax = pylab.ylim()[1]
pylab.xlim(energy_min, energy_max)
pylab.ylim(ymin, ymax)
xticks_new = list(pylab.xticks()[0])
xticks_new.extend([energy_min, energy_max])
pylab.xticks(xticks_new)
pylab.xlim(energy_min, energy_max) # limit setting again is needed.
yticks_new = list(pylab.yticks()[0])
yticks_new.extend([ymin, ymax])
pylab.yticks(yticks_new)
pylab.ylim(ymin, ymax) # limit setting again is needed.
pylab.rcParams.update({'font.size': 10})
if to_label:
j = fst_filter
for x, y in zip(eigenvalues, eigenstate_msd):
if energy_min <= x <= energy_max and ymin <= y <= ymax:
pylab.text(x, y, str(j))
j += 1
# num_points = len(filter(
# lambda (x, y): energy_min <= x and x <= energy_max and ymin <= y and y <= ymax,
# zip(eigenvalues, eigenstate_msd)))
#
if title is None:
title = wavekernel_out_path
pylab.xlabel('Energy [a.u.]')
pylab.ylabel('Mean x [$\AA$]')
pylab.title(title)
if out_filename is None:
out_filename = re.sub("\.[^.]+$", "",
wavekernel_out_path) + "_eigenvalue_vs_mean.png"
pylab.savefig(out_filename)
def segment_and_group_sources(image, T, name=None, ps=None, plots=False):
'''
*image*: binary image that defines "blobs"
*T*: source table; only ".ibx" and ".iby" elements are used (x,y integer
pix pos). Note: ".blob" field is added.
*name*: for debugging only
Returns: (blobs, blobsrcs, blobslices)
*blobs*: image, values -1 = no blob, integer blob indices
*blobsrcs*: list of np arrays of integers, elements in T within each blob
*blobslices*: list of slice objects for blob bounding-boxes.
'''
from scipy.ndimage.morphology import binary_fill_holes
from scipy.ndimage.measurements import label, find_objects
image = binary_fill_holes(image)
blobs, nblobs = label(image)
#print('Detected blobs:', nblobs)
H, W = image.shape
del image
blobslices = find_objects(blobs)
clipx = np.clip(T.ibx, 0, W - 1)
clipy = np.clip(T.iby, 0, H - 1)
T.blob = blobs[clipy, clipx]
if plots:
import pylab as plt
from astrometry.util.plotutils import dimshow
plt.clf()
dimshow(blobs > 0, vmin=0, vmax=1)
ax = plt.axis()
for i, bs in enumerate(blobslices):
sy, sx = bs
by0, by1 = sy.start, sy.stop
bx0, bx1 = sx.start, sx.stop
plt.plot([bx0, bx0, bx1, bx1, bx0], [by0, by1, by1, by0, by0],
'r-')
plt.text((bx0 + bx1) / 2.,
by0,
'%i' % (i + 1),
ha='center',
va='bottom',
color='r')
plt.plot(T.ibx, T.iby, 'rx')
for i, t in enumerate(T):
plt.text(t.ibx,
t.iby,
'src %i' % i,
color='red',
ha='left',
va='center')
plt.axis(ax)
plt.title('Blobs')
ps.savefig()
# Find sets of sources within blobs
blobsrcs = []
keepslices = []
blobmap = {}
for blob in range(1, nblobs + 1):
Isrcs, = np.nonzero(T.blob == blob)
if len(Isrcs) == 0:
blobmap[blob] = -1
continue
blobmap[blob] = len(blobsrcs)
blobsrcs.append(Isrcs)
bslc = blobslices[blob - 1]
keepslices.append(bslc)
blobslices = keepslices
# Find sources that do not belong to a blob and add them as
# singleton "blobs"; otherwise they don't get optimized.
# for sources outside the image bounds, what should we do?
inblobs = np.zeros(len(T), bool)
for Isrcs in blobsrcs:
inblobs[Isrcs] = True
noblobs = np.flatnonzero(np.logical_not(inblobs))
del inblobs
#print(len(noblobs), 'sources are not in blobs')
# Remap the "blobs" image so that empty regions are = -1 and the blob values
# correspond to their indices in the "blobsrcs" list.
if len(blobmap):
maxblob = max(blobmap.keys())
else:
maxblob = 0
maxblob = max(maxblob, blobs.max())
bm = np.zeros(maxblob + 1, int)
for k, v in blobmap.items():
bm[k] = v
bm[0] = -1
# Remap blob numbers
blobs = bm[blobs]
if plots:
from astrometry.util.plotutils import dimshow
plt.clf()
dimshow(blobs > -1, vmin=0, vmax=1)
ax = plt.axis()
for i, bs in enumerate(blobslices):
sy, sx = bs
by0, by1 = sy.start, sy.stop
bx0, bx1 = sx.start, sx.stop
plt.plot([bx0, bx0, bx1, bx1, bx0], [by0, by1, by1, by0, by0],
'r-')
plt.text((bx0 + bx1) / 2.,
by0,
'%i' % (i + 1),
ha='center',
va='bottom',
color='r')
plt.plot(T.ibx, T.iby, 'rx')
for i, t in enumerate(T):
plt.text(t.ibx,
t.iby,
'src %i' % i,
color='red',
ha='left',
va='center')
plt.axis(ax)
plt.title('Blobs')
ps.savefig()
for j, Isrcs in enumerate(blobsrcs):
for i in Isrcs:
if (blobs[clipy[i], clipx[i]] != j):
print(
'---------------------------!!!-------------------------')
print('Blob', j, 'sources', Isrcs)
print('Source', i, 'coords x,y', T.ibx[i], T.iby[i])
print('Expected blob value', j, 'but got', blobs[clipy[i],
clipx[i]])
T.blob = blobs[clipy, clipx]
assert (len(blobsrcs) == len(blobslices))
return blobs, blobsrcs, blobslices
def Plot_PS_W(xps1, xps1_w, xps2, xps2_w, freqs, signal_amp, sampling_rate,
fft_size, noise_type, window_type, m1, m2):
xps1_dBm = xps1[0]
xps1_w_dBm = xps1_w[0]
xps2_dBm = xps2[0]
xps2_w_dBm = xps2_w[0]
xps1_watt = xps1[1]
xps1_w_watt = xps1_w[1]
xps2_watt = xps2[1]
xps2_w_watt = xps2_w[1]
a1 = max(xps1_dBm)
a1_w = max(xps1_w_dBm)
b1 = numpy.argmax(xps1_dBm)
b1_w = numpy.argmax(xps1_w_dBm)
a2 = max(xps2_dBm)
a2_w = max(xps2_w_dBm)
b2 = numpy.argmax(xps2_dBm)
b2_w = numpy.argmax(xps2_w_dBm)
signal_power = signal_amp * signal_amp / 2
accuracy1 = abs(xps1_watt[b1] - signal_power) / signal_power
accuracy1_w = abs(xps1_w_watt[b1_w] - signal_power) / signal_power
accuracy2 = abs(xps2_watt[b2] - signal_power) / signal_power
accuracy2_w = abs(xps2_w_watt[b2_w] - signal_power) / signal_power
print(a2, b2)
pl.figure(figsize=(8, 6))
pl.subplot(211)
pl.step(freqs,
xps1_dBm,
color='green',
label=u"Without noise, without window")
pl.step(freqs,
xps1_w_dBm,
label=u'Without noise, with %s window' % (window_type))
pl.ylabel(u'Power(dBm)')
pl.annotate('%s Hz, %s dBm' % (b1 * sampling_rate / fft_size, a1),
xy=(freqs[b1], xps1_dBm[b1]),
xytext=(freqs[b1] + 5, xps1_dBm[b1] - 30))
props = dict(boxstyle='round', facecolor='none', alpha=0.5)
pl.text(1500,
-120,
'accuracy:%s\naccuracy_w:%s' % (accuracy1, accuracy1_w),
size=10,
bbox=props)
pl.legend(fontsize=8)
pl.title(u"Power Spectrum(fs=%sHz, n=%s)" % (sampling_rate, fft_size))
pl.subplot(212)
pl.step(freqs,
xps2_dBm,
color='green',
label=u"With %s, without window" % (noise_type))
pl.step(freqs,
xps2_w_dBm,
label=u'With %s, with %s window' % (noise_type, window_type))
pl.xlabel(u'Frequency(Hz)')
pl.ylabel(u'Power(dBm)')
pl.annotate('%s Hz, %s dBm' % (b2 * sampling_rate / fft_size, a2),
xy=(freqs[b2], xps2_dBm[b2]),
xytext=(freqs[b2] + 5, xps2_dBm[b2] - 10))
props = dict(boxstyle='round', facecolor='none', alpha=0.5)
pl.text(
1500,
-100,
'mean/lower-boundary:%s\nstd/higher-boundary:%s\naccuracy:%s\naccuracy_w:%s'
% (m1, m2, accuracy2, accuracy2_w),
size=10,
bbox=props)
pl.legend(fontsize=8)
pl.savefig('power_spectrum_wind.pdf')
else:
algorithm.fit(X)
t1 = time.time()
if hasattr(algorithm, 'labels_'):
y_pred = algorithm.labels_.astype(np.int)
else:
y_pred = algorithm.predict(X)
# plot
pl.subplot(4, 6, plot_num)
if i_dataset == 0:
pl.title(str(algorithm).split('(')[0], size=18)
pl.scatter(X[:, 0], X[:, 1], color=colors[y_pred].tolist(), s=10)
if hasattr(algorithm, 'cluster_centers_'):
centers = algorithm.cluster_centers_
center_colors = colors[:len(centers)]
pl.scatter(centers[:, 0], centers[:, 1], s=100, c=center_colors)
pl.xlim(-2, 2)
pl.ylim(-2, 2)
pl.xticks(())
pl.yticks(())
pl.text(.99,
.01, ('%.2fs' % (t1 - t0)).lstrip('0'),
transform=pl.gca().transAxes,
size=15,
horizontalalignment='right')
plot_num += 1
pl.show()
def plot_monte_histograms(data, graph_name='gene_histogram.png', bins=20,\
normal_fit=True, normed=True, colors=None, linecolors=None, \
alpha=0.75, prob_axes=True, series_names=None, show_legend=False,\
y_label=None, x_label=None, **kwargs):
"""Outputs a histogram with multiple series (must provide a list of series).
Differs from regular histogram in that p-value works w/exactly two
datasets, where the first dataset is the reference set. Calculates the
mean of the reference set, and compares this to the second set (which is
assumed to contain the means of many runs producing data comparable to the
data in the reference set).
takes: data: list of arrays of values to plot (needs to be list of arrays
so you can pass in arrays with different numbers of elements)
graph_name: filename to write graph to
bins: number of bins to use
normal_fit: whether to show the normal curve best fitting the data
normed: whether to normalize the histogram (e.g. so bars sum to 1)
colors: list of colors to use for bars
linecolors: list of colors to use for fit lines
**kwargs are passed on to init_graph_display.
"""
rc('patch', linewidth=.2)
rc('font', size='x-small')
rc('axes', linewidth=.2)
rc('axes', labelsize=7)
rc('xtick', labelsize=7)
rc('ytick', labelsize=7)
if y_label is None:
if normed:
y_label = 'Frequency'
else:
y_label = 'Count'
num_series = len(data)
if colors is None:
if num_series == 1:
colors = ['white']
else:
colors = standard_series_colors
if linecolors is None:
if num_series == 1:
linecolors = ['red']
else:
linecolors = standard_series_colors
init_graph_display(prob_axes=prob_axes, y_label=y_label, **kwargs)
all_patches = []
for i, d in enumerate(data):
fc = colors[i % len(colors)]
lc = linecolors[i % len(linecolors)]
counts, x_bins, patches = hist(d, bins=bins, normed=normed, \
alpha=alpha, facecolor=fc)
all_patches.append(patches[0])
if normal_fit and len(d) > 1:
mu = mean(d)
sigma = std(d)
minv = min(d)
maxv = max(d)
bin_width = x_bins[-1] - x_bins[-2]
#set range for normpdf
normpdf_bins = arange(minv, maxv, 0.01 * (maxv - minv))
y = normpdf(normpdf_bins, mu, sigma)
orig_area = sum(counts) * bin_width
y = y * orig_area #normpdf area is 1 by default
plot(normpdf_bins, y, linestyle='--', color=lc, linewidth=1)
font = {'color': lc, 'fontsize': 11}
text(mu,
0.0,
"*",
font,
verticalalignment='center',
horizontalalignment='center')
xlabel(x_label)
if show_legend and series_names:
fp = FontProperties()
fp.set_size('x-small')
legend(all_patches, series_names, prop=fp)
#output figure if graph name set -- otherwise, leave for further changes
if graph_name is not None:
savefig(graph_name)
fig = pl.figure(figsize=(8,5))
pl.rc('font',size=18)
pl.rc('mathtext', default='regular')
pl.xlim(-2000,2000)
pl.ylim(-0.4,1.2)
# pl.plot(CO54_vel, CO54_spectra_smooth_W/max(CO54_spectra_smooth_W), 'r-', label='CO(5-4)')
# pl.plot(NII_vel, NII_spectra_smooth_W/max(NII_spectra_smooth_W), 'g-', label='[NII]')
# pl.plot(CII_vel, CII_spectra_smooth_W/max(CII_spectra_smooth_W), 'b-', label='[CII]')
pl.plot(CO54_vel, CO54_spectra_smooth_W/max(CO54_spectra_smooth_W), c='r',ls='steps', label='CO(5-4)')
pl.plot(NII_vel, NII_spectra_smooth_W/max(NII_spectra_smooth_W), c='g',ls='steps', label='[NII]')
pl.plot(CII_vel, CII_spectra_smooth_W/max(CII_spectra_smooth_W), c='b',ls='steps', label='[CII]')
pl.text(-1750, 1, 'SPT0348-W', size=18)
pl.ylabel('Fraction of peak')
pl.xlabel(r'Radio velocity (km s$^{-1}$)')
pl.legend(fontsize=14,loc=0,ncol=1,frameon=True,numpoints=1,scatterpoints=1)
pl.savefig('spectra_smooth_W.pdf', bbox_inches='tight')
pl.close()
################
# plot spectra E
################
eeg_data = csp_multi_class.apply(eeg_data)[1000:]
labels = labels[1000:]
n_samples = len(eeg_data)
eeg_data = InstantaneousVarianceFilter(eeg_data.shape[1],
n_taps=fs // 2).apply(eeg_data)
#eeg_data = eeg_data/eeg_data.std(0)
plt.plot(eeg_data / eeg_data.std(0) / 5 + np.arange(len(eeg_data[0])))
j = 0
for label in [1, 2, 6]:
for ch in bands:
j += 2
plt.text(0, j, str(label) + str(ch))
from sklearn import datasets
from sklearn.multiclass import OneVsRestClassifier
from sklearn.svm import LinearSVC
from sklearn.linear_model import LogisticRegression
#clf = OneVsRestClassifier(LogisticRegression(random_state=0, penalty='l1'))
clf = MLPClassifier(hidden_layer_sizes=(), early_stopping=True, verbose=True)
k = 2 * n_samples // 3
train_slice = slice(None, k)
test_slice = slice(k, None)
acc_train = sum(
clf.fit(eeg_data[train_slice], labels[train_slice]).predict(
eeg_data[train_slice]) == labels[train_slice]) / len(
labels[train_slice])
'''
Plot a sine function.
-----------------------------------------------------------
(c) 2013 Allegra Via and Kristian Rother
Licensed under the conditions of the Python License
This code appears in section 16.4.1 of the book
"Managing Biological Data with Python".
-----------------------------------------------------------
'''
from pylab import figure, plot, text, axis, savefig
import math
figure()
xdata = [0.1 * i for i in range(100)]
ydata = [math.sin(j) for j in xdata]
plot(xdata, ydata, 'kd', linewidth=1)
text(4.8, 0, "$y = sin(x)$", horizontalalignment='center', fontsize=20)
axis([0, 3 * math.pi, -1.2, 1.2])
savefig('sinfunc.png')
for j in range(16):
new_pos = seq.predict(track[np.newaxis, ::, ::, ::, ::])
new = new_pos[::, -1, ::, ::, ::]
track = np.concatenate((track, new), axis=0)
# And then compare the predictions
# to the ground truth
track2 = noisy_movies[which][::, ::, ::, ::]
for i in range(15):
fig = plt.figure(figsize=(10, 5))
ax = fig.add_subplot(121)
if i >= 7:
ax.text(1, 3, 'Predictions !', fontsize=20, color='w')
else:
ax.text(1, 3, 'Initial trajectory', fontsize=20)
toplot = track[i, ::, ::, 0]
plt.imshow(toplot)
ax = fig.add_subplot(122)
plt.text(1, 3, 'Ground truth', fontsize=20)
toplot = track2[i, ::, ::, 0]
if i >= 2:
toplot = shifted_movies[which][i - 1, ::, ::, 0]
plt.imshow(toplot)
plt.savefig('%i_animate.png' % (i + 1))
def graphPer(StatTable,
Fields=[
'_Efficiency',
'_NavConf',
'_DirRtng',
'_TgtFound',
],
TestName='',
StatType='A_',
StdType=None,
Range=None):
if not graph: return
Fig = pylab.figure()
Names = []
maxStat = 0
if Range:
l = len(Range) + 1
FollowerNames = [StatTable['/FollowerID'][i] for i in Range]
else:
l = len(StatTable['/FollowerID']) + 1
FollowerNames = StatTable['/FollowerID']
for field, Line in zip(Fields, [
'-.',
':',
'-',
'--',
]):
if len(FollowerNames) > 6: offset = .5
else: offset = 1
for Giver, Fmt in zip(LogAnalyzer.Directors, Formats):
Name = StatType + Giver + field
Names.append(Name)
if Range:
StatX = [StatTable[Name][i] for i in Range]
if StdType:
StatY = [
StatTable[StdType + Giver + field][i] for i in Range
]
else:
StatX = StatTable[Name]
if StdType: StatY = StatTable[StdType + Giver + field]
Length = len(StatX)
if StdType: maxStat = max(maxStat, max(StatX + StatY))
else: maxStat = max(maxStat, max(StatX))
Xvals = [x + offset for x in range(Length)]
if StdType:
pylab.errorbar(Xvals, StatX, yerr=StatY, fmt=Fmt) #+Line)
else:
pylab.plot(Xvals, StatX, Fmt) #+Line)
#pylab.semilogy(Xvals,StatX,Fmt+Line)
if len(FollowerNames) < 6: offset += 0.1
if Range:
l = len(Range) + 1
FollowerNames = [StatTable['/FollowerID'][i] for i in Range]
else:
l = len(StatTable['/FollowerID']) + 1
FollowerNames = StatTable['/FollowerID']
if len(FollowerNames) > 6:
for num, Follower in zip(range(1, l), FollowerNames):
pylab.text(num, -0.5, Follower[0:7], rotation='vertical')
pylab.text(num, 6.5, Follower[0:7], rotation='vertical')
else:
for num, Follower in zip(range(1, l), FollowerNames):
pylab.text(num, -0.5, Follower, rotation='horizontal')
pylab.text(num, 6.5, Follower, rotation='horizontal')
pylab.axis([0, Length * 2, 0, max(int(math.ceil(maxStat)), 6)])
pylab.grid(1)
Axis = pylab.gca()
Axis.set_yticks(range(int(math.ceil(maxStat + 2))))
#Axis.set_xticks([])
adornGraph(
Title='Results per direction giver: ' + StatType + TestName,
Filename='Results_per_' + StatType + TestName,
Legend=Names,
)
def ResVectorPlot(root='./',
align='align/align_t',
poly='polyfit_d/fit',
points='points_d/',
useAccFits=False,
numEpochs=7,
TargetName='OB120169_R',
radCut_pix=10000,
magCut=22):
print 'Creating quiver plot of residuals...'
s = starset.StarSet(root + align)
s.loadPolyfit(root + poly, accel=0, arcsec=0)
try:
pointsFile = root + points + TargetName + '.points'
if os.path.exists(pointsFile + '.orig'):
pointsTab = Table.read(pointsFile + '.orig', format='ascii')
else:
pointsTab = Table.read(pointsFile, format='ascii')
times = pointsTab[pointsTab.colnames[0]]
except:
print 'Star ' + TargetName + ' not in list'
py.clf()
# for ee in range(1):
py.close(1)
py.figure(1, figsize=(10, 10))
for ee in range(numEpochs):
# Observed data
x = s.getArrayFromEpoch(ee, 'xpix')
y = s.getArrayFromEpoch(ee, 'ypix')
m = s.getArrayFromEpoch(ee, 'mag')
rad = np.hypot(x - 512, y - 512)
good = (rad < radCut_pix) & (m < magCut)
idx = np.where(good)[0]
stars = [s.stars[i] for i in idx]
x = x[idx]
y = y[idx]
rad = rad[idx]
Nstars = len(x)
x_fit = np.zeros(Nstars, dtype=float)
y_fit = np.zeros(Nstars, dtype=float)
residsX = np.zeros(Nstars, dtype=float)
residsY = np.zeros(Nstars, dtype=float)
idx2 = []
for i in range(Nstars):
fitx = stars[i].fitXv
fity = stars[i].fitYv
StarName = stars[i].name
dt = times[ee] - fitx.t0
fitLineX = fitx.p + (fitx.v * dt)
fitSigX = np.sqrt(fitx.perr**2 + (dt * fitx.verr)**2)
fitLineY = fity.p + (fity.v * dt)
fitSigY = np.sqrt(fity.perr**2 + (dt * fity.verr)**2)
x_fit[i] = fitLineX
y_fit[i] = fitLineY
residsX[i] = x[i] - fitLineX
residsY[i] = y[i] - fitLineY
idx = np.where((np.abs(residsX) < 10.0) & (np.abs(residsY) < 10.0))[0]
py.subplot(3, 3, ee + 1)
py.ylim(0, 1100)
py.xlim(0, 1100)
py.yticks(fontsize=10)
py.xticks([200, 400, 600, 800, 1000], fontsize=10)
q = py.quiver(x_fit[idx],
y_fit[idx],
residsX[idx],
residsY[idx],
scale_units='width',
scale=0.5)
py.quiver([850, 0], [100, 0], [0.05, 0.05], [0, 0],
color='red',
scale=0.5,
scale_units='width')
py.text(600, 100, '0.5 mas', color='red', fontsize=8)
# py.quiverkey(q, 0.85, 0.1, 0.02, '0.2 mas', color='red', fontsize=6)
fname = 'quiverplot_all.png'
py.subplots_adjust(bottom=0.1, right=0.97, top=0.97, left=0.05)
if os.path.exists(root + 'plots/' + fname):
os.remove(root + 'plots/' + fname)
py.savefig(root + 'plots/' + fname)
return
pl.figure(2)
pl.subplot("111", aspect=1.0)
pl.errorbar([np.mean(cdtwResult)], [np.mean(gemResult)],
[np.std(cdtwResult)], [np.std(gemResult)],
c="grey")
pl.plot([np.mean(cdtwResult)], [np.mean(gemResult)], "o", c="blue")
pl.figure(1)
pl.subplot("111", aspect=1.0)
#pl.title("Texas Sharpshooter Plot")
pl.plot([-0.25, 0.25], [0, 0], c="black")
pl.plot([0, 0], [-0.25, 0.25], c="black")
pl.xlabel('relative error difference on training set')
pl.ylabel('relative error difference on test set')
pl.axis((-0.25, 0.25, -0.25, 0.25))
pl.text(+0.2, +0.2, r'TP', fontsize=20)
pl.text(-0.23, -0.23, r'TN', fontsize=20)
pl.text(+0.2, -0.23, r'FP', fontsize=20)
pl.text(-0.23, +0.2, r'FN', fontsize=20)
pl.annotate('Lighting7',
xy=(-0.12, -0.1),
xytext=(-0.244, -0.04),
arrowprops=dict(facecolor='lightgrey', shrink=0.05),
color="grey")
pl.annotate('Fish',
xy=(0.095, 0.115),
xytext=(0.01, 0.16),
arrowprops=dict(facecolor='lightgrey', shrink=0.05),
color="grey")
pl.figure(2)
cluster_center = k_means_cluster_centers[k]
ax.plot(X[my_members, 0],
X[my_members, 1],
'w',
markerfacecolor=col,
marker='.')
ax.plot(cluster_center[0],
cluster_center[1],
'o',
markerfacecolor=col,
markeredgecolor='k',
markersize=6)
ax.set_title('KMeans')
ax.set_xticks(())
ax.set_yticks(())
pl.text(-3.5, 1.8,
'train time: %.2fs\ninertia: %f' % (t_batch, k_means.inertia_))
# MiniBatchKMeans
ax = fig.add_subplot(1, 3, 2)
for k, col in zip(range(n_clusters), colors):
my_members = mbk_means_labels == order[k]
cluster_center = mbk_means_cluster_centers[order[k]]
ax.plot(X[my_members, 0],
X[my_members, 1],
'w',
markerfacecolor=col,
marker='.')
ax.plot(cluster_center[0],
cluster_center[1],
'o',
markerfacecolor=col,
minX, minY = 300, 800
sizeX, sizeY = 215, 200
maxX, maxY = minX+sizeX, minY+sizeY
data_raw = data[minX:maxX, minY:maxY]
#sizeX, sizeY = data_raw.shape
# Create x and y indices
x = np.arange(0, sizeX)
y = np.arange(0, sizeY)
x, y = np.meshgrid(x, y)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13, 8))
plt.gcf().canvas.set_window_title('2D Gaussian Fitting')
ax1.set_title("raw 2D Gaussian")
ax2.set_title("fitted 2D Gaussian")
popt, pcov = opt.curve_fit(twoD_Gaussian, (x, y), np.ravel(data_raw), p0=initial_guess())
data_fitted = twoD_Gaussian((x, y), *popt)
print(getResult(popt))
ax2.imshow(data_raw.reshape(sizeX, sizeY), cmap=plt.cm.jet)
ax2.contour(y.transpose(), x.transpose(), data_fitted.reshape(sizeX, sizeY), 4, colors='w')
ax1.imshow(data_raw)
plt.text(25, 170, getResult(popt)[0], bbox=dict(facecolor='yellow', alpha=0.5), fontsize=12)
plt.text(25, 185, getResult(popt)[1], bbox=dict(facecolor='yellow', alpha=0.5), fontsize=12)
plt.show()
def plotcc(current_data):
from pylab import plot, text
plot([235.8162], [41.745616], 'wo')
text(235.8, 41.9, 'Cr.City', color='w', fontsize=10)
def plot(self, fig_number=322):
"""plot the stored data in figure fig_number.
Dependencies: matlabplotlib/pylab.
Example
=======
::
>> import barecmaes2 as cma
>> es = cma.CMAES(3 * [0.1], 1)
>> logger = cma.CMAESDataLogger().register(es)
>> while not es.stop():
>> X = es.ask()
>> es.tell(X, [bc.Fcts.elli(x) for x in X])
>> logger.add()
>> logger.plot()
"""
if pylab is None:
return None
pylab.figure(fig_number)
from pylab import text, hold, plot, ylabel, grid, semilogy, \
xlabel, draw, show, subplot
dat = self.dat # dictionary with entries as given in __init__
if not dat:
return
try: # a hack to get the presumable population size lambda
strpopsize = ' (popsize~' + str(dat['eval'][-2] -
dat['eval'][-3]) + ')'
except IndexError:
strpopsize = ''
# plot fit, Delta fit, sigma
subplot(221)
hold(False)
if dat['fit'][0] is None:
dat['fit'][0] = dat['fit'][1]
# should be reverted later, but let's be lazy
assert dat['fit'].count(None) == 0
dmin = min(dat['fit'])
i = dat['fit'].index(dmin)
dat['fit'][i] = max(dat['fit'])
dmin2 = min(dat['fit'])
dat['fit'][i] = dmin
semilogy(dat['iter'], [d - dmin + 1e-19 if d >= dmin2 else
dmin2 - dmin for d in dat['fit']], 'c',
linewidth=1)
hold(True)
semilogy(dat['iter'], [abs(d) for d in dat['fit']], 'b')
semilogy(dat['iter'][i], abs(dmin), 'r*')
semilogy(dat['iter'], dat['sig'], 'g')
ylabel('f-value, Delta-f-value, sigma')
grid(True)
# plot xmean
subplot(222)
hold(False)
plot(dat['iter'], dat['xm'])
hold(True)
for i in range(len(dat['xm'][-1])):
text(dat['iter'][0], dat['xm'][0][i], str(i))
text(dat['iter'][-1], dat['xm'][-1][i], str(i))
ylabel('mean solution', ha='center')
grid(True)
# plot D
subplot(223)
hold(False)
semilogy(dat['iter'], dat['D'], 'm')
xlabel('iterations' + strpopsize)
ylabel('axes lengths')
grid(True)
# plot stds
subplot(224)
# if len(gcf().axes) > 1:
# sca(pylab.gcf().axes[1])
# else:
# twinx()
hold(False)
semilogy(dat['iter'], dat['stds'])
for i in range(len(dat['stds'][-1])):
text(dat['iter'][-1], dat['stds'][-1][i], str(i))
ylabel('coordinate stds disregarding sigma', ha='center')
grid(True)
xlabel('iterations' + strpopsize)
sys.stdout.flush()
draw()
show()
CMAESDataLogger.plotted += 1
])
error_ve = np.array([
6.84474776e-07, 4.39461983e-07, 3.23219057e-07, 2.25775400e-07,
1.64093065e-07, 1.26171326e-07, 9.91391890e-08, 7.91353527e-08,
6.54439517e-08
])
error_vi = np.array([
4.34199278e-06, 3.12494787e-06, 2.28197680e-06, 1.65532127e-06,
1.22502294e-06, 9.39663685e-07, 7.40306682e-07, 5.96650924e-07,
4.90344023e-07
])
pl.loglog(N, error_ve, '-o', label=r'$v_e$')
pl.loglog(N, error_vi, '-o', label=r'$v_i$')
pl.loglog(N, error_E, '-o', label=r'$E$')
pl.loglog(N, error_B, '-o', label=r'$B$')
pl.loglog(N[-4:],
2e-2 / N[-4:]**2,
'--',
color='black',
label=r'$\mathcal{O}(N^{-2})$')
pl.text(2**7, 1.3e-6, r'$\mathcal{O}(N^{-2})$', fontsize=20)
pl.text(2**6 - 8, 2.5e-7, r'$e^-$', fontsize=20)
pl.text(2**6 - 8, 3.2e-6, r'$p^+$', fontsize=20)
pl.text(2**6 - 8, 4.5e-7, r'$E$', fontsize=20)
pl.text(2**6 - 8, 1.3e-6, r'$B$', fontsize=20)
pl.xlabel(r'$N$')
pl.ylabel('Error')
pl.xscale('log', basex=2)
pl.savefig('plot.png', bbox_inches='tight')
# first, create a figure.
fig = figure()
# background color will be used for 'wet' areas.
fig.add_axes([0.1, 0.1, 0.8, 0.8], axisbg='aqua')
# draw colored markers.
# use zorder=10 to make sure markers are drawn last.
# (otherwise they are covered up when continents are filled)
#m.scatter(x,y,25,z,cmap=cm.jet,marker='o',faceted=False,zorder=10)
# create a list of strings containing z values
# or, plot actual numbers as color-coded text strings.
zn = ['%2i' % zz for zz in z]
# plot numbers on map, colored by value.
for numstr, zval, xpt, ypt in zip(zn, z, x, y):
# only plot values inside map region.
if xpt > m.xmin and xpt < m.xmax and ypt > m.ymin and ypt < m.ymax:
hexcolor = rgb2hex(cm.jet(zval / 100.)[:3])
text(xpt, ypt, numstr, fontsize=9, weight='bold', color=hexcolor)
# draw coasts and fill continents.
m.drawcoastlines(linewidth=0.5)
m.fillcontinents(color='coral')
# draw parallels and meridians.
delat = 20.
circles = arange(0.,90.,delat).tolist()+\
arange(-delat,-90,-delat).tolist()
m.drawparallels(circles)
delon = 45.
meridians = arange(0, 360, delon)
m.drawmeridians(meridians, labels=[1, 1, 1, 1])
title('Random Points', y=1.075)
show()
print('no solution found')
sys.exit()
pyschedule.plotters.matplotlib.plot(S,resource_height=1.0,)
# plot tours
import pylab
sol = S.solution()
blue_tour = [ coords[str(city)] for (city,resource,start,end) in sol if str(city) in coords and str(resource) == 'blue' ]
pylab.plot([ x for x,y in blue_tour ],[ y for x,y in blue_tour],linewidth=2.0,color='blue')
red_tour = [ coords[str(city)] for (city,resource,start,end) in sol if str(city) in coords if str(resource) == 'red' ]
pylab.plot([ x for x,y in red_tour ],[ y for x,y in red_tour],linewidth=2.0,color='red')
# plot city names
for city in cities : pylab.text(coords[str(city)][0], coords[str(city)][1], city,color='black',fontsize=10)
pylab.title('VRP Germany')
pylab.show()
pl.figure(6)
pl.subplot(7, 7, cntfit)
pl.plot(xs, fits[:, 4], '*-')
pl.plot(xs[ok], fits[ok, 4], 'or-')
pl.ylim([2.9, 4])
cntfit += 1
delta = (extent[0] + ff[0]) - pos[0]
poss.append(extent[0] + ff[0])
deltas.append(delta)
q = np.degrees(ff[1]) - np.degrees(CSU.rotation)
qs.append(q)
pl.figure(1)
pl.text(pos[0],
pos[1],
'b%2.0i: w=%3.2f p=%5.2f q=%3.2f d=%1.3f' %
(bar, np.mean(fits[:, 4]), extent[0] + ff[0], q, delta),
fontsize=11,
family='monospace',
horizontalalignment='center')
pl.xlim([0, 2048])
pl.ylim([0, 2048])
means = np.array(means)
sds = np.array(sds)
pl.draw()
def plot_topo_file(topoplotdata):
"""
Read in a topo or bathy file and produce a pcolor map.
"""
import os
import pylab
from clawpack.clawutil.data import ClawData
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
cmap = topoplotdata.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 cmap is None:
cmap = colormaps.make_colormap({
-1: [0.3, 0.2, 0.1],
-0.00001: [0.95, 0.9, 0.7],
0.00001: [.5, .7, 0],
1: [.2, .5, .2]
})
#cmap = colormaps.make_colormap({-1:[0,0,1],0:[1,1,1],1:[1,0,0]})
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)
#pylab.clim([cmin,cmax])
if addcolorbar:
pylab.colorbar()
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')
patchedges_show = True
if patchedges_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 = ClawData()
topodata.x = x
topodata.y = y
topodata.topo = topo
return topodata
def fit_exp(o_xdata, o_ydata, o_yerr):
import scipy, pylab
a = scipy.array(o_ydata)
a, b, varp = pylab.hist(a, bins=scipy.arange(-2, 2, 0.05))
pylab.xlabel("shear")
pylab.ylabel("Number of Galaxies")
#pylab.show()
o_xdata = scipy.array(o_xdata)
o_ydata = scipy.array(o_ydata)
o_yerr = scipy.array(o_yerr)
print o_yerr
#o_xdata = o_xdata[abs(o_ydata) > 0.05]
#o_yerr = o_yerr[abs(o_ydata) > 0.05]
#o_ydata = o_ydata[abs(o_ydata) > 0.05]
both = 0
As = []
for z in range(25):
xdata = []
ydata = []
yerr = []
for i in range(len(o_xdata)):
rand = int(random.random() * len(o_xdata)) - 1
#print rand, len(o_xdata)
xdata.append(o_xdata[rand])
ydata.append(o_ydata[rand])
yerr.append(o_yerr[rand])
xdata = scipy.array(xdata)
ydata = scipy.array(ydata)
##########
# Fitting the data -- Least Squares Method
##########
# Power-law fitting is best done by first converting
# to a linear equation and then fitting to a straight line.
#
# y = a * x^b
# log(y) = log(a) + b*log(x)
#
powerlaw = lambda x, amp, index: amp * (x**index)
#print xdata
#print ydata
ydata = ydata / (scipy.ones(len(ydata)) + abs(ydata))
# define our (line) fitting function
#fitfunc = lambda p, x: p[0]*x**p[1]
if both:
fitfunc = lambda p, x: p[0] * x**p[1]
errfunc = lambda p, x, y, err: (y - fitfunc(p, x)) / err
pinit = [1., -1.]
else:
fitfunc = lambda p, x: p[0] * x**-1.
errfunc = lambda p, x, y, err: (y - fitfunc(p, x)) / err
pinit = [1.] #, -1.0]
#ydataerr = scipy.ones(len(ydata)) * 0.3
out = scipy.optimize.leastsq(errfunc,
pinit,
args=(scipy.array(xdata),
scipy.array(ydata),
scipy.array(yerr)),
full_output=1)
if both:
pfinal = out[0]
covar = out[1]
print pfinal
print covar
index = pfinal[1]
amp = pfinal[0]
ampErr = covar[0][0]
indexErr = covar[1][1]
else:
pfinal = out[0]
covar = out[1]
print pfinal
print covar
index = -1. #pfinal[1]
amp = pfinal #[0]
ampErr = covar[0][0]**0.5
indexErr = 0 #covar
#indexErr = scipy.sqrt( covar[0][0] )
#ampErr = scipy.sqrt( covar[1][1] ) * amp
##########
# Plotting data
##########
import pylab
pylab.clf()
#pylab.subplot(2, 1, 1)
pylab.errorbar(xdata, ydata, yerr=yerr, fmt='k.') # Data
from copy import copy
xdataline = copy(xdata)
xdataline.sort()
pylab.plot(xdataline, powerlaw(xdataline, amp, index)) # Fit
pylab.text(5, 6.5, 'Ampli = %5.2f +/- %5.2f' % (amp, ampErr))
pylab.text(5, 5.5, 'Index = %5.2f +/- %5.2f' % (index, indexErr))
pylab.title('Best Fit Power Law')
pylab.xlabel('X')
pylab.ylabel('Y')
#pylab.subplot(2, 1, 2)
#pylab.loglog(xdataline, powerlaw(xdataline, amp, index))
#pylab.errorbar(xdata, ydata, yerr=ydataerr, fmt='k.') # Data
#pylab.xlabel('X (log scale)')
#pylab.ylabel('Y (log scale)')
#pylab.xlim(1.0, 11)
print z
if both:
pylab.show()
#pylab.show()
pylab.clf()
As.append(amp)
As = scipy.array(As)
return As.mean(), As.std()
while True:
print 'Turn', n, 'Prepare to engage.'
bombX = float(raw_input("Select x-coordinate of shell:"))
bombY = float(raw_input("Select y-coordinate of shell:"))
n = n + 1
flyerOne.advance()
flyerOne.makePathX()
flyerOne.makePathY()
pylab.plot(flyerOne.pathX, flyerOne.pathY, 'ro')
pylab.plot(bombX, bombY, 'bo')
# inform player where his bomb is
pylab.text(5,10,'Sensor position of bomb at' + str(bombX) + \
',' + str(bombY), size = 'x-large')
pylab.show()
pylab.ylim(0, 100) # the gameplay boundary
pylab.xlim(0, 100)
if (bombX - float(flyerOne.positionList[0]))**2 +\
(bombY - float(flyerOne.positionList[1]))**2 < frag_range:
pylab.text(40, 40, 'HOSTILE TARGET DESTROYED', size='x-large')
print "You win! threat neutralised"
break
if float(flyerOne.positionList[0]) > 100 or float(
flyerOne.positionList[1]) > 100:
print "You are dead! threat bypassed your defenses"
break # Game over for n00bs
if n > 1000:
break # Safety fuse during development process
widths=d_widths,
gap=0.005,
scale=False)
for i in range(len(A2)):
Ai = [x for x in A2[i] if x > 0]
y = [x / 2.0 for x in Ai]
for j in range(len(Ai)):
if j > 0:
yy = y[j] + np.sum(Ai[0:j])
else:
yy = y[j]
if int(Ai[j]) > 10:
pl.text(0.5 * i - 0.02,
yy - 1.2,
str(Ai[j]),
fontsize=12,
zorder=10)
ax1.axes.get_xaxis().set_visible(False)
ax1.set_title('n = 5, number of graphs per u = 100', multialignment='center')
ax1.set_ylabel('u = 2', rotation=90)
#graph subplot for u = 3
A3 = makeA(2)
mycolorlistu3 = [
(0.9769448735268946, 0.6468696110452877, 0.2151452804329661),
(0.37645505989354233, 0.6875228836084111, 0.30496111115768654),
(0.6151274326753975, 0.496189476149738, 0.15244053646953548),
(0.1562085876188265, 0.44786703170340336, 0.9887241674046707),
(0.4210506028639077, 0.2200011667972023, 0.37841949185273394),
